From 4974971f500180c1856c2cb2bfdf00019c079d2c Mon Sep 17 00:00:00 2001
From: the-hampel <ahampel@flatironinstitute.org>
Date: Fri, 22 Mar 2024 18:18:38 +0000
Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20TRIQS/so?=
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diff --git a/.buildinfo b/.buildinfo
new file mode 100644
index 00000000..e87ca38c
--- /dev/null
+++ b/.buildinfo
@@ -0,0 +1,4 @@
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+config: 3ab99140c30a554ec0069b9809580ceb
+tags: 645f666f9bcd5a90fca523b33c5a78b7
diff --git a/.doctrees/ChangeLog.doctree b/.doctrees/ChangeLog.doctree
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diff --git a/.doctrees/nbsphinx/tutorials/Ce2O3_csc_w90/tutorial.ipynb b/.doctrees/nbsphinx/tutorials/Ce2O3_csc_w90/tutorial.ipynb
new file mode 100644
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+++ b/.doctrees/nbsphinx/tutorials/Ce2O3_csc_w90/tutorial.ipynb
@@ -0,0 +1,530 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "1cc005bd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.ticker as ticker\n",
+    "\n",
+    "from triqs.gf import *\n",
+    "from h5 import HDFArchive"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f93f161b",
+   "metadata": {},
+   "source": [
+    "# 3. CSC with QE/W90 and HubbardI: total energy in Ce2O3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c1dbd052",
+   "metadata": {},
+   "source": [
+    "Disclaimer:\n",
+    "\n",
+    "* These can be heavy calculations. Current parameters won't give converged solutions, but are simplified to deliver results on 10 cores in 10 minutes.\n",
+    "* The interaction values, results etc. might not be 100% physical and are only for demonstrative purposes!\n",
+    "\n",
+    "The goal of this tutorial is to demonstrate how to perform fully charge self-consistent DFT+DMFT calculations in solid_dmft using [Quantum Espresso](https://www.quantum-espresso.org/) (QE) and [Wannier90](http://www.wannier.org/) (W90) for the DFT electronic structure using the [HubbardI solver](https://triqs.github.io/hubbardI/latest/index.html).\n",
+    "\n",
+    "We will use Ce$_2$O$_3$ as an example and compute the total energy for the $s=0\\%$ experimental ground state structure. To find the equilibrium structure in DFT+DMFT one then repeats these calculations variing the strain in DFT as was done in Fig. 7 of [arxiv:2111.10289 (2021)](https://arxiv.org/abs/2111.10289.pdf):\n",
+    "\n",
+    "<img src=\"./tenergy_ce2o3.png\" alt=\"drawing\" width=\"800\"/>\n",
+    "\n",
+    "In the case of Ce$_2$O$_3$ it turns out that in fact DFT+DMFT predicts the same ground state as is found experimentally, while DFT underestimates, and DFT+DMFT in the one-shot approximation overestimates the lattice parameter, respectively.\n",
+    "\n",
+    "The tutorial will guide you through the following steps: \n",
+    "\n",
+    "* perpare the input for the DFT and DMFT calculations using Quantum Espresso and Wannier90 and TRIQS\n",
+    "* run a charge self-consistent calculation for Ce$_2$O$_3$\n",
+    "* analyse the change in the non-interacting part of the charge density using TRIQS\n",
+    "* analyse the convergence of the total energy and the DMFT self-consistency\n",
+    "\n",
+    "We set `path` variables to the reference files:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "8681be23",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path = './ref/'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "10d286f9",
+   "metadata": {},
+   "source": [
+    "## 1. Input file preparation\n",
+    "\n",
+    "The primitive cell of Ce$_2$O$_3$ contains 2 Ce atoms with 7 $f$-electrons each, so 14 in total. They are relatively flat, so there is no entanglement with any other band.\n",
+    "We start from relaxed structure as usual. All files corresponding to this structure should be prepared and stored in a separate directory (`save/` in this case). For details please look at Section III in [arxiv:2111.10289 (2021)](https://arxiv.org/abs/2111.10289.pdf).\n",
+    "\n",
+    "### DFT files\n",
+    "\n",
+    "All input files are of the same kind as usual, unless stated otherwise:\n",
+    "\n",
+    "Quantum Espresso:\n",
+    "\n",
+    "1. [ce2o3.scf.in](./dft_input/ce2o3.scf.in)\n",
+    "2. [ce2o3.nscf.in](./dft_input/ce2o3.nscf.in)\n",
+    "\n",
+    "    - explicit k-mesh\n",
+    "    ```\n",
+    "    &system\n",
+    "        nosym            = .true.\n",
+    "        dmft             = .true.\n",
+    "    ```\n",
+    "3. [ce2o3.mod_scf.in](./dft_input/ce2o3.mod_scf.in): new!\n",
+    "\n",
+    "    - explicit k-mesh\n",
+    "    ```\n",
+    "    &system\n",
+    "        nosym            = .true.\n",
+    "        dmft             = .true.\n",
+    "        dmft_prefix      = seedname\n",
+    "    &electrons\n",
+    "        electron_maxstep = 1\n",
+    "        mixing_beta      = 0.3\n",
+    "    ```\n",
+    "\n",
+    "Optionally:\n",
+    "\n",
+    "- `seedname.bnd.in`\n",
+    "- `seedname.bands.in`\n",
+    "- `seedname.proj.in`\n",
+    "\n",
+    "Wannier90:\n",
+    "\n",
+    "1. [ce2o3.win](./dft_input/ce2o3.win)\n",
+    "\n",
+    "    ```\n",
+    "        write_u_matrices = .true.\n",
+    "    ```\n",
+    "2. [ce2o3.pw2wan.in](./dft_input/ce2o3.pw2wan.in)\n",
+    "\n",
+    "### DMFT\n",
+    "\n",
+    "1. Wannier90Converter: [ce2o3.inp](./dft_input/ce2o3.inp)\n",
+    "2. solid_dmft: [dmft_config.toml](./dmft_config.toml)\n",
+    "\n",
+    "Here we'll discuss the most important input flags for solid_dmft:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "165c087b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[general]\n",
+      "seedname = \"ce2o3\"\n",
+      "jobname = \"b10-U6.46-J0.46\"\n",
+      "csc = true\n",
+      "\n",
+      "eta = 0.5\n",
+      "n_iw = 100\n",
+      "n_tau = 5001\n",
+      "\n",
+      "n_iter_dmft_first = 2\n",
+      "n_iter_dmft_per = 1\n",
+      "n_iter_dmft = 5\n",
+      "\n",
+      "block_threshold = 1e-03\n",
+      "\n",
+      "h_int_type = \"density_density\"\n",
+      "U = 6.46\n",
+      "J = 0.46\n",
+      "beta = 10\n",
+      "prec_mu = 0.1\n",
+      "\n",
+      "sigma_mix = 1.0\n",
+      "g0_mix = 1.0\n",
+      "dc_type = 0\n",
+      "dc = true\n",
+      "dc_dmft = true\n",
+      "calc_energies = true\n",
+      "\n",
+      "h5_save_freq = 1\n",
+      "\n",
+      "[solver]\n",
+      "type = \"hubbardI\"\n",
+      "n_l = 15\n",
+      "store_solver = false\n",
+      "measure_G_l = false\n",
+      "measure_density_matrix = true\n",
+      "\n",
+      "[dft]\n",
+      "dft_code = \"qe\"\n",
+      "n_cores = 10\n",
+      "mpi_env = \"default\"\n",
+      "projector_type = \"w90\"\n",
+      "dft_exec = \"pw.x\"\n",
+      "w90_tolerance = 1.e-1\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./dmft_config.toml"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f970c47",
+   "metadata": {},
+   "source": [
+    "Of course you'll have to switch `csc` on to perform the charge self-consistent calculations. Then we choose the HubbardI Solver, set the number of Legendre polynomials, Matsubara frequencies $i\\omega_n$ and imaginary time grid points $\\tau$. In this calculation we perform five iterations in total, of which the two first ones are one-shot DMFT iterations, followed by three DFT and three DMFT steps.\n",
+    "For the interaction Hamiltonian we use `density_density`. Note that you unlike the Kanamori Hamiltonian, this one is not rotationally invariant, so the correct order of the orbitals must be set (inspect the projections card in `ce2o3.win`). We must also use `dc_dmft` and `calc_energies`, since we are interested in total energies.\n",
+    "Finally, we will specify some details for the DFT manager, i.e. to use QE, W90 and the tolerance for the mapping of shells. Note that this value should in general be $1e-6$, but for demonstration purposes we reduce it here. If `dft_exec` is empty, it will assume that `pw.x` and other QE executables are available."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "47bb27d5",
+   "metadata": {},
+   "source": [
+    "## 2. Running DFT+DMFT\n",
+    "\n",
+    "Now that everything is set up, copy all files from `./dft_input` and start the calculation:\n",
+    "```\n",
+    "cp dft_input/* .\n",
+    "mpirun solid_dmft > dmft.out &\n",
+    "```\n",
+    "\n",
+    "You will note that for each DFT step solid_dmft will append the filenames of the DFT Ouput with a unique identifier `_itXY`, where `XY` is the total iteration number. This allows the user to keep track of the changes within DFT. For the W90 `seedname.wout` and `seedname_hr.dat` files the seedname will be renamed to `seedname_itXY`. If the QE `seedname_bands.dat`, and `seedname_bands.proj` are present, they will be saved, too.\n",
+    "\n",
+    "You can check the output of the calculations while they are running, but since this might take a few minutes, we'll analyse the results of the reference data in `/ref/ce2o3.h5`. You should check if the current calculation reproduces these results."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c74f73cb",
+   "metadata": {},
+   "source": [
+    "## 3. Non-interacting Hamiltonian and convergence analysis\n",
+    "### Tight-binding Hamiltonian"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7f6d9a1",
+   "metadata": {},
+   "source": [
+    "Disclaimer: the bands shown here are only the non-interacting part of the charge density. Only the first iteration corresponds to a physical charge density, namely the Kohn-Sham ground state charge density.\n",
+    "\n",
+    "The first thing to check is whether the DFT Hamiltonian obtained from Wannier90 is correct. For this we use the tools available in `triqs.lattice.utils`.\n",
+    "Let us first get the number of iterations and Fermi levels from DFT:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "1f204686",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fermi levels: [14.3557, 14.42, 14.4619, 14.495]\n",
+      "iteration counts: [1, 3, 4, 5]\n"
+     ]
+    }
+   ],
+   "source": [
+    "e_fermi_run = !grep \"DFT Fermi energy\" triqs.out\n",
+    "e_fermi_run = [float(x.split('DFT Fermi energy')[1].split('eV')[0]) for x in e_fermi_run]\n",
+    "n_iter_run = !ls ce2o3_it*_hr.dat\n",
+    "n_iter_run = sorted([int(x.split('_it')[-1].split('_')[0]) for x in n_iter_run])\n",
+    "print(f'Fermi levels: {e_fermi_run}')\n",
+    "print(f'iteration counts: {n_iter_run}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "7fa4150b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warning: could not identify MPI environment!\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Starting serial run at: 2022-03-25 12:42:36.663824\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f4a21de2160>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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YaW9vz7kPq1ev5sEHH5x2Pzdt2jS7F/YyIo5WgiAIgiAIC5Cu6xw5OsSjj7fw+BMtDPvOvXd2NoKhJA/95SgP/eUoNpuRq66o5obr67jmqhosFnHqKMydRCKNP5AgGEygadNHmkaTgt1pxuY0ISsykq5j0OIY1TiKnpqwrK5DJpYhHUmTjmZQzzGb8pl0HTJJjUxy4voMFgWT04jJYUQ2ych6BpOawaRGUSUjacWO5LRid5pJxNNEggkSORqoVFWjfyCCbyROUaEdl8ssMiTPkjhCCYIgCIIgLCDpjMpfHz3Oz3+5l7aT/hk/z2hWKKv2UFaTh9NjAUlCItvbNHY7k9EY6A7S3xVkuDd81oAiFkvzxFMneOKpE7hdZl77mqXcc9dyKivc5/UahVeusYzH/sAMemklsNnNONxmTGYDSKBoSYzpOAYtgXTaUGItrZOKpEhHM2RimVn3yp6PTEIlk1CJDSUwmGWMo4GuYlayPbqZACbC2SDXasNic5JOqYQDCWLh5KT1pdMqPb0hfCMGioocOMS8+BkTwa0gCIIgCMICkEqpPPSXI/zkZ3vo6wtPu6wkS9QtLaSyIZ/y2jzKa/MoKHEgTzNf70zplMpQb4i+zgB9HUFOHBigu21kyuWDoSQ//+Vefv7LvVyxsYo33rOCKzdWTTtHUBDGpFIqqgotJ3xnbVRRDDIOlwW7a6yXVsWoRjCqMWRODevVNZ1UKEUymCaTOHvSKQDZoKBYzSgWM4rFgmI1I5tNo61AWdLEf9DSadR4EjWRRI0nsreTqVyrH+3ZTRIfTqKYZMweE2a3CVlWMashTGqYtGJDMtrxFtlxeiwEfXESscnrSyQydHYGcDhMFBc5xBSBGRDvkCAIgiAIwkWUSGT48/8d5oGf72FwKDrtshV1XtZdU82aK6tx5VnH/65oSRzJLpzJPhzJAUxqONurpeujvVs6kq6jSQoxUwFRUxFRUxGWyiLKavLG1zMyFOXg9i4ObO+m/ejQlL1fW7Z2smVrJ6WlTu55/XJef+cyPG7LXLwdwsuEpukcODTA88+fpKgwRWGBlbw8O7IydWBrsRlxuCxYbMbRXtoUpnQUg5aYsFwmliEZTJEKp8/aQ2t02DC6nRgcNhSLGdk4+/BHNhow2KwT/qZrGmoiSSYaJ+UPkQ5FJj1PTWnEBhPEfUksHhOWPDOSAiY1ikmNkpatSEYnBaUOkvEMQV8sZ4blSCRFNDqC12ujIN8mGpSmIYJbQRAEQRCEiyCZzPC7PxzkZ7/Yg28kPuVy3iI7666uYd01NRSVuwAwZcIUBnbgSnbjTPZhTY/MuLxJXqJj/LYOJBUXUXMRfmstZs8SvK9ewjWvXkI4EOfQSz3s29JJy4GBnOvq6wvzze9s4wc/2slrXt3Mm+9bTXWVZ4Z7IrzcxBNpduzo5rkX2nn+hXZG/NnP9Yc/0DzlcxRFxu4yY3OaMRjH5tLGMKlRZP1UoKdldJKBJMlQCi09dUQrKTJGlxOTx4nR5TinYHYmJFnGYLNisFmxFHrRMhlS/lDOQFdXdeK+JImRJGa3CYvXjGyUMWpxDKlEdriyxYG53EU8liLoi0+ql6vr4PPFCAYTFBXZcbssYj5uDiK4FQRBEOZEOJIkFEoSi6WJxdPEY2niiTSxWJp4PE08nkFRJCwWIxaLAbNZGb9ttRiwmA3k59tEAg3hZU/XdZ56ppWvf3MrvdMMP65uKuCme5azZG1pNiOslqYgfJDi8H7y4m3IOUqXhHvi+A75iA0l0FUdTdXRNX38tmyQcFXacdc6cdU4MZgVLGoISyxEfuwE9b4nCZtLGbIvZdi+BOfNDWy4uYGhvjBbn2jhpWfaiEcnJ8JJJDP8/o+H+MOfDnH1VTW85U2rWb+uTHyXX8Y0TaevP0xb2witJ/3s29fH9h3dJKao7Xomi92Ew2ke76WVtQzGTBijGp8wlzYdzZD0J0lFp16vbFAweT2YPNkeWkm+8D2bssGApdCbDXTTGVKByYGurkMikCIRSGF2GbF4zShmBdPokOukwQk2G1abiWg4SXAkjqZOTF6VyWj09obx+xOUFDuwWo0X+qUuaCK4FQRBEGZE13VGRuJ0dQfp7gnS1RXKXncH6e4OEQgmzr6SGbBYDBQXOSgudlBcZKe4yEFRkYPSEge1tV5KSxzihFlYtA4fGeSr/72Zvfv6plymfnkRN92znIYVxUiShDveQUl4P4XRIxi0U8lnkmGVoX0+fIdHGDkWYORYkPQ0AcCZJBnsJTbctU48dU6K1xVQuMqLK9mHK9lH3cgzRE1FDDqWYyxcw2vfvo5X3beKvS92sPnxFnraJie70nV4frTXbklzAW++bzW33NSAyaTM7o0SLihN00kkNNIZnb6+MJKcIJ1SSaVVkimVdEolFk/T2Rmg7aSf1rYRTrb7Z1S253SSDG6vFZvTjGKQQdcxanGMamxCxmNdg2QwSTKQQk1NnenY6HJgLsjD5HFelIB2KrLxVKCbiSdI9A2RHAlOWCYZSpMMpTG7jVgLLMgGsGSCmKQoScWJ3WXB5jAR8scJBxOTyvDG42lOtvvxeCwUFdoxGMR3DERwKwiCIEwhkchw+Mgg+/b3s+9AP/v3989ZAHu27XZ0BujoDOR83GE3UV/vpbEhn4b6fBpGbzud5nnfN0E4V4ODEb713e385a/HplymaXUJN9+zgtqlhQDkR49RM/I8zlT/+DJaRqf7hT7aHu1iYNcw+nlUONE1iPTGiPTG6Nk8wKGfn8CcZ6LiqhIqry6haF0+jtQgjpFBakaeZ8ixlB73pVx2Yz2X3lBHZ4uPzY8eZ++WTjR1ci/y0WPD/Pv9T/O1r2/mda9Zyt2vX0ZFuciyfDpd1wmHkwz7Yvh8cUb8MaLRiaNfVDU7d3osB5PBIGMyKhhN2WuTScFgGLuW0TQdXddRVZ10WiUSSRGJJIlEU0QiKcLhZPZvo/cjkRTRWGp87urXvvGHOX2NxRUu8kscOPMsmC1GnHnWKXtp1bROwpcgFUpNOZdWNhowF+RhLshDMS/8LMIGqwVHXSXWsiLi/cMkff4JgWoymCYVTmP1mjF7zchksGb8qKqJpMGFO9+G3Wkm4IvlLB8UCCQIhZIUFtjJ81qRX+GNvyK4FQRBEADwB+Ls3tPL/v397N3fz5GjQ2Qyc1MbcC5FoqlswL2/f8LfS0ocrF1dyto1ZaxfV0ZNtUf08AoXXTyR5ue/2MtPf75nyl6u2iWFvPpta6huKgByB7Whrjgn/q+N9id7SIUmn+DmIptNKGZTNomOxYxiNqHrOtH2HvRM7n1J+lO0PtxJ68OdmFxGyq8opuqGMkovLaQ4cpDiyEFC5lJ6XZcgN6yg+iNXcMdb1rD5seNsfeJEziHLgUCCB36+h5/9Yg8bN1TxhruWc9WV1a+opDihUIKW1hFaW0fo7ArQ3ROiuztIT2+IZHJmWX4XC0mWqF1SyPJLy1l+STkFpU5sgT4MaMio2FLDKPppnxMdUpEMSX+CdHzq98LkdmAu9GJ0OxflsV2xmHHUlGMtLSQxMExyyI8+GsHrGsSGkySDKayFVkxOI4qewpYeJi3bSBqdFJQ6SUTTBHxRMmfU7dU0nYHBCP5AnJJiJw7Hwg/654sIbgVBEF6hdF3n5Ek/z7+YHUK4/+DAWcszzIQkgclswGQxYLaMXRsxmpVs2YakSjqVyV4nM6RS2eszf6xnq78/wqP9LTz6eAsAeXlW1q0pZd3aMtavLaO+3vuKOpkWLi5N03nsiRa++e2tDAzmzoDsLbJzx1vXsGpDJZIkkR89Ts3IcxOC2s7n+jnyqxP4jwdzrmOMe1kDBVeuo/Cq9RReuQ5HbcWUwzTVVIrIiU6Ch0+MXloJHjpB8FALp3eXpUJpTj7WzcnHunGU22i8s4a62ypw0Ydr6GHqfE/R51pPt/tSbv+bNdx413J2bjrJ848cw9c/OXOsrp/KslxS7OA1r17CzTfWU1/nXZTBylT8/jiHDg9y8PAAhw8PcrzFd9Ys2IuZ1W6kuNJNcYWb2iWFLF1fhn10JI0xE6EgtBstCZJiRTY4xwNbLQOJkTjJYGrKEQiSLGEuyMNSlI9ieXmMzlHMJuxVZVhLCon3D5EYPFV+S03rRHpjGK0KtiIrikXBqMUwpBLZ+bh2G8U2N5FAkpA/Ph4cj0mlVDq7AjidZoqL7JhMr7xQ75X3ii8CSZKqgA8DdwBVQBI4AfwO+I6u67E53NZNwFuAq4BSIAMMAPuBp4Gf67o++RdHEBYZXdfHAzFdzwZUInA5u3RaZfeeXp5/oZ0XNnfQ3ROa9ToUg0x+sYP8YgcFpQ7yi53klzgoKHHg8towmZVzOlHNpFWCI3GCvhgBX+y06ziB4RjD/eGcQ7Km4vfHefrZNp5+tg0At8vMNVfXcP21dWy4vBKLRfwECvNj34F+/uu/N3PwUO4Mw2argZvuXs5VtzdjNCm44p00+J7AlTw1D7d32xD7f3iUQGvu76jJ66H2ba+j5MaNFFyxFrPXM+P9U0wm3MsacC9rmPD3eP8Q3X9+ks4/PsHgph3o6qletEhPjD3fPsyBHx+j5tYKGu+swV0N1YEXqQhuZcCxkm7PRq68rYmNtzRweFcvL/z1GK0HB3PuQ/9AhB/8aCc/+NFOqqs83HB9HTdeX8fSJYWLKtCNxdIcOTbEoUMDHDo8yKHDg9MmCVtsZEVCUWQMRhmDUSGv0E5ZTR7FFS6KK92UVLpxeiZm7bWlhsn376QgegxXsgcJaLVeR1rJlopKRVQSvvi0dWllkxFLkRdzQR6y4eV5rJZNRuxVZZgLvcQ6eklHToUD6bhKsCOC2W3EVmhFUjQsmSBGNUbC4MaZZ8HmNBH0xYlFkpPWPTb0PN9rpaDAhryA5iPPt5fnp2UBkSTpDuCXwOmTTGzApaOX90iSdLuu623nuZ084CfA63I87AIagbuBrcDe89mWIFwI8Xiajs4A7e0BTnb46eoKMjwcxR9IMOKPEwjEJ83HsdmM5Hms5OVZyMuzUlXpob7OS32dl7raPOz2V+YwnUQiw+atHTz9TBsvbu4gEs1deD4XSZYorXJTs6SQmuYCqpsKyCuwIY82JEh6Bks6iDXjx5LuxRSLIEczyFoaRc8g6xlkPY2sZW/rkkJGNqPKptHr0duSmYxiJc/tJlHgIa0UTdoXXdcJDMfo6wzQ3xmkryNAf2eAgZ5Qzvl+ZwqGkjz8yDEefuQYFouBKzdWcf11dVx9ZbWYryvMid6+MN/89lYef/JEzsclWeLyG+u49d6VOD1WTJkQ9QNPUxQ5OF7GZ+hgkL3fO4Tv0ORkTQDeS1bQ9MG/oere2zFY57aurLWkkMb3v5nG97+ZxPAIPQ89Q+cfHmfgqa1o6WzDUiaucuLBDk482EHx+gKa76mlbEMRZeG9lIb34rM10uXZyIrLqllxWQWDPSG2PtHCzk0ncw5ZBujoDPCTB3bzkwd2U1bq5Ibrsw1QK5YV4XItnNq5qZRKy4lhDh0e4tDhQQ4fGeRku39ORrxAtgfU4bZgc5gxWRTMFiMms5I93kqMf0bUjEYmo6GmNTIZlUxaI51SiYQSxCMpkonMjI6JM6GpOpqaTSgFaSLBBJIEngIbDpeZfEcKR7wXW2oYe2oIT6ITW9o3YR06CumEnK3fqmWIRKfuYzE6bJiL8jHluRZVI8f5MFgtOJtrSY0EiXX3o6VPTRlIBtOkIxlsRRZMLhOKnsaeHial2EgpTrzFdhxuM/7hKOnkmaWDdIZ9MQLBBIWFdjzuV0bpIBHcziNJklaT7Z21ARHgi8CzgBW4D/hboBl4RJKkS8+1R1WSJDfwJLB+9E+PAL8h2zusANVkA+l7zvnFCMI8SmdU2tr8HDg4wKHD2dbvtpOzP2GIxbJlZ3p6x3o6OiY83lDvzQ5RXVfGujVl5Ofb5ugVLDzxRJrNmzt58pkTvLi5g3h8ZhktjWaF2uZCapYUULOkkKqGfCw2I4qWxJXowZnci83nw5L2Y0kHMKvhGdfWnI2MZCRh9JAweMavo6YCTN5S8grLWba+/NSyaZXBnhAdx320HR6k9fAgoWlqhkI24B/r1TUYZC5ZX87NN9Zzy00Nr9hGEOHchUIJHvjFXn71m31Tzp9sXFnMa96+lrKaPCQ9Q6V/M1X+FzCMDtEMtMfZ8639DOwanvRc2Wyi+r47aPzAmym4bNW8vpYxlgIv9e+6h/p33UNieITWH/6Blm//klj3qSHTA7uGGdg1jLvOyZJ766i+oYyCWAsFsRZC5jK63ZcjlS3lde9cz21vXs3eFzvY8sQJultHptxub1+YX/xqH7/41T4Aaqo9rFhezMoVxaxYXkxDgxfjBcgKG44ksyVu2vwcOz7M4SODHG8ZJn0e0ycUg0xRuYviChcFJdkRL/nFDjyFdpxuC8ZzyCg9Mhhh25Mn2P50G9HQ5B68uabr0HHcR8dxH4/9ej/F7jRXL41x1dIol9THsRizv9saVnwnNU784SBdT56g9KtLsDdZMOX42ZUkCVO+G0thPga7dd5fw0IkSRLmfA9Gj5N47yCJgVMNBJqqE+mLYwylsRdbkY0yJjWGUU2QNLjAYqW43D1aOig2qWEjk9Ho6wvj88UoLLC/7MvtSWeO1RbmjiRJzwLXkR0afI2u61vPePwfgK+M3v20ruufPcft/Ax46+h23qLr+m+nWE4CFF3XZ5e3/ezbrwC6AI4fP05jY+Ncrl5YBOLxOE888QQAt9xyC1br1D9Ouq7T2xfm4MEBDh4e5NChAY4eG55xXby5tKS5kOuuqeG6a2ppbMyf94P9bN6ncxGJpNiyrZOnn2nlhc0dMy7R4M63seySMpatL6dhRTFGk4I15cOV7Mad6MaV6MaeGpqQ0XKMmtaID8aJDmQvyWAKLaOjZzS0jIaW0cevdVXDYDVgchoxuUyYnEbMTiMmlxGT04Qlz4TBOn2bqw6kFCdhcwkRc+nodQlJw6nBMbquMzIYpe3wYPZyZCjn/L9crFYDt97cyJ2vXcrK0TIsC9V8f54Wuwvx/sTjaX792/088Iu9hMO5A4uCUieveftalq0vG59XW+97Els6G+ClYxp7vn2Qtke7JpX6kE1Gmj/yNpZ+8j1YCrxzvv+zpaXTdP35SY59/WcMb9kz6XFbkYWme2qpf3UVxtHvclJx0OdaS69rHSmDC4CuEz52v9DO/m3dBH2zm5llMimUljgpKXFQUuygpMQ5fl2QbxutoT1aR9tsIJ1O8uSTTwLZz4HJZCaVVgkFk/gDcfz+OCP+OH19YXp6Q/T0hOjqDk45T3qmjGaFilovlY1eKuu9lFbnUVjqzJa/OZOuYVIjmDOR8XqnipZC0VMoWgpZVwEtewwenYfT3qPxq4fivLR/6qzCF5rZqLOhMs4lXTuQtuyb8Hku//onsTdVYyvIp5hsXVbFbMRcmI+5wPOyHXp8rjLxBLHOXtLhid8PSQJrvhmL1zLela9KJhIGN5psQFN1Qv44kWkqG5jNBooK7Tgcpln9xrW0tJDJZDAYDGc9129paaGpqWnsbqWu690z3tB5EsHtPJEk6VJgx+jd7+u6/nc5lpGBg8BSwA8U67o+8wll2XVcBbwwevczuq7ff+57fW5eTsGtruvEYml8vlg2Lf9IjFAoicViwGE3YT/9YjPidJpF7T5yn0Tquk4gmKCzK0jnaFmX48eHOXh4kEBg/svJzFZZqZObbqzn1psbWdJcMC9BzVyfbOu6TkdngBc3d/DC5g727O2bcXbjygYvy9aXs+yScspqPBi1OPmxE+RHW/DE2zFpE39Q474EvsPZOpqR3ijRgTixwTiJkeR5lSI5kyXfjKvCjqPCjrPCjrPSjrPCgbPchpzrpHBUSrERsFTjt9YSsNYQN+VPeHxkMMKhl3o4sL2bk0eH0GcwKqC+zsudr13KHbc343EvnKGRY0RwO735fH9SKZU/PXiIH/54FyP+3KMErHYjN79hBVfc2ojBmG0wavA9QX7s1JDlk0/0sufbB3NmP66851bWfOkTOOur5my/55Jv5wGOfePndP7mr+NDlscYHQYaX1dD4+urseZnvzsaMsP2ZnrclxK0VgPZY1jXiRH2b+vkwLZufANznxJEliWU0Z9pTSNbVmcetlFS5aaqMZ/K+nwqG7wUV7on5YEwZiI4UgPYU0NY0yNY0yPY0iOYM6GcjYe59I4Y+N6TXh7d40TXp/+dcuVZqVtaiLfYgctrxZ2XrS8ryxKSlB0mL8kSsnTqfjyaJuSPj1/C/jhBf5ywP4F/KDo6PPns6v2tbOh9idJodt756cFtpTsPc1E+RpeoWT4dXddJ+vzEuvrR1Yk/tIpZxl5iw2A5dQ6aUuykFCe6JJFOqQSGYyTjU4cVVquRwkI7jhmOVhLB7SucJEmfB/559O4GXde3T7HcP5Edrgxwi67rT85yO78B7iU77Ln0YiSLWqzB7cBAhL37+9i3v5+jx4YZHo4y7IvNqiC5JEFVpYempnyaGgtobiyguamAggLby/KA7ffHOX7CRyAQJxRKEgxla6uNjERpbe0mHteQDTYi4RTBUOK8hm9B9v31FNopKnNRVO4kr8COw23B4TZjd5oxmE4lLtJ1nVg4RSSUIBJMEvTFGOgO0t8VxNcfmdUQ56pKN7fe3MitNzdQVzd3vSVzcbKdSqns2t2TDWi3dNDdPbOEUJIsUbeskFUbqlhxWTlurw1baoj86HHyYy24E93jJ1dqSmXkeBDf4QC+IwF8h/3EBi9ug4QkS7iq7OQvyyN/qYf85V7c1XYkOff3LGFw4bfWELDW4rfWjPcYAUSCCQ7vzAa6x/f3o56lQcBolLnhujruuWs569aWLZjvtghupzcf7086o/LXR4/z/R++RP8UowFkWWLjrQ3c8saV2J1mFC1Jtf8FKgLbkcl+1sJ9KbZ/YSfDByfPq/VesoJ1//0piq665Lz390KI9Qxw9L9/yonv/YZMdGKjmGyQqLy2lMbX11CwPG/87xFTEQPOlQzal5E0eoDRUT3tAQ5s66LlwAA9J0fOO4P6fCksc1JZ76WyIZ/Kei9ltXmYzBN7Hg1qDFeiB1eyB2eiF0eqH7M6fW+wpkqkEzLpBGQSGpm4SjqmoqkQS8n8+mABfz3hJaNNfQyqX17E6iuqaFhRTGFZ7pI5kq4i6RqgI+nZHmEJDXQdVTajycac686kVVoPDXJkVy+HdvXgn0HvdnWwkw29O7jqf/4Wx9I6rF4PTc3NZ32eADU1NXR0dPDUE09waXUj6dDkY47ZbcJWZEUabUfRkUkaXKQVK+iQiKcJjsRJTzM6zmYzkpdnxenINnxMRQS3r3CSJD0PXA1EAc9UQ4ElSdoIbBm9+1ld1z89i22YgCBgAX6v6/obR/9uAMrJDgjp13V95tljzsFiCG51XaflhI+9+/rZt7+Pvfv76ZvHbIZ5eVaWNBdw+aUVXLGxalGWOUhnVFpafOw/OMCBg/0cODgw40DqXBWUOKhsyM+2fjfkU1bjmXTCMEbWUih6+rQfaVDlbFKiSa8lpdJz0p+dj3lokPajQyRn2IjRUO/llpsbuOWmRqoq3Wd/wjRmO3y7rz9Ca6uP1rYRTrSO0No2wsl2P6kZtpxLskTD8iJWbqhk5eUVON0W3IlOCqNHyY8ex5oJjC8baAvRu22Qvm2D+I4E0DJn/20welzYK0uwVZZiG7025bmQFCVbgkSSRnsGsrcBUiNBEgPDxPuHSfQPkRjwZYvaD/rQtdmdyBosCnnNbvKXeihcVUjR2nyMlsnfMx2ImooYsi9lyLGUmKlw/LFEPM3RPX3seaGdI7t6z9oIsnRJIW/7mzXceEM9hml6ki8EEdxOby7fn6HhKH9+8DB/fPAwQ9OUdFlzRRW33LuSovJsY0pxeD91vqcxq9mTUlWVOPijoxz9XSv6GT2ItooSVn/xY9S8+TVTlvBZyJIjAY5/+5cc//rPSPoCkx73NrtpvKuGquvLUIzZ16cDYXMZQ46lEwJdyAZSfR0BOlp8dLb46Dw+zPAMpxfMFVmRKCx1UlzppqIuO7y4ot6L9YyeLklXsScHcCV7xgNaa3okZz6CTEoi0p8m2BbAf3SIUGeESF+M+FCCdDT371Kru4anam4gbHbmfNxiM3LJtbVsuKWBktHfKaMaxRPvwB3vwJoJYM6EMGfCGLT4tHkSdLLDXFMGJynFTnL0OqU4iRu9+K01qIoFXdcZ6A5xZFcPh3f10n5seNoRMf/6T6uoq3Vjt1sW5HniQjQW3D777LNce+21JIf9PPHnh3hx5w5WNjVz+3U3AtnPqa3EhsmRPV/q6e3nR7/4A9v3HOLY0WP4fMPEolHc7jyWLl3JnXfey6tum5x/1qDIeDwWPHlWTMbJoxJFcPsKJ0nSEFAA7NN1fc00y+UBY5kVxgPUGW7j9KHPHwN+TbYX+A2AffTvCbJJrD6n6/qWSSuZAws1uNV1nSNHh3jy6VaefOrERU3NX1RoZ8PllVyxsYrLL63AvQCHOOq6zvEWHy+82M6WbV0cOTo4r4Xl7S4zVQ35VDbmU9WQbQG3n5Gx1pQJY08NYk8NYUsNYU37MasRTJkwip7K+QOtI5GRLSQNDuLGfGLGAqKmAiLmkvGgRs1onDw6xKGXujm4owf/DOsPLltayK03N3LzTfWUFOc+yRijqhojI3H6ByIMDkboH4jQ2xtg/4E2ojEVr9eLwWBAlgBJQpal8dvBYIK2thGisyh7M0YxyNQvK2LVxkpWXFaBw2XOBrSRwxRGj546yU6pDOz20bttkN5tg8QGpk7AZMpzk79hNQWXr8Z7yQoctRXYKkswOh2z3r+paKpKvHeQwIHjBA8cw7//GIH9xwkdbUPPzKwhQpIlvEs9lF1eROUN1TjLjORqU4oaCxl0LGXIsWxCoBscibFz00l2PN121uGRJSUO3nzfal7/2qUXLQGVCG6nd77vj67r7N7bx+/+cIBnnz1JRp268WXpujJe9aZVlNdmeycdiV4ahx/HnTx1Pte/L8KOz28nNnTGKAhJovnDb2XV5/4eo8POYpeJxmj98R858tUfE+vsnfS42WOi/tVV1L6qAmf5qdd7eqDrt9YRMRUx3h01KhpKMtgTIuCL4h+KERiO4R+OEhiOERiOTpmNeSqSLGFzmHC4zHgKbHiLHHiL7HiLHBRVuCgsdWLIcZJvTgcmBLKOZD9Kjj6MTAr8JyIM7OzDfzyI/0Ro2mPtmaIGG89WX8PR/Nw9nWaLgWtft5RrXt2MxWrElAlTGtpLYfQw9tTgvCT705AJWirx2RsZsTWMH0OjoSSbH2/hxUeOEYtM7lP58Aeaqap0UlDgoKmpSZRim4Ebb7yRnp4efvazn3HZZZcB8O//+m/8x+c/x313vJZvfebzE5Y32g04Su088sRTvPbedwGQ7/VSXFqKLMt0dXYSDAZH130b//W1H2A05u6ldzrM5OVZsNtPzcsVwe0rmCRJFmDs6PWIruuvPsvyEbLB6DZd1zfOYjtvB346evefyQa4BVMsrgEf13X9f2a6/tO2U3GWRUqAlwAOHDhAfX39bDcxZ7I9tCM8s6mdZ549SU/vuQe0ZqsBp8eKzWEinVJJxtMk4mkSsfR5pdiXZYmlSwq4cmMlV15RSX1d3kXr1U0mM+za3cfmrV1s2drF4NCclVweN1YTtaDUOWEol7fIPuF1S7qKI9mPK3EqiZFFnb6nWE1raCmNTFJFS6koFgNGq4Jizj0POiVbCVkqCVirGLHVEzMVjQ+F27elk72bOxiZYRKRuloPJcUOiorsFBTYiEbTDA1FGRyMMjAUZXg4Ni9zu3Jx5VlZsq6UpevKaFxVgsViyBnQpsJpul/sp/vFfgZ2+1Bz1RiUZTyrmsm7bCXeS1eSd9kqHA1VF+0zqiZTRI63EzxwnJHt+xh+YRfhozOrnGZyGSm5pJDq25soXuXCYJz8escC3QHnKhLGbGCiaTqthwbY/lQrB7Z3Tzts2W438rrXNPOGu5ZRVHRhA5NEIsHzzz8PwDXXXIPFsvAazS6mc31/+vsjbN7axYMPHaXtZGDaZeuWFXLbm1dTuyR7gm/KhKgd2URJeP/4MP9kTGbb53bQt3VyzVtHUw3rvvcZ8jesmfkLWyS0dJqePz9F67d+iX/nwZzL5DW5qLq+jKrryrCXTGx8yEhGwpZyQuYKgpZyQpYKMsr0Ge41TSeTVkknVdKpDOmUOn4BMBhkFKOCwShjtZmwOkzTDsOEU42szmTfeDBrmmJ4cXQ4w9CeQYYO+Bg+6CfUET7nnAStnloeq72JuHHya1YMMlfc2siNdy3D4bbgibVRHtpFfvT4+ND38fdE1YgPJ4kPJ4gNJbJJ/9Iauqqjqdnr7G0dXdMxWA1YPCbMoxeLx4zZY8LkyB0ExQ0efLYGfPYm/NY6EokM2544wXMPHyV8Wn6NseA2L8+O2VKI12ulIN921vdfmOj+++/ns5/9LG954718/R//bVJvuSRBR6CH3UcPcPMNV1NZUYaORFqxE9Mt/OLnP+Oj/++DqKrKP3zyM7zjHe+fdntGo4LTmc0z09fXSSaTQVEUqqqmzwXQ2trKypUrx+6K4HaxkySpEBirWv5bXdfvO8vyA0ARcFDX9ZXTLXvG8z4KfG30bhIwA38BPkM2UZWbbG3bL5GtdasDd+i6/uiMX0x2OzP+kPzwhz+koGCq+Hr+RCIqew/E2Lsvhm9k5nNmLTYj1U0F1C4poKgiW4g8e7FinqJVUdd1MmmNRDyNfyhKX3uAnnY/ve1++toDMx7uOsbtUmhqtNDUYKGm2ozBML8H+pGRDCfaErS2JWlrT5KZwfDT0xnNCnkFdmxOEzaHGZvDNH6xnnHf5sy2hp+ZVAOy85GyQWwX7kQ3zmRvzpZvgMDJMEP7RwicCGaz8vZnkxmpydxnDZIiYXaZcJTbcJTZcJbbcdc5yV/qGU9uAqf/KDfjt9agI9F1YoS9mzvYu6XzrOVkLhZJgqrGfJasLWPp+jLKa/NQ9Ax58ZPjc2jHAtpkMEXP5gE6N/UxuGc493BjqxllbTOGy5ahrGtGci3s3iPNH0Y72Ip6sBX1QCt67+TSKWeSZInidflU391MxXoPZzZW64DfWkeP+xJ8tsbxHqNoOMmuTSd58dHj0zZ8yDKsWGblyg1OiopynwQKC1M8rtHekT0etp1MMOI/+4iV6qYCbnnjCppWlyBJEoqaoCqwmYrgjvHjmKbBkb/0cfg7eycfq2QZ413XYXzjjUiml//nRT3WQfrhF1G3HMi+MTnkL/VQdX0pldeXYSuY3BChA3FjPlFTAUmDm8Rpl6TRTVo59+OWQY1jzoSwpv1YMn6saT+21DCO1CBGLffvQCap4Tsewrd/mOFDfnyHAySD5zgLzGZB8rqQPA5Ut5tNchO7kiU5F21YUcw977uUglIn7ngHtSPP4kl0jT+uZTSGD/oZ3OdjcO8IvsN+1NT5z12WjTL2EivF6wsovaSQonX54xmxx0SNBXR6rmDAuZJ0WmfHM208++BhAsOxScEtgKJIuF0KFsviG4Z/sXzpS1/iy1/+Mm9605v49te/gT7oR49PztSumGXsZQ4MplPnlNn5uA4+9Pf/yI/+9/tcctllPPzXZ4mEEqRmcO6aTAwRDkcJBuN0dg1SVmqcsnFieHiY97znPWN3RXC72EmSVAl0jt79ua7rbzvL8p1AJdCq63rDLLbzr8B/nPanh4E7dX1iO+FoRuXngLHszKv0WfzHL9TgVtN02k4m2b03yrGWxFS/lxPkFdqpXVpITXMBtUsKKa50T9tqqKgJTGoUTVJQZROqZEKXpx5Ko2k6IwMRuk/6OXGgn6N7+ggMz7w31GiUqKk2UVluprLCRFmpEZPp/A76yaTGyfYkJ9qyJ27+wOyGGheWOaluKqCqMZ/qpgJKqiZngDwboxrFlhrGlhrGlezBnejGmvblHDKlZnRGjgUZ2jvM0EE/vkN+UuHZD8+diq3YSv4yD8Vr8ym9rAh7cba3ICVbGbY3M+RYht9ai6ZLnDw6xN7NHezf2nVB6gfmYrEZKa3yUFLlpqTSTUmVh9IqNzanGVMmRH60hYLYcTzx9vGT6mQwRfcL/XQ918fAHt+kuX0AUoEH5bJlKJcuQ1lRh2RcvEPENF8Qdf8J1B2HUXcfheT0nxfFJFN6XQW1dzdT0mBGkSe+PwnFSZ9rLX2uteOJqDRV48CObp576CidLb5cqx3X1Gjhyg0OqirN0y4nzD9d18lkIBpTiUQ0olGVSFQjEs3e7+tP0duXnlEpFaNJYd3VNWy8tYGK0URzkp6hPPgS1f7NE4KgwR6Nl/7lRcIdk0cPybVlmD70BpS68kmPvdxpQwEyj20l/fg2iEzReCiBq9pBwfK87GVVPq7ys9ckVyUjadmCJhvRJCOqZECTjaiSEU3KHt9kPTN6UTFoCYxqDKMam9TbmUt4IMHwPh++QyMMHwoQPBmeUdb1cbKEVFaIXFOKXFWCXJaPVFKAXJqPNFrbdXg4zR8e9DMwOPkYZnWYeM3b1nLp9bU4UoPUjTyNN9Y6/jsa7IjQ9tcu2p/oJhmY11Qr2ZdjkChYnkfJpYWUXFqIt+lUToq4wU2XZyN9zrVkNJndL7RTYQpTWmSbENyOsVhk3C4FRRG9uKdbtWoVXV1dPPzww1x11VXk5eVNuWxlaRl7Hnp80t/NeWasBRZOn8b/je89wN9/8t9Ytnw5W3fvBbK5SaLBJNFIcsrPdTIxhN8fpbMrzDe+cwyzWaKywoTLqWC3Kzjs8vh1Munnk/8wXihGBLeL3QXsuf0E8J+n/WmJruvHplj298A9o3dX6bp+YBbbWVDDkgcHozzyWAsPP3KcgYGzDyHNL3aw+opKVm+soqx28hBgcyaIO96JPTWIORPGrIYxZ8KYMiEMOSozaUjZH07ZRNLgImoqImIqImouJmIqmjBsStd1hnrDHNvbx9E9fbQdHpxxGn0ARZZoaPCyYnkRK5YXUlRox2434XSYsNuN2GxGFEVG13WCwSTdPaHRS5junhBdXSFaTvhmNTw2v9jBskvKaFpdSnVjPjbn5BN0RU1g1OIY1DhGLY5RjWPQ4hjVBAYtNnodx6jGsKVHpmz5BkgndHyHAvTv7Gf4oJ+RY0G0C5gh01XloPTyQiquKaFwRfaENSXbGLY3M+hYRsBag6rBiQMD7NvSyf5tXSRmMRdWMci4vVbc+TY8+TY8BTZceVYkWULXdHRdR9fJ3iZ7bTDKFFdkg1mX1zr+mbWkR3AkB3Am+/HGTuBI9Y+f2IwlhOrdOojvcCDnj5OtuoyKN7yK8rtuwb26edElOZuJTCzO4JNb6X3oafr/+hzp4PRzZ412A81vXU/da6qx2SYeTzQkfLYmet3r8duyxzVd12k/NsxzDx3l0Evd0wZFq1YU8ZY3r2Ljhop5GXp3MYYl67pOOJyibyBCX2+YYChJNJoiFksTH235l0dLiygGCZfTjMtlxu0y4/Vayc+3YbebsFoMZDIakUiKcCRFOJwkEkmduh9JEYlk/xYOZ9efTKmkprhkVA1N09E1UDUNXWdWGdKnU1Di4IpbG7nk+jpsDtPYG0Fx5AA1I5uwZoLjy6qSnQM/aeXoA7smrUcyGljyqffR9PF3IE8xz+2VIhOL0//Ic3T//jEGntiMlpr+mGp2m8hf5qFgRR7uOjfOcifWQhPGeejxS0U0In0x/MdGCLSGCLSFCbSGpkz2NBV7fSXey1Zlp3dcsgLX8gYU69Tf0U3Pt/O5L75APD55Oysuq+Du916C26VQO7KJ8uAO5NFh773bBznyyxMMHZiceXuMuSgf96pm3KuacDRUo9gsyCYjssmUzTiv6ySTSQ4dOIgeilBl85DqGyLeM0Csq4949wBaYvoGXovXTMNrqmh8fQ1md/Z7klQcdLsvp9e9HlPAhx5PYbWYMJsLJz1fliUKCmzkeSwvy9+mc1FXV0dHRwdPP/001113Hddccw2dnZ10dXVRVFQ0Yd5rcVERP/7CV8lEJneoSBLYK9yYbBKgc987PsDv/vQX3v6W+/ju//4vadkynvRR105VnjjzfPXM4HY6qZSfQ3v/deyuCG4Xuws45/Z9wPdG757Udb1ummXfA/xg9O67dV3/8Uy3M4P9mPeEUpmMxuYtHfzp/w6zeUvnWU9a8grtrL6ikjVXVFN+xpxWS3oET7wDT6ITd7wTSyYwZ0kXdCClOIiYSwhYqxmx1hM1F48/nk6ptB0e5PCuHg7v7J1xIqPp2G1GJEkiEj23llpJlqhpLmDZ+jKWXVJOUblrwvulqInTEmd040r0Thusnk0yIjF0YIS+rd0MHRgh1BFhhuX9cDRU42ysxtlQjaO+EktxAUaXA5PbgcFhR0unUeNJ1HiCdDBMtLOPaEcv0fZugodbibR2Trt+a4GZiqtLqby2hMKVXiRZIqXYGLYvYdC+jAGpnD2bO9n5zElGhiJEw6kJ8zFlWcLhtpBXaKO40kN1Uz61zQUUlDqRz+zt1nXGXvjp9Q11XUPR0tjT2SFxjtQA9uQA9tTghMYWNa0xsHs4m+F46yDRKZKUWIoLqHrjbVS/6Q4KNqx5RZ00qKkUA89so+tPT9L9pydyZnA9XcGaYtZ9/GryKlNI+sQf9bCphM68KxiyLxs/CRjqC/PCX47x0rNt0zZaNdR7edtb1nLLTQ1zWhd7PhJKBYNxDhwc5NCRQVrbfPT1RfCNxAiHkyQSmQs2j/xiszlNNK0q4dLr62hcVTLeOCFrKUrC+ykP7sCePtWDr8tGhk46eeGDvyMdnRwE5K1ZyoYHvkTeqiUX7DUsFqlAiK4/P0nHb/7KwNNb0dWZNwAb7QZsxVbsJVbsxTZMTiMGi4JillHMCgarEYPNiGyWQQc9raEmMmSSGVLhNEl/ioQ/STKYItofJ9IbJROffSJFo8tB/uWrKdiwmvwNa8i/bCWWgpmVkdM0ne/+7w5+9JPJDSIGk8Jr376Wjbc0kB9roWn4MSyjjSn9O4c48NMWfIdyB7UFV6yl8u5bqbzzJhx1lWfdj+mOJ7qukxz2EzzUQs/Dz9L15yeJnswdqxgsCnV3VLLkjXXYirLrSMsWjjruQDO6MRuNmM2FE6dw6TqKnv0Ns1oMlJQ4MeZI5LUoGG1I0tw0upyeLfm6664D4DOf+Qz3338/b3/72/npT386YXld10kO+oj1DExq4I4nEnQP9/OLv/6Jb//gJ5SWFLHlqQeprqpARyal2EkrNvRTNYVIJTPEo2mS8TSpZEYEt690Fyhb8u3AI6N3X9B1/Zpplr0VeGz07qd0Xf/STLczg/2Yt+C2tzfEgw8d4f/+cnTa8guQHTK2+soqLr+xnprmggkn8c5EL0WRgxRGjpw1SZGOjCbZSMcVYiMZ4kPx7F8VkBSQRy+SDCa7hM0LimHqnsak4sBvrcNvq2XEWkfakM0wq+s6/V1BDu/s4ciuXjqOD89oaNxccHosNK0qoXltKUvWlE7onVXUBN54G3mxVtyJbmzp4VkH/5pmQM0opKIaoc4Iw3v78B0cINAWJjEy8+G9zsYaim+4nJIbN1J03eVYCs+v5mxyJMDIzoP4duxn4NntDL24a8oeA0uemYqri6m4ppSh/DIe2uXm8X1OYsnZ/2iZjFCUB4VulSJnCq8tSSIlEY7LhBMK4bhMJJG9hOMKaVXCYtSwmTWsJh2rScMsqRhSSZRkElMkgrOnh4LgAIWxYUzaxNdgdDupvPsWat70aoquuwzZsHiHHM8VNZmi+/+eovUHv6f/qekTx1sKbKz/55spX29B1ib2/MaMXro8G+l3rkaXsidf0VCSFx89zuZHj+fMEjrG47Zw26uauPO1S2lsyD/v1zTb4FbXdXy+GEePD9PS4qO9I0Bvb5D+gSj+QJx4fGbDc1+ODCaFuiWFNK4qpmlVCaU1eRN6283pAOXBlygN78WoTcx2nKCGzZ96iqFtk0/2JEVh+b/8Hcv/5e9QTBcnq/Zikhj00fmHx+h7/EWGt+whOTx1b+TFIikKnpVN4xnk8y9fjau59pzKN4XDSf7l35/kxS2TG15Lqtz8zd9fQUWFlcahRymOHEACgu1hdn/zEAO7J0+PsFWU0PiBN1P79juxlRVPenw6sy1VFzx4nK4Hn6L7wafx7z40aRnZIFF9YzlL3lSPu9pBq/U6UuYiFKuHspoGouFsPXpN01G0GI1D35jV/i5U0hWfRTLNTSWB2Qa3Y9REkmh7D+lIjJV33Ejf4OD4Y4qi8O43v5l/+7ePUV4y+XcoLVtJK3bUM2oda6pOz8lWAsE4HZ0hvvq1yf/npxPB7cvQBapzWw20j97douv6ldMse3og/A+6rn91ptuZwX6MB7e/+NUmLlm/DKfDjMNhwm43zWp+pq7rdHYF2be/n8eeaGH7jq6znmyV1XjYcHMDa6+qnlB7zpryURw5SFHkILb0SM7n6rKZeMzF8JEww3v7GdrdTuBwz6yzG1q8Ztw1Dty1TvKa8slrzsNVYUY+o+FRByKmEnz2Rny2RsKWU3OuIsEELQf66Tjmo/3YML3t/jkbVqcYZGqXFNK8poTmNaWUVnsmBP/WlI/82HHyoy24E11Tzj9KJYyEetJE+xPEfTHigxFivQFigxFSoRTJYIp0JDO7eUinsVWUUHzjRopv2EDx9Zdjryw9p/XMVCYaY2DTDnoffZ7uPz9JvHdwwuM9jlJeqLiCbtfZRuZfXJ5EgKLkCA3VbtbetJKr33IdRaWei71bC1bkZBetP/4jbT/+46T/89NJisyqv7+JhtdWYtT6JzyWVJx0eS6n17UeTR4dghdPs+OZNp57+OhZ59ovW1rIna9dyq03N+LMMfR/Kum0SjicJBxJ0dcX4LHHNhMMa1RU1KBpEqlUhmgsTTCYGB/qGwwliEbTM66P/HKlGOQJSQOdbgsur5XapYXUNhdgzFFT2x3voCK4g/zosfFhoGM0WwVH/jDIgf/8c87tuZc3svGBL+Fdv2JeXs/Lna7rhI+fZGjLHoY372Zo825CM8yUPlckRcHVXIt7ZRP5l6wg//LVeNcvx2A7/1ES3T1BPvzRR2jvCEx67JLra7n7PZeQr/ezdODPWDMBMgmVQz9r4djv2yYlBiy8aj3NH3kbFXfedM6NmeczEiTa2UvXn5/k+Ld+SeREx8QHJSi/opjCz/495BVjtLioriolaXChahJBX4xE0C+C2xzONbiF7PcnMeDjDW++j+GREcLRCB29PURjMcqKivn4u9/HB//+7zDZJdAmN/KrkpG0Yicjm8d7c3tOtpFIpUki0ae5aT00yHBfmHAwQSSQmHAdDg6J4PblRpKkLwCfGr27Qdf17VMs909ka9MC3Krr+hOz3E4HUAUM6LqeO7VedrkPAWNHjjfruv7r2WznLPswHtwuX/M5TKaJE94NBhm320J1lZvGhgIqK1xUVLipKHfh9do43jLM/gP97D8wwMGDAwSCiVybmcBsMbD26mouv7GeinrveKBmUOMUh/dTEt4/YT7iqZ01kDGW4D+Zpv2vRzn5+x1o6dnNo5kpxaJQuNJL8bp8Si8vwVM7OZNjUrEzYmvAZ2tixFY3fpIMkExk6Drho+PYMO3Hh+nvDBKPpkhO07uiGGS8RXbyS5wUlDgoKMmW36lZUjgh+7OspfDEO8iLt5EfO5Ez+Nc0hcgQDO330ft8K8OHRkj65zZJhbkgLxvIjl6cDdUXbdisrmkMbdlN5+8fY8eD23naspSTnpqLsi/nS5Jg7Zoybrqxnhuvr6OwYGFnP75YtEyGvsde4MQPfkfvXzahT5OVruHNV7DiXcuwKH0T/p6WrXS7L6XHfRkZJXsymEmr7NncwaYHjzDQPf1IEbNZ4cbr61mzuoRIND0akCYJh7PzUIPBBCP+OKFwkng8gzpNrVVhapKcHd1jsWYzudvdZlx5VvIKbBRXuimt8lBU7sKkqHji7XhjbXjjrbkbRl01DLTa2fKBH+TsWZRkmaWffA8rP/MhFLPorZ1LSZ+f4OFWoh09o1NORq9H72vJ2f9GGRw2zIVe7FVlOOorcdRV4qivwr20HteSunn5P9x/oJ+P/sOj+P0Tp5PIisRr37GOK2+tpyawmWr/88jo9O0YZOfXDk6afpJ/+WpWf/6jlNw44xltU5qLaQ5aJkPn7x/j0Oe/R/BQy4THKr7+SVyr67EX5dNQ4RnN3OskrdhIhQJ4D37hvF/DQrBQgtsxajJFrLOPVDCMqqr89pGH+JevfYVwNMLnPvpJPviud+OoKkQxZEDNff6tSkYysoX2rgHiqQxJJEL2InpO+hkZjBDyJ/D1h/ENRAmOxIiGkgRHBti385/HViGC25cDSZIuA8YC2u/ruv53OZYZy168FAgARbqeI4PR9Nv5GvDR0btX6rqec7ydJEnPAteN3p3TD9nZgtu5VNmQz4ab61lzRRVm66khE85EN2WhXRRFDucoJyORNpTSsyPM0R9vJ3Do5Lzt33TMbhNFa/MpWV9A2cZirPkTe2s0FIKWCoKWSkLWSoLmClRlcvIJTdNJJTIkYinisWzdXTWjkVdon7LsDrqOI9k3Oty4DXeiG5nJvTixgEzPi310PtHK8KHcSYnOh9HtpOiaS8aDWc+KpnMayjVfAsEE//ONLTz0l6NTLiPJEs2rS1i5oZL8Ygd5hXbcXivRUJKhvjDDfWEGe0L0dQbo7wxOqPN3MUgSrFldys031nPj9fUUFopAN5fwiQ6OfPVHtP30z9OeINfcdRlrPrQeC12cPlk8Ixnpc62jy7PhVIZlTefIrh6ee/gYbYen7iFeqCT5VIKR818ZuDwWTBYjikFGlrJ1thOxNMlEBvUCJpGbiqJARaFOU0mcppI4zWVJ1tbGsZr07AvIX47fV8Lez/6GgWdztlfjXb+cy77/WdFbexHomoYaT5CJJUav46jxJDF/gB0vZk+NLr3yCmwuB7LZhMnjwlzoxTBNkqf58OTTJ/j3+58mmZz4G2x3mXnbJ66iudnBsoE/4Y2fJJNQ2fu9I5z4v4m9oe4VTaz+wkcpf/X1c9YgPJdz+HVNo/uhZzj0+e8yMlrjuPzrn8TeVI2tMJ/6EhcmV7bRQJVMJHQF2y4R3J5pLoJbyPbipgNhop29aOkMf3z8r7zvX/8Rh93O0cefw2I2Y3TYsJZ6MZhUyEweeaSq0NITIBSM0tkb4yv/fQx1mlKSF3NYspiINU90Xd8hSdILZIcmv1uSpAd0Xd96xmIfJxvYAnz9zMBWkqR3AD8ZvXu/ruufybGp/wHeD1iAb0iSdK2u6xMmp0qS9BZOBbaPXMgP2Fyw2o2su6aGy2+sp6zmVOCsaEmKwwcoDe3GmRqY9DzdUoC/187B722l98m/zGqbkixjr63AtaQOZ0MViiX3sMF0KEK4tYvIiQ6i7T3T9vwkgym6NvXRtakPOICnwUXZhiLKNhSRv9SDLKvkJTrIS3RAIHvaHDUVErJUErRUZjMxy1YyigXZZsZiM+KZ9KI1LGk/lrQfayaAJe3Hlh7BHe/AlCMJlKZJ+NtStD92gp4X+4hNkZRoxiQJY54LS34e5oI8LEVe3CsayVuzlLy1y3DUViyoYHaMrus8+ngL//U/mye1pI9x59vYeHM9l1xfhyffhi01lH2vM12YQ2HSioV4rZdEYx5xYzXa6HyVcCDOYE+YkD9GcCROaCROLJLCbDVgsZmw2oxY7SYs9uy11WbEYFRIJTOkEhmSiex1NplDhmQ8zXBfmN6OAIPdobMOXdd12LO3jz17+/jPr73I6lWl3HFbE6++vRlzjmGYr1TOhmou+95nWfmZD3HsGz+n5Tu/Ih2cXMal/U87aP/TDqrvvoK1H74Ui34SdBWDnqYyuJ3y4E76nSvp8lxB3JTP8ksrWH5pBcN9YV56to2XNp1cMPWTFYOM0aRgMMrIskQmrU2YM3wuQa3ZYqCw3EXRGZeCEgeGaRLE6LpOOJCgs8VHd9sIA11BfAMRQv448Wh6QuK2+aKq0NEv0dFv40myWe8NBlhVb6BZCuF5/s/Y2o/nzEFgcNhY/fmP0vjBv0FWFmkinEVOkmUMdhsG+8SyQdZ4HCU8BGSTLM1F4rVz9bNf7OF/vnnmqSCUVrl516eupdIVYnn3D7FmgviOBtj2hb2Eu06d0hmcdlZ99sM0/b+3LOhcCpIsU3nnTVS87kb6n9zMoc9/79SDOkT64hiDKWxFVhRzCpuuk1r7cVKKnXg0RdAXJ1fnm81mpLTEgbKQv2PGs5etutAkScKU58LgshPvHeTWq68DIBKN0trZzvLGZtKRGOmWGAabBWtpPooZIuE4kYRMLCWTzkAyJRNNyESj+rSB7cW2cL8ZLw8fATYDVuCJ0aHKz47evw947+hyx4H/OpcN6LreKUnSvwNfAdYDOyRJ+grZHmE3cBcw1msc4lQv76KQV2hj2SXlNK4swZOfPWA4kv3ZXtrwQQz6GT0sspGUqYG2R3s49PU/5Tw5PZNsNlF8wwaKrlqfDWaba3E2VM96KJKaShHt6CVyooPQ8XZ82/cz+NyOKef0BU6ECJwIcfgXJzC7TZReVkjJJQUUrsrHXmJFAhypIRypIcpCuyc8V0Mmo1jIyNZsXT/JgCUTxJwJTVuvT9ch5oPebf10P9vO8MGZF3eXFAXPqmbyL1+Fo64Sc74Hc0Ee5nwPusPGlv17wG7l1ttedVFPHmaruyfIF778PNu2d+V83OmxcMPrl7HxlgbsRCiO7Kakcz+29NS1TnUkEgYPUVMhUVMRsap8UrV2UoqXlMFBRragaEkMWhKDlshe1BgGbQSDlkDWVVTJgCqbxstOqbIRVbKiyiYShiVospF0SmWgO0jvST+9HQF62wP0tI1MzEJ5+n7psHdfH3v39fG9/93Bm+5dxRvuXjGrOZ8vd9aSQtZ84WMs/6f3cuJ/f8vR//5pzu9wxx+30PHHLVS/4VrWfuRyLGoLaClkVMrCeykN72PIvpSOvCsJG4tJp1Xc+TbqlhbScmBgTusmS1K2l1WSJGRZQpIljEYFk0XBaDZgNhswW40YzQqyrhMNJxjsDROLZs4raDRbDVTUeals8FJZn09FvRdvkX3mPUmaijERwqyGsBChXI6xojGJoT6BrKVIKw4ShjJSBgcx7HR0q7S2hultDzLcH8kOfwtnp2po85TFOZOB3ccy7MYGRbfhKr6RmpFW6oZOUBPswKCrVN51C+u//i/YKqacGSS8wmmazte/uYWf/2rfpMea15Ty1o9dSbV6mKaev6LoGY78ppX9Pzw2oUZ59X13sPa//nHWiaIuJkmSKL3lKkpvuYqDW3eQjJ7qEUzHVEIdESx5Ziz5ZsyyhoEkkseD0eFmZCBKKjnxtyycgHh3mopyKzbbK3PI//n01MuKgr2ylPhpbQPqGZ0ysYTKSG+EhGxGX6Rh4uLc60VC1/U9kiTdC/wCcAG5xlwcB+7Qdf3sUdjU2/lPSZK8wD8Cy4Cf5lhsELhT1/WWHI/NmeJyJ5m0mXg0TWYOhpn5h2JsfrSFzY+2IElQWwaX1gRYXxfHWKVSmB39h27Ow9/n4cB3t9H31INnXa+lpJDyV19H+auvo/jGjRgd5z9UUzGZcDXW4Gqsoey2a7P7petET3Yz+PxL2csLuyYnWyDbq9v+ZA/tT/Zk9y/fTOGKPApWeilc4cXT4EI+rbi5jIZJjWFSp09ao+sSiRAM7h2me1MHA7uHSYVmNvLdWlpIwca14xkhveuXT2oRHxOPx5Haj89ovQtFOqPyy1/t4/s/3EkyOTkYNFsN3HjXcq68rYl8BqkZ/gP5sYm9NmpKJT6UIOFPYbApWAssmF0mJHSsGT/WjJ+C2Ny/LxoScWM+EXMx0bxiIsVFRK5tJGVwkU6pHN/Xx76tXRx6qZtkjpqJAL6RON/67nZ+8sBu7r5rOX9z32oxZPk0RpeDpZ94N00ffivtP/8/Dn7uu0TbeyYt1/H75+j4/XPU3HcTy9+5AoelE0lLEk3A/v0dPHfEx4vHnITPv+rXBJIEDo+FvDwLeV4juqqRyahkUirJpEo4lCbsT5Geo9Z1g0mhvMZDZX1+NphtyM+WuDpL/V6DlsKeHsaW6MOW9mHOhDBnQlgyIYxqZFKSpulsVIAm0JsgbvAQMZUQNRURN3pJKVYiaTP+kI7PrxKLqaRTGsmkRiyWYXgghn8oRtAXJRxIEoskzykzdEg3sT9vKfvzlmJVNK5dk8/y91+PtXzxBBzChZXOqHz2c8/yyKOTfws23NzA69+9jsbAM1QGt5GOpNny5X30vHhqNJq1tJDLf/T58fOKxcpckIfsdiBrOlKS8Rrv8ZEkqXAae4kVgw3sqWESBjdF5S6CI3HCgYkjXTIZjfaOACXFDvLyrBctT8fFMtZ5EI+f+wigB/+azS9rs9poamgkIykkTFYSBivqeRTHVAwydqcZl9eC2Wrl0N5zXtV5EcHtPNN1/WFJklaR7cW9A6gAUsAJ4PfAt3Rdnz5Cmdl2PiVJ0kNkhyhfDZQCCbLB80PAN3VdD06zijkx0BPGZJqfj5WuQ1sPtPV4+O1mDwAFbokaaxz7gf0U+bopiQ4wVR+UtbyYunfeReWdN5G3dtkFGR4rSVI2OUVdJXXvuAuAWHc/fY+/QO9jL9D/5JacvcsJX5Ku5/rpei6bodVgUXDVOjG7TZjdRswu0+htM9ZiBwabkWhfhHB7gOhAnGh/9pIYScw487OkKBRetZ6yO66l7PZrcS9reNn+aBw8NMB/fGETLSdy976uuKyCO9+9ngp7gBr/H8iPtSABmXiG3u1DdD/fR/+uUw0Fis2CmkyDqqJYFGyFFpyVdpwVDlyVdhxlNixeM5Y883hxe4BMUiUdSZOOZkhF0qQjaVKRDFpKRTbKyEYFxSSPXhRkk4zBomTrOjKMPT0MnErHn5KthCwVVDQvZdW6tSTUy84a6EZjaX72i738+rf7efXtzbztLWuprvLM5du9qCkmE/XvfgM1b30dbT/+Iwc/913iPZOnQbT/5in2/mkH7cUN9C1bw7GQk8w8JifWdQj7E4T9CTrnOIGsLEuUVLmpbMinsj4byJZUulEMUx8zZT2Trcuc6MGeGsKWyn4+jWp0zuqIj5EAWyaALROAWI758Y7Ry2n0ldka5Amjh6ghn6ihiM6gixf2Smx9ph1ff2Tyes4irso8tsvPY+/5E2XlLl5zezN33NZERbn7XF6W8DIUj6f5x39+PGepnzvespobXlPPssE/Uhg7hr81xOZP7yLSc+qUsPpNr+aSb/0bZq/nAu71/JEkCcVsxN1QRbSzj3Qo+71T0xqhrihWrxlrgRlrxk9atiF5XVhsBkYGopOS6fUPRIjHM5SWOpAX4HSn+VJfXw/Ajh07iEaj2O2TG6U/8YlPcP3113PTTTdhNp86K04kEvz85z/nIx/5CADveOd7SBRW5WzgnwlvsYOrX1VPY52JivwUxaYAjvQgjlQXvp52/vDjc1rteRMJpYTzdiETSs1EnhbFE/XhiY3gSQapW17B+jfdyJo33YjFtrCGX2qZDL7t++h97AX6Hn2ekV3T1w2ba5biAspuu4ay26+h5OYrMXlc57SeuUxEMZ8ikRTf/t52fveHAzl7bdxeK69/zyWsXeeh3vfUeF3BkeNBjv2+je4X+lGT2R9YR0M1Fa+7kfI7rqXwqvXoqoZ/7xF8O/aPXg4QbmmftA3ZIGGwGcjEVbRzHN0gyeAos+Ouc+KuceKpc+KudeIot4/38GvI+G11DNmXMmxvJq6aOLa3jy2PtXB8f//U65bglpsa+NAHN1JW6jyn/Xs5UxNJWr7/Gw5/4ftEh/z0OMpod1fTVtTEsOHcvj8Xk8VmpLTKQ2mNh7JqD6XV2etcZXHG6Tr2jB9nvBNXohtXshdbanBWvbDTkhQw2MBoHb22gWIBxQSyAWQjyEYk2UgmrRHvHSbhC5EaCZIOREgFI6ipNGoyTXAkAKk0ZklCTybQ06lsvXJDttGo+PJS9A1X8GxPPU8/H+Hgju7znt+7enUpb3rjSq6/rhajYQHPDXyFuFi/T5FIig9/7BH27puYYV2WJd74gcu54qp8Vvb9Fmeqn45netnxlX3jvy9Gj4vLvnc/1ffefkH2Feb/fWppaSGTyWAwGGhsbETXdVL+ELGuvglVKwwWBXupDcUko0kG4gYPGV3BNxAlGZ888sxsNlBR4cI8Tx0rF1OuhFLhcJi6ujqGh4fJy8ujubkZs9lMSUkJv/nNbwBYs2YN+/btw2g0Ul9fj8fjIRaL0dLSMt7je/sdr+fzn/8GplnU304mhvD7o/iCKQKagZtqTmLa/hzxDh+ZeIZMXCUTz6BmJAbiGe54aLwAjMiWLCwuMwlujWYFi9WIyWLAbMnO/zJbjQx0BfEPTT1er7wuD7PFQOeJETLnWaNRksDtseJ2mXG7LXhGL263BbfLjMNhxmYzYrcbsdlMOEav7bbstc1mPOsQvPOV9PkZ2ryboRd2MvjCLkZ2HULPzF2pInO+h6LrLqf4+uzFtbR+TnpnF0Nw++xzbXz5P19gMMfnTZLgilc1ctt9q6jL7KN25FmMWpK+l4Y4+ptWBnZne3hls4mqe26l4b33Unj1JWd971L+IL6dBwnsO0q0s4/wyS4GDregh6I4CryY89wYPU5MHhcmjxOj24nJ40QyGkn5g6T82RP21EiAlD9EciRIcthPOpC7vIxikvE2uym/uoTKa0qxF2f/H7KBbi1D9mUMOpbTeTLMMw8e5sD27imTBpnNCm998xre8bZ12GzGnMu8EnV1B9m6rYsXX2zjpZ09JNMz/w1VDPKUgZMkS5jMCiazAZPZgNGsYDQZMJmzCZ+ioRTBkRiRYOKchtM6PRa8RQ68RXYKy5yU1eRRWu2Z0RxZsxrFlejOBrPJXhzJPgyzS+x/iskJ5jyw5IHZg2T2jN/G5MwGs4ppTo9Lekbl6hWrUYf8RDt6CR8/SfBQK5GTXYRbu7DYVepur6Tk9Ws46b6UR3Y52fTo1Im/XHlWQlMknjtdfoGNN9y1gntevwyvd+ElmXmluBi/T8Fggg9+5GEOHxma8HejSeFtn7iK9SuMrOr9FRY1xIEfH+PQz0+ML5N/2Squ/O1/46i5sPXVL3RwO0bLqMR7B0gMniq7JUlgK7Ji9ozWEFdcpBR7zmHKkG0wKC9zvezyR+QKbgH27dvHpz/9abZu3YrP50NVVaqrq2lvbwfgiSee4JFHHmHLli10d3fj8/kwGo2UlpazfMUaXvfaN7Jh4zWz2hdJkkinh8lkEphSPuRffIcTD3UQ92mYC/NwVJfjbKrGs2oJzoYqBvQU626/eezpIrgVFpfTg9tP/ufDlNVUYXOasTvN2J0mbA7zlMPZMmmV7U+38dQfDk5ZMqW4QOFd764mZi3h2LEIrYcG6DoxQiJ2jidX58FiMWCzjwW8RpxOM0WFdooL7RQWOSguslNUaKeoyIE3z5q7LM8sZKIxhrfvY+iFXfj3HCbWO0i8d5BE/zC6OnWwLxuNWEoKsJYWYqsqpfCq9RRff/m8ld5ZyMHtwECEr/zXCzz7XO4SUKVVbu75u8tYUZWkaeivOFP9DO7zse9/j+I7HACyc56a//7t1L/7Hsz55z4yYa7ep1jPAL6XDuDbsZ+Rlw7ge+lAzuHt3iVuKq8tpfKaUhxl2ZPrlGylx30pPe5L6R9U2fTQUXZuaptyjnxBgY0PfWADd9zWPO+NOwuRzxdj7/4+duzsYcvWTnp6pq9bezrFIFPdlE8ykaGnbXI91LFlrr6jmZvuWobFfvYWdDWjEQ4mCPpihPxxgiNxoqEkikHGYJQxGBVMJgW724LDZcbhtuBwWybUuZ6OQU/hTA7girXjTPbgSvZiUmc7YVgCaz7YSsBegmQvBnsJWIuQlMkNJZqqkhgYJjnsH23MCZIca9QZCZL0BUgHw6iJJGo8iRpPjN/OxBNoiSRaOoOu6eiqiq5p6KqGrmlomQwzHR8uKRJlG4uofU0d0Stv5btPeNn8eGvOxgSn28TSdeWcPDbMUO/0KTMMRpmbbmzgb+5dyfJlYm7uhXahf59GRmK8/0MPT5r2YnWYePenrmV1VZQV/b9DikXZ9sW9dD9/aiTNko+9k9Vf/BjKLHrT5srFCm7HpMMRIid70FKnzu1MDgP2EhuSIpGRLSQMHmLRNCND0ZwNswUFdgoLbC/bKVXnIpNR8Y3E8fvjZ62ukIskSbjyLHjsGt2dncSHfIQOtNH9oS9P+zwfaT7M+HmXCG6FxeX04PY32w9QWFp+1ueY0mEc6QEMagKfvZF4WmHzo8d55sEjxE8rRTFGluFdN4zw5lshYq8mYCqj1e/hWIdO5wk/XSd89LYHLki5iJkyGhWqqj001ntprM+nvt5LfZ2X0pKzJ2E5G01VSQ6NEB8NdtOhCOZCL9bSQqylhZi8ngt6cF+Iwa2qavz+T4f41ne2EcvREGIwKdzyhhXceHs1jaFNlIb2EGwNsu8HR+nbnm1tdy2pY+k/vJuav3ntrLNn5zJf75OuaYRPdODbsZ/BTTvo+tOTpPwTp9h7GlxUXVdK3W2VWLxmVMlIn3MNXZ4NDEXMvPjXY2x5/MSUjUbLlhbyiY9exZrVpXOyzwuRpumcbPezd18fe/b1sXd/P72zCGYhW6ty6boylq0rBn8ff/htF7FY7uPS8kvKufvNdWwwbSc/dpyoqYiwuZSwuYywuZSoqTA7Bn2eWLQojuQAjngnjmQ/juQAZjU0yzmyUjZwdVUjuarBUQ62IiTl1PdF1zQiJ7uJtHYS7ewj2tFDtKOX2OjtWPfAnI5QmQveZjer/+Vqtubdzo8e6Kevc3LKClmGd9xto2h5I89vi7F3cwex8NQ1kgGWLy/mXW9fy7VX174iG4suhgv5+zQ4GOHvPvQQ7e2BCX93eiy899+vZ6W3n6WDfyY1HOH5T72EvyV7fDG6HGx44MtU3nnTvO3b2Vzs4BayU7WiHb2k/KeOu7JBwl5iw2g3jA5TziOVkRjuj5BJT264cjhMlJe5zrtzYbFTVY2h4Rh+f+6ySmMURZ40n3mM2WKgsMCAOeYnORKnOxonNuwjeryDno98Zdrti+BWWNRyBre6jpE0Ri2JSY1iSQ5iT/bhSA5gTw1OqLmqSkaG7Evpd62iTyvnuYeP8vxfjpHKUc6kxhLkgzX7aGpSyGtyYypyEjGXEjKXMaKUcmzAQc8gDPeH8fVHGO6P4BsIEwnOXdmN82WxGGhoyGfd6lLWrill9epSPO4LW0R+ri204PZ4yzCf++ImDh7KXYapaVUJd733ElbYWqnzPY024mf/D49x8tEudA1cS+tZdf+HqLz71jnt6b5Q75OaStH/xGbaf/0Xev7vGTKnlV+QjTI1t5Sz5I11uKocaMgMOZbR6dnIYCqPJ353gK2Pn5iyhfeWmxr4yIc2Ulqy+OfjBoIJDh0e5NDhAfYdHODAgX4iZwlOcqmo89K8poSVq92sL/ehDLTw7V8m2Hw49xC5ogoXr3/bCm6tbqE8uANFz92zqEoGIqZiIuZiksY8kqZ8kkYPScVBUrKinSXwldAx6ilMWhxr2o8lOYg1OYQt7cOeGsConcNx0eQ8Fcg6q8FZiWTIvk5d10n0DxE42ELgwHGCB48TONhC8NAJ1NjCqO07JUnC2ViNZ1UzeauXYK8p59jXH6CgNkbR/3stP9q9hEf/2JJzeswVhT7+4d4o8WUbefxoEc893kX7seFpN1dZ6eYdb13LHbc1YzKJebnz6UIdd3v7wrzvg/83aXSHO9/G3336elbZW2gafpRQe4jn/uml8bryruZarn7w27iX1M/Lfs3UQghuIXscSfkCRDv70E8rU2MtMGPNt4yX2ktLZvxD0Qm1uceYTAqVFe5XZC13Xdfx++MMDUdRpymRZrYYyKTVnMtIkoTbayHfEifV5ycVzjZ6D5AWwa3wynB6cHvkT/9Abb4RgxY/pwyZcYObAecqDsSbeeC7B2k/OvkEwYjKDW3PsGL4MNY8M3lNLrxNbvKa3OQ1urGUOEgY8ogbs5eEMY+RtItuv5lARCIUhVg4RSySIhZJEh29nYylSSbSJOMZEvE0yfjclDOaieoaD+tWl7F2TSnr15UtusBhoQS3kUiK7//wJX7zu/05D9h2l5nXvWMdV11mocn3GK5oBy1/7uDgA8dJRzI4G2tY8ekPUn3fHcjzUCT+YrxPmVicnr88S8evH6H3r8+dGvIlQfkVxSy5r47CFV50wGdrpC3/RtoGTDz8wG6O7c2deMpqNfD+917GfW9chWGaDLoLSSyW5sixIQ4dGmD/4UEOHx6kv+/cKrA53GaaV5fSvLqIS5szVBu7yIu1kTrcwkNPG/lJ5zJimcmfH6vdyK33ruCuy0M0hF7ApGUbHTTJgiypoM18qoUOZGQrCYOLtGJD1lUkXUXWMyh6GqMaw6Alzi9TsWQAZ8WpYNZVnZ0nOzoqJBUIMbx9H8Nb9zK8dS8jOw+SGgmczxbnlWK1YKsswV5Vhq2yBFtlKfbqMtwrmvCsaMRgm/h91DIZWr7zK1q+8T1Wvn8Z7Wtfz3d+laDlwORs2VX2NPd1/oHVry7Gdc9NbI+t5LFnQux9sWPa35H8fBt/86ZV3P365TgdL6/5ggvFhTjudnYGeN//e4iBgYlZt71Fdt736RtYY9pH3cgzDO7x8eK/7yQdyTbel91xHVf88quY3Bf/N3+hBLdj1GSKSFsXmeiphjGj3YCjNDtMOaXYSSpOIsEkAd/koiOyLFFR7sLxCvle6bpOJJpiYCBCapocNVaHCasJ/P5UzikXJouBogID5sgI8eEE1opSjC4HaDon2lpJJ5NIaZXCpI4aS5D0BYh29hLr6ifW2Ut09Lp3eFAEt8LidXpw2/G791BROIuDtMkNihHUJKip7DWQkYwc99zEr56x8MTvDubsRVrjinFndA9qZxfx3kHGvqWyUcZeYsVeYsNRasVeasNeYsNeYsXsMmFwGJGcDlSjjYxiJS1byShWVNlERjKhymZU2YQqm0iqRiIphVhKIZqUiSVkYkmIJSXiCYiFkwRH4gR9sez1SHYenDZNa9lMVNd4uGpjNVddUcXaNWULvmX/Yge3uq7z2BMtfO3rW/Dl+JEDuPT6Wl73lmUsT2+jPPgSg7sG2f2tQ4TaI1hKCll1/4eoe9fdyIb5a+m92O9TKhDixP/+lqP//QCJ/lOJTgpW5LHk3joqripBQ6bPtY6T3ms5sC/Aww/sYXCKoblLmgv4109dx7KlRRfqJcyYzxdj774+Xtrdy+59vbSe8M24LNaZzBYD1U0F1K8oYu1yMysKBylInEQ6dojh3QMM7PFx8kCMv5ZcT4uzJuc61l5VzTvu87Am9Rz29DDR4TSxaD5y6Rrcl12P0WkF/3H04YMwfAgyc1wY96xksBeBowLJWQmuGnCUIsnZ74OuaYSOtjG8dc94MBs80so5ZbeaiiRhynNj8roxe7PX2dsejG4HBpsVxWpBsZpRLOYJt2WTEUmWkRQZZBlJUUilUmx7aQeSLHP93a/DXV5yTtM1Yj0D7Pr7z8PQPur/8Ta+uX89j/9pcsn4vDwLH6jxoTz8Z8ousVP+1ivoKr+Sv2y3sPmJNoJTHJsAbDYjb7xnBW998xry8i7+tI6Xk/k+7ra1jfC+//fQpN+ewjIn7/v0DaxlG9WBzXQ83cP2L+8fz5K//J//jlX/8ZELUpZwJhZacAvZ3/Z431D2HG+UYpSwl9kxWBRUyZTtxEho+AbCOc+9iooc5Htf3vVwE4kM/QMRYrGpRx7ZnGa8LoiH4vjCkz9zZ/bWSkYbtspSZOOpc6LZ/B8ePXCApatWjd0Vwa2wuMwquLUVg7sOyVOXvbbkEW7tpPP3j9H5u0fx7z2MYlYwu02sfGcTztdfzxPBK/nZt/cxnKOXpbLSzX99+VXUVjqJdw8Q7ewl2tE7Pqdr7BLr7J2QpAAJDFYDJocBo8OIyWHEYFFQLAqG0YtiVjBYR68thlOPj/5dNhtQ8lxIRfloTi8pg5OkwUlccjAUs9I5bKKjV6O/O0R/Z5D+zgDx6OyTYJktBtavK+PqK6q56spqyssWXrmRixm0HTg4wNe/tYXde/pyPl5Y5uTu917KxpphGoafJN09wN7vHqH7hX4MDhtLP/keln7snRjs85/N9GIHt2PURJKTP3uQw1/5IZHWU/UX3TUOVr9vKWUbikjLZjryrqbTfgmbn2jjid8fnGI+vMS996zg/e+7HIfjwidBGdPdE2Tn7l5e2t3Dvn299PbMvm7pGE+BjZrmAmqb81leD0sLA+SlelDajuDf0c7gHh+De0dQdTP5l66ktWo1v2i1EI5Nbi23u8y88T2reEPdPgx7ttLzYj/dL/QTbD9t/ySJvLXLqHnTHVS/+dVYSwog1D4a6B6ERO56zOdMMoCjdDSQLQdHRTbx02nzZNOhyGiv7Ggwu23flFm6Z8roceGoKcdeXYatugx7VRn26uzFVlWGuSBvTkdMzPX3reevz7Hvk59j+fuaeMx7Nz/+fgvp5MT/c4NR5lMfv5LVoRMc//YvMaR6qLtvOZkbb+GRoxU89XA7fR2BKbdhNhu4567lvP2taynIFxmW58J8HnePHhviAx9+mMAZCTFLq9y899+vZ236OSpCL3H8TyfZ/a3DoINsMnL5jz5P7VteN2f7MRcWYnA7Jh2JEmnrQktle7yz2ZQtmD1mdGTixjySqgFff5h0jl5Lt8tCaen55ztZaDIZjcGhyKTP3+msdhP5HglzMkT/iExUm5zUz2g2UFxgwBQdIeFLYqsozVkacjb/hy0tLTQ1NY3dFcGtsLicHty2//HDVFZWZOdlGR1gciKZnGArAlcNksmBrmn49xym99Hn6X7wqWlru5ZuKGLNJy+npeZOfvKHKNufap20jNli4F//8VruuL15yvXomkZi0EfKHyIdjpIJRUifcRnLvKnGE9mMnInkqdunZedUx5ZJpEiHIuiqimKWsXiz80EsXjPWAguOMhvOujyMTTWoxZVEjQX0xDwc7zNzojXOyaNDdB735TwQT6emNo/rr6nhmqtrWbGsaEEkTbgYQVt7h59vfWcbz2zKnQXZYJS5/s5l3HFHMctCT+Lyt3DkVyc4+ts2NE2m4X33svLfP4ilKH/e93XMQglux2iqStcfH+fwl36Af8/h8b8Xry9g7fuX4ql3ETPk0ZZ/Ix16HY/+ah/bnpz8HQQoKrTzyU9czQ3X1V2Qfc9kNPbu6+OZ59t44YU2enrOrafTYFKoqM2jssFLXZ2NVTUp6uyDGHtOkNx3hOARHyPHQ4Q641hrG8i/bCX5l64k/7JVZPKL+OJXX+DpZ9pyrnv1FVW8601ulvc+zL4vbRlPVDYtSaLkxo3UvPV1VL7+JgwOOyQDkPBD0g8JP/robS0+ghbzYZDGjiFytg6sYho//mJygMmFZC3IZjC2FoA5D0k+FUSqyRTBQy2M7D6Mb8f+bK/soZZz7pU12G24lzfgXtGIZ0UTnpVNuFc0YikuWPSJ7tKRKNve+Sm8xb103vEuvvqdIfyDkz977373ej7wt5cxsvMALd/9NYNPPkXda6twvvkOnhxYyuOP9NB6MHdOAMjOGbzzzqW8663rKCpynPd+v5LN13H3wMEB/t/fP0z4jHn6FXVe/vZfr2Fd4glKw/s58JNjHPpZttSPOd/D1X/+NkVXXzIn+zCXFnJwC6ClM0ROdpMOnWoYNLuM2IqtSLJEwuAmJVkZGYwSj05uiLVYDFRWuDEaF/ZIuJnQdZ1AMMHAQGTK/BhGs4LXa8SphYkNRBhWPKTUycdfm8NEiTtNus+HZHJgqyyZcgSbCG6FV4zTg9vjx4/n/MAnfX76nthM76PP0//4iyQGZ94TYbQbWPOBZdjuvYM/t63i19/bnbMH6e67lvMPH73qgg7h1TWNlD9IvH+YxMAwiQHf+HXkZDeho22Ej51EVlScVQ7c1Q7ctU5ca6uQVyzHb6ni4ICHw606J48O03Z4MGdyhKm4PRauuqKa666pYcNlldhnUEpkPlzIoK21bYSf/WIPjzx6fMqD+pJ1Zbz+HStZb95HVWArXc90se/7R4gNJqi851ZWf+FjuBpr5m0fp7LQgtsxuq7T/+RmDv7Hdxh6cReQTdJb+6pKVr6rCWu+hYClipaCV3HopMzvv/8SA12Ts8cCXHdNLZ/8xFWUFM/9HLJgMMGLWzp4+tlj7Hipj1iOntLpyLJESZWbyoZ8amttLK3M0JTvx9R7kszBo0QO9hDqTaMpBVhranEvrce1tB730nps1WUTehWffqaVz335OYI5WsztLjNveNcq7mk8SOyPj7LnW4fI33DZeMbgmVKsFipefxO1b3ktxTdunFQeJPt5ehwZjZtuvgWr7exBUCaeILD/GP7dhxjZfZiR3YcJHjiOlj630mqyyYh3/QoKNq6hYOMavOuWY68pXxBDLectO7muc/iL38f/7O9wfeqd/McvrLTmSF53zxtW8E8fuxpZlkj6/LR87ze0/fDXlG90UPSu29iWWc9fHvVxcHvXlO0IBqPMa1+9hHe9fT1lpRd/XuZiNB+fg117evnIxx6ZlIm/prmA93zqKtZFHqEwfJhd3zzEiQc7AHA21XDdI/+Ls6H6vLc/HxZ6cAtjw5QHifeeaihUzDKOMhuKSSEt20gYXIQDCYI56lQbFJmKChc228UbZXS+kskMvX1h4vHcx2xFkfF4zXiMMdIDAZJY8Uk2Mjmqibi9FgpMERK9YazlZZg80x9jRHArvGKcHtxu//WfKZZNxHsGifUMEO8dJHyig5GdB2fcC2ApyqfynltxLanj5M8ezD4XKLm0gFX/fA07i+7iO99spbttZNJzlywt5KtffNWCOgnQNY1YVx/B0UA3ePgEvpcOEjp8DE+tg4IVeXhXl2C+bCWRwgb29hWx61CaY3v76Drhm3HniaJIrFhZwtVXVHHFhiqaGgsu2BCc+f5R1HWdnbt6+dkv97B5y9TBQV6RnTvfuY7rmv3UjzxN/HgPu795iKF9IxRddxlrvvhxCjasmdN9m42FGtyO0XWd7v97mr2f/E/CLe0AGCwKS+6rY8m99cgWAz3uy2lxXc2mR7JDlXNlj7VaDXzgfZdz7xtWnnfCKX8gzmOPH+WRvxzk6Ikw2izmzZotBqqbC6hv9rCyTmOZ24ep8wSpgy2ovhSqIR9DfiWOhlqcjdW4mmvP2rsYCCb40n8+zxNPnsj5+MoNlbz7b7ys7H+YfV/eTKBT5rIffo6yW68GID4wnB3mu3kXQ1v2MLLz4MQpE1OQZBlbVSmOusrxi6mymAMD3cgFHq695lqMkoSWzI4oSQyOjDe0Rdt7CJ/oINLaRbSz97zmydoqSkYD2bUUbFxD3tplc1Imaz7M9/et56/PcfjfPk3T/XfwtZdW8MJjk0eR3PqqJv7j324Y/x6oiSQnf/EQx/7nx+RVpKj8uxs45LmaPz0aZffz7VM22CmKxO23N/Oed6ynssI9p6/j5W6uPwfbtnfx0X94lGRyYkWH+hVFvOeTV7Au+GfyQi1s++I+Op/pBaBg41quffi751Unfb4thuB2TCoYJnqyG220hrUkg6PUhtFhRJWMxI15xGIaI4ORSfVwJQlKSpzkeRbW7+/ZaJrOsC+GzxfNeQiXJAmnx0y+PYU+7CedlNG8BfSPpCaVApIkiYJCC86Mn5Q/jb2uCsU0ebjymURwK7xinB7cfoNa8jn7F+RM5kIvlXffQvUbb6Pwmksn9JCM7D7Eie//lvZfPgx6inUfXUXirrfwnT8Z2ZbjBNPpNPO5z9zI1VfVnPNruhAysTgjuw4xvG0vvm3ZuW2KHKXkkkIKrqrBuPFSegx1bGuxc2jfCEf39hENzbx0hyfPyobLK7lqYxWrVhZTXuaatyGB8/WjeLLdz2NPtPDYEy10TdFTCNlhNTfdvZybr3OwNPQUpq4WDvz4GO1PdONZs5w1X/wYJTdfedETSiz04HaMmkrR8t1fc/D+b4/Xy7UWmFn9t0uouaWChMFFS8GrOBYu408/2MnxfbmzKp9rwqlwJMljD+/lkceOcvB4dMYBrd1lpn5ZEU2NNlZWxqlOdKK0dyKFNYylS3AuXYqrqRZrWdE59Sw++1wbn/vic/j9k3sEbA4Td797NW9oOkTywUfZ881DVL7hdaz9r3+aNhOqmkwx9OIu2n/5EJ1/eJxM+EInkpqabDKSt245BRvXUDga0NoqSi72bs3Yhfi+hVra2f6WD9P8nnp+k7mD3z5wfNKJ55VXVfPVL9w6oSyJrmn0PLKJo//1YwzJDmr/33W0VNzIHx9PsvO5k1PWbJdliVtvbeRv37memuqFGygtJHP5OXju+ZN88p8fJ31GBuzmNaW86xOXsd73exyBk2z+9C76dmR7F8tfewNX/vprk7JxLzSLKbiF3NmUrfmj5YIkmbjRSyIj4+sP58xY7s2zUlTsQF4Eiaai0RS9U8wnhuy82sI8HSXoJxXKYCkpJoqR/oHJuScUg0xxkQlTYAhNNc1qpI0IboVXjHMNbp2NNZTedjUVr7mBousuO2uW2nQ4Qvuv/sKRr/6YgroMNf92D79qXc/vf7g35xf+Dfes4KMfugKLZXHUOdN1nWh7N/1Pb6P/qS0MPrsVRxEUX1ZE3o2rSS5Zw66+EnYeSHFoZ++Uw0Kn4nSZWbasiFXLi1ixrJjly4rweucmYclc/SgODUfZt6+ffQf62bGzm5aW6YevG00K17y6mdtvL2JpYjOewQMc/XUrx/7Qhq26itWf+/s5r1V7PhZLcDsm5Q9y8HPf5fg3fzE+bLVwtZf1H1mBp9b5/9l77/g47jr//zlle19Jq14syZIsS+41jh3bcXonCRAIEGqA4+B+x3EHXIHjuAPyPe6Ao/cQAqQ30qvtxN2WuyzJ6nXVtvfdmd8fK7lEki3bsi07ej4e45ndHe3Mjqd8Xp/P+/16M2isoDHjOrbv8PLM7/aMW09aFAU++P5aPveZZacMm/d4I7z+xBZe3tzBvqYoyeTknk35sxzULsxgaXGY8ngHOk8YfWYJlpr5WOeUjQnlPRt8vijf+/5mXnp5rEMuwNyl+XzqI1nMG/gr+7/3Np42gWW/+g/yrl9zRttJhiN0P/cmrQ89Q+9Lm1FTZxZ2fa6Yy4txLqomY/n8dIjxwmok/aVbRuNCXW+JQJDtn/gaWRVeXq/4GL/9RdMY19b5C3P58fdvGvcaGNy+j0P/+TOS3fsp+5s1tFVcx+OvJNj+esuEIlcQYMPV5Xzmk0soK3Wel991uTBV58FLLzfxr//+2pgyczXLCrjviwtYNPgI+sEONn19J4MHPQCUf+YDLPnJv51XF/6p4lITtzASGdfVR7T/eCTfaLkgJIGYbEuH5bqDxMYJ4zUZteTnW6dtSbtkUsHdH8TnG98wSpJFMjK0WFI+ogNhdBmZ6LIc9A+Gx60codXJ5GYJKL39yFbnGfsgzIjbGd4zTFbcSgY92etXkHfDGnKvX42lrOistpcMR9j3z/9Lz5NPsPi/rmZ77l387EcN47opl8xy8L1vX8vs8gtnGjRVqIqCZ98R+l7bQt+rW/Dt34er1oZrXRnaq1bSlCxly2GZQ7vdtNQPTNgIOhUOp4HMTBNZmUZcWSZcmSayskxkZpowGjSIIoiiiCgJSKKAODKpalqMpyeIRKJs374dVYWlS5eh0WpBVVEUUEmvoyoqyaRCJJogHE4QiSQYGo7Q2xugu9dPd0+AwYHJjVoZzVquuH42V29wUatsx9lXR9NTrTQ81oJkcVL7zb+l9L47pl2D4lITt6MEmjvY+0//TecTLwMgSAIVd86i5r7ZCEYDbc6raJQW8vzD+8c1fQNwuUx89R/WsGZ1CV1dPhqaBjm4p5ND+9to7Yvj8U9OyMlaidm12Syaq2dxxhB5cT+2/EoyrrgC2aCfst8M6YbFU88c5me/3DGuG6XBrOXO+2r5wJzDRJ98kbqfHJ7UaO1kiA4M0/7IC7Q99AxDO/af03eNQRCwVpXiXFSNY9Hc9Hxh9bSotTmVXMjrTVVVDnzzRwj9m9mz9n5+8uOWMSH7FVWZ/PxHt2K3jX+eevbWc+i/fk6kYQdln1tFV+0NPPGawtbXxn7XiVy9vozPfXoppTMid1ym4jx48unD/Od33xozKr/wymI+8tkaFg38Cam3k7f+cQe+lnRbpPbf/5aaf/2bix4xNFkuRXE7SmzYS6it+1gIsqQZycPVH8/D9Q1FCIwjEjUaicIC27QaCFFVFZ8/htsdGNOZMorFpiPTFCfZN4xktKIfiUjq6Q2MK4aNZi051jjxbg/6/Lyzut/PiNsZ3jOcKG5/oq+isLAIQ54LQ74LY54LQ3429poKsq5cPKWjAP2bdrL9U1+j/CYHkXs+yQ//GOfA9rHXjkYj8sUvrORDH5h3yTxkxiMZjtC/cQc9L26m9+VN6HV+clblY7l+Bb68uexos7PvYIiGvb0M9Z19GZTpTGaOmdU3VbJ+hYay2G5s7oM0PdFM4xNtSGY7Vf/wSWZ/7p4pFzlTxaUqbkdxv7WdXV/4j7STLmDI1LPw83MoWpdHUJtNY+b17Gsz8PgvduDuGr9sjFYWiE9yVHYUjVZi7uIcVlYLLHX4cFmdZK1cjcZ6fspiqarKW5ta+cH/bZ0wHH7O4jw+c6+TWvdf2fe9Lfj7tCz9+TfPeLR2MsS9foItnQSbO9Lzli4CzR0EmjsId/Ty7rhtUaNB50r3yutdTgx5LizlxVjKizCXFWGpKEFjNk35fk43Lsb11viTh/Fv+TOtd32B//1RJ9F3GQ4VFtv59U9vIytz4uPvq2/m0Hd+QWD3ZsruX07/kpt5/E2RLa8cJR5Njvs3ggDXXjObz356KcVF9qn8SZc853oe/PFPe/mfH24Z8/6y9aV8+JMVLHT/CaWtkze/sp1QbwRBFFn6i29R/qm7p2T/LxSXsriFtFle8GgHqVjalFMQwJRjQGvVHquHGwom8AyMzVkVRYG8XAtW68VvO8TjacOod5uVjaLVyWRlSGgDwyTDYCjMRTboURSFrm4/wXFMSa0OPVmaANH+MKaSwrNuI82I2xneM0zGLfl8kQiG2PtP/0143+vM+q8P8ceGeTzzhwPjhikvW17Af35jAxmXSe3AwNF2el7aTO+Lm/Dt34trroXMtbORr1zO0WQJOxq01O8fpL1x8IxydacbFrueBauKWHxFHoty3RT4d5E63MDRZ9ppf60bfX4B1f/4KWZ99PZpa2wzyqUubgGURILGHz/M/m/86Fh+aPbiTBZ/cS6WIjP95hoabOt59fkuXn384Li5TpNBkkXmLMhmxVyJKwsSzKqdh2XW+b+37D/Qx/d/tIUD+8fPI9YbNdx5Xw0fmnMQ359e5MBvmij91D3M+48vXXDBGIlEePmFF1H9IdZtuBqTw4ak1027iIWLxcW63tofeYGeP/4Qz/1f4Ds/7B9z/80rsPKbn95G9mkcxQPNHRz+7i8Z3vwa5Z9awvCqW3h8o5a3X2oiFplA5IoCN15fwf2fWkJB/ozxFJz9eaCqKr/8zS5+8audYz5bfVMF77+nmIV9DxNt7OKtf9xBdDiGZNCz6pH/peCW9VP6Gy4El7q4BVCSKUJtXcS9xyP59A4txiw9iiAT1TiIxAWG+oKkUmOfTaORbBdjIERRVYaGwgwOTmwYZXfqcMgh4v0BdK5stI60l0oyqdDR5SU6zn3BmanHnvKSCCiYZhWc0/NhRtzO8J7hYorbUfpe28Ler3yTyk9UcmjuB/n5L9rpafOOWc9q0/E39y/n9tvmoJEv/VpnoyQjUfo37aT3xU30vLQJWRkme1k2tg2LYE4V7dEcDnfpaG6N0XF0iK6WYRKxC5vPN1n0Rg1FszMoqcpi9hw7SwuHyQnXY+5voHdLN83PdTB40INr7TIqPv8hCt537UkGZNOZy0HcjhLp7afuH/8fbX98FgBRI1K8Po/Smwqx1eTwRugKXqkzsvPNViKhyZWZkTUiZXMyWVmrYe0cPVXLl6G1XhjTnI5OHz/48Vbeemv8mrUA1Uvy+cyHzFR3Pkfdd7aTIIvlv/42GUvnXZB9fDeX0/l0PriYx6f31Xc4+sA3Sf39/Xzrx0G8gyfnv+XkWfjNz24nN+f0oYGhjh7q/99vcL/0POUfX0Bg/W08/o6BTX9tGjMyPIokCdx8YyWf/uTSaVU94GJwNueBqqr84EdbeOhP+8Z8tuGuudx5eybz+/6Mb28Xm76+k0QwidZpZ+3zv7iojvznwuUgbiH9fxftGyDcfbw8l8YgYcozIsgSMdlGVNUx1BckHhsrBs1mLfl5ViTpwuXhhsNxenoDxCc0jNKQZVNhcAhRZ0af6zrW7oknUnR0eMf8rSBAZpYBc2QQJaXFWJR3zqJ9RtzO8J5hOohbgIQ/yMH//CmRQ6+T8Y8f5ucbc9n41/ENYFxZej732ZXcfEPlBb2BTQWqqpIKR0iGIiRDYZKhCEo8gWwyIJtNaCwmIu5B+l55h56XNjOwZTdGq4Kzyo5tQQH6JXNJFJVx1JdBr0dmyC/i8aUIeCL4hyP4vRH8nijJRApVUVEUNT1X1WOvBUFAEICRufCuOYKAQHoUAThWkkiURHR6Ga1eRqeXMZi0OLJMOLKMODMN5GepVGT6sMd7sMS6obWV3q29dL/jZmD/MLLZTOl9d1B+/wexzSm7aP8HZ8ulLkbiXj+++mb8I5OvvoVox1G01hTh+fNot5dzYMjGoaNxQoHJCVqtTmJ2lY0NSw2sW55HbvV8NPKFGYFXFJW6vT08+Ww9r7zSNGFuU0GpkzvvKeYG1276f/sK9Y90UP21zzPnyx9H1Jy5O/xUcamfT+ebi318hnbu5+A//AP6r32Mf/1ZisF3pYu4csz89me3k5c3ufD6SG8/9d//HT1PPU3ZR2oIX38Hj242sun5xglHcmVZ5LZbq/jUfUvIzj59HeTLkTM9D1Iphe88sIknnz485rObPrKA2681Udv3F/q3dPLON3eTiimYivNZ+9KvsFVdes+lUS4XcTtK3Bcg2NqFOlIuSJQFzHlGZINMXDIRFS14B8OEAmMj2y5UHm4qlTaMGs/TAdI1a50ZWsxxL8lgCkNB7kkhxdFogvZO3xjPFVEUcGXr0Hv7EXRW9DlZUzIaPSNuZ3jPMF3E7Sih9m4Offt/sVXG2VZ6F7/9xVECE9w4cjM1fHidi7s+ew1a8/kJV07F4ySDYZKBEInReSBEMhhOzwMhksGx77173WNTOHKsTqUgCkg6EUEUSEaSqCfc3ySjAY3FhNZpw1iQg6TXkgiECLZ2Ee3pwzbLjCnbiCFDhz7bgqbIhZyXhejKAocTNBpUUUQQRVRBQhUEVMR0d6AKAiqgIKhqellVEFARUGDkvZOWVQUYXVYBFTEVQ/QOow4OkuofJjIQxnvUj+eoH2+zn7g/gS7DTv5tV1P4vmvJuXrljHvreUZVVaLuwbR4Pdx8TMwGj7ag1Uexl9tIzZ1Nu7OKo2EnjV0ira3hCUeQzoQ5VVncdEMFV68rO68N8Z4eP888f4Rnn2/APY4R3SgOl4lb31/OnVWNKK+/zoFfHUFfXM3SX3wL6+yS87Z/k+VSOJ8uJtPh+PiONLPn/r/F+k938/VfyQz0nHy+ZbpM/Pbnt59RCHF0cJiGHzxI518eo+wjNfivvZPHN2rY/OLEObmyRuR9t8/lk/ctOmW+7+XImZwHiWSKb37rDV58lzO6IMAdn1rCzVfCXPfjdLzYyo7/PoCaUrHPr2LtC7/EmJd9Xn/H+eZyE7cwUi6ouYNkON0GFAQwuvTo7DqSgpaoxkHAF8c7jrOwIKTzcG0TGMCdC6qq4g/E6JsgPBrAbNORaYyRcnvRZh0PQR4lGIzT1e0bUxtbkkRysjVI/W40jix0mVMX/TQjbmd4zzDdxO0ow3sO0f6rH5K8Yz0/elbPoZ09E65bmC2zTttOua8Xm92MLsOOLsOBNsOO1m5BTaZIRWOkYnGUWJxUNIYSi5OMxI4L1wkEqxKfuNEvG2V0Vg1aqwadVYvGbkDKzyTszMFnzMQjWfEoJoajWrxhgUgcojE1PUUVotEU0UiSVErFaJIxm2TMJhGTXsWsVTDLCSxqGNdgO5a63aSa+lEVFUGrwZifjS7DjmQ0oCYSRHoHCHf0oo5XVFRIC2lBFBCkE3r/Ru8f6ti3JnrjxJeqop70twD6nCwyl88jY9k8MlctImvVossmh3A6NLZPJBWN4Tt8FM/eejx7j+DZW4/vYBMCERxlVmyzbcg1ZXQ6ZtPgz6ChU6S5NYpncGyd19MhySJ5JQ56O7yndH4dpaYmmw3rSrl6fRn5kxzZOhWRSIJX32jmqefq2VfXe8p1DWYt194xmw8v68W4/VUO/vIQkYCR2m99kVkfuW3aGNNNt/NpujFdjk+4q48dH/sstv/vZr72Wz393SebrWVkGfnNT2+n6AyNoGJDHur/32/oevRxyj+1kOG17+OxN0S2vHx0wnqYWq3EXXfW8ImPLpyyUnDTncmeB6FQnK987WW2be886X1RFPjAF5Zzw8IQVf3PcOThRvb/ugGA7PUrWP3kjy8Lp/HLUdxCuvJEqKOH2KD32Hs6mwajy4AqykQ0DiIxGHIHx5TwAnA6jWS7TFN234/HU/T2BQiFxho/QdpAMStDQuMdonr1Wjq6unjzzTdZu3btsXU83nSliXcjayTyXCJqTz+63LNzRD4Vl4q4vTxajDPMMA7ORXNx/PQX9L74Ov9ctZG6dWt49Olh2hvH1k7tdCf5A/kIQj4FNgNzdApV0T4Kt7xD8kgXqWgqXfZGUVFTaUGmKioIoDHKaIwysknGZNKgccnIBh0akxnZpEFyWBEdNgSrhajeil8wM5wyMRjV0+2TGfDCkEdh2JPAOxwj0BB5V09cbGQ6NQFfnIBvvJulFpgNttk4r9NRlCtRYAiT5+/EePgQyR0taKMRjC4n+beuxzqnFEOuC12GnVR0RLyHIiPzcLoHdFShCsKImUGSru70fauosAhZIx/7/MS5kI5dHnkPZIMefW5W2lU7z4WppABDnmvaiIfLiWQkyvDugwxt339MzPqPtKC3yzgrbTgq7RR/oIKez6/h8JCDTR0izS1Rep8LjJRXOLO6ygBZuWbmzTWwvCLGquJeMoVWWsRqnq0vZMvmgXGvxVEOHnRz8KCbH/zfVioqMrn26jLWrS0lP8+KVnvqHGuvL8rRo0M0Hh3iSOMgTc1DtLYMEz9Nnrkki6y+oYyPXh0it/5RDn1hH74ekZp/+RKln7hzSmrmzvDew1iQw4o//4bt997Pd794DV//vY2+E1y4hwbCfPz+p/jNz2+npHjyoyy6DAcLvvsPVP7dx9LGU/f+M5/97Aru/N5tPPaqytZXj44xdIvHU/zpz/t44slD3PPBWj724YXnZWTqUmNwKMwX/7+/cqRh8KT3JVnk3v/vCq6rcjO7/yXqfnyQxifaACj+4E2s+P13p72R4XsdQRQxlxQgm02E23tQVZWYL0EqpmDOM2JkCElrQy6wjZuHOzwcJhpNUJBvRT4HrxZVVRkajjAwEGK8gUVBELA5dDjEAMnBOLr8fIR3+YmoqspTT7/E66+9QVXVXK7ecOOxz7R6mTynQrJ7AGNREbLp5M4JRVG44oor2L59OwC/+93vuO+++87690xnZsTtDJc1giCQd+MGclLrcL61kaXXuXn72lIef85PT7t3zPqqCp2dETo74RUciOLVFMyzkuHUYDSIGA0CRoOIyQAmPei0EE2IxJIi0bhALAGxkXkwlCIQSBFqiBH0Rwn5YyO96SoQHJkuLMNDMYaHYC8AhSAVwryRUB29iCmQQvd2BF3wKKIAWK2oJhOKTo8imUgoAvF4asyNWQUSlCII4AybsdsM2O16Mp1GqioyqJ7jonSWc9oWSj/fxAIhBlr76GropuWtRuIxheyEk/nXLMRgOz/ht+FuN4Nb9jCwpY7BLXV46urRmAQyqu04Ku3kfLoUb+ENHPRmsr1DS3NrnI5HPCNGY4On/f7xsDr0zJ5tZV4BVHjr0bz1CImmKMlIih2RJFqLhtl3tPClDfnct2A224PzeXN7gt0b2/B7Jh4NbmwcpLFxkB//LP1Q1molTGYtZrMWs1mH2azFYtERCMRoaRlmaHBsiNmpyC6wsnxtITcsDFDa9RJHvraLloYk1V/9DFfd/8FLOhR+humB3pXBir/8mu33fo7v3L+arz9so7f9uMD1DEf4+P1P8euf3kZZ6ZnVZTfkZLH4B/9M6Muf4NB//gzfL77G57+wivc9cBuPvZRg++vNY3LyYrEkv3+wjkcfO8hH713Ahz4wH7P5vSnS2ju8/M2XnqPnXSHjGp3EfV9ZzTXFzRT1vs7W7+6j4410BFj1P32a+f/19wjie/OZdimiz3QgG/UEmztJxeIkoyn87UFMeUb0Ri+iaETKs+IZCo9xOA+HE7S0eijIt2I0nvl1Eokk6On1E5ugg1Vv1JBlUxAG+xGdWZjL0iHIZWVl6PV6jEYjiqLS0+vn9dfe4Kc//W9uu/0Dx8StwaQl1xoj3uvHVFoybofLD37wg2PC9nJnRtzO8J5AlCRcV6/HxXpKU0lurH2bZ3daeOwF75gcqBNRFJWOZh8dzRdwZy8CqgqhiEIIATCCZiRcLQyElZGFyeEdHh73fa1GpDBbx2ybQrngo0QZRhsJHQvzTkVjCIKAqNchjUxahxVDbla6bnJuFoa8bAy56RyS6dqoaN/Xwtt/3UNju4cun0K3HwY8qRPCsdP5bo8cOID4/QNk2iRyrCr5FolZ+TaqF5VQvXoO9ozJhxOpqkqgsRX3m9vp37iTgS11hDt6sBSYyKxxMGt9NqVf+TD1qRI2txtoaldof947kot+dkJWp5eZVW6nak4WtTV5LF2QS0HOyeHDce+X6P7rm3Q+8Qq9L20m1OdnxwP7OfDbBirubGXDzUdYfVUB7Tcv5+2WDOre6eTwru7TuivH4yniwxE8w2ceHj2KwaRh4ZVFbFgusSKjidS239P+rXY27w1R9ZVPs/KFDyMbZ8J8Z5g6dE47K//yK7bd+zm++4nlfP0vTrpbvcc+93mifOL+p/nZ/91CdZXrjL/fVJjLsp9/i2DLpznwrZ8Q+slX+OLfraXrgVt4/MUIO95oGRN2GQ4n+Pkvd/Knv+zn4x9dyPvvrsWgv3gmaReaXbu7+crXXsbnO9mXw2TV8cmvXcVaZx25XZvZ/G+7ce8eRBBFlvz4X5n9uQ9dpD2e4VyQjQas1WWE2rqJe/woKZVAZwhDpg6DEyQ1gZjpQKuT8QyGTkqbSiYV2tq9ZDiNZGWZjhllnopkUqF/IITXO/6zSpREMpwaTFEvBHXoZ806qfrD66+/fux72ju8RCJjn40Wuw6XNkTMHcFcNgtRM1baNTc386//+q8sWbKEvr4+urouWITwRWFG3M7wnkOUZFwL1vKpBfCBu3p55OkDvP6Ol5YGz4R5ShcDWRaxZ+jJcOrIyNSTmWHAYtFgNGgwGTWYjNr06JVJhyyJeLwhPN4wHm8Unz+Gz5/A70/Q2xehuyMwpuf+QhNPKDR3RWjuAtAjCHlkZ2rItItkZKtkmJLYtXEsYhQTUSyEMMV7kTsPEt0foD+QIOqNExmMEvUmEQ1WDPk5GPOzMRZkYyzIwVCQc3w5z3VBwkjbD3XwyiNb2dfio3FAZdA7+XNIUaDfk6LfA/tR4OAgvDwI39lFYabAHFOExXk6llw5m4x5szGXFSFKEqqqEmzpxP3mdtxvbqP/rR1E+/qxl1nJmp/BvM+VEam+g73DubzdpqGhKUrXRg9KygN4zvg3iqJAQYmNiqosaubmsHheDpWlztOGaGntVmbdexuz7r2NRDBEx2Mv0fjjh/HsOcS+Xxzh0ENHKbu5iMq72pidk8XNH6zC/bF57DpqZP/2Hg7s6CQcGD8v6WwQRIHK+TmsXmXj2ooezPV/pesPR9n6RjdaVyGzPnYPS5+4B43lvekqO8P5R2M1s/Ivv2T7R77Adz5Uyz8/kU1n8/FrMuCP8ZnPP8NPfngL82tzzmob5tJCVv7+u/iONHPgmz8m+sMv83dfvpq2793E488H2b2xbSTV4Dh+f4wf/ngbD/1pH5/42CLed/vc8+4Ue7F5/MmDfO+/N49xSc/INvPpf17NldJb2Ft38cZXd+Bp9CMZDVz5yP+Sf/O6i7PDM0wJoiRhLi0k1j9EqKsPVIgMxkiGU5hyDRjVQUSzHY3WypA7OKbtNDQcJhCMkZ9nxWAYvyNIUVSGh8MMDoXHGD6NYrZqydDHUL1BtHk5J7kgn0g8nqS9w0ciMbZtcayGrRcs5bMQxqn+oaoqn/rUp4jH4/zqV7/i9ttvP/UBugyYMZSa4ZyZroZSZ4ovGGT37la27+5l34Fhmhu9500QarQizgwjGZlGXC4zrmwL2S4zuTlm8rPN5GWbcTrPrZB4JJrgjTdbeGtzG1u3dRCeZK3R6YYgCpitOmx2HQ6nBpdTJNuhkGtP4NIEyFKHMHvcJLvdRN1BIkNRIoMxIkNRknEZQWdJG4Rlpg3CRpd1GXa0Ths6Z3o+uixbTm0cERr2s/mZXWx6p5X9vUl6Bs9/h4heJ1BuDFLUcoC5phiGITf+3gGE8lykpXNIzCohYMliMGLA7ZNpaQ6eMiLhdGRlm5hdlUXN3GwW1eZQMycLo2FqOgpUVWVo+z4af/IwHY++iBJPIMoCRevyKLgqB9f8DESLEa9hFv36cna0O9m9c5CD27smdD0fD0kWyS6wkltkp7BQT2luirm5fvKH9tL/1320v9ZNQrFScs9NFH/oZuy1lZdcrvd0MUyarkzn45OKxtj+ib/Ddkc5//JswZj8c51e5offv4llS/LPeVue/Uc48G8/IlS/i/J/vI6m8ht44lkPe99pH2sAOILDYeBj9y7g7jtrJmzAXyq8+zyQNVr++3/e4bEnDo5Zt6DUyWf+aTkro88iNx9m4z/tINgdRu/K4Kq//vyi1bS+EFyuhlKnIhEME2zpPGb8KUoCplwDGpOGmGQhKhgZ6gsSm8CFPCPDSFbm8VHcURdktztIcoL2o6yVyHJIaH3DyLaMMS7IJ1JcXExHRwe/+/2TLFu2irnVEztyFxcX09bWNub9X/ziF3z2s5/lK1/5Cg888AAlJSW0t7efVc7tpWIoNSNuZzhnLhdx+27CkTg76no40jREIBgnFIwTDMYIheKEQnHCoTjxWAqdXkanS096/fHJaNCQ4TDgdBjJdBrIyDDisBtwOPSYTdrz1pBuaBzkiacO8cJLjYSnoDzLpYBGK+HIMuHM1JHllMjOEMhxJMmzxcgzB3EmhhD6+0l0DxAdiqTF73CUmC9B3Bcn5o8T88VJhJIIopQWuw4rWqcdrcOGx5zBgYCORslJs1dDPHH29029UYOsEQn6Tm8SNhE6rUAsPjX3bpNZS3llJrU12SysyWF+TfYFc1GN9g/R/JvHafrZnwl3ph2MBRHs5VayF2biWpBB5jwnMXsBg8Zy3FEHgahIICIQikAwIhAKK4TCKqFwCkmE4lyYnROl3OHFEu0j2dRKsMGNt9nP0BEfQbdK0d03UPyhm8m6YtG0DW+fDNNZvE0HpvvxURIJdn76H7Fe7+LfXimn+dDASZ9rtBL//b3rWX1F8ZRsb3DHfvb/6w9J9hyi9Gu3cDBnA08+5ebgjonbnDabno98eD4fuKsWk+nSzMk98TzIy1/AD3+yk8OH+8esV70kn49/vppl3scJ7jjCO9/cTdyfwFJRwroXf425tPBC7/oF5WKKW1VVSUwwwnm+UZJJwp19JHzHfVD0Di16p46kpCMq2Qj64wS8kTHVHSB9nebmWFBVlf6BEPFokvFad4IgYLNrsRFASMnoc7JOCkE+kdH6t0uX1NDT03lM3N577y309XbT29tNVkYG5WXlx8KQc3Nzeeyxx076nq6uLubOnUtmZiYHDhzAaDS+J8Tt5R1zMsMM54DRoGXtFSWsvaLkYu/KaQmHE7z8ahOPPXWII/UDp/8DQGeQyS22gwqplIqSUkamFIlYEs9QdMJe/elGIp6iv9tPfzccGedzSc7AnlGII9NIRqYG17z06K/TnMJpTpJvTmDRRBFiUZL+AAlvkMM9Jt7qdrC/TWSo/cRe29MfFL1RQ2GZg8J8HcU5AuX5GvJyjTgcero7OlBUyMzMpbk9QGtfiq4B6OlL4O4OMNgXGLccwYmcrbAVRYGSMic1NdksqM1hYU02RUX2izZiqXdlMPdr9zPnK5+k5/m36H5+I549h/AeaMTT2MKRR1oQJAFnpY3shRm48k3kSAKCLCLK6bJUojSyrBdJxVL4tgTwNvs5fNSPvzOEIS8H56JqHIuuYt5nFpC9bjmi5tIeiZrh8kDUaFj6m/9mzxf/jW9fdZh/19RwZK/72OeJeIovf+VF/uvb17BhXdk5by9z2TzWv/wb3G9uY+9Xv49VeYFvfP197L5lDU890UXD3rElsny+KD/+6XZ+/1Ad994zn7vvrMFhn16dBJPB60vy+pt+Dh5+ftzP199Rzd13OJnX/xBdzxxm9w8PoiRVslYtYs0zP0WXMXW1QmcYS0JRebvnzB35pwydBVzv8rk4Fvw04jlimvi50T0cSi/IAi4YI26NZi0ZpiSiz4suJ3vCEGRIl6Tq7vWPcToHeOTRF/jdrx/gO999gOuvvY4//OnhU/6s+++/H7/fz+OPP47R+N4o/QUz4naGGS5p2ju8/PFPe3nhpUYikfHDZk4kt8hG1cJcFs+VWZLTi1R/kEjbAOG2IUJdgyQjMoaSSpwr5yOVzaY1IdHgjtPUHqK5JUh/b+iSEbwnkkoqDLmDDLmDHD3lmgJgHZkAlJHp1BhMGsqrM6kuFamU3eQNtqGNdeGoXobrqmUYcrKAdM943aG02+aiKxZSOd+Aqqr4DjXR9/Ir+LTtpJaY6MidS12PmUP7PfS0ec/6d+v0MpXVLhbMz2XZwjwW1OZgNE4/YSfKMgW3baDgtg0ApOJx/IebGd5ziOE9h/HsOUzDk/WkIieHJQuiiKjTImo1iFoNGpsFx4IqnOvnMusfqnEumos+y3kxftIMM0wKUZJY/ONvs/+fH+Abi3fzHc1S9u88LjKTSYWvfv0VvvFv67nlhsop2Wb2uhVcu+1Rup56lX1f+x9cruf49lfvZutty3j+mU4a9vaN+ZtgIM7Pf7mTX/92N2tWl3DbzVWsXFF03h3wFUVl2BOhvz9If38Id3+Q4eEIwVCcYDAdRZVejhEMxYlGkySTCqmUOjJXSCaViUNENSLv//xybpo3RFn3H9j/0wPHSv2UfPgWlv/6P2fc0mc4a7R6mQybiDbgRRYcaEuKJuxMVhQFd38IzwSVA/RGDbn2JKo/LbZF7amf5X/84x954YUXuPfee7nmmmvO7YdcYsyI2xlmuATZd6CP3z9Ux6ZNracUm4IAFfNzWbAsh5WVEYqTTURef5C+B1qoqxvGsaCWrCsXkXvrHWQsn48x/+R8jkrg+hNe+wIxDjf1MdjvZ9iTbmR4vFG8vhgeT4xINIUkiSOTMGZSFJVYXCGWgGhUxd3lI3oGodM6vYTFqkeSBKLRJAF/7LSjnOcLo1lLXrGN6hory6sV5jq6cSbdiIN9BIQAvrYA/Y9so+k7flKSA/viRThWLkBRQggOCwObduDduJnwkT3oTTEc5VZyF1nRFukp1ngwZ+gwGrM4YNTQ3jhIKnnq36nVSZhNEg6DSmWBhffds4R5S0svyfJLklaLY8EcHAvmUPaJ9HtKMknc40fUyMcF7QQhXTPMcCkhCALz/+ufqH/g53y14m2+r13N7nd6jn2uKCrf+ObrdPcGuP/ji6ck0kIQBArfdy35t66n5fdPsf/+/6Ok+km+/fd3s+N9y/nrM53U7+4Z83fJpMIbb7bwxpstZGQYufmGCm69ZQ6zSs5+ZDOVUuju9tPa5jk2dXb6cPeHGBgMTShMz5XMXAsf/ttlXG3fgb1+Kxu/XcfA/mEEUWTBA1+h6u8/fsnl4c8wPRAlAadThynqQ0oY0BUWnTIFJhyO090TGNc0CsBi1ZJviRDrDaNx2k+7/f7+fv7u7/6OjIwM/vd///dsf8Yly4y4nWGGSwRFUdn8dhu/e6iO/fvH9qyfiM1pYNn6Uq5dCeW+XfiefpCe/+7hnQYfmSsWUvSBT7Hs0evHiNnTYbPoWLlobP7XmebqqEqKSEc9XQfrOBpxcmDITv2RMA17e4mdYgQ6Fk0Ri4ZOes9q0WCz6TBZ9Chqun5jJJIkHEoQDsUndCo8V8LBOEcPDXD00ADPkh4ByCkqo3DWQkoLRKrWxJhz1zCVqhtjfBAlFiA6/Bp2TwylS8VWbCbvWgOBW1bRlczl7W479R0yLZtDtDUMEg2fWXpKPJZiOJZiGGjuHmbTgdeY60qyJE/DNe9fReGKuRe9oaYkkyRDEVLhCKlIjGQ4QioSPbasxBNobBZ0mQ70WU60ThuinH5MibI8Mwo7w2XNnH/8LEd/9Rf+3vUaP153LVvfPPke8Mtf7KC1zcO3/3U9Gs3UdOyIskz5p+6m5MO30Ph/D1H3oV9SuORxvvXlO9n1vhU8/0zXhDm5Q0NhHvzjXh78414qKzIpL3OSn2+lIN9GYUF67nQaUFXweCK4+4P0uYO43el5X1+AljYPHR1eEuOEYJ4v9EYN195dw9VXZ1Az/ByhV+t4+bv7iPniaB02Vj3yv+Res+qC7c8Mlx9KSmV4MIpqt6fNQU8QtqqqkkopxGKpdHslmhxTimqU0Ue2OeElPhTHPHvWpDp1v/CFLzA0NMSDDz5IZmbmlPymS4kZcTvDDNOcRDLFCy828rs/1NHR4Z1wPUEUmLMoj9XrclhX0oX4+p9o+9sDvLN3COeSGoo/eT+r774eU/G5u2+eK4IoYSypoaKkhgpgTcNBhqxv4r3Wzp5QMTv2Rji0s2tSpkv+QAJ/IAEET7vu+SSZUOhqHqareZitI++JooCroJD8klpyXDIuu4orM4FOo9DQqaNxU5KOo8MM9XkB75TuTzCssL1NZHtbip9u3USpaxPzHAnWrp7NktuWo890TrnYTfiDhNq7CbX3pKeOnuPLQ4OEVQVBKyHoNYg6GUGrQdBKI2HFEkgS+uEB5KFh4p44UW8MZGPa3TrTgT47E2vlLGw1s7HPnY21qnQmZHCGy4ryT3+Q9kctfO7Qs+iuu4W3Xu446fNXX26it9fP//33TdhsE+ftnSmyQU/1P36a8k+/n8Pf+xW77/g5BVc+wTf//n3UvW8FLz7fy/7tXSQnKJfX0DhIQ+PYmtkGg0wyqVxQ8Xo6UskUUtcByg820vrYIY482gIqOBZWc+VjP8RSVnSxd/E9h0YUuDLPdrF3YwyqohDtGyA86COsMxERTv+8GX2qKioMeSIMeSJYrTokSSQWSxKLJceUnxrzHYKAzalDEkfWk02Yy4sn9cx+7rnneOyxx9iwYQMf/ehHT7v+5ciMuJ1hhmlKLJbkmefq+e0f6uh3Tyzc9EYNV1xbzo1XyZQHdjP4x19x8PkOElGBWR+9jSUP3Yu9pmLCv58OmCtrMFfWUKSqFO/byxr/bsIrnRxWytm2P8HBHT0MneIYTCU2pwFXlhazMe3I6/cn8XljRM7CeVpRVPo6fPR1TI1RRnGpgyWL8jGZtdQ3DNBydIihgfBp/05VodkNzW4NTx1pQ/dgO9lWlWxdnDxdkgK7jorqPMoXl+EoLwRRQE0pqKnUmHnc4yfc0UOgrZv+5j7cHQP4/EECspaw2UrEbCckmwiqOvxxB/6wA79cSdzxrsZtCoiMTO9CNovYCnQ4HDIOq0iGRcGhS+CUQ+QEuwg+9gaN/zWIvz2EPj8fe00Ftrnl2Gsr0uH1hbkXfZR6hhnOluL330TPyxbue/V3ZH7o/Tzx55PTTw7ud3PPfY/x0x/cTEnx1BodaR02Fnz3H6j4249w4N9/zI5bfkLhuif55y/dQu+HlvHGHomdb7bSeXTo9F8Gk/KCuNAk4gpPva7w3GtFLO3pZ5nQwfx//Di13/riBamNPsNYBEFAK02ve7aqqoRjCj5VR1CfjhqaKLBYZ9Ck87sn6Pzx+ydfHUGrk3FlSmiDQ6gjIljnnLzx4+7duwHYsWMHOTlja2UPDKRNR7/0pS/x1a9+lZqaGl577bVJ79+lwIy4nWGGaUYkkuCJpw7x+z/uZXhoYuFiyzBy1U2zuXV5GMfuv9L299t5Z+cAxqJ85vzz31H2iTvROqZfT+ipEAQB+4KF2BcsBKCksZErQq8TmSfQoZ/FgU4Djc0x2hoHcXeem2DUGzXkldgpLjZSnpei0pWkujKbrMJyRJNrzIMkEIzR2xugp8dPR7eflnYvra0eOjs8eD2Tr796JuRlG1iyrJgrVhaxdFE+DsfYcO9hb5Q9Bzo4sL+dhtYQrc2B09a4jcVVOgahAy0w0pjb40b8k5sMi4AspsOhBEFNz0m/FgVIKCKBaHpkWFGcwEiocIK0UD1m1p0amc6cZEJhaCDC0BjjbwkoRtbMIq/WxKwbNZRmRCjGjaZnL96fPsWev/GgyhYyViwgc8V8MpbPJ2NJDbLpveMUOcOlT951a9BYLVz70/8k/wsf56e/7CIRO3499fUE+MgnnuD737thSmrhvhtjfjbLf/kfzPnyx9n3Lz9g600/xbXwET74wfnc8w/XUue/knfeHmT3xtZzKmt2MUkKMlvzV9Bcs4aCa9fNuKjPAKSNnTzDITzeGPHTRBwYTFpsdi1GOY4iaAnGZIK+6Bl5iRxDAJtDT6YhSry3n6TOfKzMz0mrTVLk+v1+/H7/aT8fTwBf6syI2xlmmCYEg3EeefwAf/zTvgnzLwByimysv6WcG+f2o33tDzR9eA9HGv1kr1/B6qf+nbyb1l42RjuWigosI3XSylMJVtTXEXS1EF6ZxG/IpC9motevo2dIYGBYIR5XSMRSxOMpUkkFrU7CYJQx6AVMBoEcR4o8a4JCu0K2Q0dGXgE6ZzGCznqaPQGLWYe5XEuO4qew+SgVR/cw8PZuAo1tRCQdA8YsPGWzGcqbRY9qxz2cOiNnaVkjUJItUpzsxdXRgL2+AVMshPyGEeNNV+G/81rMN16Fxmw66e+cdj0bVlewYXX6OEVCwxw8dJSN24epOxSm6dDAhL3J70ZRYMB3up2++OGFyYRCR2uAjlbYCIAVQViKK3cdVffpmJcfJjvSSHD7E7T96pf420PY5laQuXIBWauX4Fq9BGPB5fdAn+HyImvlQox5PyTxD1/mW1+6g+/8Oox/+HioQygY52++9Cyf+cwy7vvQginLwz0Ra2Upqx/7EZ699Rx+4Nfs/PqL6L77DqU3FrL8w+sZvHUZO9od9HUFGeoLMtAbwN3pw+eJoJ4HvwOtXsaRacRs12O26jFZdZitOkwjywajBlkrIcsisiwiadLL0UiCzc83ULe5fYwPQ78nzle+9jJLl+Tzt59fQc3cM/OimO4oqRTxYR+xgWFigx4SgRBKPAaJCKTCkIpBKkEyHiGzsx1Fhb7BHjQWB6LejGi0IhnNyGYjsimdJqKxmi+76JhYNMHgoJ9ASDmtV4fRosNhFTFKEaRUACWcQtKK6CQZa5aRsGIh4IsTDsRRJ2gICIKArBHRaGU0WhGLUUUX9hBpC2MoyEPnHH9wYtTTJBIZ31X5m9/8Jt/85jcn3PdzqXN7qTAjbmeY4SLj80X58yP7+dMj+wkG4xOuV1KZyXW3z2JDYQvxx/+Ppq8dItgTJv+WdSx96PNkLpt3Aff6wiNKGsw1yzDXLDvpfTUZJeVzkxjoQkklUZUUiqqgqio6qw2tPQNBawKNCTSTfyCrqkrUPYh3fwOevfUMbqljYEsdsYFhACStiKPCRt49ZVhXziExp5b93mz2t8j4jvgQ/YOkziDPLJVUiWitxGpKsLzvCpYV9GPYvYWBV+rpeeFVOh59EVGnJfe6Kyl837XkXnflsRJDJ2IwOVm6bBlLl0EiHqGvp41N23vYcTDGwb0DePpD42z90kdVwd0TxN0THBG8hWTmVDHnDiPzS2KUKK1o6nbT9J2X2PZRL4bCfFxrluJavZis1UuwzC657BprM1z6mIrzWfK737D3i1/lgY/P4T+etNPd6j32eSqp8rOfbufZ5+r5+leuYuXywvOyH44Fc1j1p+8z/z//jiP/+yBNv36MI3/6JdlLnqJqzRxUbTk9fVbaj0I0dm6i1mzTkZVnJSvXgtNlwpltJiPbjDPbjNmqS1+nqoJGiaBJRZBTYTRKFE1qGEmJIaopBFKIqoKgphDVFCmtzOz759F5dw1vPHWYnW+2jhHfO3d189FPPMHqVcV89jPLmFM19v46XUn4g/jqm/EdPkrwaBvJ4W6IDCERRGtMoc/Qoct1kMjIIqS1ExQM+BUd/rhMJK4lntITS1qJO3ORRJXmVApzJIFV7cec6sEciWLsD2NIBGDIQ9ztIxkVSCV1KKIJdFZEcyayMwdjQR6mknxMRXnIxulbE1lVVaLhKH5/mFBEJRo79fNaEARMFg0Os4I25iM1FCMSSZGMJlGVtEOy1qJBa4tj18uY7XqiDhOBgEI8lkQQQKOVkLUSOllFJ6WQ1ASiEkYJx4j3xogpGiyVZUi6icPjy8rSNa937NhBKBTCZDJNuO57FWGiHoUZZpgsgiAUAJ0AjY2NzJ49+yLv0aXB8HCYh/60j0cfP0gkMnEIy+zabG64vZirnAcJ/uE5Gh9tJOaJU3jnddT8y+dwLJhzAfd6fM7ULXm6kYrG8NU3493fkBaz+47g3d9wTMgC6DN0ZM51kDE/C+PKWvx5VdR12znYItDc4KGzeXhKyxKJosCs6iwWLbRyVZWf3M49DL24h54tboLd6XB1e20FOdesIufaVbhWLzllQ0JRUvg93TS3uWlqC9Dem6SrL0lfb5j+Lj8B79mHVhvMWsxWHRabHqtVg80m47BpcNi1ZDr1ZGSYyMqy43I5MRm1CIAogoB4LNxZFASSSQX3YIje/iC97gD9/X4GB4IMDoYZGorQ1xPCMzR+b/VkyMwxM6/GxPLyMHOjh4ls3sfAnkE8TT50WZlkrV48IniXYKutmPYREJf6dXe+uZyOj6ooHPqvH0GWhwf2zePgbve4662+qoSv/v1qcnMs521fvL4o77xxhFcffoe9rWH88tk1rh1ZJnIKbbgKrLjyrWTnW8nKt2Ky6BCVOIaEB33Siz7hHZn7MCS96JI+ZCXKmXZFqYDHUEq3bSkHBrJ4+rd7aDncP+H6q68s5p73z2PZ0gJEcXp0fKmqSqTbzfDug3jqDhHtOIIYdaOzpjDOziGUV0qvkEWnx0DXsEzvgMrgcAq/P0HAGz1lNYLJYjBrsTn02GwanHYJhxWyrCkyrQmyzTEyRS+OkBulq4+EN05SMaDKVuLrrkfKyERnMVNWXIKo1SLKF+4eq6oqqVgUvy9MKKYSiakkT1NiD0CWRSwWGYucQIolUBQhfe6lc3dGv5yEL0Aykg7Rl7QiOpsWrUWDqtWRErUIqoqoJhCScZLhJMlIkmQkRSqWQtRq0WU50WdnnNTJOjrK+uabb7J27VoAAoEApaWlDA4O4nA4qKysRKfTkZOTw1/+8pfT/p5zGbltamoimUwiy/Jp2/pNTU1UVBzzeylUVfXMSkCcAzPidoZzZkbcnhn9/UEe/GMdTzx9mHhs4nDRqkV53HhbIavN+/D85mmaHmsmEU5R/IEbmfvPn51WJlHTvRGpqiqxIQ/B5k6CLZ0EmzuOLQeaO4h0n9xQNGUbsJdbcZRbMdcWINfMoUsuZk+7icPNKVqODNLfNXEuy/kgr8TO/MWZXFUToSpxhNBbu3Dv6GNg3zCJUBJRqyHrysXkXLOK7HXLsc+rRDac2k1VVVXisQDhoIf+IS9H2wP0DcRRlHRDcHSuKul1FVVAlgScdplMp4GsDCNZmVZMZis6nRGtJJ330c9BT4T99f0cqnfT2NBPa4uH3q7AGYWAQ7p0U1mVk8VzZa4oHiK7dQ/D77TQu6OfYHcYjc1C1qpFuNYsIWv1EpxLaqad2cx0v+4uNpfj8el47Hl8B57jIflmXnu+Y9x1tDqJ++5bzCfuXYhWe+7iweeLcqRhgF17eti2vZPD9f1ndL1Jskh2gZW8WQ7ySxzklTjILbZjNGvRJIMYE4OY4oMYE4MY40MY44PoUv6TxGsqoRDpjxAeiBIejBIPJEgEEyRCSRKhBPFgep6MpFBS6ogBnoqSVFFTKoIkkLMkk6K1eWTWOPDrcjnq3MCmvSLPPViH7xT+FkWFNt5/Vw0331iJ1Tp1DtWTIRWL46k7zODW3USO7oNgF9o8E8NFc2gnl6MDBjr6wN2fZLA/dFJe9sVEFAWsTgPOTD0ZThmXU+CqlVk4bAaMJiMl2U7EVAI1kQRVQEUCUUKQNAiyjCDJCLKMqJERZAlBFCf1bFFVBTURR43HSMYTxJIKsZRAIgXRuHDa0dkT0RtlrAYwyyAbjCgqIAioCCNzAAFVVVFVFUEUIJkk5Q+Q9PpJhdMdxhqjjKQTURU1LWbjCoIooLGY0djMaKzmCV3/xxO3APv27eMb3/gGW7duZWhoiFQqRXFxMW1tbaf9XTPi9nxtVBAygXwgC8jguA3JANCiqurFT+qaYdLMiNvJ0dHh5fd/rOOvLzSQPEW4au3yAm66NY8V2j0M/vJZjj7VSiqmUvLhW5j79fuxVpZewL2eHBerEZmKx4l7/Ol8okEPkZ5+wt1uIj39REbmo69TkbEjkzq7FkuBCUuhCdssC+baYqTqCnrUXA71mmnqFGlvC9HVPIzfc+YjhoIAJbMcLFyQR+XsDDKdOpqb96GRBTKy5nDg8CB79/fRcnTotKUBTsSeaaRynouFVSLLSzzY2w/i3XiA/l0DDB32oCRVBEnCVl2Gc3ENjsVzcS6qxrFgzrQOEztbIpEEBxsH2LG7m7q6buoPDpyxu7Ujy0TtfDtrauMs1jUQ2VRH3/Z++uuGSEZTSHodWWuWknfjGvJvWoulfGy95wvN5SjeppLL9fgM1x2m7SffpveG9/GLxxN0tXjGXc/uNFBd7WJuVRbVVS4qKzLIdk2cmqEoKoODIY40DnLkyAANjYPUNwzQ13fmTvUFpU4q5udQMT+HkspMZI2EIT6EOe7GHOvDEuvFHO9DmzouKmP+OP6OIP72IP6OEKG+MGF3hHB/NF0WbIqaq8YsPWW3FFF+RwkB11zqzWt5/c1h3ny6ntApXG0lSWDpkgI2rC9j7ZoSnM6pN6kLd7sZ3rGLSMMe4kPteE0W+uwltEedNPfJdHQn6esJkUpees3kL36+kqJCCw6HCZ0+CwSQJBFJEpBlAVkCSVSRxLSZoSiAiIKoqogoCCN6RSEtMBUEFDUtMhU1vZxICcQTKomEelY17kfzYCUxfT0oyuj8bL4rXaJRFAREETSSil5NoImGkQURUacdKYOnQdJpkYz6Y3Xdpysz4vbEjQiCBbgNWAusBspPsXoI2AZsBp5XVXXPed/BGc6JGXF7ag7X9/PbB/fw5lstE/Z2C6LAgiuKuOlmF0vUHfT/4q80P9uOkhQo/djtVH/t/mlRey8VixMb8pAMhkmFoyRDYZKhCGGPj73bd6DGk8ytrkYz6jo58oOP3WfeNT/+fnpZTSZJRWKkojFSkSipaPz4ciRG3JsWsvFh37H9OBWCCDqHDqPLgCnHgLXAhLnAhGF2PuKsIsImF60eG60DOpq7BTpag3S1DJ91iK4oCpRXZLB4YR7LFuezcH7uST39EzW2I9EEhw/3s2dfL7vqetlb10NikiZQkB7VrarNYHFFiiXZfWgO7ye4txVvkw/PUT+BrhCoIIgi1qpSLJWzMBXnpaei9NxYlIcu03FZ5J2mUgqHGwZ5Z2cne/Z0c/igm3Bw8mJXb9Qwd6GLK+bBmuIetPv30L+li56tbgKd6Zxly+wS8m5cQ96NV+Fas/Si1Nu9XMXbVHE5H5/owDCHvvkAhpIIL5uv54nHuwmfwrNhFItVR1lZBrIkEArFCYUThENxwuEE4bNxeB3B5jSMiNlcZtdmY7HKWKNdOCJt2CIdWOJ9yEpaOCpJBW9LgOEGL96jfvztQXwdQWKe0+8/gCDLaB1WNDYLWrsFrf34smQyIGo0iLKEIEuIGg0pVaGpsQmlvg31cCtKPP07NSaZ2bcXU3F3Ob78BTQYlvPa617eeqb+tMdSFAVKy5wsmJfDwvm51NZkk5drPaPw5bjXi2/3DgINB+ga8NKlWuhSnXT59HT0KfT1RE4Z3XWpMUbcvofRakX0WjAQRxMOkQrHUVIqkkGHxmxENpuQLcZpFy00I24BQRAWA18C7gRGW3iTufJP3KkG4CfA71VVvTydUC5xZsTtWFRVZfuOLn77hz3s2tU94XqiJLB4TQm33JjB/NhWen/6PK0vdKIiUfbJu6j+p09jKj51mQdVVUlFoqiKAoqKqijHJlRG3ldQRz7j2OcqqXCEuC9Awhck4QuQ8AWIjyzHBj1E+4eIDYzOh0n4L0yt2dMhGyR0Vi1amwatRYvOqkFn12HM0mN06dHnWJEKciAri5jWxlDMQrvHSHu/hg439PeEcHf5GHIHzylPVquTqJrjYvGCPJYszGX+vFyMxonLSUy2sR2JJNi6vZNX32phyzvtBM6gRp4kixSWOykotlJaIFCRG6XCMoCmvYVwXRPehmECXSEiA1EiQ1GUE3KOJKMBY0E2WrsV2WJCYzEdm2sspnT4lFGPIEkIkjgyl9INyRNeH18WR0LMRMSR1xq7FX12BnpXxgXrpVYUlfqGAd54u42tW9tprB+cdE+8JItU1LpYukDP1VWDZHfvwf16M52b+/AeTYemS0YDOVevoPB911JwxzVobecv3/FELmfxNhW8F47P4I79HP2fB9DfsYzf7C9ly+sdZxyifzZotBJlc13HBG12vgVrvA97pBVHpBVbtBNJTed3+juCDB3xMnzEx/ARL56jfpRTRC9JBj2WihLMpYWYinIxFuVhKso91gmnz3IiiBNVHB3LiefB+iuuxPtOHe1/eo7OJ19FTaWQ9BIl1+RTflsJydolNOqX89b2OFteasR9BuknsiyQlWUgL8dAVqYeo15ErxPQyipqLEIoECUSTRJNqPgiIsNBAY83hc8bP6uRwUuNGXE7PoIgoNNLmHQKxlQE1R8kGUlXW5C0mrRLtcWEbDIg6XVndO5PNe9pcTsiar8NXDv61si8F9gJ7Ab6gWHAAxhIF0t0ABXAUmAeMNpKVEfW/X/AD1VVvaSKqgmCUAR8EbgJKAJiwFHgUeCnqqqeevjp7LaZC9QDo17iG1VVXTvV2xnZ1kURt6qqEu7sxVN3mOG6ejx1h/HubyAZCCEZ9COT7thcNhowlxWRd8NqstetOC8jLYlEildfb+YPD++lsXFwwvUkWWTZ+lJuvd5Gtf9tun7yIofe8OAxZmG7YT2W9asJC1o8vghDfT6G3V6CoRgaQUEnKmiFJDoSaJU4WiWGXo1jiXixBAYx9fci+72koilS0VT6Jnm2D04hnS+iMWvQmGS0Zs2x/BFJJyFp03NRIyJp06/T6SjCsb+Hd70WTujhGimimv5YQJSF49+rlZB0IqJOQjToEPQ6RLsVrFYUvZmEaCSKHm9Mz3BYy1BIQ79Xon8YhoYTeAZCeAbDeAfDxKPnbqIBYLHpmFebw+KFeSxekEdVVSaaMzDEOJvGdjKpsHdfL6+91cJbm1rpP4sQQUEUcOVZyCuxU1Igk5+RItOcwGWK4BS9aIaHSPYOkGjvI9rnP2ZykYyl56loOk8oGU2hJhUEWUSUBURZRJSE9GtJQJAFBK0MRgNRo4OQ1kxYYyYkGwkJesLoiSUFYkmBeFIgrkokVCk9V0SSqoDJoMXpspGZ58BpN+CwG7BZddhsepwOA7NmOc655EkgGGPz1g42bWln5/bOSZtUiaLA7HnZXLncyDWVbjK799L3Ziudm/sYOuwBFUSdlrwbr6LknpvIu3ndafOez4X3gng7F94rx0dVFFr/+AzeTY/Sd8Od/OqpFO1NQ1O6DVkjkltsp7wmm4r5OcyqysKqDuOItGIPt2GPtqNR0hEvge4Q/XVDuPcM4t47NOGIrGw2Yp9XiW1OGdaRyTanFFNx/pQ24Cc6D8Ldbpp+/meO/uKRY+aBWbUOym4vwb5hIW7HQrZ05LD5lXYO7+4+ZTrRxcBk0aIzyETDyUmN2k+EVitiNGkwmWR0OkgmVQKBFD5ffEpNEi9ncSsIwoQlf84UnUHGYgATEVR/iEQocawTWhAEJKMe2WRIi13jiOC9QBFX71lxKwjC74CPAKN3pj3Aw8ATqqqO734w/vdogTXAh4A7SIs0FegAPqKq6ttTud/nC0EQbiL9+8cvWJUemb5RVdWWKd7u46RHzEe55MWtqigM7TxA93NvMLhtH566euLD3mOfG116Muc6MGTq0Zg1yCYZOcOOmpGFX5+BO2VFjcaQ+9wIbT3YTDZcq1aTd9M6DNmZ57RvHm+EJ548xCOPH2ToFMYUOoPMsvWlLKxQSTXs5+Dr3bT6LQwaMggrUzeKpdVJ2J06HA4tTruIzSxgNylY9SlsugR2KYpNjGCUY8iSgEYrIGvSuSajIkXU61B0OmKKlpiiIZqSiac0xFJSOq8lJZBIMJLjAokUJJPC8XwYdcR0YSQnRlVJjyQDcPJ7o02GZEognoRYnJG8mZF5PEUiniISjBMKxAgHYoSCcSLn8EA/HXqDTPnsTKqrsqid46K62kVJsf2cHiLn2thWVZXmlmG2bOvgne1d7N/XS2wKhLvBpMHiMGC1G7DaNMiygCSO5D9JIIkgiyqSqKKq6VIf0ZhKNKoQjaWIRRVi0RSxWIpwKEE0FD9vo0darURVVRYL5+Uwb14O82pyyMg4+9w3VVVpah7itY2tbNzUStORiTulTkTWSsxdnMeaZVrWl/Zg791P92uttL3chbc5kF7HbKTg9g0U33MzuddcgaiZeFT/bLjY4i0VjZHwB0kEQiSDYZKhcNrIR1GOpR5IRgOyUY9sNKQbZUYDkkE35cdiPC728TlblFSKqHuQcJebSLeb2KCHuNdPwhtIp2acsCzptBgKcjAV5qLLcuDZtQfL7BTuxes53GfmaLdAd5uPnjYv0UmGHeuNGvJK7OTPcpA/y0n+LAeuPDNm1Yct2oEj0oY90oYule5oC3aHGDzkwV03hLtuiLB7bGeRZNDjXFSNc0nNsclaMeuCjEKd7jxIxeL0PP8WbQ8/R/df30SJJ5ANEnkrXOSvL0K+ei3d+kq2HtGxf0cf9Xt6pqzDdDLo9DK5Li3ZmRKiXsuQX6Cz1UckdGZh5BmZesrL7VRVuSgtyaKk0EZhoR3bSHmldx8nnU6PPxBjaCiMeyBE/2CQvj4/XV3DdPcE6O0NMTgYnbQAns7iVpJFZElAGnn2yZKAJI3OBWQpnYc70j8PaVupNKqanoTRPGBOmlIKJJIq4UiKaCR1RiJYZ5CxGAXMUgwhFCIZPm6aNoogishGPaJeh6TXIul0iHotkk475dfXe1ncKkAceBD4vqqqjVPwnTrgbuDrQBXwTVVVv3Wu33u+EQRhPrAFMAJB4DvAm6RHqj8IfHpk1SPAUlVVpyTmUxCEW4BnSY+Ou0beviTFbSoao++NbXQ/8zrdz71BpHfg2GeWQhNZ85y45jmxLplFML+KPT3ZtLkl+gYU3ANxBvtCDLmDEz6INFoRo0HCoBfIdsjMrcln0dIyqiqzyM05fU3U5pZhHv7LPl54sZH4KXIkdXqZjBwzqUiY/v7z1/CfCkRJQJRElJQypb220xm700BxsZ3qqixq5riYU5VFUaF9yss/THVjO5FIceCgm3e2d7BtRxcN9QPvifC2d5Ofb2XBvBzmz8tlzZXFuFzms/6ugYEQr77VwmtvtnBgb8+kjL4MJg3zVhSwfpnEVblHEQ/U0fZSB+2v9xDzpjtgdBl2ij54E7M+ejsZS2unpKf9fIs3JZkk2NKJv74Z/5EW/A2thDv70kZtPf0kvGfvGC7IcjrU/VjOpBmt3ZpetltOzqe0W9HazOn5yHuy1XzaUk3TUdwmgiEi3e600V23m3B3/7teu4n2DaKmxn+eiLKAzq5D79Sit+tQVYj54sR9caK+OKlo+u9EjUjG3Aycc2xYV85BnFdNp5JHfa+J7gEBWSOhM0joDRr0Bhm9QcIwMuVYIphTw2nn4sQghsQQatBDIAjxuIqnJchQo5/howGGWoJE/Un0yRj2mPdYg1/rsJG9bjnZ61fgWrME65yyi2aWcybnQdzrp/PJV+h+9g3cG3eS8PqR9BJZNQ4yF7gwrFtKuHw+B9126lsU2hqHaWsYIBw4945Wk1VHdqaWLG2cjMgwxbYgjopMWoRcdtcrNNcPTfqZLAhQWmZj4aI8Vi4tYUFtDg7Hqc//s40s6nMH6Oj009oxTHvHIF1dPnp7Q/T1hYlFj5/HF1PcimK6A18ji2hkAY1GRKsR0WkltNoRd2bh/He0KIpCKBQjGIoTCifPKKdaq5Mx6EWMegUDMdRwhFQ4QSKcPCm96EQkrQZRP2JcJY86UEvpuSwjaKT0dSkIk3omvZfF7U+A75yPHyGkj/zdgKSq6p+n+vunGkEQ3iRtopUE1qiquvVdn38FeGDk5TemQrALgmAGDgOFwEeBP4x8dMmI24Q/SPdf36TzqVfpfWnzMdMgSS+RtzyLgjW5ZCzJJ5JbSUMwn22NRg4c8HP0gHtKRrFGMZs1zK7IorI8A0VRCYXjhEKJY/NgMEZHh2/KtjfD+UUQBXJzLRQX2ymd5aR8loNZJQ5Kiu0XrLzD+W5sB4Nx9u7r4XDDIPWNAzQ1DdHb7Z/WnSlTjSDAvNocrrm6jKvXlZGdffZCNxCIsemddl55o5nt2zqJx05/f8nKs3DFunxuWhSgIrUP39sNtL7cRc+2/mN5htbKWcz66O2U3HsrpqK8s96/qT6fIu5BBjbvYmj7Pga37WN496FxXcYhLZ4MTl06ZcEoIZs0yAYJURQQRiaAVDwd0p6KnTAfWU5EkiSCyVPmX54K2WI6ZiYkG/XpsiEaOW0opJFRBIGB4SGQRXLz8pBHG3GieLwxJ47Mj73Psc9Pek8QYLQciTDy+8b5LoBkMDx2lNUXID7kPbVvgQB6pw5zrhFzvhFzrhFjoQ25KI+UM5MhMYP+uJkerwa3R2LAo5JKqei1oNepGLQqellBJyTRESczOoB94xsMbGpD43Bir8jElJHEnKtBa5SR9RKyQR6ZJASthDusp6VHoq1PpmNIR3fYQm/MiC92emGq10Bpto75tTksWzeH2tqc8+IofKac7XWipFJ499bT9/pWhnYcwF/fTKCpHdQklgITpiIbxkUVyHMr8Vtz6Y8bcAc19PsE/EGFeFwhHlOIx9KpQToNaDWgk8AgpjDHA2h6ejC4+zB3tZKZ6sd1bRU981ezczCLun1h3N2T70Cy2XUsW57PhrWzWbGsAIv5zFKvpvp+oqoqXm+Uji4vrR1eTPoAJqOM2azHZM4mlVRJKerZp02NIAgCogSSmHZd1sjiMfGq1crodJq058M0NEtMJhUCoRgBf4xQKDH5UV0BdDoZvV7EpFMwSHGIxVGicZS4kr7vxs5sgEIQxfR9bWQuiOLI/XFkyBqB7rCfqM9PrGeA6MMvHbsXnnwPTb/XG/Tz/hd/P/r1l7a4nSGNIAhLgR0jL3+hqupnx1lHBA4Cc0jnHmerqnr2doXp7/wR8LfAm6qqrhcEYfQ/eFqL27gvQPdzb9Dx2Ev0vvw2SizdCzoqaAvX5pKxqox+Ry1vt+ex+0CMhn19DPQEpvS3XGw0Wgl7phGjWYvBpMVo1mI0yViMKmadQjyhpENBowqRqEIsqhCNpggFE3g9MYK+SyodfUoxGDVkZprIyTGTm2MhL9dCfq6FnBwLuTkWsrKMZ5Qfez64GCNJ4XCCpuYhjjQMcKhhgJYWD0NDYTzD4QvixKnVSRjNWsyWdE6XwSCh1YrodCJarZDuPdcJaLXpsPhgVMIfFgiGUkQCcULBGOFAnHDw7M7v2tpsrrm6nA3rS8nJPnujp0gkwZsbW3nupQZ27eg67YiuKAlUL85n7WoLG0o6cLr30fVaG20vdzHcMNIpJghkr1vOrI/eTuH7rkFjOTMhfq7nk5JKMbzrID3Pv0XPCxsZ3n3opM81JhlriQVbsRlLsQVNZREBez79goP+qIk+r4w/JBCMQDCsEg4rJJMKyaRKMpVutB4r8yELaEbKfcgyaCTQacGgUzFoFAxSCp2YwCDE0aVi6FJRtLEQ2mgITciPxu9D8vtRgzGS4XRN00QoLY4ToQSp+PTKhxwPSS+lTe+y9BizDBhdegwuA7o8B3JRLtGMPNqDNjqGDXQOyPQMKPT3xRgeCOH3RM5KBGh1MrPLjcxx+Cls3IXmjR045s7BuayGiMVJkzvBka4ILREtXQkDCXVqR69c2SYWLczng3fVUFuTfVEExlTed5VEIl0rvbUrXYbO4yPu8ZMMRdLGepr0iJhk1GPIyUKfk4lsMuA7fBT369voeWEjscF06SZBEsiqdWK+eQX12YvY0WrgwD7PGY0CF8+ycuWqWVyztoyaudnnFG10vp9PE436KYpKMqWQSiqklHSI77G0JdI1ZNMpTSqiIIyEEEtIkoAoilMeYXWxSCkKgWAcvy96ZkJ3BFkjIWtEtBoBjayik1W0YhIpGUeNJ1CTKdRUuu7zaC1oZeS1qowc49Ns0k2C8OAQocZ2ur/0wCnXHSLBF2kdfTkjbi8HBEH4T9Jh1AArVFXdPsF6XyUdrgxwraqqr57DNpcBW0mPFM9TVbVhOovb2JCHnhc2Hhe0I/b8JwrazCtmMeScy5beIt7eEWX/ts5T1qE7FbKc7o06kxIr5wtBFMjMMZNbZCen2E5BgYHynCilliE0w30I/hBSCvRGG8aS2RiKKxEMGQjixL3nqqoSjiXp6QvQ6/bT5/bT1xdkaDiM1xvF543i88Xwe2ME/HGS51gnT9aIaEZuphpZQiWFJAnodLpjD5u0X5TAiSZTIx186ffh2GfCSM07nU5Cp5PTk1ZCr5PR62X0OhmrWYvDYcBu02O367HZ9NhHDIfO1WToQjCdwiRVVSUYijM4GGZwMIx7IEhff4hhb4RUalSgKOnlkdeplIIggMGgwWDQYDRqMBpkjEYtRoMGk0mDxaTDYdenJ5seg37yuZWqqqBGPcTaGwgcPUw06CGlEcBpBUcm7qST/Z0Wjh4N0t4wSEfT0BlFa8yrzebG6yu57ppybLazH633+qK88tpR/vpSIwf39512favTwLK1xdywLMl8w2E4coi2l7toe7Wb6FD6fibpdWRvuIKCW9eTf/NaDLmu03zr2Z1PsSEPva+8Q88LG+l9afOxhjakUz1c8504azIRF1RzRC2lvtdASzd0dsVwd/kvuqmOKArpcFqdhE4vYtAJ6HUCBn16rtMK6LUgSyAKChIqIioCKqKQXhZHqmSmP1PQiim0wshEEg0JNGoCrZo27dMoMbSpOHIyjpyMoUTj6SqbooAgnTDCIaZN8WSrCdFmQrCYSRpMxHVGYpKBsKLHHdTT79cy6BcZ8gkMe1P4vTE8A+GzqqV9puj0EoVZMOxTGPZf2PZfRWUGH7lnAdduKL+g9+uLcd8NHG2n+7k36f7rm/Rv2oWaTN+nNGaZnFX5JK+5ijpms7MejtYPT7purSyL1M7PYu1V5Vy9ZhZ5udYp2+eLJW5nGIsyInR9vhihUPyczKoEUUCS0p0AosgJc5AEFXF0YBY1be450qlwjBO23T/YTygYxu8LcnhPK+pobrFC2l9FhdSISPb6PDzwwy+P/umlL25HRg//oKrqrin/8ksEQRA2ka7pGwLsqqqO2wITBGEl6bxcgG+pqvqNs9yeDOwC5gP/oarqv428P23EbTIUpv/t3bhf30rf69vw1B0+bjpygqDNuqIYj7OaXUOz2Lgzwb6tnXgHJ28oXVzioLDIRmGBjaJCGyWFNooK7biyTEiSSCyWxOOPMTTsZajPjbu9lwFPlFavlvaOON2tw2ds1HAqREkgp9BGQamT/FInRaVWZmeHyVTdGAOdpA42kHSHkF2VWJetx75g3nnv3R4VNtFIckS4jIiYpEIqpZJMppAkEa1GQqOV0GoktFoJzbG5eNI+TifRNp2ZOU5nRzISZXjnXnzbXkP1NGBeUERq9hyGtcXUDzo4ejRMa/0A9Xt6JmWaI8sia64s5uYbq1h1RdE5NbR7+wK88HIjTz93hO7O06cplNdkc+XabK6rclMQ3od3ewutL3XR/Y77pNFH59LatNC9ZT32eZXj3hMmcz6pqopnbz09L2yk54WNDG3blzZ8Ih1a7FrgJHdlNqa1i6iLz2bnUT31jVE6jnom3eB+LyGKArJWQpLFkY464Vgo9minXjKRIhFLEY+fg1P9JcDobxYEzqjTw+7QcecdNXzwrtpzMoObLBfivquqKr6DjbQ/+iKdj7+M/8hxj1BrsRnXNRX0LljNzqFs9uyP0Nc5+XBjq03L0uUFbFg7m9Uri05Zbu5cmBG30xNFUQiHkyN5uvEpTb87U2LRATyeEB2dAX7004ZTrhuPezi0919GX14W4lYhLfsbSOd8PqyqaueUb2gaIwjCAJAJ7FNVdcEp1nOQLnME8Jiqqu8/y+2NjgA3AzWqqkZH3j9ncTsiXk9FDukST+zfv5+iTBfR3oFjU6i1i4FNuxjevg81cfyilA0SucvSgta1shBvRjX7AqVs3K1S907npEOOrTYdS5bks2p5PsuX5p/WNGEiokMeXn/wOV4+lGJPK6hn0K6TZBGb04A904jNacSeacSRZaJwlo3y3DhOxY011oPc00poVwNBt4qQUY558RVkrlmObLq0RU40GmXTpk0ArFmzBr3+wuSwXmrMHKdzR1VVfHuP0P/iS6Q663CUyNhXVRLMmE2PtpKdTTr2b+vi4I6uSQldq1XHNVeXcsN15VRVZpx1x5Kqquw/0M/Tzzew8a02YqcJ+TaYtSxeXcyGVVqW2ZuwDR7Gvb2Xnq1uerb1E/cf33dDYS4ZK+ZjKMrFWJiLsTgPY1EuosvJlt07gfT5pNPpSPqD+OubGd62j+Ht+xnaupdY//GyMIZMPXkrXWSvyiexeDkbO/LZdlDlcF3/pN10Z7h46PQSmVkmMl1msrNN6PUaotEE4XCcaCRBJBxnaCiC1xMjGjm7RrDJqiMrz0JWroWsPCvZ2TqKshLk2aLopCSSoCALqfQouJpCQKHbb+RQl57G1iQdTcN0tQyf1lFYlgXWrS3m059YQn7e+asNfb7uu6qq4j/YRPeTr9L91KsEG9uAkU6jhZmYblzKkaxFbG8xcGCf94yizgqKLKxcWcS61cXUVrsuSOjt+X4+dXR0kEql0Gg0lJWVTel3v5dIpRTCkSShcPq6vxDpRaPMiNs06gnzjaQdlJ9QVTU05RudRgiCoAdG44ueV1X15tOsHwRMwDZVVVeexfZKSefuGoDrVFV95YTPpkLcTvok+ZGmgowJ2keiRiSz2k72okyyF2Viq87EY6mgPjabN+tk6rZ00t3iGf+P30V+oYGKUg3lpTpyczTnNNLp8SSp2xdm38EIfv/pGwOCAAVlGVQuyKFiXjb5WQIZpigGJYgu6R+ZAmjjwyQPH2X4wACD3Uk8Qgbx4hLEmlJE50SVoWaYYYbJoqoqSlsvytYD2AZaKKw1krOhnED+fHr1Vexs1LFvWyeHdnRNKhojM1Nmfo2ReTVGrNazH82NxRQOHo5QdyBGd9fpQ00LSp0sX1vImpoY5bpWHIEmfAf66N7qpvsdN4HOUzwyrSYEuwVCEVRfEJInN3QEETKqHeStcOFaVUi4YhGvH81l084E9XW9ZxVmLGtEHE49Nkc6l1qvFzHoRfS6tCOpJIIkCQiCSkoRUJT0bqUUlVQKUikVJQWJZNpwJxFXiMUVYrH061g0NTIlL+ooxcXC5tCRkanD6ZBx2kXsFhW7TcRmlTAYxDHPu1RKpbk1xqHDYY40xoifQ/6xKAmUVGZSVWVmYXmSpZkdWPsOE2jxMnjQQ9gdIRFJkoykSIZH5tEkOpsWW4kFS7kT3cIqKCunJZHD9iMaNr5y6ugrSRJYssjMlStNmM3TP71E6XST3LSX5Dv7UHvSpcP0GTpy1xQSX7eGPclSdh6GpsNDk45+kGSR8goL5aV6yovBbrs4ztLnk4yMDAwGA0ajkdzc3Iu9O5cNiqKOpBGl77PJpEoqqZJMKlNuJvleF7frgXtJ11kd7Y4b3VAYeAp4CHhNvQyTfgVByCJdhgfgEVVVP3ia9d2kS/YcVFW19iy29yqwYbxtXXBxyyyyDXoMGXoMGTr0GXrMuUZcC5w4a7MI2woZ0pVwaCibvQ0qB3b20Fo/cPovBlzZOuZW6aipNuB0nNuNP5FQOdIYYc++CG1t47uBnojVYaBiQQ6V83NYWCVSKHVh9Tej7DtAuNtHdChGZChKJKQSS2mIoiOqtSBUzEKsnRGzM8xwvlFVFaW5i9TGPWT42yle4SRrbTk+Vy09+ip2NmjYvamdQzu7TivoBAFmFeuYV2tkTqUerfbsTXbc/Qnq9kfYfzBMJHz6HvbCcic1S/JYMU+k1tFJVriJZFs3g4c9hPsihNzpKTwyf7fTsMYsYy+1klnjIKvWiX1eLsOZ1WxszWfTrgQHd3YTm+SIniBAboGZ3Dw92VkyWU6VTLuAyTRWYJ0vVCCREoknRRIpgURSIJZI19iOJ1WScYVEQiU+Io5jsRTxeNooZbTcrqKox+aKCqqippcVUFIqiYRyfIorx+pqT6YM1JliNGuwWjVYrBosFgmzScBsFLAYVZx2CYdDRqM5/bFVVZXWthgHD0c40hglEjk/IeSyVqJktpO5lToWl4ap0beT3LWf4b39DBzw4GsLnJSehyhAhg3BakZj1eLM05Bx+0p26Jfy8uteWg73T7gtrVZkxRIjK1dY0OvPf1mWM0FNJEltOUDipa0o9W0AGLMN5N9YiXvFerb3u9i9N3RG4cZmi4bKOVbKZmkoLRDQ6abXb55qZsTthUVVT+hMHMmNVVUhff9TR++N6fvhmDudetJs1CyZUMCNzx+mzx3isWe70/m6I54pgiggjnqoiBAODfPEHz8/+o2Xvrg99uXpEczbgY8A1wCjimR0o73Aw8BDqqoePG87coERBKEQ6Bh5+ZCqqh89zfodpEv3NKuqWn6G2/oo6RFxP1Clqmrvuz6/oGHJRx/8OMVFGcRkM3HJTFy2ENE4OBrMYVeThsYDgzQdcE86PMeVbebaq2dx7YZSykqdZ7P7J9HYNMRzzzfy8msthIKndiTUaCXmrSjkirW5rCgawOptJrmrjuFdXQzsHyapcZF7y9VkXLkYY0EO+pxMJP2ZWe9fLsyE206OmeN0YVASCfrf2E7XY88juA9ScGUWGavLGcyYT6tYzbZdIXZtbKXtyOBpv0uvl1m7ppgbry9n4YLcsw4PjMWSbHq7nWeeb6JuT+/p/wDIyDYzd2k+ixaYWFAYwEwAfdJ3bNIm/cSHo0Q8MXQWDVq7FlVvIqDLpU8oZnubjT37o+zf3jXpe+6scieLF+exdGEOC2uzMZm0Z/V7pwPner2lUmmxHI0licZTRKPH56mUMiKQ1RHxnC7Lo6qg00ppozW9jEE/Ukd2xBhPls9NwHi8UZ5/oZGnn2ugt/cUZYVOQKOVKChzUjLbQXVxknKHh7qjGrbuTXD0iPeM8oK1epny6izmzdWxojxMhbaD+O4DDNW56ds1gOeo/ySxq7FbcCyuQWfXYplrprN2Nc/v0rHn7Y4JO5mMRokP3jGbD39kKXr9uY9gnst5EGzppO03j9P+0LPEBz0YMnXkX19O/6preLsvmx07/WfkCZJfZGHFikLWry6htjoLSZo+gnYmLHmG09Hc3EwikUCSJIqKik67bm3tsfG6y0fcnrQhQXABHyI9orvohI9Gd2AfaZH2Z1VVJ+7auwS4UCO3giBkAvWkc3v/VlXVH4+zzgU1lPr3Hz+HKmXi90TwD0fweyIMuYMM908+Et3hMHDNhnJuvG72lJQOaO/w8ubGVl56pYnGxtM3ZgvKnCxbP4urF6Uo8OzG++gbdL3VjfeoH11WBrM+ciulH78TW/UZ9UNc1swYJU2OmeN04UkEgnQ+9Rqdjz2HNtlOwdoczKtq6bct4GCohB1vu9m9sXVS96jsbDM3Xl/BzTdWMqvEcdb71N3j5+ln63n6uXqGJtkwFgSw2A04sow4Mk3Ys0w4s4y4nOAwK3iDIgNe8A5FaW8cpKV+YFIhkZIssmBRHuuvmsXVa2bhcp19XeDpxuVyvamqyt59fTz2xEFef7OZxCQiD0rnuqhZWkBVmZaaTDfZ8WYsgVYG6/rp2dpP3B9HNkiEnLnsM9VS53VS354glTyzNqE900hFbTYLqjVcWe4l21ePf/MB+nYO0rdrgJj35E5kQ56LnPXlJK9bzdMteWx+pW1CkWuzSHzinrl88GMrz6mM25meB0oySfdf36LpZ3+m75W30dm1FFxXyuDqDbwzkMf2XQE8A5O7biVJYG5tFqvXlHHt2lIK86dvFNeModTUo6oqkUiScDiOysioJsdHN0crR4iigMGgOefOr/PNmfwfNjU1UVFRMfry8hS3J21UEOYAHyUtdgtH3h7dkRTwCmkjqmdUVb3kCndeqJxbQRD+QHpUfBewXFXHWiBdaHE7d8G30WrPvNFnMmlZv66UG6+bzeJF+ed0gSuKyuH6fl5/q4U3N7bS0e497d8IQtrgJSNTj8uewhwahC43Stsg+miQ+YsLWf2ZG8m/YQ2i5vw4FV7KXC6NyPPNzHG6uEQHh+l68lV6nnsRvdRL4fp8pBXL6DXXsqvTya6NHezb2jEpY6Wqqkxuu3kO1107G/tZlhVKpRS2buvkuRcb2LKlg1Bo8vUtzwVBgNr5udx03WyuvfrcyiJNZy716y0STfDX5xt49PGDNLcMn3b9kspMFqwqZvliE7PlRlyhw0iDffRsddO9tZ++nQMkIyeHxjsWzSX/pqvIu/EqtNWV7Nrbx5YdndTt6aatxXNGOXuiJFA6x0XtogxWV0eYY+6A/fsY2NZF784BBg96UE8I9TaXOTH9y6d54kg22zd2TDiC7HLKfPqeudxx7xVnFTkx2fMg3O2m+dePcfRXjxLtdZO9JAvh7ut4K1zFOztCDE9S0JqtWpYsy+fqq8q56opizOZLI/phRtxOHcmkgs8XxeONED+D8pMmkxarVYfFrJuWQndG3E52BwRhLWmBdicwWrBrdKf8wKOkQ3vfvuA7dw6cb7dkQRDygO6Rlw8AdROs+ueReT3wrZHl1onq7p4NZytuc3ItrFhWwJpVJVyxsgit9sx7ZlPRGP6OXhr2tLP/yAD1nSH2tkfxBabePS4zy8TqK4u5em0pSxbln9X+Xq5c6o3IC8XMcZo+RNyDdD7xMu6XXsZkGiDv2nIS81fQqZvL1v2we2MrDfv6ThuyKYkwr1Di6morV15TTd6iSmTDmYvFRCLFnroe3tjYylubWhk4g2iXyVJRlcWN183mhmtmk5VlmvLvn25cqtdbIBjjsccP8sc/78PrPbUnRG6xnUVrSli2zEGVvgVX8CCagU463+ql4/VuBg95TnL+F2SZnGuuoOjOa8m9YQ3GvOyJ9yMQY8fubrbu6mLPnh7amk8vsE/EVWBl7uJcltVILM3vw+E5gn9bI+6dA/TuGCDUlx4DsNbkIHz5MzxeZ2bftu4Jv6/QJXH/B+dyw4dWnVFE16nOA1VR6HttC00//wvdz76BxiSS/4H5HKy+llfrtBw9MjmTy9x8M1eunsU1a0tZOC93WoUbT5YZcXtuqKpKOJzA440QCMTO2czJPCp0Lbppcz7NiNsz5BT5uQCKqqqXlHXc+a5zKwhCCdB6lrv3oKqq953l3463L5MSt1arjqVLC1i5rJBlS/MpmER4jqqqxAaG8Te0EmhsxdvQxtG2YdpFM12Cla6Inq6BJMkzDKU6V/QGmWXLClm/poSr15Vd0nlpU8Gl2oi80Mwcp+lJuMdN97Nv4Nu5Bb2mh8yrKwlWruBAoJTXXu7h4PYuAqcRGaNkOUSKTFEKkkPkJ/1UlrgouKKG7KuWYSyYnImKqqo0NA6ycVMrb2xspalp6PR/NA6SJDC3JpsrVxZz7YZyigqnb0jk+eBSu96Gh8M8/Jf9PPr4wVOO4ssakfkri1h17SyW5vaQGzyA2d9G71Y3ba9207u9H+WEZ6IgirjWLaf4gzdReMcGdBlnF1I/PBxmy45O3tnWyc6dXQyfQa6p0aJlzsI85i+0sarcT4HagnT0EAO73PTvG2Jg7zD68lyin/8kj26RaNznnvC7ZucKvH+BmSvXVWKrLkefdWo/jvHOg+jgMC2/e5Kjv3iEYHMHthIz5o+s5zVhCW9sDhCZRBRFTp6ZdevLuPW6Sipmn30ZsenCjLg9O5JJBa83gscbJZGY2oGVazYsoaenk7888hw33HANFrP2op5nl4q4nTaCcaQu61+AvwiCMHdkuZq0SdeleMd4m7S4NQGLgYlGSq86Yfmd871T5xtXtpniohyyskxkZ5rIyjKRmWmirNTJ7PKMU4YUxb1+PPuO4Nlbj3ffEbwHm4gEPQxX19CVWU5z2EJLl41w6MQRh4tTlzEaSbJpYyubNrby//73He75wDzu/eA8rNbLM7xvhhkuZ9REkghxWvMraAnNpfEFHS2/7sfnPfNn8YBHYcCjZTe5QC4MQkZLP2UvP0VZoJX85noyTCaci6rJXrecnGtXobWeXONTEASqKrOoqszi/k8vIxCM0dsToLcvPfX0pqeuHh/dXV7C4RR2hwGXy0y2y0R+npUli/JYurjgkgmJfC/T2xfgoYf38uTTh08ZwpiRY2blNeWsXmWnggPkBB7Ct7GXppc66Xirl0Tw5D70rCsXU3zPTRTeeR2G7EwgHerc3uHFatFht+vPqKHsdBq5+fpKbr6+Mu3W3Ophy/ZONm/tYO+e7lPmAocDcXZvamP3JnhQFimtzmHuogWsuD3F7I/2URttR206yuD+Ryk2Wqj/3O08/lqUjnE6dpp6Vf6zN0D+th0savof5vraccyZhbWqFHNpIbLZiGzUIxkNyEY9KUkkdagVpW+IfS/uxrf3CJ49h1ESCbLmOxG/93Ge7ihhz6seVOXUHUnZuSbWri/j1uurqLoMBO0MZ080lmR4OIzPFz3tKK2sEdFoZVQ1bTrHyFwlvZxMKJxqsDESSdDV5UOWRQ4f3sme3VtZtGgRt99++7jrt7W1MWvWrFPu0/Lly9m2bdupd/wSZdqIW0EQdMCtpEdur2Ma7dtZ8jTwtZHljzOOuBUEQSSdewzgBd6c7JerqtrGJET/hci5PZFf/N+Np+3NURIJAkc78B1qYnjPYYa278N7sAk16se6pIj+RavYZ6vicEEl7t4ISqc6Mi48+V7i8bDY9bgKrJjMOoxmDSaThMUkYDGCyQDRuEAwDMEIhMIK4VCCSChBX4f3lGYzoWCcX/9mFw//aR933VXDxz40H6fTeE77OsMMM5xflGSSlr++yqu7u3irTc/RphCqKgEKxy0Tpoah4QRDw7CDWWCYhdWiZV4K5r26Gee/f4dkqwet04GlvBjHomqy163AtXYZGlP6PmIx67BU6KioyDzpey+1kckZTmZwKMxvfrebJ546RPIU5l+VC3JYc3MlK8oCFAZ2YR04Sucb3bzxVBuexpNLz+gy7My6733k3Xs7Q1o7Da0eXnysiZbW7bS2euju8R9riOt0EtkuM9nZZnKyLeRkm8nNtbBsaQF5uZZx9uQ4giBQWuqktNTJvffMJxJJsHV7J29uauXtd9rxnSLSIZVUaNrvpmm/m6cBR5aJ2fNqqZx7FYvvjlGs7ebWyBGuy47z3MASHntmCHfX2BI73R6R7sw1bCoRWB46yOzfPYshdWqblhYAAXKWufDddTu/OZhD4xM+jmeHjcVk0XL1NWXcfetcqquyZgTtexhVVQmF4gwNR07vkSCA0aTFZNFi1iYRSaGOjNepwsh8pBmfRCYcUYgE40TDiWNCt7CwGJ1Oh16fvrcnkwqvvvIGP/3pf3PX3fdw7bU3YjBoTnlOrlq1atz3586de8a//1LhogtIQRDWkBa0d3E853b0f8kLPEbaRfmSQlXVHYIgbCY9evtJQRAeVFV167tW+zIwZ2T5h6qqnjQMKQjCfcDvRl7+u6qq3zyPuzxlxANBIl19hDr7iPb2E+5y4z3QgL+hjXBnL/FhH6IEtlkWMpbmEb3hCnYsvob9jSn63WHUTSrpaO5zRxAFXLlmaucaWJXXz+zAHmSfDyUcR0joEWMWpLgNWbGjJiKokQAkQiDHIVtEtJoQ7yrjcKKMtw/rOLB7kI6mwXF76SKRBA89VMcjj+7n9lur+fhHFpKdffk4j84ww+VAoL2LVx97lTfbNezcFyIeS3GhI0D8vjhv18Hb5IPxAziv0jOvQuYKWxv6A2+z8+G/EO6LIOq06F0ZmMuLMc/Kx5CbhbEgB2NRHuZZBUg5GRd0v2eYGvz+KA/+cS9/fmQ/0ej4NYcFAWpXFLLhtgqWZrZS4HsC9VAvR5/poPn5jjEuxNnrlpP1kbtoMBXz87fa2PO5N1BOky8ei6Xo6PTR0ekbs+2li/O59ZY5rFs7C4P+9CaKBoOG9WtLWb+2FEVROXS4n7c2t/LWpjZaT2OG5RkIseP1Fna83sJDQG6RjfLaJZRW2qksS/Jf3zSzcZfCc0+24Rmno9kXVHmFuby1soZl5l7K+o+Q6+1B9vqJ+eLHTKysRWbyNhTQMPdqfrzLQdtjAcA35vtGqV3g4o7ba7lhfRk63UVvLs9wEVEUFZ8vytBQiPhp3coFRElAEiEVj+Pvj+JTVERVQSMqaEUFjaSikxVkTdqITa+TMGq1JDP1xAUL4bBCOBjnt79/grFFaNMkEgpt7V50Ohmnw4DNph83OvLtty8py6Ip4aJcrYIgVJIWtB8GRgsljf6PJEm7JT8IPHspuiWfwJdIhxobgFcEQfgv0qOzBuCDwGdG1msEvn9R9nCKeaniOjI4+UEoakQcs60ULHdh+uQy6g1zeavJwaGGKAOtYZSjKmnvsKlHVVTc3QHc3QFeA/T6WtZdmcV9f3Mls8tdp/37ZCTK0PY6Cre8xIcCTZjvrMadWcumBhs7d3k5sqdnzN/EYykefewATz51iFtvncP9n1xCVublb94ywwzTFSWVYtcTL/Pi7iHePpxkqD/C2YzOCqMNh5HeLfXYP+fG8GCUtwbhLTIRxJvIucLIsnkaVud0U9Czj4EtHbhf2EfMGycZHSdsVRR5Rv5nEAUEUUSQJSS9HtlkQGM1oct0YikrxDZ3NvaFc8hYWos8U2P5ghMOJ/jzo/t58KE6ghPUWRclgcVrSthwWzkLTQ0U+B4i8E4Pex5poWtz30luw7LZSO5976dn/pU8ecDLjl91kUqde1qbqsKOXd3s2NWNyaTl2g3l3HpLFfMmWZpPFAVqa7Kprcnmbz+3gp7eAJs2t/Lmpjbq6npOOUoN0Nvho7fDx+bn06+NZi0FZU4WrCwkHErQtL9v3GiqeFzl7eEc3pZzIBNyqiXmFqeY5xpmttXDtkA5//OOBu/BOBAYd9s6vcz6a0r5+IcXUT7r1Lm8M1yeqKpKbKSedSQUIxqJE0uoTLYctKqqpJIqx+/Uo2ZQElGFdHBQEoiBoKpIagpZSWGUI9gsYcwWGaNOS9KgJ46VUDhFyB8jHhu/IywWS9LbF6B/IITTacDpmInguZB1bjOBe0iL2sWjb5+wymid2z9d6nVuT0QQhFuAP3J8VPrdNAI3qap6dJy/vY9zHLm90KWAdv/7XRSV55Ky2BhW7PTHzHQEbGyrl2k4EsQzGD5nB7mporrKzD1313Dd9QsmZbmuKgoDW/bgfvF5GDyI/coyDrnW8ejLCfZt7ZzQVVWrk7j7rho++bHFZ10yZLozEx45OWaO04VFVVXefuRlfv/mEHV7Jx6hGQ+NTiK/xEHV/EwWlidZlDOAXk6gCBpSggZFHJkLGpKChu1tNl57voOGvb2MLcp29siySGGplZpKHcWuJPmWAHnGIC7Bg+gZJDEYIDIYJRlJkkoopGIKyWiSyGCUuD9BPJAg6okR7o+inDDiIBn1GPJc2GsqyLvxKko+fDOy8fJKp5gu11sikeKJpw/zq9/swuMZv1NF1ogs31DGhptnUas5QL5vJ979PRz6w1H6dg6ctK4uJ4vUPfeyRclj666e09a9nSqKi+18+IPzue2WKjSas6sWEArF2ba9k7c2t/LO1g68nskZtb0bUUyPjk1UI/dMsTn13Hl3DR+9ex5Wy+X5nD4d70VDKVVVicdThMMJIpH0FE+kLlo7VVBVDKkoVk0ck0WkcuVa2ju7eOmlv7J45XqybBPfo/PyCnn1tV2IokDA38+yZTUAp8zlPVNmDKU4lkd7G2lBe+0J2xsVtb3An0i79x48n/tysVBV9TlBEOaRHsW9CSgA4sBR0iHXP1ZV9dySSacRX962mvhbBiKh0TC/FKfKZTkXNDoJm9OI1WHA5jTgtAtkmuIMDCscaYzQ2+E75Q3q8JEg//of2/ifH+/hxtUZfPQzG8jKmjjPSBBFXFcuwXXlElRFoX/zLlwP/5Z/W2ig+abrePRVhd2b2lFSJ280Hkvx8MP7ePLJw3z4Q/P46IcWzpi8zDDDeWbni+/w62fa2LX31PeBUQRRoGZZPpXzXNTOSlJt78Pc30Bg0xO4Xxpg/95BQEDv0KKz69A7tejtOnR2LYYMPR9Y6uKmT9RwKLWAV7ak2PbqUfzD5567m0wqtDZ6aW088V0tgpCN2VaM02UkM1OPTicgmQQ0MmhlsCxUsBlS2IxJMk1xym1BXOIw4qCbSMcw/fuGGTrspf/1jXQ9/Ro7PvOvaDPsOJfUUPbJuyi88zpEcXqUn7hUUVWVV147yk9+tp2u7vGjk0RRYOm6Uq5/Xxnz5TryfL9maE8vm//QRP/ek82NzFWlBO/8ME92aql/ZxA4dVtREAUyss1kF1jJLrCRXWglO99GRo6ZaDiBdyiMdzCMdzCUXh4I01Lff8Lz+2Ta27381/c28psHd/OZTyzh5psq0chnJnJNJi1Xry/j6vVlqKrK0eZhduzsYtvOLvbU9RCZRI1pSIeJni7sejJkZOpZoXYw78Bb2Pw51G8pw1pVinVOGY4FczDmT1wqaYapYzTs90JsJxpLEIkkCYcTRKPJKTmPTsRs1p5VPWYAVRAIywbCqgGNJ0FqxPVcDPlxCsOsXLmczs5uurq6yMjIoqj4uGlUVmb6XFUUFa/v+LPnb/7mCzQ1NSIIAiUlJVx33XXcfvvtl/X9/byM3AqCcBXH82hH1cLo/3QEeAb4A/CKqk5lH/cMF4OzrXP7bnQGGY1GJOifOEl/NGTr+htymOPowxwfQGluwf/2Ifq39TBwYBgloWCd7US7YSkdefNoCNk50pKgu/XUBek1WpFrl5v52//vBlz5k89lG959kLZf/RxbFfQsuJFH3pDY/norqQlCr8wWLfd9dBH33F2LwXD6XKZLgekyQjLdmTlO55/97+zjF386yLbd/kn1WGt1Eiuvm83NVxupTu0muX0P/du66ds1iK91/NDF8RA1Ivmrsim7uQjrigo6jIt4vTGLTa+003xw4mAknV4ilVKnbATqlPsoCdgzjGTnmykp1FCZG6U6x88stZ1gXTOdb/XSv2eIqCeGoJFxLJxD+Wc+SOnH33dJNoQu5vW2a3c3P/jxVg4fnvj/fsEVRVz//jksMh2iyLuVge1dHPpDE4MHT66tal1cw+BtH+aZ/VHaO7yn3K5GK1G9JJ8Fq4qoXJDL/8/eWcfJUZ9//D2zsy63d7fn7pJLcnEXCEGCBoproUJ/bam7F+qlQoG2FEqhWIt7gsTdLnLu7rLuu/P7Y5OQkL3kLrmEQPN+vfa1crMzs3O7M9/P93mez6NSSwhyEL1/AIOvF4OvD72/H1kQCYpaAgotAYWOgBi5HxKT2VXhZufaZhr29x73mpmcYuRzd87g0hXjF7nRCARDVFf3s2NnFxX7eqiu6cduPz2VaQaTmpUXKPmUei2OuhGG6qxYG+24etxHtVHSpiYSP2sy8bOnkLBwBvFzpqJQfzInpz/KyO3IiIdlFz8+yjs/Xjz99HXExGj5cAZ/MBgm6A8RDIRO6I58iEOtgJ599FmWzy5Hb5b41UMP8fPf/Imbb7mJP/zlUZw2L94PTQp1dbVz4fJZo663vLycl1566YSOyh/mfz1yu5ZIJdKhf60MbCQiaP8ry/LYRwzn+MSiN6rIK4xj+rQ02putrFvXjM8TvaZApZGYe0EelyyPoVzYiWf1U3Ss6aSyYgi/PfKjFpVKUi5eQuLSOcjBICGPjwS7lameXmSLH+vUONY609i4zY7LcewFM+AP8+ZGO+9uf54LZ2n54pcvIikn+YSfI25GGXEzHoyYZv3t73w100XfL6/g6dUKdqxtOWZW0Onw8+BD23jyqQpuvamcG66d/D/fJ/cc5zhV6iubefCRbWzZaRvTTLzOqGLJZcWsWKygwLqJnofXsemlVgKuyDlIVCqxzJtGwqIZJC6eRfysyQTsTlytXThbu3C1duJq6448b+7A091Px7oeOtb1YEg9QO6lO7hjRS5X/980Ng/N4sXn22nYf2zvTt/BGtrSSYmkZ5mprx+gvcV6TAbIRBAOyQz3uxjud1FTAW8ffF2rz6Rg0nSmXCEw9ysjFAZq6F1VS8uqZnZ85gfs/MJPSFm+gPLffAtz2dmRTni20tA4xAMPbWXzlvZRlymensolN5QxK76FrJEncGzsYP0/ahnYd3SWk6Ywl56r7+DflV763ugddX0KSaR4WgrlC7IonZGKRiMS527EYnsLo68HnX8QkcjkiWfYy3CtjaA3hEIlolCJ6FQKxIOP81P1TM3O4PIvT6LOez5bNw+yc20zQ73OY7bb2+Pg579Yx6OP7+Fzd81gxcVFYyrxGQ2lpGDqlBSmTon0g5ZlmZ4eB9U1A1TX9lNV3U917QCuUeqVx4PT7uPfL/nYWnIRF8yTuOSmPhLlLsSAG1e3G0enC0eHC0eHE0drBXUbN7HvBz4UGjWWeeUkLp1NysWLiZ9ZhvAxnPg5x+kjQeMkVhs47IR8yB05LEgHy1o0hIVDk5ohgv4wHrf/GIF6JF5JTb8iFtOQHe9IZPyqCPqIV4xgStDjCplwWL24D/42FAqJC5ZfypVXXEtR8SQSEpKxWofZsX0d99//C/bu3cuFF17I7t27MZlGq5r8+HK6IreHpqCbiAjafx9sXXOOTyBjjdxqdEqKShNYviyfC5fk4veHeOKpCl5+pXpUgwmdUcXCFUVculiiyLON4Zc2Uf98M86uSCa3oFCQvHw+WdevIP2qC1CZj/8j9VvtNL6xijVdId7bJ9PeZB11WbVGwUXTldz9ufNJLsk+8YE4iL2+heaH/4Y+3UnH9Mt55q0QFZvaRp0BNxhU3HTjVG6+fgpGo3rM2zmbOBeRHBvnjtPEMzzs4Pe/eYt3Nw4TGoMgjEvUs+SKEi6eEyRnYD1dT26m/sUWAq4gCYtmknzBPBIXzSR+zlQk3dj+P7IsM1JRTfPjL9H6zBv4h60AiJJA6vwkCq/JJTx/Mau6JvHKc810Hsc9trDQwq03TiU+Uc87a5vZurWN/m7HGa0BM8VpmTE7nvMnOZilraH/tf3Uv9SKd9iHNjWR0u9/noIv3HTWR3PP5O+tq9vOI4/u5I236kb9X2UVxnPpLeXMyRwkZ3gtgYY29j9aR9fmoyc9tGlJBO78HE9VMmo6M0B8koElVxQzbWEWWp0Ss7eNREclCa4alGEvIX+I4XobQzXWyK3airvv+KnyoiSQMDWetPlJpM5PIphdRJ++lE0tCax6sZGW2oFR35uRGcM9X5jL+eflnrZ2ObIsMzTsobvbTk+Pg7a6HlorW+npHWHALeAMSgTCAoEgBINj/9GoNRLZxRaMJjUmo0CsQcZsCBGnC2DR+7DoPFjCA8iNbdirurE22BmqGSEU0pF66RLSLjuP5OXzURo/vh0SzkVuJ4YXfjOX2Bg1gggIAsLB+nCFVoFSK6HQKEAUD4pdiZCojGRRBMFl9+Fy+AiHImPiQ5Hbx//1ErNnR1r6/P2BX/HA3/7EDZdewSO/+w2aODUKnQq/Qo8nrMFui6xjNLPDtrYWrr/uQhwOO/feey8//OEPx/zZPi6R29Mlbv8GPCnL8pYJX/k5zjo+LG4NRgtJyUayss0U5MeTnx3LpOJE0tNNNDWPsG59C2vWN1NXNzjqOrV6JUuvLOGqxWFyBjbS/exWGl5pO9z+IG5mGfmfvY70q5ejsZyco6GtqYkNa3bxZrXErm39o0Z7NFoFF08VuOv2haRNLx3z+p2tnbT87W9oU100lqzg2dc9HNg++m9bq1Ny4/VTuPXGqcR8zIynzom2sXHuOE0cwWCIx//2Dv9+teu4pQyH0OqVXHLjFC6b6yejbz1dT22j7oWIqE25aCGTf/plLHPLT3m/Qj4/Xa+voflfL9Pz9gbkcGSQkjTDwqS7SvDNWsprDfm8/p8GBntGT2JKSNRzyw1TWXllKWqNgvZ2K3VNw9Q1DFJbN0hLywhD/c7TLnp1BhVzFiRy1fRBCgZ2Uv/vKro296HQasn/3PVM+eXXzlrn5TPxexsYcPHo47t4+dWaUSdpE1KNrLhpKgunBMgbXoPU0cyBf9XTurrrKCNCVWwMlns+x0vWJDZt7Rh1m6nZZs67qpQp8zIwhoZItu8l0VmNJmQnHAzTs32Alnc66d7af5SJmCEvk6Tz5pC4dDb6zFQUGhUKjRpRo0Zx8OYfsTGwcRf9G3bRv34nksJO2rwk0s7LwD1tMe92FPLmC8201Y9+/S4uSeCrX5rH7JnpJ3FER8c7OIytshZ3bQX+kQ4GtEY6NWm0Ok209Ah0dXoY6nOOmgE2ERhi1JjjtMTHS6TGy6SbvCR4e4lpqSO8oxZVajGply4l7bLzMORmnLb9OB2cE7cTw6oXrsdsVCGHQgdvYcL+AAG7k5DPjyCAQq1A0iqQtBKSVoEgRcoE/Ao9IUHC6wrgtPtYvGDqMeL2oQd/x8MP/57rLruKh39yLwCSVoE2To1kUBFQ6PDIOhx2P067L6rZ6R/+cC+PPfogZWVT2L17DyrV2MoK/qfF7Tn+tzhS3N5y+6MUFuYSH6cjLl5HXKwWrUZi5+4u1q5roav7+C1/VBqJRZcWcfUyBXm979P26Gaa3+4g5ItcoFMvXUrJt+4icfGsCZsZDoVCbH/jHZ5a72b71v5RXY+1OolLyoLcevUMss6bM+b1u7v6aH3076hT3BzIWM7zrzuo2X1sC6FDqDUSl19WzG03TSU9LWbcn+ej4JxoGxvnjtPEsP79ffzhb3vpaB9bP+wZS7K57vo0pjlW0f3kJuqebybgDJJ8wXwm/+zLJMyfflr209PTT8tTr9H4t+dwNkfESsrcRCbdNQn7tGW8UJHGqhfqsI/ioAug0yu5+spSJpUkYrP7sDu82O0+bDYfw8Mumpp78HpldDotoihGogQCkWalQsRcxOcL4nIFTjmdMyUzhgsW6bg6t4aR/0Qi3qEA5Ny+kpkP/uisE7mn8/c2YvXwryf28J8XKvH7o7RnAoxmDcuvLWPZQj0FtrXo+mqpebqJxldaCfmPdK7WknPP7WxPnsHTL9SMur6ckgTOX1lK8bQUYnxdZI5sJt5djwAM1VppfaeL9jXd+GyR/7MuPZmkZfNIOn8uSefNQZ+RMu7P6Wrron/jLnrf24Kndjv512QjnL+M1e1FvPFiC+0NQ6O+d8asNL7+pfmUFCeMeXuyLOPt7cPVUIu7tYYhq50en5LekJ7+gJ4+p4qu3hBdnS48E5CePJHojUoy0zVkxnhJdnSR1ttB8YKZZF2/AmN+1ke9eyfkoxS3JzKUirTnCeJwBnA6vfh94/MoUKpE1FolGpWARgqhkEMoRAVBhZrefveo69MblMRrvQQH7ISDApLJgKTXIum1iEpl1HHoaP1mAUJeHwG7k4DNQcDhOjzeVBkkNPEaJI2CkBCJwgZFDWWFeXS0t0cVt1dedT1//tmv0fudhxOgJY2IJk6D0qgkKOrwCnrs9gAOm/eose2aNav48pduR6fTs2t3M+YYLfHxWlSq41ernhO35/ifYSIMpSSlyLwLC7j6Ej3Fg2tof2QdTW92EA6EEZVKsm+5nOJv3Il50umr9wqHw2xbvZ4nVg+za1vfqBERvUHikmIv111QTN5Vy8cssr39Q7Q9/ghSvJOqtPN54S0XlTtG/60LIixalM2nb5l2uAbpbOWcaBsb547TqdHaPsSvf/0OO3aPnHhhICndxDWfKWd5chXiO2+y+4/7cfd5SDpvcGTKcgABAABJREFUDpN/dg+Ji2ae5j2OEA4EaHnyFQ78/GHc7ZGJrbSFSZR8ZgoDpct5ZY+FdW82MdR3bF3j6UIUBdRqBYFAaFzpmxAx4Vq0LIVbZnah37SW/Y/U4nOEyP/89cz44/cRpdPaiGHMnI7fm9Xm5dnn9vPUs/vweKLXyKk0EkuvKOaiS5Ipdm8kbvAADS82U/Ns0+GabgBBksj73PUMLrucB584QG+UulaArEILl91aTk5JAnGuBjKtW4jxthP0BGl+o52mNzuwt0Xeq9Bpybz2YnI/fTWJi2ZOaD2ob9hK06PP0/vaS2QtjUF1xUWsai/mtRda6WoZ/TdZXp5AUoIWo0HEpJYxKIMYFH50og+ny8uwG6xeBXa/ArtXxO4WsNpDDPZ78Z7GKOyZwGRSkpskkyNYmZFpZuGnL8J0lgrds7EVkN8fwmbzYrV5xtXySiGJaLRKtFoRnTKEFA4iSUoUOgOCqCAclhkYdDI0FH1iUSGJJMSJqO3DBJwhNMkJqOPNE/p7ksNhAk43Aasd36AVORxGqZPQxKlR6iXCKMgtm0t7ewcvvvI2pcUzCIflo8TtL3/5AEpCmDxWpPAHk2IKlYg2Xo3SqCKo0OIV9NhsQZw2L7Iss3HjGu7+/I2o1Rr2VLQdfp/JpMYSr0OjiW52+nERtx/JFUgQBBFYCswDkgEd8ENZlnuOWEZ1cP9CsiyfHru8c3zkaA0qyhdkcuWKWCY71tP1m3dZ93o7IX8YSa+j6Gs3UfSV29Clnn47flEUmX/Jecy9KMy2tdt4/NVu9uzoP0bkupxBXtglsaqujUve+SOXlSVQdNMVqGKPH2XVJMZT9J3v4R+xEf7no/yoZID6y5bxwtte9m/rOGY7chg2rG9lw/pWSkoTuP3maZy/NPeUDDvOcY6PI26Pnz/9YTWvruomMEpU60iUagXLP1XGyvNFctufpeqbW2hf003stFLmPfddkpaOPfNiIhCVSvLuupbsW6+k+bEXqLzvr3Rt6qNr87tkLKnks58u4/qfLmVVQzHvv9F63JTPiSIclvGcpHDw+0K8/1Yna96GybNu5OZ/OCmvfpf9f3mRpn88T9E9tzL1198862tyx4osy+zb38sLL1Xx3pqmUSOrklJk/kUFXHhFNmWh7ST3vELrm21sfbIB79DRw5ismy4j59tf4IH/NvPOb7ZGXZ/RrOHSW8qZviiLZHc1mR0vY/D34bX6OPBiK42vtuF3RAS2Zf408u68hszrLjltdZ/qODOl3/4sxV//NI3Pv8uen71EcuJ6br9gMZvbs9i9tRdnFHfjvXtHr9M9ncTGaUlMMpCQoCPkH0GlhPSMLMKyyEBrF3trrQyOjP4bUKkVaHUqXA7fqCnnJ8JuD7DXDnsx8HJ9EOO2VRQnBJmWquKy288jfWr+yX68TyzBYBi7w4fV6sHrHeM5SuCgmJXQqcOoCSASRNIZEZVHG3a63QG6e+yjZ1yYVMSJDoI9bhSJFnRZsafFNEwQRVQmAyqTAW1KIt7eAbwDwzg6XUgaBZo4NcLBwlmtFpIyYhjudx4TUAmgYFgbjwkvGlckOzLkD+Ps8aAY9KGJ92MwedCYtJhiDNhtARobawFISjo6cGK3+7DbfRj0KuItOnTa6NHps50zLm4FQbgUeADI/tCf7ifS9/YQdwEPAk5BEFJlWR5b/tk5znrMFh1ls9OZMjOJWZkjJAzuoedPq9nwahshbwhRpaTwnpuZ9P270SZZzvj+iaLI/GXzmXe+zNaNu3jsv21U7Dy2nYPTEeD5PRper3Ezd++zLFF3M/OS80i5aNFxIxeq2BgKvvENAg4nwuP/4ntZrbSsWM5/VwfYt6Ujau1vTfUA3/3BOyQmG7j5+imsvKL0XK/cc3zikWWZ51/Yyd//WcnI8Nh6IE6amca1txcyR9iA45n3ef+haoI+gfJff4Pib9z5kUYVFSoVBV+4iZw7rqbx789R/atHIg7L63tInbuPT91SzOX3LOKd9mm8v7qH+v29o5qCnA3IMuzf0cP+HZCafRE3/PpS5g++R+WfnqX+wacp+fZnKPvxFz+2Itfp9PPWqnpeeKmSxqbRTcBEhcDs8/O4eGUekxV7SbM+QvfaNlY/Vo+j8+ihS+qKJUz9xdeoDxi58wfv0z9w7NBGFAUWrihk+bVlJIu95Hc/htHfi7PHza7/NtNysFRH0uso+upN5H/+emKK8ybkM4dCYYaG3PT0OenucdDZNUJ31zA9vQ4Gh7zY7H4c9gB+fxiYBoNAtRdoO8GaTw9qjYLMrBhy8y0UFVgoyY8nLdVEYoLhcB3h0RHJmYcjkuFQiC1/fZbNbQ7e2qfCYTtamPt9IUQxwNe/NptLlpfS1++ks9dBZ4+Dji47He3DdHfZ6etxjWnSDcDhDLHTKbCzJcA/trxDftoa5mSrueLmReRPz53AI/PxIhyWcTp9jFg9uEbps/xhRFFAo1Oh04nopUAkOqtRIWoMUQVZOBymr9/JyEj0a4mkVJAQI6MY6keMjcNUmnbGnLBFpYQuIwVNsgVPzyC+gWGc3W7CBydVNEE7OpwoUozExEU6rPq8H0SdZcCGhkCcDpPPgeyKnFdCgTCuXg/eoYjINZo8hMQwzz//BAALFiyNuj9Olx+ny49Wq8QSr8NgUH2sRO4ZTUsWBOEzwN/5oEXQIGAh8n+ZLMty9RHLqoiIXTNwuyzLT52xHT3HuDgyLfkLP3geQTThtHqxDXsY7HHgdQcQRAFJKSIqBAiFCIdkgsEwsgyWoIPM/gZy7B0svHIG03/+JfRZaR/xp/oAWZbZvHkvjz7TxP7do/csBMjK0LDI0s/8mDBFN1+NeXLRCU8IQbeHtieeIuCoon/mcl7erGLb+y3HNcXQ6pSsvLKUm2+YQkqycdTlzhTn0m3HxrnjNHb27Ovk179dS2Pj2DrHafVKVt41g8sm95Nc9zZ7freL3p2DJCyayZxH78NUOL5+fqcbvz9Exc5WVj3yLhXVgzhFLW5BiU9WHNFC4mg0WomEVBOJ6Sb0RjVavQqdQYVGpyQsy4SDMqFQmIA/hM8TwOsJ4HUHcNl9OG1e7FYvDquX4BgH4ieDSq2grFTLbHUDyjc2oxqwM+0nX6D0G3edtm2Oxnh+b06nn84uG52ddjq7bDQ1D7NmXfNxo9uCANMXZXPxpwop11STbttK/8Z2Kp+sZ6T+aH8Jy/xplP/qG8TMmcaDf93O08/ui7rOvLJEVt41k6xkyBt6jwRXNc5OF5VP1NO+pgc5LKNOiKPonlsp+L+bUMeZx39gAI83QGurleaWYeqbhmhqHqS1dYS+XveYXMfPJKIoEBuvITFRS0qaifT0OIoLLEwqSiAtxTRqfeMhTvQ98I/Y2PXr+3nOX8qmLdEnMebOT+EXP7mYWPOx36FQKExzp40DlZ3U1PbQ0GSnudGG0za+pMOMZCWzcrVccf1cJs/JO+Ni4kynJcuyjNsdwGr14HD6x9TCTSGJaPUq9FrQiX4UMpHorOr4nSacLh/dPY5R+4mbYpSYAzYUKj2axDgExan3bT4VQv4A3p4BSubNoqOnm7eefIKLLl+KLCl57vV13HLTTaSlZ/LKK+vQ6fRHvVehEPjlvd9i+aw5nD9zDmrVB4GQps5WvvrLn7J1524MBj2bt+8m1pwSMZ46jh4UhEgZi9vVh9frRxAUFBYVYDSMftz/J2puBUHIB6qIRIvXAl+SZbn2YNugY8Ttwfc8AnwGeEqW5dvOyI6eY9wcKW63v/Vn7P5EXl/rY+tO97hrudRqBTOmp7FgXibz5maQlWk+a2aLZFlm45YDPPrvOiorjp9mpVIrmFUsMincQak6QMbc2SQtm3/c9OqQz0/7cy/hqV9H4IKlvFqVzLq3W7EOukd9jygKnHdeLp++dRqlJYkn/dlOlXOibWycO04npm/AyW9/9w7rNvSO2Qm4cGoyt36uhLmB1fQ/tY4D/6wHSc20336L/M/fcFb0oQyHZerqB9m+o4OtOzvZt68Hv+/kRGZyooIrz1eyfI6AThlCkEMgCIQFBTLiwfYSaoIHbwGFjoBCj1+hx6fQM+xQMNTnZKjXefDeEbnvc457QD4WVATRBr3EJcVgSLGgUikiN2XkXnnwXhAi0WBZlg/eA8iEwwefI4P8oWWQkcNHPz+0TDAUpL9/AGSwWCyIonhwmQ+WPyRqrdaxZQZAZIA9ZW4Gy68uYrqpnsyRLQxsaqHyiQZG6m1HLRtTVsjUX36NtMvOo6FhiB/89D2aokSBtQYVK++awYz5qWTZtpJh3ULQ5qbqyQYaX20jHJQx5GZQ8s07ybnjaiTt2My7HA4fza0jNDcP09gyQmPzEG2tw/T3jX5dOZMolSKx8Ros8WpMRgmzSUVcvAFLspmMjHhyMsykpxhQKU8+42Ks593WZ19hc30r/9plZqj/2ONjjlXzkx+dx5IFJ46wBoJBamqb2bG7nf3VDqqr7Qz3jz0BMSFOYm6RnqtvnMuU2aevtdKRnClxK4oKzOYUbDYvwTFMpIiigM6gwqAT0Ak+FIKIZIhBUJz4OxEKhenptWMfxVFfqVKQYAii9AbQpiQhqqLXmn5UZGdl09bexit/+yeL58xGn6zFG/aRN3URg4NDxJjNZGfloVKpsVgS+P39jwBw9crzqaurQqlUkpuZhUGrY9g6QktnxNjQbDLx9N/+wsWXnne4JtfuiNTkHq/Hus87wMiIi/YOBw88XEdaqpHMTDMKUYjM9nHQy1AQsFn7+Ndjdx566ydW3D4I/B9QCcyUZdl/8PXjidtbgSeASlmWp5yRHT3HuDlS3F5z+b00951ca55oJCUbmD83k/lzM5g9K/24s0RnClmWWbe5msefqqZq78CYBuBpaRpKk7wUhnqYFCOROnsW5inFGPKzED80QyjLMj2r1jK85nmkpVNYM1LK6rd76Wgc3ZUSYPKUZG67aSpLF+egUJzZwfw50TY2zh2n0fH7gzzyzy08/WwtvjHWWSnVCi6/bRpXzXORXvsqu+7bQf/eIVIuWczsv/0MfWbqad7r4zMw6GLzlnY2bW1j584uHI6JFY4mrcxFM9xcMd9HUXYMIiEIByAcPHgfgFAAwkcP7IKCEq8yFo8yFq8UuY88NjPk1dHT6aC3zUpPu5WeNhu9HdbT2l7l40J8soG5y/OZtziFfKGWDOs2hjY3Ufmveobrjha1+uw0ptz7FbJuvIzePhevvlHLv57cE9UUp3BKMtd/cQ756lbyht5F6bHS8HIrVf9uIOAMElOaT9mPv0jGNRdGTauXZZnhYQ8trSM0t4zQ1DJMY/Mwra0jjAwfv6/tqaKQRPQmNTqDCrVaQqURUatENBoRjUogEIZ9Owfwuo+fajplSjKfvXMm8+dmTLiQG89519XRw57f/J7/auayeWP0SeyrVhbyna8tRa0eu+D2eaxUVjawcccAFTU+avf1jzmVOSFOybxSA1ffNI/J07NOm9A9ndengUEXVZU1iKKM0ahDrTm+g7YggEanwqAXMSj8SIKIZIxBEMceUbXZPPT2OaNnIghgNkmY/A7UFgtKg/7YZc4CsrOzaWtrY/WrrzMrM49wMIQ6RkVDXzM//fWf2LqzguGhIUKhEKmpGbz73i4A3n77FTZtXEtNzX6GhgawWkfQabTkZGRwwbyF3HntjSRZLCiUIiqzGqVJRUihxifocLhkXHYfodCx56oPi9vj4fePULX3cA/dT6y4rQEKgc/KsvzPI14/nridD2wC7LIsm8/Ijp5j3EyEW/JYEEWBsrIkFsyNRHVLihPOuIg7ElmWqWvp5fkX97F2bQ/WUVz3PowgQGqqhpTYMMlKF8miizRVgOyMRBInl2EszEGXnowoSQzvrqTrP/9EVRbPvth5vPmuk8odnccV1MkpBm66YSorLy9Brz8zdbnnRNvYOHecjiUclnn5zUoeeWQXA/1jH4hnF1m4+f+mslCxHs8ba9j5hwOg0DLzLz8k+5YrP5KMj2AwTGVVHxs3t7FxSxuNx2mTMhbUGgmdUY3fF8QVxajnEKIcJlW2kyS6SVF4yVD7MBjUSAYdkkGP0mxAk2BAHadFHaNGZRARRR/4bMg+O7LPDiEfsgxhRDxSDB4pInY9UgxuRQyDDiXD/S6G+pwM9zkZ7ncyMnB2RP9OJ4IoUDwthbnL85ldEiLDVUG8rZK+Ld1UP9PEcI31qOV1mamUfuczmFdeyppNHax+p4F9+3ujrltSilx6SzlLL0yjeOhtEly1dKzvYd8jtTi73egyUpj8sy+Tc9tViAoFXm+Qzi4b7R2RW1u7laaWYdparRM+cSIpRcwWPbEWHbHxaiwxEK/xY8SDPuBE6xzB4HOhDcuolVokpRaCAQiHIRSK9HcOhwiM9KCcmcom9RzWbLDTVn/830R+QTx33T6dC87Pm7Dr+3jPu+FgkMr7/sROfRyPrwrjjtJyKD3TyG9/cQnFheP3BQkFffR2NLBmQxtbKgMc2DMYdRvRSIhXMr/MxDU3zWPS1ImdCJjo65PbHeDdd2p56cUKKhucfPkLRWRmGImN1Y8qbtVaCYNewqAKokRGOcYI7ZEEAiG6e2y4XNEn5FRqBRaNH41ShdoSd9ZkB56IcCCIq70b/4gdhVJAn6xDoVPiVxjxClpGBtx4XNG/RyajGhkI+YMEfAHCYZmwICAL4/uNnRO3H96QIDgBLTBLluU9R7x+PHE7FagAgrIsn3PPOUsZi7iVJJHkDCNpWSayck2kZejRqAWcXoGaagd1e3vpbBoacxoigClGzZxZGcyfm8H8uZkkJHx0M28ef4DV71fy6pvNHNjdP6bakWgYTRIWs4IYXRizKoRZFSBWHcasCGKwdhGfo8IxZRGrdirZua4V/3EiXFqdxFVXlnLz9VNJTTm9dbnnRNvYOHecPkCWZdZuauaBv2ymvW3sLXAUksjFN0xm5TKJgo5XOHD/dlrf7SJh0Uzm//u3Z7xef8TqYfOWNtZvamPb9o5x9ZIVBEjLiSN/chLZOXpi9DIxhjBmbRCN6KOx1srevQ6qm4JYHeDzBMZ1jjzH+JFUCuSwTGgUd1ylEEYRCqAOeNEF3AdvHmJjVOQtm0ni7Mms39zOjp2dx70OpGabuekr85kc20nBwCqcjX2s+1Mjbc0hfLGJGC+7ELGsjMERL53dDto7rAyMI611LAgCxCUaSEwzkZKmJSNBJk3jIN43gKa3G19tG57qLhSaWEwlhcRMysdYmI0xPwt9TvqYU6MHd+yn6c9/IaZcR0vZRby5VcGeje3HjeampZu45YapXHxhATExp9Y/+WTPu52vv0/N+tU8bp1B7YFjRbkkiXzuczO489aZJ6z7HY1w0I+1p571m5vZVA0Vu4ePW4p0JIkWFXMLNVxx+VSmnVd2yiJtIq5PwWCYzesbeOG5XeysseMPfPAbuOf/ootbSaXAYFBiVIdQC2EkvQlRGn96sCzLDA25GBzyRP3tCYKA2SQSI/vQJCWeNS3LxoMsy3iGbDi6BgjKgE4NKiVhQSQsqgiGwOcNnDYjwnPi9sMbEgQHkZY/s2VZ3n3E68cTtxcA7wDDsiyfedvcc4yJk4ncalVhyrM9zMjzMLVEhaUgk/ZQFrsa1NTsH6B+by/2kfGlU+XmxrFgbgbz52VSPjVlXClDE4Usy7R2DfPS6/up2DtIY+3IcQXoySIIoNNLIIh43cGo6SNHEmtWkpasIyPdSE5uPDnZCWRlx5OYoEejUaJUiqd0YTwn2sbGueMUYdfeLv745w3UVI+tX+0hEtNN3HrPLJYYdyJtfo/tv9yLe8DP5J99mdLvfu6YFP/TRVu7lXUbWlizvoXKyj7kcUxmWZINFExJpmSSiZm5TjIVnZg9rWiCdnpGJDbW6NlQrWN3s5ZA6KOvFT4TqLUSMXFazPF6dEYVWr0KpVqBgACHS7kEIqXTQuS5IHDwZQTh6Nei/T0YDOF2+HE5fLgdPlx2Hy6HH4fVM6rJzOnCaNZEzt9eF35fCI9HxhsSYRQjsVNFVAhYUowkpZlITtOTmQyZJhfJvh6E+iac22qw7ekgJBiInz2Z2PISzJMLMU8ujFo6c7L0b9xFwx/+gmWKgsDFl/FyRQJr3mjCNjS6mJMkkQXzs7jysmIWzM9EqRz/vpzKedfZ0sHub/+InXMu57+vDET9rkyemsBv7r2Y5KRTm0SWw0Hs3bVs2d7M+mqBXTuGx1yna4lTMi1D5Pw5GSy9cTFq3fgnBE72OAUCITa+tY/Vq6rYXufG7oqebn2kuNXpk9AZlBi1MloxhFJnQFSefNmZ1xugq9uObxQfA41WIl7pRW82I+l1J72dM0U4LOP3B/H5Qnh9Qfz+EH5/iEAgdNLBk4ngROJWZ1BhtuiIidchKV08+cAth/70iRW3dUA+cIssy88e8frxxO29wA+APbIszzwjO3qOcTMRaclxah+LStwsnuqjsCwed0wulUPJVFQHqNvXQ0tN9IvKaBwyppo/N4N5czPJzvpojKmc3gA79rSwbUc7+w8M01w/clqdSk8WQQCVSkSpFFCrRFRKIXJTiagPvvbBTYFSKaKQRCSFgKRUIAoyDocNhSSQkpqMVqdFqZTQajUkp1lITjKRlKj/SCYczib+18VtffMQ9/9xHTt39I37vXOX53PTDclMGX6F5n9sp+aZJvQ5Gcx/5n4ss0+vJUMoFGb/gT7Wbmhh3YYWOjtsJ37TQTQ6JQVTkpk0NY5ZBT4K9N0RMesbwtbsYNd2P2trTOzzJNMnTZxfwccdUXHw/KNWoNNLaNSRjo9yGMLyIROpDx6HZZlwODIoDIdBDsuHHweDYXzes++8O9FIKgWJqUaS0mNISjOQmQw5Fi+p4X5obsFTUYdtZzPWJjsyKuJnTSZ+9pTDN21a0mm/TsqyTN/7W6m7/0ESJ4Pi6it4tTqd915vYaD7+M7oJpOai5YXcNmKIiaVJo45Wnqq592Q18eeb97HQG48D29LoLvt2N+/Tq/ke99ZwqUXFUZZw/iRwyEcXdVs2nZQ6O4aYWSMQlenFSlNhqnJSpYuL6V4+QwUqhMnP47nOPU1dvHW01vYUjtCVVcIr+/EWuKrXy4mN9dMnFlLbloSKq0WUXVq18BQKEx/v50Rmz9qtFIUBeJMAjEqEVVc7FmXghyWZfy+ED5f8OAtImYDgbPzfHUucvvhDQnCP4j0rn1TluXLj3g9qrgVBMECVAPxwP2yLH/7jOzoOcbNRNfcKoUQUxKsnD/Dx/wZElJqDj2KXHa1GKg5METd3l76u+wnXtERJCYZWDA3g3lzMpkxPZXY2DMvKmRZxurysauilcamQdrabXR1e+jrdTPc5/pIZ+POFDEmJXl5JkpLkplSlkZpadJpT5k+m/hfFbfV9QM88ug2Nm7oGDWtVqlSRDVY0RpUXHf3bFYUd5BQ+Rbb7t3FcI2V3DuvYcafvo/SaDgt+xwIhNixs5N31zSxfmMrtnG46aZkxlA8LZXpZRKz0gZI9Dehc3YxUmdlYP8wlfuDbOlPpNqQz7D2oxe0ggCiQkShECI9aYUPop/AURHQI98DEAyE8XuD/xPnr7MFU6wWS4oBS4qRhBQDGckiuYk+0tVDCG1t+CobcOxuxFZvxd7mREZB7NSio4SssSjnjGU6REOWZXpWb6T+j38lsSSI5obLebsln1WvtdHVfOKMDqNJzeSyJKZPTWHqlGRKSxPRaqKnsk7Uebfl36/Q9t4rvJ17Ne+9Hd374vxlOfzke+djNE6c+aUsh3F0VLNpSwPrawR27baOq949KU5BbkyAfF2QqYUJlC0uxVSYhTr+aLEX7TiFQyHcHT30V7VQtbuVyjYbe4cUNPVFJo5OhCgKFE5NZuFsHfNKdKgMJlQaHQUFBeM+Dh/GZnPR1+8hOEoJgV4vEa8KoLNYEKWPtrUPRIS41xfE5w3i9QYjj33BM15qIogCJrMWlUZCFAUEEQ7NEx36Ohxx+iccBp8/cp4f6u9geNhJW9s5cRvZkCDMArYTEbKfkWX58YOvHyNuD4qll4CZQBAolWW58Yzs6DnGzek2lMrU2FhY4OSCRTLpBQlYDQXUu9LYWxOkbl8vDft7T+jE+GFycmKZNSONWTPSmD7toxG7hwjJMja3n+bmPppb++nvdTA45GHEGsBqD2KzBQ73pxytDuzjTFqankULc1m6OIdpU1NOKu3s48L/kriVZZnNOzp47PHt7DtO6yy9UUXA68cf5SecW5rIbf83hfnhd3C8uYldfziAoNIz+5Gfk/mpiyd8nz3eAFu2tvPemmY2bmrFPcbzilKloHBKMmXTLcwv8pCvaSPO3YyndYCenQP07hygvjpAtSGf2rhCBvTHdwr9KFGpFeQUxTFrYQplUy0YTEqEI8YJsiAQFqQjbsqIUUkwjM8TxOWI9NR12X3YRjyM9LsYHnAx0G1noNtOaJzt4T4qJEkgPcdEbkEMmXlxJGbFERKUBAIyAV8It9OHw+rFPuKhq2WEvk4btiHPKZ+jFZKI0azBFKuN3OK0xMRpSUzSkGEJkRnnJlYYQeztIlDfint/E9baIayNdtz9kQkYY0E2cbPKIpHZOVOJLS8Zc33smUaWZXrf3Uzd/Q9jyfMSc8slvNdfxob1Q1Tt7BxzxpZCIVBQaKGsJJH4eB3mGE3kZtag0Qjs27cdtUpk+fJlGI16JOnkSnGsB+rY/aXvMHTb9fz1RV/UlOo4i5Z7f3wB8+ZkjHv9J0KWwzg6a9m8tZ51lTI7d9vGXKN7CI1GJNkskKTykiS4SJS9mIQwZqMKm2MApyOIXxtPj2SkO6Sn26VkcGR8kcSM/Djmz4vl/EIvxQWZGLKm0NjUfFSf25PF5w/Q02PH7Y6+TwpJxGKQiYkxIOk+mmtsKBTG4w3i9QQi994JjsYKIEkKJKWIpFQgiAJyWI60ODvcGk1GDsv4owhoQYBEc4hYfQhhDAW6gbCIw6ukq2+AwSEX7e3nxO0HGxOEh4G7iYjZl4DngecOPr/54P2FwA3AoTPxb2VZ/t4Z28lzjJsz5ZYMYBR9zEwY4oI5AWbO0uCPz6Vfnc/ezpjDUd3xGlMB5OTGMnt6GjNnpDF9emrURu0fFbIsE5RlvIEQDqeboSErQz0jDA3Ysdm82JxB7M4gDk8Yp0fG5ZFxOYO4nX7sVg9e1/iE/0eJTi9x8YWFXHd1GYUn4UJ5tvO/IG6DwTBvvlvPE0/uprV59PRdnUFFcjw0tx1rwCSKAhdeV8anLlJR2PUqlX/aQfNbHSQsmM78Z+6f0BY/Hk+ADRtbWfVeI1u3tY+596wpVkvpzFSmT9UzP2eI1EAD6qF2+vdExGzPzkG6R5Q0xOVTH1dAjyF5wvb5w8ycY2HBNAGnX4Pdp6ShJUBjVf+E9KyNj1exoMzPFWXdTE5382EDWxmO6K2rjPTUFSN9dX2S8XCbIY8yDrcYw3C/m/bGIap2ddHZMMTIoGtMEaCPGkmCkmyYnWtnWq6fLpuWDTV6dtcIY0rJ1OiUTJ2fSUqWOdLjVyOhVEuoVArUKhmtMkSCMUCcxoM27EATtKPyWwn3DeBv6cZV04m9xYG12YG9zUn4oOjTJCdgmTOFuEMpxjPLUMXGnOajMfHIskzfmm3U/u4h4lJtJF6/gM646by338DODV201B6/t/zJoNZI6PUqjAYVRqOauFgtiQl6EhP0WCx6UlONZKTHkJRoOCoF2m9zsP3O7yDOiOPxgZns2dIddf3XXDOJb9yzAI3m9JTjyLKMd6SNLZuqWV8ZYM8+F92t1tOyrRMhCJBZEE95eQyLioPMKE3GlDkVQfFBNP1Qn9uTFbfhcJiBfjvD1gCjaReTUcKiE1DHnrlStFAojNcbPErMTpSQVUgiSpUCpUqBpFQgSSKSJKCSwohypMe5KAeJdPgWI+7Hgnj4sYyIxy8w1OeMOvFmlALEKdxwsD84RMo9QrKAX1bglyW8ohpfMHIsz6UlR9uYICiAfwK3cnwvr0PfyH8Bd8lncifPMW6OFLd33P139u0Z3X49GAhPWPqaiEyW1sHsLBuL5gnkliXiNOXREUyjok6gbl8vdft6sZ9En7/cvDhmTU9j5ozUSGT3LBK7xyMcCmGvaWJ49x5c7U143CM0Fc7h7d1Kqnb3HNf4JjVZzaJyLYkWFd4gBMIC/tCHbkEBf1AmFIJQSP7gFj7icYiDtzA+TwDbsGfckXWA4pIErrt6EisuLkKl+mREcz/J4tbp9PP8a1U89+ze47b0UWkk5s40Urd3gD7nsYO+2EQ9t9wzh/MtezFUvM/Wn+/B3u5i0vc+z+SffXlCHC59viBbtrXz9uoGNm5uG3Nf3ZTMGCbNSmXOZJEZiV0keBoJNHfStaWP7q39DFaOMKSKoSE2n/r4Qvp0px6hVUgiokDU/qh6o5r/u0HJp7L3EPaHUeoix8anMNISt4Qabz6r/lPJ7vWtp7wfEGkPk5chsSyjh4UpXSSYgkgaBUqdhFIvoTjB7zSMiE+K+UDsKuNwKeNpG9ZR3+ilq2WE3g4bQ71OXHYvgUAI+WMgfEdDEKBgSjKzzsth7hQFGb5KjL5eFLIPKexDDHgJDjnxDnvwDHlxdrtxdrpwdLlxdrlw9XmQj+jRqTQZIiL2kJCdNfmM1MmeSWRZpn/DThr+9izBjgoyL8pAvXwxVYFi1uyQ2bOhlcHesburTwSSUiQlxUhuThyF+fEU5MWTnxeL4+n/MLjpTWqv+QJPPdONJ8pEclqGid/ceyGlJYmndR9lWSZg76aysp5N+1zsrQtSVzmIZxzu7eMlkrGSyMwyDYsmKykuykIbn4MgRj9Hn4q4ddhc9A54op4HIeKzYjHIGONjT2u6/ekSskeJWJUClVJEJclIQghRDiAeFLGCfHSkVQ7JhAIRAwJBFBAUAqJC+CCnGAgJSlxiDIP9XnyeY7+jSjmE1u8iqFASlFQEUIwq0M6J2+NtVBCuAb4HTB9lkWrgPlmWnztze3WOk+VIcVv/+KepM1zEQ88G6Os8ti5WoYCpU2PoHRaOO8MoHXTvHWuTcwClKFNkdjAnz86smUpS85NwmbKoHkqgojpI/b5eWusGT8q9uKQkgfOX5nLekhxycz76GrmxEg6FaHvmNfrfe4nAVRfxZl0SW9Z24DhO/eD8BZl87UvzycuN/jnlcBg5FEIOhQkHg8ihMG6Hk3Xvr0EOBpk/fSaiP4jf4cI9PIDHMYTdH2QorKIbMy0jGtrb3HQ0D4/pwpuQqOezn57BlZeXfOxTlj9p4jYcltmxp5PnXz7Apg3tBPyjKxG9Uc3y80woGhp5td5MMHTstWfawixuviOXGY7X6X1+O3sfrkZpjmXeU78jZfmCU9rXQDDEjh2dvP1OA+s2tOAeY0ZDVqGFKbNTWDwlQKm+FbOjAdv+bro2RwStvdPFoDaehth8GhKLGBhj5opaKxHwhwhHOQ6H/p6UrKO9Jbq/QPnsRH58WQvqqgPsvP8Azm43CpWIKdvApFsKSF+UjFOVSFXiSla9M8wb/957zORWXKwCtUFHb5dzXI7Ph4gxSeQJw+Qe2ExaXxNqSUYTq0JtVqOJVaFL0KBP1mFI1aFP0WFI0aGOiW5sExKUuJXxuFXxuJWWg/fxDAVMDA/6sA65cYxE0oDtVg/2YQ/2g88dVs+ox/GjwBSnZfb5uSw6L4UidSPJjv3ILS20vdfFUI0Vz5AP75APn803qnjXJFkwTy0mdkoh5imRelljQTaC+L/hoA3gHRym9anXaH/2JcypXjKuLMVXvoAmdwr7W9W01NtoqRugp9X6kdR86w0q8tO0xNXsoHhlMe80WmiqOjbCrFAI3PnpmXz20zOQpDPz/wsH3NgGO9i5r4uKWg91LX5aGmzHdaY+EaIokJBmIjffxNzJSpbMSSA1LRdJO7Zz3smIW5/XT0+fY9QU5EOGUXFmPdIEX09lWcbnC+J2B3AfErITYAiqVCkOZ20o1SIa5REiNhxElIOIRE4Mckgm5A8TDoQIB8KEAjLhYJhwIHIbTcIJIhGhKwooDUo08Vq8UgxDNvm4478TcU7cjmXjgpBKpK42EVAAQ0CFLMtNH9lOnWPcHClu/x5TxLK7J2O6dDYP75rKWy+1RD0ZTMsOctWVZl7ZbqRiW8+o6zbEaMgqiKe/235CJ8UPIwqQFR9kapaH6cV+sooSEBKz2N8bT1W9n6aqflpqBwiMMQ3xEJmZZpYtzWHpktxxOTZ+lIR8fhr/8R+G17yI+dMXscZexqo3Okc17hBEgRWXFPKFz8wiNdV0wvWPVbQ5WzvpWb0Ra90+ghYdXdlT2d1poKpigM7m4eNuIzHJwOfunMkVlxWfsQHCRPNJEbfdPXaef7Wat9+qpb/v+IOl+CQ9ly3RUNZbwX+6S9jVeKzjp0ojsfIzM7hsmpXs9jfZ/ZvddG7oJWnZPOY/9Tu0yScXAZVlmeqafl57o47V7zZgt584TVcUBfImJTJlVjJLyjwUKJswjdTTv72brk19dG/vx+EUaI/JoMWcQ4slD4cwtnpGnUFFbmkiPW0jDPVFdz7Vm9TMW5xM474eWjuOnfzRGVR8+mYL1ydtpPLhfTS/1RF1PQlT4ij/QgkxpUlUJ65kS72ep/645ZgsijRHF7cb9uC/8hJ22FPYX+nEOjj+bBdBgKR4JQW+TrL3bCRxpCtqYxulXooI3VQdxjQ9xgw9xnQ9xjQ9mrhjTXhkwCuZ8SrNeKUYvFIMPikGn2QgoNDjV+jxizp8Pjli1OIO4PMECAXDhEIy4VAYrzvAYK+DgR4HA90OBnscpzTAG+vxyE0XKVX2klRZgaGqAUk+9lqjjDFiKs7FVJyLeXIhsVOLiZlciDbpk1eWcbLIsszQ9n00Pfo8w5vXYSk1YJkSh35WCb7kfPpIZX+XgcZGDyMDblwO3wc3uw+3039Skzcng8mswmEPRN1eXn48P/n+UsomJZ2RfTmScCiI19FHc1svtU02mju8dHQH6OrxYhv24rB5j+rkoDOqSMuKITNdQ26miuI8A8V5ccSaY1HpLIiK8WfQHBK3CoWCwsLju0qHQiH6em3YHKMbLhkMEgkGEY05ZkKyF0KhMB5PRMi63UE83uj/x7EiCKBUSajUEkq1ArUK1FIYiYMCNhxAJBRxdveHCQXChP0hQocfhyds0k6hEtElacFgZMSrZXjAPWpq9/HweQcYHnHRcRxxq5BEUjLNxFiCPPXgrYde/t8Rt+f4ZHCkuH2AHOJRYimLZdrXyqnJvoz7H3PS0XiscIkxSHyxsIq4i+bwr3c1VO7qHXUbxZNiWHxZEb29Aap3d9NSO3BSxh0xRpGiDJnS7CA5ORoseRk02y1U13tPSuxaLDouW1HEtdeUkZJ89rv+Bpwu6v78BNYNr5L85RW8PTKd155vYagvepqXQiFw6aVFfO7TM48rck9GtAXdHtqfX0XnSy+jmZKIc94SVlfq2b627bip5Pn58Xz/24spn5pywm2cbXycxa3T6ef9Dc288loV+/f2nbCuPTPPzBVzw0zr2kNP7oX84ZUehqP8X9Nz47jtnhkslNYjbN/E1vv24hnwMfnn95x079qBARdvvl3HK2/U0t5mPeHyoihQMCWJaXOTWVLiJFdswDDUSO+Wbjo29NK9Y4A+wUxLTBatKcV0Ki2Ex9iTVGdUUTY7neLyFFr2t7PpveiO0UazhosvzyDTU8df31Dg9h67UNnMFL5z9RDGrWvY9adKvEPHF+uCCJPvLKLk5gKa4pexw1rM33++FteHRH6au5fru95FpxRQmlRoV0ylNn02m2sUNNfbTirtTlKKZCRJlDibSdu8hhjXiR1wlXopInQP3Q4KX0O6HpU+uhMuRARwWFASFNWERDVBUYWMIlJzJoiEBCV+hQG/ZMCvMOCTjNh8GjoHoKMrSHebje7WEbrbrKctjVMlCeSaw8yM97NsVjLJU/IwFeWgSbJ8otKKTzdBt4fBLRX0b9hJ//qdeNrriS8ykDA5DtOMHBRpyYTUBgIKbaT+W6HDhxabT40vKBIICoTC4A8KePwCTo+AywsuV/iwQZh92INt2M1Qr3PMJQtjRRBg5cpJfOWLczEaJs5R+WQJh4IE/C58PjeDQzb2H2hAkmD+vBmYY5MQxYnLlmppacHrjUwq5efno1Qe+5uWZZmhATtDVj+hUYSdUqUgwQAxlliED5sAjINwWMbtCeBy+nG6/Ph8p/C/PixkFajUEhoVqKUQCjmAQg5E6mLDYUK+MCF/KCJm/aHI81Pssy0qJUSlhCBJiJICQXnwXpIQJQlZDuPtGyLo8qA2KVEmGLGGDIwM+8YlnmU5jM83wOCgi6YmG/94vAmlSkFqtpm03DjSc2LJytKQkxTEiA1rZwMLL/rsobefE7fn+HhxpLh9SFuC2RMZCAmiQMHKLBK/fAUPbCxgzevHGl4LAlxeJnJ98nZaZ17Kv98WqN0bvQemKMIViwWuvtKC05DB/jYttQeGaTjQS3fLyEnbqScmKMnLkijIVZOZH4tDSqS+0TMusSuKAksWZ3PjdVOYMT31rB+s+IZGqP7NI3jr1hP7pWt4qTGft19qPGbQewiFQmDFiiI+f2d0kXuqos1W3UjdA0/gadtD3BevZou7hHff7qK5enQTkUsvKeSrX55PfPzZ35D9EB83cdvX5+Dd9S2sWdvIgX19ow42DiEqBCZNS+Ty0hHymipJXnkrz+3x8u9n9kVdfumVxVy3Mo7JA6/Q/MRuKv/VgDY1ifnP3k/iwvG1Nvd6g6zf0MIrb9Swc2fXCdMUBQFyShKZPj+V88tc5It16Psb6N7SQ/v6Hqoqw7RrU+hMKaRTl4wzNPZIhdGsoWxOOlPmpDMt10vLtir+8ZyLYeuxgxhBgPkXZHNbSTMvv2RndZf5mHOZpBS54bY8bk9dT/XvN9Kx/oOJQCE9EeXKpSy89Vo0Bx1x/cM2bDVN2GuaaHvuLRInqZj97Sn0Jcxig3sBf/vZmmN+67NmpvHAHy49qhe1LMt4B5rZuHobaxo07N7rHDXifCL0BiUl6QJT2ncRv3M7smt86ZEqoxJNvBptnBqtRYM2XhN5Hq9Ba1GjMqpR6iWUOglJIyKMMaMmjIhXacajjMMlxdLlNNHQraK5Ezq7ffR12OjvdkyoU71areC8ZXlcv3ISUyYnn/XXi7OZkNfH0I79EbG7YRfOlk6CDisK/KhiVGjMKtQxKtRmFQqVeLAmUUSQBBRqBUqthKRToNCpUFjiEOLjEGLNBPVmPFIM/W4jncMq2vtFujo99LRb6WmznrJZW4xZw43XT2Hl5SUkJOgn6GicGqf7+jQ4OMjAQOSartVqSUxMRKvVHv7+O2xO+ga9+EcpbxFFAbNRxBKnR9KM3/37UJqx0+XH6Qzg8YxuTHVcjhSyKgUaFWiUocMiVgwHCPtCH4jXQ48D49uWIAiIaiWiSoWoUkZE7KF7pRJRFRG0o50/ZFnG5w/h8QTweiM3vz98wut49HWFCQYdhMNehkY8jDgDGLQe8uPsmEKDaAMjaAIjSPIHk4OdAw6yrnv00NOPt7gVBOFaWZafn9CVHr3+dCBTluUtp2sb5xgfR4rb6t0ViJv2U//Q0zjqWwEwZRmYee9i3tFczqP/aIl6USgpjucO7T5ycoaoLryEJ1/101wzGHV7Rp3IzcU9rJjUh7owA09CHp3BVHY3qqmtsdFWN0hPu+2k00mUKpG8XB0F+Vqy8824FLHUVo5QubNzTOZUeXlx3HDtZFZcXIhWO3q04WzA3dVH1X0PIjgPoPrMjTyzI4F1bzSOWpd8SOTeflM5OTkf9MibqIuiq62Lql/+Hev2NWR+/xq2aebz+ovtozpl6g0qvvGVBVx5efHHYoB4totbWZZpbhlm1ftNrN/QRGP9iaNtAEnpJpbO0zE/eABjv4usO+7Ebk7mez98h9q6Y3/HRrOGG780l+WZDSQ1vsv2X+6hb88QqZedx7x//Qp1/Ngd15tbhnn+pSreeLMOl+vEUbfMgnimzU9nabmfYmU9MUN1dG7qYvv7bvZ2m+hMLqRDacEdGN/3KTZBT9nsNKbOTmZGhpUETwPeqmp++6qFHV3RszqSM2P4zM3xxL71PP/0LKOq3nrMMvHJBr7xOTNFu//L7vv34rdH0orjZkwi/xt3UqkJIIjiqN8nd1cf6y+/G1xdLLpvJtbcuaz3LORvPzs2gnvekhx+88uLRk37l0M+2nZv5r2dg6zfL9NQazspDwOVRsH0YjUzencSt78aldGAu6OXgHV8/cuPhyCCKImISvHgvRD1uaRRoNQrURoklHolKoOE0qBEZVSiitcjZaQQSkqlzR1HY5+GjTt91NZGapTVOiUarRKNToVaq0SSRHzeIG31g2OuzcvMNnP1laVccWkx5pizs13Px5GQz49vcATfwDD2zh52rdmA7PNTnFeAQpYJ+/yEfH7C/kDk3uvDb3XgH7HhH7YRsNsQQ05UBtAlatAn6dDlJ6EszIbMTLoCCRxo11LbHKStYZjO5pGTmgARRYHZc9P51JWTWDg/6yM1Tzzd16dgMEhjY+NRgvLQdftEoiviECyclKFgMBgmGAwTCo1eo3o8BBFEUUQUBURRRiEAhCPGTuFwpAVPmMiYMyxHtjGW7QhEauhFAUE82JpKjEzMCaJ4lCnUWAiHI+ae4VA4ch+Wx7Yfo+3eof7noowshxCRkf1OZK+d+IZ3CXb34h3y4R324XcGCLiCh+8DrgA9Njd39lQdWt3HXtyGgSrg5xMpcgVByCRiQvVp4JeyLP98otZ9jlPjKEOp+noKCgqQw2F6399K/V/+TdfraxGVIpM/U4zv+hv47VMS9fuPjc7qDCq+cWM+Ke/+k+QLEnhHfSHPPtczqgFCUUkCP/3uYjKUw/jaqwhZWwlJIQKxFoZViRzo1FPTItDcaKOt/uRdAwUBMnNMTC43E5MUR3ePj8odnSesATYYVNxy01RuubEcne7sFrn2+haq7n0AbUwvnk/dyH82aNnybvNRNTgfJsasZsrUFGbPSGfKpHiaGnciCMKEXBSdrZ3s+8Ef8HfsIf1HN7PePY2Xnqqjtz16a5m5czL48Q+Wkpx0dqeGn43idmDAxdadHWze3s7ePd0M9I8toqbRKZk1N4Fl2QOkVmzHNGkhWTdfhSo2htfeqOW392/E4zlW+JRMT+WWu0uZ7Xkbz/qdbP/1PgIumfLffpOir9w+pkmKQCDE2vUt/OeFSioqorfhOJK4RD0zlmSzbJbAZH0jwfYGNq/1sKclhmZ/LF1uFb5xGnorJJHckgSKp6dSXqamxNxHrLWOcFUDPm8MWx1F/G1DAIfj2POOpFJwyTX53JC4m50v9vIvVxnWkWNrQCfPTuWHK3sZ+fOLNL7WDkDi0tlM+t7nSV6+AK/XO6bvU8DpYsvN38S6Yyvn/3Eu9oJ5rPcu5q8/PTaCe/mlxfzkh+eNyUsg4Oxn75advF/hZfs+L51t449wmuM0XDLFR+6b/0Uj6Em9ZDEIAiMVNdhrmnB3jl6ucibxmmN4p2gF9fLorrcxWkgxBikrkMicls7+DjW1B0boahk+4aBakkSWX5jP5++cRWbGx6+Vz9nMqZx3Aw4nnp4B3J29uFq7cLZ04mxux9/XhegfRJ+qQT+ziHBJMQ2BNCrb1TQ0eWiq6sMd5bd/PDRaibnzMrhkeSEL52ei1ZzZccOZuD55vV46OzsJBAL4fQHsDh+B40Q0lSoRg1pAo9eOWeyFwzJeX6S3rM8XHJ/AEyLndkmpQCmBSpJRCCHEcBDZH4yYOQXlw/fH+2ELohiJrEqKw2nChx8rFKdkDhcOy/j9IXz+iFtz8DjmUuNGAK1GRK8Mgs9PKBAi7AsSsLsIefwM/v0FXFuiZ2MdyRAB7qHl0NOPvbitAwqIfJ06gGeAp2VZrjruG6OvSw+sJNIDdxkR06kwcLssy89M2E6f45SIJm6PpOvNdez47A/x9AyQNMNC6c8u5ZGG2bz+34ao+f5XXFnC9YlD2N54DMuXLuexXfm8/0Zz1GbuCoXAzTeVc/dnZh3VS04OeMDdh+zqw+0cxuby0jigoLpDor5dprXZRVfryHHF22jExGooK48nKSuWrk4PFZvajhu9iI3T8vm7ZrHyqhKU0tnt9ju8p4rqX/wJY5YX92Wf4r+btGx+t2VMx0mrVZCVpSU5ORalSkJ5sCebUhIPNhtXoFJJGA1qDHoVBl2kv6Bef/CmU2IwqDAY1IcH1gObd7Pn678gLjeA/ut38fyuRFb9pxJfFNGk0yn56pfncdUVpWet4dTZIG5HrB627+xky44O9uzupLtr7G01FJJIwSQLS8tlpvZuRRpRknnj9VjmTUMQBBwOH7/49Xreee/YEgSFJHLZreVctThMYe/rVP+tgroXWtBnp7PwP38kftaUE26/t8/Biy9X89Kr1YycIItCrZGYMi+D8qkGEoLt9LTZqGwSaegUsNpPzvHSbNFRPC2VsqkxzM5xkhxoRdXVgRA2IKWUYpy2CEdQ4pe/2cD7a6L7IhZOTebua9Vo//Uo7xov5+V9vmOyTESFwMob8rgzZR0V33sXa5OdxCWzmfqrr5Mwb9rh5cbzfQqHQuy+5z66X3yZ8/80F0fBXN5zRCK4HzaZuumGKXzjqwvGnQ3hcQyyY+s+Vm+2UrHfRX+Pc8wDLoUkMm9ODBcPr8H16m7SLltG+a++gdJkwFbdiKOh9aCw6MTZ3IG7sxdvzwAh76n38gVQaDVoUxM/uKUkoEmyoIo1salXwWNrR3BHOe+MRqxeYIV9M0uWGgjPn8NeRwqbdvqo3t193JR5URRYfmE+d981i6xM8wR8snOcrvOuLMt4evqx1zRjPVCH7UA14eEWlPEK1Itn0hZTxJ5WLVUHbDRV9Y/L0VmlVjBrdhoXLy9k6cJs9ProLuMTyZm6PnW29/LiSztweAQSLGrUUaLVKrWCtDiB5IzEMUVr/f4QQ0NuBofdEePAsR5qITJRqzeqiNELGDUBVGEvstNJ0HkwIukMEvQee80QRBFJp0Gh1aDQaZB02g8eazUIEzjec7sD2Bw+bDYPTqcfX5T9mWhUQoh41xBGNYRcHrwHGnGs34W3vm1M7/+kiVsJuAf4LmDhg69YA7AN2AlUAP3AyMGbFogDYoFCYBYw++BNwwd9b98GviPLcuWE7vQ5TokTiVsA/4iN3V/9JS1PvoLKpGTG92ezv/R6/vJIL8P9x9ZwZWWb+ekXpjD083vJXKymd9HVPPKyQOXOrqj7kJJq4sffW8Kc2RnH3Vc55Ad3PwHXAFaHk5pWF5VNfmqbgzQ0uujvGp8js6QUKZ6SiD5WT1NVH4M9owuFtHQTX7p7DsuX5Z/1DsvDFdU0PvwEktiO71Of4r9bjWMWuaeKIIDBoMRgVGIyKjEalWhlH9JwP8nZWsLp+eze66DxQPTa7Iw0I//3hbln5XE+0+LW5wtSVz/A3sp+9lX2UFfTPy4xC5EL/6RyC/OKg5Ra9yEeaCVm7kXk3HwFqtgPIkx7Krr50U/foydKD8rEdBO33jObxcadmGo2sPXeCobrbGRedwmzH7kXVczoEfdwWGbrtnaefbaCrTu7jy+WBEhKjyHOoiXs89Ld6cJhH3+P5UOYLTpySxMpLDZRnhugQNeLwW1Db05BkzcdwZCKcIThyvtrmvjFb9ZjjeLEqzOquO62IlaIa9n3p528XnYz+2uiGO3Fafnq3QmUVTzH7t/uIRwSKf/1Nyn6ym3HzPSP9/skh8Ps+PyP6X39Dc7741zshQt4e3A2/7hv3TG/7f/7/Gw+c+f46p4/zPBQP++v2c+qNYNU19ijTkodgwAzZsVxnWEX9r+uRpuTy/Tff5fkZfOO/TyyTMDuxNPTj3/ETsBqx29zErQ7I2mpbg911dUQkimeWoY21oyk1yIZdAfv9ShNerQpiShNhmPE/MCAi5/8Yg3btkZ3pB4LhfFhFq57mjS9i6xPTSJ4xWW8VZvI5nU9DB2nX6sgClywLI/P3zXzY9V+7mzkTJ93gx4vw7sqGdi4E2dNBWJsiOCy89nUl8auHUM0VPaNK5ooKUWmz0jhogsKWbYkB5Pp9KSvn+7jNDRk4y9/XM2qDSP4R/Ey0eqVXLlIx2duX0hcbtZx19fcMsyadc28t7aZ+ijlL6MRn2SgcGoyk0s1zM6xkeprxrd9LwM7exisGsHaaCd8MJgiSBKmohzMkwsxleZhzMtEn5OOISf9tJnBud0BKqv72FHRzd59XdTVDOJynvx1TKtX4vMGRzWPyi1NoK1+aNSsm7lzM/jW1xaSkx0pFwr5/QTsToJ2F36bg6DTTcjrI+TxHrz3EfL6aG1v58JffufQaj7e4vbwiiNR1/8DvghkHnx5nMkBAISAV4HfybK8feL28BwTxVjE7SG63ljLjs/9CE/PADmXZhD79Rv43euJVGw59juvUin46pfnUrxvDcPvvcik75/P6uD5PP1Ux6gpwZdcXMg3v7qA2NjxnZTlcIiwe5Dunj627+1nX6WN2mYvbS2OqBHjaBhi1OQUJ+C0+2ipGd0IqajIwle/NO+EQvxswNM7QOPfn8bbtoPgNVfy/O5Y9mzpwj4y/lYhZ5r0eJFlJXqKZ+RSMr+YtIxYFKfgrDgRnM7BQygUpqV1hH2Vfeyt7KW6qo/2VutJmUfExGmZOiOeeTlO8tq2E6wfwTznPDKuvhB9VtpRyzqcPv7y0DZeeCl6cs7c5XnccFMG5dZXGXp7Lzv/UIkcVjD9T98n/3PXHzU4CLrcOBrasNe10FXZwruVTjaNGBjyHH8AIYoCMvKoPUPHSnySgdzSBEoKVEzL8ZMX5yfOqEdjTEQwpYM6Nupgxmrz8uvfbeCdd4+NWAOUzU7ni9cKCA//nX3OMp4JFmKLkoZcNDWJH15nxfGnZ2l+swPz5ELmP/17zJOLoq73ZL5P4VCIbXd8l/5332XZn+YyVLyMN1pL+NdvNx4z+PnONxdx/bWTT7jOsRAMhti4qYbnXqqhYs/QmM6rk8vjuDm3Ds8DLxMK6yn51mco+urtiGNM5zvZ35ssy7z2Zi2/u39D1P6aRrOGeRcVMNzvpKfNSl+H7bifR6EQuKBQYvLrjyPah0lfmkryZy5gj3IGqzcHOLC9c9T3CwKcd34eX/zc7MODy3OMj486Y8Y7MMyBn/6FgXffJO32BXTMvJSnXrBTv3/8KfeiQmBqeRIXX1DE8mV5E1qnfbqOU2tbH4/9fT3vb7HiHWWCS6VWcOF8A3euLCF7zvSoy4TDMlXV/axZ18z765rp7IhepvRhRFEgpzSB8pmJzC/xUqjpILyngqGtLfTtGWS4zoYcktEkJxA7rQTz5MLIbUoRpqJcFOrTFzWXZZmubjt7D/Sye28P+/f30NZy8n2bTbFaMnMM5KUJxIWGeW9HkI5RdH9Suombb81guWUfB/ylPPGSi6pRAkgKhcDtt07js3fOPMp08Hg0NDQc2fLpkyFuD29AEERgOXAdcB6QPYa3eYAdwJvAM7Isn7io6hwfGeMRt3B0FNeYoWfaLy7ged9FPPdkY1Rn4nkLsvjKBbHUfeVH5CwzoLttJY9syuD9VxuizjSZTGq+9fWFrLi48JRn1ZxuH2tf3czGnb3sbfAx2D82UZeYbkRAoK9zdIOUxYuy+fbXF46pj+xHTcjro/WZ1xjZ/CaqeUUMJBZS2W+iuiFAc83gqK2EziYkSSApTiLVoiQrI4biqTnkFSSRmWHGbNacEUOqiRo8yLJMd4+D/QeFbFV1H00NQyedqqTRKckvjWdyoYLJpkEse7cS6AsTv/h8MlZeiDYlep3h+g0t/PK3GxgYODb7QmtQcd3ds7mopI+srlXs/fNeWt7uxFSSy7TffQcEAUd9C/a6Fhz1rTjqW3F19tKXkktV+TIO2PQEg6fv+qTRKcnIjyMvV09xjsiUPImMBA0mfQySKQ1UY+uduHZ9M/f9aj0jUSZ8dAYV195RypW69Rz4zivsWf5/vFbhOib6LIgCl12Ty2ezNrHvu29ja3ZQ/PVPM/UXX0OhGb1dyMl+n8LBIJtv/DrW7Zu44C/z6cq5mFcq03j2ga3H7Nt9P7uAFRcfvyflePH5grz+diWPP7mPnq4Tuy8XlsZyW3kX4l//i7XNS9aNlzLjD99DZT7+ufNkjk/Dvjbu+/X7HGiO3gd3+qIs7r4GEiveRrTEIaYk49HG0zpsoKFH4v11Npqqo48mY+M03DRZQewzj+Ht6iOuxEzezZNxLriIpzfHsOmd1lFLXARR4NIVRXzxc7NJSjKc8HOc4wM+anF7CFt1I3u++Rvse3ZS/Nvr2KA7jxeebjyhf8doiAqB8vIkVlxUzAXn5Z5yRHeij9OOHQ089q+d7K6wjSrWFJLI0nkm7rw4g5ILFh3zd5fLz/adnWzc3MbGzW0Mj+LB8mEklYKiqclMmxHLkmI7yUOVWFdtp3dbP4OVw4R8YUzFuSQsmknCwhkkLpyBPif9tI8DBofcVFX1sbeqj8qqPupqB3COszb7EJJKQVaemcIciQKdlUx3J2a7DaU+mTptHn9+dwiX69jziVorccmnCrmlrAHPyxsxnXcz/votqApUrBeX8Nwz7XS3WqNuMyleyfe/uoBFF5aecP8+0eL2mA0KQhowH0gHEoikI3uBgYO3A8AuWZZPPgZ/jjPKeMXtITpfX8POz/8Y38Agk+4qZXDlbfz+MTc9UfpSxsRq+OHX5qJ+/B+49m9h2k8WsstyOY8+2U9rbfSBxJLF2fzwu0snrFVMOBRi+6tbWLupkX3d0NTuPWGPMIVSRKNVjtpiR6VScNcdM7jtlvIxz4Z9lMiyjKu1k5F9ddgrqwgMtyIn6hlJzqHOn0TXiIJAEIJBmVAYQkGZUAiCIZlQUCYQCOPzBvF6Avg8h+4DY0tXPI1otQqSE9WkpZnIyo4nOzuBnEwzWZlm4uK0E3bBO9nBw/Cwm8rqfvZW9nKgso/6ugEc9pPvx6lUK8gttjC5WMWU+GFS2/bi2dmElFhM4tL5pFyyGI1l9FTIoSE3v71/I+++H72uNLc0gVu/OI254feRDuxk688rsLc7UZpNBJ1u5GAQUSmiSzfgm1TCYFoevcoE9nSq6eqemBrKI1FpJFIyY8jM1FGcr2FKoY6i7BhijLFI+gQExfhn5m02L7+5fyOrVjdE/fukWWncfZMe8wuPseelIVYvuoPqKG7IpjgtX/5sAlP2PEvFHypQxlmY98RvoqbhfphTGYyG/H7WX3Y3ga4qlt4/l5b0y3h+i4mXH9t91HIKhcD9v72ExQuzx7zu8bD3QC9/fWwbO7d1nzC3K7vAzO0LrBiefI6+7f3ETiul9LufJevaS6IuP9bjEw6F6Hh7I489V8GqFk3USRWjWcPNny5gmecdvG0a4heeh7d/CHdXH77+XmTHAPht6At0bEhewdP/HRjVXX/23HQ+U+ij98F/Yq9tRpegoeSuMjyXXMkzm2PZuKpl1P6qSpWC6z5VxmfumEHMOXflMXG2iFuIXENbn36NXV/8OakL44n7wWd4dncqq5+vGlPbwdFQKASmzUjm0ouKOX9p7kn10J2I4xQMhnnjzT089Vw1zc2jT3qLosD82THcvjSe6VddePgaG3HsH2HTljbWb2rlwIG+MZvUafVKSmakMXO6kUV5g5gadzO0qoKuTX3YWhyYpxaTfMF8EhbOIGHBdDQJpy/dPxyW6e6x09A4RF3DENV1A9TWDDAYZSJ4rMTE68gtMFGcKVOg6CO5swZ1UEvs7IUknT8PbXICwWCYPz+0maefORB1HdMXZXHX5SFi3nwBTeFFZH/6hsO95L0Dg/T+9xECBXG80lHGG/+txxGlxAZgXnaYr9wylYLLFo46PvqfErfn+ORxsuIWwDdsZfdXf0nrv18lsTye3Ps+xZ+3F7NxdWvU5a9aWcrVxh5qvvdrJt+Zh/GmS3lyXzGvP1N9jCkKRKK43/v2Yi5aPvZ9Ggshv5+6V9bzylvV7LQbaOs68YBcUoqjpp6lpZn47rcWs2BeZtS/n+3Y+wd57/GnoX+YSSVFKEUBORSCUJBwKAgHb6GAn3DQTygcIiyECSMjKwRCoohXkHCjwokat0KHS1bjDClx+hU4fQIOt4zNHsJu9WEf9uA+Sffr8aLWKEhJ0ZOSYiA5xUxGWgzpKSZSU4wkJxmIjR27+B3L4MHl8lNd08++qj72V/ZRV9s/ZgfjaAgCJKaZyM4zUZgJhSYrqb01uDbsQ9RnEr9gPsnLF2Aqzj3h55BlmdffrOP+P23G4Tj2O69UK1hx01RWLFUxaeBVOl48QO3zzWjTzQQnlzCclEO3aKHDoaajN0x3p2vMaf9jJSZeR3qmgcwUiYIsDZPKkijIScQQY0GSxj/gi8b6DS3c+6t1DEcRL1qDimvumMSnUnfT+L3nqNLN4AXlFBxRJriKy5P4/jXD2H73DG3vdZN53SXM+utPUceZx7QfpzoY9dscvLfoJnR6Kwvum0ldytU8sxpWPXf0wEilUvDQA5czY1rquNY/Hjq6bPzhoc1sWNN6QhOqtCwTty7zkb3hNdpebSQcUpC0bC7Zt1xJxjUXolBGXGaPd3xCXh+OhlbaX1jNlh0NvCJOoX8w+pz6tAUZfOECGwldQ6Rcd+dxa8TdXX00P/AnhDIDT7aVs+btjqiDc51eyde/Np9ZoW6qf/UIQzv2Y0jVUfrZybiWXc6zW2LZsKol6nXt0Ptvv3Uat9w49Yy76n7cOJvE7SGcrZ1sve072Cv3M/kH59E672aeeraPyh3Rx/9KlYJQKHzCCXWIOG/PmJnCiouLOG9RLgbD2CbvTuU4NTYN8cZbFbzxdhvDQ6OPhwQBZkw3c+tcHQtvuQIEIZKWu6+X3RXdbNnWzkAUH5bRMMVpKZuVzuxyFfPSe1Hu2cbA6iq6tvThd4kkL59P6oolpK5Ygi4taczrHSuyLDM46Ka9w0p94xC1DUM0NA7S2jIyagr2WBAVAmnZseQX6ChN9ZMXaEOzZw+yW4t51jxSLlxITNnR2Yl9vXa++uUXqWs/9rpkSTZwx51ZlNe9TMiWQNG3vjxq5kvAbqP3lccZyEjn6S2xrH+jPmrk3aBXcLmhhRULcrHMmYqpKAdNcsLhfTonbs/xseZUxO0hOl97nx2f/wlhl5Xy789nU/a1PPZYe9SIZ1pGDN+/KZf+r/8AY6KPad+fT2XSpTz2nIP926Ibfyw7P5fvf3vJuGtxx4Ktpol1f36BtX0K9jpjsJ/IwEZg1AjF0qU5fOtrC0lJPrtb2nyYiRw8yOEwvmEr3r4hums6aKvppLNlkJ6OEQY6h3CLEn5DDB6NHr9STUBUEkSBP6zA65dPafb7ZFCqRCwJOhITDSSnmEhNNpKWYiQ1yUhiop7EBMPhwcWHj5MkqWhoHGLfgV72VfVTXd1LV4f9lCz9YxP0ZOaZKchWUJTgIsvTBrv34ajsRWHJJW7WdBKXzMIyZyqicmyD4uFhN2vfPMCrb9ZS2RJ94FE4NZkrby7BZK1loH2Y1j4V3VaJnv4g/T3uMff9HCsKSSQpzUhGqpJ0g48U3wDZBpmCeTNIO2/BmD/beLDbvfz2D5t46+36qH8vnZnGZ2+NI3Pr02y/dzvbLvo8G5qPFTaiKHDldTncHreGvd9/F59TZOZDPyb75ivGlSUwEb87V0cP78y5luSpamZ9ZxoHkq7liRecbHij7qjl9HoVjzx8JSXFCePexnjo7rHzwCPbeXdV4wl7lZtiNSxbbOQiUyXOp95nYO8QIX8YTZIFfU46KkssAyEvgslAekwcgb4h3J29uDt7CdisSFOzeadkJTurok+UmWK13HxTGissPSSffx0KzdiPr62qgc4nH2Bw1gz+tlpP3f7oPgzTpyVw388uJlxRwYGfPsjQ9n2Ysg1M+twUnItX8NTGWNa91TTqec0cp+XGayfzqZWTTsv17ZPA2ShuIZIxUPO7x9j/oz+TMtdC5n13sGpwKi8+foCRUSJ8cXFKYhKMdDTbCAZOfE6VlCKzZ6ex4qIili7KOW5bwvEcJ1mWqakd4I3VVWxY30Z39/EnYFVqiQWzDFxZpiRu5gL2Vfaza28PBw70Mjw0Pg+PhFQjZbPSmF8uMs3cRnjjZvrebaB7Wz+atHRSVywh7dKlJCyaOSH1srIsMzTsob3dSnuHlbYOGy3tVjo6bHR32UfNshgPepOarII4ivNUlCY7ybbV41u3C3dPGEPJVJKXLyDpvDmoYmMOR1oP7184zGv3v8L9b/Th9B57zpwyN50vLe4j9NpuSr7/bUxFuWPap5DbTfc7z1EhpfPYsyN0NA5FXa4kV8WicA0Je7YR6nejS0tBl5GMzaTm6hf+dmixc+L2HB8vJkLcwsEo7ld+QetTr5F1YRr6b9zG7182Ub3nWNMFhSTw6ZsnU/TmvxnZuJkZX5mE7opl/LepnP8+diCqKDabNfzgO0tYdn7eSe3fifCP2Kh/9AVWv7Sb/SmTqOoVxzTL+mFUKgW33zqNO26dhlb78ZiRP5nBg98foq/fSW+vk55eB729jsh9n5OeHgd9fU58Z8Cd+Uyg1UrEWXTEx+sQRS+SAqw2Ba3NIwTH2Rf0SHRGFZn58eRlqyhO85Mn9KCpOYB1Yw0+hxrjpMmHU7BiJhWMqa+ed2CYkf117N1Yx9aqYfaPSHQ4pFEzRpUqBckZMbgcXoZPIcJ8PCSVgoycGHJTIE0YIWGgDe2evXj3tqA0Gcj77HUUfulmDNnpp2X7ABs3tXLvr9YxOHjsZ9Tqlay8YwrXFVTR89vn2LZZZvWcm+iNUqNvtuj46mdjyX3331Q+Ukn8vBnMe/I3J7XvEzVoH66o5r1FN1N4VQqTPlvG3qQb+ce/etm1tuWo5WLNGh57ZCXZWaff2Kivz8GDj+7k7TfrxnQezS2KZcYkBdPj+khp3Il9ZzveYR/ekUifRlO2EWFKAW1Jk6jzJlDbDu1trqjrFkSBpRdmsFK7h4EH30IOSSg0qkibD40ahUaNZNChNBlQmvRIRv3Bx5GbKtaEOj4WVVwMrqZmnNXvsDX9fB5/dijqtUmrk/jinVO44ebZ9L6ziQM/+QtDO/YTW2ii9PPl9M+8lCff1bL1/eZRj4VSKXL+Bfncev0USktG78X7v8jZKm4PMby7ki23fAtPRzuTvzEX11W38exqmXWv1Yz6/85LCJE7JYEeu5ravX1jyoBRqkRmz8ng0gsLmTM7nVjz0cfhRMfJ4wlQXdvPqvfqWb+hdUweJMYYNYXpArEaFX0BHfV1g5Hes+NAIYnkFCdQWp7A3NIgxWI93nc20bOmlYEqG5YFs0m7NBKdNeYf32F5NGRZZmTEQ3uHjbZ2K63tERHb0WGju9uOZ5QMipNBUookpseQmW2kNE+gPNVG0kA9nq37Gdjdy0iDnYAzOOrknqhUotCqQavFoY9jf1wBGxV5x0yKKySRlTcXcXHf6/S+XIdCp0cOBgkHQ8jB0OHH4UAQORhEDoYO/i14+F4OhhAUkPGlC9iUczmvvNg6ahmZKArk5OopywhQSgdixUY+9c6aQ38+J27P8fFiosTtIQ5FccWwgyk/X85L8oU8/3RL1MhPQVE8d6YNM/zbB0hfksTUb81lf9wK/vGMjQOjRHEvWp7Pd761eEJdBo8kHArR8cJqttz3KLszprE7mIzNNv4TY5xZzT1fns9lK4rPupY2H8blcvPSS6uwO0KUl89AFCX8gTBeTwCrzYvt4G1w2E1Pj5PePgdDQ+6Jazr+P4BSrSAjL47sHD3FGSEKdQOY2+twbtqPrd6GlJiDZfYU4ueWY5lbfsIUrIDDia2qEWtlAz1762nuHKFXoaFVSqRmUI3d+RFNLAiQnBFDVkE8BdkKJidaSWjcRc9j6xiptR5ezJCfRdFXbiP39qtQGk+fwc6I1cMf/7yFN96qi/r34ump3Hl7MpOaXmTnD9eyKe0C1oSzog5Ky2Ym890V3fT+9Fn69lqZcu9XKPnWXcfMxI+ViRy0d721ng2X3820L5WQe00Ru5Nu5q8PNh+TJpmUZODxf6wkOenMZJcMDLr462M7ef21GkJjNBhTqhRYkvWYzUpiYxQIyDQ0eejvPrHxXVZBLJ9b5iZr79sE7V5C/jAhf5iwP3T4ccgfIuQNEXAFCXiCBF1BAu4gQU9wVMduQ7qR1O9fxd/rStm9NbpLbk6GhmWFKi49Px/RYaf61/9gaMd+LGWxlHxxFk0FF/PkmzJ7N7cf9zMU5Bq56drJrLhi8lnfW/1McLaLW4Cg20PFt39Hw0NPk1geT9F9K9lpWM6zTzTSVNU/6vvitSEWF/kxlOaypw5q9429RjUjM4Zp01KZNjmZxAQ9RqOC/fu24/PJZGZPprvHTVPrCK3tVro6rAwNjj3CqlRFyrBO9hp/qKd4eZmGuTkjGJv3Y393Fz3bevE6NYdTjZOXzUPSj89XJRQK09Zupa5+kOraAaprB2hsHIpaOnImUEgieqMao0mJ0ShhMorEGARMujAxqgAmpR/8fnpsEn1Wkb4Rmf6hEMPW4KjHNzZBzxc/m0LG83/Fur8HRAFRISAoBESFiHDosSR+8LokIBz824eXFRUCgkqBbd5y/rErhf07Tuz0LWNn747vHXp6Ttye4+PFRItbOBjFvec+Wp95nfyVuXjuupP7/x2ks3nkmGUlpchNF6aR9uCvUIpu5v6gnNDcJTzfUMYL/9yHO4oTXWyclh99dylLl+Sc8r6ORjgYpPWZN9j3s4eoNudwIHc29e3jrxPNTlTxra8tYN75JadhL8eHy+Wnrn6Q9g4r7R022ttttHdG0nN8Zzgd+IwjgN6gQlIpCAXDuB3+k7brPxGiQiAly0xWXgxFmVAcZyNluBHP1v1Yd3cQkM3EzijHMiciZs1lBaOm4QY9Xuy1zYxU1tO6p4m2ASc9sop+wUB/QEfvSPikJl8mGlOslvOuLOKyOV7SQs0om6poebGGllUdBJwfzBQnnT+X4q/dQeqKJWOKRJ8s4bDMK6/V8MBDW7FHGfRodEquvKOc6yc34f7nf9n4ry7eXfJpmgePHVgqVQquuymLq0NvsefHa1EnpTP/6d8TN33SKe3jRA/aa//4Lyq++Svm/Wg6yefnsSPhFh78fRWNlUf3lM5Ij+Eff72SxMQz59o7POzm70/s4eUXKye8ThsitYB6kxqdUY1SpUBSiEhKAUmK3JRH3itArQKNCrRqOXJThtGpw2jFAGoCaPChDnlRBz2o/B6UXic4XKh0sC3hAh77jwOHLbpZi1YnMbtQZF6giYShEQKDNkb2VmMpMVB0z3z2Jizn6Vec1O09/gBTqxHJShDJNoUpiJOYUpJIzrQ8jPlZKE3/O47LHwdxe4juVRvYefdP8fb2MOmuUrR33cjq1jxef6aavuO0vhEEyNV7KM0XUWRnUdsG9fv7Tip77KNAb4q0UswrMjO7OECR1IJ/wzYG1jUyVOPCPH0aScvmkbJ8/jG1psfjUGeBPRXd7D3QS03tIM3NQ6P22f0kYLboKJ0Sj14TRiWBJEW+H8gfVMTJsnDEYwiHiZiAhiOPw2EIhSLXwQ9elw++LjMy7KOnzXrcc7HfP0LV3h8eenpO3J7j48XpELeH6Hz1PXZ8/icolR6Kf3E5j3fNZfVrrVHTNQoK47i8cTWKHVuZdFs+uXfMYKf2Eh7/d/+oJg0rLink219feNqaogOEAwGan3iFyp8/RJdPom7pFezq04zbbKA8OcQdF2cx54YlqGNjTtPeHk1/v5OKfT3s29/L3n091DcMnTZBNxqiKBATr8Ns0WGK1aIzqtAb1OgMKlQaCVEhIkkigigQ8AXxeYP4PAGG+pw0HOjDGiWN9BDHM/kaC2nJSjLSVMQm6lFoNVidIkNDfqwDLmzjML1KSDWSmRdLQbZEcbKbbH8b4T2VjGypx94ZwFhUgmVeOZZ55cTPmRrVcCjk92OtbaFpWy0N9T102Xz0+yUG/EoGXQoGhwMEToMo+DCiKGBJ0pGSoiMhXkt7m5P6+uFRlzdbdJx/VTFXzLSR79zC0MZGGl5upXf34FG16WmXn8ekH/4fltlTTvtnqK0b4Je/WU/lKBGTovIU7rgzi+lDr7H/p++xbjib99MW4Y3Siik9N5Zv3Syj+ucTNL3SSuEXb6H8N99E0p36AHuiB+2yLLPjsz+k5cmXWPrb2Zhm5LA17mYe+OVuOhqP/h9mpMfwyMNXnvG2NFabl6f+s4+XXq7GOoob8dmKpBTR6lVo9SrUGhHbsBd7lH7HR5KTb2JRoZ8UTw8Wjw0qW1B4e8n7yvlUGBbwxroAFZujm1ZFI8akJDMBkpQe4hU+EpSQGqsnpzQdS0k2xvwsVGfo+nKm+DiJW4j0+z7w84eovf9xYnL0lH1lFq5Fl/P6vgRW/afyuNe0QyhEGUuMiMqkxekG6+DZlSmVkGokpySBgnwd5dlecqQuQvuqsG2upn/PEMrEXJIumE/yBfOInz0FhWpstbOyLNPSOsKeih52VXSxp6LnlByKz3HynBO35/hYczrFLYBvaIRd99xH+3NvUnxbCR1X3MFDT9qj9oZTqkSuyPKT9fTDJE02M+cH0xnJX8gLtYW89M+9eKKIjfh4HT/+/lIWnaZWF4cI+fw0/uO/VP78IZwjLtovvoJdqjw6Osc3QDPqRQotYcpS1Cy+aApTL5p+0mmNhwgGw3R22WhpHaGlZYSGxiH2Huilr/f0969VayViE/TEWvSYE3TEWvTEJugwW/TEJuiJM4EubEcdtKEOOlCG3CjDbpQhD2I4gEgQUQ4hyGFCooqgqCYoqvEo4xnS5tE+rKFqRyfvvVgV1XlUoxVZsDwbtdFIZ/MwXS0jDJ3E5xYFSDf4KVL1UZbqo6TMgCo1kWEplqGQgQGnigGbyJBVxuMJk5KkoDTdT57Ug6q2GvumKoYrhxAMSVjmlmOZNw3LvHJMJXlH/X/9vgCNO+qo3d1Ma+cQPbYgfS6BATsMjgTGnLo5EUgqBWlZJnJSRAozNMxaMpWyknRCQZkn/l3Bk09XjBrRj0vUs2xlMZfNcJDn2MzI1kYOPFbHcN3R0YmMay6i7IdfILb89GcuOJw+/vr3Hfz3hcqokzhqrcQVt0/j2mndqN95ifd/W8s75ddT6zh2ckwQBS66Ios7UzdT88O3CYb1zHn0PlIvWTJh+3s6Bu0hv5+1y+9kZHcF5/95HuribDYZb+TPP9t6TN/u9HQT/3j4qo+k72o4LLNtZyfPvnCAbVvaxyzuPu6oNRLxCVoscQoSDSHSY33kFRnZ3h7Lhve7Rm0/NBYMBon4GJE4bYh4dYhELaTE6ckpTKVgdiGWgozTmi1xuvi4idtDjOyvZefdP2VwawXJMy0Uf2U+A1NW8M5+ExvfaqCzafRJw7OJ+GQDqdmxpGcZyUuDyWkO4oYb8W3fz8iONmwdATRZhYczkRIXzRhXqUl3j4MtW9vYsr2DPRU92EfJiJgIBFFAZ1BhMKnRmzQYTUpMBgGTJoQiHCQUlAkGZcKIhGWBECIhBIJBcDgCOOwBHA4/Trv/Ex09hnPi9hwfc063uD1ExyvvsfPun6DWesm772oebZrG2reit43IzdCzZM3jJAcGmP3NKcRfMIltykt4/F8d1Ozujrr+yy8t4ptfW4jRODHtQkbDb3NQ9cu/UfenJwj5A1hnzaJy8nnsrgucVBTRaJQoSJOwxKgwxxqJT7MQnxJLXIwWc4wGpVLE6QrgcvlxOn04XAGcTh9Op5+OLjutrSN0d9lPydjoEJJKgUqtQFIqUEgiSpUCneFgpNWoQm/SEGvRYU7QE2vREWvREaPxoQnaUQdsaII2NEErmqANddCGJmBDkiMTEkFPEPegF5/Nj8/qx2fzE3QHI/VwgTDhoIykVaDUSyj1SowZeuKLzXgkMwOGEvbLM3n5ySr2bYlerzY/286Xb9WgTk+jL5DA3jYtdfUumqsH6GwaHnfEWhAgJVYkHQeJHfXEWdtJMvhIipfRGiXsHU6cfSFM06ZimDUV3dRSNEWF+CUVQwMOWuu66O0cpH/Yw4gjyIgzjNURwuEcvc7mVBBFAa1Bidd9fIE8ZXYqcyaLFBn6MVbsJNQrUH7/L9AkxhMOy7z5dh1/eXhbVOMliAx0LrimlEum2slzbMK1t4X9j9bSv++DgZogimTesIJJ378b86TTcz45ElmWeXt1A3/48+ao7X0Aps7P5LqbspnleZumP7zLa9VprE2ZGzVaG5ug5547TGS+8QS1/64l57aVTP/j90ZtvXCynK5Bu3dwmNWzryVkH2D5g/ORM3NYp72Oh+/dTH/XsQL37w9d+ZE6vPt8QfYd6GXT9g62buugpfHMZ5h81FgSNJTmq9EY1dS2BGlvmHjho9criI9RYDGKJMaqSE+LI7csi9z8JNLSTOj1p+5Mezr4uIpbiJybOl95j/0/+jO26gYyz0sl99ZyXNOWsrU3h3Wr2qna1TXhbvQng0ojkZhmIjXLTGaGmsK0IIUWO/rBdvyVDTj3NuNocxNSJWIunxIRs3OmYizIHpdLfCAQomJfD5s2t7FpSzutrceWq50qGp2S5MwYUjLNpKdryEsJkBfvJCY0QrirD299J84D7dgO9BASTMTNLCdhwXQs86cTU1YwpoCD2+NnaDjiRTI47GZoxMPQsIuRYRcjI56DXiU+7DYfoZCMTi8xPOTF7fzoy4jGwjlxe46PNWdK3EIkirvn67+m9alXKbipmM4r7+Dhf9sZjBJpExUCC6Vupm96kcIVqUz70mS6khbyYmUWL/+zImoULzFRz49/cB7z557+frOOpnb2fvt3dLwUuegGUxLpuf56tnTH0NF6bFT6bEFUCCSkmkhIMWJJMR6+t6QYiDOBgiCiHImminIQZciDMuRGCntQhdwR0XpQuKqDdkQiF+WAO4irz4Orxx257/Xg6nUfvveP1mJJEFDFxqAyG0GI9NeVQ2E83f2oTRIpcxLIWJJC/IJcGi0XsaHezIuP7Iya2qUNeblgaAuLLT3E5pswTctEKp+CLTaHvZ0mqhqCNNcM0t4weNLpzKIY6Q8XCoHPHyZ4BiOtEDGmSkoxkJqqJjNVTU6mDrcLXn6tg+6e0dPdymanc8OlKia3vUn1X3Yy0uCk/Lffouie2xAEgX0HevnN7zdSWxu95YlGp2T5tWVctShAgX0DvpoW9j9WT8+2I1J/BYGcW69k0g/uxlR4+urhj2RPRTcP/nUbe/dFr1+0pBi5+q5pLM9swrTnbVb/so5XMq+g2Rl9Emz24jTumVFL249fwOvSMPsf95G2YuKitUdyOgft1qoG3pl3PbpYgWUPzMOblMtG7ad4+GcbjhG4iQl6HvzzZeTnxU/Y9k+FcFjGZvfS3T3Cu+9uwuOHKZMno9NqUEgCClFEoRCQJBEZCPjD+PxB/IEwvkDwqOf+g/eBQAi/P0QgEMYfCOH1BvB6g7jdATwePz5vEI8ngNcTxOcL4vUEP7J6R1EUyMzSk5KsQmdS094VpKPZOmrP3IkixqQkNVlLVnY8eYVJFOTGkZMVS2qKEYXio4v4fpzF7SHCoRBtz73JgZ8+iLOxjbjiGPKvyUdz2TJ6tcXsatFTuWeAmj3do7YRmih0BhVJ6TEkphlJT1WSlRQi1+IhKdhHuK0D7/4G7NV9eD1alAmZmEoLMU8uxFxWgLEoZ8wpxkfS1+dk89Z2Nm1uY/vOTjyeifsuq7US2UUWsooSyMmQKEj2kW0YROfqIVhZh62ig8GqEYZqrPgdYWJnTCJh/rSImJ1Xjjbl9LqT9/ba+cXPX2Xz7ujjQkkpsmxpBrGJZvz+EH5/mEAwRMAfipy3jmgZJQgCwgdPIo8FkBQioiigkCLnxsPnSEXEcEpSiCgUB/926HVRRJKEg6+LSIqD7xcjfx8Y6OLzdy0/tLVz4vaThiAImcA9wKVAJuADGoH/Ag/LsnzS/TMEQTABK4BlwAwgF9ABNqAKeAN4VJZl6yl8hBPtwxkTt4fofX8rO+7+CXgGyL/3Kh5tncH61e1RI1oJZolle1+mVNvPvB9NQyopZJt0EU/8s5naip6o6195ZQlf/8qCMzIT3bd+B3u+9itGKqoBMGYaUN/zKdbaC9i81YbT9tE4+B1Co1OSXWQhpziB7KJ4ijPDmMURNIEPoqwRoWpFkqNfcALuIH67H6/Vj7vfg6vPg/uQgD342O+I/l5dejKGvEyM+Znos9PQZaSgS09Gm5aINsmCMsYYNVXO1dFD49+eo/GR/+AbHCF5loUZ95ThKyxnn+4C/vNEA3s2tEbd5uQ8E59daCDONYi7vQdvbxeirw9NvIB2/mQCBZOosiawrwEaqwdpqx86q9Ii1VqJhGQDSYkqUhIk0lPVZKcbyMuKIy09EZUuDlFU0No2wu//sIktoziLA0yamcZVVyWxRFpP0x/fo+mNdkxFuSx49g/ElpcwPOzmgYe28dobtVHfL4gC85bns/KqBKb516JoquXAY3W0r+s5qqY2dcUSyn/9DcyTiyb6cESlpnaAh/+2nc1bo0fyJZWCC64u5YoLdZQMvEXTo9v4z4FM1pum4vcf+7/Wm9R8+pZEZlU8Q9XDe8m+5Sqm/2Hio7VHcroH7YcclOOKTZx3/1zssQVs1lzJX3+2/hiBazSq+MPvVjBjWuqE7sOp8FGLmkAghMcTwO0J4HYHcDr9jNh9WO1eRmxebHYvNpsbu81DVeUAfT0TL0p0eiU3XD+Ja1ck0tkzwr6aERpa3PQPBhgcCjA86InanmgiUSpF0jNM5BUkUFQQT2mBhYICC/Fx2nFF7E6Wj/p7MJGEQyF639tCy79epuPld5HUMimzEkiclYR+yXRcKSX0+C20D0h09sv0d7uwj3jwH/Si8HqCBP1BQmGZcEgmFAoT9IfweYP4D/ZrFQSIidcRn2QgPslAYoJEqkUmLc5PhtGJwd5HqLEVz4FmXJ1OQkIMoikFQ142xoIsYiYVEDMp/5Rc7APBEPv397JpSzubtrTRNIEp2KZYLTklCeQUxzMpV6A0YYj/Z+88w+O4zrN9z+xs78Aueu+NvVMkRYpUb5ZkybJky7binjhOPsdpLnHixOmxk9ixE/cqq/dGihIpUuwNJArRe9+G7X2+HwtQpAiQIAmQhMT7uuaamcXs7tnB7sx5znnf502P9iH1tOI5NoCjyY2z0Y2n04cqzYp97RJsE0v68gUoNHMb3Xc6j/9uL9/7UT2RyNT9i8ULzXzjr2+huPjqGFg8nba2NioqKiZ3r4nb9xOCINwO/BaYzqGhBbhNluXOi3jtW4FngfP90kaAj8qy/NaFvscM23HZxS2kXGAb/+GHNP3LTyj5UAkDH/4UP/pdAMfQ1KNbSzRO1h94ilWfLqH8vjI6rdfzzNEsnv/FkSnrdmVlGfjWN25g5fK5q505STKRoOuXz1L/V/9BeDRVKDtndQYVf7qOt6NLeXWfxImDUwvx2URvUpOZZyIj10xOoZmaUgUVdi/m2DDGyCD66CjhYR/B0dBEDckIYVeEkCu1jniixCOJVJhwLEkikiTqj5E8xyynQqNGX5R7ajGWF2EszcdQVoihJB9Je2lmX4lwhLYfPcaxv/x3SMapfrCEikfq6My+la2NVp7+34NTGj9Jksidt1fxsYcWUVyUqus56Tw83thGoL0F2d+HkGMmUlpGSyyX+g6R9iYXve1zL3a1eiW2SQFrE8m1KyjKN1BWkk52Vjpqgw1Rmvrc+fwRfvLTQ/zu8eMkppldql6aw20fLmOj5TDJbW9w+D+OExwLU/FHKUMkQa3mmeea+O//2Yd/GuOsikVZ3PuxKq7THyR95AhNv26l9enuM74PaSsWsORfvkrmxlWXflJmQFe3mx/93wG2be+Y9pjqpTnc98kaVkl7UOzdwWv/NcBzhXfR65061Gz5uhz+aHUXI3//BH6nklU//vas5tZOx+XotDf/x885+pV/InuVnfV/vxyHuZZ9qlv537/bcVYOrkql4B/+bgubN81NLfELZT6JGlmWOXKin+dfqGfXO6OMu6bPG9QblFQUSIiSSEtPfEYDoAajioc/upiPPbjw1KCtLMvEw16cLid9Ay76+r30DwYZdsYYdSZxOKO4HSF8nrnJYTSa1BSXpFFemk5VRToVZTZKS9LQ6Wa3vvt8+h5cCFGPl94nXmVo6zs4D54g2DuIMU+PpcyELkOLLkuPuiQH0WYFtRqUSmSVGkGSkJMpl1wZUuFEKhUJhZJwUokqEUYxOky8q59Iax8RT4KkaETQ2VFlZKcGm8uLMFUUocvPnrU87LGxAO/s7eWdPT3sO9BHIDA7s7NavZLyBVlULs5gSXmSMsMolkgvyv52HIeHGD3qZPSok8BICHNt+UR4cUrMGssKL8sAzHsJhWN87S+eYse+qUOus3INfPa+fD708KbL3LKZc03cvk8RBGERsIfUTKof+EfgLUALPAh8ZuLQk8AKWZYvyMVGEISPAb8GksA24DWgHvAAecDDwEcmDg8C18myfOyiP9D07bgi4nYST2MbBz77DYLtzZR98w5+N76WrS/1ThkSZtQruL53F5tsPaz6y4WEs8rYI97Ib37cSuvxqcMSH/hwHX/8h2tm/YY7FVGPlxPf+m9av/9b5EQCUSlScV8RlZ+sY2f4On7ydJTuFseUzxUEsGfr0Zu0hIMxQsEYIX+URCKJWqtEo1Oi0SpP25YwmtXkZkkUZCYpzoiQofahibuRPGMk+/rxd3vw9frx9vrx9gXw9QdITJFjeDoKrQalUY9k1J+1VlmM6Ivy0BflYpgQs5pM22W5eXga29jz0FfwHG/BmK9n5VcXIqxazQFpM0/+opkT+6e/7q5bW8iDH1nAwrosDIazZ/Nj/gDu+mZ6jxyhzRGiNZrGiV7o7wtesGmEIAooVQpMFg22TC22NIk0I6QbZexGyExTkV9gJa8wB5U+HUFlnPH5SyZlXnjpJP/9P/twu6fOLS2sSOfuTyxmbWYn2T3bOf6fR+h5YwBtbiarf/6PZN94HQ2NI3znX96eNgQ5I9fEXY8sZEtxL0We3fS92kH9j1sIu97thBtKC1j0nT+l4P5bL8v/f3DIx//95CAvvdIybT6m1a7n7k8tZXPlGIWDWzn2g3p+P7KI/WLhlIMA5jQtn/mYldrdv6P5pyco+vg9LP33v5zT2drTuRyddlmW2f/pr9H5s6fJ35jNmq8vYdiyhKPam/jZP75NT+uZ1yNBgC98diWPfnLZFa/PPV9FTSwW4+23DvDy9kH2H/QQmqaTX12h5xbnTgw1ObSm1XK0Q6TjpPucA2tGk5qPP7yYhx5YeN57WjIRIxby4Ha56O4cprffx5AzxqgHxjwyY44Y7rEg3mmuJRdLZraR0pI0KitsVJalU16WTn6eGUm6OBE1X78HF0poeAznwRP4WruJOD1EXR4iTg9xfxBBSlUWECQJUaVEMuhS92WDDoVaBYJALB6nrb0NQath2S2bSasoRl+Yc8F1ZGdKOBzn6LFB9u3vY8++Pjo6Z2d2VhAgrzSdqiXZLKzVsSTbgT3aiXakjbGDQ4weczJyxEFgOELa8joyNizHvn459uuWTlmJ4HLT0tjDn33tDQaGpilBt9nMFz63GZP96putPZ0PjLgVBKGIVOhsGZANqEiJrl7gMHBQluULLwR6lSIIwlvARiAObJBlee97/v5V4F8mdv9GluW/u8DX/wiwCfiOLMtTxtYJgvAl4L8mdt+UZXnzhbzHDNtxRcUtgJxM0v5/j3Psr/4D+yIDsS8+yg+el+htn3rUq9iS4Kam57jzT3KxLc+mI20zzxyw8uKvjp4KzTmdnBwj3/jrTaxaMfezuACehlYOfenbjO44AIAmXc2SL1STdstiHu9eyZOPD+AcmXosJDNDw+dvUrMow0c0FkJOJpHDIeRQEDkYmtgOIcQiJPwhIo4QIVeEkDNC1JsgEpQRBAmlxYTaZkVjt6K2p01sp9ZKs/EM4RpXKth1YD9oVdx8661XbechEY5w7K//g5bv/gJBhPJ7iqj+3FK6c27mrTYbz/70MO7Rc4cGZucYKStLx2hUEwjGCASj+HwR+ns9BObA6CE7x0hNdQZ1NRnUVGVQVWXDaJh5WFQikeREwwhv7ejkzZ1dDLwnnHQSk1XL7R9bxA3LklS4tuLcdpzD/9VIxB2l8KN3sOIH3yQoqvnvH+zluReap0wBUGslbn5gAXdugCrPGwRPdHL4v5twNXvePcZmpe5v/oiyzz5wUblXF0pj0whPPNXAa1vbpi2FZLRo2HxfLZs2mKnxvE5wx0F+/bjEtrTVjPunfs66zXl8tvIEPd96BlmbyYoffovM61fO5Uc5i8vVaU9Eo7y55VOM7TpE8a15rPrzRQyYltFovJHffm8vjYcGznrO+nWFfPtvNs9pmbXzMd9FjSzLDLQe5z9+3srOnY4pS+CJCoEVCw2sHdlDgasZ+0PraTQu5JV9EvX7p4/2MVs0/OHnV3HPXdUXlQ8rJ+MkxocY72rFNTpKjzNBv19Nv1dF/yiMDEcYG/TOuBza+VAqRQoKrZSVpVNZlkZ+npncHBO5OabzmkDO9+/B5WKuz5Msy3R0uti7r4+9+/s4cnSQ6CwZYam1EtVLc6hblsXKigiFim7Sgp2EmroZ2jfK4L5RPF0B0lctIWPDcjI2rCB99SKUBv2svP9s8dgPX+Y/H+ufclB89YYcHl0tsvy+u65Ayy6cD4S4FQShDzhfMk4YeBX4mSzLr8x9q+YOQRBWAAcmdv9XluXPT3GMCDQA1YAbyJTlaZIWL60tB4HlpGZ4M2RZds7y619xcTtJeNTJsb/6d3ofe46KL1/Htuy7efrpwSkFqygKrJIGeTjnKCs/U4bbWM4ebuDXP2qks2nq+pZ33VHFn/7xWszmue+wybJM7xOvcvTP/plgf2pWOWNxOku/XIu/cjW/OFzKa0+3TWsSsnZtAX/xlfXk550dEZ+IRomN+5GTSRQaNQq1ClGlvOjwovnWeRh5ax/7P/11/J19GHJ0LP1SLfrrl9BouolnX3Dw9kstV1UO7XvJzTVRUmyltCSN4iIrxUVWigqtp2aVI5E4Bw8N8NbOTna83T3tLC2AQhLZcEclt9+VS23gLbSdRzn6/SYG3hlBaTGx4offIvuem3nymUZ+8rNDeKfJz1uyrpD7PlrEisQOdP2N1P/fSbq3DZzKqxVEkfI/fJiFf/ulOa+jGYnE2fpGO0881UDjNL9lSIWrbfpQDZtuzKIquBtr/2Fe/v4gvxc20DUuTfmc9EwDX3hQQ95zv6Tr5T7qvv6HVH3lU5dFqL+Xy/m7izjdbF3zIL62bsrvLWLZl2oZMdTSmH4nT//kKPvfODvMOy/XxL/+0y1UVtjmrF3nYr5dl6ZDlpMc2LWXf/9JD+0tnmmPq6oxs9nYQ/a2F6j6WDVdi27nV69LnDgwdYUAgOISK1/9k3WsXpU/K21NRsP4G4/g72gkFA3gEjT0JNPpdOroHpQZ6Asw3OchNotlUPQGFdnZRnJyTGRnGjDoVeh1SnQ6FTqdhEIh09x8HJVSYNPGdVitBnR6FTqtEpXq0srovZ+Yi7rZnV1uDh8Z5PDRAQ4fGZzWjf5iMFo01K7IZfEyG6uLXWRHWzE42hg9NMLgvlGGDoyhySkk66Z1ZN24loz1y2elvvhcEPT6+eZfPcmbh84+P2qtxCc+bOejty7AXFp2BVp3cXxQxO1Me4qTDToCfEqW5YY5atKcIgjCPwB/PbG7Wpbl/dMc95ekwpUBbpJledsctOVfgT+b2F0hy/KhWX79q0bcTuLYd4yDf/h3yON9WL/5CP+zN4fGIyNTHms2KLgteojPfE5EW5BOS9otPLtbxSu/rZ/SXj8tTctf/Nl6ttxQelnCKWP+AI3f+V9O/vvPSEZjiJJA+b3FVD1ax0nTBn7+qpq92zqmHNVXKhU88rHFPPrJpWg1cxdWPR87kfFAkPqvfZeW//o1yDKZS9NZ9IUawkuup55VvLVtkL3b2qesjXyhGC0aCkotFOUrKc9NkKGP0OE20tor0NvlZaDbQ3wWRrDtdj25OUZaWp0zcpOsWZ7L3Y8sYJn6GDlj79D2+1aafttOIpIk66Z1LP/x3/NWvYcf/fggI9NECmTkmbjv0SVszmsjz7mLtifaafxVG/HQu5/Hvm4Zy7//TayLqi75M56LgUEvTz7dwPMvnmT8HLUOVRqJ9bdXsvn2QqpiB8ka28fxx7v5RccCDoQzpwxbFhUCm2/J4+PWd2j/9quYl61gxQ/+BkPJ7IiCi+Fy/+587T1sXfMRIg43VR8pYfHnq3FqS2nI/DDbX2jn1ceOn3UdkiSRP/jUMh79xFKUyssrJObjdelcJGJhfv/ELn7ym75z5uTm5Bu5uTbE0o5tlN+WzxHzJn77apLGg2fPsE+ydm0Bf/Yn11FUaJ31dscDQTyHDhI4eYho2Eki3cKoKZdOr4XOIYne/ihDvR4cw/4p72NziSSJaLQSWq0Sq1VLRoaBrEwD2RkGMjMNZGbosdsNZNj1aDRTD3i9X7jU30syKdPe4eTI0UEOHxnkyNFB3LOcr52eaaBuVR5Ll5pYkTNCZqgF1WAnA+8M0b9rhPEByNy8luwb15K1Ze2cOxnPBife2MvXf9hMX//Z5yq7wMxX7laz8aP3zKi80NXEB0XcHgEOkXLw7QccpGYS9UA+sAS4ntQs5iRh4O65EHxzjSAIbwPrgQBgkWX57KnD1HFrSOXlAvydLMt/Mwdt+S/gSxO7y2RZPjLLr3/ViVtImTR1/N/j1H/je+RtyaHlpo/zy6d9U5aAASi3JflC5TE23GViVF/NO/EN/O5Hx+maJq/w+vVF/NWfbyAj4+IdAS8Eb1s3h7/8Dwy9+jYAWpuGxV+oJv2WRWwPrOfXvxmk++TU+bj2DD1/8kdruOWm8jkR5PO5Ezn2zmEO/dG3cR9rRhCh6MY8qj9eQaR8Md3qhWzdL3LgrU6GejwzKu2h1kpk5VsoKtRQW5SgWj+Mpb2BWI8LwZpPV0JFMs1KhRwjMdiEKs+AuKSOHiGfxgEd7T1x+jvdDHZ75mz2ODPPxJ2fWMKGMhclzu243mnnyPcb8Q8EkQw6Fv3TV+ivWcMPfnRg2hqCKo3ETffXcecmBdXurQSOdHDouycY735XBGuy7Cz5tz+n6KE752wgaMwRYPc7Pbz5Vid79k3tmD6JQhJZe3MZW+6uoFo4Tr7nHXpf7+R3b1jZrl+CPzD1AEPFAjtfuHEcfvhbXK0xlv3n18j/8C1XxGjkdK7E725s71G2b3qEZCRK+YcKWfblOsbVeZzIfpDmxnF+8709U7rvlpak8c2vbWJBXeact3GS+XxdOhfjbjf/+cO3efnV4XPWN9UZVdTUmFhSGGFNTYReinj8BT/NR6aeyVUoBO67t5ZPf2o5tvS5ybOE1Iyev7MP9769hDuPk4yMoSiwES8poztip21YQ/cgDPb6GOr1zJmh1YViNKmx2fVk2PVkTgjgrEwDmRkG7HY9er0SnTY1YzxdfnAikSQaTRCZcCiORBNEInGCoVQt+mAghj8QJRCI4POntieXQCBKMBglGIyjlETUagVqlYRWK5GTZSQvz0xeromCAgv5eeYLynn3OBzUH23l6OF2ZFFgyYoFlFcUk5U1fRknpzNIQ+MIJxpHaGgYobF5jEBg9jMLTVYti9YWsHqVmRWZfWQEW6C3m4Fdw/TvGiahzSP39o3k3rEJ65KaK35dninxcJhf/NNv+NmOJOEpTE3XbsjiLx4qJn/JkivQukvnAyFuZ4ogCFXAl4E/ACTAB1TLsjx9XM1ViCAIY4ANqJdlefE5jrMCk1n0T8qy/MActKUeWEgq9zddluWpk+4u/vWvSnE7Sdjhov6v/oO+J5+n+M9u5BlhI1tfGZxSOIiiwLr8AH/9kX6MGSaa02/jlT0iL//m2JSOynq9ij/6wiruu6f2os0uLgRZlhl46S2O/Ml38HemyrdkLk1nyR/XEa5Zye+banj+dyen7QzU1Gbw53+6joULsma1XfO9EynLMoMv76Dh2/+D88BxAKwVZgo2ZZN5Sw2BwiV4hDS6RtV0Dsj0DcSQk0m0atCpkuhUCTItMUo0TqwjHYSPtBAPa1Dm1pC+egX265aiyUif8jwFB0cYfOVtRrfvIDHSgqXKgu66hcRKq2gdt9Hcr6any09/h5uh3osXvEqVAkmlQBQFLGYFRRY/OUon8WO9cHIAc2SciusXov/SF/m/J9vOGc67cE0+932snBXyLoz9x6j/32a6X393VkiQJCq//AgLvvmHKE2zO/gjyzLNJ8fY9U4Pb+/qpnmawafTsdh0rLmpjJU3FFMhnqTI9Taew9387nGRbcZVjI5PfU4t6Toe+bCJhfufoP03DZR8+kEW/t2XUZmNs/qZLpYr9bvrfeo1dt//ZQBKbstnxVcWEFal0ZD1AAN+A7/6t930tp2d/SII8KG7qvn0o8vJzpr7czjfr0vnY3TEyS9+u5+XXh3E7z2/qMjMNVJba0BrMXBwzwij/VN3BQSg0K5kUbbIihyB8kITGosJyaBDkBSIkoQgKVLmRJLijMfe/ds5Hpsi/SXm8+M8eAL3wYPE+psRomNoa3NR1ZbjVOfSMmakY0hBf3+U0UEvrtEAXlfwnINZV5LJep+iQkAUBZJJmVgsQeIy1TU3GtUsWZzNkkXZLFyQRVqaFoNehdGoxuHyc/hgC8eO9dHaFaJ/MMK4e+p0E1EhkJ6uITvbSF6elewMA929HhoaRqaN5pkNtHolC1cXsGKtjevyh8gKNEFPJ31vDjKw34W2bDG5d2wk57br0WbZ56wdc0X7G2/zz491cLjh7DBkpVrBI/dl8LnP3YKkmb/XrGvidgoEQVhLKv/WAPyXLMt/eoWbNGMEQdAAk9/Yl2VZvuM8x/tJzWDvk2V5zSy35XZStW5n1JZpXuN8LkpZwEGAEydOUFp6dZSBeC+eYyc58Rf/Sny4Hc1ffoL/22On9cTUM506nYKHV3l49MYRHJbFHGQdT/70OM2Hpx5jKSu18idfWs2SxbMrGqcjEY7Q9r1f0fqvPyURCiNKAhX3FVP5qTraLNfzi9fV7H61fVpX2C1bSvjiZ5eROUuzzuFwmLffTs0ob9iwAY3mypnIXAqyLDP25j46fvQ4zj1HiLm9IIClxIQ+S4varEJtVqEyq0lE4oQcYULOMPG4Csmej3npEtLXLsG6vA5Jf/ZN6XznKRGJ4tx9mOHXdjGybTcqhRdbnRXL6jKUS+vwmYtoHrXQNaxgZNDPaL+XkQEvzmHfZenkldRkcMfDC1ib2UGhYyfdL7Rz4qctZ9Qotm9axcJ/+3NM1bNzHYjHk/T2jdPR6ebI0SH27OvHMU30xXupWJjF2lvKWbDYQl6gnlzvYaKdAzz1iyAvKtfQ55l6hF8hidxyew73a3fR+c/bMC5cSt0/fwVTVcmsfKbZ4kr+7jp++BjHv/LPABRuyWHVXy4iKalpybiLIXUlW584wY7nT055DVIqRe66o5JHPrZwTmcI3y/XpfMRCkV59oWjPPV8NyODM/ttQMqZHThvKLDJpKSuWEFlWhAFSRKyQDwpkJAhkRQmFkjIAomknPpbcuJvp15aQBBIzaoJIApCyhleTKIRk6iFBFoxgUaU0ShktJKAPhJBOeZA6R5FJbrRmeOYa7NRVhUhZGfjF630e/X0u9UMuxSMjMUJ+iKEQ7FUXdeJ2q6ntk+r5XqNy4dCEmc0KCspRWpX5LHiuizWlbrIizShHmynb8cA/fs8aOtWkXv3FmzXr0g5PM9D4r4Az//P7/nxOxp8UwxIZeYa+ItP5rPqxtVXoHWzS0dHBwsWLJjcvSZuJxEE4cvAd4F2WZYrznf81YIgCHZgctrjcVmWHzzP8SNABtAgy/KCcx17ge1II5W7XAgkSOXbHr2I15nxl+QnP/kJNtuVMQ+ZCbIsk9jXQPQXL5O3Ip22Ox7h188FGJ/G5CAnU8lX7xpheVWSZvudvH1U4NmfHp626H1ttZYbbzBhNl+e3JzkmJvoz18isecEkApVXvLFatJvWshb4Q38+nfDdDRMPQOnVIqsWWVg7So9avXczzrPJ2RZJhZNwoiDRGsfcscAYiiMoFGCUkJQSghWE2JBJmJhFpgNcxIKlRwYI3G4mfihkySbutDbVNjq0rBWWdEvLaNbV8KTu9TsOTjzDu3FIClF7DlGctPj5KkdmCJufO90QZ8TfTSAIeZHb1ajefR2FGsWXNS5SCRk/IEEo2NxRkdjjI7FGBmN4XDGSVxAOrJGp2TFpmLW3FROsS1I3vgBMvyN+NpdbHvaxzPJ62hzT//7rF2awefWjhD+998w5pRQfeoOpGVzmys8X4k+/gaxx1Izo1kr7Fz3N0uQ9Ep6LdfRlbaJgS43T/zwAANdU4e2SxLUVGlZulhPQb5q3oQTXq3Iskx3X4I9B4K0t05d730+olSKGEwqjAYRk1bAqE5iUEQxiRFMiiBpiiDpmgCZ1jjmdAm1WYdk1CLo9MgaDQlBRUJMLVFZRSgu4Y9IBGMSwYhIICLiD4u4veB0Jxl3BRl3hvC4gnhdoWlNG68xNTqDClEhzKj+MkBhhY3lGwvZtDhGWaIBw9hJBt8eoGe3A6c+F+mGFYgVBQgX4ep9VdHQzJuDmeyawjQKYNUaOzeuU6C4zN4Ec4XD4eDTn/705O41cTuJIAjLSTkOh2VZnrvh3VlGEIR8UuWNAH4ty/Ij5zm+l1TecYcsy7NihSYIgoLUjO0tEw/9rSzL37rI13rfiNtJ5Fic2Iu7kV/aQcFn1/GSfgtbXxsiPk2pkKW1Kr56axeasoWcUF7HC785waG3uqY8VpIE1q01sHaVEaXy8nTWEvVtRH78PHJ/SshmLk1n6ZfrCFct5/nuBTz92zacw1OHEOl0Ctau0rNimR6Vap7fPGaILMv4A0mczjhOV2rxeJP4fEl8vjg+X5zke74KarVAVoaSrCwl2Vkq8nNVpKVdPoMRORgmUd9G/OBJOlt9HNBX02MuuGzvfz4EAfQ6Ea1WRJIEJElAObGWpNTvQiEJxGMy4XCScGRiPbEdi138vUijU1K5OJua5TnUrcglL9FO7vgBzOE+BveOsPtZN6+ab6DBO/1tJLfIwsduEyl65Xd0vtaH8oEtSLesQZDeHx2NuUCWZaI/f4n4C7sAMBUZ2PCdFRiydXg0+bTY78QvWtn54km2PdlwzvzQtDQFi+p0lJdpyMpUXhO6l4hnPEnvUJKurgitrX6CgQ/WjKWoEFAqRSTpvdcjUComFglUkoxKklFLSXQqGb0miV4vYTApSbMqSLer0GiVjMc0jHmVjI4rcHrA5Y4z7gwy7gricQYJ+i4t11SlkSbq0EtT1KRXotYp0WhEdFoRtUogLotEYiLxWIKAN0Jfu5ORAS/+8fCMvCGmQhDAYFIhKkT83uiMZltFhYBGq0zNziaSMz4PFpuOZRuKWLfWzGJDG/bx47j2ddP1xhDDMSvinRtRlOW9L64D8riPQHMvvziRhXPs7JQxvVHFA3dYKCieO9PPK8E1cTsNgiB8C/gm4JZl+equVnwaV8PMrSAI/wt8dmL3ZVLGXBdlx/p+CUueivCIk+a/+wFjW1/H/NUH+HVHOUf3T+2qLIoCN62V+MyNTsZK7+RYu4Lnf3aYod7xKY/PzjLwmUeXsmVz8UXVEbxQkrEYnT/6Pc1//yPivkDKVfmeIqo/UU1/xlp+tz+LrU+fnHYU2mzW8LGH6rj37uoLdoW8GsP/EokkDmeQoWE/w8N++ge89PR56e3zMtDvnZGb8PmorEhnyw3FbLmhhIyM89fLu5TzFInE2f5WF08+3UzrFPmMk4gKgfIFmehNGrR6JVqdCo1eiVavQqNTolJLjA16Ob63j95250V3hK40tiwDNctzqV6WQ0l1Bpb4ELZAK5m+eiS/m67X+3nztSi7Cm6gxauZNmw7PdPAg3fqWdLwPJ2/OEbeQ/dQ9bXPo063XNbPczFcDb87WZY5+sW/o+eXzwKgtqhY9+1l2OvSSAgSXWmb6DevwusJ8+YzTezd1n7eTnOaVcuqlbksWphJZUU6xUWWi3JavhrOz9VAIpGk8aSD3fu6OXR4kI628WkHcq8xNWqNggy7imyrTJ4+SJ7aQ5nBTXa+HtGeTsJgJJRUE0pKhOJKwrFUnm0ywURovoxKCSpJQKUEpZhALUdRJiIoExGEaBQ5FEYOhUkGJ9b+EMlAaiEWRZSTCKKALCnoF9NooJTjHjutPYlpa3hfTWj1SupW5rH8uhyuKxwhJ3AcOtroerWPsXaBoi8+St59NyPMM1fg6UjG43T+5rf8rkPD1t1T54cvWJzO176ymvz8q9/V+UL5QIclC4LwG2Al0ErKRTlAKs92BbBo4rDHZFn+2JVp4YVzpXNuBUH4R+AvJ3Z3kyoxNHvFxc5+v6vaUGomjDe1c+yv/p1IbwP+z3+Kn7+pZqB7atGq1ih4YHOSW262MWhfx57tfbz2++PTlowpKrTw6UeXc/ONZZdF5IaGxzj2F/9G16+eA0BlUlL3iQoK7q3muPp6fv0K7Htj6tJBAFarlkc/sYR776mdUfmgcDjO8Iib117bQSQis2rVSvR6LQqFMDFqLiIpxFPbSlXK4VGpSj1+ISOzsiwTjsQJBlLOku7xMG5XCLcnhMMRZGDIy8Cgj6EhH6OjfuKXqVatIMCSxTncclM5WzaXYpmmDvLFGNz09Y/z1DONvPBiM+PThMMDqDUSq28sY8PtpZRoh1DHfUjJEMpEEGUihDIZJDge4tmdAk+9rSEYmV8j4uZ0HdkFZsoWZFKzLJesbC3WUBfpgVbSg22oE378AwHaXuzlzQMa9pVtmrZWLaTKM91zZxqbRl+n4wfvYL9hIwu//WXMVfNncO5qMUxKJhLsf/SvT11zRKXIki9WU/6hIgA8mnxa7bcRVGXgHgvwxtONHNrRNWNzNKVSpLQkjfw8Mzk5JnJzjGRmGLBatVgsGixmLTqd8ix32Kvl/FxtJJMyDmeAo0+8Sf3zO/EZLfj1VjyCjj4HuBwzcycWRLBY1KTbtal6spKAQkzNkioUqUVSpHJskWXkpIxMKsdXlmWSSYhEkoQiSSLhJKFIgkg4STgcJxyMn3Om/2rBaFRSlCVQpA5SGhun0q5GEY8gx6LICO8Wt0Q4414nKCYNtlLGXGcYb02IOzkeR04kiYRitA4EafaItMcN9PglwtH5MSip1krUrchj8do8VpcHyAk3YXY0MfhWL12v9qEsWET1V/+AjA0r3heztJMMv7WDV3Y28Ov9uinLdqk1Ep/6WCmf/oNNiFMYrL0f+EAbSgmC8BjwEU67BEz+aeKx14GPybLseu9zr2aulFuyIAh/AfzTxO4R4AZZlqdWabPE+0HcTjK66xBH/+Jf0WZEqN/0EE++7Js2b8RiVfHJW2Ms2lJHr1DOa78/zr5p6s1CSuR+5g+Wc9OWyyNyx/Yc4fAf/z2uw40AmAoMLPp8FabrF7IztI4nn3HQeGj6uocAKpUCrVaJTjdZ4kCJWi3h90fxjIfweMKEL8GgQxBS76FUKlCqFCgUInJSJjnREUqtmegIyYTD8WlNsq4WFAqRNavz2XR9MYsXZVNUaDl1055pZzuRSLJ7Ty9PPt3Anr29Ux4zicGsZv1tlay7sYCyxAnyxvejTqRC0EOuMJ7eELtOqNneZedYIJMEU3/3BAEWrSlg84drMafpCPoiBHwRgv7ou9u+KH5vGK87jNcdwusO4feEZ/V/otZKZBdYyCqwkF1gJqvQQnaBBZ1BhTrmwRrqwhZoxRrqQiHHcLeN0797mN7doxxOlHCofAOD3uk7SRqdkttuy+R2YRd9330DQ81iFn3nT0lfsXDWPsPl4moSb3IyyaE/+jvafvjYqcdyVmew8s8XorGqkREYNi6g27qRiNKMfzzMoZ1dHNjeyejA7Bj4S5KIUimimrieKCWRaDSMUilQW5NHVVUG5WXplJelk5U5N3ny843ouI/6v/6P1P9NltHaNFj+6HZaMpdytENNW5Nz2vJ5kwgCrLuukNtvrWTd2kJ0uksPr0wmk3jHvYyOuhkbG2fMGcDhCOJ0hXB7Yoz74oz7Eox74/h8MQK+6GWvkTsVkiRQlKOkNl/D+s0LWbWxCr3+wsyPvN4wx44Pc+TYIIePDtHSMnbBM+0KSaSozMLCCpHaojgJvR2/yk4wpqTl2BBtJ0YYG/TOSeSOSiNRuzyXRWsLWFEZJy/ahG28EfehPrq3DzC410nefbdR9ZVHsdTO3/7iVAS6e9n329/xs458mhun7nqXVVj4+2/dQEXp5TEgvZz4fBHa2p20d7jYt7+B7/7bRyf/9IETt8tJ5YWuBTYAOlKi9m3gq7IsH7qCzbtorkSdW0EQvgj8YGK3Gdggy/LUdsCzyPtJ3EJKSPU/u43j3/wPTJuL2Gq7ia1bx6YdRc7N1fCJ22Vyr19Hx5CS5352mM6m6cuTFBVZ+Myjy9lyQ+lFhdpdCHIySddvXqD+r/6d0OBEPu4yG4u/UE1ywRJ2uJfx3HPD09Y9fL+jVCmwZRuxZxuxZRswp+tJsyhIt4BBJxIXlCQEFTGUjA0HGOhyM9DporvFMa2p2HuxmDXU1mQgCOAZDzE05CYaS2Iy6tHqlGjUSjQaCY0mVa9QrZI4fGSQoeFzG8LYsgxcf1c1a9bZKQ4dJsd7CEUsRO/2Qdqe66F1WMVxYyXN6VUEldPnmgoCLFpbwJYP11GYJZPv2Yc64SUuqkmIGuKiemLREBc1xBRaogojEclIUlSRTCQJ+CJ4XSG8njCRUIxYNEEsmiAeSxCPJc/YV6kVaHQqtHpVKmx6Ilx6ct9g1qCQY+ijoxiio+gjIxiiI+ijYyiTqXwyR4OL/t0jDOwexuGQaapYw/GsRTj909/PNDolm7Zkcm9aPSP//gKitYDF//gVsrasndH/8WrkahK3kLp2HvvLf6P5X35y6jG1VcWqP19EzupU2F1CUDBoWk6fZQ1RyZgyQTrpoH5PLyePDuKYxhtgtjEYVJSXpVNZYeOGjSUsW5rzgRa7Y3uPcvAL38JTfxIAY76euk/XoLv5Ok6Eq9jfpmXvtk4Guz3nfB2VSsHaNQXcuLmU9dcVYTBcHlfbSDSCc9TFuDdIOBIjGo0RjiSIxuIEgzEGh0aIJwR0OiOxeJJIJEE0Jk+sk0QiSYKhBIFAnEAwtR53RwhcYi6tIEBhjoq6ChsltQVkZxrJyDCgUon4fFF8/gjj4xF6+zy0dbjo7HIxNhq4qPfJLbZSW61hgdlF6Ug9ifomvL1+HKZMhooqaIpmcqJfSzI5e99zo0VDYYWNsroMLDY9RouG0pw4+ZFmMvwNhJp66N42QO+bgyTiSsq/8FEq/vjj6HIuX73ry0E8FKb5xz/iyREbr77lJ5E4ezBCIYl89CNlfOmLm1C+D7wcYrEEe/b2crxhZELQOhk+7fodjbppPPb1yd0Plrg9HUEQ1MDtwB8BG4Ew8EeyLP/sSrbrYhAE4TvAX03srpZlef80x/0l8I8TuzfLsrz1It/v48AvSc14dwLrL1dt4PebuJ0kGYvR8dOnOPlvP0L38HqeDS5l767haXP3Sku0PHyPHsvK9Zw44mDrEw3TuoRCKq/s9tsq+dBd1RQXWefoU6SIB4I0/etPaf6Xn5AIhRFEKNycS80j5UQrlrB1ZCFPPNZLf8e8CpCYEUqVAmuGnjS7Hlu2kcwsLXmZMoW2KDkGH4aEG23MhegaRfZ6SQaixEJxFEoRrU2TmnUSRfzqLDzaIjyaQpyqfNqaPRzb3cPxfX2X1U1zsrzN0kU6Cr37yPLVQyRCw4tD7HnRQ0c4jSZbFaP6c+fwCAIsvq6QzffVUpwZp8Czm0xfAyIzmyGQgYSoJjIhdCcFb0JUkxAkkoJEUpRICkqSgnTqMYUcQ0pGkJJhpEQ4tT5t0UZdaONuJrtfIUcYd4cXT7sXd4eX0aNOwp4oA6Y8GhZs4mQynfg5Zh+MFg233GzjVu1hRr7/GrGElYX/8Kfk33vTvBczV5u4hZTAbfyHH3L8G/95xuOFm3NY/MVatGkpsZNExKGvZNC0DI+u+NRxjiEfJ48O0dk8ykCXe1ojvNmmIN/Mh+6u4c7bKkmfw/JEVzPJRIKuXzxD/df/k/BwaoA2rdLMos9WYV1ewKBxCTsHinnr9X4aD/aft/yYUimyelU+GzcUs3BBFkWFlssStfReLvZ3kkwm8fvGGRwYo6fXQVubk+7BCAMjcfp6/ZdsInWpZOaZqKyxsLQ0SnWkldi2vYjaIqhcwNtbm6gfjNNtKiB0jsHN2UKvE7lpeZQPr3CQmxil540Bet4YxNvrR5eXReWffIKyzzww63XPrzTJRIL+J5/krS43v9ylweOcOgOwui6Nr//VDVSXzf/c2s4uF8+/0MyLr7Tg8UyfwvCBELeCIIiyLM84rkIQhA+TEmsa4EZZlt+cs8bNAYIgrAQmBe3/yrL8+SmOEYEGoBrwABmyLF9wL1kQhHuBJwAFqbzl9bIsd19cyy+c96u4nSTmD3DyP35O9y9/C5+9j8fa8zl5fHpDn7oaHR/+cDb6umU0HR46r8gFWLwom3vurmbLDaVotXPnmBfsH6b+a9+l81fP4dCm02MpZLS4nA4yiMQvrdMhiCnXRJBJJGQS8STJpDynoWJKtQKDSYPBrEZv0mBJ15GWoceeriArHXItYTK1XnTxlIAVRoYId48QGAjiGwjgHwjgGwjiHwgQD009My8oBPSZWjKX2chZlUHmMhtoNTgM1QwbF+GQ8jl5dIgju7ppOjxIfA7yxJRqBRWLsliyrpCqrCDa4RM4e4boHJBobEzS7dbgk2bWaZCUIgvXFLD53hpK7GEK3buxB5pBlhncO8bJ37fhGwgiKgSEiUVUiKe2VXoJrV2DNl2D1ja5qNHZNGjSNSiUF/Y9SiZkYv4YUX+MqC+GfyCAu/1dMRtxv9uBDCtUNBcu43j+MsZC5x75tmUZuOMWC5si79D3gzeJyxbqvv4Fij52F6J0+Vyu55KrUdxO0vWb59n/6a+TjLz7/1OoRaofKqfm4XJExbvXhaAynRFDHU59OX519hmvEwpEGehyM9LvxTXqxzUawDXqx+cJE/BGZpy3O1MkhciG9UXc86EaVq/MuyJi7EoT8wdo/pef0PxvPyMRSnVe7YvSqHqghOy12YwaajkWXcjWNz0c2N4548E9nU5JTXUGC2ozqKvLpK42E7vt/EZ8l8pc/E6iYT8nG9s4dLifpu4orZ0RBnrG5+x+JwiQXWihpCqNuhKZZZkODPX7GX7lBDF1EeHrb6ZDn8f+I8OcbLn4gD1BAIVSkUoLusj7d5HKz8ZDz1JWbqP6q39A4YO3IyrfX07AcjLJwLNPs79jkCea7LQ1e6Y8zpKm4Q+/uJR7bl84r3NrQ6EY27Z38NzzTRw7Pjyj53xQxO1x4I9lWd5xAc/5EvCfwJuyLG+Zq7bNFaeFJsdJhQjvfc/fvwr8y8TuWaV6BEH4JPDz6f4+ccxNwIuAipRD8wZZlltm71Ocn/e7uJ0kNOKg4dv/w+gbrzP+6Uf43X49g73T54qtWGbg3nuykSqW0HhoiK1PnDhvSJdep+SGTSUsW5rL0iXZ5OaYZmV2KZmUaWt3cvjIAIePDnL4YB/eOSoNIQgp0y21WkKjk0izqbFlaEmz6Uiz68jMN6LWqIglIB5LlYCJxxPEYjKynHq+IIogCIhCEhEZhZBAJIFGSqJXJzGo4xjUMQxiEFXch3piEf1u4oNjhIb9hMbCEwJ2QsgOBkmE3xWekkGHsaIYY3khpooitDmpEVU5kSQZj+Nr7ca5vx53fQty/N1zpVCJZK/KoPjWPLJXZhBRWxk2LmTEuBBP3MCBNzo4sruH0f5xopGrxxAlVUuwmEVrC8iSRilw7yY92EYyLtP1ci8nn+zEP3Dp9XIVKhGFRoFCpUChFlGoFUhqBQp1aj8RThD1x0+J2XgofrbjwmkkEegx59NSsYYWKYvYeU5pXrGVD92sZfXIdjp/uAdZY6Pu61+g8KN3vG9E7SRXs7gFcB48ztv3/BGhgTMd6LU2DYv/eAUF69MQOPM6FFYYcenL8WgK8WpyCCvTpn19WZaJhuMEfBGikQSJWIJ4PEk8lkyFxMdT66AvylCvh6Ge1DJTMZadbeSeu6u5566aD+Rs7uRg6KRRGKS8GyrvL6bwplz8piLaVMvZfkzN8X39tJ8YueD8e71BRWaGgcwMPVmZRjIz9GRmGsiw69HplGg0StTqifuJOpW+ISlFkEGWJ/wYZBnk1H1u8rHU46ntUCjMW2++BcD6DdejUatTfg4yp0yuJp/LxOud/bd3X3tyP56QU9+xeJJYJMxQTzdNrU66R2QGhqO4RgIX7UcgqRQUlKVTUpVOXYnM0jwn1tEWPFuPMLRnmFE5F9eaLbQrMznc4CAQmPkssqgQyC60kF+aTn5ZGln5ZvQmNTqDGs17jNlkWYZEHDmZRE4mcLsiHHirl4NvdeI7x6ydQiHw0EOL+MKnV15w9YWrGTmZZOiF59jX1s8TTTZamzxTHicqBO68s4T/96VNGC9TaP5c0HxyjGeea+S119sIzPC6qZBEMnJNmNLi/P5/PzH58PtW3CZJdWFeIZVbenAGz5msc+uXZdk0x02cdQRBWAK8A2gBP/Ad4K2J/Qd5t1RPK7BclmXfe57/Sc4hbgVBWA1sJ5WnHAM+CRw/T7P6ZVn2XMznmY4PiridxNvWzfGvfRdf53F6P/JJntoFzpGpc2QEAVavMHDnh3JRli2m8dAgO184SdfJ6XNyTyfDrmfpkhyWLslmyeIcsrOMaLXStII3kUjicAQZGvYxOORjcMhLY+MoR+uH8M4wR/RykGtPsig/wOKiENdVBsi0vKtYkgmZRDRBIpIgGU0SjyRIRJMkIgligThRb5SINyWMQo4wwbEwwZEQwdEQsfcIdlGtwlhWiLG8EGNFEcbyIkwVRRjLC9Fk2Wc0cBAPhhjbfZjOnz9D3zNbSUbfvcBrbWqKbsqDleXsH7OxvSmNjoGrJ9TVnK5j+fVFLLu+mNwsBZm+E2T56jFER4kGkrQ80Ub7871Exq9seN17kYEBQw4tRUtotZRxvn6bQhJZvCqHO1ZHKT3xOh0/OYjCkk3t17+QmjV4n5SWeC9Xu7iFlHv7rnu/hGPv0bP+prZoWfntO8hZLCHEpx4ojIlafOpsAio7IaWVkNJKWLISkUwkxQufDZJlGY8jyFCPh4EuN0ff6WG0/9yGVpIkcsPGEu7/cB1LF2fP+3D2C8V1uIFjf/1dhrfuPvWY2qqi/O7ClCN2WhoOfSVdchl7GkRO7Bug9fjwrM+qv99QSCIqjYTOoCI900B+aRp5eVqKMyJUpHtID3cSPXCc4T2DnNzvp0tTgrt2Oe1JE31DFzYQacs2UrU4m4rFWZTV2EgTXRgiwxgjQ+hiztNSRCKIcgxBTiKQ5L3f9ISgwK0tZVBTzdvNBvZs76G1fvqZvMwsPX/95xtZf13hRZyhqwdZlhl++QX2t/TweKOdlsbpo/GqatL4+l9vpqbcfhlbOHvE40ne2tnJY78/PqNZ2qIqG8VVdrInzB/tOUYkpYKxoQEeXPU+LwUkCEIjqfDbyTfcDfwUeE6W5SnvLIIgfBP4FhCQZdl4Odo52wiCcCfwG2A6cd4K3C7LcvsUz/0k5xa33wIu1IDqU7Is/+ICn3NOPmjidhLnoRMc//r3iMdHaLzpIZ57M+UiOxXvitx8VOWLGB30ceDNDg6+1TVjY6JJFAoBvV6FQa/CYFBhMKgRBRge8TM8cvHlbwQB8krTqViURcXCLCSliHPEj3PYf8ba6579qlKZCQ+lY62UuTuw+0bPuqGeC1WaBV1+Frr8bIyl+WeIWG1e1qwKm4jTTdevn6fth48x1DVGU3oVjbYqnDrbrL3HpSCKArZsI/llaSy7vpiyGhv2SAdZ3nrSg22IJPF0hWh7po3urQMkomd/VwRRRFeYgybThjYzHU2mDc1pa1EpERp2EBocJTQ0llpPbIdHHJw3EW+qdquUKK1mXLZ8TuYu5FjChtt3/sgCW5aB6zfZuaOsj+RTr9D5XAea3AJqv/FFCu6/5X0raieZD+IWIBGJ0vB3P6Dpn3+MnDh76l1p0rPsbx+gYHM+orcFYjPLs00ISqIKHTGFnrioJikoSYgqEoISWRBTCyIJUU1QmUZQmU5QZSMpvjuTIssy3S0O9r/RQf2e3vOWnyktSePD99Zy+62Vl80s6WrBfayZpn/9Cb2Pv3rq/6hQixRszCF/UzZZy2zE1UYc+kp6KGdPk0TjoUF6287vuHwNUKkV5GSrybfGSHoDOEci+EMCAYUWd/TCrmVqjURZXSaVS7KpXJRFocWHNdSNNdSJOdyHYmqPU6L+GPFgnHg4QTycIBFOEI8kiIdSA85yIokxz4C51Iio0+DSldIYKOO1fbD/rZ5pZ3M3XF/I1766Ebt97sPQZ5NEKMzQi09zdNDF7xvsNJ+Y3pfEkqbhi19cwX131M3LAbDx8TDPvtDME0+eYHjk3Ndgg1nN8o0lrLyhhIzc98gbOYk25sbX18zNm+6afPR9K24l4KvA13jXERkgSapkzVGgg1TuqYGUc/KdpAySjsiyvOKyNHQOEAShEPgyKbOsPCAKtANPAt+XZXnKq/41cTs/GN11iOPf+B6iPc6BJffw4hs+QtNMNQkCrF1p4N7bTSgrlhCU9TQfHmT/9g5ajg1djC64JKwZeioWpsRseZ2dbOUI6cF20gPtqBJ+ogo9UYWe2MQ6KunxxvR0OrR0jygYHgwy0u9ldMCLc9g/KyVh0pJ+Fno7WKoYw25WorIYUVlMKM0GNFl29PlZ6Apy0OVnoc/PRtJfvnDBUDjGjp1dvPRyC/sP9DEb6VWSUsRg1mC0aDFaNBgtGlRqiVAwSsgfJRSIEvSntoOBKLFIAlGRErFZeWYy881k5pnIzDdjzzYiSSL66AhZvhNk+k+gSgSIBmQ6X+6m89VevN1T37QkvY7Sz9xP5ZcfwVCUd1GfJRmPE/cHiQdDJIJh4oHQadtB4sEwkk6DympGZTUhmU20j0TZsa+f7W91MnCeGTRIhXstWJHLretElkcOMPjzXfTtHCJt2QJq/uIz5H1oSyqs/QPAfBG3kzgPnWDfJ/6S8aazxnIBUJqNFH/8Lio/tR69JQjj3eDrheTsGbbJQERhJKRKJ6C049SX4daWgCASCkQ5squH/W+0nzeFRK1WsHJFHtevL2b9usLLkjt6tRDoGeDkd39B+4+fJBF8d7BTZVKSty6L/I3ZZC5NJ67U49KV4dXk0h+00dynoKfNTV+7k74O12U14vsgkFNkoWpJNhWLsqkuEbHHerCEurGGulEm3/0/jXf7cLd58XR68XT48A8EiHijxINxZuyMI4AhR4e1zISlzIypIo3IolX89FAR21/unrLEkFYn8fnPreSh+xde9bnsvrZWera/yFveDLYfFentnL6ipsWq5qGHF/LxB5agVs+/EOzOLhe/f+IEL73Scs7SjoIAlYuzWbW5lOplOUhKBWIyii3QiiE6jC7qRBdzoom5EUnSP+aj8IFTzvnvT3F76g0FIZOUwP0MoJ54eLpGTNa6/cx8dEz+oPBBF7cwEbLyxh7qv/EfqGot7Cy8ha1vuomEpr5QCAKsXKrlwc0JLLV1OHXluB0hGg7009k8RmfT6AXP6M6E9EwDpbUZlNRkUFKbQaZVJi3YQXqwDWuoE2UyQtQfY2j/KN4eP4IkolCKiJKAqBQRJRFJJ2HM02PM15M0WAiqbASUNsaFdIa8WoJRBcGoSDgq4PaLjI2GcQ77cQz7GexxE7uAPNSlS3O470M1bN5Uikp1ZWbhkkmZo/VDvPTySd7Y3jGjvBO9SU1ZXSZ5pWnkFVvJmXDDDgWjRIKpUjk6oxqjRYNGp0QQBMRk7JRjsCgnzii/w2mjwPFYAkEUUChE1PFx9NEx9NHR1Doyii7mQCHHSSZgcL+Djhe7GD4wNq0xiDbbTuWXH6Hssx9BZTXPzkk7B/F4kqPHhtj+Vgdv7ujEMcMZnfyyNFautnFr7RjGd7bR8VgjrmYPObddT/Wff5qMDSvm5Wj5pTDfxC2kZnFP/O33OflvPyMZm/63lL56MUUP30nurevQ2wXwDyGHRiE4CsExiLghOTu+AVGFnlF9NaPGOryafGRZpq/Dxd7X2zj6Tu+MTOJqqu1sWFfE9RuKqShP/0B8FyNON20/fIy2//kdoaEzU21UJiV567PIvS4TW40VtVlFQlDgV2Xh0+TgUebQPW5mxKPA7YrhcQbxOIKMO4N4nEF8nhCxSKqE2NVQ21wQBYSJNUwYLylS90SFInU9FhWp7dRjIuLE4wqFSDQST9UFHw/P2iC2zqiiclE2lYuzqV1golA1hDXUhSXUhTaeEmPJeBJXyzhjJ1yMHXfhaHAT9c1sUEGTkYahojiVylNWiLGsAENpAYlwJBW1MzRGeGiM0NBEFM/QKOb8KMFHH+WHz0L3NMZWZeUWvv3Nm6isuDoiniZJxmIMv/YsR7odbO21cWC/h2hk+muMyaLm4YcX8PEHlqDRzC/DrEQiye53enj8qQb27e8757GmNC2rt5Sy8oYSLDY9ghwnPdBOhr+B9GDbtFEAHyhxe+qNBSEN+BRwP7CMlNPve4kA35Fl+duXs23XuDCuidt3kWWZgRff5MS3vodmVQ47sjaz9S3PtCIXoLZayyMb3ZQuKsBhrMGvykRGYGzQR2fTKJ1No3Q0jTHunHlYl6QUsdr1WO160rMMFFfaKanNIN2qwBzqmQhP6kIfTYUAB0ZCDOwZYeCdEcbqnSTjM7guCKCzaTAVGjAWGDAVGNBnalHqJZQ6CaVeQmVVI+tMBJVphJRpeKQsmsfSaO5M0nVyjJb6YUL+8+d7msxq7rq9ins/VENR4dyWTZqkp9fDy6+08MprrQwOnbvmLIDFpmPBqjzqVuZTVq7DFu3BFB7EGBnAEBlBFsSJGXAdCVGNIhlBmQgjJUNIyfC0N4jJkjuxCaEbFzWIchxdzIEy+e4ASDIhEHQmcbe5GTkwQO+OIaLe6Tsx5roKqv/sUQo/ejsK1dyGV0ajCfYf6GP7jk527Oyacf53Zp6JZWtzuHFRkIrgMYaf3EvXq31EPHEKP3o7NX/+aSwLKue07Vcz81HcTuLv7ufEt75P96+fR06ee7rIWFFEzq0bsK1ZgnVpDcbSgpS6SIQh6oOIN7UdD0MiklrkBLFohM6OdiQhQWGWBUVsPCWKY9PXEQ1JFkYNtYwY6wiqMgj6Ihza0cWere04ZnAdANBoJLKyDGRlGsnOMpKdZSAry0BGhgG1WkIhCoinLQqFmFqLAqJCQBTFU9sKUUCtltBNDIJdjSQTCcbePkjP46/Q9/RWIo6z8xENOTrSa6yk11hIr7ZgLTMhSuJpJcVMRCQjYclERDIRVRgmSolJRBMSkbhAJCYSjonEEgKCICMgIwogICOIE2tAFGQEIbUGUAgyCDIiqeeJk8fLMqKYes6pxwU5NdgopByrJl9LmHCwEpABGUGWUchRFMnYxDqKKMdQJFPbp/6WjCImIkTHIwTHk/jHZYZdSjqiGXRG0mkbU9LVEz5nH2ESlUaiqNJGWV0mFbVp1GV7SY+ceS+PheI4G90TYtaNs9lNInIRqUqSAuuCStKW1Z5aLAsqUWjU53yaY98x6v/yn7GuNvF28T089UQ/wSnu8aJC4KEH6/jDz625orOdyUQCz+E99DfWs33EyhuHkwz1nvt3bjKrefChOj7xkaVzWt1iLnC7Qzz3QjNPPdvI0HmuZwXl6ay/vYKFqwtQKMAa6iLD34A90IKUPMc9XFSBxkqvK0HxrV+bfPSDIW7PaIQgGIFaoAyYnDroA3ZMl497jauHa+L2bORkkt4nX6Phb7+LblMpb2VsZtubbiLnCPkoLNDwyEY3N1ePEdTnMa4pYFybj1edS0KQCAVihAJRwsHYxJLaDgVjJOJJzGnaU4LWYNYgigLq+DjaqAtLuAdrqAtjeBCRJHJSxnnSw/CBMQb2jOBum5ufmSCCLkOLqcCAqdBAeo2FjIXpSDYT45p8RtVlHOi3UX/UTcP+flwzKF6/dGkO999Ty6aNJbM+m+sZD/P61jZefrWVhsaR8x6v1kosWlPAsuuLqSjXkhFqxR5oxhLqOaNebHA0RDIhI2kVKHUSCpWCyHiUsDtCxBMl7IkQdkeJTKwTkQRKgxKVQYnKqERtUqFJ16G2alCZVCSiSTwdHtwnnYx3+Rjv9hEYDp3TdRhAoVGTe/dmSj51L9k3rZvTznIwGOOdvT1sf6uT3e/0EJxhCKLVrmfJdflsWpZkqb6V0NbddL3UzchRJ5JOR+mnP0zVn34SfWHunLV9vjCfxe0k403tHP/Gf9L37LYZ52srTQYsCyrQl+RjKMpFX5SLNtuOKs2CKi0V8q7QqIkmEmx7600EQTjj/MiJCIQ9EHYhe7vB2Qz+M/tdMuBXZTFsWsSIoY6ooKW9YYS9r7fReHDgss8mqlQK0tN0pKdrSUvTkZ6WWufmGKmuyqCkxIpSuvI55sl4nJE396WE7jPbiHmmvrcoVCLmYiO6TC06uwadXYsu4921Jk2NKF1doauJ6Gl5qJNLKH5qHXZFCLsihFwRws6JtSt1jZ+MnFEY9URWr2OgaCHNEQMtHR5isQsTn/YcIwXlaZQXq6kpjFGZ7kHr7CfW0o63foDAYJCQM0zYEyXijhLxRpHPUQt8pgiShKWunPTVi8i6YTUZm1ahsZ3tZi7LMj2Pv0Lrv/8Xhs/dyq9aSzmwc+rZwewcPX/z9c2sXHZxqTAXQ9zrZnTXa5zo83HYaeJYq0xnm2fKUOrTsVjV3P9ALZ/86LJ5J2obGkd44qkGtr7RTvQckSiiQmDh6nzW315JYYUNXdRBtvcImf4GVIn39M8EBVhKEawVoLWDxppaJB2CINDW1kZFRcXk0R88cXuN+c01cTs9yXic7t+8QMPf/xfGm6t4076ZN3ZMH64MYLOruXtthAcXdWHVJ0giTriFZpAQ1SnDFFFJQlBNmKeokIVUiKo2lqrnqo250cQ9Z8wGhlxhhg+MMXRgjOHDjiln9SSjnrwPbaHwI7dhXVxNMholEY6SCEdIRlLrqMeHt6kdz4lWPCda8bZ0nVEqZyYY8/VkLkknZ20mGUttBA15jOqq2DeUy77dY9Tv6SEUOLcYMps13HFrBTffVE5tTcZFCzW/P8re/b28+lobu/f0nNeQSxAFyhdksnxjMQuWZ5GT6CDLdxxrsANxQl06GtwMHRzDddKDq2X8irsRZ1y/kuKP30X+h29BZZ47bz7PeJi3d3Xz5o5O9u7vO685zyT2HCMLVuWxdrHECns36sYD9DzfSs8bA0S9MfRFuVT+8SOUPHrfnLZ/vvF+ELeT+Lv66PjpU3T+7OmzQlwvGUmBOt2Kxp6G2mZBbbOeWjSZNiy1ZViqc1ExjOxsBncLxN/NUUyiwKkvZ9i4CKeujKA/xsmjQzQdGuDksaGrIndUrVZQUW6jpjqD2ho7NVUZFBZarmhuYyIaZWz3YRx7j+HcX49jXz2RsekNed6LIAqpcmKnlRVTqFKhwJNle+TkRLmaiTI9nL4/UbJnsmwQp5UK4j3Pk5PvOY7Tjk/VFJp5PuqpDyBgqixGrltIb1Y5zUkLJ1rH8c1yypGkUpCVZyarwExOrobirCTZlijZpjBWVQBV3I8YD0MkjBAOQTSauleJMqKcmtUWSM1QI8sQjRHzh4kHYsQCcWKBGDF//NR2cCyMrz+Ary+ArriUzBtWk7l5DRkblqM0vJt7Hg+FafneLxnd+RKjD32Knz0bYGxw6tnCO24v46v/73qMhnPPDF8MyUgIX/MR+k62sH9IyZE+NQ0Nfnzj05cymkQUBRYvtnH/A0vYvL4E6SobcDkXfn+UbdvbefrZRpqaz31N1RlVrN5SxtpbykmzKrEHmsj2HsUc7jvT4FNtgbRqSKsiFDThPt5BoGeQqNNDxOEmctq6f2iIzw7tm3zmNXF7jfnFNXF7fhLRKJ0/fYrG7/wAy+017C3Ywms7Aue8uEpKkZXVIh9bMcCKch8Xot2ivhjeHj/ePj/j3X5GjjjwdHinnNlT6LTk3rmJwo/cRs6tG84bdnTWZ4tEGatv5p3fP0NyyEmBPRM5ECI27ifYP8x4Uztx3/Qzskq9RM6aDPI3ZpO9KpNxYym9mjp2NhnZ+0Y37Q3nn0HNyjJw05Yy1qwuoKrChtmsmfbYYDBGR6eLw0cGeGdvL8fqh0kkzt9rycwzsXxjMUvXF5Gvd5LlO47d34QyGUZOyowccdC/a5j+d0YIO6982SVTZTFFH7+boofvvGiDqJkQiyV4e3c3L73cwu49PSRmOEOQW2xlwao81ixWsMjUhXn4BENbW+neNoCr2QNAxsaVVH75E+Teuel973x8MbyfxO0kyXicwZd30PfMNgZfffuCxNClos3NxLKwEuviSjKXZmHJTaCS+xGizlPHRBR6HPpq3NpiPNpCwkkVXc1jNB0eoOnQIM7zuIxeTswmNRvWF7Hp+hJWrcpDe4XzAmVZJtDVj2PfMRz763EfaSLYN0xwYOSCB0ivFhRaDZosG9psO4aSfAylBYh5uXQkzRwbhQNHhhgauHIBiBqdEpNVi1avRKNTotGpUKkVKCQRUUzlBZ8KhZcmwuMVE+HyooxClFEqkihEGUlMIglJTNoENkMMuy6EVTGONDZGtHuIUNsQgmhAnV2MOr8UQ0Ul5tpyktEYx7/5PaImH6+m3cLWl3qmnCW1pqn5i69ez+brSy5qUEaWZRLeUcabjtLRNUq7U6bLo6ZnTKSvP8LooG/G+c7pNg233lrOQ/cvISvTcMFtuVLIsszRY0M8/2Iz27Z3nNMgCiCvJI21t5Sz5LoCLDjI8R4lw3/ijJQnTIVE5FxcXQlGD/bgPtKM+1jzlOkHp+Mkxh/TNbl7TdxeY35xTdzOnHgoTMdPnqT5X/8P67pcji+9ixd3xXAMnzscNzdD4jpFJyuDh9FrUiFdCtXEKLZKRBAE/ENBvL1+vL1+Iu5zzxRKRj25t28k754t5N6+8ZIdh8/VyZZlmWDfEJ6GNhzvHGH4zX24Dp6YsiSI2qyicEsOxTfnYajMZMS4kKOBKnbsdHFox8zLJmVlGagst50ROuQPROnsdM0of3YSg1nNknWFLLu+mJI8kazACbJ8x9HFUh1ud7uX7q399GwfJOw6d9sEheLMzywIKLQaFBo1Co1qYq1G1KiR43HCI07Co87zhmoKCgWGklQJJFNlMcaKItJXLMC6pGbOwo5lWab55BgvvtzCa1vbGJ/BCLggQGGljQWr8lmzUKRG30mauxHHzna6tw0wfHCMZFxGVKsoeugOKr/8CayLquak/e8X3o/i9nTkZBLXkUYGX30bx95juA83pn4TlxlTkZnCG4vIXm3HWqQ6ZSokI+BXZeLWFePWFuFR5+N2x3GNBvA4grgdk+sg7rEAPneIRCJJMpmaKZxcX45umFotsXZNPpuuL2b9dUXnHAC83MjJJOFRJ8H+YYL9IwT7hwkPj50VNZRaosixGIgigiimjJ4UipRDuihMPDaxPfF4IplkcGgQBIG8ggKUatVZx0++DpPboohCp0Uy6FAadEiTiz61Vhr1aDLTkQx6RkcDHK0f5ODRQY7VD9Pd6bqg/6koCmQXWcjOM1CYBaX2IOpkEK8/gWssxshwgg6Hkp7hJPGZ+GFcZtQaCYNFg9GswWhSYjGJWI0yVn0CixTCLAewCGHMyRByRycjKzfxvy8J9LZPPXClVIrk5OooytdRUqintFBPRZGRPJueuNfN+JiT0TEvY+MxHAFwh0TcIRGnX0HfUJzB/sBFRVMoJJFlS9J58KMr2LCmEFG8OnPcp2J01M9Lr7Tw/Isn6euf3tkZJurCry1g7S3lFJcayAw0ke09gjEydGqWNiZbcHYIdL7UzuAbx4kHLryU1zVxe415zTVxe+EkIlG6fvUcDf/4IyzVWrpvup9n96no6/Sc83kqlUiFKUJZ20EK24+glGfuPKy2p5F392by772RzBvWoFDPnonQhXayY14/Izv20/fMNvqf3z5lXpa13ETpnQUUbMklaC2hR7uEt5qM7N3WSUfj6Ky1/b1IKgV1y3NZen0R1QvSyYq0kOU7jiXUjQAEHeGUoN02wPg0ZXUAjOVFZN24lqwta8ncuBKV1UwwGGTra6+DLHPTrbeg0517UCEZjxNxuAkPOwiNOAgPOwgPjyFIUkrMVhRhKMlHVF6eGRmHM8grr7bw4sstdHSef0ZNVAiU1WVStzKP1QsVlCvbsfma8B7qovuNAfrfHiYWSI0s64tyKfvcg5Q+eh+ajPS5/ijvC97v4va9yLJMaHAU15FG/O29+Lv7CXQPEOgeIOJwE3V7SYTOP9ByKahMSjKX2rAUGzEXGzEVGTDm6hFEgSQiAVUGUclATKEjJuqIKXQTtXh1KefzMz/R5AcjkRRIyCIJWSQuiySSIglEErKCQFSBdzyOfzyMzxPGNx7G5wnhc4cZ6vFc8GyxQiGwYnket91SwQ0bS9Dp5lf+4IUym7+TaDRBZ5eLEw0jHDw6yPHjw4xexGy9LdtI5QI7S6sEVhc5yaMHyTXEWL2D0WNOHE0evF0+4uF37/EJQWRUZ8dVvpCxgir6ohpGXFc25eVCkZQiKrWEpFKQiCcJ+iIzHghQayRESZyRCeVMsWdoWLrIzqYttaxbmYdON39qV8diCd7e1c3zLzazZ1/feX0ALDYda28uZ+UNJWSrnWR7j5Dhb0KSU+cz7JMY2Ofi5G/r8fVcerTBNXF7jXnNNXF78STjcXoee4nGf/gRWlsU3/338XpPFof2j5E4T+6nWiVSoQtQ1rqfwp4GpPcIXVWahbSlNaQtqyXn9o3Y1i6Zs9DOS+k8JKJRRrbvpef3r9D71Otn1E0EkHQShZtzKLuzAH1VNsPGxRz1V7B3j5tje3pn7GB6LhSSSEmNncXXFbJoVQ45Qj8Z/iZsgZNIcpRYKEHfjkG6tw4wWu+c1rhJk2Wn6KE7KP743VgWVZ01czpfxUhT8yi/fayerW+0nzfsWFIpqFqczYJVeayoEyiS28gINBNu66d7az+92wcJjk0IEEEg57brKf/iQ2TfvO5a6PEFMl+/T3NJPBQm5vGSiEQJev28s3MnciTG0opqZF+AyJgrlRPmcBMZc+Pv6me8sY1k9OLzZhUqEWOBAXOREVO+HlGVCvcUFCLCRNjn5PrdHM53c0LlZCqyQdJKSFoFklZCqVNM7EuorSqSGiMRKeUoHJlwFI5IJnzqbBwRE/2dLvraXfR1uOjvdOGZYYktjUbiho0l3HZrBatW5F319Ucvhov5nYTDcfr6x+npcdPV66Gtw0Vbu5P+Xs+MUy9OR2dQUb4gk4U1GtaUeanQ9aJxdOOoH2P0mIvRo07Gu7xn5fUKkoR1STW2NYuxX7cU+9ql6PKyTv19fDzMyVYHHR1OWjtctHc46R/w4vXM7SDPfEWlUlBdYWTtdcVs2VxNcaHlSjfpgmnvcPL8iyd5+dUWPOf5PwuiQFGljboVudQty6BA0UtJ7ASW2CCiHCM4EqJ/9zBdr/fjuQhjUaXZiLm2DE1GesrHIN2CKj3lazAcCbLuC49MHnpN3F5jfnFN3F46yUSC/me20vQvPyE20oX10c3s1q7kjV0BXGPn76Ro1CKldpGaDAUr6jJYcesSTMW5sx6WKssyHk8YhzOIyxXE6QrhdAYZGfXS2NhJKCJjt9tQqZQoRAFJEtFplWi1SnRaJXq9EqtVS5pVm1qnaUmz6k65Hse8fnoef4WOnz2Nc9+xs94/rcpM2V2F5G/KJWApYkRfQ70zl8P7U2WFhns9xGfgPCmIArYsA2ULMqlakkNFTRqZDGDzN2MPtKBMptyNhw6M0r1tgMF3RkhEp35dhU5L/r03Uvzxu8ncvOacAm0+iZFkUmbX7m5+/bt6jhwdPO/xxdV2lm8sZs1SNUXJFuz+Jhgepmf7IN3bBlI53xOo7WmUfvp+yj77wJzmA7/fmU/fp8tB2OVh6NW3cew7RmTERcjhwtk3gByOYjAZUUhSSkWmnHMmBGaSZDxBfNxHzBcgEYqckf8paRQoDRJKvTJV5sygRLJbSCQEwmNBwqN+QmNBkpE4yYlCMZKcYDavvIIIWrsWQ44OY64OQ64eQ44eU4EeU4GBuKTBp87Fq8nFq87Fq8lj1JWk8UD/qdrp09W5Pp30dB233lzOLTeVU11lv2pLD82UeDyJ1xtmZHScN97YRSwus2jREkRRIhZL4vdH8HgjjHvDeLwRxsYCjI0FcIwFGL9EcaiQRIqqbNTUmllVEWGJfQCjqx33kUFG652MHnPi6ThbzGpzM7GtWYxt9WJsqxdhXVqLpL3wEPJIJM7IaICRET8uVxB/IIrfH8XnjxAKx0kkkiTiSeIJmUQitY5EYoyMjJJMgtliRZYF4vFkakkkSSSSxGJJvONhPO7Qec0XrzQ6vURutpaiPB2V1bksWFzAgir7vKtJC+DzRXh9WxvPvXCSpuZLi1wTRAGtXonOoEJvUKI3SNisEhkWmQx9hAzJhz3iQO0YJtY9SLh3jLAzQtgRJuSKkJzoYwmShNJqQqnXIiqlibB+YeK6ITAaCfBo+47Jt70mbq8xv7gmbmcPWZYZe+cwLd/9JcNbd5B3dyXd6+7glWN6musdMy4/oVIpqKjJYNnibJYuzCIn24TdpsNoVM+owxKLJxgY8NLV7aa720Nnt5vObje9PR4CgdkPg9LrVVisGuw2Pfl5ZgryzaQlAsR37iT03EtIPs8Zxyv1Evkbsym8IQf7YhsefQkuXQluRRYdTgN93T6cw/4zwp1EUcCebSSrwExWpgqL4MYc6iEt1Ikp3IdCTpCIyQwfGKV/1zADe0emrxMrCGRtXkPxI3eTd8+NZzhEnov5IEZCoRgvvtzCb39fT1/fuXN3rHY9yzcWs2pdFrX6LrJ89WjG++nfNZya5T7qOKPzZl+3jPIvfJT8+26e1bD4Dyrz4fs0VySTSQZf3kn3r59jbO9RwiNO5NiEKBU4VWtbqVci6SUUShGFUkRUpnwKJrclrYTaokJlN6PIskFaGl6NFVfCiCOsZcwn4fAKOD0yLk8CjyuK1xMiFkmQPC1v9nR0BiUFuXqKcg1U5JuoLrFQXmnHYDGQTCRIRmNnLTGvn+DACKHT8k6D/cME+4YJjzimPQ8qoxLbAisZi9KxL0zDWm5GUAgEVBk49JU49FWMRKw0HR6gYX8/LceHic/AxTw3x8jmG0rZckPpJbnRzyXJpEx//zht7U6a2xy0tDoYHQvgHQ/j9UYIXUYXa61eSVGlnfIKPQuLYyzOcpDmbcd3qIPRY05G65142s8Us6JaRdqy2pSQnRC0p8/KXm4u5HoiyzJ+fxSnK4jLFcLhDDLiCDDmDOJwBnC5ArhdQdyuMN7xCNFpBocvBYVCwGRWYTIoMBskzEYl+TkG6paUsGhxHlkZhqvyeztTJs2hnn6uie1vdpyzhM9coNZKpGcaSLPryLBLZKUL5KTFydaHyBRcKN1jJAaGiQ+OEfeFSUZOL5UVZ8gb4iNHD06+3DVxe435xTVxOzf4u/po+a9f0/HTJ7HVGNA9sImD0kJ2H0vS3uyc0Uj8e1EqRaxpOtJtOmw2PUqliN8XxR+IEPDHCASiBALRy9opmAl6jYgl5sM81o/dP4ot5MAedKCPBdGmqynYmE32qgzSqy0oDSqCShtBVRpJQUJGRBZEQEYb86CLOVEmAqSqHsiMd/kZOepgrN7F8KEx4qHpbyDmmjKKP/Ehih6+C11u5gV/jqtZjHjGw/z2sXqeeKoBn296cyylSsGitQUs31jE8iIfOf56bIFWXE0OOl/uo/etwTPOoWTUU/zxuyn//INYFlRejo/ygeFq/j7NBaHhMZr/9af0PbuNQM8gJJNorGpy12ViW2xDW1NEIjsfhzIXT0SDP6LEF1EQjAjEYjLRaPK0dZJoVCYYTOB2xxh3Rxh3BfF5wudNCbkYBAGyc00sW5rLrVvKWL4sd8ZlRcJjLlyHG3AdbsR1qAHXoQaC/cNTHivpJOx1VjKX28hfn4U+S0dIMp8SuqNk03R4kMM7u2mtH57RgGlWloEtN5Sy+YZSaqszrkg5lGg0QVu7g4bGUZpaxmhtd9Ld6SYSuTIuy+mZBgor0qkqU7G4KESNdRjNUDveo72MNbhxNLjwtPvOuE/ri3KxrVmCbfUi0lcvxrq4CoXq6hnkm8vrSTAYw+EM4PSECIXjBENxguEYQW8Qv8dH0BfEPephT2OQsZHQOV+rrNzMn315AyuW581r8TodXm+Yl15p4clnG+np9lzp5kyL0aIhPdOAOV2HRiuhVotoNSIajYBaLRIJOvjWVx6YPPyauL3G/OKauJ1bouM+On76FB3/9zjBvj5yN+agvOsG9kQq2X0kRtdJ52Vx27waMRAh29NPrneAHN8QmaFRrAU6bLVWzIWG1KzNRO6aQikScoQJjIQIjoYIDIdwnvQQ85+7c6S2WSl86A5KHvkQ1qW1l3QzvRrFiM8X4TeP1fPbx+oJnmNQw2TVct0t5Vy/yU653ECm7ziC20n3tgE6X+5jvOvM3GfLoirKv/BRih66A6Vx/pRSmE9cjd+n2cbb1k3TP/+YwZfeIjziRKmXKLwxl6ybKojXLOS4v5jDLSKdbV5GB3yMu4IzSk240hhNajZeX8wtW8pYvjwXpXRh+eah4TGc++sZ2voOg6++TaBr6n6jtcJE3vps8jdkYSowEBV1OAxVjBjq6I9kcHR3D0fe7qF/BgZxADqdkoULsliyKJvFi7Kpq804w5V+NohGE3R1u2lsGuFE0yiNTaN0dbnnZNDhfJjTtNhyjNizDBTmKqjIjlKd6cXGCInmFtz1IzgaU2I2OPpuKLNCpyV9xQJsqxdhW7OY9FWL0GbZL3v7L4Sr4XoSHvfx5Pef4fUuFU0npi81Iwhw45Yi/t8fbyAjY/7fX2RZ5viJYZ58ppE3ts/uLK1KnTLvuphc8UshGnXTeOzrk7vXxO015hfXxO3lQZZlHHuP0vHTp+h9/FWU2gT5NxYi334D9cECjndKtLX6GOm/PDX1dEYVRos2Zf9v0WAwa9CbUjVyk4kkyYRMPJ4kGomnlnCccCCG3xvG743gHw9PWevuUlATp8TVSZmzjeLxHlTJi5uBlox6cm7dQNHDd5Jz64ZZcyO+GjoPkwQCUR574ji/+s0x/Odwn8wpsrDhjirWL1dQ7D+ALXCSsSNjdL7SR9/bw6fybwBElZKCB26l/IsPYVu9+H05qn41cTV9n2YTx/56mv/1pwxv30vM40WXpaXsnhJMt6/mGAvZcUSg+dgYY4O+GadqXM0YjSmhe8etlSxflnPBvxtZlvG1dTP02i4GX32b0R0HSITPjr4wFRnI35BF/vXZWEpMhBUmRo21jBpqaR/VceTtbur39uIcnrn7r0IhUFVpp7Ymg6xMAzabnowMPRl2PRl2wxlOzLKcuieEI3EikQTBQJT+QS+9vR66ejx093ro7RtndMR/UZFJ50KtkVBpJBRSqp6rQiGi1kpo9Sp0ehVavRKLWcRmBrs5QaYpSoHFT5roRnQMEm0fwNvlxd3uxdPuZbzL964PgyBgqigifdWiU7OylgUViJI0q59hrrmarifOI43sfvIl3hYr2LvHMW1pH5Va5KEH6/jMp1bN+iDL5cDnj/DyKy08+UwjXV3nrhs7FZJKQWlVOlUlCtKNMSxSGG1oHLVrDKl3gERDJ8GWUWKhJLrFdWhXL0VRV0tIVDHU0s9Y1wBBhQKv3oozrmXElcTjjs7KhMk1cXuNec01cXv5ifkD9D7xKh0/fQrHnqNobWqyltlJX19CbPESjrqyOd6loq3Nz9iQj6Dv4vJkjRYN9hwTGbkmMnIN5GWKFGbEyTKG0QpBVPEAqkQAZWJinUyZX02GAidRkBRVJAQlCVFJXNRMlMbQExV1uMMaXAEJl1/C4xNwOGM4R/w4hlM5s25H8KI7OQqSlI53s3jwKPm+/vMavOgLc8m5bQO5d28mc+OqOckJvRo6D6FwjCeebOCXvzl6TqfF6qU5bLijktXFLgrG96P39dCzbYCWp7rwvqcEkqEkn7LPP0jJp+5FY0ub649wjQmuhu/TbJBMJul57CXaf/wErgMnSITCpNdYKP1IDfE1a9k5XMzbe3x0NI4SjVzevLPTEQQwWrVk5JrIz1NTnh0nVxhF6O4hMexGTgooM824c0vpDlrpHISBvgCOId+MO4u5eSY+/KEa7ry9irS0i6s/Hg+FGdm+l76nt9L//Hai7rNz501FBgo25VCwKRtTvoGA0saooZZRfSXtgyqO7+2lfm/fJbvR63RKJEkkEk0QiybmZDBCb1KTU2Qhp8BEXqaAxZDErE1g0cax6qJY1GF0igiKZBRRjiPICQSSCJEQss9H0uMj4QkQdoUJjoUJjoYJjYXwDwbx9QdOlSsDUGjUmBdUkLakBuviKiyLq7EurLzkWvFXA1fb9SSZSND6w9/R336cIyWbeO310WldwC1WFX/4+dV86M6aq97xW5ZlGhpHefKZBra+0X7B17ScIgs1dVZWVoRYaekm/OZ+BncN4m4bJ+R4d1DLsqiKrM1ryLxhNemrFuI63Mjo69sI951Au7iQ0fw6WoM22voVDAxGGOkfxz8+fUrShXJN3F5jXnNN3F5ZvC2dqXqxz72B88BxEMBSaiJruQ3b2iJUVcVEDekMBQ2M+rWM+lQ4vBIuLyRkAZ1WRK8V0GkF9BrQa0GvkckyR7GpxtFFnWhjTrQxDyIJErEkEXeEsCdKxDO5jhJ2R4j5YwiKlFHLpGGLQjVh4qIUUeok1GYVaosKjUWF2qxC0r47uh0X1YQkKyFlGiGlFZ9gpX9cS79TyfBogqFeD8O9HoZ6x4mGZ55rlRb3sXjgMLVjzRjNWjSZ6WhzMlJmHhM5UNrsjLn495zBlew8xGIJnn62kZ/8/DAu19Q5TYIAi68r5KZ7K1hs6iRv/ACCY5j253toe76HiPvdQRJBFMm9cxNlX/go2TdehyBe3R2K9yNXW2f0Qoh6vLT816/oefxVvCc7UagE8jZkkXv/cnrzVvBmo4kDex2M9I9f1WkXKo1EYXk6ZRVGFhRGKY/3kNh9AO/+VvQ5ejI2V8LiBbT4sjjYaeDwAQf9HecPAZYkkes3FHHfh2pZuSIPUby4KIhkLMbIjgP0Pf06/c++QXjUedYxljIThTekhK4+S0dQmYZDV8GYroLWURP1+/o4sa/vskUFTYcggD3HRF5pGgX5GspyElRk+clTO9CMDxBu6sHb4yXiiRLxxoiOR4l4392OhxIkYgmScRk5niQZn/6LpbanYSjJx1hRhLG8EGN5EdaFlRgriubdjOxMuVqvJ8HBEY786T+QKNbypm0TW1/sJTLN/b+wyMhn/2AVN20uu+pErt8f5dXXW3nimUY62s/+HZ6LqqU5XLfKwNoSFwWhkzjfOE7/ziFGjjiRJyLgjOVFZG5eTdYNq8nYtApRqWTk9a342o4wqtHTrS2iZUxHe3eUvk7PBfWhLoZr4vYa85pr4vbqITg4wsALb9L//HZG3tx3qnaj0iBhzDdgytdjzDdgzNNjzNej1ChSJVsnay9CqiMpy0TGowSGgviHQgSGgwSGQwSGgoSc4bPKF1wKCrWI2qJGY1GhtWvQZ+nQZ2kxZOkw5usx5OgQJZG4oCKgysCvzsQrZdDpsdDUJdDd6qS7xcHIeZx9AQwGFQ99ZCEPPbgQk+nCyytcKlei85BMymx7o53v/3A/A4PTd04Xrs7nlvsrWWE8Sd74PkLdDlqe6qL79f4zyiBpMtIp/ewDlH32I+jzs+e8/deYnqu1MzoV8XCY7t++RN9Tr+E6eIKI04M+U0vpfWUob1zLvkA1Ow9GaTkxRugcYfLTIQhgtmjJyzNRXWmjMN+CQa+gtbURpVJg0eKlyLJIOBwnEokTDscJheK4PCEGhnwMDvkYGfZdcgkYURTILUmjus7M0sIwJe4mgq++gzzuI2dtJtZbV9As1/Bmg4Ej+0bom0EnNzvbyAMfruOeu6ov6bqVTCQY232Yvqe30vfMVkIDI2cdYyk1krM6k5w1KYO+mNKAQ1eOU19Jf9hOe5ufrpNjdJ0cY6DTPadh4emZBvJL0ygsMVCZF6cue5xMYRjlcBf+5mHcbeOnwoT9Q8Gz6o8rLSbUNivKNDPeZAy0KnILC1BqU6VLlEY9KqsJldWMympCk21Hl5uJNifzA+nmfrVfT0Z27Of4N/8d8Z41PDtWzt63+qaN7MrM0vKJjy3lnrtqUauv7GBEU/MoTz7TyOtb2whfgKBUSCLL1hdy72aRFeIBHK/V07djiJGjKUGrsprJ2rKG7JvXkXXTOvT52QT7Bhjd+TJtzjCNERuN/SpaW7z4ZqnusSCA2arFZtNjs+nIsOuxpevQapRo1Qq0WiVqtYRarcDhGOTBBzZMPvWauL3G/OKauL06iXn9DG3dzdieo7gONeA+0kQ8cP6auedDodNiKMlHX5iDviAbfWEOymw7J4Z6Eawm1m9Yj0ohIccTJGMx5Fic5MQSD4aIjfuIeXxEPV6iHh8xj5fouI+o20t4xElocDRV+mLi2iQoBAw5OkyFBiwlJqzlJqxlJvRZOuKCEp8ml3FNAZ3hXPadkGk82E97w+g5zUd0eiUfvX8BDz+0GIv58oncy915OHCon+/9915Onhyb9pia5bnc+kAVq6xt5Hv24m0cpPHX7QzuGz2js2iqLqXq/32K4o/dhUKjntN2X2NmXK2d0Xg0yujOgwy9soOx3UfwtfUQG/ehtqrIXZeF/fYl9GYuYmd7Ggf2uxjpu/DZWUkpUlKaxvXrithwXSEVFbazTJku5vyEQjGGhn0MDvpo63DSeHKMllYHgxc5g6xUKSiptrOgRsvCdBeZ7YdRD3aSsSgNxcplNIYreatezf4dPYyeZ2ZUrZG447ZKHvrIQoqLrBfemNOQk0mcB47T9/Tr9D69dUpDKrVZRfYqOzlrMshabkdpUBJSpp+qpztGFk09CrpbnDhH/HhdIcZdQcZdoWlzJN+LRqfEnmPEnm3Elm0kN0OgwBajKC1ApjiC3t9HsLEXZ7MbZ5MHZ5P7DOMmpdlI2tIarEtrSVtag7muHE2mDXWa+ZRXwtX6O7namA/nSU4m6f7tC7T96GeEH72f3+3T0np8+rqvZrOKBx+o46EHl2A0XL77VjAYOzVL29Y6fSmvqdDolKy9qZR7N8SoDuyh77HDtD7dTdQfJ33lInJuXU/2zetIW7EABIHx44c4dqiR+lGJxiE1LS2+SwovliSRnDwTxcVplJakUV5sJTvLSEaGnvR03YzN79ra2qioqJjcvSZurzG/uCZu5wfJRAJvcweuQw04D57AdbiRiMNNIhwhGY4QD0VIhMKnRKVCo8ZUXYq5tgxzbTnm2jIsteXoi3LPCkGd7ZtiMh4nPOzA39WPv7MPf0cvvvZexhta8Z7sIhmLoTarsNVZsS9Mw77AirXCQkxlxKGvoJcyth+EPa+3MzowfYdRp1fyyMOLefjBRej1cz9Sf7k6D61tDv7z+3vZu69v2mMqF2dxywM1rM3oIN+zF1/DAA2/aGPowJlCOPOG1VR95VFybll/LfT4KuNKdUaT8Ti+jl58Ld24j5/Ed7ITf1c/wf5hwqMukuEISr2EtcJM1sYipOULaJNKONxjpP64j8FuD7GLcAO1pmlZv76Ie+6ooq4287xhh7N5foLBGG0dTk6eHONE0yj1x4cYuIgwXYNZTUWdnYXlIgs1/eQHW7DnwXjuEnaNlPPWTjf1e/vOW4t29ap8Hn5wIWtWF1x0yPIksizjPtZM39OvM/TaLlxHmnivkhdEAVOhgbQKM9YKM2kVZixlJgStGp86m4hkTnkpKPTEFHp8cQ2jXgnneKoMm1opo5ESqBUJNMoEWimORhHHrPCij7vQRR0Io8P4uscZ7/bh7fbhahnH0+E9FT4sqpSkLa/Dft1S0lcswLq0FkNJ/nkNuOaDaLsamE/nKR4McfK7v2D47Tfof+ARHn/l3GaaWq2CD91VwccfXk5WpnHO2tXS6uCJpxt4bWvbBZdUtNh0XH9bGXev9FHk2EX37+ppe7YbJC113/gipY/eh8pqJh7ycuj1XRxo9tAwIHKyNXjRYtaeoaOiKoOaKjuVpemUlaSRk2OalXJf18TtNeY1p4vbX1RvIduajqTTotBrkfQ6lEY92twM9PnZKC0mVBZjKgzIYkRpMaE0ze9C2+8nZFkm0DPA2O7DqZlV1zgRh4eIw31qibrHkeMJZFkGWUZOysjJJJFI6uKqNehRaNQo1CoUWjWqdCtqmwW1zYomIz0141uUi6EoD022HVFxYSUwEtEovtZu3EebGHvnCGO7jzDe2IbKpCRnTQZ512WRtcJOzJDGgHEpu/ry2fl6Lw0Hpr+ums0a/uCTS7n/vro5DWGa687D0LCP//nf/bzyauu0s0yFFTbueLiO9bk95Hv24G/oP0vUCpJE4Udupeorj5K2pGZW23iNcyPLMnF/gPCoi/Cok/CIk/DwGBGnh6hrnOhE5EPM6yc67sM9MgrRGDqtDkHg1Gz7qXv7aV+Edx879ci7q4m/yZx5jBxPkIhESUZjyPFUBAayjCgJqC0qtHYt5lIjppocFGUFjBrz6Ylk0DmqorU9xECPF6/74kLiBFGgpDSNO26t4KbNZWRnXVindK5/b253iKP1Qxw8OsjRowN0tLsuuNyGPcdIVa2VxUVRVmYNkWWL0ynW8tpRHfve6GKo99zpFgUFZj76wELuvL3qDGfiSyHi8jDy1n6G39jDyPZ9+Nq6pzxOEAVMBXqsFWZ0dg0qowqVSYnalFqrTCrURiVyUibqjxELxIkFUuvJ/cBwaELM+omMnxmOrkqzYL9uCfbrlmJft4y0ZXUXFTUyn0TblWQ+nqfQ8BjHv/mfhOIjnFxzN6+86T9vWauaagu33lLNLTdWkp5+6UZgwWCMrdvbefzpBlqap4+Smg6lWsGN99Xw0fVecgd30vnbE7Q/30MSJdVf+RSVf/oJ2jr72LmzleOdUU62hS5KzOr0EuUV6dTWZbN0YTaLajNn5fNPxzVxe415zenidsc3/gC7pCTuCxL3BIh7gkQ9IaLjMWL+2Ls3OH/sVB6fQqNGm5Nx1qLLzURfnIexrBC1zXpNAM8RwcERRnceZHTHAUZ27MfX2o3KpERr06CxqicWFZo0Neo0LUq7GUGtBElCUCkRlAoESQJJAoUI8QRyIgGxOHI0RtIXIOnxE/eFifpihJxhQs4IIUeYiC+JKjMPU1U5lgUVmBdUYFlQgTY744L+3xGXh+Fte+h7dhuDL+9AjoXJ35hN2R2FWBfYGDPUste3iJefH6B+b++0ws9u1/O5T6/gzjsqL7ju5EyYq86D0xnkp784zNPPNhKbpsanPcfIbQ8t5MbqMYrcuwg09J4lahUaNaWfeYDqr/7BtXzaOUKWZSJjLrwtXXhbuvC1dOJr6yE4OEpkQtAmQu+KQUknoTYrUeqVaK1q1FYVGqsapVWD0m5FSLOQ0OlJIqQSouDUWkacWAOikNoQhImVgCxP/E0AEJHld19CnthICBIRhYYQGgJo8MU1jEfUOL0CDleC0bEobmcY/3iYcDB2yeZPeoOKFSvyuPv2SlauyEOruXjBdrk766FQjPrjw+ze18v+A710drgv6HyICoGiChuL6rSsqQySn6/h0FAO27cOc3xf3znzW/V6FffcXc2D9y8gJ8c0C5/mXQK9gwxv34v7aDPjjW2MN7anUkdmEV1eVsp5eHEV1kVVWBZVYSwtmJVokfko2q4E8/k8eRpaafqn/yUUHGLo9g/z4l6ZthNn55SfjiDAwgVp3HpLNTdtqbygFKV4PMmBQ/288PJJdu7sJhK5OHOmRWsLeOiBDJYHXqPrJ3tpe76HWFJC8/8+Ta81n8aOEG2dQfzeC/chsKapqVtgY9WqEtYsz6OowHJZ+9HXxO015jWni9sFS/4BrSEdSalAqRRRqiSUagV6owqbTY3ZJGLUgVErY9AkMEhxjGIQc9yPLuQCh5P4gINI/xiRER+B4RDB0RCCSouxtABDWcHEuhBzTSnmmjJUltm9kb/fSUSijLy5N2U69dZ+Eu5hrBVmzAtz0C6pIJhTxEDMijuoxBtS4AuK+ELgD8gEgknCwRiJeIJkQiaRkEkmUzVtkwkZWZYRFSKScmKRFBPbCiRJQCXJ6LQCOg1o1TJ6dRKjOo5OjKCNB1CHfSjHPSh9XrSCgEZnRl9YhmnhEkzV5TNyqUyEIwxt3U3HT55k4KUdmIsMlN1VQNGtBbjSF7AvuJSXnh/k2Ds903Y88/LM/OHnVnLjlrJLDvk7ndnuPHi9YX71m2P87vHj0xpVGC0abnpgAbetilI2voNIYycnftbC0P7TRK1WQ/kXPkr1nz16WVyjPyjEA0Gchxpw7juGp7EdX0sX3tZuYh4vSr2EIVePMVeHvsCCMs+OIt1M3GzFrzHjE/W4E0Y6R1X0DsTx+xOEQglCwQSR8ETt6EiCWCTBfL6PCwIUlaRx841l3Ly5jIJ886x1wK50Z90zHmb/wX527+3m0KF+RoYvzPPAlKalemE6S+vU2PLTeWefn31vtJ+ztJsoCmxYX8TDDy5k6ZILr5k7UyJON+ON7Smx29RBeMxF3B9MLYHJdYi4P4hCo0KdkY7Gnobanobabj21bSwrxLq4CnX6peUQn4sr/T2YL7wfzlOgd5CT3/slrvoD+B68nxcb9BzfP3DeQSaFQmDRwjRqa7IoLc6gsMhKYYHlDMEryzInWxy89GoLr21twz1N1YGZkJln4r5P1XFrdj0jT+1g9wkT/RkVdIcN9PSGph2kPhcWq5raSiOr1pawfm05BXmmKzopdE3cXmNec7q4rV3896hUF3+T0hpUGExqDCYNRpOSNKsCu0XGro+RrghgibowuIZIdPUTONGLr9eHLBkxV5edmRu6oAKl0TBbH3HeEw8EGXxtF33Pvk6w+QjG9RW4KpcwJNroH9cwMJJkZDiMY8hLKHBheSJziVorodFIaLQKtBoRrRo0KhGdVoFOr0Jn1KLTqdHoNGh1GrRaFTqNEr1WQq9TYYz48D33MoO/fAoxGaTi3iLK7ilmPHMBu/3LeP6pPhoPDUz7/kVFVj73B8vZsrl0VsoKzFbnIRSK8djjx/nFr4/in8ZZVq2R2Hh3NXdu0VId2InQ1sKJn7XQ9/bwqZBTSa+j/A8fovorj6LJSL+otlwjhSzL+Nq6ceyrx7H3KM599XhOtKLPVGOtNGOsykYsK8RjzWVESGMooGXQITLiSOIYi+AbDxP0RYhfRKdmPiEIkJltZPnyPLZcX8SK5Zc2O3surrbOev/AOHv29bF7bxfHjg7j98/8WiuKAoWV6VTVphGTJRoODDB8Hof48vJ0Pv7QYm6+sQylcvYjUeYLV9v34EojyzJOV4juHjftPW4czhBeb5jx8RDDw04kSaC6Op/igjSK883k55lJT9fN6kDvXBN1j9P2o9/T9+LLJD52J68N5HB4z+CMjc4mMRok8vIMFOSncbLFQU/vpZXDUqkV1CzPJc2QYLh3nL6BCD7vxfW5LGlqqos1LKmzc8PtyykuuLoiHK+J22vMa2ZT3M7s/cBk1WLLMpCZoSTHBtn6IBlxF2muXuSTHXiP95NUWDFWVqQKrS9KhTrp8rOvqh//XBJ2uBh6dSdju3bg0ykYyKuhLZhGa0+cnva5r3H2/9n76/BIsvTA//1GJKMypUwxMxYzQzNO45BnxuMZM61xDb+7a1/f9XoNd9fPGtb2ejwznvFgMxZ0MYMKxMycKCVT/P6Qqrq6W1xSlVQVn+fJJyOVkRGRoYjI88Y55z0riVqjwGJWkSwFsAUdlKWNs/uAiHVdFadca3nzx5101M+ccTEnN4lf+LlNPPZo8V01V77bQlYoFOPNtxv5l29dxe2e/q6xQimy/dFinn02lXXx02g662j4ThvdhwduD5ugNBko+42vUPafvobWlrzo7/Ow8/cMMPjBKQY/OMXYmVoUYghreRKWrcWMF1fTJWbSNqKhozfO6LAfjyMw49AVD6okq5bi4hQ2b8pi09oMKivS0GrvzdAcKzmoSSQkmlvGOH2+h/MXumhscBKbZdzVTzNZtGTmW/BPRBjodM1aM2W3G/ji59fw0ucqMZkevkznK/k4WG6SJNHb66G2bphrdcM0NQ7T3ztOOLSwhG4qlYg9TU96homy0lT278pn7Zr0Zem+s5Ti4Qjd33uL9v/7H2j2l9Oet5ULrQrqrwytujKQJVlLWb6a6iwle/fXULWjckWXZ+XgVraq3evgdi5JyTps6QbSU1VkWOKkqX3YgqOY+zuRWgcQ1VZMFZPBrnVdBeaKogdiTLtbGS8Hj52gy+unXbTROWGkrSvE6MDE/d68FUmjESnJV1JcoidmTOfapREGOt0zzm9PNfClV2t48XNViyokLraQ5fYE+clr9fzwx3V4ZhivThAFNu8r4Jnns9iovIy5t5bG77XR8W4vianaQJXFTPl/+hplv/EV1NakBW//wy4eieA4W8vg+ycZfP8UMdcAabtziezaTqcqj9YxHZ09Ufq6xxc1VutqJIhgMGqwJutISzNSVmKjqtxOUUEyuTlJ93WMydUU1ASDUS5e7uf4qXYuXOxnbHT+TR5FhYDZqsPnDc1a86/Tqfjcc+V8+Qtrl7xf7kq2mo6DpTDhC3PqXC/HT3dy5VI/457FDwszG51eyboNmezZmc/enXnLmoX4bkmShLu2gb7XjzBy4hSq/dW0ZG3iYjM01A7PmZ38fsjINlKcKVBqibK2wMqGp/egT1s9Lazk4Fa2qt0Z3H7xZ/8vCqWVUChKaKpfWDgcY8IbvutEI0shKUVPWrqODLuCdGOE1ISbZFc/yV4XKqWZpIoKrOsqsKwtWxU1Wv6+YbrOX+VG2xCt42o6nCq6On133bRYoRRJTtZhNGkwTT3MZg1JJg1JZg0ajRKlQkShFFEpBBLxGE3NjQiCQFlZJYKgIBKJEY7Epx4xwuE4gVAMfyBCIBDF74/g90cI+COEglGCgRiR+/wDI4hgTzcR8EdmzUao1Sp57plyXvxcJaUltnkvf6GFrL5+L9/7wQ3efqeJcHjmfbN2Ry7PvJjHNn0t1v6rtPy4nbbXu4lN3Z1XJ1uo+J2vU/KrX0adtHILICtRxDNO/5tH6X/7GI4zF7CWG1E/uo12axXXBw00NPpwDPmWdRsUShGNVoFKpUCtUaLRKNHqlOi0SmKxIBqNiM2WjEJUTCaHEkCYzBKFeEeOKUEQ7nhvclq89ebHTwjirc8IKBQi1iQtJqMao0GN2aQhxarFYtFjSZq8NqzU2oPVGtRIkkR3j5vjp7s4c6aD+gbXgpqrK5TirON8i6LAwf2FfOXL66iuSluKTV7RVutxMF+SJNHZ7ebwyS7Onu2iuWGMxAKzdi+FnLwktm3L5YmDxaxbk75irwuSJOFtbKfvtUOMnDqLa30lJ8PFtHSGcTsC96WsqlIryCswUpwap1TnozpVT/G+7SRVl67Y/TgXObiVrWrzGec2Go3T0eWmvnmUplYHbW0Oujtd884AJ4iTxbHZskXeDYVSxJZunKrtlbArfKTF3KQTJEVnxFxUiKm0EHNp/n1LYBULh+k4d5MbNzppHY3Q7RLpHYziGFlYkpI7Jdu05OQkkZuXTGGelaJ8K/l5FjLSTQvqY7pUhYdYLEEgEMHni+D1hfGMhxmfCOAZHcM97GLC6yMQiBGISIRiApGESCQhEo5AJAqRSIJIVCISjhMKRmdNvDIvdwytMpOSUhsvPlfBE4+VkDRHtsX57qe6+hG+/b1rnDjROesPbfmGTJ59pYhdlpsk91+i9UdttL3RTSw4GdRq7MlU/O7PUfLLX5T7oC9AzB+g/53j9P7wPTxXLmF7uor+qh3cdNuoawnT3+lZ1gJQZraJmrXpbFqfzdYNmWRlTp8Y5EEvtN+tB2X/BENRLlzq58SpVi5eHGB0dHFDK01n/boMvvKldezZnb+q+lQuxINyHNwpHI5x9nI/x091cOliP2PD/gUvQ6kSSUk3kZyiwWgQMeoF9DrwB2HUmcAxGsQ16l9UrWZGlonnn63ghWcqsNsNC/78corFEty4OcSx092cOdtDX4/nnq4/Jc1ATraW3JQ42UoP2UyQrddh27gJ+46NixruaiW6n8Ht/WsvJHuoqFQKykttlJd+XMslSRKdXW4Onejg1Kku2podMxYYpcTt0RdJzzVTWaTAJngY6Aoz7FfjDCnwjC++1i8eSzDSP85IP9y4/VcDYEBnVJPS6CPZ0kCKoZ5kdRibKopNEyc9SUtKchJmux19RiraDDuaZMuihy9IxOOMdgzQfLOdnn43Q84wIxMw6pUYHo3gdiw+O59KJVJYnERNdTobN+SyaW3Gso5xthhKpYjZrMVs1pL5iXdKPzNvxDOO+1ojrtoGxhsaCPZ3kphwoktWo03Vo1ljQ5GbgdechlO00ufW0TWipHcwzlC/D49jHjcF5hHAtLU6+B9/fZq//p9nWLM2gwN7C9i3p4CsBTT7GxnxcbN+mJt1I1ypHaClZfZhNkrWpPHkS8XsT2smpe9fafvXVs7fEdRq0+1U/v43Kf6FV1EaVtb/eKWKh8IMfnianh++x9jJMyQfLKJr10HO5+/n5nU34esxYPahJT5jHjdHAGypevbuL2L7pmw2rs2Y8yaJ7OGi06rYv6eA/XsKkCSJnl4Px091cOpUG/UNngWPrXuna9eHuHZ9iNzcJH7mi2t5+qmyZUvwJbs7QyM+Dp/s4OyZTm5eHyUyS2ueTzNZtOQVJVGWL1Ka7CMtOoLVO4xiPIioMiOoDSTUetr7h1HGwuQmayA5QGKrgXFTCmMaO6MRE12jKuoa/Ax2e2bf1oEJ/s//ucQ//fMlNm3O5uXnK9m3p+C+JTZzOgOcv9THsVNdXLrUT2AB3UZS0gysXWOiJj+KSowTiQlcuh7myo3gnP3kFUqRogobGyqgUtFHhn8Yk9qMrnAtyZvWo89dvozmDzO55lZ21+ZTczsfTqefI6e6OH6yi+tXB4nOcbdQZ1CxZ386r64fQnP0CE2vd9PvVCPt20d04yYckpqBwQmGBv24XcvT5+QWjVaJ3qjCYFBi1IsYtaBVgShONgsUhckhYAUBFAJE4hCIQDAMgbBEMJQgGEwQCEQJLCCD5mysKRrKS5NYuzaDzZsKqS5PXbYflpVyZzwWDDHe2I77RjOemy2TzzdaiI6PY8o2YCkyYyk2Y67OYqKwkhuOVOq61bR0hOltdy1pU66MLDOVlanUVKWxtioVk0lDOBzm7NmzBIMJ1Pp8GpudNDSM4JpHoC0qBNbtzOPRp7LYZmkhpf8C7T9qpvWNHmKBycQYxsIcKn7/mxR+7YUH5u7vcpISCcbOXKXzO28w9N5RTNuz6NrwKBeHk6m76Z3zGnQnvUFJil2LPyDhGp27FiXJqmXf/kKef7KMtTWLa8K3Us67leph2D8+X4STJ9v54K1arrf6CATvLtt2UpKWV1+u5vMvV5Oc/GDcGFutx0EsFudq3TDHTkzW2Pd2zT9Tr0IpUlyRzOYqBRvMA6R0NEBIhSa3BuvmTSRVlXymFdpM+ykejuDvGcDX2Yuv8SbhkSZ8pSVcixZwtVmguW5sXjkGTGYNTzxewkvPVVJaOv/uPAuVSEh0dLq4dmOIK9cHqasbYWRo/nlHRIVAQbmNjdVq9hWPkd52keFjXQxdHuMGuZzI2c24cuaWUPZME2Vr09lUIbE7s4fEsVO4+y2s+es/f6i6BcnNkmWr2lIFt3cKBqO8d6SdN95qoGmWLLYwVejfmskr+yKU956g+0d1DJ4fQZedSemvfomib75CQmegt89LW5eDzs4Rurs99A/4GBz0E/Cvrox501EoBHLzDFQUG9i4KYctW8vISr93CYNWcuFBkiSCAyO4ahtwXq7DeakO1+U6ol4vliIz9rUp2NfbkdbVcMWVw5V2LfVNwTnvTN8rGp2SbY8W8+RBE2sU19G1X6P9zS463u29HdRa1pRR+Qe/QO4rT8xrLOCH3URHL13ffZOeH76NJk9N15bHOT9qo6FhYta+indSqURKcpSUlGhxxwxcv+ZhfI5xD/UGFbv2FvDcE2Vs3ZR118NLreTzbiV42PZPNBrno29/xKHDTdR7NDjHFx/oqlQizzxVxs98aR0F+fc3SeTdWk3Hgdsb4qNTbZw+08W12tF5d90CMFu1rF1rZku+j3LnTRQDXrTFm0l/bP+8+m4uZD/5uvsZeOMDAi3nUJWn0525ngudRi5fdDI2OHcgWVySwgvPVfDU46V31VJFkiSGR3y0tjloaB7j2s1hmhpGCCww74jBrKFiXTqbq0V25wyguXSOwY+6GTw/QtQfw51RwIniR+gIT3/DR6tXceCFSnZuVFOla8M23sDw4TaaX+un7A9+h8KfffGhq6GVg1vZqrYcwe2dunvc/OjNRj78oAWve/a+RgXlNp45qOcR83VGvneazg/6kSQl+T/zHGW//jNYaso+Mb8kSbg9Qdo7nbS3D9HV7aS3b4KBwQCjIyFi8yzo3ktKlUh2tp7CLBUVRUbWbS6iqroIre7+1dStpsIDTNbYeRvbGTlxidHjFxk9eYmwy0NSgYnUtSnYNqQRWbOWy2NZXG1X09C0/EmDPk2rV7F2ezbPH1SxRXeD0LnrtL7RzdCFUaSpw9K+ayOVf/iLZD6556H74Vyo6LiP3p98QNd330CKDjPyyJOc82RSe903r+Z9ggDZdiVlBg/bqmNEiyr48IJI3eXBOWv816xL5wuvrOHAngLU6qVrPbHazrt77WHeP64bTXz0Z9/hQm+YTnsJvU4W3U98+8Y0vv6NbWzcsDqbUK7k4yCRSNDUNsZHx5s5f3GIthb3vFsQCQLkFSWxsVLBRn0vac3XEfS5JO/aR/ojOxdcS7jY/RToH6bv9UOMXz6OvtpCW/E+Dl1Wcf38AOE5httRKkX27Mnnhecq2bYle8YbfomExPhEmO5uNy1tDprbnLS1O+nuci04kL0lM99CzfoUdlSEWWfsJnz6EkMn+hi6OEYsnCBlcw3B7Xs4HkzlzA3ntOePIMDWR4p46XOpbIx+hDHQT/fhARq/347KnsfOH/7/MZcVLmr7Vjs5uJWtassd3N4SiyU4caabH71ez9WLs58jyakGHnk0lZcquoi+dYS217vwDwVJ3beFst/4KlnPHUBUzF7IjMcT9Pd76Ogcon/AxcDgOMPDfkYdYRyuKG5XeNkyEgrCZLNFu01FWrJIerKCbLuamjU5VG2qRq1dOT/OsLILD/MhJRJ46lsZPXGJkeMXGT15mYjHi6XQROp6G/ZNGXjK1nFpKI3aNiXNTRN456ilW0panYJ0q4JUZYhk3xiFdi27vrSf8ie23bNtWI3ikQhDH56m+z/eJdh1nYnHDnA+UsTF2iC+8bm7KoiiQFGWinJ/Jweze7G9uIt32vM4ftzJ6MDsTQQNRhVPPFnGl1+pIT9veWq+Vvt5t9zk/QPjbd00/Y9/oe4/DtOZW01vyTpaXeoFjal7S2G6yKsvVvPMK1vR61dPv9yVdhwkEgmuXO/h0JEWzl0YYWRw/smgtDoV1TUmNuX4qXRcR+vwoy/dTPpje7Cuq1h0vg9Ymv3kqWuh7e//DcHXjPbVxzk2VsypMx46G8fm/GyKTc+uHXmEQjE84yG83jDj4yEmJsL4fJG7HiNco1VSXJ3Khhotu8s8pPXfwHWsjqGLY7hbPKiTrWQ8vou0J3bTnVzED95uo/ba0IzLK6xM5cWvVbLHdJWMiev0HO2n7lst+IeClP2nr7HuL373gRhmcrHk4Fa2qt2r4PZOXT1uvv396xz6sHXWWhetXsWOfVl8focb+9WPaPthE2M3XBjysij51S9R9I2X0SRbFrUN8XgCl2sCp8ON0zmOyzmBc2wCtzuIdzxCKJwgLkEiLpGQIJGAuCSRSIBKCXqNgEEjYNSJGHUiJr0Sk1FNdq6NopoS9GbrqrlLvtIKD3crEY/judnCyPGLU8HuJeL+ANZSM2nrbaRszsRTsIYbngzqe7V0docZHRhnYoYxaJeL0awmM1NPXpaOwnwTZWUZVNXkY7PqV82xs9SkRILR01fo/v7bjNeeI/7YNi6pqzlzLYpjHhlFFQqB0jwtpd4OdqrrKHmhikbrZt45p+Lq2YE5++EWl9n4wkvVPPV4KVrt8jYRf9DOu6Um75+P+fuGaPrrf6XjX35CMBxnoHI9nZVbqOtXLjjQ1agFdlYqefWVjWw+uH7FX2tWwnEQjcU5d6GZw8c6uXjJiWts/qMcpGcZWV+hYK26n/z+JjTJRdj27iF1zyZUxqXLRLyU+yk4PEbbP3wf1/H3yHihiqGaA3xwVc+lU/14nYsf4WEhrHYD+WUpVBQpWZsXpFTTT/T8VUZP9zJ0eYywN0rKljVkPrmHjCf3YFpTzqGjnXz3+9fonGW8e6vdwDNfXcfjlWMUuo4T6Bjl6t/WM3bDhcaezLZv/wVZT+29J99xJZODW9mqdj+C21u83hA/eK2en/y0HvcsF0xBFFizOYOXDiTYOHGOru/X0ntsCEGpnrHJsmz+VkLhYTklYjFctY2MHr/AyPGLjJ2pJeYPYMzUk1JhIak6DWV5IT3aPGqHLbT3g9ebwOeLEQxECQWjJBISUkKaHM5qmS+7lhQd+TlaCtOgOAXKM3RkZGRgKshDm2Gfs9XCaiNJEu7ahslMx8ePotpXxTXbFk7fhJ4Oz5yfF0WBsiI9FROdbPRfoezRTLyb9vJevZ2zpx2M9Hln/bxareDAI0V8+dU1VFWkLtG3mtuDft7dLXn/fFZo1Enz//oObX//faLjPhImHeMHd1BnreZqq0AsurCLU0aqikfWKXnl1c1k11Qv01bfnft1HMTiMc6ea+DdQ11cvuJifI5uVbcolCLlFSbWZQao8jSRGoekTTvIeHQX+uz0Zdve5dhPUX+Aq//0Jhd+cARXTiYjKYV0DYu4HKElHdpRq1eRkWshp8BEdaHE+hwvqZ52ghfqcdwYxVHnxjcYQGNPJuPxXWQ+uYf0x3aitFhobhnjwsU+fvp6A6NjM98A1eiU7H++gqceS6J6/DA6Vw91354cfk+KS2Q9d4At/+dP0WXcu9+AlUwObmWr2v0Mbm+JxuIcOtLOd75/nY4256zz5pWk8MyjRp6w38TxozO0v91L2BMhbf9WSn/jq2Q9u/+BK/wvt4etEBmPRrlx9AbnT7bQMhSg2w3DztiimvrdKylpBvJy9RTYYhRox8nHgzkG2vQCzGWFmErz0aamrPhamFvCLg/DR84x+MFJfPVX0W0vprFgJ6eaNTTXOedVcMrL0VMtDbJh5Bzlm3Ron9nHR0OFHD8XouX68JzLyMpJ4pWXqnjhmQpMpnvf5/1hO+8WSt4/M4t4xmn9++/T8j+/TdjpQVAI2Pfk4NyxhRNj+dQ2xIlG559zQhAFykv0bCsVeHJfAUU7diCIK+N39F4eB4lEgqamDt54t4VT55zzai0CkGTVsLZczRrlAOUT/VgKq0l/dPddNzVeiLvdT5Ik0T8wTlPzGHVNozS3OGhrdTDuXdoWTQazBlu6kcJCAzWlIhUZfjLjQ8Rb2hi/3oOz3oWz0UNkIoohPwv7zg3Yd27AtmMD5qoS2jpcXLk6wOWrA9ReG8Lvnz1plzlZx+6ny9i9P52K8Hkyx2vpOdLP9f/TRMgVRpeZyqb//f8h+4VHV83v570gB7eyVW0lBLe3SJJE7fVB/u171zl/tmfWBBpWu4FHHkvjlZpupEPHaftpB56OiSVpsvywWa2FyEQ0SnTcN/mY8E9NTz7Hpv4e9k7gkiQ6olq6Ahp63Ap6BsMPRJZte6aJ/AIDhalx8hUOMl1daIadoEpGn1uAuawAU1kBppJ8lLr7O/ZqIh7HdbWBoQ9PMfLRGZRKJ8Jju7mpLuNyq4KWeue8hu6x2TSsM3nYFqxl7QYR9e5tXPbmceKagqtnB+Yc0kKhENixK58vv1rD5o1Z97Uws1rPu3tF3j9zi/kDtP/Lj2n6q38lODg5MkH6Zhu5L5Vw07CGDxtsXLvpJ77AG3f5BUa2Vog8sjmZdQf3oNDMPHTKcrsXx8HQwABvvFfPsdMuOltnbtJ6J1uqjk1FCdZFuyg2aEjfv4fUPZvv29jkC91PkiTR1eXm4tV+Ll4Z4Mb1Ibz3uGuOggQpUS827zD2iBtzRgraglw0OZmos9KRNDqi0ckbNaNjPq7WDjI+j3wLAGnZZvY9X8GOrSYKfBdJ891kotNzuwkygkDJr3yJtf/ttx6qIX7mSw5uZavaSgpu79Tb5+G7P7jJ++83EwrOHIhodEp27M/mCzu8ZLadpPMnzfSfHkZUayj4yvOU/vrPYKkunfHzspVZiIyHwky0deNt6mC8qRNfZx+hUSehESehUScRpweBGBqLGo1FjTrdgrIwG09KNqMaO/0xC11OJd09ASY8yztO8t3S6hSo1Qp8E9G7auoliAJpWWbyCvQUZ0oUal1kujqIXakn6kqAPg1DUT7GwhyMBdmTj8IcVOalLbjGwxG8DW24rzXiutaE52YjkncAy7Z8HBt3ccWVxtWbQfo65leINBpVrEsPsUvZxLYdSoJVmzndZefizThN14YITMw93EZqupHnnqnglRcqsduWrp/b3ViJ591KIu+f+YuHI3R9900a/+Kf8XX2AZBcnkTFF4tI2pLN+10lvF9rprHOteBuFamZBtaUathULLJ/bwm2omoExb1LSLVcx4HHOcr7R+o5esZJ3TXnvIYRy8jUsSE7zPpYH+WFuWQ9+wjmssJpb5KFQjF6+zz09npxugI43QEcziBub4h4LEE8IZFITK7TYFBjMmowmdQkmbVkZ5jIyjSTmWHCatXN6ybcXPvpVjB74cpkMHvzxiDeu/xtVKpEUtJN2NKNpKZqCQQTdDSO4Ri+t6MT3Kmw0s6+5yvYXJEgf/wCNn8L3g4vzT/ppOfoIFJcIqm6lK3/8mfYtq27b9u50snBrWxVW6nB7S0TE2F+9EYDP/pxHc5Z+lMIokDVhnSe3atgr/4aQz+5RMc7fYTcYVL3bqH4l75AzguPPtTZ72ayEgqRYZeHsdNXGDlxCcfZK8SdfWizTOgK09HmpxK12fCqkhlXGHFHdbhDStw+Ebc3gdsbxzUWxDXiu+t+QEnJOjJyk7Db1VjNIlYzWM6F5/MAAGTKSURBVIwJFAoBSRKQBAEQploV3LGuO67FDq9IR3+C/q5xhno98x57FaCk0ECuXQSFQOdQnL7+ILEFNC/8NFEUSMtJIr/AQEmORLHZS1ZkEEVnJ4GGHvz9PkJ+JQqTDWNBDvqcDLRpKWjTbGhTkyef02xo7FYUajWSJBH1ThAacRAadU0+T91wCPQO4W1sQhFxYC5OQr+tirGMMponUmjoEmisc+NxzC8ZiVotsq5Y5GDuKNv2mBgQsjnVbObajQna60fntU81WiX79hXy0nMVbFifiSiurCZnK+G8W8nk/bNwiViMnh+9T+N//2e8DW0AmHIMlL9aSN5jWXTH83i9oYDjZycYG1x4ACIqBApLklhbrGRrpY4de6rRpeQgiMuXfG0pjwP/hJOjH9Vx6LSTa1edhGe5cX5LapqWrVkBNokOqnesJ/OJPWhSPplB3esNUd8wQl3DCPWNo3R1uxkemlj08E130miV5OQmUVCYQllJChUlNirL7Z8ZX3a6/RSNxblaO8jh4x2cPdPN2OjikkFp9SqyCqxk5ZnISRPIsUXJSw6SpXehHBsm3ttPeMCDPseCuH4t7dECLnebaW7209EwimNo7rFz70Zqlpni6jQ27s1nQ5aLHM85kkJ9DF8eo+XHnQxfcQCg0Gqo/q+/RsXvfB1RtXoyht8PcnArW9VWenB7SyyW4PCxyX65bc2OWedNzTbzyMEUXqjqQfjoJO1vdOGod6OxWSn8uZco/oXPYyrKXdbtDYaidHe7ae1w0dLhYmh4AqVSRKtVotMq0elUkw+tEpNJw5oKO4X5yfelAH6/CpH+3kF6XzuEu/UGXnsy7uRsRgULw+MqRhwJxj0RJjxBxj0hInOMt7cYJouWomITpTlQbA2Qr/FijflRhEFQ6kGpA5UWQaEhEYsR8fvpbG2GRIKCwgKUGh2iSomgVCGoVIjKyen4uIPoaAcJnUQ0PZ1+dRYtowba+6Gr1cNA19w1lslWNV94pZpXXl5HX7+Da9e7aWgYpbVjgr6+APG7HMYqOdVARo6Z7EwVBRkJCu1BshlBOTRAwuEiPuoi6g0THo8SmYgQ9kaJhSUUSlCbVZM15mY16hQ9ygwbYqqNiC2d1nAmdT0aWjrCdLc48c+zCRlMDqFVXWlg20YNpmQjHcMaOruC9LY5FzR0U/XadF58toLHDhav6CFP5OBtdvL+WTwpkaD/7WM0/Ld/xHWlHgCNRU3J83kUfy6PREoaJ101fHhFy9UzfYQCi7u+arRK8ovMlOUrqM5Xs21LDhn5hSi0liX7Lnd7HIT9Lk6fbeSDE2Ncuuya1zXJYFSxqSDOdtUY2x7dQtZT+1BoP+6X7/YEuXJ1gEuX+7l0ZYC+OZLWLQd7qp6SUhtVlWmsrUqjMN/ExYunCIcTaAylnDzTw6WLAwseR9Zg1pBdaCU3z0hxdoKKzACZkX4Sza34b3bjHwrgHw7iHwkSHAuhsSVjKsnDUJiDp66FYE8nWdvTyNyRhnlHGV5LKR3hXK53aRnsmbzhO9TjmfeNzunY0o0UVadRXJ1KcaWNLL0bS7CXtIk6tIERej8aoPknXXg7J4NqQakk7/NPUvOnv7HsZb8HhRzcyla11RLc3iJJEtdvDvOt713j3JmeWcdO0+pVbN2bxYvb/ZS6LtD3Wj1dh/qJ+mKkP7KD4l/6AtnPHbjrO3ixWILa64OcOtdLW4eTnm43YyO+Bd+11RtUlJTbqFmTwaa1GWyoScdoXP5EN/eyEBlyuGl460NuDPrpipjoHBXp7RxfUBC0GFqdkrx8A8U5KspzldQUGSkpzkKTWYSonF9/1MXsp0Q8jq+tm/G664T7m4grQ/RlVnK0xcrFsyNz/sDrdAoe22jhF359Pxn5k1kcI5E4LW0Ort3so75hmNZWF/19/ruutRZEgWS7AXOyjqRkHVarihSLSEqShD0pjt0UxRdWMuJVMuZR4PAkcLmieJwBvM4g7jH/gmqpAdQaBdm5RlLSdMRQM9jjZbTfu+BzJ7/QyoF9hXzumXKys5IW9uH7RA7eZifvn7snSRLDR8/R+N//iZHjFwFQqEXyHs2i7JVCDPnJ9GjX8n5zFqdPjMxrPNO5pGWbKCnUUZQpUpyno6oyjaysdJSG1EXV8C70OIhHQwwPdHH8TC8Xbvipr/PMK9OxUiWyplDJVp2DPTvKKHzxUVQm49Q2RKm9PsSly/1cvNRHW7tzSWpll5rVqmZ8IrqgPtZJKXqKq+zUlCpZnzNOuqeD6M1mJuoH8LSP4+2aAIUGc3kB5ooiTKX5mEryMZfkYSzO+0x/1fHWLrr/4116/uNdAn19pG+0kbUzjbRt6STScpjQpOPTZDAcS6FzWMNAr4+hXg+jAxOAhFKpQKESUaoUKJXi5LRSgVqjIDPfSnGVjTyjm6RQD5ZgD0mhfhRSlMBYiJ6jA7S+3kXQMVmeUFnMlPzi5yn9tZ9Z1kzVDyI5uJWtaqstuL1T/4CX7/zHDd57r4VQcPa7kwUVdh7Zreepwi6iR07T+U4Pjno32nQ7Rd94ieKffxVDXta81x2Nxbl8ZYAPjrRz+nQX496lD84EAXLyk9i2PZcnHyllTWXqsiTAWe5CZDQa5eRrJzhX56CuD7raPSTusuZxNkaTiswsPaXFZqrKUli/NovComyUyrvL/rlU+ynscDH64ZsEIiPc1Jbx0XUVNy4OEZ6ldlqhFNiWB5/fmU7NgTWozEZUSUaUhskxcYOhKE0tDm40DFNfP0hbq5OB/oXfYLkXRIUAEncVjCuUImvWZnBwbwH79hSQmbH6EoLIwdvs5P2ztNzXm2j+n9+m5wfvkYhGQYCMLXbKXi4gbZMdt66AK4EaDp9LcPlE15LecDRZtGRkG8jJUJGXriDDriYz00R2honk5CS0WjOi1jxt8DvTcSBJCeIhL/5xN519Dq7ddNLQEaK9K8RAl2de1xdBgNJ8DZtMbg7UZFDxpSfR2pKJxuI0NIxy8XI/ly73U1c/QmyBN+5usdj0WG0GDGYNxiQtRpMStVJCFEAUEiBJBEISgUCCQCDOhDeEZyyA2+Fflt/JpBQ9JVU2qksUrE93k9LXiO/ENZw3R/GNJEiqLCGpsghzZTFJFYUkVRajz8lYcNZnSZJwXa2n+/vv0PPD9wkNj2FI15FSYSGl0kpKhQVLiYWQMRWfOgO/2o4kCAiShEACQUogICFICSCBKMUxRkYwh/oR41E8XRM46t046l046tz4Rz5u3WMszKHsP32Nwq+/uKRjCT9M5OD2AScIQi7wG8DTQC4QBtqBHwP/IEnSkoxoLQjCF4CvA2sAKzAMnAb+XpKkC0uxjhnWu2qD21v8/ghvvtfMD39cx8AczYO0ehWbd2Xy/NYAleMXGXirgd5jgwSdYTKe2E3JL32BzKf2Iio/+yMbicS5cKmPD462c+Z0N/45MrMutWSbju07c3n8YCnbNmahVC7NEAPLUYiMxxOcPHydQ8fauNIYwO2Yf7PS2Wh1SiwWDdZkLSkpemw2I3a7ibRUA8V5VvLzLJjNy5MZeDn2U9jhwHH0DVxSmLP+PA6fCtLdMnOze0GAqkyJzV2nSb5+DVEUUZqNqJNMqMwGBJWK0NAowWEHEUHJqDmdsfwKxmw5DAlmHOOJFRnwzofBqGb79lwe2VfIjm25GI2ru/+8HLzNTt4/yyM4NErr33+ftn/8IRGXB5jsl1v8fB4Fj2eTsKTQq9/AkbZ0zh8foPXG3MNq3Q2VRoElRY8lWUOSSYFaJaBRC3c8g1JMkEhAJC4SCksEQxJj7hhjIyFcowtvMZKXpWGzdZz95Sms+8qTKFOSaWwapfbaELXXBqm9PkRwjhvmn6ZUK8gusJJbmkJOnon81ChFKT5SRAe6qBtNbILE2BjBfhcRb4SYP0Y0ECMWiiMoBBQqEUEpIiaZUGSmkbCl4lQm0zthpmNETfdggoG+AMO9ngXnYcguTGb9hiS253jI6K5l/Ng1PO0+dDnFJG+qJnljFcmbqjEV5y3L0EWJeBzH2VqGPzrP8NFzOC/eRIrHEVUiliITKZVWLIWmyX2gEEAQEEQmb+YLAoICSIC3a4KxejfORjfRaUY8sO1YT8Xv/BxZzx+Uh4S8S3Jw+wATBOFp4PvATO3cWoCnJEnqvIt1aIGfAM/MMEsC+BNJkv5sseuYY/2rPri9RZIkLlzq599/dJOL52YfSgggu8jKhrUGNud5KXVfx/X+DfpODqMwJ1P0cy+RdnA7hppyLt5w8MHRNs6f6yUYWNgPXnKqgfRsExmpCkQBQmGJcEQiHEkQDk8+XGNBvM6F3SMxmtRs2prJY4+UsW97Llrt4ptWL1UhMpGQOHe+g3ffus6lunE8roUNK6BWi6Sn68nKNpGTk0xGehKpNj1pdsNkEGvTo9Pdvz6Uy13YDo+NMnb2Xa74Tbx+TkH9leFZ58/O0fFE2QTrOo/jrZ1sQnZnM32NVY0hVYc+TYc+VYchTYc6w4TLmEKXmE+Tw0rnkILevjDeBf6vlptCIVBQlEJVZSrra9KorkojP8+64pJC3Q05eJudvH+WVywQpOvf36Llb7/LeFMHAEqtgrxHsyj5XB7mQisOQxnNiUpOXBW4crKLwW7P/d3ou5CWqmazPcC+YhNrP/8Y7c7EZDB7fZC6+hEi8xiK7E4qjYLCCjvFNelUlumpTPOQEu0jKdSHenwET5sXZ4sHV7MXb9cEvqEA8dDH61AlmdBl2NGm2VBo1ZN5G1RKkCRivgDRCT+xCT9xnxelOowp24ChyI5iXTmD5iLa/VY6BkS6uwMM9niI3bH9CqVIUZWdzWvUbNL3oT5xikBnAFPNeuw7N5C8qRpzacE9G4P306ITPkZPXWH4o/OMfHQez82WBS9DZTGTvLGKlE3VJG+qJmVzzYJa38lmJwe3DyhBENYC5wA94AP+O3Ac0AFfAH5+atZmYLMkSYvKfS4IwveBL029PA78LTAI1AB/BBRNvffzkiT938WsY471PzDB7Z0GB8f53o9v8v77rfMahFyhFMkvNFFdEKci0YWry0utVERdn4JwZH53SbMKrFRWJ1GQHqc4NUiuOISiox3fjW68HeMkIglElYigFFAoJ59FpYgm00Kgeh3tyjwaB7W0dETp7/bO+260WqNg/YZUDj5SzmN7CzCbFlZzeTeFyERC4vyVPt59s5aLtU487vk1ZVMoRYoK9FSWW1m3Lpf16/LJzjKv6EHU71VhW5IkQv11XKxt46fnVVw8PTjrsWBM0vDIPjMvrx0ipfcGGpMSdYaVmM5KWGkirDARVpoJK02MhC009Yj0dIzT2+agt90159iwy0kQBZKTdaSmGcnMNLOmMpU1NemUldjQapcvA+tKIAdvs5P3z70hSRKjJy/R9o8/oO/1I0ixyRox+9pkip/NJXt3OlGdlRHTGq64Czl3zsW10z2Mu5emNc5yUSpFSrIVlOn95NktiMVFdDtiNDaN0d7hIh5fWO2nKArklqRQsiadsqpk1uaMkxrpIjnQiTrgwFHvZqTWwXCtE3eL9/aNRo09mZSta7GuLcOypgxLTRmGvEyU+vkfz/FwhODQKIH+Yfxd/Uy09eDr6CHuGUDURlDWlDCWUUJ/PBmNGKfY3Yx4rQVNajH2PVtJ3bMZXbp9Qd/3XgqNOnFeriPiGZ8co/7WuPVTAX503E8iEsFcVkDyphqSN1VjLMxZ0eWF1U4Obh9QgiAcB/YBMWCPJEnnP/X+7wF/OfXyv0qS9P9dxDr2AiemXr4DvCBJUvyO923AVSabQ7uBQkmSPAtdzxzb8EAGt7dEo3GOnezix280cP3qwJI3ycwpTmHTZguPVHrI7L6E46NG3G1ePB3jRH1xkmpKse9Yj237OjT25MmMuiol4u2HiqjPz3hjB576VsL9HSgSTpSlGQzkrOGyI5VL14M4hmceBulOCoVARaWVXbuL2Lopl6oy+5zNlxdaiAyHY1y42sd7b9/g0jUH4575BUe2VC3ry9Ts3ZnDnke2YDStrsLq/ShsJ0Jumm5e4/uH/Zz4aJjQLC0HFEqRtVszMZi1BINxgv4IoUD09iMYiMxrTNj5Uqknx+dVqSaTfajVCtRq5dTz5EOjmfybRqNAp1WRkWYkM91IRrqZ9HQjdpthyZrXrzZy8DY7ef/ce8GhUdr/709o/6cfERwYAUBlVJJ3MIvCJ7OxllnwanMYNKzlYp+dm1dGqLvYf1eZb5eKXq/AZpQwizE0okhcZ2RoQmJkdH6/ndPJyLNQUpNGcXUaa4riZNJLcqADc6if4LCPwQujDF4YZfSak/jUTXC1NYn0R3eQtm8Lqfu2Yi6ffhzcpSJJEqFRJ476Vi68/SGiTs2BX/pZrPnZy7ZO2YNPDm4fQIIgbAYuTb38J0mSfmmaeUSgHqhgMvBMkyRpQW1WBUF4D3gKiAP50x08U31xfzD18nclSfqbhaxjHtvwQAe3dxocHOfHbzby7rvNuBbYDPhO+eU2Nm1O5mClh8zeywy/c52+k0NEJhKk7t2CfdcG7Ds2kLJlMvHPYoTGXHjqWnCevUB0pJ6RvGKuRAq4eDNGf/f4vJej1ijIzzVQVmxh07Yitm3OxZ7yyQQLsxUiJUnC6QrQ0DLG+VNN3Ghw0tnlIzrPPj9pGXq2VSl4YpudzU89gqhYGbVxsWCI0PAYwaExQsMOIm4vEa+PqHeC6LiPRDhCIhqbekSRojGi4Qijg0OQSGCz21GqVQgKxeRDFKamRRBFSCSQJAkSElIigZRIgDQ5REciGiMeDE0+QpGp5zDxYJh4KIwgCAhKxe2bH4JSgVKnwvTcBs4b13D0fBDHyOILbPNlTtJSXJJCRamNijIbZaV2UlMNaNRKVCpRvmt+l+TgbXby/rl/ErEYA+8cp/2ff8TQoTO3x/G2FJkoeCKH/EezUFp0uHWFjOrLuTGaxo0rY9Rd6mdkCYbFEQRQCJOXUlGY/IMggHBrbPGphHSxBMQSS3MdEgRIz0mioDKVwgo7FeUGclUDJAc7sAa6UEb9OBvcDJwfYejCKN7ujxvrJVUWk/XsfjKf3odt+7ppc3Ysh54+Lxdq+7h+rY/GRgcD/T60WgUbN6Xz4ovr2L0l54HqyiG7d+Tg9gEkCMJ/Y7JJMMA2SZIuzjDfHzDZXBngMUmSjixgHUbAAWiADyVJenKG+dTAGGAGzkmStHO+65jndjw0we0t8XiCcxf6OHm+h2vXh+jucM06pJAgQGFlKls3GthX6iFt6AZj79+g98QQQVeUjMd2kfvqk2Q/dwC1xbws2xx2eRg5dhZ/w1lGNRquUsz5JpHOFveCa6M1WgUGgwqjQYHZoMCoV6ASo6hVAiq1jmAoQSAcwz0eZ2gkhN+3sH7G6dlGtlWJHCwT2PLsk6jMyQvbwCUiSRK+jl5ctQ2MN3cy0dbDRGs3E209RNz3flzC2Si0CjQmFWqzGm2yBp1Ngy5Zi86mQZusRWvTokxPQUpJJSAZOdGZytvHQ3S2Ls33sFp1VFWlUVOZSnmZjbJSG3a7QQ5gl5EcvM1O3j8rg79viK7vvknnt17D19kHgKgSSd9sI3dfJlk7UlEYNLh1BYwZK2gaz6Kx3ktn4yidjaNMeFZWf/5bRIVAdmEyhZV2CipSKSnRk6UYmhpepgdDZIyIN8LQ5TGGLowydGmMyMTHv4XJG6vIeelxcl58FHNZ4T3Z5lAoxtkLnXx0uIGrdW7GRmfft9ZkLfv2ZfPqy5soK7o/v8Oy1el+BrcrowrkwbR76tnPZLPgmZy8Y3oXMO/gFtjCZGD76eV8giRJEUEQLgCPAVsEQVAttIZ4vqLRKJHIwpstKhQKFDNkpotGoyz2JsxyLVcURXbvzGP3zjzC4RjvHGrlJ6/X097imEw9/6ny/OTQJXE8E3E63XoM2UVoy4bRd/kJBsdBpyGhVjDbDeRYLEYisbihBERRRJNsIfflp+HlpymXJNa3dPKC7Qgja5xcjWVxqUtLS+P8+unGojG8nhgeNyQ+s9FTg54LErdyTcwn6WB6lpEt1Up2ZvjYsHctlrK1084Xj8eJxxeWuONOavX0GXITiQRBrxfHueuMnbmK60o97uvNRL2freUWVSIquw6lTolCq0CpVaJRTyZTUU39TVQIk1UGAiSmamYRpo4F7txnk8fg7UNRAkEEUSlOrkcBCpVw+7WoFFHqFaiNakSTGpVJidqoAo2GqEJPVNQTVpoJKi2EVUmMxU0MuNQMOgScfUEcl8YZG5xgqM9JKDDd0EHSnP8vrUZBaamdqspUKivsVFakkpY6GcgqlUrEGZKMLObacMtqvEYoZ6h9udtzeTUtVxAEVDOMA3435/J8lhuJRG4vPxKJzPh//rTZrhGx2MzDbc1FpVJNe7NnuZYrSRLR6OJ/6mc6lxe6XFVaCqW/9w1KfufrjJ2rpec7b9H/kw8ZPDfK4LlRRJVIxhY7Ofv6yNnaQr5eze7ydDzr83FrK+lyW+locdHd4qCreQyvc/q+uokESNL0P6IKUfrkZXcBJg99AXuWmewCK9mFyWQWWCnM12CL9WIO9mIJnUc34kSKS7jbvHRcdzF8eQxXs+fjG9+CgG3PJrKfP0jWcwcw5Wffk3PZ5fLz3o/PcvLKCA1tvk9kSp7rlBj3Bnn7rTbefquN3Dwjj+zN5ZXPb8Nm/eSNotV6jVgs+RoxaamuEUtNrrldJoIgjAE24IYkSetmmc8KuKZe/kSSpFcXsI5fBf5u6uULkiS9Ocu8f8vkcEQAVZIkNS5gPXN1vEgHLgN897vfJT194QNdFxUVkZGRMe17Fy9eXPRJkpubS25u7rTv1dbWEggsrmlxeno6nnE9b3/YztkzvZ/ox5iTFUKvn/2iqVAIpGaayc0SydW5ibd14+3wIHW4KS0qJu9nniP14DaEO355mpubcThmHuJlNklJSdTU1Ez7XkdHB4P9A4QHegm4RxmM6BiaUON0xwjMUeMaiYh09UxfG5KSHMGWMvPnBcBs0ZBphzxjBFtaCurULJj6Qdq6deu0nxsaGqKjo2PW7ZrNrl27bk9L8TiuSzcZ/vAMPVdvMiiFQZIQFAIa82QtqMasRp2kQqVXotQpUemViGoREJAEBRIiCUFkU74ShVJFQlCSEJVIiEiCiDcg0TwQnJr/VmB7q3Hcx3+Tpv6OcMd7CJQWpaLRqJGEqXUiEhM1TEREWloGCUYFghGRSDhBJBwjEooR8EcI+iIEfJEZx77t7dcSDH62ZKNQSBQXzn5emM0aMtKNUw8TOt3HBany8nJsNtu0nztz5sysy53NartGZGRkUFRUNO17dXV1eL2LqzG32WyUl5cDEAqFOHXqFAB79uyhu7t72a4RQ0NDi1quXq9nw4YN077X29tLb2/vopY7n2tELBajp6cHgLy8vBkDiU+78xpxJ4fDQXNz86K2F2DLli3TFoq9Xi91dXWLXu769esxGD47Fqff7+fatWuLXm5NTQ1JSZ8d6CESiXDp0qVpPjE/5eXlJKk0DL59jP6ffMjY8UtIU4GG6+AajFl6TNkGjBl61EYVCUTCShMhpYWQyoIvrsM/EcM/Ef74MR6mu1vA7Z7+f1xcGEChmL28KwiTQ/1p9Sq0OhV6owaDWYMtN5eC8lysjGEKD91+6KIurgyocY1G8Q0H8A8F8A8HSdwRPAoKEX1eFqbSfIyl+SiN+tvvLdc1IiUlBXNYwZEjDZxti9LcEbg93m1megiTaXFBXSCgoG9Ai0Ipsq7GzJN7snn02Q0olYpVe41YLPkaMWm2a8Tbb7/NV7/61Vt/kpslr3ZTQ/PcurX4niRJMw3Rc2t+H2AALkiStH0B6/kL4D9PvdwsSdKVWeb9XeCvpl4+IUnSoQWsZ94HyZ//+Z9jtVrnO/ttZrN52hMPYGRkZNF3BI1GIyaTadr3xsbGFnUnzOGK09wm0tg4/W6ZT3B7J5VaQXq2iYIMiQqDA+uJ8/QeHcDnV6DcvxHlgY2IWam43W5CocU1z1Kr1aSkpEz7ntfr/UwBXohG0AQmf1i9CT3usBqHFzyuyCcyRC4kuBUVAkaTGrtVxKYLY1EGSKh1RLSf7VMsiiJpaWnTLtfv9zM+Pv8+w5+WbkkmfrWJ2OUm4rUtCP4ASYVmNBsy8RZno03RoEw2E1PqiIlaYgotwbiaYExJOCoSjolEYgLxuEQiISElJBLxBHFNHvG4QCyaIBZLABKSBGIihFoaQUK6VUn7CdInJj6uxY3HEyTiEm5fMuGISDQSJxpJEIvECAaiRAIBcrIW32/2boLbTzMYlFgsCpJMIpkZyaSk6Ke9S7zYwg+srmsETBbYpvvRB3A6nYuuxdZqtTNeY+/lNWK+lEoldvv0WVYnJibw+RY1SMCyXiNmuokSDAbxeDyLXm5qauq0tcfhcBiXyzXNJ+bHZrNNW0MVjUYXfbMDIDk5GY1G85m/x+NxRkdHF71ci8XyybwMHh+xczeJnb7OWNonz0W1SYUxU48hQ48xQ49CLSIBMVF7u6VKVKEnojBgyC4FXSqhQJRwMEooGCUcjE0mwxtrgUQcQRQQFQKiIKBQTSW00yhQqhVoVKCSwigTIZSJMKp4EFXcT5E5QLF5AgFIxBN4OydwNnsYu+GiMZZESPhUQK1WImTYEbNtiOkpMMNNlSW9RkRjxEddTMRU9I2buXRNJB7/7A/OdMGtWqMgOVlNiknCqgjgiWvpGYEJ7yfXfyu4vZM1RcuWcgV5qROQpOAzzdbmQb5GfOxBuka0tLTwR390q3em3Cz5QXDn1Xk+Z6WfyeB2oZmDFrKeO0vBi8tQ9BCTJBhzxujrj+HzRfF4lHzcIvyTRFEgI12NSRlhbFyBZzw+XUxzWzQSp6/TQ18nHCeJ3LJf5dF/DLLDW8vYm5fp/S/nCNrSiT+5GQozQLX8p62kUhNKmvyx0TBZNZ9tD6MOhYgkFLcfMUnFpjVJTIQUjAcEAiEJrUZAr5UwqP2olX6MqhhGRRi1IkFMJRBWGQAlIaYPVJZFPE5iyEGid4TAj05hTteQu8lG6u9VYNpSTCClkGankf5OH26fiL8r+nGm4KBvXgPet3eOE49/9oddp4uTm734PmNdPREikc82+1GrFxfMfXY5AunJCjLMMbJMYZJStQxEdTjdcXzj8ytc+f0x/P4YA8DR4w4CIRVJFjUWi4oks4IkE1jMImpVHJVKQCFOtl5QKATu0zCJMplshRAsRlRP7UD11A6UHV1IfSMkBsaQHF4iE1FcLV5cLV4QQJcylUfAqkFr0WC0qlGoJi8iRSOXSU8SiYsaYmotca2aWIqWmKihVh0kFpem2swkECQJUYohSjEUiRhiIIrIJ4O+eDRB2B1hrM7FRMcw7lYvrrbxT4w1G91sAIMSTHrEDBtilh3BbllUkLdQkteHNOrCp9QzEjUy4tQTj0tMTISJx2cezk8QwJKsJS1ZIFXlRxebIKzUIZlNoEwiJRIlQxojmqGmP5xE72CUSHj6G/ZuZ4hDZyEtNUpxUYJMQxizOobCbpnK5CWT3XtycLs87ryqzKd0eGtgz4Vmu1jIeu4cPHSh68mZ4/3bzZKzs7MfqGbJiYREW4eLG3Uj+GYZAkUQoKgimYPrY6S7axmtGyHkDJOdYUdTlE8gOZXRgMDwiA+POzhjAqfARJhj73Zy7F1IzSpjy/ZdPP4NL9nd56k93c5QSyeGwjyS1pRhyM+a9w/ocjc5DIVCnD51EgVxduzeh1anv+/NiaRYDF97H+PNHfjaetBalJhzjez53nOMZ1dzbSiFd3pUdL49QU+rAzExSGbG/MbYXY0MehVmswazSU1Skpav/kwl5WXZpKYaEEWBRCyGp7aR3rc/QtPbTqVFQFmTg0trZ3RCjcsVxeMKzqtPdjwm4XKEcTk+uT/LSj57vglMDkGk/NRDpbo1rUClFHF5TZjNKaSnGcnKMpGVaSYlWYcoCnKzZORmyXeSmyVPWsnNkmftulBTBUA8FMbf1Y+/ow9fRy9xf5CgI0TQ8cmbhSqjEq1VA6ogAWMMtVGFyqhEbZh8NhpVGHwmYglhsrVNfKrFTSRBNBwnFI4TCyeI+WNE/FGivigRX5RYYDKg03WPoO/+ZE21JjUF+77NGLeWo8xNQ5U0feuP2Sz0GiHFE/i7+wl09uCOx+lX2Bh0pUyNPDD79U+pVlBZZWVNvoeMpBiWtDSUpk8mr7x9vqhVZGzZjlKppCAaI9DTTp87QYdTz9Ao09/slcDhCONwgEqtITMQJUcXJj3FjLEoF3GWm/Jys+SPPUjXCL9/+UdkmIncLHkZCIJgB25dCX8kSdIX5ph/BEgF6iVJmr50Mf3n/h74lamXFZIkzXhGCYLwy8A/TL18WZKk1+a7nnlsx+1syQ0NDRQXFy94GSstWUw4HOOt95r5/g9v4Bj9bOFWkiYTKaWkGTmwP4WnS3tRHTlG64/bmBiLkPPy41T+wS+QVPHZHy6XK8DR4518eLSdlqaxaZf7aTnFyWzbauKRUgfmm+cZ/KAFz6BI7itPU/Azz2EsnL5wfstyJ4uZLivp/UgEEQ+FGT56jr7XDzN86BTWYh0Zu7PR7N9GfTCX2g4tTY0T9LQ6PxOk3ZkAazEmN2m6mw1zJ2i62+Vq1CLWZD1Wiw6rRUtqqpGcbDPZWWayMpPIyDCh1X7y/z9bIohIJILrSj29P/2QgXeOkJQhkfZIGaqdm2jyZ3GjW01r8zg9zWOEP3VHf9akLnP0eZvNdMtVqxWkZ5jIzjKSnWUmPzeJmup0CgusKBTz+2eu5oRSnz7vVCqVnFDqjuUGg0E++ugjAA4ePDjvbMlysphJy5UsZjFJ56REAs+NZgYPncF56Saea02ExpyfmEeIJxBmGLUgoVr8RVihUGCpKCZl6xrsO9Zj37kRQ0E2giAs+zUiFgwxfPQ8g+8ewT0xzM2CHZxr1zA6MnNroFvlCKVKpLrGwp4qJY/tLSSjet1kd5cZzuU5z5d4lL4TH/HOFQ/HbkJ/78cNBkXxs4k0AWzperaWCGxWDFNcUkzmU3vRWD8ZDMkJpT72IF0jmpqaqKqquvUnuc/taveg9bmdx3Y8MEMBBQJRfvhaHd/7/g087ukzMgKkZpv53NPJPJ95g9EfnKHtzR6ivji5X3iK6j/+ZZIq5xfgDwyO896Hrbx/qJXebs+c8wsC5Jfb2b7ZwIGiEQwXTtP/TjMJQz75X3qWnJceR2u/9+n67+eQG/FwhOEjZ+n50fsMHz6BvcpAxoFC4jt2cHYgiws34zTWDhPwLT5T7y2iKGA2azCZNOh0KlRKcbLPlkqBSjU1rVSgVIooFLd+SCafBQESiTj9AwMIQFZWFqrbd7OF2wUDQZhcj0ajRK1WoJ161miUaDSTz3q9mpRkHclWHVarDp1u+h/wpSAlEoydraXnR+/T99ohNPow9rUpJO8qQVi/jgZ/Ftfa1bQ0j9PVPEY4uPgf4KWiN6ipqUlj0/pMNq7PpLIiFbX6Lu4wrFDyUDezk/fPg0uSJIKDo7hqG3DXNuK6Wo+rtpHgwMiil6mymDEV5WAsysVYlEtSVTHWteWYywoQZwiSlkPU52fw/ZP0/fQQvv4merbt44wri8Zm/5zD9omiQEWVhf1brTz73HrsM/Q5nc5CzpdYJMD5d4/x1jkv5676Z8i8/0kllVa25wRY427Bakolfd827Ds3oDTo5/ysbPWRx7l9AN2jbMm/BvzvqZfLli15Htux6oPbiYkw3/3hTX7045v4xmdumppVYOWFpy08lXKVge+cpePtHuIRibwvPUP1//PLdzVWXVu7k3c+bOHQoTbGRuduziEIUFBuZ/tmPfvzhtGcOcnIsS6E5BJyX3mS7BceRZNsWfT2LMS9LkTGIxFGPjpPz48+YOzESdJqDKQ+XoWnYgsn2lO4cj1IW93IvPrK3kmjVZKba6GoMJnCPAvp6UbSUk2kpRpISdGj109/93O+VnthW0ok8NS1MnLsAsPHLjB68hL6ZIHUtclYdpag3LgGl8rOgEfHkEfFiEtkzBXHNRbAPebH4/Dfl+BXpVZQUWFn88YsDuwtpLzM9kCMv7vaj6flJu+fh08iHifqGSfs8hJxeQk7PfiGx6g7fxESCSpqatCajYgaNWqLGY09Ga3disaejMp4D/NAfErEM87AO8foe+0wvqZrcHADp/WbOHU1SnCOwFEQoLjMwoHdqTz3zAYy0hd3g3ux54vf4+T1n5zlvdNeWlsm5pxfoRQpr05hc16Isv5a1J1jJG/eRNr+rdi2r0epm7m/sGz1kMe5fTA1MTnWbbEgCEpJkma6OpV/6jMLcWeAWj7jXJ98Pwa0L3A9D6zhkQm++8ObvPVmI8HAzE0z8stsvPi0iUcNl+j91jlOvtdLIi6Q/zPPUfVHv4i5tOCut6WkOIXf/rUd/NavbufGzWHe+qCF48c6GPdOH2xLEnQ2jdHZBP8hQF7pM2z8goHdRR5izSe5+NI/QFIJ2S88SebTe9HaVvcA7FGfn6EPTtH3xhEmrl/CvsZExpMbiHz+tznUZOLaRS+933MiSfPry6hQCBQWp7CmOo31a9KpqU4nK9OMKCfBmJEgiljXlmNdW075b/0siVgM15V6Ro5doO/wBRz/9R+AGIZ0HeUZejZm6DFkGtGUZaB8NBMxPY242kggppp8hJUEogqCUQWB8OSQRsGoSDAMoYhAOCwRDHN7iCP/eBjnqA/XyPySfN0SjcS5eWOYmzeG+ddvXcVmN7Bndz6P7i9kw4ZMVMoHr1ZXJnsYiQoFmhQrmpSPM4oHg0GaUyZrXgtX0E2OkMNF/5sf0ffaITxXa0ndn0Pblsc4ZNpO+9UJPm4AOD17uoED+zN5+XNrKcpPvTcbPQ2DJYWv/PxzfOXnoautm5+8fp2PzroZG5l+++OxBA3Xx2i4DqJYSnHVdraaYc3JD4n/3h+SEKxY11WSvLEK64YqLGvK5IBXtiBycLt8zjAZ3BqAjcDFGebbe8f02QWu4zKTiaTUU8v5i+lmEgRBDWy79RlJku6+feYqd7N+mG997zpnTnaRmKGPDkBJTSqvPKljl3iRnn87z/EP+5ESAgVf/RxVf/RLmIrzlnzbBEFg3doM1q3N4I9/dw8XL/fx1getnD3dPWMALknQ3eKgu8XBa4A9cytr9z/JzvIgqc6r3PjFfyYaTcayZQeZT+3Dur5yVdRchcZcDLz9Ef1vH0VytJC6LZ2ML29j5MU9/LBJw403HDiGxoCxOZcFkJNnYeuWbHZuzWHThiwMhun7zcjmR1QqsW1bh23bOqr+6JdIRKP4uvoZb+liorWb8ZYuBuu6Gf/pdULDRwAQRAGVQTn1mEz4YjUoSTOoUBlVt99TT00rDSoUyXoUZj1iSjIJqx2/wsJQwEyfS8uAQ8GwM4Fj2E9fuwvnyNwJ6h1jfl5/vYHXX2/AYFSzbXsuj+4vZNf2PPT6e9f8UCaTPVyCQ6P0vXGEvtcO4zx3hfTtdnhiH5c3HODUeT+Bt6LAzLWfWp2Krbsy+NyzVezenDdj3+X7paAkn9//z/n8niRx8UITr7/dxNkLzhlrnxMJidY6B611IAhpFNT8CuurtKxJGkNsPU3XP/5v3G3jmEoKsW6owrquHHNFEUkVRehzMlZFOUZ278nB7fJ5E/jDqemvM01wKwiCCNwa4dgDHF/ICiRJmhAE4SPgSeARQRCyZ6j2fxG4lRbvjYWs40ESiyU4fKyD7/7HdVqbZg+GKjdk8IXHBTZOnKHzH69y/MQQCAoKv/YiVX/0SxgL50ogvTSUSpGd2/PYuT2PcDjG2Qu9fHCknbNnugnN0rxzbHCCo4MTHH0P9KZ8Sqs3s7ZMwcY0B+Khf6Ltz3tQpFVi37sb+86N6LPm3y9nOSXicdy1DQwfOYfz/Fk04gi2R6sw/OoeznQ+z7WGKK1/NUI4ODiv5SkUAtVrM3hkbwH79xSQmWme+0OyRRNVKsylBdO2ZIiO+5ho7yE47CA86iQ04iA06iI06iQ06sIz4iBcPzktzZLoQ2VUYsw0YMrSU5ZlYFPWZO2w+kAmkS/m0xXM5VqPgca2KF1NYwz3eWftp+b3RfjoSDsfHWlHpVaweWsOTz9Wwr7d+cval1kmkz0c/D0D9L1+hL7XDjF2/hr2GisZz1bR9+Lv83eXVLS/5mGyCDg9QYDyGjtPPVnCc09UYtKv/JuygiCwbXsl27ZXEgpFOXzkJm9/0MbNG56pceA/a7I1moPOJngNMFk2U/74U2z4TcjXD6C5cpnB/zhNXZOHqC+G0qDHXF6AubKYpIqiqaC3cDI78zwzo8seTPJ/f5lIknRJEITTTNbefkMQhO9IknT+U7P9DlAxNf23kiR9olpOEISfBf5t6uWfSpL0J9Os6q+ZDG6VwN8LgvCiJEm3S4aCINiA/zH10gP830V/qVVqdMzHj99s5K23GnGOzTyshyDA2m1ZfPmRKMUdh2j/s5scr3UiatQUfeNVKv/wFzDmZ9/DLf8kjUbJgb2FHNhbOBnonu/lvSNtnD/TQyg0c6AbmIhw/fwA18/DdwCrfSNlVY+yrlRinaIf//ePEG4ZRNBnoCteQ8q2jVjXV6CYIRvgUkpEo7ivNzF2rpaJm7VI3i7Ma9JQblzPWMnLXGxWU3/SxfC/e4H5NTfW6pRs3prDY/sK2b0zD7NZbs60EqjMRpI3VM05n5RIEHF7bwe+4VEnwZHJZ3/PIOMtXYw3deBu/ezQNHq7lpRqKweqrby0NgPxyTKGpBxqe83caE1Qf3UY5/DMNbvRSJxzp7s5d7obtVrBlu25PP1YCXt25aHTyoGuTCabn/G2bvpeO0zfa4dwXanHlGMg7/FcjL/yK7zdksXps24CE7Pn1jAlaTjwWAFffHkdpfmrt1uRVqviuWc38tyzGxmfCHH0WDOHP2rn2jUH0cjM3UsmPCEun+rj8qnJ8llW4aNUvfAq638/xjr9IGL9TcbPt+C6dILeH7xNIjZ5F1NUqTCV5GGuKMJcUUjSVPBrKiuQmzc/JOTgdnn9JpNNjXXAYUEQ/pzJ2lkd8AXgF6bmawX+ZjErkCTpmCAIP5xa3nPAEUEQ/hcwCNQAfwzcGifmDyRJci/uq6wusViCE2e7+fHr9dReGpi16bFKo2Dbnixe3eEl7dJrtP6nJs52jKOxJ1PzJ79OyS9/EW1qyj3c+rlpNEoO7CvkwL7JQPfilX6OHO/k7NkePK7Z++m4x/xcOOHnwonJ10kpa8kp2EthrooKe4Ti3nOMnvw20ogXVEkoLemoMwowFBViKspFm25fcFOgRDxOeNTJeGs3443NhHpbkTwDiNoImrWljJcU0pzyKPVdStpaJ+g/5CIeG5j38pNtenbtyuPRvYVs3pT9QGbGfVgIoni7z9x0Q2nBZKbU0PAY482djDd34m3qZLypA+eVevqOD9F3fAhoRKk9QXKFhXVVVh5Zl4rq99ZT5y/gVL2W61ed9He4pl0+QCQS58zJLs6c7EKjVbB1ex5PPVbC3p15aDTyT6dMJvtYIh7Heekmg++eYOCdY3jqWlGbVeQdyKT0117iVGw93zsr0fa3DmD2jM6Va1P53OeqefaRYjTqB+taYzZpefH5dbz4/DqCwSjHTnVy+EgTly6PEA7N3GJHkqC/w0V/h4tDgFIlkle2l7Jdn2P91xNsSh1F1d6E/1Ij7kY3zqZB+l77VHoZQSCpqgT7jvXYph6m4jy5afMDSM6WvMwEQXgW+B4fNwv+tFbgaUmSPpPkaZ41twiCoAN+Cjw1wzoSwJ/N9Pm7tZKyJfcNePnB6w0cer8Zt2vmceAAklL0PHIwhRdLu4i+eZTOd3oIjIVIqiym/Ld/lvwvP4dCq7lHW740EgmJxqZRjpzo5NTpLnq6PItajsWmJz0nCbtdRXqKQEZynAx9gFRc6N2jEAghRaIIsQREY0hRCa/PT0ISSbaYEaUECSkOSpD0agSTASnZilttoz+cRL9TxcAo9PUHGehyE5hYeDfwguJkdu3I47H9hVRWpK6KHyg5e+vykhIJvE0dOM5dY+xsLY5z15ho6779vkKrIG1dCunbUrHur6bPVMWJpiSuXPPR3jBKIj7376FWp2TrjtzJQHdH/n29kSIfT7OT948Mlu84iI77GDpyloF3jjP4/knCYy4UGpHMbalkP1XMYMVu3q5N5tyZMfyzjMIAYDRreOTxYr708hqK862zzrtc7uf5cqvb1YnTHVy6NMDoyMyt7Kaj1iopKLdTWpXM+pIE1WkOTK4uwrWNuBscOJs8uFo8RMY/mbdEY7Ni27F+MuDduQHb1rX3dNinB5k8FNADThCEPCZrcZ8GsplMAtUO/AT4O0mSpj2L5xvc3jH/l4CfBdYCFiZvD56eWsenm0Qvmfsd3I6M+XnnUAtHj7bS2jRzTcwtucVWnjugYU/8AkM/uMTAmWESMYn0x3ZR/ltfI+Px3asiUJqPwcFxzl7s4+zFPq7XDsyYeXkhtHoVOqMajVaJWjP10CrRaERUKoFYTCIWk4hGE0RCMXzeEL7xMIGJ8Jxj9M1Gb1CzaXMWe3flsXtHPraU1Tc2nlzYvvdCo04c568xevoqw0fP4bnRfPs9c66RjK127HuKmKjazEetds5dDdFeNzpra49bdHoVW3dk8/Tj5ezZnotKdW8DXfl4mp28f2SwdMdBIh7Hc7OFkeMXGfrwNKMnLpGIRlFoRDK2ppJzIAtpx3Y+6Mjlo7NBulvnLo9Urknj5RereOpgMer7XEu7ks6X3j4vp851c/psNzdvDM9aqzsdnUFFUVUaxZUprCuFypQxLOE+aGpk7PIQw1cdjN5wEf/UclVJJjKf2kv28wfJeGI36iTTUn6th4oc3MpWtfsR3DpdAd4+1MLRo200NzjmDJrUWiWbtqXyzPpxcq8eovMnzYz3+NBlpVH0cy9R+PUXMRbcmyRR94skSbS1Ozl3qY/zl/qpuzE0a1Kq+00QJ4fq2bk9h30786muSkOpXFmZIRdqJRUeHlbBEQfDR88xfPgsw0fOEhyaTC6n1CvJ2GwjY18ewS07Od6TydkrIdobHUjzCHT1BhUbN6ezZ3cRB3cXYkla/r5d8vE0O3n/yGDxx0F0wofragPOy3U4ztYyeuoKEfdk7ofbAe3+TAz7NnLBU8oHF0SunR8kMksODACjSc1jT5byxZdqKLpPtbTTWannSyQS59qNQU6f7+PylX7a2xxICxvCHmOShuKqNMrXprKtMkq+sgfLeCuB610MX3UwfNWBu8X7iWu9oFSStm8LWc8fJPvZ/Rjyspb4mz3Y5OBWtqrdi+A2kUjQ1O7gxMlWLlwapKneOa+alZxCC4/tVLErcQ3vm+cYOjdCIi6S/dwBCr/xMhmP70JUPJz9M+PxBD29HuoaRrnZOEJT8yidbS4ikYXdIV0qOr2KopIUNqzLYMuGLNZUp2M0rvyskAuxUgsPDytJkvA2tjN8+CxDR84yeuIS8WAIQRSwVVnI2JtDdM9uTo3kcOZqlM6muW+kAYiiQEm5lW1bs3jkQDmVJbZlaQ0iH0+zk/ePDGY/DhLxOOExF8GBkcmcELf68de34W3q4M4TXp+qJWOLnbTNqZh3V1MXreTwdQNXzg8zNjjz8D23VNak8cqL1Tz5SPGKzAuxWs6XCV+Y2tpBzl/u5/KVAbo6564hv5MgQG6JjcpNmWxca6AqeRBbsAPdWCfD54foOznE0KUxEp8aS926roK8Lz1D/pefRZ+5MkaYWMnk4Fa2qi1XcOv0BDhxuplzF/q5fs2J2zl7oqRbtHoV27dbeTxvgKSjh+k/0kvYGyFlyxpyX3mC/K88jy7NtiTb+KCJxRJ097hpaBqlp99L/8AEg0PjjAxN4HIG7qpZ8S1anZKMLDM52Unk5VqoKrdTWW4nK9P8wDQHn8lqKTw8rOLhCI5ztQwdOcfw4TO4ahtBkjBlG8jYlUni4G7O+Ys5Uxuls3n+BaoUu54NG21sWJfO1s1F5GVZluRYl4+n2cn758EgSRKxCT8Rt5eIe5yId4LYhJ+oL0Bswk/MFyA64SPmCxALhEhEop94RIMhxoaGkGJxLEYTxOIkQmHCDvesQ4/p7VpsNcnYqq3YN6QilVVS583jVKOeKxcdDHTNnZ8zyaLl8SdLefVzlRSu8IzHq/V8cbuDXK0d5MLlPi5dGaC/b34jK9ySnGqgclMW1RtS2ZLnITPUiMnZwvC5QfpODjN0cZT4HVmdBVEk7eB2Cr76PDkvPIrSsPq6SN0LcnArW9WWIriNRGM0t/Ry7eYAjS1u2ton6O0cn1ftLIBGq2TdBgu78r0Ut5xi5O16fAMBUratI/eVJ8h96TG5ScldikbjDA37GBnx4feHCQRj+AIRAsEo4xMh2jt6iMchJzsTvV6NWq1Ep1Vis+qw2QwkW3WkJOuwWnUPfBA7k9VaeHhYhRwuRj66wPCRswwdPkugbwi1SUX69jR4bA+XpHLOXE/Q3bawJPRWm47SMjM1lVY2rstibU0BWs3CWynIx9Ps5P2zstwKUoMjU2Ndj7qIuDyTAat7/HbwGnZ5b09H3V4inolZx76+WyqDEn2qjqR8I0kFJpIKTViKrSRy8xkRcrnab+VKE9RfGcYxy1BitwiiwOat2bz8fCV7d+ejUq68WtrpPCjny+ioj8tXB7g4VbM7MjL3/+wWg1nD2h25bN6RzqasUdL8jRhdHQxfGKb3xBCD50c/UaOrNOjJeekxCr76OVL3bXloWwJORw5uZavafIPbRDxGKDDOwJCD3j4PvYM+evoDtLb76e4cJxSITvu5majUCtauTWJHpovC+hM4j7QQckexbV9HzkuPkfPS4xhyMu76+8nm9qD8KC43eT+tXpIkMdHadbtWd+T4RRKhIPa1KQhP7qLOUMXVTjXNjV5i0YUVxDVaJTkFJjIzdGRnaMnP1lOQa6GwwI7ZYkOhmD57p3w8zU7eP/eOlEgQmhqLOtA7iL9n6tE7SGjYQWjESWjEQTw0/6SGokpEqVOgMihR6ZUodZPPCq0ChVqBQiNOPqtFFGoRUa1AoRIRlAKiUkS841lQiIgqAVEx9XeViNaqQWvTIZmSCCmT8KnsdPtSaBnU0dqToL3JRW+7c15Z1AEyMk0892wFLz5bgd1uWOyuvG8e1PNlYHCcS5f7OXO+lwsX+ggG51fWtKYa2LArjy07Uqmx9pPqa0A31knvsUG6PuzD1fzJGmJ9djqFX3/xocjhMh9ycCtb1e4Mbv/yb/4dvT6FYDhBMBQnFJYIBBM4XDHGxkK4xgLE7qJPpyVZy5oKDdX6UfKuncB9oh1tRiYZj+0k4/FdpO7bKme3uw8e1B/FpSbvpwdHIhrFcfHGVH/dc7iu1KMxK7DsLqKvZjc3g2lcawjjmWNIstkIooDVpic1XYfZqECnFdFpRfRaEb1uclogjFoNVosZpVJEFAQUooioEBAVoBBFVEowaBXotAqMOgGdRoVarUZUKlEo1CiVahRKDSi0oFAjiA9G7YN8vi29iNuLt7mTiZau2/1Tx5s78XUPkAjPPqSbUqtAZ9eis2nR27RoUzRoktRozGrUZhUasxqVWYXSYkBMMiOptcRFFXFBTWLqefK1CklQEJVEYgkF0YSCWEIgGp98jschFod4Yuo5DrEEROMKogmRWEIkmlDg8Io43QncziDOER9DPZ4F32RPsup45EARzzxRwpqa9FXdKulhOF8ikThXagc4dbqbE6e6GB31z+tzmfkW1u/OZ+f2JMoUzaRP3CDUMUTnB/30HBkg5P7kTZv0R3ZQ9M1XyP7cIygW0SrnQSAHt7JV7c7gtmrd/w+1eumy/ymUIqUlBqptPooHb6A6dgFJYcK2fT3pB7eR/tguTEW5S7Y+2eI8DD+KS0HeTw+uWCCI60o9Y+eu4Tg3OcauUhslcnAnrak1tLp0tHeFCPoXVnheLkq1ArVGgUarwmDSYExSkWRWYjWLpFok0o0R0rVB7OIE2gkfYlhA1KeiSsvFWFyAMT9rxY8HKZ9vi5eIxRhv7sR9rRH39Wbc1xrxNrQTGnXO+BlRKWDI0GPKMmDM0mPKNmDMMqC3a1GnmkkkpRBWmgkrzbhjRoa8WjwBFZ6AyHhAYNwHE74Efl+UoD9KJBQjEp56hGJEInEioRjRSJxEPLEkOSAWI9mmZ9fOPJ44WMymjVmrPov/LQ/b+SJJEm1tTk6d6eb4yS6amsfm/IyoEKjalMXWg4VsL/GR6buBdaKFkYsjdH3Yz8C5EaQ7avrVyRYKvvIcRd94GUtN2XJ+nRXnfga393dQLZnsU8xJagqyFeRrfWS6u0m+dAmzkE2KbR22Fz+H7S//BH12+v3eTJlMJvsEpV5H6p7NpO7ZDHzcjHns3DVyz11jY10z/p5uQmsrGCqooVOy0T4IjrHF1+zejVgkTiwSJzARwT02c+2FqBBIy84mK89MQY6SMuMEJTffQ/jOdUJdHmKCDUNpFSmba0jeWI02NeUefgvZUkjE43gb2nBeuIHzSj3u601461pnbEIsiAKmXAOWQjOWIhOWQjPmHAPqjCRCOjsBlQ2vkEy700jPqJJhJ7i6IngcAdxjfjwOP0H//PtB3m8KhUBpmZ3t23I4uLeQ8rLlyX4uu7cEQaC01EZpqY1v/twmevu8HDrcxnsfttLb65n2M4m4RN3Ffuou9vOa3cCWA5Xs2Luf8mfa2LD3OptGBuk61E/n+31M9PuJuDy0/O13afnb75KyZQ1F33yFvC88hcpkvLdf9iEj19zK7tpiam6VKhFbsooUE2Rq/GS6e8l0DJJmTyGpqoikiiLMFUUkVRWjUD+cTTpWk4ftju9iyfvp4SYlEvi6+vHUteKtb8VT18pw3zCDqZk4LRk4BTOjARWjrjjj4yujhnc6SrWCvNIUyiuS2FQWo1psJ3TsHKNn+wj4jKTu20HGoztI278Nlfn+FeLk8216waFRHBdv4rx4A8eF67gu1xPzB6adV1SJWIvNJFdYsBabsRSZMeebiBhs+NTpeJTptDnNdA4r6R+MMtLnZaTfi3PYN++EkCuN0aSmqDiFNVVpbN2czbo1Gej1K7uVwlKQz5dJkiTR3OLgg0OtfHi4DYdj+nPjFkEUqNiQybZHithSHiXbfx27vwnntRE63uul/9TwJ7ItKw16cj//JEXfeBnb9vUP7I0SueZW9sBIt6uwmFSoFRIahYRaiKMW4piFMMmJILZEiDSdgvRcO4ZMC7oMO6aSfEzFuSu+iZtMJpPdDUEUMRXlYirKJedzj9z+ezwSITQ0hr9viED/CIG+IRzdbgacAYYFNS5RTwglIZSEEwrCkoJwXCAUEwhHIRwFKSGRkCaH5UwkpNvPicTkEF9LeR87FonTUT9KR/0o7wHmZCM1m3+WHf9ZwTZ7D/EjJ2j774c586ob++7N5L36JNkvPIrWvrKHQnkQSZLERHsPY6cuM3r6KqOnLuPvmqGMKYAp20BKhYWUCgvJ5RYsRWai2iS8ulzG1Rlc8lhp7lPSc2Gcvg4nA10DRMO99/ZLLYAggEIhIoqTrRDUaiVKpQKFQsCcpCU9zUhmuonMdCN5uVbKymykpxkf2IBDNjdBEKgot1NRbuc3f207tdeGePf9Zg4fbScc/mzOGCkh0XhlgMYrA7yWrGPzgUJ27NtNxcF21my+xgbnAD1HB+h4rw9v5wQxf4DOb71G57dew1xRRNE3X6HgK8/L18clJNfcyu7anTW31w8fp6SiHIVOg0KrQdRq5NToDwH5ju/8yPtJdrcS8TjxYIh4IITP5eb00WNIkSjbt21Ho9VM+xlJkojGJILhOKFwnFAkTjAcJxhJ4AvGcHqCuMZDuMYjOMejjLkijLoiBAILT/5nturYsi+XJ7fFqYjfxPXmOTrf7mFiIEjqvi0UfPVz5L78OEr98h/7D+P5lojH8da1Mnr6CqOnLjN2+iqhEce084oqkeSyJOxTY7naqq2ozWpCSiseXS6jQjY3Bs20toXoah6jt8255H3G9QY1ZrMGs1lLUpKGpCQtVosWS5IWg16NXqdCq1Wi0ynR6VRoNUq0WiVKpTj5UIgfT99+KFAqRRQKAYVCfCiPg8WQ99PsJnxhPjzUxmtvNNLaNv05dYsgQOnaDLY9WsTWygS5wevYfY14GsfoeLeP3uODxIIfX19FlYrMp/eS/+VnyXx6H0qddrm/zrKTa25lDwx9fpbcJ1Ymk8mWiahQIBoNqIwGJJMeMScNAMv6iiUvjI6Ph+jodNHQ4qCpZYzm5lG6O92z1gKPu4McfaOFo29ASU0B+x7ZzpNf60J17iSdb7Rx8ef+gKu//mfkffFpir7xMsmbauRasrsQj0RwXaln9NQVxk5fYexsLVHvxLTzqgxK7DXJ2NckY6uxklyahKhWEFDZ8OjyuBHL5FqPgdZWH93NY/R3thGPJaZd1nzZUg3kZCeRkW4iK8NERrqJjAwTGWlGUlONaLVyMVS2OpiMGl55qZqXX6yiqXmM195o5MPDbdMOLSRJ0HJ9iJbrQ7xu0bJ5fyHb9++iancnlWuvsf5XB+g9NkjHe724mr0kolH63zxK/5tHUZmN5Lz4GHlfeoa0A9vkCqJFkK8qMplMJpPJPsNs1rJ+XSbr12Xe/tvERJirtQOcvzLA+Qu99Pd6Z/x8W90IbXUjvJFuZN/TP8tzf+mksu80nf9+g85v/4T2f/oRyRurKPmVL5H3hafvSW3uahed8OE4f53R01cYO3MV54UbMyZ+UptU2NckY1+bTOraFCxFZgSFiE+dhkeXy5V4Fte6dbQ0euloGGGop2nRzdftqQYKC5MpnnoUFiRTkG/FaJRzZsgeLIIgUFmRSmVFKr/9mzs4fLSd195spLFxdNr5Jzwhjr3RyLE3GilZk8bWR55g+xqB3C9e58CzDfjanXS810fP0QEiE1Gi4z46v/06nd9+HW26nbzPP0n+l5+VbwQugBzcymQymUwmmxeTScO+vYXs21sIQGeXiyPHOjlyrIPO9umHiXEM+/jpv97gfZOaPU8/w8t/eJBHf/40vT+4TtubLVz8xh9T+zv/g8Kvv0jJL38Rc0n+PfxGK1twxDFZI3vmKqOnr+K53oSUmL42VZOkngpkk7GvTcFSYEISFUxoMvDq8miTsrjeraW10U1H/SgD3fVIi0j6ZLFqqaxIpaYylarKNKoqU7Fa5RsTsoePwaDmhecreeH5SlpaHbz+ZiPvf9iK3z/9mM9tN0douznCG2YNm/YVsOPAdqq39lBaUcu6Xx5k+NIY3R8NMHhuhHg4QWh47Ha2ZX12OpnP7CPr2QOkH9iGYoZuKDK5z61sCdzZ57a1tZWSkpL7vEWye03uqzM/8n6SLaWVdjy1tjl47Vbhzjd94Q5AZ1Cx++lSXtodpWT8DAM/vUbLT7sIOSdrINMf20Xpr3yRzGf231WTvJW2f+ZyO/nTmatTAW0tE23dM86vsapJXZvyiWA2LigZ12Th1eUxImRxs1tDW6OT9vpR+jtcC85gLIoCJSUpbFiXwbq1GVRXpa26hEur7Ti4X+T9tDSCwShHPurg9TcbuVk3POf8hZWpbHukiK3r1eREGkmdaEAx4aL/zDA9RwcYuer8zE0ohV5HxqM7yHp2P5nP7EeXZluur7Nocp9bmUwmk8lkq1ppiY0//L09/Nav7+DosXa+8/0bdExTmxv0Rzn84wZOvati15P7ePHn9nLgi+cYe+caTT/oYPjwGYYPn0Gfm0nJL36ewm+8vCILb3crOuHDfa0J55V6HOevMXamltDw2IzzG7P0H/eZrbZizjESE9R4tTm4dLk0kU19r5r2iw46Gkboa7+64D6zao2C6uo0Nq7LYP3aTGqq0zAY5KbFMtl86XQqnnumnOeeKaej08Xrbzby7vstTExM332gs3GUzsZRXterqN6azYZdr7KxOERGWgM7n2gi7vDSe2KQ/tMjjNW5kOIS8UCQ/rc+ov+tj0AQSN5UPTnO+u6N2HZuQGt7uDMvy8GtTCaTyWSyJaPVKnnmqXKefrKMy1cH+Pa/X+PChb7PzBcKRDn6WiOn31Oy88mdvPDqHnZ/7jyeQ1dp/I8OvJ2D3Pjj/0ndn/wdOS8/TuHXXyRt/1ZE5eorukQnfHjqWnFdqcd5pR7XlXrGmzuZqZOrIApYis3Ya6yT/Wark9Ema4iKOjzaXEZ1udSRRUO3go4LY3Q0jNLXcXHBwaxKNRnMbt2UxaaNWVRXpaFWywlsZLKlUFSYzO/99i5+41e38dHxTl5/s4Haa0PTzhsKRLlyvIsrx7swW3Ws25nLhl1bWVPhJD2ngcKX2kiMBxi6NMrghVGGLo4RmYiCJOG6XIfrch3Nf/MtAMwVRdh3bcS+ayOpuzZiKMheVa0t7tbq+4WQyWQymUy24gmCwJZN2WzZlE1rq4N/+tYVjh/v/Mx84VCMY280cuYDJTse38znntvFjqcv4Dt+ldafdjFS66TnB+/S84N30aamkPvqk+R94SlStq1bcZlEwy4P400deBs78Da2423sYLyxnUD/7M0TlVoFKZUWbNXJ2GuspFRZUemUhBVGvNpcenW5jJJNYzd0nB+lo2GU3vazJOILa2asVIpUV6WxeVMWmzdmUVOdhkYjFwVlsuWk0Sh56olSnnqilK5uN2++1cg777fg8YSmnX/cHeTUuy2cercFe6aJ9bvLWLN5HxUlLmxZ7ax/vJWtYQ+OejeD5yeD3fEe38efb+pgvKmDjn/58eT6bVaSKotJqiomqaqEpMoikqpK0NiTH8igV76iyWQymUwmW1alpTb+5i+eoL3DyT//6xU+OtbxmUrLSCjGibeaOPuBgu2Pr+X5x3ay7sBFpPqbtP20i56PBgmNOmn9u+/R+nffQ2Uxk35gG+mPbCd17xZMJXmIKtWybH8iFiPi8hJ2egg73AT6h/H3DBLoHcTfM4i/d4hA7yDRcd+cyxJVIpZCE9bSJJJLk7CWJmEpMoFCgV+dhlebTZs2h6F4Gm1dUTobb9XMdiw4mFUoJjO73gpm16xJR6ddnn0kk8nmVpBv5bd+cye/+svbOH6ykzfebOTy1YEZM5WPDU5w+Ed1HP5RHRabnsqNWVRuWsuaYki3d1CwpY21vzRA2BPGUe/GUe9mrM6Fu9VLIja50LDDzeipy4yeuvyJZWtSLCRVlWDIz0KXmYouKw391LMuMxVdum3ZrqnLSQ5uZTKZTCaT3RPFRSn85Z8/Tmeni3/5tyscOdrxmSRH0UicU++0cO6Qgu2PVvLsEztYW3OJdb/SRO+xQbo+6MPV4iXqGafv9cP0vT6ZBEdUqTCXF5BUVYI2w46g1xIdGgCdmq7+CVQKBVI8QSIWQ4onkOJxpGiMWCBIzB8kHgjdMR0k4pm4HcxGPeOL+r76VC3mPCPmXCOWQjPWUjNJ+SYEpUhIaWVCk45Hk0GPJoN2p5mutnF6Whx0t/Yy2l+34KF5RFGgssLOpo1ZbNyQxfq1Gej1q69wKpM96NRqBY8/WsLjj5YwOurj0JF23v+wlZZWx4yf8TgCnDvUxrlDbag0CkrXpFO5cS81ay3kpo1hzuuj4GAfa8JDJMJRXM2TtbvOJg/jPT58g4FPJKcKOz2TAe+ngt7bBAGNzYoqyYTKbERlMqAyG1CaDFPTRhR6HaJKiahUICiVCEoFolJJv3Pm77Hc5OBWJpPJZDLZPVVYmMx//7PH+MVvuvmXb13l0OG2zwS5sUic0++1cv6wyNZHSnjs8V3UfK2FvS/eJNQxTP+ZEYYujuJs9CAlJBLRKJ66Vjx1rZ9Z33VeW5bvISgEdDYthlQt+jQdhjQ95lwD5jwjplwjKp2SuKAkqErBr7bj1KTTrU6n22Omt8fPQKebgS43fe3XCMySYXomokKgqiKVTRsy2bgxi7U16XICKJlslUlNNfKVL6/jK19eR2eXiw8PtfHB4TYGBma+qRYNx2m4PEDD5QF+AqSkGckvs5FfvofCUgvFeT6sGQOkbu+jODyAKh4gEYkz0efH2+NjvGcCb7eP8V4fgeEgsVD8syuRJMJjLsJjrgV/JyfRBX9mqcjBrUwmk8lksvsiP8/Kf/vTR/jFb27mX//tKu990PLZIDea4OwHbZz9oI2SNWlsPfgiezYGyaxupeSr7eD1MnLNiafDi6djAk/HOEFH6HaTvHkRQKFRoNQoUGgnn1VGJRqzGnWSavLZrEZjVqE2q9HbtOjTtGiTtYgKgQQiYaWJsDKJgCoFl9pGvyqFsYiZvhGR0e4Jhvs8DHS5Gey6StC/uIKfQiFQVZnKxg1ZbNqQydo1cs2sTPYgKSxI5ld+aSu//ItbqKsf4cPDbZw81cXQ8OxdHpwjPpwjPq6e6gYmh1zLK7WRX15DTtFeMjPUZJl8GDId6Dc4yIo4KIk40MTHEYCoP0rQGZ56hAg6QoRcYUKuMBF/jFggRjQw9RycfI6HF5bA7l6Rg1uZTCaTyWT3VW5OEn/6Xw7w89/YyLe+Xcs77zUTn6Z/advNEdpujvC6ST3Z92zjejaWJcjM7SU9MkxxeARtzIMAxEJxor4o0UAMhMkmu4JCQBDveCgFlFoFimkyBCcQiSl0REUdUYWO2NRzVNThUZoYViYREEyM+dSMuQU8I0E8Y37GBicYHRhndHAQ//j0w3/Ml1qtoKLMzoYNmXIwK5M9RARBYE1NOmtq0vm9395FR6eL02d6OHm6m7r64Tm7LAT9UZqvDdF8R3ZmtVZJaqaZ1GwzaVnZpGabycjUkW6JYVQE0BT4UMd8qOMTpEw9q+N+lIkwikQYUYpxZ/qpRFwiHo4jxSUS8QRSXJqalugfm4Df7FqenTMHObiVyWQymUy2ImRnJfFf/ng/3/z6Rr713VrefqeZ2DTD2wQmIlw50cWVE118XymSV5pCVkExmQWbyMnRk2qJY9LG0RBGkYiAABIiICAJIhICkiAgoSCGglBUQSgqEo4JhKIC4YhAMAQBX5iAL0LAFyE4Ne2fiDDumsDjHGHCHfxMTfPdsNv0rF2TwZo16aypTqO8zC4PzSOTPeQEQaC4KIXiohS+/rUNuD1Bzp7r5fTZbs6d78Pvn1+XhkgoRn+ni/7OzzYz1upVmCxaTBYdZqsRs9V++7VWr0KtVaLRiOg0oNck0KkTGDUJ1Io4gpRAIDH1LCFICUaGh4H/WOI9MT9ycCuTyWQymWxFycw08//8wT6++fWNfPd713n3/RZ8M/RJjccSdDaO0dk49pn3NDolGp0KJEgkEkgSJBLSZB/dhEQ8lljw2LBLJSlJS0W5nYoyG2Vldmqq00hPMz6QQ3PIZLKlY7XoeOapMp55qox4PEF7h4vrN4a4cXOY6zeHGJ6jCfN0QoEooUCUscGJBX1OEAVEUUBU3HoWEUWBaMS94G1YKnJwK5PJZDKZbEVKTzPx+7+zm9/41e0cO9HJG281crV2cN6fDwdjhIOxZdzCuSkUAjnZSeTnWSkpSaGizE55uY20VDmQlclkd0ehECkrtVFWauPzr9QAMDLi4/rNyWC3rn6Ezi43weDyJHiSEhLxhET8U5fZSOTuumTcDTm4lclkMplMtqJptUqeeqKUp54oZWBwnNNnujl5upurtYPTNlu+19RqBal2A2mpRrKzzeTnWcnPs5CfbyUz04RKKTctlslk90ZamvH2MEMAkiQxOuqnq9v98aPLTVePG5creJ+3dunJwa1MJpPJZLJVIyvTzBdeXcMXXl2D3x/hytUBmlsdtEw9hoYW1qxuOqJCQK9TYUnSYk7SkmTWkGTWYp56tibrSE81kpZmJC3VgNWqk2thZTLZiiQIwuS1Ks3Itq05n3jP54vgcPpxOgM4HAHGnIGpaT8OZwCXO0gwECUQjBIMRgne55Yw8yEHtzKZTCaTyVYlg0HN3j0F7N1TcPtvoVCMiYkwY45xjh07TTgssXHTRnQ6LYqpLMmKqX5iSqWIVqNEo1VOPmuUaLQKuaZVJpM9FIxGNUajmvw867zmj8cThEIxAsEogUCUcChGPCGRSCSITWVKjscT9PR08fJLy7zxM5CDW5lMJpPJZA8MrVaJVqvEaBTJydYAsHVzFjqd7j5vmUwmk61uCoWIwaDGYFDPOl+SOXCPtuizxPu2ZplMJpPJZDKZTCaTyZaIHNzKZDKZTCaTyWQymWzVk4NbmUwmk8lkMplMJpOtenJwK5PJZDKZTCaTyWSyVU8ObmUymUwmk8lkMplMturJwa1MJpPJZDKZTCaTyVY9ObiVyWQymUwmk8lkMtmqJwe3MplMJpPJZDKZTCZb9eTgViaTyWQymUwmk8lkq54c3C4zQRD0giD8niAIlwRBcAmC4BMEoUkQhL8WBCF3CZavFAThUUEQ/koQhNOCIIwJghAVBMEjCELt1HqKluK7yGQymUwmk8lkMtlKpbzfG/Agmwoq3wPKPvVW+dTjm4IgfEmSpPcXuXw70ASkTPN2ErB+6vHrgiD8viRJf7uY9chkMplMJpPJZDLZSifX3C4TQRCMwLt8HNj+C3AQ2AH8MeBjMgD9iSAIaxa5Gg0fB7bXgT8FngI2AgeAvwJCgBr4X4Ig/MIi1yOTyWQymUwmk8lkK5pcc7t8fpfJ2lmA35ck6a/ueO+8IAjHgVOAHvhfTAajCyUBR4D/IknShWnePy4IwmvAcUAH/KUgCD+QJGliEeuSyWQymUwmk8lkshVLrrldBoIgqIDfnHrZBPzNp+eRJOk88K9TL/cLgrBxoeuRJGlAkqTHZghsb81zEfiHqZdJwCMLXY9MJpPJZDKZTCaTrXRycLs89gGWqenvSJKUmGG+b98x/eIybs/xO6bl5FIymUwmk8lkMpnsgSMHt8tj9x3TJ2eZ7wrgn5retXybg+aO6ZkCbZlMJpPJZDKZTCZbteTgdnlU3DHdPNNMkiTFgI5pPrPU9s5ne2QymUwmk8lkMplstZITSi2PnKlnvyRJnjnm7QPWAHZBEDSSJIWXckMEQcgAvj710sEnmyjPdxnZc8ySdWuip6dnoYuXPQDC4TAOhwOAjo4ONBrNHJ94OMn7SbaU5ONpdvL+kYF8HMyXvJ9kS+lT8YDiXq5bDm6Xh2nq2TePef13TBuBJQtuBUEQgP9zx/b8mSRJwUUsqm++Mz766KOLWLxMJpPJZDKZTCZ7ANmBe1b7JTdLXh7aqefIPOa9M5jVLfF2/BHw3NT0ceDvlnj5MplMJpPJZDKZTDaT1Hu5soe65lYQBCUQXYJFfV2SpG/f8To09ayex2fvbPexmFrVaQmC8GXgz6ZedgNfmiVr81xy5ng/Fzg7Nb0NGFjkemSrVzpweWp6MzB8H7dlJZP3k2wpycfT7OT9IwP5OJgveT/JllIWcGuo0nua7+ehDm6X0cTUs3Ee8xrumJ5PM+Y5CYLwNPBvgACMAI9KkrToi5QkSf1zrO/OlwNzzS978HzqGBiWj4HpyftJtpTk42l28v6RgXwczJe8n2RL6VPH03xasi6Zhzq4lSQpJgjCUmQpHvrU635gK2AQBMEyR1KpW7WiY0uRTEoQhH3ATwEV4AYekySp/W6XK5PJZDKZTCaTyWQr2UMd3AJIkrQcVeWNwEtT0+V8XC3/CVPNooumXjbd7UoFQdgCvMNkn18f8KQkSTfvdrkymUwmk8lkMplMttLJCaWWx5k7pvfOOBds4uNmyWdnmW9OgiCsAT5ksil0CHhWkqSLd7NMmUwmk8lkMplMJlst5OB2eZwAvFPTXxM+1fD8Dj97x/Qbi12ZIAilwGHAymSCrJckSTqx2OXJ/t/27i9GrqoO4Pj3Fwi2KIRI0tJYq8SCQSAVY2KMRKrYGn2gjVIIbaqoiaYqYhAwJiaWBxLxTwwNsYkEDdJCo/JHkzYRFVvBB6KJRpHwUB8KaAEfREookJSfD/eMOx23u7M7O3vnzHw/yc3MOffcnV+nk9/ub+6590iSJEmqjcXtEGTmq8CO0jwPuL53TES8F/hMaR7IzD9MM+atEZFl2z/da0XEKuDXwHLgGM1dkfcN/q+QJEmSpHpM/DW3Q/Rt4ErgXOBbEbEa2EOz3M8HaNagPbm0vzyfF4iIM2kK285Nqb4LPBERF8xw2L8z06V6JEmSJI0Vi9shycwjZUmefcA5wGfL1u0FYEtm/nmeL3Nh+dkdN5ZtJndy/HRoSZIkSaqexe0QZebBiLgI+AKwCVgNnAI8RVP03pqZh1oMcUGUtdBOdF2xJoCfgf74Pmkh+Xmame+PwM9Bv3yftJDa/DxFZrbxupIkSZIkLRhvKCVJkiRJqp7FrSRJkiSpeha3kiRJkqTqWdxKkiRJkqpncStJkiRJqp7FrSRJkiSpeha3kiRJkqTqWdxKkiRJkqpncas5i4i1EZFz3K5uO24NZpr/9yMRcWofxy2NiP/0HLt2+BG3r+c92z7L2OUR8VjX+J0REYsUqiph/u1fRFzc8z68v+2YtDjmknsnzXzem4jYPmm/vzWYNvOvxa2k+XoDsLGPcRuA04cbSt0iYgWwHzi/dN2amdsyM9uLSqreJ2ZpS5KGo7X8e/JivZDG1k7g+32Me3rYgWhRvQwsAbYCd88ydmvPMeoSESuBh4BzStd3MvOGFkNSPcy/JxARrwM2leaLNF/GbYqIazLzaHuRSdJ4azv/WtxqUM9l5mNtB6FF9wvgCmBdRJyVmc9MNygilgHrS/PnwJWLFF8VIuItwG+Bs0vXzZn59RZDUl3Mvye2ATijPL8WuINmBskGYE9LMUnSJGg1/zotWdJ8PAg8A5wEXDXDuKtovkR7FvjVIsRVjYh4G/A7pgrb7Ra20oL5ZHl8PDN/CDxe2k5NlqThajX/WtxKmo9jwD3l+dYZxnUS2d3lGAERcS5wAFhVur6WmTe1GJI0NnpmjOwqj7vL4/qIWL74UUnS+BuF/GtxK2m+7iqPF0XE+b07I+IdwLt6xk68iDiPprB9U+m6LjO/2WJI0rjZQjNjJJn6o2p3aZ9U9kuSFl7r+dfiVtK8ZOafgM71ftOdve30/a2MnXgRcSHNXZHPokn012Tm91oNSho/nSlxD2fmkwCZeQh4pPQ7NVmShqP1/GtxK2kQnTOyWyLif/mkrM+6pWfMpFtDc/OoZTSF7ecy87Z2Q5LGS/kCaU1p7urZ3WmvKeOkSbcsIi6YbaP5vSXNaFTyr8WtpEHsBl4DVgKXdPWvBd5c9u3+/8Mm0kbgzPL885l5e4uxSOOqc9bgFeCnPft+Uvq7x0mTbBvw1z62bW0FqKqMRP61uJU0b5n5D5qzkXD81OTO8/2ZOXFrbJ5Adj2/LCJOaS0SaQxFxEnA5tLcm5nPd+8v7X2lubmMlyQNaJTyr8WtpEH9uDxeHhFLI2Ip8PHS55TkKTuZuh3+R4B7IsK1xqWFsx5YUZ73Tomjp38F8KGhRySNtpsyM2bbAO/mr9mMTP61uJU0qPuAl4DTaBbo3kizWPdR4N72who5/6JJ5gdL+2PAnd3XKksaSOdGJc8De08wZm/Z3z1ekjSYkcm//lElaSCZ+SJwf2luZWpK8gOZeaSdqEZTZh4GPggcKl2bgdvLDbgkzVNEnE7z5RrAGcArEZG9G/By2Q+wMSJOW/xoJWl8jFr+tbiVtBA6U5PXA+vKc6ckTyMznwIuBf5Zuj4N7GgvImksXAEsneMxpwKXDyEWSZokI5V/vd5L0kL4DXCYqestngUebC+c0ZaZf4+IS4EDNEssfDEijmbmjS2HJtWqM8XtMHBdH+NvAVaV4340rKAkaQKMVP61uJU0sMw8FhF3AdeWrl2ZeazNmEZdZj4REeto7jb9RuCGiHgpM7e3G5lUl4g4G7i4NO/NzD19HPNu4CvAJRGxKjOfHGaMkjSORjH/Oi1Z0oLIzK9m5pKyXd92PDXIzL8AHwZeKF3fiAjP3kpzsxXoXLf+sz6P6YwLjl/GTJLUv5HLv565laQWZeYfI+KjwC+B1wO3lDO4t7UcmlSLzh9HzwEP93nMo8DTwMpy/M1DiEuj4Z0RcXUf4x7JzIOzD5PUZeTyr8WtJLUsM38fEZfR3CZ/CbCjXIN7R8uhSSMtIt4HrC7N+zPztX6Oy8yMiPuALwFvj4j3ZOajw4pTrdrA1J1cZ/IpppZqkzSLUc2/TkuWpBGQmQ/RrH37Ks1UnR9ExJZ2o5JGXvdaiXNdV7t7vGveStLcjGT+jcxcyJ8nSZIkSdKi88ytJEmSJKl6FreSJEmSpOpZ3EqSJEmSqmdxK0mSJEmqnsWtJEmSJKl6FreSJEmSpOpZ3EqSJEmSqmdxK0mSJEmqnsWtJEmSJKl6FreSJEmSpOpZ3EqSJEmSqmdxK0mSJEmqnsWtJEmSJKl6FreSJEmSpOpZ3EqSJEmSqmdxK0mSJEmqnsWtJEmSJKl6FreSJEmSpOpZ3EqSJEmSqmdxK0mSJEmqnsWtJEmSJKl6FreSJEmSpOpZ3EqSJEmSqmdxK0mSJEmqnsWtJEmSJKl6/wVdYdFqHXH58AAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 1000x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from matplotlib import cm\n",
+    "from triqs.lattice.utils import TB_from_wannier90, k_space_path\n",
+    "\n",
+    "# define a path in BZ\n",
+    "G = np.array([ 0.00,  0.00,  0.00])\n",
+    "M = np.array([ 0.50,  0.00,  0.00])\n",
+    "K = np.array([ 0.33,  0.33,  0.00])\n",
+    "A = np.array([ 0.00,  0.00,  0.50])\n",
+    "L = np.array([ 0.50,  0.00,  0.50])\n",
+    "H = np.array([ 0.33,  0.33,  0.50])\n",
+    "k_path = [(G, M), (M, K), (K, G), (G, A), (A, L), (L, H), (H, A)]\n",
+    "n_bnd = 14\n",
+    "n_k = 20\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 1, figsize=(5,2), dpi=200)\n",
+    "\n",
+    "for (fermi, n_iter, cycle) in [(e_fermi_run, n_iter_run, cm.RdYlBu)]:\n",
+    "\n",
+    "    col_it = np.linspace(0, 1, len(n_iter))\n",
+    "    for ct, it in enumerate(n_iter):\n",
+    "\n",
+    "        # compute TB model\n",
+    "        h_loc_add = - fermi[ct] * np.eye(n_bnd) # to center bands around 0\n",
+    "        tb = TB_from_wannier90(path='./', seed=f'ce2o3_it{it}', extend_to_spin=False, add_local=h_loc_add)\n",
+    "\n",
+    "        # compute dispersion on specified path\n",
+    "        k_vec, k_1d, special_k = k_space_path(k_path, num=n_k, bz=tb.bz)\n",
+    "        e_val = tb.dispersion(k_vec)\n",
+    "\n",
+    "        # plot\n",
+    "        for band in range(n_bnd):\n",
+    "            ax.plot(k_1d, e_val[:,band].real, c=cycle(col_it[ct]), label=f'it{it}' if band == 0 else '')\n",
+    "\n",
+    "    \n",
+    "ax.axhline(y=0,zorder=2,color='gray',alpha=0.5,ls='--')\n",
+    "ax.set_ylim(-0.2,0.8)\n",
+    "ax.grid(zorder=0)\n",
+    "ax.set_xticks(special_k)\n",
+    "ax.set_xticklabels([r'$\\Gamma$', 'M', 'K', r'$\\Gamma$', 'A', 'L', 'H', 'A'])\n",
+    "ax.set_xlim([special_k.min(), special_k.max()])\n",
+    "ax.set_ylabel(r'$\\omega$ (eV)')\n",
+    "ax.legend(fontsize='small')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "45062ca5",
+   "metadata": {},
+   "source": [
+    "Note that since this is an isolated set of bands, we don't have to worry about the disentanglement window here. Pay attention if you do need to use disentanglement though, and make sure that the configuration of Wannier90 works throughout the calculation!\n",
+    "\n",
+    "You see that one of the effects of charge self-consistency is the modificiation of the non-interacting bandstructure. The current results are far from converged, so make sure to carefully go through convergence tests as usual if you want reliable results. The figure below shows the difference to the reference data, which is quite substantial already at the DFT level."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "a3d760e5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f49a4157c40>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 1, figsize=(5,2), dpi=200)\n",
+    "\n",
+    "e_fermi_ref = [14.7437]\n",
+    "for (fermi, n_iter, path_w90, cycle, label) in [(e_fermi_ref, [1], path, cm.GnBu_r, 'reference'), (e_fermi_run, [1], './', cm.RdYlBu, 'run')]:\n",
+    "\n",
+    "    col_it = np.linspace(0, 1, len(n_iter))\n",
+    "    for ct, it in enumerate(n_iter):\n",
+    "\n",
+    "        # compute TB model\n",
+    "        h_loc_add = - fermi[ct] * np.eye(n_bnd) # to center bands around 0\n",
+    "        tb = TB_from_wannier90(path=path_w90, seed=f'ce2o3_it{it}', extend_to_spin=False, add_local=h_loc_add)\n",
+    "\n",
+    "        # compute dispersion on specified path\n",
+    "        k_vec, k_1d, special_k = k_space_path(k_path, num=n_k, bz=tb.bz)\n",
+    "        e_val = tb.dispersion(k_vec)\n",
+    "\n",
+    "        # plot\n",
+    "        for band in range(n_bnd):\n",
+    "            ax.plot(k_1d, e_val[:,band].real, c=cycle(col_it[ct]), label=f'it{it} - {label}' if band == 0 else '')\n",
+    "\n",
+    "    \n",
+    "ax.axhline(y=0,zorder=2,color='gray',alpha=0.5,ls='--')\n",
+    "ax.set_ylim(-0.2,0.8)\n",
+    "ax.grid(zorder=0)\n",
+    "ax.set_xticks(special_k)\n",
+    "ax.set_xticklabels([r'$\\Gamma$', 'M', 'K', r'$\\Gamma$', 'A', 'L', 'H', 'A'])\n",
+    "ax.set_xlim([special_k.min(), special_k.max()])\n",
+    "ax.set_ylabel(r'$\\omega$ (eV)')\n",
+    "ax.legend(fontsize='small')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fcc962ef",
+   "metadata": {},
+   "source": [
+    "### Convergence"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "192ebb20",
+   "metadata": {},
+   "source": [
+    "To check the convergence of the impurity Green's function and total energy you can look into the hdf5 Archive:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "57fbd7ae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with HDFArchive('./ce2o3.h5','r') as h5:\n",
+    "    observables = h5['DMFT_results']['observables']\n",
+    "    convergence = h5['DMFT_results']['convergence_obs']\n",
+    "    \n",
+    "with HDFArchive(path + 'ce2o3.h5','r') as h5:\n",
+    "    ref_observables = h5['DMFT_results']['observables']\n",
+    "    ref_convergence = h5['DMFT_results']['convergence_obs']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "fae94579",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1600x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1,2, figsize=(8, 2), dpi=200)\n",
+    "\n",
+    "ax[0].plot(ref_observables['E_tot']-np.min(ref_observables['E_tot']), 'x-', label='reference')\n",
+    "ax[0].plot(observables['E_tot']-np.min(observables['E_tot']), 'x-', label='result')\n",
+    "\n",
+    "ax[1].plot(ref_convergence['d_G0'][0], 'x-', label='reference')\n",
+    "ax[1].plot(convergence['d_G0'][0], 'x-', label='result')\n",
+    "\n",
+    "ax[0].set_ylabel('total energy (eV)')\n",
+    "ax[1].set_ylabel(r'convergence $G_0$')\n",
+    "\n",
+    "for it in range(2):\n",
+    "    ax[it].set_xlabel('# iteration')\n",
+    "    ax[it].xaxis.set_major_locator(ticker.MultipleLocator(5))\n",
+    "    ax[it].grid()\n",
+    "    ax[it].legend(fontsize='small')\n",
+    "\n",
+    "fig.subplots_adjust(wspace=0.3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4952537b",
+   "metadata": {},
+   "source": [
+    "Note that the total energy jumps quite a bit in the first iteration and is constant for the first two (three) one-shot iterations in this run (the reference data) as expected. Since the HubbardI solver essentially yields DMFT-convergence after one iteration (you may try to confirm this), the total number of iterations necessary to achieve convergence is relatively low."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9afd381d",
+   "metadata": {},
+   "source": [
+    "This concludes the tutorial. The following is a list of things you can try next:\n",
+    "\n",
+    "* improve the accuracy of the results by tuning the parameters until the results agree with the reference    \n",
+    "* try to fnd the equilibrium lattice paramter by repeating the above calculation of the total energy for different cell volumes"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/.doctrees/nbsphinx/tutorials/NNO_os_plo_mag/tutorial.ipynb b/.doctrees/nbsphinx/tutorials/NNO_os_plo_mag/tutorial.ipynb
new file mode 100644
index 00000000..1d3cd1f9
--- /dev/null
+++ b/.doctrees/nbsphinx/tutorials/NNO_os_plo_mag/tutorial.ipynb
@@ -0,0 +1,846 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "40ad917b-d7a1-4950-8593-abb9b4934e8a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warning: could not identify MPI environment!\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Starting serial run at: 2023-11-24 09:49:44.156139\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "np.set_printoptions(precision=6,suppress=True)\n",
+    "from triqs.plot.mpl_interface import plt,oplot\n",
+    "\n",
+    "from h5 import HDFArchive\n",
+    "\n",
+    "from triqs_dft_tools.converters.vasp import VaspConverter \n",
+    "import triqs_dft_tools.converters.plovasp.converter as plo_converter\n",
+    "\n",
+    "import pymatgen.io.vasp.outputs as vio\n",
+    "from pymatgen.electronic_structure.dos import CompleteDos\n",
+    "from pymatgen.electronic_structure.core import Spin, Orbital, OrbitalType\n",
+    "\n",
+    "import warnings \n",
+    "warnings.filterwarnings(\"ignore\") #ignore some matplotlib warnings"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c24d5aa3-8bf2-471e-868d-32a0d4bb99f7",
+   "metadata": {},
+   "source": [
+    "# 4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aed6468d-cb0b-4eee-93b4-665f4f80ac2d",
+   "metadata": {},
+   "source": [
+    "In this tutorial we will take a look at a magnetic DMFT calculation for NdNiO2 in the antiferromagnetic phase. NdNiO2 shows a clear AFM phase at lower temperatures in DFT+DMFT calculation. The calculations will be performed for a large energy window with all Ni-$d$ orbitals treated as interacting with a density-density type interaction. \n",
+    "\n",
+    "Disclaimer: the interaction values, results etc. might not be 100% physical and are only for demonstrative purposes!\n",
+    "\n",
+    "This tutorial will guide you through the following steps: \n",
+    "\n",
+    "* run a non-magnetic Vasp calculation for NdNiO2 with a two atom supercell allowing magnetic order\n",
+    "* create projectors in a large energy window for all Ni-$d$ orbitals and all O-$p$ orbitals\n",
+    "* create the hdf5 input via the Vasp converter for solid_dmft\n",
+    "* run a AFM DMFT one-shot calculation\n",
+    "* take a look at the output and analyse the multiplets of the Ni-d states\n",
+    "\n",
+    "Warning: the DMFT calculations here are very heavy requiring ~2500 core hours for the DMFT job.\n",
+    "\n",
+    "We set a `path` variable here to the reference files, which should be changed when doing the actual calculations do the work directory:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "6dde4dcd-c06a-45e0-9c06-ca11be265713",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path = './ref/'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1356ed1-b4a6-48d2-8058-863b9e70a0be",
+   "metadata": {},
+   "source": [
+    "## 1. Run DFT \n",
+    "\n",
+    "We start by running Vasp to create the raw projectors. The [INCAR](INCAR), [POSCAR](POSCAR), and [KPOINTS](KPOINTS) file are kept relatively simple. For the POTCAR the `PBE Nd_3`, `PBE Ni_pv` and `PBE O` pseudo potentials are used. Here we make sure that the Kohn-Sham eigenstates are well converged (rms), by performing a few extra SCF steps by setting `NELMIN=30`. Then, the INCAR flag `LOCPROJ = LOCPROJ = 3 4 : d : Pr` instructs Vasp to create projectors for the Ni-$d$ shell of the two Ni sties. More information can be found on the [DFTTools webpage of the Vasp converter](https://triqs.github.io/dft_tools/unstable/guide/conv_vasp.html).\n",
+    "\n",
+    "Next, run Vasp with \n",
+    "```\n",
+    "mpirun vasp_std 1>out.vasp 2>err.vasp &\n",
+    "```\n",
+    "and monitor the output. After Vasp is finished the result should look like this: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "bf1b811e-af03-4714-a644-ad7a7b57c42b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DAV:  25    -0.569483098581E+02   -0.31832E-09    0.42131E-12 29952   0.148E-06    0.488E-07\n",
+      "DAV:  26    -0.569483098574E+02    0.75124E-09    0.25243E-12 30528   0.511E-07    0.226E-07\n",
+      "DAV:  27    -0.569483098574E+02   -0.12733E-10    0.17328E-12 28448   0.285E-07    0.826E-08\n",
+      "DAV:  28    -0.569483098578E+02   -0.41837E-09    0.17366E-12 29536   0.151E-07    0.370E-08\n",
+      "DAV:  29    -0.569483098576E+02    0.22192E-09    0.19300E-12 29280   0.689E-08    0.124E-08\n",
+      "DAV:  30    -0.569483098572E+02    0.38563E-09    0.27026E-12 28576   0.388E-08    0.598E-09\n",
+      "DAV:  31    -0.569483098573E+02   -0.92768E-10    0.34212E-12 29024   0.218E-08\n",
+      " LOCPROJ mode\n",
+      " Computing AMN (projections onto localized orbitals)\n",
+      "   1 F= -.56948310E+02 E0= -.56941742E+02  d E =-.131358E-01\n"
+     ]
+    }
+   ],
+   "source": [
+    "!tail -n 10 ref/out.vasp"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3364605b-c105-4ad8-9350-6569b506df07",
+   "metadata": {},
+   "source": [
+    "let us take a look at the density of states from Vasp:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "3529d644-40f5-4b6b-98f0-2d3a6acdb524",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vasprun = vio.Vasprun(path+'/vasprun.xml')\n",
+    "dos = vasprun.complete_dos\n",
+    "Ni_spd_dos = dos.get_element_spd_dos(\"Ni\")\n",
+    "O_spd_dos = dos.get_element_spd_dos(\"O\")\n",
+    "Nd_spd_dos = dos.get_element_spd_dos(\"Nd\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "5fec0ad8-7ab4-4a02-bd72-b679f6ce6ed4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1050x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1,dpi=150,figsize=(7,4))\n",
+    "\n",
+    "ax.plot(vasprun.tdos.energies - vasprun.efermi , vasprun.tdos.densities[Spin.up], label=r'total DOS', lw = 2) \n",
+    "ax.plot(vasprun.tdos.energies - vasprun.efermi , Ni_spd_dos[OrbitalType.d].densities[Spin.up], label=r'Ni-d', lw = 2) \n",
+    "ax.plot(vasprun.tdos.energies - vasprun.efermi , O_spd_dos[OrbitalType.p].densities[Spin.up], label=r'O-p', lw = 2)\n",
+    "ax.plot(vasprun.tdos.energies - vasprun.efermi , Nd_spd_dos[OrbitalType.d].densities[Spin.up], label=r'Nd-d', lw = 2)\n",
+    "\n",
+    "ax.axvline(0, c='k', lw=1)\n",
+    "ax.set_xlabel('Energy relative to Fermi energy (eV)')\n",
+    "ax.set_ylabel('DOS (1/eV)')\n",
+    "ax.set_xlim(-9,8.5)\n",
+    "ax.set_ylim(0,20)\n",
+    "ax.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d42627e-84c4-4386-92bd-f1193e9fd8fd",
+   "metadata": {},
+   "source": [
+    "We see that the Ni-$d$ states are entangled / hybridizing with O-$p$ states and Nd-$d$ states in the energy range between -9 and 9 eV. Hence, we orthonormalize our projectors considering all states in this energy window to create well localized real-space states. These projectors will be indeed quite similar to the internal DFT+$U$ projectors used in VASP due to the large energy window. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "19285c12-c23a-4739-b5b1-56aa724bfb7f",
+   "metadata": {},
+   "source": [
+    "## 2. Creating the hdf5 archive / DMFT input\n",
+    "\n",
+    "Next we run the [Vasp converter](https://triqs.github.io/dft_tools/unstable/guide/conv_vasp.html) to create an input h5 archive for solid_dmft. The [plo.cfg](plo.cfg) looks like this: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "825c6168-97a7-4d2d-9699-b1d1e9af95dd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[General]\n",
+      "BASENAME = nno\n",
+      "\n",
+      "[Group 1]\n",
+      "SHELLS = 1\n",
+      "NORMALIZE = True\n",
+      "NORMION = False\n",
+      "EWINDOW = -10 10\n",
+      "\n",
+      "[Shell 1]\n",
+      "LSHELL = 2\n",
+      "IONS = 3 4\n",
+      "TRANSFORM = 0.0  0.0  0.0  0.0  1.0\n",
+      "            0.0  1.0  0.0  0.0  0.0\n",
+      "            0.0  0.0  1.0  0.0  0.0\n",
+      "            0.0  0.0  0.0  1.0  0.0\n",
+      "            1.0  0.0  0.0  0.0  0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat plo.cfg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2c3f2892-bb0a-4b8d-99af-76cff53b194b",
+   "metadata": {},
+   "source": [
+    "we create $d$ like projectors within a large energy window from -10 to 10 eV for very localized states for both Ni sites. Important: the sites are markes as non equivalent, so that we can later have different spin orientations on them. The flag `TRANSFORM` swaps the $d_{xy}$ and $d_{x^2 - y^2}$ orbitals, since the orientation in the unit cell of the oxygen bonds is rotated by 45 degreee. Vasp always performs projections in a global cartesian coordinate frame, so one has to rotate the orbitals manually with the octahedra orientation. \n",
+    "\n",
+    "Let's run the converter:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "8c687309-93f0-48b0-8862-85eca6c572e5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Read parameters: LOCPROJ\n",
+      "0  ->  {'label': 'dxy', 'isite': 3, 'l': 2, 'm': 0}\n",
+      "1  ->  {'label': 'dyz', 'isite': 3, 'l': 2, 'm': 1}\n",
+      "2  ->  {'label': 'dz2', 'isite': 3, 'l': 2, 'm': 2}\n",
+      "3  ->  {'label': 'dxz', 'isite': 3, 'l': 2, 'm': 3}\n",
+      "4  ->  {'label': 'dx2-y2', 'isite': 3, 'l': 2, 'm': 4}\n",
+      "5  ->  {'label': 'dxy', 'isite': 4, 'l': 2, 'm': 0}\n",
+      "6  ->  {'label': 'dyz', 'isite': 4, 'l': 2, 'm': 1}\n",
+      "7  ->  {'label': 'dz2', 'isite': 4, 'l': 2, 'm': 2}\n",
+      "8  ->  {'label': 'dxz', 'isite': 4, 'l': 2, 'm': 3}\n",
+      "9  ->  {'label': 'dx2-y2', 'isite': 4, 'l': 2, 'm': 4}\n",
+      "  Found POSCAR, title line: NdNiO2 SC\n",
+      "  Total number of ions: 8\n",
+      "  Number of types: 3\n",
+      "  Number of ions for each type: [2, 2, 4]\n",
+      "\n",
+      "    Total number of k-points: 405\n",
+      "  No tetrahedron data found in IBZKPT. Skipping...\n",
+      "[WARNING]: Error reading from EIGENVAL, trying LOCPROJ...\n",
+      "[WARNING]: Error reading Efermi from DOSCAR, trying LOCPROJ...\n",
+      "eigvals from LOCPROJ\n",
+      "\n",
+      "  Unorthonormalized density matrices and overlaps:\n",
+      "  Spin: 1\n",
+      "  Site: 3\n",
+      "  Density matrix                                                  Overlap\n",
+      "   1.1544881   0.0000000  -0.0000000   0.0000000  -0.0000000       0.9626619  -0.0000000   0.0000000   0.0000002  -0.0000000\n",
+      "   0.0000000   1.7591058  -0.0000000   0.0000000  -0.0000000      -0.0000000   0.9464342  -0.0000000   0.0000000  -0.0000000\n",
+      "  -0.0000000  -0.0000000   1.5114185   0.0000000  -0.0000000       0.0000000  -0.0000000   0.9548582  -0.0000000   0.0000000\n",
+      "   0.0000000   0.0000000   0.0000000   1.7591058  -0.0000000       0.0000002   0.0000000  -0.0000000   0.9464339   0.0000000\n",
+      "  -0.0000000  -0.0000000  -0.0000000  -0.0000000   1.8114830      -0.0000000  -0.0000000   0.0000000   0.0000000   0.9495307\n",
+      "  Site: 4\n",
+      "  Density matrix                                                  Overlap\n",
+      "   1.1544881  -0.0000000   0.0000000   0.0000000   0.0000000       0.9626621   0.0000000  -0.0000000  -0.0000001  -0.0000000\n",
+      "  -0.0000000   1.7591058  -0.0000000  -0.0000000   0.0000000       0.0000000   0.9464343  -0.0000000  -0.0000000   0.0000000\n",
+      "   0.0000000  -0.0000000   1.5114185  -0.0000000  -0.0000000      -0.0000000  -0.0000000   0.9548582   0.0000000   0.0000000\n",
+      "   0.0000000  -0.0000000  -0.0000000   1.7591058   0.0000000      -0.0000001  -0.0000000   0.0000000   0.9464344   0.0000000\n",
+      "   0.0000000   0.0000000  -0.0000000   0.0000000   1.8114830      -0.0000000   0.0000000   0.0000000   0.0000000   0.9495307\n",
+      "\n",
+      "  Generating 1 shell...\n",
+      "\n",
+      "    Shell         : 1\n",
+      "    Orbital l     : 2\n",
+      "    Number of ions: 2\n",
+      "    Dimension     : 5\n",
+      "    Correlated    : True\n",
+      "    Ion sort      : [3, 4]\n",
+      "Density matrix:\n",
+      "  Shell 1\n",
+      "Site diag : True\n",
+      "    Site 1\n",
+      "     1.9468082    -0.0000000    -0.0000000     0.0000000    -0.0000000\n",
+      "    -0.0000000     1.8880488    -0.0000000     0.0000000     0.0000000\n",
+      "    -0.0000000    -0.0000000     1.5912192     0.0000000     0.0000000\n",
+      "     0.0000000     0.0000000     0.0000000     1.8880488     0.0000000\n",
+      "    -0.0000000     0.0000000     0.0000000     0.0000000     1.1979419\n",
+      "      trace:  8.512066911392091\n",
+      "    Site 2\n",
+      "     1.9468082     0.0000000    -0.0000000    -0.0000000    -0.0000000\n",
+      "     0.0000000     1.8880488    -0.0000000    -0.0000000    -0.0000000\n",
+      "    -0.0000000    -0.0000000     1.5912192    -0.0000000    -0.0000000\n",
+      "    -0.0000000    -0.0000000    -0.0000000     1.8880488    -0.0000000\n",
+      "    -0.0000000    -0.0000000    -0.0000000    -0.0000000     1.1979419\n",
+      "      trace:  8.512066911289741\n",
+      "\n",
+      "  Impurity density: 17.024133822681833\n",
+      "\n",
+      "Overlap:\n",
+      "  Site 1\n",
+      "[[ 1. -0. -0. -0. -0.]\n",
+      " [-0.  1. -0. -0. -0.]\n",
+      " [-0. -0.  1. -0. -0.]\n",
+      " [-0. -0. -0.  1.  0.]\n",
+      " [-0. -0. -0.  0.  1.]]\n",
+      "\n",
+      "Local Hamiltonian:\n",
+      "  Shell 1\n",
+      "    Site 1 (real | complex part)\n",
+      "    -1.5179223     0.0000000     0.0000000    -0.0000000     0.0000000 |    -0.0000000    -0.0000000    -0.0000000    -0.0000000    -0.0000000\n",
+      "     0.0000000    -1.2888643     0.0000000    -0.0000000    -0.0000000 |     0.0000000     0.0000000    -0.0000000    -0.0000000    -0.0000000\n",
+      "     0.0000000     0.0000000    -0.9927644    -0.0000000    -0.0000000 |     0.0000000     0.0000000    -0.0000000     0.0000000     0.0000000\n",
+      "    -0.0000000    -0.0000000    -0.0000000    -1.2888643     0.0000000 |     0.0000000     0.0000000    -0.0000000     0.0000000     0.0000000\n",
+      "     0.0000000    -0.0000000    -0.0000000     0.0000000    -1.0828254 |     0.0000000     0.0000000    -0.0000000    -0.0000000     0.0000000\n",
+      "    Site 2 (real | complex part)\n",
+      "    -1.5179223    -0.0000000    -0.0000000    -0.0000000    -0.0000000 |     0.0000000     0.0000000    -0.0000000    -0.0000000    -0.0000000\n",
+      "    -0.0000000    -1.2888643     0.0000000    -0.0000000     0.0000000 |    -0.0000000    -0.0000000     0.0000000     0.0000000    -0.0000000\n",
+      "    -0.0000000     0.0000000    -0.9927644     0.0000000     0.0000000 |     0.0000000    -0.0000000     0.0000000    -0.0000000    -0.0000000\n",
+      "    -0.0000000    -0.0000000     0.0000000    -1.2888643     0.0000000 |     0.0000000    -0.0000000     0.0000000    -0.0000000     0.0000000\n",
+      "    -0.0000000     0.0000000     0.0000000     0.0000000    -1.0828254 |     0.0000000     0.0000000     0.0000000    -0.0000000     0.0000000\n",
+      "  Storing ctrl-file...\n",
+      "  Storing PLO-group file 'nno.pg1'...\n",
+      "  Density within window: 42.00000000005771\n",
+      "Reading input from nno.ctrl...\n",
+      "{\n",
+      "    \"ngroups\": 1,\n",
+      "    \"nk\": 405,\n",
+      "    \"ns\": 1,\n",
+      "    \"kvec1\": [\n",
+      "        0.1803844533789928,\n",
+      "        0.0,\n",
+      "        0.0\n",
+      "    ],\n",
+      "    \"kvec2\": [\n",
+      "        0.0,\n",
+      "        0.1803844533789928,\n",
+      "        0.0\n",
+      "    ],\n",
+      "    \"kvec3\": [\n",
+      "        0.0,\n",
+      "        0.0,\n",
+      "        0.30211493941280826\n",
+      "    ],\n",
+      "    \"nc_flag\": 0\n",
+      "}\n",
+      "\n",
+      "  No. of inequivalent shells: 2\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Generate and store PLOs\n",
+    "plo_converter.generate_and_output_as_text('plo.cfg', vasp_dir=path)\n",
+    "\n",
+    "# run the archive creat routine\n",
+    "conv = VaspConverter('nno')\n",
+    "conv.convert_dft_input()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bee3bf4f-0b75-445c-b3d3-7402f778fff4",
+   "metadata": {},
+   "source": [
+    "We can here cross check the quality of our projectors by making sure that there are not imaginary elements in both the local Hamiltonian and the density matrix. Furthermore, we see that the occupation of the Ni-$d$ shell is roughly 8.5 electrons which is a bit different from the nominal charge of $d^9$ for the system due to the large hybridization with the other states. For mor physical insights into the systems and a discussion on the appropriate choice of projectors see this research article [PRB 103 195101 2021](https://doi.org/10.1103/PhysRevB.103.195101)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18739e80-3c9e-4bea-9e0b-677421ec99aa",
+   "metadata": {},
+   "source": [
+    "## 3. Running the AFM calculation\n",
+    "\n",
+    "now we run the calculation at around 290 K, which should be below the ordering temperature of NdNiO2 in DMFT. The config file [config.toml](config.toml) for solid_dmft looks like this: \n",
+    "\n",
+    "    [general]\n",
+    "    seedname = \"nno\"\n",
+    "    jobname = \"NNO_lowT\"\n",
+    "\n",
+    "    enforce_off_diag = false\n",
+    "    block_threshold = 0.001\n",
+    "\n",
+    "    \n",
+    "    n_iw = 2001\n",
+    "    n_tau = 20001\n",
+    "\n",
+    "    prec_mu = 0.001\n",
+    "\n",
+    "    h_int_type = \"density_density\"\n",
+    "    U = 8.0\n",
+    "    J = 1.0\n",
+    "\n",
+    "    # temperature ~290 K\n",
+    "    beta = 40\n",
+    "\n",
+    "    magnetic = true\n",
+    "    magmom = -0.3, 0.3\n",
+    "    afm_order = true\n",
+    "\n",
+    "    n_iter_dmft = 14\n",
+    "\n",
+    "    g0_mix = 0.9\n",
+    "\n",
+    "    dc_type = 0\n",
+    "    dc = true\n",
+    "    dc_dmft = false\n",
+    "\n",
+    "    [solver]\n",
+    "    type = \"cthyb\"\n",
+    "    length_cycle = 2000\n",
+    "    n_warmup_cycles = 5e+3\n",
+    "    n_cycles_tot = 1e+7\n",
+    "    imag_threshold = 1e-5\n",
+    "\n",
+    "    perform_tail_fit = true\n",
+    "    fit_max_moment = 6\n",
+    "    fit_min_w = 10\n",
+    "    fit_max_w = 16\n",
+    "    measure_density_matrix = true"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "26910f2d-fd3d-4d72-adc5-99e79f72452d",
+   "metadata": {},
+   "source": [
+    "Let's go through some special options we set in the config file: \n",
+    "\n",
+    "* we changed `n_iw=2000` because the large energy window of the calculation requires more Matsubara frequencies\n",
+    "* `h_int_type` is set to `density_density` to reduce complexity of the problem\n",
+    "* `beta=40` here we set the temperature to ~290K\n",
+    "* `magnetic=true` lift spin degeneracy\n",
+    "* `magmom` here we specify the magnetic order. Here, we say that both Ni sites have the same spin, which should average to 0 at this high temperature. The magnetic moment is specified as an potential in eV splitting up / down channel of the initial self-energy\n",
+    "* `afm_order=true` tells solid_dmft to not solve impurities with the same `magmom` but rather copy the self-energy and if necessary flip the spin accordingly\n",
+    "* `length_cycle=2000` is the length between two Green's function measurements in cthyb. This number has to be choosen carefully to give an autocorrelation time ~1 for all orbitals\n",
+    "*  `perform_tail_fit=true` : here we use tail fitting to get good high frequency self-energy behavior\n",
+    "* `measure_density_matrix = true ` measures the impurity many-body density matrix in the Fock basis for a multiplet analysis\n",
+    "\n",
+    "By setting the flag magmom to a small value with a flipped sign on both sites we tell solid_dmft that both sites are related by flipping the down and up channel. Now we run solid_dmft simply by executing `mpirun solid_dmft config.toml`. \n",
+    "\n",
+    "Caution: this is a very heavy job, which should be submitted on a cluster. \n",
+    "\n",
+    "In the beginning of the calculation we find the following lines:\n",
+    "\n",
+    "    AFM calculation selected, mapping self energies as follows:\n",
+    "    imp  [copy sigma, source imp, switch up/down]\n",
+    "    ---------------------------------------------\n",
+    "    0: [False, 0, False]\n",
+    "    1: [True, 0, True]\n",
+    "\n",
+    "this tells us that solid_dmft detected correctly how we want to orientate the spin moments. This also reflects itself during the iterations when the second impurity problem is not solved, but instead all properties of the first impurity are copied and the spin channels are flipped: \n",
+    "\n",
+    "    ...\n",
+    "    copying the self-energy for shell 1 from shell 0\n",
+    "    inverting spin channels: False\n",
+    "    ...\n",
+    "\n",
+    "After the calculation is running or is finished we can take a look at the results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "f42d62cc-f8b4-4fc7-af76-cdf7ba13e8ea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with HDFArchive(path+'/nno.h5','r') as ar:\n",
+    "    Sigma_iw = ar['DMFT_results/last_iter/Sigma_freq_0']\n",
+    "    obs = ar['DMFT_results/observables']\n",
+    "    conv_obs = ar['DMFT_results/convergence_obs']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "65dba97b-a64c-4d88-b7cc-3607605a9aa3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1050x1200 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(nrows=4, dpi=150, figsize=(7,8), sharex=True)\n",
+    "fig.subplots_adjust(hspace=0.1)\n",
+    "# imp occupation\n",
+    "ax[0].plot(obs['iteration'], np.array(obs['imp_occ'][0]['up'])+np.array(obs['imp_occ'][0]['down']), '-o', label=r'Ni$_0$')\n",
+    "ax[0].plot(obs['iteration'], np.array(obs['imp_occ'][1]['up'])+np.array(obs['imp_occ'][1]['down']), '-o', label=r'Ni$_1$')\n",
+    "\n",
+    "# imp magnetization\n",
+    "ax[1].plot(obs['iteration'], (np.array(obs['imp_occ'][0]['up'])-np.array(obs['imp_occ'][0]['down'])), '-o', label=r'Ni$_0$')\n",
+    "ax[1].plot(obs['iteration'], (np.array(obs['imp_occ'][1]['up'])-np.array(obs['imp_occ'][1]['down'])), '-o', label=r'Ni$_1$')\n",
+    "\n",
+    "# dxy, dyz, dz2, dxz, dx2-y2 orbital magnetization\n",
+    "ax[2].plot(obs['iteration'], abs(np.array(obs['orb_occ'][0]['up'])[:,4]-np.array(obs['orb_occ'][0]['down'])[:,4]), '-o', label=r'$d_{x^2-y^2}$')\n",
+    "ax[2].plot(obs['iteration'], abs(np.array(obs['orb_occ'][0]['up'])[:,2]-np.array(obs['orb_occ'][0]['down'])[:,2]), '-o', label=r'$d_{z^2}$')\n",
+    "ax[2].plot(obs['iteration'], abs(np.array(obs['orb_occ'][0]['up'])[:,0]-np.array(obs['orb_occ'][0]['down'])[:,0]), '-o', label=r'$d_{xy}$')\n",
+    "ax[2].plot(obs['iteration'], abs(np.array(obs['orb_occ'][0]['up'])[:,1]-np.array(obs['orb_occ'][0]['down'])[:,1]), '-o', label=r'$d_{yz/xz}$')\n",
+    "\n",
+    "ax[3].semilogy(conv_obs['d_Gimp'][0], '-o')\n",
+    "\n",
+    "ax[0].set_ylabel('Imp. occupation')\n",
+    "ax[1].set_ylabel(r'magnetization $\\mu_B$')\n",
+    "ax[2].set_ylabel(r'magnetization $\\mu_B$')\n",
+    "ax[-1].set_xticks(range(0,len(obs['iteration'])))\n",
+    "ax[-1].set_xlabel('Iterations')\n",
+    "ax[0].set_ylim(8.4,8.6)\n",
+    "ax[0].legend();ax[1].legend();ax[2].legend()\n",
+    "\n",
+    "ax[3].set_ylabel(r'|G$_{imp}$-G$_{loc}$|')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5d0d0238-d573-4e18-9785-79408d6ac73d",
+   "metadata": {},
+   "source": [
+    "Let's take a look at the self-energy of the two Ni $e_g$ orbitals:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "daf0c1d8-a1fe-413d-a7b2-2eed78258e9f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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yJVqtTLwKIYQQQpgynU7H3/72N6qqqpg7dy6+vr76r5deeqm3wxPdSGbmTMGVPXMAg32c2H++CIC6Ri0ZxdUM8nTorciEEEIIIUQ3U6lUlJeX93YYohdIMmcKdM3J3BC/5mQOICm3QpI5IYQQQohuNnDgQKQUhehpsszSFFzYrf82vEU1S4DkHPmURgghhBBCCFMkyZwpqGluShnu3XoW7qQUQRFCCCGEEMIkSTJnCq4UQAEI9XJA1eLU6ZxyKYIihBBCCCGECZJkzhS0WJ9tZ2VBgKut/nF1vYZLJTW9EZUQQgghhBCiG0kyZwpazMwBRPg4tXp8KrusB4MRQgghhBBC9ARJ5kzBVcncYN/WRVBOZ0sRFCGEEEIIIUyNJHOmwCCZaz0zd1oqWgohhBBCCGFyJJkzBVf1NJkQ6sFrt8XqHydJERQhhBBCCCFMjiRzJqH1zJyzrSW3RPugulLWsrpew4Wi6l6ISwghhBBCCNFdJJkzBTrDWTd7awtCPZt7ziXJUkshhBBCCCFMiiRzpsBIMgcQE+Cs//6UFEERQgghhBDCpEgyZwqsHY0ejvZrLoRyOqesh4IRQgghhBBC9ARJ5lo4ePAgc+fOxc3NDQcHB0aPHs1HH33U6fscO3aMv/zlL0yaNAk/Pz+sra0ZMGAAK1as4NSpU10fuN8wg0PJueW8vu2c/nFSTjkaKYIihBBCCCGEyZBk7ooNGzYQHx/P1q1bGTp0KHPmzOHcuXOsXLmSJ554osP3aWxsZOTIkTz33HOkpKQwbNgwFi5ciLW1NZ9++ikjR45k3bp1XRu8TmNwyN/Flqq6Rv3j2gYtFwqruvZ5hRBCCCFEr1q+fDkqlYojR470diiiF0gyB5SWlnLvvfei0WhYt24du3fvZt26daSkpBAaGsobb7zBrl27Ony/MWPGsHnzZvLz8/n2229Zu3YtaWlpPP300zQ0NHDfffdRVFTUdS9AqzU45GJnha+zTatjJ2XfnBBCCCGESTl+/Djm5ubExMT0diiiF0gyB6xevZry8nIWLVrEkiVL9Me9vb155ZVXAHj99dc7dC8LCwsSEhKYN28eZmbN//eamZnx17/+lcjISCorK/n222+77gUYmZkDiPRpvZfuZFZZ1z2nEEIIIYToVWVlZaSnpxMZGYmtrW1vhyN6gSRzwObNmwFYtmyZwbl58+ZhY2PD9u3bUavVN/Q8KpVK/6lJbm7uDd2rldpy0DQaHI70dWr1OFGSOSGEEEIIk3H8+HEAhg0zrJ8gbg6SzIG+KMnw4cMNzllZWREdHY1arSY1NfWGn+vChQsA+Pj43PC99ArPQF2FweHBVyVzZ/MqUDcYn8UTQgghhBCK3bt3o1KpWLlypdHzK1euRKVSsXv3bv2xjIwMVCoVU6ZMoaKigl//+tcMGDAAGxsbBg8ezBtvvIHWyNaYjti6dSszZszAyckJd3d3Vq5cSWlpKceOHQOMv4cVNweL3g6gt1VUVFBWVgZAQECA0TEBAQEcPXqUzMxMYmNjr/u59u/fz7Fjx7CysmLOnDkdvi4qKsro8fT0dEKa8jWtYZI2+Kpllo1aHcm5FYwIcu3wcwshhBBCiI6rq6tj2rRppKenM23aNOrr69mxYwdPPPEEp06d4oMPPujU/f785z/z/PPPY21tzdSpU7G0tGTDhg0kJSUxcOBAoOMzc2lpaTzwwAMUFRVhbm7OL37xCx555JHOvsQb1lfiMAU3fTJXVdVc4dHOzs7oGHt7e4OxnVVRUcF9990HwOOPP46vr+9138soreEyy2APe6wszKhvbP4UKDGrTJI5IYQQQohukpCQwNChQzl37hweHh6A8gF8fHw8H374IbfeeisLFy7s0L0++eQTnn/+eUaMGMHXX3+tn3i4ePEiI0eO1C+zjIuL69D9rK2t+de//kV0dDRVVVWMGDGCKVOmtDlx0F36ShymwCSSuWXLlpGUlNSpa9asWcPo0aPR6a7de60jY9qj0Wi48847OXfuHKNHj+b555/v1PXJyclGj0dFRUFhivLASDJnYW5GuLcDSTnNSzBl35wQQgghOkynA3U/q4Zt4wwqVa+G8Nprr+kTOYCQkBCeeeYZHnroIf75z392KJkrLy/n0UcfxcnJiU2bNrWaCAgODubBBx/k5ZdfJjg4GBcXlw7FFRQUpP/ewcGB8PBwMjMzezyJ6itxmAKTSOYyMjI6vZ+tpqYGAEdHx1bHnJyc2hzr4OBwXfH97Gc/49tvvyUiIoJvv/0WKyur67pPu4wkcwBDfJ1aJXNS0VIIIYQQHaYuh5eDrj2uL3nqEti69NrTu7m5MXPmTIPjd955Jw899BAHDx5Ep9OhukbC+c4771BaWsrTTz9tdEVXaGgocP375dLT0zl27Bhjx469ruu7Sl+Jo78yiQIoR48eRafTdeprypQpADg5OeHs7AxAdna20fs3HQ8MDOx0bL/73e94//33GTBgANu2bWv1KU2XMrJnDpRkrqXMkhqKq+q6JwYhhBBCiJtcy1mnlpycnHBxcaGqqoqKCsPCdVdrqrZ+xx13GD3ftP3neipZlpeXs3TpUt5++21cXXtv+01fiaM/M4lk7kY1FTVpWnfcUkNDA0lJSVhbWxMREdGp+/7973/ntddew8vLi23btjFgwIAuideoNmbmJoZ58OTsCJxsLPXHTmaXdV8cQgghhBAm7nqrUnZm605iYiLW1tZtLj08evQo0Plkrq6ujsWLF3PPPfe06q/c0/pKHP2dSSyzvFHz5s1j7969rFu3jhUrVrQ6t3nzZtRqNXPnzsXGxqbD93z33Xf54x//iIuLC99//32nE8FO0zYYPRzq5UiolyPHM8vYfjYfgMSscqZFendvPEIIIYTo/2yclWWL/YmN8w3fomlLTFvF77Kystq8NjMz0+jxiooKysvLsbe3N7qtp6WGhgYqKytxdnY2uhyzoKCAr776CmidzG3bto1Zs2axf/9+JkyYAMDGjRu5/fbbWbt2LXPnzuXOO+9kzJgxPP744+3GcCMGDx7M/PnzefXVV/XH6uvriY6O5q677uKZZ57pkThuBjIzBzzwwAM4OTnxzTff6H8xQPlFefLJJwF44oknDK6LjIwkMjKSnJycVsfXrVvHQw89hIODA999912HKwzdkDZm5prEDWj+wyZFUIQQQgjRISqVsv+sP311QfGTpj1qaWlpBueKi4uNruZqeX779u0Gxz///HMAxo8ff839cpaWlri6ulJeXk5+fr7B+ddee43a2lq8vb1b7aebOXMms2fP5g9/+AMAe/bs4Sc/+QmrV69m/vz5bNmyhQ0bNrB161bi4uKIi4tj48aN7cZyPSZMmEBCQoJBzI2NjTz11FM9FsfNQGbmUDaqvv/++yxfvpxly5YxefJkPDw82L59O2VlZTz66KNMnz7d4LqmoisNDc2zYgUFBdx1111otVqCg4N55513eOeddwyuXbx4MYsXL+66F9HGnrkmcQOa1yGfzCrr0MZbIYQQQoibUXBwMIGBgZw+fZpvvvmGRYsWAVBdXc2DDz54zT1vv/vd79i+fTvu7u6A0krgr3/9KwAPP/xwh2IYPnw4O3bs4Nlnn+U///mP/n3be++9x6pVq/Rjrvbqq68SFxfH3/72N15++WVeeeUV7rrrLkBZjdbRJaIlJSWUlJS0O8bJyQkvLy+D4xMmTOCzzz6jsbERCwsLsrKyePHFF/nss8+wsbHpVByifZLMXbF06VL27t3LCy+8QEJCAvX19QwePJhHHnmEe++9t8P3qampob6+HoDTp09z+vRpo+MGDhzYxclc+zNzQ1vMzJXXNnCxqJpBntdXnVMIIYQQwtT95S9/4b777mPp0qXEx8fj4ODAkSNHcHJyYuHChW3OJI0dO5b6+nrCwsJaNQ2vqalhxYoVHX7/9+yzz7Jz507effddDhw4QFRUFElJSZw5c4YxY8Zw+PBho/vlYmJiuOOOO3j66ad5/vnnr7sZ91tvvcVzzz3X7ph77rmHDz/80OD4hAkTqK2t5dSpUwwfPpzHH3+c+Ph4fVIsuo4kcy1MmDCBLVu2dHi8sU2sAwcOvOG+dJ1m7QjuoW2ezi6tYdUPaViZm1GvUT4FOZ5ZJsmcEEIIIUQb7r33XlQqFatWreLAgQO4urqyYMECXnrpJX7zm9+0eZ21tTVbt27lj3/8I19//TVFRUX6vnCPPfZYh58/Pj6etWvX8txzz5GSkkJubi7Dhw/npZdeYu/evW0mcydPnuS7777D0tIST0/P63npgJJM/ulPf2p3jJmZ8R1b4eHheHh4kJCQQFFREZs2bep0T2jRMSpdj2ceoqs0NQ1P/k0gPHWxzXEFlWpGv7ij1bGfjB7A35cM7e4QhRBCCNFHabVa/ZaRiIiINt+Yi47JyMggODiYyZMns3v37l6J4fz580ycOJH7778fCwsL3nnnHc6fP3/dvZJvxKJFi7Czs+PEiRPceuut/P3vf+/xGLpCZ39PmqqPJicnd3tsIAVQTIOu/f1yXo42eDpatzp27FJpd0YkhBBCCCF6UE5ODjNnzmTx4sW8+OKL/Pa3v0Wj0bSqKNmTJkyYwBdffEF1dfU1Z/jE9ZNkzhR0YAPp1c3D0/KrKK8x3s5ACCGEEEL0H8XFxcyaNYuRI0fyr3/9CwBHR0eefvppVq1aRV5eXo/HNHz4cHQ6HatWrcLe3r7Hn/9mIcmcKbjGzBxAlJ9hP5PjmTI7J4QQQgjR37m7u5OcnMzatWtbLQN87LHHqKqqatW+oKd89NFHTJ06leXLl/f4c99MpACKKWhUQ9aPMGBUm0Oi/AwbaB69VMLUSMNyskIIIYQQonN6pQheH6PRaCguLmb9+vVs2LCBEydO9HZIJk+SOVOg00J5ZrvJ3BAjM3NHM2RmTgghhBBCdI09e/YwY8YMBg0axNq1awkLC+vtkEyeJHOmQtN+n7kgNzscrC2oqmsedzK7jAaNFktzWW0rhBBCCCFuzLRp06QZeA+Td/GmQtt+MRMzM5VBERR1g5YzuRXdGZUQQgghhBCim0gyZyo0165MGRNgbN+cLLUUQgghhBCiP5JkzlR0IJkbaiSZO3appDuiEUIIIYQQQnQzSeZMhab+mkOGBrgwIsiVaS0qWB67VHrTV14SQgghhBCiP5JkzlR0YGYu2MOe9Q+N55VlQ/XH8ivqyC6t7c7IhBBCCCGEEN1AkjlTcY0CKC15OFgT7GGvf3xM9s0JIYQQQgjR70gyZyo6sMyypeGBrvrvj8q+OSGEEEIIIfodSeZMxTX6zF1t5MAWyZw0DxdCCCGEEKLfkWTOVJhbdmhYo0bL5lO5nMhsTuBS8yupVHd8maYQQgghhBCi91n0dgCii4y8r0PDzM1U/PGr01Som2fydDo4kVlGfLhnd0UnhBBCCCGE6GIyM2cqdJoODVOpVNI8XAghhBBCCBMgyZyp0HYsmQOI8XcxOCbNw4UQQgghhOhfJJkzFR2cmQOI8TecmTuRWUajRtuVEQkhhBBCCCG6kSRzpqK64zNrQ40ss6yp15ByubIrIxJCCCGEEEJ0I0nmTMX+1zo8NMDVFhc7w+qX0jxcCCGEEOLaVq5ciUqlYvfu3b0dSq9Yvnw5KpWKI0eO9HYoNz1J5kxFY12Hh6pUKmIDXAyOSxEUIYQQQghxLcePH8fc3JyYmJjeDuWmJ8mcqehEMgcQN8DF4NixDCmCIoQQQggh2lZWVkZ6ejqRkZHY2tr2djg3PUnmTEUXJHO55WqyS2u6KCAhhBBCCGFqjh8/DsCwYcN6ORIBksyZDk19p4bHGknmAA6eL+6CYIQQQggh+r/169czevRobG1t8fb25u677yY3N7fdaw4dOsSiRYvw9PTE2tqagQMH8vDDD7e6Tq1WY2NjQ3BwsMH18+fPR6VSMXXqVINz0dHRWFhYUFFRoT+WkZGBSqViypQp1NbW8vvf/56goCCsra0JDQ3l5ZdfRqfTdfq1b926lRkzZuDk5IS7uzsrV66ktLSUY8eOATB8+PBO31N0PUnmTIWmczNzbvZWBLnbEehmR5C7nf74gfSiro5MCCGEEKLfefvtt1m2bBnHjx9n/PjxTJkyhe3btzN27FiKi41/+P3JJ58wadIkNm3aREREBEuWLMHa2pp///vfDB8+nJSUFABsbGwYM2YMGRkZZGRk6K/XaDTs378fUJJCtVqtP1dUVMSZM2eIi4vDycnJ4Lnr6+uZNWsW7777LoMHD2bq1Knk5OTw+9//nmeeeaZTr/3Pf/4zt9xyC/v372fChAlMmDCBDRs2MHPmTA4fPgx0fGYuLS2N+Ph4hgwZQkxMDP/85z87FYtonyRzpqKxczNzAFt/Hc/eJ6fy1JxI/bGD6cXX9emNEEIIIYSpyMjI4Le//S3W1tZs376dHTt28MUXX3D+/HkiIyPZvHmzwTVZWVn87Gc/Q6VSsXHjRvbv38/nn3/O2bNneeyxx8jPz+fuu+/Wj58yZQpAq4qYJ06coLy8nKioKOrq6khISNCf2717NzqdTn/d1Q4dOoRKpSItLY2tW7eydetW9u3bh4WFBW+88QZVVVUdeu2ffPIJzz//PCNGjOD8+fNs2bKFjRs3kpiYyMWLF/nqq68AiIuL69D9rK2t+de//sWZM2c4dOgQb731FsnJyR26VlybJHOmopPLLAFsrcwBGDfIHZVKOVZYWcf5go79sgshhBDi5qBu0FBe29DhL2MfDFeoO359bb3G4Pr6Rm2716gbDK+5Xu+//z51dXXcfffdrZInOzs7/vGPf6BqeuPUwurVq6mtreUnP/kJ8+fP1x83MzPjpZdews/Pjx9//FGfoE2ePBlonczt2bMHgGeffbbNc20lc2ZmZqxevRoPDw/9sZEjR3LLLbdQU1PD0aNHr/m6y8vLefTRR3FycmLTpk0EBATozwUHB/Pggw+i0+kIDg7GxcXlmvcDCAoKIjo6GgAHBwfCw8PJzMzs0LXi2ix6OwDRRa4jmWviam/FEF8nknOV9dcHzhcR5u3YVZEJIYQQop/79+503txxrsPjT/55Fs62rXvaTnhpJ5Xqxg5dv3R4AKuWx7Y69k1iDr9bd6rNa349PYzHZ4Z3OMb2NC11XL58ucG5iIgIhg0bpi8E0mTfvn0A3HXXXQbXWFtbc9ttt/Hmm2+yb98+xo4dy/jx47G2tm6VsO3evRsXFxeWLVtGQECAwTkzMzMmTpxoNOaBAwcSHm74+puO5eXltf+igXfeeYfS0lKefvppfH19Dc6HhoYC179fLj09nWPHjjF27Njrul4Ykpk5U3EDyRzAhNDmT3EOpEsRFCGEEELcvJqKlQQGBho9b+x40zUDBw40ek3T8aZxNjY2jB49mkuXLpGRkYFWq2X//v3Ex8djZmbG5MmTSUhIQK1WU1RURHJyMnFxcW3OiLWcRWvJwcEBgLq6a9dXaFo+escddxg937RU83oqWZaXl7N06VLefvttXF1dO329ME6SOVOhabiuy2rqGzlysYTymuZkMOFCMY0abVdFJoQQQgjRrzQtEzW2nPJarnVNy/Mtl1omJiZSVlamX0Y5ZcoU/b65vXv3trtf7npjvVpiYiLW1tZERUUZPd+0VLOzyVxdXR2LFy/mnnvuYcmSJTccp2gmyyxNxdDbr+uyl7aksObQJQBUgA6oVDeSlFthtBedEEIIIW4+D00J4b6JhmX02+JkY/gW88Dvp9HRGmtW5obzDYvi/JkV5dPmNdYWXTdH4efnR1paGpcuXSIsLMzgvLE9X35+fqSmpnLx4kWjyx0vXVLeb7VcvjhlyhReeOEFdu/eTUlJif5Yy/9tea4p+esODQ0NVFZW4uzsbDQxLCgo0Bc/uTqZe/fdd/n5z39ucE1MTAyJiYnceeedjBkzhscff7x7gr+JycycqQifc12XxQa46L+3tmz+cThwXloUCCGEEEJhY2mOs61lh7+MJQNONh2/vqlIW0tWFmbtXmNjaXjN9Wral7Z27VqDc2lpaSQmJhocnzRpEgCffvqpwbn6+nr9vZrGAYwfPx4rKyt2797N7t27cXV1JTZW2SsYGhqq3zfXtF8uPj7+hl9bWywtLXF1daW8vJz8/HyD86+99hq1tbV4e3sb7Ke78847ycvL0399/PHH2NjY8NRTT7FlyxY2bNjA1q1biYuLIy4ujo0bN3bb67jZSDJnKnTXV8EpLtBF/726oXlp5UHpNyeEEEKIm9S9996LlZUVa9as0Rc2AaitreXXv/41Wq3hdpT7778fW1tbPv/8c7799lv9ca1Wyx//+EdycnIYNWpUq+Iftra2jBo1ikuXLrFt2zb9frkmkydP5tChQyQlJREbG9vhCpLXq6mwybPPPtuqIul7773HqlWrWo1pycHBAR8fH3x8fDhy5AgPP/wwH3/8MXfddRfz5s1Dq9WSmJio/1q4cGG3vo6biSRzpkJ7fclcsLu9QbUpgKMZpV1a4lcIIYQQor8YNGgQL7/8Mmq1mqlTpzJjxgzuuOMOQkNDSUpKatV6oElgYCDvvvsuOp2OBQsWMGnSJO68806GDBnCqlWr8Pb2Zs2aNQbXNS2nVKvVBnvipkyZQn19PTqdrluXWDZ59tlnUalUvPvuu8TExHD77bcTFRXFAw88wKhRo4D298v973//Y8WKFXz++ecsW7as2+MVksyZDt31FSwxM1MxrMXsnIWZsiyirlHL8UulXRGZEEIIIUS/89hjj/Hll18SFxfH/v372bFjB1OmTCEhIQF3d3ej16xYsYK9e/cyf/58zp49y7p166itreWhhx7i2LFjREZGGlzTMoEzlsy1da47xMfHs3btWmJiYkhLS2Pbtm34+vqyceNG/fLQtpK59957j5///Od8/fXXzJs3r9tjFQqVzlhXR9EvREVFQWEKyQ87QNgsWPwfsDf+x6U9/9hxjlXb0gBwtbOktEapjPnI1BB+N9vwj44QQggh+j+tVktqaiqg9E5rubxPiM548803ee655/j2228ZN25cb4fTpTr7e9JUCTQ5ObnbYwOZmTMd536AmuvrDzc8qLnXR01989LKA+el35wQQgghhGjbyy+/zJ/+9Cc+/fRTgoODuXz5MpcvX6a8vLy3Q7spSDJnShrV13VZ7AAXrqyupK6xebnmqewyKtTX179OCCGEEEKYNp1Ox9/+9jeqqqqYO3cuvr6++q+XXnqpt8O7KUifOVPSWHddlzlYWxDh48TZvAoA7KzMqanXoNXB4QslzBzi3ZVRCiGEEEIIE6BSqWQGrpfJzJwp0VxfMgcwIshF/72bvZX+e2lRIIQQQgghRN8kyZwpuc5llgDDA5V9c442Fvg62+iPH5R9c0IIIYQQQvRJsszSlFznMkuA6ZHe/PB4PKGeDlwqqWHqa7sBSM2vpLCyDk9H6y4KUgghhBBCCNEVZGauhYMHDzJ37lzc3NxwcHBg9OjRfPTRR11y7/vuuw+VSoVKpSIhIaFL7mngBmbmnO0sCfd2xMxMxUB3O/xazs7JUkshhBBCCCH6HEnmrtiwYQPx8fFs3bqVoUOHMmfOHM6dO8fKlSt54oknbujeu3bt4oMPPkClUnVRtG1orO+S26hUKsaHeugfy1JLIYQQQggh+h5J5oDS0lLuvfdeNBoN69atY/fu3axbt46UlBRCQ0N544032LVr13XdW61W8/Of/5yoqKjub6J4AzNzVxsf0tx8fP/5IqS3vBBCCCGEEH2LJHPA6tWrKS8vZ9GiRSxZskR/3Nvbm1deeQWA119//bru/de//pXz58/zn//8B0tLyy6Jt003sGeuiVar43xBJSXVzbN8OWW1pBdW3fC9hRBCCCGEEF1HCqAAmzdvBmDZsmUG5+bNm4eNjQ3bt29HrVZjY2NjMKYtSUlJvPrqq9x3331MnDixy+Jt0w20JgBo1GgZ+/edFFUp9xnkYc+FomoAdqYUEOrleMMhCiGEEEIIIbqGzMwBp06dAmD48OEG56ysrIiOjkatVpOamtrhe2q1Wh588EGcnZ31s3vdasrTMOHXN3QLC3Mzgtzt9I/9XJoT110phTd0byGEEEIIIUTXuumTuYqKCsrKygAICAgwOqbpeGZmZofv+89//pOEhARWrVqFm5vbDcd5TV1UW2V0cHOsdY3N++R+zCihQt3QNU8ihBBCCCGEuGE3/TLLqqrmvWB2dnZGx9jb2xuMbU92djZPP/00U6ZM4e67777hGKOiooweT09PJ8TpygOt5oafB2D0QDf+TToA5wsqcbG1oKy2kUatjgPnirglxrdLnkcIIYQQQghxY0wimVu2bBlJSUmdumbNmjWMHj26Q1UaO1vJ8ZFHHqGuro5///vfnbruhui6JpkbMdAVlQp0OiitaWBahCc7U5UlljtTCiSZE0IIIYQQoo8wiWQuIyOjU/vZAGpqagBwdHRsdczJyanNsQ4ODte87/r169m4cSPPPPMMkZGRnYqpLcnJyUaPR0VFQWGK8kCrAa0WzG5s5ayTjSWDfZw4k1cBgLujtf7c7rRCtFodZmbd3C9PCCGEEEIIcU0mkcwdPXr0uq91cnLC2dmZ8vJysrOzGTJkiMGY7OxsAAIDA695v02bNgGwbds29u7d2+pcYmIiAA8//DBOTk788pe/NFpB87rsfwPKMmHZezd8q9HBbvpkrlLdiJkKtDoorKwjObeCmADnG34OIYQQQgghxI256QugAMTGxgJw/Phxg3MNDQ0kJSVhbW1NREREh++ZkJDAnj17Wn2Vl5cDcOLECfbs2aNPEruG7oZbEzRpWQQlMbOMYQNc9I93pRZ0yXMIIYQQQvRXK1euRKVSsXv37t4OpVcsX74clUrFkSNHejuUm54kcyi95ADWrVtncG7z5s2o1WqmT5/eoR5zH374ITqdzujX5MmTATh06BA6nY7HHnusS19HVzQNBxg1sDmZu1yhZniQq/7xzhRJ5oQQQgghbmbHjx/H3NycmJiY3g7lpifJHPDAAw/g5OTEN998w1dffaU/XlBQwJNPPgnAE088YXBdZGQkkZGR5OTk9Fis7WpUd8ltPB2tGeRpr3/saGOp//5kdhnFVV2TNAohhBBCiP6lrKyM9PR0IiMjsbW17e1wbnqSzAFubm68//77mJmZsWzZMqZOncptt91GREQE58+f59FHH2X69OkG16WmppKamkpDQx/pv9ZFM3OgtCgAsDRXYaYCHydlVlKngz1p0kBcCCGEEOJm1LQtadiwYb0ciQBJ5vSWLl3K3r17mT17NomJiXz33XeEhITw/vvv8+abb/Z2eB3TRTNzAHePG8jnD47l9F9m88tpYUyN9NSfk6WWQgghhLgZrF+/ntGjR2Nra4u3tzd33303ubm57V5z6NAhFi1ahKenJ9bW1gwcOJCHH3641XVqtRobGxuCg4MNrp8/fz4qlYqpU6canIuOjsbCwoKKigr9sYyMDFQqFVOmTKG2tpbf//73BAUFYW1tTWhoKC+//HKn22wBbN26lRkzZuDk5IS7uzsrV66ktLSUY8eOATB8+PBO31N0PZOoZtlVJkyYwJYtWzo8vrO/GN2+SbaxvstuNcSvdYuGKRFefH4kC4C9aYU0arRYmMtnAUIIIYQwTW+//Ta/+tWvMDc3Z/LkyXh4eLB9+3bGjh2rL553tU8++YSVK1ei1WoZP348AwYM4Pjx4/z73//mq6++Yvfu3URGRmJjY8OYMWPYu3cvGRkZDBw4EACNRsP+/fsBJSlsSvoAioqKOHPmDMOHDzfaSqu+vp5Zs2aRnJzM6NGjGTx4MHv27OH3v/89lZWVvPDCCx1+7X/+8595/vnnsba2ZurUqVhaWrJhwwaSkpL0sXZmZi4tLY0HHniAoqIizM3N+cUvfsEjjzzS4eu7Sl+JoyvJu3FT0oUzc1ebGOqBpbnSX65C3cjxzLJuey4hhBBCiN6UkZHBb3/7W6ytrdm+fTs7duzgiy++4Pz580RGRrJ582aDa7KysvjZz36GSqVi48aN7N+/n88//5yzZ8/y2GOPkZ+fz913360fP2XKFKD1h/0nTpygvLycqKgo6urqSEhI0J/bvXs3Op1Of93VDh06hEqlIi0tja1bt7J161b27duHhYUFb7zxBlVVVR167Z988gnPP/88I0aM4Pz582zZsoWNGzeSmJjIxYsX9fUl4uLiOnQ/AGtra/71r39x5swZDh06xFtvvdVmH+Xu1Ffi6EqSzJmSLtwzdzV7awvGBLvrH0uLAiGEEOIm0qCG2rKOfxlbvaQu7/j19TWG1zfWt39NQ9d9qP3+++9TV1fH3Xff3Sp5srOz4x//+AcqlcrgmtWrV1NbW8tPfvIT5s+frz9uZmbGSy+9hJ+fHz/++KM+QWuqct4ymduzZw8Azz77bJvn2krmzMzMWL16NR4eHvpjI0eO5JZbbqGmpqZDfZnLy8t59NFHcXJyYtOmTQQEBOjPBQcH8+CDD6LT6QgODsbFxeWa92sSFBREdHQ0AA4ODoSHh5OZmdnh67tKX4mjK8kyS1PSTTNzBZVqEjPLmBrpxf7zRQDsSingqTmR3fJ8QgghhOhj9r8Be17q+PinLoGtS+tjb8RAXXnHro+9E279d+tjp9fCNw+3fc3k38PUP3Q8xnY0LXVcvny5wbmIiAiGDRtm0J943759ANx1110G11hbW3Pbbbfx5ptvsm/fPsaOHcv48eOxtrZulbDt3r0bFxcXli1bRkBAgME5MzMzJk6caDTmgQMHEh4ebnC86VheXl77Lxp45513KC0t5emnn8bX19fgfGhoKHBj++XS09M5duwYY8eOve57dIW+EseNkpk5U9LFM3MV6gamr9rN6Bd38LOPjxHdYh9dyuVKcstqu/T5hBBCCCH6gqZiJYGBgUbPGzvedE3TnrKrNR1vGmdjY8Po0aO5dOkSGRkZaLVa9u/fT3x8PGZmZkyePJmEhATUajVFRUUkJycTFxfX5oxYy1m0lhwcHACoq7v2+8Sm5aN33HGH0fNNSzWvt5JleXk5S5cu5e2338bV1fXaF3STvhJHV5BkzlTYusGo+7v0lk42ltRrtPrHeeVqBrrb6R/LUkshhBBCmKKmInfGllNey7WuaXm+5VLLxMREysrK9Msop0yZot83t3fv3nb3y11vrFdLTEzE2tqaqKgoo+eblmpeTzJXV1fH4sWLueeee1iyZMkNxXkj+kocXUWWWZoKOzeY9dcuv+3YYHeySrIBSLhQzJQILz48mAEoSy3vGhPU5c8phBBCiD5m4uMw9qGOj7dxNjz2+Gnje+mMMbcyPBZzG0TOa/saC5uO3bsD/Pz8SEtL49KlS4SFhRmcN7bPys/Pj9TUVC5evGh0ueOlS5cAWi1fnDJlCi+88AK7d++mpKREf6zl/7Y815T8dYeGhgYqKytxdnY2mhgWFBToi59cncxt27aNWbNmsX//fiZMmADAxo0buf3221m7di1z587lzjvvZMyYMTz++OPd9hoGDx7M/PnzefXVV/XH6uvriY6O5q677uKZZ57pkTh6kszMmYpuKn4ydlBz0ZOEC8VMH+ylf7zvXBE19Y3d8rxCCCGE6EMsbZQ9cB39MjZLZOPc8eut7Ayvt7Bq/xrLrkvmmvalrV271uBcWloaiYmJBscnTZoEwKeffmpwrr6+Xn+vpnEA48ePx8rKit27d7N7925cXV31bQ9CQ0P1++aa9svFx8ff8Gtri6WlJa6urpSXl5Ofn29w/rXXXqO2thZvb2+D/XQzZ85k9uzZ/OEPyp7FPXv28JOf/ITVq1czf/58tmzZwoYNG9i6dStxcXHExcWxcePGLn8NEyZMaFUBtCnuxsZGnnrqqR6LoyfJzJyp6K5kLqQ5mcsoriHIzQ5Hawsq6xqpa9SyN62IOdE+3fLcQgghhBC94d577+WVV15hzZo1rFixQp+A1dbW8utf/xqtVmtwzf3338+rr77K559/zu233868ecosolar5Y9//CM5OTmMGjWqVcENW1tbRo0axYEDB8jPz2f27NmYmTXPtUyePJm1a9fS0NDQ7n65rjJ8+HB27NjBs88+y3/+8x/9DN17773HqlWr9GOMefXVV4mLi+Nvf/sbL7/8Mq+88oq+GMy8efOM/n9mTElJiX4msi1OTk54eXkZHJ8wYQKfffYZjY2NWFhYkJWVxYsvvshnn32GjY1Np+LoL2RmzlR0UzLn72JLoFvzp2PHr1S1bPLDmcvd8rxCCCGEEL1l0KBBvPzyy6jVaqZOncqMGTO44447CA0NJSkpqVXrgSaBgYG8++676HQ6FixYwKRJk7jzzjsZMmQIq1atwtvbmzVr1hhc17ScUq1WG+yJmzJlCvX19eh0um5dYtnk2WefRaVS8e677xITE8Ptt99OVFQUDzzwAKNGjQLa3i8XExPDHXfcwdNPP81vf/vb627G/dZbbxEWFtbu15NPPmn02gkTJlBbW8upU6cAePzxx4mPj2fRokXXFUt/IMmcqaivhoNvQ21pl9967CA3/feH0ouZFeWtf7zjbAGNGtP6hEMIIYQQ4rHHHuPLL78kLi6O/fv3s2PHDqZMmUJCQgLu7u5Gr1mxYgV79+5l/vz5nD17lnXr1lFbW8tDDz3EsWPHiIw0bOvUMoEzlsy1da47xMfHs3btWmJiYkhLS2Pbtm34+vqyceNG/exkW8ncyZMn+e6777C0tMTT0/O6Y3j22WdpaGho9+v99983em14eDgeHh4kJCTwww8/sGnTJt56663rjqU/UOl0Hd2JKvqaqKgoKEwh+WGH5oMPHwavru3/9tXxbJ748iQAA9xs+e7RSYz463Z9pcvPHhzD+BCP9m4hhBBCiD5Gq9WSmpoKKL3TWi7vE6Izzp8/z8SJE7n//vuxsLDgnXfe4fz58/q2CD1p0aJF2NnZceLECW699Vb+/ve/39D9Ovt70lQJNDk5+Yaet6Pkt9bUdEPj8JaJWlZJLaXVDUwIbf5E6odkw02yQgghhBDC9OXk5DBz5kwWL17Miy++yG9/+1s0Gk2ripI9acKECXzxxRdUV1fzpz/9qVdi6EmSzJmabtg75+NsQ4invf7xgfQiZkU1Fz3ZdiYfmeAVQgghhLi5FBcXM2vWLEaOHMm//vUvABwdHXn66adZtWoVeXl5PR7T8OHD0el0rFq1Cnt7+2tf0M9JMmdqumFmDmBiaPPs3MWiaqYP9tJXHc4pqyU5t6JbnlcIIYQQQvRN7u7uJCcns3bt2lbLDx977DGqqqoMWhj0hI8++oipU6eyfPnyHn/u3iCtCUxNN1W1vG3kAEYOdGN8iDvuDtYADA905dglpeDKD8mXifY30iBUCCGEEEKIbqTRaCguLmb9+vVs2LCBEydO9HZIPUZm5vq9q5pydtPMXLS/Mwti/fSJHMCsIc1VLb+XfXNCCCGEEKIX7NmzBx8fH1atWsXatWsJCwvr7ZB6jMzM9Xeqq5I5TX2PPfXsKB/+viUFgNT8StILqwjx7PmqRUIIIYQQ4uY1bdo0k2sG3lEyM9fv9czMnDEDPewZ4uukf7w1SRqICyGEEEII0VMkmevvVFf9J+yBZK66rpFdqQWoGzTMjWmuavnd6Z6vWCSEEEIIIcTNSpK5/u7qZZbdVAAFQKfTsfKDI8Q9/wP3fvAjxzNLmRPdXKUoObeCzOKabnt+IYQQQgghRDNJ5vq9nltmqVKp0OmgQaP0lDtwvohQLwfCvZv3yW1Jktk5IYQQQggheoIkc/1dy5m5W16B0T/r1qdr2W9u/7ki5WlbzM59J/vmhBBCCCGE6BGSzPV3LffMWTuBtWO3Pt3EsOZk7lROOSXV9cyNaU7mTmaVkVNW260xCCGEEEIIISSZMwEtZuZ6oPhJpI8jXo5KrzmdDvadKyTc24FBHvb6MVukEIoQQgjR56larO65Wcu6C3EtLX83VFfXqugDJJnr71r+UHVj8ZPmp1MxOdxT/3hPWiEqlYpbWlS13HQyt9vjEEIIIcSNUalUWFlZAVBdXd3L0QjRNzX9blhZWfXJZE6ahvd3KhWgFCTpqR5zkyM8WXssG4C9aUVotToWxvrzz13pAJzMLudCYRWDpIG4EEII0ac5OjpSXFxMfn4+APb29piZyWf9Qmi1Wqqrq/W/G46O3buV6XpJMtfvtUjm9r0OpRmw4P+69RknhnpgpgKtDoqq6jiTV0G0vzODfZ04m1cBwNeJuTwxM7xb4xBCCCHEjXF3d6e6uhq1Wk1urqysEcIYGxsb3N3dezsMo+Sjl/6uZQGUunKoKuj2p3SxsyJugIv+8Z60QgBuHeanP/b1iRx0Ol23xyKEEEKI62dubk5gYCDu7u76JZdCCIWVlRXu7u4EBgZibm7e2+EYJTNz/d3Va3c13b9vDmByuBfHM8sAJZl7ZGooC2P9+fuWFHQ6yCyp4XhmGSOCXHskHiGEEEJcH3Nzc7y8vPDy8kKn08mHsUKg7Cnti3vkribJXL93ddPwHkrmIjx5Y3saAIWVdTRotPg42zA+xJ0D54sBZXZOkjkhhBCi/+gvb2CFEApZZtnfXf0Ht4eKoMT4O/P8oih2/mYyO38zGUtz5UdpUZy/fszmU7k0aKTUsRBCCCGEEN1Bkrn+TnXVf8IeSubMzVTcPW4ggzwdWn2CNyfaB2sLJabSmgb2pBb2SDxCCCGEEELcbCSZ6/d6Z5llW5xsLJkxxFv/eO2xrF6MRgghhBBCCNMlyVx/Z7DMsneTOYBlIwL03+84W0BBRc/MFgohhBBCCHEzkWSuv+sDyZxGq+PHjBL2XmlREB/mib+LrRKOVqdvMC6EEEIIIYToOpLM9Xu9s2euya7UAka9uJ3b/nOIv29JAZT9dLeNbJ6d++LHLLRaKXMshBBCCCFEV5Jkrr/r5Zm5IDc7SqrrATibV0F2aQ0Ay0cOwOxKaJklNRy6UNyjcQkhhBBCCGHqJJnr71omc1YOEDm3R59+kKcDgzzt9Y93nC0AwM/FlikRXvrjnx/J7NG4hBBCCCGE6G6V6gYSLhSzet8Ffv2/E1wqru7R55em4f1ei2TO3hOWvd/jEcwc7M07hRcA2H42n3vGDwTgjlED2JmiJHffJ1+muKoOdwfrHo9PCCGEEEKIG1WpbiApp4KknHJO55STlFPOxeJqdC12E9U39uzWIknm+ruWM3O9VMlyxhBv3tmrJHMJF4qpUDfgZGPJtEgvvBytKaiso0GjY8OJHB6YNKhXYhRCCCGEEKKjaus1nMkr52RWOaeyyziVXc6Fop6ddesISeb6u5ZNw3u4+EmT4YGuuNpZUlrTQINGx960QuYP9cPC3IylIwL49+50AL5JzJVkTgghhBBC9CkNGi2plys5mV3GqaxyTmaXca6gCk0HC/g5WlsQ5e9EjL8z76y16eZoW5Nkrt/r/Zk5czMV0yK9WX9caUGw/Uw+84f6AXDrMH99Mnc6p5zzBVWEejn0SpxCCCGEEOLmptHquFBYxclsZcbtZHY5Z/MqqG/Uduh6RxsLov2ciQlwJtrfmRh/Z4Lc7DC7Uvnvsyd7Nr2SZK6/a7nMsqEWkr6C0Blg49SjYcwc4qVP5namFNCg0WJpbka4tyNDfJ04k1cBwDeJOfxmVkSPxiaEEEIIIW4+Op2OrJJaZcbtSuKWnFNOdb2mQ9fbWpoT468kbkMDnBka4NIqcesLJJnr71ous0QL6+6Fhw6BzZAeDWNSmCdW5mbUa7RUqBs5mlHKuBB3ABYP82uRzOXyxMxwVFe3VBBCCCGEEOIGFFSoScxS9redyinndHYZpTUNHbrW0lzFYF8nfdIWG+BCqJcD5n0ocTNGkrl+z8gPWC/snbO3tmBciDt70goB2HE2X5/MLYz15+9bUtDplJ5zxzPLGBHk2uMxCiGEEEII06Bu0HA6p5zEzDJOZJWSmFlGbnnH3gObqSDMy1FJ3Aa4EBvgTISPI9YW5t0cddeTZK6FgwcP8sILL5CQkEB9fT1DhgzhkUce4Z577rmu+2m1Wt577z0++ugjkpOTUavV+Pr6Mm7cOP74xz8SFRV140Ebm+HqxaqWe9IKcba1xMK8ecbQx9mGscHu+sbh3yTmSDInhBBCCCE6RKfTcbGomhOZZSRmKclbSl4ljR0sUDLQ3Y6hAS4MDXAmdoALUX5O2FmZRhpkGq+iC2zYsIHbbrsNrVZLfHw8Hh4e7Nixg5UrV3Ly5Elef/31Tt2vpqaGBQsWsHPnTlxdXZk4cSI2NjZcvHiRL774gltuuaUbk7neqWo5N9qHMC8HRga5tkrmQFlq2ZTMbT6VxzPzh2BpLj3rhRBCCCFEa2U19UrSdiV5S8wqo7y2Y8slfZ1tWi2VjPF3xtnOspsj7j2SzAGlpaXce++9aDQa1q9fz5IlSwDIz89n4sSJvPHGGyxYsICpU6d2+J733nsvO3fu5L777uMf//gHdnZ2+nN5eXk0NHTsB/LaVFe+Wnwy0Uszc+4O1m02BZ8T7csz3yRT36ilpLqevWmFTB/s3cMRCiGEEEKIvqRBoyUlr5LErFJ98tbRfm62luYMDXAmLtCFYQNcGRbogrdTz7YG6G2SzAGrV6+mvLycRYsW6RM5AG9vb1555RWWLFnC66+/3uFkbufOnXz55ZeMGjWK//73v5iZtZ6B8vX17dL4sbCBxtrmx700M9ceZ1tLZgz24rvTlwH48miWJHNCCCGEEDcRnU5HXrn6StKmJG+nc8qp62BbgDAvB+IGuOiTt3BvB4PVYDcbSeaAzZs3A7Bs2TKDc/PmzcPGxobt27ejVquxsbl2tv/OO+8A8Pjjjxskct3Cwrp1Mqep7/7nvA7LRw7QJ3M7zhZQUKnGy/Hm+vRECCGEEOJmUd+o5UxeBcculXL8UinHLpVyuaJjkw5u9lYMG+BC3AAXhgW6MnSAM042prtc8npJMgecOnUKgOHDhxucs7KyIjo6mqNHj5KamkpsbOw177dz504AZsyYQVJSEmvXruXy5cv4+Phwyy23MHbs2K59ARZXJUR9YGYuo6ia7WfzCfVyYEqEF6C0L/BztiG3XE2jVsdXx3P4xeSQXo5UCCGEEEJ0hZLqeiVpy1QSt5NZZR2adbM0VzHEz5lhA1wYdmXWbYCbrbSy6oCbPpmrqKigrKwMgICAAKNjAgICOHr0KJmZmddM5vLz8ykqKsLV1ZX33nuPp59+Gq22+Yf4+eefZ8WKFbz//vtYWnbs04W2CqWkp6cTEhKizMy11Et75pr83/Y0/m/7OQCmRXrpkzlzMxW3jRzAmzuUc1/8mMXP4wfJL6oQQgghRD+j1eo4X1jFsSszbscvlXZ4r1uAqy3DAl2VmbdAF4b4OmFj2f/aAvQFN30yV1VVpf++ZZGSluzt7Q3GtqW0tBSAyspK/vCHP/DTn/6UP/3pT3h5ebFjxw5+8Ytf8Mknn+Dv789LL73UBa+APjczN3qgm/77fecKqVA36KfFbxsZwFs7z6HTwcWiao5cLGHMIPfeClUIIYQQQnRAVV0jJ7PKmpO3zFIq1Y3XvM7K3IyYAGdGBLkyPNCV4UEuJrXN5nK5mhOZpVdaJpSRW1aLn4ttjz2/SSRzy5YtIykpqVPXrFmzhtGjR6PTXbs/RUfGNNFoNAA0NjYybtw41qxZoz+3dOlSbGxsmD9/Pm+99RZ//OMfcXJyuuY9k5OTjR7Xz9hdPTPX0MvJXLAbbvZWlFTX06DRsf1MPkuGK7OeAa52TArzZO+V5uJf/JglyZwQQgghRB+TW1bLjxklHM1QkreUyxV0pK2bh4M1I4NcleQtyJVof6d+2YzbmPpGLcm55RzPLON4ZiknLpUaNCqvrdf0aEwmkcxlZGSQmpraqWtqamoAcHR0bHXMWHLVNNbBweGa9215v/vuu8/g/Lx58/D29iY/P58jR44wY8aMTsVtVMtkbsofYPjdN37PG2BhbsbsKB8+P5IJwHenL+uTOYA7Rg3QJ3Pfns7jzwuiTLr/hxBCCCFEX9a0ZPLIxRKOZpTwY0YpOWW117zOTAURPk6MCHJhRJArIwLdTHKv21fHs/n0cCanc8qpv8YewA72Me8yJpHMHT169LqvdXJywtnZmfLycrKzsxkyZIjBmOzsbAACAwOveT8/Pz+srKyor68nKCjI6JigoCDy8/MpKCi47rhbabnM0skfHHu/5P/cmOZkbu+5QirVDTheWWo5Y7C3fuaurlHLVyeyuXdCcG+GK4QQQghx06hv1JKUW86PF0uU2bdLpZTVXLsHsqO1BcOCXBkRqMy8xQ5w1r+/6+8aNFrO5FZgYa4iys+51bniqnqOXSpt81o/ZxtlD2CgC3/7queWWIKJJHM3KjY2lr1793L8+HGDZK6hoYGkpCSsra2JiIi45r0sLCyIjo7m+PHjlJSUGB1TXFwMdGymr0Nazsz1gUqWAGMHueNiZ0lZTQP1jVp2phSwKM4fACsLM24bEcA7ey8A8EnCJVaOH2hyn+IIIYQQQvQFVXWNHL9UytGMEo5klJCYVYa64dpVJgPd7Bg50JWRQW6MCHIl1MsBczPTeL9WWFnH8Uxl79/xS6Wcylb63c0f6svbd7aucD88yEX/vZWFGTH+zgwPdLmyB9C1VaPyN3q4kIskcyhLH/fu3cu6detYsWJFq3ObN29GrVYzd+7cDvWYA1i4cCHHjx9n165d3H777a3OZWRkkJGRAcCwYcO6JP5WM3O9XMmyiaW5GbOH+PDF0SwAvj2Vp0/mAO4cE6hP5tILqzl0oZjxIR69EqsQQgghhCkprKzTJ25HM0pJzi2/5vI/lQoifZwYPdCVUcFujAxyw8fZNAqVNGi0pORVNidvmaVklRhfRnois8zgWJSfM8/MH8LwQBeG+PWtPYCSzAEPPPAAL774It988w1fffUVS5YsAaCgoIAnn3wSgCeeeMLgusjISAB27NiBv39zovLwww/z+uuv88EHH7B06VJmzpwJKNUwH3roITQaDfPmzWPAgAFd8wJazsxp+kYyB3BLTHMytzutkKq6RhyslR+5IHd7Jod7sufK3rlPEzIlmRNCCCGEuA7ZpTUkXCjhyMVifswo5WIHWgRYWZgRF+DCqGBXRg5UZt5MtSn3vR/8yP7zRdccZ2VuhpeTNTX1jdhZNadJNpbm3D+xb24JkmQOcHNz4/3332f58uUsW7aMyZMn4+Hhwfbt2ykrK+PRRx9l+vTpBtc1FV1paGi9xtjT05MPP/yQ5cuXM2fOHMaOHYuXlxcJCQlcvnyZ4OBg3nnnna57AS1n5n5cDTnH4Y5Pu+7+12l8iAdONhZUqBv1Sy0Xxvrpz/90bJA+mfs++TIFFWq8nEzjEyAhhBBCiO6g0+nIKqkl4WIxCReKOXyhpEPFSpxsLBg50I2RA10ZPdCNmADnPjXDdCPyymv5MUNZRjpjsDfx4Z6tzg8NcDaazPk42TA8SFkuOSywf1belGTuiqVLl7J3715eeOEFEhISqK+vZ/DgwTzyyCPce++9nb7frbfeysGDB3nxxRfZv38/R48eZcCAAfzmN7/hD3/4A+7uXViOv+XMXEUuqPrGf1YrCzNmRfmw7phSQGbTydxWydzUSC/8XWzJKaulUavjfz9m8ej0sN4KVwghhBCiz9HpdGQU13D4wpXk7WIJeeXXrpHg62zDqIFujLqybDLcyxEzE9jvptXqSCuo1CdvR6+qvKkCg2Ru1EA3LM0vEOXnrO91NzzQtUf7wXWXvvGuv4+YMGECW7Zs6fD4a/WfGzVqFF9//fUNRtUBVzcNryvv/ufsoAWxfnx1PJsJoR7Mi/Ftdc7cTMWdYwJ59XtlhvOzw5k8PCUEC3Oz3ghVCCGEEKLX6XQ60gurOXyxmIQLJRy+UExB5bW30QR72DN2kNuVBM6NAFfTaRFw5ErVzR8zSjh2qf1m5T9mGFadnBDqwem/zMamh4uT9ARJ5kyBuVXrx3WVoNMpO1l72YQQdxL+OB0vR+PLJ5ePHMD/bU+jQaPjcoWa7WcLmBPt08NRCiGEEEL0Dp1Ox7mCqiszbyUcvlhCUdW1k7cQT3vGDnJnzCB3xgS7taqoaGqe3nCacwVV7Y4Z4GbLqCA3Rge7GZyzsjDdiQJJ5kzB1TNzOi3UV4N1F7U+uAEW5mZtJnIAno7WzIn2ZdPJXAA+PXxJkjkhhBBCmCydTsf5gioOpjcvmyyprr/mdeHeDkryFuzO6GA3PB2tr3lNf1BQoebwxRKOXCzh8MVi/rIwyqAo3siBbq2SOTMVDPFzYmSQMgs5cqCrSSez7ZFkzhRYGPllrqvsE8lcR6wYE6hP5vadK+JiUTXBHva9HJUQQgghxI1rKlhyML2Ig+nFHEwv7tDMW6SPI2MHueuXTro7mEbyll1aoyRuF5TWCVdX3jx8ocQgmZsQ6k5mSbU+eYsLdNFXSL/Zyf8LpuDqmTmAugrA1/B4H6DV6lptwB0d7Ea4twNp+conLp8mXOJP84e0dbkQQgghRJ92uVzNoQtFHDyvJG/XqjapUsEQXyfGBCvJ2+hgN1zsrNq9pr9o0GhZfyz7yszbtStvHrlYYnBs/lA/5g/1MzJaSDJnCtqametD1A0adqUU8E1iLiXV9Xz5i3H6cyqVihVjg3j2m2QA1h7L5rezI0xyk6oQQgghTE9JdT0JF4r1s28XCtvv89aUvI0b5M64EHdGDnTD2bb/93jTanWoVLQqvGJhpuLV71Mpbmcpqb+LLWOClSR27KAurPh+E5BkzhS0OTPXd1wqruGhT4/rH1+9lPLWYf68tCWFmnoN5bUNbDqZy20ju6ipuhBCCCFEF6pUN3DkYol+2eTZvGu/7wr1cmB8iDvjQ5R9b672/X/mTaPVcTavgoQLxfqKk58+MJYhfk76MSqVitHBbmxJuqw/Fuxhz+iBboy5MgsZ4GrXG+GbBEnmTIGxmTl130rmInwcifB2JDVfmTHccDybJ2ZF6M872liyeJg/nx3OBOCThEuSzAkhhBCiT1A3aDiaUaqfeTudU45G236LqgFutowf5MH4UHfGDXLHywQKdGi1OlLzKzl0JYk9crGYiqvaBBy+WNwqmQO4JcYXdwcrffGWm7VYSXeQZM4UGJ2Z61vLLAFuHa7MvgGsP57DYzPCW+2dWzEmSJ/Mncwu52hGCSMHGpaXFUIIIYToTlqtjjN5Few/X8T+c0X8mFFCXaO23Ws8Ha2ZEOLO+BAPxoW4M8DNNGabauobWX8sm0NXWidcq/LmkYsl3DshuNWxhbF+LIyVPW/dQZI5U9ByZs7cGgJGgV3fS4KWDPPn1e9T0Wh15JTVcuhCMRNCm6sVDfFzYnSwm37j63/2pLNakjkhhBBC9IDcslr2nyti3/kiDp4vanePF4CLnSXjBinLJseFeBDiad/vm3TrdDqD12CmUvHXb89S30Yya26mIsbfmTGD3Bgb7M6Iga49Eaq4QpI5U9AymXPwhnu/7b1Y2uHlZMPkcE92phQAsO5YdqtkDuChySH6ZG772QJSL1cS4ePY47EKIYQQwrRVqhs4lF6sn327UNR+0RI7K3NGB7sx4crM2xBfp1YrjPqrrJIaZdbtSt+7DY9MaLUM0sbSnBGBrhy6UAwoPd6i/Z0ZN8idsSHujBroJm0CepH8P28KWi6zbFT3XhwdsGxEgD6Z25KUx3OLonCyaa7eNCXCk0gfR1IuK8tE/7MnnTduj+uNUIUQQghhQho0Wk5mlbHvXBH7zxeRmFXW7r43MxUMDXBhUpgHE0M9GBboipWFWQ9G3D3yyms5lF6sfF0oJru0dauAQ+nFLB7m3+rYrcP8GezrxLgQZc+bKVTeNBWSzJmCljNzmms3oexN0wd74WJnSVlNA+oGLd+eyuMnowP151UqFQ9NCeHX/0sEYOPJXJ6YGW4y686FEEII0TN0Oh0XiqqVpZPniki4UExVXWO71wx0t2NimAcTQz0ZN8gdZ7v+n7SoGzTsSSvkwHklib1W24SD6UUGydzyUVKUrq+SZM4UtJqZ69vJnLWFOYti/fjo0CVAWWrZMpkDmBfjy2s/pJJVUotGq2P1vgs8tyi6N8IVQgghRD9SXtPAgfQi9qQWsu9cIbnl7a9YcrGzZEKIx5UEzsMkPzyuqmvk5x8fa3fMIA97xoYoVTelz1v/IsmcKWg5M9eohoz9SiGUAaN6L6Z23DZygD6ZO3aplPTCKkI8HfTnLczN+Fl8CM98nQTA/37M4pFpoXg5ShlbIYQQQjTTanWczilnT1ohe9IKOZFZSnsdA6zMzRg50JWJYR5MCvUkyq//73vTanWkXK7kwPkiDqYX8c+7hmNn1fwW38PBmsG+Tq164Q1ws9U3LB83yAMfZ3mP1V9JMmcKrm5N8OE88B8JD+7onXiuIcrPqdW+uH1pha2SOYDbRgTw5vZzFFXVUdeoZfW+i/xx7uDeCFcIIYQQfUhBpZp9aUXsSVNm30prGtodH+njqOx7C/Nk9EA3bK3MeyjS7pNdWnNl2WSxQeXNIxdLmBLh1Wr8vBgfQjztmRjqwQQTnYHsE+qrlS8r+x57SknmTIG5leGxur7VNLwllUrFfRODSS+oYtmIAMK8DatV2lia8/P4Qbz43VkAPj50iZ/FD8LDwUiDdCGEEEKYrAaNlmOXStmTVsjetEKSc9t/j+PhYMWkME/iw5W9b56O/f+9Q3lNAwfTlT1vB84XkVFc0+bYA+eLDJK5X04L6+4Qb071NZB9BC7ug4x9kHMMysvAM7LHQpBkzhT0k6bhLS0fee2NtHeNDeQ/e9Iprq6ntkHD6n0X+f0tPffLIYQQQojekVVSo0/eDqa3X7jE3EzFiEBXJkd4Mjnc02RaBjTJKqkh/tVd6NpZPmqmgpgAFyaFejBziHfPBXezaVBD9o9K4nZxH+QcBU37/Qi7myRzpsDCyCdO6r47M9dRdlYWPBg/iJe2pACw5lAGP4sfhJu9kZlIIYQQQvRbtfUaEi4Ws/fK3rdrVVz0d7ElPlxJ3saHurdqc9RfXSquZv/5In4yKrBVMhrgaouPkw15VxVzGeRhz4QryyZNpfJmn6NphLxEuLALLuyBrCPXrhyv6tn2FZLMmQIzczCzBG2LNeMN1aDVKOf6sZ+ODeKdPemU1jRQU6/hvf0X+N1smZ0TQggh+ruskhp2phSwK7WAQ+nF1DVq2xxrZWHG2EHuTA73ZHK4ByGeDqhU/Xv2raqukUPpSgK791whl64snYzxd2ZogIt+nEqlYkKoB7tTC/TJ24RQD/xdbHspchOm00FhipK4XdyjFBW81tYlKwcIHAfBk2DgJFj7056J9QpJ5kyFhQ3UX7UBuK4CbF17J55O0Gp1HL5Ygk6nY3yoR6tz9tYWPDBpEK9+nwrAhwcyWDk+2CTWvwshhBA3kwaNlh8zStiVUsDOlALSrzH7FuJpz+RwL+LDPRgT7N7vC5dotTrO5FXol48ezyylQWO4dnJvWmGrZA7gLwujsLca2u8T2D6pLLM5ebu4F6ry2x9vaQ+BY2HgRAiOB984MO+9lEqSOVNhYQ31V+2Tq6vs88nc3rRCnv76NFkltcQGOPPNLycajLl7XBCr912gtKaB6noNb+5I44XFMb0QrRBCCCE6o6BSze7UQnalFLD/XBGV7ex9c7C2YHyIO5MjPIkP8zSZiosl1fX8dfMZ9p0rpKiq/f1VYV4OONsZbidxsJa37F2muvhK4rZHSeJKL7Y/3swSBoyG4MkwaDL4jwDzvrOkVX4yTIWxIij9YN+cp6M1WSW1AJzMLiflcgWRPk6txjjaWPKraWE8v/kMAJ8fyWLl+GBCvRwM7ieEEEKI3qPV6jiVU87OlAJ2pxZwKru83fGhXg5Mi/RiSoQnI4PcsLLo2f1GXa1Bo8XSvPVrcLC24Ifky1TXawzGO9taMjHUg/hwDyaFeeInSye7Xl0VZB6CC7uVBO7y6WtcoALfoc3JW+C4Hm010FmSzJkKY0VQ+nhFS4DBvk7E+DtzOkf5Y/9pQiZ/XRxtMG7F2CA+OpTBpeIaNFodL29N4b93j+zpcIUQQghxlfLaBvadK2RnSgF7Ugtb9Ty7mpWFGeND3Jka4cW0SC+TmH3LK6/Vzz4eOF/E1sfiW70uKwszxoV4sP1sPmYqiBvgQny4J/HhnsQGuGBuQpU3+wRNI+Qeh/SdSgKX/SNo254RBsAtREncgicrSyft3Hok1K4gyZypMJrM9f2ZOYAVYwN5ar3yKclXx7N56pZIg+UEVhZmPDk7kkc+Ow7AtjP5HL5QzJhB7j0erxBCCHEz0+l0nCuoYueVvW/HLpWi0bZdN9/fxZapkZ5MjfBifIhHv9/71qjRciKrjF0pBexKLeRsXuv3W3vPFXLXmKBWx+6fGMytw/yZGOohVSe7Q+klJXlL3wEX9kJd+zPCOPg0J2+DJoNzQM/E2Q0kmTMV/XRmDmBhrD8vfHuWSnUj1fUaNpzI4adjgwzGzY3xYVigCycyywD425YUvn54vGwGFkIIIbpZfaOWIxdL2H42n+1n88kurW1zrLmZihFBrvrZt3Dv/l95sriqjj1pyuzj3rRCKtRtz/QcOF9kkMyNC5EPn7tUXaVSafL8DiWJK0lvf7y1s1KwpCmB84yAfv4z2USSOVPRcs/cqAcgfA74DO29eDrB1sqc20YM4P0DygbUTxMusWJMoMEffpVKxdNzB7PsP4cAOJlVxu60QqZGePV4zEIIIYSpK6upZ3dqIdvP5rMntbDd4iVu9lZMCfdkaqQX8WGeJjX7VFXXyJi/7aCxndnHAW62TIvwYkqkF+Nk1VDX02qVfm/pO5WvrMPtL500s1QqTg6aAoOmgm9sr1ac7E6m+apuRi1n5nxiIGxm78VyHe4aG6hP5lIuV3L0UimjBhquVx450I0pEZ7sTi0E4M3t55gS7tnvP/ETQggh+oKMomr97NuPGe0vn4zxd2ZqhJLADTWBvV/ltQ3sP1fE5AjPVts9HKwtiBvgwtFLpfpjluYqxgS7M+XK6x/kYS/vRbpaeY7SrDt9J6TvgtqS9se7h0HodAiZBkETwPrmKJQnyZypaDkz13iNzvR9UIinAxNC3TlwvhiAjw9dMprMAfx6epg+mUvMKmPvuSImh3v2WKxCCCGEqdBodZzILGXb2Xy2n8lvt/ebtYUZE0M9mD7Ym+mDvfB2MlJJux/R6XSk5VexK7X13r//rBjBnGifVmOnRnqRXVrL1EhPpkR4MSHUQ9oFdLX6Grh0sHn2rfBs++NtnJWZt5DpEDIVXAJ7JMy+Rn4KTUXLmblGde/FcQN+OjZIn8xtScqjoGIwXkb+oRgW6Ep8uCd705SE7v+2pxEf5iGfiAkhhBAdUF3XyL5zhWw7U8Cu1AJK2qk+6eFgxfRIb2YM8WZCqDt2Vv37rWNdo4aECyXsOJvPjrMF5JQZ7v3bnVpgkMw9MCmYh6eEyHuNrqTTQfF5OPcDnNumJHKadiYkVOYQMKp59s1vGJj172I6XaF//0aKZi1n5hra3pTcl80Y7I2Pkw2XK9Q0aHR8eDCDJ+dEGh376+lh+mTuRGYZ+84VES+zc0IIIYRReeW1bD9bwPYz+RxKL6Zeo21zbKSPI9MHezFjsDexAS6Y9fPlkxqtjq9P5LD9bD570wqN9ntr4m5vhZOt4X4/awtJGrpEfQ1k7FOSt3M/QNml9se7Drwy8zYNgicps3F9mbpc6WvXg0s8JZkzFdaOzd+fXgfZR8E7CmY+13sxdZKFuRn3ThjIG9vTWD5yALePGtDm2BFBrkwK82DfuSIAXt6awvgQdyzM+3ezUSGEEKIr6HQ6UvMr+SE5n++TL5Oc23a7IgszFWMHuesTOFPo/daSmQre3HGOzJIao+eHBjgzNcJL2fvn79zvk9c+RaeD4nQ4v01J4DL2tz/7ZuWo9HkLnaYkcG6Dei7W69FYr/Sxu7BL6WmXcwwqKsDT+GREd5BkzlRYOzV/X3xO+eqHyy1XjA1i2YgA3B2MtFq4ymMzwvXJXHJuBe8fuMjP4kO6O0QhhBCiT9JodRzPLOWH5Mv8cCafS8XGkxcAZ1tLpkZ4Mn2wN5MjPHGy6d/VJxs1Wo5eKmX7mXxuHe5PlF/zDI5KpWL6YC8+OJABgI2lGZPCPJkxWEngvBz7996/Pqe+5krbgCsJXOnF9sd7x0DYDAidCQNGg3k/+FlM/hpOfAKXDkBD279nPUGSOVPRcmauifoaDRP7IHtrC+w7uKF4RJAry0YEsO5YNgCvb0tjdpQPQe723RmiEEII0WeoGzQcSi/m++TLbD+bT1FV2/vfBrrbMWOwsv9tZJBrv1/NUqFuYE9qITvO5rMrtZDy2gYArC3NWiVzAPOH+qJu0DJziNK43MZSlk12qeJ0JXE7f2X2rb0JBWsnpXBJ2EwInQFOfj0W5nXRasHsqt+VwhTltRqj6tmfLUnmTIWNk+GxftI0/Eb8ad5gdqcWUFRVj7pByx83nOaT+8fIBmUhhBAmq0LdwK6UAn44k8/ulIJ294DFDnBh1hBvZkd5E+LZ/5t3ZxbXsP1sPjtS8jl8ocRo77ftZwr43ezWy9xGBLkxIsh4lWxxHRrrIfMQpH0PaVuv3bTbK0pJ3sJmwoAxfXv2rbEeshKuNCTfoRRdmf9G6zGDpsLuvyvfW9hA4LgrlTWnwto7ezRcSeZMhbWxZK7t9fH9hVar42B6MeND3I2uYXexs+IvC6P45WcnADhwvpi1x7JZPrLt/XZCCCFEf1NQoeaHM/n8cCafQ+lFNGiM939r2v82O0qZgfN1tu3hSLvHv3af5+sTOaTlV7U5xsrcjHEh7swY4o1Wq5O9b12tuliZjUrdorQOaO99ppUjDJoMYbOU2Tdn/56Ls7N0Oii50Jy8XdwHDS1adKjLlTEtPwjxHwGTfqsUZRkwFix7b6muJHOmwmgy179n5tYezeKdvRc4X1DFv+8azi0xvkbHzYvx5evBuWw/mw/Ay1tSmBPt0+/X/wshhLi5XSis4oczSgGTE5llbY6ztTRnSoQns6K8mRbhjbOd6f37l5xbYTSRc7O3YlqkFzMGezExzFN6v3UlnQ4Kziozb2nfQ/YR0LVdBRWvIUriFjZLmX2zsOq5WDtLXQ4X9ypJ6fkd7VfVLMtUkj33FnUZzC1g+jPdH2cHyE+8qTC2Z05TrzQQt7h2MZG+aMfZAs4XKH+4X9+WxqwoH8yNfMqmUqn46+IoDpwvorZBQ3F1Pf/YcY6n5w3p6ZCFEEKI66bT6UjKqWBrch4/JOdzrqDtWShXO0tmDPZmdpQPE8P6/x4wdYOGvWmFfJ+cz/gQd5aOCGh1ftYQb749lQdAmJcD0wd7M3OIF3EDXI2+NxDXqbFOaR3QtHyyLLPtsebWyuxb+BwlgXPpR6uidvwVfvxv2+ctbGHgBKUtQuj0Pl1VU5I5U2FszxyAugIc+mf/tcdnhvP9mcvodHCuoIpNJ3NZPMz4NL2vsy2PTA3htR/SAPjgQAZ3jA4kxLPn+nwIIYQQnaXV6jiRVcbWpDy2JF0mu7TtXrH+LrbMjvJhVpRpFDApr2lgZ2o+3yflsyetkNoGZe9ffoXaIJmbGunF03MHM3OINwM9pNBZl6oqUHq+pW6B9F2tlxhezcEHwmdDxC1KCwGrPvzfoiJPmXmzsoOoW1ufC51umMx5DVHaIYROh8Dxvbp0sjMkmTMVxmbmQFnP3E+TuQgfRxbG+vFNYi4A/7c9jXlDfbFs4x+vByYN4vMjWeSU1dKo1fHit2d5f+WongxZCCGEuCaNVsfRjBK2JF1ma9JlLle0Xfkv0sdRn8AN8XXq9wVMLper2XbmMt8n55NwodhoAZOEC8WU1zS0Wi7qZGPJg/F9d3akX2laPpn6rZLA5Rxrf7zfMGX2LXwO+Ma23jvWl2gaIeeokpie+wEun1aO+w03TOYGTlQS04ETlAQuZFrfr6rZBknmTIWxPXPQ74ug/Hp6GJtP5aHR6sgoruGr49ncPirQ6FgbS3OenjeYhz89DsDOlAJ2pRYwNcKrJ0MWQgghDDRotBy+UMJ3SXn8kHy5zRYCKhWMCHRldpQPs6N8CHQ3jQbe3yTm8MGBDBKzytoc42BtwZQIT2ZH+WBt2b9nHfscrQayDkPKt8pXe73fLO2Uao3hs5UvR5+ei7OzqgqVoiXnflD2vqnLDMfknlCKt9i7Nx+zdoTfpPTdxLQTJJkzFW3OzPXvIiiDPB1YOtyfL48qveTe2nGeJcMD2pyduyXah9HBbhy5WALAs98k8f1j8dhZyY+6EEKInlXXqOHg+WK2JOXxw5l8ymoajI4zU8GYYHduiVESOG+n/rG8qzPyK9RGEzl3eytmDlH2/o0Pdcfaon/v/etT6mvgwi5I+Q7StkBNcdtjnQIg4srs28BJfX+JYfIGOPgPyDkOGK/sCoBLkLJs0ljfOxNI5ECSOdNhbql8ktLUhd7RB+w8wKz//yd+dHoYG07k0KDRkVNWy8bEXIO19E1UKhV/WRDFgrf3o9HqyCqp5fUf0vjTfCmGIoQQovupGzTsSStka9Jltp/Jp7Ku0eg4CzMV40M9uCXah1lDvHF36J/FyprodDpOZpez5XQe1pbmPDEzvNX52VE+/O27FAAGuNkye4gPs6N9GB4oBUy6VHWxUrgk5Vtlv1hj23sw8RsGEfOU/W/eUX03uTHWtLuuyvjyUDNLZelk2Czlyz20776uLtL/3+mLZtaOzcncbR9B4NjejaeLBLjasTjOn7XHlNm5/+xJ59Zh/m32jxni58TP4wfxr91KA8v3D1xk3lBfhgW69ljMQgghbh7VdY3sSi1gS9JldqUUUNNGE28rCzPiwzyYE+3LzMH9v4WAUryllO9OX2bL6Txyy5XZD1c7Sx6dFtqqQEuQuz1/WTCE0cHuDPZ17Pd7//qUkotK8pb6ndLIu632AWYWyqxb5DyImNt3e7/pdJCf3Lz3zc4d7vi09ZjQGc3fO/opzcjDZytFWdparWaiJJkzJdZOUKX0Wuvvyyuv9vPJg1h3PFtf2XJHSgEzh3i3Of7R6WFsTbrMhaJqtDr4/frTbPrVRKwsZA2+EEKIG1ehbmDH2Xy2nL7MnrRC6hqNv4G2sTRjaoQXc6J9mBbphWM/74HasnjLlqQ88ivqDMaU1jSQcKGEiWEerY6vnBDcU2GaNp0O8hKv7H/7DgqS2x5r5agkOpHzlATI1qWnouycuiq4sPtKArcNKnObz1nYQoO69dJPJ19Y8Cb4j+zbs4o9QJK5Fg4ePMgLL7xAQkIC9fX1DBkyhEceeYR77rmn0/cqLS3lpZde4ptvvuHSJaUR4aBBg7j11lt58skncXJqo2DJjWj5SYS6vOvv34tCvRyZNcSb75OVZPXdventJnM2lua8tHQoy985BEBqfiX/3p3Or2eE9Ui8QgghTE95bQPbz+Tz7ek89p0rpEFjfK+OvZU50wd7c0u0D5MjPE1i3/b5gio+OpjB1uTLFFYaJnCgvJ8ePdCNuTG+RPreXLMj3U6rVQqYnN2kfJW30//N0VdZOhk5T5mJ66v9hsuylCWhqVuU3nYa40WBaKyFS/tbz8YBjFjZ7SH2B/3/r0sX2bBhA7fddhtarZb4+Hg8PDzYsWMHK1eu5OTJk7z++usdvldhYSHjxo0jPT0dPz8/5syZQ2NjI4cOHeLFF19k/fr1HDp0CBcXl659ES17zZnYzBzALyaHsDu1kOUjB/DgpGuXJx4d7MaKsYF8kqD8wXt71zluifEh3Fv+gRFCCNExleoGtp/N59tTeexNK6JeY3wGzsnGgplDfLgl2jSaeF+tsLKOjxMuGRw3U8G4EHduifZlVpQ3Xo59vHBGf6JpVJKYMxshZXPz6itjPCOvLJ+cp+yFu3qPWV/z42r49jftDFCB//Are99mgu+wHgutv+m2ZK6hoYHU1FQKCwspLy/H2dkZT09PIiIisLTsW0sMSktLuffee9FoNKxfv54lS5YAkJ+fz8SJE3njjTdYsGABU6dO7dD9/v73v5Oens6tt97K559/jrW18olIZWUlc+fOZf/+/bzxxhs899xzXftCWs7MVeRA3ilAp/QEMQHDAl05/MfpuNhZdfiap+ZEsuNsAXnlaho0Op5af4p1vxgvm62FEEK0qaqukR1n89l8Ko89aYXUt7GE0s3eitlR3syJ9mXcIPd+v5S/vlHLwfQijl8q5YlZEa3OjQ52w93eiuLqen3xlrnRPsw0geItfUpjHVzYA2e/UZZQ1pa0MVAFA8YoCVzkPHAP6dEwO6y+BkougE906+MDxhiOtXFWZt/CZkHI9H7bJ7mndWkyV1hYyIcffsi3337LkSNHqKsznIa3sbFh9OjRzJs3j3vuuQdPz97/D7V69WrKy8tZtGiRPpED8Pb25pVXXmHJkiW8/vrrHU7m9u7dC8BTTz2lT+QAHB0d+c1vfsP+/fv58ccfu/ZFAFg7twjiVeXLfyQ8uKPrn6uXdCaRA3C0seTFW6O578OjAJzILOOjgxncN1HW7QshhGhWXdfIjpQCvj2Vy67UthM4d3sr5kT7MC/Gl9HBbq2KfPRHDRot+88XsflkHtvOXKZCrVTfXDzMn0GeDvpx5mYqHpsRhrWlObOGeHf632PRjvoaOL8dzm6EtO/b7hGsMleaXQ9ZCJHz+27/t4o8Zflk2lZlH5ytKzxxtvW+Nu9opR2ChbWyJDTiFhgwFsxl0WBndcn/Y+fOnePZZ59lw4YN1Ncr6109PDwYMWIEbm5uODk5UV5eTmlpKSkpKezZs4c9e/bwpz/9iSVLlvD8888TGhraFaFcl82bNwOwbNkyg3Pz5s3DxsaG7du3o1arsbG59vKBlglcW9zc3Dof6DWf2MjywerCrn+efmZapDeL4vz4JlHZTPvq96nMHOLNADfTaMQqhBDi+tTUN7IzpYBvT+WxM6WgzSImrnaWzIn2Zf5QX8aYQAKn0epIuFDMppO5bE2+bLT/3ZakyzwytfV7s5+OG9hDEd4E1BVKsY8z3yiJXFM18quZWykNvAcvUGbg7Lrh/eON0ung8ilI3ar0s8s90fp8ZZ5SsMWvxVJJlQp+sa9vvp5+5oaTuV/96le8++67aDQapk6dyp133smUKVMIDm575uPChQvs2rWLzz77jC+//JL169fzs5/9jH/84x83Gs51OXXqFADDhw83OGdlZUV0dDRHjx4lNTWV2NhrL1mcOXMmBw8e5OWXXzZYZvnaa68BXFdRlWuyMVJUxYSTOa1Wx3dJeQS42hE3wKXdsc/OH8K+c0WUVNdT26DhuU1nWH3PyJ4JVAghRJ9RW69hV6qSwO1IyUfdYDyBc7GzZE6UD/OG+jJ2kDuW/TyB02p1HL1UyuZTuXx3Oo+iKuPFJqwtzJgS4Um0v7PR8+IG1JYp7QPOfKP0gGur4IeFLYTNgMGLIHyWsvywr9E0wKUDzRU1K7LbHmvlqLRP8Ltq35skcl3ihpO59957j4ceeognn3wSPz+/Dl0zaNAgBg0axP33309OTg6vvPIKq1ev7pVkrqKigrKyMgACAow3og4ICODo0aNkZmZ2KJn77W9/y65du9iwYQODBg1izJgxNDY2cvDgQczNzXnvvfeYOXNmV74MhbGZuYYaqK8GK/uuf75elHChmL9sTCblciVjB7nxv5+Na3e8u4M1z8wfzONfnARg+9l8dqUUMDXSqyfCFUII0YvUDRp2pxaw+VQeO84WUNtgvA+ck40Fs68kcBNCPfp9AtdSeW0Dd/43gUatYQVOKwszpkV4MW+oL9MivbC3lqVuXUZdoVRrTN4A6TvaTuCsHJU+aUMWKvvG+vr7tvRd8NltbZ93CVKWTobPgaAJYCHLcrvLDf+2XrhwAR+f61+z6+/vz5tvvskf/vCHGw3lulRVVem/t7MzvuzO3t7eYGx7HBwc2Lp1Kw8++CCffvopGzZs0J9buHAhI0aM6FSMUVFRRo+np6cTEtJiw6t1G+0Oqgv7/h+FTtLqdKRcVip2Jlwo4WB6EeNDPNq9ZnGcP58fyeLIRWUz8XObkhkf6o61hWlVHBNCCKEkcHvTCvn2dB7bz+RT3UYjb0cbC2YN8WH+lQSuvxcx0el0nMmrINjDvlVLBFd7KyaGebA7VVmxY2muIj7Mk/mxvswY7N3v+9/1KXWVyt635A1KzzSN8VYO2Loq1SeHLIRBU/pmC4HqIqUR+eAFrY8Hx4OVA9Q3vTdWQcAoiJijNCT3jLype7/1pBtO5m4kkeuq+yxbtoykpKROXbNmzRpGjx6NTme8R0xLHRnTUmZmJvPmzSMvL481a9YwZ84cALZs2cLjjz/OxIkT2bZtG2PHju3Ufa+p5cycyhx0V/7hqi4C14Fd+1y9bHyIB2MHuZFwQUnMXv8hjXG/cEfVzh8OlUrFcwujmP+P/Wi0OjKKa1i976LBngAhhBD9U1Mxj02JufxwJp+qukaj4xytLZg5xJt5Q32ZGOZhEh/qncuvZNOpPDafyuVCYTVv3hHHojj/VmMWx/mj0epYMNSP2VE+ONtJAtdl6quvJHBfKQlco9r4ODt3GLwQhixSipmY98H/BqUZV5ZPfqskcjqtUsDEqcUKPEsbJcGrLVX28oXfItUne0m3zaOXlJRw+fJltFot3t7e3Vq1MiMjg9TU1E5dU1OjbDR1dHRsdcxYM++msQ4ODgbnjLnnnntISkri66+/ZtGiRfrjd999Nw4ODixdupQnnniCgwcPduh+ycnJRo8bzNi13DOnMmuRzJnmvrknZkbom4IfvVTKvnNFxIe3/3M22NeJn44N4sODGQD8Y+c5FsX5EeAqxVCEEKI/0mh1HLlYwqZTuWw5nUepkWIeoDTyVhI4PyaZSB+4jKJqNp/KZfOpPP1qlSabTuYZJnPD/Fk8rPUxcQPqa+D8Nkj6SknkGmuNj7N1VRK4qFuVJt59rWKjTgeXT19J4DZDvpEJkpRvYfSDrY8t/rfMvvUBXf7TdOutt3Lw4EGKiopaHffz82PGjBncddddzJgxo42rr8/Ro0ev+1onJyecnZ0pLy8nOzubIUOGGIzJzlY2dQYGBl7zfllZWezevRtra2sWLFhgcH7RokVYW1uTkJDQ4eqYHdbWMsuqgq57jj5kdLAbk8I82HdO+VlbtS2NSWEe7c7OATw+M5zNp3IpqqpH3aDlqfWn+Pi+MZhJ7zkhhOgXdDodiVllbDqpzEQVVBpfxmZnZc6MwcoM3ORwT5NI4C6Xq9l0MpeNJ3M5nVPe5rhKdQNarU7+betqDWql+mTyV0r1xoZq4+NsnJWZq6hbIXhy35uB02ogM0FJ3lI2Q1lm22Md26iJIYlcn9Dlydw333wDgLm5OU5OTmi1WsrLy8nJyeGjjz5izZo1jBs3jjVr1jBo0KCufvrrEhsby969ezl+/LhBMtfQ0EBSUhLW1tZERES0cYdmTYmfvb09ZmaG6+7Nzc2xs7Ojrq6OsrKyLlumCrRO5nQt9gaY6MwcKIlZUzJ3MquMnSkFTB/s3e41zraWPD2vuRjKgfPFfJxwiXvGD+zucIUQQlwn3ZW90ptO5rLpVC5ZJcZnQZqKeSyM82NqhBe2Vv0/gWvyu7UnWXc8m7Z2f0T7OzF/qB/zYnyl/U5X0jQoBT9Or1WKmdRXGh9n7awsOYy69coeuD5c9KO6ED6cB7Txw+QZ2dyQ3G+4JG59WJcnc+vWrWP48OEEBgbqk5na2lpOnjzJ999/z5o1azh48CDx8fEcOXKkwxUwu9O8efPYu3cv69atY8WKFa3Obd68GbVazdy5czs0i9aUnJWUlHDx4kWDFg3p6emUlpZib2+Ph0f7BTs6reUyS12LUsvVRYZjTcTwQFemRniy68qG7te3pTEt0uuas3OL4/zZmnSZ75PzAfj7lrNMDPMgxLNjS2mFEEL0jItF1UoCdzKXcwXGC5GZm6mYFObBgqF+zIoy3WIevs42BolcuLcDC4b6MT/Wj2AP0yp21qu0Wsg6rCRwyRugtsT4OCtHiJyrJHAh0/peEZPGeri4R1neadnifayjDwwYA1kJzccCRl9J4OaDh9QT6C9Uus5W97hBGo2GZ555hpdeeon77ruP1atX9+TTG1VSUkJwcDAVFRWsX7+eJUuWAFBQUMCECRM4f/4827dvZ/r06a2ui4yMBGDHjh34+zevQY+NjeXUqVPMnDmTtWvX4uys9AcpKytj2bJl7Nixg7vuuotPPvnkhuJu2jOn31NXVwl/N9JeIXoZLHvvhp6rLzudXc6Ct/frH/9nxQjmRF97xrO4qo7Z/7dX32sndoAL638xrt83gxVCiP4ut6yWzady2XQyr82lhCoVjAl2Y0GsH7dE++Jm34dnQTqoqZn3N4k5BLja8ej0sFbnzxdUMeP1PQxws2VRrD8LYv2I8DHSlkhcH51O2S92eh0krYfyLOPjLO2VsvvRSyBkeuskqS9oqFX62J35RlkKWlcOd3yuJJ0tHfmv0vdu8AKlAqVjF64Wu4kZvD/vZj2ezDUZN24cGRkZ5OXl9cbTG1i/fj3Lly9Hp9MxefJkPDw82L59O2VlZTz66KO8+eabBtc0zf5cvHiRgQMH6o8fPnyYGTNmUFVVhYeHB2PGjAEgISGB4uJiBg4cyIEDB254VtLgh0Wng+dc0U+ZT35KmSb3CAOfmBt6rr7uZ2uO8sMZZZYt0seR7x6d1KF9Aj8kX+ZnHx/TP/7NzHB+ddU/nkIIIbpfUVUdW07nsfFkLj9mlLY5LnaACwtjlaWEPs597E30ddDpdCTlVPBNYg6bTuWSX6Hs//N1tuHAU9MM/i1Lyiknys/pmitQRCeUXISkdUoSV5hifIy5tdIHLmYZhM0CS9uejfFa6quVKppnvoFzP7RoGXBF7E/g1v/0Tmw3mZ5O5nqtnI67u3un2wl0p6VLl7J3715eeOEFEhISqK+vZ/DgwTzyyCPce++9nbrXmDFjSExM5OWXX2bHjh1s374dMzMzgoODefDBB/nd736Hm1s3dL1XqZR9c3VXPsWMmAt+cV3/PH3Q4zPD9clcemEVybkVxAQ4X/O6WVE+LBsRwLpjyl7HN3ecY2qkF9H+175WCCHEjSmvbeD75MtsOpnLgfNFGOlnDSgf0i2I9WPBUD8C3U1jL9il4mq+Sczl68QcLhQaFtHIK1dz+GIJ40LcWx2Xf5+6SFUBJH+tLKPMPmJ8jMpMKV4ScxsMnq8UNelL1BVKFc2z38C57W1X07SwVVpWCZPU7clcYmIiL730EuHh4Xh6eqLRaDh06BDfffcdU6dO7e6n75QJEyawZcuWDo9vb1IzJCSEd999tyvC6hybFslcXRsbdE3QYF8nFsb6YWdlziNTQzu18fvZBUM4lF5MTlktjVodj3+RyKZfTTSJqmdCCNHX1NQ3sv1sAZtO5rIntZB6jdbouCB3OxbG+rEg1o9wb9NYSlhcVcemk7l8nZhLYlaZ0TEWZiriwz1ZFOdH7IA+ljz0d+oKpXLj6bVwYU/rYnEtBYxSErghi8Gx/aJqveq/U6H4vPFzVg7KDOKQRRA2E6xkP6Wp6vZkrqqqii+//BJoXpYYHR3NHXfcwd///vfufvqbT8vG4XUVvRdHL3jzjrjrWnbiZGPJq7cN5c7/HgbgXEEVq35I5el5hm0qhBBCdF5do4Y9qYVsOpXH9jP51DYYfxPt42TDglhfFsT6EePvbHJLCY9dKuUvm84YPTcyyJVFw/yZF2Ma+//6jMZ6Zdnh6S+v9IJro5m3RwQMvQ2il4Jb36i2rldXBdoGpV9dS2GzWydz1s7KXr4hC5ViLH1tKajoFt2ezE2cOJG8vDwOHDjA5s2bWb9+PTU1NSxfvpygoKDufvqbT8v2BDfRzBxwQ//ojw/x4L4Jwbx/4CIAq/dfZP5QP2IHuHRRdEIIcXNp1Gg5dKGYjYm5bE2+TKW60eg4N3sr5sb4sDDWn5FBribRF02j1XHGyHL/yRGeONlYUHHl/4twbwcWxfmzMNZPWgl0JZ0Oco7Dyc+VQiZtVaJ0CoCYpcosnHd03yq/X1+jJKHJX0HaDzD+VzDt6dZjhixUXmPkPGUGLnhy326HILpFjxdAqaio4De/+Q0ffPAB69atY/HixT359CbF6AbLT5bB+W3K98FTwN5D6SUy/w1wD+nxGPsTdYOGeW/tI/3K3oXRA9344udjTe6TYSGE6C5arY5jmaVsTMzlu9N5FFfXGx3naG3B7GgfFsT6MSHE3WSqCKflV/LV8Ry+PpFDQaWaQ3+YjrdT6yItr/+QSp1Gy6JYfwb7Osq/MV2pLBNOfQEn/9f28kNbV6WNQMxtMGAsGOkJ3Gvaa0juHga//LF1wqnVKq2ozHutBIYwot8XQBk5ciRvvfUW48ePN3reycmJ//73vyQnJ/P8889LMtfVWvaayz3evNSyIuemSuZ0Oh3fJ+fz2ZFM/nv3CKwtrr3/zcbSnOcWRrPiPWW55ZGMEr5Pzu9QmwMhhLhZNVVj3HQql80nc8ktN76MzcbSjBmDvVkQ68fkcE+T2ZdcVFXHxsRcvjqRTVJO6+0N3yTm8LP41v/2PjEroifDM33qCqWC46kvIGOf8TEWNsrsVczyK73g+tDsVWO90kYg+StI+a7thuTVBVCZB04tKqGbmQF9KBkVvaLLk7njx48zadIkZs6cyW9+8xtmzpxpdJyLiwt79uzp6qcXLffMWdg0J3PVhb0TTy/QaHX85N0EjmQoyyo+Scjk/onB17hKMTHMo1UT8pe2nGVapBdWFvLHUgghWjqXX6k08z6Vx8Uiw2qMAJbmKiaHe7Ig1o8Zg72xtzaNGQR1g4btZ/P56ngOe9IK0Rgpw2lhptK3GRBdTNMIF3YrSwxTNre9Dy5oIsTeoSxH7GuVKAEOvwu7XgC18V6KzQ3Jl/S9JFT0GV3+V/XAgQP84he/4IcffmDbtm34+voyY8YMhg8fTkBAAFqtlp07d/L999/Lnrnu0HLPnHmLX/rqop6PpZeYm6kY7OuoT+b+ues8y0cG4Ghj2aHr/zh3MHvPFaHR6sgoruGThEvc18FkUAghTFlWSQ0bT+ay6WQuKZeNzyCYqZR9yAtifZkT5YuzXcf+9vYHGq2OP32dxOZTuW3uARwa4MySYUpDb3cH6x6O0MRdPq0soTy9FqryjY9xD1USuJjl4NrH32fauhomcpb2EDFHSeBCZ/S9huSiz+nyZG7cuHEkJibyySef8NJLL3H27FnWrFnDxx9/rB/TtE3vl7/8ZVc/vWiZzJm1WMJSVdDzsfSiX04LY+2xbGrqNZRU17N630UenxneoWvDvB25Y9QAPj2cCcBbO8+xdHiASb0hEUKIjsqvUPPtKaWZd1vl9EGpxrgg1o+5Mb54OppmEmNupuJ8QaVBIufrbMPiYf4sGeZPmIm0UegzKi8rydvJ/0F+G/2JbV2VKpSxPwH/EX2nkIlOp8R86kulKN2C/2t9PmKOsooKFYTPUhK4sFlgJcVwRMd1y3oHlUrFT3/6U376059y+PBhvv/+exITE8nKyqKhoYHAwEDuuusubr/99u54+ptbyz1zLf+Y3UTLLAE8Ha15YGIwb+1UNkCv3neBn44LwqODn5I+PjOcbxJzqaprpKymgX/sPMef5kurAiHEzaG0up4tSUoz74SLxbRVKi3a34kFQ/2YH+uHv4vplEEvq6ln86k8Bvs6MSKodTn4JcMD+DGjFDsrc26J9mXJcH/GDnLH3ASqcPYZjfWQtgVOfKIUBNEZ6UVoZgnhs5UELmxW31qCWHIRktbB6XVQmKIcM7OA6c+CnVvzOGtH+OkG8BkK1g69E6vo97p98fqYMWMYM2ZMdz+NaNJyz1zLf31vomWWTR6MH8THCZcorWmgul7D2zvP85eFUR261sPBmoemhPDq96kAfHQog5+OCyLIXZpuCiFMU1VdIz8kKwncvnNFNBrZBwYQ4mnPwlh/5sf6EuJpOm9AGzVa9qQVsu5YNjvOFlCv0bIw1s8gmZsb44uNpRmzo3ywszKNPYB9xuUkJYE79UXb7QQCRinLKKOWtE6MeltVISRvUGYRs48Yntc2wpmvYeR9rY8HGS8YKERH3fBfoUcffZTRo0ezbNkybGxkXW+va7nMUttiGchNNjMH4GhjySNTQ3nh27MAfHr4EvdPDO5wL5/7Jwbz2eFMcspqadDoeHlrCv+6a0R3hiyEED1K3aBhV0oBG0/msjOlgLpGIzMgQICrLQti/Vgw1M/kyumn5Vey7lg2Xx3PoaiqdcGS75MvU6luaLXn2tnWkluHBfR0mKartlSZwTrxCeQlGh/jHAixt8PQO8AjtEfDa1ddFZzdpCRwF3aDTmN8XMAoZQ9f5IIeDU/cHG44mXv77bdRqVSMHj2a8PBwZs+eTVxcHEOHDiUuLo7IyEjMzU2j/HC/0HJmrrFFf5+bMJkDWDE2iPf3XyS3XE2DRscb29J4/fa4Dl1rY2nO72ZH8NgXiQB8d/oyRzNKGDmwD30SKIQQndSg0bL/XBGbTubyw5l8quqMF/LwdLRmXowvC+P8GDbAxaQSuPKaBjaezGHdsWxOZhuvJOjhYMWiOH/q20hwxQ3QapTkJ/FTOLsZNEaqflrYKI2wh61QqlL2pX5wTSpy4OtfGD/nEQFDb4PoZeAmRdRE97nhZG7Tpk38+OOPODsrJV+3bdvGtm3b9H/0raysGDJkCHFxccTGxuq/XFxcbvSphTEt98w11jZ/fxMuswQlIXtsZjhPrjsFwIbEHH42eRCRPk7XuFKxMNaP9w9c5NSVf+z/uvkM6x8abzINboUQNweNVsfhi8VsOpnHlqQ8ymoajI5ztrVkbowPC4b6McZE94F9+WMWf/omyWiSZmmuYuYQb5aNCCA+zFP+1ne1kouQ+JnyVZFtfIz/CCWBi17ad9oJaLXK0smA0a2TSs8IZb/bZeU9Bk7+StxDl4N3dN8pxCJMmkqna2tb8/W5dOkSiYmJJCYmcvLkSRITE8nIyGh+wis/2AMGDCAuLo6vv/66K5/+pmK0w3xxOvxj+JUHZkCLf6yevgyWprNBvaM0Wh1z/m8v5wqqAJgx2IvV94zq8PVHLpaw/J1D+scrxw/s8N47IYToLTqdjsSsMjaezOXbU3kUVBrveWZvZc6sKB8WxPoyMdTT5PtqnswqY9E/D7Q6Fu3vxG0jBrAw1g9X+z5USMMU1NfA2Y3KMsq2mnrbeSj74IatAK/BPRtfe4rTlSqap/4HZZmw8jsYOKH1mKMfQN5JiLkNAsf1zRlE0aOMvj/vRl2+czcoKIigoCAWLVqkP1ZRUaFP7JqSvOTkZDZt2tTVTy9a7plDqyRv9p7KV331TZnMmZup+O3sCH7+8TEszFT4ONvQqNF2+BPX0cFuLBnmz1cncgD48GAGYd4O3DWmj/evEULcdHQ6HSmXm5p555JVUmt0nJWFGdMivFgY58fUCC9srUxrO0SFuoFvT+Xx48USVi2PbbVEdGiAM+HeDhRX1bN4mD/LRgQw2LdjqzVEB+l0kHMMTnwMp9dDvZGehCpzpRpl3F3K/5r3kfY/NSVKIZOT/zMsZHLyc8NkbuS9PRebEEb0SBkmJycnJk2axKRJk/THNBoNKSkpPfH0Nxfrq/rb/OoEOPn2Tix9yKwh3vxqWihLhwcw0KPzFSlfvDWGcwVVnM5Rllv++ZtkBnk4MC7EvatDFUKITrtYVK0kcCdz9asQrmZhpmJimAcLhvoxK8q7VVEPU6DV6jiYXsy6Y1lsTb6MukFZmXLP+IHEDnDRj1OpVKy+exS+LjZYyjLKrlVbpvRUO/5R2z3hPMKVGbihd4Cjd4+G16bGeji/TUnW0r4HTb3hGJWZ0itOiD6m12rqmpub66chRReytAFzq+Y/RHWVgCRzKpWK38yKuO7rba3M+e/dI1n49n4KKuto1Or41ecn2PfkVJP7RFsI0T9kldTw3ek8Np/K03/QdDWVCsYEu7Eg1o9bon1xM8ElhJeKq1l3LJv1x7LJLVcbnF97LKtVMgcQ6C5NmbuMTgdZR+DYh8qMVqOR2WArR4heoiRxAaP61l6yHc8rSyXbaoXgNUTpZRdzm3w4LvqkG07mUlJSiIyMvOFAuuo+AmV2rqZY+b6uondjMSE+zja8e/dIlr9ziPpGLUVVdaw7ns1Px8pySyFEz8gpq+W7U3lsPp3HyayyNsfFDXBhQawf82J88XE2vbZBVXWNfHc6j3VHszmSYfxNuIudJYvj/LltpLQR6Ba1pfD/7N13WFTX1sDh3wy9dwQURVHBCjbsxiQm0dhiYnoxMb2Ynpt+b25y86XdJDe9N1M1RZOYYoy9V7BXBAQEAekdZub7YwMzwwwIwgxtvc/Dwzlnn3Nmgwizzt57rd2LVBCXfdD6OT3HwfAbYOAscG6ndVpLsi0DOY8gVUog5ioIGdK+gk8h6mlxMDd48GCuvPJKHn/8cQYPHtzs6xMSEnjxxRf58ccfqaqynl1LNJOLtzGYK7f+tFaoKTknC8ro4df0J7Sx4b5cPSqcLzanAPDx+uNcE9ezU2Z8E0K0Dyfzy/h9bwa/7c0g/kR+g+dFh3jV1YLrzCNPFdU6Jr28mtwSy6lwWg1Mjgrm8hE9OG9AMC6OMnOiVRkMcGIz7PxCFcCuthwJxc0fYq+B4fMgqL/du2hVRZGqB+fiBQPq1XobehXsWqhKIURdrEbhIs8DBykILzqGFv+kPv3007z66qt89913xMTEcO2113LOOecQExODk5PlfPyKigri4+NZvXo133zzDQcOHMDDw4N//vOfLe2KqGW6bq68EIqzVBYmfTX0HNN2/WonDAYDa49k8/Kfhyksr2LVQ5Oblb3tlol9+HJLCnoDpJwu5a/9mUwbIlMvhBCtJ7OgvC6A25mS1+B5kUEeTB8axoyhofTv5tXgeZ2Ji6MDE/oG8svuk3XH+gZ7cvmIHswZ1p1g7843EtnmSnPVerKdn0POEevnREyEETeqYMnRxZ69s85ggJRNKovmgaVQVQqhMZbBXM+xMOcDiJrWfkohCNEMrVKaICsri+eff56FCxdSUFCARqPBycmJiIgI/Pz88PLyorCwkNzcXFJSUqiursZgMODj48NNN93E448/TlBQUGt8PV1Kg6lPP59hTP877AaIX6i2w4bBbWvs18F26mR+GZNeXk21Xv3oPz1jIDdPaF5Bz7u/2cVvezIANVq35K5xnaqgrhDC/k4VqgDu970ZbE9uOIDrE+jB9KGhTB8aSlQ3r075u6dap2ftkWwWbU/lokEhXDbCfKrkpmM53P7VTmbFhHH5yHBievh0yu9DmzIYIGWjCuAO/Gw9KYh7oHEULrCv3btoVUE67P4G4r+GvCTL9ru2tK/yB6LT6ZClCYKDg3njjTd48cUXWbx4McuWLWPjxo0cOWL59CYkJISJEycyffp0rrjiClxd5Qlaq3PzNW6bThPIS7F7V9qjMF83rooL56stJwB4c+VRLhveHV/3picGuH1Sn7pgLiE1nx0peYyK8LdJf4UQnVdWYTl/7MtUafRTcmno8WpEgDszhoZx8ZBQBoR2zgAOIDmnhMU7UvlxVxqnClVdvNMllRbB3Jg+AWx/cgquTjKNstWVnFbB0M7P4fQx6+f0maxG4aKmg2M7SKpTVQ6Hf1ejcImrACv/kVx9VEFvR3nfKTqXVp0Q7Obmxrx585g3bx4A2dnZZGVlUVBQgI+PD8HBwTICZw8eJt9jvUnR8LJcNW+8fvmCLuj+Kf35Of4kRRXVFJRV8cbKo/xrZtOzqw7t4cuYPv5sOa4WTb+16hhf3DSq077BEkK0npP5ZSzfn8kf+zLZntxwANfT350ZNSNwA0O9O+3vl7JKHX/sy2DR9lS2JlkmM9mZksexrGL6BnvWHdNqNbhqJZBrNQYDpO2A7R+rjJQ6KwXmPYJh2LUqoYl/H/v3sSFJ62HRdVCeb6VRA5HnqiyaUdNVxm8hOhmbru4MCgqS4K0tmAZzVWXmpQryT0A3KQkR6OnC3ef15cU/VK3DLzencN2YXkQGeZ7hSqPbJ0XWBXPrjmTz8vLDPDpVMrIKISwl5ZTw575M/tyXwe60hhNThfu7MX2IWgM3KKzzBnAGg4G96QUs2p7KL7tPUlRebXGOk4OGKQO6ccWocHqfRX1Q0QSVJbD3e9j+CWTusXKCRiUDGXGjWlPWXgp7mwoeqL4OU34REHudykbpG94m3RLCXiRVT2dkGsyV5oBPOOQmqv28FAnmatw4LoKvtqSQlldGtd7AC78f5ON5o5p8/Tn9g5jYL5D1R3MAeG9NImE+rlw/NsJGPRZCdBQGg4GDGUX8uT+T5fsyOXyq4WLD3X3d6kbghnTvGmu/vt2WyhNL9lpt6xfsyZWjwpkzrDsBnu0gkUZnlH0EdnwCCd9ChZWHCx7BMPx6NQrnF2H37lnQVavpk3u/h5lvgLNJtlaPABVoHvsbBs5Wo3A9x4FWCsKLrqHFwdz8+fOZMGEC8+fPb9Z1L730EsuXL2fVqlUt7YKozyPQuF2SDX69jMFcvqybq+Xq5MDj0wZw9ze7APj7YBZrj2RzTv+mjSZrtRreuXY4V7y/mUOZ6o3av37ZT5CXK1MHh9is30KI9kmvN5CQls/yfZn8uT+TlNOlDZ4bEeDO1MGhTB0c0iWTd0wZEMzTP2vQ1SSi8nB2YGZMGFeMCmdYuG+X+37Yha4KDv2mplLWJkmrr9cEGHUzRM9oH2vhTieqdXC7v4UitU6dvlMg5krz8y76P5j9Drh627+PQrSxFgdzn3/+OUCzg7lDhw6xdu3alr68sMZ0ZK4kB7qPMO7nn7B/f9qxi4eEMLKXHztqUn8/tXQvf91/Dm7OTVuL4e3qxGc3jWLOO5vILCxHb4B7vtnFG1cNY/pQKVcgRGdXrdOzLTmX5fsyWb7/FJmFVupu1YgO8WLq4BCmDQ6lfzfPTh+wpOeX8cOONFYeOsX3d4w1q/kW7O3KuVFB5JdWccWocKYPCcXDRSYL2URBOuz6QtWGK860bHf2UtMRR93cPrI8VleomnC7voCkdZbt8V9aBnMylVJ0YTb9zZmbm4u/v2T4szuzYC4bfHsa9yWjpRmNRsOzswcz8+0N6PQGUnPL+N/KIzw+rel/0EJ93Ph8/iguf38zReXVVOsNLPh2F2VVMcytl4FNCNHxFZVXse5IDn8fPMWqQ1kUlFU1eG5suC9TB4cwdVAIEV1g3VdFtY6/D2SxaEcq649m1yV3+ftAlsUDrneuHS5FvW3FYICktWoU7tDvYNBZntNtsArghlzePhKjZR9RAVzCNyphW31aRzWdctj19u+bEO2YzYK5//73vzz99NM88cQTPPnkk2hl7rL9mGWzrDLfl2mWFgaGeXPrxD68v1ZNRf1hRxoLzuuHZzOeEkeHePP1LaO54dNt5JdWoTfAw9/vpqxKx/Vjetmq60IIO0nPL2PlwVOsOHCKLcdPU6WznoJSq4G43v5MHRTCRYNDCPVxs3NP28bhzCIWbU9lSXwaeaWWwe3iHakWwZwEcjZQUQx7voNtH0H2Ict2B2cYeIkK4sJHQ3sZHf7xFrUezprAKLV2L+Yq82UkQgjABsGcTqfjjjvu4NNPP8VgMPDMM8+wfPlyvvrqKyIiIlr75YQ1rr6gcTA+iTN94pZ/Qj2xay+/wNuJ+87vx+97M4gN9+XpGQObFcjVGtrDl+9uG8N1H28jp1ildX566T7KK3XcOqkdpXEWQpyRwWBgX3ohKw6e4u8DpziQUdjguU4OGsb3DWTa4BCmDOjWZZJ2FJVX8evuDBbtSGV3ar7Vc3oFuHPFyHCZpWBrucdh28dqfZm1hCY+PWHkTWpUy7MdZhkPqFdw3NENBs2BEfPaV9ApRDvUqsFcbm4uc+fOZc2aNfTr149XX32Vp556ik2bNhETE8Obb75ZV4NO2JBWq55eFZ9S+w4mbywqCqEsD9xl+qspN2cHfr1nAj7uLUu7HB3izeLbx3Dtx1vJKFBrZ57//SDFFdXcc15fnBxkhFqI9qqkoprNiadZfTiLlQezGl3/5uvuxHlRwUwZ2I2J/QLxcm2HKdttyGAwMOvtjSTllFi0uTppuXhwKFeMCmd0b/9OvzawzRgMKsPjtg/hyHKsFsruOwXiblOf27ouX2UJ7PtJPWgecaN527DrYO1LEDxIBXBDLgc337bopRAdTqsFc0eOHGHGjBkcO3aM8847jx9++AFfX18uuugiHn/8cV5//XXmz5/PsmXL+PDDD1vrZUVDPIKMwZyuEqa+qEoU+PZsH3Pj26GWBnK1+gR5svj2sVz78VZO5Kpsdm+sPMp7axLpE+TBgFBvokK8iArxYkh3HwIbeIpfrdOz4VgO3bxdGRBqzNC1KTGHn+NPUl6tw9lBi5uzA+MiA5gyoBuOEiwK0WQGg4FjWcWsOZzNmiNZbE/Ko1Knb/D8iAB3LhjYjSkDujGil1+X/v+m0WiYNjiEd9ck1h0b2sOHK0aGMys2DO8uFtzaVUUR7P4Otn4Ap49atjt7qeLeo26FwL6W7fZ2MkGthdvzPVQWqbIHsdea16zz6QF3b4eASBmFE6KZNAaDwfrE/ybSarXExMRw4sQJ8vLyuP3223n77bdxcDB/ArRy5UrmzZtHRkYGoaGhBAcHs3v3bnQ6K4tyRZMMGqTqxe3fv9+yceFsOL5GbU9/Tc2PF812LKuI8io9g7v7NPvazIJyrv14C4nZlk+ua2k1MLFfEFeMDGfKwOC6NSS5JZXc8dVOtiWpReDjIgO4bkwvlsan89eBU1bv1d3XjevH9uKa0T3ljZQQDSipqGbjsRzWHMlm7eFs0vPLGjxXo4ERPf2YUhPARQZ5dKlRpmqdnjWHs1m0I5Uh3X249/x+Zu3JOSVc8u5GLontzhUjwxkYJmnhbep0oloLl/C1mmVTX0A/GH27WlvW1g9tK4rVGridn0NGgmX7lV/BgJn27pUQdtHo+3MbaJVgTqPRoNVqefXVV7n33nsbPDcvL49bb72Vn376qe4PogRzZ6/RHxbTxcSTn4DJj9qxZ53D3wdOcf+iBLxcHfnt3on4ezS/5k5OcQWP/biH1Yez6+opNcTX3YlLYrszoW8gzy47UDeq11zdvF14eW5Mk+vlCdGZGQwGjpwqZu2RLNYczmZ7cm6DyUsAvFwdmdgvkMlRwZwXHdzgyHlnlpRTwuIdqfy4M42sIrX+N9THlQ2PnoeD1jyYrdLpZfq4Len1airl1vfh2AorJ2ig34UqiOtzbtsXys46CNs/USOHlUWW7S7eagrl6DsgqL/9+yeEHXTIYM7b25tFixZx0UUXNemaTz/9lPvuu4/S0lIJ5lqg0R+WPx+HLe+q7VG3wvT/2rFnHd/J/DLOeWV13Zu+86OD+XjeyLN+Kl9RrSMxq4TDpwo5lFnEoYwiDmQUkl3zRqm5hvf05dyoYKp0ehJzSli+L5PqesHitaN78uT0Abg7S+0m0bWk55ex8VgOG4/lsCnx9Bn/nw0K8+ac/kFMjgpmeE/fLjl9sqxSxx/7Mli0PZWtSVbSwgNf3hzHxH7ykMguKktUoewt71ufSunirZKZxN0C/u0gwVb+CVhyB6RstN7eI06tkxt0CTh3/hIdomuzdzDX4nd5en3D6wsaMn/+/GYXGRfNZJq+tyS77frRQYX5uvHo1Gj+89tBAFYeyuKTDUncMvHs/mi6ODowMMzbbBqSXm9ga1Iu3+9M5fe9GZRXmf9f0mjg0anRxPTw5d01x1h/NIfuvm78Y2oUs2LCzALLzIJyvticzCfrk+rW/Hy99QT7Txby7a1jmlwEXYiOKK+kks3HT9cFcMmnGx/V9nZ1ZGL/ICb3D+Kc/kEEe7vaqaftS23Gzu+2n+CXhJMUVVRbnOPkoOGCgd24clRPxkVKWnibKzypEprs+AzK8y3bA6Ng9G0w9Cpw8bR79xrk2c2yFIKLN8RcrYK4bgPbpFtCdAUtHpkTbafRyH/XQvhlgdruNQFmvAZrX1Z15hxc4Kbf7NjTjslgMHDzFztYdSgLUG9qfrxzHEN7+Lb6axWVV7FsTwbf70hl14l8vFwdefXyGC4cFFJ3TkFpFV6ujmi1DY8OHsos5IFFuzlokkb9woHdeO+6ERbTo4ToqLKLKtienMu2pFy2JuVyKLOQxv6SaTQwOMynZvQtiNjwrjn6Vt/fB05xy8IdVtv6d/PkipHhzBnWvcuUWmhTJ+Nh87uw/yfQ1w+qNdB/as1UysltmyBEr4fElRAaa1ni4O9nYMPrEDIURt0CQ+bKKJzokjrcNEvRdhr9YTn8B3x7ldoOjILLPoYPJqp9Rzd4MkMyRjVBbkklF7+xvi5FeXdfN765dTS9Amz3ByqrqBw3J4ezTnVeWa3n6aX7WLQjte7Y/PG9+edMeTIqOqa0vFK2JeXWfRy3kg6/vt6BHozvG8D4yEDGRgbg6978Na+dicFgsJgmXl6lY/T/raSgTBX59nB2YGZMGFeMCmdYuG+XSvbSJvQ6OPy7CuJObLJsd/JQKftH366yPLalkhxVw27Hp+qh8HlPw6SHzc8pPKk+uo+Q9xeiS+tw0yzr0+v1vPvuu/z4449kZGQQHBzM4MGDiY2NJTY2lqFDh+Lq2jWntNiVh8kTs5Js8Otl3K8uU8c8g+3frw7G38OZN66K5eqPtqA3qLU4c9/fzML5cWblAlpTsFfL/n84O2p5fs5gcoorWFkzqvjpxiR6B7pz/diIVuihELZTXqVj/8kC4k/kE5+aT3xKHicLGq73VivIy4XxkQGM6xvI+L6BdPd1s0Nv27+MgjJ+2JHGkvh0Ft0+liAv4yibq5MDc4Z1Z296AVeOCmf6kFA8XGSNrc1VFKnAaOv7kJds2e4TrmrDDb+hbWutGQyQulUlNDmwVJU5qrXzc5jwgHntOu8w9SGEsKtW/639zDPP8Pzzz1M74HfkyBE2bNhQ94RPq9XSr18/YmNjGTZsGI888khrd0GA+Zq5slz1hM/V1zgHPy9ZgrkmGt0ngH/NHMS/flFPWLKLKrjig818euMoRkW0z+Lrjg5a3rpmGFd+sIW96QWAKl4+OSqYcH/3Nu6dEIrBYCApp4SE1HwSUvOJP5HPwYxCi2Q+1oT6uBLX2199RPjTN9hTRpJqVFbrWXXoFN9tT2XdkWxqv50/7Urj9nPMR3iemj5AppzaS16KWg+3a6H10gI9RsGYu2DALHBow6C6ohj2LFJBXJaVkQWNFkJjoCwfPALs3j0hhLlWn2YZERFBRkYGX3/9NVOnTiU/P5+9e/eSkJBAQkICu3fv5tixY+j1ejQajWSzbIFGh3ErS+D/TJ6QPXQEvrsa0neq/dnvqOkbosm+35HKYz/trSsx4OSg4f3rRnD+gG5t3LOGZRWVc/EbG8gpVtn8LhjYjY9uGNnGvRJdUWW1nmNZxRzIKORgRiEHThZyIKOwborfmfQO9CAuwr8ugOvh5ybBWz3HsopYtD2Vn3alc7qk0qK9X7AnKx48pw161sWl7YBNb8LBX8FQL2mcxgEGzoIxd0P4qLbpX63cJBVsxn9lPdj0DIER82D4PPDpbv/+CdFBdPhplrm5uUydOpW5c+cC4OnpSY8ePZg2bVrdOaWlpezZs4fdu3e39suLWs4e4OQOVTVZ3Uqy1dq52mAu+3Db9a2DunxkOD5uTtzzbTyV1XqCvVwZ3tOvrbvVqGAvV56cHs0Di9T/tRUHTrHy4Kl2HYCKjs1gMJBVVEFiVjGHMlUJjgMnCzmaVdRofTdTbk4ODOnhw7BwX2LDfRnRy6/LZpw8k5KKan7bk8GiHansTMmzek64vxtXjAhn7sgedu5dF6bXw5E/VRB3YrNlu4sPjLgB4m4H33D798+a42uMJY1M9Z4EI2+G6OngcHZruYUQttPqwdyQIUPO+LTU3d2dMWPGMGbMmNZ+eWHKI1DVfgEVzAVFGdskmDsrFw4K4ZtbRvPA4gTeu3YEfmdRSNzeLontznfbjLWjnvl1P+P7BuLqJOUKxNmrqNaRcrqUxKxiErOLOZ5dQmJ2MYnZJRRbSXHfmL7BnsSG+zKspwreorp5ydS/Jrrlix1sPn7a4rizo5Zpg0O4cmQ4Y/oENJoFV7SiqnLY8x1sett6fTi/3jDmToi9Bly87N+/Wroqy8Bs6BUqI2V5viorEHstjJwvxb2FaOdaPZi7++67ueOOO8jJySEwUGrStCmPYJNgLsc8mMuRYO5sjYzwZ9VDk3Gy8mbzZH4ZYe0s8YJGo+G5SwZz8RvrqdYbSM0t47UVR3ji4gFt3TXRjpVV6kjPL1MfeWWk55dyMr+8ZruMjIIymrC0zYyTg4a+wV4MDFU1F2s/+7jJ0/6msJaRcmZMmFkwNzDUmytHhXNJbHd83OX7ajelubDjE9j6IZRkWbaHj4FxCyBqmnnSEHvLOaqmUu5fCvdsAzeT2SXOHjD5MdA6qvpw7amOnRCiQa0ezF1zzTX8/vvvzJ49m59++olu3WQ6V5upn9Gy+3Djfl4KVJWBU/sKPDoKa4Fcck4JM97awPQhofxr1kDcndtPVrj+3byYP6E3H647DsCH647TL9iTy0e2k+k97ZxOb6CkspqSCvVRXKGjslqtfam/7NhBq8HZUas+HLS4ODng7KD2XWqO2XOUxGAwUFGtp6SimtJKHSWV1eSVVJFbUkluSQU5xZU125WcLqngdHElOcUV5JU2bS1bQwI9nYkM8jQL2voFe+HsKCNuzVFZrWflwVN8vzMNP3dnXr0ixqx9Zkwob6w8wgUDu3HVqJ4M7u7TRj3tovKSVWmB+C+NyxrqaGDADBh3L4THtUXvFL0ejq2ArR+oGnG1dn0J4+81P3fMnfbtmxCixWzybvPf//43U6dOZciQIdxyyy3MnDmTYcOGSUkCezPNaFmSDX4RqmC4rgIwqCd0oUPbqnedSmW1ngXfxlNcUa3WrpzI4+1rhhEdYpvyBWfjvvP7sepQFseyigF4/Ke9deuQ/tqfSbXOwB2TI+kd2PWKvFZU6zh6qphjWeojJbeUzIIyMgrKOV1cSVlV6yZqcjQJ+JwctDhpNTg6aHF00OCkrfnsoMXJQYOjyb5Wo8FgMKAzGNDpDehrP+upO1ZZraesSlcXvJVWVjd7BK05X0evAHcigzyJDPakT6AHkcGeRAZ6yqhQC+1LL+CHnWn8nJBeF1i7Omn516yBeJvUoPRydWLTY+fjINMo7etkPGx8U6Xsr5/UxNFVTVEce3fb1ocrL4D4r2H7R5B73LJ97/eWwZwQosNp9WDuzz//5NJLL6WiogKDwcCLL77ISy+9hFarJSoqimHDhtWVJYiNjcXfv+1Tu5eUlPDTTz+xbds2tm7dyu7du6msrOSFF17gscceO+v7Llu2jFdeeYWEhAQMBkNdKYYZM2a0Yu8bUX9kTusAgf3g1D51LPuwBHOtpKCsyuzN1LGsYma/vZGnZwzk2tE920XWPQ8XRz67cRRz3t1ITnEl1XoD8z7dZnbO0oR07j2/H7dN6mN19LGzqKjWsfV4LhuO5bAzJY+9aQVU6vRnvrCVVOsNVFfqKK1s39l8HbUaQnxc6e7rRnc/N/W5ZruHnzs9/Nw69c+JvZ0uruDnhJN8vzONgxmW2QTLq/T8tf8Uc0eYJzKRQM5ODAY4ukIlNUleb9nu5q/qw8Xdav4w1d6yD6uplAnfQlWJZXvQABh9Gwy90v59E0K0ulYP5h5//HHKy8u5+OKLmTp1KgUFBezevZuEhAQOHjzIgQMH+OabbwC1lqe6unkL5W3h6NGj3HDDDa16zzfffJP77rsPR0dHpkyZgouLC3/99RczZ87kjTfe4N577fA0zCyYy1Gfw+PA2VOtn/Ptafs+dBFBXi58f8dYXv3rCO+vTQSgolrPU0v3sfFYDi9eNrRdrAsK93fn43mjuOrDzZRXWQYvFdV6Xll+mJ9rgrppg0M7zRvFap2eVYey+DnhJGuPZDc7SUctZwctHi4OuDg6UBujm36HqvVqWmNltZ5Knb6ulEV74eqkxdfNGX8PZwI8nQnwcMbfw8VkWx0P9XGjm7drp/n3b6+qdHrWHs7m+52prDqUZTXjp0tNMpPLR4Yzto/U9bI7XRXs+wk2/g+yDli2+/WGcfdAzDXg3MZ1PH+9TxX0rk+jhaiLYfTtEDER2sEDRiFE62j1YO7w4cPExMSwbNkyi7bi4uK6wC4+Pr7dlCbw8vLi5ptvJi4ujlGjRvHjjz/y/PPPn/X9jhw5wkMPPYSLiwurV69m7NixdcfHjRvHQw89xLRp0+jXr19rfQnW1R+ZA5jxum1fswtzctDy2LRoxkUG8ODiBHKKVZ2nP/ZlsietgA+uH9Eu1rPEhvvyvyuHcfc3u9DpDTg7apnUL5DdaQVkF6l6dEdOFXPPN/H0CjjMrRP7MHdED7Psl7XrxNrDiOOZZBdV8OWWFBZvTyWzsLzB85wdtUQGedK3Zrpgd183QnxcCfJywdPFEU8XRzxcHJu95qt26mNltZ4Knc64XfO5Wq+nSmegWmegSq+nWmegWqcCwWqdwaRdj84ADhrQajVoNRoctBocNBq0Wg0OWtBqNDg7aHF3ccTD2QE3Zwc8nB1xd3HA3dkRNycHCc7amQMnC7ll4Q6rbcN7+nL5yHCmDw01m1op7KSyFBK+ViNxtcnETHUfqaYpRs9o26QmpoIHme+7+qracCNvBr9ebdIlIYRttXowFxISwoAB1rPkeXp6Mn78eMaPH9/aL9sikZGRfPzxx3X7P//8c4vu98Ybb1BdXc3dd99dF8gB9O/fnyeffJIHH3yQN998k7feeqtFr3NG9dfMCbuY1D+I3++byIOLdrPhmBoRTc8v45qPtrDw5tHEhvu2bQeBqYNDWH7/RNLyyhgZ4Y+niyMFZVW89OchvtlqfNOScrqUp5bu439/H+Gm8b1xd3ZgxYFTbEvKpX83Lz64fgTh/m38JLoB+aWVvL/2OF9sSra65s3VScuEvkGMjQxgRC8/BoZ62yQ5h4NWg1tNYAXyhrwryy+txMfNyewhyNAePvQL9uRozVrWYC8XLh3eg7kjetA3WLIJtomyfLXObMv7UJpj2R51sUpq0nNM241wFWVCxh7of6H58dhrYPV/wCdcTfkccnnbjxYKIWyq1YO5OXPm8Msvv7T2bTuU2lHJ2sLppi6//HIefPBBfv31VzsEc1amWQq7CPZyZeH8ON5bm8hrK46g0xsoLK/muo+38vlNoxgZ0fZrRfsGe9E32FjnyMfNif+bM4TrRvfi/bWJLNtzsi5xRk5xJa8sNy9ncSCjkKs+3MLXt4wmwNOZt1cd47e9GYzpE8DTMwa22bRSnd7A55uS+d+KIxTVm0rp5KDhwkEhzIntzoR+UmtP2F5FtY5VB7P4KT6dNYez+P6OcWYPdDQaDdeM7sn25FwuHxHOxH6BUmOvrRRlwuZ3YMdnUFlk3qZ1VGvMxt9nXubH3jJ2q+yZ+35UiVYePACuJom2XDzh9vVqGUUHmDkhhGi5Vg/m/vnPf/LTTz/xwgsv8Pjjj7f27du9/Px8TpxQIxvDhg2zaO/RoweBgYGkpKRQUFCAj48Np92ZBnNVJVBZourICLvQajXcfW5f+gV7cvc3u6jSGajU6Vs9M2JrGxjmzZtXD+ORi6L4aP1xFm1PpaLaenKQ9PwyrvhgMwaom6L5w840thw/zdvXDLf7KGRidjH/+GEPO1PyzI4HeDhz88TeXDEynEBPF7v2SXQ9BoOBHSl5/LQrnd/2nKSw3PhQYcmuNIv/FzeN781N43vbuZeizulENZUy4RvQVZq3ObnD8HkqM6VvG5Vy0evhyJ+w5V3zxCuVVaokwti7zc+X6ZRCdCmtHsxddtllDBs2jKeeeoqDBw/y5JNPEhXVhk+x7Kw2kPPz88PDw3rg1KNHD3Jycjhx4gRDhgyxXWfc6y2UL8lRwdzpRMjcA9lHoO/50GOk7foguHBQCB9eP5IF38bz5tWxTOwXdOaL2oFwf3eenT2Ye8/vxxebkvltTwYOWg3nDQjGzcmB//19FICsmiDOVFpeGXPf28TlI3swuncAo3r7071eMXWDwUBRRXWrrQX6ZusJnvl1f139NwBvV0duPyeSG8dF4OHSfur+ic7peHYxS+LTWRKfTlpemdVzthzPtVr8W7SBjN2w4X/Wywu4+qpkIXG3g0cbJZ2pLFEB5pb3IDfRst0rDFy8LI8LIbqUVn93s2rVqrrtr776iq+//pq+ffsycuRIhg0bVleSICCgc2bkKi5W6x7c3Rueo14b5NWeeyaDBg2yejwxMZHIyEZq2Dg4qlTJZblqvyRHPbFb8U84VJOgRqOVYM4Ozo0OZsOj5+Lr7tzWXWm2QE8XHrowiocuNH8o083blSeW7KW2ZraLo5YLBnbj970Z6A0qq+O321L5dlsqABP7BfLPGQPpG+zJn/syef73g5zML2P++N48OX3AWb+5rdLpeW7ZARZuTjE7PismjGdmDcLfo+N9z0XHsmj7Cb7Zlsru1Hyr7W5ODlw0qBtzhvdgfGSABHJtyWCAlI2w4XU49rdlu1cojL1HJQ1pq0CpIB22faCyUpYXWLaHDVN9HDgbHGQdrhBdXasHc0lJSSQkJJCQkFCXufLo0aMcPXqUb7/9tu6PWPfu3Rk2bFiLk42AWpu2b9++Zl2zcOFC4uLiWvza9TUly1/tOXbhEWQM5opPqc9BUcZgLvuQ/frSxVkL5HR6A1lF5YT6uFm5on27Oq4nPm5OvLvmGP27efHgBf3p4efOtqRc7v023iJz5PqjOUx9Yz0DQr3Yl26sofXxhiQqdXr+PWtQs9/kFpRWcdc3O9l47HTdsUBPF56fM5iLBoW07AsUook2J562COQ0GhgfGcicYd25aHAInjIy3LYMBji2Eta9AqlbLNv9I2HC/WpdnGMbTsU2GGDhLDh9zPy4RgvR02HM3W2beEUI0e60+l+XXr160atXL2bPnl13rLCwsC6wqw3y9u/fb7V8wdlITk7m8OHDZz7RRGlpaau8dn1eXupJXkmJlUKd9V7b07Npmcr2799v9XhDI3ZmvEMhp+Z7U5CmPgdFG9uzm/d9E62nSqfnocW72Zp0msW3j6VXQMdbz3jxkFAuHhJqdiyutz8rHpzEH3sz2Zacy/bkXFJOq595nd5gFsjVWrg5Ba1Gw79mDmxyQFdQVsV1n2xlb7rxyfXQHj58eP1IQnxcW/BVCWGpSqdnw7EcHDQaJvU3nyo9Z3gPliacBCA6xIs5w7ozO7a7/By2B3o9HP5dBXEZCZbtoTEw4UEYMLN9lBfQaFQZgeU1OQecPWHY9WrKp7+sqxRCWLLLo0Jvb28mTpzIxIkT647pdDoOHWqdUaEdO6zX6GkLPXuqQtx5eXmUlJRYXTeXlpZmdq5N+UUYt/NrpqEF9jceO30U9Lr28UesCzEYDCz4Jp4/92cCcM1HW/n+jrGE+Xa8ETprvFyduGJUOFeMCsdgMLDiwCn+89tBTuQaH6JM7BdIYXl13YjG55uSqdTpeXbWoDNm8yuuqObGz7aZBXIzY8J4Ze5QyVApWo1Ob2BbUi6/7jnJH3szyCutYlhPX4tgbnxkAAvO68u0waEMDPNu4G7CrvQ62L8E1r9qvdB3xESY+CD0ObdtRrmqK2Dv91BVBnG3mrcNu07Vt4u5GoZfD65tX59UCNF+tTiYe+GFF4iJiSEmJobu3bs3+ToHB4emjSx1ML6+vvTs2ZMTJ04QHx/PhAkTzNrT0tLIycmhZ8+ets1kWdchk6xWecnqc2B/QAMYoLoccpMgsK/t+yLqaDQaJkcF1QVz6fllXPvxVj67cRQRgR1vhK4xGo0qBzCpfxBfbUlh14k8Zg4NY+rgEArLq7n+k63sSVNB2TdbT5CWV8Y71wzDq4HEKKWV1cz/bDvxJ/Lrjs0f35unZ5z9ujshahkMBnanFfBLwkl+23uSU4XmCX7iT+STmltqVl/R0UFrsaZUtBFdFexZDBtes5yqCNDvQpj4MPQcbf++gapht/MzVcOuOBNcfCDmKvP1ea7ecMcGmUophGiSFgdzTz75ZN0bKH9//7rArvZj0KBBODp2rbUC06dP57333uOHH36wCOa+//57AGbMmGGfzpiOzNUGc87uENgPco6o/RObJZhrA1fF9aSsSse/f1VPjZNySpj+5nqeu2Qwlw7v0ca9a32uTg7cMrGP2TEfNye+nD+aeZ9tI6FmhG7dkWwuf38zC+fHEextPk3NYDDw0OLdbEvOrTt2/ZheEsiJFjEYDBw+VcSvu0/y6+4MsxFkU86OWs6NCqK8nZcX6ZKqKyD+K5WdsuCEZfuAmSqIC4u1d8+UgjSVlXLnF+Y17CoKYNdCy/IC8vtMCNFELY6y7rrrLvbs2cOePXs4ffo0q1atYtWqVXVvrBwdHRkwYEBdcBcbG0tMTEynyGYZHa3Wnq1cudJsVPK+++7jww8/5P333+eqq65izJgxABw9epTnn38eBwcH7r33Xvt00iyYS1GLqzUa6DXeGMylbFRTOYTd3TS+N2VVOl7+U61dLKnU8eDi3aw6lMWC8/oRFdL50077uDvx3W1jeHBxAr/vVSOVhzKL+PeyA7xzzXCzc99bm8gf+zLr9q8aFX5WiVOEMPVzwknuX5Rgtc1Bq2FC30BmxoRx4aBurVZKQ7SSylKV9XHTm1CUYd6m0cLgy9SauG4D26R7ZO6DTW/Bvh9AX23e5uCiRuX6XdQ2fRNCdAoaQyumVkxKSmL37t11HwkJCSQnJxtfzOQNV2hoKLGxsa2WBKWl5syZQ0aG+kOQlpZGeno64eHhhIWFAaq/S5YsMbum9utJSkoiIiLCrO3111/nwQcfxNHRkQsuuABnZ2f++usvysrKeO2113jggQda3OfaaaoNJUgBoDQXXjZZNP3IcVUzZ+8P8OPN6phPT3hgb4v7I87e73szeOzHPWbFhQHG9gngtkl9ODc6uI16Zj96vYEX/zzEh+uOA+Co1bDp8fMI9lKjc+uOZHPjZ9vQ1/zGOqd/EJ/eOAoHrQRyomn0egNJp0uIDDJPPpVTXEHc83/X/WxpNBAX4c/MmDAuHhIq5S3ao/JC2PEJbHobSnPM27SOMPQqmPBA28w6MRggaR1sfAMSV1q2u/rCqFtUUhPPzv+7XYiupknvz1tRqwZz1hQVFZkFd7WZLMvKytBoNOh07WO6SkREBCkpKQ229+rVyywwhcaDOYBff/2VV155hfj4eABiY2N55JFHmDVrVqv0uUk/LAYDvNgTKmoyCN6yCnqMgKJMeNVkjcd9e1QNOtFm0vPLuP+7eLYn55kdD/F2ZfXDk3Fz7vyJPXR6AxNeWkVGgSpr8MhFUdx9bl/S8kqZ8dYG8kurAAj3d+PXeyZ0yLp9wr6qdXq2JuXy575Mlu/PpLC8ivinL7T4/3TVh5spq9Izc2goM4aGSSbK9qq8UNVg2/Q2lOebtzk4q8yP4+9r279nuip4IxYK08yP+/SEsXepPro0LZu1EKLjsXcwZ/PFbF5eXkyYMMFs7Zher+fIkSPs3r3b1i/fZPUDtaY4Uxw8c+ZMZs6ceZY9aiUajfqjllkz8paXpII5rxAI6GtcIJ6yUYK5Ntbd141vbx3Dkvh0Pt+UzP6TKgD/v0sHd4lADtSUtitHhfO/v48C8N32E9xxTiT/+GFPXSDn6qTlg+tGSiAnGlRepWPjsRyW789kxYFT5NX87NRaeySLqYPNS2p8flOcZEJtzxoL4hzdYOR8GLdAleNpaw5OMOZO+OtJtR8yVAWYAy8Bh66VQ0AIYXtt8ltFq9USHR1dt+ZM2JhfhDGYyzcZfew1XgVzAf3apFvCkqODlstHhjN3RA92puSxPTmP86K7WZxXWa3H2bHx9P0d1RUjw3lz5VH0BkjNLeOhxQlsSjQWBX/+kiGS/l1YyCosZ+WhLFYePMWGYzmUV+mtnueg1ZCYbVkHVAK5dqqxIM7ZU6X1H3M3eAZZvdymSnNh6wcq+2T9BCYj5qnkYnG3Qu9zJKGJEMJm5BFRV2AtoyXApIfh3CfUKJ1oVzQaDSMj/BkZ4W/RlllQzqXvbuTu8/py9aieaDvZmrEwXzfOjQpm5aEsgLpizABTBnTj0uFNL4EiuobU3FImvry6wXZnBy0T+gUydXAIFwzohp+sgWv/Gg3ivNR6s7F3g7vl70ibKzyp+rXzc6gqUWvghs8znzrp4gVXfW3/vgkhuhwJ5roCa7XmAHztULRctCqDwcCTS/ZysqCcJ5fs49fdJ3nx0qGdrjbd1XE964K5Wl4ujvznksGSubILKyitYmNiDhcM7IaTSWH5Hn5uRAS4k3zaWFLAw9mBSf2DmDo4hHOjgyULZUfRnoO404mw4XXY/R3oTabulufDri8sR+eEEMIOJJjrCvxMslmaBnOiw0nLK2NHijFBypbjuUx9Yx0PXRDF/Am9O01mx8lRQYR4u5JZWF537InpAyQpRRdTrdOzOy2fdUdyWHc0m92p+egNsPj2scT1Nr6Z12g0nBfdjeX7M5kyIJjzB3RjdB9/XBxl6mSH0Z6DuIzdKojbvxSot1bezR9G3wExV9u/X0IIgQRzXYPpNMuCdJVpy0GeUndE4f7urHhwEv9cup8/96t6a+VVep7//SC/7c3g5blD6d+t49emc3TQclWcMRHKmD7+XDUqvI17JWzNYDCQfLqUzYmnWX80m43HcizKdQCsP5ptFswB/GNqlBSP74jaaxBnMEDKJtjwGhz727LduzuMvUetjXPuXDMjhBAdiwRzXYFvOKABDGDQQUEa+JuM1lWVQeo2yNyjsoGJdi3Yy5X3rx/B73sz+OfP+8gprgQgITWf6W+uZ2ZMGOdGBTOpXxA+7h03aL9rcl/yS6sorqjm8WnR8ia9EyuuqObJJXvZcvw0pworGj030NMZR61l8h9JYNLBtNcgrpauCn64CYpPmR8P6Avj74ehV4KjrL0UQrQ9Cea6AkcX8A6DwnS1n5dsDOZKcuC1AaBTAQGDLgUfSTDREVw8JJSxfQJ4btkBfopX/7ZVOgM/7Urnp13pBHm5sO2J8ztsEOTsqOWZWYPauhuiFen1BhKzi4kM8jRL3OPh7MCGozmcLqm0uMbJQcPIXv5M6h/ExH6BDAz17nRJf7qU9h7E1XJ0Vv1Y8U+1HxoDEx6EATNBKw8OhBDthwRzXYVfhDGYMy1P4BGoAr3atXQpG2HoFfbunThLfh7OvHZlLDNjwnhiyd66YtsAE/oGWgRyX25O5r01iVwyrDvzxkXQzVvWoAnbySmuIOFEPgmp+cSn5rEntYCiimp+v3eiWXkJjUbD6D7+/L5XTR3uF+zJuMgAJvUPYkyfADxc5E9Vh1dZAts+go3/g7I887a2DOKqyiHha1U6YOR887aR8yGlprxA5HlSXkAI0S7JX8iuwi9CBWpgmQSl1wTjseT1Esx1QOdGB7PyoXP4+2AWaw5nse5INpOjLOsuZRdVcLKgnHfXJPLR+uPMjAnjqlE9GdnLDwOw5nAWP+xMw0Gr4R8XRdMzwN3+X4zokArLqzicWcSetAISUvNJSM0jNbfM6rlbk05b1AqcNzaCGUPDiOvtT6Cniz26LOyhqlyl8F//KpSYZ6ht0yCuohh2fAqb31ZTKd381dRJ0/VvLl5wzXf27ZcQQjSTBHNdRUPlCQAiJkDCV2o7eaPduiRal7uzI7NiwpgVE4Zeb0BnMFick2Myjc10SmYPPze0Gg0nco2p3U8VlrP49rEddpqmsA293mAxzTHldAnnvLKmSddrNVgN8kb3CWiN7on2QlcF8V/BuleMs0JqOXuqDJBtEcSVF8C2D2Hzu1CWazxelgu7FsKYO+3bHyGEaCEJ5rqKhgqHA0SMN27nJkJhBniH2qNXwka0Wg1aLIOwe87tSy9/dz7flGw2JTMtz/zNtYezAy9eNlQCuS6sqLyK5JxSjucUk5RTQnJOCceyi8ksKGfbE1PMAroefu64Omkpr9Jb3CfYy4XYcF9ie/oSG+7L0B6+eMq0yc5Lr4M9i2Hti5Z/axzd1JTF8feDh52D99Jc2Pq++igvMG9zcIHh10PUNPv2SQghWoH8Re0qzIK5FPM2357qI/+E2k/ZCEPm2q1rwn7CfN24/ZxI5k/oze97M/hhZxobjuVQO4in1YCTg5aHLowiMsizbTsr7GbXiTz+2JvByfxy0vLLSM8rrcuSak16fhnh/sYpuA5aDVHdvDieXUJ0qJcK3sL9GNbTl1AfV3ko0BXo9XBgKax5AXKOmLc5OMOIm2Dig+AVYt9+FWerqZTbP4bKYvM2Jw8YdbMqMeDVzb79EkKIViLBXFdhGsyV5aonk64+xmMRE9UicFDr5iSY69ScHLTMju3O7NjuZBaU89eBTKp1Bi4c1I0eftbXyRWUVuHm7ICzo2Va+Fo6vaFDFS7PLqrgaFYRx7NLSM0tJbuoguziCvJKKymr1FFepaeiWkdFlZ7yah37/n2RRSHq819dQ0mFDlcnLa5ODrg4OeDqqK377OrkUNcW08OXy0b0MLs+NbeU+NR8k3ON57s4auu+33qDqsMW7OVq8W+w60QeReXVVFTpKK3UUVheRWFZFUXl1TXb1ZwuqeB0cSUT+wXxz5kDza4/klnER+uTmvx9O5hRaBbMASycPxpvN0cJ3LoagwEO/wGrn4dT+8zbNA4w7DqY9EhNiRw70+vho/Og4IT5cRdvtVZv9J32HyEUQohWJsFcV+EZrKa4VNdMp8tLVqmWa/UabxLMybq5riTEx5UbxkY0ek5BWRXXfrKF0godM4aGEhnsiZerIzo9lFZWE38iny3HT3Mos4ixfQJ46bKhHSJ5ymsrDvPtttQmn19epbcI5rKLKqwWtrbmktgqi2Bue3IuDy7e3eQ+LL9/ElEh5oXhH1yUQPLp0gauMNfT3/Lfpbufm9Vz3ZwciAj0oHegO70DPYgI8CA6xJt+3SxHbTtyTUNxFgwGSFwFq/4DJ3fVa9SoZCLn/AMCItukewBotTDyJlj5b7Xv5gdj7lZTPd18265fQgjRiiSY6yo0GvVHtfbJadZB82AuYoJx+/RRKMq0/3QY0S6VVFRz02fb2JdeCMCbq441ev7OE3mUV+vs0bUzMhgMxKfms2RXOudGB3FetPlUqj6BzZtKWlGtA8yDlmq9ZaKZhlgrbG1tnVljDFi+Xv0AszE5xZZFufsEeXJ1XDjdfd0Iq/mICPCgm7eLjLQJS8kbVRB3YpNl28BLYPLjEBxt3z6dToTcJOg3xfz4qFtg97cQe62aUuniZf16IYTooCSY60pChhqDuZMJEHOVsc2vF/iEQ0HNKEXyBplqKQA1JbM59egevrA//bu17Rum8iodP+1K56stKRzIUEFoYXmVRTDXO9CDQE8X+gR5EBHgToi3K0HergR4OOPm5ICLkxYXR+O0R393Z4vX+vbWMZRX6Siv1lNR87m8SkdFlY6Kmu3yKvU5tqevxfW+7k7E9PBR55hM6ay9rj4rSUrp4edGpU6Pi6MWN2cHvF2d8HZzwsvVEW9X9dnfw5lATxfCfC3/Lbv7uvHCpUOb+N0VXVbaDhXEHV9t2dZ/Gpz7BITa+eco+7Aqe7D3e3APgPv2gLPJ6LOrN9y9TWrECSE6LQnmupKwWNj9jdrOsDKtK2KCeoLpHggVRXbtmmi/nB21vHvtcNYeySb+RD7HsotJyi6hUqfHQaPBQauhd5AHY/sEoDcYuHZ0L4t7bDqWQ7i/u8U6q9am0xv4cWcar604QmZhuVnb6kNZVOn0ODkY15udPyCYHU9NqX+bZokJ923R9RcPCeXiIdazxxoMBqp0BrQaVVhbg/X3pJ/cOKpFfRCiUVkHVRB3aJllW59z4bynoMdI+/Ypc58qe3DgZ6gdrS7Jhl1fWJYXkEBOCNGJSTDXlYTGGrcz96jF4VqTRAoTHlApo4Oi5I+fMKPRaJgcFczkqOBmX1tepeOh73dTVqXjw+tHEtfbNnWlthw/zdNL93E0q9iibXRvfy4b3gN9vWGt9j6FUKPR4OzYvvsoOrH8E7D6BdjzHRjqjRL3HKuCONMp+vaQuRfWvgQHf7Vs8+8DXlJWRwjRtUgw15WEDAaNVv1RriyG08cgqL+xPSiq7fomOi3TmnbXfbyVV6+IYWZMWKvdv7xKx2srjvDR+uNmUxCdHbRcOSqc68f2avNpn0J0KMXZaurijk9AV69ERWgsnP80RJ5v34d+jQVxgVEw6WEYdCk4yNsaIUTXIr/1uhJnDwjsD9mH1H7GbvNgTggbCPVRqfQrq/VU6vQs+DaerKIKbp7Qu8X3Tsop4Y4vd3L4lHFasEYDc2K78+CF/RsssyCEsKK8UNVk2/yOZU22gH4qiBswy/4zN36+B+K/tDzebbAqezBglvksEyGE6EIkmOtqQmNMgrkEGHp5m3ZHdH6zY7vT3deNWxfuIK+0CoDnlh0gxNuV6UNbNiXKy9WRsipj5szIIA9euyK2xevYhOhSqsrVKNy6/6o6pKa8u8PkxyDmmrYb9fKttw6322A451GIniFBnBCiy5Pfgl2N6bo5a0lQQK2ly9gD2z+2njpPiGYaGeHPT3eNJ9zfWM/swcUJ7DqR16L7Bnq68PlNo/D3cObGcRH8du9ECeSEaCpdNez6Et4aAcufMA/k3PzgwudhwS4YfoP9ArnyQstjo28DV18VxF3xJdy+HgbKaJwQQgBoDAZ5t95RDRo0CID9+/c3/aLkjfD5xWrbxRseTTH/g1hRBG/EQOlptb9gV9sWfRWdSmJ2MZe+u4mCMjVCF+jpzJK7xrc4y2VBWRU+blK0WogmMRjU2rNVz0HOEfM2Jw8YezeMuwdcfezXp8x9ak1c+k71d8epXgmNnKPgHykBnBCi3Tur9+ctIL8VuxrTGkAVhZCXZN7u4gUeQcb9pHX26ZfoEiKDPHn/uhE4Oag1NznFldzw6TayisrPcKWyKTGHgpqpmqYkkBOiiY6vhY/Ph8XXmwdyWicYfQfclwDnPWm/QC5zHyy6Ht4fDwd/gcJ02LXQ8rzAfhLICSGEFfKbsatx8YKAvsb9jATLc3qfY9xOWmvzLomuZWxkgFmB6qScEq77eCvFFdWNXrfl+Glu/Gw7V3ywmcyCpgV/Qoga6btg4WxYOEuNftXRQMzVsGAnTHsJPJtffuSs1A/iTB39yz59EEKITkCCua7IdN3cyQTL9j6mwdw6tYZOiFY0d0QP/jHVWArjgoHd8HB2aPD8I6eKuPWLHVRW6zl8qoj5n29Hr5cZ4kKcUfYRWHwDfHQuHF9j3hZ1Mdy5Cea8D369rF7e6hoL4mrXxF2z2D59EUKITkCyWXZFoTGw7we1bS0JSq/xxnp0pachaz+EDLFvH0Wnd9fkvpRV6nB1cuDuc/tatJdXqbbSymru+noXRTUjdy6OWv49exBarRTTFqJBBemw5gVI+Nqy4Hev8TDlGQiPs19/Tu2HNS9aBnAg2SmFEKIFJJjrisJijdsZu9VieNO6QW6+avTu5C61f3ytBHPCJh660Hqh+p0pedz51U7evHoYS+PTOZZlrHn11tXDGBXhb68uCtGxlBfAhtdhy3tQXW86csgQOP8Z6Gvngt8AB362PhInQZwQQrSIBHNdUYhJEpTyfJUExb+P+Tl9zjEGc0lrVWYzIexgZ0ou8z7dTnFFNTd8so1KnXFU4fZz+nDhoJA27J0Q7VR1Jez4VGWErF8rzq83nPcUDLq07YKmMXeqALOiEIIHqdp1EsQJIUSLSTDXFbn5qhTPuYlqP22HZTDX+xz1dBfUOovCk+AdZs9eii6qolpPdc06TdNALjbcl4cbGMkTossyGGD/Elj5b8hLNm/zCFZB0/AbwMFOGV/zkmHbR3D+v8DR2XjczQ8ufE59jp4pQZwQQrQS+W3aVYWPNm6nbrNs7zUevGqCN10lbPifXbolxLjIQD6ZNwoXR+OvJy8XR966ehhODvIrS4g6yRtVmYEfbjIP5Jw8YPITcG88jLrZPoFcQRr8ep8qQL75bYj/0vKcETfCwNkSyAkhRCuS36hdVfgo43aalWDO0RkmPGDc3/k5FGXavFtCAIzvqwI6X3cnXBy1vHpFTIsLiwvRaWQdgm+ugs8vNi8zoHGAkTerIG7yo+Diafu+FGXC7/+AN4epvxP6mhIj6/4LVVJCRAghbE2mWXZVPUyymGXug8oScPYwP2f4DbD+VdU2+jZwdLFvH0WXNqFfIJseOw+DATxc5FeVEBRlwur/U6Ne9TNURs9QUxuD+tunLyU5air+9o8tE6349IRz/gFa+X8rhBC2Jr9pu6rgAeDsBZVFYNCpgrK9J5qf4+QKV36pioy7S/ZAYX/uzvIrSggqimDTW+qjqtS8rccouOA56DXWPn0pzVX92PoBVJWYt3mFwaSHYdj15uvlhBBC2Iy8U+qqtA7QY4SxiGzqVstgDuxbh0gIIYSRrgp2faHqs5Vkm7f591G14gbMsl+ZgcRVsHieykhpyiMYJj4II25SDwGFEELYjQRzXVn4aGMwl7a9TbsihBCihsEAh5bB38/A6WPmbe6BKkPliBvtl6GyVkiM+fRON3+YcD+MuhWcZU2rEEK0BQnmujLTdXOp2yyLh1uj10FhOvj2tG3fhBCiK0rdBn89DalbzI87usHYu2H8feDqbft+VJWB1gkcTN4meATA6Dtg+0cwboHadvGyfV+EEEI0SIK5rqzHCON2WS6cToTAvtbPrSiCXV/Ctg9Ao4V7dkp6aSGEaC05x1StuIO/mB/XaCH2Wjj3CfvU+qyugJ1fwPr/wnlPw/DrzdvH36cCOTdf2/dFCCHEGUkw15W5+UFQNGQfUvtp2xoO5kpz4a8njVNsjq2A/hfZp59CCNFZFWfD2pdg52fGtP61+l2k1sV1G2j7fuiqIP4rVVKgME0dW/syDL3SPJmJPUYFhRBCNJkMrXR1PUzqzVkrHl7LrxdEXWzc3/q+7fokhBCdXWUJrH0F3oxV0xZNA7nQWJj3K1y72PaBnF4HCd/C2yNh2f3GQA6g+BScjLft6wshhGgRGZnr6sJHq5pF0HgwB2p9xKFlajtxFWQfhqAo2/ZPCCE6E101JHyt6sUVZ5q3+faC8/8Jgy61/TR2vR4OLIU1L0DOEfM2rZNKsDLxIfAOtW0/hBBCtIgEc12daemBrANQlt/wWoiICRA8CLL2q/1tH8L0V23dQyGE6PgMBjj6F6z4F2QfNG9z9VVFtkfdAo4utu/HkT9h1fNwaq95m8YBhl0Lkx6RJFdCCNFByDTLri6gn0p1DYABjq9u+FyNBkbfZtw/9Jt6YyCEEKJh6bvgi5nwzRXmgZyDi0oocl+CylRp60AOoCQHvr+pXiCnUWvjFuyAWW9JICeEEB2IBHNdnVYLfacY94+uaPz86BnG7aIMy+k5QgghlNwk+GE+fHQuJK83adBAzNWwYCdc8KxKRmUvnkEQd6txf+BsuGsLXPqhKkQuhBCiQ5FplgL6XQB7vlPbR1eotRQNrdfwCISQIZBZ81T3+BpZNyeEEKZKc2HdK7DtI9BXmbdFngdT/g2hQ23fj5MJUFEIvSeZH5/wAOQlqzVxYbG274cQQgibkZE5oKSkhC+//JIFCxYQFxeHi4sLGo2GF1988azut3PnTp555hkmTpxIWFgYLi4uhIeHc91117Fnz55W7n0r6Hu+qmUEUJIFGQmNn99nsnH7+BobdUoIITqYqjLY8Dq8EQtb3jUP5LoNget+guuX2D6QyzoIi66HD8+BX+9TZQdMufvDlV9KICeEEJ2AjMwBR48e5YYbbmiVe1VXVzNy5EgAAgMDiYuLw93dnfj4eL7++msWL17MN998w9y5c1vl9VqFm5/Kanlis9o/ugK6D2/4/D6TYdNbajt5g8rO5iA/SkKILkqvgz2LYdV/zFP7A3j3gPOegqFXgNbBtv04nahq1u1ZDNSsZ849DgnfwIh5tn1tIYQQbUJG5gAvLy9uvvlmPvjgA3bt2sWTTz7ZovuNHj2aZcuWcerUKX777Te+//57jhw5wpNPPklVVRXz588nJyenlXrfSvpdYNw++lfj5/YcCw41RWQrSyDnsO36JYQQ7dmxlfDBObD0DvNAzsVHTadcsANir7ZtIFeQBr/cC2+Pgj2LqAvkQE2L9+tlu9cWQgjRpmQ4BYiMjOTjjz+u2//555/P+l6Ojo5s2bLF4rhWq+W5557jxx9/5NChQ/z222/Mm9eOnpT2uxBWPqu203eqjGcegdbPdfZQi/Z9e0HEeHD1sV8/hRCiPcjYAyv+aZkBWOsEcbfBpIfVdEZbKjoFG16DHZ+CrtK8LTAKzn0CBsyyfc06IYQQbUaCOTvSaDQMGTKEQ4cOcfLkybbujrlug8ErDIpOAgY49jfEXNXw+WPutFvXhBCi3chPVdMp64+AAQyeC+c/DX4Rtu/H6hdg05tQVWp+3LcXTH7cPtM6hRBCtDkJ5uzs+PHjAISEhLRxT+rRaNRUy11fqP2jfzUezAkhRFdSlq9Gwba8D7oK87aIiWq2QmNrjVtbeb55IOcVBuc8AsOuBwcn+/VDCCFEm5Jgzo42bNjAzp07cXZ2ZurUqU2+btCgQVaPJyYmEhkZ2VrdU1Mt64K5v6GiCFy8Wu/+QgjR0VRXwPaPVamBsjzztqABKojrd4F6IGZPEx6EXQvByV2VGBg5H5xc7dsHIYQQbU6COTspLCxk/vz5ADzwwAOEhoa2cY+s6DMZnDygqgQqCmDbh+pNwpmU5UHhSehmPegUQogOR6+H/T/Byn9D/gnzNq9QtR4t9lrbTmWsroCdX8ChX+H6peav5dUNrlkEYcPBxdN2fRBCCNGudYpgbu7cuezbt69Z1yxcuJC4uDgb9cicTqfjmmuu4ejRo8TFxfHss8826/r9+/dbPd7QiN1Zc/GE0berqUQAG9+EUbc0nOAkbQf88Q84GQ/ugfDAfnB0bt0+CSGEvSWtg7+etqy56ewFE+6DMXepRFC2oquG3d/A2pehIFUd2/sDxFxpfl79YuBCCCG6nE4RzCUnJ3P4cPPS45eWlp75pFZy22238dtvvxEVFcVvv/2Gs3M7DnjGLYBtH0FlkVqTseV9mPyo9XP9ekPmPjDoVbHxg7/AkHZUP08IIZrj1AH4+xk4utz8uNZRTWOc9A/wDLLd6+v1sO9HWPN/qj6cqY3/U0lN7D2dUwghRLvWKYK5HTt2tHUXGvTII4/w6aefEh4ezooVKwgMbCDdf3vh7q8yVa57We1vfgdG36YKi9fnEQCDL1NPkEFNy5RgTgjR0RSehNX/Bwlfq4dTpgbOhvP/BQGtuD65PoMBDi1Tfcg6YN6mdYIRN6pSBxLICSGEqKdTBHPt1QsvvMB///tfgoODWbFiBeHh4W3dpaYZexds/UCtm6sogM3vwnkNFFKPu9UYzKVuhYzdEBpjv74KIcTZKi+ADf+DLe9BdZl5W/gYuPA5CLfhdHyDQRUdX/Wc5ZROjRZir1GjgVL0WwghRAOkkqiNfPjhhzzxxBP4+vqyfPlyoqKi2rpLTefmB+PuMe5v+xB0VdbP7T4cuo80Ofcj2/ZNCCFaqrpSBXBvxKo1wqaBXEBfuPJrmP+nbQM5UHXivr6sXiCnUfXq7t4Os9+RQE4IIUSjJJhrgejoaKKjo0lPTzc7/sMPP3DnnXfi6enJ77//TmxsbNt0sCVG3w4OLmq7PB9SNjZ8btxtxu2930Nprk27JoQQZ0WvV4lE3hkFfz4GZSa/qzyCYfprcNcWGDDDPlMaB881/p4FiJoOd26EuZ9AYF/bv74QQogOT2MwGAxt3Yn2YM6cOWRkZACQlpZGeno64eHhhIWFARAaGsqSJUvMrtHU/LFPSkoiIiICgKysLMLDw6msrGTIkCEMH269iOwll1zCJZdc0qI+12azbCjbZYt9fYUxEcCoW2H6f62fV10Brw2E0hy1P+UZmPCAbfokhBBnI2kdrPinyr5ryskDxt8LY++xbYr/zH0QPBC09Z6h/vkEZB+E856C7iNs9/pCCCHswubvz+uRNXM14uPjSUlJMTuWmppKaqpKC92rV9OmupSWllJZWQnA3r172bt3r9XzIiIiWhzM2dyAGcZg7tBvMO1lyzciAI4uaoH++ppgb9PbKviT2kdCiLZ2aj+s+BccW2F+XOOgfm9Nfgw8g233+pl7Yc2LKsHJ3E9V0ihTFzwLDvKnWAghxNmRvyA1kpOTm32NtUHNiIgIq8c7pKiLQXOfyu5WdFI90e7RwJPj0bfDlnehqlSN0G3/SEbnhBBtpyCtJkPlN0C938kDZqkMlbacynjqAKx9EQ78bDy25kUYeIl58W8J5IQQQrSArJkTDfMIVBndah1a1vC5nsEqs2WtjW9AZYnt+iaEENaU5auRuLdGqFIDpoFcz7Fw8wq48kvbBXLZh+GH+fDeOPNADsDRFYpP2eZ1hRBCdEnySFA0bsAMOLFJbR9aBlP+1fC54+6D7Z9AyFA49wlw9rBPH4UQoroCtn8M616BsjzztsD+ai1v1MW2S2yScwzWvqSSQNUfCew2GCY/DtHTpVacEEKIViXBnGhc9AxY/oTazjkC2UcgqL/1cz0CVCY2317yhkUIYR96Pez7EVY9C/knzNs8u6kgatj1tpvOWHgSVv0Hdn9rWXA8KFq9/oBZ1tcbCyGEEC0kwZxonF8vCBmiFvEDHPoVgh5q5PwIu3RLCCE4vkZlqMzYbX7c2RPG3wdj77b9DAF9NexZbB7IBfRTiVUGzTFfHyeEEEK0MgnmxJlFzzAGc3t/hAkPNm/kraoMnNxs0zchRNeTuVeti0tcaX5c6wgj58Okf4BnkH364tsTRsxTUzz9+8A5j8GQuRLECSGEsAuZ9yHObOAlxu2s/XBkedOvTVoPb8RCciNFx4UQoilyj8OPt8D7EywDuYGXwN3b4OJXbBPIFZ6E3x+BQ79btk18CGa/C3dvh5grJZATQghhNzIyJ84sOBr6T4Ujf6r9dS9D/4vOPDoX/zX8sgAMOlh8g1pP5xVi+/4KITqXokyV2GTn52pao6le41Wtth4jbfPa+amw8X+wayHoKtWDqf5TzdfAeYfBsGtt8/pCCCFEI2RkTjTNpH8Yt9N3Wj4VtyYo2viEujRHTUMSQoimKsuHlc/Cm8PU7w/TQC5oAFz9Hdz4m20Cubxk+OVe42vrKtXxrP2Nl2kRQggh7EiCOdE0PUZA5PnG/bUvw5mKo/cYYR4EHv7DNn0TQnQuVWWqVuUbMbD+VagqNbb59oQ5H6iR/qhprZ8593QiLL0L3hwOu74AfZWxzSsMpr0C/S5s3dcUQgghzpJMsxRNd86jxhG51K2QtA76nNP4NYPmwOr/qO1T+9TTbsl4KYSwRlcNCV/BmhehKMO8zT0QzvkHjLgRHF1a/7Wzj8D6/6o6cfVLDPiEw4QHYNh1tnltIYQQ4ixJMCearudo6H0OJK1V+xteO3MwF9hXFezNOaL2D/8BY+60bT+FEB2LXg8Hf1b12k4fM29z9oLx96rfGy5etnn90lyVVEVXYX7ctxdMehiGXgWOzrZ5bSGEEKIFZJqlaJ5Jjxi3j6+B7MNnvibqYuP2od9avUtCiA7KYIBjK+GjyfD9jeaBnIMLjL0H7tutRuRsFcgBuPtDzFXGff9IuOQ9WLATht8ggZwQQoh2S4I50TwRE6DbYOP+to/OfE30dON2yib1FFwI0bWlboMvZsJXl5oX/dZo1XTGBTvhoufBI6B1Xzd9F5zYYnl84oMQPAgu/UiVOIi9BhycWve1hRBCiFYmwZxoHo0G4m4z7u/+FsoLG7+m+0jwCFbbBh0cXWG7/gkh2reT8fD15fDJBZC83rxtwEy4awvMfgd8w1vvNQ0GOL4WFs6Gj86F3x62TODkF6GSqgy9AhxkBYIQQoiOQYI50XxDLgdXH7VdWawCusZotRA11bh/WKZaCtHlnNoP310LH06Go3+Zt0VMhFtWwZVfQVBU672mXg8HfoGPzoOFs9TUcIBTe61n123tzJhCCCGEjcnjR9F8zu4w7HrY/Lba3/YhjLrVvIhufVHTVdFdUGtkqiskK5wQXUH2EVjzAuxfAtQbDes+Es57Evqc27qBVHUl7F0MG/4Hp49atocNNz6QEkIIITowCebE2Rl1C2x+BzCopAXHV0Pf8xs+v8850HMsRJ6nEqI4SEIBITq13OOqHuWeRZap/kOGwnlPqXptrRnEVRSr2nCb3oaik5btfc5VJQZ6T5JROCGEEJ2CBHPi7Pj3hv4XwZE/1f6WdxsP5pzcYP6f9umbEKLt5KfCulcg4WvQV5u3BQ2Ac59Qa+NaO5iqKoe3RkBxZr0GDQycDRPuh7BhrfuaQgghRBuTYE6cvdF3GIO5Y3+rjHShMW3bJyFE2yjKhPWvws7PQVdp3hbQFyY/DoPmgNbBNq/v5Ar9LoD4L9W+1glir4Zx96l6l0IIIUQnJMGcOHt9JqvgrTat+IbX4fLPz+5eBoNMexKiIyrJUf/3t38M1eXmbb494ZzHYOiVrZshMnWbWnfbe6L58fH3wYGfYcQ8GHMXeIe13msKIYQQ7ZAEc+LsaTQw8SFYfIPa378Uzj3WtKfgej2UZIFXCOz4FP76J0ROhssXNp5IRQjRPhRnwaY3YfsnUFVq3ubdHSY9DLHXtV7Bbb0ODv6qEi+lbVf1Lu/YYP4QKLAfPHRYJWkSQgghugAJ5kTLRM+EgH41GeMMsPF/MPvths/PTYKNb6jpmW5+KhX5bw9DzzHg1xvK88Hd306dF0I0W1EmbHxTPYSpLjNv8whWD3hG3KimPbaGiiKI/1qty81PMR4/tQ+S1qoZAqYkkBNCCNGFSDAnWkarVdnhfr5L7e/+Tq2N8elu/XyDHnZ+praLMuDHW1Qh8ayD6mm+m599+i2EaJ7Ck+pBzM7PLadTugeoKY6jbm29YCo3SU3d3PUlVBRYtvcaD45urfNaQgghRAclwZxouSGXw+r/g8I00FfBqudgzvvWzw2IhKBoyD6k9k/uUp/LciFqmqybE6K9KUhTa+J2LbRMbOIRBOPuhZHzwcWz5a9lMEDiKtj2UU1ypXp16TQOMOgSGHsPdB/e8tcTQgghOjgJ5kTLOTqrp/J/PKL2d38L0TNgwAzr50ddbAzmavWaoDLdCSHah7wUFcTFf6Ue0pjy7Kb+z4+4qXWnNZblwXfXWI78uXjD8BtUBl3f8NZ7PSGEEKKDk2BOtI6R82HPd5C+U+3/eh+EjwbPIMtzo6fDhteM+xotTHtRRuWEaA9yj6sgLuEbyzpxXqFqWvXwG1TtyJaqn8XW3R8Gz4WEr9R+QF+Iuw1irgZX75a/nhBCCNHJSDAnWoeDI8z5EN6foJIilOaogO6qry2DtLDh4BliLO474kb15H3P9yoYjJ5umXJcCGFbJxNUAqMDP6u1raa8u6sgbtj1LU9sUl2hslLu/BwGXwYjbzJvH32b+v0Rdxv0OVey2wohhBCNkGBOtJ7AvnDhc/D7w2r/8G/w24Mw9UVwdDGep9Wq85beCaGxcN7T6rz9S1S7g5NlMFdeqGpL9RzTOmtzxNmrKoMjyyEoCoIHtHVvREsYDHB8jQrijq+xbPcJh4kPQuy15v+Hz0b2Edj1hRrxK8tVxyoKLYO50Bi4ZlHLXksIIYToIiSYE61r5M1w6Dc4vlrt7/gU0nepYuL+vY3nDb1CPZUH0DpA9xHGYC59l/k9K4rhnTiV/bLPuXDDUlt/FaIxK5+DLe+oZBT37wGfHm3dI9Fcep0agdv4P8jYbdnu3wfG36+mN7akTlxVGRz4RY3Cndhk2Z6xGzL3QsiQs38NIYQQoguTYE60Lq0WLv0Qvr4cMhLUsYwE+HAy3L4O/HqZnOtg3O4+0rh9Ml692axtd3BW6+8OLFVBYmEGeIfa9usQDdvyjvps0MHW9+HC/7Rtf0TTVZVBwtew6S3IS7ZsDxumgrgBM83/fzaHwaCCtN3fqlIl5fmW5zi6qYc5I25Uxb+FEEIIcVYkmBOtzzMY5i+Hv55UdaJAvaHb8p5KdGJNaIwa6THooKpEZbvsNki1OTpD2nbjuSd3gfd0m34JogG6elkNDQbr54n2pSxP/V/c+gGUZFu2R54PE+6HiIktT0S0ZxEsud16W7fBKoAbcjm4+bbsdYQQQgghwZywESdXmP4qeIXAqpqRm0PLYOoL1t8sOrtDt4FqyhWoRCi1wRyomlKF6Wo7bYdKkiLsryDVfH/8/W3SDdFEBemw5V01zbGy2LxNo4VBl6oSA6FDz+7+umqV/MhUvwtB62QsZ+DkXjMKd5P6fyxZa4UQQohWI8GcsK1hN8Cq5wGDCgQyEtRULmu6jzQGc78sUEkXaqd6dR+pMuCBsfyBsL/cJOO2dw/rpSdE28s+DBvfgD2LLWvEObqqrJTj7gG/iObfW1cFSWth7w9wbCXcG2+elMjdH/pfBCU5EHOlKjUgZQWEEEIIm5BgTtiWVze13i11i9o/+GsjwdwI2PmZcX/pnXDJe8YEKbVOxoNeLynL20KeSTBnmtBGtA8ntqqkJod/t2xz9VXp/kffDh6BzbuvrgqOr4UDS1SCo7I8Y9uhZRBzlfn5cz9rWeIUIYQQQjSJBHPC9gbMNAnmlsH5/7R+Xo+R5vt7Fqm1PDFXQlgsoAEMKp356aMqNb6wr1wJ5todvR6O/qWCuBObLdu9e8DYu1Wh7+aU9aiuVCNw+5eqgM1aIhNQWTHrB3MSyAkhhBB2IcGcsL0BM1QyFICcw2oKmLVALLA/eAQZEzSMvkOVMABw8YKgaMg+qPbTd0ow1xZMgzk/CebalK5KTXXc+Ibx/4WpoAFqPdyQuap2Y1PpdfDLvXDoVygvsH6Ogwv0nQJDL4f+086u/0IIIYRoMQnmhO35Rag6UrXr4Q7+aj0Q0zqoaZWb3oKoaSqYM02W0GOEeTAXe43Nuy7qMZ1mufLfKvCe+kLb9acrqiiGXQth8ztQmGbZ3nOcykzZ78KmJRsxGMzP0zpA5m7LQM7BBfpdAAMvUWviZB2cEEII0eYkmBP2MWCWMZg7tAwmPWz9vH4XqA9ruo+A+K/UtiRBsT+DwXxkDiD/RNv0pSsqyVF1/bZ9ZH3KY9R0FcSFxzV+H10VnNgCR5fDkeUqGO87xfyc/lPV/1dHV9U2aI4K4Fy8WuurEUIIIUQrkGBO2MeAmbD6ebV9Ml4FAb49m3cP0yQomfugqlyVQBD2odfBzDdgxT+hOFMdqyxp2z51BblJsPlt9SCjuty8TesEQ6+E8fc2PO1Yr4dT+yB5PSSth5SNat1prSN/WQZzQ65Q05r7T23eOjshhBBC2JUEc8I+gqLBPxJyE9X+in+qjHfNqTkVPBCcPdW0ze7DVd0sCebsx8FRJaPBYCwKXVXapl3q1DL2qKQm+5eAQW/e5uypim+PuQt8ultem3UIktZB8jpI3mCefbK+I3/CtJfM/y8G9VcfQgghhGjXJJgT9qHRQNyt8Odjan//Eoi62JjgpCkcnOCRRAng2pqTu3FbgrnWZTCoIGzj/yBxlWW7R5BaSzrqZnDza/g+310Nuccbbndwgd4T1chbU9fWCSGEEKLdkWBO2E/cbSr5ScpGtf/bw9BzLPiGN/0eEsi1PWeTYK5SgrlWodep/xsb/6emIdfn1xvGLYCYq6EoQ02NTNsO6TvgvKeh7/nm5/eeZB7MabSqvmPERNXWcww4e9j0SxJCCCGE7UkwJ+xH6wBz3of3xqs1OxUF8NOtcMVC8Axu696JppKRudZTXanqKW543TgF2VRgfzWC5uCigr1V/4GyXPNzUrdaBnN9JkP6LhW49Z6kHppI9kkhhBCi05FgTtiXb0+4+BXjmqsTm+HNYWrUYezdZ58tT6+D3x+BnCMw5RnLAuSi5X57GPTV5skzJJg7O5WlEP8lbHzTenkBJ3eV7CTniPpoTNp2y2OD5qgPIYQQQnRq2rbuQHtQUlLCl19+yYIFC4iLi8PFxQWNRsOLL77Yaq8xf/58NBoNGo2GLVu2tNp9O6ShV8KQy437lcWw5gV4bxyctjI6UV9lCRxbCSv+BUf/Vsf2fg87PlEZ+768FLKsFFEWLbPvB9j5Gez70XhMplk2ncEA2Ydh6Z3wSiT88Q/zQE6jVQHYbWvBPdAy6YmpwP4Qcw1Mfw0u/I/t+y6EEEKIdklG5oCjR49yww032Oz+q1ev5rPPPkOj0WAwGGz2Oh2GRgOXvA9hw2HdK8ZpY/kn4LNpcMPPEDyg4ev/fBx2faG2S7Kh3xQ1Ba1WRQF8fTncshK8utnu6+hKyvKsZ0TUV6m6ZQ5O9u9Te1ZeqB4oZO2HUwcgYzdk7rEsLQCgcYBh18L4+yEgUh3rNhAKTqg6b0FREDxIHes2SK19ayz5iRBCCCG6DAnmAC8vL26++Wbi4uIYNWoUP/74I88//3yr3Lu8vJzbb7+dQYMG4ePjw6ZNm1rlvh2egyOMvUu9iV3/mkr8AFB8Cj67GK5fAmGx1q+NmGgM5pLWqXpziavNzylIhc8vVtn/sg+BVyhc/a0qayCar36xcFNVpeDgY7++tHcHl8Gia5t+/nlPwcQHzY+d/y814ubfR601FUIIIYSwQoI5IDIyko8//rhu/+eff261ez/33HMcO3aMdevW8dRTT7XafTsNVx+44N8Q2A9+WaCmlpXlwpdzYMFOcPe3vKb3RON2QSqkbAD/3qowskZrnJ52+pj6ADWqtPkdtV5PNF+eSTAX0Ff9uzm5q4/GpgN2NqW5KuFIyiaVddLVB6762vyc2tG1BmnArxdEng/dR0DEBMtTug1stS4LIYQQovOSYM6G9u3bxyuvvML8+fOZMMHKGzZhNOw6cHKDn25TSTbKcmH9q3CRlRFSrxAIjIKcw2q/IA3u3Ah5KWpq2/HVsPV94/mhsXD5Zyq9u1D0elj1nJraev7TZx6xNB2ZC4q2DGA6q6JTah1myiaVrCfrgHm7q49aC1dbp628EPb9ZP1eLt4w+naV7MdVRjKFEEII0XISzNmIXq/n1ltvxcfHh5dffrmtu9MxDL4M8pJh5bNqf9tHMOZO8OlheW7vScZgLmk9jLhRjXb49YJ+F6jRo9zj6nPwAPAKk8LIpg4sgQ2vqe2SLJj3a+Pnm47M+XfyoPjUfjjwCxxdbr3mm6nyAhUQe4XC9o9h3cuWawvd/GHC/TDqVvMafUIIIYQQLSTBnI288847bNmyhS+++AJ/fytTBYV1o+9UQVxRBugqVJbL2e9Yntd7Emz/SG0nrTMfHdE6QNyt9utzR7TZ5HuatO7M5xeeNG779Gz9/rQnuxaaj+zW5x6oim73HAOhMZC+U41ymhbpBjX6Nm4BjL7j7EtuCCGEEEI0QoI5G0hLS+PJJ59k8uTJrZIlc9CgQVaPJyYmEhl5pvU5HYyzO5zzKCy7X+0nfANjF0BwtPl5ERMADWBQI0vZhy3PEQ1zdG3e+ZUlxu3OMkVQVw3FmZYjv4Pnmgdz3j3Uz1uvsdBznFrfqdGoUbvlT0LKRvPrnT1hzF2qbqKbr82/DCGEEEJ0XZ0imJs7dy779u1r1jULFy4kLi7OJv25++67qaio4L333rPJ/Tu9YdfD5rdV8hKDXk27vPob83Pc/SFkiEr3DpC48szBnK4aTh9tvOxBV+Hg3LzzTYM5Z3eVbr80B6rK1L+Dd1jz+5CxB07ugoGz7ZtqX1cFu7+D9f9Vgelta82n4PYYCQNmQffh0H+qWiNo2l6cDSueht3fmt9Xo4URN8Hkx8EzyD5fixBCCCG6tE4RzCUnJ3P48OFmXVNaaptixz/++CO//PILTz/9NNHRrTNStH//fqvHGxqx6/AcHFW69u9vVPuHf1NJJQZfan5et0HGYG75E2rNnVeI5f1KcuCHmyBtB1RXwGMnwMWzaX0pzgb3ANBqz/rLaZfqj8yZTlO1prLYuO3soTKPpu9Q+5d+DEMvt35dQ4qz4JMLVN2142tVghpb0+thz3ew+gVVw61W+i7oMcK4r9HAlV9aXm8wQMLXajSuPN+8re8FcOFz8qBACCGEEHbVKYK5HTt2tHUX6vz6q0oksWLFCtatM1+LlJCQAMBdd92Ft7c399xzD3PnzrV3FzuGgZdA+BhI3aL2l90P4aPBp7vxnCFzjaMjAX3Bs4EC4W5+cDJB1UMDFYT0mXzmPqz6jypqHjYcbl6hgszOwrHeyFxlcePruly81ChWRbGaRujkZmyrOosHI0f/MhbQPlOSkdaQvhN+/4cxAK2lcYCMePNgzprTibDsAUhaa348eKCqB9f3/NbtrxBCCCFEE3Sid6fty5YtWxpsi49Xb14vueQSO/WmA9JoYM778P4EFWiUF8DSO+D6n42jZJHnq4QpqVvVqEhDI0taBwiPg2N/q/0TW88czFWWqEAO1FTA5PUQeW6rfGntwoz/wYGaeopaRxWkNRbM3bFBfTYY1GdnD2Pb2QRz+mrjtrVspa2lqgxW/Au2fQgYjMe1jhBztSrW7d+n4et1VWrK75oXjcEnqID2/H/CqFukqLcQQggh2owEc63s888/5/PPP7faNnnyZNauXcvmzZsZM2aMfTvWEfn3hmkvw893qf2kdbD5LRh/n9rXaGDai027V/gYk2Bu85nPd3RT68p0lWo/L7lZXW/33P3hqWzLEbozqQ2YnUxS7Juup2uq4mzjtoeN1pdl7IGfboXsQ+bHB86GC549c229rEPw0y2Qudf8eP9pMP2/tg1ChRBCCCGaoJMtBLKv6OhooqOjSU9Pb+uudF6x16hkFLX+/jccX9P8+/Q0CZ7TtqtkKI3RatVUz1oFac1/zfauuYGcKdNgrqqs+deXZBm3PYPPvh8N2fk5fHSeeSAXFA03/AJXLGw8kDMYVHmMD88xD+Q8guHyz+HqbyWQE0IIIUS7ICNzNebMmUNGRgagSgsAvPvuuyxduhSA0NBQlixZYnZNbdKVqqoq+3W0q9FoYOYbKnlJ0Ukw6GDxPLh1FQQ0oyxD9xFqap2+Wk3bzNqvaoQ1xrQ4tmnRbGFe/PpsplmWmI7MBba8P/W5B5pP5Ry3AM57GhxdGr+uLA+W3gWHfzc/PvwGNZpnz6ybQgghhBBnIMFcjfj4eFJSUsyOpaamkpqaCkCvXr3aolsC1JTAq7+BT6dBdZnKJPjNlXDL302v4+XsDiFD1fo3gJRNZw7mTEdvOts0y+aorlTBl7OHWivm4NjyBCim0yxX/Uet2bvg3y3va60BM1TwtfkdtfayKesdM3bDoush3+T3gEcQzH4X+l/Yen0TQgghhGglEszVSE5ObvY1BoPhzCeZWLNmTbNfQ9QIGwZz3jOWKzh9FN4bB2PvgRHzzBNyNCRivDGYO/AzjLnT+nmluWoExs9kZC63DUbmDAaVdMM0cGotn06D/BNq5HPKM9DnXPAIsH5u9iH4YKLadvGGx1PByeT7XdnCkTmA1G3Nv8eZjFsAw69v2mhawjcqW6VpkpO+F8Al79pmGqgQQgghRCuQNXOi4xg0RxVkrlWYDssfh9cHw9G/m3C9SZ26E5sbHm379ip1z01vGo+V5aqMmvaUkQCv9IW/nga97uzvk30Edi8yT1RSdBIK06AgFX68WSWWaYhZwfCaIK7F0yyzzPdbMo21sgR2fGrMtFlLozlzIKfXw9/PwNI7jYGcRqsyVV6zWAI5IYQQQrRrEsyJjuWcR2H8/aB1Mh4ry1VFwQvOkIgmbBgE9DPu7/ne8pzSXJUgpTDNct2UraZa/vmEGin77lo4usJ4fPcitb5v05twfPXZ3bs4Cz6eAktug5/vMR6vn7SkLK/he1gL5loyzVJXZfl6RRlnl0ilqkwF38seUMW8mzNaXlWmfm42vG485h4A1/0EEx/qfIXihRBCCNHpyLsV0bFoNGpt1f171BRLx5qgoqJQFRZv7M28RgMxVxr3T+2zPOfY32DQq23PbhAYZWyzVTCXsRtObIJDy6DwpDpmMMDW94znZB85u3tv+B9U1Iwo7v/JeLz+1Miy/IbvUVls3K4L5lowzbIkx/rxvBTrxxui18NPt6mSFQBb3oG9PzTt2rJ8WDgbDiw1HgseCLet6Vz1BIUQQgjRqcmaOdExeYfBRc9D8AD4+W517OhfsPs7iL264euGXqmCj6FXQnC0ZfuR5cbtfhdAyWnIUVlLbbZurvS0cbs2s6NGA0OugL2L1X5RxtndO3Wr5TGDwXI0rckjc57qc8R4leLfyaP52SjrT7GslZdk/d+kIev/Cwd/Me4PvRIGX9rw+XWvnwNfXmJediDyPLj8C3D1bvrrCyGEEEK0MQnmRMcWey3s+wkSV6r9Px8FfZWqCRbYz7J8gW9PmPIv6/cyGCB5vXG/34VwMkElCvGLOHOR6bNVajJS5W6ShMS/j3G7KPPs7n1qv/m+Xq9S9hvqrcErz2/4HtamWfr2VB9nIzAK7tiogrov5xiPNydYPvQbrH7euB95vso6qXVo/LrCk7DwEmOADqrswPTXwMGpwcuEEEIIIdojCeZEx6bRwKw34Z0xUFmkkpT8ssDYPuN1GDm/afcqSIXiU8b9XuNh4OyGg7/WoNerdXq13E1GubxCjNtnOzI36y346RbjflWJ9WQqjY7MWZlm2RJOrhAyWG2PvBl2fKK2m5oEJfuwml5Zyz8S5n6iSiY0pugUfD4dco8bj41bABc8p36OhBBCCCE6GFkzJzo+nx5w0X+sty1/Uo2sNUXaduO2X2/bFLOurzzffJTMtDyAV6hx+2yDuQEzzPcrS6wnLGl0zZyVkbnW4t/M8g/VlfDjLcYA09kLrv72zFkrS3PV1ErTQG7yExLICSGEEKJDk2BOdA7D56lRuAGz1Iha7dquqlL4/RHriVH0OkjbAUf+UvtpO4xtPUbZvs9gvl5O4wCuvsZ9b9Ng7iynWTq6qvvWqii2njWyohB01dbvYW3NXGsxreV3Mh5ObGm8DMP6/0LmHuP+pR9AUFTD5wNUFMHXcyHrgPHY+f+CyY9KICeEEEKIDk2mWYrOQaNR0ylrp1QmfAtL71DbR/6Eg7/CwFnG85PWw+Lr1fRC317Qb7f5yJy9grmSeuvlTIML05G5ymIoL2x+gg6NRgVgtRktK4sbXldWXmC9cLi1aZalufDrvSqZTFUZzPv1zNMcrTEdmSvNgU8vUqNs0dPh/GfAM8jYfjIe1v3XuD/iRnVeY3RVquRD+k7jsYkPwcQHm99XIYQQQoh2RkbmROcUcxVETDTu//GoedHvgEjjOrH8FMg6qEoE1Oox0ridnwqJq2D7J5BztOHXzE+FA7+o0a+mspbJspZ7IGhNAqSzHZ0znRpZWdJwPbeGkqBYm2ZpMKgAOXGlKqvQnFpzP90GH0yCry5T5QgcXc3by/Ig/itYXW/q7OoXjFNSfXvChQ1Mra1lMKhR2aS1xmNxt8F5Tze9r0IIIYQQ7ZgEc6Jz0mjUtEsHZ7VfdBI+Og/SdqqAbOmd5ud/eA7oKtW2oyt0G2xsW3KHyrr424MqMMhPhU1vw+lE4zlleSpAWXw9/HxX0/vZUCZLUEWrPc8yCcrJBJUU5pd7YejlcNcWuH+fClJdfWDwXIi62Pya2uC2LM98xHDolWpa4sSHIXyMOubsbn5tc4K5UwdU4Hzsb/U9H3a99fNMC6iDSnIy4kZAozJXung1/jpbP4Cdnxn3B18GU1+SqZVCCCGE6DRkmqXovAL7qQBkzf+p/dPH4JMLQKNV5QtM1QZyERMhKBocnY1t/hGQskFtH18LK59Vo3w7PoG7t6mU9geXQVlNVsoDP6vROZcmrC+rP82yPq8QNXrnFWLZ58akboXsg+qj+0i44FljW1CUCoxATVs06NXURp8eKtj99EL1PZq3DHqOhqip6sOUoyugAWrWIpqO3p1JSbZx2yMIck2CYo3WWLS9MF0lLKkt0eDiBTPfUMXiA/s1/hpH/4bljxv3u4+E2e+oAFkIIYQQopOQYE50bpMeVun4N74JGNQ0vfo11kydPgbX/Wh+zDRJR22R6qFXwejbjbXJ6qfVT9umClGfiVcI9Byrgjprdezm/QpObs0fTTItFh4+uuHzJj1svv/huaoOHcBvD8GdG6xfp9GAk7v63kLDUzfr0+vNgznPYPMRztpArtbyJ1W2SlNnCuTyU+HHm4338u4BV32jvo9CCCGEEJ2IPKYWnZvWQY1K3fS7SnRSyycc5n6mAhJTRRmwZ5H5MWtB1p7vzNPcnzpg3u7UxBT+w66D+X/Cgh1wwb8t253dz25aYOo243bPRoK5+qpMRthO7W38XNOplk2dZlmWZx5Mu/o0Xjri8O9qOmZT6apUIFe7/s/JHa75Dry6Nf0eQgghhBAdhARzomvoNQ7u3AQXPg/TXoG7t8LgSyFiguW5G/5nnh7fNONirWHXw5C5xv1Mk8Dn6kXNC6DOhsEAecmQut38uF4PK59TBdBrNTYyV9+UZ4zbpglkrDENhJs6zdJ0VM7BGUpOUzdVE43xuKmvLoOq8qbdf/Xz5qOS01+DkCFNu1YIIYQQooORaZai63DxhHH3mB+LPB+O/mV+LDdRTaccNEft+9UL5gL7w7SXjPuluVCYZty3ZfBwfC1s/J/KrgkQ0E+N6oGa6rj0Tti/xHh++GhI+EaNNlaWwPAbVOKT7ENq2qFvLwgxSfbi5m/crh3d+utpNcLp7AEjbjJm3TQN5po6zbIky7hdf71cQCRc/jksvsF81BPg1Si4f48ayWvIsb9hw+vG/ZhrIPbqpvVLCCGEEKIDkmBOdG19z7d+fP1rMPASNcXRzQ8CoyDnsEr8MfczY4r+qnJV/6yWewB4h7VuH3VVqixB6Wk17bA2kAM4fVSNbrn7w6LrzKckBkXDpR/Btg9V8AZQnKVKCqx9Ue37R6oRxrI8CB6oAqpapXlqBHDzO8apkYMuNQZzZzPNsrixYK6vCoQ9QyyDufC4xgO5snz42SRQD+wPF7/StD4JIYQQQnRQEsyJri2gr/XjmXtUsfGoaSqgu3Yx7FkM/S6EboNg2YMqyUnWQZXmv1bIEPM1bgaDCr7yU9RIkVO9mmqH/1SBmHuAGiWrX3g7+wi8U1PA3MHZ+pTJtG0qMDIN5CLPU6Ncrj716swVmycCyU2EtTWjjFEXQ48njG1leVBdYb7GzfRe1qZZVpaqpDC1iWHqM83e6RGkEs7Uqv238DIpx9B9pEp4cuHz1u9X66+njKUbHJxVwN2UbKJCCCGEEB2YBHOia9NoYOqL8Odj4OINocMguabI9N/PQN8LVIDlFwHn/MN4Xdp24zq5Pd8Zj3cbrI6nbFbBUMEJVQAbIG0HXPKu8dzKUvj2SuP+Q4fNAxlQ2R5r6SohZZPl15C6Vd2rVo9RcM1iY0DlbBLUVBab77v5GevLHf7dfJ1cVYmxrZZpAFd/muXKZ2H9q4AGPLupIHXcveZTHU2nWdbPZFk7KugVajwWGgMzXrP8mk0lroL4L4375zxqPnVUCCGEEKKTkmBOiDF3Qt8paqSoIBXenwgY1NTE3d+odWb1hcWq0bu6/eGQdUCtM3u/JqmKac00UGvXJjxgTK1vWjAcrNeZc/WpKQFQE6xZK6twYqt5kDTsevORMbORuRLz9W3e3c0DNtPabGCeSKX+vXy6q/WEzh4qscn6V2saDFCcqT6W3qGmQI6+XdV4qz/N0nQ00b82mDMtlJ5p+fWaqiiGX+4z7ocMgfH3NXy+EEIIIUQnItkshQAVYLn5qmAg5irj8dX/Zz7qVSs01rjdfQTcthoeT4cL/wPammck9WumYTAJeDCfcujqY31qokZjOVpX38ldcOtquGIhDJwNA2eZt7t4Gbcris3Xt51pfZ9p2QBHN5UIpdaM1+G+BLh9HRxd3vA9lj+mCpFnH4bgASrpTMgQ8A6F4lPG8+qmWZqMzNVOnWzI6v9To5+gvu+z3214iqcQQgghRCcjwZwQ9Z37JDi4qO2iDNjwmkpCYios1riduU+1OziqaYseQQ3fe/ciY3KP0lzjcffAhq8xDW5qdR9h7GN1OeQcUYHcFQtVH0w1OjJ3hmDOdGTOuYHaeTs/My/NMOsty3PStsPn09X6wut/gjs2QPgYY7uTu/HrbOrIXPYR2PaBcX/8/RA6tOHzhRBCCCE6GQnmhKjPN1xNC6y17hX4vzD4YJIKxkCtjdPWjADpKozZInVVUF7QyM31KlMmmE+ztDbFspa1YC5kKHQfbtw/saXh682CuSLzkTmvBoK5qS/BVd+Ab0/r96lVclrVtas19CroNR40DlbOzVbTLvU1I5amyU/8I9U0TDD/eotPGc+vb/kToK9W2749YdIj1s8TQgghhOikJJgTwpqJD4Krr3FfVwkZu1Udt4I0cHRRUwZrnUxQnw/+apmm3yPYfH/3t2r6ollmx8ZG5qxMswweYJ7Z0rRQdn3OJtMsLUbmrASKXqEw5g6Inq5KMdTdx0p2yDX/Z6xH5+wFFzyrEpmYJi3xMQkIE1fBlpokMNaSnwB4dTNuG3SWawsBjq6AYyuM+xf+xzJTqBBCCCFEJyfBnBDWuPnB3E9VNkWtSZ4ggw4O/6G2TadaHvhZlSHY/rHxmHuAusdV36ggx6u7Oq6vhl/vN08GUlmiAhSDwbIv1kbmgqKhp8k0xdSt1q8FURbkyQAAJmNJREFUK9MsTYJNN3/zgA3MyzXUlhyofx+AiiJVrqHW5MeMgVjwIOPxkhyImm7c//sZFRiblSUwCeZcvMwDx/rr5nRValSuVq8JMKDeOkEhhBBCiC5AgjkhGtL3fJXc44mTMOIm4/HaYC7EZH3WsRUqsEnZaDx29SIYfBmEj1IZFieblDZIXAmHlhn3k9bC13NV4e/60zStjZ4FD4Qeccb9iiLzZCJb3oNPp6k6dqZBWHU5VBQa953cVEkGU7XZNkGVMqhlWiQcVDFx03sNu86kf9Emr1mqpkDWjhCGj4bcJMuC4aYaWze341O1RhAADUx9wby2nxBCCCFEFyHBnBBn4uiigrJayetV8NStXi2z5A3G7ZCh0GOkefuwG2DgJcZ902Cm1qFl8OFklVSlVv2ROfcA8AwCjwBj2YSeY4zTOU8nqrp5JzbBT7eqkcVeE6D/VPV1lJsEYPmpUHba/P4BJsFcdYVxu/40S9Pg0dFVZQOt5eKliqDXKjoJV38DaCBlA6x72XrBcGtfs+nIXFWZeUbQ4TdI0hMhhBBCdFlSZ06Ipug5Rq2hK89X6+cSV0P0DBUkpWxQtd32/2Q8P+5Wy9EirRbmfKCmHaZsoEG5x+GTC9WoYGBfyzVz1/5g3J71Fpz7lCrAXft6iauM7RWFatTwpt+Mx07tVyUKji6HP/4B+nq160pz4Od7VP25wP7wzzxVQLz+eYUnjdumUzWrylVfug2G/BTja57zD1XM/NjfMGgOfDbVeE1TR+Z2fGoMIh1d4dwnEEIIIYToqmRkToimcHCCfhcY94/8qYKzG5fBAwdUiv/aKYmuPioFf0Nir7HMIjn6Dpj8BFATkFWVwOaaFP9eYTDpH6qu29XfqRptpry6mQeOyevN23d+Yb7fbZDq6/pXVSbO+jQOEP+lGiVM2aS+Thcv85E3gLwU47ZpbbdNb8F/guHIH8Zjp/arz/0vhItfNi9y7uoL7v71viYrwVxlKWz4n/H4yPlnrsEnhBBCCNGJycicEE3Vfyrs/V5tH1muRqq0DmqEaPM7xvNG32G5vgzg+FpYfL1aE+cRDH3OVQlG+k1RUzC9Q1VQ8/vD6vy9P8JFL6h7nfdk0/qo15tP9wRI36GmbYbUTAutKIJf7zO2O3upkgW1PE2ySZaZ1MIzex2dWvdWxyT5Su06OtOi6VkHjNu6KvNyBhETLO9vNs2yJpjb+ZkxCHR0VXXlhBBCCCG6MBmZE6Kp+k4xZrYszYG0HWp74+smo3K+MPZu69d3G2TMDlmSBePvhVtWwDmPGpOcxF5jTBRSWQQHf2leH7MPQulpy+O7TEbnVj5rLAaudYL5f4Bfb2O7aQKW0gaCuezDoCs37ptOwawosjz/9DFjSYSdn8Ppo2pbo1VF2uszDSiLMqyMyt1sXsJACCGEEKILkmBOiKZy84Ve44z7R/6AwgzY9pHx2IT71TRLazwCod+Fxv3d31me4+wBgy817sd/1bw+Jq0zbmtM/nsnfK3KJ6z+P9j2ofH4xIfUtM0Qk2QupolNSnOsF+0+uct8v9Kk3IFpMFdbPNygVwFgeQGsecHYPux66DbQ8v71R+Z2fm4yKuemsoMKIYQQQnRxEswJ0Rz9pxm3t7yvEpVU14xQeQRD3G2NXx9zlXH74K/WR7GGXW/cTl6vEqI0VZLJerkRN4JTTVmCyhJYfAOsfcnY7uQBEx5Q28EmAVV+qvk9n/WD/+sBCd8aj6XXC+Z0FcbRuYoGpmxmHYANrxtHDp08Gk5gYroWriQLtr5n3B85X0blhBBCCCGQYE6I5okyycBYXQYFJ4z7kx62LKxdX/+pxpG7qlIV0NXXY6QqCl4r/uum9U2vM8+SGXUxDLms4fMNOnCqyUIZPMB4/PQx6hKx1KosUusDa9UfmQNjMXLTYM63p3F701uw8U3j/vj7Gk5gYnrcoIf8mu+zxgHG3mX9GiGEEEKILkaCOSGaw7+PSnBSn19vNRJ2JvVr1i29E9a9Yn6ORmNegDvhG8uyANZk7jWud9M4qHIKI+djEZjVMq0bZzoyl5tofapoSTYUpKvac6Z18GpVWgnm/E3W4mUdUAEkqGmU4+5p+Gtx9gAXK30YOAt8ejR8nRBCCCFEFyLBnBDNNe0l+EcS3PQHTH8Npvwb5v2qArWmiLnafH/P95bnDL3SmGyl6CQcWHrm+5qul+s+XJUTCBsGl39unuCklpNJxk3/PioZCqiAq36BcIDlT8AbQ2tq01VZtteNzJkUJQ/sb3medw9VYuFMo5jWRu3GyKicEEIIIUQtCeaEOBvu/ioZyqibVdIT3/CmX9tjlPl+dbnlOZ7BED3duP/X06rQd2NM68v1nmTcHnQJ9D3f8nwnN+O2g5N54OXgbP019NUqGUktn3CY+SZc+jG4B6hjpiNzoTHGoBSg7wVwx3oIi238awHLdXHdR1h+74QQQgghujAJ5oSwN40GLnjWuN9Q0pTz/2UMqgrTYf1/G76nrhpSNhv3Iyaat1sbBTMN5sB83Zxp3bjG9J8KI+bB0MvB1RsMBss1c9Nehu4j4cLn4ZrFlgXCG2Ka0RLUqJymgSmjQgghhBBdkBQNF6ItjK1ZL6arqlnXZkVAJIxbAOtfVfub3oaYayDIytTFrP3Gwt9aRwgfbd5eW7vOlFO9wuamwZyuEtz8oCzPeMzBRSVnSdloPNZjpPk9qivMp2C6eKnRy1E3W/8aG2M6zdIrFAbObv49hBBCCCE6MRmZE6ItaB1UNsdJD4Oze8PnTXxITWUEFST98Yga/aqvtoA5qLpx9e/ZpJE5kyQoWkfLtX1T/qXWBp77JLh4Q484GHhJvZsaYPLjKlgdfkPDNfeaot9Fxu1z/qGmggohhBBCiDoyMidEe+bsAVNfgEU12S2Pr1FlAbqPMD8vfadxu3u90bLa+1gca2RkLj8Ftrxr3j7yZhWEnvMPFWSaliqo5eQGkx9r8MtplojxcMcGlSWz5+gzny+EEEII0cVIMCdEexc9A0JjISNB7Z+Mtwzm0rYbt+tPfQRwsZKdsv40S99e6lhtVkpTWkfzbJ21gdzBXyFxFZTkQL8L1GhcawoZ0rr3E0IIIYToRGSapRDtnUajSg3Uyjpo3l6WDzlHjPvWMj5aKzVQf5qlVmterNzseg/ryUdSt8KOT+HgL+ZTPYUQQgghhM3JyJwQHYHperb6wdzJXcZtNz9VM66+7iPg2h9hz3dw7G+VZdK3l/XXqb2fxkEFhgad9ambAB5Bxu3S0037WoQQQgghRKuQYE6IjsB0PVvWAZUEpXakLM10vdwI6yNoHoHQb4r6aMzw6yHhKxXIGXSQukVlwrzlb+vnuwcat0ty1BTQI8tVFkv/SIia2rSvTwghhBBCNJtMswRKSkr48ssvWbBgAXFxcbi4uKDRaHjxxRdbdF+9Xs9HH33EhAkT8PPzw83NjT59+nDttdeyf//+Vuq96BKCTIK5sjwoPmXcN10v19AIWlP1HAMPHoQbfzMeqyyC6krr53uYBnPZaqrlmhdg+ROw/aOW9UUIIYQQQjRKRuaAo0ePcsMNrZu4obS0lJkzZ7Jq1Sr8/PyYMGECrq6uJCUlsWjRIqZNm8agQYNa9TVFJ+YRAJ7djEFc1gFVh81ggHSTtWrW1ss1l3eYKj1gqiwPvLpZ6ZdJMFeaY14w3MVKbTshhBBCCNFqJJgDvLy8uPnmm4mLi2PUqFH8+OOPPP/88y2650033cSqVauYP38+b731Fu7uxsyBGRkZVFVVNXK1EFYEDzAJ5g5C5HmQl2y+Vs00UUpLOHuAg7MqHg5Qlms9mDOdZlleYN4XCeaEEEIIIWxKgjkgMjKSjz/+uG7/559/btH9Vq1axeLFixk1ahQfffQRWq35bNbQ0NAW3V90UcEDVZ05UCNzYF5fzj8S3P2tX2swwNeXQ9JaFaBd9Q1ETGi4qLdGYwzkAA79Zr5ur5bpyByo4LJW/dE9IYQQQgjRqmTNnA188MEHADzwwAMWgZwQZ80sCUpNRkvTcgDW6svV0mggZZMxQPvuGmNg2BT1C4jXcvYwr1dnFszJyJwQQgghhC3JyJwNrFq1CoApU6awb98+vv/+ezIzMwkJCWHatGmMGTOmjXsoOiSz8gSHQK+vVyz8DOvlnD2gqsS4X79oeGOqKxpucw+EghNqOzfJeFyCOSGEEEIIm5JgrpWdOnWKnJwc/Pz8+OSTT3jyySfR6/V17c8++yzXXXcdn376KU5OTk26Z0OJUhITE4mMjGyVfosOICjKuF1VogK5jN3GY91HNH69iyeUZBn3mxPMObo23OYRYAzmTINFCeaEEEIIIWxK5gC2sry8PACKiop4/PHHufbaazl8+DB5eXn88MMPBAYG8tVXX/H000+3cU9Fh+PipYp911p2P+hrEul4hULIkMavd/Yw33dya/z8i14wbl/SwDRLAI9gtfYuoK9lf4UQQgghhM10ipG5uXPnsm/fvmZds3DhQuLi4lq9LzqdDoDq6mrGjh3LwoUL69ouu+wyXF1dmTFjBm+++SZPPPEE3t5nThLRUE06KW3QBQUPhPyaUbDaJCgA4xaAwxlGep09zffPNDI36mZw9VaJTPpe0PB5V38HtWtD3xwOuYlqW4I5IYQQQgib6hTBXHJyMocPH27WNaWlpTbpi5eX8Q3s/PnzLdqnT59Ot27dOHXqFNu2bWPKlCk26YfopIIHwJE/zY+5+cOIG898bf2RuDONzDm6wLDrznxf0yQ/ZnXmJJulEEIIIYQtdYpgbseOHWc+yU7CwsJwdnamsrKSXr16WT2nV69enDp1iqysLKvtQjTINAlKrTF3WU6htEZb7797c9bMNZUUDRdCCCGEsJtOEcy1J46OjgwePJhdu3aRm5tr9ZzTp1VhZU9PT6vtQjSofq03F2+Iu7Vp12rqLZF1tkEw92iyCugqCsGnR+vfXwghhBBC1JEEKDYwa9YsAFavXm3RlpycTHJyMgDDhg2zZ7dEZxDYHzQOxv1Rt4Cbb9OurR/MOZ5hmmVTGQxQXgi5xyHnCHgGQUCkmqYphBBCCCFsRoK5FoiOjiY6Opr09HSz43fddRfe3t589tlnrFixou54cXExd955JzqdjunTpxMeHm7vLouOztEF+k9V257dYOzdTb+2fjDXWgXtT8bDi+Hw5jD4Ykbr3FMIIYQQQpyRTLOsMWfOHDIyMgBIS0sD4N1332Xp0qUAhIaGsmTJErNrapOuVFVVmR0PCgri888/54orrmDq1KmMGTOG4OBgtmzZQmZmJr179+aDDz6w8VckOq25n8Lx1RAyFDwCm36dm59t+mPah/IC0FWdObOmEEIIIYRoMQnmasTHx5OSkmJ2LDU1ldTUVIAGk5k0ZM6cOWzatInnn3+eDRs2sGPHDsLDw3nooYd4/PHHCQgIaLW+iy7GyRWipjX/ugv/A9EzoKoUMLRef9zrBZSlp8ErpPXuL4QQQgghrNIYDIZWfFcn7Km2zlxDdeiEsJvnw6CqxLgfeT5c/1Pb9UcIIYQQog3Y+/25rJkTQrScR72R5vL8NumGEEIIIURXIsGcEKLl6k+1lBpzQgghhBA2J8GcEKLlPILM9yWYE0IIIYSwOQnmhBAtVz+rpot32/RDCCGEEKILkWBOCNFy7vXWzMnInBBCCCGEzUkwJ4RoOZlmKYQQQghhdxLMCSFazmKapQRzQgghhBC2JsGcEKLluo8w35dgTgghhBDC5iSYE0K0XFCUeUAnCVCEEEIIIWxOgjkhROuoKDJuy8icEEIIIYTNObZ1B4QQncTgy6AgVQV1PuFt3RshhBBCiE5PgjkhROuY/Fhb90AIIYQQokuRaZZCCCGEEEII0QFJMCeEEEIIIYQQHZAEc0IIIYQQQgjRAUkwJ4QQQgghhBAdkARzQgghhBBCCNEBSTAnhBBCCCGEEB2QBHNCCCGEEEII0QFJMCeEEEIIIYQQHZAEc0IIIYQQQgjRAUkwJ4QQQgghhBAdkARzQgghhBBCCNEBSTAnhBBCCCGEEB2QBHNCCCGEEEII0QFJMCeEEEIIIYQQHZAEc0IIIYQQQgjRAUkwJ4QQQgghhBAdkARzQgghhBBCCNEBSTAnhBBCCCGEEB2QBHNCCCGEEEII0QFJMCeEEEIIIYQQHZAEc0IIIYQQQgjRAUkwJ4QQQgghhBAdkARzQgghhBBCCNEBSTAnhBBCCCGEEB2QBHNCCCGEEEII0QFJMCeEEEIIIYQQHZAEc0IIIYQQQgjRAUkwJ4QQQgghhBAdkARzQgghhBBCCNEBSTAnhBBCCCGEEB2QBHNCCCGEEEII0QFJMCeEEEIIIYQQHZAEc0BJSQlffvklCxYsIC4uDhcXFzQaDS+++OJZ3zMvL49HH32U6Oho3NzccHNzY9CgQTz11FMUFha2Yu+FEEIIIYQQXZFjW3egPTh69Cg33HBDq90vOzubsWPHkpiYSFhYGFOnTqW6uprNmzfz/PPP8+OPP7J582Z8fX1b7TWFEEIIIYQQXYuMzAFeXl7cfPPNfPDBB+zatYsnn3yyRfd74YUXSExMZM6cORw/fpwlS5bw66+/kpSUxIQJEzh06BCvv/56K/VeCCGEEEII0RXJyBwQGRnJxx9/XLf/888/t+h+69atA+DRRx/FxcWl7riXlxcPPfQQGzZsYPv27S16DSGEEEIIIUTXJiNzNmAawDXE39/fDj0RQgghhBBCdFYSzNnABRdcAMBLL71ERUVF3fGioiL++9//AjBv3rw26ZsQQgghhBCic5Bpljbw8MMPs3r1apYsWUKfPn0YPXo01dXVbNq0CQcHBz755JO6gE8IIYQQQgghzoYEczbg6enJn3/+ya233srXX3/NkiVL6tpmzZrFiBEjmnW/QYMGWT1+6NAhnJycGmwXQgghhBBC2E9iYiJOTk52e71OEczNnTuXffv2NeuahQsXEhcXZ5P+nDhxgunTp5ORkcHChQuZOnUqAH/88QcPPPAAEyZMYMWKFYwZM6ZFr6PX66mqqmqNLosmSkxMBFTSHGEf8j23P/me2598z+1Pvuf2J99z+5Pvuf1VVVVRXV1tt9frFMFccnIyhw8fbtY1paWlNuqNWg+3b98+li5dyuzZs+uO33DDDXh6enLZZZfx4IMPsmnTpibdb//+/VaP147INdQuWp98z+1Pvuf2J99z+5Pvuf3J99z+5Htuf/I9tz97z5jrFMHcjh072roLdVJTU1mzZg0uLi7MnDnTon327Nm4uLiwZcsWysvLcXV1bYNeCiGEEEIIITo6yWbZytLS0gDw8PBAq7X89jo4OODu7o7BYCA/P9/OvRNCCCGEEEJ0FhLMtbKQkBAAcnNzSUpKsmhPTEwkLy8PDw8PAgMD7d09IYQQQgghRCchwVwLREdHEx0dTXp6et2x3r17M3ToUABuv/12CgoK6try8/O5/fbbAbjkkktwdOwUs1yFEEIIIYQQbUCiiRpz5swhIyMDME6VfPfdd1m6dCkAoaGhZiUGgLqkK/UzSn744YdMmTKFFStW0LdvX0aPHg3Ali1bOH36NBEREbz88su2/HKEEEIIIYQQnZzGYDAY2roT7UFERAQpKSkNtvfq1Yvk5GSzYxqNBoCkpCQiIiLM2hITE3nppZdYuXIl6enpaLVaevfuzaxZs3jkkUfw9/dv7S9BCCGEEEII0YVIMCeEEEIIIYQQHZCsmRNCCCGEEEKIDkiCOSGEEEIIIYTogCSYE0IIIYQQQogOSII5IYQQQgghhOiAJJgTQgghhBBCiA5IgjkhhBBCCCGE6IAkmOtgysvL+de//kX//v1xdXUlLCyM+fPn1xU6F61v8uTJaDSaBj/+/PPPtu5ih7Rz505efPFFLr30Urp3745Go8HV1fWM1y1cuJC4uDg8PT3x9/fn4osvZtOmTXboccfX3O/5M8880+jP/mOPPWbH3nc8paWlLF26lJtvvpmhQ4fi7e2Nh4cHMTExPPvssxQXFzd4rfycn52z+Z7Lz3nLvfbaa1x66aX069cPHx8fXFxc6NWrF/PmzWP//v0NXic/52evud9z+TlvXbm5uQQHB6PRaIiOjm70XFv/nDu22p2EzZWXl3P++eezadMmQkNDmT17NsnJyXz22WcsW7aMzZs3ExkZ2dbd7LQuu+wyPD09LY537/7/7d19TJXlH8fxjyCSaEAgKqhsCsGKogmtmQS6Mh+yMh9WtmrKysq2yqlbWwWuYeUSZtRvudWcIokucdZ82NRkPqKUglr41BRFpiwgQAQBiev3R+PkCeTIw7nxxvdr4w+u67rd93z3cfrlnPtmWA9UY38pKSn66aefOnTNwoULtWLFCvXv318TJ05UfX29du3apZ07d2rjxo2aPn26m6rtHTrTc0mKi4tTeHh4q/XY2NjuKKvXysrK0rx58yRJUVFRmjx5sq5evarc3FwtWbJE69ev1969ezV48GCn68h553W25xI574rPPvtMtbW1io6O1sMPPyxJKiws1Nq1a7Vhwwb9+OOPmjJlitM15LxrOtNziZx3l4ULF6q8vPy2zrk95wa2kZSUZCSZxx9/3NTU1DjW09LSjCSTkJDQg9X1XuPGjTOSTFFRUU+X0qssW7bMJCcnmy1btpjS0lIjyXh7e9/y/O7du40kExgYaM6ePetYz83NNf369TN+fn7mr7/+sqJ02+poz5csWWIkmdWrV1tXZC+SkZFh5s+f75RXY4y5fPmyGT16tJFkXn75Zac9ct41nek5Oe+6AwcOmOvXr7da/+abb4wkExISYpqamhzr5LzrOtpzct59fv75ZyPJvPnmm0aSiYyMbPOcVTlnmLOJxsZG4+/vbySZ/Pz8VvvR0dFGkjly5EgPVNe7McxZw9Vg8cwzzxhJZsWKFa323nvvPSPJpKamurHC3odhrufk5uY6+t/Q0OBYJ+fuc6uek3P3Cg8PN5JMYWGhY42cu1dbPSfn3aOurs6Eh4ebBx980Jw9e7bdYc6qnHPPnE0cOHBAVVVVCgsL0+jRo1vtz5o1S5K0ZcsWq0sD3K6+vl67d++W9G/Wb0b+YTePPPKIJKmhoUEVFRWSyLm7tdVzuJ+np6ckqV+/fpLIuRX+23N0n08++UTnzp3TypUr5eXldctzVuace+Zs4vjx45KkmJiYNvdb1lvOofutWrVKFRUV8vDwUEREhF544QWFhob2dFl3hdOnT6uhoUFBQUEaPnx4q/2W/J84ccLq0u4KOTk5OnbsmOrr6zV8+HBNmTKF+yu66Pz585IkLy8vBQQESCLn7tZWz29Gzrvf2rVrdebMGUVERGjUqFGSyLm7tdXzm5Hzzjtx4oTS0tKUmJiohIQEXbhw4ZZnrcw5w5xNFBcXS1Kbgbh5veUcut/SpUudvl+8eLGSkpKUlJTUQxXdPVzlf8CAAfL391dlZaVqamp07733Wller5eZmen0fVJSkmbOnKk1a9a0+VAguJaeni5Jmjx5sry9vSWRc3drq+c3I+ddt3z5chUWFqq2tlanTp1SYWGhQkJClJWVJQ+Pfz4MRs671+30/GbkvHOam5s1b948+fv764svvnB53sqc8zFLm2h5nLKPj0+b+wMGDHA6h+6TkJCgzMxMnTt3TnV1dTpz5ow+/fRT9e3bV8nJyY7/IMB9XOVf4u+AO4SHhys1NVWFhYW6du2aLl26pHXr1mnYsGHatGmTXnvttZ4u0Za2b9+uVatWycvLSykpKY51cu4+t+q5RM67044dO5SRkaHs7GwVFhZqxIgRysrKcnrnh5x3r9vpuUTOu+rrr7/WL7/8ouXLlyswMNDleUtz3uW77mCJN954w0gyH3/8cZv7LTdhRkREWFzZ3WvHjh1GkvHz8zN1dXU9XY7tqZ2HcXz//fdGknniiSdueX1ISIiRZC5fvuyuEnud9nrensuXL5vAwEAjyRw8eNANlfVeJ0+eNPfdd5+RZL788kunPXLuHu31vD3kvPMqKyvNvn37zIQJE4wks3TpUsceOXeP9nreHnLuWnFxsRk4cKAZN26c03pRUdEtH4BiZc55Z84mWt5+ra2tbXO/rq5OkniL3EITJ07Uo48+qurqah0+fLiny+nVXOVf4u+AlYKDg5WYmCjpn58K4/aUlJRo8uTJqqys1MKFC/X+++877ZPz7ueq5+0h553n7++v+Ph4bd++XbGxsUpKStKvv/4qiZy7S3s9bw85d+2dd95RY2OjVq5cedvXWJlzhjmbaHnQRklJSZv7Les8kMNa999/vyTpypUrPVxJ7+Yq/7W1taqqqpK/vz/3V1iE7HdMeXm5nn76aRUXFysxMVGpqamtzpDz7nU7PXeFnHeNl5eXXnrpJRljHE/tI+fu1VbPXSHn7du6dat8fHw0f/58jR8/3vE1e/ZsSf/cH9ey1vKRSStzzgNQbKLlkcr5+flt7resR0dHW1YTpMrKSkn89NDdIiMj5e3trbKyMpWUlLS6oZj8W4/s376amhpNmTJFp0+f1owZM/Tdd9+pT58+rc6R8+5zuz13hZx33aBBgyRJZWVlksi5Ff7bc1fIuWtVVVXau3dvm3vXr1937DU1NUmyNue8M2cTcXFx8vPz07lz51RQUNBqPzs7W5L07LPPWl3aXausrEz79++XdOtfGYHu0b9/fz355JOS/s36zci/tYwx2rx5syTxSGsXGhoaNG3aNB05ckSTJk3S+vXrHb8D6r/IeffoSM/bQ867R8t/csPCwiSRcyv8t+ftIeeuGWPa/CoqKpL0z+DWsubv7y/J4px36Y47WOqjjz4ykszYsWPNtWvXHOtpaWkub7JE5xw6dMjk5OSY5uZmp/WioiITFxdnJJnnn3++h6rrXeTiYRy7du0ykkxgYKA5e/asYz03N9d4e3sbX19fU1FRYUWpvUZ7PS8rKzMZGRmmvr7eab2mpsa89dZbRpIZOnSoqa2ttaJUW2pqajLTp083kkx8fPxt9Yqcd01He07Ou27fvn1mw4YN5saNG07rjY2N5quvvjIeHh6mf//+pri42LFHzrumoz0n5+7R3gNQjLEu532MMabrIyGsUF9fr/HjxysvL0/BwcGKj4/XxYsXlZeXp8DAQB0+fFjh4eE9XWavsmbNGiUmJio4OFgREREaOnSoSkpKdPToUdXX1ysqKko5OTkaPHhwT5dqO9u2bXN6RHheXp769Omjxx57zLGWlJSkqVOnOr5fsGCB0tPT5ePjo6efflqNjY3atWuXmpub9cMPP2jmzJmWvga76UjPL1y4oJEjR8rX11cPPPCAQkNDVVVVpfz8fFVUVMjf319bt25VXFxcT7wUW0hPT9eCBQskSdOnT5evr2+b51JTUx0fi5LIeVd0tOfkvOta/p0cNGiQYmNjFRgYqPLycv3222+6cuWK7rnnHmVkZOjFF190uo6cd15He07O3aOlr5GRkTp9+nSbZyzJeZfHQViqrq7OJCUlmbCwMNOvXz8zZMgQM2fOHKefeKH7nDx50syfP9/ExMSYoKAg07dvX+Pn52fGjBlj0tLS+JUEXbB69Wojqd2v1atXt3ldbGys8fHxMX5+fmbSpElm//791r8AG+pIz69evWo++OADM27cODNs2DDj7e1tfHx8TFRUlFm0aJEpKSnp2RdjA0uWLHHZb0mmqKio1bXkvHM62nNy3nXnz583H374oYmLizPBwcHGy8vLDBgwwERFRZl3333X/PHHH7e8lpx3Tkd7Ts7dw9U7cy3cnXPemQMAAAAAG+IBKAAAAABgQwxzAAAAAGBDDHMAAAAAYEMMcwAAAABgQwxzAAAAAGBDDHMAAAAAYEMMcwAAAABgQwxzAAAAAGBDDHMAAAAAYEMMcwAAAABgQwxzAAAAAGBDDHMAAAAAYEMMcwAAAABgQwxzAAC40aFDh+Th4aGYmBg1Nzff8tymTZvUp08fTZs2zcLqAAB2xjAHAICbNDc36+2335YxRunp6fLwuPU/uzExMZKk3Nxcq8oDANgcwxwAAG6ybt06nThxQhMmTFB8fHy7Z0NDQ+Xp6any8nKVl5dbVCEAwM4Y5gAAcJNly5ZJkhYtWuTyrKenp3x9fSVJpaWlbq0LANA7MMwBAOAGhw8f1smTJxUcHKyJEyc67e3fv18FBQWtrmm5p669e+sAAGjBMAcAgBvs2LFDkjRhwgSne+VKS0uVkJCgzz//3Ol8XV2dqqurJUlDhgyxrlAAgG0xzAEA4AbHjh2T9O+DTVocPHhQkhQREeG0fvz4cUlSSEgIwxwA4LYwzAEA4AaXLl2SJA0fPtxpfdu2bZKkoKAgp/WdO3dKkp566inHWm1trTw9PbVmzRrNmTNHAQEBCggIUEpKijtLBwDYBMMcAABucOPGDUnO979VV1crOztbkvT333871puampSRkSFJevXVVx3rx44dU3Nzs1asWKHnnntOR44c0eLFi5WcnKzz589b8TIAAHcwhjkAANwgNDRUkpSTk+NYW7x4sYKCgvTQQw/p0KFDjvXk5GQVFRVpzJgxTg9LKSgokIeHhzIzMzVr1iyNGjVKr7/+uiTpzz//tOiVAADuVH17ugAAAHqjV155RVu3btW3336rixcvqqqqSnl5ecrOztbGjRu1YcMGTZ06VRUVFcrLy9PQoUOVmZnp9Gfk5+dr7Nixio6Odqy1vCMXFhZm6esBANx5GOYAAHCD2bNnq6SkRP/73/+0Z88eRUZGauPGjZoxY4ZiYmJUWlqqPXv2aODAgZo7d65SUlJa3V9XUFCgSZMmOa0dPXpUI0aMaHXPHQDg7tPHGGN6uggAAOCssbFRAwcOVFZWlmbNmuVYnzt3rqqrq7V58+YerA4AcCfgnjkAAO5Av//+u27cuKHY2Fin9aNHj7ZaAwDcnRjmAAC4AxUUFCggIEAjR450rF2/fl2nTp1imAMASOJjlgAAAABgS7wzBwAAAAA2xDAHAAAAADbEMAcAAAAANsQwBwAAAAA2xDAHAAAAADbEMAcAAAAANsQwBwAAAAA2xDAHAAAAADbEMAcAAAAANsQwBwAAAAA2xDAHAAAAADbEMAcAAAAANsQwBwAAAAA2xDAHAAAAADbEMAcAAAAANsQwBwAAAAA2xDAHAAAAADb0fyCvjlT/pwbbAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 960x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1,dpi=150)\n",
+    "\n",
+    "ax.oplot(Sigma_iw['up_2'].imag, '-', color='C0', label=r'up $d_{z^2}$')\n",
+    "ax.oplot(Sigma_iw['up_4'].imag, '-', color='C1', label=r'up $d_{x^2-y^2}$')\n",
+    "\n",
+    "ax.oplot(Sigma_iw['down_2'].imag, '--', color='C0', label=r'down $d_{z^2}$')\n",
+    "ax.oplot(Sigma_iw['down_4'].imag, '--', color='C1', label=r'down $d_{x^2-y^2}$')\n",
+    "\n",
+    "ax.set_ylabel(r\"$Im \\Sigma (i \\omega)$\")\n",
+    "\n",
+    "ax.set_xlim(0,40)\n",
+    "ax.set_ylim(-1.8,0)\n",
+    "ax.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2a07a928-e69f-4ad1-91ea-0386024ed5de",
+   "metadata": {},
+   "source": [
+    "We can clearly see that a $\\omega_n=8$ the self-energy is replaced by the tail-fit as specified in the input config file. This cut is rather early, but ensures convergence. For higher sampling rates this has to be changed. We can also nicely observe a splitting of the spin channels indicating a magnetic solution, but we still have a metallic solution with both self-energies approaching 0 for small omega walues. However, the QMC noise is still rather high, especially in the $d_{x^2-y^2}$ orbital. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b22265a-4138-4d9c-8315-917320f27cb3",
+   "metadata": {},
+   "source": [
+    "## 5. Multiplet analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d3c2f507-757a-4880-b9dc-1f254c78c512",
+   "metadata": {},
+   "source": [
+    "We follow now the triqs/cthyb tutorial on the [multiplet analysis](https://triqs.github.io/cthyb/unstable/guide/multiplet_analysis_notebook.html) to analyze the multiplets of the Ni-d orbitals: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "c00e89e4-cf2e-4fca-84b1-11cb42072217",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "pd.set_option('display.width', 130)\n",
+    "\n",
+    "from triqs.operators.util import make_operator_real\n",
+    "from triqs.operators.util.observables import S_op\n",
+    "from triqs.atom_diag import quantum_number_eigenvalues\n",
+    "from triqs.operators import n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe674d6b-dae6-4497-82f5-6b8004afb275",
+   "metadata": {},
+   "source": [
+    "first we have to load the measured density matrix and the local Hamiltonian of the impurity problem from the h5 archive, which we stored by setting `measure_density_matrix=true` in the config file: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "786a549c-9306-4099-a4f0-3f19d2bdbb36",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with HDFArchive(path+'/nno.h5','r') as ar:\n",
+    "    rho = ar['DMFT_results/last_iter/full_dens_mat_0'] \n",
+    "    h_loc = ar['DMFT_results/last_iter/h_loc_diag_0']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "585625be-0888-460e-879b-2a60215a69bb",
+   "metadata": {},
+   "source": [
+    "`rho` is just a list of arrays containing the weights of each of the impurity eigenstates (many body states), and `h_loc` is a: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "efeafafa-502b-4acd-8e76-4f7eab6eb9c3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'triqs.atom_diag.atom_diag.AtomDiagReal'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(type(h_loc))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "72450efb-b8b8-4169-9c01-6fb6259a3178",
+   "metadata": {},
+   "source": [
+    "containing the local Hamiltonian of the impurity including eigenstates, eigenvalues etc."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "d767053a-f785-44d1-8a82-eafc5c8b9911",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res = [] \n",
+    "# get fundamental operators from atom_diag object\n",
+    "occ_operators = [n(*op) for op in h_loc.fops]\n",
+    "\n",
+    "# construct total occupation operator from list\n",
+    "N_op = sum(occ_operators)\n",
+    "\n",
+    "# create Sz operator and get eigenvalues\n",
+    "Sz=S_op('z', spin_names=['up','down'], n_orb=5, off_diag=False)\n",
+    "Sz = make_operator_real(Sz)\n",
+    "Sz_states = quantum_number_eigenvalues(Sz, h_loc)\n",
+    "\n",
+    "# get particle numbers from h_loc_diag\n",
+    "particle_numbers = quantum_number_eigenvalues(N_op, h_loc)\n",
+    "N_max = int(max(map(max, particle_numbers)))\n",
+    "\n",
+    "for sub in range(0,h_loc.n_subspaces):\n",
+    "\n",
+    "    # first get Fock space spanning the subspace\n",
+    "    fs_states = []\n",
+    "    for ind, fs in enumerate(h_loc.fock_states[sub]):\n",
+    "        state = bin(int(fs))[2:].rjust(N_max, '0')\n",
+    "        fs_states.append(\"|\"+state+\">\")\n",
+    "\n",
+    "    for ind in range(h_loc.get_subspace_dim(sub)):\n",
+    "\n",
+    "        # get particle number\n",
+    "        particle_number = round(particle_numbers[sub][ind])\n",
+    "        if abs(particle_number-particle_numbers[sub][ind]) > 1e-8:\n",
+    "            raise ValueError('round error for particle number to large!',\n",
+    "                             particle_numbers[sub][ind])\n",
+    "        else:\n",
+    "            particle_number = int(particle_number)\n",
+    "        eng=h_loc.energies[sub][ind]\n",
+    "\n",
+    "        # construct eigenvector in Fock state basis:\n",
+    "        ev_state = ''\n",
+    "        for i, elem in enumerate(h_loc.unitary_matrices[sub][:,ind]):\n",
+    "            ev_state += ' {:+1.4f}'.format(elem)+fs_states[i]\n",
+    "\n",
+    "        # get spin state\n",
+    "        ms=Sz_states[sub][ind]\n",
+    "\n",
+    "        # add to dict which becomes later the pandas data frame\n",
+    "        res.append({\"Sub#\" : sub,\n",
+    "                    \"EV#\" : ind,\n",
+    "                    \"N\" : particle_number,\n",
+    "                    \"energy\" : eng,\n",
+    "                    \"prob\": rho[sub][ind,ind],\n",
+    "                    \"m_s\": round(ms,1),\n",
+    "                    \"|m_s|\": abs(round(ms,1)),\n",
+    "                    \"state\": ev_state})\n",
+    "# panda data frame from res\n",
+    "res = pd.DataFrame(res, columns=res[0].keys())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "54f249f9-15b8-4b1c-bebb-7b63952e875e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    Sub#  EV#   N        energy      prob  m_s  |m_s|                 state\n",
+      "4      4    0   9  3.640517e-01  0.310283 -0.5    0.5   +1.0000|0111111111>\n",
+      "0      0    0   8  0.000000e+00  0.125113 -1.0    1.0   +1.0000|0101111111>\n",
+      "5      5    0   9  3.640517e-01  0.083760  0.5    0.5   +1.0000|1111101111>\n",
+      "20    20    0   8  8.851884e-01  0.074717  0.0    0.0   +1.0000|0111111011>\n",
+      "2      2    0   9  2.739907e-01  0.044306 -0.5    0.5   +1.0000|1101111111>\n",
+      "55    55    0  10  7.125334e+00  0.038609  0.0    0.0   +1.0000|1111111111>\n",
+      "3      3    0   9  2.739907e-01  0.035831  0.5    0.5   +1.0000|1111111011>\n",
+      "51    51    0   8  2.745626e+00  0.033932  0.0    0.0   +1.0000|0111101111>\n",
+      "1      1    0   8  4.903654e-09  0.031693  1.0    1.0   +1.0000|1111101011>\n",
+      "21    21    0   8  8.851884e-01  0.019748  0.0    0.0   +1.0000|1101101111>\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(res.sort_values('prob', ascending=False)[:10])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2af9aa9e-481b-48fb-952e-0d53080236c3",
+   "metadata": {},
+   "source": [
+    "This table shows the eigenstates of the impurity with the highest weight / occurence probability. Each row shows the state of the system, where the 1/0 indicates if an orbital is occupied. The orbitals are ordered as given in the projectors (dxy, dyz, dz2, dxz, dx2-y2) from right to left, first one spin-channel, then the other. Additionally each row shows the particle sector of the state, the energy, and the `m_s` quantum number.\n",
+    "\n",
+    "It can be seen, that the state with the highest weight is a state with one hole (N=9 electrons) in the $d_{x^2-y^2, up}$ orbital carrying a spin of `0.5`. The second state in the list is a state with two holes (N=8). One in the $d_{x^2-y^2, up}$ and one in the $d_{z^2, up}$ giving a magnetic moment of 1. This is because the impurity occupation is somewhere between 8 and 9. We can also create a nice state histogram from this: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "52d1d26d-587f-4b4d-a46a-f71850423b7d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# split into ms occupations\n",
+    "fig, (ax1) = plt.subplots(1,1,figsize=(6,4), dpi=150)\n",
+    "\n",
+    "spin_occ_five = res.groupby(['N', '|m_s|']).sum()\n",
+    "pivot_df = spin_occ_five.pivot_table(index='N', columns='|m_s|', values='prob')\n",
+    "pivot_df.plot.bar(stacked = True, rot=0, ax = ax1)\n",
+    "\n",
+    "ax1.set_ylabel(r'prob amplitude')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f5111521-4e2b-4bce-8270-654883a31cd6",
+   "metadata": {},
+   "source": [
+    "This concludes the tutorial. This you can try next:\n",
+    "\n",
+    "* try to find the transition temperature of the system by increasing the temperature in DMFT\n",
+    "* improve the accuracy of the resulting self-energy by restarting the dmft calculation with more n_cycles_tot "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/.doctrees/nbsphinx/tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.ipynb b/.doctrees/nbsphinx/tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.ipynb
new file mode 100644
index 00000000..f53ec5dc
--- /dev/null
+++ b/.doctrees/nbsphinx/tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.ipynb
@@ -0,0 +1,471 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a661f418-c4f0-435e-8db9-ff074ad58b49",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from h5 import HDFArchive\n",
+    "from triqs.gf import BlockGf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "55c5a91d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.rcParams['figure.figsize'] = (8, 4)\n",
+    "plt.rcParams['figure.dpi'] = 150"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0275b487",
+   "metadata": {},
+   "source": [
+    "Disclaimer: charge self-consistent (CSC) calculations are heavy. The current parameters won't give well converged solution but are tuned down to give results in roughly 150 core hours.\n",
+    "\n",
+    "# 2. CSC with VASP PLOs: charge order in PrNiO3\n",
+    "\n",
+    "Set the variable `read_from_ref` below to False if you want to plot your own calculated results. Otherwise, the provided reference files are used."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "1e21a834-d629-43a6-8c93-6f7e786aeca0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Reads files from reference? Otherwise, uses your simulation results\n",
+    "read_from_ref = True\n",
+    "path_mod = '/ref' if read_from_ref else ''"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "13dd34fd",
+   "metadata": {},
+   "source": [
+    "PrNiO3 is a perovskite that exhibits a metal-insulator transition coupled to a breathing distortion and charge disproportionation, see [here](https://doi.org/10.1038/s41535-019-0145-4).\n",
+    "In this tutorial, we will run DMFT calculation on the low-temperature insulating state. We will do this in a CSC way, where the correlated orbitals are defined by [projected localized orbitals (PLOs)](https://doi.org/10.1088/1361-648x/aae80a) calculated with VASP.\n",
+    "\n",
+    "## 1. Running the initial scf DFT calculation \n",
+    "\n",
+    "(~ 2 core hours)\n",
+    "\n",
+    "To get started, we run a self-consistent field (scf) DFT calculation:\n",
+    "\n",
+    "* Go into folder `1_dft_scf`\n",
+    "* Insert the POTCAR as concatenation of the files `PAW_PBE Pr_3`, `PAW_PBE Ni_pv` and `PAW_PBE O` distributed with VASP\n",
+    "* Goal: get a well-converged charge density (CHGCAR) and understand where the correlated bands are (DOSCAR and potentially PROCAR and band structure)\n",
+    "\n",
+    "Other input files are:\n",
+    "\n",
+    "* [INCAR](1_dft_scf/INCAR): using a large number of steps for good convergence. Compared to the DMFT calculation, it is relatively cheap and it is good to have a well converged starting point for DMFT.\n",
+    "* [POSCAR](1_dft_scf/POSCAR): PrNiO3 close to the experimental low-temperature structure (P21/n symmetry)\n",
+    "* [KPOINTS](1_dft_scf/KPOINTS): approximately unidistant grid of 6 x 6 x 4\n",
+    "\n",
+    "Then run Vasp with the command `mpirun -n 8 vasp_std`.\n",
+    "\n",
+    "The main output here is:\n",
+    "\n",
+    "* CHGCAR: the converged charge density to start the DMFT calculation from\n",
+    "* DOSCAR: to identify the energy range of the correlated subspace. (A partial DOS and band structure can be very helpful to identify the correlated subspace as well. The partial DOS can be obtained by uncommenting the LORBIT parameter in the INCAR but then the below functions to plot the DOS need to be adapted.)\n",
+    "\n",
+    "We now plot the DFT DOS and discuss the correlated subspace."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f4a4fc12",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dft_energy, dft_dos = np.loadtxt(f'1_dft_scf{path_mod}/DOSCAR',\n",
+    "                                 skiprows=6, unpack=True, usecols=(0, 1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "f1c5c3ca",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fermi_energy = 5.012206 # can be read from DOSCAR header or OUTCAR\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(dft_energy-fermi_energy, dft_dos)\n",
+    "ax.axhline(0, c='k')\n",
+    "ax.axvline(0, c='k')\n",
+    "ax.set_xlabel('Energy relative to Fermi energy (eV)')\n",
+    "ax.set_ylabel('DOS (1/eV)')\n",
+    "ax.set_xlim(-8, 5)\n",
+    "ax.set_ylim(0, 50);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "892a15f1",
+   "metadata": {},
+   "source": [
+    "The DOS contains (you can check this with the partial DOS):\n",
+    "\n",
+    "* Ni-eg bands in the range -0.4 to 2.5 eV with a small gap at around 0.6 eV\n",
+    "* mainly Ni-t2g bands between -1.5 and -0.5 eV\n",
+    "* mainly O-p bands between -7 and -1.5 eV\n",
+    "The Ni-d and O-p orbitals are hybridized, with an overlap in the DOS betwen Ni-t2g and O-p.\n",
+    "\n",
+    "DFT does not describe the system correctly in predicting a metallic state. In a simplified picture, the paramagnetism in DMFT will be able to split the correlated bands and push the Fermi energy into the gap of the eg orbitals, as we will see below.\n",
+    "\n",
+    "We will use the Ni-eg range to construct our correlated subspace.\n",
+    "\n",
+    "Note: with the coarse k-point mesh used in the tutorial the DOS will look much worse. We show here the DOS with converged number of kpoints for illustration."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "afb54167",
+   "metadata": {},
+   "source": [
+    "## 2. Running the CSC DMFT calculations\n",
+    "\n",
+    "(~ 150 core hours)\n",
+    "\n",
+    "We now run the DMFT calculation. In CSC calculations, the corrected charge density from DMFT is fed back into the DFT calculation to re-calculate the Kohn-Sham energies and projectors onto correlated orbitals.\n",
+    "\n",
+    "With VASP, the procedure works as described [here](https://triqs.github.io/dft_tools/latest/guide/dftdmft_selfcons.html#vasp-plovasp), where the GAMMA file written by DMFT contains the charge density *correction*. In the VASP-CSC implementation, we first converge a non-scf DFT calculation based on the CHGCAR from before, then run DMFT on the results. The VASP process stays alive but idle during the DMFT calculation. Then, when we want to update the DFT-derived quantities energies, we need to run multiple DFT steps in between the DMFT steps because the density correction is fed into VASP iteratively through mixing to ensure stability. \n",
+    "\n",
+    "### Input files for CSC DMFT calculations\n",
+    "\n",
+    "We first take a look into the input file [dmft_config.toml](2_dmft_csc/dmft_config.toml) and discuss some parameters. Please make sure you understand the role of the other parameters as well, as documented in the [reference manual of the read_config.py](https://triqs.github.io/solid_dmft/_ref/read_config.html) on the solid_dmft website. This is a selection of parameters from the dmft_config.toml:\n",
+    "\n",
+    "Group [general]:\n",
+    "\n",
+    "* `set_rot = \"hloc\"`: rotates the local impurity problem into a basis where the local Hamiltonian is diagonal\n",
+    "* `n_l = 35`: the number of Legendre coefficients to measure the imaginary-time Green's function in. Too few resulting in a \"bumpy\" Matsubara self-energy, too many include simulation noise. See also https://doi.org/10.1103/PhysRevB.84.075145.\n",
+    "* `dc_dmft = true`: using the DMFT occupations for the double counting is mandatory in CSC calculations. The DFT occupations are not well defined after the first density correction anymore\n",
+    "\n",
+    "Group [solver]:\n",
+    "\n",
+    "* `legendre_fit = true`: turns on measuring the Green's function in Legendre coefficients\n",
+    "\n",
+    "Group [dft]:\n",
+    "\n",
+    "* `n_iter = 4`: number of DFT iterations between the DMFT occupations. Should be large enough for the density correction to be fully mixed into the DFT calculation\n",
+    "* `n_cores = 32`: number of cores that DFT is run on. Check how many cores achieve the optimal DFT performance\n",
+    "* `dft_code = \"vasp\"`: we are running VASP\n",
+    "* `dft_exec = \"vasp_std\"`: the executable is vasp_std and its path is in the ROOT variable in our docker setup  \n",
+    "* `mpi_env = \"default\"`: sets the mpi environment\n",
+    "* `plo_cfg = \"plo.cfg\"`: the name of the config file for constructing the PLOs\n",
+    "* `projector_type = \"plo\"`: chooses PLO projectors\n",
+    "\n",
+    "The [plo.cfg](2_dmft_csc/plo.cfg) file is described [here](https://triqs.github.io/dft_tools/latest/guide/conv_vasp.html). The [rotations.dat](2_dmft_csc/rotations.dat) file is generated by diagonalizing the local d-shell density matrix and identifying the least occupied eigenstates as eg states. This we have limited k-point resolution wie also specify the band indices that describe our target space (isolated set of correlated states).\n",
+    "\n",
+    "### Starting the calculations\n",
+    "\n",
+    "Now we can start the calculations:\n",
+    "\n",
+    "* Go into the folder `2_dmft_csc`\n",
+    "* Link relevant files like CHGCAR, KPOINTS, POSCAR, POTCAR from previous directory by running `./2_link_files.sh`\n",
+    "* Run with `mpirun -n 32 python3 solid_dmft`\n",
+    "\n",
+    "### Analyzing the projectors\n",
+    "\n",
+    "Now we plot the DOS of the PLOs we are using to make sure that our correlated subspace works out as expected. You can speed up the calculation of the PLOs by removing the calculation of the DOS from the plo.cfg file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "2bba493e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "energies = []\n",
+    "doss = []\n",
+    "for imp in range(4):\n",
+    "    data = np.loadtxt(f'2_dmft_csc{path_mod}/pdos_0_{imp}.dat', unpack=True)\n",
+    "    energies.append(data[0])\n",
+    "    doss.append(data[1:])\n",
+    "    \n",
+    "energies = np.array(energies)\n",
+    "doss = np.array(doss)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "4ffe8e91",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "ax.plot(dft_energy-fermi_energy, dft_dos, label='Initial DFT total')\n",
+    "ax.plot(energies[0], np.sum(doss, axis=(0, 1)), label='PLO from CSC')\n",
+    "#for energy, dos in zip(energies, doss):\n",
+    "#    ax.plot(energy, dos.T)\n",
+    "ax.axhline(0, c='k')\n",
+    "ax.axvline(0, c='k')\n",
+    "ax.set_xlim(-8, 5)\n",
+    "ax.set_ylim(0,)\n",
+    "ax.set_xlabel('Energy relative to Fermi energy (eV)')\n",
+    "ax.set_ylabel('DOS (1/eV)')\n",
+    "ax.legend()\n",
+    "pass"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2a2a3293-3ef7-4457-942d-8a6bdcaabe29",
+   "metadata": {},
+   "source": [
+    "This plot shows the original DFT charge density and the PLO-DOS after applying the DMFT charge corrections. It proves that we are capturing indeed capturing the eg bands with the projectors, where the partial DOS differs a bit because of the changes from the charge self-consistency. Note that this quantity in the CSC DMFT formalism does not have any real meaning, it mainly serves as a check of the method. The correct quantity to analyze are the lattice or impurity Green's functions.\n",
+    "\n",
+    "## 3. Plotting the results: observables\n",
+    "\n",
+    "We first read in the pre-computed observables from the h5 archive and print their names:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d353c296-868a-45b5-bda6-2481d4df74ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with HDFArchive(f'2_dmft_csc{path_mod}/vasp.h5', 'r') as archive:\n",
+    "    observables = archive['DMFT_results/observables']\n",
+    "    conv_obs = archive['DMFT_results/convergence_obs']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ad578719-aa61-4560-baba-f01a4f28b726",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['E_DC', 'E_bandcorr', 'E_corr_en', 'E_dft', 'E_int', 'E_tot', 'imp_gb2', 'imp_occ', 'iteration', 'mu', 'orb_Z', 'orb_gb2', 'orb_occ'])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "observables.keys()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd150f57-3a8c-418a-a088-470180c86d87",
+   "metadata": {},
+   "source": [
+    "We will now use this to plot the occupation per impurity `imp_occ` (to see if there is charge disproportionation), the impurity Green's function at $\\tau=\\beta/2$ `imp_gb2` (to see if the system becomes insulating), the total energy `E_tot`, the DFT energy `E_dft`, and DMFT self-consistency condition over the iterations:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "41e955de-7a19-4e1f-bf27-f6973a8855d1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x1400 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(nrows=4, sharex=True, dpi=200,figsize=(7,7))\n",
+    "\n",
+    "for i in range(2):\n",
+    "    axes[0].plot(np.array(observables['imp_occ'][i]['up'])+np.array(observables['imp_occ'][i]['down']), '.-', c=f'C{i}',\n",
+    "                label=f'Impurity {i}')\n",
+    "    axes[1].plot(np.array(observables['imp_gb2'][i]['up'])+np.array(observables['imp_gb2'][i]['down']), '.-', c=f'C{i}')\n",
+    "    \n",
+    "    axes[3].semilogy(conv_obs['d_Gimp'][i], '.-', color=f'C{i}', label=f'Impurity {i}')\n",
+    "    \n",
+    "# Not impurity-dependent\n",
+    "axes[2].plot(observables['E_tot'], '.-', c='k', label='Total energy')\n",
+    "axes[2].plot(observables['E_dft'], 'x--', c='k', label='DFT energy')\n",
+    "\n",
+    "\n",
+    "axes[0].set_ylabel('Imp. occupation\\n')\n",
+    "axes[0].set_ylim(0, 2)\n",
+    "axes[0].legend()\n",
+    "axes[1].set_ylabel(r'$G(\\beta/2)$')\n",
+    "axes[2].set_ylabel('Energy')\n",
+    "axes[2].legend()\n",
+    "axes[3].set_ylabel(r'|G$_{imp}$-G$_{loc}$|')\n",
+    "axes[3].legend()\n",
+    "\n",
+    "axes[-1].set_xlabel('Iterations')\n",
+    "fig.subplots_adjust(hspace=.08)\n",
+    "pass"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "599730cc-8214-48cd-80a6-14676f2e23c0",
+   "metadata": {},
+   "source": [
+    "These plots show:\n",
+    "\n",
+    "* The occupation converges towards a disproportionated 1.6+0.4 electrons state\n",
+    "* Both sites become insulating, which we can deduce from $G(\\beta/2)$ from its relation to the spectral function at the Fermi energy $A(\\omega = 0) \\approx -(\\beta/\\pi) G(\\beta/2)$\n",
+    "* convergence is only setting in at around 20 DMFT iterations, which can be also seen from the column `rms(c)` in the Vasp OSZICAR file, and more DMFT iterations should be done ideally\n",
+    "\n",
+    "Therefore, we can conclude that we managed to capture the desired paramagnetic, insulating state that PrNiO3 shows in the experiments.\n",
+    "\n",
+    "## 4. Plotting the results: the Legendre Green's function\n",
+    "\n",
+    "We now take a look at the imaginary-time Green's function expressed in Legendre coefficients $G_l$. This is the main solver output (if we are measuring it) and also saved in the h5 archive."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "91f19160-3f34-4738-a9fa-8fe9c4289b0c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "legendre_gf = []\n",
+    "with HDFArchive(f'2_dmft_csc{path_mod}/vasp.h5') as archive:\n",
+    "    for i in range(2):\n",
+    "        legendre_gf.append(archive[f'DMFT_results/last_iter/Gimp_l_{i}'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "50176755-edbb-41ed-9656-5c648a08a6c0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "for i, legendre_coefficients_per_imp in enumerate(legendre_gf):\n",
+    "    if len(legendre_coefficients_per_imp) != 2:\n",
+    "        raise ValueError('Only blocks up_0 and down_0 supported')\n",
+    "\n",
+    "    data = (legendre_coefficients_per_imp['up_0'].data + legendre_coefficients_per_imp['down_0'].data).T\n",
+    "\n",
+    "    l_max = data.shape[2]\n",
+    "\n",
+    "    ax.semilogy(np.arange(0, l_max, 2), np.abs(np.trace(data[:, :, ::2].real, axis1=0, axis2=1)), 'x-',\n",
+    "                c=f'C{i}', label=f'Imp. {i}, even indices')\n",
+    "    ax.semilogy(np.arange(1, l_max, 2), np.abs(np.trace(data[:, :, 1::2].real, axis1=0, axis2=1)), '.:',\n",
+    "                c=f'C{i}', label=f'Imp. {i}, odd indices')\n",
+    "\n",
+    "ax.legend()\n",
+    "\n",
+    "ax.set_ylabel('Legendre coefficient $G_l$ (eV$^{-1}$)')\n",
+    "ax.set_xlabel(r'Index $l$')\n",
+    "pass"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8308345c-3f72-476c-8f58-583f9aeb1ccf",
+   "metadata": {},
+   "source": [
+    "The choice of the correct `n_l`, i.e., the Legendre cutoff is important. If it is too small, we are ignoring potential information about the Green's function. If it is too large, the noise filtering is not efficient. This can be seen by first running a few iterations with large `n_l`, e.g., 50. Then, the coefficients will first decay exponentially as in the plot above and then at higher $l$ starting showing noisy behavior. For more information about the Legendre coefficients, take a look [here](https://doi.org/10.1103/PhysRevB.84.075145).\n",
+    "\n",
+    "The noise itself should reduce with sqrt(`n_cycles_tot`) for QMC calculations but the prefactor always depends on material and its Hamiltonian, the electron filling, etc. But if you increase `n_cycles_tot`, make sure to test if you can include more Legendre coefficients.\n",
+    "\n",
+    "## 5. Next steps to try\n",
+    "\n",
+    "Here are some suggestions on how continue on this type of DMFT calculations:\n",
+    "\n",
+    "* change U and J and try to see if you can reach a metallic state. What does the occupation look like?\n",
+    "* try for better convergence: change `n_cycles_tot`, `n_iter_dmft` and `n_l`\n",
+    "* play around with the other parameters in the dmft_config.toml\n",
+    "* analyze other quantities or have a look at the spectral functions from analytical continuation\n",
+    "* try other ways to construct the correlated orbitals in CSC, e.g., with Wannier90\n",
+    "* apply this to the material of your choice!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "550fa534-c187-49d7-96e4-0848f53dd854",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/.doctrees/nbsphinx/tutorials/SVO_os_qe/tutorial.ipynb b/.doctrees/nbsphinx/tutorials/SVO_os_qe/tutorial.ipynb
new file mode 100644
index 00000000..ce1a444d
--- /dev/null
+++ b/.doctrees/nbsphinx/tutorials/SVO_os_qe/tutorial.ipynb
@@ -0,0 +1,194 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "df9acc44",
+   "metadata": {},
+   "source": [
+    "*Disclaimer:*\n",
+    "\n",
+    "Heavy calculations (~ 800 core hours): Current tutorial is best performed on an HPC facility.\n",
+    "\n",
+    "# 1. OS with QE/W90 and cthyb: SrVO3 MIT"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bbec1073",
+   "metadata": {},
+   "source": [
+    "Hello and welcome to the first part of the tutorial for solid_dmft. Here we will guide to set up and run your first DMFT calculations. \n",
+    "\n",
+    "To begin your DMFT journey we will immediately start a DMFT run on strontium vanadate (SVO). SVO is a member of a family of material known as complex perovskite oxides with 1 electron occupying the t2g manifold. Below, we show the band structure of the frontier (anti-bonding) t2g bands for SVO.\n",
+    "\n",
+    "![svobands](./ref/bnd_structure.png \"SVO band structure\")\n",
+    "\n",
+    "In these materials, the electrons sitting on the transition metal ions (V in this case) are fairly localized, and the fully delocalized picture of DFT is insufficient to describe their physics. DMFT accounts for the electron-electron interaction by providing a fully interacting many body correction to the DFT non-interacting problem.\n",
+    "\n",
+    "If you want to generate the h5 archive `svo.h5` yourself, all the necessary files are in the `./quantum_espresso_files/` folder. We used quantum espresso in this tutorial.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5aa9cda6",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## 1. Starting out with DMFT\n",
+    "\n",
+    "\n",
+    "To start your first calculation run:\n",
+    "\n",
+    "```\n",
+    "mpirun solid_dmft\n",
+    "\n",
+    "```\n",
+    "\n",
+    "Once the calculation is finished, inspect the `/out/` folder: our file of interest for the moment will be `observables_imp0.dat`, open the file:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2bc83f5f",
+   "metadata": {},
+   "source": [
+    "```\n",
+    " it |        mu |            G(beta/2) per orbital |               orbital occs up+down |impurity occ\n",
+    "  0 |  12.29775 | -0.10489   -0.10489     -0.10489 |  0.33366      0.33366      0.33366 |     1.00097\n",
+    "  1 |  12.29775 | -0.09467   -0.09488     -0.09529 |  0.36155      0.35073      0.36169 |     1.07397\n",
+    "  2 |  12.31989 | -0.08451   -0.08363     -0.08463 |  0.33581      0.34048      0.34488 |     1.02117\n",
+    "  3 |  12.29775 | -0.08282   -0.08296     -0.08254 |  0.32738      0.34572      0.34479 |     1.01789\n",
+    "  4 |  12.28973 | -0.08617   -0.08595     -0.08620 |  0.33546      0.33757      0.33192 |     1.00494\n",
+    "  5 |  12.28825 | -0.08410   -0.08458     -0.08510 |  0.33582      0.33402      0.33759 |     1.00743\n",
+    "  6 |  12.28486 | -0.08474   -0.08549     -0.08618 |  0.32276      0.33028      0.32760 |     0.98063\n",
+    "  7 |  12.29097 | -0.08172   -0.08220     -0.08118 |  0.32072      0.33046      0.33529 |     0.98647\n",
+    "  8 |  12.29497 | -0.08318   -0.08254     -0.08332 |  0.34075      0.32957      0.33089 |     1.00120\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b50e7e1",
+   "metadata": {},
+   "source": [
+    "The meaning of the column names is the following:\n",
+    "\n",
+    "* **it**: number of the DMFT iteration\n",
+    "* **mu**: value of the chemical potential\n",
+    "* **G(beta/2) per orbital**: Green's function evaluated at $\\tau=\\beta/2$, this value is proportional to the projected density of states at the fermi level, the first objective of this tutorial would be to try and drive this value to 0\n",
+    "* **orbital occs up+down:** occupations of the various states in the manifold\n",
+    "* **impurity occ**: number of electrons in each site\n",
+    "---\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b5182921",
+   "metadata": {},
+   "source": [
+    "## 2. Looking at the Metal-Insulator Transition\n",
+    "\n",
+    "In the following steps we will try to drive the system towards a Mott-insulating state. \n",
+    "\n",
+    "Inspect the script `run_MIT_coarse.sh`, we iterate the same type of calculation that was performed in the last step for a series of value of U {2-10} and J {0.0-1.0}. \n",
+    "\n",
+    "Run the script, sit back and have a long coffee break, this is going to take a while (about 6 hours on 30 cores).\n",
+    "\n",
+    "Once the run is finished run \n",
+    "\n",
+    "`python3 ./collect_results_coarse.py`\n",
+    "\n",
+    "The script will produce a heatmap image of the value of G(beta/2) for each pair of U and J. The darker area corresponds to an insulating state.\n",
+    "\n",
+    "![coarsegrid](./ref/MIT_coarse.jpg \"Coarser grid\")\n",
+    "\n",
+    "Do you notice anything strange? (hint: look at the bottom right corner and check the output file `observables_imp0.dat` for U = 2 J=1.0. )\n",
+    "\n",
+    "We have seen that for 1 electron per system U and J are competing against each other: larger J favor the metallic state. The coulomb integral U wants to repel neighbouring electrons while J would like to bring electrons together on one site,. When the latter component dominates the resulting phase is known as a charge disproportionated state which is also insulating. What is happening in the bottom right corner is that the J favors here charge disproportionation but the unit cell has a single site, therefore the system has trouble converging and oscillates between a high occupation and a low occupation state."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b6bdb73",
+   "metadata": {},
+   "source": [
+    "## 3. Refining the diagram\n",
+    "\n",
+    "In order to get better resolution in terms of the diagram you can run the script `run_MIT_fine.sh` and plot the result with \n",
+    "\n",
+    "`python3 ./collect_results_fine.py`\n",
+    "\n",
+    "The result is also visible here:\n",
+    "\n",
+    "![finegrid](./ref/MIT_fine.jpg \"Finer grid\")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dfabf20a",
+   "metadata": {},
+   "source": [
+    "## 4. Plotting the spectral function\n",
+    "\n",
+    "The spectral function in DMFT represents the local density of states of the impurity site.\n",
+    "In order to plot it we need to use one of the scripts that implements the maximum entropy method ( [Maxent](https://triqs.github.io/maxent/latest/) ), while in the folder run (be aware that you need to substitute `/path_to_solid_dmft/` with the path where you have installed solid_dmft) :\n",
+    "\n",
+    "`mpirun -n 30 python3 /path_to_solid_dmft/python/solid_dmft/postprocessing/maxent_gf_imp.py ./J0.0/U4/out/svo.h5`\n",
+    "\n",
+    "and plot the result by running in the docker container:\n",
+    "\n",
+    "`python3 read_spectral_function.py`\n",
+    "\n",
+    "\n",
+    "![Afunc](./ref/A_func_J=0.0_U=4.jpg \"Afunc\")\n",
+    "\n",
+    "\n",
+    "Take care to edit the values of J and U in the python file. What is happing to the spectral function (density of states) as one cranks U up?\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "566941ec",
+   "metadata": {},
+   "source": [
+    "## 5 Visualizing the MIT\n",
+    "\n",
+    "We will now plot the spectral function at different U values for J = 0.0 eV:\n",
+    "\n",
+    "Run the script `run_maxent_scan.sh`.\n",
+    "\n",
+    "Then collect the data:\n",
+    "\n",
+    "`python3 read_spectral_function_transition.py`\n",
+    "\n",
+    "![MIT](./ref/A_func_transition.jpg \"MIT\")\n",
+    "\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/.doctrees/nbsphinx/tutorials/correlated_bandstructure/plot_correlated_bands.ipynb b/.doctrees/nbsphinx/tutorials/correlated_bandstructure/plot_correlated_bands.ipynb
new file mode 100644
index 00000000..691622bc
--- /dev/null
+++ b/.doctrees/nbsphinx/tutorials/correlated_bandstructure/plot_correlated_bands.ipynb
@@ -0,0 +1,480 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "50bbc308",
+   "metadata": {},
+   "source": [
+    "# 5. Plotting the spectral function"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e8d5feac",
+   "metadata": {},
+   "source": [
+    "In this tutorial we go through the steps to plot tight-binding bands from a Wannier90 Hamiltonian and spectralfunctions with analytically continued (real-frequency) self-energies obtained from DMFT."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "0d69c4d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "from IPython.display import display\n",
+    "from IPython.display import Image\n",
+    "import numpy as np\n",
+    "import importlib, sys\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib import cm\n",
+    "from timeit import default_timer as timer\n",
+    "\n",
+    "from ase.io.espresso import read_espresso_in\n",
+    "\n",
+    "from h5 import HDFArchive\n",
+    "from solid_dmft.postprocessing import plot_correlated_bands as pcb"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c3ce4f44",
+   "metadata": {},
+   "source": [
+    "## 1. Configuration"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "42a860c4",
+   "metadata": {},
+   "source": [
+    "The script makes use of the `triqs.lattice.utils` class, which allows to set up a tight-binding model based on a Wannier90 Hamiltonian. Additionally, you may upload a self-energy in the usual `solid_dmft` format to compute correlated spectral properties.\n",
+    "Currently, the following options are implemented:\n",
+    "<ol>\n",
+    "<li>bandstructure</li>\n",
+    "<li>Fermi slice</li>\n",
+    "</ol>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b8d962f9",
+   "metadata": {},
+   "source": [
+    "### Basic options"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b652e03a",
+   "metadata": {},
+   "source": [
+    "We start with configuring these options. For this example we try a tight-binding bandstructure including the correlated bands (`kslice = False`, `'tb': True`, `'alatt': True`), but feel free to come back here to explore. Alternatively to an intensity plot of the correlated bands (`qp_bands`), you can compute the correlated quasiparticle bands assuming a Fermi liquid regime.\\\n",
+    "The options for $\\Sigma(\\omega)$ are `calc` or `model`, which performs a Fermi liquid linearization in the low-frequency regime. The latter will be reworked, so better stick with `calc` for now."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b8f73a48",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kslice = False\n",
+    "\n",
+    "bands_config = {'tb': True, 'alatt': True, 'qp_bands': False, 'sigma': 'calc'}\n",
+    "kslice_config = {'tb': True, 'alatt': True, 'qp_bands': False, 'sigma': 'calc'}\n",
+    "config = kslice_config if kslice else bands_config"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3c6ece97",
+   "metadata": {},
+   "source": [
+    "### Wannier90"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6d0ce79b",
+   "metadata": {},
+   "source": [
+    "Next we will set up the Wannier90 Input. Provide the path, seedname, chemical potential and orbital order used in Wannier90. You may add a spin-component, and any other local Hamiltonian. For `t2g` models the orbital order can be changed (to `orbital_order_to`) and a local spin-orbit coupling term can be added (`add_lambda`). The spectral properties can be viewed projected on a specific orbital."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "27a94d47",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "w90_path = './'\n",
+    "w90_dict = {'w90_seed': 'svo', 'w90_path': w90_path, 'mu_tb': 12.3958, 'n_orb': 3,\n",
+    "            'orbital_order_w90': ['dxz', 'dyz', 'dxy'], 'add_spin': False}\n",
+    "\n",
+    "orbital_order_to = ['dxy', 'dxz', 'dyz']\n",
+    "proj_on_orb = None # or 'dxy' etc"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "57f41c87",
+   "metadata": {},
+   "source": [
+    "### BZ configuration"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f23d7e3a",
+   "metadata": {},
+   "source": [
+    "#### Optional: ASE Brillouin Zone"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fc7b2fac",
+   "metadata": {},
+   "source": [
+    "It might be helpful to have a brief look at the Brillouin Zone by loading an input file of your favorite DFT code (Quantum Espresso in this case). ASE will write out the special $k$-points, which we can use to configure the BZ path. Alternatively, you can of course define the dictionary `kpts_dict` yourself. Careful, it might not define $Z$, which is needed and added below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "c6e46f88",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "G [0. 0. 0.]\n",
+      "M [0.5 0.5 0. ]\n",
+      "R [0.5 0.5 0.5]\n",
+      "X [0.  0.5 0. ]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes3DSubplot:>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "scf_in = './svo.scf.in'\n",
+    "\n",
+    "# read scf file\n",
+    "atoms = read_espresso_in(scf_in)\n",
+    "# set up cell and path\n",
+    "lat = atoms.cell.get_bravais_lattice()\n",
+    "path = atoms.cell.bandpath('', npoints=100)\n",
+    "kpts_dict = path.todict()['special_points']\n",
+    "\n",
+    "for key, value in kpts_dict.items():\n",
+    "    print(key, value)\n",
+    "lat.plot_bz()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "31956a53",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e47c2a48",
+   "metadata": {},
+   "source": [
+    "Depending on whether you select `kslice=True` or `False`, a corresponding `tb_config` needs to be provided containing information about the $k$-points, resolution (`n_k`) or `kz`-plane in the case of the Fermi slice. Here we just import the $k$-point dictionary provided by ASE above and add the $Z$-point. If you are unhappy with the resolution of the final plot, come back here and crank up `n_k`. For the kslice, the first letter corresponds to the upper left corner of the plotted Brillouin zone, followed by the lower left corner and the lower right one ($Y$, $\\Gamma$, and $X$ in this case)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "68c0f047",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# band specs\n",
+    "tb_bands = {'bands_path': [('R', 'G'), ('G', 'X'), ('X', 'M'), ('M', 'G')], 'Z': np.array([0,0,0.5]), 'n_k': 50}\n",
+    "tb_bands.update(kpts_dict)\n",
+    "\n",
+    "# kslice specs\n",
+    "tb_kslice = {key: tb_bands[key] for key in list(tb_bands.keys()) if key.isupper()}\n",
+    "kslice_update = {'bands_path': [('Y', 'G'),('G', 'X')], 'Y': np.array([0.5,0.0,0]), 'n_k': 50, 'kz': 0.0}\n",
+    "tb_kslice.update(kslice_update)\n",
+    "\n",
+    "tb_config = tb_kslice if kslice else tb_bands"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bf58de16",
+   "metadata": {},
+   "source": [
+    "### Self-energy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "67e42361",
+   "metadata": {},
+   "source": [
+    "Here we provide the info needed from the h5Archive, like the self-energy, iteration count, spin and block component and the frequency mesh used for the interpolation. The values for the mesh of course depend on the quantity of interest. For a kslice the resolution around $\\omega=0$ is crucial and we need only a small energy window, while for a bandstructure we are also interested in high energy features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "70fd0787",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "freq_mesh_kslice = {'window': [-0.5, 0.5], 'n_w': int(1e6)}\n",
+    "freq_mesh_bands = {'window': [-5, 5], 'n_w': int(1e3)}\n",
+    "freq_mesh = freq_mesh_kslice if kslice else freq_mesh_bands\n",
+    "\n",
+    "dmft_path = './svo_example.h5'\n",
+    "\n",
+    "proj_on_orb = orbital_order_to.index(proj_on_orb) if proj_on_orb else None\n",
+    "sigma_dict = {'dmft_path': dmft_path, 'it': 'last_iter', 'orbital_order_dmft': orbital_order_to, 'spin': 'up',\n",
+    "              'block': 0, 'eta': 0.0, 'w_mesh': freq_mesh, 'linearize': False, 'proj_on_orb' : proj_on_orb}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e314f15",
+   "metadata": {},
+   "source": [
+    "__Optional__: for completeness and as a sanity check we quickly take a look at the self-energy. Make sure you provide a physical one!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e7cb04b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 2000x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "with HDFArchive(dmft_path, 'r') as h5:\n",
+    "    sigma_freq = h5['DMFT_results']['last_iter']['Sigma_freq_0']\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 2, figsize=(10,2), squeeze=False, dpi=200)\n",
+    "\n",
+    "orb = 0\n",
+    "sp = 'up_0'\n",
+    "freq_mesh = np.array([w.value for w in sigma_freq[sp][orb,orb].mesh])\n",
+    "\n",
+    "ax[0,0].plot(freq_mesh, sigma_freq[sp][orb,orb].data.real)\n",
+    "ax[0,1].plot(freq_mesh, -sigma_freq[sp][orb,orb].data.imag)\n",
+    "\n",
+    "ax[0,0].set_ylabel(r'Re$\\Sigma(\\omega)$')\n",
+    "ax[0,1].set_ylabel(r'Im$\\Sigma(\\omega)$')\n",
+    "for ct in range(2):\n",
+    "    ax[0,ct].grid()\n",
+    "    ax[0,ct].set_xlim(-2, 2)\n",
+    "    ax[0,ct].set_xlabel(r'$\\omega$ (eV)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c249dc9",
+   "metadata": {},
+   "source": [
+    "### Plotting options"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93d1db24",
+   "metadata": {},
+   "source": [
+    "Finally, you can choose colormaps for each of the functionalities from any of the available on <a href=\"https://matplotlib.org/stable/tutorials/colors/colormaps.html\">matplotlib colormaps</a>. `vmin` determines the scaling of the logarithmically scaled colorplots. The corresponding tight-binding bands will have the maximum value of the colormap. By the way, colormaps can be reversed by appending `_r` to the identifier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a2d79e6d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_config = {'colorscheme_alatt': 'coolwarm', 'colorscheme_bands': 'coolwarm', 'colorscheme_kslice': 'PuBuGn',\n",
+    "               'colorscheme_qpbands': 'Greens', 'vmin': 0.0}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b2e5a0e",
+   "metadata": {},
+   "source": [
+    "## 2. Run and Plotting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "89a67dd6",
+   "metadata": {},
+   "source": [
+    "Now that everything is set up we may hit run. Caution, if you use a lot of $k$-points, this may take a while! In the current example, it should be done within a second."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "7e875f21",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warning: could not identify MPI environment!\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Starting serial run at: 2022-08-01 11:33:20.627842\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "H(R=0):\n",
+      "     12.9769  0.0000  0.0000\n",
+      "      0.0000 12.9769  0.0000\n",
+      "      0.0000  0.0000 12.9769\n",
+      "Setting Sigma from ./svo_example.h5\n",
+      "Adding mu_tb to DMFT μ; assuming DMFT was run with subtracted dft μ.\n",
+      "μ=12.2143 eV set for calculating A(k,ω)\n",
+      "Run took 0.588 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "start_time = timer()\n",
+    "\n",
+    "tb_data, alatt_k_w, freq_dict = pcb.get_dmft_bands(fermi_slice=kslice, with_sigma=bands_config['sigma'], add_mu_tb=True,\n",
+    "                                                   orbital_order_to=orbital_order_to, qp_bands=config['qp_bands'],\n",
+    "                                                   **w90_dict, **tb_config, **sigma_dict)\n",
+    "\n",
+    "print('Run took {0:.3f} s'.format(timer() - start_time))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7780b5d",
+   "metadata": {},
+   "source": [
+    "That's it. Now you can look at the output:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "1936db33",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1200x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "if kslice:\n",
+    "    fig, ax = plt.subplots(1, figsize=(3,3), dpi=200)\n",
+    "\n",
+    "    pcb.plot_kslice(fig, ax, alatt_k_w, tb_data, freq_dict, w90_dict['n_orb'], tb_config,\n",
+    "                    tb=config['tb'], alatt=config['alatt'], quarter=0, **plot_config)\n",
+    "\n",
+    "else:\n",
+    "    fig, ax = plt.subplots(1, figsize=(6,3), dpi=200)\n",
+    "\n",
+    "    pcb.plot_bands(fig, ax, alatt_k_w, tb_data, freq_dict, w90_dict['n_orb'], dft_mu=0.,\n",
+    "                   tb=config['tb'], alatt=config['alatt'], qp_bands=config['qp_bands'], **plot_config)\n",
+    "\n",
+    "    ax.set_ylim(-1.25,1.75)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "186cf322",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/.doctrees/nbsphinx/tutorials_Ce2O3_csc_w90_tutorial_11_3.png b/.doctrees/nbsphinx/tutorials_Ce2O3_csc_w90_tutorial_11_3.png
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diff --git a/.github/ISSUE_TEMPLATE/bug.md b/.github/ISSUE_TEMPLATE/bug.md
new file mode 100644
index 00000000..7e2b5c01
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/bug.md
@@ -0,0 +1,45 @@
+---
+name: Bug report
+about: Create a report to help us improve
+title: Bug report
+labels: bug
+
+---
+
+### Prerequisites
+
+* Please check that a similar issue isn't already filed: https://github.com/issues?q=is%3Aissue+user%3Atriqs
+
+### Description
+
+[Description of the issue]
+
+### Steps to Reproduce
+
+1. [First Step]
+2. [Second Step]
+3. [and so on...]
+
+or paste a minimal code example to reproduce the issue.
+
+**Expected behavior:** [What you expect to happen]
+
+**Actual behavior:** [What actually happens]
+
+### Versions
+
+Please provide the application version that you used.
+
+You can get this information from copy and pasting the output of
+```bash
+python -c "from solid_dmft.version import *; show_version(); show_git_hash();"
+```
+from the command line. Also, please include the OS you are running and its version.
+
+### Formatting
+
+Please use markdown in your issue message. A useful summary of commands can be found [here](https://guides.github.com/pdfs/markdown-cheatsheet-online.pdf).
+
+### Additional Information
+
+Any additional information, configuration or data that might be necessary to reproduce the issue.
diff --git a/.github/ISSUE_TEMPLATE/feature.md b/.github/ISSUE_TEMPLATE/feature.md
new file mode 100644
index 00000000..0ca4d25b
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/feature.md
@@ -0,0 +1,23 @@
+---
+name: Feature request
+about: Suggest an idea for this project
+title: Feature request
+labels: feature
+
+---
+
+### Summary
+
+One paragraph explanation of the feature.
+
+### Motivation
+
+Why is this feature of general interest?
+
+### Implementation
+
+What user interface do you suggest?
+
+### Formatting
+
+Please use markdown in your issue message. A useful summary of commands can be found [here](https://guides.github.com/pdfs/markdown-cheatsheet-online.pdf).
diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
new file mode 100644
index 00000000..88e9157c
--- /dev/null
+++ b/.github/workflows/build.yml
@@ -0,0 +1,94 @@
+name: build
+
+on:
+  push:
+    branches: [ unstable ]
+  pull_request:
+    branches: [ unstable ]
+
+jobs:
+  build:
+
+    strategy:
+      fail-fast: false
+      matrix:
+        include:
+          - {os: ubuntu-20.04, cc: gcc-10, cxx: g++-10}
+          - {os: ubuntu-20.04, cc: clang-12, cxx: clang++-12}
+
+    runs-on: ${{ matrix.os }}
+
+    steps:
+    - uses: actions/checkout@v2
+
+    - name: Install ubuntu dependencies
+      if: matrix.os == 'ubuntu-20.04'
+      run: >
+        sudo apt-get update &&
+        sudo apt-get install lsb-release wget software-properties-common &&
+        wget -O /tmp/llvm.sh https://apt.llvm.org/llvm.sh && sudo chmod +x /tmp/llvm.sh && sudo /tmp/llvm.sh 12 &&
+        curl -L https://users.flatironinstitute.org/~ccq/triqs3/focal/public.gpg | sudo apt-key add - &&
+        sudo add-apt-repository "deb https://users.flatironinstitute.org/~ccq/triqs3/focal/ /" &&
+        sudo apt-get update &&
+        sudo apt-get install
+        clang-12
+        g++-10
+        gfortran
+        hdf5-tools
+        libblas-dev
+        libboost-dev
+        libclang-12-dev
+        libc++-12-dev
+        libc++abi-12-dev
+        libfftw3-dev
+        libgfortran5
+        libgmp-dev
+        libhdf5-dev
+        liblapack-dev
+        libopenmpi-dev
+        openmpi-bin
+        openmpi-common
+        openmpi-doc
+        python3-clang-12
+        python3-dev
+        python3-mako
+        python3-matplotlib
+        python3-mpi4py
+        python3-numpy
+        python3-pip
+        python3-scipy
+        python3-sphinx
+        python3-nbsphinx
+        triqs
+        triqs_dft_tools
+        triqs_cthyb
+        triqs_maxent
+
+    - name: Build & Install HubbardI
+      env:
+        CPLUS_INCLUDE_PATH: /usr/include/openmpi:/usr/include/hdf5/serial/:$CPLUS_INCLUDE_PATH
+        CC: ${{ matrix.cc }}
+        CXX: ${{ matrix.cxx }}
+      run: |
+        git clone https://github.com/TRIQS/hubbardI hubbardI.src
+        mkdir hubbardI.build && cd hubbardI.build
+        cmake ../hubbardI.src -DBuild_Tests=OFF
+        sudo make -j1 install VERBOSE=1
+        cd ../
+
+    - name: Build solid_dmft
+      env:
+        CPLUS_INCLUDE_PATH: /usr/include/openmpi:/usr/include/hdf5/serial/:$CPLUS_INCLUDE_PATH
+        CC: ${{ matrix.cc }}
+        CXX: ${{ matrix.cxx }}
+        LIBRARY_PATH: /usr/local/opt/llvm/lib
+      run: |
+        mkdir build && cd build && cmake ..
+        make -j2 || make -j1 VERBOSE=1
+
+    - name: Test solid_dmft
+      env:
+        DYLD_FALLBACK_LIBRARY_PATH: /usr/local/opt/llvm/lib
+      run: |
+        cd build
+        ctest --output-on-failure
diff --git a/.nojekyll b/.nojekyll
new file mode 100644
index 00000000..e69de29b
diff --git a/ChangeLog.html b/ChangeLog.html
new file mode 100644
index 00000000..99c20193
--- /dev/null
+++ b/ChangeLog.html
@@ -0,0 +1,581 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" >
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Changelog &mdash; solid_dmft  documentation</title>
+      <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
+      <link rel="stylesheet" href="_static/css/custom.css" type="text/css" />
+      <link rel="stylesheet" href="_static/plot_directive.css" type="text/css" />
+    <link rel="shortcut icon" href="_static/favicon.ico"/>
+  
+        <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
+        <script src="_static/jquery.js"></script>
+        <script src="_static/underscore.js"></script>
+        <script src="_static/doctools.js"></script>
+        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+    <script src="_static/js/theme.js"></script>
+    <link rel="index" title="Index" href="genindex.html" />
+    <link rel="search" title="Search" href="search.html" />
+    <link rel="prev" title="Support &amp; contribute" href="issues.html" /> 
+</head>
+
+<body class="wy-body-for-nav"> 
+  <div class="wy-grid-for-nav">
+    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
+      <div class="wy-side-scroll">
+        <div class="wy-side-nav-search"  style="background: #7E588A" >
+            <a href="index.html" class="icon icon-home"> solid_dmft
+            <img src="_static/logo_no_text.png" class="logo" alt="Logo"/>
+          </a>
+              <div class="version">
+                3.2.0
+              </div>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" />
+    <input type="hidden" name="check_keywords" value="yes" />
+    <input type="hidden" name="area" value="default" />
+  </form>
+</div>
+        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+              <ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="install.html">Installation</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="install.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="install.html#installation-via-pip">Installation via pip</a></li>
+<li class="toctree-l2"><a class="reference internal" href="install.html#manual-installation-via-cmake">Manual installation via CMake</a></li>
+<li class="toctree-l2"><a class="reference internal" href="install.html#docker-files-images">Docker files &amp; images</a></li>
+<li class="toctree-l2"><a class="reference internal" href="install.html#version-compatibility">Version compatibility</a></li>
+<li class="toctree-l2"><a class="reference internal" href="install.html#custom-cmake-options">Custom CMake options</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="documentation.html">Documentation</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="documentation.html#code-structure">Code structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="documentation.html#dft-interface-notes">DFT interface notes</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="md_notes/w90_interface.html">Wannier90 interface</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/w90_interface.html#orbital-order-in-the-w90-converter">orbital order in the W90 converter</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="md_notes/vasp_csc.html">Interface to VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/vasp_csc.html#general-remarks">General remarks</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="cRPA_VASP/README.html#file-description">file description</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cRPA_VASP/README.html#workflow">Workflow:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cRPA_VASP/README.html#important-flags">important flags:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cRPA_VASP/README.html#version-and-compilation">version and compilation:</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="documentation.html#input-output">Input/Output</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="input_output/DMFT_input/input.html">Input</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="input_output/DMFT_input/general.html">[general]: General parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="input_output/DMFT_input/solver.html">[solver]: solver specific parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="input_output/DMFT_input/dft.html">[dft]: DFT related inputs</a></li>
+<li class="toctree-l4"><a class="reference internal" href="input_output/DMFT_input/advanced.html">[advanced]: Advanced inputs</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="input_output/DMFT_output/results.html">Output / results</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="input_output/DMFT_output/results.html#group-structure">Group structure</a></li>
+<li class="toctree-l4"><a class="reference internal" href="input_output/DMFT_output/results.html#subgroups">Subgroups</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="documentation.html#further-details-for-running">Further details for running</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="md_notes/docker.html">Docker</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/docker.html#building-the-docker-image">Building the docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/docker.html#pulling-a-docker-image">Pulling a docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="md_notes/run_locally.html">Run on your machine</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="documentation.html#module-reference-manual">Module reference manual</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="_ref/csc_flow.html">csc_flow</a></li>
+<li class="toctree-l3"><a class="reference internal" href="_ref/dft_managers.html">dft_managers</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dft_managers.mpi_helpers.html">dft_managers.mpi_helpers</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dft_managers.qe_manager.html">dft_managers.qe_manager</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dft_managers.vasp_manager.html">dft_managers.vasp_manager</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="_ref/dmft_cycle.html">dmft_cycle</a></li>
+<li class="toctree-l3"><a class="reference internal" href="_ref/dmft_tools.html">dmft_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.afm_mapping.html">dmft_tools.afm_mapping</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.convergence.html">dmft_tools.convergence</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.formatter.html">dmft_tools.formatter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.greens_functions_mixer.html">dmft_tools.greens_functions_mixer</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.initial_self_energies.html">dmft_tools.initial_self_energies</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.interaction_hamiltonian.html">dmft_tools.interaction_hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.legendre_filter.html">dmft_tools.legendre_filter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.manipulate_chemical_potential.html">dmft_tools.manipulate_chemical_potential</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.matheval.html">dmft_tools.matheval</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.observables.html">dmft_tools.observables</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.results_to_archive.html">dmft_tools.results_to_archive</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.solver.html">dmft_tools.solver</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="_ref/io_tools.html">io_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="_ref/io_tools.dict_to_h5.html">io_tools.dict_to_h5</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/io_tools.postproc_toml_dict.html">io_tools.postproc_toml_dict</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/io_tools.verify_input_params.html">io_tools.verify_input_params</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="_ref/postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="_ref/postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/postprocessing.eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/postprocessing.maxent_gf_imp.html">postprocessing.maxent_gf_imp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/postprocessing.maxent_gf_latt.html">postprocessing.maxent_gf_latt</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/postprocessing.maxent_sigma.html">postprocessing.maxent_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="_ref/util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="_ref/util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="issues.html">Support &amp; contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="issues.html#seeking-help">Seeking help</a></li>
+<li class="toctree-l2"><a class="reference internal" href="issues.html#improving-solid-dmft">Improving solid_dmft</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="issues.html#reporting-issues">Reporting issues</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Changelog</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#version-3-2-0">Version 3.2.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#general">General</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#fix">fix</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#build">build</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#feat">feat</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#doc">doc</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#test">test</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#version-3-1-5">Version 3.1.5</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#version-3-1-4">Version 3.1.4</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#version-3-1-3">Version 3.1.3</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#version-3-1-2">Version 3.1.2</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#version-3-1-1">Version 3.1.1</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#version-3-1-0">Version 3.1.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#version-3-0-0">Version 3.0.0</a></li>
+</ul>
+</li>
+</ul>
+
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+<div class="tex2jax_ignore mathjax_ignore section" id="changelog">
+<span id="id1"></span><h1>Changelog<a class="headerlink" href="#changelog" title="Permalink to this headline"></a></h1>
+<div class="section" id="version-3-2-0">
+<h2>Version 3.2.0<a class="headerlink" href="#version-3-2-0" title="Permalink to this headline"></a></h2>
+<p>solid_dmft version 3.2.0 is a release that</p>
+<ul class="simple">
+<li>adds Jenkins CI support via flatiron-jenkins</li>
+<li>includes several fixes to match the latest triqs 3.2.x release</li>
+<li>changes the Z estimate to a correct linear fit of the first two Matsubara frequencies</li>
+<li>fixes for QE and Vasp CSC</li>
+<li>add option to add a magnetic field in DMFT</li>
+<li>add solid_dmft JOSS paper reference (<a class="reference external" href="http://doi.org/10.21105/joss.04623" rel="noopener noreferrer" target="_blank">doi.org/10.21105/joss.04623</a>)</li>
+<li>add simple Ntot interaction</li>
+<li>allow Uprime!=U-2J in Kanamori</li>
+<li>updates the tutorials</li>
+<li>introduces input output documentation</li>
+<li>add support for the TRIQS Hartree Solver</li>
+<li>add RESPACK support</li>
+</ul>
+<p>We thank all contributors: Sophie Beck, Alberto Carta, Alexander Hampel, Max Merkel, Harrison LaBollita, Nils Wentzell</p>
+<p>Find below an itemized list of changes in this release.</p>
+</div>
+<div class="section" id="general">
+<h2>General<a class="headerlink" href="#general" title="Permalink to this headline"></a></h2>
+<ul class="simple">
+<li>fix SzSz measurement in triqs unstable</li>
+<li>Updated mpich VASP5 docker file to include HF solver</li>
+<li>add hartree solver</li>
+<li>feat: add regular kmesh option to pcb postproc</li>
+<li>Fix to charge-self-consistency with Vasp (#48)</li>
+<li>removed QE fix files which are now in official release</li>
+<li>Modified dockerfile to add pmi support for cray supercomputing environments</li>
+<li>add RESPACK postprocessing routines (#38)</li>
+<li>Added correction to energy calculation</li>
+<li>add triqs logos to skeleton and include ico in install directive of doc</li>
+<li>change name of dft_mu to mu_initial_guess</li>
+<li>support different DFT cubic basis conventions (#36)</li>
+<li>allow magnetic calculation for CSC (output den correction is always averaged)</li>
+<li>fix sym bug in hubbardI postprocessing</li>
+<li>always calculate dft_mu at start of calculation</li>
+<li>add h_field_it to remove magnetic field after x iterations</li>
+<li>Write solid_dmft hash to h5</li>
+<li>fix delta interface of cthyb for multiple sites with different block structures</li>
+<li>correctly use tail fitted Sigma from cthyb not via double dyson equation</li>
+<li>add paper ref to toml</li>
+<li>minor addition of post-processing script: add_local Hamiltonian, separate from add_lambda. We might remove add_lambda</li>
+<li>update doc with JOSS references</li>
+<li>Bug fix for changes in sumk mesh definition in maxent_gf_latt</li>
+<li>adapt vasp patch files for ver6.3.2</li>
+<li>function to det n_orb_solver, fix test</li>
+<li>apply block picker before block mapping</li>
+<li>fix header writing for obs file</li>
+<li>add pick solver struct option to select specific blocks for the impurity problem</li>
+<li>fix print for failing comparison test</li>
+<li>allow different interaction Hamiltonians per impurity</li>
+<li>enforce PEP standard in interaction Hamiltonian</li>
+<li>print optimal alpha in other maxent scripts</li>
+<li>final corrections for PCB functions</li>
+<li>add proj_on_orb functionality to Akw</li>
+<li>fix bug in max_G_diff function ignoring norm_temp</li>
+<li>change Sigma_imp_iw / _w to Sigma_imp (DFTTools unstable)</li>
+<li>fix load Sigma with new gf_struct in triqs 3.1.x</li>
+<li>adapt to sumk mesh changes in dfttools</li>
+<li>Made the way mesh is stored in maxent_gf_latt consistent with maxent_gf_imp</li>
+</ul>
+</div>
+<div class="section" id="fix">
+<h2>fix<a class="headerlink" href="#fix" title="Permalink to this headline"></a></h2>
+<ul class="simple">
+<li>fix deg shells in magnetic calculations</li>
+<li>fix parameter n_orb in hint construction</li>
+<li>doc strings of cRPA avering for Slater</li>
+<li>critical bug in hubbardI interface</li>
+<li>PCB fermi surface plot</li>
+<li>updates from triqs unstable</li>
+<li>simple Z estimate as linear fit</li>
+<li>PCB: removing “linearize” function, changing the model</li>
+<li>delta_interface with SOC and store solver options</li>
+<li>convert warmup cycles to int automatically</li>
+<li>problem with ish vs icrsh in PCB Thanks &#64;HenryScottx for reporting!</li>
+<li>h_int uses now n_orb instead of orb_names</li>
+</ul>
+</div>
+<div class="section" id="build">
+<h2>build<a class="headerlink" href="#build" title="Permalink to this headline"></a></h2>
+<ul class="simple">
+<li>adapt jenkins CI files</li>
+<li>simplify docker image</li>
+<li>update openmpi docker file with clang-15</li>
+<li>update CI dockerfile</li>
+<li>Updated docker file to ubuntu 22</li>
+</ul>
+</div>
+<div class="section" id="feat">
+<h2>feat<a class="headerlink" href="#feat" title="Permalink to this headline"></a></h2>
+<ul class="simple">
+<li>enable MPI for maxent_gf_imp post-processing routines</li>
+<li>add possibility to specify Uprime in Kanamori interaction</li>
+<li>add loc_n_min / max arg for cthyb</li>
+<li>add additional support for hartree when computing DC from the solver</li>
+<li>add Ntot interaction</li>
+</ul>
+</div>
+<div class="section" id="doc">
+<h2>doc<a class="headerlink" href="#doc" title="Permalink to this headline"></a></h2>
+<ul class="simple">
+<li>Added observables documentation for DMFT output</li>
+<li>Updated tutorial svo one-shot</li>
+</ul>
+</div>
+<div class="section" id="test">
+<h2>test<a class="headerlink" href="#test" title="Permalink to this headline"></a></h2>
+<ul class="simple">
+<li>fix tests after Hartree additions</li>
+<li>add Hartree Solver test</li>
+<li>Integration test for maxent gf imp and latt, bug fixes to both scripts (#30)</li>
+<li>add new test for pcb get_dmft_bands function</li>
+</ul>
+</div>
+<div class="section" id="version-3-1-5">
+<h2>Version 3.1.5<a class="headerlink" href="#version-3-1-5" title="Permalink to this headline"></a></h2>
+<p>solid_dmft version 3.1.5 is a patch-release that improves / fixes the following issues:</p>
+<ul class="simple">
+<li>fix to charge-self-consistency with Vasp and QE</li>
+<li>feat add loc_n_min / max arg for cthyb</li>
+<li>fix simple Z estimate as linear fit</li>
+<li>adapt docker images for ubuntu 22.04</li>
+</ul>
+<p>Contributors: Sophie Beck, Alberto Carta, Alexander Hampel, Max Merkel:</p>
+</div>
+<div class="section" id="version-3-1-4">
+<h2>Version 3.1.4<a class="headerlink" href="#version-3-1-4" title="Permalink to this headline"></a></h2>
+<p>solid_dmft version 3.1.4 is a patch-release that improves / fixes the following issues:</p>
+<ul class="simple">
+<li>fix and improve rootfinder in PCB for quasiparticle dispersion</li>
+<li>fix pypi package <a class="reference external" href="http://version.py" rel="noopener noreferrer" target="_blank">version.py</a> module</li>
+</ul>
+<p>Contributors: Sophie Beck, Alberto Carta, Alexander Hampel, Max Merkel:</p>
+</div>
+<div class="section" id="version-3-1-3">
+<h2>Version 3.1.3<a class="headerlink" href="#version-3-1-3" title="Permalink to this headline"></a></h2>
+<p>solid_dmft version 3.1.3 is a patch-release that improves / fixes the following issues:</p>
+<ul class="simple">
+<li>fix delta interface of cthyb for multiple sites with different block structures</li>
+<li>correctly use tail fitted Sigma from cthyb not via double dyson equation</li>
+<li>magnetic param not available in CSC crash PM calc</li>
+<li>improve PCB script from unstable branch</li>
+<li>convert warmup cycles to int automatically</li>
+<li>fix function calls in gap finder</li>
+<li>fix delta_interface with SOC and store solver options</li>
+<li>fix: update svo example for PCB test from unstable</li>
+</ul>
+<p>Contributors: Sophie Beck, Alberto Carta, Alexander Hampel, Max Merkel</p>
+</div>
+<div class="section" id="version-3-1-2">
+<h2>Version 3.1.2<a class="headerlink" href="#version-3-1-2" title="Permalink to this headline"></a></h2>
+<p>solid_dmft version 3.1.1 is a patch-release that improves / fixes the following issues:</p>
+<ul class="simple">
+<li>fix deg shells in magnetic calculations</li>
+<li>fix bug in max_G_diff function ignoring norm_temp</li>
+<li>fix load Sigma with new gf_struct in triqs 3.1.x</li>
+<li>Made the way mesh is stored in maxent_gf_latt consistent with maxent_gf_imp</li>
+<li>adapt vasp patch files for ver6.3.2</li>
+<li>update <a class="reference external" href="http://README.md" rel="noopener noreferrer" target="_blank">README.md</a> for Joss publication</li>
+<li>print optimal alpha in other maxent scripts</li>
+<li>update postprocessing routines for plotting spectral functions</li>
+<li>add new test for pcb get_dmft_bands function</li>
+<li>DOC: extend install instructions &amp; improve readme for #21 #22</li>
+<li>DOC: update support &amp; contribute section, bump ver to 3.1.1</li>
+<li>add proj_on_orb functionality to Akw</li>
+<li>Added observables documentation for DMFT output</li>
+<li>Added input documentation</li>
+<li>Added ETH logo to website, small fixes to documentation</li>
+<li>rename examples to debbuging_examples</li>
+<li>pip package build files</li>
+</ul>
+<p>Contributors: Sophie Beck, Alberto Carta, Alexander Hampel, Max Merkel</p>
+</div>
+<div class="section" id="version-3-1-1">
+<h2>Version 3.1.1<a class="headerlink" href="#version-3-1-1" title="Permalink to this headline"></a></h2>
+<p>solid_dmft version 3.1.1 is a patch-release that improves / fixes the following issues:</p>
+<ul class="simple">
+<li>delete obsolete make_spaghetti.py</li>
+<li>SOC self energies can be continued in maxent</li>
+<li>run hubbardI solver on all nodes due to slow bcast performance of atomdiag object</li>
+<li>fix DFT energy read when running CSC QE</li>
+<li>updated documentation, small fixes to tutorials</li>
+<li>exposed params of maxent_gf_imp</li>
+<li>fix the way dft_mu is loaded in PCB</li>
+<li>fix executable in SVO tutorial</li>
+<li>fix shift in sigma continuator to remove dft_mu</li>
+<li>fix chemical potential in plot Akw and minor fixes</li>
+<li>correct plotlabels in postprocessing</li>
+<li>tiny modification of printing H_loc in postprocessing</li>
+</ul>
+<p>Contributors: Sophie Beck, Alberto Carta, Max Merkel</p>
+</div>
+<div class="section" id="version-3-1-0">
+<h2>Version 3.1.0<a class="headerlink" href="#version-3-1-0" title="Permalink to this headline"></a></h2>
+<p>solid_dmft version 3.1.0 is a major release that provides tutorials in the documentation, changes to app4triqs skeleton, allows CSC calculations with QE, improves postprocessing routines, and add functionality for SOC calculations.</p>
+<ul class="simple">
+<li>all new tutorials</li>
+<li>generalize measure_chi functionality</li>
+<li>CSC with Vasp 6.3.0 works, examples updated</li>
+<li>fix two bugs in w90 interface in vasp</li>
+<li>Renamed files</li>
+<li>fix Fermi level print in mlwf.F LPRJ_WRITE call</li>
+<li>Automatic patching of vasp 6.3.0 with Docker</li>
+<li>Updated tutorial</li>
+<li>Added check on all mpi ranks if dmft_config exists at beginning of run</li>
+<li>fix small bug in <a class="reference external" href="http://convergence.py" rel="noopener noreferrer" target="_blank">convergence.py</a> thanks &#64;merkelm</li>
+<li>Rework convergence metrics</li>
+<li>remove gf_struct_flatten from solver in accordance with latest dfttools version</li>
+<li>Renaming to solid_dmft</li>
+<li>Update of maxent_gf_latt.py: more parameters exposed and spin averaging is not default anymore</li>
+<li>fix bug in afm calculation when measuring density matrix</li>
+<li>Add w90_tolerance flag for CSC</li>
+<li>use sphinx autosummary for module reference</li>
+<li>small changes in IO, additional mpi barriers in csc flow for better stability</li>
+<li>With SOC now program prints real and imag part of matrices</li>
+<li>Fixed creation of Kanamori Hamiltonian with SOC</li>
+<li>Improvements in plot_correlated_bands.py and updated tutorial</li>
+<li>change output name of MaxEnt Sigma to Sigma_maxent</li>
+<li>change to develop version of w90 because of mpi bug in openmpi dockerfile</li>
+<li>bugfix in plot_correlated_bands and cleaning up</li>
+<li>update OpenMPI Dockerfile to latest Ubuntu</li>
+<li>Tutorial to explore correlated bands using the postprocessing script</li>
+<li>check in CSC with QE if optional files are presesnt, otherwise skip calculation</li>
+<li>Updated maxent_sigma: mpi parallelization, continuator types, bug fixes, parameters exposed</li>
+<li>update installation instructions</li>
+<li>add workflow and code structure images</li>
+<li>Updated maxent sigma script</li>
+<li>W90 runs in parallel</li>
+<li>Fixing a bug related to measure_pert_order and measure_chi_SzSz for afm_order</li>
+<li>add vasp crpa scripts and tutorials</li>
+<li>add delta interface for cthyb</li>
+<li>fix get_dmft_bands and pass eta to alatt_k_w correctly</li>
+<li>allows to recompute rotation matrix even if W90 is used</li>
+<li>bugfix in initial_self_energies.py in case dc = False</li>
+<li>flatten gf_struct for triqs solvers to remove depracted warning</li>
+<li>add example files for SVO and LNO</li>
+<li>bump triqs and package version to 3.1</li>
+</ul>
+<p>Contributors: Sophie Beck, Alberto Carta, Max Merkel</p>
+</div>
+<div class="section" id="version-3-0-0">
+<h2>Version 3.0.0<a class="headerlink" href="#version-3-0-0" title="Permalink to this headline"></a></h2>
+<p>solid_dmft version 3.0.0 is a compatibility
+release for TRIQS version 3.0.0 that</p>
+<ul class="simple">
+<li>introduces compatibility with Python 3 (Python 2 no longer supported)</li>
+<li>adds a cmake-based dependency management</li>
+<li>fixes several application issues</li>
+</ul>
+</div>
+</div>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="issues.html" class="btn btn-neutral float-left" title="Support &amp; contribute" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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+
+  <div role="contentinfo">
+    <p>&#169; Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.</p>
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diff --git a/_images/tutorials_PrNiO3_csc_vasp_plo_cthyb_tutorial_10_0.png b/_images/tutorials_PrNiO3_csc_vasp_plo_cthyb_tutorial_10_0.png
new file mode 100644
index 0000000000000000000000000000000000000000..e1118927536fac1e1eb695c2ae752c5353f4ab53
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diff --git a/_modules/csc_flow.html b/_modules/csc_flow.html
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+<li class="toctree-l3"><a class="reference internal" href="../md_notes/w90_interface.html">Wannier90 interface</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/w90_interface.html#orbital-order-in-the-w90-converter">orbital order in the W90 converter</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../md_notes/vasp_csc.html">Interface to VASP</a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#file-description">file description</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#workflow">Workflow:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#important-flags">important flags:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#version-and-compilation">version and compilation:</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#input-output">Input/Output</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../input_output/DMFT_input/input.html">Input</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/general.html">[general]: General parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/solver.html">[solver]: solver specific parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/dft.html">[dft]: DFT related inputs</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/advanced.html">[advanced]: Advanced inputs</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../input_output/DMFT_output/results.html">Output / results</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_output/results.html#group-structure">Group structure</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_output/results.html#subgroups">Subgroups</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#further-details-for-running">Further details for running</a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#building-the-docker-image">Building the docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#pulling-a-docker-image">Pulling a docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../md_notes/run_locally.html">Run on your machine</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#module-reference-manual">Module reference manual</a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="../_ref/dft_managers.vasp_manager.html">dft_managers.vasp_manager</a></li>
+</ul>
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+<li class="toctree-l3"><a class="reference internal" href="../_ref/dmft_cycle.html">dmft_cycle</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../_ref/dmft_tools.initial_self_energies.html">dmft_tools.initial_self_energies</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../_ref/dmft_tools.legendre_filter.html">dmft_tools.legendre_filter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/dmft_tools.manipulate_chemical_potential.html">dmft_tools.manipulate_chemical_potential</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/dmft_tools.matheval.html">dmft_tools.matheval</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/dmft_tools.observables.html">dmft_tools.observables</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/dmft_tools.results_to_archive.html">dmft_tools.results_to_archive</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/dmft_tools.solver.html">dmft_tools.solver</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../_ref/io_tools.html">io_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/io_tools.dict_to_h5.html">io_tools.dict_to_h5</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/io_tools.postproc_toml_dict.html">io_tools.postproc_toml_dict</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/io_tools.verify_input_params.html">io_tools.verify_input_params</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../_ref/postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/postprocessing.eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/postprocessing.maxent_gf_imp.html">postprocessing.maxent_gf_imp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/postprocessing.maxent_gf_latt.html">postprocessing.maxent_gf_latt</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/postprocessing.maxent_sigma.html">postprocessing.maxent_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../_ref/util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../issues.html">Support &amp; contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../issues.html#seeking-help">Seeking help</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../issues.html#improving-solid-dmft">Improving solid_dmft</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../issues.html#reporting-issues">Reporting issues</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../ChangeLog.html">Changelog</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-2-0">Version 3.2.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#general">General</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#fix">fix</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#build">build</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#feat">feat</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#doc">doc</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#test">test</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-5">Version 3.1.5</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-4">Version 3.1.4</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-3">Version 3.1.3</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-2">Version 3.1.2</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-1">Version 3.1.1</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-0">Version 3.1.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-0-0">Version 3.0.0</a></li>
+</ul>
+</li>
+</ul>
+
+        </div>
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu"  style="background: #7E588A" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="../index.html">solid_dmft</a>
+      </nav>
+
+      <div class="wy-nav-content">
+        <div class="rst-content">
+          <div role="navigation" aria-label="Page navigation">
+  <ul class="wy-breadcrumbs">
+      <li><a href="../index.html" class="icon icon-home"></a> &raquo;</li>
+          <li><a href="index.html">Module code</a> &raquo;</li>
+      <li>csc_flow</li>
+      <li class="wy-breadcrumbs-aside">
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <h1>Source code for csc_flow</h1><div class="highlight"><pre>
+<span></span><span class="c1">################################################################################</span>
+<span class="c1">#</span>
+<span class="c1"># solid_dmft - A versatile python wrapper to perform DFT+DMFT calculations</span>
+<span class="c1">#              utilizing the TRIQS software library</span>
+<span class="c1">#</span>
+<span class="c1"># Copyright (C) 2018-2020, ETH Zurich</span>
+<span class="c1"># Copyright (C) 2021, The Simons Foundation</span>
+<span class="c1">#      authors: A. Hampel, M. Merkel, and S. Beck</span>
+<span class="c1">#</span>
+<span class="c1"># solid_dmft is free software: you can redistribute it and/or modify it under the</span>
+<span class="c1"># terms of the GNU General Public License as published by the Free Software</span>
+<span class="c1"># Foundation, either version 3 of the License, or (at your option) any later</span>
+<span class="c1"># version.</span>
+<span class="c1">#</span>
+<span class="c1"># solid_dmft is distributed in the hope that it will be useful, but WITHOUT ANY</span>
+<span class="c1"># WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A</span>
+<span class="c1"># PARTICULAR PURPOSE. See the GNU General Public License for more details.</span>
+
+<span class="c1"># You should have received a copy of the GNU General Public License along with</span>
+<span class="c1"># solid_dmft (in the file COPYING.txt in this directory). If not, see</span>
+<span class="c1"># &lt;http://www.gnu.org/licenses/&gt;.</span>
+<span class="c1">#</span>
+<span class="c1">################################################################################</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">contains the charge self-consistency flow control functions</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="kn">from</span> <span class="nn">timeit</span> <span class="kn">import</span> <span class="n">default_timer</span> <span class="k">as</span> <span class="n">timer</span>
+<span class="kn">import</span> <span class="nn">subprocess</span>
+<span class="kn">import</span> <span class="nn">shlex</span>
+<span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<span class="c1"># triqs</span>
+<span class="kn">from</span> <span class="nn">h5</span> <span class="kn">import</span> <span class="n">HDFArchive</span>
+<span class="kn">import</span> <span class="nn">triqs.utility.mpi</span> <span class="k">as</span> <span class="nn">mpi</span>
+
+<span class="kn">from</span> <span class="nn">triqs_dft_tools.converters.wannier90</span> <span class="kn">import</span> <span class="n">Wannier90Converter</span>
+<span class="kn">from</span> <span class="nn">triqs_dft_tools.converters.vasp</span> <span class="kn">import</span> <span class="n">VaspConverter</span>
+<span class="kn">from</span> <span class="nn">triqs_dft_tools.converters.plovasp.vaspio</span> <span class="kn">import</span> <span class="n">VaspData</span>
+<span class="kn">import</span> <span class="nn">triqs_dft_tools.converters.plovasp.converter</span> <span class="k">as</span> <span class="nn">plo_converter</span>
+
+<span class="kn">from</span> <span class="nn">solid_dmft.dmft_cycle</span> <span class="kn">import</span> <span class="n">dmft_cycle</span>
+<span class="kn">from</span> <span class="nn">solid_dmft.dft_managers</span> <span class="kn">import</span> <span class="n">vasp_manager</span> <span class="k">as</span> <span class="n">vasp</span>
+<span class="kn">from</span> <span class="nn">solid_dmft.dft_managers</span> <span class="kn">import</span> <span class="n">qe_manager</span> <span class="k">as</span> <span class="n">qe</span>
+
+<span class="k">def</span> <span class="nf">_run_plo_converter</span><span class="p">(</span><span class="n">general_params</span><span class="p">,</span> <span class="n">dft_params</span><span class="p">):</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+        <span class="k">return</span>
+
+    <span class="c1"># Checks for plo file for projectors</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">dft_params</span><span class="p">[</span><span class="s1">&#39;plo_cfg&#39;</span><span class="p">]):</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;*** Input PLO config file not found! &#39;</span>
+              <span class="o">+</span> <span class="s1">&#39;I was looking for </span><span class="si">{}</span><span class="s1"> ***&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">dft_params</span><span class="p">[</span><span class="s1">&#39;plo_cfg&#39;</span><span class="p">]))</span>
+        <span class="n">mpi</span><span class="o">.</span><span class="n">MPI</span><span class="o">.</span><span class="n">COMM_WORLD</span><span class="o">.</span><span class="n">Abort</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+
+    <span class="c1"># Runs plo converter</span>
+    <span class="n">plo_converter</span><span class="o">.</span><span class="n">generate_and_output_as_text</span><span class="p">(</span><span class="n">dft_params</span><span class="p">[</span><span class="s1">&#39;plo_cfg&#39;</span><span class="p">],</span> <span class="n">vasp_dir</span><span class="o">=</span><span class="s1">&#39;./&#39;</span><span class="p">)</span>
+    <span class="c1"># Writes new H(k) to h5 archive</span>
+    <span class="n">converter</span> <span class="o">=</span> <span class="n">VaspConverter</span><span class="p">(</span><span class="n">filename</span><span class="o">=</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;seedname&#39;</span><span class="p">])</span>
+    <span class="n">converter</span><span class="o">.</span><span class="n">convert_dft_input</span><span class="p">()</span>
+
+<span class="k">def</span> <span class="nf">_run_wannier90</span><span class="p">(</span><span class="n">general_params</span><span class="p">,</span> <span class="n">dft_params</span><span class="p">):</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+        <span class="k">return</span>
+
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;seedname&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39;.win&#39;</span><span class="p">):</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;*** Wannier input file not found! &#39;</span>
+              <span class="o">+</span> <span class="s1">&#39;I was looking for </span><span class="si">{0}</span><span class="s1">.win ***&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;seedname&#39;</span><span class="p">]))</span>
+        <span class="n">mpi</span><span class="o">.</span><span class="n">MPI</span><span class="o">.</span><span class="n">COMM_WORLD</span><span class="o">.</span><span class="n">Abort</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+
+    <span class="c1"># Runs wannier90 twice:</span>
+    <span class="c1"># First preprocessing to write nnkp file, then normal run</span>
+    <span class="n">command</span> <span class="o">=</span> <span class="n">shlex</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">dft_params</span><span class="p">[</span><span class="s1">&#39;w90_exec&#39;</span><span class="p">])</span>
+    <span class="n">subprocess</span><span class="o">.</span><span class="n">check_call</span><span class="p">(</span><span class="n">command</span> <span class="o">+</span> <span class="p">[</span><span class="s1">&#39;-pp&#39;</span><span class="p">,</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;seedname&#39;</span><span class="p">]],</span> <span class="n">shell</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+    <span class="n">subprocess</span><span class="o">.</span><span class="n">check_call</span><span class="p">(</span><span class="n">command</span> <span class="o">+</span> <span class="p">[</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;seedname&#39;</span><span class="p">]],</span> <span class="n">shell</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+<span class="k">def</span> <span class="nf">_run_w90converter</span><span class="p">(</span><span class="n">seedname</span><span class="p">,</span> <span class="n">tolerance</span><span class="p">):</span>
+    <span class="k">if</span> <span class="p">(</span><span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">seedname</span> <span class="o">+</span> <span class="s1">&#39;.win&#39;</span><span class="p">)</span>
+        <span class="ow">or</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">seedname</span> <span class="o">+</span> <span class="s1">&#39;.inp&#39;</span><span class="p">)):</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;*** Wannier input/converter config file not found! &#39;</span>
+              <span class="o">+</span> <span class="s1">&#39;I was looking for </span><span class="si">{0}</span><span class="s1">.win and </span><span class="si">{0}</span><span class="s1">.inp ***&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">seedname</span><span class="p">))</span>
+        <span class="n">mpi</span><span class="o">.</span><span class="n">MPI</span><span class="o">.</span><span class="n">COMM_WORLD</span><span class="o">.</span><span class="n">Abort</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+
+    <span class="c1">#TODO: choose rot_mat_type with general_params[&#39;set_rot&#39;]</span>
+    <span class="n">converter</span> <span class="o">=</span> <span class="n">Wannier90Converter</span><span class="p">(</span><span class="n">seedname</span><span class="p">,</span> <span class="n">rot_mat_type</span><span class="o">=</span><span class="s1">&#39;hloc_diag&#39;</span><span class="p">,</span> <span class="n">bloch_basis</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">w90zero</span><span class="o">=</span><span class="n">tolerance</span><span class="p">)</span>
+    <span class="n">converter</span><span class="o">.</span><span class="n">convert_dft_input</span><span class="p">()</span>
+    <span class="n">mpi</span><span class="o">.</span><span class="n">barrier</span><span class="p">()</span>
+
+    <span class="c1"># Checks if creating of rot_mat succeeded</span>
+    <span class="k">if</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+        <span class="k">with</span> <span class="n">HDFArchive</span><span class="p">(</span><span class="n">seedname</span><span class="o">+</span><span class="s1">&#39;.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">archive</span><span class="p">:</span>
+            <span class="k">assert</span> <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;dft_input&#39;</span><span class="p">][</span><span class="s1">&#39;use_rotations&#39;</span><span class="p">],</span> <span class="s1">&#39;Creation of rot_mat failed in W90 converter&#39;</span>
+    <span class="n">mpi</span><span class="o">.</span><span class="n">barrier</span><span class="p">()</span>
+
+<span class="k">def</span> <span class="nf">_full_qe_run</span><span class="p">(</span><span class="n">seedname</span><span class="p">,</span> <span class="n">dft_params</span><span class="p">,</span> <span class="n">mode</span><span class="p">):</span>
+    <span class="k">assert</span> <span class="n">mode</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">&#39;initial&#39;</span><span class="p">,</span> <span class="s1">&#39;restart&#39;</span><span class="p">,</span> <span class="s1">&#39;update&#39;</span><span class="p">)</span>
+
+    <span class="c1"># runs a full iteration of DFT</span>
+    <span class="n">qe_wrapper</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">calc_type</span><span class="p">:</span> <span class="n">qe</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">dft_params</span><span class="p">[</span><span class="s1">&#39;n_cores&#39;</span><span class="p">],</span> <span class="n">calc_type</span><span class="p">,</span> <span class="n">dft_params</span><span class="p">[</span><span class="s1">&#39;dft_exec&#39;</span><span class="p">],</span>
+                                          <span class="n">dft_params</span><span class="p">[</span><span class="s1">&#39;mpi_env&#39;</span><span class="p">],</span> <span class="n">seedname</span><span class="p">)</span>
+
+    <span class="c1"># Initially run an scf calculation</span>
+    <span class="k">if</span> <span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;initial&#39;</span><span class="p">:</span>
+        <span class="n">qe_wrapper</span><span class="p">(</span><span class="s1">&#39;scf&#39;</span><span class="p">)</span>
+    <span class="c1"># For charge update, use mode scf</span>
+    <span class="k">elif</span> <span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;update&#39;</span><span class="p">:</span>
+        <span class="n">qe_wrapper</span><span class="p">(</span><span class="s1">&#39;mod_scf&#39;</span><span class="p">)</span>
+
+    <span class="c1"># Rest is executed regardless of mode</span>
+    <span class="c1"># Optionally does bnd, bands, proj if files are present</span>
+    <span class="k">for</span> <span class="n">nscf</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;bnd&#39;</span><span class="p">,</span> <span class="s1">&#39;bands&#39;</span><span class="p">,</span> <span class="s1">&#39;proj&#39;</span><span class="p">]:</span>
+        <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">seedname</span><span class="si">}</span><span class="s1">.</span><span class="si">{</span><span class="n">nscf</span><span class="si">}</span><span class="s1">.in&#39;</span><span class="p">):</span>
+            <span class="n">qe_wrapper</span><span class="p">(</span><span class="n">nscf</span><span class="p">)</span>
+
+    <span class="c1"># nscf</span>
+    <span class="n">qe_wrapper</span><span class="p">(</span><span class="s1">&#39;nscf&#39;</span><span class="p">)</span>
+    <span class="c1"># w90 parts</span>
+    <span class="n">qe_wrapper</span><span class="p">(</span><span class="s1">&#39;win_pp&#39;</span><span class="p">)</span>
+    <span class="n">qe_wrapper</span><span class="p">(</span><span class="s1">&#39;pw2wan&#39;</span><span class="p">)</span>
+    <span class="n">qe_wrapper</span><span class="p">(</span><span class="s1">&#39;win&#39;</span><span class="p">)</span>
+    <span class="n">_run_w90converter</span><span class="p">(</span><span class="n">seedname</span><span class="p">,</span> <span class="n">dft_params</span><span class="p">[</span><span class="s1">&#39;w90_tolerance&#39;</span><span class="p">])</span>
+
+
+<span class="k">def</span> <span class="nf">_store_dft_eigvals</span><span class="p">(</span><span class="n">path_to_h5</span><span class="p">,</span> <span class="n">iteration</span><span class="p">,</span> <span class="n">projector_type</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    save the eigenvalues from LOCPROJ/wannier90 file to h5 archive</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">with</span> <span class="n">HDFArchive</span><span class="p">(</span><span class="n">path_to_h5</span><span class="p">,</span> <span class="s1">&#39;a&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">archive</span><span class="p">:</span>
+        <span class="k">if</span> <span class="s1">&#39;dft_eigvals&#39;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">archive</span><span class="p">:</span>
+            <span class="n">archive</span><span class="o">.</span><span class="n">create_group</span><span class="p">(</span><span class="s1">&#39;dft_eigvals&#39;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">projector_type</span> <span class="o">==</span> <span class="s1">&#39;plo&#39;</span><span class="p">:</span>
+            <span class="n">vasp_data</span> <span class="o">=</span> <span class="n">VaspData</span><span class="p">(</span><span class="s1">&#39;./&#39;</span><span class="p">)</span>
+            <span class="n">eigenvals</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">vasp_data</span><span class="o">.</span><span class="n">plocar</span><span class="o">.</span><span class="n">eigs</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">])</span> <span class="o">-</span> <span class="n">vasp_data</span><span class="o">.</span><span class="n">plocar</span><span class="o">.</span><span class="n">efermi</span>
+        <span class="k">elif</span> <span class="n">projector_type</span> <span class="o">==</span> <span class="s1">&#39;w90&#39;</span><span class="p">:</span>
+            <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;LOCPROJ&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">locproj_file</span><span class="p">:</span>
+                <span class="n">fermi_energy</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">locproj_file</span><span class="o">.</span><span class="n">readline</span><span class="p">()</span><span class="o">.</span><span class="n">split</span><span class="p">()[</span><span class="mi">4</span><span class="p">])</span>
+            <span class="n">n_k</span> <span class="o">=</span> <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;dft_input&#39;</span><span class="p">][</span><span class="s1">&#39;n_k&#39;</span><span class="p">]</span>
+            <span class="n">num_ks_bands</span> <span class="o">=</span> <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;dft_input&#39;</span><span class="p">][</span><span class="s1">&#39;n_orbitals&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
+            <span class="n">eigenvals</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">loadtxt</span><span class="p">(</span><span class="s1">&#39;wannier90.eig&#39;</span><span class="p">,</span> <span class="n">usecols</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+            <span class="n">eigenvals</span> <span class="o">=</span> <span class="n">eigenvals</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="n">n_k</span><span class="p">,</span> <span class="n">num_ks_bands</span><span class="p">))</span> <span class="o">-</span> <span class="n">fermi_energy</span>
+
+        <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;dft_eigvals&#39;</span><span class="p">][</span><span class="s1">&#39;it_&#39;</span><span class="o">+</span><span class="nb">str</span><span class="p">(</span><span class="n">iteration</span><span class="p">)]</span> <span class="o">=</span> <span class="n">eigenvals</span>
+
+<span class="k">def</span> <span class="nf">_full_vasp_run</span><span class="p">(</span><span class="n">general_params</span><span class="p">,</span> <span class="n">dft_params</span><span class="p">,</span> <span class="n">initial_run</span><span class="p">,</span> <span class="n">n_iter_dft</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">sum_k</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Performs a complete DFT cycle in Vasp and the correct converter. If</span>
+<span class="sd">    initial_run, Vasp is starting and performing a normal scf calculation</span>
+<span class="sd">    followed by a converter run. Otherwise, it performs n_iter_dft runs of DFT,</span>
+<span class="sd">    generating the projectors with the converter, and recalculating the charge</span>
+<span class="sd">    density correction with the new projectors.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    general_params : dict</span>
+<span class="sd">        general parameters as a dict</span>
+<span class="sd">    dft_params : dict</span>
+<span class="sd">        dft parameters as a dict</span>
+<span class="sd">    initial_run : bool</span>
+<span class="sd">        True when VASP is called for the first time. initial_run = True requires</span>
+<span class="sd">        n_iter_dft = 1.</span>
+<span class="sd">    n_iter_dft : int, optional</span>
+<span class="sd">        Number of DFT iterations to perform. The default is 1.</span>
+<span class="sd">    sum_k : SumkDFT, optional</span>
+<span class="sd">        The SumkDFT object required to recalculate the charge-density correction</span>
+<span class="sd">        if n_iter_dft &gt; 1. The default is None.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    vasp_process_id : int</span>
+<span class="sd">        The process ID of the forked VASP process.</span>
+<span class="sd">    irred_indices : np.array</span>
+<span class="sd">        Integer indices of kpts in the irreducible Brillouin zone. Only needed</span>
+<span class="sd">        for Wannier projectors, which are normally run with symmetries.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+
+    <span class="k">if</span> <span class="n">initial_run</span><span class="p">:</span>
+        <span class="k">assert</span> <span class="n">n_iter_dft</span> <span class="o">==</span> <span class="mi">1</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">assert</span> <span class="n">n_iter_dft</span> <span class="o">==</span> <span class="mi">1</span> <span class="ow">or</span> <span class="n">sum_k</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">,</span> <span class="s1">&#39;Sumk object needed to run multiple DFT iterations&#39;</span>
+
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_iter_dft</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">initial_run</span><span class="p">:</span>
+            <span class="n">vasp_process_id</span> <span class="o">=</span> <span class="n">vasp</span><span class="o">.</span><span class="n">run_initial_scf</span><span class="p">(</span><span class="n">dft_params</span><span class="p">[</span><span class="s1">&#39;n_cores&#39;</span><span class="p">],</span> <span class="n">dft_params</span><span class="p">[</span><span class="s1">&#39;dft_exec&#39;</span><span class="p">],</span>
+                                                   <span class="n">dft_params</span><span class="p">[</span><span class="s1">&#39;mpi_env&#39;</span><span class="p">])</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">vasp_process_id</span> <span class="o">=</span> <span class="kc">None</span>
+            <span class="n">vasp</span><span class="o">.</span><span class="n">run_charge_update</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="n">dft_params</span><span class="p">[</span><span class="s1">&#39;projector_type&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;plo&#39;</span><span class="p">:</span>
+            <span class="n">_run_plo_converter</span><span class="p">(</span><span class="n">general_params</span><span class="p">,</span> <span class="n">dft_params</span><span class="p">)</span>
+            <span class="n">irred_indices</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="k">elif</span> <span class="n">dft_params</span><span class="p">[</span><span class="s1">&#39;projector_type&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;w90&#39;</span><span class="p">:</span>
+            <span class="n">_run_wannier90</span><span class="p">(</span><span class="n">general_params</span><span class="p">,</span> <span class="n">dft_params</span><span class="p">)</span>
+            <span class="n">mpi</span><span class="o">.</span><span class="n">barrier</span><span class="p">()</span>
+            <span class="n">_run_w90converter</span><span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;seedname&#39;</span><span class="p">],</span> <span class="n">dft_params</span><span class="p">[</span><span class="s1">&#39;w90_tolerance&#39;</span><span class="p">])</span>
+            <span class="n">mpi</span><span class="o">.</span><span class="n">barrier</span><span class="p">()</span>
+            <span class="n">kpts</span> <span class="o">=</span> <span class="kc">None</span>
+            <span class="k">if</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+                <span class="k">with</span> <span class="n">HDFArchive</span><span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;seedname&#39;</span><span class="p">]</span><span class="o">+</span><span class="s1">&#39;.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">archive</span><span class="p">:</span>
+                    <span class="n">kpts</span> <span class="o">=</span> <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;dft_input/kpts&#39;</span><span class="p">]</span>
+            <span class="n">irred_indices</span> <span class="o">=</span> <span class="n">vasp</span><span class="o">.</span><span class="n">read_irred_kpoints</span><span class="p">(</span><span class="n">kpts</span><span class="p">)</span>
+
+        <span class="c1"># No need for recalculation of density correction if we run DMFT next</span>
+        <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="n">n_iter_dft</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="k">break</span>
+
+        <span class="c1"># Recalculates the density correction</span>
+        <span class="c1"># Reads in new projectors and hopping and updates chemical potential</span>
+        <span class="c1"># rot_mat is not updated since it&#39;s more closely related to the local problem than DFT</span>
+        <span class="c1"># New fermi weights are directly read in calc_density_correction</span>
+        <span class="n">mpi</span><span class="o">.</span><span class="n">barrier</span><span class="p">()</span>
+        <span class="k">if</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+            <span class="k">with</span> <span class="n">HDFArchive</span><span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;seedname&#39;</span><span class="p">]</span><span class="o">+</span><span class="s1">&#39;.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">archive</span><span class="p">:</span>
+                <span class="n">sum_k</span><span class="o">.</span><span class="n">proj_mat</span> <span class="o">=</span> <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;dft_input/proj_mat&#39;</span><span class="p">]</span>
+                <span class="n">sum_k</span><span class="o">.</span><span class="n">hopping</span> <span class="o">=</span> <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;dft_input/hopping&#39;</span><span class="p">]</span>
+        <span class="n">sum_k</span><span class="o">.</span><span class="n">proj_mat</span> <span class="o">=</span> <span class="n">mpi</span><span class="o">.</span><span class="n">bcast</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">proj_mat</span><span class="p">)</span>
+        <span class="n">sum_k</span><span class="o">.</span><span class="n">hopping</span> <span class="o">=</span> <span class="n">mpi</span><span class="o">.</span><span class="n">bcast</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">hopping</span><span class="p">)</span>
+        <span class="n">sum_k</span><span class="o">.</span><span class="n">calc_mu</span><span class="p">(</span><span class="n">precision</span><span class="o">=</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;prec_mu&#39;</span><span class="p">])</span>
+
+        <span class="c1"># Writes out GAMMA file</span>
+        <span class="n">sum_k</span><span class="o">.</span><span class="n">calc_density_correction</span><span class="p">(</span><span class="n">dm_type</span><span class="o">=</span><span class="s1">&#39;vasp&#39;</span><span class="p">,</span>  <span class="n">kpts_to_write</span><span class="o">=</span><span class="n">irred_indices</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">vasp_process_id</span><span class="p">,</span> <span class="n">irred_indices</span>
+
+
+<span class="c1"># Main CSC flow method</span>
+<div class="viewcode-block" id="csc_flow_control"><a class="viewcode-back" href="../_ref/csc_flow.html#csc_flow.csc_flow_control">[docs]</a><span class="k">def</span> <span class="nf">csc_flow_control</span><span class="p">(</span><span class="n">general_params</span><span class="p">,</span> <span class="n">solver_params</span><span class="p">,</span> <span class="n">dft_params</span><span class="p">,</span> <span class="n">advanced_params</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Function to run the csc cycle. It writes and removes the vasp.lock file to</span>
+<span class="sd">    start and stop Vasp, run the converter, run the dmft cycle and abort the job</span>
+<span class="sd">    if all iterations are finished.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    general_params : dict</span>
+<span class="sd">        general parameters as a dict</span>
+<span class="sd">    solver_params : dict</span>
+<span class="sd">        solver parameters as a dict</span>
+<span class="sd">    dft_params : dict</span>
+<span class="sd">        dft parameters as a dict</span>
+<span class="sd">    advanced_params : dict</span>
+<span class="sd">        advanced parameters as a dict</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># Removes legacy file vasp.suppress_projs if present</span>
+    <span class="n">vasp</span><span class="o">.</span><span class="n">remove_legacy_projections_suppressed</span><span class="p">()</span>
+
+    <span class="c1"># if GAMMA file already exists, load it by doing extra DFT iterations</span>
+    <span class="k">if</span> <span class="n">dft_params</span><span class="p">[</span><span class="s1">&#39;dft_code&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;vasp&#39;</span> <span class="ow">and</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="s1">&#39;GAMMA&#39;</span><span class="p">):</span>
+        <span class="c1"># TODO: implement</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">&#39;GAMMA file found but restarting from updated &#39;</span>
+                                  <span class="o">+</span> <span class="s1">&#39;charge density not yet implemented for Vasp.&#39;</span><span class="p">)</span>
+
+    <span class="c1"># Reads in iteration offset if restarting</span>
+    <span class="n">iteration_offset</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="k">if</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">()</span> <span class="ow">and</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;seedname&#39;</span><span class="p">]</span><span class="o">+</span><span class="s1">&#39;.h5&#39;</span><span class="p">):</span>
+        <span class="k">with</span> <span class="n">HDFArchive</span><span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;seedname&#39;</span><span class="p">]</span><span class="o">+</span><span class="s1">&#39;.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">archive</span><span class="p">:</span>
+            <span class="k">if</span> <span class="s1">&#39;DMFT_results&#39;</span> <span class="ow">in</span> <span class="n">archive</span> <span class="ow">and</span> <span class="s1">&#39;iteration_count&#39;</span> <span class="ow">in</span> <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_results&#39;</span><span class="p">]:</span>
+                <span class="n">iteration_offset</span> <span class="o">=</span> <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_results&#39;</span><span class="p">][</span><span class="s1">&#39;iteration_count&#39;</span><span class="p">]</span>
+    <span class="n">iteration_offset</span> <span class="o">=</span> <span class="n">mpi</span><span class="o">.</span><span class="n">bcast</span><span class="p">(</span><span class="n">iteration_offset</span><span class="p">)</span>
+
+    <span class="n">iter_dmft</span> <span class="o">=</span> <span class="n">iteration_offset</span><span class="o">+</span><span class="mi">1</span>
+
+    <span class="c1"># Runs DFT once and converter</span>
+    <span class="n">mpi</span><span class="o">.</span><span class="n">barrier</span><span class="p">()</span>
+    <span class="n">irred_indices</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="n">start_time_dft</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+    <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;  solid_dmft: Running </span><span class="si">{}</span><span class="s1">...&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">dft_params</span><span class="p">[</span><span class="s1">&#39;dft_code&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">upper</span><span class="p">()))</span>
+
+    <span class="k">if</span> <span class="n">dft_params</span><span class="p">[</span><span class="s1">&#39;dft_code&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;qe&#39;</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">iteration_offset</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">_full_qe_run</span><span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;seedname&#39;</span><span class="p">],</span> <span class="n">dft_params</span><span class="p">,</span> <span class="s1">&#39;initial&#39;</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">_full_qe_run</span><span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;seedname&#39;</span><span class="p">],</span> <span class="n">dft_params</span><span class="p">,</span> <span class="s1">&#39;restart&#39;</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="n">dft_params</span><span class="p">[</span><span class="s1">&#39;dft_code&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;vasp&#39;</span><span class="p">:</span>
+        <span class="n">vasp_process_id</span><span class="p">,</span> <span class="n">irred_indices</span> <span class="o">=</span> <span class="n">_full_vasp_run</span><span class="p">(</span><span class="n">general_params</span><span class="p">,</span> <span class="n">dft_params</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
+
+    <span class="n">mpi</span><span class="o">.</span><span class="n">barrier</span><span class="p">()</span>
+    <span class="n">end_time_dft</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+    <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;  solid_dmft: DFT cycle took </span><span class="si">{:10.3f}</span><span class="s1"> seconds&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">end_time_dft</span><span class="o">-</span><span class="n">start_time_dft</span><span class="p">))</span>
+
+    <span class="c1"># Now that everything is ready, starts DFT+DMFT loop</span>
+    <span class="k">while</span> <span class="kc">True</span><span class="p">:</span>
+        <span class="n">dft_energy</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="k">if</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+            <span class="c1"># Writes eigenvals to archive if requested</span>
+            <span class="k">if</span> <span class="n">dft_params</span><span class="p">[</span><span class="s1">&#39;store_eigenvals&#39;</span><span class="p">]:</span>
+                <span class="k">if</span> <span class="n">dft_params</span><span class="p">[</span><span class="s1">&#39;dft_code&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;qe&#39;</span><span class="p">:</span>
+                    <span class="c1"># TODO: implement</span>
+                    <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">&#39;store_eigenvals not yet compatible with dft_code = qe&#39;</span><span class="p">)</span>
+                <span class="n">_store_dft_eigvals</span><span class="p">(</span><span class="n">path_to_h5</span><span class="o">=</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;seedname&#39;</span><span class="p">]</span><span class="o">+</span><span class="s1">&#39;.h5&#39;</span><span class="p">,</span>
+                                   <span class="n">iteration</span><span class="o">=</span><span class="n">iter_dmft</span><span class="p">,</span>
+                                   <span class="n">projector_type</span><span class="o">=</span><span class="n">dft_params</span><span class="p">[</span><span class="s1">&#39;projector_type&#39;</span><span class="p">])</span>
+
+            <span class="c1"># Reads the DFT energy</span>
+            <span class="k">if</span> <span class="n">dft_params</span><span class="p">[</span><span class="s1">&#39;dft_code&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;vasp&#39;</span><span class="p">:</span>
+                <span class="n">dft_energy</span> <span class="o">=</span> <span class="n">vasp</span><span class="o">.</span><span class="n">read_dft_energy</span><span class="p">()</span>
+            <span class="k">elif</span> <span class="n">dft_params</span><span class="p">[</span><span class="s1">&#39;dft_code&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;qe&#39;</span><span class="p">:</span>
+                <span class="n">dft_energy</span> <span class="o">=</span> <span class="n">qe</span><span class="o">.</span><span class="n">read_dft_energy</span><span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;seedname&#39;</span><span class="p">],</span> <span class="n">iter_dmft</span><span class="p">)</span>
+        <span class="n">dft_energy</span> <span class="o">=</span> <span class="n">mpi</span><span class="o">.</span><span class="n">bcast</span><span class="p">(</span><span class="n">dft_energy</span><span class="p">)</span>
+
+        <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="s1">&#39;#&#39;</span><span class="o">*</span><span class="mi">80</span><span class="p">,</span> <span class="s1">&#39;Calling dmft_cycle&#39;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+            <span class="n">start_time_dmft</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+
+        <span class="c1"># Determines number of DMFT steps</span>
+        <span class="k">if</span> <span class="n">iter_dmft</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="n">iter_one_shot</span> <span class="o">=</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;n_iter_dmft_first&#39;</span><span class="p">]</span>
+        <span class="k">elif</span> <span class="n">iteration_offset</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">iter_dmft</span> <span class="o">==</span> <span class="n">iteration_offset</span> <span class="o">+</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="n">iter_one_shot</span> <span class="o">=</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;n_iter_dmft_per&#39;</span><span class="p">]</span> <span class="o">-</span> <span class="p">(</span><span class="n">iter_dmft</span> <span class="o">-</span> <span class="mi">1</span>
+                            <span class="o">-</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;n_iter_dmft_first&#39;</span><span class="p">])</span><span class="o">%</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;n_iter_dmft_per&#39;</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">iter_one_shot</span> <span class="o">=</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;n_iter_dmft_per&#39;</span><span class="p">]</span>
+        <span class="c1"># Maximum total number of iterations is n_iter_dmft+iteration_offset</span>
+        <span class="n">iter_one_shot</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">iter_one_shot</span><span class="p">,</span>
+                            <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;n_iter_dmft&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="n">iteration_offset</span> <span class="o">-</span> <span class="n">iter_dmft</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
+
+        <span class="c1">############################################################</span>
+        <span class="c1"># run the dmft_cycle</span>
+        <span class="n">is_converged</span><span class="p">,</span> <span class="n">sum_k</span> <span class="o">=</span> <span class="n">dmft_cycle</span><span class="p">(</span><span class="n">general_params</span><span class="p">,</span> <span class="n">solver_params</span><span class="p">,</span> <span class="n">advanced_params</span><span class="p">,</span>
+                                         <span class="n">dft_params</span><span class="p">,</span> <span class="n">iter_one_shot</span><span class="p">,</span> <span class="n">irred_indices</span><span class="p">,</span> <span class="n">dft_energy</span><span class="p">)</span>
+        <span class="c1">############################################################</span>
+
+        <span class="n">iter_dmft</span> <span class="o">+=</span> <span class="n">iter_one_shot</span>
+
+        <span class="k">if</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+            <span class="n">end_time_dmft</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span> <span class="o">+</span> <span class="s1">&#39;=&#39;</span><span class="o">*</span><span class="mi">80</span><span class="p">)</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;DMFT cycle took </span><span class="si">{:10.3f}</span><span class="s1"> seconds&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">end_time_dmft</span><span class="o">-</span><span class="n">start_time_dmft</span><span class="p">))</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;=&#39;</span><span class="o">*</span><span class="mi">80</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
+
+        <span class="c1"># If all steps are executed or calculation is converged, finish DFT+DMFT loop</span>
+        <span class="k">if</span> <span class="n">is_converged</span> <span class="ow">or</span> <span class="n">iter_dmft</span> <span class="o">&gt;</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;n_iter_dmft&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="n">iteration_offset</span><span class="p">:</span>
+            <span class="k">break</span>
+
+        <span class="c1"># Restarts DFT</span>
+        <span class="n">mpi</span><span class="o">.</span><span class="n">barrier</span><span class="p">()</span>
+        <span class="n">start_time_dft</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+        <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;  solid_dmft: Running </span><span class="si">{}</span><span class="s1">...&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">dft_params</span><span class="p">[</span><span class="s1">&#39;dft_code&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">upper</span><span class="p">()))</span>
+
+        <span class="c1"># Runs DFT and converter</span>
+        <span class="k">if</span> <span class="n">dft_params</span><span class="p">[</span><span class="s1">&#39;dft_code&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;qe&#39;</span><span class="p">:</span>
+            <span class="n">_full_qe_run</span><span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;seedname&#39;</span><span class="p">],</span> <span class="n">dft_params</span><span class="p">,</span> <span class="s1">&#39;update&#39;</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">dft_params</span><span class="p">[</span><span class="s1">&#39;dft_code&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;vasp&#39;</span><span class="p">:</span>
+            <span class="c1"># Determines number of DFT steps</span>
+            <span class="k">if</span> <span class="n">iter_dmft</span> <span class="o">==</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;n_iter_dmft_first&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="n">n_iter_dft</span> <span class="o">=</span> <span class="n">dft_params</span><span class="p">[</span><span class="s1">&#39;n_iter_first&#39;</span><span class="p">]</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">n_iter_dft</span> <span class="o">=</span> <span class="n">dft_params</span><span class="p">[</span><span class="s1">&#39;n_iter&#39;</span><span class="p">]</span>
+            <span class="n">_</span><span class="p">,</span> <span class="n">irred_indices</span> <span class="o">=</span> <span class="n">_full_vasp_run</span><span class="p">(</span><span class="n">general_params</span><span class="p">,</span> <span class="n">dft_params</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <span class="n">n_iter_dft</span><span class="p">,</span> <span class="n">sum_k</span><span class="p">)</span>
+
+        <span class="n">mpi</span><span class="o">.</span><span class="n">barrier</span><span class="p">()</span>
+        <span class="n">end_time_dft</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+        <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;  solid_dmft: DFT cycle took </span><span class="si">{:10.3f}</span><span class="s1"> seconds&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">end_time_dft</span><span class="o">-</span><span class="n">start_time_dft</span><span class="p">))</span>
+
+    <span class="c1"># Kills background VASP process for clean end</span>
+    <span class="k">if</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">()</span> <span class="ow">and</span> <span class="n">dft_params</span><span class="p">[</span><span class="s1">&#39;dft_code&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;vasp&#39;</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;  solid_dmft: Stopping VASP</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">flush</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+        <span class="n">vasp</span><span class="o">.</span><span class="n">kill</span><span class="p">(</span><span class="n">vasp_process_id</span><span class="p">)</span></div>
+</pre></div>
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.</p>
+  </div>
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
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\ No newline at end of file
diff --git a/_modules/dft_managers/mpi_helpers.html b/_modules/dft_managers/mpi_helpers.html
new file mode 100644
index 00000000..e8edaa8b
--- /dev/null
+++ b/_modules/dft_managers/mpi_helpers.html
@@ -0,0 +1,461 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" >
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>dft_managers.mpi_helpers &mdash; solid_dmft  documentation</title>
+      <link rel="stylesheet" href="../../_static/pygments.css" type="text/css" />
+      <link rel="stylesheet" href="../../_static/css/custom.css" type="text/css" />
+      <link rel="stylesheet" href="../../_static/plot_directive.css" type="text/css" />
+    <link rel="shortcut icon" href="../../_static/favicon.ico"/>
+  
+        <script data-url_root="../../" id="documentation_options" src="../../_static/documentation_options.js"></script>
+        <script src="../../_static/jquery.js"></script>
+        <script src="../../_static/underscore.js"></script>
+        <script src="../../_static/doctools.js"></script>
+        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+    <script src="../../_static/js/theme.js"></script>
+    <link rel="index" title="Index" href="../../genindex.html" />
+    <link rel="search" title="Search" href="../../search.html" /> 
+</head>
+
+<body class="wy-body-for-nav"> 
+  <div class="wy-grid-for-nav">
+    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
+      <div class="wy-side-scroll">
+        <div class="wy-side-nav-search"  style="background: #7E588A" >
+            <a href="../../index.html" class="icon icon-home"> solid_dmft
+            <img src="../../_static/logo_no_text.png" class="logo" alt="Logo"/>
+          </a>
+              <div class="version">
+                3.2.0
+              </div>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="../../search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" />
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+  </form>
+</div>
+        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+              <ul>
+<li class="toctree-l1"><a class="reference internal" href="../../install.html">Installation</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#installation-via-pip">Installation via pip</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#manual-installation-via-cmake">Manual installation via CMake</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#docker-files-images">Docker files &amp; images</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#version-compatibility">Version compatibility</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#custom-cmake-options">Custom CMake options</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../documentation.html">Documentation</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#code-structure">Code structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#dft-interface-notes">DFT interface notes</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/w90_interface.html">Wannier90 interface</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/w90_interface.html#orbital-order-in-the-w90-converter">orbital order in the W90 converter</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/vasp_csc.html">Interface to VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#general-remarks">General remarks</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#file-description">file description</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#workflow">Workflow:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#important-flags">important flags:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#version-and-compilation">version and compilation:</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#input-output">Input/Output</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../input_output/DMFT_input/input.html">Input</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/general.html">[general]: General parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/solver.html">[solver]: solver specific parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/dft.html">[dft]: DFT related inputs</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/advanced.html">[advanced]: Advanced inputs</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../input_output/DMFT_output/results.html">Output / results</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_output/results.html#group-structure">Group structure</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_output/results.html#subgroups">Subgroups</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#further-details-for-running">Further details for running</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/docker.html">Docker</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#building-the-docker-image">Building the docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#pulling-a-docker-image">Pulling a docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_locally.html">Run on your machine</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#module-reference-manual">Module reference manual</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/csc_flow.html">csc_flow</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/dft_managers.html">dft_managers</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.mpi_helpers.html">dft_managers.mpi_helpers</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.qe_manager.html">dft_managers.qe_manager</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.vasp_manager.html">dft_managers.vasp_manager</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/dmft_cycle.html">dmft_cycle</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/dmft_tools.html">dmft_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.afm_mapping.html">dmft_tools.afm_mapping</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.convergence.html">dmft_tools.convergence</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.formatter.html">dmft_tools.formatter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.greens_functions_mixer.html">dmft_tools.greens_functions_mixer</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.initial_self_energies.html">dmft_tools.initial_self_energies</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.interaction_hamiltonian.html">dmft_tools.interaction_hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.legendre_filter.html">dmft_tools.legendre_filter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.manipulate_chemical_potential.html">dmft_tools.manipulate_chemical_potential</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.matheval.html">dmft_tools.matheval</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.observables.html">dmft_tools.observables</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.results_to_archive.html">dmft_tools.results_to_archive</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.solver.html">dmft_tools.solver</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/io_tools.html">io_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.dict_to_h5.html">io_tools.dict_to_h5</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.postproc_toml_dict.html">io_tools.postproc_toml_dict</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.verify_input_params.html">io_tools.verify_input_params</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_gf_imp.html">postprocessing.maxent_gf_imp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_gf_latt.html">postprocessing.maxent_gf_latt</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_sigma.html">postprocessing.maxent_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../issues.html">Support &amp; contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../issues.html#seeking-help">Seeking help</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../issues.html#improving-solid-dmft">Improving solid_dmft</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../issues.html#reporting-issues">Reporting issues</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../ChangeLog.html">Changelog</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-2-0">Version 3.2.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#general">General</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#fix">fix</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#build">build</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#feat">feat</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#doc">doc</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#test">test</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-1-5">Version 3.1.5</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-1-4">Version 3.1.4</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-1-3">Version 3.1.3</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-1-2">Version 3.1.2</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-1-1">Version 3.1.1</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-1-0">Version 3.1.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-0-0">Version 3.0.0</a></li>
+</ul>
+</li>
+</ul>
+
+        </div>
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+      <li><a href="../../index.html" class="icon icon-home"></a> &raquo;</li>
+          <li><a href="../index.html">Module code</a> &raquo;</li>
+      <li>dft_managers.mpi_helpers</li>
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+           <div itemprop="articleBody">
+             
+  <h1>Source code for dft_managers.mpi_helpers</h1><div class="highlight"><pre>
+<span></span>
+<span class="c1">#!/usr/bin/env python3</span>
+<span class="c1"># -*- coding: utf-8 -*-</span>
+<span class="c1">################################################################################</span>
+<span class="c1">#</span>
+<span class="c1"># solid_dmft - A versatile python wrapper to perform DFT+DMFT calculations</span>
+<span class="c1">#              utilizing the TRIQS software library</span>
+<span class="c1">#</span>
+<span class="c1"># Copyright (C) 2018-2020, ETH Zurich</span>
+<span class="c1"># Copyright (C) 2021, The Simons Foundation</span>
+<span class="c1">#      authors: A. Hampel, M. Merkel, and S. Beck</span>
+<span class="c1">#</span>
+<span class="c1"># solid_dmft is free software: you can redistribute it and/or modify it under the</span>
+<span class="c1"># terms of the GNU General Public License as published by the Free Software</span>
+<span class="c1"># Foundation, either version 3 of the License, or (at your option) any later</span>
+<span class="c1"># version.</span>
+<span class="c1">#</span>
+<span class="c1"># solid_dmft is distributed in the hope that it will be useful, but WITHOUT ANY</span>
+<span class="c1"># WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A</span>
+<span class="c1"># PARTICULAR PURPOSE. See the GNU General Public License for more details.</span>
+
+<span class="c1"># You should have received a copy of the GNU General Public License along with</span>
+<span class="c1"># solid_dmft (in the file COPYING.txt in this directory). If not, see</span>
+<span class="c1"># &lt;http://www.gnu.org/licenses/&gt;.</span>
+<span class="c1">#</span>
+<span class="c1">################################################################################</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Contains the handling of the QE process. It can start QE, reactivate it,</span>
+<span class="sd">check if the lock file is there and finally kill QE. Needed for CSC calculations.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">import</span> <span class="nn">socket</span>
+<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">defaultdict</span>
+<span class="kn">import</span> <span class="nn">shlex</span>
+<span class="kn">import</span> <span class="nn">time</span>
+
+<span class="kn">import</span> <span class="nn">triqs.utility.mpi</span> <span class="k">as</span> <span class="nn">mpi</span>
+
+
+<div class="viewcode-block" id="create_hostfile"><a class="viewcode-back" href="../../_ref/dft_managers.mpi_helpers.html#dft_managers.mpi_helpers.create_hostfile">[docs]</a><span class="k">def</span> <span class="nf">create_hostfile</span><span class="p">(</span><span class="n">number_cores</span><span class="p">,</span> <span class="n">cluster_name</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Writes a host file for the mpirun. This tells mpi which nodes to ssh into</span>
+<span class="sd">    and start VASP on. The format of the hist file depends on the type of MPI</span>
+<span class="sd">    that is used.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    number_cores: int, the number of cores that vasp runs on</span>
+<span class="sd">    cluster_name: string, the name of the server</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    string: name of the hostfile if not run locally and if called by master node</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">if</span> <span class="n">cluster_name</span> <span class="o">==</span> <span class="s1">&#39;default&#39;</span><span class="p">:</span>
+        <span class="k">return</span> <span class="kc">None</span>
+
+    <span class="n">hostnames</span> <span class="o">=</span> <span class="n">mpi</span><span class="o">.</span><span class="n">world</span><span class="o">.</span><span class="n">gather</span><span class="p">(</span><span class="n">socket</span><span class="o">.</span><span class="n">gethostname</span><span class="p">(),</span> <span class="n">root</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+        <span class="c1"># create hostfile based on first number_cores ranks</span>
+        <span class="n">hosts</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">hostname</span> <span class="ow">in</span> <span class="n">hostnames</span><span class="p">[:</span><span class="n">number_cores</span><span class="p">]:</span>
+            <span class="n">hosts</span><span class="p">[</span><span class="n">hostname</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">1</span>
+
+        <span class="n">mask_hostfile</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;openmpi&#39;</span><span class="p">:</span> <span class="s1">&#39;</span><span class="si">{}</span><span class="s1"> slots=</span><span class="si">{}</span><span class="s1">&#39;</span><span class="p">,</span>  <span class="c1"># OpenMPI format</span>
+                         <span class="s1">&#39;openmpi-intra&#39;</span><span class="p">:</span> <span class="s1">&#39;</span><span class="si">{}</span><span class="s1"> slots=</span><span class="si">{}</span><span class="s1">&#39;</span><span class="p">,</span>  <span class="c1"># OpenMPI format</span>
+                         <span class="s1">&#39;mpich&#39;</span><span class="p">:</span> <span class="s1">&#39;</span><span class="si">{}</span><span class="s1">:</span><span class="si">{}</span><span class="s1">&#39;</span><span class="p">,</span>  <span class="c1"># MPICH format</span>
+                         <span class="p">}[</span><span class="n">cluster_name</span><span class="p">]</span>
+
+        <span class="n">hostfile</span> <span class="o">=</span> <span class="s1">&#39;dft.hostfile&#39;</span>
+        <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">hostfile</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">file</span><span class="p">:</span>
+            <span class="n">file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">mask_hostfile</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="o">*</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">hosts</span><span class="o">.</span><span class="n">items</span><span class="p">()))</span>
+        <span class="k">return</span> <span class="n">hostfile</span>
+
+    <span class="k">return</span> <span class="kc">None</span></div>
+
+
+<div class="viewcode-block" id="find_path_to_mpi_command"><a class="viewcode-back" href="../../_ref/dft_managers.mpi_helpers.html#dft_managers.mpi_helpers.find_path_to_mpi_command">[docs]</a><span class="k">def</span> <span class="nf">find_path_to_mpi_command</span><span class="p">(</span><span class="n">env_vars</span><span class="p">,</span> <span class="n">mpi_exe</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Finds the complete path for the mpi executable by scanning the directories</span>
+<span class="sd">    of $PATH.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    env_vars: dict of string, environment variables containing PATH</span>
+<span class="sd">    mpi_exe: string, mpi command</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    string: absolute path to mpi command</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">for</span> <span class="n">path_directory</span> <span class="ow">in</span> <span class="n">env_vars</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;PATH&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">pathsep</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">path_directory</span><span class="p">:</span>
+            <span class="n">potential_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">path_directory</span><span class="p">,</span> <span class="n">mpi_exe</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">access</span><span class="p">(</span><span class="n">potential_path</span><span class="p">,</span> <span class="n">os</span><span class="o">.</span><span class="n">F_OK</span> <span class="o">|</span> <span class="n">os</span><span class="o">.</span><span class="n">X_OK</span><span class="p">):</span>
+                <span class="k">return</span> <span class="n">potential_path</span>
+
+    <span class="k">return</span> <span class="kc">None</span></div>
+
+
+<div class="viewcode-block" id="get_mpi_arguments"><a class="viewcode-back" href="../../_ref/dft_managers.mpi_helpers.html#dft_managers.mpi_helpers.get_mpi_arguments">[docs]</a><span class="k">def</span> <span class="nf">get_mpi_arguments</span><span class="p">(</span><span class="n">mpi_profile</span><span class="p">,</span> <span class="n">mpi_exe</span><span class="p">,</span> <span class="n">number_cores</span><span class="p">,</span> <span class="n">dft_exe</span><span class="p">,</span> <span class="n">hostfile</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Depending on the settings of the cluster and the type of MPI used,</span>
+<span class="sd">    the arguments to the mpi call have to be different. The most technical part</span>
+<span class="sd">    of the vasp handler.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    cluster_name: string, name of the cluster so that settings can be tailored to it</span>
+<span class="sd">    mpi_exe: string, mpi command</span>
+<span class="sd">    number_cores: int, the number of cores that vasp runs on</span>
+<span class="sd">    dft_exe: string, the command to start the DFT code</span>
+<span class="sd">    hostfile: string, name of the hostfile</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    list of string: arguments to start mpi with</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">if</span> <span class="n">mpi_profile</span> <span class="o">==</span> <span class="s1">&#39;default&#39;</span><span class="p">:</span>
+        <span class="k">return</span> <span class="p">[</span><span class="n">mpi_exe</span><span class="p">,</span> <span class="s1">&#39;-np&#39;</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">number_cores</span><span class="p">)]</span> <span class="o">+</span> <span class="n">shlex</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">dft_exe</span><span class="p">)</span>
+
+    <span class="c1"># For the second node, mpirun starts DFT by using ssh</span>
+    <span class="c1"># Therefore we need to handover the env variables with -x</span>
+    <span class="k">if</span> <span class="n">mpi_profile</span> <span class="o">==</span> <span class="s1">&#39;openmpi&#39;</span><span class="p">:</span>
+        <span class="k">return</span> <span class="p">[</span><span class="n">mpi_exe</span><span class="p">,</span> <span class="s1">&#39;-hostfile&#39;</span><span class="p">,</span> <span class="n">hostfile</span><span class="p">,</span> <span class="s1">&#39;-np&#39;</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">number_cores</span><span class="p">),</span>
+                <span class="s1">&#39;-mca&#39;</span><span class="p">,</span> <span class="s1">&#39;mtl&#39;</span><span class="p">,</span> <span class="s1">&#39;^psm,psm2,ofi&#39;</span><span class="p">,</span>
+                <span class="s1">&#39;-mca&#39;</span><span class="p">,</span> <span class="s1">&#39;btl&#39;</span><span class="p">,</span> <span class="s1">&#39;^vader,openib,usnix&#39;</span><span class="p">,</span>
+                <span class="s1">&#39;-x&#39;</span><span class="p">,</span> <span class="s1">&#39;LD_LIBRARY_PATH&#39;</span><span class="p">,</span>
+                <span class="s1">&#39;-x&#39;</span><span class="p">,</span> <span class="s1">&#39;PATH&#39;</span><span class="p">,</span> <span class="s1">&#39;-x&#39;</span><span class="p">,</span> <span class="s1">&#39;OMP_NUM_THREADS&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="n">shlex</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">dft_exe</span><span class="p">)</span>
+    <span class="c1"># Run mpi with intra-node communication among ranks (on a single node).</span>
+    <span class="k">if</span> <span class="n">mpi_profile</span> <span class="o">==</span> <span class="s1">&#39;openmpi-intra&#39;</span><span class="p">:</span>
+        <span class="k">return</span> <span class="p">[</span><span class="n">mpi_exe</span><span class="p">,</span> <span class="s1">&#39;-np&#39;</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">number_cores</span><span class="p">),</span>
+                <span class="s1">&#39;--mca&#39;</span><span class="p">,</span> <span class="s1">&#39;pml&#39;</span><span class="p">,</span> <span class="s1">&#39;ob1&#39;</span><span class="p">,</span> <span class="s1">&#39;--mca&#39;</span><span class="p">,</span> <span class="s1">&#39;btl&#39;</span><span class="p">,</span> <span class="s1">&#39;self,vader&#39;</span><span class="p">,</span>
+                <span class="s1">&#39;-x&#39;</span><span class="p">,</span> <span class="s1">&#39;LD_LIBRARY_PATH&#39;</span><span class="p">,</span>
+                <span class="s1">&#39;-x&#39;</span><span class="p">,</span> <span class="s1">&#39;PATH&#39;</span><span class="p">,</span> <span class="s1">&#39;-x&#39;</span><span class="p">,</span> <span class="s1">&#39;OMP_NUM_THREADS&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="n">shlex</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">dft_exe</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">mpi_profile</span> <span class="o">==</span> <span class="s1">&#39;mpich&#39;</span><span class="p">:</span>
+        <span class="k">return</span> <span class="p">[</span><span class="n">mpi_exe</span><span class="p">,</span> <span class="s1">&#39;-launcher&#39;</span><span class="p">,</span> <span class="s1">&#39;ssh&#39;</span><span class="p">,</span> <span class="s1">&#39;-hostfile&#39;</span><span class="p">,</span> <span class="n">hostfile</span><span class="p">,</span>
+                <span class="s1">&#39;-np&#39;</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">number_cores</span><span class="p">),</span> <span class="s1">&#39;-envlist&#39;</span><span class="p">,</span> <span class="s1">&#39;PATH&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="n">shlex</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">dft_exe</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="kc">None</span></div>
+
+
+<div class="viewcode-block" id="poll_barrier"><a class="viewcode-back" href="../../_ref/dft_managers.mpi_helpers.html#dft_managers.mpi_helpers.poll_barrier">[docs]</a><span class="k">def</span> <span class="nf">poll_barrier</span><span class="p">(</span><span class="n">comm</span><span class="p">,</span> <span class="n">poll_interval</span><span class="o">=</span><span class="mf">0.1</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Use asynchronous synchronization, otherwise mpi.barrier uses up all the CPU time during</span>
+<span class="sd">    the run of subprocess.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    comm: MPI communicator</span>
+<span class="sd">    poll_interval: float, time step for pinging the status of the sleeping ranks</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">req</span> <span class="o">=</span> <span class="n">comm</span><span class="o">.</span><span class="n">Ibarrier</span><span class="p">()</span>
+    <span class="k">while</span> <span class="ow">not</span> <span class="n">req</span><span class="o">.</span><span class="n">Test</span><span class="p">():</span>
+        <span class="n">time</span><span class="o">.</span><span class="n">sleep</span><span class="p">(</span><span class="n">poll_interval</span><span class="p">)</span></div>
+</pre></div>
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.</p>
+  </div>
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
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+<!DOCTYPE html>
+<html class="writer-html5" lang="en" >
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>dft_managers.qe_manager &mdash; solid_dmft  documentation</title>
+      <link rel="stylesheet" href="../../_static/pygments.css" type="text/css" />
+      <link rel="stylesheet" href="../../_static/css/custom.css" type="text/css" />
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+    <script src="../../_static/js/theme.js"></script>
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+            <img src="../../_static/logo_no_text.png" class="logo" alt="Logo"/>
+          </a>
+              <div class="version">
+                3.2.0
+              </div>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="../../search.html" method="get">
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+              <ul>
+<li class="toctree-l1"><a class="reference internal" href="../../install.html">Installation</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#installation-via-pip">Installation via pip</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#manual-installation-via-cmake">Manual installation via CMake</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#docker-files-images">Docker files &amp; images</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#version-compatibility">Version compatibility</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#custom-cmake-options">Custom CMake options</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../documentation.html">Documentation</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#code-structure">Code structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#dft-interface-notes">DFT interface notes</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/w90_interface.html">Wannier90 interface</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/w90_interface.html#orbital-order-in-the-w90-converter">orbital order in the W90 converter</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/vasp_csc.html">Interface to VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#general-remarks">General remarks</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#file-description">file description</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#workflow">Workflow:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#important-flags">important flags:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#version-and-compilation">version and compilation:</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#input-output">Input/Output</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../input_output/DMFT_input/input.html">Input</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/general.html">[general]: General parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/solver.html">[solver]: solver specific parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/dft.html">[dft]: DFT related inputs</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/advanced.html">[advanced]: Advanced inputs</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../input_output/DMFT_output/results.html">Output / results</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_output/results.html#group-structure">Group structure</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_output/results.html#subgroups">Subgroups</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#further-details-for-running">Further details for running</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/docker.html">Docker</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#building-the-docker-image">Building the docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#pulling-a-docker-image">Pulling a docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_locally.html">Run on your machine</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#module-reference-manual">Module reference manual</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/csc_flow.html">csc_flow</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/dft_managers.html">dft_managers</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.mpi_helpers.html">dft_managers.mpi_helpers</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.qe_manager.html">dft_managers.qe_manager</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.vasp_manager.html">dft_managers.vasp_manager</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/dmft_cycle.html">dmft_cycle</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/dmft_tools.html">dmft_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.afm_mapping.html">dmft_tools.afm_mapping</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.convergence.html">dmft_tools.convergence</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.formatter.html">dmft_tools.formatter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.greens_functions_mixer.html">dmft_tools.greens_functions_mixer</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.initial_self_energies.html">dmft_tools.initial_self_energies</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.interaction_hamiltonian.html">dmft_tools.interaction_hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.legendre_filter.html">dmft_tools.legendre_filter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.manipulate_chemical_potential.html">dmft_tools.manipulate_chemical_potential</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.matheval.html">dmft_tools.matheval</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.observables.html">dmft_tools.observables</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.results_to_archive.html">dmft_tools.results_to_archive</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.solver.html">dmft_tools.solver</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/io_tools.html">io_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.dict_to_h5.html">io_tools.dict_to_h5</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.postproc_toml_dict.html">io_tools.postproc_toml_dict</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.verify_input_params.html">io_tools.verify_input_params</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_gf_imp.html">postprocessing.maxent_gf_imp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_gf_latt.html">postprocessing.maxent_gf_latt</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_sigma.html">postprocessing.maxent_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../issues.html">Support &amp; contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../issues.html#seeking-help">Seeking help</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../issues.html#improving-solid-dmft">Improving solid_dmft</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../issues.html#reporting-issues">Reporting issues</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../ChangeLog.html">Changelog</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-2-0">Version 3.2.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#general">General</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#fix">fix</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#test">test</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-1-5">Version 3.1.5</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-1-4">Version 3.1.4</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-1-3">Version 3.1.3</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-1-2">Version 3.1.2</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-1-1">Version 3.1.1</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-1-0">Version 3.1.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-0-0">Version 3.0.0</a></li>
+</ul>
+</li>
+</ul>
+
+        </div>
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu"  style="background: #7E588A" >
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+  <ul class="wy-breadcrumbs">
+      <li><a href="../../index.html" class="icon icon-home"></a> &raquo;</li>
+          <li><a href="../index.html">Module code</a> &raquo;</li>
+      <li>dft_managers.qe_manager</li>
+      <li class="wy-breadcrumbs-aside">
+      </li>
+  </ul>
+  <hr/>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <h1>Source code for dft_managers.qe_manager</h1><div class="highlight"><pre>
+<span></span><span class="ch">#!/usr/bin/env python3</span>
+<span class="c1"># -*- coding: utf-8 -*-</span>
+<span class="c1">################################################################################</span>
+<span class="c1">#</span>
+<span class="c1"># solid_dmft - A versatile python wrapper to perform DFT+DMFT calculations</span>
+<span class="c1">#              utilizing the TRIQS software library</span>
+<span class="c1">#</span>
+<span class="c1"># Copyright (C) 2018-2020, ETH Zurich</span>
+<span class="c1"># Copyright (C) 2021, The Simons Foundation</span>
+<span class="c1">#      authors: A. Hampel, M. Merkel, and S. Beck</span>
+<span class="c1">#</span>
+<span class="c1"># solid_dmft is free software: you can redistribute it and/or modify it under the</span>
+<span class="c1"># terms of the GNU General Public License as published by the Free Software</span>
+<span class="c1"># Foundation, either version 3 of the License, or (at your option) any later</span>
+<span class="c1"># version.</span>
+<span class="c1">#</span>
+<span class="c1"># solid_dmft is distributed in the hope that it will be useful, but WITHOUT ANY</span>
+<span class="c1"># WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A</span>
+<span class="c1"># PARTICULAR PURPOSE. See the GNU General Public License for more details.</span>
+
+<span class="c1"># You should have received a copy of the GNU General Public License along with</span>
+<span class="c1"># solid_dmft (in the file COPYING.txt in this directory). If not, see</span>
+<span class="c1"># &lt;http://www.gnu.org/licenses/&gt;.</span>
+<span class="c1">#</span>
+<span class="c1">################################################################################</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Contains the function to run a QuantumEspresso iteration. Needed for CSC calculations.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">import</span> <span class="nn">subprocess</span>
+<span class="kn">import</span> <span class="nn">sys</span>
+
+<span class="kn">import</span> <span class="nn">triqs.utility.mpi</span> <span class="k">as</span> <span class="nn">mpi</span>
+
+<span class="kn">from</span> <span class="nn">solid_dmft.dft_managers</span> <span class="kn">import</span> <span class="n">mpi_helpers</span>
+
+
+<span class="k">def</span> <span class="nf">_start_with_piping</span><span class="p">(</span><span class="n">mpi_exe</span><span class="p">,</span> <span class="n">mpi_arguments</span><span class="p">,</span> <span class="n">qe_file_ext</span><span class="p">,</span> <span class="n">env_vars</span><span class="p">,</span> <span class="n">seedname</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Handles the piping of the output when starting QE.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    mpi_exe: string, mpi command</span>
+<span class="sd">    mpi_arguments: list of string, arguments to start mpi with</span>
+<span class="sd">    qe_file_ext : string, file name for QE</span>
+<span class="sd">    env_vars: dict of string, environment variables containing PATH</span>
+<span class="sd">    seedname: string, QE input file</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    int: id of the VASP child process</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">if</span> <span class="n">qe_file_ext</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;scf&#39;</span><span class="p">,</span> <span class="s1">&#39;nscf&#39;</span><span class="p">,</span> <span class="s1">&#39;pw2wan&#39;</span><span class="p">,</span> <span class="s1">&#39;mod_scf&#39;</span><span class="p">,</span> <span class="s1">&#39;bnd&#39;</span><span class="p">,</span> <span class="s1">&#39;bands&#39;</span><span class="p">,</span> <span class="s1">&#39;proj&#39;</span><span class="p">]:</span>
+
+        <span class="n">inp</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">seedname</span><span class="si">}</span><span class="s1">.</span><span class="si">{</span><span class="n">qe_file_ext</span><span class="si">}</span><span class="s1">.in&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span>
+        <span class="n">out</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">seedname</span><span class="si">}</span><span class="s1">.</span><span class="si">{</span><span class="n">qe_file_ext</span><span class="si">}</span><span class="s1">.out&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span>
+        <span class="n">err</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">seedname</span><span class="si">}</span><span class="s1">.</span><span class="si">{</span><span class="n">qe_file_ext</span><span class="si">}</span><span class="s1">.err&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span>
+
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;  solid_dmft: Starting </span><span class="si">{}</span><span class="s1"> calculation...&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">qe_file_ext</span><span class="p">))</span>
+
+        <span class="c1"># start subprocess</span>
+        <span class="n">qe_result</span> <span class="o">=</span> <span class="n">subprocess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">mpi_arguments</span><span class="p">,</span> <span class="n">stdin</span><span class="o">=</span><span class="n">inp</span><span class="p">,</span> <span class="n">env</span><span class="o">=</span><span class="n">env_vars</span><span class="p">,</span> <span class="n">capture_output</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+                                   <span class="n">text</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">shell</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+        <span class="c1"># write output and error file</span>
+        <span class="n">output</span> <span class="o">=</span> <span class="n">qe_result</span><span class="o">.</span><span class="n">stdout</span>
+        <span class="n">error</span> <span class="o">=</span> <span class="n">qe_result</span><span class="o">.</span><span class="n">stderr</span>
+        <span class="n">out</span><span class="o">.</span><span class="n">writelines</span><span class="p">(</span><span class="n">output</span><span class="p">)</span>
+        <span class="n">err</span><span class="o">.</span><span class="n">writelines</span><span class="p">(</span><span class="n">error</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">qe_result</span><span class="o">.</span><span class="n">returncode</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;QE calculation failed. Exiting programm.&#39;</span><span class="p">)</span>
+            <span class="n">sys</span><span class="o">.</span><span class="n">exit</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+
+    <span class="k">elif</span> <span class="s1">&#39;win&#39;</span> <span class="ow">in</span> <span class="n">qe_file_ext</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;  solid_dmft: Starting Wannier90 </span><span class="si">{}</span><span class="s1">...&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">qe_file_ext</span><span class="p">))</span>
+        <span class="c1"># don&#39;t need any piping for Wannier90</span>
+        <span class="n">subprocess</span><span class="o">.</span><span class="n">check_call</span><span class="p">(</span><span class="n">mpi_arguments</span> <span class="o">+</span> <span class="p">[</span><span class="n">seedname</span><span class="p">],</span> <span class="n">env</span><span class="o">=</span><span class="n">env_vars</span><span class="p">,</span> <span class="n">shell</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+
+<div class="viewcode-block" id="run"><a class="viewcode-back" href="../../_ref/dft_managers.qe_manager.html#dft_managers.qe_manager.run">[docs]</a><span class="k">def</span> <span class="nf">run</span><span class="p">(</span><span class="n">number_cores</span><span class="p">,</span> <span class="n">qe_file_ext</span><span class="p">,</span> <span class="n">qe_exec</span><span class="p">,</span> <span class="n">mpi_profile</span><span class="p">,</span> <span class="n">seedname</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Starts the VASP child process. Takes care of initializing a clean</span>
+<span class="sd">    environment for the child process. This is needed so that VASP does not</span>
+<span class="sd">    get confused with all the standard slurm environment variables.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    number_cores: int, the number of cores that vasp runs on</span>
+<span class="sd">    qe_file_ext: string, qe executable</span>
+<span class="sd">    qe_exec: string, path to qe executables</span>
+<span class="sd">    mpi_profile: string, name of the cluster so that settings can be tailored to it</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># get MPI env</span>
+    <span class="n">hostfile</span> <span class="o">=</span> <span class="n">mpi_helpers</span><span class="o">.</span><span class="n">create_hostfile</span><span class="p">(</span><span class="n">number_cores</span><span class="p">,</span> <span class="n">mpi_profile</span><span class="p">)</span>
+    <span class="n">qe_exec_path</span> <span class="o">=</span> <span class="n">qe_exec</span><span class="o">.</span><span class="n">strip</span><span class="p">(</span><span class="n">qe_exec</span><span class="o">.</span><span class="n">rsplit</span><span class="p">(</span><span class="s1">&#39;/&#39;</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+    <span class="n">qe_exec</span> <span class="o">=</span> <span class="n">qe_exec_path</span>
+
+    <span class="k">if</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+        <span class="c1"># clean environment</span>
+        <span class="n">env_vars</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="k">for</span> <span class="n">var_name</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;PATH&#39;</span><span class="p">,</span> <span class="s1">&#39;LD_LIBRARY_PATH&#39;</span><span class="p">,</span> <span class="s1">&#39;SHELL&#39;</span><span class="p">,</span> <span class="s1">&#39;PWD&#39;</span><span class="p">,</span> <span class="s1">&#39;HOME&#39;</span><span class="p">,</span>
+                         <span class="s1">&#39;OMP_NUM_THREADS&#39;</span><span class="p">,</span> <span class="s1">&#39;OMPI_MCA_btl_vader_single_copy_mechanism&#39;</span><span class="p">]:</span>
+            <span class="n">var</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="n">var_name</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">var</span><span class="p">:</span>
+                <span class="n">env_vars</span><span class="p">[</span><span class="n">var_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">var</span>
+
+        <span class="c1"># assuming that mpirun points to the correct mpi env</span>
+        <span class="n">mpi_exe</span> <span class="o">=</span> <span class="n">mpi_helpers</span><span class="o">.</span><span class="n">find_path_to_mpi_command</span><span class="p">(</span><span class="n">env_vars</span><span class="p">,</span> <span class="s1">&#39;mpirun&#39;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">qe_file_ext</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;scf&#39;</span><span class="p">,</span> <span class="s1">&#39;nscf&#39;</span><span class="p">,</span> <span class="s1">&#39;mod_scf&#39;</span><span class="p">,</span> <span class="s1">&#39;bnd&#39;</span><span class="p">]:</span>
+            <span class="n">qe_exec</span> <span class="o">+=</span> <span class="sa">f</span><span class="s1">&#39;pw.x -nk </span><span class="si">{</span><span class="n">number_cores</span><span class="si">}</span><span class="s1">&#39;</span>
+        <span class="k">elif</span> <span class="n">qe_file_ext</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;pw2wan&#39;</span><span class="p">]:</span>
+            <span class="n">qe_exec</span> <span class="o">+=</span> <span class="s1">&#39;pw2wannier90.x -nk 1 -pd .true.&#39;</span>
+        <span class="k">elif</span> <span class="n">qe_file_ext</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;bands&#39;</span><span class="p">]:</span>
+            <span class="n">qe_exec</span> <span class="o">+=</span> <span class="sa">f</span><span class="s1">&#39;bands.x -nk </span><span class="si">{</span><span class="n">number_cores</span><span class="si">}</span><span class="s1">&#39;</span>
+        <span class="k">elif</span> <span class="n">qe_file_ext</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;proj&#39;</span><span class="p">]:</span>
+            <span class="n">qe_exec</span> <span class="o">+=</span> <span class="sa">f</span><span class="s1">&#39;projwfc.x -nk </span><span class="si">{</span><span class="n">number_cores</span><span class="si">}</span><span class="s1">&#39;</span>
+        <span class="k">elif</span> <span class="n">qe_file_ext</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;win_pp&#39;</span><span class="p">]:</span>
+            <span class="n">qe_exec</span> <span class="o">+=</span> <span class="s1">&#39;wannier90.x -pp&#39;</span>
+        <span class="k">elif</span> <span class="n">qe_file_ext</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;win&#39;</span><span class="p">]:</span>
+            <span class="n">qe_exec</span> <span class="o">+=</span> <span class="s1">&#39;wannier90.x&#39;</span>
+
+        <span class="n">arguments</span> <span class="o">=</span> <span class="n">mpi_helpers</span><span class="o">.</span><span class="n">get_mpi_arguments</span><span class="p">(</span><span class="n">mpi_profile</span><span class="p">,</span> <span class="n">mpi_exe</span><span class="p">,</span> <span class="n">number_cores</span><span class="p">,</span> <span class="n">qe_exec</span><span class="p">,</span> <span class="n">hostfile</span><span class="p">)</span>
+        <span class="n">_start_with_piping</span><span class="p">(</span><span class="n">mpi_exe</span><span class="p">,</span> <span class="n">arguments</span><span class="p">,</span> <span class="n">qe_file_ext</span><span class="p">,</span> <span class="n">env_vars</span><span class="p">,</span> <span class="n">seedname</span><span class="p">)</span>
+
+    <span class="n">mpi_helpers</span><span class="o">.</span><span class="n">poll_barrier</span><span class="p">(</span><span class="n">mpi</span><span class="o">.</span><span class="n">MPI</span><span class="o">.</span><span class="n">COMM_WORLD</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="read_dft_energy"><a class="viewcode-back" href="../../_ref/dft_managers.qe_manager.html#dft_managers.qe_manager.read_dft_energy">[docs]</a><span class="k">def</span> <span class="nf">read_dft_energy</span><span class="p">(</span><span class="n">seedname</span><span class="p">,</span> <span class="n">iter_dmft</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Reads DFT energy from quantum espresso&#39;s out files</span>
+
+<span class="sd">    1. At the first iteration, the DFT energy is read from the scf file.</span>
+
+<span class="sd">    2. After the first iteration the band energy computed in the mod_scf calculation is wrong,</span>
+<span class="sd">       and needs to be subtracted from the reported total energy. The correct band energy</span>
+<span class="sd">       is computed in the nscf calculation.</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">dft_energy</span> <span class="o">=</span> <span class="mf">0.0</span>
+    <span class="n">RYDBERG</span> <span class="o">=</span> <span class="mf">13.605693123</span> <span class="c1"># eV</span>
+
+    <span class="k">if</span> <span class="n">iter_dmft</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">seedname</span><span class="si">}</span><span class="s1">.scf.out&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">file</span><span class="p">:</span>
+            <span class="n">dft_output</span> <span class="o">=</span> <span class="n">file</span><span class="o">.</span><span class="n">readlines</span><span class="p">()</span>
+        <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">dft_output</span><span class="p">:</span>
+            <span class="k">if</span> <span class="s1">&#39;!&#39;</span> <span class="ow">in</span> <span class="n">line</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Reading total energy from the scf calculation </span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
+                <span class="n">dft_energy</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">()[</span><span class="o">-</span><span class="mi">2</span><span class="p">])</span> <span class="o">*</span> <span class="n">RYDBERG</span>
+                <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;The DFT energy is: </span><span class="si">{</span><span class="n">dft_energy</span><span class="si">}</span><span class="s2"> eV&quot;</span><span class="p">)</span>
+                <span class="k">break</span>
+            <span class="k">if</span>  <span class="n">line</span> <span class="o">==</span><span class="s2">&quot;&quot;</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">EOFError</span><span class="p">(</span><span class="s2">&quot;Did not find scf total energy&quot;</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">seedname</span><span class="si">}</span><span class="s1">.mod_scf.out&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">file</span><span class="p">:</span>
+            <span class="n">dft_output</span> <span class="o">=</span> <span class="n">file</span><span class="o">.</span><span class="n">readlines</span><span class="p">()</span>
+        <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">dft_output</span><span class="p">:</span>
+            <span class="c1">#if &#39;eband, Ef (eV)&#39; in line:</span>
+            <span class="k">if</span> <span class="s2">&quot;(sum(wg*et))&quot;</span> <span class="ow">in</span> <span class="n">line</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Reading band energy from the mod_scf calculation </span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
+                <span class="c1">#band_energy = float(line.split())</span>
+                <span class="n">band_energy_modscf</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">()[</span><span class="o">-</span><span class="mi">2</span><span class="p">])</span><span class="o">*</span><span class="n">RYDBERG</span>
+                <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;The mod_scf band energy is: </span><span class="si">{</span><span class="n">band_energy_modscf</span><span class="si">}</span><span class="s2"> eV&quot;</span><span class="p">)</span>
+            <span class="k">if</span> <span class="s1">&#39;total energy&#39;</span> <span class="ow">in</span> <span class="n">line</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Reading total energy from the mod_scf calculation </span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
+                <span class="n">dft_energy</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">()[</span><span class="o">-</span><span class="mi">2</span><span class="p">])</span> <span class="o">*</span> <span class="n">RYDBERG</span>
+                <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;The uncorrected DFT energy is: </span><span class="si">{</span><span class="n">dft_energy</span><span class="si">}</span><span class="s2"> eV&quot;</span><span class="p">)</span>
+        <span class="n">dft_energy</span> <span class="o">-=</span> <span class="n">band_energy_modscf</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;The DFT energy without kinetic part is: </span><span class="si">{</span><span class="n">dft_energy</span><span class="si">}</span><span class="s2"> eV&quot;</span><span class="p">)</span>
+
+        <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">seedname</span><span class="si">}</span><span class="s1">.nscf.out&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">file</span><span class="p">:</span>
+            <span class="n">dft_output</span> <span class="o">=</span> <span class="n">file</span><span class="o">.</span><span class="n">readlines</span><span class="p">()</span>
+        <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">dft_output</span><span class="p">:</span>
+            <span class="k">if</span> <span class="s1">&#39;The nscf band energy&#39;</span> <span class="ow">in</span> <span class="n">line</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Reading band energy from the nscf calculation</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
+                <span class="n">band_energy_nscf</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">()[</span><span class="o">-</span><span class="mi">2</span><span class="p">])</span> <span class="o">*</span> <span class="n">RYDBERG</span>
+                <span class="n">dft_energy</span> <span class="o">+=</span> <span class="n">band_energy_nscf</span>
+                <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;The nscf band energy is: </span><span class="si">{</span><span class="n">band_energy_nscf</span><span class="si">}</span><span class="s2"> eV&quot;</span><span class="p">)</span>
+                <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;The corrected DFT energy is: </span><span class="si">{</span><span class="n">dft_energy</span><span class="si">}</span><span class="s2"> eV&quot;</span><span class="p">)</span>
+                <span class="k">break</span>
+    <span class="k">return</span> <span class="n">dft_energy</span></div>
+</pre></div>
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.</p>
+  </div>
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+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/w90_interface.html#orbital-order-in-the-w90-converter">orbital order in the W90 converter</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/vasp_csc.html">Interface to VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#general-remarks">General remarks</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#file-description">file description</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#workflow">Workflow:</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#version-and-compilation">version and compilation:</a></li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#input-output">Input/Output</a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/general.html">[general]: General parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/solver.html">[solver]: solver specific parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/dft.html">[dft]: DFT related inputs</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/advanced.html">[advanced]: Advanced inputs</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../input_output/DMFT_output/results.html">Output / results</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_output/results.html#group-structure">Group structure</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_output/results.html#subgroups">Subgroups</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#further-details-for-running">Further details for running</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/docker.html">Docker</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#building-the-docker-image">Building the docker image</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
+</ul>
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+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_locally.html">Run on your machine</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#module-reference-manual">Module reference manual</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/csc_flow.html">csc_flow</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.vasp_manager.html">dft_managers.vasp_manager</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/dmft_cycle.html">dmft_cycle</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/dmft_tools.html">dmft_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.afm_mapping.html">dmft_tools.afm_mapping</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.formatter.html">dmft_tools.formatter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.greens_functions_mixer.html">dmft_tools.greens_functions_mixer</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.initial_self_energies.html">dmft_tools.initial_self_energies</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.interaction_hamiltonian.html">dmft_tools.interaction_hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.legendre_filter.html">dmft_tools.legendre_filter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.manipulate_chemical_potential.html">dmft_tools.manipulate_chemical_potential</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.matheval.html">dmft_tools.matheval</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.observables.html">dmft_tools.observables</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.results_to_archive.html">dmft_tools.results_to_archive</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.solver.html">dmft_tools.solver</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/io_tools.html">io_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.dict_to_h5.html">io_tools.dict_to_h5</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.postproc_toml_dict.html">io_tools.postproc_toml_dict</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.verify_input_params.html">io_tools.verify_input_params</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_gf_imp.html">postprocessing.maxent_gf_imp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_gf_latt.html">postprocessing.maxent_gf_latt</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_sigma.html">postprocessing.maxent_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../issues.html">Support &amp; contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../issues.html#seeking-help">Seeking help</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../issues.html#improving-solid-dmft">Improving solid_dmft</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../issues.html#reporting-issues">Reporting issues</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../ChangeLog.html">Changelog</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-2-0">Version 3.2.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#general">General</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#fix">fix</a></li>
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+      <li>dft_managers.vasp_manager</li>
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+             
+  <h1>Source code for dft_managers.vasp_manager</h1><div class="highlight"><pre>
+<span></span><span class="ch">#!/usr/bin/env python3</span>
+<span class="c1"># -*- coding: utf-8 -*-</span>
+<span class="c1">################################################################################</span>
+<span class="c1">#</span>
+<span class="c1"># solid_dmft - A versatile python wrapper to perform DFT+DMFT calculations</span>
+<span class="c1">#              utilizing the TRIQS software library</span>
+<span class="c1">#</span>
+<span class="c1"># Copyright (C) 2018-2020, ETH Zurich</span>
+<span class="c1"># Copyright (C) 2021, The Simons Foundation</span>
+<span class="c1">#      authors: A. Hampel, M. Merkel, and S. Beck</span>
+<span class="c1">#</span>
+<span class="c1"># solid_dmft is free software: you can redistribute it and/or modify it under the</span>
+<span class="c1"># terms of the GNU General Public License as published by the Free Software</span>
+<span class="c1"># Foundation, either version 3 of the License, or (at your option) any later</span>
+<span class="c1"># version.</span>
+<span class="c1">#</span>
+<span class="c1"># solid_dmft is distributed in the hope that it will be useful, but WITHOUT ANY</span>
+<span class="c1"># WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A</span>
+<span class="c1"># PARTICULAR PURPOSE. See the GNU General Public License for more details.</span>
+
+<span class="c1"># You should have received a copy of the GNU General Public License along with</span>
+<span class="c1"># solid_dmft (in the file COPYING.txt in this directory). If not, see</span>
+<span class="c1"># &lt;http://www.gnu.org/licenses/&gt;.</span>
+<span class="c1">#</span>
+<span class="c1">################################################################################</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Contains the handling of the VASP process. It can start VASP, reactivate it,</span>
+<span class="sd">check if the lock file is there and finally kill VASP. Needed for CSC calculations.</span>
+
+<span class="sd">This functionality is contained in the simpler public functions.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">import</span> <span class="nn">signal</span>
+<span class="kn">import</span> <span class="nn">time</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<span class="kn">import</span> <span class="nn">triqs.utility.mpi</span> <span class="k">as</span> <span class="nn">mpi</span>
+
+<span class="kn">from</span> <span class="nn">solid_dmft.dft_managers</span> <span class="kn">import</span> <span class="n">mpi_helpers</span>
+
+
+<span class="k">def</span> <span class="nf">_fork_and_start_vasp</span><span class="p">(</span><span class="n">mpi_exe</span><span class="p">,</span> <span class="n">arguments</span><span class="p">,</span> <span class="n">env_vars</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Forks a process from the master process that then calls mpi to start vasp.</span>
+<span class="sd">    The child process running VASP never leaves this function whereas the main</span>
+<span class="sd">    process returns the child&#39;s process id and continues. We use explicitly</span>
+<span class="sd">    os.fork as os.execve here because subprocess does not return, and as VASP</span>
+<span class="sd">    needs to keep running throughout the whole DFT+DMFT calculation that is</span>
+<span class="sd">    not a viable solution here.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    mpi_exe: string, mpi command</span>
+<span class="sd">    arguments: list of string, arguments to start mpi with</span>
+<span class="sd">    env_vars: dict of string, environment variables containing PATH</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    int: id of the VASP child process</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># fork process</span>
+    <span class="n">vasp_process_id</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">fork</span><span class="p">()</span>
+    <span class="k">if</span> <span class="n">vasp_process_id</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="c1"># close file descriptors, if rank0 had open files</span>
+        <span class="k">for</span> <span class="n">fd</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">256</span><span class="p">):</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="n">os</span><span class="o">.</span><span class="n">close</span><span class="p">(</span><span class="n">fd</span><span class="p">)</span>
+            <span class="k">except</span> <span class="ne">OSError</span><span class="p">:</span>
+                <span class="k">pass</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1"> Starting VASP now</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
+        <span class="n">os</span><span class="o">.</span><span class="n">execve</span><span class="p">(</span><span class="n">mpi_exe</span><span class="p">,</span> <span class="n">arguments</span><span class="p">,</span> <span class="n">env_vars</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1"> VASP exec failed</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
+        <span class="n">os</span><span class="o">.</span><span class="n">_exit</span><span class="p">(</span><span class="mi">127</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">vasp_process_id</span>
+
+
+<span class="k">def</span> <span class="nf">_is_lock_file_present</span><span class="p">():</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Checks if the lock file &#39;vasp.lock&#39; is there, i.e. if VASP is still working.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">res_bool</span> <span class="o">=</span> <span class="kc">False</span>
+    <span class="k">if</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+        <span class="n">res_bool</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="s1">&#39;./vasp.lock&#39;</span><span class="p">)</span>
+    <span class="n">res_bool</span> <span class="o">=</span> <span class="n">mpi</span><span class="o">.</span><span class="n">bcast</span><span class="p">(</span><span class="n">res_bool</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">res_bool</span>
+
+
+<div class="viewcode-block" id="remove_legacy_projections_suppressed"><a class="viewcode-back" href="../../_ref/dft_managers.vasp_manager.html#dft_managers.vasp_manager.remove_legacy_projections_suppressed">[docs]</a><span class="k">def</span> <span class="nf">remove_legacy_projections_suppressed</span><span class="p">():</span>
+    <span class="sd">&quot;&quot;&quot; Removes legacy file vasp.suppress_projs if present. &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+        <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="s1">&#39;./vasp.suppress_projs&#39;</span><span class="p">):</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;  solid_dmft: Removing legacy file vasp.suppress_projs&#39;</span><span class="p">,</span> <span class="n">flush</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+            <span class="n">os</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="s1">&#39;./vasp.suppress_projs&#39;</span><span class="p">)</span>
+    <span class="n">mpi</span><span class="o">.</span><span class="n">barrier</span><span class="p">()</span></div>
+
+
+<div class="viewcode-block" id="run_initial_scf"><a class="viewcode-back" href="../../_ref/dft_managers.vasp_manager.html#dft_managers.vasp_manager.run_initial_scf">[docs]</a><span class="k">def</span> <span class="nf">run_initial_scf</span><span class="p">(</span><span class="n">number_cores</span><span class="p">,</span> <span class="n">vasp_command</span><span class="p">,</span> <span class="n">cluster_name</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Starts the VASP child process. Takes care of initializing a clean</span>
+<span class="sd">    environment for the child process. This is needed so that VASP does not</span>
+<span class="sd">    get confused with all the standard slurm environment variables. Returns when</span>
+<span class="sd">    VASP has completed its initial scf cycle.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    number_cores: int, the number of cores that vasp runs on</span>
+<span class="sd">    vasp_command: string, the command to start vasp</span>
+<span class="sd">    cluster_name: string, name of the cluster so that settings can be tailored to it</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># Removes STOPCAR</span>
+    <span class="k">if</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">()</span> <span class="ow">and</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="s1">&#39;STOPCAR&#39;</span><span class="p">):</span>
+        <span class="n">os</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="s1">&#39;STOPCAR&#39;</span><span class="p">)</span>
+    <span class="n">mpi</span><span class="o">.</span><span class="n">barrier</span><span class="p">()</span>
+
+    <span class="c1"># get MPI env</span>
+    <span class="n">vasp_process_id</span> <span class="o">=</span> <span class="mi">0</span>
+
+    <span class="n">hostfile</span> <span class="o">=</span> <span class="n">mpi_helpers</span><span class="o">.</span><span class="n">create_hostfile</span><span class="p">(</span><span class="n">number_cores</span><span class="p">,</span> <span class="n">cluster_name</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+        <span class="c1"># clean environment</span>
+        <span class="n">env_vars</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="k">for</span> <span class="n">var_name</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;PATH&#39;</span><span class="p">,</span> <span class="s1">&#39;LD_LIBRARY_PATH&#39;</span><span class="p">,</span> <span class="s1">&#39;SHELL&#39;</span><span class="p">,</span> <span class="s1">&#39;PWD&#39;</span><span class="p">,</span> <span class="s1">&#39;HOME&#39;</span><span class="p">,</span>
+                         <span class="s1">&#39;OMP_NUM_THREADS&#39;</span><span class="p">,</span> <span class="s1">&#39;OMPI_MCA_btl_vader_single_copy_mechanism&#39;</span><span class="p">]:</span>
+            <span class="n">var</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="n">var_name</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">var</span><span class="p">:</span>
+                <span class="n">env_vars</span><span class="p">[</span><span class="n">var_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">var</span>
+
+        <span class="c1"># assuming that mpirun points to the correct mpi env</span>
+        <span class="n">mpi_exe</span> <span class="o">=</span> <span class="n">mpi_helpers</span><span class="o">.</span><span class="n">find_path_to_mpi_command</span><span class="p">(</span><span class="n">env_vars</span><span class="p">,</span> <span class="s1">&#39;mpirun&#39;</span><span class="p">)</span>
+
+        <span class="n">arguments</span> <span class="o">=</span> <span class="n">mpi_helpers</span><span class="o">.</span><span class="n">get_mpi_arguments</span><span class="p">(</span><span class="n">cluster_name</span><span class="p">,</span> <span class="n">mpi_exe</span><span class="p">,</span> <span class="n">number_cores</span><span class="p">,</span> <span class="n">vasp_command</span><span class="p">,</span> <span class="n">hostfile</span><span class="p">)</span>
+        <span class="n">vasp_process_id</span> <span class="o">=</span> <span class="n">_fork_and_start_vasp</span><span class="p">(</span><span class="n">mpi_exe</span><span class="p">,</span> <span class="n">arguments</span><span class="p">,</span> <span class="n">env_vars</span><span class="p">)</span>
+
+    <span class="n">mpi_helpers</span><span class="o">.</span><span class="n">poll_barrier</span><span class="p">(</span><span class="n">mpi</span><span class="o">.</span><span class="n">MPI</span><span class="o">.</span><span class="n">COMM_WORLD</span><span class="p">)</span>
+    <span class="n">vasp_process_id</span> <span class="o">=</span> <span class="n">mpi</span><span class="o">.</span><span class="n">bcast</span><span class="p">(</span><span class="n">vasp_process_id</span><span class="p">)</span>
+
+    <span class="c1"># Waits for VASP to start</span>
+    <span class="k">while</span> <span class="ow">not</span> <span class="n">_is_lock_file_present</span><span class="p">():</span>
+        <span class="n">time</span><span class="o">.</span><span class="n">sleep</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">mpi</span><span class="o">.</span><span class="n">barrier</span><span class="p">()</span>
+
+    <span class="c1"># Waits for VASP to finish</span>
+    <span class="k">while</span> <span class="n">_is_lock_file_present</span><span class="p">():</span>
+        <span class="n">time</span><span class="o">.</span><span class="n">sleep</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">mpi</span><span class="o">.</span><span class="n">barrier</span><span class="p">()</span>
+
+    <span class="k">return</span> <span class="n">vasp_process_id</span></div>
+
+
+<div class="viewcode-block" id="run_charge_update"><a class="viewcode-back" href="../../_ref/dft_managers.vasp_manager.html#dft_managers.vasp_manager.run_charge_update">[docs]</a><span class="k">def</span> <span class="nf">run_charge_update</span><span class="p">():</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Performs one step of the charge update with VASP by creating the vasp.lock</span>
+<span class="sd">    file and then waiting until it gets delete by VASP when it has finished.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+        <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;./vasp.lock&#39;</span><span class="p">,</span> <span class="s1">&#39;a&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
+    <span class="n">mpi</span><span class="o">.</span><span class="n">barrier</span><span class="p">()</span>
+
+    <span class="c1"># Waits for VASP to finish</span>
+    <span class="k">while</span> <span class="n">_is_lock_file_present</span><span class="p">():</span>
+        <span class="n">time</span><span class="o">.</span><span class="n">sleep</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">mpi</span><span class="o">.</span><span class="n">barrier</span><span class="p">()</span></div>
+
+
+<div class="viewcode-block" id="read_dft_energy"><a class="viewcode-back" href="../../_ref/dft_managers.vasp_manager.html#dft_managers.vasp_manager.read_dft_energy">[docs]</a><span class="k">def</span> <span class="nf">read_dft_energy</span><span class="p">():</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Reads DFT energy from the last line of Vasp&#39;s OSZICAR.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;OSZICAR&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">file</span><span class="p">:</span>
+        <span class="n">nextline</span> <span class="o">=</span> <span class="n">file</span><span class="o">.</span><span class="n">readline</span><span class="p">()</span>
+        <span class="k">while</span> <span class="n">nextline</span><span class="o">.</span><span class="n">strip</span><span class="p">():</span>
+            <span class="n">line</span> <span class="o">=</span> <span class="n">nextline</span>
+            <span class="n">nextline</span> <span class="o">=</span> <span class="n">file</span><span class="o">.</span><span class="n">readline</span><span class="p">()</span>
+    <span class="n">dft_energy</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">()[</span><span class="mi">2</span><span class="p">])</span>
+
+    <span class="k">return</span> <span class="n">dft_energy</span></div>
+
+
+<div class="viewcode-block" id="read_irred_kpoints"><a class="viewcode-back" href="../../_ref/dft_managers.vasp_manager.html#dft_managers.vasp_manager.read_irred_kpoints">[docs]</a><span class="k">def</span> <span class="nf">read_irred_kpoints</span><span class="p">(</span><span class="n">kpts</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot; Reads the indices of the irreducible k-points from the OUTCAR. &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="nf">read_outcar</span><span class="p">(</span><span class="n">file</span><span class="p">):</span>
+        <span class="n">has_started_reading</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">file</span><span class="p">:</span>
+            <span class="k">if</span> <span class="s1">&#39;IBZKPT_HF&#39;</span> <span class="ow">in</span> <span class="n">line</span><span class="p">:</span>
+                <span class="n">has_started_reading</span> <span class="o">=</span> <span class="kc">True</span>
+                <span class="k">continue</span>
+
+            <span class="k">if</span> <span class="ow">not</span> <span class="n">has_started_reading</span><span class="p">:</span>
+                <span class="k">continue</span>
+
+            <span class="k">if</span> <span class="s1">&#39;t-inv&#39;</span> <span class="ow">in</span> <span class="n">line</span><span class="p">:</span>
+                <span class="k">yield</span> <span class="n">line</span>
+                <span class="k">continue</span>
+
+            <span class="k">if</span> <span class="s1">&#39;-&#39;</span><span class="o">*</span><span class="mi">10</span> <span class="ow">in</span> <span class="n">line</span><span class="p">:</span>
+                <span class="k">break</span>
+
+    <span class="n">irred_indices</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="k">if</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+        <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;OUTCAR&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">file</span><span class="p">:</span>
+            <span class="n">outcar_data_raw</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">loadtxt</span><span class="p">(</span><span class="n">read_outcar</span><span class="p">(</span><span class="n">file</span><span class="p">),</span> <span class="n">usecols</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">])</span>
+        <span class="n">outcar_kpoints</span> <span class="o">=</span> <span class="n">outcar_data_raw</span><span class="p">[:,</span> <span class="p">:</span><span class="mi">3</span><span class="p">]</span>
+        <span class="n">outcar_indices</span> <span class="o">=</span> <span class="p">(</span><span class="n">outcar_data_raw</span><span class="p">[:,</span> <span class="mi">3</span><span class="p">]</span><span class="o">-</span><span class="mf">.5</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
+        <span class="k">assert</span> <span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">outcar_kpoints</span><span class="p">,</span> <span class="n">kpts</span><span class="p">)</span>
+
+        <span class="n">symmetry_mapping</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">outcar_kpoints</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">int</span><span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">kpt_outcar</span><span class="p">,</span> <span class="n">outcar_index</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">outcar_kpoints</span><span class="p">,</span> <span class="n">outcar_indices</span><span class="p">)):</span>
+            <span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">kpt</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">kpts</span><span class="p">):</span>
+                <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">kpt_outcar</span><span class="p">,</span> <span class="n">kpt</span><span class="p">):</span>
+                    <span class="c1"># Symmetry-irreducible k points</span>
+                    <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="n">outcar_index</span><span class="p">:</span>
+                        <span class="n">symmetry_mapping</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">outcar_index</span>
+                    <span class="c1"># Symmetry-reducible</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="n">symmetry_mapping</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">outcar_index</span>
+                    <span class="k">break</span>
+
+            <span class="c1"># Asserts that loop left through break, i.e. a pair was found</span>
+            <span class="k">assert</span> <span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">kpt_outcar</span><span class="p">,</span> <span class="n">kpt</span><span class="p">)</span>
+
+        <span class="n">irreds</span><span class="p">,</span> <span class="n">irred_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">symmetry_mapping</span><span class="p">,</span> <span class="n">return_index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+        <span class="k">assert</span> <span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">diff</span><span class="p">(</span><span class="n">irreds</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="k">assert</span> <span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">symmetry_mapping</span> <span class="o">&gt;=</span> <span class="mi">0</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">mpi</span><span class="o">.</span><span class="n">bcast</span><span class="p">(</span><span class="n">irred_indices</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="kill"><a class="viewcode-back" href="../../_ref/dft_managers.vasp_manager.html#dft_managers.vasp_manager.kill">[docs]</a><span class="k">def</span> <span class="nf">kill</span><span class="p">(</span><span class="n">vasp_process_id</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot; Kills the VASP process. &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: if we kill the process in the next step, does it make a difference if we write the STOPCAR</span>
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;STOPCAR&#39;</span><span class="p">,</span> <span class="s1">&#39;wt&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f_stop</span><span class="p">:</span>
+        <span class="n">f_stop</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s1">&#39;LABORT = .TRUE.</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
+    <span class="n">os</span><span class="o">.</span><span class="n">kill</span><span class="p">(</span><span class="n">vasp_process_id</span><span class="p">,</span> <span class="n">signal</span><span class="o">.</span><span class="n">SIGTERM</span><span class="p">)</span>
+    <span class="n">mpi</span><span class="o">.</span><span class="n">MPI</span><span class="o">.</span><span class="n">COMM_WORLD</span><span class="o">.</span><span class="n">Abort</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span></div>
+</pre></div>
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.</p>
+  </div>
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diff --git a/_modules/dmft_cycle.html b/_modules/dmft_cycle.html
new file mode 100644
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+<!DOCTYPE html>
+<html class="writer-html5" lang="en" >
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>dmft_cycle &mdash; solid_dmft  documentation</title>
+      <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
+      <link rel="stylesheet" href="../_static/css/custom.css" type="text/css" />
+      <link rel="stylesheet" href="../_static/plot_directive.css" type="text/css" />
+    <link rel="shortcut icon" href="../_static/favicon.ico"/>
+  
+        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
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+    <script src="../_static/js/theme.js"></script>
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+            <img src="../_static/logo_no_text.png" class="logo" alt="Logo"/>
+          </a>
+              <div class="version">
+                3.2.0
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+              <ul>
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+<li class="toctree-l2"><a class="reference internal" href="../install.html#installation-via-pip">Installation via pip</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../install.html#manual-installation-via-cmake">Manual installation via CMake</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../install.html#docker-files-images">Docker files &amp; images</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../install.html#version-compatibility">Version compatibility</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../install.html#custom-cmake-options">Custom CMake options</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../documentation.html">Documentation</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#code-structure">Code structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#dft-interface-notes">DFT interface notes</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/w90_interface.html">Wannier90 interface</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/w90_interface.html#orbital-order-in-the-w90-converter">orbital order in the W90 converter</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/vasp_csc.html">Interface to VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#general-remarks">General remarks</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#file-description">file description</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#workflow">Workflow:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#important-flags">important flags:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#version-and-compilation">version and compilation:</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#input-output">Input/Output</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../input_output/DMFT_input/input.html">Input</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/general.html">[general]: General parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/solver.html">[solver]: solver specific parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/dft.html">[dft]: DFT related inputs</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/advanced.html">[advanced]: Advanced inputs</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../input_output/DMFT_output/results.html">Output / results</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_output/results.html#group-structure">Group structure</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_output/results.html#subgroups">Subgroups</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#further-details-for-running">Further details for running</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/docker.html">Docker</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#building-the-docker-image">Building the docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#pulling-a-docker-image">Pulling a docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/run_locally.html">Run on your machine</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#module-reference-manual">Module reference manual</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../_ref/csc_flow.html">csc_flow</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../_ref/dft_managers.html">dft_managers</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/dft_managers.mpi_helpers.html">dft_managers.mpi_helpers</a></li>
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+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../_ref/dmft_cycle.html">dmft_cycle</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../_ref/dmft_tools.html">dmft_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/dmft_tools.afm_mapping.html">dmft_tools.afm_mapping</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../_ref/dmft_tools.initial_self_energies.html">dmft_tools.initial_self_energies</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../_ref/dmft_tools.results_to_archive.html">dmft_tools.results_to_archive</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/dmft_tools.solver.html">dmft_tools.solver</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../_ref/io_tools.html">io_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/io_tools.dict_to_h5.html">io_tools.dict_to_h5</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/io_tools.postproc_toml_dict.html">io_tools.postproc_toml_dict</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/io_tools.verify_input_params.html">io_tools.verify_input_params</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../_ref/postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/postprocessing.eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/postprocessing.maxent_gf_imp.html">postprocessing.maxent_gf_imp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/postprocessing.maxent_gf_latt.html">postprocessing.maxent_gf_latt</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/postprocessing.maxent_sigma.html">postprocessing.maxent_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../_ref/util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../issues.html">Support &amp; contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../issues.html#seeking-help">Seeking help</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../issues.html#improving-solid-dmft">Improving solid_dmft</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../issues.html#reporting-issues">Reporting issues</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../ChangeLog.html">Changelog</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-2-0">Version 3.2.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#general">General</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#fix">fix</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#build">build</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#feat">feat</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#doc">doc</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#test">test</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-5">Version 3.1.5</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-4">Version 3.1.4</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-3">Version 3.1.3</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-2">Version 3.1.2</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-1">Version 3.1.1</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-0">Version 3.1.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-0-0">Version 3.0.0</a></li>
+</ul>
+</li>
+</ul>
+
+        </div>
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu"  style="background: #7E588A" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="../index.html">solid_dmft</a>
+      </nav>
+
+      <div class="wy-nav-content">
+        <div class="rst-content">
+          <div role="navigation" aria-label="Page navigation">
+  <ul class="wy-breadcrumbs">
+      <li><a href="../index.html" class="icon icon-home"></a> &raquo;</li>
+          <li><a href="index.html">Module code</a> &raquo;</li>
+      <li>dmft_cycle</li>
+      <li class="wy-breadcrumbs-aside">
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <h1>Source code for dmft_cycle</h1><div class="highlight"><pre>
+<span></span><span class="c1">################################################################################</span>
+<span class="c1">#</span>
+<span class="c1"># solid_dmft - A versatile python wrapper to perform DFT+DMFT calculations</span>
+<span class="c1">#              utilizing the TRIQS software library</span>
+<span class="c1">#</span>
+<span class="c1"># Copyright (C) 2018-2020, ETH Zurich</span>
+<span class="c1"># Copyright (C) 2021, The Simons Foundation</span>
+<span class="c1">#      authors: A. Hampel, M. Merkel, and S. Beck</span>
+<span class="c1">#</span>
+<span class="c1"># solid_dmft is free software: you can redistribute it and/or modify it under the</span>
+<span class="c1"># terms of the GNU General Public License as published by the Free Software</span>
+<span class="c1"># Foundation, either version 3 of the License, or (at your option) any later</span>
+<span class="c1"># version.</span>
+<span class="c1">#</span>
+<span class="c1"># solid_dmft is distributed in the hope that it will be useful, but WITHOUT ANY</span>
+<span class="c1"># WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A</span>
+<span class="c1"># PARTICULAR PURPOSE. See the GNU General Public License for more details.</span>
+
+<span class="c1"># You should have received a copy of the GNU General Public License along with</span>
+<span class="c1"># solid_dmft (in the file COPYING.txt in this directory). If not, see</span>
+<span class="c1"># &lt;http://www.gnu.org/licenses/&gt;.</span>
+<span class="c1">#</span>
+<span class="c1">################################################################################</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">main DMFT cycle, DMFT step, and helper functions</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+
+<span class="c1"># system</span>
+<span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">from</span> <span class="nn">copy</span> <span class="kn">import</span> <span class="n">deepcopy</span>
+<span class="kn">from</span> <span class="nn">timeit</span> <span class="kn">import</span> <span class="n">default_timer</span> <span class="k">as</span> <span class="n">timer</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<span class="c1"># triqs</span>
+<span class="kn">from</span> <span class="nn">triqs.operators.util.observables</span> <span class="kn">import</span> <span class="n">S_op</span><span class="p">,</span> <span class="n">N_op</span>
+<span class="kn">from</span> <span class="nn">triqs.version</span> <span class="kn">import</span> <span class="n">git_hash</span> <span class="k">as</span> <span class="n">triqs_hash</span>
+<span class="kn">from</span> <span class="nn">triqs.version</span> <span class="kn">import</span> <span class="n">version</span> <span class="k">as</span> <span class="n">triqs_version</span>
+<span class="kn">from</span> <span class="nn">h5</span> <span class="kn">import</span> <span class="n">HDFArchive</span>
+<span class="kn">import</span> <span class="nn">triqs.utility.mpi</span> <span class="k">as</span> <span class="nn">mpi</span>
+<span class="kn">from</span> <span class="nn">triqs.gf</span> <span class="kn">import</span> <span class="n">Gf</span><span class="p">,</span> <span class="n">make_hermitian</span><span class="p">,</span> <span class="n">MeshReFreq</span><span class="p">,</span> <span class="n">MeshImFreq</span>
+<span class="kn">from</span> <span class="nn">triqs.gf.tools</span> <span class="kn">import</span> <span class="n">inverse</span>
+<span class="kn">from</span> <span class="nn">triqs_dft_tools</span> <span class="kn">import</span> <span class="n">BlockStructure</span>
+<span class="kn">from</span> <span class="nn">triqs_dft_tools.sumk_dft</span> <span class="kn">import</span> <span class="n">SumkDFT</span>
+
+<span class="c1"># own modules</span>
+<span class="kn">from</span> <span class="nn">solid_dmft.version</span> <span class="kn">import</span> <span class="n">solid_dmft_hash</span>
+<span class="kn">from</span> <span class="nn">solid_dmft.version</span> <span class="kn">import</span> <span class="n">version</span> <span class="k">as</span> <span class="n">solid_dmft_version</span>
+<span class="kn">from</span> <span class="nn">solid_dmft.dmft_tools.observables</span> <span class="kn">import</span> <span class="p">(</span><span class="n">calc_dft_kin_en</span><span class="p">,</span> <span class="n">add_dmft_observables</span><span class="p">,</span> <span class="n">calc_bandcorr_man</span><span class="p">,</span> <span class="n">write_obs</span><span class="p">,</span>
+                                         <span class="n">add_dft_values_as_zeroth_iteration</span><span class="p">,</span> <span class="n">write_header_to_file</span><span class="p">,</span> <span class="n">prep_observables</span><span class="p">)</span>
+<span class="kn">from</span> <span class="nn">solid_dmft.dmft_tools.solver</span> <span class="kn">import</span> <span class="n">SolverStructure</span>
+<span class="kn">from</span> <span class="nn">solid_dmft.dmft_tools</span> <span class="kn">import</span> <span class="n">convergence</span>
+<span class="kn">from</span> <span class="nn">solid_dmft.dmft_tools</span> <span class="kn">import</span> <span class="n">formatter</span>
+<span class="kn">from</span> <span class="nn">solid_dmft.dmft_tools</span> <span class="kn">import</span> <span class="n">interaction_hamiltonian</span>
+<span class="kn">from</span> <span class="nn">solid_dmft.dmft_tools</span> <span class="kn">import</span> <span class="n">results_to_archive</span>
+<span class="kn">from</span> <span class="nn">solid_dmft.dmft_tools</span> <span class="kn">import</span> <span class="n">afm_mapping</span>
+<span class="kn">from</span> <span class="nn">solid_dmft.dmft_tools</span> <span class="kn">import</span> <span class="n">manipulate_chemical_potential</span> <span class="k">as</span> <span class="n">manipulate_mu</span>
+<span class="kn">from</span> <span class="nn">solid_dmft.dmft_tools</span> <span class="kn">import</span> <span class="n">initial_self_energies</span> <span class="k">as</span> <span class="n">initial_sigma</span>
+<span class="kn">from</span> <span class="nn">solid_dmft.dmft_tools</span> <span class="kn">import</span> <span class="n">greens_functions_mixer</span> <span class="k">as</span> <span class="n">gf_mixer</span>
+<span class="kn">from</span> <span class="nn">solid_dmft.io_tools</span> <span class="kn">import</span> <span class="n">verify_input_params</span><span class="p">,</span> <span class="n">dict_to_h5</span>
+
+<span class="k">def</span> <span class="nf">_extract_quantity_per_inequiv</span><span class="p">(</span><span class="n">param_name</span><span class="p">,</span> <span class="n">n_inequiv_shells</span><span class="p">,</span> <span class="n">general_params</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    For quantities that can be different for each inequivalent shell, this</span>
+<span class="sd">    function checks if the quantity is a single value or a list of the correct</span>
+<span class="sd">    length. If just a single value is given, this value is applied to each shell.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    param_name : str</span>
+<span class="sd">        name of the parameter to be checked</span>
+<span class="sd">    n_inequiv_shells : int</span>
+<span class="sd">        number of inequivalent shells</span>
+<span class="sd">    general_params : dict</span>
+<span class="sd">        general parameters as a dict</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    general_params : dict</span>
+<span class="sd">        updated general parameters as a dict</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+
+    <span class="k">def</span> <span class="nf">formatter</span><span class="p">(</span><span class="n">value</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="nb">float</span><span class="p">):</span>
+            <span class="k">return</span> <span class="s1">&#39;</span><span class="si">{:.2f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
+        <span class="k">return</span> <span class="nb">str</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
+
+    <span class="n">param</span> <span class="o">=</span> <span class="n">general_params</span><span class="p">[</span><span class="n">param_name</span><span class="p">]</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">param</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+        <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;Assuming </span><span class="si">{}</span><span class="s1"> = &#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">param_name</span><span class="p">)</span>
+                <span class="o">+</span> <span class="s1">&#39;</span><span class="si">{}</span><span class="s1"> for all correlated shells&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">formatter</span><span class="p">(</span><span class="n">param</span><span class="p">)))</span>
+        <span class="n">general_params</span><span class="p">[</span><span class="n">param_name</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">param</span><span class="p">]</span> <span class="o">*</span> <span class="n">n_inequiv_shells</span>
+    <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">param</span><span class="p">)</span> <span class="o">==</span> <span class="n">n_inequiv_shells</span><span class="p">:</span>
+        <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">param_name</span><span class="si">}</span><span class="s1"> list for correlated shells: &#39;</span>
+                   <span class="o">+</span> <span class="s1">&#39;, &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="n">formatter</span><span class="p">(</span><span class="n">p</span><span class="p">)</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">param</span><span class="p">]))</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">IndexError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">param_name</span><span class="si">}</span><span class="s1"> must be list of length &#39;</span>
+                         <span class="sa">f</span><span class="s1">&#39;n_inequiv_shells=</span><span class="si">{</span><span class="n">n_inequiv_shells</span><span class="si">}</span><span class="s1"> &#39;</span>
+                         <span class="s1">&#39;or single value but is &#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="n">param_name</span><span class="p">]))</span>
+
+    <span class="k">return</span> <span class="n">general_params</span>
+
+<span class="k">def</span> <span class="nf">_determine_block_structure</span><span class="p">(</span><span class="n">sum_k</span><span class="p">,</span> <span class="n">general_params</span><span class="p">,</span> <span class="n">advanced_params</span><span class="p">,</span> <span class="n">solver_type_per_imp</span><span class="p">,</span> <span class="n">dens_mat</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Determines block structrure and degenerate deg_shells</span>
+<span class="sd">    computes first DFT density matrix to determine block structure and changes</span>
+<span class="sd">    the density matrix according to needs i.e. magnetic calculations, or keep</span>
+<span class="sd">    off-diag elements</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    sum_k : SumK Object instances</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    sum_k : SumK Object instances</span>
+<span class="sd">        updated sum_k Object</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1"> *** Determination of block structure ***&#39;</span><span class="p">)</span>
+
+    <span class="c1"># Adds spin degeneracies if not spin-orbit coupled</span>
+    <span class="k">if</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">SO</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">sum_k</span><span class="o">.</span><span class="n">block_structure</span><span class="o">.</span><span class="n">deg_shells</span> <span class="o">=</span> <span class="p">[[[</span><span class="s1">&#39;up_0&#39;</span><span class="p">,</span> <span class="s1">&#39;down_0&#39;</span><span class="p">]]</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span><span class="p">)]</span>
+
+    <span class="c1"># Evaluates degeneracies for ftps</span>
+    <span class="n">imp_ftps</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">s</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">solver_type_per_imp</span><span class="p">)</span> <span class="k">if</span> <span class="n">s</span> <span class="o">==</span> <span class="s1">&#39;ftps&#39;</span><span class="p">]</span>
+    <span class="k">if</span> <span class="n">imp_ftps</span><span class="p">:</span>
+        <span class="n">mock_sumk</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">sum_k</span><span class="p">)</span>
+        <span class="n">mock_sumk</span><span class="o">.</span><span class="n">analyse_block_structure</span><span class="p">(</span><span class="n">dm</span><span class="o">=</span><span class="n">dens_mat</span><span class="p">,</span> <span class="n">threshold</span><span class="o">=</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;block_threshold&#39;</span><span class="p">],</span> <span class="n">include_shells</span><span class="o">=</span><span class="n">imp_ftps</span><span class="p">)</span>
+        <span class="n">deg_orbs_ftps</span> <span class="o">=</span> <span class="p">[</span><span class="n">mock_sumk</span><span class="o">.</span><span class="n">deg_shells</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span> <span class="k">if</span> <span class="n">icrsh</span> <span class="ow">in</span> <span class="n">imp_ftps</span> <span class="k">else</span> <span class="kc">None</span>
+                         <span class="k">for</span> <span class="n">icrsh</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span><span class="p">)]</span>
+        <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;Block structure written to &quot;deg_orbs_ftps&quot;:&#39;</span><span class="p">)</span>
+        <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="n">deg_orbs_ftps</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">deg_orbs_ftps</span> <span class="o">=</span> <span class="kc">None</span>
+
+    <span class="c1"># Only removes the off-diagonal terms for the selected impurities</span>
+    <span class="n">imp_to_analyze</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">offdiag</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;enforce_off_diag&#39;</span><span class="p">])</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">offdiag</span><span class="p">]</span>
+    <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;using 1-particle density matrix and Hloc (atomic levels) to determine the block structure&#39;</span><span class="p">)</span>
+    <span class="n">sum_k</span><span class="o">.</span><span class="n">analyse_block_structure</span><span class="p">(</span><span class="n">dm</span><span class="o">=</span><span class="n">dens_mat</span><span class="p">,</span> <span class="n">threshold</span><span class="o">=</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;block_threshold&#39;</span><span class="p">],</span> <span class="n">include_shells</span><span class="o">=</span><span class="n">imp_to_analyze</span><span class="p">)</span>
+
+    <span class="c1"># Applies manual selection of the solver struct</span>
+    <span class="k">if</span> <span class="nb">any</span><span class="p">(</span><span class="n">s</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">advanced_params</span><span class="p">[</span><span class="s1">&#39;pick_solver_struct&#39;</span><span class="p">]):</span>
+        <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;selecting subset of orbital space for gf_struct_solver from input:&#39;</span><span class="p">)</span>
+        <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="n">advanced_params</span><span class="p">[</span><span class="s1">&#39;pick_solver_struct&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">])</span>
+        <span class="n">sum_k</span><span class="o">.</span><span class="n">block_structure</span><span class="o">.</span><span class="n">pick_gf_struct_solver</span><span class="p">(</span><span class="n">advanced_params</span><span class="p">[</span><span class="s1">&#39;pick_solver_struct&#39;</span><span class="p">])</span>
+
+    <span class="c1"># Applies the manual mapping to each inequivalent shell</span>
+    <span class="k">if</span> <span class="nb">any</span><span class="p">(</span><span class="n">s</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">advanced_params</span><span class="p">[</span><span class="s1">&#39;map_solver_struct&#39;</span><span class="p">]):</span>
+        <span class="n">sum_k</span><span class="o">.</span><span class="n">block_structure</span><span class="o">.</span><span class="n">map_gf_struct_solver</span><span class="p">(</span><span class="n">advanced_params</span><span class="p">[</span><span class="s1">&#39;map_solver_struct&#39;</span><span class="p">])</span>
+        <span class="k">if</span> <span class="nb">any</span><span class="p">(</span><span class="n">s</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">advanced_params</span><span class="p">[</span><span class="s1">&#39;mapped_solver_struct_degeneracies&#39;</span><span class="p">]):</span>
+            <span class="n">sum_k</span><span class="o">.</span><span class="n">block_structure</span><span class="o">.</span><span class="n">deg_shells</span> <span class="o">=</span> <span class="n">advanced_params</span><span class="p">[</span><span class="s1">&#39;mapped_solver_struct_degeneracies&#39;</span><span class="p">]</span>
+
+    <span class="c1"># if we want to do a magnetic calculation we need to lift up/down degeneracy</span>
+    <span class="k">if</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;magnetic&#39;</span><span class="p">]</span> <span class="ow">and</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">SO</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;Magnetic calculation: removing the spin degeneracy from the block structure&#39;</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">icrsh</span><span class="p">,</span> <span class="n">deg_shells_site</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">block_structure</span><span class="o">.</span><span class="n">deg_shells</span><span class="p">):</span>
+            <span class="n">deg_shell_mag</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="c1"># find degenerate orbitals that do not simply connect different spin channels</span>
+            <span class="k">for</span> <span class="n">deg_orbs</span> <span class="ow">in</span> <span class="n">deg_shells_site</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">spin</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;up&#39;</span><span class="p">,</span> <span class="s1">&#39;down&#39;</span><span class="p">]:</span>
+                    <span class="c1"># create a list of all up / down orbitals</span>
+                    <span class="n">deg</span> <span class="o">=</span> <span class="p">[</span><span class="n">orb</span> <span class="k">for</span> <span class="n">orb</span> <span class="ow">in</span> <span class="n">deg_orbs</span> <span class="k">if</span> <span class="n">spin</span> <span class="ow">in</span> <span class="n">orb</span><span class="p">]</span>
+                    <span class="c1"># if longer than one than we have two deg orbitals also in a magnetic calculation</span>
+                    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">deg</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+                        <span class="n">deg_shell_mag</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">deg</span><span class="p">)</span>
+            <span class="n">sum_k</span><span class="o">.</span><span class="n">block_structure</span><span class="o">.</span><span class="n">deg_shells</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span> <span class="o">=</span> <span class="n">deg_shell_mag</span>
+    <span class="c1"># for SOC we remove all degeneracies</span>
+    <span class="k">elif</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">SO</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="n">sum_k</span><span class="o">.</span><span class="n">block_structure</span><span class="o">.</span><span class="n">deg_shells</span> <span class="o">=</span> <span class="p">[[]</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span><span class="p">)]</span>
+
+    <span class="k">return</span> <span class="n">sum_k</span><span class="p">,</span> <span class="n">deg_orbs_ftps</span>
+
+
+<span class="k">def</span> <span class="nf">_calculate_rotation_matrix</span><span class="p">(</span><span class="n">general_params</span><span class="p">,</span> <span class="n">sum_k</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Applies rotation matrix to make the DMFT calculations easier for the solver.</span>
+<span class="sd">    Possible are rotations diagonalizing either the local Hamiltonian or the</span>
+<span class="sd">    density. Diagonalizing the density has not proven really helpful but</span>
+<span class="sd">    diagonalizing the local Hamiltonian has.</span>
+<span class="sd">    Note that the interaction Hamiltonian has to be rotated if it is not fully</span>
+<span class="sd">    orbital-gauge invariant (only the Kanamori fulfills that).</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    general_params : dict</span>
+<span class="sd">        general parameters as a dict</span>
+<span class="sd">    sum_k : SumkDFT object</span>
+<span class="sd">        Sumk Object</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    sum_k : SumkDFT Object</span>
+<span class="sd">        updated Sumk object with the new rotation matrices</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># Extracts new rotation matrices from density_mat or local Hamiltonian</span>
+    <span class="k">if</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;set_rot&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;hloc&#39;</span><span class="p">:</span>
+        <span class="n">q_diag</span> <span class="o">=</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">eff_atomic_levels</span><span class="p">()</span>
+    <span class="k">elif</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;set_rot&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;den&#39;</span><span class="p">:</span>
+        <span class="n">q_diag</span> <span class="o">=</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">density_matrix</span><span class="p">(</span><span class="n">method</span><span class="o">=</span><span class="s1">&#39;using_gf&#39;</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Parameter set_rot set to wrong value.&#39;</span><span class="p">)</span>
+
+    <span class="n">chnl</span> <span class="o">=</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">spin_block_names</span><span class="p">[</span><span class="n">sum_k</span><span class="o">.</span><span class="n">SO</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+
+    <span class="n">rot_mat</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">icrsh</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_corr_shells</span><span class="p">):</span>
+        <span class="n">ish</span> <span class="o">=</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">corr_to_inequiv</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span>
+        <span class="n">eigvec</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">eigh</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">q_diag</span><span class="p">[</span><span class="n">ish</span><span class="p">][</span><span class="n">chnl</span><span class="p">]))[</span><span class="mi">1</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">complex</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">use_rotations</span><span class="p">:</span>
+            <span class="n">rot_mat</span><span class="o">.</span><span class="n">append</span><span class="p">(</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">rot_mat</span><span class="p">[</span><span class="n">icrsh</span><span class="p">],</span> <span class="n">eigvec</span><span class="p">)</span> <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">rot_mat</span><span class="o">.</span><span class="n">append</span><span class="p">(</span> <span class="n">eigvec</span> <span class="p">)</span>
+
+    <span class="n">sum_k</span><span class="o">.</span><span class="n">rot_mat</span> <span class="o">=</span> <span class="n">rot_mat</span>
+    <span class="c1"># in case sum_k.use_rotations == False before:</span>
+    <span class="n">sum_k</span><span class="o">.</span><span class="n">use_rotations</span> <span class="o">=</span> <span class="kc">True</span>
+    <span class="c1"># sum_k.eff_atomic_levels() needs to be recomputed if rot_mat were changed</span>
+    <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">sum_k</span><span class="p">,</span> <span class="s2">&quot;Hsumk&quot;</span><span class="p">):</span> <span class="nb">delattr</span><span class="p">(</span><span class="n">sum_k</span><span class="p">,</span> <span class="s2">&quot;Hsumk&quot;</span><span class="p">)</span>
+    <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;Updating rotation matrices using dft </span><span class="si">{}</span><span class="s1"> eigenbasis to maximise sign&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;set_rot&#39;</span><span class="p">]))</span>
+
+    <span class="c1"># Prints matrices</span>
+    <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">New rotation matrices&#39;</span><span class="p">)</span>
+    <span class="n">formatter</span><span class="o">.</span><span class="n">print_rotation_matrix</span><span class="p">(</span><span class="n">sum_k</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">sum_k</span>
+
+
+<span class="k">def</span> <span class="nf">_chi_setup</span><span class="p">(</span><span class="n">sum_k</span><span class="p">,</span> <span class="n">solver_params</span><span class="p">,</span> <span class="n">map_imp_solver</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    sum_k : SumkDFT object</span>
+<span class="sd">        Sumk object with the information about the correct block structure</span>
+<span class="sd">    solver_params: solver params dict</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    ops_chi_measurement : list of one-particle operators to measure per impurity</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">ops_chi_measure</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span>
+
+    <span class="k">for</span> <span class="n">icrsh</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span><span class="p">):</span>
+        <span class="n">isolv</span> <span class="o">=</span> <span class="n">map_imp_solver</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span>
+        <span class="k">if</span> <span class="s1">&#39;measure_chi&#39;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">solver_params</span><span class="p">[</span><span class="n">isolv</span><span class="p">]</span> <span class="ow">or</span> <span class="n">solver_params</span><span class="p">[</span><span class="n">isolv</span><span class="p">][</span><span class="s1">&#39;measure_chi&#39;</span><span class="p">]</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">continue</span>
+
+        <span class="n">n_orb</span> <span class="o">=</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">corr_shells</span><span class="p">[</span><span class="n">icrsh</span><span class="p">][</span><span class="s1">&#39;dim&#39;</span><span class="p">]</span>
+        <span class="k">if</span> <span class="n">solver_params</span><span class="p">[</span><span class="n">isolv</span><span class="p">][</span><span class="s1">&#39;measure_chi&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;SzSz&#39;</span><span class="p">:</span>
+            <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">Imp </span><span class="si">{</span><span class="n">icrsh</span><span class="si">}</span><span class="s1"> with solver #</span><span class="si">{</span><span class="n">isolv</span><span class="si">}</span><span class="s1">: Setting up Chi(S_z(tau),S_z(0)) measurement&#39;</span><span class="p">)</span>
+
+            <span class="n">ops_chi_measure</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span> <span class="o">=</span> <span class="n">S_op</span><span class="p">(</span><span class="s1">&#39;z&#39;</span><span class="p">,</span> <span class="n">spin_names</span><span class="o">=</span><span class="n">sum_k</span><span class="o">.</span><span class="n">spin_block_names</span><span class="p">[</span><span class="n">sum_k</span><span class="o">.</span><span class="n">SO</span><span class="p">],</span>
+                                          <span class="n">n_orb</span><span class="o">=</span><span class="n">n_orb</span><span class="p">,</span> <span class="n">map_operator_structure</span><span class="o">=</span><span class="n">sum_k</span><span class="o">.</span><span class="n">sumk_to_solver</span><span class="p">[</span><span class="n">icrsh</span><span class="p">])</span>
+        <span class="k">elif</span> <span class="n">solver_params</span><span class="p">[</span><span class="n">isolv</span><span class="p">][</span><span class="s1">&#39;measure_chi&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;NN&#39;</span><span class="p">:</span>
+            <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">Imp </span><span class="si">{</span><span class="n">icrsh</span><span class="si">}</span><span class="s1"> with solver #</span><span class="si">{</span><span class="n">isolv</span><span class="si">}</span><span class="s1">: Setting up Chi(n(tau),n(0)) measurement&#39;</span><span class="p">)</span>
+
+            <span class="n">ops_chi_measure</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span> <span class="o">=</span> <span class="n">N_op</span><span class="p">(</span><span class="n">spin_names</span><span class="o">=</span><span class="n">sum_k</span><span class="o">.</span><span class="n">spin_block_names</span><span class="p">[</span><span class="n">sum_k</span><span class="o">.</span><span class="n">SO</span><span class="p">],</span>
+                                          <span class="n">n_orb</span><span class="o">=</span><span class="n">n_orb</span><span class="p">,</span> <span class="n">map_operator_structure</span><span class="o">=</span><span class="n">sum_k</span><span class="o">.</span><span class="n">sumk_to_solver</span><span class="p">[</span><span class="n">icrsh</span><span class="p">])</span>
+
+    <span class="k">return</span> <span class="n">ops_chi_measure</span>
+
+
+<div class="viewcode-block" id="dmft_cycle"><a class="viewcode-back" href="../_ref/dmft_cycle.html#dmft_cycle.dmft_cycle">[docs]</a><span class="k">def</span> <span class="nf">dmft_cycle</span><span class="p">(</span><span class="n">general_params</span><span class="p">,</span> <span class="n">solver_params</span><span class="p">,</span> <span class="n">advanced_params</span><span class="p">,</span> <span class="n">dft_params</span><span class="p">,</span>
+               <span class="n">n_iter</span><span class="p">,</span> <span class="n">dft_irred_kpt_indices</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dft_energy</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    main dmft cycle that works for one shot and CSC equally</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    general_params : dict</span>
+<span class="sd">        general parameters as a dict</span>
+<span class="sd">    solver_params : dict</span>
+<span class="sd">        solver parameters as a dict</span>
+<span class="sd">    advanced_params : dict</span>
+<span class="sd">        advanced parameters as a dict</span>
+<span class="sd">    observables : dict</span>
+<span class="sd">        current observable array for calculation</span>
+<span class="sd">    n_iter : int</span>
+<span class="sd">        number of iterations to be executed</span>
+<span class="sd">    dft_irred_kpt_indices: iterable of int</span>
+<span class="sd">        If given, writes density correction for csc calculations only for</span>
+<span class="sd">        irreducible kpoints</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    ---------</span>
+<span class="sd">    observables : dict</span>
+<span class="sd">        updated observable array for calculation</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># Creates real- or imaginary-frequency mesh to store Green functions on</span>
+    <span class="k">if</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;beta&#39;</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;Running solid_dmft on imag-freq grid because &quot;general.beta&quot; specified&#39;</span><span class="p">)</span>
+        <span class="n">sumk_mesh</span> <span class="o">=</span> <span class="n">MeshImFreq</span><span class="p">(</span><span class="n">beta</span><span class="o">=</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;beta&#39;</span><span class="p">],</span>
+                               <span class="n">S</span><span class="o">=</span><span class="s1">&#39;Fermion&#39;</span><span class="p">,</span>
+                               <span class="n">n_iw</span><span class="o">=</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;n_iw&#39;</span><span class="p">])</span>
+        <span class="n">broadening</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;Running solid_dmft on real-freq grid because &quot;general.beta&quot; not specified&#39;</span><span class="p">)</span>
+        <span class="n">sumk_mesh</span> <span class="o">=</span> <span class="n">MeshReFreq</span><span class="p">(</span><span class="n">window</span><span class="o">=</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;w_range&#39;</span><span class="p">],</span>
+                               <span class="n">n_w</span><span class="o">=</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;n_w&#39;</span><span class="p">])</span>
+        <span class="n">broadening</span> <span class="o">=</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;eta&#39;</span><span class="p">]</span>
+
+    <span class="c1"># Creates SumkDFT object</span>
+    <span class="n">sum_k</span> <span class="o">=</span> <span class="n">SumkDFT</span><span class="p">(</span><span class="n">hdf_file</span><span class="o">=</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;jobname&#39;</span><span class="p">]</span><span class="o">+</span><span class="s1">&#39;/&#39;</span><span class="o">+</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;seedname&#39;</span><span class="p">]</span><span class="o">+</span><span class="s1">&#39;.h5&#39;</span><span class="p">,</span>
+                    <span class="n">mesh</span><span class="o">=</span><span class="n">sumk_mesh</span><span class="p">,</span> <span class="n">use_dft_blocks</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">h_field</span><span class="o">=</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;h_field&#39;</span><span class="p">])</span>
+
+    <span class="n">iteration_offset</span> <span class="o">=</span> <span class="mi">0</span>
+
+    <span class="c1"># determine chemical potential for bare DFT sum_k object</span>
+    <span class="k">if</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+        <span class="n">archive</span> <span class="o">=</span> <span class="n">HDFArchive</span><span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;jobname&#39;</span><span class="p">]</span><span class="o">+</span><span class="s1">&#39;/&#39;</span><span class="o">+</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;seedname&#39;</span><span class="p">]</span><span class="o">+</span><span class="s1">&#39;.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;a&#39;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="s1">&#39;DMFT_results&#39;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">archive</span><span class="p">:</span>
+            <span class="n">archive</span><span class="o">.</span><span class="n">create_group</span><span class="p">(</span><span class="s1">&#39;DMFT_results&#39;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="s1">&#39;last_iter&#39;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_results&#39;</span><span class="p">]:</span>
+            <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_results&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">create_group</span><span class="p">(</span><span class="s1">&#39;last_iter&#39;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="s1">&#39;DMFT_input&#39;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">archive</span><span class="p">:</span>
+            <span class="n">archive</span><span class="o">.</span><span class="n">create_group</span><span class="p">(</span><span class="s1">&#39;DMFT_input&#39;</span><span class="p">)</span>
+            <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_input&#39;</span><span class="p">][</span><span class="s1">&#39;program&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;solid_dmft&#39;</span>
+            <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_input&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">create_group</span><span class="p">(</span><span class="s1">&#39;solver&#39;</span><span class="p">)</span>
+            <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_input&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">create_group</span><span class="p">(</span><span class="s1">&#39;version&#39;</span><span class="p">)</span>
+            <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_input&#39;</span><span class="p">][</span><span class="s1">&#39;version&#39;</span><span class="p">][</span><span class="s1">&#39;triqs_hash&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">triqs_hash</span>
+            <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_input&#39;</span><span class="p">][</span><span class="s1">&#39;version&#39;</span><span class="p">][</span><span class="s1">&#39;triqs_version&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">triqs_version</span>
+            <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_input&#39;</span><span class="p">][</span><span class="s1">&#39;version&#39;</span><span class="p">][</span><span class="s1">&#39;solid_dmft_hash&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">solid_dmft_hash</span>
+            <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_input&#39;</span><span class="p">][</span><span class="s1">&#39;version&#39;</span><span class="p">][</span><span class="s1">&#39;solid_dmft_version&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">solid_dmft_version</span>
+
+        <span class="k">if</span> <span class="s1">&#39;iteration_count&#39;</span> <span class="ow">in</span> <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_results&#39;</span><span class="p">]:</span>
+            <span class="n">iteration_offset</span> <span class="o">=</span> <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_results/iteration_count&#39;</span><span class="p">]</span>
+            <span class="n">sum_k</span><span class="o">.</span><span class="n">chemical_potential</span> <span class="o">=</span> <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_results/last_iter/chemical_potential_post&#39;</span><span class="p">]</span>
+            <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;RESTARTING DMFT RUN at iteration </span><span class="si">{</span><span class="n">iteration_offset</span><span class="o">+</span><span class="mi">1</span><span class="si">}</span><span class="s1"> using last self-energy&#39;</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;INITIAL DMFT RUN&#39;</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;#&#39;</span><span class="o">*</span><span class="mi">80</span><span class="p">,</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">archive</span> <span class="o">=</span> <span class="kc">None</span>
+
+    <span class="n">iteration_offset</span> <span class="o">=</span> <span class="n">mpi</span><span class="o">.</span><span class="n">bcast</span><span class="p">(</span><span class="n">iteration_offset</span><span class="p">)</span>
+    <span class="n">sum_k</span><span class="o">.</span><span class="n">chemical_potential</span> <span class="o">=</span> <span class="n">mpi</span><span class="o">.</span><span class="n">bcast</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">chemical_potential</span><span class="p">)</span>
+
+    <span class="c1"># Checks the general parameters that are impurity-dependent and ensures they are a list</span>
+    <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;Checking parameters that are impurity-dependent&#39;</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;U&#39;</span><span class="p">,</span> <span class="s1">&#39;J&#39;</span><span class="p">,</span> <span class="s1">&#39;U_prime&#39;</span><span class="p">,</span> <span class="s1">&#39;ratio_F4_F2&#39;</span><span class="p">,</span> <span class="s1">&#39;h_int_type&#39;</span><span class="p">,</span> <span class="s1">&#39;enforce_off_diag&#39;</span><span class="p">,</span> <span class="s1">&#39;dc_type&#39;</span><span class="p">]:</span>
+        <span class="n">general_params</span> <span class="o">=</span> <span class="n">_extract_quantity_per_inequiv</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span><span class="p">,</span> <span class="n">general_params</span><span class="p">)</span>
+    <span class="c1"># Same for advanced params</span>
+    <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;dc_U&#39;</span><span class="p">,</span> <span class="s1">&#39;dc_J&#39;</span><span class="p">,</span> <span class="s1">&#39;dc_fixed_occ&#39;</span><span class="p">,</span> <span class="s1">&#39;map_solver_struct&#39;</span><span class="p">,</span> <span class="s1">&#39;pick_solver_struct&#39;</span><span class="p">,</span> <span class="s1">&#39;mapped_solver_struct_degeneracies&#39;</span><span class="p">]:</span>
+        <span class="n">advanced_params</span> <span class="o">=</span> <span class="n">_extract_quantity_per_inequiv</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span><span class="p">,</span> <span class="n">advanced_params</span><span class="p">)</span>
+
+    <span class="c1"># Checks that all impurities have an associated solver</span>
+    <span class="c1"># and creates a map between impurity index and solver index</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">solver_params</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span> <span class="ow">and</span> <span class="n">solver_params</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="s1">&#39;idx_impurities&#39;</span><span class="p">]</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">map_imp_solver</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">all_idx_imp</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">entry</span> <span class="ow">in</span> <span class="n">solver_params</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">entry</span><span class="p">[</span><span class="s1">&#39;idx_impurities&#39;</span><span class="p">]]</span>
+        <span class="k">if</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">all_idx_imp</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span><span class="p">)):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;All impurities must be listed exactly once in solver.idx_impurities&#39;</span>
+                             <span class="sa">f</span><span class="s1">&#39;but instead got </span><span class="si">{</span><span class="n">all_idx_imp</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+
+        <span class="n">map_imp_solver</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">iineq</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span><span class="p">):</span>
+            <span class="k">for</span> <span class="n">isolver</span><span class="p">,</span> <span class="n">entry</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">solver_params</span><span class="p">):</span>
+                <span class="k">if</span> <span class="n">iineq</span> <span class="ow">in</span> <span class="n">entry</span><span class="p">[</span><span class="s1">&#39;idx_impurities&#39;</span><span class="p">]:</span>
+                    <span class="n">map_imp_solver</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">isolver</span><span class="p">)</span>
+                    <span class="k">break</span>
+    <span class="n">solver_type_per_imp</span> <span class="o">=</span> <span class="p">[</span><span class="n">solver_params</span><span class="p">[</span><span class="n">map_imp_solver</span><span class="p">[</span><span class="n">iineq</span><span class="p">]][</span><span class="s1">&#39;type&#39;</span><span class="p">]</span> <span class="k">for</span> <span class="n">iineq</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span><span class="p">)]</span>
+    <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">Solver type per impurity: </span><span class="si">{</span><span class="n">solver_type_per_imp</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+
+    <span class="c1"># Checks all parameters that need to be checked against info in SumkDFT object</span>
+    <span class="n">verify_input_params</span><span class="o">.</span><span class="n">verify_h5_dependent</span><span class="p">(</span><span class="n">sum_k</span><span class="p">,</span> <span class="n">solver_type_per_imp</span><span class="p">,</span> <span class="n">general_params</span><span class="p">)</span>
+
+    <span class="c1"># Initializes chemical potential with mu_initial_guess if this is the first iteration</span>
+    <span class="k">if</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;mu_initial_guess&#39;</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">iteration_offset</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">sum_k</span><span class="o">.</span><span class="n">chemical_potential</span> <span class="o">=</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;mu_initial_guess&#39;</span><span class="p">]</span>
+        <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">Initial chemical potential set to </span><span class="si">{:.3f}</span><span class="s1"> eV</span><span class="se">\n</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">chemical_potential</span><span class="p">))</span>
+
+    <span class="n">dft_mu</span> <span class="o">=</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">calc_mu</span><span class="p">(</span><span class="n">precision</span><span class="o">=</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;prec_mu&#39;</span><span class="p">],</span> <span class="n">method</span><span class="o">=</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;calc_mu_method&#39;</span><span class="p">],</span>
+                           <span class="n">broadening</span><span class="o">=</span><span class="n">broadening</span><span class="p">)</span>
+
+    <span class="c1"># calculate E_kin_dft for one shot calculations</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;csc&#39;</span><span class="p">]</span> <span class="ow">and</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;calc_energies&#39;</span><span class="p">]:</span>
+        <span class="n">E_kin_dft</span> <span class="o">=</span> <span class="n">calc_dft_kin_en</span><span class="p">(</span><span class="n">general_params</span><span class="p">,</span> <span class="n">sum_k</span><span class="p">,</span> <span class="n">dft_mu</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">E_kin_dft</span> <span class="o">=</span> <span class="kc">None</span>
+
+    <span class="c1"># check for previous broyden data oterhwise initialize it:</span>
+    <span class="k">if</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">()</span> <span class="ow">and</span>  <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;g0_mix_type&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;broyden&#39;</span><span class="p">:</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="s1">&#39;broyler&#39;</span> <span class="ow">in</span> <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_results&#39;</span><span class="p">]:</span>
+            <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_results&#39;</span><span class="p">][</span><span class="s1">&#39;broyler&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[{</span><span class="s1">&#39;mu&#39;</span> <span class="p">:</span> <span class="p">[],</span><span class="s1">&#39;V&#39;</span><span class="p">:</span> <span class="p">[],</span> <span class="s1">&#39;dV&#39;</span><span class="p">:</span> <span class="p">[],</span> <span class="s1">&#39;F&#39;</span><span class="p">:</span> <span class="p">[],</span> <span class="s1">&#39;dF&#39;</span><span class="p">:</span> <span class="p">[]}</span>
+                                                  <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span><span class="p">)]</span>
+
+    <span class="c1"># Generates a rotation matrix to change the basis</span>
+    <span class="k">if</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;set_rot&#39;</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="c1"># calculate new rotation matrices</span>
+        <span class="n">sum_k</span> <span class="o">=</span> <span class="n">_calculate_rotation_matrix</span><span class="p">(</span><span class="n">general_params</span><span class="p">,</span> <span class="n">sum_k</span><span class="p">)</span>
+    <span class="c1"># Saves rotation matrix to h5 archive:</span>
+    <span class="k">if</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">()</span> <span class="ow">and</span> <span class="n">iteration_offset</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_input&#39;</span><span class="p">][</span><span class="s1">&#39;rot_mat&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">rot_mat</span>
+    <span class="n">mpi</span><span class="o">.</span><span class="n">barrier</span><span class="p">()</span>
+
+    <span class="c1"># Calculates density in default block structure</span>
+    <span class="n">local_gf_dft_corr</span> <span class="o">=</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">extract_G_loc</span><span class="p">(</span><span class="n">broadening</span><span class="o">=</span><span class="n">broadening</span><span class="p">,</span> <span class="n">transform_to_solver_blocks</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">with_Sigma</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+    <span class="n">dens_mat_dft</span> <span class="o">=</span> <span class="p">[</span><span class="n">gf</span><span class="o">.</span><span class="n">density</span><span class="p">()</span> <span class="k">for</span> <span class="n">gf</span> <span class="ow">in</span> <span class="n">local_gf_dft_corr</span><span class="p">]</span>
+
+    <span class="k">for</span> <span class="n">iineq</span><span class="p">,</span> <span class="n">gf</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">local_gf_dft_corr</span><span class="p">):</span>
+        <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Total density for imp </span><span class="si">{</span><span class="n">iineq</span><span class="si">}</span><span class="s1"> from DFT: &#39;</span>
+                   <span class="s1">&#39;</span><span class="si">{:10.6f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">gf</span><span class="o">.</span><span class="n">total_density</span><span class="p">())))</span>
+
+    <span class="c1"># determine block structure for solver</span>
+    <span class="n">det_blocks</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="c1"># load previous block_structure if possible</span>
+    <span class="k">if</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+        <span class="n">det_blocks</span> <span class="o">=</span> <span class="s1">&#39;block_structure&#39;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_input&#39;</span><span class="p">]</span>
+    <span class="n">det_blocks</span> <span class="o">=</span> <span class="n">mpi</span><span class="o">.</span><span class="n">bcast</span><span class="p">(</span><span class="n">det_blocks</span><span class="p">)</span>
+
+    <span class="c1"># Previous rot_mat only not None if the rot_mat changed from load_sigma or previous run</span>
+    <span class="n">previous_rot_mat</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="n">deg_orbs_ftps</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="c1"># determine block structure for GF and Hyb function</span>
+    <span class="k">if</span> <span class="n">det_blocks</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;load_sigma&#39;</span><span class="p">]:</span>
+        <span class="n">sum_k</span><span class="p">,</span> <span class="n">deg_orbs_ftps</span> <span class="o">=</span> <span class="n">_determine_block_structure</span><span class="p">(</span><span class="n">sum_k</span><span class="p">,</span> <span class="n">general_params</span><span class="p">,</span> <span class="n">advanced_params</span><span class="p">,</span> <span class="n">solver_type_per_imp</span><span class="p">,</span> <span class="n">dens_mat_dft</span><span class="p">)</span>
+    <span class="c1"># if load sigma we need to load everything from this h5 archive</span>
+    <span class="k">elif</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;load_sigma&#39;</span><span class="p">]:</span>
+        <span class="c1">#loading block_struc and rot_mat and deg_shells</span>
+        <span class="k">if</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+            <span class="k">with</span> <span class="n">HDFArchive</span><span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;path_to_sigma&#39;</span><span class="p">],</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">old_calc</span><span class="p">:</span>
+                <span class="n">sum_k</span><span class="o">.</span><span class="n">block_structure</span> <span class="o">=</span> <span class="n">old_calc</span><span class="p">[</span><span class="s1">&#39;DMFT_input/block_structure&#39;</span><span class="p">]</span>
+                <span class="n">sum_k</span><span class="o">.</span><span class="n">deg_shells</span> <span class="o">=</span> <span class="n">old_calc</span><span class="p">[</span><span class="s1">&#39;DMFT_input/deg_shells&#39;</span><span class="p">]</span>
+                <span class="n">previous_rot_mat</span> <span class="o">=</span> <span class="n">old_calc</span><span class="p">[</span><span class="s1">&#39;DMFT_input/rot_mat&#39;</span><span class="p">]</span>
+                <span class="k">if</span> <span class="n">deg_orbs_ftps</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                    <span class="n">deg_orbs_ftps</span> <span class="o">=</span> <span class="n">old_calc</span><span class="p">[</span><span class="s1">&#39;DMFT_input/solver_struct_ftps&#39;</span><span class="p">]</span>
+
+            <span class="k">if</span> <span class="ow">not</span> <span class="nb">all</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">rot_mat</span><span class="p">,</span> <span class="n">previous_rot_mat</span><span class="p">)):</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;WARNING: rot_mat in current run is different from loaded_sigma run.&#39;</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">previous_rot_mat</span> <span class="o">=</span> <span class="kc">None</span>
+
+        <span class="n">sum_k</span><span class="o">.</span><span class="n">block_structure</span> <span class="o">=</span> <span class="n">mpi</span><span class="o">.</span><span class="n">bcast</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">block_structure</span><span class="p">)</span>
+        <span class="n">sum_k</span><span class="o">.</span><span class="n">deg_shells</span> <span class="o">=</span> <span class="n">mpi</span><span class="o">.</span><span class="n">bcast</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">deg_shells</span><span class="p">)</span>
+        <span class="n">previous_rot_mat</span> <span class="o">=</span> <span class="n">mpi</span><span class="o">.</span><span class="n">bcast</span><span class="p">(</span><span class="n">previous_rot_mat</span><span class="p">)</span>
+        <span class="n">deg_orbs_ftps</span> <span class="o">=</span> <span class="n">mpi</span><span class="o">.</span><span class="n">bcast</span><span class="p">(</span><span class="n">deg_orbs_ftps</span><span class="p">)</span>
+
+        <span class="c1"># In a magnetic calculation, no shells are degenerate</span>
+        <span class="k">if</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;magnetic&#39;</span><span class="p">]</span> <span class="ow">and</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">SO</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">sum_k</span><span class="o">.</span><span class="n">deg_shells</span> <span class="o">=</span> <span class="p">[[]</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span><span class="p">)]</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="c1"># Master node checks if rot_mat stayed the same</span>
+        <span class="k">if</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+            <span class="n">sum_k</span><span class="o">.</span><span class="n">block_structure</span> <span class="o">=</span> <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_input&#39;</span><span class="p">][</span><span class="s1">&#39;block_structure&#39;</span><span class="p">]</span>
+            <span class="n">sum_k</span><span class="o">.</span><span class="n">deg_shells</span> <span class="o">=</span> <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_input/deg_shells&#39;</span><span class="p">]</span>
+            <span class="n">previous_rot_mat</span> <span class="o">=</span> <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_input&#39;</span><span class="p">][</span><span class="s1">&#39;rot_mat&#39;</span><span class="p">]</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="nb">all</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">rot_mat</span><span class="p">,</span> <span class="n">previous_rot_mat</span><span class="p">)):</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;WARNING: rot_mat in current step is different from previous step.&#39;</span><span class="p">)</span>
+                <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_input&#39;</span><span class="p">][</span><span class="s1">&#39;rot_mat&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">rot_mat</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">previous_rot_mat</span> <span class="o">=</span> <span class="kc">None</span>
+            <span class="k">if</span> <span class="s1">&#39;solver_struct_ftps&#39;</span> <span class="ow">in</span> <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_input&#39;</span><span class="p">]:</span>
+                <span class="n">deg_orbs_ftps</span> <span class="o">=</span> <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_input/solver_struct_ftps&#39;</span><span class="p">]</span>
+
+        <span class="n">sum_k</span><span class="o">.</span><span class="n">block_structure</span> <span class="o">=</span> <span class="n">mpi</span><span class="o">.</span><span class="n">bcast</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">block_structure</span><span class="p">)</span>
+        <span class="n">sum_k</span><span class="o">.</span><span class="n">deg_shells</span> <span class="o">=</span> <span class="n">mpi</span><span class="o">.</span><span class="n">bcast</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">deg_shells</span><span class="p">)</span>
+        <span class="n">previous_rot_mat</span> <span class="o">=</span> <span class="n">mpi</span><span class="o">.</span><span class="n">bcast</span><span class="p">(</span><span class="n">previous_rot_mat</span><span class="p">)</span>
+        <span class="n">deg_orbs_ftps</span> <span class="o">=</span> <span class="n">mpi</span><span class="o">.</span><span class="n">bcast</span><span class="p">(</span><span class="n">deg_orbs_ftps</span><span class="p">)</span>
+
+    <span class="c1"># Compatibility with h5 archives from the triqs2 version</span>
+    <span class="c1"># Sumk doesn&#39;t hold corr_to_inequiv anymore, which is in block_structure now</span>
+    <span class="k">if</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">block_structure</span><span class="o">.</span><span class="n">corr_to_inequiv</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+            <span class="n">sum_k</span><span class="o">.</span><span class="n">block_structure</span><span class="o">.</span><span class="n">corr_to_inequiv</span> <span class="o">=</span> <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;dft_input/corr_to_inequiv&#39;</span><span class="p">]</span>
+        <span class="n">sum_k</span><span class="o">.</span><span class="n">block_structure</span> <span class="o">=</span> <span class="n">mpi</span><span class="o">.</span><span class="n">bcast</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">block_structure</span><span class="p">)</span>
+
+    <span class="c1"># Determination of shell_multiplicity</span>
+    <span class="n">shell_multiplicity</span> <span class="o">=</span> <span class="p">[</span><span class="n">sum_k</span><span class="o">.</span><span class="n">corr_to_inequiv</span><span class="o">.</span><span class="n">count</span><span class="p">(</span><span class="n">icrsh</span><span class="p">)</span> <span class="k">for</span> <span class="n">icrsh</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span><span class="p">)]</span>
+
+    <span class="c1"># print block structure and DFT input quantitites!</span>
+    <span class="n">formatter</span><span class="o">.</span><span class="n">print_block_sym</span><span class="p">(</span><span class="n">sum_k</span><span class="p">,</span> <span class="n">dens_mat_dft</span><span class="p">,</span> <span class="n">general_params</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;magnetic&#39;</span><span class="p">]:</span>
+        <span class="n">sum_k</span><span class="o">.</span><span class="n">SP</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="k">if</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;afm_order&#39;</span><span class="p">]:</span>
+            <span class="n">general_params</span> <span class="o">=</span> <span class="n">afm_mapping</span><span class="o">.</span><span class="n">determine</span><span class="p">(</span><span class="n">general_params</span><span class="p">,</span> <span class="n">archive</span><span class="p">,</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span><span class="p">)</span>
+
+    <span class="c1"># Constructs interaction Hamiltonian and writes it to the h5 archive</span>
+    <span class="n">h_int</span> <span class="o">=</span> <span class="n">interaction_hamiltonian</span><span class="o">.</span><span class="n">construct</span><span class="p">(</span><span class="n">sum_k</span><span class="p">,</span> <span class="n">general_params</span><span class="p">,</span> <span class="n">solver_type_per_imp</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+        <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_input&#39;</span><span class="p">][</span><span class="s1">&#39;h_int&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">h_int</span>
+
+    <span class="c1"># If new calculation, writes input parameters and sum_k &lt;-&gt; solver mapping to archive</span>
+    <span class="k">if</span> <span class="n">iteration_offset</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+            <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_input&#39;</span><span class="p">][</span><span class="s1">&#39;general_params&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">dict_to_h5</span><span class="o">.</span><span class="n">prep_params_for_h5</span><span class="p">(</span><span class="n">general_params</span><span class="p">)</span>
+            <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_input&#39;</span><span class="p">][</span><span class="s1">&#39;solver_params&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">dict_to_h5</span><span class="o">.</span><span class="n">prep_params_for_h5</span><span class="p">(</span><span class="n">solver_params</span><span class="p">)</span>
+            <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_input&#39;</span><span class="p">][</span><span class="s1">&#39;dft_params&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">dict_to_h5</span><span class="o">.</span><span class="n">prep_params_for_h5</span><span class="p">(</span><span class="n">dft_params</span><span class="p">)</span>
+            <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_input&#39;</span><span class="p">][</span><span class="s1">&#39;advanced_params&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">dict_to_h5</span><span class="o">.</span><span class="n">prep_params_for_h5</span><span class="p">(</span><span class="n">advanced_params</span><span class="p">)</span>
+
+            <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_input&#39;</span><span class="p">][</span><span class="s1">&#39;block_structure&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">block_structure</span>
+            <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_input&#39;</span><span class="p">][</span><span class="s1">&#39;deg_shells&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">deg_shells</span>
+            <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_input&#39;</span><span class="p">][</span><span class="s1">&#39;shell_multiplicity&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">shell_multiplicity</span>
+            <span class="k">if</span> <span class="n">deg_orbs_ftps</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_input&#39;</span><span class="p">][</span><span class="s1">&#39;solver_struct_ftps&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">deg_orbs_ftps</span>
+    <span class="n">mpi</span><span class="o">.</span><span class="n">barrier</span><span class="p">()</span>
+
+    <span class="n">solvers</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span>
+    <span class="k">for</span> <span class="n">icrsh</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span><span class="p">):</span>
+        <span class="c1"># Construct the Solver instances</span>
+        <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span> <span class="o">=</span> <span class="n">SolverStructure</span><span class="p">(</span><span class="n">general_params</span><span class="p">,</span> <span class="n">solver_params</span><span class="p">[</span><span class="n">map_imp_solver</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]],</span>
+                                         <span class="n">advanced_params</span><span class="p">,</span> <span class="n">sum_k</span><span class="p">,</span> <span class="n">icrsh</span><span class="p">,</span> <span class="n">h_int</span><span class="p">[</span><span class="n">icrsh</span><span class="p">],</span>
+                                         <span class="n">iteration_offset</span><span class="p">,</span> <span class="n">deg_orbs_ftps</span><span class="p">)</span>
+
+    <span class="c1"># store solver hash to archive</span>
+    <span class="k">if</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+        <span class="k">if</span> <span class="s1">&#39;version&#39;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_input&#39;</span><span class="p">]:</span>
+            <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_input&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">create_group</span><span class="p">(</span><span class="s1">&#39;version&#39;</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">iineq</span><span class="p">,</span> <span class="n">solver</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">solvers</span><span class="p">):</span>
+            <span class="k">if</span> <span class="sa">f</span><span class="s1">&#39;solver </span><span class="si">{</span><span class="n">iineq</span><span class="si">}</span><span class="s1">&#39;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_input&#39;</span><span class="p">][</span><span class="s1">&#39;version&#39;</span><span class="p">]:</span>
+                <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_input&#39;</span><span class="p">][</span><span class="s1">&#39;version&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">create_group</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;solver </span><span class="si">{</span><span class="n">iineq</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+            <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_input&#39;</span><span class="p">][</span><span class="s1">&#39;version&#39;</span><span class="p">][</span><span class="sa">f</span><span class="s1">&#39;solver </span><span class="si">{</span><span class="n">iineq</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">][</span><span class="s1">&#39;name&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">solver</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;type&#39;</span><span class="p">]</span>
+            <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_input&#39;</span><span class="p">][</span><span class="s1">&#39;version&#39;</span><span class="p">][</span><span class="sa">f</span><span class="s1">&#39;solver </span><span class="si">{</span><span class="n">iineq</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">][</span><span class="s1">&#39;hash&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">solver</span><span class="o">.</span><span class="n">git_hash</span>
+            <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_input&#39;</span><span class="p">][</span><span class="s1">&#39;version&#39;</span><span class="p">][</span><span class="sa">f</span><span class="s1">&#39;solver </span><span class="si">{</span><span class="n">iineq</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">][</span><span class="s1">&#39;version&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">solver</span><span class="o">.</span><span class="n">version</span>
+
+    <span class="c1"># Extracts local GF per *inequivalent* shell</span>
+    <span class="n">local_gf_dft</span> <span class="o">=</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">extract_G_loc</span><span class="p">(</span><span class="n">broadening</span><span class="o">=</span><span class="n">broadening</span><span class="p">,</span> <span class="n">with_Sigma</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">mu</span><span class="o">=</span><span class="n">dft_mu</span><span class="p">)</span>
+    <span class="n">density_mat_dft</span> <span class="o">=</span> <span class="p">[</span><span class="n">gf</span><span class="o">.</span><span class="n">density</span><span class="p">()</span> <span class="k">for</span> <span class="n">gf</span> <span class="ow">in</span> <span class="n">local_gf_dft</span><span class="p">]</span>
+
+    <span class="c1"># Determines initial Sigma and DC</span>
+    <span class="n">sum_k</span><span class="p">,</span> <span class="n">solvers</span> <span class="o">=</span> <span class="n">initial_sigma</span><span class="o">.</span><span class="n">determine_dc_and_initial_sigma</span><span class="p">(</span><span class="n">general_params</span><span class="p">,</span> <span class="n">advanced_params</span><span class="p">,</span> <span class="n">sum_k</span><span class="p">,</span>
+                                                                  <span class="n">archive</span><span class="p">,</span> <span class="n">iteration_offset</span><span class="p">,</span> <span class="n">density_mat_dft</span><span class="p">,</span> <span class="n">solvers</span><span class="p">,</span>
+                                                                  <span class="n">solver_type_per_imp</span><span class="p">)</span>
+
+    <span class="n">sum_k</span> <span class="o">=</span> <span class="n">manipulate_mu</span><span class="o">.</span><span class="n">set_initial_mu</span><span class="p">(</span><span class="n">general_params</span><span class="p">,</span> <span class="n">sum_k</span><span class="p">,</span> <span class="n">iteration_offset</span><span class="p">,</span> <span class="n">archive</span><span class="p">,</span> <span class="n">broadening</span><span class="p">)</span>
+
+
+    <span class="c1"># setup of measurement of chi(SzSz(tau) if requested</span>
+    <span class="n">ops_chi_measure</span> <span class="o">=</span> <span class="n">_chi_setup</span><span class="p">(</span><span class="n">sum_k</span><span class="p">,</span> <span class="n">solver_params</span><span class="p">,</span> <span class="n">map_imp_solver</span><span class="p">)</span>
+
+    <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1"> </span><span class="si">{}</span><span class="s1"> DMFT cycles requested. Starting with iteration  </span><span class="si">{}</span><span class="s1">.</span><span class="se">\n</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">n_iter</span><span class="p">,</span> <span class="n">iteration_offset</span><span class="o">+</span><span class="mi">1</span><span class="p">))</span>
+
+    <span class="c1"># Prepares observable and conv dicts</span>
+    <span class="n">observables</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="n">conv_obs</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="k">if</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+        <span class="n">observables</span> <span class="o">=</span> <span class="n">prep_observables</span><span class="p">(</span><span class="n">archive</span><span class="p">,</span> <span class="n">sum_k</span><span class="p">)</span>
+        <span class="n">conv_obs</span> <span class="o">=</span> <span class="n">convergence</span><span class="o">.</span><span class="n">prep_conv_obs</span><span class="p">(</span><span class="n">archive</span><span class="p">)</span>
+    <span class="n">observables</span> <span class="o">=</span> <span class="n">mpi</span><span class="o">.</span><span class="n">bcast</span><span class="p">(</span><span class="n">observables</span><span class="p">)</span>
+    <span class="n">conv_obs</span> <span class="o">=</span> <span class="n">mpi</span><span class="o">.</span><span class="n">bcast</span><span class="p">(</span><span class="n">conv_obs</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">()</span> <span class="ow">and</span> <span class="n">iteration_offset</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">write_header_to_file</span><span class="p">(</span><span class="n">general_params</span><span class="p">,</span> <span class="n">sum_k</span><span class="p">)</span>
+        <span class="n">observables</span> <span class="o">=</span> <span class="n">add_dft_values_as_zeroth_iteration</span><span class="p">(</span><span class="n">observables</span><span class="p">,</span> <span class="n">general_params</span><span class="p">,</span> <span class="n">solver_type_per_imp</span><span class="p">,</span> <span class="n">dft_mu</span><span class="p">,</span> <span class="n">dft_energy</span><span class="p">,</span> <span class="n">sum_k</span><span class="p">,</span>
+                                                         <span class="n">local_gf_dft</span><span class="p">,</span> <span class="n">density_mat_dft</span><span class="p">,</span> <span class="n">shell_multiplicity</span><span class="p">)</span>
+        <span class="n">write_obs</span><span class="p">(</span><span class="n">observables</span><span class="p">,</span> <span class="n">sum_k</span><span class="p">,</span> <span class="n">general_params</span><span class="p">)</span>
+        <span class="c1"># write convergence file</span>
+        <span class="n">convergence</span><span class="o">.</span><span class="n">prep_conv_file</span><span class="p">(</span><span class="n">general_params</span><span class="p">,</span> <span class="n">sum_k</span><span class="p">)</span>
+
+    <span class="c1"># The infamous DMFT self consistency cycle</span>
+    <span class="n">is_converged</span> <span class="o">=</span> <span class="kc">False</span>
+    <span class="k">for</span> <span class="n">it</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">iteration_offset</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">iteration_offset</span> <span class="o">+</span> <span class="n">n_iter</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
+
+        <span class="c1"># remove h_field when number of iterations is reached</span>
+        <span class="k">if</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">h_field</span> <span class="o">!=</span> <span class="mf">0.0</span> <span class="ow">and</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;h_field_it&#39;</span><span class="p">]</span> <span class="o">!=</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">it</span> <span class="o">&gt;</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;h_field_it&#39;</span><span class="p">]:</span>
+            <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">Removing magnetic field now.</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
+            <span class="n">sum_k</span><span class="o">.</span><span class="n">h_field</span> <span class="o">=</span> <span class="mf">0.0</span>
+            <span class="c1"># enforce recomputation of eff_atomic_levels</span>
+            <span class="nb">delattr</span><span class="p">(</span><span class="n">sum_k</span><span class="p">,</span> <span class="s1">&#39;Hsumk&#39;</span><span class="p">)</span>
+
+        <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;#&#39;</span><span class="o">*</span><span class="mi">80</span><span class="p">)</span>
+        <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;Running iteration: </span><span class="si">{}</span><span class="s1"> / </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">it</span><span class="p">,</span> <span class="n">iteration_offset</span> <span class="o">+</span> <span class="n">n_iter</span><span class="p">))</span>
+        <span class="p">(</span><span class="n">sum_k</span><span class="p">,</span> <span class="n">solvers</span><span class="p">,</span>
+         <span class="n">observables</span><span class="p">,</span> <span class="n">is_converged</span><span class="p">)</span> <span class="o">=</span> <span class="n">_dmft_step</span><span class="p">(</span><span class="n">sum_k</span><span class="p">,</span> <span class="n">solvers</span><span class="p">,</span> <span class="n">it</span><span class="p">,</span> <span class="n">general_params</span><span class="p">,</span>
+                                                 <span class="n">solver_params</span><span class="p">,</span> <span class="n">advanced_params</span><span class="p">,</span> <span class="n">dft_params</span><span class="p">,</span> <span class="n">map_imp_solver</span><span class="p">,</span> <span class="n">solver_type_per_imp</span><span class="p">,</span>
+                                                 <span class="n">h_int</span><span class="p">,</span> <span class="n">archive</span><span class="p">,</span> <span class="n">shell_multiplicity</span><span class="p">,</span> <span class="n">E_kin_dft</span><span class="p">,</span>
+                                                 <span class="n">observables</span><span class="p">,</span> <span class="n">conv_obs</span><span class="p">,</span> <span class="n">ops_chi_measure</span><span class="p">,</span> <span class="n">dft_irred_kpt_indices</span><span class="p">,</span> <span class="n">dft_energy</span><span class="p">,</span> <span class="n">broadening</span><span class="p">,</span>
+                                                 <span class="n">is_converged</span><span class="p">,</span> <span class="n">is_sampling</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">is_converged</span><span class="p">:</span>
+            <span class="k">break</span>
+
+    <span class="k">if</span> <span class="n">is_converged</span><span class="p">:</span>
+        <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;*** Required convergence reached ***&#39;</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;** All requested iterations finished ***&#39;</span><span class="p">)</span>
+    <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;#&#39;</span><span class="o">*</span><span class="mi">80</span><span class="p">)</span>
+
+    <span class="c1"># Starts the sampling dmft iterations if requested</span>
+    <span class="k">if</span> <span class="n">is_converged</span> <span class="ow">and</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;sampling_iterations&#39;</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;*** Sampling now for </span><span class="si">{}</span><span class="s1"> iterations ***&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;sampling_iterations&#39;</span><span class="p">]))</span>
+        <span class="n">iteration_offset</span> <span class="o">=</span> <span class="n">it</span>
+
+        <span class="k">for</span> <span class="n">it</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">iteration_offset</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span>
+                        <span class="n">iteration_offset</span> <span class="o">+</span> <span class="mi">1</span> <span class="o">+</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;sampling_iterations&#39;</span><span class="p">]):</span>
+            <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;#&#39;</span><span class="o">*</span><span class="mi">80</span><span class="p">)</span>
+            <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;Running iteration: </span><span class="si">{}</span><span class="s1"> / </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">it</span><span class="p">,</span> <span class="n">iteration_offset</span><span class="o">+</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;sampling_iterations&#39;</span><span class="p">]))</span>
+            <span class="n">sum_k</span><span class="p">,</span> <span class="n">solvers</span><span class="p">,</span> <span class="n">observables</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">_dmft_step</span><span class="p">(</span><span class="n">sum_k</span><span class="p">,</span> <span class="n">solvers</span><span class="p">,</span> <span class="n">it</span><span class="p">,</span> <span class="n">general_params</span><span class="p">,</span>
+                                                        <span class="n">solver_params</span><span class="p">,</span> <span class="n">advanced_params</span><span class="p">,</span> <span class="n">dft_params</span><span class="p">,</span> <span class="n">map_imp_solver</span><span class="p">,</span> <span class="n">solver_type_per_imp</span><span class="p">,</span>
+                                                        <span class="n">h_int</span><span class="p">,</span> <span class="n">archive</span><span class="p">,</span> <span class="n">shell_multiplicity</span><span class="p">,</span> <span class="n">E_kin_dft</span><span class="p">,</span>
+                                                        <span class="n">observables</span><span class="p">,</span> <span class="n">conv_obs</span><span class="p">,</span> <span class="n">ops_chi_measure</span><span class="p">,</span> <span class="n">dft_irred_kpt_indices</span><span class="p">,</span> <span class="n">dft_energy</span><span class="p">,</span> <span class="n">broadening</span><span class="p">,</span>
+                                                        <span class="n">is_converged</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">is_sampling</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+        <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;** Sampling finished ***&#39;</span><span class="p">)</span>
+        <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;#&#39;</span><span class="o">*</span><span class="mi">80</span><span class="p">)</span>
+
+    <span class="n">mpi</span><span class="o">.</span><span class="n">barrier</span><span class="p">()</span>
+
+    <span class="c1"># close the h5 archive</span>
+    <span class="k">if</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+        <span class="k">del</span> <span class="n">archive</span>
+
+    <span class="k">return</span> <span class="n">is_converged</span><span class="p">,</span> <span class="n">sum_k</span></div>
+
+
+<span class="k">def</span> <span class="nf">_dmft_step</span><span class="p">(</span><span class="n">sum_k</span><span class="p">,</span> <span class="n">solvers</span><span class="p">,</span> <span class="n">it</span><span class="p">,</span> <span class="n">general_params</span><span class="p">,</span>
+               <span class="n">solver_params</span><span class="p">,</span> <span class="n">advanced_params</span><span class="p">,</span> <span class="n">dft_params</span><span class="p">,</span> <span class="n">map_imp_solver</span><span class="p">,</span> <span class="n">solver_type_per_imp</span><span class="p">,</span>
+               <span class="n">h_int</span><span class="p">,</span> <span class="n">archive</span><span class="p">,</span> <span class="n">shell_multiplicity</span><span class="p">,</span> <span class="n">E_kin_dft</span><span class="p">,</span>
+               <span class="n">observables</span><span class="p">,</span> <span class="n">conv_obs</span><span class="p">,</span> <span class="n">ops_chi_measure</span><span class="p">,</span> <span class="n">dft_irred_kpt_indices</span><span class="p">,</span> <span class="n">dft_energy</span><span class="p">,</span> <span class="n">broadening</span><span class="p">,</span>
+               <span class="n">is_converged</span><span class="p">,</span> <span class="n">is_sampling</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Contains the actual dmft steps when all the preparation is done</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># init local density matrices for observables</span>
+    <span class="n">density_tot</span> <span class="o">=</span> <span class="mf">0.0</span>
+    <span class="n">density_shell</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span><span class="p">)</span>
+    <span class="n">density_mat</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span>
+    <span class="n">density_mat_unsym</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span>
+    <span class="n">density_shell_pre</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span><span class="p">)</span>
+    <span class="n">density_mat_pre</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span>
+
+    <span class="n">mpi</span><span class="o">.</span><span class="n">barrier</span><span class="p">()</span>
+
+    <span class="k">if</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">SO</span><span class="p">:</span>
+        <span class="n">printed</span> <span class="o">=</span> <span class="p">((</span><span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">,</span> <span class="s1">&#39;real&#39;</span><span class="p">),</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">imag</span><span class="p">,</span> <span class="s1">&#39;imaginary&#39;</span><span class="p">))</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">printed</span> <span class="o">=</span> <span class="p">((</span><span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">,</span> <span class="s1">&#39;real&#39;</span><span class="p">),</span> <span class="p">)</span>
+
+    <span class="c1"># Extracts G local</span>
+    <span class="n">G_loc_all</span> <span class="o">=</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">extract_G_loc</span><span class="p">(</span><span class="n">broadening</span><span class="o">=</span><span class="n">broadening</span><span class="p">)</span>
+
+    <span class="c1"># Copies Sigma and G0 before Solver run for mixing later</span>
+    <span class="n">Sigma_freq_previous</span> <span class="o">=</span> <span class="p">[</span><span class="n">solvers</span><span class="p">[</span><span class="n">iineq</span><span class="p">]</span><span class="o">.</span><span class="n">Sigma_freq</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span> <span class="k">for</span> <span class="n">iineq</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span><span class="p">)]</span>
+    <span class="n">G0_freq_previous</span> <span class="o">=</span> <span class="p">[</span><span class="n">solvers</span><span class="p">[</span><span class="n">iineq</span><span class="p">]</span><span class="o">.</span><span class="n">G0_freq</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span> <span class="k">for</span> <span class="n">iineq</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span><span class="p">)]</span>
+
+    <span class="c1"># looping over inequiv shells and solving for each site seperately</span>
+    <span class="k">for</span> <span class="n">icrsh</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span><span class="p">):</span>
+        <span class="c1"># copy the block of G_loc into the corresponding instance of the impurity solver</span>
+        <span class="c1"># TODO: why do we set solvers.G_freq? Isn&#39;t that simply an output of the solver?</span>
+        <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">G_freq</span> <span class="o">&lt;&lt;</span> <span class="n">G_loc_all</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span>
+
+        <span class="n">density_shell_pre</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">G_freq</span><span class="o">.</span><span class="n">total_density</span><span class="p">())</span>
+        <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1"> *** Correlated Shell type #</span><span class="si">{:3d}</span><span class="s1"> : &#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">icrsh</span><span class="p">)</span>
+                   <span class="o">+</span> <span class="s1">&#39;Estimated total charge of impurity problem = </span><span class="si">{:.6f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">density_shell_pre</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]))</span>
+        <span class="n">density_mat_pre</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span> <span class="o">=</span> <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">G_freq</span><span class="o">.</span><span class="n">density</span><span class="p">()</span>
+        <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;Estimated density matrix:&#39;</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">density_mat_pre</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()):</span>
+            <span class="k">for</span> <span class="n">func</span><span class="p">,</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">printed</span><span class="p">:</span>
+                <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{}</span><span class="s1">, </span><span class="si">{}</span><span class="s1"> part&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">name</span><span class="p">))</span>
+                <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="n">func</span><span class="p">(</span><span class="n">value</span><span class="p">))</span>
+
+        <span class="c1"># dyson equation to extract G0_freq, using Hermitian symmetry</span>
+        <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">G0_freq</span> <span class="o">&lt;&lt;</span> <span class="n">inverse</span><span class="p">(</span><span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">Sigma_freq</span> <span class="o">+</span> <span class="n">inverse</span><span class="p">(</span><span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">G_freq</span><span class="p">))</span>
+
+        <span class="c1"># mixing of G0 if wanted from the second iteration on</span>
+        <span class="k">if</span> <span class="n">it</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span> <span class="o">=</span> <span class="n">gf_mixer</span><span class="o">.</span><span class="n">mix_g0</span><span class="p">(</span><span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">],</span> <span class="n">general_params</span><span class="p">,</span> <span class="n">icrsh</span><span class="p">,</span> <span class="n">archive</span><span class="p">,</span>
+                                             <span class="n">G0_freq_previous</span><span class="p">[</span><span class="n">icrsh</span><span class="p">],</span> <span class="n">it</span><span class="p">,</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">deg_shells</span><span class="p">[</span><span class="n">icrsh</span><span class="p">])</span>
+
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">mesh</span><span class="p">,</span> <span class="n">MeshImFreq</span><span class="p">):</span>
+            <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">G0_freq</span> <span class="o">&lt;&lt;</span> <span class="n">make_hermitian</span><span class="p">(</span><span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">G0_freq</span><span class="p">)</span>
+        <span class="n">sum_k</span><span class="o">.</span><span class="n">symm_deg_gf</span><span class="p">(</span><span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">G0_freq</span><span class="p">,</span> <span class="n">ish</span><span class="o">=</span><span class="n">icrsh</span><span class="p">)</span>
+
+         <span class="c1"># store solver to h5 archive</span>
+        <span class="k">if</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;store_solver&#39;</span><span class="p">]</span> <span class="ow">and</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+            <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_input/solver&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">create_group</span><span class="p">(</span><span class="s1">&#39;it_&#39;</span><span class="o">+</span><span class="nb">str</span><span class="p">(</span><span class="n">it</span><span class="p">))</span>
+            <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_input/solver/it_&#39;</span><span class="o">+</span><span class="nb">str</span><span class="p">(</span><span class="n">it</span><span class="p">)][</span><span class="s1">&#39;S_&#39;</span><span class="o">+</span><span class="nb">str</span><span class="p">(</span><span class="n">icrsh</span><span class="p">)]</span> <span class="o">=</span> <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">triqs_solver</span>
+
+        <span class="c1"># store DMFT input directly in last_iter</span>
+        <span class="k">if</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+            <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_results/last_iter&#39;</span><span class="p">][</span><span class="s1">&#39;G0_freq_</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">icrsh</span><span class="p">)]</span> <span class="o">=</span> <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">G0_freq</span>
+
+        <span class="c1"># setup of measurement of chi(SzSz(tau) if requested</span>
+        <span class="c1"># TODO: move this into solver class?</span>
+        <span class="k">if</span> <span class="n">ops_chi_measure</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;measure_O_tau&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">ops_chi_measure</span><span class="p">[</span><span class="n">icrsh</span><span class="p">],</span> <span class="n">ops_chi_measure</span><span class="p">[</span><span class="n">icrsh</span><span class="p">])</span>
+
+        <span class="k">if</span> <span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;magnetic&#39;</span><span class="p">]</span> <span class="ow">and</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;afm_order&#39;</span><span class="p">]</span> <span class="ow">and</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;afm_mapping&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">][</span><span class="mi">0</span><span class="p">]):</span>
+            <span class="c1"># If we do a AFM calculation we can use the init magnetic moments to</span>
+            <span class="c1"># copy the self energy instead of solving it explicitly</span>
+            <span class="n">solvers</span> <span class="o">=</span> <span class="n">afm_mapping</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">general_params</span><span class="p">,</span> <span class="n">icrsh</span><span class="p">,</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">gf_struct_solver</span><span class="p">[</span><span class="n">icrsh</span><span class="p">],</span> <span class="n">solvers</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="c1"># Solve the impurity problem for this shell</span>
+            <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">Solving the impurity problem for shell </span><span class="si">{}</span><span class="s1"> ...&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">icrsh</span><span class="p">))</span>
+            <span class="n">mpi</span><span class="o">.</span><span class="n">barrier</span><span class="p">()</span>
+            <span class="n">start_time</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+            <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">solve</span><span class="p">(</span><span class="n">it</span><span class="o">=</span><span class="n">it</span><span class="p">)</span>
+            <span class="n">mpi</span><span class="o">.</span><span class="n">barrier</span><span class="p">()</span>
+            <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;Actual time for solver: </span><span class="si">{:.2f}</span><span class="s1"> s&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start_time</span><span class="p">))</span>
+
+        <span class="c1"># some printout of the obtained density matrices and some basic checks from the unsymmetrized solver output</span>
+        <span class="n">density_shell</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">G_freq_unsym</span><span class="o">.</span><span class="n">total_density</span><span class="p">())</span>
+        <span class="n">density_tot</span> <span class="o">+=</span> <span class="n">density_shell</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">*</span><span class="n">shell_multiplicity</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span>
+        <span class="n">density_mat_unsym</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span> <span class="o">=</span> <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">G_freq_unsym</span><span class="o">.</span><span class="n">density</span><span class="p">()</span>
+        <span class="n">density_mat</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span> <span class="o">=</span> <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">G_freq</span><span class="o">.</span><span class="n">density</span><span class="p">()</span>
+        <span class="n">formatter</span><span class="o">.</span><span class="n">print_local_density</span><span class="p">(</span><span class="n">density_shell</span><span class="p">[</span><span class="n">icrsh</span><span class="p">],</span> <span class="n">density_shell_pre</span><span class="p">[</span><span class="n">icrsh</span><span class="p">],</span>
+                                      <span class="n">density_mat_unsym</span><span class="p">[</span><span class="n">icrsh</span><span class="p">],</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">SO</span><span class="p">)</span>
+
+        <span class="c1"># update solver in h5 archive</span>
+        <span class="k">if</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;store_solver&#39;</span><span class="p">]</span> <span class="ow">and</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+            <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_input/solver/it_&#39;</span><span class="o">+</span><span class="nb">str</span><span class="p">(</span><span class="n">it</span><span class="p">)][</span><span class="s1">&#39;S_&#39;</span><span class="o">+</span><span class="nb">str</span><span class="p">(</span><span class="n">icrsh</span><span class="p">)]</span> <span class="o">=</span> <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">triqs_solver</span>
+
+    <span class="c1"># Done with loop over impurities</span>
+
+    <span class="k">if</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+        <span class="c1"># Done. Now do post-processing:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1"> *** Post-processing the solver output ***&#39;</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Total charge of all correlated shells : </span><span class="si">{:.6f}</span><span class="se">\n</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">density_tot</span><span class="p">))</span>
+
+    <span class="n">solvers</span> <span class="o">=</span> <span class="n">gf_mixer</span><span class="o">.</span><span class="n">mix_sigma</span><span class="p">(</span><span class="n">general_params</span><span class="p">,</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span><span class="p">,</span> <span class="n">solvers</span><span class="p">,</span> <span class="n">Sigma_freq_previous</span><span class="p">)</span>
+
+    <span class="c1"># calculate new DC</span>
+    <span class="c1"># for the hartree solver the DC potential will be formally set to zero as it is already present in the Sigma</span>
+    <span class="k">if</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;dc&#39;</span><span class="p">]</span> <span class="ow">and</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;dc_dmft&#39;</span><span class="p">]:</span>
+        <span class="n">sum_k</span> <span class="o">=</span> <span class="n">initial_sigma</span><span class="o">.</span><span class="n">calculate_double_counting</span><span class="p">(</span><span class="n">sum_k</span><span class="p">,</span> <span class="n">density_mat</span><span class="p">,</span>
+                                                        <span class="n">general_params</span><span class="p">,</span> <span class="n">advanced_params</span><span class="p">,</span>
+                                                        <span class="n">solver_type_per_imp</span><span class="p">)</span>
+
+    <span class="c1">#The hartree solver computes the DC energy internally, set it in sum_k</span>
+    <span class="k">for</span> <span class="n">icrsh</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_corr_shells</span><span class="p">):</span>
+        <span class="n">iineq</span> <span class="o">=</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">corr_to_inequiv</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span>
+        <span class="k">if</span> <span class="n">solver_type_per_imp</span><span class="p">[</span><span class="n">iineq</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;hartree&#39;</span><span class="p">:</span>
+            <span class="n">sum_k</span><span class="o">.</span><span class="n">dc_energ</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span> <span class="o">=</span> <span class="n">solvers</span><span class="p">[</span><span class="n">iineq</span><span class="p">]</span><span class="o">.</span><span class="n">DC_energy</span>
+
+    <span class="c1"># doing the dmft loop and set new sigma into sumk</span>
+    <span class="n">sum_k</span><span class="o">.</span><span class="n">put_Sigma</span><span class="p">([</span><span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">Sigma_freq</span> <span class="k">for</span> <span class="n">icrsh</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span><span class="p">)])</span>
+
+    <span class="c1"># saving previous mu for writing to observables file</span>
+    <span class="n">previous_mu</span> <span class="o">=</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">chemical_potential</span>
+    <span class="n">sum_k</span> <span class="o">=</span> <span class="n">manipulate_mu</span><span class="o">.</span><span class="n">update_mu</span><span class="p">(</span><span class="n">general_params</span><span class="p">,</span> <span class="n">sum_k</span><span class="p">,</span> <span class="n">it</span><span class="p">,</span> <span class="n">archive</span><span class="p">,</span> <span class="n">broadening</span><span class="p">)</span>
+
+    <span class="c1"># if we do a CSC calculation we need always an updated GAMMA file</span>
+    <span class="n">E_bandcorr</span> <span class="o">=</span> <span class="mf">0.0</span>
+    <span class="n">deltaN</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="n">dens</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="k">if</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;csc&#39;</span><span class="p">]:</span>
+        <span class="c1"># handling the density correction for fcsc calculations</span>
+        <span class="k">assert</span> <span class="n">dft_irred_kpt_indices</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">dft_params</span><span class="p">[</span><span class="s1">&#39;dft_code&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;vasp&#39;</span>
+        <span class="n">deltaN</span><span class="p">,</span> <span class="n">dens</span><span class="p">,</span> <span class="n">E_bandcorr</span> <span class="o">=</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">calc_density_correction</span><span class="p">(</span><span class="n">dm_type</span><span class="o">=</span><span class="n">dft_params</span><span class="p">[</span><span class="s1">&#39;dft_code&#39;</span><span class="p">],</span>
+                                                                 <span class="n">kpts_to_write</span><span class="o">=</span><span class="n">dft_irred_kpt_indices</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;calc_energies&#39;</span><span class="p">]:</span>
+        <span class="c1"># for a one shot calculation we are using our own method</span>
+        <span class="n">E_bandcorr</span> <span class="o">=</span> <span class="n">calc_bandcorr_man</span><span class="p">(</span><span class="n">general_params</span><span class="p">,</span> <span class="n">sum_k</span><span class="p">,</span> <span class="n">E_kin_dft</span><span class="p">)</span>
+
+    <span class="c1"># Writes results to h5 archive</span>
+    <span class="n">results_to_archive</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">archive</span><span class="p">,</span> <span class="n">sum_k</span><span class="p">,</span> <span class="n">general_params</span><span class="p">,</span> <span class="n">solver_params</span><span class="p">,</span> <span class="n">solvers</span><span class="p">,</span> <span class="n">map_imp_solver</span><span class="p">,</span> <span class="n">solver_type_per_imp</span><span class="p">,</span> <span class="n">it</span><span class="p">,</span>
+                             <span class="n">is_sampling</span><span class="p">,</span> <span class="n">previous_mu</span><span class="p">,</span> <span class="n">density_mat_pre</span><span class="p">,</span> <span class="n">density_mat</span><span class="p">,</span> <span class="n">deltaN</span><span class="p">,</span> <span class="n">dens</span><span class="p">)</span>
+
+    <span class="n">mpi</span><span class="o">.</span><span class="n">barrier</span><span class="p">()</span>
+
+    <span class="c1"># calculate observables and write them to file</span>
+    <span class="k">if</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1"> *** calculation of observables ***&#39;</span><span class="p">)</span>
+        <span class="n">observables</span> <span class="o">=</span> <span class="n">add_dmft_observables</span><span class="p">(</span><span class="n">observables</span><span class="p">,</span>
+                                           <span class="n">general_params</span><span class="p">,</span>
+                                           <span class="n">solver_params</span><span class="p">,</span>
+                                           <span class="n">map_imp_solver</span><span class="p">,</span>
+                                           <span class="n">solver_type_per_imp</span><span class="p">,</span>
+                                           <span class="n">dft_energy</span><span class="p">,</span>
+                                           <span class="n">it</span><span class="p">,</span>
+                                           <span class="n">solvers</span><span class="p">,</span>
+                                           <span class="n">h_int</span><span class="p">,</span>
+                                           <span class="n">previous_mu</span><span class="p">,</span>
+                                           <span class="n">sum_k</span><span class="p">,</span>
+                                           <span class="n">density_mat</span><span class="p">,</span>
+                                           <span class="n">shell_multiplicity</span><span class="p">,</span>
+                                           <span class="n">E_bandcorr</span><span class="p">)</span>
+
+        <span class="n">write_obs</span><span class="p">(</span><span class="n">observables</span><span class="p">,</span> <span class="n">sum_k</span><span class="p">,</span> <span class="n">general_params</span><span class="p">)</span>
+
+        <span class="c1"># write the new observable array to h5 archive</span>
+        <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_results&#39;</span><span class="p">][</span><span class="s1">&#39;observables&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">observables</span>
+
+    <span class="c1"># Computes convergence quantities and writes them to file</span>
+    <span class="k">if</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+        <span class="n">conv_obs</span> <span class="o">=</span> <span class="n">convergence</span><span class="o">.</span><span class="n">calc_convergence_quantities</span><span class="p">(</span><span class="n">sum_k</span><span class="p">,</span> <span class="n">general_params</span><span class="p">,</span> <span class="n">conv_obs</span><span class="p">,</span> <span class="n">observables</span><span class="p">,</span>
+                                                           <span class="n">solvers</span><span class="p">,</span> <span class="n">G0_freq_previous</span><span class="p">,</span> <span class="n">G_loc_all</span><span class="p">,</span> <span class="n">Sigma_freq_previous</span><span class="p">)</span>
+        <span class="n">convergence</span><span class="o">.</span><span class="n">write_conv</span><span class="p">(</span><span class="n">conv_obs</span><span class="p">,</span> <span class="n">sum_k</span><span class="p">,</span> <span class="n">general_params</span><span class="p">)</span>
+        <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_results&#39;</span><span class="p">][</span><span class="s1">&#39;convergence_obs&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">conv_obs</span>
+    <span class="n">conv_obs</span> <span class="o">=</span> <span class="n">mpi</span><span class="o">.</span><span class="n">bcast</span><span class="p">(</span><span class="n">conv_obs</span><span class="p">)</span>
+
+    <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;*** iteration finished ***&#39;</span><span class="p">)</span>
+
+    <span class="c1"># Checks for convergence</span>
+    <span class="n">is_now_converged</span> <span class="o">=</span> <span class="n">convergence</span><span class="o">.</span><span class="n">check_convergence</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span><span class="p">,</span> <span class="n">general_params</span><span class="p">,</span> <span class="n">conv_obs</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">is_now_converged</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">is_converged</span> <span class="o">=</span> <span class="kc">False</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="c1"># if convergency criteria was already reached don&#39;t overwrite it!</span>
+        <span class="n">is_converged</span> <span class="o">=</span> <span class="n">is_converged</span> <span class="ow">or</span> <span class="n">is_now_converged</span>
+
+    <span class="c1"># Final prints</span>
+    <span class="n">formatter</span><span class="o">.</span><span class="n">print_summary_observables</span><span class="p">(</span><span class="n">observables</span><span class="p">,</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span><span class="p">,</span>
+                                        <span class="n">sum_k</span><span class="o">.</span><span class="n">spin_block_names</span><span class="p">[</span><span class="n">sum_k</span><span class="o">.</span><span class="n">SO</span><span class="p">])</span>
+    <span class="k">if</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;calc_energies&#39;</span><span class="p">]:</span>
+        <span class="n">formatter</span><span class="o">.</span><span class="n">print_summary_energetics</span><span class="p">(</span><span class="n">observables</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;magnetic&#39;</span><span class="p">]</span> <span class="ow">and</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">SO</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="c1"># if a magnetic calculation is done print out a summary of up/down occ</span>
+        <span class="n">formatter</span><span class="o">.</span><span class="n">print_summary_magnetic_occ</span><span class="p">(</span><span class="n">observables</span><span class="p">,</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span><span class="p">)</span>
+    <span class="n">formatter</span><span class="o">.</span><span class="n">print_summary_convergence</span><span class="p">(</span><span class="n">conv_obs</span><span class="p">,</span> <span class="n">general_params</span><span class="p">,</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">sum_k</span><span class="p">,</span> <span class="n">solvers</span><span class="p">,</span> <span class="n">observables</span><span class="p">,</span> <span class="n">is_converged</span>
+</pre></div>
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.</p>
+  </div>
+
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diff --git a/_modules/dmft_tools/afm_mapping.html b/_modules/dmft_tools/afm_mapping.html
new file mode 100644
index 00000000..4c6a1af7
--- /dev/null
+++ b/_modules/dmft_tools/afm_mapping.html
@@ -0,0 +1,425 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" >
+<head>
+  <meta charset="utf-8" />
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+  <title>dmft_tools.afm_mapping &mdash; solid_dmft  documentation</title>
+      <link rel="stylesheet" href="../../_static/pygments.css" type="text/css" />
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+            <img src="../../_static/logo_no_text.png" class="logo" alt="Logo"/>
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+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
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+</ul>
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+<li class="toctree-l3"><a class="reference internal" href="../../_ref/postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_gf_imp.html">postprocessing.maxent_gf_imp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_gf_latt.html">postprocessing.maxent_gf_latt</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
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+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../issues.html">Support &amp; contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../issues.html#seeking-help">Seeking help</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../../ChangeLog.html">Changelog</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-2-0">Version 3.2.0</a></li>
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+  <h1>Source code for dmft_tools.afm_mapping</h1><div class="highlight"><pre>
+<span></span><span class="c1"># -*- coding: utf-8 -*-</span>
+<span class="c1">################################################################################</span>
+<span class="c1">#</span>
+<span class="c1"># solid_dmft - A versatile python wrapper to perform DFT+DMFT calculations</span>
+<span class="c1">#              utilizing the TRIQS software library</span>
+<span class="c1">#</span>
+<span class="c1"># Copyright (C) 2018-2020, ETH Zurich</span>
+<span class="c1"># Copyright (C) 2021, The Simons Foundation</span>
+<span class="c1">#      authors: A. Hampel, M. Merkel, and S. Beck</span>
+<span class="c1">#</span>
+<span class="c1"># solid_dmft is free software: you can redistribute it and/or modify it under the</span>
+<span class="c1"># terms of the GNU General Public License as published by the Free Software</span>
+<span class="c1"># Foundation, either version 3 of the License, or (at your option) any later</span>
+<span class="c1"># version.</span>
+<span class="c1">#</span>
+<span class="c1"># solid_dmft is distributed in the hope that it will be useful, but WITHOUT ANY</span>
+<span class="c1"># WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A</span>
+<span class="c1"># PARTICULAR PURPOSE. See the GNU General Public License for more details.</span>
+
+<span class="c1"># You should have received a copy of the GNU General Public License along with</span>
+<span class="c1"># solid_dmft (in the file COPYING.txt in this directory). If not, see</span>
+<span class="c1"># &lt;http://www.gnu.org/licenses/&gt;.</span>
+<span class="c1">#</span>
+<span class="c1">################################################################################</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">triqs.utility.mpi</span> <span class="k">as</span> <span class="nn">mpi</span>
+
+<div class="viewcode-block" id="determine"><a class="viewcode-back" href="../../_ref/dmft_tools.afm_mapping.html#dmft_tools.afm_mapping.determine">[docs]</a><span class="k">def</span> <span class="nf">determine</span><span class="p">(</span><span class="n">general_params</span><span class="p">,</span> <span class="n">archive</span><span class="p">,</span> <span class="n">n_inequiv_shells</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Determines the symmetries that are used in AFM calculations. These</span>
+<span class="sd">    symmetries can then be used to copy the self-energies from one impurity to</span>
+<span class="sd">    another by exchanging up/down channels for speedup and accuracy.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">afm_mapping</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="k">if</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+        <span class="c1"># Reads mapping from h5 archive if it exists already from a previous run</span>
+        <span class="k">if</span> <span class="s1">&#39;afm_mapping&#39;</span> <span class="ow">in</span> <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_input&#39;</span><span class="p">]:</span>
+            <span class="n">afm_mapping</span> <span class="o">=</span> <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_input&#39;</span><span class="p">][</span><span class="s1">&#39;afm_mapping&#39;</span><span class="p">]</span>
+        <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;magmom&#39;</span><span class="p">])</span> <span class="o">==</span> <span class="n">n_inequiv_shells</span><span class="p">:</span>
+            <span class="c1"># find equal or opposite spin imps, where we use the magmom array to</span>
+            <span class="c1"># identity those with equal numbers or opposite</span>
+            <span class="c1"># [copy Yes/False, from where, switch up/down channel]</span>
+            <span class="n">afm_mapping</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="n">n_inequiv_shells</span>
+            <span class="n">abs_moms</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;magmom&#39;</span><span class="p">])</span>
+
+            <span class="k">for</span> <span class="n">icrsh</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_inequiv_shells</span><span class="p">):</span>
+                <span class="c1"># if the moment was seen before ...</span>
+                <span class="n">previous_occurences</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nonzero</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">isclose</span><span class="p">(</span><span class="n">abs_moms</span><span class="p">[:</span><span class="n">icrsh</span><span class="p">],</span> <span class="n">abs_moms</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]))[</span><span class="mi">0</span><span class="p">]</span>
+                <span class="k">if</span> <span class="n">previous_occurences</span><span class="o">.</span><span class="n">size</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="c1"># find the source imp to copy from</span>
+                    <span class="n">source</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">previous_occurences</span><span class="p">)</span>
+                    <span class="c1"># determine if we need to switch up and down channel</span>
+                    <span class="n">switch</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">isclose</span><span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;magmom&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">],</span> <span class="o">-</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;magmom&#39;</span><span class="p">][</span><span class="n">source</span><span class="p">])</span>
+
+                    <span class="n">afm_mapping</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="kc">True</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">switch</span><span class="p">]</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">afm_mapping</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="kc">False</span><span class="p">,</span> <span class="n">icrsh</span><span class="p">,</span> <span class="kc">False</span><span class="p">]</span>
+
+
+            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;AFM calculation selected, mapping self energies as follows:&#39;</span><span class="p">)</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;imp  [copy sigma, source imp, switch up/down]&#39;</span><span class="p">)</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;---------------------------------------------&#39;</span><span class="p">)</span>
+            <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">elem</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">afm_mapping</span><span class="p">):</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{}</span><span class="s1">: </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">elem</span><span class="p">))</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
+
+            <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_input&#39;</span><span class="p">][</span><span class="s1">&#39;afm_mapping&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">afm_mapping</span>
+
+        <span class="c1"># if anything did not work set afm_order false</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;WARNING: couldn</span><span class="se">\&#39;</span><span class="s1">t determine afm mapping. No mapping used.&#39;</span><span class="p">)</span>
+            <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;afm_order&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">False</span>
+
+    <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;afm_order&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">mpi</span><span class="o">.</span><span class="n">bcast</span><span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;afm_order&#39;</span><span class="p">])</span>
+    <span class="k">if</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;afm_order&#39;</span><span class="p">]:</span>
+        <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;afm_mapping&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">mpi</span><span class="o">.</span><span class="n">bcast</span><span class="p">(</span><span class="n">afm_mapping</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">general_params</span></div>
+
+
+<span class="k">def</span> <span class="nf">apply</span><span class="p">(</span><span class="n">general_params</span><span class="p">,</span> <span class="n">icrsh</span><span class="p">,</span> <span class="n">gf_struct_solver</span><span class="p">,</span> <span class="n">solvers</span><span class="p">):</span>
+    <span class="n">imp_source</span> <span class="o">=</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;afm_mapping&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
+    <span class="n">invert_spin</span> <span class="o">=</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;afm_mapping&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span>
+    <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">copying the self-energy for shell </span><span class="si">{}</span><span class="s1"> from shell </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">icrsh</span><span class="p">,</span> <span class="n">imp_source</span><span class="p">))</span>
+    <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;inverting spin channels: &#39;</span><span class="o">+</span><span class="nb">str</span><span class="p">(</span><span class="n">invert_spin</span><span class="p">))</span>
+
+    <span class="k">if</span> <span class="n">invert_spin</span><span class="p">:</span>
+        <span class="k">for</span> <span class="n">spin_channel</span> <span class="ow">in</span> <span class="n">gf_struct_solver</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+            <span class="k">if</span> <span class="s1">&#39;up&#39;</span> <span class="ow">in</span> <span class="n">spin_channel</span><span class="p">:</span>
+                <span class="n">target_channel</span> <span class="o">=</span> <span class="n">spin_channel</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;up&#39;</span><span class="p">,</span> <span class="s1">&#39;down&#39;</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">target_channel</span> <span class="o">=</span> <span class="n">spin_channel</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;down&#39;</span><span class="p">,</span> <span class="s1">&#39;up&#39;</span><span class="p">)</span>
+
+            <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">Sigma_freq</span><span class="p">[</span><span class="n">spin_channel</span><span class="p">]</span> <span class="o">&lt;&lt;</span> <span class="n">solvers</span><span class="p">[</span><span class="n">imp_source</span><span class="p">]</span><span class="o">.</span><span class="n">Sigma_freq</span><span class="p">[</span><span class="n">target_channel</span><span class="p">]</span>
+            <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">G_freq</span><span class="p">[</span><span class="n">spin_channel</span><span class="p">]</span> <span class="o">&lt;&lt;</span> <span class="n">solvers</span><span class="p">[</span><span class="n">imp_source</span><span class="p">]</span><span class="o">.</span><span class="n">G_freq</span><span class="p">[</span><span class="n">target_channel</span><span class="p">]</span>
+            <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">G_freq_unsym</span><span class="p">[</span><span class="n">spin_channel</span><span class="p">]</span> <span class="o">&lt;&lt;</span> <span class="n">solvers</span><span class="p">[</span><span class="n">imp_source</span><span class="p">]</span><span class="o">.</span><span class="n">G_freq_unsym</span><span class="p">[</span><span class="n">target_channel</span><span class="p">]</span>
+            <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">G0_freq</span><span class="p">[</span><span class="n">spin_channel</span><span class="p">]</span> <span class="o">&lt;&lt;</span> <span class="n">solvers</span><span class="p">[</span><span class="n">imp_source</span><span class="p">]</span><span class="o">.</span><span class="n">G0_freq</span><span class="p">[</span><span class="n">target_channel</span><span class="p">]</span>
+            <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">G_time</span><span class="p">[</span><span class="n">spin_channel</span><span class="p">]</span> <span class="o">&lt;&lt;</span> <span class="n">solvers</span><span class="p">[</span><span class="n">imp_source</span><span class="p">]</span><span class="o">.</span><span class="n">G_time</span><span class="p">[</span><span class="n">target_channel</span><span class="p">]</span>
+
+            <span class="k">if</span> <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;measure_pert_order&#39;</span><span class="p">]:</span>
+                <span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">],</span> <span class="s1">&#39;perturbation_order&#39;</span><span class="p">):</span>
+                    <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">perturbation_order</span> <span class="o">=</span> <span class="p">{}</span>
+                <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">perturbation_order</span><span class="p">[</span><span class="n">spin_channel</span><span class="p">]</span> <span class="o">=</span> <span class="n">solvers</span><span class="p">[</span><span class="n">imp_source</span><span class="p">]</span><span class="o">.</span><span class="n">perturbation_order</span><span class="p">[</span><span class="n">target_channel</span><span class="p">]</span>
+                <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">perturbation_order_total</span> <span class="o">=</span> <span class="n">solvers</span><span class="p">[</span><span class="n">imp_source</span><span class="p">]</span><span class="o">.</span><span class="n">perturbation_order_total</span>
+
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">Sigma_freq</span> <span class="o">&lt;&lt;</span> <span class="n">solvers</span><span class="p">[</span><span class="n">imp_source</span><span class="p">]</span><span class="o">.</span><span class="n">Sigma_freq</span>
+        <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">G_freq</span> <span class="o">&lt;&lt;</span> <span class="n">solvers</span><span class="p">[</span><span class="n">imp_source</span><span class="p">]</span><span class="o">.</span><span class="n">G_freq</span>
+        <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">G_freq_unsym</span> <span class="o">&lt;&lt;</span> <span class="n">solvers</span><span class="p">[</span><span class="n">imp_source</span><span class="p">]</span><span class="o">.</span><span class="n">G_freq_unsym</span>
+        <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">G0_freq</span> <span class="o">&lt;&lt;</span> <span class="n">solvers</span><span class="p">[</span><span class="n">imp_source</span><span class="p">]</span><span class="o">.</span><span class="n">G0_freq</span>
+        <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">G_time</span> <span class="o">&lt;&lt;</span> <span class="n">solvers</span><span class="p">[</span><span class="n">imp_source</span><span class="p">]</span><span class="o">.</span><span class="n">G_time</span>
+
+        <span class="k">if</span> <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;measure_pert_order&#39;</span><span class="p">]:</span>
+            <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">perturbation_order</span> <span class="o">=</span> <span class="n">solvers</span><span class="p">[</span><span class="n">imp_source</span><span class="p">]</span><span class="o">.</span><span class="n">perturbation_order</span>
+            <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">perturbation_order_total</span> <span class="o">=</span> <span class="n">solvers</span><span class="p">[</span><span class="n">imp_source</span><span class="p">]</span><span class="o">.</span><span class="n">perturbation_order_total</span>
+
+    <span class="k">if</span> <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;measure_density_matrix&#39;</span><span class="p">]:</span>
+        <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">density_matrix</span> <span class="o">=</span> <span class="n">solvers</span><span class="p">[</span><span class="n">imp_source</span><span class="p">]</span><span class="o">.</span><span class="n">density_matrix</span>
+        <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">h_loc_diagonalization</span> <span class="o">=</span> <span class="n">solvers</span><span class="p">[</span><span class="n">imp_source</span><span class="p">]</span><span class="o">.</span><span class="n">h_loc_diagonalization</span>
+
+    <span class="k">if</span> <span class="s1">&#39;measure_chi&#39;</span> <span class="ow">in</span> <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">solver_params</span> <span class="ow">and</span> <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;measure_chi&#39;</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">O_time</span> <span class="o">=</span> <span class="n">solvers</span><span class="p">[</span><span class="n">imp_source</span><span class="p">]</span><span class="o">.</span><span class="n">O_time</span>
+
+    <span class="k">return</span> <span class="n">solvers</span>
+</pre></div>
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
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+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/vasp_csc.html">Interface to VASP</a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#workflow">Workflow:</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#version-and-compilation">version and compilation:</a></li>
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+</li>
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+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_output/results.html#group-structure">Group structure</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#further-details-for-running">Further details for running</a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#module-reference-manual">Module reference manual</a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.vasp_manager.html">dft_managers.vasp_manager</a></li>
+</ul>
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+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.greens_functions_mixer.html">dmft_tools.greens_functions_mixer</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.initial_self_energies.html">dmft_tools.initial_self_energies</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.interaction_hamiltonian.html">dmft_tools.interaction_hamiltonian</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.manipulate_chemical_potential.html">dmft_tools.manipulate_chemical_potential</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.matheval.html">dmft_tools.matheval</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.observables.html">dmft_tools.observables</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.results_to_archive.html">dmft_tools.results_to_archive</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.solver.html">dmft_tools.solver</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/io_tools.html">io_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.dict_to_h5.html">io_tools.dict_to_h5</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.postproc_toml_dict.html">io_tools.postproc_toml_dict</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.verify_input_params.html">io_tools.verify_input_params</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_gf_imp.html">postprocessing.maxent_gf_imp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_gf_latt.html">postprocessing.maxent_gf_latt</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_sigma.html">postprocessing.maxent_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../issues.html">Support &amp; contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../issues.html#seeking-help">Seeking help</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../issues.html#improving-solid-dmft">Improving solid_dmft</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../issues.html#reporting-issues">Reporting issues</a></li>
+</ul>
+</li>
+</ul>
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+<li class="toctree-l1"><a class="reference internal" href="../../ChangeLog.html">Changelog</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-2-0">Version 3.2.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#general">General</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#fix">fix</a></li>
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+             
+  <h1>Source code for dmft_tools.convergence</h1><div class="highlight"><pre>
+<span></span><span class="c1">################################################################################</span>
+<span class="c1">#</span>
+<span class="c1"># solid_dmft - A versatile python wrapper to perform DFT+DMFT calculations</span>
+<span class="c1">#              utilizing the TRIQS software library</span>
+<span class="c1">#</span>
+<span class="c1"># Copyright (C) 2018-2020, ETH Zurich</span>
+<span class="c1"># Copyright (C) 2021, The Simons Foundation</span>
+<span class="c1">#      authors: A. Hampel, M. Merkel, and S. Beck</span>
+<span class="c1">#</span>
+<span class="c1"># solid_dmft is free software: you can redistribute it and/or modify it under the</span>
+<span class="c1"># terms of the GNU General Public License as published by the Free Software</span>
+<span class="c1"># Foundation, either version 3 of the License, or (at your option) any later</span>
+<span class="c1"># version.</span>
+<span class="c1">#</span>
+<span class="c1"># solid_dmft is distributed in the hope that it will be useful, but WITHOUT ANY</span>
+<span class="c1"># WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A</span>
+<span class="c1"># PARTICULAR PURPOSE. See the GNU General Public License for more details.</span>
+
+<span class="c1"># You should have received a copy of the GNU General Public License along with</span>
+<span class="c1"># solid_dmft (in the file COPYING.txt in this directory). If not, see</span>
+<span class="c1"># &lt;http://www.gnu.org/licenses/&gt;.</span>
+<span class="c1">#</span>
+<span class="c1">################################################################################</span>
+<span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">contain helper functions to check convergence</span>
+<span class="sd">&#39;&#39;&#39;</span>
+<span class="c1"># system</span>
+<span class="kn">import</span> <span class="nn">os.path</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<span class="c1"># triqs</span>
+<span class="kn">from</span> <span class="nn">triqs.gf</span> <span class="kn">import</span> <span class="n">MeshImFreq</span><span class="p">,</span> <span class="n">MeshImTime</span><span class="p">,</span> <span class="n">MeshReFreq</span><span class="p">,</span> <span class="n">BlockGf</span>
+<span class="kn">from</span> <span class="nn">solid_dmft.dmft_tools</span> <span class="kn">import</span> <span class="n">solver</span>
+
+<span class="k">def</span> <span class="nf">_generate_header</span><span class="p">(</span><span class="n">general_params</span><span class="p">,</span> <span class="n">sum_k</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Generates the headers that are used in write_header_to_file.</span>
+<span class="sd">    Returns a dict with {file_name: header_string}</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">n_orb</span> <span class="o">=</span> <span class="n">solver</span><span class="o">.</span><span class="n">get_n_orbitals</span><span class="p">(</span><span class="n">sum_k</span><span class="p">)</span>
+
+    <span class="n">header_energy_mask</span> <span class="o">=</span> <span class="s1">&#39;| </span><span class="si">{:&gt;11}</span><span class="s1"> &#39;</span>
+    <span class="n">header_energy</span> <span class="o">=</span> <span class="n">header_energy_mask</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s1">&#39;δE_tot&#39;</span><span class="p">)</span>
+
+    <span class="n">headers</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="k">for</span> <span class="n">iineq</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span><span class="p">):</span>
+        <span class="n">number_spaces</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="mi">13</span><span class="o">*</span><span class="n">n_orb</span><span class="p">[</span><span class="n">iineq</span><span class="p">][</span><span class="s1">&#39;up&#39;</span><span class="p">]</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">21</span><span class="p">)</span>
+        <span class="n">header_basic_mask</span> <span class="o">=</span> <span class="s1">&#39;{{:&gt;3}} | {{:&gt;11}} | {{:&gt;</span><span class="si">{0}</span><span class="s1">}} | {{:&gt;11}} | {{:&gt;11}} | {{:&gt;11}} | {{:&gt;11}} &#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">number_spaces</span><span class="p">)</span>
+
+        <span class="n">file_name</span> <span class="o">=</span> <span class="s1">&#39;conv_imp</span><span class="si">{}</span><span class="s1">.dat&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">iineq</span><span class="p">)</span>
+        <span class="n">headers</span><span class="p">[</span><span class="n">file_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">header_basic_mask</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s1">&#39;it&#39;</span><span class="p">,</span> <span class="s1">&#39;δμ&#39;</span><span class="p">,</span><span class="s1">&#39;δocc orb&#39;</span><span class="p">,</span><span class="s1">&#39;δimp occ&#39;</span><span class="p">,</span><span class="s1">&#39;δGimp&#39;</span><span class="p">,</span> <span class="s1">&#39;δG0&#39;</span><span class="p">,</span><span class="s1">&#39;δΣ&#39;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;calc_energies&#39;</span><span class="p">]:</span>
+            <span class="n">headers</span><span class="p">[</span><span class="n">file_name</span><span class="p">]</span> <span class="o">+=</span> <span class="n">header_energy</span>
+
+
+    <span class="k">return</span> <span class="n">headers</span>
+
+<div class="viewcode-block" id="write_conv"><a class="viewcode-back" href="../../_ref/dmft_tools.convergence.html#dmft_tools.convergence.write_conv">[docs]</a><span class="k">def</span> <span class="nf">write_conv</span><span class="p">(</span><span class="n">conv_obs</span><span class="p">,</span> <span class="n">sum_k</span><span class="p">,</span> <span class="n">general_params</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    writes the last entries of the conv arrays to the files</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    conv_obs : list of dicts</span>
+<span class="sd">        convergence observable arrays/dicts</span>
+
+<span class="sd">    sum_k : SumK Object instances</span>
+
+<span class="sd">    general_params : dict</span>
+
+<span class="sd">    __Returns:__</span>
+
+<span class="sd">    nothing</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">n_orb</span> <span class="o">=</span> <span class="n">solver</span><span class="o">.</span><span class="n">get_n_orbitals</span><span class="p">(</span><span class="n">sum_k</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">icrsh</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span><span class="p">):</span>
+        <span class="n">line</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{:3d}</span><span class="s1"> | &#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">conv_obs</span><span class="p">[</span><span class="s1">&#39;iteration&#39;</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+        <span class="n">line</span> <span class="o">+=</span> <span class="s1">&#39;</span><span class="si">{:10.5e}</span><span class="s1"> | &#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">conv_obs</span><span class="p">[</span><span class="s1">&#39;d_mu&#39;</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+
+        <span class="c1"># Adds spaces for header to fit in properly</span>
+        <span class="k">if</span> <span class="n">n_orb</span><span class="p">[</span><span class="n">icrsh</span><span class="p">][</span><span class="s1">&#39;up&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="n">line</span> <span class="o">+=</span> <span class="s1">&#39; &#39;</span><span class="o">*</span><span class="mi">11</span>
+        <span class="c1"># Adds up the spin channels</span>
+        <span class="k">for</span> <span class="n">iorb</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_orb</span><span class="p">[</span><span class="n">icrsh</span><span class="p">][</span><span class="s1">&#39;up&#39;</span><span class="p">]):</span>
+            <span class="n">line</span> <span class="o">+=</span> <span class="s1">&#39;</span><span class="si">{:10.5e}</span><span class="s1">   &#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">conv_obs</span><span class="p">[</span><span class="s1">&#39;d_orb_occ&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="n">iorb</span><span class="p">])</span>
+        <span class="n">line</span> <span class="o">=</span> <span class="n">line</span><span class="p">[:</span><span class="o">-</span><span class="mi">3</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; | &#39;</span>
+
+        <span class="c1"># imp occupation change</span>
+        <span class="n">line</span> <span class="o">+=</span> <span class="s1">&#39;</span><span class="si">{:10.5e}</span><span class="s1"> | &#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">conv_obs</span><span class="p">[</span><span class="s1">&#39;d_imp_occ&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+        <span class="c1"># Gimp change</span>
+        <span class="n">line</span> <span class="o">+=</span> <span class="s1">&#39;</span><span class="si">{:10.5e}</span><span class="s1"> | &#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">conv_obs</span><span class="p">[</span><span class="s1">&#39;d_Gimp&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+        <span class="c1"># G0 change</span>
+        <span class="n">line</span> <span class="o">+=</span> <span class="s1">&#39;</span><span class="si">{:10.5e}</span><span class="s1"> | &#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">conv_obs</span><span class="p">[</span><span class="s1">&#39;d_G0&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+        <span class="c1"># Σ change</span>
+        <span class="n">line</span> <span class="o">+=</span> <span class="s1">&#39;</span><span class="si">{:10.5e}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">conv_obs</span><span class="p">[</span><span class="s1">&#39;d_Sigma&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+
+        <span class="k">if</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;calc_energies&#39;</span><span class="p">]:</span>
+            <span class="n">line</span> <span class="o">+=</span> <span class="s1">&#39; | </span><span class="si">{:10.5e}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">conv_obs</span><span class="p">[</span><span class="s1">&#39;d_Etot&#39;</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+
+        <span class="n">file_name</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{}</span><span class="s1">/conv_imp</span><span class="si">{}</span><span class="s1">.dat&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;jobname&#39;</span><span class="p">],</span> <span class="n">icrsh</span><span class="p">)</span>
+        <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">file_name</span><span class="p">,</span> <span class="s1">&#39;a&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">obs_file</span><span class="p">:</span>
+            <span class="n">obs_file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">line</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="max_G_diff"><a class="viewcode-back" href="../../_ref/dmft_tools.convergence.html#dmft_tools.convergence.max_G_diff">[docs]</a><span class="k">def</span> <span class="nf">max_G_diff</span><span class="p">(</span><span class="n">G1</span><span class="p">,</span> <span class="n">G2</span><span class="p">,</span> <span class="n">norm_temp</span> <span class="o">=</span> <span class="kc">True</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    calculates difference between two block Gfs</span>
+<span class="sd">    uses numpy linalg norm on the last two indices first</span>
+<span class="sd">    and then the norm along the mesh axis. The result is divided</span>
+<span class="sd">    by sqrt(beta) for MeshImFreq and by sqrt(beta/#taupoints) for</span>
+<span class="sd">    MeshImTime.</span>
+
+<span class="sd">    1/ (2* sqrt(beta)) sqrt( sum_n sum_ij [abs(G1 - G2)_ij(w_n)]^2 )</span>
+
+<span class="sd">    this is only done for MeshImFreq Gf objects, for all other</span>
+<span class="sd">    meshes the weights are set to 1</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    G1 : Gf or BlockGf to compare</span>
+
+<span class="sd">    G2 : Gf or BlockGf to compare</span>
+
+<span class="sd">    norm_temp: bool, default = True</span>
+<span class="sd">       divide by an additional sqrt(beta) to account for temperature scaling</span>
+<span class="sd">       only correct for uniformly distributed error.</span>
+
+<span class="sd">    __Returns:__</span>
+
+<span class="sd">    diff : float</span>
+<span class="sd">           difference between the two Gfs</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">G1</span><span class="p">,</span> <span class="n">BlockGf</span><span class="p">):</span>
+        <span class="n">diff</span> <span class="o">=</span> <span class="mf">0.0</span>
+        <span class="k">for</span> <span class="n">block</span><span class="p">,</span> <span class="n">gf</span> <span class="ow">in</span> <span class="n">G1</span><span class="p">:</span>
+            <span class="n">diff</span> <span class="o">+=</span> <span class="n">max_G_diff</span><span class="p">(</span><span class="n">G1</span><span class="p">[</span><span class="n">block</span><span class="p">],</span> <span class="n">G2</span><span class="p">[</span><span class="n">block</span><span class="p">],</span> <span class="n">norm_temp</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">diff</span>
+
+    <span class="k">assert</span> <span class="n">G1</span><span class="o">.</span><span class="n">mesh</span> <span class="o">==</span> <span class="n">G2</span><span class="o">.</span><span class="n">mesh</span><span class="p">,</span> <span class="s1">&#39;mesh of two input Gfs does not match&#39;</span>
+    <span class="k">assert</span> <span class="n">G1</span><span class="o">.</span><span class="n">target_shape</span> <span class="o">==</span> <span class="n">G2</span><span class="o">.</span><span class="n">target_shape</span><span class="p">,</span> <span class="s1">&#39;can only compare Gfs with same shape&#39;</span>
+
+    <span class="c1"># subtract largest real value to make sure that G1-G2 falls off to 0</span>
+    <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">G1</span><span class="o">.</span><span class="n">mesh</span><span class="p">)</span> <span class="ow">is</span> <span class="n">MeshImFreq</span><span class="p">:</span>
+        <span class="n">offset</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">diag</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">diag</span><span class="p">(</span><span class="n">G1</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,:,:]</span><span class="o">.</span><span class="n">real</span> <span class="o">-</span> <span class="n">G2</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,:,:]</span><span class="o">.</span><span class="n">real</span><span class="p">))</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">offset</span> <span class="o">=</span> <span class="mf">0.0</span>
+
+    <span class="c1">#  calculate norm over all axis but the first one which are frequencies</span>
+    <span class="n">norm_grid</span> <span class="o">=</span> <span class="nb">abs</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">G1</span><span class="o">.</span><span class="n">data</span> <span class="o">-</span> <span class="n">G2</span><span class="o">.</span><span class="n">data</span> <span class="o">-</span> <span class="n">offset</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="nb">tuple</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">G1</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">ndim</span><span class="p">))))</span>
+    <span class="c1"># now calculate Frobenius norm over grid points</span>
+    <span class="n">norm</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">norm_grid</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">G1</span><span class="o">.</span><span class="n">mesh</span><span class="p">)</span> <span class="ow">is</span> <span class="n">MeshImFreq</span><span class="p">:</span>
+        <span class="n">norm</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">norm_grid</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">G1</span><span class="o">.</span><span class="n">mesh</span><span class="o">.</span><span class="n">beta</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="nb">type</span><span class="p">(</span><span class="n">G1</span><span class="o">.</span><span class="n">mesh</span><span class="p">)</span> <span class="ow">is</span> <span class="n">MeshImTime</span><span class="p">:</span>
+        <span class="n">norm</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">norm_grid</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">G1</span><span class="o">.</span><span class="n">mesh</span><span class="o">.</span><span class="n">beta</span><span class="o">/</span><span class="nb">len</span><span class="p">(</span><span class="n">G1</span><span class="o">.</span><span class="n">mesh</span><span class="p">))</span>
+    <span class="k">elif</span> <span class="nb">type</span><span class="p">(</span><span class="n">G1</span><span class="o">.</span><span class="n">mesh</span><span class="p">)</span> <span class="ow">is</span> <span class="n">MeshReFreq</span><span class="p">:</span>
+        <span class="n">norm</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">norm_grid</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">G1</span><span class="o">.</span><span class="n">mesh</span><span class="p">))</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;MeshReTime is not implemented&#39;</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">G1</span><span class="o">.</span><span class="n">mesh</span><span class="p">)</span> <span class="ow">in</span> <span class="p">(</span><span class="n">MeshImFreq</span><span class="p">,</span> <span class="n">MeshImTime</span><span class="p">)</span> <span class="ow">and</span> <span class="n">norm_temp</span><span class="p">:</span>
+        <span class="n">norm</span> <span class="o">=</span> <span class="n">norm</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">G1</span><span class="o">.</span><span class="n">mesh</span><span class="o">.</span><span class="n">beta</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">norm</span></div>
+
+<div class="viewcode-block" id="prep_conv_obs"><a class="viewcode-back" href="../../_ref/dmft_tools.convergence.html#dmft_tools.convergence.prep_conv_obs">[docs]</a><span class="k">def</span> <span class="nf">prep_conv_obs</span><span class="p">(</span><span class="n">h5_archive</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    prepares the conv arrays and files for the DMFT calculation</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    h5_archive: hdf archive instance</span>
+<span class="sd">        hdf archive for calculation</span>
+
+<span class="sd">    __Returns:__</span>
+<span class="sd">    conv_obs : dict</span>
+<span class="sd">        conv array for calculation</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># determine number of impurities</span>
+    <span class="n">n_inequiv_shells</span> <span class="o">=</span> <span class="n">h5_archive</span><span class="p">[</span><span class="s1">&#39;dft_input&#39;</span><span class="p">][</span><span class="s1">&#39;n_inequiv_shells&#39;</span><span class="p">]</span>
+
+    <span class="c1"># check for previous iterations</span>
+    <span class="n">conv_prev</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">if</span> <span class="s1">&#39;convergence_obs&#39;</span> <span class="ow">in</span> <span class="n">h5_archive</span><span class="p">[</span><span class="s1">&#39;DMFT_results&#39;</span><span class="p">]:</span>
+        <span class="n">conv_prev</span> <span class="o">=</span> <span class="n">h5_archive</span><span class="p">[</span><span class="s1">&#39;DMFT_results&#39;</span><span class="p">][</span><span class="s1">&#39;convergence_obs&#39;</span><span class="p">]</span>
+
+    <span class="c1"># prepare observable dicts</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">conv_prev</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">conv_obs</span> <span class="o">=</span> <span class="n">conv_prev</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">conv_obs</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
+        <span class="n">conv_obs</span><span class="p">[</span><span class="s1">&#39;iteration&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">conv_obs</span><span class="p">[</span><span class="s1">&#39;d_mu&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">conv_obs</span><span class="p">[</span><span class="s1">&#39;d_Etot&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">conv_obs</span><span class="p">[</span><span class="s1">&#39;d_orb_occ&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[[]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_inequiv_shells</span><span class="p">)]</span>
+        <span class="n">conv_obs</span><span class="p">[</span><span class="s1">&#39;d_imp_occ&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[[]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_inequiv_shells</span><span class="p">)]</span>
+
+        <span class="n">conv_obs</span><span class="p">[</span><span class="s1">&#39;d_Gimp&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[[]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_inequiv_shells</span><span class="p">)]</span>
+        <span class="n">conv_obs</span><span class="p">[</span><span class="s1">&#39;d_G0&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[[]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_inequiv_shells</span><span class="p">)]</span>
+        <span class="n">conv_obs</span><span class="p">[</span><span class="s1">&#39;d_Sigma&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[[]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_inequiv_shells</span><span class="p">)]</span>
+
+    <span class="k">return</span> <span class="n">conv_obs</span></div>
+
+<div class="viewcode-block" id="prep_conv_file"><a class="viewcode-back" href="../../_ref/dmft_tools.convergence.html#dmft_tools.convergence.prep_conv_file">[docs]</a><span class="k">def</span> <span class="nf">prep_conv_file</span><span class="p">(</span><span class="n">general_params</span><span class="p">,</span> <span class="n">sum_k</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Writes the header to the conv files</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    general_params : dict</span>
+<span class="sd">        general parameters as a dict</span>
+<span class="sd">    n_inequiv_shells : int</span>
+<span class="sd">        number of impurities for calculations</span>
+
+
+<span class="sd">    __Returns:__</span>
+<span class="sd">    nothing</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">headers</span> <span class="o">=</span> <span class="n">_generate_header</span><span class="p">(</span><span class="n">general_params</span><span class="p">,</span> <span class="n">sum_k</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">file_name</span><span class="p">,</span> <span class="n">header</span> <span class="ow">in</span> <span class="n">headers</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+        <span class="n">path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;jobname&#39;</span><span class="p">],</span> <span class="n">file_name</span><span class="p">)</span>
+        <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">conv_file</span><span class="p">:</span>
+            <span class="n">conv_file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">header</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="calc_convergence_quantities"><a class="viewcode-back" href="../../_ref/dmft_tools.convergence.html#dmft_tools.convergence.calc_convergence_quantities">[docs]</a><span class="k">def</span> <span class="nf">calc_convergence_quantities</span><span class="p">(</span><span class="n">sum_k</span><span class="p">,</span> <span class="n">general_params</span><span class="p">,</span> <span class="n">conv_obs</span><span class="p">,</span> <span class="n">observables</span><span class="p">,</span>
+                                     <span class="n">solvers</span><span class="p">,</span> <span class="n">G0_old</span><span class="p">,</span> <span class="n">G_loc_all</span><span class="p">,</span> <span class="n">Sigma_freq_previous</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Calculations convergence quantities, i.e. the difference in observables</span>
+<span class="sd">    between the last and second to last iteration.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    sum_k : SumK Object instances</span>
+
+<span class="sd">    general_params : dict</span>
+<span class="sd">        general parameters as a dict</span>
+
+<span class="sd">    conv_obs : list of dicts</span>
+<span class="sd">        convergence observable arrays</span>
+
+<span class="sd">    observables : list of dicts</span>
+<span class="sd">        observable arrays</span>
+
+<span class="sd">    solvers : solver objects</span>
+
+<span class="sd">    G0_old : list of block Gf object</span>
+<span class="sd">            last G0_freq</span>
+
+<span class="sd">    G_loc_all : list of block Gf objects</span>
+<span class="sd">            G_loc extracted from before imp solver</span>
+
+<span class="sd">    Sigma_freq_previous : list of block Gf objects</span>
+<span class="sd">            previous impurity sigma to compare with</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    conv_obs : list of dicts</span>
+<span class="sd">        updated convergence observable arrays</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">conv_obs</span><span class="p">[</span><span class="s1">&#39;iteration&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">observables</span><span class="p">[</span><span class="s1">&#39;iteration&#39;</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+    <span class="n">conv_obs</span><span class="p">[</span><span class="s1">&#39;d_mu&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">observables</span><span class="p">[</span><span class="s1">&#39;mu&#39;</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;mu&#39;</span><span class="p">][</span><span class="o">-</span><span class="mi">2</span><span class="p">]</span> <span class="p">))</span>
+    <span class="k">for</span> <span class="n">icrsh</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span><span class="p">):</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">corr_shells</span><span class="p">[</span><span class="n">icrsh</span><span class="p">][</span><span class="s1">&#39;SO&#39;</span><span class="p">]:</span>
+            <span class="c1"># difference in imp occupation</span>
+            <span class="n">conv_obs</span><span class="p">[</span><span class="s1">&#39;d_imp_occ&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">abs</span><span class="p">((</span><span class="n">observables</span><span class="p">[</span><span class="s1">&#39;imp_occ&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">][</span><span class="s1">&#39;up&#39;</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">+</span>
+                                                 <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;imp_occ&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">][</span><span class="s1">&#39;down&#39;</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span><span class="o">-</span>
+                                                <span class="p">(</span><span class="n">observables</span><span class="p">[</span><span class="s1">&#39;imp_occ&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">][</span><span class="s1">&#39;up&#39;</span><span class="p">][</span><span class="o">-</span><span class="mi">2</span><span class="p">]</span><span class="o">+</span>
+                                                 <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;imp_occ&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">][</span><span class="s1">&#39;down&#39;</span><span class="p">][</span><span class="o">-</span><span class="mi">2</span><span class="p">])))</span>
+            <span class="c1"># difference in orb occ spin absolute</span>
+            <span class="n">conv_obs</span><span class="p">[</span><span class="s1">&#39;d_orb_occ&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">observables</span><span class="p">[</span><span class="s1">&#39;orb_occ&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">][</span><span class="s1">&#39;up&#39;</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span>
+                                                    <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;orb_occ&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">][</span><span class="s1">&#39;up&#39;</span><span class="p">][</span><span class="o">-</span><span class="mi">2</span><span class="p">])</span><span class="o">+</span>
+                                                <span class="nb">abs</span><span class="p">(</span><span class="n">observables</span><span class="p">[</span><span class="s1">&#39;orb_occ&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">][</span><span class="s1">&#39;down&#39;</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span>
+                                                    <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;orb_occ&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">][</span><span class="s1">&#39;down&#39;</span><span class="p">][</span><span class="o">-</span><span class="mi">2</span><span class="p">]))</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">conv_obs</span><span class="p">[</span><span class="s1">&#39;d_imp_occ&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">observables</span><span class="p">[</span><span class="s1">&#39;imp_occ&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">][</span><span class="s1">&#39;ud&#39;</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span>
+                                                    <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;imp_occ&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">][</span><span class="s1">&#39;ud&#39;</span><span class="p">][</span><span class="o">-</span><span class="mi">2</span><span class="p">]))</span>
+            <span class="n">conv_obs</span><span class="p">[</span><span class="s1">&#39;d_orb_occ&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">observables</span><span class="p">[</span><span class="s1">&#39;orb_occ&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">][</span><span class="s1">&#39;ud&#39;</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">+</span>
+                                                    <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;imp_occ&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">][</span><span class="s1">&#39;ud&#39;</span><span class="p">][</span><span class="o">-</span><span class="mi">2</span><span class="p">]))</span>
+
+        <span class="n">conv_obs</span><span class="p">[</span><span class="s1">&#39;d_Gimp&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">max_G_diff</span><span class="p">(</span><span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">G_freq</span><span class="p">,</span> <span class="n">G_loc_all</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]))</span>
+        <span class="n">conv_obs</span><span class="p">[</span><span class="s1">&#39;d_G0&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">max_G_diff</span><span class="p">(</span><span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">G0_freq</span><span class="p">,</span> <span class="n">G0_old</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]))</span>
+        <span class="n">conv_obs</span><span class="p">[</span><span class="s1">&#39;d_Sigma&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">max_G_diff</span><span class="p">(</span><span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">Sigma_freq</span><span class="p">,</span> <span class="n">Sigma_freq_previous</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]))</span>
+
+    <span class="k">if</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;calc_energies&#39;</span><span class="p">]:</span>
+        <span class="n">conv_obs</span><span class="p">[</span><span class="s1">&#39;d_Etot&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">observables</span><span class="p">[</span><span class="s1">&#39;E_tot&#39;</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span><span class="n">observables</span><span class="p">[</span><span class="s1">&#39;E_tot&#39;</span><span class="p">][</span><span class="o">-</span><span class="mi">2</span><span class="p">]))</span>
+
+    <span class="k">return</span> <span class="n">conv_obs</span></div>
+
+
+<div class="viewcode-block" id="check_convergence"><a class="viewcode-back" href="../../_ref/dmft_tools.convergence.html#dmft_tools.convergence.check_convergence">[docs]</a><span class="k">def</span> <span class="nf">check_convergence</span><span class="p">(</span><span class="n">n_inequiv_shells</span><span class="p">,</span> <span class="n">general_params</span><span class="p">,</span> <span class="n">conv_obs</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    check last iteration for convergence</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    n_inequiv_shells : int</span>
+<span class="sd">        Number of inequivalent shells as saved in SumkDFT object</span>
+
+<span class="sd">    general_params : dict</span>
+<span class="sd">        general parameters as a dict</span>
+
+<span class="sd">    conv_obs : list of dicts</span>
+<span class="sd">        convergence observable arrays</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    is_converged : bool</span>
+<span class="sd">        true if desired accuracy is reached. None if no convergence criterion</span>
+<span class="sd">        is set</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># If no convergence criterion is set, convergence is undefined and returns None</span>
+    <span class="k">if</span> <span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;occ_conv_crit&#39;</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="mf">0.0</span> <span class="ow">and</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;gimp_conv_crit&#39;</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="mf">0.0</span>
+            <span class="ow">and</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;g0_conv_crit&#39;</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="mf">0.0</span> <span class="ow">and</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;sigma_conv_crit&#39;</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="mf">0.0</span><span class="p">):</span>
+        <span class="k">return</span> <span class="kc">None</span>
+
+    <span class="c1"># Checks convergence criteria</span>
+    <span class="k">for</span> <span class="n">icrsh</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_inequiv_shells</span><span class="p">):</span>
+        <span class="c1"># Checks imp occ</span>
+        <span class="k">if</span> <span class="p">(</span><span class="n">conv_obs</span><span class="p">[</span><span class="s1">&#39;d_imp_occ&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;occ_conv_crit&#39;</span><span class="p">]</span>
+                <span class="ow">and</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;occ_conv_crit&#39;</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mf">0.0</span><span class="p">):</span>
+            <span class="k">return</span> <span class="kc">False</span>
+
+        <span class="c1"># Checks Gimp</span>
+        <span class="k">if</span> <span class="p">(</span><span class="n">conv_obs</span><span class="p">[</span><span class="s1">&#39;d_Gimp&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;gimp_conv_crit&#39;</span><span class="p">]</span>
+                <span class="ow">and</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;gimp_conv_crit&#39;</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mf">0.0</span><span class="p">):</span>
+            <span class="k">return</span> <span class="kc">False</span>
+
+        <span class="c1"># Checks G0</span>
+        <span class="k">if</span> <span class="p">(</span><span class="n">conv_obs</span><span class="p">[</span><span class="s1">&#39;d_G0&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;g0_conv_crit&#39;</span><span class="p">]</span>
+                <span class="ow">and</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;g0_conv_crit&#39;</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mf">0.0</span><span class="p">):</span>
+            <span class="k">return</span> <span class="kc">False</span>
+
+        <span class="c1"># Checks Sigma</span>
+        <span class="k">if</span> <span class="p">(</span><span class="n">conv_obs</span><span class="p">[</span><span class="s1">&#39;d_Sigma&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;sigma_conv_crit&#39;</span><span class="p">]</span>
+                <span class="ow">and</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;sigma_conv_crit&#39;</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mf">0.0</span><span class="p">):</span>
+            <span class="k">return</span> <span class="kc">False</span>
+
+    <span class="k">return</span> <span class="kc">True</span></div>
+</pre></div>
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.</p>
+  </div>
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
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diff --git a/_modules/dmft_tools/formatter.html b/_modules/dmft_tools/formatter.html
new file mode 100644
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+++ b/_modules/dmft_tools/formatter.html
@@ -0,0 +1,505 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" >
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>dmft_tools.formatter &mdash; solid_dmft  documentation</title>
+      <link rel="stylesheet" href="../../_static/pygments.css" type="text/css" />
+      <link rel="stylesheet" href="../../_static/css/custom.css" type="text/css" />
+      <link rel="stylesheet" href="../../_static/plot_directive.css" type="text/css" />
+    <link rel="shortcut icon" href="../../_static/favicon.ico"/>
+  
+        <script data-url_root="../../" id="documentation_options" src="../../_static/documentation_options.js"></script>
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+        <script src="../../_static/doctools.js"></script>
+        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+    <script src="../../_static/js/theme.js"></script>
+    <link rel="index" title="Index" href="../../genindex.html" />
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+            <a href="../../index.html" class="icon icon-home"> solid_dmft
+            <img src="../../_static/logo_no_text.png" class="logo" alt="Logo"/>
+          </a>
+              <div class="version">
+                3.2.0
+              </div>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="../../search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" />
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+              <ul>
+<li class="toctree-l1"><a class="reference internal" href="../../install.html">Installation</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#installation-via-pip">Installation via pip</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#manual-installation-via-cmake">Manual installation via CMake</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#docker-files-images">Docker files &amp; images</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#version-compatibility">Version compatibility</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#custom-cmake-options">Custom CMake options</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../documentation.html">Documentation</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#code-structure">Code structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#dft-interface-notes">DFT interface notes</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/w90_interface.html">Wannier90 interface</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/w90_interface.html#orbital-order-in-the-w90-converter">orbital order in the W90 converter</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/vasp_csc.html">Interface to VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#general-remarks">General remarks</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#file-description">file description</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#workflow">Workflow:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#important-flags">important flags:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#version-and-compilation">version and compilation:</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#input-output">Input/Output</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../input_output/DMFT_input/input.html">Input</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/general.html">[general]: General parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/solver.html">[solver]: solver specific parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/dft.html">[dft]: DFT related inputs</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/advanced.html">[advanced]: Advanced inputs</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../input_output/DMFT_output/results.html">Output / results</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_output/results.html#group-structure">Group structure</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_output/results.html#subgroups">Subgroups</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#further-details-for-running">Further details for running</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/docker.html">Docker</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#building-the-docker-image">Building the docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#pulling-a-docker-image">Pulling a docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_locally.html">Run on your machine</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#module-reference-manual">Module reference manual</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/csc_flow.html">csc_flow</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/dft_managers.html">dft_managers</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.mpi_helpers.html">dft_managers.mpi_helpers</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.vasp_manager.html">dft_managers.vasp_manager</a></li>
+</ul>
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+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_gf_imp.html">postprocessing.maxent_gf_imp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_gf_latt.html">postprocessing.maxent_gf_latt</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_sigma.html">postprocessing.maxent_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../issues.html">Support &amp; contribute</a><ul>
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+</ul>
+</li>
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+<li class="toctree-l1"><a class="reference internal" href="../../ChangeLog.html">Changelog</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-2-0">Version 3.2.0</a></li>
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+      <li><a href="../../index.html" class="icon icon-home"></a> &raquo;</li>
+          <li><a href="../index.html">Module code</a> &raquo;</li>
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+             
+  <h1>Source code for dmft_tools.formatter</h1><div class="highlight"><pre>
+<span></span><span class="c1">################################################################################</span>
+<span class="c1">#</span>
+<span class="c1"># solid_dmft - A versatile python wrapper to perform DFT+DMFT calculations</span>
+<span class="c1">#              utilizing the TRIQS software library</span>
+<span class="c1">#</span>
+<span class="c1"># Copyright (C) 2018-2020, ETH Zurich</span>
+<span class="c1"># Copyright (C) 2021, The Simons Foundation</span>
+<span class="c1">#      authors: A. Hampel, M. Merkel, and S. Beck</span>
+<span class="c1">#</span>
+<span class="c1"># solid_dmft is free software: you can redistribute it and/or modify it under the</span>
+<span class="c1"># terms of the GNU General Public License as published by the Free Software</span>
+<span class="c1"># Foundation, either version 3 of the License, or (at your option) any later</span>
+<span class="c1"># version.</span>
+<span class="c1">#</span>
+<span class="c1"># solid_dmft is distributed in the hope that it will be useful, but WITHOUT ANY</span>
+<span class="c1"># WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A</span>
+<span class="c1"># PARTICULAR PURPOSE. See the GNU General Public License for more details.</span>
+
+<span class="c1"># You should have received a copy of the GNU General Public License along with</span>
+<span class="c1"># solid_dmft (in the file COPYING.txt in this directory). If not, see</span>
+<span class="c1"># &lt;http://www.gnu.org/licenses/&gt;.</span>
+<span class="c1">#</span>
+<span class="c1">################################################################################</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Contains formatters for things that need to be printed in DMFT calculations.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">triqs.utility.mpi</span> <span class="k">as</span> <span class="nn">mpi</span>
+
+<div class="viewcode-block" id="print_rotation_matrix"><a class="viewcode-back" href="../../_ref/dmft_tools.formatter.html#dmft_tools.formatter.print_rotation_matrix">[docs]</a><span class="k">def</span> <span class="nf">print_rotation_matrix</span><span class="p">(</span><span class="n">sum_k</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Prints the rotation matrix, real and imaginary part separately.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+        <span class="k">return</span>
+
+    <span class="k">for</span> <span class="n">icrsh</span><span class="p">,</span> <span class="n">rot_crsh</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">rot_mat</span><span class="p">):</span>
+        <span class="n">n_orb</span> <span class="o">=</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">corr_shells</span><span class="p">[</span><span class="n">icrsh</span><span class="p">][</span><span class="s1">&#39;dim&#39;</span><span class="p">]</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;rot_mat[</span><span class="si">{:2d}</span><span class="s1">] &#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">icrsh</span><span class="p">)</span><span class="o">+</span><span class="s1">&#39;real part&#39;</span><span class="o">.</span><span class="n">center</span><span class="p">(</span><span class="mi">9</span><span class="o">*</span><span class="n">n_orb</span><span class="p">)</span><span class="o">+</span><span class="s1">&#39;  &#39;</span><span class="o">+</span><span class="s1">&#39;imaginary part&#39;</span><span class="o">.</span><span class="n">center</span><span class="p">(</span><span class="mi">9</span><span class="o">*</span><span class="n">n_orb</span><span class="p">))</span>
+        <span class="n">fmt</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{:9.5f}</span><span class="s1">&#39;</span> <span class="o">*</span> <span class="n">n_orb</span>
+        <span class="k">for</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">rot_crsh</span><span class="p">:</span>
+            <span class="n">row</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">row</span><span class="o">.</span><span class="n">real</span><span class="p">,</span> <span class="n">row</span><span class="o">.</span><span class="n">imag</span><span class="p">))</span>
+            <span class="nb">print</span><span class="p">((</span><span class="s1">&#39; &#39;</span><span class="o">*</span><span class="mi">11</span> <span class="o">+</span> <span class="n">fmt</span> <span class="o">+</span> <span class="s1">&#39;  &#39;</span> <span class="o">+</span> <span class="n">fmt</span><span class="p">)</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="o">*</span><span class="n">row</span><span class="p">))</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="print_block_sym"><a class="viewcode-back" href="../../_ref/dmft_tools.formatter.html#dmft_tools.formatter.print_block_sym">[docs]</a><span class="k">def</span> <span class="nf">print_block_sym</span><span class="p">(</span><span class="n">sum_k</span><span class="p">,</span> <span class="n">dm</span><span class="p">,</span> <span class="n">general_params</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Prints a summary of block structure finder, determination of</span>
+<span class="sd">    shell_multiplicity, local Hamiltonian, DFT density matrix.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+        <span class="k">return</span>
+
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1"> number of ineq. correlated shells: </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span><span class="p">))</span>
+    <span class="c1"># correlated shells and their structure</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1"> block structure summary&#39;</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">icrsh</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span><span class="p">):</span>
+        <span class="n">shlst</span> <span class="o">=</span> <span class="p">[</span><span class="n">ish</span> <span class="k">for</span> <span class="n">ish</span><span class="p">,</span> <span class="n">ineq_shell</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">corr_to_inequiv</span><span class="p">)</span> <span class="k">if</span> <span class="n">ineq_shell</span> <span class="o">==</span> <span class="n">icrsh</span><span class="p">]</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39; -- Shell type #</span><span class="si">{:3d}</span><span class="s1">: &#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">icrsh</span><span class="p">)</span> <span class="o">+</span> <span class="nb">format</span><span class="p">(</span><span class="n">shlst</span><span class="p">))</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;  | shell multiplicity &#39;</span><span class="o">+</span><span class="nb">str</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">shlst</span><span class="p">)))</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;  | block struct. sum_k : &#39;</span> <span class="o">+</span> <span class="nb">format</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">gf_struct_sumk</span><span class="p">[</span><span class="n">sum_k</span><span class="o">.</span><span class="n">inequiv_to_corr</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]]))</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;  | block struct. solver: &#39;</span> <span class="o">+</span> <span class="nb">format</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">gf_struct_solver</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]))</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;  | deg. orbitals : &#39;</span> <span class="o">+</span> <span class="nb">format</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">deg_shells</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]))</span>
+
+    <span class="c1"># Prints matrices</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">Rotation matrices&#39;</span><span class="p">)</span>
+    <span class="n">print_rotation_matrix</span><span class="p">(</span><span class="n">sum_k</span><span class="p">)</span>
+
+    <span class="c1"># Prints dft local Hamiltonian and occupations</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">Effective atomic levels&#39;</span><span class="p">)</span>
+    <span class="n">eal</span> <span class="o">=</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">eff_atomic_levels</span><span class="p">()</span>
+    <span class="k">for</span> <span class="n">icrsh</span><span class="p">,</span> <span class="n">eal_crsh</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">eal</span><span class="p">):</span>
+        <span class="n">shlst</span> <span class="o">=</span> <span class="p">[</span><span class="n">ish</span> <span class="k">for</span> <span class="n">ish</span><span class="p">,</span> <span class="n">ineq_shell</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">corr_to_inequiv</span><span class="p">)</span> <span class="k">if</span> <span class="n">ineq_shell</span> <span class="o">==</span> <span class="n">icrsh</span><span class="p">]</span>
+        <span class="n">n_orb</span> <span class="o">=</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">corr_shells</span><span class="p">[</span><span class="n">sum_k</span><span class="o">.</span><span class="n">inequiv_to_corr</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]][</span><span class="s1">&#39;dim&#39;</span><span class="p">]</span>
+        <span class="n">spins</span> <span class="o">=</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">spin_block_names</span><span class="p">[</span><span class="n">sum_k</span><span class="o">.</span><span class="n">SO</span><span class="p">]</span>
+        <span class="k">if</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">SO</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">eal_blocks</span> <span class="o">=</span> <span class="p">{</span><span class="n">spin</span><span class="p">:</span> <span class="n">eal_crsh</span><span class="p">[</span><span class="n">spin</span><span class="p">]</span> <span class="k">for</span> <span class="n">spin</span> <span class="ow">in</span> <span class="n">spins</span><span class="p">}</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">eal_blocks</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;ud real&#39;</span><span class="p">:</span> <span class="n">eal_crsh</span><span class="p">[</span><span class="s1">&#39;ud&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">real</span><span class="p">,</span> <span class="s1">&#39;ud imag&#39;</span><span class="p">:</span> <span class="n">eal_crsh</span><span class="p">[</span><span class="s1">&#39;ud&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">imag</span><span class="p">}</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;H_loc[</span><span class="si">{:2d}</span><span class="s1">] &#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">icrsh</span><span class="p">)</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span><span class="s1">&#39; &#39;</span><span class="o">+</span><span class="s1">&#39; &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="n">sp</span><span class="o">.</span><span class="n">center</span><span class="p">(</span><span class="mi">9</span><span class="o">*</span><span class="n">n_orb</span><span class="p">)</span> <span class="k">for</span> <span class="n">sp</span> <span class="ow">in</span> <span class="n">eal_blocks</span><span class="o">.</span><span class="n">keys</span><span class="p">()]))</span>
+        <span class="n">fmt</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{:9.5f}</span><span class="s1">&#39;</span> <span class="o">*</span> <span class="n">n_orb</span>
+        <span class="k">for</span> <span class="n">block_1</span><span class="p">,</span> <span class="n">block_2</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">eal_blocks</span><span class="o">.</span><span class="n">values</span><span class="p">()):</span>
+            <span class="n">row</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">block_1</span><span class="o">.</span><span class="n">real</span><span class="p">,</span> <span class="n">block_2</span><span class="o">.</span><span class="n">real</span><span class="p">))</span>
+            <span class="nb">print</span><span class="p">((</span><span class="s1">&#39; &#39;</span><span class="o">*</span><span class="mi">11</span> <span class="o">+</span> <span class="n">fmt</span> <span class="o">+</span> <span class="s1">&#39;  &#39;</span> <span class="o">+</span> <span class="n">fmt</span><span class="p">)</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="o">*</span><span class="n">row</span><span class="p">))</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">dm</span><span class="p">:</span>
+        <span class="c1"># Prints dft density matrix</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">DFT density matrix&#39;</span><span class="p">)</span>
+        <span class="c1"># Double loop to sort by impurities</span>
+        <span class="k">for</span> <span class="n">icrsh</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span><span class="p">):</span>
+            <span class="n">spins</span> <span class="o">=</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">spin_block_names</span><span class="p">[</span><span class="n">sum_k</span><span class="o">.</span><span class="n">SO</span><span class="p">]</span>
+            <span class="n">shlst</span> <span class="o">=</span> <span class="p">[</span><span class="n">ish</span> <span class="k">for</span> <span class="n">ish</span><span class="p">,</span> <span class="n">ineq_shell</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">corr_to_inequiv</span><span class="p">)</span> <span class="k">if</span> <span class="n">ineq_shell</span> <span class="o">==</span> <span class="n">icrsh</span><span class="p">]</span>
+            <span class="k">for</span> <span class="n">sh</span> <span class="ow">in</span> <span class="n">shlst</span><span class="p">:</span>
+                <span class="n">n_orb</span> <span class="o">=</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">corr_shells</span><span class="p">[</span><span class="n">sh</span><span class="p">][</span><span class="s1">&#39;dim&#39;</span><span class="p">]</span>
+                <span class="k">if</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">SO</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="n">dm_blocks</span> <span class="o">=</span> <span class="p">{</span><span class="n">spin</span><span class="p">:</span> <span class="n">dm</span><span class="p">[</span><span class="n">sh</span><span class="p">][</span><span class="n">spin</span><span class="p">]</span> <span class="k">for</span> <span class="n">spin</span> <span class="ow">in</span> <span class="n">spins</span><span class="p">}</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">dm_blocks</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;ud real&#39;</span><span class="p">:</span> <span class="n">dm</span><span class="p">[</span><span class="n">sh</span><span class="p">][</span><span class="s1">&#39;ud&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">real</span><span class="p">,</span> <span class="s1">&#39;ud imag&#39;</span><span class="p">:</span> <span class="n">dm</span><span class="p">[</span><span class="n">sh</span><span class="p">][</span><span class="s1">&#39;ud&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">imag</span><span class="p">}</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;rho[</span><span class="si">{0:2d}</span><span class="s1">] &#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">sh</span><span class="p">)</span><span class="o">+</span><span class="mi">4</span><span class="o">*</span><span class="s1">&#39; &#39;</span><span class="o">+</span><span class="s1">&#39; &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="n">sp</span><span class="o">.</span><span class="n">center</span><span class="p">(</span><span class="mi">9</span><span class="o">*</span><span class="n">n_orb</span><span class="p">)</span> <span class="k">for</span> <span class="n">sp</span> <span class="ow">in</span> <span class="n">dm_blocks</span><span class="o">.</span><span class="n">keys</span><span class="p">()]))</span>
+                <span class="n">fmt</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{:9.5f}</span><span class="s1">&#39;</span> <span class="o">*</span> <span class="n">n_orb</span>
+                <span class="k">for</span> <span class="n">block_1</span><span class="p">,</span> <span class="n">block_2</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">dm_blocks</span><span class="o">.</span><span class="n">values</span><span class="p">()):</span>
+                    <span class="n">row</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">block_1</span><span class="o">.</span><span class="n">real</span><span class="p">,</span> <span class="n">block_2</span><span class="o">.</span><span class="n">real</span><span class="p">))</span>
+                    <span class="nb">print</span><span class="p">((</span><span class="s1">&#39; &#39;</span><span class="o">*</span><span class="mi">11</span> <span class="o">+</span> <span class="n">fmt</span> <span class="o">+</span> <span class="s1">&#39;  &#39;</span> <span class="o">+</span> <span class="n">fmt</span><span class="p">)</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="o">*</span><span class="n">row</span><span class="p">))</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span></div>
+
+
+<span class="k">def</span> <span class="nf">print_local_density</span><span class="p">(</span><span class="n">density</span><span class="p">,</span> <span class="n">density_pre</span><span class="p">,</span> <span class="n">density_mat</span><span class="p">,</span> <span class="n">spin_orbit</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+        <span class="k">return</span>
+
+    <span class="k">if</span> <span class="n">spin_orbit</span><span class="p">:</span>
+        <span class="n">printed</span> <span class="o">=</span> <span class="p">((</span><span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">,</span> <span class="s1">&#39;real&#39;</span><span class="p">),</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">imag</span><span class="p">,</span> <span class="s1">&#39;imaginary&#39;</span><span class="p">))</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">printed</span> <span class="o">=</span> <span class="p">((</span><span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">,</span> <span class="s1">&#39;real&#39;</span><span class="p">),</span> <span class="p">)</span>
+
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">Total charge of impurity problem: </span><span class="si">{:7.5f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">density</span><span class="p">))</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Total charge convergency of impurity problem: </span><span class="si">{:7.5f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">density</span><span class="o">-</span><span class="n">density_pre</span><span class="p">))</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">Density matrix:&#39;</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">density_mat</span><span class="o">.</span><span class="n">items</span><span class="p">()):</span>
+        <span class="k">for</span> <span class="n">func</span><span class="p">,</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">printed</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{}</span><span class="s1">, </span><span class="si">{}</span><span class="s1"> part&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">name</span><span class="p">))</span>
+            <span class="nb">print</span><span class="p">(</span><span class="n">func</span><span class="p">(</span><span class="n">value</span><span class="p">))</span>
+        <span class="n">eigenvalues</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">eigvalsh</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;eigenvalues: </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">eigenvalues</span><span class="p">))</span>
+        <span class="c1"># check for large off-diagonal elements and write out a warning</span>
+        <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">value</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">diag</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">diag</span><span class="p">(</span><span class="n">value</span><span class="p">))))</span> <span class="o">&gt;=</span> <span class="mf">0.1</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">!!! WARNING !!!&#39;</span><span class="p">)</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;!!! large off diagonal elements in density matrix detected! I hope you know what you are doing !!!&#39;</span><span class="p">)</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;!!! WARNING !!!</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">print_summary_energetics</span><span class="p">(</span><span class="n">observables</span><span class="p">):</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+        <span class="k">return</span>
+
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span> <span class="o">+</span> <span class="s1">&#39;=&#39;</span><span class="o">*</span><span class="mi">60</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;summary of energetics:&#39;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;total energy: &#39;</span><span class="p">,</span> <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;E_tot&#39;</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;DFT energy: &#39;</span><span class="p">,</span> <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;E_dft&#39;</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;correlation energy: &#39;</span><span class="p">,</span> <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;E_corr_en&#39;</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;DFT band correction: &#39;</span><span class="p">,</span> <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;E_bandcorr&#39;</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;=&#39;</span><span class="o">*</span><span class="mi">60</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">print_summary_observables</span><span class="p">(</span><span class="n">observables</span><span class="p">,</span> <span class="n">n_inequiv_shells</span><span class="p">,</span> <span class="n">spin_block_names</span><span class="p">):</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+        <span class="k">return</span>
+
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;=&#39;</span><span class="o">*</span><span class="mi">60</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;summary of impurity observables:&#39;</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">icrsh</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_inequiv_shells</span><span class="p">):</span>
+        <span class="n">total_occ</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">([</span><span class="n">observables</span><span class="p">[</span><span class="s1">&#39;imp_occ&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">][</span><span class="n">spin</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">spin</span> <span class="ow">in</span> <span class="n">spin_block_names</span><span class="p">])</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;total occupany of impurity </span><span class="si">{}</span><span class="s1">: </span><span class="si">{:7.4f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">icrsh</span><span class="p">,</span> <span class="n">total_occ</span><span class="p">))</span>
+    <span class="k">for</span> <span class="n">icrsh</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_inequiv_shells</span><span class="p">):</span>
+        <span class="n">total_gb2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">([</span><span class="n">observables</span><span class="p">[</span><span class="s1">&#39;imp_gb2&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">][</span><span class="n">spin</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">spin</span> <span class="ow">in</span> <span class="n">spin_block_names</span><span class="p">])</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;G(beta/2) occ of impurity </span><span class="si">{}</span><span class="s1">: </span><span class="si">{:8.4f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">icrsh</span><span class="p">,</span> <span class="n">total_gb2</span><span class="p">))</span>
+    <span class="k">for</span> <span class="n">icrsh</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_inequiv_shells</span><span class="p">):</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Z (simple estimate) of impurity </span><span class="si">{}</span><span class="s1"> per orb:&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">icrsh</span><span class="p">))</span>
+        <span class="k">for</span> <span class="n">spin</span> <span class="ow">in</span> <span class="n">spin_block_names</span><span class="p">:</span>
+            <span class="n">Z_spin</span> <span class="o">=</span> <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;orb_Z&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">][</span><span class="n">spin</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{:&gt;5}</span><span class="s1">: &#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">spin</span><span class="p">)</span> <span class="o">+</span> <span class="s1">&#39; &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{:6.3f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">Z_orb</span><span class="p">)</span> <span class="k">for</span> <span class="n">Z_orb</span> <span class="ow">in</span> <span class="n">Z_spin</span><span class="p">))</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;=&#39;</span><span class="o">*</span><span class="mi">60</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">print_summary_magnetic_occ</span><span class="p">(</span><span class="n">observables</span><span class="p">,</span> <span class="n">n_inequiv_shells</span><span class="p">):</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+        <span class="k">return</span>
+
+    <span class="n">occ</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;up&#39;</span><span class="p">:</span> <span class="mf">0.0</span><span class="p">,</span> <span class="s1">&#39;down&#39;</span><span class="p">:</span> <span class="mf">0.0</span><span class="p">}</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span> <span class="o">+</span> <span class="s1">&#39;=&#39;</span><span class="o">*</span><span class="mi">60</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1"> *** summary of magnetic occupations: ***&#39;</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">icrsh</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_inequiv_shells</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">spin</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;up&#39;</span><span class="p">,</span> <span class="s1">&#39;down&#39;</span><span class="p">]:</span>
+            <span class="n">temp</span> <span class="o">=</span> <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;imp_occ&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">][</span><span class="n">spin</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;imp &#39;</span><span class="o">+</span><span class="nb">str</span><span class="p">(</span><span class="n">icrsh</span><span class="p">)</span><span class="o">+</span><span class="s1">&#39; spin &#39;</span><span class="o">+</span><span class="n">spin</span><span class="o">+</span><span class="s1">&#39;: </span><span class="si">{:6.4f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">temp</span><span class="p">))</span>
+            <span class="n">occ</span><span class="p">[</span><span class="n">spin</span><span class="p">]</span> <span class="o">+=</span> <span class="n">temp</span>
+
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;total spin up   occ: &#39;</span><span class="o">+</span><span class="s1">&#39;</span><span class="si">{:6.4f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">occ</span><span class="p">[</span><span class="s1">&#39;up&#39;</span><span class="p">]))</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;total spin down occ: &#39;</span><span class="o">+</span><span class="s1">&#39;</span><span class="si">{:6.4f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">occ</span><span class="p">[</span><span class="s1">&#39;down&#39;</span><span class="p">]))</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;=&#39;</span><span class="o">*</span><span class="mi">60</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">print_summary_convergence</span><span class="p">(</span><span class="n">conv_obs</span><span class="p">,</span> <span class="n">general_params</span><span class="p">,</span> <span class="n">n_inequiv_shells</span><span class="p">):</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+        <span class="k">return</span>
+
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;=&#39;</span><span class="o">*</span><span class="mi">60</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;convergence:&#39;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;δμ:      </span><span class="si">{:.4e}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">conv_obs</span><span class="p">[</span><span class="s1">&#39;d_mu&#39;</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">]))</span>
+    <span class="c1"># if calc energies calc /print also the diff in Etot</span>
+    <span class="k">if</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;calc_energies&#39;</span><span class="p">]:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;δE_tot:  </span><span class="si">{:.4e}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">conv_obs</span><span class="p">[</span><span class="s1">&#39;d_Etot&#39;</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">]))</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;---&quot;</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">icrsh</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_inequiv_shells</span><span class="p">):</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Impurity &#39;</span><span class="o">+</span><span class="nb">str</span><span class="p">(</span><span class="n">icrsh</span><span class="p">)</span><span class="o">+</span><span class="s1">&#39;:&#39;</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;δn imp : </span><span class="si">{:.4e}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">conv_obs</span><span class="p">[</span><span class="s1">&#39;d_imp_occ&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">]))</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;δn orb : &#39;</span><span class="o">+</span><span class="s1">&#39;  &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{:.4e}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">orb</span><span class="p">)</span> <span class="k">for</span> <span class="n">orb</span> <span class="ow">in</span> <span class="n">conv_obs</span><span class="p">[</span><span class="s1">&#39;d_orb_occ&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">]))</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;δ Gimp : </span><span class="si">{:.4e}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">conv_obs</span><span class="p">[</span><span class="s1">&#39;d_Gimp&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">]))</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;δ G0   : </span><span class="si">{:.4e}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">conv_obs</span><span class="p">[</span><span class="s1">&#39;d_G0&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">]))</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;δ Σ    : </span><span class="si">{:.4e}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">conv_obs</span><span class="p">[</span><span class="s1">&#39;d_Sigma&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">]))</span>
+
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;=&#39;</span><span class="o">*</span><span class="mi">60</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
+</pre></div>
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.</p>
+  </div>
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diff --git a/_modules/dmft_tools/initial_self_energies.html b/_modules/dmft_tools/initial_self_energies.html
new file mode 100644
index 00000000..a8e8c825
--- /dev/null
+++ b/_modules/dmft_tools/initial_self_energies.html
@@ -0,0 +1,802 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" >
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>dmft_tools.initial_self_energies &mdash; solid_dmft  documentation</title>
+      <link rel="stylesheet" href="../../_static/pygments.css" type="text/css" />
+      <link rel="stylesheet" href="../../_static/css/custom.css" type="text/css" />
+      <link rel="stylesheet" href="../../_static/plot_directive.css" type="text/css" />
+    <link rel="shortcut icon" href="../../_static/favicon.ico"/>
+  
+        <script data-url_root="../../" id="documentation_options" src="../../_static/documentation_options.js"></script>
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+    <script src="../../_static/js/theme.js"></script>
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+              <div class="version">
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+              <ul>
+<li class="toctree-l1"><a class="reference internal" href="../../install.html">Installation</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#installation-via-pip">Installation via pip</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#manual-installation-via-cmake">Manual installation via CMake</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#docker-files-images">Docker files &amp; images</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#dft-interface-notes">DFT interface notes</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/w90_interface.html">Wannier90 interface</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/w90_interface.html#orbital-order-in-the-w90-converter">orbital order in the W90 converter</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/vasp_csc.html">Interface to VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#general-remarks">General remarks</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#file-description">file description</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#workflow">Workflow:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#important-flags">important flags:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#version-and-compilation">version and compilation:</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#input-output">Input/Output</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../input_output/DMFT_input/input.html">Input</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/general.html">[general]: General parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/solver.html">[solver]: solver specific parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/dft.html">[dft]: DFT related inputs</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/advanced.html">[advanced]: Advanced inputs</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../input_output/DMFT_output/results.html">Output / results</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_output/results.html#group-structure">Group structure</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_output/results.html#subgroups">Subgroups</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#further-details-for-running">Further details for running</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/docker.html">Docker</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#building-the-docker-image">Building the docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#pulling-a-docker-image">Pulling a docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_locally.html">Run on your machine</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#module-reference-manual">Module reference manual</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/csc_flow.html">csc_flow</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/dft_managers.html">dft_managers</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.mpi_helpers.html">dft_managers.mpi_helpers</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.qe_manager.html">dft_managers.qe_manager</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.vasp_manager.html">dft_managers.vasp_manager</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/dmft_cycle.html">dmft_cycle</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/dmft_tools.html">dmft_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.afm_mapping.html">dmft_tools.afm_mapping</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.formatter.html">dmft_tools.formatter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.greens_functions_mixer.html">dmft_tools.greens_functions_mixer</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.initial_self_energies.html">dmft_tools.initial_self_energies</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.interaction_hamiltonian.html">dmft_tools.interaction_hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.legendre_filter.html">dmft_tools.legendre_filter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.manipulate_chemical_potential.html">dmft_tools.manipulate_chemical_potential</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.matheval.html">dmft_tools.matheval</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.observables.html">dmft_tools.observables</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.results_to_archive.html">dmft_tools.results_to_archive</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.solver.html">dmft_tools.solver</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/io_tools.html">io_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.dict_to_h5.html">io_tools.dict_to_h5</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.postproc_toml_dict.html">io_tools.postproc_toml_dict</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.verify_input_params.html">io_tools.verify_input_params</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_gf_imp.html">postprocessing.maxent_gf_imp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_gf_latt.html">postprocessing.maxent_gf_latt</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_sigma.html">postprocessing.maxent_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
+</li>
+</ul>
+</li>
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+          <li><a href="../index.html">Module code</a> &raquo;</li>
+      <li>dmft_tools.initial_self_energies</li>
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+      </li>
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+             
+  <h1>Source code for dmft_tools.initial_self_energies</h1><div class="highlight"><pre>
+<span></span><span class="c1">################################################################################</span>
+<span class="c1">#</span>
+<span class="c1"># solid_dmft - A versatile python wrapper to perform DFT+DMFT calculations</span>
+<span class="c1">#              utilizing the TRIQS software library</span>
+<span class="c1">#</span>
+<span class="c1"># Copyright (C) 2018-2020, ETH Zurich</span>
+<span class="c1"># Copyright (C) 2021, The Simons Foundation</span>
+<span class="c1">#      authors: A. Hampel, M. Merkel, and S. Beck</span>
+<span class="c1">#</span>
+<span class="c1"># solid_dmft is free software: you can redistribute it and/or modify it under the</span>
+<span class="c1"># terms of the GNU General Public License as published by the Free Software</span>
+<span class="c1"># Foundation, either version 3 of the License, or (at your option) any later</span>
+<span class="c1"># version.</span>
+<span class="c1">#</span>
+<span class="c1"># solid_dmft is distributed in the hope that it will be useful, but WITHOUT ANY</span>
+<span class="c1"># WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A</span>
+<span class="c1"># PARTICULAR PURPOSE. See the GNU General Public License for more details.</span>
+
+<span class="c1"># You should have received a copy of the GNU General Public License along with</span>
+<span class="c1"># solid_dmft (in the file COPYING.txt in this directory). If not, see</span>
+<span class="c1"># &lt;http://www.gnu.org/licenses/&gt;.</span>
+<span class="c1">#</span>
+<span class="c1">################################################################################</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Contains all functions related to determining the double counting and the</span>
+<span class="sd">initial self-energy.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="c1"># system</span>
+<span class="kn">from</span> <span class="nn">copy</span> <span class="kn">import</span> <span class="n">deepcopy</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<span class="c1"># triqs</span>
+<span class="kn">from</span> <span class="nn">h5</span> <span class="kn">import</span> <span class="n">HDFArchive</span>
+<span class="kn">import</span> <span class="nn">triqs.utility.mpi</span> <span class="k">as</span> <span class="nn">mpi</span>
+<span class="kn">from</span> <span class="nn">triqs.gf</span> <span class="kn">import</span> <span class="n">BlockGf</span><span class="p">,</span> <span class="n">Gf</span>
+
+
+<div class="viewcode-block" id="calculate_double_counting"><a class="viewcode-back" href="../../_ref/dmft_tools.initial_self_energies.html#dmft_tools.initial_self_energies.calculate_double_counting">[docs]</a><span class="k">def</span> <span class="nf">calculate_double_counting</span><span class="p">(</span><span class="n">sum_k</span><span class="p">,</span> <span class="n">density_matrix</span><span class="p">,</span> <span class="n">general_params</span><span class="p">,</span> <span class="n">advanced_params</span><span class="p">,</span> <span class="n">solver_type_per_imp</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Calculates the double counting, including all manipulations from advanced_params.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    sum_k : SumkDFT object</span>
+<span class="sd">    density_matrix : list of gf_struct_solver like</span>
+<span class="sd">        List of density matrices for all inequivalent shells</span>
+<span class="sd">    general_params : dict</span>
+<span class="sd">        general parameters as a dict</span>
+<span class="sd">    advanced_params : dict</span>
+<span class="sd">        advanced parameters as a dict</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    --------</span>
+<span class="sd">    sum_k : SumKDFT object</span>
+<span class="sd">        The SumKDFT object containing the updated double counting</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">*** DC determination ***&#39;</span><span class="p">)</span>
+
+    <span class="c1"># copy the density matrix to not change it</span>
+    <span class="n">density_matrix_DC</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">density_matrix</span><span class="p">)</span>
+
+    <span class="c1"># TODO: suppress print when reseting DC to zero</span>
+    <span class="c1">#       and add a final print of the DC pot/energy at the end of the whole function</span>
+    <span class="n">icrsh_hartree</span> <span class="o">=</span> <span class="p">[</span><span class="n">icrsh</span> <span class="k">for</span> <span class="n">icrsh</span><span class="p">,</span> <span class="nb">type</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">solver_type_per_imp</span><span class="p">)</span> <span class="k">if</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">&#39;hartree&#39;</span><span class="p">]</span>
+    <span class="n">icrsh_not_hartree</span> <span class="o">=</span> <span class="p">[</span><span class="n">icrsh</span> <span class="k">for</span> <span class="n">icrsh</span><span class="p">,</span> <span class="nb">type</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">solver_type_per_imp</span><span class="p">)</span> <span class="k">if</span> <span class="nb">type</span> <span class="o">!=</span> <span class="s1">&#39;hartree&#39;</span><span class="p">]</span>
+    <span class="k">if</span> <span class="n">icrsh_hartree</span><span class="p">:</span>
+        <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">SOLID_DMFT: Hartree solver for impurities </span><span class="si">{</span><span class="n">icrsh_hartree</span><span class="si">}</span><span class="s1"> detected. &#39;</span>
+                   <span class="s1">&#39;Zeroing out the DC correction there, which gets computed at the solver level.&#39;</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">icrsh</span> <span class="ow">in</span> <span class="n">icrsh_hartree</span><span class="p">:</span>
+            <span class="n">sum_k</span><span class="o">.</span><span class="n">calc_dc</span><span class="p">(</span><span class="n">density_matrix_DC</span><span class="p">[</span><span class="n">icrsh</span><span class="p">],</span> <span class="n">orb</span><span class="o">=</span><span class="n">icrsh</span><span class="p">,</span>
+                          <span class="n">use_dc_value</span><span class="o">=</span><span class="mf">0.0</span><span class="p">)</span>
+
+    <span class="c1"># Sets the DC and exits the function if advanced_params[&#39;dc_fixed_value&#39;] is specified</span>
+    <span class="k">if</span> <span class="n">advanced_params</span><span class="p">[</span><span class="s1">&#39;dc_fixed_value&#39;</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">for</span> <span class="n">icrsh</span> <span class="ow">in</span> <span class="n">icrsh_not_hartree</span><span class="p">:</span>
+            <span class="n">sum_k</span><span class="o">.</span><span class="n">calc_dc</span><span class="p">(</span><span class="n">density_matrix_DC</span><span class="p">[</span><span class="n">icrsh</span><span class="p">],</span> <span class="n">orb</span><span class="o">=</span><span class="n">icrsh</span><span class="p">,</span>
+                          <span class="n">use_dc_value</span><span class="o">=</span><span class="n">advanced_params</span><span class="p">[</span><span class="s1">&#39;dc_fixed_value&#39;</span><span class="p">])</span>
+        <span class="k">return</span> <span class="n">sum_k</span>
+
+    <span class="k">for</span> <span class="n">icrsh</span> <span class="ow">in</span> <span class="n">icrsh_not_hartree</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">advanced_params</span><span class="p">[</span><span class="s1">&#39;dc_fixed_occ&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Fixing occupation for DC for imp </span><span class="si">{</span><span class="n">icrsh</span><span class="si">}</span><span class="s1"> to n=</span><span class="si">{</span><span class="n">advanced_params</span><span class="p">[</span><span class="s2">&quot;dc_fixed_occ&quot;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">]</span><span class="si">:</span><span class="s1">.4f</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+            <span class="n">n_orb</span> <span class="o">=</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">corr_shells</span><span class="p">[</span><span class="n">icrsh</span><span class="p">][</span><span class="s1">&#39;dim&#39;</span><span class="p">]</span>
+            <span class="c1"># we need to handover a matrix to calc_dc so calc occ per orb per spin channel</span>
+            <span class="n">orb_occ</span> <span class="o">=</span> <span class="n">advanced_params</span><span class="p">[</span><span class="s1">&#39;dc_fixed_occ&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">]</span><span class="o">/</span><span class="p">(</span><span class="n">n_orb</span><span class="o">*</span><span class="mi">2</span><span class="p">)</span>
+            <span class="c1"># setting occ of each diag orb element to calc value</span>
+            <span class="k">for</span> <span class="n">inner</span> <span class="ow">in</span> <span class="n">density_matrix_DC</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">():</span>
+                <span class="n">np</span><span class="o">.</span><span class="n">fill_diagonal</span><span class="p">(</span><span class="n">inner</span><span class="p">,</span> <span class="n">orb_occ</span><span class="o">+</span><span class="mf">0.0</span><span class="n">j</span><span class="p">)</span>
+
+    <span class="c1"># The regular way: calculates the DC based on U, J and the dc_type</span>
+    <span class="k">for</span> <span class="n">icrsh</span> <span class="ow">in</span> <span class="n">icrsh_not_hartree</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;dc_type&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">]</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+            <span class="c1"># this is FLL for eg orbitals only as done in Seth PRB 96 205139 2017 eq 10</span>
+            <span class="c1"># this setting for U and J is reasonable as it is in the spirit of F0 and Javg</span>
+            <span class="c1"># for the 5 orb case</span>
+            <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;Doing FLL DC for eg orbitals only with Uavg=U-J and Javg=2*J&#39;</span><span class="p">)</span>
+            <span class="n">Uavg</span> <span class="o">=</span> <span class="n">advanced_params</span><span class="p">[</span><span class="s1">&#39;dc_U&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">]</span> <span class="o">-</span> <span class="n">advanced_params</span><span class="p">[</span><span class="s1">&#39;dc_J&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">]</span>
+            <span class="n">Javg</span> <span class="o">=</span> <span class="mi">2</span><span class="o">*</span><span class="n">advanced_params</span><span class="p">[</span><span class="s1">&#39;dc_J&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">]</span>
+            <span class="n">sum_k</span><span class="o">.</span><span class="n">calc_dc</span><span class="p">(</span><span class="n">density_matrix_DC</span><span class="p">[</span><span class="n">icrsh</span><span class="p">],</span> <span class="n">U_interact</span><span class="o">=</span><span class="n">Uavg</span><span class="p">,</span> <span class="n">J_hund</span><span class="o">=</span><span class="n">Javg</span><span class="p">,</span>
+                          <span class="n">orb</span><span class="o">=</span><span class="n">icrsh</span><span class="p">,</span> <span class="n">use_dc_formula</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">Calculating standard DC for impurity </span><span class="si">{</span><span class="n">icrsh</span><span class="si">}</span><span class="s1"> with U=</span><span class="si">{</span><span class="n">advanced_params</span><span class="p">[</span><span class="s2">&quot;dc_U&quot;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">]</span><span class="si">}</span><span class="s1"> and J=</span><span class="si">{</span><span class="n">advanced_params</span><span class="p">[</span><span class="s2">&quot;dc_J&quot;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">]</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+            <span class="n">sum_k</span><span class="o">.</span><span class="n">calc_dc</span><span class="p">(</span><span class="n">density_matrix_DC</span><span class="p">[</span><span class="n">icrsh</span><span class="p">],</span> <span class="n">U_interact</span><span class="o">=</span><span class="n">advanced_params</span><span class="p">[</span><span class="s1">&#39;dc_U&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">],</span>
+                          <span class="n">J_hund</span><span class="o">=</span><span class="n">advanced_params</span><span class="p">[</span><span class="s1">&#39;dc_J&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">],</span> <span class="n">orb</span><span class="o">=</span><span class="n">icrsh</span><span class="p">,</span>
+                          <span class="n">use_dc_formula</span><span class="o">=</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;dc_type&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">])</span>
+
+    <span class="c1"># for the fixed DC according to https://doi.org/10.1103/PhysRevB.90.075136</span>
+    <span class="c1"># dc_imp is calculated with fixed occ but dc_energ is calculated with given n</span>
+    <span class="k">if</span> <span class="n">advanced_params</span><span class="p">[</span><span class="s1">&#39;dc_nominal&#39;</span><span class="p">]:</span>
+        <span class="k">if</span> <span class="s1">&#39;Hartree&#39;</span> <span class="ow">in</span> <span class="n">solver_type_per_imp</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">&#39;dc_nominal not implemented in presence of Hartree solver&#39;</span><span class="p">)</span>
+        <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">calculating DC energy with fixed DC potential from above</span><span class="se">\n</span><span class="s1">&#39;</span>
+                   <span class="o">+</span> <span class="s1">&#39; for the original density matrix doi.org/10.1103/PhysRevB.90.075136</span><span class="se">\n</span><span class="s1">&#39;</span>
+                   <span class="o">+</span> <span class="s1">&#39; aka nominal DC&#39;</span><span class="p">)</span>
+        <span class="n">dc_imp</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">dc_imp</span><span class="p">)</span>
+        <span class="n">dc_new_en</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">dc_energ</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">ish</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_corr_shells</span><span class="p">):</span>
+            <span class="n">n_DC</span> <span class="o">=</span> <span class="mf">0.0</span>
+            <span class="k">for</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">density_matrix</span><span class="p">[</span><span class="n">sum_k</span><span class="o">.</span><span class="n">corr_to_inequiv</span><span class="p">[</span><span class="n">ish</span><span class="p">]]</span><span class="o">.</span><span class="n">values</span><span class="p">():</span>
+                <span class="n">n_DC</span> <span class="o">+=</span> <span class="n">np</span><span class="o">.</span><span class="n">trace</span><span class="p">(</span><span class="n">value</span><span class="o">.</span><span class="n">real</span><span class="p">)</span>
+
+            <span class="c1"># calculate new DC_energ as n*V_DC</span>
+            <span class="c1"># average over blocks in case blocks have different imp</span>
+            <span class="n">dc_new_en</span><span class="p">[</span><span class="n">ish</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.0</span>
+            <span class="k">for</span> <span class="n">spin</span><span class="p">,</span> <span class="n">dc_per_spin</span> <span class="ow">in</span> <span class="n">dc_imp</span><span class="p">[</span><span class="n">ish</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+                <span class="c1"># assuming that the DC potential is the same for all orbitals</span>
+                <span class="c1"># dc_per_spin is a list for each block containing on the diag</span>
+                <span class="c1"># elements the DC potential for the self-energy correction</span>
+                <span class="n">dc_new_en</span><span class="p">[</span><span class="n">ish</span><span class="p">]</span> <span class="o">+=</span> <span class="n">n_DC</span> <span class="o">*</span> <span class="n">dc_per_spin</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+            <span class="n">dc_new_en</span><span class="p">[</span><span class="n">ish</span><span class="p">]</span> <span class="o">=</span> <span class="n">dc_new_en</span><span class="p">[</span><span class="n">ish</span><span class="p">]</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">dc_imp</span><span class="p">[</span><span class="n">ish</span><span class="p">])</span>
+        <span class="n">sum_k</span><span class="o">.</span><span class="n">set_dc</span><span class="p">(</span><span class="n">dc_imp</span><span class="p">,</span> <span class="n">dc_new_en</span><span class="p">)</span>
+
+        <span class="c1"># Print new DC values</span>
+        <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">Fixed occ, new DC values:&#39;</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">icrsh</span><span class="p">,</span> <span class="p">(</span><span class="n">dc_per_shell</span><span class="p">,</span> <span class="n">energy_per_shell</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">dc_imp</span><span class="p">,</span> <span class="n">dc_new_en</span><span class="p">)):</span>
+            <span class="k">for</span> <span class="n">spin</span><span class="p">,</span> <span class="n">dc_per_spin</span> <span class="ow">in</span> <span class="n">dc_per_shell</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+                <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;DC for shell </span><span class="si">{}</span><span class="s1"> and block </span><span class="si">{}</span><span class="s1"> = </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">icrsh</span><span class="p">,</span> <span class="n">spin</span><span class="p">,</span> <span class="n">dc_per_spin</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]))</span>
+            <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;DC energy for shell </span><span class="si">{}</span><span class="s1"> = </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">icrsh</span><span class="p">,</span> <span class="n">energy_per_shell</span><span class="p">))</span>
+
+    <span class="c1"># Rescales DC if advanced_params[&#39;dc_factor&#39;] is given</span>
+    <span class="k">if</span> <span class="n">advanced_params</span><span class="p">[</span><span class="s1">&#39;dc_factor&#39;</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="c1"># Here, no check for Hartree since its DC is 0 and the scaling doesn&#39;t change that</span>
+        <span class="n">rescaled_dc_imp</span> <span class="o">=</span> <span class="p">[{</span><span class="n">spin</span><span class="p">:</span> <span class="n">advanced_params</span><span class="p">[</span><span class="s1">&#39;dc_factor&#39;</span><span class="p">]</span> <span class="o">*</span> <span class="n">dc_per_spin</span>
+                            <span class="k">for</span> <span class="n">spin</span><span class="p">,</span> <span class="n">dc_per_spin</span> <span class="ow">in</span> <span class="n">dc_per_shell</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
+                          <span class="k">for</span> <span class="n">dc_per_shell</span> <span class="ow">in</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">dc_imp</span><span class="p">]</span>
+        <span class="n">rescaled_dc_energy</span> <span class="o">=</span> <span class="p">[</span><span class="n">advanced_params</span><span class="p">[</span><span class="s1">&#39;dc_factor&#39;</span><span class="p">]</span> <span class="o">*</span> <span class="n">energy_per_shell</span>
+                              <span class="k">for</span> <span class="n">energy_per_shell</span> <span class="ow">in</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">dc_energ</span><span class="p">]</span>
+        <span class="n">sum_k</span><span class="o">.</span><span class="n">set_dc</span><span class="p">(</span><span class="n">rescaled_dc_imp</span><span class="p">,</span> <span class="n">rescaled_dc_energy</span><span class="p">)</span>
+
+        <span class="c1"># Print new DC values</span>
+        <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">Rescaled DC, new DC values:&#39;</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">icrsh</span><span class="p">,</span> <span class="p">(</span><span class="n">dc_per_shell</span><span class="p">,</span> <span class="n">energy_per_shell</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">rescaled_dc_imp</span><span class="p">,</span> <span class="n">rescaled_dc_energy</span><span class="p">)):</span>
+            <span class="k">for</span> <span class="n">spin</span><span class="p">,</span> <span class="n">dc_per_spin</span> <span class="ow">in</span> <span class="n">dc_per_shell</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+                <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;DC for shell </span><span class="si">{}</span><span class="s1"> and block </span><span class="si">{}</span><span class="s1"> = </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">icrsh</span><span class="p">,</span> <span class="n">spin</span><span class="p">,</span> <span class="n">dc_per_spin</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]))</span>
+            <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;DC energy for shell </span><span class="si">{}</span><span class="s1"> = </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">icrsh</span><span class="p">,</span> <span class="n">energy_per_shell</span><span class="p">))</span>
+
+    <span class="k">if</span> <span class="n">advanced_params</span><span class="p">[</span><span class="s1">&#39;dc_orb_shift&#39;</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="s1">&#39;Hartree&#39;</span> <span class="ow">in</span> <span class="n">solver_type_per_imp</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">&#39;dc_orb_shift not implemented in presence of Hartree solver&#39;</span><span class="p">)</span>
+        <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;adding an extra orbital dependent shift per impurity&#39;</span><span class="p">)</span>
+        <span class="n">tot_norb</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="n">dc_orb_shift</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">dc_orb_shift_orig</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">advanced_params</span><span class="p">[</span><span class="s1">&#39;dc_orb_shift&#39;</span><span class="p">])</span>
+        <span class="k">for</span> <span class="n">icrsh</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span><span class="p">):</span>
+            <span class="n">tot_norb</span> <span class="o">+=</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">corr_shells</span><span class="p">[</span><span class="n">icrsh</span><span class="p">][</span><span class="s1">&#39;dim&#39;</span><span class="p">]</span>
+            <span class="n">dc_orb_shift</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dc_orb_shift_orig</span><span class="p">[:</span><span class="n">sum_k</span><span class="o">.</span><span class="n">corr_shells</span><span class="p">[</span><span class="n">icrsh</span><span class="p">][</span><span class="s1">&#39;dim&#39;</span><span class="p">]])</span>
+            <span class="k">del</span> <span class="n">dc_orb_shift_orig</span><span class="p">[:</span><span class="n">sum_k</span><span class="o">.</span><span class="n">corr_shells</span><span class="p">[</span><span class="n">icrsh</span><span class="p">][</span><span class="s1">&#39;dim&#39;</span><span class="p">]]</span>
+
+        <span class="n">dc_orb_shift</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">dc_orb_shift</span><span class="p">)</span>
+        <span class="n">dc</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">icrsh</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span><span class="p">):</span>
+            <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;shift on imp </span><span class="si">{</span><span class="n">icrsh</span><span class="si">}</span><span class="s1">: </span><span class="si">{</span><span class="n">dc_orb_shift</span><span class="p">[</span><span class="n">icrsh</span><span class="p">,:]</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+            <span class="n">dc</span><span class="o">.</span><span class="n">append</span><span class="p">({})</span>
+            <span class="k">for</span> <span class="n">spin</span><span class="p">,</span> <span class="n">dc_per_spin</span> <span class="ow">in</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">dc_imp</span><span class="p">[</span><span class="n">sum_k</span><span class="o">.</span><span class="n">inequiv_to_corr</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]]</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+                <span class="n">dc</span><span class="p">[</span><span class="n">icrsh</span><span class="p">][</span><span class="n">spin</span><span class="p">]</span> <span class="o">=</span> <span class="n">dc_per_spin</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">diag</span><span class="p">(</span><span class="n">dc_orb_shift</span><span class="p">[</span><span class="n">icrsh</span><span class="p">,:])</span>
+
+        <span class="k">for</span> <span class="n">ish</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_corr_shells</span><span class="p">):</span>
+            <span class="n">sum_k</span><span class="o">.</span><span class="n">dc_imp</span><span class="p">[</span><span class="n">ish</span><span class="p">]</span> <span class="o">=</span> <span class="n">dc</span><span class="p">[</span><span class="n">sum_k</span><span class="o">.</span><span class="n">corr_to_inequiv</span><span class="p">[</span><span class="n">ish</span><span class="p">]]</span>
+
+    <span class="k">return</span> <span class="n">sum_k</span></div>
+
+
+<span class="k">def</span> <span class="nf">_load_sigma_from_h5</span><span class="p">(</span><span class="n">h5_archive</span><span class="p">,</span> <span class="n">iteration</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Reads impurity self-energy for all impurities from file and returns them as a list</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    h5_archive : HDFArchive</span>
+<span class="sd">        HDFArchive to read from</span>
+<span class="sd">    iteration : int</span>
+<span class="sd">        at which iteration will sigma be loaded</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    --------</span>
+<span class="sd">    self_energies : list of green functions</span>
+
+<span class="sd">    dc_imp : numpy array</span>
+<span class="sd">        DC potentials</span>
+<span class="sd">    dc_energy : numpy array</span>
+<span class="sd">        DC energies per impurity</span>
+<span class="sd">    density_matrix : numpy arrays</span>
+<span class="sd">        Density matrix from the previous self-energy</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">internal_path</span> <span class="o">=</span> <span class="s1">&#39;DMFT_results/&#39;</span>
+    <span class="n">internal_path</span> <span class="o">+=</span> <span class="s1">&#39;last_iter&#39;</span> <span class="k">if</span> <span class="n">iteration</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span> <span class="k">else</span> <span class="s1">&#39;it_</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">iteration</span><span class="p">)</span>
+
+    <span class="n">n_inequiv_shells</span> <span class="o">=</span> <span class="n">h5_archive</span><span class="p">[</span><span class="s1">&#39;dft_input&#39;</span><span class="p">][</span><span class="s1">&#39;n_inequiv_shells&#39;</span><span class="p">]</span>
+
+    <span class="c1"># Loads previous self-energies and DC</span>
+    <span class="n">self_energies</span> <span class="o">=</span> <span class="p">[</span><span class="n">h5_archive</span><span class="p">[</span><span class="n">internal_path</span><span class="p">][</span><span class="s1">&#39;Sigma_freq_</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">iineq</span><span class="p">)]</span>
+                     <span class="k">for</span> <span class="n">iineq</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_inequiv_shells</span><span class="p">)]</span>
+    <span class="n">last_g0</span> <span class="o">=</span> <span class="p">[</span><span class="n">h5_archive</span><span class="p">[</span><span class="n">internal_path</span><span class="p">][</span><span class="s1">&#39;G0_freq_</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">iineq</span><span class="p">)]</span>
+                     <span class="k">for</span> <span class="n">iineq</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_inequiv_shells</span><span class="p">)]</span>
+    <span class="n">dc_imp</span> <span class="o">=</span> <span class="n">h5_archive</span><span class="p">[</span><span class="n">internal_path</span><span class="p">][</span><span class="s1">&#39;DC_pot&#39;</span><span class="p">]</span>
+    <span class="n">dc_energy</span> <span class="o">=</span> <span class="n">h5_archive</span><span class="p">[</span><span class="n">internal_path</span><span class="p">][</span><span class="s1">&#39;DC_energ&#39;</span><span class="p">]</span>
+
+    <span class="c1"># Loads density_matrix to recalculate DC if dc_dmft</span>
+    <span class="n">density_matrix</span> <span class="o">=</span> <span class="n">h5_archive</span><span class="p">[</span><span class="n">internal_path</span><span class="p">][</span><span class="s1">&#39;dens_mat_post&#39;</span><span class="p">]</span>
+
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Loaded Sigma_imp0...imp</span><span class="si">{}</span><span class="s1"> &#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">n_inequiv_shells</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+          <span class="o">+</span> <span class="p">(</span><span class="s1">&#39;at last it &#39;</span> <span class="k">if</span> <span class="n">iteration</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span> <span class="k">else</span> <span class="s1">&#39;at it </span><span class="si">{}</span><span class="s1"> &#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">iteration</span><span class="p">)))</span>
+
+    <span class="k">return</span> <span class="n">self_energies</span><span class="p">,</span> <span class="n">dc_imp</span><span class="p">,</span> <span class="n">dc_energy</span><span class="p">,</span> <span class="n">last_g0</span><span class="p">,</span> <span class="n">density_matrix</span>
+
+
+<span class="k">def</span> <span class="nf">_sumk_sigma_to_solver_struct</span><span class="p">(</span><span class="n">sum_k</span><span class="p">,</span> <span class="n">start_sigma</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Extracts the local Sigma. Copied from SumkDFT.extract_G_loc, version 2.1.x.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    sum_k : SumkDFT object</span>
+<span class="sd">        Sumk object with the information about the correct block structure</span>
+<span class="sd">    start_sigma : list of BlockGf (Green&#39;s function) objects</span>
+<span class="sd">        List of Sigmas in sum_k block structure that are to be converted.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    Sigma_inequiv : list of BlockGf (Green&#39;s function) objects</span>
+<span class="sd">        List of Sigmas that can be used to initialize the solver</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">Sigma_local</span> <span class="o">=</span> <span class="p">[</span><span class="n">start_sigma</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span> <span class="k">for</span> <span class="n">icrsh</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_corr_shells</span><span class="p">)]</span>
+    <span class="n">Sigma_inequiv</span> <span class="o">=</span> <span class="p">[</span><span class="n">BlockGf</span><span class="p">(</span><span class="n">name_block_generator</span><span class="o">=</span><span class="p">[(</span><span class="n">block</span><span class="p">,</span> <span class="n">Gf</span><span class="p">(</span><span class="n">mesh</span><span class="o">=</span><span class="n">Sigma_local</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">mesh</span><span class="p">,</span> <span class="n">target_shape</span><span class="o">=</span><span class="p">(</span><span class="n">dim</span><span class="p">,</span> <span class="n">dim</span><span class="p">)))</span>
+                                                   <span class="k">for</span> <span class="n">block</span><span class="p">,</span> <span class="n">dim</span> <span class="ow">in</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">gf_struct_solver</span><span class="p">[</span><span class="n">ish</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()],</span>
+                             <span class="n">make_copies</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> <span class="k">for</span> <span class="n">ish</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span><span class="p">)]</span>
+
+    <span class="c1"># G_loc is rotated to the local coordinate system</span>
+    <span class="k">if</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">use_rotations</span><span class="p">:</span>
+        <span class="k">for</span> <span class="n">icrsh</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_corr_shells</span><span class="p">):</span>
+            <span class="k">for</span> <span class="n">bname</span><span class="p">,</span> <span class="n">gf</span> <span class="ow">in</span> <span class="n">Sigma_local</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]:</span>
+                <span class="n">Sigma_local</span><span class="p">[</span><span class="n">icrsh</span><span class="p">][</span><span class="n">bname</span><span class="p">]</span> <span class="o">&lt;&lt;</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">rotloc</span><span class="p">(</span>
+                    <span class="n">icrsh</span><span class="p">,</span> <span class="n">gf</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="s1">&#39;toLocal&#39;</span><span class="p">)</span>
+
+    <span class="c1"># transform to CTQMC blocks</span>
+    <span class="k">for</span> <span class="n">ish</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">block</span><span class="p">,</span> <span class="n">dim</span> <span class="ow">in</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">gf_struct_solver</span><span class="p">[</span><span class="n">ish</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+            <span class="k">for</span> <span class="n">ind1</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">dim</span><span class="p">):</span>
+                <span class="k">for</span> <span class="n">ind2</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">dim</span><span class="p">):</span>
+                    <span class="n">block_sumk</span><span class="p">,</span> <span class="n">ind1_sumk</span> <span class="o">=</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">solver_to_sumk</span><span class="p">[</span><span class="n">ish</span><span class="p">][(</span><span class="n">block</span><span class="p">,</span> <span class="n">ind1</span><span class="p">)]</span>
+                    <span class="n">block_sumk</span><span class="p">,</span> <span class="n">ind2_sumk</span> <span class="o">=</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">solver_to_sumk</span><span class="p">[</span><span class="n">ish</span><span class="p">][(</span><span class="n">block</span><span class="p">,</span> <span class="n">ind2</span><span class="p">)]</span>
+                    <span class="n">Sigma_inequiv</span><span class="p">[</span><span class="n">ish</span><span class="p">][</span><span class="n">block</span><span class="p">][</span><span class="n">ind1</span><span class="p">,</span> <span class="n">ind2</span><span class="p">]</span> <span class="o">&lt;&lt;</span> <span class="n">Sigma_local</span><span class="p">[</span>
+                        <span class="n">sum_k</span><span class="o">.</span><span class="n">inequiv_to_corr</span><span class="p">[</span><span class="n">ish</span><span class="p">]][</span><span class="n">block_sumk</span><span class="p">][</span><span class="n">ind1_sumk</span><span class="p">,</span> <span class="n">ind2_sumk</span><span class="p">]</span>
+
+    <span class="c1"># return only the inequivalent shells</span>
+    <span class="k">return</span> <span class="n">Sigma_inequiv</span>
+
+
+<span class="k">def</span> <span class="nf">_set_loaded_sigma</span><span class="p">(</span><span class="n">sum_k</span><span class="p">,</span> <span class="n">loaded_sigma</span><span class="p">,</span> <span class="n">loaded_dc_imp</span><span class="p">,</span> <span class="n">general_params</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Adjusts for the Hartree shift when loading a self energy Sigma_freq from a</span>
+<span class="sd">    previous calculation that was run with a different U, J or double counting.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    sum_k : SumkDFT object</span>
+<span class="sd">        Sumk object with the information about the correct block structure</span>
+<span class="sd">    loaded_sigma : list of BlockGf (Green&#39;s function) objects</span>
+<span class="sd">        List of Sigmas loaded from the previous calculation</span>
+<span class="sd">    loaded_dc_imp : list of dicts</span>
+<span class="sd">        List of dicts containing the loaded DC. Used to adjust the Hartree shift.</span>
+<span class="sd">    general_params : dict</span>
+<span class="sd">        general parameters as a dict</span>
+
+<span class="sd">    Raises</span>
+<span class="sd">    ------</span>
+<span class="sd">    ValueError</span>
+<span class="sd">        Raised if the block structure between the loaded and the Sumk DC_imp</span>
+<span class="sd">        does not agree.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    start_sigma : list of BlockGf (Green&#39;s function) objects</span>
+<span class="sd">        List of Sigmas, loaded Sigma adjusted for the new Hartree term</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># Compares loaded and new double counting</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">loaded_dc_imp</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">dc_imp</span><span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Loaded double counting has a different number of &#39;</span>
+                         <span class="o">+</span> <span class="s1">&#39;correlated shells than current calculation.&#39;</span><span class="p">)</span>
+
+    <span class="n">has_double_counting_changed</span> <span class="o">=</span> <span class="kc">False</span>
+    <span class="k">for</span> <span class="n">loaded_dc_shell</span><span class="p">,</span> <span class="n">calc_dc_shell</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">loaded_dc_imp</span><span class="p">,</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">dc_imp</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">loaded_dc_shell</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span> <span class="o">!=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">calc_dc_shell</span><span class="o">.</span><span class="n">keys</span><span class="p">()):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Loaded double counting has a different block &#39;</span>
+                             <span class="o">+</span> <span class="s1">&#39;structure than current calculation.&#39;</span><span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">channel</span> <span class="ow">in</span> <span class="n">loaded_dc_shell</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">loaded_dc_shell</span><span class="p">[</span><span class="n">channel</span><span class="p">],</span> <span class="n">calc_dc_shell</span><span class="p">[</span><span class="n">channel</span><span class="p">],</span>
+                               <span class="n">atol</span><span class="o">=</span><span class="mf">1e-4</span><span class="p">,</span> <span class="n">rtol</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
+                <span class="n">has_double_counting_changed</span> <span class="o">=</span> <span class="kc">True</span>
+                <span class="k">break</span>
+
+    <span class="c1"># Sets initial Sigma</span>
+    <span class="n">start_sigma</span> <span class="o">=</span> <span class="n">loaded_sigma</span>
+
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">has_double_counting_changed</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;DC remained the same. Using loaded Sigma as initial Sigma.&#39;</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">start_sigma</span>
+
+    <span class="c1"># Uses the SumkDFT add_dc routine to correctly substract the DC shift</span>
+    <span class="n">sum_k</span><span class="o">.</span><span class="n">put_Sigma</span><span class="p">(</span><span class="n">start_sigma</span><span class="p">)</span>
+    <span class="n">calculated_dc_imp</span> <span class="o">=</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">dc_imp</span>
+    <span class="n">sum_k</span><span class="o">.</span><span class="n">dc_imp</span> <span class="o">=</span> <span class="p">[{</span><span class="n">channel</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">loaded_dc_shell</span><span class="p">[</span><span class="n">channel</span><span class="p">])</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">calc_dc_shell</span><span class="p">[</span><span class="n">channel</span><span class="p">])</span>
+                     <span class="k">for</span> <span class="n">channel</span> <span class="ow">in</span> <span class="n">loaded_dc_shell</span><span class="p">}</span>
+                    <span class="k">for</span> <span class="n">calc_dc_shell</span><span class="p">,</span> <span class="n">loaded_dc_shell</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">dc_imp</span><span class="p">,</span> <span class="n">loaded_dc_imp</span><span class="p">)]</span>
+    <span class="n">start_sigma</span> <span class="o">=</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">add_dc</span><span class="p">()</span>
+    <span class="n">start_sigma</span> <span class="o">=</span> <span class="n">_sumk_sigma_to_solver_struct</span><span class="p">(</span><span class="n">sum_k</span><span class="p">,</span> <span class="n">start_sigma</span><span class="p">)</span>
+
+    <span class="c1"># Prints information on correction of Hartree shift</span>
+    <span class="n">first_block</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">key</span> <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">loaded_sigma</span><span class="p">[</span><span class="mi">0</span><span class="p">])[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;DC changed, initial Sigma is the loaded Sigma with corrected Hartree shift:&#39;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;    Sigma for imp0, block &quot;</span><span class="si">{}</span><span class="s1">&quot;, orbital 0 &#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">first_block</span><span class="p">)</span>
+          <span class="o">+</span> <span class="s1">&#39;shifted from </span><span class="si">{:.3f}</span><span class="s1"> eV &#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">loaded_sigma</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">first_block</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">real</span><span class="p">)</span>
+          <span class="o">+</span> <span class="s1">&#39;to </span><span class="si">{:.3f}</span><span class="s1"> eV&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">start_sigma</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">first_block</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">real</span><span class="p">))</span>
+
+    <span class="c1"># Cleans up</span>
+    <span class="n">sum_k</span><span class="o">.</span><span class="n">dc_imp</span> <span class="o">=</span> <span class="n">calculated_dc_imp</span>
+    <span class="p">[</span><span class="n">sigma_freq</span><span class="o">.</span><span class="n">zero</span><span class="p">()</span> <span class="k">for</span> <span class="n">sigma_freq</span> <span class="ow">in</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">Sigma_imp</span><span class="p">]</span>
+
+    <span class="k">return</span> <span class="n">start_sigma</span>
+
+
+<div class="viewcode-block" id="determine_dc_and_initial_sigma"><a class="viewcode-back" href="../../_ref/dmft_tools.initial_self_energies.html#dmft_tools.initial_self_energies.determine_dc_and_initial_sigma">[docs]</a><span class="k">def</span> <span class="nf">determine_dc_and_initial_sigma</span><span class="p">(</span><span class="n">general_params</span><span class="p">,</span> <span class="n">advanced_params</span><span class="p">,</span> <span class="n">sum_k</span><span class="p">,</span>
+                                   <span class="n">archive</span><span class="p">,</span> <span class="n">iteration_offset</span><span class="p">,</span> <span class="n">density_mat_dft</span><span class="p">,</span> <span class="n">solvers</span><span class="p">,</span>
+                                   <span class="n">solver_type_per_imp</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Determines the double counting (DC) and the initial Sigma. This can happen</span>
+<span class="sd">    in five different ways:</span>
+<span class="sd">    * Calculation resumed: use the previous DC and the Sigma of the last complete calculation.</span>
+
+<span class="sd">    * Calculation initialized with load_sigma: use the DC and Sigma from the previous file.</span>
+<span class="sd">      If the DC changed (and therefore the Hartree shift), the initial Sigma is adjusted by that.</span>
+
+<span class="sd">    * New calculation, with DC: calculate the DC, then initialize the Sigma as the DC,</span>
+<span class="sd">      effectively starting the calculation from the DFT Green&#39;s function.</span>
+<span class="sd">      Also breaks magnetic symmetry if calculation is magnetic.</span>
+
+<span class="sd">    * New calculation, without DC: Sigma is initialized as 0,</span>
+<span class="sd">      starting the calculation from the DFT Green&#39;s function.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    general_params : dict</span>
+<span class="sd">        general parameters as a dict</span>
+<span class="sd">    advanced_params : dict</span>
+<span class="sd">        advanced parameters as a dict</span>
+<span class="sd">    sum_k : SumkDFT object</span>
+<span class="sd">        Sumk object with the information about the correct block structure</span>
+<span class="sd">    archive : HDFArchive</span>
+<span class="sd">        the archive of the current calculation</span>
+<span class="sd">    iteration_offset : int</span>
+<span class="sd">        the iterations done before this calculation</span>
+<span class="sd">    density_mat_dft : numpy array</span>
+<span class="sd">        DFT density matrix</span>
+<span class="sd">    solvers : list</span>
+<span class="sd">        list of Solver instances</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    sum_k : SumkDFT object</span>
+<span class="sd">        the SumkDFT object, updated by the initial Sigma and the DC</span>
+<span class="sd">    solvers : list</span>
+<span class="sd">        list of Solver instances, updated by the initial Sigma</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">start_sigma</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="n">last_g0</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="k">if</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+        <span class="c1"># Resumes previous calculation</span>
+        <span class="k">if</span> <span class="n">iteration_offset</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">From previous calculation:&#39;</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="s1">&#39; &#39;</span><span class="p">)</span>
+            <span class="n">start_sigma</span><span class="p">,</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">dc_imp</span><span class="p">,</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">dc_energ</span><span class="p">,</span> <span class="n">last_g0</span><span class="p">,</span>  <span class="n">_</span> <span class="o">=</span> <span class="n">_load_sigma_from_h5</span><span class="p">(</span><span class="n">archive</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;csc&#39;</span><span class="p">]</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;dc_dmft&#39;</span><span class="p">]:</span>
+                <span class="n">sum_k</span> <span class="o">=</span> <span class="n">calculate_double_counting</span><span class="p">(</span><span class="n">sum_k</span><span class="p">,</span> <span class="n">density_mat_dft</span><span class="p">,</span> <span class="n">general_params</span><span class="p">,</span> <span class="n">advanced_params</span><span class="p">,</span> <span class="n">solver_type_per_imp</span><span class="p">)</span>
+        <span class="c1"># Loads Sigma from different calculation</span>
+        <span class="k">elif</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;load_sigma&#39;</span><span class="p">]:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">From </span><span class="si">{}</span><span class="s1">:&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;path_to_sigma&#39;</span><span class="p">]),</span> <span class="n">end</span><span class="o">=</span><span class="s1">&#39; &#39;</span><span class="p">)</span>
+            <span class="k">with</span> <span class="n">HDFArchive</span><span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;path_to_sigma&#39;</span><span class="p">],</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">sigma_archive</span><span class="p">:</span>
+                <span class="p">(</span><span class="n">loaded_sigma</span><span class="p">,</span> <span class="n">loaded_dc_imp</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span>
+                 <span class="n">_</span><span class="p">,</span> <span class="n">loaded_density_matrix</span><span class="p">)</span> <span class="o">=</span> <span class="n">_load_sigma_from_h5</span><span class="p">(</span><span class="n">sigma_archive</span><span class="p">,</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;load_sigma_iter&#39;</span><span class="p">])</span>
+
+            <span class="c1"># Recalculate double counting in case U, J or DC formula changed</span>
+            <span class="k">if</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;dc&#39;</span><span class="p">]:</span>
+                <span class="k">if</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;dc_dmft&#39;</span><span class="p">]:</span>
+                    <span class="n">sum_k</span> <span class="o">=</span> <span class="n">calculate_double_counting</span><span class="p">(</span><span class="n">sum_k</span><span class="p">,</span> <span class="n">loaded_density_matrix</span><span class="p">,</span>
+                                                      <span class="n">general_params</span><span class="p">,</span> <span class="n">advanced_params</span><span class="p">,</span>
+                                                      <span class="n">solver_type_per_imp</span><span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">sum_k</span> <span class="o">=</span> <span class="n">calculate_double_counting</span><span class="p">(</span><span class="n">sum_k</span><span class="p">,</span> <span class="n">density_mat_dft</span><span class="p">,</span>
+                                                      <span class="n">general_params</span><span class="p">,</span> <span class="n">advanced_params</span><span class="p">,</span>
+                                                      <span class="n">solver_type_per_imp</span><span class="p">)</span>
+
+            <span class="n">start_sigma</span> <span class="o">=</span> <span class="n">_set_loaded_sigma</span><span class="p">(</span><span class="n">sum_k</span><span class="p">,</span> <span class="n">loaded_sigma</span><span class="p">,</span> <span class="n">loaded_dc_imp</span><span class="p">,</span> <span class="n">general_params</span><span class="p">)</span>
+
+        <span class="c1"># Sets DC as Sigma because no initial Sigma given</span>
+        <span class="k">elif</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;dc&#39;</span><span class="p">]:</span>
+            <span class="n">sum_k</span> <span class="o">=</span> <span class="n">calculate_double_counting</span><span class="p">(</span><span class="n">sum_k</span><span class="p">,</span> <span class="n">density_mat_dft</span><span class="p">,</span> <span class="n">general_params</span><span class="p">,</span> <span class="n">advanced_params</span><span class="p">,</span>
+                                              <span class="n">solver_type_per_imp</span><span class="p">)</span>
+
+            <span class="c1"># initialize Sigma from sum_k</span>
+            <span class="n">start_sigma</span> <span class="o">=</span> <span class="p">[</span><span class="n">sum_k</span><span class="o">.</span><span class="n">block_structure</span><span class="o">.</span><span class="n">create_gf</span><span class="p">(</span><span class="n">ish</span><span class="o">=</span><span class="n">iineq</span><span class="p">,</span> <span class="n">gf_function</span><span class="o">=</span><span class="n">Gf</span><span class="p">,</span> <span class="n">space</span><span class="o">=</span><span class="s1">&#39;solver&#39;</span><span class="p">,</span>
+                                                           <span class="n">mesh</span><span class="o">=</span><span class="n">sum_k</span><span class="o">.</span><span class="n">mesh</span><span class="p">)</span>
+                           <span class="k">for</span> <span class="n">iineq</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span><span class="p">)]</span>
+            <span class="k">for</span> <span class="n">icrsh</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span><span class="p">):</span>
+                <span class="n">dc_value</span> <span class="o">=</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">dc_imp</span><span class="p">[</span><span class="n">sum_k</span><span class="o">.</span><span class="n">inequiv_to_corr</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]][</span><span class="n">sum_k</span><span class="o">.</span><span class="n">spin_block_names</span><span class="p">[</span><span class="n">sum_k</span><span class="o">.</span><span class="n">SO</span><span class="p">][</span><span class="mi">0</span><span class="p">]][</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
+
+                <span class="k">if</span> <span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;magnetic&#39;</span><span class="p">]</span> <span class="ow">and</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;magmom&#39;</span><span class="p">]</span> <span class="ow">and</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">SO</span> <span class="o">==</span> <span class="mi">0</span><span class="p">):</span>
+                    <span class="c1"># if we are doing a magnetic calculation and initial magnetic moments</span>
+                    <span class="c1"># are set, manipulate the initial sigma accordingly</span>
+                    <span class="n">fac</span> <span class="o">=</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;magmom&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">]</span>
+
+                    <span class="c1"># init self energy according to factors in magmoms</span>
+                    <span class="c1"># if magmom positive the up channel will be favored</span>
+                    <span class="k">for</span> <span class="n">spin_channel</span> <span class="ow">in</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">gf_struct_solver</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+                        <span class="k">if</span> <span class="s1">&#39;up&#39;</span> <span class="ow">in</span> <span class="n">spin_channel</span><span class="p">:</span>
+                            <span class="n">start_sigma</span><span class="p">[</span><span class="n">icrsh</span><span class="p">][</span><span class="n">spin_channel</span><span class="p">]</span> <span class="o">&lt;&lt;</span> <span class="o">-</span><span class="n">fac</span> <span class="o">+</span> <span class="n">dc_value</span>
+                        <span class="k">else</span><span class="p">:</span>
+                            <span class="n">start_sigma</span><span class="p">[</span><span class="n">icrsh</span><span class="p">][</span><span class="n">spin_channel</span><span class="p">]</span> <span class="o">&lt;&lt;</span> <span class="n">fac</span> <span class="o">+</span> <span class="n">dc_value</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">start_sigma</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span> <span class="o">&lt;&lt;</span> <span class="n">dc_value</span>
+        <span class="c1"># Sets Sigma to zero because neither initial Sigma nor DC given</span>
+
+        <span class="k">elif</span> <span class="p">(</span><span class="ow">not</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;dc&#39;</span><span class="p">]</span> <span class="ow">and</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;magnetic&#39;</span><span class="p">]):</span>
+            <span class="n">start_sigma</span> <span class="o">=</span> <span class="p">[</span><span class="n">sum_k</span><span class="o">.</span><span class="n">block_structure</span><span class="o">.</span><span class="n">create_gf</span><span class="p">(</span><span class="n">ish</span><span class="o">=</span><span class="n">iineq</span><span class="p">,</span> <span class="n">gf_function</span><span class="o">=</span><span class="n">Gf</span><span class="p">,</span> <span class="n">space</span><span class="o">=</span><span class="s1">&#39;solver&#39;</span><span class="p">,</span> <span class="n">mesh</span><span class="o">=</span><span class="n">sum_k</span><span class="o">.</span><span class="n">mesh</span><span class="p">)</span>
+                                <span class="k">for</span> <span class="n">iineq</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span><span class="p">)]</span>
+            <span class="k">for</span> <span class="n">icrsh</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span><span class="p">):</span>
+                <span class="k">if</span> <span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;magnetic&#39;</span><span class="p">]</span> <span class="ow">and</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;magmom&#39;</span><span class="p">]</span> <span class="ow">and</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">SO</span> <span class="o">==</span> <span class="mi">0</span><span class="p">):</span>
+                    <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">*** Adding magnetic bias to initial sigma for impurity </span><span class="si">{</span><span class="n">icrsh</span><span class="si">}</span><span class="s1"> ***&#39;</span><span class="p">)</span>
+                    <span class="c1"># if we are doing a magnetic calculation and initial magnetic moments</span>
+                    <span class="c1"># are set, manipulate the initial sigma accordingly</span>
+                    <span class="n">fac</span> <span class="o">=</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;magmom&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">]</span>
+
+                    <span class="c1"># if magmom positive the up channel will be favored</span>
+                    <span class="k">for</span> <span class="n">spin_channel</span> <span class="ow">in</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">gf_struct_solver</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+                        <span class="k">if</span> <span class="s1">&#39;up&#39;</span> <span class="ow">in</span> <span class="n">spin_channel</span><span class="p">:</span>
+                            <span class="n">start_sigma</span><span class="p">[</span><span class="n">icrsh</span><span class="p">][</span><span class="n">spin_channel</span><span class="p">]</span> <span class="o">&lt;&lt;</span> <span class="o">-</span><span class="n">fac</span>
+                        <span class="k">else</span><span class="p">:</span>
+                            <span class="n">start_sigma</span><span class="p">[</span><span class="n">icrsh</span><span class="p">][</span><span class="n">spin_channel</span><span class="p">]</span> <span class="o">&lt;&lt;</span> <span class="n">fac</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">start_sigma</span> <span class="o">=</span> <span class="p">[</span><span class="n">sum_k</span><span class="o">.</span><span class="n">block_structure</span><span class="o">.</span><span class="n">create_gf</span><span class="p">(</span><span class="n">ish</span><span class="o">=</span><span class="n">iineq</span><span class="p">,</span> <span class="n">gf_function</span><span class="o">=</span><span class="n">Gf</span><span class="p">,</span> <span class="n">space</span><span class="o">=</span><span class="s1">&#39;solver&#39;</span><span class="p">,</span> <span class="n">mesh</span><span class="o">=</span><span class="n">sum_k</span><span class="o">.</span><span class="n">mesh</span><span class="p">)</span>
+                           <span class="k">for</span> <span class="n">iineq</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span><span class="p">)]</span>
+
+    <span class="c1"># Adds random, frequency-independent noise in zeroth iteration to break symmetries</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">isclose</span><span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;noise_level_initial_sigma&#39;</span><span class="p">],</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">and</span> <span class="n">iteration_offset</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+            <span class="k">for</span> <span class="n">start_sigma_per_imp</span> <span class="ow">in</span> <span class="n">start_sigma</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">block</span> <span class="ow">in</span> <span class="n">start_sigma_per_imp</span><span class="p">:</span>
+                    <span class="n">noise</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="n">scale</span><span class="o">=</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;noise_level_initial_sigma&#39;</span><span class="p">],</span>
+                                             <span class="n">size</span><span class="o">=</span><span class="n">block</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">:])</span>
+                    <span class="c1"># Makes the noise hermitian</span>
+                    <span class="n">noise</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">broadcast_to</span><span class="p">(</span><span class="mf">.5</span> <span class="o">*</span> <span class="p">(</span><span class="n">noise</span> <span class="o">+</span> <span class="n">noise</span><span class="o">.</span><span class="n">T</span><span class="p">),</span> <span class="n">block</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+                    <span class="n">block</span> <span class="o">+=</span> <span class="n">Gf</span><span class="p">(</span><span class="n">indices</span><span class="o">=</span><span class="n">block</span><span class="o">.</span><span class="n">indices</span><span class="p">,</span> <span class="n">mesh</span><span class="o">=</span><span class="n">block</span><span class="o">.</span><span class="n">mesh</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">noise</span><span class="p">)</span>
+
+    <span class="c1"># bcast everything to other nodes</span>
+    <span class="n">sum_k</span><span class="o">.</span><span class="n">dc_imp</span> <span class="o">=</span> <span class="n">mpi</span><span class="o">.</span><span class="n">bcast</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">dc_imp</span><span class="p">)</span>
+    <span class="n">sum_k</span><span class="o">.</span><span class="n">dc_energ</span> <span class="o">=</span> <span class="n">mpi</span><span class="o">.</span><span class="n">bcast</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">dc_energ</span><span class="p">)</span>
+    <span class="n">start_sigma</span> <span class="o">=</span> <span class="n">mpi</span><span class="o">.</span><span class="n">bcast</span><span class="p">(</span><span class="n">start_sigma</span><span class="p">)</span>
+    <span class="n">last_g0</span> <span class="o">=</span> <span class="n">mpi</span><span class="o">.</span><span class="n">bcast</span><span class="p">(</span><span class="n">last_g0</span><span class="p">)</span>
+    <span class="c1"># Loads everything now to the solver</span>
+    <span class="k">for</span> <span class="n">icrsh</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span><span class="p">):</span>
+        <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">Sigma_freq</span> <span class="o">=</span> <span class="n">start_sigma</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span>
+        <span class="k">if</span> <span class="n">last_g0</span><span class="p">:</span>
+            <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">G0_freq</span> <span class="o">=</span> <span class="n">last_g0</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span>
+
+    <span class="c1"># Updates the sum_k object with the Matsubara self-energy</span>
+    <span class="n">sum_k</span><span class="o">.</span><span class="n">put_Sigma</span><span class="p">([</span><span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">Sigma_freq</span> <span class="k">for</span> <span class="n">icrsh</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span><span class="p">)])</span>
+
+    <span class="c1"># load sigma as first guess in the hartree solver if applicable</span>
+    <span class="k">for</span> <span class="n">icrsh</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span><span class="p">):</span>
+        <span class="c1"># TODO:</span>
+        <span class="c1"># should this be moved to before the solve() call? Having it only here means there is a mismatch</span>
+        <span class="c1"># between the mixing at the level of the solver and the sumk (solver mixes always 100%)</span>
+        <span class="k">if</span> <span class="n">solver_type_per_imp</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;hartree&#39;</span><span class="p">:</span>
+            <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;SOLID_DMFT: setting first guess hartree solver for impurity </span><span class="si">{</span><span class="n">icrsh</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+            <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">reinitialize_sigma</span><span class="p">(</span><span class="n">start_sigma</span><span class="p">[</span><span class="n">icrsh</span><span class="p">])</span>
+
+    <span class="k">return</span> <span class="n">sum_k</span><span class="p">,</span> <span class="n">solvers</span></div>
+</pre></div>
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.</p>
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diff --git a/_modules/dmft_tools/interaction_hamiltonian.html b/_modules/dmft_tools/interaction_hamiltonian.html
new file mode 100644
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+<!DOCTYPE html>
+<html class="writer-html5" lang="en" >
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+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#file-description">file description</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#workflow">Workflow:</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#version-and-compilation">version and compilation:</a></li>
+</ul>
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+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#input-output">Input/Output</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../input_output/DMFT_input/input.html">Input</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/general.html">[general]: General parameters</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_output/results.html#group-structure">Group structure</a></li>
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+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../../_ref/postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
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+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
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+      <li>dmft_tools.interaction_hamiltonian</li>
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+      </li>
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+             
+  <h1>Source code for dmft_tools.interaction_hamiltonian</h1><div class="highlight"><pre>
+<span></span><span class="ch">#!/usr/bin/env python3</span>
+<span class="c1"># -*- coding: utf-8 -*-</span>
+<span class="c1">################################################################################</span>
+<span class="c1">#</span>
+<span class="c1"># solid_dmft - A versatile python wrapper to perform DFT+DMFT calculations</span>
+<span class="c1">#              utilizing the TRIQS software library</span>
+<span class="c1">#</span>
+<span class="c1"># Copyright (C) 2018-2020, ETH Zurich</span>
+<span class="c1"># Copyright (C) 2021, The Simons Foundation</span>
+<span class="c1">#      authors: A. Hampel, M. Merkel, and S. Beck</span>
+<span class="c1">#</span>
+<span class="c1"># solid_dmft is free software: you can redistribute it and/or modify it under the</span>
+<span class="c1"># terms of the GNU General Public License as published by the Free Software</span>
+<span class="c1"># Foundation, either version 3 of the License, or (at your option) any later</span>
+<span class="c1"># version.</span>
+<span class="c1">#</span>
+<span class="c1"># solid_dmft is distributed in the hope that it will be useful, but WITHOUT ANY</span>
+<span class="c1"># WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A</span>
+<span class="c1"># PARTICULAR PURPOSE. See the GNU General Public License for more details.</span>
+
+<span class="c1"># You should have received a copy of the GNU General Public License along with</span>
+<span class="c1"># solid_dmft (in the file COPYING.txt in this directory). If not, see</span>
+<span class="c1"># &lt;http://www.gnu.org/licenses/&gt;.</span>
+<span class="c1">#</span>
+<span class="c1">################################################################################</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Contains all functions related to constructing the interaction Hamiltonian.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="c1"># system</span>
+<span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">itertools</span> <span class="kn">import</span> <span class="n">product</span>
+
+<span class="c1"># triqs</span>
+<span class="kn">from</span> <span class="nn">h5</span> <span class="kn">import</span> <span class="n">HDFArchive</span>
+<span class="kn">import</span> <span class="nn">triqs.utility.mpi</span> <span class="k">as</span> <span class="nn">mpi</span>
+<span class="kn">from</span> <span class="nn">triqs.operators</span> <span class="kn">import</span> <span class="n">util</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">c_dag</span><span class="p">,</span> <span class="n">Operator</span>
+<span class="kn">from</span> <span class="nn">solid_dmft.dmft_tools</span> <span class="kn">import</span> <span class="n">solver</span>
+<span class="k">try</span><span class="p">:</span>
+    <span class="kn">import</span> <span class="nn">forktps</span> <span class="k">as</span> <span class="nn">ftps</span>
+<span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
+    <span class="k">pass</span>
+
+<span class="k">def</span> <span class="nf">_load_crpa_interaction_matrix</span><span class="p">(</span><span class="n">sum_k</span><span class="p">,</span> <span class="n">filename</span><span class="o">=</span><span class="s1">&#39;UIJKL&#39;</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Loads VASP cRPA data to use as an interaction Hamiltonian.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="nf">_round_to_int</span><span class="p">(</span><span class="n">data</span><span class="p">):</span>
+        <span class="k">return</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="o">+</span> <span class="mf">.5</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
+
+    <span class="c1"># Loads data from VASP cRPA file</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Loading cRPA matrix from file: &#39;</span><span class="o">+</span><span class="nb">str</span><span class="p">(</span><span class="n">filename</span><span class="p">))</span>
+    <span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">loadtxt</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="n">unpack</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="n">u_matrix_four_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">_round_to_int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">data</span><span class="p">[:</span><span class="mi">4</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">complex</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">entry</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">T</span><span class="p">:</span>
+        <span class="c1"># VASP switches the order of the indices, ijkl -&gt; ikjl</span>
+        <span class="n">i</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">l</span> <span class="o">=</span> <span class="n">_round_to_int</span><span class="p">(</span><span class="n">entry</span><span class="p">[:</span><span class="mi">4</span><span class="p">])</span><span class="o">-</span><span class="mi">1</span>
+        <span class="n">u_matrix_four_indices</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">l</span><span class="p">]</span> <span class="o">=</span> <span class="n">entry</span><span class="p">[</span><span class="mi">4</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="n">j</span> <span class="o">*</span> <span class="n">entry</span><span class="p">[</span><span class="mi">5</span><span class="p">]</span>
+
+    <span class="c1"># Slices up the four index U-matrix, separating shells</span>
+    <span class="n">u_matrix_four_indices_per_shell</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span>
+    <span class="n">first_index_shell</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="k">for</span> <span class="n">ish</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_corr_shells</span><span class="p">):</span>
+        <span class="n">icrsh</span> <span class="o">=</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">corr_to_inequiv</span><span class="p">[</span><span class="n">ish</span><span class="p">]</span>
+        <span class="n">n_orb</span> <span class="o">=</span> <span class="n">solver</span><span class="o">.</span><span class="n">get_n_orbitals</span><span class="p">(</span><span class="n">sum_k</span><span class="p">)[</span><span class="n">icrsh</span><span class="p">][</span><span class="s1">&#39;up&#39;</span><span class="p">]</span>
+        <span class="n">u_matrix_temp</span> <span class="o">=</span> <span class="n">u_matrix_four_indices</span><span class="p">[</span><span class="n">first_index_shell</span><span class="p">:</span><span class="n">first_index_shell</span><span class="o">+</span><span class="n">n_orb</span><span class="p">,</span>
+                                              <span class="n">first_index_shell</span><span class="p">:</span><span class="n">first_index_shell</span><span class="o">+</span><span class="n">n_orb</span><span class="p">,</span>
+                                              <span class="n">first_index_shell</span><span class="p">:</span><span class="n">first_index_shell</span><span class="o">+</span><span class="n">n_orb</span><span class="p">,</span>
+                                              <span class="n">first_index_shell</span><span class="p">:</span><span class="n">first_index_shell</span><span class="o">+</span><span class="n">n_orb</span><span class="p">]</span>
+        <span class="c1"># I think for now we should stick with real interactions make real</span>
+        <span class="n">u_matrix_temp</span><span class="o">.</span><span class="n">imag</span> <span class="o">=</span> <span class="mf">0.0</span>
+
+        <span class="k">if</span> <span class="n">ish</span> <span class="o">==</span> <span class="n">icrsh</span><span class="p">:</span>
+            <span class="n">u_matrix_four_indices_per_shell</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span> <span class="o">=</span> <span class="n">u_matrix_temp</span>
+        <span class="k">elif</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">u_matrix_four_indices_per_shell</span><span class="p">[</span><span class="n">icrsh</span><span class="p">],</span> <span class="n">u_matrix_temp</span><span class="p">,</span> <span class="n">atol</span><span class="o">=</span><span class="mf">1e-6</span><span class="p">,</span> <span class="n">rtol</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
+            <span class="c1"># TODO: for some reason, some entries in the matrices differ by a sign. Check that</span>
+            <span class="c1"># mpi.report(np.allclose(np.abs(u_matrix_four_indices_per_shell[icrsh]), np.abs(u_matrix_temp),</span>
+            <span class="c1"># atol=1e-6, rtol=0))</span>
+            <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;Warning: cRPA matrix for impurity </span><span class="si">{}</span><span class="s1"> &#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">icrsh</span><span class="p">)</span>
+                       <span class="o">+</span> <span class="s1">&#39;differs for shells </span><span class="si">{}</span><span class="s1"> and </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">inequiv_to_corr</span><span class="p">[</span><span class="n">icrsh</span><span class="p">],</span> <span class="n">ish</span><span class="p">))</span>
+
+        <span class="n">first_index_shell</span> <span class="o">+=</span> <span class="n">n_orb</span>
+
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">u_matrix_four_indices</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">first_index_shell</span><span class="p">):</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Warning: different number of orbitals in cRPA matrix than in calculation.&#39;</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">u_matrix_four_indices_per_shell</span>
+
+
+<span class="c1"># def _adapt_U_2index_for_SO(Umat, Upmat):</span>
+<span class="c1">#     &quot;&quot;&quot;</span>
+<span class="c1">#     Changes the two-index U matrices such that for a system consisting of a</span>
+<span class="c1">#     single block &#39;ud&#39; with the entries (1, up), (1, down), (2, up), (2, down),</span>
+<span class="c1">#     ... the matrices are consistent with the case without spin-orbit coupling.</span>
+
+<span class="c1">#     Parameters</span>
+<span class="c1">#     ----------</span>
+<span class="c1">#     Umat : numpy array</span>
+<span class="c1">#         The two-index interaction matrix for parallel spins without SO.</span>
+<span class="c1">#     Upmat : numpy array</span>
+<span class="c1">#         The two-index interaction matrix for antiparallel spins without SO.</span>
+
+<span class="c1">#     Returns</span>
+<span class="c1">#     -------</span>
+<span class="c1">#     Umat_SO : numpy array</span>
+<span class="c1">#         The two-index interaction matrix for parallel spins. Because in SO all</span>
+<span class="c1">#         entries have nominal spin &#39;ud&#39;, this matrix now contains the original</span>
+<span class="c1">#         Umat and Upmat.</span>
+<span class="c1">#     Upmat_SO : numpy array</span>
+<span class="c1">#         The two-index interaction matrix for antiparallel spins. Unused because</span>
+<span class="c1">#         in SO, all spins have the same nominal spin &#39;ud&#39;.</span>
+<span class="c1">#     &quot;&quot;&quot;</span>
+
+<span class="c1">#     Umat_SO = np.zeros(np.array(Umat.shape)*2, dtype=Umat.dtype)</span>
+<span class="c1">#     Umat_SO[::2, ::2] = Umat_SO[1::2, 1::2] = Umat</span>
+<span class="c1">#     Umat_SO[::2, 1::2] = Umat_SO[1::2, ::2] = Upmat</span>
+<span class="c1">#     Upmat_SO = None</span>
+
+<span class="c1">#     return Umat_SO, Upmat_SO</span>
+
+
+<span class="k">def</span> <span class="nf">_adapt_U_4index_for_SO</span><span class="p">(</span><span class="n">Umat_full</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Changes the four-index U matrix such that for a system consisting of a</span>
+<span class="sd">    single block &#39;ud&#39; with the entries (1, up), (1, down), (2, up), (2, down),</span>
+<span class="sd">    ... the matrix is consistent with the case without spin-orbit coupling.</span>
+<span class="sd">    This can be derived directly from the definition of the Slater Hamiltonian.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    Umat_full : numpy array</span>
+<span class="sd">       The four-index interaction matrix without SO.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    Umat_full_SO : numpy array</span>
+<span class="sd">        The four-index interaction matrix with SO. For a matrix U_ijkl, the</span>
+<span class="sd">        indices i, k correspond to spin sigma, and indices j, l to sigma&#39;.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">Umat_full_SO</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">Umat_full</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span><span class="o">*</span><span class="mi">2</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">Umat_full</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">spin</span><span class="p">,</span> <span class="n">spin_prime</span> <span class="ow">in</span> <span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)):</span>
+        <span class="n">Umat_full_SO</span><span class="p">[</span><span class="n">spin</span><span class="p">::</span><span class="mi">2</span><span class="p">,</span> <span class="n">spin_prime</span><span class="p">::</span><span class="mi">2</span><span class="p">,</span> <span class="n">spin</span><span class="p">::</span><span class="mi">2</span><span class="p">,</span> <span class="n">spin_prime</span><span class="p">::</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="n">Umat_full</span>
+
+    <span class="k">return</span> <span class="n">Umat_full_SO</span>
+
+
+<span class="k">def</span> <span class="nf">_construct_kanamori</span><span class="p">(</span><span class="n">sum_k</span><span class="p">,</span> <span class="n">general_params</span><span class="p">,</span> <span class="n">solver_type_per_imp</span><span class="p">,</span> <span class="n">icrsh</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Constructs the Kanamori interaction Hamiltonian. Only Kanamori does not</span>
+<span class="sd">    need the full four-index matrix. Therefore, we can construct it directly</span>
+<span class="sd">    from the parameters U and J.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">n_orb</span> <span class="o">=</span> <span class="n">solver</span><span class="o">.</span><span class="n">get_n_orbitals</span><span class="p">(</span><span class="n">sum_k</span><span class="p">)[</span><span class="n">icrsh</span><span class="p">][</span><span class="s1">&#39;up&#39;</span><span class="p">]</span>
+    <span class="k">if</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">SO</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="k">assert</span> <span class="n">n_orb</span> <span class="o">%</span> <span class="mi">2</span> <span class="o">==</span> <span class="mi">0</span>
+        <span class="n">n_orb</span> <span class="o">=</span> <span class="n">n_orb</span> <span class="o">//</span> <span class="mi">2</span>
+
+    <span class="k">if</span> <span class="n">n_orb</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">):</span>
+        <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;warning: are you sure you want to use the kanamori hamiltonian &#39;</span>
+                   <span class="o">+</span> <span class="s1">&#39;outside the t2g or eg manifold?&#39;</span><span class="p">)</span>
+
+    <span class="c1"># check if Uprime has been specified manually</span>
+    <span class="k">if</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;U_prime&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">]</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">U_prime</span> <span class="o">=</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;U&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">]</span> <span class="o">-</span> <span class="mf">2.0</span> <span class="o">*</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;J&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">]</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">U_prime</span> <span class="o">=</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;U_prime&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">]</span>
+    <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;U = </span><span class="si">{:.2f}</span><span class="s1">, U</span><span class="se">\&#39;</span><span class="s1"> = </span><span class="si">{:.2f}</span><span class="s1">, J = </span><span class="si">{:.2f}</span><span class="se">\n</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;U&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">],</span> <span class="n">U_prime</span><span class="p">,</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;J&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">]))</span>
+
+    <span class="k">if</span> <span class="n">solver_type_per_imp</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;ftps&#39;</span><span class="p">:</span>
+        <span class="c1"># 1-band modell requires J and U&#39; equals zero</span>
+        <span class="k">if</span> <span class="n">n_orb</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="n">up</span><span class="p">,</span> <span class="n">j</span> <span class="o">=</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">up</span> <span class="o">=</span> <span class="n">U_prime</span>
+            <span class="n">j</span> <span class="o">=</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;J&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">]</span>
+        <span class="n">h_int</span> <span class="o">=</span> <span class="n">ftps</span><span class="o">.</span><span class="n">solver_core</span><span class="o">.</span><span class="n">HInt</span><span class="p">(</span><span class="n">u</span><span class="o">=</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;U&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">],</span> <span class="n">j</span><span class="o">=</span><span class="n">j</span><span class="p">,</span> <span class="n">up</span><span class="o">=</span><span class="n">up</span><span class="p">,</span> <span class="n">dd</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">SO</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="c1"># Constructs U matrix</span>
+        <span class="n">Umat</span><span class="p">,</span> <span class="n">Upmat</span> <span class="o">=</span> <span class="n">util</span><span class="o">.</span><span class="n">U_matrix_kanamori</span><span class="p">(</span><span class="n">n_orb</span><span class="o">=</span><span class="n">n_orb</span><span class="p">,</span> <span class="n">U_int</span><span class="o">=</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;U&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">],</span>
+                                             <span class="n">J_hund</span><span class="o">=</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;J&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">],</span>
+                                             <span class="n">Up_int</span><span class="o">=</span><span class="n">U_prime</span><span class="p">)</span>
+
+        <span class="n">h_int</span> <span class="o">=</span> <span class="n">util</span><span class="o">.</span><span class="n">h_int_kanamori</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">spin_block_names</span><span class="p">[</span><span class="n">sum_k</span><span class="o">.</span><span class="n">SO</span><span class="p">],</span> <span class="n">n_orb</span><span class="p">,</span>
+                                    <span class="n">map_operator_structure</span><span class="o">=</span><span class="n">sum_k</span><span class="o">.</span><span class="n">sumk_to_solver</span><span class="p">[</span><span class="n">icrsh</span><span class="p">],</span>
+                                    <span class="n">U</span><span class="o">=</span><span class="n">Umat</span><span class="p">,</span> <span class="n">Uprime</span><span class="o">=</span><span class="n">Upmat</span><span class="p">,</span> <span class="n">J_hund</span><span class="o">=</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;J&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">],</span>
+                                    <span class="n">H_dump</span><span class="o">=</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;jobname&#39;</span><span class="p">],</span> <span class="sa">f</span><span class="s1">&#39;H_imp</span><span class="si">{</span><span class="n">icrsh</span><span class="si">}</span><span class="s1">.txt&#39;</span><span class="p">))</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">h_int</span> <span class="o">=</span> <span class="n">_construct_kanamori_soc</span><span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;U&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">],</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;J&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">],</span>
+                                        <span class="n">n_orb</span><span class="p">,</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">sumk_to_solver</span><span class="p">[</span><span class="n">icrsh</span><span class="p">],</span>
+                                        <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;jobname&#39;</span><span class="p">],</span> <span class="sa">f</span><span class="s1">&#39;H_imp</span><span class="si">{</span><span class="n">icrsh</span><span class="si">}</span><span class="s1">.txt&#39;</span><span class="p">))</span>
+    <span class="k">return</span> <span class="n">h_int</span>
+
+
+<span class="k">def</span> <span class="nf">_construct_kanamori_soc</span><span class="p">(</span><span class="n">U_int</span><span class="p">,</span> <span class="n">J_hund</span><span class="p">,</span> <span class="n">n_orb</span><span class="p">,</span> <span class="n">map_operator_structure</span><span class="p">,</span> <span class="n">H_dump</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Adapted from triqs.operators.util.hamiltonians.h_int_kanamori. Assumes</span>
+<span class="sd">    that spin_names == [&#39;ud&#39;] and that map_operator_structure is given.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">orb_names</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">n_orb</span><span class="p">))</span>
+
+    <span class="k">if</span> <span class="n">H_dump</span><span class="p">:</span>
+        <span class="n">H_dump_file</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="n">H_dump</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span>
+        <span class="n">H_dump_file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s2">&quot;Kanamori Hamiltonian:&quot;</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
+
+    <span class="n">H</span> <span class="o">=</span> <span class="n">Operator</span><span class="p">()</span>
+    <span class="n">mkind</span> <span class="o">=</span> <span class="n">util</span><span class="o">.</span><span class="n">op_struct</span><span class="o">.</span><span class="n">get_mkind</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="n">map_operator_structure</span><span class="p">)</span>
+
+    <span class="n">s</span> <span class="o">=</span> <span class="s1">&#39;ud&#39;</span>
+
+    <span class="c1"># density terms:</span>
+    <span class="c1"># TODO: reformulate in terms of Umat and Upmat for consistency with triqs?</span>
+    <span class="k">if</span> <span class="n">H_dump</span><span class="p">:</span>
+        <span class="n">H_dump_file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s2">&quot;Density-density terms:&quot;</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">a1</span><span class="p">,</span> <span class="n">a2</span> <span class="ow">in</span> <span class="n">product</span><span class="p">(</span><span class="n">orb_names</span><span class="p">,</span> <span class="n">orb_names</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">a1</span> <span class="o">==</span> <span class="n">a2</span><span class="p">:</span>  <span class="c1"># same spin and orbital</span>
+            <span class="k">continue</span>
+
+        <span class="k">if</span> <span class="n">a1</span> <span class="o">//</span> <span class="mi">2</span> <span class="o">==</span> <span class="n">a2</span> <span class="o">//</span> <span class="mi">2</span><span class="p">:</span>  <span class="c1"># same orbital (, different spins)</span>
+            <span class="n">U_val</span> <span class="o">=</span> <span class="n">U_int</span>
+        <span class="k">elif</span> <span class="n">a1</span> <span class="o">%</span> <span class="mi">2</span> <span class="o">!=</span> <span class="n">a2</span> <span class="o">%</span> <span class="mi">2</span><span class="p">:</span>  <span class="c1"># different spins (, different orbitals)</span>
+            <span class="n">U_val</span> <span class="o">=</span> <span class="n">U_int</span> <span class="o">-</span> <span class="mi">2</span><span class="o">*</span><span class="n">J_hund</span>
+        <span class="k">else</span><span class="p">:</span>  <span class="c1"># same spins (, different orbitals)</span>
+            <span class="n">U_val</span> <span class="o">=</span> <span class="n">U_int</span> <span class="o">-</span> <span class="mi">3</span><span class="o">*</span><span class="n">J_hund</span>
+
+        <span class="n">H_term</span> <span class="o">=</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="n">U_val</span> <span class="o">*</span> <span class="n">n</span><span class="p">(</span><span class="o">*</span><span class="n">mkind</span><span class="p">(</span><span class="n">s</span><span class="p">,</span> <span class="n">a1</span><span class="p">))</span> <span class="o">*</span> <span class="n">n</span><span class="p">(</span><span class="o">*</span><span class="n">mkind</span><span class="p">(</span><span class="n">s</span><span class="p">,</span> <span class="n">a2</span><span class="p">))</span>
+        <span class="n">H</span> <span class="o">+=</span> <span class="n">H_term</span>
+
+        <span class="c1"># Dump terms of H</span>
+        <span class="k">if</span> <span class="n">H_dump</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">H_term</span><span class="o">.</span><span class="n">is_zero</span><span class="p">():</span>
+            <span class="n">H_dump_file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">%s</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="n">mkind</span><span class="p">(</span><span class="n">s</span><span class="p">,</span> <span class="n">a1</span><span class="p">),</span> <span class="p">)</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\t</span><span class="s1">&#39;</span><span class="p">)</span>
+            <span class="n">H_dump_file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">%s</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="n">mkind</span><span class="p">(</span><span class="n">s</span><span class="p">,</span> <span class="n">a2</span><span class="p">),</span> <span class="p">)</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\t</span><span class="s1">&#39;</span><span class="p">)</span>
+            <span class="n">H_dump_file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">U_val</span><span class="p">)</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
+
+    <span class="c1"># spin-flip terms:</span>
+    <span class="k">if</span> <span class="n">H_dump</span><span class="p">:</span>
+        <span class="n">H_dump_file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s2">&quot;Spin-flip terms:&quot;</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">a1</span><span class="p">,</span> <span class="n">a2</span><span class="p">,</span> <span class="n">a3</span><span class="p">,</span> <span class="n">a4</span> <span class="ow">in</span> <span class="n">product</span><span class="p">(</span><span class="n">orb_names</span><span class="p">,</span> <span class="n">orb_names</span><span class="p">,</span> <span class="n">orb_names</span><span class="p">,</span> <span class="n">orb_names</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">a1</span> <span class="o">==</span> <span class="n">a2</span> <span class="ow">or</span> <span class="n">a1</span> <span class="o">==</span> <span class="n">a3</span> <span class="ow">or</span> <span class="n">a1</span> <span class="o">==</span> <span class="n">a4</span> <span class="ow">or</span> <span class="n">a2</span> <span class="o">==</span> <span class="n">a3</span> <span class="ow">or</span> <span class="n">a2</span> <span class="o">==</span> <span class="n">a4</span> <span class="ow">or</span> <span class="n">a3</span> <span class="o">==</span> <span class="n">a4</span><span class="p">:</span>
+            <span class="k">continue</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="n">a1</span><span class="o">//</span><span class="mi">2</span> <span class="o">==</span> <span class="n">a2</span><span class="o">//</span><span class="mi">2</span> <span class="ow">and</span> <span class="n">a3</span><span class="o">//</span><span class="mi">2</span> <span class="o">==</span> <span class="n">a4</span><span class="o">//</span><span class="mi">2</span> <span class="ow">and</span> <span class="n">a1</span><span class="o">//</span><span class="mi">2</span> <span class="o">!=</span> <span class="n">a3</span><span class="o">//</span><span class="mi">2</span> <span class="ow">and</span> <span class="n">a1</span> <span class="o">%</span> <span class="mi">2</span> <span class="o">!=</span> <span class="n">a3</span> <span class="o">%</span> <span class="mi">2</span><span class="p">):</span>
+            <span class="k">continue</span>
+
+        <span class="n">H_term</span> <span class="o">=</span> <span class="o">-</span><span class="mf">0.5</span> <span class="o">*</span> <span class="n">J_hund</span> <span class="o">*</span> <span class="n">c_dag</span><span class="p">(</span><span class="o">*</span><span class="n">mkind</span><span class="p">(</span><span class="n">s</span><span class="p">,</span> <span class="n">a1</span><span class="p">))</span> <span class="o">*</span> <span class="n">c</span><span class="p">(</span><span class="o">*</span><span class="n">mkind</span><span class="p">(</span><span class="n">s</span><span class="p">,</span> <span class="n">a2</span><span class="p">))</span> <span class="o">*</span> <span class="n">c_dag</span><span class="p">(</span><span class="o">*</span><span class="n">mkind</span><span class="p">(</span><span class="n">s</span><span class="p">,</span> <span class="n">a3</span><span class="p">))</span> <span class="o">*</span> <span class="n">c</span><span class="p">(</span><span class="o">*</span><span class="n">mkind</span><span class="p">(</span><span class="n">s</span><span class="p">,</span> <span class="n">a4</span><span class="p">))</span>
+        <span class="n">H</span> <span class="o">+=</span> <span class="n">H_term</span>
+
+        <span class="c1"># Dump terms of H</span>
+        <span class="k">if</span> <span class="n">H_dump</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">H_term</span><span class="o">.</span><span class="n">is_zero</span><span class="p">():</span>
+            <span class="n">H_dump_file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">%s</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="n">mkind</span><span class="p">(</span><span class="n">s</span><span class="p">,</span> <span class="n">a1</span><span class="p">),</span> <span class="p">)</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\t</span><span class="s1">&#39;</span><span class="p">)</span>
+            <span class="n">H_dump_file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">%s</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="n">mkind</span><span class="p">(</span><span class="n">s</span><span class="p">,</span> <span class="n">a2</span><span class="p">),</span> <span class="p">)</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\t</span><span class="s1">&#39;</span><span class="p">)</span>
+            <span class="n">H_dump_file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">%s</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="n">mkind</span><span class="p">(</span><span class="n">s</span><span class="p">,</span> <span class="n">a3</span><span class="p">),</span> <span class="p">)</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\t</span><span class="s1">&#39;</span><span class="p">)</span>
+            <span class="n">H_dump_file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">%s</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="n">mkind</span><span class="p">(</span><span class="n">s</span><span class="p">,</span> <span class="n">a4</span><span class="p">),</span> <span class="p">)</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\t</span><span class="s1">&#39;</span><span class="p">)</span>
+            <span class="n">H_dump_file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="o">-</span><span class="n">J_hund</span><span class="p">)</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
+
+    <span class="c1"># pair-hopping terms:</span>
+    <span class="k">if</span> <span class="n">H_dump</span><span class="p">:</span>
+        <span class="n">H_dump_file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s2">&quot;Pair-hopping terms:&quot;</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">a1</span><span class="p">,</span> <span class="n">a2</span><span class="p">,</span> <span class="n">a3</span><span class="p">,</span> <span class="n">a4</span> <span class="ow">in</span> <span class="n">product</span><span class="p">(</span><span class="n">orb_names</span><span class="p">,</span> <span class="n">orb_names</span><span class="p">,</span> <span class="n">orb_names</span><span class="p">,</span> <span class="n">orb_names</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">a1</span> <span class="o">==</span> <span class="n">a2</span> <span class="ow">or</span> <span class="n">a1</span> <span class="o">==</span> <span class="n">a3</span> <span class="ow">or</span> <span class="n">a1</span> <span class="o">==</span> <span class="n">a4</span> <span class="ow">or</span> <span class="n">a2</span> <span class="o">==</span> <span class="n">a3</span> <span class="ow">or</span> <span class="n">a2</span> <span class="o">==</span> <span class="n">a4</span> <span class="ow">or</span> <span class="n">a3</span> <span class="o">==</span> <span class="n">a4</span><span class="p">:</span>
+            <span class="k">continue</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="n">a1</span><span class="o">//</span><span class="mi">2</span> <span class="o">==</span> <span class="n">a2</span><span class="o">//</span><span class="mi">2</span> <span class="ow">and</span> <span class="n">a3</span><span class="o">//</span><span class="mi">2</span> <span class="o">==</span> <span class="n">a4</span><span class="o">//</span><span class="mi">2</span> <span class="ow">and</span> <span class="n">a1</span><span class="o">//</span><span class="mi">2</span> <span class="o">!=</span> <span class="n">a3</span><span class="o">//</span><span class="mi">2</span> <span class="ow">and</span> <span class="n">a1</span> <span class="o">%</span> <span class="mi">2</span> <span class="o">!=</span> <span class="n">a3</span> <span class="o">%</span> <span class="mi">2</span><span class="p">):</span>
+            <span class="k">continue</span>
+
+        <span class="n">H_term</span> <span class="o">=</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="n">J_hund</span> <span class="o">*</span> <span class="n">c_dag</span><span class="p">(</span><span class="o">*</span><span class="n">mkind</span><span class="p">(</span><span class="n">s</span><span class="p">,</span> <span class="n">a1</span><span class="p">))</span> <span class="o">*</span> <span class="n">c_dag</span><span class="p">(</span><span class="o">*</span><span class="n">mkind</span><span class="p">(</span><span class="n">s</span><span class="p">,</span> <span class="n">a2</span><span class="p">))</span> <span class="o">*</span> <span class="n">c</span><span class="p">(</span><span class="o">*</span><span class="n">mkind</span><span class="p">(</span><span class="n">s</span><span class="p">,</span> <span class="n">a4</span><span class="p">))</span> <span class="o">*</span> <span class="n">c</span><span class="p">(</span><span class="o">*</span><span class="n">mkind</span><span class="p">(</span><span class="n">s</span><span class="p">,</span> <span class="n">a3</span><span class="p">))</span>
+        <span class="n">H</span> <span class="o">+=</span> <span class="n">H_term</span>
+
+        <span class="c1"># Dump terms of H</span>
+        <span class="k">if</span> <span class="n">H_dump</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">H_term</span><span class="o">.</span><span class="n">is_zero</span><span class="p">():</span>
+            <span class="n">H_dump_file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">%s</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="n">mkind</span><span class="p">(</span><span class="n">s</span><span class="p">,</span> <span class="n">a1</span><span class="p">),</span> <span class="p">)</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\t</span><span class="s1">&#39;</span><span class="p">)</span>
+            <span class="n">H_dump_file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">%s</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="n">mkind</span><span class="p">(</span><span class="n">s</span><span class="p">,</span> <span class="n">a2</span><span class="p">),</span> <span class="p">)</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\t</span><span class="s1">&#39;</span><span class="p">)</span>
+            <span class="n">H_dump_file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">%s</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="n">mkind</span><span class="p">(</span><span class="n">s</span><span class="p">,</span> <span class="n">a3</span><span class="p">),</span> <span class="p">)</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\t</span><span class="s1">&#39;</span><span class="p">)</span>
+            <span class="n">H_dump_file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">%s</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="n">mkind</span><span class="p">(</span><span class="n">s</span><span class="p">,</span> <span class="n">a4</span><span class="p">),</span> <span class="p">)</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\t</span><span class="s1">&#39;</span><span class="p">)</span>
+            <span class="n">H_dump_file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="o">-</span><span class="n">J_hund</span><span class="p">)</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">H</span>
+
+
+<span class="k">def</span> <span class="nf">_construct_dynamic</span><span class="p">(</span><span class="n">sum_k</span><span class="p">,</span> <span class="n">general_params</span><span class="p">,</span> <span class="n">icrsh</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Constructs the interaction Hamiltonian for a frequency-dependent interaction.</span>
+<span class="sd">    Works only without spin-orbit coupling and only for one orbital.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;###### Dynamic U calculation ######, load parameters from input archive.&#39;</span><span class="p">)</span>
+    <span class="n">U_onsite</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="k">if</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+        <span class="k">with</span> <span class="n">HDFArchive</span><span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;jobname&#39;</span><span class="p">]</span><span class="o">+</span><span class="s1">&#39;/&#39;</span><span class="o">+</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;seedname&#39;</span><span class="p">]</span><span class="o">+</span><span class="s1">&#39;.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">archive</span><span class="p">:</span>
+            <span class="n">U_onsite</span> <span class="o">=</span> <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;dynamic_U&#39;</span><span class="p">][</span><span class="s1">&#39;U_scr&#39;</span><span class="p">]</span>
+    <span class="n">U_onsite</span> <span class="o">=</span> <span class="n">mpi</span><span class="o">.</span><span class="n">bcast</span><span class="p">(</span><span class="n">U_onsite</span><span class="p">)</span>
+
+    <span class="n">n_orb</span> <span class="o">=</span> <span class="n">solver</span><span class="o">.</span><span class="n">get_n_orbitals</span><span class="p">(</span><span class="n">sum_k</span><span class="p">)[</span><span class="n">icrsh</span><span class="p">][</span><span class="s1">&#39;up&#39;</span><span class="p">]</span>
+    <span class="k">if</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">SO</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;dynamic U not implemented for SO!=0&#39;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">n_orb</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;dynamic U not implemented for more than one orbital&#39;</span><span class="p">)</span>
+
+    <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;onsite interaction value for imp </span><span class="si">{}</span><span class="s1">: </span><span class="si">{:.3f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">icrsh</span><span class="p">,</span> <span class="n">U_onsite</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]))</span>
+    <span class="n">h_int</span> <span class="o">=</span> <span class="n">util</span><span class="o">.</span><span class="n">h_int_density</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">spin_block_names</span><span class="p">[</span><span class="n">sum_k</span><span class="o">.</span><span class="n">SO</span><span class="p">],</span> <span class="n">n_orb</span><span class="p">,</span>
+                               <span class="n">map_operator_structure</span><span class="o">=</span><span class="n">sum_k</span><span class="o">.</span><span class="n">sumk_to_solver</span><span class="p">[</span><span class="n">icrsh</span><span class="p">],</span>
+                               <span class="n">U</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">0</span><span class="p">]]),</span> <span class="n">Uprime</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="n">U_onsite</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]]]),</span> <span class="n">H_dump</span><span class="o">=</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;jobname&#39;</span><span class="p">],</span> <span class="sa">f</span><span class="s1">&#39;H_imp</span><span class="si">{</span><span class="n">icrsh</span><span class="si">}</span><span class="s1">.txt&#39;</span><span class="p">))</span>
+
+    <span class="k">return</span> <span class="n">h_int</span>
+
+
+<span class="k">def</span> <span class="nf">_generate_four_index_u_matrix</span><span class="p">(</span><span class="n">sum_k</span><span class="p">,</span> <span class="n">general_params</span><span class="p">,</span> <span class="n">icrsh</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Generates the four-index interaction matrix per impurity with the interaction</span>
+<span class="sd">    parameters U and J (and ratio_F4_F2 for the d shell).</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># ish points to the shell representative of the current group</span>
+    <span class="n">ish</span> <span class="o">=</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">inequiv_to_corr</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span>
+    <span class="n">n_orb</span> <span class="o">=</span> <span class="n">solver</span><span class="o">.</span><span class="n">get_n_orbitals</span><span class="p">(</span><span class="n">sum_k</span><span class="p">)[</span><span class="n">icrsh</span><span class="p">][</span><span class="s1">&#39;up&#39;</span><span class="p">]</span>
+    <span class="k">if</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">SO</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="k">assert</span> <span class="n">n_orb</span> <span class="o">%</span> <span class="mi">2</span> <span class="o">==</span> <span class="mi">0</span>
+        <span class="n">n_orb</span> <span class="o">=</span> <span class="n">n_orb</span> <span class="o">//</span> <span class="mi">2</span>
+
+    <span class="n">l</span> <span class="o">=</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">corr_shells</span><span class="p">[</span><span class="n">ish</span><span class="p">][</span><span class="s1">&#39;l&#39;</span><span class="p">]</span>
+    <span class="k">if</span> <span class="n">l</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">:</span>
+        <span class="n">slater_integrals</span> <span class="o">=</span> <span class="n">util</span><span class="o">.</span><span class="n">U_J_to_radial_integrals</span><span class="p">(</span><span class="n">l</span><span class="o">=</span><span class="n">l</span><span class="p">,</span> <span class="n">U_int</span><span class="o">=</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;U&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">],</span>
+                                                        <span class="n">J_hund</span><span class="o">=</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;J&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">])</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="c1"># Implements parameter R=F4/F2. For R=0.63 equivalent to util.U_J_to_radial_integrals</span>
+        <span class="n">U</span> <span class="o">=</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;U&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">]</span>
+        <span class="n">J</span> <span class="o">=</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;J&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">]</span>
+        <span class="n">R</span> <span class="o">=</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;ratio_F4_F2&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">]</span>
+        <span class="n">R</span> <span class="o">=</span> <span class="mf">0.63</span> <span class="k">if</span> <span class="n">R</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">R</span>
+        <span class="n">slater_integrals</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">U</span><span class="p">,</span> <span class="mi">14</span><span class="o">*</span><span class="n">J</span><span class="o">/</span><span class="p">(</span><span class="mi">1</span><span class="o">+</span><span class="n">R</span><span class="p">),</span> <span class="mi">14</span><span class="o">*</span><span class="n">J</span><span class="o">*</span><span class="n">R</span><span class="o">/</span><span class="p">(</span><span class="mi">1</span><span class="o">+</span><span class="n">R</span><span class="p">)])</span>
+
+    <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">Impurity </span><span class="si">{}</span><span class="s1">: The corresponding slater integrals are&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">icrsh</span><span class="p">))</span>
+    <span class="n">formatted_slater_integrals</span> <span class="o">=</span> <span class="p">[</span><span class="n">y</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">([</span><span class="mi">2</span><span class="o">*</span><span class="n">x</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">slater_integrals</span><span class="p">))],</span> <span class="n">slater_integrals</span><span class="p">))</span> <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="n">x</span><span class="p">]</span>
+    <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">((</span><span class="s1">&#39;F</span><span class="si">{:d}</span><span class="s1"> = </span><span class="si">{:.2f}</span><span class="s1">, &#39;</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">slater_integrals</span><span class="p">))</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="o">*</span><span class="n">formatted_slater_integrals</span><span class="p">))</span>
+
+    <span class="c1"># Constructs U matrix</span>
+    <span class="c1"># construct full spherical symmetric U matrix and transform to cubic basis</span>
+    <span class="c1"># the order for the cubic orbitals is given by the convention. The TRIQS</span>
+    <span class="c1"># convention is as follows (&quot;xy&quot;,&quot;yz&quot;,&quot;z^2&quot;,&quot;xz&quot;,&quot;x^2-y^2&quot;)</span>
+    <span class="c1"># this is consistent with the order of orbitals in the VASP interface</span>
+    <span class="c1"># but not necessarily with wannier90, qe, and wien2k!</span>
+    <span class="c1"># This is also true for the f-shell.</span>
+    <span class="n">Umat_full</span> <span class="o">=</span> <span class="n">util</span><span class="o">.</span><span class="n">U_matrix_slater</span><span class="p">(</span><span class="n">l</span><span class="o">=</span><span class="n">l</span><span class="p">,</span>
+                              <span class="n">radial_integrals</span><span class="o">=</span><span class="n">slater_integrals</span><span class="p">,</span> <span class="n">basis</span><span class="o">=</span><span class="s1">&#39;spherical&#39;</span><span class="p">)</span>
+    <span class="n">Umat_full</span> <span class="o">=</span> <span class="n">util</span><span class="o">.</span><span class="n">transform_U_matrix</span><span class="p">(</span><span class="n">Umat_full</span><span class="p">,</span>
+                                        <span class="n">util</span><span class="o">.</span><span class="n">spherical_to_cubic</span><span class="p">(</span><span class="n">l</span><span class="o">=</span><span class="n">l</span><span class="p">,</span> <span class="n">convention</span><span class="o">=</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;h_int_basis&#39;</span><span class="p">]))</span>
+
+    <span class="k">if</span> <span class="n">l</span> <span class="o">==</span> <span class="mi">2</span> <span class="ow">and</span> <span class="n">n_orb</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+        <span class="n">Umat_full</span> <span class="o">=</span> <span class="n">util</span><span class="o">.</span><span class="n">eg_submatrix</span><span class="p">(</span><span class="n">Umat_full</span><span class="p">)</span>
+        <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;Using eg subspace of interaction Hamiltonian&#39;</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="n">l</span> <span class="o">==</span> <span class="mi">2</span> <span class="ow">and</span> <span class="n">n_orb</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+        <span class="n">Umat_full</span> <span class="o">=</span> <span class="n">util</span><span class="o">.</span><span class="n">t2g_submatrix</span><span class="p">(</span><span class="n">Umat_full</span><span class="p">)</span>
+        <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;Using t2g subspace of interaction Hamiltonian&#39;</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="n">n_orb</span> <span class="o">!=</span> <span class="mi">2</span><span class="o">*</span><span class="n">l</span><span class="o">+</span><span class="mi">1</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Imp </span><span class="si">{</span><span class="n">icrsh</span><span class="si">}</span><span class="s1">: for the Slater Hamiltonian, please use either &#39;</span>
+                         <span class="sa">f</span><span class="s1">&#39;the full shell with 2l+1=</span><span class="si">{</span><span class="mi">2</span><span class="o">*</span><span class="n">l</span><span class="o">+</span><span class="mi">1</span><span class="si">}</span><span class="s1"> orbitals &#39;</span>
+                         <span class="s1">&#39;or the t2g or eg subspace of the d shell with 3 or 2 orbitals.&#39;</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">Umat_full</span>
+
+
+<span class="k">def</span> <span class="nf">_rotate_four_index_matrix</span><span class="p">(</span><span class="n">sum_k</span><span class="p">,</span> <span class="n">general_params</span><span class="p">,</span> <span class="n">Umat_full</span><span class="p">,</span> <span class="n">icrsh</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot; Rotates the four index matrix into the local frame. &quot;&quot;&quot;</span>
+
+    <span class="n">ish</span> <span class="o">=</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">inequiv_to_corr</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span>
+    <span class="c1"># Transposes rotation matrix here because TRIQS has a slightly different definition</span>
+    <span class="n">Umat_full_rotated</span> <span class="o">=</span> <span class="n">util</span><span class="o">.</span><span class="n">transform_U_matrix</span><span class="p">(</span><span class="n">Umat_full</span><span class="p">,</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">rot_mat</span><span class="p">[</span><span class="n">ish</span><span class="p">]</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;h_int_type&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">]</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">&#39;density_density&#39;</span><span class="p">,</span> <span class="s1">&#39;crpa_density_density&#39;</span><span class="p">):</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">Umat_full_rotated</span><span class="p">,</span> <span class="n">Umat_full</span><span class="p">):</span>
+            <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;WARNING: applying a rotation matrix changes the dens-dens Hamiltonian.</span><span class="se">\n</span><span class="s1">&#39;</span>
+                       <span class="o">+</span> <span class="s1">&#39;This changes the definition of the ignored spin flip and pair hopping.&#39;</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;h_int_type&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">]</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">&#39;full_slater&#39;</span><span class="p">,</span> <span class="s1">&#39;crpa&#39;</span><span class="p">):</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">Umat_full_rotated</span><span class="p">,</span> <span class="n">Umat_full</span><span class="p">):</span>
+            <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;WARNING: applying a rotation matrix changes the interaction Hamiltonian.</span><span class="se">\n</span><span class="s1">&#39;</span>
+                       <span class="o">+</span> <span class="s1">&#39;Please ensure that the rotation is correct!&#39;</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">Umat_full_rotated</span>
+
+
+<span class="k">def</span> <span class="nf">_construct_density_density</span><span class="p">(</span><span class="n">sum_k</span><span class="p">,</span> <span class="n">general_params</span><span class="p">,</span> <span class="n">Umat_full_rotated</span><span class="p">,</span> <span class="n">icrsh</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Constructs the density-density Slater-Hamiltonian from the four-index</span>
+<span class="sd">    interaction matrix.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># Constructs Hamiltonian from Umat_full_rotated</span>
+    <span class="n">n_orb</span> <span class="o">=</span> <span class="n">solver</span><span class="o">.</span><span class="n">get_n_orbitals</span><span class="p">(</span><span class="n">sum_k</span><span class="p">)[</span><span class="n">icrsh</span><span class="p">][</span><span class="s1">&#39;up&#39;</span><span class="p">]</span>
+
+    <span class="n">Umat</span><span class="p">,</span> <span class="n">Upmat</span> <span class="o">=</span> <span class="n">util</span><span class="o">.</span><span class="n">reduce_4index_to_2index</span><span class="p">(</span><span class="n">Umat_full_rotated</span><span class="p">)</span>
+    <span class="n">h_int</span> <span class="o">=</span> <span class="n">util</span><span class="o">.</span><span class="n">h_int_density</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">spin_block_names</span><span class="p">[</span><span class="n">sum_k</span><span class="o">.</span><span class="n">SO</span><span class="p">],</span> <span class="n">n_orb</span><span class="p">,</span>
+                               <span class="n">map_operator_structure</span><span class="o">=</span><span class="n">sum_k</span><span class="o">.</span><span class="n">sumk_to_solver</span><span class="p">[</span><span class="n">icrsh</span><span class="p">],</span>
+                               <span class="n">U</span><span class="o">=</span><span class="n">Umat</span><span class="p">,</span> <span class="n">Uprime</span><span class="o">=</span><span class="n">Upmat</span><span class="p">,</span> <span class="n">H_dump</span><span class="o">=</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;jobname&#39;</span><span class="p">],</span> <span class="sa">f</span><span class="s1">&#39;H_imp</span><span class="si">{</span><span class="n">icrsh</span><span class="si">}</span><span class="s1">.txt&#39;</span><span class="p">))</span>
+
+    <span class="k">return</span> <span class="n">h_int</span>
+
+
+<span class="k">def</span> <span class="nf">_construct_slater</span><span class="p">(</span><span class="n">sum_k</span><span class="p">,</span> <span class="n">general_params</span><span class="p">,</span> <span class="n">Umat_full_rotated</span><span class="p">,</span> <span class="n">icrsh</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Constructs the full Slater-Hamiltonian from the four-index interaction</span>
+<span class="sd">    matrix.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">n_orb</span> <span class="o">=</span> <span class="n">solver</span><span class="o">.</span><span class="n">get_n_orbitals</span><span class="p">(</span><span class="n">sum_k</span><span class="p">)[</span><span class="n">icrsh</span><span class="p">][</span><span class="s1">&#39;up&#39;</span><span class="p">]</span>
+
+    <span class="n">h_int</span> <span class="o">=</span> <span class="n">util</span><span class="o">.</span><span class="n">h_int_slater</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">spin_block_names</span><span class="p">[</span><span class="n">sum_k</span><span class="o">.</span><span class="n">SO</span><span class="p">],</span> <span class="n">n_orb</span><span class="p">,</span>
+                              <span class="n">map_operator_structure</span><span class="o">=</span><span class="n">sum_k</span><span class="o">.</span><span class="n">sumk_to_solver</span><span class="p">[</span><span class="n">icrsh</span><span class="p">],</span>
+                              <span class="n">U_matrix</span><span class="o">=</span><span class="n">Umat_full_rotated</span><span class="p">,</span>
+                              <span class="n">H_dump</span><span class="o">=</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;jobname&#39;</span><span class="p">],</span> <span class="sa">f</span><span class="s1">&#39;H_imp</span><span class="si">{</span><span class="n">icrsh</span><span class="si">}</span><span class="s1">.txt&#39;</span><span class="p">))</span>
+
+    <span class="k">return</span> <span class="n">h_int</span>
+
+<div class="viewcode-block" id="h_int_simple_intra"><a class="viewcode-back" href="../../_ref/dmft_tools.interaction_hamiltonian.html#dmft_tools.interaction_hamiltonian.h_int_simple_intra">[docs]</a><span class="k">def</span> <span class="nf">h_int_simple_intra</span><span class="p">(</span><span class="n">spin_names</span><span class="p">,</span><span class="n">n_orb</span><span class="p">,</span><span class="n">U</span><span class="p">,</span><span class="n">off_diag</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span><span class="n">map_operator_structure</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span><span class="n">H_dump</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Create a simple intra orbital density-density Hamiltonian.</span>
+<span class="sd">    (no inter orbital terms)</span>
+
+<span class="sd">    .. math::</span>
+<span class="sd">        H = \frac{1}{2} \sum_{i \sigma \neq \sigma&#39;)} U_{i i}^{\sigma \sigma&#39;} n_{i \sigma} n_{i \sigma&#39;}.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    spin_names : list of strings</span>
+<span class="sd">               Names of the spins, e.g. [&#39;up&#39;,&#39;down&#39;].</span>
+<span class="sd">    n_orb : int</span>
+<span class="sd">               Number of orbitals.</span>
+<span class="sd">    U : float</span>
+<span class="sd">               U value</span>
+<span class="sd">    off_diag : boolean</span>
+<span class="sd">               Do we have (orbital) off-diagonal elements?</span>
+<span class="sd">               If yes, the operators and blocks are denoted by (&#39;spin&#39;, &#39;orbital&#39;),</span>
+<span class="sd">               otherwise by (&#39;spin_orbital&#39;,0).</span>
+<span class="sd">    map_operator_structure : dict</span>
+<span class="sd">               Mapping of names of GF blocks names from one convention to another,</span>
+<span class="sd">               e.g. {(&#39;up&#39;, 0): (&#39;up_0&#39;, 0), (&#39;down&#39;, 0): (&#39;down_0&#39;,0)}.</span>
+<span class="sd">               If provided, the operators and blocks are denoted by the mapping of ``(&#39;spin&#39;, &#39;orbital&#39;)``.</span>
+<span class="sd">    H_dump : string</span>
+<span class="sd">               Name of the file to which the Hamiltonian should be written.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    H : Operator</span>
+<span class="sd">        The Hamiltonian.</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="kn">from</span> <span class="nn">triqs.operators.util.op_struct</span> <span class="kn">import</span> <span class="n">get_mkind</span>
+
+    <span class="k">if</span> <span class="n">H_dump</span><span class="p">:</span>
+        <span class="n">H_dump_file</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="n">H_dump</span><span class="p">,</span><span class="s1">&#39;w&#39;</span><span class="p">)</span>
+        <span class="n">H_dump_file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s2">&quot;Density-density Hamiltonian:&quot;</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
+
+    <span class="n">H</span> <span class="o">=</span> <span class="n">Operator</span><span class="p">()</span>
+    <span class="n">mkind</span> <span class="o">=</span> <span class="n">get_mkind</span><span class="p">(</span><span class="n">off_diag</span><span class="p">,</span><span class="n">map_operator_structure</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">H_dump</span><span class="p">:</span> <span class="n">H_dump_file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s2">&quot;Density-density terms:&quot;</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">s1</span><span class="p">,</span> <span class="n">s2</span> <span class="ow">in</span> <span class="n">product</span><span class="p">(</span><span class="n">spin_names</span><span class="p">,</span><span class="n">spin_names</span><span class="p">):</span>
+        <span class="k">if</span> <span class="p">(</span><span class="n">s1</span> <span class="ow">is</span> <span class="ow">not</span> <span class="n">s2</span><span class="p">):</span>
+            <span class="k">for</span> <span class="n">a1</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_orb</span><span class="p">):</span>
+                <span class="n">H_term</span> <span class="o">=</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="n">U</span> <span class="o">*</span> <span class="n">n</span><span class="p">(</span><span class="o">*</span><span class="n">mkind</span><span class="p">(</span><span class="n">s1</span><span class="p">,</span><span class="n">a1</span><span class="p">))</span> <span class="o">*</span> <span class="n">n</span><span class="p">(</span><span class="o">*</span><span class="n">mkind</span><span class="p">(</span><span class="n">s2</span><span class="p">,</span><span class="n">a1</span><span class="p">))</span>
+                <span class="n">H</span> <span class="o">+=</span> <span class="n">H_term</span>
+
+                <span class="c1"># Dump terms of H</span>
+                <span class="k">if</span> <span class="n">H_dump</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">H_term</span><span class="o">.</span><span class="n">is_zero</span><span class="p">():</span>
+                    <span class="n">H_dump_file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">%s</span><span class="s1">&#39;</span><span class="o">%</span><span class="p">(</span><span class="n">mkind</span><span class="p">(</span><span class="n">s1</span><span class="p">,</span><span class="n">a1</span><span class="p">),)</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\t</span><span class="s1">&#39;</span><span class="p">)</span>
+                    <span class="n">H_dump_file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">%s</span><span class="s1">&#39;</span><span class="o">%</span><span class="p">(</span><span class="n">mkind</span><span class="p">(</span><span class="n">s2</span><span class="p">,</span><span class="n">a1</span><span class="p">),)</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\t</span><span class="s1">&#39;</span><span class="p">)</span>
+                    <span class="n">H_dump_file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">U</span><span class="p">)</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">H</span></div>
+
+
+<div class="viewcode-block" id="construct"><a class="viewcode-back" href="../../_ref/dmft_tools.interaction_hamiltonian.html#dmft_tools.interaction_hamiltonian.construct">[docs]</a><span class="k">def</span> <span class="nf">construct</span><span class="p">(</span><span class="n">sum_k</span><span class="p">,</span> <span class="n">general_params</span><span class="p">,</span> <span class="n">solver_type_per_imp</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Constructs the interaction Hamiltonian. Currently implemented are the</span>
+<span class="sd">    Kanamori Hamiltonian (usually for 2 or 3 orbitals), the density-density and</span>
+<span class="sd">    the full Slater Hamiltonian (for 2, 3, or 5 orbitals).</span>
+<span class="sd">    If sum_k.rot_mat is non-identity, we have to consider rotating the interaction</span>
+<span class="sd">    Hamiltonian: the Kanamori Hamiltonian does not change because it is invariant</span>
+<span class="sd">    under orbital mixing but all the other Hamiltonians are at most invariant</span>
+<span class="sd">    under rotations in space. Therefore, sum_k.rot_mat has to be correct before</span>
+<span class="sd">    calling this method.</span>
+
+<span class="sd">    The parameters U and J will be interpreted differently depending on the</span>
+<span class="sd">    type of the interaction Hamiltonian: it is either the Kanamori parameters</span>
+<span class="sd">    for the Kanamori Hamiltonian or the orbital-averaged parameters (consistent</span>
+<span class="sd">    with DFT+U, https://cms.mpi.univie.ac.at/wiki/index.php/LDAUTYPE ) for all</span>
+<span class="sd">    other Hamiltonians.</span>
+
+<span class="sd">    Note also that for all Hamiltonians except Kanamori, the order of the</span>
+<span class="sd">    orbitals matters. The correct order is specified here:</span>
+<span class="sd">    triqs.github.io/triqs/unstable/documentation/python_api/triqs.operators.util.U_matrix.spherical_to_cubic.html</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># Extracts U and J</span>
+    <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;*** interaction parameters ***&#39;</span><span class="p">)</span>
+
+    <span class="c1"># Constructs the interaction Hamiltonian. Needs to come after setting sum_k.rot_mat</span>
+    <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">Constructing the interaction Hamiltonians&#39;</span><span class="p">)</span>
+    <span class="n">h_int</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span>
+    <span class="k">for</span> <span class="n">icrsh</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span><span class="p">):</span>
+        <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">Impurity </span><span class="si">{}</span><span class="s1">: constructing a </span><span class="si">{}</span><span class="s1"> type interaction Hamiltonian&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">icrsh</span><span class="p">,</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;h_int_type&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">]))</span>
+
+        <span class="c1"># Kanamori</span>
+        <span class="k">if</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;h_int_type&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;kanamori&#39;</span><span class="p">:</span>
+            <span class="n">h_int</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span> <span class="o">=</span> <span class="n">_construct_kanamori</span><span class="p">(</span><span class="n">sum_k</span><span class="p">,</span> <span class="n">general_params</span><span class="p">,</span> <span class="n">solver_type_per_imp</span><span class="p">,</span> <span class="n">icrsh</span><span class="p">)</span>
+            <span class="k">continue</span>
+
+        <span class="c1"># for density density or full slater get full four-index U matrix</span>
+        <span class="k">if</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;h_int_type&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">]</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">&#39;density_density&#39;</span><span class="p">,</span> <span class="s1">&#39;full_slater&#39;</span><span class="p">):</span>
+            <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">Note: The input parameters U and J here are orbital-averaged parameters.&#39;</span><span class="p">)</span>
+            <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;Note: The order of the orbitals is important. See also the doc string of this method.&#39;</span><span class="p">)</span>
+            <span class="c1"># Checks that all entries are l == 2 or R is None</span>
+            <span class="k">if</span> <span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">corr_shells</span><span class="p">[</span><span class="n">sum_k</span><span class="o">.</span><span class="n">inequiv_to_corr</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]][</span><span class="s1">&#39;l&#39;</span><span class="p">]</span> <span class="o">!=</span> <span class="mi">2</span>
+                    <span class="ow">and</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;ratio_F4_F2&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">):</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Ratio F4/F2 only implemented for d-shells &#39;</span>
+                                 <span class="o">+</span> <span class="s1">&#39;but set in impurity </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">icrsh</span><span class="p">))</span>
+
+            <span class="k">if</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;h_int_type&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;density_density&#39;</span> <span class="ow">and</span> <span class="n">solver_type_per_imp</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;ftps&#39;</span><span class="p">:</span>
+                <span class="c1"># TODO: implement</span>
+                <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">&#39;Note: Density-density not implemented for ftps.&#39;</span><span class="p">)</span>
+
+            <span class="n">Umat_full</span> <span class="o">=</span> <span class="n">_generate_four_index_u_matrix</span><span class="p">(</span><span class="n">sum_k</span><span class="p">,</span> <span class="n">general_params</span><span class="p">,</span> <span class="n">icrsh</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">SO</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="n">Umat_full</span> <span class="o">=</span> <span class="p">[</span><span class="n">_adapt_U_4index_for_SO</span><span class="p">(</span><span class="n">Umat_full_per_imp</span><span class="p">)</span>
+                             <span class="k">for</span> <span class="n">Umat_full_per_imp</span> <span class="ow">in</span> <span class="n">Umat_full</span><span class="p">]</span>
+
+            <span class="c1"># Rotates the interaction matrix</span>
+            <span class="n">Umat_full_rotated</span> <span class="o">=</span> <span class="n">_rotate_four_index_matrix</span><span class="p">(</span><span class="n">sum_k</span><span class="p">,</span> <span class="n">general_params</span><span class="p">,</span> <span class="n">Umat_full</span><span class="p">,</span> <span class="n">icrsh</span><span class="p">)</span>
+
+            <span class="c1"># construct slater / density density from U tensor</span>
+            <span class="k">if</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;h_int_type&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;full_slater&#39;</span><span class="p">:</span>
+                <span class="n">h_int</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span> <span class="o">=</span> <span class="n">_construct_slater</span><span class="p">(</span><span class="n">sum_k</span><span class="p">,</span> <span class="n">general_params</span><span class="p">,</span> <span class="n">Umat_full_rotated</span><span class="p">,</span> <span class="n">icrsh</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">h_int</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span> <span class="o">=</span> <span class="n">_construct_density_density</span><span class="p">(</span><span class="n">sum_k</span><span class="p">,</span> <span class="n">general_params</span><span class="p">,</span> <span class="n">Umat_full_rotated</span><span class="p">,</span> <span class="n">icrsh</span><span class="p">)</span>
+
+            <span class="k">continue</span>
+
+        <span class="c1"># simple total impurity occupation interation: U/2 (Ntot^2 - Ntot)</span>
+        <span class="k">if</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;h_int_type&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;ntot&#39;</span><span class="p">:</span>
+            <span class="n">n_tot_op</span> <span class="o">=</span> <span class="n">Operator</span><span class="p">()</span>
+            <span class="k">for</span> <span class="n">block</span><span class="p">,</span> <span class="n">n_orb</span> <span class="ow">in</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">gf_struct_solver</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+                <span class="n">n_tot_op</span> <span class="o">+=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">n</span><span class="p">(</span><span class="n">block</span><span class="p">,</span> <span class="n">orb</span><span class="p">)</span> <span class="k">for</span> <span class="n">orb</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_orb</span><span class="p">))</span>
+            <span class="n">h_int</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span> <span class="o">=</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;U&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">]</span><span class="o">/</span><span class="mf">2.0</span> <span class="o">*</span> <span class="p">(</span><span class="n">n_tot_op</span><span class="o">*</span><span class="n">n_tot_op</span> <span class="o">-</span> <span class="n">n_tot_op</span><span class="p">)</span>
+            <span class="k">continue</span>
+
+        <span class="k">if</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;h_int_type&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;simple_intra&#39;</span><span class="p">:</span>
+            <span class="n">h_int</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span> <span class="o">=</span> <span class="n">h_int_simple_intra</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">spin_block_names</span><span class="p">[</span><span class="n">sum_k</span><span class="o">.</span><span class="n">SO</span><span class="p">],</span>
+                                              <span class="n">solver</span><span class="o">.</span><span class="n">get_n_orbitals</span><span class="p">(</span><span class="n">sum_k</span><span class="p">)[</span><span class="n">icrsh</span><span class="p">][</span><span class="s1">&#39;up&#39;</span><span class="p">],</span>
+                                              <span class="n">map_operator_structure</span><span class="o">=</span><span class="n">sum_k</span><span class="o">.</span><span class="n">sumk_to_solver</span><span class="p">[</span><span class="n">icrsh</span><span class="p">],</span>
+                                              <span class="n">U</span><span class="o">=</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;U&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">],</span>
+                                              <span class="n">H_dump</span><span class="o">=</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;jobname&#39;</span><span class="p">],</span> <span class="sa">f</span><span class="s1">&#39;H_imp</span><span class="si">{</span><span class="n">icrsh</span><span class="si">}</span><span class="s1">.txt&#39;</span><span class="p">))</span>
+            <span class="k">continue</span>
+
+
+        <span class="c1"># read from file options</span>
+        <span class="k">if</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;h_int_type&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">]</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">&#39;crpa&#39;</span><span class="p">,</span> <span class="s1">&#39;crpa_density_density&#39;</span><span class="p">):</span>
+            <span class="n">Umat_full</span> <span class="o">=</span> <span class="n">_load_crpa_interaction_matrix</span><span class="p">(</span><span class="n">sum_k</span><span class="p">,</span> <span class="n">icrsh</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">SO</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="n">Umat_full</span> <span class="o">=</span> <span class="p">[</span><span class="n">_adapt_U_4index_for_SO</span><span class="p">(</span><span class="n">Umat_full_per_imp</span><span class="p">)</span>
+                             <span class="k">for</span> <span class="n">Umat_full_per_imp</span> <span class="ow">in</span> <span class="n">Umat_full</span><span class="p">]</span>
+
+            <span class="c1"># Rotates the interaction matrix</span>
+            <span class="n">Umat_full_rotated</span> <span class="o">=</span> <span class="n">_rotate_four_index_matrix</span><span class="p">(</span><span class="n">sum_k</span><span class="p">,</span> <span class="n">general_params</span><span class="p">,</span> <span class="n">Umat_full</span><span class="p">,</span> <span class="n">icrsh</span><span class="p">)</span>
+
+            <span class="c1"># construct slater / density density from U tensor</span>
+            <span class="k">if</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;h_int_type&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;crpa&#39;</span><span class="p">:</span>
+                <span class="n">h_int</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span> <span class="o">=</span> <span class="n">_construct_slater</span><span class="p">(</span><span class="n">sum_k</span><span class="p">,</span> <span class="n">general_params</span><span class="p">,</span> <span class="n">Umat_full_rotated</span><span class="p">,</span> <span class="n">icrsh</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">h_int</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span> <span class="o">=</span> <span class="n">_construct_density_density</span><span class="p">(</span><span class="n">sum_k</span><span class="p">,</span> <span class="n">general_params</span><span class="p">,</span> <span class="n">Umat_full_rotated</span><span class="p">,</span> <span class="n">icrsh</span><span class="p">)</span>
+            <span class="k">continue</span>
+
+        <span class="c1"># dynamic interaction from file</span>
+        <span class="k">if</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;h_int_type&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;dynamic&#39;</span><span class="p">:</span>
+            <span class="n">h_int</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span> <span class="o">=</span> <span class="n">_construct_dynamic</span><span class="p">(</span><span class="n">sum_k</span><span class="p">,</span> <span class="n">general_params</span><span class="p">,</span> <span class="n">icrsh</span><span class="p">)</span>
+            <span class="k">continue</span>
+
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">&#39;Error when constructing the interaction Hamiltonian.&#39;</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">h_int</span></div>
+</pre></div>
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.</p>
+  </div>
+
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new file mode 100644
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+<!DOCTYPE html>
+<html class="writer-html5" lang="en" >
+<head>
+  <meta charset="utf-8" />
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+      <link rel="stylesheet" href="../../_static/pygments.css" type="text/css" />
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+    <script src="../../_static/js/theme.js"></script>
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+            <a href="../../index.html" class="icon icon-home"> solid_dmft
+            <img src="../../_static/logo_no_text.png" class="logo" alt="Logo"/>
+          </a>
+              <div class="version">
+                3.2.0
+              </div>
+<div role="search">
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+              <ul>
+<li class="toctree-l1"><a class="reference internal" href="../../install.html">Installation</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#installation-via-pip">Installation via pip</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#manual-installation-via-cmake">Manual installation via CMake</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#docker-files-images">Docker files &amp; images</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#version-compatibility">Version compatibility</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#custom-cmake-options">Custom CMake options</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../documentation.html">Documentation</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#code-structure">Code structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#dft-interface-notes">DFT interface notes</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/w90_interface.html">Wannier90 interface</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/w90_interface.html#orbital-order-in-the-w90-converter">orbital order in the W90 converter</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/vasp_csc.html">Interface to VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#general-remarks">General remarks</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#file-description">file description</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#workflow">Workflow:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#important-flags">important flags:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#version-and-compilation">version and compilation:</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#input-output">Input/Output</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../input_output/DMFT_input/input.html">Input</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/general.html">[general]: General parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/solver.html">[solver]: solver specific parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/dft.html">[dft]: DFT related inputs</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/advanced.html">[advanced]: Advanced inputs</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../input_output/DMFT_output/results.html">Output / results</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_output/results.html#group-structure">Group structure</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_output/results.html#subgroups">Subgroups</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#further-details-for-running">Further details for running</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/docker.html">Docker</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#building-the-docker-image">Building the docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#pulling-a-docker-image">Pulling a docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_locally.html">Run on your machine</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#module-reference-manual">Module reference manual</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/csc_flow.html">csc_flow</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/dft_managers.html">dft_managers</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.mpi_helpers.html">dft_managers.mpi_helpers</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.qe_manager.html">dft_managers.qe_manager</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.vasp_manager.html">dft_managers.vasp_manager</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/dmft_cycle.html">dmft_cycle</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/dmft_tools.html">dmft_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.afm_mapping.html">dmft_tools.afm_mapping</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.convergence.html">dmft_tools.convergence</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.formatter.html">dmft_tools.formatter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.greens_functions_mixer.html">dmft_tools.greens_functions_mixer</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.initial_self_energies.html">dmft_tools.initial_self_energies</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.interaction_hamiltonian.html">dmft_tools.interaction_hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.legendre_filter.html">dmft_tools.legendre_filter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.manipulate_chemical_potential.html">dmft_tools.manipulate_chemical_potential</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.matheval.html">dmft_tools.matheval</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.observables.html">dmft_tools.observables</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.results_to_archive.html">dmft_tools.results_to_archive</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.solver.html">dmft_tools.solver</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/io_tools.html">io_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.dict_to_h5.html">io_tools.dict_to_h5</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.postproc_toml_dict.html">io_tools.postproc_toml_dict</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.verify_input_params.html">io_tools.verify_input_params</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_gf_imp.html">postprocessing.maxent_gf_imp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_gf_latt.html">postprocessing.maxent_gf_latt</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_sigma.html">postprocessing.maxent_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
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+      <li>dmft_tools.legendre_filter</li>
+      <li class="wy-breadcrumbs-aside">
+      </li>
+  </ul>
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+             
+  <h1>Source code for dmft_tools.legendre_filter</h1><div class="highlight"><pre>
+<span></span><span class="c1">################################################################################</span>
+<span class="c1">#</span>
+<span class="c1"># solid_dmft - A versatile python wrapper to perform DFT+DMFT calculations</span>
+<span class="c1">#              utilizing the TRIQS software library</span>
+<span class="c1">#</span>
+<span class="c1"># Copyright (C) 2018-2020, ETH Zurich</span>
+<span class="c1"># Copyright (C) 2021, The Simons Foundation</span>
+<span class="c1">#      authors: A. Hampel, M. Merkel, and S. Beck</span>
+<span class="c1">#</span>
+<span class="c1"># solid_dmft is free software: you can redistribute it and/or modify it under the</span>
+<span class="c1"># terms of the GNU General Public License as published by the Free Software</span>
+<span class="c1"># Foundation, either version 3 of the License, or (at your option) any later</span>
+<span class="c1"># version.</span>
+<span class="c1">#</span>
+<span class="c1"># solid_dmft is distributed in the hope that it will be useful, but WITHOUT ANY</span>
+<span class="c1"># WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A</span>
+<span class="c1"># PARTICULAR PURPOSE. See the GNU General Public License for more details.</span>
+
+<span class="c1"># You should have received a copy of the GNU General Public License along with</span>
+<span class="c1"># solid_dmft (in the file COPYING.txt in this directory). If not, see</span>
+<span class="c1"># &lt;http://www.gnu.org/licenses/&gt;.</span>
+<span class="c1">#</span>
+<span class="c1">################################################################################</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<span class="c1"># triqs</span>
+<span class="kn">from</span> <span class="nn">triqs.gf</span> <span class="kn">import</span> <span class="n">BlockGf</span>
+<span class="kn">from</span> <span class="nn">triqs.gf.tools</span> <span class="kn">import</span> <span class="n">fit_legendre</span>
+
+
+<div class="viewcode-block" id="apply"><a class="viewcode-back" href="../../_ref/dmft_tools.legendre_filter.html#dmft_tools.legendre_filter.apply">[docs]</a><span class="k">def</span> <span class="nf">apply</span><span class="p">(</span><span class="n">G_tau</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">G_l_cut</span><span class="o">=</span><span class="mf">1e-19</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot; Filter binned imaginary time Green&#39;s function</span>
+<span class="sd">    using a Legendre filter of given order and coefficient threshold.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    G_tau : TRIQS imaginary time Block Green&#39;s function</span>
+<span class="sd">    auto : determines automatically the cut-off nl</span>
+<span class="sd">    order : int</span>
+<span class="sd">        Legendre expansion order in the filter</span>
+<span class="sd">    G_l_cut : float</span>
+<span class="sd">        Legendre coefficient cut-off</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    G_l : TRIQS Legendre Block Green&#39;s function</span>
+<span class="sd">        Fitted Green&#39;s function on a Legendre mesh</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">l_g_l</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">g</span> <span class="ow">in</span> <span class="n">G_tau</span><span class="p">:</span>
+
+        <span class="n">g_l</span> <span class="o">=</span> <span class="n">fit_legendre</span><span class="p">(</span><span class="n">g</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="n">order</span><span class="p">)</span>
+        <span class="n">g_l</span><span class="o">.</span><span class="n">data</span><span class="p">[:]</span> <span class="o">*=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">g_l</span><span class="o">.</span><span class="n">data</span><span class="p">)</span> <span class="o">&gt;</span> <span class="n">G_l_cut</span><span class="p">)</span>
+        <span class="n">g_l</span><span class="o">.</span><span class="n">enforce_discontinuity</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">identity</span><span class="p">(</span><span class="n">g</span><span class="o">.</span><span class="n">target_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
+
+        <span class="n">l_g_l</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">g_l</span><span class="p">)</span>
+
+    <span class="n">G_l</span> <span class="o">=</span> <span class="n">BlockGf</span><span class="p">(</span><span class="n">name_list</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="n">G_tau</span><span class="o">.</span><span class="n">indices</span><span class="p">),</span> <span class="n">block_list</span><span class="o">=</span><span class="n">l_g_l</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">G_l</span></div>
+</pre></div>
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.</p>
+  </div>
+
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diff --git a/_modules/dmft_tools/manipulate_chemical_potential.html b/_modules/dmft_tools/manipulate_chemical_potential.html
new file mode 100644
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+<!DOCTYPE html>
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+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/w90_interface.html">Wannier90 interface</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/w90_interface.html#orbital-order-in-the-w90-converter">orbital order in the W90 converter</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/vasp_csc.html">Interface to VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#general-remarks">General remarks</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#file-description">file description</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#workflow">Workflow:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#important-flags">important flags:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#version-and-compilation">version and compilation:</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#input-output">Input/Output</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../input_output/DMFT_input/input.html">Input</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/general.html">[general]: General parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/solver.html">[solver]: solver specific parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/dft.html">[dft]: DFT related inputs</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/advanced.html">[advanced]: Advanced inputs</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../input_output/DMFT_output/results.html">Output / results</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_output/results.html#group-structure">Group structure</a></li>
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+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#further-details-for-running">Further details for running</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/docker.html">Docker</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#building-the-docker-image">Building the docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#pulling-a-docker-image">Pulling a docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_locally.html">Run on your machine</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#module-reference-manual">Module reference manual</a><ul>
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+</ul>
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+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.dict_to_h5.html">io_tools.dict_to_h5</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.postproc_toml_dict.html">io_tools.postproc_toml_dict</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.verify_input_params.html">io_tools.verify_input_params</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_gf_imp.html">postprocessing.maxent_gf_imp</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
+</li>
+</ul>
+</li>
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+          <li><a href="../index.html">Module code</a> &raquo;</li>
+      <li>dmft_tools.manipulate_chemical_potential</li>
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+      </li>
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+             
+  <h1>Source code for dmft_tools.manipulate_chemical_potential</h1><div class="highlight"><pre>
+<span></span><span class="ch">#!/usr/bin/env python3</span>
+<span class="c1"># -*- coding: utf-8 -*-</span>
+<span class="c1">################################################################################</span>
+<span class="c1">#</span>
+<span class="c1"># solid_dmft - A versatile python wrapper to perform DFT+DMFT calculations</span>
+<span class="c1">#              utilizing the TRIQS software library</span>
+<span class="c1">#</span>
+<span class="c1"># Copyright (C) 2018-2020, ETH Zurich</span>
+<span class="c1"># Copyright (C) 2021, The Simons Foundation</span>
+<span class="c1">#      authors: A. Hampel, M. Merkel, and S. Beck</span>
+<span class="c1">#</span>
+<span class="c1"># solid_dmft is free software: you can redistribute it and/or modify it under the</span>
+<span class="c1"># terms of the GNU General Public License as published by the Free Software</span>
+<span class="c1"># Foundation, either version 3 of the License, or (at your option) any later</span>
+<span class="c1"># version.</span>
+<span class="c1">#</span>
+<span class="c1"># solid_dmft is distributed in the hope that it will be useful, but WITHOUT ANY</span>
+<span class="c1"># WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A</span>
+<span class="c1"># PARTICULAR PURPOSE. See the GNU General Public License for more details.</span>
+
+<span class="c1"># You should have received a copy of the GNU General Public License along with</span>
+<span class="c1"># solid_dmft (in the file COPYING.txt in this directory). If not, see</span>
+<span class="c1"># &lt;http://www.gnu.org/licenses/&gt;.</span>
+<span class="c1">#</span>
+<span class="c1">################################################################################</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Contains all the functions related to setting the chemical potential in the</span>
+<span class="sd">next iteration.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<span class="kn">import</span> <span class="nn">triqs.utility.mpi</span> <span class="k">as</span> <span class="nn">mpi</span>
+<span class="kn">from</span> <span class="nn">triqs.gf</span> <span class="kn">import</span> <span class="n">BlockGf</span><span class="p">,</span> <span class="n">GfImFreq</span><span class="p">,</span> <span class="n">GfImTime</span><span class="p">,</span> <span class="n">Fourier</span><span class="p">,</span> <span class="n">MeshImFreq</span>
+<span class="k">try</span><span class="p">:</span>
+    <span class="k">if</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+        <span class="kn">from</span> <span class="nn">solid_dmft.postprocessing</span> <span class="kn">import</span> <span class="n">maxent_gf_latt</span>
+    <span class="n">imported_maxent</span> <span class="o">=</span> <span class="kc">True</span>
+<span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
+    <span class="n">imported_maxent</span> <span class="o">=</span> <span class="kc">False</span>
+
+<span class="k">def</span> <span class="nf">_mix_chemical_potential</span><span class="p">(</span><span class="n">general_params</span><span class="p">,</span> <span class="n">density_tot</span><span class="p">,</span> <span class="n">density_required</span><span class="p">,</span>
+                            <span class="n">previous_mu</span><span class="p">,</span> <span class="n">predicted_mu</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Mixes the previous chemical potential and the predicted potential with linear</span>
+<span class="sd">    mixing:</span>
+<span class="sd">    new_mu = factor * predicted_mu + (1-factor) * previous_mu, with</span>
+<span class="sd">    factor = mu_mix_per_occupation_offset * |density_tot - density_required| + mu_mix_const</span>
+<span class="sd">    under the constrain of 0 &lt;= factor &lt;= 1.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    general_params : dict</span>
+<span class="sd">        general parameters as a dict</span>
+<span class="sd">    density_tot : float</span>
+<span class="sd">        total occupation of the correlated system</span>
+<span class="sd">    density_required : float</span>
+<span class="sd">        required density for the impurity problem</span>
+<span class="sd">    previous_mu : float</span>
+<span class="sd">        the chemical potential from the previous iteration</span>
+<span class="sd">    predicted_mu : float</span>
+<span class="sd">        the chemical potential predicted by methods like the SumkDFT dichotomy</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    new_mu : float</span>
+<span class="sd">        the chemical potential that results from the mixing</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">mu_mixing</span> <span class="o">=</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;mu_mix_per_occupation_offset&#39;</span><span class="p">]</span> <span class="o">*</span> <span class="nb">abs</span><span class="p">(</span><span class="n">density_tot</span> <span class="o">-</span> <span class="n">density_required</span><span class="p">)</span>
+    <span class="n">mu_mixing</span> <span class="o">+=</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;mu_mix_const&#39;</span><span class="p">]</span>
+    <span class="n">mu_mixing</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="nb">min</span><span class="p">(</span><span class="n">mu_mixing</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="mi">0</span><span class="p">)</span>
+    <span class="n">new_mu</span> <span class="o">=</span> <span class="n">mu_mixing</span> <span class="o">*</span> <span class="n">predicted_mu</span> <span class="o">+</span> <span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">mu_mixing</span><span class="p">)</span> <span class="o">*</span> <span class="n">previous_mu</span>
+
+    <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;Mixing dichotomy mu with previous iteration by factor </span><span class="si">{:.3f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">mu_mixing</span><span class="p">))</span>
+    <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;New chemical potential: </span><span class="si">{:.3f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">new_mu</span><span class="p">))</span>
+    <span class="k">return</span> <span class="n">new_mu</span>
+
+
+
+<span class="k">def</span> <span class="nf">_initialize_lattice_gf</span><span class="p">(</span><span class="n">sum_k</span><span class="p">,</span> <span class="n">general_params</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Creates lattice Green&#39;s function (GF) that is averaged over orbitals,</span>
+<span class="sd">    blocks and spins. Returns lattice GF as input for an analytical</span>
+<span class="sd">    continuation as well as G_lattice(tau=beta/2) (proxy for the spectral</span>
+<span class="sd">    weight) and G_lattice(beta) (proxy for the total occupation).</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    sum_k : SumkDFT object</span>
+<span class="sd">        Sumk object to generate the lattice GF from.</span>
+<span class="sd">    general_params : dict</span>
+<span class="sd">        general parameters as dict.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    gf_lattice_iw : triqs.gf.BlockGf</span>
+<span class="sd">        trace of the lattice GF over all blocks, orbitals and spins in</span>
+<span class="sd">        Matsubara frequency.</span>
+<span class="sd">    g_betahalf : complex</span>
+<span class="sd">        the Fourier transform of gf_lattice_iw evaluated at tau=beta/2.</span>
+<span class="sd">    occupation : complex</span>
+<span class="sd">        the total density from gf_lattice_iw</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># Initializes lattice GF to zero for each process</span>
+    <span class="n">mesh</span> <span class="o">=</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">Sigma_imp</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">mesh</span>
+    <span class="n">trace_gf_latt</span> <span class="o">=</span> <span class="n">GfImFreq</span><span class="p">(</span><span class="n">mesh</span><span class="o">=</span><span class="n">mesh</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">mesh</span><span class="p">),</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">complex</span><span class="p">))</span>
+    <span class="n">occupation</span> <span class="o">=</span> <span class="mi">0</span>
+
+    <span class="c1"># Takes trace over orbitals and spins</span>
+    <span class="n">ikarray</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_k</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">ik</span> <span class="ow">in</span> <span class="n">mpi</span><span class="o">.</span><span class="n">slice_array</span><span class="p">(</span><span class="n">ikarray</span><span class="p">):</span>
+        <span class="n">gf_latt</span> <span class="o">=</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">lattice_gf</span><span class="p">(</span><span class="n">ik</span><span class="p">)</span><span class="o">*</span><span class="n">sum_k</span><span class="o">.</span><span class="n">bz_weights</span><span class="p">[</span><span class="n">ik</span><span class="p">]</span>
+        <span class="n">trace_gf_latt</span><span class="o">.</span><span class="n">data</span><span class="p">[:]</span> <span class="o">+=</span> <span class="n">np</span><span class="o">.</span><span class="n">trace</span><span class="p">(</span><span class="nb">sum</span><span class="p">(</span><span class="n">g</span><span class="o">.</span><span class="n">data</span> <span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">g</span> <span class="ow">in</span> <span class="n">gf_latt</span><span class="p">),</span> <span class="n">axis1</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">axis2</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="n">occupation</span> <span class="o">+=</span> <span class="n">gf_latt</span><span class="o">.</span><span class="n">total_density</span><span class="p">()</span>
+
+    <span class="n">trace_gf_latt</span> <span class="o">&lt;&lt;</span> <span class="n">mpi</span><span class="o">.</span><span class="n">all_reduce</span><span class="p">(</span><span class="n">trace_gf_latt</span><span class="p">)</span>
+    <span class="n">occupation</span> <span class="o">=</span> <span class="n">mpi</span><span class="o">.</span><span class="n">all_reduce</span><span class="p">(</span><span class="n">occupation</span><span class="p">)</span>
+
+    <span class="c1"># Lattice GF as BlockGf, required for compatibility with MaxEnt functions</span>
+    <span class="n">gf_lattice_iw</span> <span class="o">=</span> <span class="n">BlockGf</span><span class="p">(</span><span class="n">name_list</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;total&#39;</span><span class="p">],</span> <span class="n">block_list</span><span class="o">=</span><span class="p">[</span><span class="n">trace_gf_latt</span><span class="p">])</span>
+
+    <span class="c1"># Fourier transforms the lattice GF</span>
+    <span class="n">gf_tau</span> <span class="o">=</span> <span class="n">GfImTime</span><span class="p">(</span><span class="n">beta</span><span class="o">=</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;beta&#39;</span><span class="p">],</span> <span class="n">n_points</span><span class="o">=</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;n_tau&#39;</span><span class="p">],</span> <span class="n">indices</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+    <span class="n">gf_tau</span> <span class="o">&lt;&lt;</span> <span class="n">Fourier</span><span class="p">(</span><span class="n">gf_lattice_iw</span><span class="p">[</span><span class="s1">&#39;total&#39;</span><span class="p">])</span>
+
+    <span class="n">tau_mesh</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">m</span><span class="p">)</span> <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">gf_tau</span><span class="o">.</span><span class="n">mesh</span><span class="p">])</span>
+    <span class="n">middle_index</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmin</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">tau_mesh</span><span class="o">-</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;beta&#39;</span><span class="p">]</span><span class="o">/</span><span class="mi">2</span><span class="p">))</span>
+    <span class="n">samp</span> <span class="o">=</span> <span class="mi">10</span>
+
+    <span class="c1"># Extracts G_latt(tau) at beta/2</span>
+    <span class="n">g_betahalf</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">gf_tau</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">middle_index</span><span class="o">-</span><span class="n">samp</span><span class="p">:</span><span class="n">middle_index</span><span class="o">+</span><span class="n">samp</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
+    <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;Lattice Gf: occupation = </span><span class="si">{:.5f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">occupation</span><span class="p">))</span>
+    <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;            G(beta/2)  = </span><span class="si">{:.5f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">g_betahalf</span><span class="p">))</span>
+
+    <span class="k">return</span> <span class="n">gf_lattice_iw</span><span class="p">,</span> <span class="n">g_betahalf</span><span class="p">,</span> <span class="n">occupation</span>
+
+<span class="k">def</span> <span class="nf">_determine_band_edge</span><span class="p">(</span><span class="n">mesh</span><span class="p">,</span> <span class="n">spectral_function</span><span class="p">,</span> <span class="n">spectral_func_threshold</span><span class="p">,</span> <span class="n">valence_band</span><span class="p">,</span> <span class="n">edge_threshold</span><span class="o">=</span><span class="mf">.2</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Finds the band edge of a spectral function. This is done in two steps:</span>
+<span class="sd">    starting from the Fermi energy, looks for the first peak</span>
+<span class="sd">    (&gt;spectral_func_threshold and local maximum on discrete grid). Then moves</span>
+<span class="sd">    back towards Fermi energy until the spectral function is smaller than the</span>
+<span class="sd">    fraction edge_threshold of the peak value.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    mesh : numpy.ndarray of float</span>
+<span class="sd">        the real frequencies grid.</span>
+<span class="sd">    spectral_function : numpy.ndarray of float</span>
+<span class="sd">        the values of the spectral function on the grid.</span>
+<span class="sd">    spectral_func_threshold : float</span>
+<span class="sd">        Threshold for spectral function to cross before looking for peaks.</span>
+<span class="sd">    valence_band : bool</span>
+<span class="sd">        Determines if looking for valence band (i.e. the upper band edge) or</span>
+<span class="sd">        the conduction band (i.e. the lower band edge).</span>
+<span class="sd">    edge_threshold : float</span>
+<span class="sd">        Fraction of the peak value that defines the band edge value.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    float</span>
+<span class="sd">        The frequency value of the band edge.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># Determines direction to move away from Fermi energy to find band edge</span>
+    <span class="n">direction</span> <span class="o">=</span> <span class="mi">1</span> <span class="k">if</span> <span class="n">valence_band</span> <span class="k">else</span> <span class="o">-</span><span class="mi">1</span>
+
+    <span class="c1"># Starts closest to the Fermi energy</span>
+    <span class="n">starting_index</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmin</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">mesh</span><span class="p">))</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Starting at index </span><span class="si">{}</span><span class="s1"> with A(omega=</span><span class="si">{:.3f}</span><span class="s1">)=</span><span class="si">{:.3f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">starting_index</span><span class="p">,</span> <span class="n">mesh</span><span class="p">[</span><span class="n">starting_index</span><span class="p">],</span> <span class="n">spectral_function</span><span class="p">[</span><span class="n">starting_index</span><span class="p">]))</span>
+    <span class="k">assert</span> <span class="n">spectral_function</span><span class="p">[</span><span class="n">starting_index</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">spectral_func_threshold</span>
+
+    <span class="c1"># Finds peak</span>
+    <span class="n">peak_index</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">starting_index</span><span class="o">+</span><span class="n">direction</span><span class="p">,</span> <span class="n">mesh</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">if</span> <span class="n">valence_band</span> <span class="k">else</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">direction</span><span class="p">):</span>
+        <span class="c1"># If A(omega) low, go further</span>
+        <span class="k">if</span> <span class="n">spectral_function</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">spectral_func_threshold</span><span class="p">:</span>
+            <span class="k">continue</span>
+
+        <span class="c1"># If spectral function still increasing, go further</span>
+        <span class="k">if</span> <span class="n">spectral_function</span><span class="p">[</span><span class="n">i</span><span class="o">-</span><span class="n">direction</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">spectral_function</span><span class="p">[</span><span class="n">i</span><span class="p">]:</span>
+            <span class="k">continue</span>
+
+        <span class="n">peak_index</span> <span class="o">=</span> <span class="n">i</span><span class="o">-</span><span class="n">direction</span>
+        <span class="k">break</span>
+
+    <span class="k">assert</span> <span class="n">peak_index</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">,</span> <span class="s1">&#39;Band peak not found. Check frequency range of MaxEnt&#39;</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Peak at index </span><span class="si">{}</span><span class="s1"> with A(omega=</span><span class="si">{:.3f}</span><span class="s1">)=</span><span class="si">{:.3f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">peak_index</span><span class="p">,</span> <span class="n">mesh</span><span class="p">[</span><span class="n">peak_index</span><span class="p">],</span> <span class="n">spectral_function</span><span class="p">[</span><span class="n">peak_index</span><span class="p">]))</span>
+
+    <span class="c1"># Finds band edge</span>
+    <span class="n">edge_index</span> <span class="o">=</span> <span class="n">starting_index</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">peak_index</span><span class="o">-</span><span class="n">direction</span><span class="p">,</span> <span class="n">starting_index</span><span class="o">-</span><span class="n">direction</span><span class="p">,</span> <span class="o">-</span><span class="n">direction</span><span class="p">):</span>
+        <span class="c1"># If above ratio edge_threshold of peak height, go further back to starting index</span>
+        <span class="k">if</span> <span class="n">spectral_function</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">edge_threshold</span> <span class="o">*</span> <span class="n">spectral_function</span><span class="p">[</span><span class="n">peak_index</span><span class="p">]:</span>
+            <span class="k">continue</span>
+
+        <span class="n">edge_index</span> <span class="o">=</span> <span class="n">i</span>
+        <span class="k">break</span>
+
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Band edge at index </span><span class="si">{}</span><span class="s1"> with A(omega=</span><span class="si">{:.3f}</span><span class="s1">)=</span><span class="si">{:.3f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">edge_index</span><span class="p">,</span> <span class="n">mesh</span><span class="p">[</span><span class="n">edge_index</span><span class="p">],</span> <span class="n">spectral_function</span><span class="p">[</span><span class="n">edge_index</span><span class="p">]))</span>
+    <span class="k">return</span> <span class="n">mesh</span><span class="p">[</span><span class="n">edge_index</span><span class="p">]</span>
+
+<span class="k">def</span> <span class="nf">_set_mu_to_gap_middle_with_maxent</span><span class="p">(</span><span class="n">general_params</span><span class="p">,</span> <span class="n">sum_k</span><span class="p">,</span> <span class="n">gf_lattice_iw</span><span class="p">,</span> <span class="n">archive</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Bundles running maxent on the total lattice GF, analyzing the spectral</span>
+<span class="sd">    function and determining the new chemical potential.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    general_params : dict</span>
+<span class="sd">        general parameters as dict.</span>
+<span class="sd">    sum_k : SumkDFT object</span>
+<span class="sd">        SumkDFT object needed for original chemical potential and frequency</span>
+<span class="sd">        range of MaxEnt continuation.</span>
+<span class="sd">    gf_lattice_iw : BlockGf</span>
+<span class="sd">        trace of the lattice GF over all blocks, orbitals and spins in</span>
+<span class="sd">        Matsubara frequency.</span>
+<span class="sd">    archive : HDFArchive, optional</span>
+<span class="sd">        If given, writes spectral function (i.e. MaxEnt result) to archive.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    float</span>
+<span class="sd">        new chemical potential located in the middle of the gap from MaxEnt.</span>
+<span class="sd">        None if not master node or if something went wrong.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+        <span class="k">return</span> <span class="kc">None</span>
+
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">imported_maxent</span><span class="p">:</span>
+        <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;WARNING: cannot find gap with MaxEnt, MaxEnt not found&#39;</span><span class="p">)</span>
+        <span class="k">return</span> <span class="kc">None</span>
+
+    <span class="c1"># Runs MaxEnt using the Chi2Curvature analyzer</span>
+    <span class="n">maxent_results</span><span class="p">,</span> <span class="n">mesh</span> <span class="o">=</span> <span class="n">maxent_gf_latt</span><span class="o">.</span><span class="n">_run_maxent</span><span class="p">(</span><span class="n">gf_lattice_iw</span><span class="p">,</span> <span class="n">sum_k</span><span class="p">,</span> <span class="mf">.02</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">200</span><span class="p">,</span> <span class="mi">30</span><span class="p">)</span>
+    <span class="n">mesh</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">mesh</span><span class="p">)</span>
+    <span class="n">spectral_function</span> <span class="o">=</span> <span class="n">maxent_results</span><span class="p">[</span><span class="s1">&#39;total&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">get_A_out</span><span class="p">(</span><span class="s1">&#39;Chi2CurvatureAnalyzer&#39;</span><span class="p">)</span>
+
+    <span class="c1"># Writes spectral function to archive</span>
+    <span class="k">if</span> <span class="n">archive</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">unpacked_results</span> <span class="o">=</span> <span class="n">maxent_gf_latt</span><span class="o">.</span><span class="n">_unpack_maxent_results</span><span class="p">(</span><span class="n">maxent_results</span><span class="p">,</span> <span class="n">mesh</span><span class="p">)</span>
+        <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_results/last_iter&#39;</span><span class="p">][</span><span class="s1">&#39;Alatt_w&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">unpacked_results</span>
+
+    <span class="c1"># Checks if spectral function at Fermi energy below threshold</span>
+    <span class="n">spectral_func_threshold</span> <span class="o">=</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;beta&#39;</span><span class="p">]</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;mu_gap_gb2_threshold&#39;</span><span class="p">]</span>
+    <span class="k">if</span> <span class="n">spectral_function</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argmin</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">mesh</span><span class="p">))]</span> <span class="o">&gt;</span> <span class="n">spectral_func_threshold</span><span class="p">:</span>
+        <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;WARNING: cannot find gap with MaxEnt, spectral function not gapped at Fermi energy&#39;</span><span class="p">)</span>
+        <span class="k">return</span> <span class="kc">None</span>
+
+    <span class="c1"># Determines band edges for conduction and valence band</span>
+    <span class="n">edge_threshold</span> <span class="o">=</span> <span class="mf">0.2</span>
+    <span class="n">conduction_edge</span> <span class="o">=</span> <span class="n">_determine_band_edge</span><span class="p">(</span><span class="n">mesh</span><span class="p">,</span> <span class="n">spectral_function</span><span class="p">,</span> <span class="n">spectral_func_threshold</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <span class="n">edge_threshold</span><span class="p">)</span>
+    <span class="n">valence_edge</span> <span class="o">=</span> <span class="n">_determine_band_edge</span><span class="p">(</span><span class="n">mesh</span><span class="p">,</span> <span class="n">spectral_function</span><span class="p">,</span> <span class="n">spectral_func_threshold</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="n">edge_threshold</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">chemical_potential</span> <span class="o">+</span> <span class="p">(</span><span class="n">valence_edge</span> <span class="o">+</span> <span class="n">conduction_edge</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span>
+
+<div class="viewcode-block" id="set_initial_mu"><a class="viewcode-back" href="../../_ref/dmft_tools.manipulate_chemical_potential.html#dmft_tools.manipulate_chemical_potential.set_initial_mu">[docs]</a><span class="k">def</span> <span class="nf">set_initial_mu</span><span class="p">(</span><span class="n">general_params</span><span class="p">,</span> <span class="n">sum_k</span><span class="p">,</span> <span class="n">iteration_offset</span><span class="p">,</span> <span class="n">archive</span><span class="p">,</span> <span class="n">broadening</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Handles the different ways of setting the initial chemical potential mu:</span>
+<span class="sd">    * Chemical potential set to fixed value: uses this value</span>
+
+<span class="sd">    * New calculation: determines mu from dichotomy method</span>
+
+<span class="sd">    * Resuming calculation and chemical potential not updated this iteration:</span>
+<span class="sd">       loads calculation before previous iteration.</span>
+
+<span class="sd">    * Resuming calculation and chemical potential is updated:</span>
+<span class="sd">        checks if the system is gapped and potentially run MaxEnt to find gap</span>
+<span class="sd">        middle. Otherwise, gets mu from dichotomy and applies mu mixing to result.</span>
+
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    general_params : dict</span>
+<span class="sd">        general parameters as dict.</span>
+<span class="sd">    sum_k : SumkDFT object</span>
+<span class="sd">        contains system information necessary to determine the initial mu.</span>
+<span class="sd">    iteration_offset : int</span>
+<span class="sd">        the number of iterations executed in previous calculations.</span>
+<span class="sd">    archive : HDFArchive</span>
+<span class="sd">        needed to potentially load previous results and write MaxEnt results to.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    sum_k : SumkDFT object</span>
+<span class="sd">        the altered SumkDFT object with the initial mu set correctly.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># Uses fixed_mu_value as chemical potential if parameter is given</span>
+    <span class="k">if</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;fixed_mu_value&#39;</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">sum_k</span><span class="o">.</span><span class="n">set_mu</span><span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;fixed_mu_value&#39;</span><span class="p">])</span>
+        <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;+++ Keeping the chemical potential fixed at </span><span class="si">{:.3f}</span><span class="s1"> eV +++&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;fixed_mu_value&#39;</span><span class="p">]))</span>
+        <span class="k">return</span> <span class="n">sum_k</span>
+
+    <span class="c1"># In first iteration, determines mu and returns</span>
+    <span class="k">if</span> <span class="n">iteration_offset</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">sum_k</span><span class="o">.</span><span class="n">calc_mu</span><span class="p">(</span><span class="n">precision</span><span class="o">=</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;prec_mu&#39;</span><span class="p">],</span> <span class="n">method</span><span class="o">=</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;calc_mu_method&#39;</span><span class="p">],</span>
+                      <span class="n">broadening</span><span class="o">=</span><span class="n">broadening</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">sum_k</span>
+
+    <span class="c1"># If continuing calculation and not updating mu, loads sold value</span>
+    <span class="k">if</span> <span class="n">iteration_offset</span> <span class="o">%</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;mu_update_freq&#39;</span><span class="p">]</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+            <span class="n">sum_k</span><span class="o">.</span><span class="n">chemical_potential</span> <span class="o">=</span> <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_results/last_iter/chemical_potential_pre&#39;</span><span class="p">]</span>
+        <span class="n">sum_k</span><span class="o">.</span><span class="n">chemical_potential</span> <span class="o">=</span> <span class="n">mpi</span><span class="o">.</span><span class="n">bcast</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">chemical_potential</span><span class="p">)</span>
+        <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;Chemical potential not updated this step, &#39;</span>
+                   <span class="o">+</span> <span class="s1">&#39;reusing loaded one of </span><span class="si">{:.3f}</span><span class="s1"> eV&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">chemical_potential</span><span class="p">))</span>
+        <span class="k">return</span> <span class="n">sum_k</span>
+
+    <span class="c1"># If continuing calculation and updating mu, reads in occupation and</span>
+    <span class="c1"># chemical_potential_pre from the last run</span>
+    <span class="n">previous_mu</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="k">if</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+        <span class="n">previous_mu</span> <span class="o">=</span> <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_results/last_iter/chemical_potential_pre&#39;</span><span class="p">]</span>
+    <span class="n">previous_mu</span> <span class="o">=</span> <span class="n">mpi</span><span class="o">.</span><span class="n">bcast</span><span class="p">(</span><span class="n">previous_mu</span><span class="p">)</span>
+
+    <span class="c1"># Runs maxent if spectral weight too low and occupation is close to desired one</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">mesh</span><span class="p">,</span> <span class="n">MeshImFreq</span><span class="p">)</span> <span class="ow">and</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;mu_gap_gb2_threshold&#39;</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">sum_k</span><span class="o">.</span><span class="n">chemical_potential</span> <span class="o">=</span> <span class="n">previous_mu</span>
+        <span class="n">gf_lattice_iw</span><span class="p">,</span> <span class="n">g_betahalf</span><span class="p">,</span> <span class="n">occupation</span> <span class="o">=</span> <span class="n">_initialize_lattice_gf</span><span class="p">(</span><span class="n">sum_k</span><span class="p">,</span> <span class="n">general_params</span><span class="p">)</span>
+        <span class="n">fulfills_occupation_crit</span> <span class="o">=</span> <span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;mu_gap_occ_deviation&#39;</span><span class="p">]</span> <span class="ow">is</span> <span class="kc">None</span>
+                                    <span class="ow">or</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">occupation</span> <span class="o">-</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">density_required</span><span class="p">)</span> <span class="o">&lt;</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;mu_gap_occ_deviation&#39;</span><span class="p">])</span>
+
+        <span class="k">if</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">g_betahalf</span><span class="p">)</span> <span class="o">&lt;</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;mu_gap_gb2_threshold&#39;</span><span class="p">]</span> <span class="ow">and</span> <span class="n">fulfills_occupation_crit</span><span class="p">:</span>
+            <span class="n">new_mu</span> <span class="o">=</span> <span class="n">_set_mu_to_gap_middle_with_maxent</span><span class="p">(</span><span class="n">general_params</span><span class="p">,</span> <span class="n">sum_k</span><span class="p">,</span> <span class="n">gf_lattice_iw</span><span class="p">,</span> <span class="n">archive</span><span class="p">)</span>
+            <span class="n">new_mu</span> <span class="o">=</span> <span class="n">mpi</span><span class="o">.</span><span class="n">bcast</span><span class="p">(</span><span class="n">new_mu</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">new_mu</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">sum_k</span><span class="o">.</span><span class="n">chemical_potential</span> <span class="o">=</span> <span class="n">new_mu</span>
+                <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;New chemical potential in the gap: </span><span class="si">{:.3f}</span><span class="s1"> eV&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">new_mu</span><span class="p">))</span>
+                <span class="k">return</span> <span class="n">sum_k</span>
+    <span class="c1"># Calculates occupation for mu mixing below</span>
+    <span class="k">elif</span> <span class="n">np</span><span class="o">.</span><span class="n">isclose</span><span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;mu_mix_per_occupation_offset&#39;</span><span class="p">],</span> <span class="mi">0</span><span class="p">):</span>
+        <span class="n">occupation</span> <span class="o">=</span> <span class="mi">0</span> <span class="c1"># The occupation does not matter in this case</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">occupation</span> <span class="o">=</span> <span class="n">_initialize_lattice_gf</span><span class="p">(</span><span class="n">sum_k</span><span class="p">,</span> <span class="n">general_params</span><span class="p">)</span>
+
+    <span class="c1"># If system not gapped, gets chemical potential from dichotomy method</span>
+    <span class="n">sum_k</span><span class="o">.</span><span class="n">calc_mu</span><span class="p">(</span><span class="n">precision</span><span class="o">=</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;prec_mu&#39;</span><span class="p">],</span> <span class="n">method</span><span class="o">=</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;calc_mu_method&#39;</span><span class="p">],</span>
+                  <span class="n">broadening</span><span class="o">=</span><span class="n">broadening</span><span class="p">)</span>
+
+    <span class="c1"># Applies mu mixing to dichotomy result</span>
+    <span class="n">sum_k</span><span class="o">.</span><span class="n">chemical_potential</span> <span class="o">=</span> <span class="n">_mix_chemical_potential</span><span class="p">(</span><span class="n">general_params</span><span class="p">,</span> <span class="n">occupation</span><span class="p">,</span>
+                                                       <span class="n">sum_k</span><span class="o">.</span><span class="n">density_required</span><span class="p">,</span>
+                                                       <span class="n">previous_mu</span><span class="p">,</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">chemical_potential</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">sum_k</span></div>
+
+<div class="viewcode-block" id="update_mu"><a class="viewcode-back" href="../../_ref/dmft_tools.manipulate_chemical_potential.html#dmft_tools.manipulate_chemical_potential.update_mu">[docs]</a><span class="k">def</span> <span class="nf">update_mu</span><span class="p">(</span><span class="n">general_params</span><span class="p">,</span> <span class="n">sum_k</span><span class="p">,</span> <span class="n">it</span><span class="p">,</span> <span class="n">archive</span><span class="p">,</span> <span class="n">broadening</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Handles the different ways of updating the chemical potential mu:</span>
+<span class="sd">    * Chemical potential set to fixed value: uses this value</span>
+
+<span class="sd">    * Chemical potential not updated this iteration: nothing happens.</span>
+
+<span class="sd">    * Chemical potential is updated: checks if the system is gapped and</span>
+<span class="sd">        potentially run MaxEnt to find gap middle. Otherwise, gets mu from</span>
+<span class="sd">        dichotomy and applies mu mixing to result.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    general_params : dict</span>
+<span class="sd">        general parameters as dict.</span>
+<span class="sd">    sum_k : SumkDFT object</span>
+<span class="sd">        contains system information necessary to update mu.</span>
+<span class="sd">    it : int</span>
+<span class="sd">        the number of the current iteration.</span>
+<span class="sd">    archive : HDFArchive</span>
+<span class="sd">        needed to potentially write MaxEnt results to.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    sum_k : SumkDFT object</span>
+<span class="sd">        the altered SumkDFT object with the updated mu.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># Uses fixed_mu_value as chemical potential if parameter is given</span>
+    <span class="k">if</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;fixed_mu_value&#39;</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">sum_k</span><span class="o">.</span><span class="n">set_mu</span><span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;fixed_mu_value&#39;</span><span class="p">])</span>
+        <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;+++ Keeping the chemical potential fixed at </span><span class="si">{:.3f}</span><span class="s1"> eV +++&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;fixed_mu_value&#39;</span><span class="p">]))</span>
+        <span class="k">return</span> <span class="n">sum_k</span>
+
+    <span class="c1"># If mu won&#39;t be updated this step, don&#39;t update it...</span>
+    <span class="k">if</span> <span class="n">it</span> <span class="o">%</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;mu_update_freq&#39;</span><span class="p">]</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;Chemical potential not updated this step, &#39;</span>
+                   <span class="o">+</span> <span class="s1">&#39;reusing previous one of </span><span class="si">{:.3f}</span><span class="s1"> eV&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">chemical_potential</span><span class="p">))</span>
+        <span class="k">return</span> <span class="n">sum_k</span>
+
+    <span class="c1"># Runs maxent if spectral weight too low and occupation is close to desired one</span>
+    <span class="c1"># TODO: which solvers work?</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">mesh</span><span class="p">,</span> <span class="n">MeshImFreq</span><span class="p">)</span> <span class="ow">and</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;mu_gap_gb2_threshold&#39;</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">gf_lattice_iw</span><span class="p">,</span> <span class="n">g_betahalf</span><span class="p">,</span> <span class="n">occupation</span> <span class="o">=</span> <span class="n">_initialize_lattice_gf</span><span class="p">(</span><span class="n">sum_k</span><span class="p">,</span> <span class="n">general_params</span><span class="p">)</span>
+        <span class="n">fulfills_occupation_crit</span> <span class="o">=</span> <span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;mu_gap_occ_deviation&#39;</span><span class="p">]</span> <span class="ow">is</span> <span class="kc">None</span>
+                                    <span class="ow">or</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">occupation</span> <span class="o">-</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">density_required</span><span class="p">)</span> <span class="o">&lt;</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;mu_gap_occ_deviation&#39;</span><span class="p">])</span>
+
+        <span class="k">if</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">g_betahalf</span><span class="p">)</span> <span class="o">&lt;</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;mu_gap_gb2_threshold&#39;</span><span class="p">]</span> <span class="ow">and</span> <span class="n">fulfills_occupation_crit</span><span class="p">:</span>
+            <span class="n">new_mu</span> <span class="o">=</span> <span class="n">_set_mu_to_gap_middle_with_maxent</span><span class="p">(</span><span class="n">general_params</span><span class="p">,</span> <span class="n">sum_k</span><span class="p">,</span> <span class="n">gf_lattice_iw</span><span class="p">,</span> <span class="n">archive</span><span class="p">)</span>
+            <span class="n">new_mu</span> <span class="o">=</span> <span class="n">mpi</span><span class="o">.</span><span class="n">bcast</span><span class="p">(</span><span class="n">new_mu</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">new_mu</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">sum_k</span><span class="o">.</span><span class="n">chemical_potential</span> <span class="o">=</span> <span class="n">new_mu</span>
+                <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;New chemical potential in the gap: </span><span class="si">{:.3f}</span><span class="s1"> eV&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">new_mu</span><span class="p">))</span>
+                <span class="k">return</span> <span class="n">sum_k</span>
+    <span class="c1"># Calculates occupation for mu mixing below</span>
+    <span class="k">elif</span> <span class="n">np</span><span class="o">.</span><span class="n">isclose</span><span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;mu_mix_per_occupation_offset&#39;</span><span class="p">],</span> <span class="mi">0</span><span class="p">):</span>
+        <span class="n">occupation</span> <span class="o">=</span> <span class="mi">0</span> <span class="c1"># The occupation does not matter in this case</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">occupation</span> <span class="o">=</span> <span class="n">_initialize_lattice_gf</span><span class="p">(</span><span class="n">sum_k</span><span class="p">,</span> <span class="n">general_params</span><span class="p">)</span>
+
+    <span class="c1"># If system not gapped, gets chemical potential from dichotomy method</span>
+    <span class="n">previous_mu</span> <span class="o">=</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">chemical_potential</span>
+    <span class="n">sum_k</span><span class="o">.</span><span class="n">calc_mu</span><span class="p">(</span><span class="n">precision</span><span class="o">=</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;prec_mu&#39;</span><span class="p">],</span> <span class="n">method</span><span class="o">=</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;calc_mu_method&#39;</span><span class="p">],</span>
+                  <span class="n">broadening</span><span class="o">=</span><span class="n">broadening</span><span class="p">)</span>
+
+    <span class="c1"># Applies mu mixing to dichotomy result</span>
+    <span class="n">sum_k</span><span class="o">.</span><span class="n">chemical_potential</span> <span class="o">=</span> <span class="n">_mix_chemical_potential</span><span class="p">(</span><span class="n">general_params</span><span class="p">,</span> <span class="n">occupation</span><span class="p">,</span>
+                                                       <span class="n">sum_k</span><span class="o">.</span><span class="n">density_required</span><span class="p">,</span>
+                                                       <span class="n">previous_mu</span><span class="p">,</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">chemical_potential</span><span class="p">)</span>
+    <span class="n">mpi</span><span class="o">.</span><span class="n">barrier</span><span class="p">()</span>
+    <span class="k">return</span> <span class="n">sum_k</span></div>
+</pre></div>
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.</p>
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+            <img src="../../_static/logo_no_text.png" class="logo" alt="Logo"/>
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+              <div class="version">
+                3.2.0
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+<li class="toctree-l1"><a class="reference internal" href="../../install.html">Installation</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#installation-via-pip">Installation via pip</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#manual-installation-via-cmake">Manual installation via CMake</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#docker-files-images">Docker files &amp; images</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#version-compatibility">Version compatibility</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#custom-cmake-options">Custom CMake options</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../documentation.html">Documentation</a><ul>
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+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#dft-interface-notes">DFT interface notes</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/w90_interface.html">Wannier90 interface</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/w90_interface.html#orbital-order-in-the-w90-converter">orbital order in the W90 converter</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/vasp_csc.html">Interface to VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#general-remarks">General remarks</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#file-description">file description</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#workflow">Workflow:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#important-flags">important flags:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#version-and-compilation">version and compilation:</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#input-output">Input/Output</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../input_output/DMFT_input/input.html">Input</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/general.html">[general]: General parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/solver.html">[solver]: solver specific parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/dft.html">[dft]: DFT related inputs</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/advanced.html">[advanced]: Advanced inputs</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../input_output/DMFT_output/results.html">Output / results</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_output/results.html#group-structure">Group structure</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_output/results.html#subgroups">Subgroups</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#further-details-for-running">Further details for running</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/docker.html">Docker</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#building-the-docker-image">Building the docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#pulling-a-docker-image">Pulling a docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_locally.html">Run on your machine</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#module-reference-manual">Module reference manual</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/csc_flow.html">csc_flow</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/dft_managers.html">dft_managers</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.mpi_helpers.html">dft_managers.mpi_helpers</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.qe_manager.html">dft_managers.qe_manager</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.vasp_manager.html">dft_managers.vasp_manager</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/dmft_cycle.html">dmft_cycle</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/dmft_tools.html">dmft_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.afm_mapping.html">dmft_tools.afm_mapping</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.formatter.html">dmft_tools.formatter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.greens_functions_mixer.html">dmft_tools.greens_functions_mixer</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.initial_self_energies.html">dmft_tools.initial_self_energies</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.interaction_hamiltonian.html">dmft_tools.interaction_hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.legendre_filter.html">dmft_tools.legendre_filter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.manipulate_chemical_potential.html">dmft_tools.manipulate_chemical_potential</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.matheval.html">dmft_tools.matheval</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.observables.html">dmft_tools.observables</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.results_to_archive.html">dmft_tools.results_to_archive</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.solver.html">dmft_tools.solver</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/io_tools.html">io_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.dict_to_h5.html">io_tools.dict_to_h5</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.postproc_toml_dict.html">io_tools.postproc_toml_dict</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.verify_input_params.html">io_tools.verify_input_params</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_gf_imp.html">postprocessing.maxent_gf_imp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_gf_latt.html">postprocessing.maxent_gf_latt</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_sigma.html">postprocessing.maxent_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
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+  <ul class="wy-breadcrumbs">
+      <li><a href="../../index.html" class="icon icon-home"></a> &raquo;</li>
+          <li><a href="../index.html">Module code</a> &raquo;</li>
+      <li>dmft_tools.matheval</li>
+      <li class="wy-breadcrumbs-aside">
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <h1>Source code for dmft_tools.matheval</h1><div class="highlight"><pre>
+<span></span><span class="c1"># https://stackoverflow.com/a/30516254</span>
+
+<span class="kn">import</span> <span class="nn">ast</span>
+<span class="kn">import</span> <span class="nn">math</span>
+
+
+<div class="viewcode-block" id="MathExpr"><a class="viewcode-back" href="../../_ref/dmft_tools.matheval.MathExpr.html#dmft_tools.matheval.MathExpr">[docs]</a><span class="k">class</span> <span class="nc">MathExpr</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
+    <span class="n">allowed_nodes</span> <span class="o">=</span> <span class="p">(</span>
+        <span class="n">ast</span><span class="o">.</span><span class="n">Module</span><span class="p">,</span>
+        <span class="n">ast</span><span class="o">.</span><span class="n">Expr</span><span class="p">,</span>
+        <span class="n">ast</span><span class="o">.</span><span class="n">Load</span><span class="p">,</span>
+        <span class="n">ast</span><span class="o">.</span><span class="n">Expression</span><span class="p">,</span>
+        <span class="n">ast</span><span class="o">.</span><span class="n">Add</span><span class="p">,</span>
+        <span class="n">ast</span><span class="o">.</span><span class="n">Sub</span><span class="p">,</span>
+        <span class="n">ast</span><span class="o">.</span><span class="n">UnaryOp</span><span class="p">,</span>
+        <span class="n">ast</span><span class="o">.</span><span class="n">Num</span><span class="p">,</span>
+        <span class="n">ast</span><span class="o">.</span><span class="n">BinOp</span><span class="p">,</span>
+        <span class="n">ast</span><span class="o">.</span><span class="n">Mult</span><span class="p">,</span>
+        <span class="n">ast</span><span class="o">.</span><span class="n">Div</span><span class="p">,</span>
+        <span class="n">ast</span><span class="o">.</span><span class="n">Pow</span><span class="p">,</span>
+        <span class="n">ast</span><span class="o">.</span><span class="n">BitOr</span><span class="p">,</span>
+        <span class="n">ast</span><span class="o">.</span><span class="n">BitAnd</span><span class="p">,</span>
+        <span class="n">ast</span><span class="o">.</span><span class="n">BitXor</span><span class="p">,</span>
+        <span class="n">ast</span><span class="o">.</span><span class="n">USub</span><span class="p">,</span>
+        <span class="n">ast</span><span class="o">.</span><span class="n">UAdd</span><span class="p">,</span>
+        <span class="n">ast</span><span class="o">.</span><span class="n">FloorDiv</span><span class="p">,</span>
+        <span class="n">ast</span><span class="o">.</span><span class="n">Mod</span><span class="p">,</span>
+        <span class="n">ast</span><span class="o">.</span><span class="n">LShift</span><span class="p">,</span>
+        <span class="n">ast</span><span class="o">.</span><span class="n">RShift</span><span class="p">,</span>
+        <span class="n">ast</span><span class="o">.</span><span class="n">Invert</span><span class="p">,</span>
+        <span class="n">ast</span><span class="o">.</span><span class="n">Call</span><span class="p">,</span>
+        <span class="n">ast</span><span class="o">.</span><span class="n">Name</span><span class="p">,</span>
+    <span class="p">)</span>
+
+    <span class="n">functions</span> <span class="o">=</span> <span class="p">{</span>
+        <span class="s2">&quot;abs&quot;</span><span class="p">:</span> <span class="nb">abs</span><span class="p">,</span>
+        <span class="s2">&quot;complex&quot;</span><span class="p">:</span> <span class="nb">complex</span><span class="p">,</span>
+        <span class="s2">&quot;min&quot;</span><span class="p">:</span> <span class="nb">min</span><span class="p">,</span>
+        <span class="s2">&quot;max&quot;</span><span class="p">:</span> <span class="nb">max</span><span class="p">,</span>
+        <span class="s2">&quot;pow&quot;</span><span class="p">:</span> <span class="nb">pow</span><span class="p">,</span>
+        <span class="s2">&quot;round&quot;</span><span class="p">:</span> <span class="nb">round</span><span class="p">,</span>
+    <span class="p">}</span>
+    <span class="n">functions</span><span class="o">.</span><span class="n">update</span><span class="p">(</span>
+        <span class="p">{</span><span class="n">key</span><span class="p">:</span> <span class="n">value</span> <span class="k">for</span> <span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">vars</span><span class="p">(</span><span class="n">math</span><span class="p">)</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">key</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s2">&quot;_&quot;</span><span class="p">)}</span>
+    <span class="p">)</span>
+
+<div class="viewcode-block" id="MathExpr.__init__"><a class="viewcode-back" href="../../_ref/dmft_tools.matheval.MathExpr.__init__.html#dmft_tools.matheval.MathExpr.__init__">[docs]</a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">expr</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">any</span><span class="p">(</span><span class="n">elem</span> <span class="ow">in</span> <span class="n">expr</span> <span class="k">for</span> <span class="n">elem</span> <span class="ow">in</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">#&quot;</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="n">expr</span><span class="p">)</span>
+
+        <span class="n">node</span> <span class="o">=</span> <span class="n">ast</span><span class="o">.</span><span class="n">parse</span><span class="p">(</span><span class="n">expr</span><span class="o">.</span><span class="n">strip</span><span class="p">(),</span> <span class="n">mode</span><span class="o">=</span><span class="s2">&quot;eval&quot;</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">curr</span> <span class="ow">in</span> <span class="n">ast</span><span class="o">.</span><span class="n">walk</span><span class="p">(</span><span class="n">node</span><span class="p">):</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">curr</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">allowed_nodes</span><span class="p">):</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="n">curr</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">code</span> <span class="o">=</span> <span class="nb">compile</span><span class="p">(</span><span class="n">node</span><span class="p">,</span> <span class="s2">&quot;&lt;string&gt;&quot;</span><span class="p">,</span> <span class="s2">&quot;eval&quot;</span><span class="p">)</span></div>
+
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">return</span> <span class="nb">eval</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">code</span><span class="p">,</span> <span class="p">{</span><span class="s2">&quot;__builtins__&quot;</span><span class="p">:</span> <span class="kc">None</span><span class="p">},</span> <span class="p">{</span><span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">functions</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">})</span></div>
+</pre></div>
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.</p>
+  </div>
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
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diff --git a/_modules/dmft_tools/observables.html b/_modules/dmft_tools/observables.html
new file mode 100644
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+<!DOCTYPE html>
+<html class="writer-html5" lang="en" >
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+<li class="toctree-l2"><a class="reference internal" href="../../install.html#manual-installation-via-cmake">Manual installation via CMake</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#docker-files-images">Docker files &amp; images</a></li>
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+</ul>
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+<li class="toctree-l1"><a class="reference internal" href="../../documentation.html">Documentation</a><ul>
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+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#dft-interface-notes">DFT interface notes</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/w90_interface.html">Wannier90 interface</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/w90_interface.html#orbital-order-in-the-w90-converter">orbital order in the W90 converter</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/vasp_csc.html">Interface to VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#general-remarks">General remarks</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#file-description">file description</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#workflow">Workflow:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#important-flags">important flags:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#version-and-compilation">version and compilation:</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#input-output">Input/Output</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../input_output/DMFT_input/input.html">Input</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/general.html">[general]: General parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/solver.html">[solver]: solver specific parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/dft.html">[dft]: DFT related inputs</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/advanced.html">[advanced]: Advanced inputs</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../input_output/DMFT_output/results.html">Output / results</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_output/results.html#group-structure">Group structure</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_output/results.html#subgroups">Subgroups</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#further-details-for-running">Further details for running</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/docker.html">Docker</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#building-the-docker-image">Building the docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#pulling-a-docker-image">Pulling a docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_locally.html">Run on your machine</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#module-reference-manual">Module reference manual</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/csc_flow.html">csc_flow</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/dft_managers.html">dft_managers</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.mpi_helpers.html">dft_managers.mpi_helpers</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.qe_manager.html">dft_managers.qe_manager</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.vasp_manager.html">dft_managers.vasp_manager</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/dmft_cycle.html">dmft_cycle</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/dmft_tools.html">dmft_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.afm_mapping.html">dmft_tools.afm_mapping</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.convergence.html">dmft_tools.convergence</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.formatter.html">dmft_tools.formatter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.greens_functions_mixer.html">dmft_tools.greens_functions_mixer</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.initial_self_energies.html">dmft_tools.initial_self_energies</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.interaction_hamiltonian.html">dmft_tools.interaction_hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.legendre_filter.html">dmft_tools.legendre_filter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.manipulate_chemical_potential.html">dmft_tools.manipulate_chemical_potential</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.matheval.html">dmft_tools.matheval</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.observables.html">dmft_tools.observables</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.results_to_archive.html">dmft_tools.results_to_archive</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.solver.html">dmft_tools.solver</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/io_tools.html">io_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.dict_to_h5.html">io_tools.dict_to_h5</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.postproc_toml_dict.html">io_tools.postproc_toml_dict</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.verify_input_params.html">io_tools.verify_input_params</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_gf_imp.html">postprocessing.maxent_gf_imp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_gf_latt.html">postprocessing.maxent_gf_latt</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_sigma.html">postprocessing.maxent_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../issues.html">Support &amp; contribute</a><ul>
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+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-2-0">Version 3.2.0</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-1-4">Version 3.1.4</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-1-2">Version 3.1.2</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-1-0">Version 3.1.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-0-0">Version 3.0.0</a></li>
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+        </div>
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+  <ul class="wy-breadcrumbs">
+      <li><a href="../../index.html" class="icon icon-home"></a> &raquo;</li>
+          <li><a href="../index.html">Module code</a> &raquo;</li>
+      <li>dmft_tools.observables</li>
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+      </li>
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+             
+  <h1>Source code for dmft_tools.observables</h1><div class="highlight"><pre>
+<span></span><span class="c1">################################################################################</span>
+<span class="c1">#</span>
+<span class="c1"># solid_dmft - A versatile python wrapper to perform DFT+DMFT calculations</span>
+<span class="c1">#              utilizing the TRIQS software library</span>
+<span class="c1">#</span>
+<span class="c1"># Copyright (C) 2018-2020, ETH Zurich</span>
+<span class="c1"># Copyright (C) 2021, The Simons Foundation</span>
+<span class="c1">#      authors: A. Hampel, M. Merkel, and S. Beck</span>
+<span class="c1">#</span>
+<span class="c1"># solid_dmft is free software: you can redistribute it and/or modify it under the</span>
+<span class="c1"># terms of the GNU General Public License as published by the Free Software</span>
+<span class="c1"># Foundation, either version 3 of the License, or (at your option) any later</span>
+<span class="c1"># version.</span>
+<span class="c1">#</span>
+<span class="c1"># solid_dmft is distributed in the hope that it will be useful, but WITHOUT ANY</span>
+<span class="c1"># WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A</span>
+<span class="c1"># PARTICULAR PURPOSE. See the GNU General Public License for more details.</span>
+
+<span class="c1"># You should have received a copy of the GNU General Public License along with</span>
+<span class="c1"># solid_dmft (in the file COPYING.txt in this directory). If not, see</span>
+<span class="c1"># &lt;http://www.gnu.org/licenses/&gt;.</span>
+<span class="c1">#</span>
+<span class="c1">################################################################################</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Contains all functions related to the observables.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="c1"># system</span>
+<span class="kn">import</span> <span class="nn">os.path</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<span class="c1"># triqs</span>
+<span class="kn">import</span> <span class="nn">triqs.utility.mpi</span> <span class="k">as</span> <span class="nn">mpi</span>
+<span class="kn">from</span> <span class="nn">triqs.gf</span> <span class="kn">import</span> <span class="n">Gf</span><span class="p">,</span> <span class="n">MeshImTime</span>
+<span class="kn">from</span> <span class="nn">triqs.atom_diag</span> <span class="kn">import</span> <span class="n">trace_rho_op</span>
+<span class="kn">from</span> <span class="nn">triqs.gf.descriptors</span> <span class="kn">import</span> <span class="n">Fourier</span>
+<span class="kn">from</span> <span class="nn">solid_dmft.dmft_tools</span> <span class="kn">import</span> <span class="n">solver</span>
+
+<div class="viewcode-block" id="prep_observables"><a class="viewcode-back" href="../../_ref/dmft_tools.observables.html#dmft_tools.observables.prep_observables">[docs]</a><span class="k">def</span> <span class="nf">prep_observables</span><span class="p">(</span><span class="n">h5_archive</span><span class="p">,</span> <span class="n">sum_k</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    prepares the observable arrays and files for the DMFT calculation</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    h5_archive: hdf archive instance</span>
+<span class="sd">        hdf archive for calculation</span>
+<span class="sd">    sum_k : SumK Object instances</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    observables : dict</span>
+<span class="sd">        observable array for calculation</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># determine number of impurities</span>
+    <span class="n">n_inequiv_shells</span> <span class="o">=</span> <span class="n">h5_archive</span><span class="p">[</span><span class="s1">&#39;dft_input&#39;</span><span class="p">][</span><span class="s1">&#39;n_inequiv_shells&#39;</span><span class="p">]</span>
+
+    <span class="c1"># check for previous iterations</span>
+    <span class="n">obs_prev</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">if</span> <span class="s1">&#39;observables&#39;</span> <span class="ow">in</span> <span class="n">h5_archive</span><span class="p">[</span><span class="s1">&#39;DMFT_results&#39;</span><span class="p">]:</span>
+        <span class="n">obs_prev</span> <span class="o">=</span> <span class="n">h5_archive</span><span class="p">[</span><span class="s1">&#39;DMFT_results&#39;</span><span class="p">][</span><span class="s1">&#39;observables&#39;</span><span class="p">]</span>
+
+    <span class="c1"># prepare observable dicts</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">obs_prev</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">observables</span> <span class="o">=</span> <span class="n">obs_prev</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">observables</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
+        <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;iteration&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;mu&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;E_tot&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;E_bandcorr&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;E_int&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[[]</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_inequiv_shells</span><span class="p">)]</span>
+        <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;E_corr_en&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;E_dft&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;E_DC&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[[]</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_inequiv_shells</span><span class="p">)]</span>
+        <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;orb_gb2&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[{</span><span class="n">spin</span><span class="p">:</span> <span class="p">[]</span> <span class="k">for</span> <span class="n">spin</span> <span class="ow">in</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">spin_block_names</span><span class="p">[</span><span class="n">sum_k</span><span class="o">.</span><span class="n">SO</span><span class="p">]}</span>
+                                  <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_inequiv_shells</span><span class="p">)]</span>
+        <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;imp_gb2&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[{</span><span class="n">spin</span><span class="p">:</span> <span class="p">[]</span> <span class="k">for</span> <span class="n">spin</span> <span class="ow">in</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">spin_block_names</span><span class="p">[</span><span class="n">sum_k</span><span class="o">.</span><span class="n">SO</span><span class="p">]}</span>
+                                  <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_inequiv_shells</span><span class="p">)]</span>
+        <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;orb_occ&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[{</span><span class="n">spin</span><span class="p">:</span> <span class="p">[]</span> <span class="k">for</span> <span class="n">spin</span> <span class="ow">in</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">spin_block_names</span><span class="p">[</span><span class="n">sum_k</span><span class="o">.</span><span class="n">SO</span><span class="p">]}</span>
+                                  <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_inequiv_shells</span><span class="p">)]</span>
+        <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;orb_Z&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[{</span><span class="n">spin</span><span class="p">:</span> <span class="p">[]</span> <span class="k">for</span> <span class="n">spin</span> <span class="ow">in</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">spin_block_names</span><span class="p">[</span><span class="n">sum_k</span><span class="o">.</span><span class="n">SO</span><span class="p">]}</span>
+                                  <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_inequiv_shells</span><span class="p">)]</span>
+        <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;imp_occ&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[{</span><span class="n">spin</span><span class="p">:</span> <span class="p">[]</span> <span class="k">for</span> <span class="n">spin</span> <span class="ow">in</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">spin_block_names</span><span class="p">[</span><span class="n">sum_k</span><span class="o">.</span><span class="n">SO</span><span class="p">]}</span>
+                                  <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_inequiv_shells</span><span class="p">)]</span>
+
+    <span class="k">return</span> <span class="n">observables</span></div>
+
+<span class="k">def</span> <span class="nf">_generate_header</span><span class="p">(</span><span class="n">general_params</span><span class="p">,</span> <span class="n">sum_k</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Generates the headers that are used in write_header_to_file.</span>
+<span class="sd">    Returns a dict with {file_name: header_string}</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">n_orb</span> <span class="o">=</span> <span class="n">solver</span><span class="o">.</span><span class="n">get_n_orbitals</span><span class="p">(</span><span class="n">sum_k</span><span class="p">)</span>
+
+    <span class="n">header_energy_mask</span> <span class="o">=</span> <span class="s1">&#39; | </span><span class="si">{:&gt;10}</span><span class="s1"> | </span><span class="si">{:&gt;10}</span><span class="s1">   </span><span class="si">{:&gt;10}</span><span class="s1">   </span><span class="si">{:&gt;10}</span><span class="s1">   </span><span class="si">{:&gt;10}</span><span class="s1">&#39;</span>
+    <span class="n">header_energy</span> <span class="o">=</span> <span class="n">header_energy_mask</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s1">&#39;E_tot&#39;</span><span class="p">,</span> <span class="s1">&#39;E_DFT&#39;</span><span class="p">,</span> <span class="s1">&#39;E_bandcorr&#39;</span><span class="p">,</span> <span class="s1">&#39;E_int_imp&#39;</span><span class="p">,</span> <span class="s1">&#39;E_DC&#39;</span><span class="p">)</span>
+
+    <span class="n">headers</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="k">for</span> <span class="n">iineq</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span><span class="p">):</span>
+        <span class="n">number_spaces</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="mi">10</span><span class="o">*</span><span class="n">n_orb</span><span class="p">[</span><span class="n">iineq</span><span class="p">][</span><span class="s1">&#39;up&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="mi">3</span><span class="o">*</span><span class="p">(</span><span class="n">n_orb</span><span class="p">[</span><span class="n">iineq</span><span class="p">][</span><span class="s1">&#39;up&#39;</span><span class="p">]</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mi">21</span><span class="p">)</span>
+        <span class="n">header_basic_mask</span> <span class="o">=</span> <span class="s1">&#39;{{:&gt;3}} | {{:&gt;10}} | {{:&gt;</span><span class="si">{0}</span><span class="s1">}} | {{:&gt;</span><span class="si">{0}</span><span class="s1">}} | {{:&gt;17}}&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">number_spaces</span><span class="p">)</span>
+
+        <span class="c1"># If magnetic calculation is done create two obs files per imp</span>
+        <span class="k">if</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;magnetic&#39;</span><span class="p">]</span> <span class="ow">and</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">SO</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">spin</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">&#39;up&#39;</span><span class="p">,</span> <span class="s1">&#39;down&#39;</span><span class="p">):</span>
+                <span class="n">file_name</span> <span class="o">=</span> <span class="s1">&#39;observables_imp</span><span class="si">{}</span><span class="s1">_</span><span class="si">{}</span><span class="s1">.dat&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">iineq</span><span class="p">,</span> <span class="n">spin</span><span class="p">)</span>
+                <span class="n">headers</span><span class="p">[</span><span class="n">file_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">header_basic_mask</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s1">&#39;it&#39;</span><span class="p">,</span> <span class="s1">&#39;mu&#39;</span><span class="p">,</span> <span class="s1">&#39;G(beta/2) per orbital&#39;</span><span class="p">,</span>
+                                                             <span class="s1">&#39;orbital occs &#39;</span><span class="o">+</span><span class="n">spin</span><span class="p">,</span> <span class="s1">&#39;impurity occ &#39;</span><span class="o">+</span><span class="n">spin</span><span class="p">)</span>
+
+                <span class="k">if</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;calc_energies&#39;</span><span class="p">]:</span>
+                    <span class="n">headers</span><span class="p">[</span><span class="n">file_name</span><span class="p">]</span> <span class="o">+=</span> <span class="n">header_energy</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">file_name</span> <span class="o">=</span> <span class="s1">&#39;observables_imp</span><span class="si">{}</span><span class="s1">.dat&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">iineq</span><span class="p">)</span>
+            <span class="n">headers</span><span class="p">[</span><span class="n">file_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">header_basic_mask</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s1">&#39;it&#39;</span><span class="p">,</span> <span class="s1">&#39;mu&#39;</span><span class="p">,</span> <span class="s1">&#39;G(beta/2) per orbital&#39;</span><span class="p">,</span>
+                                                         <span class="s1">&#39;orbital occs up+down&#39;</span><span class="p">,</span> <span class="s1">&#39;impurity occ&#39;</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;calc_energies&#39;</span><span class="p">]:</span>
+                <span class="n">headers</span><span class="p">[</span><span class="n">file_name</span><span class="p">]</span> <span class="o">+=</span> <span class="n">header_energy</span>
+
+    <span class="k">return</span> <span class="n">headers</span>
+
+
+<div class="viewcode-block" id="write_header_to_file"><a class="viewcode-back" href="../../_ref/dmft_tools.observables.html#dmft_tools.observables.write_header_to_file">[docs]</a><span class="k">def</span> <span class="nf">write_header_to_file</span><span class="p">(</span><span class="n">general_params</span><span class="p">,</span> <span class="n">sum_k</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Writes the header to the observable files</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    general_params : dict</span>
+<span class="sd">        general parameters as a dict</span>
+<span class="sd">    n_inequiv_shells : int</span>
+<span class="sd">        number of impurities for calculations</span>
+
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    nothing</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">headers</span> <span class="o">=</span> <span class="n">_generate_header</span><span class="p">(</span><span class="n">general_params</span><span class="p">,</span> <span class="n">sum_k</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">file_name</span><span class="p">,</span> <span class="n">header</span> <span class="ow">in</span> <span class="n">headers</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+        <span class="n">path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;jobname&#39;</span><span class="p">],</span> <span class="n">file_name</span><span class="p">)</span>
+        <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">obs_file</span><span class="p">:</span>
+            <span class="n">obs_file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">header</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="add_dft_values_as_zeroth_iteration"><a class="viewcode-back" href="../../_ref/dmft_tools.observables.html#dmft_tools.observables.add_dft_values_as_zeroth_iteration">[docs]</a><span class="k">def</span> <span class="nf">add_dft_values_as_zeroth_iteration</span><span class="p">(</span><span class="n">observables</span><span class="p">,</span> <span class="n">general_params</span><span class="p">,</span> <span class="n">solver_type_per_imp</span><span class="p">,</span> <span class="n">dft_mu</span><span class="p">,</span> <span class="n">dft_energy</span><span class="p">,</span>
+                                       <span class="n">sum_k</span><span class="p">,</span> <span class="n">G_loc_all_dft</span><span class="p">,</span> <span class="n">density_mat_dft</span><span class="p">,</span> <span class="n">shell_multiplicity</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Calculates the DFT observables that should be written as the zeroth iteration.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    observables : observable arrays/dicts</span>
+
+<span class="sd">    general_params : general parameters as a dict</span>
+
+<span class="sd">    dft_mu : dft chemical potential</span>
+
+<span class="sd">    sum_k : SumK Object instances</span>
+
+<span class="sd">    G_loc_all_dft : Gloc from DFT for G(beta/2)</span>
+
+<span class="sd">    density_mat_dft : occupations from DFT</span>
+
+<span class="sd">    shell_multiplicity : degeneracy of impurities</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+
+<span class="sd">    observables: list of dicts</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">dft_energy</span> <span class="o">=</span> <span class="mf">0.0</span> <span class="k">if</span> <span class="n">dft_energy</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">dft_energy</span>
+    <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;iteration&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+    <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;mu&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="n">dft_mu</span><span class="p">))</span>
+
+    <span class="k">if</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;calc_energies&#39;</span><span class="p">]:</span>
+        <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;E_bandcorr&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mf">0.0</span><span class="p">)</span>
+        <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;E_corr_en&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mf">0.0</span><span class="p">)</span>
+        <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;E_dft&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dft_energy</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;E_bandcorr&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;none&#39;</span><span class="p">)</span>
+        <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;E_corr_en&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;none&#39;</span><span class="p">)</span>
+        <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;E_dft&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;none&#39;</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">iineq</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;calc_energies&#39;</span><span class="p">]:</span>
+            <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;E_int&#39;</span><span class="p">][</span><span class="n">iineq</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mf">0.0</span><span class="p">)</span>
+            <span class="n">double_counting_energy</span> <span class="o">=</span> <span class="n">shell_multiplicity</span><span class="p">[</span><span class="n">iineq</span><span class="p">]</span><span class="o">*</span><span class="n">sum_k</span><span class="o">.</span><span class="n">dc_energ</span><span class="p">[</span><span class="n">sum_k</span><span class="o">.</span><span class="n">inequiv_to_corr</span><span class="p">[</span><span class="n">iineq</span><span class="p">]]</span> <span class="k">if</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;dc&#39;</span><span class="p">]</span> <span class="k">else</span> <span class="mf">0.0</span>
+            <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;E_DC&#39;</span><span class="p">][</span><span class="n">iineq</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">double_counting_energy</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;E_int&#39;</span><span class="p">][</span><span class="n">iineq</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;none&#39;</span><span class="p">)</span>
+            <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;E_DC&#39;</span><span class="p">][</span><span class="n">iineq</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;none&#39;</span><span class="p">)</span>
+
+        <span class="c1"># Collect all occupation and G(beta/2) for spin up and down separately</span>
+        <span class="k">for</span> <span class="n">spin</span> <span class="ow">in</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">spin_block_names</span><span class="p">[</span><span class="n">sum_k</span><span class="o">.</span><span class="n">SO</span><span class="p">]:</span>
+            <span class="n">g_beta_half_per_impurity</span> <span class="o">=</span> <span class="mf">0.0</span>
+            <span class="n">g_beta_half_per_orbital</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="n">occupation_per_impurity</span> <span class="o">=</span> <span class="mf">0.0</span>
+            <span class="n">occupation_per_orbital</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="n">Z_per_orbital</span> <span class="o">=</span> <span class="p">[]</span>
+
+            <span class="c1"># iterate over all spin channels and add the to up or down</span>
+            <span class="c1"># we need only the keys of the blocks, but sorted! Otherwise</span>
+            <span class="c1"># orbitals will be mixed up_0 to down_0 etc.</span>
+            <span class="k">for</span> <span class="n">spin_channel</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">gf_struct_solver</span><span class="p">[</span><span class="n">iineq</span><span class="p">]</span><span class="o">.</span><span class="n">keys</span><span class="p">()):</span>
+                <span class="k">if</span> <span class="ow">not</span> <span class="n">spin</span> <span class="ow">in</span> <span class="n">spin_channel</span><span class="p">:</span>
+                    <span class="k">continue</span>
+
+                <span class="k">if</span> <span class="n">solver_type_per_imp</span><span class="p">[</span><span class="n">iineq</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;ftps&#39;</span><span class="p">:</span>
+                    <span class="n">freq_mesh</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">w</span><span class="o">.</span><span class="n">value</span> <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">G_loc_all_dft</span><span class="p">[</span><span class="n">iineq</span><span class="p">][</span><span class="n">spin_channel</span><span class="p">]</span><span class="o">.</span><span class="n">mesh</span><span class="p">])</span>
+                    <span class="n">fermi_idx</span> <span class="o">=</span> <span class="nb">abs</span><span class="p">(</span><span class="n">freq_mesh</span><span class="p">)</span><span class="o">.</span><span class="n">argmin</span><span class="p">()</span>
+                    <span class="n">gb2_averaged</span> <span class="o">=</span> <span class="n">G_loc_all_dft</span><span class="p">[</span><span class="n">iineq</span><span class="p">][</span><span class="n">spin_channel</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">fermi_idx</span><span class="p">]</span><span class="o">.</span><span class="n">imag</span>
+
+                    <span class="c1"># Z is not defined without Sigma, adding 1</span>
+                    <span class="n">Z_per_orbital</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">]</span> <span class="o">*</span> <span class="n">G_loc_all_dft</span><span class="p">[</span><span class="n">iineq</span><span class="p">][</span><span class="n">spin_channel</span><span class="p">]</span><span class="o">.</span><span class="n">target_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>  <span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="c1"># G(beta/2)</span>
+                    <span class="n">mesh</span> <span class="o">=</span> <span class="n">MeshImTime</span><span class="p">(</span><span class="n">beta</span><span class="o">=</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;beta&#39;</span><span class="p">],</span> <span class="n">S</span><span class="o">=</span><span class="s2">&quot;Fermion&quot;</span><span class="p">,</span>
+                                      <span class="n">n_max</span><span class="o">=</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;n_tau&#39;</span><span class="p">])</span>
+                    <span class="n">G_time</span> <span class="o">=</span> <span class="n">Gf</span><span class="p">(</span><span class="n">mesh</span><span class="o">=</span><span class="n">mesh</span><span class="p">,</span> <span class="n">indices</span><span class="o">=</span><span class="n">G_loc_all_dft</span><span class="p">[</span><span class="n">iineq</span><span class="p">][</span><span class="n">spin_channel</span><span class="p">]</span><span class="o">.</span><span class="n">indices</span><span class="p">)</span>
+                    <span class="n">G_time</span> <span class="o">&lt;&lt;</span> <span class="n">Fourier</span><span class="p">(</span><span class="n">G_loc_all_dft</span><span class="p">[</span><span class="n">iineq</span><span class="p">][</span><span class="n">spin_channel</span><span class="p">])</span>
+
+                    <span class="c1"># since G(tau) has always 10001 values we are sampling +-10 values</span>
+                    <span class="c1"># hard coded around beta/2, for beta=40 this corresponds to approx +-0.05</span>
+                    <span class="n">mesh_mid</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">G_time</span><span class="o">.</span><span class="n">data</span><span class="p">)</span> <span class="o">//</span> <span class="mi">2</span>
+                    <span class="n">samp</span> <span class="o">=</span> <span class="mi">10</span>
+                    <span class="n">gg</span> <span class="o">=</span> <span class="n">G_time</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">mesh_mid</span><span class="o">-</span><span class="n">samp</span><span class="p">:</span><span class="n">mesh_mid</span><span class="o">+</span><span class="n">samp</span><span class="p">]</span>
+                    <span class="n">gb2_averaged</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">gg</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+
+                    <span class="c1"># Z is not defined without Sigma, adding 1</span>
+                    <span class="n">Z_per_orbital</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">]</span> <span class="o">*</span> <span class="n">G_time</span><span class="o">.</span><span class="n">target_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>  <span class="p">)</span>
+
+                <span class="n">g_beta_half_per_orbital</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">diag</span><span class="p">(</span><span class="n">gb2_averaged</span><span class="p">))</span>
+                <span class="n">g_beta_half_per_impurity</span> <span class="o">+=</span> <span class="n">np</span><span class="o">.</span><span class="n">trace</span><span class="p">(</span><span class="n">gb2_averaged</span><span class="p">)</span>
+
+                <span class="c1"># occupation per orbital</span>
+                <span class="n">den_mat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">density_mat_dft</span><span class="p">[</span><span class="n">iineq</span><span class="p">][</span><span class="n">spin_channel</span><span class="p">])</span>
+                <span class="n">occupation_per_orbital</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">diag</span><span class="p">(</span><span class="n">den_mat</span><span class="p">))</span>
+                <span class="n">occupation_per_impurity</span> <span class="o">+=</span> <span class="n">np</span><span class="o">.</span><span class="n">trace</span><span class="p">(</span><span class="n">den_mat</span><span class="p">)</span>
+
+            <span class="c1"># adding those values to the observable object</span>
+            <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;orb_gb2&#39;</span><span class="p">][</span><span class="n">iineq</span><span class="p">][</span><span class="n">spin</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">g_beta_half_per_orbital</span><span class="p">))</span>
+            <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;imp_gb2&#39;</span><span class="p">][</span><span class="n">iineq</span><span class="p">][</span><span class="n">spin</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">g_beta_half_per_impurity</span><span class="p">)</span>
+            <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;orb_occ&#39;</span><span class="p">][</span><span class="n">iineq</span><span class="p">][</span><span class="n">spin</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">occupation_per_orbital</span><span class="p">))</span>
+            <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;imp_occ&#39;</span><span class="p">][</span><span class="n">iineq</span><span class="p">][</span><span class="n">spin</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">occupation_per_impurity</span><span class="p">)</span>
+            <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;orb_Z&#39;</span><span class="p">][</span><span class="n">iineq</span><span class="p">][</span><span class="n">spin</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">Z_per_orbital</span><span class="p">))</span>
+
+    <span class="c1"># for it 0 we just subtract E_DC from E_DFT</span>
+    <span class="k">if</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;calc_energies&#39;</span><span class="p">]:</span>
+        <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;E_tot&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dft_energy</span> <span class="o">-</span> <span class="nb">sum</span><span class="p">([</span><span class="n">dc_per_imp</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">dc_per_imp</span> <span class="ow">in</span> <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;E_DC&#39;</span><span class="p">]]))</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;E_tot&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;none&#39;</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">observables</span></div>
+
+
+<div class="viewcode-block" id="add_dmft_observables"><a class="viewcode-back" href="../../_ref/dmft_tools.observables.html#dmft_tools.observables.add_dmft_observables">[docs]</a><span class="k">def</span> <span class="nf">add_dmft_observables</span><span class="p">(</span><span class="n">observables</span><span class="p">,</span> <span class="n">general_params</span><span class="p">,</span> <span class="n">solver_params</span><span class="p">,</span> <span class="n">map_imp_solver</span><span class="p">,</span> <span class="n">solver_type_per_imp</span><span class="p">,</span> <span class="n">dft_energy</span><span class="p">,</span> <span class="n">it</span><span class="p">,</span> <span class="n">solvers</span><span class="p">,</span> <span class="n">h_int</span><span class="p">,</span>
+                         <span class="n">previous_mu</span><span class="p">,</span> <span class="n">sum_k</span><span class="p">,</span> <span class="n">density_mat</span><span class="p">,</span> <span class="n">shell_multiplicity</span><span class="p">,</span> <span class="n">E_bandcorr</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    calculates the observables for given Input, I decided to calculate the observables</span>
+<span class="sd">    not adhoc since it should be done only once by the master_node</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    observables : observable arrays/dicts</span>
+
+<span class="sd">    general_params : general parameters as a dict</span>
+
+<span class="sd">    solver_params : solver parameters as a dict</span>
+
+<span class="sd">    it : iteration counter</span>
+
+<span class="sd">    solvers : Solver instances</span>
+
+<span class="sd">    h_int : interaction hamiltonian</span>
+
+<span class="sd">    previous_mu : dmft chemical potential for which the calculation was just done</span>
+
+<span class="sd">    sum_k : SumK Object instances</span>
+
+<span class="sd">    density_mat : DMFT occupations</span>
+
+<span class="sd">    shell_multiplicity : degeneracy of impurities</span>
+
+<span class="sd">    E_bandcorr : E_kin_dmft - E_kin_dft, either calculated man or from sum_k method if CSC</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    observables: list of dicts</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># init energy values</span>
+    <span class="n">E_corr_en</span> <span class="o">=</span> <span class="mf">0.0</span>
+    <span class="c1"># Read energy from OSZICAR</span>
+    <span class="n">dft_energy</span> <span class="o">=</span> <span class="mf">0.0</span> <span class="k">if</span> <span class="n">dft_energy</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">dft_energy</span>
+
+    <span class="c1"># now the normal output from each iteration</span>
+    <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;iteration&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">it</span><span class="p">)</span>
+    <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;mu&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="n">previous_mu</span><span class="p">))</span>
+    <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;E_bandcorr&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">E_bandcorr</span><span class="p">)</span>
+    <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;E_dft&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dft_energy</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;calc_energies&#39;</span><span class="p">]:</span>
+        <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">Calculating interaction energies&#39;</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">icrsh</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span><span class="p">):</span>
+            <span class="k">if</span> <span class="p">(</span><span class="n">solver_type_per_imp</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;cthyb&#39;</span><span class="p">,</span> <span class="s1">&#39;hubbardI&#39;</span><span class="p">]</span>
+                    <span class="ow">and</span> <span class="n">solver_params</span><span class="p">[</span><span class="n">map_imp_solver</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]][</span><span class="s2">&quot;measure_density_matrix&quot;</span><span class="p">]):</span>
+                <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;    Imp </span><span class="si">{</span><span class="n">icrsh</span><span class="si">}</span><span class="s1">: from impurity density matrix&#39;</span><span class="p">)</span>
+                <span class="c1"># Extract accumulated density matrix</span>
+                <span class="n">density_matrix</span> <span class="o">=</span> <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">density_matrix</span>
+                <span class="c1"># Object containing eigensystem of the local Hamiltonian</span>
+                <span class="n">diag_local_ham</span> <span class="o">=</span> <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">h_loc_diagonalization</span>
+                <span class="n">E_int</span> <span class="o">=</span> <span class="n">trace_rho_op</span><span class="p">(</span><span class="n">density_matrix</span><span class="p">,</span> <span class="n">h_int</span><span class="p">[</span><span class="n">icrsh</span><span class="p">],</span> <span class="n">diag_local_ham</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="n">solver_type_per_imp</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;hartree&#39;</span><span class="p">:</span>
+                <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;    Imp </span><span class="si">{</span><span class="n">icrsh</span><span class="si">}</span><span class="s1">: from Hartree&#39;</span><span class="p">)</span>
+                <span class="n">E_int</span> <span class="o">=</span> <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">interaction_energy</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;    Imp </span><span class="si">{</span><span class="n">icrsh</span><span class="si">}</span><span class="s1">: from Migdal formula. &#39;</span>
+                           <span class="s1">&#39;WARNING: less stable than measuring density matrix and using trace_rho_op!&#39;</span><span class="p">)</span>
+                <span class="c1"># calc energy for given S and G</span>
+                <span class="c1"># dmft interaction energy with E_int = 0.5 * Tr[Sigma * G]</span>
+                <span class="n">E_int</span> <span class="o">=</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">((</span><span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">G_freq</span> <span class="o">*</span> <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">Sigma_freq</span><span class="p">)</span><span class="o">.</span><span class="n">total_density</span><span class="p">())</span>
+
+            <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;E_int&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">shell_multiplicity</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">*</span><span class="n">E_int</span><span class="o">.</span><span class="n">real</span><span class="p">)</span>
+            <span class="n">E_corr_en</span> <span class="o">+=</span> <span class="n">shell_multiplicity</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span> <span class="o">*</span> <span class="p">(</span><span class="n">E_int</span><span class="o">.</span><span class="n">real</span> <span class="o">-</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">dc_energ</span><span class="p">[</span><span class="n">sum_k</span><span class="o">.</span><span class="n">inequiv_to_corr</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]])</span>
+
+    <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;E_corr_en&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">E_corr_en</span><span class="p">)</span>
+
+    <span class="c1"># calc total energy</span>
+    <span class="n">E_tot</span> <span class="o">=</span> <span class="n">dft_energy</span> <span class="o">+</span> <span class="n">E_bandcorr</span> <span class="o">+</span> <span class="n">E_corr_en</span>
+    <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;E_tot&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">E_tot</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">icrsh</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;dc&#39;</span><span class="p">]:</span>
+            <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;E_DC&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">shell_multiplicity</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">*</span><span class="n">sum_k</span><span class="o">.</span><span class="n">dc_energ</span><span class="p">[</span><span class="n">sum_k</span><span class="o">.</span><span class="n">inequiv_to_corr</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]])</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;E_DC&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mf">0.0</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">solver_type_per_imp</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;ftps&#39;</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">G_time</span><span class="p">:</span>
+                <span class="n">G_time</span> <span class="o">=</span> <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">G_time</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">G_time</span> <span class="o">=</span> <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">G_time_orig</span>
+
+        <span class="c1"># Collect all occupation and G(beta/2) for spin up and down separately</span>
+        <span class="k">for</span> <span class="n">spin</span> <span class="ow">in</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">spin_block_names</span><span class="p">[</span><span class="n">sum_k</span><span class="o">.</span><span class="n">SO</span><span class="p">]:</span>
+            <span class="n">g_beta_half_per_impurity</span> <span class="o">=</span> <span class="mf">0.0</span>
+            <span class="n">g_beta_half_per_orbital</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="n">occupation_per_impurity</span> <span class="o">=</span> <span class="mf">0.0</span>
+            <span class="n">occupation_per_orbital</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="n">Z_per_orbital</span> <span class="o">=</span> <span class="p">[]</span>
+
+            <span class="c1"># iterate over all spin channels and add the to up or down</span>
+            <span class="k">for</span> <span class="n">spin_channel</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">gf_struct_solver</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">keys</span><span class="p">()):</span>
+                <span class="k">if</span> <span class="ow">not</span> <span class="n">spin</span> <span class="ow">in</span> <span class="n">spin_channel</span><span class="p">:</span>
+                    <span class="k">continue</span>
+                <span class="k">if</span> <span class="n">solver_type_per_imp</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;ftps&#39;</span><span class="p">:</span>
+                    <span class="n">freq_mesh</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">w</span><span class="o">.</span><span class="n">value</span> <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">G_freq</span><span class="p">[</span><span class="n">spin_channel</span><span class="p">]</span><span class="o">.</span><span class="n">mesh</span><span class="p">])</span>
+                    <span class="n">fermi_idx</span> <span class="o">=</span> <span class="nb">abs</span><span class="p">(</span><span class="n">freq_mesh</span><span class="p">)</span><span class="o">.</span><span class="n">argmin</span><span class="p">()</span>
+                    <span class="n">gb2_averaged</span> <span class="o">=</span> <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">G_freq</span><span class="p">[</span><span class="n">spin_channel</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">fermi_idx</span><span class="p">]</span><span class="o">.</span><span class="n">imag</span>
+
+                    <span class="c1"># Z is not defined without Sigma, adding 1</span>
+                    <span class="n">Z_per_orbital</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">]</span> <span class="o">*</span> <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">G_freq</span><span class="p">[</span><span class="n">spin_channel</span><span class="p">]</span><span class="o">.</span><span class="n">target_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>  <span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="c1"># G(beta/2)</span>
+                    <span class="c1"># since G(tau) has always 10001 values we are sampling +-10 values</span>
+                    <span class="c1"># hard coded around beta/2, for beta=40 this corresponds to approx +-0.05</span>
+                    <span class="n">mesh_mid</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">G_time</span><span class="p">[</span><span class="n">spin_channel</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="p">)</span> <span class="o">//</span> <span class="mi">2</span>
+                    <span class="n">samp</span> <span class="o">=</span> <span class="mi">10</span>
+                    <span class="n">gg</span> <span class="o">=</span> <span class="n">G_time</span><span class="p">[</span><span class="n">spin_channel</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">mesh_mid</span><span class="o">-</span><span class="n">samp</span><span class="p">:</span><span class="n">mesh_mid</span><span class="o">+</span><span class="n">samp</span><span class="p">]</span>
+                    <span class="n">gb2_averaged</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">gg</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+
+                    <span class="c1"># get Z</span>
+                    <span class="n">Z_per_orbital</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span> <span class="n">calc_Z</span><span class="p">(</span><span class="n">Sigma</span><span class="o">=</span> <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">Sigma_freq</span><span class="p">[</span><span class="n">spin_channel</span><span class="p">])</span> <span class="p">)</span>
+
+                <span class="n">g_beta_half_per_orbital</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">diag</span><span class="p">(</span><span class="n">gb2_averaged</span><span class="p">))</span>
+                <span class="n">g_beta_half_per_impurity</span> <span class="o">+=</span> <span class="n">np</span><span class="o">.</span><span class="n">trace</span><span class="p">(</span><span class="n">gb2_averaged</span><span class="p">)</span>
+
+                <span class="c1"># occupation per orbital and impurity</span>
+                <span class="n">den_mat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">density_mat</span><span class="p">[</span><span class="n">icrsh</span><span class="p">][</span><span class="n">spin_channel</span><span class="p">])</span>
+                <span class="n">occupation_per_orbital</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">diag</span><span class="p">(</span><span class="n">den_mat</span><span class="p">))</span>
+                <span class="n">occupation_per_impurity</span> <span class="o">+=</span> <span class="n">np</span><span class="o">.</span><span class="n">trace</span><span class="p">(</span><span class="n">den_mat</span><span class="p">)</span>
+
+            <span class="c1"># adding those values to the observable object</span>
+            <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;orb_gb2&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">][</span><span class="n">spin</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">g_beta_half_per_orbital</span><span class="p">))</span>
+            <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;imp_gb2&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">][</span><span class="n">spin</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">g_beta_half_per_impurity</span><span class="p">)</span>
+            <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;orb_occ&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">][</span><span class="n">spin</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">occupation_per_orbital</span><span class="p">))</span>
+            <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;imp_occ&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">][</span><span class="n">spin</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">occupation_per_impurity</span><span class="p">)</span>
+            <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;orb_Z&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">][</span><span class="n">spin</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">Z_per_orbital</span><span class="p">))</span>
+
+    <span class="k">return</span> <span class="n">observables</span></div>
+
+<div class="viewcode-block" id="write_obs"><a class="viewcode-back" href="../../_ref/dmft_tools.observables.html#dmft_tools.observables.write_obs">[docs]</a><span class="k">def</span> <span class="nf">write_obs</span><span class="p">(</span><span class="n">observables</span><span class="p">,</span> <span class="n">sum_k</span><span class="p">,</span> <span class="n">general_params</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    writes the last entries of the observable arrays to the files</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    observables : list of dicts</span>
+<span class="sd">        observable arrays/dicts</span>
+
+<span class="sd">    sum_k : SumK Object instances</span>
+
+<span class="sd">    general_params : dict</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    nothing</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">n_orb</span> <span class="o">=</span> <span class="n">solver</span><span class="o">.</span><span class="n">get_n_orbitals</span><span class="p">(</span><span class="n">sum_k</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">icrsh</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;magnetic&#39;</span><span class="p">]</span> <span class="ow">and</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">SO</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">spin</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">&#39;up&#39;</span><span class="p">,</span> <span class="s1">&#39;down&#39;</span><span class="p">):</span>
+                <span class="n">line</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{:3d}</span><span class="s1"> | &#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">observables</span><span class="p">[</span><span class="s1">&#39;iteration&#39;</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+                <span class="n">line</span> <span class="o">+=</span> <span class="s1">&#39;</span><span class="si">{:10.5f}</span><span class="s1"> | &#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">observables</span><span class="p">[</span><span class="s1">&#39;mu&#39;</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+
+                <span class="k">if</span> <span class="n">n_orb</span><span class="p">[</span><span class="n">icrsh</span><span class="p">][</span><span class="n">spin</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+                    <span class="n">line</span> <span class="o">+=</span> <span class="s1">&#39; &#39;</span><span class="o">*</span><span class="mi">11</span>
+                <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;orb_gb2&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">][</span><span class="n">spin</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">]:</span>
+                    <span class="n">line</span> <span class="o">+=</span> <span class="s1">&#39;</span><span class="si">{:10.5f}</span><span class="s1">   &#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">item</span><span class="p">)</span>
+                <span class="n">line</span> <span class="o">=</span> <span class="n">line</span><span class="p">[:</span><span class="o">-</span><span class="mi">3</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; | &#39;</span>
+
+                <span class="k">if</span> <span class="n">n_orb</span><span class="p">[</span><span class="n">icrsh</span><span class="p">][</span><span class="n">spin</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+                    <span class="n">line</span> <span class="o">+=</span> <span class="s1">&#39; &#39;</span><span class="o">*</span><span class="mi">11</span>
+                <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="n">observables</span><span class="p">[</span><span class="s1">&#39;orb_occ&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">][</span><span class="n">spin</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">]:</span>
+                    <span class="n">line</span> <span class="o">+=</span> <span class="s1">&#39;</span><span class="si">{:10.5f}</span><span class="s1">   &#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">item</span><span class="p">)</span>
+                <span class="n">line</span> <span class="o">=</span> <span class="n">line</span><span class="p">[:</span><span class="o">-</span><span class="mi">3</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; | &#39;</span>
+
+                <span class="n">line</span> <span class="o">+=</span> <span class="s1">&#39;</span><span class="si">{:17.5f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">observables</span><span class="p">[</span><span class="s1">&#39;imp_occ&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">][</span><span class="n">spin</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+
+                <span class="k">if</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;calc_energies&#39;</span><span class="p">]:</span>
+                    <span class="n">line</span> <span class="o">+=</span> <span class="s1">&#39; | </span><span class="si">{:10.5f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">observables</span><span class="p">[</span><span class="s1">&#39;E_tot&#39;</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+                    <span class="n">line</span> <span class="o">+=</span> <span class="s1">&#39; | </span><span class="si">{:10.5f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">observables</span><span class="p">[</span><span class="s1">&#39;E_dft&#39;</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+                    <span class="n">line</span> <span class="o">+=</span> <span class="s1">&#39;   </span><span class="si">{:10.5f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">observables</span><span class="p">[</span><span class="s1">&#39;E_bandcorr&#39;</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+                    <span class="n">line</span> <span class="o">+=</span> <span class="s1">&#39;   </span><span class="si">{:10.5f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">observables</span><span class="p">[</span><span class="s1">&#39;E_int&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+                    <span class="n">line</span> <span class="o">+=</span> <span class="s1">&#39;   </span><span class="si">{:10.5f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">observables</span><span class="p">[</span><span class="s1">&#39;E_DC&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+
+                <span class="n">file_name</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{}</span><span class="s1">/observables_imp</span><span class="si">{}</span><span class="s1">_</span><span class="si">{}</span><span class="s1">.dat&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;jobname&#39;</span><span class="p">],</span> <span class="n">icrsh</span><span class="p">,</span> <span class="n">spin</span><span class="p">)</span>
+                <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">file_name</span><span class="p">,</span> <span class="s1">&#39;a&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">obs_file</span><span class="p">:</span>
+                    <span class="n">obs_file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">line</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">line</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{:3d}</span><span class="s1"> | &#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">observables</span><span class="p">[</span><span class="s1">&#39;iteration&#39;</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+            <span class="n">line</span> <span class="o">+=</span> <span class="s1">&#39;</span><span class="si">{:10.5f}</span><span class="s1"> | &#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">observables</span><span class="p">[</span><span class="s1">&#39;mu&#39;</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+
+            <span class="c1"># Adds spaces for header to fit in properly</span>
+            <span class="k">if</span> <span class="n">n_orb</span><span class="p">[</span><span class="n">icrsh</span><span class="p">][</span><span class="s1">&#39;up&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="n">line</span> <span class="o">+=</span> <span class="s1">&#39; &#39;</span><span class="o">*</span><span class="mi">11</span>
+            <span class="c1"># Adds up the spin channels</span>
+            <span class="k">for</span> <span class="n">iorb</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_orb</span><span class="p">[</span><span class="n">icrsh</span><span class="p">][</span><span class="s1">&#39;up&#39;</span><span class="p">]):</span>
+                <span class="n">val</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">([</span><span class="n">observables</span><span class="p">[</span><span class="s1">&#39;orb_gb2&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">][</span><span class="n">spin</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="n">iorb</span><span class="p">]</span> <span class="k">for</span> <span class="n">spin</span> <span class="ow">in</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">spin_block_names</span><span class="p">[</span><span class="n">sum_k</span><span class="o">.</span><span class="n">SO</span><span class="p">]])</span>
+                <span class="n">line</span> <span class="o">+=</span> <span class="s1">&#39;</span><span class="si">{:10.5f}</span><span class="s1">   &#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">val</span><span class="p">)</span>
+            <span class="n">line</span> <span class="o">=</span> <span class="n">line</span><span class="p">[:</span><span class="o">-</span><span class="mi">3</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; | &#39;</span>
+
+            <span class="c1"># Adds spaces for header to fit in properly</span>
+            <span class="k">if</span> <span class="n">n_orb</span><span class="p">[</span><span class="n">icrsh</span><span class="p">][</span><span class="s1">&#39;up&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="n">line</span> <span class="o">+=</span> <span class="s1">&#39; &#39;</span><span class="o">*</span><span class="mi">11</span>
+            <span class="c1"># Adds up the spin channels</span>
+            <span class="k">for</span> <span class="n">iorb</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_orb</span><span class="p">[</span><span class="n">icrsh</span><span class="p">][</span><span class="s1">&#39;up&#39;</span><span class="p">]):</span>
+                <span class="n">val</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">([</span><span class="n">observables</span><span class="p">[</span><span class="s1">&#39;orb_occ&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">][</span><span class="n">spin</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="n">iorb</span><span class="p">]</span> <span class="k">for</span> <span class="n">spin</span> <span class="ow">in</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">spin_block_names</span><span class="p">[</span><span class="n">sum_k</span><span class="o">.</span><span class="n">SO</span><span class="p">]])</span>
+                <span class="n">line</span> <span class="o">+=</span> <span class="s1">&#39;</span><span class="si">{:10.5f}</span><span class="s1">   &#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">val</span><span class="p">)</span>
+            <span class="n">line</span> <span class="o">=</span> <span class="n">line</span><span class="p">[:</span><span class="o">-</span><span class="mi">3</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; | &#39;</span>
+
+            <span class="c1"># Adds up the spin channels</span>
+            <span class="n">val</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">([</span><span class="n">observables</span><span class="p">[</span><span class="s1">&#39;imp_occ&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">][</span><span class="n">spin</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">spin</span> <span class="ow">in</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">spin_block_names</span><span class="p">[</span><span class="n">sum_k</span><span class="o">.</span><span class="n">SO</span><span class="p">]])</span>
+            <span class="n">line</span> <span class="o">+=</span> <span class="s1">&#39;</span><span class="si">{:17.5f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">val</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;calc_energies&#39;</span><span class="p">]:</span>
+                <span class="n">line</span> <span class="o">+=</span> <span class="s1">&#39; | </span><span class="si">{:10.5f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">observables</span><span class="p">[</span><span class="s1">&#39;E_tot&#39;</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+                <span class="n">line</span> <span class="o">+=</span> <span class="s1">&#39; | </span><span class="si">{:10.5f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">observables</span><span class="p">[</span><span class="s1">&#39;E_dft&#39;</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+                <span class="n">line</span> <span class="o">+=</span> <span class="s1">&#39;   </span><span class="si">{:10.5f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">observables</span><span class="p">[</span><span class="s1">&#39;E_bandcorr&#39;</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+                <span class="n">line</span> <span class="o">+=</span> <span class="s1">&#39;   </span><span class="si">{:10.5f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">observables</span><span class="p">[</span><span class="s1">&#39;E_int&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+                <span class="n">line</span> <span class="o">+=</span> <span class="s1">&#39;   </span><span class="si">{:10.5f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">observables</span><span class="p">[</span><span class="s1">&#39;E_DC&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+
+            <span class="n">file_name</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{}</span><span class="s1">/observables_imp</span><span class="si">{}</span><span class="s1">.dat&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;jobname&#39;</span><span class="p">],</span> <span class="n">icrsh</span><span class="p">)</span>
+            <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">file_name</span><span class="p">,</span> <span class="s1">&#39;a&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">obs_file</span><span class="p">:</span>
+                <span class="n">obs_file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">line</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="calc_dft_kin_en"><a class="viewcode-back" href="../../_ref/dmft_tools.observables.html#dmft_tools.observables.calc_dft_kin_en">[docs]</a><span class="k">def</span> <span class="nf">calc_dft_kin_en</span><span class="p">(</span><span class="n">general_params</span><span class="p">,</span> <span class="n">sum_k</span><span class="p">,</span> <span class="n">dft_mu</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Calculates the kinetic energy from DFT for target states</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    general_params : dict</span>
+<span class="sd">        general parameters as a dict</span>
+
+<span class="sd">    sum_k : SumK Object instances</span>
+
+<span class="sd">    dft_mu: float</span>
+<span class="sd">        DFT fermi energy</span>
+
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    E_kin_dft: float</span>
+<span class="sd">        kinetic energy from DFT</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">H_ks</span> <span class="o">=</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">hopping</span>
+    <span class="n">num_kpts</span> <span class="o">=</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">n_k</span>
+    <span class="n">E_kin</span> <span class="o">=</span> <span class="mf">0.0</span>
+    <span class="n">ikarray</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_k</span><span class="p">)))</span>
+    <span class="k">for</span> <span class="n">ik</span> <span class="ow">in</span> <span class="n">mpi</span><span class="o">.</span><span class="n">slice_array</span><span class="p">(</span><span class="n">ikarray</span><span class="p">):</span>
+        <span class="n">nb</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_orbitals</span><span class="p">[</span><span class="n">ik</span><span class="p">])</span>
+        <span class="c1"># calculate lattice greens function need here to set sigma other n_iw is assumend to be 1025!</span>
+        <span class="c1"># TODO: implement here version for FTPS!</span>
+        <span class="n">G_freq_lat</span> <span class="o">=</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">lattice_gf</span><span class="p">(</span><span class="n">ik</span><span class="p">,</span> <span class="n">with_Sigma</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">mu</span><span class="o">=</span><span class="n">dft_mu</span><span class="p">)</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+        <span class="c1"># # calculate G(beta) via the function density, which is the same as fourier trafo G(w) and taking G(b)</span>
+        <span class="n">G_freq_lat_beta</span> <span class="o">=</span> <span class="n">G_freq_lat</span><span class="o">.</span><span class="n">density</span><span class="p">()</span>
+        <span class="k">for</span> <span class="n">spin</span> <span class="ow">in</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">spin_block_names</span><span class="p">[</span><span class="n">sum_k</span><span class="o">.</span><span class="n">SO</span><span class="p">]:</span>
+            <span class="n">E_kin</span> <span class="o">+=</span> <span class="n">np</span><span class="o">.</span><span class="n">trace</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">H_ks</span><span class="p">[</span><span class="n">ik</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="p">:</span><span class="n">nb</span><span class="p">,</span> <span class="p">:</span><span class="n">nb</span><span class="p">],</span> <span class="n">G_freq_lat_beta</span><span class="p">[</span><span class="n">spin</span><span class="p">][:,</span> <span class="p">:]))</span>
+    <span class="n">E_kin</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">E_kin</span><span class="p">)</span>
+    <span class="c1"># collect data and put into E_kin_dft</span>
+    <span class="n">E_kin_dft</span> <span class="o">=</span> <span class="n">mpi</span><span class="o">.</span><span class="n">all_reduce</span><span class="p">(</span><span class="n">E_kin</span><span class="p">)</span>
+    <span class="n">mpi</span><span class="o">.</span><span class="n">barrier</span><span class="p">()</span>
+    <span class="c1"># E_kin should be divided by the number of k-points</span>
+    <span class="n">E_kin_dft</span> <span class="o">=</span> <span class="n">E_kin_dft</span><span class="o">/</span><span class="n">num_kpts</span>
+
+    <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Kinetic energy contribution dft part: </span><span class="si">{</span><span class="n">E_kin_dft</span><span class="si">:</span><span class="s1">.8f</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">E_kin_dft</span></div>
+
+<div class="viewcode-block" id="calc_bandcorr_man"><a class="viewcode-back" href="../../_ref/dmft_tools.observables.html#dmft_tools.observables.calc_bandcorr_man">[docs]</a><span class="k">def</span> <span class="nf">calc_bandcorr_man</span><span class="p">(</span><span class="n">general_params</span><span class="p">,</span> <span class="n">sum_k</span><span class="p">,</span> <span class="n">E_kin_dft</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Calculates the correlated kinetic energy from DMFT for target states</span>
+<span class="sd">    and then determines the band correction energy</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    general_params : dict</span>
+<span class="sd">        general parameters as a dict</span>
+
+<span class="sd">    sum_k : SumK Object instances</span>
+
+<span class="sd">    E_kin_dft: float</span>
+<span class="sd">        kinetic energy from DFT</span>
+
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    E_bandcorr: float</span>
+<span class="sd">        band energy correction E_kin_dmft - E_kin_dft</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">E_kin_dmft</span> <span class="o">=</span> <span class="mf">0.0</span><span class="n">j</span>
+    <span class="n">E_kin</span> <span class="o">=</span> <span class="mf">0.0</span><span class="n">j</span>
+    <span class="n">H_ks</span> <span class="o">=</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">hopping</span>
+    <span class="n">num_kpts</span> <span class="o">=</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">n_k</span>
+
+    <span class="c1"># kinetic energy from dmft lattice Greens functions</span>
+    <span class="n">ikarray</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_k</span><span class="p">)))</span>
+    <span class="k">for</span> <span class="n">ik</span> <span class="ow">in</span> <span class="n">mpi</span><span class="o">.</span><span class="n">slice_array</span><span class="p">(</span><span class="n">ikarray</span><span class="p">):</span>
+        <span class="n">nb</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_orbitals</span><span class="p">[</span><span class="n">ik</span><span class="p">])</span>
+        <span class="c1"># calculate lattice greens function</span>
+        <span class="n">G_freq_lat</span> <span class="o">=</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">lattice_gf</span><span class="p">(</span><span class="n">ik</span><span class="p">,</span> <span class="n">with_Sigma</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">with_dc</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+        <span class="c1"># calculate G(beta) via the function density, which is the same as fourier trafo G(w) and taking G(b)</span>
+        <span class="n">G_freq_lat_beta</span> <span class="o">=</span> <span class="n">G_freq_lat</span><span class="o">.</span><span class="n">density</span><span class="p">()</span>
+        <span class="k">for</span> <span class="n">spin</span> <span class="ow">in</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">spin_block_names</span><span class="p">[</span><span class="n">sum_k</span><span class="o">.</span><span class="n">SO</span><span class="p">]:</span>
+            <span class="n">E_kin</span> <span class="o">+=</span> <span class="n">np</span><span class="o">.</span><span class="n">trace</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">H_ks</span><span class="p">[</span><span class="n">ik</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="p">:</span><span class="n">nb</span><span class="p">,</span> <span class="p">:</span><span class="n">nb</span><span class="p">],</span> <span class="n">G_freq_lat_beta</span><span class="p">[</span><span class="n">spin</span><span class="p">][:,</span> <span class="p">:]))</span>
+    <span class="n">E_kin</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">E_kin</span><span class="p">)</span>
+
+    <span class="c1"># collect data and put into E_kin_dmft</span>
+    <span class="n">E_kin_dmft</span> <span class="o">=</span> <span class="n">mpi</span><span class="o">.</span><span class="n">all_reduce</span><span class="p">(</span><span class="n">E_kin</span><span class="p">)</span>
+    <span class="n">mpi</span><span class="o">.</span><span class="n">barrier</span><span class="p">()</span>
+    <span class="c1"># E_kin should be divided by the number of k-points</span>
+    <span class="n">E_kin_dmft</span> <span class="o">=</span> <span class="n">E_kin_dmft</span><span class="o">/</span><span class="n">num_kpts</span>
+
+    <span class="k">if</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Kinetic energy contribution dmft part: &#39;</span><span class="o">+</span><span class="nb">str</span><span class="p">(</span><span class="n">E_kin_dmft</span><span class="p">))</span>
+
+    <span class="n">E_bandcorr</span> <span class="o">=</span> <span class="n">E_kin_dmft</span> <span class="o">-</span> <span class="n">E_kin_dft</span>
+
+    <span class="k">return</span> <span class="n">E_bandcorr</span></div>
+
+<div class="viewcode-block" id="calc_Z"><a class="viewcode-back" href="../../_ref/dmft_tools.observables.html#dmft_tools.observables.calc_Z">[docs]</a><span class="k">def</span> <span class="nf">calc_Z</span><span class="p">(</span><span class="n">Sigma</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    calculates the inverse mass enhancement from the impurity</span>
+<span class="sd">    self-energy by a simple linear fit estimate:</span>
+<span class="sd">    [ 1 - ((Im S_iw[n_iw0+1]-S_iw[n_iw0])/(iw[n_iw0+1]-iw[n_iw0])) ]^-1</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    Sigma: Gf on MeshImFreq</span>
+<span class="sd">        self-energy on Matsubara mesh</span>
+
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    orb_Z: 1d numpy array</span>
+<span class="sd">        list of Z values per orbital in Sigma</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">orb_Z</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="n">iw</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">imag</span><span class="p">(</span><span class="n">n</span><span class="p">)</span> <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="n">Sigma</span><span class="o">.</span><span class="n">mesh</span><span class="p">]</span>
+    <span class="c1"># find smallest iw_n</span>
+    <span class="n">n_iw0</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="mf">0.5</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">iw</span><span class="p">))</span>
+
+    <span class="k">for</span> <span class="n">orb</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="n">Sigma</span><span class="o">.</span><span class="n">target_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
+        <span class="n">Im_S_iw</span> <span class="o">=</span> <span class="n">Sigma</span><span class="p">[</span><span class="n">orb</span><span class="p">,</span><span class="n">orb</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">imag</span>
+        <span class="c1"># simple extraction from linear fit to first two Matsubara freq of Sigma</span>
+        <span class="c1"># assuming Fermi liquid like self energy</span>
+        <span class="n">Z</span> <span class="o">=</span> <span class="mi">1</span><span class="o">/</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="p">(</span><span class="n">Im_S_iw</span><span class="p">[</span><span class="n">n_iw0</span><span class="o">+</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span><span class="n">Im_S_iw</span><span class="p">[</span><span class="n">n_iw0</span><span class="p">])</span> <span class="o">/</span> <span class="p">(</span><span class="n">iw</span><span class="p">[</span><span class="n">n_iw0</span><span class="o">+</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span><span class="n">iw</span><span class="p">[</span><span class="n">n_iw0</span><span class="p">])</span> <span class="p">)</span>
+        <span class="n">orb_Z</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">Z</span><span class="p">))</span>
+
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">orb_Z</span><span class="p">)</span></div>
+</pre></div>
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.</p>
+  </div>
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diff --git a/_modules/dmft_tools/results_to_archive.html b/_modules/dmft_tools/results_to_archive.html
new file mode 100644
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@@ -0,0 +1,443 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" >
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>dmft_tools.results_to_archive &mdash; solid_dmft  documentation</title>
+      <link rel="stylesheet" href="../../_static/pygments.css" type="text/css" />
+      <link rel="stylesheet" href="../../_static/css/custom.css" type="text/css" />
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+    <link rel="shortcut icon" href="../../_static/favicon.ico"/>
+  
+        <script data-url_root="../../" id="documentation_options" src="../../_static/documentation_options.js"></script>
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+    <script src="../../_static/js/theme.js"></script>
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+              <div class="version">
+                3.2.0
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+              <ul>
+<li class="toctree-l1"><a class="reference internal" href="../../install.html">Installation</a><ul>
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+<li class="toctree-l2"><a class="reference internal" href="../../install.html#installation-via-pip">Installation via pip</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#manual-installation-via-cmake">Manual installation via CMake</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#docker-files-images">Docker files &amp; images</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#version-compatibility">Version compatibility</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#custom-cmake-options">Custom CMake options</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../documentation.html">Documentation</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#code-structure">Code structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#dft-interface-notes">DFT interface notes</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/w90_interface.html">Wannier90 interface</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/w90_interface.html#orbital-order-in-the-w90-converter">orbital order in the W90 converter</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/vasp_csc.html">Interface to VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#general-remarks">General remarks</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#file-description">file description</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#workflow">Workflow:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#important-flags">important flags:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#version-and-compilation">version and compilation:</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#input-output">Input/Output</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../input_output/DMFT_input/input.html">Input</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/general.html">[general]: General parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/solver.html">[solver]: solver specific parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/dft.html">[dft]: DFT related inputs</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/advanced.html">[advanced]: Advanced inputs</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../input_output/DMFT_output/results.html">Output / results</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_output/results.html#group-structure">Group structure</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_output/results.html#subgroups">Subgroups</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#further-details-for-running">Further details for running</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/docker.html">Docker</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#building-the-docker-image">Building the docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#pulling-a-docker-image">Pulling a docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#module-reference-manual">Module reference manual</a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.vasp_manager.html">dft_managers.vasp_manager</a></li>
+</ul>
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+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.solver.html">dmft_tools.solver</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/io_tools.html">io_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.dict_to_h5.html">io_tools.dict_to_h5</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.postproc_toml_dict.html">io_tools.postproc_toml_dict</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.verify_input_params.html">io_tools.verify_input_params</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_gf_imp.html">postprocessing.maxent_gf_imp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_gf_latt.html">postprocessing.maxent_gf_latt</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_sigma.html">postprocessing.maxent_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../issues.html">Support &amp; contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../issues.html#seeking-help">Seeking help</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../issues.html#improving-solid-dmft">Improving solid_dmft</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../issues.html#reporting-issues">Reporting issues</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../ChangeLog.html">Changelog</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-2-0">Version 3.2.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#general">General</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#fix">fix</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-1-4">Version 3.1.4</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-1-3">Version 3.1.3</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-1-2">Version 3.1.2</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-1-0">Version 3.1.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-0-0">Version 3.0.0</a></li>
+</ul>
+</li>
+</ul>
+
+        </div>
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+    </nav>
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+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu"  style="background: #7E588A" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
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+      <li><a href="../../index.html" class="icon icon-home"></a> &raquo;</li>
+          <li><a href="../index.html">Module code</a> &raquo;</li>
+      <li>dmft_tools.results_to_archive</li>
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+      </li>
+  </ul>
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+             
+  <h1>Source code for dmft_tools.results_to_archive</h1><div class="highlight"><pre>
+<span></span><span class="c1">################################################################################</span>
+<span class="c1">#</span>
+<span class="c1"># solid_dmft - A versatile python wrapper to perform DFT+DMFT calculations</span>
+<span class="c1">#              utilizing the TRIQS software library</span>
+<span class="c1">#</span>
+<span class="c1"># Copyright (C) 2018-2020, ETH Zurich</span>
+<span class="c1"># Copyright (C) 2021, The Simons Foundation</span>
+<span class="c1">#      authors: A. Hampel, M. Merkel, and S. Beck</span>
+<span class="c1">#</span>
+<span class="c1"># solid_dmft is free software: you can redistribute it and/or modify it under the</span>
+<span class="c1"># terms of the GNU General Public License as published by the Free Software</span>
+<span class="c1"># Foundation, either version 3 of the License, or (at your option) any later</span>
+<span class="c1"># version.</span>
+<span class="c1">#</span>
+<span class="c1"># solid_dmft is distributed in the hope that it will be useful, but WITHOUT ANY</span>
+<span class="c1"># WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A</span>
+<span class="c1"># PARTICULAR PURPOSE. See the GNU General Public License for more details.</span>
+
+<span class="c1"># You should have received a copy of the GNU General Public License along with</span>
+<span class="c1"># solid_dmft (in the file COPYING.txt in this directory). If not, see</span>
+<span class="c1"># &lt;http://www.gnu.org/licenses/&gt;.</span>
+<span class="c1">#</span>
+<span class="c1">################################################################################</span>
+
+<span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">import</span> <span class="nn">triqs.utility.mpi</span> <span class="k">as</span> <span class="nn">mpi</span>
+
+
+<span class="k">def</span> <span class="nf">_compile_information</span><span class="p">(</span><span class="n">sum_k</span><span class="p">,</span> <span class="n">general_params</span><span class="p">,</span> <span class="n">solver_params</span><span class="p">,</span> <span class="n">solvers</span><span class="p">,</span> <span class="n">map_imp_solver</span><span class="p">,</span> <span class="n">solver_type_per_imp</span><span class="p">,</span>
+                         <span class="n">previous_mu</span><span class="p">,</span> <span class="n">density_mat_pre</span><span class="p">,</span> <span class="n">density_mat</span><span class="p">,</span> <span class="n">deltaN</span><span class="p">,</span> <span class="n">dens</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot; Collects all results in a dictonary. &quot;&quot;&quot;</span>
+
+    <span class="n">write_to_h5</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;chemical_potential_post&#39;</span><span class="p">:</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">chemical_potential</span><span class="p">,</span>
+                   <span class="s1">&#39;chemical_potential_pre&#39;</span><span class="p">:</span> <span class="n">previous_mu</span><span class="p">,</span>
+                   <span class="s1">&#39;DC_pot&#39;</span><span class="p">:</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">dc_imp</span><span class="p">,</span>
+                   <span class="s1">&#39;DC_energ&#39;</span><span class="p">:</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">dc_energ</span><span class="p">,</span>
+                   <span class="s1">&#39;dens_mat_pre&#39;</span><span class="p">:</span> <span class="n">density_mat_pre</span><span class="p">,</span>
+                   <span class="s1">&#39;dens_mat_post&#39;</span><span class="p">:</span> <span class="n">density_mat</span><span class="p">,</span>
+                  <span class="p">}</span>
+
+    <span class="k">if</span> <span class="n">deltaN</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">write_to_h5</span><span class="p">[</span><span class="s1">&#39;deltaN&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">deltaN</span>
+    <span class="k">if</span> <span class="n">dens</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">write_to_h5</span><span class="p">[</span><span class="s1">&#39;deltaN_trace&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">dens</span>
+
+    <span class="k">for</span> <span class="n">icrsh</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span><span class="p">):</span>
+        <span class="n">isolvsec</span> <span class="o">=</span> <span class="n">map_imp_solver</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span>
+        <span class="k">if</span> <span class="n">solver_type_per_imp</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;cthyb&#39;</span><span class="p">,</span> <span class="s1">&#39;hubbardI&#39;</span><span class="p">]:</span>
+            <span class="n">write_to_h5</span><span class="p">[</span><span class="s1">&#39;Delta_time_</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">icrsh</span><span class="p">)]</span> <span class="o">=</span> <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">Delta_time</span>
+
+            <span class="c1"># Write the full density matrix to last_iter only - it is large</span>
+            <span class="k">if</span> <span class="n">solver_params</span><span class="p">[</span><span class="n">isolvsec</span><span class="p">][</span><span class="s1">&#39;measure_density_matrix&#39;</span><span class="p">]:</span>
+                <span class="n">write_to_h5</span><span class="p">[</span><span class="s1">&#39;full_dens_mat_</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">icrsh</span><span class="p">)]</span> <span class="o">=</span> <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">density_matrix</span>
+                <span class="n">write_to_h5</span><span class="p">[</span><span class="s1">&#39;h_loc_diag_</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">icrsh</span><span class="p">)]</span> <span class="o">=</span> <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">h_loc_diagonalization</span>
+
+        <span class="k">elif</span> <span class="n">solver_type_per_imp</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;ftps&#39;</span><span class="p">:</span>
+            <span class="n">write_to_h5</span><span class="p">[</span><span class="s1">&#39;Delta_freq_</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">icrsh</span><span class="p">)]</span> <span class="o">=</span> <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">Delta_freq</span>
+
+        <span class="n">write_to_h5</span><span class="p">[</span><span class="s1">&#39;Gimp_time_</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">icrsh</span><span class="p">)]</span> <span class="o">=</span> <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">G_time</span>
+        <span class="n">write_to_h5</span><span class="p">[</span><span class="s1">&#39;G0_freq_</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">icrsh</span><span class="p">)]</span> <span class="o">=</span> <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">G0_freq</span>
+        <span class="n">write_to_h5</span><span class="p">[</span><span class="s1">&#39;Gimp_freq_</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">icrsh</span><span class="p">)]</span> <span class="o">=</span> <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">G_freq</span>
+        <span class="n">write_to_h5</span><span class="p">[</span><span class="s1">&#39;Sigma_freq_</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">icrsh</span><span class="p">)]</span> <span class="o">=</span> <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">Sigma_freq</span>
+
+        <span class="k">if</span> <span class="n">solver_type_per_imp</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;cthyb&#39;</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">solver_params</span><span class="p">[</span><span class="n">isolvsec</span><span class="p">][</span><span class="s1">&#39;measure_pert_order&#39;</span><span class="p">]:</span>
+                <span class="n">write_to_h5</span><span class="p">[</span><span class="s1">&#39;pert_order_imp_</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">icrsh</span><span class="p">)]</span> <span class="o">=</span> <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">perturbation_order</span>
+                <span class="n">write_to_h5</span><span class="p">[</span><span class="s1">&#39;pert_order_total_imp_</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">icrsh</span><span class="p">)]</span> <span class="o">=</span> <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">perturbation_order_total</span>
+
+            <span class="k">if</span> <span class="n">solver_params</span><span class="p">[</span><span class="n">isolvsec</span><span class="p">][</span><span class="s1">&#39;measure_chi&#39;</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">write_to_h5</span><span class="p">[</span><span class="s1">&#39;O_</span><span class="si">{}</span><span class="s1">_time_</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">solver_params</span><span class="p">[</span><span class="n">isolvsec</span><span class="p">][</span><span class="s1">&#39;measure_chi&#39;</span><span class="p">],</span> <span class="n">icrsh</span><span class="p">)]</span> <span class="o">=</span> <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">O_time</span>
+
+            <span class="c1"># if legendre was set, that we have both now!</span>
+            <span class="k">if</span> <span class="p">(</span><span class="n">solver_params</span><span class="p">[</span><span class="n">isolvsec</span><span class="p">][</span><span class="s1">&#39;measure_G_l&#39;</span><span class="p">]</span>
+                <span class="ow">or</span> <span class="ow">not</span> <span class="n">solver_params</span><span class="p">[</span><span class="n">isolvsec</span><span class="p">][</span><span class="s1">&#39;perform_tail_fit&#39;</span><span class="p">]</span> <span class="ow">and</span> <span class="n">solver_params</span><span class="p">[</span><span class="n">isolvsec</span><span class="p">][</span><span class="s1">&#39;legendre_fit&#39;</span><span class="p">]):</span>
+                <span class="n">write_to_h5</span><span class="p">[</span><span class="s1">&#39;G_time_orig_</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">icrsh</span><span class="p">)]</span> <span class="o">=</span> <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">G_time_orig</span>
+                <span class="n">write_to_h5</span><span class="p">[</span><span class="s1">&#39;Gimp_l_</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">icrsh</span><span class="p">)]</span> <span class="o">=</span> <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">G_l</span>
+
+        <span class="k">if</span> <span class="n">solver_type_per_imp</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;ctint&#39;</span> <span class="ow">and</span> <span class="n">solver_params</span><span class="p">[</span><span class="n">isolvsec</span><span class="p">][</span><span class="s1">&#39;measure_histogram&#39;</span><span class="p">]:</span>
+            <span class="n">write_to_h5</span><span class="p">[</span><span class="s1">&#39;pert_order_imp_</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">icrsh</span><span class="p">)]</span> <span class="o">=</span> <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">perturbation_order</span>
+
+        <span class="k">if</span> <span class="n">solver_type_per_imp</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;hubbardI&#39;</span><span class="p">:</span>
+            <span class="n">write_to_h5</span><span class="p">[</span><span class="s1">&#39;G0_Refreq_</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">icrsh</span><span class="p">)]</span> <span class="o">=</span> <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">G0_Refreq</span>
+            <span class="n">write_to_h5</span><span class="p">[</span><span class="s1">&#39;Gimp_Refreq_</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">icrsh</span><span class="p">)]</span> <span class="o">=</span> <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">G_Refreq</span>
+            <span class="n">write_to_h5</span><span class="p">[</span><span class="s1">&#39;Sigma_Refreq_</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">icrsh</span><span class="p">)]</span> <span class="o">=</span> <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">Sigma_Refreq</span>
+
+            <span class="k">if</span> <span class="n">solver_params</span><span class="p">[</span><span class="n">isolvsec</span><span class="p">][</span><span class="s1">&#39;measure_G_l&#39;</span><span class="p">]:</span>
+                <span class="n">write_to_h5</span><span class="p">[</span><span class="s1">&#39;Gimp_l_</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">icrsh</span><span class="p">)]</span> <span class="o">=</span> <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">G_l</span>
+
+        <span class="k">if</span> <span class="n">solver_type_per_imp</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;hartree&#39;</span><span class="p">:</span>
+            <span class="n">write_to_h5</span><span class="p">[</span><span class="s1">&#39;Sigma_Refreq_</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">icrsh</span><span class="p">)]</span> <span class="o">=</span> <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">Sigma_Refreq</span>
+
+        <span class="k">if</span> <span class="n">solver_type_per_imp</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;ctseg&#39;</span><span class="p">:</span>
+            <span class="c1"># if legendre was set, that we have both now!</span>
+            <span class="k">if</span> <span class="p">(</span><span class="n">solver_params</span><span class="p">[</span><span class="n">isolvsec</span><span class="p">][</span><span class="s1">&#39;measure_gl&#39;</span><span class="p">]</span> <span class="ow">or</span> <span class="n">solver_params</span><span class="p">[</span><span class="n">isolvsec</span><span class="p">][</span><span class="s1">&#39;legendre_fit&#39;</span><span class="p">]):</span>
+                <span class="n">write_to_h5</span><span class="p">[</span><span class="s1">&#39;G_time_orig_</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">icrsh</span><span class="p">)]</span> <span class="o">=</span> <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">G_time_orig</span>
+                <span class="n">write_to_h5</span><span class="p">[</span><span class="s1">&#39;Gimp_l_</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">icrsh</span><span class="p">)]</span> <span class="o">=</span> <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">G_l</span>
+            <span class="k">if</span> <span class="n">solver_params</span><span class="p">[</span><span class="n">isolvsec</span><span class="p">][</span><span class="s1">&#39;measure_ft&#39;</span><span class="p">]:</span>
+                <span class="n">write_to_h5</span><span class="p">[</span><span class="s1">&#39;F_freq_</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">icrsh</span><span class="p">)]</span> <span class="o">=</span> <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">F_freq</span>
+                <span class="n">write_to_h5</span><span class="p">[</span><span class="s1">&#39;F_time_</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">icrsh</span><span class="p">)]</span> <span class="o">=</span> <span class="n">solvers</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">F_time</span>
+
+    <span class="k">return</span> <span class="n">write_to_h5</span>
+
+<div class="viewcode-block" id="write"><a class="viewcode-back" href="../../_ref/dmft_tools.results_to_archive.html#dmft_tools.results_to_archive.write">[docs]</a><span class="k">def</span> <span class="nf">write</span><span class="p">(</span><span class="n">archive</span><span class="p">,</span> <span class="n">sum_k</span><span class="p">,</span> <span class="n">general_params</span><span class="p">,</span> <span class="n">solver_params</span><span class="p">,</span> <span class="n">solvers</span><span class="p">,</span> <span class="n">map_imp_solver</span><span class="p">,</span> <span class="n">solver_type_per_imp</span><span class="p">,</span> <span class="n">it</span><span class="p">,</span> <span class="n">is_sampling</span><span class="p">,</span>
+          <span class="n">previous_mu</span><span class="p">,</span> <span class="n">density_mat_pre</span><span class="p">,</span> <span class="n">density_mat</span><span class="p">,</span> <span class="n">deltaN</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dens</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Collects and writes results to archive.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+        <span class="k">return</span>
+
+    <span class="n">write_to_h5</span> <span class="o">=</span> <span class="n">_compile_information</span><span class="p">(</span><span class="n">sum_k</span><span class="p">,</span> <span class="n">general_params</span><span class="p">,</span> <span class="n">solver_params</span><span class="p">,</span> <span class="n">solvers</span><span class="p">,</span> <span class="n">map_imp_solver</span><span class="p">,</span> <span class="n">solver_type_per_imp</span><span class="p">,</span>
+                                       <span class="n">previous_mu</span><span class="p">,</span> <span class="n">density_mat_pre</span><span class="p">,</span> <span class="n">density_mat</span><span class="p">,</span> <span class="n">deltaN</span><span class="p">,</span> <span class="n">dens</span><span class="p">)</span>
+
+    <span class="c1"># Saves the results to last_iter</span>
+    <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_results&#39;</span><span class="p">][</span><span class="s1">&#39;iteration_count&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">it</span>
+    <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">write_to_h5</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+        <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_results/last_iter&#39;</span><span class="p">][</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
+
+    <span class="c1"># Permanently saves to h5 archive every h5_save_freq iterations</span>
+    <span class="k">if</span> <span class="p">((</span><span class="ow">not</span> <span class="n">is_sampling</span> <span class="ow">and</span> <span class="n">it</span> <span class="o">%</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;h5_save_freq&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)</span>
+            <span class="ow">or</span> <span class="p">(</span><span class="n">is_sampling</span> <span class="ow">and</span> <span class="n">it</span> <span class="o">%</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;sampling_h5_save_freq&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)):</span>
+
+        <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_results&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">create_group</span><span class="p">(</span><span class="s1">&#39;it_</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">it</span><span class="p">))</span>
+        <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">write_to_h5</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+            <span class="c1"># Full density matrix only written to last_iter - it is large</span>
+            <span class="k">if</span> <span class="s1">&#39;full_dens_mat_&#39;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">key</span> <span class="ow">and</span> <span class="s1">&#39;h_loc_diag_&#39;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">key</span><span class="p">:</span>
+                <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_results/it_</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">it</span><span class="p">)][</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
+
+        <span class="c1"># Saves CSC input</span>
+        <span class="k">if</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;csc&#39;</span><span class="p">]:</span>
+            <span class="k">for</span> <span class="n">dft_var</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;dft_update&#39;</span><span class="p">,</span> <span class="s1">&#39;dft_input&#39;</span><span class="p">,</span> <span class="s1">&#39;dft_misc_input&#39;</span><span class="p">]:</span>
+                <span class="k">if</span> <span class="n">dft_var</span> <span class="ow">in</span> <span class="n">archive</span><span class="p">:</span>
+                    <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_results/it_</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">it</span><span class="p">)]</span><span class="o">.</span><span class="n">create_group</span><span class="p">(</span><span class="n">dft_var</span><span class="p">)</span>
+                    <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">archive</span><span class="p">[</span><span class="n">dft_var</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+                        <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_results/it_</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">it</span><span class="p">)][</span><span class="n">dft_var</span><span class="p">][</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
+            <span class="k">for</span> <span class="n">band_elem</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;_bands.dat&#39;</span><span class="p">,</span> <span class="s1">&#39;_bands.dat.gnu&#39;</span><span class="p">,</span> <span class="s1">&#39;_bands.projwfc_up&#39;</span><span class="p">,</span> <span class="s1">&#39;_band.dat&#39;</span><span class="p">]:</span>
+                <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="s1">&#39;./</span><span class="si">{}{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;seedname&#39;</span><span class="p">],</span> <span class="n">band_elem</span><span class="p">)):</span>
+                    <span class="n">os</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span><span class="s1">&#39;./</span><span class="si">{}{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;seedname&#39;</span><span class="p">],</span> <span class="n">band_elem</span><span class="p">),</span>
+                              <span class="s1">&#39;./</span><span class="si">{}{}</span><span class="s1">_it</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;seedname&#39;</span><span class="p">],</span> <span class="n">band_elem</span><span class="p">,</span> <span class="n">it</span><span class="p">))</span>
+            <span class="k">for</span> <span class="n">w90_elem</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;_hr.dat&#39;</span><span class="p">,</span> <span class="s1">&#39;.wout&#39;</span><span class="p">]:</span>
+                <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="s1">&#39;./</span><span class="si">{}{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;seedname&#39;</span><span class="p">],</span> <span class="n">w90_elem</span><span class="p">)):</span>
+                    <span class="n">os</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span><span class="s1">&#39;./</span><span class="si">{}{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;seedname&#39;</span><span class="p">],</span> <span class="n">w90_elem</span><span class="p">),</span>
+                              <span class="s1">&#39;./</span><span class="si">{}</span><span class="s1">_it</span><span class="si">{}{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;seedname&#39;</span><span class="p">],</span> <span class="n">it</span><span class="p">,</span> <span class="n">w90_elem</span><span class="p">))</span></div>
+</pre></div>
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.</p>
+  </div>
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diff --git a/_modules/dmft_tools/solver.html b/_modules/dmft_tools/solver.html
new file mode 100644
index 00000000..3617ced9
--- /dev/null
+++ b/_modules/dmft_tools/solver.html
@@ -0,0 +1,1548 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" >
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>dmft_tools.solver &mdash; solid_dmft  documentation</title>
+      <link rel="stylesheet" href="../../_static/pygments.css" type="text/css" />
+      <link rel="stylesheet" href="../../_static/css/custom.css" type="text/css" />
+      <link rel="stylesheet" href="../../_static/plot_directive.css" type="text/css" />
+    <link rel="shortcut icon" href="../../_static/favicon.ico"/>
+  
+        <script data-url_root="../../" id="documentation_options" src="../../_static/documentation_options.js"></script>
+        <script src="../../_static/jquery.js"></script>
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+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
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+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
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+      <li>dmft_tools.solver</li>
+      <li class="wy-breadcrumbs-aside">
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <h1>Source code for dmft_tools.solver</h1><div class="highlight"><pre>
+<span></span><span class="c1">################################################################################</span>
+<span class="c1">#</span>
+<span class="c1"># solid_dmft - A versatile python wrapper to perform DFT+DMFT calculations</span>
+<span class="c1">#              utilizing the TRIQS software library</span>
+<span class="c1">#</span>
+<span class="c1"># Copyright (C) 2018-2020, ETH Zurich</span>
+<span class="c1"># Copyright (C) 2021, The Simons Foundation</span>
+<span class="c1">#      authors: A. Hampel, M. Merkel, and S. Beck</span>
+<span class="c1">#</span>
+<span class="c1"># solid_dmft is free software: you can redistribute it and/or modify it under the</span>
+<span class="c1"># terms of the GNU General Public License as published by the Free Software</span>
+<span class="c1"># Foundation, either version 3 of the License, or (at your option) any later</span>
+<span class="c1"># version.</span>
+<span class="c1">#</span>
+<span class="c1"># solid_dmft is distributed in the hope that it will be useful, but WITHOUT ANY</span>
+<span class="c1"># WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A</span>
+<span class="c1"># PARTICULAR PURPOSE. See the GNU General Public License for more details.</span>
+
+<span class="c1"># You should have received a copy of the GNU General Public License along with</span>
+<span class="c1"># solid_dmft (in the file COPYING.txt in this directory). If not, see</span>
+<span class="c1"># &lt;http://www.gnu.org/licenses/&gt;.</span>
+<span class="c1">#</span>
+<span class="c1">################################################################################</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">itertools</span> <span class="kn">import</span> <span class="n">product</span>
+
+<span class="kn">from</span> <span class="nn">triqs.gf</span> <span class="kn">import</span> <span class="n">MeshImTime</span><span class="p">,</span> <span class="n">MeshReTime</span><span class="p">,</span> <span class="n">MeshReFreq</span><span class="p">,</span> <span class="n">MeshLegendre</span><span class="p">,</span> <span class="n">Gf</span><span class="p">,</span> <span class="n">BlockGf</span><span class="p">,</span> <span class="n">make_hermitian</span><span class="p">,</span> <span class="n">Omega</span><span class="p">,</span> <span class="n">iOmega_n</span><span class="p">,</span> <span class="n">make_gf_from_fourier</span><span class="p">,</span> <span class="n">fit_hermitian_tail</span>
+<span class="kn">from</span> <span class="nn">triqs.gf.tools</span> <span class="kn">import</span> <span class="n">inverse</span><span class="p">,</span> <span class="n">make_zero_tail</span>
+<span class="kn">from</span> <span class="nn">triqs.gf.descriptors</span> <span class="kn">import</span> <span class="n">Fourier</span>
+<span class="kn">from</span> <span class="nn">triqs.operators</span> <span class="kn">import</span> <span class="n">c_dag</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">Operator</span>
+<span class="kn">import</span> <span class="nn">triqs.utility.mpi</span> <span class="k">as</span> <span class="nn">mpi</span>
+<span class="kn">from</span> <span class="nn">h5</span> <span class="kn">import</span> <span class="n">HDFArchive</span>
+
+<span class="kn">from</span> <span class="nn">solid_dmft.io_tools.dict_to_h5</span> <span class="kn">import</span> <span class="n">prep_params_for_h5</span>
+
+<span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">legendre_filter</span>
+<span class="kn">from</span> <span class="nn">.matheval</span> <span class="kn">import</span> <span class="n">MathExpr</span>
+
+<div class="viewcode-block" id="get_n_orbitals"><a class="viewcode-back" href="../../_ref/dmft_tools.solver.html#dmft_tools.solver.get_n_orbitals">[docs]</a><span class="k">def</span> <span class="nf">get_n_orbitals</span><span class="p">(</span><span class="n">sum_k</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    determines the number of orbitals within the</span>
+<span class="sd">    solver block structure.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    sum_k : dft_tools sumk object</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    n_orb : dict of int</span>
+<span class="sd">        number of orbitals for up / down as dict for SOC calculation</span>
+<span class="sd">        without up / down block up holds the number of orbitals</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">n_orbitals</span> <span class="o">=</span> <span class="p">[{</span><span class="s1">&#39;up&#39;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="s1">&#39;down&#39;</span><span class="p">:</span> <span class="mi">0</span><span class="p">}</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span><span class="p">)]</span>
+    <span class="k">for</span> <span class="n">icrsh</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_inequiv_shells</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">block</span><span class="p">,</span> <span class="n">n_orb</span> <span class="ow">in</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">gf_struct_solver</span><span class="p">[</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+            <span class="k">if</span> <span class="s1">&#39;down&#39;</span> <span class="ow">in</span> <span class="n">block</span><span class="p">:</span>
+                <span class="n">n_orbitals</span><span class="p">[</span><span class="n">icrsh</span><span class="p">][</span><span class="s1">&#39;down&#39;</span><span class="p">]</span> <span class="o">+=</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">gf_struct_solver</span><span class="p">[</span><span class="n">icrsh</span><span class="p">][</span><span class="n">block</span><span class="p">]</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">n_orbitals</span><span class="p">[</span><span class="n">icrsh</span><span class="p">][</span><span class="s1">&#39;up&#39;</span><span class="p">]</span> <span class="o">+=</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">gf_struct_solver</span><span class="p">[</span><span class="n">icrsh</span><span class="p">][</span><span class="n">block</span><span class="p">]</span>
+
+    <span class="k">return</span> <span class="n">n_orbitals</span></div>
+
+<span class="k">def</span> <span class="nf">_gf_fit_tail_fraction</span><span class="p">(</span><span class="n">Gf</span><span class="p">,</span> <span class="n">fraction</span><span class="o">=</span><span class="mf">0.4</span><span class="p">,</span> <span class="n">replace</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">known_moments</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    fits the tail of Gf object by making a polynomial</span>
+<span class="sd">    fit of the Gf on the given fraction of the Gf mesh</span>
+<span class="sd">    and replacing that part of the Gf by the fit</span>
+
+<span class="sd">    0.4 fits the last 40% of the Gf and replaces the</span>
+<span class="sd">    part with the tail</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    Gf : BlockGf (Green&#39;s function) object</span>
+<span class="sd">    fraction: float, optional default 0.4</span>
+<span class="sd">        fraction of the Gf to fit</span>
+<span class="sd">    replace: float, optional default fraction</span>
+<span class="sd">        fraction of the Gf to replace</span>
+<span class="sd">    known_moments: np.array</span>
+<span class="sd">        known moments as numpy array</span>
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    Gf_fit : BlockGf (Green&#39;s function) object</span>
+<span class="sd">            fitted Gf</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">Gf_fit</span> <span class="o">=</span> <span class="n">Gf</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+    <span class="c1"># if no replace factor is given use the same fraction</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">replace</span><span class="p">:</span>
+        <span class="n">replace</span> <span class="o">=</span> <span class="n">fraction</span>
+
+    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">bl</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">Gf_fit</span><span class="o">.</span><span class="n">indices</span><span class="p">):</span>
+        <span class="n">Gf_fit</span><span class="p">[</span><span class="n">bl</span><span class="p">]</span><span class="o">.</span><span class="n">mesh</span><span class="o">.</span><span class="n">set_tail_fit_parameters</span><span class="p">(</span><span class="n">tail_fraction</span><span class="o">=</span><span class="n">fraction</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">known_moments</span><span class="p">:</span>
+            <span class="n">tail</span> <span class="o">=</span> <span class="n">Gf_fit</span><span class="p">[</span><span class="n">bl</span><span class="p">]</span><span class="o">.</span><span class="n">fit_hermitian_tail</span><span class="p">(</span><span class="n">known_moments</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">tail</span> <span class="o">=</span> <span class="n">Gf_fit</span><span class="p">[</span><span class="n">bl</span><span class="p">]</span><span class="o">.</span><span class="n">fit_hermitian_tail</span><span class="p">()</span>
+        <span class="n">nmax_frac</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">Gf_fit</span><span class="p">[</span><span class="n">bl</span><span class="p">]</span><span class="o">.</span><span class="n">mesh</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">replace</span><span class="p">))</span>
+        <span class="n">Gf_fit</span><span class="p">[</span><span class="n">bl</span><span class="p">]</span><span class="o">.</span><span class="n">replace_by_tail</span><span class="p">(</span><span class="n">tail</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">n_min</span><span class="o">=</span><span class="n">nmax_frac</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">Gf_fit</span>
+
+<div class="viewcode-block" id="SolverStructure"><a class="viewcode-back" href="../../_ref/dmft_tools.solver.SolverStructure.html#dmft_tools.solver.SolverStructure">[docs]</a><span class="k">class</span> <span class="nc">SolverStructure</span><span class="p">:</span>
+
+    <span class="sa">r</span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">    Handles all solid_dmft solver objects and contains TRIQS solver instance.</span>
+
+<span class="sd">    Attributes</span>
+<span class="sd">    ----------</span>
+
+<span class="sd">    Methods</span>
+<span class="sd">    -------</span>
+<span class="sd">    solve(self, **kwargs)</span>
+<span class="sd">        solve impurity problem</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+
+<div class="viewcode-block" id="SolverStructure.__init__"><a class="viewcode-back" href="../../_ref/dmft_tools.solver.SolverStructure.__init__.html#dmft_tools.solver.SolverStructure.__init__">[docs]</a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">general_params</span><span class="p">,</span> <span class="n">solver_params</span><span class="p">,</span> <span class="n">advanced_params</span><span class="p">,</span> <span class="n">sum_k</span><span class="p">,</span> <span class="n">icrsh</span><span class="p">,</span> <span class="n">h_int</span><span class="p">,</span> <span class="n">iteration_offset</span><span class="p">,</span> <span class="n">deg_orbs_ftps</span><span class="p">):</span>
+        <span class="sa">r</span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Initialisation of the solver instance with h_int for impurity &quot;icrsh&quot; based on soliDMFT parameters.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        general_paramuters: dict</span>
+<span class="sd">                           general parameters as dict</span>
+<span class="sd">        solver_params: dict</span>
+<span class="sd">                           solver-specific parameters as dict</span>
+<span class="sd">        sum_k: triqs.dft_tools.sumk object</span>
+<span class="sd">               SumkDFT instance</span>
+<span class="sd">        icrsh: int</span>
+<span class="sd">               correlated shell index</span>
+<span class="sd">        h_int: triqs.operator object</span>
+<span class="sd">               interaction Hamiltonian of correlated shell</span>
+<span class="sd">        iteration_offset: int</span>
+<span class="sd">               number of iterations this run is based on</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">general_params</span> <span class="o">=</span> <span class="n">general_params</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span> <span class="o">=</span> <span class="n">solver_params</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">advanced_params</span> <span class="o">=</span> <span class="n">advanced_params</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">sum_k</span> <span class="o">=</span> <span class="n">sum_k</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span> <span class="o">=</span> <span class="n">icrsh</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">h_int</span> <span class="o">=</span> <span class="n">h_int</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">iteration_offset</span> <span class="o">=</span> <span class="n">iteration_offset</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">deg_orbs_ftps</span> <span class="o">=</span> <span class="n">deg_orbs_ftps</span>
+
+        <span class="c1"># Stores random_seed as MathExpr object to evaluate it at runtime</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;random_seed&#39;</span><span class="p">)</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">random_seed_generator</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">random_seed_generator</span> <span class="o">=</span> <span class="n">MathExpr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;random_seed&#39;</span><span class="p">])</span>
+
+        <span class="c1"># initialize solver object, options are cthyb</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;type&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;cthyb&#39;</span><span class="p">:</span>
+            <span class="kn">from</span> <span class="nn">triqs_cthyb.version</span> <span class="kn">import</span> <span class="n">triqs_cthyb_hash</span><span class="p">,</span> <span class="n">version</span>
+
+            <span class="c1"># sets up necessary GF objects on ImFreq</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_init_ImFreq_objects</span><span class="p">()</span>
+            <span class="c1"># sets up solver</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_create_cthyb_solver</span><span class="p">()</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">git_hash</span> <span class="o">=</span> <span class="n">triqs_cthyb_hash</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">version</span> <span class="o">=</span> <span class="n">version</span>
+
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;type&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;ctint&#39;</span><span class="p">:</span>
+            <span class="kn">from</span> <span class="nn">triqs_ctint.version</span> <span class="kn">import</span> <span class="n">triqs_ctint_hash</span><span class="p">,</span> <span class="n">version</span>
+
+            <span class="c1"># sets up necessary GF objects on ImFreq</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_init_ImFreq_objects</span><span class="p">()</span>
+            <span class="c1"># sets up solver</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_create_ctint_solver</span><span class="p">()</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;measure_histogram&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">&#39;measure_pert_order&#39;</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;use_double_insertion&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">&#39;move_double&#39;</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">git_hash</span> <span class="o">=</span> <span class="n">triqs_ctint_hash</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">version</span> <span class="o">=</span> <span class="n">version</span>
+
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;type&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;hubbardI&#39;</span><span class="p">:</span>
+            <span class="kn">from</span> <span class="nn">triqs_hubbardI.version</span> <span class="kn">import</span> <span class="n">triqs_hubbardI_hash</span><span class="p">,</span> <span class="n">version</span>
+
+            <span class="c1"># sets up necessary GF objects on ImFreq</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_init_ImFreq_objects</span><span class="p">()</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_init_ReFreq_hubbardI</span><span class="p">()</span>
+            <span class="c1"># sets up solver</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_create_hubbardI_solver</span><span class="p">()</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">git_hash</span> <span class="o">=</span> <span class="n">triqs_hubbardI_hash</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">version</span> <span class="o">=</span> <span class="n">version</span>
+
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;type&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;hartree&#39;</span><span class="p">:</span>
+            <span class="kn">from</span> <span class="nn">triqs_hartree_fock.version</span> <span class="kn">import</span> <span class="n">triqs_hartree_fock_hash</span><span class="p">,</span> <span class="n">version</span>
+
+            <span class="c1"># sets up necessary GF objects on ImFreq</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_init_ImFreq_objects</span><span class="p">()</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_init_ReFreq_hartree</span><span class="p">()</span>
+            <span class="c1"># sets up solver</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_create_hartree_solver</span><span class="p">()</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">git_hash</span> <span class="o">=</span> <span class="n">triqs_hartree_fock_hash</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">version</span> <span class="o">=</span> <span class="n">version</span>
+
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;type&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;ftps&#39;</span><span class="p">:</span>
+            <span class="kn">from</span> <span class="nn">forktps.version</span> <span class="kn">import</span> <span class="n">forktps_hash</span><span class="p">,</span> <span class="n">version</span>
+
+            <span class="c1"># additional parameters</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">bathfit_adjusted</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">iteration_offset</span> <span class="o">!=</span> <span class="mi">0</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">path_to_gs_accepted</span> <span class="o">=</span> <span class="nb">bool</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;path_to_gs&#39;</span><span class="p">])</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">convert_ftps</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;up&#39;</span><span class="p">:</span> <span class="s1">&#39;up&#39;</span><span class="p">,</span> <span class="s1">&#39;down&#39;</span><span class="p">:</span> <span class="s1">&#39;dn&#39;</span><span class="p">,</span> <span class="s1">&#39;ud&#39;</span><span class="p">:</span> <span class="s1">&#39;ud&#39;</span><span class="p">,</span> <span class="s1">&#39;ud_0&#39;</span><span class="p">:</span> <span class="s1">&#39;ud_0&#39;</span><span class="p">,</span> <span class="s1">&#39;ud_1&#39;</span><span class="p">:</span> <span class="s1">&#39;ud_1&#39;</span><span class="p">}</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">gf_struct</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sum_k</span><span class="o">.</span><span class="n">gf_struct_solver_list</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">]</span>
+            <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">block</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">gf_struct</span><span class="p">):</span>
+                <span class="n">spin</span> <span class="o">=</span> <span class="n">block</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;_&#39;</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">sum_k</span><span class="o">.</span><span class="n">corr_shells</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">][</span><span class="s1">&#39;SO&#39;</span><span class="p">]</span> <span class="k">else</span> <span class="n">block</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                <span class="c1"># FTPS solver does not know a more complicated gf_struct list of indices, so make sure the order is correct!</span>
+                <span class="n">indices</span> <span class="o">=</span> <span class="n">block</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">sum_k</span><span class="o">.</span><span class="n">corr_shells</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">][</span><span class="s1">&#39;SO&#39;</span><span class="p">]</span> <span class="k">else</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">3</span><span class="p">))</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">gf_struct</span><span class="p">[</span><span class="n">ct</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">convert_ftps</span><span class="p">[</span><span class="n">spin</span><span class="p">],</span> <span class="n">indices</span><span class="p">)</span>
+            <span class="c1"># sets up necessary GF objects on ReFreq</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_init_ReFreq_objects</span><span class="p">()</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">bathfit_adjusted</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">iteration_offset</span> <span class="o">!=</span> <span class="mi">0</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">path_to_gs_accepted</span> <span class="o">=</span> <span class="nb">bool</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;path_to_gs&#39;</span><span class="p">])</span>
+            <span class="c1"># sets up solver</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">sector_params</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">dmrg_params</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tevo_params</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">calc_me</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">calc_mapping</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_create_ftps_solver</span><span class="p">()</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">git_hash</span> <span class="o">=</span> <span class="n">forktps_hash</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">version</span> <span class="o">=</span> <span class="n">version</span>
+
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;type&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;inchworm&#39;</span><span class="p">:</span>
+
+            <span class="c1"># sets up necessary GF objects on ImFreq</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_init_ImFreq_objects</span><span class="p">()</span>
+            <span class="c1"># sets up solver</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_create_inchworm_solver</span><span class="p">()</span>
+            <span class="c1"># self.git_hash = inchworm_hash</span>
+
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;type&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;ctseg&#39;</span><span class="p">:</span>
+            <span class="kn">from</span> <span class="nn">triqs_ctseg.version</span> <span class="kn">import</span> <span class="n">triqs_ctseg_hash</span><span class="p">,</span> <span class="n">version</span>
+
+            <span class="c1"># sets up necessary GF objects on ImFreq</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_init_ImFreq_objects</span><span class="p">()</span>
+            <span class="c1"># sets up solver</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_create_ctseg_solver</span><span class="p">()</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">git_hash</span> <span class="o">=</span> <span class="n">triqs_ctseg_hash</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">version</span> <span class="o">=</span> <span class="n">version</span></div>
+
+    <span class="c1"># ********************************************************************</span>
+    <span class="c1"># initialize Freq and Time objects</span>
+    <span class="c1"># ********************************************************************</span>
+
+    <span class="k">def</span> <span class="nf">_init_ImFreq_objects</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="sa">r</span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Initialize all ImFreq objects</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+
+        <span class="c1"># create all ImFreq instances</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">n_iw</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;n_iw&#39;</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sum_k</span><span class="o">.</span><span class="n">block_structure</span><span class="o">.</span><span class="n">create_gf</span><span class="p">(</span><span class="n">ish</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">,</span> <span class="n">gf_function</span><span class="o">=</span><span class="n">Gf</span><span class="p">,</span> <span class="n">space</span><span class="o">=</span><span class="s1">&#39;solver&#39;</span><span class="p">,</span>
+                                                           <span class="n">mesh</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">sum_k</span><span class="o">.</span><span class="n">mesh</span><span class="p">)</span>
+        <span class="c1"># copy</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">Sigma_freq</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">G0_freq</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">G_freq_unsym</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">Delta_freq</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+
+        <span class="c1"># create all ImTime instances</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">n_tau</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;n_tau&#39;</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">G_time</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sum_k</span><span class="o">.</span><span class="n">block_structure</span><span class="o">.</span><span class="n">create_gf</span><span class="p">(</span><span class="n">ish</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">,</span> <span class="n">gf_function</span><span class="o">=</span><span class="n">Gf</span><span class="p">,</span> <span class="n">space</span><span class="o">=</span><span class="s1">&#39;solver&#39;</span><span class="p">,</span>
+                                                           <span class="n">mesh</span><span class="o">=</span><span class="n">MeshImTime</span><span class="p">(</span><span class="n">beta</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;beta&#39;</span><span class="p">],</span>
+                                                                           <span class="n">S</span><span class="o">=</span><span class="s1">&#39;Fermion&#39;</span><span class="p">,</span> <span class="n">n_tau</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_tau</span><span class="p">)</span>
+                                                           <span class="p">)</span>
+        <span class="c1"># copy</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">Delta_time</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">G_time</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+
+        <span class="c1"># create all Legendre instances</span>
+        <span class="k">if</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;type&#39;</span><span class="p">]</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;cthyb&#39;</span><span class="p">,</span> <span class="s1">&#39;ctseg&#39;</span><span class="p">]</span>
+                <span class="ow">and</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;measure_G_l&#39;</span><span class="p">]</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;legendre_fit&#39;</span><span class="p">])</span>
+                <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;type&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;hubbardI&#39;</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;measure_G_l&#39;</span><span class="p">]):</span>
+
+            <span class="bp">self</span><span class="o">.</span><span class="n">n_l</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;n_l&#39;</span><span class="p">]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">G_l</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sum_k</span><span class="o">.</span><span class="n">block_structure</span><span class="o">.</span><span class="n">create_gf</span><span class="p">(</span><span class="n">ish</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">,</span> <span class="n">gf_function</span><span class="o">=</span><span class="n">Gf</span><span class="p">,</span> <span class="n">space</span><span class="o">=</span><span class="s1">&#39;solver&#39;</span><span class="p">,</span>
+                                                            <span class="n">mesh</span><span class="o">=</span><span class="n">MeshLegendre</span><span class="p">(</span><span class="n">beta</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;beta&#39;</span><span class="p">],</span>
+                                                                              <span class="n">max_n</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_l</span><span class="p">,</span> <span class="n">S</span><span class="o">=</span><span class="s1">&#39;Fermion&#39;</span><span class="p">)</span>
+                                                            <span class="p">)</span>
+            <span class="c1"># move original G_freq to G_freq_orig</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">G_time_orig</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">G_time</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;type&#39;</span><span class="p">]</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;cthyb&#39;</span><span class="p">,</span> <span class="s1">&#39;hubbardI&#39;</span><span class="p">]</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;measure_density_matrix&#39;</span><span class="p">]:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">density_matrix</span> <span class="o">=</span> <span class="kc">None</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">h_loc_diagonalization</span> <span class="o">=</span> <span class="kc">None</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;type&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;cthyb&#39;</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;measure_chi&#39;</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">O_time</span> <span class="o">=</span> <span class="kc">None</span>
+
+    <span class="k">def</span> <span class="nf">_init_ReFreq_objects</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="sa">r</span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Initialize all ReFreq objects</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+
+        <span class="c1"># create all ReFreq instances</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">n_w</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;n_w&#39;</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sum_k</span><span class="o">.</span><span class="n">block_structure</span><span class="o">.</span><span class="n">create_gf</span><span class="p">(</span><span class="n">ish</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">,</span> <span class="n">gf_function</span><span class="o">=</span><span class="n">Gf</span><span class="p">,</span> <span class="n">space</span><span class="o">=</span><span class="s1">&#39;solver&#39;</span><span class="p">,</span>
+                                                           <span class="n">mesh</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">sum_k</span><span class="o">.</span><span class="n">mesh</span><span class="p">)</span>
+        <span class="c1"># copy</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">Sigma_freq</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">G0_freq</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">Delta_freq</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">G_freq_unsym</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+
+        <span class="c1"># create another Delta_freq for the solver, which uses different spin indices</span>
+        <span class="n">n_orb</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sum_k</span><span class="o">.</span><span class="n">corr_shells</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">][</span><span class="s1">&#39;dim&#39;</span><span class="p">]</span>
+        <span class="n">n_orb</span> <span class="o">=</span> <span class="n">n_orb</span><span class="o">//</span><span class="mi">2</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">sum_k</span><span class="o">.</span><span class="n">corr_shells</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">][</span><span class="s1">&#39;SO&#39;</span><span class="p">]</span> <span class="k">else</span> <span class="n">n_orb</span>
+        <span class="n">gf</span> <span class="o">=</span> <span class="n">Gf</span><span class="p">(</span><span class="n">target_shape</span> <span class="o">=</span> <span class="p">(</span><span class="n">n_orb</span><span class="p">,</span> <span class="n">n_orb</span><span class="p">),</span> <span class="n">mesh</span><span class="o">=</span><span class="n">MeshReFreq</span><span class="p">(</span><span class="n">n_w</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_w</span><span class="p">,</span> <span class="n">window</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;w_range&#39;</span><span class="p">]))</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">Delta_freq_solver</span> <span class="o">=</span> <span class="n">BlockGf</span><span class="p">(</span><span class="n">name_list</span> <span class="o">=</span><span class="nb">tuple</span><span class="p">([</span><span class="n">block</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">block</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">gf_struct</span><span class="p">]),</span> <span class="n">block_list</span> <span class="o">=</span> <span class="p">(</span><span class="n">gf</span><span class="p">,</span> <span class="n">gf</span><span class="p">),</span> <span class="n">make_copies</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span>
+
+        <span class="c1"># create all ReTime instances</span>
+        <span class="c1"># FIXME: dummy G_time, since time_steps will be recalculated during run</span>
+        <span class="c1">#time_steps = int(2 * self.solver_params[&#39;time_steps&#39;] * self.solver_params[&#39;refine_factor&#39;]) if self.solver_params[&#39;n_bath&#39;] != 0 else int(2 * self.solver_params[&#39;time_steps&#39;])</span>
+        <span class="n">time_steps</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="mi">1</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;refine_factor&#39;</span><span class="p">])</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;n_bath&#39;</span><span class="p">]</span> <span class="o">!=</span> <span class="mi">0</span> <span class="k">else</span> <span class="nb">int</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">G_time</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sum_k</span><span class="o">.</span><span class="n">block_structure</span><span class="o">.</span><span class="n">create_gf</span><span class="p">(</span><span class="n">ish</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">,</span> <span class="n">gf_function</span><span class="o">=</span><span class="n">Gf</span><span class="p">,</span> <span class="n">space</span><span class="o">=</span><span class="s1">&#39;solver&#39;</span><span class="p">,</span>
+                                                           <span class="n">mesh</span><span class="o">=</span><span class="n">MeshReTime</span><span class="p">(</span><span class="n">n_t</span><span class="o">=</span><span class="n">time_steps</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span>
+                                                           <span class="n">window</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="n">time_steps</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;dt&#39;</span><span class="p">]])</span>
+                                                           <span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">_init_ReFreq_hubbardI</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="sa">r</span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Initialize all ReFreq objects</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+
+        <span class="c1"># create all ReFreq instances</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">n_w</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;n_w&#39;</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">G_Refreq</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sum_k</span><span class="o">.</span><span class="n">block_structure</span><span class="o">.</span><span class="n">create_gf</span><span class="p">(</span><span class="n">ish</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">,</span> <span class="n">gf_function</span><span class="o">=</span><span class="n">Gf</span><span class="p">,</span> <span class="n">space</span><span class="o">=</span><span class="s1">&#39;solver&#39;</span><span class="p">,</span>
+                                                             <span class="n">mesh</span><span class="o">=</span><span class="n">MeshReFreq</span><span class="p">(</span><span class="n">n_w</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_w</span><span class="p">,</span> <span class="n">window</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;w_range&#39;</span><span class="p">])</span>
+                                                             <span class="p">)</span>
+        <span class="c1"># copy</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">Sigma_Refreq</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">G_Refreq</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">G0_Refreq</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">G_Refreq</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+
+    <span class="k">def</span> <span class="nf">_init_ReFreq_hartree</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="sa">r</span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Initialize all ReFreq objects</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+
+        <span class="c1"># create all ReFreq instances</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">n_w</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;n_w&#39;</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">Sigma_Refreq</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sum_k</span><span class="o">.</span><span class="n">block_structure</span><span class="o">.</span><span class="n">create_gf</span><span class="p">(</span><span class="n">ish</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">,</span> <span class="n">gf_function</span><span class="o">=</span><span class="n">Gf</span><span class="p">,</span> <span class="n">space</span><span class="o">=</span><span class="s1">&#39;solver&#39;</span><span class="p">,</span>
+                                                                 <span class="n">mesh</span><span class="o">=</span><span class="n">MeshReFreq</span><span class="p">(</span><span class="n">n_w</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_w</span><span class="p">,</span> <span class="n">window</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;w_range&#39;</span><span class="p">])</span>
+                                                                 <span class="p">)</span>
+
+    <span class="c1"># ********************************************************************</span>
+    <span class="c1"># solver-specific solve() command</span>
+    <span class="c1"># ********************************************************************</span>
+
+<div class="viewcode-block" id="SolverStructure.solve"><a class="viewcode-back" href="../../_ref/dmft_tools.solver.SolverStructure.solve.html#dmft_tools.solver.SolverStructure.solve">[docs]</a>    <span class="k">def</span> <span class="nf">solve</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="sa">r</span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        solve impurity problem with current solver</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">random_seed_generator</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver_params</span><span class="p">[</span><span class="s1">&#39;random_seed&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">random_seed_generator</span><span class="p">(</span><span class="n">it</span><span class="o">=</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;it&quot;</span><span class="p">],</span> <span class="n">rank</span><span class="o">=</span><span class="n">mpi</span><span class="o">.</span><span class="n">rank</span><span class="p">))</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">assert</span> <span class="s1">&#39;random_seed&#39;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver_params</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;type&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;cthyb&#39;</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;delta_interface&#39;</span><span class="p">]:</span>
+                <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1"> Using the delta interface for cthyb passing Delta(tau) and Hloc0 directly.&#39;</span><span class="p">)</span>
+                 <span class="c1"># prepare solver input</span>
+                <span class="n">sumk_eal</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sum_k</span><span class="o">.</span><span class="n">eff_atomic_levels</span><span class="p">()[</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">]</span>
+                <span class="n">solver_eal</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sum_k</span><span class="o">.</span><span class="n">block_structure</span><span class="o">.</span><span class="n">convert_matrix</span><span class="p">(</span><span class="n">sumk_eal</span><span class="p">,</span> <span class="n">space_from</span><span class="o">=</span><span class="s1">&#39;sumk&#39;</span><span class="p">,</span> <span class="n">ish_from</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">sum_k</span><span class="o">.</span><span class="n">inequiv_to_corr</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">])</span>
+                <span class="c1"># fill Delta_time from Delta_freq sum_k to solver</span>
+                <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">g0</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">G0_freq</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">Delta_freq</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">&lt;&lt;</span> <span class="n">iOmega_n</span> <span class="o">-</span> <span class="n">inverse</span><span class="p">(</span><span class="n">g0</span><span class="p">)</span> <span class="o">-</span> <span class="n">solver_eal</span><span class="p">[</span><span class="n">name</span><span class="p">]</span>
+                    <span class="n">known_moments</span> <span class="o">=</span> <span class="n">make_zero_tail</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">Delta_freq</span><span class="p">[</span><span class="n">name</span><span class="p">],</span> <span class="mi">1</span><span class="p">)</span>
+                    <span class="n">tail</span><span class="p">,</span> <span class="n">err</span> <span class="o">=</span> <span class="n">fit_hermitian_tail</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">Delta_freq</span><span class="p">[</span><span class="n">name</span><span class="p">],</span> <span class="n">known_moments</span><span class="p">)</span>
+                    <span class="c1"># without SOC delta_tau needs to be real</span>
+                    <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">sum_k</span><span class="o">.</span><span class="n">SO</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+                        <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">Delta_tau</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">&lt;&lt;</span> <span class="n">make_gf_from_fourier</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">Delta_freq</span><span class="p">[</span><span class="n">name</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">Delta_tau</span><span class="o">.</span><span class="n">mesh</span><span class="p">,</span> <span class="n">tail</span><span class="p">)</span><span class="o">.</span><span class="n">real</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">Delta_tau</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">&lt;&lt;</span> <span class="n">make_gf_from_fourier</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">Delta_freq</span><span class="p">[</span><span class="n">name</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">Delta_tau</span><span class="o">.</span><span class="n">mesh</span><span class="p">,</span> <span class="n">tail</span><span class="p">)</span>
+
+                <span class="c1"># Make non-interacting operator for Hloc0</span>
+                <span class="n">Hloc_0</span> <span class="o">=</span> <span class="n">Operator</span><span class="p">()</span>
+                <span class="k">for</span> <span class="n">spin</span><span class="p">,</span> <span class="n">spin_block</span> <span class="ow">in</span> <span class="n">solver_eal</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+                    <span class="k">for</span> <span class="n">o1</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">spin_block</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
+                        <span class="k">for</span> <span class="n">o2</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">spin_block</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]):</span>
+                            <span class="c1"># check if off-diag element is larger than threshold</span>
+                            <span class="k">if</span> <span class="n">o1</span> <span class="o">!=</span> <span class="n">o2</span> <span class="ow">and</span> <span class="nb">abs</span><span class="p">(</span><span class="n">spin_block</span><span class="p">[</span><span class="n">o1</span><span class="p">,</span><span class="n">o2</span><span class="p">])</span> <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;off_diag_threshold&#39;</span><span class="p">]:</span>
+                                <span class="k">continue</span>
+                            <span class="k">else</span><span class="p">:</span>
+                                <span class="c1"># TODO: adapt for SOC calculations, which should keep the imag part</span>
+                                <span class="n">Hloc_0</span> <span class="o">+=</span> <span class="n">spin_block</span><span class="p">[</span><span class="n">o1</span><span class="p">,</span><span class="n">o2</span><span class="p">]</span><span class="o">.</span><span class="n">real</span><span class="o">/</span><span class="mi">2</span> <span class="o">*</span> <span class="p">(</span><span class="n">c_dag</span><span class="p">(</span><span class="n">spin</span><span class="p">,</span><span class="n">o1</span><span class="p">)</span> <span class="o">*</span> <span class="n">c</span><span class="p">(</span><span class="n">spin</span><span class="p">,</span><span class="n">o2</span><span class="p">)</span> <span class="o">+</span> <span class="n">c_dag</span><span class="p">(</span><span class="n">spin</span><span class="p">,</span><span class="n">o2</span><span class="p">)</span> <span class="o">*</span> <span class="n">c</span><span class="p">(</span><span class="n">spin</span><span class="p">,</span><span class="n">o1</span><span class="p">))</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver_params</span><span class="p">[</span><span class="s1">&#39;h_loc0&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">Hloc_0</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="c1"># fill G0_freq from sum_k to solver</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">G0_iw</span> <span class="o">&lt;&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">G0_freq</span>
+
+            <span class="c1"># update solver in h5 archive one last time for debugging if solve command crashes</span>
+            <span class="k">if</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+                <span class="k">with</span> <span class="n">HDFArchive</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;jobname&#39;</span><span class="p">]</span><span class="o">+</span><span class="s1">&#39;/&#39;</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;seedname&#39;</span><span class="p">]</span><span class="o">+</span><span class="s1">&#39;.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;a&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">archive</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="ow">not</span> <span class="s1">&#39;it_-1&#39;</span> <span class="ow">in</span> <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_input/solver&#39;</span><span class="p">]:</span>
+                        <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_input/solver&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">create_group</span><span class="p">(</span><span class="s1">&#39;it_-1&#39;</span><span class="p">)</span>
+                    <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_input/solver/it_-1&#39;</span><span class="p">][</span><span class="sa">f</span><span class="s1">&#39;triqs_solver_params_</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">prep_params_for_h5</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver_params</span><span class="p">)</span>
+                    <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_input/solver/it_-1&#39;</span><span class="p">][</span><span class="s1">&#39;mpi_size&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">mpi</span><span class="o">.</span><span class="n">size</span>
+                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;store_solver&#39;</span><span class="p">]:</span>
+                        <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_input/solver/it_-1&#39;</span><span class="p">][</span><span class="sa">f</span><span class="s1">&#39;S_</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span>
+            <span class="n">mpi</span><span class="o">.</span><span class="n">barrier</span><span class="p">()</span>
+
+            <span class="c1"># Solve the impurity problem for icrsh shell</span>
+            <span class="c1"># *************************************</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">solve</span><span class="p">(</span><span class="n">h_int</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">h_int</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver_params</span><span class="p">)</span>
+            <span class="c1"># *************************************</span>
+
+            <span class="c1"># call postprocessing</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_cthyb_postprocessing</span><span class="p">()</span>
+
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;type&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;ctint&#39;</span><span class="p">:</span>
+            <span class="c1"># fill G0_freq from sum_k to solver</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">G0_iw</span> <span class="o">&lt;&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">G0_freq</span>
+
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;h_int_type&#39;</span><span class="p">][</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;dynamic&#39;</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">b1</span><span class="p">,</span> <span class="n">b2</span> <span class="ow">in</span> <span class="n">product</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sum_k</span><span class="o">.</span><span class="n">gf_struct_solver_dict</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">keys</span><span class="p">(),</span> <span class="n">repeat</span><span class="o">=</span><span class="mi">2</span><span class="p">):</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">D0_iw</span><span class="p">[</span><span class="n">b1</span><span class="p">,</span><span class="n">b2</span><span class="p">]</span> <span class="o">&lt;&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">U_iw</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">]</span>
+
+            <span class="c1"># Solve the impurity problem for icrsh shell</span>
+            <span class="c1"># *************************************</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">solve</span><span class="p">(</span><span class="n">h_int</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">h_int</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver_params</span><span class="p">)</span>
+            <span class="c1"># *************************************</span>
+
+            <span class="c1"># call postprocessing</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_ctint_postprocessing</span><span class="p">()</span>
+
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;type&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;hubbardI&#39;</span><span class="p">:</span>
+            <span class="c1"># fill G0_freq from sum_k to solver</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">G0_iw</span> <span class="o">&lt;&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">G0_freq</span>
+
+            <span class="c1"># Solve the impurity problem for icrsh shell</span>
+            <span class="c1"># *************************************</span>
+            <span class="c1"># this is done on every node due to very slow bcast of the AtomDiag object as of now</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">solve</span><span class="p">(</span><span class="n">h_int</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">h_int</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver_params</span><span class="p">)</span>
+            <span class="c1"># if density matrix is measured, get this too. Needs to be done here,</span>
+            <span class="c1"># because solver property &#39;dm&#39; is not initialized/broadcastable</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;measure_density_matrix&#39;</span><span class="p">]:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">density_matrix</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">dm</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">h_loc_diagonalization</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">ad</span>
+            <span class="c1"># *************************************</span>
+
+            <span class="c1"># call postprocessing</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_hubbardI_postprocessing</span><span class="p">()</span>
+
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;type&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;hartree&#39;</span><span class="p">:</span>
+            <span class="c1"># fill G0_freq from sum_k to solver</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">G0_iw</span> <span class="o">&lt;&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">G0_freq</span>
+
+            <span class="c1"># Solve the impurity problem for icrsh shell</span>
+            <span class="c1"># *************************************</span>
+            <span class="c1"># this is done on every node due to very slow bcast of the AtomDiag object as of now</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">solve</span><span class="p">(</span><span class="n">h_int</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">h_int</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver_params</span><span class="p">)</span>
+
+            <span class="c1"># call postprocessing</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_hartree_postprocessing</span><span class="p">()</span>
+
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;type&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;ftps&#39;</span><span class="p">:</span>
+            <span class="kn">import</span> <span class="nn">forktps</span> <span class="k">as</span> <span class="nn">ftps</span>
+            <span class="kn">from</span> <span class="nn">forktps.DiscreteBath</span> <span class="kn">import</span> <span class="n">DiscretizeBath</span><span class="p">,</span> <span class="n">TimeStepEstimation</span>
+            <span class="kn">from</span> <span class="nn">forktps.BathFitting</span> <span class="kn">import</span> <span class="n">BathFitter</span>
+            <span class="kn">from</span> <span class="nn">forktps.Helpers</span> <span class="kn">import</span> <span class="n">MakeGFstruct</span>
+            <span class="c1"># from . import OffDiagFitter as off_fitter</span>
+
+            <span class="k">def</span> <span class="nf">make_positive_definite</span><span class="p">(</span><span class="n">G</span><span class="p">):</span>
+                <span class="c1"># ensure that Delta is positive definite</span>
+                <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">gf</span> <span class="ow">in</span> <span class="n">G</span><span class="p">:</span>
+                    <span class="k">for</span> <span class="n">orb</span><span class="p">,</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">product</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">gf</span><span class="o">.</span><span class="n">target_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">gf</span><span class="o">.</span><span class="n">mesh</span><span class="p">):</span>
+                        <span class="k">if</span> <span class="n">gf</span><span class="p">[</span><span class="n">orb</span><span class="p">,</span><span class="n">orb</span><span class="p">][</span><span class="n">w</span><span class="p">]</span><span class="o">.</span><span class="n">imag</span> <span class="o">&gt;</span> <span class="mf">0.0</span><span class="p">:</span>
+                            <span class="n">gf</span><span class="p">[</span><span class="n">orb</span><span class="p">,</span><span class="n">orb</span><span class="p">][</span><span class="n">w</span><span class="p">]</span> <span class="o">=</span> <span class="n">gf</span><span class="p">[</span><span class="n">orb</span><span class="p">,</span><span class="n">orb</span><span class="p">][</span><span class="n">w</span><span class="p">]</span><span class="o">.</span><span class="n">real</span> <span class="o">+</span> <span class="mf">0.0</span><span class="n">j</span>
+                <span class="k">return</span> <span class="n">G</span>
+
+            <span class="c1"># create h_loc solver object</span>
+            <span class="n">h_loc</span> <span class="o">=</span> <span class="n">ftps</span><span class="o">.</span><span class="n">solver_core</span><span class="o">.</span><span class="n">Hloc</span><span class="p">(</span><span class="n">MakeGFstruct</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">Delta_freq_solver</span><span class="p">),</span> <span class="n">SO</span><span class="o">=</span><span class="nb">bool</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sum_k</span><span class="o">.</span><span class="n">corr_shells</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">][</span><span class="s1">&#39;SO&#39;</span><span class="p">]))</span>
+            <span class="c1"># need eff_atomic_levels</span>
+            <span class="n">sumk_eal</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sum_k</span><span class="o">.</span><span class="n">eff_atomic_levels</span><span class="p">()[</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">]</span>
+
+            <span class="c1"># fill Delta_time from Delta_freq sum_k to solver</span>
+            <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">g0</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">G0_freq</span><span class="p">:</span>
+                <span class="n">spin</span> <span class="o">=</span> <span class="n">name</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;_&#39;</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">sum_k</span><span class="o">.</span><span class="n">corr_shells</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">][</span><span class="s1">&#39;SO&#39;</span><span class="p">]</span> <span class="k">else</span> <span class="n">name</span>
+                <span class="n">ftps_name</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">convert_ftps</span><span class="p">[</span><span class="n">spin</span><span class="p">]</span>
+                <span class="n">solver_eal</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sum_k</span><span class="o">.</span><span class="n">block_structure</span><span class="o">.</span><span class="n">convert_matrix</span><span class="p">(</span><span class="n">sumk_eal</span><span class="p">,</span> <span class="n">space_from</span><span class="o">=</span><span class="s1">&#39;sumk&#39;</span><span class="p">,</span> <span class="n">ish_from</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">sum_k</span><span class="o">.</span><span class="n">inequiv_to_corr</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">])[</span><span class="n">name</span><span class="p">]</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">Delta_freq</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">&lt;&lt;</span> <span class="n">Omega</span> <span class="o">+</span> <span class="mi">1</span><span class="n">j</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;eta&#39;</span><span class="p">]</span> <span class="o">-</span> <span class="n">inverse</span><span class="p">(</span><span class="n">g0</span><span class="p">)</span> <span class="o">-</span> <span class="n">solver_eal</span>
+                <span class="c1"># solver Delta is symmetrized by just using &#39;up_0&#39; channel</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">Delta_freq_solver</span><span class="p">[</span><span class="n">ftps_name</span><span class="p">]</span> <span class="o">&lt;&lt;</span> <span class="n">Omega</span> <span class="o">+</span> <span class="mi">1</span><span class="n">j</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;eta&#39;</span><span class="p">]</span> <span class="o">-</span> <span class="n">inverse</span><span class="p">(</span><span class="n">g0</span><span class="p">)</span> <span class="o">-</span> <span class="n">solver_eal</span>
+
+            <span class="c1"># ensure that Delta is positive definite</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">Delta_freq_solver</span> <span class="o">=</span> <span class="n">make_positive_definite</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">Delta_freq_solver</span><span class="p">)</span>
+
+            <span class="c1"># remove off-diagonal terms</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;diag_delta&#39;</span><span class="p">]:</span>
+                <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">delta</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">Delta_freq_solver</span><span class="p">:</span>
+                    <span class="k">for</span> <span class="n">i_orb</span><span class="p">,</span> <span class="n">j_orb</span> <span class="ow">in</span> <span class="n">product</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">delta</span><span class="o">.</span><span class="n">target_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span><span class="nb">range</span><span class="p">(</span><span class="n">delta</span><span class="o">.</span><span class="n">target_shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])):</span>
+                        <span class="k">if</span> <span class="n">i_orb</span> <span class="o">!=</span> <span class="n">j_orb</span><span class="p">:</span>
+                            <span class="n">delta</span><span class="p">[</span><span class="n">i_orb</span><span class="p">,</span><span class="n">j_orb</span><span class="p">]</span> <span class="o">&lt;&lt;</span> <span class="mf">0.0</span> <span class="o">+</span> <span class="mf">0.0</span><span class="n">j</span>
+
+            <span class="c1"># option to increase bath sites, but run with previous eta to get increased accuracy</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;n_bath&#39;</span><span class="p">]</span> <span class="o">!=</span> <span class="mi">0</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;refine_factor&#39;</span><span class="p">]</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">bathfit_adjusted</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">bathfit_adjusted</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">iteration_offset</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;Rescaling &quot;n_bath&quot; with a factor of </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;refine_factor&#39;</span><span class="p">]))</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;n_bath&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;refine_factor&#39;</span><span class="p">]</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;n_bath&#39;</span><span class="p">])</span>
+
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;bath_fit&#39;</span><span class="p">]:</span>
+
+                <span class="c1"># bathfitter</span>
+                <span class="c1"># FIXME: this is temporary, since off-diagonal Bathfitter is not yet integrated in FTPS</span>
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">sum_k</span><span class="o">.</span><span class="n">corr_shells</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">][</span><span class="s1">&#39;SO&#39;</span><span class="p">]:</span>
+                    <span class="n">fitter</span> <span class="o">=</span> <span class="n">off_fitter</span><span class="o">.</span><span class="n">OffDiagBathFitter</span><span class="p">(</span><span class="n">Nb</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;n_bath&#39;</span><span class="p">])</span> <span class="k">if</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;refine_factor&#39;</span><span class="p">]</span> <span class="o">!=</span> <span class="mi">1</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;n_bath&#39;</span><span class="p">]</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">)</span> <span class="k">else</span> <span class="n">off_fitter</span><span class="o">.</span><span class="n">OffDiagBathFitter</span><span class="p">(</span><span class="n">Nb</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+                    <span class="n">Delta_discrete</span> <span class="o">=</span> <span class="n">fitter</span><span class="o">.</span><span class="n">FitBath</span><span class="p">(</span><span class="n">Delta</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">Delta_freq_solver</span><span class="p">,</span> <span class="n">eta</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;eta&#39;</span><span class="p">],</span> <span class="n">ignoreWeight</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;ignore_weight&#39;</span><span class="p">],</span>
+                                                    <span class="n">SO</span><span class="o">=</span><span class="nb">bool</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sum_k</span><span class="o">.</span><span class="n">corr_shells</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">][</span><span class="s1">&#39;SO&#39;</span><span class="p">]))</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">fitter</span> <span class="o">=</span> <span class="n">BathFitter</span><span class="p">(</span><span class="n">Nb</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;n_bath&#39;</span><span class="p">])</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;n_bath&#39;</span><span class="p">]</span> <span class="o">!=</span> <span class="mi">0</span> <span class="k">else</span> <span class="n">BathFitter</span><span class="p">(</span><span class="n">Nb</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+                    <span class="n">Delta_discrete</span> <span class="o">=</span> <span class="n">fitter</span><span class="o">.</span><span class="n">FitBath</span><span class="p">(</span><span class="n">Delta</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">Delta_freq_solver</span><span class="p">,</span> <span class="n">eta</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;eta&#39;</span><span class="p">],</span> <span class="n">ignoreWeight</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;ignore_weight&#39;</span><span class="p">])</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="c1"># discretizebath</span>
+                <span class="n">gap_interval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;enforce_gap&#39;</span><span class="p">]</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;enforce_gap&#39;</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="kc">None</span>
+                <span class="n">Delta_discrete</span> <span class="o">=</span> <span class="n">DiscretizeBath</span><span class="p">(</span><span class="n">Delta</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">Delta_freq_solver</span><span class="p">,</span> <span class="n">Nb</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;n_bath&#39;</span><span class="p">],</span> <span class="n">gap</span><span class="o">=</span><span class="n">gap_interval</span><span class="p">,</span>
+                                                <span class="n">SO</span><span class="o">=</span><span class="nb">bool</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sum_k</span><span class="o">.</span><span class="n">corr_shells</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">][</span><span class="s1">&#39;SO&#39;</span><span class="p">]))</span>
+
+            <span class="c1"># should be done only once after the first iteration</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;n_bath&#39;</span><span class="p">]</span> <span class="o">!=</span> <span class="mi">0</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;refine_factor&#39;</span><span class="p">]</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">bathfit_adjusted</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">bathfit_adjusted</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">iteration_offset</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;Rescaling &quot;1/eta&quot; with a factor of </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;refine_factor&#39;</span><span class="p">]))</span>
+                    <span class="c1"># rescaling eta</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;eta&#39;</span><span class="p">]</span> <span class="o">/=</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;refine_factor&#39;</span><span class="p">]</span>
+
+                    <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">bathfit_adjusted</span><span class="p">:</span>
+                        <span class="bp">self</span><span class="o">.</span><span class="n">bathfit_adjusted</span> <span class="o">=</span> <span class="kc">True</span>
+
+            <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">b</span> <span class="o">=</span> <span class="n">Delta_discrete</span>
+            <span class="c1"># calculate time_steps</span>
+            <span class="n">time_steps</span> <span class="o">=</span> <span class="n">TimeStepEstimation</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">b</span><span class="p">,</span> <span class="n">eta</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;eta&#39;</span><span class="p">],</span> <span class="n">dt</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;dt&#39;</span><span class="p">])</span>
+            <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;TimeStepEstimation returned </span><span class="si">{}</span><span class="s1"> with given bath, &quot;eta&quot; = </span><span class="si">{}</span><span class="s1"> and &quot;dt&quot; = </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">time_steps</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;eta&#39;</span><span class="p">],</span>
+                                                                                                         <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;dt&#39;</span><span class="p">]))</span>
+            <span class="c1"># need to update tevo_params and G_time</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">tevo_params</span><span class="o">.</span><span class="n">time_steps</span> <span class="o">=</span> <span class="n">time_steps</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">G_time</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sum_k</span><span class="o">.</span><span class="n">block_structure</span><span class="o">.</span><span class="n">create_gf</span><span class="p">(</span><span class="n">ish</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">,</span> <span class="n">gf_function</span><span class="o">=</span><span class="n">Gf</span><span class="p">,</span> <span class="n">space</span><span class="o">=</span><span class="s1">&#39;solver&#39;</span><span class="p">,</span>
+                                                               <span class="n">mesh</span><span class="o">=</span><span class="n">MeshReTime</span><span class="p">(</span><span class="n">n_t</span><span class="o">=</span><span class="mi">2</span><span class="o">*</span><span class="n">time_steps</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span>
+                                                                               <span class="n">window</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">2</span><span class="o">*</span><span class="n">time_steps</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;dt&#39;</span><span class="p">]])</span>
+                                                               <span class="p">)</span>
+
+
+            <span class="c1"># fill Hloc FTPS object</span>
+            <span class="c1"># get hloc_dft from effective atomic levels</span>
+            <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">gf</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">Delta_freq</span><span class="p">:</span>
+                <span class="n">solver_eal</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sum_k</span><span class="o">.</span><span class="n">block_structure</span><span class="o">.</span><span class="n">convert_matrix</span><span class="p">(</span><span class="n">sumk_eal</span><span class="p">,</span> <span class="n">space_from</span><span class="o">=</span><span class="s1">&#39;sumk&#39;</span><span class="p">,</span> <span class="n">ish_from</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">sum_k</span><span class="o">.</span><span class="n">inequiv_to_corr</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">])[</span><span class="n">name</span><span class="p">]</span>
+                <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">sum_k</span><span class="o">.</span><span class="n">corr_shells</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">][</span><span class="s1">&#39;SO&#39;</span><span class="p">]:</span>
+                    <span class="n">name</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">convert_ftps</span><span class="p">[</span><span class="n">name</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;_&#39;</span><span class="p">)[</span><span class="mi">0</span><span class="p">]]</span>
+                    <span class="n">solver_eal</span> <span class="o">=</span> <span class="n">solver_eal</span><span class="o">.</span><span class="n">real</span>
+                    <span class="c1"># remove off-diagonal terms</span>
+                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;diag_delta&#39;</span><span class="p">]:</span>
+                        <span class="n">solver_eal</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">diag</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">diag</span><span class="p">(</span><span class="n">solver_eal</span><span class="p">))</span>
+                <span class="n">h_loc</span><span class="o">.</span><span class="n">Fill</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">solver_eal</span><span class="p">)</span>
+
+            <span class="c1"># fill solver h_loc</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">e0</span> <span class="o">=</span> <span class="n">h_loc</span>
+
+            <span class="c1"># FIXME: unfortunately, in the current implementation the solver initializations aren&#39;t included yet in dmft_cycle,</span>
+            <span class="c1"># so for debugging it is done here again</span>
+            <span class="c1"># store solver to h5 archive</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;store_solver&#39;</span><span class="p">]</span> <span class="ow">and</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+                <span class="k">with</span> <span class="n">HDFArchive</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;jobname&#39;</span><span class="p">]</span><span class="o">+</span><span class="s1">&#39;/&#39;</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;seedname&#39;</span><span class="p">]</span><span class="o">+</span><span class="s1">&#39;.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;a&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">archive</span><span class="p">:</span>
+                    <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_input/solver&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">create_group</span><span class="p">(</span><span class="s1">&#39;it_-1&#39;</span><span class="p">)</span>
+                    <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_input/solver/it_-1&#39;</span><span class="p">][</span><span class="s1">&#39;Delta&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">Delta_freq_solver</span>
+                    <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_input/solver/it_-1&#39;</span><span class="p">][</span><span class="s1">&#39;S_&#39;</span><span class="o">+</span><span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">)]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span>
+
+            <span class="c1"># Solve the impurity problem for icrsh shell</span>
+            <span class="c1"># *************************************</span>
+            <span class="n">path_to_gs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;path_to_gs&#39;</span><span class="p">]</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;path_to_gs&#39;</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">path_to_gs_accepted</span> <span class="k">else</span> <span class="kc">None</span>
+            <span class="c1"># fix to make sure this is only done in iteration 1</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">path_to_gs_accepted</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">path_to_gs_accepted</span> <span class="o">=</span> <span class="kc">False</span>
+            <span class="k">if</span> <span class="n">path_to_gs</span> <span class="o">!=</span> <span class="s1">&#39;postprocess&#39;</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">solve</span><span class="p">(</span><span class="n">h_int</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">h_int</span><span class="p">,</span> <span class="n">params_GS</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dmrg_params</span><span class="p">,</span> <span class="n">params_partSector</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">sector_params</span><span class="p">,</span>
+                                        <span class="n">tevo</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">tevo_params</span><span class="p">,</span> <span class="n">eta</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;eta&#39;</span><span class="p">],</span> <span class="n">calc_me</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">calc_me</span><span class="p">,</span>
+                                        <span class="n">state_storage</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;state_storage&#39;</span><span class="p">],</span><span class="n">path_to_gs</span><span class="o">=</span><span class="n">path_to_gs</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">post_process</span><span class="p">(</span><span class="n">h_int</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">h_int</span><span class="p">,</span> <span class="n">params_GS</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dmrg_params</span><span class="p">,</span> <span class="n">params_partSector</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dmrg_params</span><span class="p">,</span>
+                                               <span class="n">tevo</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">tevo_params</span><span class="p">,</span> <span class="n">eta</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;eta&#39;</span><span class="p">],</span> <span class="n">calc_me</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">calc_me</span><span class="p">,</span>
+                                               <span class="n">state_storage</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;state_storage&#39;</span><span class="p">])</span>
+            <span class="c1"># *************************************</span>
+
+            <span class="c1"># call postprocessing</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_ftps_postprocessing</span><span class="p">()</span>
+
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;type&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;inchworm&#39;</span><span class="p">:</span>
+            <span class="c1"># fill Delta_time from Delta_freq sum_k to solver</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">Delta_tau</span> <span class="o">&lt;&lt;</span> <span class="n">make_gf_from_fourier</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">Delta_freq</span><span class="p">)</span><span class="o">.</span><span class="n">real</span>
+
+            <span class="c1"># Solve the impurity problem for icrsh shell</span>
+            <span class="c1"># *************************************</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">solve</span><span class="p">(</span><span class="n">h_int</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">h_int</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver_params</span><span class="p">)</span>
+            <span class="c1"># *************************************</span>
+
+            <span class="c1"># call postprocessing</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_inchworm_postprocessing</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;type&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;ctseg&#39;</span><span class="p">:</span>
+            <span class="c1"># fill G0_freq from sum_k to solver</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">G0_iw</span> <span class="o">&lt;&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">G0_freq</span>
+
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;h_int_type&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;dynamic&#39;</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">b1</span><span class="p">,</span> <span class="n">b2</span> <span class="ow">in</span> <span class="n">product</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sum_k</span><span class="o">.</span><span class="n">gf_struct_solver_dict</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">keys</span><span class="p">(),</span> <span class="n">repeat</span><span class="o">=</span><span class="mi">2</span><span class="p">):</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">D0_iw</span><span class="p">[</span><span class="n">b1</span><span class="o">+</span><span class="s2">&quot;|&quot;</span><span class="o">+</span><span class="n">b2</span><span class="p">]</span> <span class="o">&lt;&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">U_iw</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">]</span>
+
+            <span class="c1"># Solve the impurity problem for icrsh shell</span>
+            <span class="c1"># *************************************</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">solve</span><span class="p">(</span><span class="n">h_int</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">h_int</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver_params</span><span class="p">)</span>
+            <span class="c1"># *************************************</span>
+
+            <span class="c1"># call postprocessing</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_ctseg_postprocessing</span><span class="p">()</span>
+
+        <span class="k">return</span></div>
+
+    <span class="c1"># ********************************************************************</span>
+    <span class="c1"># create solvers objects</span>
+    <span class="c1"># ********************************************************************</span>
+
+    <span class="k">def</span> <span class="nf">_create_cthyb_solver</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="sa">r</span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Initialize cthyb solver instance</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="kn">from</span> <span class="nn">triqs_cthyb.solver</span> <span class="kn">import</span> <span class="n">Solver</span> <span class="k">as</span> <span class="n">cthyb_solver</span>
+
+        <span class="c1"># Separately stores all params that go into solve() call of solver</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver_params</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="n">keys_to_pass</span> <span class="o">=</span> <span class="p">(</span><span class="s1">&#39;imag_threshold&#39;</span><span class="p">,</span> <span class="s1">&#39;length_cycle&#39;</span><span class="p">,</span> <span class="s1">&#39;max_time&#39;</span><span class="p">,</span> <span class="s1">&#39;measure_density_matrix&#39;</span><span class="p">,</span>
+                        <span class="s1">&#39;measure_G_l&#39;</span><span class="p">,</span> <span class="s1">&#39;measure_pert_order&#39;</span><span class="p">,</span> <span class="s1">&#39;move_double&#39;</span><span class="p">,</span> <span class="s1">&#39;move_shift&#39;</span><span class="p">,</span>
+                        <span class="s1">&#39;off_diag_threshold&#39;</span><span class="p">,</span> <span class="s1">&#39;perform_tail_fit&#39;</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">keys_to_pass</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver_params</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
+
+        <span class="c1"># Calculates number of sweeps per rank</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver_params</span><span class="p">[</span><span class="s1">&#39;n_cycles&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;n_cycles_tot&#39;</span><span class="p">]</span> <span class="o">/</span> <span class="n">mpi</span><span class="o">.</span><span class="n">size</span><span class="p">)</span>
+        <span class="c1"># cast warmup cycles to int in case given in scientific notation</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver_params</span><span class="p">[</span><span class="s1">&#39;n_warmup_cycles&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;n_warmup_cycles&#39;</span><span class="p">])</span>
+
+        <span class="c1"># Renames measure chi param</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver_params</span><span class="p">[</span><span class="s1">&#39;measure_O_tau_min_ins&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;measure_chi_insertions&#39;</span><span class="p">]</span>
+
+        <span class="c1"># use_norm_as_weight also required to measure the density matrix</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver_params</span><span class="p">[</span><span class="s1">&#39;use_norm_as_weight&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver_params</span><span class="p">[</span><span class="s1">&#39;measure_density_matrix&#39;</span><span class="p">]</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver_params</span><span class="p">[</span><span class="s1">&#39;perform_tail_fit&#39;</span><span class="p">]:</span>
+            <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">&#39;fit_max_moment&#39;</span><span class="p">,</span> <span class="s1">&#39;fit_max_n&#39;</span><span class="p">,</span> <span class="s1">&#39;fit_max_w&#39;</span><span class="p">,</span> <span class="s1">&#39;fit_min_n&#39;</span><span class="p">,</span> <span class="s1">&#39;fit_min_w&#39;</span><span class="p">):</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver_params</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
+
+        <span class="c1"># set loc_n_min and loc_n_max</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;loc_n_min&#39;</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver_params</span><span class="p">[</span><span class="s1">&#39;loc_n_min&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;loc_n_min&#39;</span><span class="p">]</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;loc_n_max&#39;</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver_params</span><span class="p">[</span><span class="s1">&#39;loc_n_max&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;loc_n_max&#39;</span><span class="p">]</span>
+
+        <span class="n">gf_struct</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sum_k</span><span class="o">.</span><span class="n">gf_struct_solver_list</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">]</span>
+        <span class="c1"># Construct the triqs_solver instances</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;measure_G_l&#39;</span><span class="p">]:</span>
+            <span class="n">triqs_solver</span> <span class="o">=</span> <span class="n">cthyb_solver</span><span class="p">(</span><span class="n">beta</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;beta&#39;</span><span class="p">],</span> <span class="n">gf_struct</span><span class="o">=</span><span class="n">gf_struct</span><span class="p">,</span>
+                            <span class="n">n_iw</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;n_iw&#39;</span><span class="p">],</span> <span class="n">n_tau</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;n_tau&#39;</span><span class="p">],</span>
+                            <span class="n">n_l</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;n_l&#39;</span><span class="p">],</span> <span class="n">delta_interface</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;delta_interface&#39;</span><span class="p">])</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">triqs_solver</span> <span class="o">=</span> <span class="n">cthyb_solver</span><span class="p">(</span><span class="n">beta</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;beta&#39;</span><span class="p">],</span> <span class="n">gf_struct</span><span class="o">=</span><span class="n">gf_struct</span><span class="p">,</span>
+                            <span class="n">n_iw</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;n_iw&#39;</span><span class="p">],</span> <span class="n">n_tau</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;n_tau&#39;</span><span class="p">],</span>
+                            <span class="n">delta_interface</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;delta_interface&#39;</span><span class="p">])</span>
+
+        <span class="k">return</span> <span class="n">triqs_solver</span>
+
+    <span class="k">def</span> <span class="nf">_create_ctint_solver</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="sa">r</span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Initialize ctint solver instance</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="kn">from</span> <span class="nn">triqs_ctint</span> <span class="kn">import</span> <span class="n">Solver</span> <span class="k">as</span> <span class="n">ctint_solver</span>
+
+        <span class="c1"># Separately stores all params that go into solve() call of solver</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver_params</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="n">keys_to_pass</span> <span class="o">=</span> <span class="p">(</span><span class="s1">&#39;length_cycle&#39;</span><span class="p">,</span> <span class="s1">&#39;max_time&#39;</span><span class="p">,</span> <span class="s1">&#39;measure_pert_order&#39;</span><span class="p">,</span> <span class="s1">&#39;move_double&#39;</span><span class="p">,</span> <span class="s1">&#39;n_warmup_cycles&#39;</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">keys_to_pass</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver_params</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
+
+        <span class="c1"># Calculates number of sweeps per rank</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver_params</span><span class="p">[</span><span class="s1">&#39;n_cycles&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;n_cycles_tot&#39;</span><span class="p">]</span> <span class="o">/</span> <span class="n">mpi</span><span class="o">.</span><span class="n">size</span><span class="p">)</span>
+
+        <span class="n">gf_struct</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sum_k</span><span class="o">.</span><span class="n">gf_struct_solver_list</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">]</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;h_int_type&#39;</span><span class="p">][</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;dynamic&#39;</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">U_iw</span> <span class="o">=</span> <span class="kc">None</span>
+            <span class="k">if</span>  <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+                <span class="k">with</span> <span class="n">HDFArchive</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;jobname&#39;</span><span class="p">]</span><span class="o">+</span><span class="s1">&#39;/&#39;</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;seedname&#39;</span><span class="p">]</span><span class="o">+</span><span class="s1">&#39;.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">archive</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">U_iw</span> <span class="o">=</span> <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;dynamic_U&#39;</span><span class="p">][</span><span class="s1">&#39;U_iw&#39;</span><span class="p">]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">U_iw</span> <span class="o">=</span> <span class="n">mpi</span><span class="o">.</span><span class="n">bcast</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">U_iw</span><span class="p">)</span>
+            <span class="n">n_iw_dyn</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">U_iw</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">mesh</span><span class="o">.</span><span class="n">last_index</span><span class="p">()</span><span class="o">+</span><span class="mi">1</span>
+            <span class="c1"># Construct the triqs_solver instances</span>
+            <span class="n">triqs_solver</span> <span class="o">=</span> <span class="n">ctint_solver</span><span class="p">(</span><span class="n">beta</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;beta&#39;</span><span class="p">],</span> <span class="n">gf_struct</span><span class="o">=</span><span class="n">gf_struct</span><span class="p">,</span>
+                            <span class="n">n_iw</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;n_iw&#39;</span><span class="p">],</span> <span class="n">n_tau</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;n_tau&#39;</span><span class="p">],</span> <span class="n">use_D</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">use_Jperp</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                            <span class="n">n_iw_dynamical_interactions</span><span class="o">=</span><span class="n">n_iw_dyn</span><span class="p">,</span><span class="n">n_tau_dynamical_interactions</span><span class="o">=</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">n_iw_dyn</span><span class="o">*</span><span class="mf">2.5</span><span class="p">)))</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="c1"># Construct the triqs_solver instances</span>
+            <span class="n">triqs_solver</span> <span class="o">=</span> <span class="n">ctint_solver</span><span class="p">(</span><span class="n">beta</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;beta&#39;</span><span class="p">],</span> <span class="n">gf_struct</span><span class="o">=</span><span class="n">gf_struct</span><span class="p">,</span>
+                            <span class="n">n_iw</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;n_iw&#39;</span><span class="p">],</span> <span class="n">n_tau</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;n_tau&#39;</span><span class="p">],</span> <span class="n">use_D</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">use_Jperp</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">triqs_solver</span>
+
+    <span class="k">def</span> <span class="nf">_create_hubbardI_solver</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="sa">r</span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Initialize hubbardI solver instance</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="kn">from</span> <span class="nn">triqs_hubbardI</span> <span class="kn">import</span> <span class="n">Solver</span> <span class="k">as</span> <span class="n">hubbardI_solver</span>
+
+        <span class="c1"># Separately stores all params that go into solve() call of solver</span>
+        <span class="c1"># All params need to be renamed</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver_params</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver_params</span><span class="p">[</span><span class="s1">&#39;calc_gtau&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;measure_G_tau&#39;</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver_params</span><span class="p">[</span><span class="s1">&#39;calc_gw&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver_params</span><span class="p">[</span><span class="s1">&#39;calc_gl&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;measure_G_l&#39;</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver_params</span><span class="p">[</span><span class="s1">&#39;calc_dm&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;measure_density_matrix&#39;</span><span class="p">]</span>
+
+        <span class="n">gf_struct</span> <span class="o">=</span>  <span class="bp">self</span><span class="o">.</span><span class="n">sum_k</span><span class="o">.</span><span class="n">gf_struct_solver_list</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">]</span>
+        <span class="c1"># Construct the triqs_solver instances</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;measure_G_l&#39;</span><span class="p">]:</span>
+            <span class="n">triqs_solver</span> <span class="o">=</span> <span class="n">hubbardI_solver</span><span class="p">(</span><span class="n">beta</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;beta&#39;</span><span class="p">],</span> <span class="n">gf_struct</span><span class="o">=</span><span class="n">gf_struct</span><span class="p">,</span>
+                                           <span class="n">n_iw</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;n_iw&#39;</span><span class="p">],</span> <span class="n">n_tau</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;n_tau&#39;</span><span class="p">],</span>
+                                           <span class="n">n_l</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;n_l&#39;</span><span class="p">],</span> <span class="n">n_w</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;n_w&#39;</span><span class="p">],</span>
+                                           <span class="n">w_min</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;w_range&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">w_max</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;w_range&#39;</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span>
+                                           <span class="n">idelta</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;eta&#39;</span><span class="p">])</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">triqs_solver</span> <span class="o">=</span> <span class="n">hubbardI_solver</span><span class="p">(</span><span class="n">beta</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;beta&#39;</span><span class="p">],</span> <span class="n">gf_struct</span><span class="o">=</span><span class="n">gf_struct</span><span class="p">,</span>
+                                           <span class="n">n_iw</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;n_iw&#39;</span><span class="p">],</span> <span class="n">n_tau</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;n_tau&#39;</span><span class="p">],</span>
+                                           <span class="n">n_w</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;n_w&#39;</span><span class="p">],</span> <span class="n">idelta</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;eta&#39;</span><span class="p">],</span>
+                                           <span class="n">w_min</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;w_range&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">w_max</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;w_range&#39;</span><span class="p">][</span><span class="mi">1</span><span class="p">])</span>
+
+        <span class="k">return</span> <span class="n">triqs_solver</span>
+
+    <span class="k">def</span> <span class="nf">_make_spin_equal</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">Sigma</span><span class="p">):</span>
+
+        <span class="c1"># if not SOC than average up and down</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;magnetic&#39;</span><span class="p">]</span> <span class="ow">and</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">sum_k</span><span class="o">.</span><span class="n">SO</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="n">Sigma</span><span class="p">[</span><span class="s1">&#39;up_0&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.5</span><span class="o">*</span><span class="p">(</span><span class="n">Sigma</span><span class="p">[</span><span class="s1">&#39;up_0&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="n">Sigma</span><span class="p">[</span><span class="s1">&#39;down_0&#39;</span><span class="p">])</span>
+            <span class="n">Sigma</span><span class="p">[</span><span class="s1">&#39;down_0&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">Sigma</span><span class="p">[</span><span class="s1">&#39;up_0&#39;</span><span class="p">]</span>
+
+        <span class="k">return</span> <span class="n">Sigma</span>
+
+    <span class="k">def</span> <span class="nf">_create_hartree_solver</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="sa">r</span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Initialize hartree_fock solver instance</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="kn">from</span> <span class="nn">triqs_hartree_fock</span> <span class="kn">import</span> <span class="n">ImpuritySolver</span> <span class="k">as</span> <span class="n">hartree_solver</span>
+
+        <span class="c1"># Separately stores all params that go into solve() call of solver</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver_params</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="n">keys_to_pass</span> <span class="o">=</span> <span class="p">(</span><span class="s1">&#39;method&#39;</span><span class="p">,</span> <span class="s1">&#39;one_shot&#39;</span><span class="p">,</span> <span class="s1">&#39;tol&#39;</span><span class="p">,</span> <span class="s1">&#39;with_fock&#39;</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">keys_to_pass</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver_params</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
+
+        <span class="n">gf_struct</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sum_k</span><span class="o">.</span><span class="n">gf_struct_solver_list</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">]</span>
+        <span class="c1"># Construct the triqs_solver instances</span>
+        <span class="c1"># Always initialize the solver with dc_U and dc_J equal to U and J and let the _interface_hartree_dc function</span>
+        <span class="c1"># take care of changing the parameters</span>
+        <span class="n">triqs_solver</span> <span class="o">=</span> <span class="n">hartree_solver</span><span class="p">(</span><span class="n">beta</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;beta&#39;</span><span class="p">],</span> <span class="n">gf_struct</span><span class="o">=</span><span class="n">gf_struct</span><span class="p">,</span>
+                                      <span class="n">n_iw</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;n_iw&#39;</span><span class="p">],</span> <span class="n">force_real</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;force_real&#39;</span><span class="p">],</span>
+                                      <span class="n">symmetries</span><span class="o">=</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_make_spin_equal</span><span class="p">],</span>
+                                      <span class="n">dc_U</span><span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;U&#39;</span><span class="p">][</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">],</span>
+                                      <span class="n">dc_J</span><span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;J&#39;</span><span class="p">][</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">]</span>
+                                      <span class="p">)</span>
+
+        <span class="k">def</span> <span class="nf">_interface_hartree_dc</span><span class="p">(</span><span class="n">hartree_instance</span><span class="p">,</span> <span class="n">general_params</span><span class="p">,</span> <span class="n">advanced_params</span><span class="p">,</span> <span class="n">icrsh</span><span class="p">):</span>
+            <span class="sd">&quot;&quot;&quot; Modifies in-place class attributes to infercace with options in solid_dmft</span>
+<span class="sd">                for the moment supports only DC-relevant parameters</span>
+
+<span class="sd">            Parameters</span>
+<span class="sd">            ----------</span>
+<span class="sd">                general_params : dict</span>
+<span class="sd">                    solid_dmft general parameter dictionary</span>
+<span class="sd">                advanced_params : dict</span>
+<span class="sd">                    solid_dmft advanced parameter dictionary</span>
+<span class="sd">                icrsh : int</span>
+<span class="sd">                    correlated shell number</span>
+<span class="sd">            &quot;&quot;&quot;</span>
+            <span class="nb">setattr</span><span class="p">(</span><span class="n">hartree_instance</span><span class="p">,</span> <span class="s1">&#39;dc&#39;</span><span class="p">,</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;dc&#39;</span><span class="p">])</span>
+            <span class="k">if</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;dc_type&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="nb">setattr</span><span class="p">(</span><span class="n">hartree_instance</span><span class="p">,</span> <span class="s1">&#39;dc_type&#39;</span><span class="p">,</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;dc_type&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">])</span>
+
+            <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;dc_factor&#39;</span><span class="p">,</span> <span class="s1">&#39;dc_fixed_value&#39;</span><span class="p">]:</span>
+                <span class="k">if</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">advanced_params</span> <span class="ow">and</span> <span class="n">advanced_params</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                    <span class="nb">setattr</span><span class="p">(</span><span class="n">hartree_instance</span><span class="p">,</span> <span class="n">key</span><span class="p">,</span> <span class="n">advanced_params</span><span class="p">[</span><span class="n">key</span><span class="p">])</span>
+
+            <span class="c1">#list valued keys</span>
+            <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;dc_U&#39;</span><span class="p">,</span> <span class="s1">&#39;dc_J&#39;</span><span class="p">,</span> <span class="s1">&#39;dc_fixed_occ&#39;</span><span class="p">]:</span>
+                <span class="k">if</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">advanced_params</span> <span class="ow">and</span> <span class="n">advanced_params</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="n">icrsh</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                    <span class="nb">setattr</span><span class="p">(</span><span class="n">hartree_instance</span><span class="p">,</span> <span class="n">key</span><span class="p">,</span> <span class="n">advanced_params</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="n">icrsh</span><span class="p">])</span>
+
+            <span class="c1"># Handle special cases</span>
+            <span class="k">if</span> <span class="s1">&#39;dc_dmft&#39;</span> <span class="ow">in</span> <span class="n">general_params</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;dc_dmft&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="kc">False</span><span class="p">:</span>
+                    <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;HARTREE SOLVER: Warning dft occupation in the DC calculations are meaningless for the hartree solver, reverting to dmft occupations&#39;</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="n">hartree_instance</span><span class="o">.</span><span class="n">dc_type</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">and</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;magnetic&#39;</span><span class="p">]:</span>
+                    <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;HARTREE SOLVER: Detected dc_type = </span><span class="si">{</span><span class="n">hartree_instance</span><span class="o">.</span><span class="n">dc_type</span><span class="si">}</span><span class="s2">, changing to &#39;cFLL&#39;&quot;</span><span class="p">)</span>
+                    <span class="n">hartree_instance</span><span class="o">.</span><span class="n">dc_type</span> <span class="o">=</span> <span class="s1">&#39;cFLL&#39;</span>
+            <span class="k">elif</span> <span class="n">hartree_instance</span><span class="o">.</span><span class="n">dc_type</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;magnetic&#39;</span><span class="p">]:</span>
+                    <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;HARTREE SOLVER: Detected dc_type = </span><span class="si">{</span><span class="n">hartree_instance</span><span class="o">.</span><span class="n">dc_type</span><span class="si">}</span><span class="s2">, changing to &#39;sFLL&#39;&quot;</span><span class="p">)</span>
+                    <span class="n">hartree_instance</span><span class="o">.</span><span class="n">dc_type</span> <span class="o">=</span> <span class="s1">&#39;sFLL&#39;</span>
+            <span class="k">elif</span> <span class="n">hartree_instance</span><span class="o">.</span><span class="n">dc_type</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+                    <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;HARTREE SOLVER: Detected dc_type = </span><span class="si">{</span><span class="n">hartree_instance</span><span class="o">.</span><span class="n">dc_type</span><span class="si">}</span><span class="s2">, changing to &#39;cHeld&#39;&quot;</span><span class="p">)</span>
+                    <span class="n">hartree_instance</span><span class="o">.</span><span class="n">dc_type</span> <span class="o">=</span> <span class="s1">&#39;cHeld&#39;</span>
+            <span class="k">elif</span> <span class="n">hartree_instance</span><span class="o">.</span><span class="n">dc_type</span> <span class="o">==</span> <span class="mi">2</span> <span class="ow">and</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;magnetic&#39;</span><span class="p">]:</span>
+                    <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;HARTREE SOLVER: Detected dc_type = </span><span class="si">{</span><span class="n">hartree_instance</span><span class="o">.</span><span class="n">dc_type</span><span class="si">}</span><span class="s2">, changing to &#39;cAMF&#39;&quot;</span><span class="p">)</span>
+                    <span class="n">hartree_instance</span><span class="o">.</span><span class="n">dc_type</span> <span class="o">=</span> <span class="s1">&#39;cAMF&#39;</span>
+            <span class="k">elif</span> <span class="n">hartree_instance</span><span class="o">.</span><span class="n">dc_type</span> <span class="o">==</span> <span class="mi">2</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;magnetic&#39;</span><span class="p">]:</span>
+                    <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;HARTREE SOLVER: Detected dc_type = </span><span class="si">{</span><span class="n">hartree_instance</span><span class="o">.</span><span class="n">dc_type</span><span class="si">}</span><span class="s2">, changing to &#39;sAMF&#39;&quot;</span><span class="p">)</span>
+                    <span class="n">hartree_instance</span><span class="o">.</span><span class="n">dc_type</span> <span class="o">=</span> <span class="s1">&#39;sAMF&#39;</span>
+
+        <span class="c1"># Give dc information to the solver in order to customize DC calculation</span>
+        <span class="n">_interface_hartree_dc</span><span class="p">(</span><span class="n">triqs_solver</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">advanced_params</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">triqs_solver</span>
+
+    <span class="k">def</span> <span class="nf">_create_inchworm_solver</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="sa">r</span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Initialize inchworm solver instance</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+
+        <span class="k">return</span> <span class="n">triqs_solver</span>
+
+    <span class="k">def</span> <span class="nf">_create_ctseg_solver</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="sa">r</span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Initialize cthyb solver instance</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="kn">from</span> <span class="nn">triqs_ctseg</span> <span class="kn">import</span> <span class="n">Solver</span> <span class="k">as</span> <span class="n">ctseg_solver</span>
+
+        <span class="c1"># Separately stores all params that go into solve() call of solver</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver_params</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="n">keys_to_pass</span> <span class="o">=</span> <span class="p">(</span><span class="s1">&#39;improved_estimator&#39;</span><span class="p">,</span> <span class="s1">&#39;length_cycle&#39;</span><span class="p">,</span> <span class="s1">&#39;max_time&#39;</span><span class="p">,</span> <span class="s1">&#39;measure_pert_order&#39;</span><span class="p">,</span> <span class="s1">&#39;n_warmup_cycles&#39;</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">keys_to_pass</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver_params</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
+
+        <span class="c1"># Calculates number of sweeps per rank</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver_params</span><span class="p">[</span><span class="s1">&#39;n_cycles&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;n_cycles_tot&#39;</span><span class="p">]</span> <span class="o">/</span> <span class="n">mpi</span><span class="o">.</span><span class="n">size</span><span class="p">)</span>
+
+        <span class="c1"># Rename parameters that are differentin ctseg than cthyb</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver_params</span><span class="p">[</span><span class="s1">&#39;measure_gt&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;measure_G_tau&#39;</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver_params</span><span class="p">[</span><span class="s1">&#39;measure_gw&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;measure_G_iw&#39;</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver_params</span><span class="p">[</span><span class="s1">&#39;measure_gl&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;measure_G_l&#39;</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver_params</span><span class="p">[</span><span class="s1">&#39;measure_hist&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;measure_pert_order&#39;</span><span class="p">]</span>
+
+        <span class="c1"># Makes sure measure_gw is true if improved estimators are used</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver_params</span><span class="p">[</span><span class="s1">&#39;improved_estimator&#39;</span><span class="p">]:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver_params</span><span class="p">[</span><span class="s1">&#39;measure_gt&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver_params</span><span class="p">[</span><span class="s1">&#39;measure_ft&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver_params</span><span class="p">[</span><span class="s1">&#39;measure_ft&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">False</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;h_int_type&#39;</span><span class="p">][</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;dynamic&#39;</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">U_iw</span> <span class="o">=</span> <span class="kc">None</span>
+            <span class="k">if</span>  <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+                <span class="k">with</span> <span class="n">HDFArchive</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;jobname&#39;</span><span class="p">]</span><span class="o">+</span><span class="s1">&#39;/&#39;</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;seedname&#39;</span><span class="p">]</span><span class="o">+</span><span class="s1">&#39;.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">archive</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">U_iw</span> <span class="o">=</span> <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;dynamic_U&#39;</span><span class="p">][</span><span class="s1">&#39;U_iw&#39;</span><span class="p">]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">U_iw</span> <span class="o">=</span> <span class="n">mpi</span><span class="o">.</span><span class="n">bcast</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">U_iw</span><span class="p">)</span>
+            <span class="n">n_w_b_nn</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">U_iw</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">]</span><span class="o">.</span><span class="n">mesh</span><span class="o">.</span><span class="n">last_index</span><span class="p">()</span><span class="o">+</span><span class="mi">1</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">n_w_b_nn</span> <span class="o">=</span> <span class="mi">1001</span>
+
+        <span class="n">gf_struct</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sum_k</span><span class="o">.</span><span class="n">gf_struct_solver_list</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">]</span>
+        <span class="c1"># Construct the triqs_solver instances</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;measure_gl&#39;</span><span class="p">]:</span>
+            <span class="n">triqs_solver</span> <span class="o">=</span> <span class="n">ctseg_solver</span><span class="p">(</span><span class="n">beta</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;beta&#39;</span><span class="p">],</span> <span class="n">gf_struct</span><span class="o">=</span><span class="n">gf_struct</span><span class="p">,</span>
+                            <span class="n">n_iw</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;n_iw&#39;</span><span class="p">],</span> <span class="n">n_tau</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;n_tau&#39;</span><span class="p">],</span>
+                            <span class="n">n_legendre_g</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;n_l&#39;</span><span class="p">],</span> <span class="n">n_w_b_nn</span> <span class="o">=</span> <span class="n">n_w_b_nn</span><span class="p">,</span> <span class="n">n_tau_k</span><span class="o">=</span><span class="nb">int</span><span class="p">(</span><span class="n">n_w_b_nn</span><span class="o">*</span><span class="mf">2.5</span><span class="p">),</span> <span class="n">n_tau_jperp</span><span class="o">=</span><span class="nb">int</span><span class="p">(</span><span class="n">n_w_b_nn</span><span class="o">*</span><span class="mf">2.5</span><span class="p">))</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">triqs_solver</span> <span class="o">=</span> <span class="n">ctseg_solver</span><span class="p">(</span><span class="n">beta</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;beta&#39;</span><span class="p">],</span> <span class="n">gf_struct</span><span class="o">=</span><span class="n">gf_struct</span><span class="p">,</span>
+                            <span class="n">n_iw</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;n_iw&#39;</span><span class="p">],</span> <span class="n">n_tau</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;n_tau&#39;</span><span class="p">],</span>
+                            <span class="n">n_w_b_nn</span> <span class="o">=</span> <span class="n">n_w_b_nn</span><span class="p">,</span> <span class="n">n_tau_k</span><span class="o">=</span><span class="nb">int</span><span class="p">(</span><span class="n">n_w_b_nn</span><span class="o">*</span><span class="mf">2.5</span><span class="p">),</span> <span class="n">n_tau_jperp</span><span class="o">=</span><span class="nb">int</span><span class="p">(</span><span class="n">n_w_b_nn</span><span class="o">*</span><span class="mf">2.5</span><span class="p">))</span>
+
+        <span class="k">return</span> <span class="n">triqs_solver</span>
+
+    <span class="k">def</span> <span class="nf">_create_ftps_solver</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="sa">r</span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Initialize ftps solver instance</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="kn">import</span> <span class="nn">forktps</span> <span class="k">as</span> <span class="nn">ftps</span>
+
+        <span class="c1"># TODO: add triqs_solver_params for ftps as well to make it analogous to other similars</span>
+        <span class="c1"># Not necessary but nicer. For now, just keep an empty dummy dict</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver_params</span> <span class="o">=</span> <span class="p">{}</span>
+
+        <span class="c1"># convert self.deg_orbs_ftps to mapping and solver-friendly list</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">sum_k</span><span class="o">.</span><span class="n">corr_shells</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">][</span><span class="s1">&#39;SO&#39;</span><span class="p">]:</span>
+            <span class="c1"># mapping dictionary</span>
+            <span class="n">calc_mapping</span> <span class="o">=</span> <span class="p">{</span><span class="bp">self</span><span class="o">.</span><span class="n">deg_orbs_ftps</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">][</span><span class="n">deg_shell</span><span class="p">][</span><span class="mi">0</span><span class="p">]:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">deg_orbs_ftps</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">][</span><span class="n">deg_shell</span><span class="p">][</span><span class="mi">1</span><span class="p">:]</span> <span class="k">for</span> <span class="n">deg_shell</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">deg_orbs_ftps</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">]))}</span>
+            <span class="c1"># make solver-friendly list from mapping keys</span>
+            <span class="n">calc_me</span> <span class="o">=</span> <span class="p">[[</span><span class="n">item</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;_&#39;</span><span class="p">)[</span><span class="mi">0</span><span class="p">],</span> <span class="nb">int</span><span class="p">(</span><span class="n">item</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;_&#39;</span><span class="p">)[</span><span class="mi">1</span><span class="p">])]</span> <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="n">calc_mapping</span><span class="o">.</span><span class="n">keys</span><span class="p">()]</span>
+            <span class="c1"># replace &#39;down&#39; with &#39;dn&#39;</span>
+            <span class="n">calc_me</span> <span class="o">=</span> <span class="p">[[</span><span class="n">item</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;down&#39;</span><span class="p">,</span><span class="s1">&#39;dn&#39;</span><span class="p">),</span><span class="n">item</span><span class="p">[</span><span class="mi">1</span><span class="p">]]</span> <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="n">calc_me</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="c1"># for SOC we just end up calculating everything for now</span>
+            <span class="c1"># TODO: perhaps skip down channel</span>
+            <span class="n">calc_mapping</span> <span class="o">=</span> <span class="kc">None</span>
+            <span class="n">calc_me</span> <span class="o">=</span> <span class="p">[[</span><span class="sa">f</span><span class="s1">&#39;ud_</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">,</span><span class="n">j</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">j</span> <span class="ow">in</span> <span class="n">product</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">),</span> <span class="nb">range</span><span class="p">(</span><span class="mi">3</span><span class="p">))]</span>
+
+        <span class="c1"># create solver</span>
+        <span class="n">triqs_solver</span> <span class="o">=</span> <span class="n">ftps</span><span class="o">.</span><span class="n">Solver</span><span class="p">(</span><span class="n">gf_struct</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">gf_struct</span><span class="p">,</span> <span class="n">nw</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;n_w&#39;</span><span class="p">],</span>
+                                   <span class="n">wmin</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;w_range&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">wmax</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;w_range&#39;</span><span class="p">][</span><span class="mi">1</span><span class="p">])</span>
+
+
+        <span class="c1"># create partSector params</span>
+        <span class="n">sector_params</span> <span class="o">=</span> <span class="n">ftps</span><span class="o">.</span><span class="n">solver</span><span class="o">.</span><span class="n">DMRGParams</span><span class="p">(</span><span class="n">maxmI</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">maxmIB</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">maxmB</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">tw</span><span class="o">=</span><span class="mf">1e-10</span><span class="p">,</span> <span class="n">nmax</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">sweeps</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
+
+        <span class="c1"># for now prep_imagTevo, prep_method and nmax hard-coded</span>
+        <span class="c1"># create DMRG params</span>
+        <span class="n">dmrg_params</span> <span class="o">=</span> <span class="n">ftps</span><span class="o">.</span><span class="n">solver</span><span class="o">.</span><span class="n">DMRGParams</span><span class="p">(</span><span class="n">maxmI</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;dmrg_maxmI&#39;</span><span class="p">],</span> <span class="n">maxmIB</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;dmrg_maxmIB&#39;</span><span class="p">],</span>
+                                             <span class="n">maxmB</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;dmrg_maxmB&#39;</span><span class="p">],</span> <span class="n">tw</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;dmrg_tw&#39;</span><span class="p">],</span>
+                                             <span class="n">prep_imagTevo</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">prep_method</span><span class="o">=</span><span class="s1">&#39;TEBD&#39;</span><span class="p">,</span> <span class="n">sweeps</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;sweeps&#39;</span><span class="p">],</span> <span class="n">nmax</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+                                             <span class="n">prep_time_steps</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">napph</span><span class="o">=</span><span class="mi">2</span>
+                                             <span class="p">)</span>
+
+        <span class="c1"># create TEVO params</span>
+        <span class="n">tevo_params</span> <span class="o">=</span> <span class="n">ftps</span><span class="o">.</span><span class="n">solver</span><span class="o">.</span><span class="n">TevoParams</span><span class="p">(</span><span class="n">dt</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;dt&#39;</span><span class="p">],</span> <span class="n">time_steps</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="c1">#dummy, will be updated during the run</span>
+                                             <span class="n">maxmI</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;maxmI&#39;</span><span class="p">],</span> <span class="n">maxmIB</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;maxmIB&#39;</span><span class="p">],</span>
+                                             <span class="n">maxmB</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;maxmB&#39;</span><span class="p">],</span> <span class="n">tw</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;tw&#39;</span><span class="p">])</span>
+
+        <span class="k">return</span> <span class="n">triqs_solver</span><span class="p">,</span> <span class="n">sector_params</span><span class="p">,</span> <span class="n">dmrg_params</span><span class="p">,</span> <span class="n">tevo_params</span><span class="p">,</span> <span class="n">calc_me</span><span class="p">,</span> <span class="n">calc_mapping</span>
+
+    <span class="c1"># ********************************************************************</span>
+    <span class="c1"># post-processing of solver output</span>
+    <span class="c1"># ********************************************************************</span>
+
+    <span class="k">def</span> <span class="nf">_cthyb_postprocessing</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="sa">r</span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Organize G_freq, G_time, Sigma_freq and G_l from cthyb solver</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+
+        <span class="k">def</span> <span class="nf">set_Gs_from_G_l</span><span class="p">():</span>
+
+            <span class="c1"># create new G_freq and G_time</span>
+            <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">g</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">G_l</span><span class="p">:</span>
+                <span class="n">g</span><span class="o">.</span><span class="n">enforce_discontinuity</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">identity</span><span class="p">(</span><span class="n">g</span><span class="o">.</span><span class="n">target_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
+                <span class="c1"># set G_freq from Legendre and Fouriertransform to get G_time</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">set_from_legendre</span><span class="p">(</span><span class="n">g</span><span class="p">)</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">G_time</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">set_from_legendre</span><span class="p">(</span><span class="n">g</span><span class="p">)</span>
+
+            <span class="c1"># Symmetrize</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span> <span class="o">&lt;&lt;</span> <span class="n">make_hermitian</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">G_freq_unsym</span> <span class="o">&lt;&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">sum_k</span><span class="o">.</span><span class="n">symm_deg_gf</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span><span class="p">,</span> <span class="n">ish</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">sum_k</span><span class="o">.</span><span class="n">symm_deg_gf</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">G_time</span><span class="p">,</span> <span class="n">ish</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">)</span>
+            <span class="c1"># Dyson equation to get Sigma_freq</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">Sigma_freq</span> <span class="o">&lt;&lt;</span> <span class="n">inverse</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">G0_freq</span><span class="p">)</span> <span class="o">-</span> <span class="n">inverse</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span><span class="p">)</span>
+
+            <span class="k">return</span>
+
+        <span class="c1"># get Delta_time from solver</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">Delta_time</span> <span class="o">&lt;&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">Delta_tau</span>
+
+        <span class="c1"># if measured in Legendre basis, get G_l from solver too</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;measure_G_l&#39;</span><span class="p">]:</span>
+            <span class="c1"># store original G_time into G_time_orig</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">G_time_orig</span> <span class="o">&lt;&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">G_tau</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">G_l</span> <span class="o">&lt;&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">G_l</span>
+            <span class="c1"># get G_time, G_freq, Sigma_freq from G_l</span>
+            <span class="n">set_Gs_from_G_l</span><span class="p">()</span>
+
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span> <span class="o">&lt;&lt;</span> <span class="n">make_hermitian</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">G_iw</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">G_freq_unsym</span> <span class="o">&lt;&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">sum_k</span><span class="o">.</span><span class="n">symm_deg_gf</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span><span class="p">,</span> <span class="n">ish</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">)</span>
+            <span class="c1"># set G_time</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">G_time</span> <span class="o">&lt;&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">G_tau</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">sum_k</span><span class="o">.</span><span class="n">symm_deg_gf</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">G_time</span><span class="p">,</span> <span class="n">ish</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;legendre_fit&#39;</span><span class="p">]:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">G_time_orig</span> <span class="o">&lt;&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">G_tau</span>
+                <span class="c1"># run the filter</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">G_l</span> <span class="o">&lt;&lt;</span> <span class="n">legendre_filter</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">G_time</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;n_l&#39;</span><span class="p">])</span>
+                <span class="c1"># get G_time, G_freq, Sigma_freq from G_l</span>
+                <span class="n">set_Gs_from_G_l</span><span class="p">()</span>
+            <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;perform_tail_fit&#39;</span><span class="p">]</span> <span class="ow">and</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;legendre_fit&#39;</span><span class="p">]:</span>
+                <span class="c1"># if tailfit has been used replace Sigma with the tail fitted Sigma from cthyb</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">Sigma_freq</span> <span class="o">&lt;&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">Sigma_iw</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">sum_k</span><span class="o">.</span><span class="n">symm_deg_gf</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">Sigma_freq</span><span class="p">,</span> <span class="n">ish</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="c1"># obtain Sigma via dyson from symmetrized G_freq</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">Sigma_freq</span> <span class="o">&lt;&lt;</span> <span class="n">inverse</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">G0_freq</span><span class="p">)</span> <span class="o">-</span> <span class="n">inverse</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span><span class="p">)</span>
+
+        <span class="c1"># if density matrix is measured, get this too</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;measure_density_matrix&#39;</span><span class="p">]:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">density_matrix</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">density_matrix</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">h_loc_diagonalization</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">h_loc_diagonalization</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;measure_pert_order&#39;</span><span class="p">]:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">perturbation_order</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">perturbation_order</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">perturbation_order_total</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">perturbation_order_total</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;measure_chi&#39;</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">O_time</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">O_tau</span>
+
+        <span class="k">return</span>
+
+    <span class="k">def</span> <span class="nf">_ctint_postprocessing</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="sa">r</span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Organize G_freq, G_time, Sigma_freq and G_l from cthyb solver</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="c1">#TODO</span>
+
+        <span class="c1"># def set_Gs_from_G_l():</span>
+
+        <span class="c1">#     # create new G_freq and G_time</span>
+        <span class="c1">#     for i, g in self.G_l:</span>
+        <span class="c1">#         g.enforce_discontinuity(np.identity(g.target_shape[0]))</span>
+        <span class="c1">#         # set G_freq from Legendre and Fouriertransform to get G_time</span>
+        <span class="c1">#         self.G_freq[i].set_from_legendre(g)</span>
+        <span class="c1">#         self.G_time[i] &lt;&lt; Fourier(self.G_freq[i])</span>
+        <span class="c1">#     # Symmetrize</span>
+        <span class="c1">#     self.G_freq &lt;&lt; make_hermitian(self.G_freq)</span>
+        <span class="c1">#     # Dyson equation to get Sigma_freq</span>
+        <span class="c1">#     self.Sigma_freq &lt;&lt; inverse(self.G0_freq) - inverse(self.G_freq)</span>
+
+        <span class="c1">#     return</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span> <span class="o">&lt;&lt;</span> <span class="n">make_hermitian</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">G_iw</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">G_freq_unsym</span> <span class="o">&lt;&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">sum_k</span><span class="o">.</span><span class="n">symm_deg_gf</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span><span class="p">,</span> <span class="n">ish</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">Sigma_freq</span> <span class="o">&lt;&lt;</span> <span class="n">inverse</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">G0_freq</span><span class="p">)</span> <span class="o">-</span> <span class="n">inverse</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">G_time</span> <span class="o">&lt;&lt;</span> <span class="n">Fourier</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span><span class="p">)</span>
+
+        <span class="c1"># TODO: probably not needed/sensible</span>
+        <span class="c1"># if self.solver_params[&#39;legendre_fit&#39;]:</span>
+        <span class="c1">#     self.G_freq_orig &lt;&lt; self.triqs_solver.G_iw</span>
+        <span class="c1">#     # run the filter</span>
+        <span class="c1">#     self.G_l &lt;&lt; legendre_filter.apply(self.G_time, self.solver_params[&#39;n_l&#39;])</span>
+        <span class="c1">#     # get G_time, G_freq, Sigma_freq from G_l</span>
+        <span class="c1">#     set_Gs_from_G_l()</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;measure_histogram&#39;</span><span class="p">]:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">perturbation_order</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">histogram</span>
+
+        <span class="k">return</span>
+
+    <span class="k">def</span> <span class="nf">_hubbardI_postprocessing</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="sa">r</span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Organize G_freq, G_time, Sigma_freq and G_l from hubbardI solver</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+
+        <span class="c1"># get everything from solver</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">Sigma_freq</span> <span class="o">&lt;&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">Sigma_iw</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">G0_freq</span> <span class="o">&lt;&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">G0_iw</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">G0_Refreq</span> <span class="o">&lt;&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">G0_w</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span> <span class="o">&lt;&lt;</span> <span class="n">make_hermitian</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">G_iw</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">G_freq_unsym</span> <span class="o">&lt;&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">G_iw</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">sum_k</span><span class="o">.</span><span class="n">symm_deg_gf</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span><span class="p">,</span> <span class="n">ish</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span> <span class="o">&lt;&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">G_Refreq</span> <span class="o">&lt;&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">G_w</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">Sigma_Refreq</span> <span class="o">&lt;&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">Sigma_w</span>
+
+        <span class="c1"># if measured in Legendre basis, get G_l from solver too</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;measure_G_l&#39;</span><span class="p">]:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">G_l</span> <span class="o">&lt;&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">G_l</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;measure_G_tau&#39;</span><span class="p">]:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">G_time</span> <span class="o">&lt;&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">G_tau</span>
+
+        <span class="k">return</span>
+
+    <span class="k">def</span> <span class="nf">_hartree_postprocessing</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="sa">r</span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Organize G_freq, G_time, Sigma_freq and G_l from hartree solver</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+
+        <span class="c1"># get everything from solver</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">G0_freq</span> <span class="o">&lt;&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">G0_iw</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">G_freq_unsym</span> <span class="o">&lt;&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">G_iw</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">sum_k</span><span class="o">.</span><span class="n">symm_deg_gf</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span><span class="p">,</span> <span class="n">ish</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span> <span class="o">&lt;&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span>
+        <span class="k">for</span> <span class="n">bl</span><span class="p">,</span> <span class="n">gf</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">Sigma_freq</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">Sigma_freq</span><span class="p">[</span><span class="n">bl</span><span class="p">]</span> <span class="o">&lt;&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">Sigma_HF</span><span class="p">[</span><span class="n">bl</span><span class="p">]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">Sigma_Refreq</span><span class="p">[</span><span class="n">bl</span><span class="p">]</span> <span class="o">&lt;&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">Sigma_HF</span><span class="p">[</span><span class="n">bl</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">G_time</span> <span class="o">&lt;&lt;</span> <span class="n">Fourier</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">interaction_energy</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">interaction_energy</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">DC_energy</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">DC_energy</span><span class="p">()</span>
+
+        <span class="k">return</span>
+
+    <span class="k">def</span> <span class="nf">_inchworm_postprocessing</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="sa">r</span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Organize G_freq, G_time, Sigma_freq and G_l from inchworm solver</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+
+        <span class="k">return</span>
+
+    <span class="k">def</span> <span class="nf">_ftps_postprocessing</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="sa">r</span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Organize G_freq, G_time, Sigma_freq and G_l from ftps solver</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="kn">from</span> <span class="nn">forktps.DiscreteBath</span> <span class="kn">import</span> <span class="n">SigmaDyson</span>
+
+        <span class="c1"># symmetrization of reduced solver G</span>
+        <span class="k">def</span> <span class="nf">symmetrize_opt</span><span class="p">(</span><span class="n">G_in</span><span class="p">,</span> <span class="n">soc</span><span class="p">):</span>
+            <span class="n">G</span> <span class="o">=</span> <span class="n">G_in</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+            <span class="k">if</span> <span class="n">soc</span><span class="p">:</span>
+                <span class="k">def</span> <span class="nf">swap_2</span><span class="p">():</span>
+                    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">):</span>
+                        <span class="n">G</span><span class="p">[</span><span class="s1">&#39;ud_1&#39;</span><span class="p">][</span><span class="n">i</span><span class="p">,</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="n">G</span><span class="p">[</span><span class="s1">&#39;ud_1&#39;</span><span class="p">][</span><span class="n">i</span><span class="p">,</span><span class="mi">2</span><span class="p">]</span>
+                        <span class="n">G</span><span class="p">[</span><span class="s1">&#39;ud_1&#39;</span><span class="p">][</span><span class="mi">2</span><span class="p">,</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="n">G</span><span class="p">[</span><span class="s1">&#39;ud_1&#39;</span><span class="p">][</span><span class="mi">2</span><span class="p">,</span><span class="n">i</span><span class="p">]</span>
+                <span class="n">swap_2</span><span class="p">()</span>
+                <span class="n">G</span><span class="p">[</span><span class="s1">&#39;ud_0&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.5</span><span class="o">*</span><span class="p">(</span><span class="n">G</span><span class="p">[</span><span class="s1">&#39;ud_0&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="n">G</span><span class="p">[</span><span class="s1">&#39;ud_1&#39;</span><span class="p">])</span>
+                <span class="n">G</span><span class="p">[</span><span class="s1">&#39;ud_1&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">G</span><span class="p">[</span><span class="s1">&#39;ud_0&#39;</span><span class="p">]</span>
+                <span class="k">for</span> <span class="n">name</span> <span class="p">,</span> <span class="n">g</span> <span class="ow">in</span> <span class="n">G</span><span class="p">:</span>
+                    <span class="n">g</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.5</span><span class="o">*</span><span class="p">(</span><span class="n">g</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span><span class="o">+</span><span class="n">g</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">])</span>
+                    <span class="n">g</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="n">g</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span>
+                <span class="n">swap_2</span><span class="p">()</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">switch</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">spin</span><span class="p">:</span> <span class="s1">&#39;dn&#39;</span> <span class="k">if</span> <span class="n">spin</span> <span class="o">==</span> <span class="s1">&#39;down&#39;</span> <span class="k">else</span> <span class="s1">&#39;up&#39;</span>
+                <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">mapto</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">calc_mapping</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+                    <span class="n">spin</span><span class="p">,</span> <span class="n">block</span> <span class="o">=</span> <span class="n">key</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;_&#39;</span><span class="p">)</span>
+                    <span class="k">for</span> <span class="n">deg_item</span> <span class="ow">in</span> <span class="n">mapto</span><span class="p">:</span>
+                        <span class="n">map_spin</span><span class="p">,</span> <span class="n">map_block</span> <span class="o">=</span> <span class="n">deg_item</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;_&#39;</span><span class="p">)</span>
+                        <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;mapping </span><span class="si">{</span><span class="n">spin</span><span class="si">}</span><span class="s1">-</span><span class="si">{</span><span class="n">block</span><span class="si">}</span><span class="s1"> to </span><span class="si">{</span><span class="n">map_spin</span><span class="si">}</span><span class="s1">-</span><span class="si">{</span><span class="n">map_block</span><span class="si">}</span><span class="s1">...&#39;</span><span class="p">)</span>
+                        <span class="n">G</span><span class="p">[</span><span class="n">switch</span><span class="p">(</span><span class="n">map_spin</span><span class="p">)]</span><span class="o">.</span><span class="n">data</span><span class="p">[:,</span><span class="nb">int</span><span class="p">(</span><span class="n">map_block</span><span class="p">),</span><span class="nb">int</span><span class="p">(</span><span class="n">map_block</span><span class="p">)]</span> <span class="o">=</span> <span class="n">G</span><span class="p">[</span><span class="n">switch</span><span class="p">(</span><span class="n">spin</span><span class="p">)]</span><span class="o">.</span><span class="n">data</span><span class="p">[:,</span><span class="nb">int</span><span class="p">(</span><span class="n">block</span><span class="p">),</span><span class="nb">int</span><span class="p">(</span><span class="n">block</span><span class="p">)]</span>
+                <span class="c1"># particle-hole symmetry: enforce mirror/point symmetry of G(w)</span>
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;ph_symm&#39;</span><span class="p">]:</span>
+                    <span class="k">for</span> <span class="n">block</span><span class="p">,</span> <span class="n">gf</span> <span class="ow">in</span> <span class="n">G</span><span class="p">:</span>
+                        <span class="n">gf</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">real</span> <span class="o">=</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="p">(</span> <span class="n">gf</span><span class="o">.</span><span class="n">data</span><span class="p">[::</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">real</span> <span class="o">-</span> <span class="n">gf</span><span class="o">.</span><span class="n">data</span><span class="p">[::</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">real</span> <span class="p">)</span>
+                        <span class="n">gf</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">imag</span> <span class="o">=</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="p">(</span> <span class="n">gf</span><span class="o">.</span><span class="n">data</span><span class="p">[::</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">imag</span> <span class="o">+</span> <span class="n">gf</span><span class="o">.</span><span class="n">data</span><span class="p">[::</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">imag</span> <span class="p">)</span>
+            <span class="k">return</span> <span class="n">G</span>
+
+        <span class="k">def</span> <span class="nf">symmetrize</span><span class="p">(</span><span class="n">G</span><span class="p">):</span>
+            <span class="k">return</span> <span class="n">symmetrize_opt</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">soc</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">sum_k</span><span class="o">.</span><span class="n">corr_shells</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">][</span><span class="s1">&#39;SO&#39;</span><span class="p">])</span>
+
+        <span class="k">def</span> <span class="nf">make_positive_definite</span><span class="p">(</span><span class="n">G</span><span class="p">):</span>
+            <span class="c1"># ensure that Delta is positive definite</span>
+            <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">gf</span> <span class="ow">in</span> <span class="n">G</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">orb</span><span class="p">,</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">product</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">gf</span><span class="o">.</span><span class="n">target_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">gf</span><span class="o">.</span><span class="n">mesh</span><span class="p">):</span>
+                    <span class="k">if</span> <span class="n">gf</span><span class="p">[</span><span class="n">orb</span><span class="p">,</span><span class="n">orb</span><span class="p">][</span><span class="n">w</span><span class="p">]</span><span class="o">.</span><span class="n">imag</span> <span class="o">&gt;</span> <span class="mf">0.0</span><span class="p">:</span>
+                        <span class="n">gf</span><span class="p">[</span><span class="n">orb</span><span class="p">,</span><span class="n">orb</span><span class="p">][</span><span class="n">w</span><span class="p">]</span> <span class="o">=</span> <span class="n">gf</span><span class="p">[</span><span class="n">orb</span><span class="p">,</span><span class="n">orb</span><span class="p">][</span><span class="n">w</span><span class="p">]</span><span class="o">.</span><span class="n">real</span> <span class="o">+</span> <span class="mf">0.0</span><span class="n">j</span>
+            <span class="k">return</span> <span class="n">G</span>
+
+        <span class="n">G_w</span> <span class="o">=</span> <span class="n">symmetrize</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">G_w</span><span class="p">)</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">sum_k</span><span class="o">.</span><span class="n">corr_shells</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">][</span><span class="s1">&#39;SO&#39;</span><span class="p">]:</span>
+            <span class="n">G_w</span> <span class="o">=</span> <span class="n">make_positive_definite</span><span class="p">(</span><span class="n">G_w</span><span class="p">)</span>
+
+        <span class="c1"># calculate Sigma_freq via Dyson</span>
+        <span class="c1"># do not use Dyson equation directly, as G0 might have wrong eta</span>
+        <span class="n">Sigma_w_symm</span> <span class="o">=</span> <span class="n">SigmaDyson</span><span class="p">(</span><span class="n">Gret</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">G_ret</span><span class="p">,</span> <span class="n">bath</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">b</span><span class="p">,</span>
+                                  <span class="n">hloc</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">e0</span><span class="p">,</span> <span class="n">mesh</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">Delta_freq_solver</span><span class="o">.</span><span class="n">mesh</span><span class="p">,</span>
+                                  <span class="n">eta</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;eta&#39;</span><span class="p">],</span> <span class="n">symmG</span><span class="o">=</span><span class="n">symmetrize</span><span class="p">)</span>
+
+        <span class="c1"># convert everything to solver objects</span>
+        <span class="k">for</span> <span class="n">block</span><span class="p">,</span> <span class="n">gf</span> <span class="ow">in</span> <span class="n">G_w</span><span class="p">:</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">sum_k</span><span class="o">.</span><span class="n">corr_shells</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">][</span><span class="s1">&#39;SO&#39;</span><span class="p">]:</span>
+                <span class="n">reverse_convert</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="nb">reversed</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">convert_ftps</span><span class="o">.</span><span class="n">items</span><span class="p">()))</span>
+                <span class="n">sumk_name</span> <span class="o">=</span> <span class="n">reverse_convert</span><span class="p">[</span><span class="n">block</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;_&#39;</span><span class="p">)[</span><span class="mi">0</span><span class="p">]]</span> <span class="o">+</span> <span class="s1">&#39;_0&#39;</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">sumk_name</span> <span class="o">=</span> <span class="n">block</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span><span class="p">[</span><span class="n">sumk_name</span><span class="p">]</span> <span class="o">&lt;&lt;</span> <span class="n">gf</span>
+            <span class="c1"># in FTPS the unsym result is not calculated. Symmetries are used by construction</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">G_freq_unsym</span><span class="p">[</span><span class="n">sumk_name</span><span class="p">]</span> <span class="o">&lt;&lt;</span> <span class="n">gf</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">Sigma_freq</span><span class="p">[</span><span class="n">sumk_name</span><span class="p">]</span> <span class="o">&lt;&lt;</span> <span class="n">Sigma_w_symm</span><span class="p">[</span><span class="n">block</span><span class="p">]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">G_time</span><span class="p">[</span><span class="n">sumk_name</span><span class="p">]</span> <span class="o">&lt;&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">G_ret</span><span class="p">[</span><span class="n">block</span><span class="p">]</span>
+
+        <span class="k">return</span>
+
+
+    <span class="k">def</span> <span class="nf">_ctseg_postprocessing</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="sa">r</span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Organize G_freq, G_time, Sigma_freq and G_l from cthyb solver</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+
+        <span class="k">def</span> <span class="nf">set_Gs_from_G_l</span><span class="p">():</span>
+
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;measure_ft&#39;</span><span class="p">]</span> <span class="ow">and</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1"> !!!!WARNING!!!! </span><span class="se">\n</span><span class="s1"> you enabled both improved estimators and legendre based filtering / sampling. Sigma will be overwritten by legendre result.  </span><span class="se">\n</span><span class="s1"> !!!!WARNING!!!!</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
+
+            <span class="c1"># create new G_freq and G_time</span>
+            <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">g</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">G_l</span><span class="p">:</span>
+                <span class="n">g</span><span class="o">.</span><span class="n">enforce_discontinuity</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">identity</span><span class="p">(</span><span class="n">g</span><span class="o">.</span><span class="n">target_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
+                <span class="c1"># set G_freq from Legendre and Fouriertransform to get G_time</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">set_from_legendre</span><span class="p">(</span><span class="n">g</span><span class="p">)</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">G_time</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">set_from_legendre</span><span class="p">(</span><span class="n">g</span><span class="p">)</span>
+            <span class="c1"># Symmetrize</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span> <span class="o">&lt;&lt;</span> <span class="n">make_hermitian</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">G_freq_unsym</span> <span class="o">&lt;&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">sum_k</span><span class="o">.</span><span class="n">symm_deg_gf</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span><span class="p">,</span> <span class="n">ish</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">sum_k</span><span class="o">.</span><span class="n">symm_deg_gf</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">G_time</span><span class="p">,</span> <span class="n">ish</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">)</span>
+            <span class="c1"># Dyson equation to get Sigma_freq</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">Sigma_freq</span> <span class="o">&lt;&lt;</span> <span class="n">inverse</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">G0_freq</span><span class="p">)</span> <span class="o">-</span> <span class="n">inverse</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span><span class="p">)</span>
+
+            <span class="k">return</span>
+
+        <span class="c1"># first print average sign</span>
+        <span class="k">if</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">Average sign: </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">average_sign</span><span class="p">))</span>
+        <span class="c1"># get Delta_time from solver</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">Delta_time</span> <span class="o">&lt;&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">Delta_tau</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">G_time</span> <span class="o">&lt;&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">G_tau</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">sum_k</span><span class="o">.</span><span class="n">symm_deg_gf</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">G_time</span><span class="p">,</span> <span class="n">ish</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;measure_gw&#39;</span><span class="p">]:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span> <span class="o">&lt;&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">G_iw</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+                <span class="c1"># create empty moment container (list of np.arrays)</span>
+                <span class="n">Gf_known_moments</span> <span class="o">=</span> <span class="n">make_zero_tail</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span><span class="p">,</span><span class="n">n_moments</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+                <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">bl</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span><span class="o">.</span><span class="n">indices</span><span class="p">):</span>
+                    <span class="c1"># 0 moment is 0, dont touch it, but first moment is 1 for the Gf</span>
+                    <span class="n">Gf_known_moments</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span><span class="p">[</span><span class="n">bl</span><span class="p">]</span><span class="o">.</span><span class="n">target_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span><span class="p">[</span><span class="n">bl</span><span class="p">]</span> <span class="o">&lt;&lt;</span> <span class="n">Fourier</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">G_time</span><span class="p">[</span><span class="n">bl</span><span class="p">],</span> <span class="n">Gf_known_moments</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span> <span class="o">&lt;&lt;</span> <span class="n">mpi</span><span class="o">.</span><span class="n">bcast</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span> <span class="o">&lt;&lt;</span> <span class="n">make_hermitian</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">G_freq_unsym</span> <span class="o">&lt;&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">sum_k</span><span class="o">.</span><span class="n">symm_deg_gf</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span><span class="p">,</span> <span class="n">ish</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">icrsh</span><span class="p">)</span>
+
+        <span class="c1"># if measured in Legendre basis, get G_l from solver too</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;measure_gl&#39;</span><span class="p">]:</span>
+            <span class="c1"># store original G_time into G_time_orig</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">G_time_orig</span> <span class="o">&lt;&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">G_tau</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">G_l</span> <span class="o">&lt;&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">G_l</span>
+            <span class="c1"># get G_time, G_freq, Sigma_freq from G_l</span>
+            <span class="n">set_Gs_from_G_l</span><span class="p">()</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;legendre_fit&#39;</span><span class="p">]:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">G_time_orig</span> <span class="o">&lt;&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">G_tau</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">G_l</span> <span class="o">&lt;&lt;</span> <span class="n">legendre_filter</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">G_time</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;n_l&#39;</span><span class="p">])</span>
+            <span class="c1"># get G_time, G_freq, Sigma_freq from G_l</span>
+            <span class="n">set_Gs_from_G_l</span><span class="p">()</span>
+        <span class="c1"># if improved estimators are turned on calc Sigma from F_tau, otherwise:</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;measure_ft&#39;</span><span class="p">]:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">F_freq</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">F_freq</span> <span class="o">&lt;&lt;</span> <span class="mf">0.0</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">F_time</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">G_time</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">F_time</span> <span class="o">&lt;&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">F_tau</span>
+            <span class="n">F_known_moments</span> <span class="o">=</span> <span class="n">make_zero_tail</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">F_freq</span><span class="p">,</span> <span class="n">n_moments</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+                <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">bl</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">F_freq</span><span class="o">.</span><span class="n">indices</span><span class="p">):</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">F_freq</span><span class="p">[</span><span class="n">bl</span><span class="p">]</span> <span class="o">&lt;&lt;</span> <span class="n">Fourier</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">F_tau</span><span class="p">[</span><span class="n">bl</span><span class="p">],</span> <span class="n">F_known_moments</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+                <span class="c1"># fit tail of improved estimator and G_freq</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">F_freq</span> <span class="o">&lt;&lt;</span> <span class="n">_gf_fit_tail_fraction</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">F_freq</span><span class="p">,</span> <span class="n">fraction</span><span class="o">=</span><span class="mf">0.9</span><span class="p">,</span> <span class="n">replace</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">known_moments</span><span class="o">=</span><span class="n">F_known_moments</span><span class="p">)</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span> <span class="o">&lt;&lt;</span> <span class="n">_gf_fit_tail_fraction</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span> <span class="p">,</span><span class="n">fraction</span><span class="o">=</span><span class="mf">0.9</span><span class="p">,</span> <span class="n">replace</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">known_moments</span><span class="o">=</span><span class="n">Gf_known_moments</span><span class="p">)</span>
+
+            <span class="bp">self</span><span class="o">.</span><span class="n">F_freq</span> <span class="o">&lt;&lt;</span> <span class="n">mpi</span><span class="o">.</span><span class="n">bcast</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">F_freq</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span> <span class="o">&lt;&lt;</span> <span class="n">mpi</span><span class="o">.</span><span class="n">bcast</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span><span class="p">)</span>
+            <span class="k">for</span> <span class="n">block</span><span class="p">,</span> <span class="n">fw</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">F_freq</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">iw</span> <span class="ow">in</span> <span class="n">fw</span><span class="o">.</span><span class="n">mesh</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">Sigma_freq</span><span class="p">[</span><span class="n">block</span><span class="p">][</span><span class="n">iw</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">F_freq</span><span class="p">[</span><span class="n">block</span><span class="p">][</span><span class="n">iw</span><span class="p">]</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span><span class="p">[</span><span class="n">block</span><span class="p">][</span><span class="n">iw</span><span class="p">]</span>
+
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">!!!! WARNING !!!! tail of solver output not handled! Turn on either measure_ft, legendre_fit, or measure_gl</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">Sigma_freq</span> <span class="o">&lt;&lt;</span> <span class="n">inverse</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">G0_freq</span><span class="p">)</span> <span class="o">-</span> <span class="n">inverse</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">G_freq</span><span class="p">)</span>
+
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver_params</span><span class="p">[</span><span class="s1">&#39;measure_hist&#39;</span><span class="p">]:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">perturbation_order</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">triqs_solver</span><span class="o">.</span><span class="n">histogram</span>
+
+        <span class="k">return</span></div>
+</pre></div>
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.</p>
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+<head>
+  <meta charset="utf-8" />
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+              <div class="version">
+                3.2.0
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+<li class="toctree-l2"><a class="reference internal" href="../install.html#installation-via-pip">Installation via pip</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#dft-interface-notes">DFT interface notes</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/w90_interface.html">Wannier90 interface</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/w90_interface.html#orbital-order-in-the-w90-converter">orbital order in the W90 converter</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/vasp_csc.html">Interface to VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#general-remarks">General remarks</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#file-description">file description</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#workflow">Workflow:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#important-flags">important flags:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#version-and-compilation">version and compilation:</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#input-output">Input/Output</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../input_output/DMFT_input/input.html">Input</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/general.html">[general]: General parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/solver.html">[solver]: solver specific parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/dft.html">[dft]: DFT related inputs</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/advanced.html">[advanced]: Advanced inputs</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../input_output/DMFT_output/results.html">Output / results</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_output/results.html#group-structure">Group structure</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_output/results.html#subgroups">Subgroups</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#further-details-for-running">Further details for running</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/docker.html">Docker</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#building-the-docker-image">Building the docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#pulling-a-docker-image">Pulling a docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../md_notes/run_locally.html">Run on your machine</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#module-reference-manual">Module reference manual</a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="../_ref/dmft_tools.manipulate_chemical_potential.html">dmft_tools.manipulate_chemical_potential</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/dmft_tools.matheval.html">dmft_tools.matheval</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../_ref/dmft_tools.solver.html">dmft_tools.solver</a></li>
+</ul>
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+<li class="toctree-l4"><a class="reference internal" href="../_ref/io_tools.dict_to_h5.html">io_tools.dict_to_h5</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/io_tools.postproc_toml_dict.html">io_tools.postproc_toml_dict</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/io_tools.verify_input_params.html">io_tools.verify_input_params</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../_ref/postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/postprocessing.eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/postprocessing.maxent_gf_imp.html">postprocessing.maxent_gf_imp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/postprocessing.maxent_gf_latt.html">postprocessing.maxent_gf_latt</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/postprocessing.maxent_sigma.html">postprocessing.maxent_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../_ref/util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../issues.html">Support &amp; contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../issues.html#seeking-help">Seeking help</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../issues.html#improving-solid-dmft">Improving solid_dmft</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../issues.html#reporting-issues">Reporting issues</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../ChangeLog.html">Changelog</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-2-0">Version 3.2.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#general">General</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#fix">fix</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#build">build</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#doc">doc</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#test">test</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-5">Version 3.1.5</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-4">Version 3.1.4</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-3">Version 3.1.3</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-2">Version 3.1.2</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-1">Version 3.1.1</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-0">Version 3.1.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-0-0">Version 3.0.0</a></li>
+</ul>
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+             
+  <h1>All modules for which code is available</h1>
+<ul><li><a href="csc_flow.html">csc_flow</a></li>
+<li><a href="dft_managers/mpi_helpers.html">dft_managers.mpi_helpers</a></li>
+<li><a href="dft_managers/qe_manager.html">dft_managers.qe_manager</a></li>
+<li><a href="dft_managers/vasp_manager.html">dft_managers.vasp_manager</a></li>
+<li><a href="dmft_cycle.html">dmft_cycle</a></li>
+<li><a href="dmft_tools/afm_mapping.html">dmft_tools.afm_mapping</a></li>
+<li><a href="dmft_tools/convergence.html">dmft_tools.convergence</a></li>
+<li><a href="dmft_tools/formatter.html">dmft_tools.formatter</a></li>
+<li><a href="dmft_tools/initial_self_energies.html">dmft_tools.initial_self_energies</a></li>
+<li><a href="dmft_tools/interaction_hamiltonian.html">dmft_tools.interaction_hamiltonian</a></li>
+<li><a href="dmft_tools/legendre_filter.html">dmft_tools.legendre_filter</a></li>
+<li><a href="dmft_tools/manipulate_chemical_potential.html">dmft_tools.manipulate_chemical_potential</a></li>
+<li><a href="dmft_tools/matheval.html">dmft_tools.matheval</a></li>
+<li><a href="dmft_tools/observables.html">dmft_tools.observables</a></li>
+<li><a href="dmft_tools/results_to_archive.html">dmft_tools.results_to_archive</a></li>
+<li><a href="dmft_tools/solver.html">dmft_tools.solver</a></li>
+<li><a href="io_tools/dict_to_h5.html">io_tools.dict_to_h5</a></li>
+<li><a href="io_tools/postproc_toml_dict.html">io_tools.postproc_toml_dict</a></li>
+<li><a href="io_tools/verify_input_params.html">io_tools.verify_input_params</a></li>
+<li><a href="postprocessing/eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li><a href="postprocessing/eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
+<li><a href="postprocessing/maxent_gf_imp.html">postprocessing.maxent_gf_imp</a></li>
+<li><a href="postprocessing/maxent_gf_latt.html">postprocessing.maxent_gf_latt</a></li>
+<li><a href="postprocessing/maxent_sigma.html">postprocessing.maxent_sigma</a></li>
+<li><a href="postprocessing/pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li><a href="postprocessing/plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+<li><a href="util/write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+
+           </div>
+          </div>
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+    <p>&#169; Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.</p>
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diff --git a/_modules/io_tools/dict_to_h5.html b/_modules/io_tools/dict_to_h5.html
new file mode 100644
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+<!DOCTYPE html>
+<html class="writer-html5" lang="en" >
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>io_tools.dict_to_h5 &mdash; solid_dmft  documentation</title>
+      <link rel="stylesheet" href="../../_static/pygments.css" type="text/css" />
+      <link rel="stylesheet" href="../../_static/css/custom.css" type="text/css" />
+      <link rel="stylesheet" href="../../_static/plot_directive.css" type="text/css" />
+    <link rel="shortcut icon" href="../../_static/favicon.ico"/>
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+    <script src="../../_static/js/theme.js"></script>
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+            <a href="../../index.html" class="icon icon-home"> solid_dmft
+            <img src="../../_static/logo_no_text.png" class="logo" alt="Logo"/>
+          </a>
+              <div class="version">
+                3.2.0
+              </div>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="../../search.html" method="get">
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+<li class="toctree-l1"><a class="reference internal" href="../../install.html">Installation</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#installation-via-pip">Installation via pip</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#manual-installation-via-cmake">Manual installation via CMake</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#docker-files-images">Docker files &amp; images</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#version-compatibility">Version compatibility</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#custom-cmake-options">Custom CMake options</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../documentation.html">Documentation</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#code-structure">Code structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#dft-interface-notes">DFT interface notes</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/w90_interface.html">Wannier90 interface</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/w90_interface.html#orbital-order-in-the-w90-converter">orbital order in the W90 converter</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/vasp_csc.html">Interface to VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#general-remarks">General remarks</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#file-description">file description</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#workflow">Workflow:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#important-flags">important flags:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#version-and-compilation">version and compilation:</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#input-output">Input/Output</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../input_output/DMFT_input/input.html">Input</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/general.html">[general]: General parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/solver.html">[solver]: solver specific parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/dft.html">[dft]: DFT related inputs</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/advanced.html">[advanced]: Advanced inputs</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../input_output/DMFT_output/results.html">Output / results</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_output/results.html#group-structure">Group structure</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_output/results.html#subgroups">Subgroups</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#further-details-for-running">Further details for running</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/docker.html">Docker</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#building-the-docker-image">Building the docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#pulling-a-docker-image">Pulling a docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_locally.html">Run on your machine</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#module-reference-manual">Module reference manual</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/csc_flow.html">csc_flow</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/dft_managers.html">dft_managers</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.mpi_helpers.html">dft_managers.mpi_helpers</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.qe_manager.html">dft_managers.qe_manager</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.vasp_manager.html">dft_managers.vasp_manager</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/dmft_cycle.html">dmft_cycle</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/dmft_tools.html">dmft_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.afm_mapping.html">dmft_tools.afm_mapping</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.convergence.html">dmft_tools.convergence</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.formatter.html">dmft_tools.formatter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.greens_functions_mixer.html">dmft_tools.greens_functions_mixer</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.initial_self_energies.html">dmft_tools.initial_self_energies</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.interaction_hamiltonian.html">dmft_tools.interaction_hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.legendre_filter.html">dmft_tools.legendre_filter</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.observables.html">dmft_tools.observables</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.results_to_archive.html">dmft_tools.results_to_archive</a></li>
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+</ul>
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+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.dict_to_h5.html">io_tools.dict_to_h5</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.verify_input_params.html">io_tools.verify_input_params</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_gf_imp.html">postprocessing.maxent_gf_imp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_gf_latt.html">postprocessing.maxent_gf_latt</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_sigma.html">postprocessing.maxent_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-0-0">Version 3.0.0</a></li>
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+  <ul class="wy-breadcrumbs">
+      <li><a href="../../index.html" class="icon icon-home"></a> &raquo;</li>
+          <li><a href="../index.html">Module code</a> &raquo;</li>
+      <li>io_tools.dict_to_h5</li>
+      <li class="wy-breadcrumbs-aside">
+      </li>
+  </ul>
+  <hr/>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <h1>Source code for io_tools.dict_to_h5</h1><div class="highlight"><pre>
+<span></span><span class="kn">from</span> <span class="nn">copy</span> <span class="kn">import</span> <span class="n">deepcopy</span>
+
+<span class="k">def</span> <span class="nf">_iteratively_replace_none</span><span class="p">(</span><span class="n">to_write</span><span class="p">,</span> <span class="n">replace_from</span><span class="p">,</span> <span class="n">replace_with</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot; Limitation: can only replace None with a string, or a string with None. &quot;&quot;&quot;</span>
+    <span class="c1"># First two checks needed because comparison to triqs many-body operator fails</span>
+    <span class="k">if</span> <span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">to_write</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span> <span class="ow">or</span> <span class="n">to_write</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">and</span> <span class="n">to_write</span> <span class="o">==</span> <span class="n">replace_from</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">replace_with</span>
+
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">to_write</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">to_write</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+            <span class="n">to_write</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">_iteratively_replace_none</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">replace_from</span><span class="p">,</span> <span class="n">replace_with</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">to_write</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">to_write</span><span class="p">):</span>
+            <span class="n">to_write</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">_iteratively_replace_none</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">replace_from</span><span class="p">,</span> <span class="n">replace_with</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">to_write</span>
+
+<div class="viewcode-block" id="prep_params_for_h5"><a class="viewcode-back" href="../../_ref/io_tools.dict_to_h5.html#io_tools.dict_to_h5.prep_params_for_h5">[docs]</a><span class="k">def</span> <span class="nf">prep_params_for_h5</span><span class="p">(</span><span class="n">dict_to_write</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot; Replace all NoneType with a string &#39;none&#39; to be able to write to h5. &quot;&quot;&quot;</span>
+    <span class="k">return</span> <span class="n">_iteratively_replace_none</span><span class="p">(</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">dict_to_write</span><span class="p">),</span> <span class="kc">None</span><span class="p">,</span> <span class="s1">&#39;none&#39;</span><span class="p">)</span></div>
+
+<span class="c1"># Not sure if the reverse route is actually needed</span>
+<div class="viewcode-block" id="prep_params_from_h5"><a class="viewcode-back" href="../../_ref/io_tools.dict_to_h5.html#io_tools.dict_to_h5.prep_params_from_h5">[docs]</a><span class="k">def</span> <span class="nf">prep_params_from_h5</span><span class="p">(</span><span class="n">dict_to_read</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot; Replace all &#39;none&#39; strings with NoneType to parse the dict coming from h5. &quot;&quot;&quot;</span>
+    <span class="k">return</span> <span class="n">_iteratively_replace_none</span><span class="p">(</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">dict_to_read</span><span class="p">),</span> <span class="s1">&#39;none&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span></div>
+</pre></div>
+
+           </div>
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+
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+
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+    <p>&#169; Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.</p>
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+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#code-structure">Code structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#dft-interface-notes">DFT interface notes</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/w90_interface.html">Wannier90 interface</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/w90_interface.html#orbital-order-in-the-w90-converter">orbital order in the W90 converter</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/vasp_csc.html">Interface to VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#general-remarks">General remarks</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#file-description">file description</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#workflow">Workflow:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#important-flags">important flags:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#version-and-compilation">version and compilation:</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#input-output">Input/Output</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../input_output/DMFT_input/input.html">Input</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/general.html">[general]: General parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/solver.html">[solver]: solver specific parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/dft.html">[dft]: DFT related inputs</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/advanced.html">[advanced]: Advanced inputs</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../input_output/DMFT_output/results.html">Output / results</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_output/results.html#group-structure">Group structure</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_output/results.html#subgroups">Subgroups</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#further-details-for-running">Further details for running</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/docker.html">Docker</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#building-the-docker-image">Building the docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#pulling-a-docker-image">Pulling a docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_locally.html">Run on your machine</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#module-reference-manual">Module reference manual</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/csc_flow.html">csc_flow</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/dft_managers.html">dft_managers</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.mpi_helpers.html">dft_managers.mpi_helpers</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.qe_manager.html">dft_managers.qe_manager</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.vasp_manager.html">dft_managers.vasp_manager</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/dmft_cycle.html">dmft_cycle</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/dmft_tools.html">dmft_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.afm_mapping.html">dmft_tools.afm_mapping</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.convergence.html">dmft_tools.convergence</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.formatter.html">dmft_tools.formatter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.greens_functions_mixer.html">dmft_tools.greens_functions_mixer</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.initial_self_energies.html">dmft_tools.initial_self_energies</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.interaction_hamiltonian.html">dmft_tools.interaction_hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.legendre_filter.html">dmft_tools.legendre_filter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.manipulate_chemical_potential.html">dmft_tools.manipulate_chemical_potential</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.matheval.html">dmft_tools.matheval</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.observables.html">dmft_tools.observables</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.results_to_archive.html">dmft_tools.results_to_archive</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.solver.html">dmft_tools.solver</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/io_tools.html">io_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.dict_to_h5.html">io_tools.dict_to_h5</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.postproc_toml_dict.html">io_tools.postproc_toml_dict</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.verify_input_params.html">io_tools.verify_input_params</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_gf_imp.html">postprocessing.maxent_gf_imp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_gf_latt.html">postprocessing.maxent_gf_latt</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_sigma.html">postprocessing.maxent_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../issues.html">Support &amp; contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../issues.html#seeking-help">Seeking help</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../issues.html#improving-solid-dmft">Improving solid_dmft</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../issues.html#reporting-issues">Reporting issues</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../ChangeLog.html">Changelog</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-2-0">Version 3.2.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#general">General</a></li>
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+          <li><a href="../index.html">Module code</a> &raquo;</li>
+      <li>io_tools.postproc_toml_dict</li>
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+             
+  <h1>Source code for io_tools.postproc_toml_dict</h1><div class="highlight"><pre>
+<span></span><span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span><span class="p">,</span> <span class="n">Dict</span><span class="p">,</span> <span class="n">List</span><span class="p">,</span> <span class="n">Union</span>
+<span class="kn">import</span> <span class="nn">copy</span>
+
+<span class="n">ParamDict</span> <span class="o">=</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">]</span>
+<span class="n">FullConfig</span> <span class="o">=</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Union</span><span class="p">[</span><span class="n">ParamDict</span><span class="p">,</span> <span class="n">List</span><span class="p">[</span><span class="n">ParamDict</span><span class="p">]]]</span>
+
+<span class="k">def</span> <span class="nf">_verify_dict_is_param_dict</span><span class="p">(</span><span class="n">d</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+    <span class="sd">&quot;&quot;&quot; Checks that the input is of type ParamDict. &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">d</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Expected a dict, but got </span><span class="si">{</span><span class="n">d</span><span class="si">}</span><span class="s1"> of type </span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">d</span><span class="p">)</span><span class="si">}</span><span class="s1">.&#39;</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">d</span><span class="p">:</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Expected a string as key, but got </span><span class="si">{</span><span class="n">key</span><span class="si">}</span><span class="s1"> of type </span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">key</span><span class="p">)</span><span class="si">}</span><span class="s1">.&#39;</span><span class="p">)</span>
+
+<span class="k">def</span> <span class="nf">_verify_dict_is_full_config</span><span class="p">(</span><span class="n">d</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+    <span class="sd">&quot;&quot;&quot; Checks that dict is of type FullConfig. &quot;&quot;&quot;</span>
+    <span class="c1"># Checks that no keys outside a section</span>
+    <span class="c1"># This is, that d is of type Dict[str, Union[Dict, List]]</span>
+    <span class="k">for</span> <span class="n">section_name</span><span class="p">,</span> <span class="n">section</span> <span class="ow">in</span> <span class="n">d</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">section</span><span class="p">,</span> <span class="p">(</span><span class="nb">dict</span><span class="p">,</span> <span class="nb">list</span><span class="p">)):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Key &quot;</span><span class="si">{</span><span class="n">section_name</span><span class="si">}</span><span class="s1">&quot; found outside of a section.&#39;</span><span class="p">)</span>
+
+    <span class="c1"># Checks that entries are ParamDicts or List[ParamDict]</span>
+    <span class="k">for</span> <span class="n">section</span> <span class="ow">in</span> <span class="n">d</span><span class="o">.</span><span class="n">values</span><span class="p">():</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">section</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+            <span class="k">for</span> <span class="n">entry</span> <span class="ow">in</span> <span class="n">section</span><span class="p">:</span>
+                <span class="n">_verify_dict_is_param_dict</span><span class="p">(</span><span class="n">entry</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">_verify_dict_is_param_dict</span><span class="p">(</span><span class="n">section</span><span class="p">)</span>
+
+<span class="k">def</span> <span class="nf">_verify_restrictions_on_default_and_config</span><span class="p">(</span><span class="n">cfg_inp</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">],</span> <span class="n">cfg_def</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">],</span> <span class="n">match_key</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">str</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+    <span class="sd">&quot;&quot;&quot; Checks that the restrictions described in the docstring of merge_config_with_default are met. &quot;&quot;&quot;</span>
+    <span class="c1"># Checks that type of cfg_def dict is FullConfig</span>
+    <span class="n">_verify_dict_is_full_config</span><span class="p">(</span><span class="n">cfg_def</span><span class="p">)</span>
+
+    <span class="c1"># Checks that keys listed in match_key are lists and all other are dicts</span>
+    <span class="k">for</span> <span class="n">section_name</span><span class="p">,</span> <span class="n">section</span> <span class="ow">in</span> <span class="n">cfg_def</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+        <span class="k">if</span> <span class="n">section_name</span> <span class="ow">in</span> <span class="n">match_key</span> <span class="ow">and</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">section</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;&quot;</span><span class="si">{</span><span class="n">section_name</span><span class="si">}</span><span class="s1">&quot; is in match_key so it should be a list in the default config.&#39;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">section_name</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">match_key</span> <span class="ow">and</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">section</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;&quot;</span><span class="si">{</span><span class="n">section_name</span><span class="si">}</span><span class="s1">&quot; is not in match_key so it should be a dict in the default config.&#39;</span><span class="p">)</span>
+
+    <span class="c1"># Checks that no sections in config that are not in default</span>
+    <span class="n">unknown_sections</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">cfg_inp</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span> <span class="o">-</span> <span class="nb">set</span><span class="p">(</span><span class="n">cfg_def</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
+    <span class="k">if</span> <span class="n">unknown_sections</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Unknown sections were found in the config file: &#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">unknown_sections</span><span class="p">)</span>
+                         <span class="o">+</span> <span class="s1">&#39;. Please refer to the default config file for valid keys.&#39;</span><span class="p">)</span>
+
+    <span class="c1"># Checks that all sections listed in match_key are in default and config</span>
+    <span class="n">unmatched_sections</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">match_key</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span> <span class="o">-</span> <span class="nb">set</span><span class="p">(</span><span class="n">cfg_def</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
+    <span class="k">if</span> <span class="n">unmatched_sections</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Sections &#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">unmatched_sections</span><span class="p">)</span> <span class="o">+</span> <span class="s1">&#39; found in match_key &#39;</span>
+                            <span class="o">+</span> <span class="s1">&#39;that are not in the default config.&#39;</span><span class="p">)</span>
+
+    <span class="n">unmatched_sections</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">match_key</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span> <span class="o">-</span> <span class="nb">set</span><span class="p">(</span><span class="n">cfg_inp</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
+    <span class="k">if</span> <span class="n">unmatched_sections</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Sections &#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">unmatched_sections</span><span class="p">)</span> <span class="o">+</span> <span class="s1">&#39; found in match_key &#39;</span>
+                            <span class="o">+</span> <span class="s1">&#39;that are not in the config.&#39;</span><span class="p">)</span>
+
+    <span class="c1"># Checks type of config dict</span>
+    <span class="n">_verify_dict_is_full_config</span><span class="p">(</span><span class="n">cfg_inp</span><span class="p">)</span>
+
+<span class="k">def</span> <span class="nf">_apply_default_values</span><span class="p">(</span><span class="n">cfg_inp</span><span class="p">:</span> <span class="n">FullConfig</span><span class="p">,</span> <span class="n">cfg_def</span><span class="p">:</span> <span class="n">FullConfig</span><span class="p">,</span> <span class="n">match_key</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">str</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="n">FullConfig</span><span class="p">:</span>
+    <span class="sd">&quot;&quot;&quot; Fills in the default values where the input config does not specify a value. &quot;&quot;&quot;</span>
+    <span class="n">output</span><span class="p">:</span> <span class="n">FullConfig</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="k">for</span> <span class="n">section_name</span><span class="p">,</span> <span class="n">section</span> <span class="ow">in</span> <span class="n">cfg_def</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">section</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+            <span class="n">key</span> <span class="o">=</span> <span class="n">match_key</span><span class="p">[</span><span class="n">section_name</span><span class="p">]</span>
+            <span class="n">output</span><span class="p">[</span><span class="n">section_name</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="k">for</span> <span class="n">entry</span> <span class="ow">in</span> <span class="n">cfg_inp</span><span class="p">[</span><span class="n">section_name</span><span class="p">]:</span>
+                <span class="c1"># Finds matching section through match_key in cfg_def</span>
+                <span class="k">for</span> <span class="n">default_entry</span> <span class="ow">in</span> <span class="n">section</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="n">default_entry</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">==</span> <span class="n">entry</span><span class="p">[</span><span class="n">key</span><span class="p">]:</span>
+                        <span class="n">output</span><span class="p">[</span><span class="n">section_name</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">default_entry</span><span class="p">))</span>
+                        <span class="k">break</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;No matching section with same &quot;</span><span class="si">{</span><span class="n">section_name</span><span class="si">}</span><span class="s1">.</span><span class="si">{</span><span class="n">key</span><span class="si">}</span><span class="s1">&quot;=&quot;</span><span class="si">{</span><span class="n">entry</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="si">}</span><span class="s1">&quot; found in defaults.&#39;</span><span class="p">)</span>
+                <span class="c1"># Updates config values in output</span>
+                <span class="n">unknown_keys</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">entry</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span> <span class="o">-</span> <span class="nb">set</span><span class="p">(</span><span class="n">output</span><span class="p">[</span><span class="n">section_name</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
+                <span class="k">if</span> <span class="n">unknown_keys</span><span class="p">:</span>
+                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Unknown keys </span><span class="si">{</span><span class="n">unknown_keys</span><span class="si">}</span><span class="s1"> found in section &quot;</span><span class="si">{</span><span class="n">section_name</span><span class="si">}</span><span class="s1">&quot;. &#39;</span>
+                                     <span class="s1">&#39;All valid keys have to be in the default config.&#39;</span><span class="p">)</span>
+                <span class="n">output</span><span class="p">[</span><span class="n">section_name</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">entry</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">entry</span> <span class="o">=</span> <span class="n">cfg_inp</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">section_name</span><span class="p">,</span> <span class="p">{})</span>
+            <span class="n">output</span><span class="p">[</span><span class="n">section_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">section</span><span class="p">)</span>
+            <span class="n">unknown_keys</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">entry</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span> <span class="o">-</span> <span class="nb">set</span><span class="p">(</span><span class="n">output</span><span class="p">[</span><span class="n">section_name</span><span class="p">]</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
+            <span class="k">if</span> <span class="n">unknown_keys</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Unknown keys </span><span class="si">{</span><span class="n">unknown_keys</span><span class="si">}</span><span class="s1"> found in section &quot;</span><span class="si">{</span><span class="n">section_name</span><span class="si">}</span><span class="s1">&quot;. &#39;</span>
+                                 <span class="s1">&#39;All valid keys have to be in the default config.&#39;</span><span class="p">)</span>
+            <span class="n">output</span><span class="p">[</span><span class="n">section_name</span><span class="p">]</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">entry</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">output</span>
+
+<span class="k">def</span> <span class="nf">_replace_none</span><span class="p">(</span><span class="n">d</span><span class="p">:</span> <span class="n">ParamDict</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+    <span class="sd">&quot;&quot;&quot; Replace &#39;&lt;none&gt;&#39; by None in a ParamDict. This also works inside lists. &quot;&quot;&quot;</span>
+    <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">d</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+        <span class="k">if</span> <span class="n">value</span> <span class="o">==</span> <span class="s1">&#39;&lt;none&gt;&#39;</span><span class="p">:</span>
+            <span class="n">d</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+            <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">value</span><span class="p">):</span>
+                <span class="k">if</span> <span class="n">v</span> <span class="o">==</span> <span class="s1">&#39;&lt;none&gt;&#39;</span><span class="p">:</span>
+                    <span class="n">value</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
+
+<span class="k">def</span> <span class="nf">_verify_all_mandatory_fields_present</span><span class="p">(</span><span class="n">d</span><span class="p">:</span> <span class="n">ParamDict</span><span class="p">,</span> <span class="n">section_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+    <span class="sd">&quot;&quot;&quot; Verifies that all fields with &quot;&lt;no default&gt;&quot; have been replaced after reading in the config. &quot;&quot;&quot;</span>
+    <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">d</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+        <span class="k">if</span> <span class="n">value</span> <span class="o">==</span> <span class="s1">&#39;&lt;no default&gt;&#39;</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;&quot;</span><span class="si">{</span><span class="n">key</span><span class="si">}</span><span class="s1">&quot; in section &quot;</span><span class="si">{</span><span class="n">section_name</span><span class="si">}</span><span class="s1">&quot; is mandatory and was left empty.&#39;</span><span class="p">)</span>
+
+<span class="k">def</span> <span class="nf">_resolve_references</span><span class="p">(</span><span class="n">d</span><span class="p">:</span> <span class="n">ParamDict</span><span class="p">,</span> <span class="n">section_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">output</span><span class="p">:</span> <span class="n">FullConfig</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+    <span class="sd">&quot;&quot;&quot; Resolve all references of type &quot;&lt;section.key&gt;&quot; in a ParamDict. &quot;&quot;&quot;</span>
+    <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">d</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span> <span class="ow">and</span> <span class="n">value</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">&#39;&lt;&#39;</span><span class="p">)</span> <span class="ow">and</span> <span class="n">value</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s1">&#39;&gt;&#39;</span><span class="p">):</span>
+            <span class="n">ref_key</span> <span class="o">=</span> <span class="n">value</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;.&#39;</span><span class="p">)</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">ref_key</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Invalid reference &quot;</span><span class="si">{</span><span class="n">value</span><span class="si">}</span><span class="s1">&quot; in section &quot;</span><span class="si">{</span><span class="n">section_name</span><span class="si">}</span><span class="s1">&quot;.&#39;</span><span class="p">)</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">output</span><span class="p">[</span><span class="n">ref_key</span><span class="p">[</span><span class="mi">0</span><span class="p">]],</span> <span class="nb">list</span><span class="p">):</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Invalid reference &quot;</span><span class="si">{</span><span class="n">value</span><span class="si">}</span><span class="s1">&quot; to listed section &quot;</span><span class="si">{</span><span class="n">section_name</span><span class="si">}</span><span class="s1">&quot;.&#39;</span><span class="p">)</span>
+
+            <span class="n">referenced_val</span> <span class="o">=</span> <span class="n">output</span><span class="p">[</span><span class="n">ref_key</span><span class="p">[</span><span class="mi">0</span><span class="p">]][</span><span class="n">ref_key</span><span class="p">[</span><span class="mi">1</span><span class="p">]]</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">referenced_val</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span> <span class="ow">and</span> <span class="n">referenced_val</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">&#39;&lt;&#39;</span><span class="p">)</span> <span class="ow">and</span> <span class="n">referenced_val</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s1">&#39;&gt;&#39;</span><span class="p">):</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;&quot;</span><span class="si">{</span><span class="n">ref_key</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="si">}</span><span class="s1">&quot; in section &quot;</span><span class="si">{</span><span class="n">ref_key</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s1">&quot; is a reference itself.&#39;</span><span class="p">)</span>
+            <span class="n">d</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">referenced_val</span>
+
+<span class="c1"># type hints currently not supported by sphinx autodoc</span>
+<span class="c1"># def merge_config_with_default(cfg_inp: Dict[str, Any], cfg_def: Dict[str, Any],</span>
+<span class="c1">#                               match_key: Dict[str, str] = {}) -&gt; FullConfig:</span>
+<div class="viewcode-block" id="merge_config_with_default"><a class="viewcode-back" href="../../_ref/io_tools.postproc_toml_dict.html#io_tools.postproc_toml_dict.merge_config_with_default">[docs]</a><span class="k">def</span> <span class="nf">merge_config_with_default</span><span class="p">(</span><span class="n">cfg_inp</span><span class="p">,</span> <span class="n">cfg_def</span><span class="p">,</span> <span class="n">match_key</span><span class="o">=</span><span class="p">{}):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Merge a TOML config dict with a default TOML dict.</span>
+<span class="sd">    The default dict dictates the structure of the input:</span>
+
+<span class="sd">    - Only sections and keys in the default are allowed in the input</span>
+<span class="sd">    - All sections listed in match_key must be lists of dicts in the default</span>
+<span class="sd">      and can be lists of dicts or dicts in the config</span>
+
+<span class="sd">    The dicts allows for the following extensions:</span>
+
+<span class="sd">    - Mandatory inputs for all calculations indicated by &quot;&lt;no default&gt;&quot;</span>
+<span class="sd">    - None indicated by &quot;&lt;none&gt;&quot;. Also works inside lists</span>
+<span class="sd">    - References within the dictionary indicated by &quot;&lt;section.key&gt;&quot;</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    cfg_inp : dict</span>
+<span class="sd">        The input config dict</span>
+<span class="sd">    cfg_def : dict</span>
+<span class="sd">        The default config dict</span>
+<span class="sd">    match_key : dict, optional</span>
+<span class="sd">        A dictionary that contains section/key pairs to map entries in listed sections</span>
+<span class="sd">        between the input and default config.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    dict</span>
+<span class="sd">        The merged config dict</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># Check restrictions and makes sure that config and default are of type FullConfig</span>
+    <span class="n">_verify_restrictions_on_default_and_config</span><span class="p">(</span><span class="n">cfg_inp</span><span class="p">,</span> <span class="n">cfg_def</span><span class="p">,</span> <span class="n">match_key</span><span class="p">)</span>
+
+    <span class="c1"># Checks that keys not listed in match_key are dicts</span>
+    <span class="c1"># The others can be lists or dicts. This differs from cfg_def</span>
+    <span class="c1"># to allow users to use multiple sections or not</span>
+    <span class="k">for</span> <span class="n">section_name</span><span class="p">,</span> <span class="n">section</span> <span class="ow">in</span> <span class="n">cfg_inp</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+        <span class="k">if</span> <span class="n">section_name</span> <span class="ow">in</span> <span class="n">match_key</span> <span class="ow">and</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">section</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+            <span class="n">cfg_inp</span><span class="p">[</span><span class="n">section_name</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">section</span><span class="p">]</span>
+        <span class="k">if</span> <span class="n">section_name</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">match_key</span> <span class="ow">and</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">section</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;&quot;</span><span class="si">{</span><span class="n">section_name</span><span class="si">}</span><span class="s1">&quot; should be a dict and not a list in the config.&#39;</span><span class="p">)</span>
+
+    <span class="c1"># Merges config with default</span>
+    <span class="n">output</span> <span class="o">=</span> <span class="n">_apply_default_values</span><span class="p">(</span><span class="n">cfg_inp</span><span class="p">,</span> <span class="n">cfg_def</span><span class="p">,</span> <span class="n">match_key</span><span class="p">)</span>
+
+    <span class="c1"># Converts &quot;&lt;none&gt;&quot; to None, checks that no mandatory fields were left empty</span>
+    <span class="c1"># and resolves referencing defaults</span>
+    <span class="k">for</span> <span class="n">section_name</span><span class="p">,</span> <span class="n">section</span> <span class="ow">in</span> <span class="n">output</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">section</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
+            <span class="n">_replace_none</span><span class="p">(</span><span class="n">section</span><span class="p">)</span>
+            <span class="n">_verify_all_mandatory_fields_present</span><span class="p">(</span><span class="n">section</span><span class="p">,</span> <span class="n">section_name</span><span class="p">)</span>
+            <span class="n">_resolve_references</span><span class="p">(</span><span class="n">section</span><span class="p">,</span> <span class="n">section_name</span><span class="p">,</span> <span class="n">output</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">entry</span> <span class="ow">in</span> <span class="n">section</span><span class="p">:</span>
+                <span class="n">_replace_none</span><span class="p">(</span><span class="n">entry</span><span class="p">)</span>
+                <span class="n">_verify_all_mandatory_fields_present</span><span class="p">(</span><span class="n">entry</span><span class="p">,</span> <span class="n">section_name</span><span class="p">)</span>
+                <span class="n">_resolve_references</span><span class="p">(</span><span class="n">entry</span><span class="p">,</span> <span class="n">section_name</span><span class="p">,</span> <span class="n">output</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">output</span></div>
+</pre></div>
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.</p>
+  </div>
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
+
+</footer>
+        </div>
+      </div>
+    </section>
+  </div>
+  <script>
+      jQuery(function () {
+          SphinxRtdTheme.Navigation.enable(true);
+      });
+  </script> 
+
+</body>
+</html>
\ No newline at end of file
diff --git a/_modules/io_tools/verify_input_params.html b/_modules/io_tools/verify_input_params.html
new file mode 100644
index 00000000..36e6a1a2
--- /dev/null
+++ b/_modules/io_tools/verify_input_params.html
@@ -0,0 +1,410 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" >
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>io_tools.verify_input_params &mdash; solid_dmft  documentation</title>
+      <link rel="stylesheet" href="../../_static/pygments.css" type="text/css" />
+      <link rel="stylesheet" href="../../_static/css/custom.css" type="text/css" />
+      <link rel="stylesheet" href="../../_static/plot_directive.css" type="text/css" />
+    <link rel="shortcut icon" href="../../_static/favicon.ico"/>
+  
+        <script data-url_root="../../" id="documentation_options" src="../../_static/documentation_options.js"></script>
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+        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+    <script src="../../_static/js/theme.js"></script>
+    <link rel="index" title="Index" href="../../genindex.html" />
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+            <img src="../../_static/logo_no_text.png" class="logo" alt="Logo"/>
+          </a>
+              <div class="version">
+                3.2.0
+              </div>
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+<li class="toctree-l1"><a class="reference internal" href="../../install.html">Installation</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#installation-via-pip">Installation via pip</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#manual-installation-via-cmake">Manual installation via CMake</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#docker-files-images">Docker files &amp; images</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#version-compatibility">Version compatibility</a></li>
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+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#dft-interface-notes">DFT interface notes</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/w90_interface.html">Wannier90 interface</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/w90_interface.html#orbital-order-in-the-w90-converter">orbital order in the W90 converter</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/vasp_csc.html">Interface to VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#general-remarks">General remarks</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#file-description">file description</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#workflow">Workflow:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#important-flags">important flags:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#version-and-compilation">version and compilation:</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#input-output">Input/Output</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../input_output/DMFT_input/input.html">Input</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/general.html">[general]: General parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/solver.html">[solver]: solver specific parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/dft.html">[dft]: DFT related inputs</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/advanced.html">[advanced]: Advanced inputs</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../input_output/DMFT_output/results.html">Output / results</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_output/results.html#group-structure">Group structure</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_output/results.html#subgroups">Subgroups</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#further-details-for-running">Further details for running</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/docker.html">Docker</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#building-the-docker-image">Building the docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#pulling-a-docker-image">Pulling a docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_locally.html">Run on your machine</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#module-reference-manual">Module reference manual</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/csc_flow.html">csc_flow</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/dft_managers.html">dft_managers</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.mpi_helpers.html">dft_managers.mpi_helpers</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.qe_manager.html">dft_managers.qe_manager</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.vasp_manager.html">dft_managers.vasp_manager</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/dmft_cycle.html">dmft_cycle</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/dmft_tools.html">dmft_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.afm_mapping.html">dmft_tools.afm_mapping</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.formatter.html">dmft_tools.formatter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.greens_functions_mixer.html">dmft_tools.greens_functions_mixer</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.initial_self_energies.html">dmft_tools.initial_self_energies</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.interaction_hamiltonian.html">dmft_tools.interaction_hamiltonian</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.manipulate_chemical_potential.html">dmft_tools.manipulate_chemical_potential</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.observables.html">dmft_tools.observables</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.results_to_archive.html">dmft_tools.results_to_archive</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.solver.html">dmft_tools.solver</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/io_tools.html">io_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.dict_to_h5.html">io_tools.dict_to_h5</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.postproc_toml_dict.html">io_tools.postproc_toml_dict</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.verify_input_params.html">io_tools.verify_input_params</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_gf_imp.html">postprocessing.maxent_gf_imp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_gf_latt.html">postprocessing.maxent_gf_latt</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_sigma.html">postprocessing.maxent_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../issues.html">Support &amp; contribute</a><ul>
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+<li class="toctree-l1"><a class="reference internal" href="../../ChangeLog.html">Changelog</a><ul>
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+          <li><a href="../index.html">Module code</a> &raquo;</li>
+      <li>io_tools.verify_input_params</li>
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+      </li>
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+             
+  <h1>Source code for io_tools.verify_input_params</h1><div class="highlight"><pre>
+<span></span><span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span><span class="p">,</span> <span class="n">Dict</span><span class="p">,</span> <span class="n">List</span><span class="p">,</span> <span class="n">Union</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<span class="n">ParamDict</span> <span class="o">=</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">]</span>
+<span class="n">FullConfig</span> <span class="o">=</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Union</span><span class="p">[</span><span class="n">ParamDict</span><span class="p">,</span> <span class="n">List</span><span class="p">[</span><span class="n">ParamDict</span><span class="p">]]]</span>
+
+<span class="k">def</span> <span class="nf">_verify_input_params_general</span><span class="p">(</span><span class="n">params</span><span class="p">:</span> <span class="n">FullConfig</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+    <span class="c1"># Checks that grid parameters are specified completely</span>
+    <span class="k">if</span> <span class="n">params</span><span class="p">[</span><span class="s1">&#39;general&#39;</span><span class="p">][</span><span class="s1">&#39;beta&#39;</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="p">(</span><span class="n">params</span><span class="p">[</span><span class="s1">&#39;general&#39;</span><span class="p">][</span><span class="s1">&#39;n_iw&#39;</span><span class="p">]</span> <span class="ow">is</span> <span class="kc">None</span>
+                                                  <span class="ow">or</span> <span class="n">params</span><span class="p">[</span><span class="s1">&#39;general&#39;</span><span class="p">][</span><span class="s1">&#39;n_tau&#39;</span><span class="p">]</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Imaginary-frequency grid chosen, specify &quot;n_iw&quot; and &quot;n_tau&quot;.&#39;</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">params</span><span class="p">[</span><span class="s1">&#39;general&#39;</span><span class="p">][</span><span class="s1">&#39;beta&#39;</span><span class="p">]</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="p">(</span><span class="n">params</span><span class="p">[</span><span class="s1">&#39;general&#39;</span><span class="p">][</span><span class="s1">&#39;eta&#39;</span><span class="p">]</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">params</span><span class="p">[</span><span class="s1">&#39;general&#39;</span><span class="p">][</span><span class="s1">&#39;n_w&#39;</span><span class="p">]</span> <span class="ow">is</span> <span class="kc">None</span>
+                                              <span class="ow">or</span> <span class="n">params</span><span class="p">[</span><span class="s1">&#39;general&#39;</span><span class="p">][</span><span class="s1">&#39;w_range&#39;</span><span class="p">]</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Real-frequency grid chosen, specify &quot;eta&quot;, &quot;n_w&quot;, and &quot;w_range&quot;.&#39;</span><span class="p">)</span>
+
+    <span class="c1"># warning if sigma mixing is used, remove in future versions</span>
+    <span class="k">if</span> <span class="n">params</span><span class="p">[</span><span class="s1">&#39;general&#39;</span><span class="p">][</span><span class="s1">&#39;sigma_mix&#39;</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mf">1.0</span> <span class="ow">and</span> <span class="n">params</span><span class="p">[</span><span class="s1">&#39;general&#39;</span><span class="p">][</span><span class="s1">&#39;g0_mix&#39;</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mf">1.0</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;You shall not use Sigma and G0 mixing together!&#39;</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">params</span><span class="p">[</span><span class="s1">&#39;general&#39;</span><span class="p">][</span><span class="s1">&#39;calc_energies&#39;</span><span class="p">]</span> <span class="ow">and</span> <span class="nb">any</span><span class="p">(</span><span class="n">entry</span><span class="p">[</span><span class="s1">&#39;type&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;ftps&#39;</span> <span class="k">for</span> <span class="n">entry</span> <span class="ow">in</span> <span class="n">params</span><span class="p">[</span><span class="s1">&#39;solver&#39;</span><span class="p">]):</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;&quot;calc_energies&quot; is not valid for solver of type = &quot;ftps&quot;&#39;</span><span class="p">)</span>
+
+    <span class="c1"># Checks validity of other general params</span>
+    <span class="n">h_int_type_options</span> <span class="o">=</span> <span class="p">(</span><span class="s1">&#39;density_density&#39;</span><span class="p">,</span> <span class="s1">&#39;kanamori&#39;</span><span class="p">,</span> <span class="s1">&#39;full_slater&#39;</span><span class="p">,</span> <span class="s1">&#39;crpa&#39;</span><span class="p">,</span>
+                          <span class="s1">&#39;crpa_density_density&#39;</span><span class="p">,</span> <span class="s1">&#39;dynamic&#39;</span><span class="p">,</span> <span class="s1">&#39;ntot&#39;</span><span class="p">,</span> <span class="s1">&#39;simple_intra&#39;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">params</span><span class="p">[</span><span class="s1">&#39;general&#39;</span><span class="p">][</span><span class="s1">&#39;h_int_type&#39;</span><span class="p">],</span> <span class="nb">str</span><span class="p">):</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">params</span><span class="p">[</span><span class="s1">&#39;general&#39;</span><span class="p">][</span><span class="s1">&#39;h_int_type&#39;</span><span class="p">]</span> <span class="ow">in</span> <span class="n">h_int_type_options</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Invalid &quot;h_int_type&quot; = </span><span class="si">{</span><span class="n">params</span><span class="p">[</span><span class="s2">&quot;general&quot;</span><span class="p">][</span><span class="s2">&quot;h_int_type&quot;</span><span class="p">]</span><span class="si">}</span><span class="s1">.&#39;</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">params</span><span class="p">[</span><span class="s1">&#39;general&#39;</span><span class="p">][</span><span class="s1">&#39;h_int_type&#39;</span><span class="p">],</span> <span class="nb">list</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">any</span><span class="p">(</span><span class="n">entry</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">h_int_type_options</span> <span class="k">for</span> <span class="n">entry</span> <span class="ow">in</span> <span class="n">params</span><span class="p">[</span><span class="s1">&#39;general&#39;</span><span class="p">][</span><span class="s1">&#39;h_int_type&#39;</span><span class="p">]):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Invalid &quot;h_int_type&quot; in input list.&#39;</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Invalid &quot;h_int_type&quot; input type. String or list expected.&#39;</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">params</span><span class="p">[</span><span class="s1">&#39;general&#39;</span><span class="p">][</span><span class="s1">&#39;g0_mix_type&#39;</span><span class="p">]</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">&#39;linear&#39;</span><span class="p">,</span> <span class="s1">&#39;broyden&#39;</span><span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Invalid &quot;g0_mix_type&quot; = </span><span class="si">{</span><span class="n">params</span><span class="p">[</span><span class="s2">&quot;general&quot;</span><span class="p">][</span><span class="s2">&quot;g0_mix_type&quot;</span><span class="p">]</span><span class="si">}</span><span class="s1">.&#39;</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">params</span><span class="p">[</span><span class="s1">&#39;general&#39;</span><span class="p">][</span><span class="s1">&#39;calc_mu_method&#39;</span><span class="p">]</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">&#39;dichotomy&#39;</span><span class="p">,</span> <span class="s1">&#39;newton&#39;</span><span class="p">,</span> <span class="s1">&#39;brent&#39;</span><span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Invalid &quot;calc_mu_method&quot; = </span><span class="si">{</span><span class="n">params</span><span class="p">[</span><span class="s2">&quot;general&quot;</span><span class="p">][</span><span class="s2">&quot;calc_mu_method&quot;</span><span class="p">]</span><span class="si">}</span><span class="s1">.&#39;</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">params</span><span class="p">[</span><span class="s1">&#39;general&#39;</span><span class="p">][</span><span class="s1">&#39;set_rot&#39;</span><span class="p">]</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="s1">&#39;den&#39;</span><span class="p">,</span> <span class="s1">&#39;hloc&#39;</span><span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Invalid &quot;set_rot&quot; = </span><span class="si">{</span><span class="n">params</span><span class="p">[</span><span class="s2">&quot;general&quot;</span><span class="p">][</span><span class="s2">&quot;set_rot&quot;</span><span class="p">]</span><span class="si">}</span><span class="s1">.&#39;</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">params</span><span class="p">[</span><span class="s1">&#39;general&#39;</span><span class="p">][</span><span class="s1">&#39;h_int_basis&#39;</span><span class="p">]</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">&#39;triqs&#39;</span><span class="p">,</span> <span class="s1">&#39;wien2k&#39;</span><span class="p">,</span> <span class="s1">&#39;wannier90&#39;</span><span class="p">,</span> <span class="s1">&#39;qe&#39;</span><span class="p">,</span> <span class="s1">&#39;vasp&#39;</span><span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Invalid &quot;h_int_basis&quot; = </span><span class="si">{</span><span class="n">params</span><span class="p">[</span><span class="s2">&quot;general&quot;</span><span class="p">][</span><span class="s2">&quot;h_int_basis&quot;</span><span class="p">]</span><span class="si">}</span><span class="s1">.&#39;</span><span class="p">)</span>
+
+<span class="k">def</span> <span class="nf">_verify_input_params_dft</span><span class="p">(</span><span class="n">params</span><span class="p">:</span> <span class="n">FullConfig</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+    <span class="k">if</span> <span class="n">params</span><span class="p">[</span><span class="s1">&#39;dft&#39;</span><span class="p">][</span><span class="s1">&#39;dft_code&#39;</span><span class="p">]</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">&#39;vasp&#39;</span><span class="p">,</span> <span class="s1">&#39;qe&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Invalid &quot;dft.dft_code&quot; = </span><span class="si">{</span><span class="n">params</span><span class="p">[</span><span class="s2">&quot;dft&quot;</span><span class="p">][</span><span class="s2">&quot;dft_code&quot;</span><span class="p">]</span><span class="si">}</span><span class="s1">.&#39;</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">params</span><span class="p">[</span><span class="s1">&#39;dft&#39;</span><span class="p">][</span><span class="s1">&#39;mpi_env&#39;</span><span class="p">]</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">&#39;default&#39;</span><span class="p">,</span> <span class="s1">&#39;openmpi&#39;</span><span class="p">,</span> <span class="s1">&#39;openmpi-intra&#39;</span><span class="p">,</span> <span class="s1">&#39;mpich&#39;</span><span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Invalid &quot;dft.mpi_env&quot; = </span><span class="si">{</span><span class="n">params</span><span class="p">[</span><span class="s2">&quot;dft&quot;</span><span class="p">][</span><span class="s2">&quot;mpi_env&quot;</span><span class="p">]</span><span class="si">}</span><span class="s1">.&#39;</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">params</span><span class="p">[</span><span class="s1">&#39;dft&#39;</span><span class="p">][</span><span class="s1">&#39;projector_type&#39;</span><span class="p">]</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">&#39;w90&#39;</span><span class="p">,</span> <span class="s1">&#39;plo&#39;</span><span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Invalid &quot;dft.projector_type&quot; = </span><span class="si">{</span><span class="n">params</span><span class="p">[</span><span class="s2">&quot;dft&quot;</span><span class="p">][</span><span class="s2">&quot;projector_type&quot;</span><span class="p">]</span><span class="si">}</span><span class="s1">.&#39;</span><span class="p">)</span>
+
+<span class="k">def</span> <span class="nf">_verify_input_params_solver</span><span class="p">(</span><span class="n">params</span><span class="p">:</span> <span class="n">FullConfig</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+    <span class="n">solver_params</span> <span class="o">=</span> <span class="n">params</span><span class="p">[</span><span class="s1">&#39;solver&#39;</span><span class="p">]</span>
+
+    <span class="c1"># Checks that the solver impurities index are all lists if there are multiple solvers</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">solver_params</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="ow">or</span> <span class="n">solver_params</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="s1">&#39;idx_impurities&#39;</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="nb">any</span><span class="p">(</span><span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">entry</span><span class="p">[</span><span class="s1">&#39;idx_impurities&#39;</span><span class="p">],</span> <span class="nb">list</span><span class="p">)</span> <span class="k">for</span> <span class="n">entry</span> <span class="ow">in</span> <span class="n">solver_params</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;All &quot;solver.idx_impurities&quot; need to specify the impurities &#39;</span>
+                             <span class="s1">&#39;as a list of ints when there are multiple solvers.&#39;</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">entry</span> <span class="ow">in</span> <span class="n">solver_params</span><span class="p">:</span>
+            <span class="k">if</span> <span class="nb">any</span><span class="p">(</span><span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">imp</span><span class="p">,</span> <span class="nb">int</span><span class="p">)</span> <span class="k">for</span> <span class="n">imp</span> <span class="ow">in</span> <span class="n">entry</span><span class="p">[</span><span class="s1">&#39;idx_impurities&#39;</span><span class="p">]):</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;All &quot;solver.idx_impurities&quot; need to specify the impurities &#39;</span>
+                                 <span class="s1">&#39;as a list of ints when there are multiple solvers.&#39;</span><span class="p">)</span>
+
+    <span class="c1"># Checks that all solvers support the specified grid</span>
+    <span class="c1"># TODO: add real-frequency support for solvers that do both (e.g., hartree)</span>
+    <span class="n">supported_grids</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;real&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;ftps&#39;</span><span class="p">],</span>
+                       <span class="s1">&#39;imag&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;cthyb&#39;</span><span class="p">,</span> <span class="s1">&#39;ctint&#39;</span><span class="p">,</span> <span class="s1">&#39;ctseg&#39;</span><span class="p">,</span> <span class="s1">&#39;hubbardI&#39;</span><span class="p">,</span> <span class="s1">&#39;hartree&#39;</span><span class="p">]}</span>
+    <span class="k">if</span> <span class="n">params</span><span class="p">[</span><span class="s1">&#39;general&#39;</span><span class="p">][</span><span class="s1">&#39;beta&#39;</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">for</span> <span class="n">entry</span> <span class="ow">in</span> <span class="n">solver_params</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">entry</span><span class="p">[</span><span class="s1">&#39;type&#39;</span><span class="p">]</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">supported_grids</span><span class="p">[</span><span class="s1">&#39;imag&#39;</span><span class="p">]:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Solver </span><span class="si">{</span><span class="n">entry</span><span class="p">[</span><span class="s2">&quot;type&quot;</span><span class="p">]</span><span class="si">}</span><span class="s1"> does not support real-frequency grid.&#39;</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">for</span> <span class="n">entry</span> <span class="ow">in</span> <span class="n">solver_params</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">entry</span><span class="p">[</span><span class="s1">&#39;type&#39;</span><span class="p">]</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">supported_grids</span><span class="p">[</span><span class="s1">&#39;real&#39;</span><span class="p">]:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Solver </span><span class="si">{</span><span class="n">entry</span><span class="p">[</span><span class="s2">&quot;type&quot;</span><span class="p">]</span><span class="si">}</span><span class="s1"> does not support imaginary-frequency grid.&#39;</span><span class="p">)</span>
+
+<span class="k">def</span> <span class="nf">_verify_input_params_advanced</span><span class="p">(</span><span class="n">params</span><span class="p">:</span> <span class="n">FullConfig</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+    <span class="k">pass</span>
+
+<div class="viewcode-block" id="verify_before_dmft_cycle"><a class="viewcode-back" href="../../_ref/io_tools.verify_input_params.html#io_tools.verify_input_params.verify_before_dmft_cycle">[docs]</a><span class="k">def</span> <span class="nf">verify_before_dmft_cycle</span><span class="p">(</span><span class="n">params</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot; All checks of params that can be done before dmft_cycle is called. &quot;&quot;&quot;</span>
+    <span class="n">_verify_input_params_general</span><span class="p">(</span><span class="n">params</span><span class="p">)</span>
+    <span class="n">_verify_input_params_dft</span><span class="p">(</span><span class="n">params</span><span class="p">)</span>
+    <span class="n">_verify_input_params_solver</span><span class="p">(</span><span class="n">params</span><span class="p">)</span>
+    <span class="n">_verify_input_params_advanced</span><span class="p">(</span><span class="n">params</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="verify_h5_dependent"><a class="viewcode-back" href="../../_ref/io_tools.verify_input_params.html#io_tools.verify_input_params.verify_h5_dependent">[docs]</a><span class="k">def</span> <span class="nf">verify_h5_dependent</span><span class="p">(</span><span class="n">sum_k</span><span class="p">,</span> <span class="n">solver_type_per_imp</span><span class="p">,</span> <span class="n">general_params</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot; All checks of params that depend on the h5 file content that is stored in the SumkDFT object. &quot;&quot;&quot;</span>
+    <span class="c1"># Incompatabilities for SO coupling</span>
+    <span class="k">if</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">SO</span> <span class="o">==</span> <span class="mi">1</span> <span class="ow">and</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;magnetic&#39;</span><span class="p">]</span> <span class="ow">and</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;afm_order&#39;</span><span class="p">]:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;AFM order not supported with SO coupling&#39;</span><span class="p">)</span>
+
+    <span class="c1"># Checks that enforce_off_diag true for ftps and hartree</span>
+    <span class="k">if</span> <span class="nb">any</span><span class="p">(</span><span class="n">s</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;ftps&#39;</span><span class="p">,</span> <span class="s1">&#39;hartree&#39;</span><span class="p">]</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">e</span> <span class="k">for</span> <span class="n">s</span><span class="p">,</span> <span class="n">e</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">solver_type_per_imp</span><span class="p">,</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;enforce_off_diag&#39;</span><span class="p">])):</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;enforce_off_diag must be True for a impurities solver by ftps or hartree solvers&#39;</span><span class="p">)</span>
+
+    <span class="c1"># Checks that the interaction Hamiltonian and the parameters match</span>
+    <span class="k">if</span> <span class="nb">any</span><span class="p">(</span><span class="n">h</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;density_density&#39;</span><span class="p">,</span> <span class="s1">&#39;slater&#39;</span><span class="p">]</span> <span class="ow">and</span> <span class="n">r</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
+        <span class="k">for</span> <span class="n">h</span><span class="p">,</span> <span class="n">r</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;h_int_type&#39;</span><span class="p">],</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;ratio_F4_F2&#39;</span><span class="p">])):</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;&quot;ratio_F4_F2&quot; only considered for interaction Hamiltonians &quot;density_density&quot; and &quot;slater&quot;. &#39;</span>
+                        <span class="s1">&#39;Please set to None for all other Hamiltonians.&#39;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="nb">any</span><span class="p">(</span><span class="n">h</span> <span class="o">!=</span> <span class="s1">&#39;kanamori&#39;</span> <span class="ow">and</span> <span class="n">up</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">for</span> <span class="n">h</span><span class="p">,</span> <span class="n">up</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;h_int_type&#39;</span><span class="p">],</span> <span class="n">general_params</span><span class="p">[</span><span class="s1">&#39;U_prime&#39;</span><span class="p">])):</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;&quot;U_prime&quot; only considered for interaction Hamiltonian &quot;kanamori&quot;. &#39;</span>
+                         <span class="s1">&#39;Please set to None for all other Hamiltonians.&#39;</span><span class="p">)</span></div>
+</pre></div>
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.</p>
+  </div>
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
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diff --git a/_modules/postprocessing/eval_U_cRPA_RESPACK.html b/_modules/postprocessing/eval_U_cRPA_RESPACK.html
new file mode 100644
index 00000000..6fefb33e
--- /dev/null
+++ b/_modules/postprocessing/eval_U_cRPA_RESPACK.html
@@ -0,0 +1,604 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" >
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>postprocessing.eval_U_cRPA_RESPACK &mdash; solid_dmft  documentation</title>
+      <link rel="stylesheet" href="../../_static/pygments.css" type="text/css" />
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+              <div class="version">
+                3.2.0
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+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#input-output">Input/Output</a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
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+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_gf_imp.html">postprocessing.maxent_gf_imp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_gf_latt.html">postprocessing.maxent_gf_latt</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_sigma.html">postprocessing.maxent_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
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+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../issues.html">Support &amp; contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../issues.html#seeking-help">Seeking help</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../issues.html#improving-solid-dmft">Improving solid_dmft</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../issues.html#reporting-issues">Reporting issues</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../ChangeLog.html">Changelog</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-2-0">Version 3.2.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#general">General</a></li>
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+      <li>postprocessing.eval_U_cRPA_RESPACK</li>
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+             
+  <h1>Source code for postprocessing.eval_U_cRPA_RESPACK</h1><div class="highlight"><pre>
+<span></span><span class="ch">#!@TRIQS_PYTHON_EXECUTABLE@</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">itertools</span> <span class="kn">import</span> <span class="n">product</span>
+
+
+<div class="viewcode-block" id="respack_data"><a class="viewcode-back" href="../../_ref/postprocessing.eval_U_cRPA_RESPACK.respack_data.html#postprocessing.eval_U_cRPA_RESPACK.respack_data">[docs]</a><span class="k">class</span> <span class="nc">respack_data</span><span class="p">:</span>
+    <span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">    respack data class</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+
+<div class="viewcode-block" id="respack_data.__init__"><a class="viewcode-back" href="../../_ref/postprocessing.eval_U_cRPA_RESPACK.respack_data.__init__.html#postprocessing.eval_U_cRPA_RESPACK.respack_data.__init__">[docs]</a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">path</span><span class="p">,</span> <span class="n">seed</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">path</span> <span class="o">=</span> <span class="n">path</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">seed</span> <span class="o">=</span> <span class="n">seed</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">freq</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">n_orb</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="c1"># Uijij</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">U_R</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="c1"># Vijij</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">V_R</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="c1"># Uijji = Uiijj</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">J_R</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="c1"># Vijji = Viijj</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">X_R</span> <span class="o">=</span> <span class="kc">None</span>
+
+        <span class="c1"># full Uijkl reconstructed</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">Uijkl</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">Vijkl</span> <span class="o">=</span> <span class="kc">None</span>
+
+        <span class="c1"># freq dependent direction Coulomb</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">Uij_w</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">Jij_w</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">w_mesh</span> <span class="o">=</span> <span class="kc">None</span></div></div>
+
+
+<span class="k">def</span> <span class="nf">_read_R_file</span><span class="p">(</span><span class="n">file</span><span class="p">):</span>
+    <span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">    read respack Wmat, Jmat, Vmat, Xmat file format</span>
+
+<span class="sd">    Parameters:</span>
+<span class="sd">    -----------</span>
+<span class="sd">    file: string</span>
+<span class="sd">        string to file</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">    --------</span>
+<span class="sd">    U_R: dict of np.ndarray</span>
+<span class="sd">        keys are tuples of 3d integers representing the R vectors</span>
+<span class="sd">        values are n_orb x n_orb np.ndarray complex</span>
+<span class="sd">    n_orb: int</span>
+<span class="sd">        number of orbitals</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+    <span class="c1"># read X_R from file</span>
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fd</span><span class="p">:</span>
+        <span class="c1"># eliminate header</span>
+        <span class="n">fd</span><span class="o">.</span><span class="n">readline</span><span class="p">()</span>
+        <span class="n">fd</span><span class="o">.</span><span class="n">readline</span><span class="p">()</span>
+        <span class="n">fd</span><span class="o">.</span><span class="n">readline</span><span class="p">()</span>
+
+        <span class="c1"># get number of R vectors</span>
+        <span class="n">r_vec_max</span> <span class="o">=</span> <span class="p">[</span><span class="nb">abs</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">i</span><span class="p">))</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">fd</span><span class="o">.</span><span class="n">readline</span><span class="p">()</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span><span class="o">.</span><span class="n">split</span><span class="p">()]</span>
+        <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">r_vec_max</span><span class="p">)</span> <span class="o">==</span> <span class="mi">3</span>
+
+        <span class="c1"># determine number of orbitals</span>
+        <span class="k">while</span> <span class="kc">True</span><span class="p">:</span>
+            <span class="n">line</span> <span class="o">=</span> <span class="n">fd</span><span class="o">.</span><span class="n">readline</span><span class="p">()</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span><span class="o">.</span><span class="n">split</span><span class="p">()</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="n">line</span><span class="p">:</span>
+                <span class="k">break</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">n_orb</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">line</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+
+    <span class="c1"># open again and read whole file</span>
+    <span class="n">U_R</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fd</span><span class="p">:</span>
+        <span class="c1"># eliminate header</span>
+        <span class="n">fd</span><span class="o">.</span><span class="n">readline</span><span class="p">()</span>
+        <span class="n">fd</span><span class="o">.</span><span class="n">readline</span><span class="p">()</span>
+        <span class="n">fd</span><span class="o">.</span><span class="n">readline</span><span class="p">()</span>
+
+        <span class="k">for</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span> <span class="ow">in</span> <span class="n">product</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="n">r_vec_max</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">r_vec_max</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">+</span><span class="mi">1</span><span class="p">),</span>
+                               <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="n">r_vec_max</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">r_vec_max</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">+</span><span class="mi">1</span><span class="p">),</span>
+                               <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="n">r_vec_max</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">r_vec_max</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
+                               <span class="p">):</span>
+            <span class="n">fd</span><span class="o">.</span><span class="n">readline</span><span class="p">()</span>  <span class="c1"># remove rvec line</span>
+            <span class="n">U_R</span><span class="p">[</span><span class="nb">tuple</span><span class="p">([</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="p">])]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">n_orb</span><span class="p">,</span> <span class="n">n_orb</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">complex</span><span class="p">)</span>
+            <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">product</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">n_orb</span><span class="p">),</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_orb</span><span class="p">)):</span>
+                <span class="n">line</span> <span class="o">=</span> <span class="n">fd</span><span class="o">.</span><span class="n">readline</span><span class="p">()</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span><span class="o">.</span><span class="n">split</span><span class="p">()</span>
+                <span class="n">U_R</span><span class="p">[</span><span class="nb">tuple</span><span class="p">([</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="p">])][</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">line</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span><span class="o">+</span><span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="nb">float</span><span class="p">(</span><span class="n">line</span><span class="p">[</span><span class="mi">3</span><span class="p">])</span>
+            <span class="n">fd</span><span class="o">.</span><span class="n">readline</span><span class="p">()</span>  <span class="c1"># remove empty line before next block</span>
+
+    <span class="k">return</span> <span class="n">U_R</span><span class="p">,</span> <span class="n">n_orb</span>
+
+
+<span class="k">def</span> <span class="nf">_read_freq_int</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">n_orb</span><span class="p">,</span> <span class="n">U_or_J</span><span class="p">,</span> <span class="n">w_or_iw</span><span class="o">=</span><span class="s1">&#39;w&#39;</span><span class="p">):</span>
+    <span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">    read frequency dependent files from disk</span>
+
+<span class="sd">    Parameters:</span>
+<span class="sd">    -----------</span>
+<span class="sd">    path: string</span>
+<span class="sd">        path to respack calculations</span>
+<span class="sd">    n_orb: int</span>
+<span class="sd">        number of orbitals</span>
+<span class="sd">    U_or_J: string</span>
+<span class="sd">        pass either U or J for reading U or reading J</span>
+<span class="sd">    w_or_iw: string, optional default=&#39;w&#39;</span>
+<span class="sd">        read either real frequency axis or Matsubara axis results</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">    --------</span>
+<span class="sd">    Uij_w: np.ndarray, dtype=complex shape=(n_w, n_orb, n_orb)</span>
+<span class="sd">        direct freq dependent Coulomb integrals between orbitals</span>
+<span class="sd">    w_mesh: np.ndarry, shape=(n_w)</span>
+<span class="sd">        frequency mesh of Uij_w tensor</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+
+    <span class="k">assert</span> <span class="n">U_or_J</span> <span class="o">==</span> <span class="s1">&#39;U&#39;</span> <span class="ow">or</span> <span class="n">U_or_J</span> <span class="o">==</span> <span class="s1">&#39;J&#39;</span>
+
+    <span class="k">if</span> <span class="n">U_or_J</span> <span class="o">==</span> <span class="s1">&#39;U&#39;</span><span class="p">:</span>
+        <span class="n">file</span> <span class="o">=</span> <span class="n">path</span><span class="o">+</span><span class="s1">&#39;/dir-intW/dat.UvsE.&#39;</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">file</span> <span class="o">=</span> <span class="n">path</span><span class="o">+</span><span class="s1">&#39;/dir-intJ/dat.JvsE.&#39;</span>
+
+    <span class="c1"># read first w_mesh</span>
+    <span class="k">if</span> <span class="n">w_or_iw</span> <span class="o">==</span> <span class="s1">&#39;w&#39;</span><span class="p">:</span>
+        <span class="n">w_mesh</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">loadtxt</span><span class="p">(</span><span class="n">file</span><span class="o">+</span><span class="s1">&#39;001-001&#39;</span><span class="p">)[:,</span> <span class="mi">0</span><span class="p">]</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">w_mesh</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">loadtxt</span><span class="p">(</span><span class="n">file</span><span class="o">+</span><span class="s1">&#39;001-001&#39;</span><span class="p">)[:,</span> <span class="mi">1</span><span class="p">]</span>
+
+    <span class="n">Uij_w</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">w_mesh</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">n_orb</span><span class="p">,</span> <span class="n">n_orb</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">complex</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">n_orb</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">n_orb</span><span class="p">):</span>  <span class="c1"># only the upper triangle part is calculated by RESPACK</span>
+            <span class="n">temp_u</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">loadtxt</span><span class="p">(</span><span class="n">file</span><span class="o">+</span><span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">zfill</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span><span class="o">+</span><span class="s1">&#39;-&#39;</span><span class="o">+</span><span class="nb">str</span><span class="p">(</span><span class="n">j</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">zfill</span><span class="p">(</span><span class="mi">3</span><span class="p">))</span>
+            <span class="n">Uij_w</span><span class="p">[:,</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">temp_u</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">temp_u</span><span class="p">[:,</span> <span class="mi">3</span><span class="p">]</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="n">i</span> <span class="o">==</span> <span class="n">j</span><span class="p">:</span>  <span class="c1"># set the lower triangle with the complex conj</span>
+                <span class="n">Uij_w</span><span class="p">[:,</span> <span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">temp_u</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="o">-</span> <span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">temp_u</span><span class="p">[:,</span> <span class="mi">3</span><span class="p">]</span>
+
+    <span class="k">return</span> <span class="n">Uij_w</span><span class="p">,</span> <span class="n">w_mesh</span>
+
+
+<div class="viewcode-block" id="read_interaction"><a class="viewcode-back" href="../../_ref/postprocessing.eval_U_cRPA_RESPACK.html#postprocessing.eval_U_cRPA_RESPACK.read_interaction">[docs]</a><span class="k">def</span> <span class="nf">read_interaction</span><span class="p">(</span><span class="n">seed</span><span class="p">,</span> <span class="n">path</span><span class="o">=</span><span class="s1">&#39;./&#39;</span><span class="p">):</span>
+    <span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">    Parameters:</span>
+<span class="sd">    -----------</span>
+<span class="sd">    seed: string</span>
+<span class="sd">        seed of QE / w90 file names</span>
+<span class="sd">    path: string</span>
+<span class="sd">        path to respack calculations</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">    --------</span>
+<span class="sd">    res: respack data class</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+
+    <span class="n">res</span> <span class="o">=</span> <span class="n">respack_data</span><span class="p">(</span><span class="n">seed</span><span class="p">,</span> <span class="n">path</span><span class="p">)</span>
+
+    <span class="c1"># read w3d.out file for frequency info</span>
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">path</span><span class="o">+</span><span class="s1">&#39;/&#39;</span><span class="o">+</span><span class="n">seed</span><span class="o">+</span><span class="s1">&#39;.w3d.out&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">w3d</span><span class="p">:</span>
+        <span class="n">lines</span> <span class="o">=</span> <span class="n">w3d</span><span class="o">.</span><span class="n">readlines</span><span class="p">()</span>
+        <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">lines</span><span class="p">:</span>
+            <span class="k">if</span> <span class="s1">&#39;CALC_IFREQ=&#39;</span> <span class="ow">in</span> <span class="n">line</span><span class="p">:</span>
+                <span class="n">res</span><span class="o">.</span><span class="n">freq</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">line</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39; &#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;=&#39;</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;,&#39;</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span>
+                <span class="k">break</span>
+
+    <span class="c1"># read R dependent matrices and IFREQ</span>
+    <span class="n">res</span><span class="o">.</span><span class="n">U_R</span><span class="p">,</span> <span class="n">res</span><span class="o">.</span><span class="n">n_orb</span> <span class="o">=</span> <span class="n">_read_R_file</span><span class="p">(</span><span class="n">file</span><span class="o">=</span><span class="n">path</span><span class="o">+</span><span class="s1">&#39;/dir-intW/dat.Wmat&#39;</span><span class="p">)</span>
+    <span class="n">res</span><span class="o">.</span><span class="n">V_R</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">_read_R_file</span><span class="p">(</span><span class="n">file</span><span class="o">=</span><span class="n">path</span><span class="o">+</span><span class="s1">&#39;/dir-intW/dat.Vmat&#39;</span><span class="p">)</span>
+    <span class="n">res</span><span class="o">.</span><span class="n">J_R</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">_read_R_file</span><span class="p">(</span><span class="n">file</span><span class="o">=</span><span class="n">path</span><span class="o">+</span><span class="s1">&#39;/dir-intJ/dat.Jmat&#39;</span><span class="p">)</span>
+    <span class="n">res</span><span class="o">.</span><span class="n">X_R</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">_read_R_file</span><span class="p">(</span><span class="n">file</span><span class="o">=</span><span class="n">path</span><span class="o">+</span><span class="s1">&#39;/dir-intJ/dat.Xmat&#39;</span><span class="p">)</span>
+
+    <span class="c1"># create R=0 matrices for user convenience</span>
+    <span class="n">res</span><span class="o">.</span><span class="n">Uijij</span> <span class="o">=</span> <span class="n">res</span><span class="o">.</span><span class="n">U_R</span><span class="p">[(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span>
+    <span class="n">res</span><span class="o">.</span><span class="n">Uijji</span> <span class="o">=</span> <span class="n">res</span><span class="o">.</span><span class="n">J_R</span><span class="p">[(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span>
+    <span class="n">res</span><span class="o">.</span><span class="n">Vijij</span> <span class="o">=</span> <span class="n">res</span><span class="o">.</span><span class="n">V_R</span><span class="p">[(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span>
+    <span class="n">res</span><span class="o">.</span><span class="n">Vijji</span> <span class="o">=</span> <span class="n">res</span><span class="o">.</span><span class="n">X_R</span><span class="p">[(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span>
+
+    <span class="c1"># reconstruct full Uijkl tensor assuming Uijji == Uiijj</span>
+    <span class="n">res</span><span class="o">.</span><span class="n">Uijkl</span> <span class="o">=</span> <span class="n">construct_Uijkl</span><span class="p">(</span><span class="n">res</span><span class="o">.</span><span class="n">U_R</span><span class="p">[(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)],</span> <span class="n">res</span><span class="o">.</span><span class="n">J_R</span><span class="p">[(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)])</span>
+    <span class="n">res</span><span class="o">.</span><span class="n">Vijkl</span> <span class="o">=</span> <span class="n">construct_Uijkl</span><span class="p">(</span><span class="n">res</span><span class="o">.</span><span class="n">V_R</span><span class="p">[(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)],</span> <span class="n">res</span><span class="o">.</span><span class="n">X_R</span><span class="p">[(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)])</span>
+
+    <span class="c1"># read freq dependent results</span>
+    <span class="n">res</span><span class="o">.</span><span class="n">Uij_w</span><span class="p">,</span> <span class="n">res</span><span class="o">.</span><span class="n">w_mesh</span> <span class="o">=</span> <span class="n">_read_freq_int</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">res</span><span class="o">.</span><span class="n">n_orb</span><span class="p">,</span> <span class="n">U_or_J</span><span class="o">=</span><span class="s1">&#39;U&#39;</span><span class="p">)</span>
+    <span class="n">res</span><span class="o">.</span><span class="n">Jij_w</span><span class="p">,</span> <span class="n">res</span><span class="o">.</span><span class="n">w_mesh</span> <span class="o">=</span> <span class="n">_read_freq_int</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">res</span><span class="o">.</span><span class="n">n_orb</span><span class="p">,</span> <span class="n">U_or_J</span><span class="o">=</span><span class="s1">&#39;J&#39;</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">res</span></div>
+
+
+<div class="viewcode-block" id="construct_Uijkl"><a class="viewcode-back" href="../../_ref/postprocessing.eval_U_cRPA_RESPACK.html#postprocessing.eval_U_cRPA_RESPACK.construct_Uijkl">[docs]</a><span class="k">def</span> <span class="nf">construct_Uijkl</span><span class="p">(</span><span class="n">Uijij</span><span class="p">,</span> <span class="n">Uiijj</span><span class="p">):</span>
+    <span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">    construct full 4 index Uijkl tensor from respack data</span>
+<span class="sd">    assuming Uijji = Uiijj</span>
+
+<span class="sd">    ----------</span>
+<span class="sd">    Uijij: np.ndarray</span>
+<span class="sd">        Uijij matrix</span>
+<span class="sd">    Uiijj: np.ndarray</span>
+<span class="sd">        Uiijj matrix</span>
+
+<span class="sd">    -------</span>
+<span class="sd">    uijkl : numpy array</span>
+<span class="sd">        uijkl Coulomb tensor</span>
+
+<span class="sd">    &#39;&#39;&#39;</span>
+
+    <span class="n">n_orb</span> <span class="o">=</span> <span class="n">Uijij</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">orb_range</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">n_orb</span><span class="p">)</span>
+    <span class="n">Uijkl</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">n_orb</span><span class="p">,</span> <span class="n">n_orb</span><span class="p">,</span> <span class="n">n_orb</span><span class="p">,</span> <span class="n">n_orb</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">complex</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">product</span><span class="p">(</span><span class="n">orb_range</span><span class="p">,</span> <span class="n">orb_range</span><span class="p">,</span> <span class="n">orb_range</span><span class="p">,</span> <span class="n">orb_range</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="n">j</span> <span class="o">==</span> <span class="n">k</span> <span class="o">==</span> <span class="n">l</span><span class="p">:</span>  <span class="c1"># Uiiii</span>
+            <span class="n">Uijkl</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">l</span><span class="p">]</span> <span class="o">=</span> <span class="n">Uijij</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span>
+        <span class="k">elif</span> <span class="n">i</span> <span class="o">==</span> <span class="n">k</span> <span class="ow">and</span> <span class="n">j</span> <span class="o">==</span> <span class="n">l</span><span class="p">:</span>  <span class="c1"># Uijij</span>
+            <span class="n">Uijkl</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">l</span><span class="p">]</span> <span class="o">=</span> <span class="n">Uijij</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span>
+        <span class="k">elif</span> <span class="n">i</span> <span class="o">==</span> <span class="n">l</span> <span class="ow">and</span> <span class="n">j</span> <span class="o">==</span> <span class="n">k</span><span class="p">:</span>  <span class="c1"># Uijji</span>
+            <span class="n">Uijkl</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">l</span><span class="p">]</span> <span class="o">=</span> <span class="n">Uiijj</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span>
+        <span class="k">elif</span> <span class="n">i</span> <span class="o">==</span> <span class="n">j</span> <span class="ow">and</span> <span class="n">k</span> <span class="o">==</span> <span class="n">l</span><span class="p">:</span>  <span class="c1"># Uiijj</span>
+            <span class="n">Uijkl</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">l</span><span class="p">]</span> <span class="o">=</span> <span class="n">Uiijj</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">k</span><span class="p">]</span>
+    <span class="k">return</span> <span class="n">Uijkl</span></div>
+
+
+<div class="viewcode-block" id="fit_slater"><a class="viewcode-back" href="../../_ref/postprocessing.eval_U_cRPA_RESPACK.html#postprocessing.eval_U_cRPA_RESPACK.fit_slater">[docs]</a><span class="k">def</span> <span class="nf">fit_slater</span><span class="p">(</span><span class="n">u_ijij_crpa</span><span class="p">,</span> <span class="n">u_ijji_crpa</span><span class="p">,</span> <span class="n">U_init</span><span class="p">,</span> <span class="n">J_init</span><span class="p">,</span> <span class="n">fixed_F4_F2</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+    <span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">    finds best Slater parameters U, J for given Uijij and Uijji matrices</span>
+<span class="sd">    using the triqs U_matrix operator routine assumes F4/F2=0.625</span>
+
+<span class="sd">    Parameters:</span>
+<span class="sd">    -----------</span>
+<span class="sd">    u_ijij_crpa: np.ndarray of shape (3,3) or (5, 5)</span>
+<span class="sd">        Uijij matrix</span>
+<span class="sd">    u_ijji_crpa: np.ndarray of shape (3,3) or (5, 5)</span>
+<span class="sd">        Uijji matrix</span>
+<span class="sd">    U_init: float</span>
+<span class="sd">        inital value of U for optimization</span>
+<span class="sd">    J_init: float</span>
+<span class="sd">        inital value of J for optimization</span>
+<span class="sd">    fixed_F4_F2: bool, optional default=True</span>
+<span class="sd">        fix F4/F2 ratio to 0.625</span>
+<span class="sd">    Returns:</span>
+<span class="sd">    --------</span>
+<span class="sd">    U_int: float</span>
+<span class="sd">        averaged U value</span>
+<span class="sd">    J_hund: float</span>
+<span class="sd">        averaged J value</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+
+    <span class="kn">from</span> <span class="nn">triqs.operators.util.U_matrix</span> <span class="kn">import</span> <span class="n">U_matrix_slater</span><span class="p">,</span> <span class="n">spherical_to_cubic</span><span class="p">,</span> <span class="n">reduce_4index_to_2index</span><span class="p">,</span> <span class="n">transform_U_matrix</span>
+    <span class="kn">from</span> <span class="nn">scipy.optimize</span> <span class="kn">import</span> <span class="n">minimize</span>
+
+    <span class="c1"># input checks</span>
+    <span class="k">if</span> <span class="n">u_ijij_crpa</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">):</span>
+        <span class="n">l</span><span class="o">=</span><span class="mi">1</span>
+        <span class="c1"># for p shell there are only 2 parameters</span>
+        <span class="n">fixed_F4_F2</span> <span class="o">=</span> <span class="kc">True</span>
+    <span class="k">elif</span> <span class="n">u_ijij_crpa</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="p">(</span><span class="mi">5</span><span class="p">,</span><span class="mi">5</span><span class="p">):</span>
+        <span class="n">l</span><span class="o">=</span><span class="mi">2</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;fit slater only implemented for full p or d shell&#39;</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">minimizer</span><span class="p">(</span><span class="n">parameters</span><span class="p">):</span>
+        <span class="n">U_int</span><span class="p">,</span> <span class="n">J_hund</span> <span class="o">=</span> <span class="n">parameters</span>
+        <span class="n">Umat_full</span> <span class="o">=</span> <span class="n">U_matrix_slater</span><span class="p">(</span><span class="n">l</span><span class="o">=</span><span class="n">l</span><span class="p">,</span> <span class="n">U_int</span><span class="o">=</span><span class="n">U_int</span><span class="p">,</span> <span class="n">J_hund</span><span class="o">=</span><span class="n">J_hund</span><span class="p">,</span> <span class="n">basis</span><span class="o">=</span><span class="s1">&#39;spherical&#39;</span><span class="p">)</span>
+        <span class="n">Umat_full</span> <span class="o">=</span> <span class="n">transform_U_matrix</span><span class="p">(</span><span class="n">Umat_full</span><span class="p">,</span> <span class="n">T</span><span class="p">)</span>
+
+        <span class="n">Umat</span><span class="p">,</span> <span class="n">u_ijij_slater</span> <span class="o">=</span> <span class="n">reduce_4index_to_2index</span><span class="p">(</span><span class="n">Umat_full</span><span class="p">)</span>
+        <span class="n">u_iijj_slater</span> <span class="o">=</span> <span class="n">u_ijij_slater</span> <span class="o">-</span> <span class="n">Umat</span>
+        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">u_ijji_crpa</span> <span class="o">-</span> <span class="n">u_iijj_slater</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="p">(</span><span class="n">u_ijij_crpa</span> <span class="o">-</span> <span class="n">u_ijij_slater</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">minimizer_radial</span><span class="p">(</span><span class="n">parameters</span><span class="p">):</span>
+        <span class="n">Umat_full</span> <span class="o">=</span> <span class="n">U_matrix_slater</span><span class="p">(</span><span class="n">l</span><span class="o">=</span><span class="n">l</span><span class="p">,</span> <span class="n">radial_integrals</span><span class="o">=</span><span class="n">parameters</span><span class="p">,</span> <span class="n">basis</span><span class="o">=</span><span class="s1">&#39;spherical&#39;</span><span class="p">)</span>
+        <span class="n">Umat_full</span> <span class="o">=</span> <span class="n">transform_U_matrix</span><span class="p">(</span><span class="n">Umat_full</span><span class="p">,</span> <span class="n">T</span><span class="p">)</span>
+
+        <span class="n">Umat</span><span class="p">,</span> <span class="n">u_ijij_slater</span> <span class="o">=</span> <span class="n">reduce_4index_to_2index</span><span class="p">(</span><span class="n">Umat_full</span><span class="p">)</span>
+        <span class="n">u_iijj_slater</span> <span class="o">=</span> <span class="n">u_ijij_slater</span> <span class="o">-</span> <span class="n">Umat</span>
+        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">u_ijji_crpa</span> <span class="o">-</span> <span class="n">u_iijj_slater</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="p">(</span><span class="n">u_ijij_crpa</span> <span class="o">-</span> <span class="n">u_ijij_slater</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+
+    <span class="c1"># transformation matrix from spherical to w90 basis</span>
+    <span class="n">T</span> <span class="o">=</span> <span class="n">spherical_to_cubic</span><span class="p">(</span><span class="n">l</span><span class="o">=</span><span class="n">l</span><span class="p">,</span> <span class="n">convention</span><span class="o">=</span><span class="s1">&#39;wannier90&#39;</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">fixed_F4_F2</span><span class="p">:</span>
+        <span class="n">result</span> <span class="o">=</span> <span class="n">minimize</span><span class="p">(</span><span class="n">minimizer</span><span class="p">,</span> <span class="p">(</span><span class="n">U_init</span><span class="p">,</span> <span class="n">J_init</span><span class="p">))</span>
+
+        <span class="n">U_int</span><span class="p">,</span> <span class="n">J_hund</span> <span class="o">=</span> <span class="n">result</span><span class="o">.</span><span class="n">x</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Final results from fit: U = </span><span class="si">{:.3f}</span><span class="s1"> eV, J = </span><span class="si">{:.3f}</span><span class="s1"> eV&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">U_int</span><span class="p">,</span> <span class="n">J_hund</span><span class="p">))</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;optimize error&#39;</span><span class="p">,</span> <span class="n">result</span><span class="o">.</span><span class="n">fun</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="c1"># start with 0.63 as guess</span>
+        <span class="n">F0</span> <span class="o">=</span> <span class="n">U_init</span>
+        <span class="n">F2</span> <span class="o">=</span> <span class="n">J_init</span> <span class="o">*</span> <span class="mf">14.0</span> <span class="o">/</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">+</span> <span class="mf">0.63</span><span class="p">)</span>
+        <span class="n">F4</span> <span class="o">=</span> <span class="mf">0.630</span> <span class="o">*</span> <span class="n">F2</span>
+
+        <span class="n">initial_guess</span> <span class="o">=</span> <span class="p">[</span><span class="n">F0</span><span class="p">,</span> <span class="n">F2</span><span class="p">,</span> <span class="n">F4</span><span class="p">]</span>
+
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Initial guess: F0 = </span><span class="si">{0[0]:.3f}</span><span class="s1"> eV, F2 = </span><span class="si">{0[1]:.3f}</span><span class="s1"> eV, F4 = </span><span class="si">{0[2]:.3f}</span><span class="s1"> eV&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">initial_guess</span><span class="p">))</span>
+
+        <span class="n">result</span> <span class="o">=</span> <span class="n">minimize</span><span class="p">(</span><span class="n">minimizer_radial</span><span class="p">,</span> <span class="n">initial_guess</span><span class="p">)</span>
+        <span class="n">F0</span><span class="p">,</span> <span class="n">F2</span><span class="p">,</span> <span class="n">F4</span> <span class="o">=</span> <span class="n">result</span><span class="o">.</span><span class="n">x</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Final results from fit: F0 = </span><span class="si">{:.3f}</span><span class="s1"> eV, F2 = </span><span class="si">{:.3f}</span><span class="s1"> eV, F4 = </span><span class="si">{:.3f}</span><span class="s1"> eV&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">F0</span><span class="p">,</span> <span class="n">F2</span><span class="p">,</span> <span class="n">F4</span><span class="p">))</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;(F2+F4)/14 = </span><span class="si">{:.3f}</span><span class="s1"> eV&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">((</span><span class="n">F2</span><span class="o">+</span><span class="n">F4</span><span class="p">)</span><span class="o">/</span><span class="mi">14</span><span class="p">))</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;F4/F2 = </span><span class="si">{:.3f}</span><span class="s1"> eV&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">F4</span><span class="o">/</span><span class="n">F2</span><span class="p">))</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;optimize error&#39;</span><span class="p">,</span> <span class="n">result</span><span class="o">.</span><span class="n">fun</span><span class="p">)</span>
+        <span class="n">U_int</span> <span class="o">=</span> <span class="n">F0</span>
+        <span class="n">J_hund</span> <span class="o">=</span> <span class="p">(</span><span class="n">F2</span><span class="o">+</span><span class="n">F4</span><span class="p">)</span><span class="o">/</span><span class="mi">14</span>
+
+    <span class="k">return</span> <span class="n">U_int</span><span class="p">,</span> <span class="n">J_hund</span></div>
+</pre></div>
+
+           </div>
+          </div>
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+    <p>&#169; Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.</p>
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new file mode 100644
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+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#workflow">Workflow:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#important-flags">important flags:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#version-and-compilation">version and compilation:</a></li>
+</ul>
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+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#input-output">Input/Output</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../input_output/DMFT_input/input.html">Input</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/general.html">[general]: General parameters</a></li>
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+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../input_output/DMFT_output/results.html">Output / results</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_output/results.html#group-structure">Group structure</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#further-details-for-running">Further details for running</a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#pulling-a-docker-image">Pulling a docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
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+</ul>
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+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
+</li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#module-reference-manual">Module reference manual</a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.observables.html">dmft_tools.observables</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.results_to_archive.html">dmft_tools.results_to_archive</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.solver.html">dmft_tools.solver</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/io_tools.html">io_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.dict_to_h5.html">io_tools.dict_to_h5</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.postproc_toml_dict.html">io_tools.postproc_toml_dict</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.verify_input_params.html">io_tools.verify_input_params</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_gf_imp.html">postprocessing.maxent_gf_imp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_gf_latt.html">postprocessing.maxent_gf_latt</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_sigma.html">postprocessing.maxent_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
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+          <li><a href="../index.html">Module code</a> &raquo;</li>
+      <li>postprocessing.eval_U_cRPA_Vasp</li>
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+             
+  <h1>Source code for postprocessing.eval_U_cRPA_Vasp</h1><div class="highlight"><pre>
+<span></span><span class="ch">#!@TRIQS_PYTHON_EXECUTABLE@</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">collections</span>
+<span class="kn">from</span> <span class="nn">itertools</span> <span class="kn">import</span> <span class="n">product</span>
+
+<span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">python functions for reading Uijkl from a VASP cRPA run and the evaluating the matrix</span>
+<span class="sd">elements for different basis sets.</span>
+
+<span class="sd">Copyright (C) 2020, A. Hampel and M. Merkel from Materials Theory Group</span>
+<span class="sd">at ETH Zurich</span>
+<span class="sd">&#39;&#39;&#39;</span>
+
+
+<div class="viewcode-block" id="read_uijkl"><a class="viewcode-back" href="../../_ref/postprocessing.eval_U_cRPA_Vasp.html#postprocessing.eval_U_cRPA_Vasp.read_uijkl">[docs]</a><span class="k">def</span> <span class="nf">read_uijkl</span><span class="p">(</span><span class="n">path_to_uijkl</span><span class="p">,</span> <span class="n">n_sites</span><span class="p">,</span> <span class="n">n_orb</span><span class="p">):</span>
+    <span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">    reads the VASP UIJKL files or the vijkl file if wanted</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    path_to_uijkl : string</span>
+<span class="sd">        path to Uijkl like file</span>
+<span class="sd">    n_sites: int</span>
+<span class="sd">        number of different atoms (Wannier centers)</span>
+<span class="sd">    n_orb : int</span>
+<span class="sd">        number of orbitals per atom</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    uijkl : numpy array</span>
+<span class="sd">        uijkl Coulomb tensor</span>
+
+<span class="sd">    &#39;&#39;&#39;</span>
+    <span class="n">dim</span> <span class="o">=</span> <span class="n">n_sites</span><span class="o">*</span><span class="n">n_orb</span>
+    <span class="n">uijkl</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">dim</span><span class="p">,</span> <span class="n">dim</span><span class="p">,</span> <span class="n">dim</span><span class="p">,</span> <span class="n">dim</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">complex</span><span class="p">)</span>
+    <span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">loadtxt</span><span class="p">(</span><span class="n">path_to_uijkl</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">])):</span>
+        <span class="n">i</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">line</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span><span class="o">-</span><span class="mi">1</span>
+        <span class="n">j</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">line</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span><span class="o">-</span><span class="mi">1</span>
+        <span class="n">k</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">line</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span><span class="o">-</span><span class="mi">1</span>
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">line</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span><span class="o">-</span><span class="mi">1</span>
+        <span class="n">uijkl</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">l</span><span class="p">]</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">line</span><span class="p">,</span> <span class="mi">4</span><span class="p">]</span><span class="o">+</span><span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">data</span><span class="p">[</span><span class="n">line</span><span class="p">,</span> <span class="mi">5</span><span class="p">]</span>
+
+    <span class="k">return</span> <span class="n">uijkl</span></div>
+
+
+<div class="viewcode-block" id="construct_U_kan"><a class="viewcode-back" href="../../_ref/postprocessing.eval_U_cRPA_Vasp.html#postprocessing.eval_U_cRPA_Vasp.construct_U_kan">[docs]</a><span class="k">def</span> <span class="nf">construct_U_kan</span><span class="p">(</span><span class="n">n_orb</span><span class="p">,</span> <span class="n">U</span><span class="p">,</span> <span class="n">J</span><span class="p">,</span> <span class="n">Up</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">Jc</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">    construct Kanamori Uijkl tensor for given U, J, Up, and Jc</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    n_orb : int</span>
+<span class="sd">        number of orbitals</span>
+<span class="sd">    U : float</span>
+<span class="sd">        U value for elements Uiiii</span>
+<span class="sd">    J : float</span>
+<span class="sd">        Hunds coupling J for tensor elements Uijji</span>
+<span class="sd">    Up : float, optional, default=U-2J</span>
+<span class="sd">        inter orbital exchange term Uijij</span>
+<span class="sd">    Jc : float, optional, default=J</span>
+<span class="sd">        Uiijj term, is the same as J for real valued wave functions</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    uijkl : numpy array</span>
+<span class="sd">        uijkl Coulomb tensor</span>
+
+<span class="sd">    &#39;&#39;&#39;</span>
+
+    <span class="n">orb_range</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">n_orb</span><span class="p">)</span>
+    <span class="n">U_kan</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">n_orb</span><span class="p">,</span> <span class="n">n_orb</span><span class="p">,</span> <span class="n">n_orb</span><span class="p">,</span> <span class="n">n_orb</span><span class="p">))</span>
+
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">Up</span><span class="p">:</span>
+        <span class="n">Up</span> <span class="o">=</span> <span class="n">U</span><span class="o">-</span><span class="mi">2</span><span class="o">*</span><span class="n">J</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">Jc</span><span class="p">:</span>
+        <span class="n">Jc</span> <span class="o">=</span> <span class="n">J</span>
+
+    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">product</span><span class="p">(</span><span class="n">orb_range</span><span class="p">,</span> <span class="n">orb_range</span><span class="p">,</span> <span class="n">orb_range</span><span class="p">,</span> <span class="n">orb_range</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="n">j</span> <span class="o">==</span> <span class="n">k</span> <span class="o">==</span> <span class="n">l</span><span class="p">:</span>  <span class="c1"># Uiiii</span>
+            <span class="n">U_kan</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">l</span><span class="p">]</span> <span class="o">=</span> <span class="n">U</span>
+        <span class="k">elif</span> <span class="n">i</span> <span class="o">==</span> <span class="n">k</span> <span class="ow">and</span> <span class="n">j</span> <span class="o">==</span> <span class="n">l</span><span class="p">:</span>  <span class="c1"># Uijij</span>
+            <span class="n">U_kan</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">l</span><span class="p">]</span> <span class="o">=</span> <span class="n">Up</span>
+        <span class="k">elif</span> <span class="n">i</span> <span class="o">==</span> <span class="n">l</span> <span class="ow">and</span> <span class="n">j</span> <span class="o">==</span> <span class="n">k</span><span class="p">:</span>  <span class="c1"># Uijji</span>
+            <span class="n">U_kan</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">l</span><span class="p">]</span> <span class="o">=</span> <span class="n">J</span>
+        <span class="k">elif</span> <span class="n">i</span> <span class="o">==</span> <span class="n">j</span> <span class="ow">and</span> <span class="n">k</span> <span class="o">==</span> <span class="n">l</span><span class="p">:</span>  <span class="c1"># Uiijj</span>
+            <span class="n">U_kan</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">l</span><span class="p">]</span> <span class="o">=</span> <span class="n">Jc</span>
+    <span class="k">return</span> <span class="n">U_kan</span></div>
+
+
+<div class="viewcode-block" id="red_to_2ind"><a class="viewcode-back" href="../../_ref/postprocessing.eval_U_cRPA_Vasp.html#postprocessing.eval_U_cRPA_Vasp.red_to_2ind">[docs]</a><span class="k">def</span> <span class="nf">red_to_2ind</span><span class="p">(</span><span class="n">uijkl</span><span class="p">,</span> <span class="n">n_sites</span><span class="p">,</span> <span class="n">n_orb</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+    <span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">    reduces the 4index coulomb matrix to a 2index matrix and</span>
+<span class="sd">    follows the procedure given in PRB96 seth,peil,georges:</span>
+<span class="sd">    U_antipar = U_mm&#39;^oo&#39; = U_mm&#39;mm&#39; (Coulomb Int)</span>
+<span class="sd">    U_par = U_mm&#39;^oo = U_mm&#39;mm&#39; - U_mm&#39;m&#39;m (for intersite interaction)</span>
+<span class="sd">    U_ijij (Hunds coupling)</span>
+<span class="sd">    the indices in VASP are switched: U_ijkl ---VASP--&gt; U_ikjl</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    uijkl : numpy array</span>
+<span class="sd">        4d numpy array of Coulomb tensor</span>
+<span class="sd">    n_sites: int</span>
+<span class="sd">        number of different atoms (Wannier centers)</span>
+<span class="sd">    n_orb : int</span>
+<span class="sd">        number of orbitals per atom</span>
+<span class="sd">    out : bool</span>
+<span class="sd">        verbose mode</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    Uij_anti : numpy array</span>
+<span class="sd">        red 2 index matrix U_mm&#39;mm&#39;</span>
+<span class="sd">    Uiijj : numpy array</span>
+<span class="sd">        red 2 index matrix U_iijj</span>
+<span class="sd">    Uijji : numpy array</span>
+<span class="sd">        red 2 index matrix Uijji</span>
+<span class="sd">    Uij_par : numpy array</span>
+<span class="sd">        red 2 index matrix U_mm\&#39;mm\&#39; - U_mm\&#39;m\&#39;m</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+    <span class="n">dim</span> <span class="o">=</span> <span class="n">n_sites</span><span class="o">*</span><span class="n">n_orb</span>
+
+    <span class="c1"># create 2 index matrix</span>
+    <span class="n">Uij_anti</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">dim</span><span class="p">,</span> <span class="n">dim</span><span class="p">))</span>
+    <span class="n">Uij_par</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">dim</span><span class="p">,</span> <span class="n">dim</span><span class="p">))</span>
+    <span class="n">Uiijj</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">dim</span><span class="p">,</span> <span class="n">dim</span><span class="p">))</span>
+    <span class="n">Uijji</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">dim</span><span class="p">,</span> <span class="n">dim</span><span class="p">))</span>
+
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">dim</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">dim</span><span class="p">):</span>
+            <span class="c1"># the indices in VASP are switched: U_ijkl ---VASP--&gt; U_ikjl</span>
+            <span class="n">Uij_anti</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">uijkl</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span>
+            <span class="n">Uij_par</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">uijkl</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span><span class="o">-</span><span class="n">uijkl</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">]</span>
+            <span class="n">Uiijj</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">uijkl</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span>
+            <span class="n">Uijji</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">uijkl</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">]</span>
+
+    <span class="n">np</span><span class="o">.</span><span class="n">set_printoptions</span><span class="p">(</span><span class="n">precision</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">suppress</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">out</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;reduced U anti-parallel = U_mm</span><span class="se">\&#39;</span><span class="s1">\^oo</span><span class="se">\&#39;</span><span class="s1"> = U_mm</span><span class="se">\&#39;</span><span class="s1">mm</span><span class="se">\&#39;</span><span class="s1"> matrix : </span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">Uij_anti</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;reduced U parallel = U_mm</span><span class="se">\&#39;</span><span class="s1">\^oo = U_mm</span><span class="se">\&#39;</span><span class="s1">mm</span><span class="se">\&#39;</span><span class="s1"> - U_mm</span><span class="se">\&#39;</span><span class="s1">m</span><span class="se">\&#39;</span><span class="s1">m matrix : </span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">Uij_par</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;reduced Uijji : </span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">Uijji</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;reduced Uiijj : </span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">Uiijj</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">Uij_anti</span><span class="p">,</span> <span class="n">Uiijj</span><span class="p">,</span> <span class="n">Uijji</span><span class="p">,</span> <span class="n">Uij_par</span></div>
+
+
+<div class="viewcode-block" id="calc_kan_params"><a class="viewcode-back" href="../../_ref/postprocessing.eval_U_cRPA_Vasp.html#postprocessing.eval_U_cRPA_Vasp.calc_kan_params">[docs]</a><span class="k">def</span> <span class="nf">calc_kan_params</span><span class="p">(</span><span class="n">uijkl</span><span class="p">,</span> <span class="n">n_sites</span><span class="p">,</span> <span class="n">n_orb</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+    <span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">    calculates the kanamori interaction parameters from a</span>
+<span class="sd">    given Uijkl matrix. Follows the procedure given in</span>
+<span class="sd">    PHYSICAL REVIEW B 86, 165105 (2012) Vaugier,Biermann</span>
+<span class="sd">    formula 30,31,32</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    uijkl : numpy array</span>
+<span class="sd">        4d numpy array of Coulomb tensor</span>
+<span class="sd">    n_sites: int</span>
+<span class="sd">        number of different atoms (Wannier centers)</span>
+<span class="sd">    n_orb : int</span>
+<span class="sd">        number of orbitals per atom</span>
+<span class="sd">    out : bool</span>
+<span class="sd">        verbose mode</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    int_params : direct</span>
+<span class="sd">        kanamori parameters</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+
+    <span class="n">int_params</span> <span class="o">=</span> <span class="n">collections</span><span class="o">.</span><span class="n">OrderedDict</span><span class="p">()</span>
+
+    <span class="c1"># calculate intra-orbital U</span>
+    <span class="n">U</span> <span class="o">=</span> <span class="mf">0.0</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">n_orb</span><span class="p">):</span>
+        <span class="n">U</span> <span class="o">+=</span> <span class="n">uijkl</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">i</span><span class="p">]</span>
+    <span class="n">U</span> <span class="o">=</span> <span class="n">U</span><span class="o">/</span><span class="p">(</span><span class="n">n_orb</span><span class="p">)</span>
+    <span class="n">int_params</span><span class="p">[</span><span class="s1">&#39;U&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">U</span>
+
+    <span class="c1"># calculate the U&#39;</span>
+    <span class="n">Uprime</span> <span class="o">=</span> <span class="mf">0.0</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">n_orb</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">n_orb</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">i</span> <span class="o">!=</span> <span class="n">j</span><span class="p">:</span>
+                <span class="n">Uprime</span> <span class="o">+=</span> <span class="n">uijkl</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span>
+    <span class="n">Uprime</span> <span class="o">=</span> <span class="n">Uprime</span> <span class="o">/</span> <span class="p">(</span><span class="n">n_orb</span><span class="o">*</span><span class="p">(</span><span class="n">n_orb</span><span class="o">-</span><span class="mi">1</span><span class="p">))</span>
+    <span class="n">int_params</span><span class="p">[</span><span class="s1">&#39;Uprime&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">Uprime</span>
+
+    <span class="c1"># calculate J</span>
+    <span class="n">J</span> <span class="o">=</span> <span class="mf">0.0</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">n_orb</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">n_orb</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">i</span> <span class="o">!=</span> <span class="n">j</span><span class="p">:</span>
+                <span class="n">J</span> <span class="o">+=</span> <span class="n">uijkl</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span>
+    <span class="n">J</span> <span class="o">=</span> <span class="n">J</span> <span class="o">/</span> <span class="p">(</span><span class="n">n_orb</span><span class="o">*</span><span class="p">(</span><span class="n">n_orb</span><span class="o">-</span><span class="mi">1</span><span class="p">))</span>
+    <span class="n">int_params</span><span class="p">[</span><span class="s1">&#39;J&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">J</span>
+
+    <span class="k">if</span> <span class="n">out</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;U= &#39;</span><span class="p">,</span> <span class="s2">&quot;</span><span class="si">{:.4f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">U</span><span class="p">))</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;U</span><span class="se">\&#39;</span><span class="s1">= &#39;</span><span class="p">,</span> <span class="s2">&quot;</span><span class="si">{:.4f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">Uprime</span><span class="p">))</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;J= &#39;</span><span class="p">,</span> <span class="s2">&quot;</span><span class="si">{:.4f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">J</span><span class="p">))</span>
+
+    <span class="k">return</span> <span class="n">int_params</span></div>
+
+
+<div class="viewcode-block" id="fit_kanamori"><a class="viewcode-back" href="../../_ref/postprocessing.eval_U_cRPA_Vasp.html#postprocessing.eval_U_cRPA_Vasp.fit_kanamori">[docs]</a><span class="k">def</span> <span class="nf">fit_kanamori</span><span class="p">(</span><span class="n">uijkl</span><span class="p">,</span> <span class="n">n_orb</span><span class="p">,</span> <span class="n">switch_jk</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">fit_2</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">fit_3</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">fit_4</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+    <span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">    Fit Kanamori Hamiltonian with scipy to 2,3, and / or 4 parameters</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    -----------</span>
+<span class="sd">    uijkl: np.array (n_orb x n_orb x n_orb x n_orb)</span>
+<span class="sd">            input four index tensor</span>
+<span class="sd">    n_orb: int</span>
+<span class="sd">            number of orbitals</span>
+<span class="sd">    switch_jk: bool, default=False</span>
+<span class="sd">            flip two inner indices in input U tensor (for Vasp)</span>
+<span class="sd">    fit_2: bool, default=True</span>
+<span class="sd">            fit two parameter form</span>
+<span class="sd">    fit_3: bool, default=False</span>
+<span class="sd">            fit three parameter form (U,Up,J=Jc)</span>
+<span class="sd">    fit_4: bool, default=True</span>
+<span class="sd">            fit four parameter form</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    Uijkl_fit: np.array (n_orb x n_orb x n_orb x n_orb)</span>
+<span class="sd">            fitted Uijkl tensor</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+    <span class="kn">from</span> <span class="nn">scipy.optimize</span> <span class="kn">import</span> <span class="n">minimize</span>
+
+    <span class="k">def</span> <span class="nf">minimizer_2params</span><span class="p">(</span><span class="n">parameters</span><span class="p">):</span>
+        <span class="n">U</span><span class="p">,</span> <span class="n">J</span> <span class="o">=</span> <span class="n">parameters</span>
+
+        <span class="n">Uijkl_fit</span> <span class="o">=</span> <span class="n">construct_U_kan</span><span class="p">(</span><span class="n">n_orb</span><span class="p">,</span> <span class="n">U</span><span class="p">,</span> <span class="n">J</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">uijkl</span> <span class="o">-</span> <span class="n">Uijkl_fit</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">minimizer_3params</span><span class="p">(</span><span class="n">parameters</span><span class="p">):</span>
+        <span class="n">U</span><span class="p">,</span> <span class="n">J</span><span class="p">,</span> <span class="n">Up</span> <span class="o">=</span> <span class="n">parameters</span>
+
+        <span class="n">Uijkl_fit</span> <span class="o">=</span> <span class="n">construct_U_kan</span><span class="p">(</span><span class="n">n_orb</span><span class="p">,</span> <span class="n">U</span><span class="p">,</span> <span class="n">J</span><span class="p">,</span> <span class="n">Up</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">uijkl</span> <span class="o">-</span> <span class="n">Uijkl_fit</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">minimizer_4params</span><span class="p">(</span><span class="n">parameters</span><span class="p">):</span>
+        <span class="n">U</span><span class="p">,</span> <span class="n">J</span><span class="p">,</span> <span class="n">Up</span><span class="p">,</span> <span class="n">Jc</span> <span class="o">=</span> <span class="n">parameters</span>
+
+        <span class="n">Uijkl_fit</span> <span class="o">=</span> <span class="n">construct_U_kan</span><span class="p">(</span><span class="n">n_orb</span><span class="p">,</span> <span class="n">U</span><span class="p">,</span> <span class="n">J</span><span class="p">,</span> <span class="n">Up</span><span class="p">,</span> <span class="n">Jc</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">uijkl</span> <span class="o">-</span> <span class="n">Uijkl_fit</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+
+    <span class="c1"># check if J = JC (Hunds exchange and pair hopping have same amplitude)</span>
+    <span class="c1"># true for real values wave functions</span>
+    <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">uijkl</span><span class="o">.</span><span class="n">imag</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mf">0.0</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Largest imaginary part of Uijkl: </span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">uijkl</span><span class="o">.</span><span class="n">imag</span><span class="p">)</span><span class="si">}</span><span class="s2">. Kanamori Hint assumed to be real valued. Neglecting imag part&quot;</span><span class="p">)</span>
+        <span class="n">uijkl</span> <span class="o">=</span> <span class="n">uijkl</span><span class="o">.</span><span class="n">real</span>
+
+    <span class="k">if</span> <span class="n">switch_jk</span><span class="p">:</span>
+        <span class="n">uijkl</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">uijkl</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+
+    <span class="c1"># fit U, J</span>
+    <span class="k">if</span> <span class="n">fit_2</span><span class="p">:</span>
+        <span class="n">initial_guess</span> <span class="o">=</span> <span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="n">result</span> <span class="o">=</span> <span class="n">minimize</span><span class="p">(</span><span class="n">minimizer_2params</span><span class="p">,</span> <span class="n">initial_guess</span><span class="p">)</span>
+        <span class="n">U</span><span class="p">,</span> <span class="n">J</span> <span class="o">=</span> <span class="n">result</span><span class="o">.</span><span class="n">x</span>
+        <span class="n">Uijkl_fit</span> <span class="o">=</span> <span class="n">construct_U_kan</span><span class="p">(</span><span class="n">n_orb</span><span class="p">,</span> <span class="n">U</span><span class="p">,</span> <span class="n">J</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Result 2 parameter fit: </span><span class="se">\n</span><span class="s1">U = </span><span class="si">{:.4f}</span><span class="s1"> eV, J = </span><span class="si">{:.4f}</span><span class="s1"> eV&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">U</span><span class="p">,</span> <span class="n">J</span><span class="p">))</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;optimize error </span><span class="si">{</span><span class="n">result</span><span class="o">.</span><span class="n">fun</span><span class="si">:</span><span class="s1">.3e</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+        <span class="n">max_ind</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unravel_index</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">Uijkl_fit</span><span class="o">-</span><span class="n">uijkl</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="kc">None</span><span class="p">),</span> <span class="n">Uijkl_fit</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;U max diff: U</span><span class="si">{</span><span class="n">max_ind</span><span class="si">}</span><span class="s1">= </span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">Uijkl_fit</span><span class="o">-</span><span class="n">uijkl</span><span class="p">)[</span><span class="n">max_ind</span><span class="p">]</span><span class="si">:</span><span class="s1">.4e</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">-------------------------</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
+
+    <span class="c1"># fit U, J, Up</span>
+    <span class="k">if</span> <span class="n">fit_3</span><span class="p">:</span>
+        <span class="n">initial_guess</span> <span class="o">=</span> <span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+        <span class="n">result</span> <span class="o">=</span> <span class="n">minimize</span><span class="p">(</span><span class="n">minimizer_3params</span><span class="p">,</span> <span class="n">initial_guess</span><span class="p">)</span>
+        <span class="n">U</span><span class="p">,</span> <span class="n">J</span><span class="p">,</span> <span class="n">Up</span> <span class="o">=</span> <span class="n">result</span><span class="o">.</span><span class="n">x</span>
+        <span class="n">Uijkl_fit</span> <span class="o">=</span> <span class="n">construct_U_kan</span><span class="p">(</span><span class="n">n_orb</span><span class="p">,</span> <span class="n">U</span><span class="p">,</span> <span class="n">J</span><span class="p">,</span> <span class="n">Up</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Result 3 parameter fit: </span><span class="se">\n</span><span class="s1">U = </span><span class="si">{:.4f}</span><span class="s1"> eV, U</span><span class="se">\&#39;</span><span class="s1"> = </span><span class="si">{:.4f}</span><span class="s1"> eV J = </span><span class="si">{:.4f}</span><span class="s1"> eV&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">U</span><span class="p">,</span> <span class="n">Up</span><span class="p">,</span> <span class="n">J</span><span class="p">))</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;optimize error </span><span class="si">{</span><span class="n">result</span><span class="o">.</span><span class="n">fun</span><span class="si">:</span><span class="s1">.3e</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+        <span class="n">max_ind</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unravel_index</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">Uijkl_fit</span><span class="o">-</span><span class="n">uijkl</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="kc">None</span><span class="p">),</span> <span class="n">Uijkl_fit</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;U max diff: U</span><span class="si">{</span><span class="n">max_ind</span><span class="si">}</span><span class="s1">= </span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">Uijkl_fit</span><span class="o">-</span><span class="n">uijkl</span><span class="p">)[</span><span class="n">max_ind</span><span class="p">]</span><span class="si">:</span><span class="s1">.4e</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;U=U</span><span class="se">\&#39;</span><span class="s1">-2J deviation: </span><span class="si">{</span><span class="n">U</span><span class="o">-</span><span class="n">Up</span><span class="o">-</span><span class="mi">2</span><span class="o">*</span><span class="n">J</span><span class="si">:</span><span class="s1">.4f</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">-------------------------</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">fit_4</span><span class="p">:</span>
+        <span class="c1"># fit U, J, Up, Jc</span>
+        <span class="n">initial_guess</span> <span class="o">=</span> <span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="n">result</span> <span class="o">=</span> <span class="n">minimize</span><span class="p">(</span><span class="n">minimizer_4params</span><span class="p">,</span> <span class="n">initial_guess</span><span class="p">)</span>
+        <span class="n">U</span><span class="p">,</span> <span class="n">J</span><span class="p">,</span> <span class="n">Up</span><span class="p">,</span> <span class="n">Jc</span> <span class="o">=</span> <span class="n">result</span><span class="o">.</span><span class="n">x</span>
+        <span class="n">Uijkl_fit</span> <span class="o">=</span> <span class="n">construct_U_kan</span><span class="p">(</span><span class="n">n_orb</span><span class="p">,</span> <span class="n">U</span><span class="p">,</span> <span class="n">Up</span><span class="p">,</span> <span class="n">J</span><span class="p">,</span> <span class="n">Jc</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Result 4 parameter fit: </span><span class="se">\n</span><span class="s1">U = </span><span class="si">{:.4f}</span><span class="s1"> eV, U</span><span class="se">\&#39;</span><span class="s1"> = </span><span class="si">{:.4f}</span><span class="s1"> eV, J = </span><span class="si">{:.4f}</span><span class="s1"> eV, Jc = </span><span class="si">{:.4f}</span><span class="s1"> eV&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">U</span><span class="p">,</span> <span class="n">Up</span><span class="p">,</span> <span class="n">J</span><span class="p">,</span> <span class="n">Jc</span><span class="p">))</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;optimize error </span><span class="si">{</span><span class="n">result</span><span class="o">.</span><span class="n">fun</span><span class="si">:</span><span class="s1">.3e</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;U max diff: U</span><span class="si">{</span><span class="n">max_ind</span><span class="si">}</span><span class="s1">= </span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">Uijkl_fit</span><span class="o">-</span><span class="n">uijkl</span><span class="p">)[</span><span class="n">max_ind</span><span class="p">]</span><span class="si">:</span><span class="s1">.4e</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+        <span class="n">max_ind</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unravel_index</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">Uijkl_fit</span><span class="o">-</span><span class="n">uijkl</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="kc">None</span><span class="p">),</span> <span class="n">Uijkl_fit</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;U=U</span><span class="se">\&#39;</span><span class="s1">-2J deviation: </span><span class="si">{</span><span class="n">U</span><span class="o">-</span><span class="n">Up</span><span class="o">-</span><span class="mi">2</span><span class="o">*</span><span class="n">J</span><span class="si">:</span><span class="s1">.4f</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;J=Jc deviation: </span><span class="si">{</span><span class="n">J</span><span class="o">-</span><span class="n">Jc</span><span class="si">:</span><span class="s1">.4f</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">Uijkl_fit</span></div>
+
+
+<div class="viewcode-block" id="calc_u_avg_fulld"><a class="viewcode-back" href="../../_ref/postprocessing.eval_U_cRPA_Vasp.html#postprocessing.eval_U_cRPA_Vasp.calc_u_avg_fulld">[docs]</a><span class="k">def</span> <span class="nf">calc_u_avg_fulld</span><span class="p">(</span><span class="n">uijkl</span><span class="p">,</span> <span class="n">n_sites</span><span class="p">,</span> <span class="n">n_orb</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+    <span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">    calculates the coulomb integrals from a</span>
+<span class="sd">    given Uijkl matrix for full d shells. Follows the procedure given</span>
+<span class="sd">    in Pavarini  - 2014 - arXiv - 1411 6906 - julich school U matrix</span>
+<span class="sd">    page 8 or as done in</span>
+<span class="sd">    PHYSICAL REVIEW B 86, 165105 (2012) Vaugier,Biermann</span>
+<span class="sd">    formula 23, 25</span>
+<span class="sd">    works atm only for full d shell (l=2)</span>
+
+<span class="sd">    Returns F0=U, and J=(F2+F4)/14</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    uijkl : numpy array</span>
+<span class="sd">        4d numpy array of Coulomb tensor</span>
+<span class="sd">    n_sites: int</span>
+<span class="sd">        number of different atoms (Wannier centers)</span>
+<span class="sd">    n_orb : int</span>
+<span class="sd">        number of orbitals per atom</span>
+<span class="sd">    out : bool</span>
+<span class="sd">        verbose mode</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    int_params : direct</span>
+<span class="sd">        Slater parameters</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+
+    <span class="n">int_params</span> <span class="o">=</span> <span class="n">collections</span><span class="o">.</span><span class="n">OrderedDict</span><span class="p">()</span>
+    <span class="n">Uij_anti</span><span class="p">,</span> <span class="n">Uiijj</span><span class="p">,</span> <span class="n">Uijji</span><span class="p">,</span> <span class="n">Uij_par</span> <span class="o">=</span> <span class="n">red_to_2ind</span><span class="p">(</span><span class="n">uijkl</span><span class="p">,</span> <span class="n">n_sites</span><span class="p">,</span> <span class="n">n_orb</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+    <span class="c1"># U_antipar = U_mm&#39;^oo&#39; = U_mm&#39;mm&#39; (Coulomb Int)</span>
+    <span class="c1"># U_par = U_mm&#39;^oo = U_mm&#39;mm&#39; - U_mm&#39;m&#39;m (for intersite interaction)</span>
+    <span class="c1"># here we assume cubic harmonics (real harmonics) as basis functions in the order</span>
+    <span class="c1"># dz2 dxz dyz dx2-y2 dxy</span>
+    <span class="c1"># triqs basis: basis ordered as (xy,yz,z^2,xz,x^2-y^2)</span>
+
+    <span class="c1"># calculate J</span>
+    <span class="n">J_cubic</span> <span class="o">=</span> <span class="mf">0.0</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">n_orb</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">n_orb</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">i</span> <span class="o">!=</span> <span class="n">j</span><span class="p">:</span>
+                <span class="n">J_cubic</span> <span class="o">+=</span> <span class="n">Uijji</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span>
+    <span class="n">J_cubic</span> <span class="o">=</span> <span class="n">J_cubic</span><span class="o">/</span><span class="p">(</span><span class="mf">20.0</span><span class="p">)</span>
+    <span class="c1"># 20 for 2l(2l+1)</span>
+    <span class="n">int_params</span><span class="p">[</span><span class="s1">&#39;J_cubic&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">J_cubic</span>
+
+    <span class="c1"># conversion from cubic to spherical:</span>
+    <span class="n">J</span> <span class="o">=</span> <span class="mf">7.0</span> <span class="o">*</span> <span class="n">J_cubic</span> <span class="o">/</span> <span class="mf">5.0</span>
+
+    <span class="n">int_params</span><span class="p">[</span><span class="s1">&#39;J&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">J</span>
+
+    <span class="c1"># calculate intra-orbital U</span>
+    <span class="n">U_0</span> <span class="o">=</span> <span class="mf">0.0</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">n_orb</span><span class="p">):</span>
+        <span class="n">U_0</span> <span class="o">+=</span> <span class="n">Uij_anti</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">i</span><span class="p">]</span>
+    <span class="n">U_0</span> <span class="o">=</span> <span class="n">U_0</span> <span class="o">/</span> <span class="nb">float</span><span class="p">(</span><span class="n">n_orb</span><span class="p">)</span>
+    <span class="n">int_params</span><span class="p">[</span><span class="s1">&#39;U_0&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">U_0</span>
+
+    <span class="c1"># now conversion from cubic to spherical</span>
+    <span class="n">U</span> <span class="o">=</span> <span class="n">U_0</span> <span class="o">-</span> <span class="p">(</span><span class="mf">8.0</span><span class="o">*</span><span class="n">J_cubic</span><span class="o">/</span><span class="mf">5.0</span><span class="p">)</span>
+
+    <span class="n">int_params</span><span class="p">[</span><span class="s1">&#39;U&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">U</span>
+
+    <span class="k">if</span> <span class="n">out</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;cubic U_0= &#39;</span><span class="p">,</span> <span class="s2">&quot;</span><span class="si">{:.4f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">U_0</span><span class="p">))</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;cubic J_cubic= &#39;</span><span class="p">,</span> <span class="s2">&quot;</span><span class="si">{:.4f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">J_cubic</span><span class="p">))</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;spherical F0=U= &#39;</span><span class="p">,</span> <span class="s2">&quot;</span><span class="si">{:.4f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">U</span><span class="p">))</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;spherical J=(F2+f4)/14 = &#39;</span><span class="p">,</span> <span class="s2">&quot;</span><span class="si">{:.4f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">J</span><span class="p">))</span>
+
+    <span class="k">return</span> <span class="n">int_params</span></div>
+
+
+<div class="viewcode-block" id="calculate_interaction_from_averaging"><a class="viewcode-back" href="../../_ref/postprocessing.eval_U_cRPA_Vasp.html#postprocessing.eval_U_cRPA_Vasp.calculate_interaction_from_averaging">[docs]</a><span class="k">def</span> <span class="nf">calculate_interaction_from_averaging</span><span class="p">(</span><span class="n">uijkl</span><span class="p">,</span> <span class="n">n_sites</span><span class="p">,</span> <span class="n">n_orb</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+    <span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">    calculates U,J by averaging directly the Uijkl matrix</span>
+<span class="sd">    ignoring if tensor is given in spherical or cubic basis.</span>
+<span class="sd">    The assumption here is that the averaging gives indepentendly</span>
+<span class="sd">    of the choosen basis (cubic or spherical harmonics) the same results</span>
+<span class="sd">    if Uijkl is a true Slater matrix.</span>
+
+<span class="sd">    Returns F0=U, and J=(F2+F4)/14</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    uijkl : numpy array</span>
+<span class="sd">        4d numpy array of Coulomb tensor</span>
+<span class="sd">    n_sites: int</span>
+<span class="sd">        number of different atoms (Wannier centers)</span>
+<span class="sd">    n_orb : int</span>
+<span class="sd">        number of orbitals per atom</span>
+<span class="sd">    out : bool</span>
+<span class="sd">        verbose mode</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    U, J: tuple</span>
+<span class="sd">        Slater parameters</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+
+    <span class="n">l</span> <span class="o">=</span> <span class="mi">2</span>
+
+    <span class="n">Uij_anti</span><span class="p">,</span> <span class="n">Uiijj</span><span class="p">,</span> <span class="n">Uijji</span><span class="p">,</span> <span class="n">Uij_par</span> <span class="o">=</span> <span class="n">red_to_2ind</span><span class="p">(</span><span class="n">uijkl</span><span class="p">,</span> <span class="n">n_sites</span><span class="p">,</span> <span class="n">n_orb</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
+
+    <span class="c1"># Calculates Slater-averaged parameters directly</span>
+    <span class="n">U</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="n">n_sites</span>
+    <span class="n">J</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="n">n_sites</span>
+    <span class="k">for</span> <span class="n">impurity</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_sites</span><span class="p">):</span>
+        <span class="n">u_ijij_imp</span> <span class="o">=</span> <span class="n">Uij_anti</span><span class="p">[</span><span class="n">impurity</span><span class="o">*</span><span class="n">n_orb</span><span class="p">:(</span><span class="n">impurity</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span><span class="o">*</span><span class="n">n_orb</span><span class="p">,</span> <span class="n">impurity</span><span class="o">*</span><span class="n">n_orb</span><span class="p">:(</span><span class="n">impurity</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span><span class="o">*</span><span class="n">n_orb</span><span class="p">]</span>
+        <span class="n">U</span><span class="p">[</span><span class="n">impurity</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">u_ijij_imp</span><span class="p">)</span>
+
+        <span class="n">u_iijj_imp</span> <span class="o">=</span> <span class="n">Uiijj</span><span class="p">[</span><span class="n">impurity</span><span class="o">*</span><span class="n">n_orb</span><span class="p">:(</span><span class="n">impurity</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span><span class="o">*</span><span class="n">n_orb</span><span class="p">,</span> <span class="n">impurity</span><span class="o">*</span><span class="n">n_orb</span><span class="p">:(</span><span class="n">impurity</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span><span class="o">*</span><span class="n">n_orb</span><span class="p">]</span>
+        <span class="n">J</span><span class="p">[</span><span class="n">impurity</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">u_iijj_imp</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">l</span><span class="o">*</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">l</span><span class="o">+</span><span class="mi">1</span><span class="p">))</span> <span class="o">-</span> <span class="n">U</span><span class="p">[</span><span class="n">impurity</span><span class="p">]</span> <span class="o">/</span> <span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">l</span><span class="p">)</span>
+    <span class="n">U</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">U</span><span class="p">)</span>
+    <span class="n">J</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">J</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">out</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;spherical F0=U= &#39;</span><span class="p">,</span> <span class="s2">&quot;</span><span class="si">{:.4f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">U</span><span class="p">))</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;spherical J=(F2+f4)/14 = &#39;</span><span class="p">,</span> <span class="s2">&quot;</span><span class="si">{:.4f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">J</span><span class="p">))</span>
+
+    <span class="k">return</span> <span class="n">U</span><span class="p">,</span> <span class="n">J</span></div>
+
+
+<div class="viewcode-block" id="fit_slater_fulld"><a class="viewcode-back" href="../../_ref/postprocessing.eval_U_cRPA_Vasp.html#postprocessing.eval_U_cRPA_Vasp.fit_slater_fulld">[docs]</a><span class="k">def</span> <span class="nf">fit_slater_fulld</span><span class="p">(</span><span class="n">uijkl</span><span class="p">,</span> <span class="n">n_sites</span><span class="p">,</span> <span class="n">U_init</span><span class="p">,</span> <span class="n">J_init</span><span class="p">,</span> <span class="n">fixed_F4_F2</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+    <span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">    finds best Slater parameters U, J for given Uijkl tensor</span>
+<span class="sd">    using the triqs U_matrix operator routine</span>
+<span class="sd">    assumes F4/F2=0.625</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+
+    <span class="kn">from</span> <span class="nn">triqs.operators.util.U_matrix</span> <span class="kn">import</span> <span class="n">U_matrix_slater</span><span class="p">,</span> <span class="n">reduce_4index_to_2index</span>
+    <span class="kn">from</span> <span class="nn">scipy.optimize</span> <span class="kn">import</span> <span class="n">minimize</span>
+    <span class="c1"># transform U matrix orbital basis ijkl to nmop, note the last two indices need to be switched in the T matrices</span>
+
+    <span class="k">def</span> <span class="nf">transformU</span><span class="p">(</span><span class="n">U_matrix</span><span class="p">,</span> <span class="n">T</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s2">&quot;im,jn,ijkl,lo,kp-&gt;mnpo&quot;</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">conj</span><span class="p">(</span><span class="n">T</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">conj</span><span class="p">(</span><span class="n">T</span><span class="p">),</span> <span class="n">U_matrix</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="n">T</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">minimizer</span><span class="p">(</span><span class="n">parameters</span><span class="p">):</span>
+        <span class="n">U_int</span><span class="p">,</span> <span class="n">J_hund</span> <span class="o">=</span> <span class="n">parameters</span>
+        <span class="n">Umat_full</span> <span class="o">=</span> <span class="n">U_matrix_slater</span><span class="p">(</span><span class="n">l</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">U_int</span><span class="o">=</span><span class="n">U_int</span><span class="p">,</span> <span class="n">J_hund</span><span class="o">=</span><span class="n">J_hund</span><span class="p">,</span> <span class="n">basis</span><span class="o">=</span><span class="s1">&#39;cubic&#39;</span><span class="p">)</span>
+        <span class="n">Umat_full</span> <span class="o">=</span> <span class="n">transformU</span><span class="p">(</span><span class="n">Umat_full</span><span class="p">,</span> <span class="n">rot_def_to_w90</span><span class="p">)</span>
+
+        <span class="n">Umat</span><span class="p">,</span> <span class="n">Upmat</span> <span class="o">=</span> <span class="n">reduce_4index_to_2index</span><span class="p">(</span><span class="n">Umat_full</span><span class="p">)</span>
+        <span class="n">u_iijj_crpa</span> <span class="o">=</span> <span class="n">Uiijj</span><span class="p">[:</span><span class="mi">5</span><span class="p">,</span> <span class="p">:</span><span class="mi">5</span><span class="p">]</span>
+        <span class="n">u_iijj_slater</span> <span class="o">=</span> <span class="n">Upmat</span> <span class="o">-</span> <span class="n">Umat</span>
+        <span class="n">u_ijij_crpa</span> <span class="o">=</span> <span class="n">Uij_anti</span><span class="p">[:</span><span class="mi">5</span><span class="p">,</span> <span class="p">:</span><span class="mi">5</span><span class="p">]</span>
+        <span class="n">u_ijij_slater</span> <span class="o">=</span> <span class="n">Upmat</span>
+        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">u_iijj_crpa</span> <span class="o">-</span> <span class="n">u_iijj_slater</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="p">(</span><span class="n">u_ijij_crpa</span> <span class="o">-</span> <span class="n">u_ijij_slater</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">minimizer_radial</span><span class="p">(</span><span class="n">parameters</span><span class="p">):</span>
+        <span class="n">F0</span><span class="p">,</span> <span class="n">F2</span><span class="p">,</span> <span class="n">F4</span> <span class="o">=</span> <span class="n">parameters</span>
+        <span class="n">Umat_full</span> <span class="o">=</span> <span class="n">U_matrix_slater</span><span class="p">(</span><span class="n">l</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">radial_integrals</span><span class="o">=</span><span class="p">[</span><span class="n">F0</span><span class="p">,</span> <span class="n">F2</span><span class="p">,</span> <span class="n">F4</span><span class="p">],</span> <span class="n">basis</span><span class="o">=</span><span class="s1">&#39;cubic&#39;</span><span class="p">)</span>
+        <span class="n">Umat_full</span> <span class="o">=</span> <span class="n">transformU</span><span class="p">(</span><span class="n">Umat_full</span><span class="p">,</span> <span class="n">rot_def_to_w90</span><span class="p">)</span>
+
+        <span class="n">Umat</span><span class="p">,</span> <span class="n">Upmat</span> <span class="o">=</span> <span class="n">reduce_4index_to_2index</span><span class="p">(</span><span class="n">Umat_full</span><span class="p">)</span>
+        <span class="n">u_iijj_crpa</span> <span class="o">=</span> <span class="n">Uiijj</span><span class="p">[:</span><span class="mi">5</span><span class="p">,</span> <span class="p">:</span><span class="mi">5</span><span class="p">]</span>
+        <span class="n">u_iijj_slater</span> <span class="o">=</span> <span class="n">Upmat</span> <span class="o">-</span> <span class="n">Umat</span>
+        <span class="n">u_ijij_crpa</span> <span class="o">=</span> <span class="n">Uij_anti</span><span class="p">[:</span><span class="mi">5</span><span class="p">,</span> <span class="p">:</span><span class="mi">5</span><span class="p">]</span>
+        <span class="n">u_ijij_slater</span> <span class="o">=</span> <span class="n">Upmat</span>
+        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">u_iijj_crpa</span> <span class="o">-</span> <span class="n">u_iijj_slater</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="p">(</span><span class="n">u_ijij_crpa</span> <span class="o">-</span> <span class="n">u_ijij_slater</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+
+    <span class="c1"># rot triqs d basis to w90 default basis!</span>
+    <span class="c1"># check your order of orbitals assuming:</span>
+    <span class="c1"># dz2, dxz, dyz, dx2-y2, dxy</span>
+    <span class="n">rot_def_to_w90</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
+                               <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
+                               <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
+                               <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
+                               <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]])</span>
+
+    <span class="n">Uij_anti</span><span class="p">,</span> <span class="n">Uiijj</span><span class="p">,</span> <span class="n">Uijji</span><span class="p">,</span> <span class="n">Uij_par</span> <span class="o">=</span> <span class="n">red_to_2ind</span><span class="p">(</span><span class="n">uijkl</span><span class="p">,</span> <span class="n">n_sites</span><span class="p">,</span> <span class="n">n_orb</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">fixed_F4_F2</span><span class="p">:</span>
+        <span class="n">result</span> <span class="o">=</span> <span class="n">minimize</span><span class="p">(</span><span class="n">minimizer</span><span class="p">,</span> <span class="p">(</span><span class="n">U_init</span><span class="p">,</span> <span class="n">J_init</span><span class="p">))</span>
+
+        <span class="n">U_int</span><span class="p">,</span> <span class="n">J_hund</span> <span class="o">=</span> <span class="n">result</span><span class="o">.</span><span class="n">x</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Final results from fit: U = </span><span class="si">{:.3f}</span><span class="s1"> eV, J = </span><span class="si">{:.3f}</span><span class="s1"> eV&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">U_int</span><span class="p">,</span> <span class="n">J_hund</span><span class="p">))</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;optimize error&#39;</span><span class="p">,</span> <span class="n">result</span><span class="o">.</span><span class="n">fun</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="c1"># start with 0.63 as guess</span>
+        <span class="n">F0</span> <span class="o">=</span> <span class="n">U_init</span>
+        <span class="n">F2</span> <span class="o">=</span> <span class="n">J_init</span> <span class="o">*</span> <span class="mf">14.0</span> <span class="o">/</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">+</span> <span class="mf">0.63</span><span class="p">)</span>
+        <span class="n">F4</span> <span class="o">=</span> <span class="mf">0.630</span> <span class="o">*</span> <span class="n">F2</span>
+
+        <span class="n">initial_guess</span> <span class="o">=</span> <span class="p">(</span><span class="n">F0</span><span class="p">,</span> <span class="n">F2</span><span class="p">,</span> <span class="n">F4</span><span class="p">)</span>
+
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Initial guess: F0 = </span><span class="si">{0[0]:.3f}</span><span class="s1"> eV, F2 = </span><span class="si">{0[1]:.3f}</span><span class="s1"> eV, F4 = </span><span class="si">{0[2]:.3f}</span><span class="s1"> eV&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">initial_guess</span><span class="p">))</span>
+
+        <span class="n">result</span> <span class="o">=</span> <span class="n">minimize</span><span class="p">(</span><span class="n">minimizer_radial</span><span class="p">,</span> <span class="n">initial_guess</span><span class="p">)</span>
+        <span class="n">F0</span><span class="p">,</span> <span class="n">F2</span><span class="p">,</span> <span class="n">F4</span> <span class="o">=</span> <span class="n">result</span><span class="o">.</span><span class="n">x</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Final results from fit: F0 = </span><span class="si">{:.3f}</span><span class="s1"> eV, F2 = </span><span class="si">{:.3f}</span><span class="s1"> eV, F4 = </span><span class="si">{:.3f}</span><span class="s1"> eV&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">F0</span><span class="p">,</span> <span class="n">F2</span><span class="p">,</span> <span class="n">F4</span><span class="p">))</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;(F2+F4)/14 = </span><span class="si">{:.3f}</span><span class="s1"> eV&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">((</span><span class="n">F2</span><span class="o">+</span><span class="n">F4</span><span class="p">)</span><span class="o">/</span><span class="mi">14</span><span class="p">))</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;F4/F2 = </span><span class="si">{:.3f}</span><span class="s1"> eV&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">F4</span><span class="o">/</span><span class="n">F2</span><span class="p">))</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;optimize error&#39;</span><span class="p">,</span> <span class="n">result</span><span class="o">.</span><span class="n">fun</span><span class="p">)</span>
+        <span class="n">U_int</span> <span class="o">=</span> <span class="n">F0</span>
+        <span class="n">J_hund</span> <span class="o">=</span> <span class="p">(</span><span class="n">F2</span><span class="o">+</span><span class="n">F4</span><span class="p">)</span><span class="o">/</span><span class="mi">14</span>
+
+    <span class="k">return</span> <span class="n">U_int</span><span class="p">,</span> <span class="n">J_hund</span></div>
+
+<span class="c1"># example for a five orbital model</span>
+<span class="c1"># uijkl=read_uijkl(&#39;UIJKL&#39;,1,5)</span>
+<span class="c1"># calc_u_avg_fulld(uijkl,n_sites=1,n_orb=5,out=True)</span>
+<span class="c1"># print(&#39;now via fitting with fixed F4/F2=0.63 ratio&#39;)</span>
+<span class="c1"># fit_slater_fulld(uijkl,1,3,1, fixed_F4_F2 = True)</span>
+<span class="c1"># print(&#39;now directly fitting F0,F2,F4&#39;)</span>
+<span class="c1"># fit_slater_fulld(uijkl,1,3,1, fixed_F4_F2 = False)</span>
+
+<span class="c1"># calculate_interaction_from_averaging(uijkl, 1, 5, out=True)</span>
+
+
+<span class="c1"># example for 3 orbital kanamori</span>
+<span class="c1"># uijkl=read_uijkl(&#39;UIJKL&#39;,1,3)</span>
+<span class="c1"># calc_kan_params(uijkl,1,3,out=True)</span>
+</pre></div>
+
+           </div>
+          </div>
+          <footer>
+
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+
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+    <p>&#169; Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.</p>
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diff --git a/_modules/postprocessing/maxent_gf_imp.html b/_modules/postprocessing/maxent_gf_imp.html
new file mode 100644
index 00000000..8c69336a
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+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
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+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
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+      </li>
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+             
+  <h1>Source code for postprocessing.maxent_gf_imp</h1><div class="highlight"><pre>
+<span></span><span class="c1">################################################################################</span>
+<span class="c1">#</span>
+<span class="c1"># TRIQS: a Toolbox for Research in Interacting Quantum Systems</span>
+<span class="c1">#</span>
+<span class="c1"># Copyright (C) 2016-2018, N. Wentzell</span>
+<span class="c1"># Copyright (C) 2018-2019, Simons Foundation</span>
+<span class="c1">#   author: N. Wentzell</span>
+<span class="c1">#</span>
+<span class="c1"># TRIQS is free software: you can redistribute it and/or modify it under the</span>
+<span class="c1"># terms of the GNU General Public License as published by the Free Software</span>
+<span class="c1"># Foundation, either version 3 of the License, or (at your option) any later</span>
+<span class="c1"># version.</span>
+<span class="c1">#</span>
+<span class="c1"># TRIQS is distributed in the hope that it will be useful, but WITHOUT ANY</span>
+<span class="c1"># WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS</span>
+<span class="c1"># FOR A PARTICULAR PURPOSE. See the GNU General Public License for more</span>
+<span class="c1"># details.</span>
+<span class="c1">#</span>
+<span class="c1"># You should have received a copy of the GNU General Public License along with</span>
+<span class="c1"># TRIQS. If not, see &lt;http://www.gnu.org/licenses/&gt;.</span>
+<span class="c1">#</span>
+<span class="c1">################################################################################</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Analytic continuation of the impurity Green&#39;s function to the impurity spectral</span>
+<span class="sd">function using maxent.</span>
+
+<span class="sd">Reads G_imp(i omega) from the h5 archive and writes A_imp(omega) back. See</span>
+<span class="sd">the docstring of main() for more information.</span>
+
+<span class="sd">Not mpi parallelized.</span>
+
+<span class="sd">Author: Maximilian Merkel, Materials Theory Group, ETH Zurich, 2020 - 2022</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="kn">import</span> <span class="nn">sys</span>
+<span class="kn">import</span> <span class="nn">time</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<span class="kn">from</span> <span class="nn">triqs_maxent.elementwise_maxent</span> <span class="kn">import</span> <span class="n">PoormanMaxEnt</span>
+<span class="kn">from</span> <span class="nn">triqs_maxent.omega_meshes</span> <span class="kn">import</span> <span class="n">HyperbolicOmegaMesh</span>
+<span class="kn">from</span> <span class="nn">triqs_maxent.alpha_meshes</span> <span class="kn">import</span> <span class="n">LogAlphaMesh</span>
+<span class="kn">from</span> <span class="nn">triqs_maxent.logtaker</span> <span class="kn">import</span> <span class="n">VerbosityFlags</span>
+<span class="kn">from</span> <span class="nn">h5</span> <span class="kn">import</span> <span class="n">HDFArchive</span>
+<span class="kn">from</span> <span class="nn">triqs.utility</span> <span class="kn">import</span> <span class="n">mpi</span>
+<span class="kn">from</span> <span class="nn">triqs.gf</span> <span class="kn">import</span> <span class="n">BlockGf</span>
+
+
+<span class="k">def</span> <span class="nf">_read_h5</span><span class="p">(</span><span class="n">external_path</span><span class="p">,</span> <span class="n">iteration</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Reads the h5 archive to get the impurity Green&#39;s functions.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    external_path : string</span>
+<span class="sd">        path to h5 archive</span>
+<span class="sd">    iteration : int</span>
+<span class="sd">        The iteration that is being read from, None corresponds to &#39;last_iter&#39;</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    gf_imp_tau : list</span>
+<span class="sd">        Impurity Green&#39;s function as block Green&#39;s function for each impurity</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="sd">&quot;&quot;&quot;Reads the block Green&#39;s function G(tau) from h5 archive.&quot;&quot;&quot;</span>
+
+    <span class="n">h5_internal_path</span> <span class="o">=</span> <span class="s1">&#39;DMFT_results/&#39;</span> <span class="o">+</span> <span class="p">(</span><span class="s1">&#39;last_iter&#39;</span> <span class="k">if</span> <span class="n">iteration</span> <span class="ow">is</span> <span class="kc">None</span>
+                                          <span class="k">else</span> <span class="sa">f</span><span class="s1">&#39;it_</span><span class="si">{</span><span class="n">iteration</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+
+    <span class="k">with</span> <span class="n">HDFArchive</span><span class="p">(</span><span class="n">external_path</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">archive</span><span class="p">:</span>
+        <span class="n">impurity_paths</span> <span class="o">=</span> <span class="p">[</span><span class="n">key</span> <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">archive</span><span class="p">[</span><span class="n">h5_internal_path</span><span class="p">]</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span>
+                          <span class="k">if</span> <span class="s1">&#39;Gimp_time_&#39;</span> <span class="ow">in</span> <span class="n">key</span> <span class="ow">and</span> <span class="s1">&#39;orig&#39;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">key</span><span class="p">]</span>
+        <span class="c1"># Sorts impurity paths by their indices, not sure if necessary</span>
+        <span class="n">impurity_indices</span> <span class="o">=</span> <span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">s</span><span class="p">[</span><span class="n">s</span><span class="o">.</span><span class="n">rfind</span><span class="p">(</span><span class="s1">&#39;_&#39;</span><span class="p">)</span><span class="o">+</span><span class="mi">1</span><span class="p">:])</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">impurity_paths</span><span class="p">]</span>
+        <span class="n">impurity_paths</span> <span class="o">=</span> <span class="p">[</span><span class="n">impurity_paths</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">impurity_indices</span><span class="p">)]</span>
+
+        <span class="n">gf_imp_tau</span> <span class="o">=</span> <span class="p">[</span><span class="n">archive</span><span class="p">[</span><span class="n">h5_internal_path</span><span class="p">][</span><span class="n">p</span><span class="p">]</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">impurity_paths</span><span class="p">]</span>
+    <span class="k">return</span> <span class="n">gf_imp_tau</span>
+
+
+<span class="k">def</span> <span class="nf">_sum_greens_functions</span><span class="p">(</span><span class="n">block_gf</span><span class="p">,</span> <span class="n">sum_spins</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Sums over spin channels if sum_spins. It combines &quot;up&quot; and &quot;down&quot; into one</span>
+<span class="sd">    block &quot;total&quot;, or for SOC, simply renames the blocks ud into &quot;total&quot;.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">sum_spins</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">block_gf</span>
+
+    <span class="k">for</span> <span class="n">ind</span> <span class="ow">in</span> <span class="n">block_gf</span><span class="o">.</span><span class="n">indices</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">ind</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">&#39;up_&#39;</span><span class="p">):</span>
+            <span class="k">assert</span> <span class="n">ind</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;up&#39;</span><span class="p">,</span> <span class="s1">&#39;down&#39;</span><span class="p">)</span> <span class="ow">in</span> <span class="n">block_gf</span><span class="o">.</span><span class="n">indices</span>
+        <span class="k">elif</span> <span class="n">ind</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">&#39;down_&#39;</span><span class="p">):</span>
+            <span class="k">assert</span> <span class="n">ind</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;down&#39;</span><span class="p">,</span> <span class="s1">&#39;up&#39;</span><span class="p">)</span> <span class="ow">in</span> <span class="n">block_gf</span><span class="o">.</span><span class="n">indices</span>
+        <span class="k">elif</span> <span class="ow">not</span> <span class="n">ind</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">&#39;ud_&#39;</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Block </span><span class="si">{</span><span class="n">ind</span><span class="si">}</span><span class="s1"> in G(tau) has unknown spin type. &#39;</span>
+                             <span class="o">+</span> <span class="s1">&#39;Check G(tau) or turn off sum_spins.&#39;</span><span class="p">)</span>
+
+    <span class="n">summed_gf_imp</span> <span class="o">=</span> <span class="p">{}</span>
+
+    <span class="k">for</span> <span class="n">block_name</span><span class="p">,</span> <span class="n">block</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">block_gf</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">block_name</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">&#39;up_&#39;</span><span class="p">):</span>
+            <span class="n">new_block_name</span> <span class="o">=</span> <span class="n">block_name</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;up&#39;</span><span class="p">,</span> <span class="s1">&#39;total&#39;</span><span class="p">)</span>
+            <span class="n">opp_spin_block_name</span> <span class="o">=</span> <span class="n">block_name</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;up&#39;</span><span class="p">,</span> <span class="s1">&#39;down&#39;</span><span class="p">)</span>
+            <span class="n">summed_gf_imp</span><span class="p">[</span><span class="n">new_block_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">block</span> <span class="o">+</span> <span class="n">block_gf</span><span class="p">[</span><span class="n">opp_spin_block_name</span><span class="p">]</span>
+        <span class="k">elif</span> <span class="n">block_name</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">&#39;ud_&#39;</span><span class="p">):</span>
+            <span class="n">summed_gf_imp</span><span class="p">[</span><span class="n">block_name</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;ud&#39;</span><span class="p">,</span> <span class="s1">&#39;total&#39;</span><span class="p">)]</span> <span class="o">=</span> <span class="n">block</span>
+
+    <span class="k">return</span> <span class="n">BlockGf</span><span class="p">(</span><span class="n">name_list</span><span class="o">=</span><span class="n">summed_gf_imp</span><span class="o">.</span><span class="n">keys</span><span class="p">(),</span> <span class="n">block_list</span><span class="o">=</span><span class="n">summed_gf_imp</span><span class="o">.</span><span class="n">values</span><span class="p">())</span>
+
+
+<span class="k">def</span> <span class="nf">_run_maxent</span><span class="p">(</span><span class="n">gf_imp_list</span><span class="p">,</span> <span class="n">maxent_error</span><span class="p">,</span> <span class="n">n_points_maxent</span><span class="p">,</span> <span class="n">n_points_alpha</span><span class="p">,</span>
+                <span class="n">omega_min</span><span class="p">,</span> <span class="n">omega_max</span><span class="p">,</span> <span class="n">analyzer</span><span class="o">=</span><span class="s1">&#39;LineFitAnalyzer&#39;</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Runs maxent to get the spectral functions from the list of block GFs.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">omega_mesh</span> <span class="o">=</span> <span class="n">HyperbolicOmegaMesh</span><span class="p">(</span><span class="n">omega_min</span><span class="o">=</span><span class="n">omega_min</span><span class="p">,</span> <span class="n">omega_max</span><span class="o">=</span><span class="n">omega_max</span><span class="p">,</span>
+                                     <span class="n">n_points</span><span class="o">=</span><span class="n">n_points_maxent</span><span class="p">)</span>
+
+    <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Continuing impurities with blocks: &#39;</span><span class="p">)</span>
+    <span class="n">imps_blocks</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">block_gf</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">gf_imp_list</span><span class="p">):</span>
+        <span class="n">blocks</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">block_gf</span><span class="o">.</span><span class="n">indices</span><span class="p">)</span>
+        <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;- Imp </span><span class="si">{}</span><span class="s1">: </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">blocks</span><span class="p">))</span>
+        <span class="k">for</span> <span class="n">block</span> <span class="ow">in</span> <span class="n">blocks</span><span class="p">:</span>
+            <span class="n">imps_blocks</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">block</span><span class="p">))</span>
+    <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;-&#39;</span><span class="o">*</span><span class="mi">50</span><span class="p">)</span>
+    <span class="n">imps_blocks_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">imps_blocks</span><span class="p">))</span>
+
+    <span class="c1"># Initialize collective results</span>
+    <span class="n">data_linefit</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">imps_blocks</span><span class="p">)</span>
+    <span class="n">data_chi2</span> <span class="o">=</span>  <span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">imps_blocks</span><span class="p">)</span>
+
+    <span class="n">mpi</span><span class="o">.</span><span class="n">barrier</span><span class="p">()</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">mpi</span><span class="o">.</span><span class="n">slice_array</span><span class="p">(</span><span class="n">imps_blocks_indices</span><span class="p">):</span>
+        <span class="n">imp</span><span class="p">,</span> <span class="n">block</span> <span class="o">=</span> <span class="n">imps_blocks</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Rank </span><span class="si">{</span><span class="n">mpi</span><span class="o">.</span><span class="n">rank</span><span class="si">}</span><span class="s2">: solving impurity </span><span class="si">{</span><span class="n">imp</span><span class="o">+</span><span class="mi">1</span><span class="si">}</span><span class="s2"> block &#39;</span><span class="si">{</span><span class="n">block</span><span class="si">}</span><span class="s2">&#39;&quot;</span><span class="p">)</span>
+
+        <span class="n">solver</span> <span class="o">=</span> <span class="n">PoormanMaxEnt</span><span class="p">(</span><span class="n">use_complex</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+        <span class="n">solver</span><span class="o">.</span><span class="n">set_G_tau</span><span class="p">(</span><span class="n">gf_imp_list</span><span class="p">[</span><span class="n">imp</span><span class="p">][</span><span class="n">block</span><span class="p">])</span>
+        <span class="n">solver</span><span class="o">.</span><span class="n">set_error</span><span class="p">(</span><span class="n">maxent_error</span><span class="p">)</span>
+        <span class="n">solver</span><span class="o">.</span><span class="n">omega</span> <span class="o">=</span> <span class="n">omega_mesh</span>
+        <span class="n">solver</span><span class="o">.</span><span class="n">alpha_mesh</span> <span class="o">=</span> <span class="n">LogAlphaMesh</span><span class="p">(</span><span class="n">alpha_min</span><span class="o">=</span><span class="mf">1e-6</span><span class="p">,</span> <span class="n">alpha_max</span><span class="o">=</span><span class="mf">1e2</span><span class="p">,</span>
+                                         <span class="n">n_points</span><span class="o">=</span><span class="n">n_points_alpha</span><span class="p">)</span>
+        <span class="c1"># silence output</span>
+        <span class="n">solver</span><span class="o">.</span><span class="n">maxent_diagonal</span><span class="o">.</span><span class="n">logtaker</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">VerbosityFlags</span><span class="o">.</span><span class="n">Quiet</span>
+        <span class="n">solver</span><span class="o">.</span><span class="n">maxent_offdiagonal</span><span class="o">.</span><span class="n">logtaker</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">VerbosityFlags</span><span class="o">.</span><span class="n">Quiet</span>
+        <span class="n">results</span> <span class="o">=</span> <span class="n">solver</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
+
+        <span class="n">n_orb</span> <span class="o">=</span> <span class="n">gf_imp_list</span><span class="p">[</span><span class="n">imp</span><span class="p">][</span><span class="n">block</span><span class="p">]</span><span class="o">.</span><span class="n">target_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">opt_alpha</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">n_orb</span><span class="p">,</span> <span class="n">n_orb</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">int</span><span class="p">)</span>
+        <span class="n">opt_alpha</span><span class="p">[:,</span> <span class="p">:,</span> <span class="p">:]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>  <span class="c1"># set results to -1 to distinguish them from 0</span>
+        <span class="k">for</span> <span class="n">i_orb</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_orb</span><span class="p">):</span>
+            <span class="k">for</span> <span class="n">j_orb</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_orb</span><span class="p">):</span>
+                <span class="k">for</span> <span class="n">l_com</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">):</span>  <span class="c1"># loop over complex numbers</span>
+                    <span class="k">if</span> <span class="n">results</span><span class="o">.</span><span class="n">analyzer_results</span><span class="p">[</span><span class="n">i_orb</span><span class="p">][</span><span class="n">j_orb</span><span class="p">][</span><span class="n">l_com</span><span class="p">]</span> <span class="o">==</span> <span class="p">{}:</span>
+                        <span class="k">continue</span>
+                    <span class="n">opt_alpha</span><span class="p">[</span><span class="n">i_orb</span><span class="p">,</span> <span class="n">j_orb</span><span class="p">,</span>
+                              <span class="n">l_com</span><span class="p">]</span> <span class="o">=</span> <span class="n">results</span><span class="o">.</span><span class="n">analyzer_results</span><span class="p">[</span><span class="n">i_orb</span><span class="p">][</span><span class="n">j_orb</span><span class="p">][</span><span class="n">l_com</span><span class="p">][</span><span class="n">analyzer</span><span class="p">][</span><span class="s1">&#39;alpha_index&#39;</span><span class="p">]</span>
+
+        <span class="nb">print</span><span class="p">(</span>
+            <span class="sa">f</span><span class="s2">&quot;Optimal alphas , Imp </span><span class="si">{</span><span class="n">imp</span><span class="o">+</span><span class="mi">1</span><span class="si">}</span><span class="s2"> block &#39;</span><span class="si">{</span><span class="n">block</span><span class="si">}</span><span class="s2">&#39;: </span><span class="se">\n</span><span class="s2">--- Real part ---</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">opt_alpha</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">])</span>
+        <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">opt_alpha</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">!=</span> <span class="o">-</span><span class="mi">1</span><span class="p">):</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Imp {i+1} block </span><span class="si">{block}</span><span class="s1"> Imag part ---</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">opt_alpha</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">1</span><span class="p">])</span>
+        <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">opt_alpha</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span><span class="p">):</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;(a -1 indicates that maxent did not run for this block due to symmetry)&#39;</span><span class="p">)</span>
+
+        <span class="c1"># store unpacked data in flatted list / maxent res object not bcastable</span>
+        <span class="n">data_linefit</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">results</span><span class="o">.</span><span class="n">get_A_out</span><span class="p">(</span><span class="s1">&#39;LineFitAnalyzer&#39;</span><span class="p">)</span>
+        <span class="n">data_chi2</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">results</span><span class="o">.</span><span class="n">get_A_out</span><span class="p">(</span><span class="s1">&#39;Chi2CurvatureAnalyzer&#39;</span><span class="p">)</span>
+
+    <span class="c1"># slow barrier to reduce CPU load of waiting ranks</span>
+    <span class="n">mpi</span><span class="o">.</span><span class="n">barrier</span><span class="p">(</span><span class="mi">1000</span><span class="p">)</span>
+    <span class="c1"># Synchronizes information between ranks</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">imps_blocks_indices</span><span class="p">:</span>
+        <span class="n">data_linefit</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">mpi</span><span class="o">.</span><span class="n">all_reduce</span><span class="p">(</span><span class="n">data_linefit</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+        <span class="n">data_chi2</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">mpi</span><span class="o">.</span><span class="n">all_reduce</span><span class="p">(</span><span class="n">data_chi2</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+
+    <span class="c1"># final result list</span>
+    <span class="n">unpacked_results</span> <span class="o">=</span> <span class="p">[{</span><span class="s1">&#39;mesh&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">omega_mesh</span><span class="p">),</span>
+                         <span class="s1">&#39;Aimp_w_line_fit&#39;</span><span class="p">:</span> <span class="p">{},</span>
+                         <span class="s1">&#39;Aimp_w_chi2_curvature&#39;</span><span class="p">:</span> <span class="p">{}</span>
+                         <span class="p">}</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">gf_imp_list</span><span class="p">))]</span>
+
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">imps_blocks_indices</span><span class="p">:</span>
+        <span class="n">imp</span><span class="p">,</span> <span class="n">block</span> <span class="o">=</span> <span class="n">imps_blocks</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+        <span class="n">unpacked_results</span><span class="p">[</span><span class="n">imp</span><span class="p">][</span><span class="s1">&#39;Aimp_w_line_fit&#39;</span><span class="p">][</span><span class="n">block</span><span class="p">]</span> <span class="o">=</span> <span class="n">data_linefit</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+        <span class="n">unpacked_results</span><span class="p">[</span><span class="n">imp</span><span class="p">][</span><span class="s1">&#39;Aimp_w_chi2_curvature&#39;</span><span class="p">][</span><span class="n">block</span><span class="p">]</span> <span class="o">=</span> <span class="n">data_chi2</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+
+    <span class="k">return</span> <span class="n">unpacked_results</span>
+
+
+<span class="k">def</span> <span class="nf">_write_spectral_function_to_h5</span><span class="p">(</span><span class="n">unpacked_results</span><span class="p">,</span> <span class="n">external_path</span><span class="p">,</span> <span class="n">iteration</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot; Writes the mesh and the maxent result for each analyzer to h5 archive. &quot;&quot;&quot;</span>
+
+    <span class="n">h5_internal_path</span> <span class="o">=</span> <span class="s1">&#39;DMFT_results/&#39;</span> <span class="o">+</span> <span class="p">(</span><span class="s1">&#39;last_iter&#39;</span> <span class="k">if</span> <span class="n">iteration</span> <span class="ow">is</span> <span class="kc">None</span>
+                                          <span class="k">else</span> <span class="sa">f</span><span class="s1">&#39;it_</span><span class="si">{</span><span class="n">iteration</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+
+    <span class="k">with</span> <span class="n">HDFArchive</span><span class="p">(</span><span class="n">external_path</span><span class="p">,</span> <span class="s1">&#39;a&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">archive</span><span class="p">:</span>
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">res</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">unpacked_results</span><span class="p">):</span>
+            <span class="n">archive</span><span class="p">[</span><span class="n">h5_internal_path</span><span class="p">][</span><span class="sa">f</span><span class="s1">&#39;Aimp_maxent_</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">res</span>
+
+
+<div class="viewcode-block" id="main"><a class="viewcode-back" href="../../_ref/postprocessing.maxent_gf_imp.html#postprocessing.maxent_gf_imp.main">[docs]</a><span class="k">def</span> <span class="nf">main</span><span class="p">(</span><span class="n">external_path</span><span class="p">,</span> <span class="n">iteration</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sum_spins</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">maxent_error</span><span class="o">=</span><span class="mf">0.02</span><span class="p">,</span>
+         <span class="n">n_points_maxent</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span> <span class="n">n_points_alpha</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">omega_min</span><span class="o">=-</span><span class="mi">20</span><span class="p">,</span> <span class="n">omega_max</span><span class="o">=</span><span class="mi">20</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Main function that reads the impurity Greens (GF) function from h5,</span>
+<span class="sd">    analytically continues it, writes the result back to the h5 archive and</span>
+<span class="sd">    also returns the results.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    external_path : string</span>
+<span class="sd">        Path to the h5 archive to read from and write to.</span>
+<span class="sd">    iteration : int/string</span>
+<span class="sd">        Iteration to read from and write to. Defaults to last_iter.</span>
+<span class="sd">    sum_spins : bool</span>
+<span class="sd">        Whether to sum over the spins or continue the impurity GF</span>
+<span class="sd">        for the up and down spin separately, for example for magnetized results.</span>
+<span class="sd">    maxent_error : float</span>
+<span class="sd">        The error that is used for the analyzers.</span>
+<span class="sd">    n_points_maxent : int</span>
+<span class="sd">        Number of omega points on the hyperbolic mesh used in the continuation.</span>
+<span class="sd">    n_points_alpha : int</span>
+<span class="sd">        Number of points that the MaxEnt alpha parameter is varied on logarithmically.</span>
+<span class="sd">    omega_min : float</span>
+<span class="sd">        Lower end of range where the GF is being continued. Range has to comprise</span>
+<span class="sd">        all features of the impurity GF for correct normalization.</span>
+<span class="sd">    omega_max : float</span>
+<span class="sd">        Upper end of range where the GF is being continued. See omega_min.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    maxent_results : list</span>
+<span class="sd">        The omega mesh and impurity spectral function from two different analyzers</span>
+<span class="sd">        in a dict for each impurity</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">gf_imp_tau</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">if</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+        <span class="n">start_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+
+        <span class="n">gf_imp_tau</span> <span class="o">=</span> <span class="n">_read_h5</span><span class="p">(</span><span class="n">external_path</span><span class="p">,</span> <span class="n">iteration</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">gf</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">gf_imp_tau</span><span class="p">):</span>
+            <span class="n">gf_imp_tau</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">_sum_greens_functions</span><span class="p">(</span><span class="n">gf</span><span class="p">,</span> <span class="n">sum_spins</span><span class="p">)</span>
+    <span class="n">gf_imp_tau</span> <span class="o">=</span> <span class="n">mpi</span><span class="o">.</span><span class="n">bcast</span><span class="p">(</span><span class="n">gf_imp_tau</span><span class="p">)</span>
+
+    <span class="n">maxent_results</span> <span class="o">=</span> <span class="n">_run_maxent</span><span class="p">(</span><span class="n">gf_imp_tau</span><span class="p">,</span> <span class="n">maxent_error</span><span class="p">,</span> <span class="n">n_points_maxent</span><span class="p">,</span>
+                                             <span class="n">n_points_alpha</span><span class="p">,</span> <span class="n">omega_min</span><span class="p">,</span> <span class="n">omega_max</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+        <span class="n">_write_spectral_function_to_h5</span><span class="p">(</span><span class="n">maxent_results</span><span class="p">,</span> <span class="n">external_path</span><span class="p">,</span> <span class="n">iteration</span><span class="p">)</span>
+
+        <span class="n">total_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">start_time</span>
+        <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;-&#39;</span><span class="o">*</span><span class="mi">80</span><span class="p">,</span> <span class="s1">&#39;DONE&#39;</span><span class="p">)</span>
+        <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Total run time: </span><span class="si">{</span><span class="n">total_time</span><span class="si">:</span><span class="s1">.0f</span><span class="si">}</span><span class="s1"> s.&#39;</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">maxent_results</span></div>
+
+
+<span class="k">def</span> <span class="nf">_strtobool</span><span class="p">(</span><span class="n">val</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;Convert a string representation of truth to true (1) or false (0).</span>
+<span class="sd">    True values are &#39;y&#39;, &#39;yes&#39;, &#39;t&#39;, &#39;true&#39;, &#39;on&#39;, and &#39;1&#39;; false values</span>
+<span class="sd">    are &#39;n&#39;, &#39;no&#39;, &#39;f&#39;, &#39;false&#39;, &#39;off&#39;, and &#39;0&#39;.  Raises ValueError if</span>
+<span class="sd">    &#39;val&#39; is anything else.</span>
+<span class="sd">    Copied from distutils.util in python 3.10.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">val</span> <span class="o">=</span> <span class="n">val</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span>
+    <span class="k">if</span> <span class="n">val</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="s1">&#39;yes&#39;</span><span class="p">,</span> <span class="s1">&#39;t&#39;</span><span class="p">,</span> <span class="s1">&#39;true&#39;</span><span class="p">,</span> <span class="s1">&#39;on&#39;</span><span class="p">,</span> <span class="s1">&#39;1&#39;</span><span class="p">):</span>
+        <span class="k">return</span> <span class="mi">1</span>
+    <span class="k">elif</span> <span class="n">val</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">&#39;n&#39;</span><span class="p">,</span> <span class="s1">&#39;no&#39;</span><span class="p">,</span> <span class="s1">&#39;f&#39;</span><span class="p">,</span> <span class="s1">&#39;false&#39;</span><span class="p">,</span> <span class="s1">&#39;off&#39;</span><span class="p">,</span> <span class="s1">&#39;0&#39;</span><span class="p">):</span>
+        <span class="k">return</span> <span class="mi">0</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;invalid truth value </span><span class="si">{!r}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">val</span><span class="p">))</span>
+
+
+<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
+    <span class="c1"># Casts input parameters</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">2</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="o">==</span> <span class="s1">&#39;none&#39;</span><span class="p">:</span>
+            <span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">3</span><span class="p">:</span>
+        <span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span> <span class="o">=</span> <span class="n">_strtobool</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">3</span><span class="p">])</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">4</span><span class="p">:</span>
+        <span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">4</span><span class="p">]</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">4</span><span class="p">])</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">5</span><span class="p">:</span>
+        <span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">5</span><span class="p">]</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">5</span><span class="p">])</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">6</span><span class="p">:</span>
+        <span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">6</span><span class="p">]</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">6</span><span class="p">])</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">7</span><span class="p">:</span>
+        <span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">7</span><span class="p">]</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">7</span><span class="p">])</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">8</span><span class="p">:</span>
+        <span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">8</span><span class="p">]</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">8</span><span class="p">])</span>
+
+    <span class="n">main</span><span class="p">(</span><span class="o">*</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">1</span><span class="p">:])</span>
+</pre></div>
+
+           </div>
+          </div>
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+
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+
+  <div role="contentinfo">
+    <p>&#169; Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.</p>
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diff --git a/_modules/postprocessing/maxent_gf_latt.html b/_modules/postprocessing/maxent_gf_latt.html
new file mode 100644
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+<html class="writer-html5" lang="en" >
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+            <img src="../../_static/logo_no_text.png" class="logo" alt="Logo"/>
+          </a>
+              <div class="version">
+                3.2.0
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+              <ul>
+<li class="toctree-l1"><a class="reference internal" href="../../install.html">Installation</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#installation-via-pip">Installation via pip</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#manual-installation-via-cmake">Manual installation via CMake</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#docker-files-images">Docker files &amp; images</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#version-compatibility">Version compatibility</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#custom-cmake-options">Custom CMake options</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#dft-interface-notes">DFT interface notes</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/w90_interface.html">Wannier90 interface</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/w90_interface.html#orbital-order-in-the-w90-converter">orbital order in the W90 converter</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/vasp_csc.html">Interface to VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#general-remarks">General remarks</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#file-description">file description</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#workflow">Workflow:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#important-flags">important flags:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#version-and-compilation">version and compilation:</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#input-output">Input/Output</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../input_output/DMFT_input/input.html">Input</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/general.html">[general]: General parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/solver.html">[solver]: solver specific parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/dft.html">[dft]: DFT related inputs</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/advanced.html">[advanced]: Advanced inputs</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../input_output/DMFT_output/results.html">Output / results</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_output/results.html#group-structure">Group structure</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_output/results.html#subgroups">Subgroups</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#further-details-for-running">Further details for running</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/docker.html">Docker</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#building-the-docker-image">Building the docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#pulling-a-docker-image">Pulling a docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_locally.html">Run on your machine</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#module-reference-manual">Module reference manual</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/csc_flow.html">csc_flow</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/dft_managers.html">dft_managers</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.mpi_helpers.html">dft_managers.mpi_helpers</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.qe_manager.html">dft_managers.qe_manager</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.vasp_manager.html">dft_managers.vasp_manager</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/dmft_cycle.html">dmft_cycle</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/dmft_tools.html">dmft_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.afm_mapping.html">dmft_tools.afm_mapping</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.convergence.html">dmft_tools.convergence</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.formatter.html">dmft_tools.formatter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.greens_functions_mixer.html">dmft_tools.greens_functions_mixer</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.initial_self_energies.html">dmft_tools.initial_self_energies</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.interaction_hamiltonian.html">dmft_tools.interaction_hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.legendre_filter.html">dmft_tools.legendre_filter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.manipulate_chemical_potential.html">dmft_tools.manipulate_chemical_potential</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.matheval.html">dmft_tools.matheval</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.observables.html">dmft_tools.observables</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.results_to_archive.html">dmft_tools.results_to_archive</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.solver.html">dmft_tools.solver</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/io_tools.html">io_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.dict_to_h5.html">io_tools.dict_to_h5</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.postproc_toml_dict.html">io_tools.postproc_toml_dict</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.verify_input_params.html">io_tools.verify_input_params</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_gf_imp.html">postprocessing.maxent_gf_imp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_gf_latt.html">postprocessing.maxent_gf_latt</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_sigma.html">postprocessing.maxent_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
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+      <li>postprocessing.maxent_gf_latt</li>
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+             
+  <h1>Source code for postprocessing.maxent_gf_latt</h1><div class="highlight"><pre>
+<span></span><span class="c1">################################################################################</span>
+<span class="c1">#</span>
+<span class="c1"># TRIQS: a Toolbox for Research in Interacting Quantum Systems</span>
+<span class="c1">#</span>
+<span class="c1"># Copyright (C) 2016-2018, N. Wentzell</span>
+<span class="c1"># Copyright (C) 2018-2019, Simons Foundation</span>
+<span class="c1">#   author: N. Wentzell</span>
+<span class="c1">#</span>
+<span class="c1"># TRIQS is free software: you can redistribute it and/or modify it under the</span>
+<span class="c1"># terms of the GNU General Public License as published by the Free Software</span>
+<span class="c1"># Foundation, either version 3 of the License, or (at your option) any later</span>
+<span class="c1"># version.</span>
+<span class="c1">#</span>
+<span class="c1"># TRIQS is distributed in the hope that it will be useful, but WITHOUT ANY</span>
+<span class="c1"># WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS</span>
+<span class="c1"># FOR A PARTICULAR PURPOSE. See the GNU General Public License for more</span>
+<span class="c1"># details.</span>
+<span class="c1">#</span>
+<span class="c1"># You should have received a copy of the GNU General Public License along with</span>
+<span class="c1"># TRIQS. If not, see &lt;http://www.gnu.org/licenses/&gt;.</span>
+<span class="c1">#</span>
+<span class="c1">################################################################################</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Analytic continuation of the lattice Green&#39;s function to the lattice spectral</span>
+<span class="sd">function using maxent.</span>
+
+<span class="sd">Reads G_latt(i omega) from the h5 archive and writes A_latt(omega) back. See</span>
+<span class="sd">the docstring of main() for more information.</span>
+
+<span class="sd">mpi parallelized for the generation of the imaginary-frequency lattice GF over</span>
+<span class="sd">k points.</span>
+
+<span class="sd">Author: Maximilian Merkel, Materials Theory Group, ETH Zurich, 2020 - 2022</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="kn">import</span> <span class="nn">sys</span>
+<span class="kn">import</span> <span class="nn">time</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<span class="kn">from</span> <span class="nn">triqs_maxent.tau_maxent</span> <span class="kn">import</span> <span class="n">TauMaxEnt</span>
+<span class="kn">from</span> <span class="nn">triqs_maxent.omega_meshes</span> <span class="kn">import</span> <span class="n">HyperbolicOmegaMesh</span>
+<span class="kn">from</span> <span class="nn">triqs_maxent.alpha_meshes</span> <span class="kn">import</span> <span class="n">LogAlphaMesh</span>
+<span class="kn">from</span> <span class="nn">triqs_dft_tools.sumk_dft</span> <span class="kn">import</span> <span class="n">SumkDFT</span>
+<span class="kn">from</span> <span class="nn">h5</span> <span class="kn">import</span> <span class="n">HDFArchive</span>
+<span class="kn">from</span> <span class="nn">triqs.utility</span> <span class="kn">import</span> <span class="n">mpi</span>
+<span class="kn">from</span> <span class="nn">triqs.gf</span> <span class="kn">import</span> <span class="n">Gf</span><span class="p">,</span> <span class="n">BlockGf</span>
+
+
+<span class="k">def</span> <span class="nf">_read_h5</span><span class="p">(</span><span class="n">external_path</span><span class="p">,</span> <span class="n">iteration</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Reads the h5 archive to get the Matsubara self energy, the double-counting potential,</span>
+<span class="sd">    the chemical potential and the block structure.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    external_path : string</span>
+<span class="sd">        path to h5 archive</span>
+<span class="sd">    iteration : int</span>
+<span class="sd">        The iteration that is being read from, None corresponds to &#39;last_iter&#39;</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    sigma_iw : list</span>
+<span class="sd">        Self energy as block Green&#39;s function for each impurity</span>
+<span class="sd">    chemical_potential : float</span>
+<span class="sd">        The chemical potential of the problem. Should be approximately real</span>
+<span class="sd">    dc_potential : list</span>
+<span class="sd">        Double counting for each impurity</span>
+<span class="sd">    block_structure : triqs_dft_tools.BlockStructure</span>
+<span class="sd">        Block structure mapping from the DMFT calculation</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">h5_internal_path</span> <span class="o">=</span> <span class="s1">&#39;DMFT_results/&#39;</span> <span class="o">+</span> <span class="p">(</span><span class="s1">&#39;last_iter&#39;</span> <span class="k">if</span> <span class="n">iteration</span> <span class="ow">is</span> <span class="kc">None</span>
+                                          <span class="k">else</span> <span class="sa">f</span><span class="s1">&#39;it_</span><span class="si">{</span><span class="n">iteration</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+
+    <span class="k">with</span> <span class="n">HDFArchive</span><span class="p">(</span><span class="n">external_path</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">archive</span><span class="p">:</span>
+        <span class="n">impurity_paths</span> <span class="o">=</span> <span class="p">[</span><span class="n">key</span> <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">archive</span><span class="p">[</span><span class="n">h5_internal_path</span><span class="p">]</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span> <span class="k">if</span> <span class="s1">&#39;Sigma_freq_&#39;</span> <span class="ow">in</span> <span class="n">key</span><span class="p">]</span>
+        <span class="c1"># Sorts impurity paths by their indices, not sure if necessary</span>
+        <span class="n">impurity_indices</span> <span class="o">=</span> <span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">s</span><span class="p">[</span><span class="n">s</span><span class="o">.</span><span class="n">rfind</span><span class="p">(</span><span class="s1">&#39;_&#39;</span><span class="p">)</span><span class="o">+</span><span class="mi">1</span><span class="p">:])</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">impurity_paths</span><span class="p">]</span>
+        <span class="n">impurity_paths</span> <span class="o">=</span> <span class="p">[</span><span class="n">impurity_paths</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">impurity_indices</span><span class="p">)]</span>
+
+        <span class="n">sigma_iw</span> <span class="o">=</span> <span class="p">[</span><span class="n">archive</span><span class="p">[</span><span class="n">h5_internal_path</span><span class="p">][</span><span class="n">p</span><span class="p">]</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">impurity_paths</span><span class="p">]</span>
+
+        <span class="n">block_structure</span> <span class="o">=</span> <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_input&#39;</span><span class="p">][</span><span class="s1">&#39;block_structure&#39;</span><span class="p">]</span>
+        <span class="c1"># Fix for archives from triqs 2 when corr_to_inequiv was in SumkDFT, not in BlockStructure</span>
+        <span class="k">if</span> <span class="n">block_structure</span><span class="o">.</span><span class="n">corr_to_inequiv</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">block_structure</span><span class="o">.</span><span class="n">corr_to_inequiv</span> <span class="o">=</span> <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;dft_input/corr_to_inequiv&#39;</span><span class="p">]</span>
+
+        <span class="n">dc_potential</span> <span class="o">=</span> <span class="n">archive</span><span class="p">[</span><span class="n">h5_internal_path</span><span class="p">][</span><span class="s1">&#39;DC_pot&#39;</span><span class="p">]</span>
+
+        <span class="k">if</span> <span class="s1">&#39;chemical_potential_post&#39;</span> <span class="ow">in</span> <span class="n">archive</span><span class="p">[</span><span class="n">h5_internal_path</span><span class="p">]:</span>
+            <span class="n">chemical_potential</span> <span class="o">=</span> <span class="n">archive</span><span class="p">[</span><span class="n">h5_internal_path</span><span class="p">][</span><span class="s1">&#39;chemical_potential_post&#39;</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="c1"># Old name for chemical_potential_post</span>
+            <span class="n">chemical_potential</span> <span class="o">=</span> <span class="n">archive</span><span class="p">[</span><span class="n">h5_internal_path</span><span class="p">][</span><span class="s1">&#39;chemical_potential&#39;</span><span class="p">]</span>
+
+    <span class="k">return</span> <span class="n">sigma_iw</span><span class="p">,</span> <span class="n">chemical_potential</span><span class="p">,</span> <span class="n">dc_potential</span><span class="p">,</span> <span class="n">block_structure</span>
+
+
+<span class="k">def</span> <span class="nf">_generate_lattice_gf</span><span class="p">(</span><span class="n">sum_k</span><span class="p">,</span> <span class="n">sum_spins</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Generates the lattice GF from the SumkDFT object. If sum_spins, it</span>
+<span class="sd">    has one block &quot;total&quot;. Otherwise, the block names are the spins.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># Initializes lattice GF to zero for each process</span>
+    <span class="n">spin_blocks</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;total&#39;</span><span class="p">]</span> <span class="k">if</span> <span class="n">sum_spins</span> <span class="k">else</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">spin_block_names</span><span class="p">[</span><span class="n">sum_k</span><span class="o">.</span><span class="n">SO</span><span class="p">]</span>
+    <span class="n">mesh</span> <span class="o">=</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">Sigma_imp</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">mesh</span>
+    <span class="n">trace_gf_latt</span> <span class="o">=</span> <span class="p">{</span><span class="n">key</span><span class="p">:</span> <span class="n">Gf</span><span class="p">(</span><span class="n">mesh</span><span class="o">=</span><span class="n">mesh</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">mesh</span><span class="p">),</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">complex</span><span class="p">))</span>
+                     <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">spin_blocks</span><span class="p">}</span>
+
+    <span class="c1"># Takes trace over orbitals (and spins). Individual entries do not make sense</span>
+    <span class="c1"># because the KS Hamiltonian ususally has the bands sorted by energy</span>
+    <span class="k">for</span> <span class="n">ik</span> <span class="ow">in</span> <span class="n">mpi</span><span class="o">.</span><span class="n">slice_array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">n_k</span><span class="p">)):</span>
+        <span class="n">gf_latt</span> <span class="o">=</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">lattice_gf</span><span class="p">(</span><span class="n">ik</span><span class="p">)</span> <span class="o">*</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">bz_weights</span><span class="p">[</span><span class="n">ik</span><span class="p">]</span>
+        <span class="k">if</span> <span class="n">sum_spins</span><span class="p">:</span>
+            <span class="n">trace_gf_latt</span><span class="p">[</span><span class="s1">&#39;total&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="p">[:]</span> <span class="o">+=</span> <span class="n">np</span><span class="o">.</span><span class="n">trace</span><span class="p">(</span><span class="nb">sum</span><span class="p">(</span><span class="n">g</span><span class="o">.</span><span class="n">data</span> <span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">g</span> <span class="ow">in</span> <span class="n">gf_latt</span><span class="p">),</span>
+                                                       <span class="n">axis1</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">axis2</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">spin_blocks</span><span class="p">:</span>
+                <span class="n">trace_gf_latt</span><span class="p">[</span><span class="n">s</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="p">[:]</span> <span class="o">+=</span> <span class="n">np</span><span class="o">.</span><span class="n">trace</span><span class="p">(</span><span class="n">gf_latt</span><span class="p">[</span><span class="n">s</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="p">,</span>
+                                                     <span class="n">axis1</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">axis2</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">spin_blocks</span><span class="p">:</span>
+        <span class="n">trace_gf_latt</span><span class="p">[</span><span class="n">s</span><span class="p">]</span> <span class="o">&lt;&lt;</span> <span class="n">mpi</span><span class="o">.</span><span class="n">all_reduce</span><span class="p">(</span><span class="n">trace_gf_latt</span><span class="p">[</span><span class="n">s</span><span class="p">])</span>
+
+    <span class="c1"># Lattice GF as BlockGf, required for compatibility with MaxEnt functions</span>
+    <span class="n">gf_lattice_iw</span> <span class="o">=</span> <span class="n">BlockGf</span><span class="p">(</span><span class="n">name_list</span><span class="o">=</span><span class="n">trace_gf_latt</span><span class="o">.</span><span class="n">keys</span><span class="p">(),</span>
+                            <span class="n">block_list</span><span class="o">=</span><span class="n">trace_gf_latt</span><span class="o">.</span><span class="n">values</span><span class="p">())</span>
+    <span class="k">return</span> <span class="n">gf_lattice_iw</span>
+
+
+<span class="k">def</span> <span class="nf">_run_maxent</span><span class="p">(</span><span class="n">gf_lattice_iw</span><span class="p">,</span> <span class="n">sum_k</span><span class="p">,</span> <span class="n">error</span><span class="p">,</span> <span class="n">omega_min</span><span class="p">,</span> <span class="n">omega_max</span><span class="p">,</span>
+                <span class="n">n_points_maxent</span><span class="p">,</span> <span class="n">n_points_alpha</span><span class="p">,</span> <span class="n">analyzer</span><span class="o">=</span><span class="s1">&#39;LineFitAnalyzer&#39;</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Runs maxent to get the spectral function from the block GF.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># Automatic determination of energy range from hopping matrix</span>
+    <span class="k">if</span> <span class="n">omega_max</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">num_ks_orbitals</span> <span class="o">=</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">hopping</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
+        <span class="n">hopping_diagonal</span> <span class="o">=</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">hopping</span><span class="p">[:,</span> <span class="p">:,</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">num_ks_orbitals</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">num_ks_orbitals</span><span class="p">)]</span>
+        <span class="n">hopping_min</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">hopping_diagonal</span><span class="p">)</span>
+        <span class="n">hopping_max</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">hopping_diagonal</span><span class="p">)</span>
+        <span class="n">omega_min</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="o">-</span><span class="mi">20</span><span class="p">,</span> <span class="n">hopping_min</span> <span class="o">-</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">chemical_potential</span><span class="p">)</span>
+        <span class="n">omega_max</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="n">hopping_max</span> <span class="o">-</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">chemical_potential</span><span class="p">)</span>
+        <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;Set omega range to </span><span class="si">{:.3f}</span><span class="s1">...</span><span class="si">{:.3f}</span><span class="s1"> eV&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">omega_min</span><span class="p">,</span> <span class="n">omega_max</span><span class="p">))</span>
+
+    <span class="n">omega_mesh</span> <span class="o">=</span> <span class="n">HyperbolicOmegaMesh</span><span class="p">(</span><span class="n">omega_min</span><span class="o">=</span><span class="n">omega_min</span><span class="p">,</span> <span class="n">omega_max</span><span class="o">=</span><span class="n">omega_max</span><span class="p">,</span>
+                                     <span class="n">n_points</span><span class="o">=</span><span class="n">n_points_maxent</span><span class="p">)</span>
+
+    <span class="c1"># Prints information on the blocks found</span>
+    <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;Found blocks </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">gf_lattice_iw</span><span class="o">.</span><span class="n">indices</span><span class="p">)))</span>
+
+    <span class="c1"># Initializes and runs the maxent solver</span>
+    <span class="c1"># TODO: parallelization over blocks</span>
+    <span class="n">results</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="k">for</span> <span class="n">block</span><span class="p">,</span> <span class="n">gf</span> <span class="ow">in</span> <span class="n">gf_lattice_iw</span><span class="p">:</span>
+        <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;-&#39;</span><span class="o">*</span><span class="mi">80</span><span class="p">,</span> <span class="sa">f</span><span class="s1">&#39;Running MaxEnt on block &quot;</span><span class="si">{</span><span class="n">block</span><span class="si">}</span><span class="s1">&quot; now&#39;</span><span class="p">,</span> <span class="s1">&#39;-&#39;</span><span class="o">*</span><span class="mi">80</span><span class="p">)</span>
+        <span class="n">solver</span> <span class="o">=</span> <span class="n">TauMaxEnt</span><span class="p">()</span>
+        <span class="n">solver</span><span class="o">.</span><span class="n">set_G_iw</span><span class="p">(</span><span class="n">gf</span><span class="p">)</span>
+        <span class="n">solver</span><span class="o">.</span><span class="n">set_error</span><span class="p">(</span><span class="n">error</span><span class="p">)</span>
+        <span class="n">solver</span><span class="o">.</span><span class="n">omega</span> <span class="o">=</span> <span class="n">omega_mesh</span>
+        <span class="n">solver</span><span class="o">.</span><span class="n">alpha_mesh</span> <span class="o">=</span> <span class="n">LogAlphaMesh</span><span class="p">(</span><span class="n">alpha_min</span><span class="o">=</span><span class="mf">1e-6</span><span class="p">,</span> <span class="n">alpha_max</span><span class="o">=</span><span class="mf">1e2</span><span class="p">,</span> <span class="n">n_points</span><span class="o">=</span><span class="n">n_points_alpha</span><span class="p">)</span>
+        <span class="n">results</span><span class="p">[</span><span class="n">block</span><span class="p">]</span> <span class="o">=</span> <span class="n">solver</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
+
+        <span class="n">opt_alpha</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="n">block</span><span class="p">]</span><span class="o">.</span><span class="n">analyzer_results</span><span class="p">[</span><span class="n">analyzer</span><span class="p">][</span><span class="s1">&#39;alpha_index&#39;</span><span class="p">]</span>
+        <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Optimal alpha, block &quot;</span><span class="si">{</span><span class="n">block</span><span class="si">}</span><span class="s1">&quot; from </span><span class="si">{</span><span class="n">analyzer</span><span class="si">}</span><span class="s1">: </span><span class="si">{</span><span class="n">opt_alpha</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">results</span><span class="p">,</span> <span class="n">omega_mesh</span>
+
+
+<span class="k">def</span> <span class="nf">_unpack_maxent_results</span><span class="p">(</span><span class="n">results</span><span class="p">,</span> <span class="n">omega_mesh</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Converts maxent result to dict with mesh and spectral function from each</span>
+<span class="sd">    analyzer.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">data_linefit</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="n">data_chi2</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">result</span> <span class="ow">in</span> <span class="n">results</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+        <span class="n">data_linefit</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">result</span><span class="o">.</span><span class="n">get_A_out</span><span class="p">(</span><span class="s1">&#39;LineFitAnalyzer&#39;</span><span class="p">)</span>
+        <span class="n">data_chi2</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">result</span><span class="o">.</span><span class="n">get_A_out</span><span class="p">(</span><span class="s1">&#39;Chi2CurvatureAnalyzer&#39;</span><span class="p">)</span>
+
+    <span class="n">data</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;mesh&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">omega_mesh</span><span class="p">),</span> <span class="s1">&#39;Alatt_w_line_fit&#39;</span><span class="p">:</span> <span class="n">data_linefit</span><span class="p">,</span>
+            <span class="s1">&#39;Alatt_w_chi2_curvature&#39;</span><span class="p">:</span> <span class="n">data_chi2</span><span class="p">}</span>
+    <span class="k">return</span> <span class="n">data</span>
+
+
+<span class="k">def</span> <span class="nf">_write_spectral_function_to_h5</span><span class="p">(</span><span class="n">unpacked_results</span><span class="p">,</span> <span class="n">external_path</span><span class="p">,</span> <span class="n">iteration</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot; Writes the mesh and the maxent result for each analyzer to h5 archive. &quot;&quot;&quot;</span>
+
+    <span class="n">h5_internal_path</span> <span class="o">=</span> <span class="s1">&#39;DMFT_results/&#39;</span> <span class="o">+</span> <span class="p">(</span><span class="s1">&#39;last_iter&#39;</span> <span class="k">if</span> <span class="n">iteration</span> <span class="ow">is</span> <span class="kc">None</span>
+                                          <span class="k">else</span> <span class="sa">f</span><span class="s1">&#39;it_</span><span class="si">{</span><span class="n">iteration</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+
+    <span class="k">with</span> <span class="n">HDFArchive</span><span class="p">(</span><span class="n">external_path</span><span class="p">,</span> <span class="s1">&#39;a&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">archive</span><span class="p">:</span>
+        <span class="n">archive</span><span class="p">[</span><span class="n">h5_internal_path</span><span class="p">][</span><span class="s1">&#39;Alatt_maxent&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">unpacked_results</span>
+
+
+<div class="viewcode-block" id="main"><a class="viewcode-back" href="../../_ref/postprocessing.maxent_gf_latt.html#postprocessing.maxent_gf_latt.main">[docs]</a><span class="k">def</span> <span class="nf">main</span><span class="p">(</span><span class="n">external_path</span><span class="p">,</span> <span class="n">iteration</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sum_spins</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">maxent_error</span><span class="o">=</span><span class="mf">.02</span><span class="p">,</span>
+         <span class="n">n_points_maxent</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span> <span class="n">n_points_alpha</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">omega_min</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">omega_max</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Main function that reads the lattice Green&#39;s function (GF) from h5,</span>
+<span class="sd">    analytically continues it, writes the result back to the h5 archive and</span>
+<span class="sd">    also returns the results.</span>
+<span class="sd">    Only the trace can be used because the Kohn-Sham energies (&quot;hopping&quot;) are not</span>
+<span class="sd">    sorted by &quot;orbital&quot; but by energy, leading to crossovers.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    external_path: string</span>
+<span class="sd">        Path to the h5 archive to read from and write to.</span>
+<span class="sd">    iteration: int/string</span>
+<span class="sd">        Iteration to read from and write to. Defaults to last_iter.</span>
+<span class="sd">    sum_spins: bool</span>
+<span class="sd">        Whether to sum over the spins or continue the lattice GF</span>
+<span class="sd">        for the up and down spin separately, for example for magnetized results.</span>
+<span class="sd">    maxent_error : float</span>
+<span class="sd">        The error that is used for the analyzers.</span>
+<span class="sd">    n_points_maxent : int</span>
+<span class="sd">        Number of omega points on the hyperbolic mesh used in the continuation.</span>
+<span class="sd">    n_points_alpha : int</span>
+<span class="sd">        Number of points that the MaxEnt alpha parameter is varied on logarithmically.</span>
+<span class="sd">    omega_min : float</span>
+<span class="sd">        Lower end of range where the GF is being continued. Range has to comprise</span>
+<span class="sd">        all features of the lattice GF for correct normalization.</span>
+<span class="sd">        If omega_min and omega_max are None, they are chosen automatically based</span>
+<span class="sd">        on the diagonal entries in the hopping matrix but at least to -20...20 eV.</span>
+<span class="sd">    omega_max : float</span>
+<span class="sd">        Upper end of range where the GF is being continued. See omega_min.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    unpacked_results : dict</span>
+<span class="sd">        The omega mesh and lattice spectral function from two different analyzers</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">if</span> <span class="p">(</span><span class="n">omega_max</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">omega_min</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
+            <span class="ow">or</span> <span class="n">omega_max</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">omega_min</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Both or neither of omega_max and omega_min have to be None&#39;</span><span class="p">)</span>
+
+    <span class="n">start_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+
+    <span class="c1"># Sets up the SumkDFT object</span>
+    <span class="n">h5_content</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="k">if</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+        <span class="n">h5_content</span> <span class="o">=</span> <span class="n">_read_h5</span><span class="p">(</span><span class="n">external_path</span><span class="p">,</span> <span class="n">iteration</span><span class="p">)</span>
+    <span class="n">sigma_iw</span><span class="p">,</span> <span class="n">chemical_potential</span><span class="p">,</span> <span class="n">dc_potential</span><span class="p">,</span> <span class="n">block_structure</span> <span class="o">=</span> <span class="n">mpi</span><span class="o">.</span><span class="n">bcast</span><span class="p">(</span><span class="n">h5_content</span><span class="p">)</span>
+    <span class="n">sum_k</span> <span class="o">=</span> <span class="n">SumkDFT</span><span class="p">(</span><span class="n">external_path</span><span class="p">,</span> <span class="n">mesh</span><span class="o">=</span><span class="n">sigma_iw</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">mesh</span><span class="p">,</span> <span class="n">use_dft_blocks</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+    <span class="n">sum_k</span><span class="o">.</span><span class="n">block_structure</span> <span class="o">=</span> <span class="n">block_structure</span>
+    <span class="n">sum_k</span><span class="o">.</span><span class="n">put_Sigma</span><span class="p">(</span><span class="n">sigma_iw</span><span class="p">)</span>
+    <span class="n">sum_k</span><span class="o">.</span><span class="n">set_mu</span><span class="p">(</span><span class="n">chemical_potential</span><span class="p">)</span>
+    <span class="n">sum_k</span><span class="o">.</span><span class="n">set_dc</span><span class="p">(</span><span class="n">dc_potential</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+    <span class="c1"># Generates the lattice GF</span>
+    <span class="n">gf_lattice_iw</span> <span class="o">=</span> <span class="n">_generate_lattice_gf</span><span class="p">(</span><span class="n">sum_k</span><span class="p">,</span> <span class="n">sum_spins</span><span class="p">)</span>
+    <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;Generated the lattice GF.&#39;</span><span class="p">)</span>
+
+    <span class="c1"># Runs MaxEnt</span>
+    <span class="n">unpacked_results</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="k">if</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+        <span class="n">maxent_results</span><span class="p">,</span> <span class="n">omega_mesh</span> <span class="o">=</span> <span class="n">_run_maxent</span><span class="p">(</span><span class="n">gf_lattice_iw</span><span class="p">,</span> <span class="n">sum_k</span><span class="p">,</span> <span class="n">maxent_error</span><span class="p">,</span>
+                                                 <span class="n">omega_min</span><span class="p">,</span> <span class="n">omega_max</span><span class="p">,</span> <span class="n">n_points_maxent</span><span class="p">,</span>
+                                                 <span class="n">n_points_alpha</span><span class="p">)</span>
+        <span class="n">unpacked_results</span> <span class="o">=</span> <span class="n">_unpack_maxent_results</span><span class="p">(</span><span class="n">maxent_results</span><span class="p">,</span> <span class="n">omega_mesh</span><span class="p">)</span>
+        <span class="n">_write_spectral_function_to_h5</span><span class="p">(</span><span class="n">unpacked_results</span><span class="p">,</span> <span class="n">external_path</span><span class="p">,</span> <span class="n">iteration</span><span class="p">)</span>
+    <span class="n">unpacked_results</span> <span class="o">=</span> <span class="n">mpi</span><span class="o">.</span><span class="n">bcast</span><span class="p">(</span><span class="n">unpacked_results</span><span class="p">)</span>
+
+    <span class="n">total_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">start_time</span>
+    <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;-&#39;</span><span class="o">*</span><span class="mi">80</span><span class="p">,</span> <span class="s1">&#39;DONE&#39;</span><span class="p">)</span>
+    <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Total run time: </span><span class="si">{</span><span class="n">total_time</span><span class="si">:</span><span class="s1">.0f</span><span class="si">}</span><span class="s1"> s.&#39;</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">unpacked_results</span></div>
+
+
+<span class="k">def</span> <span class="nf">_strtobool</span><span class="p">(</span><span class="n">val</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;Convert a string representation of truth to true (1) or false (0).</span>
+<span class="sd">    True values are &#39;y&#39;, &#39;yes&#39;, &#39;t&#39;, &#39;true&#39;, &#39;on&#39;, and &#39;1&#39;; false values</span>
+<span class="sd">    are &#39;n&#39;, &#39;no&#39;, &#39;f&#39;, &#39;false&#39;, &#39;off&#39;, and &#39;0&#39;.  Raises ValueError if</span>
+<span class="sd">    &#39;val&#39; is anything else.</span>
+<span class="sd">    Copied from distutils.util in python 3.10.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">val</span> <span class="o">=</span> <span class="n">val</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span>
+    <span class="k">if</span> <span class="n">val</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="s1">&#39;yes&#39;</span><span class="p">,</span> <span class="s1">&#39;t&#39;</span><span class="p">,</span> <span class="s1">&#39;true&#39;</span><span class="p">,</span> <span class="s1">&#39;on&#39;</span><span class="p">,</span> <span class="s1">&#39;1&#39;</span><span class="p">):</span>
+        <span class="k">return</span> <span class="mi">1</span>
+    <span class="k">elif</span> <span class="n">val</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">&#39;n&#39;</span><span class="p">,</span> <span class="s1">&#39;no&#39;</span><span class="p">,</span> <span class="s1">&#39;f&#39;</span><span class="p">,</span> <span class="s1">&#39;false&#39;</span><span class="p">,</span> <span class="s1">&#39;off&#39;</span><span class="p">,</span> <span class="s1">&#39;0&#39;</span><span class="p">):</span>
+        <span class="k">return</span> <span class="mi">0</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;invalid truth value </span><span class="si">{!r}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">val</span><span class="p">))</span>
+
+
+<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
+    <span class="c1"># Casts input parameters</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">2</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="o">==</span> <span class="s1">&#39;none&#39;</span><span class="p">:</span>
+            <span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">3</span><span class="p">:</span>
+        <span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span> <span class="o">=</span> <span class="n">_strtobool</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">3</span><span class="p">])</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">4</span><span class="p">:</span>
+        <span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">4</span><span class="p">]</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">4</span><span class="p">])</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">5</span><span class="p">:</span>
+        <span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">5</span><span class="p">]</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">5</span><span class="p">])</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">6</span><span class="p">:</span>
+        <span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">6</span><span class="p">]</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">6</span><span class="p">])</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">7</span><span class="p">:</span>
+        <span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">7</span><span class="p">]</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">7</span><span class="p">])</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">8</span><span class="p">:</span>
+        <span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">8</span><span class="p">]</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">8</span><span class="p">])</span>
+
+    <span class="n">main</span><span class="p">(</span><span class="o">*</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">1</span><span class="p">:])</span>
+</pre></div>
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.</p>
+  </div>
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
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+      jQuery(function () {
+          SphinxRtdTheme.Navigation.enable(true);
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+  </script> 
+
+</body>
+</html>
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diff --git a/_modules/postprocessing/maxent_sigma.html b/_modules/postprocessing/maxent_sigma.html
new file mode 100644
index 00000000..02655253
--- /dev/null
+++ b/_modules/postprocessing/maxent_sigma.html
@@ -0,0 +1,690 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" >
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>postprocessing.maxent_sigma &mdash; solid_dmft  documentation</title>
+      <link rel="stylesheet" href="../../_static/pygments.css" type="text/css" />
+      <link rel="stylesheet" href="../../_static/css/custom.css" type="text/css" />
+      <link rel="stylesheet" href="../../_static/plot_directive.css" type="text/css" />
+    <link rel="shortcut icon" href="../../_static/favicon.ico"/>
+  
+        <script data-url_root="../../" id="documentation_options" src="../../_static/documentation_options.js"></script>
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+    <script src="../../_static/js/theme.js"></script>
+    <link rel="index" title="Index" href="../../genindex.html" />
+    <link rel="search" title="Search" href="../../search.html" /> 
+</head>
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+<body class="wy-body-for-nav"> 
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+        <div class="wy-side-nav-search"  style="background: #7E588A" >
+            <a href="../../index.html" class="icon icon-home"> solid_dmft
+            <img src="../../_static/logo_no_text.png" class="logo" alt="Logo"/>
+          </a>
+              <div class="version">
+                3.2.0
+              </div>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="../../search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" />
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+              <ul>
+<li class="toctree-l1"><a class="reference internal" href="../../install.html">Installation</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#installation-via-pip">Installation via pip</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#manual-installation-via-cmake">Manual installation via CMake</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#docker-files-images">Docker files &amp; images</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#version-compatibility">Version compatibility</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#custom-cmake-options">Custom CMake options</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../documentation.html">Documentation</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#code-structure">Code structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#dft-interface-notes">DFT interface notes</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/w90_interface.html">Wannier90 interface</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/w90_interface.html#orbital-order-in-the-w90-converter">orbital order in the W90 converter</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/vasp_csc.html">Interface to VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#general-remarks">General remarks</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#file-description">file description</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#workflow">Workflow:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#important-flags">important flags:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#version-and-compilation">version and compilation:</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#input-output">Input/Output</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../input_output/DMFT_input/input.html">Input</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/general.html">[general]: General parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/solver.html">[solver]: solver specific parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/dft.html">[dft]: DFT related inputs</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/advanced.html">[advanced]: Advanced inputs</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../input_output/DMFT_output/results.html">Output / results</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_output/results.html#group-structure">Group structure</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_output/results.html#subgroups">Subgroups</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#further-details-for-running">Further details for running</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/docker.html">Docker</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#building-the-docker-image">Building the docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#pulling-a-docker-image">Pulling a docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_locally.html">Run on your machine</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#module-reference-manual">Module reference manual</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/csc_flow.html">csc_flow</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/dft_managers.html">dft_managers</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.mpi_helpers.html">dft_managers.mpi_helpers</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.qe_manager.html">dft_managers.qe_manager</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.vasp_manager.html">dft_managers.vasp_manager</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/dmft_cycle.html">dmft_cycle</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/dmft_tools.html">dmft_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.afm_mapping.html">dmft_tools.afm_mapping</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.convergence.html">dmft_tools.convergence</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.formatter.html">dmft_tools.formatter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.greens_functions_mixer.html">dmft_tools.greens_functions_mixer</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.initial_self_energies.html">dmft_tools.initial_self_energies</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.interaction_hamiltonian.html">dmft_tools.interaction_hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.legendre_filter.html">dmft_tools.legendre_filter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.manipulate_chemical_potential.html">dmft_tools.manipulate_chemical_potential</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.matheval.html">dmft_tools.matheval</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.observables.html">dmft_tools.observables</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.results_to_archive.html">dmft_tools.results_to_archive</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.solver.html">dmft_tools.solver</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/io_tools.html">io_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.dict_to_h5.html">io_tools.dict_to_h5</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.postproc_toml_dict.html">io_tools.postproc_toml_dict</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.verify_input_params.html">io_tools.verify_input_params</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_gf_imp.html">postprocessing.maxent_gf_imp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_gf_latt.html">postprocessing.maxent_gf_latt</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_sigma.html">postprocessing.maxent_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../issues.html">Support &amp; contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../issues.html#seeking-help">Seeking help</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../issues.html#improving-solid-dmft">Improving solid_dmft</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../issues.html#reporting-issues">Reporting issues</a></li>
+</ul>
+</li>
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+<li class="toctree-l1"><a class="reference internal" href="../../ChangeLog.html">Changelog</a><ul>
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+        </div>
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+      <li><a href="../../index.html" class="icon icon-home"></a> &raquo;</li>
+          <li><a href="../index.html">Module code</a> &raquo;</li>
+      <li>postprocessing.maxent_sigma</li>
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+      </li>
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+             
+  <h1>Source code for postprocessing.maxent_sigma</h1><div class="highlight"><pre>
+<span></span><span class="c1">################################################################################</span>
+<span class="c1">#</span>
+<span class="c1"># TRIQS: a Toolbox for Research in Interacting Quantum Systems</span>
+<span class="c1">#</span>
+<span class="c1"># Copyright (C) 2016-2018, N. Wentzell</span>
+<span class="c1"># Copyright (C) 2018-2019, Simons Foundation</span>
+<span class="c1">#   author: N. Wentzell</span>
+<span class="c1">#</span>
+<span class="c1"># TRIQS is free software: you can redistribute it and/or modify it under the</span>
+<span class="c1"># terms of the GNU General Public License as published by the Free Software</span>
+<span class="c1"># Foundation, either version 3 of the License, or (at your option) any later</span>
+<span class="c1"># version.</span>
+<span class="c1">#</span>
+<span class="c1"># TRIQS is distributed in the hope that it will be useful, but WITHOUT ANY</span>
+<span class="c1"># WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS</span>
+<span class="c1"># FOR A PARTICULAR PURPOSE. See the GNU General Public License for more</span>
+<span class="c1"># details.</span>
+<span class="c1">#</span>
+<span class="c1"># You should have received a copy of the GNU General Public License along with</span>
+<span class="c1"># TRIQS. If not, see &lt;http://www.gnu.org/licenses/&gt;.</span>
+<span class="c1">#</span>
+<span class="c1">################################################################################</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Analytic continuation of the self-energy using maxent on an auxiliary Green&#39;s</span>
+<span class="sd">function.</span>
+
+<span class="sd">Reads Sigma(i omega) from the h5 archive and writes Sigma(omega) back. See</span>
+<span class="sd">the docstring of main() for more information.</span>
+
+<span class="sd">mpi parallelized for the maxent routine over all blocks and for the continuator</span>
+<span class="sd">extraction over omega points.</span>
+
+<span class="sd">Author: Maximilian Merkel, Materials Theory Group, ETH Zurich, 2020 - 2022</span>
+
+<span class="sd">Warnings:</span>
+<span class="sd">    * When using this on self-energies with SOC, please check that the formalism</span>
+<span class="sd">      is correct, in particular the Kramers-Kronig relation.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="kn">import</span> <span class="nn">time</span>
+<span class="kn">import</span> <span class="nn">sys</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+
+<span class="kn">from</span> <span class="nn">triqs.utility</span> <span class="kn">import</span> <span class="n">mpi</span>
+<span class="kn">from</span> <span class="nn">triqs_maxent.sigma_continuator</span> <span class="kn">import</span> <span class="n">InversionSigmaContinuator</span><span class="p">,</span> <span class="n">DirectSigmaContinuator</span>
+<span class="kn">from</span> <span class="nn">triqs_maxent.elementwise_maxent</span> <span class="kn">import</span> <span class="n">PoormanMaxEnt</span>
+<span class="kn">from</span> <span class="nn">triqs_maxent.omega_meshes</span> <span class="kn">import</span> <span class="n">HyperbolicOmegaMesh</span>
+<span class="kn">from</span> <span class="nn">triqs_maxent.alpha_meshes</span> <span class="kn">import</span> <span class="n">LogAlphaMesh</span>
+<span class="kn">from</span> <span class="nn">triqs_maxent.logtaker</span> <span class="kn">import</span> <span class="n">VerbosityFlags</span>
+<span class="kn">from</span> <span class="nn">h5</span> <span class="kn">import</span> <span class="n">HDFArchive</span>
+
+
+<span class="k">def</span> <span class="nf">_read_h5</span><span class="p">(</span><span class="n">external_path</span><span class="p">,</span> <span class="n">iteration</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Reads the h5 archive to get the Matsubara self energy, the double-counting potential</span>
+<span class="sd">    and the chemical potential.</span>
+
+<span class="sd">    Parameters:</span>
+<span class="sd">    -----------</span>
+<span class="sd">    external_path : string</span>
+<span class="sd">        path to h5 archive</span>
+<span class="sd">    iteration : int</span>
+<span class="sd">        The iteration that is being read from, None corresponds to &#39;last_iter&#39;</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">    --------</span>
+<span class="sd">    sigma_iw : list</span>
+<span class="sd">        Self energy as block Green&#39;s function for each impurity</span>
+<span class="sd">    dc_potential : list</span>
+<span class="sd">        Double counting for each impurity</span>
+<span class="sd">    chemical_potential : float</span>
+<span class="sd">        The chemical potential of the problem. Should be approximately real</span>
+<span class="sd">    chemical_potential_zero : float</span>
+<span class="sd">        The chemical potential at 0 iteration. Should be approximately real</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">h5_internal_path</span> <span class="o">=</span> <span class="s1">&#39;DMFT_results/&#39;</span> <span class="o">+</span> <span class="p">(</span><span class="s1">&#39;last_iter&#39;</span> <span class="k">if</span> <span class="n">iteration</span> <span class="ow">is</span> <span class="kc">None</span>
+                                          <span class="k">else</span> <span class="sa">f</span><span class="s1">&#39;it_</span><span class="si">{</span><span class="n">iteration</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+
+    <span class="k">with</span> <span class="n">HDFArchive</span><span class="p">(</span><span class="n">external_path</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">archive</span><span class="p">:</span>
+        <span class="n">impurity_paths</span> <span class="o">=</span> <span class="p">[</span><span class="n">key</span> <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">archive</span><span class="p">[</span><span class="n">h5_internal_path</span><span class="p">]</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span> <span class="k">if</span> <span class="s1">&#39;Sigma_freq_&#39;</span> <span class="ow">in</span> <span class="n">key</span><span class="p">]</span>
+        <span class="c1"># Sorts impurity paths by their indices, not sure if necessary</span>
+        <span class="n">impurity_indices</span> <span class="o">=</span> <span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">s</span><span class="p">[</span><span class="n">s</span><span class="o">.</span><span class="n">rfind</span><span class="p">(</span><span class="s1">&#39;_&#39;</span><span class="p">)</span><span class="o">+</span><span class="mi">1</span><span class="p">:])</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">impurity_paths</span><span class="p">]</span>
+        <span class="n">impurity_paths</span> <span class="o">=</span> <span class="p">[</span><span class="n">impurity_paths</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">impurity_indices</span><span class="p">)]</span>
+        <span class="n">sigma_iw</span> <span class="o">=</span> <span class="p">[</span><span class="n">archive</span><span class="p">[</span><span class="n">h5_internal_path</span><span class="p">][</span><span class="n">p</span><span class="p">]</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">impurity_paths</span><span class="p">]</span>
+
+        <span class="n">inequiv_to_corr</span> <span class="o">=</span> <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;dft_input&#39;</span><span class="p">][</span><span class="s1">&#39;inequiv_to_corr&#39;</span><span class="p">]</span>
+        <span class="n">dc_potential</span> <span class="o">=</span> <span class="p">[</span><span class="n">archive</span><span class="p">[</span><span class="n">h5_internal_path</span><span class="p">][</span><span class="s1">&#39;DC_pot&#39;</span><span class="p">][</span><span class="n">icrsh</span><span class="p">]</span>
+                        <span class="k">for</span> <span class="n">icrsh</span> <span class="ow">in</span> <span class="n">inequiv_to_corr</span><span class="p">]</span>
+
+        <span class="k">if</span> <span class="s1">&#39;chemical_potential_post&#39;</span> <span class="ow">in</span> <span class="n">archive</span><span class="p">[</span><span class="n">h5_internal_path</span><span class="p">]:</span>
+            <span class="n">chemical_potential</span> <span class="o">=</span> <span class="n">archive</span><span class="p">[</span><span class="n">h5_internal_path</span><span class="p">][</span><span class="s1">&#39;chemical_potential_post&#39;</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="c1"># Old name for chemical_potential_post</span>
+            <span class="n">chemical_potential</span> <span class="o">=</span> <span class="n">archive</span><span class="p">[</span><span class="n">h5_internal_path</span><span class="p">][</span><span class="s1">&#39;chemical_potential&#39;</span><span class="p">]</span>
+        <span class="n">chemical_potential_zero</span> <span class="o">=</span> <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_results/observables&#39;</span><span class="p">][</span><span class="s1">&#39;mu&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+
+    <span class="k">return</span> <span class="n">sigma_iw</span><span class="p">,</span> <span class="n">dc_potential</span><span class="p">,</span> <span class="n">chemical_potential</span><span class="p">,</span> <span class="n">chemical_potential_zero</span>
+
+
+<span class="k">def</span> <span class="nf">_create_sigma_continuator</span><span class="p">(</span><span class="n">sigma_iw</span><span class="p">,</span> <span class="n">dc_potential</span><span class="p">,</span> <span class="n">chemical_potential</span><span class="p">,</span> <span class="n">chemical_potential_zero</span><span class="p">,</span> <span class="n">continuator_type</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Initializes the inversion and direct sigma continuator. Returns a list of</span>
+<span class="sd">    continuators. Types of supported auxiliary Green&#39;s functions:</span>
+<span class="sd">    * &#39;inversion_dc&#39;: inversion continuator, constant C = dc_potential for the impurity</span>
+<span class="sd">    * &#39;inversion_sigmainf&#39;: inversion continuator, constant C = Sigma(i infinity) + chemical potential</span>
+<span class="sd">    * &#39;direct&#39;: direct continuator</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">for</span> <span class="n">sigma_imp</span> <span class="ow">in</span> <span class="n">sigma_iw</span><span class="p">:</span>
+        <span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">sigma_block</span> <span class="ow">in</span> <span class="n">sigma_imp</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">sigma_block</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">&#39;Continuation of matrix-valued selfenergies &#39;</span>
+                              <span class="o">+</span> <span class="s1">&#39;with nonzero offdiagonal components can be &#39;</span>
+                              <span class="o">+</span> <span class="s1">&#39;unstable since MaxEnt matrix continuation &#39;</span>
+                              <span class="o">+</span> <span class="s1">&#39;does not guarantee a positive semi-definite, &#39;</span>
+                              <span class="o">+</span> <span class="s1">&#39;Hermitian output.&#39;</span><span class="p">)</span>
+
+    <span class="n">n_inequiv_shells</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">sigma_iw</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">continuator_type</span> <span class="o">==</span> <span class="s1">&#39;inversion_dc&#39;</span><span class="p">:</span>
+        <span class="n">shifts</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="n">n_inequiv_shells</span>
+        <span class="k">for</span> <span class="n">iineq</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_inequiv_shells</span><span class="p">):</span>
+            <span class="k">for</span> <span class="n">dc_block</span> <span class="ow">in</span> <span class="n">dc_potential</span><span class="p">[</span><span class="n">iineq</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">():</span>
+                <span class="c1"># Reads first element from matrix for shift</span>
+                <span class="k">if</span> <span class="n">shifts</span><span class="p">[</span><span class="n">iineq</span><span class="p">]</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                    <span class="n">shifts</span><span class="p">[</span><span class="n">iineq</span><span class="p">]</span> <span class="o">=</span> <span class="n">dc_block</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
+                <span class="c1"># Checks that matrix for up and down is unit matrix * shift</span>
+                <span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">dc_block</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">dc_block</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span><span class="o">*</span><span class="n">shifts</span><span class="p">[</span><span class="n">iineq</span><span class="p">]):</span>
+                    <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">&#39;Only scalar dc per impurity supported&#39;</span><span class="p">)</span>
+
+        <span class="n">continuators</span> <span class="o">=</span> <span class="p">[</span><span class="n">InversionSigmaContinuator</span><span class="p">(</span><span class="n">sigma_imp</span><span class="p">,</span> <span class="n">shift</span><span class="p">)</span>
+                        <span class="k">for</span> <span class="n">sigma_imp</span><span class="p">,</span> <span class="n">shift</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">sigma_iw</span><span class="p">,</span> <span class="n">shifts</span><span class="p">)]</span>
+    <span class="k">elif</span> <span class="n">continuator_type</span> <span class="o">==</span> <span class="s1">&#39;inversion_sigmainf&#39;</span><span class="p">:</span>
+        <span class="n">shifts</span> <span class="o">=</span> <span class="p">[{</span><span class="n">key</span><span class="p">:</span> <span class="n">sigma_block</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">real</span> <span class="o">+</span> <span class="p">(</span><span class="n">chemical_potential</span> <span class="o">-</span> <span class="n">chemical_potential_zero</span><span class="p">)</span>
+                   <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">sigma_block</span> <span class="ow">in</span> <span class="n">sigma_imp</span><span class="p">}</span> <span class="k">for</span> <span class="n">sigma_imp</span> <span class="ow">in</span> <span class="n">sigma_iw</span><span class="p">]</span>
+        <span class="n">continuators</span> <span class="o">=</span> <span class="p">[</span><span class="n">InversionSigmaContinuator</span><span class="p">(</span><span class="n">sigma_imp</span><span class="p">,</span> <span class="n">shift</span><span class="p">)</span>
+                        <span class="k">for</span> <span class="n">sigma_imp</span><span class="p">,</span> <span class="n">shift</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">sigma_iw</span><span class="p">,</span> <span class="n">shifts</span><span class="p">)]</span>
+    <span class="k">elif</span> <span class="n">continuator_type</span> <span class="o">==</span> <span class="s1">&#39;direct&#39;</span><span class="p">:</span>
+        <span class="k">for</span> <span class="n">sigma_imp</span> <span class="ow">in</span> <span class="n">sigma_iw</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">sigma_block</span> <span class="ow">in</span> <span class="n">sigma_imp</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">sigma_block</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+                    <span class="c1"># TODO: implement making input diagonal if it is not</span>
+                    <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">&#39;Continuing only diagonal elements of non-diagonal &#39;</span>
+                                              <span class="s1">&#39;matrix not implemented yet&#39;</span><span class="p">)</span>
+        <span class="n">continuators</span> <span class="o">=</span> <span class="p">[</span><span class="n">DirectSigmaContinuator</span><span class="p">(</span><span class="n">sigma_imp</span><span class="p">)</span> <span class="k">for</span> <span class="n">sigma_imp</span> <span class="ow">in</span> <span class="n">sigma_iw</span><span class="p">]</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span>
+
+    <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Created sigma continuator of type &quot;</span><span class="si">{</span><span class="n">continuator_type</span><span class="si">}</span><span class="s1">&quot;&#39;</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">continuators</span>
+
+
+<span class="k">def</span> <span class="nf">_run_maxent</span><span class="p">(</span><span class="n">continuators</span><span class="p">,</span> <span class="n">error</span><span class="p">,</span> <span class="n">omega_min</span><span class="p">,</span> <span class="n">omega_max</span><span class="p">,</span> <span class="n">n_points_maxent</span><span class="p">,</span>
+                <span class="n">n_points_alpha</span><span class="p">,</span> <span class="n">analyzer</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Uses maxent to continue the auxiliary Green&#39;s function obtained from the</span>
+<span class="sd">    continuator. The range for alpha is set to 1e-6 to 1e2.</span>
+<span class="sd">    Returns the real-frequency auxiliary Green&#39;s function</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># Finds blocks of impurities and prints summary</span>
+    <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;Continuing impurities with blocks:&#39;</span><span class="p">)</span>
+    <span class="n">imps_blocks</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">continuator</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">continuators</span><span class="p">):</span>
+        <span class="n">blocks</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">continuator</span><span class="o">.</span><span class="n">Gaux_iw</span><span class="o">.</span><span class="n">indices</span><span class="p">)</span>
+        <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;- Imp </span><span class="si">{}</span><span class="s1">: </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">blocks</span><span class="p">))</span>
+        <span class="k">for</span> <span class="n">block</span> <span class="ow">in</span> <span class="n">blocks</span><span class="p">:</span>
+            <span class="n">imps_blocks</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">block</span><span class="p">))</span>
+
+    <span class="c1"># Initializes arrays to save results in</span>
+    <span class="n">spectral_funcs</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">1</span><span class="p">)]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">imps_blocks</span><span class="p">)</span>
+    <span class="n">opt_alphas</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">int</span><span class="p">)]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">imps_blocks</span><span class="p">)</span>
+    <span class="n">omega_mesh</span> <span class="o">=</span> <span class="n">HyperbolicOmegaMesh</span><span class="p">(</span><span class="n">omega_min</span><span class="o">=</span><span class="n">omega_min</span><span class="p">,</span> <span class="n">omega_max</span><span class="o">=</span><span class="n">omega_max</span><span class="p">,</span> <span class="n">n_points</span><span class="o">=</span><span class="n">n_points_maxent</span><span class="p">)</span>
+
+    <span class="c1"># Runs MaxEnt while parallelizing over impurities and blocks</span>
+    <span class="n">imps_blocks_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">imps_blocks</span><span class="p">))</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">mpi</span><span class="o">.</span><span class="n">slice_array</span><span class="p">(</span><span class="n">imps_blocks_indices</span><span class="p">):</span>
+        <span class="n">imp</span><span class="p">,</span> <span class="n">block</span> <span class="o">=</span> <span class="n">imps_blocks</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+        <span class="n">g_aux_block</span> <span class="o">=</span> <span class="n">continuators</span><span class="p">[</span><span class="n">imp</span><span class="p">]</span><span class="o">.</span><span class="n">Gaux_iw</span><span class="p">[</span><span class="n">block</span><span class="p">]</span>
+        <span class="n">solver</span> <span class="o">=</span> <span class="n">PoormanMaxEnt</span><span class="p">(</span><span class="n">use_complex</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+        <span class="n">solver</span><span class="o">.</span><span class="n">set_G_iw</span><span class="p">(</span><span class="n">g_aux_block</span><span class="p">)</span>
+        <span class="n">solver</span><span class="o">.</span><span class="n">set_error</span><span class="p">(</span><span class="n">error</span><span class="p">)</span>
+        <span class="n">solver</span><span class="o">.</span><span class="n">omega</span> <span class="o">=</span> <span class="n">omega_mesh</span>
+        <span class="n">solver</span><span class="o">.</span><span class="n">alpha_mesh</span> <span class="o">=</span> <span class="n">LogAlphaMesh</span><span class="p">(</span><span class="n">alpha_min</span><span class="o">=</span><span class="mf">1e-6</span><span class="p">,</span> <span class="n">alpha_max</span><span class="o">=</span><span class="mf">1e2</span><span class="p">,</span> <span class="n">n_points</span><span class="o">=</span><span class="n">n_points_alpha</span><span class="p">)</span>
+        <span class="c1"># Turns off MaxEnt output, it&#39;s far too messy in the parallel mode</span>
+        <span class="c1"># For some reason, MaxEnt still prints &quot;appending&quot;</span>
+        <span class="n">solver</span><span class="o">.</span><span class="n">maxent_diagonal</span><span class="o">.</span><span class="n">logtaker</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">VerbosityFlags</span><span class="o">.</span><span class="n">Quiet</span>
+        <span class="n">solver</span><span class="o">.</span><span class="n">maxent_offdiagonal</span><span class="o">.</span><span class="n">logtaker</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">VerbosityFlags</span><span class="o">.</span><span class="n">Quiet</span>
+        <span class="n">result</span> <span class="o">=</span> <span class="n">solver</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
+
+        <span class="n">spectral_funcs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">result</span><span class="o">.</span><span class="n">get_A_out</span><span class="p">(</span><span class="n">analyzer</span><span class="p">)</span>
+
+        <span class="n">opt_alphas</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">g_aux_block</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span> <span class="o">+</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="p">),</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">int</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">opt_alphas</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
+            <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">j</span><span class="o">+</span><span class="mi">1</span><span class="p">):</span>
+                <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">):</span> <span class="c1"># loop over complex numbers</span>
+                    <span class="k">if</span> <span class="n">result</span><span class="o">.</span><span class="n">analyzer_results</span><span class="p">[</span><span class="n">k</span><span class="p">][</span><span class="n">j</span><span class="p">][</span><span class="n">l</span><span class="p">]</span> <span class="o">==</span> <span class="p">{}:</span>
+                        <span class="k">continue</span>
+                    <span class="n">opt_alphas</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">k</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">l</span><span class="p">]</span> <span class="o">=</span> <span class="n">result</span><span class="o">.</span><span class="n">analyzer_results</span><span class="p">[</span><span class="n">k</span><span class="p">][</span><span class="n">j</span><span class="p">][</span><span class="n">l</span><span class="p">][</span><span class="n">analyzer</span><span class="p">][</span><span class="s1">&#39;alpha_index&#39;</span><span class="p">]</span>
+
+    <span class="c1"># Synchronizes information between ranks</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">imps_blocks_indices</span><span class="p">:</span>
+        <span class="n">spectral_funcs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">mpi</span><span class="o">.</span><span class="n">all_reduce</span><span class="p">(</span><span class="n">spectral_funcs</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+        <span class="n">opt_alphas</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">mpi</span><span class="o">.</span><span class="n">all_reduce</span><span class="p">(</span><span class="n">opt_alphas</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+
+    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">block_index</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">imps_blocks</span><span class="p">):</span>
+        <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Optimal alphas, block </span><span class="si">{</span><span class="n">block_index</span><span class="si">}</span><span class="s1">:&#39;</span><span class="p">)</span>
+        <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;--- Real part ---&#39;</span><span class="p">,</span> <span class="n">opt_alphas</span><span class="p">[</span><span class="n">i</span><span class="p">][:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">])</span>
+        <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">opt_alphas</span><span class="p">[</span><span class="n">i</span><span class="p">][:,</span> <span class="p">:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">!=</span> <span class="o">-</span><span class="mi">1</span><span class="p">):</span>
+            <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;--- Imag part ---&#39;</span><span class="p">,</span> <span class="n">opt_alphas</span><span class="p">[</span><span class="n">i</span><span class="p">][:,</span> <span class="p">:,</span> <span class="mi">1</span><span class="p">])</span>
+
+    <span class="c1"># Sorts results into original order of impurities and blocks</span>
+    <span class="c1"># and adds information from Hermitian conjugate of off-diagonal elements</span>
+    <span class="n">sorted_spectral_funcs</span> <span class="o">=</span> <span class="p">[{}</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">continuators</span><span class="p">))]</span>
+    <span class="k">for</span> <span class="p">(</span><span class="n">imp</span><span class="p">,</span> <span class="n">block</span><span class="p">),</span> <span class="n">val</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">imps_blocks</span><span class="p">,</span> <span class="n">spectral_funcs</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">val</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
+            <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">i</span><span class="p">):</span>
+                <span class="n">val</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">val</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">conj</span><span class="p">()</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">val</span><span class="o">.</span><span class="n">imag</span><span class="p">,</span> <span class="mi">0</span><span class="p">):</span>
+            <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;The result is complex. This might be correct but comes &#39;</span>
+                       <span class="o">+</span> <span class="s1">&#39;without guarantuee of formal correctness.&#39;</span><span class="p">)</span>
+        <span class="n">sorted_spectral_funcs</span><span class="p">[</span><span class="n">imp</span><span class="p">][</span><span class="n">block</span><span class="p">]</span> <span class="o">=</span> <span class="n">val</span>
+
+    <span class="k">return</span> <span class="n">sorted_spectral_funcs</span><span class="p">,</span> <span class="n">omega_mesh</span>
+
+
+<span class="k">def</span> <span class="nf">_get_sigma_omega_from_aux</span><span class="p">(</span><span class="n">continuators</span><span class="p">,</span> <span class="n">aux_spectral_funcs</span><span class="p">,</span> <span class="n">aux_omega_mesh</span><span class="p">,</span>
+                              <span class="n">omega_min</span><span class="p">,</span> <span class="n">omega_max</span><span class="p">,</span> <span class="n">n_points_interp</span><span class="p">,</span> <span class="n">n_points_final</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot; Extracts the real-frequency self energy from the auxiliary Green&#39;s function. &quot;&quot;&quot;</span>
+    <span class="k">for</span> <span class="n">cont_imp</span><span class="p">,</span> <span class="n">spec_imp</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">continuators</span><span class="p">,</span> <span class="n">aux_spectral_funcs</span><span class="p">):</span>
+        <span class="n">cont_imp</span><span class="o">.</span><span class="n">set_Gaux_w_from_Aaux_w</span><span class="p">(</span><span class="n">spec_imp</span><span class="p">,</span> <span class="n">aux_omega_mesh</span><span class="p">,</span> <span class="n">np_interp_A</span><span class="o">=</span><span class="n">n_points_interp</span><span class="p">,</span>
+                                        <span class="n">np_omega</span><span class="o">=</span><span class="n">n_points_final</span><span class="p">,</span> <span class="n">w_min</span><span class="o">=</span><span class="n">omega_min</span><span class="p">,</span> <span class="n">w_max</span><span class="o">=</span><span class="n">omega_max</span><span class="p">)</span>
+
+    <span class="n">g_aux_w</span> <span class="o">=</span> <span class="p">[</span><span class="n">continuator</span><span class="o">.</span><span class="n">Gaux_w</span> <span class="k">for</span> <span class="n">continuator</span> <span class="ow">in</span> <span class="n">continuators</span><span class="p">]</span>
+    <span class="n">sigma_w</span> <span class="o">=</span> <span class="p">[</span><span class="n">continuator</span><span class="o">.</span><span class="n">S_w</span> <span class="k">for</span> <span class="n">continuator</span> <span class="ow">in</span> <span class="n">continuators</span><span class="p">]</span>
+    <span class="k">return</span> <span class="n">g_aux_w</span><span class="p">,</span> <span class="n">sigma_w</span>
+
+
+<span class="k">def</span> <span class="nf">_write_sigma_omega_to_h5</span><span class="p">(</span><span class="n">g_aux_w</span><span class="p">,</span> <span class="n">sigma_w</span><span class="p">,</span> <span class="n">external_path</span><span class="p">,</span> <span class="n">iteration</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot; Writes real-frequency self energy to h5 archive. &quot;&quot;&quot;</span>
+    <span class="n">h5_internal_path</span> <span class="o">=</span> <span class="s1">&#39;DMFT_results/&#39;</span> <span class="o">+</span> <span class="p">(</span><span class="s1">&#39;last_iter&#39;</span> <span class="k">if</span> <span class="n">iteration</span> <span class="ow">is</span> <span class="kc">None</span>
+                                          <span class="k">else</span> <span class="sa">f</span><span class="s1">&#39;it_</span><span class="si">{</span><span class="n">iteration</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+
+    <span class="k">with</span> <span class="n">HDFArchive</span><span class="p">(</span><span class="n">external_path</span><span class="p">,</span> <span class="s1">&#39;a&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">archive</span><span class="p">:</span>
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">g_aux_imp</span><span class="p">,</span> <span class="n">sigma_imp</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">g_aux_w</span><span class="p">,</span> <span class="n">sigma_w</span><span class="p">)):</span>
+            <span class="n">archive</span><span class="p">[</span><span class="n">h5_internal_path</span><span class="p">][</span><span class="sa">f</span><span class="s1">&#39;Sigma_maxent_</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">sigma_imp</span>
+            <span class="n">archive</span><span class="p">[</span><span class="n">h5_internal_path</span><span class="p">][</span><span class="sa">f</span><span class="s1">&#39;G_aux_for_Sigma_maxent_</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">g_aux_imp</span>
+
+
+<div class="viewcode-block" id="main"><a class="viewcode-back" href="../../_ref/postprocessing.maxent_sigma.html#postprocessing.maxent_sigma.main">[docs]</a><span class="k">def</span> <span class="nf">main</span><span class="p">(</span><span class="n">external_path</span><span class="p">,</span> <span class="n">iteration</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">continuator_type</span><span class="o">=</span><span class="s1">&#39;inversion_sigmainf&#39;</span><span class="p">,</span> <span class="n">maxent_error</span><span class="o">=</span><span class="mf">.02</span><span class="p">,</span>
+         <span class="n">omega_min</span><span class="o">=-</span><span class="mf">12.</span><span class="p">,</span> <span class="n">omega_max</span><span class="o">=</span><span class="mf">12.</span><span class="p">,</span> <span class="n">n_points_maxent</span><span class="o">=</span><span class="mi">400</span><span class="p">,</span> <span class="n">n_points_alpha</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>
+         <span class="n">analyzer</span><span class="o">=</span><span class="s1">&#39;LineFitAnalyzer&#39;</span><span class="p">,</span> <span class="n">n_points_interp</span><span class="o">=</span><span class="mi">2000</span><span class="p">,</span> <span class="n">n_points_final</span><span class="o">=</span><span class="mi">1000</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Main function that reads the Matsubara self-energy from h5, analytically continues it,</span>
+<span class="sd">    writes the results back to the h5 archive and also returns the results.</span>
+
+<span class="sd">    Function parallelizes using MPI over impurities and blocks.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    external_path : string</span>
+<span class="sd">        Path to the h5 archive to read from and write to</span>
+<span class="sd">    iteration : int/string</span>
+<span class="sd">        Iteration to read from and write to. Default to last_iter</span>
+<span class="sd">    continuator_type : string</span>
+<span class="sd">        Type of continuator to use, one of &#39;inversion_sigmainf&#39;, &#39;inversion_dc&#39;, &#39;direct&#39;</span>
+<span class="sd">    maxent_error : float</span>
+<span class="sd">        The error that is used for the analyzers.</span>
+<span class="sd">    omega_min : float</span>
+<span class="sd">        Lower end of range where Sigma is being continued. Range has to comprise</span>
+<span class="sd">        all features of the self-energy because the real part of it comes from</span>
+<span class="sd">        the Kramers-Kronig relation applied to the auxiliary spectral function.</span>
+<span class="sd">        For example, if the real-frequency self-energy bends at omega_min or</span>
+<span class="sd">        omega_max, there are neglegcted features and the range should be extended.</span>
+<span class="sd">    omega_max : float</span>
+<span class="sd">        Upper end of range where Sigma is being continued. See omega_min.</span>
+<span class="sd">    n_points_maxent : int</span>
+<span class="sd">        Number of omega points on the hyperbolic mesh used in analytically</span>
+<span class="sd">        continuing the auxiliary GF</span>
+<span class="sd">    n_points_alpha : int</span>
+<span class="sd">        Number of points that the MaxEnt alpha parameter is varied on logarithmically</span>
+<span class="sd">    analyzer : string</span>
+<span class="sd">        Analyzer used int MaxEnt, one of &#39;LineFitAnalyzer&#39;, &#39;Chi2CurvatureAnalyzer&#39;,</span>
+<span class="sd">        &#39;ClassicAnalyzer&#39;, &#39;EntropyAnalyzer&#39;, &#39;BryanAnalyzer&#39;</span>
+<span class="sd">    n_points_interp : int</span>
+<span class="sd">        Number of points where auxiliary GF is interpolated to integrate over</span>
+<span class="sd">        it for the Kramers-Kronig relation</span>
+<span class="sd">    n_points_final : int</span>
+<span class="sd">        Number of omega points the complex auxiliary GF and therefore the</span>
+<span class="sd">        continued self-energy has on a linear grid between omega_min and omega_max</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    sigma_w : list of triqs.gf.BlockGf</span>
+<span class="sd">        Sigma(omega) per inequivalent shell</span>
+<span class="sd">    g_aux_w : list of triqs.gf.BlockGf</span>
+<span class="sd">        G_aux(omega) per inequivalent shell</span>
+
+<span class="sd">    Raises</span>
+<span class="sd">    ------</span>
+<span class="sd">    NotImplementedError</span>
+<span class="sd">        -- When a wrong continuator type or maxent analyzer is chosen</span>
+<span class="sd">        -- For direct continuator: when the self energy contains blocks larger</span>
+<span class="sd">        than 1x1 (no off-diagonal continuation possible)</span>
+<span class="sd">        -- For inversion_dc continuator: when the DC is not a diagonal matrix with</span>
+<span class="sd">        the same entry for all blocks of an impurity. Otherwise, issues like</span>
+<span class="sd">        the global frame violating the block structure would come up.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># Checks on input parameters</span>
+    <span class="k">if</span> <span class="n">continuator_type</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">&#39;inversion_sigmainf&#39;</span><span class="p">,</span> <span class="s1">&#39;inversion_dc&#39;</span><span class="p">,</span> <span class="s1">&#39;direct&#39;</span><span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">&#39;Unsupported type of continuator chosen&#39;</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">analyzer</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">&#39;LineFitAnalyzer&#39;</span><span class="p">,</span> <span class="s1">&#39;Chi2CurvatureAnalyzer&#39;</span><span class="p">,</span> <span class="s1">&#39;ClassicAnalyzer&#39;</span><span class="p">,</span>
+                        <span class="s1">&#39;EntropyAnalyzer&#39;</span><span class="p">,</span> <span class="s1">&#39;BryanAnalyzer&#39;</span><span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">&#39;Unsupported type of analyzer chosen&#39;</span><span class="p">)</span>
+
+    <span class="k">assert</span> <span class="n">omega_min</span> <span class="o">&lt;</span> <span class="n">omega_max</span>
+
+    <span class="c1"># Reads in data and initializes continuator object</span>
+    <span class="n">start_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+    <span class="n">continuators</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="k">if</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+        <span class="n">sigma_iw</span><span class="p">,</span> <span class="n">dc_potential</span><span class="p">,</span> <span class="n">chemical_potential</span><span class="p">,</span> <span class="n">chemical_potential_zero</span> <span class="o">=</span> <span class="n">_read_h5</span><span class="p">(</span><span class="n">external_path</span><span class="p">,</span> <span class="n">iteration</span><span class="p">)</span>
+        <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;Finished reading h5 archive. Found </span><span class="si">{}</span><span class="s1"> impurities.&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">sigma_iw</span><span class="p">)))</span>
+        <span class="n">continuators</span> <span class="o">=</span> <span class="n">_create_sigma_continuator</span><span class="p">(</span><span class="n">sigma_iw</span><span class="p">,</span> <span class="n">dc_potential</span><span class="p">,</span>
+                                                 <span class="n">chemical_potential</span><span class="p">,</span> <span class="n">chemical_potential_zero</span><span class="p">,</span> <span class="n">continuator_type</span><span class="p">)</span>
+    <span class="n">continuators</span> <span class="o">=</span> <span class="n">mpi</span><span class="o">.</span><span class="n">bcast</span><span class="p">(</span><span class="n">continuators</span><span class="p">)</span>
+    <span class="n">init_end_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+
+    <span class="c1"># Runs MaxEnt</span>
+    <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;Starting run of maxent now.&#39;</span><span class="p">)</span>
+    <span class="n">aux_spectral_funcs</span><span class="p">,</span> <span class="n">aux_omega_mesh</span> <span class="o">=</span> <span class="n">_run_maxent</span><span class="p">(</span><span class="n">continuators</span><span class="p">,</span> <span class="n">maxent_error</span><span class="p">,</span>
+                                                     <span class="n">omega_min</span><span class="p">,</span> <span class="n">omega_max</span><span class="p">,</span>
+                                                     <span class="n">n_points_maxent</span><span class="p">,</span> <span class="n">n_points_alpha</span><span class="p">,</span>
+                                                     <span class="n">analyzer</span><span class="p">)</span>
+    <span class="n">maxent_end_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+
+    <span class="c1"># Extracts Sigma(omega)</span>
+    <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Extracting Σ(ω) now with </span><span class="si">{</span><span class="n">mpi</span><span class="o">.</span><span class="n">size</span><span class="si">}</span><span class="s1"> process(es).&#39;</span><span class="p">)</span>
+    <span class="n">g_aux_w</span><span class="p">,</span> <span class="n">sigma_w</span> <span class="o">=</span> <span class="n">_get_sigma_omega_from_aux</span><span class="p">(</span><span class="n">continuators</span><span class="p">,</span> <span class="n">aux_spectral_funcs</span><span class="p">,</span>
+                                                 <span class="n">aux_omega_mesh</span><span class="p">,</span> <span class="n">omega_min</span><span class="p">,</span> <span class="n">omega_max</span><span class="p">,</span>
+                                                 <span class="n">n_points_interp</span><span class="p">,</span> <span class="n">n_points_final</span><span class="p">)</span>
+    <span class="n">extract_end_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+
+    <span class="c1"># Writes results into h5 archive</span>
+    <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;Writing results to h5 archive now.&#39;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+        <span class="n">_write_sigma_omega_to_h5</span><span class="p">(</span><span class="n">g_aux_w</span><span class="p">,</span> <span class="n">sigma_w</span><span class="p">,</span> <span class="n">external_path</span><span class="p">,</span> <span class="n">iteration</span><span class="p">)</span>
+    <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;Finished writing Σ(ω) to archive.&#39;</span><span class="p">)</span>
+
+    <span class="n">all_end_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+
+    <span class="c1"># Prints timing summary</span>
+    <span class="n">run_time_report</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="si">{:&lt;8}</span><span class="s1"> | </span><span class="si">{:&lt;10}</span><span class="se">\n</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s1">&#39;Task&#39;</span><span class="p">,</span> <span class="s1">&#39;Duration (s)&#39;</span><span class="p">)</span>
+    <span class="n">length_table</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">run_time_report</span><span class="p">)</span> <span class="o">-</span> <span class="mi">2</span>
+    <span class="n">run_time_report</span> <span class="o">+=</span> <span class="s1">&#39;-&#39;</span><span class="o">*</span><span class="n">length_table</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span>
+    <span class="n">run_time_report</span> <span class="o">+=</span> <span class="s1">&#39;</span><span class="si">{:&lt;8}</span><span class="s1"> | </span><span class="si">{:10.4f}</span><span class="se">\n</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s1">&#39;Reading&#39;</span><span class="p">,</span> <span class="n">init_end_time</span> <span class="o">-</span> <span class="n">start_time</span><span class="p">)</span>
+    <span class="n">run_time_report</span> <span class="o">+=</span> <span class="s1">&#39;</span><span class="si">{:&lt;8}</span><span class="s1"> | </span><span class="si">{:10.4f}</span><span class="se">\n</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s1">&#39;MaxEnt&#39;</span><span class="p">,</span> <span class="n">maxent_end_time</span> <span class="o">-</span> <span class="n">init_end_time</span><span class="p">)</span>
+    <span class="n">run_time_report</span> <span class="o">+=</span> <span class="s1">&#39;</span><span class="si">{:&lt;8}</span><span class="s1"> | </span><span class="si">{:10.4f}</span><span class="se">\n</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s1">&#39;Extract.&#39;</span><span class="p">,</span> <span class="n">extract_end_time</span> <span class="o">-</span> <span class="n">maxent_end_time</span><span class="p">)</span>
+    <span class="n">run_time_report</span> <span class="o">+=</span> <span class="s1">&#39;</span><span class="si">{:&lt;8}</span><span class="s1"> | </span><span class="si">{:10.4f}</span><span class="se">\n</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s1">&#39;Writing&#39;</span><span class="p">,</span> <span class="n">all_end_time</span> <span class="o">-</span> <span class="n">extract_end_time</span><span class="p">)</span>
+    <span class="n">run_time_report</span> <span class="o">+=</span> <span class="s1">&#39;-&#39;</span><span class="o">*</span><span class="n">length_table</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span>
+    <span class="n">run_time_report</span> <span class="o">+=</span> <span class="s1">&#39;</span><span class="si">{:&lt;8}</span><span class="s1"> | </span><span class="si">{:10.4f}</span><span class="se">\n</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s1">&#39;Total&#39;</span><span class="p">,</span> <span class="n">all_end_time</span> <span class="o">-</span> <span class="n">start_time</span><span class="p">)</span>
+
+    <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="n">run_time_report</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">sigma_w</span><span class="p">,</span> <span class="n">g_aux_w</span></div>
+
+
+<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
+    <span class="c1"># Casts input parameters</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">2</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="o">==</span> <span class="s1">&#39;none&#39;</span><span class="p">:</span>
+            <span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">4</span><span class="p">:</span>
+        <span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">4</span><span class="p">]</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">4</span><span class="p">])</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">5</span><span class="p">:</span>
+        <span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">5</span><span class="p">]</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">5</span><span class="p">])</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">6</span><span class="p">:</span>
+        <span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">6</span><span class="p">]</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">6</span><span class="p">])</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">7</span><span class="p">:</span>
+        <span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">7</span><span class="p">]</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">7</span><span class="p">])</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">8</span><span class="p">:</span>
+        <span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">8</span><span class="p">]</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">8</span><span class="p">])</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">10</span><span class="p">:</span>
+        <span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">10</span><span class="p">]</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">10</span><span class="p">])</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">11</span><span class="p">:</span>
+        <span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">11</span><span class="p">]</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">11</span><span class="p">])</span>
+
+    <span class="n">main</span><span class="p">(</span><span class="o">*</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">1</span><span class="p">:])</span>
+</pre></div>
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.</p>
+  </div>
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
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diff --git a/_modules/postprocessing/pade_sigma.html b/_modules/postprocessing/pade_sigma.html
new file mode 100644
index 00000000..e48c99eb
--- /dev/null
+++ b/_modules/postprocessing/pade_sigma.html
@@ -0,0 +1,405 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" >
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>postprocessing.pade_sigma &mdash; solid_dmft  documentation</title>
+      <link rel="stylesheet" href="../../_static/pygments.css" type="text/css" />
+      <link rel="stylesheet" href="../../_static/css/custom.css" type="text/css" />
+      <link rel="stylesheet" href="../../_static/plot_directive.css" type="text/css" />
+    <link rel="shortcut icon" href="../../_static/favicon.ico"/>
+  
+        <script data-url_root="../../" id="documentation_options" src="../../_static/documentation_options.js"></script>
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+        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+    <script src="../../_static/js/theme.js"></script>
+    <link rel="index" title="Index" href="../../genindex.html" />
+    <link rel="search" title="Search" href="../../search.html" /> 
+</head>
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+<body class="wy-body-for-nav"> 
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+            <a href="../../index.html" class="icon icon-home"> solid_dmft
+            <img src="../../_static/logo_no_text.png" class="logo" alt="Logo"/>
+          </a>
+              <div class="version">
+                3.2.0
+              </div>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="../../search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" />
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+        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+              <ul>
+<li class="toctree-l1"><a class="reference internal" href="../../install.html">Installation</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#installation-via-pip">Installation via pip</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#manual-installation-via-cmake">Manual installation via CMake</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#docker-files-images">Docker files &amp; images</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#version-compatibility">Version compatibility</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#custom-cmake-options">Custom CMake options</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../documentation.html">Documentation</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#code-structure">Code structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#dft-interface-notes">DFT interface notes</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/w90_interface.html">Wannier90 interface</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/w90_interface.html#orbital-order-in-the-w90-converter">orbital order in the W90 converter</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/vasp_csc.html">Interface to VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#general-remarks">General remarks</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#file-description">file description</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#workflow">Workflow:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#important-flags">important flags:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#version-and-compilation">version and compilation:</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#input-output">Input/Output</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../input_output/DMFT_input/input.html">Input</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/general.html">[general]: General parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/solver.html">[solver]: solver specific parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/dft.html">[dft]: DFT related inputs</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/advanced.html">[advanced]: Advanced inputs</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../input_output/DMFT_output/results.html">Output / results</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_output/results.html#group-structure">Group structure</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_output/results.html#subgroups">Subgroups</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#further-details-for-running">Further details for running</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/docker.html">Docker</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#building-the-docker-image">Building the docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#pulling-a-docker-image">Pulling a docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_locally.html">Run on your machine</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#module-reference-manual">Module reference manual</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/csc_flow.html">csc_flow</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/dft_managers.html">dft_managers</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.mpi_helpers.html">dft_managers.mpi_helpers</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.qe_manager.html">dft_managers.qe_manager</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.vasp_manager.html">dft_managers.vasp_manager</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/dmft_cycle.html">dmft_cycle</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/dmft_tools.html">dmft_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.afm_mapping.html">dmft_tools.afm_mapping</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.convergence.html">dmft_tools.convergence</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.formatter.html">dmft_tools.formatter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.greens_functions_mixer.html">dmft_tools.greens_functions_mixer</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.initial_self_energies.html">dmft_tools.initial_self_energies</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.interaction_hamiltonian.html">dmft_tools.interaction_hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.legendre_filter.html">dmft_tools.legendre_filter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.manipulate_chemical_potential.html">dmft_tools.manipulate_chemical_potential</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.matheval.html">dmft_tools.matheval</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.observables.html">dmft_tools.observables</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.results_to_archive.html">dmft_tools.results_to_archive</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.solver.html">dmft_tools.solver</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/io_tools.html">io_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.dict_to_h5.html">io_tools.dict_to_h5</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.postproc_toml_dict.html">io_tools.postproc_toml_dict</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.verify_input_params.html">io_tools.verify_input_params</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_gf_imp.html">postprocessing.maxent_gf_imp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_gf_latt.html">postprocessing.maxent_gf_latt</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_sigma.html">postprocessing.maxent_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
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+      <li>postprocessing.pade_sigma</li>
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+             
+  <h1>Source code for postprocessing.pade_sigma</h1><div class="highlight"><pre>
+<span></span><span class="c1"># pyright: reportUnusedExpression=false</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<span class="kn">from</span> <span class="nn">triqs.utility</span> <span class="kn">import</span> <span class="n">mpi</span>
+<span class="kn">from</span> <span class="nn">h5</span> <span class="kn">import</span> <span class="n">HDFArchive</span>
+<span class="kn">from</span> <span class="nn">triqs.gf</span> <span class="kn">import</span> <span class="n">Gf</span><span class="p">,</span> <span class="n">MeshReFreq</span><span class="p">,</span> <span class="n">BlockGf</span>
+
+<span class="kn">from</span> <span class="nn">solid_dmft.postprocessing.maxent_sigma</span> <span class="kn">import</span> <span class="n">_read_h5</span>
+
+<span class="k">def</span> <span class="nf">_write_sigma_omega_to_h5</span><span class="p">(</span><span class="n">sigma_w</span><span class="p">,</span> <span class="n">external_path</span><span class="p">,</span> <span class="n">iteration</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot; Writes real-frequency self energy to h5 archive. &quot;&quot;&quot;</span>
+    <span class="n">h5_internal_path</span> <span class="o">=</span> <span class="s1">&#39;DMFT_results/&#39;</span> <span class="o">+</span> <span class="p">(</span><span class="s1">&#39;last_iter&#39;</span> <span class="k">if</span> <span class="n">iteration</span> <span class="ow">is</span> <span class="kc">None</span>
+                                          <span class="k">else</span> <span class="sa">f</span><span class="s1">&#39;it_</span><span class="si">{</span><span class="n">iteration</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+
+    <span class="k">with</span> <span class="n">HDFArchive</span><span class="p">(</span><span class="n">external_path</span><span class="p">,</span> <span class="s1">&#39;a&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">archive</span><span class="p">:</span>
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">sigma_imp</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">sigma_w</span><span class="p">):</span>
+            <span class="n">archive</span><span class="p">[</span><span class="n">h5_internal_path</span><span class="p">][</span><span class="sa">f</span><span class="s1">&#39;Sigma_Refreq_</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">sigma_imp</span>
+
+<span class="k">def</span> <span class="nf">_run_pade</span><span class="p">(</span><span class="n">sigma_iw_list</span><span class="p">,</span> <span class="n">n_w</span><span class="p">,</span> <span class="n">w_min</span><span class="p">,</span> <span class="n">w_max</span><span class="p">,</span> <span class="n">n_iw</span><span class="p">,</span> <span class="n">eta</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Run pade in parallel. Call via main function.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;Continuing impurities with blocks:&#39;</span><span class="p">)</span>
+
+    <span class="n">imps_blocks</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">sigma_iw_flat_list</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="c1"># create flattened list of self-energies</span>
+    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">sigma_iw</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">sigma_iw_list</span><span class="p">):</span>
+        <span class="n">blocks</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">sigma_iw</span><span class="o">.</span><span class="n">indices</span><span class="p">)</span>
+        <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;- Imp </span><span class="si">{}</span><span class="s1">: </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">blocks</span><span class="p">))</span>
+        <span class="k">for</span> <span class="n">block</span> <span class="ow">in</span> <span class="n">blocks</span><span class="p">:</span>
+            <span class="n">imps_blocks</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">block</span><span class="p">))</span>
+            <span class="n">sigma_iw_flat_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">sigma_iw</span><span class="p">[</span><span class="n">block</span><span class="p">])</span>
+
+    <span class="n">sigma_w_flat_list</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">wmesh</span> <span class="o">=</span> <span class="n">MeshReFreq</span><span class="p">(</span><span class="n">w_min</span><span class="o">=</span><span class="n">w_min</span><span class="p">,</span><span class="n">w_max</span><span class="o">=</span><span class="n">w_max</span><span class="p">,</span><span class="n">n_w</span><span class="o">=</span><span class="n">n_w</span><span class="p">)</span>
+    <span class="n">imps_blocks_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">imps_blocks</span><span class="p">))</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">imps_blocks_indices</span><span class="p">:</span>
+        <span class="n">sigma_w_flat_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">Gf</span><span class="p">(</span><span class="n">mesh</span><span class="o">=</span><span class="n">wmesh</span><span class="p">,</span> <span class="n">target_shape</span><span class="o">=</span><span class="n">sigma_iw_flat_list</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">target_shape</span><span class="p">))</span>
+
+    <span class="c1"># Runs Pade while parallelizing over impurities and blocks</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">mpi</span><span class="o">.</span><span class="n">slice_array</span><span class="p">(</span><span class="n">imps_blocks_indices</span><span class="p">):</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Rank </span><span class="si">{</span><span class="n">mpi</span><span class="o">.</span><span class="n">rank</span><span class="si">}</span><span class="s1"> continuing Σ </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">/</span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">imps_blocks</span><span class="p">)</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+        <span class="n">sigma_w_flat_list</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">set_from_pade</span><span class="p">(</span><span class="n">sigma_iw_flat_list</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="n">n_points</span><span class="o">=</span><span class="n">n_iw</span><span class="p">,</span> <span class="n">freq_offset</span><span class="o">=</span><span class="n">eta</span><span class="p">)</span>
+
+    <span class="c1"># sync Pade data</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">imps_blocks_indices</span><span class="p">:</span>
+        <span class="n">sigma_w_flat_list</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">mpi</span><span class="o">.</span><span class="n">all_reduce</span><span class="p">(</span><span class="n">sigma_w_flat_list</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+
+    <span class="c1"># Create list of BlockGf</span>
+    <span class="n">sigma_w_list</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">sigma_iw</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">sigma_iw_list</span><span class="p">):</span>
+        <span class="n">block_list</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">block</span> <span class="ow">in</span> <span class="n">sigma_iw</span><span class="o">.</span><span class="n">indices</span><span class="p">:</span>
+            <span class="n">block_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">sigma_w_flat_list</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="mi">0</span><span class="p">))</span>
+        <span class="n">sigma_w_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">BlockGf</span><span class="p">(</span><span class="n">name_list</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="n">sigma_iw</span><span class="o">.</span><span class="n">indices</span><span class="p">),</span> <span class="n">block_list</span><span class="o">=</span><span class="n">block_list</span><span class="p">,</span> <span class="n">make_copies</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
+
+    <span class="k">return</span> <span class="n">sigma_w_list</span>
+
+<div class="viewcode-block" id="main"><a class="viewcode-back" href="../../_ref/postprocessing.pade_sigma.html#postprocessing.pade_sigma.main">[docs]</a><span class="k">def</span> <span class="nf">main</span><span class="p">(</span><span class="n">external_path</span><span class="p">,</span> <span class="n">n_w</span><span class="p">,</span> <span class="n">w_min</span><span class="p">,</span> <span class="n">w_max</span><span class="p">,</span> <span class="n">n_iw</span><span class="p">,</span> <span class="n">iteration</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">eta</span><span class="o">=</span><span class="mf">0.0</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Main function that reads the Matsubara self-energy from h5, analytically continues it,</span>
+<span class="sd">    writes the results back to the h5 archive and also returns the results.</span>
+
+<span class="sd">    Function parallelizes using MPI over impurities and blocks.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    external_path : string</span>
+<span class="sd">        Path to the h5 archive to read from and write to</span>
+<span class="sd">    n_w : int</span>
+<span class="sd">        number of real frequencies of the final self-energies returned</span>
+<span class="sd">    w_min : float</span>
+<span class="sd">        Lower end of range where Sigma is being continued.</span>
+<span class="sd">    w_max : float</span>
+<span class="sd">        Upper end of range where Sigma is being continued.</span>
+<span class="sd">    n_iw : int</span>
+<span class="sd">        number of Matsubara frequencies to consider for the Pade approximant</span>
+<span class="sd">    iteration : int/string</span>
+<span class="sd">        Iteration to read from and write to. Default to last_iter</span>
+<span class="sd">    eta : float</span>
+<span class="sd">        frequency offset within Pade</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    sigma_w : list of triqs.gf.BlockGf</span>
+<span class="sd">        Sigma(omega) per inequivalent shell</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">sigma_iw</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="k">if</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+        <span class="n">sigma_iw</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">_read_h5</span><span class="p">(</span><span class="n">external_path</span><span class="p">,</span> <span class="n">iteration</span><span class="p">)</span>
+    <span class="n">sigma_iw</span> <span class="o">=</span> <span class="n">mpi</span><span class="o">.</span><span class="n">bcast</span><span class="p">(</span><span class="n">sigma_iw</span><span class="p">)</span>
+
+    <span class="c1"># run pade in parallel</span>
+    <span class="n">sigma_w</span> <span class="o">=</span> <span class="n">_run_pade</span><span class="p">(</span><span class="n">sigma_iw</span><span class="p">,</span> <span class="n">n_w</span><span class="p">,</span> <span class="n">w_min</span><span class="p">,</span> <span class="n">w_max</span><span class="p">,</span> <span class="n">n_iw</span><span class="p">,</span> <span class="n">eta</span><span class="p">)</span>
+
+    <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;Writing results to h5 archive now.&#39;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">mpi</span><span class="o">.</span><span class="n">is_master_node</span><span class="p">():</span>
+        <span class="n">_write_sigma_omega_to_h5</span><span class="p">(</span><span class="n">sigma_w</span><span class="p">,</span> <span class="n">external_path</span><span class="p">,</span> <span class="n">iteration</span><span class="p">)</span>
+    <span class="n">mpi</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="s1">&#39;Finished writing Σ(ω) to archive.&#39;</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">sigma_w</span></div>
+
+</pre></div>
+
+           </div>
+          </div>
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new file mode 100644
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+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#input-output">Input/Output</a><ul>
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+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#further-details-for-running">Further details for running</a><ul>
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+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
+</li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#module-reference-manual">Module reference manual</a><ul>
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+</ul>
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+<li class="toctree-l3"><a class="reference internal" href="../../_ref/io_tools.html">io_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.dict_to_h5.html">io_tools.dict_to_h5</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../../_ref/postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_gf_latt.html">postprocessing.maxent_gf_latt</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_sigma.html">postprocessing.maxent_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
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+          <li><a href="../index.html">Module code</a> &raquo;</li>
+      <li>postprocessing.plot_correlated_bands</li>
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+             
+  <h1>Source code for postprocessing.plot_correlated_bands</h1><div class="highlight"><pre>
+<span></span><span class="ch">#!/usr/bin/env python3</span>
+<span class="c1">################################################################################</span>
+<span class="c1">#</span>
+<span class="c1"># TRIQS: a Toolbox for Research in Interacting Quantum Systems</span>
+<span class="c1">#</span>
+<span class="c1"># Copyright (C) 2016-2018, N. Wentzell</span>
+<span class="c1"># Copyright (C) 2018-2019, Simons Foundation</span>
+<span class="c1">#   author: N. Wentzell</span>
+<span class="c1">#</span>
+<span class="c1"># TRIQS is free software: you can redistribute it and/or modify it under the</span>
+<span class="c1"># terms of the GNU General Public License as published by the Free Software</span>
+<span class="c1"># Foundation, either version 3 of the License, or (at your option) any later</span>
+<span class="c1"># version.</span>
+<span class="c1">#</span>
+<span class="c1"># TRIQS is distributed in the hope that it will be useful, but WITHOUT ANY</span>
+<span class="c1"># WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS</span>
+<span class="c1"># FOR A PARTICULAR PURPOSE. See the GNU General Public License for more</span>
+<span class="c1"># details.</span>
+<span class="c1">#</span>
+<span class="c1"># You should have received a copy of the GNU General Public License along with</span>
+<span class="c1"># TRIQS. If not, see &lt;http://www.gnu.org/licenses/&gt;.</span>
+<span class="c1">#</span>
+<span class="c1">################################################################################</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Reads DMFT_ouput observables such as real-frequency Sigma and a Wannier90</span>
+<span class="sd">TB Hamiltonian to compute spectral properties. It runs in two modes,</span>
+<span class="sd">either calculating the bandstructure or Fermi slice.</span>
+
+<span class="sd">Written by Sophie Beck, 2021-2022</span>
+
+<span class="sd">TODO:</span>
+<span class="sd">- extend to multi impurity systems</span>
+<span class="sd">- make proper use of rot_mat from DFT_Tools (atm it assumed that wannier_hr and Sigma are written in the same basis)</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">from</span> <span class="nn">matplotlib.ticker</span> <span class="kn">import</span> <span class="n">MaxNLocator</span>
+<span class="kn">from</span> <span class="nn">matplotlib.colors</span> <span class="kn">import</span> <span class="n">Normalize</span>
+<span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">cm</span>
+<span class="kn">from</span> <span class="nn">scipy.optimize</span> <span class="kn">import</span> <span class="n">brentq</span>
+<span class="kn">from</span> <span class="nn">scipy.interpolate</span> <span class="kn">import</span> <span class="n">interp1d</span>
+<span class="kn">from</span> <span class="nn">scipy.signal</span> <span class="kn">import</span> <span class="n">argrelextrema</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">itertools</span>
+<span class="kn">import</span> <span class="nn">skimage.measure</span>
+<span class="kn">from</span> <span class="nn">warnings</span> <span class="kn">import</span> <span class="n">warn</span>
+
+<span class="kn">from</span> <span class="nn">h5</span> <span class="kn">import</span> <span class="n">HDFArchive</span>
+<span class="kn">from</span> <span class="nn">triqs.gf</span> <span class="kn">import</span> <span class="n">BlockGf</span><span class="p">,</span> <span class="n">MeshReFreq</span><span class="p">,</span> <span class="n">Gf</span>
+<span class="kn">from</span> <span class="nn">triqs.lattice.utils</span> <span class="kn">import</span> <span class="n">TB_from_wannier90</span><span class="p">,</span> <span class="n">k_space_path</span>
+<span class="kn">from</span> <span class="nn">triqs_dft_tools.sumk_dft</span> <span class="kn">import</span> <span class="n">SumkDFT</span>
+
+
+<span class="k">def</span> <span class="nf">lambda_matrix_w90_t2g</span><span class="p">(</span><span class="n">add_lambda</span><span class="p">):</span>
+
+    <span class="n">lambda_x</span><span class="p">,</span> <span class="n">lambda_y</span><span class="p">,</span> <span class="n">lambda_z</span> <span class="o">=</span> <span class="n">add_lambda</span>
+
+    <span class="n">lambda_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">6</span><span class="p">,</span> <span class="mi">6</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">complex</span><span class="p">)</span>
+    <span class="n">lambda_matrix</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="o">+</span><span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">lambda_z</span><span class="o">/</span><span class="mf">2.0</span>
+    <span class="n">lambda_matrix</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">5</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">lambda_x</span><span class="o">/</span><span class="mf">2.0</span>
+    <span class="n">lambda_matrix</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">]</span> <span class="o">=</span>    <span class="o">-</span><span class="n">lambda_y</span><span class="o">/</span><span class="mf">2.0</span>
+    <span class="n">lambda_matrix</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span> <span class="o">=</span> <span class="o">+</span><span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">lambda_x</span><span class="o">/</span><span class="mf">2.0</span>
+    <span class="n">lambda_matrix</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">]</span> <span class="o">=</span>    <span class="o">+</span><span class="n">lambda_y</span><span class="o">/</span><span class="mf">2.0</span>
+    <span class="n">lambda_matrix</span><span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">lambda_z</span><span class="o">/</span><span class="mf">2.0</span>
+    <span class="n">lambda_matrix</span> <span class="o">+=</span> <span class="n">np</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">conjugate</span><span class="p">(</span><span class="n">lambda_matrix</span><span class="p">))</span>
+
+    <span class="k">return</span> <span class="n">lambda_matrix</span>
+
+
+<span class="k">def</span> <span class="nf">change_basis</span><span class="p">(</span><span class="n">n_orb</span><span class="p">,</span> <span class="n">orbital_order_to</span><span class="p">,</span> <span class="n">orbital_order_from</span><span class="p">):</span>
+
+    <span class="n">change_of_basis</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">n_orb</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">orb</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">orbital_order_to</span><span class="p">):</span>
+        <span class="n">orb_idx</span> <span class="o">=</span> <span class="n">orbital_order_from</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">orb</span><span class="p">)</span>
+        <span class="n">change_of_basis</span><span class="p">[</span><span class="n">orb_idx</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">roll</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">n_orb</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">ct</span><span class="p">)[:,</span> <span class="mi">0</span><span class="p">]</span>
+
+    <span class="k">return</span> <span class="n">change_of_basis</span>
+
+
+<span class="k">def</span> <span class="nf">print_matrix</span><span class="p">(</span><span class="n">matrix</span><span class="p">,</span> <span class="n">n_orb</span><span class="p">,</span> <span class="n">text</span><span class="p">):</span>
+
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{}</span><span class="s1">:&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">text</span><span class="p">))</span>
+
+    <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">matrix</span><span class="o">.</span><span class="n">imag</span> <span class="o">&gt;</span> <span class="mf">1e-4</span><span class="p">):</span>
+        <span class="n">fmt</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{:16.4f}</span><span class="s1">&#39;</span> <span class="o">*</span> <span class="n">n_orb</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">fmt</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{:8.4f}</span><span class="s1">&#39;</span> <span class="o">*</span> <span class="n">n_orb</span>
+        <span class="n">matrix</span> <span class="o">=</span> <span class="n">matrix</span><span class="o">.</span><span class="n">real</span>
+
+    <span class="k">for</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">matrix</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">((</span><span class="s1">&#39; &#39;</span><span class="o">*</span><span class="mi">4</span> <span class="o">+</span> <span class="n">fmt</span><span class="p">)</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="o">*</span><span class="n">row</span><span class="p">))</span>
+
+
+<span class="k">def</span> <span class="nf">_sigma_from_dmft</span><span class="p">(</span><span class="n">n_orb</span><span class="p">,</span> <span class="n">orbital_order</span><span class="p">,</span> <span class="n">with_sigma</span><span class="p">,</span> <span class="n">spin</span><span class="p">,</span> <span class="n">orbital_order_dmft</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">specs</span><span class="p">):</span>
+
+    <span class="k">if</span> <span class="n">orbital_order_dmft</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">orbital_order_dmft</span> <span class="o">=</span> <span class="n">orbital_order</span>
+
+    <span class="k">if</span> <span class="n">with_sigma</span> <span class="o">==</span> <span class="s1">&#39;calc&#39;</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Setting Sigma from </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">specs</span><span class="p">[</span><span class="s1">&#39;dmft_path&#39;</span><span class="p">]))</span>
+
+        <span class="n">sigma_imp_list</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">dc_imp_list</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">with</span> <span class="n">HDFArchive</span><span class="p">(</span><span class="n">specs</span><span class="p">[</span><span class="s1">&#39;dmft_path&#39;</span><span class="p">],</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">ar</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">icrsh</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">ar</span><span class="p">[</span><span class="s1">&#39;dft_input&#39;</span><span class="p">][</span><span class="s1">&#39;n_inequiv_shells&#39;</span><span class="p">]):</span>
+                <span class="k">try</span><span class="p">:</span>
+                    <span class="n">sigma</span> <span class="o">=</span> <span class="n">ar</span><span class="p">[</span><span class="s1">&#39;DMFT_results&#39;</span><span class="p">][</span><span class="n">specs</span><span class="p">[</span><span class="s1">&#39;it&#39;</span><span class="p">]][</span><span class="sa">f</span><span class="s1">&#39;Sigma_freq_</span><span class="si">{</span><span class="n">icrsh</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">]</span>
+                    <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">sigma</span><span class="o">.</span><span class="n">mesh</span><span class="p">,</span> <span class="n">MeshReFreq</span><span class="p">),</span> <span class="s1">&#39;Imported Greens function must be real frequency&#39;</span>
+                <span class="k">except</span><span class="p">(</span><span class="ne">KeyError</span><span class="p">,</span> <span class="ne">AssertionError</span><span class="p">):</span>
+                    <span class="k">try</span><span class="p">:</span>
+                        <span class="n">sigma</span> <span class="o">=</span> <span class="n">ar</span><span class="p">[</span><span class="s1">&#39;DMFT_results&#39;</span><span class="p">][</span><span class="n">specs</span><span class="p">[</span><span class="s1">&#39;it&#39;</span><span class="p">]][</span><span class="sa">f</span><span class="s1">&#39;Sigma_maxent_</span><span class="si">{</span><span class="n">icrsh</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">]</span>
+                    <span class="k">except</span> <span class="ne">KeyError</span><span class="p">:</span>
+                        <span class="k">try</span><span class="p">:</span>
+                            <span class="n">sigma</span> <span class="o">=</span> <span class="n">ar</span><span class="p">[</span><span class="s1">&#39;DMFT_results&#39;</span><span class="p">][</span><span class="n">specs</span><span class="p">[</span><span class="s1">&#39;it&#39;</span><span class="p">]][</span><span class="sa">f</span><span class="s1">&#39;Sigma_Refreq_</span><span class="si">{</span><span class="n">icrsh</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">]</span>
+                        <span class="k">except</span> <span class="ne">KeyError</span><span class="p">:</span>
+                            <span class="k">raise</span> <span class="ne">KeyError</span><span class="p">(</span><span class="s1">&#39;Provide either &quot;Sigma_freq_0&quot; in real frequency, &quot;Sigma_Refreq_0&quot; or &quot;Sigma_maxent_0&quot;.&#39;</span><span class="p">)</span>
+                <span class="n">sigma_imp_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">sigma</span><span class="p">)</span>
+
+            <span class="k">for</span> <span class="n">ish</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">ar</span><span class="p">[</span><span class="s1">&#39;dft_input&#39;</span><span class="p">][</span><span class="s1">&#39;n_corr_shells&#39;</span><span class="p">]):</span>
+                <span class="n">dc_imp_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">ar</span><span class="p">[</span><span class="s1">&#39;DMFT_results&#39;</span><span class="p">][</span><span class="n">specs</span><span class="p">[</span><span class="s1">&#39;it&#39;</span><span class="p">]][</span><span class="s1">&#39;DC_pot&#39;</span><span class="p">][</span><span class="n">ish</span><span class="p">])</span>
+
+            <span class="n">mu_dmft</span> <span class="o">=</span> <span class="n">ar</span><span class="p">[</span><span class="s1">&#39;DMFT_results&#39;</span><span class="p">][</span><span class="n">specs</span><span class="p">[</span><span class="s1">&#39;it&#39;</span><span class="p">]][</span><span class="s1">&#39;chemical_potential_post&#39;</span><span class="p">]</span>
+
+            <span class="n">sum_k</span> <span class="o">=</span> <span class="n">SumkDFT</span><span class="p">(</span><span class="n">specs</span><span class="p">[</span><span class="s1">&#39;dmft_path&#39;</span><span class="p">],</span> <span class="n">mesh</span><span class="o">=</span><span class="n">sigma</span><span class="o">.</span><span class="n">mesh</span><span class="p">)</span>
+            <span class="n">sum_k</span><span class="o">.</span><span class="n">block_structure</span> <span class="o">=</span> <span class="n">ar</span><span class="p">[</span><span class="s1">&#39;DMFT_input/block_structure&#39;</span><span class="p">]</span>
+            <span class="n">sum_k</span><span class="o">.</span><span class="n">deg_shells</span> <span class="o">=</span> <span class="n">ar</span><span class="p">[</span><span class="s1">&#39;DMFT_input/deg_shells&#39;</span><span class="p">]</span>
+            <span class="n">sum_k</span><span class="o">.</span><span class="n">set_mu</span><span class="p">(</span><span class="n">mu_dmft</span><span class="p">)</span>
+            <span class="c1"># set Sigma and DC into sum_k</span>
+            <span class="n">sum_k</span><span class="o">.</span><span class="n">dc_imp</span> <span class="o">=</span> <span class="n">dc_imp_list</span>
+            <span class="n">sum_k</span><span class="o">.</span><span class="n">set_Sigma</span><span class="p">(</span><span class="n">sigma_imp_list</span><span class="p">)</span>
+
+            <span class="c1"># use add_dc function to rotate to sumk block structure and subtract the DC</span>
+            <span class="n">sigma_sumk</span> <span class="o">=</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">add_dc</span><span class="p">()</span>
+
+            <span class="k">assert</span> <span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">sum_k</span><span class="o">.</span><span class="n">proj_mat</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">proj_mat</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span> <span class="s1">&#39;upfolding works only when proj_mat is the same for all kpoints (wannier mode)&#39;</span>
+
+            <span class="c1"># now upfold with proj_mat to band basis, this only works for the</span>
+            <span class="c1"># case where proj_mat is equal for all k points (wannier mode)</span>
+            <span class="n">sigma</span> <span class="o">=</span> <span class="n">Gf</span><span class="p">(</span><span class="n">mesh</span><span class="o">=</span><span class="n">sigma</span><span class="o">.</span><span class="n">mesh</span><span class="p">,</span> <span class="n">target_shape</span><span class="o">=</span><span class="p">[</span><span class="n">n_orb</span><span class="p">,</span> <span class="n">n_orb</span><span class="p">])</span>
+            <span class="k">for</span> <span class="n">ish</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">ar</span><span class="p">[</span><span class="s1">&#39;dft_input&#39;</span><span class="p">][</span><span class="s1">&#39;n_corr_shells&#39;</span><span class="p">]):</span>
+                <span class="n">sigma</span> <span class="o">+=</span> <span class="n">sum_k</span><span class="o">.</span><span class="n">upfold</span><span class="p">(</span><span class="n">ik</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">ish</span><span class="o">=</span><span class="n">ish</span><span class="p">,</span>
+                                      <span class="n">bname</span><span class="o">=</span><span class="n">spin</span><span class="p">,</span> <span class="n">gf_to_upfold</span><span class="o">=</span><span class="n">sigma_sumk</span><span class="p">[</span><span class="n">ish</span><span class="p">][</span><span class="n">spin</span><span class="p">],</span>
+                                      <span class="n">gf_inp</span><span class="o">=</span><span class="n">sigma</span><span class="p">)</span>
+
+        <span class="c1"># already subtracted</span>
+        <span class="n">dc</span> <span class="o">=</span> <span class="mf">0.0</span>
+
+    <span class="k">else</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Setting Sigma from memory&#39;</span><span class="p">)</span>
+
+        <span class="n">sigma</span> <span class="o">=</span> <span class="n">with_sigma</span><span class="p">[</span><span class="n">spin</span><span class="p">]</span>
+        <span class="n">dc</span> <span class="o">=</span> <span class="n">specs</span><span class="p">[</span><span class="s1">&#39;dc&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="n">spin</span><span class="p">][</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
+        <span class="n">mu_dmft</span> <span class="o">=</span> <span class="n">specs</span><span class="p">[</span><span class="s1">&#39;mu_dmft&#39;</span><span class="p">]</span>
+
+    <span class="n">SOC</span> <span class="o">=</span> <span class="p">(</span><span class="n">spin</span> <span class="o">==</span> <span class="s1">&#39;ud&#39;</span><span class="p">)</span>
+    <span class="n">w_mesh_dmft</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="n">sigma</span><span class="o">.</span><span class="n">mesh</span><span class="o">.</span><span class="n">w_min</span><span class="p">,</span> <span class="n">sigma</span><span class="o">.</span><span class="n">mesh</span><span class="o">.</span><span class="n">w_max</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">sigma</span><span class="o">.</span><span class="n">mesh</span><span class="p">))</span>
+    <span class="k">assert</span> <span class="n">sigma</span><span class="o">.</span><span class="n">target_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">n_orb</span><span class="p">,</span> <span class="sa">f</span><span class="s1">&#39;Number of Wannier orbitals: </span><span class="si">{</span><span class="n">n_orb</span><span class="si">}</span><span class="s1"> and self-energy target_shape </span><span class="si">{</span><span class="n">sigma</span><span class="o">.</span><span class="n">target_shape</span><span class="si">}</span><span class="s1"> does not match&#39;</span>
+
+    <span class="n">sigma_mat</span> <span class="o">=</span> <span class="n">sigma</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">real</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">n_orb</span><span class="p">)</span> <span class="o">*</span> <span class="n">dc</span> <span class="o">+</span> <span class="mi">1</span><span class="n">j</span> <span class="o">*</span> <span class="n">sigma</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">imag</span>
+
+    <span class="c1"># rotate sigma from orbital_order_dmft to orbital_order</span>
+    <span class="n">change_of_basis</span> <span class="o">=</span> <span class="n">change_basis</span><span class="p">(</span><span class="n">n_orb</span><span class="p">,</span> <span class="n">orbital_order</span><span class="p">,</span> <span class="n">orbital_order_dmft</span><span class="p">)</span>
+    <span class="n">sigma_mat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;ij, kjl -&gt; kil&#39;</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">inv</span><span class="p">(</span><span class="n">change_of_basis</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;ijk, kl -&gt; ijl&#39;</span><span class="p">,</span> <span class="n">sigma_mat</span><span class="p">,</span> <span class="n">change_of_basis</span><span class="p">))</span>
+
+    <span class="c1"># set up mesh</span>
+    <span class="k">if</span> <span class="s1">&#39;w_mesh&#39;</span> <span class="ow">in</span> <span class="n">specs</span><span class="p">:</span>
+        <span class="n">freq_dict</span> <span class="o">=</span> <span class="n">specs</span><span class="p">[</span><span class="s1">&#39;w_mesh&#39;</span><span class="p">]</span>
+        <span class="n">w_mesh</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">*</span><span class="n">freq_dict</span><span class="p">[</span><span class="s1">&#39;window&#39;</span><span class="p">],</span> <span class="n">freq_dict</span><span class="p">[</span><span class="s1">&#39;n_w&#39;</span><span class="p">])</span>
+        <span class="n">freq_dict</span><span class="o">.</span><span class="n">update</span><span class="p">({</span><span class="s1">&#39;w_mesh&#39;</span><span class="p">:</span> <span class="n">w_mesh</span><span class="p">})</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">w_mesh</span> <span class="o">=</span> <span class="n">w_mesh_dmft</span>
+        <span class="n">freq_dict</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;w_mesh&#39;</span><span class="p">:</span> <span class="n">w_mesh_dmft</span><span class="p">,</span> <span class="s1">&#39;n_w&#39;</span><span class="p">:</span> <span class="nb">len</span><span class="p">(</span><span class="n">sigma</span><span class="o">.</span><span class="n">mesh</span><span class="p">),</span> <span class="s1">&#39;window&#39;</span><span class="p">:</span> <span class="p">[</span><span class="n">sigma</span><span class="o">.</span><span class="n">mesh</span><span class="o">.</span><span class="n">w_min</span><span class="p">,</span> <span class="n">sigma</span><span class="o">.</span><span class="n">mesh</span><span class="o">.</span><span class="n">w_max</span><span class="p">]}</span>
+
+    <span class="n">sigma_interpolated</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">n_orb</span><span class="p">,</span> <span class="n">n_orb</span><span class="p">,</span> <span class="n">freq_dict</span><span class="p">[</span><span class="s1">&#39;n_w&#39;</span><span class="p">]),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">complex</span><span class="p">)</span>
+
+    <span class="c1"># interpolate sigma</span>
+    <span class="k">def</span> <span class="nf">interpolate_sigma</span><span class="p">(</span><span class="n">w_mesh</span><span class="p">,</span> <span class="n">w_mesh_dmft</span><span class="p">,</span> <span class="n">orb1</span><span class="p">,</span> <span class="n">orb2</span><span class="p">):</span> <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">w_mesh</span><span class="p">,</span> <span class="n">w_mesh_dmft</span><span class="p">,</span> <span class="n">sigma_mat</span><span class="p">[:,</span> <span class="n">orb1</span><span class="p">,</span> <span class="n">orb2</span><span class="p">])</span>
+
+    <span class="k">for</span> <span class="n">ct1</span><span class="p">,</span> <span class="n">ct2</span> <span class="ow">in</span> <span class="n">itertools</span><span class="o">.</span><span class="n">product</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">n_orb</span><span class="p">),</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_orb</span><span class="p">)):</span>
+        <span class="k">if</span> <span class="n">ct1</span> <span class="o">!=</span> <span class="n">ct2</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">SOC</span><span class="p">:</span>
+            <span class="k">continue</span>
+        <span class="n">sigma_interpolated</span><span class="p">[</span><span class="n">ct1</span><span class="p">,</span> <span class="n">ct2</span><span class="p">]</span> <span class="o">=</span> <span class="n">interpolate_sigma</span><span class="p">(</span><span class="n">w_mesh</span><span class="p">,</span> <span class="n">w_mesh_dmft</span><span class="p">,</span> <span class="n">ct1</span><span class="p">,</span> <span class="n">ct2</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">sigma_interpolated</span><span class="p">,</span> <span class="n">mu_dmft</span><span class="p">,</span> <span class="n">freq_dict</span>
+
+
+<span class="k">def</span> <span class="nf">sigma_FL</span><span class="p">(</span><span class="n">n_orb</span><span class="p">,</span> <span class="n">orbital_order</span><span class="p">,</span> <span class="n">Sigma_0</span><span class="p">,</span> <span class="n">Sigma_Z</span><span class="p">,</span> <span class="n">freq_dict</span><span class="p">,</span> <span class="n">eta</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">mu_dmft</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Setting Re[Sigma] with Fermi liquid approximation&#39;</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">Sigma_0</span><span class="p">)</span> <span class="ow">and</span> <span class="n">mu_dmft</span> <span class="o">==</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Sigma_0 does not preserve electron count. Please provide &quot;mu_dmft&quot;.&#39;</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">Sigma_0</span><span class="p">)</span> <span class="ow">and</span> <span class="n">mu_dmft</span> <span class="o">==</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">mu_dmft</span> <span class="o">=</span> <span class="mf">0.</span>
+
+    <span class="n">eta</span> <span class="o">=</span> <span class="n">eta</span> <span class="o">*</span> <span class="mi">1</span><span class="n">j</span>
+
+    <span class="c1"># set up mesh</span>
+    <span class="n">w_mesh</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">*</span><span class="n">freq_dict</span><span class="p">[</span><span class="s1">&#39;window&#39;</span><span class="p">],</span> <span class="n">freq_dict</span><span class="p">[</span><span class="s1">&#39;n_w&#39;</span><span class="p">])</span>
+    <span class="n">freq_dict</span><span class="o">.</span><span class="n">update</span><span class="p">({</span><span class="s1">&#39;w_mesh&#39;</span><span class="p">:</span> <span class="n">w_mesh</span><span class="p">})</span>
+
+    <span class="c1"># setting up sigma</span>
+    <span class="n">sigma_array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">n_orb</span><span class="p">,</span> <span class="n">n_orb</span><span class="p">,</span> <span class="n">freq_dict</span><span class="p">[</span><span class="s1">&#39;n_w&#39;</span><span class="p">]),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">complex</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">approximate_sigma</span><span class="p">(</span><span class="n">orb</span><span class="p">):</span> <span class="k">return</span> <span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="mi">1</span><span class="o">/</span><span class="n">Sigma_Z</span><span class="p">[</span><span class="n">orb</span><span class="p">])</span> <span class="o">*</span> <span class="n">freq_dict</span><span class="p">[</span><span class="s1">&#39;w_mesh&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="n">Sigma_0</span><span class="p">[</span><span class="n">orb</span><span class="p">]</span> <span class="o">-</span> <span class="n">mu_dmft</span>
+    <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">orb</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">orbital_order</span><span class="p">):</span>
+        <span class="n">sigma_array</span><span class="p">[</span><span class="n">ct</span><span class="p">,</span> <span class="n">ct</span><span class="p">]</span> <span class="o">=</span> <span class="n">approximate_sigma</span><span class="p">(</span><span class="n">ct</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="n">j</span> <span class="o">*</span> <span class="n">eta</span>
+
+    <span class="k">return</span> <span class="n">sigma_array</span><span class="p">,</span> <span class="n">freq_dict</span>
+
+
+<span class="k">def</span> <span class="nf">_calc_alatt</span><span class="p">(</span><span class="n">n_orb</span><span class="p">,</span> <span class="n">mu</span><span class="p">,</span> <span class="n">eta</span><span class="p">,</span> <span class="n">e_mat</span><span class="p">,</span> <span class="n">sigma</span><span class="p">,</span> <span class="n">qp_bands</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">e_vecs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                <span class="n">proj_nuk</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">trace</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">freq_dict</span><span class="p">):</span>
+    <span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">    calculate slice of lattice spectral function for given TB dispersion / e_mat and self-energy</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    n_orb : int</span>
+<span class="sd">          number of Wannier orbitals</span>
+<span class="sd">    proj_nuk : optinal, 2D numpy array (n_orb, n_k)</span>
+<span class="sd">          projections to be applied on A(k,w) in band basis. Only works when band_basis=True</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    alatt_k_w : numpy array, either (n_k, n_w) or if trace=False (n_k, n_w, n_orb)</span>
+<span class="sd">            Lattice Green&#39;s function on specified k-path / mesh</span>
+
+<span class="sd">    &#39;&#39;&#39;</span>
+
+    <span class="c1"># adjust to system size</span>
+    <span class="k">def</span> <span class="nf">upscale</span><span class="p">(</span><span class="n">quantity</span><span class="p">,</span> <span class="n">n_orb</span><span class="p">):</span> <span class="k">return</span> <span class="n">quantity</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">identity</span><span class="p">(</span><span class="n">n_orb</span><span class="p">)</span>
+    <span class="n">mu</span> <span class="o">=</span> <span class="n">upscale</span><span class="p">(</span><span class="n">mu</span><span class="p">,</span> <span class="n">n_orb</span><span class="p">)</span>
+    <span class="n">eta</span> <span class="o">=</span> <span class="n">upscale</span><span class="p">(</span><span class="n">eta</span><span class="p">,</span> <span class="n">n_orb</span><span class="p">)</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">e_vecs</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+        <span class="n">sigma_rot</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">sigma</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">complex</span><span class="p">)</span>
+
+    <span class="n">w_vec</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">upscale</span><span class="p">(</span><span class="n">freq_dict</span><span class="p">[</span><span class="s1">&#39;w_mesh&#39;</span><span class="p">][</span><span class="n">w</span><span class="p">],</span> <span class="n">n_orb</span><span class="p">)</span> <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">freq_dict</span><span class="p">[</span><span class="s1">&#39;n_w&#39;</span><span class="p">])])</span>
+    <span class="n">n_k</span> <span class="o">=</span> <span class="n">e_mat</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
+
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">qp_bands</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">trace</span><span class="p">:</span>
+            <span class="n">alatt_k_w</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">n_k</span><span class="p">,</span> <span class="n">freq_dict</span><span class="p">[</span><span class="s1">&#39;n_w&#39;</span><span class="p">]))</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">alatt_k_w</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">n_k</span><span class="p">,</span> <span class="n">freq_dict</span><span class="p">[</span><span class="s1">&#39;n_w&#39;</span><span class="p">],</span> <span class="n">n_orb</span><span class="p">))</span>
+
+        <span class="k">def</span> <span class="nf">invert_and_trace</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">eta</span><span class="p">,</span> <span class="n">mu</span><span class="p">,</span> <span class="n">e_mat</span><span class="p">,</span> <span class="n">sigma</span><span class="p">,</span> <span class="n">trace</span><span class="p">,</span> <span class="n">proj</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+            <span class="c1"># inversion is automatically vectorized over first axis of 3D array (omega first index now)</span>
+            <span class="n">Glatt</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">inv</span><span class="p">(</span><span class="n">w</span> <span class="o">+</span> <span class="n">eta</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="o">...</span><span class="p">]</span> <span class="o">+</span> <span class="n">mu</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="o">...</span><span class="p">]</span> <span class="o">-</span> <span class="n">e_mat</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="o">...</span><span class="p">]</span> <span class="o">-</span> <span class="n">sigma</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
+            <span class="c1"># TODO: fix this is only correct for diagonal Sigma</span>
+            <span class="n">A_w_nu</span> <span class="o">=</span> <span class="o">-</span><span class="mf">1.0</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">diagonal</span><span class="p">(</span><span class="n">Glatt</span><span class="p">,</span> <span class="n">axis1</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">axis2</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">imag</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">proj</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+                <span class="n">A_w_nu</span> <span class="o">=</span> <span class="n">A_w_nu</span> <span class="o">*</span> <span class="n">proj</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span>
+            <span class="k">if</span> <span class="n">trace</span><span class="p">:</span>
+                <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">A_w_nu</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">return</span> <span class="n">A_w_nu</span>
+
+        <span class="k">for</span> <span class="n">ik</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_k</span><span class="p">):</span>
+            <span class="c1"># if evecs are given transform sigma into band basis</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">e_vecs</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+                <span class="n">sigma_rot</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;ij,jkw-&gt;ikw&#39;</span><span class="p">,</span> <span class="n">e_vecs</span><span class="p">[:,</span> <span class="p">:,</span> <span class="n">ik</span><span class="p">]</span><span class="o">.</span><span class="n">conjugate</span><span class="p">()</span><span class="o">.</span><span class="n">transpose</span><span class="p">(),</span> <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;ijw,jk-&gt;ikw&#39;</span><span class="p">,</span> <span class="n">sigma</span><span class="p">,</span> <span class="n">e_vecs</span><span class="p">[:,</span> <span class="p">:,</span> <span class="n">ik</span><span class="p">]))</span>
+                <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">proj_nuk</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+                    <span class="n">alatt_k_w</span><span class="p">[</span><span class="n">ik</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">invert_and_trace</span><span class="p">(</span><span class="n">w_vec</span><span class="p">,</span> <span class="n">eta</span><span class="p">,</span> <span class="n">mu</span><span class="p">,</span> <span class="n">e_mat</span><span class="p">[:,</span> <span class="p">:,</span> <span class="n">ik</span><span class="p">],</span> <span class="n">sigma_rot</span><span class="p">,</span> <span class="n">trace</span><span class="p">,</span> <span class="n">proj_nuk</span><span class="p">[:,</span> <span class="n">ik</span><span class="p">])</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">alatt_k_w</span><span class="p">[</span><span class="n">ik</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">invert_and_trace</span><span class="p">(</span><span class="n">w_vec</span><span class="p">,</span> <span class="n">eta</span><span class="p">,</span> <span class="n">mu</span><span class="p">,</span> <span class="n">e_mat</span><span class="p">[:,</span> <span class="p">:,</span> <span class="n">ik</span><span class="p">],</span> <span class="n">sigma_rot</span><span class="p">,</span> <span class="n">trace</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">alatt_k_w</span><span class="p">[</span><span class="n">ik</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">invert_and_trace</span><span class="p">(</span><span class="n">w_vec</span><span class="p">,</span> <span class="n">eta</span><span class="p">,</span> <span class="n">mu</span><span class="p">,</span> <span class="n">e_mat</span><span class="p">[:,</span> <span class="p">:,</span> <span class="n">ik</span><span class="p">],</span> <span class="n">sigma</span><span class="p">,</span> <span class="n">trace</span><span class="p">)</span>
+
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">alatt_k_w</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">n_k</span><span class="p">,</span> <span class="n">n_orb</span><span class="p">))</span>
+        <span class="n">kslice</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">freq_dict</span><span class="p">[</span><span class="s1">&#39;n_w&#39;</span><span class="p">],</span> <span class="n">n_orb</span><span class="p">))</span>
+        <span class="k">def</span> <span class="nf">kslice_interp</span><span class="p">(</span><span class="n">orb</span><span class="p">):</span> <span class="k">return</span> <span class="n">interp1d</span><span class="p">(</span><span class="n">freq_dict</span><span class="p">[</span><span class="s1">&#39;w_mesh&#39;</span><span class="p">],</span> <span class="n">kslice</span><span class="p">[:,</span> <span class="n">orb</span><span class="p">])</span>
+
+        <span class="k">for</span> <span class="n">ik</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_k</span><span class="p">):</span>
+            <span class="k">for</span> <span class="n">iw</span><span class="p">,</span> <span class="n">w</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">freq_dict</span><span class="p">[</span><span class="s1">&#39;w_mesh&#39;</span><span class="p">]):</span>
+                <span class="n">np</span><span class="o">.</span><span class="n">fill_diagonal</span><span class="p">(</span><span class="n">sigma</span><span class="p">[:,</span> <span class="p">:,</span> <span class="n">iw</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">diag</span><span class="p">(</span><span class="n">sigma</span><span class="p">[:,</span> <span class="p">:,</span> <span class="n">iw</span><span class="p">])</span><span class="o">.</span><span class="n">real</span><span class="p">)</span>
+                <span class="c1">#sigma[:,:,iw] = sigma[:,:,iw].real</span>
+                <span class="n">kslice</span><span class="p">[</span><span class="n">iw</span><span class="p">],</span> <span class="n">_</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">eigh</span><span class="p">(</span><span class="n">upscale</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">n_orb</span><span class="p">)</span> <span class="o">+</span> <span class="n">eta</span> <span class="o">+</span> <span class="n">mu</span> <span class="o">-</span> <span class="n">e_mat</span><span class="p">[:,</span> <span class="p">:,</span> <span class="n">ik</span><span class="p">]</span> <span class="o">-</span> <span class="n">sigma</span><span class="p">[:,</span> <span class="p">:,</span> <span class="n">iw</span><span class="p">])</span>
+
+            <span class="k">for</span> <span class="n">orb</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_orb</span><span class="p">):</span>
+                <span class="n">w_min</span><span class="p">,</span> <span class="n">w_max</span> <span class="o">=</span> <span class="n">freq_dict</span><span class="p">[</span><span class="s1">&#39;window&#39;</span><span class="p">]</span>
+                <span class="k">try</span><span class="p">:</span>
+                    <span class="n">x0</span> <span class="o">=</span> <span class="n">brentq</span><span class="p">(</span><span class="n">kslice_interp</span><span class="p">(</span><span class="n">orb</span><span class="p">),</span> <span class="n">w_min</span><span class="p">,</span> <span class="n">w_max</span><span class="p">)</span>
+                    <span class="n">w_bin</span> <span class="o">=</span> <span class="nb">int</span><span class="p">((</span><span class="n">x0</span> <span class="o">-</span> <span class="n">w_min</span><span class="p">)</span> <span class="o">/</span> <span class="p">((</span><span class="n">w_max</span> <span class="o">-</span> <span class="n">w_min</span><span class="p">)</span> <span class="o">/</span> <span class="n">freq_dict</span><span class="p">[</span><span class="s1">&#39;n_w&#39;</span><span class="p">]))</span>
+                    <span class="n">alatt_k_w</span><span class="p">[</span><span class="n">ik</span><span class="p">,</span> <span class="n">orb</span><span class="p">]</span> <span class="o">=</span> <span class="n">freq_dict</span><span class="p">[</span><span class="s1">&#39;w_mesh&#39;</span><span class="p">][</span><span class="n">w_bin</span><span class="p">]</span>
+                <span class="k">except</span> <span class="ne">ValueError</span><span class="p">:</span>
+                    <span class="k">pass</span>
+
+    <span class="k">return</span> <span class="n">alatt_k_w</span>
+
+
+<span class="k">def</span> <span class="nf">_calc_kslice</span><span class="p">(</span><span class="n">n_orb</span><span class="p">,</span> <span class="n">mu</span><span class="p">,</span> <span class="n">eta</span><span class="p">,</span> <span class="n">e_mat</span><span class="p">,</span> <span class="n">sigma</span><span class="p">,</span> <span class="n">qp_bands</span><span class="p">,</span> <span class="n">e_vecs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">proj_nuk</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">freq_dict</span><span class="p">):</span>
+    <span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">    calculate lattice spectral function for given TB dispersion / e_mat and self-energy</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    n_orb : int</span>
+<span class="sd">          number of Wannier orbitals</span>
+<span class="sd">    proj_nuk : optinal, 2D numpy array (n_orb, n_k)</span>
+<span class="sd">          projections to be applied on A(k,w) in band basis. Only works when band_basis=True</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    alatt_k_w : numpy array, either (n_k, n_w) or if trace=False (n_k, n_w, n_orb)</span>
+<span class="sd">            Lattice Green&#39;s function on specified k-path / mesh</span>
+
+<span class="sd">    &#39;&#39;&#39;</span>
+
+    <span class="c1"># adjust to system size</span>
+    <span class="k">def</span> <span class="nf">upscale</span><span class="p">(</span><span class="n">quantity</span><span class="p">,</span> <span class="n">n_orb</span><span class="p">):</span> <span class="k">return</span> <span class="n">quantity</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">identity</span><span class="p">(</span><span class="n">n_orb</span><span class="p">)</span>
+    <span class="n">mu</span> <span class="o">=</span> <span class="n">upscale</span><span class="p">(</span><span class="n">mu</span><span class="p">,</span> <span class="n">n_orb</span><span class="p">)</span>
+    <span class="n">eta</span> <span class="o">=</span> <span class="n">upscale</span><span class="p">(</span><span class="n">eta</span><span class="p">,</span> <span class="n">n_orb</span><span class="p">)</span>
+
+    <span class="n">iw0</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sign</span><span class="p">(</span><span class="n">freq_dict</span><span class="p">[</span><span class="s1">&#39;w_mesh&#39;</span><span class="p">])</span> <span class="o">==</span> <span class="kc">True</span><span class="p">)[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">-</span><span class="mi">1</span>
+    <span class="n">print_matrix</span><span class="p">(</span><span class="n">sigma</span><span class="p">[:,</span> <span class="p">:,</span> <span class="n">iw0</span><span class="p">],</span> <span class="n">n_orb</span><span class="p">,</span> <span class="s1">&#39;Zero-frequency Sigma&#39;</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">e_vecs</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+        <span class="n">sigma_rot</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">sigma</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">complex</span><span class="p">)</span>
+
+    <span class="n">n_kx</span><span class="p">,</span> <span class="n">n_ky</span> <span class="o">=</span> <span class="n">e_mat</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">:</span><span class="mi">4</span><span class="p">]</span>
+
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">qp_bands</span><span class="p">:</span>
+        <span class="n">alatt_k_w</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">n_kx</span><span class="p">,</span> <span class="n">n_ky</span><span class="p">))</span>
+
+        <span class="k">def</span> <span class="nf">invert_and_trace</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">eta</span><span class="p">,</span> <span class="n">mu</span><span class="p">,</span> <span class="n">e_mat</span><span class="p">,</span> <span class="n">sigma</span><span class="p">,</span> <span class="n">proj</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+            <span class="c1"># inversion is automatically vectorized over first axis of 3D array (omega first index now)</span>
+            <span class="n">Glatt</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">inv</span><span class="p">(</span><span class="n">w</span> <span class="o">+</span> <span class="n">eta</span> <span class="o">+</span> <span class="n">mu</span> <span class="o">-</span> <span class="n">e_mat</span> <span class="o">-</span> <span class="n">sigma</span><span class="p">)</span>
+            <span class="n">A_nu</span> <span class="o">=</span> <span class="o">-</span><span class="mf">1.0</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">diagonal</span><span class="p">(</span><span class="n">Glatt</span><span class="p">)</span><span class="o">.</span><span class="n">imag</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">proj</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+                <span class="n">A_nu</span> <span class="o">=</span> <span class="n">A_nu</span> <span class="o">*</span> <span class="n">proj</span>
+            <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">A_nu</span><span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">ikx</span><span class="p">,</span> <span class="n">iky</span> <span class="ow">in</span> <span class="n">itertools</span><span class="o">.</span><span class="n">product</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">n_kx</span><span class="p">),</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_ky</span><span class="p">)):</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">e_vecs</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+                <span class="n">sigma_rot</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;ij,jk-&gt;ik&#39;</span><span class="p">,</span>
+                                      <span class="n">e_vecs</span><span class="p">[:,</span> <span class="p">:,</span> <span class="n">ikx</span><span class="p">,</span> <span class="n">iky</span><span class="p">]</span><span class="o">.</span><span class="n">conjugate</span><span class="p">()</span><span class="o">.</span><span class="n">transpose</span><span class="p">(),</span>
+                                      <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;ij,jk-&gt;ik&#39;</span><span class="p">,</span> <span class="n">sigma</span><span class="p">[:,</span> <span class="p">:,</span> <span class="n">iw0</span><span class="p">],</span> <span class="n">e_vecs</span><span class="p">[:,</span> <span class="p">:,</span> <span class="n">ikx</span><span class="p">,</span> <span class="n">iky</span><span class="p">]))</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">sigma_rot</span> <span class="o">=</span> <span class="n">sigma</span><span class="p">[:,</span> <span class="p">:,</span> <span class="n">iw0</span><span class="p">]</span>
+
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">proj_nuk</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+                <span class="n">alatt_k_w</span><span class="p">[</span><span class="n">ikx</span><span class="p">,</span> <span class="n">iky</span><span class="p">]</span> <span class="o">=</span> <span class="n">invert_and_trace</span><span class="p">(</span><span class="n">upscale</span><span class="p">(</span><span class="n">freq_dict</span><span class="p">[</span><span class="s1">&#39;w_mesh&#39;</span><span class="p">][</span><span class="n">iw0</span><span class="p">],</span> <span class="n">n_orb</span><span class="p">),</span> <span class="n">eta</span><span class="p">,</span> <span class="n">mu</span><span class="p">,</span>
+                                                       <span class="n">e_mat</span><span class="p">[:,</span> <span class="p">:,</span> <span class="n">ikx</span><span class="p">,</span> <span class="n">iky</span><span class="p">],</span> <span class="n">sigma_rot</span><span class="p">,</span> <span class="n">proj_nuk</span><span class="p">[:,</span> <span class="n">ikx</span><span class="p">,</span> <span class="n">iky</span><span class="p">])</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">alatt_k_w</span><span class="p">[</span><span class="n">ikx</span><span class="p">,</span> <span class="n">iky</span><span class="p">]</span> <span class="o">=</span> <span class="n">invert_and_trace</span><span class="p">(</span><span class="n">upscale</span><span class="p">(</span><span class="n">freq_dict</span><span class="p">[</span><span class="s1">&#39;w_mesh&#39;</span><span class="p">][</span><span class="n">iw0</span><span class="p">],</span> <span class="n">n_orb</span><span class="p">),</span> <span class="n">eta</span><span class="p">,</span> <span class="n">mu</span><span class="p">,</span> <span class="n">e_mat</span><span class="p">[:,</span> <span class="p">:,</span> <span class="n">ikx</span><span class="p">,</span> <span class="n">iky</span><span class="p">],</span> <span class="n">sigma_rot</span><span class="p">)</span>
+
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">assert</span> <span class="n">n_kx</span> <span class="o">==</span> <span class="n">n_ky</span><span class="p">,</span> <span class="s1">&#39;Not implemented for N_kx != N_ky&#39;</span>
+
+        <span class="k">def</span> <span class="nf">search_for_extrema</span><span class="p">(</span><span class="n">data</span><span class="p">):</span>
+            <span class="c1"># return None for no extrema, [] if ends of interval are the only extrema,</span>
+            <span class="c1"># list of indices if local extrema are present</span>
+            <span class="n">answer</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">data</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">or</span> <span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">data</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">answer</span><span class="p">:</span>
+                <span class="k">return</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">roots</span> <span class="o">=</span> <span class="p">[]</span>
+                <span class="n">roots</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">argrelextrema</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">greater</span><span class="p">)[</span><span class="mi">0</span><span class="p">]))</span>
+                <span class="n">roots</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">argrelextrema</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">less</span><span class="p">)[</span><span class="mi">0</span><span class="p">]))</span>
+                <span class="n">roots</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">([</span><span class="n">item</span> <span class="k">for</span> <span class="n">sublist</span> <span class="ow">in</span> <span class="n">roots</span> <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="n">sublist</span><span class="p">])</span>
+            <span class="k">return</span> <span class="n">roots</span>
+
+        <span class="n">alatt_k_w</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">n_kx</span><span class="p">,</span> <span class="n">n_ky</span><span class="p">,</span> <span class="n">n_orb</span><span class="p">))</span>
+        <span class="c1"># go through grid horizontally, then vertically</span>
+        <span class="k">for</span> <span class="n">it</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">):</span>
+            <span class="n">kslice</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">n_kx</span><span class="p">,</span> <span class="n">n_ky</span><span class="p">,</span> <span class="n">n_orb</span><span class="p">))</span>
+
+            <span class="k">for</span> <span class="n">ik1</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_kx</span><span class="p">):</span>
+                <span class="n">e_temp</span> <span class="o">=</span> <span class="n">e_mat</span><span class="p">[:,</span> <span class="p">:,</span> <span class="p">:,</span> <span class="n">ik1</span><span class="p">]</span> <span class="k">if</span> <span class="n">it</span> <span class="o">==</span> <span class="mi">0</span> <span class="k">else</span> <span class="n">e_mat</span><span class="p">[:,</span> <span class="p">:,</span> <span class="n">ik1</span><span class="p">,</span> <span class="p">:]</span>
+                <span class="k">for</span> <span class="n">ik2</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_kx</span><span class="p">):</span>
+                    <span class="n">e_val</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">eigh</span><span class="p">(</span><span class="n">eta</span> <span class="o">+</span> <span class="n">mu</span> <span class="o">-</span> <span class="n">e_temp</span><span class="p">[:,</span> <span class="p">:,</span> <span class="n">ik2</span><span class="p">]</span> <span class="o">-</span> <span class="n">sigma</span><span class="p">[:,</span> <span class="p">:,</span> <span class="n">iw0</span><span class="p">])</span>
+                    <span class="n">k1</span><span class="p">,</span> <span class="n">k2</span> <span class="o">=</span> <span class="p">[</span><span class="n">ik2</span><span class="p">,</span> <span class="n">ik1</span><span class="p">]</span> <span class="k">if</span> <span class="n">it</span> <span class="o">==</span> <span class="mi">0</span> <span class="k">else</span> <span class="p">[</span><span class="n">ik1</span><span class="p">,</span> <span class="n">ik2</span><span class="p">]</span>
+                    <span class="n">kslice</span><span class="p">[</span><span class="n">k1</span><span class="p">,</span> <span class="n">k2</span><span class="p">]</span> <span class="o">=</span> <span class="n">e_val</span>
+
+                <span class="k">for</span> <span class="n">orb</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_orb</span><span class="p">):</span>
+                    <span class="n">temp_kslice</span> <span class="o">=</span> <span class="n">kslice</span><span class="p">[:,</span><span class="n">ik1</span><span class="p">,</span><span class="n">orb</span><span class="p">]</span> <span class="k">if</span> <span class="n">it</span> <span class="o">==</span> <span class="mi">0</span> <span class="k">else</span> <span class="n">kslice</span><span class="p">[</span><span class="n">ik1</span><span class="p">,:,</span><span class="n">orb</span><span class="p">]</span>
+                    <span class="n">roots</span> <span class="o">=</span> <span class="n">search_for_extrema</span><span class="p">(</span><span class="n">temp_kslice</span><span class="p">)</span>
+                    <span class="c1"># iterate through sections between extrema</span>
+                    <span class="k">if</span> <span class="n">roots</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                        <span class="n">idx_1</span> <span class="o">=</span> <span class="mi">0</span>
+                        <span class="k">for</span> <span class="n">root_ct</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">roots</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
+                            <span class="n">idx_2</span> <span class="o">=</span> <span class="n">roots</span><span class="p">[</span><span class="n">root_ct</span><span class="p">]</span> <span class="k">if</span> <span class="n">root_ct</span> <span class="o">&lt;</span> <span class="nb">len</span><span class="p">(</span><span class="n">roots</span><span class="p">)</span> <span class="k">else</span> <span class="n">n_kx</span>
+                            <span class="n">root_section</span> <span class="o">=</span> <span class="n">temp_kslice</span><span class="p">[</span><span class="n">idx_1</span><span class="p">:</span><span class="n">idx_2</span><span class="o">+</span><span class="mi">1</span><span class="p">]</span>
+                            <span class="k">try</span><span class="p">:</span>
+                                <span class="n">x0</span> <span class="o">=</span> <span class="n">brentq</span><span class="p">(</span><span class="n">interp1d</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="n">idx_1</span><span class="p">,</span> <span class="n">idx_2</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">root_section</span><span class="p">)),</span> <span class="n">root_section</span><span class="p">),</span> <span class="n">idx_1</span><span class="p">,</span> <span class="n">idx_2</span><span class="p">)</span>
+                                <span class="n">k1</span><span class="p">,</span> <span class="n">k2</span> <span class="o">=</span> <span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">x0</span><span class="p">)),</span> <span class="n">ik1</span><span class="p">]</span> <span class="k">if</span> <span class="n">it</span> <span class="o">==</span> <span class="mi">0</span> <span class="k">else</span> <span class="p">[</span><span class="n">ik1</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">x0</span><span class="p">))]</span>
+                                <span class="n">alatt_k_w</span><span class="p">[</span><span class="n">k1</span><span class="p">,</span> <span class="n">k2</span><span class="p">,</span> <span class="n">orb</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">1</span>
+                            <span class="k">except</span><span class="p">(</span><span class="ne">ValueError</span><span class="p">):</span>
+                                <span class="k">pass</span>
+                            <span class="n">idx_1</span> <span class="o">=</span> <span class="n">idx_2</span>
+
+        <span class="n">alatt_k_w</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">alatt_k_w</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">)]</span> <span class="o">=</span> <span class="mi">1</span>
+
+    <span class="k">return</span> <span class="n">alatt_k_w</span>
+
+
+<div class="viewcode-block" id="get_tb_bands"><a class="viewcode-back" href="../../_ref/postprocessing.plot_correlated_bands.html#postprocessing.plot_correlated_bands.get_tb_bands">[docs]</a><span class="k">def</span> <span class="nf">get_tb_bands</span><span class="p">(</span><span class="n">e_mat</span><span class="p">,</span> <span class="n">proj_on_orb</span><span class="o">=</span><span class="p">[</span><span class="kc">None</span><span class="p">],</span> <span class="o">**</span><span class="n">specs</span><span class="p">):</span>
+    <span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">    calculate eigenvalues and eigenvectors for given list of e_mat on kmesh</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    e_mat : numpy array of shape (n_orb, n_orb, nk) or (n_orb, n_orb, nk, nk)</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    e_val : numpy array of shape (n_orb, n_orb, nk) or (n_orb, n_orb, nk, nk)</span>
+<span class="sd">        eigenvalues as matrix</span>
+<span class="sd">    e_vec : numpy array of shape (n_orb, n_orb, nk) or (n_orb, n_orb, nk, nk)</span>
+<span class="sd">        eigenvectors as matrix</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+
+    <span class="n">e_val</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">e_mat</span><span class="o">.</span><span class="n">shape</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">complex</span><span class="p">)</span>
+    <span class="n">e_vec</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">e_mat</span><span class="o">.</span><span class="n">shape</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">complex</span><span class="p">)</span>
+    <span class="n">n_orb</span> <span class="o">=</span> <span class="n">e_mat</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+    <span class="k">for</span> <span class="n">ikx</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">e_mat</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">]):</span>
+        <span class="c1"># if we have a 2d kmesh e_mat is dim=4</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">e_mat</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">4</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">iky</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">e_mat</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">3</span><span class="p">]):</span>
+                <span class="n">e_val</span><span class="p">[</span><span class="nb">range</span><span class="p">(</span><span class="n">n_orb</span><span class="p">),</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_orb</span><span class="p">),</span> <span class="n">ikx</span><span class="p">,</span> <span class="n">iky</span><span class="p">],</span> <span class="n">e_vec</span><span class="p">[:,</span> <span class="p">:,</span> <span class="n">ikx</span><span class="p">,</span> <span class="n">iky</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">eigh</span><span class="p">(</span><span class="n">e_mat</span><span class="p">[:,</span> <span class="p">:,</span> <span class="n">ikx</span><span class="p">,</span> <span class="n">iky</span><span class="p">])</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">e_val</span><span class="p">[</span><span class="nb">range</span><span class="p">(</span><span class="n">n_orb</span><span class="p">),</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_orb</span><span class="p">),</span> <span class="n">ikx</span><span class="p">],</span> <span class="n">e_vec</span><span class="p">[:,</span> <span class="p">:,</span> <span class="n">ikx</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">eigh</span><span class="p">(</span><span class="n">e_mat</span><span class="p">[:,</span> <span class="p">:,</span> <span class="n">ikx</span><span class="p">])</span>
+
+    <span class="k">if</span> <span class="n">proj_on_orb</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;calculating projection on orbitals </span><span class="si">{</span><span class="n">proj_on_orb</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+        <span class="n">total_proj</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">shape</span><span class="p">(</span><span class="n">e_vec</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
+        <span class="k">for</span> <span class="n">band</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_orb</span><span class="p">):</span>
+            <span class="k">for</span> <span class="n">orb</span> <span class="ow">in</span> <span class="n">proj_on_orb</span><span class="p">:</span>
+                <span class="n">total_proj</span><span class="p">[</span><span class="n">band</span><span class="p">]</span> <span class="o">+=</span> <span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">e_vec</span><span class="p">[</span><span class="n">orb</span><span class="p">,</span> <span class="n">band</span><span class="p">]</span> <span class="o">*</span> <span class="n">e_vec</span><span class="p">[</span><span class="n">orb</span><span class="p">,</span> <span class="n">band</span><span class="p">]</span><span class="o">.</span><span class="n">conjugate</span><span class="p">())</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">total_proj</span> <span class="o">=</span> <span class="kc">None</span>
+
+    <span class="k">return</span> <span class="n">e_val</span><span class="p">,</span> <span class="n">e_vec</span><span class="p">,</span> <span class="n">total_proj</span></div>
+
+
+<span class="k">def</span> <span class="nf">get_tb_kslice</span><span class="p">(</span><span class="n">tb</span><span class="p">,</span> <span class="n">mu_tb</span><span class="p">,</span> <span class="o">**</span><span class="n">specs</span><span class="p">):</span>
+
+    <span class="n">w90_paths</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">section</span><span class="p">:</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">specs</span><span class="p">[</span><span class="n">section</span><span class="p">[</span><span class="mi">0</span><span class="p">]]),</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">specs</span><span class="p">[</span><span class="n">section</span><span class="p">[</span><span class="mi">1</span><span class="p">]])),</span> <span class="n">specs</span><span class="p">[</span><span class="s1">&#39;bands_path&#39;</span><span class="p">]))</span>
+    <span class="n">upper_left</span> <span class="o">=</span> <span class="n">w90_paths</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">lower_right</span> <span class="o">=</span> <span class="n">w90_paths</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+    <span class="n">origin</span> <span class="o">=</span> <span class="n">w90_paths</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
+    <span class="k">assert</span> <span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">origin</span><span class="p">,</span> <span class="n">w90_paths</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]),</span> <span class="s1">&#39;&quot;bands_path&quot; coordinates for origin of Fermi surface needs to be consistent&#39;</span>
+    <span class="k">if</span> <span class="s1">&#39;kz&#39;</span> <span class="ow">in</span> <span class="n">specs</span> <span class="ow">and</span> <span class="n">specs</span><span class="p">[</span><span class="s1">&#39;kz&#39;</span><span class="p">]</span> <span class="o">!=</span> <span class="mf">0.</span><span class="p">:</span>
+        <span class="k">assert</span> <span class="s1">&#39;Z&#39;</span> <span class="ow">in</span> <span class="n">specs</span><span class="p">,</span> <span class="s1">&#39;Please provide Z point coordinate in tb_data_dict as input coordinate&#39;</span>
+        <span class="n">Z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">specs</span><span class="p">[</span><span class="s1">&#39;Z&#39;</span><span class="p">])</span>
+        <span class="n">kz</span> <span class="o">=</span> <span class="n">specs</span><span class="p">[</span><span class="s1">&#39;kz&#39;</span><span class="p">]</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">kz</span> <span class="o">=</span> <span class="mf">0.</span>
+        <span class="n">Z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">3</span><span class="p">))</span>
+
+    <span class="c1"># calculate FS at the mu_tb value</span>
+    <span class="n">FS_kx_ky</span><span class="p">,</span> <span class="n">band_char</span> <span class="o">=</span> <span class="n">get_kx_ky_FS</span><span class="p">(</span><span class="n">lower_right</span><span class="p">,</span> <span class="n">upper_left</span><span class="p">,</span> <span class="n">origin</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">tb</span><span class="p">,</span> <span class="n">N_kxy</span><span class="o">=</span><span class="n">specs</span><span class="p">[</span><span class="s1">&#39;n_k&#39;</span><span class="p">],</span> <span class="n">kz</span><span class="o">=</span><span class="n">kz</span><span class="p">,</span> <span class="n">fermi</span><span class="o">=</span><span class="n">mu_tb</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">FS_kx_ky</span><span class="p">,</span> <span class="n">band_char</span>
+
+
+<span class="k">def</span> <span class="nf">_fract_ind_to_val</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">ind</span><span class="p">):</span>
+    <span class="n">ind</span><span class="p">[</span><span class="n">ind</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">-</span><span class="mi">1</span><span class="o">-</span><span class="mf">1e-6</span>
+    <span class="n">int_ind</span> <span class="o">=</span> <span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">indi</span><span class="p">)</span> <span class="k">for</span> <span class="n">indi</span> <span class="ow">in</span> <span class="n">ind</span><span class="p">]</span>
+    <span class="n">int_ind_p1</span> <span class="o">=</span> <span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">indi</span><span class="p">)</span><span class="o">+</span><span class="mi">1</span> <span class="k">for</span> <span class="n">indi</span> <span class="ow">in</span> <span class="n">ind</span><span class="p">]</span>
+    <span class="k">return</span> <span class="n">x</span><span class="p">[</span><span class="n">int_ind</span><span class="p">]</span> <span class="o">+</span> <span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="n">int_ind_p1</span><span class="p">]</span> <span class="o">-</span> <span class="n">x</span><span class="p">[</span><span class="n">int_ind</span><span class="p">])</span><span class="o">*</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ind</span><span class="p">)</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">int_ind</span><span class="p">))</span>
+
+
+<span class="k">def</span> <span class="nf">get_kx_ky_FS</span><span class="p">(</span><span class="n">lower_right</span><span class="p">,</span> <span class="n">upper_left</span><span class="p">,</span> <span class="n">origin</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">tb</span><span class="p">,</span> <span class="n">select</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">N_kxy</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">kz</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">fermi</span><span class="o">=</span><span class="mf">0.0</span><span class="p">):</span>
+
+    <span class="c1"># create mesh</span>
+    <span class="n">kx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">N_kxy</span><span class="p">)</span>
+    <span class="n">ky</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">N_kxy</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">select</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">select</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">tb</span><span class="o">.</span><span class="n">n_orbitals</span><span class="p">))</span>
+
+    <span class="c1"># go in horizontal arrays from bottom to top</span>
+    <span class="n">E_FS</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">tb</span><span class="o">.</span><span class="n">n_orbitals</span><span class="p">,</span> <span class="n">N_kxy</span><span class="p">,</span> <span class="n">N_kxy</span><span class="p">))</span>
+    <span class="k">for</span> <span class="n">kyi</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N_kxy</span><span class="p">):</span>
+        <span class="n">path_FS</span> <span class="o">=</span> <span class="p">[((</span><span class="n">upper_left</span> <span class="o">-</span> <span class="n">origin</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">N_kxy</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">*</span><span class="n">kyi</span><span class="o">+</span><span class="n">kz</span><span class="o">*</span><span class="n">Z</span><span class="o">+</span><span class="n">origin</span><span class="p">,</span> <span class="n">origin</span><span class="o">+</span><span class="p">(</span><span class="n">lower_right</span><span class="o">-</span><span class="n">origin</span><span class="p">)</span><span class="o">+</span><span class="p">(</span><span class="n">upper_left</span><span class="o">-</span><span class="n">origin</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">N_kxy</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">*</span><span class="n">kyi</span><span class="o">+</span><span class="n">kz</span><span class="o">*</span><span class="n">Z</span><span class="p">)]</span>
+        <span class="n">k_vec</span><span class="p">,</span> <span class="n">dst</span><span class="p">,</span> <span class="n">tks</span> <span class="o">=</span> <span class="n">k_space_path</span><span class="p">(</span><span class="n">path_FS</span><span class="p">,</span> <span class="n">num</span><span class="o">=</span><span class="n">N_kxy</span><span class="p">)</span>
+        <span class="n">E_FS</span><span class="p">[:,</span> <span class="p">:,</span> <span class="n">kyi</span><span class="p">]</span> <span class="o">=</span> <span class="n">tb</span><span class="o">.</span><span class="n">dispersion</span><span class="p">(</span><span class="n">k_vec</span><span class="p">)</span><span class="o">.</span><span class="n">transpose</span><span class="p">()</span> <span class="o">-</span> <span class="n">fermi</span>
+
+    <span class="n">contours</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="n">FS_kx_ky</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="n">FS_kx_ky_prim</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="n">band_char</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="c1"># contour for each sheet</span>
+    <span class="k">for</span> <span class="n">sheet</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">tb</span><span class="o">.</span><span class="n">n_orbitals</span><span class="p">):</span>
+        <span class="n">contours</span><span class="p">[</span><span class="n">sheet</span><span class="p">]</span> <span class="o">=</span> <span class="n">skimage</span><span class="o">.</span><span class="n">measure</span><span class="o">.</span><span class="n">find_contours</span><span class="p">(</span><span class="n">E_FS</span><span class="p">[</span><span class="n">sheet</span><span class="p">,</span> <span class="p">:,</span> <span class="p">:],</span> <span class="mf">0.0</span><span class="p">)</span>
+
+    <span class="n">sheet_ct</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="k">for</span> <span class="n">sheet</span> <span class="ow">in</span> <span class="n">contours</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+        <span class="k">for</span> <span class="n">sec_per_sheet</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">contours</span><span class="p">[</span><span class="n">sheet</span><span class="p">])):</span>
+            <span class="c1"># once on 2D cubic mesh</span>
+            <span class="n">FS_kx_ky</span><span class="p">[</span><span class="n">sheet_ct</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">([</span><span class="n">_fract_ind_to_val</span><span class="p">(</span><span class="n">kx</span><span class="p">,</span> <span class="n">contours</span><span class="p">[</span><span class="n">sheet</span><span class="p">][</span><span class="n">sec_per_sheet</span><span class="p">][:,</span> <span class="mi">0</span><span class="p">]),</span>
+                                            <span class="n">_fract_ind_to_val</span><span class="p">(</span><span class="n">ky</span><span class="p">,</span> <span class="n">contours</span><span class="p">[</span><span class="n">sheet</span><span class="p">][</span><span class="n">sec_per_sheet</span><span class="p">][:,</span> <span class="mi">1</span><span class="p">]),</span>
+                                            <span class="n">kz</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">contours</span><span class="p">[</span><span class="n">sheet</span><span class="p">][</span><span class="n">sec_per_sheet</span><span class="p">][:,</span> <span class="mi">0</span><span class="p">]))])</span><span class="o">.</span><span class="n">T</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+            <span class="c1"># repeat on actual mesh for computing the weights</span>
+            <span class="n">ks_skimage</span> <span class="o">=</span> <span class="n">contours</span><span class="p">[</span><span class="n">sheet</span><span class="p">][</span><span class="n">sec_per_sheet</span><span class="p">]</span><span class="o">/</span><span class="p">(</span><span class="n">N_kxy</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+            <span class="n">FS_kx_ky_prim</span><span class="p">[</span><span class="n">sheet_ct</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;i,j-&gt;ij&#39;</span><span class="p">,</span> <span class="n">ks_skimage</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">lower_right</span><span class="p">)</span>
+                                       <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;i,j-&gt;ij&#39;</span><span class="p">,</span> <span class="n">ks_skimage</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">upper_left</span><span class="p">)</span>
+                                       <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;i,j-&gt;ij&#39;</span><span class="p">,</span> <span class="n">kz</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">ks_skimage</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">Z</span><span class="p">))</span>
+            <span class="n">band_char</span><span class="p">[</span><span class="n">sheet_ct</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
+            <span class="c1"># compute the weight aka band character</span>
+            <span class="k">for</span> <span class="n">ct_k</span><span class="p">,</span> <span class="n">k_on_sheet</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">FS_kx_ky_prim</span><span class="p">[</span><span class="n">sheet_ct</span><span class="p">]):</span>
+                <span class="n">E_mat</span> <span class="o">=</span> <span class="n">tb</span><span class="o">.</span><span class="n">fourier</span><span class="p">(</span><span class="n">k_on_sheet</span><span class="p">)</span>
+                <span class="n">e_val</span><span class="p">,</span> <span class="n">e_vec</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">eigh</span><span class="p">(</span><span class="n">E_mat</span><span class="p">[</span><span class="n">select</span><span class="p">[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span> <span class="n">select</span><span class="p">])</span>
+                <span class="n">orb_on_FS</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmin</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">e_val</span><span class="p">))</span>
+
+                <span class="n">band_char</span><span class="p">[</span><span class="n">sheet_ct</span><span class="p">][</span><span class="n">ct_k</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">e_vec</span><span class="p">[</span><span class="n">orb</span><span class="p">,</span> <span class="n">orb_on_FS</span><span class="p">]</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">conjugate</span><span class="p">(</span><span class="n">e_vec</span><span class="p">[</span><span class="n">orb</span><span class="p">,</span> <span class="n">orb_on_FS</span><span class="p">])),</span> <span class="mi">4</span><span class="p">)</span> <span class="k">for</span> <span class="n">orb</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">select</span><span class="p">))]</span>
+            <span class="n">sheet_ct</span> <span class="o">+=</span> <span class="mi">1</span>
+
+    <span class="k">return</span> <span class="n">FS_kx_ky</span><span class="p">,</span> <span class="n">band_char</span>
+
+
+<span class="k">def</span> <span class="nf">_setup_plot_bands</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">special_k</span><span class="p">,</span> <span class="n">k_points_labels</span><span class="p">,</span> <span class="n">freq_dict</span><span class="p">):</span>
+
+    <span class="n">ax</span><span class="o">.</span><span class="n">axhline</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s1">&#39;gray&#39;</span><span class="p">,</span> <span class="n">ls</span><span class="o">=</span><span class="s1">&#39;--&#39;</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="mf">0.8</span><span class="p">,</span> <span class="n">zorder</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$\omega - \mu$ (eV)&#39;</span><span class="p">)</span>
+<span class="c1">#     ax.set_ylim(*freq_dict[&#39;window&#39;])</span>
+    <span class="k">for</span> <span class="n">ik</span> <span class="ow">in</span> <span class="n">special_k</span><span class="p">:</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">ik</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">zorder</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(</span><span class="n">special_k</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="n">special_k</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">special_k</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+    <span class="n">k_points_labels</span> <span class="o">=</span> <span class="p">[</span><span class="sa">r</span><span class="s1">&#39;$\Gamma$&#39;</span> <span class="k">if</span> <span class="n">k</span> <span class="o">==</span> <span class="s1">&#39;G&#39;</span> <span class="k">else</span> <span class="n">k</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">k_points_labels</span><span class="p">]</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">(</span><span class="n">k_points_labels</span><span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">setup_plot_kslice</span><span class="p">(</span><span class="n">ax</span><span class="p">):</span>
+
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_aspect</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+    <span class="c1"># ax.set_xlim(0,1)</span>
+    <span class="c1"># ax.set_ylim(0,1)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">set_major_locator</span><span class="p">(</span><span class="n">MaxNLocator</span><span class="p">(</span><span class="n">integer</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">yaxis</span><span class="o">.</span><span class="n">set_major_locator</span><span class="p">(</span><span class="n">MaxNLocator</span><span class="p">(</span><span class="n">integer</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$k_x\pi/a$&#39;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$k_y\pi/b$&#39;</span><span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">plot_bands</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span><span class="p">,</span> <span class="n">alatt_k_w</span><span class="p">,</span> <span class="n">tb_data</span><span class="p">,</span> <span class="n">freq_dict</span><span class="p">,</span> <span class="n">n_orb</span><span class="p">,</span> <span class="n">tb</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">alatt</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">qp_bands</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">plot_dict</span><span class="p">):</span>
+
+    <span class="k">assert</span> <span class="n">tb_data</span><span class="p">[</span><span class="s1">&#39;special_k&#39;</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">,</span> <span class="s1">&#39;a regular k point mesh has been used, please call plot_dos&#39;</span>
+
+    <span class="n">proj_on_orb</span> <span class="o">=</span> <span class="n">tb_data</span><span class="p">[</span><span class="s1">&#39;proj_on_orb&#39;</span><span class="p">]</span>
+    <span class="n">total_proj</span> <span class="o">=</span> <span class="n">tb_data</span><span class="p">[</span><span class="s1">&#39;proj_nuk&#39;</span><span class="p">]</span>
+
+    <span class="k">if</span> <span class="n">alatt</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">alatt_k_w</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;A(k,w) unknown. Specify &quot;with_sigma = True&quot;&#39;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">qp_bands</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">orb</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_orb</span><span class="p">):</span>
+                <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">tb_data</span><span class="p">[</span><span class="s1">&#39;k_mesh&#39;</span><span class="p">],</span> <span class="n">alatt_k_w</span><span class="p">[:,</span> <span class="n">orb</span><span class="p">]</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="nb">eval</span><span class="p">(</span><span class="s1">&#39;cm.&#39;</span><span class="o">+</span><span class="n">plot_dict</span><span class="p">[</span><span class="s1">&#39;colorscheme_qpbands&#39;</span><span class="p">])(</span><span class="mf">1.0</span><span class="p">)]),</span> <span class="n">zorder</span><span class="o">=</span><span class="mf">2.</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mf">1.</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">kw_x</span><span class="p">,</span> <span class="n">kw_y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">tb_data</span><span class="p">[</span><span class="s1">&#39;k_mesh&#39;</span><span class="p">],</span> <span class="n">freq_dict</span><span class="p">[</span><span class="s1">&#39;w_mesh&#39;</span><span class="p">])</span>
+
+            <span class="n">vmax</span> <span class="o">=</span> <span class="n">plot_dict</span><span class="p">[</span><span class="s1">&#39;vmax&#39;</span><span class="p">]</span> <span class="k">if</span> <span class="s1">&#39;vmax&#39;</span> <span class="ow">in</span> <span class="n">plot_dict</span> <span class="k">else</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">alatt_k_w</span><span class="p">)</span>
+            <span class="n">vmin</span> <span class="o">=</span> <span class="n">plot_dict</span><span class="p">[</span><span class="s1">&#39;vmin&#39;</span><span class="p">]</span> <span class="k">if</span> <span class="s1">&#39;vmin&#39;</span> <span class="ow">in</span> <span class="n">plot_dict</span> <span class="k">else</span> <span class="mf">0.0</span>
+
+            <span class="n">graph</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">pcolormesh</span><span class="p">(</span><span class="n">kw_x</span><span class="p">,</span> <span class="n">kw_y</span><span class="p">,</span> <span class="n">alatt_k_w</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plot_dict</span><span class="p">[</span><span class="s1">&#39;colorscheme_alatt&#39;</span><span class="p">],</span>
+                                  <span class="n">norm</span><span class="o">=</span><span class="n">Normalize</span><span class="p">(</span><span class="n">vmin</span><span class="o">=</span><span class="n">vmin</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="n">vmax</span><span class="p">),</span> <span class="n">shading</span><span class="o">=</span><span class="s1">&#39;gouraud&#39;</span><span class="p">)</span>
+            <span class="n">colorbar</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">graph</span><span class="p">)</span>
+            <span class="n">colorbar</span><span class="o">.</span><span class="n">set_label</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$A(k, \omega)$&#39;</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">tb</span><span class="p">:</span>
+        <span class="c1"># if projection is requested, _get_tb_bands() ran already</span>
+        <span class="k">if</span> <span class="n">proj_on_orb</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">eps_nuk</span> <span class="o">=</span> <span class="n">tb_data</span><span class="p">[</span><span class="s1">&#39;e_mat&#39;</span><span class="p">]</span>
+            <span class="n">evec_nuk</span> <span class="o">=</span> <span class="n">tb_data</span><span class="p">[</span><span class="s1">&#39;e_vecs&#39;</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">eps_nuk</span><span class="p">,</span> <span class="n">evec_nuk</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">get_tb_bands</span><span class="p">(</span><span class="o">**</span><span class="n">tb_data</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">band</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_orb</span><span class="p">):</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="n">proj_on_orb</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">plot_dict</span><span class="p">[</span><span class="s1">&#39;colorscheme_bands&#39;</span><span class="p">],</span> <span class="nb">str</span><span class="p">):</span>
+                    <span class="n">color</span> <span class="o">=</span> <span class="nb">eval</span><span class="p">(</span><span class="s1">&#39;cm.&#39;</span><span class="o">+</span><span class="n">plot_dict</span><span class="p">[</span><span class="s1">&#39;colorscheme_bands&#39;</span><span class="p">])(</span><span class="mf">1.0</span><span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">color</span> <span class="o">=</span> <span class="n">plot_dict</span><span class="p">[</span><span class="s1">&#39;colorscheme_bands&#39;</span><span class="p">]</span>
+                <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">tb_data</span><span class="p">[</span><span class="s1">&#39;k_mesh&#39;</span><span class="p">],</span> <span class="n">eps_nuk</span><span class="p">[</span><span class="n">band</span><span class="p">,</span> <span class="n">band</span><span class="p">]</span><span class="o">.</span><span class="n">real</span> <span class="o">-</span> <span class="n">tb_data</span><span class="p">[</span><span class="s1">&#39;mu_tb&#39;</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">color</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">r</span><span class="s1">&#39;tight-binding&#39;</span><span class="p">,</span> <span class="n">zorder</span><span class="o">=</span><span class="mf">1.</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">color</span> <span class="o">=</span> <span class="nb">eval</span><span class="p">(</span><span class="s1">&#39;cm.&#39;</span><span class="o">+</span><span class="n">plot_dict</span><span class="p">[</span><span class="s1">&#39;colorscheme_bands&#39;</span><span class="p">])(</span><span class="n">total_proj</span><span class="p">[</span><span class="n">band</span><span class="p">])</span>
+                <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">tb_data</span><span class="p">[</span><span class="s1">&#39;k_mesh&#39;</span><span class="p">],</span> <span class="n">eps_nuk</span><span class="p">[</span><span class="n">band</span><span class="p">,</span> <span class="n">band</span><span class="p">]</span><span class="o">.</span><span class="n">real</span> <span class="o">-</span> <span class="n">tb_data</span><span class="p">[</span><span class="s1">&#39;mu_tb&#39;</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">color</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">r</span><span class="s1">&#39;tight-binding&#39;</span><span class="p">,</span> <span class="n">zorder</span><span class="o">=</span><span class="mf">1.</span><span class="p">)</span>
+
+    <span class="n">_setup_plot_bands</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">tb_data</span><span class="p">[</span><span class="s1">&#39;special_k&#39;</span><span class="p">],</span> <span class="n">tb_data</span><span class="p">[</span><span class="s1">&#39;k_points_labels&#39;</span><span class="p">],</span> <span class="n">freq_dict</span><span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">plot_dos</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span><span class="p">,</span> <span class="n">alatt_k_w</span><span class="p">,</span> <span class="n">tb_data</span><span class="p">,</span> <span class="n">freq_dict</span><span class="p">,</span> <span class="n">tb</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">alatt</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+
+    <span class="k">assert</span> <span class="n">tb</span> <span class="o">==</span> <span class="kc">False</span><span class="p">,</span> <span class="s1">&#39;plotting TB DOS is not supported yet.&#39;</span>
+
+    <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">alatt_k_w</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span><span class="p">,</span> <span class="s1">&#39;input Akw should only have a k and omega index&#39;</span>
+
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">label</span><span class="p">:</span>
+        <span class="n">label</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span>
+
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">color</span><span class="p">:</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">freq_dict</span><span class="p">[</span><span class="s1">&#39;w_mesh&#39;</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">alatt_k_w</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">/</span><span class="n">alatt_k_w</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">freq_dict</span><span class="p">[</span><span class="s1">&#39;w_mesh&#39;</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">alatt_k_w</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="o">/</span>
+                <span class="n">alatt_k_w</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">)</span>
+
+    <span class="n">ax</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s1">&#39;gray&#39;</span><span class="p">,</span> <span class="n">ls</span><span class="o">=</span><span class="s1">&#39;--&#39;</span><span class="p">,</span> <span class="n">zorder</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$\omega - \mu$ (eV)&#39;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;A($\omega$)&#39;</span><span class="p">)</span>
+
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="o">*</span><span class="n">freq_dict</span><span class="p">[</span><span class="s1">&#39;window&#39;</span><span class="p">])</span>
+
+    <span class="k">return</span>
+
+<span class="k">def</span> <span class="nf">plot_kslice</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span><span class="p">,</span> <span class="n">alatt_k_w</span><span class="p">,</span> <span class="n">tb_data</span><span class="p">,</span> <span class="n">freq_dict</span><span class="p">,</span> <span class="n">n_orb</span><span class="p">,</span> <span class="n">tb_dict</span><span class="p">,</span> <span class="n">tb</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">alatt</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">quarter</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="o">**</span><span class="n">plot_dict</span><span class="p">):</span>
+
+    <span class="n">proj_on_orb</span> <span class="o">=</span> <span class="n">tb_data</span><span class="p">[</span><span class="s1">&#39;proj_on_orb&#39;</span><span class="p">]</span>
+    <span class="k">if</span> <span class="n">quarter</span><span class="p">:</span>
+        <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">quarter</span><span class="p">,</span> <span class="nb">int</span><span class="p">)</span> <span class="ow">or</span> <span class="nb">all</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="nb">int</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">quarter</span><span class="p">),</span> <span class="s1">&#39;quarter should be&#39;</span>\
+            <span class="sa">f</span><span class="s1">&#39;an integer or list of integers, but is </span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">quarter</span><span class="p">)</span><span class="si">}</span><span class="s1">.&#39;</span>
+
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">quarter</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span>
+        <span class="n">quarter</span> <span class="o">=</span> <span class="p">[</span><span class="n">quarter</span><span class="p">]</span>
+
+    <span class="n">sign</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+    <span class="n">quarters</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">sign</span><span class="p">,</span> <span class="n">sign</span><span class="p">])</span>
+    <span class="n">four_quarters</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">itertools</span><span class="o">.</span><span class="n">product</span><span class="p">(</span><span class="o">*</span><span class="n">quarters</span><span class="p">))</span>
+    <span class="n">used_quarters</span> <span class="o">=</span> <span class="p">[</span><span class="n">four_quarters</span><span class="p">[</span><span class="n">x</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">quarter</span><span class="p">]</span>
+
+    <span class="n">vmax</span> <span class="o">=</span> <span class="n">plot_dict</span><span class="p">[</span><span class="s1">&#39;vmax&#39;</span><span class="p">]</span> <span class="k">if</span> <span class="s1">&#39;vmax&#39;</span> <span class="ow">in</span> <span class="n">plot_dict</span> <span class="k">else</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">alatt_k_w</span><span class="p">)</span>
+    <span class="n">vmin</span> <span class="o">=</span> <span class="n">plot_dict</span><span class="p">[</span><span class="s1">&#39;vmin&#39;</span><span class="p">]</span> <span class="k">if</span> <span class="s1">&#39;vmin&#39;</span> <span class="ow">in</span> <span class="n">plot_dict</span> <span class="k">else</span> <span class="mf">0.0</span>
+    <span class="k">assert</span> <span class="n">vmax</span> <span class="o">&gt;</span> <span class="n">vmin</span><span class="p">,</span> <span class="s1">&#39;vmax needs to be larger than vmin&#39;</span>
+
+    <span class="k">if</span> <span class="n">alatt</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">alatt_k_w</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;A(k,w) unknown. Specify &quot;with_sigma = True&quot;&#39;</span><span class="p">)</span>
+        <span class="n">n_kx</span><span class="p">,</span> <span class="n">n_ky</span> <span class="o">=</span> <span class="n">tb_data</span><span class="p">[</span><span class="s1">&#39;e_mat&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">:</span><span class="mi">4</span><span class="p">]</span>
+        <span class="n">kx</span><span class="p">,</span> <span class="n">ky</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">n_kx</span><span class="p">),</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_ky</span><span class="p">))</span>
+        <span class="k">for</span> <span class="p">(</span><span class="n">qx</span><span class="p">,</span> <span class="n">qy</span><span class="p">)</span> <span class="ow">in</span> <span class="n">used_quarters</span><span class="p">:</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">alatt_k_w</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">2</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">orb</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_orb</span><span class="p">):</span>
+                    <span class="n">ax</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">qx</span> <span class="o">*</span> <span class="n">kx</span><span class="o">/</span><span class="p">(</span><span class="n">n_kx</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="n">qy</span> <span class="o">*</span> <span class="n">ky</span><span class="o">/</span><span class="p">(</span><span class="n">n_ky</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="n">alatt_k_w</span><span class="p">[:,</span> <span class="p">:,</span> <span class="n">orb</span><span class="p">]</span><span class="o">.</span><span class="n">T</span><span class="p">,</span>
+                               <span class="n">colors</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="nb">eval</span><span class="p">(</span><span class="s1">&#39;cm.&#39;</span><span class="o">+</span><span class="n">plot_dict</span><span class="p">[</span><span class="s1">&#39;colorscheme_qpbands&#39;</span><span class="p">])(</span><span class="mf">0.7</span><span class="p">)]),</span> <span class="n">levels</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">zorder</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">graph</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">pcolormesh</span><span class="p">(</span><span class="n">qx</span> <span class="o">*</span> <span class="n">kx</span><span class="o">/</span><span class="p">(</span><span class="n">n_kx</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="n">qy</span> <span class="o">*</span> <span class="n">ky</span><span class="o">/</span><span class="p">(</span><span class="n">n_ky</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="n">alatt_k_w</span><span class="o">.</span><span class="n">T</span><span class="p">,</span>
+                                      <span class="n">cmap</span><span class="o">=</span><span class="n">plot_dict</span><span class="p">[</span><span class="s1">&#39;colorscheme_kslice&#39;</span><span class="p">],</span>
+                                      <span class="n">norm</span><span class="o">=</span><span class="n">Normalize</span><span class="p">(</span><span class="n">vmin</span><span class="o">=</span><span class="n">vmin</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="n">vmax</span><span class="p">),</span>
+                                      <span class="n">shading</span><span class="o">=</span><span class="s1">&#39;gouraud&#39;</span><span class="p">)</span>
+                <span class="c1">#colorbar = plt.colorbar(graph)</span>
+                <span class="c1">#colorbar.set_label(r&#39;$A(k, 0$)&#39;)</span>
+
+    <span class="k">if</span> <span class="n">tb</span><span class="p">:</span>
+        <span class="n">FS_kx_ky</span><span class="p">,</span> <span class="n">band_char</span> <span class="o">=</span> <span class="n">get_tb_kslice</span><span class="p">(</span><span class="n">tb_data</span><span class="p">[</span><span class="s1">&#39;tb&#39;</span><span class="p">],</span> <span class="n">tb_data</span><span class="p">[</span><span class="s1">&#39;mu_tb&#39;</span><span class="p">],</span> <span class="o">**</span><span class="n">tb_dict</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">sheet</span> <span class="ow">in</span> <span class="n">FS_kx_ky</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+            <span class="k">for</span> <span class="n">k_on_sheet</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">FS_kx_ky</span><span class="p">[</span><span class="n">sheet</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
+                <span class="k">if</span> <span class="ow">not</span> <span class="n">proj_on_orb</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">plot_dict</span><span class="p">[</span><span class="s1">&#39;colorscheme_bands&#39;</span><span class="p">],</span> <span class="nb">str</span><span class="p">):</span>
+                        <span class="n">color</span> <span class="o">=</span> <span class="nb">eval</span><span class="p">(</span><span class="s1">&#39;cm.&#39;</span><span class="o">+</span><span class="n">plot_dict</span><span class="p">[</span><span class="s1">&#39;colorscheme_bands&#39;</span><span class="p">])(</span><span class="mf">1.0</span><span class="p">)</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="n">color</span> <span class="o">=</span> <span class="n">plot_dict</span><span class="p">[</span><span class="s1">&#39;colorscheme_bands&#39;</span><span class="p">]</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">total_proj</span> <span class="o">=</span> <span class="mi">0</span>
+                    <span class="k">for</span> <span class="n">orb</span> <span class="ow">in</span> <span class="n">proj_on_orb</span><span class="p">:</span>
+                        <span class="n">total_proj</span> <span class="o">+=</span> <span class="n">band_char</span><span class="p">[</span><span class="n">sheet</span><span class="p">][</span><span class="n">k_on_sheet</span><span class="p">][</span><span class="n">orb</span><span class="p">]</span>
+                    <span class="n">color</span> <span class="o">=</span> <span class="nb">eval</span><span class="p">(</span><span class="s1">&#39;cm.&#39;</span><span class="o">+</span><span class="n">plot_dict</span><span class="p">[</span><span class="s1">&#39;colorscheme_bands&#39;</span><span class="p">])(</span><span class="n">total_proj</span><span class="p">)</span>
+                <span class="k">for</span> <span class="p">(</span><span class="n">qx</span><span class="p">,</span> <span class="n">qy</span><span class="p">)</span> <span class="ow">in</span> <span class="n">used_quarters</span><span class="p">:</span>
+                    <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">qx</span> <span class="o">*</span> <span class="n">FS_kx_ky</span><span class="p">[</span><span class="n">sheet</span><span class="p">][</span><span class="n">k_on_sheet</span><span class="p">:</span><span class="n">k_on_sheet</span><span class="o">+</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="mi">2</span><span class="o">*</span><span class="n">qy</span> <span class="o">*</span> <span class="n">FS_kx_ky</span><span class="p">[</span><span class="n">sheet</span><span class="p">][</span><span class="n">k_on_sheet</span><span class="p">:</span><span class="n">k_on_sheet</span><span class="o">+</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="s1">&#39;-&#39;</span><span class="p">,</span>
+                            <span class="n">solid_capstyle</span><span class="o">=</span><span class="s1">&#39;round&#39;</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="n">color</span><span class="p">,</span> <span class="n">zorder</span><span class="o">=</span><span class="mf">1.</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">plot_dict</span><span class="p">[</span><span class="s1">&#39;label&#39;</span><span class="p">]</span> <span class="k">if</span> <span class="s1">&#39;label&#39;</span> <span class="ow">in</span> <span class="n">plot_dict</span> <span class="k">else</span> <span class="s1">&#39;&#39;</span><span class="p">)</span>
+
+    <span class="n">setup_plot_kslice</span><span class="p">(</span><span class="n">ax</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">ax</span>
+
+
+<div class="viewcode-block" id="get_dmft_bands"><a class="viewcode-back" href="../../_ref/postprocessing.plot_correlated_bands.html#postprocessing.plot_correlated_bands.get_dmft_bands">[docs]</a><span class="k">def</span> <span class="nf">get_dmft_bands</span><span class="p">(</span><span class="n">n_orb</span><span class="p">,</span> <span class="n">w90_path</span><span class="p">,</span> <span class="n">w90_seed</span><span class="p">,</span> <span class="n">mu_tb</span><span class="p">,</span> <span class="n">add_spin</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">add_lambda</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">add_local</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                   <span class="n">with_sigma</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">fermi_slice</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">qp_bands</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">orbital_order_to</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                   <span class="n">add_mu_tb</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">band_basis</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">proj_on_orb</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">trace</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">eta</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span>
+                   <span class="n">mu_shift</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">proj_nuk</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">specs</span><span class="p">):</span>
+    <span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">    Extract tight-binding from given w90 seed_hr.dat and seed.wout files, and then extract from</span>
+<span class="sd">    given solid_dmft calculation the self-energy and construct the spectral function A(k,w) on</span>
+<span class="sd">    given k-path.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    n_orb : int</span>
+<span class="sd">        Number of Wannier orbitals in seed_hr.dat</span>
+<span class="sd">    w90_path : string</span>
+<span class="sd">        Path to w90 files</span>
+<span class="sd">    w90_seed : string</span>
+<span class="sd">        Seed of wannier90 calculation, i.e. seed_hr.dat and seed.wout</span>
+<span class="sd">    add_spin : bool, default=False</span>
+<span class="sd">        Extend w90 Hamiltonian by spin indices</span>
+<span class="sd">    add_lambda : float, default=None</span>
+<span class="sd">        Add SOC term with strength add_lambda (works only for t2g shells)</span>
+<span class="sd">    add_local : numpy array, default=None</span>
+<span class="sd">        Add local term of dimension (n_orb x n_orb)</span>
+<span class="sd">    with_sigma : str, or BlockGf, default=None</span>
+<span class="sd">        Add self-energy to spectral function? Can be either directly take</span>
+<span class="sd">        a triqs BlockGf object or can be either &#39;calc&#39; or &#39;model&#39;</span>
+<span class="sd">        &#39;calc&#39; reads results from h5 archive (solid_dmft)</span>
+<span class="sd">        in case &#39;calc&#39; or &#39;model&#39; are specified a extra kwargs dict has</span>
+<span class="sd">        to be given sigma_dict containing information about the self-energy</span>
+<span class="sd">    add_mu_tb : bool, default=False</span>
+<span class="sd">        Add the TB specified chemical potential to the lattice Green function</span>
+<span class="sd">        set to True if DMFT calculation was performed with DFT fermi subtracted.</span>
+<span class="sd">    proj_on_orb : int or list of int, default=None</span>
+<span class="sd">        orbital projections to be made for the spectral function and TB bands</span>
+<span class="sd">        the integer refer to the orbitals read</span>
+<span class="sd">    trace : bool, default=True</span>
+<span class="sd">        Return trace over orbitals for spectral function. For special</span>
+<span class="sd">        post-processing purposes this can be set to False giving the returned</span>
+<span class="sd">        alatt_k_w an extra dimension n_orb</span>
+<span class="sd">    eta : float, default=0.0</span>
+<span class="sd">        Broadening of spectral function, finitie shift on imaginary axis</span>
+<span class="sd">        if with_sigma=None it has to be provided !=0.0</span>
+<span class="sd">    mu_shift : float, default=0.0</span>
+<span class="sd">        Manual extra shift when calculating the spectral function</span>
+<span class="sd">    proj_nuk : numpy array, default [None]</span>
+<span class="sd">        Extra projections to be applied to the final spectral function</span>
+<span class="sd">        per orbital and k-point. Has to match shape of final lattice Green</span>
+<span class="sd">        function. Will be applied together with proj_on_orb if specified.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    tb_data : dict</span>
+<span class="sd">       tight binding dict containing the kpoint mesh, dispersion / emat, and eigenvectors</span>
+
+<span class="sd">    alatt_k_w : numpy array (float) of dim n_k x n_w ( x n_orb if trace=False)</span>
+<span class="sd">        lattice spectral function data on the kpoint mesh defined in tb_data and frequency</span>
+<span class="sd">        mesh defined in freq_dict</span>
+
+<span class="sd">    freq_dict : dict</span>
+<span class="sd">        frequency mesh information on which alatt_k_w is evaluated</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+
+    <span class="c1"># set default ordering</span>
+    <span class="k">if</span> <span class="s1">&#39;orbital_order_w90&#39;</span> <span class="ow">in</span> <span class="n">specs</span><span class="p">:</span>
+        <span class="n">orbital_order_w90</span> <span class="o">=</span> <span class="n">specs</span><span class="p">[</span><span class="s1">&#39;orbital_order_w90&#39;</span><span class="p">]</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">orbital_order_w90</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">n_orb</span><span class="p">))</span>
+
+    <span class="k">if</span> <span class="n">orbital_order_to</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">orbital_order_to</span> <span class="o">=</span> <span class="n">orbital_order_w90</span>
+
+    <span class="c1"># checks</span>
+    <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">orbital_order_to</span><span class="p">))</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">orbital_order_to</span><span class="p">),</span> <span class="s1">&#39;Please provide a unique identifier for each orbital.&#39;</span>
+
+    <span class="k">assert</span> <span class="nb">set</span><span class="p">(</span><span class="n">orbital_order_w90</span><span class="p">)</span> <span class="o">==</span> <span class="nb">set</span><span class="p">(</span><span class="n">orbital_order_to</span><span class="p">),</span> <span class="sa">f</span><span class="s1">&#39;Identifiers of orbital_order_to and orbital_order_w90&#39;</span>\
+        <span class="sa">f</span><span class="s1">&#39;do not match! orbital_order_to is </span><span class="si">{</span><span class="n">orbital_order_to</span><span class="si">}</span><span class="s1">, but orbital_order_w90 is </span><span class="si">{</span><span class="n">orbital_order_w90</span><span class="si">}</span><span class="s1">.&#39;</span>
+
+    <span class="k">assert</span> <span class="n">with_sigma</span> <span class="ow">or</span> <span class="n">eta</span> <span class="o">!=</span> <span class="mf">0.0</span><span class="p">,</span> <span class="s1">&#39;if no Sigma is provided eta has to be different from 0.0&#39;</span>
+
+    <span class="c1"># proj_on_orb</span>
+    <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">proj_on_orb</span><span class="p">,</span> <span class="p">(</span><span class="nb">int</span><span class="p">,</span> <span class="nb">type</span><span class="p">(</span><span class="kc">None</span><span class="p">)))</span> <span class="ow">or</span> <span class="nb">all</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="p">(</span><span class="nb">int</span><span class="p">,</span> <span class="nb">type</span><span class="p">(</span><span class="kc">None</span><span class="p">)))</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">proj_on_orb</span><span class="p">),</span> <span class="s1">&#39;proj_on_orb should be &#39;</span>\
+        <span class="sa">f</span><span class="s1">&#39;an integer or list of integers, but is </span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">specs</span><span class="p">[</span><span class="s2">&quot;proj_on_orb&quot;</span><span class="p">])</span><span class="si">}</span><span class="s1">.&#39;</span>
+
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">proj_on_orb</span><span class="p">,</span> <span class="p">(</span><span class="nb">int</span><span class="p">,</span> <span class="nb">type</span><span class="p">(</span><span class="kc">None</span><span class="p">))):</span>
+        <span class="n">proj_on_orb</span> <span class="o">=</span> <span class="p">[</span><span class="n">proj_on_orb</span><span class="p">]</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">proj_on_orb</span> <span class="o">=</span> <span class="n">proj_on_orb</span>
+
+    <span class="c1"># if projection is requested we have to use band_basis</span>
+    <span class="k">if</span> <span class="n">proj_on_orb</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">band_basis</span> <span class="o">=</span> <span class="kc">True</span>
+
+    <span class="c1"># if proj_nuk is given we need to use the band_basis</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">proj_nuk</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">band_basis</span><span class="p">:</span>
+        <span class="n">band_basis</span> <span class="o">=</span> <span class="kc">True</span>
+
+    <span class="c1"># set up Wannier Hamiltonian</span>
+    <span class="n">n_orb</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">n_orb</span> <span class="k">if</span> <span class="n">add_spin</span> <span class="k">else</span> <span class="n">n_orb</span>
+    <span class="n">change_of_basis</span> <span class="o">=</span> <span class="n">change_basis</span><span class="p">(</span><span class="n">n_orb</span><span class="p">,</span> <span class="n">orbital_order_to</span><span class="p">,</span> <span class="n">orbital_order_w90</span><span class="p">)</span>
+    <span class="n">H_add_loc</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">n_orb</span><span class="p">,</span> <span class="n">n_orb</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">complex</span><span class="p">)</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">add_local</span><span class="p">,</span> <span class="nb">type</span><span class="p">(</span><span class="kc">None</span><span class="p">)):</span>
+        <span class="k">assert</span> <span class="n">np</span><span class="o">.</span><span class="n">shape</span><span class="p">(</span><span class="n">add_local</span><span class="p">)</span> <span class="o">==</span> <span class="p">(</span><span class="n">n_orb</span><span class="p">,</span> <span class="n">n_orb</span><span class="p">),</span> <span class="s1">&#39;add_local must have dimension (n_orb, n_orb), but has &#39;</span>\
+                <span class="sa">f</span><span class="s1">&#39;dimension </span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">shape</span><span class="p">(</span><span class="n">add_local</span><span class="p">)</span><span class="si">}</span><span class="s1">&#39;</span>
+        <span class="n">H_add_loc</span> <span class="o">+=</span> <span class="n">add_local</span>
+    <span class="k">if</span> <span class="n">add_spin</span> <span class="ow">and</span> <span class="n">add_lambda</span><span class="p">:</span>
+        <span class="n">H_add_loc</span> <span class="o">+=</span> <span class="n">lambda_matrix_w90_t2g</span><span class="p">(</span><span class="n">add_lambda</span><span class="p">)</span>
+    <span class="n">eta</span> <span class="o">=</span> <span class="n">eta</span> <span class="o">*</span> <span class="mi">1</span><span class="n">j</span>
+
+    <span class="n">tb</span> <span class="o">=</span> <span class="n">TB_from_wannier90</span><span class="p">(</span><span class="n">path</span><span class="o">=</span><span class="n">w90_path</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="n">w90_seed</span><span class="p">,</span> <span class="n">extend_to_spin</span><span class="o">=</span><span class="n">add_spin</span><span class="p">,</span> <span class="n">add_local</span><span class="o">=</span><span class="n">H_add_loc</span><span class="p">)</span>
+    <span class="c1"># print local H(R)</span>
+    <span class="n">h_of_r</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;ij, jk -&gt; ik&#39;</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">inv</span><span class="p">(</span><span class="n">change_of_basis</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;ij, jk -&gt; ik&#39;</span><span class="p">,</span> <span class="n">tb</span><span class="o">.</span><span class="n">hoppings</span><span class="p">[(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)],</span> <span class="n">change_of_basis</span><span class="p">))</span>
+    <span class="k">if</span> <span class="n">n_orb</span> <span class="o">&lt;=</span> <span class="mi">12</span><span class="p">:</span>
+        <span class="n">print_matrix</span><span class="p">(</span><span class="n">h_of_r</span><span class="p">,</span> <span class="n">n_orb</span><span class="p">,</span> <span class="s1">&#39;H(R=0)&#39;</span><span class="p">)</span>
+
+    <span class="c1"># kmesh prep</span>
+    <span class="k">if</span> <span class="p">(</span><span class="s1">&#39;bands_path&#39;</span> <span class="ow">in</span> <span class="n">specs</span> <span class="ow">and</span> <span class="s1">&#39;kmesh&#39;</span> <span class="ow">in</span> <span class="n">specs</span><span class="p">)</span> <span class="ow">or</span> <span class="p">(</span><span class="s1">&#39;bands_path&#39;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">specs</span> <span class="ow">and</span> <span class="s1">&#39;kmesh&#39;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">specs</span><span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;choose either a bands_path or kmesh!&#39;</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="s1">&#39;bands_path&#39;</span> <span class="ow">in</span> <span class="n">specs</span><span class="p">:</span>
+        <span class="n">w90_paths</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">section</span><span class="p">:</span> <span class="p">(</span>
+            <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">specs</span><span class="p">[</span><span class="n">section</span><span class="p">[</span><span class="mi">0</span><span class="p">]]),</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">specs</span><span class="p">[</span><span class="n">section</span><span class="p">[</span><span class="mi">1</span><span class="p">]])),</span> <span class="n">specs</span><span class="p">[</span><span class="s1">&#39;bands_path&#39;</span><span class="p">]))</span>
+        <span class="n">k_points_labels</span> <span class="o">=</span> <span class="p">[</span><span class="n">k</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">specs</span><span class="p">[</span><span class="s1">&#39;bands_path&#39;</span><span class="p">]]</span> <span class="o">+</span> <span class="p">[</span><span class="n">specs</span><span class="p">[</span><span class="s1">&#39;bands_path&#39;</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">]]</span>
+        <span class="n">n_k</span> <span class="o">=</span> <span class="n">specs</span><span class="p">[</span><span class="s1">&#39;n_k&#39;</span><span class="p">]</span>
+        <span class="n">k_vec</span><span class="p">,</span> <span class="n">k_1d</span><span class="p">,</span> <span class="n">special_k</span> <span class="o">=</span> <span class="n">k_space_path</span><span class="p">(</span><span class="n">w90_paths</span><span class="p">,</span> <span class="n">bz</span><span class="o">=</span><span class="n">tb</span><span class="o">.</span><span class="n">bz</span><span class="p">,</span> <span class="n">num</span><span class="o">=</span><span class="n">n_k</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="s1">&#39;kmesh&#39;</span> <span class="ow">in</span> <span class="n">specs</span><span class="p">:</span>
+        <span class="k">assert</span> <span class="s1">&#39;reg&#39;</span> <span class="ow">in</span> <span class="n">specs</span><span class="p">[</span><span class="s1">&#39;kmesh&#39;</span><span class="p">],</span> <span class="s1">&#39;only regular kmesh is implemented&#39;</span>
+
+        <span class="n">special_k</span> <span class="o">=</span> <span class="n">k_points_labels</span> <span class="o">=</span> <span class="kc">None</span>
+
+        <span class="c1"># read kmesh size</span>
+        <span class="k">if</span> <span class="s1">&#39;n_k&#39;</span> <span class="ow">in</span> <span class="n">specs</span><span class="p">:</span>
+            <span class="n">k_dim</span> <span class="o">=</span> <span class="n">specs</span><span class="p">[</span><span class="s1">&#39;n_k&#39;</span><span class="p">]</span>
+        <span class="k">elif</span> <span class="s1">&#39;k_dim&#39;</span> <span class="ow">in</span> <span class="n">specs</span><span class="p">:</span>
+            <span class="n">k_dim</span> <span class="o">=</span> <span class="n">specs</span><span class="p">[</span><span class="s1">&#39;k_dim&#39;</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;please specify either n_k or k_dim&#39;</span><span class="p">)</span>
+
+        <span class="c1"># create regular kmesh</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">k_dim</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span>
+            <span class="n">k_spacing</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">k_dim</span><span class="p">,</span> <span class="n">endpoint</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+            <span class="n">k_vec</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">k_spacing</span><span class="p">,</span> <span class="n">k_spacing</span><span class="p">,</span> <span class="n">k_spacing</span><span class="p">))</span><span class="o">.</span><span class="n">T</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+            <span class="n">n_k</span> <span class="o">=</span> <span class="n">k_dim</span><span class="o">**</span><span class="mi">3</span>
+            <span class="n">k_1d</span> <span class="o">=</span> <span class="p">(</span><span class="n">k_dim</span><span class="p">,</span> <span class="n">k_dim</span><span class="p">,</span> <span class="n">k_dim</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="nb">all</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="nb">int</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">k_dim</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">k_dim</span><span class="p">)</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+            <span class="n">k_x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">k_dim</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">endpoint</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+            <span class="n">k_y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">k_dim</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">endpoint</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+            <span class="n">k_z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">k_dim</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">endpoint</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+            <span class="n">k_vec</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">k_x</span><span class="p">,</span> <span class="n">k_y</span><span class="p">,</span> <span class="n">k_z</span><span class="p">))</span><span class="o">.</span><span class="n">T</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+            <span class="n">n_k</span> <span class="o">=</span> <span class="n">k_dim</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="n">k_dim</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="n">k_dim</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
+            <span class="n">k_1d</span> <span class="o">=</span> <span class="n">k_dim</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="s1">&#39;k_dim / n_k needs to be either an int or a list / tuple of int length 3&#39;</span><span class="p">)</span>
+
+    <span class="c1"># calculate tight-binding eigenvalues for non slices</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">fermi_slice</span><span class="p">:</span>
+        <span class="c1"># Fourier trafo on input grid / path</span>
+        <span class="n">e_mat</span> <span class="o">=</span> <span class="n">tb</span><span class="o">.</span><span class="n">fourier</span><span class="p">(</span><span class="n">k_vec</span><span class="p">)</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
+        <span class="n">e_mat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;ij, jkl -&gt; ikl&#39;</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">inv</span><span class="p">(</span><span class="n">change_of_basis</span><span class="p">),</span>
+                          <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;ijk, jm -&gt; imk&#39;</span><span class="p">,</span> <span class="n">e_mat</span><span class="p">,</span> <span class="n">change_of_basis</span><span class="p">))</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">if</span> <span class="s1">&#39;kz&#39;</span> <span class="ow">in</span> <span class="n">specs</span> <span class="ow">and</span> <span class="n">specs</span><span class="p">[</span><span class="s1">&#39;kz&#39;</span><span class="p">]</span> <span class="o">!=</span> <span class="mf">0.</span><span class="p">:</span>
+            <span class="k">assert</span> <span class="s1">&#39;Z&#39;</span> <span class="ow">in</span> <span class="n">specs</span><span class="p">,</span> <span class="s1">&#39;Please provide Z point coordinate in tb_data_dict as input coordinate&#39;</span>
+            <span class="n">Z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">specs</span><span class="p">[</span><span class="s1">&#39;Z&#39;</span><span class="p">])</span>
+            <span class="n">kz</span> <span class="o">=</span> <span class="n">specs</span><span class="p">[</span><span class="s1">&#39;kz&#39;</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">kz</span> <span class="o">=</span> <span class="mf">0.</span>
+            <span class="n">Z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">3</span><span class="p">))</span>
+
+        <span class="n">k_vec</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">n_k</span><span class="o">*</span><span class="n">n_k</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
+        <span class="n">e_mat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">n_orb</span><span class="p">,</span> <span class="n">n_orb</span><span class="p">,</span> <span class="n">n_k</span><span class="p">,</span> <span class="n">n_k</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">complex</span><span class="p">)</span>
+
+        <span class="n">upper_left</span> <span class="o">=</span> <span class="n">w90_paths</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">lower_right</span> <span class="o">=</span> <span class="n">w90_paths</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+        <span class="n">origin</span> <span class="o">=</span> <span class="n">w90_paths</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
+        <span class="k">for</span> <span class="n">ik_y</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_k</span><span class="p">):</span>
+            <span class="n">path_along_x</span> <span class="o">=</span> <span class="p">[((</span><span class="n">upper_left</span> <span class="o">-</span> <span class="n">origin</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">n_k</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">*</span><span class="n">ik_y</span><span class="o">+</span><span class="n">kz</span><span class="o">*</span><span class="n">Z</span><span class="o">+</span><span class="n">origin</span><span class="p">,</span> <span class="n">origin</span><span class="o">+</span><span class="p">(</span><span class="n">lower_right</span><span class="o">-</span><span class="n">origin</span><span class="p">)</span><span class="o">+</span><span class="p">(</span><span class="n">upper_left</span><span class="o">-</span><span class="n">origin</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">n_k</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">*</span><span class="n">ik_y</span><span class="o">+</span><span class="n">kz</span><span class="o">*</span><span class="n">Z</span><span class="p">)]</span>
+            <span class="n">k_vec</span><span class="p">[</span><span class="n">ik_y</span><span class="o">*</span><span class="n">n_k</span><span class="p">:</span><span class="n">ik_y</span><span class="o">*</span><span class="n">n_k</span><span class="o">+</span><span class="n">n_k</span><span class="p">,</span> <span class="p">:],</span> <span class="n">k_1d</span><span class="p">,</span> <span class="n">special_k</span> <span class="o">=</span> <span class="n">k_space_path</span><span class="p">(</span><span class="n">path_along_x</span><span class="p">,</span> <span class="n">bz</span><span class="o">=</span><span class="n">tb</span><span class="o">.</span><span class="n">bz</span><span class="p">,</span> <span class="n">num</span><span class="o">=</span><span class="n">n_k</span><span class="p">)</span>
+            <span class="n">e_mat</span><span class="p">[:,</span> <span class="p">:,</span> <span class="p">:,</span> <span class="n">ik_y</span><span class="p">]</span> <span class="o">=</span> <span class="n">tb</span><span class="o">.</span><span class="n">fourier</span><span class="p">(</span><span class="n">k_vec</span><span class="p">[</span><span class="n">ik_y</span><span class="o">*</span><span class="n">n_k</span><span class="p">:</span><span class="n">ik_y</span><span class="o">*</span><span class="n">n_k</span><span class="o">+</span><span class="n">n_k</span><span class="p">,</span> <span class="p">:])</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
+        <span class="c1">#if add_spin:</span>
+        <span class="c1">#    e_mat = e_mat[2:5, 2:5]</span>
+        <span class="n">e_mat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;ij, jklm -&gt; iklm&#39;</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">inv</span><span class="p">(</span><span class="n">change_of_basis</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;ijkl, jm -&gt; imkl&#39;</span><span class="p">,</span> <span class="n">e_mat</span><span class="p">,</span> <span class="n">change_of_basis</span><span class="p">))</span>
+
+    <span class="k">if</span> <span class="n">band_basis</span><span class="p">:</span>
+        <span class="n">e_mat</span><span class="p">,</span> <span class="n">e_vecs</span><span class="p">,</span> <span class="n">orb_proj</span> <span class="o">=</span> <span class="n">get_tb_bands</span><span class="p">(</span><span class="n">e_mat</span><span class="p">,</span> <span class="n">proj_on_orb</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">e_vecs</span> <span class="o">=</span> <span class="n">total_proj</span> <span class="o">=</span> <span class="n">orb_proj</span> <span class="o">=</span> <span class="kc">None</span>
+
+    <span class="c1"># now we merge proj_nuk and orb_proj (has reverse shape)</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">proj_nuk</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">orb_proj</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+        <span class="n">proj_nuk</span> <span class="o">=</span> <span class="n">proj_nuk</span> <span class="o">*</span> <span class="n">orb_proj</span>
+    <span class="k">elif</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">proj_nuk</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">orb_proj</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+        <span class="n">proj_nuk</span> <span class="o">=</span> <span class="n">orb_proj</span>
+
+    <span class="c1"># dmft output</span>
+    <span class="k">if</span> <span class="n">with_sigma</span><span class="p">:</span>
+        <span class="n">sigma_types</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;calc&#39;</span><span class="p">,</span> <span class="s1">&#39;model&#39;</span><span class="p">]</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">with_sigma</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">with_sigma</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">sigma_types</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Invalid sigma type. Expected one of: </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">sigma_types</span><span class="p">))</span>
+        <span class="k">elif</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">with_sigma</span><span class="p">,</span> <span class="n">BlockGf</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Invalid sigma type. Expected BlockGf.&#39;</span><span class="p">)</span>
+
+        <span class="c1"># get sigma</span>
+        <span class="k">if</span> <span class="n">with_sigma</span> <span class="o">==</span> <span class="s1">&#39;model&#39;</span><span class="p">:</span>
+            <span class="n">mu_dmft</span> <span class="o">=</span> <span class="kc">None</span> <span class="k">if</span> <span class="s1">&#39;mu_dmft&#39;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">specs</span> <span class="k">else</span> <span class="n">specs</span><span class="p">[</span><span class="s1">&#39;mu_dmft&#39;</span><span class="p">]</span>
+            <span class="n">delta_sigma</span><span class="p">,</span> <span class="n">freq_dict</span> <span class="o">=</span> <span class="n">sigma_FL</span><span class="p">(</span><span class="n">n_orb</span><span class="p">,</span> <span class="n">orbital_order_to</span><span class="p">,</span> <span class="n">specs</span><span class="p">[</span><span class="s1">&#39;Sigma_0&#39;</span><span class="p">],</span> <span class="n">specs</span><span class="p">[</span><span class="s1">&#39;Sigma_Z&#39;</span><span class="p">],</span> <span class="n">specs</span><span class="p">[</span><span class="s1">&#39;w_mesh&#39;</span><span class="p">],</span> <span class="n">eta</span><span class="o">=</span><span class="n">eta</span><span class="p">,</span> <span class="n">mu_dmft</span><span class="o">=</span><span class="n">mu_dmft</span><span class="p">)</span>
+            <span class="n">mu</span> <span class="o">=</span> <span class="n">mu_tb</span> <span class="o">+</span> <span class="n">mu_shift</span>
+        <span class="c1"># else is from dmft or memory:</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">delta_sigma</span><span class="p">,</span> <span class="n">mu_dmft</span><span class="p">,</span> <span class="n">freq_dict</span> <span class="o">=</span> <span class="n">_sigma_from_dmft</span><span class="p">(</span><span class="n">n_orb</span><span class="p">,</span> <span class="n">orbital_order_to</span><span class="p">,</span> <span class="n">with_sigma</span><span class="p">,</span> <span class="o">**</span><span class="n">specs</span><span class="p">)</span>
+            <span class="n">mu</span> <span class="o">=</span> <span class="n">mu_dmft</span> <span class="o">+</span> <span class="n">mu_shift</span>
+
+        <span class="k">if</span> <span class="n">add_mu_tb</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Adding mu_tb to DMFT μ; assuming DMFT was run with subtracted dft μ.&#39;</span><span class="p">)</span>
+            <span class="n">mu</span> <span class="o">+=</span> <span class="n">mu_tb</span>
+
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;μ=</span><span class="si">{:2.4f}</span><span class="s1"> eV set for calculating A(k,ω)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">mu</span><span class="p">))</span>
+
+        <span class="k">assert</span> <span class="n">n_orb</span> <span class="o">==</span> <span class="n">delta_sigma</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">and</span> <span class="n">n_orb</span> <span class="o">==</span> <span class="n">delta_sigma</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span>
+            <span class="mi">1</span><span class="p">],</span> <span class="sa">f</span><span class="s1">&#39;Number of orbitals n_orb=</span><span class="si">{</span><span class="n">n_orb</span><span class="si">}</span><span class="s1"> and shape of sigma: </span><span class="si">{</span><span class="n">delta_sigma</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s1"> does not match&#39;</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">proj_nuk</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+            <span class="k">assert</span> <span class="n">n_orb</span> <span class="o">==</span> <span class="n">proj_nuk</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="sa">f</span><span class="s1">&#39;Number of orbitals n_orb=</span><span class="si">{</span><span class="n">n_orb</span><span class="si">}</span><span class="s1"> does not match shape of proj_nuk: </span><span class="si">{</span><span class="n">proj_nuk</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s1">&#39;</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="n">fermi_slice</span><span class="p">:</span>
+                <span class="k">assert</span> <span class="n">proj_nuk</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">e_vecs</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span>
+                    <span class="mi">2</span><span class="p">],</span> <span class="sa">f</span><span class="s1">&#39;Number of kpoints in proj_nuk : </span><span class="si">{</span><span class="n">proj_nuk</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="si">}</span><span class="s1"> does not match number of kpoints in e_vecs: </span><span class="si">{</span><span class="n">e_vecs</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="si">}</span><span class="s1">&#39;</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">assert</span> <span class="n">proj_nuk</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="nb">tuple</span><span class="p">([</span><span class="n">n_orb</span><span class="p">,</span> <span class="n">e_vecs</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">e_vecs</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">3</span><span class="p">]]</span>
+                                               <span class="p">),</span> <span class="sa">f</span><span class="s1">&#39;shape of projectors </span><span class="si">{</span><span class="n">proj_nuk</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s1"> does not match expected shape of [</span><span class="si">{</span><span class="n">n_orb</span><span class="si">}</span><span class="s1">,</span><span class="si">{</span><span class="n">e_vecs</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="si">}</span><span class="s1">,</span><span class="si">{</span><span class="n">e_vecs</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span><span class="si">}</span><span class="s1">]&#39;</span>
+
+        <span class="c1"># calculate alatt</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">fermi_slice</span><span class="p">:</span>
+            <span class="n">alatt_k_w</span> <span class="o">=</span> <span class="n">_calc_alatt</span><span class="p">(</span><span class="n">n_orb</span><span class="p">,</span> <span class="n">mu</span><span class="p">,</span> <span class="n">eta</span><span class="p">,</span> <span class="n">e_mat</span><span class="p">,</span> <span class="n">delta_sigma</span><span class="p">,</span> <span class="n">qp_bands</span><span class="p">,</span> <span class="n">e_vecs</span><span class="o">=</span><span class="n">e_vecs</span><span class="p">,</span>
+                                    <span class="n">trace</span><span class="o">=</span><span class="n">trace</span><span class="p">,</span> <span class="n">proj_nuk</span><span class="o">=</span><span class="n">proj_nuk</span><span class="p">,</span> <span class="o">**</span><span class="n">freq_dict</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">alatt_k_w</span> <span class="o">=</span> <span class="n">_calc_kslice</span><span class="p">(</span><span class="n">n_orb</span><span class="p">,</span> <span class="n">mu</span><span class="p">,</span> <span class="n">eta</span><span class="p">,</span> <span class="n">e_mat</span><span class="p">,</span> <span class="n">delta_sigma</span><span class="p">,</span> <span class="n">qp_bands</span><span class="p">,</span> <span class="n">e_vecs</span><span class="o">=</span><span class="n">e_vecs</span><span class="p">,</span>
+                                     <span class="n">proj_nuk</span><span class="o">=</span><span class="n">proj_nuk</span><span class="p">,</span> <span class="o">**</span><span class="n">freq_dict</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">freq_dict</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="n">freq_dict</span><span class="p">[</span><span class="s1">&#39;w_mesh&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="n">freq_dict</span><span class="p">[</span><span class="s1">&#39;window&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="n">alatt_k_w</span> <span class="o">=</span> <span class="kc">None</span>
+
+    <span class="n">tb_data</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;k_mesh&#39;</span><span class="p">:</span> <span class="n">k_1d</span><span class="p">,</span> <span class="s1">&#39;special_k&#39;</span><span class="p">:</span> <span class="n">special_k</span><span class="p">,</span> <span class="s1">&#39;k_points&#39;</span><span class="p">:</span> <span class="n">k_vec</span><span class="p">,</span>
+               <span class="s1">&#39;k_points_labels&#39;</span><span class="p">:</span> <span class="n">k_points_labels</span><span class="p">,</span> <span class="s1">&#39;e_mat&#39;</span><span class="p">:</span> <span class="n">e_mat</span><span class="p">,</span>
+               <span class="s1">&#39;e_vecs&#39;</span><span class="p">:</span> <span class="n">e_vecs</span><span class="p">,</span> <span class="s1">&#39;tb&#39;</span><span class="p">:</span> <span class="n">tb</span><span class="p">,</span> <span class="s1">&#39;mu_tb&#39;</span><span class="p">:</span> <span class="n">mu_tb</span><span class="p">,</span>
+               <span class="s1">&#39;proj_on_orb&#39;</span><span class="p">:</span> <span class="n">proj_on_orb</span><span class="p">,</span> <span class="s1">&#39;proj_nuk&#39;</span><span class="p">:</span> <span class="n">proj_nuk</span><span class="p">}</span>
+
+    <span class="k">return</span> <span class="n">tb_data</span><span class="p">,</span> <span class="n">alatt_k_w</span><span class="p">,</span> <span class="n">freq_dict</span></div>
+</pre></div>
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.</p>
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+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/w90_interface.html#orbital-order-in-the-w90-converter">orbital order in the W90 converter</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/vasp_csc.html">Interface to VASP</a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
+</ul>
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+<li class="toctree-l3"><a class="reference internal" href="../../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#file-description">file description</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#workflow">Workflow:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#important-flags">important flags:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#version-and-compilation">version and compilation:</a></li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#input-output">Input/Output</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../input_output/DMFT_input/input.html">Input</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/general.html">[general]: General parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/solver.html">[solver]: solver specific parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/dft.html">[dft]: DFT related inputs</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/advanced.html">[advanced]: Advanced inputs</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../input_output/DMFT_output/results.html">Output / results</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_output/results.html#group-structure">Group structure</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_output/results.html#subgroups">Subgroups</a></li>
+</ul>
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+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#further-details-for-running">Further details for running</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/docker.html">Docker</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#building-the-docker-image">Building the docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#pulling-a-docker-image">Pulling a docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_locally.html">Run on your machine</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#module-reference-manual">Module reference manual</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/csc_flow.html">csc_flow</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/dft_managers.html">dft_managers</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.mpi_helpers.html">dft_managers.mpi_helpers</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.qe_manager.html">dft_managers.qe_manager</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.vasp_manager.html">dft_managers.vasp_manager</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/dmft_cycle.html">dmft_cycle</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/dmft_tools.html">dmft_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.afm_mapping.html">dmft_tools.afm_mapping</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.convergence.html">dmft_tools.convergence</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.formatter.html">dmft_tools.formatter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.greens_functions_mixer.html">dmft_tools.greens_functions_mixer</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.initial_self_energies.html">dmft_tools.initial_self_energies</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.interaction_hamiltonian.html">dmft_tools.interaction_hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.legendre_filter.html">dmft_tools.legendre_filter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.manipulate_chemical_potential.html">dmft_tools.manipulate_chemical_potential</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.matheval.html">dmft_tools.matheval</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.observables.html">dmft_tools.observables</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.results_to_archive.html">dmft_tools.results_to_archive</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.solver.html">dmft_tools.solver</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/io_tools.html">io_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.dict_to_h5.html">io_tools.dict_to_h5</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.postproc_toml_dict.html">io_tools.postproc_toml_dict</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.verify_input_params.html">io_tools.verify_input_params</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_gf_imp.html">postprocessing.maxent_gf_imp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_gf_latt.html">postprocessing.maxent_gf_latt</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_sigma.html">postprocessing.maxent_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../issues.html">Support &amp; contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../issues.html#seeking-help">Seeking help</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../issues.html#improving-solid-dmft">Improving solid_dmft</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../issues.html#reporting-issues">Reporting issues</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../ChangeLog.html">Changelog</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-2-0">Version 3.2.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#general">General</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#fix">fix</a></li>
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+          <li><a href="../index.html">Module code</a> &raquo;</li>
+      <li>util.write_kslice_to_h5</li>
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+      </li>
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+             
+  <h1>Source code for util.write_kslice_to_h5</h1><div class="highlight"><pre>
+<span></span><span class="ch">#!/usr/bin/env python3</span>
+<span class="c1">################################################################################</span>
+<span class="c1">#</span>
+<span class="c1"># TRIQS: a Toolbox for Research in Interacting Quantum Systems</span>
+<span class="c1">#</span>
+<span class="c1"># Copyright (C) 2016-2018, N. Wentzell</span>
+<span class="c1"># Copyright (C) 2018-2019, Simons Foundation</span>
+<span class="c1">#   author: N. Wentzell</span>
+<span class="c1">#</span>
+<span class="c1"># TRIQS is free software: you can redistribute it and/or modify it under the</span>
+<span class="c1"># terms of the GNU General Public License as published by the Free Software</span>
+<span class="c1"># Foundation, either version 3 of the License, or (at your option) any later</span>
+<span class="c1"># version.</span>
+<span class="c1">#</span>
+<span class="c1"># TRIQS is distributed in the hope that it will be useful, but WITHOUT ANY</span>
+<span class="c1"># WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS</span>
+<span class="c1"># FOR A PARTICULAR PURPOSE. See the GNU General Public License for more</span>
+<span class="c1"># details.</span>
+<span class="c1">#</span>
+<span class="c1"># You should have received a copy of the GNU General Public License along with</span>
+<span class="c1"># TRIQS. If not, see &lt;http://www.gnu.org/licenses/&gt;.</span>
+<span class="c1">#</span>
+<span class="c1">################################################################################</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Reads the -kslice-bands.dat and the -kslice-coord.dat file (as Wannier90 writes them).</span>
+<span class="sd">The -kslice-bands.dat contains the band energies corresponding to the slices through</span>
+<span class="sd">k-space given in _kslice-coords.dat. The latter has the list of k points in 2D direct</span>
+<span class="sd">coordinates.</span>
+
+<span class="sd">This only works for k independent projectors as from a TB model or from Wannier90.</span>
+
+<span class="sd">Writes all the information back into the h5 archive in the group &#39;dft_bands_input&#39;,</span>
+<span class="sd">which is needed for plotting DMFT bands with SumkDFTTools spaghettis.</span>
+
+<span class="sd">Adapted from &quot;write_bands_to_h5.py&quot; by Sophie Beck, 2021</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="kn">import</span> <span class="nn">sys</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">h5</span> <span class="kn">import</span> <span class="n">HDFArchive</span>
+
+
+<span class="k">def</span> <span class="nf">_read_bands</span><span class="p">(</span><span class="n">seedname</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot; Reads the -kslice-bands.dat and the -kslice-coord.dat file. &quot;&quot;&quot;</span>
+
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Reading </span><span class="si">{0}</span><span class="s1">-kslice-bands.dat and </span><span class="si">{0}</span><span class="s1">-kslice-coord.dat&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">seedname</span><span class="p">))</span>
+
+    <span class="n">kpoints</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">loadtxt</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{}</span><span class="s1">-kslice-coord.dat&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">seedname</span><span class="p">),</span> <span class="n">skiprows</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">usecols</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
+    <span class="c1"># to avoid issues with scientific notation of decimals</span>
+    <span class="n">kpoints</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">around</span><span class="p">(</span><span class="n">kpoints</span><span class="p">,</span> <span class="n">decimals</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+    <span class="n">band_energies</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">loadtxt</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{}</span><span class="s1">-kslice-bands.dat&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">seedname</span><span class="p">),</span> <span class="n">skiprows</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">usecols</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">))</span>
+
+    <span class="c1"># reshape to band indices</span>
+    <span class="n">sub_bands</span> <span class="o">=</span> <span class="n">band_energies</span><span class="o">.</span><span class="n">size</span><span class="o">//</span><span class="nb">len</span><span class="p">(</span><span class="n">kpoints</span><span class="p">)</span>
+    <span class="n">band_energies</span> <span class="o">=</span> <span class="n">band_energies</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">kpoints</span><span class="p">),</span> <span class="n">sub_bands</span><span class="p">))</span>
+
+    <span class="c1"># blow up to mimic using projectors</span>
+    <span class="n">band_energies</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">np</span><span class="o">.</span><span class="n">diag</span><span class="p">(</span><span class="n">e</span><span class="p">)</span> <span class="k">for</span> <span class="n">e</span> <span class="ow">in</span> <span class="n">band_energies</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">complex</span><span class="p">)</span>
+    <span class="c1"># add dummy spin components</span>
+    <span class="n">band_energies</span> <span class="o">=</span> <span class="n">band_energies</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="n">kpoints</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">,</span> <span class="n">band_energies</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">band_energies</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
+    <span class="n">kpoints</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">kpoints</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">kpoints</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">)),</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">kpoints</span><span class="p">,</span> <span class="n">band_energies</span>
+
+
+<span class="k">def</span> <span class="nf">_read_h5_dft_input_proj_mat</span><span class="p">(</span><span class="n">archive_name</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Reads the projection matrix from the h5. In the following,</span>
+<span class="sd">    it is assumed to be k independent.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">with</span> <span class="n">HDFArchive</span><span class="p">(</span><span class="n">archive_name</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">archive</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;dft_input/proj_mat&#39;</span><span class="p">]</span>
+
+
+<span class="k">def</span> <span class="nf">_write_dft_bands_input_to_h5</span><span class="p">(</span><span class="n">archive_name</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;Writes all the information back to the h5 archive. data is a dict. &quot;&quot;&quot;</span>
+    <span class="k">with</span> <span class="n">HDFArchive</span><span class="p">(</span><span class="n">archive_name</span><span class="p">,</span> <span class="s1">&#39;a&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">archive</span><span class="p">:</span>
+        <span class="k">if</span> <span class="s1">&#39;dft_bands_input&#39;</span> <span class="ow">in</span> <span class="n">archive</span><span class="p">:</span>
+            <span class="k">del</span> <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;dft_bands_input&#39;</span><span class="p">]</span>
+        <span class="n">archive</span><span class="o">.</span><span class="n">create_group</span><span class="p">(</span><span class="s1">&#39;dft_bands_input&#39;</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">data</span><span class="p">:</span>
+            <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;dft_bands_input&#39;</span><span class="p">][</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Written results to </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">archive_name</span><span class="p">))</span>
+
+
+<div class="viewcode-block" id="main"><a class="viewcode-back" href="../../_ref/util.write_kslice_to_h5.html#util.write_kslice_to_h5.main">[docs]</a><span class="k">def</span> <span class="nf">main</span><span class="p">(</span><span class="n">seedname</span><span class="p">,</span> <span class="n">filename_archive</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Executes the program on the band data from the files &lt;seedname&gt;_bands.dat and</span>
+<span class="sd">    &lt;seedname&gt;_bands.kpt. If no seedname_archive is specified, &lt;seedname&gt;.h5 is used.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">if</span> <span class="n">filename_archive</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">filename_archive</span> <span class="o">=</span> <span class="n">seedname</span> <span class="o">+</span> <span class="s1">&#39;.h5&#39;</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Using the archive &quot;</span><span class="si">{}</span><span class="s1">&quot;&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">filename_archive</span><span class="p">))</span>
+
+    <span class="n">kpoints</span><span class="p">,</span> <span class="n">band_energies</span> <span class="o">=</span> <span class="n">_read_bands</span><span class="p">(</span><span class="n">seedname</span><span class="p">)</span>
+    <span class="n">dft_proj_mat</span> <span class="o">=</span> <span class="n">_read_h5_dft_input_proj_mat</span><span class="p">(</span><span class="n">filename_archive</span><span class="p">)</span>
+
+    <span class="n">data</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;n_k&#39;</span><span class="p">:</span> <span class="n">kpoints</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
+            <span class="s1">&#39;n_orbitals&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">kpoints</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">int</span><span class="p">)</span> <span class="o">*</span> <span class="mi">3</span><span class="p">,</span> <span class="c1"># The 1 in here only works for SO == 0</span>
+            <span class="s1">&#39;proj_mat&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">broadcast_to</span><span class="p">(</span><span class="n">dft_proj_mat</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
+                                        <span class="p">(</span><span class="n">kpoints</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="p">)</span> <span class="o">+</span> <span class="n">dft_proj_mat</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">:]),</span>
+            <span class="s1">&#39;hopping&#39;</span><span class="p">:</span> <span class="n">band_energies</span><span class="p">,</span>
+            <span class="c1"># Quantities are not used for unprojected spaghetti</span>
+            <span class="s1">&#39;n_parproj&#39;</span><span class="p">:</span> <span class="s1">&#39;none&#39;</span><span class="p">,</span>
+            <span class="s1">&#39;proj_mat_all&#39;</span><span class="p">:</span> <span class="s1">&#39;none&#39;</span><span class="p">,</span>
+            <span class="c1"># Quantity that SumkDFTTools does not need but that is nice for plots</span>
+            <span class="s1">&#39;kpoints&#39;</span><span class="p">:</span> <span class="n">kpoints</span><span class="p">}</span>
+
+    <span class="n">_write_dft_bands_input_to_h5</span><span class="p">(</span><span class="n">filename_archive</span><span class="p">,</span> <span class="n">data</span><span class="p">)</span></div>
+
+<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+        <span class="n">main</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+    <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">)</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+        <span class="n">main</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Please give a seedname (and optionally an archive to write to). Exiting.&#39;</span><span class="p">)</span>
+        <span class="n">sys</span><span class="o">.</span><span class="n">exit</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
+</pre></div>
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.</p>
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+<li class="toctree-l4"><a class="reference internal" href="../md_notes/w90_interface.html#orbital-order-in-the-w90-converter">orbital order in the W90 converter</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/vasp_csc.html">Interface to VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#general-remarks">General remarks</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#file-description">file description</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#workflow">Workflow:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#important-flags">important flags:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#version-and-compilation">version and compilation:</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#input-output">Input/Output</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../input_output/DMFT_input/input.html">Input</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/general.html">[general]: General parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/solver.html">[solver]: solver specific parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/dft.html">[dft]: DFT related inputs</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/advanced.html">[advanced]: Advanced inputs</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../input_output/DMFT_output/results.html">Output / results</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_output/results.html#group-structure">Group structure</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_output/results.html#subgroups">Subgroups</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#further-details-for-running">Further details for running</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/docker.html">Docker</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#building-the-docker-image">Building the docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#pulling-a-docker-image">Pulling a docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/run_locally.html">Run on your machine</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="reference internal" href="../documentation.html#module-reference-manual">Module reference manual</a><ul class="current">
+<li class="toctree-l3 current"><a class="current reference internal" href="#">csc_flow</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dft_managers.html">dft_managers</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="dft_managers.mpi_helpers.html">dft_managers.mpi_helpers</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dft_managers.qe_manager.html">dft_managers.qe_manager</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dft_managers.vasp_manager.html">dft_managers.vasp_manager</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="dmft_cycle.html">dmft_cycle</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dmft_tools.html">dmft_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.afm_mapping.html">dmft_tools.afm_mapping</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.formatter.html">dmft_tools.formatter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.greens_functions_mixer.html">dmft_tools.greens_functions_mixer</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.initial_self_energies.html">dmft_tools.initial_self_energies</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.legendre_filter.html">dmft_tools.legendre_filter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.manipulate_chemical_potential.html">dmft_tools.manipulate_chemical_potential</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.matheval.html">dmft_tools.matheval</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.observables.html">dmft_tools.observables</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.results_to_archive.html">dmft_tools.results_to_archive</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.solver.html">dmft_tools.solver</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="io_tools.html">io_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="io_tools.dict_to_h5.html">io_tools.dict_to_h5</a></li>
+<li class="toctree-l4"><a class="reference internal" href="io_tools.postproc_toml_dict.html">io_tools.postproc_toml_dict</a></li>
+<li class="toctree-l4"><a class="reference internal" href="io_tools.verify_input_params.html">io_tools.verify_input_params</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.maxent_gf_imp.html">postprocessing.maxent_gf_imp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.maxent_gf_latt.html">postprocessing.maxent_gf_latt</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.maxent_sigma.html">postprocessing.maxent_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../issues.html">Support &amp; contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../issues.html#seeking-help">Seeking help</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../issues.html#improving-solid-dmft">Improving solid_dmft</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../issues.html#reporting-issues">Reporting issues</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../ChangeLog.html">Changelog</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-2-0">Version 3.2.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#general">General</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#fix">fix</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#test">test</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-5">Version 3.1.5</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-4">Version 3.1.4</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-3">Version 3.1.3</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-2">Version 3.1.2</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-1">Version 3.1.1</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-0">Version 3.1.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-0-0">Version 3.0.0</a></li>
+</ul>
+</li>
+</ul>
+
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+/* CSS overrides for sphinx_rtd_theme */
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+.nbinput.nblast.container,
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+    margin-bottom: 19px;  /* padding has already 5px */
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+/* ... except between code cells! */
+.nblast.container + .nbinput.container {
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+<div class="section" id="module-csc_flow">
+<span id="csc-flow"></span><h1>csc_flow<a class="headerlink" href="#module-csc_flow" title="Permalink to this headline"></a></h1>
+<p>contains the charge self-consistency flow control functions</p>
+<dl class="py function">
+<dt class="sig sig-object py" id="csc_flow.csc_flow_control">
+<code class="sig-prename descclassname"><span class="pre">csc_flow.</span></code><code class="sig-name descname"><span class="pre">csc_flow_control</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">general_params</span></span></em>, <em><span class="n"><span class="pre">solver_params</span></span></em>, <em><span class="n"><span class="pre">dft_params</span></span></em>, <em><span class="n"><span class="pre">advanced_params</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/csc_flow.html#csc_flow_control"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#csc_flow.csc_flow_control" title="Permalink to this definition"></a></dt>
+<dd><p>Function to run the csc cycle. It writes and removes the vasp.lock file to
+start and stop Vasp, run the converter, run the dmft cycle and abort the job
+if all iterations are finished.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first last docutils">
+<dt><strong>general_params</strong><span class="classifier">dict</span></dt><dd><p>general parameters as a dict</p>
+</dd>
+<dt><strong>solver_params</strong><span class="classifier">dict</span></dt><dd><p>solver parameters as a dict</p>
+</dd>
+<dt><strong>dft_params</strong><span class="classifier">dict</span></dt><dd><p>dft parameters as a dict</p>
+</dd>
+<dt><strong>advanced_params</strong><span class="classifier">dict</span></dt><dd><p>advanced parameters as a dict</p>
+</dd>
+</dl>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+</div>
+
+
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+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="../md_notes/run_cluster.html" class="btn btn-neutral float-left" title="Running solid_dmft on a cluster" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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+
+  <div role="contentinfo">
+    <p>&#169; Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.</p>
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diff --git a/_ref/dft_managers.html b/_ref/dft_managers.html
new file mode 100644
index 00000000..123825fa
--- /dev/null
+++ b/_ref/dft_managers.html
@@ -0,0 +1,350 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" >
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>dft_managers &mdash; solid_dmft  documentation</title>
+      <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
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+</head>
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+            <img src="../_static/logo_no_text.png" class="logo" alt="Logo"/>
+          </a>
+              <div class="version">
+                3.2.0
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+              <ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../install.html">Installation</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../install.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../install.html#installation-via-pip">Installation via pip</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../install.html#manual-installation-via-cmake">Manual installation via CMake</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../install.html#docker-files-images">Docker files &amp; images</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../install.html#version-compatibility">Version compatibility</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../install.html#custom-cmake-options">Custom CMake options</a></li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../documentation.html">Documentation</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#code-structure">Code structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#dft-interface-notes">DFT interface notes</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/w90_interface.html">Wannier90 interface</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/w90_interface.html#orbital-order-in-the-w90-converter">orbital order in the W90 converter</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/vasp_csc.html">Interface to VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#general-remarks">General remarks</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#file-description">file description</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#workflow">Workflow:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#important-flags">important flags:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#version-and-compilation">version and compilation:</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#input-output">Input/Output</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../input_output/DMFT_input/input.html">Input</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/general.html">[general]: General parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/solver.html">[solver]: solver specific parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/dft.html">[dft]: DFT related inputs</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/advanced.html">[advanced]: Advanced inputs</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../input_output/DMFT_output/results.html">Output / results</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_output/results.html#group-structure">Group structure</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_output/results.html#subgroups">Subgroups</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#further-details-for-running">Further details for running</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/docker.html">Docker</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#building-the-docker-image">Building the docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#pulling-a-docker-image">Pulling a docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/run_locally.html">Run on your machine</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="reference internal" href="../documentation.html#module-reference-manual">Module reference manual</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="csc_flow.html">csc_flow</a></li>
+<li class="toctree-l3 current"><a class="current reference internal" href="#">dft_managers</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="dft_managers.mpi_helpers.html">dft_managers.mpi_helpers</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dft_managers.qe_manager.html">dft_managers.qe_manager</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dft_managers.vasp_manager.html">dft_managers.vasp_manager</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="dmft_cycle.html">dmft_cycle</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dmft_tools.html">dmft_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.afm_mapping.html">dmft_tools.afm_mapping</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.convergence.html">dmft_tools.convergence</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.formatter.html">dmft_tools.formatter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.greens_functions_mixer.html">dmft_tools.greens_functions_mixer</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.initial_self_energies.html">dmft_tools.initial_self_energies</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.interaction_hamiltonian.html">dmft_tools.interaction_hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.legendre_filter.html">dmft_tools.legendre_filter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.manipulate_chemical_potential.html">dmft_tools.manipulate_chemical_potential</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.matheval.html">dmft_tools.matheval</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.observables.html">dmft_tools.observables</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.results_to_archive.html">dmft_tools.results_to_archive</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.solver.html">dmft_tools.solver</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="io_tools.html">io_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="io_tools.dict_to_h5.html">io_tools.dict_to_h5</a></li>
+<li class="toctree-l4"><a class="reference internal" href="io_tools.postproc_toml_dict.html">io_tools.postproc_toml_dict</a></li>
+<li class="toctree-l4"><a class="reference internal" href="io_tools.verify_input_params.html">io_tools.verify_input_params</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.maxent_gf_imp.html">postprocessing.maxent_gf_imp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.maxent_gf_latt.html">postprocessing.maxent_gf_latt</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.maxent_sigma.html">postprocessing.maxent_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
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+<div class="section" id="module-dft_managers">
+<span id="dft-managers"></span><h1>dft_managers<a class="headerlink" href="#module-dft_managers" title="Permalink to this headline"></a></h1>
+<p>DFT code driver modules</p>
+<p class="rubric">Modules</p>
+<table border="1" class="longtable docutils align-default">
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+<col width="10%" />
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+<tr class="row-odd"><td><a class="reference internal" href="dft_managers.mpi_helpers.html#module-dft_managers.mpi_helpers" title="dft_managers.mpi_helpers"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dft_managers.mpi_helpers</span></code></a></td>
+<td>Contains the handling of the QE process.</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="dft_managers.qe_manager.html#module-dft_managers.qe_manager" title="dft_managers.qe_manager"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dft_managers.qe_manager</span></code></a></td>
+<td>Contains the function to run a QuantumEspresso iteration.</td>
+</tr>
+<tr class="row-odd"><td><a class="reference internal" href="dft_managers.vasp_manager.html#module-dft_managers.vasp_manager" title="dft_managers.vasp_manager"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dft_managers.vasp_manager</span></code></a></td>
+<td>Contains the handling of the VASP process.</td>
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+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
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+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
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+</ul>
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+<div class="section" id="module-dft_managers.mpi_helpers">
+<span id="dft-managers-mpi-helpers"></span><h1>dft_managers.mpi_helpers<a class="headerlink" href="#module-dft_managers.mpi_helpers" title="Permalink to this headline"></a></h1>
+<p>Contains the handling of the QE process. It can start QE, reactivate it,
+check if the lock file is there and finally kill QE. Needed for CSC calculations.</p>
+<dl class="py function">
+<dt class="sig sig-object py" id="dft_managers.mpi_helpers.create_hostfile">
+<code class="sig-prename descclassname"><span class="pre">dft_managers.mpi_helpers.</span></code><code class="sig-name descname"><span class="pre">create_hostfile</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">number_cores</span></span></em>, <em><span class="n"><span class="pre">cluster_name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dft_managers/mpi_helpers.html#create_hostfile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dft_managers.mpi_helpers.create_hostfile" title="Permalink to this definition"></a></dt>
+<dd><p>Writes a host file for the mpirun. This tells mpi which nodes to ssh into
+and start VASP on. The format of the hist file depends on the type of MPI
+that is used.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
+<dt><strong>number_cores: int, the number of cores that vasp runs on</strong></dt><dd></dd>
+<dt><strong>cluster_name: string, the name of the server</strong></dt><dd></dd>
+</dl>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
+<dt>string: name of the hostfile if not run locally and if called by master node</dt><dd></dd>
+</dl>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="dft_managers.mpi_helpers.find_path_to_mpi_command">
+<code class="sig-prename descclassname"><span class="pre">dft_managers.mpi_helpers.</span></code><code class="sig-name descname"><span class="pre">find_path_to_mpi_command</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">env_vars</span></span></em>, <em><span class="n"><span class="pre">mpi_exe</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dft_managers/mpi_helpers.html#find_path_to_mpi_command"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dft_managers.mpi_helpers.find_path_to_mpi_command" title="Permalink to this definition"></a></dt>
+<dd><p>Finds the complete path for the mpi executable by scanning the directories
+of $PATH.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
+<dt><strong>env_vars: dict of string, environment variables containing PATH</strong></dt><dd></dd>
+<dt><strong>mpi_exe: string, mpi command</strong></dt><dd></dd>
+</dl>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
+<dt>string: absolute path to mpi command</dt><dd></dd>
+</dl>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="dft_managers.mpi_helpers.get_mpi_arguments">
+<code class="sig-prename descclassname"><span class="pre">dft_managers.mpi_helpers.</span></code><code class="sig-name descname"><span class="pre">get_mpi_arguments</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">mpi_profile</span></span></em>, <em><span class="n"><span class="pre">mpi_exe</span></span></em>, <em><span class="n"><span class="pre">number_cores</span></span></em>, <em><span class="n"><span class="pre">dft_exe</span></span></em>, <em><span class="n"><span class="pre">hostfile</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dft_managers/mpi_helpers.html#get_mpi_arguments"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dft_managers.mpi_helpers.get_mpi_arguments" title="Permalink to this definition"></a></dt>
+<dd><p>Depending on the settings of the cluster and the type of MPI used,
+the arguments to the mpi call have to be different. The most technical part
+of the vasp handler.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
+<dt><strong>cluster_name: string, name of the cluster so that settings can be tailored to it</strong></dt><dd></dd>
+<dt><strong>mpi_exe: string, mpi command</strong></dt><dd></dd>
+<dt><strong>number_cores: int, the number of cores that vasp runs on</strong></dt><dd></dd>
+<dt><strong>dft_exe: string, the command to start the DFT code</strong></dt><dd></dd>
+<dt><strong>hostfile: string, name of the hostfile</strong></dt><dd></dd>
+</dl>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
+<dt>list of string: arguments to start mpi with</dt><dd></dd>
+</dl>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="dft_managers.mpi_helpers.poll_barrier">
+<code class="sig-prename descclassname"><span class="pre">dft_managers.mpi_helpers.</span></code><code class="sig-name descname"><span class="pre">poll_barrier</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">comm</span></span></em>, <em><span class="n"><span class="pre">poll_interval</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dft_managers/mpi_helpers.html#poll_barrier"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dft_managers.mpi_helpers.poll_barrier" title="Permalink to this definition"></a></dt>
+<dd><p>Use asynchronous synchronization, otherwise mpi.barrier uses up all the CPU time during
+the run of subprocess.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first last docutils">
+<dt><strong>comm: MPI communicator</strong></dt><dd></dd>
+<dt><strong>poll_interval: float, time step for pinging the status of the sleeping ranks</strong></dt><dd></dd>
+</dl>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
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+<li class="toctree-l1 current"><a class="reference internal" href="../documentation.html">Documentation</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#code-structure">Code structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#dft-interface-notes">DFT interface notes</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/w90_interface.html">Wannier90 interface</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/w90_interface.html#orbital-order-in-the-w90-converter">orbital order in the W90 converter</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/vasp_csc.html">Interface to VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#general-remarks">General remarks</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#file-description">file description</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#workflow">Workflow:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#important-flags">important flags:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#version-and-compilation">version and compilation:</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#input-output">Input/Output</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../input_output/DMFT_input/input.html">Input</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/general.html">[general]: General parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/solver.html">[solver]: solver specific parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/dft.html">[dft]: DFT related inputs</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/advanced.html">[advanced]: Advanced inputs</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../input_output/DMFT_output/results.html">Output / results</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_output/results.html#group-structure">Group structure</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_output/results.html#subgroups">Subgroups</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#further-details-for-running">Further details for running</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/docker.html">Docker</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#building-the-docker-image">Building the docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#pulling-a-docker-image">Pulling a docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/run_locally.html">Run on your machine</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="reference internal" href="../documentation.html#module-reference-manual">Module reference manual</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="csc_flow.html">csc_flow</a></li>
+<li class="toctree-l3 current"><a class="reference internal" href="dft_managers.html">dft_managers</a><ul class="current">
+<li class="toctree-l4"><a class="reference internal" href="dft_managers.mpi_helpers.html">dft_managers.mpi_helpers</a></li>
+<li class="toctree-l4 current"><a class="current reference internal" href="#">dft_managers.qe_manager</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dft_managers.vasp_manager.html">dft_managers.vasp_manager</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="dmft_cycle.html">dmft_cycle</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dmft_tools.html">dmft_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.afm_mapping.html">dmft_tools.afm_mapping</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.convergence.html">dmft_tools.convergence</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.formatter.html">dmft_tools.formatter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.greens_functions_mixer.html">dmft_tools.greens_functions_mixer</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.initial_self_energies.html">dmft_tools.initial_self_energies</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.interaction_hamiltonian.html">dmft_tools.interaction_hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.legendre_filter.html">dmft_tools.legendre_filter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.manipulate_chemical_potential.html">dmft_tools.manipulate_chemical_potential</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.matheval.html">dmft_tools.matheval</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.observables.html">dmft_tools.observables</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.results_to_archive.html">dmft_tools.results_to_archive</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.solver.html">dmft_tools.solver</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="io_tools.html">io_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="io_tools.dict_to_h5.html">io_tools.dict_to_h5</a></li>
+<li class="toctree-l4"><a class="reference internal" href="io_tools.postproc_toml_dict.html">io_tools.postproc_toml_dict</a></li>
+<li class="toctree-l4"><a class="reference internal" href="io_tools.verify_input_params.html">io_tools.verify_input_params</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.maxent_gf_imp.html">postprocessing.maxent_gf_imp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.maxent_gf_latt.html">postprocessing.maxent_gf_latt</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.maxent_sigma.html">postprocessing.maxent_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../issues.html">Support &amp; contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../issues.html#seeking-help">Seeking help</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../issues.html#improving-solid-dmft">Improving solid_dmft</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../issues.html#reporting-issues">Reporting issues</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../ChangeLog.html">Changelog</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-2-0">Version 3.2.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#general">General</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#fix">fix</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#build">build</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#doc">doc</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#test">test</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-5">Version 3.1.5</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-4">Version 3.1.4</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-3">Version 3.1.3</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-2">Version 3.1.2</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-1">Version 3.1.1</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-0">Version 3.1.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-0-0">Version 3.0.0</a></li>
+</ul>
+</li>
+</ul>
+
+        </div>
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+      <li>dft_managers.qe_manager</li>
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+            <a href="../_sources/_ref/dft_managers.qe_manager.rst.txt" rel="nofollow"> View page source</a>
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+             
+  
+<style>
+/* CSS overrides for sphinx_rtd_theme */
+
+/* 24px margin */
+.nbinput.nblast.container,
+.nboutput.nblast.container {
+    margin-bottom: 19px;  /* padding has already 5px */
+}
+
+/* ... except between code cells! */
+.nblast.container + .nbinput.container {
+    margin-top: -19px;
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+    margin-right: 4px;  /* make room for the exclamation icon */
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+.math {
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+</style>
+<div class="section" id="module-dft_managers.qe_manager">
+<span id="dft-managers-qe-manager"></span><h1>dft_managers.qe_manager<a class="headerlink" href="#module-dft_managers.qe_manager" title="Permalink to this headline"></a></h1>
+<p>Contains the function to run a QuantumEspresso iteration. Needed for CSC calculations.</p>
+<dl class="py function">
+<dt class="sig sig-object py" id="dft_managers.qe_manager.read_dft_energy">
+<code class="sig-prename descclassname"><span class="pre">dft_managers.qe_manager.</span></code><code class="sig-name descname"><span class="pre">read_dft_energy</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">seedname</span></span></em>, <em><span class="n"><span class="pre">iter_dmft</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dft_managers/qe_manager.html#read_dft_energy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dft_managers.qe_manager.read_dft_energy" title="Permalink to this definition"></a></dt>
+<dd><p>Reads DFT energy from quantum espresso’s out files</p>
+<ol class="arabic simple">
+<li>At the first iteration, the DFT energy is read from the scf file.</li>
+<li>After the first iteration the band energy computed in the mod_scf calculation is wrong,
+and needs to be subtracted from the reported total energy. The correct band energy
+is computed in the nscf calculation.</li>
+</ol>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="dft_managers.qe_manager.run">
+<code class="sig-prename descclassname"><span class="pre">dft_managers.qe_manager.</span></code><code class="sig-name descname"><span class="pre">run</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">number_cores</span></span></em>, <em><span class="n"><span class="pre">qe_file_ext</span></span></em>, <em><span class="n"><span class="pre">qe_exec</span></span></em>, <em><span class="n"><span class="pre">mpi_profile</span></span></em>, <em><span class="n"><span class="pre">seedname</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dft_managers/qe_manager.html#run"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dft_managers.qe_manager.run" title="Permalink to this definition"></a></dt>
+<dd><p>Starts the VASP child process. Takes care of initializing a clean
+environment for the child process. This is needed so that VASP does not
+get confused with all the standard slurm environment variables.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first last docutils">
+<dt><strong>number_cores: int, the number of cores that vasp runs on</strong></dt><dd></dd>
+<dt><strong>qe_file_ext: string, qe executable</strong></dt><dd></dd>
+<dt><strong>qe_exec: string, path to qe executables</strong></dt><dd></dd>
+<dt><strong>mpi_profile: string, name of the cluster so that settings can be tailored to it</strong></dt><dd></dd>
+</dl>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+</div>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
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+
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+    <p>&#169; Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.</p>
+  </div>
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diff --git a/_ref/dft_managers.vasp_manager.html b/_ref/dft_managers.vasp_manager.html
new file mode 100644
index 00000000..9a7812a2
--- /dev/null
+++ b/_ref/dft_managers.vasp_manager.html
@@ -0,0 +1,388 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" >
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>dft_managers.vasp_manager &mdash; solid_dmft  documentation</title>
+      <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
+      <link rel="stylesheet" href="../_static/css/custom.css" type="text/css" />
+      <link rel="stylesheet" href="../_static/plot_directive.css" type="text/css" />
+    <link rel="shortcut icon" href="../_static/favicon.ico"/>
+  
+        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
+        <script src="../_static/jquery.js"></script>
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+    <script src="../_static/js/theme.js"></script>
+    <link rel="index" title="Index" href="../genindex.html" />
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+    <link rel="next" title="dmft_cycle" href="dmft_cycle.html" />
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+</head>
+
+<body class="wy-body-for-nav"> 
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+      <div class="wy-side-scroll">
+        <div class="wy-side-nav-search"  style="background: #7E588A" >
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+            <img src="../_static/logo_no_text.png" class="logo" alt="Logo"/>
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+              <div class="version">
+                3.2.0
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+              <ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../install.html">Installation</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../install.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../install.html#installation-via-pip">Installation via pip</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../install.html#manual-installation-via-cmake">Manual installation via CMake</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../install.html#docker-files-images">Docker files &amp; images</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../install.html#version-compatibility">Version compatibility</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../install.html#custom-cmake-options">Custom CMake options</a></li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../documentation.html">Documentation</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#code-structure">Code structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#dft-interface-notes">DFT interface notes</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/w90_interface.html">Wannier90 interface</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/w90_interface.html#orbital-order-in-the-w90-converter">orbital order in the W90 converter</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/vasp_csc.html">Interface to VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#general-remarks">General remarks</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#file-description">file description</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#workflow">Workflow:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#important-flags">important flags:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#version-and-compilation">version and compilation:</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#input-output">Input/Output</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../input_output/DMFT_input/input.html">Input</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/general.html">[general]: General parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/solver.html">[solver]: solver specific parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/dft.html">[dft]: DFT related inputs</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/advanced.html">[advanced]: Advanced inputs</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../input_output/DMFT_output/results.html">Output / results</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_output/results.html#group-structure">Group structure</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_output/results.html#subgroups">Subgroups</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#further-details-for-running">Further details for running</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/docker.html">Docker</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#building-the-docker-image">Building the docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#pulling-a-docker-image">Pulling a docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/run_locally.html">Run on your machine</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="reference internal" href="../documentation.html#module-reference-manual">Module reference manual</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="csc_flow.html">csc_flow</a></li>
+<li class="toctree-l3 current"><a class="reference internal" href="dft_managers.html">dft_managers</a><ul class="current">
+<li class="toctree-l4"><a class="reference internal" href="dft_managers.mpi_helpers.html">dft_managers.mpi_helpers</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dft_managers.qe_manager.html">dft_managers.qe_manager</a></li>
+<li class="toctree-l4 current"><a class="current reference internal" href="#">dft_managers.vasp_manager</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="dmft_cycle.html">dmft_cycle</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dmft_tools.html">dmft_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.afm_mapping.html">dmft_tools.afm_mapping</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.convergence.html">dmft_tools.convergence</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.formatter.html">dmft_tools.formatter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.greens_functions_mixer.html">dmft_tools.greens_functions_mixer</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.initial_self_energies.html">dmft_tools.initial_self_energies</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.interaction_hamiltonian.html">dmft_tools.interaction_hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.legendre_filter.html">dmft_tools.legendre_filter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.manipulate_chemical_potential.html">dmft_tools.manipulate_chemical_potential</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.matheval.html">dmft_tools.matheval</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.observables.html">dmft_tools.observables</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.results_to_archive.html">dmft_tools.results_to_archive</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.solver.html">dmft_tools.solver</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="io_tools.html">io_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="io_tools.dict_to_h5.html">io_tools.dict_to_h5</a></li>
+<li class="toctree-l4"><a class="reference internal" href="io_tools.postproc_toml_dict.html">io_tools.postproc_toml_dict</a></li>
+<li class="toctree-l4"><a class="reference internal" href="io_tools.verify_input_params.html">io_tools.verify_input_params</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.maxent_gf_imp.html">postprocessing.maxent_gf_imp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.maxent_gf_latt.html">postprocessing.maxent_gf_latt</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.maxent_sigma.html">postprocessing.maxent_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
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+<div class="section" id="module-dft_managers.vasp_manager">
+<span id="dft-managers-vasp-manager"></span><h1>dft_managers.vasp_manager<a class="headerlink" href="#module-dft_managers.vasp_manager" title="Permalink to this headline"></a></h1>
+<p>Contains the handling of the VASP process. It can start VASP, reactivate it,
+check if the lock file is there and finally kill VASP. Needed for CSC calculations.</p>
+<p>This functionality is contained in the simpler public functions.</p>
+<dl class="py function">
+<dt class="sig sig-object py" id="dft_managers.vasp_manager.kill">
+<code class="sig-prename descclassname"><span class="pre">dft_managers.vasp_manager.</span></code><code class="sig-name descname"><span class="pre">kill</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">vasp_process_id</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dft_managers/vasp_manager.html#kill"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dft_managers.vasp_manager.kill" title="Permalink to this definition"></a></dt>
+<dd><p>Kills the VASP process.</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="dft_managers.vasp_manager.read_dft_energy">
+<code class="sig-prename descclassname"><span class="pre">dft_managers.vasp_manager.</span></code><code class="sig-name descname"><span class="pre">read_dft_energy</span></code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dft_managers/vasp_manager.html#read_dft_energy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dft_managers.vasp_manager.read_dft_energy" title="Permalink to this definition"></a></dt>
+<dd><p>Reads DFT energy from the last line of Vasp’s OSZICAR.</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="dft_managers.vasp_manager.read_irred_kpoints">
+<code class="sig-prename descclassname"><span class="pre">dft_managers.vasp_manager.</span></code><code class="sig-name descname"><span class="pre">read_irred_kpoints</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">kpts</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dft_managers/vasp_manager.html#read_irred_kpoints"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dft_managers.vasp_manager.read_irred_kpoints" title="Permalink to this definition"></a></dt>
+<dd><p>Reads the indices of the irreducible k-points from the OUTCAR.</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="dft_managers.vasp_manager.remove_legacy_projections_suppressed">
+<code class="sig-prename descclassname"><span class="pre">dft_managers.vasp_manager.</span></code><code class="sig-name descname"><span class="pre">remove_legacy_projections_suppressed</span></code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dft_managers/vasp_manager.html#remove_legacy_projections_suppressed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dft_managers.vasp_manager.remove_legacy_projections_suppressed" title="Permalink to this definition"></a></dt>
+<dd><p>Removes legacy file vasp.suppress_projs if present.</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="dft_managers.vasp_manager.run_charge_update">
+<code class="sig-prename descclassname"><span class="pre">dft_managers.vasp_manager.</span></code><code class="sig-name descname"><span class="pre">run_charge_update</span></code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dft_managers/vasp_manager.html#run_charge_update"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dft_managers.vasp_manager.run_charge_update" title="Permalink to this definition"></a></dt>
+<dd><p>Performs one step of the charge update with VASP by creating the vasp.lock
+file and then waiting until it gets delete by VASP when it has finished.</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="dft_managers.vasp_manager.run_initial_scf">
+<code class="sig-prename descclassname"><span class="pre">dft_managers.vasp_manager.</span></code><code class="sig-name descname"><span class="pre">run_initial_scf</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">number_cores</span></span></em>, <em><span class="n"><span class="pre">vasp_command</span></span></em>, <em><span class="n"><span class="pre">cluster_name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dft_managers/vasp_manager.html#run_initial_scf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dft_managers.vasp_manager.run_initial_scf" title="Permalink to this definition"></a></dt>
+<dd><p>Starts the VASP child process. Takes care of initializing a clean
+environment for the child process. This is needed so that VASP does not
+get confused with all the standard slurm environment variables. Returns when
+VASP has completed its initial scf cycle.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first last docutils">
+<dt><strong>number_cores: int, the number of cores that vasp runs on</strong></dt><dd></dd>
+<dt><strong>vasp_command: string, the command to start vasp</strong></dt><dd></dd>
+<dt><strong>cluster_name: string, name of the cluster so that settings can be tailored to it</strong></dt><dd></dd>
+</dl>
+</td>
+</tr>
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+<li class="toctree-l3"><a class="reference internal" href="../md_notes/w90_interface.html">Wannier90 interface</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/w90_interface.html#orbital-order-in-the-w90-converter">orbital order in the W90 converter</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/vasp_csc.html">Interface to VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#general-remarks">General remarks</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#file-description">file description</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#workflow">Workflow:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#important-flags">important flags:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#version-and-compilation">version and compilation:</a></li>
+</ul>
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+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#input-output">Input/Output</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../input_output/DMFT_input/input.html">Input</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/general.html">[general]: General parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/solver.html">[solver]: solver specific parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/dft.html">[dft]: DFT related inputs</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/advanced.html">[advanced]: Advanced inputs</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../input_output/DMFT_output/results.html">Output / results</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_output/results.html#group-structure">Group structure</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#further-details-for-running">Further details for running</a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/run_locally.html">Run on your machine</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="reference internal" href="../documentation.html#module-reference-manual">Module reference manual</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="csc_flow.html">csc_flow</a></li>
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+</ul>
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+</ul>
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+<li class="toctree-l3"><a class="reference internal" href="postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../issues.html">Support &amp; contribute</a><ul>
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+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-0">Version 3.1.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-0-0">Version 3.0.0</a></li>
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+<div class="section" id="module-dmft_cycle">
+<span id="dmft-cycle"></span><h1>dmft_cycle<a class="headerlink" href="#module-dmft_cycle" title="Permalink to this headline"></a></h1>
+<p>main DMFT cycle, DMFT step, and helper functions</p>
+<dl class="py function">
+<dt class="sig sig-object py" id="dmft_cycle.dmft_cycle">
+<code class="sig-prename descclassname"><span class="pre">dmft_cycle.</span></code><code class="sig-name descname"><span class="pre">dmft_cycle</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">general_params</span></span></em>, <em><span class="n"><span class="pre">solver_params</span></span></em>, <em><span class="n"><span class="pre">advanced_params</span></span></em>, <em><span class="n"><span class="pre">dft_params</span></span></em>, <em><span class="n"><span class="pre">n_iter</span></span></em>, <em><span class="n"><span class="pre">dft_irred_kpt_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em><span class="n"><span class="pre">dft_energy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dmft_cycle.html#dmft_cycle"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dmft_cycle.dmft_cycle" title="Permalink to this definition"></a></dt>
+<dd><p>main dmft cycle that works for one shot and CSC equally</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
+<dt><strong>general_params</strong><span class="classifier">dict</span></dt><dd><p>general parameters as a dict</p>
+</dd>
+<dt><strong>solver_params</strong><span class="classifier">dict</span></dt><dd><p>solver parameters as a dict</p>
+</dd>
+<dt><strong>advanced_params</strong><span class="classifier">dict</span></dt><dd><p>advanced parameters as a dict</p>
+</dd>
+<dt><strong>observables</strong><span class="classifier">dict</span></dt><dd><p>current observable array for calculation</p>
+</dd>
+<dt><strong>n_iter</strong><span class="classifier">int</span></dt><dd><p>number of iterations to be executed</p>
+</dd>
+<dt><strong>dft_irred_kpt_indices: iterable of int</strong></dt><dd><p>If given, writes density correction for csc calculations only for
+irreducible kpoints</p>
+</dd>
+</dl>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
+<dt><strong>observables</strong><span class="classifier">dict</span></dt><dd><p>updated observable array for calculation</p>
+</dd>
+</dl>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+</div>
+
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+    <p>&#169; Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.</p>
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+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#dft-interface-notes">DFT interface notes</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/w90_interface.html">Wannier90 interface</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/w90_interface.html#orbital-order-in-the-w90-converter">orbital order in the W90 converter</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/vasp_csc.html">Interface to VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#general-remarks">General remarks</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#file-description">file description</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#workflow">Workflow:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#important-flags">important flags:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#version-and-compilation">version and compilation:</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#input-output">Input/Output</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../input_output/DMFT_input/input.html">Input</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/general.html">[general]: General parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/solver.html">[solver]: solver specific parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/dft.html">[dft]: DFT related inputs</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/advanced.html">[advanced]: Advanced inputs</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../input_output/DMFT_output/results.html">Output / results</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_output/results.html#group-structure">Group structure</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_output/results.html#subgroups">Subgroups</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#further-details-for-running">Further details for running</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/docker.html">Docker</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#building-the-docker-image">Building the docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#pulling-a-docker-image">Pulling a docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/run_locally.html">Run on your machine</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="reference internal" href="../documentation.html#module-reference-manual">Module reference manual</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="csc_flow.html">csc_flow</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dft_managers.html">dft_managers</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="dft_managers.mpi_helpers.html">dft_managers.mpi_helpers</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dft_managers.qe_manager.html">dft_managers.qe_manager</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dft_managers.vasp_manager.html">dft_managers.vasp_manager</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="dmft_cycle.html">dmft_cycle</a></li>
+<li class="toctree-l3 current"><a class="reference internal" href="dmft_tools.html">dmft_tools</a><ul class="current">
+<li class="toctree-l4 current"><a class="current reference internal" href="#">dmft_tools.afm_mapping</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.convergence.html">dmft_tools.convergence</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.formatter.html">dmft_tools.formatter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.greens_functions_mixer.html">dmft_tools.greens_functions_mixer</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.initial_self_energies.html">dmft_tools.initial_self_energies</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.interaction_hamiltonian.html">dmft_tools.interaction_hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.legendre_filter.html">dmft_tools.legendre_filter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.manipulate_chemical_potential.html">dmft_tools.manipulate_chemical_potential</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.matheval.html">dmft_tools.matheval</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.observables.html">dmft_tools.observables</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.results_to_archive.html">dmft_tools.results_to_archive</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.solver.html">dmft_tools.solver</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="io_tools.html">io_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="io_tools.dict_to_h5.html">io_tools.dict_to_h5</a></li>
+<li class="toctree-l4"><a class="reference internal" href="io_tools.postproc_toml_dict.html">io_tools.postproc_toml_dict</a></li>
+<li class="toctree-l4"><a class="reference internal" href="io_tools.verify_input_params.html">io_tools.verify_input_params</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.maxent_gf_imp.html">postprocessing.maxent_gf_imp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.maxent_gf_latt.html">postprocessing.maxent_gf_latt</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.maxent_sigma.html">postprocessing.maxent_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../issues.html">Support &amp; contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../issues.html#seeking-help">Seeking help</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../issues.html#improving-solid-dmft">Improving solid_dmft</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../issues.html#reporting-issues">Reporting issues</a></li>
+</ul>
+</li>
+</ul>
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+<li class="toctree-l1"><a class="reference internal" href="../ChangeLog.html">Changelog</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-2-0">Version 3.2.0</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-4">Version 3.1.4</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-3">Version 3.1.3</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-2">Version 3.1.2</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-1">Version 3.1.1</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-0">Version 3.1.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-0-0">Version 3.0.0</a></li>
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+<div class="section" id="module-dmft_tools.afm_mapping">
+<span id="dmft-tools-afm-mapping"></span><h1>dmft_tools.afm_mapping<a class="headerlink" href="#module-dmft_tools.afm_mapping" title="Permalink to this headline"></a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="dmft_tools.afm_mapping.determine">
+<code class="sig-prename descclassname"><span class="pre">dmft_tools.afm_mapping.</span></code><code class="sig-name descname"><span class="pre">determine</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">general_params</span></span></em>, <em><span class="n"><span class="pre">archive</span></span></em>, <em><span class="n"><span class="pre">n_inequiv_shells</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dmft_tools/afm_mapping.html#determine"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dmft_tools.afm_mapping.determine" title="Permalink to this definition"></a></dt>
+<dd><p>Determines the symmetries that are used in AFM calculations. These
+symmetries can then be used to copy the self-energies from one impurity to
+another by exchanging up/down channels for speedup and accuracy.</p>
+</dd></dl>
+
+</div>
+
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+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
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+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
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+<li class="toctree-l3"><a class="reference internal" href="util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
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+</li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../issues.html">Support &amp; contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../issues.html#seeking-help">Seeking help</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../issues.html#improving-solid-dmft">Improving solid_dmft</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../issues.html#reporting-issues">Reporting issues</a></li>
+</ul>
+</li>
+</ul>
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+<div class="section" id="module-dmft_tools.convergence">
+<span id="dmft-tools-convergence"></span><h1>dmft_tools.convergence<a class="headerlink" href="#module-dmft_tools.convergence" title="Permalink to this headline"></a></h1>
+<p>contain helper functions to check convergence</p>
+<dl class="py function">
+<dt class="sig sig-object py" id="dmft_tools.convergence.calc_convergence_quantities">
+<code class="sig-prename descclassname"><span class="pre">dmft_tools.convergence.</span></code><code class="sig-name descname"><span class="pre">calc_convergence_quantities</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">sum_k</span></span></em>, <em><span class="n"><span class="pre">general_params</span></span></em>, <em><span class="n"><span class="pre">conv_obs</span></span></em>, <em><span class="n"><span class="pre">observables</span></span></em>, <em><span class="n"><span class="pre">solvers</span></span></em>, <em><span class="n"><span class="pre">G0_old</span></span></em>, <em><span class="n"><span class="pre">G_loc_all</span></span></em>, <em><span class="n"><span class="pre">Sigma_freq_previous</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dmft_tools/convergence.html#calc_convergence_quantities"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dmft_tools.convergence.calc_convergence_quantities" title="Permalink to this definition"></a></dt>
+<dd><p>Calculations convergence quantities, i.e. the difference in observables
+between the last and second to last iteration.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
+<dt><strong>sum_k</strong><span class="classifier">SumK Object instances</span></dt><dd></dd>
+<dt><strong>general_params</strong><span class="classifier">dict</span></dt><dd><p>general parameters as a dict</p>
+</dd>
+<dt><strong>conv_obs</strong><span class="classifier">list of dicts</span></dt><dd><p>convergence observable arrays</p>
+</dd>
+<dt><strong>observables</strong><span class="classifier">list of dicts</span></dt><dd><p>observable arrays</p>
+</dd>
+<dt><strong>solvers</strong><span class="classifier">solver objects</span></dt><dd></dd>
+<dt><strong>G0_old</strong><span class="classifier">list of block Gf object</span></dt><dd><p>last G0_freq</p>
+</dd>
+<dt><strong>G_loc_all</strong><span class="classifier">list of block Gf objects</span></dt><dd><p>G_loc extracted from before imp solver</p>
+</dd>
+<dt><strong>Sigma_freq_previous</strong><span class="classifier">list of block Gf objects</span></dt><dd><p>previous impurity sigma to compare with</p>
+</dd>
+</dl>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
+<dt><strong>conv_obs</strong><span class="classifier">list of dicts</span></dt><dd><p>updated convergence observable arrays</p>
+</dd>
+</dl>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="dmft_tools.convergence.check_convergence">
+<code class="sig-prename descclassname"><span class="pre">dmft_tools.convergence.</span></code><code class="sig-name descname"><span class="pre">check_convergence</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">n_inequiv_shells</span></span></em>, <em><span class="n"><span class="pre">general_params</span></span></em>, <em><span class="n"><span class="pre">conv_obs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dmft_tools/convergence.html#check_convergence"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dmft_tools.convergence.check_convergence" title="Permalink to this definition"></a></dt>
+<dd><p>check last iteration for convergence</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
+<dt><strong>n_inequiv_shells</strong><span class="classifier">int</span></dt><dd><p>Number of inequivalent shells as saved in SumkDFT object</p>
+</dd>
+<dt><strong>general_params</strong><span class="classifier">dict</span></dt><dd><p>general parameters as a dict</p>
+</dd>
+<dt><strong>conv_obs</strong><span class="classifier">list of dicts</span></dt><dd><p>convergence observable arrays</p>
+</dd>
+</dl>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
+<dt><strong>is_converged</strong><span class="classifier">bool</span></dt><dd><p>true if desired accuracy is reached. None if no convergence criterion
+is set</p>
+</dd>
+</dl>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="dmft_tools.convergence.max_G_diff">
+<code class="sig-prename descclassname"><span class="pre">dmft_tools.convergence.</span></code><code class="sig-name descname"><span class="pre">max_G_diff</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">G1</span></span></em>, <em><span class="n"><span class="pre">G2</span></span></em>, <em><span class="n"><span class="pre">norm_temp</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dmft_tools/convergence.html#max_G_diff"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dmft_tools.convergence.max_G_diff" title="Permalink to this definition"></a></dt>
+<dd><p>calculates difference between two block Gfs
+uses numpy linalg norm on the last two indices first
+and then the norm along the mesh axis. The result is divided
+by sqrt(beta) for MeshImFreq and by sqrt(beta/#taupoints) for
+MeshImTime.</p>
+<p>1/ (2* sqrt(beta)) sqrt( sum_n sum_ij [abs(G1 - G2)_ij(w_n)]^2 )</p>
+<p>this is only done for MeshImFreq Gf objects, for all other
+meshes the weights are set to 1</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first last docutils">
+<dt><strong>G1</strong><span class="classifier">Gf or BlockGf to compare</span></dt><dd></dd>
+<dt><strong>G2</strong><span class="classifier">Gf or BlockGf to compare</span></dt><dd></dd>
+<dt><strong>norm_temp: bool, default = True</strong></dt><dd><p>divide by an additional sqrt(beta) to account for temperature scaling
+only correct for uniformly distributed error.</p>
+</dd>
+<dt><strong>__Returns:__</strong></dt><dd></dd>
+<dt><strong>diff</strong><span class="classifier">float</span></dt><dd><p>difference between the two Gfs</p>
+</dd>
+</dl>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="dmft_tools.convergence.prep_conv_file">
+<code class="sig-prename descclassname"><span class="pre">dmft_tools.convergence.</span></code><code class="sig-name descname"><span class="pre">prep_conv_file</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">general_params</span></span></em>, <em><span class="n"><span class="pre">sum_k</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dmft_tools/convergence.html#prep_conv_file"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dmft_tools.convergence.prep_conv_file" title="Permalink to this definition"></a></dt>
+<dd><p>Writes the header to the conv files</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first last docutils">
+<dt><strong>general_params</strong><span class="classifier">dict</span></dt><dd><p>general parameters as a dict</p>
+</dd>
+<dt><strong>n_inequiv_shells</strong><span class="classifier">int</span></dt><dd><p>number of impurities for calculations</p>
+</dd>
+<dt><strong>__Returns:__</strong></dt><dd></dd>
+<dt><strong>nothing</strong></dt><dd></dd>
+</dl>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="dmft_tools.convergence.prep_conv_obs">
+<code class="sig-prename descclassname"><span class="pre">dmft_tools.convergence.</span></code><code class="sig-name descname"><span class="pre">prep_conv_obs</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">h5_archive</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dmft_tools/convergence.html#prep_conv_obs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dmft_tools.convergence.prep_conv_obs" title="Permalink to this definition"></a></dt>
+<dd><p>prepares the conv arrays and files for the DMFT calculation</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first last docutils">
+<dt><strong>h5_archive: hdf archive instance</strong></dt><dd><p>hdf archive for calculation</p>
+</dd>
+<dt><strong>__Returns:__</strong></dt><dd></dd>
+<dt><strong>conv_obs</strong><span class="classifier">dict</span></dt><dd><p>conv array for calculation</p>
+</dd>
+</dl>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="dmft_tools.convergence.write_conv">
+<code class="sig-prename descclassname"><span class="pre">dmft_tools.convergence.</span></code><code class="sig-name descname"><span class="pre">write_conv</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">conv_obs</span></span></em>, <em><span class="n"><span class="pre">sum_k</span></span></em>, <em><span class="n"><span class="pre">general_params</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dmft_tools/convergence.html#write_conv"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dmft_tools.convergence.write_conv" title="Permalink to this definition"></a></dt>
+<dd><p>writes the last entries of the conv arrays to the files</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first last docutils">
+<dt><strong>conv_obs</strong><span class="classifier">list of dicts</span></dt><dd><p>convergence observable arrays/dicts</p>
+</dd>
+<dt><strong>sum_k</strong><span class="classifier">SumK Object instances</span></dt><dd></dd>
+<dt><strong>general_params</strong><span class="classifier">dict</span></dt><dd></dd>
+<dt><strong>__Returns:__</strong></dt><dd></dd>
+<dt><strong>nothing</strong></dt><dd></dd>
+</dl>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
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+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#workflow">Workflow:</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#version-and-compilation">version and compilation:</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#input-output">Input/Output</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../input_output/DMFT_input/input.html">Input</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/general.html">[general]: General parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/solver.html">[solver]: solver specific parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/dft.html">[dft]: DFT related inputs</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/advanced.html">[advanced]: Advanced inputs</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../input_output/DMFT_output/results.html">Output / results</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_output/results.html#group-structure">Group structure</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_output/results.html#subgroups">Subgroups</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#further-details-for-running">Further details for running</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/docker.html">Docker</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#building-the-docker-image">Building the docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#pulling-a-docker-image">Pulling a docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/run_locally.html">Run on your machine</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="reference internal" href="../documentation.html#module-reference-manual">Module reference manual</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="csc_flow.html">csc_flow</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dft_managers.html">dft_managers</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="dft_managers.mpi_helpers.html">dft_managers.mpi_helpers</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dft_managers.qe_manager.html">dft_managers.qe_manager</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dft_managers.vasp_manager.html">dft_managers.vasp_manager</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="dmft_cycle.html">dmft_cycle</a></li>
+<li class="toctree-l3 current"><a class="reference internal" href="dmft_tools.html">dmft_tools</a><ul class="current">
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+<li class="toctree-l4 current"><a class="current reference internal" href="#">dmft_tools.formatter</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="io_tools.verify_input_params.html">io_tools.verify_input_params</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.maxent_gf_imp.html">postprocessing.maxent_gf_imp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.maxent_gf_latt.html">postprocessing.maxent_gf_latt</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.maxent_sigma.html">postprocessing.maxent_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../issues.html">Support &amp; contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../issues.html#seeking-help">Seeking help</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../issues.html#improving-solid-dmft">Improving solid_dmft</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../issues.html#reporting-issues">Reporting issues</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../ChangeLog.html">Changelog</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-2-0">Version 3.2.0</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-5">Version 3.1.5</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-4">Version 3.1.4</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-3">Version 3.1.3</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-2">Version 3.1.2</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-1">Version 3.1.1</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-0">Version 3.1.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-0-0">Version 3.0.0</a></li>
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+<div class="section" id="module-dmft_tools.formatter">
+<span id="dmft-tools-formatter"></span><h1>dmft_tools.formatter<a class="headerlink" href="#module-dmft_tools.formatter" title="Permalink to this headline"></a></h1>
+<p>Contains formatters for things that need to be printed in DMFT calculations.</p>
+<dl class="py function">
+<dt class="sig sig-object py" id="dmft_tools.formatter.print_block_sym">
+<code class="sig-prename descclassname"><span class="pre">dmft_tools.formatter.</span></code><code class="sig-name descname"><span class="pre">print_block_sym</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">sum_k</span></span></em>, <em><span class="n"><span class="pre">dm</span></span></em>, <em><span class="n"><span class="pre">general_params</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dmft_tools/formatter.html#print_block_sym"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dmft_tools.formatter.print_block_sym" title="Permalink to this definition"></a></dt>
+<dd><p>Prints a summary of block structure finder, determination of
+shell_multiplicity, local Hamiltonian, DFT density matrix.</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="dmft_tools.formatter.print_rotation_matrix">
+<code class="sig-prename descclassname"><span class="pre">dmft_tools.formatter.</span></code><code class="sig-name descname"><span class="pre">print_rotation_matrix</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">sum_k</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dmft_tools/formatter.html#print_rotation_matrix"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dmft_tools.formatter.print_rotation_matrix" title="Permalink to this definition"></a></dt>
+<dd><p>Prints the rotation matrix, real and imaginary part separately.</p>
+</dd></dl>
+
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diff --git a/_ref/dmft_tools.greens_functions_mixer.html b/_ref/dmft_tools.greens_functions_mixer.html
new file mode 100644
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+<li class="toctree-l3"><a class="reference internal" href="../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/solver.html">[solver]: solver specific parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/dft.html">[dft]: DFT related inputs</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="postprocessing.maxent_sigma.html">postprocessing.maxent_sigma</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
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+</li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../issues.html">Support &amp; contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../issues.html#seeking-help">Seeking help</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../issues.html#improving-solid-dmft">Improving solid_dmft</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../issues.html#reporting-issues">Reporting issues</a></li>
+</ul>
+</li>
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+<li class="toctree-l1"><a class="reference internal" href="../ChangeLog.html">Changelog</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-2-0">Version 3.2.0</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-3">Version 3.1.3</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-2">Version 3.1.2</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-1">Version 3.1.1</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-0">Version 3.1.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-0-0">Version 3.0.0</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../md_notes/w90_interface.html#orbital-order-in-the-w90-converter">orbital order in the W90 converter</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../md_notes/vasp_csc.html">Interface to VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#general-remarks">General remarks</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
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+</li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
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+<div class="section" id="module-dmft_tools">
+<span id="dmft-tools"></span><h1>dmft_tools<a class="headerlink" href="#module-dmft_tools" title="Permalink to this headline"></a></h1>
+<p>DMFT routine helper functions used during solid_dmft run</p>
+<p class="rubric">Modules</p>
+<table border="1" class="longtable docutils align-default">
+<colgroup>
+<col width="10%" />
+<col width="90%" />
+</colgroup>
+<tbody valign="top">
+<tr class="row-odd"><td><a class="reference internal" href="dmft_tools.afm_mapping.html#module-dmft_tools.afm_mapping" title="dmft_tools.afm_mapping"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dmft_tools.afm_mapping</span></code></a></td>
+<td></td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="dmft_tools.convergence.html#module-dmft_tools.convergence" title="dmft_tools.convergence"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dmft_tools.convergence</span></code></a></td>
+<td>contain helper functions to check convergence</td>
+</tr>
+<tr class="row-odd"><td><a class="reference internal" href="dmft_tools.formatter.html#module-dmft_tools.formatter" title="dmft_tools.formatter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dmft_tools.formatter</span></code></a></td>
+<td>Contains formatters for things that need to be printed in DMFT calculations.</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="dmft_tools.greens_functions_mixer.html#module-dmft_tools.greens_functions_mixer" title="dmft_tools.greens_functions_mixer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dmft_tools.greens_functions_mixer</span></code></a></td>
+<td></td>
+</tr>
+<tr class="row-odd"><td><a class="reference internal" href="dmft_tools.initial_self_energies.html#module-dmft_tools.initial_self_energies" title="dmft_tools.initial_self_energies"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dmft_tools.initial_self_energies</span></code></a></td>
+<td>Contains all functions related to determining the double counting and the initial self-energy.</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="dmft_tools.interaction_hamiltonian.html#module-dmft_tools.interaction_hamiltonian" title="dmft_tools.interaction_hamiltonian"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dmft_tools.interaction_hamiltonian</span></code></a></td>
+<td>Contains all functions related to constructing the interaction Hamiltonian.</td>
+</tr>
+<tr class="row-odd"><td><a class="reference internal" href="dmft_tools.legendre_filter.html#module-dmft_tools.legendre_filter" title="dmft_tools.legendre_filter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dmft_tools.legendre_filter</span></code></a></td>
+<td></td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="dmft_tools.manipulate_chemical_potential.html#module-dmft_tools.manipulate_chemical_potential" title="dmft_tools.manipulate_chemical_potential"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dmft_tools.manipulate_chemical_potential</span></code></a></td>
+<td>Contains all the functions related to setting the chemical potential in the next iteration.</td>
+</tr>
+<tr class="row-odd"><td><a class="reference internal" href="dmft_tools.matheval.html#module-dmft_tools.matheval" title="dmft_tools.matheval"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dmft_tools.matheval</span></code></a></td>
+<td></td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="dmft_tools.observables.html#module-dmft_tools.observables" title="dmft_tools.observables"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dmft_tools.observables</span></code></a></td>
+<td>Contains all functions related to the observables.</td>
+</tr>
+<tr class="row-odd"><td><a class="reference internal" href="dmft_tools.results_to_archive.html#module-dmft_tools.results_to_archive" title="dmft_tools.results_to_archive"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dmft_tools.results_to_archive</span></code></a></td>
+<td></td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="dmft_tools.solver.html#module-dmft_tools.solver" title="dmft_tools.solver"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dmft_tools.solver</span></code></a></td>
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diff --git a/_ref/dmft_tools.initial_self_energies.html b/_ref/dmft_tools.initial_self_energies.html
new file mode 100644
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+<li class="toctree-l4"><a class="reference internal" href="../md_notes/w90_interface.html#orbital-order-in-the-w90-converter">orbital order in the W90 converter</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../md_notes/vasp_csc.html">Interface to VASP</a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#workflow">Workflow:</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#input-output">Input/Output</a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/general.html">[general]: General parameters</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/dft.html">[dft]: DFT related inputs</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/advanced.html">[advanced]: Advanced inputs</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../md_notes/run_locally.html">Run on your machine</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
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+</ul>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
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+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
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+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
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+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
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+<div class="section" id="module-dmft_tools.initial_self_energies">
+<span id="dmft-tools-initial-self-energies"></span><h1>dmft_tools.initial_self_energies<a class="headerlink" href="#module-dmft_tools.initial_self_energies" title="Permalink to this headline"></a></h1>
+<p>Contains all functions related to determining the double counting and the
+initial self-energy.</p>
+<dl class="py function">
+<dt class="sig sig-object py" id="dmft_tools.initial_self_energies.calculate_double_counting">
+<code class="sig-prename descclassname"><span class="pre">dmft_tools.initial_self_energies.</span></code><code class="sig-name descname"><span class="pre">calculate_double_counting</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">sum_k</span></span></em>, <em><span class="n"><span class="pre">density_matrix</span></span></em>, <em><span class="n"><span class="pre">general_params</span></span></em>, <em><span class="n"><span class="pre">advanced_params</span></span></em>, <em><span class="n"><span class="pre">solver_type_per_imp</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dmft_tools/initial_self_energies.html#calculate_double_counting"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dmft_tools.initial_self_energies.calculate_double_counting" title="Permalink to this definition"></a></dt>
+<dd><p>Calculates the double counting, including all manipulations from advanced_params.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
+<dt><strong>sum_k</strong><span class="classifier">SumkDFT object</span></dt><dd></dd>
+<dt><strong>density_matrix</strong><span class="classifier">list of gf_struct_solver like</span></dt><dd><p>List of density matrices for all inequivalent shells</p>
+</dd>
+<dt><strong>general_params</strong><span class="classifier">dict</span></dt><dd><p>general parameters as a dict</p>
+</dd>
+<dt><strong>advanced_params</strong><span class="classifier">dict</span></dt><dd><p>advanced parameters as a dict</p>
+</dd>
+</dl>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
+<dt><strong>sum_k</strong><span class="classifier">SumKDFT object</span></dt><dd><p>The SumKDFT object containing the updated double counting</p>
+</dd>
+</dl>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="dmft_tools.initial_self_energies.determine_dc_and_initial_sigma">
+<code class="sig-prename descclassname"><span class="pre">dmft_tools.initial_self_energies.</span></code><code class="sig-name descname"><span class="pre">determine_dc_and_initial_sigma</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">general_params</span></span></em>, <em><span class="n"><span class="pre">advanced_params</span></span></em>, <em><span class="n"><span class="pre">sum_k</span></span></em>, <em><span class="n"><span class="pre">archive</span></span></em>, <em><span class="n"><span class="pre">iteration_offset</span></span></em>, <em><span class="n"><span class="pre">density_mat_dft</span></span></em>, <em><span class="n"><span class="pre">solvers</span></span></em>, <em><span class="n"><span class="pre">solver_type_per_imp</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dmft_tools/initial_self_energies.html#determine_dc_and_initial_sigma"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dmft_tools.initial_self_energies.determine_dc_and_initial_sigma" title="Permalink to this definition"></a></dt>
+<dd><p>Determines the double counting (DC) and the initial Sigma. This can happen
+in five different ways:
+* Calculation resumed: use the previous DC and the Sigma of the last complete calculation.</p>
+<ul class="simple">
+<li>Calculation initialized with load_sigma: use the DC and Sigma from the previous file.
+If the DC changed (and therefore the Hartree shift), the initial Sigma is adjusted by that.</li>
+<li>New calculation, with DC: calculate the DC, then initialize the Sigma as the DC,
+effectively starting the calculation from the DFT Green’s function.
+Also breaks magnetic symmetry if calculation is magnetic.</li>
+<li>New calculation, without DC: Sigma is initialized as 0,
+starting the calculation from the DFT Green’s function.</li>
+</ul>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
+<dt><strong>general_params</strong><span class="classifier">dict</span></dt><dd><p>general parameters as a dict</p>
+</dd>
+<dt><strong>advanced_params</strong><span class="classifier">dict</span></dt><dd><p>advanced parameters as a dict</p>
+</dd>
+<dt><strong>sum_k</strong><span class="classifier">SumkDFT object</span></dt><dd><p>Sumk object with the information about the correct block structure</p>
+</dd>
+<dt><strong>archive</strong><span class="classifier">HDFArchive</span></dt><dd><p>the archive of the current calculation</p>
+</dd>
+<dt><strong>iteration_offset</strong><span class="classifier">int</span></dt><dd><p>the iterations done before this calculation</p>
+</dd>
+<dt><strong>density_mat_dft</strong><span class="classifier">numpy array</span></dt><dd><p>DFT density matrix</p>
+</dd>
+<dt><strong>solvers</strong><span class="classifier">list</span></dt><dd><p>list of Solver instances</p>
+</dd>
+</dl>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
+<dt><strong>sum_k</strong><span class="classifier">SumkDFT object</span></dt><dd><p>the SumkDFT object, updated by the initial Sigma and the DC</p>
+</dd>
+<dt><strong>solvers</strong><span class="classifier">list</span></dt><dd><p>list of Solver instances, updated by the initial Sigma</p>
+</dd>
+</dl>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+</div>
+
+
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
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+<div class="section" id="module-dmft_tools.interaction_hamiltonian">
+<span id="dmft-tools-interaction-hamiltonian"></span><h1>dmft_tools.interaction_hamiltonian<a class="headerlink" href="#module-dmft_tools.interaction_hamiltonian" title="Permalink to this headline"></a></h1>
+<p>Contains all functions related to constructing the interaction Hamiltonian.</p>
+<dl class="py function">
+<dt class="sig sig-object py" id="dmft_tools.interaction_hamiltonian.construct">
+<code class="sig-prename descclassname"><span class="pre">dmft_tools.interaction_hamiltonian.</span></code><code class="sig-name descname"><span class="pre">construct</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">sum_k</span></span></em>, <em><span class="n"><span class="pre">general_params</span></span></em>, <em><span class="n"><span class="pre">solver_type_per_imp</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dmft_tools/interaction_hamiltonian.html#construct"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dmft_tools.interaction_hamiltonian.construct" title="Permalink to this definition"></a></dt>
+<dd><p>Constructs the interaction Hamiltonian. Currently implemented are the
+Kanamori Hamiltonian (usually for 2 or 3 orbitals), the density-density and
+the full Slater Hamiltonian (for 2, 3, or 5 orbitals).
+If sum_k.rot_mat is non-identity, we have to consider rotating the interaction
+Hamiltonian: the Kanamori Hamiltonian does not change because it is invariant
+under orbital mixing but all the other Hamiltonians are at most invariant
+under rotations in space. Therefore, sum_k.rot_mat has to be correct before
+calling this method.</p>
+<p>The parameters U and J will be interpreted differently depending on the
+type of the interaction Hamiltonian: it is either the Kanamori parameters
+for the Kanamori Hamiltonian or the orbital-averaged parameters (consistent
+with DFT+U, <a class="reference external" href="https://cms.mpi.univie.ac.at/wiki/index.php/LDAUTYPE" rel="noopener noreferrer" target="_blank">https://cms.mpi.univie.ac.at/wiki/index.php/LDAUTYPE</a> ) for all
+other Hamiltonians.</p>
+<p>Note also that for all Hamiltonians except Kanamori, the order of the
+orbitals matters. The correct order is specified here:
+triqs.github.io/triqs/unstable/documentation/python_api/triqs.operators.util.U_matrix.spherical_to_cubic.html</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="dmft_tools.interaction_hamiltonian.h_int_simple_intra">
+<code class="sig-prename descclassname"><span class="pre">dmft_tools.interaction_hamiltonian.</span></code><code class="sig-name descname"><span class="pre">h_int_simple_intra</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">spin_names</span></span></em>, <em><span class="n"><span class="pre">n_orb</span></span></em>, <em><span class="n"><span class="pre">U</span></span></em>, <em><span class="n"><span class="pre">off_diag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em><span class="n"><span class="pre">map_operator_structure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em><span class="n"><span class="pre">H_dump</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dmft_tools/interaction_hamiltonian.html#h_int_simple_intra"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dmft_tools.interaction_hamiltonian.h_int_simple_intra" title="Permalink to this definition"></a></dt>
+<dd><p>Create a simple intra orbital density-density Hamiltonian.
+(no inter orbital terms)</p>
+<div class="math notranslate nohighlight">
+\[H = \frac{1}{2} \sum_{i \sigma \neq \sigma')} U_{i i}^{\sigma \sigma'} n_{i \sigma} n_{i \sigma'}.\]</div>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
+<dt><strong>spin_names</strong><span class="classifier">list of strings</span></dt><dd><p>Names of the spins, e.g. [‘up’,’down’].</p>
+</dd>
+<dt><strong>n_orb</strong><span class="classifier">int</span></dt><dd><p>Number of orbitals.</p>
+</dd>
+<dt><strong>U</strong><span class="classifier">float</span></dt><dd><p>U value</p>
+</dd>
+<dt><strong>off_diag</strong><span class="classifier">boolean</span></dt><dd><p>Do we have (orbital) off-diagonal elements?
+If yes, the operators and blocks are denoted by (‘spin’, ‘orbital’),
+otherwise by (‘spin_orbital’,0).</p>
+</dd>
+<dt><strong>map_operator_structure</strong><span class="classifier">dict</span></dt><dd><p>Mapping of names of GF blocks names from one convention to another,
+e.g. {(‘up’, 0): (‘up_0’, 0), (‘down’, 0): (‘down_0’,0)}.
+If provided, the operators and blocks are denoted by the mapping of <code class="docutils literal notranslate"><span class="pre">('spin',</span> <span class="pre">'orbital')</span></code>.</p>
+</dd>
+<dt><strong>H_dump</strong><span class="classifier">string</span></dt><dd><p>Name of the file to which the Hamiltonian should be written.</p>
+</dd>
+</dl>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
+<dt><strong>H</strong><span class="classifier">Operator</span></dt><dd><p>The Hamiltonian.</p>
+</dd>
+</dl>
+</td>
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+<li class="toctree-l2"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
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+<div class="section" id="module-dmft_tools.legendre_filter">
+<span id="dmft-tools-legendre-filter"></span><h1>dmft_tools.legendre_filter<a class="headerlink" href="#module-dmft_tools.legendre_filter" title="Permalink to this headline"></a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="dmft_tools.legendre_filter.apply">
+<code class="sig-prename descclassname"><span class="pre">dmft_tools.legendre_filter.</span></code><code class="sig-name descname"><span class="pre">apply</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">G_tau</span></span></em>, <em><span class="n"><span class="pre">order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em><span class="n"><span class="pre">G_l_cut</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-19</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dmft_tools/legendre_filter.html#apply"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dmft_tools.legendre_filter.apply" title="Permalink to this definition"></a></dt>
+<dd><p>Filter binned imaginary time Green’s function
+using a Legendre filter of given order and coefficient threshold.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
+<dt><strong>G_tau</strong><span class="classifier">TRIQS imaginary time Block Green’s function</span></dt><dd></dd>
+<dt><strong>auto</strong><span class="classifier">determines automatically the cut-off nl</span></dt><dd></dd>
+<dt><strong>order</strong><span class="classifier">int</span></dt><dd><p>Legendre expansion order in the filter</p>
+</dd>
+<dt><strong>G_l_cut</strong><span class="classifier">float</span></dt><dd><p>Legendre coefficient cut-off</p>
+</dd>
+</dl>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
+<dt><strong>G_l</strong><span class="classifier">TRIQS Legendre Block Green’s function</span></dt><dd><p>Fitted Green’s function on a Legendre mesh</p>
+</dd>
+</dl>
+</td>
+</tr>
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+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
+</ul>
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+<li class="toctree-l3"><a class="reference internal" href="../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#file-description">file description</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#workflow">Workflow:</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
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+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
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+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
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+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
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+<div class="section" id="module-dmft_tools.manipulate_chemical_potential">
+<span id="dmft-tools-manipulate-chemical-potential"></span><h1>dmft_tools.manipulate_chemical_potential<a class="headerlink" href="#module-dmft_tools.manipulate_chemical_potential" title="Permalink to this headline"></a></h1>
+<p>Contains all the functions related to setting the chemical potential in the
+next iteration.</p>
+<dl class="py function">
+<dt class="sig sig-object py" id="dmft_tools.manipulate_chemical_potential.set_initial_mu">
+<code class="sig-prename descclassname"><span class="pre">dmft_tools.manipulate_chemical_potential.</span></code><code class="sig-name descname"><span class="pre">set_initial_mu</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">general_params</span></span></em>, <em><span class="n"><span class="pre">sum_k</span></span></em>, <em><span class="n"><span class="pre">iteration_offset</span></span></em>, <em><span class="n"><span class="pre">archive</span></span></em>, <em><span class="n"><span class="pre">broadening</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dmft_tools/manipulate_chemical_potential.html#set_initial_mu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dmft_tools.manipulate_chemical_potential.set_initial_mu" title="Permalink to this definition"></a></dt>
+<dd><p>Handles the different ways of setting the initial chemical potential mu:
+* Chemical potential set to fixed value: uses this value</p>
+<ul class="simple">
+<li>New calculation: determines mu from dichotomy method</li>
+<li><dl class="first docutils">
+<dt>Resuming calculation and chemical potential not updated this iteration:</dt><dd>loads calculation before previous iteration.</dd>
+</dl>
+</li>
+<li><dl class="first docutils">
+<dt>Resuming calculation and chemical potential is updated:</dt><dd>checks if the system is gapped and potentially run MaxEnt to find gap
+middle. Otherwise, gets mu from dichotomy and applies mu mixing to result.</dd>
+</dl>
+</li>
+</ul>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
+<dt><strong>general_params</strong><span class="classifier">dict</span></dt><dd><p>general parameters as dict.</p>
+</dd>
+<dt><strong>sum_k</strong><span class="classifier">SumkDFT object</span></dt><dd><p>contains system information necessary to determine the initial mu.</p>
+</dd>
+<dt><strong>iteration_offset</strong><span class="classifier">int</span></dt><dd><p>the number of iterations executed in previous calculations.</p>
+</dd>
+<dt><strong>archive</strong><span class="classifier">HDFArchive</span></dt><dd><p>needed to potentially load previous results and write MaxEnt results to.</p>
+</dd>
+</dl>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
+<dt><strong>sum_k</strong><span class="classifier">SumkDFT object</span></dt><dd><p>the altered SumkDFT object with the initial mu set correctly.</p>
+</dd>
+</dl>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="dmft_tools.manipulate_chemical_potential.update_mu">
+<code class="sig-prename descclassname"><span class="pre">dmft_tools.manipulate_chemical_potential.</span></code><code class="sig-name descname"><span class="pre">update_mu</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">general_params</span></span></em>, <em><span class="n"><span class="pre">sum_k</span></span></em>, <em><span class="n"><span class="pre">it</span></span></em>, <em><span class="n"><span class="pre">archive</span></span></em>, <em><span class="n"><span class="pre">broadening</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dmft_tools/manipulate_chemical_potential.html#update_mu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dmft_tools.manipulate_chemical_potential.update_mu" title="Permalink to this definition"></a></dt>
+<dd><p>Handles the different ways of updating the chemical potential mu:
+* Chemical potential set to fixed value: uses this value</p>
+<ul class="simple">
+<li>Chemical potential not updated this iteration: nothing happens.</li>
+<li><dl class="first docutils">
+<dt>Chemical potential is updated: checks if the system is gapped and</dt><dd>potentially run MaxEnt to find gap middle. Otherwise, gets mu from
+dichotomy and applies mu mixing to result.</dd>
+</dl>
+</li>
+</ul>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
+<dt><strong>general_params</strong><span class="classifier">dict</span></dt><dd><p>general parameters as dict.</p>
+</dd>
+<dt><strong>sum_k</strong><span class="classifier">SumkDFT object</span></dt><dd><p>contains system information necessary to update mu.</p>
+</dd>
+<dt><strong>it</strong><span class="classifier">int</span></dt><dd><p>the number of the current iteration.</p>
+</dd>
+<dt><strong>archive</strong><span class="classifier">HDFArchive</span></dt><dd><p>needed to potentially write MaxEnt results to.</p>
+</dd>
+</dl>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
+<dt><strong>sum_k</strong><span class="classifier">SumkDFT object</span></dt><dd><p>the altered SumkDFT object with the updated mu.</p>
+</dd>
+</dl>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
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+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="postprocessing.html">postprocessing</a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
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+</li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../issues.html">Support &amp; contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../issues.html#seeking-help">Seeking help</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../issues.html#improving-solid-dmft">Improving solid_dmft</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../issues.html#reporting-issues">Reporting issues</a></li>
+</ul>
+</li>
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+</ul>
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+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
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+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
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+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
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+<h1>dmft_tools.matheval.MathExpr.allowed_nodes<a class="headerlink" href="#dmft-tools-matheval-mathexpr-allowed-nodes" title="Permalink to this headline"></a></h1>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="dmft_tools.matheval.MathExpr.allowed_nodes">
+<code class="sig-prename descclassname"><span class="pre">MathExpr.</span></code><code class="sig-name descname"><span class="pre">allowed_nodes</span></code><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">(&lt;class</span> <span class="pre">'ast.Module'&gt;,</span> <span class="pre">&lt;class</span> <span class="pre">'ast.Expr'&gt;,</span> <span class="pre">&lt;class</span> <span class="pre">'ast.Load'&gt;,</span> <span class="pre">&lt;class</span> <span class="pre">'ast.Expression'&gt;,</span> <span class="pre">&lt;class</span> <span class="pre">'ast.Add'&gt;,</span> <span class="pre">&lt;class</span> <span class="pre">'ast.Sub'&gt;,</span> <span class="pre">&lt;class</span> <span class="pre">'ast.UnaryOp'&gt;,</span> <span class="pre">&lt;class</span> <span class="pre">'ast.Num'&gt;,</span> <span class="pre">&lt;class</span> <span class="pre">'ast.BinOp'&gt;,</span> <span class="pre">&lt;class</span> <span class="pre">'ast.Mult'&gt;,</span> <span class="pre">&lt;class</span> <span class="pre">'ast.Div'&gt;,</span> <span class="pre">&lt;class</span> <span class="pre">'ast.Pow'&gt;,</span> <span class="pre">&lt;class</span> <span class="pre">'ast.BitOr'&gt;,</span> <span class="pre">&lt;class</span> <span class="pre">'ast.BitAnd'&gt;,</span> <span class="pre">&lt;class</span> <span class="pre">'ast.BitXor'&gt;,</span> <span class="pre">&lt;class</span> <span class="pre">'ast.USub'&gt;,</span> <span class="pre">&lt;class</span> <span class="pre">'ast.UAdd'&gt;,</span> <span class="pre">&lt;class</span> <span class="pre">'ast.FloorDiv'&gt;,</span> <span class="pre">&lt;class</span> <span class="pre">'ast.Mod'&gt;,</span> <span class="pre">&lt;class</span> <span class="pre">'ast.LShift'&gt;,</span> <span class="pre">&lt;class</span> <span class="pre">'ast.RShift'&gt;,</span> <span class="pre">&lt;class</span> <span class="pre">'ast.Invert'&gt;,</span> <span class="pre">&lt;class</span> <span class="pre">'ast.Call'&gt;,</span> <span class="pre">&lt;class</span> <span class="pre">'ast.Name'&gt;)</span></em><a class="headerlink" href="#dmft_tools.matheval.MathExpr.allowed_nodes" title="Permalink to this definition"></a></dt>
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+<li class="toctree-l2"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
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+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
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+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
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+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
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+<div class="section" id="dmft-tools-matheval-mathexpr-functions">
+<h1>dmft_tools.matheval.MathExpr.functions<a class="headerlink" href="#dmft-tools-matheval-mathexpr-functions" title="Permalink to this headline"></a></h1>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="dmft_tools.matheval.MathExpr.functions">
+<code class="sig-prename descclassname"><span class="pre">MathExpr.</span></code><code class="sig-name descname"><span class="pre">functions</span></code><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">{'abs':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">abs&gt;,</span> <span class="pre">'acos':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">acos&gt;,</span> <span class="pre">'acosh':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">acosh&gt;,</span> <span class="pre">'asin':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">asin&gt;,</span> <span class="pre">'asinh':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">asinh&gt;,</span> <span class="pre">'atan':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">atan&gt;,</span> <span class="pre">'atan2':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">atan2&gt;,</span> <span class="pre">'atanh':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">atanh&gt;,</span> <span class="pre">'ceil':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">ceil&gt;,</span> <span class="pre">'comb':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">comb&gt;,</span> <span class="pre">'complex':</span> <span class="pre">&lt;class</span> <span class="pre">'complex'&gt;,</span> <span class="pre">'copysign':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">copysign&gt;,</span> <span class="pre">'cos':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">cos&gt;,</span> <span class="pre">'cosh':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">cosh&gt;,</span> <span class="pre">'degrees':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">degrees&gt;,</span> <span class="pre">'dist':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">dist&gt;,</span> <span class="pre">'e':</span> <span class="pre">2.718281828459045,</span> <span class="pre">'erf':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">erf&gt;,</span> <span class="pre">'erfc':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">erfc&gt;,</span> <span class="pre">'exp':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">exp&gt;,</span> <span class="pre">'expm1':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">expm1&gt;,</span> <span class="pre">'fabs':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">fabs&gt;,</span> <span class="pre">'factorial':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">factorial&gt;,</span> <span class="pre">'floor':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">floor&gt;,</span> <span class="pre">'fmod':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">fmod&gt;,</span> <span class="pre">'frexp':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">frexp&gt;,</span> <span class="pre">'fsum':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">fsum&gt;,</span> <span class="pre">'gamma':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">gamma&gt;,</span> <span class="pre">'gcd':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">gcd&gt;,</span> <span class="pre">'hypot':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">hypot&gt;,</span> <span class="pre">'inf':</span> <span class="pre">inf,</span> <span class="pre">'isclose':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">isclose&gt;,</span> <span class="pre">'isfinite':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">isfinite&gt;,</span> <span class="pre">'isinf':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">isinf&gt;,</span> <span class="pre">'isnan':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">isnan&gt;,</span> <span class="pre">'isqrt':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">isqrt&gt;,</span> <span class="pre">'lcm':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">lcm&gt;,</span> <span class="pre">'ldexp':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">ldexp&gt;,</span> <span class="pre">'lgamma':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">lgamma&gt;,</span> <span class="pre">'log':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">log&gt;,</span> <span class="pre">'log10':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">log10&gt;,</span> <span class="pre">'log1p':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">log1p&gt;,</span> <span class="pre">'log2':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">log2&gt;,</span> <span class="pre">'max':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">max&gt;,</span> <span class="pre">'min':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">min&gt;,</span> <span class="pre">'modf':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">modf&gt;,</span> <span class="pre">'nan':</span> <span class="pre">nan,</span> <span class="pre">'nextafter':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">nextafter&gt;,</span> <span class="pre">'perm':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">perm&gt;,</span> <span class="pre">'pi':</span> <span class="pre">3.141592653589793,</span> <span class="pre">'pow':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">pow&gt;,</span> <span class="pre">'prod':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">prod&gt;,</span> <span class="pre">'radians':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">radians&gt;,</span> <span class="pre">'remainder':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">remainder&gt;,</span> <span class="pre">'round':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">round&gt;,</span> <span class="pre">'sin':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">sin&gt;,</span> <span class="pre">'sinh':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">sinh&gt;,</span> <span class="pre">'sqrt':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">sqrt&gt;,</span> <span class="pre">'tan':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">tan&gt;,</span> <span class="pre">'tanh':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">tanh&gt;,</span> <span class="pre">'tau':</span> <span class="pre">6.283185307179586,</span> <span class="pre">'trunc':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">trunc&gt;,</span> <span class="pre">'ulp':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">ulp&gt;}</span></em><a class="headerlink" href="#dmft_tools.matheval.MathExpr.functions" title="Permalink to this definition"></a></dt>
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+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
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+<li class="toctree-l2"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
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+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
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+<div class="section" id="dmft-tools-matheval-mathexpr">
+<h1>dmft_tools.matheval.MathExpr<a class="headerlink" href="#dmft-tools-matheval-mathexpr" title="Permalink to this headline"></a></h1>
+<dl class="py class">
+<dt class="sig sig-object py">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><code class="sig-prename descclassname"><span class="pre">dmft_tools.matheval.</span></code><code class="sig-name descname"><span class="pre">MathExpr</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">expr</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dmft_tools/matheval.html#MathExpr"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
+<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3.11/library/functions.html#object" rel="noopener noreferrer" target="_blank" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
+<p class="rubric">Methods</p>
+<table border="1" class="longtable docutils align-default">
+<colgroup>
+<col width="10%" />
+<col width="90%" />
+</colgroup>
+<tbody valign="top">
+<tr class="row-odd"><td><code class="xref py py-obj docutils literal notranslate"><span class="pre">__call__</span></code>(**kwargs)</td>
+<td>Call self as a function.</td>
+</tr>
+</tbody>
+</table>
+<dl class="py method">
+<dt class="sig sig-object py">
+<code class="sig-name descname"><span class="pre">__init__</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">expr</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dmft_tools/matheval.html#MathExpr.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<table border="1" class="longtable docutils align-default">
+<colgroup>
+<col width="10%" />
+<col width="90%" />
+</colgroup>
+<tbody valign="top">
+<tr class="row-odd"><td><a class="reference internal" href="dmft_tools.matheval.MathExpr.__init__.html#dmft_tools.matheval.MathExpr.__init__" title="dmft_tools.matheval.MathExpr.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(expr)</td>
+<td></td>
+</tr>
+</tbody>
+</table>
+<p class="rubric">Attributes</p>
+<table border="1" class="longtable docutils align-default">
+<colgroup>
+<col width="10%" />
+<col width="90%" />
+</colgroup>
+<tbody valign="top">
+<tr class="row-odd"><td><a class="reference internal" href="dmft_tools.matheval.MathExpr.allowed_nodes.html#dmft_tools.matheval.MathExpr.allowed_nodes" title="dmft_tools.matheval.MathExpr.allowed_nodes"><code class="xref py py-obj docutils literal notranslate"><span class="pre">allowed_nodes</span></code></a></td>
+<td></td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="dmft_tools.matheval.MathExpr.functions.html#dmft_tools.matheval.MathExpr.functions" title="dmft_tools.matheval.MathExpr.functions"><code class="xref py py-obj docutils literal notranslate"><span class="pre">functions</span></code></a></td>
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+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
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+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
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+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
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+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/vasp_csc.html">Interface to VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#general-remarks">General remarks</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#file-description">file description</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#workflow">Workflow:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#important-flags">important flags:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#version-and-compilation">version and compilation:</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#input-output">Input/Output</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../input_output/DMFT_input/input.html">Input</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/general.html">[general]: General parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/solver.html">[solver]: solver specific parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/dft.html">[dft]: DFT related inputs</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/advanced.html">[advanced]: Advanced inputs</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../input_output/DMFT_output/results.html">Output / results</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_output/results.html#group-structure">Group structure</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_output/results.html#subgroups">Subgroups</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#further-details-for-running">Further details for running</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/docker.html">Docker</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#building-the-docker-image">Building the docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#pulling-a-docker-image">Pulling a docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/run_locally.html">Run on your machine</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="reference internal" href="../documentation.html#module-reference-manual">Module reference manual</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="csc_flow.html">csc_flow</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dft_managers.html">dft_managers</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="dft_managers.mpi_helpers.html">dft_managers.mpi_helpers</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dft_managers.qe_manager.html">dft_managers.qe_manager</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dft_managers.vasp_manager.html">dft_managers.vasp_manager</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="dmft_cycle.html">dmft_cycle</a></li>
+<li class="toctree-l3 current"><a class="reference internal" href="dmft_tools.html">dmft_tools</a><ul class="current">
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.afm_mapping.html">dmft_tools.afm_mapping</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.convergence.html">dmft_tools.convergence</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.formatter.html">dmft_tools.formatter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.greens_functions_mixer.html">dmft_tools.greens_functions_mixer</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.initial_self_energies.html">dmft_tools.initial_self_energies</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.interaction_hamiltonian.html">dmft_tools.interaction_hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.legendre_filter.html">dmft_tools.legendre_filter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.manipulate_chemical_potential.html">dmft_tools.manipulate_chemical_potential</a></li>
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+<li class="toctree-l4 current"><a class="current reference internal" href="#">dmft_tools.observables</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="io_tools.dict_to_h5.html">io_tools.dict_to_h5</a></li>
+<li class="toctree-l4"><a class="reference internal" href="io_tools.postproc_toml_dict.html">io_tools.postproc_toml_dict</a></li>
+<li class="toctree-l4"><a class="reference internal" href="io_tools.verify_input_params.html">io_tools.verify_input_params</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.maxent_gf_imp.html">postprocessing.maxent_gf_imp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.maxent_gf_latt.html">postprocessing.maxent_gf_latt</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.maxent_sigma.html">postprocessing.maxent_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../issues.html">Support &amp; contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../issues.html#seeking-help">Seeking help</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../issues.html#improving-solid-dmft">Improving solid_dmft</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../issues.html#reporting-issues">Reporting issues</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../ChangeLog.html">Changelog</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-2-0">Version 3.2.0</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-4">Version 3.1.4</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-3">Version 3.1.3</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-1">Version 3.1.1</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-0">Version 3.1.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-0-0">Version 3.0.0</a></li>
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+<div class="section" id="module-dmft_tools.observables">
+<span id="dmft-tools-observables"></span><h1>dmft_tools.observables<a class="headerlink" href="#module-dmft_tools.observables" title="Permalink to this headline"></a></h1>
+<p>Contains all functions related to the observables.</p>
+<dl class="py function">
+<dt class="sig sig-object py" id="dmft_tools.observables.add_dft_values_as_zeroth_iteration">
+<code class="sig-prename descclassname"><span class="pre">dmft_tools.observables.</span></code><code class="sig-name descname"><span class="pre">add_dft_values_as_zeroth_iteration</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">observables</span></span></em>, <em><span class="n"><span class="pre">general_params</span></span></em>, <em><span class="n"><span class="pre">solver_type_per_imp</span></span></em>, <em><span class="n"><span class="pre">dft_mu</span></span></em>, <em><span class="n"><span class="pre">dft_energy</span></span></em>, <em><span class="n"><span class="pre">sum_k</span></span></em>, <em><span class="n"><span class="pre">G_loc_all_dft</span></span></em>, <em><span class="n"><span class="pre">density_mat_dft</span></span></em>, <em><span class="n"><span class="pre">shell_multiplicity</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dmft_tools/observables.html#add_dft_values_as_zeroth_iteration"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dmft_tools.observables.add_dft_values_as_zeroth_iteration" title="Permalink to this definition"></a></dt>
+<dd><p>Calculates the DFT observables that should be written as the zeroth iteration.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
+<dt><strong>observables</strong><span class="classifier">observable arrays/dicts</span></dt><dd></dd>
+<dt><strong>general_params</strong><span class="classifier">general parameters as a dict</span></dt><dd></dd>
+<dt><strong>dft_mu</strong><span class="classifier">dft chemical potential</span></dt><dd></dd>
+<dt><strong>sum_k</strong><span class="classifier">SumK Object instances</span></dt><dd></dd>
+<dt><strong>G_loc_all_dft</strong><span class="classifier">Gloc from DFT for G(beta/2)</span></dt><dd></dd>
+<dt><strong>density_mat_dft</strong><span class="classifier">occupations from DFT</span></dt><dd></dd>
+<dt><strong>shell_multiplicity</strong><span class="classifier">degeneracy of impurities</span></dt><dd></dd>
+</dl>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
+<dt>observables: list of dicts</dt><dd></dd>
+</dl>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="dmft_tools.observables.add_dmft_observables">
+<code class="sig-prename descclassname"><span class="pre">dmft_tools.observables.</span></code><code class="sig-name descname"><span class="pre">add_dmft_observables</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">observables</span></span></em>, <em><span class="n"><span class="pre">general_params</span></span></em>, <em><span class="n"><span class="pre">solver_params</span></span></em>, <em><span class="n"><span class="pre">map_imp_solver</span></span></em>, <em><span class="n"><span class="pre">solver_type_per_imp</span></span></em>, <em><span class="n"><span class="pre">dft_energy</span></span></em>, <em><span class="n"><span class="pre">it</span></span></em>, <em><span class="n"><span class="pre">solvers</span></span></em>, <em><span class="n"><span class="pre">h_int</span></span></em>, <em><span class="n"><span class="pre">previous_mu</span></span></em>, <em><span class="n"><span class="pre">sum_k</span></span></em>, <em><span class="n"><span class="pre">density_mat</span></span></em>, <em><span class="n"><span class="pre">shell_multiplicity</span></span></em>, <em><span class="n"><span class="pre">E_bandcorr</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dmft_tools/observables.html#add_dmft_observables"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dmft_tools.observables.add_dmft_observables" title="Permalink to this definition"></a></dt>
+<dd><p>calculates the observables for given Input, I decided to calculate the observables
+not adhoc since it should be done only once by the master_node</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
+<dt><strong>observables</strong><span class="classifier">observable arrays/dicts</span></dt><dd></dd>
+<dt><strong>general_params</strong><span class="classifier">general parameters as a dict</span></dt><dd></dd>
+<dt><strong>solver_params</strong><span class="classifier">solver parameters as a dict</span></dt><dd></dd>
+<dt><strong>it</strong><span class="classifier">iteration counter</span></dt><dd></dd>
+<dt><strong>solvers</strong><span class="classifier">Solver instances</span></dt><dd></dd>
+<dt><strong>h_int</strong><span class="classifier">interaction hamiltonian</span></dt><dd></dd>
+<dt><strong>previous_mu</strong><span class="classifier">dmft chemical potential for which the calculation was just done</span></dt><dd></dd>
+<dt><strong>sum_k</strong><span class="classifier">SumK Object instances</span></dt><dd></dd>
+<dt><strong>density_mat</strong><span class="classifier">DMFT occupations</span></dt><dd></dd>
+<dt><strong>shell_multiplicity</strong><span class="classifier">degeneracy of impurities</span></dt><dd></dd>
+<dt><strong>E_bandcorr</strong><span class="classifier">E_kin_dmft - E_kin_dft, either calculated man or from sum_k method if CSC</span></dt><dd></dd>
+</dl>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
+<dt>observables: list of dicts</dt><dd></dd>
+</dl>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="dmft_tools.observables.calc_Z">
+<code class="sig-prename descclassname"><span class="pre">dmft_tools.observables.</span></code><code class="sig-name descname"><span class="pre">calc_Z</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">Sigma</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dmft_tools/observables.html#calc_Z"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dmft_tools.observables.calc_Z" title="Permalink to this definition"></a></dt>
+<dd><p>calculates the inverse mass enhancement from the impurity
+self-energy by a simple linear fit estimate:
+[ 1 - ((Im S_iw[n_iw0+1]-S_iw[n_iw0])/(iw[n_iw0+1]-iw[n_iw0])) ]^-1</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
+<dt><strong>Sigma: Gf on MeshImFreq</strong></dt><dd><p>self-energy on Matsubara mesh</p>
+</dd>
+</dl>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
+<dt>orb_Z: 1d numpy array</dt><dd><p>list of Z values per orbital in Sigma</p>
+</dd>
+</dl>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="dmft_tools.observables.calc_bandcorr_man">
+<code class="sig-prename descclassname"><span class="pre">dmft_tools.observables.</span></code><code class="sig-name descname"><span class="pre">calc_bandcorr_man</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">general_params</span></span></em>, <em><span class="n"><span class="pre">sum_k</span></span></em>, <em><span class="n"><span class="pre">E_kin_dft</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dmft_tools/observables.html#calc_bandcorr_man"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dmft_tools.observables.calc_bandcorr_man" title="Permalink to this definition"></a></dt>
+<dd><p>Calculates the correlated kinetic energy from DMFT for target states
+and then determines the band correction energy</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
+<dt><strong>general_params</strong><span class="classifier">dict</span></dt><dd><p>general parameters as a dict</p>
+</dd>
+<dt><strong>sum_k</strong><span class="classifier">SumK Object instances</span></dt><dd></dd>
+<dt><strong>E_kin_dft: float</strong></dt><dd><p>kinetic energy from DFT</p>
+</dd>
+</dl>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
+<dt>E_bandcorr: float</dt><dd><p>band energy correction E_kin_dmft - E_kin_dft</p>
+</dd>
+</dl>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="dmft_tools.observables.calc_dft_kin_en">
+<code class="sig-prename descclassname"><span class="pre">dmft_tools.observables.</span></code><code class="sig-name descname"><span class="pre">calc_dft_kin_en</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">general_params</span></span></em>, <em><span class="n"><span class="pre">sum_k</span></span></em>, <em><span class="n"><span class="pre">dft_mu</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dmft_tools/observables.html#calc_dft_kin_en"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dmft_tools.observables.calc_dft_kin_en" title="Permalink to this definition"></a></dt>
+<dd><p>Calculates the kinetic energy from DFT for target states</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
+<dt><strong>general_params</strong><span class="classifier">dict</span></dt><dd><p>general parameters as a dict</p>
+</dd>
+<dt><strong>sum_k</strong><span class="classifier">SumK Object instances</span></dt><dd></dd>
+<dt><strong>dft_mu: float</strong></dt><dd><p>DFT fermi energy</p>
+</dd>
+</dl>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
+<dt>E_kin_dft: float</dt><dd><p>kinetic energy from DFT</p>
+</dd>
+</dl>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="dmft_tools.observables.prep_observables">
+<code class="sig-prename descclassname"><span class="pre">dmft_tools.observables.</span></code><code class="sig-name descname"><span class="pre">prep_observables</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">h5_archive</span></span></em>, <em><span class="n"><span class="pre">sum_k</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dmft_tools/observables.html#prep_observables"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dmft_tools.observables.prep_observables" title="Permalink to this definition"></a></dt>
+<dd><p>prepares the observable arrays and files for the DMFT calculation</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
+<dt><strong>h5_archive: hdf archive instance</strong></dt><dd><p>hdf archive for calculation</p>
+</dd>
+<dt><strong>sum_k</strong><span class="classifier">SumK Object instances</span></dt><dd></dd>
+</dl>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
+<dt><strong>observables</strong><span class="classifier">dict</span></dt><dd><p>observable array for calculation</p>
+</dd>
+</dl>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="dmft_tools.observables.write_header_to_file">
+<code class="sig-prename descclassname"><span class="pre">dmft_tools.observables.</span></code><code class="sig-name descname"><span class="pre">write_header_to_file</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">general_params</span></span></em>, <em><span class="n"><span class="pre">sum_k</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dmft_tools/observables.html#write_header_to_file"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dmft_tools.observables.write_header_to_file" title="Permalink to this definition"></a></dt>
+<dd><p>Writes the header to the observable files</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
+<dt><strong>general_params</strong><span class="classifier">dict</span></dt><dd><p>general parameters as a dict</p>
+</dd>
+<dt><strong>n_inequiv_shells</strong><span class="classifier">int</span></dt><dd><p>number of impurities for calculations</p>
+</dd>
+</dl>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
+<dt>nothing</dt><dd></dd>
+</dl>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="dmft_tools.observables.write_obs">
+<code class="sig-prename descclassname"><span class="pre">dmft_tools.observables.</span></code><code class="sig-name descname"><span class="pre">write_obs</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">observables</span></span></em>, <em><span class="n"><span class="pre">sum_k</span></span></em>, <em><span class="n"><span class="pre">general_params</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dmft_tools/observables.html#write_obs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dmft_tools.observables.write_obs" title="Permalink to this definition"></a></dt>
+<dd><p>writes the last entries of the observable arrays to the files</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
+<dt><strong>observables</strong><span class="classifier">list of dicts</span></dt><dd><p>observable arrays/dicts</p>
+</dd>
+<dt><strong>sum_k</strong><span class="classifier">SumK Object instances</span></dt><dd></dd>
+<dt><strong>general_params</strong><span class="classifier">dict</span></dt><dd></dd>
+</dl>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
+<dt>nothing</dt><dd></dd>
+</dl>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+</div>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
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+
+  <div role="contentinfo">
+    <p>&#169; Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.</p>
+  </div>
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diff --git a/_ref/dmft_tools.results_to_archive.html b/_ref/dmft_tools.results_to_archive.html
new file mode 100644
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--- /dev/null
+++ b/_ref/dmft_tools.results_to_archive.html
@@ -0,0 +1,338 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" >
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>dmft_tools.results_to_archive &mdash; solid_dmft  documentation</title>
+      <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
+      <link rel="stylesheet" href="../_static/css/custom.css" type="text/css" />
+      <link rel="stylesheet" href="../_static/plot_directive.css" type="text/css" />
+    <link rel="shortcut icon" href="../_static/favicon.ico"/>
+  
+        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
+        <script src="../_static/jquery.js"></script>
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+        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+    <script src="../_static/js/theme.js"></script>
+    <link rel="index" title="Index" href="../genindex.html" />
+    <link rel="search" title="Search" href="../search.html" />
+    <link rel="next" title="dmft_tools.solver" href="dmft_tools.solver.html" />
+    <link rel="prev" title="dmft_tools.observables" href="dmft_tools.observables.html" /> 
+</head>
+
+<body class="wy-body-for-nav"> 
+  <div class="wy-grid-for-nav">
+    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
+      <div class="wy-side-scroll">
+        <div class="wy-side-nav-search"  style="background: #7E588A" >
+            <a href="../index.html" class="icon icon-home"> solid_dmft
+            <img src="../_static/logo_no_text.png" class="logo" alt="Logo"/>
+          </a>
+              <div class="version">
+                3.2.0
+              </div>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" />
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+              <ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../install.html">Installation</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../install.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../install.html#installation-via-pip">Installation via pip</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../install.html#manual-installation-via-cmake">Manual installation via CMake</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../install.html#docker-files-images">Docker files &amp; images</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../install.html#version-compatibility">Version compatibility</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../install.html#custom-cmake-options">Custom CMake options</a></li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../documentation.html">Documentation</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#code-structure">Code structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#dft-interface-notes">DFT interface notes</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/w90_interface.html">Wannier90 interface</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/w90_interface.html#orbital-order-in-the-w90-converter">orbital order in the W90 converter</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/vasp_csc.html">Interface to VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#general-remarks">General remarks</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#file-description">file description</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#workflow">Workflow:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#important-flags">important flags:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#version-and-compilation">version and compilation:</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#input-output">Input/Output</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../input_output/DMFT_input/input.html">Input</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/general.html">[general]: General parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/solver.html">[solver]: solver specific parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/dft.html">[dft]: DFT related inputs</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/advanced.html">[advanced]: Advanced inputs</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../input_output/DMFT_output/results.html">Output / results</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_output/results.html#group-structure">Group structure</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_output/results.html#subgroups">Subgroups</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#further-details-for-running">Further details for running</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/docker.html">Docker</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#building-the-docker-image">Building the docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#pulling-a-docker-image">Pulling a docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/run_locally.html">Run on your machine</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="reference internal" href="../documentation.html#module-reference-manual">Module reference manual</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="csc_flow.html">csc_flow</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="dft_managers.mpi_helpers.html">dft_managers.mpi_helpers</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="dft_managers.vasp_manager.html">dft_managers.vasp_manager</a></li>
+</ul>
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+<li class="toctree-l4"><a class="reference internal" href="io_tools.verify_input_params.html">io_tools.verify_input_params</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="postprocessing.maxent_gf_latt.html">postprocessing.maxent_gf_latt</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../issues.html">Support &amp; contribute</a><ul>
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+<li class="toctree-l3"><a class="reference internal" href="../issues.html#reporting-issues">Reporting issues</a></li>
+</ul>
+</li>
+</ul>
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+<li class="toctree-l1"><a class="reference internal" href="../ChangeLog.html">Changelog</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-2-0">Version 3.2.0</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-1">Version 3.1.1</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-0">Version 3.1.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-0-0">Version 3.0.0</a></li>
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+<style>
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+}
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+/* ... except between code cells! */
+.nblast.container + .nbinput.container {
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+<div class="section" id="module-dmft_tools.results_to_archive">
+<span id="dmft-tools-results-to-archive"></span><h1>dmft_tools.results_to_archive<a class="headerlink" href="#module-dmft_tools.results_to_archive" title="Permalink to this headline"></a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="dmft_tools.results_to_archive.write">
+<code class="sig-prename descclassname"><span class="pre">dmft_tools.results_to_archive.</span></code><code class="sig-name descname"><span class="pre">write</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">archive</span></span></em>, <em><span class="n"><span class="pre">sum_k</span></span></em>, <em><span class="n"><span class="pre">general_params</span></span></em>, <em><span class="n"><span class="pre">solver_params</span></span></em>, <em><span class="n"><span class="pre">solvers</span></span></em>, <em><span class="n"><span class="pre">map_imp_solver</span></span></em>, <em><span class="n"><span class="pre">solver_type_per_imp</span></span></em>, <em><span class="n"><span class="pre">it</span></span></em>, <em><span class="n"><span class="pre">is_sampling</span></span></em>, <em><span class="n"><span class="pre">previous_mu</span></span></em>, <em><span class="n"><span class="pre">density_mat_pre</span></span></em>, <em><span class="n"><span class="pre">density_mat</span></span></em>, <em><span class="n"><span class="pre">deltaN</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em><span class="n"><span class="pre">dens</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dmft_tools/results_to_archive.html#write"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dmft_tools.results_to_archive.write" title="Permalink to this definition"></a></dt>
+<dd><p>Collects and writes results to archive.</p>
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+
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diff --git a/_ref/dmft_tools.solver.SolverStructure.__init__.html b/_ref/dmft_tools.solver.SolverStructure.__init__.html
new file mode 100644
index 00000000..8427ad54
--- /dev/null
+++ b/_ref/dmft_tools.solver.SolverStructure.__init__.html
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+<li class="toctree-l2"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
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+<div class="section" id="dmft-tools-solver-solverstructure-init">
+<h1>dmft_tools.solver.SolverStructure.__init__<a class="headerlink" href="#dmft-tools-solver-solverstructure-init" title="Permalink to this headline"></a></h1>
+<dl class="py method">
+<dt class="sig sig-object py" id="dmft_tools.solver.SolverStructure.__init__">
+<code class="sig-prename descclassname"><span class="pre">SolverStructure.</span></code><code class="sig-name descname"><span class="pre">__init__</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">general_params</span></span></em>, <em><span class="n"><span class="pre">solver_params</span></span></em>, <em><span class="n"><span class="pre">advanced_params</span></span></em>, <em><span class="n"><span class="pre">sum_k</span></span></em>, <em><span class="n"><span class="pre">icrsh</span></span></em>, <em><span class="n"><span class="pre">h_int</span></span></em>, <em><span class="n"><span class="pre">iteration_offset</span></span></em>, <em><span class="n"><span class="pre">deg_orbs_ftps</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dmft_tools/solver.html#SolverStructure.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dmft_tools.solver.SolverStructure.__init__" title="Permalink to this definition"></a></dt>
+<dd><p>Initialisation of the solver instance with h_int for impurity “icrsh” based on soliDMFT parameters.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first last docutils">
+<dt><strong>general_paramuters: dict</strong></dt><dd><p>general parameters as dict</p>
+</dd>
+<dt><strong>solver_params: dict</strong></dt><dd><p>solver-specific parameters as dict</p>
+</dd>
+<dt><strong>sum_k: triqs.dft_tools.sumk object</strong></dt><dd><p>SumkDFT instance</p>
+</dd>
+<dt><strong>icrsh: int</strong></dt><dd><p>correlated shell index</p>
+</dd>
+<dt><strong>h_int: triqs.operator object</strong></dt><dd><p>interaction Hamiltonian of correlated shell</p>
+</dd>
+<dt><strong>iteration_offset: int</strong></dt><dd><p>number of iterations this run is based on</p>
+</dd>
+</dl>
+</td>
+</tr>
+</tbody>
+</table>
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+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
+</ul>
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+<li class="toctree-l3"><a class="reference internal" href="../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#workflow">Workflow:</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
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+<div class="section" id="dmft-tools-solver-solverstructure">
+<h1>dmft_tools.solver.SolverStructure<a class="headerlink" href="#dmft-tools-solver-solverstructure" title="Permalink to this headline"></a></h1>
+<dl class="py class">
+<dt class="sig sig-object py">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><code class="sig-prename descclassname"><span class="pre">dmft_tools.solver.</span></code><code class="sig-name descname"><span class="pre">SolverStructure</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">general_params</span></span></em>, <em><span class="n"><span class="pre">solver_params</span></span></em>, <em><span class="n"><span class="pre">advanced_params</span></span></em>, <em><span class="n"><span class="pre">sum_k</span></span></em>, <em><span class="n"><span class="pre">icrsh</span></span></em>, <em><span class="n"><span class="pre">h_int</span></span></em>, <em><span class="n"><span class="pre">iteration_offset</span></span></em>, <em><span class="n"><span class="pre">deg_orbs_ftps</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dmft_tools/solver.html#SolverStructure"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
+<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3.11/library/functions.html#object" rel="noopener noreferrer" target="_blank" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
+<p>Handles all solid_dmft solver objects and contains TRIQS solver instance.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Attributes:</th><td class="field-body"><blockquote class="first last">
+<div></div></blockquote>
+</td>
+</tr>
+</tbody>
+</table>
+<p class="rubric">Methods</p>
+<table border="1" class="docutils align-default">
+<colgroup>
+<col width="53%" />
+<col width="47%" />
+</colgroup>
+<tbody valign="top">
+<tr class="row-odd"><td><strong>solve(self, **kwargs)</strong></td>
+<td>solve impurity problem</td>
+</tr>
+</tbody>
+</table>
+<dl class="py method">
+<dt class="sig sig-object py">
+<code class="sig-name descname"><span class="pre">__init__</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">general_params</span></span></em>, <em><span class="n"><span class="pre">solver_params</span></span></em>, <em><span class="n"><span class="pre">advanced_params</span></span></em>, <em><span class="n"><span class="pre">sum_k</span></span></em>, <em><span class="n"><span class="pre">icrsh</span></span></em>, <em><span class="n"><span class="pre">h_int</span></span></em>, <em><span class="n"><span class="pre">iteration_offset</span></span></em>, <em><span class="n"><span class="pre">deg_orbs_ftps</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dmft_tools/solver.html#SolverStructure.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
+<dd><p>Initialisation of the solver instance with h_int for impurity “icrsh” based on soliDMFT parameters.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first last docutils">
+<dt><strong>general_paramuters: dict</strong></dt><dd><p>general parameters as dict</p>
+</dd>
+<dt><strong>solver_params: dict</strong></dt><dd><p>solver-specific parameters as dict</p>
+</dd>
+<dt><strong>sum_k: triqs.dft_tools.sumk object</strong></dt><dd><p>SumkDFT instance</p>
+</dd>
+<dt><strong>icrsh: int</strong></dt><dd><p>correlated shell index</p>
+</dd>
+<dt><strong>h_int: triqs.operator object</strong></dt><dd><p>interaction Hamiltonian of correlated shell</p>
+</dd>
+<dt><strong>iteration_offset: int</strong></dt><dd><p>number of iterations this run is based on</p>
+</dd>
+</dl>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py">
+<code class="sig-name descname"><span class="pre">solve</span></code><span class="sig-paren">(</span><em><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dmft_tools/solver.html#SolverStructure.solve"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
+<dd><p>solve impurity problem with current solver</p>
+</dd></dl>
+
+</dd></dl>
+
+<table border="1" class="longtable docutils align-default">
+<colgroup>
+<col width="10%" />
+<col width="90%" />
+</colgroup>
+<tbody valign="top">
+<tr class="row-odd"><td><a class="reference internal" href="dmft_tools.solver.SolverStructure.__init__.html#dmft_tools.solver.SolverStructure.__init__" title="dmft_tools.solver.SolverStructure.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(general_params,&nbsp;solver_params,&nbsp;...)</td>
+<td>Initialisation of the solver instance with h_int for impurity &quot;icrsh&quot; based on soliDMFT parameters.</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="dmft_tools.solver.SolverStructure.solve.html#dmft_tools.solver.SolverStructure.solve" title="dmft_tools.solver.SolverStructure.solve"><code class="xref py py-obj docutils literal notranslate"><span class="pre">solve</span></code></a>(**kwargs)</td>
+<td>solve impurity problem with current solver</td>
+</tr>
+</tbody>
+</table>
+</div>
+
+
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+<li class="toctree-l4"><a class="reference internal" href="../md_notes/w90_interface.html#orbital-order-in-the-w90-converter">orbital order in the W90 converter</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#building-the-docker-image">Building the docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#pulling-a-docker-image">Pulling a docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
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+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
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+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
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+<div class="section" id="dmft-tools-solver-solverstructure-solve">
+<h1>dmft_tools.solver.SolverStructure.solve<a class="headerlink" href="#dmft-tools-solver-solverstructure-solve" title="Permalink to this headline"></a></h1>
+<dl class="py method">
+<dt class="sig sig-object py" id="dmft_tools.solver.SolverStructure.solve">
+<code class="sig-prename descclassname"><span class="pre">SolverStructure.</span></code><code class="sig-name descname"><span class="pre">solve</span></code><span class="sig-paren">(</span><em><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dmft_tools/solver.html#SolverStructure.solve"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dmft_tools.solver.SolverStructure.solve" title="Permalink to this definition"></a></dt>
+<dd><p>solve impurity problem with current solver</p>
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+<li class="toctree-l2"><a class="reference internal" href="../install.html#version-compatibility">Version compatibility</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../md_notes/w90_interface.html">Wannier90 interface</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/w90_interface.html#orbital-order-in-the-w90-converter">orbital order in the W90 converter</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/vasp_csc.html">Interface to VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#general-remarks">General remarks</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#file-description">file description</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#workflow">Workflow:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#important-flags">important flags:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#version-and-compilation">version and compilation:</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#input-output">Input/Output</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../input_output/DMFT_input/input.html">Input</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/general.html">[general]: General parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/solver.html">[solver]: solver specific parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/dft.html">[dft]: DFT related inputs</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/advanced.html">[advanced]: Advanced inputs</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../input_output/DMFT_output/results.html">Output / results</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_output/results.html#group-structure">Group structure</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_output/results.html#subgroups">Subgroups</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#further-details-for-running">Further details for running</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/docker.html">Docker</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#building-the-docker-image">Building the docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#pulling-a-docker-image">Pulling a docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/run_locally.html">Run on your machine</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="reference internal" href="../documentation.html#module-reference-manual">Module reference manual</a><ul class="current">
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+<li class="toctree-l4"><a class="reference internal" href="dft_managers.vasp_manager.html">dft_managers.vasp_manager</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../issues.html">Support &amp; contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../issues.html#seeking-help">Seeking help</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../issues.html#improving-solid-dmft">Improving solid_dmft</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../issues.html#reporting-issues">Reporting issues</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../ChangeLog.html">Changelog</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-2-0">Version 3.2.0</a></li>
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+<dl class="py class">
+<dt class="sig sig-object py" id="dmft_tools.solver.SolverStructure">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><code class="sig-prename descclassname"><span class="pre">dmft_tools.solver.</span></code><code class="sig-name descname"><span class="pre">SolverStructure</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">general_params</span></span></em>, <em><span class="n"><span class="pre">solver_params</span></span></em>, <em><span class="n"><span class="pre">advanced_params</span></span></em>, <em><span class="n"><span class="pre">sum_k</span></span></em>, <em><span class="n"><span class="pre">icrsh</span></span></em>, <em><span class="n"><span class="pre">h_int</span></span></em>, <em><span class="n"><span class="pre">iteration_offset</span></span></em>, <em><span class="n"><span class="pre">deg_orbs_ftps</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dmft_tools/solver.html#SolverStructure"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dmft_tools.solver.SolverStructure" title="Permalink to this definition"></a></dt>
+<dd><p>Handles all solid_dmft solver objects and contains TRIQS solver instance.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Attributes:</th><td class="field-body"><blockquote class="first last">
+<div></div></blockquote>
+</td>
+</tr>
+</tbody>
+</table>
+<p class="rubric">Methods</p>
+<table border="1" class="docutils align-default">
+<colgroup>
+<col width="53%" />
+<col width="47%" />
+</colgroup>
+<tbody valign="top">
+<tr class="row-odd"><td><strong>solve(self, **kwargs)</strong></td>
+<td>solve impurity problem</td>
+</tr>
+</tbody>
+</table>
+<dl class="py method">
+<dt class="sig sig-object py" id="dmft_tools.solver.SolverStructure.solve">
+<code class="sig-name descname"><span class="pre">solve</span></code><span class="sig-paren">(</span><em><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dmft_tools/solver.html#SolverStructure.solve"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dmft_tools.solver.SolverStructure.solve" title="Permalink to this definition"></a></dt>
+<dd><p>solve impurity problem with current solver</p>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="dmft_tools.solver.get_n_orbitals">
+<code class="sig-prename descclassname"><span class="pre">dmft_tools.solver.</span></code><code class="sig-name descname"><span class="pre">get_n_orbitals</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">sum_k</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/dmft_tools/solver.html#get_n_orbitals"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#dmft_tools.solver.get_n_orbitals" title="Permalink to this definition"></a></dt>
+<dd><p>determines the number of orbitals within the
+solver block structure.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
+<dt><strong>sum_k</strong><span class="classifier">dft_tools sumk object</span></dt><dd></dd>
+</dl>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
+<dt><strong>n_orb</strong><span class="classifier">dict of int</span></dt><dd><p>number of orbitals for up / down as dict for SOC calculation
+without up / down block up holds the number of orbitals</p>
+</dd>
+</dl>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+<p class="rubric">Classes</p>
+<table border="1" class="longtable docutils align-default">
+<colgroup>
+<col width="10%" />
+<col width="90%" />
+</colgroup>
+<tbody valign="top">
+<tr class="row-odd"><td><a class="reference internal" href="#dmft_tools.solver.SolverStructure" title="dmft_tools.solver.SolverStructure"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SolverStructure</span></code></a>(general_params,&nbsp;...)</td>
+<td>Handles all solid_dmft solver objects and contains TRIQS solver instance.</td>
+</tr>
+</tbody>
+</table>
+</div>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
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+    </div>
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+
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+    <p>&#169; Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.</p>
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diff --git a/_ref/io_tools.dict_to_h5.html b/_ref/io_tools.dict_to_h5.html
new file mode 100644
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+<!DOCTYPE html>
+<html class="writer-html5" lang="en" >
+<head>
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+              <div class="version">
+                3.2.0
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+<li class="toctree-l1"><a class="reference internal" href="../install.html">Installation</a><ul>
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+<li class="toctree-l2"><a class="reference internal" href="../install.html#installation-via-pip">Installation via pip</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../install.html#manual-installation-via-cmake">Manual installation via CMake</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../install.html#docker-files-images">Docker files &amp; images</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../install.html#version-compatibility">Version compatibility</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../install.html#custom-cmake-options">Custom CMake options</a></li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../documentation.html">Documentation</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#code-structure">Code structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#dft-interface-notes">DFT interface notes</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/w90_interface.html">Wannier90 interface</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/w90_interface.html#orbital-order-in-the-w90-converter">orbital order in the W90 converter</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/vasp_csc.html">Interface to VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#general-remarks">General remarks</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#file-description">file description</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#workflow">Workflow:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#important-flags">important flags:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#version-and-compilation">version and compilation:</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#input-output">Input/Output</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../input_output/DMFT_input/input.html">Input</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/general.html">[general]: General parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/solver.html">[solver]: solver specific parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/dft.html">[dft]: DFT related inputs</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/advanced.html">[advanced]: Advanced inputs</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../input_output/DMFT_output/results.html">Output / results</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_output/results.html#group-structure">Group structure</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_output/results.html#subgroups">Subgroups</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#further-details-for-running">Further details for running</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/docker.html">Docker</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#building-the-docker-image">Building the docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#pulling-a-docker-image">Pulling a docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/run_locally.html">Run on your machine</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="reference internal" href="../documentation.html#module-reference-manual">Module reference manual</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="csc_flow.html">csc_flow</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dft_managers.html">dft_managers</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="dft_managers.mpi_helpers.html">dft_managers.mpi_helpers</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="dft_managers.vasp_manager.html">dft_managers.vasp_manager</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="dmft_cycle.html">dmft_cycle</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dmft_tools.html">dmft_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.afm_mapping.html">dmft_tools.afm_mapping</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.initial_self_energies.html">dmft_tools.initial_self_energies</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.solver.html">dmft_tools.solver</a></li>
+</ul>
+</li>
+<li class="toctree-l3 current"><a class="reference internal" href="io_tools.html">io_tools</a><ul class="current">
+<li class="toctree-l4 current"><a class="current reference internal" href="#">io_tools.dict_to_h5</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="io_tools.verify_input_params.html">io_tools.verify_input_params</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="postprocessing.maxent_sigma.html">postprocessing.maxent_sigma</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
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+<li class="toctree-l4"><a class="reference internal" href="util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
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+<div class="section" id="module-io_tools.dict_to_h5">
+<span id="io-tools-dict-to-h5"></span><h1>io_tools.dict_to_h5<a class="headerlink" href="#module-io_tools.dict_to_h5" title="Permalink to this headline"></a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="io_tools.dict_to_h5.prep_params_for_h5">
+<code class="sig-prename descclassname"><span class="pre">io_tools.dict_to_h5.</span></code><code class="sig-name descname"><span class="pre">prep_params_for_h5</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">dict_to_write</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/io_tools/dict_to_h5.html#prep_params_for_h5"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#io_tools.dict_to_h5.prep_params_for_h5" title="Permalink to this definition"></a></dt>
+<dd><p>Replace all NoneType with a string ‘none’ to be able to write to h5.</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="io_tools.dict_to_h5.prep_params_from_h5">
+<code class="sig-prename descclassname"><span class="pre">io_tools.dict_to_h5.</span></code><code class="sig-name descname"><span class="pre">prep_params_from_h5</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">dict_to_read</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/io_tools/dict_to_h5.html#prep_params_from_h5"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#io_tools.dict_to_h5.prep_params_from_h5" title="Permalink to this definition"></a></dt>
+<dd><p>Replace all ‘none’ strings with NoneType to parse the dict coming from h5.</p>
+</dd></dl>
+
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+
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+<li class="toctree-l4"><a class="reference internal" href="../md_notes/w90_interface.html#orbital-order-in-the-w90-converter">orbital order in the W90 converter</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../md_notes/vasp_csc.html">Interface to VASP</a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#workflow">Workflow:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#important-flags">important flags:</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#version-and-compilation">version and compilation:</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
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+<li class="toctree-l2 current"><a class="reference internal" href="../documentation.html#module-reference-manual">Module reference manual</a><ul class="current">
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+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#important-flags">important flags:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#version-and-compilation">version and compilation:</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#input-output">Input/Output</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../input_output/DMFT_input/input.html">Input</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/general.html">[general]: General parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/solver.html">[solver]: solver specific parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/dft.html">[dft]: DFT related inputs</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/advanced.html">[advanced]: Advanced inputs</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../input_output/DMFT_output/results.html">Output / results</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_output/results.html#group-structure">Group structure</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_output/results.html#subgroups">Subgroups</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#further-details-for-running">Further details for running</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/docker.html">Docker</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#building-the-docker-image">Building the docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#pulling-a-docker-image">Pulling a docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/run_locally.html">Run on your machine</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="reference internal" href="../documentation.html#module-reference-manual">Module reference manual</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="csc_flow.html">csc_flow</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dft_managers.html">dft_managers</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="dft_managers.mpi_helpers.html">dft_managers.mpi_helpers</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dft_managers.qe_manager.html">dft_managers.qe_manager</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dft_managers.vasp_manager.html">dft_managers.vasp_manager</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="dmft_cycle.html">dmft_cycle</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dmft_tools.html">dmft_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.afm_mapping.html">dmft_tools.afm_mapping</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.convergence.html">dmft_tools.convergence</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.formatter.html">dmft_tools.formatter</a></li>
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+</ul>
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+<li class="toctree-l3"><a class="reference internal" href="postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../issues.html">Support &amp; contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../issues.html#seeking-help">Seeking help</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../issues.html#improving-solid-dmft">Improving solid_dmft</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../issues.html#reporting-issues">Reporting issues</a></li>
+</ul>
+</li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-2-0">Version 3.2.0</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-4">Version 3.1.4</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-3">Version 3.1.3</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-2">Version 3.1.2</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-1">Version 3.1.1</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-0">Version 3.1.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-0-0">Version 3.0.0</a></li>
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+<div class="section" id="module-io_tools.postproc_toml_dict">
+<span id="io-tools-postproc-toml-dict"></span><h1>io_tools.postproc_toml_dict<a class="headerlink" href="#module-io_tools.postproc_toml_dict" title="Permalink to this headline"></a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="io_tools.postproc_toml_dict.merge_config_with_default">
+<code class="sig-prename descclassname"><span class="pre">io_tools.postproc_toml_dict.</span></code><code class="sig-name descname"><span class="pre">merge_config_with_default</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">cfg_inp</span></span></em>, <em><span class="n"><span class="pre">cfg_def</span></span></em>, <em><span class="n"><span class="pre">match_key</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/io_tools/postproc_toml_dict.html#merge_config_with_default"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#io_tools.postproc_toml_dict.merge_config_with_default" title="Permalink to this definition"></a></dt>
+<dd><p>Merge a TOML config dict with a default TOML dict.
+The default dict dictates the structure of the input:</p>
+<ul class="simple">
+<li>Only sections and keys in the default are allowed in the input</li>
+<li>All sections listed in match_key must be lists of dicts in the default
+and can be lists of dicts or dicts in the config</li>
+</ul>
+<p>The dicts allows for the following extensions:</p>
+<ul class="simple">
+<li>Mandatory inputs for all calculations indicated by “&lt;no default&gt;”</li>
+<li>None indicated by “&lt;none&gt;”. Also works inside lists</li>
+<li>References within the dictionary indicated by “&lt;section.key&gt;”</li>
+</ul>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
+<dt><strong>cfg_inp</strong><span class="classifier">dict</span></dt><dd><p>The input config dict</p>
+</dd>
+<dt><strong>cfg_def</strong><span class="classifier">dict</span></dt><dd><p>The default config dict</p>
+</dd>
+<dt><strong>match_key</strong><span class="classifier">dict, optional</span></dt><dd><p>A dictionary that contains section/key pairs to map entries in listed sections
+between the input and default config.</p>
+</dd>
+</dl>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
+<dt>dict</dt><dd><p>The merged config dict</p>
+</dd>
+</dl>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+</div>
+
+
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+<li class="toctree-l4"><a class="reference internal" href="../md_notes/w90_interface.html#orbital-order-in-the-w90-converter">orbital order in the W90 converter</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../md_notes/vasp_csc.html">Interface to VASP</a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
+</ul>
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+<li class="toctree-l3"><a class="reference internal" href="../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#file-description">file description</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#workflow">Workflow:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#important-flags">important flags:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#version-and-compilation">version and compilation:</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#input-output">Input/Output</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../input_output/DMFT_input/input.html">Input</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/general.html">[general]: General parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/solver.html">[solver]: solver specific parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/dft.html">[dft]: DFT related inputs</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/advanced.html">[advanced]: Advanced inputs</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../input_output/DMFT_output/results.html">Output / results</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_output/results.html#group-structure">Group structure</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_output/results.html#subgroups">Subgroups</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="reference internal" href="../documentation.html#module-reference-manual">Module reference manual</a><ul class="current">
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+<li class="toctree-l4"><a class="reference internal" href="dft_managers.vasp_manager.html">dft_managers.vasp_manager</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="dmft_cycle.html">dmft_cycle</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dmft_tools.html">dmft_tools</a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.initial_self_energies.html">dmft_tools.initial_self_energies</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.interaction_hamiltonian.html">dmft_tools.interaction_hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.legendre_filter.html">dmft_tools.legendre_filter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.manipulate_chemical_potential.html">dmft_tools.manipulate_chemical_potential</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.matheval.html">dmft_tools.matheval</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.observables.html">dmft_tools.observables</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.results_to_archive.html">dmft_tools.results_to_archive</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.solver.html">dmft_tools.solver</a></li>
+</ul>
+</li>
+<li class="toctree-l3 current"><a class="reference internal" href="io_tools.html">io_tools</a><ul class="current">
+<li class="toctree-l4"><a class="reference internal" href="io_tools.dict_to_h5.html">io_tools.dict_to_h5</a></li>
+<li class="toctree-l4"><a class="reference internal" href="io_tools.postproc_toml_dict.html">io_tools.postproc_toml_dict</a></li>
+<li class="toctree-l4 current"><a class="current reference internal" href="#">io_tools.verify_input_params</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.maxent_gf_imp.html">postprocessing.maxent_gf_imp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.maxent_gf_latt.html">postprocessing.maxent_gf_latt</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.maxent_sigma.html">postprocessing.maxent_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../issues.html">Support &amp; contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../issues.html#seeking-help">Seeking help</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../issues.html#improving-solid-dmft">Improving solid_dmft</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../issues.html#reporting-issues">Reporting issues</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../ChangeLog.html">Changelog</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-2-0">Version 3.2.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#general">General</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#fix">fix</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#build">build</a></li>
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+<span id="io-tools-verify-input-params"></span><h1>io_tools.verify_input_params<a class="headerlink" href="#module-io_tools.verify_input_params" title="Permalink to this headline"></a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="io_tools.verify_input_params.verify_before_dmft_cycle">
+<code class="sig-prename descclassname"><span class="pre">io_tools.verify_input_params.</span></code><code class="sig-name descname"><span class="pre">verify_before_dmft_cycle</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/io_tools/verify_input_params.html#verify_before_dmft_cycle"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#io_tools.verify_input_params.verify_before_dmft_cycle" title="Permalink to this definition"></a></dt>
+<dd><p>All checks of params that can be done before dmft_cycle is called.</p>
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+<code class="sig-prename descclassname"><span class="pre">io_tools.verify_input_params.</span></code><code class="sig-name descname"><span class="pre">verify_h5_dependent</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">sum_k</span></span></em>, <em><span class="n"><span class="pre">solver_type_per_imp</span></span></em>, <em><span class="n"><span class="pre">general_params</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/io_tools/verify_input_params.html#verify_h5_dependent"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#io_tools.verify_input_params.verify_h5_dependent" title="Permalink to this definition"></a></dt>
+<dd><p>All checks of params that depend on the h5 file content that is stored in the SumkDFT object.</p>
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+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
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+<div class="section" id="module-postprocessing.eval_U_cRPA_RESPACK">
+<span id="postprocessing-eval-u-crpa-respack"></span><h1>postprocessing.eval_U_cRPA_RESPACK<a class="headerlink" href="#module-postprocessing.eval_U_cRPA_RESPACK" title="Permalink to this headline"></a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="postprocessing.eval_U_cRPA_RESPACK.construct_Uijkl">
+<code class="sig-prename descclassname"><span class="pre">postprocessing.eval_U_cRPA_RESPACK.</span></code><code class="sig-name descname"><span class="pre">construct_Uijkl</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">Uijij</span></span></em>, <em><span class="n"><span class="pre">Uiijj</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/postprocessing/eval_U_cRPA_RESPACK.html#construct_Uijkl"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#postprocessing.eval_U_cRPA_RESPACK.construct_Uijkl" title="Permalink to this definition"></a></dt>
+<dd><p>construct full 4 index Uijkl tensor from respack data
+assuming Uijji = Uiijj</p>
+<dl class="docutils">
+<dt>Uijij: np.ndarray</dt><dd>Uijij matrix</dd>
+<dt>Uiijj: np.ndarray</dt><dd>Uiijj matrix</dd>
+</dl>
+<dl class="docutils">
+<dt>uijkl<span class="classifier">numpy array</span></dt><dd>uijkl Coulomb tensor</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="postprocessing.eval_U_cRPA_RESPACK.fit_slater">
+<code class="sig-prename descclassname"><span class="pre">postprocessing.eval_U_cRPA_RESPACK.</span></code><code class="sig-name descname"><span class="pre">fit_slater</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">u_ijij_crpa</span></span></em>, <em><span class="n"><span class="pre">u_ijji_crpa</span></span></em>, <em><span class="n"><span class="pre">U_init</span></span></em>, <em><span class="n"><span class="pre">J_init</span></span></em>, <em><span class="n"><span class="pre">fixed_F4_F2</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/postprocessing/eval_U_cRPA_RESPACK.html#fit_slater"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#postprocessing.eval_U_cRPA_RESPACK.fit_slater" title="Permalink to this definition"></a></dt>
+<dd><p>finds best Slater parameters U, J for given Uijij and Uijji matrices
+using the triqs U_matrix operator routine assumes F4/F2=0.625</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="postprocessing.eval_U_cRPA_RESPACK.read_interaction">
+<code class="sig-prename descclassname"><span class="pre">postprocessing.eval_U_cRPA_RESPACK.</span></code><code class="sig-name descname"><span class="pre">read_interaction</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">seed</span></span></em>, <em><span class="n"><span class="pre">path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'./'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/postprocessing/eval_U_cRPA_RESPACK.html#read_interaction"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#postprocessing.eval_U_cRPA_RESPACK.read_interaction" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="postprocessing.eval_U_cRPA_RESPACK.respack_data">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><code class="sig-prename descclassname"><span class="pre">postprocessing.eval_U_cRPA_RESPACK.</span></code><code class="sig-name descname"><span class="pre">respack_data</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">path</span></span></em>, <em><span class="n"><span class="pre">seed</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/postprocessing/eval_U_cRPA_RESPACK.html#respack_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#postprocessing.eval_U_cRPA_RESPACK.respack_data" title="Permalink to this definition"></a></dt>
+<dd><p>respack data class</p>
+</dd></dl>
+
+<p class="rubric">Classes</p>
+<table border="1" class="longtable docutils align-default">
+<colgroup>
+<col width="10%" />
+<col width="90%" />
+</colgroup>
+<tbody valign="top">
+<tr class="row-odd"><td><a class="reference internal" href="#postprocessing.eval_U_cRPA_RESPACK.respack_data" title="postprocessing.eval_U_cRPA_RESPACK.respack_data"><code class="xref py py-obj docutils literal notranslate"><span class="pre">respack_data</span></code></a>(path,&nbsp;seed)</td>
+<td>respack data class</td>
+</tr>
+</tbody>
+</table>
+</div>
+
+
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+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#workflow">Workflow:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#important-flags">important flags:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#input-output">Input/Output</a><ul>
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+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
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+<div class="section" id="postprocessing-eval-u-crpa-respack-respack-data">
+<h1>postprocessing.eval_U_cRPA_RESPACK.respack_data<a class="headerlink" href="#postprocessing-eval-u-crpa-respack-respack-data" title="Permalink to this headline"></a></h1>
+<dl class="py class">
+<dt class="sig sig-object py">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><code class="sig-prename descclassname"><span class="pre">postprocessing.eval_U_cRPA_RESPACK.</span></code><code class="sig-name descname"><span class="pre">respack_data</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">path</span></span></em>, <em><span class="n"><span class="pre">seed</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/postprocessing/eval_U_cRPA_RESPACK.html#respack_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
+<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3.11/library/functions.html#object" rel="noopener noreferrer" target="_blank" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
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+<code class="sig-name descname"><span class="pre">__init__</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">path</span></span></em>, <em><span class="n"><span class="pre">seed</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/postprocessing/eval_U_cRPA_RESPACK.html#respack_data.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
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+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#file-description">file description</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#workflow">Workflow:</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#version-and-compilation">version and compilation:</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#input-output">Input/Output</a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
+</li>
+</ul>
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+      <li>postprocessing.eval_U_cRPA_Vasp</li>
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+<div class="section" id="module-postprocessing.eval_U_cRPA_Vasp">
+<span id="postprocessing-eval-u-crpa-vasp"></span><h1>postprocessing.eval_U_cRPA_Vasp<a class="headerlink" href="#module-postprocessing.eval_U_cRPA_Vasp" title="Permalink to this headline"></a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="postprocessing.eval_U_cRPA_Vasp.calc_kan_params">
+<code class="sig-prename descclassname"><span class="pre">postprocessing.eval_U_cRPA_Vasp.</span></code><code class="sig-name descname"><span class="pre">calc_kan_params</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">uijkl</span></span></em>, <em><span class="n"><span class="pre">n_sites</span></span></em>, <em><span class="n"><span class="pre">n_orb</span></span></em>, <em><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/postprocessing/eval_U_cRPA_Vasp.html#calc_kan_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#postprocessing.eval_U_cRPA_Vasp.calc_kan_params" title="Permalink to this definition"></a></dt>
+<dd><p>calculates the kanamori interaction parameters from a
+given Uijkl matrix. Follows the procedure given in
+PHYSICAL REVIEW B 86, 165105 (2012) Vaugier,Biermann
+formula 30,31,32</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
+<dt><strong>uijkl</strong><span class="classifier">numpy array</span></dt><dd><p>4d numpy array of Coulomb tensor</p>
+</dd>
+<dt><strong>n_sites: int</strong></dt><dd><p>number of different atoms (Wannier centers)</p>
+</dd>
+<dt><strong>n_orb</strong><span class="classifier">int</span></dt><dd><p>number of orbitals per atom</p>
+</dd>
+<dt><strong>out</strong><span class="classifier">bool</span></dt><dd><p>verbose mode</p>
+</dd>
+</dl>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
+<dt><strong>int_params</strong><span class="classifier">direct</span></dt><dd><p>kanamori parameters</p>
+</dd>
+</dl>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="postprocessing.eval_U_cRPA_Vasp.calc_u_avg_fulld">
+<code class="sig-prename descclassname"><span class="pre">postprocessing.eval_U_cRPA_Vasp.</span></code><code class="sig-name descname"><span class="pre">calc_u_avg_fulld</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">uijkl</span></span></em>, <em><span class="n"><span class="pre">n_sites</span></span></em>, <em><span class="n"><span class="pre">n_orb</span></span></em>, <em><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/postprocessing/eval_U_cRPA_Vasp.html#calc_u_avg_fulld"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#postprocessing.eval_U_cRPA_Vasp.calc_u_avg_fulld" title="Permalink to this definition"></a></dt>
+<dd><p>calculates the coulomb integrals from a
+given Uijkl matrix for full d shells. Follows the procedure given
+in Pavarini  - 2014 - arXiv - 1411 6906 - julich school U matrix
+page 8 or as done in
+PHYSICAL REVIEW B 86, 165105 (2012) Vaugier,Biermann
+formula 23, 25
+works atm only for full d shell (l=2)</p>
+<p>Returns F0=U, and J=(F2+F4)/14</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
+<dt><strong>uijkl</strong><span class="classifier">numpy array</span></dt><dd><p>4d numpy array of Coulomb tensor</p>
+</dd>
+<dt><strong>n_sites: int</strong></dt><dd><p>number of different atoms (Wannier centers)</p>
+</dd>
+<dt><strong>n_orb</strong><span class="classifier">int</span></dt><dd><p>number of orbitals per atom</p>
+</dd>
+<dt><strong>out</strong><span class="classifier">bool</span></dt><dd><p>verbose mode</p>
+</dd>
+</dl>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
+<dt><strong>int_params</strong><span class="classifier">direct</span></dt><dd><p>Slater parameters</p>
+</dd>
+</dl>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="postprocessing.eval_U_cRPA_Vasp.calculate_interaction_from_averaging">
+<code class="sig-prename descclassname"><span class="pre">postprocessing.eval_U_cRPA_Vasp.</span></code><code class="sig-name descname"><span class="pre">calculate_interaction_from_averaging</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">uijkl</span></span></em>, <em><span class="n"><span class="pre">n_sites</span></span></em>, <em><span class="n"><span class="pre">n_orb</span></span></em>, <em><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/postprocessing/eval_U_cRPA_Vasp.html#calculate_interaction_from_averaging"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#postprocessing.eval_U_cRPA_Vasp.calculate_interaction_from_averaging" title="Permalink to this definition"></a></dt>
+<dd><p>calculates U,J by averaging directly the Uijkl matrix
+ignoring if tensor is given in spherical or cubic basis.
+The assumption here is that the averaging gives indepentendly
+of the choosen basis (cubic or spherical harmonics) the same results
+if Uijkl is a true Slater matrix.</p>
+<p>Returns F0=U, and J=(F2+F4)/14</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
+<dt><strong>uijkl</strong><span class="classifier">numpy array</span></dt><dd><p>4d numpy array of Coulomb tensor</p>
+</dd>
+<dt><strong>n_sites: int</strong></dt><dd><p>number of different atoms (Wannier centers)</p>
+</dd>
+<dt><strong>n_orb</strong><span class="classifier">int</span></dt><dd><p>number of orbitals per atom</p>
+</dd>
+<dt><strong>out</strong><span class="classifier">bool</span></dt><dd><p>verbose mode</p>
+</dd>
+</dl>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
+<dt>U, J: tuple</dt><dd><p>Slater parameters</p>
+</dd>
+</dl>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="postprocessing.eval_U_cRPA_Vasp.construct_U_kan">
+<code class="sig-prename descclassname"><span class="pre">postprocessing.eval_U_cRPA_Vasp.</span></code><code class="sig-name descname"><span class="pre">construct_U_kan</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">n_orb</span></span></em>, <em><span class="n"><span class="pre">U</span></span></em>, <em><span class="n"><span class="pre">J</span></span></em>, <em><span class="n"><span class="pre">Up</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em><span class="n"><span class="pre">Jc</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/postprocessing/eval_U_cRPA_Vasp.html#construct_U_kan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#postprocessing.eval_U_cRPA_Vasp.construct_U_kan" title="Permalink to this definition"></a></dt>
+<dd><p>construct Kanamori Uijkl tensor for given U, J, Up, and Jc</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
+<dt><strong>n_orb</strong><span class="classifier">int</span></dt><dd><p>number of orbitals</p>
+</dd>
+<dt><strong>U</strong><span class="classifier">float</span></dt><dd><p>U value for elements Uiiii</p>
+</dd>
+<dt><strong>J</strong><span class="classifier">float</span></dt><dd><p>Hunds coupling J for tensor elements Uijji</p>
+</dd>
+<dt><strong>Up</strong><span class="classifier">float, optional, default=U-2J</span></dt><dd><p>inter orbital exchange term Uijij</p>
+</dd>
+<dt><strong>Jc</strong><span class="classifier">float, optional, default=J</span></dt><dd><p>Uiijj term, is the same as J for real valued wave functions</p>
+</dd>
+</dl>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
+<dt><strong>uijkl</strong><span class="classifier">numpy array</span></dt><dd><p>uijkl Coulomb tensor</p>
+</dd>
+</dl>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="postprocessing.eval_U_cRPA_Vasp.fit_kanamori">
+<code class="sig-prename descclassname"><span class="pre">postprocessing.eval_U_cRPA_Vasp.</span></code><code class="sig-name descname"><span class="pre">fit_kanamori</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">uijkl</span></span></em>, <em><span class="n"><span class="pre">n_orb</span></span></em>, <em><span class="n"><span class="pre">switch_jk</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em><span class="n"><span class="pre">fit_2</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em><span class="n"><span class="pre">fit_3</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em><span class="n"><span class="pre">fit_4</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/postprocessing/eval_U_cRPA_Vasp.html#fit_kanamori"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#postprocessing.eval_U_cRPA_Vasp.fit_kanamori" title="Permalink to this definition"></a></dt>
+<dd><p>Fit Kanamori Hamiltonian with scipy to 2,3, and / or 4 parameters</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
+<dt><strong>uijkl: np.array (n_orb x n_orb x n_orb x n_orb)</strong></dt><dd><p>input four index tensor</p>
+</dd>
+<dt><strong>n_orb: int</strong></dt><dd><p>number of orbitals</p>
+</dd>
+<dt><strong>switch_jk: bool, default=False</strong></dt><dd><p>flip two inner indices in input U tensor (for Vasp)</p>
+</dd>
+<dt><strong>fit_2: bool, default=True</strong></dt><dd><p>fit two parameter form</p>
+</dd>
+<dt><strong>fit_3: bool, default=False</strong></dt><dd><p>fit three parameter form (U,Up,J=Jc)</p>
+</dd>
+<dt><strong>fit_4: bool, default=True</strong></dt><dd><p>fit four parameter form</p>
+</dd>
+</dl>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
+<dt>Uijkl_fit: np.array (n_orb x n_orb x n_orb x n_orb)</dt><dd><p>fitted Uijkl tensor</p>
+</dd>
+</dl>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="postprocessing.eval_U_cRPA_Vasp.fit_slater_fulld">
+<code class="sig-prename descclassname"><span class="pre">postprocessing.eval_U_cRPA_Vasp.</span></code><code class="sig-name descname"><span class="pre">fit_slater_fulld</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">uijkl</span></span></em>, <em><span class="n"><span class="pre">n_sites</span></span></em>, <em><span class="n"><span class="pre">U_init</span></span></em>, <em><span class="n"><span class="pre">J_init</span></span></em>, <em><span class="n"><span class="pre">fixed_F4_F2</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/postprocessing/eval_U_cRPA_Vasp.html#fit_slater_fulld"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#postprocessing.eval_U_cRPA_Vasp.fit_slater_fulld" title="Permalink to this definition"></a></dt>
+<dd><p>finds best Slater parameters U, J for given Uijkl tensor
+using the triqs U_matrix operator routine
+assumes F4/F2=0.625</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="postprocessing.eval_U_cRPA_Vasp.read_uijkl">
+<code class="sig-prename descclassname"><span class="pre">postprocessing.eval_U_cRPA_Vasp.</span></code><code class="sig-name descname"><span class="pre">read_uijkl</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">path_to_uijkl</span></span></em>, <em><span class="n"><span class="pre">n_sites</span></span></em>, <em><span class="n"><span class="pre">n_orb</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/postprocessing/eval_U_cRPA_Vasp.html#read_uijkl"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#postprocessing.eval_U_cRPA_Vasp.read_uijkl" title="Permalink to this definition"></a></dt>
+<dd><p>reads the VASP UIJKL files or the vijkl file if wanted</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
+<dt><strong>path_to_uijkl</strong><span class="classifier">string</span></dt><dd><p>path to Uijkl like file</p>
+</dd>
+<dt><strong>n_sites: int</strong></dt><dd><p>number of different atoms (Wannier centers)</p>
+</dd>
+<dt><strong>n_orb</strong><span class="classifier">int</span></dt><dd><p>number of orbitals per atom</p>
+</dd>
+</dl>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
+<dt><strong>uijkl</strong><span class="classifier">numpy array</span></dt><dd><p>uijkl Coulomb tensor</p>
+</dd>
+</dl>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="postprocessing.eval_U_cRPA_Vasp.red_to_2ind">
+<code class="sig-prename descclassname"><span class="pre">postprocessing.eval_U_cRPA_Vasp.</span></code><code class="sig-name descname"><span class="pre">red_to_2ind</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">uijkl</span></span></em>, <em><span class="n"><span class="pre">n_sites</span></span></em>, <em><span class="n"><span class="pre">n_orb</span></span></em>, <em><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/postprocessing/eval_U_cRPA_Vasp.html#red_to_2ind"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#postprocessing.eval_U_cRPA_Vasp.red_to_2ind" title="Permalink to this definition"></a></dt>
+<dd><p>reduces the 4index coulomb matrix to a 2index matrix and
+follows the procedure given in PRB96 seth,peil,georges:
+U_antipar = U_mm’^oo’ = U_mm’mm’ (Coulomb Int)
+U_par = U_mm’^oo = U_mm’mm’ - U_mm’m’m (for intersite interaction)
+U_ijij (Hunds coupling)
+the indices in VASP are switched: U_ijkl —VASP–&gt; U_ikjl</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
+<dt><strong>uijkl</strong><span class="classifier">numpy array</span></dt><dd><p>4d numpy array of Coulomb tensor</p>
+</dd>
+<dt><strong>n_sites: int</strong></dt><dd><p>number of different atoms (Wannier centers)</p>
+</dd>
+<dt><strong>n_orb</strong><span class="classifier">int</span></dt><dd><p>number of orbitals per atom</p>
+</dd>
+<dt><strong>out</strong><span class="classifier">bool</span></dt><dd><p>verbose mode</p>
+</dd>
+</dl>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
+<dt><strong>Uij_anti</strong><span class="classifier">numpy array</span></dt><dd><p>red 2 index matrix U_mm’mm’</p>
+</dd>
+<dt><strong>Uiijj</strong><span class="classifier">numpy array</span></dt><dd><p>red 2 index matrix U_iijj</p>
+</dd>
+<dt><strong>Uijji</strong><span class="classifier">numpy array</span></dt><dd><p>red 2 index matrix Uijji</p>
+</dd>
+<dt><strong>Uij_par</strong><span class="classifier">numpy array</span></dt><dd><p>red 2 index matrix U_mm’mm’ - U_mm’m’m</p>
+</dd>
+</dl>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+</div>
+
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+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/vasp_csc.html">Interface to VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#general-remarks">General remarks</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#file-description">file description</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#workflow">Workflow:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#important-flags">important flags:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#input-output">Input/Output</a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
+</ul>
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+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="reference internal" href="../documentation.html#module-reference-manual">Module reference manual</a><ul class="current">
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+</ul>
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+<li class="toctree-l3 current"><a class="current reference internal" href="#">postprocessing</a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
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+</li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
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+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
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+<div class="section" id="module-postprocessing">
+<span id="postprocessing"></span><h1>postprocessing<a class="headerlink" href="#module-postprocessing" title="Permalink to this headline"></a></h1>
+<p>Postprocessing tools</p>
+<p class="rubric">Modules</p>
+<table border="1" class="longtable docutils align-default">
+<colgroup>
+<col width="10%" />
+<col width="90%" />
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+<tbody valign="top">
+<tr class="row-odd"><td><a class="reference internal" href="postprocessing.eval_U_cRPA_RESPACK.html#module-postprocessing.eval_U_cRPA_RESPACK" title="postprocessing.eval_U_cRPA_RESPACK"><code class="xref py py-obj docutils literal notranslate"><span class="pre">postprocessing.eval_U_cRPA_RESPACK</span></code></a></td>
+<td></td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="postprocessing.eval_U_cRPA_Vasp.html#module-postprocessing.eval_U_cRPA_Vasp" title="postprocessing.eval_U_cRPA_Vasp"><code class="xref py py-obj docutils literal notranslate"><span class="pre">postprocessing.eval_U_cRPA_Vasp</span></code></a></td>
+<td></td>
+</tr>
+<tr class="row-odd"><td><a class="reference internal" href="postprocessing.maxent_gf_imp.html#module-postprocessing.maxent_gf_imp" title="postprocessing.maxent_gf_imp"><code class="xref py py-obj docutils literal notranslate"><span class="pre">postprocessing.maxent_gf_imp</span></code></a></td>
+<td>Analytic continuation of the impurity Green's function to the impurity spectral function using maxent.</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="postprocessing.maxent_gf_latt.html#module-postprocessing.maxent_gf_latt" title="postprocessing.maxent_gf_latt"><code class="xref py py-obj docutils literal notranslate"><span class="pre">postprocessing.maxent_gf_latt</span></code></a></td>
+<td>Analytic continuation of the lattice Green's function to the lattice spectral function using maxent.</td>
+</tr>
+<tr class="row-odd"><td><a class="reference internal" href="postprocessing.maxent_sigma.html#module-postprocessing.maxent_sigma" title="postprocessing.maxent_sigma"><code class="xref py py-obj docutils literal notranslate"><span class="pre">postprocessing.maxent_sigma</span></code></a></td>
+<td>Analytic continuation of the self-energy using maxent on an auxiliary Green's function.</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="postprocessing.pade_sigma.html#module-postprocessing.pade_sigma" title="postprocessing.pade_sigma"><code class="xref py py-obj docutils literal notranslate"><span class="pre">postprocessing.pade_sigma</span></code></a></td>
+<td></td>
+</tr>
+<tr class="row-odd"><td><a class="reference internal" href="postprocessing.plot_correlated_bands.html#module-postprocessing.plot_correlated_bands" title="postprocessing.plot_correlated_bands"><code class="xref py py-obj docutils literal notranslate"><span class="pre">postprocessing.plot_correlated_bands</span></code></a></td>
+<td>Reads DMFT_ouput observables such as real-frequency Sigma and a Wannier90 TB Hamiltonian to compute spectral properties.</td>
+</tr>
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+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#file-description">file description</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#workflow">Workflow:</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
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+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
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+</ul>
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+</li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
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+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
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+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
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+<div class="section" id="module-postprocessing.maxent_gf_imp">
+<span id="postprocessing-maxent-gf-imp"></span><h1>postprocessing.maxent_gf_imp<a class="headerlink" href="#module-postprocessing.maxent_gf_imp" title="Permalink to this headline"></a></h1>
+<p>Analytic continuation of the impurity Green’s function to the impurity spectral
+function using maxent.</p>
+<p>Reads G_imp(i omega) from the h5 archive and writes A_imp(omega) back. See
+the docstring of main() for more information.</p>
+<p>Not mpi parallelized.</p>
+<p>Author: Maximilian Merkel, Materials Theory Group, ETH Zurich, 2020 - 2022</p>
+<dl class="py function">
+<dt class="sig sig-object py" id="postprocessing.maxent_gf_imp.main">
+<code class="sig-prename descclassname"><span class="pre">postprocessing.maxent_gf_imp.</span></code><code class="sig-name descname"><span class="pre">main</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">external_path</span></span></em>, <em><span class="n"><span class="pre">iteration</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em><span class="n"><span class="pre">sum_spins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em><span class="n"><span class="pre">maxent_error</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.02</span></span></em>, <em><span class="n"><span class="pre">n_points_maxent</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">200</span></span></em>, <em><span class="n"><span class="pre">n_points_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">50</span></span></em>, <em><span class="n"><span class="pre">omega_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-</span> <span class="pre">20</span></span></em>, <em><span class="n"><span class="pre">omega_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/postprocessing/maxent_gf_imp.html#main"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#postprocessing.maxent_gf_imp.main" title="Permalink to this definition"></a></dt>
+<dd><p>Main function that reads the impurity Greens (GF) function from h5,
+analytically continues it, writes the result back to the h5 archive and
+also returns the results.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
+<dt><strong>external_path</strong><span class="classifier">string</span></dt><dd><p>Path to the h5 archive to read from and write to.</p>
+</dd>
+<dt><strong>iteration</strong><span class="classifier">int/string</span></dt><dd><p>Iteration to read from and write to. Defaults to last_iter.</p>
+</dd>
+<dt><strong>sum_spins</strong><span class="classifier">bool</span></dt><dd><p>Whether to sum over the spins or continue the impurity GF
+for the up and down spin separately, for example for magnetized results.</p>
+</dd>
+<dt><strong>maxent_error</strong><span class="classifier">float</span></dt><dd><p>The error that is used for the analyzers.</p>
+</dd>
+<dt><strong>n_points_maxent</strong><span class="classifier">int</span></dt><dd><p>Number of omega points on the hyperbolic mesh used in the continuation.</p>
+</dd>
+<dt><strong>n_points_alpha</strong><span class="classifier">int</span></dt><dd><p>Number of points that the MaxEnt alpha parameter is varied on logarithmically.</p>
+</dd>
+<dt><strong>omega_min</strong><span class="classifier">float</span></dt><dd><p>Lower end of range where the GF is being continued. Range has to comprise
+all features of the impurity GF for correct normalization.</p>
+</dd>
+<dt><strong>omega_max</strong><span class="classifier">float</span></dt><dd><p>Upper end of range where the GF is being continued. See omega_min.</p>
+</dd>
+</dl>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
+<dt><strong>maxent_results</strong><span class="classifier">list</span></dt><dd><p>The omega mesh and impurity spectral function from two different analyzers
+in a dict for each impurity</p>
+</dd>
+</dl>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+</div>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="postprocessing.eval_U_cRPA_Vasp.html" class="btn btn-neutral float-left" title="postprocessing.eval_U_cRPA_Vasp" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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+    </div>
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+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.</p>
+  </div>
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+<li class="toctree-l2"><a class="reference internal" href="../install.html#docker-files-images">Docker files &amp; images</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../install.html#version-compatibility">Version compatibility</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../install.html#custom-cmake-options">Custom CMake options</a></li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../documentation.html">Documentation</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#code-structure">Code structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#dft-interface-notes">DFT interface notes</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/w90_interface.html">Wannier90 interface</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/w90_interface.html#orbital-order-in-the-w90-converter">orbital order in the W90 converter</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/vasp_csc.html">Interface to VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#general-remarks">General remarks</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#file-description">file description</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#workflow">Workflow:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#important-flags">important flags:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#version-and-compilation">version and compilation:</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#input-output">Input/Output</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../input_output/DMFT_input/input.html">Input</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/general.html">[general]: General parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/solver.html">[solver]: solver specific parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/dft.html">[dft]: DFT related inputs</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/advanced.html">[advanced]: Advanced inputs</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../input_output/DMFT_output/results.html">Output / results</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_output/results.html#group-structure">Group structure</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_output/results.html#subgroups">Subgroups</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#further-details-for-running">Further details for running</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/docker.html">Docker</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#building-the-docker-image">Building the docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#pulling-a-docker-image">Pulling a docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/run_locally.html">Run on your machine</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="reference internal" href="../documentation.html#module-reference-manual">Module reference manual</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="csc_flow.html">csc_flow</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dft_managers.html">dft_managers</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="dft_managers.mpi_helpers.html">dft_managers.mpi_helpers</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dft_managers.qe_manager.html">dft_managers.qe_manager</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dft_managers.vasp_manager.html">dft_managers.vasp_manager</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="dmft_cycle.html">dmft_cycle</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dmft_tools.html">dmft_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.afm_mapping.html">dmft_tools.afm_mapping</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.convergence.html">dmft_tools.convergence</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.formatter.html">dmft_tools.formatter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.greens_functions_mixer.html">dmft_tools.greens_functions_mixer</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.legendre_filter.html">dmft_tools.legendre_filter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.manipulate_chemical_potential.html">dmft_tools.manipulate_chemical_potential</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.results_to_archive.html">dmft_tools.results_to_archive</a></li>
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+</ul>
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+<li class="toctree-l4"><a class="reference internal" href="io_tools.dict_to_h5.html">io_tools.dict_to_h5</a></li>
+<li class="toctree-l4"><a class="reference internal" href="io_tools.postproc_toml_dict.html">io_tools.postproc_toml_dict</a></li>
+<li class="toctree-l4"><a class="reference internal" href="io_tools.verify_input_params.html">io_tools.verify_input_params</a></li>
+</ul>
+</li>
+<li class="toctree-l3 current"><a class="reference internal" href="postprocessing.html">postprocessing</a><ul class="current">
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.maxent_gf_imp.html">postprocessing.maxent_gf_imp</a></li>
+<li class="toctree-l4 current"><a class="current reference internal" href="#">postprocessing.maxent_gf_latt</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.maxent_sigma.html">postprocessing.maxent_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../issues.html">Support &amp; contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../issues.html#seeking-help">Seeking help</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../issues.html#improving-solid-dmft">Improving solid_dmft</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../issues.html#reporting-issues">Reporting issues</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../ChangeLog.html">Changelog</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-2-0">Version 3.2.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#general">General</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#fix">fix</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-5">Version 3.1.5</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-4">Version 3.1.4</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-3">Version 3.1.3</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-2">Version 3.1.2</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-1">Version 3.1.1</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-0">Version 3.1.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-0-0">Version 3.0.0</a></li>
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+      <li>postprocessing.maxent_gf_latt</li>
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+<div class="section" id="module-postprocessing.maxent_gf_latt">
+<span id="postprocessing-maxent-gf-latt"></span><h1>postprocessing.maxent_gf_latt<a class="headerlink" href="#module-postprocessing.maxent_gf_latt" title="Permalink to this headline"></a></h1>
+<p>Analytic continuation of the lattice Green’s function to the lattice spectral
+function using maxent.</p>
+<p>Reads G_latt(i omega) from the h5 archive and writes A_latt(omega) back. See
+the docstring of main() for more information.</p>
+<p>mpi parallelized for the generation of the imaginary-frequency lattice GF over
+k points.</p>
+<p>Author: Maximilian Merkel, Materials Theory Group, ETH Zurich, 2020 - 2022</p>
+<dl class="py function">
+<dt class="sig sig-object py" id="postprocessing.maxent_gf_latt.main">
+<code class="sig-prename descclassname"><span class="pre">postprocessing.maxent_gf_latt.</span></code><code class="sig-name descname"><span class="pre">main</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">external_path</span></span></em>, <em><span class="n"><span class="pre">iteration</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em><span class="n"><span class="pre">sum_spins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em><span class="n"><span class="pre">maxent_error</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.02</span></span></em>, <em><span class="n"><span class="pre">n_points_maxent</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">200</span></span></em>, <em><span class="n"><span class="pre">n_points_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">50</span></span></em>, <em><span class="n"><span class="pre">omega_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em><span class="n"><span class="pre">omega_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/postprocessing/maxent_gf_latt.html#main"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#postprocessing.maxent_gf_latt.main" title="Permalink to this definition"></a></dt>
+<dd><p>Main function that reads the lattice Green’s function (GF) from h5,
+analytically continues it, writes the result back to the h5 archive and
+also returns the results.
+Only the trace can be used because the Kohn-Sham energies (“hopping”) are not
+sorted by “orbital” but by energy, leading to crossovers.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
+<dt><strong>external_path: string</strong></dt><dd><p>Path to the h5 archive to read from and write to.</p>
+</dd>
+<dt><strong>iteration: int/string</strong></dt><dd><p>Iteration to read from and write to. Defaults to last_iter.</p>
+</dd>
+<dt><strong>sum_spins: bool</strong></dt><dd><p>Whether to sum over the spins or continue the lattice GF
+for the up and down spin separately, for example for magnetized results.</p>
+</dd>
+<dt><strong>maxent_error</strong><span class="classifier">float</span></dt><dd><p>The error that is used for the analyzers.</p>
+</dd>
+<dt><strong>n_points_maxent</strong><span class="classifier">int</span></dt><dd><p>Number of omega points on the hyperbolic mesh used in the continuation.</p>
+</dd>
+<dt><strong>n_points_alpha</strong><span class="classifier">int</span></dt><dd><p>Number of points that the MaxEnt alpha parameter is varied on logarithmically.</p>
+</dd>
+<dt><strong>omega_min</strong><span class="classifier">float</span></dt><dd><p>Lower end of range where the GF is being continued. Range has to comprise
+all features of the lattice GF for correct normalization.
+If omega_min and omega_max are None, they are chosen automatically based
+on the diagonal entries in the hopping matrix but at least to -20…20 eV.</p>
+</dd>
+<dt><strong>omega_max</strong><span class="classifier">float</span></dt><dd><p>Upper end of range where the GF is being continued. See omega_min.</p>
+</dd>
+</dl>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
+<dt><strong>unpacked_results</strong><span class="classifier">dict</span></dt><dd><p>The omega mesh and lattice spectral function from two different analyzers</p>
+</dd>
+</dl>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+</div>
+
+
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+    <p>&#169; Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.</p>
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+<li class="toctree-l3"><a class="reference internal" href="../md_notes/w90_interface.html">Wannier90 interface</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/w90_interface.html#orbital-order-in-the-w90-converter">orbital order in the W90 converter</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/vasp_csc.html">Interface to VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#general-remarks">General remarks</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#file-description">file description</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#workflow">Workflow:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#important-flags">important flags:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#version-and-compilation">version and compilation:</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#input-output">Input/Output</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../input_output/DMFT_input/input.html">Input</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/general.html">[general]: General parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/solver.html">[solver]: solver specific parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/dft.html">[dft]: DFT related inputs</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/advanced.html">[advanced]: Advanced inputs</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../input_output/DMFT_output/results.html">Output / results</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_output/results.html#group-structure">Group structure</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_output/results.html#subgroups">Subgroups</a></li>
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+</li>
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+</li>
+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#further-details-for-running">Further details for running</a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#building-the-docker-image">Building the docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#pulling-a-docker-image">Pulling a docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/run_locally.html">Run on your machine</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="reference internal" href="../documentation.html#module-reference-manual">Module reference manual</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="csc_flow.html">csc_flow</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="dft_managers.vasp_manager.html">dft_managers.vasp_manager</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="dmft_cycle.html">dmft_cycle</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.manipulate_chemical_potential.html">dmft_tools.manipulate_chemical_potential</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.observables.html">dmft_tools.observables</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.solver.html">dmft_tools.solver</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="io_tools.html">io_tools</a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="io_tools.verify_input_params.html">io_tools.verify_input_params</a></li>
+</ul>
+</li>
+<li class="toctree-l3 current"><a class="reference internal" href="postprocessing.html">postprocessing</a><ul class="current">
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
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+<li class="toctree-l4 current"><a class="current reference internal" href="#">postprocessing.maxent_sigma</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
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+</li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
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+<div class="section" id="module-postprocessing.maxent_sigma">
+<span id="postprocessing-maxent-sigma"></span><h1>postprocessing.maxent_sigma<a class="headerlink" href="#module-postprocessing.maxent_sigma" title="Permalink to this headline"></a></h1>
+<p>Analytic continuation of the self-energy using maxent on an auxiliary Green’s
+function.</p>
+<p>Reads Sigma(i omega) from the h5 archive and writes Sigma(omega) back. See
+the docstring of main() for more information.</p>
+<p>mpi parallelized for the maxent routine over all blocks and for the continuator
+extraction over omega points.</p>
+<p>Author: Maximilian Merkel, Materials Theory Group, ETH Zurich, 2020 - 2022</p>
+<dl class="docutils">
+<dt>Warnings:</dt><dd><ul class="simple">
+<li>When using this on self-energies with SOC, please check that the formalism
+is correct, in particular the Kramers-Kronig relation.</li>
+</ul>
+</dd>
+</dl>
+<dl class="py function">
+<dt class="sig sig-object py" id="postprocessing.maxent_sigma.main">
+<code class="sig-prename descclassname"><span class="pre">postprocessing.maxent_sigma.</span></code><code class="sig-name descname"><span class="pre">main</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">external_path</span></span></em>, <em><span class="n"><span class="pre">iteration</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em><span class="n"><span class="pre">continuator_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'inversion_sigmainf'</span></span></em>, <em><span class="n"><span class="pre">maxent_error</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.02</span></span></em>, <em><span class="n"><span class="pre">omega_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-</span> <span class="pre">12.0</span></span></em>, <em><span class="n"><span class="pre">omega_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">12.0</span></span></em>, <em><span class="n"><span class="pre">n_points_maxent</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">400</span></span></em>, <em><span class="n"><span class="pre">n_points_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">50</span></span></em>, <em><span class="n"><span class="pre">analyzer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'LineFitAnalyzer'</span></span></em>, <em><span class="n"><span class="pre">n_points_interp</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2000</span></span></em>, <em><span class="n"><span class="pre">n_points_final</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/postprocessing/maxent_sigma.html#main"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#postprocessing.maxent_sigma.main" title="Permalink to this definition"></a></dt>
+<dd><p>Main function that reads the Matsubara self-energy from h5, analytically continues it,
+writes the results back to the h5 archive and also returns the results.</p>
+<p>Function parallelizes using MPI over impurities and blocks.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
+<dt><strong>external_path</strong><span class="classifier">string</span></dt><dd><p>Path to the h5 archive to read from and write to</p>
+</dd>
+<dt><strong>iteration</strong><span class="classifier">int/string</span></dt><dd><p>Iteration to read from and write to. Default to last_iter</p>
+</dd>
+<dt><strong>continuator_type</strong><span class="classifier">string</span></dt><dd><p>Type of continuator to use, one of ‘inversion_sigmainf’, ‘inversion_dc’, ‘direct’</p>
+</dd>
+<dt><strong>maxent_error</strong><span class="classifier">float</span></dt><dd><p>The error that is used for the analyzers.</p>
+</dd>
+<dt><strong>omega_min</strong><span class="classifier">float</span></dt><dd><p>Lower end of range where Sigma is being continued. Range has to comprise
+all features of the self-energy because the real part of it comes from
+the Kramers-Kronig relation applied to the auxiliary spectral function.
+For example, if the real-frequency self-energy bends at omega_min or
+omega_max, there are neglegcted features and the range should be extended.</p>
+</dd>
+<dt><strong>omega_max</strong><span class="classifier">float</span></dt><dd><p>Upper end of range where Sigma is being continued. See omega_min.</p>
+</dd>
+<dt><strong>n_points_maxent</strong><span class="classifier">int</span></dt><dd><p>Number of omega points on the hyperbolic mesh used in analytically
+continuing the auxiliary GF</p>
+</dd>
+<dt><strong>n_points_alpha</strong><span class="classifier">int</span></dt><dd><p>Number of points that the MaxEnt alpha parameter is varied on logarithmically</p>
+</dd>
+<dt><strong>analyzer</strong><span class="classifier">string</span></dt><dd><p>Analyzer used int MaxEnt, one of ‘LineFitAnalyzer’, ‘Chi2CurvatureAnalyzer’,
+‘ClassicAnalyzer’, ‘EntropyAnalyzer’, ‘BryanAnalyzer’</p>
+</dd>
+<dt><strong>n_points_interp</strong><span class="classifier">int</span></dt><dd><p>Number of points where auxiliary GF is interpolated to integrate over
+it for the Kramers-Kronig relation</p>
+</dd>
+<dt><strong>n_points_final</strong><span class="classifier">int</span></dt><dd><p>Number of omega points the complex auxiliary GF and therefore the
+continued self-energy has on a linear grid between omega_min and omega_max</p>
+</dd>
+</dl>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first docutils">
+<dt><strong>sigma_w</strong><span class="classifier">list of triqs.gf.BlockGf</span></dt><dd><p>Sigma(omega) per inequivalent shell</p>
+</dd>
+<dt><strong>g_aux_w</strong><span class="classifier">list of triqs.gf.BlockGf</span></dt><dd><p>G_aux(omega) per inequivalent shell</p>
+</dd>
+</dl>
+</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">Raises:</th><td class="field-body"><dl class="first last docutils">
+<dt>NotImplementedError</dt><dd><p>– When a wrong continuator type or maxent analyzer is chosen
+– For direct continuator: when the self energy contains blocks larger
+than 1x1 (no off-diagonal continuation possible)
+– For inversion_dc continuator: when the DC is not a diagonal matrix with
+the same entry for all blocks of an impurity. Otherwise, issues like
+the global frame violating the block structure would come up.</p>
+</dd>
+</dl>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+</div>
+
+
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+    <p>&#169; Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.</p>
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+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_output/results.html#group-structure">Group structure</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
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+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
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+<li class="toctree-l4 current"><a class="current reference internal" href="#">postprocessing.pade_sigma</a></li>
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+</ul>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
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+</ul>
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+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
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+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
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+<div class="section" id="module-postprocessing.pade_sigma">
+<span id="postprocessing-pade-sigma"></span><h1>postprocessing.pade_sigma<a class="headerlink" href="#module-postprocessing.pade_sigma" title="Permalink to this headline"></a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="postprocessing.pade_sigma.main">
+<code class="sig-prename descclassname"><span class="pre">postprocessing.pade_sigma.</span></code><code class="sig-name descname"><span class="pre">main</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">external_path</span></span></em>, <em><span class="n"><span class="pre">n_w</span></span></em>, <em><span class="n"><span class="pre">w_min</span></span></em>, <em><span class="n"><span class="pre">w_max</span></span></em>, <em><span class="n"><span class="pre">n_iw</span></span></em>, <em><span class="n"><span class="pre">iteration</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em><span class="n"><span class="pre">eta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/postprocessing/pade_sigma.html#main"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#postprocessing.pade_sigma.main" title="Permalink to this definition"></a></dt>
+<dd><p>Main function that reads the Matsubara self-energy from h5, analytically continues it,
+writes the results back to the h5 archive and also returns the results.</p>
+<p>Function parallelizes using MPI over impurities and blocks.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
+<dt><strong>external_path</strong><span class="classifier">string</span></dt><dd><p>Path to the h5 archive to read from and write to</p>
+</dd>
+<dt><strong>n_w</strong><span class="classifier">int</span></dt><dd><p>number of real frequencies of the final self-energies returned</p>
+</dd>
+<dt><strong>w_min</strong><span class="classifier">float</span></dt><dd><p>Lower end of range where Sigma is being continued.</p>
+</dd>
+<dt><strong>w_max</strong><span class="classifier">float</span></dt><dd><p>Upper end of range where Sigma is being continued.</p>
+</dd>
+<dt><strong>n_iw</strong><span class="classifier">int</span></dt><dd><p>number of Matsubara frequencies to consider for the Pade approximant</p>
+</dd>
+<dt><strong>iteration</strong><span class="classifier">int/string</span></dt><dd><p>Iteration to read from and write to. Default to last_iter</p>
+</dd>
+<dt><strong>eta</strong><span class="classifier">float</span></dt><dd><p>frequency offset within Pade</p>
+</dd>
+</dl>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
+<dt><strong>sigma_w</strong><span class="classifier">list of triqs.gf.BlockGf</span></dt><dd><p>Sigma(omega) per inequivalent shell</p>
+</dd>
+</dl>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+</div>
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+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
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+<li class="toctree-l3"><a class="reference internal" href="../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.results_to_archive.html">dmft_tools.results_to_archive</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.solver.html">dmft_tools.solver</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="io_tools.html">io_tools</a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="io_tools.verify_input_params.html">io_tools.verify_input_params</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="postprocessing.maxent_gf_latt.html">postprocessing.maxent_gf_latt</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.maxent_sigma.html">postprocessing.maxent_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4 current"><a class="current reference internal" href="#">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
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+</li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../issues.html">Support &amp; contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../issues.html#seeking-help">Seeking help</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../issues.html#improving-solid-dmft">Improving solid_dmft</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../issues.html#reporting-issues">Reporting issues</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../ChangeLog.html">Changelog</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-2-0">Version 3.2.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#general">General</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#fix">fix</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-5">Version 3.1.5</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-4">Version 3.1.4</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-3">Version 3.1.3</a></li>
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+<div class="section" id="module-postprocessing.plot_correlated_bands">
+<span id="postprocessing-plot-correlated-bands"></span><h1>postprocessing.plot_correlated_bands<a class="headerlink" href="#module-postprocessing.plot_correlated_bands" title="Permalink to this headline"></a></h1>
+<p>Reads DMFT_ouput observables such as real-frequency Sigma and a Wannier90
+TB Hamiltonian to compute spectral properties. It runs in two modes,
+either calculating the bandstructure or Fermi slice.</p>
+<p>Written by Sophie Beck, 2021-2022</p>
+<p>TODO:
+- extend to multi impurity systems
+- make proper use of rot_mat from DFT_Tools (atm it assumed that wannier_hr and Sigma are written in the same basis)</p>
+<dl class="py function">
+<dt class="sig sig-object py" id="postprocessing.plot_correlated_bands.get_dmft_bands">
+<code class="sig-prename descclassname"><span class="pre">postprocessing.plot_correlated_bands.</span></code><code class="sig-name descname"><span class="pre">get_dmft_bands</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">n_orb</span></span></em>, <em><span class="n"><span class="pre">w90_path</span></span></em>, <em><span class="n"><span class="pre">w90_seed</span></span></em>, <em><span class="n"><span class="pre">mu_tb</span></span></em>, <em><span class="n"><span class="pre">add_spin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em><span class="n"><span class="pre">add_lambda</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em><span class="n"><span class="pre">add_local</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em><span class="n"><span class="pre">with_sigma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em><span class="n"><span class="pre">fermi_slice</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em><span class="n"><span class="pre">qp_bands</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em><span class="n"><span class="pre">orbital_order_to</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em><span class="n"><span class="pre">add_mu_tb</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em><span class="n"><span class="pre">band_basis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em><span class="n"><span class="pre">proj_on_orb</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em><span class="n"><span class="pre">trace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em><span class="n"><span class="pre">eta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em><span class="n"><span class="pre">mu_shift</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em><span class="n"><span class="pre">proj_nuk</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">specs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/postprocessing/plot_correlated_bands.html#get_dmft_bands"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#postprocessing.plot_correlated_bands.get_dmft_bands" title="Permalink to this definition"></a></dt>
+<dd><p>Extract tight-binding from given w90 seed_hr.dat and seed.wout files, and then extract from
+given solid_dmft calculation the self-energy and construct the spectral function A(k,w) on
+given k-path.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
+<dt><strong>n_orb</strong><span class="classifier">int</span></dt><dd><p>Number of Wannier orbitals in seed_hr.dat</p>
+</dd>
+<dt><strong>w90_path</strong><span class="classifier">string</span></dt><dd><p>Path to w90 files</p>
+</dd>
+<dt><strong>w90_seed</strong><span class="classifier">string</span></dt><dd><p>Seed of wannier90 calculation, i.e. seed_hr.dat and seed.wout</p>
+</dd>
+<dt><strong>add_spin</strong><span class="classifier">bool, default=False</span></dt><dd><p>Extend w90 Hamiltonian by spin indices</p>
+</dd>
+<dt><strong>add_lambda</strong><span class="classifier">float, default=None</span></dt><dd><p>Add SOC term with strength add_lambda (works only for t2g shells)</p>
+</dd>
+<dt><strong>add_local</strong><span class="classifier">numpy array, default=None</span></dt><dd><p>Add local term of dimension (n_orb x n_orb)</p>
+</dd>
+<dt><strong>with_sigma</strong><span class="classifier">str, or BlockGf, default=None</span></dt><dd><p>Add self-energy to spectral function? Can be either directly take
+a triqs BlockGf object or can be either ‘calc’ or ‘model’
+‘calc’ reads results from h5 archive (solid_dmft)
+in case ‘calc’ or ‘model’ are specified a extra kwargs dict has
+to be given sigma_dict containing information about the self-energy</p>
+</dd>
+<dt><strong>add_mu_tb</strong><span class="classifier">bool, default=False</span></dt><dd><p>Add the TB specified chemical potential to the lattice Green function
+set to True if DMFT calculation was performed with DFT fermi subtracted.</p>
+</dd>
+<dt><strong>proj_on_orb</strong><span class="classifier">int or list of int, default=None</span></dt><dd><p>orbital projections to be made for the spectral function and TB bands
+the integer refer to the orbitals read</p>
+</dd>
+<dt><strong>trace</strong><span class="classifier">bool, default=True</span></dt><dd><p>Return trace over orbitals for spectral function. For special
+post-processing purposes this can be set to False giving the returned
+alatt_k_w an extra dimension n_orb</p>
+</dd>
+<dt><strong>eta</strong><span class="classifier">float, default=0.0</span></dt><dd><p>Broadening of spectral function, finitie shift on imaginary axis
+if with_sigma=None it has to be provided !=0.0</p>
+</dd>
+<dt><strong>mu_shift</strong><span class="classifier">float, default=0.0</span></dt><dd><p>Manual extra shift when calculating the spectral function</p>
+</dd>
+<dt><strong>proj_nuk</strong><span class="classifier">numpy array, default [None]</span></dt><dd><p>Extra projections to be applied to the final spectral function
+per orbital and k-point. Has to match shape of final lattice Green
+function. Will be applied together with proj_on_orb if specified.</p>
+</dd>
+</dl>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
+<dt><strong>tb_data</strong><span class="classifier">dict</span></dt><dd><p>tight binding dict containing the kpoint mesh, dispersion / emat, and eigenvectors</p>
+</dd>
+<dt><strong>alatt_k_w</strong><span class="classifier">numpy array (float) of dim n_k x n_w ( x n_orb if trace=False)</span></dt><dd><p>lattice spectral function data on the kpoint mesh defined in tb_data and frequency
+mesh defined in freq_dict</p>
+</dd>
+<dt><strong>freq_dict</strong><span class="classifier">dict</span></dt><dd><p>frequency mesh information on which alatt_k_w is evaluated</p>
+</dd>
+</dl>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="postprocessing.plot_correlated_bands.get_tb_bands">
+<code class="sig-prename descclassname"><span class="pre">postprocessing.plot_correlated_bands.</span></code><code class="sig-name descname"><span class="pre">get_tb_bands</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">e_mat</span></span></em>, <em><span class="n"><span class="pre">proj_on_orb</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[None]</span></span></em>, <em><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">specs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/postprocessing/plot_correlated_bands.html#get_tb_bands"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#postprocessing.plot_correlated_bands.get_tb_bands" title="Permalink to this definition"></a></dt>
+<dd><p>calculate eigenvalues and eigenvectors for given list of e_mat on kmesh</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
+<dt><strong>e_mat</strong><span class="classifier">numpy array of shape (n_orb, n_orb, nk) or (n_orb, n_orb, nk, nk)</span></dt><dd></dd>
+</dl>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
+<dt><strong>e_val</strong><span class="classifier">numpy array of shape (n_orb, n_orb, nk) or (n_orb, n_orb, nk, nk)</span></dt><dd><p>eigenvalues as matrix</p>
+</dd>
+<dt><strong>e_vec</strong><span class="classifier">numpy array of shape (n_orb, n_orb, nk) or (n_orb, n_orb, nk, nk)</span></dt><dd><p>eigenvectors as matrix</p>
+</dd>
+</dl>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+</div>
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+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
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+</li>
+</ul>
+</li>
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+<li class="toctree-l4"><a class="reference internal" href="util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
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+<div class="section" id="module-util">
+<span id="util"></span><h1>util<a class="headerlink" href="#module-util" title="Permalink to this headline"></a></h1>
+<p>external helper functions, not used by any DMFT routine</p>
+<p class="rubric">Modules</p>
+<table border="1" class="longtable docutils align-default">
+<colgroup>
+<col width="10%" />
+<col width="90%" />
+</colgroup>
+<tbody valign="top">
+<tr class="row-odd"><td><a class="reference internal" href="util.symmetrize_gamma_file.html#module-util.symmetrize_gamma_file" title="util.symmetrize_gamma_file"><code class="xref py py-obj docutils literal notranslate"><span class="pre">util.symmetrize_gamma_file</span></code></a></td>
+<td></td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="util.write_kslice_to_h5.html#module-util.write_kslice_to_h5" title="util.write_kslice_to_h5"><code class="xref py py-obj docutils literal notranslate"><span class="pre">util.write_kslice_to_h5</span></code></a></td>
+<td>Reads the -kslice-bands.dat and the -kslice-coord.dat file (as Wannier90 writes them).</td>
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+</table>
+</div>
+
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+<li class="toctree-l3"><a class="reference internal" href="../md_notes/w90_interface.html">Wannier90 interface</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/w90_interface.html#orbital-order-in-the-w90-converter">orbital order in the W90 converter</a></li>
+</ul>
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+<li class="toctree-l3"><a class="reference internal" href="../md_notes/vasp_csc.html">Interface to VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#general-remarks">General remarks</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#file-description">file description</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#workflow">Workflow:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#important-flags">important flags:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#version-and-compilation">version and compilation:</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#input-output">Input/Output</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../input_output/DMFT_input/input.html">Input</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/general.html">[general]: General parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/solver.html">[solver]: solver specific parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/dft.html">[dft]: DFT related inputs</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/advanced.html">[advanced]: Advanced inputs</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../input_output/DMFT_output/results.html">Output / results</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_output/results.html#group-structure">Group structure</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_output/results.html#subgroups">Subgroups</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#further-details-for-running">Further details for running</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/docker.html">Docker</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#building-the-docker-image">Building the docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#pulling-a-docker-image">Pulling a docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/run_locally.html">Run on your machine</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="reference internal" href="../documentation.html#module-reference-manual">Module reference manual</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="csc_flow.html">csc_flow</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dft_managers.html">dft_managers</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="dft_managers.mpi_helpers.html">dft_managers.mpi_helpers</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="dft_managers.vasp_manager.html">dft_managers.vasp_manager</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="dmft_cycle.html">dmft_cycle</a></li>
+<li class="toctree-l3"><a class="reference internal" href="dmft_tools.html">dmft_tools</a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="dmft_tools.results_to_archive.html">dmft_tools.results_to_archive</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="io_tools.postproc_toml_dict.html">io_tools.postproc_toml_dict</a></li>
+<li class="toctree-l4"><a class="reference internal" href="io_tools.verify_input_params.html">io_tools.verify_input_params</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="postprocessing.maxent_gf_latt.html">postprocessing.maxent_gf_latt</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.maxent_sigma.html">postprocessing.maxent_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3 current"><a class="reference internal" href="util.html">util</a><ul class="current">
+<li class="toctree-l4 current"><a class="current reference internal" href="#">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../issues.html">Support &amp; contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../issues.html#seeking-help">Seeking help</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../issues.html#improving-solid-dmft">Improving solid_dmft</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../issues.html#reporting-issues">Reporting issues</a></li>
+</ul>
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+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#input-output">Input/Output</a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/general.html">[general]: General parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/solver.html">[solver]: solver specific parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/dft.html">[dft]: DFT related inputs</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="io_tools.verify_input_params.html">io_tools.verify_input_params</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
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+<li class="toctree-l4 current"><a class="current reference internal" href="#">util.write_kslice_to_h5</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../issues.html">Support &amp; contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../issues.html#seeking-help">Seeking help</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../issues.html#improving-solid-dmft">Improving solid_dmft</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../issues.html#reporting-issues">Reporting issues</a></li>
+</ul>
+</li>
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+<li class="toctree-l1"><a class="reference internal" href="../ChangeLog.html">Changelog</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-2-0">Version 3.2.0</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-0-0">Version 3.0.0</a></li>
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+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu"  style="background: #7E588A" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="../index.html">solid_dmft</a>
+      </nav>
+
+      <div class="wy-nav-content">
+        <div class="rst-content">
+          <div role="navigation" aria-label="Page navigation">
+  <ul class="wy-breadcrumbs">
+      <li><a href="../index.html" class="icon icon-home"></a> &raquo;</li>
+          <li><a href="../documentation.html">Documentation</a> &raquo;</li>
+          <li><a href="util.html">util</a> &raquo;</li>
+      <li>util.write_kslice_to_h5</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../_sources/_ref/util.write_kslice_to_h5.rst.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  
+<style>
+/* CSS overrides for sphinx_rtd_theme */
+
+/* 24px margin */
+.nbinput.nblast.container,
+.nboutput.nblast.container {
+    margin-bottom: 19px;  /* padding has already 5px */
+}
+
+/* ... except between code cells! */
+.nblast.container + .nbinput.container {
+    margin-top: -19px;
+}
+
+.admonition > p:before {
+    margin-right: 4px;  /* make room for the exclamation icon */
+}
+
+/* Fix math alignment, see https://github.com/rtfd/sphinx_rtd_theme/pull/686 */
+.math {
+    text-align: unset;
+}
+</style>
+<div class="section" id="module-util.write_kslice_to_h5">
+<span id="util-write-kslice-to-h5"></span><h1>util.write_kslice_to_h5<a class="headerlink" href="#module-util.write_kslice_to_h5" title="Permalink to this headline"></a></h1>
+<p>Reads the -kslice-bands.dat and the -kslice-coord.dat file (as Wannier90 writes them).
+The -kslice-bands.dat contains the band energies corresponding to the slices through
+k-space given in _kslice-coords.dat. The latter has the list of k points in 2D direct
+coordinates.</p>
+<p>This only works for k independent projectors as from a TB model or from Wannier90.</p>
+<p>Writes all the information back into the h5 archive in the group ‘dft_bands_input’,
+which is needed for plotting DMFT bands with SumkDFTTools spaghettis.</p>
+<p>Adapted from “write_bands_to_h5.py” by Sophie Beck, 2021</p>
+<dl class="py function">
+<dt class="sig sig-object py" id="util.write_kslice_to_h5.main">
+<code class="sig-prename descclassname"><span class="pre">util.write_kslice_to_h5.</span></code><code class="sig-name descname"><span class="pre">main</span></code><span class="sig-paren">(</span><em><span class="n"><span class="pre">seedname</span></span></em>, <em><span class="n"><span class="pre">filename_archive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/util/write_kslice_to_h5.html#main"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#util.write_kslice_to_h5.main" title="Permalink to this definition"></a></dt>
+<dd><p>Executes the program on the band data from the files &lt;seedname&gt;_bands.dat and
+&lt;seedname&gt;_bands.kpt. If no seedname_archive is specified, &lt;seedname&gt;.h5 is used.</p>
+</dd></dl>
+
+</div>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="util.symmetrize_gamma_file.html" class="btn btn-neutral float-left" title="util.symmetrize_gamma_file" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="../tutorials.html" class="btn btn-neutral float-right" title="Tutorials" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+    </div>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.</p>
+  </div>
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
+
+</footer>
+        </div>
+      </div>
+    </section>
+  </div>
+  <script>
+      jQuery(function () {
+          SphinxRtdTheme.Navigation.enable(true);
+      });
+  </script> 
+
+</body>
+</html>
\ No newline at end of file
diff --git a/_sources/ChangeLog.md.txt b/_sources/ChangeLog.md.txt
new file mode 100644
index 00000000..3c7712a3
--- /dev/null
+++ b/_sources/ChangeLog.md.txt
@@ -0,0 +1,246 @@
+(changelog)=
+
+# Changelog
+
+
+## Version 3.2.0
+
+solid_dmft version 3.2.0 is a release that
+* adds Jenkins CI support via flatiron-jenkins
+* includes several fixes to match the latest triqs 3.2.x release
+* changes the Z estimate to a correct linear fit of the first two Matsubara frequencies
+* fixes for QE and Vasp CSC
+* add option to add a magnetic field in DMFT
+* add solid_dmft JOSS paper reference (doi.org/10.21105/joss.04623)
+* add simple Ntot interaction
+* allow Uprime!=U-2J in Kanamori
+* updates the tutorials
+* introduces input output documentation
+* add support for the TRIQS Hartree Solver
+* add RESPACK support
+
+We thank all contributors: Sophie Beck, Alberto Carta, Alexander Hampel, Max Merkel, Harrison LaBollita, Nils Wentzell
+
+Find below an itemized list of changes in this release.
+
+General
+-------
+* fix SzSz measurement in triqs unstable
+* Updated mpich VASP5 docker file to include HF solver
+* add hartree solver
+* feat: add regular kmesh option to pcb postproc
+* Fix to charge-self-consistency with Vasp (#48)
+* removed QE fix files which are now in official release
+* Modified dockerfile to add pmi support for cray supercomputing environments
+* add RESPACK postprocessing routines (#38)
+* Added correction to energy calculation
+* add triqs logos to skeleton and include ico in install directive of doc
+* change name of dft_mu to mu_initial_guess
+* support different DFT cubic basis conventions (#36)
+* allow magnetic calculation for CSC (output den correction is always averaged)
+* fix sym bug in hubbardI postprocessing
+* always calculate dft_mu at start of calculation
+* add h_field_it to remove magnetic field after x iterations
+* Write solid_dmft hash to h5
+* fix delta interface of cthyb for multiple sites with different block structures
+* correctly use tail fitted Sigma from cthyb not via double dyson equation
+* add paper ref to toml
+* minor addition of post-processing script: add_local Hamiltonian, separate from add_lambda. We might remove add_lambda
+* update doc with JOSS references
+* Bug fix for changes in sumk mesh definition in maxent_gf_latt
+* adapt vasp patch files for ver6.3.2
+* function to det n_orb_solver, fix test
+* apply block picker before block mapping
+* fix header writing for obs file
+* add pick solver struct option to select specific blocks for the impurity problem
+* fix print for failing comparison test
+* allow different interaction Hamiltonians per impurity
+* enforce PEP standard in interaction Hamiltonian
+* print optimal alpha in other maxent scripts
+* final corrections for PCB functions
+* add proj_on_orb functionality to Akw
+* fix bug in max_G_diff function ignoring norm_temp
+* change Sigma_imp_iw / _w to Sigma_imp (DFTTools unstable)
+* fix load Sigma with new gf_struct in triqs 3.1.x
+* adapt to sumk mesh changes in dfttools
+* Made the way mesh is stored in maxent_gf_latt consistent with maxent_gf_imp
+
+fix
+---
+* fix deg shells in magnetic calculations
+* fix parameter n_orb in hint construction
+* doc strings of cRPA avering for Slater
+* critical bug in hubbardI interface
+* PCB fermi surface plot
+* updates from triqs unstable
+* simple Z estimate as linear fit
+* PCB: removing "linearize" function, changing the model
+* delta_interface with SOC and store solver options
+* convert warmup cycles to int automatically
+* problem with ish vs icrsh in PCB Thanks @HenryScottx for reporting!
+* h_int uses now n_orb instead of orb_names
+
+build
+-----
+* adapt jenkins CI files
+* simplify docker image
+* update openmpi docker file with clang-15
+* update CI dockerfile
+* Updated docker file to ubuntu 22
+
+feat
+----
+* enable MPI for maxent_gf_imp post-processing routines
+* add possibility to specify Uprime in Kanamori interaction
+* add loc_n_min / max arg for cthyb
+* add additional support for hartree when computing DC from the solver
+* add Ntot interaction
+
+doc
+---
+* Added observables documentation for DMFT output
+* Updated tutorial svo one-shot
+
+test
+----
+* fix tests after Hartree additions
+* add Hartree Solver test
+* Integration test for maxent gf imp and latt, bug fixes to both scripts (#30)
+* add new test for pcb get_dmft_bands function
+
+
+## Version 3.1.5
+
+solid_dmft version 3.1.5 is a patch-release that improves / fixes the following issues:
+
+* fix to charge-self-consistency with Vasp and QE
+* feat add loc_n_min / max arg for cthyb
+* fix simple Z estimate as linear fit
+* adapt docker images for ubuntu 22.04
+
+Contributors: Sophie Beck, Alberto Carta, Alexander Hampel, Max Merkel:
+
+## Version 3.1.4
+
+solid_dmft version 3.1.4 is a patch-release that improves / fixes the following issues:
+
+* fix and improve rootfinder in PCB for quasiparticle dispersion
+* fix pypi package version.py module
+
+Contributors: Sophie Beck, Alberto Carta, Alexander Hampel, Max Merkel:
+
+## Version 3.1.3
+
+solid_dmft version 3.1.3 is a patch-release that improves / fixes the following issues:
+
+* fix delta interface of cthyb for multiple sites with different block structures
+* correctly use tail fitted Sigma from cthyb not via double dyson equation
+* magnetic param not available in CSC crash PM calc
+* improve PCB script from unstable branch
+* convert warmup cycles to int automatically
+* fix function calls in gap finder
+* fix delta_interface with SOC and store solver options
+* fix: update svo example for PCB test from unstable
+
+Contributors: Sophie Beck, Alberto Carta, Alexander Hampel, Max Merkel
+
+## Version 3.1.2
+
+solid_dmft version 3.1.1 is a patch-release that improves / fixes the following issues:
+
+* fix deg shells in magnetic calculations
+* fix bug in max_G_diff function ignoring norm_temp
+* fix load Sigma with new gf_struct in triqs 3.1.x
+* Made the way mesh is stored in maxent_gf_latt consistent with maxent_gf_imp
+* adapt vasp patch files for ver6.3.2
+* update README.md for Joss publication
+* print optimal alpha in other maxent scripts
+* update postprocessing routines for plotting spectral functions
+* add new test for pcb get_dmft_bands function
+* DOC: extend install instructions & improve readme for #21 #22
+* DOC: update support & contribute section, bump ver to 3.1.1
+* add proj_on_orb functionality to Akw
+* Added observables documentation for DMFT output
+* Added input documentation
+* Added ETH logo to website, small fixes to documentation
+* rename examples to debbuging_examples
+* pip package build files
+
+Contributors: Sophie Beck, Alberto Carta, Alexander Hampel, Max Merkel
+
+
+## Version 3.1.1
+
+solid_dmft version 3.1.1 is a patch-release that improves / fixes the following issues:
+
+* delete obsolete make_spaghetti.py
+* SOC self energies can be continued in maxent
+* run hubbardI solver on all nodes due to slow bcast performance of atomdiag object
+* fix DFT energy read when running CSC QE
+* updated documentation, small fixes to tutorials
+* exposed params of maxent_gf_imp
+* fix the way dft_mu is loaded in PCB
+* fix executable in SVO tutorial
+* fix shift in sigma continuator to remove dft_mu
+* fix chemical potential in plot Akw and minor fixes
+* correct plotlabels in postprocessing
+* tiny modification of printing H_loc in postprocessing
+
+Contributors: Sophie Beck, Alberto Carta, Max Merkel
+
+## Version 3.1.0
+
+solid_dmft version 3.1.0 is a major release that provides tutorials in the documentation, changes to app4triqs skeleton, allows CSC calculations with QE, improves postprocessing routines, and add functionality for SOC calculations.
+
+* all new tutorials
+* generalize measure_chi functionality
+* CSC with Vasp 6.3.0 works, examples updated
+* fix two bugs in w90 interface in vasp
+* Renamed files
+* fix Fermi level print in mlwf.F LPRJ_WRITE call
+* Automatic patching of vasp 6.3.0 with Docker
+* Updated tutorial
+* Added check on all mpi ranks if dmft_config exists at beginning of run
+* fix small bug in convergence.py thanks @merkelm
+* Rework convergence metrics
+* remove gf_struct_flatten from solver in accordance with latest dfttools version
+* Renaming to solid_dmft
+* Update of maxent_gf_latt.py: more parameters exposed and spin averaging is not default anymore
+* fix bug in afm calculation when measuring density matrix
+* Add w90_tolerance flag for CSC
+* use sphinx autosummary for module reference
+* small changes in IO, additional mpi barriers in csc flow for better stability
+* With SOC now program prints real and imag part of matrices
+* Fixed creation of Kanamori Hamiltonian with SOC
+* Improvements in plot_correlated_bands.py and updated tutorial
+* change output name of MaxEnt Sigma to Sigma_maxent
+* change to develop version of w90 because of mpi bug in openmpi dockerfile
+* bugfix in plot_correlated_bands and cleaning up
+* update OpenMPI Dockerfile to latest Ubuntu
+* Tutorial to explore correlated bands using the postprocessing script
+* check in CSC with QE if optional files are presesnt, otherwise skip calculation
+* Updated maxent_sigma: mpi parallelization, continuator types, bug fixes, parameters exposed
+* update installation instructions
+* add workflow and code structure images
+* Updated maxent sigma script
+* W90 runs in parallel
+* Fixing a bug related to measure_pert_order and measure_chi_SzSz for afm_order
+* add vasp crpa scripts and tutorials
+* add delta interface for cthyb
+* fix get_dmft_bands and pass eta to alatt_k_w correctly
+* allows to recompute rotation matrix even if W90 is used
+* bugfix in initial_self_energies.py in case dc = False
+* flatten gf_struct for triqs solvers to remove depracted warning
+* add example files for SVO and LNO
+* bump triqs and package version to 3.1
+
+Contributors: Sophie Beck, Alberto Carta, Max Merkel
+
+## Version 3.0.0
+
+solid_dmft version 3.0.0 is a compatibility
+release for TRIQS version 3.0.0 that
+* introduces compatibility with Python 3 (Python 2 no longer supported)
+* adds a cmake-based dependency management
+* fixes several application issues
+
diff --git a/_sources/_ref/csc_flow.rst.txt b/_sources/_ref/csc_flow.rst.txt
new file mode 100644
index 00000000..a77add57
--- /dev/null
+++ b/_sources/_ref/csc_flow.rst.txt
@@ -0,0 +1,21 @@
+csc\_flow
+=========
+
+.. automodule:: csc_flow
+    :members:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/_sources/_ref/dft_managers.mpi_helpers.rst.txt b/_sources/_ref/dft_managers.mpi_helpers.rst.txt
new file mode 100644
index 00000000..5295cf57
--- /dev/null
+++ b/_sources/_ref/dft_managers.mpi_helpers.rst.txt
@@ -0,0 +1,21 @@
+dft\_managers.mpi\_helpers
+==========================
+
+.. automodule:: dft_managers.mpi_helpers
+    :members:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/_sources/_ref/dft_managers.qe_manager.rst.txt b/_sources/_ref/dft_managers.qe_manager.rst.txt
new file mode 100644
index 00000000..9aaff904
--- /dev/null
+++ b/_sources/_ref/dft_managers.qe_manager.rst.txt
@@ -0,0 +1,21 @@
+dft\_managers.qe\_manager
+=========================
+
+.. automodule:: dft_managers.qe_manager
+    :members:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/_sources/_ref/dft_managers.rst.txt b/_sources/_ref/dft_managers.rst.txt
new file mode 100644
index 00000000..5b5c2b40
--- /dev/null
+++ b/_sources/_ref/dft_managers.rst.txt
@@ -0,0 +1,33 @@
+dft\_managers
+=============
+
+.. automodule:: dft_managers
+    :members:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+.. rubric:: Modules
+
+.. autosummary::
+    :toctree:
+    :template: autosummary_module_template.rst 
+    :recursive:
+
+    
+    dft_managers.mpi_helpers
+    dft_managers.qe_manager
+    dft_managers.vasp_manager
+
+
diff --git a/_sources/_ref/dft_managers.vasp_manager.rst.txt b/_sources/_ref/dft_managers.vasp_manager.rst.txt
new file mode 100644
index 00000000..920c4ed0
--- /dev/null
+++ b/_sources/_ref/dft_managers.vasp_manager.rst.txt
@@ -0,0 +1,21 @@
+dft\_managers.vasp\_manager
+===========================
+
+.. automodule:: dft_managers.vasp_manager
+    :members:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/_sources/_ref/dmft_cycle.rst.txt b/_sources/_ref/dmft_cycle.rst.txt
new file mode 100644
index 00000000..975b3616
--- /dev/null
+++ b/_sources/_ref/dmft_cycle.rst.txt
@@ -0,0 +1,21 @@
+dmft\_cycle
+===========
+
+.. automodule:: dmft_cycle
+    :members:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/_sources/_ref/dmft_tools.afm_mapping.rst.txt b/_sources/_ref/dmft_tools.afm_mapping.rst.txt
new file mode 100644
index 00000000..1b5c6f10
--- /dev/null
+++ b/_sources/_ref/dmft_tools.afm_mapping.rst.txt
@@ -0,0 +1,21 @@
+dmft\_tools.afm\_mapping
+========================
+
+.. automodule:: dmft_tools.afm_mapping
+    :members:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/_sources/_ref/dmft_tools.convergence.rst.txt b/_sources/_ref/dmft_tools.convergence.rst.txt
new file mode 100644
index 00000000..dda5dd76
--- /dev/null
+++ b/_sources/_ref/dmft_tools.convergence.rst.txt
@@ -0,0 +1,21 @@
+dmft\_tools.convergence
+=======================
+
+.. automodule:: dmft_tools.convergence
+    :members:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/_sources/_ref/dmft_tools.formatter.rst.txt b/_sources/_ref/dmft_tools.formatter.rst.txt
new file mode 100644
index 00000000..4afba5a8
--- /dev/null
+++ b/_sources/_ref/dmft_tools.formatter.rst.txt
@@ -0,0 +1,21 @@
+dmft\_tools.formatter
+=====================
+
+.. automodule:: dmft_tools.formatter
+    :members:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/_sources/_ref/dmft_tools.greens_functions_mixer.rst.txt b/_sources/_ref/dmft_tools.greens_functions_mixer.rst.txt
new file mode 100644
index 00000000..dba5fe56
--- /dev/null
+++ b/_sources/_ref/dmft_tools.greens_functions_mixer.rst.txt
@@ -0,0 +1,21 @@
+dmft\_tools.greens\_functions\_mixer
+====================================
+
+.. automodule:: dmft_tools.greens_functions_mixer
+    :members:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/_sources/_ref/dmft_tools.initial_self_energies.rst.txt b/_sources/_ref/dmft_tools.initial_self_energies.rst.txt
new file mode 100644
index 00000000..d404c380
--- /dev/null
+++ b/_sources/_ref/dmft_tools.initial_self_energies.rst.txt
@@ -0,0 +1,21 @@
+dmft\_tools.initial\_self\_energies
+===================================
+
+.. automodule:: dmft_tools.initial_self_energies
+    :members:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/_sources/_ref/dmft_tools.interaction_hamiltonian.rst.txt b/_sources/_ref/dmft_tools.interaction_hamiltonian.rst.txt
new file mode 100644
index 00000000..0772c2d3
--- /dev/null
+++ b/_sources/_ref/dmft_tools.interaction_hamiltonian.rst.txt
@@ -0,0 +1,21 @@
+dmft\_tools.interaction\_hamiltonian
+====================================
+
+.. automodule:: dmft_tools.interaction_hamiltonian
+    :members:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/_sources/_ref/dmft_tools.legendre_filter.rst.txt b/_sources/_ref/dmft_tools.legendre_filter.rst.txt
new file mode 100644
index 00000000..dfd4d27d
--- /dev/null
+++ b/_sources/_ref/dmft_tools.legendre_filter.rst.txt
@@ -0,0 +1,21 @@
+dmft\_tools.legendre\_filter
+============================
+
+.. automodule:: dmft_tools.legendre_filter
+    :members:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/_sources/_ref/dmft_tools.manipulate_chemical_potential.rst.txt b/_sources/_ref/dmft_tools.manipulate_chemical_potential.rst.txt
new file mode 100644
index 00000000..778f7976
--- /dev/null
+++ b/_sources/_ref/dmft_tools.manipulate_chemical_potential.rst.txt
@@ -0,0 +1,21 @@
+dmft\_tools.manipulate\_chemical\_potential
+===========================================
+
+.. automodule:: dmft_tools.manipulate_chemical_potential
+    :members:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/_sources/_ref/dmft_tools.matheval.MathExpr.__init__.rst.txt b/_sources/_ref/dmft_tools.matheval.MathExpr.__init__.rst.txt
new file mode 100644
index 00000000..6558404f
--- /dev/null
+++ b/_sources/_ref/dmft_tools.matheval.MathExpr.__init__.rst.txt
@@ -0,0 +1,6 @@
+dmft\_tools.matheval.MathExpr.\_\_init\_\_
+==========================================
+
+.. currentmodule:: dmft_tools.matheval
+
+.. automethod:: MathExpr.__init__
\ No newline at end of file
diff --git a/_sources/_ref/dmft_tools.matheval.MathExpr.allowed_nodes.rst.txt b/_sources/_ref/dmft_tools.matheval.MathExpr.allowed_nodes.rst.txt
new file mode 100644
index 00000000..3e297bb9
--- /dev/null
+++ b/_sources/_ref/dmft_tools.matheval.MathExpr.allowed_nodes.rst.txt
@@ -0,0 +1,6 @@
+dmft\_tools.matheval.MathExpr.allowed\_nodes
+============================================
+
+.. currentmodule:: dmft_tools.matheval
+
+.. autoattribute:: MathExpr.allowed_nodes
\ No newline at end of file
diff --git a/_sources/_ref/dmft_tools.matheval.MathExpr.functions.rst.txt b/_sources/_ref/dmft_tools.matheval.MathExpr.functions.rst.txt
new file mode 100644
index 00000000..c6e7828f
--- /dev/null
+++ b/_sources/_ref/dmft_tools.matheval.MathExpr.functions.rst.txt
@@ -0,0 +1,6 @@
+dmft\_tools.matheval.MathExpr.functions
+=======================================
+
+.. currentmodule:: dmft_tools.matheval
+
+.. autoattribute:: MathExpr.functions
\ No newline at end of file
diff --git a/_sources/_ref/dmft_tools.matheval.MathExpr.rst.txt b/_sources/_ref/dmft_tools.matheval.MathExpr.rst.txt
new file mode 100644
index 00000000..f5622e1f
--- /dev/null
+++ b/_sources/_ref/dmft_tools.matheval.MathExpr.rst.txt
@@ -0,0 +1,31 @@
+dmft\_tools.matheval.MathExpr
+=============================
+
+.. currentmodule:: dmft_tools.matheval
+
+.. autoclass:: MathExpr
+    :members:                               
+    :show-inheritance:                     
+    :inherited-members:                   
+    :special-members: __init__
+    :noindex:
+
+
+
+.. autosummary::
+    :toctree:                             
+    
+      ~MathExpr.__init__
+
+
+
+
+
+.. rubric:: Attributes
+
+.. autosummary::
+    :toctree:                             
+    
+      ~MathExpr.allowed_nodes
+      ~MathExpr.functions
+
diff --git a/_sources/_ref/dmft_tools.matheval.rst.txt b/_sources/_ref/dmft_tools.matheval.rst.txt
new file mode 100644
index 00000000..f0d34aa8
--- /dev/null
+++ b/_sources/_ref/dmft_tools.matheval.rst.txt
@@ -0,0 +1,29 @@
+dmft\_tools.matheval
+====================
+
+.. automodule:: dmft_tools.matheval
+    :members:
+
+
+
+
+
+
+
+.. rubric:: Classes
+
+.. autosummary::
+    :toctree:                                         
+    :template: autosummary_class_template.rst
+    
+    MathExpr
+
+
+
+
+
+
+
+
+
+
diff --git a/_sources/_ref/dmft_tools.observables.rst.txt b/_sources/_ref/dmft_tools.observables.rst.txt
new file mode 100644
index 00000000..48a1c135
--- /dev/null
+++ b/_sources/_ref/dmft_tools.observables.rst.txt
@@ -0,0 +1,21 @@
+dmft\_tools.observables
+=======================
+
+.. automodule:: dmft_tools.observables
+    :members:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/_sources/_ref/dmft_tools.results_to_archive.rst.txt b/_sources/_ref/dmft_tools.results_to_archive.rst.txt
new file mode 100644
index 00000000..109c43cf
--- /dev/null
+++ b/_sources/_ref/dmft_tools.results_to_archive.rst.txt
@@ -0,0 +1,21 @@
+dmft\_tools.results\_to\_archive
+================================
+
+.. automodule:: dmft_tools.results_to_archive
+    :members:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/_sources/_ref/dmft_tools.rst.txt b/_sources/_ref/dmft_tools.rst.txt
new file mode 100644
index 00000000..2e7598d7
--- /dev/null
+++ b/_sources/_ref/dmft_tools.rst.txt
@@ -0,0 +1,42 @@
+dmft\_tools
+===========
+
+.. automodule:: dmft_tools
+    :members:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+.. rubric:: Modules
+
+.. autosummary::
+    :toctree:
+    :template: autosummary_module_template.rst 
+    :recursive:
+
+    
+    dmft_tools.afm_mapping
+    dmft_tools.convergence
+    dmft_tools.formatter
+    dmft_tools.greens_functions_mixer
+    dmft_tools.initial_self_energies
+    dmft_tools.interaction_hamiltonian
+    dmft_tools.legendre_filter
+    dmft_tools.manipulate_chemical_potential
+    dmft_tools.matheval
+    dmft_tools.observables
+    dmft_tools.results_to_archive
+    dmft_tools.solver
+
+
diff --git a/_sources/_ref/dmft_tools.solver.SolverStructure.__init__.rst.txt b/_sources/_ref/dmft_tools.solver.SolverStructure.__init__.rst.txt
new file mode 100644
index 00000000..4e097961
--- /dev/null
+++ b/_sources/_ref/dmft_tools.solver.SolverStructure.__init__.rst.txt
@@ -0,0 +1,6 @@
+dmft\_tools.solver.SolverStructure.\_\_init\_\_
+===============================================
+
+.. currentmodule:: dmft_tools.solver
+
+.. automethod:: SolverStructure.__init__
\ No newline at end of file
diff --git a/_sources/_ref/dmft_tools.solver.SolverStructure.rst.txt b/_sources/_ref/dmft_tools.solver.SolverStructure.rst.txt
new file mode 100644
index 00000000..1f641c58
--- /dev/null
+++ b/_sources/_ref/dmft_tools.solver.SolverStructure.rst.txt
@@ -0,0 +1,24 @@
+dmft\_tools.solver.SolverStructure
+==================================
+
+.. currentmodule:: dmft_tools.solver
+
+.. autoclass:: SolverStructure
+    :members:                               
+    :show-inheritance:                     
+    :inherited-members:                   
+    :special-members: __init__
+    :noindex:
+
+
+
+.. autosummary::
+    :toctree:                             
+    
+      ~SolverStructure.__init__
+      ~SolverStructure.solve
+
+
+
+
+
diff --git a/_sources/_ref/dmft_tools.solver.SolverStructure.solve.rst.txt b/_sources/_ref/dmft_tools.solver.SolverStructure.solve.rst.txt
new file mode 100644
index 00000000..7d3ee9a7
--- /dev/null
+++ b/_sources/_ref/dmft_tools.solver.SolverStructure.solve.rst.txt
@@ -0,0 +1,6 @@
+dmft\_tools.solver.SolverStructure.solve
+========================================
+
+.. currentmodule:: dmft_tools.solver
+
+.. automethod:: SolverStructure.solve
\ No newline at end of file
diff --git a/_sources/_ref/dmft_tools.solver.rst.txt b/_sources/_ref/dmft_tools.solver.rst.txt
new file mode 100644
index 00000000..e977c714
--- /dev/null
+++ b/_sources/_ref/dmft_tools.solver.rst.txt
@@ -0,0 +1,29 @@
+dmft\_tools.solver
+==================
+
+.. automodule:: dmft_tools.solver
+    :members:
+
+
+
+
+
+
+
+.. rubric:: Classes
+
+.. autosummary::
+    :toctree:                                         
+    :template: autosummary_class_template.rst
+    
+    SolverStructure
+
+
+
+
+
+
+
+
+
+
diff --git a/_sources/_ref/io_tools.dict_to_h5.rst.txt b/_sources/_ref/io_tools.dict_to_h5.rst.txt
new file mode 100644
index 00000000..379812c3
--- /dev/null
+++ b/_sources/_ref/io_tools.dict_to_h5.rst.txt
@@ -0,0 +1,21 @@
+io\_tools.dict\_to\_h5
+======================
+
+.. automodule:: io_tools.dict_to_h5
+    :members:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/_sources/_ref/io_tools.postproc_toml_dict.rst.txt b/_sources/_ref/io_tools.postproc_toml_dict.rst.txt
new file mode 100644
index 00000000..409375dc
--- /dev/null
+++ b/_sources/_ref/io_tools.postproc_toml_dict.rst.txt
@@ -0,0 +1,21 @@
+io\_tools.postproc\_toml\_dict
+==============================
+
+.. automodule:: io_tools.postproc_toml_dict
+    :members:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/_sources/_ref/io_tools.rst.txt b/_sources/_ref/io_tools.rst.txt
new file mode 100644
index 00000000..ba51d0e4
--- /dev/null
+++ b/_sources/_ref/io_tools.rst.txt
@@ -0,0 +1,33 @@
+io\_tools
+=========
+
+.. automodule:: io_tools
+    :members:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+.. rubric:: Modules
+
+.. autosummary::
+    :toctree:
+    :template: autosummary_module_template.rst 
+    :recursive:
+
+    
+    io_tools.dict_to_h5
+    io_tools.postproc_toml_dict
+    io_tools.verify_input_params
+
+
diff --git a/_sources/_ref/io_tools.verify_input_params.rst.txt b/_sources/_ref/io_tools.verify_input_params.rst.txt
new file mode 100644
index 00000000..bd358a8f
--- /dev/null
+++ b/_sources/_ref/io_tools.verify_input_params.rst.txt
@@ -0,0 +1,21 @@
+io\_tools.verify\_input\_params
+===============================
+
+.. automodule:: io_tools.verify_input_params
+    :members:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/_sources/_ref/postprocessing.eval_U_cRPA_RESPACK.respack_data.__init__.rst.txt b/_sources/_ref/postprocessing.eval_U_cRPA_RESPACK.respack_data.__init__.rst.txt
new file mode 100644
index 00000000..855b070b
--- /dev/null
+++ b/_sources/_ref/postprocessing.eval_U_cRPA_RESPACK.respack_data.__init__.rst.txt
@@ -0,0 +1,6 @@
+postprocessing.eval\_U\_cRPA\_RESPACK.respack\_data.\_\_init\_\_
+================================================================
+
+.. currentmodule:: postprocessing.eval_U_cRPA_RESPACK
+
+.. automethod:: respack_data.__init__
\ No newline at end of file
diff --git a/_sources/_ref/postprocessing.eval_U_cRPA_RESPACK.respack_data.rst.txt b/_sources/_ref/postprocessing.eval_U_cRPA_RESPACK.respack_data.rst.txt
new file mode 100644
index 00000000..c6843a98
--- /dev/null
+++ b/_sources/_ref/postprocessing.eval_U_cRPA_RESPACK.respack_data.rst.txt
@@ -0,0 +1,23 @@
+postprocessing.eval\_U\_cRPA\_RESPACK.respack\_data
+===================================================
+
+.. currentmodule:: postprocessing.eval_U_cRPA_RESPACK
+
+.. autoclass:: respack_data
+    :members:                               
+    :show-inheritance:                     
+    :inherited-members:                   
+    :special-members: __init__
+    :noindex:
+
+
+
+.. autosummary::
+    :toctree:                             
+    
+      ~respack_data.__init__
+
+
+
+
+
diff --git a/_sources/_ref/postprocessing.eval_U_cRPA_RESPACK.rst.txt b/_sources/_ref/postprocessing.eval_U_cRPA_RESPACK.rst.txt
new file mode 100644
index 00000000..6b2f03cc
--- /dev/null
+++ b/_sources/_ref/postprocessing.eval_U_cRPA_RESPACK.rst.txt
@@ -0,0 +1,29 @@
+postprocessing.eval\_U\_cRPA\_RESPACK
+=====================================
+
+.. automodule:: postprocessing.eval_U_cRPA_RESPACK
+    :members:
+
+
+
+
+
+
+
+.. rubric:: Classes
+
+.. autosummary::
+    :toctree:                                         
+    :template: autosummary_class_template.rst
+    
+    respack_data
+
+
+
+
+
+
+
+
+
+
diff --git a/_sources/_ref/postprocessing.eval_U_cRPA_Vasp.rst.txt b/_sources/_ref/postprocessing.eval_U_cRPA_Vasp.rst.txt
new file mode 100644
index 00000000..5c8b998b
--- /dev/null
+++ b/_sources/_ref/postprocessing.eval_U_cRPA_Vasp.rst.txt
@@ -0,0 +1,21 @@
+postprocessing.eval\_U\_cRPA\_Vasp
+==================================
+
+.. automodule:: postprocessing.eval_U_cRPA_Vasp
+    :members:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/_sources/_ref/postprocessing.maxent_gf_imp.rst.txt b/_sources/_ref/postprocessing.maxent_gf_imp.rst.txt
new file mode 100644
index 00000000..91100ec5
--- /dev/null
+++ b/_sources/_ref/postprocessing.maxent_gf_imp.rst.txt
@@ -0,0 +1,21 @@
+postprocessing.maxent\_gf\_imp
+==============================
+
+.. automodule:: postprocessing.maxent_gf_imp
+    :members:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/_sources/_ref/postprocessing.maxent_gf_latt.rst.txt b/_sources/_ref/postprocessing.maxent_gf_latt.rst.txt
new file mode 100644
index 00000000..df7153a9
--- /dev/null
+++ b/_sources/_ref/postprocessing.maxent_gf_latt.rst.txt
@@ -0,0 +1,21 @@
+postprocessing.maxent\_gf\_latt
+===============================
+
+.. automodule:: postprocessing.maxent_gf_latt
+    :members:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/_sources/_ref/postprocessing.maxent_sigma.rst.txt b/_sources/_ref/postprocessing.maxent_sigma.rst.txt
new file mode 100644
index 00000000..f7babca8
--- /dev/null
+++ b/_sources/_ref/postprocessing.maxent_sigma.rst.txt
@@ -0,0 +1,21 @@
+postprocessing.maxent\_sigma
+============================
+
+.. automodule:: postprocessing.maxent_sigma
+    :members:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/_sources/_ref/postprocessing.pade_sigma.rst.txt b/_sources/_ref/postprocessing.pade_sigma.rst.txt
new file mode 100644
index 00000000..a0856d9d
--- /dev/null
+++ b/_sources/_ref/postprocessing.pade_sigma.rst.txt
@@ -0,0 +1,21 @@
+postprocessing.pade\_sigma
+==========================
+
+.. automodule:: postprocessing.pade_sigma
+    :members:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/_sources/_ref/postprocessing.plot_correlated_bands.rst.txt b/_sources/_ref/postprocessing.plot_correlated_bands.rst.txt
new file mode 100644
index 00000000..fb89bb5f
--- /dev/null
+++ b/_sources/_ref/postprocessing.plot_correlated_bands.rst.txt
@@ -0,0 +1,21 @@
+postprocessing.plot\_correlated\_bands
+======================================
+
+.. automodule:: postprocessing.plot_correlated_bands
+    :members:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/_sources/_ref/postprocessing.rst.txt b/_sources/_ref/postprocessing.rst.txt
new file mode 100644
index 00000000..55a45bc7
--- /dev/null
+++ b/_sources/_ref/postprocessing.rst.txt
@@ -0,0 +1,37 @@
+postprocessing
+==============
+
+.. automodule:: postprocessing
+    :members:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+.. rubric:: Modules
+
+.. autosummary::
+    :toctree:
+    :template: autosummary_module_template.rst 
+    :recursive:
+
+    
+    postprocessing.eval_U_cRPA_RESPACK
+    postprocessing.eval_U_cRPA_Vasp
+    postprocessing.maxent_gf_imp
+    postprocessing.maxent_gf_latt
+    postprocessing.maxent_sigma
+    postprocessing.pade_sigma
+    postprocessing.plot_correlated_bands
+
+
diff --git a/_sources/_ref/util.rst.txt b/_sources/_ref/util.rst.txt
new file mode 100644
index 00000000..fb0cd3b1
--- /dev/null
+++ b/_sources/_ref/util.rst.txt
@@ -0,0 +1,32 @@
+util
+====
+
+.. automodule:: util
+    :members:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+.. rubric:: Modules
+
+.. autosummary::
+    :toctree:
+    :template: autosummary_module_template.rst 
+    :recursive:
+
+    
+    util.symmetrize_gamma_file
+    util.write_kslice_to_h5
+
+
diff --git a/_sources/_ref/util.symmetrize_gamma_file.rst.txt b/_sources/_ref/util.symmetrize_gamma_file.rst.txt
new file mode 100644
index 00000000..f70df010
--- /dev/null
+++ b/_sources/_ref/util.symmetrize_gamma_file.rst.txt
@@ -0,0 +1,21 @@
+util.symmetrize\_gamma\_file
+============================
+
+.. automodule:: util.symmetrize_gamma_file
+    :members:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/_sources/_ref/util.write_kslice_to_h5.rst.txt b/_sources/_ref/util.write_kslice_to_h5.rst.txt
new file mode 100644
index 00000000..a2cfb44b
--- /dev/null
+++ b/_sources/_ref/util.write_kslice_to_h5.rst.txt
@@ -0,0 +1,21 @@
+util.write\_kslice\_to\_h5
+==========================
+
+.. automodule:: util.write_kslice_to_h5
+    :members:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/_sources/cRPA_VASP/README.md.txt b/_sources/cRPA_VASP/README.md.txt
new file mode 100644
index 00000000..7d54158e
--- /dev/null
+++ b/_sources/cRPA_VASP/README.md.txt
@@ -0,0 +1,109 @@
+# How to do cRPA calculations with VASP
+
+This is just a small tutorial and help on how to do cRPA calculations within
+VASP (https://cms.mpi.univie.ac.at/wiki/index.php/CRPA_of_SrVO3) . Moreover, the
+python script `eval_U.py` contains helper functions to extract the full
+$U$ matrix tensor from the `Uijkl` or `Vijkl` file from a VASP cRPA run. There
+are also some general remarks on the notation in VASP for the Coulomb tensor in
+the pdf included in this folder. Moreover, there is a small collection of
+examples for SrVO3 and LuNiO3. For more details please take a look at the PhD
+thesis of Merzuk Kaltak (http://othes.univie.ac.at/38099/).
+
+## file description
+ * `eval_U.py` extraction of Coulomb tensor, calculation of reduced two-index matrices, and calculation / fitting of Kanamori or Slater parameters
+ * `ext_eps.sh` a small bash script that can extract $\epsilon^-1(|q+G|)=[1-VP^r]^-1$ from a given vasprun.xml file
+
+## Workflow:
+1. DFT NM normal like:
+    * SYSTEM = SrVO3
+    * ISMEAR =  0
+    * SIGMA =  0.05
+    * EDIFF = 1E-8
+2. optical part (larger nbands) and optical properties for generating the linear response integrals needed for cRPA or GW
+    1. nbands: ~100 bands per atoms, but not larger than number of plane waves generated from ENCUT
+    2. example:
+        * SYSTEM = SrVO3
+        * ISMEAR =  0
+        * ENCUT = high value!
+        * SIGMA =  0.05
+        * EDIFF = 1E-8
+        * ALGO = Exact ; NELM=1
+        * LOPTICS = .TRUE.
+        * LWAVE = .TRUE.
+        * NBANDS =96
+        * LMAXMIX=4
+3. if needed generate wannier functions with ALGO=none (read wavecar and chgcar additionally) and do 0 steps to get the wannier functions correct - this step is not needed, if one has already a wannier90.win file
+4. ALGO=CRPA to make vasp calculate U matrices (bare, screened etc. )
+    1. omegamax=0 (default) for frequency depend U matrix
+    2. NCRPA_BANDS for selecting bands in a non-disentagled workflow (vasp.at/wiki/index.php/NCRPA_BANDS)
+    3. or set NTARGET STATES= # of target states for using the KUBO formalism for disentanglement. Works directly with the wannier functions as basis. The states not listet will be included in screening.
+    4. example file:
+        * SYSTEM = SrVO3
+        * ISMEAR =  0
+        * ENCUT = high value!
+        * VCUTOFF = reasonable high value!
+        * SIGMA =  0.05
+        * EDIFF = 1E-8
+        * NBANDS =96
+        * ALGO = CRPA
+        * NTARGET_STATES = 1 2 3
+        * LWAVE = .FALSE.
+        * NCSHMEM=1
+        * LMAXMIX=4
+
+## important flags:
+if you get sigsevs while calculating the polarization make sure your local stack
+size is large enough by setting:
+```
+ulimit -s unlimited
+```
+
+* ALGO=CRPA (automatically calls wannier90 and calculates the U matrix)
+* NTARGET_STATES= # number of target Wannier funcitons if more target states than basis functions for U matrix one specify the one to exclude from screening as integer list: `1 2 3`. This would build the U matrix for the first 3 Wannier functions in wannier90.win, where 5 Wannier functions are specified there in total and the last 2 are included for the calculation of screening.
+* for the disentanglement with `NTARGET_STATES` there are 3 options in cRPA:
+    * LPROJECTED (default): Kubo method by Merzuk (http://othes.univie.ac.at/38099/)
+    * LDISENTANGLED: disentanglement of Miyake (doi.org/10.1103/PhysRevB.80.155134)
+    * LWEIGHTED: weighted method of Friedrich and Shih
+* LOPTICS= TRUE for calculating the necessary response integrals withing the Kohn-Sham Basis W000x.tmp
+* NCSHMEM=1 nodody knows, but it is needed!
+* VCUTOFF cuttoff for bare interaction V. This tests your convergency
+and is written in the OUTCAR as two sets of bare interaction, where for one of them
+it says: low cutoff result for V_ijkl. Here ENCUT was used and for the one above 1.1*ENCUT or VCUTOFF was used.
+* usually a converged ENCUT gives also a reasonably high VCUTOFF, so that explicitly setting VCUTOFF is not necessary. Moreover, the effect of the VCUTOFF convergence is included by subtracting the constant shift between LOW and HIGH VCUTOFF test output in the OUTCAR
+* One can see in the convergence plot "debugging_examples/LaTiO3/VCUTOFF_convergence.png" the effect of ENCUT and VCUTOFF:
+
+![vcutoff_test](VCUTOFF_convergence.png)
+
+## convergency tests:
+$`E_{corr}^{RPA}`$  converges for NBANDS,ENCUT to $`\infty`$, where the asymptotic
+behavior goes like $`1/N_{bands} \approx ENCUT^{-3/2} `$. The ENCUT for the GW part
+is set automatically by VASP with the ratio: $`ENCUTGW = 2/3 \ ENCUT`$. Moreover,
+it is crucial to first converge the bare interaction V that does not depend on the
+polarization. To do these tests set in the INCAR file:
+* ALGO = 2E4W  # calculates only the V
+* LWPOT = .FALSE # avoid errors
+* VCUTOFF # vary the cut-off until convergency is reached, default is 1.1*ENCUT
+* NBANDS # minor effect on V then on W, but nevertheless a reasonable amount of
+bands must be used. A good choice is 3*NELECT (# of electrons in the systems).
+
+The procedure is then to first convergence KPOINTS and ENCUT, where KPOINTS dependency of the results seems to be weak. Then increase NBANDS until U does not change anymore.
+
+## Parameterization of U and J from cRPA calculations
+`eval_u.py` provides four different methods:
+- Kanamori: `calc_kan_params(...)` for extracting Kanamori parameters for a cubic system 
+- Slater 1: `calc_u_avg_fulld(...)` using averaging and symmetries: $`U_\mathrm{cubic} = \frac1{2l+1} \sum_i (U_{iiii})`$, $`J_\mathrm{cubic} = \frac1{2l(2l+1)} \sum_{i, j\neq i} U_{ijji}`$. Then, the interaction parameters follow from the conversion $`U = U_\mathrm{cubic} - \frac85 J_\mathrm{cubic}, J = \frac75 J_\mathrm{cubic}`$.
+- Slater 2: `calculate_interaction_from_averaging(...)` using direct averaging: $`U = \frac1{(2l+1)^2} \sum_{i, j} U_{iijj}`$ and $`J = U - \frac1{2l(2l+1)} \sum_{i, j} U_{ijij}`$. This is more straight forward that Slater 1, but ignores the basis in which the cRPA Uijkl matrix is written. For a perfect Slater matrix this gives the same results if applied in cubic or spherical harmonics basis.
+- Slater 3: `fit_slater_fulld(...) `using an least-square fit (summed over the matrix elements) of the two-index matrices $`U_{iijj}`$ and $`U_{ijij}`$ to the Slater Hamiltonian. 
+
+These three methods give the same results if the cRPA matrix is of the Slater type already. Be aware of the order of your basis functions and the basis in which the $U$ tensor is written!
+
+## general sidemarks:
+* careful with the averaged U,u,J values in the end of the OUTCAR, because they sum all off-diagonal elements! Also inter-site, if the unit cell contains more than one target atom
+* in VASP the two inner indices are exchanged compared to the notation in PRB 86, 165105 (2012): U_ijkl = U_ikjl^VASP
+* when specifying bands, always start with 1 not 0.
+* GW pseudopotentials can be more accurate, since they provide higher cut-offs e.g. , test this...
+* NCRPA_BANDS and NTARGET_STATES gives the same result in non-entangled bands
+
+## version and compilation:
+* supported vasp version 6 or higher
+* wannier90 upto v3.1 works, if no features exclusively to wannier90 v3 are used
diff --git a/_sources/documentation.rst.txt b/_sources/documentation.rst.txt
new file mode 100644
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--- /dev/null
+++ b/_sources/documentation.rst.txt
@@ -0,0 +1,66 @@
+.. _documentation:
+
+***************
+Documentation
+***************
+
+Code structure
+==============
+
+.. image:: _static/code_structure.png
+   :width: 100%
+   :align: center
+
+more details in the reference manual below.
+
+To get started with the code after a successful :ref:`installation`, take a look at the :ref:`tutorials` section. Here we provide further special information and a reference manual for all available functions.
+
+
+
+DFT interface notes
+===================
+
+.. toctree::
+    :maxdepth: 1
+
+    md_notes/w90_interface.md
+    md_notes/vasp_csc.md
+    cRPA_VASP/README.md
+
+Input/Output
+===================
+.. toctree::
+    :maxdepth: 1
+
+    input_output/DMFT_input/input
+    input_output/DMFT_output/results
+
+Further details for running
+===========================
+
+.. toctree::
+    :maxdepth: 1
+
+    md_notes/docker.md
+    md_notes/run_locally.md
+    md_notes/run_cluster.md
+
+Module reference manual
+=======================
+
+.. autosummary::
+    :toctree: _ref
+    :template: autosummary_module_template.rst
+    :recursive:
+
+    csc_flow
+    dft_managers
+    dmft_cycle
+    dmft_tools
+    io_tools
+    postprocessing
+    util
+
+
+
+
diff --git a/_sources/index.rst.txt b/_sources/index.rst.txt
new file mode 100644
index 00000000..e520f1aa
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+++ b/_sources/index.rst.txt
@@ -0,0 +1,64 @@
+.. index:: solid_dmft
+
+.. module:: solid_dmft
+
+solid_dmft
+**********
+
+.. sidebar:: solid_dmft |PROJECT_VERSION|
+
+   This is the homepage of solid_dmft |PROJECT_VERSION|.
+   For changes see the :ref:`changelog page <changelog>`.
+   visit us on:
+      
+      .. image:: _static/logo_github.png
+         :width: 60%
+         :align: center
+         :target: https://github.com/flatironinstitute/solid_dmft
+
+
+This program allows to perform DFT+DMFT ''one-shot'' and charge self-consistent
+(CSC) calculations from h5 archives or VASP/Quantum Espresso input files for
+multiband systems using the `TRIQS software library <https://triqs.github.io>`_, and the DFT code interface
+`TRIQS/DFTTools <https://triqs.github.io/dft_tools>`_. Works with triqs >3.x.x.
+solid_dmft takes advantage of various
+`impurity solvers available <https://triqs.github.io/triqs/latest/applications.html#impurity-solvers>`_
+in triqs: cthyb, HubbardI, ForkTPS, ctint, and ctseg. Postprocessing scripts are available to
+perform analytic continuation and calculate spectral functions.
+
+For installation use the same branch / tag as your triqs installation. More
+information under :ref:`installation`. 
+
+Learn how to use solid_dmft in the :ref:`documentation` and the :ref:`tutorials`.
+
+For more technical information about the implementation check also the `solid_dmft publication <https://doi.org/10.21105/joss.04623>`_ in the JOSS journal. If you are using this code for your research, please cite the paper using this `bib file <https://github.com/TRIQS/solid_dmft/blob/unstable/cite_solid_dmft.bib>`_.
+
+Workflow of DFT+DMFT calculations with solid_dmft
+=================================================
+
+.. image:: _static/workflow.png
+   :width: 100%
+   :align: center
+
+------------
+
+.. toctree::
+   :maxdepth: 2
+   :hidden:
+
+   install
+   documentation
+   tutorials
+   issues
+   ChangeLog.md
+
+
+.. image:: logos/flatiron.png
+   :width: 300
+   :align: left
+   :target: https://www.simonsfoundation.org/flatiron/center-for-computational-quantum-physics
+.. image:: logos/eth_logo_kurz_pos.png
+   :width: 300
+   :align: right
+   :target: https://theory.mat.ethz.ch
+
diff --git a/_sources/input_output/DMFT_input/advanced.rst.txt b/_sources/input_output/DMFT_input/advanced.rst.txt
new file mode 100644
index 00000000..63a61cfb
--- /dev/null
+++ b/_sources/input_output/DMFT_input/advanced.rst.txt
@@ -0,0 +1,85 @@
+[advanced]: Advanced inputs
+---------------------------
+
+Advanced parameters, do not modify the default value unless you know what you are doing.
+
+
+
+
+
+.. admonition:: dc_factor 
+ 	:class: intag  
+ 
+            **type** = float;  **default**  = None
+
+        If given, scales the dc energy by multiplying with this factor, usually < 1.
+        If None, the dc is left unchanged, which is equivalent to dc_factor=1.
+
+.. admonition:: dc_fixed_occ 
+ 	:class: intag  
+ 
+            **type** = list of float;  **default**  = None
+
+        If given, the occupation for the DC for each impurity is set to the provided value.
+        Still uses the same kind of DC!
+
+.. admonition:: dc_fixed_value 
+ 	:class: intag  
+ 
+            **type** = float;  **default**  = None
+
+        If given, it sets the DC (energy/imp) to this fixed value. Overwrites EVERY other DC configuration parameter if DC is turned on
+
+.. admonition:: dc_nominal 
+ 	:class: intag  
+ 
+            **type** = bool;  **default**  = False
+
+        TODO: write
+
+.. admonition:: dc_orb_shift 
+ 	:class: intag  
+ 
+            **type** = list of float;  **default**  = None
+
+        extra potential shift per orbital per impurity added to the DC
+
+.. admonition:: dc_J 
+ 	:class: intag  
+ 
+            **type** = float or list of float;  **default**  = general.J
+
+        J values for DC determination. If only one value is given, the same J is assumed for all impurities
+
+.. admonition:: dc_U 
+ 	:class: intag  
+ 
+            **type** = float or list of float;  **default**  = general.U
+
+        U values for DC determination. If only one value is given, the same U is assumed for all impurities
+
+.. admonition:: map_solver_struct 
+ 	:class: intag  
+ 
+            **type** = list of dict;  **default**  = None
+
+        Additional manual mapping of the solver block structure, applied
+        after the block structure finder for each impurity.
+        Give exactly one dict per ineq impurity.
+        see also triqs.github.io/dft_tools/latest/_python_api/triqs_dft_tools.block_structure.BlockStructure.map_gf_struct_solver.html
+
+.. admonition:: mapped_solver_struct_degeneracies 
+ 	:class: intag  
+ 
+            **type** = list;  **default**  = None
+
+        Degeneracies applied when using map_solver_struct, for each impurity.
+        If not given and map_solver_struct is used, no symmetrization will happen.
+
+.. admonition:: pick_solver_struct 
+ 	:class: intag  
+ 
+            **type** = list of dict;  **default**  = None
+
+        input a solver dictionary for each ineq impurity to reduce dimensionality of
+        solver block structure. Similar to to map_solver_struct, but with simpler syntax.
diff --git a/_sources/input_output/DMFT_input/dft.rst.txt b/_sources/input_output/DMFT_input/dft.rst.txt
new file mode 100644
index 00000000..f83c57d1
--- /dev/null
+++ b/_sources/input_output/DMFT_input/dft.rst.txt
@@ -0,0 +1,103 @@
+
+[dft]: DFT related inputs
+-------------------------
+
+List of parameters for the DFT calculation in charge-self-consistent calculations.
+The parameters are ignored in one-shot calculations.
+
+
+
+
+
+.. admonition:: dft_code 
+ 	:class: intag  
+ 
+            **type** = string
+
+        choose the DFT code interface, for now Quantum Espresso and Vasp are available.
+        Possible values:
+
+        * 'vasp'
+        * 'qe'
+
+.. admonition:: dft_exec 
+ 	:class: intag  
+ 
+            **type** = string;  **default**  = 'vasp_std'
+
+        command for the DFT executable
+
+.. admonition:: mpi_env 
+ 	:class: intag  
+ 
+            **type** = string;  **default** '
+
+        selection for mpi env for DFT / VASP in default this will only call VASP as mpirun -np n_cores_dft dft_exec
+        Possible values:
+
+        * 'default'
+        * 'openmpi'
+        * 'openmpi-intra'
+        * 'mpich'
+
+.. admonition:: n_cores 
+ 	:class: intag  
+ 
+            **type** = int, None
+
+        number of cores for the DFT code (VASP). Mandatory for charge-self-consistent calculations
+
+.. admonition:: n_iter 
+ 	:class: intag  
+ 
+            **type** = int;  **default**  = 4
+
+        only needed for VASP. Number of DFT iterations to feed the DMFT
+        charge density into DFT, which generally takes multiple Davidson steps.
+        For every DFT iterations, the charge-density correction is recalculated
+        using newly generated projectors and hoppings from the previous DFT run
+
+.. admonition:: n_iter_first 
+ 	:class: intag  
+ 
+            **type** = int;  **default**  = dft.n_iter
+
+        number of DFT iterations in the first charge correction because this
+        first charge correction usually changes the DFT wave functions the most.
+
+.. admonition:: plo_cfg 
+ 	:class: intag  
+ 
+            **type** = str;  **default**  = 'plo.cfg'
+
+        config file for PLOs for the converter
+
+.. admonition:: projector_type 
+ 	:class: intag  
+ 
+            **type** = string;  **default**  = 'w90'
+
+        plo: uses VASP's PLO formalism, requires LOCPROJ in the INCAR
+        w90: uses Wannier90 (for VASP and QuantumEspresso)
+
+.. admonition:: store_eigenvals 
+ 	:class: intag  
+ 
+            **type** = bool;  **default**  = False
+
+        stores the dft eigenvals from LOCPROJ (projector_type=plo) or
+        wannier90.eig (projector_type=w90) file in h5 archive
+
+.. admonition:: w90_exec 
+ 	:class: intag  
+ 
+            **type** = string;  **default**  ='wannier90.x'
+
+        the command to start a single-core wannier run
+
+.. admonition:: w90_tolerance 
+ 	:class: intag  
+ 
+            **type** = float;  **default**  = 1e-6
+
+        threshold for mapping of shells and checks of the Hamiltonian
diff --git a/_sources/input_output/DMFT_input/general.rst.txt b/_sources/input_output/DMFT_input/general.rst.txt
new file mode 100644
index 00000000..b830dd2c
--- /dev/null
+++ b/_sources/input_output/DMFT_input/general.rst.txt
@@ -0,0 +1,470 @@
+[general]: General parameters
+-----------------------------
+
+Frequently used parameters that apply to the whole simulation.
+
+
+
+
+
+
+.. admonition:: afm_order 
+ 	:class: intag  
+ 
+            **type** = bool;  **default**  = False
+
+        copy self energies instead of solving explicitly for afm order
+
+.. admonition:: beta 
+ 	:class: intag  
+ 
+            **type** = float;  **default**  = None
+
+        inverse temperature. If set, solid_dmft stores all Greens functions etc on an imaginary-frequency grid
+        and also n_iw and n_tau have to be specified.
+        If not set, it uses a real-frequency grid and eta, n_w and w_range have to be set
+
+.. admonition:: block_threshold 
+ 	:class: intag  
+ 
+            **type** = float;  **default**  = 1e-5
+
+        threshold for finding block structures in the input data (off-diag yes or no)
+
+.. admonition:: broy_max_it 
+ 	:class: intag  
+ 
+            **type** = int;  **default**  = -1
+
+        maximum number of iteration to be considered for broyden mixing.
+        Only used if general.g0_mix_type='broyden'.
+        1 corresponds to simple linear mixing
+
+.. admonition:: calc_energies 
+ 	:class: intag  
+ 
+            **type** = bool;  **default**  = False
+
+        Calculate the energies explicitly within the dmft loop and write them to the observables file.
+        Not compatible with 'ftps' solver
+
+.. admonition:: calc_mu_method 
+ 	:class: intag  
+ 
+            **type** = string;  **default**  = 'dichotomy'
+
+        optimization method used for finding the chemical potential:
+
+        * 'dichotomy': usual method from TRIQS, should always converge but may be slow
+        * 'newton': scipy Newton root finder, much faster but might be unstable
+        * 'brent': scipy hyperbolic Brent root finder preconditioned with dichotomy to find edge, a compromise between speed and stability
+
+.. admonition:: csc 
+ 	:class: intag  
+ 
+            **type** = bool;  **default**  = False
+
+        are we doing a CSC calculation?
+
+.. admonition:: dc 
+ 	:class: intag  
+ 
+            **type** = bool;  **default**  = True
+
+        is the double-counting correction on?
+
+.. admonition:: dc_dmft 
+ 	:class: intag  
+ 
+            **type** = bool;  **default**  = None
+
+        Calculate the double-counting (DC) correction from DMFT or DFT occupations.
+        Needs to be set if general.dc = True.
+
+        * True: DC with DMFT occupation in each iteration
+        * False: DC with DFT occupations after each DFT cycle
+
+.. admonition:: dc_type 
+ 	:class: intag  
+ 
+            **type** = int or list of int;  **default**  = None
+
+        Type of double counting correction considered.
+        Can be a list per impurity to have different types for different impurities.
+        Needs to be set if general.dc = True.
+
+        * 0: FLL
+        * 1: Held formula, needs to be used with slater-kanamori h_int_type=2
+        * 2: AMF
+        * 3: FLL for eg orbitals only with U,J for Kanamori
+
+.. admonition:: enforce_off_diag 
+ 	:class: intag  
+ 
+            **type** = bool or list of bool;  **default**  = True
+
+        only if False, the block structure can be reduced to ignore off-diagonal elements
+        if they are below the general.block_threshold of the block structure finder
+
+.. admonition:: eta 
+ 	:class: intag  
+ 
+            **type** = float;  **default**  = None
+
+        broadening of Green's function. Used when Green's functions are stored on real-frequency grid,
+        i.e., general.beta is None, and for the real-frequency solvers
+
+.. admonition:: fixed_mu_value 
+ 	:class: intag  
+ 
+            **type** = float;  **default**  = None
+
+        If given, the chemical potential remains fixed in calculations
+
+.. admonition:: g0_conv_crit 
+ 	:class: intag  
+ 
+            **type** = float;  **default**  = -1.0
+
+        stop the calculation if sum_w 1/(w^0.6) ||G0-G0_prev|| is smaller than threshold
+
+.. admonition:: g0_mix 
+ 	:class: intag  
+ 
+            **type** = float;  **default**  = 1.0
+
+        Mixing the weiss field G0 with previous iteration G0 for better convergency. 1.0 means no mixing.
+        Setting g0_mix to 0.0 with linear mixing can be used for statistic sampling when
+        restarting a calculation
+
+.. admonition:: g0_mix_type 
+ 	:class: intag  
+ 
+            **type** = string;  **default**  = 'linear'
+
+        which type of mixing is used. Possible values are:
+        linear: linear mixing
+        broyden: broyden mixing
+
+.. admonition:: gimp_conv_crit 
+ 	:class: intag  
+ 
+            **type** = float;  **default**  = -1.0
+
+        stop the calculation if  sum_w 1/(w^0.6) ||Gimp-Gloc|| is smaller than threshold
+
+.. admonition:: h_field 
+ 	:class: intag  
+ 
+            **type** = float;  **default**  = 0.0
+
+        magnetic field
+
+.. admonition:: h_field_it 
+ 	:class: intag  
+ 
+            **type** = int;  **default**  = 0
+
+        number of iterations the magnetic field is kept on
+
+.. admonition:: h_int_basis 
+ 	:class: intag  
+ 
+            **type** = string;  **default**  = 'triqs'
+
+        cubic basis convention to compute the interaction U matrix
+
+        * 'triqs'
+        * 'vasp' (equivalent to 'triqs')
+        * 'wien2k'
+        * 'wannier90'
+        * 'qe' (equivalent to 'wannier90')
+
+.. admonition:: h_int_type 
+ 	:class: intag  
+ 
+            **type** = string;  **mandatory**
+
+        interaction type:
+
+        * density_density: used for full d-shell or eg- or t2g-subset
+        * kanamori: only physical for the t2g or the eg subset
+        * full_slater: used for full d-shell or eg- or t2g-subset
+        * ntot: U/2 (Ntot^2 - Ntot) interaction
+        * simple_intra: density-density like but only intra orbital with given U value (no rotations applied)
+        * crpa: use the cRPA matrix as interaction Hamiltonian
+        * crpa_density_density: use the density-density terms of the cRPA matrix
+        * dynamic: use dynamic U from h5 archive
+
+        Needs to be stored as Matsubara Gf under dynamic_U/U_iw in the input h5
+
+.. admonition:: h5_save_freq 
+ 	:class: intag  
+ 
+            **type** = int;  **default**  = 5
+
+        how often is the output saved to the h5 archive
+
+.. admonition:: J 
+ 	:class: intag  
+ 
+            **type** = float or list of float;  **mandatory**
+
+        J interaction value. If it is a float, the same J is assumed for all impurities,
+        otherwise as a list a different J can be specified per impurity.
+
+.. admonition:: jobname 
+ 	:class: intag  
+ 
+            **type** = str;  **default**  = 'dmft_dir'
+
+        the output directory for one-shot calculations
+
+.. admonition:: load_sigma 
+ 	:class: intag  
+ 
+            **type** = bool;  **default**  = False
+
+        load a old sigma from h5 file
+
+.. admonition:: load_sigma_iter 
+ 	:class: intag  
+ 
+            **type** = int;  **default**  = -1
+
+        load the sigma from a specific iteration if wanted.
+        If it is -1, loads from the last iteration.
+
+.. admonition:: magmom 
+ 	:class: intag  
+ 
+            **type** = list of float;  **default**  = None
+
+        Initialize magnetic moments if magnetic is on. length must be #imps.
+        List composed of energetic shifts written in electronvolts.
+        This will initialize the spin blocks of the sigma with a diagonal shift
+        With -shift for the up block, and +shift for the down block
+        (positive shift favours the up spin component, not compatible with spin-orbit coupling)
+
+.. admonition:: magnetic 
+ 	:class: intag  
+ 
+            **type** = bool;  **default**  = False
+
+        are we doing a magnetic calculations? If yes put magnetic to True.
+        Not implemented for CSC calculations
+
+.. admonition:: mu_gap_gb2_threshold 
+ 	:class: intag  
+ 
+            **type** = float;  **default**  = None
+
+        Threshold of the absolute of the lattice GF at tau=beta/2 for use
+        of MaxEnt's lattice spectral function to put the chemical potential
+        into the middle of the gap. Does not work if system completely full
+        or empty, mu mixing is not applied to it. Recommended value 0.01.
+
+.. admonition:: mu_gap_occ_deviation 
+ 	:class: intag  
+ 
+            **type** = float;  **default**  = None
+
+        Only used if mu_gap_gb2_threshold != None. Sets additional criterion
+        for finding the middle of the gap through occupation deviation to
+        avoid getting stuck in an insulating state with wrong occupation.
+
+.. admonition:: mu_initial_guess 
+ 	:class: intag  
+ 
+            **type** = float;  **default**  = None
+
+        The chemical potential of the DFT calculation.
+        If not given, mu will be calculated from the DFT bands
+
+.. admonition:: mu_mix_const 
+ 	:class: intag  
+ 
+            **type** = float;  **default**  = 1.0
+
+        Constant term of the mixing of the chemical potential. See mu_mix_per_occupation_offset.
+
+.. admonition:: mu_mix_per_occupation_offset 
+ 	:class: intag  
+ 
+            **type** = float;  **default**  = 0.0
+
+        Mu mixing proportional to the occupation offset.
+        Mixing between the dichotomy result and the previous mui,
+
+        mu_next = factor * mu_dichotomy + (1-factor) * mu_previous, with
+        factor = mu_mix_per_occupation_offset * abs(n - n\_target) + mu_mix_const.
+
+        The program ensures that 0 <= factor <= 1.
+        mu_mix_const = 1.0 and mu_mix_per_occupation_offset = 0.0 means no mixing.
+
+.. admonition:: mu_update_freq 
+ 	:class: intag  
+ 
+            **type** = int;  **default**  = 1
+
+        The chemical potential will be updated every # iteration
+
+.. admonition:: n_iter_dmft 
+ 	:class: intag  
+ 
+            **type** = int;  **mandatory**
+
+        number of iterations per dmft cycle after first cycle
+
+.. admonition:: n_iter_dmft_first 
+ 	:class: intag  
+ 
+            **type** = int;  **default**  = 10
+
+        number of iterations in first dmft cycle to converge dmft solution
+
+.. admonition:: n_iter_dmft_per 
+ 	:class: intag  
+ 
+            **type** = int;  **default**  = 2
+
+        number of iterations per dmft step in CSC calculations
+
+.. admonition:: n_iw 
+ 	:class: intag  
+ 
+            **type** = int;  **default**  = 1025
+
+        number of Matsubara frequencies for the imaginary-frequency grid
+
+.. admonition:: n_tau 
+ 	:class: intag  
+ 
+            **type** = int;  **default**  = 10001
+
+        number of imaginary time points for the imaginary-time grid
+
+.. admonition:: n_w 
+ 	:class: intag  
+ 
+            **type** = int;  **default**  = 5001
+
+        number of real frequency points for the real-frequency grid
+
+.. admonition:: noise_level_initial_sigma 
+ 	:class: intag  
+ 
+            **type** = float;  **default**  = 0.0
+
+        spread of Gaussian noise applied to the initial Sigma
+
+.. admonition:: occ_conv_crit 
+ 	:class: intag  
+ 
+            **type** = float;  **default**  = -1.0
+
+        stop the calculation if a certain threshold for the imp occ change is reached
+
+.. admonition:: path_to_sigma 
+ 	:class: intag  
+ 
+            **type** = str;  **default**  = None
+
+        path to h5 file from which the sigma should be loaded.
+        Needed if load_sigma is true
+
+.. admonition:: prec_mu 
+ 	:class: intag  
+ 
+            **type** = float;  **default**  = 1e-4
+
+        general precision for determining the chemical potential at any time calc_mu is called
+
+.. admonition:: ratio_F4_F2 
+ 	:class: intag  
+ 
+            **type** = float or list of float;  **default**  = None
+
+        Ratio between the Slater integrals  F_4 and F_2. Only used for the
+        interaction Hamiltonians 'density_density' and 'full_slater' and
+        only for d-shell impurities;  **default**  is 0.63.
+
+.. admonition:: sampling_h5_save_freq 
+ 	:class: intag  
+ 
+            **type** = int;  **default**  = 5
+
+        overwrites h5_save_freq when sampling has started
+
+.. admonition:: sampling_iterations 
+ 	:class: intag  
+ 
+            **type** = int;  **default**  = 0
+
+        for how many iterations should the solution sampled after the CSC loop is converged
+
+.. admonition:: seedname 
+ 	:class: intag  
+ 
+            **type** = str;  **mandatory**
+
+        seedname for h5 archive with DMFT input and output
+
+.. admonition:: set_rot 
+ 	:class: intag  
+ 
+            **type** = string;  **default**  = None
+
+        Local orbital rotations added by solid_dmft
+
+        * None: keep the rotations stored in the h5 archive
+        * 'den' use the DFT occupations density_mat_dft for diagonalization
+        * 'hloc': use the DFT local Hamiltonian hloc_dft for diagonalization
+
+.. admonition:: sigma_conv_crit 
+ 	:class: intag  
+ 
+            **type** = float;  **default**  = -1.0
+
+        stop the calculation if sum_w 1/(w^0.6) ||Sigma-Sigma_prev|| is smaller than threshold
+
+.. admonition:: sigma_mix 
+ 	:class: intag  
+ 
+            **type** = float;  **default**  = 1.0
+
+        careful: Sigma mixing can break orbital symmetries, use G0 mixing.
+        mixing sigma with previous iteration sigma for better convergency. 1.0 means no mixing
+
+.. admonition:: store_solver 
+ 	:class: intag  
+ 
+            **type** = bool;  **default**  = False
+
+        whether to store the whole solver object under DMFT_input in h5 archive
+
+.. admonition:: U 
+ 	:class: intag  
+ 
+            **type** = float or list of float;  **mandatory**
+
+        U interaction value. If it is a float, the same U is assumed for all impurities,
+        otherwise as a list a different U can be specified per impurity.
+
+.. admonition:: U_prime 
+ 	:class: intag  
+ 
+            **type** = float or list of floats;  **default**  = None
+
+        U prime interaction value.
+        Only used for impurities where general.h_int_type is kanamori.
+        If it is a float, the same U prime is assumed for all impurities,
+        otherwise as a list a different U prime can be specified per impurity.
+        For None;  **default**  of U prime = U-2J is used.
+
+.. admonition:: w_range 
+ 	:class: intag  
+ 
+            **type** = list of int;  **default**  = [-10, 10]
+
+        Minimal and maximal range of the real-frequency grid
diff --git a/_sources/input_output/DMFT_input/input.rst.txt b/_sources/input_output/DMFT_input/input.rst.txt
new file mode 100644
index 00000000..56d1772d
--- /dev/null
+++ b/_sources/input_output/DMFT_input/input.rst.txt
@@ -0,0 +1,31 @@
+Input
+-------
+
+The aim of this section is to provide a comprehensive listing of all the input flags available for the `dmft_config.ini` input file. We begin by listing the possible sections and follow with the input parameters.
+
+.. toctree::
+    :maxdepth: 1
+
+    general
+    solver
+    dft
+    advanced
+
+Below an exhaustive list containing all the parameters marked by section.
+
+**[general]**
+
+afm_order; beta; block_threshold; broy_max_it; calc_energies; calc_mu_method; csc; dc; dc_dmft; dc_type; enforce_off_diag; eta; fixed_mu_value; g0_conv_crit; g0_mix; g0_mix_type; gimp_conv_crit; h_field; h_field_it; h_int_basis; h_int_type; h5_save_freq; J; jobname; load_sigma; load_sigma_iter; magmom; magnetic; mu_gap_gb2_threshold; mu_gap_occ_deviation; mu_initial_guess; mu_mix_const; mu_mix_per_occupation_offset; mu_update_freq; n_iter_dmft; n_iter_dmft_first; n_iter_dmft_per; n_iw; n_tau; n_w; noise_level_initial_sigma; occ_conv_crit; path_to_sigma; prec_mu; ratio_F4_F2; sampling_h5_save_freq; sampling_iterations; seedname; set_rot; sigma_conv_crit; sigma_mix; store_solver; U; U_prime; w_range; 
+
+**[solver]**
+
+
+type; idx_impurities; delta_interface; fit_max_moment; fit_max_n; fit_max_w; fit_min_n; fit_min_w; imag_threshold; legendre_fit; length_cycle; loc_n_max; loc_n_min; max_time; measure_chi_insertions; measure_chi; measure_density_matrix; measure_G_l; measure_pert_order; move_double; move_shift; n_cycles_tot; n_l; n_warmup_cycles; off_diag_threshold; perform_tail_fit; random_seed; length_cycle; max_time; measure_pert_order; move_double; n_cycles_tot; n_warmup_cycles; random_seed; improved_estimator; legendre_fit; length_cycle; max_time; measure_G_iw; measure_G_l; measure_G_tau; measure_pert_order; n_cycles_tot; n_l; n_warmup_cycles; random_seed; legendre_fit; measure_density_matrix; measure_G_l; measure_G_tau; n_l; bath_fit; calc_me; diag_delta; dmrg_maxm; dmrg_maxmB; dmrg_maxmI; dmrg_maxmIB; dmrg_tw; dt; enforce_gap; ignore_weight; maxm; maxmB; maxmI; maxmIB; n_bath; path_to_gs; ph_symm; refine_factor; state_storage; sweeps; tw; force_real; method; one_shot; tol; with_fock; 
+
+**[dft]**
+
+dft_code; dft_exec; mpi_env; n_cores; n_iter; n_iter_first; plo_cfg; projector_type; store_eigenvals; w90_exec; w90_tolerance; 
+
+**[advanced]**
+
+dc_factor; dc_fixed_occ; dc_fixed_value; dc_nominal; dc_orb_shift; dc_J; dc_U; map_solver_struct; mapped_solver_struct_degeneracies; pick_solver_struct; 
\ No newline at end of file
diff --git a/_sources/input_output/DMFT_input/solver.rst.txt b/_sources/input_output/DMFT_input/solver.rst.txt
new file mode 100644
index 00000000..7c425cf1
--- /dev/null
+++ b/_sources/input_output/DMFT_input/solver.rst.txt
@@ -0,0 +1,616 @@
+[solver]: solver specific parameters
+------------------------------------
+
+Here are the parameters that are uniquely dependent on the solver chosen. Some parameters are used within solid_dmft and some are passed directly into the triqs solver.
+To see which parameters were passed to the solver for a given calculation, look at the triqs_solver_params in DMFT_input/solver in the h5 archive.
+Solver-specific parameters are listed in the respective sections.
+
+
+
+
+
+.. admonition:: type 
+ 	:class: intag  
+ 
+            **type** = str;  **mandatory**
+
+        type of solver chosen for the calculation, currently supports:
+
+        * 'cthyb'
+        * 'ctint'
+        * 'ctseg'
+        * 'hubbardI'
+        * 'ftps'
+        * 'hartree'
+
+.. admonition:: idx_impurities 
+ 	:class: intag  
+ 
+            **type** = list of int;  **default**  = None
+
+        list of impurities this solver is supposed to solve
+
+cthyb
+=====
+
+.. admonition:: delta_interface 
+ 	:class: intag  
+ 
+            **type** = bool;  **default**  = False
+
+        use delta interface in cthyb instead of input G0
+
+.. admonition:: fit_max_moment 
+ 	:class: intag  
+ 
+            **type** = int;  **default**  = None
+
+        max moment to be fitted. Only used if solver.perform_tail_fit = True
+
+.. admonition:: fit_max_n 
+ 	:class: intag  
+ 
+            **type** = int;  **default**  = None
+
+        number of highest matsubara frequency to fit. Only used if solver.perform_tail_fit = True
+
+.. admonition:: fit_max_w 
+ 	:class: intag  
+ 
+            **type** = float;  **default**  = None
+
+        highest matsubara frequency to fit. Only used if solver.perform_tail_fit = True
+
+.. admonition:: fit_min_n 
+ 	:class: intag  
+ 
+            **type** = int;  **default**  = None
+
+        number of start matsubara frequency to start with. Only used if solver.perform_tail_fit = True
+
+.. admonition:: fit_min_w 
+ 	:class: intag  
+ 
+            **type** = float;  **default**  = None
+
+        start matsubara frequency to start with. Only used if solver.perform_tail_fit = True
+
+.. admonition:: imag_threshold 
+ 	:class: intag  
+ 
+            **type** = float;  **default**  = 1e-14
+
+        threshold for imag part of G0_tau. be warned if symmetries are off in projection scheme imag parts can occur in G0_tau
+
+.. admonition:: legendre_fit 
+ 	:class: intag  
+ 
+            **type** = bool;  **default**  = False
+
+        filter noise of G(tau) with G_l, cutoff is taken from n_l
+
+.. admonition:: length_cycle 
+ 	:class: intag  
+ 
+            **type** = int;  **mandatory**
+
+        length of each cycle; number of sweeps before measurement is taken
+
+.. admonition:: loc_n_max 
+ 	:class: intag  
+ 
+            **type** = int;  **default**  = None
+
+        Restrict local Hilbert space to states with at most this number of particles
+
+.. admonition:: loc_n_min 
+ 	:class: intag  
+ 
+            **type** = int;  **default**  = None
+
+        Restrict local Hilbert space to states with at least this number of particles
+
+.. admonition:: max_time 
+ 	:class: intag  
+ 
+            **type** = int;  **default**  = -1
+
+        maximum amount the solver is allowed to spend in each iteration
+
+.. admonition:: measure_chi_insertions 
+ 	:class: intag  
+ 
+            **type** = int;  **default**  = 100
+
+        number of insertation for measurement of chi
+
+.. admonition:: measure_chi 
+ 	:class: intag  
+ 
+            **type** = str;  **default**  = None
+
+        measure the dynamic suszeptibility of an operator O, chi(O,O(tau))
+        triqs.github.io/cthyb/unstable/guide/dynamic_susceptibility_notebook.html
+        Possible values for this flag are:
+
+        * 'SzSz': spin susceptibility
+        * 'NN': occupation susceptibility
+
+.. admonition:: measure_density_matrix 
+ 	:class: intag  
+ 
+            **type** = bool;  **default**  = False
+
+        measures the impurity density matrix and sets also
+        use_norm_as_weight to true
+
+.. admonition:: measure_G_l 
+ 	:class: intag  
+ 
+            **type** = bool;  **default**  = False
+
+        measure Legendre Greens function
+
+.. admonition:: measure_pert_order 
+ 	:class: intag  
+ 
+            **type** = bool;  **default**  = False
+
+        measure perturbation order histograms: triqs.github.io/cthyb/latest/guide/perturbation_order_notebook.html.
+        The result is stored in the h5 archive under 'DMFT_results' at every iteration
+        in the subgroups 'pert_order_imp_X' and 'pert_order_total_imp_X'
+
+.. admonition:: move_double 
+ 	:class: intag  
+ 
+            **type** = bool;  **default**  = True
+
+        double moves in solver
+
+.. admonition:: move_shift 
+ 	:class: intag  
+ 
+            **type** = bool;  **default**  = False
+
+        shift moves in solver
+
+.. admonition:: n_cycles_tot 
+ 	:class: intag  
+ 
+            **type** = int;  **mandatory**
+
+        total number of sweeps
+
+.. admonition:: n_l 
+ 	:class: intag  
+ 
+            **type** = int;  **default**  = None
+
+        number of Legendre coefficients.
+        Needed if measure_G_l=True or legendre_fit=True
+
+.. admonition:: n_warmup_cycles 
+ 	:class: intag  
+ 
+            **type** = int;  **mandatory**
+
+        number of warmup cycles before real measurement sets in
+
+.. admonition:: off_diag_threshold 
+ 	:class: intag  
+ 
+            **type** = float;  **default**  = 0.0
+
+        threshold for off-diag elements in Hloc0
+
+.. admonition:: perform_tail_fit 
+ 	:class: intag  
+ 
+            **type** = bool;  **default**  = False
+
+        tail fitting if legendre is off?
+
+.. admonition:: random_seed 
+ 	:class: intag  
+ 
+            **type** = str;  **default**  = None
+
+        if None;  **default**  seed by triqs.
+        If specified the int will be used for random seeds. Careful, this will give the same random
+        numbers on all mpi ranks.
+        You can also pass a string that will convert the keywords it or rank on runtime, e.g.,
+        34788 * it + 928374 * rank will convert each iteration the variables it and rank for the random
+        seed
+
+ctint
+=====
+
+.. admonition:: length_cycle 
+ 	:class: intag  
+ 
+            **type** = int;  **mandatory**
+
+        length of each cycle; number of sweeps before measurement is taken
+
+.. admonition:: max_time 
+ 	:class: intag  
+ 
+            **type** = int;  **default**  = -1
+
+        maximum amount the solver is allowed to spend in each iteration
+
+.. admonition:: measure_pert_order 
+ 	:class: intag  
+ 
+            **type** = bool;  **default**  = False
+
+        measure perturbation order histograms: triqs.github.io/cthyb/latest/guide/perturbation_order_notebook.html.
+        The result is stored in the h5 archive under 'DMFT_results' at every iteration
+        in the subgroups 'pert_order_imp_X' and 'pert_order_total_imp_X'
+
+.. admonition:: move_double 
+ 	:class: intag  
+ 
+            **type** = bool;  **default**  = True
+
+        double moves in solver
+
+.. admonition:: n_cycles_tot 
+ 	:class: intag  
+ 
+            **type** = int;  **mandatory**
+
+        total number of sweeps
+
+.. admonition:: n_warmup_cycles 
+ 	:class: intag  
+ 
+            **type** = int;  **mandatory**
+
+        number of warmup cycles before real measurement sets in
+
+.. admonition:: random_seed 
+ 	:class: intag  
+ 
+            **type** = str;  **default**  = None
+
+        if None;  **default**  seed by triqs.
+        If specified the int will be used for random seeds. Careful, this will give the same random
+        numbers on all mpi ranks.
+        You can also pass a string that will convert the keywords it or rank on runtime, e.g.,
+        34788 * it + 928374 * rank will convert each iteration the variables it and rank for the random
+        seed
+
+ctseg
+=====
+
+.. admonition:: improved_estimator 
+ 	:class: intag  
+ 
+            **type** = bool;  **default**  = False
+
+        measure improved estimators
+        Sigma_iw will automatically be calculated via
+        http://dx.doi.org/10.1103/PhysRevB.85.205106
+
+.. admonition:: legendre_fit 
+ 	:class: intag  
+ 
+            **type** = bool;  **default**  = False
+
+        filter noise of G(tau) with G_l, cutoff is taken from n_l
+
+.. admonition:: length_cycle 
+ 	:class: intag  
+ 
+            **type** = int;  **mandatory**
+
+        length of each cycle; number of sweeps before measurement is taken
+
+.. admonition:: max_time 
+ 	:class: intag  
+ 
+            **type** = int;  **default**  = -1
+
+        maximum amount the solver is allowed to spend in each iteration
+
+.. admonition:: measure_G_iw 
+ 	:class: intag  
+ 
+            **type** = bool;  **default**  = False
+
+        should the solver measure G(iw)?
+
+.. admonition:: measure_G_l 
+ 	:class: intag  
+ 
+            **type** = bool;  **default**  = False
+
+        measure Legendre Greens function
+
+.. admonition:: measure_G_tau 
+ 	:class: intag  
+ 
+            **type** = bool;  **default**  = True
+
+        should the solver measure G(tau)?
+
+.. admonition:: measure_pert_order 
+ 	:class: intag  
+ 
+            **type** = bool;  **default**  = False
+
+        measure perturbation order histograms: triqs.github.io/cthyb/latest/guide/perturbation_order_notebook.html.
+        The result is stored in the h5 archive under 'DMFT_results' at every iteration
+        in the subgroups 'pert_order_imp_X' and 'pert_order_total_imp_X'
+
+.. admonition:: n_cycles_tot 
+ 	:class: intag  
+ 
+            **type** = int;  **mandatory**
+
+        total number of sweeps
+
+.. admonition:: n_l 
+ 	:class: intag  
+ 
+            **type** = int;  **default**  = None
+
+        number of Legendre coefficients.
+        Needed if measure_G_l=True or legendre_fit=True
+
+.. admonition:: n_warmup_cycles 
+ 	:class: intag  
+ 
+            **type** = int;  **mandatory**
+
+        number of warmup cycles before real measurement sets in
+
+.. admonition:: random_seed 
+ 	:class: intag  
+ 
+            **type** = str;  **default**  = None
+
+        if None;  **default**  seed by triqs.
+        If specified the int will be used for random seeds. Careful, this will give the same random
+        numbers on all mpi ranks.
+        You can also pass a string that will convert the keywords it or rank on runtime, e.g.,
+        34788 * it + 928374 * rank will convert each iteration the variables it and rank for the random
+        seed
+
+hubbardI
+========
+
+.. admonition:: legendre_fit 
+ 	:class: intag  
+ 
+            **type** = bool;  **default**  = False
+
+        filter noise of G(tau) with G_l, cutoff is taken from n_l
+
+.. admonition:: measure_density_matrix 
+ 	:class: intag  
+ 
+            **type** = bool;  **default**  = False
+
+        measures the impurity density matrix and sets also
+        use_norm_as_weight to true
+
+.. admonition:: measure_G_l 
+ 	:class: intag  
+ 
+            **type** = bool;  **default**  = False
+
+        measure Legendre Greens function
+
+.. admonition:: measure_G_tau 
+ 	:class: intag  
+ 
+            **type** = bool;  **default**  = True
+
+        should the solver measure G(tau)?
+
+.. admonition:: n_l 
+ 	:class: intag  
+ 
+            **type** = int;  **default**  = None
+
+        number of Legendre coefficients.
+        needed if measure_G_l=True or legendre_fit=True
+
+ftps parameters
+===============
+
+.. admonition:: bath_fit 
+ 	:class: intag  
+ 
+            **type** = bool;  **mandatory**
+
+        DiscretizeBath vs BathFitter
+
+.. admonition:: calc_me 
+ 	:class: intag  
+ 
+            **type** = bool;  **default**  = True
+
+        calculate only symmetry-inequivalent spins/orbitals, symmetrized afterwards
+
+.. admonition:: diag_delta 
+ 	:class: intag  
+ 
+            **type** = bool;  **default**  = False
+
+        option to remove off-diagonal terms in the hybridization function
+        available for ftps
+
+.. admonition:: dmrg_maxm 
+ 	:class: intag  
+ 
+            **type** = int;  **default**  = 100
+
+        TODO: add description
+
+.. admonition:: dmrg_maxmB 
+ 	:class: intag  
+ 
+            **type** = int;  **default**  = 100
+
+        maximal bath-bath bond dimensions
+
+.. admonition:: dmrg_maxmI 
+ 	:class: intag  
+ 
+            **type** = int;  **default**  = 100
+
+        maximal imp-imp bond dimensions
+
+.. admonition:: dmrg_maxmIB 
+ 	:class: intag  
+ 
+            **type** = int;  **default**  = 100
+
+        maximal imp-bath bond dimensions
+
+.. admonition:: dmrg_tw 
+ 	:class: intag  
+ 
+            **type** = float;  **default**  1e-9
+
+        truncated weight for every link
+
+.. admonition:: dt 
+ 	:class: intag  
+ 
+            **type** = float;  **mandatory**
+
+        time step
+
+.. admonition:: enforce_gap 
+ 	:class: intag  
+ 
+            **type** = list of float;  **default**  = None
+
+        enforce gap in DiscretizeBath between interval
+
+.. admonition:: ignore_weight 
+ 	:class: intag  
+ 
+            **type** = float;  **default**  = 0.0
+
+        ignore weight of peaks for bath fitter
+
+.. admonition:: maxm 
+ 	:class: intag  
+ 
+            **type** = int;  **default**  = 100
+
+        TODO: write description
+
+.. admonition:: maxmB 
+ 	:class: intag  
+ 
+            **type** = int;  **default**  = 100
+
+        maximal bath-bath bond dimensions
+
+.. admonition:: maxmI 
+ 	:class: intag  
+ 
+            **type** = int;  **default**  = 100
+
+        maximal imp-imp bond dimensions
+
+.. admonition:: maxmIB 
+ 	:class: intag  
+ 
+            **type** = int;  **default**  = 100
+
+        maximal imp-bath bond dimensions
+
+.. admonition:: n_bath 
+ 	:class: intag  
+ 
+            **type** = int;  **default**  = 0
+
+        number of bath sites
+
+.. admonition:: path_to_gs 
+ 	:class: intag  
+ 
+            **type** = string;  **default**  = None
+
+        location of GS if already present. Use 'postprocess' to skip solver and go directly to post-processing
+        of previously terminated time-evolved state
+
+.. admonition:: ph_symm 
+ 	:class: intag  
+ 
+            **type** = bool;  **default**  = False
+
+        particle-hole symmetric problem
+
+.. admonition:: refine_factor 
+ 	:class: intag  
+ 
+            **type** = int;  **default**  = 1
+
+        rerun ftps cycle with increased accuracy
+
+.. admonition:: state_storage 
+ 	:class: intag  
+ 
+            **type** = string;  **default**  = './'
+
+        location of large MPS states
+
+.. admonition:: sweeps 
+ 	:class: intag  
+ 
+            **type** = int;  **default**  = 10
+
+        Number of DMRG sweeps
+
+.. admonition:: tw 
+ 	:class: intag  
+ 
+            **type** = float;  **default**  1e-9
+
+        truncated weight for every link
+
+hartree
+================
+
+.. admonition:: force_real 
+ 	:class: intag  
+ 
+            **type** = bool;  **default**  = True
+
+        force the self energy from Hartree fock to be real
+
+.. admonition:: method 
+ 	:class: intag  
+ 
+            **type** = str;  **default**  = "krylov"
+
+        method for root finder. Only used if one_shot=False, see scipy.optimize.root for options.
+
+.. admonition:: one_shot 
+ 	:class: intag  
+ 
+            **type** = bool;  **default**  = False
+
+        Perform a one-shot or self-consitent root finding in each DMFT step of the Hartree solver.
+
+.. admonition:: tol 
+ 	:class: intag  
+ 
+            **type** = float;  **default**  = 1e-5
+
+        tolerance for root finder if one_shot=False.
+
+.. admonition:: with_fock 
+ 	:class: intag  
+ 
+            **type** = bool;  **default**  = False
+
+        include Fock exchange terms in the self-energy
diff --git a/_sources/input_output/DMFT_output/iterations.rst.txt b/_sources/input_output/DMFT_output/iterations.rst.txt
new file mode 100644
index 00000000..8b36ac3d
--- /dev/null
+++ b/_sources/input_output/DMFT_output/iterations.rst.txt
@@ -0,0 +1,150 @@
+
+Iterations
+----------
+
+List of the main outputs for solid_dmft for every iteration.
+
+.. warning::
+
+  According to the symmetries found by the solver, the resulting indexing of the triqs.Gf objects might vary.
+  In order to retrieve the indices call the Gf.indices method.
+
+
+Legend:
+
+* iiter = iteration number: range(0, n_dmft_iter)
+* ish = shell number: range(0, n_shells)
+* icrsh = correlated shell number: range(0, n_corr_shells)
+* iineq = inequivalent correlated shell number: range(0, n_inequiv_shells)
+* iorb = orbital number: range(0, n_orbitals)
+* sp = spin label
+* ikpt = k-point label, the order is the same as given in the wannier90 input: range(0, n_kpt)
+* iband = band label before downfolding, n_bands = number of bands included in the disentanglement window during the wannierization: range(0, n_bands)
+
+
+[observables]
+=============
+
+.. admonition:: chemical_potential_pre: 
+  :class: intag
+            **type=** float;
+
+            Chemical potential before the solver iteration.
+
+.. admonition:: chemical_potential_post: 
+  :class: intag
+            **type=** float;
+
+            Chemical potential after the solver iteration.
+
+.. admonition:: DC_energ: 
+  :class: intag
+            **type=** arr(float);
+
+            **indices=** [iorb]
+
+            Double counting correction.
+
+.. admonition:: DC_pot: 
+  :class: intag
+ 
+            **type=** arr(float);
+
+            **indices=** [iiter]
+
+            Double counting potential.**what exactly is the indexing here?**
+
+.. admonition:: Delta_time_{iimp}: 
+  :class: intag
+ 
+            **type=** triqs.gf.block_gf.BlockGf
+
+
+            Imaginary time hybridization function.
+
+.. admonition:: G0_freq_{iimp}: 
+  :class: intag
+ 
+            **type=** triqs.gf.block_gf.BlockGf
+
+
+            Imaginary frequency Weiss field.
+
+.. admonition:: G0_time_orig_{iimp}: 
+  :class: intag
+ 
+            **type=** triqs.gf.block_gf.BlockGf
+
+
+            ??
+
+.. admonition:: G_imp_freq_{iimp}: 
+  :class: intag
+ 
+            **type=** triqs.gf.block_gf.BlockGf
+
+
+            Imaginary frequency impurity green function.
+
+.. admonition:: G_imp_l_{iimp}: 
+  :class: intag
+ 
+            **type=** triqs.gf.block_gf.BlockGf
+
+
+            Legendre representation of the impurity green function.
+
+.. admonition:: G_imp_time_{iimp}: 
+  :class: intag
+ 
+            **type=** triqs.gf.block_gf.BlockGf
+
+
+            Imaginary time representation of the impurity green function.
+
+.. admonition:: Sigma_freq_{iimp}: 
+  :class: intag
+ 
+            **type=** triqs.gf.block_gf.BlockGf
+
+
+            Imaginary frequency self-energy obtained from the Dyson equation.
+
+.. admonition:: deltaN: 
+  :class: intag
+ 
+            **type=** dict(arr(float))
+            
+            **indices=** [ispin][ikpt][iband, iband]
+
+
+            Correction to the DFT occupation of a particular band: 
+
+.. admonition:: deltaN_trace: 
+  :class: intag
+ 
+            **type=** dict
+            
+            **indices=** [ispin]
+
+
+            Total sum of the charge correction for an impurity.
+
+.. admonition:: dens_mat_pre: 
+  :class: intag
+ 
+            **type=** arr(dict) 
+
+            **indices=** [iimp][*same as block structure Gf*]
+
+            Density matrix before the solver iteration.
+
+.. admonition:: dens_mat_post: 
+  :class: intag
+ 
+            **type=** arr(dict) 
+
+            **indices=** [ispin][iimp]
+
+            Density matrix after the solver iteration.
+
diff --git a/_sources/input_output/DMFT_output/observables.rst.txt b/_sources/input_output/DMFT_output/observables.rst.txt
new file mode 100644
index 00000000..9fbf5687
--- /dev/null
+++ b/_sources/input_output/DMFT_output/observables.rst.txt
@@ -0,0 +1,202 @@
+
+Observables/convergence_obs
+---------------------------
+
+List of the single-particle observables obtained in a single DMFT iteration
+
+
+Legend:
+
+* iiter = iteration number: range(0, n_dmft_iter)
+* iimp = impurity number: range(0, n_imp)
+* iorb = orbital number: range(0, n_orbitals)
+* ispin = spin label, 'up' or 'down' in collinear calculations
+
+
+[observables]
+=============
+
+.. admonition:: iteration: 
+  :class: intag
+ 
+            **type=** arr(int);
+
+            **indices=** [iiter]
+
+            Number of the iteration.
+
+.. admonition:: mu: 
+  :class: intag
+ 
+            **type=** arr(float);
+
+            **indices=** [iiter]
+
+            Chemical potential fed to the solver at the present iteration (pre-dichotomy adjustment).
+
+.. admonition:: orb_gb2: 
+  :class: intag
+ 
+            **type=** arr(dict)
+
+            **indices=** [iimp][ispin][iiter, iorb]
+
+            Orbital resolved G(beta/2), proxy for projected density of states at the Fermi level. Low value of orb_gb2 correlate with the presence of a gap.
+
+.. admonition:: imp_gb2: 
+  :class: intag
+ 
+            **type=** arr(dict)
+
+            **indices=** [iimp][ispin][iiter]
+
+            Site G(beta/2), proxy for total density of states at the Fermi level. Low values correlate with the presence of a gap.
+
+.. admonition:: orb_Z: 
+  :class: intag
+ 
+            **type=** arr(dict)
+
+            **indices=** [iimp][ispin][iiter, iorb]
+
+            Orbital resolved quasiparticle weight (eff_mass/renormalized_mass). As obtained by linearizing the self-energy around :math:`\omega = 0`
+
+            .. math::
+
+              Z = \bigg( 1- \frac{\partial Re[\Sigma]}{\partial \omega} \bigg|_{\omega \rightarrow 0} \bigg)^{-1} \\
+
+
+.. admonition:: orb_occ: 
+  :class: intag
+ 
+            **type=** arr(dict)
+
+            **indices=** [iimp][ispin][iiter, iorb]
+
+            Orbital resolved mean site occupation.
+
+.. admonition:: imp_occ: 
+  :class: intag
+ 
+            **type=** arr(dict)
+
+            **indices=** [iimp][ispin][iiter]
+
+            Total mean site occupation.
+
+
+.. admonition:: E_tot: 
+  :class: intag
+ 
+            **type=** arr(float)
+
+            **indices=** [iiter]
+
+            Total energy, computed as:
+            
+            .. math::
+
+              E_{tot} = E_{DFT} + E_{corr} + E_{int} -E_{DC}
+
+
+.. admonition:: E_dft: 
+  :class: intag
+ 
+            **type=** arr(float)
+
+            **indices=** [iiter]
+
+            :math:`E_{DFT}` in the total energy expression. System energy as computed by the DFT code at every csc iteration.
+
+
+
+.. admonition:: E_bandcorr: 
+  :class: intag
+ 
+            **type=** arr(float)
+
+            **indices=** [iiter]
+
+            :math:`E_{corr}` in the total energy expression. DMFT correction to the kinetic energy.
+
+.. admonition:: E_corr_en: 
+  :class: intag
+ 
+            **type=** arr(float)
+
+            **indices=** [iiter]
+
+            Sum of the E_DC and E_int_imp terms.
+
+.. admonition:: E_int_imp: 
+  :class: intag
+ 
+            **type=** arr(float)
+
+            **indices=** [iiter]
+
+            :math:`E_{int}` in the total energy expression. Energy contribution from the electronic interactions within the single impurity.
+
+
+.. admonition:: E_DC: 
+  :class: intag
+ 
+            **type=** arr(float)
+
+            **indices=** [iiter]
+
+            :math:`E_{DC}` in the total energy expression. Double counting energy contribution.
+
+
+
+
+[convergence_obs]
+=================
+
+.. admonition:: iteration: 
+  :class: intag
+ 
+            **type=** arr(int);
+
+            **indices=** [iiter]
+
+            Number of the iteration.
+
+.. admonition:: d_mu: 
+  :class: intag
+ 
+            **type=** arr(float)
+
+            **indices=** [iiter]
+
+            Chemical potential stepwise difference.
+
+
+.. admonition:: d_orb_occ: 
+  :class: intag
+ 
+            **type=** arr(dict)
+
+            **indices=** [iimp][ispin][iiter,iorb]
+
+            Orbital occupation stepwise difference.
+
+.. admonition:: d_imp_occ: 
+  :class: intag
+ 
+            **type=** arr(dict)
+
+            **indices=** [iimp][ispin][iiter]
+
+            Impurity occupation stepwise difference.
+
+.. admonition:: d_Etot: 
+  :class: intag
+ 
+            **type=** arr(float)
+
+            **indices=** [iiter]
+
+            Total energy stepwise difference.
+            
+
diff --git a/_sources/input_output/DMFT_output/results.rst.txt b/_sources/input_output/DMFT_output/results.rst.txt
new file mode 100644
index 00000000..c1568797
--- /dev/null
+++ b/_sources/input_output/DMFT_output/results.rst.txt
@@ -0,0 +1,28 @@
+
+*************************************
+Output / results
+*************************************
+The *DMFT_results* group contains the output of the DMFT iterations. The subgroups contained here fall under two main categories:
+
+* **Iterations**: relevant quantities for the DMFT solutions, such as Weiss field, Green function, extracted self-energy, etc.
+  Normally these are solver dependent.
+
+* **Observables**: Single-particles quantities that can be measured with the aid of the green function. Includes chemical potential, estimate of the quasiparticle weight, impurity occupation, total energy, energy contributions, etc. The convergence_obs subgroup lists the stepwise difference in the observables' value as the calculation progresses and can be used as a proxy for convergence.
+
+Group structure
+===============
+
+.. image:: ./group_structure.png
+   :width: 100%
+   :align: center
+
+
+Subgroups
+===================
+.. toctree::
+    :maxdepth: 1
+
+    iterations
+    observables
+
+
diff --git a/_sources/install.rst.txt b/_sources/install.rst.txt
new file mode 100644
index 00000000..ef7acb9a
--- /dev/null
+++ b/_sources/install.rst.txt
@@ -0,0 +1,109 @@
+.. highlight:: bash
+.. _installation:
+
+Installation 
+#############
+
+Prerequisites
+-------------
+
+#. The :ref:`TRIQS <triqslibs:welcome>` library, see `TRIQS installation instruction <https://triqs.github.io/triqs/latest/install.html>`_.
+   In the following, we assume that :ref:`TRIQS <triqslibs:welcome>`, `triqs/dft_tools <https://triqs.github.io/dft_tools>`_, and at least one of the impurity solvers `available in TRIQS <https://triqs.github.io/triqs/latest/applications.html>`_, e.g. cthyb, HubbardI, ctseg, FTPS, or ctint is installed in the directory ``path_to_triqs``.
+
+#. Make sure to install besides the triqs requirements also the python packages::
+
+     $ pip3 install --user scipy argparse pytest
+
+#. To build the documentation the following extra python packages are needed::
+
+     $ pip3 install --user sphinx sphinx-autobuild pandoc nbsphinx linkify-it-py sphinx_rtd_theme myst-parser
+
+
+Installation via pip
+--------------------
+
+You can install the latest solid_dmft release simply via pip (PyPi):
+```
+pip install solid_dmft
+```
+However, please make sure that you have a valid TRIQS and TRIQS/DFTTools installation matching the version of solid_dmft. Furthermore, you need at least one of the supported DMFT impurity solvers installed to use solid_dmft. 
+
+Manual installation via CMake
+-----------------------------
+
+We provide hereafter the build instructions in the form of a documented bash script. Please change the variable INSTALL_PREFIX to point to your TRIQS installation directory::
+    
+    INSTALL_PREFIX=/path/to/triqs
+    # source the triqsvars.sh file from your TRIQS installation to load the TRIQS environment
+    source $(INSTALL_PREFIX)/share/triqs/triqsvars.sh
+
+    # clone the flatironinstitute/solid_dmft repository from GitHub
+    git clone https://github.com/flatironinstitute/solid_dmft solid_dmft.src
+
+    # checkout the branch of solid_dmft matching your triqs version. 
+    # For example if you use the 3.1.x branch of triqs, dfttools. and cthyb
+    git checkout 3.1.x
+
+    # Create and move to a new directory where you will compile the code
+    mkdir solid_dmft.build && cd solid_dmft.build
+
+    # In the build directory call cmake, including any additional custom CMake options, see below
+    cmake ../solid_dmft.src
+
+    # Compile the code, run the tests, and install the application
+    make test
+    make install
+
+This installs solid_dmft into your TRIQS installation folder.
+
+To build ``solid_dmft`` with documentation you should run:: 
+
+     $ cmake path/to/solid_dmft.src -DBuild_Documentation=ON 
+     $ make 
+     $ sphinx-autobuild path/to/solid_dmft.src/doc ./doc/html -c ./doc/
+
+The last line will automatically search for changes in your src dir, rebuild the documentation, 
+and serve it locally as under `127.0.0.1:8000`. 
+
+Docker files & images
+---------------------
+
+We `provide docker files <https://github.com/TRIQS/solid_dmft/tree/3.1.x/Docker>`_ to build solid_dmft inside a docker container with all dependencies and instructions on how to integrate the connected DFT codes as well. Additionally, we host a most recent unstable version of the docker image used for the github CI `on dockerhub <https://hub.docker.com/r/materialstheory/solid_dmft_ci>`_. To use this version, which includes the cthyb solver, the hubbardI solver, dfttools, and the maxent package, pull the following image::
+
+    $ docker pull materialstheory/solid_dmft_ci
+
+
+Version compatibility
+---------------------
+
+Keep in mind that the version of ``solid_dmft`` must be compatible with your TRIQS library version,
+see :ref:`TRIQS website <triqslibs:versions>`.
+In particular the Major Version numbers have to be the same.
+To use a particular version, go into the directory with the sources, and look at all available branches::
+
+     $ cd solid_dmft.src && git branch -vv
+
+Checkout the version of the code that you want::
+
+     $ git checkout 3.1.x
+
+and follow steps 3 to 6 above to compile the code.
+
+Custom CMake options
+--------------------
+
+The compilation of ``solid_dmft`` can be configured using CMake-options::
+
+    cmake ../solid_dmft.src -DOPTION1=value1 -DOPTION2=value2 ...
+
++-----------------------------------------------------------------+-----------------------------------------------+
+| Options                                                         | Syntax                                        |
++=================================================================+===============================================+
+| Specify an installation path other than path_to_triqs           | -DCMAKE_INSTALL_PREFIX=path_to_solid_dmft     |
++-----------------------------------------------------------------+-----------------------------------------------+
+| Build in Debugging Mode                                         | -DCMAKE_BUILD_TYPE=Debug                      |
++-----------------------------------------------------------------+-----------------------------------------------+
+| Disable testing (not recommended)                               | -DBuild_Tests=OFF                             |
++-----------------------------------------------------------------+-----------------------------------------------+
+| Build the documentation                                         | -DBuild_Documentation=ON                      |
++-----------------------------------------------------------------+-----------------------------------------------+
diff --git a/_sources/issues.rst.txt b/_sources/issues.rst.txt
new file mode 100644
index 00000000..b7036f85
--- /dev/null
+++ b/_sources/issues.rst.txt
@@ -0,0 +1,52 @@
+.. _issues:
+
+********************
+Support & contribute
+********************
+
+Seeking help
+============
+
+If you have any questions please ask them on the solid_dmft github discussion page:
+`<https://github.com/flatironinstitute/solid_dmft/discussions>`_. However, note
+that solid_dmft is targeted at experienced users of DMFT, and we can only provide
+technial support for the code itself not for theory questions about the utilized methods.
+
+Also make sure to ask only questions relevant for solid_dmft. For questions
+regarding other parts of TRIQS use the discussions page of the respective TRIQS
+application.
+
+Take also a look at the :ref:`tutorials` section of the documentation for examples, and
+the official `TRIQS tutorial page <https://github.com/TRIQS/tutorials>`_ for even more
+tutorials.
+
+
+Improving solid_dmft
+====================
+
+Please post suggestions for new features on the `github discussion page 
+<https://github.com/flatironinstitute/solid_dmft/discussions>`_ or create 
+directly a pull request with new features or helpful postprocessing scripts
+via github.
+
+Reporting issues
+****************
+
+Please report all problems and bugs directly at the github issue page
+`<https://github.com/flatironinstitute/solid_dmft/issues>`_.  In order to make
+it easier for us to solve the issue please follow these guidelines:
+
+#. In all cases specify which version of the application you are using. You can
+   find the version number in the file :file:`CMakeLists.txt` at the root of the
+   application sources.
+
+#. If you have a problem during the installation, give us information about
+   your operating system and the compiler you are using. Include the outputs of
+   the ``cmake`` and ``make`` commands as well as the ``CMakeCache.txt`` file
+   which is in the build directory. Please include these outputs in a
+   `gist <http://gist.github.com/>`_ file referenced in the issue.
+
+#. If you are experiencing a problem during the execution of the application, provide
+   a script which allows to quickly reproduce the problem.
+
+Thanks!
diff --git a/_sources/md_notes/docker.md.txt b/_sources/md_notes/docker.md.txt
new file mode 100644
index 00000000..b9c00950
--- /dev/null
+++ b/_sources/md_notes/docker.md.txt
@@ -0,0 +1,75 @@
+
+# Docker
+
+There are Dockerfiles for images based on Ubuntu 20 ("focal") with OpenMPI (for non-Cray clusters) or MPICH (for Cray clusters like Daint), IntelMKL, VASP, wannier90 2.1, triqs 3.x.x, and Triqs MaxEnt included.
+
+## Building the docker image
+The Dockerfile is built with this command, where `<version name>` could be `3.0.0`:
+```
+docker build -t triqs_mpich:<version name> -f mpich_dockerfile ./
+docker build -t triqs_openmpi:<version name> -f openmpi_dockerfile ./
+```
+Note that you need a working, modified vasp version as archive (csc_vasp.tar.gz) in this directory to make the CSC calculation work.
+
+## Pulling a docker image
+Alternatively, you can pull an already-compiled image from the ETH gitlab container registry.
+First [log in with a personal access token](https://gitlab.ethz.ch/help/user/packages/container_registry/index#authenticating-to-the-gitlab-container-registry).
+This token you can save into a file and then log in into the registry with
+```
+cat <path to token> | docker login registry.ethz.ch -u <username gitlab> --password-stdin
+```
+and then run
+```
+docker pull registry.ethz.ch/d-matl-theory/uni-dmft/<image name>:<version name>
+```
+Just make sure that the version is the one that you want to have, it might not yet contain recent changes or bug fixes. Alternatively, there is the [official triqs docker image](https://hub.docker.com/r/flatironinstitute/triqs/), which however is not optimized for use on Daint.
+
+## Getting docker images onto CSCS daint
+First, you load the desired docker images with [sarus on daint](https://user.cscs.ch/tools/containers/sarus/).
+Then there are two ways of getting the image on daint:
+
+(1) For gitlab images (don't forget that you need the personal access token) or other, public image this can be done via:
+```
+sarus pull registry.ethz.ch/d-matl-theory/uni-dmft/<image name>:<version name>
+sarus pull materialstheory/triqs
+```
+Pulling from the gitlab didn't work on daint when I tried, which leaves you with the second option.
+
+(2) If you wish to use your locally saved docker image, you first have to save it
+```
+docker save --output=docker-triqs.tar <image name>:<version name>
+```
+and then upload the tar to daint and then load it via:
+```
+sarus load docker-triqs.tar <image name>:<version name>
+```
+then you can run it as shown in the example files.
+
+### Running a docker container
+
+You can start a docker container with either of these commands
+```
+docker run --rm -it -u $(id -u) -v ~$PWD:/work <image name>:<version name> bash
+docker run --rm -it --shm-size=4g -e USER_ID=`id -u` -e GROUP_ID=`id -g` -p 8378:8378 -v $PWD:/work <image name>:<version name> bash
+```
+where the second command adds some important flags.
+- The -e flags will translate your current user and group id into the container and make sure writing permissions are correct for the mounted volumes.
+- The option --shm-size, which increases shared memory size.
+This is hard coded in Docker to 64m and is often not sufficient and will produce SIBUS 7 errors when starting programs with mpirun! (see also https://github.com/moby/moby/issues/2606).
+- The '-v' flags mounts a host directory as the docker directory given after the colon.
+This way docker can permanently save data; otherwise, it will restart with clean directories each time.
+Make sure you mount all the directories you need (where you save your data, where your uni-dmft directory is, ...)!
+- All the flags are explained in 'docker run --help'.
+
+Inside the docker, you can normally execute program. To run uni-dmft, for example, use
+```
+mpirun -n 4 python <path to uni-dmft>/run_dmft.py
+```
+To start a jupyter-lab server from the current path, use
+```
+jupyter.sh
+```
+All these commands you can execute directly by just adding them to the `docker run ... bash` command with the `-c` flag, e.g.
+```
+docker run --rm -it --shm-size=4g -e USER_ID=`id -u` -e GROUP_ID=`id -g` -p 8378:8378 -v $PWD:/work <image name>:<version name> bash -c 'cd /work && mpirun -n 4 python <path to uni-dmft>/run_dmft.py'
+```
diff --git a/_sources/md_notes/run_cluster.md.txt b/_sources/md_notes/run_cluster.md.txt
new file mode 100644
index 00000000..f17a69bf
--- /dev/null
+++ b/_sources/md_notes/run_cluster.md.txt
@@ -0,0 +1,70 @@
+# Running solid_dmft on a cluster
+
+## Running on CSCS daint
+
+in some directories one can also find example job files to run everything on
+daint. Note, that one has to first load the desired docker images with sarus
+on daint: https://user.cscs.ch/tools/containers/sarus/, see the README.md in the `/Docker` folder.
+
+## one shot job on daint
+
+one shot is quite straight forward. Just get the newest version of these
+scripts, go to a working directory and then create job file that looks like
+this:
+```
+#!/bin/bash
+#SBATCH --job-name="svo-test"
+#SBATCH --time=1:00:00
+#SBATCH --nodes=2
+#SBATCH --ntasks-per-node=36
+#SBATCH --account=eth3
+#SBATCH --ntasks-per-core=1
+#SBATCH --constraint=mc
+#SBATCH --partition=normal
+#SBATCH --output=out.%j
+#SBATCH --error=err.%j
+
+#======START=====
+
+srun sarus run --mpi --mount=type=bind,source=$SCRATCH,destination=$SCRATCH --mount=type=bind,source=/apps,destination=/apps load/library/triqs-2.1-vasp bash -c "cd $PWD ; python /apps/ethz/eth3/dmatl-theory-git/solid_dmft/solid_dmft.py"
+```
+thats it. This line automatically runs the docker image and executes the
+`solid_dmft.py` script. Unfortunately the new sarus container enginge does not mounts automatically user directories. Therefore, one needs to specify with `--mount` to mount the scratch and apps folder manually. Then, one executes in the container bash to first go into the current dir and then executes python and the dmft script.
+
+## CSC calculations on daint
+
+CSC calculations need the parameter `csc = True` and the mandatory parameters from the group `dft`.
+Then, solid_dmft automatically starts VASP on as many cores as specified.
+Note that VASP runs on cores that are already used by solid_dmft.
+This minimizes the time that cores are idle while not harming the performance because these two processes are never active at the same time.
+
+For the latest version in the Dockerfile_MPICH, we need the sarus version >= 1.3.2, which can be loaded from the daint modules as `sarus/1.3.2` but isn't the default version.
+The reason for this is that only from this sarus version on, having more than one version of libgfortran in the docker image is supported, which comes from Vasp requiring the use of gfortran7 and everything else using gfortran9.
+
+A slurm job script should look like this:
+```
+#!/bin/bash
+#SBATCH --job-name="svo-csc-test"
+#SBATCH --time=4:00:00
+#SBATCH --nodes=4
+#SBATCH --ntasks-per-node=36
+#SBATCH --account=eth3
+#SBATCH --ntasks-per-core=1
+#SBATCH --constraint=mc
+#SBATCH --partition=normal
+#SBATCH --output=out.%j
+#SBATCH --error=err.%j
+
+# path to solid_dmft.py script
+SCRIPTDIR=/apps/ethz/eth3/dmatl-theory-git/solid_dmft/solid_dmft.py
+# Sarus image that is utilized
+IMAGE=load/library/triqs_mpich
+
+srun --cpu-bind=none --mpi=pmi2 sarus run --mount=type=bind,source=/apps,destination=/apps --mount=type=bind,source=$SCRATCH,destination=$SCRATCH --workdir=$PWD $IMAGE python3 $SCRIPTDIR
+```
+Note that here the mpi option is given to the `srun` command and not the sarus command, as for one-shot calculations.
+This is important for the python to be able to start VASP.
+
+In general I found 1 node for Vasp is in most cases enough, which means that we set `n_cores` in the dmft\_config.ini to 36 here.
+Using more than one node results in a lot of MPI communication, which in turn slows down the calculation significantly.
+For a 80 atom unit cell 2 nodes are useful, but for a 20 atom unit cell not at all!
diff --git a/_sources/md_notes/run_locally.md.txt b/_sources/md_notes/run_locally.md.txt
new file mode 100644
index 00000000..8eeaccc2
--- /dev/null
+++ b/_sources/md_notes/run_locally.md.txt
@@ -0,0 +1,25 @@
+# Run on your machine
+
+## CSC calculations locally
+
+Here one needs a special docker image with vasp included. This can be done by
+building the Dockerfile in `/Docker`.
+Then start this docker image as done above and go to the directory with all
+necessary input files (start with `svo-csc` example). You need a pre-converged
+CHGCAR and preferably a WAVECAR, a set of INCAR, POSCAR, KPOINTS and POTCAR
+files, the PLO cfg file `plo.cfg` and the usual DMFT input file
+`dmft_config.ini`, which specifies the number of ranks for the DFT code and the DFT code executable in the `[dft]` section.
+
+The whole machinery is started by calling `solid_dmft.py` as for one-shot calculations. Importantly the flag `csc = True` has to be set in the general section in the config file. Then:
+```
+mpirun -n 12 /work/solid_dmft.py
+```
+The programm will then run the `csc_flow_control` routine, which starts VASP accordingly by spawning a new child process. After VASP is finished it will run the converter, run the dmft_cycle, and then VASP again until the given
+limit of DMFT iterations is reached. This should also work on most HPC systems (tested on slurm with OpenMPI), as the the child mpirun call is performed without the slurm environment variables. This tricks slrum into starting more ranks than it has available. Note, that maybe a slight adaption of the environment variables is needed to make sure VASP is found on the second node. The variables are stored `args` in the function `start_vasp_from_master_node` of the module `csc_flow.py`
+
+One remark regarding the number of iterations per DFT cycle. Since VASP uses a
+block Davidson scheme for minimizing the energy functional not all eigenvalues
+of the Hamiltonian are updated simultaneously therefore one has to make several
+iterations before the changes from DMFT in the charge density are completely
+considered. The default value are __6__ DFT iterations, which is very
+conservative, and can be changed by changing the config parameter `n_iter` in the `[dft]` section. In general one should use `IALGO=90` in VASP, which performs an exact diagonalization rather than a partial diagonalization scheme, but this is very slow for larger systems.
diff --git a/_sources/md_notes/vasp_csc.md.txt b/_sources/md_notes/vasp_csc.md.txt
new file mode 100644
index 00000000..9b950e93
--- /dev/null
+++ b/_sources/md_notes/vasp_csc.md.txt
@@ -0,0 +1,107 @@
+# Interface to VASP
+
+
+## General remarks
+
+One can use the official Vasp 5.4.4 patch 1 version with a few modifications:
+
+- there is a bug in `fileio.F` around line 1710 where the code tries print out something like "reading the density matrix from Gamma", but this should be done only by the master node. Adding a `IF (IO%IU0>=0) THEN ... ENDIF` around it fixes this
+- in the current version of the dft_tools interface the file `LOCPROJ` should contain the fermi energy in the header. Therefore  one should replace the following line in `locproj.F`:
+```
+WRITE(99,'(4I6,"  # of spin, # of k-points, # of bands, # of proj" )') NS,NK,NB,NF
+```
+by
+```
+WRITE(99,'(4I6,F12.7,"  # of spin, # of k-points, # of bands, # of proj, Efermi" )') W%WDES%NCDIJ,NK,NB,NF,EFERMI
+```
+and add the variable `EFERMI` accordingly in the function call.
+- Vasp gets sometimes stuck and does not write the `OSZICAR` file correctly due to a stuck buffer. Adding a flush to the buffer to have a correctly written `OSZICAR` to extract the DFT energy helps, by adding in `electron.F` around line 580 after
+```
+CALL STOP_TIMING("G",IO%IU6,"DOS")
+```
+two lines:
+```
+flush(17)
+print *, ' '
+```
+- this one is __essential__ for the current version of the DMFT code. Vasp spends a very long time in the function `LPRJ_LDApU` and this function is not needed! It is used for some basic checks and a manual LDA+U implementation. Removing the call to this function in `electron.F` in line 644 speeds up the calculation by up to 30%! If this is not done, Vasp will create a GAMMA file each iteration which needs to be removed manually to not overwrite the DMFT GAMMA file!
+- make sure that mixing in VASP stays turned on. Don't set IMIX or the DFT steps won't converge!
+
+## LOCPROJ bug for individual projections:
+
+
+Example use of LOCPROJ for t2g manifold of SrVO3 (the order of the orbitals seems to be mixed up... this example leads to x^2 -y^2, z^2, yz... )
+In the current version there is some mix up in the mapping between selected orbitals in the INCAR and actual selected in the LOCPROJ. This is 
+what the software does (left side is INCAR, right side is resulting in the LOCPROJ)
+
+* xy -> x2-y2
+* yz -> z2
+* xz -> yz
+* x2-y2 -> xz
+* z2 -> xy
+
+```
+LOCPROJ = 2 : dxz : Pr 1
+LOCPROJ = 2 : dx2-y2 : Pr 1
+LOCPROJ = 2 : dz2 : Pr 1
+```
+However, if the complete d manifold is chosen, the usual VASP order (xy, yz, z2, xz, x2-y2) is obtained in the LOCPROJ. This is done as shown below
+```
+LOCPROJ = 2 : d : Pr 1
+```
+
+## convergence of projectors with Vasp
+
+
+for a good convergence of the projectors it is important to convergence the wavefunctions to high accuracy. Otherwise this often leads to off-diagonal elements in the the local Green's function. To check convergence pay attention to the rms and rms(c) values in the Vasp output. The former specifies the convergence of the KS wavefunction and the latter is difference of the input and out charge density. Note, this does not necessarily coincide with good convergence of the total energy in DFT! Here an example of two calculations for the same system, both converged down to `EDIFF= 1E-10` and Vasp stopped. First run:
+
+```
+       N       E                     dE             d eps       ncg     rms          rms(c)
+...
+DAV:  25    -0.394708006287E+02   -0.65893E-09   -0.11730E-10 134994   0.197E-06  0.992E-05
+...
+```
+second run with different smearing:
+```
+...
+DAV:  31    -0.394760088659E+02    0.39472E-09    0.35516E-13 132366   0.110E-10  0.245E-10
+...
+```
+The total energy is lower as well. But more importantly the second calculation produces well converged projectors preserving symmetries way better, with less off-diagonal elements in Gloc, making it way easier for the solver. Always pay attention to rms.
+
+## Enabling CSC calculations with Wannier90 projectors
+
+
+You basically only need to add two things to have W90 run in Vasp's CSC mode, all in `electron.F`:
+
+- the line `USE mlwf` at the top of the `SUBROUTINE ELMIN` together with all the other `USE ...` statements.
+- right below where you removed the call to `LPRJ_LDApU` (see above, around line 650), there is the line `CALL LPRJ_DEALLOC_COVL`. Just add the following block right below, inside the same "if" as the `CALL LPRJ_DEALLOC_COVL`:
+```
+IF (WANNIER90()) THEN
+   CALL KPAR_SYNC_ALL(WDES,W)
+   CALL MLWF_WANNIER90(WDES,W,P,CQIJ,T_INFO,LATT_CUR,INFO,IO)
+ENDIF
+```
+Then, the only problem you'll have is the order of compilation in the `.objects` file. It has to change because now electron.F references mlwf. For that move the entries `twoelectron4o.o` and `mlwf.o` (in this order) up right behind `linear_optics.o`. Then, move the lines from `electron.o` to `stm.o` behind the new position of `mlwf.o`.
+
+Remarks:
+
+- W90-CSC requires Wannier90 v3, in v2 the tag write_u_matrices does not work correctly. Until now, linking W90 v3 to Vasp with the `DVASP2WANNIER90v2` has worked without any problems even though it is not officially supported
+- symmetries in Vasp should remain turned on, otherwise the determination of rotation matrices in dft_tools' wannier converter will most likely fail
+
+## Speeding up by not writing projectors at every step
+
+
+This is very important for CSC calculations with W90 but also speeds up the PLO-based ones.
+
+Writing the Wannier projectors is a time consuming step (and to a lesser extent, the PLO projectors) and basically needs only to be done in the DFT iteration right before a DMFT iteration. Therefore, solid_dmft writes the file `vasp.suppress_projs` that tells Vasp when __not__ to compute/write the projectors. This requires two small changes in `electron.F` in the Vasp source code:
+
+- adding the definition of a logical variable where all other variables are defined for `SUBROUTINE ELMIN`, e.g. around line 150, by inserting `LOGICAL :: LSUPPRESS_PROJS_EXISTS`
+- go to the place where you removed the call to `LPRJ_LDApU` (see above, around line 650). This is inside a `IF (MOD(INFO%ICHARG,10)==5) THEN ... ENDIF` block. This whole block has to be disabled when the file `vasp.suppress_projs` exists. So, right under this block's "IF", add the lines
+```
+INQUIRE(FILE='vasp.suppress_projs',&
+        EXIST=LSUPPRESS_PROJS_EXISTS)
+
+IF (.NOT. LSUPPRESS_PROJS_EXISTS) THEN
+```
+and right before this block's "ENDIF", add another `ENDIF`.
diff --git a/_sources/md_notes/w90_interface.md.txt b/_sources/md_notes/w90_interface.md.txt
new file mode 100644
index 00000000..52675070
--- /dev/null
+++ b/_sources/md_notes/w90_interface.md.txt
@@ -0,0 +1,38 @@
+# Wannier90 interface
+
+## orbital order in the W90 converter
+
+Some interaction Hamiltonians are sensitive to the order of orbitals (i.e. density-density or Slater Hamiltonian), others are invariant under rotations in orbital space (i.e. the Kanamori Hamiltonian).
+For the former class and W90-based DMFT calculations, we need to be careful because the order of W90 (z^2, xz, yz, x^2-y^2, xy) is different from the order expected by TRIQS (xy, yz, z^2, xz, x^2-y^2).
+Therefore, we need to specify the order of orbitals in the projections block (example for Pbnm or P21/n cell, full d shell):
+```
+begin projections
+# site 0
+f=0.5,0.0,0.0:dxy
+f=0.5,0.0,0.0:dyz
+f=0.5,0.0,0.0:dz2
+f=0.5,0.0,0.0:dxz
+f=0.5,0.0,0.0:dx2-y2
+# site 1
+f=0.5,0.0,0.5:dxy
+f=0.5,0.0,0.5:dyz
+f=0.5,0.0,0.5:dz2
+f=0.5,0.0,0.5:dxz
+f=0.5,0.0,0.5:dx2-y2
+# site 2
+f=0.0,0.5,0.0:dxy
+f=0.0,0.5,0.0:dyz
+f=0.0,0.5,0.0:dz2
+f=0.0,0.5,0.0:dxz
+f=0.0,0.5,0.0:dx2-y2
+# site 3
+f=0.0,0.5,0.5:dxy
+f=0.0,0.5,0.5:dyz
+f=0.0,0.5,0.5:dz2
+f=0.0,0.5,0.5:dxz
+f=0.0,0.5,0.5:dx2-y2
+end projections
+```
+Warning: simply using `Fe:dxy,dyz,dz2,dxz,dx2-y2` does not work, VASP/W90 brings the d orbitals back to W90 standard order.
+
+The 45-degree rotation for the sqrt2 x sqrt2 x 2 cell can be ignored because the interaction Hamiltonian is invariant under swapping x^2-y^2 and xy.
diff --git a/_sources/tutorials.rst.txt b/_sources/tutorials.rst.txt
new file mode 100644
index 00000000..e0862429
--- /dev/null
+++ b/_sources/tutorials.rst.txt
@@ -0,0 +1,28 @@
+.. _tutorials:
+
+Tutorials
+==========
+
+These tutorials provide an overview about typical workflows to perform DFT+DMFT calculations with solid_dmft. The tutorials are sorted by complexity and introduce one after another more available features. 
+
+.. note::
+   The tutorials are run with the 3.1.x branch of triqs. Please use the 3.1.x branch for triqs and all applications to reproduce the results shown here. 
+
+Short description of the tutorials linked below: 
+
+1. Typical one-shot (OS) DMFT calculation based on prepared hdf5 archive for SrVO3
+2. Full charge self-consistent (CSC) DFT+DMFT calculation using the PLO formalism with Vasp for PrNiO3
+3. Full CSC DFT+DMFT calculation using w90 in combination with Quantum Espresso utilizing the lighter HubbardI solver
+4. OS magnetic DMFT calculation for NdNiO2 in a large energy window for 5 d orbitals
+5. Postprocessing: plot the spectral function after a DFT+DMFT calculation
+
+----
+
+.. toctree::
+    :maxdepth: 2
+
+    tutorials/SVO_os_qe/tutorial
+    tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial
+    tutorials/Ce2O3_csc_w90/tutorial
+    tutorials/NNO_os_plo_mag/tutorial
+    tutorials/correlated_bandstructure/plot_correlated_bands
diff --git a/_sources/tutorials/Ce2O3_csc_w90/tutorial.ipynb.txt b/_sources/tutorials/Ce2O3_csc_w90/tutorial.ipynb.txt
new file mode 100644
index 00000000..6e154533
--- /dev/null
+++ b/_sources/tutorials/Ce2O3_csc_w90/tutorial.ipynb.txt
@@ -0,0 +1,530 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "1cc005bd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.ticker as ticker\n",
+    "\n",
+    "from triqs.gf import *\n",
+    "from h5 import HDFArchive"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f93f161b",
+   "metadata": {},
+   "source": [
+    "# 3. CSC with QE/W90 and HubbardI: total energy in Ce2O3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c1dbd052",
+   "metadata": {},
+   "source": [
+    "Disclaimer:\n",
+    "\n",
+    "* These can be heavy calculations. Current parameters won't give converged solutions, but are simplified to deliver results on 10 cores in 10 minutes.\n",
+    "* The interaction values, results etc. might not be 100% physical and are only for demonstrative purposes!\n",
+    "\n",
+    "The goal of this tutorial is to demonstrate how to perform fully charge self-consistent DFT+DMFT calculations in solid_dmft using [Quantum Espresso](https://www.quantum-espresso.org/) (QE) and [Wannier90](http://www.wannier.org/) (W90) for the DFT electronic structure using the [HubbardI solver](https://triqs.github.io/hubbardI/latest/index.html).\n",
+    "\n",
+    "We will use Ce$_2$O$_3$ as an example and compute the total energy for the $s=0\\%$ experimental ground state structure. To find the equilibrium structure in DFT+DMFT one then repeats these calculations variing the strain in DFT as was done in Fig. 7 of [arxiv:2111.10289 (2021)](https://arxiv.org/abs/2111.10289.pdf):\n",
+    "\n",
+    "<img src=\"./tenergy_ce2o3.png\" alt=\"drawing\" width=\"800\"/>\n",
+    "\n",
+    "In the case of Ce$_2$O$_3$ it turns out that in fact DFT+DMFT predicts the same ground state as is found experimentally, while DFT underestimates, and DFT+DMFT in the one-shot approximation overestimates the lattice parameter, respectively.\n",
+    "\n",
+    "The tutorial will guide you through the following steps: \n",
+    "\n",
+    "* perpare the input for the DFT and DMFT calculations using Quantum Espresso and Wannier90 and TRIQS\n",
+    "* run a charge self-consistent calculation for Ce$_2$O$_3$\n",
+    "* analyse the change in the non-interacting part of the charge density using TRIQS\n",
+    "* analyse the convergence of the total energy and the DMFT self-consistency\n",
+    "\n",
+    "We set `path` variables to the reference files:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "8681be23",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path = './ref/'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "10d286f9",
+   "metadata": {},
+   "source": [
+    "## 1. Input file preparation\n",
+    "\n",
+    "The primitive cell of Ce$_2$O$_3$ contains 2 Ce atoms with 7 $f$-electrons each, so 14 in total. They are relatively flat, so there is no entanglement with any other band.\n",
+    "We start from relaxed structure as usual. All files corresponding to this structure should be prepared and stored in a separate directory (`save/` in this case). For details please look at Section III in [arxiv:2111.10289 (2021)](https://arxiv.org/abs/2111.10289.pdf).\n",
+    "\n",
+    "### DFT files\n",
+    "\n",
+    "All input files are of the same kind as usual, unless stated otherwise:\n",
+    "\n",
+    "Quantum Espresso:\n",
+    "\n",
+    "1. [ce2o3.scf.in](./dft_input/ce2o3.scf.in)\n",
+    "2. [ce2o3.nscf.in](./dft_input/ce2o3.nscf.in)\n",
+    "\n",
+    "    - explicit k-mesh\n",
+    "    ```\n",
+    "    &system\n",
+    "        nosym            = .true.\n",
+    "        dmft             = .true.\n",
+    "    ```\n",
+    "3. [ce2o3.mod_scf.in](./dft_input/ce2o3.mod_scf.in): new!\n",
+    "\n",
+    "    - explicit k-mesh\n",
+    "    ```\n",
+    "    &system\n",
+    "        nosym            = .true.\n",
+    "        dmft             = .true.\n",
+    "        dmft_prefix      = seedname\n",
+    "    &electrons\n",
+    "        electron_maxstep = 1\n",
+    "        mixing_beta      = 0.3\n",
+    "    ```\n",
+    "\n",
+    "Optionally:\n",
+    "\n",
+    "- `seedname.bnd.in`\n",
+    "- `seedname.bands.in`\n",
+    "- `seedname.proj.in`\n",
+    "\n",
+    "Wannier90:\n",
+    "\n",
+    "1. [ce2o3.win](./dft_input/ce2o3.win)\n",
+    "\n",
+    "    ```\n",
+    "        write_u_matrices = .true.\n",
+    "    ```\n",
+    "2. [ce2o3.pw2wan.in](./dft_input/ce2o3.pw2wan.in)\n",
+    "\n",
+    "### DMFT\n",
+    "\n",
+    "1. Wannier90Converter: [ce2o3.inp](./dft_input/ce2o3.inp)\n",
+    "2. solid_dmft: [dmft_config.toml](./dmft_config.toml)\n",
+    "\n",
+    "Here we'll discuss the most important input flags for solid_dmft:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "165c087b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[general]\n",
+      "seedname = \"ce2o3\"\n",
+      "jobname = \"b10-U6.46-J0.46\"\n",
+      "csc = true\n",
+      "\n",
+      "eta = 0.5\n",
+      "n_iw = 100\n",
+      "n_tau = 5001\n",
+      "\n",
+      "n_iter_dmft_first = 2\n",
+      "n_iter_dmft_per = 1\n",
+      "n_iter_dmft = 5\n",
+      "\n",
+      "block_threshold = 1e-03\n",
+      "\n",
+      "h_int_type = \"density_density\"\n",
+      "U = 6.46\n",
+      "J = 0.46\n",
+      "beta = 10\n",
+      "prec_mu = 0.1\n",
+      "\n",
+      "sigma_mix = 1.0\n",
+      "g0_mix = 1.0\n",
+      "dc_type = 0\n",
+      "dc = true\n",
+      "dc_dmft = true\n",
+      "calc_energies = true\n",
+      "\n",
+      "h5_save_freq = 1\n",
+      "\n",
+      "[solver]\n",
+      "type = \"hubbardI\"\n",
+      "n_l = 15\n",
+      "store_solver = false\n",
+      "measure_G_l = false\n",
+      "measure_density_matrix = true\n",
+      "\n",
+      "[dft]\n",
+      "dft_code = \"qe\"\n",
+      "n_cores = 10\n",
+      "mpi_env = \"default\"\n",
+      "projector_type = \"w90\"\n",
+      "dft_exec = \"pw.x\"\n",
+      "w90_tolerance = 1.e-1\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./dmft_config.toml"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f970c47",
+   "metadata": {},
+   "source": [
+    "Of course you'll have to switch `csc` on to perform the charge self-consistent calculations. Then we choose the HubbardI Solver, set the number of Legendre polynomials, Matsubara frequencies $i\\omega_n$ and imaginary time grid points $\\tau$. In this calculation we perform five iterations in total, of which the two first ones are one-shot DMFT iterations, followed by three DFT and three DMFT steps.\n",
+    "For the interaction Hamiltonian we use `density_density`. Note that you unlike the Kanamori Hamiltonian, this one is not rotationally invariant, so the correct order of the orbitals must be set (inspect the projections card in `ce2o3.win`). We must also use `dc_dmft` and `calc_energies`, since we are interested in total energies.\n",
+    "Finally, we will specify some details for the DFT manager, i.e. to use QE, W90 and the tolerance for the mapping of shells. Note that this value should in general be $1e-6$, but for demonstration purposes we reduce it here. If `dft_exec` is empty, it will assume that `pw.x` and other QE executables are available."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "47bb27d5",
+   "metadata": {},
+   "source": [
+    "## 2. Running DFT+DMFT\n",
+    "\n",
+    "Now that everything is set up, copy all files from `./dft_input` and start the calculation:\n",
+    "```\n",
+    "cp dft_input/* .\n",
+    "mpirun solid_dmft > dmft.out &\n",
+    "```\n",
+    "\n",
+    "You will note that for each DFT step solid_dmft will append the filenames of the DFT Ouput with a unique identifier `_itXY`, where `XY` is the total iteration number. This allows the user to keep track of the changes within DFT. For the W90 `seedname.wout` and `seedname_hr.dat` files the seedname will be renamed to `seedname_itXY`. If the QE `seedname_bands.dat`, and `seedname_bands.proj` are present, they will be saved, too.\n",
+    "\n",
+    "You can check the output of the calculations while they are running, but since this might take a few minutes, we'll analyse the results of the reference data in `/ref/ce2o3.h5`. You should check if the current calculation reproduces these results."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c74f73cb",
+   "metadata": {},
+   "source": [
+    "## 3. Non-interacting Hamiltonian and convergence analysis\n",
+    "### Tight-binding Hamiltonian"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7f6d9a1",
+   "metadata": {},
+   "source": [
+    "Disclaimer: the bands shown here are only the non-interacting part of the charge density. Only the first iteration corresponds to a physical charge density, namely the Kohn-Sham ground state charge density.\n",
+    "\n",
+    "The first thing to check is whether the DFT Hamiltonian obtained from Wannier90 is correct. For this we use the tools available in `triqs.lattice.utils`.\n",
+    "Let us first get the number of iterations and Fermi levels from DFT:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "1f204686",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fermi levels: [14.3557, 14.42, 14.4619, 14.495]\n",
+      "iteration counts: [1, 3, 4, 5]\n"
+     ]
+    }
+   ],
+   "source": [
+    "e_fermi_run = !grep \"DFT Fermi energy\" triqs.out\n",
+    "e_fermi_run = [float(x.split('DFT Fermi energy')[1].split('eV')[0]) for x in e_fermi_run]\n",
+    "n_iter_run = !ls ce2o3_it*_hr.dat\n",
+    "n_iter_run = sorted([int(x.split('_it')[-1].split('_')[0]) for x in n_iter_run])\n",
+    "print(f'Fermi levels: {e_fermi_run}')\n",
+    "print(f'iteration counts: {n_iter_run}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "7fa4150b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warning: could not identify MPI environment!\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Starting serial run at: 2022-03-25 12:42:36.663824\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f4a21de2160>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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YaW9vz7kPq1ev5sEHH5x2Pzdt2jS7F/YyIo5WgiAIgiAIC5Cu6xw5OsSjj7fw+BMtDPvOvXd2NoKhJA/95SgP/eUoNpuRq66o5obr67jmqhosFnHqKMydRCKNP5AgGEygadNHmkaTgt1pxuY0ISsykq5j0OIY1TiKnpqwrK5DJpYhHUmTjmZQzzGb8pl0HTJJjUxy4voMFgWT04jJYUQ2ych6BpOawaRGUSUjacWO5LRid5pJxNNEggkSORqoVFWjfyCCbyROUaEdl8ssMiTPkjhCCYIgCIIgLCDpjMpfHz3Oz3+5l7aT/hk/z2hWKKv2UFaTh9NjAUlCItvbNHY7k9EY6A7S3xVkuDd81oAiFkvzxFMneOKpE7hdZl77mqXcc9dyKivc5/UahVeusYzH/sAMemklsNnNONxmTGYDSKBoSYzpOAYtgXTaUGItrZOKpEhHM2RimVn3yp6PTEIlk1CJDSUwmGWMo4GuYlayPbqZACbC2SDXasNic5JOqYQDCWLh5KT1pdMqPb0hfCMGioocOMS8+BkTwa0gCIIgCMICkEqpPPSXI/zkZ3vo6wtPu6wkS9QtLaSyIZ/y2jzKa/MoKHEgTzNf70zplMpQb4i+zgB9HUFOHBigu21kyuWDoSQ//+Vefv7LvVyxsYo33rOCKzdWTTtHUBDGpFIqqgotJ3xnbVRRDDIOlwW7a6yXVsWoRjCqMWRODevVNZ1UKEUymCaTOHvSKQDZoKBYzSgWM4rFgmI1I5tNo61AWdLEf9DSadR4EjWRRI0nsreTqVyrH+3ZTRIfTqKYZMweE2a3CVlWMashTGqYtGJDMtrxFtlxeiwEfXESscnrSyQydHYGcDhMFBc5xBSBGRDvkCAIgiAIwkWUSGT48/8d5oGf72FwKDrtshV1XtZdU82aK6tx5VnH/65oSRzJLpzJPhzJAUxqONurpeujvVs6kq6jSQoxUwFRUxFRUxGWyiLKavLG1zMyFOXg9i4ObO+m/ejQlL1fW7Z2smVrJ6WlTu55/XJef+cyPG7LXLwdwsuEpukcODTA88+fpKgwRWGBlbw8O7IydWBrsRlxuCxYbMbRXtoUpnQUg5aYsFwmliEZTJEKp8/aQ2t02DC6nRgcNhSLGdk4+/BHNhow2KwT/qZrGmoiSSYaJ+UPkQ5FJj1PTWnEBhPEfUksHhOWPDOSAiY1ikmNkpatSEYnBaUOkvEMQV8sZ4blSCRFNDqC12ujIN8mGpSmIYJbQRAEQRCEiyCZzPC7PxzkZ7/Yg28kPuVy3iI7666uYd01NRSVuwAwZcIUBnbgSnbjTPZhTY/MuLxJXqJj/LYOJBUXUXMRfmstZs8SvK9ewjWvXkI4EOfQSz3s29JJy4GBnOvq6wvzze9s4wc/2slrXt3Mm+9bTXWVZ4Z7IrzcxBNpduzo5rkX2nn+hXZG/NnP9Yc/0DzlcxRFxu4yY3OaMRjH5tLGMKlRZP1UoKdldJKBJMlQCi09dUQrKTJGlxOTx4nR5TinYHYmJFnGYLNisFmxFHrRMhlS/lDOQFdXdeK+JImRJGa3CYvXjGyUMWpxDKlEdriyxYG53EU8liLoi0+ql6vr4PPFCAYTFBXZcbssYj5uDiK4FQRBEOZEOJIkFEoSi6WJxdPEY2niiTSxWJp4PE08nkFRJCwWIxaLAbNZGb9ttRiwmA3k59tEAg3hZU/XdZ56ppWvf3MrvdMMP65uKuCme5azZG1pNiOslqYgfJDi8H7y4m3IOUqXhHvi+A75iA0l0FUdTdXRNX38tmyQcFXacdc6cdU4MZgVLGoISyxEfuwE9b4nCZtLGbIvZdi+BOfNDWy4uYGhvjBbn2jhpWfaiEcnJ8JJJDP8/o+H+MOfDnH1VTW85U2rWb+uTHyXX8Y0TaevP0xb2witJ/3s29fH9h3dJKao7Xomi92Ew2ke76WVtQzGTBijGp8wlzYdzZD0J0lFp16vbFAweT2YPNkeWkm+8D2bssGApdCbDXTTGVKByYGurkMikCIRSGF2GbF4zShmBdPokOukwQk2G1abiWg4SXAkjqZOTF6VyWj09obx+xOUFDuwWo0X+qUuaCK4FQRBEGZE13VGRuJ0dQfp7gnS1RXKXncH6e4OEQgmzr6SGbBYDBQXOSgudlBcZKe4yEFRkYPSEge1tV5KSxzihFlYtA4fGeSr/72Zvfv6plymfnkRN92znIYVxUiShDveQUl4P4XRIxi0U8lnkmGVoX0+fIdHGDkWYORYkPQ0AcCZJBnsJTbctU48dU6K1xVQuMqLK9mHK9lH3cgzRE1FDDqWYyxcw2vfvo5X3beKvS92sPnxFnraJie70nV4frTXbklzAW++bzW33NSAyaTM7o0SLihN00kkNNIZnb6+MJKcIJ1SSaVVkimVdEolFk/T2Rmg7aSf1rYRTrb7Z1S253SSDG6vFZvTjGKQQdcxanGMamxCxmNdg2QwSTKQQk1NnenY6HJgLsjD5HFelIB2KrLxVKCbiSdI9A2RHAlOWCYZSpMMpTG7jVgLLMgGsGSCmKQoScWJ3WXB5jAR8scJBxOTyvDG42lOtvvxeCwUFdoxGMR3DERwKwiCIEwhkchw+Mgg+/b3s+9AP/v3989ZAHu27XZ0BujoDOR83GE3UV/vpbEhn4b6fBpGbzud5nnfN0E4V4ODEb713e385a/HplymaXUJN9+zgtqlhQDkR49RM/I8zlT/+DJaRqf7hT7aHu1iYNcw+nlUONE1iPTGiPTG6Nk8wKGfn8CcZ6LiqhIqry6haF0+jtQgjpFBakaeZ8ixlB73pVx2Yz2X3lBHZ4uPzY8eZ++WTjR1ci/y0WPD/Pv9T/O1r2/mda9Zyt2vX0ZFuciyfDpd1wmHkwz7Yvh8cUb8MaLRiaNfVDU7d3osB5PBIGMyKhhN2WuTScFgGLuW0TQdXddRVZ10WiUSSRGJJIlEU0QiKcLhZPZvo/cjkRTRWGp87urXvvGHOX2NxRUu8kscOPMsmC1GnHnWKXtp1bROwpcgFUpNOZdWNhowF+RhLshDMS/8LMIGqwVHXSXWsiLi/cMkff4JgWoymCYVTmP1mjF7zchksGb8qKqJpMGFO9+G3Wkm4IvlLB8UCCQIhZIUFtjJ81qRX+GNvyK4FQRBEADwB+Ls3tPL/v397N3fz5GjQ2Qyc1MbcC5FoqlswL2/f8LfS0ocrF1dyto1ZaxfV0ZNtUf08AoXXTyR5ue/2MtPf75nyl6u2iWFvPpta6huKgByB7Whrjgn/q+N9id7SIUmn+DmIptNKGZTNomOxYxiNqHrOtH2HvRM7n1J+lO0PtxJ68OdmFxGyq8opuqGMkovLaQ4cpDiyEFC5lJ6XZcgN6yg+iNXcMdb1rD5seNsfeJEziHLgUCCB36+h5/9Yg8bN1TxhruWc9WV1a+opDihUIKW1hFaW0fo7ArQ3ROiuztIT2+IZHJmWX4XC0mWqF1SyPJLy1l+STkFpU5sgT4MaMio2FLDKPppnxMdUpEMSX+CdHzq98LkdmAu9GJ0OxflsV2xmHHUlGMtLSQxMExyyI8+GsHrGsSGkySDKayFVkxOI4qewpYeJi3bSBqdFJQ6SUTTBHxRMmfU7dU0nYHBCP5AnJJiJw7Hwg/654sIbgVBEF6hdF3n5Ek/z7+YHUK4/+DAWcszzIQkgclswGQxYLaMXRsxmpVs2YakSjqVyV4nM6RS2eszf6xnq78/wqP9LTz6eAsAeXlW1q0pZd3aMtavLaO+3vuKOpkWLi5N03nsiRa++e2tDAzmzoDsLbJzx1vXsGpDJZIkkR89Ts3IcxOC2s7n+jnyqxP4jwdzrmOMe1kDBVeuo/Cq9RReuQ5HbcWUwzTVVIrIiU6Ch0+MXloJHjpB8FALp3eXpUJpTj7WzcnHunGU22i8s4a62ypw0Ydr6GHqfE/R51pPt/tSbv+bNdx413J2bjrJ848cw9c/OXOsrp/KslxS7OA1r17CzTfWU1/nXZTBylT8/jiHDg9y8PAAhw8PcrzFd9Ys2IuZ1W6kuNJNcYWb2iWFLF1fhn10JI0xE6EgtBstCZJiRTY4xwNbLQOJkTjJYGrKEQiSLGEuyMNSlI9ieXmMzlHMJuxVZVhLCon3D5EYPFV+S03rRHpjGK0KtiIrikXBqMUwpBLZ+bh2G8U2N5FAkpA/Ph4cj0mlVDq7AjidZoqL7JhMr7xQ75X3ii8CSZKqgA8DdwBVQBI4AfwO+I6u67E53NZNwFuAq4BSIAMMAPuBp4Gf67o++RdHEBYZXdfHAzFdzwZUInA5u3RaZfeeXp5/oZ0XNnfQ3ROa9ToUg0x+sYP8YgcFpQ7yi53klzgoKHHg8towmZVzOlHNpFWCI3GCvhgBX+y06ziB4RjD/eGcQ7Km4vfHefrZNp5+tg0At8vMNVfXcP21dWy4vBKLRfwECvNj34F+/uu/N3PwUO4Mw2argZvuXs5VtzdjNCm44p00+J7AlTw1D7d32xD7f3iUQGvu76jJ66H2ba+j5MaNFFyxFrPXM+P9U0wm3MsacC9rmPD3eP8Q3X9+ks4/PsHgph3o6qletEhPjD3fPsyBHx+j5tYKGu+swV0N1YEXqQhuZcCxkm7PRq68rYmNtzRweFcvL/z1GK0HB3PuQ/9AhB/8aCc/+NFOqqs83HB9HTdeX8fSJYWLKtCNxdIcOTbEoUMDHDo8yKHDg9MmCVtsZEVCUWQMRhmDUSGv0E5ZTR7FFS6KK92UVLpxeiZm7bWlhsn376QgegxXsgcJaLVeR1rJlopKRVQSvvi0dWllkxFLkRdzQR6y4eV5rJZNRuxVZZgLvcQ6eklHToUD6bhKsCOC2W3EVmhFUjQsmSBGNUbC4MaZZ8HmNBH0xYlFkpPWPTb0PN9rpaDAhryA5iPPt5fnp2UBkSTpDuCXwOmTTGzApaOX90iSdLuu623nuZ084CfA63I87AIagbuBrcDe89mWIFwI8Xiajs4A7e0BTnb46eoKMjwcxR9IMOKPEwjEJ83HsdmM5Hms5OVZyMuzUlXpob7OS32dl7raPOz2V+YwnUQiw+atHTz9TBsvbu4gEs1deD4XSZYorXJTs6SQmuYCqpsKyCuwIY82JEh6Bks6iDXjx5LuxRSLIEczyFoaRc8g6xlkPY2sZW/rkkJGNqPKptHr0duSmYxiJc/tJlHgIa0UTdoXXdcJDMfo6wzQ3xmkryNAf2eAgZ5Qzvl+ZwqGkjz8yDEefuQYFouBKzdWcf11dVx9ZbWYryvMid6+MN/89lYef/JEzsclWeLyG+u49d6VOD1WTJkQ9QNPUxQ5OF7GZ+hgkL3fO4Tv0ORkTQDeS1bQ9MG/oere2zFY57aurLWkkMb3v5nG97+ZxPAIPQ89Q+cfHmfgqa1o6WzDUiaucuLBDk482EHx+gKa76mlbEMRZeG9lIb34rM10uXZyIrLqllxWQWDPSG2PtHCzk0ncw5ZBujoDPCTB3bzkwd2U1bq5Ibrsw1QK5YV4XItnNq5qZRKy4lhDh0e4tDhQQ4fGeRku39ORrxAtgfU4bZgc5gxWRTMFiMms5I93kqMf0bUjEYmo6GmNTIZlUxaI51SiYQSxCMpkonMjI6JM6GpOpqaTSgFaSLBBJIEngIbDpeZfEcKR7wXW2oYe2oIT6ITW9o3YR06CumEnK3fqmWIRKfuYzE6bJiL8jHluRZVI8f5MFgtOJtrSY0EiXX3o6VPTRlIBtOkIxlsRRZMLhOKnsaeHial2EgpTrzFdhxuM/7hKOnkmaWDdIZ9MQLBBIWFdjzuV0bpIBHcziNJklaT7Z21ARHgi8CzgBW4D/hboBl4RJKkS8+1R1WSJDfwJLB+9E+PAL8h2zusANVkA+l7zvnFCMI8SmdU2tr8HDg4wKHD2dbvtpOzP2GIxbJlZ3p6x3o6OiY83lDvzQ5RXVfGujVl5Ofb5ugVLDzxRJrNmzt58pkTvLi5g3h8ZhktjWaF2uZCapYUULOkkKqGfCw2I4qWxJXowZnci83nw5L2Y0kHMKvhGdfWnI2MZCRh9JAweMavo6YCTN5S8grLWba+/NSyaZXBnhAdx320HR6k9fAgoWlqhkI24B/r1TUYZC5ZX87NN9Zzy00Nr9hGEOHchUIJHvjFXn71m31Tzp9sXFnMa96+lrKaPCQ9Q6V/M1X+FzCMDtEMtMfZ8639DOwanvRc2Wyi+r47aPzAmym4bNW8vpYxlgIv9e+6h/p33UNieITWH/6Blm//klj3qSHTA7uGGdg1jLvOyZJ766i+oYyCWAsFsRZC5jK63ZcjlS3lde9cz21vXs3eFzvY8sQJultHptxub1+YX/xqH7/41T4Aaqo9rFhezMoVxaxYXkxDgxfjBcgKG44ksyVu2vwcOz7M4SODHG8ZJn0e0ycUg0xRuYviChcFJdkRL/nFDjyFdpxuC8ZzyCg9Mhhh25Mn2P50G9HQ5B68uabr0HHcR8dxH4/9ej/F7jRXL41x1dIol9THsRizv9saVnwnNU784SBdT56g9KtLsDdZMOX42ZUkCVO+G0thPga7dd5fw0IkSRLmfA9Gj5N47yCJgVMNBJqqE+mLYwylsRdbkY0yJjWGUU2QNLjAYqW43D1aOig2qWEjk9Ho6wvj88UoLLC/7MvtSWeO1RbmjiRJzwLXkR0afI2u61vPePwfgK+M3v20ruufPcft/Ax46+h23qLr+m+nWE4CFF3XZ5e3/ezbrwC6AI4fP05jY+Ncrl5YBOLxOE888QQAt9xyC1br1D9Ouq7T2xfm4MEBDh4e5NChAY4eG55xXby5tKS5kOuuqeG6a2ppbMyf94P9bN6ncxGJpNiyrZOnn2nlhc0dMy7R4M63seySMpatL6dhRTFGk4I15cOV7Mad6MaV6MaeGpqQ0XKMmtaID8aJDmQvyWAKLaOjZzS0jIaW0cevdVXDYDVgchoxuUyYnEbMTiMmlxGT04Qlz4TBOn2bqw6kFCdhcwkRc+nodQlJw6nBMbquMzIYpe3wYPZyZCjn/L9crFYDt97cyJ2vXcrK0TIsC9V8f54Wuwvx/sTjaX792/088Iu9hMO5A4uCUieveftalq0vG59XW+97Els6G+ClYxp7vn2Qtke7JpX6kE1Gmj/yNpZ+8j1YCrxzvv+zpaXTdP35SY59/WcMb9kz6XFbkYWme2qpf3UVxtHvclJx0OdaS69rHSmDC4CuEz52v9DO/m3dBH2zm5llMimUljgpKXFQUuygpMQ5fl2QbxutoT1aR9tsIJ1O8uSTTwLZz4HJZCaVVgkFk/gDcfz+OCP+OH19YXp6Q/T0hOjqDk45T3qmjGaFilovlY1eKuu9lFbnUVjqzJa/OZOuYVIjmDOR8XqnipZC0VMoWgpZVwEtewwenYfT3qPxq4fivLR/6qzCF5rZqLOhMs4lXTuQtuyb8Hku//onsTdVYyvIp5hsXVbFbMRcmI+5wPOyHXp8rjLxBLHOXtLhid8PSQJrvhmL1zLela9KJhIGN5psQFN1Qv44kWkqG5jNBooK7Tgcpln9xrW0tJDJZDAYDGc9129paaGpqWnsbqWu690z3tB5EsHtPJEk6VJgx+jd7+u6/nc5lpGBg8BSwA8U67o+8wll2XVcBbwwevczuq7ff+57fW5eTsGtruvEYml8vlg2Lf9IjFAoicViwGE3YT/9YjPidJpF7T5yn0Tquk4gmKCzK0jnaFmX48eHOXh4kEBg/svJzFZZqZObbqzn1psbWdJcMC9BzVyfbOu6TkdngBc3d/DC5g727O2bcXbjygYvy9aXs+yScspqPBi1OPmxE+RHW/DE2zFpE39Q474EvsPZOpqR3ijRgTixwTiJkeR5lSI5kyXfjKvCjqPCjrPCjrPSjrPCgbPchpzrpHBUSrERsFTjt9YSsNYQN+VPeHxkMMKhl3o4sL2bk0eH0GcwKqC+zsudr13KHbc343EvnKGRY0RwO735fH9SKZU/PXiIH/54FyP+3KMErHYjN79hBVfc2ojBmG0wavA9QX7s1JDlk0/0sufbB3NmP66851bWfOkTOOur5my/55Jv5wGOfePndP7mr+NDlscYHQYaX1dD4+urseZnvzsaMsP2ZnrclxK0VgPZY1jXiRH2b+vkwLZufANznxJEliWU0Z9pTSNbVmcetlFS5aaqMZ/K+nwqG7wUV7on5YEwZiI4UgPYU0NY0yNY0yPY0iOYM6GcjYe59I4Y+N6TXh7d40TXp/+dcuVZqVtaiLfYgctrxZ2XrS8ryxKSlB0mL8kSsnTqfjyaJuSPj1/C/jhBf5ywP4F/KDo6PPns6v2tbOh9idJodt756cFtpTsPc1E+RpeoWT4dXddJ+vzEuvrR1Yk/tIpZxl5iw2A5dQ6aUuykFCe6JJFOqQSGYyTjU4cVVquRwkI7jhmOVhLB7SucJEmfB/559O4GXde3T7HcP5Edrgxwi67rT85yO78B7iU77Ln0YiSLWqzB7cBAhL37+9i3v5+jx4YZHo4y7IvNqiC5JEFVpYempnyaGgtobiyguamAggLby/KA7ffHOX7CRyAQJxRKEgxla6uNjERpbe0mHteQDTYi4RTBUOK8hm9B9v31FNopKnNRVO4kr8COw23B4TZjd5oxmE4lLtJ1nVg4RSSUIBJMEvTFGOgO0t8VxNcfmdUQ56pKN7fe3MitNzdQVzd3vSVzcbKdSqns2t2TDWi3dNDdPbOEUJIsUbeskFUbqlhxWTlurw1baoj86HHyYy24E93jJ1dqSmXkeBDf4QC+IwF8h/3EBi9ug4QkS7iq7OQvyyN/qYf85V7c1XYkOff3LGFw4bfWELDW4rfWjPcYAUSCCQ7vzAa6x/f3o56lQcBolLnhujruuWs569aWLZjvtghupzcf7086o/LXR4/z/R++RP8UowFkWWLjrQ3c8saV2J1mFC1Jtf8FKgLbkcl+1sJ9KbZ/YSfDByfPq/VesoJ1//0piq665Lz390KI9Qxw9L9/yonv/YZMdGKjmGyQqLy2lMbX11CwPG/87xFTEQPOlQzal5E0eoDRUT3tAQ5s66LlwAA9J0fOO4P6fCksc1JZ76WyIZ/Kei9ltXmYzBN7Hg1qDFeiB1eyB2eiF0eqH7M6fW+wpkqkEzLpBGQSGpm4SjqmoqkQS8n8+mABfz3hJaNNfQyqX17E6iuqaFhRTGFZ7pI5kq4i6RqgI+nZHmEJDXQdVTajycac686kVVoPDXJkVy+HdvXgn0HvdnWwkw29O7jqf/4Wx9I6rF4PTc3NZ32eADU1NXR0dPDUE09waXUj6dDkY47ZbcJWZEUabUfRkUkaXKQVK+iQiKcJjsRJTzM6zmYzkpdnxenINnxMRQS3r3CSJD0PXA1EAc9UQ4ElSdoIbBm9+1ld1z89i22YgCBgAX6v6/obR/9uAMrJDgjp13V95tljzsFiCG51XaflhI+9+/rZt7+Pvfv76ZvHbIZ5eVaWNBdw+aUVXLGxalGWOUhnVFpafOw/OMCBg/0cODgw40DqXBWUOKhsyM+2fjfkU1bjmXTCMEbWUih6+rQfaVDlbFKiSa8lpdJz0p+dj3lokPajQyRn2IjRUO/llpsbuOWmRqoq3Wd/wjRmO3y7rz9Ca6uP1rYRTrSO0No2wsl2P6kZtpxLskTD8iJWbqhk5eUVON0W3IlOCqNHyY8ex5oJjC8baAvRu22Qvm2D+I4E0DJn/20welzYK0uwVZZiG7025bmQFCVbgkSSRnsGsrcBUiNBEgPDxPuHSfQPkRjwZYvaD/rQtdmdyBosCnnNbvKXeihcVUjR2nyMlsnfMx2ImooYsi9lyLGUmKlw/LFEPM3RPX3seaGdI7t6z9oIsnRJIW/7mzXceEM9hml6ki8EEdxOby7fn6HhKH9+8DB/fPAwQ9OUdFlzRRW33LuSovJsY0pxeD91vqcxq9mTUlWVOPijoxz9XSv6GT2ItooSVn/xY9S8+TVTlvBZyJIjAY5/+5cc//rPSPoCkx73NrtpvKuGquvLUIzZ16cDYXMZQ46lEwJdyAZSfR0BOlp8dLb46Dw+zPAMpxfMFVmRKCx1UlzppqIuO7y4ot6L9YyeLklXsScHcCV7xgNaa3okZz6CTEoi0p8m2BbAf3SIUGeESF+M+FCCdDT371Kru4anam4gbHbmfNxiM3LJtbVsuKWBktHfKaMaxRPvwB3vwJoJYM6EMGfCGLT4tHkSdLLDXFMGJynFTnL0OqU4iRu9+K01qIoFXdcZ6A5xZFcPh3f10n5seNoRMf/6T6uoq3Vjt1sW5HniQjQW3D777LNce+21JIf9PPHnh3hx5w5WNjVz+3U3AtnPqa3EhsmRPV/q6e3nR7/4A9v3HOLY0WP4fMPEolHc7jyWLl3JnXfey6tum5x/1qDIeDwWPHlWTMbJoxJFcPsKJ0nSEFAA7NN1fc00y+UBY5kVxgPUGW7j9KHPHwN+TbYX+A2AffTvCbJJrD6n6/qWSSuZAws1uNV1nSNHh3jy6VaefOrERU3NX1RoZ8PllVyxsYrLL63AvQCHOOq6zvEWHy+82M6WbV0cOTo4r4Xl7S4zVQ35VDbmU9WQbQG3n5Gx1pQJY08NYk8NYUsNYU37MasRTJkwip7K+QOtI5GRLSQNDuLGfGLGAqKmAiLmkvGgRs1onDw6xKGXujm4owf/DOsPLltayK03N3LzTfWUFOc+yRijqhojI3H6ByIMDkboH4jQ2xtg/4E2ojEVr9eLwWBAlgBJQpal8dvBYIK2thGisyh7M0YxyNQvK2LVxkpWXFaBw2XOBrSRwxRGj546yU6pDOz20bttkN5tg8QGpk7AZMpzk79hNQWXr8Z7yQoctRXYKkswOh2z3r+paKpKvHeQwIHjBA8cw7//GIH9xwkdbUPPzKwhQpIlvEs9lF1eROUN1TjLjORqU4oaCxl0LGXIsWxCoBscibFz00l2PN121uGRJSUO3nzfal7/2qUXLQGVCG6nd77vj67r7N7bx+/+cIBnnz1JRp268WXpujJe9aZVlNdmeycdiV4ahx/HnTx1Pte/L8KOz28nNnTGKAhJovnDb2XV5/4eo8POYpeJxmj98R858tUfE+vsnfS42WOi/tVV1L6qAmf5qdd7eqDrt9YRMRUx3h01KhpKMtgTIuCL4h+KERiO4R+OEhiOERiOTpmNeSqSLGFzmHC4zHgKbHiLHHiL7HiLHBRVuCgsdWLIcZJvTgcmBLKOZD9Kjj6MTAr8JyIM7OzDfzyI/0Ro2mPtmaIGG89WX8PR/Nw9nWaLgWtft5RrXt2MxWrElAlTGtpLYfQw9tTgvCT705AJWirx2RsZsTWMH0OjoSSbH2/hxUeOEYtM7lP58Aeaqap0UlDgoKmpSZRim4Ebb7yRnp4efvazn3HZZZcB8O//+m/8x+c/x313vJZvfebzE5Y32g04Su088sRTvPbedwGQ7/VSXFqKLMt0dXYSDAZH130b//W1H2A05u6ldzrM5OVZsNtPzcsVwe0rmCRJFmDs6PWIruuvPsvyEbLB6DZd1zfOYjtvB346evefyQa4BVMsrgEf13X9f2a6/tO2U3GWRUqAlwAOHDhAfX39bDcxZ7I9tCM8s6mdZ549SU/vuQe0ZqsBp8eKzWEinVJJxtMk4mkSsfR5pdiXZYmlSwq4cmMlV15RSX1d3kXr1U0mM+za3cfmrV1s2drF4NCclVweN1YTtaDUOWEol7fIPuF1S7qKI9mPK3EqiZFFnb6nWE1raCmNTFJFS6koFgNGq4Jizj0POiVbCVkqCVirGLHVEzMVjQ+F27elk72bOxiZYRKRuloPJcUOiorsFBTYiEbTDA1FGRyMMjAUZXg4Ni9zu3Jx5VlZsq6UpevKaFxVgsViyBnQpsJpul/sp/vFfgZ2+1Bz1RiUZTyrmsm7bCXeS1eSd9kqHA1VF+0zqiZTRI63EzxwnJHt+xh+YRfhozOrnGZyGSm5pJDq25soXuXCYJz8escC3QHnKhLGbGCiaTqthwbY/lQrB7Z3Tzts2W438rrXNPOGu5ZRVHRhA5NEIsHzzz8PwDXXXIPFsvAazS6mc31/+vsjbN7axYMPHaXtZGDaZeuWFXLbm1dTuyR7gm/KhKgd2URJeP/4MP9kTGbb53bQt3VyzVtHUw3rvvcZ8jesmfkLWyS0dJqePz9F67d+iX/nwZzL5DW5qLq+jKrryrCXTGx8yEhGwpZyQuYKgpZyQpYKMsr0Ge41TSeTVkknVdKpDOmUOn4BMBhkFKOCwShjtZmwOkzTDsOEU42szmTfeDBrmmJ4cXQ4w9CeQYYO+Bg+6CfUET7nnAStnloeq72JuHHya1YMMlfc2siNdy3D4bbgibVRHtpFfvT4+ND38fdE1YgPJ4kPJ4gNJbJJ/9Iauqqjqdnr7G0dXdMxWA1YPCbMoxeLx4zZY8LkyB0ExQ0efLYGfPYm/NY6EokM2544wXMPHyV8Wn6NseA2L8+O2VKI12ulIN921vdfmOj+++/ns5/9LG954718/R//bVJvuSRBR6CH3UcPcPMNV1NZUYaORFqxE9Mt/OLnP+Oj/++DqKrKP3zyM7zjHe+fdntGo4LTmc0z09fXSSaTQVEUqqqmzwXQ2trKypUrx+6K4HaxkySpEBirWv5bXdfvO8vyA0ARcFDX9ZXTLXvG8z4KfG30bhIwA38BPkM2UZWbbG3bL5GtdasDd+i6/uiMX0x2OzP+kPzwhz+koGCq+Hr+RCIqew/E2Lsvhm9k5nNmLTYj1U0F1C4poKgiW4g8e7FinqJVUdd1MmmNRDyNfyhKX3uAnnY/ve1++toDMx7uOsbtUmhqtNDUYKGm2ozBML8H+pGRDCfaErS2JWlrT5KZwfDT0xnNCnkFdmxOEzaHGZvDNH6xnnHf5sy2hp+ZVAOy85GyQWwX7kQ3zmRvzpZvgMDJMEP7RwicCGaz8vZnkxmpydxnDZIiYXaZcJTbcJTZcJbbcdc5yV/qGU9uAqf/KDfjt9agI9F1YoS9mzvYu6XzrOVkLhZJgqrGfJasLWPp+jLKa/NQ9Ax58ZPjc2jHAtpkMEXP5gE6N/UxuGc493BjqxllbTOGy5ahrGtGci3s3iPNH0Y72Ip6sBX1QCt67+TSKWeSZInidflU391MxXoPZzZW64DfWkeP+xJ8tsbxHqNoOMmuTSd58dHj0zZ8yDKsWGblyg1OiopynwQKC1M8rtHekT0etp1MMOI/+4iV6qYCbnnjCppWlyBJEoqaoCqwmYrgjvHjmKbBkb/0cfg7eycfq2QZ413XYXzjjUiml//nRT3WQfrhF1G3HMi+MTnkL/VQdX0pldeXYSuY3BChA3FjPlFTAUmDm8Rpl6TRTVo59+OWQY1jzoSwpv1YMn6saT+21DCO1CBGLffvQCap4Tsewrd/mOFDfnyHAySD5zgLzGZB8rqQPA5Ut5tNchO7kiU5F21YUcw977uUglIn7ngHtSPP4kl0jT+uZTSGD/oZ3OdjcO8IvsN+1NT5z12WjTL2EivF6wsovaSQonX54xmxx0SNBXR6rmDAuZJ0WmfHM208++BhAsOxScEtgKJIuF0KFsviG4Z/sXzpS1/iy1/+Mm9605v49te/gT7oR49PztSumGXsZQ4MplPnlNn5uA4+9Pf/yI/+9/tcctllPPzXZ4mEEqRmcO6aTAwRDkcJBuN0dg1SVmqcsnFieHiY97znPWN3RXC72EmSVAl0jt79ua7rbzvL8p1AJdCq63rDLLbzr8B/nPanh4E7dX1iO+FoRuXngLHszKv0WfzHL9TgVtN02k4m2b03yrGWxFS/lxPkFdqpXVpITXMBtUsKKa50T9tqqKgJTGoUTVJQZROqZEKXpx5Ko2k6IwMRuk/6OXGgn6N7+ggMz7w31GiUqKk2UVluprLCRFmpEZPp/A76yaTGyfYkJ9qyJ27+wOyGGheWOaluKqCqMZ/qpgJKqiZngDwboxrFlhrGlhrGlezBnejGmvblHDKlZnRGjgUZ2jvM0EE/vkN+UuHZD8+diq3YSv4yD8Vr8ym9rAh7cba3ICVbGbY3M+RYht9ai6ZLnDw6xN7NHezf2nVB6gfmYrEZKa3yUFLlpqTSTUmVh9IqNzanGVMmRH60hYLYcTzx9vGT6mQwRfcL/XQ918fAHt+kuX0AUoEH5bJlKJcuQ1lRh2RcvEPENF8Qdf8J1B2HUXcfheT0nxfFJFN6XQW1dzdT0mBGkSe+PwnFSZ9rLX2uteOJqDRV48CObp576CidLb5cqx3X1Gjhyg0OqirN0y4nzD9d18lkIBpTiUQ0olGVSFQjEs3e7+tP0duXnlEpFaNJYd3VNWy8tYGK0URzkp6hPPgS1f7NE4KgwR6Nl/7lRcIdk0cPybVlmD70BpS68kmPvdxpQwEyj20l/fg2iEzReCiBq9pBwfK87GVVPq7ys9ckVyUjadmCJhvRJCOqZECTjaiSEU3KHt9kPTN6UTFoCYxqDKMam9TbmUt4IMHwPh++QyMMHwoQPBmeUdb1cbKEVFaIXFOKXFWCXJaPVFKAXJqPNFrbdXg4zR8e9DMwOPkYZnWYeM3b1nLp9bU4UoPUjTyNN9Y6/jsa7IjQ9tcu2p/oJhmY11Qr2ZdjkChYnkfJpYWUXFqIt+lUToq4wU2XZyN9zrVkNJndL7RTYQpTWmSbENyOsVhk3C4FRRG9uKdbtWoVXV1dPPzww1x11VXk5eVNuWxlaRl7Hnp80t/NeWasBRZOn8b/je89wN9/8t9Ytnw5W3fvBbK5SaLBJNFIcsrPdTIxhN8fpbMrzDe+cwyzWaKywoTLqWC3Kzjs8vh1Munnk/8wXihGBLeL3QXsuf0E8J+n/WmJruvHplj298A9o3dX6bp+YBbbWVDDkgcHozzyWAsPP3KcgYGzDyHNL3aw+opKVm+soqx28hBgcyaIO96JPTWIORPGrIYxZ8KYMiEMOSozaUjZH07ZRNLgImoqImIqImouJmIqmjBsStd1hnrDHNvbx9E9fbQdHpxxGn0ARZZoaPCyYnkRK5YXUlRox2434XSYsNuN2GxGFEVG13WCwSTdPaHRS5junhBdXSFaTvhmNTw2v9jBskvKaFpdSnVjPjbn5BN0RU1g1OIY1DhGLY5RjWPQ4hjVBAYtNnodx6jGsKVHpmz5BkgndHyHAvTv7Gf4oJ+RY0G0C5gh01XloPTyQiquKaFwRfaENSXbGLY3M+hYRsBag6rBiQMD7NvSyf5tXSRmMRdWMci4vVbc+TY8+TY8BTZceVYkWULXdHRdR9fJ3iZ7bTDKFFdkg1mX1zr+mbWkR3AkB3Am+/HGTuBI9Y+f2IwlhOrdOojvcCDnj5OtuoyKN7yK8rtuwb26edElOZuJTCzO4JNb6X3oafr/+hzp4PRzZ412A81vXU/da6qx2SYeTzQkfLYmet3r8duyxzVd12k/NsxzDx3l0Evd0wZFq1YU8ZY3r2Ljhop5GXp3MYYl67pOOJyibyBCX2+YYChJNJoiFksTH235l0dLiygGCZfTjMtlxu0y4/Vayc+3YbebsFoMZDIakUiKcCRFOJwkEkmduh9JEYlk/xYOZ9efTKmkprhkVA1N09E1UDUNXWdWGdKnU1Di4IpbG7nk+jpsDtPYG0Fx5AA1I5uwZoLjy6qSnQM/aeXoA7smrUcyGljyqffR9PF3IE8xz+2VIhOL0//Ic3T//jEGntiMlpr+mGp2m8hf5qFgRR7uOjfOcifWQhPGeejxS0U0In0x/MdGCLSGCLSFCbSGpkz2NBV7fSXey1Zlp3dcsgLX8gYU69Tf0U3Pt/O5L75APD55Oysuq+Du916C26VQO7KJ8uAO5NFh773bBznyyxMMHZiceXuMuSgf96pm3KuacDRUo9gsyCYjssmUzTiv6ySTSQ4dOIgeilBl85DqGyLeM0Csq4949wBaYvoGXovXTMNrqmh8fQ1md/Z7klQcdLsvp9e9HlPAhx5PYbWYMJsLJz1fliUKCmzkeSwvy9+mc1FXV0dHRwdPP/001113Hddccw2dnZ10dXVRVFQ0Yd5rcVERP/7CV8lEJneoSBLYK9yYbBKgc987PsDv/vQX3v6W+/ju//4vadkynvRR105VnjjzfPXM4HY6qZSfQ3v/deyuCG4Xuws45/Z9wPdG757Udb1ummXfA/xg9O67dV3/8Uy3M4P9mPeEUpmMxuYtHfzp/w6zeUvnWU9a8grtrL6ikjVXVFN+xpxWS3oET7wDT6ITd7wTSyYwZ0kXdCClOIiYSwhYqxmx1hM1F48/nk6ptB0e5PCuHg7v7J1xIqPp2G1GJEkiEj23llpJlqhpLmDZ+jKWXVJOUblrwvulqInTEmd040r0Thusnk0yIjF0YIS+rd0MHRgh1BFhhuX9cDRU42ysxtlQjaO+EktxAUaXA5PbgcFhR0unUeNJ1HiCdDBMtLOPaEcv0fZugodbibR2Trt+a4GZiqtLqby2hMKVXiRZIqXYGLYvYdC+jAGpnD2bO9n5zElGhiJEw6kJ8zFlWcLhtpBXaKO40kN1Uz61zQUUlDqRz+zt1nXGXvjp9Q11XUPR0tjT2SFxjtQA9uQA9tTghMYWNa0xsHs4m+F46yDRKZKUWIoLqHrjbVS/6Q4KNqx5RZ00qKkUA89so+tPT9L9pydyZnA9XcGaYtZ9/GryKlNI+sQf9bCphM68KxiyLxs/CRjqC/PCX47x0rNt0zZaNdR7edtb1nLLTQ1zWhd7PhJKBYNxDhwc5NCRQVrbfPT1RfCNxAiHkyQSmQs2j/xiszlNNK0q4dLr62hcVTLeOCFrKUrC+ykP7sCePtWDr8tGhk46eeGDvyMdnRwE5K1ZyoYHvkTeqiUX7DUsFqlAiK4/P0nHb/7KwNNb0dWZNwAb7QZsxVbsJVbsxTZMTiMGi4JillHMCgarEYPNiGyWQQc9raEmMmSSGVLhNEl/ioQ/STKYItofJ9IbJROffSJFo8tB/uWrKdiwmvwNa8i/bCWWgpmVkdM0ne/+7w5+9JPJDSIGk8Jr376Wjbc0kB9roWn4MSyjjSn9O4c48NMWfIdyB7UFV6yl8u5bqbzzJhx1lWfdj+mOJ7qukxz2EzzUQs/Dz9L15yeJnswdqxgsCnV3VLLkjXXYirLrSMsWjjruQDO6MRuNmM2FE6dw6TqKnv0Ns1oMlJQ4MeZI5LUoGG1I0tw0upyeLfm6664D4DOf+Qz3338/b3/72/npT386YXld10kO+oj1DExq4I4nEnQP9/OLv/6Jb//gJ5SWFLHlqQeprqpARyal2EkrNvRTNYVIJTPEo2mS8TSpZEYEt690Fyhb8u3AI6N3X9B1/Zpplr0VeGz07qd0Xf/STLczg/2Yt+C2tzfEgw8d4f/+cnTa8guQHTK2+soqLr+xnprmggkn8c5EL0WRgxRGjpw1SZGOjCbZSMcVYiMZ4kPx7F8VkBSQRy+SDCa7hM0LimHqnsak4sBvrcNvq2XEWkfakM0wq+s6/V1BDu/s4ciuXjqOD89oaNxccHosNK0qoXltKUvWlE7onVXUBN54G3mxVtyJbmzp4VkH/5pmQM0opKIaoc4Iw3v78B0cINAWJjEy8+G9zsYaim+4nJIbN1J03eVYCs+v5mxyJMDIzoP4duxn4NntDL24a8oeA0uemYqri6m4ppSh/DIe2uXm8X1OYsnZ/2iZjFCUB4VulSJnCq8tSSIlEY7LhBMK4bhMJJG9hOMKaVXCYtSwmTWsJh2rScMsqRhSSZRkElMkgrOnh4LgAIWxYUzaxNdgdDupvPsWat70aoquuwzZsHiHHM8VNZmi+/+eovUHv6f/qekTx1sKbKz/55spX29B1ib2/MaMXro8G+l3rkaXsidf0VCSFx89zuZHj+fMEjrG47Zw26uauPO1S2lsyD/v1zTb4FbXdXy+GEePD9PS4qO9I0Bvb5D+gSj+QJx4fGbDc1+ODCaFuiWFNK4qpmlVCaU1eRN6283pAOXBlygN78WoTcx2nKCGzZ96iqFtk0/2JEVh+b/8Hcv/5e9QTBcnq/Zikhj00fmHx+h7/EWGt+whOTx1b+TFIikKnpVN4xnk8y9fjau59pzKN4XDSf7l35/kxS2TG15Lqtz8zd9fQUWFlcahRymOHEACgu1hdn/zEAO7J0+PsFWU0PiBN1P79juxlRVPenw6sy1VFzx4nK4Hn6L7wafx7z40aRnZIFF9YzlL3lSPu9pBq/U6UuYiFKuHspoGouFsPXpN01G0GI1D35jV/i5U0hWfRTLNTSWB2Qa3Y9REkmh7D+lIjJV33Ejf4OD4Y4qi8O43v5l/+7ePUV4y+XcoLVtJK3bUM2oda6pOz8lWAsE4HZ0hvvq1yf/npxPB7cvQBapzWw20j97douv6ldMse3og/A+6rn91ptuZwX6MB7e/+NUmLlm/DKfDjMNhwm43zWp+pq7rdHYF2be/n8eeaGH7jq6znmyV1XjYcHMDa6+qnlB7zpryURw5SFHkILb0SM7n6rKZeMzF8JEww3v7GdrdTuBwz6yzG1q8Ztw1Dty1TvKa8slrzsNVYUY+o+FRByKmEnz2Rny2RsKWU3OuIsEELQf66Tjmo/3YML3t/jkbVqcYZGqXFNK8poTmNaWUVnsmBP/WlI/82HHyoy24E11Tzj9KJYyEetJE+xPEfTHigxFivQFigxFSoRTJYIp0JDO7eUinsVWUUHzjRopv2EDx9Zdjryw9p/XMVCYaY2DTDnoffZ7uPz9JvHdwwuM9jlJeqLiCbtfZRuZfXJ5EgKLkCA3VbtbetJKr33IdRaWei71bC1bkZBetP/4jbT/+46T/89NJisyqv7+JhtdWYtT6JzyWVJx0eS6n17UeTR4dghdPs+OZNp57+OhZ59ovW1rIna9dyq03N+LMMfR/Kum0SjicJBxJ0dcX4LHHNhMMa1RU1KBpEqlUhmgsTTCYGB/qGwwliEbTM66P/HKlGOQJSQOdbgsur5XapYXUNhdgzFFT2x3voCK4g/zosfFhoGM0WwVH/jDIgf/8c87tuZc3svGBL+Fdv2JeXs/Lna7rhI+fZGjLHoY372Zo825CM8yUPlckRcHVXIt7ZRP5l6wg//LVeNcvx2A7/1ES3T1BPvzRR2jvCEx67JLra7n7PZeQr/ezdODPWDMBMgmVQz9r4djv2yYlBiy8aj3NH3kbFXfedM6NmeczEiTa2UvXn5/k+Ld+SeREx8QHJSi/opjCz/495BVjtLioriolaXChahJBX4xE0C+C2xzONbiF7PcnMeDjDW++j+GREcLRCB29PURjMcqKivn4u9/HB//+7zDZJdAmN/KrkpG0Yicjm8d7c3tOtpFIpUki0ae5aT00yHBfmHAwQSSQmHAdDg6J4PblRpKkLwCfGr27Qdf17VMs909ka9MC3Krr+hOz3E4HUAUM6LqeO7VedrkPAWNHjjfruv7r2WznLPswHtwuX/M5TKaJE94NBhm320J1lZvGhgIqK1xUVLipKHfh9do43jLM/gP97D8wwMGDAwSCiVybmcBsMbD26mouv7GeinrveKBmUOMUh/dTEt4/YT7iqZ01kDGW4D+Zpv2vRzn5+x1o6dnNo5kpxaJQuNJL8bp8Si8vwVM7OZNjUrEzYmvAZ2tixFY3fpIMkExk6Drho+PYMO3Hh+nvDBKPpkhO07uiGGS8RXbyS5wUlDgoKMmW36lZUjgh+7OspfDEO8iLt5EfO5Ez+Nc0hcgQDO330ft8K8OHRkj65zZJhbkgLxvIjl6cDdUXbdisrmkMbdlN5+8fY8eD23naspSTnpqLsi/nS5Jg7Zoybrqxnhuvr6OwYGFnP75YtEyGvsde4MQPfkfvXzahT5OVruHNV7DiXcuwKH0T/p6WrXS7L6XHfRkZJXsymEmr7NncwaYHjzDQPf1IEbNZ4cbr61mzuoRIND0akCYJh7PzUIPBBCP+OKFwkng8gzpNrVVhapKcHd1jsWYzudvdZlx5VvIKbBRXuimt8lBU7sKkqHji7XhjbXjjrbkbRl01DLTa2fKBH+TsWZRkmaWffA8rP/MhFLPorZ1LSZ+f4OFWoh09o1NORq9H72vJ2f9GGRw2zIVe7FVlOOorcdRV4qivwr20HteSunn5P9x/oJ+P/sOj+P0Tp5PIisRr37GOK2+tpyawmWr/88jo9O0YZOfXDk6afpJ/+WpWf/6jlNw44xltU5qLaQ5aJkPn7x/j0Oe/R/BQy4THKr7+SVyr67EX5dNQ4RnN3OskrdhIhQJ4D37hvF/DQrBQgtsxajJFrLOPVDCMqqr89pGH+JevfYVwNMLnPvpJPviud+OoKkQxZEDNff6tSkYysoX2rgHiqQxJJEL2InpO+hkZjBDyJ/D1h/ENRAmOxIiGkgRHBti385/HViGC25cDSZIuA8YC2u/ruv53OZYZy168FAgARbqeI4PR9Nv5GvDR0btX6rqec7ydJEnPAteN3p3TD9nZgtu5VNmQz4ab61lzRRVm66khE85EN2WhXRRFDucoJyORNpTSsyPM0R9vJ3Do5Lzt33TMbhNFa/MpWV9A2cZirPkTe2s0FIKWCoKWSkLWSoLmClRlcvIJTdNJJTIkYinisWzdXTWjkVdon7LsDrqOI9k3Oty4DXeiG5nJvTixgEzPi310PtHK8KHcSYnOh9HtpOiaS8aDWc+KpnMayjVfAsEE//ONLTz0l6NTLiPJEs2rS1i5oZL8Ygd5hXbcXivRUJKhvjDDfWEGe0L0dQbo7wxOqPN3MUgSrFldys031nPj9fUUFopAN5fwiQ6OfPVHtP30z9OeINfcdRlrPrQeC12cPlk8Ixnpc62jy7PhVIZlTefIrh6ee/gYbYen7iFeqCT5VIKR818ZuDwWTBYjikFGlrJ1thOxNMlEBvUCJpGbiqJARaFOU0mcppI4zWVJ1tbGsZr07AvIX47fV8Lez/6GgWdztlfjXb+cy77/WdFbexHomoYaT5CJJUav46jxJDF/gB0vZk+NLr3yCmwuB7LZhMnjwlzoxTBNkqf58OTTJ/j3+58mmZz4G2x3mXnbJ66iudnBsoE/4Y2fJJNQ2fu9I5z4v4m9oe4VTaz+wkcpf/X1c9YgPJdz+HVNo/uhZzj0+e8yMlrjuPzrn8TeVI2tMJ/6EhcmV7bRQJVMJHQF2y4R3J5pLoJbyPbipgNhop29aOkMf3z8r7zvX/8Rh93O0cefw2I2Y3TYsJZ6MZhUyEweeaSq0NITIBSM0tkb4yv/fQx1mlKSF3NYspiINU90Xd8hSdILZIcmv1uSpAd0Xd96xmIfJxvYAnz9zMBWkqR3AD8ZvXu/ruufybGp/wHeD1iAb0iSdK2u6xMmp0qS9BZOBbaPXMgP2Fyw2o2su6aGy2+sp6zmVOCsaEmKwwcoDe3GmRqY9DzdUoC/187B722l98m/zGqbkixjr63AtaQOZ0MViiX3sMF0KEK4tYvIiQ6i7T3T9vwkgym6NvXRtakPOICnwUXZhiLKNhSRv9SDLKvkJTrIS3RAIHvaHDUVErJUErRUZjMxy1YyigXZZsZiM+KZ9KI1LGk/lrQfayaAJe3Hlh7BHe/AlCMJlKZJ+NtStD92gp4X+4hNkZRoxiQJY54LS34e5oI8LEVe3CsayVuzlLy1y3DUViyoYHaMrus8+ngL//U/mye1pI9x59vYeHM9l1xfhyffhi01lH2vM12YQ2HSioV4rZdEYx5xYzXa6HyVcCDOYE+YkD9GcCROaCROLJLCbDVgsZmw2oxY7SYs9uy11WbEYFRIJTOkEhmSiex1NplDhmQ8zXBfmN6OAIPdobMOXdd12LO3jz17+/jPr73I6lWl3HFbE6++vRlzjmGYr1TOhmou+95nWfmZD3HsGz+n5Tu/Ih2cXMal/U87aP/TDqrvvoK1H74Ui34SdBWDnqYyuJ3y4E76nSvp8lxB3JTP8ksrWH5pBcN9YV56to2XNp1cMPWTFYOM0aRgMMrIskQmrU2YM3wuQa3ZYqCw3EXRGZeCEgeGaRLE6LpOOJCgs8VHd9sIA11BfAMRQv448Wh6QuK2+aKq0NEv0dFv40myWe8NBlhVb6BZCuF5/s/Y2o/nzEFgcNhY/fmP0vjBv0FWFmkinEVOkmUMdhsG+8SyQdZ4HCU8BGSTLM1F4rVz9bNf7OF/vnnmqSCUVrl516eupdIVYnn3D7FmgviOBtj2hb2Eu06d0hmcdlZ99sM0/b+3LOhcCpIsU3nnTVS87kb6n9zMoc9/79SDOkT64hiDKWxFVhRzCpuuk1r7cVKKnXg0RdAXJ1fnm81mpLTEgbKQv2PGs5etutAkScKU58LgshPvHeTWq68DIBKN0trZzvLGZtKRGOmWGAabBWtpPooZIuE4kYRMLCWTzkAyJRNNyESj+rSB7cW2cL8ZLw8fATYDVuCJ0aHKz47evw947+hyx4H/OpcN6LreKUnSvwNfAdYDOyRJ+grZHmE3cBcw1msc4lQv76KQV2hj2SXlNK4swZOfPWA4kv3ZXtrwQQz6GT0sspGUqYG2R3s49PU/5Tw5PZNsNlF8wwaKrlqfDWaba3E2VM96KJKaShHt6CVyooPQ8XZ82/cz+NyOKef0BU6ECJwIcfgXJzC7TZReVkjJJQUUrsrHXmJFAhypIRypIcpCuyc8V0Mmo1jIyNZsXT/JgCUTxJwJTVuvT9ch5oPebf10P9vO8MGZF3eXFAXPqmbyL1+Fo64Sc74Hc0Ee5nwPusPGlv17wG7l1ttedVFPHmaruyfIF778PNu2d+V83OmxcMPrl7HxlgbsRCiO7Kakcz+29NS1TnUkEgYPUVMhUVMRsap8UrV2UoqXlMFBRragaEkMWhKDlshe1BgGbQSDlkDWVVTJgCqbxstOqbIRVbKiyiYShiVospF0SmWgO0jvST+9HQF62wP0tI1MzEJ5+n7psHdfH3v39fG9/93Bm+5dxRvuXjGrOZ8vd9aSQtZ84WMs/6f3cuJ/f8vR//5pzu9wxx+30PHHLVS/4VrWfuRyLGoLaClkVMrCeykN72PIvpSOvCsJG4tJp1Xc+TbqlhbScmBgTusmS1K2l1WSJGRZQpIljEYFk0XBaDZgNhswW40YzQqyrhMNJxjsDROLZs4raDRbDVTUeals8FJZn09FvRdvkX3mPUmaijERwqyGsBChXI6xojGJoT6BrKVIKw4ShjJSBgcx7HR0q7S2hultDzLcH8kOfwtnp2po85TFOZOB3ccy7MYGRbfhKr6RmpFW6oZOUBPswKCrVN51C+u//i/YKqacGSS8wmmazte/uYWf/2rfpMea15Ty1o9dSbV6mKaev6LoGY78ppX9Pzw2oUZ59X13sPa//nHWiaIuJkmSKL3lKkpvuYqDW3eQjJ7qEUzHVEIdESx5Ziz5ZsyyhoEkkseD0eFmZCBKKjnxtyycgHh3mopyKzbbK3PI//n01MuKgr2ylPhpbQPqGZ0ysYTKSG+EhGxGX6Rh4uLc60VC1/U9kiTdC/wCcAG5xlwcB+7Qdf3sUdjU2/lPSZK8wD8Cy4Cf5lhsELhT1/WWHI/NmeJyJ5m0mXg0TWYOhpn5h2JsfrSFzY+2IElQWwaX1gRYXxfHWKVSmB39h27Ow9/n4cB3t9H31INnXa+lpJDyV19H+auvo/jGjRgd5z9UUzGZcDXW4Gqsoey2a7P7petET3Yz+PxL2csLuyYnWyDbq9v+ZA/tT/Zk9y/fTOGKPApWeilc4cXT4EI+rbi5jIZJjWFSp09ao+sSiRAM7h2me1MHA7uHSYVmNvLdWlpIwca14xkhveuXT2oRHxOPx5Haj89ovQtFOqPyy1/t4/s/3EkyOTkYNFsN3HjXcq68rYl8BqkZ/gP5sYm9NmpKJT6UIOFPYbApWAssmF0mJHSsGT/WjJ+C2Ny/LxoScWM+EXMx0bxiIsVFRK5tJGVwkU6pHN/Xx76tXRx6qZtkjpqJAL6RON/67nZ+8sBu7r5rOX9z32oxZPk0RpeDpZ94N00ffivtP/8/Dn7uu0TbeyYt1/H75+j4/XPU3HcTy9+5AoelE0lLEk3A/v0dPHfEx4vHnITPv+rXBJIEDo+FvDwLeV4juqqRyahkUirJpEo4lCbsT5Geo9Z1g0mhvMZDZX1+NphtyM+WuDpL/V6DlsKeHsaW6MOW9mHOhDBnQlgyIYxqZFKSpulsVIAm0JsgbvAQMZUQNRURN3pJKVYiaTP+kI7PrxKLqaRTGsmkRiyWYXgghn8oRtAXJRxIEoskzykzdEg3sT9vKfvzlmJVNK5dk8/y91+PtXzxBBzChZXOqHz2c8/yyKOTfws23NzA69+9jsbAM1QGt5GOpNny5X30vHhqNJq1tJDLf/T58fOKxcpckIfsdiBrOlKS8Rrv8ZEkqXAae4kVgw3sqWESBjdF5S6CI3HCgYkjXTIZjfaOACXFDvLyrBctT8fFMtZ5EI+f+wigB/+azS9rs9poamgkIykkTFYSBivqeRTHVAwydqcZl9eC2Wrl0N5zXtV5EcHtPNN1/WFJklaR7cW9A6gAUsAJ4PfAt3Rdnz5Cmdl2PiVJ0kNkhyhfDZQCCbLB80PAN3VdD06zijkx0BPGZJqfj5WuQ1sPtPV4+O1mDwAFbokaaxz7gf0U+bopiQ4wVR+UtbyYunfeReWdN5G3dtkFGR4rSVI2OUVdJXXvuAuAWHc/fY+/QO9jL9D/5JacvcsJX5Ku5/rpei6bodVgUXDVOjG7TZjdRswu0+htM9ZiBwabkWhfhHB7gOhAnGh/9pIYScw487OkKBRetZ6yO66l7PZrcS9reNn+aBw8NMB/fGETLSdy976uuKyCO9+9ngp7gBr/H8iPtSABmXiG3u1DdD/fR/+uUw0Fis2CmkyDqqJYFGyFFpyVdpwVDlyVdhxlNixeM5Y883hxe4BMUiUdSZOOZkhF0qQjaVKRDFpKRTbKyEYFxSSPXhRkk4zBomTrOjKMPT0MnErHn5KthCwVVDQvZdW6tSTUy84a6EZjaX72i738+rf7efXtzbztLWuprvLM5du9qCkmE/XvfgM1b30dbT/+Iwc/913iPZOnQbT/5in2/mkH7cUN9C1bw7GQk8w8JifWdQj7E4T9CTrnOIGsLEuUVLmpbMinsj4byJZUulEMUx8zZT2Trcuc6MGeGsKWyn4+jWp0zuqIj5EAWyaALROAWI758Y7Ry2n0ldka5Amjh6ghn6ihiM6gixf2Smx9ph1ff2Tyes4irso8tsvPY+/5E2XlLl5zezN33NZERbn7XF6W8DIUj6f5x39+PGepnzvespobXlPPssE/Uhg7hr81xOZP7yLSc+qUsPpNr+aSb/0bZq/nAu71/JEkCcVsxN1QRbSzj3Qo+71T0xqhrihWrxlrgRlrxk9atiF5XVhsBkYGopOS6fUPRIjHM5SWOpAX4HSn+VJfXw/Ajh07iEaj2O2TG6U/8YlPcP3113PTTTdhNp86K04kEvz85z/nIx/5CADveOd7SBRW5WzgnwlvsYOrX1VPY52JivwUxaYAjvQgjlQXvp52/vDjc1rteRMJpYTzdiETSs1EnhbFE/XhiY3gSQapW17B+jfdyJo33YjFtrCGX2qZDL7t++h97AX6Hn2ekV3T1w2ba5biAspuu4ay26+h5OYrMXlc57SeuUxEMZ8ikRTf/t52fveHAzl7bdxeK69/zyWsXeeh3vfUeF3BkeNBjv2+je4X+lGT2R9YR0M1Fa+7kfI7rqXwqvXoqoZ/7xF8O/aPXg4QbmmftA3ZIGGwGcjEVbRzHN0gyeAos+Ouc+KuceKpc+KudeIot4/38GvI+G11DNmXMmxvJq6aOLa3jy2PtXB8f//U65bglpsa+NAHN1JW6jyn/Xs5UxNJWr7/Gw5/4ftEh/z0OMpod1fTVtTEsOHcvj8Xk8VmpLTKQ2mNh7JqD6XV2etcZXHG6Tr2jB9nvBNXohtXshdbanBWvbDTkhQw2MBoHb22gWIBxQSyAWQjyEYk2UgmrRHvHSbhC5EaCZIOREgFI6ipNGoyTXAkAKk0ZklCTybQ06lsvXJDttGo+PJS9A1X8GxPPU8/H+Hgju7znt+7enUpb3rjSq6/rhajYQHPDXyFuFi/T5FIig9/7BH27puYYV2WJd74gcu54qp8Vvb9Fmeqn45netnxlX3jvy9Gj4vLvnc/1ffefkH2Feb/fWppaSGTyWAwGGhsbETXdVL+ELGuvglVKwwWBXupDcUko0kG4gYPGV3BNxAlGZ888sxsNlBR4cI8Tx0rF1OuhFLhcJi6ujqGh4fJy8ujubkZs9lMSUkJv/nNbwBYs2YN+/btw2g0Ul9fj8fjIRaL0dLSMt7je/sdr+fzn/8GplnU304mhvD7o/iCKQKagZtqTmLa/hzxDh+ZeIZMXCUTz6BmJAbiGe54aLwAjMiWLCwuMwlujWYFi9WIyWLAbMnO/zJbjQx0BfEPTT1er7wuD7PFQOeJETLnWaNRksDtseJ2mXG7LXhGL263BbfLjMNhxmYzYrcbsdlMOEav7bbstc1mPOsQvPOV9PkZ2ryboRd2MvjCLkZ2HULPzF2pInO+h6LrLqf4+uzFtbR+TnpnF0Nw++xzbXz5P19gMMfnTZLgilc1ctt9q6jL7KN25FmMWpK+l4Y4+ptWBnZne3hls4mqe26l4b33Unj1JWd971L+IL6dBwnsO0q0s4/wyS4GDregh6I4CryY89wYPU5MHhcmjxOj24nJ40QyGkn5g6T82RP21EiAlD9EciRIcthPOpC7vIxikvE2uym/uoTKa0qxF2f/H7KBbi1D9mUMOpbTeTLMMw8e5sD27imTBpnNCm998xre8bZ12GzGnMu8EnV1B9m6rYsXX2zjpZ09JNMz/w1VDPKUgZMkS5jMCiazAZPZgNGsYDQZMJmzCZ+ioRTBkRiRYOKchtM6PRa8RQ68RXYKy5yU1eRRWu2Z0RxZsxrFlejOBrPJXhzJPgyzS+x/iskJ5jyw5IHZg2T2jN/G5MwGs4ppTo9Lekbl6hWrUYf8RDt6CR8/SfBQK5GTXYRbu7DYVepur6Tk9Ws46b6UR3Y52fTo1Im/XHlWQlMknjtdfoGNN9y1gntevwyvd+ElmXmluBi/T8Fggg9+5GEOHxma8HejSeFtn7iK9SuMrOr9FRY1xIEfH+PQz0+ML5N/2Squ/O1/46i5sPXVL3RwO0bLqMR7B0gMniq7JUlgK7Ji9ozWEFdcpBR7zmHKkG0wKC9zvezyR+QKbgH27dvHpz/9abZu3YrP50NVVaqrq2lvbwfgiSee4JFHHmHLli10d3fj8/kwGo2UlpazfMUaXvfaN7Jh4zWz2hdJkkinh8lkEphSPuRffIcTD3UQ92mYC/NwVJfjbKrGs2oJzoYqBvQU626/eezpIrgVFpfTg9tP/ufDlNVUYXOasTvN2J0mbA7zlMPZMmmV7U+38dQfDk5ZMqW4QOFd764mZi3h2LEIrYcG6DoxQiJ2jidX58FiMWCzjwW8RpxOM0WFdooL7RQWOSguslNUaKeoyIE3z5q7LM8sZKIxhrfvY+iFXfj3HCbWO0i8d5BE/zC6OnWwLxuNWEoKsJYWYqsqpfCq9RRff/m8ld5ZyMHtwECEr/zXCzz7XO4SUKVVbu75u8tYUZWkaeivOFP9DO7zse9/j+I7HACyc56a//7t1L/7Hsz55z4yYa7ep1jPAL6XDuDbsZ+Rlw7ge+lAzuHt3iVuKq8tpfKaUhxl2ZPrlGylx30pPe5L6R9U2fTQUXZuaptyjnxBgY0PfWADd9zWPO+NOwuRzxdj7/4+duzsYcvWTnp6pq9bezrFIFPdlE8ykaGnbXI91LFlrr6jmZvuWobFfvYWdDWjEQ4mCPpihPxxgiNxoqEkikHGYJQxGBVMJgW724LDZcbhtuBwWybUuZ6OQU/hTA7girXjTPbgSvZiUmc7YVgCaz7YSsBegmQvBnsJWIuQlMkNJZqqkhgYJjnsH23MCZIca9QZCZL0BUgHw6iJJGo8iRpPjN/OxBNoiSRaOoOu6eiqiq5p6KqGrmlomQwzHR8uKRJlG4uofU0d0Stv5btPeNn8eGvOxgSn28TSdeWcPDbMUO/0KTMMRpmbbmzgb+5dyfJlYm7uhXahf59GRmK8/0MPT5r2YnWYePenrmV1VZQV/b9DikXZ9sW9dD9/aiTNko+9k9Vf/BjKLHrT5srFCm7HpMMRIid70FKnzu1MDgP2EhuSIpGRLSQMHmLRNCND0ZwNswUFdgoLbC/bKVXnIpNR8Y3E8fvjZ62ukIskSbjyLHjsGt2dncSHfIQOtNH9oS9P+zwfaT7M+HmXCG6FxeX04PY32w9QWFp+1ueY0mEc6QEMagKfvZF4WmHzo8d55sEjxE8rRTFGluFdN4zw5lshYq8mYCqj1e/hWIdO5wk/XSd89LYHLki5iJkyGhWqqj001ntprM+nvt5LfZ2X0pKzJ2E5G01VSQ6NEB8NdtOhCOZCL9bSQqylhZi8ngt6cF+Iwa2qavz+T4f41ne2EcvREGIwKdzyhhXceHs1jaFNlIb2EGwNsu8HR+nbnm1tdy2pY+k/vJuav3ntrLNn5zJf75OuaYRPdODbsZ/BTTvo+tOTpPwTp9h7GlxUXVdK3W2VWLxmVMlIn3MNXZ4NDEXMvPjXY2x5/MSUjUbLlhbyiY9exZrVpXOyzwuRpumcbPezd18fe/b1sXd/P72zCGYhW6ty6boylq0rBn8ff/htF7FY7uPS8kvKufvNdWwwbSc/dpyoqYiwuZSwuYywuZSoqTA7Bn2eWLQojuQAjngnjmQ/juQAZjU0yzmyUjZwdVUjuarBUQ62IiTl1PdF1zQiJ7uJtHYS7ewj2tFDtKOX2OjtWPfAnI5QmQveZjer/+Vqtubdzo8e6Kevc3LKClmGd9xto2h5I89vi7F3cwex8NQ1kgGWLy/mXW9fy7VX174iG4suhgv5+zQ4GOHvPvQQ7e2BCX93eiy899+vZ6W3n6WDfyY1HOH5T72EvyV7fDG6HGx44MtU3nnTvO3b2Vzs4BayU7WiHb2k/KeOu7JBwl5iw2g3jA5TziOVkRjuj5BJT264cjhMlJe5zrtzYbFTVY2h4Rh+f+6ySmMURZ40n3mM2WKgsMCAOeYnORKnOxonNuwjeryDno98Zdrti+BWWNRyBre6jpE0Ri2JSY1iSQ5iT/bhSA5gTw1OqLmqSkaG7Evpd62iTyvnuYeP8vxfjpHKUc6kxhLkgzX7aGpSyGtyYypyEjGXEjKXMaKUcmzAQc8gDPeH8fVHGO6P4BsIEwnOXdmN82WxGGhoyGfd6lLWrill9epSPO4LW0R+ri204PZ4yzCf++ImDh7KXYapaVUJd733ElbYWqnzPY024mf/D49x8tEudA1cS+tZdf+HqLz71jnt6b5Q75OaStH/xGbaf/0Xev7vGTKnlV+QjTI1t5Sz5I11uKocaMgMOZbR6dnIYCqPJ353gK2Pn5iyhfeWmxr4yIc2Ulqy+OfjBoIJDh0e5NDhAfYdHODAgX4iZwlOcqmo89K8poSVq92sL/ehDLTw7V8m2Hw49xC5ogoXr3/bCm6tbqE8uANFz92zqEoGIqZiIuZiksY8kqZ8kkYPScVBUrKinSXwldAx6ilMWhxr2o8lOYg1OYQt7cOeGsConcNx0eQ8Fcg6q8FZiWTIvk5d10n0DxE42ELgwHGCB48TONhC8NAJ1NjCqO07JUnC2ViNZ1UzeauXYK8p59jXH6CgNkbR/3stP9q9hEf/2JJzeswVhT7+4d4o8WUbefxoEc893kX7seFpN1dZ6eYdb13LHbc1YzKJebnz6UIdd3v7wrzvg/83aXSHO9/G3336elbZW2gafpRQe4jn/uml8bryruZarn7w27iX1M/Lfs3UQghuIXscSfkCRDv70E8rU2MtMGPNt4yX2ktLZvxD0Qm1uceYTAqVFe5XZC13Xdfx++MMDUdRpymRZrYYyKTVnMtIkoTbayHfEifV5ycVzjZ6D5AWwa3wynB6cHvkT/9Abb4RgxY/pwyZcYObAecqDsSbeeC7B2k/OvkEwYjKDW3PsGL4MNY8M3lNLrxNbvKa3OQ1urGUOEgY8ogbs5eEMY+RtItuv5lARCIUhVg4RSySIhZJEh29nYylSSbSJOMZEvE0yfjclDOaieoaD+tWl7F2TSnr15UtusBhoQS3kUiK7//wJX7zu/05D9h2l5nXvWMdV11mocn3GK5oBy1/7uDgA8dJRzI4G2tY8ekPUn3fHcjzUCT+YrxPmVicnr88S8evH6H3r8+dGvIlQfkVxSy5r47CFV50wGdrpC3/RtoGTDz8wG6O7c2deMpqNfD+917GfW9chWGaDLoLSSyW5sixIQ4dGmD/4UEOHx6kv+/cKrA53GaaV5fSvLqIS5szVBu7yIu1kTrcwkNPG/lJ5zJimcmfH6vdyK33ruCuy0M0hF7ApGUbHTTJgiypoM18qoUOZGQrCYOLtGJD1lUkXUXWMyh6GqMaw6Alzi9TsWQAZ8WpYNZVnZ0nOzoqJBUIMbx9H8Nb9zK8dS8jOw+SGgmczxbnlWK1YKsswV5Vhq2yBFtlKfbqMtwrmvCsaMRgm/h91DIZWr7zK1q+8T1Wvn8Z7Wtfz3d+laDlwORs2VX2NPd1/oHVry7Gdc9NbI+t5LFnQux9sWPa35H8fBt/86ZV3P365TgdL6/5ggvFhTjudnYGeN//e4iBgYlZt71Fdt736RtYY9pH3cgzDO7x8eK/7yQdyTbel91xHVf88quY3Bf/N3+hBLdj1GSKSFsXmeiphjGj3YCjNDtMOaXYSSpOIsEkAd/koiOyLFFR7sLxCvle6bpOJJpiYCBCapocNVaHCasJ/P5UzikXJouBogID5sgI8eEE1opSjC4HaDon2lpJJ5NIaZXCpI4aS5D0BYh29hLr6ifW2Ut09Lp3eFAEt8LidXpw2/G791BROIuDtMkNihHUJKip7DWQkYwc99zEr56x8MTvDubsRVrjinFndA9qZxfx3kHGvqWyUcZeYsVeYsNRasVeasNeYsNeYsXsMmFwGJGcDlSjjYxiJS1byShWVNlERjKhymZU2YQqm0iqRiIphVhKIZqUiSVkYkmIJSXiCYiFkwRH4gR9sez1SHYenDZNa9lMVNd4uGpjNVddUcXaNWULvmX/Yge3uq7z2BMtfO3rW/Dl+JEDuPT6Wl73lmUsT2+jPPgSg7sG2f2tQ4TaI1hKCll1/4eoe9fdyIb5a+m92O9TKhDixP/+lqP//QCJ/lOJTgpW5LHk3joqripBQ6bPtY6T3ms5sC/Aww/sYXCKoblLmgv4109dx7KlRRfqJcyYzxdj774+Xtrdy+59vbSe8M24LNaZzBYD1U0F1K8oYu1yMysKBylInEQ6dojh3QMM7PFx8kCMv5ZcT4uzJuc61l5VzTvu87Am9Rz29DDR4TSxaD5y6Rrcl12P0WkF/3H04YMwfAgyc1wY96xksBeBowLJWQmuGnCUIsnZ74OuaYSOtjG8dc94MBs80so5ZbeaiiRhynNj8roxe7PX2dsejG4HBpsVxWpBsZpRLOYJt2WTEUmWkRQZZBlJUUilUmx7aQeSLHP93a/DXV5yTtM1Yj0D7Pr7z8PQPur/8Ta+uX89j/9pcsn4vDwLH6jxoTz8Z8ousVP+1ivoKr+Sv2y3sPmJNoJTHJsAbDYjb7xnBW998xry8i7+tI6Xk/k+7ra1jfC+//fQpN+ewjIn7/v0DaxlG9WBzXQ83cP2L+8fz5K//J//jlX/8ZELUpZwJhZacAvZ3/Z431D2HG+UYpSwl9kxWBRUyZTtxEho+AbCOc+9iooc5Htf3vVwE4kM/QMRYrGpRx7ZnGa8LoiH4vjCkz9zZ/bWSkYbtspSZOOpc6LZ/B8ePXCApatWjd0Vwa2wuMwquLUVg7sOyVOXvbbkEW7tpPP3j9H5u0fx7z2MYlYwu02sfGcTztdfzxPBK/nZt/cxnKOXpbLSzX99+VXUVjqJdw8Q7ewl2tE7Pqdr7BLr7J2QpAAJDFYDJocBo8OIyWHEYFFQLAqG0YtiVjBYR68thlOPj/5dNhtQ8lxIRfloTi8pg5OkwUlccjAUs9I5bKKjV6O/O0R/Z5D+zgDx6OyTYJktBtavK+PqK6q56spqyssWXrmRixm0HTg4wNe/tYXde/pyPl5Y5uTu917KxpphGoafJN09wN7vHqH7hX4MDhtLP/keln7snRjs85/N9GIHt2PURJKTP3uQw1/5IZHWU/UX3TUOVr9vKWUbikjLZjryrqbTfgmbn2jjid8fnGI+vMS996zg/e+7HIfjwidBGdPdE2Tn7l5e2t3Dvn299PbMvm7pGE+BjZrmAmqb81leD0sLA+SlelDajuDf0c7gHh+De0dQdTP5l66ktWo1v2i1EI5Nbi23u8y88T2reEPdPgx7ttLzYj/dL/QTbD9t/ySJvLXLqHnTHVS/+dVYSwog1D4a6B6ERO56zOdMMoCjdDSQLQdHRTbx02nzZNOhyGiv7Ggwu23flFm6Z8roceGoKcdeXYatugx7VRn26uzFVlWGuSBvTkdMzPX3reevz7Hvk59j+fuaeMx7Nz/+fgvp5MT/c4NR5lMfv5LVoRMc//YvMaR6qLtvOZkbb+GRoxU89XA7fR2BKbdhNhu4567lvP2taynIFxmW58J8HnePHhviAx9+mMAZCTFLq9y899+vZ236OSpCL3H8TyfZ/a3DoINsMnL5jz5P7VteN2f7MRcWYnA7Jh2JEmnrQktle7yz2ZQtmD1mdGTixjySqgFff5h0jl5Lt8tCaen55ztZaDIZjcGhyKTP3+msdhP5HglzMkT/iExUm5zUz2g2UFxgwBQdIeFLYqsozVkacjb/hy0tLTQ1NY3dFcGtsLicHty2//HDVFZWZOdlGR1gciKZnGArAlcNksmBrmn49xym99Hn6X7wqWlru5ZuKGLNJy+npeZOfvKHKNufap20jNli4F//8VruuL15yvXomkZi0EfKHyIdjpIJRUifcRnLvKnGE9mMnInkqdunZedUx5ZJpEiHIuiqimKWsXiz80EsXjPWAguOMhvOujyMTTWoxZVEjQX0xDwc7zNzojXOyaNDdB735TwQT6emNo/rr6nhmqtrWbGsaEEkTbgYQVt7h59vfWcbz2zKnQXZYJS5/s5l3HFHMctCT+Lyt3DkVyc4+ts2NE2m4X33svLfP4ilKH/e93XMQglux2iqStcfH+fwl36Af8/h8b8Xry9g7fuX4ql3ETPk0ZZ/Ix16HY/+ah/bnpz8HQQoKrTzyU9czQ3X1V2Qfc9kNPbu6+OZ59t44YU2enrOrafTYFKoqM2jssFLXZ2NVTUp6uyDGHtOkNx3hOARHyPHQ4Q641hrG8i/bCX5l64k/7JVZPKL+OJXX+DpZ9pyrnv1FVW8601ulvc+zL4vbRlPVDYtSaLkxo3UvPV1VL7+JgwOOyQDkPBD0g8JP/robS0+ghbzYZDGjiFytg6sYho//mJygMmFZC3IZjC2FoA5D0k+FUSqyRTBQy2M7D6Mb8f+bK/soZZz7pU12G24lzfgXtGIZ0UTnpVNuFc0YikuWPSJ7tKRKNve+Sm8xb103vEuvvqdIfyDkz977373ej7wt5cxsvMALd/9NYNPPkXda6twvvkOnhxYyuOP9NB6MHdOAMjOGbzzzqW8663rKCpynPd+v5LN13H3wMEB/t/fP0z4jHn6FXVe/vZfr2Fd4glKw/s58JNjHPpZttSPOd/D1X/+NkVXXzIn+zCXFnJwC6ClM0ROdpMOnWoYNLuM2IqtSLJEwuAmJVkZGYwSj05uiLVYDFRWuDEaF/ZIuJnQdZ1AMMHAQGTK/BhGs4LXa8SphYkNRBhWPKTUycdfm8NEiTtNus+HZHJgqyyZcgSbCG6FV4zTg9vjx4/n/MAnfX76nthM76PP0//4iyQGZ94TYbQbWPOBZdjuvYM/t63i19/bnbMH6e67lvMPH73qgg7h1TWNlD9IvH+YxMAwiQHf+HXkZDeho22Ej51EVlScVQ7c1Q7ctU5ca6uQVyzHb6ni4ICHw606J48O03Z4MGdyhKm4PRauuqKa666pYcNlldhnUEpkPlzIoK21bYSf/WIPjzx6fMqD+pJ1Zbz+HStZb95HVWArXc90se/7R4gNJqi851ZWf+FjuBpr5m0fp7LQgtsxuq7T/+RmDv7Hdxh6cReQTdJb+6pKVr6rCWu+hYClipaCV3HopMzvv/8SA12Ts8cCXHdNLZ/8xFWUFM/9HLJgMMGLWzp4+tlj7Hipj1iOntLpyLJESZWbyoZ8amttLK3M0JTvx9R7kszBo0QO9hDqTaMpBVhranEvrce1tB730nps1WUTehWffqaVz335OYI5WsztLjNveNcq7mk8SOyPj7LnW4fI33DZeMbgmVKsFipefxO1b3ktxTdunFQeJPt5ehwZjZtuvgWr7exBUCaeILD/GP7dhxjZfZiR3YcJHjiOlj630mqyyYh3/QoKNq6hYOMavOuWY68pXxBDLectO7muc/iL38f/7O9wfeqd/McvrLTmSF53zxtW8E8fuxpZlkj6/LR87ze0/fDXlG90UPSu29iWWc9fHvVxcHvXlO0IBqPMa1+9hHe9fT1lpRd/XuZiNB+fg117evnIxx6ZlIm/prmA93zqKtZFHqEwfJhd3zzEiQc7AHA21XDdI/+Ls6H6vLc/HxZ6cAtjw5QHifeeaihUzDKOMhuKSSEt20gYXIQDCYI56lQbFJmKChc228UbZXS+kskMvX1h4vHcx2xFkfF4zXiMMdIDAZJY8Uk2Mjmqibi9FgpMERK9YazlZZg80x9jRHArvGKcHtxu//WfKZZNxHsGifUMEO8dJHyig5GdB2fcC2ApyqfynltxLanj5M8ezD4XKLm0gFX/fA07i+7iO99spbttZNJzlywt5KtffNWCOgnQNY1YVx/B0UA3ePgEvpcOEjp8DE+tg4IVeXhXl2C+bCWRwgb29hWx61CaY3v76Drhm3HniaJIrFhZwtVXVHHFhiqaGgsu2BCc+f5R1HWdnbt6+dkv97B5y9TBQV6RnTvfuY7rmv3UjzxN/HgPu795iKF9IxRddxlrvvhxCjasmdN9m42FGtyO0XWd7v97mr2f/E/CLe0AGCwKS+6rY8m99cgWAz3uy2lxXc2mR7JDlXNlj7VaDXzgfZdz7xtWnnfCKX8gzmOPH+WRvxzk6Ikw2izmzZotBqqbC6hv9rCyTmOZ24ep8wSpgy2ovhSqIR9DfiWOhlqcjdW4mmvP2rsYCCb40n8+zxNPnsj5+MoNlbz7b7ys7H+YfV/eTKBT5rIffo6yW68GID4wnB3mu3kXQ1v2MLLz4MQpE1OQZBlbVSmOusrxi6mymAMD3cgFHq695lqMkoSWzI4oSQyOjDe0Rdt7CJ/oINLaRbSz97zmydoqSkYD2bUUbFxD3tplc1Imaz7M9/et56/PcfjfPk3T/XfwtZdW8MJjk0eR3PqqJv7j324Y/x6oiSQnf/EQx/7nx+RVpKj8uxs45LmaPz0aZffz7VM22CmKxO23N/Oed6ynssI9p6/j5W6uPwfbtnfx0X94lGRyYkWH+hVFvOeTV7Au+GfyQi1s++I+Op/pBaBg41quffi751Unfb4thuB2TCoYJnqyG220hrUkg6PUhtFhRJWMxI15xGIaI4ORSfVwJQlKSpzkeRbW7+/ZaJrOsC+GzxfNeQiXJAmnx0y+PYU+7CedlNG8BfSPpCaVApIkiYJCC86Mn5Q/jb2uCsU0ebjymURwK7xinB7cfoNa8jn7F+RM5kIvlXffQvUbb6Pwmksn9JCM7D7Eie//lvZfPgx6inUfXUXirrfwnT8Z2ZbjBNPpNPO5z9zI1VfVnPNruhAysTgjuw4xvG0vvm3ZuW2KHKXkkkIKrqrBuPFSegx1bGuxc2jfCEf39hENzbx0hyfPyobLK7lqYxWrVhZTXuaatyGB8/WjeLLdz2NPtPDYEy10TdFTCNlhNTfdvZybr3OwNPQUpq4WDvz4GO1PdONZs5w1X/wYJTdfedETSiz04HaMmkrR8t1fc/D+b4/Xy7UWmFn9t0uouaWChMFFS8GrOBYu408/2MnxfbmzKp9rwqlwJMljD+/lkceOcvB4dMYBrd1lpn5ZEU2NNlZWxqlOdKK0dyKFNYylS3AuXYqrqRZrWdE59Sw++1wbn/vic/j9k3sEbA4Td797NW9oOkTywUfZ881DVL7hdaz9r3+aNhOqmkwx9OIu2n/5EJ1/eJxM+EInkpqabDKSt245BRvXUDga0NoqSi72bs3Yhfi+hVra2f6WD9P8nnp+k7mD3z5wfNKJ55VXVfPVL9w6oSyJrmn0PLKJo//1YwzJDmr/33W0VNzIHx9PsvO5k1PWbJdliVtvbeRv37memuqFGygtJHP5OXju+ZN88p8fJ31GBuzmNaW86xOXsd73exyBk2z+9C76dmR7F8tfewNX/vprk7JxLzSLKbiF3NmUrfmj5YIkmbjRSyIj4+sP58xY7s2zUlTsQF4Eiaai0RS9U8wnhuy82sI8HSXoJxXKYCkpJoqR/oHJuScUg0xxkQlTYAhNNc1qpI0IboVXjHMNbp2NNZTedjUVr7mBousuO2uW2nQ4Qvuv/sKRr/6YgroMNf92D79qXc/vf7g35xf+Dfes4KMfugKLZXHUOdN1nWh7N/1Pb6P/qS0MPrsVRxEUX1ZE3o2rSS5Zw66+EnYeSHFoZ++Uw0Kn4nSZWbasiFXLi1ixrJjly4rweucmYclc/SgODUfZt6+ffQf62bGzm5aW6YevG00K17y6mdtvL2JpYjOewQMc/XUrx/7Qhq26itWf+/s5r1V7PhZLcDsm5Q9y8HPf5fg3fzE+bLVwtZf1H1mBp9b5/9l77/g47jr//zlle19Jq14syZIsS+41jh3bcXonCRAIEGqA4+B+x3EHXIHjuAPyPe6Ao/cQAqQ30qvtxN2WuyzJ6nXVtvfdmd8fK7lEki3bsi07ej4e45ndHe3Mjqd8Xp/P+/16M2isoDHjOrbv8PLM7/aMW09aFAU++P5aPveZZacMm/d4I7z+xBZe3tzBvqYoyeTknk35sxzULsxgaXGY8ngHOk8YfWYJlpr5WOeUjQnlPRt8vijf+/5mXnp5rEMuwNyl+XzqI1nMG/gr+7/3Np42gWW/+g/yrl9zRttJhiN0P/cmrQ89Q+9Lm1FTZxZ2fa6Yy4txLqomY/n8dIjxwmok/aVbRuNCXW+JQJDtn/gaWRVeXq/4GL/9RdMY19b5C3P58fdvGvcaGNy+j0P/+TOS3fsp+5s1tFVcx+OvJNj+esuEIlcQYMPV5Xzmk0soK3Wel991uTBV58FLLzfxr//+2pgyczXLCrjviwtYNPgI+sEONn19J4MHPQCUf+YDLPnJv51XF/6p4lITtzASGdfVR7T/eCTfaLkgJIGYbEuH5bqDxMYJ4zUZteTnW6dtSbtkUsHdH8TnG98wSpJFMjK0WFI+ogNhdBmZ6LIc9A+Gx60codXJ5GYJKL39yFbnGfsgzIjbGd4zTFbcSgY92etXkHfDGnKvX42lrOistpcMR9j3z/9Lz5NPsPi/rmZ77l387EcN47opl8xy8L1vX8vs8gtnGjRVqIqCZ98R+l7bQt+rW/Dt34er1oZrXRnaq1bSlCxly2GZQ7vdtNQPTNgIOhUOp4HMTBNZmUZcWSZcmSayskxkZpowGjSIIoiiiCgJSKKAODKpalqMpyeIRKJs374dVYWlS5eh0WpBVVEUUEmvoyoqyaRCJJogHE4QiSQYGo7Q2xugu9dPd0+AwYHJjVoZzVquuH42V29wUatsx9lXR9NTrTQ81oJkcVL7zb+l9L47pl2D4lITt6MEmjvY+0//TecTLwMgSAIVd86i5r7ZCEYDbc6raJQW8vzD+8c1fQNwuUx89R/WsGZ1CV1dPhqaBjm4p5ND+9to7Yvj8U9OyMlaidm12Syaq2dxxhB5cT+2/EoyrrgC2aCfst8M6YbFU88c5me/3DGuG6XBrOXO+2r5wJzDRJ98kbqfHJ7UaO1kiA4M0/7IC7Q99AxDO/af03eNQRCwVpXiXFSNY9Hc9Hxh9bSotTmVXMjrTVVVDnzzRwj9m9mz9n5+8uOWMSH7FVWZ/PxHt2K3jX+eevbWc+i/fk6kYQdln1tFV+0NPPGawtbXxn7XiVy9vozPfXoppTMid1ym4jx48unD/Od33xozKr/wymI+8tkaFg38Cam3k7f+cQe+lnRbpPbf/5aaf/2bix4xNFkuRXE7SmzYS6it+1gIsqQZycPVH8/D9Q1FCIwjEjUaicIC27QaCFFVFZ8/htsdGNOZMorFpiPTFCfZN4xktKIfiUjq6Q2MK4aNZi051jjxbg/6/Lyzut/PiNsZ3jOcKG5/oq+isLAIQ54LQ74LY54LQ3429poKsq5cPKWjAP2bdrL9U1+j/CYHkXs+yQ//GOfA9rHXjkYj8sUvrORDH5h3yTxkxiMZjtC/cQc9L26m9+VN6HV+clblY7l+Bb68uexos7PvYIiGvb0M9Z19GZTpTGaOmdU3VbJ+hYay2G5s7oM0PdFM4xNtSGY7Vf/wSWZ/7p4pFzlTxaUqbkdxv7WdXV/4j7STLmDI1LPw83MoWpdHUJtNY+b17Gsz8PgvduDuGr9sjFYWiE9yVHYUjVZi7uIcVlYLLHX4cFmdZK1cjcZ6fspiqarKW5ta+cH/bZ0wHH7O4jw+c6+TWvdf2fe9Lfj7tCz9+TfPeLR2MsS9foItnQSbO9Lzli4CzR0EmjsId/Ty7rhtUaNB50r3yutdTgx5LizlxVjKizCXFWGpKEFjNk35fk43Lsb11viTh/Fv+TOtd32B//1RJ9F3GQ4VFtv59U9vIytz4uPvq2/m0Hd+QWD3ZsruX07/kpt5/E2RLa8cJR5Njvs3ggDXXjObz356KcVF9qn8SZc853oe/PFPe/mfH24Z8/6y9aV8+JMVLHT/CaWtkze/sp1QbwRBFFn6i29R/qm7p2T/LxSXsriFtFle8GgHqVjalFMQwJRjQGvVHquHGwom8AyMzVkVRYG8XAtW68VvO8TjacOod5uVjaLVyWRlSGgDwyTDYCjMRTboURSFrm4/wXFMSa0OPVmaANH+MKaSwrNuI82I2xneM0zGLfl8kQiG2PtP/0143+vM+q8P8ceGeTzzhwPjhikvW17Af35jAxmXSe3AwNF2el7aTO+Lm/Dt34trroXMtbORr1zO0WQJOxq01O8fpL1x8IxydacbFrueBauKWHxFHoty3RT4d5E63MDRZ9ppf60bfX4B1f/4KWZ99PZpa2wzyqUubgGURILGHz/M/m/86Fh+aPbiTBZ/cS6WIjP95hoabOt59fkuXn384Li5TpNBkkXmLMhmxVyJKwsSzKqdh2XW+b+37D/Qx/d/tIUD+8fPI9YbNdx5Xw0fmnMQ359e5MBvmij91D3M+48vXXDBGIlEePmFF1H9IdZtuBqTw4ak1027iIWLxcW63tofeYGeP/4Qz/1f4Ds/7B9z/80rsPKbn95G9mkcxQPNHRz+7i8Z3vwa5Z9awvCqW3h8o5a3X2oiFplA5IoCN15fwf2fWkJB/ozxFJz9eaCqKr/8zS5+8audYz5bfVMF77+nmIV9DxNt7OKtf9xBdDiGZNCz6pH/peCW9VP6Gy4El7q4BVCSKUJtXcS9xyP59A4txiw9iiAT1TiIxAWG+oKkUmOfTaORbBdjIERRVYaGwgwOTmwYZXfqcMgh4v0BdK5stI60l0oyqdDR5SU6zn3BmanHnvKSCCiYZhWc0/NhRtzO8J7hYorbUfpe28Ler3yTyk9UcmjuB/n5L9rpafOOWc9q0/E39y/n9tvmoJEv/VpnoyQjUfo37aT3xU30vLQJWRkme1k2tg2LYE4V7dEcDnfpaG6N0XF0iK6WYRKxC5vPN1n0Rg1FszMoqcpi9hw7SwuHyQnXY+5voHdLN83PdTB40INr7TIqPv8hCt537UkGZNOZy0HcjhLp7afuH/8fbX98FgBRI1K8Po/Smwqx1eTwRugKXqkzsvPNViKhyZWZkTUiZXMyWVmrYe0cPVXLl6G1XhjTnI5OHz/48Vbeemv8mrUA1Uvy+cyHzFR3Pkfdd7aTIIvlv/42GUvnXZB9fDeX0/l0PriYx6f31Xc4+sA3Sf39/Xzrx0G8gyfnv+XkWfjNz24nN+f0oYGhjh7q/99vcL/0POUfX0Bg/W08/o6BTX9tGjMyPIokCdx8YyWf/uTSaVU94GJwNueBqqr84EdbeOhP+8Z8tuGuudx5eybz+/6Mb28Xm76+k0QwidZpZ+3zv7iojvznwuUgbiH9fxftGyDcfbw8l8YgYcozIsgSMdlGVNUx1BckHhsrBs1mLfl5ViTpwuXhhsNxenoDxCc0jNKQZVNhcAhRZ0af6zrW7oknUnR0eMf8rSBAZpYBc2QQJaXFWJR3zqJ9RtzO8J5hOohbgIQ/yMH//CmRQ6+T8Y8f5ucbc9n41/ENYFxZej732ZXcfEPlBb2BTQWqqpIKR0iGIiRDYZKhCEo8gWwyIJtNaCwmIu5B+l55h56XNjOwZTdGq4Kzyo5tQQH6JXNJFJVx1JdBr0dmyC/i8aUIeCL4hyP4vRH8nijJRApVUVEUNT1X1WOvBUFAEICRufCuOYKAQHoUAThWkkiURHR6Ga1eRqeXMZi0OLJMOLKMODMN5GepVGT6sMd7sMS6obWV3q29dL/jZmD/MLLZTOl9d1B+/wexzSm7aP8HZ8ulLkbiXj+++mb8I5OvvoVox1G01hTh+fNot5dzYMjGoaNxQoHJCVqtTmJ2lY0NSw2sW55HbvV8NPKFGYFXFJW6vT08+Ww9r7zSNGFuU0GpkzvvKeYG1276f/sK9Y90UP21zzPnyx9H1Jy5O/xUcamfT+ebi318hnbu5+A//AP6r32Mf/1ZisF3pYu4csz89me3k5c3ufD6SG8/9d//HT1PPU3ZR2oIX38Hj242sun5xglHcmVZ5LZbq/jUfUvIzj59HeTLkTM9D1Iphe88sIknnz485rObPrKA2681Udv3F/q3dPLON3eTiimYivNZ+9KvsFVdes+lUS4XcTtK3Bcg2NqFOlIuSJQFzHlGZINMXDIRFS14B8OEAmMj2y5UHm4qlTaMGs/TAdI1a50ZWsxxL8lgCkNB7kkhxdFogvZO3xjPFVEUcGXr0Hv7EXRW9DlZUzIaPSNuZ3jPMF3E7Sih9m4Offt/sVXG2VZ6F7/9xVECE9w4cjM1fHidi7s+ew1a8/kJV07F4ySDYZKBEInReSBEMhhOzwMhksGx77173WNTOHKsTqUgCkg6EUEUSEaSqCfc3ySjAY3FhNZpw1iQg6TXkgiECLZ2Ee3pwzbLjCnbiCFDhz7bgqbIhZyXhejKAocTNBpUUUQQRVRBQhUEVMR0d6AKAiqgIKhqellVEFARUGDkvZOWVQUYXVYBFTEVQ/QOow4OkuofJjIQxnvUj+eoH2+zn7g/gS7DTv5tV1P4vmvJuXrljHvreUZVVaLuwbR4Pdx8TMwGj7ag1Uexl9tIzZ1Nu7OKo2EnjV0ira3hCUeQzoQ5VVncdEMFV68rO68N8Z4eP888f4Rnn2/APY4R3SgOl4lb31/OnVWNKK+/zoFfHUFfXM3SX3wL6+yS87Z/k+VSOJ8uJtPh+PiONLPn/r/F+k938/VfyQz0nHy+ZbpM/Pbnt59RCHF0cJiGHzxI518eo+wjNfivvZPHN2rY/OLEObmyRuR9t8/lk/ctOmW+7+XImZwHiWSKb37rDV58lzO6IMAdn1rCzVfCXPfjdLzYyo7/PoCaUrHPr2LtC7/EmJd9Xn/H+eZyE7cwUi6ouYNkON0GFAQwuvTo7DqSgpaoxkHAF8c7jrOwIKTzcG0TGMCdC6qq4g/E6JsgPBrAbNORaYyRcnvRZh0PQR4lGIzT1e0bUxtbkkRysjVI/W40jix0mVMX/TQjbmd4zzDdxO0ow3sO0f6rH5K8Yz0/elbPoZ09E65bmC2zTttOua8Xm92MLsOOLsOBNsOO1m5BTaZIRWOkYnGUWJxUNIYSi5OMxI4L1wkEqxKfuNEvG2V0Vg1aqwadVYvGbkDKzyTszMFnzMQjWfEoJoajWrxhgUgcojE1PUUVotEU0UiSVErFaJIxm2TMJhGTXsWsVTDLCSxqGNdgO5a63aSa+lEVFUGrwZifjS7DjmQ0oCYSRHoHCHf0oo5XVFRIC2lBFBCkE3r/Ru8f6ti3JnrjxJeqop70twD6nCwyl88jY9k8MlctImvVossmh3A6NLZPJBWN4Tt8FM/eejx7j+DZW4/vYBMCERxlVmyzbcg1ZXQ6ZtPgz6ChU6S5NYpncGyd19MhySJ5JQ56O7yndH4dpaYmmw3rSrl6fRn5kxzZOhWRSIJX32jmqefq2VfXe8p1DWYt194xmw8v68W4/VUO/vIQkYCR2m99kVkfuW3aGNNNt/NpujFdjk+4q48dH/sstv/vZr72Wz393SebrWVkGfnNT2+n6AyNoGJDHur/32/oevRxyj+1kOG17+OxN0S2vHx0wnqYWq3EXXfW8ImPLpyyUnDTncmeB6FQnK987WW2be886X1RFPjAF5Zzw8IQVf3PcOThRvb/ugGA7PUrWP3kjy8Lp/HLUdxCuvJEqKOH2KD32Hs6mwajy4AqykQ0DiIxGHIHx5TwAnA6jWS7TFN234/HU/T2BQiFxho/QdpAMStDQuMdonr1Wjq6unjzzTdZu3btsXU83nSliXcjayTyXCJqTz+63LNzRD4Vl4q4vTxajDPMMA7ORXNx/PQX9L74Ov9ctZG6dWt49Olh2hvH1k7tdCf5A/kIQj4FNgNzdApV0T4Kt7xD8kgXqWgqXfZGUVFTaUGmKioIoDHKaIwysknGZNKgccnIBh0akxnZpEFyWBEdNgSrhajeil8wM5wyMRjV0+2TGfDCkEdh2JPAOxwj0BB5V09cbGQ6NQFfnIBvvJulFpgNttk4r9NRlCtRYAiT5+/EePgQyR0taKMRjC4n+beuxzqnFEOuC12GnVR0RLyHIiPzcLoHdFShCsKImUGSru70fauosAhZIx/7/MS5kI5dHnkPZIMefW5W2lU7z4WppABDnmvaiIfLiWQkyvDugwxt339MzPqPtKC3yzgrbTgq7RR/oIKez6/h8JCDTR0izS1Rep8LjJRXOLO6ygBZuWbmzTWwvCLGquJeMoVWWsRqnq0vZMvmgXGvxVEOHnRz8KCbH/zfVioqMrn26jLWrS0lP8+KVnvqHGuvL8rRo0M0Hh3iSOMgTc1DtLYMEz9Nnrkki6y+oYyPXh0it/5RDn1hH74ekZp/+RKln7hzSmrmzvDew1iQw4o//4bt997Pd794DV//vY2+E1y4hwbCfPz+p/jNz2+npHjyoyy6DAcLvvsPVP7dx9LGU/f+M5/97Aru/N5tPPaqytZXj44xdIvHU/zpz/t44slD3PPBWj724YXnZWTqUmNwKMwX/7+/cqRh8KT3JVnk3v/vCq6rcjO7/yXqfnyQxifaACj+4E2s+P13p72R4XsdQRQxlxQgm02E23tQVZWYL0EqpmDOM2JkCElrQy6wjZuHOzwcJhpNUJBvRT4HrxZVVRkajjAwEGK8gUVBELA5dDjEAMnBOLr8fIR3+YmoqspTT7/E66+9QVXVXK7ecOOxz7R6mTynQrJ7AGNREbLp5M4JRVG44oor2L59OwC/+93vuO+++87690xnZsTtDJc1giCQd+MGclLrcL61kaXXuXn72lIef85PT7t3zPqqCp2dETo74RUciOLVFMyzkuHUYDSIGA0CRoOIyQAmPei0EE2IxJIi0bhALAGxkXkwlCIQSBFqiBH0Rwn5YyO96SoQHJkuLMNDMYaHYC8AhSAVwryRUB29iCmQQvd2BF3wKKIAWK2oJhOKTo8imUgoAvF4asyNWQUSlCII4AybsdsM2O16Mp1GqioyqJ7jonSWc9oWSj/fxAIhBlr76GropuWtRuIxheyEk/nXLMRgOz/ht+FuN4Nb9jCwpY7BLXV46urRmAQyqu04Ku3kfLoUb+ENHPRmsr1DS3NrnI5HPCNGY4On/f7xsDr0zJ5tZV4BVHjr0bz1CImmKMlIih2RJFqLhtl3tPClDfnct2A224PzeXN7gt0b2/B7Jh4NbmwcpLFxkB//LP1Q1molTGYtZrMWs1mH2azFYtERCMRoaRlmaHBsiNmpyC6wsnxtITcsDFDa9RJHvraLloYk1V/9DFfd/8FLOhR+humB3pXBir/8mu33fo7v3L+arz9so7f9uMD1DEf4+P1P8euf3kZZ6ZnVZTfkZLH4B/9M6Muf4NB//gzfL77G57+wivc9cBuPvZRg++vNY3LyYrEkv3+wjkcfO8hH713Ahz4wH7P5vSnS2ju8/M2XnqPnXSHjGp3EfV9ZzTXFzRT1vs7W7+6j4410BFj1P32a+f/19wjie/OZdimiz3QgG/UEmztJxeIkoyn87UFMeUb0Ri+iaETKs+IZCo9xOA+HE7S0eijIt2I0nvl1Eokk6On1E5ugg1Vv1JBlUxAG+xGdWZjL0iHIZWVl6PV6jEYjiqLS0+vn9dfe4Kc//W9uu/0Dx8StwaQl1xoj3uvHVFoybofLD37wg2PC9nJnRtzO8J5AlCRcV6/HxXpKU0lurH2bZ3daeOwF75gcqBNRFJWOZh8dzRdwZy8CqgqhiEIIATCCZiRcLQyElZGFyeEdHh73fa1GpDBbx2ybQrngo0QZRhsJHQvzTkVjCIKAqNchjUxahxVDbla6bnJuFoa8bAy56RyS6dqoaN/Xwtt/3UNju4cun0K3HwY8qRPCsdP5bo8cOID4/QNk2iRyrCr5FolZ+TaqF5VQvXoO9ozJhxOpqkqgsRX3m9vp37iTgS11hDt6sBSYyKxxMGt9NqVf+TD1qRI2txtoaldof947kot+dkJWp5eZVW6nak4WtTV5LF2QS0HOyeHDce+X6P7rm3Q+8Qq9L20m1OdnxwP7OfDbBirubGXDzUdYfVUB7Tcv5+2WDOre6eTwru7TuivH4yniwxE8w2ceHj2KwaRh4ZVFbFgusSKjidS239P+rXY27w1R9ZVPs/KFDyMbZ8J8Z5g6dE47K//yK7bd+zm++4nlfP0vTrpbvcc+93mifOL+p/nZ/91CdZXrjL/fVJjLsp9/i2DLpznwrZ8Q+slX+OLfraXrgVt4/MUIO95oGRN2GQ4n+Pkvd/Knv+zn4x9dyPvvrsWgv3gmaReaXbu7+crXXsbnO9mXw2TV8cmvXcVaZx25XZvZ/G+7ce8eRBBFlvz4X5n9uQ9dpD2e4VyQjQas1WWE2rqJe/woKZVAZwhDpg6DEyQ1gZjpQKuT8QyGTkqbSiYV2tq9ZDiNZGWZjhllnopkUqF/IITXO/6zSpREMpwaTFEvBHXoZ806qfrD66+/fux72ju8RCJjn40Wuw6XNkTMHcFcNgtRM1baNTc386//+q8sWbKEvr4+urouWITwRWFG3M7wnkOUZFwL1vKpBfCBu3p55OkDvP6Ol5YGz4R5ShcDWRaxZ+jJcOrIyNSTmWHAYtFgNGgwGTWYjNr06JVJhyyJeLwhPN4wHm8Unz+Gz5/A70/Q2xehuyMwpuf+QhNPKDR3RWjuAtAjCHlkZ2rItItkZKtkmJLYtXEsYhQTUSyEMMV7kTsPEt0foD+QIOqNExmMEvUmEQ1WDPk5GPOzMRZkYyzIwVCQc3w5z3VBwkjbD3XwyiNb2dfio3FAZdA7+XNIUaDfk6LfA/tR4OAgvDwI39lFYabAHFOExXk6llw5m4x5szGXFSFKEqqqEmzpxP3mdtxvbqP/rR1E+/qxl1nJmp/BvM+VEam+g73DubzdpqGhKUrXRg9KygN4zvg3iqJAQYmNiqosaubmsHheDpWlztOGaGntVmbdexuz7r2NRDBEx2Mv0fjjh/HsOcS+Xxzh0ENHKbu5iMq72pidk8XNH6zC/bF57DpqZP/2Hg7s6CQcGD8v6WwQRIHK+TmsXmXj2ooezPV/pesPR9n6RjdaVyGzPnYPS5+4B43lvekqO8P5R2M1s/Ivv2T7R77Adz5Uyz8/kU1n8/FrMuCP8ZnPP8NPfngL82tzzmob5tJCVv7+u/iONHPgmz8m+sMv83dfvpq2793E488H2b2xbSTV4Dh+f4wf/ngbD/1pH5/42CLed/vc8+4Ue7F5/MmDfO+/N49xSc/INvPpf17NldJb2Ft38cZXd+Bp9CMZDVz5yP+Sf/O6i7PDM0wJoiRhLi0k1j9EqKsPVIgMxkiGU5hyDRjVQUSzHY3WypA7OKbtNDQcJhCMkZ9nxWAYvyNIUVSGh8MMDoXHGD6NYrZqydDHUL1BtHk5J7kgn0g8nqS9w0ciMbZtcayGrRcs5bMQxqn+oaoqn/rUp4jH4/zqV7/i9ttvP/UBugyYMZSa4ZyZroZSZ4ovGGT37la27+5l34Fhmhu9500QarQizgwjGZlGXC4zrmwL2S4zuTlm8rPN5GWbcTrPrZB4JJrgjTdbeGtzG1u3dRCeZK3R6YYgCpitOmx2HQ6nBpdTJNuhkGtP4NIEyFKHMHvcJLvdRN1BIkNRIoMxIkNRknEZQWdJG4Rlpg3CRpd1GXa0Ths6Z3o+uixbTm0cERr2s/mZXWx6p5X9vUl6Bs9/h4heJ1BuDFLUcoC5phiGITf+3gGE8lykpXNIzCohYMliMGLA7ZNpaQ6eMiLhdGRlm5hdlUXN3GwW1eZQMycLo2FqOgpUVWVo+z4af/IwHY++iBJPIMoCRevyKLgqB9f8DESLEa9hFv36cna0O9m9c5CD27smdD0fD0kWyS6wkltkp7BQT2luirm5fvKH9tL/1320v9ZNQrFScs9NFH/oZuy1lZdcrvd0MUyarkzn45OKxtj+ib/Ddkc5//JswZj8c51e5offv4llS/LPeVue/Uc48G8/IlS/i/J/vI6m8ht44lkPe99pH2sAOILDYeBj9y7g7jtrJmzAXyq8+zyQNVr++3/e4bEnDo5Zt6DUyWf+aTkro88iNx9m4z/tINgdRu/K4Kq//vyi1bS+EFyuhlKnIhEME2zpPGb8KUoCplwDGpOGmGQhKhgZ6gsSm8CFPCPDSFbm8VHcURdktztIcoL2o6yVyHJIaH3DyLaMMS7IJ1JcXExHRwe/+/2TLFu2irnVEztyFxcX09bWNub9X/ziF3z2s5/lK1/5Cg888AAlJSW0t7efVc7tpWIoNSNuZzhnLhdx+27CkTg76no40jREIBgnFIwTDMYIheKEQnHCoTjxWAqdXkanS096/fHJaNCQ4TDgdBjJdBrIyDDisBtwOPSYTdrz1pBuaBzkiacO8cJLjYSnoDzLpYBGK+HIMuHM1JHllMjOEMhxJMmzxcgzB3EmhhD6+0l0DxAdiqTF73CUmC9B3Bcn5o8T88VJhJIIopQWuw4rWqcdrcOGx5zBgYCORslJs1dDPHH29029UYOsEQn6Tm8SNhE6rUAsPjX3bpNZS3llJrU12SysyWF+TfYFc1GN9g/R/JvHafrZnwl3ph2MBRHs5VayF2biWpBB5jwnMXsBg8Zy3FEHgahIICIQikAwIhAKK4TCKqFwCkmE4lyYnROl3OHFEu0j2dRKsMGNt9nP0BEfQbdK0d03UPyhm8m6YtG0DW+fDNNZvE0HpvvxURIJdn76H7Fe7+LfXimn+dDASZ9rtBL//b3rWX1F8ZRsb3DHfvb/6w9J9hyi9Gu3cDBnA08+5ebgjonbnDabno98eD4fuKsWk+nSzMk98TzIy1/AD3+yk8OH+8esV70kn49/vppl3scJ7jjCO9/cTdyfwFJRwroXf425tPBC7/oF5WKKW1VVSUwwwnm+UZJJwp19JHzHfVD0Di16p46kpCMq2Qj64wS8kTHVHSB9nebmWFBVlf6BEPFokvFad4IgYLNrsRFASMnoc7JOCkE+kdH6t0uX1NDT03lM3N577y309XbT29tNVkYG5WXlx8KQc3Nzeeyxx076nq6uLubOnUtmZiYHDhzAaDS+J8Tt5R1zMsMM54DRoGXtFSWsvaLkYu/KaQmHE7z8ahOPPXWII/UDp/8DQGeQyS22gwqplIqSUkamFIlYEs9QdMJe/elGIp6iv9tPfzccGedzSc7AnlGII9NIRqYG17z06K/TnMJpTpJvTmDRRBFiUZL+AAlvkMM9Jt7qdrC/TWSo/cRe29MfFL1RQ2GZg8J8HcU5AuX5GvJyjTgcero7OlBUyMzMpbk9QGtfiq4B6OlL4O4OMNgXGLccwYmcrbAVRYGSMic1NdksqM1hYU02RUX2izZiqXdlMPdr9zPnK5+k5/m36H5+I549h/AeaMTT2MKRR1oQJAFnpY3shRm48k3kSAKCLCLK6bJUojSyrBdJxVL4tgTwNvs5fNSPvzOEIS8H56JqHIuuYt5nFpC9bjmi5tIeiZrh8kDUaFj6m/9mzxf/jW9fdZh/19RwZK/72OeJeIovf+VF/uvb17BhXdk5by9z2TzWv/wb3G9uY+9Xv49VeYFvfP197L5lDU890UXD3rElsny+KD/+6XZ+/1Ad994zn7vvrMFhn16dBJPB60vy+pt+Dh5+ftzP199Rzd13OJnX/xBdzxxm9w8PoiRVslYtYs0zP0WXMXW1QmcYS0JRebvnzB35pwydBVzv8rk4Fvw04jlimvi50T0cSi/IAi4YI26NZi0ZpiSiz4suJ3vCEGRIl6Tq7vWPcToHeOTRF/jdrx/gO999gOuvvY4//OnhU/6s+++/H7/fz+OPP47R+N4o/QUz4naGGS5p2ju8/PFPe3nhpUYikfHDZk4kt8hG1cJcFs+VWZLTi1R/kEjbAOG2IUJdgyQjMoaSSpwr5yOVzaY1IdHgjtPUHqK5JUh/b+iSEbwnkkoqDLmDDLmDHD3lmgJgHZkAlJHp1BhMGsqrM6kuFamU3eQNtqGNdeGoXobrqmUYcrKAdM943aG02+aiKxZSOd+Aqqr4DjXR9/Ir+LTtpJaY6MidS12PmUP7PfS0ec/6d+v0MpXVLhbMz2XZwjwW1OZgNE4/YSfKMgW3baDgtg0ApOJx/IebGd5ziOE9h/HsOUzDk/WkIieHJQuiiKjTImo1iFoNGpsFx4IqnOvnMusfqnEumos+y3kxftIMM0wKUZJY/ONvs/+fH+Abi3fzHc1S9u88LjKTSYWvfv0VvvFv67nlhsop2Wb2uhVcu+1Rup56lX1f+x9cruf49lfvZutty3j+mU4a9vaN+ZtgIM7Pf7mTX/92N2tWl3DbzVWsXFF03h3wFUVl2BOhvz9If38Id3+Q4eEIwVCcYDAdRZVejhEMxYlGkySTCqmUOjJXSCaViUNENSLv//xybpo3RFn3H9j/0wPHSv2UfPgWlv/6P2fc0mc4a7R6mQybiDbgRRYcaEuKJuxMVhQFd38IzwSVA/RGDbn2JKo/LbZF7amf5X/84x954YUXuPfee7nmmmvO7YdcYsyI2xlmuATZd6CP3z9Ux6ZNracUm4IAFfNzWbAsh5WVEYqTTURef5C+B1qoqxvGsaCWrCsXkXvrHWQsn48x/+R8jkrg+hNe+wIxDjf1MdjvZ9iTbmR4vFG8vhgeT4xINIUkiSOTMGZSFJVYXCGWgGhUxd3lI3oGodM6vYTFqkeSBKLRJAF/7LSjnOcLo1lLXrGN6hory6sV5jq6cSbdiIN9BIQAvrYA/Y9so+k7flKSA/viRThWLkBRQggOCwObduDduJnwkT3oTTEc5VZyF1nRFukp1ngwZ+gwGrM4YNTQ3jhIKnnq36nVSZhNEg6DSmWBhffds4R5S0svyfJLklaLY8EcHAvmUPaJ9HtKMknc40fUyMcF7QQhXTPMcCkhCALz/+ufqH/g53y14m2+r13N7nd6jn2uKCrf+ObrdPcGuP/ji6ck0kIQBArfdy35t66n5fdPsf/+/6Ok+km+/fd3s+N9y/nrM53U7+4Z83fJpMIbb7bwxpstZGQYufmGCm69ZQ6zSs5+ZDOVUuju9tPa5jk2dXb6cPeHGBgMTShMz5XMXAsf/ttlXG3fgb1+Kxu/XcfA/mEEUWTBA1+h6u8/fsnl4c8wPRAlAadThynqQ0oY0BUWnTIFJhyO090TGNc0CsBi1ZJviRDrDaNx2k+7/f7+fv7u7/6OjIwM/vd///dsf8Yly4y4nWGGSwRFUdn8dhu/e6iO/fvH9qyfiM1pYNn6Uq5dCeW+XfiefpCe/+7hnQYfmSsWUvSBT7Hs0evHiNnTYbPoWLlobP7XmebqqEqKSEc9XQfrOBpxcmDITv2RMA17e4mdYgQ6Fk0Ri4ZOes9q0WCz6TBZ9Chqun5jJJIkHEoQDsUndCo8V8LBOEcPDXD00ADPkh4ByCkqo3DWQkoLRKrWxJhz1zCVqhtjfBAlFiA6/Bp2TwylS8VWbCbvWgOBW1bRlczl7W479R0yLZtDtDUMEg2fWXpKPJZiOJZiGGjuHmbTgdeY60qyJE/DNe9fReGKuRe9oaYkkyRDEVLhCKlIjGQ4QioSPbasxBNobBZ0mQ70WU60ThuinH5MibI8Mwo7w2XNnH/8LEd/9Rf+3vUaP153LVvfPPke8Mtf7KC1zcO3/3U9Gs3UdOyIskz5p+6m5MO30Ph/D1H3oV9SuORxvvXlO9n1vhU8/0zXhDm5Q0NhHvzjXh78414qKzIpL3OSn2+lIN9GYUF67nQaUFXweCK4+4P0uYO43el5X1+AljYPHR1eEuOEYJ4v9EYN195dw9VXZ1Az/ByhV+t4+bv7iPniaB02Vj3yv+Res+qC7c8Mlx9KSmV4MIpqt6fNQU8QtqqqkkopxGKpdHslmhxTimqU0Ue2OeElPhTHPHvWpDp1v/CFLzA0NMSDDz5IZmbmlPymS4kZcTvDDNOcRDLFCy828rs/1NHR4Z1wPUEUmLMoj9XrclhX0oX4+p9o+9sDvLN3COeSGoo/eT+r774eU/G5u2+eK4IoYSypoaKkhgpgTcNBhqxv4r3Wzp5QMTv2Rji0s2tSpkv+QAJ/IAEET7vu+SSZUOhqHqareZitI++JooCroJD8klpyXDIuu4orM4FOo9DQqaNxU5KOo8MM9XkB75TuTzCssL1NZHtbip9u3USpaxPzHAnWrp7NktuWo890TrnYTfiDhNq7CbX3pKeOnuPLQ4OEVQVBKyHoNYg6GUGrQdBKI2HFEkgS+uEB5KFh4p44UW8MZGPa3TrTgT47E2vlLGw1s7HPnY21qnQmZHCGy4ryT3+Q9kctfO7Qs+iuu4W3Xu446fNXX26it9fP//33TdhsE+ftnSmyQU/1P36a8k+/n8Pf+xW77/g5BVc+wTf//n3UvW8FLz7fy/7tXSQnKJfX0DhIQ+PYmtkGg0wyqVxQ8Xo6UskUUtcByg820vrYIY482gIqOBZWc+VjP8RSVnSxd/E9h0YUuDLPdrF3YwyqohDtGyA86COsMxERTv+8GX2qKioMeSIMeSJYrTokSSQWSxKLJceUnxrzHYKAzalDEkfWk02Yy4sn9cx+7rnneOyxx9iwYQMf/ehHT7v+5ciMuJ1hhmlKLJbkmefq+e0f6uh3Tyzc9EYNV1xbzo1XyZQHdjP4x19x8PkOElGBWR+9jSUP3Yu9pmLCv58OmCtrMFfWUKSqFO/byxr/bsIrnRxWytm2P8HBHT0MneIYTCU2pwFXlhazMe3I6/cn8XljRM7CeVpRVPo6fPR1TI1RRnGpgyWL8jGZtdQ3DNBydIihgfBp/05VodkNzW4NTx1pQ/dgO9lWlWxdnDxdkgK7jorqPMoXl+EoLwRRQE0pqKnUmHnc4yfc0UOgrZv+5j7cHQP4/EECspaw2UrEbCckmwiqOvxxB/6wA79cSdzxrsZtCoiMTO9CNovYCnQ4HDIOq0iGRcGhS+CUQ+QEuwg+9gaN/zWIvz2EPj8fe00Ftrnl2Gsr0uH1hbkXfZR6hhnOluL330TPyxbue/V3ZH7o/Tzx55PTTw7ud3PPfY/x0x/cTEnx1BodaR02Fnz3H6j4249w4N9/zI5bfkLhuif55y/dQu+HlvHGHomdb7bSeXTo9F8Gk/KCuNAk4gpPva7w3GtFLO3pZ5nQwfx//Di13/riBamNPsNYBEFAK02ve7aqqoRjCj5VR1CfjhqaKLBYZ9Ck87sn6Pzx+ydfHUGrk3FlSmiDQ6gjIljnnLzx4+7duwHYsWMHOTlja2UPDKRNR7/0pS/x1a9+lZqaGl577bVJ79+lwIy4nWGGaUYkkuCJpw7x+z/uZXhoYuFiyzBy1U2zuXV5GMfuv9L299t5Z+cAxqJ85vzz31H2iTvROqZfT+ipEAQB+4KF2BcsBKCksZErQq8TmSfQoZ/FgU4Djc0x2hoHcXeem2DUGzXkldgpLjZSnpei0pWkujKbrMJyRJNrzIMkEIzR2xugp8dPR7eflnYvra0eOjs8eD2Tr796JuRlG1iyrJgrVhaxdFE+DsfYcO9hb5Q9Bzo4sL+dhtYQrc2B09a4jcVVOgahAy0w0pjb40b8k5sMi4AspsOhBEFNz0m/FgVIKCKBaHpkWFGcwEiocIK0UD1m1p0amc6cZEJhaCDC0BjjbwkoRtbMIq/WxKwbNZRmRCjGjaZnL96fPsWev/GgyhYyViwgc8V8MpbPJ2NJDbLpveMUOcOlT951a9BYLVz70/8k/wsf56e/7CIRO3499fUE+MgnnuD737thSmrhvhtjfjbLf/kfzPnyx9n3Lz9g600/xbXwET74wfnc8w/XUue/knfeHmT3xtZzKmt2MUkKMlvzV9Bcs4aCa9fNuKjPAKSNnTzDITzeGPHTRBwYTFpsdi1GOY4iaAnGZIK+6Bl5iRxDAJtDT6YhSry3n6TOfKzMz0mrTVLk+v1+/H7/aT8fTwBf6syI2xlmmCYEg3EeefwAf/zTvgnzLwByimysv6WcG+f2o33tDzR9eA9HGv1kr1/B6qf+nbyb1l42RjuWigosI3XSylMJVtTXEXS1EF6ZxG/IpC9motevo2dIYGBYIR5XSMRSxOMpUkkFrU7CYJQx6AVMBoEcR4o8a4JCu0K2Q0dGXgE6ZzGCznqaPQGLWYe5XEuO4qew+SgVR/cw8PZuAo1tRCQdA8YsPGWzGcqbRY9qxz2cOiNnaVkjUJItUpzsxdXRgL2+AVMshPyGEeNNV+G/81rMN16Fxmw66e+cdj0bVlewYXX6OEVCwxw8dJSN24epOxSm6dDAhL3J70ZRYMB3up2++OGFyYRCR2uAjlbYCIAVQViKK3cdVffpmJcfJjvSSHD7E7T96pf420PY5laQuXIBWauX4Fq9BGPB5fdAn+HyImvlQox5PyTxD1/mW1+6g+/8Oox/+HioQygY52++9Cyf+cwy7vvQginLwz0Ra2Upqx/7EZ699Rx+4Nfs/PqL6L77DqU3FrL8w+sZvHUZO9od9HUFGeoLMtAbwN3pw+eJoJ4HvwOtXsaRacRs12O26jFZdZitOkwjywajBlkrIcsisiwiadLL0UiCzc83ULe5fYwPQ78nzle+9jJLl+Tzt59fQc3cM/OimO4oqRTxYR+xgWFigx4SgRBKPAaJCKTCkIpBKkEyHiGzsx1Fhb7BHjQWB6LejGi0IhnNyGYjsimdJqKxmi+76JhYNMHgoJ9ASDmtV4fRosNhFTFKEaRUACWcQtKK6CQZa5aRsGIh4IsTDsRRJ2gICIKArBHRaGU0WhGLUUUX9hBpC2MoyEPnHH9wYtTTJBIZ31X5m9/8Jt/85jcn3PdzqXN7qTAjbmeY4SLj80X58yP7+dMj+wkG4xOuV1KZyXW3z2JDYQvxx/+Ppq8dItgTJv+WdSx96PNkLpt3Aff6wiNKGsw1yzDXLDvpfTUZJeVzkxjoQkklUZUUiqqgqio6qw2tPQNBawKNCTSTfyCrqkrUPYh3fwOevfUMbqljYEsdsYFhACStiKPCRt49ZVhXziExp5b93mz2t8j4jvgQ/YOkziDPLJVUiWitxGpKsLzvCpYV9GPYvYWBV+rpeeFVOh59EVGnJfe6Kyl837XkXnflsRJDJ2IwOVm6bBlLl0EiHqGvp41N23vYcTDGwb0DePpD42z90kdVwd0TxN0THBG8hWTmVDHnDiPzS2KUKK1o6nbT9J2X2PZRL4bCfFxrluJavZis1UuwzC657BprM1z6mIrzWfK737D3i1/lgY/P4T+etNPd6j32eSqp8rOfbufZ5+r5+leuYuXywvOyH44Fc1j1p+8z/z//jiP/+yBNv36MI3/6JdlLnqJqzRxUbTk9fVbaj0I0dm6i1mzTkZVnJSvXgtNlwpltJiPbjDPbjNmqS1+nqoJGiaBJRZBTYTRKFE1qGEmJIaopBFKIqoKgphDVFCmtzOz759F5dw1vPHWYnW+2jhHfO3d189FPPMHqVcV89jPLmFM19v46XUn4g/jqm/EdPkrwaBvJ4W6IDCERRGtMoc/Qoct1kMjIIqS1ExQM+BUd/rhMJK4lntITS1qJO3ORRJXmVApzJIFV7cec6sEciWLsD2NIBGDIQ9ztIxkVSCV1KKIJdFZEcyayMwdjQR6mknxMRXnIxulbE1lVVaLhKH5/mFBEJRo79fNaEARMFg0Os4I25iM1FCMSSZGMJlGVtEOy1qJBa4tj18uY7XqiDhOBgEI8lkQQQKOVkLUSOllFJ6WQ1ASiEkYJx4j3xogpGiyVZUi6icPjy8rSNa937NhBKBTCZDJNuO57FWGiHoUZZpgsgiAUAJ0AjY2NzJ49+yLv0aXB8HCYh/60j0cfP0gkMnEIy+zabG64vZirnAcJ/uE5Gh9tJOaJU3jnddT8y+dwLJhzAfd6fM7ULXm6kYrG8NU3493fkBaz+47g3d9wTMgC6DN0ZM51kDE/C+PKWvx5VdR12znYItDc4KGzeXhKyxKJosCs6iwWLbRyVZWf3M49DL24h54tboLd6XB1e20FOdesIufaVbhWLzllQ0JRUvg93TS3uWlqC9Dem6SrL0lfb5j+Lj8B79mHVhvMWsxWHRabHqtVg80m47BpcNi1ZDr1ZGSYyMqy43I5MRm1CIAogoB4LNxZFASSSQX3YIje/iC97gD9/X4GB4IMDoYZGorQ1xPCMzR+b/VkyMwxM6/GxPLyMHOjh4ls3sfAnkE8TT50WZlkrV48IniXYKutmPYREJf6dXe+uZyOj6ooHPqvH0GWhwf2zePgbve4662+qoSv/v1qcnMs521fvL4o77xxhFcffoe9rWH88tk1rh1ZJnIKbbgKrLjyrWTnW8nKt2Ky6BCVOIaEB33Siz7hHZn7MCS96JI+ZCXKmXZFqYDHUEq3bSkHBrJ4+rd7aDncP+H6q68s5p73z2PZ0gJEcXp0fKmqSqTbzfDug3jqDhHtOIIYdaOzpjDOziGUV0qvkEWnx0DXsEzvgMrgcAq/P0HAGz1lNYLJYjBrsTn02GwanHYJhxWyrCkyrQmyzTEyRS+OkBulq4+EN05SMaDKVuLrrkfKyERnMVNWXIKo1SLKF+4eq6oqqVgUvy9MKKYSiakkT1NiD0CWRSwWGYucQIolUBQhfe6lc3dGv5yEL0Aykg7Rl7QiOpsWrUWDqtWRErUIqoqoJhCScZLhJMlIkmQkRSqWQtRq0WU50WdnnNTJOjrK+uabb7J27VoAAoEApaWlDA4O4nA4qKysRKfTkZOTw1/+8pfT/p5zGbltamoimUwiy/Jp2/pNTU1UVBzzeylUVfXMSkCcAzPidoZzZkbcnhn9/UEe/GMdTzx9mHhs4nDRqkV53HhbIavN+/D85mmaHmsmEU5R/IEbmfvPn51WJlHTvRGpqiqxIQ/B5k6CLZ0EmzuOLQeaO4h0n9xQNGUbsJdbcZRbMdcWINfMoUsuZk+7icPNKVqODNLfNXEuy/kgr8TO/MWZXFUToSpxhNBbu3Dv6GNg3zCJUBJRqyHrysXkXLOK7HXLsc+rRDac2k1VVVXisQDhoIf+IS9H2wP0DcRRlHRDcHSuKul1FVVAlgScdplMp4GsDCNZmVZMZis6nRGtJJ330c9BT4T99f0cqnfT2NBPa4uH3q7AGYWAQ7p0U1mVk8VzZa4oHiK7dQ/D77TQu6OfYHcYjc1C1qpFuNYsIWv1EpxLaqad2cx0v+4uNpfj8el47Hl8B57jIflmXnu+Y9x1tDqJ++5bzCfuXYhWe+7iweeLcqRhgF17eti2vZPD9f1ndL1Jskh2gZW8WQ7ySxzklTjILbZjNGvRJIMYE4OY4oMYE4MY40MY44PoUv6TxGsqoRDpjxAeiBIejBIPJEgEEyRCSRKhBPFgep6MpFBS6ogBnoqSVFFTKoIkkLMkk6K1eWTWOPDrcjnq3MCmvSLPPViH7xT+FkWFNt5/Vw0331iJ1Tp1DtWTIRWL46k7zODW3USO7oNgF9o8E8NFc2gnl6MDBjr6wN2fZLA/dFJe9sVEFAWsTgPOTD0ZThmXU+CqlVk4bAaMJiMl2U7EVAI1kQRVQEUCUUKQNAiyjCDJCLKMqJERZAlBFCf1bFFVBTURR43HSMYTxJIKsZRAIgXRuHDa0dkT0RtlrAYwyyAbjCgqIAioCCNzAAFVVVFVFUEUIJkk5Q+Q9PpJhdMdxhqjjKQTURU1LWbjCoIooLGY0djMaKzmCV3/xxO3APv27eMb3/gGW7duZWhoiFQqRXFxMW1tbaf9XTPi9nxtVBAygXwgC8jguA3JANCiqurFT+qaYdLMiNvJ0dHh5fd/rOOvLzSQPEW4au3yAm66NY8V2j0M/vJZjj7VSiqmUvLhW5j79fuxVpZewL2eHBerEZmKx4l7/Ol8okEPkZ5+wt1uIj39REbmo69TkbEjkzq7FkuBCUuhCdssC+baYqTqCnrUXA71mmnqFGlvC9HVPIzfc+YjhoIAJbMcLFyQR+XsDDKdOpqb96GRBTKy5nDg8CB79/fRcnTotKUBTsSeaaRynouFVSLLSzzY2w/i3XiA/l0DDB32oCRVBEnCVl2Gc3ENjsVzcS6qxrFgzrQOEztbIpEEBxsH2LG7m7q6buoPDpyxu7Ujy0TtfDtrauMs1jUQ2VRH3/Z++uuGSEZTSHodWWuWknfjGvJvWoulfGy95wvN5SjeppLL9fgM1x2m7SffpveG9/GLxxN0tXjGXc/uNFBd7WJuVRbVVS4qKzLIdk2cmqEoKoODIY40DnLkyAANjYPUNwzQ13fmTvUFpU4q5udQMT+HkspMZI2EIT6EOe7GHOvDEuvFHO9DmzouKmP+OP6OIP72IP6OEKG+MGF3hHB/NF0WbIqaq8YsPWW3FFF+RwkB11zqzWt5/c1h3ny6ntApXG0lSWDpkgI2rC9j7ZoSnM6pN6kLd7sZ3rGLSMMe4kPteE0W+uwltEedNPfJdHQn6esJkUpees3kL36+kqJCCw6HCZ0+CwSQJBFJEpBlAVkCSVSRxLSZoSiAiIKoqogoCCN6RSEtMBUEFDUtMhU1vZxICcQTKomEelY17kfzYCUxfT0oyuj8bL4rXaJRFAREETSSil5NoImGkQURUacdKYOnQdJpkYz6Y3Xdpysz4vbEjQiCBbgNWAusBspPsXoI2AZsBp5XVXXPed/BGc6JGXF7ag7X9/PbB/fw5lstE/Z2C6LAgiuKuOlmF0vUHfT/4q80P9uOkhQo/djtVH/t/mlRey8VixMb8pAMhkmFoyRDYZKhCGGPj73bd6DGk8ytrkYz6jo58oOP3WfeNT/+fnpZTSZJRWKkojFSkSipaPz4ciRG3JsWsvFh37H9OBWCCDqHDqPLgCnHgLXAhLnAhGF2PuKsIsImF60eG60DOpq7BTpag3S1DJ91iK4oCpRXZLB4YR7LFuezcH7uST39EzW2I9EEhw/3s2dfL7vqetlb10NikiZQkB7VrarNYHFFiiXZfWgO7ye4txVvkw/PUT+BrhCoIIgi1qpSLJWzMBXnpaei9NxYlIcu03FZ5J2mUgqHGwZ5Z2cne/Z0c/igm3Bw8mJXb9Qwd6GLK+bBmuIetPv30L+li56tbgKd6Zxly+wS8m5cQ96NV+Fas/Si1Nu9XMXbVHE5H5/owDCHvvkAhpIIL5uv54nHuwmfwrNhFItVR1lZBrIkEArFCYUThENxwuEE4bNxeB3B5jSMiNlcZtdmY7HKWKNdOCJt2CIdWOJ9yEpaOCpJBW9LgOEGL96jfvztQXwdQWKe0+8/gCDLaB1WNDYLWrsFrf34smQyIGo0iLKEIEuIGg0pVaGpsQmlvg31cCtKPP07NSaZ2bcXU3F3Ob78BTQYlvPa617eeqb+tMdSFAVKy5wsmJfDwvm51NZkk5drPaPw5bjXi2/3DgINB+ga8NKlWuhSnXT59HT0KfT1RE4Z3XWpMUbcvofRakX0WjAQRxMOkQrHUVIqkkGHxmxENpuQLcZpFy00I24BQRAWA18C7gRGW3iTufJP3KkG4CfA71VVvTydUC5xZsTtWFRVZfuOLn77hz3s2tU94XqiJLB4TQm33JjB/NhWen/6PK0vdKIiUfbJu6j+p09jKj51mQdVVUlFoqiKAoqKqijHJlRG3ldQRz7j2OcqqXCEuC9Awhck4QuQ8AWIjyzHBj1E+4eIDYzOh0n4L0yt2dMhGyR0Vi1amwatRYvOqkFn12HM0mN06dHnWJEKciAri5jWxlDMQrvHSHu/hg439PeEcHf5GHIHzylPVquTqJrjYvGCPJYszGX+vFyMxonLSUy2sR2JJNi6vZNX32phyzvtBM6gRp4kixSWOykotlJaIFCRG6XCMoCmvYVwXRPehmECXSEiA1EiQ1GUE3KOJKMBY0E2WrsV2WJCYzEdm2sspnT4lFGPIEkIkjgyl9INyRNeH18WR0LMRMSR1xq7FX12BnpXxgXrpVYUlfqGAd54u42tW9tprB+cdE+8JItU1LpYukDP1VWDZHfvwf16M52b+/AeTYemS0YDOVevoPB911JwxzVobecv3/FELmfxNhW8F47P4I79HP2fB9DfsYzf7C9ly+sdZxyifzZotBJlc13HBG12vgVrvA97pBVHpBVbtBNJTed3+juCDB3xMnzEx/ARL56jfpRTRC9JBj2WihLMpYWYinIxFuVhKso91gmnz3IiiBNVHB3LiefB+iuuxPtOHe1/eo7OJ19FTaWQ9BIl1+RTflsJydolNOqX89b2OFteasR9BuknsiyQlWUgL8dAVqYeo15ErxPQyipqLEIoECUSTRJNqPgiIsNBAY83hc8bP6uRwUuNGXE7PoIgoNNLmHQKxlQE1R8kGUlXW5C0mrRLtcWEbDIg6XVndO5PNe9pcTsiar8NXDv61si8F9gJ7Ab6gWHAAxhIF0t0ABXAUmAeMNpKVEfW/X/AD1VVvaSKqgmCUAR8EbgJKAJiwFHgUeCnqqqeevjp7LaZC9QDo17iG1VVXTvV2xnZ1kURt6qqEu7sxVN3mOG6ejx1h/HubyAZCCEZ9COT7thcNhowlxWRd8NqstetOC8jLYlEildfb+YPD++lsXFwwvUkWWTZ+lJuvd5Gtf9tun7yIofe8OAxZmG7YT2W9asJC1o8vghDfT6G3V6CoRgaQUEnKmiFJDoSaJU4WiWGXo1jiXixBAYx9fci+72koilS0VT6Jnm2D04hnS+iMWvQmGS0Zs2x/BFJJyFp03NRIyJp06/T6SjCsb+Hd70WTujhGimimv5YQJSF49+rlZB0IqJOQjToEPQ6RLsVrFYUvZmEaCSKHm9Mz3BYy1BIQ79Xon8YhoYTeAZCeAbDeAfDxKPnbqIBYLHpmFebw+KFeSxekEdVVSaaMzDEOJvGdjKpsHdfL6+91cJbm1rpP4sQQUEUcOVZyCuxU1Igk5+RItOcwGWK4BS9aIaHSPYOkGjvI9rnP2ZykYyl56loOk8oGU2hJhUEWUSUBURZRJSE9GtJQJAFBK0MRgNRo4OQ1kxYYyYkGwkJesLoiSUFYkmBeFIgrkokVCk9V0SSqoDJoMXpspGZ58BpN+CwG7BZddhsepwOA7NmOc655EkgGGPz1g42bWln5/bOSZtUiaLA7HnZXLncyDWVbjK799L3Ziudm/sYOuwBFUSdlrwbr6LknpvIu3ndafOez4X3gng7F94rx0dVFFr/+AzeTY/Sd8Od/OqpFO1NQ1O6DVkjkltsp7wmm4r5OcyqysKqDuOItGIPt2GPtqNR0hEvge4Q/XVDuPcM4t47NOGIrGw2Yp9XiW1OGdaRyTanFFNx/pQ24Cc6D8Ldbpp+/meO/uKRY+aBWbUOym4vwb5hIW7HQrZ05LD5lXYO7+4+ZTrRxcBk0aIzyETDyUmN2k+EVitiNGkwmWR0OkgmVQKBFD5ffEpNEi9ncSsIwoQlf84UnUHGYgATEVR/iEQocawTWhAEJKMe2WRIi13jiOC9QBFX71lxKwjC74CPAKN3pj3Aw8ATqqqO734w/vdogTXAh4A7SIs0FegAPqKq6ttTud/nC0EQbiL9+8cvWJUemb5RVdWWKd7u46RHzEe55MWtqigM7TxA93NvMLhtH566euLD3mOfG116Muc6MGTq0Zg1yCYZOcOOmpGFX5+BO2VFjcaQ+9wIbT3YTDZcq1aTd9M6DNmZ57RvHm+EJ548xCOPH2ToFMYUOoPMsvWlLKxQSTXs5+Dr3bT6LQwaMggrUzeKpdVJ2J06HA4tTruIzSxgNylY9SlsugR2KYpNjGCUY8iSgEYrIGvSuSajIkXU61B0OmKKlpiiIZqSiac0xFJSOq8lJZBIMJLjAokUJJPC8XwYdcR0YSQnRlVJjyQDcPJ7o02GZEognoRYnJG8mZF5PEUiniISjBMKxAgHYoSCcSLn8EA/HXqDTPnsTKqrsqid46K62kVJsf2cHiLn2thWVZXmlmG2bOvgne1d7N/XS2wKhLvBpMHiMGC1G7DaNMiygCSO5D9JIIkgiyqSqKKq6VIf0ZhKNKoQjaWIRRVi0RSxWIpwKEE0FD9vo0darURVVRYL5+Uwb14O82pyyMg4+9w3VVVpah7itY2tbNzUStORiTulTkTWSsxdnMeaZVrWl/Zg791P92uttL3chbc5kF7HbKTg9g0U33MzuddcgaiZeFT/bLjY4i0VjZHwB0kEQiSDYZKhcNrIR1GOpR5IRgOyUY9sNKQbZUYDkkE35cdiPC728TlblFSKqHuQcJebSLeb2KCHuNdPwhtIp2acsCzptBgKcjAV5qLLcuDZtQfL7BTuxes53GfmaLdAd5uPnjYv0UmGHeuNGvJK7OTPcpA/y0n+LAeuPDNm1Yct2oEj0oY90oYule5oC3aHGDzkwV03hLtuiLB7bGeRZNDjXFSNc0nNsclaMeuCjEKd7jxIxeL0PP8WbQ8/R/df30SJJ5ANEnkrXOSvL0K+ei3d+kq2HtGxf0cf9Xt6pqzDdDLo9DK5Li3ZmRKiXsuQX6Cz1UckdGZh5BmZesrL7VRVuSgtyaKk0EZhoR3bSHmldx8nnU6PPxBjaCiMeyBE/2CQvj4/XV3DdPcE6O0NMTgYnbQAns7iVpJFZElAGnn2yZKAJI3OBWQpnYc70j8PaVupNKqanoTRPGBOmlIKJJIq4UiKaCR1RiJYZ5CxGAXMUgwhFCIZPm6aNoogishGPaJeh6TXIul0iHotkk475dfXe1ncKkAceBD4vqqqjVPwnTrgbuDrQBXwTVVVv3Wu33u+EQRhPrAFMAJB4DvAm6RHqj8IfHpk1SPAUlVVpyTmUxCEW4BnSY+Ou0beviTFbSoao++NbXQ/8zrdz71BpHfg2GeWQhNZ85y45jmxLplFML+KPT3ZtLkl+gYU3ANxBvtCDLmDEz6INFoRo0HCoBfIdsjMrcln0dIyqiqzyM05fU3U5pZhHv7LPl54sZH4KXIkdXqZjBwzqUiY/v7z1/CfCkRJQJRElJQypb220xm700BxsZ3qqixq5riYU5VFUaF9yss/THVjO5FIceCgm3e2d7BtRxcN9QPvifC2d5Ofb2XBvBzmz8tlzZXFuFzms/6ugYEQr77VwmtvtnBgb8+kjL4MJg3zVhSwfpnEVblHEQ/U0fZSB+2v9xDzpjtgdBl2ij54E7M+ejsZS2unpKf9fIs3JZkk2NKJv74Z/5EW/A2thDv70kZtPf0kvGfvGC7IcjrU/VjOpBmt3ZpetltOzqe0W9HazOn5yHuy1XzaUk3TUdwmgiEi3e600V23m3B3/7teu4n2DaKmxn+eiLKAzq5D79Sit+tQVYj54sR9caK+OKlo+u9EjUjG3Aycc2xYV85BnFdNp5JHfa+J7gEBWSOhM0joDRr0Bhm9QcIwMuVYIphTw2nn4sQghsQQatBDIAjxuIqnJchQo5/howGGWoJE/Un0yRj2mPdYg1/rsJG9bjnZ61fgWrME65yyi2aWcybnQdzrp/PJV+h+9g3cG3eS8PqR9BJZNQ4yF7gwrFtKuHw+B9126lsU2hqHaWsYIBw4945Wk1VHdqaWLG2cjMgwxbYgjopMWoRcdtcrNNcPTfqZLAhQWmZj4aI8Vi4tYUFtDg7Hqc//s40s6nMH6Oj009oxTHvHIF1dPnp7Q/T1hYlFj5/HF1PcimK6A18ji2hkAY1GRKsR0WkltNoRd2bh/He0KIpCKBQjGIoTCifPKKdaq5Mx6EWMegUDMdRwhFQ4QSKcPCm96EQkrQZRP2JcJY86UEvpuSwjaKT0dSkIk3omvZfF7U+A75yPHyGkj/zdgKSq6p+n+vunGkEQ3iRtopUE1qiquvVdn38FeGDk5TemQrALgmAGDgOFwEeBP4x8dMmI24Q/SPdf36TzqVfpfWnzMdMgSS+RtzyLgjW5ZCzJJ5JbSUMwn22NRg4c8HP0gHtKRrFGMZs1zK7IorI8A0VRCYXjhEKJY/NgMEZHh2/KtjfD+UUQBXJzLRQX2ymd5aR8loNZJQ5Kiu0XrLzD+W5sB4Nx9u7r4XDDIPWNAzQ1DdHb7Z/WnSlTjSDAvNocrrm6jKvXlZGdffZCNxCIsemddl55o5nt2zqJx05/f8nKs3DFunxuWhSgIrUP39sNtL7cRc+2/mN5htbKWcz66O2U3HsrpqK8s96/qT6fIu5BBjbvYmj7Pga37WN496FxXcYhLZ4MTl06ZcEoIZs0yAYJURQQRiaAVDwd0p6KnTAfWU5EkiSCyVPmX54K2WI6ZiYkG/XpsiEaOW0opJFRBIGB4SGQRXLz8pBHG3GieLwxJ47Mj73Psc9Pek8QYLQciTDy+8b5LoBkMDx2lNUXID7kPbVvgQB6pw5zrhFzvhFzrhFjoQ25KI+UM5MhMYP+uJkerwa3R2LAo5JKqei1oNepGLQqellBJyTRESczOoB94xsMbGpD43Bir8jElJHEnKtBa5SR9RKyQR6ZJASthDusp6VHoq1PpmNIR3fYQm/MiC92emGq10Bpto75tTksWzeH2tqc8+IofKac7XWipFJ499bT9/pWhnYcwF/fTKCpHdQklgITpiIbxkUVyHMr8Vtz6Y8bcAc19PsE/EGFeFwhHlOIx9KpQToNaDWgk8AgpjDHA2h6ejC4+zB3tZKZ6sd1bRU981ezczCLun1h3N2T70Cy2XUsW57PhrWzWbGsAIv5zFKvpvp+oqoqXm+Uji4vrR1eTPoAJqOM2azHZM4mlVRJKerZp02NIAgCogSSmHZd1sjiMfGq1crodJq058M0NEtMJhUCoRgBf4xQKDH5UV0BdDoZvV7EpFMwSHGIxVGicZS4kr7vxs5sgEIQxfR9bWQuiOLI/XFkyBqB7rCfqM9PrGeA6MMvHbsXnnwPTb/XG/Tz/hd/P/r1l7a4nSGNIAhLgR0jL3+hqupnx1lHBA4Cc0jnHmerqnr2doXp7/wR8LfAm6qqrhcEYfQ/eFqL27gvQPdzb9Dx2Ev0vvw2SizdCzoqaAvX5pKxqox+Ry1vt+ex+0CMhn19DPQEpvS3XGw0Wgl7phGjWYvBpMVo1mI0yViMKmadQjyhpENBowqRqEIsqhCNpggFE3g9MYK+SyodfUoxGDVkZprIyTGTm2MhL9dCfq6FnBwLuTkWsrKMZ5Qfez64GCNJ4XCCpuYhjjQMcKhhgJYWD0NDYTzD4QvixKnVSRjNWsyWdE6XwSCh1YrodCJarZDuPdcJaLXpsPhgVMIfFgiGUkQCcULBGOFAnHDw7M7v2tpsrrm6nA3rS8nJPnujp0gkwZsbW3nupQZ27eg67YiuKAlUL85n7WoLG0o6cLr30fVaG20vdzHcMNIpJghkr1vOrI/eTuH7rkFjOTMhfq7nk5JKMbzrID3Pv0XPCxsZ3n3opM81JhlriQVbsRlLsQVNZREBez79goP+qIk+r4w/JBCMQDCsEg4rJJMKyaRKMpVutB4r8yELaEbKfcgyaCTQacGgUzFoFAxSCp2YwCDE0aVi6FJRtLEQ2mgITciPxu9D8vtRgzGS4XRN00QoLY4ToQSp+PTKhxwPSS+lTe+y9BizDBhdegwuA7o8B3JRLtGMPNqDNjqGDXQOyPQMKPT3xRgeCOH3RM5KBGh1MrPLjcxx+Cls3IXmjR045s7BuayGiMVJkzvBka4ILREtXQkDCXVqR69c2SYWLczng3fVUFuTfVEExlTed5VEIl0rvbUrXYbO4yPu8ZMMRdLGepr0iJhk1GPIyUKfk4lsMuA7fBT369voeWEjscF06SZBEsiqdWK+eQX12YvY0WrgwD7PGY0CF8+ycuWqWVyztoyaudnnFG10vp9PE436KYpKMqWQSiqklHSI77G0JdI1ZNMpTSqiIIyEEEtIkoAoilMeYXWxSCkKgWAcvy96ZkJ3BFkjIWtEtBoBjayik1W0YhIpGUeNJ1CTKdRUuu7zaC1oZeS1qowc49Ns0k2C8OAQocZ2ur/0wCnXHSLBF2kdfTkjbi8HBEH4T9Jh1AArVFXdPsF6XyUdrgxwraqqr57DNpcBW0mPFM9TVbVhOovb2JCHnhc2Hhe0I/b8JwrazCtmMeScy5beIt7eEWX/ts5T1qE7FbKc7o06kxIr5wtBFMjMMZNbZCen2E5BgYHynCilliE0w30I/hBSCvRGG8aS2RiKKxEMGQjixL3nqqoSjiXp6QvQ6/bT5/bT1xdkaDiM1xvF543i88Xwe2ME/HGS51gnT9aIaEZuphpZQiWFJAnodLpjD5u0X5TAiSZTIx186ffh2GfCSM07nU5Cp5PTk1ZCr5PR62X0OhmrWYvDYcBu02O367HZ9NhHDIfO1WToQjCdwiRVVSUYijM4GGZwMIx7IEhff4hhb4RUalSgKOnlkdeplIIggMGgwWDQYDRqMBpkjEYtRoMGk0mDxaTDYdenJ5seg37yuZWqqqBGPcTaGwgcPUw06CGlEcBpBUcm7qST/Z0Wjh4N0t4wSEfT0BlFa8yrzebG6yu57ppybLazH633+qK88tpR/vpSIwf39512favTwLK1xdywLMl8w2E4coi2l7toe7Wb6FD6fibpdWRvuIKCW9eTf/NaDLmu03zr2Z1PsSEPva+8Q88LG+l9afOxhjakUz1c8504azIRF1RzRC2lvtdASzd0dsVwd/kvuqmOKArpcFqdhE4vYtAJ6HUCBn16rtMK6LUgSyAKChIqIioCKqKQXhZHqmSmP1PQiim0wshEEg0JNGoCrZo27dMoMbSpOHIyjpyMoUTj6SqbooAgnTDCIaZN8WSrCdFmQrCYSRpMxHVGYpKBsKLHHdTT79cy6BcZ8gkMe1P4vTE8A+GzqqV9puj0EoVZMOxTGPZf2PZfRWUGH7lnAdduKL+g9+uLcd8NHG2n+7k36f7rm/Rv2oWaTN+nNGaZnFX5JK+5ijpms7MejtYPT7purSyL1M7PYu1V5Vy9ZhZ5udYp2+eLJW5nGIsyInR9vhihUPyczKoEUUCS0p0AosgJc5AEFXF0YBY1be450qlwjBO23T/YTygYxu8LcnhPK+pobrFC2l9FhdSISPb6PDzwwy+P/umlL25HRg//oKrqrin/8ksEQRA2ka7pGwLsqqqO2wITBGEl6bxcgG+pqvqNs9yeDOwC5gP/oarqv428P23EbTIUpv/t3bhf30rf69vw1B0+bjpygqDNuqIYj7OaXUOz2Lgzwb6tnXgHJ28oXVzioLDIRmGBjaJCGyWFNooK7biyTEiSSCyWxOOPMTTsZajPjbu9lwFPlFavlvaOON2tw2ds1HAqREkgp9BGQamT/FInRaVWZmeHyVTdGAOdpA42kHSHkF2VWJetx75g3nnv3R4VNtFIckS4jIiYpEIqpZJMppAkEa1GQqOV0GoktFoJzbG5eNI+TifRNp2ZOU5nRzISZXjnXnzbXkP1NGBeUERq9hyGtcXUDzo4ejRMa/0A9Xt6JmWaI8sia64s5uYbq1h1RdE5NbR7+wK88HIjTz93hO7O06cplNdkc+XabK6rclMQ3od3ewutL3XR/Y77pNFH59LatNC9ZT32eZXj3hMmcz6pqopnbz09L2yk54WNDG3blzZ8Ih1a7FrgJHdlNqa1i6iLz2bnUT31jVE6jnom3eB+LyGKArJWQpLFkY464Vgo9minXjKRIhFLEY+fg1P9JcDobxYEzqjTw+7QcecdNXzwrtpzMoObLBfivquqKr6DjbQ/+iKdj7+M/8hxj1BrsRnXNRX0LljNzqFs9uyP0Nc5+XBjq03L0uUFbFg7m9Uri05Zbu5cmBG30xNFUQiHkyN5uvEpTb87U2LRATyeEB2dAX7004ZTrhuPezi0919GX14W4lYhLfsbSOd8PqyqaueUb2gaIwjCAJAJ7FNVdcEp1nOQLnME8Jiqqu8/y+2NjgA3AzWqqkZH3j9ncTsiXk9FDukST+zfv5+iTBfR3oFjU6i1i4FNuxjevg81cfyilA0SucvSgta1shBvRjX7AqVs3K1S907npEOOrTYdS5bks2p5PsuX5p/WNGEiokMeXn/wOV4+lGJPK6hn0K6TZBGb04A904jNacSeacSRZaJwlo3y3DhOxY011oPc00poVwNBt4qQUY558RVkrlmObLq0RU40GmXTpk0ArFmzBr3+wuSwXmrMHKdzR1VVfHuP0P/iS6Q663CUyNhXVRLMmE2PtpKdTTr2b+vi4I6uSQldq1XHNVeXcsN15VRVZpx1x5Kqquw/0M/Tzzew8a02YqcJ+TaYtSxeXcyGVVqW2ZuwDR7Gvb2Xnq1uerb1E/cf33dDYS4ZK+ZjKMrFWJiLsTgPY1EuosvJlt07gfT5pNPpSPqD+OubGd62j+Ht+xnaupdY//GyMIZMPXkrXWSvyiexeDkbO/LZdlDlcF3/pN10Z7h46PQSmVkmMl1msrNN6PUaotEE4XCcaCRBJBxnaCiC1xMjGjm7RrDJqiMrz0JWroWsPCvZ2TqKshLk2aLopCSSoCALqfQouJpCQKHbb+RQl57G1iQdTcN0tQyf1lFYlgXWrS3m059YQn7e+asNfb7uu6qq4j/YRPeTr9L91KsEG9uAkU6jhZmYblzKkaxFbG8xcGCf94yizgqKLKxcWcS61cXUVrsuSOjt+X4+dXR0kEql0Gg0lJWVTel3v5dIpRTCkSShcPq6vxDpRaPMiNs06gnzjaQdlJ9QVTU05RudRgiCoAdG44ueV1X15tOsHwRMwDZVVVeexfZKSefuGoDrVFV95YTPpkLcTvok+ZGmgowJ2keiRiSz2k72okyyF2Viq87EY6mgPjabN+tk6rZ00t3iGf+P30V+oYGKUg3lpTpyczTnNNLp8SSp2xdm38EIfv/pGwOCAAVlGVQuyKFiXjb5WQIZpigGJYgu6R+ZAmjjwyQPH2X4wACD3Uk8Qgbx4hLEmlJE50SVoWaYYYbJoqoqSlsvytYD2AZaKKw1krOhnED+fHr1Vexs1LFvWyeHdnRNKhojM1Nmfo2ReTVGrNazH82NxRQOHo5QdyBGd9fpQ00LSp0sX1vImpoY5bpWHIEmfAf66N7qpvsdN4HOUzwyrSYEuwVCEVRfEJInN3QEETKqHeStcOFaVUi4YhGvH81l084E9XW9ZxVmLGtEHE49Nkc6l1qvFzHoRfS6tCOpJIIkCQiCSkoRUJT0bqUUlVQKUikVJQWJZNpwJxFXiMUVYrH061g0NTIlL+ooxcXC5tCRkanD6ZBx2kXsFhW7TcRmlTAYxDHPu1RKpbk1xqHDYY40xoifQ/6xKAmUVGZSVWVmYXmSpZkdWPsOE2jxMnjQQ9gdIRFJkoykSIZH5tEkOpsWW4kFS7kT3cIqKCunJZHD9iMaNr5y6ugrSRJYssjMlStNmM3TP71E6XST3LSX5Dv7UHvSpcP0GTpy1xQSX7eGPclSdh6GpsNDk45+kGSR8goL5aV6yovBbrs4ztLnk4yMDAwGA0ajkdzc3Iu9O5cNiqKOpBGl77PJpEoqqZJMKlNuJvleF7frgXtJ11kd7Y4b3VAYeAp4CHhNvQyTfgVByCJdhgfgEVVVP3ia9d2kS/YcVFW19iy29yqwYbxtXXBxyyyyDXoMGXoMGTr0GXrMuUZcC5w4a7MI2woZ0pVwaCibvQ0qB3b20Fo/cPovBlzZOuZW6aipNuB0nNuNP5FQOdIYYc++CG1t47uBnojVYaBiQQ6V83NYWCVSKHVh9Tej7DtAuNtHdChGZChKJKQSS2mIoiOqtSBUzEKsnRGzM8xwvlFVFaW5i9TGPWT42yle4SRrbTk+Vy09+ip2NmjYvamdQzu7TivoBAFmFeuYV2tkTqUerfbsTXbc/Qnq9kfYfzBMJHz6HvbCcic1S/JYMU+k1tFJVriJZFs3g4c9hPsihNzpKTwyf7fTsMYsYy+1klnjIKvWiX1eLsOZ1WxszWfTrgQHd3YTm+SIniBAboGZ3Dw92VkyWU6VTLuAyTRWYJ0vVCCREoknRRIpgURSIJZI19iOJ1WScYVEQiU+Io5jsRTxeNooZbTcrqKox+aKCqqippcVUFIqiYRyfIorx+pqT6YM1JliNGuwWjVYrBosFgmzScBsFLAYVZx2CYdDRqM5/bFVVZXWthgHD0c40hglEjk/IeSyVqJktpO5lToWl4ap0beT3LWf4b39DBzw4GsLnJSehyhAhg3BakZj1eLM05Bx+0p26Jfy8uteWg73T7gtrVZkxRIjK1dY0OvPf1mWM0FNJEltOUDipa0o9W0AGLMN5N9YiXvFerb3u9i9N3RG4cZmi4bKOVbKZmkoLRDQ6abXb55qZsTthUVVT+hMHMmNVVUhff9TR++N6fvhmDudetJs1CyZUMCNzx+mzx3isWe70/m6I54pgiggjnqoiBAODfPEHz8/+o2Xvrg99uXpEczbgY8A1wCjimR0o73Aw8BDqqoePG87coERBKEQ6Bh5+ZCqqh89zfodpEv3NKuqWn6G2/oo6RFxP1Clqmrvuz6/oGHJRx/8OMVFGcRkM3HJTFy2ENE4OBrMYVeThsYDgzQdcE86PMeVbebaq2dx7YZSykqdZ7P7J9HYNMRzzzfy8msthIKndiTUaCXmrSjkirW5rCgawOptJrmrjuFdXQzsHyapcZF7y9VkXLkYY0EO+pxMJP2ZWe9fLsyE206OmeN0YVASCfrf2E7XY88juA9ScGUWGavLGcyYT6tYzbZdIXZtbKXtyOBpv0uvl1m7ppgbry9n4YLcsw4PjMWSbHq7nWeeb6JuT+/p/wDIyDYzd2k+ixaYWFAYwEwAfdJ3bNIm/cSHo0Q8MXQWDVq7FlVvIqDLpU8oZnubjT37o+zf3jXpe+6scieLF+exdGEOC2uzMZm0Z/V7pwPner2lUmmxHI0licZTRKPH56mUMiKQ1RHxnC7Lo6qg00ppozW9jEE/Ukd2xBhPls9NwHi8UZ5/oZGnn2ugt/cUZYVOQKOVKChzUjLbQXVxknKHh7qjGrbuTXD0iPeM8oK1epny6izmzdWxojxMhbaD+O4DDNW56ds1gOeo/ySxq7FbcCyuQWfXYplrprN2Nc/v0rHn7Y4JO5mMRokP3jGbD39kKXr9uY9gnst5EGzppO03j9P+0LPEBz0YMnXkX19O/6preLsvmx07/WfkCZJfZGHFikLWry6htjoLSZo+gnYmLHmG09Hc3EwikUCSJIqKik67bm3tsfG6y0fcnrQhQXABHyI9orvohI9Gd2AfaZH2Z1VVJ+7auwS4UCO3giBkAvWkc3v/VlXVH4+zzgU1lPr3Hz+HKmXi90TwD0fweyIMuYMM908+Et3hMHDNhnJuvG72lJQOaO/w8ubGVl56pYnGxtM3ZgvKnCxbP4urF6Uo8OzG++gbdL3VjfeoH11WBrM+ciulH78TW/UZ9UNc1swYJU2OmeN04UkEgnQ+9Rqdjz2HNtlOwdoczKtq6bct4GCohB1vu9m9sXVS96jsbDM3Xl/BzTdWMqvEcdb71N3j5+ln63n6uXqGJtkwFgSw2A04sow4Mk3Ys0w4s4y4nOAwK3iDIgNe8A5FaW8cpKV+YFIhkZIssmBRHuuvmsXVa2bhcp19XeDpxuVyvamqyt59fTz2xEFef7OZxCQiD0rnuqhZWkBVmZaaTDfZ8WYsgVYG6/rp2dpP3B9HNkiEnLnsM9VS53VS354glTyzNqE900hFbTYLqjVcWe4l21ePf/MB+nYO0rdrgJj35E5kQ56LnPXlJK9bzdMteWx+pW1CkWuzSHzinrl88GMrz6mM25meB0oySfdf36LpZ3+m75W30dm1FFxXyuDqDbwzkMf2XQE8A5O7biVJYG5tFqvXlHHt2lIK86dvFNeModTUo6oqkUiScDiOysioJsdHN0crR4iigMGgOefOr/PNmfwfNjU1UVFRMfry8hS3J21UEOYAHyUtdgtH3h7dkRTwCmkjqmdUVb3kCndeqJxbQRD+QHpUfBewXFXHWiBdaHE7d8G30WrPvNFnMmlZv66UG6+bzeJF+ed0gSuKyuH6fl5/q4U3N7bS0e497d8IQtrgJSNTj8uewhwahC43Stsg+miQ+YsLWf2ZG8m/YQ2i5vw4FV7KXC6NyPPNzHG6uEQHh+l68lV6nnsRvdRL4fp8pBXL6DXXsqvTya6NHezb2jEpY6Wqqkxuu3kO1107G/tZlhVKpRS2buvkuRcb2LKlg1Bo8vUtzwVBgNr5udx03WyuvfrcyiJNZy716y0STfDX5xt49PGDNLcMn3b9kspMFqwqZvliE7PlRlyhw0iDffRsddO9tZ++nQMkIyeHxjsWzSX/pqvIu/EqtNWV7Nrbx5YdndTt6aatxXNGOXuiJFA6x0XtogxWV0eYY+6A/fsY2NZF784BBg96UE8I9TaXOTH9y6d54kg22zd2TDiC7HLKfPqeudxx7xVnFTkx2fMg3O2m+dePcfRXjxLtdZO9JAvh7ut4K1zFOztCDE9S0JqtWpYsy+fqq8q56opizOZLI/phRtxOHcmkgs8XxeONED+D8pMmkxarVYfFrJuWQndG3E52BwRhLWmBdicwWrBrdKf8wKOkQ3vfvuA7dw6cb7dkQRDygO6Rlw8AdROs+ueReT3wrZHl1onq7p4NZytuc3ItrFhWwJpVJVyxsgit9sx7ZlPRGP6OXhr2tLP/yAD1nSH2tkfxBabePS4zy8TqK4u5em0pSxbln9X+Xq5c6o3IC8XMcZo+RNyDdD7xMu6XXsZkGiDv2nIS81fQqZvL1v2we2MrDfv6ThuyKYkwr1Di6morV15TTd6iSmTDmYvFRCLFnroe3tjYylubWhk4g2iXyVJRlcWN183mhmtmk5VlmvLvn25cqtdbIBjjsccP8sc/78PrPbUnRG6xnUVrSli2zEGVvgVX8CCagU463+ql4/VuBg95TnL+F2SZnGuuoOjOa8m9YQ3GvOyJ9yMQY8fubrbu6mLPnh7amk8vsE/EVWBl7uJcltVILM3vw+E5gn9bI+6dA/TuGCDUlx4DsNbkIHz5MzxeZ2bftu4Jv6/QJXH/B+dyw4dWnVFE16nOA1VR6HttC00//wvdz76BxiSS/4H5HKy+llfrtBw9MjmTy9x8M1eunsU1a0tZOC93WoUbT5YZcXtuqKpKOJzA440QCMTO2czJPCp0Lbppcz7NiNsz5BT5uQCKqqqXlHXc+a5zKwhCCdB6lrv3oKqq953l3463L5MSt1arjqVLC1i5rJBlS/MpmER4jqqqxAaG8Te0EmhsxdvQxtG2YdpFM12Cla6Inq6BJMkzDKU6V/QGmWXLClm/poSr15Vd0nlpU8Gl2oi80Mwcp+lJuMdN97Nv4Nu5Bb2mh8yrKwlWruBAoJTXXu7h4PYuAqcRGaNkOUSKTFEKkkPkJ/1UlrgouKKG7KuWYSyYnImKqqo0NA6ycVMrb2xspalp6PR/NA6SJDC3JpsrVxZz7YZyigqnb0jk+eBSu96Gh8M8/Jf9PPr4wVOO4ssakfkri1h17SyW5vaQGzyA2d9G71Y3ba9207u9H+WEZ6IgirjWLaf4gzdReMcGdBlnF1I/PBxmy45O3tnWyc6dXQyfQa6p0aJlzsI85i+0sarcT4HagnT0EAO73PTvG2Jg7zD68lyin/8kj26RaNznnvC7ZucKvH+BmSvXVWKrLkefdWo/jvHOg+jgMC2/e5Kjv3iEYHMHthIz5o+s5zVhCW9sDhCZRBRFTp6ZdevLuPW6Sipmn30ZsenCjLg9O5JJBa83gscbJZGY2oGVazYsoaenk7888hw33HANFrP2op5nl4q4nTaCcaQu61+AvwiCMHdkuZq0SdeleMd4m7S4NQGLgYlGSq86Yfmd871T5xtXtpniohyyskxkZ5rIyjKRmWmirNTJ7PKMU4YUxb1+PPuO4Nlbj3ffEbwHm4gEPQxX19CVWU5z2EJLl41w6MQRh4tTlzEaSbJpYyubNrby//73He75wDzu/eA8rNbLM7xvhhkuZ9REkghxWvMraAnNpfEFHS2/7sfnPfNn8YBHYcCjZTe5QC4MQkZLP2UvP0VZoJX85noyTCaci6rJXrecnGtXobWeXONTEASqKrOoqszi/k8vIxCM0dsToLcvPfX0pqeuHh/dXV7C4RR2hwGXy0y2y0R+npUli/JYurjgkgmJfC/T2xfgoYf38uTTh08ZwpiRY2blNeWsXmWnggPkBB7Ct7GXppc66Xirl0Tw5D70rCsXU3zPTRTeeR2G7EwgHerc3uHFatFht+vPqKHsdBq5+fpKbr6+Mu3W3Ophy/ZONm/tYO+e7lPmAocDcXZvamP3JnhQFimtzmHuogWsuD3F7I/2URttR206yuD+Ryk2Wqj/3O08/lqUjnE6dpp6Vf6zN0D+th0savof5vraccyZhbWqFHNpIbLZiGzUIxkNyEY9KUkkdagVpW+IfS/uxrf3CJ49h1ESCbLmOxG/93Ge7ihhz6seVOXUHUnZuSbWri/j1uurqLoMBO0MZ080lmR4OIzPFz3tKK2sEdFoZVQ1bTrHyFwlvZxMKJxqsDESSdDV5UOWRQ4f3sme3VtZtGgRt99++7jrt7W1MWvWrFPu0/Lly9m2bdupd/wSZdqIW0EQdMCtpEdur2Ma7dtZ8jTwtZHljzOOuBUEQSSdewzgBd6c7JerqtrGJET/hci5PZFf/N+Np+3NURIJAkc78B1qYnjPYYa278N7sAk16se6pIj+RavYZ6vicEEl7t4ISqc6Mi48+V7i8bDY9bgKrJjMOoxmDSaThMUkYDGCyQDRuEAwDMEIhMIK4VCCSChBX4f3lGYzoWCcX/9mFw//aR933VXDxz40H6fTeE77OsMMM5xflGSSlr++yqu7u3irTc/RphCqKgEKxy0Tpoah4QRDw7CDWWCYhdWiZV4K5r26Gee/f4dkqwet04GlvBjHomqy163AtXYZGlP6PmIx67BU6KioyDzpey+1kckZTmZwKMxvfrebJ546RPIU5l+VC3JYc3MlK8oCFAZ2YR04Sucb3bzxVBuexpNLz+gy7My6733k3Xs7Q1o7Da0eXnysiZbW7bS2euju8R9riOt0EtkuM9nZZnKyLeRkm8nNtbBsaQF5uZZx9uQ4giBQWuqktNTJvffMJxJJsHV7J29uauXtd9rxnSLSIZVUaNrvpmm/m6cBR5aJ2fNqqZx7FYvvjlGs7ebWyBGuy47z3MASHntmCHfX2BI73R6R7sw1bCoRWB46yOzfPYshdWqblhYAAXKWufDddTu/OZhD4xM+jmeHjcVk0XL1NWXcfetcqquyZgTtexhVVQmF4gwNR07vkSCA0aTFZNFi1iYRSaGOjNepwsh8pBmfRCYcUYgE40TDiWNCt7CwGJ1Oh16fvrcnkwqvvvIGP/3pf3PX3fdw7bU3YjBoTnlOrlq1atz3586de8a//1LhogtIQRDWkBa0d3E853b0f8kLPEbaRfmSQlXVHYIgbCY9evtJQRAeVFV167tW+zIwZ2T5h6qqnjQMKQjCfcDvRl7+u6qq3zyPuzxlxANBIl19hDr7iPb2E+5y4z3QgL+hjXBnL/FhH6IEtlkWMpbmEb3hCnYsvob9jSn63WHUTSrpaO5zRxAFXLlmaucaWJXXz+zAHmSfDyUcR0joEWMWpLgNWbGjJiKokQAkQiDHIVtEtJoQ7yrjcKKMtw/rOLB7kI6mwXF76SKRBA89VMcjj+7n9lur+fhHFpKdffk4j84ww+VAoL2LVx97lTfbNezcFyIeS3GhI0D8vjhv18Hb5IPxAziv0jOvQuYKWxv6A2+z8+G/EO6LIOq06F0ZmMuLMc/Kx5CbhbEgB2NRHuZZBUg5GRd0v2eYGvz+KA/+cS9/fmQ/0ej4NYcFAWpXFLLhtgqWZrZS4HsC9VAvR5/poPn5jjEuxNnrlpP1kbtoMBXz87fa2PO5N1BOky8ei6Xo6PTR0ekbs+2li/O59ZY5rFs7C4P+9CaKBoOG9WtLWb+2FEVROXS4n7c2t/LWpjZaT2OG5RkIseP1Fna83sJDQG6RjfLaJZRW2qksS/Jf3zSzcZfCc0+24Rmno9kXVHmFuby1soZl5l7K+o+Q6+1B9vqJ+eLHTKysRWbyNhTQMPdqfrzLQdtjAcA35vtGqV3g4o7ba7lhfRk63UVvLs9wEVEUFZ8vytBQiPhp3coFRElAEiEVj+Pvj+JTVERVQSMqaEUFjaSikxVkTdqITa+TMGq1JDP1xAUL4bBCOBjnt79/grFFaNMkEgpt7V50Ohmnw4DNph83OvLtty8py6Ip4aJcrYIgVJIWtB8GRgsljf6PJEm7JT8IPHspuiWfwJdIhxobgFcEQfgv0qOzBuCDwGdG1msEvn9R9nCKeaniOjI4+UEoakQcs60ULHdh+uQy6g1zeavJwaGGKAOtYZSjKmnvsKlHVVTc3QHc3QFeA/T6WtZdmcV9f3Mls8tdp/37ZCTK0PY6Cre8xIcCTZjvrMadWcumBhs7d3k5sqdnzN/EYykefewATz51iFtvncP9n1xCVublb94ywwzTFSWVYtcTL/Pi7iHePpxkqD/C2YzOCqMNh5HeLfXYP+fG8GCUtwbhLTIRxJvIucLIsnkaVud0U9Czj4EtHbhf2EfMGycZHSdsVRR5Rv5nEAUEUUSQJSS9HtlkQGM1oct0YikrxDZ3NvaFc8hYWos8U2P5ghMOJ/jzo/t58KE6ghPUWRclgcVrSthwWzkLTQ0U+B4i8E4Pex5poWtz30luw7LZSO5976dn/pU8ecDLjl91kUqde1qbqsKOXd3s2NWNyaTl2g3l3HpLFfMmWZpPFAVqa7Kprcnmbz+3gp7eAJs2t/Lmpjbq6npOOUoN0Nvho7fDx+bn06+NZi0FZU4WrCwkHErQtL9v3GiqeFzl7eEc3pZzIBNyqiXmFqeY5xpmttXDtkA5//OOBu/BOBAYd9s6vcz6a0r5+IcXUT7r1Lm8M1yeqKpKbKSedSQUIxqJE0uoTLYctKqqpJIqx+/Uo2ZQElGFdHBQEoiBoKpIagpZSWGUI9gsYcwWGaNOS9KgJ46VUDhFyB8jHhu/IywWS9LbF6B/IITTacDpmInguZB1bjOBe0iL2sWjb5+wymid2z9d6nVuT0QQhFuAP3J8VPrdNAI3qap6dJy/vY9zHLm90KWAdv/7XRSV55Ky2BhW7PTHzHQEbGyrl2k4EsQzGD5nB7mporrKzD1313Dd9QsmZbmuKgoDW/bgfvF5GDyI/coyDrnW8ejLCfZt7ZzQVVWrk7j7rho++bHFZ10yZLozEx45OWaO04VFVVXefuRlfv/mEHV7Jx6hGQ+NTiK/xEHV/EwWlidZlDOAXk6gCBpSggZFHJkLGpKChu1tNl57voOGvb2MLcp29siySGGplZpKHcWuJPmWAHnGIC7Bg+gZJDEYIDIYJRlJkkoopGIKyWiSyGCUuD9BPJAg6okR7o+inDDiIBn1GPJc2GsqyLvxKko+fDOy8fJKp5gu11sikeKJpw/zq9/swuMZv1NF1ogs31DGhptnUas5QL5vJ979PRz6w1H6dg6ctK4uJ4vUPfeyRclj666e09a9nSqKi+18+IPzue2WKjSas6sWEArF2ba9k7c2t/LO1g68nskZtb0bUUyPjk1UI/dMsTn13Hl3DR+9ex5Wy+X5nD4d70VDKVVVicdThMMJIpH0FE+kLlo7VVBVDKkoVk0ck0WkcuVa2ju7eOmlv7J45XqybBPfo/PyCnn1tV2IokDA38+yZTUAp8zlPVNmDKU4lkd7G2lBe+0J2xsVtb3An0i79x48n/tysVBV9TlBEOaRHsW9CSgA4sBR0iHXP1ZV9dySSacRX962mvhbBiKh0TC/FKfKZTkXNDoJm9OI1WHA5jTgtAtkmuIMDCscaYzQ2+E75Q3q8JEg//of2/ifH+/hxtUZfPQzG8jKmjjPSBBFXFcuwXXlElRFoX/zLlwP/5Z/W2ig+abrePRVhd2b2lFSJ280Hkvx8MP7ePLJw3z4Q/P46IcWzpi8zDDDeWbni+/w62fa2LX31PeBUQRRoGZZPpXzXNTOSlJt78Pc30Bg0xO4Xxpg/95BQEDv0KKz69A7tejtOnR2LYYMPR9Y6uKmT9RwKLWAV7ak2PbqUfzD5567m0wqtDZ6aW088V0tgpCN2VaM02UkM1OPTicgmQQ0MmhlsCxUsBlS2IxJMk1xym1BXOIw4qCbSMcw/fuGGTrspf/1jXQ9/Ro7PvOvaDPsOJfUUPbJuyi88zpEcXqUn7hUUVWVV147yk9+tp2u7vGjk0RRYOm6Uq5/Xxnz5TryfL9maE8vm//QRP/ek82NzFWlBO/8ME92aql/ZxA4dVtREAUyss1kF1jJLrCRXWglO99GRo6ZaDiBdyiMdzCMdzCUXh4I01Lff8Lz+2Ta27381/c28psHd/OZTyzh5psq0chnJnJNJi1Xry/j6vVlqKrK0eZhduzsYtvOLvbU9RCZRI1pSIeJni7sejJkZOpZoXYw78Bb2Pw51G8pw1pVinVOGY4FczDmT1wqaYapYzTs90JsJxpLEIkkCYcTRKPJKTmPTsRs1p5VPWYAVRAIywbCqgGNJ0FqxPVcDPlxCsOsXLmczs5uurq6yMjIoqj4uGlUVmb6XFUUFa/v+LPnb/7mCzQ1NSIIAiUlJVx33XXcfvvtl/X9/byM3AqCcBXH82hH1cLo/3QEeAb4A/CKqk5lH/cMF4OzrXP7bnQGGY1GJOifOEl/NGTr+htymOPowxwfQGluwf/2Ifq39TBwYBgloWCd7US7YSkdefNoCNk50pKgu/XUBek1WpFrl5v52//vBlz5k89lG959kLZf/RxbFfQsuJFH3pDY/norqQlCr8wWLfd9dBH33F2LwXD6XKZLgekyQjLdmTlO55/97+zjF386yLbd/kn1WGt1Eiuvm83NVxupTu0muX0P/du66ds1iK91/NDF8RA1Ivmrsim7uQjrigo6jIt4vTGLTa+003xw4mAknV4ilVKnbATqlPsoCdgzjGTnmykp1FCZG6U6x88stZ1gXTOdb/XSv2eIqCeGoJFxLJxD+Wc+SOnH33dJNoQu5vW2a3c3P/jxVg4fnvj/fsEVRVz//jksMh2iyLuVge1dHPpDE4MHT66tal1cw+BtH+aZ/VHaO7yn3K5GK1G9JJ8Fq4qoXJDL/8/eWcfJUZ9//D2zsy63d7fn7pJLcnEXCEGCBoproUJ/bam7F+qlQoG2FEqhWIt7gsTdLnLu7rLuu/P7Y5OQkL3kLrmEQPN+vfa1crMzs3O7M9/P93mez6NSSwhyEL1/AIOvF4OvD72/H1kQCYpaAgotAYWOgBi5HxKT2VXhZufaZhr29x73mpmcYuRzd87g0hXjF7nRCARDVFf3s2NnFxX7eqiu6cduPz2VaQaTmpUXKPmUei2OuhGG6qxYG+24etxHtVHSpiYSP2sy8bOnkLBwBvFzpqJQfzInpz/KyO3IiIdlFz8+yjs/Xjz99HXExGj5cAZ/MBgm6A8RDIRO6I58iEOtgJ599FmWzy5Hb5b41UMP8fPf/Imbb7mJP/zlUZw2L94PTQp1dbVz4fJZo663vLycl1566YSOyh/mfz1yu5ZIJdKhf60MbCQiaP8ry/LYRwzn+MSiN6rIK4xj+rQ02putrFvXjM8TvaZApZGYe0EelyyPoVzYiWf1U3Ss6aSyYgi/PfKjFpVKUi5eQuLSOcjBICGPjwS7lameXmSLH+vUONY609i4zY7LcewFM+AP8+ZGO+9uf54LZ2n54pcvIikn+YSfI25GGXEzHoyYZv3t73w100XfL6/g6dUKdqxtOWZW0Onw8+BD23jyqQpuvamcG66d/D/fJ/cc5zhV6iubefCRbWzZaRvTTLzOqGLJZcWsWKygwLqJnofXsemlVgKuyDlIVCqxzJtGwqIZJC6eRfysyQTsTlytXThbu3C1duJq6448b+7A091Px7oeOtb1YEg9QO6lO7hjRS5X/980Ng/N4sXn22nYf2zvTt/BGtrSSYmkZ5mprx+gvcV6TAbIRBAOyQz3uxjud1FTAW8ffF2rz6Rg0nSmXCEw9ysjFAZq6F1VS8uqZnZ85gfs/MJPSFm+gPLffAtz2dmRTni20tA4xAMPbWXzlvZRlymensolN5QxK76FrJEncGzsYP0/ahnYd3SWk6Ywl56r7+DflV763ugddX0KSaR4WgrlC7IonZGKRiMS527EYnsLo68HnX8QkcjkiWfYy3CtjaA3hEIlolCJ6FQKxIOP81P1TM3O4PIvT6LOez5bNw+yc20zQ73OY7bb2+Pg579Yx6OP7+Fzd81gxcVFYyrxGQ2lpGDqlBSmTon0g5ZlmZ4eB9U1A1TX9lNV3U917QCuUeqVx4PT7uPfL/nYWnIRF8yTuOSmPhLlLsSAG1e3G0enC0eHC0eHE0drBXUbN7HvBz4UGjWWeeUkLp1NysWLiZ9ZhvAxnPg5x+kjQeMkVhs47IR8yB05LEgHy1o0hIVDk5ohgv4wHrf/GIF6JF5JTb8iFtOQHe9IZPyqCPqIV4xgStDjCplwWL24D/42FAqJC5ZfypVXXEtR8SQSEpKxWofZsX0d99//C/bu3cuFF17I7t27MZlGq5r8+HK6IreHpqCbiAjafx9sXXOOTyBjjdxqdEqKShNYviyfC5fk4veHeOKpCl5+pXpUgwmdUcXCFUVculiiyLON4Zc2Uf98M86uSCa3oFCQvHw+WdevIP2qC1CZj/8j9VvtNL6xijVdId7bJ9PeZB11WbVGwUXTldz9ufNJLsk+8YE4iL2+heaH/4Y+3UnH9Mt55q0QFZvaRp0BNxhU3HTjVG6+fgpGo3rM2zmbOBeRHBvnjtPEMzzs4Pe/eYt3Nw4TGoMgjEvUs+SKEi6eEyRnYD1dT26m/sUWAq4gCYtmknzBPBIXzSR+zlQk3dj+P7IsM1JRTfPjL9H6zBv4h60AiJJA6vwkCq/JJTx/Mau6JvHKc810Hsc9trDQwq03TiU+Uc87a5vZurWN/m7HGa0BM8VpmTE7nvMnOZilraH/tf3Uv9SKd9iHNjWR0u9/noIv3HTWR3PP5O+tq9vOI4/u5I236kb9X2UVxnPpLeXMyRwkZ3gtgYY29j9aR9fmoyc9tGlJBO78HE9VMmo6M0B8koElVxQzbWEWWp0Ss7eNREclCa4alGEvIX+I4XobQzXWyK3airvv+KnyoiSQMDWetPlJpM5PIphdRJ++lE0tCax6sZGW2oFR35uRGcM9X5jL+eflnrZ2ObIsMzTsobvbTk+Pg7a6HlorW+npHWHALeAMSgTCAoEgBINj/9GoNRLZxRaMJjUmo0CsQcZsCBGnC2DR+7DoPFjCA8iNbdirurE22BmqGSEU0pF66RLSLjuP5OXzURo/vh0SzkVuJ4YXfjOX2Bg1gggIAsLB+nCFVoFSK6HQKEAUD4pdiZCojGRRBMFl9+Fy+AiHImPiQ5Hbx//1ErNnR1r6/P2BX/HA3/7EDZdewSO/+w2aODUKnQq/Qo8nrMFui6xjNLPDtrYWrr/uQhwOO/feey8//OEPx/zZPi6R29Mlbv8GPCnL8pYJX/k5zjo+LG4NRgtJyUayss0U5MeTnx3LpOJE0tNNNDWPsG59C2vWN1NXNzjqOrV6JUuvLOGqxWFyBjbS/exWGl5pO9z+IG5mGfmfvY70q5ejsZyco6GtqYkNa3bxZrXErm39o0Z7NFoFF08VuOv2haRNLx3z+p2tnbT87W9oU100lqzg2dc9HNg++m9bq1Ny4/VTuPXGqcR8zIynzom2sXHuOE0cwWCIx//2Dv9+teu4pQyH0OqVXHLjFC6b6yejbz1dT22j7oWIqE25aCGTf/plLHPLT3m/Qj4/Xa+voflfL9Pz9gbkcGSQkjTDwqS7SvDNWsprDfm8/p8GBntGT2JKSNRzyw1TWXllKWqNgvZ2K3VNw9Q1DFJbN0hLywhD/c7TLnp1BhVzFiRy1fRBCgZ2Uv/vKro296HQasn/3PVM+eXXzlrn5TPxexsYcPHo47t4+dWaUSdpE1KNrLhpKgunBMgbXoPU0cyBf9XTurrrKCNCVWwMlns+x0vWJDZt7Rh1m6nZZs67qpQp8zIwhoZItu8l0VmNJmQnHAzTs32Alnc66d7af5SJmCEvk6Tz5pC4dDb6zFQUGhUKjRpRo0Zx8OYfsTGwcRf9G3bRv34nksJO2rwk0s7LwD1tMe92FPLmC8201Y9+/S4uSeCrX5rH7JnpJ3FER8c7OIytshZ3bQX+kQ4GtEY6NWm0Ok209Ah0dXoY6nOOmgE2ERhi1JjjtMTHS6TGy6SbvCR4e4lpqSO8oxZVajGply4l7bLzMORmnLb9OB2cE7cTw6oXrsdsVCGHQgdvYcL+AAG7k5DPjyCAQq1A0iqQtBKSVoEgRcoE/Ao9IUHC6wrgtPtYvGDqMeL2oQd/x8MP/57rLruKh39yLwCSVoE2To1kUBFQ6PDIOhx2P067L6rZ6R/+cC+PPfogZWVT2L17DyrV2MoK/qfF7Tn+tzhS3N5y+6MUFuYSH6cjLl5HXKwWrUZi5+4u1q5roav7+C1/VBqJRZcWcfUyBXm979P26Gaa3+4g5ItcoFMvXUrJt+4icfGsCZsZDoVCbH/jHZ5a72b71v5RXY+1OolLyoLcevUMss6bM+b1u7v6aH3076hT3BzIWM7zrzuo2X1sC6FDqDUSl19WzG03TSU9LWbcn+ej4JxoGxvnjtPEsP79ffzhb3vpaB9bP+wZS7K57vo0pjlW0f3kJuqebybgDJJ8wXwm/+zLJMyfflr209PTT8tTr9H4t+dwNkfESsrcRCbdNQn7tGW8UJHGqhfqsI/ioAug0yu5+spSJpUkYrP7sDu82O0+bDYfw8Mumpp78HpldDotoihGogQCkWalQsRcxOcL4nIFTjmdMyUzhgsW6bg6t4aR/0Qi3qEA5Ny+kpkP/uisE7mn8/c2YvXwryf28J8XKvH7o7RnAoxmDcuvLWPZQj0FtrXo+mqpebqJxldaCfmPdK7WknPP7WxPnsHTL9SMur6ckgTOX1lK8bQUYnxdZI5sJt5djwAM1VppfaeL9jXd+GyR/7MuPZmkZfNIOn8uSefNQZ+RMu7P6Wrron/jLnrf24Kndjv512QjnL+M1e1FvPFiC+0NQ6O+d8asNL7+pfmUFCeMeXuyLOPt7cPVUIu7tYYhq50en5LekJ7+gJ4+p4qu3hBdnS48E5CePJHojUoy0zVkxnhJdnSR1ttB8YKZZF2/AmN+1ke9eyfkoxS3JzKUirTnCeJwBnA6vfh94/MoUKpE1FolGpWARgqhkEMoRAVBhZrefveo69MblMRrvQQH7ISDApLJgKTXIum1iEpl1HHoaP1mAUJeHwG7k4DNQcDhOjzeVBkkNPEaJI2CkBCJwgZFDWWFeXS0t0cVt1dedT1//tmv0fudhxOgJY2IJk6D0qgkKOrwCnrs9gAOm/eose2aNav48pduR6fTs2t3M+YYLfHxWlSq41ernhO35/ifYSIMpSSlyLwLC7j6Ej3Fg2tof2QdTW92EA6EEZVKsm+5nOJv3Il50umr9wqHw2xbvZ4nVg+za1vfqBERvUHikmIv111QTN5Vy8cssr39Q7Q9/ghSvJOqtPN54S0XlTtG/60LIixalM2nb5l2uAbpbOWcaBsb547TqdHaPsSvf/0OO3aPnHhhICndxDWfKWd5chXiO2+y+4/7cfd5SDpvcGTKcgABAABJREFUDpN/dg+Ji2ae5j2OEA4EaHnyFQ78/GHc7ZGJrbSFSZR8ZgoDpct5ZY+FdW82MdR3bF3j6UIUBdRqBYFAaFzpmxAx4Vq0LIVbZnah37SW/Y/U4nOEyP/89cz44/cRpdPaiGHMnI7fm9Xm5dnn9vPUs/vweKLXyKk0EkuvKOaiS5Ipdm8kbvAADS82U/Ns0+GabgBBksj73PUMLrucB584QG+UulaArEILl91aTk5JAnGuBjKtW4jxthP0BGl+o52mNzuwt0Xeq9Bpybz2YnI/fTWJi2ZOaD2ob9hK06PP0/vaS2QtjUF1xUWsai/mtRda6WoZ/TdZXp5AUoIWo0HEpJYxKIMYFH50og+ny8uwG6xeBXa/ArtXxO4WsNpDDPZ78Z7GKOyZwGRSkpskkyNYmZFpZuGnL8J0lgrds7EVkN8fwmbzYrV5xtXySiGJaLRKtFoRnTKEFA4iSUoUOgOCqCAclhkYdDI0FH1iUSGJJMSJqO3DBJwhNMkJqOPNE/p7ksNhAk43Aasd36AVORxGqZPQxKlR6iXCKMgtm0t7ewcvvvI2pcUzCIflo8TtL3/5AEpCmDxWpPAHk2IKlYg2Xo3SqCKo0OIV9NhsQZw2L7Iss3HjGu7+/I2o1Rr2VLQdfp/JpMYSr0OjiW52+nERtx/JFUgQBBFYCswDkgEd8ENZlnuOWEZ1cP9CsiyfHru8c3zkaA0qyhdkcuWKWCY71tP1m3dZ93o7IX8YSa+j6Gs3UfSV29Clnn47flEUmX/Jecy9KMy2tdt4/NVu9uzoP0bkupxBXtglsaqujUve+SOXlSVQdNMVqGKPH2XVJMZT9J3v4R+xEf7no/yoZID6y5bxwtte9m/rOGY7chg2rG9lw/pWSkoTuP3maZy/NPeUDDvOcY6PI26Pnz/9YTWvruomMEpU60iUagXLP1XGyvNFctufpeqbW2hf003stFLmPfddkpaOPfNiIhCVSvLuupbsW6+k+bEXqLzvr3Rt6qNr87tkLKnks58u4/qfLmVVQzHvv9F63JTPiSIclvGcpHDw+0K8/1Yna96GybNu5OZ/OCmvfpf9f3mRpn88T9E9tzL1198862tyx4osy+zb38sLL1Xx3pqmUSOrklJk/kUFXHhFNmWh7ST3vELrm21sfbIB79DRw5ismy4j59tf4IH/NvPOb7ZGXZ/RrOHSW8qZviiLZHc1mR0vY/D34bX6OPBiK42vtuF3RAS2Zf408u68hszrLjltdZ/qODOl3/4sxV//NI3Pv8uen71EcuJ6br9gMZvbs9i9tRdnFHfjvXtHr9M9ncTGaUlMMpCQoCPkH0GlhPSMLMKyyEBrF3trrQyOjP4bUKkVaHUqXA7fqCnnJ8JuD7DXDnsx8HJ9EOO2VRQnBJmWquKy288jfWr+yX68TyzBYBi7w4fV6sHrHeM5SuCgmJXQqcOoCSASRNIZEZVHG3a63QG6e+yjZ1yYVMSJDoI9bhSJFnRZsafFNEwQRVQmAyqTAW1KIt7eAbwDwzg6XUgaBZo4NcLBwlmtFpIyYhjudx4TUAmgYFgbjwkvGlckOzLkD+Ps8aAY9KGJ92MwedCYtJhiDNhtARobawFISjo6cGK3+7DbfRj0KuItOnTa6NHps50zLm4FQbgUeADI/tCf7ifS9/YQdwEPAk5BEFJlWR5b/tk5znrMFh1ls9OZMjOJWZkjJAzuoedPq9nwahshbwhRpaTwnpuZ9P270SZZzvj+iaLI/GXzmXe+zNaNu3jsv21U7Dy2nYPTEeD5PRper3Ezd++zLFF3M/OS80i5aNFxIxeq2BgKvvENAg4nwuP/4ntZrbSsWM5/VwfYt6Ujau1vTfUA3/3BOyQmG7j5+imsvKL0XK/cc3zikWWZ51/Yyd//WcnI8Nh6IE6amca1txcyR9iA45n3ef+haoI+gfJff4Pib9z5kUYVFSoVBV+4iZw7rqbx789R/atHIg7L63tInbuPT91SzOX3LOKd9mm8v7qH+v29o5qCnA3IMuzf0cP+HZCafRE3/PpS5g++R+WfnqX+wacp+fZnKPvxFz+2Itfp9PPWqnpeeKmSxqbRTcBEhcDs8/O4eGUekxV7SbM+QvfaNlY/Vo+j8+ihS+qKJUz9xdeoDxi58wfv0z9w7NBGFAUWrihk+bVlJIu95Hc/htHfi7PHza7/NtNysFRH0uso+upN5H/+emKK8ybkM4dCYYaG3PT0OenucdDZNUJ31zA9vQ4Gh7zY7H4c9gB+fxiYBoNAtRdoO8GaTw9qjYLMrBhy8y0UFVgoyY8nLdVEYoLhcB3h0RHJmYcjkuFQiC1/fZbNbQ7e2qfCYTtamPt9IUQxwNe/NptLlpfS1++ks9dBZ4+Dji47He3DdHfZ6etxjWnSDcDhDLHTKbCzJcA/trxDftoa5mSrueLmReRPz53AI/PxIhyWcTp9jFg9uEbps/xhRFFAo1Oh04nopUAkOqtRIWoMUQVZOBymr9/JyEj0a4mkVJAQI6MY6keMjcNUmnbGnLBFpYQuIwVNsgVPzyC+gWGc3W7CBydVNEE7OpwoUozExEU6rPq8H0SdZcCGhkCcDpPPgeyKnFdCgTCuXg/eoYjINZo8hMQwzz//BAALFiyNuj9Olx+ny49Wq8QSr8NgUH2sRO4ZTUsWBOEzwN/5oEXQIGAh8n+ZLMty9RHLqoiIXTNwuyzLT52xHT3HuDgyLfkLP3geQTThtHqxDXsY7HHgdQcQRAFJKSIqBAiFCIdkgsEwsgyWoIPM/gZy7B0svHIG03/+JfRZaR/xp/oAWZbZvHkvjz7TxP7do/csBMjK0LDI0s/8mDBFN1+NeXLRCU8IQbeHtieeIuCoon/mcl7erGLb+y3HNcXQ6pSsvLKUm2+YQkqycdTlzhTn0m3HxrnjNHb27Ovk179dS2Pj2DrHafVKVt41g8sm95Nc9zZ7freL3p2DJCyayZxH78NUOL5+fqcbvz9Exc5WVj3yLhXVgzhFLW5BiU9WHNFC4mg0WomEVBOJ6Sb0RjVavQqdQYVGpyQsy4SDMqFQmIA/hM8TwOsJ4HUHcNl9OG1e7FYvDquX4BgH4ieDSq2grFTLbHUDyjc2oxqwM+0nX6D0G3edtm2Oxnh+b06nn84uG52ddjq7bDQ1D7NmXfNxo9uCANMXZXPxpwop11STbttK/8Z2Kp+sZ6T+aH8Jy/xplP/qG8TMmcaDf93O08/ui7rOvLJEVt41k6xkyBt6jwRXNc5OF5VP1NO+pgc5LKNOiKPonlsp+L+bUMeZx39gAI83QGurleaWYeqbhmhqHqS1dYS+XveYXMfPJKIoEBuvITFRS0qaifT0OIoLLEwqSiAtxTRqfeMhTvQ98I/Y2PXr+3nOX8qmLdEnMebOT+EXP7mYWPOx36FQKExzp40DlZ3U1PbQ0GSnudGG0za+pMOMZCWzcrVccf1cJs/JO+Ni4kynJcuyjNsdwGr14HD6x9TCTSGJaPUq9FrQiX4UMpHorOr4nSacLh/dPY5R+4mbYpSYAzYUKj2axDgExan3bT4VQv4A3p4BSubNoqOnm7eefIKLLl+KLCl57vV13HLTTaSlZ/LKK+vQ6fRHvVehEPjlvd9i+aw5nD9zDmrVB4GQps5WvvrLn7J1524MBj2bt+8m1pwSMZ46jh4UhEgZi9vVh9frRxAUFBYVYDSMftz/J2puBUHIB6qIRIvXAl+SZbn2YNugY8Ttwfc8AnwGeEqW5dvOyI6eY9wcKW63v/Vn7P5EXl/rY+tO97hrudRqBTOmp7FgXibz5maQlWk+a2aLZFlm45YDPPrvOiorjp9mpVIrmFUsMincQak6QMbc2SQtm3/c9OqQz0/7cy/hqV9H4IKlvFqVzLq3W7EOukd9jygKnHdeLp++dRqlJYkn/dlOlXOibWycO04npm/AyW9/9w7rNvSO2Qm4cGoyt36uhLmB1fQ/tY4D/6wHSc20336L/M/fcFb0oQyHZerqB9m+o4OtOzvZt68Hv+/kRGZyooIrz1eyfI6AThlCkEMgCIQFBTLiwfYSaoIHbwGFjoBCj1+hx6fQM+xQMNTnZKjXefDeEbnvc457QD4WVATRBr3EJcVgSLGgUikiN2XkXnnwXhAi0WBZlg/eA8iEwwefI4P8oWWQkcNHPz+0TDAUpL9/AGSwWCyIonhwmQ+WPyRqrdaxZQZAZIA9ZW4Gy68uYrqpnsyRLQxsaqHyiQZG6m1HLRtTVsjUX36NtMvOo6FhiB/89D2aokSBtQYVK++awYz5qWTZtpJh3ULQ5qbqyQYaX20jHJQx5GZQ8s07ybnjaiTt2My7HA4fza0jNDcP09gyQmPzEG2tw/T3jX5dOZMolSKx8Ros8WpMRgmzSUVcvAFLspmMjHhyMsykpxhQKU8+42Ks593WZ19hc30r/9plZqj/2ONjjlXzkx+dx5IFJ46wBoJBamqb2bG7nf3VDqqr7Qz3jz0BMSFOYm6RnqtvnMuU2aevtdKRnClxK4oKzOYUbDYvwTFMpIiigM6gwqAT0Ak+FIKIZIhBUJz4OxEKhenptWMfxVFfqVKQYAii9AbQpiQhqqLXmn5UZGdl09bexit/+yeL58xGn6zFG/aRN3URg4NDxJjNZGfloVKpsVgS+P39jwBw9crzqaurQqlUkpuZhUGrY9g6QktnxNjQbDLx9N/+wsWXnne4JtfuiNTkHq/Hus87wMiIi/YOBw88XEdaqpHMTDMKUYjM9nHQy1AQsFn7+Ndjdx566ydW3D4I/B9QCcyUZdl/8PXjidtbgSeASlmWp5yRHT3HuDlS3F5z+b00951ca55oJCUbmD83k/lzM5g9K/24s0RnClmWWbe5msefqqZq78CYBuBpaRpKk7wUhnqYFCOROnsW5inFGPKzED80QyjLMj2r1jK85nmkpVNYM1LK6rd76Wgc3ZUSYPKUZG67aSpLF+egUJzZwfw50TY2zh2n0fH7gzzyzy08/WwtvjHWWSnVCi6/bRpXzXORXvsqu+7bQf/eIVIuWczsv/0MfWbqad7r4zMw6GLzlnY2bW1j584uHI6JFY4mrcxFM9xcMd9HUXYMIiEIByAcPHgfgFAAwkcP7IKCEq8yFo8yFq8UuY88NjPk1dHT6aC3zUpPu5WeNhu9HdbT2l7l40J8soG5y/OZtziFfKGWDOs2hjY3Ufmveobrjha1+uw0ptz7FbJuvIzePhevvlHLv57cE9UUp3BKMtd/cQ756lbyht5F6bHS8HIrVf9uIOAMElOaT9mPv0jGNRdGTauXZZnhYQ8trSM0t4zQ1DJMY/Mwra0jjAwfv6/tqaKQRPQmNTqDCrVaQqURUatENBoRjUogEIZ9Owfwuo+fajplSjKfvXMm8+dmTLiQG89519XRw57f/J7/auayeWP0SeyrVhbyna8tRa0eu+D2eaxUVjawcccAFTU+avf1jzmVOSFOybxSA1ffNI/J07NOm9A9ndengUEXVZU1iKKM0ahDrTm+g7YggEanwqAXMSj8SIKIZIxBEMceUbXZPPT2OaNnIghgNkmY/A7UFgtKg/7YZc4CsrOzaWtrY/WrrzMrM49wMIQ6RkVDXzM//fWf2LqzguGhIUKhEKmpGbz73i4A3n77FTZtXEtNzX6GhgawWkfQabTkZGRwwbyF3HntjSRZLCiUIiqzGqVJRUihxifocLhkXHYfodCx56oPi9vj4fePULX3cA/dT6y4rQEKgc/KsvzPI14/nridD2wC7LIsm8/Ijp5j3EyEW/JYEEWBsrIkFsyNRHVLihPOuIg7ElmWqWvp5fkX97F2bQ/WUVz3PowgQGqqhpTYMMlKF8miizRVgOyMRBInl2EszEGXnowoSQzvrqTrP/9EVRbPvth5vPmuk8odnccV1MkpBm66YSorLy9Brz8zdbnnRNvYOHecjiUclnn5zUoeeWQXA/1jH4hnF1m4+f+mslCxHs8ba9j5hwOg0DLzLz8k+5YrP5KMj2AwTGVVHxs3t7FxSxuNx2mTMhbUGgmdUY3fF8QVxajnEKIcJlW2kyS6SVF4yVD7MBjUSAYdkkGP0mxAk2BAHadFHaNGZRARRR/4bMg+O7LPDiEfsgxhRDxSDB4pInY9UgxuRQyDDiXD/S6G+pwM9zkZ7ncyMnB2RP9OJ4IoUDwthbnL85ldEiLDVUG8rZK+Ld1UP9PEcI31qOV1mamUfuczmFdeyppNHax+p4F9+3ujrltSilx6SzlLL0yjeOhtEly1dKzvYd8jtTi73egyUpj8sy+Tc9tViAoFXm+Qzi4b7R2RW1u7laaWYdparRM+cSIpRcwWPbEWHbHxaiwxEK/xY8SDPuBE6xzB4HOhDcuolVokpRaCAQiHIRSK9HcOhwiM9KCcmcom9RzWbLDTVn/830R+QTx33T6dC87Pm7Dr+3jPu+FgkMr7/sROfRyPrwrjjtJyKD3TyG9/cQnFheP3BQkFffR2NLBmQxtbKgMc2DMYdRvRSIhXMr/MxDU3zWPS1ImdCJjo65PbHeDdd2p56cUKKhucfPkLRWRmGImN1Y8qbtVaCYNewqAKokRGOcYI7ZEEAiG6e2y4XNEn5FRqBRaNH41ShdoSd9ZkB56IcCCIq70b/4gdhVJAn6xDoVPiVxjxClpGBtx4XNG/RyajGhkI+YMEfAHCYZmwICAL4/uNnRO3H96QIDgBLTBLluU9R7x+PHE7FagAgrIsn3PPOUsZi7iVJJHkDCNpWSayck2kZejRqAWcXoGaagd1e3vpbBoacxoigClGzZxZGcyfm8H8uZkkJHx0M28ef4DV71fy6pvNHNjdP6bakWgYTRIWs4IYXRizKoRZFSBWHcasCGKwdhGfo8IxZRGrdirZua4V/3EiXFqdxFVXlnLz9VNJTTm9dbnnRNvYOHecPkCWZdZuauaBv2ymvW3sLXAUksjFN0xm5TKJgo5XOHD/dlrf7SJh0Uzm//u3Z7xef8TqYfOWNtZvamPb9o5x9ZIVBEjLiSN/chLZOXpi9DIxhjBmbRCN6KOx1srevQ6qm4JYHeDzBMZ1jjzH+JFUCuSwTGgUd1ylEEYRCqAOeNEF3AdvHmJjVOQtm0ni7Mms39zOjp2dx70OpGabuekr85kc20nBwCqcjX2s+1Mjbc0hfLGJGC+7ELGsjMERL53dDto7rAyMI611LAgCxCUaSEwzkZKmJSNBJk3jIN43gKa3G19tG57qLhSaWEwlhcRMysdYmI0xPwt9TvqYU6MHd+yn6c9/IaZcR0vZRby5VcGeje3HjeampZu45YapXHxhATExp9Y/+WTPu52vv0/N+tU8bp1B7YFjRbkkiXzuczO489aZJ6z7HY1w0I+1p571m5vZVA0Vu4ePW4p0JIkWFXMLNVxx+VSmnVd2yiJtIq5PwWCYzesbeOG5XeysseMPfPAbuOf/ootbSaXAYFBiVIdQC2EkvQlRGn96sCzLDA25GBzyRP3tCYKA2SQSI/vQJCWeNS3LxoMsy3iGbDi6BgjKgE4NKiVhQSQsqgiGwOcNnDYjwnPi9sMbEgQHkZY/s2VZ3n3E68cTtxcA7wDDsiyfedvcc4yJk4ncalVhyrM9zMjzMLVEhaUgk/ZQFrsa1NTsH6B+by/2kfGlU+XmxrFgbgbz52VSPjVlXClDE4Usy7R2DfPS6/up2DtIY+3IcQXoySIIoNNLIIh43cGo6SNHEmtWkpasIyPdSE5uPDnZCWRlx5OYoEejUaJUiqd0YTwn2sbGueMUYdfeLv745w3UVI+tX+0hEtNN3HrPLJYYdyJtfo/tv9yLe8DP5J99mdLvfu6YFP/TRVu7lXUbWlizvoXKyj7kcUxmWZINFExJpmSSiZm5TjIVnZg9rWiCdnpGJDbW6NlQrWN3s5ZA6KOvFT4TqLUSMXFazPF6dEYVWr0KpVqBgACHS7kEIqXTQuS5IHDwZQTh6Nei/T0YDOF2+HE5fLgdPlx2Hy6HH4fVM6rJzOnCaNZEzt9eF35fCI9HxhsSYRQjsVNFVAhYUowkpZlITtOTmQyZJhfJvh6E+iac22qw7ekgJBiInz2Z2PISzJMLMU8ujFo6c7L0b9xFwx/+gmWKgsDFl/FyRQJr3mjCNjS6mJMkkQXzs7jysmIWzM9EqRz/vpzKedfZ0sHub/+InXMu57+vDET9rkyemsBv7r2Y5KRTm0SWw0Hs3bVs2d7M+mqBXTuGx1yna4lTMi1D5Pw5GSy9cTFq3fgnBE72OAUCITa+tY/Vq6rYXufG7oqebn2kuNXpk9AZlBi1MloxhFJnQFSefNmZ1xugq9uObxQfA41WIl7pRW82I+l1J72dM0U4LOP3B/H5Qnh9Qfz+EH5/iEAgdNLBk4ngROJWZ1BhtuiIidchKV08+cAth/70iRW3dUA+cIssy88e8frxxO29wA+APbIszzwjO3qOcTMRaclxah+LStwsnuqjsCwed0wulUPJVFQHqNvXQ0tN9IvKaBwyppo/N4N5czPJzvpojKmc3gA79rSwbUc7+w8M01w/clqdSk8WQQCVSkSpFFCrRFRKIXJTiagPvvbBTYFSKaKQRCSFgKRUIAoyDocNhSSQkpqMVqdFqZTQajUkp1lITjKRlKj/SCYczib+18VtffMQ9/9xHTt39I37vXOX53PTDclMGX6F5n9sp+aZJvQ5Gcx/5n4ss0+vJUMoFGb/gT7Wbmhh3YYWOjtsJ37TQTQ6JQVTkpk0NY5ZBT4K9N0RMesbwtbsYNd2P2trTOzzJNMnTZxfwccdUXHw/KNWoNNLaNSRjo9yGMLyIROpDx6HZZlwODIoDIdBDsuHHweDYXzes++8O9FIKgWJqUaS0mNISjOQmQw5Fi+p4X5obsFTUYdtZzPWJjsyKuJnTSZ+9pTDN21a0mm/TsqyTN/7W6m7/0ESJ4Pi6it4tTqd915vYaD7+M7oJpOai5YXcNmKIiaVJo45Wnqq592Q18eeb97HQG48D29LoLvt2N+/Tq/ke99ZwqUXFUZZw/iRwyEcXdVs2nZQ6O4aYWSMQlenFSlNhqnJSpYuL6V4+QwUqhMnP47nOPU1dvHW01vYUjtCVVcIr+/EWuKrXy4mN9dMnFlLbloSKq0WUXVq18BQKEx/v50Rmz9qtFIUBeJMAjEqEVVc7FmXghyWZfy+ED5f8OAtImYDgbPzfHUucvvhDQnCP4j0rn1TluXLj3g9qrgVBMECVAPxwP2yLH/7jOzoOcbNRNfcKoUQUxKsnD/Dx/wZElJqDj2KXHa1GKg5METd3l76u+wnXtERJCYZWDA3g3lzMpkxPZXY2DMvKmRZxurysauilcamQdrabXR1e+jrdTPc5/pIZ+POFDEmJXl5JkpLkplSlkZpadJpT5k+m/hfFbfV9QM88ug2Nm7oGDWtVqlSRDVY0RpUXHf3bFYUd5BQ+Rbb7t3FcI2V3DuvYcafvo/SaDgt+xwIhNixs5N31zSxfmMrtnG46aZkxlA8LZXpZRKz0gZI9Dehc3YxUmdlYP8wlfuDbOlPpNqQz7D2oxe0ggCiQkShECI9aYUPop/AURHQI98DEAyE8XuD/xPnr7MFU6wWS4oBS4qRhBQDGckiuYk+0tVDCG1t+CobcOxuxFZvxd7mREZB7NSio4SssSjnjGU6REOWZXpWb6T+j38lsSSI5obLebsln1WvtdHVfOKMDqNJzeSyJKZPTWHqlGRKSxPRaqKnsk7Uebfl36/Q9t4rvJ17Ne+9Hd374vxlOfzke+djNE6c+aUsh3F0VLNpSwPrawR27baOq949KU5BbkyAfF2QqYUJlC0uxVSYhTr+aLEX7TiFQyHcHT30V7VQtbuVyjYbe4cUNPVFJo5OhCgKFE5NZuFsHfNKdKgMJlQaHQUFBeM+Dh/GZnPR1+8hOEoJgV4vEa8KoLNYEKWPtrUPRIS41xfE5w3i9QYjj33BM15qIogCJrMWlUZCFAUEEQ7NEx36Ohxx+iccBp8/cp4f6u9geNhJW9s5cRvZkCDMArYTEbKfkWX58YOvHyNuD4qll4CZQBAolWW58Yzs6DnGzek2lMrU2FhY4OSCRTLpBQlYDQXUu9LYWxOkbl8vDft7T+jE+GFycmKZNSONWTPSmD7toxG7hwjJMja3n+bmPppb++nvdTA45GHEGsBqD2KzBQ73pxytDuzjTFqankULc1m6OIdpU1NOKu3s48L/kriVZZnNOzp47PHt7DtO6yy9UUXA68cf5SecW5rIbf83hfnhd3C8uYldfziAoNIz+5Gfk/mpiyd8nz3eAFu2tvPemmY2bmrFPcbzilKloHBKMmXTLcwv8pCvaSPO3YyndYCenQP07hygvjpAtSGf2rhCBvTHdwr9KFGpFeQUxTFrYQplUy0YTEqEI8YJsiAQFqQjbsqIUUkwjM8TxOWI9NR12X3YRjyM9LsYHnAx0G1noNtOaJzt4T4qJEkgPcdEbkEMmXlxJGbFERKUBAIyAV8It9OHw+rFPuKhq2WEvk4btiHPKZ+jFZKI0azBFKuN3OK0xMRpSUzSkGEJkRnnJlYYQeztIlDfint/E9baIayNdtz9kQkYY0E2cbPKIpHZOVOJLS8Zc33smUaWZXrf3Uzd/Q9jyfMSc8slvNdfxob1Q1Tt7BxzxpZCIVBQaKGsJJH4eB3mGE3kZtag0Qjs27cdtUpk+fJlGI16JOnkSnGsB+rY/aXvMHTb9fz1RV/UlOo4i5Z7f3wB8+ZkjHv9J0KWwzg6a9m8tZ51lTI7d9vGXKN7CI1GJNkskKTykiS4SJS9mIQwZqMKm2MApyOIXxtPj2SkO6Sn26VkcGR8kcSM/Djmz4vl/EIvxQWZGLKm0NjUfFSf25PF5w/Q02PH7Y6+TwpJxGKQiYkxIOk+mmtsKBTG4w3i9QQi994JjsYKIEkKJKWIpFQgiAJyWI60ODvcGk1GDsv4owhoQYBEc4hYfQhhDAW6gbCIw6ukq2+AwSEX7e3nxO0HGxOEh4G7iYjZl4DngecOPr/54P2FwA3AoTPxb2VZ/t4Z28lzjJsz5ZYMYBR9zEwY4oI5AWbO0uCPz6Vfnc/ezpjDUd3xGlMB5OTGMnt6GjNnpDF9emrURu0fFbIsE5RlvIEQDqeboSErQz0jDA3Ysdm82JxB7M4gDk8Yp0fG5ZFxOYO4nX7sVg9e1/iE/0eJTi9x8YWFXHd1GYUn4UJ5tvO/IG6DwTBvvlvPE0/uprV59PRdnUFFcjw0tx1rwCSKAhdeV8anLlJR2PUqlX/aQfNbHSQsmM78Z+6f0BY/Hk+ADRtbWfVeI1u3tY+596wpVkvpzFSmT9UzP2eI1EAD6qF2+vdExGzPzkG6R5Q0xOVTH1dAjyF5wvb5w8ycY2HBNAGnX4Pdp6ShJUBjVf+E9KyNj1exoMzPFWXdTE5382EDWxmO6K2rjPTUFSN9dX2S8XCbIY8yDrcYw3C/m/bGIap2ddHZMMTIoGtMEaCPGkmCkmyYnWtnWq6fLpuWDTV6dtcIY0rJ1OiUTJ2fSUqWOdLjVyOhVEuoVArUKhmtMkSCMUCcxoM27EATtKPyWwn3DeBv6cZV04m9xYG12YG9zUn4oOjTJCdgmTOFuEMpxjPLUMXGnOajMfHIskzfmm3U/u4h4lJtJF6/gM646by338DODV201B6/t/zJoNZI6PUqjAYVRqOauFgtiQl6EhP0WCx6UlONZKTHkJRoOCoF2m9zsP3O7yDOiOPxgZns2dIddf3XXDOJb9yzAI3m9JTjyLKMd6SNLZuqWV8ZYM8+F92t1tOyrRMhCJBZEE95eQyLioPMKE3GlDkVQfFBNP1Qn9uTFbfhcJiBfjvD1gCjaReTUcKiE1DHnrlStFAojNcbPErMTpSQVUgiSpUCpUqBpFQgSSKSJKCSwohypMe5KAeJdPgWI+7Hgnj4sYyIxy8w1OeMOvFmlALEKdxwsD84RMo9QrKAX1bglyW8ohpfMHIsz6UlR9uYICiAfwK3cnwvr0PfyH8Bd8lncifPMW6OFLd33P139u0Z3X49GAhPWPqaiEyW1sHsLBuL5gnkliXiNOXREUyjok6gbl8vdft6sZ9En7/cvDhmTU9j5ozUSGT3LBK7xyMcCmGvaWJ49x5c7U143CM0Fc7h7d1Kqnb3HNf4JjVZzaJyLYkWFd4gBMIC/tCHbkEBf1AmFIJQSP7gFj7icYiDtzA+TwDbsGfckXWA4pIErrt6EisuLkKl+mREcz/J4tbp9PP8a1U89+ze47b0UWkk5s40Urd3gD7nsYO+2EQ9t9wzh/MtezFUvM/Wn+/B3u5i0vc+z+SffXlCHC59viBbtrXz9uoGNm5uG3Nf3ZTMGCbNSmXOZJEZiV0keBoJNHfStaWP7q39DFaOMKSKoSE2n/r4Qvp0px6hVUgiokDU/qh6o5r/u0HJp7L3EPaHUeoix8anMNISt4Qabz6r/lPJ7vWtp7wfEGkPk5chsSyjh4UpXSSYgkgaBUqdhFIvoTjB7zSMiE+K+UDsKuNwKeNpG9ZR3+ilq2WE3g4bQ71OXHYvgUAI+WMgfEdDEKBgSjKzzsth7hQFGb5KjL5eFLIPKexDDHgJDjnxDnvwDHlxdrtxdrpwdLlxdrlw9XmQj+jRqTQZIiL2kJCdNfmM1MmeSWRZpn/DThr+9izBjgoyL8pAvXwxVYFi1uyQ2bOhlcHesburTwSSUiQlxUhuThyF+fEU5MWTnxeL4+n/MLjpTWqv+QJPPdONJ8pEclqGid/ceyGlJYmndR9lWSZg76aysp5N+1zsrQtSVzmIZxzu7eMlkrGSyMwyDYsmKykuykIbn4MgRj9Hn4q4ddhc9A54op4HIeKzYjHIGONjT2u6/ekSskeJWJUClVJEJclIQghRDiAeFLGCfHSkVQ7JhAIRAwJBFBAUAqJC+CCnGAgJSlxiDIP9XnyeY7+jSjmE1u8iqFASlFQEUIwq0M6J2+NtVBCuAb4HTB9lkWrgPlmWnztze3WOk+VIcVv/+KepM1zEQ88G6Os8ti5WoYCpU2PoHRaOO8MoHXTvHWuTcwClKFNkdjAnz86smUpS85NwmbKoHkqgojpI/b5eWusGT8q9uKQkgfOX5nLekhxycz76GrmxEg6FaHvmNfrfe4nAVRfxZl0SW9Z24DhO/eD8BZl87UvzycuN/jnlcBg5FEIOhQkHg8ihMG6Hk3Xvr0EOBpk/fSaiP4jf4cI9PIDHMYTdH2QorKIbMy0jGtrb3HQ0D4/pwpuQqOezn57BlZeXfOxTlj9p4jYcltmxp5PnXz7Apg3tBPyjKxG9Uc3y80woGhp5td5MMHTstWfawixuviOXGY7X6X1+O3sfrkZpjmXeU78jZfmCU9rXQDDEjh2dvP1OA+s2tOAeY0ZDVqGFKbNTWDwlQKm+FbOjAdv+bro2RwStvdPFoDaehth8GhKLGBhj5opaKxHwhwhHOQ6H/p6UrKO9Jbq/QPnsRH58WQvqqgPsvP8Azm43CpWIKdvApFsKSF+UjFOVSFXiSla9M8wb/957zORWXKwCtUFHb5dzXI7Ph4gxSeQJw+Qe2ExaXxNqSUYTq0JtVqOJVaFL0KBP1mFI1aFP0WFI0aGOiW5sExKUuJXxuFXxuJWWg/fxDAVMDA/6sA65cYxE0oDtVg/2YQ/2g88dVs+ox/GjwBSnZfb5uSw6L4UidSPJjv3ILS20vdfFUI0Vz5AP75APn803qnjXJFkwTy0mdkoh5imRelljQTaC+L/hoA3gHRym9anXaH/2JcypXjKuLMVXvoAmdwr7W9W01NtoqRugp9X6kdR86w0q8tO0xNXsoHhlMe80WmiqOjbCrFAI3PnpmXz20zOQpDPz/wsH3NgGO9i5r4uKWg91LX5aGmzHdaY+EaIokJBmIjffxNzJSpbMSSA1LRdJO7Zz3smIW5/XT0+fY9QU5EOGUXFmPdIEX09lWcbnC+J2B3AfErITYAiqVCkOZ20o1SIa5REiNhxElIOIRE4Mckgm5A8TDoQIB8KEAjLhYJhwIHIbTcIJIhGhKwooDUo08Vq8UgxDNvm4478TcU7cjmXjgpBKpK42EVAAQ0CFLMtNH9lOnWPcHClu/x5TxLK7J2O6dDYP75rKWy+1RD0ZTMsOctWVZl7ZbqRiW8+o6zbEaMgqiKe/235CJ8UPIwqQFR9kapaH6cV+sooSEBKz2N8bT1W9n6aqflpqBwiMMQ3xEJmZZpYtzWHpktxxOTZ+lIR8fhr/8R+G17yI+dMXscZexqo3Okc17hBEgRWXFPKFz8wiNdV0wvWPVbQ5WzvpWb0Ra90+ghYdXdlT2d1poKpigM7m4eNuIzHJwOfunMkVlxWfsQHCRPNJEbfdPXaef7Wat9+qpb/v+IOl+CQ9ly3RUNZbwX+6S9jVeKzjp0ojsfIzM7hsmpXs9jfZ/ZvddG7oJWnZPOY/9Tu0yScXAZVlmeqafl57o47V7zZgt584TVcUBfImJTJlVjJLyjwUKJswjdTTv72brk19dG/vx+EUaI/JoMWcQ4slD4cwtnpGnUFFbmkiPW0jDPVFdz7Vm9TMW5xM474eWjuOnfzRGVR8+mYL1ydtpPLhfTS/1RF1PQlT4ij/QgkxpUlUJ65kS72ep/645ZgsijRHF7cb9uC/8hJ22FPYX+nEOjj+bBdBgKR4JQW+TrL3bCRxpCtqYxulXooI3VQdxjQ9xgw9xnQ9xjQ9mrhjTXhkwCuZ8SrNeKUYvFIMPikGn2QgoNDjV+jxizp8Pjli1OIO4PMECAXDhEIy4VAYrzvAYK+DgR4HA90OBnscpzTAG+vxyE0XKVX2klRZgaGqAUk+9lqjjDFiKs7FVJyLeXIhsVOLiZlciDbpk1eWcbLIsszQ9n00Pfo8w5vXYSk1YJkSh35WCb7kfPpIZX+XgcZGDyMDblwO3wc3uw+3039Skzcng8mswmEPRN1eXn48P/n+UsomJZ2RfTmScCiI19FHc1svtU02mju8dHQH6OrxYhv24rB5j+rkoDOqSMuKITNdQ26miuI8A8V5ccSaY1HpLIiK8WfQHBK3CoWCwsLju0qHQiH6em3YHKMbLhkMEgkGEY05ZkKyF0KhMB5PRMi63UE83uj/x7EiCKBUSajUEkq1ArUK1FIYiYMCNhxAJBRxdveHCQXChP0hQocfhyds0k6hEtElacFgZMSrZXjAPWpq9/HweQcYHnHRcRxxq5BEUjLNxFiCPPXgrYde/t8Rt+f4ZHCkuH2AHOJRYimLZdrXyqnJvoz7H3PS0XiscIkxSHyxsIq4i+bwr3c1VO7qHXUbxZNiWHxZEb29Aap3d9NSO3BSxh0xRpGiDJnS7CA5ORoseRk02y1U13tPSuxaLDouW1HEtdeUkZJ89rv+Bpwu6v78BNYNr5L85RW8PTKd155vYagvepqXQiFw6aVFfO7TM48rck9GtAXdHtqfX0XnSy+jmZKIc94SVlfq2b627bip5Pn58Xz/24spn5pywm2cbXycxa3T6ef9Dc288loV+/f2nbCuPTPPzBVzw0zr2kNP7oX84ZUehqP8X9Nz47jtnhkslNYjbN/E1vv24hnwMfnn95x079qBARdvvl3HK2/U0t5mPeHyoihQMCWJaXOTWVLiJFdswDDUSO+Wbjo29NK9Y4A+wUxLTBatKcV0Ki2Ex9iTVGdUUTY7neLyFFr2t7PpveiO0UazhosvzyDTU8df31Dg9h67UNnMFL5z9RDGrWvY9adKvEPHF+uCCJPvLKLk5gKa4pexw1rM33++FteHRH6au5fru95FpxRQmlRoV0ylNn02m2sUNNfbTirtTlKKZCRJlDibSdu8hhjXiR1wlXopInQP3Q4KX0O6HpU+uhMuRARwWFASFNWERDVBUYWMIlJzJoiEBCV+hQG/ZMCvMOCTjNh8GjoHoKMrSHebje7WEbrbrKctjVMlCeSaw8yM97NsVjLJU/IwFeWgSbJ8otKKTzdBt4fBLRX0b9hJ//qdeNrriS8ykDA5DtOMHBRpyYTUBgIKbaT+W6HDhxabT40vKBIICoTC4A8KePwCTo+AywsuV/iwQZh92INt2M1Qr3PMJQtjRRBg5cpJfOWLczEaJs5R+WQJh4IE/C58PjeDQzb2H2hAkmD+vBmYY5MQxYnLlmppacHrjUwq5efno1Qe+5uWZZmhATtDVj+hUYSdUqUgwQAxlliED5sAjINwWMbtCeBy+nG6/Ph8p/C/PixkFajUEhoVqKUQCjmAQg5E6mLDYUK+MCF/KCJm/aHI81Pssy0qJUSlhCBJiJICQXnwXpIQJQlZDuPtGyLo8qA2KVEmGLGGDIwM+8YlnmU5jM83wOCgi6YmG/94vAmlSkFqtpm03DjSc2LJytKQkxTEiA1rZwMLL/rsobefE7fn+HhxpLh9SFuC2RMZCAmiQMHKLBK/fAUPbCxgzevHGl4LAlxeJnJ98nZaZ17Kv98WqN0bvQemKMIViwWuvtKC05DB/jYttQeGaTjQS3fLyEnbqScmKMnLkijIVZOZH4tDSqS+0TMusSuKAksWZ3PjdVOYMT31rB+s+IZGqP7NI3jr1hP7pWt4qTGft19qPGbQewiFQmDFiiI+f2d0kXuqos1W3UjdA0/gadtD3BevZou7hHff7qK5enQTkUsvKeSrX55PfPzZ35D9EB83cdvX5+Dd9S2sWdvIgX19ow42DiEqBCZNS+Ty0hHymipJXnkrz+3x8u9n9kVdfumVxVy3Mo7JA6/Q/MRuKv/VgDY1ifnP3k/iwvG1Nvd6g6zf0MIrb9Swc2fXCdMUBQFyShKZPj+V88tc5It16Psb6N7SQ/v6Hqoqw7RrU+hMKaRTl4wzNPZIhdGsoWxOOlPmpDMt10vLtir+8ZyLYeuxgxhBgPkXZHNbSTMvv2RndZf5mHOZpBS54bY8bk9dT/XvN9Kx/oOJQCE9EeXKpSy89Vo0Bx1x/cM2bDVN2GuaaHvuLRInqZj97Sn0Jcxig3sBf/vZmmN+67NmpvHAHy49qhe1LMt4B5rZuHobaxo07N7rHDXifCL0BiUl6QJT2ncRv3M7smt86ZEqoxJNvBptnBqtRYM2XhN5Hq9Ba1GjMqpR6iWUOglJIyKMMaMmjIhXacajjMMlxdLlNNHQraK5Ezq7ffR12OjvdkyoU71areC8ZXlcv3ISUyYnn/XXi7OZkNfH0I79EbG7YRfOlk6CDisK/KhiVGjMKtQxKtRmFQqVeLAmUUSQBBRqBUqthKRToNCpUFjiEOLjEGLNBPVmPFIM/W4jncMq2vtFujo99LRb6WmznrJZW4xZw43XT2Hl5SUkJOgn6GicGqf7+jQ4OMjAQOSartVqSUxMRKvVHv7+O2xO+ga9+EcpbxFFAbNRxBKnR9KM3/37UJqx0+XH6Qzg8YxuTHVcjhSyKgUaFWiUocMiVgwHCPtCH4jXQ48D49uWIAiIaiWiSoWoUkZE7KF7pRJRFRG0o50/ZFnG5w/h8QTweiM3vz98wut49HWFCQYdhMNehkY8jDgDGLQe8uPsmEKDaAMjaAIjSPIHk4OdAw6yrnv00NOPt7gVBOFaWZafn9CVHr3+dCBTluUtp2sb5xgfR4rb6t0ViJv2U//Q0zjqWwEwZRmYee9i3tFczqP/aIl6USgpjucO7T5ycoaoLryEJ1/101wzGHV7Rp3IzcU9rJjUh7owA09CHp3BVHY3qqmtsdFWN0hPu+2k00mUKpG8XB0F+Vqy8824FLHUVo5QubNzTOZUeXlx3HDtZFZcXIhWO3q04WzA3dVH1X0PIjgPoPrMjTyzI4F1bzSOWpd8SOTeflM5OTkf9MibqIuiq62Lql/+Hev2NWR+/xq2aebz+ovtozpl6g0qvvGVBVx5efHHYoB4totbWZZpbhlm1ftNrN/QRGP9iaNtAEnpJpbO0zE/eABjv4usO+7Ebk7mez98h9q6Y3/HRrOGG780l+WZDSQ1vsv2X+6hb88QqZedx7x//Qp1/Ngd15tbhnn+pSreeLMOl+vEUbfMgnimzU9nabmfYmU9MUN1dG7qYvv7bvZ2m+hMLqRDacEdGN/3KTZBT9nsNKbOTmZGhpUETwPeqmp++6qFHV3RszqSM2P4zM3xxL71PP/0LKOq3nrMMvHJBr7xOTNFu//L7vv34rdH0orjZkwi/xt3UqkJIIjiqN8nd1cf6y+/G1xdLLpvJtbcuaz3LORvPzs2gnvekhx+88uLRk37l0M+2nZv5r2dg6zfL9NQazspDwOVRsH0YjUzencSt78aldGAu6OXgHV8/cuPhyCCKImISvHgvRD1uaRRoNQrURoklHolKoOE0qBEZVSiitcjZaQQSkqlzR1HY5+GjTt91NZGapTVOiUarRKNToVaq0SSRHzeIG31g2OuzcvMNnP1laVccWkx5pizs13Px5GQz49vcATfwDD2zh52rdmA7PNTnFeAQpYJ+/yEfH7C/kDk3uvDb3XgH7HhH7YRsNsQQ05UBtAlatAn6dDlJ6EszIbMTLoCCRxo11LbHKStYZjO5pGTmgARRYHZc9P51JWTWDg/6yM1Tzzd16dgMEhjY+NRgvLQdftEoiviECyclKFgMBgmGAwTCo1eo3o8BBFEUUQUBURRRiEAhCPGTuFwpAVPmMiYMyxHtjGW7QhEauhFAUE82JpKjEzMCaJ4lCnUWAiHI+ae4VA4ch+Wx7Yfo+3eof7noowshxCRkf1OZK+d+IZ3CXb34h3y4R324XcGCLiCh+8DrgA9Njd39lQdWt3HXtyGgSrg5xMpcgVByCRiQvVp4JeyLP98otZ9jlPjKEOp+noKCgqQw2F6399K/V/+TdfraxGVIpM/U4zv+hv47VMS9fuPjc7qDCq+cWM+Ke/+k+QLEnhHfSHPPtczqgFCUUkCP/3uYjKUw/jaqwhZWwlJIQKxFoZViRzo1FPTItDcaKOt/uRdAwUBMnNMTC43E5MUR3ePj8odnSesATYYVNxy01RuubEcne7sFrn2+haq7n0AbUwvnk/dyH82aNnybvNRNTgfJsasZsrUFGbPSGfKpHiaGnciCMKEXBSdrZ3s+8Ef8HfsIf1HN7PePY2Xnqqjtz16a5m5czL48Q+Wkpx0dqeGn43idmDAxdadHWze3s7ePd0M9I8toqbRKZk1N4Fl2QOkVmzHNGkhWTdfhSo2htfeqOW392/E4zlW+JRMT+WWu0uZ7Xkbz/qdbP/1PgIumfLffpOir9w+pkmKQCDE2vUt/OeFSioqorfhOJK4RD0zlmSzbJbAZH0jwfYGNq/1sKclhmZ/LF1uFb5xGnorJJHckgSKp6dSXqamxNxHrLWOcFUDPm8MWx1F/G1DAIfj2POOpFJwyTX53JC4m50v9vIvVxnWkWNrQCfPTuWHK3sZ+fOLNL7WDkDi0tlM+t7nSV6+AK/XO6bvU8DpYsvN38S6Yyvn/3Eu9oJ5rPcu5q8/PTaCe/mlxfzkh+eNyUsg4Oxn75advF/hZfs+L51t449wmuM0XDLFR+6b/0Uj6Em9ZDEIAiMVNdhrmnB3jl6ucibxmmN4p2gF9fLorrcxWkgxBikrkMicls7+DjW1B0boahk+4aBakkSWX5jP5++cRWbGx6+Vz9nMqZx3Aw4nnp4B3J29uFq7cLZ04mxux9/XhegfRJ+qQT+ziHBJMQ2BNCrb1TQ0eWiq6sMd5bd/PDRaibnzMrhkeSEL52ei1ZzZccOZuD55vV46OzsJBAL4fQHsDh+B40Q0lSoRg1pAo9eOWeyFwzJeX6S3rM8XHJ/AEyLndkmpQCmBSpJRCCHEcBDZH4yYOQXlw/fH+2ELohiJrEqKw2nChx8rFKdkDhcOy/j9IXz+iFtz8DjmUuNGAK1GRK8Mgs9PKBAi7AsSsLsIefwM/v0FXFuiZ2MdyRAB7qHl0NOPvbitAwqIfJ06gGeAp2VZrjruG6OvSw+sJNIDdxkR06kwcLssy89M2E6f45SIJm6PpOvNdez47A/x9AyQNMNC6c8u5ZGG2bz+34ao+f5XXFnC9YlD2N54DMuXLuexXfm8/0Zz1GbuCoXAzTeVc/dnZh3VS04OeMDdh+zqw+0cxuby0jigoLpDor5dprXZRVfryHHF22jExGooK48nKSuWrk4PFZvajhu9iI3T8vm7ZrHyqhKU0tnt9ju8p4rqX/wJY5YX92Wf4r+btGx+t2VMx0mrVZCVpSU5ORalSkJ5sCebUhIPNhtXoFJJGA1qDHoVBl2kv6Bef/CmU2IwqDAY1IcH1gObd7Pn678gLjeA/ut38fyuRFb9pxJfFNGk0yn56pfncdUVpWet4dTZIG5HrB627+xky44O9uzupLtr7G01FJJIwSQLS8tlpvZuRRpRknnj9VjmTUMQBBwOH7/49Xreee/YEgSFJHLZreVctThMYe/rVP+tgroXWtBnp7PwP38kftaUE26/t8/Biy9X89Kr1YycIItCrZGYMi+D8qkGEoLt9LTZqGwSaegUsNpPzvHSbNFRPC2VsqkxzM5xkhxoRdXVgRA2IKWUYpy2CEdQ4pe/2cD7a6L7IhZOTebua9Vo//Uo7xov5+V9vmOyTESFwMob8rgzZR0V33sXa5OdxCWzmfqrr5Mwb9rh5cbzfQqHQuy+5z66X3yZ8/80F0fBXN5zRCK4HzaZuumGKXzjqwvGnQ3hcQyyY+s+Vm+2UrHfRX+Pc8wDLoUkMm9ODBcPr8H16m7SLltG+a++gdJkwFbdiKOh9aCw6MTZ3IG7sxdvzwAh76n38gVQaDVoUxM/uKUkoEmyoIo1salXwWNrR3BHOe+MRqxeYIV9M0uWGgjPn8NeRwqbdvqo3t193JR5URRYfmE+d981i6xM8wR8snOcrvOuLMt4evqx1zRjPVCH7UA14eEWlPEK1Itn0hZTxJ5WLVUHbDRV9Y/L0VmlVjBrdhoXLy9k6cJs9ProLuMTyZm6PnW29/LiSztweAQSLGrUUaLVKrWCtDiB5IzEMUVr/f4QQ0NuBofdEePAsR5qITJRqzeqiNELGDUBVGEvstNJ0HkwIukMEvQee80QRBFJp0Gh1aDQaZB02g8eazUIEzjec7sD2Bw+bDYPTqcfX5T9mWhUQoh41xBGNYRcHrwHGnGs34W3vm1M7/+kiVsJuAf4LmDhg69YA7AN2AlUAP3AyMGbFogDYoFCYBYw++BNwwd9b98GviPLcuWE7vQ5TokTiVsA/4iN3V/9JS1PvoLKpGTG92ezv/R6/vJIL8P9x9ZwZWWb+ekXpjD083vJXKymd9HVPPKyQOXOrqj7kJJq4sffW8Kc2RnH3Vc55Ad3PwHXAFaHk5pWF5VNfmqbgzQ0uujvGp8js6QUKZ6SiD5WT1NVH4M9owuFtHQTX7p7DsuX5Z/1DsvDFdU0PvwEktiO71Of4r9bjWMWuaeKIIDBoMRgVGIyKjEalWhlH9JwP8nZWsLp+eze66DxQPTa7Iw0I//3hbln5XE+0+LW5wtSVz/A3sp+9lX2UFfTPy4xC5EL/6RyC/OKg5Ra9yEeaCVm7kXk3HwFqtgPIkx7Krr50U/foydKD8rEdBO33jObxcadmGo2sPXeCobrbGRedwmzH7kXVczoEfdwWGbrtnaefbaCrTu7jy+WBEhKjyHOoiXs89Ld6cJhH3+P5UOYLTpySxMpLDZRnhugQNeLwW1Db05BkzcdwZCKcIThyvtrmvjFb9ZjjeLEqzOquO62IlaIa9n3p528XnYz+2uiGO3Fafnq3QmUVTzH7t/uIRwSKf/1Nyn6ym3HzPSP9/skh8Ps+PyP6X39Dc7741zshQt4e3A2/7hv3TG/7f/7/Gw+c+f46p4/zPBQP++v2c+qNYNU19ijTkodgwAzZsVxnWEX9r+uRpuTy/Tff5fkZfOO/TyyTMDuxNPTj3/ETsBqx29zErQ7I2mpbg911dUQkimeWoY21oyk1yIZdAfv9ShNerQpiShNhmPE/MCAi5/8Yg3btkZ3pB4LhfFhFq57mjS9i6xPTSJ4xWW8VZvI5nU9DB2nX6sgClywLI/P3zXzY9V+7mzkTJ93gx4vw7sqGdi4E2dNBWJsiOCy89nUl8auHUM0VPaNK5ooKUWmz0jhogsKWbYkB5Pp9KSvn+7jNDRk4y9/XM2qDSP4R/Ey0eqVXLlIx2duX0hcbtZx19fcMsyadc28t7aZ+ijlL6MRn2SgcGoyk0s1zM6xkeprxrd9LwM7exisGsHaaCd8MJgiSBKmohzMkwsxleZhzMtEn5OOISf9tJnBud0BKqv72FHRzd59XdTVDOJynvx1TKtX4vMGRzWPyi1NoK1+aNSsm7lzM/jW1xaSkx0pFwr5/QTsToJ2F36bg6DTTcjrI+TxHrz3EfL6aG1v58JffufQaj7e4vbwiiNR1/8DvghkHnx5nMkBAISAV4HfybK8feL28BwTxVjE7SG63ljLjs/9CE/PADmXZhD79Rv43euJVGw59juvUin46pfnUrxvDcPvvcik75/P6uD5PP1Ux6gpwZdcXMg3v7qA2NjxnZTlcIiwe5Dunj627+1nX6WN2mYvbS2OqBHjaBhi1OQUJ+C0+2ipGd0IqajIwle/NO+EQvxswNM7QOPfn8bbtoPgNVfy/O5Y9mzpwj4y/lYhZ5r0eJFlJXqKZ+RSMr+YtIxYFKfgrDgRnM7BQygUpqV1hH2Vfeyt7KW6qo/2VutJmUfExGmZOiOeeTlO8tq2E6wfwTznPDKuvhB9VtpRyzqcPv7y0DZeeCl6cs7c5XnccFMG5dZXGXp7Lzv/UIkcVjD9T98n/3PXHzU4CLrcOBrasNe10FXZwruVTjaNGBjyHH8AIYoCMvKoPUPHSnySgdzSBEoKVEzL8ZMX5yfOqEdjTEQwpYM6Nupgxmrz8uvfbeCdd4+NWAOUzU7ni9cKCA//nX3OMp4JFmKLkoZcNDWJH15nxfGnZ2l+swPz5ELmP/17zJOLoq73ZL5P4VCIbXd8l/5332XZn+YyVLyMN1pL+NdvNx4z+PnONxdx/bWTT7jOsRAMhti4qYbnXqqhYs/QmM6rk8vjuDm3Ds8DLxMK6yn51mco+urtiGNM5zvZ35ssy7z2Zi2/u39D1P6aRrOGeRcVMNzvpKfNSl+H7bifR6EQuKBQYvLrjyPah0lfmkryZy5gj3IGqzcHOLC9c9T3CwKcd34eX/zc7MODy3OMj486Y8Y7MMyBn/6FgXffJO32BXTMvJSnXrBTv3/8KfeiQmBqeRIXX1DE8mV5E1qnfbqOU2tbH4/9fT3vb7HiHWWCS6VWcOF8A3euLCF7zvSoy4TDMlXV/axZ18z765rp7IhepvRhRFEgpzSB8pmJzC/xUqjpILyngqGtLfTtGWS4zoYcktEkJxA7rQTz5MLIbUoRpqJcFOrTFzWXZZmubjt7D/Sye28P+/f30NZy8n2bTbFaMnMM5KUJxIWGeW9HkI5RdH9Suombb81guWUfB/ylPPGSi6pRAkgKhcDtt07js3fOPMp08Hg0NDQc2fLpkyFuD29AEERgOXAdcB6QPYa3eYAdwJvAM7Isn7io6hwfGeMRt3B0FNeYoWfaLy7ged9FPPdkY1Rn4nkLsvjKBbHUfeVH5CwzoLttJY9syuD9VxuizjSZTGq+9fWFrLi48JRn1ZxuH2tf3czGnb3sbfAx2D82UZeYbkRAoK9zdIOUxYuy+fbXF46pj+xHTcjro/WZ1xjZ/CaqeUUMJBZS2W+iuiFAc83gqK2EziYkSSApTiLVoiQrI4biqTnkFSSRmWHGbNacEUOqiRo8yLJMd4+D/QeFbFV1H00NQyedqqTRKckvjWdyoYLJpkEse7cS6AsTv/h8MlZeiDYlep3h+g0t/PK3GxgYODb7QmtQcd3ds7mopI+srlXs/fNeWt7uxFSSy7TffQcEAUd9C/a6Fhz1rTjqW3F19tKXkktV+TIO2PQEg6fv+qTRKcnIjyMvV09xjsiUPImMBA0mfQySKQ1UY+uduHZ9M/f9aj0jUSZ8dAYV195RypW69Rz4zivsWf5/vFbhOib6LIgCl12Ty2ezNrHvu29ja3ZQ/PVPM/UXX0OhGb1dyMl+n8LBIJtv/DrW7Zu44C/z6cq5mFcq03j2ga3H7Nt9P7uAFRcfvyflePH5grz+diWPP7mPnq4Tuy8XlsZyW3kX4l//i7XNS9aNlzLjD99DZT7+ufNkjk/Dvjbu+/X7HGiO3gd3+qIs7r4GEiveRrTEIaYk49HG0zpsoKFH4v11Npqqo48mY+M03DRZQewzj+Ht6iOuxEzezZNxLriIpzfHsOmd1lFLXARR4NIVRXzxc7NJSjKc8HOc4wM+anF7CFt1I3u++Rvse3ZS/Nvr2KA7jxeebjyhf8doiAqB8vIkVlxUzAXn5Z5yRHeij9OOHQ089q+d7K6wjSrWFJLI0nkm7rw4g5ILFh3zd5fLz/adnWzc3MbGzW0Mj+LB8mEklYKiqclMmxHLkmI7yUOVWFdtp3dbP4OVw4R8YUzFuSQsmknCwhkkLpyBPif9tI8DBofcVFX1sbeqj8qqPupqB3COszb7EJJKQVaemcIciQKdlUx3J2a7DaU+mTptHn9+dwiX69jziVorccmnCrmlrAHPyxsxnXcz/votqApUrBeX8Nwz7XS3WqNuMyleyfe/uoBFF5aecP8+0eL2mA0KQhowH0gHEoikI3uBgYO3A8AuWZZPPgZ/jjPKeMXtITpfX8POz/8Y38Agk+4qZXDlbfz+MTc9UfpSxsRq+OHX5qJ+/B+49m9h2k8WsstyOY8+2U9rbfSBxJLF2fzwu0snrFVMOBRi+6tbWLupkX3d0NTuPWGPMIVSRKNVjtpiR6VScNcdM7jtlvIxz4Z9lMiyjKu1k5F9ddgrqwgMtyIn6hlJzqHOn0TXiIJAEIJBmVAYQkGZUAiCIZlQUCYQCOPzBvF6Avg8h+4DY0tXPI1otQqSE9WkpZnIyo4nOzuBnEwzWZlm4uK0E3bBO9nBw/Cwm8rqfvZW9nKgso/6ugEc9pPvx6lUK8gttjC5WMWU+GFS2/bi2dmElFhM4tL5pFyyGI1l9FTIoSE3v71/I+++H72uNLc0gVu/OI254feRDuxk688rsLc7UZpNBJ1u5GAQUSmiSzfgm1TCYFoevcoE9nSq6eqemBrKI1FpJFIyY8jM1FGcr2FKoY6i7BhijLFI+gQExfhn5m02L7+5fyOrVjdE/fukWWncfZMe8wuPseelIVYvuoPqKG7IpjgtX/5sAlP2PEvFHypQxlmY98RvoqbhfphTGYyG/H7WX3Y3ga4qlt4/l5b0y3h+i4mXH9t91HIKhcD9v72ExQuzx7zu8bD3QC9/fWwbO7d1nzC3K7vAzO0LrBiefI6+7f3ETiul9LufJevaS6IuP9bjEw6F6Hh7I489V8GqFk3USRWjWcPNny5gmecdvG0a4heeh7d/CHdXH77+XmTHAPht6At0bEhewdP/HRjVXX/23HQ+U+ij98F/Yq9tRpegoeSuMjyXXMkzm2PZuKpl1P6qSpWC6z5VxmfumEHMOXflMXG2iFuIXENbn36NXV/8OakL44n7wWd4dncqq5+vGlPbwdFQKASmzUjm0ouKOX9p7kn10J2I4xQMhnnjzT089Vw1zc2jT3qLosD82THcvjSe6VddePgaG3HsH2HTljbWb2rlwIG+MZvUafVKSmakMXO6kUV5g5gadzO0qoKuTX3YWhyYpxaTfMF8EhbOIGHBdDQJpy/dPxyW6e6x09A4RF3DENV1A9TWDDAYZSJ4rMTE68gtMFGcKVOg6CO5swZ1UEvs7IUknT8PbXICwWCYPz+0maefORB1HdMXZXHX5SFi3nwBTeFFZH/6hsO95L0Dg/T+9xECBXG80lHGG/+txxGlxAZgXnaYr9wylYLLFo46PvqfErfn+ORxsuIWwDdsZfdXf0nrv18lsTye3Ps+xZ+3F7NxdWvU5a9aWcrVxh5qvvdrJt+Zh/GmS3lyXzGvP1N9jCkKRKK43/v2Yi5aPvZ9Ggshv5+6V9bzylvV7LQbaOs68YBcUoqjpp6lpZn47rcWs2BeZtS/n+3Y+wd57/GnoX+YSSVFKEUBORSCUJBwKAgHb6GAn3DQTygcIiyECSMjKwRCoohXkHCjwokat0KHS1bjDClx+hU4fQIOt4zNHsJu9WEf9uA+Sffr8aLWKEhJ0ZOSYiA5xUxGWgzpKSZSU4wkJxmIjR27+B3L4MHl8lNd08++qj72V/ZRV9s/ZgfjaAgCJKaZyM4zUZgJhSYrqb01uDbsQ9RnEr9gPsnLF2Aqzj3h55BlmdffrOP+P23G4Tj2O69UK1hx01RWLFUxaeBVOl48QO3zzWjTzQQnlzCclEO3aKHDoaajN0x3p2vMaf9jJSZeR3qmgcwUiYIsDZPKkijIScQQY0GSxj/gi8b6DS3c+6t1DEcRL1qDimvumMSnUnfT+L3nqNLN4AXlFBxRJriKy5P4/jXD2H73DG3vdZN53SXM+utPUceZx7QfpzoY9dscvLfoJnR6Kwvum0ldytU8sxpWPXf0wEilUvDQA5czY1rquNY/Hjq6bPzhoc1sWNN6QhOqtCwTty7zkb3hNdpebSQcUpC0bC7Zt1xJxjUXolBGXGaPd3xCXh+OhlbaX1jNlh0NvCJOoX8w+pz6tAUZfOECGwldQ6Rcd+dxa8TdXX00P/AnhDIDT7aVs+btjqiDc51eyde/Np9ZoW6qf/UIQzv2Y0jVUfrZybiWXc6zW2LZsKol6nXt0Ptvv3Uat9w49Yy76n7cOJvE7SGcrZ1sve072Cv3M/kH59E672aeeraPyh3Rx/9KlYJQKHzCCXWIOG/PmJnCiouLOG9RLgbD2CbvTuU4NTYN8cZbFbzxdhvDQ6OPhwQBZkw3c+tcHQtvuQIEIZKWu6+X3RXdbNnWzkAUH5bRMMVpKZuVzuxyFfPSe1Hu2cbA6iq6tvThd4kkL59P6oolpK5Ygi4taczrHSuyLDM46Ka9w0p94xC1DUM0NA7S2jIyagr2WBAVAmnZseQX6ChN9ZMXaEOzZw+yW4t51jxSLlxITNnR2Yl9vXa++uUXqWs/9rpkSTZwx51ZlNe9TMiWQNG3vjxq5kvAbqP3lccZyEjn6S2xrH+jPmrk3aBXcLmhhRULcrHMmYqpKAdNcsLhfTonbs/xseZUxO0hOl97nx2f/wlhl5Xy789nU/a1PPZYe9SIZ1pGDN+/KZf+r/8AY6KPad+fT2XSpTz2nIP926Ibfyw7P5fvf3vJuGtxx4Ktpol1f36BtX0K9jpjsJ/IwEZg1AjF0qU5fOtrC0lJPrtb2nyYiRw8yOEwvmEr3r4hums6aKvppLNlkJ6OEQY6h3CLEn5DDB6NHr9STUBUEkSBP6zA65dPafb7ZFCqRCwJOhITDSSnmEhNNpKWYiQ1yUhiop7EBMPhwcWHj5MkqWhoHGLfgV72VfVTXd1LV4f9lCz9YxP0ZOaZKchWUJTgIsvTBrv34ajsRWHJJW7WdBKXzMIyZyqicmyD4uFhN2vfPMCrb9ZS2RJ94FE4NZkrby7BZK1loH2Y1j4V3VaJnv4g/T3uMff9HCsKSSQpzUhGqpJ0g48U3wDZBpmCeTNIO2/BmD/beLDbvfz2D5t46+36qH8vnZnGZ2+NI3Pr02y/dzvbLvo8G5qPFTaiKHDldTncHreGvd9/F59TZOZDPyb75ivGlSUwEb87V0cP78y5luSpamZ9ZxoHkq7liRecbHij7qjl9HoVjzx8JSXFCePexnjo7rHzwCPbeXdV4wl7lZtiNSxbbOQiUyXOp95nYO8QIX8YTZIFfU46KkssAyEvgslAekwcgb4h3J29uDt7CdisSFOzeadkJTurok+UmWK13HxTGissPSSffx0KzdiPr62qgc4nH2Bw1gz+tlpP3f7oPgzTpyVw388uJlxRwYGfPsjQ9n2Ysg1M+twUnItX8NTGWNa91TTqec0cp+XGayfzqZWTTsv17ZPA2ShuIZIxUPO7x9j/oz+TMtdC5n13sGpwKi8+foCRUSJ8cXFKYhKMdDTbCAZOfE6VlCKzZ6ex4qIili7KOW5bwvEcJ1mWqakd4I3VVWxY30Z39/EnYFVqiQWzDFxZpiRu5gL2Vfaza28PBw70Mjw0Pg+PhFQjZbPSmF8uMs3cRnjjZvrebaB7Wz+atHRSVywh7dKlJCyaOSH1srIsMzTsob3dSnuHlbYOGy3tVjo6bHR32UfNshgPepOarII4ivNUlCY7ybbV41u3C3dPGEPJVJKXLyDpvDmoYmMOR1oP7184zGv3v8L9b/Th9B57zpwyN50vLe4j9NpuSr7/bUxFuWPap5DbTfc7z1EhpfPYsyN0NA5FXa4kV8WicA0Je7YR6nejS0tBl5GMzaTm6hf+dmixc+L2HB8vJkLcwsEo7ld+QetTr5F1YRr6b9zG7182Ub3nWNMFhSTw6ZsnU/TmvxnZuJkZX5mE7opl/LepnP8+diCqKDabNfzgO0tYdn7eSe3fifCP2Kh/9AVWv7Sb/SmTqOoVxzTL+mFUKgW33zqNO26dhlb78ZiRP5nBg98foq/fSW+vk55eB729jsh9n5OeHgd9fU58Z8Cd+Uyg1UrEWXTEx+sQRS+SAqw2Ba3NIwTH2Rf0SHRGFZn58eRlqyhO85Mn9KCpOYB1Yw0+hxrjpMmHU7BiJhWMqa+ed2CYkf117N1Yx9aqYfaPSHQ4pFEzRpUqBckZMbgcXoZPIcJ8PCSVgoycGHJTIE0YIWGgDe2evXj3tqA0Gcj77HUUfulmDNnpp2X7ABs3tXLvr9YxOHjsZ9Tqlay8YwrXFVTR89vn2LZZZvWcm+iNUqNvtuj46mdjyX3331Q+Ukn8vBnMe/I3J7XvEzVoH66o5r1FN1N4VQqTPlvG3qQb+ce/etm1tuWo5WLNGh57ZCXZWaff2Kivz8GDj+7k7TfrxnQezS2KZcYkBdPj+khp3Il9ZzveYR/ekUifRlO2EWFKAW1Jk6jzJlDbDu1trqjrFkSBpRdmsFK7h4EH30IOSSg0qkibD40ahUaNZNChNBlQmvRIRv3Bx5GbKtaEOj4WVVwMrqZmnNXvsDX9fB5/dijqtUmrk/jinVO44ebZ9L6ziQM/+QtDO/YTW2ii9PPl9M+8lCff1bL1/eZRj4VSKXL+Bfncev0USktG78X7v8jZKm4PMby7ki23fAtPRzuTvzEX11W38exqmXWv1Yz6/85LCJE7JYEeu5ravX1jyoBRqkRmz8ng0gsLmTM7nVjz0cfhRMfJ4wlQXdvPqvfqWb+hdUweJMYYNYXpArEaFX0BHfV1g5Hes+NAIYnkFCdQWp7A3NIgxWI93nc20bOmlYEqG5YFs0m7NBKdNeYf32F5NGRZZmTEQ3uHjbZ2K63tERHb0WGju9uOZ5QMipNBUookpseQmW2kNE+gPNVG0kA9nq37Gdjdy0iDnYAzOOrknqhUotCqQavFoY9jf1wBGxV5x0yKKySRlTcXcXHf6/S+XIdCp0cOBgkHQ8jB0OHH4UAQORhEDoYO/i14+F4OhhAUkPGlC9iUczmvvNg6ahmZKArk5OopywhQSgdixUY+9c6aQ38+J27P8fFiosTtIQ5FccWwgyk/X85L8oU8/3RL1MhPQVE8d6YNM/zbB0hfksTUb81lf9wK/vGMjQOjRHEvWp7Pd761eEJdBo8kHArR8cJqttz3KLszprE7mIzNNv4TY5xZzT1fns9lK4rPupY2H8blcvPSS6uwO0KUl89AFCX8gTBeTwCrzYvt4G1w2E1Pj5PePgdDQ+6Jazr+P4BSrSAjL47sHD3FGSEKdQOY2+twbtqPrd6GlJiDZfYU4ueWY5lbfsIUrIDDia2qEWtlAz1762nuHKFXoaFVSqRmUI3d+RFNLAiQnBFDVkE8BdkKJidaSWjcRc9j6xiptR5ezJCfRdFXbiP39qtQGk+fwc6I1cMf/7yFN96qi/r34ump3Hl7MpOaXmTnD9eyKe0C1oSzog5Ky2Ym890V3fT+9Fn69lqZcu9XKPnWXcfMxI+ViRy0d721ng2X3820L5WQe00Ru5Nu5q8PNh+TJpmUZODxf6wkOenMZJcMDLr462M7ef21GkJjNBhTqhRYkvWYzUpiYxQIyDQ0eejvPrHxXVZBLJ9b5iZr79sE7V5C/jAhf5iwP3T4ccgfIuQNEXAFCXiCBF1BAu4gQU9wVMduQ7qR1O9fxd/rStm9NbpLbk6GhmWFKi49Px/RYaf61/9gaMd+LGWxlHxxFk0FF/PkmzJ7N7cf9zMU5Bq56drJrLhi8lnfW/1McLaLW4Cg20PFt39Hw0NPk1geT9F9K9lpWM6zTzTSVNU/6vvitSEWF/kxlOaypw5q9429RjUjM4Zp01KZNjmZxAQ9RqOC/fu24/PJZGZPprvHTVPrCK3tVro6rAwNjj3CqlRFyrBO9hp/qKd4eZmGuTkjGJv3Y393Fz3bevE6NYdTjZOXzUPSj89XJRQK09Zupa5+kOraAaprB2hsHIpaOnImUEgieqMao0mJ0ShhMorEGARMujAxqgAmpR/8fnpsEn1Wkb4Rmf6hEMPW4KjHNzZBzxc/m0LG83/Fur8HRAFRISAoBESFiHDosSR+8LokIBz824eXFRUCgkqBbd5y/rErhf07Tuz0LWNn747vHXp6Ttye4+PFRItbOBjFvec+Wp95nfyVuXjuupP7/x2ks3nkmGUlpchNF6aR9uCvUIpu5v6gnNDcJTzfUMYL/9yHO4oTXWyclh99dylLl+Sc8r6ORjgYpPWZN9j3s4eoNudwIHc29e3jrxPNTlTxra8tYN75JadhL8eHy+Wnrn6Q9g4r7R022ttttHdG0nN8Zzgd+IwjgN6gQlIpCAXDuB3+k7brPxGiQiAly0xWXgxFmVAcZyNluBHP1v1Yd3cQkM3EzijHMiciZs1lBaOm4QY9Xuy1zYxU1tO6p4m2ASc9sop+wUB/QEfvSPikJl8mGlOslvOuLOKyOV7SQs0om6poebGGllUdBJwfzBQnnT+X4q/dQeqKJWOKRJ8s4bDMK6/V8MBDW7FHGfRodEquvKOc6yc34f7nf9n4ry7eXfJpmgePHVgqVQquuymLq0NvsefHa1EnpTP/6d8TN33SKe3jRA/aa//4Lyq++Svm/Wg6yefnsSPhFh78fRWNlUf3lM5Ij+Eff72SxMQz59o7POzm70/s4eUXKye8ThsitYB6kxqdUY1SpUBSiEhKAUmK3JRH3itArQKNCrRqOXJThtGpw2jFAGoCaPChDnlRBz2o/B6UXic4XKh0sC3hAh77jwOHLbpZi1YnMbtQZF6giYShEQKDNkb2VmMpMVB0z3z2Jizn6Vec1O09/gBTqxHJShDJNoUpiJOYUpJIzrQ8jPlZKE3/O47LHwdxe4juVRvYefdP8fb2MOmuUrR33cjq1jxef6aavuO0vhEEyNV7KM0XUWRnUdsG9fv7Tip77KNAb4q0UswrMjO7OECR1IJ/wzYG1jUyVOPCPH0aScvmkbJ8/jG1psfjUGeBPRXd7D3QS03tIM3NQ6P22f0kYLboKJ0Sj14TRiWBJEW+H8gfVMTJsnDEYwiHiZiAhiOPw2EIhSLXwQ9elw++LjMy7KOnzXrcc7HfP0LV3h8eenpO3J7j48XpELeH6Hz1PXZ8/icolR6Kf3E5j3fNZfVrrVHTNQoK47i8cTWKHVuZdFs+uXfMYKf2Eh7/d/+oJg0rLink219feNqaogOEAwGan3iFyp8/RJdPom7pFezq04zbbKA8OcQdF2cx54YlqGNjTtPeHk1/v5OKfT3s29/L3n091DcMnTZBNxqiKBATr8Ns0WGK1aIzqtAb1OgMKlQaCVEhIkkigigQ8AXxeYP4PAGG+pw0HOjDGiWN9BDHM/kaC2nJSjLSVMQm6lFoNVidIkNDfqwDLmzjML1KSDWSmRdLQbZEcbKbbH8b4T2VjGypx94ZwFhUgmVeOZZ55cTPmRrVcCjk92OtbaFpWy0N9T102Xz0+yUG/EoGXQoGhwMEToMo+DCiKGBJ0pGSoiMhXkt7m5P6+uFRlzdbdJx/VTFXzLSR79zC0MZGGl5upXf34FG16WmXn8ekH/4fltlTTvtnqK0b4Je/WU/lKBGTovIU7rgzi+lDr7H/p++xbjib99MW4Y3Siik9N5Zv3Syj+ucTNL3SSuEXb6H8N99E0p36AHuiB+2yLLPjsz+k5cmXWPrb2Zhm5LA17mYe+OVuOhqP/h9mpMfwyMNXnvG2NFabl6f+s4+XXq7GOoob8dmKpBTR6lVo9SrUGhHbsBd7lH7HR5KTb2JRoZ8UTw8Wjw0qW1B4e8n7yvlUGBbwxroAFZujm1ZFI8akJDMBkpQe4hU+EpSQGqsnpzQdS0k2xvwsVGfo+nKm+DiJW4j0+z7w84eovf9xYnL0lH1lFq5Fl/P6vgRW/afyuNe0QyhEGUuMiMqkxekG6+DZlSmVkGokpySBgnwd5dlecqQuQvuqsG2upn/PEMrEXJIumE/yBfOInz0FhWpstbOyLNPSOsKeih52VXSxp6LnlByKz3HynBO35/hYczrFLYBvaIRd99xH+3NvUnxbCR1X3MFDT9qj9oZTqkSuyPKT9fTDJE02M+cH0xnJX8gLtYW89M+9eKKIjfh4HT/+/lIWnaZWF4cI+fw0/uO/VP78IZwjLtovvoJdqjw6Osc3QDPqRQotYcpS1Cy+aApTL5p+0mmNhwgGw3R22WhpHaGlZYSGxiH2Huilr/f0969VayViE/TEWvSYE3TEWvTEJugwW/TEJuiJM4EubEcdtKEOOlCG3CjDbpQhD2I4gEgQUQ4hyGFCooqgqCYoqvEo4xnS5tE+rKFqRyfvvVgV1XlUoxVZsDwbtdFIZ/MwXS0jDJ3E5xYFSDf4KVL1UZbqo6TMgCo1kWEplqGQgQGnigGbyJBVxuMJk5KkoDTdT57Ug6q2GvumKoYrhxAMSVjmlmOZNw3LvHJMJXlH/X/9vgCNO+qo3d1Ma+cQPbYgfS6BATsMjgTGnLo5EUgqBWlZJnJSRAozNMxaMpWyknRCQZkn/l3Bk09XjBrRj0vUs2xlMZfNcJDn2MzI1kYOPFbHcN3R0YmMay6i7IdfILb89GcuOJw+/vr3Hfz3hcqokzhqrcQVt0/j2mndqN95ifd/W8s75ddT6zh2ckwQBS66Ios7UzdT88O3CYb1zHn0PlIvWTJh+3s6Bu0hv5+1y+9kZHcF5/95HuribDYZb+TPP9t6TN/u9HQT/3j4qo+k72o4LLNtZyfPvnCAbVvaxyzuPu6oNRLxCVoscQoSDSHSY33kFRnZ3h7Lhve7Rm0/NBYMBon4GJE4bYh4dYhELaTE6ckpTKVgdiGWgozTmi1xuvi4idtDjOyvZefdP2VwawXJMy0Uf2U+A1NW8M5+ExvfaqCzafRJw7OJ+GQDqdmxpGcZyUuDyWkO4oYb8W3fz8iONmwdATRZhYczkRIXzRhXqUl3j4MtW9vYsr2DPRU92EfJiJgIBFFAZ1BhMKnRmzQYTUpMBgGTJoQiHCQUlAkGZcKIhGWBECIhBIJBcDgCOOwBHA4/Trv/Ex09hnPi9hwfc063uD1ExyvvsfPun6DWesm772oebZrG2reit43IzdCzZM3jJAcGmP3NKcRfMIltykt4/F8d1Ozujrr+yy8t4ptfW4jRODHtQkbDb3NQ9cu/UfenJwj5A1hnzaJy8nnsrgucVBTRaJQoSJOwxKgwxxqJT7MQnxJLXIwWc4wGpVLE6QrgcvlxOn04XAGcTh9Op5+OLjutrSN0d9lPydjoEJJKgUqtQFIqUEgiSpUCneFgpNWoQm/SEGvRYU7QE2vREWvREaPxoQnaUQdsaII2NEErmqANddCGJmBDkiMTEkFPEPegF5/Nj8/qx2fzE3QHI/VwgTDhoIykVaDUSyj1SowZeuKLzXgkMwOGEvbLM3n5ySr2bYlerzY/286Xb9WgTk+jL5DA3jYtdfUumqsH6GwaHnfEWhAgJVYkHQeJHfXEWdtJMvhIipfRGiXsHU6cfSFM06ZimDUV3dRSNEWF+CUVQwMOWuu66O0cpH/Yw4gjyIgzjNURwuEcvc7mVBBFAa1Bidd9fIE8ZXYqcyaLFBn6MVbsJNQrUH7/L9AkxhMOy7z5dh1/eXhbVOMliAx0LrimlEum2slzbMK1t4X9j9bSv++DgZogimTesIJJ378b86TTcz45ElmWeXt1A3/48+ao7X0Aps7P5LqbspnleZumP7zLa9VprE2ZGzVaG5ug5547TGS+8QS1/64l57aVTP/j90ZtvXCynK5Bu3dwmNWzryVkH2D5g/ORM3NYp72Oh+/dTH/XsQL37w9d+ZE6vPt8QfYd6GXT9g62buugpfHMZ5h81FgSNJTmq9EY1dS2BGlvmHjho9criI9RYDGKJMaqSE+LI7csi9z8JNLSTOj1p+5Mezr4uIpbiJybOl95j/0/+jO26gYyz0sl99ZyXNOWsrU3h3Wr2qna1TXhbvQng0ojkZhmIjXLTGaGmsK0IIUWO/rBdvyVDTj3NuNocxNSJWIunxIRs3OmYizIHpdLfCAQomJfD5s2t7FpSzutrceWq50qGp2S5MwYUjLNpKdryEsJkBfvJCY0QrirD299J84D7dgO9BASTMTNLCdhwXQs86cTU1YwpoCD2+NnaDjiRTI47GZoxMPQsIuRYRcjI56DXiU+7DYfoZCMTi8xPOTF7fzoy4jGwjlxe46PNWdK3EIkirvn67+m9alXKbipmM4r7+Dhf9sZjBJpExUCC6Vupm96kcIVqUz70mS6khbyYmUWL/+zImoULzFRz49/cB7z557+frOOpnb2fvt3dLwUuegGUxLpuf56tnTH0NF6bFT6bEFUCCSkmkhIMWJJMR6+t6QYiDOBgiCiHImminIQZciDMuRGCntQhdwR0XpQuKqDdkQiF+WAO4irz4Orxx257/Xg6nUfvveP1mJJEFDFxqAyG0GI9NeVQ2E83f2oTRIpcxLIWJJC/IJcGi0XsaHezIuP7Iya2qUNeblgaAuLLT3E5pswTctEKp+CLTaHvZ0mqhqCNNcM0t4weNLpzKIY6Q8XCoHPHyZ4BiOtEDGmSkoxkJqqJjNVTU6mDrcLXn6tg+6e0dPdymanc8OlKia3vUn1X3Yy0uCk/Lffouie2xAEgX0HevnN7zdSWxu95YlGp2T5tWVctShAgX0DvpoW9j9WT8+2I1J/BYGcW69k0g/uxlR4+urhj2RPRTcP/nUbe/dFr1+0pBi5+q5pLM9swrTnbVb/so5XMq+g2Rl9Emz24jTumVFL249fwOvSMPsf95G2YuKitUdyOgft1qoG3pl3PbpYgWUPzMOblMtG7ad4+GcbjhG4iQl6HvzzZeTnxU/Y9k+FcFjGZvfS3T3Cu+9uwuOHKZMno9NqUEgCClFEoRCQJBEZCPjD+PxB/IEwvkDwqOf+g/eBQAi/P0QgEMYfCOH1BvB6g7jdATwePz5vEI8ngNcTxOcL4vUEP7J6R1EUyMzSk5KsQmdS094VpKPZOmrP3IkixqQkNVlLVnY8eYVJFOTGkZMVS2qKEYXio4v4fpzF7SHCoRBtz73JgZ8+iLOxjbjiGPKvyUdz2TJ6tcXsatFTuWeAmj3do7YRmih0BhVJ6TEkphlJT1WSlRQi1+IhKdhHuK0D7/4G7NV9eD1alAmZmEoLMU8uxFxWgLEoZ8wpxkfS1+dk89Z2Nm1uY/vOTjyeifsuq7US2UUWsooSyMmQKEj2kW0YROfqIVhZh62ig8GqEYZqrPgdYWJnTCJh/rSImJ1Xjjbl9LqT9/ba+cXPX2Xz7ujjQkkpsmxpBrGJZvz+EH5/mEAwRMAfipy3jmgZJQgCwgdPIo8FkBQioiigkCLnxsPnSEXEcEpSiCgUB/926HVRRJKEg6+LSIqD7xcjfx8Y6OLzdy0/tLVz4vaThiAImcA9wKVAJuADGoH/Ag/LsnzS/TMEQTABK4BlwAwgF9ABNqAKeAN4VJZl6yl8hBPtwxkTt4fofX8rO+7+CXgGyL/3Kh5tncH61e1RI1oJZolle1+mVNvPvB9NQyopZJt0EU/8s5naip6o6195ZQlf/8qCMzIT3bd+B3u+9itGKqoBMGYaUN/zKdbaC9i81YbT9tE4+B1Co1OSXWQhpziB7KJ4ijPDmMURNIEPoqwRoWpFkqNfcALuIH67H6/Vj7vfg6vPg/uQgD342O+I/l5dejKGvEyM+Znos9PQZaSgS09Gm5aINsmCMsYYNVXO1dFD49+eo/GR/+AbHCF5loUZ95ThKyxnn+4C/vNEA3s2tEbd5uQ8E59daCDONYi7vQdvbxeirw9NvIB2/mQCBZOosiawrwEaqwdpqx86q9Ii1VqJhGQDSYkqUhIk0lPVZKcbyMuKIy09EZUuDlFU0No2wu//sIktoziLA0yamcZVVyWxRFpP0x/fo+mNdkxFuSx49g/ElpcwPOzmgYe28dobtVHfL4gC85bns/KqBKb516JoquXAY3W0r+s5qqY2dcUSyn/9DcyTiyb6cESlpnaAh/+2nc1bo0fyJZWCC64u5YoLdZQMvEXTo9v4z4FM1pum4vcf+7/Wm9R8+pZEZlU8Q9XDe8m+5Sqm/2Hio7VHcroH7YcclOOKTZx3/1zssQVs1lzJX3+2/hiBazSq+MPvVjBjWuqE7sOp8FGLmkAghMcTwO0J4HYHcDr9jNh9WO1eRmxebHYvNpsbu81DVeUAfT0TL0p0eiU3XD+Ja1ck0tkzwr6aERpa3PQPBhgcCjA86InanmgiUSpF0jNM5BUkUFQQT2mBhYICC/Fx2nFF7E6Wj/p7MJGEQyF639tCy79epuPld5HUMimzEkiclYR+yXRcKSX0+C20D0h09sv0d7uwj3jwH/Si8HqCBP1BQmGZcEgmFAoT9IfweYP4D/ZrFQSIidcRn2QgPslAYoJEqkUmLc5PhtGJwd5HqLEVz4FmXJ1OQkIMoikFQ142xoIsYiYVEDMp/5Rc7APBEPv397JpSzubtrTRNIEp2KZYLTklCeQUxzMpV6A0YYj/Z+88w+O4zrN9z+xs78Aueu+NvVMkRYpUb5ZkybJky7binjhOPsdpLnHixOmxk9ixE/cqq/dGihIpUuwNJArRe9+G7X2+HwtQpAiQIAmQhMT7uuaamcXs7tnB7sx5znnf502P9iH1tOI5NoCjyY2z0Y2n04cqzYp97RJsE0v68gUoNHMb3Xc6j/9uL9/7UT2RyNT9i8ULzXzjr2+huPjqGFg8nba2NioqKiZ3r4nb9xOCINwO/BaYzqGhBbhNluXOi3jtW4FngfP90kaAj8qy/NaFvscM23HZxS2kXGAb/+GHNP3LTyj5UAkDH/4UP/pdAMfQ1KNbSzRO1h94ilWfLqH8vjI6rdfzzNEsnv/FkSnrdmVlGfjWN25g5fK5q505STKRoOuXz1L/V/9BeDRVKDtndQYVf7qOt6NLeXWfxImDUwvx2URvUpOZZyIj10xOoZmaUgUVdi/m2DDGyCD66CjhYR/B0dBEDckIYVeEkCu1jniixCOJVJhwLEkikiTqj5E8xyynQqNGX5R7ajGWF2EszcdQVoihJB9Je2lmX4lwhLYfPcaxv/x3SMapfrCEikfq6My+la2NVp7+34NTGj9Jksidt1fxsYcWUVyUqus56Tw83thGoL0F2d+HkGMmUlpGSyyX+g6R9iYXve1zL3a1eiW2SQFrE8m1KyjKN1BWkk52Vjpqgw1Rmvrc+fwRfvLTQ/zu8eMkppldql6aw20fLmOj5TDJbW9w+D+OExwLU/FHKUMkQa3mmeea+O//2Yd/GuOsikVZ3PuxKq7THyR95AhNv26l9enuM74PaSsWsORfvkrmxlWXflJmQFe3mx/93wG2be+Y9pjqpTnc98kaVkl7UOzdwWv/NcBzhXfR65061Gz5uhz+aHUXI3//BH6nklU//vas5tZOx+XotDf/x885+pV/InuVnfV/vxyHuZZ9qlv537/bcVYOrkql4B/+bgubN81NLfELZT6JGlmWOXKin+dfqGfXO6OMu6bPG9QblFQUSIiSSEtPfEYDoAajioc/upiPPbjw1KCtLMvEw16cLid9Ay76+r30DwYZdsYYdSZxOKO4HSF8nrnJYTSa1BSXpFFemk5VRToVZTZKS9LQ6Wa3vvt8+h5cCFGPl94nXmVo6zs4D54g2DuIMU+PpcyELkOLLkuPuiQH0WYFtRqUSmSVGkGSkJMpl1wZUuFEKhUJhZJwUokqEUYxOky8q59Iax8RT4KkaETQ2VFlZKcGm8uLMFUUocvPnrU87LGxAO/s7eWdPT3sO9BHIDA7s7NavZLyBVlULs5gSXmSMsMolkgvyv52HIeHGD3qZPSok8BICHNt+UR4cUrMGssKL8sAzHsJhWN87S+eYse+qUOus3INfPa+fD708KbL3LKZc03cvk8RBGERsIfUTKof+EfgLUALPAh8ZuLQk8AKWZYvyMVGEISPAb8GksA24DWgHvAAecDDwEcmDg8C18myfOyiP9D07bgi4nYST2MbBz77DYLtzZR98w5+N76WrS/1ThkSZtQruL53F5tsPaz6y4WEs8rYI97Ib37cSuvxqcMSH/hwHX/8h2tm/YY7FVGPlxPf+m9av/9b5EQCUSlScV8RlZ+sY2f4On7ydJTuFseUzxUEsGfr0Zu0hIMxQsEYIX+URCKJWqtEo1Oi0SpP25YwmtXkZkkUZCYpzoiQofahibuRPGMk+/rxd3vw9frx9vrx9gXw9QdITJFjeDoKrQalUY9k1J+1VlmM6Ivy0BflYpgQs5pM22W5eXga29jz0FfwHG/BmK9n5VcXIqxazQFpM0/+opkT+6e/7q5bW8iDH1nAwrosDIazZ/Nj/gDu+mZ6jxyhzRGiNZrGiV7o7wtesGmEIAooVQpMFg22TC22NIk0I6QbZexGyExTkV9gJa8wB5U+HUFlnPH5SyZlXnjpJP/9P/twu6fOLS2sSOfuTyxmbWYn2T3bOf6fR+h5YwBtbiarf/6PZN94HQ2NI3znX96eNgQ5I9fEXY8sZEtxL0We3fS92kH9j1sIu97thBtKC1j0nT+l4P5bL8v/f3DIx//95CAvvdIybT6m1a7n7k8tZXPlGIWDWzn2g3p+P7KI/WLhlIMA5jQtn/mYldrdv6P5pyco+vg9LP33v5zT2drTuRyddlmW2f/pr9H5s6fJ35jNmq8vYdiyhKPam/jZP75NT+uZ1yNBgC98diWPfnLZFa/PPV9FTSwW4+23DvDy9kH2H/QQmqaTX12h5xbnTgw1ObSm1XK0Q6TjpPucA2tGk5qPP7yYhx5YeN57WjIRIxby4Ha56O4cprffx5AzxqgHxjwyY44Y7rEg3mmuJRdLZraR0pI0KitsVJalU16WTn6eGUm6OBE1X78HF0poeAznwRP4WruJOD1EXR4iTg9xfxBBSlUWECQJUaVEMuhS92WDDoVaBYJALB6nrb0NQath2S2bSasoRl+Yc8F1ZGdKOBzn6LFB9u3vY8++Pjo6Z2d2VhAgrzSdqiXZLKzVsSTbgT3aiXakjbGDQ4weczJyxEFgOELa8joyNizHvn459uuWTlmJ4HLT0tjDn33tDQaGpilBt9nMFz63GZP96putPZ0PjLgVBKGIVOhsGZANqEiJrl7gMHBQluULLwR6lSIIwlvARiAObJBlee97/v5V4F8mdv9GluW/u8DX/wiwCfiOLMtTxtYJgvAl4L8mdt+UZXnzhbzHDNtxRcUtgJxM0v5/j3Psr/4D+yIDsS8+yg+el+htn3rUq9iS4Kam57jzT3KxLc+mI20zzxyw8uKvjp4KzTmdnBwj3/jrTaxaMfezuACehlYOfenbjO44AIAmXc2SL1STdstiHu9eyZOPD+AcmXosJDNDw+dvUrMow0c0FkJOJpHDIeRQEDkYmtgOIcQiJPwhIo4QIVeEkDNC1JsgEpQRBAmlxYTaZkVjt6K2p01sp9ZKs/EM4RpXKth1YD9oVdx8661XbechEY5w7K//g5bv/gJBhPJ7iqj+3FK6c27mrTYbz/70MO7Rc4cGZucYKStLx2hUEwjGCASj+HwR+ns9BObA6CE7x0hNdQZ1NRnUVGVQVWXDaJh5WFQikeREwwhv7ejkzZ1dDLwnnHQSk1XL7R9bxA3LklS4tuLcdpzD/9VIxB2l8KN3sOIH3yQoqvnvH+zluReap0wBUGslbn5gAXdugCrPGwRPdHL4v5twNXvePcZmpe5v/oiyzz5wUblXF0pj0whPPNXAa1vbpi2FZLRo2HxfLZs2mKnxvE5wx0F+/bjEtrTVjPunfs66zXl8tvIEPd96BlmbyYoffovM61fO5Uc5i8vVaU9Eo7y55VOM7TpE8a15rPrzRQyYltFovJHffm8vjYcGznrO+nWFfPtvNs9pmbXzMd9FjSzLDLQe5z9+3srOnY4pS+CJCoEVCw2sHdlDgasZ+0PraTQu5JV9EvX7p4/2MVs0/OHnV3HPXdUXlQ8rJ+MkxocY72rFNTpKjzNBv19Nv1dF/yiMDEcYG/TOuBza+VAqRQoKrZSVpVNZlkZ+npncHBO5OabzmkDO9+/B5WKuz5Msy3R0uti7r4+9+/s4cnSQ6CwZYam1EtVLc6hblsXKigiFim7Sgp2EmroZ2jfK4L5RPF0B0lctIWPDcjI2rCB99SKUBv2svP9s8dgPX+Y/H+ufclB89YYcHl0tsvy+u65Ayy6cD4S4FQShDzhfMk4YeBX4mSzLr8x9q+YOQRBWAAcmdv9XluXPT3GMCDQA1YAbyJTlaZIWL60tB4HlpGZ4M2RZds7y619xcTtJeNTJsb/6d3ofe46KL1/Htuy7efrpwSkFqygKrJIGeTjnKCs/U4bbWM4ebuDXP2qks2nq+pZ33VHFn/7xWszmue+wybJM7xOvcvTP/plgf2pWOWNxOku/XIu/cjW/OFzKa0+3TWsSsnZtAX/xlfXk550dEZ+IRomN+5GTSRQaNQq1ClGlvOjwovnWeRh5ax/7P/11/J19GHJ0LP1SLfrrl9BouolnX3Dw9kstV1UO7XvJzTVRUmyltCSN4iIrxUVWigqtp2aVI5E4Bw8N8NbOTna83T3tLC2AQhLZcEclt9+VS23gLbSdRzn6/SYG3hlBaTGx4offIvuem3nymUZ+8rNDeKfJz1uyrpD7PlrEisQOdP2N1P/fSbq3DZzKqxVEkfI/fJiFf/ulOa+jGYnE2fpGO0881UDjNL9lSIWrbfpQDZtuzKIquBtr/2Fe/v4gvxc20DUuTfmc9EwDX3hQQ95zv6Tr5T7qvv6HVH3lU5dFqL+Xy/m7izjdbF3zIL62bsrvLWLZl2oZMdTSmH4nT//kKPvfODvMOy/XxL/+0y1UVtjmrF3nYr5dl6ZDlpMc2LWXf/9JD+0tnmmPq6oxs9nYQ/a2F6j6WDVdi27nV69LnDgwdYUAgOISK1/9k3WsXpU/K21NRsP4G4/g72gkFA3gEjT0JNPpdOroHpQZ6Asw3OchNotlUPQGFdnZRnJyTGRnGjDoVeh1SnQ6FTqdhEIh09x8HJVSYNPGdVitBnR6FTqtEpXq0srovZ+Yi7rZnV1uDh8Z5PDRAQ4fGZzWjf5iMFo01K7IZfEyG6uLXWRHWzE42hg9NMLgvlGGDoyhySkk66Z1ZN24loz1y2elvvhcEPT6+eZfPcmbh84+P2qtxCc+bOejty7AXFp2BVp3cXxQxO1Me4qTDToCfEqW5YY5atKcIgjCPwB/PbG7Wpbl/dMc95ekwpUBbpJledsctOVfgT+b2F0hy/KhWX79q0bcTuLYd4yDf/h3yON9WL/5CP+zN4fGIyNTHms2KLgteojPfE5EW5BOS9otPLtbxSu/rZ/SXj8tTctf/Nl6ttxQelnCKWP+AI3f+V9O/vvPSEZjiJJA+b3FVD1ax0nTBn7+qpq92zqmHNVXKhU88rHFPPrJpWg1cxdWPR87kfFAkPqvfZeW//o1yDKZS9NZ9IUawkuup55VvLVtkL3b2qesjXyhGC0aCkotFOUrKc9NkKGP0OE20tor0NvlZaDbQ3wWRrDtdj25OUZaWp0zcpOsWZ7L3Y8sYJn6GDlj79D2+1aafttOIpIk66Z1LP/x3/NWvYcf/fggI9NECmTkmbjv0SVszmsjz7mLtifaafxVG/HQu5/Hvm4Zy7//TayLqi75M56LgUEvTz7dwPMvnmT8HLUOVRqJ9bdXsvn2QqpiB8ka28fxx7v5RccCDoQzpwxbFhUCm2/J4+PWd2j/9quYl61gxQ/+BkPJ7IiCi+Fy/+587T1sXfMRIg43VR8pYfHnq3FqS2nI/DDbX2jn1ceOn3UdkiSRP/jUMh79xFKUyssrJObjdelcJGJhfv/ELn7ym75z5uTm5Bu5uTbE0o5tlN+WzxHzJn77apLGg2fPsE+ydm0Bf/Yn11FUaJ31dscDQTyHDhI4eYho2Eki3cKoKZdOr4XOIYne/ihDvR4cw/4p72NziSSJaLQSWq0Sq1VLRoaBrEwD2RkGMjMNZGbosdsNZNj1aDRTD3i9X7jU30syKdPe4eTI0UEOHxnkyNFB3LOcr52eaaBuVR5Ll5pYkTNCZqgF1WAnA+8M0b9rhPEByNy8luwb15K1Ze2cOxnPBife2MvXf9hMX//Z5yq7wMxX7laz8aP3zKi80NXEB0XcHgEOkXLw7QccpGYS9UA+sAS4ntQs5iRh4O65EHxzjSAIbwPrgQBgkWX57KnD1HFrSOXlAvydLMt/Mwdt+S/gSxO7y2RZPjLLr3/ViVtImTR1/N/j1H/je+RtyaHlpo/zy6d9U5aAASi3JflC5TE23GViVF/NO/EN/O5Hx+maJq/w+vVF/NWfbyAj4+IdAS8Eb1s3h7/8Dwy9+jYAWpuGxV+oJv2WRWwPrOfXvxmk++TU+bj2DD1/8kdruOWm8jkR5PO5Ezn2zmEO/dG3cR9rRhCh6MY8qj9eQaR8Md3qhWzdL3LgrU6GejwzKu2h1kpk5VsoKtRQW5SgWj+Mpb2BWI8LwZpPV0JFMs1KhRwjMdiEKs+AuKSOHiGfxgEd7T1x+jvdDHZ75mz2ODPPxJ2fWMKGMhclzu243mnnyPcb8Q8EkQw6Fv3TV+ivWcMPfnRg2hqCKo3ETffXcecmBdXurQSOdHDouycY735XBGuy7Cz5tz+n6KE752wgaMwRYPc7Pbz5Vid79k3tmD6JQhJZe3MZW+6uoFo4Tr7nHXpf7+R3b1jZrl+CPzD1AEPFAjtfuHEcfvhbXK0xlv3n18j/8C1XxGjkdK7E725s71G2b3qEZCRK+YcKWfblOsbVeZzIfpDmxnF+8709U7rvlpak8c2vbWJBXeact3GS+XxdOhfjbjf/+cO3efnV4XPWN9UZVdTUmFhSGGFNTYReinj8BT/NR6aeyVUoBO67t5ZPf2o5tvS5ybOE1Iyev7MP9769hDuPk4yMoSiwES8poztip21YQ/cgDPb6GOr1zJmh1YViNKmx2fVk2PVkTgjgrEwDmRkG7HY9er0SnTY1YzxdfnAikSQaTRCZcCiORBNEInGCoVQt+mAghj8QJRCI4POntieXQCBKMBglGIyjlETUagVqlYRWK5GTZSQvz0xeromCAgv5eeYLynn3OBzUH23l6OF2ZFFgyYoFlFcUk5U1fRknpzNIQ+MIJxpHaGgYobF5jEBg9jMLTVYti9YWsHqVmRWZfWQEW6C3m4Fdw/TvGiahzSP39o3k3rEJ65KaK35dninxcJhf/NNv+NmOJOEpTE3XbsjiLx4qJn/JkivQukvnAyFuZ4ogCFXAl4E/ACTAB1TLsjx9XM1ViCAIY4ANqJdlefE5jrMCk1n0T8qy/MActKUeWEgq9zddluWpk+4u/vWvSnE7Sdjhov6v/oO+J5+n+M9u5BlhI1tfGZxSOIiiwLr8AH/9kX6MGSaa02/jlT0iL//m2JSOynq9ij/6wiruu6f2os0uLgRZlhl46S2O/Ml38HemyrdkLk1nyR/XEa5Zye+banj+dyen7QzU1Gbw53+6joULsma1XfO9EynLMoMv76Dh2/+D88BxAKwVZgo2ZZN5Sw2BwiV4hDS6RtV0Dsj0DcSQk0m0atCpkuhUCTItMUo0TqwjHYSPtBAPa1Dm1pC+egX265aiyUif8jwFB0cYfOVtRrfvIDHSgqXKgu66hcRKq2gdt9Hcr6any09/h5uh3osXvEqVAkmlQBQFLGYFRRY/OUon8WO9cHIAc2SciusXov/SF/m/J9vOGc67cE0+932snBXyLoz9x6j/32a6X393VkiQJCq//AgLvvmHKE2zO/gjyzLNJ8fY9U4Pb+/qpnmawafTsdh0rLmpjJU3FFMhnqTI9Taew9387nGRbcZVjI5PfU4t6Toe+bCJhfufoP03DZR8+kEW/t2XUZmNs/qZLpYr9bvrfeo1dt//ZQBKbstnxVcWEFal0ZD1AAN+A7/6t930tp2d/SII8KG7qvn0o8vJzpr7czjfr0vnY3TEyS9+u5+XXh3E7z2/qMjMNVJba0BrMXBwzwij/VN3BQSg0K5kUbbIihyB8kITGosJyaBDkBSIkoQgKVLmRJLijMfe/ds5Hpsi/SXm8+M8eAL3wYPE+psRomNoa3NR1ZbjVOfSMmakY0hBf3+U0UEvrtEAXlfwnINZV5LJep+iQkAUBZJJmVgsQeIy1TU3GtUsWZzNkkXZLFyQRVqaFoNehdGoxuHyc/hgC8eO9dHaFaJ/MMK4e+p0E1EhkJ6uITvbSF6elewMA929HhoaRqaN5pkNtHolC1cXsGKtjevyh8gKNEFPJ31vDjKw34W2bDG5d2wk57br0WbZ56wdc0X7G2/zz491cLjh7DBkpVrBI/dl8LnP3YKkmb/XrGvidgoEQVhLKv/WAPyXLMt/eoWbNGMEQdAAk9/Yl2VZvuM8x/tJzWDvk2V5zSy35XZStW5n1JZpXuN8LkpZwEGAEydOUFp6dZSBeC+eYyc58Rf/Sny4Hc1ffoL/22On9cTUM506nYKHV3l49MYRHJbFHGQdT/70OM2Hpx5jKSu18idfWs2SxbMrGqcjEY7Q9r1f0fqvPyURCiNKAhX3FVP5qTraLNfzi9fV7H61fVpX2C1bSvjiZ5eROUuzzuFwmLffTs0ob9iwAY3mypnIXAqyLDP25j46fvQ4zj1HiLm9IIClxIQ+S4varEJtVqEyq0lE4oQcYULOMPG4Csmej3npEtLXLsG6vA5Jf/ZN6XznKRGJ4tx9mOHXdjGybTcqhRdbnRXL6jKUS+vwmYtoHrXQNaxgZNDPaL+XkQEvzmHfZenkldRkcMfDC1ib2UGhYyfdL7Rz4qctZ9Qotm9axcJ/+3NM1bNzHYjHk/T2jdPR6ebI0SH27OvHMU30xXupWJjF2lvKWbDYQl6gnlzvYaKdAzz1iyAvKtfQ55l6hF8hidxyew73a3fR+c/bMC5cSt0/fwVTVcmsfKbZ4kr+7jp++BjHv/LPABRuyWHVXy4iKalpybiLIXUlW584wY7nT055DVIqRe66o5JHPrZwTmcI3y/XpfMRCkV59oWjPPV8NyODM/ttQMqZHThvKLDJpKSuWEFlWhAFSRKyQDwpkJAhkRQmFkjIAomknPpbcuJvp15aQBBIzaoJIApCyhleTKIRk6iFBFoxgUaU0ShktJKAPhJBOeZA6R5FJbrRmeOYa7NRVhUhZGfjF630e/X0u9UMuxSMjMUJ+iKEQ7FUXdeJ2q6ntk+r5XqNy4dCEmc0KCspRWpX5LHiuizWlbrIizShHmynb8cA/fs8aOtWkXv3FmzXr0g5PM9D4r4Az//P7/nxOxp8UwxIZeYa+ItP5rPqxtVXoHWzS0dHBwsWLJjcvSZuJxEE4cvAd4F2WZYrznf81YIgCHZgctrjcVmWHzzP8SNABtAgy/KCcx17ge1II5W7XAgkSOXbHr2I15nxl+QnP/kJNtuVMQ+ZCbIsk9jXQPQXL5O3Ip22Ox7h188FGJ/G5CAnU8lX7xpheVWSZvudvH1U4NmfHp626H1ttZYbbzBhNl+e3JzkmJvoz18isecEkApVXvLFatJvWshb4Q38+nfDdDRMPQOnVIqsWWVg7So9avXczzrPJ2RZJhZNwoiDRGsfcscAYiiMoFGCUkJQSghWE2JBJmJhFpgNcxIKlRwYI3G4mfihkySbutDbVNjq0rBWWdEvLaNbV8KTu9TsOTjzDu3FIClF7DlGctPj5KkdmCJufO90QZ8TfTSAIeZHb1ajefR2FGsWXNS5SCRk/IEEo2NxRkdjjI7FGBmN4XDGSVxAOrJGp2TFpmLW3FROsS1I3vgBMvyN+NpdbHvaxzPJ62hzT//7rF2awefWjhD+998w5pRQfeoOpGVzmys8X4k+/gaxx1Izo1kr7Fz3N0uQ9Ep6LdfRlbaJgS43T/zwAANdU4e2SxLUVGlZulhPQb5q3oQTXq3Iskx3X4I9B4K0t05d730+olSKGEwqjAYRk1bAqE5iUEQxiRFMiiBpiiDpmgCZ1jjmdAm1WYdk1CLo9MgaDQlBRUJMLVFZRSgu4Y9IBGMSwYhIICLiD4u4veB0Jxl3BRl3hvC4gnhdoWlNG68xNTqDClEhzKj+MkBhhY3lGwvZtDhGWaIBw9hJBt8eoGe3A6c+F+mGFYgVBQgX4ep9VdHQzJuDmeyawjQKYNUaOzeuU6C4zN4Ec4XD4eDTn/705O41cTuJIAjLSTkOh2VZnrvh3VlGEIR8UuWNAH4ty/Ij5zm+l1TecYcsy7NihSYIgoLUjO0tEw/9rSzL37rI13rfiNtJ5Fic2Iu7kV/aQcFn1/GSfgtbXxsiPk2pkKW1Kr56axeasoWcUF7HC785waG3uqY8VpIE1q01sHaVEaXy8nTWEvVtRH78PHJ/SshmLk1n6ZfrCFct5/nuBTz92zacw1OHEOl0Ctau0rNimR6Vap7fPGaILMv4A0mczjhOV2rxeJP4fEl8vjg+X5zke74KarVAVoaSrCwl2Vkq8nNVpKVdPoMRORgmUd9G/OBJOlt9HNBX02MuuGzvfz4EAfQ6Ea1WRJIEJElAObGWpNTvQiEJxGMy4XCScGRiPbEdi138vUijU1K5OJua5TnUrcglL9FO7vgBzOE+BveOsPtZN6+ab6DBO/1tJLfIwsduEyl65Xd0vtaH8oEtSLesQZDeHx2NuUCWZaI/f4n4C7sAMBUZ2PCdFRiydXg0+bTY78QvWtn54km2PdlwzvzQtDQFi+p0lJdpyMpUXhO6l4hnPEnvUJKurgitrX6CgQ/WjKWoEFAqRSTpvdcjUComFglUkoxKklFLSXQqGb0miV4vYTApSbMqSLer0GiVjMc0jHmVjI4rcHrA5Y4z7gwy7gricQYJ+i4t11SlkSbq0EtT1KRXotYp0WhEdFoRtUogLotEYiLxWIKAN0Jfu5ORAS/+8fCMvCGmQhDAYFIhKkT83uiMZltFhYBGq0zNziaSMz4PFpuOZRuKWLfWzGJDG/bx47j2ddP1xhDDMSvinRtRlOW9L64D8riPQHMvvziRhXPs7JQxvVHFA3dYKCieO9PPK8E1cTsNgiB8C/gm4JZl+equVnwaV8PMrSAI/wt8dmL3ZVLGXBdlx/p+CUueivCIk+a/+wFjW1/H/NUH+HVHOUf3T+2qLIoCN62V+MyNTsZK7+RYu4Lnf3aYod7xKY/PzjLwmUeXsmVz8UXVEbxQkrEYnT/6Pc1//yPivkDKVfmeIqo/UU1/xlp+tz+LrU+fnHYU2mzW8LGH6rj37uoLdoW8GsP/EokkDmeQoWE/w8N++ge89PR56e3zMtDvnZGb8PmorEhnyw3FbLmhhIyM89fLu5TzFInE2f5WF08+3UzrFPmMk4gKgfIFmehNGrR6JVqdCo1eiVavQqNTolJLjA16Ob63j95250V3hK40tiwDNctzqV6WQ0l1Bpb4ELZAK5m+eiS/m67X+3nztSi7Cm6gxauZNmw7PdPAg3fqWdLwPJ2/OEbeQ/dQ9bXPo063XNbPczFcDb87WZY5+sW/o+eXzwKgtqhY9+1l2OvSSAgSXWmb6DevwusJ8+YzTezd1n7eTnOaVcuqlbksWphJZUU6xUWWi3JavhrOz9VAIpGk8aSD3fu6OXR4kI628WkHcq8xNWqNggy7imyrTJ4+SJ7aQ5nBTXa+HtGeTsJgJJRUE0pKhOJKwrFUnm0ywURovoxKCSpJQKUEpZhALUdRJiIoExGEaBQ5FEYOhUkGJ9b+EMlAaiEWRZSTCKKALCnoF9NooJTjHjutPYlpa3hfTWj1SupW5rH8uhyuKxwhJ3AcOtroerWPsXaBoi8+St59NyPMM1fg6UjG43T+5rf8rkPD1t1T54cvWJzO176ymvz8q9/V+UL5QIclC4LwG2Al0ErKRTlAKs92BbBo4rDHZFn+2JVp4YVzpXNuBUH4R+AvJ3Z3kyoxNHvFxc5+v6vaUGomjDe1c+yv/p1IbwP+z3+Kn7+pZqB7atGq1ih4YHOSW262MWhfx57tfbz2++PTlowpKrTw6UeXc/ONZZdF5IaGxzj2F/9G16+eA0BlUlL3iQoK7q3muPp6fv0K7Htj6tJBAFarlkc/sYR776mdUfmgcDjO8Iib117bQSQis2rVSvR6LQqFMDFqLiIpxFPbSlXK4VGpSj1+ISOzsiwTjsQJBlLOku7xMG5XCLcnhMMRZGDIy8Cgj6EhH6OjfuKXqVatIMCSxTncclM5WzaXYpmmDvLFGNz09Y/z1DONvPBiM+PThMMDqDUSq28sY8PtpZRoh1DHfUjJEMpEEGUihDIZJDge4tmdAk+9rSEYmV8j4uZ0HdkFZsoWZFKzLJesbC3WUBfpgVbSg22oE378AwHaXuzlzQMa9pVtmrZWLaTKM91zZxqbRl+n4wfvYL9hIwu//WXMVfNncO5qMUxKJhLsf/SvT11zRKXIki9WU/6hIgA8mnxa7bcRVGXgHgvwxtONHNrRNWNzNKVSpLQkjfw8Mzk5JnJzjGRmGLBatVgsGixmLTqd8ix32Kvl/FxtJJMyDmeAo0+8Sf3zO/EZLfj1VjyCjj4HuBwzcycWRLBY1KTbtal6spKAQkzNkioUqUVSpHJskWXkpIxMKsdXlmWSSYhEkoQiSSLhJKFIgkg4STgcJxyMn3Om/2rBaFRSlCVQpA5SGhun0q5GEY8gx6LICO8Wt0Q4414nKCYNtlLGXGcYb02IOzkeR04kiYRitA4EafaItMcN9PglwtH5MSip1krUrchj8do8VpcHyAk3YXY0MfhWL12v9qEsWET1V/+AjA0r3heztJMMv7WDV3Y28Ov9uinLdqk1Ep/6WCmf/oNNiFMYrL0f+EAbSgmC8BjwEU67BEz+aeKx14GPybLseu9zr2aulFuyIAh/AfzTxO4R4AZZlqdWabPE+0HcTjK66xBH/+Jf0WZEqN/0EE++7Js2b8RiVfHJW2Ms2lJHr1DOa78/zr5p6s1CSuR+5g+Wc9OWyyNyx/Yc4fAf/z2uw40AmAoMLPp8FabrF7IztI4nn3HQeGj6uocAKpUCrVaJTjdZ4kCJWi3h90fxjIfweMKEL8GgQxBS76FUKlCqFCgUInJSJjnREUqtmegIyYTD8WlNsq4WFAqRNavz2XR9MYsXZVNUaDl1055pZzuRSLJ7Ty9PPt3Anr29Ux4zicGsZv1tlay7sYCyxAnyxvejTqRC0EOuMJ7eELtOqNneZedYIJMEU3/3BAEWrSlg84drMafpCPoiBHwRgv7ou9u+KH5vGK87jNcdwusO4feEZ/V/otZKZBdYyCqwkF1gJqvQQnaBBZ1BhTrmwRrqwhZoxRrqQiHHcLeN0797mN7doxxOlHCofAOD3uk7SRqdkttuy+R2YRd9330DQ81iFn3nT0lfsXDWPsPl4moSb3IyyaE/+jvafvjYqcdyVmew8s8XorGqkREYNi6g27qRiNKMfzzMoZ1dHNjeyejA7Bj4S5KIUimimrieKCWRaDSMUilQW5NHVVUG5WXplJelk5U5N3ny843ouI/6v/6P1P9NltHaNFj+6HZaMpdytENNW5Nz2vJ5kwgCrLuukNtvrWTd2kJ0uksPr0wmk3jHvYyOuhkbG2fMGcDhCOJ0hXB7Yoz74oz7Eox74/h8MQK+6GWvkTsVkiRQlKOkNl/D+s0LWbWxCr3+wsyPvN4wx44Pc+TYIIePDtHSMnbBM+0KSaSozMLCCpHaojgJvR2/yk4wpqTl2BBtJ0YYG/TOSeSOSiNRuzyXRWsLWFEZJy/ahG28EfehPrq3DzC410nefbdR9ZVHsdTO3/7iVAS6e9n329/xs458mhun7nqXVVj4+2/dQEXp5TEgvZz4fBHa2p20d7jYt7+B7/7bRyf/9IETt8tJ5YWuBTYAOlKi9m3gq7IsH7qCzbtorkSdW0EQvgj8YGK3Gdggy/LUdsCzyPtJ3EJKSPU/u43j3/wPTJuL2Gq7ia1bx6YdRc7N1fCJ22Vyr19Hx5CS5352mM6m6cuTFBVZ+Myjy9lyQ+lFhdpdCHIySddvXqD+r/6d0OBEPu4yG4u/UE1ywRJ2uJfx3HPD09Y9fL+jVCmwZRuxZxuxZRswp+tJsyhIt4BBJxIXlCQEFTGUjA0HGOhyM9DporvFMa2p2HuxmDXU1mQgCOAZDzE05CYaS2Iy6tHqlGjUSjQaCY0mVa9QrZI4fGSQoeFzG8LYsgxcf1c1a9bZKQ4dJsd7CEUsRO/2Qdqe66F1WMVxYyXN6VUEldPnmgoCLFpbwJYP11GYJZPv2Yc64SUuqkmIGuKiemLREBc1xBRaogojEclIUlSRTCQJ+CJ4XSG8njCRUIxYNEEsmiAeSxCPJc/YV6kVaHQqtHpVKmx6Ilx6ct9g1qCQY+ijoxiio+gjIxiiI+ijYyiTqXwyR4OL/t0jDOwexuGQaapYw/GsRTj909/PNDolm7Zkcm9aPSP//gKitYDF//gVsrasndH/8WrkahK3kLp2HvvLf6P5X35y6jG1VcWqP19EzupU2F1CUDBoWk6fZQ1RyZgyQTrpoH5PLyePDuKYxhtgtjEYVJSXpVNZYeOGjSUsW5rzgRa7Y3uPcvAL38JTfxIAY76euk/XoLv5Ok6Eq9jfpmXvtk4Guz3nfB2VSsHaNQXcuLmU9dcVYTBcHlfbSDSCc9TFuDdIOBIjGo0RjiSIxuIEgzEGh0aIJwR0OiOxeJJIJEE0Jk+sk0QiSYKhBIFAnEAwtR53RwhcYi6tIEBhjoq6ChsltQVkZxrJyDCgUon4fFF8/gjj4xF6+zy0dbjo7HIxNhq4qPfJLbZSW61hgdlF6Ug9ifomvL1+HKZMhooqaIpmcqJfSzI5e99zo0VDYYWNsroMLDY9RouG0pw4+ZFmMvwNhJp66N42QO+bgyTiSsq/8FEq/vjj6HIuX73ry0E8FKb5xz/iyREbr77lJ5E4ezBCIYl89CNlfOmLm1C+D7wcYrEEe/b2crxhZELQOhk+7fodjbppPPb1yd0Plrg9HUEQ1MDtwB8BG4Ew8EeyLP/sSrbrYhAE4TvAX03srpZlef80x/0l8I8TuzfLsrz1It/v48AvSc14dwLrL1dt4PebuJ0kGYvR8dOnOPlvP0L38HqeDS5l767haXP3Sku0PHyPHsvK9Zw44mDrEw3TuoRCKq/s9tsq+dBd1RQXWefoU6SIB4I0/etPaf6Xn5AIhRFEKNycS80j5UQrlrB1ZCFPPNZLf8e8CpCYEUqVAmuGnjS7Hlu2kcwsLXmZMoW2KDkGH4aEG23MhegaRfZ6SQaixEJxFEoRrU2TmnUSRfzqLDzaIjyaQpyqfNqaPRzb3cPxfX2X1U1zsrzN0kU6Cr37yPLVQyRCw4tD7HnRQ0c4jSZbFaP6c+fwCAIsvq6QzffVUpwZp8Czm0xfAyIzmyGQgYSoJjIhdCcFb0JUkxAkkoJEUpRICkqSgnTqMYUcQ0pGkJJhpEQ4tT5t0UZdaONuJrtfIUcYd4cXT7sXd4eX0aNOwp4oA6Y8GhZs4mQynfg5Zh+MFg233GzjVu1hRr7/GrGElYX/8Kfk33vTvBczV5u4hZTAbfyHH3L8G/95xuOFm3NY/MVatGkpsZNExKGvZNC0DI+u+NRxjiEfJ48O0dk8ykCXe1ojvNmmIN/Mh+6u4c7bKkmfw/JEVzPJRIKuXzxD/df/k/BwaoA2rdLMos9WYV1ewKBxCTsHinnr9X4aD/aft/yYUimyelU+GzcUs3BBFkWFlssStfReLvZ3kkwm8fvGGRwYo6fXQVubk+7BCAMjcfp6/ZdsInWpZOaZqKyxsLQ0SnWkldi2vYjaIqhcwNtbm6gfjNNtKiB0jsHN2UKvE7lpeZQPr3CQmxil540Bet4YxNvrR5eXReWffIKyzzww63XPrzTJRIL+J5/krS43v9ylweOcOgOwui6Nr//VDVSXzf/c2s4uF8+/0MyLr7Tg8UyfwvCBELeCIIiyLM84rkIQhA+TEmsa4EZZlt+cs8bNAYIgrAQmBe3/yrL8+SmOEYEGoBrwABmyLF9wL1kQhHuBJwAFqbzl9bIsd19cyy+c96u4nSTmD3DyP35O9y9/C5+9j8fa8zl5fHpDn7oaHR/+cDb6umU0HR46r8gFWLwom3vurmbLDaVotXPnmBfsH6b+a9+l81fP4dCm02MpZLS4nA4yiMQvrdMhiCnXRJBJJGQS8STJpDynoWJKtQKDSYPBrEZv0mBJ15GWoceeriArHXItYTK1XnTxlIAVRoYId48QGAjiGwjgHwjgGwjiHwgQD009My8oBPSZWjKX2chZlUHmMhtoNTgM1QwbF+GQ8jl5dIgju7ppOjxIfA7yxJRqBRWLsliyrpCqrCDa4RM4e4boHJBobEzS7dbgk2bWaZCUIgvXFLD53hpK7GEK3buxB5pBlhncO8bJ37fhGwgiKgSEiUVUiKe2VXoJrV2DNl2D1ja5qNHZNGjSNSiUF/Y9SiZkYv4YUX+MqC+GfyCAu/1dMRtxv9uBDCtUNBcu43j+MsZC5x75tmUZuOMWC5si79D3gzeJyxbqvv4Fij52F6J0+Vyu55KrUdxO0vWb59n/6a+TjLz7/1OoRaofKqfm4XJExbvXhaAynRFDHU59OX519hmvEwpEGehyM9LvxTXqxzUawDXqx+cJE/BGZpy3O1MkhciG9UXc86EaVq/MuyJi7EoT8wdo/pef0PxvPyMRSnVe7YvSqHqghOy12YwaajkWXcjWNz0c2N4548E9nU5JTXUGC2ozqKvLpK42E7vt/EZ8l8pc/E6iYT8nG9s4dLifpu4orZ0RBnrG5+x+JwiQXWihpCqNuhKZZZkODPX7GX7lBDF1EeHrb6ZDn8f+I8OcbLn4gD1BAIVSkUoLusj7d5HKz8ZDz1JWbqP6q39A4YO3IyrfX07AcjLJwLNPs79jkCea7LQ1e6Y8zpKm4Q+/uJR7bl84r3NrQ6EY27Z38NzzTRw7Pjyj53xQxO1x4I9lWd5xAc/5EvCfwJuyLG+Zq7bNFaeFJsdJhQjvfc/fvwr8y8TuWaV6BEH4JPDz6f4+ccxNwIuAipRD8wZZlltm71Ocn/e7uJ0kNOKg4dv/w+gbrzP+6Uf43X49g73T54qtWGbg3nuykSqW0HhoiK1PnDhvSJdep+SGTSUsW5rL0iXZ5OaYZmV2KZmUaWt3cvjIAIePDnL4YB/eOSoNIQgp0y21WkKjk0izqbFlaEmz6Uiz68jMN6LWqIglIB5LlYCJxxPEYjKynHq+IIogCIhCEhEZhZBAJIFGSqJXJzGo4xjUMQxiEFXch3piEf1u4oNjhIb9hMbCEwJ2QsgOBkmE3xWekkGHsaIYY3khpooitDmpEVU5kSQZj+Nr7ca5vx53fQty/N1zpVCJZK/KoPjWPLJXZhBRWxk2LmTEuBBP3MCBNzo4sruH0f5xopGrxxAlVUuwmEVrC8iSRilw7yY92EYyLtP1ci8nn+zEP3Dp9XIVKhGFRoFCpUChFlGoFUhqBQp1aj8RThD1x0+J2XgofrbjwmkkEegx59NSsYYWKYvYeU5pXrGVD92sZfXIdjp/uAdZY6Pu61+g8KN3vG9E7SRXs7gFcB48ztv3/BGhgTMd6LU2DYv/eAUF69MQOPM6FFYYcenL8WgK8WpyCCvTpn19WZaJhuMEfBGikQSJWIJ4PEk8lkyFxMdT66AvylCvh6Ge1DJTMZadbeSeu6u5566aD+Rs7uRg6KRRGKS8GyrvL6bwplz8piLaVMvZfkzN8X39tJ8YueD8e71BRWaGgcwMPVmZRjIz9GRmGsiw69HplGg0StTqifuJOpW+ISlFkEGWJ/wYZBnk1H1u8rHU46ntUCjMW2++BcD6DdejUatTfg4yp0yuJp/LxOud/bd3X3tyP56QU9+xeJJYJMxQTzdNrU66R2QGhqO4RgIX7UcgqRQUlKVTUpVOXYnM0jwn1tEWPFuPMLRnmFE5F9eaLbQrMznc4CAQmPkssqgQyC60kF+aTn5ZGln5ZvQmNTqDGs17jNlkWYZEHDmZRE4mcLsiHHirl4NvdeI7x6ydQiHw0EOL+MKnV15w9YWrGTmZZOiF59jX1s8TTTZamzxTHicqBO68s4T/96VNGC9TaP5c0HxyjGeea+S119sIzPC6qZBEMnJNmNLi/P5/PzH58PtW3CZJdWFeIZVbenAGz5msc+uXZdk0x02cdQRBWAK8A2gBP/Ad4K2J/Qd5t1RPK7BclmXfe57/Sc4hbgVBWA1sJ5WnHAM+CRw/T7P6ZVn2XMznmY4PiridxNvWzfGvfRdf53F6P/JJntoFzpGpc2QEAVavMHDnh3JRli2m8dAgO184SdfJ6XNyTyfDrmfpkhyWLslmyeIcsrOMaLXStII3kUjicAQZGvYxOORjcMhLY+MoR+uH8M4wR/RykGtPsig/wOKiENdVBsi0vKtYkgmZRDRBIpIgGU0SjyRIRJMkIgligThRb5SINyWMQo4wwbEwwZEQwdEQsfcIdlGtwlhWiLG8EGNFEcbyIkwVRRjLC9Fk2Wc0cBAPhhjbfZjOnz9D3zNbSUbfvcBrbWqKbsqDleXsH7OxvSmNjoGrJ9TVnK5j+fVFLLu+mNwsBZm+E2T56jFER4kGkrQ80Ub7871Exq9seN17kYEBQw4tRUtotZRxvn6bQhJZvCqHO1ZHKT3xOh0/OYjCkk3t17+QmjV4n5SWeC9Xu7iFlHv7rnu/hGPv0bP+prZoWfntO8hZLCHEpx4ojIlafOpsAio7IaWVkNJKWLISkUwkxQufDZJlGY8jyFCPh4EuN0ff6WG0/9yGVpIkcsPGEu7/cB1LF2fP+3D2C8V1uIFjf/1dhrfuPvWY2qqi/O7ClCN2WhoOfSVdchl7GkRO7Bug9fjwrM+qv99QSCIqjYTOoCI900B+aRp5eVqKMyJUpHtID3cSPXCc4T2DnNzvp0tTgrt2Oe1JE31DFzYQacs2UrU4m4rFWZTV2EgTXRgiwxgjQ+hiztNSRCKIcgxBTiKQ5L3f9ISgwK0tZVBTzdvNBvZs76G1fvqZvMwsPX/95xtZf13hRZyhqwdZlhl++QX2t/TweKOdlsbpo/GqatL4+l9vpqbcfhlbOHvE40ne2tnJY78/PqNZ2qIqG8VVdrInzB/tOUYkpYKxoQEeXPU+LwUkCEIjqfDbyTfcDfwUeE6W5SnvLIIgfBP4FhCQZdl4Odo52wiCcCfwG2A6cd4K3C7LcvsUz/0k5xa33wIu1IDqU7Is/+ICn3NOPmjidhLnoRMc//r3iMdHaLzpIZ57M+UiOxXvitx8VOWLGB30ceDNDg6+1TVjY6JJFAoBvV6FQa/CYFBhMKgRBRge8TM8cvHlbwQB8krTqViURcXCLCSliHPEj3PYf8ba6579qlKZCQ+lY62UuTuw+0bPuqGeC1WaBV1+Frr8bIyl+WeIWG1e1qwKm4jTTdevn6fth48x1DVGU3oVjbYqnDrbrL3HpSCKArZsI/llaSy7vpiyGhv2SAdZ3nrSg22IJPF0hWh7po3urQMkomd/VwRRRFeYgybThjYzHU2mDc1pa1EpERp2EBocJTQ0llpPbIdHHJw3EW+qdquUKK1mXLZ8TuYu5FjChtt3/sgCW5aB6zfZuaOsj+RTr9D5XAea3AJqv/FFCu6/5X0raieZD+IWIBGJ0vB3P6Dpn3+MnDh76l1p0rPsbx+gYHM+orcFYjPLs00ISqIKHTGFnrioJikoSYgqEoISWRBTCyIJUU1QmUZQmU5QZSMpvjuTIssy3S0O9r/RQf2e3vOWnyktSePD99Zy+62Vl80s6WrBfayZpn/9Cb2Pv3rq/6hQixRszCF/UzZZy2zE1UYc+kp6KGdPk0TjoUF6287vuHwNUKkV5GSrybfGSHoDOEci+EMCAYUWd/TCrmVqjURZXSaVS7KpXJRFocWHNdSNNdSJOdyHYmqPU6L+GPFgnHg4QTycIBFOEI8kiIdSA85yIokxz4C51Iio0+DSldIYKOO1fbD/rZ5pZ3M3XF/I1766Ebt97sPQZ5NEKMzQi09zdNDF7xvsNJ+Y3pfEkqbhi19cwX131M3LAbDx8TDPvtDME0+eYHjk3Ndgg1nN8o0lrLyhhIzc98gbOYk25sbX18zNm+6afPR9K24l4KvA13jXERkgSapkzVGgg1TuqYGUc/KdpAySjsiyvOKyNHQOEAShEPgyKbOsPCAKtANPAt+XZXnKq/41cTs/GN11iOPf+B6iPc6BJffw4hs+QtNMNQkCrF1p4N7bTSgrlhCU9TQfHmT/9g5ajg1djC64JKwZeioWpsRseZ2dbOUI6cF20gPtqBJ+ogo9UYWe2MQ6KunxxvR0OrR0jygYHgwy0u9ldMCLc9g/KyVh0pJ+Fno7WKoYw25WorIYUVlMKM0GNFl29PlZ6Apy0OVnoc/PRtJfvnDBUDjGjp1dvPRyC/sP9DEb6VWSUsRg1mC0aDFaNBgtGlRqiVAwSsgfJRSIEvSntoOBKLFIAlGRErFZeWYy881k5pnIzDdjzzYiSSL66AhZvhNk+k+gSgSIBmQ6X+6m89VevN1T37QkvY7Sz9xP5ZcfwVCUd1GfJRmPE/cHiQdDJIJh4oHQadtB4sEwkk6DympGZTUhmU20j0TZsa+f7W91MnCeGTRIhXstWJHLretElkcOMPjzXfTtHCJt2QJq/uIz5H1oSyqs/QPAfBG3kzgPnWDfJ/6S8aazxnIBUJqNFH/8Lio/tR69JQjj3eDrheTsGbbJQERhJKRKJ6C049SX4daWgCASCkQ5squH/W+0nzeFRK1WsHJFHtevL2b9usLLkjt6tRDoGeDkd39B+4+fJBF8d7BTZVKSty6L/I3ZZC5NJ67U49KV4dXk0h+00dynoKfNTV+7k74O12U14vsgkFNkoWpJNhWLsqkuEbHHerCEurGGulEm3/0/jXf7cLd58XR68XT48A8EiHijxINxZuyMI4AhR4e1zISlzIypIo3IolX89FAR21/unrLEkFYn8fnPreSh+xde9bnsvrZWera/yFveDLYfFentnL6ipsWq5qGHF/LxB5agVs+/EOzOLhe/f+IEL73Scs7SjoIAlYuzWbW5lOplOUhKBWIyii3QiiE6jC7qRBdzoom5EUnSP+aj8IFTzvnvT3F76g0FIZOUwP0MoJ54eLpGTNa6/cx8dEz+oPBBF7cwEbLyxh7qv/EfqGot7Cy8ha1vuomEpr5QCAKsXKrlwc0JLLV1OHXluB0hGg7009k8RmfT6AXP6M6E9EwDpbUZlNRkUFKbQaZVJi3YQXqwDWuoE2UyQtQfY2j/KN4eP4IkolCKiJKAqBQRJRFJJ2HM02PM15M0WAiqbASUNsaFdIa8WoJRBcGoSDgq4PaLjI2GcQ77cQz7GexxE7uAPNSlS3O470M1bN5Uikp1ZWbhkkmZo/VDvPTySd7Y3jGjvBO9SU1ZXSZ5pWnkFVvJmXDDDgWjRIKpUjk6oxqjRYNGp0QQBMRk7JRjsCgnzii/w2mjwPFYAkEUUChE1PFx9NEx9NHR1Doyii7mQCHHSSZgcL+Djhe7GD4wNq0xiDbbTuWXH6Hssx9BZTXPzkk7B/F4kqPHhtj+Vgdv7ujEMcMZnfyyNFautnFr7RjGd7bR8VgjrmYPObddT/Wff5qMDSvm5Wj5pTDfxC2kZnFP/O33OflvPyMZm/63lL56MUUP30nurevQ2wXwDyGHRiE4CsExiLghOTu+AVGFnlF9NaPGOryafGRZpq/Dxd7X2zj6Tu+MTOJqqu1sWFfE9RuKqShP/0B8FyNON20/fIy2//kdoaEzU21UJiV567PIvS4TW40VtVlFQlDgV2Xh0+TgUebQPW5mxKPA7YrhcQbxOIKMO4N4nEF8nhCxSKqE2NVQ21wQBYSJNUwYLylS90SFInU9FhWp7dRjIuLE4wqFSDQST9UFHw/P2iC2zqiiclE2lYuzqV1golA1hDXUhSXUhTaeEmPJeBJXyzhjJ1yMHXfhaHAT9c1sUEGTkYahojiVylNWiLGsAENpAYlwJBW1MzRGeGiM0NBEFM/QKOb8KMFHH+WHz0L3NMZWZeUWvv3Nm6isuDoiniZJxmIMv/YsR7odbO21cWC/h2hk+muMyaLm4YcX8PEHlqDRzC/DrEQiye53enj8qQb27e8757GmNC2rt5Sy8oYSLDY9ghwnPdBOhr+B9GDbtFEAHyhxe+qNBSEN+BRwP7CMlNPve4kA35Fl+duXs23XuDCuidt3kWWZgRff5MS3vodmVQ47sjaz9S3PtCIXoLZayyMb3ZQuKsBhrMGvykRGYGzQR2fTKJ1No3Q0jTHunHlYl6QUsdr1WO160rMMFFfaKanNIN2qwBzqmQhP6kIfTYUAB0ZCDOwZYeCdEcbqnSTjM7guCKCzaTAVGjAWGDAVGNBnalHqJZQ6CaVeQmVVI+tMBJVphJRpeKQsmsfSaO5M0nVyjJb6YUL+8+d7msxq7rq9ins/VENR4dyWTZqkp9fDy6+08MprrQwOnbvmLIDFpmPBqjzqVuZTVq7DFu3BFB7EGBnAEBlBFsSJGXAdCVGNIhlBmQgjJUNIyfC0N4jJkjuxCaEbFzWIchxdzIEy+e4ASDIhEHQmcbe5GTkwQO+OIaLe6Tsx5roKqv/sUQo/ejsK1dyGV0ajCfYf6GP7jk527Oyacf53Zp6JZWtzuHFRkIrgMYaf3EvXq31EPHEKP3o7NX/+aSwLKue07Vcz81HcTuLv7ufEt75P96+fR06ee7rIWFFEzq0bsK1ZgnVpDcbSgpS6SIQh6oOIN7UdD0MiklrkBLFohM6OdiQhQWGWBUVsPCWKY9PXEQ1JFkYNtYwY6wiqMgj6Ihza0cWere04ZnAdANBoJLKyDGRlGsnOMpKdZSAry0BGhgG1WkIhCoinLQqFmFqLAqJCQBTFU9sKUUCtltBNDIJdjSQTCcbePkjP46/Q9/RWIo6z8xENOTrSa6yk11hIr7ZgLTMhSuJpJcVMRCQjYclERDIRVRgmSolJRBMSkbhAJCYSjonEEgKCICMgIwogICOIE2tAFGQEIbUGUAgyCDIiqeeJk8fLMqKYes6pxwU5NdgopByrJl9LmHCwEpABGUGWUchRFMnYxDqKKMdQJFPbp/6WjCImIkTHIwTHk/jHZYZdSjqiGXRG0mkbU9LVEz5nH2ESlUaiqNJGWV0mFbVp1GV7SY+ceS+PheI4G90TYtaNs9lNInIRqUqSAuuCStKW1Z5aLAsqUWjU53yaY98x6v/yn7GuNvF28T089UQ/wSnu8aJC4KEH6/jDz625orOdyUQCz+E99DfWs33EyhuHkwz1nvt3bjKrefChOj7xkaVzWt1iLnC7Qzz3QjNPPdvI0HmuZwXl6ay/vYKFqwtQKMAa6iLD34A90IKUPMc9XFSBxkqvK0HxrV+bfPSDIW7PaIQgGIFaoAyYnDroA3ZMl497jauHa+L2bORkkt4nX6Phb7+LblMpb2VsZtubbiLnCPkoLNDwyEY3N1ePEdTnMa4pYFybj1edS0KQCAVihAJRwsHYxJLaDgVjJOJJzGnaU4LWYNYgigLq+DjaqAtLuAdrqAtjeBCRJHJSxnnSw/CBMQb2jOBum5ufmSCCLkOLqcCAqdBAeo2FjIXpSDYT45p8RtVlHOi3UX/UTcP+flwzKF6/dGkO999Ty6aNJbM+m+sZD/P61jZefrWVhsaR8x6v1kosWlPAsuuLqSjXkhFqxR5oxhLqOaNebHA0RDIhI2kVKHUSCpWCyHiUsDtCxBMl7IkQdkeJTKwTkQRKgxKVQYnKqERtUqFJ16G2alCZVCSiSTwdHtwnnYx3+Rjv9hEYDp3TdRhAoVGTe/dmSj51L9k3rZvTznIwGOOdvT1sf6uT3e/0EJxhCKLVrmfJdflsWpZkqb6V0NbddL3UzchRJ5JOR+mnP0zVn34SfWHunLV9vjCfxe0k403tHP/Gf9L37LYZ52srTQYsCyrQl+RjKMpFX5SLNtuOKs2CKi0V8q7QqIkmEmx7600EQTjj/MiJCIQ9EHYhe7vB2Qz+M/tdMuBXZTFsWsSIoY6ooKW9YYS9r7fReHDgss8mqlQK0tN0pKdrSUvTkZ6WWufmGKmuyqCkxIpSuvI55sl4nJE396WE7jPbiHmmvrcoVCLmYiO6TC06uwadXYsu4921Jk2NKF1doauJ6Gl5qJNLKH5qHXZFCLsihFwRws6JtSt1jZ+MnFEY9URWr2OgaCHNEQMtHR5isQsTn/YcIwXlaZQXq6kpjFGZ7kHr7CfW0o63foDAYJCQM0zYEyXijhLxRpHPUQt8pgiShKWunPTVi8i6YTUZm1ahsZ3tZi7LMj2Pv0Lrv/8Xhs/dyq9aSzmwc+rZwewcPX/z9c2sXHZxqTAXQ9zrZnTXa5zo83HYaeJYq0xnm2fKUOrTsVjV3P9ALZ/86LJ5J2obGkd44qkGtr7RTvQckSiiQmDh6nzW315JYYUNXdRBtvcImf4GVIn39M8EBVhKEawVoLWDxppaJB2CINDW1kZFRcXk0R88cXuN+c01cTs9yXic7t+8QMPf/xfGm6t4076ZN3ZMH64MYLOruXtthAcXdWHVJ0giTriFZpAQ1SnDFFFJQlBNmKeokIVUiKo2lqrnqo250cQ9Z8wGhlxhhg+MMXRgjOHDjiln9SSjnrwPbaHwI7dhXVxNMholEY6SCEdIRlLrqMeHt6kdz4lWPCda8bZ0nVEqZyYY8/VkLkknZ20mGUttBA15jOqq2DeUy77dY9Tv6SEUOLcYMps13HFrBTffVE5tTcZFCzW/P8re/b28+lobu/f0nNeQSxAFyhdksnxjMQuWZ5GT6CDLdxxrsANxQl06GtwMHRzDddKDq2X8irsRZ1y/kuKP30X+h29BZZ47bz7PeJi3d3Xz5o5O9u7vO685zyT2HCMLVuWxdrHECns36sYD9DzfSs8bA0S9MfRFuVT+8SOUPHrfnLZ/vvF+ELeT+Lv66PjpU3T+7OmzQlwvGUmBOt2Kxp6G2mZBbbOeWjSZNiy1ZViqc1ExjOxsBncLxN/NUUyiwKkvZ9i4CKeujKA/xsmjQzQdGuDksaGrIndUrVZQUW6jpjqD2ho7NVUZFBZarmhuYyIaZWz3YRx7j+HcX49jXz2RsekNed6LIAqpcmKnlRVTqFKhwJNle+TkRLmaiTI9nL4/UbJnsmwQp5UK4j3Pk5PvOY7Tjk/VFJp5PuqpDyBgqixGrltIb1Y5zUkLJ1rH8c1yypGkUpCVZyarwExOrobirCTZlijZpjBWVQBV3I8YD0MkjBAOQTSauleJMqKcmtUWSM1QI8sQjRHzh4kHYsQCcWKBGDF//NR2cCyMrz+Ary+ArriUzBtWk7l5DRkblqM0vJt7Hg+FafneLxnd+RKjD32Knz0bYGxw6tnCO24v46v/73qMhnPPDF8MyUgIX/MR+k62sH9IyZE+NQ0Nfnzj05cymkQUBRYvtnH/A0vYvL4E6SobcDkXfn+UbdvbefrZRpqaz31N1RlVrN5SxtpbykmzKrEHmsj2HsUc7jvT4FNtgbRqSKsiFDThPt5BoGeQqNNDxOEmctq6f2iIzw7tm3zmNXF7jfnFNXF7fhLRKJ0/fYrG7/wAy+017C3Ywms7Aue8uEpKkZXVIh9bMcCKch8Xot2ivhjeHj/ePj/j3X5GjjjwdHinnNlT6LTk3rmJwo/cRs6tG84bdnTWZ4tEGatv5p3fP0NyyEmBPRM5ECI27ifYP8x4Uztx3/Qzskq9RM6aDPI3ZpO9KpNxYym9mjp2NhnZ+0Y37Q3nn0HNyjJw05Yy1qwuoKrChtmsmfbYYDBGR6eLw0cGeGdvL8fqh0kkzt9rycwzsXxjMUvXF5Gvd5LlO47d34QyGUZOyowccdC/a5j+d0YIO6982SVTZTFFH7+boofvvGiDqJkQiyV4e3c3L73cwu49PSRmOEOQW2xlwao81ixWsMjUhXn4BENbW+neNoCr2QNAxsaVVH75E+Teuel973x8MbyfxO0kyXicwZd30PfMNgZfffuCxNClos3NxLKwEuviSjKXZmHJTaCS+xGizlPHRBR6HPpq3NpiPNpCwkkVXc1jNB0eoOnQIM7zuIxeTswmNRvWF7Hp+hJWrcpDe4XzAmVZJtDVj2PfMRz763EfaSLYN0xwYOSCB0ivFhRaDZosG9psO4aSfAylBYh5uXQkzRwbhQNHhhgauHIBiBqdEpNVi1avRKNTotGpUKkVKCQRUUzlBZ8KhZcmwuMVE+HyooxClFEqkihEGUlMIglJTNoENkMMuy6EVTGONDZGtHuIUNsQgmhAnV2MOr8UQ0Ul5tpyktEYx7/5PaImH6+m3cLWl3qmnCW1pqn5i69ez+brSy5qUEaWZRLeUcabjtLRNUq7U6bLo6ZnTKSvP8LooG/G+c7pNg233lrOQ/cvISvTcMFtuVLIsszRY0M8/2Iz27Z3nNMgCiCvJI21t5Sz5LoCLDjI8R4lw3/ijJQnTIVE5FxcXQlGD/bgPtKM+1jzlOkHp+Mkxh/TNbl7TdxeY35xTdzOnHgoTMdPnqT5X/8P67pcji+9ixd3xXAMnzscNzdD4jpFJyuDh9FrUiFdCtXEKLZKRBAE/ENBvL1+vL1+Iu5zzxRKRj25t28k754t5N6+8ZIdh8/VyZZlmWDfEJ6GNhzvHGH4zX24Dp6YsiSI2qyicEsOxTfnYajMZMS4kKOBKnbsdHFox8zLJmVlGagst50ROuQPROnsdM0of3YSg1nNknWFLLu+mJI8kazACbJ8x9HFUh1ud7uX7q399GwfJOw6d9sEheLMzywIKLQaFBo1Co1qYq1G1KiR43HCI07Co87zhmoKCgWGklQJJFNlMcaKItJXLMC6pGbOwo5lWab55BgvvtzCa1vbGJ/BCLggQGGljQWr8lmzUKRG30mauxHHzna6tw0wfHCMZFxGVKsoeugOKr/8CayLquak/e8X3o/i9nTkZBLXkUYGX30bx95juA83pn4TlxlTkZnCG4vIXm3HWqQ6ZSokI+BXZeLWFePWFuFR5+N2x3GNBvA4grgdk+sg7rEAPneIRCJJMpmaKZxcX45umFotsXZNPpuuL2b9dUXnHAC83MjJJOFRJ8H+YYL9IwT7hwkPj50VNZRaosixGIgigiimjJ4UipRDuihMPDaxPfF4IplkcGgQBIG8ggKUatVZx0++DpPboohCp0Uy6FAadEiTiz61Vhr1aDLTkQx6RkcDHK0f5ODRQY7VD9Pd6bqg/6koCmQXWcjOM1CYBaX2IOpkEK8/gWssxshwgg6Hkp7hJPGZ+GFcZtQaCYNFg9GswWhSYjGJWI0yVn0CixTCLAewCGHMyRByRycjKzfxvy8J9LZPPXClVIrk5OooytdRUqintFBPRZGRPJueuNfN+JiT0TEvY+MxHAFwh0TcIRGnX0HfUJzB/sBFRVMoJJFlS9J58KMr2LCmEFG8OnPcp2J01M9Lr7Tw/Isn6euf3tkZJurCry1g7S3lFJcayAw0ke09gjEydGqWNiZbcHYIdL7UzuAbx4kHLryU1zVxe415zTVxe+EkIlG6fvUcDf/4IyzVWrpvup9n96no6/Sc83kqlUiFKUJZ20EK24+glGfuPKy2p5F392by772RzBvWoFDPnonQhXayY14/Izv20/fMNvqf3z5lXpa13ETpnQUUbMklaC2hR7uEt5qM7N3WSUfj6Ky1/b1IKgV1y3NZen0R1QvSyYq0kOU7jiXUjQAEHeGUoN02wPg0ZXUAjOVFZN24lqwta8ncuBKV1UwwGGTra6+DLHPTrbeg0517UCEZjxNxuAkPOwiNOAgPOwgPjyFIUkrMVhRhKMlHVF6eGRmHM8grr7bw4sstdHSef0ZNVAiU1WVStzKP1QsVlCvbsfma8B7qovuNAfrfHiYWSI0s64tyKfvcg5Q+eh+ajPS5/ijvC97v4va9yLJMaHAU15FG/O29+Lv7CXQPEOgeIOJwE3V7SYTOP9ByKahMSjKX2rAUGzEXGzEVGTDm6hFEgSQiAVUGUclATKEjJuqIKXQTtXh1KefzMz/R5AcjkRRIyCIJWSQuiySSIglEErKCQFSBdzyOfzyMzxPGNx7G5wnhc4cZ6vFc8GyxQiGwYnket91SwQ0bS9Dp5lf+4IUym7+TaDRBZ5eLEw0jHDw6yPHjw4xexGy9LdtI5QI7S6sEVhc5yaMHyTXEWL2D0WNOHE0evF0+4uF37/EJQWRUZ8dVvpCxgir6ohpGXFc25eVCkZQiKrWEpFKQiCcJ+iIzHghQayRESZyRCeVMsWdoWLrIzqYttaxbmYdON39qV8diCd7e1c3zLzazZ1/feX0ALDYda28uZ+UNJWSrnWR7j5Dhb0KSU+cz7JMY2Ofi5G/r8fVcerTBNXF7jXnNNXF78STjcXoee4nGf/gRWlsU3/338XpPFof2j5E4T+6nWiVSoQtQ1rqfwp4GpPcIXVWahbSlNaQtqyXn9o3Y1i6Zs9DOS+k8JKJRRrbvpef3r9D71Otn1E0EkHQShZtzKLuzAH1VNsPGxRz1V7B3j5tje3pn7GB6LhSSSEmNncXXFbJoVQ45Qj8Z/iZsgZNIcpRYKEHfjkG6tw4wWu+c1rhJk2Wn6KE7KP743VgWVZ01czpfxUhT8yi/fayerW+0nzfsWFIpqFqczYJVeayoEyiS28gINBNu66d7az+92wcJjk0IEEEg57brKf/iQ2TfvO5a6PEFMl+/T3NJPBQm5vGSiEQJev28s3MnciTG0opqZF+AyJgrlRPmcBMZc+Pv6me8sY1k9OLzZhUqEWOBAXOREVO+HlGVCvcUFCLCRNjn5PrdHM53c0LlZCqyQdJKSFoFklZCqVNM7EuorSqSGiMRKeUoHJlwFI5IJnzqbBwRE/2dLvraXfR1uOjvdOGZYYktjUbiho0l3HZrBatW5F319Ucvhov5nYTDcfr6x+npcdPV66Gtw0Vbu5P+Xs+MUy9OR2dQUb4gk4U1GtaUeanQ9aJxdOOoH2P0mIvRo07Gu7xn5fUKkoR1STW2NYuxX7cU+9ql6PKyTv19fDzMyVYHHR1OWjtctHc46R/w4vXM7SDPfEWlUlBdYWTtdcVs2VxNcaHlSjfpgmnvcPL8iyd5+dUWPOf5PwuiQFGljboVudQty6BA0UtJ7ASW2CCiHCM4EqJ/9zBdr/fjuQhjUaXZiLm2DE1GesrHIN2CKj3lazAcCbLuC49MHnpN3F5jfnFN3F46yUSC/me20vQvPyE20oX10c3s1q7kjV0BXGPn76Ro1CKldpGaDAUr6jJYcesSTMW5sx6WKssyHk8YhzOIyxXE6QrhdAYZGfXS2NhJKCJjt9tQqZQoRAFJEtFplWi1SnRaJXq9EqtVS5pVm1qnaUmz6k65Hse8fnoef4WOnz2Nc9+xs94/rcpM2V2F5G/KJWApYkRfQ70zl8P7U2WFhns9xGfgPCmIArYsA2ULMqlakkNFTRqZDGDzN2MPtKBMptyNhw6M0r1tgMF3RkhEp35dhU5L/r03Uvzxu8ncvOacAm0+iZFkUmbX7m5+/bt6jhwdPO/xxdV2lm8sZs1SNUXJFuz+Jhgepmf7IN3bBlI53xOo7WmUfvp+yj77wJzmA7/fmU/fp8tB2OVh6NW3cew7RmTERcjhwtk3gByOYjAZUUhSSkWmnHMmBGaSZDxBfNxHzBcgEYqckf8paRQoDRJKvTJV5sygRLJbSCQEwmNBwqN+QmNBkpE4yYlCMZKcYDavvIIIWrsWQ44OY64OQ64eQ44eU4EeU4GBuKTBp87Fq8nFq87Fq8lj1JWk8UD/qdrp09W5Pp30dB233lzOLTeVU11lv2pLD82UeDyJ1xtmZHScN97YRSwus2jREkRRIhZL4vdH8HgjjHvDeLwRxsYCjI0FcIwFGL9EcaiQRIqqbNTUmllVEWGJfQCjqx33kUFG652MHnPi6ThbzGpzM7GtWYxt9WJsqxdhXVqLpL3wEPJIJM7IaICRET8uVxB/IIrfH8XnjxAKx0kkkiTiSeIJmUQitY5EYoyMjJJMgtliRZYF4vFkakkkSSSSxGJJvONhPO7Qec0XrzQ6vURutpaiPB2V1bksWFzAgir7vKtJC+DzRXh9WxvPvXCSpuZLi1wTRAGtXonOoEJvUKI3SNisEhkWmQx9hAzJhz3iQO0YJtY9SLh3jLAzQtgRJuSKkJzoYwmShNJqQqnXIiqlibB+YeK6ITAaCfBo+47Jt70mbq8xv7gmbmcPWZYZe+cwLd/9JcNbd5B3dyXd6+7glWN6musdMy4/oVIpqKjJYNnibJYuzCIn24TdpsNoVM+owxKLJxgY8NLV7aa720Nnt5vObje9PR4CgdkPg9LrVVisGuw2Pfl5ZgryzaQlAsR37iT03EtIPs8Zxyv1Evkbsym8IQf7YhsefQkuXQluRRYdTgN93T6cw/4zwp1EUcCebSSrwExWpgqL4MYc6iEt1Ikp3IdCTpCIyQwfGKV/1zADe0emrxMrCGRtXkPxI3eTd8+NZzhEnov5IEZCoRgvvtzCb39fT1/fuXN3rHY9yzcWs2pdFrX6LrJ89WjG++nfNZya5T7qOKPzZl+3jPIvfJT8+26e1bD4Dyrz4fs0VySTSQZf3kn3r59jbO9RwiNO5NiEKBU4VWtbqVci6SUUShGFUkRUpnwKJrclrYTaokJlN6PIskFaGl6NFVfCiCOsZcwn4fAKOD0yLk8CjyuK1xMiFkmQPC1v9nR0BiUFuXqKcg1U5JuoLrFQXmnHYDGQTCRIRmNnLTGvn+DACKHT8k6D/cME+4YJjzimPQ8qoxLbAisZi9KxL0zDWm5GUAgEVBk49JU49FWMRKw0HR6gYX8/LceHic/AxTw3x8jmG0rZckPpJbnRzyXJpEx//zht7U6a2xy0tDoYHQvgHQ/j9UYIXUYXa61eSVGlnfIKPQuLYyzOcpDmbcd3qIPRY05G65142s8Us6JaRdqy2pSQnRC0p8/KXm4u5HoiyzJ+fxSnK4jLFcLhDDLiCDDmDOJwBnC5ArhdQdyuMN7xCNFpBocvBYVCwGRWYTIoMBskzEYl+TkG6paUsGhxHlkZhqvyeztTJs2hnn6uie1vdpyzhM9coNZKpGcaSLPryLBLZKUL5KTFydaHyBRcKN1jJAaGiQ+OEfeFSUZOL5UVZ8gb4iNHD06+3DVxe435xTVxOzf4u/po+a9f0/HTJ7HVGNA9sImD0kJ2H0vS3uyc0Uj8e1EqRaxpOtJtOmw2PUqliN8XxR+IEPDHCASiBALRy9opmAl6jYgl5sM81o/dP4ot5MAedKCPBdGmqynYmE32qgzSqy0oDSqCShtBVRpJQUJGRBZEQEYb86CLOVEmAqSqHsiMd/kZOepgrN7F8KEx4qHpbyDmmjKKP/Ehih6+C11u5gV/jqtZjHjGw/z2sXqeeKoBn296cyylSsGitQUs31jE8iIfOf56bIFWXE0OOl/uo/etwTPOoWTUU/zxuyn//INYFlRejo/ygeFq/j7NBaHhMZr/9af0PbuNQM8gJJNorGpy12ViW2xDW1NEIjsfhzIXT0SDP6LEF1EQjAjEYjLRaPK0dZJoVCYYTOB2xxh3Rxh3BfF5wudNCbkYBAGyc00sW5rLrVvKWL4sd8ZlRcJjLlyHG3AdbsR1qAHXoQaC/cNTHivpJOx1VjKX28hfn4U+S0dIMp8SuqNk03R4kMM7u2mtH57RgGlWloEtN5Sy+YZSaqszrkg5lGg0QVu7g4bGUZpaxmhtd9Ld6SYSuTIuy+mZBgor0qkqU7G4KESNdRjNUDveo72MNbhxNLjwtPvOuE/ri3KxrVmCbfUi0lcvxrq4CoXq6hnkm8vrSTAYw+EM4PSECIXjBENxguEYQW8Qv8dH0BfEPephT2OQsZHQOV+rrNzMn315AyuW581r8TodXm+Yl15p4clnG+np9lzp5kyL0aIhPdOAOV2HRiuhVotoNSIajYBaLRIJOvjWVx6YPPyauL3G/OKauJ1bouM+On76FB3/9zjBvj5yN+agvOsG9kQq2X0kRtdJ52Vx27waMRAh29NPrneAHN8QmaFRrAU6bLVWzIWG1KzNRO6aQikScoQJjIQIjoYIDIdwnvQQ85+7c6S2WSl86A5KHvkQ1qW1l3QzvRrFiM8X4TeP1fPbx+oJnmNQw2TVct0t5Vy/yU653ECm7ziC20n3tgE6X+5jvOvM3GfLoirKv/BRih66A6Vx/pRSmE9cjd+n2cbb1k3TP/+YwZfeIjziRKmXKLwxl6ybKojXLOS4v5jDLSKdbV5GB3yMu4IzSk240hhNajZeX8wtW8pYvjwXpXRh+eah4TGc++sZ2voOg6++TaBr6n6jtcJE3vps8jdkYSowEBV1OAxVjBjq6I9kcHR3D0fe7qF/BgZxADqdkoULsliyKJvFi7Kpq804w5V+NohGE3R1u2lsGuFE0yiNTaN0dbnnZNDhfJjTtNhyjNizDBTmKqjIjlKd6cXGCInmFtz1IzgaU2I2OPpuKLNCpyV9xQJsqxdhW7OY9FWL0GbZL3v7L4Sr4XoSHvfx5Pef4fUuFU0npi81Iwhw45Yi/t8fbyAjY/7fX2RZ5viJYZ58ppE3ts/uLK1KnTLvuphc8UshGnXTeOzrk7vXxO015hfXxO3lQZZlHHuP0vHTp+h9/FWU2gT5NxYi334D9cECjndKtLX6GOm/PDX1dEYVRos2Zf9v0WAwa9CbUjVyk4kkyYRMPJ4kGomnlnCccCCG3xvG743gHw9PWevuUlATp8TVSZmzjeLxHlTJi5uBlox6cm7dQNHDd5Jz64ZZcyO+GjoPkwQCUR574ji/+s0x/Odwn8wpsrDhjirWL1dQ7D+ALXCSsSNjdL7SR9/bw6fybwBElZKCB26l/IsPYVu9+H05qn41cTV9n2YTx/56mv/1pwxv30vM40WXpaXsnhJMt6/mGAvZcUSg+dgYY4O+GadqXM0YjSmhe8etlSxflnPBvxtZlvG1dTP02i4GX32b0R0HSITPjr4wFRnI35BF/vXZWEpMhBUmRo21jBpqaR/VceTtbur39uIcnrn7r0IhUFVpp7Ymg6xMAzabnowMPRl2PRl2wxlOzLKcuieEI3EikQTBQJT+QS+9vR66ejx093ro7RtndMR/UZFJ50KtkVBpJBRSqp6rQiGi1kpo9Sp0ehVavRKLWcRmBrs5QaYpSoHFT5roRnQMEm0fwNvlxd3uxdPuZbzL964PgyBgqigifdWiU7OylgUViJI0q59hrrmarifOI43sfvIl3hYr2LvHMW1pH5Va5KEH6/jMp1bN+iDL5cDnj/DyKy08+UwjXV3nrhs7FZJKQWlVOlUlCtKNMSxSGG1oHLVrDKl3gERDJ8GWUWKhJLrFdWhXL0VRV0tIVDHU0s9Y1wBBhQKv3oozrmXElcTjjs7KhMk1cXuNec01cXv5ifkD9D7xKh0/fQrHnqNobWqyltlJX19CbPESjrqyOd6loq3Nz9iQj6Dv4vJkjRYN9hwTGbkmMnIN5GWKFGbEyTKG0QpBVPEAqkQAZWJinUyZX02GAidRkBRVJAQlCVFJXNRMlMbQExV1uMMaXAEJl1/C4xNwOGM4R/w4hlM5s25H8KI7OQqSlI53s3jwKPm+/vMavOgLc8m5bQO5d28mc+OqOckJvRo6D6FwjCeebOCXvzl6TqfF6qU5bLijktXFLgrG96P39dCzbYCWp7rwvqcEkqEkn7LPP0jJp+5FY0ub649wjQmuhu/TbJBMJul57CXaf/wErgMnSITCpNdYKP1IDfE1a9k5XMzbe3x0NI4SjVzevLPTEQQwWrVk5JrIz1NTnh0nVxhF6O4hMexGTgooM824c0vpDlrpHISBvgCOId+MO4u5eSY+/KEa7ry9irS0i6s/Hg+FGdm+l76nt9L//Hai7rNz501FBgo25VCwKRtTvoGA0saooZZRfSXtgyqO7+2lfm/fJbvR63RKJEkkEk0QiybmZDBCb1KTU2Qhp8BEXqaAxZDErE1g0cax6qJY1GF0igiKZBRRjiPICQSSCJEQss9H0uMj4QkQdoUJjoUJjoYJjYXwDwbx9QdOlSsDUGjUmBdUkLakBuviKiyLq7EurLzkWvFXA1fb9SSZSND6w9/R336cIyWbeO310WldwC1WFX/4+dV86M6aq97xW5ZlGhpHefKZBra+0X7B17ScIgs1dVZWVoRYaekm/OZ+BncN4m4bJ+R4d1DLsqiKrM1ryLxhNemrFuI63Mjo69sI951Au7iQ0fw6WoM22voVDAxGGOkfxz8+fUrShXJN3F5jXnNN3F5ZvC2dqXqxz72B88BxEMBSaiJruQ3b2iJUVcVEDekMBQ2M+rWM+lQ4vBIuLyRkAZ1WRK8V0GkF9BrQa0GvkckyR7GpxtFFnWhjTrQxDyIJErEkEXeEsCdKxDO5jhJ2R4j5YwiKlFHLpGGLQjVh4qIUUeok1GYVaosKjUWF2qxC0r47uh0X1YQkKyFlGiGlFZ9gpX9cS79TyfBogqFeD8O9HoZ6x4mGZ55rlRb3sXjgMLVjzRjNWjSZ6WhzMlJmHhM5UNrsjLn495zBlew8xGIJnn62kZ/8/DAu19Q5TYIAi68r5KZ7K1hs6iRv/ACCY5j253toe76HiPvdQRJBFMm9cxNlX/go2TdehyBe3R2K9yNXW2f0Qoh6vLT816/oefxVvCc7UagE8jZkkXv/cnrzVvBmo4kDex2M9I9f1WkXKo1EYXk6ZRVGFhRGKY/3kNh9AO/+VvQ5ejI2V8LiBbT4sjjYaeDwAQf9HecPAZYkkes3FHHfh2pZuSIPUby4KIhkLMbIjgP0Pf06/c++QXjUedYxljIThTekhK4+S0dQmYZDV8GYroLWURP1+/o4sa/vskUFTYcggD3HRF5pGgX5GspyElRk+clTO9CMDxBu6sHb4yXiiRLxxoiOR4l4392OhxIkYgmScRk5niQZn/6LpbanYSjJx1hRhLG8EGN5EdaFlRgriubdjOxMuVqvJ8HBEY786T+QKNbypm0TW1/sJTLN/b+wyMhn/2AVN20uu+pErt8f5dXXW3nimUY62s/+HZ6LqqU5XLfKwNoSFwWhkzjfOE7/ziFGjjiRJyLgjOVFZG5eTdYNq8nYtApRqWTk9a342o4wqtHTrS2iZUxHe3eUvk7PBfWhLoZr4vYa85pr4vbqITg4wsALb9L//HZG3tx3qnaj0iBhzDdgytdjzDdgzNNjzNej1ChSJVsnay9CqiMpy0TGowSGgviHQgSGgwSGQwSGgoSc4bPKF1wKCrWI2qJGY1GhtWvQZ+nQZ2kxZOkw5usx5OgQJZG4oCKgysCvzsQrZdDpsdDUJdDd6qS7xcHIeZx9AQwGFQ99ZCEPPbgQk+nCyytcKlei85BMymx7o53v/3A/A4PTd04Xrs7nlvsrWWE8Sd74PkLdDlqe6qL79f4zyiBpMtIp/ewDlH32I+jzs+e8/deYnqu1MzoV8XCY7t++RN9Tr+E6eIKI04M+U0vpfWUob1zLvkA1Ow9GaTkxRugcYfLTIQhgtmjJyzNRXWmjMN+CQa+gtbURpVJg0eKlyLJIOBwnEokTDscJheK4PCEGhnwMDvkYGfZdcgkYURTILUmjus7M0sIwJe4mgq++gzzuI2dtJtZbV9As1/Bmg4Ej+0bom0EnNzvbyAMfruOeu6ov6bqVTCQY232Yvqe30vfMVkIDI2cdYyk1krM6k5w1KYO+mNKAQ1eOU19Jf9hOe5ufrpNjdJ0cY6DTPadh4emZBvJL0ygsMVCZF6cue5xMYRjlcBf+5mHcbeOnwoT9Q8Gz6o8rLSbUNivKNDPeZAy0KnILC1BqU6VLlEY9KqsJldWMympCk21Hl5uJNifzA+nmfrVfT0Z27Of4N/8d8Z41PDtWzt63+qaN7MrM0vKJjy3lnrtqUauv7GBEU/MoTz7TyOtb2whfgKBUSCLL1hdy72aRFeIBHK/V07djiJGjKUGrsprJ2rKG7JvXkXXTOvT52QT7Bhjd+TJtzjCNERuN/SpaW7z4ZqnusSCA2arFZtNjs+nIsOuxpevQapRo1Qq0WiVqtYRarcDhGOTBBzZMPvWauL3G/OKauL06iXn9DG3dzdieo7gONeA+0kQ8cP6auedDodNiKMlHX5iDviAbfWEOymw7J4Z6Eawm1m9Yj0ohIccTJGMx5Fic5MQSD4aIjfuIeXxEPV6iHh8xj5fouI+o20t4xElocDRV+mLi2iQoBAw5OkyFBiwlJqzlJqxlJvRZOuKCEp8ml3FNAZ3hXPadkGk82E97w+g5zUd0eiUfvX8BDz+0GIv58oncy915OHCon+/9915Onhyb9pia5bnc+kAVq6xt5Hv24m0cpPHX7QzuGz2js2iqLqXq/32K4o/dhUKjntN2X2NmXK2d0Xg0yujOgwy9soOx3UfwtfUQG/ehtqrIXZeF/fYl9GYuYmd7Ggf2uxjpu/DZWUkpUlKaxvXrithwXSEVFbazTJku5vyEQjGGhn0MDvpo63DSeHKMllYHgxc5g6xUKSiptrOgRsvCdBeZ7YdRD3aSsSgNxcplNIYreatezf4dPYyeZ2ZUrZG447ZKHvrIQoqLrBfemNOQk0mcB47T9/Tr9D69dUpDKrVZRfYqOzlrMshabkdpUBJSpp+qpztGFk09CrpbnDhH/HhdIcZdQcZdoWlzJN+LRqfEnmPEnm3Elm0kN0OgwBajKC1ApjiC3t9HsLEXZ7MbZ5MHZ5P7DOMmpdlI2tIarEtrSVtag7muHE2mDXWa+ZRXwtX6O7namA/nSU4m6f7tC7T96GeEH72f3+3T0np8+rqvZrOKBx+o46EHl2A0XL77VjAYOzVL29Y6fSmvqdDolKy9qZR7N8SoDuyh77HDtD7dTdQfJ33lInJuXU/2zetIW7EABIHx44c4dqiR+lGJxiE1LS2+SwovliSRnDwTxcVplJakUV5sJTvLSEaGnvR03YzN79ra2qioqJjcvSZurzG/uCZu5wfJRAJvcweuQw04D57AdbiRiMNNIhwhGY4QD0VIhMKnRKVCo8ZUXYq5tgxzbTnm2jIsteXoi3LPCkGd7ZtiMh4nPOzA39WPv7MPf0cvvvZexhta8Z7sIhmLoTarsNVZsS9Mw77AirXCQkxlxKGvoJcyth+EPa+3MzowfYdRp1fyyMOLefjBRej1cz9Sf7k6D61tDv7z+3vZu69v2mMqF2dxywM1rM3oIN+zF1/DAA2/aGPowJlCOPOG1VR95VFybll/LfT4KuNKdUaT8Ti+jl58Ld24j5/Ed7ITf1c/wf5hwqMukuEISr2EtcJM1sYipOULaJNKONxjpP64j8FuD7GLcAO1pmlZv76Ie+6ooq4287xhh7N5foLBGG0dTk6eHONE0yj1x4cYuIgwXYNZTUWdnYXlIgs1/eQHW7DnwXjuEnaNlPPWTjf1e/vOW4t29ap8Hn5wIWtWF1x0yPIksizjPtZM39OvM/TaLlxHmnivkhdEAVOhgbQKM9YKM2kVZixlJgStGp86m4hkTnkpKPTEFHp8cQ2jXgnneKoMm1opo5ESqBUJNMoEWimORhHHrPCij7vQRR0Io8P4uscZ7/bh7fbhahnH0+E9FT4sqpSkLa/Dft1S0lcswLq0FkNJ/nkNuOaDaLsamE/nKR4McfK7v2D47Tfof+ARHn/l3GaaWq2CD91VwccfXk5WpnHO2tXS6uCJpxt4bWvbBZdUtNh0XH9bGXev9FHk2EX37+ppe7YbJC113/gipY/eh8pqJh7ycuj1XRxo9tAwIHKyNXjRYtaeoaOiKoOaKjuVpemUlaSRk2OalXJf18TtNeY1p4vbX1RvIduajqTTotBrkfQ6lEY92twM9PnZKC0mVBZjKgzIYkRpMaE0ze9C2+8nZFkm0DPA2O7DqZlV1zgRh4eIw31qibrHkeMJZFkGWUZOysjJJJFI6uKqNehRaNQo1CoUWjWqdCtqmwW1zYomIz0141uUi6EoD022HVFxYSUwEtEovtZu3EebGHvnCGO7jzDe2IbKpCRnTQZ512WRtcJOzJDGgHEpu/ry2fl6Lw0Hpr+ums0a/uCTS7n/vro5DWGa687D0LCP//nf/bzyauu0s0yFFTbueLiO9bk95Hv24G/oP0vUCpJE4Udupeorj5K2pGZW23iNcyPLMnF/gPCoi/Cok/CIk/DwGBGnh6hrnOhE5EPM6yc67sM9MgrRGDqtDkHg1Gz7qXv7aV+Edx879ci7q4m/yZx5jBxPkIhESUZjyPFUBAayjCgJqC0qtHYt5lIjppocFGUFjBrz6Ylk0DmqorU9xECPF6/74kLiBFGgpDSNO26t4KbNZWRnXVindK5/b253iKP1Qxw8OsjRowN0tLsuuNyGPcdIVa2VxUVRVmYNkWWL0ynW8tpRHfve6GKo99zpFgUFZj76wELuvL3qDGfiSyHi8jDy1n6G39jDyPZ9+Nq6pzxOEAVMBXqsFWZ0dg0qowqVSYnalFqrTCrURiVyUibqjxELxIkFUuvJ/cBwaELM+omMnxmOrkqzYL9uCfbrlmJft4y0ZXUXFTUyn0TblWQ+nqfQ8BjHv/mfhOIjnFxzN6+86T9vWauaagu33lLNLTdWkp5+6UZgwWCMrdvbefzpBlqap4+Smg6lWsGN99Xw0fVecgd30vnbE7Q/30MSJdVf+RSVf/oJ2jr72LmzleOdUU62hS5KzOr0EuUV6dTWZbN0YTaLajNn5fNPxzVxe415zenidsc3/gC7pCTuCxL3BIh7gkQ9IaLjMWL+2Ls3OH/sVB6fQqNGm5Nx1qLLzURfnIexrBC1zXpNAM8RwcERRnceZHTHAUZ27MfX2o3KpERr06CxqicWFZo0Neo0LUq7GUGtBElCUCkRlAoESQJJAoUI8QRyIgGxOHI0RtIXIOnxE/eFifpihJxhQs4IIUeYiC+JKjMPU1U5lgUVmBdUYFlQgTY744L+3xGXh+Fte+h7dhuDL+9AjoXJ35hN2R2FWBfYGDPUste3iJefH6B+b++0ws9u1/O5T6/gzjsqL7ju5EyYq86D0xnkp784zNPPNhKbpsanPcfIbQ8t5MbqMYrcuwg09J4lahUaNaWfeYDqr/7BtXzaOUKWZSJjLrwtXXhbuvC1dOJr6yE4OEpkQtAmQu+KQUknoTYrUeqVaK1q1FYVGqsapVWD0m5FSLOQ0OlJIqQSouDUWkacWAOikNoQhImVgCxP/E0AEJHld19CnthICBIRhYYQGgJo8MU1jEfUOL0CDleC0bEobmcY/3iYcDB2yeZPeoOKFSvyuPv2SlauyEOruXjBdrk766FQjPrjw+ze18v+A710drgv6HyICoGiChuL6rSsqQySn6/h0FAO27cOc3xf3znzW/V6FffcXc2D9y8gJ8c0C5/mXQK9gwxv34v7aDPjjW2MN7anUkdmEV1eVsp5eHEV1kVVWBZVYSwtmJVokfko2q4E8/k8eRpaafqn/yUUHGLo9g/z4l6ZthNn55SfjiDAwgVp3HpLNTdtqbygFKV4PMmBQ/288PJJdu7sJhK5OHOmRWsLeOiBDJYHXqPrJ3tpe76HWFJC8/8+Ta81n8aOEG2dQfzeC/chsKapqVtgY9WqEtYsz6OowHJZ+9HXxO015jWni9sFS/4BrSEdSalAqRRRqiSUagV6owqbTY3ZJGLUgVErY9AkMEhxjGIQc9yPLuQCh5P4gINI/xiRER+B4RDB0RCCSouxtABDWcHEuhBzTSnmmjJUltm9kb/fSUSijLy5N2U69dZ+Eu5hrBVmzAtz0C6pIJhTxEDMijuoxBtS4AuK+ELgD8gEgknCwRiJeIJkQiaRkEkmUzVtkwkZWZYRFSKScmKRFBPbCiRJQCXJ6LQCOg1o1TJ6dRKjOo5OjKCNB1CHfSjHPSh9XrSCgEZnRl9YhmnhEkzV5TNyqUyEIwxt3U3HT55k4KUdmIsMlN1VQNGtBbjSF7AvuJSXnh/k2Ds903Y88/LM/OHnVnLjlrJLDvk7ndnuPHi9YX71m2P87vHj0xpVGC0abnpgAbetilI2voNIYycnftbC0P7TRK1WQ/kXPkr1nz16WVyjPyjEA0Gchxpw7juGp7EdX0sX3tZuYh4vSr2EIVePMVeHvsCCMs+OIt1M3GzFrzHjE/W4E0Y6R1X0DsTx+xOEQglCwQSR8ETt6EiCWCTBfL6PCwIUlaRx841l3Ly5jIJ886x1wK50Z90zHmb/wX527+3m0KF+RoYvzPPAlKalemE6S+vU2PLTeWefn31vtJ+ztJsoCmxYX8TDDy5k6ZILr5k7UyJON+ON7Smx29RBeMxF3B9MLYHJdYi4P4hCo0KdkY7Gnobanobabj21bSwrxLq4CnX6peUQn4sr/T2YL7wfzlOgd5CT3/slrvoD+B68nxcb9BzfP3DeQSaFQmDRwjRqa7IoLc6gsMhKYYHlDMEryzInWxy89GoLr21twz1N1YGZkJln4r5P1XFrdj0jT+1g9wkT/RkVdIcN9PSGph2kPhcWq5raSiOr1pawfm05BXmmKzopdE3cXmNec7q4rV3896hUF3+T0hpUGExqDCYNRpOSNKsCu0XGro+RrghgibowuIZIdPUTONGLr9eHLBkxV5edmRu6oAKl0TBbH3HeEw8EGXxtF33Pvk6w+QjG9RW4KpcwJNroH9cwMJJkZDiMY8hLKHBheSJziVorodFIaLQKtBoRrRo0KhGdVoFOr0Jn1KLTqdHoNGh1GrRaFTqNEr1WQq9TYYz48D33MoO/fAoxGaTi3iLK7ilmPHMBu/3LeP6pPhoPDUz7/kVFVj73B8vZsrl0VsoKzFbnIRSK8djjx/nFr4/in8ZZVq2R2Hh3NXdu0VId2InQ1sKJn7XQ9/bwqZBTSa+j/A8fovorj6LJSL+otlwjhSzL+Nq6ceyrx7H3KM599XhOtKLPVGOtNGOsykYsK8RjzWVESGMooGXQITLiSOIYi+AbDxP0RYhfRKdmPiEIkJltZPnyPLZcX8SK5Zc2O3surrbOev/AOHv29bF7bxfHjg7j98/8WiuKAoWV6VTVphGTJRoODDB8Hof48vJ0Pv7QYm6+sQylcvYjUeYLV9v34EojyzJOV4juHjftPW4czhBeb5jx8RDDw04kSaC6Op/igjSK883k55lJT9fN6kDvXBN1j9P2o9/T9+LLJD52J68N5HB4z+CMjc4mMRok8vIMFOSncbLFQU/vpZXDUqkV1CzPJc2QYLh3nL6BCD7vxfW5LGlqqos1LKmzc8PtyykuuLoiHK+J22vMa2ZT3M7s/cBk1WLLMpCZoSTHBtn6IBlxF2muXuSTHXiP95NUWDFWVqQKrS9KhTrp8rOvqh//XBJ2uBh6dSdju3bg0ykYyKuhLZhGa0+cnva5r3H2/9n76/BIsvTA//1GJKMypUwxMxYzQzNO45BnxuMZM61xDb+7a1/f9XoNd9fPGtb2ejwznvFgMxZ0MYMKxMycKCVT/P6Qqrq6W1xSlVQVn+fJJyOVkRGRoYjI88Y55z0riVqjwGJWkSwFsAUdlKWNs/uAiHVdFadca3nzx5101M+ccTEnN4lf+LlNPPZo8V01V77bQlYoFOPNtxv5l29dxe2e/q6xQimy/dFinn02lXXx02g662j4ThvdhwduD5ugNBko+42vUPafvobWlrzo7/Ow8/cMMPjBKQY/OMXYmVoUYghreRKWrcWMF1fTJWbSNqKhozfO6LAfjyMw49AVD6okq5bi4hQ2b8pi09oMKivS0GrvzdAcKzmoSSQkmlvGOH2+h/MXumhscBKbZdzVTzNZtGTmW/BPRBjodM1aM2W3G/ji59fw0ucqMZkevkznK/k4WG6SJNHb66G2bphrdcM0NQ7T3ztOOLSwhG4qlYg9TU96homy0lT278pn7Zr0Zem+s5Ti4Qjd33uL9v/7H2j2l9Oet5ULrQrqrwytujKQJVlLWb6a6iwle/fXULWjckWXZ+XgVraq3evgdi5JyTps6QbSU1VkWOKkqX3YgqOY+zuRWgcQ1VZMFZPBrnVdBeaKogdiTLtbGS8Hj52gy+unXbTROWGkrSvE6MDE/d68FUmjESnJV1JcoidmTOfapREGOt0zzm9PNfClV2t48XNViyokLraQ5fYE+clr9fzwx3V4ZhivThAFNu8r4Jnns9iovIy5t5bG77XR8W4vianaQJXFTPl/+hplv/EV1NakBW//wy4eieA4W8vg+ycZfP8UMdcAabtziezaTqcqj9YxHZ09Ufq6xxc1VutqJIhgMGqwJutISzNSVmKjqtxOUUEyuTlJ93WMydUU1ASDUS5e7uf4qXYuXOxnbHT+TR5FhYDZqsPnDc1a86/Tqfjcc+V8+Qtrl7xf7kq2mo6DpTDhC3PqXC/HT3dy5VI/457FDwszG51eyboNmezZmc/enXnLmoX4bkmShLu2gb7XjzBy4hSq/dW0ZG3iYjM01A7PmZ38fsjINlKcKVBqibK2wMqGp/egT1s9Lazk4Fa2qt0Z3H7xZ/8vCqWVUChKaKpfWDgcY8IbvutEI0shKUVPWrqODLuCdGOE1ISbZFc/yV4XKqWZpIoKrOsqsKwtWxU1Wv6+YbrOX+VG2xCt42o6nCq6On133bRYoRRJTtZhNGkwTT3MZg1JJg1JZg0ajRKlQkShFFEpBBLxGE3NjQiCQFlZJYKgIBKJEY7Epx4xwuE4gVAMfyBCIBDF74/g90cI+COEglGCgRiR+/wDI4hgTzcR8EdmzUao1Sp57plyXvxcJaUltnkvf6GFrL5+L9/7wQ3efqeJcHjmfbN2Ry7PvJjHNn0t1v6rtPy4nbbXu4lN3Z1XJ1uo+J2vU/KrX0adtHILICtRxDNO/5tH6X/7GI4zF7CWG1E/uo12axXXBw00NPpwDPmWdRsUShGNVoFKpUCtUaLRKNHqlOi0SmKxIBqNiM2WjEJUTCaHEkCYzBKFeEeOKUEQ7nhvclq89ebHTwjirc8IKBQi1iQtJqMao0GN2aQhxarFYtFjSZq8NqzU2oPVGtRIkkR3j5vjp7s4c6aD+gbXgpqrK5TirON8i6LAwf2FfOXL66iuSluKTV7RVutxMF+SJNHZ7ebwyS7Onu2iuWGMxAKzdi+FnLwktm3L5YmDxaxbk75irwuSJOFtbKfvtUOMnDqLa30lJ8PFtHSGcTsC96WsqlIryCswUpwap1TnozpVT/G+7SRVl67Y/TgXObiVrWrzGec2Go3T0eWmvnmUplYHbW0Oujtd884AJ4iTxbHZskXeDYVSxJZunKrtlbArfKTF3KQTJEVnxFxUiKm0EHNp/n1LYBULh+k4d5MbNzppHY3Q7RLpHYziGFlYkpI7Jdu05OQkkZuXTGGelaJ8K/l5FjLSTQvqY7pUhYdYLEEgEMHni+D1hfGMhxmfCOAZHcM97GLC6yMQiBGISIRiApGESCQhEo5AJAqRSIJIVCISjhMKRmdNvDIvdwytMpOSUhsvPlfBE4+VkDRHtsX57qe6+hG+/b1rnDjROesPbfmGTJ59pYhdlpsk91+i9UdttL3RTSw4GdRq7MlU/O7PUfLLX5T7oC9AzB+g/53j9P7wPTxXLmF7uor+qh3cdNuoawnT3+lZ1gJQZraJmrXpbFqfzdYNmWRlTp8Y5EEvtN+tB2X/BENRLlzq58SpVi5eHGB0dHFDK01n/boMvvKldezZnb+q+lQuxINyHNwpHI5x9nI/x091cOliP2PD/gUvQ6kSSUk3kZyiwWgQMeoF9DrwB2HUmcAxGsQ16l9UrWZGlonnn63ghWcqsNsNC/78corFEty4OcSx092cOdtDX4/nnq4/Jc1ATraW3JQ42UoP2UyQrddh27gJ+46NixruaiW6n8Ht/WsvJHuoqFQKykttlJd+XMslSRKdXW4Onejg1Kku2podMxYYpcTt0RdJzzVTWaTAJngY6Aoz7FfjDCnwjC++1i8eSzDSP85IP9y4/VcDYEBnVJPS6CPZ0kCKoZ5kdRibKopNEyc9SUtKchJmux19RiraDDuaZMuihy9IxOOMdgzQfLOdnn43Q84wIxMw6pUYHo3gdiw+O59KJVJYnERNdTobN+SyaW3Gso5xthhKpYjZrMVs1pL5iXdKPzNvxDOO+1ojrtoGxhsaCPZ3kphwoktWo03Vo1ljQ5GbgdechlO00ufW0TWipHcwzlC/D49jHjcF5hHAtLU6+B9/fZq//p9nWLM2gwN7C9i3p4CsBTT7GxnxcbN+mJt1I1ypHaClZfZhNkrWpPHkS8XsT2smpe9fafvXVs7fEdRq0+1U/v43Kf6FV1EaVtb/eKWKh8IMfnianh++x9jJMyQfLKJr10HO5+/n5nU34esxYPahJT5jHjdHAGypevbuL2L7pmw2rs2Y8yaJ7OGi06rYv6eA/XsKkCSJnl4Px091cOpUG/UNngWPrXuna9eHuHZ9iNzcJH7mi2t5+qmyZUvwJbs7QyM+Dp/s4OyZTm5eHyUyS2ueTzNZtOQVJVGWL1Ka7CMtOoLVO4xiPIioMiOoDSTUetr7h1HGwuQmayA5QGKrgXFTCmMaO6MRE12jKuoa/Ax2e2bf1oEJ/s//ucQ//fMlNm3O5uXnK9m3p+C+JTZzOgOcv9THsVNdXLrUT2AB3UZS0gysXWOiJj+KSowTiQlcuh7myo3gnP3kFUqRogobGyqgUtFHhn8Yk9qMrnAtyZvWo89dvozmDzO55lZ21+ZTczsfTqefI6e6OH6yi+tXB4nOcbdQZ1CxZ386r64fQnP0CE2vd9PvVCPt20d04yYckpqBwQmGBv24XcvT5+QWjVaJ3qjCYFBi1IsYtaBVgShONgsUhckhYAUBFAJE4hCIQDAMgbBEMJQgGEwQCEQJLCCD5mysKRrKS5NYuzaDzZsKqS5PXbYflpVyZzwWDDHe2I77RjOemy2TzzdaiI6PY8o2YCkyYyk2Y67OYqKwkhuOVOq61bR0hOltdy1pU66MLDOVlanUVKWxtioVk0lDOBzm7NmzBIMJ1Pp8GpudNDSM4JpHoC0qBNbtzOPRp7LYZmkhpf8C7T9qpvWNHmKBycQYxsIcKn7/mxR+7YUH5u7vcpISCcbOXKXzO28w9N5RTNuz6NrwKBeHk6m76Z3zGnQnvUFJil2LPyDhGp27FiXJqmXf/kKef7KMtTWLa8K3Us67leph2D8+X4STJ9v54K1arrf6CATvLtt2UpKWV1+u5vMvV5Oc/GDcGFutx0EsFudq3TDHTkzW2Pd2zT9Tr0IpUlyRzOYqBRvMA6R0NEBIhSa3BuvmTSRVlXymFdpM+ykejuDvGcDX2Yuv8SbhkSZ8pSVcixZwtVmguW5sXjkGTGYNTzxewkvPVVJaOv/uPAuVSEh0dLq4dmOIK9cHqasbYWRo/nlHRIVAQbmNjdVq9hWPkd52keFjXQxdHuMGuZzI2c24cuaWUPZME2Vr09lUIbE7s4fEsVO4+y2s+es/f6i6BcnNkmWr2lIFt3cKBqO8d6SdN95qoGmWLLYwVejfmskr+yKU956g+0d1DJ4fQZedSemvfomib75CQmegt89LW5eDzs4Rurs99A/4GBz0E/Cvrox501EoBHLzDFQUG9i4KYctW8vISr93CYNWcuFBkiSCAyO4ahtwXq7DeakO1+U6ol4vliIz9rUp2NfbkdbVcMWVw5V2LfVNwTnvTN8rGp2SbY8W8+RBE2sU19G1X6P9zS463u29HdRa1pRR+Qe/QO4rT8xrLOCH3URHL13ffZOeH76NJk9N15bHOT9qo6FhYta+indSqURKcpSUlGhxxwxcv+ZhfI5xD/UGFbv2FvDcE2Vs3ZR118NLreTzbiV42PZPNBrno29/xKHDTdR7NDjHFx/oqlQizzxVxs98aR0F+fc3SeTdWk3Hgdsb4qNTbZw+08W12tF5d90CMFu1rF1rZku+j3LnTRQDXrTFm0l/bP+8+m4uZD/5uvsZeOMDAi3nUJWn0525ngudRi5fdDI2OHcgWVySwgvPVfDU46V31VJFkiSGR3y0tjloaB7j2s1hmhpGCCww74jBrKFiXTqbq0V25wyguXSOwY+6GTw/QtQfw51RwIniR+gIT3/DR6tXceCFSnZuVFOla8M23sDw4TaaX+un7A9+h8KfffGhq6GVg1vZqrYcwe2dunvc/OjNRj78oAWve/a+RgXlNp45qOcR83VGvneazg/6kSQl+T/zHGW//jNYaso+Mb8kSbg9Qdo7nbS3D9HV7aS3b4KBwQCjIyFi8yzo3ktKlUh2tp7CLBUVRUbWbS6iqroIre7+1dStpsIDTNbYeRvbGTlxidHjFxk9eYmwy0NSgYnUtSnYNqQRWbOWy2NZXG1X09C0/EmDPk2rV7F2ezbPH1SxRXeD0LnrtL7RzdCFUaSpw9K+ayOVf/iLZD6556H74Vyo6LiP3p98QNd330CKDjPyyJOc82RSe903r+Z9ggDZdiVlBg/bqmNEiyr48IJI3eXBOWv816xL5wuvrOHAngLU6qVrPbHazrt77WHeP64bTXz0Z9/hQm+YTnsJvU4W3U98+8Y0vv6NbWzcsDqbUK7k4yCRSNDUNsZHx5s5f3GIthb3vFsQCQLkFSWxsVLBRn0vac3XEfS5JO/aR/ojOxdcS7jY/RToH6bv9UOMXz6OvtpCW/E+Dl1Wcf38AOE5httRKkX27Mnnhecq2bYle8YbfomExPhEmO5uNy1tDprbnLS1O+nuci04kL0lM99CzfoUdlSEWWfsJnz6EkMn+hi6OEYsnCBlcw3B7Xs4HkzlzA3ntOePIMDWR4p46XOpbIx+hDHQT/fhARq/347KnsfOH/7/MZcVLmr7Vjs5uJWtassd3N4SiyU4caabH71ez9WLs58jyakGHnk0lZcquoi+dYS217vwDwVJ3beFst/4KlnPHUBUzF7IjMcT9Pd76Ogcon/AxcDgOMPDfkYdYRyuKG5XeNkyEgrCZLNFu01FWrJIerKCbLuamjU5VG2qRq1dOT/OsLILD/MhJRJ46lsZPXGJkeMXGT15mYjHi6XQROp6G/ZNGXjK1nFpKI3aNiXNTRN456ilW0panYJ0q4JUZYhk3xiFdi27vrSf8ie23bNtWI3ikQhDH56m+z/eJdh1nYnHDnA+UsTF2iC+8bm7KoiiQFGWinJ/Jweze7G9uIt32vM4ftzJ6MDsTQQNRhVPPFnGl1+pIT9veWq+Vvt5t9zk/QPjbd00/Y9/oe4/DtOZW01vyTpaXeoFjal7S2G6yKsvVvPMK1vR61dPv9yVdhwkEgmuXO/h0JEWzl0YYWRw/smgtDoV1TUmNuX4qXRcR+vwoy/dTPpje7Cuq1h0vg9Ymv3kqWuh7e//DcHXjPbVxzk2VsypMx46G8fm/GyKTc+uHXmEQjE84yG83jDj4yEmJsL4fJG7HiNco1VSXJ3Khhotu8s8pPXfwHWsjqGLY7hbPKiTrWQ8vou0J3bTnVzED95uo/ba0IzLK6xM5cWvVbLHdJWMiev0HO2n7lst+IeClP2nr7HuL373gRhmcrHk4Fa2qt2r4PZOXT1uvv396xz6sHXWWhetXsWOfVl8focb+9WPaPthE2M3XBjysij51S9R9I2X0SRbFrUN8XgCl2sCp8ON0zmOyzmBc2wCtzuIdzxCKJwgLkEiLpGQIJGAuCSRSIBKCXqNgEEjYNSJGHUiJr0Sk1FNdq6NopoS9GbrqrlLvtIKD3crEY/judnCyPGLU8HuJeL+ANZSM2nrbaRszsRTsIYbngzqe7V0docZHRhnYoYxaJeL0awmM1NPXpaOwnwTZWUZVNXkY7PqV82xs9SkRILR01fo/v7bjNeeI/7YNi6pqzlzLYpjHhlFFQqB0jwtpd4OdqrrKHmhikbrZt45p+Lq2YE5++EWl9n4wkvVPPV4KVrt8jYRf9DOu6Um75+P+fuGaPrrf6XjX35CMBxnoHI9nZVbqOtXLjjQ1agFdlYqefWVjWw+uH7FX2tWwnEQjcU5d6GZw8c6uXjJiWts/qMcpGcZWV+hYK26n/z+JjTJRdj27iF1zyZUxqXLRLyU+yk4PEbbP3wf1/H3yHihiqGaA3xwVc+lU/14nYsf4WEhrHYD+WUpVBQpWZsXpFTTT/T8VUZP9zJ0eYywN0rKljVkPrmHjCf3YFpTzqGjnXz3+9fonGW8e6vdwDNfXcfjlWMUuo4T6Bjl6t/WM3bDhcaezLZv/wVZT+29J99xJZODW9mqdj+C21u83hA/eK2en/y0HvcsF0xBFFizOYOXDiTYOHGOru/X0ntsCEGpnrHJsmz+VkLhYTklYjFctY2MHr/AyPGLjJ2pJeYPYMzUk1JhIak6DWV5IT3aPGqHLbT3g9ebwOeLEQxECQWjJBISUkKaHM5qmS+7lhQd+TlaCtOgOAXKM3RkZGRgKshDm2Gfs9XCaiNJEu7ahslMx8ePotpXxTXbFk7fhJ4Oz5yfF0WBsiI9FROdbPRfoezRTLyb9vJevZ2zpx2M9Hln/bxareDAI0V8+dU1VFWkLtG3mtuDft7dLXn/fFZo1Enz//oObX//faLjPhImHeMHd1BnreZqq0AsurCLU0aqikfWKXnl1c1k11Qv01bfnft1HMTiMc6ea+DdQ11cvuJifI5uVbcolCLlFSbWZQao8jSRGoekTTvIeHQX+uz0Zdve5dhPUX+Aq//0Jhd+cARXTiYjKYV0DYu4HKElHdpRq1eRkWshp8BEdaHE+hwvqZ52ghfqcdwYxVHnxjcYQGNPJuPxXWQ+uYf0x3aitFhobhnjwsU+fvp6A6NjM98A1eiU7H++gqceS6J6/DA6Vw91354cfk+KS2Q9d4At/+dP0WXcu9+AlUwObmWr2v0Mbm+JxuIcOtLOd75/nY4256zz5pWk8MyjRp6w38TxozO0v91L2BMhbf9WSn/jq2Q9u/+BK/wvt4etEBmPRrlx9AbnT7bQMhSg2w3DztiimvrdKylpBvJy9RTYYhRox8nHgzkG2vQCzGWFmErz0aamrPhamFvCLg/DR84x+MFJfPVX0W0vprFgJ6eaNTTXOedVcMrL0VMtDbJh5Bzlm3Ron9nHR0OFHD8XouX68JzLyMpJ4pWXqnjhmQpMpnvf5/1hO+8WSt4/M4t4xmn9++/T8j+/TdjpQVAI2Pfk4NyxhRNj+dQ2xIlG559zQhAFykv0bCsVeHJfAUU7diCIK+N39F4eB4lEgqamDt54t4VT55zzai0CkGTVsLZczRrlAOUT/VgKq0l/dPddNzVeiLvdT5Ik0T8wTlPzGHVNozS3OGhrdTDuXdoWTQazBlu6kcJCAzWlIhUZfjLjQ8Rb2hi/3oOz3oWz0UNkIoohPwv7zg3Yd27AtmMD5qoS2jpcXLk6wOWrA9ReG8Lvnz1plzlZx+6ny9i9P52K8Hkyx2vpOdLP9f/TRMgVRpeZyqb//f8h+4VHV83v570gB7eyVW0lBLe3SJJE7fVB/u171zl/tmfWBBpWu4FHHkvjlZpupEPHaftpB56OiSVpsvywWa2FyEQ0SnTcN/mY8E9NTz7Hpv4e9k7gkiQ6olq6Ahp63Ap6BsMPRJZte6aJ/AIDhalx8hUOMl1daIadoEpGn1uAuawAU1kBppJ8lLr7O/ZqIh7HdbWBoQ9PMfLRGZRKJ8Jju7mpLuNyq4KWeue8hu6x2TSsM3nYFqxl7QYR9e5tXPbmceKagqtnB+Yc0kKhENixK58vv1rD5o1Z97Uws1rPu3tF3j9zi/kDtP/Lj2n6q38lODg5MkH6Zhu5L5Vw07CGDxtsXLvpJ77AG3f5BUa2Vog8sjmZdQf3oNDMPHTKcrsXx8HQwABvvFfPsdMuOltnbtJ6J1uqjk1FCdZFuyg2aEjfv4fUPZvv29jkC91PkiTR1eXm4tV+Ll4Z4Mb1Ibz3uGuOggQpUS827zD2iBtzRgraglw0OZmos9KRNDqi0ckbNaNjPq7WDjI+j3wLAGnZZvY9X8GOrSYKfBdJ891kotNzuwkygkDJr3yJtf/ttx6qIX7mSw5uZavaSgpu79Tb5+G7P7jJ++83EwrOHIhodEp27M/mCzu8ZLadpPMnzfSfHkZUayj4yvOU/vrPYKkunfHzspVZiIyHwky0deNt6mC8qRNfZx+hUSehESehUScRpweBGBqLGo1FjTrdgrIwG09KNqMaO/0xC11OJd09ASY8yztO8t3S6hSo1Qp8E9G7auoliAJpWWbyCvQUZ0oUal1kujqIXakn6kqAPg1DUT7GwhyMBdmTj8IcVOalLbjGwxG8DW24rzXiutaE52YjkncAy7Z8HBt3ccWVxtWbQfo65leINBpVrEsPsUvZxLYdSoJVmzndZefizThN14YITMw93EZqupHnnqnglRcqsduWrp/b3ViJ591KIu+f+YuHI3R9900a/+Kf8XX2AZBcnkTFF4tI2pLN+10lvF9rprHOteBuFamZBtaUathULLJ/bwm2omoExb1LSLVcx4HHOcr7R+o5esZJ3TXnvIYRy8jUsSE7zPpYH+WFuWQ9+wjmssJpb5KFQjF6+zz09npxugI43QEcziBub4h4LEE8IZFITK7TYFBjMmowmdQkmbVkZ5jIyjSTmWHCatXN6ybcXPvpVjB74cpkMHvzxiDeu/xtVKpEUtJN2NKNpKZqCQQTdDSO4Ri+t6MT3Kmw0s6+5yvYXJEgf/wCNn8L3g4vzT/ppOfoIFJcIqm6lK3/8mfYtq27b9u50snBrWxVW6nB7S0TE2F+9EYDP/pxHc5Z+lMIokDVhnSe3atgr/4aQz+5RMc7fYTcYVL3bqH4l75AzguPPtTZ72ayEgqRYZeHsdNXGDlxCcfZK8SdfWizTOgK09HmpxK12fCqkhlXGHFHdbhDStw+Ebc3gdsbxzUWxDXiu+t+QEnJOjJyk7Db1VjNIlYzWM6F5/MAAGTKSURBVIwJFAoBSRKQBAEQploV3LGuO67FDq9IR3+C/q5xhno98x57FaCk0ECuXQSFQOdQnL7+ILEFNC/8NFEUSMtJIr/AQEmORLHZS1ZkEEVnJ4GGHvz9PkJ+JQqTDWNBDvqcDLRpKWjTbGhTkyef02xo7FYUajWSJBH1ThAacRAadU0+T91wCPQO4W1sQhFxYC5OQr+tirGMMponUmjoEmisc+NxzC8ZiVotsq5Y5GDuKNv2mBgQsjnVbObajQna60fntU81WiX79hXy0nMVbFifiSiurCZnK+G8W8nk/bNwiViMnh+9T+N//2e8DW0AmHIMlL9aSN5jWXTH83i9oYDjZycYG1x4ACIqBApLklhbrGRrpY4de6rRpeQgiMuXfG0pjwP/hJOjH9Vx6LSTa1edhGe5cX5LapqWrVkBNokOqnesJ/OJPWhSPplB3esNUd8wQl3DCPWNo3R1uxkemlj08E130miV5OQmUVCYQllJChUlNirL7Z8ZX3a6/RSNxblaO8jh4x2cPdPN2OjikkFp9SqyCqxk5ZnISRPIsUXJSw6SpXehHBsm3ttPeMCDPseCuH4t7dECLnebaW7209EwimNo7rFz70Zqlpni6jQ27s1nQ5aLHM85kkJ9DF8eo+XHnQxfcQCg0Gqo/q+/RsXvfB1RtXoyht8PcnArW9VWenB7SyyW4PCxyX65bc2OWedNzTbzyMEUXqjqQfjoJO1vdOGod6OxWSn8uZco/oXPYyrKXdbtDYaidHe7ae1w0dLhYmh4AqVSRKtVotMq0elUkw+tEpNJw5oKO4X5yfelAH6/CpH+3kF6XzuEu/UGXnsy7uRsRgULw+MqRhwJxj0RJjxBxj0hInOMt7cYJouWomITpTlQbA2Qr/FijflRhEFQ6kGpA5UWQaEhEYsR8fvpbG2GRIKCwgKUGh2iSomgVCGoVIjKyen4uIPoaAcJnUQ0PZ1+dRYtowba+6Gr1cNA19w1lslWNV94pZpXXl5HX7+Da9e7aWgYpbVjgr6+APG7HMYqOdVARo6Z7EwVBRkJCu1BshlBOTRAwuEiPuoi6g0THo8SmYgQ9kaJhSUUSlCbVZM15mY16hQ9ygwbYqqNiC2d1nAmdT0aWjrCdLc48c+zCRlMDqFVXWlg20YNpmQjHcMaOruC9LY5FzR0U/XadF58toLHDhav6CFP5OBtdvL+WTwpkaD/7WM0/Ld/xHWlHgCNRU3J83kUfy6PREoaJ101fHhFy9UzfYQCi7u+arRK8ovMlOUrqM5Xs21LDhn5hSi0liX7Lnd7HIT9Lk6fbeSDE2Ncuuya1zXJYFSxqSDOdtUY2x7dQtZT+1BoP+6X7/YEuXJ1gEuX+7l0ZYC+OZLWLQd7qp6SUhtVlWmsrUqjMN/ExYunCIcTaAylnDzTw6WLAwseR9Zg1pBdaCU3z0hxdoKKzACZkX4Sza34b3bjHwrgHw7iHwkSHAuhsSVjKsnDUJiDp66FYE8nWdvTyNyRhnlHGV5LKR3hXK53aRnsmbzhO9TjmfeNzunY0o0UVadRXJ1KcaWNLL0bS7CXtIk6tIERej8aoPknXXg7J4NqQakk7/NPUvOnv7HsZb8HhRzcyla11RLc3iJJEtdvDvOt713j3JmeWcdO0+pVbN2bxYvb/ZS6LtD3Wj1dh/qJ+mKkP7KD4l/6AtnPHbjrO3ixWILa64OcOtdLW4eTnm43YyO+Bd+11RtUlJTbqFmTwaa1GWyoScdoXP5EN/eyEBlyuGl460NuDPrpipjoHBXp7RxfUBC0GFqdkrx8A8U5KspzldQUGSkpzkKTWYSonF9/1MXsp0Q8jq+tm/G664T7m4grQ/RlVnK0xcrFsyNz/sDrdAoe22jhF359Pxn5k1kcI5E4LW0Ort3so75hmNZWF/19/ruutRZEgWS7AXOyjqRkHVarihSLSEqShD0pjt0UxRdWMuJVMuZR4PAkcLmieJwBvM4g7jH/gmqpAdQaBdm5RlLSdMRQM9jjZbTfu+BzJ7/QyoF9hXzumXKys5IW9uH7RA7eZifvn7snSRLDR8/R+N//iZHjFwFQqEXyHs2i7JVCDPnJ9GjX8n5zFqdPjMxrPNO5pGWbKCnUUZQpUpyno6oyjaysdJSG1EXV8C70OIhHQwwPdHH8TC8Xbvipr/PMK9OxUiWyplDJVp2DPTvKKHzxUVQm49Q2RKm9PsSly/1cvNRHW7tzSWpll5rVqmZ8IrqgPtZJKXqKq+zUlCpZnzNOuqeD6M1mJuoH8LSP4+2aAIUGc3kB5ooiTKX5mEryMZfkYSzO+0x/1fHWLrr/4116/uNdAn19pG+0kbUzjbRt6STScpjQpOPTZDAcS6FzWMNAr4+hXg+jAxOAhFKpQKESUaoUKJXi5LRSgVqjIDPfSnGVjTyjm6RQD5ZgD0mhfhRSlMBYiJ6jA7S+3kXQMVmeUFnMlPzi5yn9tZ9Z1kzVDyI5uJWtaqstuL1T/4CX7/zHDd57r4VQcPa7kwUVdh7Zreepwi6iR07T+U4Pjno32nQ7Rd94ieKffxVDXta81x2Nxbl8ZYAPjrRz+nQX496lD84EAXLyk9i2PZcnHyllTWXqsiTAWe5CZDQa5eRrJzhX56CuD7raPSTusuZxNkaTiswsPaXFZqrKUli/NovComyUyrvL/rlU+ynscDH64ZsEIiPc1Jbx0XUVNy4OEZ6ldlqhFNiWB5/fmU7NgTWozEZUSUaUhskxcYOhKE0tDm40DFNfP0hbq5OB/oXfYLkXRIUAEncVjCuUImvWZnBwbwH79hSQmbH6EoLIwdvs5P2ztNzXm2j+n9+m5wfvkYhGQYCMLXbKXi4gbZMdt66AK4EaDp9LcPlE15LecDRZtGRkG8jJUJGXriDDriYz00R2honk5CS0WjOi1jxt8DvTcSBJCeIhL/5xN519Dq7ddNLQEaK9K8RAl2de1xdBgNJ8DZtMbg7UZFDxpSfR2pKJxuI0NIxy8XI/ly73U1c/QmyBN+5usdj0WG0GDGYNxiQtRpMStVJCFEAUEiBJBEISgUCCQCDOhDeEZyyA2+Fflt/JpBQ9JVU2qksUrE93k9LXiO/ENZw3R/GNJEiqLCGpsghzZTFJFYUkVRajz8lYcNZnSZJwXa2n+/vv0PPD9wkNj2FI15FSYSGl0kpKhQVLiYWQMRWfOgO/2o4kCAiShEACQUogICFICSCBKMUxRkYwh/oR41E8XRM46t046l046tz4Rz5u3WMszKHsP32Nwq+/uKRjCT9M5OD2AScIQi7wG8DTQC4QBtqBHwP/IEnSkoxoLQjCF4CvA2sAKzAMnAb+XpKkC0uxjhnWu2qD21v8/ghvvtfMD39cx8AczYO0ehWbd2Xy/NYAleMXGXirgd5jgwSdYTKe2E3JL32BzKf2Iio/+yMbicS5cKmPD462c+Z0N/45MrMutWSbju07c3n8YCnbNmahVC7NEAPLUYiMxxOcPHydQ8fauNIYwO2Yf7PS2Wh1SiwWDdZkLSkpemw2I3a7ibRUA8V5VvLzLJjNy5MZeDn2U9jhwHH0DVxSmLP+PA6fCtLdMnOze0GAqkyJzV2nSb5+DVEUUZqNqJNMqMwGBJWK0NAowWEHEUHJqDmdsfwKxmw5DAlmHOOJFRnwzofBqGb79lwe2VfIjm25GI2ru/+8HLzNTt4/yyM4NErr33+ftn/8IRGXB5jsl1v8fB4Fj2eTsKTQq9/AkbZ0zh8foPXG3MNq3Q2VRoElRY8lWUOSSYFaJaBRC3c8g1JMkEhAJC4SCksEQxJj7hhjIyFcowtvMZKXpWGzdZz95Sms+8qTKFOSaWwapfbaELXXBqm9PkRwjhvmn6ZUK8gusJJbmkJOnon81ChFKT5SRAe6qBtNbILE2BjBfhcRb4SYP0Y0ECMWiiMoBBQqEUEpIiaZUGSmkbCl4lQm0zthpmNETfdggoG+AMO9ngXnYcguTGb9hiS253jI6K5l/Ng1PO0+dDnFJG+qJnljFcmbqjEV5y3L0EWJeBzH2VqGPzrP8NFzOC/eRIrHEVUiliITKZVWLIWmyX2gEEAQEEQmb+YLAoICSIC3a4KxejfORjfRaUY8sO1YT8Xv/BxZzx+Uh4S8S3Jw+wATBOFp4PvATO3cWoCnJEnqvIt1aIGfAM/MMEsC+BNJkv5sseuYY/2rPri9RZIkLlzq599/dJOL52YfSgggu8jKhrUGNud5KXVfx/X+DfpODqMwJ1P0cy+RdnA7hppyLt5w8MHRNs6f6yUYWNgPXnKqgfRsExmpCkQBQmGJcEQiHEkQDk8+XGNBvM6F3SMxmtRs2prJY4+UsW97Llrt4ptWL1UhMpGQOHe+g3ffus6lunE8roUNK6BWi6Sn68nKNpGTk0xGehKpNj1pdsNkEGvTo9Pdvz6Uy13YDo+NMnb2Xa74Tbx+TkH9leFZ58/O0fFE2QTrOo/jrZ1sQnZnM32NVY0hVYc+TYc+VYchTYc6w4TLmEKXmE+Tw0rnkILevjDeBf6vlptCIVBQlEJVZSrra9KorkojP8+64pJC3Q05eJudvH+WVywQpOvf36Llb7/LeFMHAEqtgrxHsyj5XB7mQisOQxnNiUpOXBW4crKLwW7P/d3ou5CWqmazPcC+YhNrP/8Y7c7EZDB7fZC6+hEi8xiK7E4qjYLCCjvFNelUlumpTPOQEu0jKdSHenwET5sXZ4sHV7MXb9cEvqEA8dDH61AlmdBl2NGm2VBo1ZN5G1RKkCRivgDRCT+xCT9xnxelOowp24ChyI5iXTmD5iLa/VY6BkS6uwMM9niI3bH9CqVIUZWdzWvUbNL3oT5xikBnAFPNeuw7N5C8qRpzacE9G4P306ITPkZPXWH4o/OMfHQez82WBS9DZTGTvLGKlE3VJG+qJmVzzYJa38lmJwe3DyhBENYC5wA94AP+O3Ac0AFfAH5+atZmYLMkSYvKfS4IwveBL029PA78LTAI1AB/BBRNvffzkiT938WsY471PzDB7Z0GB8f53o9v8v77rfMahFyhFMkvNFFdEKci0YWry0utVERdn4JwZH53SbMKrFRWJ1GQHqc4NUiuOISiox3fjW68HeMkIglElYigFFAoJ59FpYgm00Kgeh3tyjwaB7W0dETp7/bO+260WqNg/YZUDj5SzmN7CzCbFlZzeTeFyERC4vyVPt59s5aLtU487vk1ZVMoRYoK9FSWW1m3Lpf16/LJzjKv6EHU71VhW5IkQv11XKxt46fnVVw8PTjrsWBM0vDIPjMvrx0ipfcGGpMSdYaVmM5KWGkirDARVpoJK02MhC009Yj0dIzT2+agt90159iwy0kQBZKTdaSmGcnMNLOmMpU1NemUldjQapcvA+tKIAdvs5P3z70hSRKjJy/R9o8/oO/1I0ixyRox+9pkip/NJXt3OlGdlRHTGq64Czl3zsW10z2Mu5emNc5yUSpFSrIVlOn95NktiMVFdDtiNDaN0d7hIh5fWO2nKArklqRQsiadsqpk1uaMkxrpIjnQiTrgwFHvZqTWwXCtE3eL9/aNRo09mZSta7GuLcOypgxLTRmGvEyU+vkfz/FwhODQKIH+Yfxd/Uy09eDr6CHuGUDURlDWlDCWUUJ/PBmNGKfY3Yx4rQVNajH2PVtJ3bMZXbp9Qd/3XgqNOnFeriPiGZ8co/7WuPVTAX503E8iEsFcVkDyphqSN1VjLMxZ0eWF1U4Obh9QgiAcB/YBMWCPJEnnP/X+7wF/OfXyv0qS9P9dxDr2AiemXr4DvCBJUvyO923AVSabQ7uBQkmSPAtdzxzb8EAGt7dEo3GOnezix280cP3qwJI3ycwpTmHTZguPVHrI7L6E46NG3G1ePB3jRH1xkmpKse9Yj237OjT25MmMuiol4u2HiqjPz3hjB576VsL9HSgSTpSlGQzkrOGyI5VL14M4hmceBulOCoVARaWVXbuL2Lopl6oy+5zNlxdaiAyHY1y42sd7b9/g0jUH4575BUe2VC3ry9Ts3ZnDnke2YDStrsLq/ShsJ0Jumm5e4/uH/Zz4aJjQLC0HFEqRtVszMZi1BINxgv4IoUD09iMYiMxrTNj5Uqknx+dVqSaTfajVCtRq5dTz5EOjmfybRqNAp1WRkWYkM91IRrqZ9HQjdpthyZrXrzZy8DY7ef/ce8GhUdr/709o/6cfERwYAUBlVJJ3MIvCJ7OxllnwanMYNKzlYp+dm1dGqLvYf1eZb5eKXq/AZpQwizE0okhcZ2RoQmJkdH6/ndPJyLNQUpNGcXUaa4riZNJLcqADc6if4LCPwQujDF4YZfSak/jUTXC1NYn0R3eQtm8Lqfu2Yi6ffhzcpSJJEqFRJ476Vi68/SGiTs2BX/pZrPnZy7ZO2YNPDm4fQIIgbAYuTb38J0mSfmmaeUSgHqhgMvBMkyRpQW1WBUF4D3gKiAP50x08U31xfzD18nclSfqbhaxjHtvwQAe3dxocHOfHbzby7rvNuBbYDPhO+eU2Nm1O5mClh8zeywy/c52+k0NEJhKk7t2CfdcG7Ds2kLJlMvHPYoTGXHjqWnCevUB0pJ6RvGKuRAq4eDNGf/f4vJej1ijIzzVQVmxh07Yitm3OxZ7yyQQLsxUiJUnC6QrQ0DLG+VNN3Ghw0tnlIzrPPj9pGXq2VSl4YpudzU89gqhYGbVxsWCI0PAYwaExQsMOIm4vEa+PqHeC6LiPRDhCIhqbekSRojGi4Qijg0OQSGCz21GqVQgKxeRDFKamRRBFSCSQJAkSElIigZRIgDQ5REciGiMeDE0+QpGp5zDxYJh4KIwgCAhKxe2bH4JSgVKnwvTcBs4b13D0fBDHyOILbPNlTtJSXJJCRamNijIbZaV2UlMNaNRKVCpRvmt+l+TgbXby/rl/ErEYA+8cp/2ff8TQoTO3x/G2FJkoeCKH/EezUFp0uHWFjOrLuTGaxo0rY9Rd6mdkCYbFEQRQCJOXUlGY/IMggHBrbPGphHSxBMQSS3MdEgRIz0mioDKVwgo7FeUGclUDJAc7sAa6UEb9OBvcDJwfYejCKN7ujxvrJVUWk/XsfjKf3odt+7ppc3Ysh54+Lxdq+7h+rY/GRgcD/T60WgUbN6Xz4ovr2L0l54HqyiG7d+Tg9gEkCMJ/Y7JJMMA2SZIuzjDfHzDZXBngMUmSjixgHUbAAWiADyVJenKG+dTAGGAGzkmStHO+65jndjw0we0t8XiCcxf6OHm+h2vXh+jucM06pJAgQGFlKls3GthX6iFt6AZj79+g98QQQVeUjMd2kfvqk2Q/dwC1xbws2xx2eRg5dhZ/w1lGNRquUsz5JpHOFveCa6M1WgUGgwqjQYHZoMCoV6ASo6hVAiq1jmAoQSAcwz0eZ2gkhN+3sH7G6dlGtlWJHCwT2PLsk6jMyQvbwCUiSRK+jl5ctQ2MN3cy0dbDRGs3E209RNz3flzC2Si0CjQmFWqzGm2yBp1Ngy5Zi86mQZusRWvTokxPQUpJJSAZOdGZytvHQ3S2Ls33sFp1VFWlUVOZSnmZjbJSG3a7QQ5gl5EcvM1O3j8rg79viK7vvknnt17D19kHgKgSSd9sI3dfJlk7UlEYNLh1BYwZK2gaz6Kx3ktn4yidjaNMeFZWf/5bRIVAdmEyhZV2CipSKSnRk6UYmhpepgdDZIyIN8LQ5TGGLowydGmMyMTHv4XJG6vIeelxcl58FHNZ4T3Z5lAoxtkLnXx0uIGrdW7GRmfft9ZkLfv2ZfPqy5soK7o/v8Oy1el+BrcrowrkwbR76tnPZLPgmZy8Y3oXMO/gFtjCZGD76eV8giRJEUEQLgCPAVsEQVAttIZ4vqLRKJHIwpstKhQKFDNkpotGoyz2JsxyLVcURXbvzGP3zjzC4RjvHGrlJ6/X097imEw9/6ny/OTQJXE8E3E63XoM2UVoy4bRd/kJBsdBpyGhVjDbDeRYLEYisbihBERRRJNsIfflp+HlpymXJNa3dPKC7Qgja5xcjWVxqUtLS+P8+unGojG8nhgeNyQ+s9FTg54LErdyTcwn6WB6lpEt1Up2ZvjYsHctlrK1084Xj8eJxxeWuONOavX0GXITiQRBrxfHueuMnbmK60o97uvNRL2freUWVSIquw6lTolCq0CpVaJRTyZTUU39TVQIk1UGAiSmamYRpo4F7txnk8fg7UNRAkEEUSlOrkcBCpVw+7WoFFHqFaiNakSTGpVJidqoAo2GqEJPVNQTVpoJKi2EVUmMxU0MuNQMOgScfUEcl8YZG5xgqM9JKDDd0EHSnP8vrUZBaamdqspUKivsVFakkpY6GcgqlUrEGZKMLObacMtqvEYoZ6h9udtzeTUtVxAEVDOMA3435/J8lhuJRG4vPxKJzPh//rTZrhGx2MzDbc1FpVJNe7NnuZYrSRLR6OJ/6mc6lxe6XFVaCqW/9w1KfufrjJ2rpec7b9H/kw8ZPDfK4LlRRJVIxhY7Ofv6yNnaQr5eze7ydDzr83FrK+lyW+locdHd4qCreQyvc/q+uokESNL0P6IKUfrkZXcBJg99AXuWmewCK9mFyWQWWCnM12CL9WIO9mIJnUc34kSKS7jbvHRcdzF8eQxXs+fjG9+CgG3PJrKfP0jWcwcw5Wffk3PZ5fLz3o/PcvLKCA1tvk9kSp7rlBj3Bnn7rTbefquN3Dwjj+zN5ZXPb8Nm/eSNotV6jVgs+RoxaamuEUtNrrldJoIgjAE24IYkSetmmc8KuKZe/kSSpFcXsI5fBf5u6uULkiS9Ocu8f8vkcEQAVZIkNS5gPXN1vEgHLgN897vfJT194QNdFxUVkZGRMe17Fy9eXPRJkpubS25u7rTv1dbWEggsrmlxeno6nnE9b3/YztkzvZ/ox5iTFUKvn/2iqVAIpGaayc0SydW5ibd14+3wIHW4KS0qJu9nniP14DaEO355mpubcThmHuJlNklJSdTU1Ez7XkdHB4P9A4QHegm4RxmM6BiaUON0xwjMUeMaiYh09UxfG5KSHMGWMvPnBcBs0ZBphzxjBFtaCurULJj6Qdq6deu0nxsaGqKjo2PW7ZrNrl27bk9L8TiuSzcZ/vAMPVdvMiiFQZIQFAIa82QtqMasRp2kQqVXotQpUemViGoREJAEBRIiCUFkU74ShVJFQlCSEJVIiEiCiDcg0TwQnJr/VmB7q3Hcx3+Tpv6OcMd7CJQWpaLRqJGEqXUiEhM1TEREWloGCUYFghGRSDhBJBwjEooR8EcI+iIEfJEZx77t7dcSDH62ZKNQSBQXzn5emM0aMtKNUw8TOt3HBany8nJsNtu0nztz5sysy53NartGZGRkUFRUNO17dXV1eL2LqzG32WyUl5cDEAqFOHXqFAB79uyhu7t72a4RQ0NDi1quXq9nw4YN077X29tLb2/vopY7n2tELBajp6cHgLy8vBkDiU+78xpxJ4fDQXNz86K2F2DLli3TFoq9Xi91dXWLXu769esxGD47Fqff7+fatWuLXm5NTQ1JSZ8d6CESiXDp0qVpPjE/5eXlJKk0DL59jP6ffMjY8UtIU4GG6+AajFl6TNkGjBl61EYVCUTCShMhpYWQyoIvrsM/EcM/Ef74MR6mu1vA7Z7+f1xcGEChmL28KwiTQ/1p9Sq0OhV6owaDWYMtN5eC8lysjGEKD91+6KIurgyocY1G8Q0H8A8F8A8HSdwRPAoKEX1eFqbSfIyl+SiN+tvvLdc1IiUlBXNYwZEjDZxti9LcEbg93m1megiTaXFBXSCgoG9Ai0Ipsq7GzJN7snn02Q0olYpVe41YLPkaMWm2a8Tbb7/NV7/61Vt/kpslr3ZTQ/PcurX4niRJMw3Rc2t+H2AALkiStH0B6/kL4D9PvdwsSdKVWeb9XeCvpl4+IUnSoQWsZ94HyZ//+Z9jtVrnO/ttZrN52hMPYGRkZNF3BI1GIyaTadr3xsbGFnUnzOGK09wm0tg4/W6ZT3B7J5VaQXq2iYIMiQqDA+uJ8/QeHcDnV6DcvxHlgY2IWam43W5CocU1z1Kr1aSkpEz7ntfr/UwBXohG0AQmf1i9CT3usBqHFzyuyCcyRC4kuBUVAkaTGrtVxKYLY1EGSKh1RLSf7VMsiiJpaWnTLtfv9zM+Pv8+w5+WbkkmfrWJ2OUm4rUtCP4ASYVmNBsy8RZno03RoEw2E1PqiIlaYgotwbiaYExJOCoSjolEYgLxuEQiISElJBLxBHFNHvG4QCyaIBZLABKSBGIihFoaQUK6VUn7CdInJj6uxY3HEyTiEm5fMuGISDQSJxpJEIvECAaiRAIBcrIW32/2boLbTzMYlFgsCpJMIpkZyaSk6Ke9S7zYwg+srmsETBbYpvvRB3A6nYuuxdZqtTNeY+/lNWK+lEoldvv0WVYnJibw+RY1SMCyXiNmuokSDAbxeDyLXm5qauq0tcfhcBiXyzXNJ+bHZrNNW0MVjUYXfbMDIDk5GY1G85m/x+NxRkdHF71ci8XyybwMHh+xczeJnb7OWNonz0W1SYUxU48hQ48xQ49CLSIBMVF7u6VKVKEnojBgyC4FXSqhQJRwMEooGCUcjE0mwxtrgUQcQRQQFQKiIKBQTSW00yhQqhVoVKCSwigTIZSJMKp4EFXcT5E5QLF5AgFIxBN4OydwNnsYu+GiMZZESPhUQK1WImTYEbNtiOkpMMNNlSW9RkRjxEddTMRU9I2buXRNJB7/7A/OdMGtWqMgOVlNiknCqgjgiWvpGYEJ7yfXfyu4vZM1RcuWcgV5qROQpOAzzdbmQb5GfOxBuka0tLTwR390q3em3Cz5QXDn1Xk+Z6WfyeB2oZmDFrKeO0vBi8tQ9BCTJBhzxujrj+HzRfF4lHzcIvyTRFEgI12NSRlhbFyBZzw+XUxzWzQSp6/TQ18nHCeJ3LJf5dF/DLLDW8vYm5fp/S/nCNrSiT+5GQozQLX8p62kUhNKmvyx0TBZNZ9tD6MOhYgkFLcfMUnFpjVJTIQUjAcEAiEJrUZAr5UwqP2olX6MqhhGRRi1IkFMJRBWGQAlIaYPVJZFPE5iyEGid4TAj05hTteQu8lG6u9VYNpSTCClkGankf5OH26fiL8r+nGm4KBvXgPet3eOE49/9oddp4uTm734PmNdPREikc82+1GrFxfMfXY5AunJCjLMMbJMYZJStQxEdTjdcXzj8ytc+f0x/P4YA8DR4w4CIRVJFjUWi4oks4IkE1jMImpVHJVKQCFOtl5QKATu0zCJMplshRAsRlRP7UD11A6UHV1IfSMkBsaQHF4iE1FcLV5cLV4QQJcylUfAqkFr0WC0qlGoJi8iRSOXSU8SiYsaYmotca2aWIqWmKihVh0kFpem2swkECQJUYohSjEUiRhiIIrIJ4O+eDRB2B1hrM7FRMcw7lYvrrbxT4w1G91sAIMSTHrEDBtilh3BbllUkLdQkteHNOrCp9QzEjUy4tQTj0tMTISJx2cezk8QwJKsJS1ZIFXlRxebIKzUIZlNoEwiJRIlQxojmqGmP5xE72CUSHj6G/ZuZ4hDZyEtNUpxUYJMQxizOobCbpnK5CWT3XtycLs87ryqzKd0eGtgz4Vmu1jIeu4cPHSh68mZ4/3bzZKzs7MfqGbJiYREW4eLG3Uj+GYZAkUQoKgimYPrY6S7axmtGyHkDJOdYUdTlE8gOZXRgMDwiA+POzhjAqfARJhj73Zy7F1IzSpjy/ZdPP4NL9nd56k93c5QSyeGwjyS1pRhyM+a9w/ocjc5DIVCnD51EgVxduzeh1anv+/NiaRYDF97H+PNHfjaetBalJhzjez53nOMZ1dzbSiFd3pUdL49QU+rAzExSGbG/MbYXY0MehVmswazSU1Skpav/kwl5WXZpKYaEEWBRCyGp7aR3rc/QtPbTqVFQFmTg0trZ3RCjcsVxeMKzqtPdjwm4XKEcTk+uT/LSj57vglMDkGk/NRDpbo1rUClFHF5TZjNKaSnGcnKMpGVaSYlWYcoCnKzZORmyXeSmyVPWsnNkmftulBTBUA8FMbf1Y+/ow9fRy9xf5CgI0TQ8cmbhSqjEq1VA6ogAWMMtVGFyqhEbZh8NhpVGHwmYglhsrVNfKrFTSRBNBwnFI4TCyeI+WNE/FGivigRX5RYYDKg03WPoO/+ZE21JjUF+77NGLeWo8xNQ5U0feuP2Sz0GiHFE/i7+wl09uCOx+lX2Bh0pUyNPDD79U+pVlBZZWVNvoeMpBiWtDSUpk8mr7x9vqhVZGzZjlKppCAaI9DTTp87QYdTz9Ao09/slcDhCONwgEqtITMQJUcXJj3FjLEoF3GWm/Jys+SPPUjXCL9/+UdkmIncLHkZCIJgB25dCX8kSdIX5ph/BEgF6iVJmr50Mf3n/h74lamXFZIkzXhGCYLwy8A/TL18WZKk1+a7nnlsx+1syQ0NDRQXFy94GSstWUw4HOOt95r5/g9v4Bj9bOFWkiYTKaWkGTmwP4WnS3tRHTlG64/bmBiLkPPy41T+wS+QVPHZHy6XK8DR4518eLSdlqaxaZf7aTnFyWzbauKRUgfmm+cZ/KAFz6BI7itPU/Azz2EsnL5wfstyJ4uZLivp/UgEEQ+FGT56jr7XDzN86BTWYh0Zu7PR7N9GfTCX2g4tTY0T9LQ6PxOk3ZkAazEmN2m6mw1zJ2i62+Vq1CLWZD1Wiw6rRUtqqpGcbDPZWWayMpPIyDCh1X7y/z9bIohIJILrSj29P/2QgXeOkJQhkfZIGaqdm2jyZ3GjW01r8zg9zWOEP3VHf9akLnP0eZvNdMtVqxWkZ5jIzjKSnWUmPzeJmup0CgusKBTz+2eu5oRSnz7vVCqVnFDqjuUGg0E++ugjAA4ePDjvbMlysphJy5UsZjFJ56REAs+NZgYPncF56Saea02ExpyfmEeIJxBmGLUgoVr8RVihUGCpKCZl6xrsO9Zj37kRQ0E2giAs+zUiFgwxfPQ8g+8ewT0xzM2CHZxr1zA6MnNroFvlCKVKpLrGwp4qJY/tLSSjet1kd5cZzuU5z5d4lL4TH/HOFQ/HbkJ/78cNBkXxs4k0AWzperaWCGxWDFNcUkzmU3vRWD8ZDMkJpT72IF0jmpqaqKqquvUnuc/taveg9bmdx3Y8MEMBBQJRfvhaHd/7/g087ukzMgKkZpv53NPJPJ95g9EfnKHtzR6ivji5X3iK6j/+ZZIq5xfgDwyO896Hrbx/qJXebs+c8wsC5Jfb2b7ZwIGiEQwXTtP/TjMJQz75X3qWnJceR2u/9+n67+eQG/FwhOEjZ+n50fsMHz6BvcpAxoFC4jt2cHYgiws34zTWDhPwLT5T7y2iKGA2azCZNOh0KlRKcbLPlkqBSjU1rVSgVIooFLd+SCafBQESiTj9AwMIQFZWFqrbd7OF2wUDQZhcj0ajRK1WoJ161miUaDSTz3q9mpRkHclWHVarDp1u+h/wpSAlEoydraXnR+/T99ohNPow9rUpJO8qQVi/jgZ/Ftfa1bQ0j9PVPEY4uPgf4KWiN6ipqUlj0/pMNq7PpLIiFbX6Lu4wrFDyUDezk/fPg0uSJIKDo7hqG3DXNuK6Wo+rtpHgwMiil6mymDEV5WAsysVYlEtSVTHWteWYywoQZwiSlkPU52fw/ZP0/fQQvv4merbt44wri8Zm/5zD9omiQEWVhf1brTz73HrsM/Q5nc5CzpdYJMD5d4/x1jkv5676Z8i8/0kllVa25wRY427Bakolfd827Ds3oDTo5/ysbPWRx7l9AN2jbMm/BvzvqZfLli15Htux6oPbiYkw3/3hTX7045v4xmdumppVYOWFpy08lXKVge+cpePtHuIRibwvPUP1//PLdzVWXVu7k3c+bOHQoTbGRuduziEIUFBuZ/tmPfvzhtGcOcnIsS6E5BJyX3mS7BceRZNsWfT2LMS9LkTGIxFGPjpPz48+YOzESdJqDKQ+XoWnYgsn2lO4cj1IW93IvPrK3kmjVZKba6GoMJnCPAvp6UbSUk2kpRpISdGj109/93O+VnthW0ok8NS1MnLsAsPHLjB68hL6ZIHUtclYdpag3LgGl8rOgEfHkEfFiEtkzBXHNRbAPebH4/Dfl+BXpVZQUWFn88YsDuwtpLzM9kCMv7vaj6flJu+fh08iHifqGSfs8hJxeQk7PfiGx6g7fxESCSpqatCajYgaNWqLGY09Ga3disaejMp4D/NAfErEM87AO8foe+0wvqZrcHADp/WbOHU1SnCOwFEQoLjMwoHdqTz3zAYy0hd3g3ux54vf4+T1n5zlvdNeWlsm5pxfoRQpr05hc16Isv5a1J1jJG/eRNr+rdi2r0epm7m/sGz1kMe5fTA1MTnWbbEgCEpJkma6OpV/6jMLcWeAWj7jXJ98Pwa0L3A9D6zhkQm++8ObvPVmI8HAzE0z8stsvPi0iUcNl+j91jlOvtdLIi6Q/zPPUfVHv4i5tOCut6WkOIXf/rUd/NavbufGzWHe+qCF48c6GPdOH2xLEnQ2jdHZBP8hQF7pM2z8goHdRR5izSe5+NI/QFIJ2S88SebTe9HaVvcA7FGfn6EPTtH3xhEmrl/CvsZExpMbiHz+tznUZOLaRS+933MiSfPry6hQCBQWp7CmOo31a9KpqU4nK9OMKCfBmJEgiljXlmNdW075b/0siVgM15V6Ro5doO/wBRz/9R+AGIZ0HeUZejZm6DFkGtGUZaB8NBMxPY242kggppp8hJUEogqCUQWB8OSQRsGoSDAMoYhAOCwRDHN7iCP/eBjnqA/XyPySfN0SjcS5eWOYmzeG+ddvXcVmN7Bndz6P7i9kw4ZMVMoHr1ZXJnsYiQoFmhQrmpSPM4oHg0GaUyZrXgtX0E2OkMNF/5sf0ffaITxXa0ndn0Pblsc4ZNpO+9UJPm4AOD17uoED+zN5+XNrKcpPvTcbPQ2DJYWv/PxzfOXnoautm5+8fp2PzroZG5l+++OxBA3Xx2i4DqJYSnHVdraaYc3JD4n/3h+SEKxY11WSvLEK64YqLGvK5IBXtiBycLt8zjAZ3BqAjcDFGebbe8f02QWu4zKTiaTUU8v5i+lmEgRBDWy79RlJku6+feYqd7N+mG997zpnTnaRmKGPDkBJTSqvPKljl3iRnn87z/EP+5ESAgVf/RxVf/RLmIrzlnzbBEFg3doM1q3N4I9/dw8XL/fx1getnD3dPWMALknQ3eKgu8XBa4A9cytr9z/JzvIgqc6r3PjFfyYaTcayZQeZT+3Dur5yVdRchcZcDLz9Ef1vH0VytJC6LZ2ML29j5MU9/LBJw403HDiGxoCxOZcFkJNnYeuWbHZuzWHThiwMhun7zcjmR1QqsW1bh23bOqr+6JdIRKP4uvoZb+liorWb8ZYuBuu6Gf/pdULDRwAQRAGVQTn1mEz4YjUoSTOoUBlVt99TT00rDSoUyXoUZj1iSjIJqx2/wsJQwEyfS8uAQ8GwM4Fj2E9fuwvnyNwJ6h1jfl5/vYHXX2/AYFSzbXsuj+4vZNf2PPT6e9f8UCaTPVyCQ6P0vXGEvtcO4zx3hfTtdnhiH5c3HODUeT+Bt6LAzLWfWp2Krbsy+NyzVezenDdj3+X7paAkn9//z/n8niRx8UITr7/dxNkLzhlrnxMJidY6B611IAhpFNT8CuurtKxJGkNsPU3XP/5v3G3jmEoKsW6owrquHHNFEUkVRehzMlZFOUZ278nB7fJ5E/jDqemvM01wKwiCCNwa4dgDHF/ICiRJmhAE4SPgSeARQRCyZ6j2fxG4lRbvjYWs40ESiyU4fKyD7/7HdVqbZg+GKjdk8IXHBTZOnKHzH69y/MQQCAoKv/YiVX/0SxgL50ogvTSUSpGd2/PYuT2PcDjG2Qu9fHCknbNnugnN0rxzbHCCo4MTHH0P9KZ8Sqs3s7ZMwcY0B+Khf6Ltz3tQpFVi37sb+86N6LPm3y9nOSXicdy1DQwfOYfz/Fk04gi2R6sw/OoeznQ+z7WGKK1/NUI4ODiv5SkUAtVrM3hkbwH79xSQmWme+0OyRRNVKsylBdO2ZIiO+5ho7yE47CA86iQ04iA06iI06iQ06sIz4iBcPzktzZLoQ2VUYsw0YMrSU5ZlYFPWZO2w+kAmkS/m0xXM5VqPgca2KF1NYwz3eWftp+b3RfjoSDsfHWlHpVaweWsOTz9Wwr7d+cval1kmkz0c/D0D9L1+hL7XDjF2/hr2GisZz1bR9+Lv83eXVLS/5mGyCDg9QYDyGjtPPVnCc09UYtKv/JuygiCwbXsl27ZXEgpFOXzkJm9/0MbNG56pceA/a7I1moPOJngNMFk2U/74U2z4TcjXD6C5cpnB/zhNXZOHqC+G0qDHXF6AubKYpIqiqaC3cDI78zwzo8seTPJ/f5lIknRJEITTTNbefkMQhO9IknT+U7P9DlAxNf23kiR9olpOEISfBf5t6uWfSpL0J9Os6q+ZDG6VwN8LgvCiJEm3S4aCINiA/zH10gP830V/qVVqdMzHj99s5K23GnGOzTyshyDA2m1ZfPmRKMUdh2j/s5scr3UiatQUfeNVKv/wFzDmZ9/DLf8kjUbJgb2FHNhbOBnonu/lvSNtnD/TQyg0c6AbmIhw/fwA18/DdwCrfSNlVY+yrlRinaIf//ePEG4ZRNBnoCteQ8q2jVjXV6CYIRvgUkpEo7ivNzF2rpaJm7VI3i7Ma9JQblzPWMnLXGxWU3/SxfC/e4H5NTfW6pRs3prDY/sK2b0zD7NZbs60EqjMRpI3VM05n5RIEHF7bwe+4VEnwZHJZ3/PIOMtXYw3deBu/ezQNHq7lpRqKweqrby0NgPxyTKGpBxqe83caE1Qf3UY5/DMNbvRSJxzp7s5d7obtVrBlu25PP1YCXt25aHTyoGuTCabn/G2bvpeO0zfa4dwXanHlGMg7/FcjL/yK7zdksXps24CE7Pn1jAlaTjwWAFffHkdpfmrt1uRVqviuWc38tyzGxmfCHH0WDOHP2rn2jUH0cjM3UsmPCEun+rj8qnJ8llW4aNUvfAq638/xjr9IGL9TcbPt+C6dILeH7xNIjZ5F1NUqTCV5GGuKMJcUUjSVPBrKiuQmzc/JOTgdnn9JpNNjXXAYUEQ/pzJ2lkd8AXgF6bmawX+ZjErkCTpmCAIP5xa3nPAEUEQ/hcwCNQAfwzcGifmDyRJci/uq6wusViCE2e7+fHr9dReGpi16bFKo2Dbnixe3eEl7dJrtP6nJs52jKOxJ1PzJ79OyS9/EW1qyj3c+rlpNEoO7CvkwL7JQPfilX6OHO/k7NkePK7Z++m4x/xcOOHnwonJ10kpa8kp2EthrooKe4Ti3nOMnvw20ogXVEkoLemoMwowFBViKspFm25fcFOgRDxOeNTJeGs3443NhHpbkTwDiNoImrWljJcU0pzyKPVdStpaJ+g/5CIeG5j38pNtenbtyuPRvYVs3pT9QGbGfVgIoni7z9x0Q2nBZKbU0PAY482djDd34m3qZLypA+eVevqOD9F3fAhoRKk9QXKFhXVVVh5Zl4rq99ZT5y/gVL2W61ed9He4pl0+QCQS58zJLs6c7EKjVbB1ex5PPVbC3p15aDTyT6dMJvtYIh7Heekmg++eYOCdY3jqWlGbVeQdyKT0117iVGw93zsr0fa3DmD2jM6Va1P53OeqefaRYjTqB+taYzZpefH5dbz4/DqCwSjHTnVy+EgTly6PEA7N3GJHkqC/w0V/h4tDgFIlkle2l7Jdn2P91xNsSh1F1d6E/1Ij7kY3zqZB+l77VHoZQSCpqgT7jvXYph6m4jy5afMDSM6WvMwEQXgW+B4fNwv+tFbgaUmSPpPkaZ41twiCoAN+Cjw1wzoSwJ/N9Pm7tZKyJfcNePnB6w0cer8Zt2vmceAAklL0PHIwhRdLu4i+eZTOd3oIjIVIqiym/Ld/lvwvP4dCq7lHW740EgmJxqZRjpzo5NTpLnq6PItajsWmJz0nCbtdRXqKQEZynAx9gFRc6N2jEAghRaIIsQREY0hRCa/PT0ISSbaYEaUECSkOSpD0agSTASnZilttoz+cRL9TxcAo9PUHGehyE5hYeDfwguJkdu3I47H9hVRWpK6KHyg5e+vykhIJvE0dOM5dY+xsLY5z15ho6779vkKrIG1dCunbUrHur6bPVMWJpiSuXPPR3jBKIj7376FWp2TrjtzJQHdH/n29kSIfT7OT948Mlu84iI77GDpyloF3jjP4/knCYy4UGpHMbalkP1XMYMVu3q5N5tyZMfyzjMIAYDRreOTxYr708hqK862zzrtc7uf5cqvb1YnTHVy6NMDoyMyt7Kaj1iopKLdTWpXM+pIE1WkOTK4uwrWNuBscOJs8uFo8RMY/mbdEY7Ni27F+MuDduQHb1rX3dNinB5k8FNADThCEPCZrcZ8GsplMAtUO/AT4O0mSpj2L5xvc3jH/l4CfBdYCFiZvD56eWsenm0Qvmfsd3I6M+XnnUAtHj7bS2jRzTcwtucVWnjugYU/8AkM/uMTAmWESMYn0x3ZR/ltfI+Px3asiUJqPwcFxzl7s4+zFPq7XDsyYeXkhtHoVOqMajVaJWjP10CrRaERUKoFYTCIWk4hGE0RCMXzeEL7xMIGJ8Jxj9M1Gb1CzaXMWe3flsXtHPraU1Tc2nlzYvvdCo04c568xevoqw0fP4bnRfPs9c66RjK127HuKmKjazEetds5dDdFeNzpra49bdHoVW3dk8/Tj5ezZnotKdW8DXfl4mp28f2SwdMdBIh7Hc7OFkeMXGfrwNKMnLpGIRlFoRDK2ppJzIAtpx3Y+6Mjlo7NBulvnLo9Urknj5RereOpgMer7XEu7ks6X3j4vp851c/psNzdvDM9aqzsdnUFFUVUaxZUprCuFypQxLOE+aGpk7PIQw1cdjN5wEf/UclVJJjKf2kv28wfJeGI36iTTUn6th4oc3MpWtfsR3DpdAd4+1MLRo200NzjmDJrUWiWbtqXyzPpxcq8eovMnzYz3+NBlpVH0cy9R+PUXMRbcmyRR94skSbS1Ozl3qY/zl/qpuzE0a1Kq+00QJ4fq2bk9h30786muSkOpXFmZIRdqJRUeHlbBEQfDR88xfPgsw0fOEhyaTC6n1CvJ2GwjY18ewS07Od6TydkrIdobHUjzCHT1BhUbN6ezZ3cRB3cXYkla/r5d8vE0O3n/yGDxx0F0wofragPOy3U4ztYyeuoKEfdk7ofbAe3+TAz7NnLBU8oHF0SunR8kMksODACjSc1jT5byxZdqKLpPtbTTWannSyQS59qNQU6f7+PylX7a2xxICxvCHmOShuKqNMrXprKtMkq+sgfLeCuB610MX3UwfNWBu8X7iWu9oFSStm8LWc8fJPvZ/Rjyspb4mz3Y5OBWtqrdi+A2kUjQ1O7gxMlWLlwapKneOa+alZxCC4/tVLErcQ3vm+cYOjdCIi6S/dwBCr/xMhmP70JUPJz9M+PxBD29HuoaRrnZOEJT8yidbS4ikYXdIV0qOr2KopIUNqzLYMuGLNZUp2M0rvyskAuxUgsPDytJkvA2tjN8+CxDR84yeuIS8WAIQRSwVVnI2JtDdM9uTo3kcOZqlM6muW+kAYiiQEm5lW1bs3jkQDmVJbZlaQ0iH0+zk/ePDGY/DhLxOOExF8GBkcmcELf68de34W3q4M4TXp+qJWOLnbTNqZh3V1MXreTwdQNXzg8zNjjz8D23VNak8cqL1Tz5SPGKzAuxWs6XCV+Y2tpBzl/u5/KVAbo6564hv5MgQG6JjcpNmWxca6AqeRBbsAPdWCfD54foOznE0KUxEp8aS926roK8Lz1D/pefRZ+5MkaYWMnk4Fa2qi1XcOv0BDhxuplzF/q5fs2J2zl7oqRbtHoV27dbeTxvgKSjh+k/0kvYGyFlyxpyX3mC/K88jy7NtiTb+KCJxRJ097hpaBqlp99L/8AEg0PjjAxN4HIG7qpZ8S1anZKMLDM52Unk5VqoKrdTWW4nK9P8wDQHn8lqKTw8rOLhCI5ztQwdOcfw4TO4ahtBkjBlG8jYlUni4G7O+Ys5Uxuls3n+BaoUu54NG21sWJfO1s1F5GVZluRYl4+n2cn758EgSRKxCT8Rt5eIe5yId4LYhJ+oL0Bswk/MFyA64SPmCxALhEhEop94RIMhxoaGkGJxLEYTxOIkQmHCDvesQ4/p7VpsNcnYqq3YN6QilVVS583jVKOeKxcdDHTNnZ8zyaLl8SdLefVzlRSu8IzHq/V8cbuDXK0d5MLlPi5dGaC/b34jK9ySnGqgclMW1RtS2ZLnITPUiMnZwvC5QfpODjN0cZT4HVmdBVEk7eB2Cr76PDkvPIrSsPq6SN0LcnArW9WWIriNRGM0t/Ry7eYAjS1u2ton6O0cn1ftLIBGq2TdBgu78r0Ut5xi5O16fAMBUratI/eVJ8h96TG5ScldikbjDA37GBnx4feHCQRj+AIRAsEo4xMh2jt6iMchJzsTvV6NWq1Ep1Vis+qw2QwkW3WkJOuwWnUPfBA7k9VaeHhYhRwuRj66wPCRswwdPkugbwi1SUX69jR4bA+XpHLOXE/Q3bawJPRWm47SMjM1lVY2rstibU0BWs3CWynIx9Ps5P2zstwKUoMjU2Ndj7qIuDyTAat7/HbwGnZ5b09H3V4inolZx76+WyqDEn2qjqR8I0kFJpIKTViKrSRy8xkRcrnab+VKE9RfGcYxy1BitwiiwOat2bz8fCV7d+ejUq68WtrpPCjny+ioj8tXB7g4VbM7MjL3/+wWg1nD2h25bN6RzqasUdL8jRhdHQxfGKb3xBCD50c/UaOrNOjJeekxCr76OVL3bXloWwJORw5uZavafIPbRDxGKDDOwJCD3j4PvYM+evoDtLb76e4cJxSITvu5majUCtauTWJHpovC+hM4j7QQckexbV9HzkuPkfPS4xhyMu76+8nm9qD8KC43eT+tXpIkMdHadbtWd+T4RRKhIPa1KQhP7qLOUMXVTjXNjV5i0YUVxDVaJTkFJjIzdGRnaMnP1lOQa6GwwI7ZYkOhmD57p3w8zU7eP/eOlEgQmhqLOtA7iL9n6tE7SGjYQWjESWjEQTw0/6SGokpEqVOgMihR6ZUodZPPCq0ChVqBQiNOPqtFFGoRUa1AoRIRlAKiUkS841lQiIgqAVEx9XeViNaqQWvTIZmSCCmT8KnsdPtSaBnU0dqToL3JRW+7c15Z1AEyMk0892wFLz5bgd1uWOyuvG8e1PNlYHCcS5f7OXO+lwsX+ggG51fWtKYa2LArjy07Uqmx9pPqa0A31knvsUG6PuzD1fzJGmJ9djqFX3/xocjhMh9ycCtb1e4Mbv/yb/4dvT6FYDhBMBQnFJYIBBM4XDHGxkK4xgLE7qJPpyVZy5oKDdX6UfKuncB9oh1tRiYZj+0k4/FdpO7bKme3uw8e1B/FpSbvpwdHIhrFcfHGVH/dc7iu1KMxK7DsLqKvZjc3g2lcawjjmWNIstkIooDVpic1XYfZqECnFdFpRfRaEb1uclogjFoNVosZpVJEFAQUooioEBAVoBBFVEowaBXotAqMOgGdRoVarUZUKlEo1CiVahRKDSi0oFAjiA9G7YN8vi29iNuLt7mTiZau2/1Tx5s78XUPkAjPPqSbUqtAZ9eis2nR27RoUzRoktRozGrUZhUasxqVWYXSYkBMMiOptcRFFXFBTWLqefK1CklQEJVEYgkF0YSCWEIgGp98jschFod4Yuo5DrEEROMKogmRWEIkmlDg8Io43QncziDOER9DPZ4F32RPsup45EARzzxRwpqa9FXdKulhOF8ikThXagc4dbqbE6e6GB31z+tzmfkW1u/OZ+f2JMoUzaRP3CDUMUTnB/30HBkg5P7kTZv0R3ZQ9M1XyP7cIygW0SrnQSAHt7JV7c7gtmrd/w+1eumy/ymUIqUlBqptPooHb6A6dgFJYcK2fT3pB7eR/tguTEW5S7Y+2eI8DD+KS0HeTw+uWCCI60o9Y+eu4Tg3OcauUhslcnAnrak1tLp0tHeFCPoXVnheLkq1ArVGgUarwmDSYExSkWRWYjWLpFok0o0R0rVB7OIE2gkfYlhA1KeiSsvFWFyAMT9rxY8HKZ9vi5eIxRhv7sR9rRH39Wbc1xrxNrQTGnXO+BlRKWDI0GPKMmDM0mPKNmDMMqC3a1GnmkkkpRBWmgkrzbhjRoa8WjwBFZ6AyHhAYNwHE74Efl+UoD9KJBQjEp56hGJEInEioRjRSJxEPLEkOSAWI9mmZ9fOPJ44WMymjVmrPov/LQ/b+SJJEm1tTk6d6eb4yS6amsfm/IyoEKjalMXWg4VsL/GR6buBdaKFkYsjdH3Yz8C5EaQ7avrVyRYKvvIcRd94GUtN2XJ+nRXnfga393dQLZnsU8xJagqyFeRrfWS6u0m+dAmzkE2KbR22Fz+H7S//BH12+v3eTJlMJvsEpV5H6p7NpO7ZDHzcjHns3DVyz11jY10z/p5uQmsrGCqooVOy0T4IjrHF1+zejVgkTiwSJzARwT02c+2FqBBIy84mK89MQY6SMuMEJTffQ/jOdUJdHmKCDUNpFSmba0jeWI02NeUefgvZUkjE43gb2nBeuIHzSj3u601461pnbEIsiAKmXAOWQjOWIhOWQjPmHAPqjCRCOjsBlQ2vkEy700jPqJJhJ7i6IngcAdxjfjwOP0H//PtB3m8KhUBpmZ3t23I4uLeQ8rLlyX4uu7cEQaC01EZpqY1v/twmevu8HDrcxnsfttLb65n2M4m4RN3Ffuou9vOa3cCWA5Xs2Luf8mfa2LD3OptGBuk61E/n+31M9PuJuDy0/O13afnb75KyZQ1F33yFvC88hcpkvLdf9iEj19zK7tpiam6VKhFbsooUE2Rq/GS6e8l0DJJmTyGpqoikiiLMFUUkVRWjUD+cTTpWk4ftju9iyfvp4SYlEvi6+vHUteKtb8VT18pw3zCDqZk4LRk4BTOjARWjrjjj4yujhnc6SrWCvNIUyiuS2FQWo1psJ3TsHKNn+wj4jKTu20HGoztI278Nlfn+FeLk8216waFRHBdv4rx4A8eF67gu1xPzB6adV1SJWIvNJFdYsBabsRSZMeebiBhs+NTpeJTptDnNdA4r6R+MMtLnZaTfi3PYN++EkCuN0aSmqDiFNVVpbN2czbo1Gej1K7uVwlKQz5dJkiTR3OLgg0OtfHi4DYdj+nPjFkEUqNiQybZHithSHiXbfx27vwnntRE63uul/9TwJ7ItKw16cj//JEXfeBnb9vUP7I0SueZW9sBIt6uwmFSoFRIahYRaiKMW4piFMMmJILZEiDSdgvRcO4ZMC7oMO6aSfEzFuSu+iZtMJpPdDUEUMRXlYirKJedzj9z+ezwSITQ0hr9viED/CIG+IRzdbgacAYYFNS5RTwglIZSEEwrCkoJwXCAUEwhHIRwFKSGRkCaH5UwkpNvPicTkEF9LeR87FonTUT9KR/0o7wHmZCM1m3+WHf9ZwTZ7D/EjJ2j774c586ob++7N5L36JNkvPIrWvrKHQnkQSZLERHsPY6cuM3r6KqOnLuPvmqGMKYAp20BKhYWUCgvJ5RYsRWai2iS8ulzG1Rlc8lhp7lPSc2Gcvg4nA10DRMO99/ZLLYAggEIhIoqTrRDUaiVKpQKFQsCcpCU9zUhmuonMdCN5uVbKymykpxkf2IBDNjdBEKgot1NRbuc3f207tdeGePf9Zg4fbScc/mzOGCkh0XhlgMYrA7yWrGPzgUJ27NtNxcF21my+xgbnAD1HB+h4rw9v5wQxf4DOb71G57dew1xRRNE3X6HgK8/L18clJNfcyu7anTW31w8fp6SiHIVOg0KrQdRq5NToDwH5ju/8yPtJdrcS8TjxYIh4IITP5eb00WNIkSjbt21Ho9VM+xlJkojGJILhOKFwnFAkTjAcJxhJ4AvGcHqCuMZDuMYjOMejjLkijLoiBAILT/5nturYsi+XJ7fFqYjfxPXmOTrf7mFiIEjqvi0UfPVz5L78OEr98h/7D+P5lojH8da1Mnr6CqOnLjN2+iqhEce084oqkeSyJOxTY7naqq2ozWpCSiseXS6jQjY3Bs20toXoah6jt8255H3G9QY1ZrMGs1lLUpKGpCQtVosWS5IWg16NXqdCq1Wi0ynR6VRoNUq0WiVKpTj5UIgfT99+KFAqRRQKAYVCfCiPg8WQ99PsJnxhPjzUxmtvNNLaNv05dYsgQOnaDLY9WsTWygS5wevYfY14GsfoeLeP3uODxIIfX19FlYrMp/eS/+VnyXx6H0qddrm/zrKTa25lDwx9fpbcJ1Ymk8mWiahQIBoNqIwGJJMeMScNAMv6iiUvjI6Ph+jodNHQ4qCpZYzm5lG6O92z1gKPu4McfaOFo29ASU0B+x7ZzpNf60J17iSdb7Rx8ef+gKu//mfkffFpir7xMsmbauRasrsQj0RwXaln9NQVxk5fYexsLVHvxLTzqgxK7DXJ2NckY6uxklyahKhWEFDZ8OjyuBHL5FqPgdZWH93NY/R3thGPJaZd1nzZUg3kZCeRkW4iK8NERrqJjAwTGWlGUlONaLVyMVS2OpiMGl55qZqXX6yiqXmM195o5MPDbdMOLSRJ0HJ9iJbrQ7xu0bJ5fyHb9++iancnlWuvsf5XB+g9NkjHe724mr0kolH63zxK/5tHUZmN5Lz4GHlfeoa0A9vkCqJFkK8qMplMJpPJPsNs1rJ+XSbr12Xe/tvERJirtQOcvzLA+Qu99Pd6Z/x8W90IbXUjvJFuZN/TP8tzf+mksu80nf9+g85v/4T2f/oRyRurKPmVL5H3hafvSW3uahed8OE4f53R01cYO3MV54UbMyZ+UptU2NckY1+bTOraFCxFZgSFiE+dhkeXy5V4Fte6dbQ0euloGGGop2nRzdftqQYKC5MpnnoUFiRTkG/FaJRzZsgeLIIgUFmRSmVFKr/9mzs4fLSd195spLFxdNr5Jzwhjr3RyLE3GilZk8bWR55g+xqB3C9e58CzDfjanXS810fP0QEiE1Gi4z46v/06nd9+HW26nbzPP0n+l5+VbwQugBzcymQymUwmmxeTScO+vYXs21sIQGeXiyPHOjlyrIPO9umHiXEM+/jpv97gfZOaPU8/w8t/eJBHf/40vT+4TtubLVz8xh9T+zv/g8Kvv0jJL38Rc0n+PfxGK1twxDFZI3vmKqOnr+K53oSUmL42VZOkngpkk7GvTcFSYEISFUxoMvDq8miTsrjeraW10U1H/SgD3fVIi0j6ZLFqqaxIpaYylarKNKoqU7Fa5RsTsoePwaDmhecreeH5SlpaHbz+ZiPvf9iK3z/9mM9tN0douznCG2YNm/YVsOPAdqq39lBaUcu6Xx5k+NIY3R8NMHhuhHg4QWh47Ha2ZX12OpnP7CPr2QOkH9iGYoZuKDK5z61sCdzZ57a1tZWSkpL7vEWye03uqzM/8n6SLaWVdjy1tjl47Vbhzjd94Q5AZ1Cx++lSXtodpWT8DAM/vUbLT7sIOSdrINMf20Xpr3yRzGf231WTvJW2f+ZyO/nTmatTAW0tE23dM86vsapJXZvyiWA2LigZ12Th1eUxImRxs1tDW6OT9vpR+jtcC85gLIoCJSUpbFiXwbq1GVRXpa26hEur7Ti4X+T9tDSCwShHPurg9TcbuVk3POf8hZWpbHukiK3r1eREGkmdaEAx4aL/zDA9RwcYuer8zE0ohV5HxqM7yHp2P5nP7EeXZluur7Nocp9bmUwmk8lkq1ppiY0//L09/Nav7+DosXa+8/0bdExTmxv0Rzn84wZOvati15P7ePHn9nLgi+cYe+caTT/oYPjwGYYPn0Gfm0nJL36ewm+8vCILb3crOuHDfa0J55V6HOevMXamltDw2IzzG7P0H/eZrbZizjESE9R4tTm4dLk0kU19r5r2iw46Gkboa7+64D6zao2C6uo0Nq7LYP3aTGqq0zAY5KbFMtl86XQqnnumnOeeKaej08Xrbzby7vstTExM332gs3GUzsZRXterqN6azYZdr7KxOERGWgM7n2gi7vDSe2KQ/tMjjNW5kOIS8UCQ/rc+ov+tj0AQSN5UPTnO+u6N2HZuQGt7uDMvy8GtTCaTyWSyJaPVKnnmqXKefrKMy1cH+Pa/X+PChb7PzBcKRDn6WiOn31Oy88mdvPDqHnZ/7jyeQ1dp/I8OvJ2D3Pjj/0ndn/wdOS8/TuHXXyRt/1ZE5eorukQnfHjqWnFdqcd5pR7XlXrGmzuZqZOrIApYis3Ya6yT/Wark9Ema4iKOjzaXEZ1udSRRUO3go4LY3Q0jNLXcXHBwaxKNRnMbt2UxaaNWVRXpaFWywlsZLKlUFSYzO/99i5+41e38dHxTl5/s4Haa0PTzhsKRLlyvIsrx7swW3Ws25nLhl1bWVPhJD2ngcKX2kiMBxi6NMrghVGGLo4RmYiCJOG6XIfrch3Nf/MtAMwVRdh3bcS+ayOpuzZiKMheVa0t7tbq+4WQyWQymUy24gmCwJZN2WzZlE1rq4N/+tYVjh/v/Mx84VCMY280cuYDJTse38znntvFjqcv4Dt+ldafdjFS66TnB+/S84N30aamkPvqk+R94SlStq1bcZlEwy4P400deBs78Da2423sYLyxnUD/7M0TlVoFKZUWbNXJ2GuspFRZUemUhBVGvNpcenW5jJJNYzd0nB+lo2GU3vazJOILa2asVIpUV6WxeVMWmzdmUVOdhkYjFwVlsuWk0Sh56olSnnqilK5uN2++1cg777fg8YSmnX/cHeTUuy2cercFe6aJ9bvLWLN5HxUlLmxZ7ax/vJWtYQ+OejeD5yeD3fEe38efb+pgvKmDjn/58eT6bVaSKotJqiomqaqEpMoikqpK0NiTH8igV76iyWQymUwmW1alpTb+5i+eoL3DyT//6xU+OtbxmUrLSCjGibeaOPuBgu2Pr+X5x3ay7sBFpPqbtP20i56PBgmNOmn9u+/R+nffQ2Uxk35gG+mPbCd17xZMJXmIKtWybH8iFiPi8hJ2egg73AT6h/H3DBLoHcTfM4i/d4hA7yDRcd+cyxJVIpZCE9bSJJJLk7CWJmEpMoFCgV+dhlebTZs2h6F4Gm1dUTobb9XMdiw4mFUoJjO73gpm16xJR6ddnn0kk8nmVpBv5bd+cye/+svbOH6ykzfebOTy1YEZM5WPDU5w+Ed1HP5RHRabnsqNWVRuWsuaYki3d1CwpY21vzRA2BPGUe/GUe9mrM6Fu9VLIja50LDDzeipy4yeuvyJZWtSLCRVlWDIz0KXmYouKw391LMuMxVdum3ZrqnLSQ5uZTKZTCaT3RPFRSn85Z8/Tmeni3/5tyscOdrxmSRH0UicU++0cO6Qgu2PVvLsEztYW3OJdb/SRO+xQbo+6MPV4iXqGafv9cP0vT6ZBEdUqTCXF5BUVYI2w46g1xIdGgCdmq7+CVQKBVI8QSIWQ4onkOJxpGiMWCBIzB8kHgjdMR0k4pm4HcxGPeOL+r76VC3mPCPmXCOWQjPWUjNJ+SYEpUhIaWVCk45Hk0GPJoN2p5mutnF6Whx0t/Yy2l+34KF5RFGgssLOpo1ZbNyQxfq1Gej1q69wKpM96NRqBY8/WsLjj5YwOurj0JF23v+wlZZWx4yf8TgCnDvUxrlDbag0CkrXpFO5cS81ay3kpo1hzuuj4GAfa8JDJMJRXM2TtbvOJg/jPT58g4FPJKcKOz2TAe+ngt7bBAGNzYoqyYTKbERlMqAyG1CaDFPTRhR6HaJKiahUICiVCEoFolJJv3Pm77Hc5OBWJpPJZDLZPVVYmMx//7PH+MVvuvmXb13l0OG2zwS5sUic0++1cv6wyNZHSnjs8V3UfK2FvS/eJNQxTP+ZEYYujuJs9CAlJBLRKJ66Vjx1rZ9Z33VeW5bvISgEdDYthlQt+jQdhjQ95lwD5jwjplwjKp2SuKAkqErBr7bj1KTTrU6n22Omt8fPQKebgS43fe3XCMySYXomokKgqiKVTRsy2bgxi7U16XICKJlslUlNNfKVL6/jK19eR2eXiw8PtfHB4TYGBma+qRYNx2m4PEDD5QF+AqSkGckvs5FfvofCUgvFeT6sGQOkbu+jODyAKh4gEYkz0efH2+NjvGcCb7eP8V4fgeEgsVD8syuRJMJjLsJjrgV/JyfRBX9mqcjBrUwmk8lksvsiP8/Kf/vTR/jFb27mX//tKu990PLZIDea4OwHbZz9oI2SNWlsPfgiezYGyaxupeSr7eD1MnLNiafDi6djAk/HOEFH6HaTvHkRQKFRoNQoUGgnn1VGJRqzGnWSavLZrEZjVqE2q9HbtOjTtGiTtYgKgQQiYaWJsDKJgCoFl9pGvyqFsYiZvhGR0e4Jhvs8DHS5Gey6StC/uIKfQiFQVZnKxg1ZbNqQydo1cs2sTPYgKSxI5ld+aSu//ItbqKsf4cPDbZw81cXQ8OxdHpwjPpwjPq6e6gYmh1zLK7WRX15DTtFeMjPUZJl8GDId6Dc4yIo4KIk40MTHEYCoP0rQGZ56hAg6QoRcYUKuMBF/jFggRjQw9RycfI6HF5bA7l6Rg1uZTCaTyWT3VW5OEn/6Xw7w89/YyLe+Xcs77zUTn6Z/advNEdpujvC6ST3Z92zjejaWJcjM7SU9MkxxeARtzIMAxEJxor4o0UAMhMkmu4JCQBDveCgFlFoFimkyBCcQiSl0REUdUYWO2NRzVNThUZoYViYREEyM+dSMuQU8I0E8Y37GBicYHRhndHAQ//j0w3/Ml1qtoKLMzoYNmXIwK5M9RARBYE1NOmtq0vm9395FR6eL02d6OHm6m7r64Tm7LAT9UZqvDdF8R3ZmtVZJaqaZ1GwzaVnZpGabycjUkW6JYVQE0BT4UMd8qOMTpEw9q+N+lIkwikQYUYpxZ/qpRFwiHo4jxSUS8QRSXJqalugfm4Df7FqenTMHObiVyWQymUy2ImRnJfFf/ng/3/z6Rr713VrefqeZ2DTD2wQmIlw50cWVE118XymSV5pCVkExmQWbyMnRk2qJY9LG0RBGkYiAABIiICAJIhICkiAgoSCGglBUQSgqEo4JhKIC4YhAMAQBX5iAL0LAFyE4Ne2fiDDumsDjHGHCHfxMTfPdsNv0rF2TwZo16aypTqO8zC4PzSOTPeQEQaC4KIXiohS+/rUNuD1Bzp7r5fTZbs6d78Pvn1+XhkgoRn+ni/7OzzYz1upVmCxaTBYdZqsRs9V++7VWr0KtVaLRiOg0oNck0KkTGDUJ1Io4gpRAIDH1LCFICUaGh4H/WOI9MT9ycCuTyWQymWxFycw08//8wT6++fWNfPd713n3/RZ8M/RJjccSdDaO0dk49pn3NDolGp0KJEgkEkgSJBLSZB/dhEQ8lljw2LBLJSlJS0W5nYoyG2Vldmqq00hPMz6QQ3PIZLKlY7XoeOapMp55qox4PEF7h4vrN4a4cXOY6zeHGJ6jCfN0QoEooUCUscGJBX1OEAVEUUBU3HoWEUWBaMS94G1YKnJwK5PJZDKZbEVKTzPx+7+zm9/41e0cO9HJG281crV2cN6fDwdjhIOxZdzCuSkUAjnZSeTnWSkpSaGizE55uY20VDmQlclkd0ehECkrtVFWauPzr9QAMDLi4/rNyWC3rn6Ezi43weDyJHiSEhLxhET8U5fZSOTuumTcDTm4lclkMplMtqJptUqeeqKUp54oZWBwnNNnujl5upurtYPTNlu+19RqBal2A2mpRrKzzeTnWcnPs5CfbyUz04RKKTctlslk90ZamvH2MEMAkiQxOuqnq9v98aPLTVePG5creJ+3dunJwa1MJpPJZLJVIyvTzBdeXcMXXl2D3x/hytUBmlsdtEw9hoYW1qxuOqJCQK9TYUnSYk7SkmTWkGTWYp56tibrSE81kpZmJC3VgNWqk2thZTLZiiQIwuS1Ks3Itq05n3jP54vgcPpxOgM4HAHGnIGpaT8OZwCXO0gwECUQjBIMRgne55Yw8yEHtzKZTCaTyVYlg0HN3j0F7N1TcPtvoVCMiYkwY45xjh07TTgssXHTRnQ6LYqpLMmKqX5iSqWIVqNEo1VOPmuUaLQKuaZVJpM9FIxGNUajmvw867zmj8cThEIxAsEogUCUcChGPCGRSCSITWVKjscT9PR08fJLy7zxM5CDW5lMJpPJZA8MrVaJVqvEaBTJydYAsHVzFjqd7j5vmUwmk61uCoWIwaDGYFDPOl+SOXCPtuizxPu2ZplMJpPJZDKZTCaTyZaIHNzKZDKZTCaTyWQymWzVk4NbmUwmk8lkMplMJpOtenJwK5PJZDKZTCaTyWSyVU8ObmUymUwmk8lkMplMturJwa1MJpPJZDKZTCaTyVY9ObiVyWQymUwmk8lkMtmqJwe3MplMJpPJZDKZTCZb9eTgViaTyWQymUwmk8lkq54c3C4zQRD0giD8niAIlwRBcAmC4BMEoUkQhL8WBCF3CZavFAThUUEQ/koQhNOCIIwJghAVBMEjCELt1HqKluK7yGQymUwmk8lkMtlKpbzfG/Agmwoq3wPKPvVW+dTjm4IgfEmSpPcXuXw70ASkTPN2ErB+6vHrgiD8viRJf7uY9chkMplMJpPJZDLZSifX3C4TQRCMwLt8HNj+C3AQ2AH8MeBjMgD9iSAIaxa5Gg0fB7bXgT8FngI2AgeAvwJCgBr4X4Ig/MIi1yOTyWQymUwmk8lkK5pcc7t8fpfJ2lmA35ck6a/ueO+8IAjHgVOAHvhfTAajCyUBR4D/IknShWnePy4IwmvAcUAH/KUgCD+QJGliEeuSyWQymUwmk8lkshVLrrldBoIgqIDfnHrZBPzNp+eRJOk88K9TL/cLgrBxoeuRJGlAkqTHZghsb81zEfiHqZdJwCMLXY9MJpPJZDKZTCaTrXRycLs89gGWqenvSJKUmGG+b98x/eIybs/xO6bl5FIymUwmk8lkMpnsgSMHt8tj9x3TJ2eZ7wrgn5retXybg+aO6ZkCbZlMJpPJZDKZTCZbteTgdnlU3DHdPNNMkiTFgI5pPrPU9s5ne2QymUwmk8lkMplstZITSi2PnKlnvyRJnjnm7QPWAHZBEDSSJIWXckMEQcgAvj710sEnmyjPdxnZc8ySdWuip6dnoYuXPQDC4TAOhwOAjo4ONBrNHJ94OMn7SbaU5ONpdvL+kYF8HMyXvJ9kS+lT8YDiXq5bDm6Xh2nq2TePef13TBuBJQtuBUEQgP9zx/b8mSRJwUUsqm++Mz766KOLWLxMJpPJZDKZTCZ7ANmBe1b7JTdLXh7aqefIPOa9M5jVLfF2/BHw3NT0ceDvlnj5MplMJpPJZDKZTDaT1Hu5soe65lYQBCUQXYJFfV2SpG/f8To09ayex2fvbPexmFrVaQmC8GXgz6ZedgNfmiVr81xy5ng/Fzg7Nb0NGFjkemSrVzpweWp6MzB8H7dlJZP3k2wpycfT7OT9IwP5OJgveT/JllIWcGuo0nua7+ehDm6X0cTUs3Ee8xrumJ5PM+Y5CYLwNPBvgACMAI9KkrToi5QkSf1zrO/OlwNzzS978HzqGBiWj4HpyftJtpTk42l28v6RgXwczJe8n2RL6VPH03xasi6Zhzq4lSQpJgjCUmQpHvrU635gK2AQBMEyR1KpW7WiY0uRTEoQhH3ATwEV4AYekySp/W6XK5PJZDKZTCaTyWQr2UMd3AJIkrQcVeWNwEtT0+V8XC3/CVPNooumXjbd7UoFQdgCvMNkn18f8KQkSTfvdrkymUwmk8lkMplMttLJCaWWx5k7pvfOOBds4uNmyWdnmW9OgiCsAT5ksil0CHhWkqSLd7NMmUwmk8lkMplMJlst5OB2eZwAvFPTXxM+1fD8Dj97x/Qbi12ZIAilwGHAymSCrJckSTqx2OXJ/t/27i9GrqoO4Pj3Fwi2KIRI0tJYq8SCQSAVY2KMRKrYGn2gjVIIbaqoiaYqYhAwJiaWBxLxTwwNsYkEDdJCo/JHkzYRFVvBB6KJRpHwUB8KaAEfREookJSfD/eMOx23u7M7O3vnzHw/yc3MOffcnV+nk9/ub+6590iSJEmqjcXtEGTmq8CO0jwPuL53TES8F/hMaR7IzD9MM+atEZFl2z/da0XEKuDXwHLgGM1dkfcN/q+QJEmSpHpM/DW3Q/Rt4ErgXOBbEbEa2EOz3M8HaNagPbm0vzyfF4iIM2kK285Nqb4LPBERF8xw2L8z06V6JEmSJI0Vi9shycwjZUmefcA5wGfL1u0FYEtm/nmeL3Nh+dkdN5ZtJndy/HRoSZIkSaqexe0QZebBiLgI+AKwCVgNnAI8RVP03pqZh1oMcUGUtdBOdF2xJoCfgf74Pmkh+Xmame+PwM9Bv3yftJDa/DxFZrbxupIkSZIkLRhvKCVJkiRJqp7FrSRJkiSpeha3kiRJkqTqWdxKkiRJkqpncStJkiRJqp7FrSRJkiSpeha3kiRJkqTqWdxKkiRJkqpncas5i4i1EZFz3K5uO24NZpr/9yMRcWofxy2NiP/0HLt2+BG3r+c92z7L2OUR8VjX+J0REYsUqiph/u1fRFzc8z68v+2YtDjmknsnzXzem4jYPmm/vzWYNvOvxa2k+XoDsLGPcRuA04cbSt0iYgWwHzi/dN2amdsyM9uLSqreJ2ZpS5KGo7X8e/JivZDG1k7g+32Me3rYgWhRvQwsAbYCd88ydmvPMeoSESuBh4BzStd3MvOGFkNSPcy/JxARrwM2leaLNF/GbYqIazLzaHuRSdJ4azv/WtxqUM9l5mNtB6FF9wvgCmBdRJyVmc9MNygilgHrS/PnwJWLFF8VIuItwG+Bs0vXzZn59RZDUl3Mvye2ATijPL8WuINmBskGYE9LMUnSJGg1/zotWdJ8PAg8A5wEXDXDuKtovkR7FvjVIsRVjYh4G/A7pgrb7Ra20oL5ZHl8PDN/CDxe2k5NlqThajX/WtxKmo9jwD3l+dYZxnUS2d3lGAERcS5wAFhVur6WmTe1GJI0NnpmjOwqj7vL4/qIWL74UUnS+BuF/GtxK2m+7iqPF0XE+b07I+IdwLt6xk68iDiPprB9U+m6LjO/2WJI0rjZQjNjJJn6o2p3aZ9U9kuSFl7r+dfiVtK8ZOafgM71ftOdve30/a2MnXgRcSHNXZHPokn012Tm91oNSho/nSlxD2fmkwCZeQh4pPQ7NVmShqP1/GtxK2kQnTOyWyLif/mkrM+6pWfMpFtDc/OoZTSF7ecy87Z2Q5LGS/kCaU1p7urZ3WmvKeOkSbcsIi6YbaP5vSXNaFTyr8WtpEHsBl4DVgKXdPWvBd5c9u3+/8Mm0kbgzPL885l5e4uxSOOqc9bgFeCnPft+Uvq7x0mTbBvw1z62bW0FqKqMRP61uJU0b5n5D5qzkXD81OTO8/2ZOXFrbJ5Adj2/LCJOaS0SaQxFxEnA5tLcm5nPd+8v7X2lubmMlyQNaJTyr8WtpEH9uDxeHhFLI2Ip8PHS55TkKTuZuh3+R4B7IsK1xqWFsx5YUZ73Tomjp38F8KGhRySNtpsyM2bbAO/mr9mMTP61uJU0qPuAl4DTaBbo3kizWPdR4N72who5/6JJ5gdL+2PAnd3XKksaSOdGJc8De08wZm/Z3z1ekjSYkcm//lElaSCZ+SJwf2luZWpK8gOZeaSdqEZTZh4GPggcKl2bgdvLDbgkzVNEnE7z5RrAGcArEZG9G/By2Q+wMSJOW/xoJWl8jFr+tbiVtBA6U5PXA+vKc6ckTyMznwIuBf5Zuj4N7GgvImksXAEsneMxpwKXDyEWSZokI5V/vd5L0kL4DXCYqestngUebC+c0ZaZf4+IS4EDNEssfDEijmbmjS2HJtWqM8XtMHBdH+NvAVaV4340rKAkaQKMVP61uJU0sMw8FhF3AdeWrl2ZeazNmEZdZj4REeto7jb9RuCGiHgpM7e3G5lUl4g4G7i4NO/NzD19HPNu4CvAJRGxKjOfHGaMkjSORjH/Oi1Z0oLIzK9m5pKyXd92PDXIzL8AHwZeKF3fiAjP3kpzsxXoXLf+sz6P6YwLjl/GTJLUv5HLv565laQWZeYfI+KjwC+B1wO3lDO4t7UcmlSLzh9HzwEP93nMo8DTwMpy/M1DiEuj4Z0RcXUf4x7JzIOzD5PUZeTyr8WtJLUsM38fEZfR3CZ/CbCjXIN7R8uhSSMtIt4HrC7N+zPztX6Oy8yMiPuALwFvj4j3ZOajw4pTrdrA1J1cZ/IpppZqkzSLUc2/TkuWpBGQmQ/RrH37Ks1UnR9ExJZ2o5JGXvdaiXNdV7t7vGveStLcjGT+jcxcyJ8nSZIkSdKi88ytJEmSJKl6FreSJEmSpOpZ3EqSJEmSqmdxK0mSJEmqnsWtJEmSJKl6FreSJEmSpOpZ3EqSJEmSqmdxK0mSJEmqnsWtJEmSJKl6FreSJEmSpOpZ3EqSJEmSqmdxK0mSJEmqnsWtJEmSJKl6FreSJEmSpOpZ3EqSJEmSqmdxK0mSJEmqnsWtJEmSJKl6FreSJEmSpOpZ3EqSJEmSqmdxK0mSJEmqnsWtJEmSJKl6FreSJEmSpOpZ3EqSJEmSqmdxK0mSJEmqnsWtJEmSJKl6/wVdYdFqHXH58AAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 1000x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from matplotlib import cm\n",
+    "from triqs.lattice.utils import TB_from_wannier90, k_space_path\n",
+    "\n",
+    "# define a path in BZ\n",
+    "G = np.array([ 0.00,  0.00,  0.00])\n",
+    "M = np.array([ 0.50,  0.00,  0.00])\n",
+    "K = np.array([ 0.33,  0.33,  0.00])\n",
+    "A = np.array([ 0.00,  0.00,  0.50])\n",
+    "L = np.array([ 0.50,  0.00,  0.50])\n",
+    "H = np.array([ 0.33,  0.33,  0.50])\n",
+    "k_path = [(G, M), (M, K), (K, G), (G, A), (A, L), (L, H), (H, A)]\n",
+    "n_bnd = 14\n",
+    "n_k = 20\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 1, figsize=(5,2), dpi=200)\n",
+    "\n",
+    "for (fermi, n_iter, cycle) in [(e_fermi_run, n_iter_run, cm.RdYlBu)]:\n",
+    "\n",
+    "    col_it = np.linspace(0, 1, len(n_iter))\n",
+    "    for ct, it in enumerate(n_iter):\n",
+    "\n",
+    "        # compute TB model\n",
+    "        h_loc_add = - fermi[ct] * np.eye(n_bnd) # to center bands around 0\n",
+    "        tb = TB_from_wannier90(path='./', seed=f'ce2o3_it{it}', extend_to_spin=False, add_local=h_loc_add)\n",
+    "\n",
+    "        # compute dispersion on specified path\n",
+    "        k_vec, k_1d, special_k = k_space_path(k_path, num=n_k, bz=tb.bz)\n",
+    "        e_val = tb.dispersion(k_vec)\n",
+    "\n",
+    "        # plot\n",
+    "        for band in range(n_bnd):\n",
+    "            ax.plot(k_1d, e_val[:,band].real, c=cycle(col_it[ct]), label=f'it{it}' if band == 0 else '')\n",
+    "\n",
+    "    \n",
+    "ax.axhline(y=0,zorder=2,color='gray',alpha=0.5,ls='--')\n",
+    "ax.set_ylim(-0.2,0.8)\n",
+    "ax.grid(zorder=0)\n",
+    "ax.set_xticks(special_k)\n",
+    "ax.set_xticklabels([r'$\\Gamma$', 'M', 'K', r'$\\Gamma$', 'A', 'L', 'H', 'A'])\n",
+    "ax.set_xlim([special_k.min(), special_k.max()])\n",
+    "ax.set_ylabel(r'$\\omega$ (eV)')\n",
+    "ax.legend(fontsize='small')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "45062ca5",
+   "metadata": {},
+   "source": [
+    "Note that since this is an isolated set of bands, we don't have to worry about the disentanglement window here. Pay attention if you do need to use disentanglement though, and make sure that the configuration of Wannier90 works throughout the calculation!\n",
+    "\n",
+    "You see that one of the effects of charge self-consistency is the modificiation of the non-interacting bandstructure. The current results are far from converged, so make sure to carefully go through convergence tests as usual if you want reliable results. The figure below shows the difference to the reference data, which is quite substantial already at the DFT level."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "a3d760e5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f49a4157c40>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 1, figsize=(5,2), dpi=200)\n",
+    "\n",
+    "e_fermi_ref = [14.7437]\n",
+    "for (fermi, n_iter, path_w90, cycle, label) in [(e_fermi_ref, [1], path, cm.GnBu_r, 'reference'), (e_fermi_run, [1], './', cm.RdYlBu, 'run')]:\n",
+    "\n",
+    "    col_it = np.linspace(0, 1, len(n_iter))\n",
+    "    for ct, it in enumerate(n_iter):\n",
+    "\n",
+    "        # compute TB model\n",
+    "        h_loc_add = - fermi[ct] * np.eye(n_bnd) # to center bands around 0\n",
+    "        tb = TB_from_wannier90(path=path_w90, seed=f'ce2o3_it{it}', extend_to_spin=False, add_local=h_loc_add)\n",
+    "\n",
+    "        # compute dispersion on specified path\n",
+    "        k_vec, k_1d, special_k = k_space_path(k_path, num=n_k, bz=tb.bz)\n",
+    "        e_val = tb.dispersion(k_vec)\n",
+    "\n",
+    "        # plot\n",
+    "        for band in range(n_bnd):\n",
+    "            ax.plot(k_1d, e_val[:,band].real, c=cycle(col_it[ct]), label=f'it{it} - {label}' if band == 0 else '')\n",
+    "\n",
+    "    \n",
+    "ax.axhline(y=0,zorder=2,color='gray',alpha=0.5,ls='--')\n",
+    "ax.set_ylim(-0.2,0.8)\n",
+    "ax.grid(zorder=0)\n",
+    "ax.set_xticks(special_k)\n",
+    "ax.set_xticklabels([r'$\\Gamma$', 'M', 'K', r'$\\Gamma$', 'A', 'L', 'H', 'A'])\n",
+    "ax.set_xlim([special_k.min(), special_k.max()])\n",
+    "ax.set_ylabel(r'$\\omega$ (eV)')\n",
+    "ax.legend(fontsize='small')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fcc962ef",
+   "metadata": {},
+   "source": [
+    "### Convergence"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "192ebb20",
+   "metadata": {},
+   "source": [
+    "To check the convergence of the impurity Green's function and total energy you can look into the hdf5 Archive:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "57fbd7ae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with HDFArchive('./ce2o3.h5','r') as h5:\n",
+    "    observables = h5['DMFT_results']['observables']\n",
+    "    convergence = h5['DMFT_results']['convergence_obs']\n",
+    "    \n",
+    "with HDFArchive(path + 'ce2o3.h5','r') as h5:\n",
+    "    ref_observables = h5['DMFT_results']['observables']\n",
+    "    ref_convergence = h5['DMFT_results']['convergence_obs']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "fae94579",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1600x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1,2, figsize=(8, 2), dpi=200)\n",
+    "\n",
+    "ax[0].plot(ref_observables['E_tot']-np.min(ref_observables['E_tot']), 'x-', label='reference')\n",
+    "ax[0].plot(observables['E_tot']-np.min(observables['E_tot']), 'x-', label='result')\n",
+    "\n",
+    "ax[1].plot(ref_convergence['d_G0'][0], 'x-', label='reference')\n",
+    "ax[1].plot(convergence['d_G0'][0], 'x-', label='result')\n",
+    "\n",
+    "ax[0].set_ylabel('total energy (eV)')\n",
+    "ax[1].set_ylabel(r'convergence $G_0$')\n",
+    "\n",
+    "for it in range(2):\n",
+    "    ax[it].set_xlabel('# iteration')\n",
+    "    ax[it].xaxis.set_major_locator(ticker.MultipleLocator(5))\n",
+    "    ax[it].grid()\n",
+    "    ax[it].legend(fontsize='small')\n",
+    "\n",
+    "fig.subplots_adjust(wspace=0.3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4952537b",
+   "metadata": {},
+   "source": [
+    "Note that the total energy jumps quite a bit in the first iteration and is constant for the first two (three) one-shot iterations in this run (the reference data) as expected. Since the HubbardI solver essentially yields DMFT-convergence after one iteration (you may try to confirm this), the total number of iterations necessary to achieve convergence is relatively low."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9afd381d",
+   "metadata": {},
+   "source": [
+    "This concludes the tutorial. The following is a list of things you can try next:\n",
+    "\n",
+    "* improve the accuracy of the results by tuning the parameters until the results agree with the reference    \n",
+    "* try to fnd the equilibrium lattice paramter by repeating the above calculation of the total energy for different cell volumes"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/_sources/tutorials/NNO_os_plo_mag/tutorial.ipynb.txt b/_sources/tutorials/NNO_os_plo_mag/tutorial.ipynb.txt
new file mode 100644
index 00000000..1d3cd1f9
--- /dev/null
+++ b/_sources/tutorials/NNO_os_plo_mag/tutorial.ipynb.txt
@@ -0,0 +1,846 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "40ad917b-d7a1-4950-8593-abb9b4934e8a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warning: could not identify MPI environment!\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Starting serial run at: 2023-11-24 09:49:44.156139\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "np.set_printoptions(precision=6,suppress=True)\n",
+    "from triqs.plot.mpl_interface import plt,oplot\n",
+    "\n",
+    "from h5 import HDFArchive\n",
+    "\n",
+    "from triqs_dft_tools.converters.vasp import VaspConverter \n",
+    "import triqs_dft_tools.converters.plovasp.converter as plo_converter\n",
+    "\n",
+    "import pymatgen.io.vasp.outputs as vio\n",
+    "from pymatgen.electronic_structure.dos import CompleteDos\n",
+    "from pymatgen.electronic_structure.core import Spin, Orbital, OrbitalType\n",
+    "\n",
+    "import warnings \n",
+    "warnings.filterwarnings(\"ignore\") #ignore some matplotlib warnings"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c24d5aa3-8bf2-471e-868d-32a0d4bb99f7",
+   "metadata": {},
+   "source": [
+    "# 4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aed6468d-cb0b-4eee-93b4-665f4f80ac2d",
+   "metadata": {},
+   "source": [
+    "In this tutorial we will take a look at a magnetic DMFT calculation for NdNiO2 in the antiferromagnetic phase. NdNiO2 shows a clear AFM phase at lower temperatures in DFT+DMFT calculation. The calculations will be performed for a large energy window with all Ni-$d$ orbitals treated as interacting with a density-density type interaction. \n",
+    "\n",
+    "Disclaimer: the interaction values, results etc. might not be 100% physical and are only for demonstrative purposes!\n",
+    "\n",
+    "This tutorial will guide you through the following steps: \n",
+    "\n",
+    "* run a non-magnetic Vasp calculation for NdNiO2 with a two atom supercell allowing magnetic order\n",
+    "* create projectors in a large energy window for all Ni-$d$ orbitals and all O-$p$ orbitals\n",
+    "* create the hdf5 input via the Vasp converter for solid_dmft\n",
+    "* run a AFM DMFT one-shot calculation\n",
+    "* take a look at the output and analyse the multiplets of the Ni-d states\n",
+    "\n",
+    "Warning: the DMFT calculations here are very heavy requiring ~2500 core hours for the DMFT job.\n",
+    "\n",
+    "We set a `path` variable here to the reference files, which should be changed when doing the actual calculations do the work directory:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "6dde4dcd-c06a-45e0-9c06-ca11be265713",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path = './ref/'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1356ed1-b4a6-48d2-8058-863b9e70a0be",
+   "metadata": {},
+   "source": [
+    "## 1. Run DFT \n",
+    "\n",
+    "We start by running Vasp to create the raw projectors. The [INCAR](INCAR), [POSCAR](POSCAR), and [KPOINTS](KPOINTS) file are kept relatively simple. For the POTCAR the `PBE Nd_3`, `PBE Ni_pv` and `PBE O` pseudo potentials are used. Here we make sure that the Kohn-Sham eigenstates are well converged (rms), by performing a few extra SCF steps by setting `NELMIN=30`. Then, the INCAR flag `LOCPROJ = LOCPROJ = 3 4 : d : Pr` instructs Vasp to create projectors for the Ni-$d$ shell of the two Ni sties. More information can be found on the [DFTTools webpage of the Vasp converter](https://triqs.github.io/dft_tools/unstable/guide/conv_vasp.html).\n",
+    "\n",
+    "Next, run Vasp with \n",
+    "```\n",
+    "mpirun vasp_std 1>out.vasp 2>err.vasp &\n",
+    "```\n",
+    "and monitor the output. After Vasp is finished the result should look like this: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "bf1b811e-af03-4714-a644-ad7a7b57c42b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DAV:  25    -0.569483098581E+02   -0.31832E-09    0.42131E-12 29952   0.148E-06    0.488E-07\n",
+      "DAV:  26    -0.569483098574E+02    0.75124E-09    0.25243E-12 30528   0.511E-07    0.226E-07\n",
+      "DAV:  27    -0.569483098574E+02   -0.12733E-10    0.17328E-12 28448   0.285E-07    0.826E-08\n",
+      "DAV:  28    -0.569483098578E+02   -0.41837E-09    0.17366E-12 29536   0.151E-07    0.370E-08\n",
+      "DAV:  29    -0.569483098576E+02    0.22192E-09    0.19300E-12 29280   0.689E-08    0.124E-08\n",
+      "DAV:  30    -0.569483098572E+02    0.38563E-09    0.27026E-12 28576   0.388E-08    0.598E-09\n",
+      "DAV:  31    -0.569483098573E+02   -0.92768E-10    0.34212E-12 29024   0.218E-08\n",
+      " LOCPROJ mode\n",
+      " Computing AMN (projections onto localized orbitals)\n",
+      "   1 F= -.56948310E+02 E0= -.56941742E+02  d E =-.131358E-01\n"
+     ]
+    }
+   ],
+   "source": [
+    "!tail -n 10 ref/out.vasp"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3364605b-c105-4ad8-9350-6569b506df07",
+   "metadata": {},
+   "source": [
+    "let us take a look at the density of states from Vasp:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "3529d644-40f5-4b6b-98f0-2d3a6acdb524",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vasprun = vio.Vasprun(path+'/vasprun.xml')\n",
+    "dos = vasprun.complete_dos\n",
+    "Ni_spd_dos = dos.get_element_spd_dos(\"Ni\")\n",
+    "O_spd_dos = dos.get_element_spd_dos(\"O\")\n",
+    "Nd_spd_dos = dos.get_element_spd_dos(\"Nd\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "5fec0ad8-7ab4-4a02-bd72-b679f6ce6ed4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1050x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1,dpi=150,figsize=(7,4))\n",
+    "\n",
+    "ax.plot(vasprun.tdos.energies - vasprun.efermi , vasprun.tdos.densities[Spin.up], label=r'total DOS', lw = 2) \n",
+    "ax.plot(vasprun.tdos.energies - vasprun.efermi , Ni_spd_dos[OrbitalType.d].densities[Spin.up], label=r'Ni-d', lw = 2) \n",
+    "ax.plot(vasprun.tdos.energies - vasprun.efermi , O_spd_dos[OrbitalType.p].densities[Spin.up], label=r'O-p', lw = 2)\n",
+    "ax.plot(vasprun.tdos.energies - vasprun.efermi , Nd_spd_dos[OrbitalType.d].densities[Spin.up], label=r'Nd-d', lw = 2)\n",
+    "\n",
+    "ax.axvline(0, c='k', lw=1)\n",
+    "ax.set_xlabel('Energy relative to Fermi energy (eV)')\n",
+    "ax.set_ylabel('DOS (1/eV)')\n",
+    "ax.set_xlim(-9,8.5)\n",
+    "ax.set_ylim(0,20)\n",
+    "ax.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d42627e-84c4-4386-92bd-f1193e9fd8fd",
+   "metadata": {},
+   "source": [
+    "We see that the Ni-$d$ states are entangled / hybridizing with O-$p$ states and Nd-$d$ states in the energy range between -9 and 9 eV. Hence, we orthonormalize our projectors considering all states in this energy window to create well localized real-space states. These projectors will be indeed quite similar to the internal DFT+$U$ projectors used in VASP due to the large energy window. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "19285c12-c23a-4739-b5b1-56aa724bfb7f",
+   "metadata": {},
+   "source": [
+    "## 2. Creating the hdf5 archive / DMFT input\n",
+    "\n",
+    "Next we run the [Vasp converter](https://triqs.github.io/dft_tools/unstable/guide/conv_vasp.html) to create an input h5 archive for solid_dmft. The [plo.cfg](plo.cfg) looks like this: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "825c6168-97a7-4d2d-9699-b1d1e9af95dd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[General]\n",
+      "BASENAME = nno\n",
+      "\n",
+      "[Group 1]\n",
+      "SHELLS = 1\n",
+      "NORMALIZE = True\n",
+      "NORMION = False\n",
+      "EWINDOW = -10 10\n",
+      "\n",
+      "[Shell 1]\n",
+      "LSHELL = 2\n",
+      "IONS = 3 4\n",
+      "TRANSFORM = 0.0  0.0  0.0  0.0  1.0\n",
+      "            0.0  1.0  0.0  0.0  0.0\n",
+      "            0.0  0.0  1.0  0.0  0.0\n",
+      "            0.0  0.0  0.0  1.0  0.0\n",
+      "            1.0  0.0  0.0  0.0  0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat plo.cfg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2c3f2892-bb0a-4b8d-99af-76cff53b194b",
+   "metadata": {},
+   "source": [
+    "we create $d$ like projectors within a large energy window from -10 to 10 eV for very localized states for both Ni sites. Important: the sites are markes as non equivalent, so that we can later have different spin orientations on them. The flag `TRANSFORM` swaps the $d_{xy}$ and $d_{x^2 - y^2}$ orbitals, since the orientation in the unit cell of the oxygen bonds is rotated by 45 degreee. Vasp always performs projections in a global cartesian coordinate frame, so one has to rotate the orbitals manually with the octahedra orientation. \n",
+    "\n",
+    "Let's run the converter:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "8c687309-93f0-48b0-8862-85eca6c572e5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Read parameters: LOCPROJ\n",
+      "0  ->  {'label': 'dxy', 'isite': 3, 'l': 2, 'm': 0}\n",
+      "1  ->  {'label': 'dyz', 'isite': 3, 'l': 2, 'm': 1}\n",
+      "2  ->  {'label': 'dz2', 'isite': 3, 'l': 2, 'm': 2}\n",
+      "3  ->  {'label': 'dxz', 'isite': 3, 'l': 2, 'm': 3}\n",
+      "4  ->  {'label': 'dx2-y2', 'isite': 3, 'l': 2, 'm': 4}\n",
+      "5  ->  {'label': 'dxy', 'isite': 4, 'l': 2, 'm': 0}\n",
+      "6  ->  {'label': 'dyz', 'isite': 4, 'l': 2, 'm': 1}\n",
+      "7  ->  {'label': 'dz2', 'isite': 4, 'l': 2, 'm': 2}\n",
+      "8  ->  {'label': 'dxz', 'isite': 4, 'l': 2, 'm': 3}\n",
+      "9  ->  {'label': 'dx2-y2', 'isite': 4, 'l': 2, 'm': 4}\n",
+      "  Found POSCAR, title line: NdNiO2 SC\n",
+      "  Total number of ions: 8\n",
+      "  Number of types: 3\n",
+      "  Number of ions for each type: [2, 2, 4]\n",
+      "\n",
+      "    Total number of k-points: 405\n",
+      "  No tetrahedron data found in IBZKPT. Skipping...\n",
+      "[WARNING]: Error reading from EIGENVAL, trying LOCPROJ...\n",
+      "[WARNING]: Error reading Efermi from DOSCAR, trying LOCPROJ...\n",
+      "eigvals from LOCPROJ\n",
+      "\n",
+      "  Unorthonormalized density matrices and overlaps:\n",
+      "  Spin: 1\n",
+      "  Site: 3\n",
+      "  Density matrix                                                  Overlap\n",
+      "   1.1544881   0.0000000  -0.0000000   0.0000000  -0.0000000       0.9626619  -0.0000000   0.0000000   0.0000002  -0.0000000\n",
+      "   0.0000000   1.7591058  -0.0000000   0.0000000  -0.0000000      -0.0000000   0.9464342  -0.0000000   0.0000000  -0.0000000\n",
+      "  -0.0000000  -0.0000000   1.5114185   0.0000000  -0.0000000       0.0000000  -0.0000000   0.9548582  -0.0000000   0.0000000\n",
+      "   0.0000000   0.0000000   0.0000000   1.7591058  -0.0000000       0.0000002   0.0000000  -0.0000000   0.9464339   0.0000000\n",
+      "  -0.0000000  -0.0000000  -0.0000000  -0.0000000   1.8114830      -0.0000000  -0.0000000   0.0000000   0.0000000   0.9495307\n",
+      "  Site: 4\n",
+      "  Density matrix                                                  Overlap\n",
+      "   1.1544881  -0.0000000   0.0000000   0.0000000   0.0000000       0.9626621   0.0000000  -0.0000000  -0.0000001  -0.0000000\n",
+      "  -0.0000000   1.7591058  -0.0000000  -0.0000000   0.0000000       0.0000000   0.9464343  -0.0000000  -0.0000000   0.0000000\n",
+      "   0.0000000  -0.0000000   1.5114185  -0.0000000  -0.0000000      -0.0000000  -0.0000000   0.9548582   0.0000000   0.0000000\n",
+      "   0.0000000  -0.0000000  -0.0000000   1.7591058   0.0000000      -0.0000001  -0.0000000   0.0000000   0.9464344   0.0000000\n",
+      "   0.0000000   0.0000000  -0.0000000   0.0000000   1.8114830      -0.0000000   0.0000000   0.0000000   0.0000000   0.9495307\n",
+      "\n",
+      "  Generating 1 shell...\n",
+      "\n",
+      "    Shell         : 1\n",
+      "    Orbital l     : 2\n",
+      "    Number of ions: 2\n",
+      "    Dimension     : 5\n",
+      "    Correlated    : True\n",
+      "    Ion sort      : [3, 4]\n",
+      "Density matrix:\n",
+      "  Shell 1\n",
+      "Site diag : True\n",
+      "    Site 1\n",
+      "     1.9468082    -0.0000000    -0.0000000     0.0000000    -0.0000000\n",
+      "    -0.0000000     1.8880488    -0.0000000     0.0000000     0.0000000\n",
+      "    -0.0000000    -0.0000000     1.5912192     0.0000000     0.0000000\n",
+      "     0.0000000     0.0000000     0.0000000     1.8880488     0.0000000\n",
+      "    -0.0000000     0.0000000     0.0000000     0.0000000     1.1979419\n",
+      "      trace:  8.512066911392091\n",
+      "    Site 2\n",
+      "     1.9468082     0.0000000    -0.0000000    -0.0000000    -0.0000000\n",
+      "     0.0000000     1.8880488    -0.0000000    -0.0000000    -0.0000000\n",
+      "    -0.0000000    -0.0000000     1.5912192    -0.0000000    -0.0000000\n",
+      "    -0.0000000    -0.0000000    -0.0000000     1.8880488    -0.0000000\n",
+      "    -0.0000000    -0.0000000    -0.0000000    -0.0000000     1.1979419\n",
+      "      trace:  8.512066911289741\n",
+      "\n",
+      "  Impurity density: 17.024133822681833\n",
+      "\n",
+      "Overlap:\n",
+      "  Site 1\n",
+      "[[ 1. -0. -0. -0. -0.]\n",
+      " [-0.  1. -0. -0. -0.]\n",
+      " [-0. -0.  1. -0. -0.]\n",
+      " [-0. -0. -0.  1.  0.]\n",
+      " [-0. -0. -0.  0.  1.]]\n",
+      "\n",
+      "Local Hamiltonian:\n",
+      "  Shell 1\n",
+      "    Site 1 (real | complex part)\n",
+      "    -1.5179223     0.0000000     0.0000000    -0.0000000     0.0000000 |    -0.0000000    -0.0000000    -0.0000000    -0.0000000    -0.0000000\n",
+      "     0.0000000    -1.2888643     0.0000000    -0.0000000    -0.0000000 |     0.0000000     0.0000000    -0.0000000    -0.0000000    -0.0000000\n",
+      "     0.0000000     0.0000000    -0.9927644    -0.0000000    -0.0000000 |     0.0000000     0.0000000    -0.0000000     0.0000000     0.0000000\n",
+      "    -0.0000000    -0.0000000    -0.0000000    -1.2888643     0.0000000 |     0.0000000     0.0000000    -0.0000000     0.0000000     0.0000000\n",
+      "     0.0000000    -0.0000000    -0.0000000     0.0000000    -1.0828254 |     0.0000000     0.0000000    -0.0000000    -0.0000000     0.0000000\n",
+      "    Site 2 (real | complex part)\n",
+      "    -1.5179223    -0.0000000    -0.0000000    -0.0000000    -0.0000000 |     0.0000000     0.0000000    -0.0000000    -0.0000000    -0.0000000\n",
+      "    -0.0000000    -1.2888643     0.0000000    -0.0000000     0.0000000 |    -0.0000000    -0.0000000     0.0000000     0.0000000    -0.0000000\n",
+      "    -0.0000000     0.0000000    -0.9927644     0.0000000     0.0000000 |     0.0000000    -0.0000000     0.0000000    -0.0000000    -0.0000000\n",
+      "    -0.0000000    -0.0000000     0.0000000    -1.2888643     0.0000000 |     0.0000000    -0.0000000     0.0000000    -0.0000000     0.0000000\n",
+      "    -0.0000000     0.0000000     0.0000000     0.0000000    -1.0828254 |     0.0000000     0.0000000     0.0000000    -0.0000000     0.0000000\n",
+      "  Storing ctrl-file...\n",
+      "  Storing PLO-group file 'nno.pg1'...\n",
+      "  Density within window: 42.00000000005771\n",
+      "Reading input from nno.ctrl...\n",
+      "{\n",
+      "    \"ngroups\": 1,\n",
+      "    \"nk\": 405,\n",
+      "    \"ns\": 1,\n",
+      "    \"kvec1\": [\n",
+      "        0.1803844533789928,\n",
+      "        0.0,\n",
+      "        0.0\n",
+      "    ],\n",
+      "    \"kvec2\": [\n",
+      "        0.0,\n",
+      "        0.1803844533789928,\n",
+      "        0.0\n",
+      "    ],\n",
+      "    \"kvec3\": [\n",
+      "        0.0,\n",
+      "        0.0,\n",
+      "        0.30211493941280826\n",
+      "    ],\n",
+      "    \"nc_flag\": 0\n",
+      "}\n",
+      "\n",
+      "  No. of inequivalent shells: 2\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Generate and store PLOs\n",
+    "plo_converter.generate_and_output_as_text('plo.cfg', vasp_dir=path)\n",
+    "\n",
+    "# run the archive creat routine\n",
+    "conv = VaspConverter('nno')\n",
+    "conv.convert_dft_input()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bee3bf4f-0b75-445c-b3d3-7402f778fff4",
+   "metadata": {},
+   "source": [
+    "We can here cross check the quality of our projectors by making sure that there are not imaginary elements in both the local Hamiltonian and the density matrix. Furthermore, we see that the occupation of the Ni-$d$ shell is roughly 8.5 electrons which is a bit different from the nominal charge of $d^9$ for the system due to the large hybridization with the other states. For mor physical insights into the systems and a discussion on the appropriate choice of projectors see this research article [PRB 103 195101 2021](https://doi.org/10.1103/PhysRevB.103.195101)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18739e80-3c9e-4bea-9e0b-677421ec99aa",
+   "metadata": {},
+   "source": [
+    "## 3. Running the AFM calculation\n",
+    "\n",
+    "now we run the calculation at around 290 K, which should be below the ordering temperature of NdNiO2 in DMFT. The config file [config.toml](config.toml) for solid_dmft looks like this: \n",
+    "\n",
+    "    [general]\n",
+    "    seedname = \"nno\"\n",
+    "    jobname = \"NNO_lowT\"\n",
+    "\n",
+    "    enforce_off_diag = false\n",
+    "    block_threshold = 0.001\n",
+    "\n",
+    "    \n",
+    "    n_iw = 2001\n",
+    "    n_tau = 20001\n",
+    "\n",
+    "    prec_mu = 0.001\n",
+    "\n",
+    "    h_int_type = \"density_density\"\n",
+    "    U = 8.0\n",
+    "    J = 1.0\n",
+    "\n",
+    "    # temperature ~290 K\n",
+    "    beta = 40\n",
+    "\n",
+    "    magnetic = true\n",
+    "    magmom = -0.3, 0.3\n",
+    "    afm_order = true\n",
+    "\n",
+    "    n_iter_dmft = 14\n",
+    "\n",
+    "    g0_mix = 0.9\n",
+    "\n",
+    "    dc_type = 0\n",
+    "    dc = true\n",
+    "    dc_dmft = false\n",
+    "\n",
+    "    [solver]\n",
+    "    type = \"cthyb\"\n",
+    "    length_cycle = 2000\n",
+    "    n_warmup_cycles = 5e+3\n",
+    "    n_cycles_tot = 1e+7\n",
+    "    imag_threshold = 1e-5\n",
+    "\n",
+    "    perform_tail_fit = true\n",
+    "    fit_max_moment = 6\n",
+    "    fit_min_w = 10\n",
+    "    fit_max_w = 16\n",
+    "    measure_density_matrix = true"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "26910f2d-fd3d-4d72-adc5-99e79f72452d",
+   "metadata": {},
+   "source": [
+    "Let's go through some special options we set in the config file: \n",
+    "\n",
+    "* we changed `n_iw=2000` because the large energy window of the calculation requires more Matsubara frequencies\n",
+    "* `h_int_type` is set to `density_density` to reduce complexity of the problem\n",
+    "* `beta=40` here we set the temperature to ~290K\n",
+    "* `magnetic=true` lift spin degeneracy\n",
+    "* `magmom` here we specify the magnetic order. Here, we say that both Ni sites have the same spin, which should average to 0 at this high temperature. The magnetic moment is specified as an potential in eV splitting up / down channel of the initial self-energy\n",
+    "* `afm_order=true` tells solid_dmft to not solve impurities with the same `magmom` but rather copy the self-energy and if necessary flip the spin accordingly\n",
+    "* `length_cycle=2000` is the length between two Green's function measurements in cthyb. This number has to be choosen carefully to give an autocorrelation time ~1 for all orbitals\n",
+    "*  `perform_tail_fit=true` : here we use tail fitting to get good high frequency self-energy behavior\n",
+    "* `measure_density_matrix = true ` measures the impurity many-body density matrix in the Fock basis for a multiplet analysis\n",
+    "\n",
+    "By setting the flag magmom to a small value with a flipped sign on both sites we tell solid_dmft that both sites are related by flipping the down and up channel. Now we run solid_dmft simply by executing `mpirun solid_dmft config.toml`. \n",
+    "\n",
+    "Caution: this is a very heavy job, which should be submitted on a cluster. \n",
+    "\n",
+    "In the beginning of the calculation we find the following lines:\n",
+    "\n",
+    "    AFM calculation selected, mapping self energies as follows:\n",
+    "    imp  [copy sigma, source imp, switch up/down]\n",
+    "    ---------------------------------------------\n",
+    "    0: [False, 0, False]\n",
+    "    1: [True, 0, True]\n",
+    "\n",
+    "this tells us that solid_dmft detected correctly how we want to orientate the spin moments. This also reflects itself during the iterations when the second impurity problem is not solved, but instead all properties of the first impurity are copied and the spin channels are flipped: \n",
+    "\n",
+    "    ...\n",
+    "    copying the self-energy for shell 1 from shell 0\n",
+    "    inverting spin channels: False\n",
+    "    ...\n",
+    "\n",
+    "After the calculation is running or is finished we can take a look at the results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "f42d62cc-f8b4-4fc7-af76-cdf7ba13e8ea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with HDFArchive(path+'/nno.h5','r') as ar:\n",
+    "    Sigma_iw = ar['DMFT_results/last_iter/Sigma_freq_0']\n",
+    "    obs = ar['DMFT_results/observables']\n",
+    "    conv_obs = ar['DMFT_results/convergence_obs']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "65dba97b-a64c-4d88-b7cc-3607605a9aa3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1050x1200 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(nrows=4, dpi=150, figsize=(7,8), sharex=True)\n",
+    "fig.subplots_adjust(hspace=0.1)\n",
+    "# imp occupation\n",
+    "ax[0].plot(obs['iteration'], np.array(obs['imp_occ'][0]['up'])+np.array(obs['imp_occ'][0]['down']), '-o', label=r'Ni$_0$')\n",
+    "ax[0].plot(obs['iteration'], np.array(obs['imp_occ'][1]['up'])+np.array(obs['imp_occ'][1]['down']), '-o', label=r'Ni$_1$')\n",
+    "\n",
+    "# imp magnetization\n",
+    "ax[1].plot(obs['iteration'], (np.array(obs['imp_occ'][0]['up'])-np.array(obs['imp_occ'][0]['down'])), '-o', label=r'Ni$_0$')\n",
+    "ax[1].plot(obs['iteration'], (np.array(obs['imp_occ'][1]['up'])-np.array(obs['imp_occ'][1]['down'])), '-o', label=r'Ni$_1$')\n",
+    "\n",
+    "# dxy, dyz, dz2, dxz, dx2-y2 orbital magnetization\n",
+    "ax[2].plot(obs['iteration'], abs(np.array(obs['orb_occ'][0]['up'])[:,4]-np.array(obs['orb_occ'][0]['down'])[:,4]), '-o', label=r'$d_{x^2-y^2}$')\n",
+    "ax[2].plot(obs['iteration'], abs(np.array(obs['orb_occ'][0]['up'])[:,2]-np.array(obs['orb_occ'][0]['down'])[:,2]), '-o', label=r'$d_{z^2}$')\n",
+    "ax[2].plot(obs['iteration'], abs(np.array(obs['orb_occ'][0]['up'])[:,0]-np.array(obs['orb_occ'][0]['down'])[:,0]), '-o', label=r'$d_{xy}$')\n",
+    "ax[2].plot(obs['iteration'], abs(np.array(obs['orb_occ'][0]['up'])[:,1]-np.array(obs['orb_occ'][0]['down'])[:,1]), '-o', label=r'$d_{yz/xz}$')\n",
+    "\n",
+    "ax[3].semilogy(conv_obs['d_Gimp'][0], '-o')\n",
+    "\n",
+    "ax[0].set_ylabel('Imp. occupation')\n",
+    "ax[1].set_ylabel(r'magnetization $\\mu_B$')\n",
+    "ax[2].set_ylabel(r'magnetization $\\mu_B$')\n",
+    "ax[-1].set_xticks(range(0,len(obs['iteration'])))\n",
+    "ax[-1].set_xlabel('Iterations')\n",
+    "ax[0].set_ylim(8.4,8.6)\n",
+    "ax[0].legend();ax[1].legend();ax[2].legend()\n",
+    "\n",
+    "ax[3].set_ylabel(r'|G$_{imp}$-G$_{loc}$|')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5d0d0238-d573-4e18-9785-79408d6ac73d",
+   "metadata": {},
+   "source": [
+    "Let's take a look at the self-energy of the two Ni $e_g$ orbitals:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "daf0c1d8-a1fe-413d-a7b2-2eed78258e9f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 960x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1,dpi=150)\n",
+    "\n",
+    "ax.oplot(Sigma_iw['up_2'].imag, '-', color='C0', label=r'up $d_{z^2}$')\n",
+    "ax.oplot(Sigma_iw['up_4'].imag, '-', color='C1', label=r'up $d_{x^2-y^2}$')\n",
+    "\n",
+    "ax.oplot(Sigma_iw['down_2'].imag, '--', color='C0', label=r'down $d_{z^2}$')\n",
+    "ax.oplot(Sigma_iw['down_4'].imag, '--', color='C1', label=r'down $d_{x^2-y^2}$')\n",
+    "\n",
+    "ax.set_ylabel(r\"$Im \\Sigma (i \\omega)$\")\n",
+    "\n",
+    "ax.set_xlim(0,40)\n",
+    "ax.set_ylim(-1.8,0)\n",
+    "ax.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2a07a928-e69f-4ad1-91ea-0386024ed5de",
+   "metadata": {},
+   "source": [
+    "We can clearly see that a $\\omega_n=8$ the self-energy is replaced by the tail-fit as specified in the input config file. This cut is rather early, but ensures convergence. For higher sampling rates this has to be changed. We can also nicely observe a splitting of the spin channels indicating a magnetic solution, but we still have a metallic solution with both self-energies approaching 0 for small omega walues. However, the QMC noise is still rather high, especially in the $d_{x^2-y^2}$ orbital. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b22265a-4138-4d9c-8315-917320f27cb3",
+   "metadata": {},
+   "source": [
+    "## 5. Multiplet analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d3c2f507-757a-4880-b9dc-1f254c78c512",
+   "metadata": {},
+   "source": [
+    "We follow now the triqs/cthyb tutorial on the [multiplet analysis](https://triqs.github.io/cthyb/unstable/guide/multiplet_analysis_notebook.html) to analyze the multiplets of the Ni-d orbitals: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "c00e89e4-cf2e-4fca-84b1-11cb42072217",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "pd.set_option('display.width', 130)\n",
+    "\n",
+    "from triqs.operators.util import make_operator_real\n",
+    "from triqs.operators.util.observables import S_op\n",
+    "from triqs.atom_diag import quantum_number_eigenvalues\n",
+    "from triqs.operators import n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe674d6b-dae6-4497-82f5-6b8004afb275",
+   "metadata": {},
+   "source": [
+    "first we have to load the measured density matrix and the local Hamiltonian of the impurity problem from the h5 archive, which we stored by setting `measure_density_matrix=true` in the config file: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "786a549c-9306-4099-a4f0-3f19d2bdbb36",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with HDFArchive(path+'/nno.h5','r') as ar:\n",
+    "    rho = ar['DMFT_results/last_iter/full_dens_mat_0'] \n",
+    "    h_loc = ar['DMFT_results/last_iter/h_loc_diag_0']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "585625be-0888-460e-879b-2a60215a69bb",
+   "metadata": {},
+   "source": [
+    "`rho` is just a list of arrays containing the weights of each of the impurity eigenstates (many body states), and `h_loc` is a: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "efeafafa-502b-4acd-8e76-4f7eab6eb9c3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'triqs.atom_diag.atom_diag.AtomDiagReal'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(type(h_loc))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "72450efb-b8b8-4169-9c01-6fb6259a3178",
+   "metadata": {},
+   "source": [
+    "containing the local Hamiltonian of the impurity including eigenstates, eigenvalues etc."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "d767053a-f785-44d1-8a82-eafc5c8b9911",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res = [] \n",
+    "# get fundamental operators from atom_diag object\n",
+    "occ_operators = [n(*op) for op in h_loc.fops]\n",
+    "\n",
+    "# construct total occupation operator from list\n",
+    "N_op = sum(occ_operators)\n",
+    "\n",
+    "# create Sz operator and get eigenvalues\n",
+    "Sz=S_op('z', spin_names=['up','down'], n_orb=5, off_diag=False)\n",
+    "Sz = make_operator_real(Sz)\n",
+    "Sz_states = quantum_number_eigenvalues(Sz, h_loc)\n",
+    "\n",
+    "# get particle numbers from h_loc_diag\n",
+    "particle_numbers = quantum_number_eigenvalues(N_op, h_loc)\n",
+    "N_max = int(max(map(max, particle_numbers)))\n",
+    "\n",
+    "for sub in range(0,h_loc.n_subspaces):\n",
+    "\n",
+    "    # first get Fock space spanning the subspace\n",
+    "    fs_states = []\n",
+    "    for ind, fs in enumerate(h_loc.fock_states[sub]):\n",
+    "        state = bin(int(fs))[2:].rjust(N_max, '0')\n",
+    "        fs_states.append(\"|\"+state+\">\")\n",
+    "\n",
+    "    for ind in range(h_loc.get_subspace_dim(sub)):\n",
+    "\n",
+    "        # get particle number\n",
+    "        particle_number = round(particle_numbers[sub][ind])\n",
+    "        if abs(particle_number-particle_numbers[sub][ind]) > 1e-8:\n",
+    "            raise ValueError('round error for particle number to large!',\n",
+    "                             particle_numbers[sub][ind])\n",
+    "        else:\n",
+    "            particle_number = int(particle_number)\n",
+    "        eng=h_loc.energies[sub][ind]\n",
+    "\n",
+    "        # construct eigenvector in Fock state basis:\n",
+    "        ev_state = ''\n",
+    "        for i, elem in enumerate(h_loc.unitary_matrices[sub][:,ind]):\n",
+    "            ev_state += ' {:+1.4f}'.format(elem)+fs_states[i]\n",
+    "\n",
+    "        # get spin state\n",
+    "        ms=Sz_states[sub][ind]\n",
+    "\n",
+    "        # add to dict which becomes later the pandas data frame\n",
+    "        res.append({\"Sub#\" : sub,\n",
+    "                    \"EV#\" : ind,\n",
+    "                    \"N\" : particle_number,\n",
+    "                    \"energy\" : eng,\n",
+    "                    \"prob\": rho[sub][ind,ind],\n",
+    "                    \"m_s\": round(ms,1),\n",
+    "                    \"|m_s|\": abs(round(ms,1)),\n",
+    "                    \"state\": ev_state})\n",
+    "# panda data frame from res\n",
+    "res = pd.DataFrame(res, columns=res[0].keys())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "54f249f9-15b8-4b1c-bebb-7b63952e875e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    Sub#  EV#   N        energy      prob  m_s  |m_s|                 state\n",
+      "4      4    0   9  3.640517e-01  0.310283 -0.5    0.5   +1.0000|0111111111>\n",
+      "0      0    0   8  0.000000e+00  0.125113 -1.0    1.0   +1.0000|0101111111>\n",
+      "5      5    0   9  3.640517e-01  0.083760  0.5    0.5   +1.0000|1111101111>\n",
+      "20    20    0   8  8.851884e-01  0.074717  0.0    0.0   +1.0000|0111111011>\n",
+      "2      2    0   9  2.739907e-01  0.044306 -0.5    0.5   +1.0000|1101111111>\n",
+      "55    55    0  10  7.125334e+00  0.038609  0.0    0.0   +1.0000|1111111111>\n",
+      "3      3    0   9  2.739907e-01  0.035831  0.5    0.5   +1.0000|1111111011>\n",
+      "51    51    0   8  2.745626e+00  0.033932  0.0    0.0   +1.0000|0111101111>\n",
+      "1      1    0   8  4.903654e-09  0.031693  1.0    1.0   +1.0000|1111101011>\n",
+      "21    21    0   8  8.851884e-01  0.019748  0.0    0.0   +1.0000|1101101111>\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(res.sort_values('prob', ascending=False)[:10])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2af9aa9e-481b-48fb-952e-0d53080236c3",
+   "metadata": {},
+   "source": [
+    "This table shows the eigenstates of the impurity with the highest weight / occurence probability. Each row shows the state of the system, where the 1/0 indicates if an orbital is occupied. The orbitals are ordered as given in the projectors (dxy, dyz, dz2, dxz, dx2-y2) from right to left, first one spin-channel, then the other. Additionally each row shows the particle sector of the state, the energy, and the `m_s` quantum number.\n",
+    "\n",
+    "It can be seen, that the state with the highest weight is a state with one hole (N=9 electrons) in the $d_{x^2-y^2, up}$ orbital carrying a spin of `0.5`. The second state in the list is a state with two holes (N=8). One in the $d_{x^2-y^2, up}$ and one in the $d_{z^2, up}$ giving a magnetic moment of 1. This is because the impurity occupation is somewhere between 8 and 9. We can also create a nice state histogram from this: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "52d1d26d-587f-4b4d-a46a-f71850423b7d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# split into ms occupations\n",
+    "fig, (ax1) = plt.subplots(1,1,figsize=(6,4), dpi=150)\n",
+    "\n",
+    "spin_occ_five = res.groupby(['N', '|m_s|']).sum()\n",
+    "pivot_df = spin_occ_five.pivot_table(index='N', columns='|m_s|', values='prob')\n",
+    "pivot_df.plot.bar(stacked = True, rot=0, ax = ax1)\n",
+    "\n",
+    "ax1.set_ylabel(r'prob amplitude')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f5111521-4e2b-4bce-8270-654883a31cd6",
+   "metadata": {},
+   "source": [
+    "This concludes the tutorial. This you can try next:\n",
+    "\n",
+    "* try to find the transition temperature of the system by increasing the temperature in DMFT\n",
+    "* improve the accuracy of the resulting self-energy by restarting the dmft calculation with more n_cycles_tot "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/_sources/tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.ipynb.txt b/_sources/tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.ipynb.txt
new file mode 100644
index 00000000..f53ec5dc
--- /dev/null
+++ b/_sources/tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.ipynb.txt
@@ -0,0 +1,471 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a661f418-c4f0-435e-8db9-ff074ad58b49",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from h5 import HDFArchive\n",
+    "from triqs.gf import BlockGf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "55c5a91d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.rcParams['figure.figsize'] = (8, 4)\n",
+    "plt.rcParams['figure.dpi'] = 150"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0275b487",
+   "metadata": {},
+   "source": [
+    "Disclaimer: charge self-consistent (CSC) calculations are heavy. The current parameters won't give well converged solution but are tuned down to give results in roughly 150 core hours.\n",
+    "\n",
+    "# 2. CSC with VASP PLOs: charge order in PrNiO3\n",
+    "\n",
+    "Set the variable `read_from_ref` below to False if you want to plot your own calculated results. Otherwise, the provided reference files are used."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "1e21a834-d629-43a6-8c93-6f7e786aeca0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Reads files from reference? Otherwise, uses your simulation results\n",
+    "read_from_ref = True\n",
+    "path_mod = '/ref' if read_from_ref else ''"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "13dd34fd",
+   "metadata": {},
+   "source": [
+    "PrNiO3 is a perovskite that exhibits a metal-insulator transition coupled to a breathing distortion and charge disproportionation, see [here](https://doi.org/10.1038/s41535-019-0145-4).\n",
+    "In this tutorial, we will run DMFT calculation on the low-temperature insulating state. We will do this in a CSC way, where the correlated orbitals are defined by [projected localized orbitals (PLOs)](https://doi.org/10.1088/1361-648x/aae80a) calculated with VASP.\n",
+    "\n",
+    "## 1. Running the initial scf DFT calculation \n",
+    "\n",
+    "(~ 2 core hours)\n",
+    "\n",
+    "To get started, we run a self-consistent field (scf) DFT calculation:\n",
+    "\n",
+    "* Go into folder `1_dft_scf`\n",
+    "* Insert the POTCAR as concatenation of the files `PAW_PBE Pr_3`, `PAW_PBE Ni_pv` and `PAW_PBE O` distributed with VASP\n",
+    "* Goal: get a well-converged charge density (CHGCAR) and understand where the correlated bands are (DOSCAR and potentially PROCAR and band structure)\n",
+    "\n",
+    "Other input files are:\n",
+    "\n",
+    "* [INCAR](1_dft_scf/INCAR): using a large number of steps for good convergence. Compared to the DMFT calculation, it is relatively cheap and it is good to have a well converged starting point for DMFT.\n",
+    "* [POSCAR](1_dft_scf/POSCAR): PrNiO3 close to the experimental low-temperature structure (P21/n symmetry)\n",
+    "* [KPOINTS](1_dft_scf/KPOINTS): approximately unidistant grid of 6 x 6 x 4\n",
+    "\n",
+    "Then run Vasp with the command `mpirun -n 8 vasp_std`.\n",
+    "\n",
+    "The main output here is:\n",
+    "\n",
+    "* CHGCAR: the converged charge density to start the DMFT calculation from\n",
+    "* DOSCAR: to identify the energy range of the correlated subspace. (A partial DOS and band structure can be very helpful to identify the correlated subspace as well. The partial DOS can be obtained by uncommenting the LORBIT parameter in the INCAR but then the below functions to plot the DOS need to be adapted.)\n",
+    "\n",
+    "We now plot the DFT DOS and discuss the correlated subspace."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f4a4fc12",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dft_energy, dft_dos = np.loadtxt(f'1_dft_scf{path_mod}/DOSCAR',\n",
+    "                                 skiprows=6, unpack=True, usecols=(0, 1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "f1c5c3ca",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fermi_energy = 5.012206 # can be read from DOSCAR header or OUTCAR\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(dft_energy-fermi_energy, dft_dos)\n",
+    "ax.axhline(0, c='k')\n",
+    "ax.axvline(0, c='k')\n",
+    "ax.set_xlabel('Energy relative to Fermi energy (eV)')\n",
+    "ax.set_ylabel('DOS (1/eV)')\n",
+    "ax.set_xlim(-8, 5)\n",
+    "ax.set_ylim(0, 50);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "892a15f1",
+   "metadata": {},
+   "source": [
+    "The DOS contains (you can check this with the partial DOS):\n",
+    "\n",
+    "* Ni-eg bands in the range -0.4 to 2.5 eV with a small gap at around 0.6 eV\n",
+    "* mainly Ni-t2g bands between -1.5 and -0.5 eV\n",
+    "* mainly O-p bands between -7 and -1.5 eV\n",
+    "The Ni-d and O-p orbitals are hybridized, with an overlap in the DOS betwen Ni-t2g and O-p.\n",
+    "\n",
+    "DFT does not describe the system correctly in predicting a metallic state. In a simplified picture, the paramagnetism in DMFT will be able to split the correlated bands and push the Fermi energy into the gap of the eg orbitals, as we will see below.\n",
+    "\n",
+    "We will use the Ni-eg range to construct our correlated subspace.\n",
+    "\n",
+    "Note: with the coarse k-point mesh used in the tutorial the DOS will look much worse. We show here the DOS with converged number of kpoints for illustration."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "afb54167",
+   "metadata": {},
+   "source": [
+    "## 2. Running the CSC DMFT calculations\n",
+    "\n",
+    "(~ 150 core hours)\n",
+    "\n",
+    "We now run the DMFT calculation. In CSC calculations, the corrected charge density from DMFT is fed back into the DFT calculation to re-calculate the Kohn-Sham energies and projectors onto correlated orbitals.\n",
+    "\n",
+    "With VASP, the procedure works as described [here](https://triqs.github.io/dft_tools/latest/guide/dftdmft_selfcons.html#vasp-plovasp), where the GAMMA file written by DMFT contains the charge density *correction*. In the VASP-CSC implementation, we first converge a non-scf DFT calculation based on the CHGCAR from before, then run DMFT on the results. The VASP process stays alive but idle during the DMFT calculation. Then, when we want to update the DFT-derived quantities energies, we need to run multiple DFT steps in between the DMFT steps because the density correction is fed into VASP iteratively through mixing to ensure stability. \n",
+    "\n",
+    "### Input files for CSC DMFT calculations\n",
+    "\n",
+    "We first take a look into the input file [dmft_config.toml](2_dmft_csc/dmft_config.toml) and discuss some parameters. Please make sure you understand the role of the other parameters as well, as documented in the [reference manual of the read_config.py](https://triqs.github.io/solid_dmft/_ref/read_config.html) on the solid_dmft website. This is a selection of parameters from the dmft_config.toml:\n",
+    "\n",
+    "Group [general]:\n",
+    "\n",
+    "* `set_rot = \"hloc\"`: rotates the local impurity problem into a basis where the local Hamiltonian is diagonal\n",
+    "* `n_l = 35`: the number of Legendre coefficients to measure the imaginary-time Green's function in. Too few resulting in a \"bumpy\" Matsubara self-energy, too many include simulation noise. See also https://doi.org/10.1103/PhysRevB.84.075145.\n",
+    "* `dc_dmft = true`: using the DMFT occupations for the double counting is mandatory in CSC calculations. The DFT occupations are not well defined after the first density correction anymore\n",
+    "\n",
+    "Group [solver]:\n",
+    "\n",
+    "* `legendre_fit = true`: turns on measuring the Green's function in Legendre coefficients\n",
+    "\n",
+    "Group [dft]:\n",
+    "\n",
+    "* `n_iter = 4`: number of DFT iterations between the DMFT occupations. Should be large enough for the density correction to be fully mixed into the DFT calculation\n",
+    "* `n_cores = 32`: number of cores that DFT is run on. Check how many cores achieve the optimal DFT performance\n",
+    "* `dft_code = \"vasp\"`: we are running VASP\n",
+    "* `dft_exec = \"vasp_std\"`: the executable is vasp_std and its path is in the ROOT variable in our docker setup  \n",
+    "* `mpi_env = \"default\"`: sets the mpi environment\n",
+    "* `plo_cfg = \"plo.cfg\"`: the name of the config file for constructing the PLOs\n",
+    "* `projector_type = \"plo\"`: chooses PLO projectors\n",
+    "\n",
+    "The [plo.cfg](2_dmft_csc/plo.cfg) file is described [here](https://triqs.github.io/dft_tools/latest/guide/conv_vasp.html). The [rotations.dat](2_dmft_csc/rotations.dat) file is generated by diagonalizing the local d-shell density matrix and identifying the least occupied eigenstates as eg states. This we have limited k-point resolution wie also specify the band indices that describe our target space (isolated set of correlated states).\n",
+    "\n",
+    "### Starting the calculations\n",
+    "\n",
+    "Now we can start the calculations:\n",
+    "\n",
+    "* Go into the folder `2_dmft_csc`\n",
+    "* Link relevant files like CHGCAR, KPOINTS, POSCAR, POTCAR from previous directory by running `./2_link_files.sh`\n",
+    "* Run with `mpirun -n 32 python3 solid_dmft`\n",
+    "\n",
+    "### Analyzing the projectors\n",
+    "\n",
+    "Now we plot the DOS of the PLOs we are using to make sure that our correlated subspace works out as expected. You can speed up the calculation of the PLOs by removing the calculation of the DOS from the plo.cfg file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "2bba493e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "energies = []\n",
+    "doss = []\n",
+    "for imp in range(4):\n",
+    "    data = np.loadtxt(f'2_dmft_csc{path_mod}/pdos_0_{imp}.dat', unpack=True)\n",
+    "    energies.append(data[0])\n",
+    "    doss.append(data[1:])\n",
+    "    \n",
+    "energies = np.array(energies)\n",
+    "doss = np.array(doss)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "4ffe8e91",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "ax.plot(dft_energy-fermi_energy, dft_dos, label='Initial DFT total')\n",
+    "ax.plot(energies[0], np.sum(doss, axis=(0, 1)), label='PLO from CSC')\n",
+    "#for energy, dos in zip(energies, doss):\n",
+    "#    ax.plot(energy, dos.T)\n",
+    "ax.axhline(0, c='k')\n",
+    "ax.axvline(0, c='k')\n",
+    "ax.set_xlim(-8, 5)\n",
+    "ax.set_ylim(0,)\n",
+    "ax.set_xlabel('Energy relative to Fermi energy (eV)')\n",
+    "ax.set_ylabel('DOS (1/eV)')\n",
+    "ax.legend()\n",
+    "pass"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2a2a3293-3ef7-4457-942d-8a6bdcaabe29",
+   "metadata": {},
+   "source": [
+    "This plot shows the original DFT charge density and the PLO-DOS after applying the DMFT charge corrections. It proves that we are capturing indeed capturing the eg bands with the projectors, where the partial DOS differs a bit because of the changes from the charge self-consistency. Note that this quantity in the CSC DMFT formalism does not have any real meaning, it mainly serves as a check of the method. The correct quantity to analyze are the lattice or impurity Green's functions.\n",
+    "\n",
+    "## 3. Plotting the results: observables\n",
+    "\n",
+    "We first read in the pre-computed observables from the h5 archive and print their names:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d353c296-868a-45b5-bda6-2481d4df74ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with HDFArchive(f'2_dmft_csc{path_mod}/vasp.h5', 'r') as archive:\n",
+    "    observables = archive['DMFT_results/observables']\n",
+    "    conv_obs = archive['DMFT_results/convergence_obs']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ad578719-aa61-4560-baba-f01a4f28b726",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['E_DC', 'E_bandcorr', 'E_corr_en', 'E_dft', 'E_int', 'E_tot', 'imp_gb2', 'imp_occ', 'iteration', 'mu', 'orb_Z', 'orb_gb2', 'orb_occ'])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "observables.keys()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd150f57-3a8c-418a-a088-470180c86d87",
+   "metadata": {},
+   "source": [
+    "We will now use this to plot the occupation per impurity `imp_occ` (to see if there is charge disproportionation), the impurity Green's function at $\\tau=\\beta/2$ `imp_gb2` (to see if the system becomes insulating), the total energy `E_tot`, the DFT energy `E_dft`, and DMFT self-consistency condition over the iterations:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "41e955de-7a19-4e1f-bf27-f6973a8855d1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x1400 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(nrows=4, sharex=True, dpi=200,figsize=(7,7))\n",
+    "\n",
+    "for i in range(2):\n",
+    "    axes[0].plot(np.array(observables['imp_occ'][i]['up'])+np.array(observables['imp_occ'][i]['down']), '.-', c=f'C{i}',\n",
+    "                label=f'Impurity {i}')\n",
+    "    axes[1].plot(np.array(observables['imp_gb2'][i]['up'])+np.array(observables['imp_gb2'][i]['down']), '.-', c=f'C{i}')\n",
+    "    \n",
+    "    axes[3].semilogy(conv_obs['d_Gimp'][i], '.-', color=f'C{i}', label=f'Impurity {i}')\n",
+    "    \n",
+    "# Not impurity-dependent\n",
+    "axes[2].plot(observables['E_tot'], '.-', c='k', label='Total energy')\n",
+    "axes[2].plot(observables['E_dft'], 'x--', c='k', label='DFT energy')\n",
+    "\n",
+    "\n",
+    "axes[0].set_ylabel('Imp. occupation\\n')\n",
+    "axes[0].set_ylim(0, 2)\n",
+    "axes[0].legend()\n",
+    "axes[1].set_ylabel(r'$G(\\beta/2)$')\n",
+    "axes[2].set_ylabel('Energy')\n",
+    "axes[2].legend()\n",
+    "axes[3].set_ylabel(r'|G$_{imp}$-G$_{loc}$|')\n",
+    "axes[3].legend()\n",
+    "\n",
+    "axes[-1].set_xlabel('Iterations')\n",
+    "fig.subplots_adjust(hspace=.08)\n",
+    "pass"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "599730cc-8214-48cd-80a6-14676f2e23c0",
+   "metadata": {},
+   "source": [
+    "These plots show:\n",
+    "\n",
+    "* The occupation converges towards a disproportionated 1.6+0.4 electrons state\n",
+    "* Both sites become insulating, which we can deduce from $G(\\beta/2)$ from its relation to the spectral function at the Fermi energy $A(\\omega = 0) \\approx -(\\beta/\\pi) G(\\beta/2)$\n",
+    "* convergence is only setting in at around 20 DMFT iterations, which can be also seen from the column `rms(c)` in the Vasp OSZICAR file, and more DMFT iterations should be done ideally\n",
+    "\n",
+    "Therefore, we can conclude that we managed to capture the desired paramagnetic, insulating state that PrNiO3 shows in the experiments.\n",
+    "\n",
+    "## 4. Plotting the results: the Legendre Green's function\n",
+    "\n",
+    "We now take a look at the imaginary-time Green's function expressed in Legendre coefficients $G_l$. This is the main solver output (if we are measuring it) and also saved in the h5 archive."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "91f19160-3f34-4738-a9fa-8fe9c4289b0c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "legendre_gf = []\n",
+    "with HDFArchive(f'2_dmft_csc{path_mod}/vasp.h5') as archive:\n",
+    "    for i in range(2):\n",
+    "        legendre_gf.append(archive[f'DMFT_results/last_iter/Gimp_l_{i}'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "50176755-edbb-41ed-9656-5c648a08a6c0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "for i, legendre_coefficients_per_imp in enumerate(legendre_gf):\n",
+    "    if len(legendre_coefficients_per_imp) != 2:\n",
+    "        raise ValueError('Only blocks up_0 and down_0 supported')\n",
+    "\n",
+    "    data = (legendre_coefficients_per_imp['up_0'].data + legendre_coefficients_per_imp['down_0'].data).T\n",
+    "\n",
+    "    l_max = data.shape[2]\n",
+    "\n",
+    "    ax.semilogy(np.arange(0, l_max, 2), np.abs(np.trace(data[:, :, ::2].real, axis1=0, axis2=1)), 'x-',\n",
+    "                c=f'C{i}', label=f'Imp. {i}, even indices')\n",
+    "    ax.semilogy(np.arange(1, l_max, 2), np.abs(np.trace(data[:, :, 1::2].real, axis1=0, axis2=1)), '.:',\n",
+    "                c=f'C{i}', label=f'Imp. {i}, odd indices')\n",
+    "\n",
+    "ax.legend()\n",
+    "\n",
+    "ax.set_ylabel('Legendre coefficient $G_l$ (eV$^{-1}$)')\n",
+    "ax.set_xlabel(r'Index $l$')\n",
+    "pass"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8308345c-3f72-476c-8f58-583f9aeb1ccf",
+   "metadata": {},
+   "source": [
+    "The choice of the correct `n_l`, i.e., the Legendre cutoff is important. If it is too small, we are ignoring potential information about the Green's function. If it is too large, the noise filtering is not efficient. This can be seen by first running a few iterations with large `n_l`, e.g., 50. Then, the coefficients will first decay exponentially as in the plot above and then at higher $l$ starting showing noisy behavior. For more information about the Legendre coefficients, take a look [here](https://doi.org/10.1103/PhysRevB.84.075145).\n",
+    "\n",
+    "The noise itself should reduce with sqrt(`n_cycles_tot`) for QMC calculations but the prefactor always depends on material and its Hamiltonian, the electron filling, etc. But if you increase `n_cycles_tot`, make sure to test if you can include more Legendre coefficients.\n",
+    "\n",
+    "## 5. Next steps to try\n",
+    "\n",
+    "Here are some suggestions on how continue on this type of DMFT calculations:\n",
+    "\n",
+    "* change U and J and try to see if you can reach a metallic state. What does the occupation look like?\n",
+    "* try for better convergence: change `n_cycles_tot`, `n_iter_dmft` and `n_l`\n",
+    "* play around with the other parameters in the dmft_config.toml\n",
+    "* analyze other quantities or have a look at the spectral functions from analytical continuation\n",
+    "* try other ways to construct the correlated orbitals in CSC, e.g., with Wannier90\n",
+    "* apply this to the material of your choice!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "550fa534-c187-49d7-96e4-0848f53dd854",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/_sources/tutorials/SVO_os_qe/tutorial.ipynb.txt b/_sources/tutorials/SVO_os_qe/tutorial.ipynb.txt
new file mode 100644
index 00000000..4303d27b
--- /dev/null
+++ b/_sources/tutorials/SVO_os_qe/tutorial.ipynb.txt
@@ -0,0 +1,185 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Disclaimer:*\n",
+    "\n",
+    "Heavy calculations (~ 800 core hours): Current tutorial is best performed on an HPC facility.\n",
+    "\n",
+    "# 1. OS with QE/W90 and cthyb: SrVO3 MIT"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Hello and welcome to the first part of the tutorial for solid_dmft. Here we will guide to set up and run your first DMFT calculations. \n",
+    "\n",
+    "To begin your DMFT journey we will immediately start a DMFT run on strontium vanadate (SVO). SVO is a member of a family of material known as complex perovskite oxides with 1 electron occupying the t2g manifold. Below, we show the band structure of the frontier (anti-bonding) t2g bands for SVO.\n",
+    "\n",
+    "![svobands](./ref/bnd_structure.png \"SVO band structure\")\n",
+    "\n",
+    "In these materials, the electrons sitting on the transition metal ions (V in this case) are fairly localized, and the fully delocalized picture of DFT is insufficient to describe their physics. DMFT accounts for the electron-electron interaction by providing a fully interacting many body correction to the DFT non-interacting problem.\n",
+    "\n",
+    "If you want to generate the h5 archive `svo.h5` yourself, all the necessary files are in the `./quantum_espresso_files/` folder. We used quantum espresso in this tutorial.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## 1. Starting out with DMFT\n",
+    "\n",
+    "\n",
+    "To start your first calculation run:\n",
+    "\n",
+    "```\n",
+    "mpirun solid_dmft\n",
+    "\n",
+    "```\n",
+    "\n",
+    "Once the calculation is finished, inspect the `/out/` folder: our file of interest for the moment will be `observables_imp0.dat`, open the file:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```\n",
+    " it |        mu |            G(beta/2) per orbital |               orbital occs up+down |impurity occ\n",
+    "  0 |  12.29775 | -0.10489   -0.10489     -0.10489 |  0.33366      0.33366      0.33366 |     1.00097\n",
+    "  1 |  12.29775 | -0.09467   -0.09488     -0.09529 |  0.36155      0.35073      0.36169 |     1.07397\n",
+    "  2 |  12.31989 | -0.08451   -0.08363     -0.08463 |  0.33581      0.34048      0.34488 |     1.02117\n",
+    "  3 |  12.29775 | -0.08282   -0.08296     -0.08254 |  0.32738      0.34572      0.34479 |     1.01789\n",
+    "  4 |  12.28973 | -0.08617   -0.08595     -0.08620 |  0.33546      0.33757      0.33192 |     1.00494\n",
+    "  5 |  12.28825 | -0.08410   -0.08458     -0.08510 |  0.33582      0.33402      0.33759 |     1.00743\n",
+    "  6 |  12.28486 | -0.08474   -0.08549     -0.08618 |  0.32276      0.33028      0.32760 |     0.98063\n",
+    "  7 |  12.29097 | -0.08172   -0.08220     -0.08118 |  0.32072      0.33046      0.33529 |     0.98647\n",
+    "  8 |  12.29497 | -0.08318   -0.08254     -0.08332 |  0.34075      0.32957      0.33089 |     1.00120\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The meaning of the column names is the following:\n",
+    "\n",
+    "* **it**: number of the DMFT iteration\n",
+    "* **mu**: value of the chemical potential\n",
+    "* **G(beta/2) per orbital**: Green's function evaluated at $\\tau=\\beta/2$, this value is proportional to the projected density of states at the fermi level, the first objective of this tutorial would be to try and drive this value to 0\n",
+    "* **orbital occs up+down:** occupations of the various states in the manifold\n",
+    "* **impurity occ**: number of electrons in each site\n",
+    "---\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Looking at the Metal-Insulator Transition\n",
+    "\n",
+    "In the following steps we will try to drive the system towards a Mott-insulating state. \n",
+    "\n",
+    "Inspect the script `run_MIT_coarse.sh`, we iterate the same type of calculation that was performed in the last step for a series of value of U {2-10} and J {0.0-1.0}. \n",
+    "\n",
+    "Run the script, sit back and have a long coffee break, this is going to take a while (about 6 hours on 30 cores).\n",
+    "\n",
+    "Once the run is finished run \n",
+    "\n",
+    "`python3 ./collect_results_coarse.py`\n",
+    "\n",
+    "The script will produce a heatmap image of the value of G(beta/2) for each pair of U and J. The darker area corresponds to an insulating state.\n",
+    "\n",
+    "![coarsegrid](./ref/MIT_coarse.jpg \"Coarser grid\")\n",
+    "\n",
+    "Do you notice anything strange? (hint: look at the bottom right corner and check the output file `observables_imp0.dat` for U = 2 J=1.0. )\n",
+    "\n",
+    "We have seen that for 1 electron per system U and J are competing against each other: larger J favor the metallic state. The coulomb integral U wants to repel neighbouring electrons while J would like to bring electrons together on one site,. When the latter component dominates the resulting phase is known as a charge disproportionated state which is also insulating. What is happening in the bottom right corner is that the J favors here charge disproportionation but the unit cell has a single site, therefore the system has trouble converging and oscillates between a high occupation and a low occupation state."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Refining the diagram\n",
+    "\n",
+    "In order to get better resolution in terms of the diagram you can run the script `run_MIT_fine.sh` and plot the result with \n",
+    "\n",
+    "`python3 ./collect_results_fine.py`\n",
+    "\n",
+    "The result is also visible here:\n",
+    "\n",
+    "![finegrid](./ref/MIT_fine.jpg \"Finer grid\")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Plotting the spectral function\n",
+    "\n",
+    "The spectral function in DMFT represents the local density of states of the impurity site.\n",
+    "In order to plot it we need to use one of the scripts that implements the maximum entropy method ( [Maxent](https://triqs.github.io/maxent/latest/) ), while in the folder run (be aware that you need to substitute `/path_to_solid_dmft/` with the path where you have installed solid_dmft) :\n",
+    "\n",
+    "`mpirun -n 30 python3 /path_to_solid_dmft/python/solid_dmft/postprocessing/maxent_gf_imp.py ./J0.0/U4/out/svo.h5`\n",
+    "\n",
+    "and plot the result by running in the docker container:\n",
+    "\n",
+    "`python3 read_spectral_function.py`\n",
+    "\n",
+    "\n",
+    "![Afunc](./ref/A_func_J=0.0_U=4.jpg \"Afunc\")\n",
+    "\n",
+    "\n",
+    "Take care to edit the values of J and U in the python file. What is happing to the spectral function (density of states) as one cranks U up?\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5 Visualizing the MIT\n",
+    "\n",
+    "We will now plot the spectral function at different U values for J = 0.0 eV:\n",
+    "\n",
+    "Run the script `run_maxent_scan.sh`.\n",
+    "\n",
+    "Then collect the data:\n",
+    "\n",
+    "`python3 read_spectral_function_transition.py`\n",
+    "\n",
+    "![MIT](./ref/A_func_transition.jpg \"MIT\")\n",
+    "\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/_sources/tutorials/correlated_bandstructure/plot_correlated_bands.ipynb.txt b/_sources/tutorials/correlated_bandstructure/plot_correlated_bands.ipynb.txt
new file mode 100644
index 00000000..691622bc
--- /dev/null
+++ b/_sources/tutorials/correlated_bandstructure/plot_correlated_bands.ipynb.txt
@@ -0,0 +1,480 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "50bbc308",
+   "metadata": {},
+   "source": [
+    "# 5. Plotting the spectral function"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e8d5feac",
+   "metadata": {},
+   "source": [
+    "In this tutorial we go through the steps to plot tight-binding bands from a Wannier90 Hamiltonian and spectralfunctions with analytically continued (real-frequency) self-energies obtained from DMFT."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "0d69c4d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "from IPython.display import display\n",
+    "from IPython.display import Image\n",
+    "import numpy as np\n",
+    "import importlib, sys\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib import cm\n",
+    "from timeit import default_timer as timer\n",
+    "\n",
+    "from ase.io.espresso import read_espresso_in\n",
+    "\n",
+    "from h5 import HDFArchive\n",
+    "from solid_dmft.postprocessing import plot_correlated_bands as pcb"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c3ce4f44",
+   "metadata": {},
+   "source": [
+    "## 1. Configuration"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "42a860c4",
+   "metadata": {},
+   "source": [
+    "The script makes use of the `triqs.lattice.utils` class, which allows to set up a tight-binding model based on a Wannier90 Hamiltonian. Additionally, you may upload a self-energy in the usual `solid_dmft` format to compute correlated spectral properties.\n",
+    "Currently, the following options are implemented:\n",
+    "<ol>\n",
+    "<li>bandstructure</li>\n",
+    "<li>Fermi slice</li>\n",
+    "</ol>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b8d962f9",
+   "metadata": {},
+   "source": [
+    "### Basic options"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b652e03a",
+   "metadata": {},
+   "source": [
+    "We start with configuring these options. For this example we try a tight-binding bandstructure including the correlated bands (`kslice = False`, `'tb': True`, `'alatt': True`), but feel free to come back here to explore. Alternatively to an intensity plot of the correlated bands (`qp_bands`), you can compute the correlated quasiparticle bands assuming a Fermi liquid regime.\\\n",
+    "The options for $\\Sigma(\\omega)$ are `calc` or `model`, which performs a Fermi liquid linearization in the low-frequency regime. The latter will be reworked, so better stick with `calc` for now."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b8f73a48",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kslice = False\n",
+    "\n",
+    "bands_config = {'tb': True, 'alatt': True, 'qp_bands': False, 'sigma': 'calc'}\n",
+    "kslice_config = {'tb': True, 'alatt': True, 'qp_bands': False, 'sigma': 'calc'}\n",
+    "config = kslice_config if kslice else bands_config"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3c6ece97",
+   "metadata": {},
+   "source": [
+    "### Wannier90"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6d0ce79b",
+   "metadata": {},
+   "source": [
+    "Next we will set up the Wannier90 Input. Provide the path, seedname, chemical potential and orbital order used in Wannier90. You may add a spin-component, and any other local Hamiltonian. For `t2g` models the orbital order can be changed (to `orbital_order_to`) and a local spin-orbit coupling term can be added (`add_lambda`). The spectral properties can be viewed projected on a specific orbital."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "27a94d47",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "w90_path = './'\n",
+    "w90_dict = {'w90_seed': 'svo', 'w90_path': w90_path, 'mu_tb': 12.3958, 'n_orb': 3,\n",
+    "            'orbital_order_w90': ['dxz', 'dyz', 'dxy'], 'add_spin': False}\n",
+    "\n",
+    "orbital_order_to = ['dxy', 'dxz', 'dyz']\n",
+    "proj_on_orb = None # or 'dxy' etc"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "57f41c87",
+   "metadata": {},
+   "source": [
+    "### BZ configuration"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f23d7e3a",
+   "metadata": {},
+   "source": [
+    "#### Optional: ASE Brillouin Zone"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fc7b2fac",
+   "metadata": {},
+   "source": [
+    "It might be helpful to have a brief look at the Brillouin Zone by loading an input file of your favorite DFT code (Quantum Espresso in this case). ASE will write out the special $k$-points, which we can use to configure the BZ path. Alternatively, you can of course define the dictionary `kpts_dict` yourself. Careful, it might not define $Z$, which is needed and added below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "c6e46f88",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "G [0. 0. 0.]\n",
+      "M [0.5 0.5 0. ]\n",
+      "R [0.5 0.5 0.5]\n",
+      "X [0.  0.5 0. ]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes3DSubplot:>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "scf_in = './svo.scf.in'\n",
+    "\n",
+    "# read scf file\n",
+    "atoms = read_espresso_in(scf_in)\n",
+    "# set up cell and path\n",
+    "lat = atoms.cell.get_bravais_lattice()\n",
+    "path = atoms.cell.bandpath('', npoints=100)\n",
+    "kpts_dict = path.todict()['special_points']\n",
+    "\n",
+    "for key, value in kpts_dict.items():\n",
+    "    print(key, value)\n",
+    "lat.plot_bz()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "31956a53",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e47c2a48",
+   "metadata": {},
+   "source": [
+    "Depending on whether you select `kslice=True` or `False`, a corresponding `tb_config` needs to be provided containing information about the $k$-points, resolution (`n_k`) or `kz`-plane in the case of the Fermi slice. Here we just import the $k$-point dictionary provided by ASE above and add the $Z$-point. If you are unhappy with the resolution of the final plot, come back here and crank up `n_k`. For the kslice, the first letter corresponds to the upper left corner of the plotted Brillouin zone, followed by the lower left corner and the lower right one ($Y$, $\\Gamma$, and $X$ in this case)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "68c0f047",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# band specs\n",
+    "tb_bands = {'bands_path': [('R', 'G'), ('G', 'X'), ('X', 'M'), ('M', 'G')], 'Z': np.array([0,0,0.5]), 'n_k': 50}\n",
+    "tb_bands.update(kpts_dict)\n",
+    "\n",
+    "# kslice specs\n",
+    "tb_kslice = {key: tb_bands[key] for key in list(tb_bands.keys()) if key.isupper()}\n",
+    "kslice_update = {'bands_path': [('Y', 'G'),('G', 'X')], 'Y': np.array([0.5,0.0,0]), 'n_k': 50, 'kz': 0.0}\n",
+    "tb_kslice.update(kslice_update)\n",
+    "\n",
+    "tb_config = tb_kslice if kslice else tb_bands"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bf58de16",
+   "metadata": {},
+   "source": [
+    "### Self-energy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "67e42361",
+   "metadata": {},
+   "source": [
+    "Here we provide the info needed from the h5Archive, like the self-energy, iteration count, spin and block component and the frequency mesh used for the interpolation. The values for the mesh of course depend on the quantity of interest. For a kslice the resolution around $\\omega=0$ is crucial and we need only a small energy window, while for a bandstructure we are also interested in high energy features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "70fd0787",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "freq_mesh_kslice = {'window': [-0.5, 0.5], 'n_w': int(1e6)}\n",
+    "freq_mesh_bands = {'window': [-5, 5], 'n_w': int(1e3)}\n",
+    "freq_mesh = freq_mesh_kslice if kslice else freq_mesh_bands\n",
+    "\n",
+    "dmft_path = './svo_example.h5'\n",
+    "\n",
+    "proj_on_orb = orbital_order_to.index(proj_on_orb) if proj_on_orb else None\n",
+    "sigma_dict = {'dmft_path': dmft_path, 'it': 'last_iter', 'orbital_order_dmft': orbital_order_to, 'spin': 'up',\n",
+    "              'block': 0, 'eta': 0.0, 'w_mesh': freq_mesh, 'linearize': False, 'proj_on_orb' : proj_on_orb}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e314f15",
+   "metadata": {},
+   "source": [
+    "__Optional__: for completeness and as a sanity check we quickly take a look at the self-energy. Make sure you provide a physical one!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e7cb04b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 2000x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "with HDFArchive(dmft_path, 'r') as h5:\n",
+    "    sigma_freq = h5['DMFT_results']['last_iter']['Sigma_freq_0']\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 2, figsize=(10,2), squeeze=False, dpi=200)\n",
+    "\n",
+    "orb = 0\n",
+    "sp = 'up_0'\n",
+    "freq_mesh = np.array([w.value for w in sigma_freq[sp][orb,orb].mesh])\n",
+    "\n",
+    "ax[0,0].plot(freq_mesh, sigma_freq[sp][orb,orb].data.real)\n",
+    "ax[0,1].plot(freq_mesh, -sigma_freq[sp][orb,orb].data.imag)\n",
+    "\n",
+    "ax[0,0].set_ylabel(r'Re$\\Sigma(\\omega)$')\n",
+    "ax[0,1].set_ylabel(r'Im$\\Sigma(\\omega)$')\n",
+    "for ct in range(2):\n",
+    "    ax[0,ct].grid()\n",
+    "    ax[0,ct].set_xlim(-2, 2)\n",
+    "    ax[0,ct].set_xlabel(r'$\\omega$ (eV)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c249dc9",
+   "metadata": {},
+   "source": [
+    "### Plotting options"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93d1db24",
+   "metadata": {},
+   "source": [
+    "Finally, you can choose colormaps for each of the functionalities from any of the available on <a href=\"https://matplotlib.org/stable/tutorials/colors/colormaps.html\">matplotlib colormaps</a>. `vmin` determines the scaling of the logarithmically scaled colorplots. The corresponding tight-binding bands will have the maximum value of the colormap. By the way, colormaps can be reversed by appending `_r` to the identifier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a2d79e6d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_config = {'colorscheme_alatt': 'coolwarm', 'colorscheme_bands': 'coolwarm', 'colorscheme_kslice': 'PuBuGn',\n",
+    "               'colorscheme_qpbands': 'Greens', 'vmin': 0.0}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b2e5a0e",
+   "metadata": {},
+   "source": [
+    "## 2. Run and Plotting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "89a67dd6",
+   "metadata": {},
+   "source": [
+    "Now that everything is set up we may hit run. Caution, if you use a lot of $k$-points, this may take a while! In the current example, it should be done within a second."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "7e875f21",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warning: could not identify MPI environment!\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Starting serial run at: 2022-08-01 11:33:20.627842\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "H(R=0):\n",
+      "     12.9769  0.0000  0.0000\n",
+      "      0.0000 12.9769  0.0000\n",
+      "      0.0000  0.0000 12.9769\n",
+      "Setting Sigma from ./svo_example.h5\n",
+      "Adding mu_tb to DMFT μ; assuming DMFT was run with subtracted dft μ.\n",
+      "μ=12.2143 eV set for calculating A(k,ω)\n",
+      "Run took 0.588 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "start_time = timer()\n",
+    "\n",
+    "tb_data, alatt_k_w, freq_dict = pcb.get_dmft_bands(fermi_slice=kslice, with_sigma=bands_config['sigma'], add_mu_tb=True,\n",
+    "                                                   orbital_order_to=orbital_order_to, qp_bands=config['qp_bands'],\n",
+    "                                                   **w90_dict, **tb_config, **sigma_dict)\n",
+    "\n",
+    "print('Run took {0:.3f} s'.format(timer() - start_time))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7780b5d",
+   "metadata": {},
+   "source": [
+    "That's it. Now you can look at the output:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "1936db33",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1200x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "if kslice:\n",
+    "    fig, ax = plt.subplots(1, figsize=(3,3), dpi=200)\n",
+    "\n",
+    "    pcb.plot_kslice(fig, ax, alatt_k_w, tb_data, freq_dict, w90_dict['n_orb'], tb_config,\n",
+    "                    tb=config['tb'], alatt=config['alatt'], quarter=0, **plot_config)\n",
+    "\n",
+    "else:\n",
+    "    fig, ax = plt.subplots(1, figsize=(6,3), dpi=200)\n",
+    "\n",
+    "    pcb.plot_bands(fig, ax, alatt_k_w, tb_data, freq_dict, w90_dict['n_orb'], dft_mu=0.,\n",
+    "                   tb=config['tb'], alatt=config['alatt'], qp_bands=config['qp_bands'], **plot_config)\n",
+    "\n",
+    "    ax.set_ylim(-1.25,1.75)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "186cf322",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/_static/basic.css b/_static/basic.css
new file mode 100644
index 00000000..603f6a87
--- /dev/null
+++ b/_static/basic.css
@@ -0,0 +1,905 @@
+/*
+ * basic.css
+ * ~~~~~~~~~
+ *
+ * Sphinx stylesheet -- basic theme.
+ *
+ * :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+/* -- main layout ----------------------------------------------------------- */
+
+div.clearer {
+    clear: both;
+}
+
+div.section::after {
+    display: block;
+    content: '';
+    clear: left;
+}
+
+/* -- relbar ---------------------------------------------------------------- */
+
+div.related {
+    width: 100%;
+    font-size: 90%;
+}
+
+div.related h3 {
+    display: none;
+}
+
+div.related ul {
+    margin: 0;
+    padding: 0 0 0 10px;
+    list-style: none;
+}
+
+div.related li {
+    display: inline;
+}
+
+div.related li.right {
+    float: right;
+    margin-right: 5px;
+}
+
+/* -- sidebar --------------------------------------------------------------- */
+
+div.sphinxsidebarwrapper {
+    padding: 10px 5px 0 10px;
+}
+
+div.sphinxsidebar {
+    float: left;
+    width: 230px;
+    margin-left: -100%;
+    font-size: 90%;
+    word-wrap: break-word;
+    overflow-wrap : break-word;
+}
+
+div.sphinxsidebar ul {
+    list-style: none;
+}
+
+div.sphinxsidebar ul ul,
+div.sphinxsidebar ul.want-points {
+    margin-left: 20px;
+    list-style: square;
+}
+
+div.sphinxsidebar ul ul {
+    margin-top: 0;
+    margin-bottom: 0;
+}
+
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+}
+
+
+img {
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+}
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+    padding: 0;
+}
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+    padding: 5px 0 5px 20px;
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+}
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+}
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+}
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+ul.keywordmatches li.goodmatch a {
+    font-weight: bold;
+}
+
+/* -- index page ------------------------------------------------------------ */
+
+table.contentstable {
+    width: 90%;
+    margin-left: auto;
+    margin-right: auto;
+}
+
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+    line-height: 150%;
+}
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+    font-size: 1.3em;
+}
+
+span.linkdescr {
+    font-style: italic;
+    padding-top: 5px;
+    font-size: 90%;
+}
+
+/* -- general index --------------------------------------------------------- */
+
+table.indextable {
+    width: 100%;
+}
+
+table.indextable td {
+    text-align: left;
+    vertical-align: top;
+}
+
+table.indextable ul {
+    margin-top: 0;
+    margin-bottom: 0;
+    list-style-type: none;
+}
+
+table.indextable > tbody > tr > td > ul {
+    padding-left: 0em;
+}
+
+table.indextable tr.pcap {
+    height: 10px;
+}
+
+table.indextable tr.cap {
+    margin-top: 10px;
+    background-color: #f2f2f2;
+}
+
+img.toggler {
+    margin-right: 3px;
+    margin-top: 3px;
+    cursor: pointer;
+}
+
+div.modindex-jumpbox {
+    border-top: 1px solid #ddd;
+    border-bottom: 1px solid #ddd;
+    margin: 1em 0 1em 0;
+    padding: 0.4em;
+}
+
+div.genindex-jumpbox {
+    border-top: 1px solid #ddd;
+    border-bottom: 1px solid #ddd;
+    margin: 1em 0 1em 0;
+    padding: 0.4em;
+}
+
+/* -- domain module index --------------------------------------------------- */
+
+table.modindextable td {
+    padding: 2px;
+    border-collapse: collapse;
+}
+
+/* -- general body styles --------------------------------------------------- */
+
+div.body {
+    min-width: 450px;
+    max-width: 800px;
+}
+
+div.body p, div.body dd, div.body li, div.body blockquote {
+    -moz-hyphens: auto;
+    -ms-hyphens: auto;
+    -webkit-hyphens: auto;
+    hyphens: auto;
+}
+
+a.headerlink {
+    visibility: hidden;
+}
+
+a.brackets:before,
+span.brackets > a:before{
+    content: "[";
+}
+
+a.brackets:after,
+span.brackets > a:after {
+    content: "]";
+}
+
+h1:hover > a.headerlink,
+h2:hover > a.headerlink,
+h3:hover > a.headerlink,
+h4:hover > a.headerlink,
+h5:hover > a.headerlink,
+h6:hover > a.headerlink,
+dt:hover > a.headerlink,
+caption:hover > a.headerlink,
+p.caption:hover > a.headerlink,
+div.code-block-caption:hover > a.headerlink {
+    visibility: visible;
+}
+
+div.body p.caption {
+    text-align: inherit;
+}
+
+div.body td {
+    text-align: left;
+}
+
+.first {
+    margin-top: 0 !important;
+}
+
+p.rubric {
+    margin-top: 30px;
+    font-weight: bold;
+}
+
+img.align-left, figure.align-left, .figure.align-left, object.align-left {
+    clear: left;
+    float: left;
+    margin-right: 1em;
+}
+
+img.align-right, figure.align-right, .figure.align-right, object.align-right {
+    clear: right;
+    float: right;
+    margin-left: 1em;
+}
+
+img.align-center, figure.align-center, .figure.align-center, object.align-center {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+
+img.align-default, figure.align-default, .figure.align-default {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+
+.align-left {
+    text-align: left;
+}
+
+.align-center {
+    text-align: center;
+}
+
+.align-default {
+    text-align: center;
+}
+
+.align-right {
+    text-align: right;
+}
+
+/* -- sidebars -------------------------------------------------------------- */
+
+div.sidebar,
+aside.sidebar {
+    margin: 0 0 0.5em 1em;
+    border: 1px solid #ddb;
+    padding: 7px;
+    background-color: #ffe;
+    width: 40%;
+    float: right;
+    clear: right;
+    overflow-x: auto;
+}
+
+p.sidebar-title {
+    font-weight: bold;
+}
+
+div.admonition, div.topic, blockquote {
+    clear: left;
+}
+
+/* -- topics ---------------------------------------------------------------- */
+
+div.topic {
+    border: 1px solid #ccc;
+    padding: 7px;
+    margin: 10px 0 10px 0;
+}
+
+p.topic-title {
+    font-size: 1.1em;
+    font-weight: bold;
+    margin-top: 10px;
+}
+
+/* -- admonitions ----------------------------------------------------------- */
+
+div.admonition {
+    margin-top: 10px;
+    margin-bottom: 10px;
+    padding: 7px;
+}
+
+div.admonition dt {
+    font-weight: bold;
+}
+
+p.admonition-title {
+    margin: 0px 10px 5px 0px;
+    font-weight: bold;
+}
+
+div.body p.centered {
+    text-align: center;
+    margin-top: 25px;
+}
+
+/* -- content of sidebars/topics/admonitions -------------------------------- */
+
+div.sidebar > :last-child,
+aside.sidebar > :last-child,
+div.topic > :last-child,
+div.admonition > :last-child {
+    margin-bottom: 0;
+}
+
+div.sidebar::after,
+aside.sidebar::after,
+div.topic::after,
+div.admonition::after,
+blockquote::after {
+    display: block;
+    content: '';
+    clear: both;
+}
+
+/* -- tables ---------------------------------------------------------------- */
+
+table.docutils {
+    margin-top: 10px;
+    margin-bottom: 10px;
+    border: 0;
+    border-collapse: collapse;
+}
+
+table.align-center {
+    margin-left: auto;
+    margin-right: auto;
+}
+
+table.align-default {
+    margin-left: auto;
+    margin-right: auto;
+}
+
+table caption span.caption-number {
+    font-style: italic;
+}
+
+table caption span.caption-text {
+}
+
+table.docutils td, table.docutils th {
+    padding: 1px 8px 1px 5px;
+    border-top: 0;
+    border-left: 0;
+    border-right: 0;
+    border-bottom: 1px solid #aaa;
+}
+
+table.footnote td, table.footnote th {
+    border: 0 !important;
+}
+
+th {
+    text-align: left;
+    padding-right: 5px;
+}
+
+table.citation {
+    border-left: solid 1px gray;
+    margin-left: 1px;
+}
+
+table.citation td {
+    border-bottom: none;
+}
+
+th > :first-child,
+td > :first-child {
+    margin-top: 0px;
+}
+
+th > :last-child,
+td > :last-child {
+    margin-bottom: 0px;
+}
+
+/* -- figures --------------------------------------------------------------- */
+
+div.figure, figure {
+    margin: 0.5em;
+    padding: 0.5em;
+}
+
+div.figure p.caption, figcaption {
+    padding: 0.3em;
+}
+
+div.figure p.caption span.caption-number,
+figcaption span.caption-number {
+    font-style: italic;
+}
+
+div.figure p.caption span.caption-text,
+figcaption span.caption-text {
+}
+
+/* -- field list styles ----------------------------------------------------- */
+
+table.field-list td, table.field-list th {
+    border: 0 !important;
+}
+
+.field-list ul {
+    margin: 0;
+    padding-left: 1em;
+}
+
+.field-list p {
+    margin: 0;
+}
+
+.field-name {
+    -moz-hyphens: manual;
+    -ms-hyphens: manual;
+    -webkit-hyphens: manual;
+    hyphens: manual;
+}
+
+/* -- hlist styles ---------------------------------------------------------- */
+
+table.hlist {
+    margin: 1em 0;
+}
+
+table.hlist td {
+    vertical-align: top;
+}
+
+/* -- object description styles --------------------------------------------- */
+
+.sig {
+	font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace;
+}
+
+.sig-name, code.descname {
+    background-color: transparent;
+    font-weight: bold;
+}
+
+.sig-name {
+	font-size: 1.1em;
+}
+
+code.descname {
+    font-size: 1.2em;
+}
+
+.sig-prename, code.descclassname {
+    background-color: transparent;
+}
+
+.optional {
+    font-size: 1.3em;
+}
+
+.sig-paren {
+    font-size: larger;
+}
+
+.sig-param.n {
+	font-style: italic;
+}
+
+/* C++ specific styling */
+
+.sig-inline.c-texpr,
+.sig-inline.cpp-texpr {
+	font-family: unset;
+}
+
+.sig.c   .k, .sig.c   .kt,
+.sig.cpp .k, .sig.cpp .kt {
+	color: #0033B3;
+}
+
+.sig.c   .m,
+.sig.cpp .m {
+	color: #1750EB;
+}
+
+.sig.c   .s, .sig.c   .sc,
+.sig.cpp .s, .sig.cpp .sc {
+	color: #067D17;
+}
+
+
+/* -- other body styles ----------------------------------------------------- */
+
+ol.arabic {
+    list-style: decimal;
+}
+
+ol.loweralpha {
+    list-style: lower-alpha;
+}
+
+ol.upperalpha {
+    list-style: upper-alpha;
+}
+
+ol.lowerroman {
+    list-style: lower-roman;
+}
+
+ol.upperroman {
+    list-style: upper-roman;
+}
+
+:not(li) > ol > li:first-child > :first-child,
+:not(li) > ul > li:first-child > :first-child {
+    margin-top: 0px;
+}
+
+:not(li) > ol > li:last-child > :last-child,
+:not(li) > ul > li:last-child > :last-child {
+    margin-bottom: 0px;
+}
+
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+ol.simple ul p,
+ul.simple ol p,
+ul.simple ul p {
+    margin-top: 0;
+}
+
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+ul.simple > li:not(:first-child) > p {
+    margin-top: 0;
+}
+
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+ul.simple p {
+    margin-bottom: 0;
+}
+
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+dl.citation > dt {
+    float: left;
+    margin-right: 0.5em;
+}
+
+dl.footnote > dd,
+dl.citation > dd {
+    margin-bottom: 0em;
+}
+
+dl.footnote > dd:after,
+dl.citation > dd:after {
+    content: "";
+    clear: both;
+}
+
+dl.field-list {
+    display: grid;
+    grid-template-columns: fit-content(30%) auto;
+}
+
+dl.field-list > dt {
+    font-weight: bold;
+    word-break: break-word;
+    padding-left: 0.5em;
+    padding-right: 5px;
+}
+
+dl.field-list > dt:after {
+    content: ":";
+}
+
+dl.field-list > dd {
+    padding-left: 0.5em;
+    margin-top: 0em;
+    margin-left: 0em;
+    margin-bottom: 0em;
+}
+
+dl {
+    margin-bottom: 15px;
+}
+
+dd > :first-child {
+    margin-top: 0px;
+}
+
+dd ul, dd table {
+    margin-bottom: 10px;
+}
+
+dd {
+    margin-top: 3px;
+    margin-bottom: 10px;
+    margin-left: 30px;
+}
+
+dl > dd:last-child,
+dl > dd:last-child > :last-child {
+    margin-bottom: 0;
+}
+
+dt:target, span.highlighted {
+    background-color: #fbe54e;
+}
+
+rect.highlighted {
+    fill: #fbe54e;
+}
+
+dl.glossary dt {
+    font-weight: bold;
+    font-size: 1.1em;
+}
+
+.versionmodified {
+    font-style: italic;
+}
+
+.system-message {
+    background-color: #fda;
+    padding: 5px;
+    border: 3px solid red;
+}
+
+.footnote:target  {
+    background-color: #ffa;
+}
+
+.line-block {
+    display: block;
+    margin-top: 1em;
+    margin-bottom: 1em;
+}
+
+.line-block .line-block {
+    margin-top: 0;
+    margin-bottom: 0;
+    margin-left: 1.5em;
+}
+
+.guilabel, .menuselection {
+    font-family: sans-serif;
+}
+
+.accelerator {
+    text-decoration: underline;
+}
+
+.classifier {
+    font-style: oblique;
+}
+
+.classifier:before {
+    font-style: normal;
+    margin: 0 0.5em;
+    content: ":";
+    display: inline-block;
+}
+
+abbr, acronym {
+    border-bottom: dotted 1px;
+    cursor: help;
+}
+
+/* -- code displays --------------------------------------------------------- */
+
+pre {
+    overflow: auto;
+    overflow-y: hidden;  /* fixes display issues on Chrome browsers */
+}
+
+pre, div[class*="highlight-"] {
+    clear: both;
+}
+
+span.pre {
+    -moz-hyphens: none;
+    -ms-hyphens: none;
+    -webkit-hyphens: none;
+    hyphens: none;
+}
+
+div[class*="highlight-"] {
+    margin: 1em 0;
+}
+
+td.linenos pre {
+    border: 0;
+    background-color: transparent;
+    color: #aaa;
+}
+
+table.highlighttable {
+    display: block;
+}
+
+table.highlighttable tbody {
+    display: block;
+}
+
+table.highlighttable tr {
+    display: flex;
+}
+
+table.highlighttable td {
+    margin: 0;
+    padding: 0;
+}
+
+table.highlighttable td.linenos {
+    padding-right: 0.5em;
+}
+
+table.highlighttable td.code {
+    flex: 1;
+    overflow: hidden;
+}
+
+.highlight .hll {
+    display: block;
+}
+
+div.highlight pre,
+table.highlighttable pre {
+    margin: 0;
+}
+
+div.code-block-caption + div {
+    margin-top: 0;
+}
+
+div.code-block-caption {
+    margin-top: 1em;
+    padding: 2px 5px;
+    font-size: small;
+}
+
+div.code-block-caption code {
+    background-color: transparent;
+}
+
+table.highlighttable td.linenos,
+span.linenos,
+div.highlight span.gp {  /* gp: Generic.Prompt */
+  user-select: none;
+  -webkit-user-select: text; /* Safari fallback only */
+  -webkit-user-select: none; /* Chrome/Safari */
+  -moz-user-select: none; /* Firefox */
+  -ms-user-select: none; /* IE10+ */
+}
+
+div.code-block-caption span.caption-number {
+    padding: 0.1em 0.3em;
+    font-style: italic;
+}
+
+div.code-block-caption span.caption-text {
+}
+
+div.literal-block-wrapper {
+    margin: 1em 0;
+}
+
+code.xref, a code {
+    background-color: transparent;
+    font-weight: bold;
+}
+
+h1 code, h2 code, h3 code, h4 code, h5 code, h6 code {
+    background-color: transparent;
+}
+
+.viewcode-link {
+    float: right;
+}
+
+.viewcode-back {
+    float: right;
+    font-family: sans-serif;
+}
+
+div.viewcode-block:target {
+    margin: -1px -10px;
+    padding: 0 10px;
+}
+
+/* -- math display ---------------------------------------------------------- */
+
+img.math {
+    vertical-align: middle;
+}
+
+div.body div.math p {
+    text-align: center;
+}
+
+span.eqno {
+    float: right;
+}
+
+span.eqno a.headerlink {
+    position: absolute;
+    z-index: 1;
+}
+
+div.math:hover a.headerlink {
+    visibility: visible;
+}
+
+/* -- printout stylesheet --------------------------------------------------- */
+
+@media print {
+    div.document,
+    div.documentwrapper,
+    div.bodywrapper {
+        margin: 0 !important;
+        width: 100%;
+    }
+
+    div.sphinxsidebar,
+    div.related,
+    div.footer,
+    #top-link {
+        display: none;
+    }
+}
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diff --git a/_static/css/badge_only.css b/_static/css/badge_only.css
new file mode 100644
index 00000000..4d153448
--- /dev/null
+++ b/_static/css/badge_only.css
@@ -0,0 +1 @@
+.fa:before{-webkit-font-smoothing:antialiased}.clearfix{*zoom:1}.clearfix:before,.clearfix:after{display:table;content:""}.clearfix:after{clear:both}@font-face{font-family:FontAwesome;font-style:normal;font-weight:normal;src:url("../fonts/fontawesome-webfont.eot?#iefix") format("embedded-opentype"),url("../fonts/fontawesome-webfont.woff2") format("woff2"),url("../fonts/fontawesome-webfont.woff") format("woff"),url("../fonts/fontawesome-webfont.ttf") format("truetype"),url("../fonts/fontawesome-webfont.svg#FontAwesome") format("svg")}.fa:before{display:inline-block;font-family:FontAwesome;font-style:normal;font-weight:normal;line-height:1;text-decoration:inherit}a .fa{display:inline-block;text-decoration:inherit}li .fa{display:inline-block}li .fa-large:before,li .fa-large:before{width:1.875em}ul.fas{list-style-type:none;margin-left:2em;text-indent:-0.8em}ul.fas li .fa{width:.8em}ul.fas li .fa-large:before,ul.fas li .fa-large:before{vertical-align:baseline}.fa-book:before{content:""}.icon-book:before{content:""}.fa-caret-down:before{content:""}.icon-caret-down:before{content:""}.fa-caret-up:before{content:""}.icon-caret-up:before{content:""}.fa-caret-left:before{content:""}.icon-caret-left:before{content:""}.fa-caret-right:before{content:""}.icon-caret-right:before{content:""}.rst-versions{position:fixed;bottom:0;left:0;width:300px;color:#fcfcfc;background:#1f1d1d;font-family:"Lato","proxima-nova","Helvetica Neue",Arial,sans-serif;z-index:400}.rst-versions a{color:#2980B9;text-decoration:none}.rst-versions .rst-badge-small{display:none}.rst-versions .rst-current-version{padding:12px;background-color:#272525;display:block;text-align:right;font-size:90%;cursor:pointer;color:#27AE60}.rst-versions .rst-current-version::after{clear:both;content:"";display:block}.rst-versions .rst-current-version .fa{color:#fcfcfc}.rst-versions .rst-current-version .fa-book{float:left}.rst-versions .rst-current-version .icon-book{float:left}.rst-versions .rst-current-version.rst-out-of-date{background-color:#E74C3C;color:#fff}.rst-versions .rst-current-version.rst-active-old-version{background-color:#F1C40F;color:#000}.rst-versions.shift-up{height:auto;max-height:100%;overflow-y:scroll}.rst-versions.shift-up .rst-other-versions{display:block}.rst-versions .rst-other-versions{font-size:90%;padding:12px;color:gray;display:none}.rst-versions .rst-other-versions hr{display:block;height:1px;border:0;margin:20px 0;padding:0;border-top:solid 1px #413d3d}.rst-versions .rst-other-versions dd{display:inline-block;margin:0}.rst-versions .rst-other-versions dd a{display:inline-block;padding:6px;color:#fcfcfc}.rst-versions.rst-badge{width:auto;bottom:20px;right:20px;left:auto;border:none;max-width:300px;max-height:90%}.rst-versions.rst-badge .icon-book{float:none;line-height:30px}.rst-versions.rst-badge .fa-book{float:none;line-height:30px}.rst-versions.rst-badge.shift-up .rst-current-version{text-align:right}.rst-versions.rst-badge.shift-up .rst-current-version .fa-book{float:left}.rst-versions.rst-badge.shift-up .rst-current-version .icon-book{float:left}.rst-versions.rst-badge>.rst-current-version{width:auto;height:30px;line-height:30px;padding:0 6px;display:block;text-align:center}@media screen and (max-width: 768px){.rst-versions{width:85%;display:none}.rst-versions.shift{display:block}}
diff --git a/_static/css/custom.css b/_static/css/custom.css
new file mode 100644
index 00000000..428cb68a
--- /dev/null
+++ b/_static/css/custom.css
@@ -0,0 +1,28 @@
+@import url("theme.css");
+
+.wy-nav-content {
+    max-width: 70em;
+}
+
+/* here we add an empty svg icon to overwrite the rst icon in order to avoid the default exclamation mark, alternatively we can add a small icon  */
+:root {
+  --icon--empty-icon: url('data:image/svg+xml;charset=utf-8,<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 576 512"><path d=""/></svg>');
+  --icon--book-icon: url('data:image/svg+xml;charset=utf-8,<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 487.5 487.5" style="disable-backgrounds" xml:space="preserve"><path d="M437 12.3C437 5.5 431.5 0 424.7 0H126.3C84.4 0 50.4 34.1 50.4 75.9v335.7c0 41.9 34.1 75.9 75.9 75.9h298.5c6.8 0 12.3-5.5 12.3-12.3V139.6c0-6.8-5.5-12.3-12.3-12.3H126.3c-28.3 0-51.4-23.1-51.4-51.4S98 24.5 126.3 24.5h298.5c6.7 0 12.2-5.5 12.2-12.2zM126.3 151.8h286.2V463H126.3c-28.3 0-51.4-23.1-51.4-51.4V131.7c13.5 12.5 31.6 20.1 51.4 20.1z"/><path d="M130.5 64.8c-6.8 0-12.3 5.5-12.3 12.3s5.5 12.3 12.3 12.3h280.1c6.8 0 12.3-5.5 12.3-12.3s-5.5-12.3-12.3-12.3H130.5zM178 397.7c6.3 2.4 13.4-.7 15.8-7.1l17.9-46.8h62.7c.5 0 .9-.1 1.3-.1l17.9 46.9c1.9 4.9 6.5 7.9 11.4 7.9 1.5 0 2.9-.3 4.4-.8 6.3-2.4 9.5-9.5 7.1-15.8l-54-141.2c-3-7.9-10.4-13-18.8-13s-15.8 5.1-18.8 13l-54 141.2c-2.4 6.3.8 13.3 7.1 15.8zM243.7 260l22.7 59.3h-45.3l22.6-59.3z"/></svg>');
+  --icon--square: url('data:image/svg+xml;charset=utf-8,<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 32 32" style="enable-background:new 0 0 32 32"><path d="M0 0h32v32H0z"/></svg>');
+}
+
+/* definining a new admonition class called 'intag' */
+.admonition.intag {
+  border-color: rgb(0, 0, 0);
+}
+
+/* title layout of the new */
+.admonition.intag > .admonition-title {
+  border-color: rgb(0, 0, 0);
+}
+
+/* Removes everything, including the icon before the title in the 'intag' admonition*/
+.admonition.intag > .admonition-title::before {
+  all:unset;
+}
+
diff --git a/_static/css/theme.css b/_static/css/theme.css
new file mode 100644
index 00000000..40606a86
--- /dev/null
+++ b/_static/css/theme.css
@@ -0,0 +1,4 @@
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+ *  Font Awesome 4.7.0 by @davegandy - http://fontawesome.io - @fontawesome
+ *  License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License)
+ */@font-face{font-family:'FontAwesome';src:url("../fonts/fontawesome-webfont.eot?v=4.7.0");src:url("../fonts/fontawesome-webfont.eot?#iefix&v=4.7.0") format("embedded-opentype"),url("../fonts/fontawesome-webfont.woff2?v=4.7.0") format("woff2"),url("../fonts/fontawesome-webfont.woff?v=4.7.0") format("woff"),url("../fonts/fontawesome-webfont.ttf?v=4.7.0") format("truetype"),url("../fonts/fontawesome-webfont.svg?v=4.7.0#fontawesomeregular") format("svg");font-weight:normal;font-style:normal}.fa,.wy-menu-vertical li button.toctree-expand,.wy-menu-vertical li.on a button.toctree-expand,.wy-menu-vertical li.current>a button.toctree-expand,.rst-content .admonition-title,.rst-content h1 .headerlink,.rst-content h2 .headerlink,.rst-content h3 .headerlink,.rst-content h4 .headerlink,.rst-content h5 .headerlink,.rst-content h6 .headerlink,.rst-content dl dt .headerlink,.rst-content p .headerlink,.rst-content p.caption .headerlink,.rst-content table>caption .headerlink,.rst-content 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diff --git a/_static/doctools.js b/_static/doctools.js
new file mode 100644
index 00000000..8cbf1b16
--- /dev/null
+++ b/_static/doctools.js
@@ -0,0 +1,323 @@
+/*
+ * doctools.js
+ * ~~~~~~~~~~~
+ *
+ * Sphinx JavaScript utilities for all documentation.
+ *
+ * :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+/**
+ * select a different prefix for underscore
+ */
+$u = _.noConflict();
+
+/**
+ * make the code below compatible with browsers without
+ * an installed firebug like debugger
+if (!window.console || !console.firebug) {
+  var names = ["log", "debug", "info", "warn", "error", "assert", "dir",
+    "dirxml", "group", "groupEnd", "time", "timeEnd", "count", "trace",
+    "profile", "profileEnd"];
+  window.console = {};
+  for (var i = 0; i < names.length; ++i)
+    window.console[names[i]] = function() {};
+}
+ */
+
+/**
+ * small helper function to urldecode strings
+ *
+ * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL
+ */
+jQuery.urldecode = function(x) {
+  if (!x) {
+    return x
+  }
+  return decodeURIComponent(x.replace(/\+/g, ' '));
+};
+
+/**
+ * small helper function to urlencode strings
+ */
+jQuery.urlencode = encodeURIComponent;
+
+/**
+ * This function returns the parsed url parameters of the
+ * current request. Multiple values per key are supported,
+ * it will always return arrays of strings for the value parts.
+ */
+jQuery.getQueryParameters = function(s) {
+  if (typeof s === 'undefined')
+    s = document.location.search;
+  var parts = s.substr(s.indexOf('?') + 1).split('&');
+  var result = {};
+  for (var i = 0; i < parts.length; i++) {
+    var tmp = parts[i].split('=', 2);
+    var key = jQuery.urldecode(tmp[0]);
+    var value = jQuery.urldecode(tmp[1]);
+    if (key in result)
+      result[key].push(value);
+    else
+      result[key] = [value];
+  }
+  return result;
+};
+
+/**
+ * highlight a given string on a jquery object by wrapping it in
+ * span elements with the given class name.
+ */
+jQuery.fn.highlightText = function(text, className) {
+  function highlight(node, addItems) {
+    if (node.nodeType === 3) {
+      var val = node.nodeValue;
+      var pos = val.toLowerCase().indexOf(text);
+      if (pos >= 0 &&
+          !jQuery(node.parentNode).hasClass(className) &&
+          !jQuery(node.parentNode).hasClass("nohighlight")) {
+        var span;
+        var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg");
+        if (isInSVG) {
+          span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
+        } else {
+          span = document.createElement("span");
+          span.className = className;
+        }
+        span.appendChild(document.createTextNode(val.substr(pos, text.length)));
+        node.parentNode.insertBefore(span, node.parentNode.insertBefore(
+          document.createTextNode(val.substr(pos + text.length)),
+          node.nextSibling));
+        node.nodeValue = val.substr(0, pos);
+        if (isInSVG) {
+          var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect");
+          var bbox = node.parentElement.getBBox();
+          rect.x.baseVal.value = bbox.x;
+          rect.y.baseVal.value = bbox.y;
+          rect.width.baseVal.value = bbox.width;
+          rect.height.baseVal.value = bbox.height;
+          rect.setAttribute('class', className);
+          addItems.push({
+              "parent": node.parentNode,
+              "target": rect});
+        }
+      }
+    }
+    else if (!jQuery(node).is("button, select, textarea")) {
+      jQuery.each(node.childNodes, function() {
+        highlight(this, addItems);
+      });
+    }
+  }
+  var addItems = [];
+  var result = this.each(function() {
+    highlight(this, addItems);
+  });
+  for (var i = 0; i < addItems.length; ++i) {
+    jQuery(addItems[i].parent).before(addItems[i].target);
+  }
+  return result;
+};
+
+/*
+ * backward compatibility for jQuery.browser
+ * This will be supported until firefox bug is fixed.
+ */
+if (!jQuery.browser) {
+  jQuery.uaMatch = function(ua) {
+    ua = ua.toLowerCase();
+
+    var match = /(chrome)[ \/]([\w.]+)/.exec(ua) ||
+      /(webkit)[ \/]([\w.]+)/.exec(ua) ||
+      /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) ||
+      /(msie) ([\w.]+)/.exec(ua) ||
+      ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) ||
+      [];
+
+    return {
+      browser: match[ 1 ] || "",
+      version: match[ 2 ] || "0"
+    };
+  };
+  jQuery.browser = {};
+  jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true;
+}
+
+/**
+ * Small JavaScript module for the documentation.
+ */
+var Documentation = {
+
+  init : function() {
+    this.fixFirefoxAnchorBug();
+    this.highlightSearchWords();
+    this.initIndexTable();
+    if (DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) {
+      this.initOnKeyListeners();
+    }
+  },
+
+  /**
+   * i18n support
+   */
+  TRANSLATIONS : {},
+  PLURAL_EXPR : function(n) { return n === 1 ? 0 : 1; },
+  LOCALE : 'unknown',
+
+  // gettext and ngettext don't access this so that the functions
+  // can safely bound to a different name (_ = Documentation.gettext)
+  gettext : function(string) {
+    var translated = Documentation.TRANSLATIONS[string];
+    if (typeof translated === 'undefined')
+      return string;
+    return (typeof translated === 'string') ? translated : translated[0];
+  },
+
+  ngettext : function(singular, plural, n) {
+    var translated = Documentation.TRANSLATIONS[singular];
+    if (typeof translated === 'undefined')
+      return (n == 1) ? singular : plural;
+    return translated[Documentation.PLURALEXPR(n)];
+  },
+
+  addTranslations : function(catalog) {
+    for (var key in catalog.messages)
+      this.TRANSLATIONS[key] = catalog.messages[key];
+    this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')');
+    this.LOCALE = catalog.locale;
+  },
+
+  /**
+   * add context elements like header anchor links
+   */
+  addContextElements : function() {
+    $('div[id] > :header:first').each(function() {
+      $('<a class="headerlink">\u00B6</a>').
+      attr('href', '#' + this.id).
+      attr('title', _('Permalink to this headline')).
+      appendTo(this);
+    });
+    $('dt[id]').each(function() {
+      $('<a class="headerlink">\u00B6</a>').
+      attr('href', '#' + this.id).
+      attr('title', _('Permalink to this definition')).
+      appendTo(this);
+    });
+  },
+
+  /**
+   * workaround a firefox stupidity
+   * see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075
+   */
+  fixFirefoxAnchorBug : function() {
+    if (document.location.hash && $.browser.mozilla)
+      window.setTimeout(function() {
+        document.location.href += '';
+      }, 10);
+  },
+
+  /**
+   * highlight the search words provided in the url in the text
+   */
+  highlightSearchWords : function() {
+    var params = $.getQueryParameters();
+    var terms = (params.highlight) ? params.highlight[0].split(/\s+/) : [];
+    if (terms.length) {
+      var body = $('div.body');
+      if (!body.length) {
+        body = $('body');
+      }
+      window.setTimeout(function() {
+        $.each(terms, function() {
+          body.highlightText(this.toLowerCase(), 'highlighted');
+        });
+      }, 10);
+      $('<p class="highlight-link"><a href="javascript:Documentation.' +
+        'hideSearchWords()">' + _('Hide Search Matches') + '</a></p>')
+          .appendTo($('#searchbox'));
+    }
+  },
+
+  /**
+   * init the domain index toggle buttons
+   */
+  initIndexTable : function() {
+    var togglers = $('img.toggler').click(function() {
+      var src = $(this).attr('src');
+      var idnum = $(this).attr('id').substr(7);
+      $('tr.cg-' + idnum).toggle();
+      if (src.substr(-9) === 'minus.png')
+        $(this).attr('src', src.substr(0, src.length-9) + 'plus.png');
+      else
+        $(this).attr('src', src.substr(0, src.length-8) + 'minus.png');
+    }).css('display', '');
+    if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) {
+        togglers.click();
+    }
+  },
+
+  /**
+   * helper function to hide the search marks again
+   */
+  hideSearchWords : function() {
+    $('#searchbox .highlight-link').fadeOut(300);
+    $('span.highlighted').removeClass('highlighted');
+  },
+
+  /**
+   * make the url absolute
+   */
+  makeURL : function(relativeURL) {
+    return DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL;
+  },
+
+  /**
+   * get the current relative url
+   */
+  getCurrentURL : function() {
+    var path = document.location.pathname;
+    var parts = path.split(/\//);
+    $.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\//), function() {
+      if (this === '..')
+        parts.pop();
+    });
+    var url = parts.join('/');
+    return path.substring(url.lastIndexOf('/') + 1, path.length - 1);
+  },
+
+  initOnKeyListeners: function() {
+    $(document).keydown(function(event) {
+      var activeElementType = document.activeElement.tagName;
+      // don't navigate when in search box, textarea, dropdown or button
+      if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT'
+          && activeElementType !== 'BUTTON' && !event.altKey && !event.ctrlKey && !event.metaKey
+          && !event.shiftKey) {
+        switch (event.keyCode) {
+          case 37: // left
+            var prevHref = $('link[rel="prev"]').prop('href');
+            if (prevHref) {
+              window.location.href = prevHref;
+              return false;
+            }
+            break;
+          case 39: // right
+            var nextHref = $('link[rel="next"]').prop('href');
+            if (nextHref) {
+              window.location.href = nextHref;
+              return false;
+            }
+            break;
+        }
+      }
+    });
+  }
+};
+
+// quick alias for translations
+_ = Documentation.gettext;
+
+$(document).ready(function() {
+  Documentation.init();
+});
diff --git a/_static/documentation_options.js b/_static/documentation_options.js
new file mode 100644
index 00000000..2fa8c97f
--- /dev/null
+++ b/_static/documentation_options.js
@@ -0,0 +1,12 @@
+var DOCUMENTATION_OPTIONS = {
+    URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
+    VERSION: '',
+    LANGUAGE: 'None',
+    COLLAPSE_INDEX: false,
+    BUILDER: 'html',
+    FILE_SUFFIX: '.html',
+    LINK_SUFFIX: '.html',
+    HAS_SOURCE: true,
+    SOURCELINK_SUFFIX: '.txt',
+    NAVIGATION_WITH_KEYS: false
+};
\ No newline at end of file
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diff --git a/_static/fonts/fontawesome-webfont.svg b/_static/fonts/fontawesome-webfont.svg
new file mode 100644
index 00000000..855c845e
--- /dev/null
+++ b/_static/fonts/fontawesome-webfont.svg
@@ -0,0 +1,2671 @@
+<?xml version="1.0" standalone="no"?>
+<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd" >
+<svg>
+<metadata>
+Created by FontForge 20120731 at Mon Oct 24 17:37:40 2016
+ By ,,,
+Copyright Dave Gandy 2016. All rights reserved.
+</metadata>
+<defs>
+<font id="FontAwesome" horiz-adv-x="1536" >
+  <font-face 
+    font-family="FontAwesome"
+    font-weight="400"
+    font-stretch="normal"
+    units-per-em="1792"
+    panose-1="0 0 0 0 0 0 0 0 0 0"
+    ascent="1536"
+    descent="-256"
+    bbox="-1.02083 -256.962 2304.6 1537.02"
+    underline-thickness="0"
+    underline-position="0"
+    unicode-range="U+0020-F500"
+  />
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+    <glyph glyph-name=".notdef" horiz-adv-x="896" 
+d="M224 112h448v1312h-448v-1312zM112 0v1536h672v-1536h-672z" />
+    <glyph glyph-name=".null" horiz-adv-x="0" 
+ />
+    <glyph glyph-name="nonmarkingreturn" horiz-adv-x="597" 
+ />
+    <glyph glyph-name="space" unicode=" " horiz-adv-x="448" 
+ />
+    <glyph glyph-name="dieresis" unicode="&#xa8;" horiz-adv-x="1792" 
+ />
+    <glyph glyph-name="copyright" unicode="&#xa9;" horiz-adv-x="1792" 
+ />
+    <glyph glyph-name="registered" unicode="&#xae;" horiz-adv-x="1792" 
+ />
+    <glyph glyph-name="acute" unicode="&#xb4;" horiz-adv-x="1792" 
+ />
+    <glyph glyph-name="AE" unicode="&#xc6;" horiz-adv-x="1792" 
+ />
+    <glyph glyph-name="Oslash" unicode="&#xd8;" horiz-adv-x="1792" 
+ />
+    <glyph glyph-name="trademark" unicode="&#x2122;" horiz-adv-x="1792" 
+ />
+    <glyph glyph-name="infinity" unicode="&#x221e;" horiz-adv-x="1792" 
+ />
+    <glyph glyph-name="notequal" unicode="&#x2260;" horiz-adv-x="1792" 
+ />
+    <glyph glyph-name="glass" unicode="&#xf000;" horiz-adv-x="1792" 
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diff --git a/_static/jquery.js b/_static/jquery.js
new file mode 100644
index 00000000..624bca82
--- /dev/null
+++ b/_static/jquery.js
@@ -0,0 +1,10879 @@
+/*!
+ * jQuery JavaScript Library v3.6.0
+ * https://jquery.com/
+ *
+ * Includes Sizzle.js
+ * https://sizzlejs.com/
+ *
+ * Copyright OpenJS Foundation and other contributors
+ * Released under the MIT license
+ * https://jquery.org/license
+ */
+( function( global, factory ) {
+
+	"use strict";
+
+	if ( typeof module === "object" && typeof module.exports === "object" ) {
+
+		// For CommonJS and CommonJS-like environments where a proper `window`
+		// is present, execute the factory and get jQuery.
+		// For environments that do not have a `window` with a `document`
+		// (such as Node.js), expose a factory as module.exports.
+		// This accentuates the need for the creation of a real `window`.
+		// e.g. var jQuery = require("jquery")(window);
+		// See ticket #14549 for more info.
+		module.exports = global.document ?
+			factory( global, true ) :
+			function( w ) {
+				if ( !w.document ) {
+					throw new Error( "jQuery requires a window with a document" );
+				}
+				return factory( w );
+			};
+	} else {
+		factory( global );
+	}
+
+// Pass this if window is not defined yet
+} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) {
+
+// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1
+// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode
+// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common
+// enough that all such attempts are guarded in a try block.
+"use strict";
+
+var arr = [];
+
+var getProto = Object.getPrototypeOf;
+
+var slice = arr.slice;
+
+var flat = arr.flat ? function( array ) {
+	return arr.flat.call( array );
+} : function( array ) {
+	return arr.concat.apply( [], array );
+};
+
+
+var push = arr.push;
+
+var indexOf = arr.indexOf;
+
+var class2type = {};
+
+var toString = class2type.toString;
+
+var hasOwn = class2type.hasOwnProperty;
+
+var fnToString = hasOwn.toString;
+
+var ObjectFunctionString = fnToString.call( Object );
+
+var support = {};
+
+var isFunction = function isFunction( obj ) {
+
+		// Support: Chrome <=57, Firefox <=52
+		// In some browsers, typeof returns "function" for HTML <object> elements
+		// (i.e., `typeof document.createElement( "object" ) === "function"`).
+		// We don't want to classify *any* DOM node as a function.
+		// Support: QtWeb <=3.8.5, WebKit <=534.34, wkhtmltopdf tool <=0.12.5
+		// Plus for old WebKit, typeof returns "function" for HTML collections
+		// (e.g., `typeof document.getElementsByTagName("div") === "function"`). (gh-4756)
+		return typeof obj === "function" && typeof obj.nodeType !== "number" &&
+			typeof obj.item !== "function";
+	};
+
+
+var isWindow = function isWindow( obj ) {
+		return obj != null && obj === obj.window;
+	};
+
+
+var document = window.document;
+
+
+
+	var preservedScriptAttributes = {
+		type: true,
+		src: true,
+		nonce: true,
+		noModule: true
+	};
+
+	function DOMEval( code, node, doc ) {
+		doc = doc || document;
+
+		var i, val,
+			script = doc.createElement( "script" );
+
+		script.text = code;
+		if ( node ) {
+			for ( i in preservedScriptAttributes ) {
+
+				// Support: Firefox 64+, Edge 18+
+				// Some browsers don't support the "nonce" property on scripts.
+				// On the other hand, just using `getAttribute` is not enough as
+				// the `nonce` attribute is reset to an empty string whenever it
+				// becomes browsing-context connected.
+				// See https://github.com/whatwg/html/issues/2369
+				// See https://html.spec.whatwg.org/#nonce-attributes
+				// The `node.getAttribute` check was added for the sake of
+				// `jQuery.globalEval` so that it can fake a nonce-containing node
+				// via an object.
+				val = node[ i ] || node.getAttribute && node.getAttribute( i );
+				if ( val ) {
+					script.setAttribute( i, val );
+				}
+			}
+		}
+		doc.head.appendChild( script ).parentNode.removeChild( script );
+	}
+
+
+function toType( obj ) {
+	if ( obj == null ) {
+		return obj + "";
+	}
+
+	// Support: Android <=2.3 only (functionish RegExp)
+	return typeof obj === "object" || typeof obj === "function" ?
+		class2type[ toString.call( obj ) ] || "object" :
+		typeof obj;
+}
+/* global Symbol */
+// Defining this global in .eslintrc.json would create a danger of using the global
+// unguarded in another place, it seems safer to define global only for this module
+
+
+
+var
+	version = "3.6.0",
+
+	// Define a local copy of jQuery
+	jQuery = function( selector, context ) {
+
+		// The jQuery object is actually just the init constructor 'enhanced'
+		// Need init if jQuery is called (just allow error to be thrown if not included)
+		return new jQuery.fn.init( selector, context );
+	};
+
+jQuery.fn = jQuery.prototype = {
+
+	// The current version of jQuery being used
+	jquery: version,
+
+	constructor: jQuery,
+
+	// The default length of a jQuery object is 0
+	length: 0,
+
+	toArray: function() {
+		return slice.call( this );
+	},
+
+	// Get the Nth element in the matched element set OR
+	// Get the whole matched element set as a clean array
+	get: function( num ) {
+
+		// Return all the elements in a clean array
+		if ( num == null ) {
+			return slice.call( this );
+		}
+
+		// Return just the one element from the set
+		return num < 0 ? this[ num + this.length ] : this[ num ];
+	},
+
+	// Take an array of elements and push it onto the stack
+	// (returning the new matched element set)
+	pushStack: function( elems ) {
+
+		// Build a new jQuery matched element set
+		var ret = jQuery.merge( this.constructor(), elems );
+
+		// Add the old object onto the stack (as a reference)
+		ret.prevObject = this;
+
+		// Return the newly-formed element set
+		return ret;
+	},
+
+	// Execute a callback for every element in the matched set.
+	each: function( callback ) {
+		return jQuery.each( this, callback );
+	},
+
+	map: function( callback ) {
+		return this.pushStack( jQuery.map( this, function( elem, i ) {
+			return callback.call( elem, i, elem );
+		} ) );
+	},
+
+	slice: function() {
+		return this.pushStack( slice.apply( this, arguments ) );
+	},
+
+	first: function() {
+		return this.eq( 0 );
+	},
+
+	last: function() {
+		return this.eq( -1 );
+	},
+
+	even: function() {
+		return this.pushStack( jQuery.grep( this, function( _elem, i ) {
+			return ( i + 1 ) % 2;
+		} ) );
+	},
+
+	odd: function() {
+		return this.pushStack( jQuery.grep( this, function( _elem, i ) {
+			return i % 2;
+		} ) );
+	},
+
+	eq: function( i ) {
+		var len = this.length,
+			j = +i + ( i < 0 ? len : 0 );
+		return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] );
+	},
+
+	end: function() {
+		return this.prevObject || this.constructor();
+	},
+
+	// For internal use only.
+	// Behaves like an Array's method, not like a jQuery method.
+	push: push,
+	sort: arr.sort,
+	splice: arr.splice
+};
+
+jQuery.extend = jQuery.fn.extend = function() {
+	var options, name, src, copy, copyIsArray, clone,
+		target = arguments[ 0 ] || {},
+		i = 1,
+		length = arguments.length,
+		deep = false;
+
+	// Handle a deep copy situation
+	if ( typeof target === "boolean" ) {
+		deep = target;
+
+		// Skip the boolean and the target
+		target = arguments[ i ] || {};
+		i++;
+	}
+
+	// Handle case when target is a string or something (possible in deep copy)
+	if ( typeof target !== "object" && !isFunction( target ) ) {
+		target = {};
+	}
+
+	// Extend jQuery itself if only one argument is passed
+	if ( i === length ) {
+		target = this;
+		i--;
+	}
+
+	for ( ; i < length; i++ ) {
+
+		// Only deal with non-null/undefined values
+		if ( ( options = arguments[ i ] ) != null ) {
+
+			// Extend the base object
+			for ( name in options ) {
+				copy = options[ name ];
+
+				// Prevent Object.prototype pollution
+				// Prevent never-ending loop
+				if ( name === "__proto__" || target === copy ) {
+					continue;
+				}
+
+				// Recurse if we're merging plain objects or arrays
+				if ( deep && copy && ( jQuery.isPlainObject( copy ) ||
+					( copyIsArray = Array.isArray( copy ) ) ) ) {
+					src = target[ name ];
+
+					// Ensure proper type for the source value
+					if ( copyIsArray && !Array.isArray( src ) ) {
+						clone = [];
+					} else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) {
+						clone = {};
+					} else {
+						clone = src;
+					}
+					copyIsArray = false;
+
+					// Never move original objects, clone them
+					target[ name ] = jQuery.extend( deep, clone, copy );
+
+				// Don't bring in undefined values
+				} else if ( copy !== undefined ) {
+					target[ name ] = copy;
+				}
+			}
+		}
+	}
+
+	// Return the modified object
+	return target;
+};
+
+jQuery.extend( {
+
+	// Unique for each copy of jQuery on the page
+	expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ),
+
+	// Assume jQuery is ready without the ready module
+	isReady: true,
+
+	error: function( msg ) {
+		throw new Error( msg );
+	},
+
+	noop: function() {},
+
+	isPlainObject: function( obj ) {
+		var proto, Ctor;
+
+		// Detect obvious negatives
+		// Use toString instead of jQuery.type to catch host objects
+		if ( !obj || toString.call( obj ) !== "[object Object]" ) {
+			return false;
+		}
+
+		proto = getProto( obj );
+
+		// Objects with no prototype (e.g., `Object.create( null )`) are plain
+		if ( !proto ) {
+			return true;
+		}
+
+		// Objects with prototype are plain iff they were constructed by a global Object function
+		Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor;
+		return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString;
+	},
+
+	isEmptyObject: function( obj ) {
+		var name;
+
+		for ( name in obj ) {
+			return false;
+		}
+		return true;
+	},
+
+	// Evaluates a script in a provided context; falls back to the global one
+	// if not specified.
+	globalEval: function( code, options, doc ) {
+		DOMEval( code, { nonce: options && options.nonce }, doc );
+	},
+
+	each: function( obj, callback ) {
+		var length, i = 0;
+
+		if ( isArrayLike( obj ) ) {
+			length = obj.length;
+			for ( ; i < length; i++ ) {
+				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
+					break;
+				}
+			}
+		} else {
+			for ( i in obj ) {
+				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
+					break;
+				}
+			}
+		}
+
+		return obj;
+	},
+
+	// results is for internal usage only
+	makeArray: function( arr, results ) {
+		var ret = results || [];
+
+		if ( arr != null ) {
+			if ( isArrayLike( Object( arr ) ) ) {
+				jQuery.merge( ret,
+					typeof arr === "string" ?
+						[ arr ] : arr
+				);
+			} else {
+				push.call( ret, arr );
+			}
+		}
+
+		return ret;
+	},
+
+	inArray: function( elem, arr, i ) {
+		return arr == null ? -1 : indexOf.call( arr, elem, i );
+	},
+
+	// Support: Android <=4.0 only, PhantomJS 1 only
+	// push.apply(_, arraylike) throws on ancient WebKit
+	merge: function( first, second ) {
+		var len = +second.length,
+			j = 0,
+			i = first.length;
+
+		for ( ; j < len; j++ ) {
+			first[ i++ ] = second[ j ];
+		}
+
+		first.length = i;
+
+		return first;
+	},
+
+	grep: function( elems, callback, invert ) {
+		var callbackInverse,
+			matches = [],
+			i = 0,
+			length = elems.length,
+			callbackExpect = !invert;
+
+		// Go through the array, only saving the items
+		// that pass the validator function
+		for ( ; i < length; i++ ) {
+			callbackInverse = !callback( elems[ i ], i );
+			if ( callbackInverse !== callbackExpect ) {
+				matches.push( elems[ i ] );
+			}
+		}
+
+		return matches;
+	},
+
+	// arg is for internal usage only
+	map: function( elems, callback, arg ) {
+		var length, value,
+			i = 0,
+			ret = [];
+
+		// Go through the array, translating each of the items to their new values
+		if ( isArrayLike( elems ) ) {
+			length = elems.length;
+			for ( ; i < length; i++ ) {
+				value = callback( elems[ i ], i, arg );
+
+				if ( value != null ) {
+					ret.push( value );
+				}
+			}
+
+		// Go through every key on the object,
+		} else {
+			for ( i in elems ) {
+				value = callback( elems[ i ], i, arg );
+
+				if ( value != null ) {
+					ret.push( value );
+				}
+			}
+		}
+
+		// Flatten any nested arrays
+		return flat( ret );
+	},
+
+	// A global GUID counter for objects
+	guid: 1,
+
+	// jQuery.support is not used in Core but other projects attach their
+	// properties to it so it needs to exist.
+	support: support
+} );
+
+if ( typeof Symbol === "function" ) {
+	jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ];
+}
+
+// Populate the class2type map
+jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ),
+	function( _i, name ) {
+		class2type[ "[object " + name + "]" ] = name.toLowerCase();
+	} );
+
+function isArrayLike( obj ) {
+
+	// Support: real iOS 8.2 only (not reproducible in simulator)
+	// `in` check used to prevent JIT error (gh-2145)
+	// hasOwn isn't used here due to false negatives
+	// regarding Nodelist length in IE
+	var length = !!obj && "length" in obj && obj.length,
+		type = toType( obj );
+
+	if ( isFunction( obj ) || isWindow( obj ) ) {
+		return false;
+	}
+
+	return type === "array" || length === 0 ||
+		typeof length === "number" && length > 0 && ( length - 1 ) in obj;
+}
+var Sizzle =
+/*!
+ * Sizzle CSS Selector Engine v2.3.6
+ * https://sizzlejs.com/
+ *
+ * Copyright JS Foundation and other contributors
+ * Released under the MIT license
+ * https://js.foundation/
+ *
+ * Date: 2021-02-16
+ */
+( function( window ) {
+var i,
+	support,
+	Expr,
+	getText,
+	isXML,
+	tokenize,
+	compile,
+	select,
+	outermostContext,
+	sortInput,
+	hasDuplicate,
+
+	// Local document vars
+	setDocument,
+	document,
+	docElem,
+	documentIsHTML,
+	rbuggyQSA,
+	rbuggyMatches,
+	matches,
+	contains,
+
+	// Instance-specific data
+	expando = "sizzle" + 1 * new Date(),
+	preferredDoc = window.document,
+	dirruns = 0,
+	done = 0,
+	classCache = createCache(),
+	tokenCache = createCache(),
+	compilerCache = createCache(),
+	nonnativeSelectorCache = createCache(),
+	sortOrder = function( a, b ) {
+		if ( a === b ) {
+			hasDuplicate = true;
+		}
+		return 0;
+	},
+
+	// Instance methods
+	hasOwn = ( {} ).hasOwnProperty,
+	arr = [],
+	pop = arr.pop,
+	pushNative = arr.push,
+	push = arr.push,
+	slice = arr.slice,
+
+	// Use a stripped-down indexOf as it's faster than native
+	// https://jsperf.com/thor-indexof-vs-for/5
+	indexOf = function( list, elem ) {
+		var i = 0,
+			len = list.length;
+		for ( ; i < len; i++ ) {
+			if ( list[ i ] === elem ) {
+				return i;
+			}
+		}
+		return -1;
+	},
+
+	booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" +
+		"ismap|loop|multiple|open|readonly|required|scoped",
+
+	// Regular expressions
+
+	// http://www.w3.org/TR/css3-selectors/#whitespace
+	whitespace = "[\\x20\\t\\r\\n\\f]",
+
+	// https://www.w3.org/TR/css-syntax-3/#ident-token-diagram
+	identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace +
+		"?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+",
+
+	// Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors
+	attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace +
+
+		// Operator (capture 2)
+		"*([*^$|!~]?=)" + whitespace +
+
+		// "Attribute values must be CSS identifiers [capture 5]
+		// or strings [capture 3 or capture 4]"
+		"*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" +
+		whitespace + "*\\]",
+
+	pseudos = ":(" + identifier + ")(?:\\((" +
+
+		// To reduce the number of selectors needing tokenize in the preFilter, prefer arguments:
+		// 1. quoted (capture 3; capture 4 or capture 5)
+		"('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" +
+
+		// 2. simple (capture 6)
+		"((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" +
+
+		// 3. anything else (capture 2)
+		".*" +
+		")\\)|)",
+
+	// Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter
+	rwhitespace = new RegExp( whitespace + "+", "g" ),
+	rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" +
+		whitespace + "+$", "g" ),
+
+	rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ),
+	rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace +
+		"*" ),
+	rdescend = new RegExp( whitespace + "|>" ),
+
+	rpseudo = new RegExp( pseudos ),
+	ridentifier = new RegExp( "^" + identifier + "$" ),
+
+	matchExpr = {
+		"ID": new RegExp( "^#(" + identifier + ")" ),
+		"CLASS": new RegExp( "^\\.(" + identifier + ")" ),
+		"TAG": new RegExp( "^(" + identifier + "|[*])" ),
+		"ATTR": new RegExp( "^" + attributes ),
+		"PSEUDO": new RegExp( "^" + pseudos ),
+		"CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" +
+			whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" +
+			whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ),
+		"bool": new RegExp( "^(?:" + booleans + ")$", "i" ),
+
+		// For use in libraries implementing .is()
+		// We use this for POS matching in `select`
+		"needsContext": new RegExp( "^" + whitespace +
+			"*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace +
+			"*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" )
+	},
+
+	rhtml = /HTML$/i,
+	rinputs = /^(?:input|select|textarea|button)$/i,
+	rheader = /^h\d$/i,
+
+	rnative = /^[^{]+\{\s*\[native \w/,
+
+	// Easily-parseable/retrievable ID or TAG or CLASS selectors
+	rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/,
+
+	rsibling = /[+~]/,
+
+	// CSS escapes
+	// http://www.w3.org/TR/CSS21/syndata.html#escaped-characters
+	runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ),
+	funescape = function( escape, nonHex ) {
+		var high = "0x" + escape.slice( 1 ) - 0x10000;
+
+		return nonHex ?
+
+			// Strip the backslash prefix from a non-hex escape sequence
+			nonHex :
+
+			// Replace a hexadecimal escape sequence with the encoded Unicode code point
+			// Support: IE <=11+
+			// For values outside the Basic Multilingual Plane (BMP), manually construct a
+			// surrogate pair
+			high < 0 ?
+				String.fromCharCode( high + 0x10000 ) :
+				String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 );
+	},
+
+	// CSS string/identifier serialization
+	// https://drafts.csswg.org/cssom/#common-serializing-idioms
+	rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g,
+	fcssescape = function( ch, asCodePoint ) {
+		if ( asCodePoint ) {
+
+			// U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER
+			if ( ch === "\0" ) {
+				return "\uFFFD";
+			}
+
+			// Control characters and (dependent upon position) numbers get escaped as code points
+			return ch.slice( 0, -1 ) + "\\" +
+				ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " ";
+		}
+
+		// Other potentially-special ASCII characters get backslash-escaped
+		return "\\" + ch;
+	},
+
+	// Used for iframes
+	// See setDocument()
+	// Removing the function wrapper causes a "Permission Denied"
+	// error in IE
+	unloadHandler = function() {
+		setDocument();
+	},
+
+	inDisabledFieldset = addCombinator(
+		function( elem ) {
+			return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset";
+		},
+		{ dir: "parentNode", next: "legend" }
+	);
+
+// Optimize for push.apply( _, NodeList )
+try {
+	push.apply(
+		( arr = slice.call( preferredDoc.childNodes ) ),
+		preferredDoc.childNodes
+	);
+
+	// Support: Android<4.0
+	// Detect silently failing push.apply
+	// eslint-disable-next-line no-unused-expressions
+	arr[ preferredDoc.childNodes.length ].nodeType;
+} catch ( e ) {
+	push = { apply: arr.length ?
+
+		// Leverage slice if possible
+		function( target, els ) {
+			pushNative.apply( target, slice.call( els ) );
+		} :
+
+		// Support: IE<9
+		// Otherwise append directly
+		function( target, els ) {
+			var j = target.length,
+				i = 0;
+
+			// Can't trust NodeList.length
+			while ( ( target[ j++ ] = els[ i++ ] ) ) {}
+			target.length = j - 1;
+		}
+	};
+}
+
+function Sizzle( selector, context, results, seed ) {
+	var m, i, elem, nid, match, groups, newSelector,
+		newContext = context && context.ownerDocument,
+
+		// nodeType defaults to 9, since context defaults to document
+		nodeType = context ? context.nodeType : 9;
+
+	results = results || [];
+
+	// Return early from calls with invalid selector or context
+	if ( typeof selector !== "string" || !selector ||
+		nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) {
+
+		return results;
+	}
+
+	// Try to shortcut find operations (as opposed to filters) in HTML documents
+	if ( !seed ) {
+		setDocument( context );
+		context = context || document;
+
+		if ( documentIsHTML ) {
+
+			// If the selector is sufficiently simple, try using a "get*By*" DOM method
+			// (excepting DocumentFragment context, where the methods don't exist)
+			if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) {
+
+				// ID selector
+				if ( ( m = match[ 1 ] ) ) {
+
+					// Document context
+					if ( nodeType === 9 ) {
+						if ( ( elem = context.getElementById( m ) ) ) {
+
+							// Support: IE, Opera, Webkit
+							// TODO: identify versions
+							// getElementById can match elements by name instead of ID
+							if ( elem.id === m ) {
+								results.push( elem );
+								return results;
+							}
+						} else {
+							return results;
+						}
+
+					// Element context
+					} else {
+
+						// Support: IE, Opera, Webkit
+						// TODO: identify versions
+						// getElementById can match elements by name instead of ID
+						if ( newContext && ( elem = newContext.getElementById( m ) ) &&
+							contains( context, elem ) &&
+							elem.id === m ) {
+
+							results.push( elem );
+							return results;
+						}
+					}
+
+				// Type selector
+				} else if ( match[ 2 ] ) {
+					push.apply( results, context.getElementsByTagName( selector ) );
+					return results;
+
+				// Class selector
+				} else if ( ( m = match[ 3 ] ) && support.getElementsByClassName &&
+					context.getElementsByClassName ) {
+
+					push.apply( results, context.getElementsByClassName( m ) );
+					return results;
+				}
+			}
+
+			// Take advantage of querySelectorAll
+			if ( support.qsa &&
+				!nonnativeSelectorCache[ selector + " " ] &&
+				( !rbuggyQSA || !rbuggyQSA.test( selector ) ) &&
+
+				// Support: IE 8 only
+				// Exclude object elements
+				( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) {
+
+				newSelector = selector;
+				newContext = context;
+
+				// qSA considers elements outside a scoping root when evaluating child or
+				// descendant combinators, which is not what we want.
+				// In such cases, we work around the behavior by prefixing every selector in the
+				// list with an ID selector referencing the scope context.
+				// The technique has to be used as well when a leading combinator is used
+				// as such selectors are not recognized by querySelectorAll.
+				// Thanks to Andrew Dupont for this technique.
+				if ( nodeType === 1 &&
+					( rdescend.test( selector ) || rcombinators.test( selector ) ) ) {
+
+					// Expand context for sibling selectors
+					newContext = rsibling.test( selector ) && testContext( context.parentNode ) ||
+						context;
+
+					// We can use :scope instead of the ID hack if the browser
+					// supports it & if we're not changing the context.
+					if ( newContext !== context || !support.scope ) {
+
+						// Capture the context ID, setting it first if necessary
+						if ( ( nid = context.getAttribute( "id" ) ) ) {
+							nid = nid.replace( rcssescape, fcssescape );
+						} else {
+							context.setAttribute( "id", ( nid = expando ) );
+						}
+					}
+
+					// Prefix every selector in the list
+					groups = tokenize( selector );
+					i = groups.length;
+					while ( i-- ) {
+						groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " +
+							toSelector( groups[ i ] );
+					}
+					newSelector = groups.join( "," );
+				}
+
+				try {
+					push.apply( results,
+						newContext.querySelectorAll( newSelector )
+					);
+					return results;
+				} catch ( qsaError ) {
+					nonnativeSelectorCache( selector, true );
+				} finally {
+					if ( nid === expando ) {
+						context.removeAttribute( "id" );
+					}
+				}
+			}
+		}
+	}
+
+	// All others
+	return select( selector.replace( rtrim, "$1" ), context, results, seed );
+}
+
+/**
+ * Create key-value caches of limited size
+ * @returns {function(string, object)} Returns the Object data after storing it on itself with
+ *	property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength)
+ *	deleting the oldest entry
+ */
+function createCache() {
+	var keys = [];
+
+	function cache( key, value ) {
+
+		// Use (key + " ") to avoid collision with native prototype properties (see Issue #157)
+		if ( keys.push( key + " " ) > Expr.cacheLength ) {
+
+			// Only keep the most recent entries
+			delete cache[ keys.shift() ];
+		}
+		return ( cache[ key + " " ] = value );
+	}
+	return cache;
+}
+
+/**
+ * Mark a function for special use by Sizzle
+ * @param {Function} fn The function to mark
+ */
+function markFunction( fn ) {
+	fn[ expando ] = true;
+	return fn;
+}
+
+/**
+ * Support testing using an element
+ * @param {Function} fn Passed the created element and returns a boolean result
+ */
+function assert( fn ) {
+	var el = document.createElement( "fieldset" );
+
+	try {
+		return !!fn( el );
+	} catch ( e ) {
+		return false;
+	} finally {
+
+		// Remove from its parent by default
+		if ( el.parentNode ) {
+			el.parentNode.removeChild( el );
+		}
+
+		// release memory in IE
+		el = null;
+	}
+}
+
+/**
+ * Adds the same handler for all of the specified attrs
+ * @param {String} attrs Pipe-separated list of attributes
+ * @param {Function} handler The method that will be applied
+ */
+function addHandle( attrs, handler ) {
+	var arr = attrs.split( "|" ),
+		i = arr.length;
+
+	while ( i-- ) {
+		Expr.attrHandle[ arr[ i ] ] = handler;
+	}
+}
+
+/**
+ * Checks document order of two siblings
+ * @param {Element} a
+ * @param {Element} b
+ * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b
+ */
+function siblingCheck( a, b ) {
+	var cur = b && a,
+		diff = cur && a.nodeType === 1 && b.nodeType === 1 &&
+			a.sourceIndex - b.sourceIndex;
+
+	// Use IE sourceIndex if available on both nodes
+	if ( diff ) {
+		return diff;
+	}
+
+	// Check if b follows a
+	if ( cur ) {
+		while ( ( cur = cur.nextSibling ) ) {
+			if ( cur === b ) {
+				return -1;
+			}
+		}
+	}
+
+	return a ? 1 : -1;
+}
+
+/**
+ * Returns a function to use in pseudos for input types
+ * @param {String} type
+ */
+function createInputPseudo( type ) {
+	return function( elem ) {
+		var name = elem.nodeName.toLowerCase();
+		return name === "input" && elem.type === type;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for buttons
+ * @param {String} type
+ */
+function createButtonPseudo( type ) {
+	return function( elem ) {
+		var name = elem.nodeName.toLowerCase();
+		return ( name === "input" || name === "button" ) && elem.type === type;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for :enabled/:disabled
+ * @param {Boolean} disabled true for :disabled; false for :enabled
+ */
+function createDisabledPseudo( disabled ) {
+
+	// Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable
+	return function( elem ) {
+
+		// Only certain elements can match :enabled or :disabled
+		// https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled
+		// https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled
+		if ( "form" in elem ) {
+
+			// Check for inherited disabledness on relevant non-disabled elements:
+			// * listed form-associated elements in a disabled fieldset
+			//   https://html.spec.whatwg.org/multipage/forms.html#category-listed
+			//   https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled
+			// * option elements in a disabled optgroup
+			//   https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled
+			// All such elements have a "form" property.
+			if ( elem.parentNode && elem.disabled === false ) {
+
+				// Option elements defer to a parent optgroup if present
+				if ( "label" in elem ) {
+					if ( "label" in elem.parentNode ) {
+						return elem.parentNode.disabled === disabled;
+					} else {
+						return elem.disabled === disabled;
+					}
+				}
+
+				// Support: IE 6 - 11
+				// Use the isDisabled shortcut property to check for disabled fieldset ancestors
+				return elem.isDisabled === disabled ||
+
+					// Where there is no isDisabled, check manually
+					/* jshint -W018 */
+					elem.isDisabled !== !disabled &&
+					inDisabledFieldset( elem ) === disabled;
+			}
+
+			return elem.disabled === disabled;
+
+		// Try to winnow out elements that can't be disabled before trusting the disabled property.
+		// Some victims get caught in our net (label, legend, menu, track), but it shouldn't
+		// even exist on them, let alone have a boolean value.
+		} else if ( "label" in elem ) {
+			return elem.disabled === disabled;
+		}
+
+		// Remaining elements are neither :enabled nor :disabled
+		return false;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for positionals
+ * @param {Function} fn
+ */
+function createPositionalPseudo( fn ) {
+	return markFunction( function( argument ) {
+		argument = +argument;
+		return markFunction( function( seed, matches ) {
+			var j,
+				matchIndexes = fn( [], seed.length, argument ),
+				i = matchIndexes.length;
+
+			// Match elements found at the specified indexes
+			while ( i-- ) {
+				if ( seed[ ( j = matchIndexes[ i ] ) ] ) {
+					seed[ j ] = !( matches[ j ] = seed[ j ] );
+				}
+			}
+		} );
+	} );
+}
+
+/**
+ * Checks a node for validity as a Sizzle context
+ * @param {Element|Object=} context
+ * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value
+ */
+function testContext( context ) {
+	return context && typeof context.getElementsByTagName !== "undefined" && context;
+}
+
+// Expose support vars for convenience
+support = Sizzle.support = {};
+
+/**
+ * Detects XML nodes
+ * @param {Element|Object} elem An element or a document
+ * @returns {Boolean} True iff elem is a non-HTML XML node
+ */
+isXML = Sizzle.isXML = function( elem ) {
+	var namespace = elem && elem.namespaceURI,
+		docElem = elem && ( elem.ownerDocument || elem ).documentElement;
+
+	// Support: IE <=8
+	// Assume HTML when documentElement doesn't yet exist, such as inside loading iframes
+	// https://bugs.jquery.com/ticket/4833
+	return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" );
+};
+
+/**
+ * Sets document-related variables once based on the current document
+ * @param {Element|Object} [doc] An element or document object to use to set the document
+ * @returns {Object} Returns the current document
+ */
+setDocument = Sizzle.setDocument = function( node ) {
+	var hasCompare, subWindow,
+		doc = node ? node.ownerDocument || node : preferredDoc;
+
+	// Return early if doc is invalid or already selected
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) {
+		return document;
+	}
+
+	// Update global variables
+	document = doc;
+	docElem = document.documentElement;
+	documentIsHTML = !isXML( document );
+
+	// Support: IE 9 - 11+, Edge 12 - 18+
+	// Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936)
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( preferredDoc != document &&
+		( subWindow = document.defaultView ) && subWindow.top !== subWindow ) {
+
+		// Support: IE 11, Edge
+		if ( subWindow.addEventListener ) {
+			subWindow.addEventListener( "unload", unloadHandler, false );
+
+		// Support: IE 9 - 10 only
+		} else if ( subWindow.attachEvent ) {
+			subWindow.attachEvent( "onunload", unloadHandler );
+		}
+	}
+
+	// Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only,
+	// Safari 4 - 5 only, Opera <=11.6 - 12.x only
+	// IE/Edge & older browsers don't support the :scope pseudo-class.
+	// Support: Safari 6.0 only
+	// Safari 6.0 supports :scope but it's an alias of :root there.
+	support.scope = assert( function( el ) {
+		docElem.appendChild( el ).appendChild( document.createElement( "div" ) );
+		return typeof el.querySelectorAll !== "undefined" &&
+			!el.querySelectorAll( ":scope fieldset div" ).length;
+	} );
+
+	/* Attributes
+	---------------------------------------------------------------------- */
+
+	// Support: IE<8
+	// Verify that getAttribute really returns attributes and not properties
+	// (excepting IE8 booleans)
+	support.attributes = assert( function( el ) {
+		el.className = "i";
+		return !el.getAttribute( "className" );
+	} );
+
+	/* getElement(s)By*
+	---------------------------------------------------------------------- */
+
+	// Check if getElementsByTagName("*") returns only elements
+	support.getElementsByTagName = assert( function( el ) {
+		el.appendChild( document.createComment( "" ) );
+		return !el.getElementsByTagName( "*" ).length;
+	} );
+
+	// Support: IE<9
+	support.getElementsByClassName = rnative.test( document.getElementsByClassName );
+
+	// Support: IE<10
+	// Check if getElementById returns elements by name
+	// The broken getElementById methods don't pick up programmatically-set names,
+	// so use a roundabout getElementsByName test
+	support.getById = assert( function( el ) {
+		docElem.appendChild( el ).id = expando;
+		return !document.getElementsByName || !document.getElementsByName( expando ).length;
+	} );
+
+	// ID filter and find
+	if ( support.getById ) {
+		Expr.filter[ "ID" ] = function( id ) {
+			var attrId = id.replace( runescape, funescape );
+			return function( elem ) {
+				return elem.getAttribute( "id" ) === attrId;
+			};
+		};
+		Expr.find[ "ID" ] = function( id, context ) {
+			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
+				var elem = context.getElementById( id );
+				return elem ? [ elem ] : [];
+			}
+		};
+	} else {
+		Expr.filter[ "ID" ] =  function( id ) {
+			var attrId = id.replace( runescape, funescape );
+			return function( elem ) {
+				var node = typeof elem.getAttributeNode !== "undefined" &&
+					elem.getAttributeNode( "id" );
+				return node && node.value === attrId;
+			};
+		};
+
+		// Support: IE 6 - 7 only
+		// getElementById is not reliable as a find shortcut
+		Expr.find[ "ID" ] = function( id, context ) {
+			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
+				var node, i, elems,
+					elem = context.getElementById( id );
+
+				if ( elem ) {
+
+					// Verify the id attribute
+					node = elem.getAttributeNode( "id" );
+					if ( node && node.value === id ) {
+						return [ elem ];
+					}
+
+					// Fall back on getElementsByName
+					elems = context.getElementsByName( id );
+					i = 0;
+					while ( ( elem = elems[ i++ ] ) ) {
+						node = elem.getAttributeNode( "id" );
+						if ( node && node.value === id ) {
+							return [ elem ];
+						}
+					}
+				}
+
+				return [];
+			}
+		};
+	}
+
+	// Tag
+	Expr.find[ "TAG" ] = support.getElementsByTagName ?
+		function( tag, context ) {
+			if ( typeof context.getElementsByTagName !== "undefined" ) {
+				return context.getElementsByTagName( tag );
+
+			// DocumentFragment nodes don't have gEBTN
+			} else if ( support.qsa ) {
+				return context.querySelectorAll( tag );
+			}
+		} :
+
+		function( tag, context ) {
+			var elem,
+				tmp = [],
+				i = 0,
+
+				// By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too
+				results = context.getElementsByTagName( tag );
+
+			// Filter out possible comments
+			if ( tag === "*" ) {
+				while ( ( elem = results[ i++ ] ) ) {
+					if ( elem.nodeType === 1 ) {
+						tmp.push( elem );
+					}
+				}
+
+				return tmp;
+			}
+			return results;
+		};
+
+	// Class
+	Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) {
+		if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) {
+			return context.getElementsByClassName( className );
+		}
+	};
+
+	/* QSA/matchesSelector
+	---------------------------------------------------------------------- */
+
+	// QSA and matchesSelector support
+
+	// matchesSelector(:active) reports false when true (IE9/Opera 11.5)
+	rbuggyMatches = [];
+
+	// qSa(:focus) reports false when true (Chrome 21)
+	// We allow this because of a bug in IE8/9 that throws an error
+	// whenever `document.activeElement` is accessed on an iframe
+	// So, we allow :focus to pass through QSA all the time to avoid the IE error
+	// See https://bugs.jquery.com/ticket/13378
+	rbuggyQSA = [];
+
+	if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) {
+
+		// Build QSA regex
+		// Regex strategy adopted from Diego Perini
+		assert( function( el ) {
+
+			var input;
+
+			// Select is set to empty string on purpose
+			// This is to test IE's treatment of not explicitly
+			// setting a boolean content attribute,
+			// since its presence should be enough
+			// https://bugs.jquery.com/ticket/12359
+			docElem.appendChild( el ).innerHTML = "<a id='" + expando + "'></a>" +
+				"<select id='" + expando + "-\r\\' msallowcapture=''>" +
+				"<option selected=''></option></select>";
+
+			// Support: IE8, Opera 11-12.16
+			// Nothing should be selected when empty strings follow ^= or $= or *=
+			// The test attribute must be unknown in Opera but "safe" for WinRT
+			// https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section
+			if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) {
+				rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" );
+			}
+
+			// Support: IE8
+			// Boolean attributes and "value" are not treated correctly
+			if ( !el.querySelectorAll( "[selected]" ).length ) {
+				rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" );
+			}
+
+			// Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+
+			if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) {
+				rbuggyQSA.push( "~=" );
+			}
+
+			// Support: IE 11+, Edge 15 - 18+
+			// IE 11/Edge don't find elements on a `[name='']` query in some cases.
+			// Adding a temporary attribute to the document before the selection works
+			// around the issue.
+			// Interestingly, IE 10 & older don't seem to have the issue.
+			input = document.createElement( "input" );
+			input.setAttribute( "name", "" );
+			el.appendChild( input );
+			if ( !el.querySelectorAll( "[name='']" ).length ) {
+				rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" +
+					whitespace + "*(?:''|\"\")" );
+			}
+
+			// Webkit/Opera - :checked should return selected option elements
+			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
+			// IE8 throws error here and will not see later tests
+			if ( !el.querySelectorAll( ":checked" ).length ) {
+				rbuggyQSA.push( ":checked" );
+			}
+
+			// Support: Safari 8+, iOS 8+
+			// https://bugs.webkit.org/show_bug.cgi?id=136851
+			// In-page `selector#id sibling-combinator selector` fails
+			if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) {
+				rbuggyQSA.push( ".#.+[+~]" );
+			}
+
+			// Support: Firefox <=3.6 - 5 only
+			// Old Firefox doesn't throw on a badly-escaped identifier.
+			el.querySelectorAll( "\\\f" );
+			rbuggyQSA.push( "[\\r\\n\\f]" );
+		} );
+
+		assert( function( el ) {
+			el.innerHTML = "<a href='' disabled='disabled'></a>" +
+				"<select disabled='disabled'><option/></select>";
+
+			// Support: Windows 8 Native Apps
+			// The type and name attributes are restricted during .innerHTML assignment
+			var input = document.createElement( "input" );
+			input.setAttribute( "type", "hidden" );
+			el.appendChild( input ).setAttribute( "name", "D" );
+
+			// Support: IE8
+			// Enforce case-sensitivity of name attribute
+			if ( el.querySelectorAll( "[name=d]" ).length ) {
+				rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" );
+			}
+
+			// FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled)
+			// IE8 throws error here and will not see later tests
+			if ( el.querySelectorAll( ":enabled" ).length !== 2 ) {
+				rbuggyQSA.push( ":enabled", ":disabled" );
+			}
+
+			// Support: IE9-11+
+			// IE's :disabled selector does not pick up the children of disabled fieldsets
+			docElem.appendChild( el ).disabled = true;
+			if ( el.querySelectorAll( ":disabled" ).length !== 2 ) {
+				rbuggyQSA.push( ":enabled", ":disabled" );
+			}
+
+			// Support: Opera 10 - 11 only
+			// Opera 10-11 does not throw on post-comma invalid pseudos
+			el.querySelectorAll( "*,:x" );
+			rbuggyQSA.push( ",.*:" );
+		} );
+	}
+
+	if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches ||
+		docElem.webkitMatchesSelector ||
+		docElem.mozMatchesSelector ||
+		docElem.oMatchesSelector ||
+		docElem.msMatchesSelector ) ) ) ) {
+
+		assert( function( el ) {
+
+			// Check to see if it's possible to do matchesSelector
+			// on a disconnected node (IE 9)
+			support.disconnectedMatch = matches.call( el, "*" );
+
+			// This should fail with an exception
+			// Gecko does not error, returns false instead
+			matches.call( el, "[s!='']:x" );
+			rbuggyMatches.push( "!=", pseudos );
+		} );
+	}
+
+	rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) );
+	rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) );
+
+	/* Contains
+	---------------------------------------------------------------------- */
+	hasCompare = rnative.test( docElem.compareDocumentPosition );
+
+	// Element contains another
+	// Purposefully self-exclusive
+	// As in, an element does not contain itself
+	contains = hasCompare || rnative.test( docElem.contains ) ?
+		function( a, b ) {
+			var adown = a.nodeType === 9 ? a.documentElement : a,
+				bup = b && b.parentNode;
+			return a === bup || !!( bup && bup.nodeType === 1 && (
+				adown.contains ?
+					adown.contains( bup ) :
+					a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16
+			) );
+		} :
+		function( a, b ) {
+			if ( b ) {
+				while ( ( b = b.parentNode ) ) {
+					if ( b === a ) {
+						return true;
+					}
+				}
+			}
+			return false;
+		};
+
+	/* Sorting
+	---------------------------------------------------------------------- */
+
+	// Document order sorting
+	sortOrder = hasCompare ?
+	function( a, b ) {
+
+		// Flag for duplicate removal
+		if ( a === b ) {
+			hasDuplicate = true;
+			return 0;
+		}
+
+		// Sort on method existence if only one input has compareDocumentPosition
+		var compare = !a.compareDocumentPosition - !b.compareDocumentPosition;
+		if ( compare ) {
+			return compare;
+		}
+
+		// Calculate position if both inputs belong to the same document
+		// Support: IE 11+, Edge 17 - 18+
+		// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+		// two documents; shallow comparisons work.
+		// eslint-disable-next-line eqeqeq
+		compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ?
+			a.compareDocumentPosition( b ) :
+
+			// Otherwise we know they are disconnected
+			1;
+
+		// Disconnected nodes
+		if ( compare & 1 ||
+			( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) {
+
+			// Choose the first element that is related to our preferred document
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			// eslint-disable-next-line eqeqeq
+			if ( a == document || a.ownerDocument == preferredDoc &&
+				contains( preferredDoc, a ) ) {
+				return -1;
+			}
+
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			// eslint-disable-next-line eqeqeq
+			if ( b == document || b.ownerDocument == preferredDoc &&
+				contains( preferredDoc, b ) ) {
+				return 1;
+			}
+
+			// Maintain original order
+			return sortInput ?
+				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
+				0;
+		}
+
+		return compare & 4 ? -1 : 1;
+	} :
+	function( a, b ) {
+
+		// Exit early if the nodes are identical
+		if ( a === b ) {
+			hasDuplicate = true;
+			return 0;
+		}
+
+		var cur,
+			i = 0,
+			aup = a.parentNode,
+			bup = b.parentNode,
+			ap = [ a ],
+			bp = [ b ];
+
+		// Parentless nodes are either documents or disconnected
+		if ( !aup || !bup ) {
+
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			/* eslint-disable eqeqeq */
+			return a == document ? -1 :
+				b == document ? 1 :
+				/* eslint-enable eqeqeq */
+				aup ? -1 :
+				bup ? 1 :
+				sortInput ?
+				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
+				0;
+
+		// If the nodes are siblings, we can do a quick check
+		} else if ( aup === bup ) {
+			return siblingCheck( a, b );
+		}
+
+		// Otherwise we need full lists of their ancestors for comparison
+		cur = a;
+		while ( ( cur = cur.parentNode ) ) {
+			ap.unshift( cur );
+		}
+		cur = b;
+		while ( ( cur = cur.parentNode ) ) {
+			bp.unshift( cur );
+		}
+
+		// Walk down the tree looking for a discrepancy
+		while ( ap[ i ] === bp[ i ] ) {
+			i++;
+		}
+
+		return i ?
+
+			// Do a sibling check if the nodes have a common ancestor
+			siblingCheck( ap[ i ], bp[ i ] ) :
+
+			// Otherwise nodes in our document sort first
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			/* eslint-disable eqeqeq */
+			ap[ i ] == preferredDoc ? -1 :
+			bp[ i ] == preferredDoc ? 1 :
+			/* eslint-enable eqeqeq */
+			0;
+	};
+
+	return document;
+};
+
+Sizzle.matches = function( expr, elements ) {
+	return Sizzle( expr, null, null, elements );
+};
+
+Sizzle.matchesSelector = function( elem, expr ) {
+	setDocument( elem );
+
+	if ( support.matchesSelector && documentIsHTML &&
+		!nonnativeSelectorCache[ expr + " " ] &&
+		( !rbuggyMatches || !rbuggyMatches.test( expr ) ) &&
+		( !rbuggyQSA     || !rbuggyQSA.test( expr ) ) ) {
+
+		try {
+			var ret = matches.call( elem, expr );
+
+			// IE 9's matchesSelector returns false on disconnected nodes
+			if ( ret || support.disconnectedMatch ||
+
+				// As well, disconnected nodes are said to be in a document
+				// fragment in IE 9
+				elem.document && elem.document.nodeType !== 11 ) {
+				return ret;
+			}
+		} catch ( e ) {
+			nonnativeSelectorCache( expr, true );
+		}
+	}
+
+	return Sizzle( expr, document, null, [ elem ] ).length > 0;
+};
+
+Sizzle.contains = function( context, elem ) {
+
+	// Set document vars if needed
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( ( context.ownerDocument || context ) != document ) {
+		setDocument( context );
+	}
+	return contains( context, elem );
+};
+
+Sizzle.attr = function( elem, name ) {
+
+	// Set document vars if needed
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( ( elem.ownerDocument || elem ) != document ) {
+		setDocument( elem );
+	}
+
+	var fn = Expr.attrHandle[ name.toLowerCase() ],
+
+		// Don't get fooled by Object.prototype properties (jQuery #13807)
+		val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ?
+			fn( elem, name, !documentIsHTML ) :
+			undefined;
+
+	return val !== undefined ?
+		val :
+		support.attributes || !documentIsHTML ?
+			elem.getAttribute( name ) :
+			( val = elem.getAttributeNode( name ) ) && val.specified ?
+				val.value :
+				null;
+};
+
+Sizzle.escape = function( sel ) {
+	return ( sel + "" ).replace( rcssescape, fcssescape );
+};
+
+Sizzle.error = function( msg ) {
+	throw new Error( "Syntax error, unrecognized expression: " + msg );
+};
+
+/**
+ * Document sorting and removing duplicates
+ * @param {ArrayLike} results
+ */
+Sizzle.uniqueSort = function( results ) {
+	var elem,
+		duplicates = [],
+		j = 0,
+		i = 0;
+
+	// Unless we *know* we can detect duplicates, assume their presence
+	hasDuplicate = !support.detectDuplicates;
+	sortInput = !support.sortStable && results.slice( 0 );
+	results.sort( sortOrder );
+
+	if ( hasDuplicate ) {
+		while ( ( elem = results[ i++ ] ) ) {
+			if ( elem === results[ i ] ) {
+				j = duplicates.push( i );
+			}
+		}
+		while ( j-- ) {
+			results.splice( duplicates[ j ], 1 );
+		}
+	}
+
+	// Clear input after sorting to release objects
+	// See https://github.com/jquery/sizzle/pull/225
+	sortInput = null;
+
+	return results;
+};
+
+/**
+ * Utility function for retrieving the text value of an array of DOM nodes
+ * @param {Array|Element} elem
+ */
+getText = Sizzle.getText = function( elem ) {
+	var node,
+		ret = "",
+		i = 0,
+		nodeType = elem.nodeType;
+
+	if ( !nodeType ) {
+
+		// If no nodeType, this is expected to be an array
+		while ( ( node = elem[ i++ ] ) ) {
+
+			// Do not traverse comment nodes
+			ret += getText( node );
+		}
+	} else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) {
+
+		// Use textContent for elements
+		// innerText usage removed for consistency of new lines (jQuery #11153)
+		if ( typeof elem.textContent === "string" ) {
+			return elem.textContent;
+		} else {
+
+			// Traverse its children
+			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
+				ret += getText( elem );
+			}
+		}
+	} else if ( nodeType === 3 || nodeType === 4 ) {
+		return elem.nodeValue;
+	}
+
+	// Do not include comment or processing instruction nodes
+
+	return ret;
+};
+
+Expr = Sizzle.selectors = {
+
+	// Can be adjusted by the user
+	cacheLength: 50,
+
+	createPseudo: markFunction,
+
+	match: matchExpr,
+
+	attrHandle: {},
+
+	find: {},
+
+	relative: {
+		">": { dir: "parentNode", first: true },
+		" ": { dir: "parentNode" },
+		"+": { dir: "previousSibling", first: true },
+		"~": { dir: "previousSibling" }
+	},
+
+	preFilter: {
+		"ATTR": function( match ) {
+			match[ 1 ] = match[ 1 ].replace( runescape, funescape );
+
+			// Move the given value to match[3] whether quoted or unquoted
+			match[ 3 ] = ( match[ 3 ] || match[ 4 ] ||
+				match[ 5 ] || "" ).replace( runescape, funescape );
+
+			if ( match[ 2 ] === "~=" ) {
+				match[ 3 ] = " " + match[ 3 ] + " ";
+			}
+
+			return match.slice( 0, 4 );
+		},
+
+		"CHILD": function( match ) {
+
+			/* matches from matchExpr["CHILD"]
+				1 type (only|nth|...)
+				2 what (child|of-type)
+				3 argument (even|odd|\d*|\d*n([+-]\d+)?|...)
+				4 xn-component of xn+y argument ([+-]?\d*n|)
+				5 sign of xn-component
+				6 x of xn-component
+				7 sign of y-component
+				8 y of y-component
+			*/
+			match[ 1 ] = match[ 1 ].toLowerCase();
+
+			if ( match[ 1 ].slice( 0, 3 ) === "nth" ) {
+
+				// nth-* requires argument
+				if ( !match[ 3 ] ) {
+					Sizzle.error( match[ 0 ] );
+				}
+
+				// numeric x and y parameters for Expr.filter.CHILD
+				// remember that false/true cast respectively to 0/1
+				match[ 4 ] = +( match[ 4 ] ?
+					match[ 5 ] + ( match[ 6 ] || 1 ) :
+					2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) );
+				match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" );
+
+				// other types prohibit arguments
+			} else if ( match[ 3 ] ) {
+				Sizzle.error( match[ 0 ] );
+			}
+
+			return match;
+		},
+
+		"PSEUDO": function( match ) {
+			var excess,
+				unquoted = !match[ 6 ] && match[ 2 ];
+
+			if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) {
+				return null;
+			}
+
+			// Accept quoted arguments as-is
+			if ( match[ 3 ] ) {
+				match[ 2 ] = match[ 4 ] || match[ 5 ] || "";
+
+			// Strip excess characters from unquoted arguments
+			} else if ( unquoted && rpseudo.test( unquoted ) &&
+
+				// Get excess from tokenize (recursively)
+				( excess = tokenize( unquoted, true ) ) &&
+
+				// advance to the next closing parenthesis
+				( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) {
+
+				// excess is a negative index
+				match[ 0 ] = match[ 0 ].slice( 0, excess );
+				match[ 2 ] = unquoted.slice( 0, excess );
+			}
+
+			// Return only captures needed by the pseudo filter method (type and argument)
+			return match.slice( 0, 3 );
+		}
+	},
+
+	filter: {
+
+		"TAG": function( nodeNameSelector ) {
+			var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase();
+			return nodeNameSelector === "*" ?
+				function() {
+					return true;
+				} :
+				function( elem ) {
+					return elem.nodeName && elem.nodeName.toLowerCase() === nodeName;
+				};
+		},
+
+		"CLASS": function( className ) {
+			var pattern = classCache[ className + " " ];
+
+			return pattern ||
+				( pattern = new RegExp( "(^|" + whitespace +
+					")" + className + "(" + whitespace + "|$)" ) ) && classCache(
+						className, function( elem ) {
+							return pattern.test(
+								typeof elem.className === "string" && elem.className ||
+								typeof elem.getAttribute !== "undefined" &&
+									elem.getAttribute( "class" ) ||
+								""
+							);
+				} );
+		},
+
+		"ATTR": function( name, operator, check ) {
+			return function( elem ) {
+				var result = Sizzle.attr( elem, name );
+
+				if ( result == null ) {
+					return operator === "!=";
+				}
+				if ( !operator ) {
+					return true;
+				}
+
+				result += "";
+
+				/* eslint-disable max-len */
+
+				return operator === "=" ? result === check :
+					operator === "!=" ? result !== check :
+					operator === "^=" ? check && result.indexOf( check ) === 0 :
+					operator === "*=" ? check && result.indexOf( check ) > -1 :
+					operator === "$=" ? check && result.slice( -check.length ) === check :
+					operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 :
+					operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" :
+					false;
+				/* eslint-enable max-len */
+
+			};
+		},
+
+		"CHILD": function( type, what, _argument, first, last ) {
+			var simple = type.slice( 0, 3 ) !== "nth",
+				forward = type.slice( -4 ) !== "last",
+				ofType = what === "of-type";
+
+			return first === 1 && last === 0 ?
+
+				// Shortcut for :nth-*(n)
+				function( elem ) {
+					return !!elem.parentNode;
+				} :
+
+				function( elem, _context, xml ) {
+					var cache, uniqueCache, outerCache, node, nodeIndex, start,
+						dir = simple !== forward ? "nextSibling" : "previousSibling",
+						parent = elem.parentNode,
+						name = ofType && elem.nodeName.toLowerCase(),
+						useCache = !xml && !ofType,
+						diff = false;
+
+					if ( parent ) {
+
+						// :(first|last|only)-(child|of-type)
+						if ( simple ) {
+							while ( dir ) {
+								node = elem;
+								while ( ( node = node[ dir ] ) ) {
+									if ( ofType ?
+										node.nodeName.toLowerCase() === name :
+										node.nodeType === 1 ) {
+
+										return false;
+									}
+								}
+
+								// Reverse direction for :only-* (if we haven't yet done so)
+								start = dir = type === "only" && !start && "nextSibling";
+							}
+							return true;
+						}
+
+						start = [ forward ? parent.firstChild : parent.lastChild ];
+
+						// non-xml :nth-child(...) stores cache data on `parent`
+						if ( forward && useCache ) {
+
+							// Seek `elem` from a previously-cached index
+
+							// ...in a gzip-friendly way
+							node = parent;
+							outerCache = node[ expando ] || ( node[ expando ] = {} );
+
+							// Support: IE <9 only
+							// Defend against cloned attroperties (jQuery gh-1709)
+							uniqueCache = outerCache[ node.uniqueID ] ||
+								( outerCache[ node.uniqueID ] = {} );
+
+							cache = uniqueCache[ type ] || [];
+							nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
+							diff = nodeIndex && cache[ 2 ];
+							node = nodeIndex && parent.childNodes[ nodeIndex ];
+
+							while ( ( node = ++nodeIndex && node && node[ dir ] ||
+
+								// Fallback to seeking `elem` from the start
+								( diff = nodeIndex = 0 ) || start.pop() ) ) {
+
+								// When found, cache indexes on `parent` and break
+								if ( node.nodeType === 1 && ++diff && node === elem ) {
+									uniqueCache[ type ] = [ dirruns, nodeIndex, diff ];
+									break;
+								}
+							}
+
+						} else {
+
+							// Use previously-cached element index if available
+							if ( useCache ) {
+
+								// ...in a gzip-friendly way
+								node = elem;
+								outerCache = node[ expando ] || ( node[ expando ] = {} );
+
+								// Support: IE <9 only
+								// Defend against cloned attroperties (jQuery gh-1709)
+								uniqueCache = outerCache[ node.uniqueID ] ||
+									( outerCache[ node.uniqueID ] = {} );
+
+								cache = uniqueCache[ type ] || [];
+								nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
+								diff = nodeIndex;
+							}
+
+							// xml :nth-child(...)
+							// or :nth-last-child(...) or :nth(-last)?-of-type(...)
+							if ( diff === false ) {
+
+								// Use the same loop as above to seek `elem` from the start
+								while ( ( node = ++nodeIndex && node && node[ dir ] ||
+									( diff = nodeIndex = 0 ) || start.pop() ) ) {
+
+									if ( ( ofType ?
+										node.nodeName.toLowerCase() === name :
+										node.nodeType === 1 ) &&
+										++diff ) {
+
+										// Cache the index of each encountered element
+										if ( useCache ) {
+											outerCache = node[ expando ] ||
+												( node[ expando ] = {} );
+
+											// Support: IE <9 only
+											// Defend against cloned attroperties (jQuery gh-1709)
+											uniqueCache = outerCache[ node.uniqueID ] ||
+												( outerCache[ node.uniqueID ] = {} );
+
+											uniqueCache[ type ] = [ dirruns, diff ];
+										}
+
+										if ( node === elem ) {
+											break;
+										}
+									}
+								}
+							}
+						}
+
+						// Incorporate the offset, then check against cycle size
+						diff -= last;
+						return diff === first || ( diff % first === 0 && diff / first >= 0 );
+					}
+				};
+		},
+
+		"PSEUDO": function( pseudo, argument ) {
+
+			// pseudo-class names are case-insensitive
+			// http://www.w3.org/TR/selectors/#pseudo-classes
+			// Prioritize by case sensitivity in case custom pseudos are added with uppercase letters
+			// Remember that setFilters inherits from pseudos
+			var args,
+				fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] ||
+					Sizzle.error( "unsupported pseudo: " + pseudo );
+
+			// The user may use createPseudo to indicate that
+			// arguments are needed to create the filter function
+			// just as Sizzle does
+			if ( fn[ expando ] ) {
+				return fn( argument );
+			}
+
+			// But maintain support for old signatures
+			if ( fn.length > 1 ) {
+				args = [ pseudo, pseudo, "", argument ];
+				return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ?
+					markFunction( function( seed, matches ) {
+						var idx,
+							matched = fn( seed, argument ),
+							i = matched.length;
+						while ( i-- ) {
+							idx = indexOf( seed, matched[ i ] );
+							seed[ idx ] = !( matches[ idx ] = matched[ i ] );
+						}
+					} ) :
+					function( elem ) {
+						return fn( elem, 0, args );
+					};
+			}
+
+			return fn;
+		}
+	},
+
+	pseudos: {
+
+		// Potentially complex pseudos
+		"not": markFunction( function( selector ) {
+
+			// Trim the selector passed to compile
+			// to avoid treating leading and trailing
+			// spaces as combinators
+			var input = [],
+				results = [],
+				matcher = compile( selector.replace( rtrim, "$1" ) );
+
+			return matcher[ expando ] ?
+				markFunction( function( seed, matches, _context, xml ) {
+					var elem,
+						unmatched = matcher( seed, null, xml, [] ),
+						i = seed.length;
+
+					// Match elements unmatched by `matcher`
+					while ( i-- ) {
+						if ( ( elem = unmatched[ i ] ) ) {
+							seed[ i ] = !( matches[ i ] = elem );
+						}
+					}
+				} ) :
+				function( elem, _context, xml ) {
+					input[ 0 ] = elem;
+					matcher( input, null, xml, results );
+
+					// Don't keep the element (issue #299)
+					input[ 0 ] = null;
+					return !results.pop();
+				};
+		} ),
+
+		"has": markFunction( function( selector ) {
+			return function( elem ) {
+				return Sizzle( selector, elem ).length > 0;
+			};
+		} ),
+
+		"contains": markFunction( function( text ) {
+			text = text.replace( runescape, funescape );
+			return function( elem ) {
+				return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1;
+			};
+		} ),
+
+		// "Whether an element is represented by a :lang() selector
+		// is based solely on the element's language value
+		// being equal to the identifier C,
+		// or beginning with the identifier C immediately followed by "-".
+		// The matching of C against the element's language value is performed case-insensitively.
+		// The identifier C does not have to be a valid language name."
+		// http://www.w3.org/TR/selectors/#lang-pseudo
+		"lang": markFunction( function( lang ) {
+
+			// lang value must be a valid identifier
+			if ( !ridentifier.test( lang || "" ) ) {
+				Sizzle.error( "unsupported lang: " + lang );
+			}
+			lang = lang.replace( runescape, funescape ).toLowerCase();
+			return function( elem ) {
+				var elemLang;
+				do {
+					if ( ( elemLang = documentIsHTML ?
+						elem.lang :
+						elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) {
+
+						elemLang = elemLang.toLowerCase();
+						return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0;
+					}
+				} while ( ( elem = elem.parentNode ) && elem.nodeType === 1 );
+				return false;
+			};
+		} ),
+
+		// Miscellaneous
+		"target": function( elem ) {
+			var hash = window.location && window.location.hash;
+			return hash && hash.slice( 1 ) === elem.id;
+		},
+
+		"root": function( elem ) {
+			return elem === docElem;
+		},
+
+		"focus": function( elem ) {
+			return elem === document.activeElement &&
+				( !document.hasFocus || document.hasFocus() ) &&
+				!!( elem.type || elem.href || ~elem.tabIndex );
+		},
+
+		// Boolean properties
+		"enabled": createDisabledPseudo( false ),
+		"disabled": createDisabledPseudo( true ),
+
+		"checked": function( elem ) {
+
+			// In CSS3, :checked should return both checked and selected elements
+			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
+			var nodeName = elem.nodeName.toLowerCase();
+			return ( nodeName === "input" && !!elem.checked ) ||
+				( nodeName === "option" && !!elem.selected );
+		},
+
+		"selected": function( elem ) {
+
+			// Accessing this property makes selected-by-default
+			// options in Safari work properly
+			if ( elem.parentNode ) {
+				// eslint-disable-next-line no-unused-expressions
+				elem.parentNode.selectedIndex;
+			}
+
+			return elem.selected === true;
+		},
+
+		// Contents
+		"empty": function( elem ) {
+
+			// http://www.w3.org/TR/selectors/#empty-pseudo
+			// :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5),
+			//   but not by others (comment: 8; processing instruction: 7; etc.)
+			// nodeType < 6 works because attributes (2) do not appear as children
+			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
+				if ( elem.nodeType < 6 ) {
+					return false;
+				}
+			}
+			return true;
+		},
+
+		"parent": function( elem ) {
+			return !Expr.pseudos[ "empty" ]( elem );
+		},
+
+		// Element/input types
+		"header": function( elem ) {
+			return rheader.test( elem.nodeName );
+		},
+
+		"input": function( elem ) {
+			return rinputs.test( elem.nodeName );
+		},
+
+		"button": function( elem ) {
+			var name = elem.nodeName.toLowerCase();
+			return name === "input" && elem.type === "button" || name === "button";
+		},
+
+		"text": function( elem ) {
+			var attr;
+			return elem.nodeName.toLowerCase() === "input" &&
+				elem.type === "text" &&
+
+				// Support: IE<8
+				// New HTML5 attribute values (e.g., "search") appear with elem.type === "text"
+				( ( attr = elem.getAttribute( "type" ) ) == null ||
+					attr.toLowerCase() === "text" );
+		},
+
+		// Position-in-collection
+		"first": createPositionalPseudo( function() {
+			return [ 0 ];
+		} ),
+
+		"last": createPositionalPseudo( function( _matchIndexes, length ) {
+			return [ length - 1 ];
+		} ),
+
+		"eq": createPositionalPseudo( function( _matchIndexes, length, argument ) {
+			return [ argument < 0 ? argument + length : argument ];
+		} ),
+
+		"even": createPositionalPseudo( function( matchIndexes, length ) {
+			var i = 0;
+			for ( ; i < length; i += 2 ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} ),
+
+		"odd": createPositionalPseudo( function( matchIndexes, length ) {
+			var i = 1;
+			for ( ; i < length; i += 2 ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} ),
+
+		"lt": createPositionalPseudo( function( matchIndexes, length, argument ) {
+			var i = argument < 0 ?
+				argument + length :
+				argument > length ?
+					length :
+					argument;
+			for ( ; --i >= 0; ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} ),
+
+		"gt": createPositionalPseudo( function( matchIndexes, length, argument ) {
+			var i = argument < 0 ? argument + length : argument;
+			for ( ; ++i < length; ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} )
+	}
+};
+
+Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ];
+
+// Add button/input type pseudos
+for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) {
+	Expr.pseudos[ i ] = createInputPseudo( i );
+}
+for ( i in { submit: true, reset: true } ) {
+	Expr.pseudos[ i ] = createButtonPseudo( i );
+}
+
+// Easy API for creating new setFilters
+function setFilters() {}
+setFilters.prototype = Expr.filters = Expr.pseudos;
+Expr.setFilters = new setFilters();
+
+tokenize = Sizzle.tokenize = function( selector, parseOnly ) {
+	var matched, match, tokens, type,
+		soFar, groups, preFilters,
+		cached = tokenCache[ selector + " " ];
+
+	if ( cached ) {
+		return parseOnly ? 0 : cached.slice( 0 );
+	}
+
+	soFar = selector;
+	groups = [];
+	preFilters = Expr.preFilter;
+
+	while ( soFar ) {
+
+		// Comma and first run
+		if ( !matched || ( match = rcomma.exec( soFar ) ) ) {
+			if ( match ) {
+
+				// Don't consume trailing commas as valid
+				soFar = soFar.slice( match[ 0 ].length ) || soFar;
+			}
+			groups.push( ( tokens = [] ) );
+		}
+
+		matched = false;
+
+		// Combinators
+		if ( ( match = rcombinators.exec( soFar ) ) ) {
+			matched = match.shift();
+			tokens.push( {
+				value: matched,
+
+				// Cast descendant combinators to space
+				type: match[ 0 ].replace( rtrim, " " )
+			} );
+			soFar = soFar.slice( matched.length );
+		}
+
+		// Filters
+		for ( type in Expr.filter ) {
+			if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] ||
+				( match = preFilters[ type ]( match ) ) ) ) {
+				matched = match.shift();
+				tokens.push( {
+					value: matched,
+					type: type,
+					matches: match
+				} );
+				soFar = soFar.slice( matched.length );
+			}
+		}
+
+		if ( !matched ) {
+			break;
+		}
+	}
+
+	// Return the length of the invalid excess
+	// if we're just parsing
+	// Otherwise, throw an error or return tokens
+	return parseOnly ?
+		soFar.length :
+		soFar ?
+			Sizzle.error( selector ) :
+
+			// Cache the tokens
+			tokenCache( selector, groups ).slice( 0 );
+};
+
+function toSelector( tokens ) {
+	var i = 0,
+		len = tokens.length,
+		selector = "";
+	for ( ; i < len; i++ ) {
+		selector += tokens[ i ].value;
+	}
+	return selector;
+}
+
+function addCombinator( matcher, combinator, base ) {
+	var dir = combinator.dir,
+		skip = combinator.next,
+		key = skip || dir,
+		checkNonElements = base && key === "parentNode",
+		doneName = done++;
+
+	return combinator.first ?
+
+		// Check against closest ancestor/preceding element
+		function( elem, context, xml ) {
+			while ( ( elem = elem[ dir ] ) ) {
+				if ( elem.nodeType === 1 || checkNonElements ) {
+					return matcher( elem, context, xml );
+				}
+			}
+			return false;
+		} :
+
+		// Check against all ancestor/preceding elements
+		function( elem, context, xml ) {
+			var oldCache, uniqueCache, outerCache,
+				newCache = [ dirruns, doneName ];
+
+			// We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching
+			if ( xml ) {
+				while ( ( elem = elem[ dir ] ) ) {
+					if ( elem.nodeType === 1 || checkNonElements ) {
+						if ( matcher( elem, context, xml ) ) {
+							return true;
+						}
+					}
+				}
+			} else {
+				while ( ( elem = elem[ dir ] ) ) {
+					if ( elem.nodeType === 1 || checkNonElements ) {
+						outerCache = elem[ expando ] || ( elem[ expando ] = {} );
+
+						// Support: IE <9 only
+						// Defend against cloned attroperties (jQuery gh-1709)
+						uniqueCache = outerCache[ elem.uniqueID ] ||
+							( outerCache[ elem.uniqueID ] = {} );
+
+						if ( skip && skip === elem.nodeName.toLowerCase() ) {
+							elem = elem[ dir ] || elem;
+						} else if ( ( oldCache = uniqueCache[ key ] ) &&
+							oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) {
+
+							// Assign to newCache so results back-propagate to previous elements
+							return ( newCache[ 2 ] = oldCache[ 2 ] );
+						} else {
+
+							// Reuse newcache so results back-propagate to previous elements
+							uniqueCache[ key ] = newCache;
+
+							// A match means we're done; a fail means we have to keep checking
+							if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) {
+								return true;
+							}
+						}
+					}
+				}
+			}
+			return false;
+		};
+}
+
+function elementMatcher( matchers ) {
+	return matchers.length > 1 ?
+		function( elem, context, xml ) {
+			var i = matchers.length;
+			while ( i-- ) {
+				if ( !matchers[ i ]( elem, context, xml ) ) {
+					return false;
+				}
+			}
+			return true;
+		} :
+		matchers[ 0 ];
+}
+
+function multipleContexts( selector, contexts, results ) {
+	var i = 0,
+		len = contexts.length;
+	for ( ; i < len; i++ ) {
+		Sizzle( selector, contexts[ i ], results );
+	}
+	return results;
+}
+
+function condense( unmatched, map, filter, context, xml ) {
+	var elem,
+		newUnmatched = [],
+		i = 0,
+		len = unmatched.length,
+		mapped = map != null;
+
+	for ( ; i < len; i++ ) {
+		if ( ( elem = unmatched[ i ] ) ) {
+			if ( !filter || filter( elem, context, xml ) ) {
+				newUnmatched.push( elem );
+				if ( mapped ) {
+					map.push( i );
+				}
+			}
+		}
+	}
+
+	return newUnmatched;
+}
+
+function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) {
+	if ( postFilter && !postFilter[ expando ] ) {
+		postFilter = setMatcher( postFilter );
+	}
+	if ( postFinder && !postFinder[ expando ] ) {
+		postFinder = setMatcher( postFinder, postSelector );
+	}
+	return markFunction( function( seed, results, context, xml ) {
+		var temp, i, elem,
+			preMap = [],
+			postMap = [],
+			preexisting = results.length,
+
+			// Get initial elements from seed or context
+			elems = seed || multipleContexts(
+				selector || "*",
+				context.nodeType ? [ context ] : context,
+				[]
+			),
+
+			// Prefilter to get matcher input, preserving a map for seed-results synchronization
+			matcherIn = preFilter && ( seed || !selector ) ?
+				condense( elems, preMap, preFilter, context, xml ) :
+				elems,
+
+			matcherOut = matcher ?
+
+				// If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results,
+				postFinder || ( seed ? preFilter : preexisting || postFilter ) ?
+
+					// ...intermediate processing is necessary
+					[] :
+
+					// ...otherwise use results directly
+					results :
+				matcherIn;
+
+		// Find primary matches
+		if ( matcher ) {
+			matcher( matcherIn, matcherOut, context, xml );
+		}
+
+		// Apply postFilter
+		if ( postFilter ) {
+			temp = condense( matcherOut, postMap );
+			postFilter( temp, [], context, xml );
+
+			// Un-match failing elements by moving them back to matcherIn
+			i = temp.length;
+			while ( i-- ) {
+				if ( ( elem = temp[ i ] ) ) {
+					matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem );
+				}
+			}
+		}
+
+		if ( seed ) {
+			if ( postFinder || preFilter ) {
+				if ( postFinder ) {
+
+					// Get the final matcherOut by condensing this intermediate into postFinder contexts
+					temp = [];
+					i = matcherOut.length;
+					while ( i-- ) {
+						if ( ( elem = matcherOut[ i ] ) ) {
+
+							// Restore matcherIn since elem is not yet a final match
+							temp.push( ( matcherIn[ i ] = elem ) );
+						}
+					}
+					postFinder( null, ( matcherOut = [] ), temp, xml );
+				}
+
+				// Move matched elements from seed to results to keep them synchronized
+				i = matcherOut.length;
+				while ( i-- ) {
+					if ( ( elem = matcherOut[ i ] ) &&
+						( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) {
+
+						seed[ temp ] = !( results[ temp ] = elem );
+					}
+				}
+			}
+
+		// Add elements to results, through postFinder if defined
+		} else {
+			matcherOut = condense(
+				matcherOut === results ?
+					matcherOut.splice( preexisting, matcherOut.length ) :
+					matcherOut
+			);
+			if ( postFinder ) {
+				postFinder( null, results, matcherOut, xml );
+			} else {
+				push.apply( results, matcherOut );
+			}
+		}
+	} );
+}
+
+function matcherFromTokens( tokens ) {
+	var checkContext, matcher, j,
+		len = tokens.length,
+		leadingRelative = Expr.relative[ tokens[ 0 ].type ],
+		implicitRelative = leadingRelative || Expr.relative[ " " ],
+		i = leadingRelative ? 1 : 0,
+
+		// The foundational matcher ensures that elements are reachable from top-level context(s)
+		matchContext = addCombinator( function( elem ) {
+			return elem === checkContext;
+		}, implicitRelative, true ),
+		matchAnyContext = addCombinator( function( elem ) {
+			return indexOf( checkContext, elem ) > -1;
+		}, implicitRelative, true ),
+		matchers = [ function( elem, context, xml ) {
+			var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || (
+				( checkContext = context ).nodeType ?
+					matchContext( elem, context, xml ) :
+					matchAnyContext( elem, context, xml ) );
+
+			// Avoid hanging onto element (issue #299)
+			checkContext = null;
+			return ret;
+		} ];
+
+	for ( ; i < len; i++ ) {
+		if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) {
+			matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ];
+		} else {
+			matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches );
+
+			// Return special upon seeing a positional matcher
+			if ( matcher[ expando ] ) {
+
+				// Find the next relative operator (if any) for proper handling
+				j = ++i;
+				for ( ; j < len; j++ ) {
+					if ( Expr.relative[ tokens[ j ].type ] ) {
+						break;
+					}
+				}
+				return setMatcher(
+					i > 1 && elementMatcher( matchers ),
+					i > 1 && toSelector(
+
+					// If the preceding token was a descendant combinator, insert an implicit any-element `*`
+					tokens
+						.slice( 0, i - 1 )
+						.concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } )
+					).replace( rtrim, "$1" ),
+					matcher,
+					i < j && matcherFromTokens( tokens.slice( i, j ) ),
+					j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ),
+					j < len && toSelector( tokens )
+				);
+			}
+			matchers.push( matcher );
+		}
+	}
+
+	return elementMatcher( matchers );
+}
+
+function matcherFromGroupMatchers( elementMatchers, setMatchers ) {
+	var bySet = setMatchers.length > 0,
+		byElement = elementMatchers.length > 0,
+		superMatcher = function( seed, context, xml, results, outermost ) {
+			var elem, j, matcher,
+				matchedCount = 0,
+				i = "0",
+				unmatched = seed && [],
+				setMatched = [],
+				contextBackup = outermostContext,
+
+				// We must always have either seed elements or outermost context
+				elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ),
+
+				// Use integer dirruns iff this is the outermost matcher
+				dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ),
+				len = elems.length;
+
+			if ( outermost ) {
+
+				// Support: IE 11+, Edge 17 - 18+
+				// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+				// two documents; shallow comparisons work.
+				// eslint-disable-next-line eqeqeq
+				outermostContext = context == document || context || outermost;
+			}
+
+			// Add elements passing elementMatchers directly to results
+			// Support: IE<9, Safari
+			// Tolerate NodeList properties (IE: "length"; Safari: <number>) matching elements by id
+			for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) {
+				if ( byElement && elem ) {
+					j = 0;
+
+					// Support: IE 11+, Edge 17 - 18+
+					// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+					// two documents; shallow comparisons work.
+					// eslint-disable-next-line eqeqeq
+					if ( !context && elem.ownerDocument != document ) {
+						setDocument( elem );
+						xml = !documentIsHTML;
+					}
+					while ( ( matcher = elementMatchers[ j++ ] ) ) {
+						if ( matcher( elem, context || document, xml ) ) {
+							results.push( elem );
+							break;
+						}
+					}
+					if ( outermost ) {
+						dirruns = dirrunsUnique;
+					}
+				}
+
+				// Track unmatched elements for set filters
+				if ( bySet ) {
+
+					// They will have gone through all possible matchers
+					if ( ( elem = !matcher && elem ) ) {
+						matchedCount--;
+					}
+
+					// Lengthen the array for every element, matched or not
+					if ( seed ) {
+						unmatched.push( elem );
+					}
+				}
+			}
+
+			// `i` is now the count of elements visited above, and adding it to `matchedCount`
+			// makes the latter nonnegative.
+			matchedCount += i;
+
+			// Apply set filters to unmatched elements
+			// NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount`
+			// equals `i`), unless we didn't visit _any_ elements in the above loop because we have
+			// no element matchers and no seed.
+			// Incrementing an initially-string "0" `i` allows `i` to remain a string only in that
+			// case, which will result in a "00" `matchedCount` that differs from `i` but is also
+			// numerically zero.
+			if ( bySet && i !== matchedCount ) {
+				j = 0;
+				while ( ( matcher = setMatchers[ j++ ] ) ) {
+					matcher( unmatched, setMatched, context, xml );
+				}
+
+				if ( seed ) {
+
+					// Reintegrate element matches to eliminate the need for sorting
+					if ( matchedCount > 0 ) {
+						while ( i-- ) {
+							if ( !( unmatched[ i ] || setMatched[ i ] ) ) {
+								setMatched[ i ] = pop.call( results );
+							}
+						}
+					}
+
+					// Discard index placeholder values to get only actual matches
+					setMatched = condense( setMatched );
+				}
+
+				// Add matches to results
+				push.apply( results, setMatched );
+
+				// Seedless set matches succeeding multiple successful matchers stipulate sorting
+				if ( outermost && !seed && setMatched.length > 0 &&
+					( matchedCount + setMatchers.length ) > 1 ) {
+
+					Sizzle.uniqueSort( results );
+				}
+			}
+
+			// Override manipulation of globals by nested matchers
+			if ( outermost ) {
+				dirruns = dirrunsUnique;
+				outermostContext = contextBackup;
+			}
+
+			return unmatched;
+		};
+
+	return bySet ?
+		markFunction( superMatcher ) :
+		superMatcher;
+}
+
+compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) {
+	var i,
+		setMatchers = [],
+		elementMatchers = [],
+		cached = compilerCache[ selector + " " ];
+
+	if ( !cached ) {
+
+		// Generate a function of recursive functions that can be used to check each element
+		if ( !match ) {
+			match = tokenize( selector );
+		}
+		i = match.length;
+		while ( i-- ) {
+			cached = matcherFromTokens( match[ i ] );
+			if ( cached[ expando ] ) {
+				setMatchers.push( cached );
+			} else {
+				elementMatchers.push( cached );
+			}
+		}
+
+		// Cache the compiled function
+		cached = compilerCache(
+			selector,
+			matcherFromGroupMatchers( elementMatchers, setMatchers )
+		);
+
+		// Save selector and tokenization
+		cached.selector = selector;
+	}
+	return cached;
+};
+
+/**
+ * A low-level selection function that works with Sizzle's compiled
+ *  selector functions
+ * @param {String|Function} selector A selector or a pre-compiled
+ *  selector function built with Sizzle.compile
+ * @param {Element} context
+ * @param {Array} [results]
+ * @param {Array} [seed] A set of elements to match against
+ */
+select = Sizzle.select = function( selector, context, results, seed ) {
+	var i, tokens, token, type, find,
+		compiled = typeof selector === "function" && selector,
+		match = !seed && tokenize( ( selector = compiled.selector || selector ) );
+
+	results = results || [];
+
+	// Try to minimize operations if there is only one selector in the list and no seed
+	// (the latter of which guarantees us context)
+	if ( match.length === 1 ) {
+
+		// Reduce context if the leading compound selector is an ID
+		tokens = match[ 0 ] = match[ 0 ].slice( 0 );
+		if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" &&
+			context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) {
+
+			context = ( Expr.find[ "ID" ]( token.matches[ 0 ]
+				.replace( runescape, funescape ), context ) || [] )[ 0 ];
+			if ( !context ) {
+				return results;
+
+			// Precompiled matchers will still verify ancestry, so step up a level
+			} else if ( compiled ) {
+				context = context.parentNode;
+			}
+
+			selector = selector.slice( tokens.shift().value.length );
+		}
+
+		// Fetch a seed set for right-to-left matching
+		i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length;
+		while ( i-- ) {
+			token = tokens[ i ];
+
+			// Abort if we hit a combinator
+			if ( Expr.relative[ ( type = token.type ) ] ) {
+				break;
+			}
+			if ( ( find = Expr.find[ type ] ) ) {
+
+				// Search, expanding context for leading sibling combinators
+				if ( ( seed = find(
+					token.matches[ 0 ].replace( runescape, funescape ),
+					rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) ||
+						context
+				) ) ) {
+
+					// If seed is empty or no tokens remain, we can return early
+					tokens.splice( i, 1 );
+					selector = seed.length && toSelector( tokens );
+					if ( !selector ) {
+						push.apply( results, seed );
+						return results;
+					}
+
+					break;
+				}
+			}
+		}
+	}
+
+	// Compile and execute a filtering function if one is not provided
+	// Provide `match` to avoid retokenization if we modified the selector above
+	( compiled || compile( selector, match ) )(
+		seed,
+		context,
+		!documentIsHTML,
+		results,
+		!context || rsibling.test( selector ) && testContext( context.parentNode ) || context
+	);
+	return results;
+};
+
+// One-time assignments
+
+// Sort stability
+support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando;
+
+// Support: Chrome 14-35+
+// Always assume duplicates if they aren't passed to the comparison function
+support.detectDuplicates = !!hasDuplicate;
+
+// Initialize against the default document
+setDocument();
+
+// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27)
+// Detached nodes confoundingly follow *each other*
+support.sortDetached = assert( function( el ) {
+
+	// Should return 1, but returns 4 (following)
+	return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1;
+} );
+
+// Support: IE<8
+// Prevent attribute/property "interpolation"
+// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx
+if ( !assert( function( el ) {
+	el.innerHTML = "<a href='#'></a>";
+	return el.firstChild.getAttribute( "href" ) === "#";
+} ) ) {
+	addHandle( "type|href|height|width", function( elem, name, isXML ) {
+		if ( !isXML ) {
+			return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 );
+		}
+	} );
+}
+
+// Support: IE<9
+// Use defaultValue in place of getAttribute("value")
+if ( !support.attributes || !assert( function( el ) {
+	el.innerHTML = "<input/>";
+	el.firstChild.setAttribute( "value", "" );
+	return el.firstChild.getAttribute( "value" ) === "";
+} ) ) {
+	addHandle( "value", function( elem, _name, isXML ) {
+		if ( !isXML && elem.nodeName.toLowerCase() === "input" ) {
+			return elem.defaultValue;
+		}
+	} );
+}
+
+// Support: IE<9
+// Use getAttributeNode to fetch booleans when getAttribute lies
+if ( !assert( function( el ) {
+	return el.getAttribute( "disabled" ) == null;
+} ) ) {
+	addHandle( booleans, function( elem, name, isXML ) {
+		var val;
+		if ( !isXML ) {
+			return elem[ name ] === true ? name.toLowerCase() :
+				( val = elem.getAttributeNode( name ) ) && val.specified ?
+					val.value :
+					null;
+		}
+	} );
+}
+
+return Sizzle;
+
+} )( window );
+
+
+
+jQuery.find = Sizzle;
+jQuery.expr = Sizzle.selectors;
+
+// Deprecated
+jQuery.expr[ ":" ] = jQuery.expr.pseudos;
+jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort;
+jQuery.text = Sizzle.getText;
+jQuery.isXMLDoc = Sizzle.isXML;
+jQuery.contains = Sizzle.contains;
+jQuery.escapeSelector = Sizzle.escape;
+
+
+
+
+var dir = function( elem, dir, until ) {
+	var matched = [],
+		truncate = until !== undefined;
+
+	while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) {
+		if ( elem.nodeType === 1 ) {
+			if ( truncate && jQuery( elem ).is( until ) ) {
+				break;
+			}
+			matched.push( elem );
+		}
+	}
+	return matched;
+};
+
+
+var siblings = function( n, elem ) {
+	var matched = [];
+
+	for ( ; n; n = n.nextSibling ) {
+		if ( n.nodeType === 1 && n !== elem ) {
+			matched.push( n );
+		}
+	}
+
+	return matched;
+};
+
+
+var rneedsContext = jQuery.expr.match.needsContext;
+
+
+
+function nodeName( elem, name ) {
+
+	return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase();
+
+}
+var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i );
+
+
+
+// Implement the identical functionality for filter and not
+function winnow( elements, qualifier, not ) {
+	if ( isFunction( qualifier ) ) {
+		return jQuery.grep( elements, function( elem, i ) {
+			return !!qualifier.call( elem, i, elem ) !== not;
+		} );
+	}
+
+	// Single element
+	if ( qualifier.nodeType ) {
+		return jQuery.grep( elements, function( elem ) {
+			return ( elem === qualifier ) !== not;
+		} );
+	}
+
+	// Arraylike of elements (jQuery, arguments, Array)
+	if ( typeof qualifier !== "string" ) {
+		return jQuery.grep( elements, function( elem ) {
+			return ( indexOf.call( qualifier, elem ) > -1 ) !== not;
+		} );
+	}
+
+	// Filtered directly for both simple and complex selectors
+	return jQuery.filter( qualifier, elements, not );
+}
+
+jQuery.filter = function( expr, elems, not ) {
+	var elem = elems[ 0 ];
+
+	if ( not ) {
+		expr = ":not(" + expr + ")";
+	}
+
+	if ( elems.length === 1 && elem.nodeType === 1 ) {
+		return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : [];
+	}
+
+	return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) {
+		return elem.nodeType === 1;
+	} ) );
+};
+
+jQuery.fn.extend( {
+	find: function( selector ) {
+		var i, ret,
+			len = this.length,
+			self = this;
+
+		if ( typeof selector !== "string" ) {
+			return this.pushStack( jQuery( selector ).filter( function() {
+				for ( i = 0; i < len; i++ ) {
+					if ( jQuery.contains( self[ i ], this ) ) {
+						return true;
+					}
+				}
+			} ) );
+		}
+
+		ret = this.pushStack( [] );
+
+		for ( i = 0; i < len; i++ ) {
+			jQuery.find( selector, self[ i ], ret );
+		}
+
+		return len > 1 ? jQuery.uniqueSort( ret ) : ret;
+	},
+	filter: function( selector ) {
+		return this.pushStack( winnow( this, selector || [], false ) );
+	},
+	not: function( selector ) {
+		return this.pushStack( winnow( this, selector || [], true ) );
+	},
+	is: function( selector ) {
+		return !!winnow(
+			this,
+
+			// If this is a positional/relative selector, check membership in the returned set
+			// so $("p:first").is("p:last") won't return true for a doc with two "p".
+			typeof selector === "string" && rneedsContext.test( selector ) ?
+				jQuery( selector ) :
+				selector || [],
+			false
+		).length;
+	}
+} );
+
+
+// Initialize a jQuery object
+
+
+// A central reference to the root jQuery(document)
+var rootjQuery,
+
+	// A simple way to check for HTML strings
+	// Prioritize #id over <tag> to avoid XSS via location.hash (#9521)
+	// Strict HTML recognition (#11290: must start with <)
+	// Shortcut simple #id case for speed
+	rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/,
+
+	init = jQuery.fn.init = function( selector, context, root ) {
+		var match, elem;
+
+		// HANDLE: $(""), $(null), $(undefined), $(false)
+		if ( !selector ) {
+			return this;
+		}
+
+		// Method init() accepts an alternate rootjQuery
+		// so migrate can support jQuery.sub (gh-2101)
+		root = root || rootjQuery;
+
+		// Handle HTML strings
+		if ( typeof selector === "string" ) {
+			if ( selector[ 0 ] === "<" &&
+				selector[ selector.length - 1 ] === ">" &&
+				selector.length >= 3 ) {
+
+				// Assume that strings that start and end with <> are HTML and skip the regex check
+				match = [ null, selector, null ];
+
+			} else {
+				match = rquickExpr.exec( selector );
+			}
+
+			// Match html or make sure no context is specified for #id
+			if ( match && ( match[ 1 ] || !context ) ) {
+
+				// HANDLE: $(html) -> $(array)
+				if ( match[ 1 ] ) {
+					context = context instanceof jQuery ? context[ 0 ] : context;
+
+					// Option to run scripts is true for back-compat
+					// Intentionally let the error be thrown if parseHTML is not present
+					jQuery.merge( this, jQuery.parseHTML(
+						match[ 1 ],
+						context && context.nodeType ? context.ownerDocument || context : document,
+						true
+					) );
+
+					// HANDLE: $(html, props)
+					if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) {
+						for ( match in context ) {
+
+							// Properties of context are called as methods if possible
+							if ( isFunction( this[ match ] ) ) {
+								this[ match ]( context[ match ] );
+
+							// ...and otherwise set as attributes
+							} else {
+								this.attr( match, context[ match ] );
+							}
+						}
+					}
+
+					return this;
+
+				// HANDLE: $(#id)
+				} else {
+					elem = document.getElementById( match[ 2 ] );
+
+					if ( elem ) {
+
+						// Inject the element directly into the jQuery object
+						this[ 0 ] = elem;
+						this.length = 1;
+					}
+					return this;
+				}
+
+			// HANDLE: $(expr, $(...))
+			} else if ( !context || context.jquery ) {
+				return ( context || root ).find( selector );
+
+			// HANDLE: $(expr, context)
+			// (which is just equivalent to: $(context).find(expr)
+			} else {
+				return this.constructor( context ).find( selector );
+			}
+
+		// HANDLE: $(DOMElement)
+		} else if ( selector.nodeType ) {
+			this[ 0 ] = selector;
+			this.length = 1;
+			return this;
+
+		// HANDLE: $(function)
+		// Shortcut for document ready
+		} else if ( isFunction( selector ) ) {
+			return root.ready !== undefined ?
+				root.ready( selector ) :
+
+				// Execute immediately if ready is not present
+				selector( jQuery );
+		}
+
+		return jQuery.makeArray( selector, this );
+	};
+
+// Give the init function the jQuery prototype for later instantiation
+init.prototype = jQuery.fn;
+
+// Initialize central reference
+rootjQuery = jQuery( document );
+
+
+var rparentsprev = /^(?:parents|prev(?:Until|All))/,
+
+	// Methods guaranteed to produce a unique set when starting from a unique set
+	guaranteedUnique = {
+		children: true,
+		contents: true,
+		next: true,
+		prev: true
+	};
+
+jQuery.fn.extend( {
+	has: function( target ) {
+		var targets = jQuery( target, this ),
+			l = targets.length;
+
+		return this.filter( function() {
+			var i = 0;
+			for ( ; i < l; i++ ) {
+				if ( jQuery.contains( this, targets[ i ] ) ) {
+					return true;
+				}
+			}
+		} );
+	},
+
+	closest: function( selectors, context ) {
+		var cur,
+			i = 0,
+			l = this.length,
+			matched = [],
+			targets = typeof selectors !== "string" && jQuery( selectors );
+
+		// Positional selectors never match, since there's no _selection_ context
+		if ( !rneedsContext.test( selectors ) ) {
+			for ( ; i < l; i++ ) {
+				for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) {
+
+					// Always skip document fragments
+					if ( cur.nodeType < 11 && ( targets ?
+						targets.index( cur ) > -1 :
+
+						// Don't pass non-elements to Sizzle
+						cur.nodeType === 1 &&
+							jQuery.find.matchesSelector( cur, selectors ) ) ) {
+
+						matched.push( cur );
+						break;
+					}
+				}
+			}
+		}
+
+		return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched );
+	},
+
+	// Determine the position of an element within the set
+	index: function( elem ) {
+
+		// No argument, return index in parent
+		if ( !elem ) {
+			return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1;
+		}
+
+		// Index in selector
+		if ( typeof elem === "string" ) {
+			return indexOf.call( jQuery( elem ), this[ 0 ] );
+		}
+
+		// Locate the position of the desired element
+		return indexOf.call( this,
+
+			// If it receives a jQuery object, the first element is used
+			elem.jquery ? elem[ 0 ] : elem
+		);
+	},
+
+	add: function( selector, context ) {
+		return this.pushStack(
+			jQuery.uniqueSort(
+				jQuery.merge( this.get(), jQuery( selector, context ) )
+			)
+		);
+	},
+
+	addBack: function( selector ) {
+		return this.add( selector == null ?
+			this.prevObject : this.prevObject.filter( selector )
+		);
+	}
+} );
+
+function sibling( cur, dir ) {
+	while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {}
+	return cur;
+}
+
+jQuery.each( {
+	parent: function( elem ) {
+		var parent = elem.parentNode;
+		return parent && parent.nodeType !== 11 ? parent : null;
+	},
+	parents: function( elem ) {
+		return dir( elem, "parentNode" );
+	},
+	parentsUntil: function( elem, _i, until ) {
+		return dir( elem, "parentNode", until );
+	},
+	next: function( elem ) {
+		return sibling( elem, "nextSibling" );
+	},
+	prev: function( elem ) {
+		return sibling( elem, "previousSibling" );
+	},
+	nextAll: function( elem ) {
+		return dir( elem, "nextSibling" );
+	},
+	prevAll: function( elem ) {
+		return dir( elem, "previousSibling" );
+	},
+	nextUntil: function( elem, _i, until ) {
+		return dir( elem, "nextSibling", until );
+	},
+	prevUntil: function( elem, _i, until ) {
+		return dir( elem, "previousSibling", until );
+	},
+	siblings: function( elem ) {
+		return siblings( ( elem.parentNode || {} ).firstChild, elem );
+	},
+	children: function( elem ) {
+		return siblings( elem.firstChild );
+	},
+	contents: function( elem ) {
+		if ( elem.contentDocument != null &&
+
+			// Support: IE 11+
+			// <object> elements with no `data` attribute has an object
+			// `contentDocument` with a `null` prototype.
+			getProto( elem.contentDocument ) ) {
+
+			return elem.contentDocument;
+		}
+
+		// Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only
+		// Treat the template element as a regular one in browsers that
+		// don't support it.
+		if ( nodeName( elem, "template" ) ) {
+			elem = elem.content || elem;
+		}
+
+		return jQuery.merge( [], elem.childNodes );
+	}
+}, function( name, fn ) {
+	jQuery.fn[ name ] = function( until, selector ) {
+		var matched = jQuery.map( this, fn, until );
+
+		if ( name.slice( -5 ) !== "Until" ) {
+			selector = until;
+		}
+
+		if ( selector && typeof selector === "string" ) {
+			matched = jQuery.filter( selector, matched );
+		}
+
+		if ( this.length > 1 ) {
+
+			// Remove duplicates
+			if ( !guaranteedUnique[ name ] ) {
+				jQuery.uniqueSort( matched );
+			}
+
+			// Reverse order for parents* and prev-derivatives
+			if ( rparentsprev.test( name ) ) {
+				matched.reverse();
+			}
+		}
+
+		return this.pushStack( matched );
+	};
+} );
+var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g );
+
+
+
+// Convert String-formatted options into Object-formatted ones
+function createOptions( options ) {
+	var object = {};
+	jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) {
+		object[ flag ] = true;
+	} );
+	return object;
+}
+
+/*
+ * Create a callback list using the following parameters:
+ *
+ *	options: an optional list of space-separated options that will change how
+ *			the callback list behaves or a more traditional option object
+ *
+ * By default a callback list will act like an event callback list and can be
+ * "fired" multiple times.
+ *
+ * Possible options:
+ *
+ *	once:			will ensure the callback list can only be fired once (like a Deferred)
+ *
+ *	memory:			will keep track of previous values and will call any callback added
+ *					after the list has been fired right away with the latest "memorized"
+ *					values (like a Deferred)
+ *
+ *	unique:			will ensure a callback can only be added once (no duplicate in the list)
+ *
+ *	stopOnFalse:	interrupt callings when a callback returns false
+ *
+ */
+jQuery.Callbacks = function( options ) {
+
+	// Convert options from String-formatted to Object-formatted if needed
+	// (we check in cache first)
+	options = typeof options === "string" ?
+		createOptions( options ) :
+		jQuery.extend( {}, options );
+
+	var // Flag to know if list is currently firing
+		firing,
+
+		// Last fire value for non-forgettable lists
+		memory,
+
+		// Flag to know if list was already fired
+		fired,
+
+		// Flag to prevent firing
+		locked,
+
+		// Actual callback list
+		list = [],
+
+		// Queue of execution data for repeatable lists
+		queue = [],
+
+		// Index of currently firing callback (modified by add/remove as needed)
+		firingIndex = -1,
+
+		// Fire callbacks
+		fire = function() {
+
+			// Enforce single-firing
+			locked = locked || options.once;
+
+			// Execute callbacks for all pending executions,
+			// respecting firingIndex overrides and runtime changes
+			fired = firing = true;
+			for ( ; queue.length; firingIndex = -1 ) {
+				memory = queue.shift();
+				while ( ++firingIndex < list.length ) {
+
+					// Run callback and check for early termination
+					if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false &&
+						options.stopOnFalse ) {
+
+						// Jump to end and forget the data so .add doesn't re-fire
+						firingIndex = list.length;
+						memory = false;
+					}
+				}
+			}
+
+			// Forget the data if we're done with it
+			if ( !options.memory ) {
+				memory = false;
+			}
+
+			firing = false;
+
+			// Clean up if we're done firing for good
+			if ( locked ) {
+
+				// Keep an empty list if we have data for future add calls
+				if ( memory ) {
+					list = [];
+
+				// Otherwise, this object is spent
+				} else {
+					list = "";
+				}
+			}
+		},
+
+		// Actual Callbacks object
+		self = {
+
+			// Add a callback or a collection of callbacks to the list
+			add: function() {
+				if ( list ) {
+
+					// If we have memory from a past run, we should fire after adding
+					if ( memory && !firing ) {
+						firingIndex = list.length - 1;
+						queue.push( memory );
+					}
+
+					( function add( args ) {
+						jQuery.each( args, function( _, arg ) {
+							if ( isFunction( arg ) ) {
+								if ( !options.unique || !self.has( arg ) ) {
+									list.push( arg );
+								}
+							} else if ( arg && arg.length && toType( arg ) !== "string" ) {
+
+								// Inspect recursively
+								add( arg );
+							}
+						} );
+					} )( arguments );
+
+					if ( memory && !firing ) {
+						fire();
+					}
+				}
+				return this;
+			},
+
+			// Remove a callback from the list
+			remove: function() {
+				jQuery.each( arguments, function( _, arg ) {
+					var index;
+					while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) {
+						list.splice( index, 1 );
+
+						// Handle firing indexes
+						if ( index <= firingIndex ) {
+							firingIndex--;
+						}
+					}
+				} );
+				return this;
+			},
+
+			// Check if a given callback is in the list.
+			// If no argument is given, return whether or not list has callbacks attached.
+			has: function( fn ) {
+				return fn ?
+					jQuery.inArray( fn, list ) > -1 :
+					list.length > 0;
+			},
+
+			// Remove all callbacks from the list
+			empty: function() {
+				if ( list ) {
+					list = [];
+				}
+				return this;
+			},
+
+			// Disable .fire and .add
+			// Abort any current/pending executions
+			// Clear all callbacks and values
+			disable: function() {
+				locked = queue = [];
+				list = memory = "";
+				return this;
+			},
+			disabled: function() {
+				return !list;
+			},
+
+			// Disable .fire
+			// Also disable .add unless we have memory (since it would have no effect)
+			// Abort any pending executions
+			lock: function() {
+				locked = queue = [];
+				if ( !memory && !firing ) {
+					list = memory = "";
+				}
+				return this;
+			},
+			locked: function() {
+				return !!locked;
+			},
+
+			// Call all callbacks with the given context and arguments
+			fireWith: function( context, args ) {
+				if ( !locked ) {
+					args = args || [];
+					args = [ context, args.slice ? args.slice() : args ];
+					queue.push( args );
+					if ( !firing ) {
+						fire();
+					}
+				}
+				return this;
+			},
+
+			// Call all the callbacks with the given arguments
+			fire: function() {
+				self.fireWith( this, arguments );
+				return this;
+			},
+
+			// To know if the callbacks have already been called at least once
+			fired: function() {
+				return !!fired;
+			}
+		};
+
+	return self;
+};
+
+
+function Identity( v ) {
+	return v;
+}
+function Thrower( ex ) {
+	throw ex;
+}
+
+function adoptValue( value, resolve, reject, noValue ) {
+	var method;
+
+	try {
+
+		// Check for promise aspect first to privilege synchronous behavior
+		if ( value && isFunction( ( method = value.promise ) ) ) {
+			method.call( value ).done( resolve ).fail( reject );
+
+		// Other thenables
+		} else if ( value && isFunction( ( method = value.then ) ) ) {
+			method.call( value, resolve, reject );
+
+		// Other non-thenables
+		} else {
+
+			// Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer:
+			// * false: [ value ].slice( 0 ) => resolve( value )
+			// * true: [ value ].slice( 1 ) => resolve()
+			resolve.apply( undefined, [ value ].slice( noValue ) );
+		}
+
+	// For Promises/A+, convert exceptions into rejections
+	// Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in
+	// Deferred#then to conditionally suppress rejection.
+	} catch ( value ) {
+
+		// Support: Android 4.0 only
+		// Strict mode functions invoked without .call/.apply get global-object context
+		reject.apply( undefined, [ value ] );
+	}
+}
+
+jQuery.extend( {
+
+	Deferred: function( func ) {
+		var tuples = [
+
+				// action, add listener, callbacks,
+				// ... .then handlers, argument index, [final state]
+				[ "notify", "progress", jQuery.Callbacks( "memory" ),
+					jQuery.Callbacks( "memory" ), 2 ],
+				[ "resolve", "done", jQuery.Callbacks( "once memory" ),
+					jQuery.Callbacks( "once memory" ), 0, "resolved" ],
+				[ "reject", "fail", jQuery.Callbacks( "once memory" ),
+					jQuery.Callbacks( "once memory" ), 1, "rejected" ]
+			],
+			state = "pending",
+			promise = {
+				state: function() {
+					return state;
+				},
+				always: function() {
+					deferred.done( arguments ).fail( arguments );
+					return this;
+				},
+				"catch": function( fn ) {
+					return promise.then( null, fn );
+				},
+
+				// Keep pipe for back-compat
+				pipe: function( /* fnDone, fnFail, fnProgress */ ) {
+					var fns = arguments;
+
+					return jQuery.Deferred( function( newDefer ) {
+						jQuery.each( tuples, function( _i, tuple ) {
+
+							// Map tuples (progress, done, fail) to arguments (done, fail, progress)
+							var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ];
+
+							// deferred.progress(function() { bind to newDefer or newDefer.notify })
+							// deferred.done(function() { bind to newDefer or newDefer.resolve })
+							// deferred.fail(function() { bind to newDefer or newDefer.reject })
+							deferred[ tuple[ 1 ] ]( function() {
+								var returned = fn && fn.apply( this, arguments );
+								if ( returned && isFunction( returned.promise ) ) {
+									returned.promise()
+										.progress( newDefer.notify )
+										.done( newDefer.resolve )
+										.fail( newDefer.reject );
+								} else {
+									newDefer[ tuple[ 0 ] + "With" ](
+										this,
+										fn ? [ returned ] : arguments
+									);
+								}
+							} );
+						} );
+						fns = null;
+					} ).promise();
+				},
+				then: function( onFulfilled, onRejected, onProgress ) {
+					var maxDepth = 0;
+					function resolve( depth, deferred, handler, special ) {
+						return function() {
+							var that = this,
+								args = arguments,
+								mightThrow = function() {
+									var returned, then;
+
+									// Support: Promises/A+ section 2.3.3.3.3
+									// https://promisesaplus.com/#point-59
+									// Ignore double-resolution attempts
+									if ( depth < maxDepth ) {
+										return;
+									}
+
+									returned = handler.apply( that, args );
+
+									// Support: Promises/A+ section 2.3.1
+									// https://promisesaplus.com/#point-48
+									if ( returned === deferred.promise() ) {
+										throw new TypeError( "Thenable self-resolution" );
+									}
+
+									// Support: Promises/A+ sections 2.3.3.1, 3.5
+									// https://promisesaplus.com/#point-54
+									// https://promisesaplus.com/#point-75
+									// Retrieve `then` only once
+									then = returned &&
+
+										// Support: Promises/A+ section 2.3.4
+										// https://promisesaplus.com/#point-64
+										// Only check objects and functions for thenability
+										( typeof returned === "object" ||
+											typeof returned === "function" ) &&
+										returned.then;
+
+									// Handle a returned thenable
+									if ( isFunction( then ) ) {
+
+										// Special processors (notify) just wait for resolution
+										if ( special ) {
+											then.call(
+												returned,
+												resolve( maxDepth, deferred, Identity, special ),
+												resolve( maxDepth, deferred, Thrower, special )
+											);
+
+										// Normal processors (resolve) also hook into progress
+										} else {
+
+											// ...and disregard older resolution values
+											maxDepth++;
+
+											then.call(
+												returned,
+												resolve( maxDepth, deferred, Identity, special ),
+												resolve( maxDepth, deferred, Thrower, special ),
+												resolve( maxDepth, deferred, Identity,
+													deferred.notifyWith )
+											);
+										}
+
+									// Handle all other returned values
+									} else {
+
+										// Only substitute handlers pass on context
+										// and multiple values (non-spec behavior)
+										if ( handler !== Identity ) {
+											that = undefined;
+											args = [ returned ];
+										}
+
+										// Process the value(s)
+										// Default process is resolve
+										( special || deferred.resolveWith )( that, args );
+									}
+								},
+
+								// Only normal processors (resolve) catch and reject exceptions
+								process = special ?
+									mightThrow :
+									function() {
+										try {
+											mightThrow();
+										} catch ( e ) {
+
+											if ( jQuery.Deferred.exceptionHook ) {
+												jQuery.Deferred.exceptionHook( e,
+													process.stackTrace );
+											}
+
+											// Support: Promises/A+ section 2.3.3.3.4.1
+											// https://promisesaplus.com/#point-61
+											// Ignore post-resolution exceptions
+											if ( depth + 1 >= maxDepth ) {
+
+												// Only substitute handlers pass on context
+												// and multiple values (non-spec behavior)
+												if ( handler !== Thrower ) {
+													that = undefined;
+													args = [ e ];
+												}
+
+												deferred.rejectWith( that, args );
+											}
+										}
+									};
+
+							// Support: Promises/A+ section 2.3.3.3.1
+							// https://promisesaplus.com/#point-57
+							// Re-resolve promises immediately to dodge false rejection from
+							// subsequent errors
+							if ( depth ) {
+								process();
+							} else {
+
+								// Call an optional hook to record the stack, in case of exception
+								// since it's otherwise lost when execution goes async
+								if ( jQuery.Deferred.getStackHook ) {
+									process.stackTrace = jQuery.Deferred.getStackHook();
+								}
+								window.setTimeout( process );
+							}
+						};
+					}
+
+					return jQuery.Deferred( function( newDefer ) {
+
+						// progress_handlers.add( ... )
+						tuples[ 0 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								isFunction( onProgress ) ?
+									onProgress :
+									Identity,
+								newDefer.notifyWith
+							)
+						);
+
+						// fulfilled_handlers.add( ... )
+						tuples[ 1 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								isFunction( onFulfilled ) ?
+									onFulfilled :
+									Identity
+							)
+						);
+
+						// rejected_handlers.add( ... )
+						tuples[ 2 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								isFunction( onRejected ) ?
+									onRejected :
+									Thrower
+							)
+						);
+					} ).promise();
+				},
+
+				// Get a promise for this deferred
+				// If obj is provided, the promise aspect is added to the object
+				promise: function( obj ) {
+					return obj != null ? jQuery.extend( obj, promise ) : promise;
+				}
+			},
+			deferred = {};
+
+		// Add list-specific methods
+		jQuery.each( tuples, function( i, tuple ) {
+			var list = tuple[ 2 ],
+				stateString = tuple[ 5 ];
+
+			// promise.progress = list.add
+			// promise.done = list.add
+			// promise.fail = list.add
+			promise[ tuple[ 1 ] ] = list.add;
+
+			// Handle state
+			if ( stateString ) {
+				list.add(
+					function() {
+
+						// state = "resolved" (i.e., fulfilled)
+						// state = "rejected"
+						state = stateString;
+					},
+
+					// rejected_callbacks.disable
+					// fulfilled_callbacks.disable
+					tuples[ 3 - i ][ 2 ].disable,
+
+					// rejected_handlers.disable
+					// fulfilled_handlers.disable
+					tuples[ 3 - i ][ 3 ].disable,
+
+					// progress_callbacks.lock
+					tuples[ 0 ][ 2 ].lock,
+
+					// progress_handlers.lock
+					tuples[ 0 ][ 3 ].lock
+				);
+			}
+
+			// progress_handlers.fire
+			// fulfilled_handlers.fire
+			// rejected_handlers.fire
+			list.add( tuple[ 3 ].fire );
+
+			// deferred.notify = function() { deferred.notifyWith(...) }
+			// deferred.resolve = function() { deferred.resolveWith(...) }
+			// deferred.reject = function() { deferred.rejectWith(...) }
+			deferred[ tuple[ 0 ] ] = function() {
+				deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments );
+				return this;
+			};
+
+			// deferred.notifyWith = list.fireWith
+			// deferred.resolveWith = list.fireWith
+			// deferred.rejectWith = list.fireWith
+			deferred[ tuple[ 0 ] + "With" ] = list.fireWith;
+		} );
+
+		// Make the deferred a promise
+		promise.promise( deferred );
+
+		// Call given func if any
+		if ( func ) {
+			func.call( deferred, deferred );
+		}
+
+		// All done!
+		return deferred;
+	},
+
+	// Deferred helper
+	when: function( singleValue ) {
+		var
+
+			// count of uncompleted subordinates
+			remaining = arguments.length,
+
+			// count of unprocessed arguments
+			i = remaining,
+
+			// subordinate fulfillment data
+			resolveContexts = Array( i ),
+			resolveValues = slice.call( arguments ),
+
+			// the primary Deferred
+			primary = jQuery.Deferred(),
+
+			// subordinate callback factory
+			updateFunc = function( i ) {
+				return function( value ) {
+					resolveContexts[ i ] = this;
+					resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value;
+					if ( !( --remaining ) ) {
+						primary.resolveWith( resolveContexts, resolveValues );
+					}
+				};
+			};
+
+		// Single- and empty arguments are adopted like Promise.resolve
+		if ( remaining <= 1 ) {
+			adoptValue( singleValue, primary.done( updateFunc( i ) ).resolve, primary.reject,
+				!remaining );
+
+			// Use .then() to unwrap secondary thenables (cf. gh-3000)
+			if ( primary.state() === "pending" ||
+				isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) {
+
+				return primary.then();
+			}
+		}
+
+		// Multiple arguments are aggregated like Promise.all array elements
+		while ( i-- ) {
+			adoptValue( resolveValues[ i ], updateFunc( i ), primary.reject );
+		}
+
+		return primary.promise();
+	}
+} );
+
+
+// These usually indicate a programmer mistake during development,
+// warn about them ASAP rather than swallowing them by default.
+var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/;
+
+jQuery.Deferred.exceptionHook = function( error, stack ) {
+
+	// Support: IE 8 - 9 only
+	// Console exists when dev tools are open, which can happen at any time
+	if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) {
+		window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack );
+	}
+};
+
+
+
+
+jQuery.readyException = function( error ) {
+	window.setTimeout( function() {
+		throw error;
+	} );
+};
+
+
+
+
+// The deferred used on DOM ready
+var readyList = jQuery.Deferred();
+
+jQuery.fn.ready = function( fn ) {
+
+	readyList
+		.then( fn )
+
+		// Wrap jQuery.readyException in a function so that the lookup
+		// happens at the time of error handling instead of callback
+		// registration.
+		.catch( function( error ) {
+			jQuery.readyException( error );
+		} );
+
+	return this;
+};
+
+jQuery.extend( {
+
+	// Is the DOM ready to be used? Set to true once it occurs.
+	isReady: false,
+
+	// A counter to track how many items to wait for before
+	// the ready event fires. See #6781
+	readyWait: 1,
+
+	// Handle when the DOM is ready
+	ready: function( wait ) {
+
+		// Abort if there are pending holds or we're already ready
+		if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) {
+			return;
+		}
+
+		// Remember that the DOM is ready
+		jQuery.isReady = true;
+
+		// If a normal DOM Ready event fired, decrement, and wait if need be
+		if ( wait !== true && --jQuery.readyWait > 0 ) {
+			return;
+		}
+
+		// If there are functions bound, to execute
+		readyList.resolveWith( document, [ jQuery ] );
+	}
+} );
+
+jQuery.ready.then = readyList.then;
+
+// The ready event handler and self cleanup method
+function completed() {
+	document.removeEventListener( "DOMContentLoaded", completed );
+	window.removeEventListener( "load", completed );
+	jQuery.ready();
+}
+
+// Catch cases where $(document).ready() is called
+// after the browser event has already occurred.
+// Support: IE <=9 - 10 only
+// Older IE sometimes signals "interactive" too soon
+if ( document.readyState === "complete" ||
+	( document.readyState !== "loading" && !document.documentElement.doScroll ) ) {
+
+	// Handle it asynchronously to allow scripts the opportunity to delay ready
+	window.setTimeout( jQuery.ready );
+
+} else {
+
+	// Use the handy event callback
+	document.addEventListener( "DOMContentLoaded", completed );
+
+	// A fallback to window.onload, that will always work
+	window.addEventListener( "load", completed );
+}
+
+
+
+
+// Multifunctional method to get and set values of a collection
+// The value/s can optionally be executed if it's a function
+var access = function( elems, fn, key, value, chainable, emptyGet, raw ) {
+	var i = 0,
+		len = elems.length,
+		bulk = key == null;
+
+	// Sets many values
+	if ( toType( key ) === "object" ) {
+		chainable = true;
+		for ( i in key ) {
+			access( elems, fn, i, key[ i ], true, emptyGet, raw );
+		}
+
+	// Sets one value
+	} else if ( value !== undefined ) {
+		chainable = true;
+
+		if ( !isFunction( value ) ) {
+			raw = true;
+		}
+
+		if ( bulk ) {
+
+			// Bulk operations run against the entire set
+			if ( raw ) {
+				fn.call( elems, value );
+				fn = null;
+
+			// ...except when executing function values
+			} else {
+				bulk = fn;
+				fn = function( elem, _key, value ) {
+					return bulk.call( jQuery( elem ), value );
+				};
+			}
+		}
+
+		if ( fn ) {
+			for ( ; i < len; i++ ) {
+				fn(
+					elems[ i ], key, raw ?
+						value :
+						value.call( elems[ i ], i, fn( elems[ i ], key ) )
+				);
+			}
+		}
+	}
+
+	if ( chainable ) {
+		return elems;
+	}
+
+	// Gets
+	if ( bulk ) {
+		return fn.call( elems );
+	}
+
+	return len ? fn( elems[ 0 ], key ) : emptyGet;
+};
+
+
+// Matches dashed string for camelizing
+var rmsPrefix = /^-ms-/,
+	rdashAlpha = /-([a-z])/g;
+
+// Used by camelCase as callback to replace()
+function fcamelCase( _all, letter ) {
+	return letter.toUpperCase();
+}
+
+// Convert dashed to camelCase; used by the css and data modules
+// Support: IE <=9 - 11, Edge 12 - 15
+// Microsoft forgot to hump their vendor prefix (#9572)
+function camelCase( string ) {
+	return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase );
+}
+var acceptData = function( owner ) {
+
+	// Accepts only:
+	//  - Node
+	//    - Node.ELEMENT_NODE
+	//    - Node.DOCUMENT_NODE
+	//  - Object
+	//    - Any
+	return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType );
+};
+
+
+
+
+function Data() {
+	this.expando = jQuery.expando + Data.uid++;
+}
+
+Data.uid = 1;
+
+Data.prototype = {
+
+	cache: function( owner ) {
+
+		// Check if the owner object already has a cache
+		var value = owner[ this.expando ];
+
+		// If not, create one
+		if ( !value ) {
+			value = {};
+
+			// We can accept data for non-element nodes in modern browsers,
+			// but we should not, see #8335.
+			// Always return an empty object.
+			if ( acceptData( owner ) ) {
+
+				// If it is a node unlikely to be stringify-ed or looped over
+				// use plain assignment
+				if ( owner.nodeType ) {
+					owner[ this.expando ] = value;
+
+				// Otherwise secure it in a non-enumerable property
+				// configurable must be true to allow the property to be
+				// deleted when data is removed
+				} else {
+					Object.defineProperty( owner, this.expando, {
+						value: value,
+						configurable: true
+					} );
+				}
+			}
+		}
+
+		return value;
+	},
+	set: function( owner, data, value ) {
+		var prop,
+			cache = this.cache( owner );
+
+		// Handle: [ owner, key, value ] args
+		// Always use camelCase key (gh-2257)
+		if ( typeof data === "string" ) {
+			cache[ camelCase( data ) ] = value;
+
+		// Handle: [ owner, { properties } ] args
+		} else {
+
+			// Copy the properties one-by-one to the cache object
+			for ( prop in data ) {
+				cache[ camelCase( prop ) ] = data[ prop ];
+			}
+		}
+		return cache;
+	},
+	get: function( owner, key ) {
+		return key === undefined ?
+			this.cache( owner ) :
+
+			// Always use camelCase key (gh-2257)
+			owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ];
+	},
+	access: function( owner, key, value ) {
+
+		// In cases where either:
+		//
+		//   1. No key was specified
+		//   2. A string key was specified, but no value provided
+		//
+		// Take the "read" path and allow the get method to determine
+		// which value to return, respectively either:
+		//
+		//   1. The entire cache object
+		//   2. The data stored at the key
+		//
+		if ( key === undefined ||
+				( ( key && typeof key === "string" ) && value === undefined ) ) {
+
+			return this.get( owner, key );
+		}
+
+		// When the key is not a string, or both a key and value
+		// are specified, set or extend (existing objects) with either:
+		//
+		//   1. An object of properties
+		//   2. A key and value
+		//
+		this.set( owner, key, value );
+
+		// Since the "set" path can have two possible entry points
+		// return the expected data based on which path was taken[*]
+		return value !== undefined ? value : key;
+	},
+	remove: function( owner, key ) {
+		var i,
+			cache = owner[ this.expando ];
+
+		if ( cache === undefined ) {
+			return;
+		}
+
+		if ( key !== undefined ) {
+
+			// Support array or space separated string of keys
+			if ( Array.isArray( key ) ) {
+
+				// If key is an array of keys...
+				// We always set camelCase keys, so remove that.
+				key = key.map( camelCase );
+			} else {
+				key = camelCase( key );
+
+				// If a key with the spaces exists, use it.
+				// Otherwise, create an array by matching non-whitespace
+				key = key in cache ?
+					[ key ] :
+					( key.match( rnothtmlwhite ) || [] );
+			}
+
+			i = key.length;
+
+			while ( i-- ) {
+				delete cache[ key[ i ] ];
+			}
+		}
+
+		// Remove the expando if there's no more data
+		if ( key === undefined || jQuery.isEmptyObject( cache ) ) {
+
+			// Support: Chrome <=35 - 45
+			// Webkit & Blink performance suffers when deleting properties
+			// from DOM nodes, so set to undefined instead
+			// https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted)
+			if ( owner.nodeType ) {
+				owner[ this.expando ] = undefined;
+			} else {
+				delete owner[ this.expando ];
+			}
+		}
+	},
+	hasData: function( owner ) {
+		var cache = owner[ this.expando ];
+		return cache !== undefined && !jQuery.isEmptyObject( cache );
+	}
+};
+var dataPriv = new Data();
+
+var dataUser = new Data();
+
+
+
+//	Implementation Summary
+//
+//	1. Enforce API surface and semantic compatibility with 1.9.x branch
+//	2. Improve the module's maintainability by reducing the storage
+//		paths to a single mechanism.
+//	3. Use the same single mechanism to support "private" and "user" data.
+//	4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData)
+//	5. Avoid exposing implementation details on user objects (eg. expando properties)
+//	6. Provide a clear path for implementation upgrade to WeakMap in 2014
+
+var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/,
+	rmultiDash = /[A-Z]/g;
+
+function getData( data ) {
+	if ( data === "true" ) {
+		return true;
+	}
+
+	if ( data === "false" ) {
+		return false;
+	}
+
+	if ( data === "null" ) {
+		return null;
+	}
+
+	// Only convert to a number if it doesn't change the string
+	if ( data === +data + "" ) {
+		return +data;
+	}
+
+	if ( rbrace.test( data ) ) {
+		return JSON.parse( data );
+	}
+
+	return data;
+}
+
+function dataAttr( elem, key, data ) {
+	var name;
+
+	// If nothing was found internally, try to fetch any
+	// data from the HTML5 data-* attribute
+	if ( data === undefined && elem.nodeType === 1 ) {
+		name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase();
+		data = elem.getAttribute( name );
+
+		if ( typeof data === "string" ) {
+			try {
+				data = getData( data );
+			} catch ( e ) {}
+
+			// Make sure we set the data so it isn't changed later
+			dataUser.set( elem, key, data );
+		} else {
+			data = undefined;
+		}
+	}
+	return data;
+}
+
+jQuery.extend( {
+	hasData: function( elem ) {
+		return dataUser.hasData( elem ) || dataPriv.hasData( elem );
+	},
+
+	data: function( elem, name, data ) {
+		return dataUser.access( elem, name, data );
+	},
+
+	removeData: function( elem, name ) {
+		dataUser.remove( elem, name );
+	},
+
+	// TODO: Now that all calls to _data and _removeData have been replaced
+	// with direct calls to dataPriv methods, these can be deprecated.
+	_data: function( elem, name, data ) {
+		return dataPriv.access( elem, name, data );
+	},
+
+	_removeData: function( elem, name ) {
+		dataPriv.remove( elem, name );
+	}
+} );
+
+jQuery.fn.extend( {
+	data: function( key, value ) {
+		var i, name, data,
+			elem = this[ 0 ],
+			attrs = elem && elem.attributes;
+
+		// Gets all values
+		if ( key === undefined ) {
+			if ( this.length ) {
+				data = dataUser.get( elem );
+
+				if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) {
+					i = attrs.length;
+					while ( i-- ) {
+
+						// Support: IE 11 only
+						// The attrs elements can be null (#14894)
+						if ( attrs[ i ] ) {
+							name = attrs[ i ].name;
+							if ( name.indexOf( "data-" ) === 0 ) {
+								name = camelCase( name.slice( 5 ) );
+								dataAttr( elem, name, data[ name ] );
+							}
+						}
+					}
+					dataPriv.set( elem, "hasDataAttrs", true );
+				}
+			}
+
+			return data;
+		}
+
+		// Sets multiple values
+		if ( typeof key === "object" ) {
+			return this.each( function() {
+				dataUser.set( this, key );
+			} );
+		}
+
+		return access( this, function( value ) {
+			var data;
+
+			// The calling jQuery object (element matches) is not empty
+			// (and therefore has an element appears at this[ 0 ]) and the
+			// `value` parameter was not undefined. An empty jQuery object
+			// will result in `undefined` for elem = this[ 0 ] which will
+			// throw an exception if an attempt to read a data cache is made.
+			if ( elem && value === undefined ) {
+
+				// Attempt to get data from the cache
+				// The key will always be camelCased in Data
+				data = dataUser.get( elem, key );
+				if ( data !== undefined ) {
+					return data;
+				}
+
+				// Attempt to "discover" the data in
+				// HTML5 custom data-* attrs
+				data = dataAttr( elem, key );
+				if ( data !== undefined ) {
+					return data;
+				}
+
+				// We tried really hard, but the data doesn't exist.
+				return;
+			}
+
+			// Set the data...
+			this.each( function() {
+
+				// We always store the camelCased key
+				dataUser.set( this, key, value );
+			} );
+		}, null, value, arguments.length > 1, null, true );
+	},
+
+	removeData: function( key ) {
+		return this.each( function() {
+			dataUser.remove( this, key );
+		} );
+	}
+} );
+
+
+jQuery.extend( {
+	queue: function( elem, type, data ) {
+		var queue;
+
+		if ( elem ) {
+			type = ( type || "fx" ) + "queue";
+			queue = dataPriv.get( elem, type );
+
+			// Speed up dequeue by getting out quickly if this is just a lookup
+			if ( data ) {
+				if ( !queue || Array.isArray( data ) ) {
+					queue = dataPriv.access( elem, type, jQuery.makeArray( data ) );
+				} else {
+					queue.push( data );
+				}
+			}
+			return queue || [];
+		}
+	},
+
+	dequeue: function( elem, type ) {
+		type = type || "fx";
+
+		var queue = jQuery.queue( elem, type ),
+			startLength = queue.length,
+			fn = queue.shift(),
+			hooks = jQuery._queueHooks( elem, type ),
+			next = function() {
+				jQuery.dequeue( elem, type );
+			};
+
+		// If the fx queue is dequeued, always remove the progress sentinel
+		if ( fn === "inprogress" ) {
+			fn = queue.shift();
+			startLength--;
+		}
+
+		if ( fn ) {
+
+			// Add a progress sentinel to prevent the fx queue from being
+			// automatically dequeued
+			if ( type === "fx" ) {
+				queue.unshift( "inprogress" );
+			}
+
+			// Clear up the last queue stop function
+			delete hooks.stop;
+			fn.call( elem, next, hooks );
+		}
+
+		if ( !startLength && hooks ) {
+			hooks.empty.fire();
+		}
+	},
+
+	// Not public - generate a queueHooks object, or return the current one
+	_queueHooks: function( elem, type ) {
+		var key = type + "queueHooks";
+		return dataPriv.get( elem, key ) || dataPriv.access( elem, key, {
+			empty: jQuery.Callbacks( "once memory" ).add( function() {
+				dataPriv.remove( elem, [ type + "queue", key ] );
+			} )
+		} );
+	}
+} );
+
+jQuery.fn.extend( {
+	queue: function( type, data ) {
+		var setter = 2;
+
+		if ( typeof type !== "string" ) {
+			data = type;
+			type = "fx";
+			setter--;
+		}
+
+		if ( arguments.length < setter ) {
+			return jQuery.queue( this[ 0 ], type );
+		}
+
+		return data === undefined ?
+			this :
+			this.each( function() {
+				var queue = jQuery.queue( this, type, data );
+
+				// Ensure a hooks for this queue
+				jQuery._queueHooks( this, type );
+
+				if ( type === "fx" && queue[ 0 ] !== "inprogress" ) {
+					jQuery.dequeue( this, type );
+				}
+			} );
+	},
+	dequeue: function( type ) {
+		return this.each( function() {
+			jQuery.dequeue( this, type );
+		} );
+	},
+	clearQueue: function( type ) {
+		return this.queue( type || "fx", [] );
+	},
+
+	// Get a promise resolved when queues of a certain type
+	// are emptied (fx is the type by default)
+	promise: function( type, obj ) {
+		var tmp,
+			count = 1,
+			defer = jQuery.Deferred(),
+			elements = this,
+			i = this.length,
+			resolve = function() {
+				if ( !( --count ) ) {
+					defer.resolveWith( elements, [ elements ] );
+				}
+			};
+
+		if ( typeof type !== "string" ) {
+			obj = type;
+			type = undefined;
+		}
+		type = type || "fx";
+
+		while ( i-- ) {
+			tmp = dataPriv.get( elements[ i ], type + "queueHooks" );
+			if ( tmp && tmp.empty ) {
+				count++;
+				tmp.empty.add( resolve );
+			}
+		}
+		resolve();
+		return defer.promise( obj );
+	}
+} );
+var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source;
+
+var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" );
+
+
+var cssExpand = [ "Top", "Right", "Bottom", "Left" ];
+
+var documentElement = document.documentElement;
+
+
+
+	var isAttached = function( elem ) {
+			return jQuery.contains( elem.ownerDocument, elem );
+		},
+		composed = { composed: true };
+
+	// Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only
+	// Check attachment across shadow DOM boundaries when possible (gh-3504)
+	// Support: iOS 10.0-10.2 only
+	// Early iOS 10 versions support `attachShadow` but not `getRootNode`,
+	// leading to errors. We need to check for `getRootNode`.
+	if ( documentElement.getRootNode ) {
+		isAttached = function( elem ) {
+			return jQuery.contains( elem.ownerDocument, elem ) ||
+				elem.getRootNode( composed ) === elem.ownerDocument;
+		};
+	}
+var isHiddenWithinTree = function( elem, el ) {
+
+		// isHiddenWithinTree might be called from jQuery#filter function;
+		// in that case, element will be second argument
+		elem = el || elem;
+
+		// Inline style trumps all
+		return elem.style.display === "none" ||
+			elem.style.display === "" &&
+
+			// Otherwise, check computed style
+			// Support: Firefox <=43 - 45
+			// Disconnected elements can have computed display: none, so first confirm that elem is
+			// in the document.
+			isAttached( elem ) &&
+
+			jQuery.css( elem, "display" ) === "none";
+	};
+
+
+
+function adjustCSS( elem, prop, valueParts, tween ) {
+	var adjusted, scale,
+		maxIterations = 20,
+		currentValue = tween ?
+			function() {
+				return tween.cur();
+			} :
+			function() {
+				return jQuery.css( elem, prop, "" );
+			},
+		initial = currentValue(),
+		unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ),
+
+		// Starting value computation is required for potential unit mismatches
+		initialInUnit = elem.nodeType &&
+			( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) &&
+			rcssNum.exec( jQuery.css( elem, prop ) );
+
+	if ( initialInUnit && initialInUnit[ 3 ] !== unit ) {
+
+		// Support: Firefox <=54
+		// Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144)
+		initial = initial / 2;
+
+		// Trust units reported by jQuery.css
+		unit = unit || initialInUnit[ 3 ];
+
+		// Iteratively approximate from a nonzero starting point
+		initialInUnit = +initial || 1;
+
+		while ( maxIterations-- ) {
+
+			// Evaluate and update our best guess (doubling guesses that zero out).
+			// Finish if the scale equals or crosses 1 (making the old*new product non-positive).
+			jQuery.style( elem, prop, initialInUnit + unit );
+			if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) {
+				maxIterations = 0;
+			}
+			initialInUnit = initialInUnit / scale;
+
+		}
+
+		initialInUnit = initialInUnit * 2;
+		jQuery.style( elem, prop, initialInUnit + unit );
+
+		// Make sure we update the tween properties later on
+		valueParts = valueParts || [];
+	}
+
+	if ( valueParts ) {
+		initialInUnit = +initialInUnit || +initial || 0;
+
+		// Apply relative offset (+=/-=) if specified
+		adjusted = valueParts[ 1 ] ?
+			initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] :
+			+valueParts[ 2 ];
+		if ( tween ) {
+			tween.unit = unit;
+			tween.start = initialInUnit;
+			tween.end = adjusted;
+		}
+	}
+	return adjusted;
+}
+
+
+var defaultDisplayMap = {};
+
+function getDefaultDisplay( elem ) {
+	var temp,
+		doc = elem.ownerDocument,
+		nodeName = elem.nodeName,
+		display = defaultDisplayMap[ nodeName ];
+
+	if ( display ) {
+		return display;
+	}
+
+	temp = doc.body.appendChild( doc.createElement( nodeName ) );
+	display = jQuery.css( temp, "display" );
+
+	temp.parentNode.removeChild( temp );
+
+	if ( display === "none" ) {
+		display = "block";
+	}
+	defaultDisplayMap[ nodeName ] = display;
+
+	return display;
+}
+
+function showHide( elements, show ) {
+	var display, elem,
+		values = [],
+		index = 0,
+		length = elements.length;
+
+	// Determine new display value for elements that need to change
+	for ( ; index < length; index++ ) {
+		elem = elements[ index ];
+		if ( !elem.style ) {
+			continue;
+		}
+
+		display = elem.style.display;
+		if ( show ) {
+
+			// Since we force visibility upon cascade-hidden elements, an immediate (and slow)
+			// check is required in this first loop unless we have a nonempty display value (either
+			// inline or about-to-be-restored)
+			if ( display === "none" ) {
+				values[ index ] = dataPriv.get( elem, "display" ) || null;
+				if ( !values[ index ] ) {
+					elem.style.display = "";
+				}
+			}
+			if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) {
+				values[ index ] = getDefaultDisplay( elem );
+			}
+		} else {
+			if ( display !== "none" ) {
+				values[ index ] = "none";
+
+				// Remember what we're overwriting
+				dataPriv.set( elem, "display", display );
+			}
+		}
+	}
+
+	// Set the display of the elements in a second loop to avoid constant reflow
+	for ( index = 0; index < length; index++ ) {
+		if ( values[ index ] != null ) {
+			elements[ index ].style.display = values[ index ];
+		}
+	}
+
+	return elements;
+}
+
+jQuery.fn.extend( {
+	show: function() {
+		return showHide( this, true );
+	},
+	hide: function() {
+		return showHide( this );
+	},
+	toggle: function( state ) {
+		if ( typeof state === "boolean" ) {
+			return state ? this.show() : this.hide();
+		}
+
+		return this.each( function() {
+			if ( isHiddenWithinTree( this ) ) {
+				jQuery( this ).show();
+			} else {
+				jQuery( this ).hide();
+			}
+		} );
+	}
+} );
+var rcheckableType = ( /^(?:checkbox|radio)$/i );
+
+var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i );
+
+var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i );
+
+
+
+( function() {
+	var fragment = document.createDocumentFragment(),
+		div = fragment.appendChild( document.createElement( "div" ) ),
+		input = document.createElement( "input" );
+
+	// Support: Android 4.0 - 4.3 only
+	// Check state lost if the name is set (#11217)
+	// Support: Windows Web Apps (WWA)
+	// `name` and `type` must use .setAttribute for WWA (#14901)
+	input.setAttribute( "type", "radio" );
+	input.setAttribute( "checked", "checked" );
+	input.setAttribute( "name", "t" );
+
+	div.appendChild( input );
+
+	// Support: Android <=4.1 only
+	// Older WebKit doesn't clone checked state correctly in fragments
+	support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked;
+
+	// Support: IE <=11 only
+	// Make sure textarea (and checkbox) defaultValue is properly cloned
+	div.innerHTML = "<textarea>x</textarea>";
+	support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue;
+
+	// Support: IE <=9 only
+	// IE <=9 replaces <option> tags with their contents when inserted outside of
+	// the select element.
+	div.innerHTML = "<option></option>";
+	support.option = !!div.lastChild;
+} )();
+
+
+// We have to close these tags to support XHTML (#13200)
+var wrapMap = {
+
+	// XHTML parsers do not magically insert elements in the
+	// same way that tag soup parsers do. So we cannot shorten
+	// this by omitting <tbody> or other required elements.
+	thead: [ 1, "<table>", "</table>" ],
+	col: [ 2, "<table><colgroup>", "</colgroup></table>" ],
+	tr: [ 2, "<table><tbody>", "</tbody></table>" ],
+	td: [ 3, "<table><tbody><tr>", "</tr></tbody></table>" ],
+
+	_default: [ 0, "", "" ]
+};
+
+wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead;
+wrapMap.th = wrapMap.td;
+
+// Support: IE <=9 only
+if ( !support.option ) {
+	wrapMap.optgroup = wrapMap.option = [ 1, "<select multiple='multiple'>", "</select>" ];
+}
+
+
+function getAll( context, tag ) {
+
+	// Support: IE <=9 - 11 only
+	// Use typeof to avoid zero-argument method invocation on host objects (#15151)
+	var ret;
+
+	if ( typeof context.getElementsByTagName !== "undefined" ) {
+		ret = context.getElementsByTagName( tag || "*" );
+
+	} else if ( typeof context.querySelectorAll !== "undefined" ) {
+		ret = context.querySelectorAll( tag || "*" );
+
+	} else {
+		ret = [];
+	}
+
+	if ( tag === undefined || tag && nodeName( context, tag ) ) {
+		return jQuery.merge( [ context ], ret );
+	}
+
+	return ret;
+}
+
+
+// Mark scripts as having already been evaluated
+function setGlobalEval( elems, refElements ) {
+	var i = 0,
+		l = elems.length;
+
+	for ( ; i < l; i++ ) {
+		dataPriv.set(
+			elems[ i ],
+			"globalEval",
+			!refElements || dataPriv.get( refElements[ i ], "globalEval" )
+		);
+	}
+}
+
+
+var rhtml = /<|&#?\w+;/;
+
+function buildFragment( elems, context, scripts, selection, ignored ) {
+	var elem, tmp, tag, wrap, attached, j,
+		fragment = context.createDocumentFragment(),
+		nodes = [],
+		i = 0,
+		l = elems.length;
+
+	for ( ; i < l; i++ ) {
+		elem = elems[ i ];
+
+		if ( elem || elem === 0 ) {
+
+			// Add nodes directly
+			if ( toType( elem ) === "object" ) {
+
+				// Support: Android <=4.0 only, PhantomJS 1 only
+				// push.apply(_, arraylike) throws on ancient WebKit
+				jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem );
+
+			// Convert non-html into a text node
+			} else if ( !rhtml.test( elem ) ) {
+				nodes.push( context.createTextNode( elem ) );
+
+			// Convert html into DOM nodes
+			} else {
+				tmp = tmp || fragment.appendChild( context.createElement( "div" ) );
+
+				// Deserialize a standard representation
+				tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase();
+				wrap = wrapMap[ tag ] || wrapMap._default;
+				tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ];
+
+				// Descend through wrappers to the right content
+				j = wrap[ 0 ];
+				while ( j-- ) {
+					tmp = tmp.lastChild;
+				}
+
+				// Support: Android <=4.0 only, PhantomJS 1 only
+				// push.apply(_, arraylike) throws on ancient WebKit
+				jQuery.merge( nodes, tmp.childNodes );
+
+				// Remember the top-level container
+				tmp = fragment.firstChild;
+
+				// Ensure the created nodes are orphaned (#12392)
+				tmp.textContent = "";
+			}
+		}
+	}
+
+	// Remove wrapper from fragment
+	fragment.textContent = "";
+
+	i = 0;
+	while ( ( elem = nodes[ i++ ] ) ) {
+
+		// Skip elements already in the context collection (trac-4087)
+		if ( selection && jQuery.inArray( elem, selection ) > -1 ) {
+			if ( ignored ) {
+				ignored.push( elem );
+			}
+			continue;
+		}
+
+		attached = isAttached( elem );
+
+		// Append to fragment
+		tmp = getAll( fragment.appendChild( elem ), "script" );
+
+		// Preserve script evaluation history
+		if ( attached ) {
+			setGlobalEval( tmp );
+		}
+
+		// Capture executables
+		if ( scripts ) {
+			j = 0;
+			while ( ( elem = tmp[ j++ ] ) ) {
+				if ( rscriptType.test( elem.type || "" ) ) {
+					scripts.push( elem );
+				}
+			}
+		}
+	}
+
+	return fragment;
+}
+
+
+var rtypenamespace = /^([^.]*)(?:\.(.+)|)/;
+
+function returnTrue() {
+	return true;
+}
+
+function returnFalse() {
+	return false;
+}
+
+// Support: IE <=9 - 11+
+// focus() and blur() are asynchronous, except when they are no-op.
+// So expect focus to be synchronous when the element is already active,
+// and blur to be synchronous when the element is not already active.
+// (focus and blur are always synchronous in other supported browsers,
+// this just defines when we can count on it).
+function expectSync( elem, type ) {
+	return ( elem === safeActiveElement() ) === ( type === "focus" );
+}
+
+// Support: IE <=9 only
+// Accessing document.activeElement can throw unexpectedly
+// https://bugs.jquery.com/ticket/13393
+function safeActiveElement() {
+	try {
+		return document.activeElement;
+	} catch ( err ) { }
+}
+
+function on( elem, types, selector, data, fn, one ) {
+	var origFn, type;
+
+	// Types can be a map of types/handlers
+	if ( typeof types === "object" ) {
+
+		// ( types-Object, selector, data )
+		if ( typeof selector !== "string" ) {
+
+			// ( types-Object, data )
+			data = data || selector;
+			selector = undefined;
+		}
+		for ( type in types ) {
+			on( elem, type, selector, data, types[ type ], one );
+		}
+		return elem;
+	}
+
+	if ( data == null && fn == null ) {
+
+		// ( types, fn )
+		fn = selector;
+		data = selector = undefined;
+	} else if ( fn == null ) {
+		if ( typeof selector === "string" ) {
+
+			// ( types, selector, fn )
+			fn = data;
+			data = undefined;
+		} else {
+
+			// ( types, data, fn )
+			fn = data;
+			data = selector;
+			selector = undefined;
+		}
+	}
+	if ( fn === false ) {
+		fn = returnFalse;
+	} else if ( !fn ) {
+		return elem;
+	}
+
+	if ( one === 1 ) {
+		origFn = fn;
+		fn = function( event ) {
+
+			// Can use an empty set, since event contains the info
+			jQuery().off( event );
+			return origFn.apply( this, arguments );
+		};
+
+		// Use same guid so caller can remove using origFn
+		fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ );
+	}
+	return elem.each( function() {
+		jQuery.event.add( this, types, fn, data, selector );
+	} );
+}
+
+/*
+ * Helper functions for managing events -- not part of the public interface.
+ * Props to Dean Edwards' addEvent library for many of the ideas.
+ */
+jQuery.event = {
+
+	global: {},
+
+	add: function( elem, types, handler, data, selector ) {
+
+		var handleObjIn, eventHandle, tmp,
+			events, t, handleObj,
+			special, handlers, type, namespaces, origType,
+			elemData = dataPriv.get( elem );
+
+		// Only attach events to objects that accept data
+		if ( !acceptData( elem ) ) {
+			return;
+		}
+
+		// Caller can pass in an object of custom data in lieu of the handler
+		if ( handler.handler ) {
+			handleObjIn = handler;
+			handler = handleObjIn.handler;
+			selector = handleObjIn.selector;
+		}
+
+		// Ensure that invalid selectors throw exceptions at attach time
+		// Evaluate against documentElement in case elem is a non-element node (e.g., document)
+		if ( selector ) {
+			jQuery.find.matchesSelector( documentElement, selector );
+		}
+
+		// Make sure that the handler has a unique ID, used to find/remove it later
+		if ( !handler.guid ) {
+			handler.guid = jQuery.guid++;
+		}
+
+		// Init the element's event structure and main handler, if this is the first
+		if ( !( events = elemData.events ) ) {
+			events = elemData.events = Object.create( null );
+		}
+		if ( !( eventHandle = elemData.handle ) ) {
+			eventHandle = elemData.handle = function( e ) {
+
+				// Discard the second event of a jQuery.event.trigger() and
+				// when an event is called after a page has unloaded
+				return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ?
+					jQuery.event.dispatch.apply( elem, arguments ) : undefined;
+			};
+		}
+
+		// Handle multiple events separated by a space
+		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
+		t = types.length;
+		while ( t-- ) {
+			tmp = rtypenamespace.exec( types[ t ] ) || [];
+			type = origType = tmp[ 1 ];
+			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
+
+			// There *must* be a type, no attaching namespace-only handlers
+			if ( !type ) {
+				continue;
+			}
+
+			// If event changes its type, use the special event handlers for the changed type
+			special = jQuery.event.special[ type ] || {};
+
+			// If selector defined, determine special event api type, otherwise given type
+			type = ( selector ? special.delegateType : special.bindType ) || type;
+
+			// Update special based on newly reset type
+			special = jQuery.event.special[ type ] || {};
+
+			// handleObj is passed to all event handlers
+			handleObj = jQuery.extend( {
+				type: type,
+				origType: origType,
+				data: data,
+				handler: handler,
+				guid: handler.guid,
+				selector: selector,
+				needsContext: selector && jQuery.expr.match.needsContext.test( selector ),
+				namespace: namespaces.join( "." )
+			}, handleObjIn );
+
+			// Init the event handler queue if we're the first
+			if ( !( handlers = events[ type ] ) ) {
+				handlers = events[ type ] = [];
+				handlers.delegateCount = 0;
+
+				// Only use addEventListener if the special events handler returns false
+				if ( !special.setup ||
+					special.setup.call( elem, data, namespaces, eventHandle ) === false ) {
+
+					if ( elem.addEventListener ) {
+						elem.addEventListener( type, eventHandle );
+					}
+				}
+			}
+
+			if ( special.add ) {
+				special.add.call( elem, handleObj );
+
+				if ( !handleObj.handler.guid ) {
+					handleObj.handler.guid = handler.guid;
+				}
+			}
+
+			// Add to the element's handler list, delegates in front
+			if ( selector ) {
+				handlers.splice( handlers.delegateCount++, 0, handleObj );
+			} else {
+				handlers.push( handleObj );
+			}
+
+			// Keep track of which events have ever been used, for event optimization
+			jQuery.event.global[ type ] = true;
+		}
+
+	},
+
+	// Detach an event or set of events from an element
+	remove: function( elem, types, handler, selector, mappedTypes ) {
+
+		var j, origCount, tmp,
+			events, t, handleObj,
+			special, handlers, type, namespaces, origType,
+			elemData = dataPriv.hasData( elem ) && dataPriv.get( elem );
+
+		if ( !elemData || !( events = elemData.events ) ) {
+			return;
+		}
+
+		// Once for each type.namespace in types; type may be omitted
+		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
+		t = types.length;
+		while ( t-- ) {
+			tmp = rtypenamespace.exec( types[ t ] ) || [];
+			type = origType = tmp[ 1 ];
+			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
+
+			// Unbind all events (on this namespace, if provided) for the element
+			if ( !type ) {
+				for ( type in events ) {
+					jQuery.event.remove( elem, type + types[ t ], handler, selector, true );
+				}
+				continue;
+			}
+
+			special = jQuery.event.special[ type ] || {};
+			type = ( selector ? special.delegateType : special.bindType ) || type;
+			handlers = events[ type ] || [];
+			tmp = tmp[ 2 ] &&
+				new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" );
+
+			// Remove matching events
+			origCount = j = handlers.length;
+			while ( j-- ) {
+				handleObj = handlers[ j ];
+
+				if ( ( mappedTypes || origType === handleObj.origType ) &&
+					( !handler || handler.guid === handleObj.guid ) &&
+					( !tmp || tmp.test( handleObj.namespace ) ) &&
+					( !selector || selector === handleObj.selector ||
+						selector === "**" && handleObj.selector ) ) {
+					handlers.splice( j, 1 );
+
+					if ( handleObj.selector ) {
+						handlers.delegateCount--;
+					}
+					if ( special.remove ) {
+						special.remove.call( elem, handleObj );
+					}
+				}
+			}
+
+			// Remove generic event handler if we removed something and no more handlers exist
+			// (avoids potential for endless recursion during removal of special event handlers)
+			if ( origCount && !handlers.length ) {
+				if ( !special.teardown ||
+					special.teardown.call( elem, namespaces, elemData.handle ) === false ) {
+
+					jQuery.removeEvent( elem, type, elemData.handle );
+				}
+
+				delete events[ type ];
+			}
+		}
+
+		// Remove data and the expando if it's no longer used
+		if ( jQuery.isEmptyObject( events ) ) {
+			dataPriv.remove( elem, "handle events" );
+		}
+	},
+
+	dispatch: function( nativeEvent ) {
+
+		var i, j, ret, matched, handleObj, handlerQueue,
+			args = new Array( arguments.length ),
+
+			// Make a writable jQuery.Event from the native event object
+			event = jQuery.event.fix( nativeEvent ),
+
+			handlers = (
+				dataPriv.get( this, "events" ) || Object.create( null )
+			)[ event.type ] || [],
+			special = jQuery.event.special[ event.type ] || {};
+
+		// Use the fix-ed jQuery.Event rather than the (read-only) native event
+		args[ 0 ] = event;
+
+		for ( i = 1; i < arguments.length; i++ ) {
+			args[ i ] = arguments[ i ];
+		}
+
+		event.delegateTarget = this;
+
+		// Call the preDispatch hook for the mapped type, and let it bail if desired
+		if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) {
+			return;
+		}
+
+		// Determine handlers
+		handlerQueue = jQuery.event.handlers.call( this, event, handlers );
+
+		// Run delegates first; they may want to stop propagation beneath us
+		i = 0;
+		while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) {
+			event.currentTarget = matched.elem;
+
+			j = 0;
+			while ( ( handleObj = matched.handlers[ j++ ] ) &&
+				!event.isImmediatePropagationStopped() ) {
+
+				// If the event is namespaced, then each handler is only invoked if it is
+				// specially universal or its namespaces are a superset of the event's.
+				if ( !event.rnamespace || handleObj.namespace === false ||
+					event.rnamespace.test( handleObj.namespace ) ) {
+
+					event.handleObj = handleObj;
+					event.data = handleObj.data;
+
+					ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle ||
+						handleObj.handler ).apply( matched.elem, args );
+
+					if ( ret !== undefined ) {
+						if ( ( event.result = ret ) === false ) {
+							event.preventDefault();
+							event.stopPropagation();
+						}
+					}
+				}
+			}
+		}
+
+		// Call the postDispatch hook for the mapped type
+		if ( special.postDispatch ) {
+			special.postDispatch.call( this, event );
+		}
+
+		return event.result;
+	},
+
+	handlers: function( event, handlers ) {
+		var i, handleObj, sel, matchedHandlers, matchedSelectors,
+			handlerQueue = [],
+			delegateCount = handlers.delegateCount,
+			cur = event.target;
+
+		// Find delegate handlers
+		if ( delegateCount &&
+
+			// Support: IE <=9
+			// Black-hole SVG <use> instance trees (trac-13180)
+			cur.nodeType &&
+
+			// Support: Firefox <=42
+			// Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861)
+			// https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click
+			// Support: IE 11 only
+			// ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343)
+			!( event.type === "click" && event.button >= 1 ) ) {
+
+			for ( ; cur !== this; cur = cur.parentNode || this ) {
+
+				// Don't check non-elements (#13208)
+				// Don't process clicks on disabled elements (#6911, #8165, #11382, #11764)
+				if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) {
+					matchedHandlers = [];
+					matchedSelectors = {};
+					for ( i = 0; i < delegateCount; i++ ) {
+						handleObj = handlers[ i ];
+
+						// Don't conflict with Object.prototype properties (#13203)
+						sel = handleObj.selector + " ";
+
+						if ( matchedSelectors[ sel ] === undefined ) {
+							matchedSelectors[ sel ] = handleObj.needsContext ?
+								jQuery( sel, this ).index( cur ) > -1 :
+								jQuery.find( sel, this, null, [ cur ] ).length;
+						}
+						if ( matchedSelectors[ sel ] ) {
+							matchedHandlers.push( handleObj );
+						}
+					}
+					if ( matchedHandlers.length ) {
+						handlerQueue.push( { elem: cur, handlers: matchedHandlers } );
+					}
+				}
+			}
+		}
+
+		// Add the remaining (directly-bound) handlers
+		cur = this;
+		if ( delegateCount < handlers.length ) {
+			handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } );
+		}
+
+		return handlerQueue;
+	},
+
+	addProp: function( name, hook ) {
+		Object.defineProperty( jQuery.Event.prototype, name, {
+			enumerable: true,
+			configurable: true,
+
+			get: isFunction( hook ) ?
+				function() {
+					if ( this.originalEvent ) {
+						return hook( this.originalEvent );
+					}
+				} :
+				function() {
+					if ( this.originalEvent ) {
+						return this.originalEvent[ name ];
+					}
+				},
+
+			set: function( value ) {
+				Object.defineProperty( this, name, {
+					enumerable: true,
+					configurable: true,
+					writable: true,
+					value: value
+				} );
+			}
+		} );
+	},
+
+	fix: function( originalEvent ) {
+		return originalEvent[ jQuery.expando ] ?
+			originalEvent :
+			new jQuery.Event( originalEvent );
+	},
+
+	special: {
+		load: {
+
+			// Prevent triggered image.load events from bubbling to window.load
+			noBubble: true
+		},
+		click: {
+
+			// Utilize native event to ensure correct state for checkable inputs
+			setup: function( data ) {
+
+				// For mutual compressibility with _default, replace `this` access with a local var.
+				// `|| data` is dead code meant only to preserve the variable through minification.
+				var el = this || data;
+
+				// Claim the first handler
+				if ( rcheckableType.test( el.type ) &&
+					el.click && nodeName( el, "input" ) ) {
+
+					// dataPriv.set( el, "click", ... )
+					leverageNative( el, "click", returnTrue );
+				}
+
+				// Return false to allow normal processing in the caller
+				return false;
+			},
+			trigger: function( data ) {
+
+				// For mutual compressibility with _default, replace `this` access with a local var.
+				// `|| data` is dead code meant only to preserve the variable through minification.
+				var el = this || data;
+
+				// Force setup before triggering a click
+				if ( rcheckableType.test( el.type ) &&
+					el.click && nodeName( el, "input" ) ) {
+
+					leverageNative( el, "click" );
+				}
+
+				// Return non-false to allow normal event-path propagation
+				return true;
+			},
+
+			// For cross-browser consistency, suppress native .click() on links
+			// Also prevent it if we're currently inside a leveraged native-event stack
+			_default: function( event ) {
+				var target = event.target;
+				return rcheckableType.test( target.type ) &&
+					target.click && nodeName( target, "input" ) &&
+					dataPriv.get( target, "click" ) ||
+					nodeName( target, "a" );
+			}
+		},
+
+		beforeunload: {
+			postDispatch: function( event ) {
+
+				// Support: Firefox 20+
+				// Firefox doesn't alert if the returnValue field is not set.
+				if ( event.result !== undefined && event.originalEvent ) {
+					event.originalEvent.returnValue = event.result;
+				}
+			}
+		}
+	}
+};
+
+// Ensure the presence of an event listener that handles manually-triggered
+// synthetic events by interrupting progress until reinvoked in response to
+// *native* events that it fires directly, ensuring that state changes have
+// already occurred before other listeners are invoked.
+function leverageNative( el, type, expectSync ) {
+
+	// Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add
+	if ( !expectSync ) {
+		if ( dataPriv.get( el, type ) === undefined ) {
+			jQuery.event.add( el, type, returnTrue );
+		}
+		return;
+	}
+
+	// Register the controller as a special universal handler for all event namespaces
+	dataPriv.set( el, type, false );
+	jQuery.event.add( el, type, {
+		namespace: false,
+		handler: function( event ) {
+			var notAsync, result,
+				saved = dataPriv.get( this, type );
+
+			if ( ( event.isTrigger & 1 ) && this[ type ] ) {
+
+				// Interrupt processing of the outer synthetic .trigger()ed event
+				// Saved data should be false in such cases, but might be a leftover capture object
+				// from an async native handler (gh-4350)
+				if ( !saved.length ) {
+
+					// Store arguments for use when handling the inner native event
+					// There will always be at least one argument (an event object), so this array
+					// will not be confused with a leftover capture object.
+					saved = slice.call( arguments );
+					dataPriv.set( this, type, saved );
+
+					// Trigger the native event and capture its result
+					// Support: IE <=9 - 11+
+					// focus() and blur() are asynchronous
+					notAsync = expectSync( this, type );
+					this[ type ]();
+					result = dataPriv.get( this, type );
+					if ( saved !== result || notAsync ) {
+						dataPriv.set( this, type, false );
+					} else {
+						result = {};
+					}
+					if ( saved !== result ) {
+
+						// Cancel the outer synthetic event
+						event.stopImmediatePropagation();
+						event.preventDefault();
+
+						// Support: Chrome 86+
+						// In Chrome, if an element having a focusout handler is blurred by
+						// clicking outside of it, it invokes the handler synchronously. If
+						// that handler calls `.remove()` on the element, the data is cleared,
+						// leaving `result` undefined. We need to guard against this.
+						return result && result.value;
+					}
+
+				// If this is an inner synthetic event for an event with a bubbling surrogate
+				// (focus or blur), assume that the surrogate already propagated from triggering the
+				// native event and prevent that from happening again here.
+				// This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the
+				// bubbling surrogate propagates *after* the non-bubbling base), but that seems
+				// less bad than duplication.
+				} else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) {
+					event.stopPropagation();
+				}
+
+			// If this is a native event triggered above, everything is now in order
+			// Fire an inner synthetic event with the original arguments
+			} else if ( saved.length ) {
+
+				// ...and capture the result
+				dataPriv.set( this, type, {
+					value: jQuery.event.trigger(
+
+						// Support: IE <=9 - 11+
+						// Extend with the prototype to reset the above stopImmediatePropagation()
+						jQuery.extend( saved[ 0 ], jQuery.Event.prototype ),
+						saved.slice( 1 ),
+						this
+					)
+				} );
+
+				// Abort handling of the native event
+				event.stopImmediatePropagation();
+			}
+		}
+	} );
+}
+
+jQuery.removeEvent = function( elem, type, handle ) {
+
+	// This "if" is needed for plain objects
+	if ( elem.removeEventListener ) {
+		elem.removeEventListener( type, handle );
+	}
+};
+
+jQuery.Event = function( src, props ) {
+
+	// Allow instantiation without the 'new' keyword
+	if ( !( this instanceof jQuery.Event ) ) {
+		return new jQuery.Event( src, props );
+	}
+
+	// Event object
+	if ( src && src.type ) {
+		this.originalEvent = src;
+		this.type = src.type;
+
+		// Events bubbling up the document may have been marked as prevented
+		// by a handler lower down the tree; reflect the correct value.
+		this.isDefaultPrevented = src.defaultPrevented ||
+				src.defaultPrevented === undefined &&
+
+				// Support: Android <=2.3 only
+				src.returnValue === false ?
+			returnTrue :
+			returnFalse;
+
+		// Create target properties
+		// Support: Safari <=6 - 7 only
+		// Target should not be a text node (#504, #13143)
+		this.target = ( src.target && src.target.nodeType === 3 ) ?
+			src.target.parentNode :
+			src.target;
+
+		this.currentTarget = src.currentTarget;
+		this.relatedTarget = src.relatedTarget;
+
+	// Event type
+	} else {
+		this.type = src;
+	}
+
+	// Put explicitly provided properties onto the event object
+	if ( props ) {
+		jQuery.extend( this, props );
+	}
+
+	// Create a timestamp if incoming event doesn't have one
+	this.timeStamp = src && src.timeStamp || Date.now();
+
+	// Mark it as fixed
+	this[ jQuery.expando ] = true;
+};
+
+// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding
+// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html
+jQuery.Event.prototype = {
+	constructor: jQuery.Event,
+	isDefaultPrevented: returnFalse,
+	isPropagationStopped: returnFalse,
+	isImmediatePropagationStopped: returnFalse,
+	isSimulated: false,
+
+	preventDefault: function() {
+		var e = this.originalEvent;
+
+		this.isDefaultPrevented = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.preventDefault();
+		}
+	},
+	stopPropagation: function() {
+		var e = this.originalEvent;
+
+		this.isPropagationStopped = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.stopPropagation();
+		}
+	},
+	stopImmediatePropagation: function() {
+		var e = this.originalEvent;
+
+		this.isImmediatePropagationStopped = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.stopImmediatePropagation();
+		}
+
+		this.stopPropagation();
+	}
+};
+
+// Includes all common event props including KeyEvent and MouseEvent specific props
+jQuery.each( {
+	altKey: true,
+	bubbles: true,
+	cancelable: true,
+	changedTouches: true,
+	ctrlKey: true,
+	detail: true,
+	eventPhase: true,
+	metaKey: true,
+	pageX: true,
+	pageY: true,
+	shiftKey: true,
+	view: true,
+	"char": true,
+	code: true,
+	charCode: true,
+	key: true,
+	keyCode: true,
+	button: true,
+	buttons: true,
+	clientX: true,
+	clientY: true,
+	offsetX: true,
+	offsetY: true,
+	pointerId: true,
+	pointerType: true,
+	screenX: true,
+	screenY: true,
+	targetTouches: true,
+	toElement: true,
+	touches: true,
+	which: true
+}, jQuery.event.addProp );
+
+jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) {
+	jQuery.event.special[ type ] = {
+
+		// Utilize native event if possible so blur/focus sequence is correct
+		setup: function() {
+
+			// Claim the first handler
+			// dataPriv.set( this, "focus", ... )
+			// dataPriv.set( this, "blur", ... )
+			leverageNative( this, type, expectSync );
+
+			// Return false to allow normal processing in the caller
+			return false;
+		},
+		trigger: function() {
+
+			// Force setup before trigger
+			leverageNative( this, type );
+
+			// Return non-false to allow normal event-path propagation
+			return true;
+		},
+
+		// Suppress native focus or blur as it's already being fired
+		// in leverageNative.
+		_default: function() {
+			return true;
+		},
+
+		delegateType: delegateType
+	};
+} );
+
+// Create mouseenter/leave events using mouseover/out and event-time checks
+// so that event delegation works in jQuery.
+// Do the same for pointerenter/pointerleave and pointerover/pointerout
+//
+// Support: Safari 7 only
+// Safari sends mouseenter too often; see:
+// https://bugs.chromium.org/p/chromium/issues/detail?id=470258
+// for the description of the bug (it existed in older Chrome versions as well).
+jQuery.each( {
+	mouseenter: "mouseover",
+	mouseleave: "mouseout",
+	pointerenter: "pointerover",
+	pointerleave: "pointerout"
+}, function( orig, fix ) {
+	jQuery.event.special[ orig ] = {
+		delegateType: fix,
+		bindType: fix,
+
+		handle: function( event ) {
+			var ret,
+				target = this,
+				related = event.relatedTarget,
+				handleObj = event.handleObj;
+
+			// For mouseenter/leave call the handler if related is outside the target.
+			// NB: No relatedTarget if the mouse left/entered the browser window
+			if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) {
+				event.type = handleObj.origType;
+				ret = handleObj.handler.apply( this, arguments );
+				event.type = fix;
+			}
+			return ret;
+		}
+	};
+} );
+
+jQuery.fn.extend( {
+
+	on: function( types, selector, data, fn ) {
+		return on( this, types, selector, data, fn );
+	},
+	one: function( types, selector, data, fn ) {
+		return on( this, types, selector, data, fn, 1 );
+	},
+	off: function( types, selector, fn ) {
+		var handleObj, type;
+		if ( types && types.preventDefault && types.handleObj ) {
+
+			// ( event )  dispatched jQuery.Event
+			handleObj = types.handleObj;
+			jQuery( types.delegateTarget ).off(
+				handleObj.namespace ?
+					handleObj.origType + "." + handleObj.namespace :
+					handleObj.origType,
+				handleObj.selector,
+				handleObj.handler
+			);
+			return this;
+		}
+		if ( typeof types === "object" ) {
+
+			// ( types-object [, selector] )
+			for ( type in types ) {
+				this.off( type, selector, types[ type ] );
+			}
+			return this;
+		}
+		if ( selector === false || typeof selector === "function" ) {
+
+			// ( types [, fn] )
+			fn = selector;
+			selector = undefined;
+		}
+		if ( fn === false ) {
+			fn = returnFalse;
+		}
+		return this.each( function() {
+			jQuery.event.remove( this, types, fn, selector );
+		} );
+	}
+} );
+
+
+var
+
+	// Support: IE <=10 - 11, Edge 12 - 13 only
+	// In IE/Edge using regex groups here causes severe slowdowns.
+	// See https://connect.microsoft.com/IE/feedback/details/1736512/
+	rnoInnerhtml = /<script|<style|<link/i,
+
+	// checked="checked" or checked
+	rchecked = /checked\s*(?:[^=]|=\s*.checked.)/i,
+	rcleanScript = /^\s*<!(?:\[CDATA\[|--)|(?:\]\]|--)>\s*$/g;
+
+// Prefer a tbody over its parent table for containing new rows
+function manipulationTarget( elem, content ) {
+	if ( nodeName( elem, "table" ) &&
+		nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) {
+
+		return jQuery( elem ).children( "tbody" )[ 0 ] || elem;
+	}
+
+	return elem;
+}
+
+// Replace/restore the type attribute of script elements for safe DOM manipulation
+function disableScript( elem ) {
+	elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type;
+	return elem;
+}
+function restoreScript( elem ) {
+	if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) {
+		elem.type = elem.type.slice( 5 );
+	} else {
+		elem.removeAttribute( "type" );
+	}
+
+	return elem;
+}
+
+function cloneCopyEvent( src, dest ) {
+	var i, l, type, pdataOld, udataOld, udataCur, events;
+
+	if ( dest.nodeType !== 1 ) {
+		return;
+	}
+
+	// 1. Copy private data: events, handlers, etc.
+	if ( dataPriv.hasData( src ) ) {
+		pdataOld = dataPriv.get( src );
+		events = pdataOld.events;
+
+		if ( events ) {
+			dataPriv.remove( dest, "handle events" );
+
+			for ( type in events ) {
+				for ( i = 0, l = events[ type ].length; i < l; i++ ) {
+					jQuery.event.add( dest, type, events[ type ][ i ] );
+				}
+			}
+		}
+	}
+
+	// 2. Copy user data
+	if ( dataUser.hasData( src ) ) {
+		udataOld = dataUser.access( src );
+		udataCur = jQuery.extend( {}, udataOld );
+
+		dataUser.set( dest, udataCur );
+	}
+}
+
+// Fix IE bugs, see support tests
+function fixInput( src, dest ) {
+	var nodeName = dest.nodeName.toLowerCase();
+
+	// Fails to persist the checked state of a cloned checkbox or radio button.
+	if ( nodeName === "input" && rcheckableType.test( src.type ) ) {
+		dest.checked = src.checked;
+
+	// Fails to return the selected option to the default selected state when cloning options
+	} else if ( nodeName === "input" || nodeName === "textarea" ) {
+		dest.defaultValue = src.defaultValue;
+	}
+}
+
+function domManip( collection, args, callback, ignored ) {
+
+	// Flatten any nested arrays
+	args = flat( args );
+
+	var fragment, first, scripts, hasScripts, node, doc,
+		i = 0,
+		l = collection.length,
+		iNoClone = l - 1,
+		value = args[ 0 ],
+		valueIsFunction = isFunction( value );
+
+	// We can't cloneNode fragments that contain checked, in WebKit
+	if ( valueIsFunction ||
+			( l > 1 && typeof value === "string" &&
+				!support.checkClone && rchecked.test( value ) ) ) {
+		return collection.each( function( index ) {
+			var self = collection.eq( index );
+			if ( valueIsFunction ) {
+				args[ 0 ] = value.call( this, index, self.html() );
+			}
+			domManip( self, args, callback, ignored );
+		} );
+	}
+
+	if ( l ) {
+		fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored );
+		first = fragment.firstChild;
+
+		if ( fragment.childNodes.length === 1 ) {
+			fragment = first;
+		}
+
+		// Require either new content or an interest in ignored elements to invoke the callback
+		if ( first || ignored ) {
+			scripts = jQuery.map( getAll( fragment, "script" ), disableScript );
+			hasScripts = scripts.length;
+
+			// Use the original fragment for the last item
+			// instead of the first because it can end up
+			// being emptied incorrectly in certain situations (#8070).
+			for ( ; i < l; i++ ) {
+				node = fragment;
+
+				if ( i !== iNoClone ) {
+					node = jQuery.clone( node, true, true );
+
+					// Keep references to cloned scripts for later restoration
+					if ( hasScripts ) {
+
+						// Support: Android <=4.0 only, PhantomJS 1 only
+						// push.apply(_, arraylike) throws on ancient WebKit
+						jQuery.merge( scripts, getAll( node, "script" ) );
+					}
+				}
+
+				callback.call( collection[ i ], node, i );
+			}
+
+			if ( hasScripts ) {
+				doc = scripts[ scripts.length - 1 ].ownerDocument;
+
+				// Reenable scripts
+				jQuery.map( scripts, restoreScript );
+
+				// Evaluate executable scripts on first document insertion
+				for ( i = 0; i < hasScripts; i++ ) {
+					node = scripts[ i ];
+					if ( rscriptType.test( node.type || "" ) &&
+						!dataPriv.access( node, "globalEval" ) &&
+						jQuery.contains( doc, node ) ) {
+
+						if ( node.src && ( node.type || "" ).toLowerCase()  !== "module" ) {
+
+							// Optional AJAX dependency, but won't run scripts if not present
+							if ( jQuery._evalUrl && !node.noModule ) {
+								jQuery._evalUrl( node.src, {
+									nonce: node.nonce || node.getAttribute( "nonce" )
+								}, doc );
+							}
+						} else {
+							DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc );
+						}
+					}
+				}
+			}
+		}
+	}
+
+	return collection;
+}
+
+function remove( elem, selector, keepData ) {
+	var node,
+		nodes = selector ? jQuery.filter( selector, elem ) : elem,
+		i = 0;
+
+	for ( ; ( node = nodes[ i ] ) != null; i++ ) {
+		if ( !keepData && node.nodeType === 1 ) {
+			jQuery.cleanData( getAll( node ) );
+		}
+
+		if ( node.parentNode ) {
+			if ( keepData && isAttached( node ) ) {
+				setGlobalEval( getAll( node, "script" ) );
+			}
+			node.parentNode.removeChild( node );
+		}
+	}
+
+	return elem;
+}
+
+jQuery.extend( {
+	htmlPrefilter: function( html ) {
+		return html;
+	},
+
+	clone: function( elem, dataAndEvents, deepDataAndEvents ) {
+		var i, l, srcElements, destElements,
+			clone = elem.cloneNode( true ),
+			inPage = isAttached( elem );
+
+		// Fix IE cloning issues
+		if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) &&
+				!jQuery.isXMLDoc( elem ) ) {
+
+			// We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2
+			destElements = getAll( clone );
+			srcElements = getAll( elem );
+
+			for ( i = 0, l = srcElements.length; i < l; i++ ) {
+				fixInput( srcElements[ i ], destElements[ i ] );
+			}
+		}
+
+		// Copy the events from the original to the clone
+		if ( dataAndEvents ) {
+			if ( deepDataAndEvents ) {
+				srcElements = srcElements || getAll( elem );
+				destElements = destElements || getAll( clone );
+
+				for ( i = 0, l = srcElements.length; i < l; i++ ) {
+					cloneCopyEvent( srcElements[ i ], destElements[ i ] );
+				}
+			} else {
+				cloneCopyEvent( elem, clone );
+			}
+		}
+
+		// Preserve script evaluation history
+		destElements = getAll( clone, "script" );
+		if ( destElements.length > 0 ) {
+			setGlobalEval( destElements, !inPage && getAll( elem, "script" ) );
+		}
+
+		// Return the cloned set
+		return clone;
+	},
+
+	cleanData: function( elems ) {
+		var data, elem, type,
+			special = jQuery.event.special,
+			i = 0;
+
+		for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) {
+			if ( acceptData( elem ) ) {
+				if ( ( data = elem[ dataPriv.expando ] ) ) {
+					if ( data.events ) {
+						for ( type in data.events ) {
+							if ( special[ type ] ) {
+								jQuery.event.remove( elem, type );
+
+							// This is a shortcut to avoid jQuery.event.remove's overhead
+							} else {
+								jQuery.removeEvent( elem, type, data.handle );
+							}
+						}
+					}
+
+					// Support: Chrome <=35 - 45+
+					// Assign undefined instead of using delete, see Data#remove
+					elem[ dataPriv.expando ] = undefined;
+				}
+				if ( elem[ dataUser.expando ] ) {
+
+					// Support: Chrome <=35 - 45+
+					// Assign undefined instead of using delete, see Data#remove
+					elem[ dataUser.expando ] = undefined;
+				}
+			}
+		}
+	}
+} );
+
+jQuery.fn.extend( {
+	detach: function( selector ) {
+		return remove( this, selector, true );
+	},
+
+	remove: function( selector ) {
+		return remove( this, selector );
+	},
+
+	text: function( value ) {
+		return access( this, function( value ) {
+			return value === undefined ?
+				jQuery.text( this ) :
+				this.empty().each( function() {
+					if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+						this.textContent = value;
+					}
+				} );
+		}, null, value, arguments.length );
+	},
+
+	append: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+				var target = manipulationTarget( this, elem );
+				target.appendChild( elem );
+			}
+		} );
+	},
+
+	prepend: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+				var target = manipulationTarget( this, elem );
+				target.insertBefore( elem, target.firstChild );
+			}
+		} );
+	},
+
+	before: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.parentNode ) {
+				this.parentNode.insertBefore( elem, this );
+			}
+		} );
+	},
+
+	after: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.parentNode ) {
+				this.parentNode.insertBefore( elem, this.nextSibling );
+			}
+		} );
+	},
+
+	empty: function() {
+		var elem,
+			i = 0;
+
+		for ( ; ( elem = this[ i ] ) != null; i++ ) {
+			if ( elem.nodeType === 1 ) {
+
+				// Prevent memory leaks
+				jQuery.cleanData( getAll( elem, false ) );
+
+				// Remove any remaining nodes
+				elem.textContent = "";
+			}
+		}
+
+		return this;
+	},
+
+	clone: function( dataAndEvents, deepDataAndEvents ) {
+		dataAndEvents = dataAndEvents == null ? false : dataAndEvents;
+		deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents;
+
+		return this.map( function() {
+			return jQuery.clone( this, dataAndEvents, deepDataAndEvents );
+		} );
+	},
+
+	html: function( value ) {
+		return access( this, function( value ) {
+			var elem = this[ 0 ] || {},
+				i = 0,
+				l = this.length;
+
+			if ( value === undefined && elem.nodeType === 1 ) {
+				return elem.innerHTML;
+			}
+
+			// See if we can take a shortcut and just use innerHTML
+			if ( typeof value === "string" && !rnoInnerhtml.test( value ) &&
+				!wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) {
+
+				value = jQuery.htmlPrefilter( value );
+
+				try {
+					for ( ; i < l; i++ ) {
+						elem = this[ i ] || {};
+
+						// Remove element nodes and prevent memory leaks
+						if ( elem.nodeType === 1 ) {
+							jQuery.cleanData( getAll( elem, false ) );
+							elem.innerHTML = value;
+						}
+					}
+
+					elem = 0;
+
+				// If using innerHTML throws an exception, use the fallback method
+				} catch ( e ) {}
+			}
+
+			if ( elem ) {
+				this.empty().append( value );
+			}
+		}, null, value, arguments.length );
+	},
+
+	replaceWith: function() {
+		var ignored = [];
+
+		// Make the changes, replacing each non-ignored context element with the new content
+		return domManip( this, arguments, function( elem ) {
+			var parent = this.parentNode;
+
+			if ( jQuery.inArray( this, ignored ) < 0 ) {
+				jQuery.cleanData( getAll( this ) );
+				if ( parent ) {
+					parent.replaceChild( elem, this );
+				}
+			}
+
+		// Force callback invocation
+		}, ignored );
+	}
+} );
+
+jQuery.each( {
+	appendTo: "append",
+	prependTo: "prepend",
+	insertBefore: "before",
+	insertAfter: "after",
+	replaceAll: "replaceWith"
+}, function( name, original ) {
+	jQuery.fn[ name ] = function( selector ) {
+		var elems,
+			ret = [],
+			insert = jQuery( selector ),
+			last = insert.length - 1,
+			i = 0;
+
+		for ( ; i <= last; i++ ) {
+			elems = i === last ? this : this.clone( true );
+			jQuery( insert[ i ] )[ original ]( elems );
+
+			// Support: Android <=4.0 only, PhantomJS 1 only
+			// .get() because push.apply(_, arraylike) throws on ancient WebKit
+			push.apply( ret, elems.get() );
+		}
+
+		return this.pushStack( ret );
+	};
+} );
+var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" );
+
+var getStyles = function( elem ) {
+
+		// Support: IE <=11 only, Firefox <=30 (#15098, #14150)
+		// IE throws on elements created in popups
+		// FF meanwhile throws on frame elements through "defaultView.getComputedStyle"
+		var view = elem.ownerDocument.defaultView;
+
+		if ( !view || !view.opener ) {
+			view = window;
+		}
+
+		return view.getComputedStyle( elem );
+	};
+
+var swap = function( elem, options, callback ) {
+	var ret, name,
+		old = {};
+
+	// Remember the old values, and insert the new ones
+	for ( name in options ) {
+		old[ name ] = elem.style[ name ];
+		elem.style[ name ] = options[ name ];
+	}
+
+	ret = callback.call( elem );
+
+	// Revert the old values
+	for ( name in options ) {
+		elem.style[ name ] = old[ name ];
+	}
+
+	return ret;
+};
+
+
+var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" );
+
+
+
+( function() {
+
+	// Executing both pixelPosition & boxSizingReliable tests require only one layout
+	// so they're executed at the same time to save the second computation.
+	function computeStyleTests() {
+
+		// This is a singleton, we need to execute it only once
+		if ( !div ) {
+			return;
+		}
+
+		container.style.cssText = "position:absolute;left:-11111px;width:60px;" +
+			"margin-top:1px;padding:0;border:0";
+		div.style.cssText =
+			"position:relative;display:block;box-sizing:border-box;overflow:scroll;" +
+			"margin:auto;border:1px;padding:1px;" +
+			"width:60%;top:1%";
+		documentElement.appendChild( container ).appendChild( div );
+
+		var divStyle = window.getComputedStyle( div );
+		pixelPositionVal = divStyle.top !== "1%";
+
+		// Support: Android 4.0 - 4.3 only, Firefox <=3 - 44
+		reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12;
+
+		// Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3
+		// Some styles come back with percentage values, even though they shouldn't
+		div.style.right = "60%";
+		pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36;
+
+		// Support: IE 9 - 11 only
+		// Detect misreporting of content dimensions for box-sizing:border-box elements
+		boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36;
+
+		// Support: IE 9 only
+		// Detect overflow:scroll screwiness (gh-3699)
+		// Support: Chrome <=64
+		// Don't get tricked when zoom affects offsetWidth (gh-4029)
+		div.style.position = "absolute";
+		scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12;
+
+		documentElement.removeChild( container );
+
+		// Nullify the div so it wouldn't be stored in the memory and
+		// it will also be a sign that checks already performed
+		div = null;
+	}
+
+	function roundPixelMeasures( measure ) {
+		return Math.round( parseFloat( measure ) );
+	}
+
+	var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal,
+		reliableTrDimensionsVal, reliableMarginLeftVal,
+		container = document.createElement( "div" ),
+		div = document.createElement( "div" );
+
+	// Finish early in limited (non-browser) environments
+	if ( !div.style ) {
+		return;
+	}
+
+	// Support: IE <=9 - 11 only
+	// Style of cloned element affects source element cloned (#8908)
+	div.style.backgroundClip = "content-box";
+	div.cloneNode( true ).style.backgroundClip = "";
+	support.clearCloneStyle = div.style.backgroundClip === "content-box";
+
+	jQuery.extend( support, {
+		boxSizingReliable: function() {
+			computeStyleTests();
+			return boxSizingReliableVal;
+		},
+		pixelBoxStyles: function() {
+			computeStyleTests();
+			return pixelBoxStylesVal;
+		},
+		pixelPosition: function() {
+			computeStyleTests();
+			return pixelPositionVal;
+		},
+		reliableMarginLeft: function() {
+			computeStyleTests();
+			return reliableMarginLeftVal;
+		},
+		scrollboxSize: function() {
+			computeStyleTests();
+			return scrollboxSizeVal;
+		},
+
+		// Support: IE 9 - 11+, Edge 15 - 18+
+		// IE/Edge misreport `getComputedStyle` of table rows with width/height
+		// set in CSS while `offset*` properties report correct values.
+		// Behavior in IE 9 is more subtle than in newer versions & it passes
+		// some versions of this test; make sure not to make it pass there!
+		//
+		// Support: Firefox 70+
+		// Only Firefox includes border widths
+		// in computed dimensions. (gh-4529)
+		reliableTrDimensions: function() {
+			var table, tr, trChild, trStyle;
+			if ( reliableTrDimensionsVal == null ) {
+				table = document.createElement( "table" );
+				tr = document.createElement( "tr" );
+				trChild = document.createElement( "div" );
+
+				table.style.cssText = "position:absolute;left:-11111px;border-collapse:separate";
+				tr.style.cssText = "border:1px solid";
+
+				// Support: Chrome 86+
+				// Height set through cssText does not get applied.
+				// Computed height then comes back as 0.
+				tr.style.height = "1px";
+				trChild.style.height = "9px";
+
+				// Support: Android 8 Chrome 86+
+				// In our bodyBackground.html iframe,
+				// display for all div elements is set to "inline",
+				// which causes a problem only in Android 8 Chrome 86.
+				// Ensuring the div is display: block
+				// gets around this issue.
+				trChild.style.display = "block";
+
+				documentElement
+					.appendChild( table )
+					.appendChild( tr )
+					.appendChild( trChild );
+
+				trStyle = window.getComputedStyle( tr );
+				reliableTrDimensionsVal = ( parseInt( trStyle.height, 10 ) +
+					parseInt( trStyle.borderTopWidth, 10 ) +
+					parseInt( trStyle.borderBottomWidth, 10 ) ) === tr.offsetHeight;
+
+				documentElement.removeChild( table );
+			}
+			return reliableTrDimensionsVal;
+		}
+	} );
+} )();
+
+
+function curCSS( elem, name, computed ) {
+	var width, minWidth, maxWidth, ret,
+
+		// Support: Firefox 51+
+		// Retrieving style before computed somehow
+		// fixes an issue with getting wrong values
+		// on detached elements
+		style = elem.style;
+
+	computed = computed || getStyles( elem );
+
+	// getPropertyValue is needed for:
+	//   .css('filter') (IE 9 only, #12537)
+	//   .css('--customProperty) (#3144)
+	if ( computed ) {
+		ret = computed.getPropertyValue( name ) || computed[ name ];
+
+		if ( ret === "" && !isAttached( elem ) ) {
+			ret = jQuery.style( elem, name );
+		}
+
+		// A tribute to the "awesome hack by Dean Edwards"
+		// Android Browser returns percentage for some values,
+		// but width seems to be reliably pixels.
+		// This is against the CSSOM draft spec:
+		// https://drafts.csswg.org/cssom/#resolved-values
+		if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) {
+
+			// Remember the original values
+			width = style.width;
+			minWidth = style.minWidth;
+			maxWidth = style.maxWidth;
+
+			// Put in the new values to get a computed value out
+			style.minWidth = style.maxWidth = style.width = ret;
+			ret = computed.width;
+
+			// Revert the changed values
+			style.width = width;
+			style.minWidth = minWidth;
+			style.maxWidth = maxWidth;
+		}
+	}
+
+	return ret !== undefined ?
+
+		// Support: IE <=9 - 11 only
+		// IE returns zIndex value as an integer.
+		ret + "" :
+		ret;
+}
+
+
+function addGetHookIf( conditionFn, hookFn ) {
+
+	// Define the hook, we'll check on the first run if it's really needed.
+	return {
+		get: function() {
+			if ( conditionFn() ) {
+
+				// Hook not needed (or it's not possible to use it due
+				// to missing dependency), remove it.
+				delete this.get;
+				return;
+			}
+
+			// Hook needed; redefine it so that the support test is not executed again.
+			return ( this.get = hookFn ).apply( this, arguments );
+		}
+	};
+}
+
+
+var cssPrefixes = [ "Webkit", "Moz", "ms" ],
+	emptyStyle = document.createElement( "div" ).style,
+	vendorProps = {};
+
+// Return a vendor-prefixed property or undefined
+function vendorPropName( name ) {
+
+	// Check for vendor prefixed names
+	var capName = name[ 0 ].toUpperCase() + name.slice( 1 ),
+		i = cssPrefixes.length;
+
+	while ( i-- ) {
+		name = cssPrefixes[ i ] + capName;
+		if ( name in emptyStyle ) {
+			return name;
+		}
+	}
+}
+
+// Return a potentially-mapped jQuery.cssProps or vendor prefixed property
+function finalPropName( name ) {
+	var final = jQuery.cssProps[ name ] || vendorProps[ name ];
+
+	if ( final ) {
+		return final;
+	}
+	if ( name in emptyStyle ) {
+		return name;
+	}
+	return vendorProps[ name ] = vendorPropName( name ) || name;
+}
+
+
+var
+
+	// Swappable if display is none or starts with table
+	// except "table", "table-cell", or "table-caption"
+	// See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display
+	rdisplayswap = /^(none|table(?!-c[ea]).+)/,
+	rcustomProp = /^--/,
+	cssShow = { position: "absolute", visibility: "hidden", display: "block" },
+	cssNormalTransform = {
+		letterSpacing: "0",
+		fontWeight: "400"
+	};
+
+function setPositiveNumber( _elem, value, subtract ) {
+
+	// Any relative (+/-) values have already been
+	// normalized at this point
+	var matches = rcssNum.exec( value );
+	return matches ?
+
+		// Guard against undefined "subtract", e.g., when used as in cssHooks
+		Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) :
+		value;
+}
+
+function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) {
+	var i = dimension === "width" ? 1 : 0,
+		extra = 0,
+		delta = 0;
+
+	// Adjustment may not be necessary
+	if ( box === ( isBorderBox ? "border" : "content" ) ) {
+		return 0;
+	}
+
+	for ( ; i < 4; i += 2 ) {
+
+		// Both box models exclude margin
+		if ( box === "margin" ) {
+			delta += jQuery.css( elem, box + cssExpand[ i ], true, styles );
+		}
+
+		// If we get here with a content-box, we're seeking "padding" or "border" or "margin"
+		if ( !isBorderBox ) {
+
+			// Add padding
+			delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
+
+			// For "border" or "margin", add border
+			if ( box !== "padding" ) {
+				delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+
+			// But still keep track of it otherwise
+			} else {
+				extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+			}
+
+		// If we get here with a border-box (content + padding + border), we're seeking "content" or
+		// "padding" or "margin"
+		} else {
+
+			// For "content", subtract padding
+			if ( box === "content" ) {
+				delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
+			}
+
+			// For "content" or "padding", subtract border
+			if ( box !== "margin" ) {
+				delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+			}
+		}
+	}
+
+	// Account for positive content-box scroll gutter when requested by providing computedVal
+	if ( !isBorderBox && computedVal >= 0 ) {
+
+		// offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border
+		// Assuming integer scroll gutter, subtract the rest and round down
+		delta += Math.max( 0, Math.ceil(
+			elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
+			computedVal -
+			delta -
+			extra -
+			0.5
+
+		// If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter
+		// Use an explicit zero to avoid NaN (gh-3964)
+		) ) || 0;
+	}
+
+	return delta;
+}
+
+function getWidthOrHeight( elem, dimension, extra ) {
+
+	// Start with computed style
+	var styles = getStyles( elem ),
+
+		// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322).
+		// Fake content-box until we know it's needed to know the true value.
+		boxSizingNeeded = !support.boxSizingReliable() || extra,
+		isBorderBox = boxSizingNeeded &&
+			jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
+		valueIsBorderBox = isBorderBox,
+
+		val = curCSS( elem, dimension, styles ),
+		offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 );
+
+	// Support: Firefox <=54
+	// Return a confounding non-pixel value or feign ignorance, as appropriate.
+	if ( rnumnonpx.test( val ) ) {
+		if ( !extra ) {
+			return val;
+		}
+		val = "auto";
+	}
+
+
+	// Support: IE 9 - 11 only
+	// Use offsetWidth/offsetHeight for when box sizing is unreliable.
+	// In those cases, the computed value can be trusted to be border-box.
+	if ( ( !support.boxSizingReliable() && isBorderBox ||
+
+		// Support: IE 10 - 11+, Edge 15 - 18+
+		// IE/Edge misreport `getComputedStyle` of table rows with width/height
+		// set in CSS while `offset*` properties report correct values.
+		// Interestingly, in some cases IE 9 doesn't suffer from this issue.
+		!support.reliableTrDimensions() && nodeName( elem, "tr" ) ||
+
+		// Fall back to offsetWidth/offsetHeight when value is "auto"
+		// This happens for inline elements with no explicit setting (gh-3571)
+		val === "auto" ||
+
+		// Support: Android <=4.1 - 4.3 only
+		// Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602)
+		!parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) &&
+
+		// Make sure the element is visible & connected
+		elem.getClientRects().length ) {
+
+		isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box";
+
+		// Where available, offsetWidth/offsetHeight approximate border box dimensions.
+		// Where not available (e.g., SVG), assume unreliable box-sizing and interpret the
+		// retrieved value as a content box dimension.
+		valueIsBorderBox = offsetProp in elem;
+		if ( valueIsBorderBox ) {
+			val = elem[ offsetProp ];
+		}
+	}
+
+	// Normalize "" and auto
+	val = parseFloat( val ) || 0;
+
+	// Adjust for the element's box model
+	return ( val +
+		boxModelAdjustment(
+			elem,
+			dimension,
+			extra || ( isBorderBox ? "border" : "content" ),
+			valueIsBorderBox,
+			styles,
+
+			// Provide the current computed size to request scroll gutter calculation (gh-3589)
+			val
+		)
+	) + "px";
+}
+
+jQuery.extend( {
+
+	// Add in style property hooks for overriding the default
+	// behavior of getting and setting a style property
+	cssHooks: {
+		opacity: {
+			get: function( elem, computed ) {
+				if ( computed ) {
+
+					// We should always get a number back from opacity
+					var ret = curCSS( elem, "opacity" );
+					return ret === "" ? "1" : ret;
+				}
+			}
+		}
+	},
+
+	// Don't automatically add "px" to these possibly-unitless properties
+	cssNumber: {
+		"animationIterationCount": true,
+		"columnCount": true,
+		"fillOpacity": true,
+		"flexGrow": true,
+		"flexShrink": true,
+		"fontWeight": true,
+		"gridArea": true,
+		"gridColumn": true,
+		"gridColumnEnd": true,
+		"gridColumnStart": true,
+		"gridRow": true,
+		"gridRowEnd": true,
+		"gridRowStart": true,
+		"lineHeight": true,
+		"opacity": true,
+		"order": true,
+		"orphans": true,
+		"widows": true,
+		"zIndex": true,
+		"zoom": true
+	},
+
+	// Add in properties whose names you wish to fix before
+	// setting or getting the value
+	cssProps: {},
+
+	// Get and set the style property on a DOM Node
+	style: function( elem, name, value, extra ) {
+
+		// Don't set styles on text and comment nodes
+		if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) {
+			return;
+		}
+
+		// Make sure that we're working with the right name
+		var ret, type, hooks,
+			origName = camelCase( name ),
+			isCustomProp = rcustomProp.test( name ),
+			style = elem.style;
+
+		// Make sure that we're working with the right name. We don't
+		// want to query the value if it is a CSS custom property
+		// since they are user-defined.
+		if ( !isCustomProp ) {
+			name = finalPropName( origName );
+		}
+
+		// Gets hook for the prefixed version, then unprefixed version
+		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
+
+		// Check if we're setting a value
+		if ( value !== undefined ) {
+			type = typeof value;
+
+			// Convert "+=" or "-=" to relative numbers (#7345)
+			if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) {
+				value = adjustCSS( elem, name, ret );
+
+				// Fixes bug #9237
+				type = "number";
+			}
+
+			// Make sure that null and NaN values aren't set (#7116)
+			if ( value == null || value !== value ) {
+				return;
+			}
+
+			// If a number was passed in, add the unit (except for certain CSS properties)
+			// The isCustomProp check can be removed in jQuery 4.0 when we only auto-append
+			// "px" to a few hardcoded values.
+			if ( type === "number" && !isCustomProp ) {
+				value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" );
+			}
+
+			// background-* props affect original clone's values
+			if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) {
+				style[ name ] = "inherit";
+			}
+
+			// If a hook was provided, use that value, otherwise just set the specified value
+			if ( !hooks || !( "set" in hooks ) ||
+				( value = hooks.set( elem, value, extra ) ) !== undefined ) {
+
+				if ( isCustomProp ) {
+					style.setProperty( name, value );
+				} else {
+					style[ name ] = value;
+				}
+			}
+
+		} else {
+
+			// If a hook was provided get the non-computed value from there
+			if ( hooks && "get" in hooks &&
+				( ret = hooks.get( elem, false, extra ) ) !== undefined ) {
+
+				return ret;
+			}
+
+			// Otherwise just get the value from the style object
+			return style[ name ];
+		}
+	},
+
+	css: function( elem, name, extra, styles ) {
+		var val, num, hooks,
+			origName = camelCase( name ),
+			isCustomProp = rcustomProp.test( name );
+
+		// Make sure that we're working with the right name. We don't
+		// want to modify the value if it is a CSS custom property
+		// since they are user-defined.
+		if ( !isCustomProp ) {
+			name = finalPropName( origName );
+		}
+
+		// Try prefixed name followed by the unprefixed name
+		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
+
+		// If a hook was provided get the computed value from there
+		if ( hooks && "get" in hooks ) {
+			val = hooks.get( elem, true, extra );
+		}
+
+		// Otherwise, if a way to get the computed value exists, use that
+		if ( val === undefined ) {
+			val = curCSS( elem, name, styles );
+		}
+
+		// Convert "normal" to computed value
+		if ( val === "normal" && name in cssNormalTransform ) {
+			val = cssNormalTransform[ name ];
+		}
+
+		// Make numeric if forced or a qualifier was provided and val looks numeric
+		if ( extra === "" || extra ) {
+			num = parseFloat( val );
+			return extra === true || isFinite( num ) ? num || 0 : val;
+		}
+
+		return val;
+	}
+} );
+
+jQuery.each( [ "height", "width" ], function( _i, dimension ) {
+	jQuery.cssHooks[ dimension ] = {
+		get: function( elem, computed, extra ) {
+			if ( computed ) {
+
+				// Certain elements can have dimension info if we invisibly show them
+				// but it must have a current display style that would benefit
+				return rdisplayswap.test( jQuery.css( elem, "display" ) ) &&
+
+					// Support: Safari 8+
+					// Table columns in Safari have non-zero offsetWidth & zero
+					// getBoundingClientRect().width unless display is changed.
+					// Support: IE <=11 only
+					// Running getBoundingClientRect on a disconnected node
+					// in IE throws an error.
+					( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ?
+					swap( elem, cssShow, function() {
+						return getWidthOrHeight( elem, dimension, extra );
+					} ) :
+					getWidthOrHeight( elem, dimension, extra );
+			}
+		},
+
+		set: function( elem, value, extra ) {
+			var matches,
+				styles = getStyles( elem ),
+
+				// Only read styles.position if the test has a chance to fail
+				// to avoid forcing a reflow.
+				scrollboxSizeBuggy = !support.scrollboxSize() &&
+					styles.position === "absolute",
+
+				// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991)
+				boxSizingNeeded = scrollboxSizeBuggy || extra,
+				isBorderBox = boxSizingNeeded &&
+					jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
+				subtract = extra ?
+					boxModelAdjustment(
+						elem,
+						dimension,
+						extra,
+						isBorderBox,
+						styles
+					) :
+					0;
+
+			// Account for unreliable border-box dimensions by comparing offset* to computed and
+			// faking a content-box to get border and padding (gh-3699)
+			if ( isBorderBox && scrollboxSizeBuggy ) {
+				subtract -= Math.ceil(
+					elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
+					parseFloat( styles[ dimension ] ) -
+					boxModelAdjustment( elem, dimension, "border", false, styles ) -
+					0.5
+				);
+			}
+
+			// Convert to pixels if value adjustment is needed
+			if ( subtract && ( matches = rcssNum.exec( value ) ) &&
+				( matches[ 3 ] || "px" ) !== "px" ) {
+
+				elem.style[ dimension ] = value;
+				value = jQuery.css( elem, dimension );
+			}
+
+			return setPositiveNumber( elem, value, subtract );
+		}
+	};
+} );
+
+jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft,
+	function( elem, computed ) {
+		if ( computed ) {
+			return ( parseFloat( curCSS( elem, "marginLeft" ) ) ||
+				elem.getBoundingClientRect().left -
+					swap( elem, { marginLeft: 0 }, function() {
+						return elem.getBoundingClientRect().left;
+					} )
+			) + "px";
+		}
+	}
+);
+
+// These hooks are used by animate to expand properties
+jQuery.each( {
+	margin: "",
+	padding: "",
+	border: "Width"
+}, function( prefix, suffix ) {
+	jQuery.cssHooks[ prefix + suffix ] = {
+		expand: function( value ) {
+			var i = 0,
+				expanded = {},
+
+				// Assumes a single number if not a string
+				parts = typeof value === "string" ? value.split( " " ) : [ value ];
+
+			for ( ; i < 4; i++ ) {
+				expanded[ prefix + cssExpand[ i ] + suffix ] =
+					parts[ i ] || parts[ i - 2 ] || parts[ 0 ];
+			}
+
+			return expanded;
+		}
+	};
+
+	if ( prefix !== "margin" ) {
+		jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber;
+	}
+} );
+
+jQuery.fn.extend( {
+	css: function( name, value ) {
+		return access( this, function( elem, name, value ) {
+			var styles, len,
+				map = {},
+				i = 0;
+
+			if ( Array.isArray( name ) ) {
+				styles = getStyles( elem );
+				len = name.length;
+
+				for ( ; i < len; i++ ) {
+					map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles );
+				}
+
+				return map;
+			}
+
+			return value !== undefined ?
+				jQuery.style( elem, name, value ) :
+				jQuery.css( elem, name );
+		}, name, value, arguments.length > 1 );
+	}
+} );
+
+
+function Tween( elem, options, prop, end, easing ) {
+	return new Tween.prototype.init( elem, options, prop, end, easing );
+}
+jQuery.Tween = Tween;
+
+Tween.prototype = {
+	constructor: Tween,
+	init: function( elem, options, prop, end, easing, unit ) {
+		this.elem = elem;
+		this.prop = prop;
+		this.easing = easing || jQuery.easing._default;
+		this.options = options;
+		this.start = this.now = this.cur();
+		this.end = end;
+		this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" );
+	},
+	cur: function() {
+		var hooks = Tween.propHooks[ this.prop ];
+
+		return hooks && hooks.get ?
+			hooks.get( this ) :
+			Tween.propHooks._default.get( this );
+	},
+	run: function( percent ) {
+		var eased,
+			hooks = Tween.propHooks[ this.prop ];
+
+		if ( this.options.duration ) {
+			this.pos = eased = jQuery.easing[ this.easing ](
+				percent, this.options.duration * percent, 0, 1, this.options.duration
+			);
+		} else {
+			this.pos = eased = percent;
+		}
+		this.now = ( this.end - this.start ) * eased + this.start;
+
+		if ( this.options.step ) {
+			this.options.step.call( this.elem, this.now, this );
+		}
+
+		if ( hooks && hooks.set ) {
+			hooks.set( this );
+		} else {
+			Tween.propHooks._default.set( this );
+		}
+		return this;
+	}
+};
+
+Tween.prototype.init.prototype = Tween.prototype;
+
+Tween.propHooks = {
+	_default: {
+		get: function( tween ) {
+			var result;
+
+			// Use a property on the element directly when it is not a DOM element,
+			// or when there is no matching style property that exists.
+			if ( tween.elem.nodeType !== 1 ||
+				tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) {
+				return tween.elem[ tween.prop ];
+			}
+
+			// Passing an empty string as a 3rd parameter to .css will automatically
+			// attempt a parseFloat and fallback to a string if the parse fails.
+			// Simple values such as "10px" are parsed to Float;
+			// complex values such as "rotate(1rad)" are returned as-is.
+			result = jQuery.css( tween.elem, tween.prop, "" );
+
+			// Empty strings, null, undefined and "auto" are converted to 0.
+			return !result || result === "auto" ? 0 : result;
+		},
+		set: function( tween ) {
+
+			// Use step hook for back compat.
+			// Use cssHook if its there.
+			// Use .style if available and use plain properties where available.
+			if ( jQuery.fx.step[ tween.prop ] ) {
+				jQuery.fx.step[ tween.prop ]( tween );
+			} else if ( tween.elem.nodeType === 1 && (
+				jQuery.cssHooks[ tween.prop ] ||
+					tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) {
+				jQuery.style( tween.elem, tween.prop, tween.now + tween.unit );
+			} else {
+				tween.elem[ tween.prop ] = tween.now;
+			}
+		}
+	}
+};
+
+// Support: IE <=9 only
+// Panic based approach to setting things on disconnected nodes
+Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = {
+	set: function( tween ) {
+		if ( tween.elem.nodeType && tween.elem.parentNode ) {
+			tween.elem[ tween.prop ] = tween.now;
+		}
+	}
+};
+
+jQuery.easing = {
+	linear: function( p ) {
+		return p;
+	},
+	swing: function( p ) {
+		return 0.5 - Math.cos( p * Math.PI ) / 2;
+	},
+	_default: "swing"
+};
+
+jQuery.fx = Tween.prototype.init;
+
+// Back compat <1.8 extension point
+jQuery.fx.step = {};
+
+
+
+
+var
+	fxNow, inProgress,
+	rfxtypes = /^(?:toggle|show|hide)$/,
+	rrun = /queueHooks$/;
+
+function schedule() {
+	if ( inProgress ) {
+		if ( document.hidden === false && window.requestAnimationFrame ) {
+			window.requestAnimationFrame( schedule );
+		} else {
+			window.setTimeout( schedule, jQuery.fx.interval );
+		}
+
+		jQuery.fx.tick();
+	}
+}
+
+// Animations created synchronously will run synchronously
+function createFxNow() {
+	window.setTimeout( function() {
+		fxNow = undefined;
+	} );
+	return ( fxNow = Date.now() );
+}
+
+// Generate parameters to create a standard animation
+function genFx( type, includeWidth ) {
+	var which,
+		i = 0,
+		attrs = { height: type };
+
+	// If we include width, step value is 1 to do all cssExpand values,
+	// otherwise step value is 2 to skip over Left and Right
+	includeWidth = includeWidth ? 1 : 0;
+	for ( ; i < 4; i += 2 - includeWidth ) {
+		which = cssExpand[ i ];
+		attrs[ "margin" + which ] = attrs[ "padding" + which ] = type;
+	}
+
+	if ( includeWidth ) {
+		attrs.opacity = attrs.width = type;
+	}
+
+	return attrs;
+}
+
+function createTween( value, prop, animation ) {
+	var tween,
+		collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ),
+		index = 0,
+		length = collection.length;
+	for ( ; index < length; index++ ) {
+		if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) {
+
+			// We're done with this property
+			return tween;
+		}
+	}
+}
+
+function defaultPrefilter( elem, props, opts ) {
+	var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display,
+		isBox = "width" in props || "height" in props,
+		anim = this,
+		orig = {},
+		style = elem.style,
+		hidden = elem.nodeType && isHiddenWithinTree( elem ),
+		dataShow = dataPriv.get( elem, "fxshow" );
+
+	// Queue-skipping animations hijack the fx hooks
+	if ( !opts.queue ) {
+		hooks = jQuery._queueHooks( elem, "fx" );
+		if ( hooks.unqueued == null ) {
+			hooks.unqueued = 0;
+			oldfire = hooks.empty.fire;
+			hooks.empty.fire = function() {
+				if ( !hooks.unqueued ) {
+					oldfire();
+				}
+			};
+		}
+		hooks.unqueued++;
+
+		anim.always( function() {
+
+			// Ensure the complete handler is called before this completes
+			anim.always( function() {
+				hooks.unqueued--;
+				if ( !jQuery.queue( elem, "fx" ).length ) {
+					hooks.empty.fire();
+				}
+			} );
+		} );
+	}
+
+	// Detect show/hide animations
+	for ( prop in props ) {
+		value = props[ prop ];
+		if ( rfxtypes.test( value ) ) {
+			delete props[ prop ];
+			toggle = toggle || value === "toggle";
+			if ( value === ( hidden ? "hide" : "show" ) ) {
+
+				// Pretend to be hidden if this is a "show" and
+				// there is still data from a stopped show/hide
+				if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) {
+					hidden = true;
+
+				// Ignore all other no-op show/hide data
+				} else {
+					continue;
+				}
+			}
+			orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop );
+		}
+	}
+
+	// Bail out if this is a no-op like .hide().hide()
+	propTween = !jQuery.isEmptyObject( props );
+	if ( !propTween && jQuery.isEmptyObject( orig ) ) {
+		return;
+	}
+
+	// Restrict "overflow" and "display" styles during box animations
+	if ( isBox && elem.nodeType === 1 ) {
+
+		// Support: IE <=9 - 11, Edge 12 - 15
+		// Record all 3 overflow attributes because IE does not infer the shorthand
+		// from identically-valued overflowX and overflowY and Edge just mirrors
+		// the overflowX value there.
+		opts.overflow = [ style.overflow, style.overflowX, style.overflowY ];
+
+		// Identify a display type, preferring old show/hide data over the CSS cascade
+		restoreDisplay = dataShow && dataShow.display;
+		if ( restoreDisplay == null ) {
+			restoreDisplay = dataPriv.get( elem, "display" );
+		}
+		display = jQuery.css( elem, "display" );
+		if ( display === "none" ) {
+			if ( restoreDisplay ) {
+				display = restoreDisplay;
+			} else {
+
+				// Get nonempty value(s) by temporarily forcing visibility
+				showHide( [ elem ], true );
+				restoreDisplay = elem.style.display || restoreDisplay;
+				display = jQuery.css( elem, "display" );
+				showHide( [ elem ] );
+			}
+		}
+
+		// Animate inline elements as inline-block
+		if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) {
+			if ( jQuery.css( elem, "float" ) === "none" ) {
+
+				// Restore the original display value at the end of pure show/hide animations
+				if ( !propTween ) {
+					anim.done( function() {
+						style.display = restoreDisplay;
+					} );
+					if ( restoreDisplay == null ) {
+						display = style.display;
+						restoreDisplay = display === "none" ? "" : display;
+					}
+				}
+				style.display = "inline-block";
+			}
+		}
+	}
+
+	if ( opts.overflow ) {
+		style.overflow = "hidden";
+		anim.always( function() {
+			style.overflow = opts.overflow[ 0 ];
+			style.overflowX = opts.overflow[ 1 ];
+			style.overflowY = opts.overflow[ 2 ];
+		} );
+	}
+
+	// Implement show/hide animations
+	propTween = false;
+	for ( prop in orig ) {
+
+		// General show/hide setup for this element animation
+		if ( !propTween ) {
+			if ( dataShow ) {
+				if ( "hidden" in dataShow ) {
+					hidden = dataShow.hidden;
+				}
+			} else {
+				dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } );
+			}
+
+			// Store hidden/visible for toggle so `.stop().toggle()` "reverses"
+			if ( toggle ) {
+				dataShow.hidden = !hidden;
+			}
+
+			// Show elements before animating them
+			if ( hidden ) {
+				showHide( [ elem ], true );
+			}
+
+			/* eslint-disable no-loop-func */
+
+			anim.done( function() {
+
+				/* eslint-enable no-loop-func */
+
+				// The final step of a "hide" animation is actually hiding the element
+				if ( !hidden ) {
+					showHide( [ elem ] );
+				}
+				dataPriv.remove( elem, "fxshow" );
+				for ( prop in orig ) {
+					jQuery.style( elem, prop, orig[ prop ] );
+				}
+			} );
+		}
+
+		// Per-property setup
+		propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim );
+		if ( !( prop in dataShow ) ) {
+			dataShow[ prop ] = propTween.start;
+			if ( hidden ) {
+				propTween.end = propTween.start;
+				propTween.start = 0;
+			}
+		}
+	}
+}
+
+function propFilter( props, specialEasing ) {
+	var index, name, easing, value, hooks;
+
+	// camelCase, specialEasing and expand cssHook pass
+	for ( index in props ) {
+		name = camelCase( index );
+		easing = specialEasing[ name ];
+		value = props[ index ];
+		if ( Array.isArray( value ) ) {
+			easing = value[ 1 ];
+			value = props[ index ] = value[ 0 ];
+		}
+
+		if ( index !== name ) {
+			props[ name ] = value;
+			delete props[ index ];
+		}
+
+		hooks = jQuery.cssHooks[ name ];
+		if ( hooks && "expand" in hooks ) {
+			value = hooks.expand( value );
+			delete props[ name ];
+
+			// Not quite $.extend, this won't overwrite existing keys.
+			// Reusing 'index' because we have the correct "name"
+			for ( index in value ) {
+				if ( !( index in props ) ) {
+					props[ index ] = value[ index ];
+					specialEasing[ index ] = easing;
+				}
+			}
+		} else {
+			specialEasing[ name ] = easing;
+		}
+	}
+}
+
+function Animation( elem, properties, options ) {
+	var result,
+		stopped,
+		index = 0,
+		length = Animation.prefilters.length,
+		deferred = jQuery.Deferred().always( function() {
+
+			// Don't match elem in the :animated selector
+			delete tick.elem;
+		} ),
+		tick = function() {
+			if ( stopped ) {
+				return false;
+			}
+			var currentTime = fxNow || createFxNow(),
+				remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ),
+
+				// Support: Android 2.3 only
+				// Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497)
+				temp = remaining / animation.duration || 0,
+				percent = 1 - temp,
+				index = 0,
+				length = animation.tweens.length;
+
+			for ( ; index < length; index++ ) {
+				animation.tweens[ index ].run( percent );
+			}
+
+			deferred.notifyWith( elem, [ animation, percent, remaining ] );
+
+			// If there's more to do, yield
+			if ( percent < 1 && length ) {
+				return remaining;
+			}
+
+			// If this was an empty animation, synthesize a final progress notification
+			if ( !length ) {
+				deferred.notifyWith( elem, [ animation, 1, 0 ] );
+			}
+
+			// Resolve the animation and report its conclusion
+			deferred.resolveWith( elem, [ animation ] );
+			return false;
+		},
+		animation = deferred.promise( {
+			elem: elem,
+			props: jQuery.extend( {}, properties ),
+			opts: jQuery.extend( true, {
+				specialEasing: {},
+				easing: jQuery.easing._default
+			}, options ),
+			originalProperties: properties,
+			originalOptions: options,
+			startTime: fxNow || createFxNow(),
+			duration: options.duration,
+			tweens: [],
+			createTween: function( prop, end ) {
+				var tween = jQuery.Tween( elem, animation.opts, prop, end,
+					animation.opts.specialEasing[ prop ] || animation.opts.easing );
+				animation.tweens.push( tween );
+				return tween;
+			},
+			stop: function( gotoEnd ) {
+				var index = 0,
+
+					// If we are going to the end, we want to run all the tweens
+					// otherwise we skip this part
+					length = gotoEnd ? animation.tweens.length : 0;
+				if ( stopped ) {
+					return this;
+				}
+				stopped = true;
+				for ( ; index < length; index++ ) {
+					animation.tweens[ index ].run( 1 );
+				}
+
+				// Resolve when we played the last frame; otherwise, reject
+				if ( gotoEnd ) {
+					deferred.notifyWith( elem, [ animation, 1, 0 ] );
+					deferred.resolveWith( elem, [ animation, gotoEnd ] );
+				} else {
+					deferred.rejectWith( elem, [ animation, gotoEnd ] );
+				}
+				return this;
+			}
+		} ),
+		props = animation.props;
+
+	propFilter( props, animation.opts.specialEasing );
+
+	for ( ; index < length; index++ ) {
+		result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts );
+		if ( result ) {
+			if ( isFunction( result.stop ) ) {
+				jQuery._queueHooks( animation.elem, animation.opts.queue ).stop =
+					result.stop.bind( result );
+			}
+			return result;
+		}
+	}
+
+	jQuery.map( props, createTween, animation );
+
+	if ( isFunction( animation.opts.start ) ) {
+		animation.opts.start.call( elem, animation );
+	}
+
+	// Attach callbacks from options
+	animation
+		.progress( animation.opts.progress )
+		.done( animation.opts.done, animation.opts.complete )
+		.fail( animation.opts.fail )
+		.always( animation.opts.always );
+
+	jQuery.fx.timer(
+		jQuery.extend( tick, {
+			elem: elem,
+			anim: animation,
+			queue: animation.opts.queue
+		} )
+	);
+
+	return animation;
+}
+
+jQuery.Animation = jQuery.extend( Animation, {
+
+	tweeners: {
+		"*": [ function( prop, value ) {
+			var tween = this.createTween( prop, value );
+			adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween );
+			return tween;
+		} ]
+	},
+
+	tweener: function( props, callback ) {
+		if ( isFunction( props ) ) {
+			callback = props;
+			props = [ "*" ];
+		} else {
+			props = props.match( rnothtmlwhite );
+		}
+
+		var prop,
+			index = 0,
+			length = props.length;
+
+		for ( ; index < length; index++ ) {
+			prop = props[ index ];
+			Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || [];
+			Animation.tweeners[ prop ].unshift( callback );
+		}
+	},
+
+	prefilters: [ defaultPrefilter ],
+
+	prefilter: function( callback, prepend ) {
+		if ( prepend ) {
+			Animation.prefilters.unshift( callback );
+		} else {
+			Animation.prefilters.push( callback );
+		}
+	}
+} );
+
+jQuery.speed = function( speed, easing, fn ) {
+	var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : {
+		complete: fn || !fn && easing ||
+			isFunction( speed ) && speed,
+		duration: speed,
+		easing: fn && easing || easing && !isFunction( easing ) && easing
+	};
+
+	// Go to the end state if fx are off
+	if ( jQuery.fx.off ) {
+		opt.duration = 0;
+
+	} else {
+		if ( typeof opt.duration !== "number" ) {
+			if ( opt.duration in jQuery.fx.speeds ) {
+				opt.duration = jQuery.fx.speeds[ opt.duration ];
+
+			} else {
+				opt.duration = jQuery.fx.speeds._default;
+			}
+		}
+	}
+
+	// Normalize opt.queue - true/undefined/null -> "fx"
+	if ( opt.queue == null || opt.queue === true ) {
+		opt.queue = "fx";
+	}
+
+	// Queueing
+	opt.old = opt.complete;
+
+	opt.complete = function() {
+		if ( isFunction( opt.old ) ) {
+			opt.old.call( this );
+		}
+
+		if ( opt.queue ) {
+			jQuery.dequeue( this, opt.queue );
+		}
+	};
+
+	return opt;
+};
+
+jQuery.fn.extend( {
+	fadeTo: function( speed, to, easing, callback ) {
+
+		// Show any hidden elements after setting opacity to 0
+		return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show()
+
+			// Animate to the value specified
+			.end().animate( { opacity: to }, speed, easing, callback );
+	},
+	animate: function( prop, speed, easing, callback ) {
+		var empty = jQuery.isEmptyObject( prop ),
+			optall = jQuery.speed( speed, easing, callback ),
+			doAnimation = function() {
+
+				// Operate on a copy of prop so per-property easing won't be lost
+				var anim = Animation( this, jQuery.extend( {}, prop ), optall );
+
+				// Empty animations, or finishing resolves immediately
+				if ( empty || dataPriv.get( this, "finish" ) ) {
+					anim.stop( true );
+				}
+			};
+
+		doAnimation.finish = doAnimation;
+
+		return empty || optall.queue === false ?
+			this.each( doAnimation ) :
+			this.queue( optall.queue, doAnimation );
+	},
+	stop: function( type, clearQueue, gotoEnd ) {
+		var stopQueue = function( hooks ) {
+			var stop = hooks.stop;
+			delete hooks.stop;
+			stop( gotoEnd );
+		};
+
+		if ( typeof type !== "string" ) {
+			gotoEnd = clearQueue;
+			clearQueue = type;
+			type = undefined;
+		}
+		if ( clearQueue ) {
+			this.queue( type || "fx", [] );
+		}
+
+		return this.each( function() {
+			var dequeue = true,
+				index = type != null && type + "queueHooks",
+				timers = jQuery.timers,
+				data = dataPriv.get( this );
+
+			if ( index ) {
+				if ( data[ index ] && data[ index ].stop ) {
+					stopQueue( data[ index ] );
+				}
+			} else {
+				for ( index in data ) {
+					if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) {
+						stopQueue( data[ index ] );
+					}
+				}
+			}
+
+			for ( index = timers.length; index--; ) {
+				if ( timers[ index ].elem === this &&
+					( type == null || timers[ index ].queue === type ) ) {
+
+					timers[ index ].anim.stop( gotoEnd );
+					dequeue = false;
+					timers.splice( index, 1 );
+				}
+			}
+
+			// Start the next in the queue if the last step wasn't forced.
+			// Timers currently will call their complete callbacks, which
+			// will dequeue but only if they were gotoEnd.
+			if ( dequeue || !gotoEnd ) {
+				jQuery.dequeue( this, type );
+			}
+		} );
+	},
+	finish: function( type ) {
+		if ( type !== false ) {
+			type = type || "fx";
+		}
+		return this.each( function() {
+			var index,
+				data = dataPriv.get( this ),
+				queue = data[ type + "queue" ],
+				hooks = data[ type + "queueHooks" ],
+				timers = jQuery.timers,
+				length = queue ? queue.length : 0;
+
+			// Enable finishing flag on private data
+			data.finish = true;
+
+			// Empty the queue first
+			jQuery.queue( this, type, [] );
+
+			if ( hooks && hooks.stop ) {
+				hooks.stop.call( this, true );
+			}
+
+			// Look for any active animations, and finish them
+			for ( index = timers.length; index--; ) {
+				if ( timers[ index ].elem === this && timers[ index ].queue === type ) {
+					timers[ index ].anim.stop( true );
+					timers.splice( index, 1 );
+				}
+			}
+
+			// Look for any animations in the old queue and finish them
+			for ( index = 0; index < length; index++ ) {
+				if ( queue[ index ] && queue[ index ].finish ) {
+					queue[ index ].finish.call( this );
+				}
+			}
+
+			// Turn off finishing flag
+			delete data.finish;
+		} );
+	}
+} );
+
+jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) {
+	var cssFn = jQuery.fn[ name ];
+	jQuery.fn[ name ] = function( speed, easing, callback ) {
+		return speed == null || typeof speed === "boolean" ?
+			cssFn.apply( this, arguments ) :
+			this.animate( genFx( name, true ), speed, easing, callback );
+	};
+} );
+
+// Generate shortcuts for custom animations
+jQuery.each( {
+	slideDown: genFx( "show" ),
+	slideUp: genFx( "hide" ),
+	slideToggle: genFx( "toggle" ),
+	fadeIn: { opacity: "show" },
+	fadeOut: { opacity: "hide" },
+	fadeToggle: { opacity: "toggle" }
+}, function( name, props ) {
+	jQuery.fn[ name ] = function( speed, easing, callback ) {
+		return this.animate( props, speed, easing, callback );
+	};
+} );
+
+jQuery.timers = [];
+jQuery.fx.tick = function() {
+	var timer,
+		i = 0,
+		timers = jQuery.timers;
+
+	fxNow = Date.now();
+
+	for ( ; i < timers.length; i++ ) {
+		timer = timers[ i ];
+
+		// Run the timer and safely remove it when done (allowing for external removal)
+		if ( !timer() && timers[ i ] === timer ) {
+			timers.splice( i--, 1 );
+		}
+	}
+
+	if ( !timers.length ) {
+		jQuery.fx.stop();
+	}
+	fxNow = undefined;
+};
+
+jQuery.fx.timer = function( timer ) {
+	jQuery.timers.push( timer );
+	jQuery.fx.start();
+};
+
+jQuery.fx.interval = 13;
+jQuery.fx.start = function() {
+	if ( inProgress ) {
+		return;
+	}
+
+	inProgress = true;
+	schedule();
+};
+
+jQuery.fx.stop = function() {
+	inProgress = null;
+};
+
+jQuery.fx.speeds = {
+	slow: 600,
+	fast: 200,
+
+	// Default speed
+	_default: 400
+};
+
+
+// Based off of the plugin by Clint Helfers, with permission.
+// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/
+jQuery.fn.delay = function( time, type ) {
+	time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time;
+	type = type || "fx";
+
+	return this.queue( type, function( next, hooks ) {
+		var timeout = window.setTimeout( next, time );
+		hooks.stop = function() {
+			window.clearTimeout( timeout );
+		};
+	} );
+};
+
+
+( function() {
+	var input = document.createElement( "input" ),
+		select = document.createElement( "select" ),
+		opt = select.appendChild( document.createElement( "option" ) );
+
+	input.type = "checkbox";
+
+	// Support: Android <=4.3 only
+	// Default value for a checkbox should be "on"
+	support.checkOn = input.value !== "";
+
+	// Support: IE <=11 only
+	// Must access selectedIndex to make default options select
+	support.optSelected = opt.selected;
+
+	// Support: IE <=11 only
+	// An input loses its value after becoming a radio
+	input = document.createElement( "input" );
+	input.value = "t";
+	input.type = "radio";
+	support.radioValue = input.value === "t";
+} )();
+
+
+var boolHook,
+	attrHandle = jQuery.expr.attrHandle;
+
+jQuery.fn.extend( {
+	attr: function( name, value ) {
+		return access( this, jQuery.attr, name, value, arguments.length > 1 );
+	},
+
+	removeAttr: function( name ) {
+		return this.each( function() {
+			jQuery.removeAttr( this, name );
+		} );
+	}
+} );
+
+jQuery.extend( {
+	attr: function( elem, name, value ) {
+		var ret, hooks,
+			nType = elem.nodeType;
+
+		// Don't get/set attributes on text, comment and attribute nodes
+		if ( nType === 3 || nType === 8 || nType === 2 ) {
+			return;
+		}
+
+		// Fallback to prop when attributes are not supported
+		if ( typeof elem.getAttribute === "undefined" ) {
+			return jQuery.prop( elem, name, value );
+		}
+
+		// Attribute hooks are determined by the lowercase version
+		// Grab necessary hook if one is defined
+		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
+			hooks = jQuery.attrHooks[ name.toLowerCase() ] ||
+				( jQuery.expr.match.bool.test( name ) ? boolHook : undefined );
+		}
+
+		if ( value !== undefined ) {
+			if ( value === null ) {
+				jQuery.removeAttr( elem, name );
+				return;
+			}
+
+			if ( hooks && "set" in hooks &&
+				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
+				return ret;
+			}
+
+			elem.setAttribute( name, value + "" );
+			return value;
+		}
+
+		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
+			return ret;
+		}
+
+		ret = jQuery.find.attr( elem, name );
+
+		// Non-existent attributes return null, we normalize to undefined
+		return ret == null ? undefined : ret;
+	},
+
+	attrHooks: {
+		type: {
+			set: function( elem, value ) {
+				if ( !support.radioValue && value === "radio" &&
+					nodeName( elem, "input" ) ) {
+					var val = elem.value;
+					elem.setAttribute( "type", value );
+					if ( val ) {
+						elem.value = val;
+					}
+					return value;
+				}
+			}
+		}
+	},
+
+	removeAttr: function( elem, value ) {
+		var name,
+			i = 0,
+
+			// Attribute names can contain non-HTML whitespace characters
+			// https://html.spec.whatwg.org/multipage/syntax.html#attributes-2
+			attrNames = value && value.match( rnothtmlwhite );
+
+		if ( attrNames && elem.nodeType === 1 ) {
+			while ( ( name = attrNames[ i++ ] ) ) {
+				elem.removeAttribute( name );
+			}
+		}
+	}
+} );
+
+// Hooks for boolean attributes
+boolHook = {
+	set: function( elem, value, name ) {
+		if ( value === false ) {
+
+			// Remove boolean attributes when set to false
+			jQuery.removeAttr( elem, name );
+		} else {
+			elem.setAttribute( name, name );
+		}
+		return name;
+	}
+};
+
+jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) {
+	var getter = attrHandle[ name ] || jQuery.find.attr;
+
+	attrHandle[ name ] = function( elem, name, isXML ) {
+		var ret, handle,
+			lowercaseName = name.toLowerCase();
+
+		if ( !isXML ) {
+
+			// Avoid an infinite loop by temporarily removing this function from the getter
+			handle = attrHandle[ lowercaseName ];
+			attrHandle[ lowercaseName ] = ret;
+			ret = getter( elem, name, isXML ) != null ?
+				lowercaseName :
+				null;
+			attrHandle[ lowercaseName ] = handle;
+		}
+		return ret;
+	};
+} );
+
+
+
+
+var rfocusable = /^(?:input|select|textarea|button)$/i,
+	rclickable = /^(?:a|area)$/i;
+
+jQuery.fn.extend( {
+	prop: function( name, value ) {
+		return access( this, jQuery.prop, name, value, arguments.length > 1 );
+	},
+
+	removeProp: function( name ) {
+		return this.each( function() {
+			delete this[ jQuery.propFix[ name ] || name ];
+		} );
+	}
+} );
+
+jQuery.extend( {
+	prop: function( elem, name, value ) {
+		var ret, hooks,
+			nType = elem.nodeType;
+
+		// Don't get/set properties on text, comment and attribute nodes
+		if ( nType === 3 || nType === 8 || nType === 2 ) {
+			return;
+		}
+
+		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
+
+			// Fix name and attach hooks
+			name = jQuery.propFix[ name ] || name;
+			hooks = jQuery.propHooks[ name ];
+		}
+
+		if ( value !== undefined ) {
+			if ( hooks && "set" in hooks &&
+				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
+				return ret;
+			}
+
+			return ( elem[ name ] = value );
+		}
+
+		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
+			return ret;
+		}
+
+		return elem[ name ];
+	},
+
+	propHooks: {
+		tabIndex: {
+			get: function( elem ) {
+
+				// Support: IE <=9 - 11 only
+				// elem.tabIndex doesn't always return the
+				// correct value when it hasn't been explicitly set
+				// https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/
+				// Use proper attribute retrieval(#12072)
+				var tabindex = jQuery.find.attr( elem, "tabindex" );
+
+				if ( tabindex ) {
+					return parseInt( tabindex, 10 );
+				}
+
+				if (
+					rfocusable.test( elem.nodeName ) ||
+					rclickable.test( elem.nodeName ) &&
+					elem.href
+				) {
+					return 0;
+				}
+
+				return -1;
+			}
+		}
+	},
+
+	propFix: {
+		"for": "htmlFor",
+		"class": "className"
+	}
+} );
+
+// Support: IE <=11 only
+// Accessing the selectedIndex property
+// forces the browser to respect setting selected
+// on the option
+// The getter ensures a default option is selected
+// when in an optgroup
+// eslint rule "no-unused-expressions" is disabled for this code
+// since it considers such accessions noop
+if ( !support.optSelected ) {
+	jQuery.propHooks.selected = {
+		get: function( elem ) {
+
+			/* eslint no-unused-expressions: "off" */
+
+			var parent = elem.parentNode;
+			if ( parent && parent.parentNode ) {
+				parent.parentNode.selectedIndex;
+			}
+			return null;
+		},
+		set: function( elem ) {
+
+			/* eslint no-unused-expressions: "off" */
+
+			var parent = elem.parentNode;
+			if ( parent ) {
+				parent.selectedIndex;
+
+				if ( parent.parentNode ) {
+					parent.parentNode.selectedIndex;
+				}
+			}
+		}
+	};
+}
+
+jQuery.each( [
+	"tabIndex",
+	"readOnly",
+	"maxLength",
+	"cellSpacing",
+	"cellPadding",
+	"rowSpan",
+	"colSpan",
+	"useMap",
+	"frameBorder",
+	"contentEditable"
+], function() {
+	jQuery.propFix[ this.toLowerCase() ] = this;
+} );
+
+
+
+
+	// Strip and collapse whitespace according to HTML spec
+	// https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace
+	function stripAndCollapse( value ) {
+		var tokens = value.match( rnothtmlwhite ) || [];
+		return tokens.join( " " );
+	}
+
+
+function getClass( elem ) {
+	return elem.getAttribute && elem.getAttribute( "class" ) || "";
+}
+
+function classesToArray( value ) {
+	if ( Array.isArray( value ) ) {
+		return value;
+	}
+	if ( typeof value === "string" ) {
+		return value.match( rnothtmlwhite ) || [];
+	}
+	return [];
+}
+
+jQuery.fn.extend( {
+	addClass: function( value ) {
+		var classes, elem, cur, curValue, clazz, j, finalValue,
+			i = 0;
+
+		if ( isFunction( value ) ) {
+			return this.each( function( j ) {
+				jQuery( this ).addClass( value.call( this, j, getClass( this ) ) );
+			} );
+		}
+
+		classes = classesToArray( value );
+
+		if ( classes.length ) {
+			while ( ( elem = this[ i++ ] ) ) {
+				curValue = getClass( elem );
+				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
+
+				if ( cur ) {
+					j = 0;
+					while ( ( clazz = classes[ j++ ] ) ) {
+						if ( cur.indexOf( " " + clazz + " " ) < 0 ) {
+							cur += clazz + " ";
+						}
+					}
+
+					// Only assign if different to avoid unneeded rendering.
+					finalValue = stripAndCollapse( cur );
+					if ( curValue !== finalValue ) {
+						elem.setAttribute( "class", finalValue );
+					}
+				}
+			}
+		}
+
+		return this;
+	},
+
+	removeClass: function( value ) {
+		var classes, elem, cur, curValue, clazz, j, finalValue,
+			i = 0;
+
+		if ( isFunction( value ) ) {
+			return this.each( function( j ) {
+				jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) );
+			} );
+		}
+
+		if ( !arguments.length ) {
+			return this.attr( "class", "" );
+		}
+
+		classes = classesToArray( value );
+
+		if ( classes.length ) {
+			while ( ( elem = this[ i++ ] ) ) {
+				curValue = getClass( elem );
+
+				// This expression is here for better compressibility (see addClass)
+				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
+
+				if ( cur ) {
+					j = 0;
+					while ( ( clazz = classes[ j++ ] ) ) {
+
+						// Remove *all* instances
+						while ( cur.indexOf( " " + clazz + " " ) > -1 ) {
+							cur = cur.replace( " " + clazz + " ", " " );
+						}
+					}
+
+					// Only assign if different to avoid unneeded rendering.
+					finalValue = stripAndCollapse( cur );
+					if ( curValue !== finalValue ) {
+						elem.setAttribute( "class", finalValue );
+					}
+				}
+			}
+		}
+
+		return this;
+	},
+
+	toggleClass: function( value, stateVal ) {
+		var type = typeof value,
+			isValidValue = type === "string" || Array.isArray( value );
+
+		if ( typeof stateVal === "boolean" && isValidValue ) {
+			return stateVal ? this.addClass( value ) : this.removeClass( value );
+		}
+
+		if ( isFunction( value ) ) {
+			return this.each( function( i ) {
+				jQuery( this ).toggleClass(
+					value.call( this, i, getClass( this ), stateVal ),
+					stateVal
+				);
+			} );
+		}
+
+		return this.each( function() {
+			var className, i, self, classNames;
+
+			if ( isValidValue ) {
+
+				// Toggle individual class names
+				i = 0;
+				self = jQuery( this );
+				classNames = classesToArray( value );
+
+				while ( ( className = classNames[ i++ ] ) ) {
+
+					// Check each className given, space separated list
+					if ( self.hasClass( className ) ) {
+						self.removeClass( className );
+					} else {
+						self.addClass( className );
+					}
+				}
+
+			// Toggle whole class name
+			} else if ( value === undefined || type === "boolean" ) {
+				className = getClass( this );
+				if ( className ) {
+
+					// Store className if set
+					dataPriv.set( this, "__className__", className );
+				}
+
+				// If the element has a class name or if we're passed `false`,
+				// then remove the whole classname (if there was one, the above saved it).
+				// Otherwise bring back whatever was previously saved (if anything),
+				// falling back to the empty string if nothing was stored.
+				if ( this.setAttribute ) {
+					this.setAttribute( "class",
+						className || value === false ?
+							"" :
+							dataPriv.get( this, "__className__" ) || ""
+					);
+				}
+			}
+		} );
+	},
+
+	hasClass: function( selector ) {
+		var className, elem,
+			i = 0;
+
+		className = " " + selector + " ";
+		while ( ( elem = this[ i++ ] ) ) {
+			if ( elem.nodeType === 1 &&
+				( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) {
+				return true;
+			}
+		}
+
+		return false;
+	}
+} );
+
+
+
+
+var rreturn = /\r/g;
+
+jQuery.fn.extend( {
+	val: function( value ) {
+		var hooks, ret, valueIsFunction,
+			elem = this[ 0 ];
+
+		if ( !arguments.length ) {
+			if ( elem ) {
+				hooks = jQuery.valHooks[ elem.type ] ||
+					jQuery.valHooks[ elem.nodeName.toLowerCase() ];
+
+				if ( hooks &&
+					"get" in hooks &&
+					( ret = hooks.get( elem, "value" ) ) !== undefined
+				) {
+					return ret;
+				}
+
+				ret = elem.value;
+
+				// Handle most common string cases
+				if ( typeof ret === "string" ) {
+					return ret.replace( rreturn, "" );
+				}
+
+				// Handle cases where value is null/undef or number
+				return ret == null ? "" : ret;
+			}
+
+			return;
+		}
+
+		valueIsFunction = isFunction( value );
+
+		return this.each( function( i ) {
+			var val;
+
+			if ( this.nodeType !== 1 ) {
+				return;
+			}
+
+			if ( valueIsFunction ) {
+				val = value.call( this, i, jQuery( this ).val() );
+			} else {
+				val = value;
+			}
+
+			// Treat null/undefined as ""; convert numbers to string
+			if ( val == null ) {
+				val = "";
+
+			} else if ( typeof val === "number" ) {
+				val += "";
+
+			} else if ( Array.isArray( val ) ) {
+				val = jQuery.map( val, function( value ) {
+					return value == null ? "" : value + "";
+				} );
+			}
+
+			hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ];
+
+			// If set returns undefined, fall back to normal setting
+			if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) {
+				this.value = val;
+			}
+		} );
+	}
+} );
+
+jQuery.extend( {
+	valHooks: {
+		option: {
+			get: function( elem ) {
+
+				var val = jQuery.find.attr( elem, "value" );
+				return val != null ?
+					val :
+
+					// Support: IE <=10 - 11 only
+					// option.text throws exceptions (#14686, #14858)
+					// Strip and collapse whitespace
+					// https://html.spec.whatwg.org/#strip-and-collapse-whitespace
+					stripAndCollapse( jQuery.text( elem ) );
+			}
+		},
+		select: {
+			get: function( elem ) {
+				var value, option, i,
+					options = elem.options,
+					index = elem.selectedIndex,
+					one = elem.type === "select-one",
+					values = one ? null : [],
+					max = one ? index + 1 : options.length;
+
+				if ( index < 0 ) {
+					i = max;
+
+				} else {
+					i = one ? index : 0;
+				}
+
+				// Loop through all the selected options
+				for ( ; i < max; i++ ) {
+					option = options[ i ];
+
+					// Support: IE <=9 only
+					// IE8-9 doesn't update selected after form reset (#2551)
+					if ( ( option.selected || i === index ) &&
+
+							// Don't return options that are disabled or in a disabled optgroup
+							!option.disabled &&
+							( !option.parentNode.disabled ||
+								!nodeName( option.parentNode, "optgroup" ) ) ) {
+
+						// Get the specific value for the option
+						value = jQuery( option ).val();
+
+						// We don't need an array for one selects
+						if ( one ) {
+							return value;
+						}
+
+						// Multi-Selects return an array
+						values.push( value );
+					}
+				}
+
+				return values;
+			},
+
+			set: function( elem, value ) {
+				var optionSet, option,
+					options = elem.options,
+					values = jQuery.makeArray( value ),
+					i = options.length;
+
+				while ( i-- ) {
+					option = options[ i ];
+
+					/* eslint-disable no-cond-assign */
+
+					if ( option.selected =
+						jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1
+					) {
+						optionSet = true;
+					}
+
+					/* eslint-enable no-cond-assign */
+				}
+
+				// Force browsers to behave consistently when non-matching value is set
+				if ( !optionSet ) {
+					elem.selectedIndex = -1;
+				}
+				return values;
+			}
+		}
+	}
+} );
+
+// Radios and checkboxes getter/setter
+jQuery.each( [ "radio", "checkbox" ], function() {
+	jQuery.valHooks[ this ] = {
+		set: function( elem, value ) {
+			if ( Array.isArray( value ) ) {
+				return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 );
+			}
+		}
+	};
+	if ( !support.checkOn ) {
+		jQuery.valHooks[ this ].get = function( elem ) {
+			return elem.getAttribute( "value" ) === null ? "on" : elem.value;
+		};
+	}
+} );
+
+
+
+
+// Return jQuery for attributes-only inclusion
+
+
+support.focusin = "onfocusin" in window;
+
+
+var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/,
+	stopPropagationCallback = function( e ) {
+		e.stopPropagation();
+	};
+
+jQuery.extend( jQuery.event, {
+
+	trigger: function( event, data, elem, onlyHandlers ) {
+
+		var i, cur, tmp, bubbleType, ontype, handle, special, lastElement,
+			eventPath = [ elem || document ],
+			type = hasOwn.call( event, "type" ) ? event.type : event,
+			namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : [];
+
+		cur = lastElement = tmp = elem = elem || document;
+
+		// Don't do events on text and comment nodes
+		if ( elem.nodeType === 3 || elem.nodeType === 8 ) {
+			return;
+		}
+
+		// focus/blur morphs to focusin/out; ensure we're not firing them right now
+		if ( rfocusMorph.test( type + jQuery.event.triggered ) ) {
+			return;
+		}
+
+		if ( type.indexOf( "." ) > -1 ) {
+
+			// Namespaced trigger; create a regexp to match event type in handle()
+			namespaces = type.split( "." );
+			type = namespaces.shift();
+			namespaces.sort();
+		}
+		ontype = type.indexOf( ":" ) < 0 && "on" + type;
+
+		// Caller can pass in a jQuery.Event object, Object, or just an event type string
+		event = event[ jQuery.expando ] ?
+			event :
+			new jQuery.Event( type, typeof event === "object" && event );
+
+		// Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true)
+		event.isTrigger = onlyHandlers ? 2 : 3;
+		event.namespace = namespaces.join( "." );
+		event.rnamespace = event.namespace ?
+			new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) :
+			null;
+
+		// Clean up the event in case it is being reused
+		event.result = undefined;
+		if ( !event.target ) {
+			event.target = elem;
+		}
+
+		// Clone any incoming data and prepend the event, creating the handler arg list
+		data = data == null ?
+			[ event ] :
+			jQuery.makeArray( data, [ event ] );
+
+		// Allow special events to draw outside the lines
+		special = jQuery.event.special[ type ] || {};
+		if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) {
+			return;
+		}
+
+		// Determine event propagation path in advance, per W3C events spec (#9951)
+		// Bubble up to document, then to window; watch for a global ownerDocument var (#9724)
+		if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) {
+
+			bubbleType = special.delegateType || type;
+			if ( !rfocusMorph.test( bubbleType + type ) ) {
+				cur = cur.parentNode;
+			}
+			for ( ; cur; cur = cur.parentNode ) {
+				eventPath.push( cur );
+				tmp = cur;
+			}
+
+			// Only add window if we got to document (e.g., not plain obj or detached DOM)
+			if ( tmp === ( elem.ownerDocument || document ) ) {
+				eventPath.push( tmp.defaultView || tmp.parentWindow || window );
+			}
+		}
+
+		// Fire handlers on the event path
+		i = 0;
+		while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) {
+			lastElement = cur;
+			event.type = i > 1 ?
+				bubbleType :
+				special.bindType || type;
+
+			// jQuery handler
+			handle = ( dataPriv.get( cur, "events" ) || Object.create( null ) )[ event.type ] &&
+				dataPriv.get( cur, "handle" );
+			if ( handle ) {
+				handle.apply( cur, data );
+			}
+
+			// Native handler
+			handle = ontype && cur[ ontype ];
+			if ( handle && handle.apply && acceptData( cur ) ) {
+				event.result = handle.apply( cur, data );
+				if ( event.result === false ) {
+					event.preventDefault();
+				}
+			}
+		}
+		event.type = type;
+
+		// If nobody prevented the default action, do it now
+		if ( !onlyHandlers && !event.isDefaultPrevented() ) {
+
+			if ( ( !special._default ||
+				special._default.apply( eventPath.pop(), data ) === false ) &&
+				acceptData( elem ) ) {
+
+				// Call a native DOM method on the target with the same name as the event.
+				// Don't do default actions on window, that's where global variables be (#6170)
+				if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) {
+
+					// Don't re-trigger an onFOO event when we call its FOO() method
+					tmp = elem[ ontype ];
+
+					if ( tmp ) {
+						elem[ ontype ] = null;
+					}
+
+					// Prevent re-triggering of the same event, since we already bubbled it above
+					jQuery.event.triggered = type;
+
+					if ( event.isPropagationStopped() ) {
+						lastElement.addEventListener( type, stopPropagationCallback );
+					}
+
+					elem[ type ]();
+
+					if ( event.isPropagationStopped() ) {
+						lastElement.removeEventListener( type, stopPropagationCallback );
+					}
+
+					jQuery.event.triggered = undefined;
+
+					if ( tmp ) {
+						elem[ ontype ] = tmp;
+					}
+				}
+			}
+		}
+
+		return event.result;
+	},
+
+	// Piggyback on a donor event to simulate a different one
+	// Used only for `focus(in | out)` events
+	simulate: function( type, elem, event ) {
+		var e = jQuery.extend(
+			new jQuery.Event(),
+			event,
+			{
+				type: type,
+				isSimulated: true
+			}
+		);
+
+		jQuery.event.trigger( e, null, elem );
+	}
+
+} );
+
+jQuery.fn.extend( {
+
+	trigger: function( type, data ) {
+		return this.each( function() {
+			jQuery.event.trigger( type, data, this );
+		} );
+	},
+	triggerHandler: function( type, data ) {
+		var elem = this[ 0 ];
+		if ( elem ) {
+			return jQuery.event.trigger( type, data, elem, true );
+		}
+	}
+} );
+
+
+// Support: Firefox <=44
+// Firefox doesn't have focus(in | out) events
+// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787
+//
+// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1
+// focus(in | out) events fire after focus & blur events,
+// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order
+// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857
+if ( !support.focusin ) {
+	jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) {
+
+		// Attach a single capturing handler on the document while someone wants focusin/focusout
+		var handler = function( event ) {
+			jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) );
+		};
+
+		jQuery.event.special[ fix ] = {
+			setup: function() {
+
+				// Handle: regular nodes (via `this.ownerDocument`), window
+				// (via `this.document`) & document (via `this`).
+				var doc = this.ownerDocument || this.document || this,
+					attaches = dataPriv.access( doc, fix );
+
+				if ( !attaches ) {
+					doc.addEventListener( orig, handler, true );
+				}
+				dataPriv.access( doc, fix, ( attaches || 0 ) + 1 );
+			},
+			teardown: function() {
+				var doc = this.ownerDocument || this.document || this,
+					attaches = dataPriv.access( doc, fix ) - 1;
+
+				if ( !attaches ) {
+					doc.removeEventListener( orig, handler, true );
+					dataPriv.remove( doc, fix );
+
+				} else {
+					dataPriv.access( doc, fix, attaches );
+				}
+			}
+		};
+	} );
+}
+var location = window.location;
+
+var nonce = { guid: Date.now() };
+
+var rquery = ( /\?/ );
+
+
+
+// Cross-browser xml parsing
+jQuery.parseXML = function( data ) {
+	var xml, parserErrorElem;
+	if ( !data || typeof data !== "string" ) {
+		return null;
+	}
+
+	// Support: IE 9 - 11 only
+	// IE throws on parseFromString with invalid input.
+	try {
+		xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" );
+	} catch ( e ) {}
+
+	parserErrorElem = xml && xml.getElementsByTagName( "parsererror" )[ 0 ];
+	if ( !xml || parserErrorElem ) {
+		jQuery.error( "Invalid XML: " + (
+			parserErrorElem ?
+				jQuery.map( parserErrorElem.childNodes, function( el ) {
+					return el.textContent;
+				} ).join( "\n" ) :
+				data
+		) );
+	}
+	return xml;
+};
+
+
+var
+	rbracket = /\[\]$/,
+	rCRLF = /\r?\n/g,
+	rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i,
+	rsubmittable = /^(?:input|select|textarea|keygen)/i;
+
+function buildParams( prefix, obj, traditional, add ) {
+	var name;
+
+	if ( Array.isArray( obj ) ) {
+
+		// Serialize array item.
+		jQuery.each( obj, function( i, v ) {
+			if ( traditional || rbracket.test( prefix ) ) {
+
+				// Treat each array item as a scalar.
+				add( prefix, v );
+
+			} else {
+
+				// Item is non-scalar (array or object), encode its numeric index.
+				buildParams(
+					prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]",
+					v,
+					traditional,
+					add
+				);
+			}
+		} );
+
+	} else if ( !traditional && toType( obj ) === "object" ) {
+
+		// Serialize object item.
+		for ( name in obj ) {
+			buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add );
+		}
+
+	} else {
+
+		// Serialize scalar item.
+		add( prefix, obj );
+	}
+}
+
+// Serialize an array of form elements or a set of
+// key/values into a query string
+jQuery.param = function( a, traditional ) {
+	var prefix,
+		s = [],
+		add = function( key, valueOrFunction ) {
+
+			// If value is a function, invoke it and use its return value
+			var value = isFunction( valueOrFunction ) ?
+				valueOrFunction() :
+				valueOrFunction;
+
+			s[ s.length ] = encodeURIComponent( key ) + "=" +
+				encodeURIComponent( value == null ? "" : value );
+		};
+
+	if ( a == null ) {
+		return "";
+	}
+
+	// If an array was passed in, assume that it is an array of form elements.
+	if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) {
+
+		// Serialize the form elements
+		jQuery.each( a, function() {
+			add( this.name, this.value );
+		} );
+
+	} else {
+
+		// If traditional, encode the "old" way (the way 1.3.2 or older
+		// did it), otherwise encode params recursively.
+		for ( prefix in a ) {
+			buildParams( prefix, a[ prefix ], traditional, add );
+		}
+	}
+
+	// Return the resulting serialization
+	return s.join( "&" );
+};
+
+jQuery.fn.extend( {
+	serialize: function() {
+		return jQuery.param( this.serializeArray() );
+	},
+	serializeArray: function() {
+		return this.map( function() {
+
+			// Can add propHook for "elements" to filter or add form elements
+			var elements = jQuery.prop( this, "elements" );
+			return elements ? jQuery.makeArray( elements ) : this;
+		} ).filter( function() {
+			var type = this.type;
+
+			// Use .is( ":disabled" ) so that fieldset[disabled] works
+			return this.name && !jQuery( this ).is( ":disabled" ) &&
+				rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) &&
+				( this.checked || !rcheckableType.test( type ) );
+		} ).map( function( _i, elem ) {
+			var val = jQuery( this ).val();
+
+			if ( val == null ) {
+				return null;
+			}
+
+			if ( Array.isArray( val ) ) {
+				return jQuery.map( val, function( val ) {
+					return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
+				} );
+			}
+
+			return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
+		} ).get();
+	}
+} );
+
+
+var
+	r20 = /%20/g,
+	rhash = /#.*$/,
+	rantiCache = /([?&])_=[^&]*/,
+	rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg,
+
+	// #7653, #8125, #8152: local protocol detection
+	rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/,
+	rnoContent = /^(?:GET|HEAD)$/,
+	rprotocol = /^\/\//,
+
+	/* Prefilters
+	 * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example)
+	 * 2) These are called:
+	 *    - BEFORE asking for a transport
+	 *    - AFTER param serialization (s.data is a string if s.processData is true)
+	 * 3) key is the dataType
+	 * 4) the catchall symbol "*" can be used
+	 * 5) execution will start with transport dataType and THEN continue down to "*" if needed
+	 */
+	prefilters = {},
+
+	/* Transports bindings
+	 * 1) key is the dataType
+	 * 2) the catchall symbol "*" can be used
+	 * 3) selection will start with transport dataType and THEN go to "*" if needed
+	 */
+	transports = {},
+
+	// Avoid comment-prolog char sequence (#10098); must appease lint and evade compression
+	allTypes = "*/".concat( "*" ),
+
+	// Anchor tag for parsing the document origin
+	originAnchor = document.createElement( "a" );
+
+originAnchor.href = location.href;
+
+// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport
+function addToPrefiltersOrTransports( structure ) {
+
+	// dataTypeExpression is optional and defaults to "*"
+	return function( dataTypeExpression, func ) {
+
+		if ( typeof dataTypeExpression !== "string" ) {
+			func = dataTypeExpression;
+			dataTypeExpression = "*";
+		}
+
+		var dataType,
+			i = 0,
+			dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || [];
+
+		if ( isFunction( func ) ) {
+
+			// For each dataType in the dataTypeExpression
+			while ( ( dataType = dataTypes[ i++ ] ) ) {
+
+				// Prepend if requested
+				if ( dataType[ 0 ] === "+" ) {
+					dataType = dataType.slice( 1 ) || "*";
+					( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func );
+
+				// Otherwise append
+				} else {
+					( structure[ dataType ] = structure[ dataType ] || [] ).push( func );
+				}
+			}
+		}
+	};
+}
+
+// Base inspection function for prefilters and transports
+function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) {
+
+	var inspected = {},
+		seekingTransport = ( structure === transports );
+
+	function inspect( dataType ) {
+		var selected;
+		inspected[ dataType ] = true;
+		jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) {
+			var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR );
+			if ( typeof dataTypeOrTransport === "string" &&
+				!seekingTransport && !inspected[ dataTypeOrTransport ] ) {
+
+				options.dataTypes.unshift( dataTypeOrTransport );
+				inspect( dataTypeOrTransport );
+				return false;
+			} else if ( seekingTransport ) {
+				return !( selected = dataTypeOrTransport );
+			}
+		} );
+		return selected;
+	}
+
+	return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" );
+}
+
+// A special extend for ajax options
+// that takes "flat" options (not to be deep extended)
+// Fixes #9887
+function ajaxExtend( target, src ) {
+	var key, deep,
+		flatOptions = jQuery.ajaxSettings.flatOptions || {};
+
+	for ( key in src ) {
+		if ( src[ key ] !== undefined ) {
+			( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ];
+		}
+	}
+	if ( deep ) {
+		jQuery.extend( true, target, deep );
+	}
+
+	return target;
+}
+
+/* Handles responses to an ajax request:
+ * - finds the right dataType (mediates between content-type and expected dataType)
+ * - returns the corresponding response
+ */
+function ajaxHandleResponses( s, jqXHR, responses ) {
+
+	var ct, type, finalDataType, firstDataType,
+		contents = s.contents,
+		dataTypes = s.dataTypes;
+
+	// Remove auto dataType and get content-type in the process
+	while ( dataTypes[ 0 ] === "*" ) {
+		dataTypes.shift();
+		if ( ct === undefined ) {
+			ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" );
+		}
+	}
+
+	// Check if we're dealing with a known content-type
+	if ( ct ) {
+		for ( type in contents ) {
+			if ( contents[ type ] && contents[ type ].test( ct ) ) {
+				dataTypes.unshift( type );
+				break;
+			}
+		}
+	}
+
+	// Check to see if we have a response for the expected dataType
+	if ( dataTypes[ 0 ] in responses ) {
+		finalDataType = dataTypes[ 0 ];
+	} else {
+
+		// Try convertible dataTypes
+		for ( type in responses ) {
+			if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) {
+				finalDataType = type;
+				break;
+			}
+			if ( !firstDataType ) {
+				firstDataType = type;
+			}
+		}
+
+		// Or just use first one
+		finalDataType = finalDataType || firstDataType;
+	}
+
+	// If we found a dataType
+	// We add the dataType to the list if needed
+	// and return the corresponding response
+	if ( finalDataType ) {
+		if ( finalDataType !== dataTypes[ 0 ] ) {
+			dataTypes.unshift( finalDataType );
+		}
+		return responses[ finalDataType ];
+	}
+}
+
+/* Chain conversions given the request and the original response
+ * Also sets the responseXXX fields on the jqXHR instance
+ */
+function ajaxConvert( s, response, jqXHR, isSuccess ) {
+	var conv2, current, conv, tmp, prev,
+		converters = {},
+
+		// Work with a copy of dataTypes in case we need to modify it for conversion
+		dataTypes = s.dataTypes.slice();
+
+	// Create converters map with lowercased keys
+	if ( dataTypes[ 1 ] ) {
+		for ( conv in s.converters ) {
+			converters[ conv.toLowerCase() ] = s.converters[ conv ];
+		}
+	}
+
+	current = dataTypes.shift();
+
+	// Convert to each sequential dataType
+	while ( current ) {
+
+		if ( s.responseFields[ current ] ) {
+			jqXHR[ s.responseFields[ current ] ] = response;
+		}
+
+		// Apply the dataFilter if provided
+		if ( !prev && isSuccess && s.dataFilter ) {
+			response = s.dataFilter( response, s.dataType );
+		}
+
+		prev = current;
+		current = dataTypes.shift();
+
+		if ( current ) {
+
+			// There's only work to do if current dataType is non-auto
+			if ( current === "*" ) {
+
+				current = prev;
+
+			// Convert response if prev dataType is non-auto and differs from current
+			} else if ( prev !== "*" && prev !== current ) {
+
+				// Seek a direct converter
+				conv = converters[ prev + " " + current ] || converters[ "* " + current ];
+
+				// If none found, seek a pair
+				if ( !conv ) {
+					for ( conv2 in converters ) {
+
+						// If conv2 outputs current
+						tmp = conv2.split( " " );
+						if ( tmp[ 1 ] === current ) {
+
+							// If prev can be converted to accepted input
+							conv = converters[ prev + " " + tmp[ 0 ] ] ||
+								converters[ "* " + tmp[ 0 ] ];
+							if ( conv ) {
+
+								// Condense equivalence converters
+								if ( conv === true ) {
+									conv = converters[ conv2 ];
+
+								// Otherwise, insert the intermediate dataType
+								} else if ( converters[ conv2 ] !== true ) {
+									current = tmp[ 0 ];
+									dataTypes.unshift( tmp[ 1 ] );
+								}
+								break;
+							}
+						}
+					}
+				}
+
+				// Apply converter (if not an equivalence)
+				if ( conv !== true ) {
+
+					// Unless errors are allowed to bubble, catch and return them
+					if ( conv && s.throws ) {
+						response = conv( response );
+					} else {
+						try {
+							response = conv( response );
+						} catch ( e ) {
+							return {
+								state: "parsererror",
+								error: conv ? e : "No conversion from " + prev + " to " + current
+							};
+						}
+					}
+				}
+			}
+		}
+	}
+
+	return { state: "success", data: response };
+}
+
+jQuery.extend( {
+
+	// Counter for holding the number of active queries
+	active: 0,
+
+	// Last-Modified header cache for next request
+	lastModified: {},
+	etag: {},
+
+	ajaxSettings: {
+		url: location.href,
+		type: "GET",
+		isLocal: rlocalProtocol.test( location.protocol ),
+		global: true,
+		processData: true,
+		async: true,
+		contentType: "application/x-www-form-urlencoded; charset=UTF-8",
+
+		/*
+		timeout: 0,
+		data: null,
+		dataType: null,
+		username: null,
+		password: null,
+		cache: null,
+		throws: false,
+		traditional: false,
+		headers: {},
+		*/
+
+		accepts: {
+			"*": allTypes,
+			text: "text/plain",
+			html: "text/html",
+			xml: "application/xml, text/xml",
+			json: "application/json, text/javascript"
+		},
+
+		contents: {
+			xml: /\bxml\b/,
+			html: /\bhtml/,
+			json: /\bjson\b/
+		},
+
+		responseFields: {
+			xml: "responseXML",
+			text: "responseText",
+			json: "responseJSON"
+		},
+
+		// Data converters
+		// Keys separate source (or catchall "*") and destination types with a single space
+		converters: {
+
+			// Convert anything to text
+			"* text": String,
+
+			// Text to html (true = no transformation)
+			"text html": true,
+
+			// Evaluate text as a json expression
+			"text json": JSON.parse,
+
+			// Parse text as xml
+			"text xml": jQuery.parseXML
+		},
+
+		// For options that shouldn't be deep extended:
+		// you can add your own custom options here if
+		// and when you create one that shouldn't be
+		// deep extended (see ajaxExtend)
+		flatOptions: {
+			url: true,
+			context: true
+		}
+	},
+
+	// Creates a full fledged settings object into target
+	// with both ajaxSettings and settings fields.
+	// If target is omitted, writes into ajaxSettings.
+	ajaxSetup: function( target, settings ) {
+		return settings ?
+
+			// Building a settings object
+			ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) :
+
+			// Extending ajaxSettings
+			ajaxExtend( jQuery.ajaxSettings, target );
+	},
+
+	ajaxPrefilter: addToPrefiltersOrTransports( prefilters ),
+	ajaxTransport: addToPrefiltersOrTransports( transports ),
+
+	// Main method
+	ajax: function( url, options ) {
+
+		// If url is an object, simulate pre-1.5 signature
+		if ( typeof url === "object" ) {
+			options = url;
+			url = undefined;
+		}
+
+		// Force options to be an object
+		options = options || {};
+
+		var transport,
+
+			// URL without anti-cache param
+			cacheURL,
+
+			// Response headers
+			responseHeadersString,
+			responseHeaders,
+
+			// timeout handle
+			timeoutTimer,
+
+			// Url cleanup var
+			urlAnchor,
+
+			// Request state (becomes false upon send and true upon completion)
+			completed,
+
+			// To know if global events are to be dispatched
+			fireGlobals,
+
+			// Loop variable
+			i,
+
+			// uncached part of the url
+			uncached,
+
+			// Create the final options object
+			s = jQuery.ajaxSetup( {}, options ),
+
+			// Callbacks context
+			callbackContext = s.context || s,
+
+			// Context for global events is callbackContext if it is a DOM node or jQuery collection
+			globalEventContext = s.context &&
+				( callbackContext.nodeType || callbackContext.jquery ) ?
+				jQuery( callbackContext ) :
+				jQuery.event,
+
+			// Deferreds
+			deferred = jQuery.Deferred(),
+			completeDeferred = jQuery.Callbacks( "once memory" ),
+
+			// Status-dependent callbacks
+			statusCode = s.statusCode || {},
+
+			// Headers (they are sent all at once)
+			requestHeaders = {},
+			requestHeadersNames = {},
+
+			// Default abort message
+			strAbort = "canceled",
+
+			// Fake xhr
+			jqXHR = {
+				readyState: 0,
+
+				// Builds headers hashtable if needed
+				getResponseHeader: function( key ) {
+					var match;
+					if ( completed ) {
+						if ( !responseHeaders ) {
+							responseHeaders = {};
+							while ( ( match = rheaders.exec( responseHeadersString ) ) ) {
+								responseHeaders[ match[ 1 ].toLowerCase() + " " ] =
+									( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] )
+										.concat( match[ 2 ] );
+							}
+						}
+						match = responseHeaders[ key.toLowerCase() + " " ];
+					}
+					return match == null ? null : match.join( ", " );
+				},
+
+				// Raw string
+				getAllResponseHeaders: function() {
+					return completed ? responseHeadersString : null;
+				},
+
+				// Caches the header
+				setRequestHeader: function( name, value ) {
+					if ( completed == null ) {
+						name = requestHeadersNames[ name.toLowerCase() ] =
+							requestHeadersNames[ name.toLowerCase() ] || name;
+						requestHeaders[ name ] = value;
+					}
+					return this;
+				},
+
+				// Overrides response content-type header
+				overrideMimeType: function( type ) {
+					if ( completed == null ) {
+						s.mimeType = type;
+					}
+					return this;
+				},
+
+				// Status-dependent callbacks
+				statusCode: function( map ) {
+					var code;
+					if ( map ) {
+						if ( completed ) {
+
+							// Execute the appropriate callbacks
+							jqXHR.always( map[ jqXHR.status ] );
+						} else {
+
+							// Lazy-add the new callbacks in a way that preserves old ones
+							for ( code in map ) {
+								statusCode[ code ] = [ statusCode[ code ], map[ code ] ];
+							}
+						}
+					}
+					return this;
+				},
+
+				// Cancel the request
+				abort: function( statusText ) {
+					var finalText = statusText || strAbort;
+					if ( transport ) {
+						transport.abort( finalText );
+					}
+					done( 0, finalText );
+					return this;
+				}
+			};
+
+		// Attach deferreds
+		deferred.promise( jqXHR );
+
+		// Add protocol if not provided (prefilters might expect it)
+		// Handle falsy url in the settings object (#10093: consistency with old signature)
+		// We also use the url parameter if available
+		s.url = ( ( url || s.url || location.href ) + "" )
+			.replace( rprotocol, location.protocol + "//" );
+
+		// Alias method option to type as per ticket #12004
+		s.type = options.method || options.type || s.method || s.type;
+
+		// Extract dataTypes list
+		s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ];
+
+		// A cross-domain request is in order when the origin doesn't match the current origin.
+		if ( s.crossDomain == null ) {
+			urlAnchor = document.createElement( "a" );
+
+			// Support: IE <=8 - 11, Edge 12 - 15
+			// IE throws exception on accessing the href property if url is malformed,
+			// e.g. http://example.com:80x/
+			try {
+				urlAnchor.href = s.url;
+
+				// Support: IE <=8 - 11 only
+				// Anchor's host property isn't correctly set when s.url is relative
+				urlAnchor.href = urlAnchor.href;
+				s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !==
+					urlAnchor.protocol + "//" + urlAnchor.host;
+			} catch ( e ) {
+
+				// If there is an error parsing the URL, assume it is crossDomain,
+				// it can be rejected by the transport if it is invalid
+				s.crossDomain = true;
+			}
+		}
+
+		// Convert data if not already a string
+		if ( s.data && s.processData && typeof s.data !== "string" ) {
+			s.data = jQuery.param( s.data, s.traditional );
+		}
+
+		// Apply prefilters
+		inspectPrefiltersOrTransports( prefilters, s, options, jqXHR );
+
+		// If request was aborted inside a prefilter, stop there
+		if ( completed ) {
+			return jqXHR;
+		}
+
+		// We can fire global events as of now if asked to
+		// Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118)
+		fireGlobals = jQuery.event && s.global;
+
+		// Watch for a new set of requests
+		if ( fireGlobals && jQuery.active++ === 0 ) {
+			jQuery.event.trigger( "ajaxStart" );
+		}
+
+		// Uppercase the type
+		s.type = s.type.toUpperCase();
+
+		// Determine if request has content
+		s.hasContent = !rnoContent.test( s.type );
+
+		// Save the URL in case we're toying with the If-Modified-Since
+		// and/or If-None-Match header later on
+		// Remove hash to simplify url manipulation
+		cacheURL = s.url.replace( rhash, "" );
+
+		// More options handling for requests with no content
+		if ( !s.hasContent ) {
+
+			// Remember the hash so we can put it back
+			uncached = s.url.slice( cacheURL.length );
+
+			// If data is available and should be processed, append data to url
+			if ( s.data && ( s.processData || typeof s.data === "string" ) ) {
+				cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data;
+
+				// #9682: remove data so that it's not used in an eventual retry
+				delete s.data;
+			}
+
+			// Add or update anti-cache param if needed
+			if ( s.cache === false ) {
+				cacheURL = cacheURL.replace( rantiCache, "$1" );
+				uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) +
+					uncached;
+			}
+
+			// Put hash and anti-cache on the URL that will be requested (gh-1732)
+			s.url = cacheURL + uncached;
+
+		// Change '%20' to '+' if this is encoded form body content (gh-2658)
+		} else if ( s.data && s.processData &&
+			( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) {
+			s.data = s.data.replace( r20, "+" );
+		}
+
+		// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
+		if ( s.ifModified ) {
+			if ( jQuery.lastModified[ cacheURL ] ) {
+				jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] );
+			}
+			if ( jQuery.etag[ cacheURL ] ) {
+				jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] );
+			}
+		}
+
+		// Set the correct header, if data is being sent
+		if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) {
+			jqXHR.setRequestHeader( "Content-Type", s.contentType );
+		}
+
+		// Set the Accepts header for the server, depending on the dataType
+		jqXHR.setRequestHeader(
+			"Accept",
+			s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ?
+				s.accepts[ s.dataTypes[ 0 ] ] +
+					( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) :
+				s.accepts[ "*" ]
+		);
+
+		// Check for headers option
+		for ( i in s.headers ) {
+			jqXHR.setRequestHeader( i, s.headers[ i ] );
+		}
+
+		// Allow custom headers/mimetypes and early abort
+		if ( s.beforeSend &&
+			( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) {
+
+			// Abort if not done already and return
+			return jqXHR.abort();
+		}
+
+		// Aborting is no longer a cancellation
+		strAbort = "abort";
+
+		// Install callbacks on deferreds
+		completeDeferred.add( s.complete );
+		jqXHR.done( s.success );
+		jqXHR.fail( s.error );
+
+		// Get transport
+		transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR );
+
+		// If no transport, we auto-abort
+		if ( !transport ) {
+			done( -1, "No Transport" );
+		} else {
+			jqXHR.readyState = 1;
+
+			// Send global event
+			if ( fireGlobals ) {
+				globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] );
+			}
+
+			// If request was aborted inside ajaxSend, stop there
+			if ( completed ) {
+				return jqXHR;
+			}
+
+			// Timeout
+			if ( s.async && s.timeout > 0 ) {
+				timeoutTimer = window.setTimeout( function() {
+					jqXHR.abort( "timeout" );
+				}, s.timeout );
+			}
+
+			try {
+				completed = false;
+				transport.send( requestHeaders, done );
+			} catch ( e ) {
+
+				// Rethrow post-completion exceptions
+				if ( completed ) {
+					throw e;
+				}
+
+				// Propagate others as results
+				done( -1, e );
+			}
+		}
+
+		// Callback for when everything is done
+		function done( status, nativeStatusText, responses, headers ) {
+			var isSuccess, success, error, response, modified,
+				statusText = nativeStatusText;
+
+			// Ignore repeat invocations
+			if ( completed ) {
+				return;
+			}
+
+			completed = true;
+
+			// Clear timeout if it exists
+			if ( timeoutTimer ) {
+				window.clearTimeout( timeoutTimer );
+			}
+
+			// Dereference transport for early garbage collection
+			// (no matter how long the jqXHR object will be used)
+			transport = undefined;
+
+			// Cache response headers
+			responseHeadersString = headers || "";
+
+			// Set readyState
+			jqXHR.readyState = status > 0 ? 4 : 0;
+
+			// Determine if successful
+			isSuccess = status >= 200 && status < 300 || status === 304;
+
+			// Get response data
+			if ( responses ) {
+				response = ajaxHandleResponses( s, jqXHR, responses );
+			}
+
+			// Use a noop converter for missing script but not if jsonp
+			if ( !isSuccess &&
+				jQuery.inArray( "script", s.dataTypes ) > -1 &&
+				jQuery.inArray( "json", s.dataTypes ) < 0 ) {
+				s.converters[ "text script" ] = function() {};
+			}
+
+			// Convert no matter what (that way responseXXX fields are always set)
+			response = ajaxConvert( s, response, jqXHR, isSuccess );
+
+			// If successful, handle type chaining
+			if ( isSuccess ) {
+
+				// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
+				if ( s.ifModified ) {
+					modified = jqXHR.getResponseHeader( "Last-Modified" );
+					if ( modified ) {
+						jQuery.lastModified[ cacheURL ] = modified;
+					}
+					modified = jqXHR.getResponseHeader( "etag" );
+					if ( modified ) {
+						jQuery.etag[ cacheURL ] = modified;
+					}
+				}
+
+				// if no content
+				if ( status === 204 || s.type === "HEAD" ) {
+					statusText = "nocontent";
+
+				// if not modified
+				} else if ( status === 304 ) {
+					statusText = "notmodified";
+
+				// If we have data, let's convert it
+				} else {
+					statusText = response.state;
+					success = response.data;
+					error = response.error;
+					isSuccess = !error;
+				}
+			} else {
+
+				// Extract error from statusText and normalize for non-aborts
+				error = statusText;
+				if ( status || !statusText ) {
+					statusText = "error";
+					if ( status < 0 ) {
+						status = 0;
+					}
+				}
+			}
+
+			// Set data for the fake xhr object
+			jqXHR.status = status;
+			jqXHR.statusText = ( nativeStatusText || statusText ) + "";
+
+			// Success/Error
+			if ( isSuccess ) {
+				deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] );
+			} else {
+				deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] );
+			}
+
+			// Status-dependent callbacks
+			jqXHR.statusCode( statusCode );
+			statusCode = undefined;
+
+			if ( fireGlobals ) {
+				globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError",
+					[ jqXHR, s, isSuccess ? success : error ] );
+			}
+
+			// Complete
+			completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] );
+
+			if ( fireGlobals ) {
+				globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] );
+
+				// Handle the global AJAX counter
+				if ( !( --jQuery.active ) ) {
+					jQuery.event.trigger( "ajaxStop" );
+				}
+			}
+		}
+
+		return jqXHR;
+	},
+
+	getJSON: function( url, data, callback ) {
+		return jQuery.get( url, data, callback, "json" );
+	},
+
+	getScript: function( url, callback ) {
+		return jQuery.get( url, undefined, callback, "script" );
+	}
+} );
+
+jQuery.each( [ "get", "post" ], function( _i, method ) {
+	jQuery[ method ] = function( url, data, callback, type ) {
+
+		// Shift arguments if data argument was omitted
+		if ( isFunction( data ) ) {
+			type = type || callback;
+			callback = data;
+			data = undefined;
+		}
+
+		// The url can be an options object (which then must have .url)
+		return jQuery.ajax( jQuery.extend( {
+			url: url,
+			type: method,
+			dataType: type,
+			data: data,
+			success: callback
+		}, jQuery.isPlainObject( url ) && url ) );
+	};
+} );
+
+jQuery.ajaxPrefilter( function( s ) {
+	var i;
+	for ( i in s.headers ) {
+		if ( i.toLowerCase() === "content-type" ) {
+			s.contentType = s.headers[ i ] || "";
+		}
+	}
+} );
+
+
+jQuery._evalUrl = function( url, options, doc ) {
+	return jQuery.ajax( {
+		url: url,
+
+		// Make this explicit, since user can override this through ajaxSetup (#11264)
+		type: "GET",
+		dataType: "script",
+		cache: true,
+		async: false,
+		global: false,
+
+		// Only evaluate the response if it is successful (gh-4126)
+		// dataFilter is not invoked for failure responses, so using it instead
+		// of the default converter is kludgy but it works.
+		converters: {
+			"text script": function() {}
+		},
+		dataFilter: function( response ) {
+			jQuery.globalEval( response, options, doc );
+		}
+	} );
+};
+
+
+jQuery.fn.extend( {
+	wrapAll: function( html ) {
+		var wrap;
+
+		if ( this[ 0 ] ) {
+			if ( isFunction( html ) ) {
+				html = html.call( this[ 0 ] );
+			}
+
+			// The elements to wrap the target around
+			wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true );
+
+			if ( this[ 0 ].parentNode ) {
+				wrap.insertBefore( this[ 0 ] );
+			}
+
+			wrap.map( function() {
+				var elem = this;
+
+				while ( elem.firstElementChild ) {
+					elem = elem.firstElementChild;
+				}
+
+				return elem;
+			} ).append( this );
+		}
+
+		return this;
+	},
+
+	wrapInner: function( html ) {
+		if ( isFunction( html ) ) {
+			return this.each( function( i ) {
+				jQuery( this ).wrapInner( html.call( this, i ) );
+			} );
+		}
+
+		return this.each( function() {
+			var self = jQuery( this ),
+				contents = self.contents();
+
+			if ( contents.length ) {
+				contents.wrapAll( html );
+
+			} else {
+				self.append( html );
+			}
+		} );
+	},
+
+	wrap: function( html ) {
+		var htmlIsFunction = isFunction( html );
+
+		return this.each( function( i ) {
+			jQuery( this ).wrapAll( htmlIsFunction ? html.call( this, i ) : html );
+		} );
+	},
+
+	unwrap: function( selector ) {
+		this.parent( selector ).not( "body" ).each( function() {
+			jQuery( this ).replaceWith( this.childNodes );
+		} );
+		return this;
+	}
+} );
+
+
+jQuery.expr.pseudos.hidden = function( elem ) {
+	return !jQuery.expr.pseudos.visible( elem );
+};
+jQuery.expr.pseudos.visible = function( elem ) {
+	return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length );
+};
+
+
+
+
+jQuery.ajaxSettings.xhr = function() {
+	try {
+		return new window.XMLHttpRequest();
+	} catch ( e ) {}
+};
+
+var xhrSuccessStatus = {
+
+		// File protocol always yields status code 0, assume 200
+		0: 200,
+
+		// Support: IE <=9 only
+		// #1450: sometimes IE returns 1223 when it should be 204
+		1223: 204
+	},
+	xhrSupported = jQuery.ajaxSettings.xhr();
+
+support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported );
+support.ajax = xhrSupported = !!xhrSupported;
+
+jQuery.ajaxTransport( function( options ) {
+	var callback, errorCallback;
+
+	// Cross domain only allowed if supported through XMLHttpRequest
+	if ( support.cors || xhrSupported && !options.crossDomain ) {
+		return {
+			send: function( headers, complete ) {
+				var i,
+					xhr = options.xhr();
+
+				xhr.open(
+					options.type,
+					options.url,
+					options.async,
+					options.username,
+					options.password
+				);
+
+				// Apply custom fields if provided
+				if ( options.xhrFields ) {
+					for ( i in options.xhrFields ) {
+						xhr[ i ] = options.xhrFields[ i ];
+					}
+				}
+
+				// Override mime type if needed
+				if ( options.mimeType && xhr.overrideMimeType ) {
+					xhr.overrideMimeType( options.mimeType );
+				}
+
+				// X-Requested-With header
+				// For cross-domain requests, seeing as conditions for a preflight are
+				// akin to a jigsaw puzzle, we simply never set it to be sure.
+				// (it can always be set on a per-request basis or even using ajaxSetup)
+				// For same-domain requests, won't change header if already provided.
+				if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) {
+					headers[ "X-Requested-With" ] = "XMLHttpRequest";
+				}
+
+				// Set headers
+				for ( i in headers ) {
+					xhr.setRequestHeader( i, headers[ i ] );
+				}
+
+				// Callback
+				callback = function( type ) {
+					return function() {
+						if ( callback ) {
+							callback = errorCallback = xhr.onload =
+								xhr.onerror = xhr.onabort = xhr.ontimeout =
+									xhr.onreadystatechange = null;
+
+							if ( type === "abort" ) {
+								xhr.abort();
+							} else if ( type === "error" ) {
+
+								// Support: IE <=9 only
+								// On a manual native abort, IE9 throws
+								// errors on any property access that is not readyState
+								if ( typeof xhr.status !== "number" ) {
+									complete( 0, "error" );
+								} else {
+									complete(
+
+										// File: protocol always yields status 0; see #8605, #14207
+										xhr.status,
+										xhr.statusText
+									);
+								}
+							} else {
+								complete(
+									xhrSuccessStatus[ xhr.status ] || xhr.status,
+									xhr.statusText,
+
+									// Support: IE <=9 only
+									// IE9 has no XHR2 but throws on binary (trac-11426)
+									// For XHR2 non-text, let the caller handle it (gh-2498)
+									( xhr.responseType || "text" ) !== "text"  ||
+									typeof xhr.responseText !== "string" ?
+										{ binary: xhr.response } :
+										{ text: xhr.responseText },
+									xhr.getAllResponseHeaders()
+								);
+							}
+						}
+					};
+				};
+
+				// Listen to events
+				xhr.onload = callback();
+				errorCallback = xhr.onerror = xhr.ontimeout = callback( "error" );
+
+				// Support: IE 9 only
+				// Use onreadystatechange to replace onabort
+				// to handle uncaught aborts
+				if ( xhr.onabort !== undefined ) {
+					xhr.onabort = errorCallback;
+				} else {
+					xhr.onreadystatechange = function() {
+
+						// Check readyState before timeout as it changes
+						if ( xhr.readyState === 4 ) {
+
+							// Allow onerror to be called first,
+							// but that will not handle a native abort
+							// Also, save errorCallback to a variable
+							// as xhr.onerror cannot be accessed
+							window.setTimeout( function() {
+								if ( callback ) {
+									errorCallback();
+								}
+							} );
+						}
+					};
+				}
+
+				// Create the abort callback
+				callback = callback( "abort" );
+
+				try {
+
+					// Do send the request (this may raise an exception)
+					xhr.send( options.hasContent && options.data || null );
+				} catch ( e ) {
+
+					// #14683: Only rethrow if this hasn't been notified as an error yet
+					if ( callback ) {
+						throw e;
+					}
+				}
+			},
+
+			abort: function() {
+				if ( callback ) {
+					callback();
+				}
+			}
+		};
+	}
+} );
+
+
+
+
+// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432)
+jQuery.ajaxPrefilter( function( s ) {
+	if ( s.crossDomain ) {
+		s.contents.script = false;
+	}
+} );
+
+// Install script dataType
+jQuery.ajaxSetup( {
+	accepts: {
+		script: "text/javascript, application/javascript, " +
+			"application/ecmascript, application/x-ecmascript"
+	},
+	contents: {
+		script: /\b(?:java|ecma)script\b/
+	},
+	converters: {
+		"text script": function( text ) {
+			jQuery.globalEval( text );
+			return text;
+		}
+	}
+} );
+
+// Handle cache's special case and crossDomain
+jQuery.ajaxPrefilter( "script", function( s ) {
+	if ( s.cache === undefined ) {
+		s.cache = false;
+	}
+	if ( s.crossDomain ) {
+		s.type = "GET";
+	}
+} );
+
+// Bind script tag hack transport
+jQuery.ajaxTransport( "script", function( s ) {
+
+	// This transport only deals with cross domain or forced-by-attrs requests
+	if ( s.crossDomain || s.scriptAttrs ) {
+		var script, callback;
+		return {
+			send: function( _, complete ) {
+				script = jQuery( "<script>" )
+					.attr( s.scriptAttrs || {} )
+					.prop( { charset: s.scriptCharset, src: s.url } )
+					.on( "load error", callback = function( evt ) {
+						script.remove();
+						callback = null;
+						if ( evt ) {
+							complete( evt.type === "error" ? 404 : 200, evt.type );
+						}
+					} );
+
+				// Use native DOM manipulation to avoid our domManip AJAX trickery
+				document.head.appendChild( script[ 0 ] );
+			},
+			abort: function() {
+				if ( callback ) {
+					callback();
+				}
+			}
+		};
+	}
+} );
+
+
+
+
+var oldCallbacks = [],
+	rjsonp = /(=)\?(?=&|$)|\?\?/;
+
+// Default jsonp settings
+jQuery.ajaxSetup( {
+	jsonp: "callback",
+	jsonpCallback: function() {
+		var callback = oldCallbacks.pop() || ( jQuery.expando + "_" + ( nonce.guid++ ) );
+		this[ callback ] = true;
+		return callback;
+	}
+} );
+
+// Detect, normalize options and install callbacks for jsonp requests
+jQuery.ajaxPrefilter( "json jsonp", function( s, originalSettings, jqXHR ) {
+
+	var callbackName, overwritten, responseContainer,
+		jsonProp = s.jsonp !== false && ( rjsonp.test( s.url ) ?
+			"url" :
+			typeof s.data === "string" &&
+				( s.contentType || "" )
+					.indexOf( "application/x-www-form-urlencoded" ) === 0 &&
+				rjsonp.test( s.data ) && "data"
+		);
+
+	// Handle iff the expected data type is "jsonp" or we have a parameter to set
+	if ( jsonProp || s.dataTypes[ 0 ] === "jsonp" ) {
+
+		// Get callback name, remembering preexisting value associated with it
+		callbackName = s.jsonpCallback = isFunction( s.jsonpCallback ) ?
+			s.jsonpCallback() :
+			s.jsonpCallback;
+
+		// Insert callback into url or form data
+		if ( jsonProp ) {
+			s[ jsonProp ] = s[ jsonProp ].replace( rjsonp, "$1" + callbackName );
+		} else if ( s.jsonp !== false ) {
+			s.url += ( rquery.test( s.url ) ? "&" : "?" ) + s.jsonp + "=" + callbackName;
+		}
+
+		// Use data converter to retrieve json after script execution
+		s.converters[ "script json" ] = function() {
+			if ( !responseContainer ) {
+				jQuery.error( callbackName + " was not called" );
+			}
+			return responseContainer[ 0 ];
+		};
+
+		// Force json dataType
+		s.dataTypes[ 0 ] = "json";
+
+		// Install callback
+		overwritten = window[ callbackName ];
+		window[ callbackName ] = function() {
+			responseContainer = arguments;
+		};
+
+		// Clean-up function (fires after converters)
+		jqXHR.always( function() {
+
+			// If previous value didn't exist - remove it
+			if ( overwritten === undefined ) {
+				jQuery( window ).removeProp( callbackName );
+
+			// Otherwise restore preexisting value
+			} else {
+				window[ callbackName ] = overwritten;
+			}
+
+			// Save back as free
+			if ( s[ callbackName ] ) {
+
+				// Make sure that re-using the options doesn't screw things around
+				s.jsonpCallback = originalSettings.jsonpCallback;
+
+				// Save the callback name for future use
+				oldCallbacks.push( callbackName );
+			}
+
+			// Call if it was a function and we have a response
+			if ( responseContainer && isFunction( overwritten ) ) {
+				overwritten( responseContainer[ 0 ] );
+			}
+
+			responseContainer = overwritten = undefined;
+		} );
+
+		// Delegate to script
+		return "script";
+	}
+} );
+
+
+
+
+// Support: Safari 8 only
+// In Safari 8 documents created via document.implementation.createHTMLDocument
+// collapse sibling forms: the second one becomes a child of the first one.
+// Because of that, this security measure has to be disabled in Safari 8.
+// https://bugs.webkit.org/show_bug.cgi?id=137337
+support.createHTMLDocument = ( function() {
+	var body = document.implementation.createHTMLDocument( "" ).body;
+	body.innerHTML = "<form></form><form></form>";
+	return body.childNodes.length === 2;
+} )();
+
+
+// Argument "data" should be string of html
+// context (optional): If specified, the fragment will be created in this context,
+// defaults to document
+// keepScripts (optional): If true, will include scripts passed in the html string
+jQuery.parseHTML = function( data, context, keepScripts ) {
+	if ( typeof data !== "string" ) {
+		return [];
+	}
+	if ( typeof context === "boolean" ) {
+		keepScripts = context;
+		context = false;
+	}
+
+	var base, parsed, scripts;
+
+	if ( !context ) {
+
+		// Stop scripts or inline event handlers from being executed immediately
+		// by using document.implementation
+		if ( support.createHTMLDocument ) {
+			context = document.implementation.createHTMLDocument( "" );
+
+			// Set the base href for the created document
+			// so any parsed elements with URLs
+			// are based on the document's URL (gh-2965)
+			base = context.createElement( "base" );
+			base.href = document.location.href;
+			context.head.appendChild( base );
+		} else {
+			context = document;
+		}
+	}
+
+	parsed = rsingleTag.exec( data );
+	scripts = !keepScripts && [];
+
+	// Single tag
+	if ( parsed ) {
+		return [ context.createElement( parsed[ 1 ] ) ];
+	}
+
+	parsed = buildFragment( [ data ], context, scripts );
+
+	if ( scripts && scripts.length ) {
+		jQuery( scripts ).remove();
+	}
+
+	return jQuery.merge( [], parsed.childNodes );
+};
+
+
+/**
+ * Load a url into a page
+ */
+jQuery.fn.load = function( url, params, callback ) {
+	var selector, type, response,
+		self = this,
+		off = url.indexOf( " " );
+
+	if ( off > -1 ) {
+		selector = stripAndCollapse( url.slice( off ) );
+		url = url.slice( 0, off );
+	}
+
+	// If it's a function
+	if ( isFunction( params ) ) {
+
+		// We assume that it's the callback
+		callback = params;
+		params = undefined;
+
+	// Otherwise, build a param string
+	} else if ( params && typeof params === "object" ) {
+		type = "POST";
+	}
+
+	// If we have elements to modify, make the request
+	if ( self.length > 0 ) {
+		jQuery.ajax( {
+			url: url,
+
+			// If "type" variable is undefined, then "GET" method will be used.
+			// Make value of this field explicit since
+			// user can override it through ajaxSetup method
+			type: type || "GET",
+			dataType: "html",
+			data: params
+		} ).done( function( responseText ) {
+
+			// Save response for use in complete callback
+			response = arguments;
+
+			self.html( selector ?
+
+				// If a selector was specified, locate the right elements in a dummy div
+				// Exclude scripts to avoid IE 'Permission Denied' errors
+				jQuery( "<div>" ).append( jQuery.parseHTML( responseText ) ).find( selector ) :
+
+				// Otherwise use the full result
+				responseText );
+
+		// If the request succeeds, this function gets "data", "status", "jqXHR"
+		// but they are ignored because response was set above.
+		// If it fails, this function gets "jqXHR", "status", "error"
+		} ).always( callback && function( jqXHR, status ) {
+			self.each( function() {
+				callback.apply( this, response || [ jqXHR.responseText, status, jqXHR ] );
+			} );
+		} );
+	}
+
+	return this;
+};
+
+
+
+
+jQuery.expr.pseudos.animated = function( elem ) {
+	return jQuery.grep( jQuery.timers, function( fn ) {
+		return elem === fn.elem;
+	} ).length;
+};
+
+
+
+
+jQuery.offset = {
+	setOffset: function( elem, options, i ) {
+		var curPosition, curLeft, curCSSTop, curTop, curOffset, curCSSLeft, calculatePosition,
+			position = jQuery.css( elem, "position" ),
+			curElem = jQuery( elem ),
+			props = {};
+
+		// Set position first, in-case top/left are set even on static elem
+		if ( position === "static" ) {
+			elem.style.position = "relative";
+		}
+
+		curOffset = curElem.offset();
+		curCSSTop = jQuery.css( elem, "top" );
+		curCSSLeft = jQuery.css( elem, "left" );
+		calculatePosition = ( position === "absolute" || position === "fixed" ) &&
+			( curCSSTop + curCSSLeft ).indexOf( "auto" ) > -1;
+
+		// Need to be able to calculate position if either
+		// top or left is auto and position is either absolute or fixed
+		if ( calculatePosition ) {
+			curPosition = curElem.position();
+			curTop = curPosition.top;
+			curLeft = curPosition.left;
+
+		} else {
+			curTop = parseFloat( curCSSTop ) || 0;
+			curLeft = parseFloat( curCSSLeft ) || 0;
+		}
+
+		if ( isFunction( options ) ) {
+
+			// Use jQuery.extend here to allow modification of coordinates argument (gh-1848)
+			options = options.call( elem, i, jQuery.extend( {}, curOffset ) );
+		}
+
+		if ( options.top != null ) {
+			props.top = ( options.top - curOffset.top ) + curTop;
+		}
+		if ( options.left != null ) {
+			props.left = ( options.left - curOffset.left ) + curLeft;
+		}
+
+		if ( "using" in options ) {
+			options.using.call( elem, props );
+
+		} else {
+			curElem.css( props );
+		}
+	}
+};
+
+jQuery.fn.extend( {
+
+	// offset() relates an element's border box to the document origin
+	offset: function( options ) {
+
+		// Preserve chaining for setter
+		if ( arguments.length ) {
+			return options === undefined ?
+				this :
+				this.each( function( i ) {
+					jQuery.offset.setOffset( this, options, i );
+				} );
+		}
+
+		var rect, win,
+			elem = this[ 0 ];
+
+		if ( !elem ) {
+			return;
+		}
+
+		// Return zeros for disconnected and hidden (display: none) elements (gh-2310)
+		// Support: IE <=11 only
+		// Running getBoundingClientRect on a
+		// disconnected node in IE throws an error
+		if ( !elem.getClientRects().length ) {
+			return { top: 0, left: 0 };
+		}
+
+		// Get document-relative position by adding viewport scroll to viewport-relative gBCR
+		rect = elem.getBoundingClientRect();
+		win = elem.ownerDocument.defaultView;
+		return {
+			top: rect.top + win.pageYOffset,
+			left: rect.left + win.pageXOffset
+		};
+	},
+
+	// position() relates an element's margin box to its offset parent's padding box
+	// This corresponds to the behavior of CSS absolute positioning
+	position: function() {
+		if ( !this[ 0 ] ) {
+			return;
+		}
+
+		var offsetParent, offset, doc,
+			elem = this[ 0 ],
+			parentOffset = { top: 0, left: 0 };
+
+		// position:fixed elements are offset from the viewport, which itself always has zero offset
+		if ( jQuery.css( elem, "position" ) === "fixed" ) {
+
+			// Assume position:fixed implies availability of getBoundingClientRect
+			offset = elem.getBoundingClientRect();
+
+		} else {
+			offset = this.offset();
+
+			// Account for the *real* offset parent, which can be the document or its root element
+			// when a statically positioned element is identified
+			doc = elem.ownerDocument;
+			offsetParent = elem.offsetParent || doc.documentElement;
+			while ( offsetParent &&
+				( offsetParent === doc.body || offsetParent === doc.documentElement ) &&
+				jQuery.css( offsetParent, "position" ) === "static" ) {
+
+				offsetParent = offsetParent.parentNode;
+			}
+			if ( offsetParent && offsetParent !== elem && offsetParent.nodeType === 1 ) {
+
+				// Incorporate borders into its offset, since they are outside its content origin
+				parentOffset = jQuery( offsetParent ).offset();
+				parentOffset.top += jQuery.css( offsetParent, "borderTopWidth", true );
+				parentOffset.left += jQuery.css( offsetParent, "borderLeftWidth", true );
+			}
+		}
+
+		// Subtract parent offsets and element margins
+		return {
+			top: offset.top - parentOffset.top - jQuery.css( elem, "marginTop", true ),
+			left: offset.left - parentOffset.left - jQuery.css( elem, "marginLeft", true )
+		};
+	},
+
+	// This method will return documentElement in the following cases:
+	// 1) For the element inside the iframe without offsetParent, this method will return
+	//    documentElement of the parent window
+	// 2) For the hidden or detached element
+	// 3) For body or html element, i.e. in case of the html node - it will return itself
+	//
+	// but those exceptions were never presented as a real life use-cases
+	// and might be considered as more preferable results.
+	//
+	// This logic, however, is not guaranteed and can change at any point in the future
+	offsetParent: function() {
+		return this.map( function() {
+			var offsetParent = this.offsetParent;
+
+			while ( offsetParent && jQuery.css( offsetParent, "position" ) === "static" ) {
+				offsetParent = offsetParent.offsetParent;
+			}
+
+			return offsetParent || documentElement;
+		} );
+	}
+} );
+
+// Create scrollLeft and scrollTop methods
+jQuery.each( { scrollLeft: "pageXOffset", scrollTop: "pageYOffset" }, function( method, prop ) {
+	var top = "pageYOffset" === prop;
+
+	jQuery.fn[ method ] = function( val ) {
+		return access( this, function( elem, method, val ) {
+
+			// Coalesce documents and windows
+			var win;
+			if ( isWindow( elem ) ) {
+				win = elem;
+			} else if ( elem.nodeType === 9 ) {
+				win = elem.defaultView;
+			}
+
+			if ( val === undefined ) {
+				return win ? win[ prop ] : elem[ method ];
+			}
+
+			if ( win ) {
+				win.scrollTo(
+					!top ? val : win.pageXOffset,
+					top ? val : win.pageYOffset
+				);
+
+			} else {
+				elem[ method ] = val;
+			}
+		}, method, val, arguments.length );
+	};
+} );
+
+// Support: Safari <=7 - 9.1, Chrome <=37 - 49
+// Add the top/left cssHooks using jQuery.fn.position
+// Webkit bug: https://bugs.webkit.org/show_bug.cgi?id=29084
+// Blink bug: https://bugs.chromium.org/p/chromium/issues/detail?id=589347
+// getComputedStyle returns percent when specified for top/left/bottom/right;
+// rather than make the css module depend on the offset module, just check for it here
+jQuery.each( [ "top", "left" ], function( _i, prop ) {
+	jQuery.cssHooks[ prop ] = addGetHookIf( support.pixelPosition,
+		function( elem, computed ) {
+			if ( computed ) {
+				computed = curCSS( elem, prop );
+
+				// If curCSS returns percentage, fallback to offset
+				return rnumnonpx.test( computed ) ?
+					jQuery( elem ).position()[ prop ] + "px" :
+					computed;
+			}
+		}
+	);
+} );
+
+
+// Create innerHeight, innerWidth, height, width, outerHeight and outerWidth methods
+jQuery.each( { Height: "height", Width: "width" }, function( name, type ) {
+	jQuery.each( {
+		padding: "inner" + name,
+		content: type,
+		"": "outer" + name
+	}, function( defaultExtra, funcName ) {
+
+		// Margin is only for outerHeight, outerWidth
+		jQuery.fn[ funcName ] = function( margin, value ) {
+			var chainable = arguments.length && ( defaultExtra || typeof margin !== "boolean" ),
+				extra = defaultExtra || ( margin === true || value === true ? "margin" : "border" );
+
+			return access( this, function( elem, type, value ) {
+				var doc;
+
+				if ( isWindow( elem ) ) {
+
+					// $( window ).outerWidth/Height return w/h including scrollbars (gh-1729)
+					return funcName.indexOf( "outer" ) === 0 ?
+						elem[ "inner" + name ] :
+						elem.document.documentElement[ "client" + name ];
+				}
+
+				// Get document width or height
+				if ( elem.nodeType === 9 ) {
+					doc = elem.documentElement;
+
+					// Either scroll[Width/Height] or offset[Width/Height] or client[Width/Height],
+					// whichever is greatest
+					return Math.max(
+						elem.body[ "scroll" + name ], doc[ "scroll" + name ],
+						elem.body[ "offset" + name ], doc[ "offset" + name ],
+						doc[ "client" + name ]
+					);
+				}
+
+				return value === undefined ?
+
+					// Get width or height on the element, requesting but not forcing parseFloat
+					jQuery.css( elem, type, extra ) :
+
+					// Set width or height on the element
+					jQuery.style( elem, type, value, extra );
+			}, type, chainable ? margin : undefined, chainable );
+		};
+	} );
+} );
+
+
+jQuery.each( [
+	"ajaxStart",
+	"ajaxStop",
+	"ajaxComplete",
+	"ajaxError",
+	"ajaxSuccess",
+	"ajaxSend"
+], function( _i, type ) {
+	jQuery.fn[ type ] = function( fn ) {
+		return this.on( type, fn );
+	};
+} );
+
+
+
+
+jQuery.fn.extend( {
+
+	bind: function( types, data, fn ) {
+		return this.on( types, null, data, fn );
+	},
+	unbind: function( types, fn ) {
+		return this.off( types, null, fn );
+	},
+
+	delegate: function( selector, types, data, fn ) {
+		return this.on( types, selector, data, fn );
+	},
+	undelegate: function( selector, types, fn ) {
+
+		// ( namespace ) or ( selector, types [, fn] )
+		return arguments.length === 1 ?
+			this.off( selector, "**" ) :
+			this.off( types, selector || "**", fn );
+	},
+
+	hover: function( fnOver, fnOut ) {
+		return this.mouseenter( fnOver ).mouseleave( fnOut || fnOver );
+	}
+} );
+
+jQuery.each(
+	( "blur focus focusin focusout resize scroll click dblclick " +
+	"mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave " +
+	"change select submit keydown keypress keyup contextmenu" ).split( " " ),
+	function( _i, name ) {
+
+		// Handle event binding
+		jQuery.fn[ name ] = function( data, fn ) {
+			return arguments.length > 0 ?
+				this.on( name, null, data, fn ) :
+				this.trigger( name );
+		};
+	}
+);
+
+
+
+
+// Support: Android <=4.0 only
+// Make sure we trim BOM and NBSP
+var rtrim = /^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g;
+
+// Bind a function to a context, optionally partially applying any
+// arguments.
+// jQuery.proxy is deprecated to promote standards (specifically Function#bind)
+// However, it is not slated for removal any time soon
+jQuery.proxy = function( fn, context ) {
+	var tmp, args, proxy;
+
+	if ( typeof context === "string" ) {
+		tmp = fn[ context ];
+		context = fn;
+		fn = tmp;
+	}
+
+	// Quick check to determine if target is callable, in the spec
+	// this throws a TypeError, but we will just return undefined.
+	if ( !isFunction( fn ) ) {
+		return undefined;
+	}
+
+	// Simulated bind
+	args = slice.call( arguments, 2 );
+	proxy = function() {
+		return fn.apply( context || this, args.concat( slice.call( arguments ) ) );
+	};
+
+	// Set the guid of unique handler to the same of original handler, so it can be removed
+	proxy.guid = fn.guid = fn.guid || jQuery.guid++;
+
+	return proxy;
+};
+
+jQuery.holdReady = function( hold ) {
+	if ( hold ) {
+		jQuery.readyWait++;
+	} else {
+		jQuery.ready( true );
+	}
+};
+jQuery.isArray = Array.isArray;
+jQuery.parseJSON = JSON.parse;
+jQuery.nodeName = nodeName;
+jQuery.isFunction = isFunction;
+jQuery.isWindow = isWindow;
+jQuery.camelCase = camelCase;
+jQuery.type = toType;
+
+jQuery.now = Date.now;
+
+jQuery.isNumeric = function( obj ) {
+
+	// As of jQuery 3.0, isNumeric is limited to
+	// strings and numbers (primitives or objects)
+	// that can be coerced to finite numbers (gh-2662)
+	var type = jQuery.type( obj );
+	return ( type === "number" || type === "string" ) &&
+
+		// parseFloat NaNs numeric-cast false positives ("")
+		// ...but misinterprets leading-number strings, particularly hex literals ("0x...")
+		// subtraction forces infinities to NaN
+		!isNaN( obj - parseFloat( obj ) );
+};
+
+jQuery.trim = function( text ) {
+	return text == null ?
+		"" :
+		( text + "" ).replace( rtrim, "" );
+};
+
+
+
+// Register as a named AMD module, since jQuery can be concatenated with other
+// files that may use define, but not via a proper concatenation script that
+// understands anonymous AMD modules. A named AMD is safest and most robust
+// way to register. Lowercase jquery is used because AMD module names are
+// derived from file names, and jQuery is normally delivered in a lowercase
+// file name. Do this after creating the global so that if an AMD module wants
+// to call noConflict to hide this version of jQuery, it will work.
+
+// Note that for maximum portability, libraries that are not jQuery should
+// declare themselves as anonymous modules, and avoid setting a global if an
+// AMD loader is present. jQuery is a special case. For more information, see
+// https://github.com/jrburke/requirejs/wiki/Updating-existing-libraries#wiki-anon
+
+if ( typeof define === "function" && define.amd ) {
+	define( "jquery", [], function() {
+		return jQuery;
+	} );
+}
+
+
+
+
+var
+
+	// Map over jQuery in case of overwrite
+	_jQuery = window.jQuery,
+
+	// Map over the $ in case of overwrite
+	_$ = window.$;
+
+jQuery.noConflict = function( deep ) {
+	if ( window.$ === jQuery ) {
+		window.$ = _$;
+	}
+
+	if ( deep && window.jQuery === jQuery ) {
+		window.jQuery = _jQuery;
+	}
+
+	return jQuery;
+};
+
+// Expose jQuery and $ identifiers, even in AMD
+// (#7102#comment:10, https://github.com/jquery/jquery/pull/557)
+// and CommonJS for browser emulators (#13566)
+if ( typeof noGlobal === "undefined" ) {
+	window.jQuery = window.$ = jQuery;
+}
+
+
+
+
+return jQuery;
+} );
diff --git a/_static/js/theme.js b/_static/js/theme.js
new file mode 100644
index 00000000..2defd10d
--- /dev/null
+++ b/_static/js/theme.js
@@ -0,0 +1,265 @@
+var jQuery = (typeof(window) != 'undefined') ? window.jQuery : require('jquery');
+
+// Sphinx theme nav state
+function ThemeNav () {
+
+    var nav = {
+        navBar: null,
+        win: null,
+        winScroll: false,
+        winResize: false,
+        linkScroll: false,
+        winPosition: 0,
+        winHeight: null,
+        docHeight: null,
+        isRunning: false
+    };
+
+    nav.enable = function (withStickyNav) {
+        var self = this;
+
+        // TODO this can likely be removed once the theme javascript is broken
+        // out from the RTD assets. This just ensures old projects that are
+        // calling `enable()` get the sticky menu on by default. All other cals
+        // to `enable` should include an argument for enabling the sticky menu.
+        if (typeof(withStickyNav) == 'undefined') {
+            withStickyNav = true;
+        }
+
+        if (self.isRunning) {
+            // Only allow enabling nav logic once
+            return;
+        }
+
+        self.isRunning = true;
+        jQuery(function ($) {
+            self.init($);
+
+            self.reset();
+            self.win.on('hashchange', self.reset);
+
+            if (withStickyNav) {
+                // Set scroll monitor
+                self.win.on('scroll', function () {
+                    if (!self.linkScroll) {
+                        if (!self.winScroll) {
+                            self.winScroll = true;
+                            requestAnimationFrame(function() { self.onScroll(); });
+                        }
+                    }
+                });
+            }
+
+            // Set resize monitor
+            self.win.on('resize', function () {
+                if (!self.winResize) {
+                    self.winResize = true;
+                    requestAnimationFrame(function() { self.onResize(); });
+                }
+            });
+
+            self.onResize();
+        });
+
+    };
+
+    // TODO remove this with a split in theme and Read the Docs JS logic as
+    // well, it's only here to support 0.3.0 installs of our theme.
+    nav.enableSticky = function() {
+        this.enable(true);
+    };
+
+    nav.init = function ($) {
+        var doc = $(document),
+            self = this;
+
+        this.navBar = $('div.wy-side-scroll:first');
+        this.win = $(window);
+
+        // Set up javascript UX bits
+        $(document)
+            // Shift nav in mobile when clicking the menu.
+            .on('click', "[data-toggle='wy-nav-top']", function() {
+                $("[data-toggle='wy-nav-shift']").toggleClass("shift");
+                $("[data-toggle='rst-versions']").toggleClass("shift");
+            })
+
+            // Nav menu link click operations
+            .on('click', ".wy-menu-vertical .current ul li a", function() {
+                var target = $(this);
+                // Close menu when you click a link.
+                $("[data-toggle='wy-nav-shift']").removeClass("shift");
+                $("[data-toggle='rst-versions']").toggleClass("shift");
+                // Handle dynamic display of l3 and l4 nav lists
+                self.toggleCurrent(target);
+                self.hashChange();
+            })
+            .on('click', "[data-toggle='rst-current-version']", function() {
+                $("[data-toggle='rst-versions']").toggleClass("shift-up");
+            })
+
+        // Make tables responsive
+        $("table.docutils:not(.field-list,.footnote,.citation)")
+            .wrap("<div class='wy-table-responsive'></div>");
+
+        // Add extra class to responsive tables that contain
+        // footnotes or citations so that we can target them for styling
+        $("table.docutils.footnote")
+            .wrap("<div class='wy-table-responsive footnote'></div>");
+        $("table.docutils.citation")
+            .wrap("<div class='wy-table-responsive citation'></div>");
+
+        // Add expand links to all parents of nested ul
+        $('.wy-menu-vertical ul').not('.simple').siblings('a').each(function () {
+            var link = $(this);
+                expand =
+                    $('<button class="toctree-expand" title="Open/close menu"></button>');
+            expand.on('click', function (ev) {
+                self.toggleCurrent(link);
+                ev.stopPropagation();
+                return false;
+            });
+            link.prepend(expand);
+        });
+    };
+
+    nav.reset = function () {
+        // Get anchor from URL and open up nested nav
+        var anchor = encodeURI(window.location.hash) || '#';
+
+        try {
+            var vmenu = $('.wy-menu-vertical');
+            var link = vmenu.find('[href="' + anchor + '"]');
+            if (link.length === 0) {
+                // this link was not found in the sidebar.
+                // Find associated id element, then its closest section
+                // in the document and try with that one.
+                var id_elt = $('.document [id="' + anchor.substring(1) + '"]');
+                var closest_section = id_elt.closest('div.section');
+                link = vmenu.find('[href="#' + closest_section.attr("id") + '"]');
+                if (link.length === 0) {
+                    // still not found in the sidebar. fall back to main section
+                    link = vmenu.find('[href="#"]');
+                }
+            }
+            // If we found a matching link then reset current and re-apply
+            // otherwise retain the existing match
+            if (link.length > 0) {
+                $('.wy-menu-vertical .current')
+                    .removeClass('current')
+                    .attr('aria-expanded','false');
+                link.addClass('current')
+                    .attr('aria-expanded','true');
+                link.closest('li.toctree-l1')
+                    .parent()
+                    .addClass('current')
+                    .attr('aria-expanded','true');
+                for (let i = 1; i <= 10; i++) {
+                    link.closest('li.toctree-l' + i)
+                        .addClass('current')
+                        .attr('aria-expanded','true');
+                }
+                link[0].scrollIntoView();
+            }
+        }
+        catch (err) {
+            console.log("Error expanding nav for anchor", err);
+        }
+
+    };
+
+    nav.onScroll = function () {
+        this.winScroll = false;
+        var newWinPosition = this.win.scrollTop(),
+            winBottom = newWinPosition + this.winHeight,
+            navPosition = this.navBar.scrollTop(),
+            newNavPosition = navPosition + (newWinPosition - this.winPosition);
+        if (newWinPosition < 0 || winBottom > this.docHeight) {
+            return;
+        }
+        this.navBar.scrollTop(newNavPosition);
+        this.winPosition = newWinPosition;
+    };
+
+    nav.onResize = function () {
+        this.winResize = false;
+        this.winHeight = this.win.height();
+        this.docHeight = $(document).height();
+    };
+
+    nav.hashChange = function () {
+        this.linkScroll = true;
+        this.win.one('hashchange', function () {
+            this.linkScroll = false;
+        });
+    };
+
+    nav.toggleCurrent = function (elem) {
+        var parent_li = elem.closest('li');
+        parent_li
+            .siblings('li.current')
+            .removeClass('current')
+            .attr('aria-expanded','false');
+        parent_li
+            .siblings()
+            .find('li.current')
+            .removeClass('current')
+            .attr('aria-expanded','false');
+        var children = parent_li.find('> ul li');
+        // Don't toggle terminal elements.
+        if (children.length) {
+            children
+                .removeClass('current')
+                .attr('aria-expanded','false');
+            parent_li
+                .toggleClass('current')
+                .attr('aria-expanded', function(i, old) {
+                    return old == 'true' ? 'false' : 'true';
+                });
+        }
+    }
+
+    return nav;
+};
+
+_ThemeNav = ThemeNav();
+
+if (typeof(window) != 'undefined') {
+    window.SphinxRtdTheme = {
+        Navigation: _ThemeNav,
+        // TODO remove this once static assets are split up between the theme
+        // and Read the Docs. For now, this patches 0.3.0 to be backwards
+        // compatible with a pre-0.3.0 layout.html
+        StickyNav: _ThemeNav,
+    };
+}
+
+
+// requestAnimationFrame polyfill by Erik Möller. fixes from Paul Irish and Tino Zijdel
+// https://gist.github.com/paulirish/1579671
+// MIT license
+
+(function() {
+    var lastTime = 0;
+    var vendors = ['ms', 'moz', 'webkit', 'o'];
+    for(var x = 0; x < vendors.length && !window.requestAnimationFrame; ++x) {
+        window.requestAnimationFrame = window[vendors[x]+'RequestAnimationFrame'];
+        window.cancelAnimationFrame = window[vendors[x]+'CancelAnimationFrame']
+                                   || window[vendors[x]+'CancelRequestAnimationFrame'];
+    }
+
+    if (!window.requestAnimationFrame)
+        window.requestAnimationFrame = function(callback, element) {
+            var currTime = new Date().getTime();
+            var timeToCall = Math.max(0, 16 - (currTime - lastTime));
+            var id = window.setTimeout(function() { callback(currTime + timeToCall); },
+              timeToCall);
+            lastTime = currTime + timeToCall;
+            return id;
+        };
+
+    if (!window.cancelAnimationFrame)
+        window.cancelAnimationFrame = function(id) {
+            clearTimeout(id);
+        };
+}());
diff --git a/_static/language_data.js b/_static/language_data.js
new file mode 100644
index 00000000..863704b3
--- /dev/null
+++ b/_static/language_data.js
@@ -0,0 +1,297 @@
+/*
+ * language_data.js
+ * ~~~~~~~~~~~~~~~~
+ *
+ * This script contains the language-specific data used by searchtools.js,
+ * namely the list of stopwords, stemmer, scorer and splitter.
+ *
+ * :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+var stopwords = ["a","and","are","as","at","be","but","by","for","if","in","into","is","it","near","no","not","of","on","or","such","that","the","their","then","there","these","they","this","to","was","will","with"];
+
+
+/* Non-minified version is copied as a separate JS file, is available */
+
+/**
+ * Porter Stemmer
+ */
+var Stemmer = function() {
+
+  var step2list = {
+    ational: 'ate',
+    tional: 'tion',
+    enci: 'ence',
+    anci: 'ance',
+    izer: 'ize',
+    bli: 'ble',
+    alli: 'al',
+    entli: 'ent',
+    eli: 'e',
+    ousli: 'ous',
+    ization: 'ize',
+    ation: 'ate',
+    ator: 'ate',
+    alism: 'al',
+    iveness: 'ive',
+    fulness: 'ful',
+    ousness: 'ous',
+    aliti: 'al',
+    iviti: 'ive',
+    biliti: 'ble',
+    logi: 'log'
+  };
+
+  var step3list = {
+    icate: 'ic',
+    ative: '',
+    alize: 'al',
+    iciti: 'ic',
+    ical: 'ic',
+    ful: '',
+    ness: ''
+  };
+
+  var c = "[^aeiou]";          // consonant
+  var v = "[aeiouy]";          // vowel
+  var C = c + "[^aeiouy]*";    // consonant sequence
+  var V = v + "[aeiou]*";      // vowel sequence
+
+  var mgr0 = "^(" + C + ")?" + V + C;                      // [C]VC... is m>0
+  var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$";    // [C]VC[V] is m=1
+  var mgr1 = "^(" + C + ")?" + V + C + V + C;              // [C]VCVC... is m>1
+  var s_v   = "^(" + C + ")?" + v;                         // vowel in stem
+
+  this.stemWord = function (w) {
+    var stem;
+    var suffix;
+    var firstch;
+    var origword = w;
+
+    if (w.length < 3)
+      return w;
+
+    var re;
+    var re2;
+    var re3;
+    var re4;
+
+    firstch = w.substr(0,1);
+    if (firstch == "y")
+      w = firstch.toUpperCase() + w.substr(1);
+
+    // Step 1a
+    re = /^(.+?)(ss|i)es$/;
+    re2 = /^(.+?)([^s])s$/;
+
+    if (re.test(w))
+      w = w.replace(re,"$1$2");
+    else if (re2.test(w))
+      w = w.replace(re2,"$1$2");
+
+    // Step 1b
+    re = /^(.+?)eed$/;
+    re2 = /^(.+?)(ed|ing)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      re = new RegExp(mgr0);
+      if (re.test(fp[1])) {
+        re = /.$/;
+        w = w.replace(re,"");
+      }
+    }
+    else if (re2.test(w)) {
+      var fp = re2.exec(w);
+      stem = fp[1];
+      re2 = new RegExp(s_v);
+      if (re2.test(stem)) {
+        w = stem;
+        re2 = /(at|bl|iz)$/;
+        re3 = new RegExp("([^aeiouylsz])\\1$");
+        re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+        if (re2.test(w))
+          w = w + "e";
+        else if (re3.test(w)) {
+          re = /.$/;
+          w = w.replace(re,"");
+        }
+        else if (re4.test(w))
+          w = w + "e";
+      }
+    }
+
+    // Step 1c
+    re = /^(.+?)y$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(s_v);
+      if (re.test(stem))
+        w = stem + "i";
+    }
+
+    // Step 2
+    re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      suffix = fp[2];
+      re = new RegExp(mgr0);
+      if (re.test(stem))
+        w = stem + step2list[suffix];
+    }
+
+    // Step 3
+    re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      suffix = fp[2];
+      re = new RegExp(mgr0);
+      if (re.test(stem))
+        w = stem + step3list[suffix];
+    }
+
+    // Step 4
+    re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
+    re2 = /^(.+?)(s|t)(ion)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(mgr1);
+      if (re.test(stem))
+        w = stem;
+    }
+    else if (re2.test(w)) {
+      var fp = re2.exec(w);
+      stem = fp[1] + fp[2];
+      re2 = new RegExp(mgr1);
+      if (re2.test(stem))
+        w = stem;
+    }
+
+    // Step 5
+    re = /^(.+?)e$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(mgr1);
+      re2 = new RegExp(meq1);
+      re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+      if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
+        w = stem;
+    }
+    re = /ll$/;
+    re2 = new RegExp(mgr1);
+    if (re.test(w) && re2.test(w)) {
+      re = /.$/;
+      w = w.replace(re,"");
+    }
+
+    // and turn initial Y back to y
+    if (firstch == "y")
+      w = firstch.toLowerCase() + w.substr(1);
+    return w;
+  }
+}
+
+
+
+
+var splitChars = (function() {
+    var result = {};
+    var singles = [96, 180, 187, 191, 215, 247, 749, 885, 903, 907, 909, 930, 1014, 1648,
+         1748, 1809, 2416, 2473, 2481, 2526, 2601, 2609, 2612, 2615, 2653, 2702,
+         2706, 2729, 2737, 2740, 2857, 2865, 2868, 2910, 2928, 2948, 2961, 2971,
+         2973, 3085, 3089, 3113, 3124, 3213, 3217, 3241, 3252, 3295, 3341, 3345,
+         3369, 3506, 3516, 3633, 3715, 3721, 3736, 3744, 3748, 3750, 3756, 3761,
+         3781, 3912, 4239, 4347, 4681, 4695, 4697, 4745, 4785, 4799, 4801, 4823,
+         4881, 5760, 5901, 5997, 6313, 7405, 8024, 8026, 8028, 8030, 8117, 8125,
+         8133, 8181, 8468, 8485, 8487, 8489, 8494, 8527, 11311, 11359, 11687, 11695,
+         11703, 11711, 11719, 11727, 11735, 12448, 12539, 43010, 43014, 43019, 43587,
+         43696, 43713, 64286, 64297, 64311, 64317, 64319, 64322, 64325, 65141];
+    var i, j, start, end;
+    for (i = 0; i < singles.length; i++) {
+        result[singles[i]] = true;
+    }
+    var ranges = [[0, 47], [58, 64], [91, 94], [123, 169], [171, 177], [182, 184], [706, 709],
+         [722, 735], [741, 747], [751, 879], [888, 889], [894, 901], [1154, 1161],
+         [1318, 1328], [1367, 1368], [1370, 1376], [1416, 1487], [1515, 1519], [1523, 1568],
+         [1611, 1631], [1642, 1645], [1750, 1764], [1767, 1773], [1789, 1790], [1792, 1807],
+         [1840, 1868], [1958, 1968], [1970, 1983], [2027, 2035], [2038, 2041], [2043, 2047],
+         [2070, 2073], [2075, 2083], [2085, 2087], [2089, 2307], [2362, 2364], [2366, 2383],
+         [2385, 2391], [2402, 2405], [2419, 2424], [2432, 2436], [2445, 2446], [2449, 2450],
+         [2483, 2485], [2490, 2492], [2494, 2509], [2511, 2523], [2530, 2533], [2546, 2547],
+         [2554, 2564], [2571, 2574], [2577, 2578], [2618, 2648], [2655, 2661], [2672, 2673],
+         [2677, 2692], [2746, 2748], [2750, 2767], [2769, 2783], [2786, 2789], [2800, 2820],
+         [2829, 2830], [2833, 2834], [2874, 2876], [2878, 2907], [2914, 2917], [2930, 2946],
+         [2955, 2957], [2966, 2968], [2976, 2978], [2981, 2983], [2987, 2989], [3002, 3023],
+         [3025, 3045], [3059, 3076], [3130, 3132], [3134, 3159], [3162, 3167], [3170, 3173],
+         [3184, 3191], [3199, 3204], [3258, 3260], [3262, 3293], [3298, 3301], [3312, 3332],
+         [3386, 3388], [3390, 3423], [3426, 3429], [3446, 3449], [3456, 3460], [3479, 3481],
+         [3518, 3519], [3527, 3584], [3636, 3647], [3655, 3663], [3674, 3712], [3717, 3718],
+         [3723, 3724], [3726, 3731], [3752, 3753], [3764, 3772], [3774, 3775], [3783, 3791],
+         [3802, 3803], [3806, 3839], [3841, 3871], [3892, 3903], [3949, 3975], [3980, 4095],
+         [4139, 4158], [4170, 4175], [4182, 4185], [4190, 4192], [4194, 4196], [4199, 4205],
+         [4209, 4212], [4226, 4237], [4250, 4255], [4294, 4303], [4349, 4351], [4686, 4687],
+         [4702, 4703], [4750, 4751], [4790, 4791], [4806, 4807], [4886, 4887], [4955, 4968],
+         [4989, 4991], [5008, 5023], [5109, 5120], [5741, 5742], [5787, 5791], [5867, 5869],
+         [5873, 5887], [5906, 5919], [5938, 5951], [5970, 5983], [6001, 6015], [6068, 6102],
+         [6104, 6107], [6109, 6111], [6122, 6127], [6138, 6159], [6170, 6175], [6264, 6271],
+         [6315, 6319], [6390, 6399], [6429, 6469], [6510, 6511], [6517, 6527], [6572, 6592],
+         [6600, 6607], [6619, 6655], [6679, 6687], [6741, 6783], [6794, 6799], [6810, 6822],
+         [6824, 6916], [6964, 6980], [6988, 6991], [7002, 7042], [7073, 7085], [7098, 7167],
+         [7204, 7231], [7242, 7244], [7294, 7400], [7410, 7423], [7616, 7679], [7958, 7959],
+         [7966, 7967], [8006, 8007], [8014, 8015], [8062, 8063], [8127, 8129], [8141, 8143],
+         [8148, 8149], [8156, 8159], [8173, 8177], [8189, 8303], [8306, 8307], [8314, 8318],
+         [8330, 8335], [8341, 8449], [8451, 8454], [8456, 8457], [8470, 8472], [8478, 8483],
+         [8506, 8507], [8512, 8516], [8522, 8525], [8586, 9311], [9372, 9449], [9472, 10101],
+         [10132, 11263], [11493, 11498], [11503, 11516], [11518, 11519], [11558, 11567],
+         [11622, 11630], [11632, 11647], [11671, 11679], [11743, 11822], [11824, 12292],
+         [12296, 12320], [12330, 12336], [12342, 12343], [12349, 12352], [12439, 12444],
+         [12544, 12548], [12590, 12592], [12687, 12689], [12694, 12703], [12728, 12783],
+         [12800, 12831], [12842, 12880], [12896, 12927], [12938, 12976], [12992, 13311],
+         [19894, 19967], [40908, 40959], [42125, 42191], [42238, 42239], [42509, 42511],
+         [42540, 42559], [42592, 42593], [42607, 42622], [42648, 42655], [42736, 42774],
+         [42784, 42785], [42889, 42890], [42893, 43002], [43043, 43055], [43062, 43071],
+         [43124, 43137], [43188, 43215], [43226, 43249], [43256, 43258], [43260, 43263],
+         [43302, 43311], [43335, 43359], [43389, 43395], [43443, 43470], [43482, 43519],
+         [43561, 43583], [43596, 43599], [43610, 43615], [43639, 43641], [43643, 43647],
+         [43698, 43700], [43703, 43704], [43710, 43711], [43715, 43738], [43742, 43967],
+         [44003, 44015], [44026, 44031], [55204, 55215], [55239, 55242], [55292, 55295],
+         [57344, 63743], [64046, 64047], [64110, 64111], [64218, 64255], [64263, 64274],
+         [64280, 64284], [64434, 64466], [64830, 64847], [64912, 64913], [64968, 65007],
+         [65020, 65135], [65277, 65295], [65306, 65312], [65339, 65344], [65371, 65381],
+         [65471, 65473], [65480, 65481], [65488, 65489], [65496, 65497]];
+    for (i = 0; i < ranges.length; i++) {
+        start = ranges[i][0];
+        end = ranges[i][1];
+        for (j = start; j <= end; j++) {
+            result[j] = true;
+        }
+    }
+    return result;
+})();
+
+function splitQuery(query) {
+    var result = [];
+    var start = -1;
+    for (var i = 0; i < query.length; i++) {
+        if (splitChars[query.charCodeAt(i)]) {
+            if (start !== -1) {
+                result.push(query.slice(start, i));
+                start = -1;
+            }
+        } else if (start === -1) {
+            start = i;
+        }
+    }
+    if (start !== -1) {
+        result.push(query.slice(start));
+    }
+    return result;
+}
+
+
diff --git a/_static/logo_cea.png b/_static/logo_cea.png
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diff --git a/_static/minus.png b/_static/minus.png
new file mode 100644
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literal 0
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diff --git a/_static/plot_directive.css b/_static/plot_directive.css
new file mode 100644
index 00000000..d45593c9
--- /dev/null
+++ b/_static/plot_directive.css
@@ -0,0 +1,16 @@
+/*
+ * plot_directive.css
+ * ~~~~~~~~~~~~
+ *
+ * Stylesheet controlling images created using the `plot` directive within
+ * Sphinx.
+ *
+ * :copyright: Copyright 2020-* by the Matplotlib development team.
+ * :license: Matplotlib, see LICENSE for details.
+ *
+ */
+
+img.plot-directive {
+    border: 0;
+    max-width: 100%;
+}
diff --git a/_static/plus.png b/_static/plus.png
new file mode 100644
index 0000000000000000000000000000000000000000..7107cec93a979b9a5f64843235a16651d563ce2d
GIT binary patch
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literal 0
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diff --git a/_static/pygments.css b/_static/pygments.css
new file mode 100644
index 00000000..691aeb82
--- /dev/null
+++ b/_static/pygments.css
@@ -0,0 +1,74 @@
+pre { line-height: 125%; }
+td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+.highlight .hll { background-color: #ffffcc }
+.highlight { background: #eeffcc; }
+.highlight .c { color: #408090; font-style: italic } /* Comment */
+.highlight .err { border: 1px solid #FF0000 } /* Error */
+.highlight .k { color: #007020; font-weight: bold } /* Keyword */
+.highlight .o { color: #666666 } /* Operator */
+.highlight .ch { color: #408090; font-style: italic } /* Comment.Hashbang */
+.highlight .cm { color: #408090; font-style: italic } /* Comment.Multiline */
+.highlight .cp { color: #007020 } /* Comment.Preproc */
+.highlight .cpf { color: #408090; font-style: italic } /* Comment.PreprocFile */
+.highlight .c1 { color: #408090; font-style: italic } /* Comment.Single */
+.highlight .cs { color: #408090; background-color: #fff0f0 } /* Comment.Special */
+.highlight .gd { color: #A00000 } /* Generic.Deleted */
+.highlight .ge { font-style: italic } /* Generic.Emph */
+.highlight .gr { color: #FF0000 } /* Generic.Error */
+.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
+.highlight .gi { color: #00A000 } /* Generic.Inserted */
+.highlight .go { color: #333333 } /* Generic.Output */
+.highlight .gp { color: #c65d09; font-weight: bold } /* Generic.Prompt */
+.highlight .gs { font-weight: bold } /* Generic.Strong */
+.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
+.highlight .gt { color: #0044DD } /* Generic.Traceback */
+.highlight .kc { color: #007020; font-weight: bold } /* Keyword.Constant */
+.highlight .kd { color: #007020; font-weight: bold } /* Keyword.Declaration */
+.highlight .kn { color: #007020; font-weight: bold } /* Keyword.Namespace */
+.highlight .kp { color: #007020 } /* Keyword.Pseudo */
+.highlight .kr { color: #007020; font-weight: bold } /* Keyword.Reserved */
+.highlight .kt { color: #902000 } /* Keyword.Type */
+.highlight .m { color: #208050 } /* Literal.Number */
+.highlight .s { color: #4070a0 } /* Literal.String */
+.highlight .na { color: #4070a0 } /* Name.Attribute */
+.highlight .nb { color: #007020 } /* Name.Builtin */
+.highlight .nc { color: #0e84b5; font-weight: bold } /* Name.Class */
+.highlight .no { color: #60add5 } /* Name.Constant */
+.highlight .nd { color: #555555; font-weight: bold } /* Name.Decorator */
+.highlight .ni { color: #d55537; font-weight: bold } /* Name.Entity */
+.highlight .ne { color: #007020 } /* Name.Exception */
+.highlight .nf { color: #06287e } /* Name.Function */
+.highlight .nl { color: #002070; font-weight: bold } /* Name.Label */
+.highlight .nn { color: #0e84b5; font-weight: bold } /* Name.Namespace */
+.highlight .nt { color: #062873; font-weight: bold } /* Name.Tag */
+.highlight .nv { color: #bb60d5 } /* Name.Variable */
+.highlight .ow { color: #007020; font-weight: bold } /* Operator.Word */
+.highlight .w { color: #bbbbbb } /* Text.Whitespace */
+.highlight .mb { color: #208050 } /* Literal.Number.Bin */
+.highlight .mf { color: #208050 } /* Literal.Number.Float */
+.highlight .mh { color: #208050 } /* Literal.Number.Hex */
+.highlight .mi { color: #208050 } /* Literal.Number.Integer */
+.highlight .mo { color: #208050 } /* Literal.Number.Oct */
+.highlight .sa { color: #4070a0 } /* Literal.String.Affix */
+.highlight .sb { color: #4070a0 } /* Literal.String.Backtick */
+.highlight .sc { color: #4070a0 } /* Literal.String.Char */
+.highlight .dl { color: #4070a0 } /* Literal.String.Delimiter */
+.highlight .sd { color: #4070a0; font-style: italic } /* Literal.String.Doc */
+.highlight .s2 { color: #4070a0 } /* Literal.String.Double */
+.highlight .se { color: #4070a0; font-weight: bold } /* Literal.String.Escape */
+.highlight .sh { color: #4070a0 } /* Literal.String.Heredoc */
+.highlight .si { color: #70a0d0; font-style: italic } /* Literal.String.Interpol */
+.highlight .sx { color: #c65d09 } /* Literal.String.Other */
+.highlight .sr { color: #235388 } /* Literal.String.Regex */
+.highlight .s1 { color: #4070a0 } /* Literal.String.Single */
+.highlight .ss { color: #517918 } /* Literal.String.Symbol */
+.highlight .bp { color: #007020 } /* Name.Builtin.Pseudo */
+.highlight .fm { color: #06287e } /* Name.Function.Magic */
+.highlight .vc { color: #bb60d5 } /* Name.Variable.Class */
+.highlight .vg { color: #bb60d5 } /* Name.Variable.Global */
+.highlight .vi { color: #bb60d5 } /* Name.Variable.Instance */
+.highlight .vm { color: #bb60d5 } /* Name.Variable.Magic */
+.highlight .il { color: #208050 } /* Literal.Number.Integer.Long */
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diff --git a/_static/searchtools.js b/_static/searchtools.js
new file mode 100644
index 00000000..7dbc787d
--- /dev/null
+++ b/_static/searchtools.js
@@ -0,0 +1,532 @@
+/*
+ * searchtools.js
+ * ~~~~~~~~~~~~~~~~
+ *
+ * Sphinx JavaScript utilities for the full-text search.
+ *
+ * :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+if (!Scorer) {
+  /**
+   * Simple result scoring code.
+   */
+  var Scorer = {
+    // Implement the following function to further tweak the score for each result
+    // The function takes a result array [filename, title, anchor, descr, score]
+    // and returns the new score.
+    /*
+    score: function(result) {
+      return result[4];
+    },
+    */
+
+    // query matches the full name of an object
+    objNameMatch: 11,
+    // or matches in the last dotted part of the object name
+    objPartialMatch: 6,
+    // Additive scores depending on the priority of the object
+    objPrio: {0:  15,   // used to be importantResults
+              1:  5,   // used to be objectResults
+              2: -5},  // used to be unimportantResults
+    //  Used when the priority is not in the mapping.
+    objPrioDefault: 0,
+
+    // query found in title
+    title: 15,
+    partialTitle: 7,
+    // query found in terms
+    term: 5,
+    partialTerm: 2
+  };
+}
+
+if (!splitQuery) {
+  function splitQuery(query) {
+    return query.split(/\s+/);
+  }
+}
+
+/**
+ * Search Module
+ */
+var Search = {
+
+  _index : null,
+  _queued_query : null,
+  _pulse_status : -1,
+
+  htmlToText : function(htmlString) {
+      var virtualDocument = document.implementation.createHTMLDocument('virtual');
+      var htmlElement = $(htmlString, virtualDocument);
+      htmlElement.find('.headerlink').remove();
+      docContent = htmlElement.find('[role=main]')[0];
+      if(docContent === undefined) {
+          console.warn("Content block not found. Sphinx search tries to obtain it " +
+                       "via '[role=main]'. Could you check your theme or template.");
+          return "";
+      }
+      return docContent.textContent || docContent.innerText;
+  },
+
+  init : function() {
+      var params = $.getQueryParameters();
+      if (params.q) {
+          var query = params.q[0];
+          $('input[name="q"]')[0].value = query;
+          this.performSearch(query);
+      }
+  },
+
+  loadIndex : function(url) {
+    $.ajax({type: "GET", url: url, data: null,
+            dataType: "script", cache: true,
+            complete: function(jqxhr, textstatus) {
+              if (textstatus != "success") {
+                document.getElementById("searchindexloader").src = url;
+              }
+            }});
+  },
+
+  setIndex : function(index) {
+    var q;
+    this._index = index;
+    if ((q = this._queued_query) !== null) {
+      this._queued_query = null;
+      Search.query(q);
+    }
+  },
+
+  hasIndex : function() {
+      return this._index !== null;
+  },
+
+  deferQuery : function(query) {
+      this._queued_query = query;
+  },
+
+  stopPulse : function() {
+      this._pulse_status = 0;
+  },
+
+  startPulse : function() {
+    if (this._pulse_status >= 0)
+        return;
+    function pulse() {
+      var i;
+      Search._pulse_status = (Search._pulse_status + 1) % 4;
+      var dotString = '';
+      for (i = 0; i < Search._pulse_status; i++)
+        dotString += '.';
+      Search.dots.text(dotString);
+      if (Search._pulse_status > -1)
+        window.setTimeout(pulse, 500);
+    }
+    pulse();
+  },
+
+  /**
+   * perform a search for something (or wait until index is loaded)
+   */
+  performSearch : function(query) {
+    // create the required interface elements
+    this.out = $('#search-results');
+    this.title = $('<h2>' + _('Searching') + '</h2>').appendTo(this.out);
+    this.dots = $('<span></span>').appendTo(this.title);
+    this.status = $('<p class="search-summary">&nbsp;</p>').appendTo(this.out);
+    this.output = $('<ul class="search"/>').appendTo(this.out);
+
+    $('#search-progress').text(_('Preparing search...'));
+    this.startPulse();
+
+    // index already loaded, the browser was quick!
+    if (this.hasIndex())
+      this.query(query);
+    else
+      this.deferQuery(query);
+  },
+
+  /**
+   * execute search (requires search index to be loaded)
+   */
+  query : function(query) {
+    var i;
+
+    // stem the searchterms and add them to the correct list
+    var stemmer = new Stemmer();
+    var searchterms = [];
+    var excluded = [];
+    var hlterms = [];
+    var tmp = splitQuery(query);
+    var objectterms = [];
+    for (i = 0; i < tmp.length; i++) {
+      if (tmp[i] !== "") {
+          objectterms.push(tmp[i].toLowerCase());
+      }
+
+      if ($u.indexOf(stopwords, tmp[i].toLowerCase()) != -1 || tmp[i] === "") {
+        // skip this "word"
+        continue;
+      }
+      // stem the word
+      var word = stemmer.stemWord(tmp[i].toLowerCase());
+      // prevent stemmer from cutting word smaller than two chars
+      if(word.length < 3 && tmp[i].length >= 3) {
+        word = tmp[i];
+      }
+      var toAppend;
+      // select the correct list
+      if (word[0] == '-') {
+        toAppend = excluded;
+        word = word.substr(1);
+      }
+      else {
+        toAppend = searchterms;
+        hlterms.push(tmp[i].toLowerCase());
+      }
+      // only add if not already in the list
+      if (!$u.contains(toAppend, word))
+        toAppend.push(word);
+    }
+    var highlightstring = '?highlight=' + $.urlencode(hlterms.join(" "));
+
+    // console.debug('SEARCH: searching for:');
+    // console.info('required: ', searchterms);
+    // console.info('excluded: ', excluded);
+
+    // prepare search
+    var terms = this._index.terms;
+    var titleterms = this._index.titleterms;
+
+    // array of [filename, title, anchor, descr, score]
+    var results = [];
+    $('#search-progress').empty();
+
+    // lookup as object
+    for (i = 0; i < objectterms.length; i++) {
+      var others = [].concat(objectterms.slice(0, i),
+                             objectterms.slice(i+1, objectterms.length));
+      results = results.concat(this.performObjectSearch(objectterms[i], others));
+    }
+
+    // lookup as search terms in fulltext
+    results = results.concat(this.performTermsSearch(searchterms, excluded, terms, titleterms));
+
+    // let the scorer override scores with a custom scoring function
+    if (Scorer.score) {
+      for (i = 0; i < results.length; i++)
+        results[i][4] = Scorer.score(results[i]);
+    }
+
+    // now sort the results by score (in opposite order of appearance, since the
+    // display function below uses pop() to retrieve items) and then
+    // alphabetically
+    results.sort(function(a, b) {
+      var left = a[4];
+      var right = b[4];
+      if (left > right) {
+        return 1;
+      } else if (left < right) {
+        return -1;
+      } else {
+        // same score: sort alphabetically
+        left = a[1].toLowerCase();
+        right = b[1].toLowerCase();
+        return (left > right) ? -1 : ((left < right) ? 1 : 0);
+      }
+    });
+
+    // for debugging
+    //Search.lastresults = results.slice();  // a copy
+    //console.info('search results:', Search.lastresults);
+
+    // print the results
+    var resultCount = results.length;
+    function displayNextItem() {
+      // results left, load the summary and display it
+      if (results.length) {
+        var item = results.pop();
+        var listItem = $('<li></li>');
+        var requestUrl = "";
+        var linkUrl = "";
+        if (DOCUMENTATION_OPTIONS.BUILDER === 'dirhtml') {
+          // dirhtml builder
+          var dirname = item[0] + '/';
+          if (dirname.match(/\/index\/$/)) {
+            dirname = dirname.substring(0, dirname.length-6);
+          } else if (dirname == 'index/') {
+            dirname = '';
+          }
+          requestUrl = DOCUMENTATION_OPTIONS.URL_ROOT + dirname;
+          linkUrl = requestUrl;
+
+        } else {
+          // normal html builders
+          requestUrl = DOCUMENTATION_OPTIONS.URL_ROOT + item[0] + DOCUMENTATION_OPTIONS.FILE_SUFFIX;
+          linkUrl = item[0] + DOCUMENTATION_OPTIONS.LINK_SUFFIX;
+        }
+        listItem.append($('<a/>').attr('href',
+            linkUrl +
+            highlightstring + item[2]).html(item[1]));
+        if (item[3]) {
+          listItem.append($('<span> (' + item[3] + ')</span>'));
+          Search.output.append(listItem);
+          setTimeout(function() {
+            displayNextItem();
+          }, 5);
+        } else if (DOCUMENTATION_OPTIONS.HAS_SOURCE) {
+          $.ajax({url: requestUrl,
+                  dataType: "text",
+                  complete: function(jqxhr, textstatus) {
+                    var data = jqxhr.responseText;
+                    if (data !== '' && data !== undefined) {
+                      var summary = Search.makeSearchSummary(data, searchterms, hlterms);
+                      if (summary) {
+                        listItem.append(summary);
+                      }
+                    }
+                    Search.output.append(listItem);
+                    setTimeout(function() {
+                      displayNextItem();
+                    }, 5);
+                  }});
+        } else {
+          // no source available, just display title
+          Search.output.append(listItem);
+          setTimeout(function() {
+            displayNextItem();
+          }, 5);
+        }
+      }
+      // search finished, update title and status message
+      else {
+        Search.stopPulse();
+        Search.title.text(_('Search Results'));
+        if (!resultCount)
+          Search.status.text(_('Your search did not match any documents. Please make sure that all words are spelled correctly and that you\'ve selected enough categories.'));
+        else
+            Search.status.text(_('Search finished, found %s page(s) matching the search query.').replace('%s', resultCount));
+        Search.status.fadeIn(500);
+      }
+    }
+    displayNextItem();
+  },
+
+  /**
+   * search for object names
+   */
+  performObjectSearch : function(object, otherterms) {
+    var filenames = this._index.filenames;
+    var docnames = this._index.docnames;
+    var objects = this._index.objects;
+    var objnames = this._index.objnames;
+    var titles = this._index.titles;
+
+    var i;
+    var results = [];
+
+    for (var prefix in objects) {
+      if (!(objects[prefix] instanceof Array)) {
+        objects[prefix] = Object.entries(objects[prefix]).map(([name, match]) => [...match, name]);
+      }
+      for (var iMatch = 0; iMatch != objects[prefix].length; ++iMatch) {
+        var match = objects[prefix][iMatch];
+        var name = match[4];
+        var fullname = (prefix ? prefix + '.' : '') + name;
+        var fullnameLower = fullname.toLowerCase()
+        if (fullnameLower.indexOf(object) > -1) {
+          var score = 0;
+          var parts = fullnameLower.split('.');
+          // check for different match types: exact matches of full name or
+          // "last name" (i.e. last dotted part)
+          if (fullnameLower == object || parts[parts.length - 1] == object) {
+            score += Scorer.objNameMatch;
+          // matches in last name
+          } else if (parts[parts.length - 1].indexOf(object) > -1) {
+            score += Scorer.objPartialMatch;
+          }
+          var objname = objnames[match[1]][2];
+          var title = titles[match[0]];
+          // If more than one term searched for, we require other words to be
+          // found in the name/title/description
+          if (otherterms.length > 0) {
+            var haystack = (prefix + ' ' + name + ' ' +
+                            objname + ' ' + title).toLowerCase();
+            var allfound = true;
+            for (i = 0; i < otherterms.length; i++) {
+              if (haystack.indexOf(otherterms[i]) == -1) {
+                allfound = false;
+                break;
+              }
+            }
+            if (!allfound) {
+              continue;
+            }
+          }
+          var descr = objname + _(', in ') + title;
+
+          var anchor = match[3];
+          if (anchor === '')
+            anchor = fullname;
+          else if (anchor == '-')
+            anchor = objnames[match[1]][1] + '-' + fullname;
+          // add custom score for some objects according to scorer
+          if (Scorer.objPrio.hasOwnProperty(match[2])) {
+            score += Scorer.objPrio[match[2]];
+          } else {
+            score += Scorer.objPrioDefault;
+          }
+          results.push([docnames[match[0]], fullname, '#'+anchor, descr, score, filenames[match[0]]]);
+        }
+      }
+    }
+
+    return results;
+  },
+
+  /**
+   * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions
+   */
+  escapeRegExp : function(string) {
+    return string.replace(/[.*+\-?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
+  },
+
+  /**
+   * search for full-text terms in the index
+   */
+  performTermsSearch : function(searchterms, excluded, terms, titleterms) {
+    var docnames = this._index.docnames;
+    var filenames = this._index.filenames;
+    var titles = this._index.titles;
+
+    var i, j, file;
+    var fileMap = {};
+    var scoreMap = {};
+    var results = [];
+
+    // perform the search on the required terms
+    for (i = 0; i < searchterms.length; i++) {
+      var word = searchterms[i];
+      var files = [];
+      var _o = [
+        {files: terms[word], score: Scorer.term},
+        {files: titleterms[word], score: Scorer.title}
+      ];
+      // add support for partial matches
+      if (word.length > 2) {
+        var word_regex = this.escapeRegExp(word);
+        for (var w in terms) {
+          if (w.match(word_regex) && !terms[word]) {
+            _o.push({files: terms[w], score: Scorer.partialTerm})
+          }
+        }
+        for (var w in titleterms) {
+          if (w.match(word_regex) && !titleterms[word]) {
+              _o.push({files: titleterms[w], score: Scorer.partialTitle})
+          }
+        }
+      }
+
+      // no match but word was a required one
+      if ($u.every(_o, function(o){return o.files === undefined;})) {
+        break;
+      }
+      // found search word in contents
+      $u.each(_o, function(o) {
+        var _files = o.files;
+        if (_files === undefined)
+          return
+
+        if (_files.length === undefined)
+          _files = [_files];
+        files = files.concat(_files);
+
+        // set score for the word in each file to Scorer.term
+        for (j = 0; j < _files.length; j++) {
+          file = _files[j];
+          if (!(file in scoreMap))
+            scoreMap[file] = {};
+          scoreMap[file][word] = o.score;
+        }
+      });
+
+      // create the mapping
+      for (j = 0; j < files.length; j++) {
+        file = files[j];
+        if (file in fileMap && fileMap[file].indexOf(word) === -1)
+          fileMap[file].push(word);
+        else
+          fileMap[file] = [word];
+      }
+    }
+
+    // now check if the files don't contain excluded terms
+    for (file in fileMap) {
+      var valid = true;
+
+      // check if all requirements are matched
+      var filteredTermCount = // as search terms with length < 3 are discarded: ignore
+        searchterms.filter(function(term){return term.length > 2}).length
+      if (
+        fileMap[file].length != searchterms.length &&
+        fileMap[file].length != filteredTermCount
+      ) continue;
+
+      // ensure that none of the excluded terms is in the search result
+      for (i = 0; i < excluded.length; i++) {
+        if (terms[excluded[i]] == file ||
+            titleterms[excluded[i]] == file ||
+            $u.contains(terms[excluded[i]] || [], file) ||
+            $u.contains(titleterms[excluded[i]] || [], file)) {
+          valid = false;
+          break;
+        }
+      }
+
+      // if we have still a valid result we can add it to the result list
+      if (valid) {
+        // select one (max) score for the file.
+        // for better ranking, we should calculate ranking by using words statistics like basic tf-idf...
+        var score = $u.max($u.map(fileMap[file], function(w){return scoreMap[file][w]}));
+        results.push([docnames[file], titles[file], '', null, score, filenames[file]]);
+      }
+    }
+    return results;
+  },
+
+  /**
+   * helper function to return a node containing the
+   * search summary for a given text. keywords is a list
+   * of stemmed words, hlwords is the list of normal, unstemmed
+   * words. the first one is used to find the occurrence, the
+   * latter for highlighting it.
+   */
+  makeSearchSummary : function(htmlText, keywords, hlwords) {
+    var text = Search.htmlToText(htmlText);
+    if (text == "") {
+      return null;
+    }
+    var textLower = text.toLowerCase();
+    var start = 0;
+    $.each(keywords, function() {
+      var i = textLower.indexOf(this.toLowerCase());
+      if (i > -1)
+        start = i;
+    });
+    start = Math.max(start - 120, 0);
+    var excerpt = ((start > 0) ? '...' : '') +
+      $.trim(text.substr(start, 240)) +
+      ((start + 240 - text.length) ? '...' : '');
+    var rv = $('<p class="context"></p>').text(excerpt);
+    $.each(hlwords, function() {
+      rv = rv.highlightText(this, 'highlighted');
+    });
+    return rv;
+  }
+};
+
+$(document).ready(function() {
+  Search.init();
+});
diff --git a/_static/triqs_logo/Icon/JPG/Triqs_Icon_RGB_Black.jpg b/_static/triqs_logo/Icon/JPG/Triqs_Icon_RGB_Black.jpg
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new file mode 100644
index 00000000..ff598488
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new file mode 100644
index 00000000..76d6707d
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new file mode 100644
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new file mode 100644
index 00000000..27ff0f46
--- /dev/null
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diff --git a/_static/underscore.js b/_static/underscore.js
new file mode 100644
index 00000000..b812b350
--- /dev/null
+++ b/_static/underscore.js
@@ -0,0 +1,2042 @@
+(function (global, factory) {
+  typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() :
+  typeof define === 'function' && define.amd ? define('underscore', factory) :
+  (global = typeof globalThis !== 'undefined' ? globalThis : global || self, (function () {
+    var current = global._;
+    var exports = global._ = factory();
+    exports.noConflict = function () { global._ = current; return exports; };
+  }()));
+}(this, (function () {
+  //     Underscore.js 1.13.2
+  //     https://underscorejs.org
+  //     (c) 2009-2021 Jeremy Ashkenas, Julian Gonggrijp, and DocumentCloud and Investigative Reporters & Editors
+  //     Underscore may be freely distributed under the MIT license.
+
+  // Current version.
+  var VERSION = '1.13.2';
+
+  // Establish the root object, `window` (`self`) in the browser, `global`
+  // on the server, or `this` in some virtual machines. We use `self`
+  // instead of `window` for `WebWorker` support.
+  var root = typeof self == 'object' && self.self === self && self ||
+            typeof global == 'object' && global.global === global && global ||
+            Function('return this')() ||
+            {};
+
+  // Save bytes in the minified (but not gzipped) version:
+  var ArrayProto = Array.prototype, ObjProto = Object.prototype;
+  var SymbolProto = typeof Symbol !== 'undefined' ? Symbol.prototype : null;
+
+  // Create quick reference variables for speed access to core prototypes.
+  var push = ArrayProto.push,
+      slice = ArrayProto.slice,
+      toString = ObjProto.toString,
+      hasOwnProperty = ObjProto.hasOwnProperty;
+
+  // Modern feature detection.
+  var supportsArrayBuffer = typeof ArrayBuffer !== 'undefined',
+      supportsDataView = typeof DataView !== 'undefined';
+
+  // All **ECMAScript 5+** native function implementations that we hope to use
+  // are declared here.
+  var nativeIsArray = Array.isArray,
+      nativeKeys = Object.keys,
+      nativeCreate = Object.create,
+      nativeIsView = supportsArrayBuffer && ArrayBuffer.isView;
+
+  // Create references to these builtin functions because we override them.
+  var _isNaN = isNaN,
+      _isFinite = isFinite;
+
+  // Keys in IE < 9 that won't be iterated by `for key in ...` and thus missed.
+  var hasEnumBug = !{toString: null}.propertyIsEnumerable('toString');
+  var nonEnumerableProps = ['valueOf', 'isPrototypeOf', 'toString',
+    'propertyIsEnumerable', 'hasOwnProperty', 'toLocaleString'];
+
+  // The largest integer that can be represented exactly.
+  var MAX_ARRAY_INDEX = Math.pow(2, 53) - 1;
+
+  // Some functions take a variable number of arguments, or a few expected
+  // arguments at the beginning and then a variable number of values to operate
+  // on. This helper accumulates all remaining arguments past the function’s
+  // argument length (or an explicit `startIndex`), into an array that becomes
+  // the last argument. Similar to ES6’s "rest parameter".
+  function restArguments(func, startIndex) {
+    startIndex = startIndex == null ? func.length - 1 : +startIndex;
+    return function() {
+      var length = Math.max(arguments.length - startIndex, 0),
+          rest = Array(length),
+          index = 0;
+      for (; index < length; index++) {
+        rest[index] = arguments[index + startIndex];
+      }
+      switch (startIndex) {
+        case 0: return func.call(this, rest);
+        case 1: return func.call(this, arguments[0], rest);
+        case 2: return func.call(this, arguments[0], arguments[1], rest);
+      }
+      var args = Array(startIndex + 1);
+      for (index = 0; index < startIndex; index++) {
+        args[index] = arguments[index];
+      }
+      args[startIndex] = rest;
+      return func.apply(this, args);
+    };
+  }
+
+  // Is a given variable an object?
+  function isObject(obj) {
+    var type = typeof obj;
+    return type === 'function' || type === 'object' && !!obj;
+  }
+
+  // Is a given value equal to null?
+  function isNull(obj) {
+    return obj === null;
+  }
+
+  // Is a given variable undefined?
+  function isUndefined(obj) {
+    return obj === void 0;
+  }
+
+  // Is a given value a boolean?
+  function isBoolean(obj) {
+    return obj === true || obj === false || toString.call(obj) === '[object Boolean]';
+  }
+
+  // Is a given value a DOM element?
+  function isElement(obj) {
+    return !!(obj && obj.nodeType === 1);
+  }
+
+  // Internal function for creating a `toString`-based type tester.
+  function tagTester(name) {
+    var tag = '[object ' + name + ']';
+    return function(obj) {
+      return toString.call(obj) === tag;
+    };
+  }
+
+  var isString = tagTester('String');
+
+  var isNumber = tagTester('Number');
+
+  var isDate = tagTester('Date');
+
+  var isRegExp = tagTester('RegExp');
+
+  var isError = tagTester('Error');
+
+  var isSymbol = tagTester('Symbol');
+
+  var isArrayBuffer = tagTester('ArrayBuffer');
+
+  var isFunction = tagTester('Function');
+
+  // Optimize `isFunction` if appropriate. Work around some `typeof` bugs in old
+  // v8, IE 11 (#1621), Safari 8 (#1929), and PhantomJS (#2236).
+  var nodelist = root.document && root.document.childNodes;
+  if (typeof /./ != 'function' && typeof Int8Array != 'object' && typeof nodelist != 'function') {
+    isFunction = function(obj) {
+      return typeof obj == 'function' || false;
+    };
+  }
+
+  var isFunction$1 = isFunction;
+
+  var hasObjectTag = tagTester('Object');
+
+  // In IE 10 - Edge 13, `DataView` has string tag `'[object Object]'`.
+  // In IE 11, the most common among them, this problem also applies to
+  // `Map`, `WeakMap` and `Set`.
+  var hasStringTagBug = (
+        supportsDataView && hasObjectTag(new DataView(new ArrayBuffer(8)))
+      ),
+      isIE11 = (typeof Map !== 'undefined' && hasObjectTag(new Map));
+
+  var isDataView = tagTester('DataView');
+
+  // In IE 10 - Edge 13, we need a different heuristic
+  // to determine whether an object is a `DataView`.
+  function ie10IsDataView(obj) {
+    return obj != null && isFunction$1(obj.getInt8) && isArrayBuffer(obj.buffer);
+  }
+
+  var isDataView$1 = (hasStringTagBug ? ie10IsDataView : isDataView);
+
+  // Is a given value an array?
+  // Delegates to ECMA5's native `Array.isArray`.
+  var isArray = nativeIsArray || tagTester('Array');
+
+  // Internal function to check whether `key` is an own property name of `obj`.
+  function has$1(obj, key) {
+    return obj != null && hasOwnProperty.call(obj, key);
+  }
+
+  var isArguments = tagTester('Arguments');
+
+  // Define a fallback version of the method in browsers (ahem, IE < 9), where
+  // there isn't any inspectable "Arguments" type.
+  (function() {
+    if (!isArguments(arguments)) {
+      isArguments = function(obj) {
+        return has$1(obj, 'callee');
+      };
+    }
+  }());
+
+  var isArguments$1 = isArguments;
+
+  // Is a given object a finite number?
+  function isFinite$1(obj) {
+    return !isSymbol(obj) && _isFinite(obj) && !isNaN(parseFloat(obj));
+  }
+
+  // Is the given value `NaN`?
+  function isNaN$1(obj) {
+    return isNumber(obj) && _isNaN(obj);
+  }
+
+  // Predicate-generating function. Often useful outside of Underscore.
+  function constant(value) {
+    return function() {
+      return value;
+    };
+  }
+
+  // Common internal logic for `isArrayLike` and `isBufferLike`.
+  function createSizePropertyCheck(getSizeProperty) {
+    return function(collection) {
+      var sizeProperty = getSizeProperty(collection);
+      return typeof sizeProperty == 'number' && sizeProperty >= 0 && sizeProperty <= MAX_ARRAY_INDEX;
+    }
+  }
+
+  // Internal helper to generate a function to obtain property `key` from `obj`.
+  function shallowProperty(key) {
+    return function(obj) {
+      return obj == null ? void 0 : obj[key];
+    };
+  }
+
+  // Internal helper to obtain the `byteLength` property of an object.
+  var getByteLength = shallowProperty('byteLength');
+
+  // Internal helper to determine whether we should spend extensive checks against
+  // `ArrayBuffer` et al.
+  var isBufferLike = createSizePropertyCheck(getByteLength);
+
+  // Is a given value a typed array?
+  var typedArrayPattern = /\[object ((I|Ui)nt(8|16|32)|Float(32|64)|Uint8Clamped|Big(I|Ui)nt64)Array\]/;
+  function isTypedArray(obj) {
+    // `ArrayBuffer.isView` is the most future-proof, so use it when available.
+    // Otherwise, fall back on the above regular expression.
+    return nativeIsView ? (nativeIsView(obj) && !isDataView$1(obj)) :
+                  isBufferLike(obj) && typedArrayPattern.test(toString.call(obj));
+  }
+
+  var isTypedArray$1 = supportsArrayBuffer ? isTypedArray : constant(false);
+
+  // Internal helper to obtain the `length` property of an object.
+  var getLength = shallowProperty('length');
+
+  // Internal helper to create a simple lookup structure.
+  // `collectNonEnumProps` used to depend on `_.contains`, but this led to
+  // circular imports. `emulatedSet` is a one-off solution that only works for
+  // arrays of strings.
+  function emulatedSet(keys) {
+    var hash = {};
+    for (var l = keys.length, i = 0; i < l; ++i) hash[keys[i]] = true;
+    return {
+      contains: function(key) { return hash[key] === true; },
+      push: function(key) {
+        hash[key] = true;
+        return keys.push(key);
+      }
+    };
+  }
+
+  // Internal helper. Checks `keys` for the presence of keys in IE < 9 that won't
+  // be iterated by `for key in ...` and thus missed. Extends `keys` in place if
+  // needed.
+  function collectNonEnumProps(obj, keys) {
+    keys = emulatedSet(keys);
+    var nonEnumIdx = nonEnumerableProps.length;
+    var constructor = obj.constructor;
+    var proto = isFunction$1(constructor) && constructor.prototype || ObjProto;
+
+    // Constructor is a special case.
+    var prop = 'constructor';
+    if (has$1(obj, prop) && !keys.contains(prop)) keys.push(prop);
+
+    while (nonEnumIdx--) {
+      prop = nonEnumerableProps[nonEnumIdx];
+      if (prop in obj && obj[prop] !== proto[prop] && !keys.contains(prop)) {
+        keys.push(prop);
+      }
+    }
+  }
+
+  // Retrieve the names of an object's own properties.
+  // Delegates to **ECMAScript 5**'s native `Object.keys`.
+  function keys(obj) {
+    if (!isObject(obj)) return [];
+    if (nativeKeys) return nativeKeys(obj);
+    var keys = [];
+    for (var key in obj) if (has$1(obj, key)) keys.push(key);
+    // Ahem, IE < 9.
+    if (hasEnumBug) collectNonEnumProps(obj, keys);
+    return keys;
+  }
+
+  // Is a given array, string, or object empty?
+  // An "empty" object has no enumerable own-properties.
+  function isEmpty(obj) {
+    if (obj == null) return true;
+    // Skip the more expensive `toString`-based type checks if `obj` has no
+    // `.length`.
+    var length = getLength(obj);
+    if (typeof length == 'number' && (
+      isArray(obj) || isString(obj) || isArguments$1(obj)
+    )) return length === 0;
+    return getLength(keys(obj)) === 0;
+  }
+
+  // Returns whether an object has a given set of `key:value` pairs.
+  function isMatch(object, attrs) {
+    var _keys = keys(attrs), length = _keys.length;
+    if (object == null) return !length;
+    var obj = Object(object);
+    for (var i = 0; i < length; i++) {
+      var key = _keys[i];
+      if (attrs[key] !== obj[key] || !(key in obj)) return false;
+    }
+    return true;
+  }
+
+  // If Underscore is called as a function, it returns a wrapped object that can
+  // be used OO-style. This wrapper holds altered versions of all functions added
+  // through `_.mixin`. Wrapped objects may be chained.
+  function _$1(obj) {
+    if (obj instanceof _$1) return obj;
+    if (!(this instanceof _$1)) return new _$1(obj);
+    this._wrapped = obj;
+  }
+
+  _$1.VERSION = VERSION;
+
+  // Extracts the result from a wrapped and chained object.
+  _$1.prototype.value = function() {
+    return this._wrapped;
+  };
+
+  // Provide unwrapping proxies for some methods used in engine operations
+  // such as arithmetic and JSON stringification.
+  _$1.prototype.valueOf = _$1.prototype.toJSON = _$1.prototype.value;
+
+  _$1.prototype.toString = function() {
+    return String(this._wrapped);
+  };
+
+  // Internal function to wrap or shallow-copy an ArrayBuffer,
+  // typed array or DataView to a new view, reusing the buffer.
+  function toBufferView(bufferSource) {
+    return new Uint8Array(
+      bufferSource.buffer || bufferSource,
+      bufferSource.byteOffset || 0,
+      getByteLength(bufferSource)
+    );
+  }
+
+  // We use this string twice, so give it a name for minification.
+  var tagDataView = '[object DataView]';
+
+  // Internal recursive comparison function for `_.isEqual`.
+  function eq(a, b, aStack, bStack) {
+    // Identical objects are equal. `0 === -0`, but they aren't identical.
+    // See the [Harmony `egal` proposal](https://wiki.ecmascript.org/doku.php?id=harmony:egal).
+    if (a === b) return a !== 0 || 1 / a === 1 / b;
+    // `null` or `undefined` only equal to itself (strict comparison).
+    if (a == null || b == null) return false;
+    // `NaN`s are equivalent, but non-reflexive.
+    if (a !== a) return b !== b;
+    // Exhaust primitive checks
+    var type = typeof a;
+    if (type !== 'function' && type !== 'object' && typeof b != 'object') return false;
+    return deepEq(a, b, aStack, bStack);
+  }
+
+  // Internal recursive comparison function for `_.isEqual`.
+  function deepEq(a, b, aStack, bStack) {
+    // Unwrap any wrapped objects.
+    if (a instanceof _$1) a = a._wrapped;
+    if (b instanceof _$1) b = b._wrapped;
+    // Compare `[[Class]]` names.
+    var className = toString.call(a);
+    if (className !== toString.call(b)) return false;
+    // Work around a bug in IE 10 - Edge 13.
+    if (hasStringTagBug && className == '[object Object]' && isDataView$1(a)) {
+      if (!isDataView$1(b)) return false;
+      className = tagDataView;
+    }
+    switch (className) {
+      // These types are compared by value.
+      case '[object RegExp]':
+        // RegExps are coerced to strings for comparison (Note: '' + /a/i === '/a/i')
+      case '[object String]':
+        // Primitives and their corresponding object wrappers are equivalent; thus, `"5"` is
+        // equivalent to `new String("5")`.
+        return '' + a === '' + b;
+      case '[object Number]':
+        // `NaN`s are equivalent, but non-reflexive.
+        // Object(NaN) is equivalent to NaN.
+        if (+a !== +a) return +b !== +b;
+        // An `egal` comparison is performed for other numeric values.
+        return +a === 0 ? 1 / +a === 1 / b : +a === +b;
+      case '[object Date]':
+      case '[object Boolean]':
+        // Coerce dates and booleans to numeric primitive values. Dates are compared by their
+        // millisecond representations. Note that invalid dates with millisecond representations
+        // of `NaN` are not equivalent.
+        return +a === +b;
+      case '[object Symbol]':
+        return SymbolProto.valueOf.call(a) === SymbolProto.valueOf.call(b);
+      case '[object ArrayBuffer]':
+      case tagDataView:
+        // Coerce to typed array so we can fall through.
+        return deepEq(toBufferView(a), toBufferView(b), aStack, bStack);
+    }
+
+    var areArrays = className === '[object Array]';
+    if (!areArrays && isTypedArray$1(a)) {
+        var byteLength = getByteLength(a);
+        if (byteLength !== getByteLength(b)) return false;
+        if (a.buffer === b.buffer && a.byteOffset === b.byteOffset) return true;
+        areArrays = true;
+    }
+    if (!areArrays) {
+      if (typeof a != 'object' || typeof b != 'object') return false;
+
+      // Objects with different constructors are not equivalent, but `Object`s or `Array`s
+      // from different frames are.
+      var aCtor = a.constructor, bCtor = b.constructor;
+      if (aCtor !== bCtor && !(isFunction$1(aCtor) && aCtor instanceof aCtor &&
+                               isFunction$1(bCtor) && bCtor instanceof bCtor)
+                          && ('constructor' in a && 'constructor' in b)) {
+        return false;
+      }
+    }
+    // Assume equality for cyclic structures. The algorithm for detecting cyclic
+    // structures is adapted from ES 5.1 section 15.12.3, abstract operation `JO`.
+
+    // Initializing stack of traversed objects.
+    // It's done here since we only need them for objects and arrays comparison.
+    aStack = aStack || [];
+    bStack = bStack || [];
+    var length = aStack.length;
+    while (length--) {
+      // Linear search. Performance is inversely proportional to the number of
+      // unique nested structures.
+      if (aStack[length] === a) return bStack[length] === b;
+    }
+
+    // Add the first object to the stack of traversed objects.
+    aStack.push(a);
+    bStack.push(b);
+
+    // Recursively compare objects and arrays.
+    if (areArrays) {
+      // Compare array lengths to determine if a deep comparison is necessary.
+      length = a.length;
+      if (length !== b.length) return false;
+      // Deep compare the contents, ignoring non-numeric properties.
+      while (length--) {
+        if (!eq(a[length], b[length], aStack, bStack)) return false;
+      }
+    } else {
+      // Deep compare objects.
+      var _keys = keys(a), key;
+      length = _keys.length;
+      // Ensure that both objects contain the same number of properties before comparing deep equality.
+      if (keys(b).length !== length) return false;
+      while (length--) {
+        // Deep compare each member
+        key = _keys[length];
+        if (!(has$1(b, key) && eq(a[key], b[key], aStack, bStack))) return false;
+      }
+    }
+    // Remove the first object from the stack of traversed objects.
+    aStack.pop();
+    bStack.pop();
+    return true;
+  }
+
+  // Perform a deep comparison to check if two objects are equal.
+  function isEqual(a, b) {
+    return eq(a, b);
+  }
+
+  // Retrieve all the enumerable property names of an object.
+  function allKeys(obj) {
+    if (!isObject(obj)) return [];
+    var keys = [];
+    for (var key in obj) keys.push(key);
+    // Ahem, IE < 9.
+    if (hasEnumBug) collectNonEnumProps(obj, keys);
+    return keys;
+  }
+
+  // Since the regular `Object.prototype.toString` type tests don't work for
+  // some types in IE 11, we use a fingerprinting heuristic instead, based
+  // on the methods. It's not great, but it's the best we got.
+  // The fingerprint method lists are defined below.
+  function ie11fingerprint(methods) {
+    var length = getLength(methods);
+    return function(obj) {
+      if (obj == null) return false;
+      // `Map`, `WeakMap` and `Set` have no enumerable keys.
+      var keys = allKeys(obj);
+      if (getLength(keys)) return false;
+      for (var i = 0; i < length; i++) {
+        if (!isFunction$1(obj[methods[i]])) return false;
+      }
+      // If we are testing against `WeakMap`, we need to ensure that
+      // `obj` doesn't have a `forEach` method in order to distinguish
+      // it from a regular `Map`.
+      return methods !== weakMapMethods || !isFunction$1(obj[forEachName]);
+    };
+  }
+
+  // In the interest of compact minification, we write
+  // each string in the fingerprints only once.
+  var forEachName = 'forEach',
+      hasName = 'has',
+      commonInit = ['clear', 'delete'],
+      mapTail = ['get', hasName, 'set'];
+
+  // `Map`, `WeakMap` and `Set` each have slightly different
+  // combinations of the above sublists.
+  var mapMethods = commonInit.concat(forEachName, mapTail),
+      weakMapMethods = commonInit.concat(mapTail),
+      setMethods = ['add'].concat(commonInit, forEachName, hasName);
+
+  var isMap = isIE11 ? ie11fingerprint(mapMethods) : tagTester('Map');
+
+  var isWeakMap = isIE11 ? ie11fingerprint(weakMapMethods) : tagTester('WeakMap');
+
+  var isSet = isIE11 ? ie11fingerprint(setMethods) : tagTester('Set');
+
+  var isWeakSet = tagTester('WeakSet');
+
+  // Retrieve the values of an object's properties.
+  function values(obj) {
+    var _keys = keys(obj);
+    var length = _keys.length;
+    var values = Array(length);
+    for (var i = 0; i < length; i++) {
+      values[i] = obj[_keys[i]];
+    }
+    return values;
+  }
+
+  // Convert an object into a list of `[key, value]` pairs.
+  // The opposite of `_.object` with one argument.
+  function pairs(obj) {
+    var _keys = keys(obj);
+    var length = _keys.length;
+    var pairs = Array(length);
+    for (var i = 0; i < length; i++) {
+      pairs[i] = [_keys[i], obj[_keys[i]]];
+    }
+    return pairs;
+  }
+
+  // Invert the keys and values of an object. The values must be serializable.
+  function invert(obj) {
+    var result = {};
+    var _keys = keys(obj);
+    for (var i = 0, length = _keys.length; i < length; i++) {
+      result[obj[_keys[i]]] = _keys[i];
+    }
+    return result;
+  }
+
+  // Return a sorted list of the function names available on the object.
+  function functions(obj) {
+    var names = [];
+    for (var key in obj) {
+      if (isFunction$1(obj[key])) names.push(key);
+    }
+    return names.sort();
+  }
+
+  // An internal function for creating assigner functions.
+  function createAssigner(keysFunc, defaults) {
+    return function(obj) {
+      var length = arguments.length;
+      if (defaults) obj = Object(obj);
+      if (length < 2 || obj == null) return obj;
+      for (var index = 1; index < length; index++) {
+        var source = arguments[index],
+            keys = keysFunc(source),
+            l = keys.length;
+        for (var i = 0; i < l; i++) {
+          var key = keys[i];
+          if (!defaults || obj[key] === void 0) obj[key] = source[key];
+        }
+      }
+      return obj;
+    };
+  }
+
+  // Extend a given object with all the properties in passed-in object(s).
+  var extend = createAssigner(allKeys);
+
+  // Assigns a given object with all the own properties in the passed-in
+  // object(s).
+  // (https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Object/assign)
+  var extendOwn = createAssigner(keys);
+
+  // Fill in a given object with default properties.
+  var defaults = createAssigner(allKeys, true);
+
+  // Create a naked function reference for surrogate-prototype-swapping.
+  function ctor() {
+    return function(){};
+  }
+
+  // An internal function for creating a new object that inherits from another.
+  function baseCreate(prototype) {
+    if (!isObject(prototype)) return {};
+    if (nativeCreate) return nativeCreate(prototype);
+    var Ctor = ctor();
+    Ctor.prototype = prototype;
+    var result = new Ctor;
+    Ctor.prototype = null;
+    return result;
+  }
+
+  // Creates an object that inherits from the given prototype object.
+  // If additional properties are provided then they will be added to the
+  // created object.
+  function create(prototype, props) {
+    var result = baseCreate(prototype);
+    if (props) extendOwn(result, props);
+    return result;
+  }
+
+  // Create a (shallow-cloned) duplicate of an object.
+  function clone(obj) {
+    if (!isObject(obj)) return obj;
+    return isArray(obj) ? obj.slice() : extend({}, obj);
+  }
+
+  // Invokes `interceptor` with the `obj` and then returns `obj`.
+  // The primary purpose of this method is to "tap into" a method chain, in
+  // order to perform operations on intermediate results within the chain.
+  function tap(obj, interceptor) {
+    interceptor(obj);
+    return obj;
+  }
+
+  // Normalize a (deep) property `path` to array.
+  // Like `_.iteratee`, this function can be customized.
+  function toPath$1(path) {
+    return isArray(path) ? path : [path];
+  }
+  _$1.toPath = toPath$1;
+
+  // Internal wrapper for `_.toPath` to enable minification.
+  // Similar to `cb` for `_.iteratee`.
+  function toPath(path) {
+    return _$1.toPath(path);
+  }
+
+  // Internal function to obtain a nested property in `obj` along `path`.
+  function deepGet(obj, path) {
+    var length = path.length;
+    for (var i = 0; i < length; i++) {
+      if (obj == null) return void 0;
+      obj = obj[path[i]];
+    }
+    return length ? obj : void 0;
+  }
+
+  // Get the value of the (deep) property on `path` from `object`.
+  // If any property in `path` does not exist or if the value is
+  // `undefined`, return `defaultValue` instead.
+  // The `path` is normalized through `_.toPath`.
+  function get(object, path, defaultValue) {
+    var value = deepGet(object, toPath(path));
+    return isUndefined(value) ? defaultValue : value;
+  }
+
+  // Shortcut function for checking if an object has a given property directly on
+  // itself (in other words, not on a prototype). Unlike the internal `has`
+  // function, this public version can also traverse nested properties.
+  function has(obj, path) {
+    path = toPath(path);
+    var length = path.length;
+    for (var i = 0; i < length; i++) {
+      var key = path[i];
+      if (!has$1(obj, key)) return false;
+      obj = obj[key];
+    }
+    return !!length;
+  }
+
+  // Keep the identity function around for default iteratees.
+  function identity(value) {
+    return value;
+  }
+
+  // Returns a predicate for checking whether an object has a given set of
+  // `key:value` pairs.
+  function matcher(attrs) {
+    attrs = extendOwn({}, attrs);
+    return function(obj) {
+      return isMatch(obj, attrs);
+    };
+  }
+
+  // Creates a function that, when passed an object, will traverse that object’s
+  // properties down the given `path`, specified as an array of keys or indices.
+  function property(path) {
+    path = toPath(path);
+    return function(obj) {
+      return deepGet(obj, path);
+    };
+  }
+
+  // Internal function that returns an efficient (for current engines) version
+  // of the passed-in callback, to be repeatedly applied in other Underscore
+  // functions.
+  function optimizeCb(func, context, argCount) {
+    if (context === void 0) return func;
+    switch (argCount == null ? 3 : argCount) {
+      case 1: return function(value) {
+        return func.call(context, value);
+      };
+      // The 2-argument case is omitted because we’re not using it.
+      case 3: return function(value, index, collection) {
+        return func.call(context, value, index, collection);
+      };
+      case 4: return function(accumulator, value, index, collection) {
+        return func.call(context, accumulator, value, index, collection);
+      };
+    }
+    return function() {
+      return func.apply(context, arguments);
+    };
+  }
+
+  // An internal function to generate callbacks that can be applied to each
+  // element in a collection, returning the desired result — either `_.identity`,
+  // an arbitrary callback, a property matcher, or a property accessor.
+  function baseIteratee(value, context, argCount) {
+    if (value == null) return identity;
+    if (isFunction$1(value)) return optimizeCb(value, context, argCount);
+    if (isObject(value) && !isArray(value)) return matcher(value);
+    return property(value);
+  }
+
+  // External wrapper for our callback generator. Users may customize
+  // `_.iteratee` if they want additional predicate/iteratee shorthand styles.
+  // This abstraction hides the internal-only `argCount` argument.
+  function iteratee(value, context) {
+    return baseIteratee(value, context, Infinity);
+  }
+  _$1.iteratee = iteratee;
+
+  // The function we call internally to generate a callback. It invokes
+  // `_.iteratee` if overridden, otherwise `baseIteratee`.
+  function cb(value, context, argCount) {
+    if (_$1.iteratee !== iteratee) return _$1.iteratee(value, context);
+    return baseIteratee(value, context, argCount);
+  }
+
+  // Returns the results of applying the `iteratee` to each element of `obj`.
+  // In contrast to `_.map` it returns an object.
+  function mapObject(obj, iteratee, context) {
+    iteratee = cb(iteratee, context);
+    var _keys = keys(obj),
+        length = _keys.length,
+        results = {};
+    for (var index = 0; index < length; index++) {
+      var currentKey = _keys[index];
+      results[currentKey] = iteratee(obj[currentKey], currentKey, obj);
+    }
+    return results;
+  }
+
+  // Predicate-generating function. Often useful outside of Underscore.
+  function noop(){}
+
+  // Generates a function for a given object that returns a given property.
+  function propertyOf(obj) {
+    if (obj == null) return noop;
+    return function(path) {
+      return get(obj, path);
+    };
+  }
+
+  // Run a function **n** times.
+  function times(n, iteratee, context) {
+    var accum = Array(Math.max(0, n));
+    iteratee = optimizeCb(iteratee, context, 1);
+    for (var i = 0; i < n; i++) accum[i] = iteratee(i);
+    return accum;
+  }
+
+  // Return a random integer between `min` and `max` (inclusive).
+  function random(min, max) {
+    if (max == null) {
+      max = min;
+      min = 0;
+    }
+    return min + Math.floor(Math.random() * (max - min + 1));
+  }
+
+  // A (possibly faster) way to get the current timestamp as an integer.
+  var now = Date.now || function() {
+    return new Date().getTime();
+  };
+
+  // Internal helper to generate functions for escaping and unescaping strings
+  // to/from HTML interpolation.
+  function createEscaper(map) {
+    var escaper = function(match) {
+      return map[match];
+    };
+    // Regexes for identifying a key that needs to be escaped.
+    var source = '(?:' + keys(map).join('|') + ')';
+    var testRegexp = RegExp(source);
+    var replaceRegexp = RegExp(source, 'g');
+    return function(string) {
+      string = string == null ? '' : '' + string;
+      return testRegexp.test(string) ? string.replace(replaceRegexp, escaper) : string;
+    };
+  }
+
+  // Internal list of HTML entities for escaping.
+  var escapeMap = {
+    '&': '&amp;',
+    '<': '&lt;',
+    '>': '&gt;',
+    '"': '&quot;',
+    "'": '&#x27;',
+    '`': '&#x60;'
+  };
+
+  // Function for escaping strings to HTML interpolation.
+  var _escape = createEscaper(escapeMap);
+
+  // Internal list of HTML entities for unescaping.
+  var unescapeMap = invert(escapeMap);
+
+  // Function for unescaping strings from HTML interpolation.
+  var _unescape = createEscaper(unescapeMap);
+
+  // By default, Underscore uses ERB-style template delimiters. Change the
+  // following template settings to use alternative delimiters.
+  var templateSettings = _$1.templateSettings = {
+    evaluate: /<%([\s\S]+?)%>/g,
+    interpolate: /<%=([\s\S]+?)%>/g,
+    escape: /<%-([\s\S]+?)%>/g
+  };
+
+  // When customizing `_.templateSettings`, if you don't want to define an
+  // interpolation, evaluation or escaping regex, we need one that is
+  // guaranteed not to match.
+  var noMatch = /(.)^/;
+
+  // Certain characters need to be escaped so that they can be put into a
+  // string literal.
+  var escapes = {
+    "'": "'",
+    '\\': '\\',
+    '\r': 'r',
+    '\n': 'n',
+    '\u2028': 'u2028',
+    '\u2029': 'u2029'
+  };
+
+  var escapeRegExp = /\\|'|\r|\n|\u2028|\u2029/g;
+
+  function escapeChar(match) {
+    return '\\' + escapes[match];
+  }
+
+  // In order to prevent third-party code injection through
+  // `_.templateSettings.variable`, we test it against the following regular
+  // expression. It is intentionally a bit more liberal than just matching valid
+  // identifiers, but still prevents possible loopholes through defaults or
+  // destructuring assignment.
+  var bareIdentifier = /^\s*(\w|\$)+\s*$/;
+
+  // JavaScript micro-templating, similar to John Resig's implementation.
+  // Underscore templating handles arbitrary delimiters, preserves whitespace,
+  // and correctly escapes quotes within interpolated code.
+  // NB: `oldSettings` only exists for backwards compatibility.
+  function template(text, settings, oldSettings) {
+    if (!settings && oldSettings) settings = oldSettings;
+    settings = defaults({}, settings, _$1.templateSettings);
+
+    // Combine delimiters into one regular expression via alternation.
+    var matcher = RegExp([
+      (settings.escape || noMatch).source,
+      (settings.interpolate || noMatch).source,
+      (settings.evaluate || noMatch).source
+    ].join('|') + '|$', 'g');
+
+    // Compile the template source, escaping string literals appropriately.
+    var index = 0;
+    var source = "__p+='";
+    text.replace(matcher, function(match, escape, interpolate, evaluate, offset) {
+      source += text.slice(index, offset).replace(escapeRegExp, escapeChar);
+      index = offset + match.length;
+
+      if (escape) {
+        source += "'+\n((__t=(" + escape + "))==null?'':_.escape(__t))+\n'";
+      } else if (interpolate) {
+        source += "'+\n((__t=(" + interpolate + "))==null?'':__t)+\n'";
+      } else if (evaluate) {
+        source += "';\n" + evaluate + "\n__p+='";
+      }
+
+      // Adobe VMs need the match returned to produce the correct offset.
+      return match;
+    });
+    source += "';\n";
+
+    var argument = settings.variable;
+    if (argument) {
+      // Insure against third-party code injection. (CVE-2021-23358)
+      if (!bareIdentifier.test(argument)) throw new Error(
+        'variable is not a bare identifier: ' + argument
+      );
+    } else {
+      // If a variable is not specified, place data values in local scope.
+      source = 'with(obj||{}){\n' + source + '}\n';
+      argument = 'obj';
+    }
+
+    source = "var __t,__p='',__j=Array.prototype.join," +
+      "print=function(){__p+=__j.call(arguments,'');};\n" +
+      source + 'return __p;\n';
+
+    var render;
+    try {
+      render = new Function(argument, '_', source);
+    } catch (e) {
+      e.source = source;
+      throw e;
+    }
+
+    var template = function(data) {
+      return render.call(this, data, _$1);
+    };
+
+    // Provide the compiled source as a convenience for precompilation.
+    template.source = 'function(' + argument + '){\n' + source + '}';
+
+    return template;
+  }
+
+  // Traverses the children of `obj` along `path`. If a child is a function, it
+  // is invoked with its parent as context. Returns the value of the final
+  // child, or `fallback` if any child is undefined.
+  function result(obj, path, fallback) {
+    path = toPath(path);
+    var length = path.length;
+    if (!length) {
+      return isFunction$1(fallback) ? fallback.call(obj) : fallback;
+    }
+    for (var i = 0; i < length; i++) {
+      var prop = obj == null ? void 0 : obj[path[i]];
+      if (prop === void 0) {
+        prop = fallback;
+        i = length; // Ensure we don't continue iterating.
+      }
+      obj = isFunction$1(prop) ? prop.call(obj) : prop;
+    }
+    return obj;
+  }
+
+  // Generate a unique integer id (unique within the entire client session).
+  // Useful for temporary DOM ids.
+  var idCounter = 0;
+  function uniqueId(prefix) {
+    var id = ++idCounter + '';
+    return prefix ? prefix + id : id;
+  }
+
+  // Start chaining a wrapped Underscore object.
+  function chain(obj) {
+    var instance = _$1(obj);
+    instance._chain = true;
+    return instance;
+  }
+
+  // Internal function to execute `sourceFunc` bound to `context` with optional
+  // `args`. Determines whether to execute a function as a constructor or as a
+  // normal function.
+  function executeBound(sourceFunc, boundFunc, context, callingContext, args) {
+    if (!(callingContext instanceof boundFunc)) return sourceFunc.apply(context, args);
+    var self = baseCreate(sourceFunc.prototype);
+    var result = sourceFunc.apply(self, args);
+    if (isObject(result)) return result;
+    return self;
+  }
+
+  // Partially apply a function by creating a version that has had some of its
+  // arguments pre-filled, without changing its dynamic `this` context. `_` acts
+  // as a placeholder by default, allowing any combination of arguments to be
+  // pre-filled. Set `_.partial.placeholder` for a custom placeholder argument.
+  var partial = restArguments(function(func, boundArgs) {
+    var placeholder = partial.placeholder;
+    var bound = function() {
+      var position = 0, length = boundArgs.length;
+      var args = Array(length);
+      for (var i = 0; i < length; i++) {
+        args[i] = boundArgs[i] === placeholder ? arguments[position++] : boundArgs[i];
+      }
+      while (position < arguments.length) args.push(arguments[position++]);
+      return executeBound(func, bound, this, this, args);
+    };
+    return bound;
+  });
+
+  partial.placeholder = _$1;
+
+  // Create a function bound to a given object (assigning `this`, and arguments,
+  // optionally).
+  var bind = restArguments(function(func, context, args) {
+    if (!isFunction$1(func)) throw new TypeError('Bind must be called on a function');
+    var bound = restArguments(function(callArgs) {
+      return executeBound(func, bound, context, this, args.concat(callArgs));
+    });
+    return bound;
+  });
+
+  // Internal helper for collection methods to determine whether a collection
+  // should be iterated as an array or as an object.
+  // Related: https://people.mozilla.org/~jorendorff/es6-draft.html#sec-tolength
+  // Avoids a very nasty iOS 8 JIT bug on ARM-64. #2094
+  var isArrayLike = createSizePropertyCheck(getLength);
+
+  // Internal implementation of a recursive `flatten` function.
+  function flatten$1(input, depth, strict, output) {
+    output = output || [];
+    if (!depth && depth !== 0) {
+      depth = Infinity;
+    } else if (depth <= 0) {
+      return output.concat(input);
+    }
+    var idx = output.length;
+    for (var i = 0, length = getLength(input); i < length; i++) {
+      var value = input[i];
+      if (isArrayLike(value) && (isArray(value) || isArguments$1(value))) {
+        // Flatten current level of array or arguments object.
+        if (depth > 1) {
+          flatten$1(value, depth - 1, strict, output);
+          idx = output.length;
+        } else {
+          var j = 0, len = value.length;
+          while (j < len) output[idx++] = value[j++];
+        }
+      } else if (!strict) {
+        output[idx++] = value;
+      }
+    }
+    return output;
+  }
+
+  // Bind a number of an object's methods to that object. Remaining arguments
+  // are the method names to be bound. Useful for ensuring that all callbacks
+  // defined on an object belong to it.
+  var bindAll = restArguments(function(obj, keys) {
+    keys = flatten$1(keys, false, false);
+    var index = keys.length;
+    if (index < 1) throw new Error('bindAll must be passed function names');
+    while (index--) {
+      var key = keys[index];
+      obj[key] = bind(obj[key], obj);
+    }
+    return obj;
+  });
+
+  // Memoize an expensive function by storing its results.
+  function memoize(func, hasher) {
+    var memoize = function(key) {
+      var cache = memoize.cache;
+      var address = '' + (hasher ? hasher.apply(this, arguments) : key);
+      if (!has$1(cache, address)) cache[address] = func.apply(this, arguments);
+      return cache[address];
+    };
+    memoize.cache = {};
+    return memoize;
+  }
+
+  // Delays a function for the given number of milliseconds, and then calls
+  // it with the arguments supplied.
+  var delay = restArguments(function(func, wait, args) {
+    return setTimeout(function() {
+      return func.apply(null, args);
+    }, wait);
+  });
+
+  // Defers a function, scheduling it to run after the current call stack has
+  // cleared.
+  var defer = partial(delay, _$1, 1);
+
+  // Returns a function, that, when invoked, will only be triggered at most once
+  // during a given window of time. Normally, the throttled function will run
+  // as much as it can, without ever going more than once per `wait` duration;
+  // but if you'd like to disable the execution on the leading edge, pass
+  // `{leading: false}`. To disable execution on the trailing edge, ditto.
+  function throttle(func, wait, options) {
+    var timeout, context, args, result;
+    var previous = 0;
+    if (!options) options = {};
+
+    var later = function() {
+      previous = options.leading === false ? 0 : now();
+      timeout = null;
+      result = func.apply(context, args);
+      if (!timeout) context = args = null;
+    };
+
+    var throttled = function() {
+      var _now = now();
+      if (!previous && options.leading === false) previous = _now;
+      var remaining = wait - (_now - previous);
+      context = this;
+      args = arguments;
+      if (remaining <= 0 || remaining > wait) {
+        if (timeout) {
+          clearTimeout(timeout);
+          timeout = null;
+        }
+        previous = _now;
+        result = func.apply(context, args);
+        if (!timeout) context = args = null;
+      } else if (!timeout && options.trailing !== false) {
+        timeout = setTimeout(later, remaining);
+      }
+      return result;
+    };
+
+    throttled.cancel = function() {
+      clearTimeout(timeout);
+      previous = 0;
+      timeout = context = args = null;
+    };
+
+    return throttled;
+  }
+
+  // When a sequence of calls of the returned function ends, the argument
+  // function is triggered. The end of a sequence is defined by the `wait`
+  // parameter. If `immediate` is passed, the argument function will be
+  // triggered at the beginning of the sequence instead of at the end.
+  function debounce(func, wait, immediate) {
+    var timeout, previous, args, result, context;
+
+    var later = function() {
+      var passed = now() - previous;
+      if (wait > passed) {
+        timeout = setTimeout(later, wait - passed);
+      } else {
+        timeout = null;
+        if (!immediate) result = func.apply(context, args);
+        // This check is needed because `func` can recursively invoke `debounced`.
+        if (!timeout) args = context = null;
+      }
+    };
+
+    var debounced = restArguments(function(_args) {
+      context = this;
+      args = _args;
+      previous = now();
+      if (!timeout) {
+        timeout = setTimeout(later, wait);
+        if (immediate) result = func.apply(context, args);
+      }
+      return result;
+    });
+
+    debounced.cancel = function() {
+      clearTimeout(timeout);
+      timeout = args = context = null;
+    };
+
+    return debounced;
+  }
+
+  // Returns the first function passed as an argument to the second,
+  // allowing you to adjust arguments, run code before and after, and
+  // conditionally execute the original function.
+  function wrap(func, wrapper) {
+    return partial(wrapper, func);
+  }
+
+  // Returns a negated version of the passed-in predicate.
+  function negate(predicate) {
+    return function() {
+      return !predicate.apply(this, arguments);
+    };
+  }
+
+  // Returns a function that is the composition of a list of functions, each
+  // consuming the return value of the function that follows.
+  function compose() {
+    var args = arguments;
+    var start = args.length - 1;
+    return function() {
+      var i = start;
+      var result = args[start].apply(this, arguments);
+      while (i--) result = args[i].call(this, result);
+      return result;
+    };
+  }
+
+  // Returns a function that will only be executed on and after the Nth call.
+  function after(times, func) {
+    return function() {
+      if (--times < 1) {
+        return func.apply(this, arguments);
+      }
+    };
+  }
+
+  // Returns a function that will only be executed up to (but not including) the
+  // Nth call.
+  function before(times, func) {
+    var memo;
+    return function() {
+      if (--times > 0) {
+        memo = func.apply(this, arguments);
+      }
+      if (times <= 1) func = null;
+      return memo;
+    };
+  }
+
+  // Returns a function that will be executed at most one time, no matter how
+  // often you call it. Useful for lazy initialization.
+  var once = partial(before, 2);
+
+  // Returns the first key on an object that passes a truth test.
+  function findKey(obj, predicate, context) {
+    predicate = cb(predicate, context);
+    var _keys = keys(obj), key;
+    for (var i = 0, length = _keys.length; i < length; i++) {
+      key = _keys[i];
+      if (predicate(obj[key], key, obj)) return key;
+    }
+  }
+
+  // Internal function to generate `_.findIndex` and `_.findLastIndex`.
+  function createPredicateIndexFinder(dir) {
+    return function(array, predicate, context) {
+      predicate = cb(predicate, context);
+      var length = getLength(array);
+      var index = dir > 0 ? 0 : length - 1;
+      for (; index >= 0 && index < length; index += dir) {
+        if (predicate(array[index], index, array)) return index;
+      }
+      return -1;
+    };
+  }
+
+  // Returns the first index on an array-like that passes a truth test.
+  var findIndex = createPredicateIndexFinder(1);
+
+  // Returns the last index on an array-like that passes a truth test.
+  var findLastIndex = createPredicateIndexFinder(-1);
+
+  // Use a comparator function to figure out the smallest index at which
+  // an object should be inserted so as to maintain order. Uses binary search.
+  function sortedIndex(array, obj, iteratee, context) {
+    iteratee = cb(iteratee, context, 1);
+    var value = iteratee(obj);
+    var low = 0, high = getLength(array);
+    while (low < high) {
+      var mid = Math.floor((low + high) / 2);
+      if (iteratee(array[mid]) < value) low = mid + 1; else high = mid;
+    }
+    return low;
+  }
+
+  // Internal function to generate the `_.indexOf` and `_.lastIndexOf` functions.
+  function createIndexFinder(dir, predicateFind, sortedIndex) {
+    return function(array, item, idx) {
+      var i = 0, length = getLength(array);
+      if (typeof idx == 'number') {
+        if (dir > 0) {
+          i = idx >= 0 ? idx : Math.max(idx + length, i);
+        } else {
+          length = idx >= 0 ? Math.min(idx + 1, length) : idx + length + 1;
+        }
+      } else if (sortedIndex && idx && length) {
+        idx = sortedIndex(array, item);
+        return array[idx] === item ? idx : -1;
+      }
+      if (item !== item) {
+        idx = predicateFind(slice.call(array, i, length), isNaN$1);
+        return idx >= 0 ? idx + i : -1;
+      }
+      for (idx = dir > 0 ? i : length - 1; idx >= 0 && idx < length; idx += dir) {
+        if (array[idx] === item) return idx;
+      }
+      return -1;
+    };
+  }
+
+  // Return the position of the first occurrence of an item in an array,
+  // or -1 if the item is not included in the array.
+  // If the array is large and already in sort order, pass `true`
+  // for **isSorted** to use binary search.
+  var indexOf = createIndexFinder(1, findIndex, sortedIndex);
+
+  // Return the position of the last occurrence of an item in an array,
+  // or -1 if the item is not included in the array.
+  var lastIndexOf = createIndexFinder(-1, findLastIndex);
+
+  // Return the first value which passes a truth test.
+  function find(obj, predicate, context) {
+    var keyFinder = isArrayLike(obj) ? findIndex : findKey;
+    var key = keyFinder(obj, predicate, context);
+    if (key !== void 0 && key !== -1) return obj[key];
+  }
+
+  // Convenience version of a common use case of `_.find`: getting the first
+  // object containing specific `key:value` pairs.
+  function findWhere(obj, attrs) {
+    return find(obj, matcher(attrs));
+  }
+
+  // The cornerstone for collection functions, an `each`
+  // implementation, aka `forEach`.
+  // Handles raw objects in addition to array-likes. Treats all
+  // sparse array-likes as if they were dense.
+  function each(obj, iteratee, context) {
+    iteratee = optimizeCb(iteratee, context);
+    var i, length;
+    if (isArrayLike(obj)) {
+      for (i = 0, length = obj.length; i < length; i++) {
+        iteratee(obj[i], i, obj);
+      }
+    } else {
+      var _keys = keys(obj);
+      for (i = 0, length = _keys.length; i < length; i++) {
+        iteratee(obj[_keys[i]], _keys[i], obj);
+      }
+    }
+    return obj;
+  }
+
+  // Return the results of applying the iteratee to each element.
+  function map(obj, iteratee, context) {
+    iteratee = cb(iteratee, context);
+    var _keys = !isArrayLike(obj) && keys(obj),
+        length = (_keys || obj).length,
+        results = Array(length);
+    for (var index = 0; index < length; index++) {
+      var currentKey = _keys ? _keys[index] : index;
+      results[index] = iteratee(obj[currentKey], currentKey, obj);
+    }
+    return results;
+  }
+
+  // Internal helper to create a reducing function, iterating left or right.
+  function createReduce(dir) {
+    // Wrap code that reassigns argument variables in a separate function than
+    // the one that accesses `arguments.length` to avoid a perf hit. (#1991)
+    var reducer = function(obj, iteratee, memo, initial) {
+      var _keys = !isArrayLike(obj) && keys(obj),
+          length = (_keys || obj).length,
+          index = dir > 0 ? 0 : length - 1;
+      if (!initial) {
+        memo = obj[_keys ? _keys[index] : index];
+        index += dir;
+      }
+      for (; index >= 0 && index < length; index += dir) {
+        var currentKey = _keys ? _keys[index] : index;
+        memo = iteratee(memo, obj[currentKey], currentKey, obj);
+      }
+      return memo;
+    };
+
+    return function(obj, iteratee, memo, context) {
+      var initial = arguments.length >= 3;
+      return reducer(obj, optimizeCb(iteratee, context, 4), memo, initial);
+    };
+  }
+
+  // **Reduce** builds up a single result from a list of values, aka `inject`,
+  // or `foldl`.
+  var reduce = createReduce(1);
+
+  // The right-associative version of reduce, also known as `foldr`.
+  var reduceRight = createReduce(-1);
+
+  // Return all the elements that pass a truth test.
+  function filter(obj, predicate, context) {
+    var results = [];
+    predicate = cb(predicate, context);
+    each(obj, function(value, index, list) {
+      if (predicate(value, index, list)) results.push(value);
+    });
+    return results;
+  }
+
+  // Return all the elements for which a truth test fails.
+  function reject(obj, predicate, context) {
+    return filter(obj, negate(cb(predicate)), context);
+  }
+
+  // Determine whether all of the elements pass a truth test.
+  function every(obj, predicate, context) {
+    predicate = cb(predicate, context);
+    var _keys = !isArrayLike(obj) && keys(obj),
+        length = (_keys || obj).length;
+    for (var index = 0; index < length; index++) {
+      var currentKey = _keys ? _keys[index] : index;
+      if (!predicate(obj[currentKey], currentKey, obj)) return false;
+    }
+    return true;
+  }
+
+  // Determine if at least one element in the object passes a truth test.
+  function some(obj, predicate, context) {
+    predicate = cb(predicate, context);
+    var _keys = !isArrayLike(obj) && keys(obj),
+        length = (_keys || obj).length;
+    for (var index = 0; index < length; index++) {
+      var currentKey = _keys ? _keys[index] : index;
+      if (predicate(obj[currentKey], currentKey, obj)) return true;
+    }
+    return false;
+  }
+
+  // Determine if the array or object contains a given item (using `===`).
+  function contains(obj, item, fromIndex, guard) {
+    if (!isArrayLike(obj)) obj = values(obj);
+    if (typeof fromIndex != 'number' || guard) fromIndex = 0;
+    return indexOf(obj, item, fromIndex) >= 0;
+  }
+
+  // Invoke a method (with arguments) on every item in a collection.
+  var invoke = restArguments(function(obj, path, args) {
+    var contextPath, func;
+    if (isFunction$1(path)) {
+      func = path;
+    } else {
+      path = toPath(path);
+      contextPath = path.slice(0, -1);
+      path = path[path.length - 1];
+    }
+    return map(obj, function(context) {
+      var method = func;
+      if (!method) {
+        if (contextPath && contextPath.length) {
+          context = deepGet(context, contextPath);
+        }
+        if (context == null) return void 0;
+        method = context[path];
+      }
+      return method == null ? method : method.apply(context, args);
+    });
+  });
+
+  // Convenience version of a common use case of `_.map`: fetching a property.
+  function pluck(obj, key) {
+    return map(obj, property(key));
+  }
+
+  // Convenience version of a common use case of `_.filter`: selecting only
+  // objects containing specific `key:value` pairs.
+  function where(obj, attrs) {
+    return filter(obj, matcher(attrs));
+  }
+
+  // Return the maximum element (or element-based computation).
+  function max(obj, iteratee, context) {
+    var result = -Infinity, lastComputed = -Infinity,
+        value, computed;
+    if (iteratee == null || typeof iteratee == 'number' && typeof obj[0] != 'object' && obj != null) {
+      obj = isArrayLike(obj) ? obj : values(obj);
+      for (var i = 0, length = obj.length; i < length; i++) {
+        value = obj[i];
+        if (value != null && value > result) {
+          result = value;
+        }
+      }
+    } else {
+      iteratee = cb(iteratee, context);
+      each(obj, function(v, index, list) {
+        computed = iteratee(v, index, list);
+        if (computed > lastComputed || computed === -Infinity && result === -Infinity) {
+          result = v;
+          lastComputed = computed;
+        }
+      });
+    }
+    return result;
+  }
+
+  // Return the minimum element (or element-based computation).
+  function min(obj, iteratee, context) {
+    var result = Infinity, lastComputed = Infinity,
+        value, computed;
+    if (iteratee == null || typeof iteratee == 'number' && typeof obj[0] != 'object' && obj != null) {
+      obj = isArrayLike(obj) ? obj : values(obj);
+      for (var i = 0, length = obj.length; i < length; i++) {
+        value = obj[i];
+        if (value != null && value < result) {
+          result = value;
+        }
+      }
+    } else {
+      iteratee = cb(iteratee, context);
+      each(obj, function(v, index, list) {
+        computed = iteratee(v, index, list);
+        if (computed < lastComputed || computed === Infinity && result === Infinity) {
+          result = v;
+          lastComputed = computed;
+        }
+      });
+    }
+    return result;
+  }
+
+  // Safely create a real, live array from anything iterable.
+  var reStrSymbol = /[^\ud800-\udfff]|[\ud800-\udbff][\udc00-\udfff]|[\ud800-\udfff]/g;
+  function toArray(obj) {
+    if (!obj) return [];
+    if (isArray(obj)) return slice.call(obj);
+    if (isString(obj)) {
+      // Keep surrogate pair characters together.
+      return obj.match(reStrSymbol);
+    }
+    if (isArrayLike(obj)) return map(obj, identity);
+    return values(obj);
+  }
+
+  // Sample **n** random values from a collection using the modern version of the
+  // [Fisher-Yates shuffle](https://en.wikipedia.org/wiki/Fisher–Yates_shuffle).
+  // If **n** is not specified, returns a single random element.
+  // The internal `guard` argument allows it to work with `_.map`.
+  function sample(obj, n, guard) {
+    if (n == null || guard) {
+      if (!isArrayLike(obj)) obj = values(obj);
+      return obj[random(obj.length - 1)];
+    }
+    var sample = toArray(obj);
+    var length = getLength(sample);
+    n = Math.max(Math.min(n, length), 0);
+    var last = length - 1;
+    for (var index = 0; index < n; index++) {
+      var rand = random(index, last);
+      var temp = sample[index];
+      sample[index] = sample[rand];
+      sample[rand] = temp;
+    }
+    return sample.slice(0, n);
+  }
+
+  // Shuffle a collection.
+  function shuffle(obj) {
+    return sample(obj, Infinity);
+  }
+
+  // Sort the object's values by a criterion produced by an iteratee.
+  function sortBy(obj, iteratee, context) {
+    var index = 0;
+    iteratee = cb(iteratee, context);
+    return pluck(map(obj, function(value, key, list) {
+      return {
+        value: value,
+        index: index++,
+        criteria: iteratee(value, key, list)
+      };
+    }).sort(function(left, right) {
+      var a = left.criteria;
+      var b = right.criteria;
+      if (a !== b) {
+        if (a > b || a === void 0) return 1;
+        if (a < b || b === void 0) return -1;
+      }
+      return left.index - right.index;
+    }), 'value');
+  }
+
+  // An internal function used for aggregate "group by" operations.
+  function group(behavior, partition) {
+    return function(obj, iteratee, context) {
+      var result = partition ? [[], []] : {};
+      iteratee = cb(iteratee, context);
+      each(obj, function(value, index) {
+        var key = iteratee(value, index, obj);
+        behavior(result, value, key);
+      });
+      return result;
+    };
+  }
+
+  // Groups the object's values by a criterion. Pass either a string attribute
+  // to group by, or a function that returns the criterion.
+  var groupBy = group(function(result, value, key) {
+    if (has$1(result, key)) result[key].push(value); else result[key] = [value];
+  });
+
+  // Indexes the object's values by a criterion, similar to `_.groupBy`, but for
+  // when you know that your index values will be unique.
+  var indexBy = group(function(result, value, key) {
+    result[key] = value;
+  });
+
+  // Counts instances of an object that group by a certain criterion. Pass
+  // either a string attribute to count by, or a function that returns the
+  // criterion.
+  var countBy = group(function(result, value, key) {
+    if (has$1(result, key)) result[key]++; else result[key] = 1;
+  });
+
+  // Split a collection into two arrays: one whose elements all pass the given
+  // truth test, and one whose elements all do not pass the truth test.
+  var partition = group(function(result, value, pass) {
+    result[pass ? 0 : 1].push(value);
+  }, true);
+
+  // Return the number of elements in a collection.
+  function size(obj) {
+    if (obj == null) return 0;
+    return isArrayLike(obj) ? obj.length : keys(obj).length;
+  }
+
+  // Internal `_.pick` helper function to determine whether `key` is an enumerable
+  // property name of `obj`.
+  function keyInObj(value, key, obj) {
+    return key in obj;
+  }
+
+  // Return a copy of the object only containing the allowed properties.
+  var pick = restArguments(function(obj, keys) {
+    var result = {}, iteratee = keys[0];
+    if (obj == null) return result;
+    if (isFunction$1(iteratee)) {
+      if (keys.length > 1) iteratee = optimizeCb(iteratee, keys[1]);
+      keys = allKeys(obj);
+    } else {
+      iteratee = keyInObj;
+      keys = flatten$1(keys, false, false);
+      obj = Object(obj);
+    }
+    for (var i = 0, length = keys.length; i < length; i++) {
+      var key = keys[i];
+      var value = obj[key];
+      if (iteratee(value, key, obj)) result[key] = value;
+    }
+    return result;
+  });
+
+  // Return a copy of the object without the disallowed properties.
+  var omit = restArguments(function(obj, keys) {
+    var iteratee = keys[0], context;
+    if (isFunction$1(iteratee)) {
+      iteratee = negate(iteratee);
+      if (keys.length > 1) context = keys[1];
+    } else {
+      keys = map(flatten$1(keys, false, false), String);
+      iteratee = function(value, key) {
+        return !contains(keys, key);
+      };
+    }
+    return pick(obj, iteratee, context);
+  });
+
+  // Returns everything but the last entry of the array. Especially useful on
+  // the arguments object. Passing **n** will return all the values in
+  // the array, excluding the last N.
+  function initial(array, n, guard) {
+    return slice.call(array, 0, Math.max(0, array.length - (n == null || guard ? 1 : n)));
+  }
+
+  // Get the first element of an array. Passing **n** will return the first N
+  // values in the array. The **guard** check allows it to work with `_.map`.
+  function first(array, n, guard) {
+    if (array == null || array.length < 1) return n == null || guard ? void 0 : [];
+    if (n == null || guard) return array[0];
+    return initial(array, array.length - n);
+  }
+
+  // Returns everything but the first entry of the `array`. Especially useful on
+  // the `arguments` object. Passing an **n** will return the rest N values in the
+  // `array`.
+  function rest(array, n, guard) {
+    return slice.call(array, n == null || guard ? 1 : n);
+  }
+
+  // Get the last element of an array. Passing **n** will return the last N
+  // values in the array.
+  function last(array, n, guard) {
+    if (array == null || array.length < 1) return n == null || guard ? void 0 : [];
+    if (n == null || guard) return array[array.length - 1];
+    return rest(array, Math.max(0, array.length - n));
+  }
+
+  // Trim out all falsy values from an array.
+  function compact(array) {
+    return filter(array, Boolean);
+  }
+
+  // Flatten out an array, either recursively (by default), or up to `depth`.
+  // Passing `true` or `false` as `depth` means `1` or `Infinity`, respectively.
+  function flatten(array, depth) {
+    return flatten$1(array, depth, false);
+  }
+
+  // Take the difference between one array and a number of other arrays.
+  // Only the elements present in just the first array will remain.
+  var difference = restArguments(function(array, rest) {
+    rest = flatten$1(rest, true, true);
+    return filter(array, function(value){
+      return !contains(rest, value);
+    });
+  });
+
+  // Return a version of the array that does not contain the specified value(s).
+  var without = restArguments(function(array, otherArrays) {
+    return difference(array, otherArrays);
+  });
+
+  // Produce a duplicate-free version of the array. If the array has already
+  // been sorted, you have the option of using a faster algorithm.
+  // The faster algorithm will not work with an iteratee if the iteratee
+  // is not a one-to-one function, so providing an iteratee will disable
+  // the faster algorithm.
+  function uniq(array, isSorted, iteratee, context) {
+    if (!isBoolean(isSorted)) {
+      context = iteratee;
+      iteratee = isSorted;
+      isSorted = false;
+    }
+    if (iteratee != null) iteratee = cb(iteratee, context);
+    var result = [];
+    var seen = [];
+    for (var i = 0, length = getLength(array); i < length; i++) {
+      var value = array[i],
+          computed = iteratee ? iteratee(value, i, array) : value;
+      if (isSorted && !iteratee) {
+        if (!i || seen !== computed) result.push(value);
+        seen = computed;
+      } else if (iteratee) {
+        if (!contains(seen, computed)) {
+          seen.push(computed);
+          result.push(value);
+        }
+      } else if (!contains(result, value)) {
+        result.push(value);
+      }
+    }
+    return result;
+  }
+
+  // Produce an array that contains the union: each distinct element from all of
+  // the passed-in arrays.
+  var union = restArguments(function(arrays) {
+    return uniq(flatten$1(arrays, true, true));
+  });
+
+  // Produce an array that contains every item shared between all the
+  // passed-in arrays.
+  function intersection(array) {
+    var result = [];
+    var argsLength = arguments.length;
+    for (var i = 0, length = getLength(array); i < length; i++) {
+      var item = array[i];
+      if (contains(result, item)) continue;
+      var j;
+      for (j = 1; j < argsLength; j++) {
+        if (!contains(arguments[j], item)) break;
+      }
+      if (j === argsLength) result.push(item);
+    }
+    return result;
+  }
+
+  // Complement of zip. Unzip accepts an array of arrays and groups
+  // each array's elements on shared indices.
+  function unzip(array) {
+    var length = array && max(array, getLength).length || 0;
+    var result = Array(length);
+
+    for (var index = 0; index < length; index++) {
+      result[index] = pluck(array, index);
+    }
+    return result;
+  }
+
+  // Zip together multiple lists into a single array -- elements that share
+  // an index go together.
+  var zip = restArguments(unzip);
+
+  // Converts lists into objects. Pass either a single array of `[key, value]`
+  // pairs, or two parallel arrays of the same length -- one of keys, and one of
+  // the corresponding values. Passing by pairs is the reverse of `_.pairs`.
+  function object(list, values) {
+    var result = {};
+    for (var i = 0, length = getLength(list); i < length; i++) {
+      if (values) {
+        result[list[i]] = values[i];
+      } else {
+        result[list[i][0]] = list[i][1];
+      }
+    }
+    return result;
+  }
+
+  // Generate an integer Array containing an arithmetic progression. A port of
+  // the native Python `range()` function. See
+  // [the Python documentation](https://docs.python.org/library/functions.html#range).
+  function range(start, stop, step) {
+    if (stop == null) {
+      stop = start || 0;
+      start = 0;
+    }
+    if (!step) {
+      step = stop < start ? -1 : 1;
+    }
+
+    var length = Math.max(Math.ceil((stop - start) / step), 0);
+    var range = Array(length);
+
+    for (var idx = 0; idx < length; idx++, start += step) {
+      range[idx] = start;
+    }
+
+    return range;
+  }
+
+  // Chunk a single array into multiple arrays, each containing `count` or fewer
+  // items.
+  function chunk(array, count) {
+    if (count == null || count < 1) return [];
+    var result = [];
+    var i = 0, length = array.length;
+    while (i < length) {
+      result.push(slice.call(array, i, i += count));
+    }
+    return result;
+  }
+
+  // Helper function to continue chaining intermediate results.
+  function chainResult(instance, obj) {
+    return instance._chain ? _$1(obj).chain() : obj;
+  }
+
+  // Add your own custom functions to the Underscore object.
+  function mixin(obj) {
+    each(functions(obj), function(name) {
+      var func = _$1[name] = obj[name];
+      _$1.prototype[name] = function() {
+        var args = [this._wrapped];
+        push.apply(args, arguments);
+        return chainResult(this, func.apply(_$1, args));
+      };
+    });
+    return _$1;
+  }
+
+  // Add all mutator `Array` functions to the wrapper.
+  each(['pop', 'push', 'reverse', 'shift', 'sort', 'splice', 'unshift'], function(name) {
+    var method = ArrayProto[name];
+    _$1.prototype[name] = function() {
+      var obj = this._wrapped;
+      if (obj != null) {
+        method.apply(obj, arguments);
+        if ((name === 'shift' || name === 'splice') && obj.length === 0) {
+          delete obj[0];
+        }
+      }
+      return chainResult(this, obj);
+    };
+  });
+
+  // Add all accessor `Array` functions to the wrapper.
+  each(['concat', 'join', 'slice'], function(name) {
+    var method = ArrayProto[name];
+    _$1.prototype[name] = function() {
+      var obj = this._wrapped;
+      if (obj != null) obj = method.apply(obj, arguments);
+      return chainResult(this, obj);
+    };
+  });
+
+  // Named Exports
+
+  var allExports = {
+    __proto__: null,
+    VERSION: VERSION,
+    restArguments: restArguments,
+    isObject: isObject,
+    isNull: isNull,
+    isUndefined: isUndefined,
+    isBoolean: isBoolean,
+    isElement: isElement,
+    isString: isString,
+    isNumber: isNumber,
+    isDate: isDate,
+    isRegExp: isRegExp,
+    isError: isError,
+    isSymbol: isSymbol,
+    isArrayBuffer: isArrayBuffer,
+    isDataView: isDataView$1,
+    isArray: isArray,
+    isFunction: isFunction$1,
+    isArguments: isArguments$1,
+    isFinite: isFinite$1,
+    isNaN: isNaN$1,
+    isTypedArray: isTypedArray$1,
+    isEmpty: isEmpty,
+    isMatch: isMatch,
+    isEqual: isEqual,
+    isMap: isMap,
+    isWeakMap: isWeakMap,
+    isSet: isSet,
+    isWeakSet: isWeakSet,
+    keys: keys,
+    allKeys: allKeys,
+    values: values,
+    pairs: pairs,
+    invert: invert,
+    functions: functions,
+    methods: functions,
+    extend: extend,
+    extendOwn: extendOwn,
+    assign: extendOwn,
+    defaults: defaults,
+    create: create,
+    clone: clone,
+    tap: tap,
+    get: get,
+    has: has,
+    mapObject: mapObject,
+    identity: identity,
+    constant: constant,
+    noop: noop,
+    toPath: toPath$1,
+    property: property,
+    propertyOf: propertyOf,
+    matcher: matcher,
+    matches: matcher,
+    times: times,
+    random: random,
+    now: now,
+    escape: _escape,
+    unescape: _unescape,
+    templateSettings: templateSettings,
+    template: template,
+    result: result,
+    uniqueId: uniqueId,
+    chain: chain,
+    iteratee: iteratee,
+    partial: partial,
+    bind: bind,
+    bindAll: bindAll,
+    memoize: memoize,
+    delay: delay,
+    defer: defer,
+    throttle: throttle,
+    debounce: debounce,
+    wrap: wrap,
+    negate: negate,
+    compose: compose,
+    after: after,
+    before: before,
+    once: once,
+    findKey: findKey,
+    findIndex: findIndex,
+    findLastIndex: findLastIndex,
+    sortedIndex: sortedIndex,
+    indexOf: indexOf,
+    lastIndexOf: lastIndexOf,
+    find: find,
+    detect: find,
+    findWhere: findWhere,
+    each: each,
+    forEach: each,
+    map: map,
+    collect: map,
+    reduce: reduce,
+    foldl: reduce,
+    inject: reduce,
+    reduceRight: reduceRight,
+    foldr: reduceRight,
+    filter: filter,
+    select: filter,
+    reject: reject,
+    every: every,
+    all: every,
+    some: some,
+    any: some,
+    contains: contains,
+    includes: contains,
+    include: contains,
+    invoke: invoke,
+    pluck: pluck,
+    where: where,
+    max: max,
+    min: min,
+    shuffle: shuffle,
+    sample: sample,
+    sortBy: sortBy,
+    groupBy: groupBy,
+    indexBy: indexBy,
+    countBy: countBy,
+    partition: partition,
+    toArray: toArray,
+    size: size,
+    pick: pick,
+    omit: omit,
+    first: first,
+    head: first,
+    take: first,
+    initial: initial,
+    last: last,
+    rest: rest,
+    tail: rest,
+    drop: rest,
+    compact: compact,
+    flatten: flatten,
+    without: without,
+    uniq: uniq,
+    unique: uniq,
+    union: union,
+    intersection: intersection,
+    difference: difference,
+    unzip: unzip,
+    transpose: unzip,
+    zip: zip,
+    object: object,
+    range: range,
+    chunk: chunk,
+    mixin: mixin,
+    'default': _$1
+  };
+
+  // Default Export
+
+  // Add all of the Underscore functions to the wrapper object.
+  var _ = mixin(allExports);
+  // Legacy Node.js API.
+  _._ = _;
+
+  return _;
+
+})));
+//# sourceMappingURL=underscore-umd.js.map
diff --git a/_static/workflow.png b/_static/workflow.png
new file mode 100644
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diff --git a/cRPA_VASP/README.html b/cRPA_VASP/README.html
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+  <title>How to do cRPA calculations with VASP &mdash; solid_dmft  documentation</title>
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+              <div class="version">
+                3.2.0
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+<li class="toctree-l2"><a class="reference internal" href="../install.html#installation-via-pip">Installation via pip</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../install.html#docker-files-images">Docker files &amp; images</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../install.html#version-compatibility">Version compatibility</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../install.html#custom-cmake-options">Custom CMake options</a></li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../documentation.html">Documentation</a><ul class="current">
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+<li class="toctree-l2 current"><a class="reference internal" href="../documentation.html#dft-interface-notes">DFT interface notes</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/w90_interface.html">Wannier90 interface</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/w90_interface.html#orbital-order-in-the-w90-converter">orbital order in the W90 converter</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/vasp_csc.html">Interface to VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#general-remarks">General remarks</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
+</ul>
+</li>
+<li class="toctree-l3 current"><a class="current reference internal" href="#">How to do cRPA calculations with VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#file-description">file description</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#workflow">Workflow:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#important-flags">important flags:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#general-sidemarks">general sidemarks:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#version-and-compilation">version and compilation:</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#input-output">Input/Output</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../input_output/DMFT_input/input.html">Input</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/general.html">[general]: General parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/solver.html">[solver]: solver specific parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/dft.html">[dft]: DFT related inputs</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/advanced.html">[advanced]: Advanced inputs</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../input_output/DMFT_output/results.html">Output / results</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_output/results.html#group-structure">Group structure</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_output/results.html#subgroups">Subgroups</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#further-details-for-running">Further details for running</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/docker.html">Docker</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#building-the-docker-image">Building the docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#pulling-a-docker-image">Pulling a docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/run_locally.html">Run on your machine</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#module-reference-manual">Module reference manual</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../_ref/csc_flow.html">csc_flow</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../_ref/dft_managers.html">dft_managers</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/dft_managers.mpi_helpers.html">dft_managers.mpi_helpers</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/dft_managers.qe_manager.html">dft_managers.qe_manager</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/dft_managers.vasp_manager.html">dft_managers.vasp_manager</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../_ref/dmft_cycle.html">dmft_cycle</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../_ref/dmft_tools.html">dmft_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/dmft_tools.afm_mapping.html">dmft_tools.afm_mapping</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/dmft_tools.convergence.html">dmft_tools.convergence</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/dmft_tools.formatter.html">dmft_tools.formatter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/dmft_tools.greens_functions_mixer.html">dmft_tools.greens_functions_mixer</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/dmft_tools.initial_self_energies.html">dmft_tools.initial_self_energies</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/dmft_tools.interaction_hamiltonian.html">dmft_tools.interaction_hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/dmft_tools.legendre_filter.html">dmft_tools.legendre_filter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/dmft_tools.manipulate_chemical_potential.html">dmft_tools.manipulate_chemical_potential</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/dmft_tools.matheval.html">dmft_tools.matheval</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/dmft_tools.observables.html">dmft_tools.observables</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/dmft_tools.results_to_archive.html">dmft_tools.results_to_archive</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/dmft_tools.solver.html">dmft_tools.solver</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../_ref/io_tools.html">io_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/io_tools.dict_to_h5.html">io_tools.dict_to_h5</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/io_tools.postproc_toml_dict.html">io_tools.postproc_toml_dict</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/io_tools.verify_input_params.html">io_tools.verify_input_params</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../_ref/postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/postprocessing.eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/postprocessing.maxent_gf_imp.html">postprocessing.maxent_gf_imp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/postprocessing.maxent_gf_latt.html">postprocessing.maxent_gf_latt</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/postprocessing.maxent_sigma.html">postprocessing.maxent_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../_ref/util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
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+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
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+<div class="tex2jax_ignore mathjax_ignore section" id="how-to-do-crpa-calculations-with-vasp">
+<h1>How to do cRPA calculations with VASP<a class="headerlink" href="#how-to-do-crpa-calculations-with-vasp" title="Permalink to this headline"></a></h1>
+<p>This is just a small tutorial and help on how to do cRPA calculations within
+VASP (<a class="reference external" href="https://cms.mpi.univie.ac.at/wiki/index.php/CRPA_of_SrVO3" rel="noopener noreferrer" target="_blank">https://cms.mpi.univie.ac.at/wiki/index.php/CRPA_of_SrVO3</a>) . Moreover, the
+python script <code class="docutils literal notranslate"><span class="pre">eval_U.py</span></code> contains helper functions to extract the full
+<span class="math notranslate nohighlight">\(U\)</span> matrix tensor from the <code class="docutils literal notranslate"><span class="pre">Uijkl</span></code> or <code class="docutils literal notranslate"><span class="pre">Vijkl</span></code> file from a VASP cRPA run. There
+are also some general remarks on the notation in VASP for the Coulomb tensor in
+the pdf included in this folder. Moreover, there is a small collection of
+examples for SrVO3 and LuNiO3. For more details please take a look at the PhD
+thesis of Merzuk Kaltak (<a class="reference external" href="http://othes.univie.ac.at/38099/" rel="noopener noreferrer" target="_blank">http://othes.univie.ac.at/38099/</a>).</p>
+<div class="section" id="file-description">
+<h2>file description<a class="headerlink" href="#file-description" title="Permalink to this headline"></a></h2>
+<ul class="simple">
+<li><code class="docutils literal notranslate"><span class="pre">eval_U.py</span></code> extraction of Coulomb tensor, calculation of reduced two-index matrices, and calculation / fitting of Kanamori or Slater parameters</li>
+<li><code class="docutils literal notranslate"><span class="pre">ext_eps.sh</span></code> a small bash script that can extract <span class="math notranslate nohighlight">\(\epsilon^-1(|q+G|)=[1-VP^r]^-1\)</span> from a given vasprun.xml file</li>
+</ul>
+</div>
+<div class="section" id="workflow">
+<h2>Workflow:<a class="headerlink" href="#workflow" title="Permalink to this headline"></a></h2>
+<ol class="arabic simple">
+<li>DFT NM normal like:<ul>
+<li>SYSTEM = SrVO3</li>
+<li>ISMEAR =  0</li>
+<li>SIGMA =  0.05</li>
+<li>EDIFF = 1E-8</li>
+</ul>
+</li>
+<li>optical part (larger nbands) and optical properties for generating the linear response integrals needed for cRPA or GW<ol class="arabic">
+<li>nbands: ~100 bands per atoms, but not larger than number of plane waves generated from ENCUT</li>
+<li>example:<ul>
+<li>SYSTEM = SrVO3</li>
+<li>ISMEAR =  0</li>
+<li>ENCUT = high value!</li>
+<li>SIGMA =  0.05</li>
+<li>EDIFF = 1E-8</li>
+<li>ALGO = Exact ; NELM=1</li>
+<li>LOPTICS = .TRUE.</li>
+<li>LWAVE = .TRUE.</li>
+<li>NBANDS =96</li>
+<li>LMAXMIX=4</li>
+</ul>
+</li>
+</ol>
+</li>
+<li>if needed generate wannier functions with ALGO=none (read wavecar and chgcar additionally) and do 0 steps to get the wannier functions correct - this step is not needed, if one has already a wannier90.win file</li>
+<li>ALGO=CRPA to make vasp calculate U matrices (bare, screened etc. )<ol class="arabic">
+<li>omegamax=0 (default) for frequency depend U matrix</li>
+<li>NCRPA_BANDS for selecting bands in a non-disentagled workflow (<a class="reference external" href="http://vasp.at/wiki/index.php/NCRPA_BANDS" rel="noopener noreferrer" target="_blank">vasp.at/wiki/index.php/NCRPA_BANDS</a>)</li>
+<li>or set NTARGET STATES= # of target states for using the KUBO formalism for disentanglement. Works directly with the wannier functions as basis. The states not listet will be included in screening.</li>
+<li>example file:<ul>
+<li>SYSTEM = SrVO3</li>
+<li>ISMEAR =  0</li>
+<li>ENCUT = high value!</li>
+<li>VCUTOFF = reasonable high value!</li>
+<li>SIGMA =  0.05</li>
+<li>EDIFF = 1E-8</li>
+<li>NBANDS =96</li>
+<li>ALGO = CRPA</li>
+<li>NTARGET_STATES = 1 2 3</li>
+<li>LWAVE = .FALSE.</li>
+<li>NCSHMEM=1</li>
+<li>LMAXMIX=4</li>
+</ul>
+</li>
+</ol>
+</li>
+</ol>
+</div>
+<div class="section" id="important-flags">
+<h2>important flags:<a class="headerlink" href="#important-flags" title="Permalink to this headline"></a></h2>
+<p>if you get sigsevs while calculating the polarization make sure your local stack
+size is large enough by setting:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">ulimit</span> <span class="o">-</span><span class="n">s</span> <span class="n">unlimited</span>
+</pre></div>
+</div>
+<ul class="simple">
+<li>ALGO=CRPA (automatically calls wannier90 and calculates the U matrix)</li>
+<li>NTARGET_STATES= # number of target Wannier funcitons if more target states than basis functions for U matrix one specify the one to exclude from screening as integer list: <code class="docutils literal notranslate"><span class="pre">1</span> <span class="pre">2</span> <span class="pre">3</span></code>. This would build the U matrix for the first 3 Wannier functions in wannier90.win, where 5 Wannier functions are specified there in total and the last 2 are included for the calculation of screening.</li>
+<li>for the disentanglement with <code class="docutils literal notranslate"><span class="pre">NTARGET_STATES</span></code> there are 3 options in cRPA:<ul>
+<li>LPROJECTED (default): Kubo method by Merzuk (<a class="reference external" href="http://othes.univie.ac.at/38099/" rel="noopener noreferrer" target="_blank">http://othes.univie.ac.at/38099/</a>)</li>
+<li>LDISENTANGLED: disentanglement of Miyake (<a class="reference external" href="http://doi.org/10.1103/PhysRevB.80.155134" rel="noopener noreferrer" target="_blank">doi.org/10.1103/PhysRevB.80.155134</a>)</li>
+<li>LWEIGHTED: weighted method of Friedrich and Shih</li>
+</ul>
+</li>
+<li>LOPTICS= TRUE for calculating the necessary response integrals withing the Kohn-Sham Basis W000x.tmp</li>
+<li>NCSHMEM=1 nodody knows, but it is needed!</li>
+<li>VCUTOFF cuttoff for bare interaction V. This tests your convergency
+and is written in the OUTCAR as two sets of bare interaction, where for one of them
+it says: low cutoff result for V_ijkl. Here ENCUT was used and for the one above 1.1*ENCUT or VCUTOFF was used.</li>
+<li>usually a converged ENCUT gives also a reasonably high VCUTOFF, so that explicitly setting VCUTOFF is not necessary. Moreover, the effect of the VCUTOFF convergence is included by subtracting the constant shift between LOW and HIGH VCUTOFF test output in the OUTCAR</li>
+<li>One can see in the convergence plot “debugging_examples/LaTiO3/VCUTOFF_convergence.png” the effect of ENCUT and VCUTOFF:</li>
+</ul>
+<p><img alt="vcutoff_test" src="cRPA_VASP/VCUTOFF_convergence.png" /></p>
+</div>
+<div class="section" id="convergency-tests">
+<h2>convergency tests:<a class="headerlink" href="#convergency-tests" title="Permalink to this headline"></a></h2>
+<p><span class="math notranslate nohighlight">\(`E_{corr}^{RPA}`\)</span>  converges for NBANDS,ENCUT to <span class="math notranslate nohighlight">\(`\infty`\)</span>, where the asymptotic
+behavior goes like <span class="math notranslate nohighlight">\(`1/N_{bands} \approx ENCUT^{-3/2} `\)</span>. The ENCUT for the GW part
+is set automatically by VASP with the ratio: <span class="math notranslate nohighlight">\(`ENCUTGW = 2/3 \ ENCUT`\)</span>. Moreover,
+it is crucial to first converge the bare interaction V that does not depend on the
+polarization. To do these tests set in the INCAR file:</p>
+<ul class="simple">
+<li>ALGO = 2E4W  # calculates only the V</li>
+<li>LWPOT = .FALSE # avoid errors</li>
+<li>VCUTOFF # vary the cut-off until convergency is reached, default is 1.1*ENCUT</li>
+<li>NBANDS # minor effect on V then on W, but nevertheless a reasonable amount of
+bands must be used. A good choice is 3*NELECT (# of electrons in the systems).</li>
+</ul>
+<p>The procedure is then to first convergence KPOINTS and ENCUT, where KPOINTS dependency of the results seems to be weak. Then increase NBANDS until U does not change anymore.</p>
+</div>
+<div class="section" id="parameterization-of-u-and-j-from-crpa-calculations">
+<h2>Parameterization of U and J from cRPA calculations<a class="headerlink" href="#parameterization-of-u-and-j-from-crpa-calculations" title="Permalink to this headline"></a></h2>
+<p><code class="docutils literal notranslate"><span class="pre">eval_u.py</span></code> provides four different methods:</p>
+<ul class="simple">
+<li>Kanamori: <code class="docutils literal notranslate"><span class="pre">calc_kan_params(...)</span></code> for extracting Kanamori parameters for a cubic system</li>
+<li>Slater 1: <code class="docutils literal notranslate"><span class="pre">calc_u_avg_fulld(...)</span></code> using averaging and symmetries: <span class="math notranslate nohighlight">\(`U_\mathrm{cubic} = \frac1{2l+1} \sum_i (U_{iiii})`\)</span>, <span class="math notranslate nohighlight">\(`J_\mathrm{cubic} = \frac1{2l(2l+1)} \sum_{i, j\neq i} U_{ijji}`\)</span>. Then, the interaction parameters follow from the conversion <span class="math notranslate nohighlight">\(`U = U_\mathrm{cubic} - \frac85 J_\mathrm{cubic}, J = \frac75 J_\mathrm{cubic}`\)</span>.</li>
+<li>Slater 2: <code class="docutils literal notranslate"><span class="pre">calculate_interaction_from_averaging(...)</span></code> using direct averaging: <span class="math notranslate nohighlight">\(`U = \frac1{(2l+1)^2} \sum_{i, j} U_{iijj}`\)</span> and <span class="math notranslate nohighlight">\(`J = U - \frac1{2l(2l+1)} \sum_{i, j} U_{ijij}`\)</span>. This is more straight forward that Slater 1, but ignores the basis in which the cRPA Uijkl matrix is written. For a perfect Slater matrix this gives the same results if applied in cubic or spherical harmonics basis.</li>
+<li>Slater 3: <code class="docutils literal notranslate"><span class="pre">fit_slater_fulld(...)</span> </code>using an least-square fit (summed over the matrix elements) of the two-index matrices <span class="math notranslate nohighlight">\(`U_{iijj}`\)</span> and <span class="math notranslate nohighlight">\(`U_{ijij}`\)</span> to the Slater Hamiltonian.</li>
+</ul>
+<p>These three methods give the same results if the cRPA matrix is of the Slater type already. Be aware of the order of your basis functions and the basis in which the <span class="math notranslate nohighlight">\(U\)</span> tensor is written!</p>
+</div>
+<div class="section" id="general-sidemarks">
+<h2>general sidemarks:<a class="headerlink" href="#general-sidemarks" title="Permalink to this headline"></a></h2>
+<ul class="simple">
+<li>careful with the averaged U,u,J values in the end of the OUTCAR, because they sum all off-diagonal elements! Also inter-site, if the unit cell contains more than one target atom</li>
+<li>in VASP the two inner indices are exchanged compared to the notation in PRB 86, 165105 (2012): U_ijkl = U_ikjl^VASP</li>
+<li>when specifying bands, always start with 1 not 0.</li>
+<li>GW pseudopotentials can be more accurate, since they provide higher cut-offs e.g. , test this…</li>
+<li>NCRPA_BANDS and NTARGET_STATES gives the same result in non-entangled bands</li>
+</ul>
+</div>
+<div class="section" id="version-and-compilation">
+<h2>version and compilation:<a class="headerlink" href="#version-and-compilation" title="Permalink to this headline"></a></h2>
+<ul class="simple">
+<li>supported vasp version 6 or higher</li>
+<li>wannier90 upto v3.1 works, if no features exclusively to wannier90 v3 are used</li>
+</ul>
+</div>
+</div>
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+<li class="toctree-l3"><a class="reference internal" href="md_notes/w90_interface.html">Wannier90 interface</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/w90_interface.html#orbital-order-in-the-w90-converter">orbital order in the W90 converter</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="md_notes/vasp_csc.html">Interface to VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/vasp_csc.html#general-remarks">General remarks</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="cRPA_VASP/README.html#file-description">file description</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cRPA_VASP/README.html#workflow">Workflow:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cRPA_VASP/README.html#important-flags">important flags:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cRPA_VASP/README.html#version-and-compilation">version and compilation:</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#input-output">Input/Output</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="input_output/DMFT_input/input.html">Input</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="input_output/DMFT_input/general.html">[general]: General parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="input_output/DMFT_input/solver.html">[solver]: solver specific parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="input_output/DMFT_input/dft.html">[dft]: DFT related inputs</a></li>
+<li class="toctree-l4"><a class="reference internal" href="input_output/DMFT_input/advanced.html">[advanced]: Advanced inputs</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="input_output/DMFT_output/results.html">Output / results</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="input_output/DMFT_output/results.html#group-structure">Group structure</a></li>
+<li class="toctree-l4"><a class="reference internal" href="input_output/DMFT_output/results.html#subgroups">Subgroups</a></li>
+</ul>
+</li>
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+</li>
+<li class="toctree-l2"><a class="reference internal" href="#further-details-for-running">Further details for running</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="md_notes/docker.html">Docker</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/docker.html#building-the-docker-image">Building the docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/docker.html#pulling-a-docker-image">Pulling a docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
+</ul>
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+<li class="toctree-l3"><a class="reference internal" href="md_notes/run_locally.html">Run on your machine</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#module-reference-manual">Module reference manual</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="_ref/csc_flow.html">csc_flow</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="_ref/dmft_cycle.html">dmft_cycle</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="_ref/io_tools.dict_to_h5.html">io_tools.dict_to_h5</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="_ref/io_tools.verify_input_params.html">io_tools.verify_input_params</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="_ref/postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="_ref/postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/postprocessing.eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/postprocessing.maxent_gf_imp.html">postprocessing.maxent_gf_imp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/postprocessing.maxent_gf_latt.html">postprocessing.maxent_gf_latt</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/postprocessing.maxent_sigma.html">postprocessing.maxent_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="_ref/util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="_ref/util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="issues.html">Support &amp; contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="issues.html#seeking-help">Seeking help</a></li>
+<li class="toctree-l2"><a class="reference internal" href="issues.html#improving-solid-dmft">Improving solid_dmft</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="issues.html#reporting-issues">Reporting issues</a></li>
+</ul>
+</li>
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+<li class="toctree-l1"><a class="reference internal" href="ChangeLog.html">Changelog</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#version-3-2-0">Version 3.2.0</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#version-3-1-4">Version 3.1.4</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#version-3-1-3">Version 3.1.3</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#version-3-1-2">Version 3.1.2</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#version-3-1-1">Version 3.1.1</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#version-3-1-0">Version 3.1.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#version-3-0-0">Version 3.0.0</a></li>
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+<div class="section" id="code-structure">
+<h2>Code structure<a class="headerlink" href="#code-structure" title="Permalink to this headline"></a></h2>
+<a class="reference internal image-reference" href="_images/code_structure.png"><img alt="_images/code_structure.png" class="align-center" src="_images/code_structure.png" style="width: 100%;" /></a>
+<p>more details in the reference manual below.</p>
+<p>To get started with the code after a successful <a class="reference internal" href="install.html#installation"><span class="std std-ref">Installation</span></a>, take a look at the <a class="reference internal" href="tutorials.html#tutorials"><span class="std std-ref">Tutorials</span></a> section. Here we provide further special information and a reference manual for all available functions.</p>
+</div>
+<div class="section" id="dft-interface-notes">
+<h2>DFT interface notes<a class="headerlink" href="#dft-interface-notes" title="Permalink to this headline"></a></h2>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="md_notes/w90_interface.html">Wannier90 interface</a></li>
+<li class="toctree-l1"><a class="reference internal" href="md_notes/vasp_csc.html">Interface to VASP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="cRPA_VASP/README.html">How to do cRPA calculations with VASP</a></li>
+</ul>
+</div>
+</div>
+<div class="section" id="input-output">
+<h2>Input/Output<a class="headerlink" href="#input-output" title="Permalink to this headline"></a></h2>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="input_output/DMFT_input/input.html">Input</a></li>
+<li class="toctree-l1"><a class="reference internal" href="input_output/DMFT_output/results.html">Output / results</a></li>
+</ul>
+</div>
+</div>
+<div class="section" id="further-details-for-running">
+<h2>Further details for running<a class="headerlink" href="#further-details-for-running" title="Permalink to this headline"></a></h2>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="md_notes/docker.html">Docker</a></li>
+<li class="toctree-l1"><a class="reference internal" href="md_notes/run_locally.html">Run on your machine</a></li>
+<li class="toctree-l1"><a class="reference internal" href="md_notes/run_cluster.html">Running solid_dmft on a cluster</a></li>
+</ul>
+</div>
+</div>
+<div class="section" id="module-reference-manual">
+<h2>Module reference manual<a class="headerlink" href="#module-reference-manual" title="Permalink to this headline"></a></h2>
+<table border="1" class="longtable docutils align-default">
+<colgroup>
+<col width="10%" />
+<col width="90%" />
+</colgroup>
+<tbody valign="top">
+<tr class="row-odd"><td><a class="reference internal" href="_ref/csc_flow.html#module-csc_flow" title="csc_flow"><code class="xref py py-obj docutils literal notranslate"><span class="pre">csc_flow</span></code></a></td>
+<td>contains the charge self-consistency flow control functions</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="_ref/dft_managers.html#module-dft_managers" title="dft_managers"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dft_managers</span></code></a></td>
+<td>DFT code driver modules</td>
+</tr>
+<tr class="row-odd"><td><a class="reference internal" href="_ref/dmft_cycle.html#module-dmft_cycle" title="dmft_cycle"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dmft_cycle</span></code></a></td>
+<td>main DMFT cycle, DMFT step, and helper functions</td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="_ref/dmft_tools.html#module-dmft_tools" title="dmft_tools"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dmft_tools</span></code></a></td>
+<td>DMFT routine helper functions used during solid_dmft run</td>
+</tr>
+<tr class="row-odd"><td><a class="reference internal" href="_ref/io_tools.html#module-io_tools" title="io_tools"><code class="xref py py-obj docutils literal notranslate"><span class="pre">io_tools</span></code></a></td>
+<td></td>
+</tr>
+<tr class="row-even"><td><a class="reference internal" href="_ref/postprocessing.html#module-postprocessing" title="postprocessing"><code class="xref py py-obj docutils literal notranslate"><span class="pre">postprocessing</span></code></a></td>
+<td>Postprocessing tools</td>
+</tr>
+<tr class="row-odd"><td><a class="reference internal" href="_ref/util.html#module-util" title="util"><code class="xref py py-obj docutils literal notranslate"><span class="pre">util</span></code></a></td>
+<td>external helper functions, not used by any DMFT routine</td>
+</tr>
+</tbody>
+</table>
+</div>
+</div>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="install.html" class="btn btn-neutral float-left" title="Installation" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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+    </div>
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+
+  <div role="contentinfo">
+    <p>&#169; Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.</p>
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diff --git a/genindex.html b/genindex.html
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+++ b/genindex.html
@@ -0,0 +1,906 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" >
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Index &mdash; solid_dmft  documentation</title>
+      <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
+      <link rel="stylesheet" href="_static/css/custom.css" type="text/css" />
+      <link rel="stylesheet" href="_static/plot_directive.css" type="text/css" />
+    <link rel="shortcut icon" href="_static/favicon.ico"/>
+  
+        <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
+        <script src="_static/jquery.js"></script>
+        <script src="_static/underscore.js"></script>
+        <script src="_static/doctools.js"></script>
+        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+    <script src="_static/js/theme.js"></script>
+    <link rel="index" title="Index" href="#" />
+    <link rel="search" title="Search" href="search.html" /> 
+</head>
+
+<body class="wy-body-for-nav"> 
+  <div class="wy-grid-for-nav">
+    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
+      <div class="wy-side-scroll">
+        <div class="wy-side-nav-search"  style="background: #7E588A" >
+            <a href="index.html" class="icon icon-home"> solid_dmft
+            <img src="_static/logo_no_text.png" class="logo" alt="Logo"/>
+          </a>
+              <div class="version">
+                3.2.0
+              </div>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" />
+    <input type="hidden" name="check_keywords" value="yes" />
+    <input type="hidden" name="area" value="default" />
+  </form>
+</div>
+        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+              <ul>
+<li class="toctree-l1"><a class="reference internal" href="install.html">Installation</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="install.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="install.html#installation-via-pip">Installation via pip</a></li>
+<li class="toctree-l2"><a class="reference internal" href="install.html#manual-installation-via-cmake">Manual installation via CMake</a></li>
+<li class="toctree-l2"><a class="reference internal" href="install.html#docker-files-images">Docker files &amp; images</a></li>
+<li class="toctree-l2"><a class="reference internal" href="install.html#version-compatibility">Version compatibility</a></li>
+<li class="toctree-l2"><a class="reference internal" href="install.html#custom-cmake-options">Custom CMake options</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="documentation.html">Documentation</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="documentation.html#code-structure">Code structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="documentation.html#dft-interface-notes">DFT interface notes</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="md_notes/w90_interface.html">Wannier90 interface</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/w90_interface.html#orbital-order-in-the-w90-converter">orbital order in the W90 converter</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="md_notes/vasp_csc.html">Interface to VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/vasp_csc.html#general-remarks">General remarks</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="cRPA_VASP/README.html#file-description">file description</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cRPA_VASP/README.html#workflow">Workflow:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cRPA_VASP/README.html#important-flags">important flags:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cRPA_VASP/README.html#version-and-compilation">version and compilation:</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="documentation.html#input-output">Input/Output</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="input_output/DMFT_input/input.html">Input</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="input_output/DMFT_input/general.html">[general]: General parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="input_output/DMFT_input/solver.html">[solver]: solver specific parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="input_output/DMFT_input/dft.html">[dft]: DFT related inputs</a></li>
+<li class="toctree-l4"><a class="reference internal" href="input_output/DMFT_input/advanced.html">[advanced]: Advanced inputs</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="input_output/DMFT_output/results.html">Output / results</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="input_output/DMFT_output/results.html#group-structure">Group structure</a></li>
+<li class="toctree-l4"><a class="reference internal" href="input_output/DMFT_output/results.html#subgroups">Subgroups</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="documentation.html#further-details-for-running">Further details for running</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="md_notes/docker.html">Docker</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/docker.html#building-the-docker-image">Building the docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/docker.html#pulling-a-docker-image">Pulling a docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="md_notes/run_locally.html">Run on your machine</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="documentation.html#module-reference-manual">Module reference manual</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="_ref/csc_flow.html">csc_flow</a></li>
+<li class="toctree-l3"><a class="reference internal" href="_ref/dft_managers.html">dft_managers</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dft_managers.mpi_helpers.html">dft_managers.mpi_helpers</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dft_managers.qe_manager.html">dft_managers.qe_manager</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dft_managers.vasp_manager.html">dft_managers.vasp_manager</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="_ref/dmft_cycle.html">dmft_cycle</a></li>
+<li class="toctree-l3"><a class="reference internal" href="_ref/dmft_tools.html">dmft_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.afm_mapping.html">dmft_tools.afm_mapping</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.convergence.html">dmft_tools.convergence</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.formatter.html">dmft_tools.formatter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.greens_functions_mixer.html">dmft_tools.greens_functions_mixer</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.initial_self_energies.html">dmft_tools.initial_self_energies</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.interaction_hamiltonian.html">dmft_tools.interaction_hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.legendre_filter.html">dmft_tools.legendre_filter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.manipulate_chemical_potential.html">dmft_tools.manipulate_chemical_potential</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.matheval.html">dmft_tools.matheval</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.observables.html">dmft_tools.observables</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.results_to_archive.html">dmft_tools.results_to_archive</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.solver.html">dmft_tools.solver</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="_ref/io_tools.html">io_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="_ref/io_tools.dict_to_h5.html">io_tools.dict_to_h5</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/io_tools.postproc_toml_dict.html">io_tools.postproc_toml_dict</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/io_tools.verify_input_params.html">io_tools.verify_input_params</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="_ref/postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="_ref/postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/postprocessing.eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/postprocessing.maxent_gf_imp.html">postprocessing.maxent_gf_imp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/postprocessing.maxent_gf_latt.html">postprocessing.maxent_gf_latt</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/postprocessing.maxent_sigma.html">postprocessing.maxent_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="_ref/util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="_ref/util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="issues.html">Support &amp; contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="issues.html#seeking-help">Seeking help</a></li>
+<li class="toctree-l2"><a class="reference internal" href="issues.html#improving-solid-dmft">Improving solid_dmft</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="issues.html#reporting-issues">Reporting issues</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="ChangeLog.html">Changelog</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#version-3-2-0">Version 3.2.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#general">General</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#fix">fix</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#build">build</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#feat">feat</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#doc">doc</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#test">test</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#version-3-1-5">Version 3.1.5</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#version-3-1-4">Version 3.1.4</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#version-3-1-3">Version 3.1.3</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#version-3-1-2">Version 3.1.2</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#version-3-1-1">Version 3.1.1</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#version-3-1-0">Version 3.1.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#version-3-0-0">Version 3.0.0</a></li>
+</ul>
+</li>
+</ul>
+
+        </div>
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu"  style="background: #7E588A" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="index.html">solid_dmft</a>
+      </nav>
+
+      <div class="wy-nav-content">
+        <div class="rst-content">
+          <div role="navigation" aria-label="Page navigation">
+  <ul class="wy-breadcrumbs">
+      <li><a href="index.html" class="icon icon-home"></a> &raquo;</li>
+      <li>Index</li>
+      <li class="wy-breadcrumbs-aside">
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+
+<h1 id="index">Index</h1>
+
+<div class="genindex-jumpbox">
+ <a href="#_"><strong>_</strong></a>
+ | <a href="#A"><strong>A</strong></a>
+ | <a href="#C"><strong>C</strong></a>
+ | <a href="#D"><strong>D</strong></a>
+ | <a href="#F"><strong>F</strong></a>
+ | <a href="#G"><strong>G</strong></a>
+ | <a href="#H"><strong>H</strong></a>
+ | <a href="#I"><strong>I</strong></a>
+ | <a href="#K"><strong>K</strong></a>
+ | <a href="#M"><strong>M</strong></a>
+ | <a href="#P"><strong>P</strong></a>
+ | <a href="#R"><strong>R</strong></a>
+ | <a href="#S"><strong>S</strong></a>
+ | <a href="#U"><strong>U</strong></a>
+ | <a href="#V"><strong>V</strong></a>
+ | <a href="#W"><strong>W</strong></a>
+ 
+</div>
+<h2 id="_">_</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="_ref/dmft_tools.matheval.MathExpr.__init__.html#dmft_tools.matheval.MathExpr.__init__">__init__() (dmft_tools.matheval.MathExpr method)</a>
+
+      <ul>
+        <li><a href="_ref/dmft_tools.solver.SolverStructure.__init__.html#dmft_tools.solver.SolverStructure.__init__">(dmft_tools.solver.SolverStructure method)</a>
+</li>
+        <li><a href="_ref/postprocessing.eval_U_cRPA_RESPACK.respack_data.__init__.html#postprocessing.eval_U_cRPA_RESPACK.respack_data.__init__">(postprocessing.eval_U_cRPA_RESPACK.respack_data method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="A">A</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="_ref/dmft_tools.observables.html#dmft_tools.observables.add_dft_values_as_zeroth_iteration">add_dft_values_as_zeroth_iteration() (in module dmft_tools.observables)</a>
+</li>
+      <li><a href="_ref/dmft_tools.observables.html#dmft_tools.observables.add_dmft_observables">add_dmft_observables() (in module dmft_tools.observables)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="_ref/dmft_tools.matheval.MathExpr.allowed_nodes.html#dmft_tools.matheval.MathExpr.allowed_nodes">allowed_nodes (dmft_tools.matheval.MathExpr attribute)</a>
+</li>
+      <li><a href="_ref/dmft_tools.legendre_filter.html#dmft_tools.legendre_filter.apply">apply() (in module dmft_tools.legendre_filter)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="C">C</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="_ref/dmft_tools.observables.html#dmft_tools.observables.calc_bandcorr_man">calc_bandcorr_man() (in module dmft_tools.observables)</a>
+</li>
+      <li><a href="_ref/dmft_tools.convergence.html#dmft_tools.convergence.calc_convergence_quantities">calc_convergence_quantities() (in module dmft_tools.convergence)</a>
+</li>
+      <li><a href="_ref/dmft_tools.observables.html#dmft_tools.observables.calc_dft_kin_en">calc_dft_kin_en() (in module dmft_tools.observables)</a>
+</li>
+      <li><a href="_ref/postprocessing.eval_U_cRPA_Vasp.html#postprocessing.eval_U_cRPA_Vasp.calc_kan_params">calc_kan_params() (in module postprocessing.eval_U_cRPA_Vasp)</a>
+</li>
+      <li><a href="_ref/postprocessing.eval_U_cRPA_Vasp.html#postprocessing.eval_U_cRPA_Vasp.calc_u_avg_fulld">calc_u_avg_fulld() (in module postprocessing.eval_U_cRPA_Vasp)</a>
+</li>
+      <li><a href="_ref/dmft_tools.observables.html#dmft_tools.observables.calc_Z">calc_Z() (in module dmft_tools.observables)</a>
+</li>
+      <li><a href="_ref/dmft_tools.initial_self_energies.html#dmft_tools.initial_self_energies.calculate_double_counting">calculate_double_counting() (in module dmft_tools.initial_self_energies)</a>
+</li>
+      <li><a href="_ref/postprocessing.eval_U_cRPA_Vasp.html#postprocessing.eval_U_cRPA_Vasp.calculate_interaction_from_averaging">calculate_interaction_from_averaging() (in module postprocessing.eval_U_cRPA_Vasp)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="_ref/dmft_tools.convergence.html#dmft_tools.convergence.check_convergence">check_convergence() (in module dmft_tools.convergence)</a>
+</li>
+      <li><a href="_ref/dmft_tools.interaction_hamiltonian.html#dmft_tools.interaction_hamiltonian.construct">construct() (in module dmft_tools.interaction_hamiltonian)</a>
+</li>
+      <li><a href="_ref/postprocessing.eval_U_cRPA_Vasp.html#postprocessing.eval_U_cRPA_Vasp.construct_U_kan">construct_U_kan() (in module postprocessing.eval_U_cRPA_Vasp)</a>
+</li>
+      <li><a href="_ref/postprocessing.eval_U_cRPA_RESPACK.html#postprocessing.eval_U_cRPA_RESPACK.construct_Uijkl">construct_Uijkl() (in module postprocessing.eval_U_cRPA_RESPACK)</a>
+</li>
+      <li><a href="_ref/dft_managers.mpi_helpers.html#dft_managers.mpi_helpers.create_hostfile">create_hostfile() (in module dft_managers.mpi_helpers)</a>
+</li>
+      <li>
+    csc_flow
+
+      <ul>
+        <li><a href="_ref/csc_flow.html#module-csc_flow">module</a>
+</li>
+      </ul></li>
+      <li><a href="_ref/csc_flow.html#csc_flow.csc_flow_control">csc_flow_control() (in module csc_flow)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="D">D</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="_ref/dmft_tools.afm_mapping.html#dmft_tools.afm_mapping.determine">determine() (in module dmft_tools.afm_mapping)</a>
+</li>
+      <li><a href="_ref/dmft_tools.initial_self_energies.html#dmft_tools.initial_self_energies.determine_dc_and_initial_sigma">determine_dc_and_initial_sigma() (in module dmft_tools.initial_self_energies)</a>
+</li>
+      <li>
+    dft_managers
+
+      <ul>
+        <li><a href="_ref/dft_managers.html#module-dft_managers">module</a>
+</li>
+      </ul></li>
+      <li>
+    dft_managers.mpi_helpers
+
+      <ul>
+        <li><a href="_ref/dft_managers.mpi_helpers.html#module-dft_managers.mpi_helpers">module</a>
+</li>
+      </ul></li>
+      <li>
+    dft_managers.qe_manager
+
+      <ul>
+        <li><a href="_ref/dft_managers.qe_manager.html#module-dft_managers.qe_manager">module</a>
+</li>
+      </ul></li>
+      <li>
+    dft_managers.vasp_manager
+
+      <ul>
+        <li><a href="_ref/dft_managers.vasp_manager.html#module-dft_managers.vasp_manager">module</a>
+</li>
+      </ul></li>
+      <li>
+    dmft_cycle
+
+      <ul>
+        <li><a href="_ref/dmft_cycle.html#module-dmft_cycle">module</a>
+</li>
+      </ul></li>
+      <li><a href="_ref/dmft_cycle.html#dmft_cycle.dmft_cycle">dmft_cycle() (in module dmft_cycle)</a>
+</li>
+      <li>
+    dmft_tools
+
+      <ul>
+        <li><a href="_ref/dmft_tools.html#module-dmft_tools">module</a>
+</li>
+      </ul></li>
+      <li>
+    dmft_tools.afm_mapping
+
+      <ul>
+        <li><a href="_ref/dmft_tools.afm_mapping.html#module-dmft_tools.afm_mapping">module</a>
+</li>
+      </ul></li>
+      <li>
+    dmft_tools.convergence
+
+      <ul>
+        <li><a href="_ref/dmft_tools.convergence.html#module-dmft_tools.convergence">module</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li>
+    dmft_tools.formatter
+
+      <ul>
+        <li><a href="_ref/dmft_tools.formatter.html#module-dmft_tools.formatter">module</a>
+</li>
+      </ul></li>
+      <li>
+    dmft_tools.greens_functions_mixer
+
+      <ul>
+        <li><a href="_ref/dmft_tools.greens_functions_mixer.html#module-dmft_tools.greens_functions_mixer">module</a>
+</li>
+      </ul></li>
+      <li>
+    dmft_tools.initial_self_energies
+
+      <ul>
+        <li><a href="_ref/dmft_tools.initial_self_energies.html#module-dmft_tools.initial_self_energies">module</a>
+</li>
+      </ul></li>
+      <li>
+    dmft_tools.interaction_hamiltonian
+
+      <ul>
+        <li><a href="_ref/dmft_tools.interaction_hamiltonian.html#module-dmft_tools.interaction_hamiltonian">module</a>
+</li>
+      </ul></li>
+      <li>
+    dmft_tools.legendre_filter
+
+      <ul>
+        <li><a href="_ref/dmft_tools.legendre_filter.html#module-dmft_tools.legendre_filter">module</a>
+</li>
+      </ul></li>
+      <li>
+    dmft_tools.manipulate_chemical_potential
+
+      <ul>
+        <li><a href="_ref/dmft_tools.manipulate_chemical_potential.html#module-dmft_tools.manipulate_chemical_potential">module</a>
+</li>
+      </ul></li>
+      <li>
+    dmft_tools.matheval
+
+      <ul>
+        <li><a href="_ref/dmft_tools.matheval.html#module-dmft_tools.matheval">module</a>
+</li>
+      </ul></li>
+      <li>
+    dmft_tools.observables
+
+      <ul>
+        <li><a href="_ref/dmft_tools.observables.html#module-dmft_tools.observables">module</a>
+</li>
+      </ul></li>
+      <li>
+    dmft_tools.results_to_archive
+
+      <ul>
+        <li><a href="_ref/dmft_tools.results_to_archive.html#module-dmft_tools.results_to_archive">module</a>
+</li>
+      </ul></li>
+      <li>
+    dmft_tools.solver
+
+      <ul>
+        <li><a href="_ref/dmft_tools.solver.html#module-dmft_tools.solver">module</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="F">F</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="_ref/dft_managers.mpi_helpers.html#dft_managers.mpi_helpers.find_path_to_mpi_command">find_path_to_mpi_command() (in module dft_managers.mpi_helpers)</a>
+</li>
+      <li><a href="_ref/postprocessing.eval_U_cRPA_Vasp.html#postprocessing.eval_U_cRPA_Vasp.fit_kanamori">fit_kanamori() (in module postprocessing.eval_U_cRPA_Vasp)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="_ref/postprocessing.eval_U_cRPA_RESPACK.html#postprocessing.eval_U_cRPA_RESPACK.fit_slater">fit_slater() (in module postprocessing.eval_U_cRPA_RESPACK)</a>
+</li>
+      <li><a href="_ref/postprocessing.eval_U_cRPA_Vasp.html#postprocessing.eval_U_cRPA_Vasp.fit_slater_fulld">fit_slater_fulld() (in module postprocessing.eval_U_cRPA_Vasp)</a>
+</li>
+      <li><a href="_ref/dmft_tools.matheval.MathExpr.functions.html#dmft_tools.matheval.MathExpr.functions">functions (dmft_tools.matheval.MathExpr attribute)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="G">G</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="_ref/postprocessing.plot_correlated_bands.html#postprocessing.plot_correlated_bands.get_dmft_bands">get_dmft_bands() (in module postprocessing.plot_correlated_bands)</a>
+</li>
+      <li><a href="_ref/dft_managers.mpi_helpers.html#dft_managers.mpi_helpers.get_mpi_arguments">get_mpi_arguments() (in module dft_managers.mpi_helpers)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="_ref/dmft_tools.solver.html#dmft_tools.solver.get_n_orbitals">get_n_orbitals() (in module dmft_tools.solver)</a>
+</li>
+      <li><a href="_ref/postprocessing.plot_correlated_bands.html#postprocessing.plot_correlated_bands.get_tb_bands">get_tb_bands() (in module postprocessing.plot_correlated_bands)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="H">H</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="_ref/dmft_tools.interaction_hamiltonian.html#dmft_tools.interaction_hamiltonian.h_int_simple_intra">h_int_simple_intra() (in module dmft_tools.interaction_hamiltonian)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="I">I</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li>
+    io_tools
+
+      <ul>
+        <li><a href="_ref/io_tools.html#module-io_tools">module</a>
+</li>
+      </ul></li>
+      <li>
+    io_tools.dict_to_h5
+
+      <ul>
+        <li><a href="_ref/io_tools.dict_to_h5.html#module-io_tools.dict_to_h5">module</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li>
+    io_tools.postproc_toml_dict
+
+      <ul>
+        <li><a href="_ref/io_tools.postproc_toml_dict.html#module-io_tools.postproc_toml_dict">module</a>
+</li>
+      </ul></li>
+      <li>
+    io_tools.verify_input_params
+
+      <ul>
+        <li><a href="_ref/io_tools.verify_input_params.html#module-io_tools.verify_input_params">module</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="K">K</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="_ref/dft_managers.vasp_manager.html#dft_managers.vasp_manager.kill">kill() (in module dft_managers.vasp_manager)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="M">M</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="_ref/postprocessing.maxent_gf_imp.html#postprocessing.maxent_gf_imp.main">main() (in module postprocessing.maxent_gf_imp)</a>
+
+      <ul>
+        <li><a href="_ref/postprocessing.maxent_gf_latt.html#postprocessing.maxent_gf_latt.main">(in module postprocessing.maxent_gf_latt)</a>
+</li>
+        <li><a href="_ref/postprocessing.maxent_sigma.html#postprocessing.maxent_sigma.main">(in module postprocessing.maxent_sigma)</a>
+</li>
+        <li><a href="_ref/postprocessing.pade_sigma.html#postprocessing.pade_sigma.main">(in module postprocessing.pade_sigma)</a>
+</li>
+        <li><a href="_ref/util.write_kslice_to_h5.html#util.write_kslice_to_h5.main">(in module util.write_kslice_to_h5)</a>
+</li>
+      </ul></li>
+      <li><a href="_ref/dmft_tools.convergence.html#dmft_tools.convergence.max_G_diff">max_G_diff() (in module dmft_tools.convergence)</a>
+</li>
+      <li><a href="_ref/io_tools.postproc_toml_dict.html#io_tools.postproc_toml_dict.merge_config_with_default">merge_config_with_default() (in module io_tools.postproc_toml_dict)</a>
+</li>
+      <li>
+    module
+
+      <ul>
+        <li><a href="_ref/csc_flow.html#module-csc_flow">csc_flow</a>
+</li>
+        <li><a href="_ref/dft_managers.html#module-dft_managers">dft_managers</a>
+</li>
+        <li><a href="_ref/dft_managers.mpi_helpers.html#module-dft_managers.mpi_helpers">dft_managers.mpi_helpers</a>
+</li>
+        <li><a href="_ref/dft_managers.qe_manager.html#module-dft_managers.qe_manager">dft_managers.qe_manager</a>
+</li>
+        <li><a href="_ref/dft_managers.vasp_manager.html#module-dft_managers.vasp_manager">dft_managers.vasp_manager</a>
+</li>
+        <li><a href="_ref/dmft_cycle.html#module-dmft_cycle">dmft_cycle</a>
+</li>
+        <li><a href="_ref/dmft_tools.html#module-dmft_tools">dmft_tools</a>
+</li>
+        <li><a href="_ref/dmft_tools.afm_mapping.html#module-dmft_tools.afm_mapping">dmft_tools.afm_mapping</a>
+</li>
+        <li><a href="_ref/dmft_tools.convergence.html#module-dmft_tools.convergence">dmft_tools.convergence</a>
+</li>
+        <li><a href="_ref/dmft_tools.formatter.html#module-dmft_tools.formatter">dmft_tools.formatter</a>
+</li>
+        <li><a href="_ref/dmft_tools.greens_functions_mixer.html#module-dmft_tools.greens_functions_mixer">dmft_tools.greens_functions_mixer</a>
+</li>
+        <li><a href="_ref/dmft_tools.initial_self_energies.html#module-dmft_tools.initial_self_energies">dmft_tools.initial_self_energies</a>
+</li>
+        <li><a href="_ref/dmft_tools.interaction_hamiltonian.html#module-dmft_tools.interaction_hamiltonian">dmft_tools.interaction_hamiltonian</a>
+</li>
+        <li><a href="_ref/dmft_tools.legendre_filter.html#module-dmft_tools.legendre_filter">dmft_tools.legendre_filter</a>
+</li>
+        <li><a href="_ref/dmft_tools.manipulate_chemical_potential.html#module-dmft_tools.manipulate_chemical_potential">dmft_tools.manipulate_chemical_potential</a>
+</li>
+        <li><a href="_ref/dmft_tools.matheval.html#module-dmft_tools.matheval">dmft_tools.matheval</a>
+</li>
+        <li><a href="_ref/dmft_tools.observables.html#module-dmft_tools.observables">dmft_tools.observables</a>
+</li>
+        <li><a href="_ref/dmft_tools.results_to_archive.html#module-dmft_tools.results_to_archive">dmft_tools.results_to_archive</a>
+</li>
+        <li><a href="_ref/dmft_tools.solver.html#module-dmft_tools.solver">dmft_tools.solver</a>
+</li>
+        <li><a href="_ref/io_tools.html#module-io_tools">io_tools</a>
+</li>
+        <li><a href="_ref/io_tools.dict_to_h5.html#module-io_tools.dict_to_h5">io_tools.dict_to_h5</a>
+</li>
+        <li><a href="_ref/io_tools.postproc_toml_dict.html#module-io_tools.postproc_toml_dict">io_tools.postproc_toml_dict</a>
+</li>
+        <li><a href="_ref/io_tools.verify_input_params.html#module-io_tools.verify_input_params">io_tools.verify_input_params</a>
+</li>
+        <li><a href="_ref/postprocessing.html#module-postprocessing">postprocessing</a>
+</li>
+        <li><a href="_ref/postprocessing.eval_U_cRPA_RESPACK.html#module-postprocessing.eval_U_cRPA_RESPACK">postprocessing.eval_U_cRPA_RESPACK</a>
+</li>
+        <li><a href="_ref/postprocessing.eval_U_cRPA_Vasp.html#module-postprocessing.eval_U_cRPA_Vasp">postprocessing.eval_U_cRPA_Vasp</a>
+</li>
+        <li><a href="_ref/postprocessing.maxent_gf_imp.html#module-postprocessing.maxent_gf_imp">postprocessing.maxent_gf_imp</a>
+</li>
+        <li><a href="_ref/postprocessing.maxent_gf_latt.html#module-postprocessing.maxent_gf_latt">postprocessing.maxent_gf_latt</a>
+</li>
+        <li><a href="_ref/postprocessing.maxent_sigma.html#module-postprocessing.maxent_sigma">postprocessing.maxent_sigma</a>
+</li>
+        <li><a href="_ref/postprocessing.pade_sigma.html#module-postprocessing.pade_sigma">postprocessing.pade_sigma</a>
+</li>
+        <li><a href="_ref/postprocessing.plot_correlated_bands.html#module-postprocessing.plot_correlated_bands">postprocessing.plot_correlated_bands</a>
+</li>
+        <li><a href="index.html#module-solid_dmft">solid_dmft</a>
+</li>
+        <li><a href="_ref/util.html#module-util">util</a>
+</li>
+        <li><a href="_ref/util.symmetrize_gamma_file.html#module-util.symmetrize_gamma_file">util.symmetrize_gamma_file</a>
+</li>
+        <li><a href="_ref/util.write_kslice_to_h5.html#module-util.write_kslice_to_h5">util.write_kslice_to_h5</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="P">P</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="_ref/dft_managers.mpi_helpers.html#dft_managers.mpi_helpers.poll_barrier">poll_barrier() (in module dft_managers.mpi_helpers)</a>
+</li>
+      <li>
+    postprocessing
+
+      <ul>
+        <li><a href="_ref/postprocessing.html#module-postprocessing">module</a>
+</li>
+      </ul></li>
+      <li>
+    postprocessing.eval_U_cRPA_RESPACK
+
+      <ul>
+        <li><a href="_ref/postprocessing.eval_U_cRPA_RESPACK.html#module-postprocessing.eval_U_cRPA_RESPACK">module</a>
+</li>
+      </ul></li>
+      <li>
+    postprocessing.eval_U_cRPA_Vasp
+
+      <ul>
+        <li><a href="_ref/postprocessing.eval_U_cRPA_Vasp.html#module-postprocessing.eval_U_cRPA_Vasp">module</a>
+</li>
+      </ul></li>
+      <li>
+    postprocessing.maxent_gf_imp
+
+      <ul>
+        <li><a href="_ref/postprocessing.maxent_gf_imp.html#module-postprocessing.maxent_gf_imp">module</a>
+</li>
+      </ul></li>
+      <li>
+    postprocessing.maxent_gf_latt
+
+      <ul>
+        <li><a href="_ref/postprocessing.maxent_gf_latt.html#module-postprocessing.maxent_gf_latt">module</a>
+</li>
+      </ul></li>
+      <li>
+    postprocessing.maxent_sigma
+
+      <ul>
+        <li><a href="_ref/postprocessing.maxent_sigma.html#module-postprocessing.maxent_sigma">module</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li>
+    postprocessing.pade_sigma
+
+      <ul>
+        <li><a href="_ref/postprocessing.pade_sigma.html#module-postprocessing.pade_sigma">module</a>
+</li>
+      </ul></li>
+      <li>
+    postprocessing.plot_correlated_bands
+
+      <ul>
+        <li><a href="_ref/postprocessing.plot_correlated_bands.html#module-postprocessing.plot_correlated_bands">module</a>
+</li>
+      </ul></li>
+      <li><a href="_ref/dmft_tools.convergence.html#dmft_tools.convergence.prep_conv_file">prep_conv_file() (in module dmft_tools.convergence)</a>
+</li>
+      <li><a href="_ref/dmft_tools.convergence.html#dmft_tools.convergence.prep_conv_obs">prep_conv_obs() (in module dmft_tools.convergence)</a>
+</li>
+      <li><a href="_ref/dmft_tools.observables.html#dmft_tools.observables.prep_observables">prep_observables() (in module dmft_tools.observables)</a>
+</li>
+      <li><a href="_ref/io_tools.dict_to_h5.html#io_tools.dict_to_h5.prep_params_for_h5">prep_params_for_h5() (in module io_tools.dict_to_h5)</a>
+</li>
+      <li><a href="_ref/io_tools.dict_to_h5.html#io_tools.dict_to_h5.prep_params_from_h5">prep_params_from_h5() (in module io_tools.dict_to_h5)</a>
+</li>
+      <li><a href="_ref/dmft_tools.formatter.html#dmft_tools.formatter.print_block_sym">print_block_sym() (in module dmft_tools.formatter)</a>
+</li>
+      <li><a href="_ref/dmft_tools.formatter.html#dmft_tools.formatter.print_rotation_matrix">print_rotation_matrix() (in module dmft_tools.formatter)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="R">R</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="_ref/dft_managers.qe_manager.html#dft_managers.qe_manager.read_dft_energy">read_dft_energy() (in module dft_managers.qe_manager)</a>
+
+      <ul>
+        <li><a href="_ref/dft_managers.vasp_manager.html#dft_managers.vasp_manager.read_dft_energy">(in module dft_managers.vasp_manager)</a>
+</li>
+      </ul></li>
+      <li><a href="_ref/postprocessing.eval_U_cRPA_RESPACK.html#postprocessing.eval_U_cRPA_RESPACK.read_interaction">read_interaction() (in module postprocessing.eval_U_cRPA_RESPACK)</a>
+</li>
+      <li><a href="_ref/dft_managers.vasp_manager.html#dft_managers.vasp_manager.read_irred_kpoints">read_irred_kpoints() (in module dft_managers.vasp_manager)</a>
+</li>
+      <li><a href="_ref/postprocessing.eval_U_cRPA_Vasp.html#postprocessing.eval_U_cRPA_Vasp.read_uijkl">read_uijkl() (in module postprocessing.eval_U_cRPA_Vasp)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="_ref/postprocessing.eval_U_cRPA_Vasp.html#postprocessing.eval_U_cRPA_Vasp.red_to_2ind">red_to_2ind() (in module postprocessing.eval_U_cRPA_Vasp)</a>
+</li>
+      <li><a href="_ref/dft_managers.vasp_manager.html#dft_managers.vasp_manager.remove_legacy_projections_suppressed">remove_legacy_projections_suppressed() (in module dft_managers.vasp_manager)</a>
+</li>
+      <li><a href="_ref/postprocessing.eval_U_cRPA_RESPACK.html#postprocessing.eval_U_cRPA_RESPACK.respack_data">respack_data (class in postprocessing.eval_U_cRPA_RESPACK)</a>
+</li>
+      <li><a href="_ref/dft_managers.qe_manager.html#dft_managers.qe_manager.run">run() (in module dft_managers.qe_manager)</a>
+</li>
+      <li><a href="_ref/dft_managers.vasp_manager.html#dft_managers.vasp_manager.run_charge_update">run_charge_update() (in module dft_managers.vasp_manager)</a>
+</li>
+      <li><a href="_ref/dft_managers.vasp_manager.html#dft_managers.vasp_manager.run_initial_scf">run_initial_scf() (in module dft_managers.vasp_manager)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="S">S</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="_ref/dmft_tools.manipulate_chemical_potential.html#dmft_tools.manipulate_chemical_potential.set_initial_mu">set_initial_mu() (in module dmft_tools.manipulate_chemical_potential)</a>
+</li>
+      <li><a href="index.html#index-0">solid_dmft</a>
+
+      <ul>
+        <li><a href="index.html#module-solid_dmft">module</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="_ref/dmft_tools.solver.SolverStructure.solve.html#dmft_tools.solver.SolverStructure.solve">solve() (dmft_tools.solver.SolverStructure method)</a>, <a href="_ref/dmft_tools.solver.html#dmft_tools.solver.SolverStructure.solve">[1]</a>
+</li>
+      <li><a href="_ref/dmft_tools.solver.html#dmft_tools.solver.SolverStructure">SolverStructure (class in dmft_tools.solver)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="U">U</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="_ref/dmft_tools.manipulate_chemical_potential.html#dmft_tools.manipulate_chemical_potential.update_mu">update_mu() (in module dmft_tools.manipulate_chemical_potential)</a>
+</li>
+      <li>
+    util
+
+      <ul>
+        <li><a href="_ref/util.html#module-util">module</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li>
+    util.symmetrize_gamma_file
+
+      <ul>
+        <li><a href="_ref/util.symmetrize_gamma_file.html#module-util.symmetrize_gamma_file">module</a>
+</li>
+      </ul></li>
+      <li>
+    util.write_kslice_to_h5
+
+      <ul>
+        <li><a href="_ref/util.write_kslice_to_h5.html#module-util.write_kslice_to_h5">module</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="V">V</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="_ref/io_tools.verify_input_params.html#io_tools.verify_input_params.verify_before_dmft_cycle">verify_before_dmft_cycle() (in module io_tools.verify_input_params)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="_ref/io_tools.verify_input_params.html#io_tools.verify_input_params.verify_h5_dependent">verify_h5_dependent() (in module io_tools.verify_input_params)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="W">W</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="_ref/dmft_tools.results_to_archive.html#dmft_tools.results_to_archive.write">write() (in module dmft_tools.results_to_archive)</a>
+</li>
+      <li><a href="_ref/dmft_tools.convergence.html#dmft_tools.convergence.write_conv">write_conv() (in module dmft_tools.convergence)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="_ref/dmft_tools.observables.html#dmft_tools.observables.write_header_to_file">write_header_to_file() (in module dmft_tools.observables)</a>
+</li>
+      <li><a href="_ref/dmft_tools.observables.html#dmft_tools.observables.write_obs">write_obs() (in module dmft_tools.observables)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.</p>
+  </div>
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
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+        </div>
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+    </section>
+  </div>
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+      jQuery(function () {
+          SphinxRtdTheme.Navigation.enable(true);
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+  </script> 
+
+</body>
+</html>
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diff --git a/index.html b/index.html
new file mode 100644
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+<!DOCTYPE html>
+<html class="writer-html5" lang="en" >
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>solid_dmft &mdash; solid_dmft  documentation</title>
+      <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
+      <link rel="stylesheet" href="_static/css/custom.css" type="text/css" />
+      <link rel="stylesheet" href="_static/plot_directive.css" type="text/css" />
+    <link rel="shortcut icon" href="_static/favicon.ico"/>
+  
+        <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
+        <script src="_static/jquery.js"></script>
+        <script src="_static/underscore.js"></script>
+        <script src="_static/doctools.js"></script>
+        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+    <script src="_static/js/theme.js"></script>
+    <link rel="index" title="Index" href="genindex.html" />
+    <link rel="search" title="Search" href="search.html" />
+    <link rel="next" title="Installation" href="install.html" /> 
+</head>
+
+<body class="wy-body-for-nav"> 
+  <div class="wy-grid-for-nav">
+    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
+      <div class="wy-side-scroll">
+        <div class="wy-side-nav-search"  style="background: #7E588A" >
+            <a href="#" class="icon icon-home"> solid_dmft
+            <img src="_static/logo_no_text.png" class="logo" alt="Logo"/>
+          </a>
+              <div class="version">
+                3.2.0
+              </div>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" />
+    <input type="hidden" name="check_keywords" value="yes" />
+    <input type="hidden" name="area" value="default" />
+  </form>
+</div>
+        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+              <ul>
+<li class="toctree-l1"><a class="reference internal" href="install.html">Installation</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="install.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="install.html#installation-via-pip">Installation via pip</a></li>
+<li class="toctree-l2"><a class="reference internal" href="install.html#manual-installation-via-cmake">Manual installation via CMake</a></li>
+<li class="toctree-l2"><a class="reference internal" href="install.html#docker-files-images">Docker files &amp; images</a></li>
+<li class="toctree-l2"><a class="reference internal" href="install.html#version-compatibility">Version compatibility</a></li>
+<li class="toctree-l2"><a class="reference internal" href="install.html#custom-cmake-options">Custom CMake options</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="documentation.html">Documentation</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="documentation.html#code-structure">Code structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="documentation.html#dft-interface-notes">DFT interface notes</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="md_notes/w90_interface.html">Wannier90 interface</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/w90_interface.html#orbital-order-in-the-w90-converter">orbital order in the W90 converter</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="md_notes/vasp_csc.html">Interface to VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/vasp_csc.html#general-remarks">General remarks</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="cRPA_VASP/README.html#file-description">file description</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cRPA_VASP/README.html#workflow">Workflow:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cRPA_VASP/README.html#important-flags">important flags:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cRPA_VASP/README.html#version-and-compilation">version and compilation:</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="documentation.html#input-output">Input/Output</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="input_output/DMFT_input/input.html">Input</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="input_output/DMFT_input/general.html">[general]: General parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="input_output/DMFT_input/solver.html">[solver]: solver specific parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="input_output/DMFT_input/dft.html">[dft]: DFT related inputs</a></li>
+<li class="toctree-l4"><a class="reference internal" href="input_output/DMFT_input/advanced.html">[advanced]: Advanced inputs</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="input_output/DMFT_output/results.html">Output / results</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="input_output/DMFT_output/results.html#group-structure">Group structure</a></li>
+<li class="toctree-l4"><a class="reference internal" href="input_output/DMFT_output/results.html#subgroups">Subgroups</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="documentation.html#further-details-for-running">Further details for running</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="md_notes/docker.html">Docker</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/docker.html#building-the-docker-image">Building the docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/docker.html#pulling-a-docker-image">Pulling a docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="md_notes/run_locally.html">Run on your machine</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="documentation.html#module-reference-manual">Module reference manual</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="_ref/csc_flow.html">csc_flow</a></li>
+<li class="toctree-l3"><a class="reference internal" href="_ref/dft_managers.html">dft_managers</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dft_managers.mpi_helpers.html">dft_managers.mpi_helpers</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dft_managers.qe_manager.html">dft_managers.qe_manager</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dft_managers.vasp_manager.html">dft_managers.vasp_manager</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="_ref/dmft_cycle.html">dmft_cycle</a></li>
+<li class="toctree-l3"><a class="reference internal" href="_ref/dmft_tools.html">dmft_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.afm_mapping.html">dmft_tools.afm_mapping</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.convergence.html">dmft_tools.convergence</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.formatter.html">dmft_tools.formatter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.greens_functions_mixer.html">dmft_tools.greens_functions_mixer</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.initial_self_energies.html">dmft_tools.initial_self_energies</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.interaction_hamiltonian.html">dmft_tools.interaction_hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.legendre_filter.html">dmft_tools.legendre_filter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.manipulate_chemical_potential.html">dmft_tools.manipulate_chemical_potential</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.matheval.html">dmft_tools.matheval</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.observables.html">dmft_tools.observables</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.results_to_archive.html">dmft_tools.results_to_archive</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.solver.html">dmft_tools.solver</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="_ref/io_tools.html">io_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="_ref/io_tools.dict_to_h5.html">io_tools.dict_to_h5</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/io_tools.postproc_toml_dict.html">io_tools.postproc_toml_dict</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/io_tools.verify_input_params.html">io_tools.verify_input_params</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="_ref/postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="_ref/postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/postprocessing.eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/postprocessing.maxent_gf_imp.html">postprocessing.maxent_gf_imp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/postprocessing.maxent_gf_latt.html">postprocessing.maxent_gf_latt</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/postprocessing.maxent_sigma.html">postprocessing.maxent_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="_ref/util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="_ref/util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="issues.html">Support &amp; contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="issues.html#seeking-help">Seeking help</a></li>
+<li class="toctree-l2"><a class="reference internal" href="issues.html#improving-solid-dmft">Improving solid_dmft</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="issues.html#reporting-issues">Reporting issues</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="ChangeLog.html">Changelog</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#version-3-2-0">Version 3.2.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#general">General</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#fix">fix</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#build">build</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#feat">feat</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#doc">doc</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#test">test</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#version-3-1-5">Version 3.1.5</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#version-3-1-4">Version 3.1.4</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#version-3-1-3">Version 3.1.3</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#version-3-1-2">Version 3.1.2</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#version-3-1-1">Version 3.1.1</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#version-3-1-0">Version 3.1.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#version-3-0-0">Version 3.0.0</a></li>
+</ul>
+</li>
+</ul>
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+<span class="target" id="module-solid_dmft"><span id="index-0"></span></span><div class="section" id="solid-dmft">
+<h1>solid_dmft<a class="headerlink" href="#solid-dmft" title="Permalink to this headline"></a></h1>
+<div class="sidebar">
+<p class="first sidebar-title">solid_dmft 3.2.0</p>
+<p>This is the homepage of solid_dmft 3.2.0.
+For changes see the <a class="reference internal" href="ChangeLog.html#changelog"><span class="std std-ref">changelog page</span></a>.
+visit us on:</p>
+<blockquote class="last">
+<div><a class="reference external image-reference" href="https://github.com/flatironinstitute/solid_dmft" rel="noopener noreferrer" target="_blank"><img alt="_images/logo_github.png" class="align-center" src="_images/logo_github.png" style="width: 60%;" /></a>
+</div></blockquote>
+</div>
+<p>This program allows to perform DFT+DMFT ‘’one-shot’’ and charge self-consistent
+(CSC) calculations from h5 archives or VASP/Quantum Espresso input files for
+multiband systems using the <a class="reference external" href="https://triqs.github.io" rel="noopener noreferrer" target="_blank">TRIQS software library</a>, and the DFT code interface
+<a class="reference external" href="https://triqs.github.io/dft_tools" rel="noopener noreferrer" target="_blank">TRIQS/DFTTools</a>. Works with triqs &gt;3.x.x.
+solid_dmft takes advantage of various
+<a class="reference external" href="https://triqs.github.io/triqs/latest/applications.html#impurity-solvers" rel="noopener noreferrer" target="_blank">impurity solvers available</a>
+in triqs: cthyb, HubbardI, ForkTPS, ctint, and ctseg. Postprocessing scripts are available to
+perform analytic continuation and calculate spectral functions.</p>
+<p>For installation use the same branch / tag as your triqs installation. More
+information under <a class="reference internal" href="install.html#installation"><span class="std std-ref">Installation</span></a>.</p>
+<p>Learn how to use solid_dmft in the <a class="reference internal" href="documentation.html#documentation"><span class="std std-ref">Documentation</span></a> and the <a class="reference internal" href="tutorials.html#tutorials"><span class="std std-ref">Tutorials</span></a>.</p>
+<p>For more technical information about the implementation check also the <a class="reference external" href="https://doi.org/10.21105/joss.04623" rel="noopener noreferrer" target="_blank">solid_dmft publication</a> in the JOSS journal. If you are using this code for your research, please cite the paper using this <a class="reference external" href="https://github.com/TRIQS/solid_dmft/blob/unstable/cite_solid_dmft.bib" rel="noopener noreferrer" target="_blank">bib file</a>.</p>
+<div class="section" id="workflow-of-dft-dmft-calculations-with-solid-dmft">
+<h2>Workflow of DFT+DMFT calculations with solid_dmft<a class="headerlink" href="#workflow-of-dft-dmft-calculations-with-solid-dmft" title="Permalink to this headline"></a></h2>
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+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/w90_interface.html">Wannier90 interface</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/w90_interface.html#orbital-order-in-the-w90-converter">orbital order in the W90 converter</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/vasp_csc.html">Interface to VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#general-remarks">General remarks</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#file-description">file description</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#workflow">Workflow:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#important-flags">important flags:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#version-and-compilation">version and compilation:</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="reference internal" href="../../documentation.html#input-output">Input/Output</a><ul class="current">
+<li class="toctree-l3 current"><a class="reference internal" href="input.html">Input</a><ul class="current">
+<li class="toctree-l4"><a class="reference internal" href="general.html">[general]: General parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="solver.html">[solver]: solver specific parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dft.html">[dft]: DFT related inputs</a></li>
+<li class="toctree-l4 current"><a class="current reference internal" href="#">[advanced]: Advanced inputs</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../DMFT_output/results.html#group-structure">Group structure</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../DMFT_output/results.html#subgroups">Subgroups</a></li>
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+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#further-details-for-running">Further details for running</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/docker.html">Docker</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#building-the-docker-image">Building the docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#pulling-a-docker-image">Pulling a docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_locally.html">Run on your machine</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#module-reference-manual">Module reference manual</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/csc_flow.html">csc_flow</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.vasp_manager.html">dft_managers.vasp_manager</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/dmft_cycle.html">dmft_cycle</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.afm_mapping.html">dmft_tools.afm_mapping</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.formatter.html">dmft_tools.formatter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.greens_functions_mixer.html">dmft_tools.greens_functions_mixer</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.initial_self_energies.html">dmft_tools.initial_self_energies</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.interaction_hamiltonian.html">dmft_tools.interaction_hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.legendre_filter.html">dmft_tools.legendre_filter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.manipulate_chemical_potential.html">dmft_tools.manipulate_chemical_potential</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.matheval.html">dmft_tools.matheval</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.observables.html">dmft_tools.observables</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.results_to_archive.html">dmft_tools.results_to_archive</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.solver.html">dmft_tools.solver</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/io_tools.html">io_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.dict_to_h5.html">io_tools.dict_to_h5</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.postproc_toml_dict.html">io_tools.postproc_toml_dict</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.verify_input_params.html">io_tools.verify_input_params</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_gf_imp.html">postprocessing.maxent_gf_imp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_gf_latt.html">postprocessing.maxent_gf_latt</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_sigma.html">postprocessing.maxent_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
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+<div class="section" id="advanced-advanced-inputs">
+<h1>[advanced]: Advanced inputs<a class="headerlink" href="#advanced-advanced-inputs" title="Permalink to this headline"></a></h1>
+<p>Advanced parameters, do not modify the default value unless you know what you are doing.</p>
+<div class="intag admonition">
+<p class="first admonition-title">dc_factor</p>
+<blockquote>
+<div><strong>type</strong> = float;  <strong>default</strong>  = None</div></blockquote>
+<p class="last">If given, scales the dc energy by multiplying with this factor, usually &lt; 1.
+If None, the dc is left unchanged, which is equivalent to dc_factor=1.</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">dc_fixed_occ</p>
+<blockquote>
+<div><strong>type</strong> = list of float;  <strong>default</strong>  = None</div></blockquote>
+<p class="last">If given, the occupation for the DC for each impurity is set to the provided value.
+Still uses the same kind of DC!</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">dc_fixed_value</p>
+<blockquote>
+<div><strong>type</strong> = float;  <strong>default</strong>  = None</div></blockquote>
+<p class="last">If given, it sets the DC (energy/imp) to this fixed value. Overwrites EVERY other DC configuration parameter if DC is turned on</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">dc_nominal</p>
+<blockquote>
+<div><strong>type</strong> = bool;  <strong>default</strong>  = False</div></blockquote>
+<p class="last">TODO: write</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">dc_orb_shift</p>
+<blockquote>
+<div><strong>type</strong> = list of float;  <strong>default</strong>  = None</div></blockquote>
+<p class="last">extra potential shift per orbital per impurity added to the DC</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">dc_J</p>
+<blockquote>
+<div><strong>type</strong> = float or list of float;  <strong>default</strong>  = general.J</div></blockquote>
+<p class="last">J values for DC determination. If only one value is given, the same J is assumed for all impurities</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">dc_U</p>
+<blockquote>
+<div><strong>type</strong> = float or list of float;  <strong>default</strong>  = general.U</div></blockquote>
+<p class="last">U values for DC determination. If only one value is given, the same U is assumed for all impurities</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">map_solver_struct</p>
+<blockquote>
+<div><strong>type</strong> = list of dict;  <strong>default</strong>  = None</div></blockquote>
+<p class="last">Additional manual mapping of the solver block structure, applied
+after the block structure finder for each impurity.
+Give exactly one dict per ineq impurity.
+see also triqs.github.io/dft_tools/latest/_python_api/triqs_dft_tools.block_structure.BlockStructure.map_gf_struct_solver.html</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">mapped_solver_struct_degeneracies</p>
+<blockquote>
+<div><strong>type</strong> = list;  <strong>default</strong>  = None</div></blockquote>
+<p class="last">Degeneracies applied when using map_solver_struct, for each impurity.
+If not given and map_solver_struct is used, no symmetrization will happen.</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">pick_solver_struct</p>
+<blockquote>
+<div><strong>type</strong> = list of dict;  <strong>default</strong>  = None</div></blockquote>
+<p class="last">input a solver dictionary for each ineq impurity to reduce dimensionality of
+solver block structure. Similar to to map_solver_struct, but with simpler syntax.</p>
+</div>
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+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
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+<div class="section" id="dft-dft-related-inputs">
+<h1>[dft]: DFT related inputs<a class="headerlink" href="#dft-dft-related-inputs" title="Permalink to this headline"></a></h1>
+<p>List of parameters for the DFT calculation in charge-self-consistent calculations.
+The parameters are ignored in one-shot calculations.</p>
+<div class="intag admonition">
+<p class="first admonition-title">dft_code</p>
+<blockquote>
+<div><strong>type</strong> = string</div></blockquote>
+<p>choose the DFT code interface, for now Quantum Espresso and Vasp are available.
+Possible values:</p>
+<ul class="last simple">
+<li>‘vasp’</li>
+<li>‘qe’</li>
+</ul>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">dft_exec</p>
+<blockquote>
+<div><strong>type</strong> = string;  <strong>default</strong>  = ‘vasp_std’</div></blockquote>
+<p class="last">command for the DFT executable</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">mpi_env</p>
+<blockquote>
+<div><strong>type</strong> = string;  <strong>default</strong> ‘</div></blockquote>
+<p>selection for mpi env for DFT / VASP in default this will only call VASP as mpirun -np n_cores_dft dft_exec
+Possible values:</p>
+<ul class="last simple">
+<li>‘default’</li>
+<li>‘openmpi’</li>
+<li>‘openmpi-intra’</li>
+<li>‘mpich’</li>
+</ul>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">n_cores</p>
+<blockquote>
+<div><strong>type</strong> = int, None</div></blockquote>
+<p class="last">number of cores for the DFT code (VASP). Mandatory for charge-self-consistent calculations</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">n_iter</p>
+<blockquote>
+<div><strong>type</strong> = int;  <strong>default</strong>  = 4</div></blockquote>
+<p class="last">only needed for VASP. Number of DFT iterations to feed the DMFT
+charge density into DFT, which generally takes multiple Davidson steps.
+For every DFT iterations, the charge-density correction is recalculated
+using newly generated projectors and hoppings from the previous DFT run</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">n_iter_first</p>
+<blockquote>
+<div><strong>type</strong> = int;  <strong>default</strong>  = dft.n_iter</div></blockquote>
+<p class="last">number of DFT iterations in the first charge correction because this
+first charge correction usually changes the DFT wave functions the most.</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">plo_cfg</p>
+<blockquote>
+<div><strong>type</strong> = str;  <strong>default</strong>  = ‘plo.cfg’</div></blockquote>
+<p class="last">config file for PLOs for the converter</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">projector_type</p>
+<blockquote>
+<div><strong>type</strong> = string;  <strong>default</strong>  = ‘w90’</div></blockquote>
+<p class="last">plo: uses VASP’s PLO formalism, requires LOCPROJ in the INCAR
+w90: uses Wannier90 (for VASP and QuantumEspresso)</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">store_eigenvals</p>
+<blockquote>
+<div><strong>type</strong> = bool;  <strong>default</strong>  = False</div></blockquote>
+<p class="last">stores the dft eigenvals from LOCPROJ (projector_type=plo) or
+wannier90.eig (projector_type=w90) file in h5 archive</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">w90_exec</p>
+<blockquote>
+<div><strong>type</strong> = string;  <strong>default</strong>  =’wannier90.x’</div></blockquote>
+<p class="last">the command to start a single-core wannier run</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">w90_tolerance</p>
+<blockquote>
+<div><strong>type</strong> = float;  <strong>default</strong>  = 1e-6</div></blockquote>
+<p class="last">threshold for mapping of shells and checks of the Hamiltonian</p>
+</div>
+</div>
+
+
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+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#file-description">file description</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#workflow">Workflow:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#important-flags">important flags:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#version-and-compilation">version and compilation:</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="reference internal" href="../../documentation.html#input-output">Input/Output</a><ul class="current">
+<li class="toctree-l3 current"><a class="reference internal" href="input.html">Input</a><ul class="current">
+<li class="toctree-l4 current"><a class="current reference internal" href="#">[general]: General parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="solver.html">[solver]: solver specific parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dft.html">[dft]: DFT related inputs</a></li>
+<li class="toctree-l4"><a class="reference internal" href="advanced.html">[advanced]: Advanced inputs</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../DMFT_output/results.html">Output / results</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../DMFT_output/results.html#group-structure">Group structure</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../DMFT_output/results.html#subgroups">Subgroups</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#further-details-for-running">Further details for running</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/docker.html">Docker</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#building-the-docker-image">Building the docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#pulling-a-docker-image">Pulling a docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_locally.html">Run on your machine</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#module-reference-manual">Module reference manual</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/csc_flow.html">csc_flow</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/dft_managers.html">dft_managers</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.mpi_helpers.html">dft_managers.mpi_helpers</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.qe_manager.html">dft_managers.qe_manager</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.vasp_manager.html">dft_managers.vasp_manager</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/dmft_cycle.html">dmft_cycle</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/dmft_tools.html">dmft_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.afm_mapping.html">dmft_tools.afm_mapping</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.convergence.html">dmft_tools.convergence</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.formatter.html">dmft_tools.formatter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.greens_functions_mixer.html">dmft_tools.greens_functions_mixer</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.initial_self_energies.html">dmft_tools.initial_self_energies</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.interaction_hamiltonian.html">dmft_tools.interaction_hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.legendre_filter.html">dmft_tools.legendre_filter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.manipulate_chemical_potential.html">dmft_tools.manipulate_chemical_potential</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.matheval.html">dmft_tools.matheval</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.observables.html">dmft_tools.observables</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.results_to_archive.html">dmft_tools.results_to_archive</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.solver.html">dmft_tools.solver</a></li>
+</ul>
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+<li class="toctree-l3"><a class="reference internal" href="../../_ref/io_tools.html">io_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.dict_to_h5.html">io_tools.dict_to_h5</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.postproc_toml_dict.html">io_tools.postproc_toml_dict</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.verify_input_params.html">io_tools.verify_input_params</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_gf_imp.html">postprocessing.maxent_gf_imp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_gf_latt.html">postprocessing.maxent_gf_latt</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_sigma.html">postprocessing.maxent_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../issues.html">Support &amp; contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../issues.html#seeking-help">Seeking help</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../issues.html#improving-solid-dmft">Improving solid_dmft</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../issues.html#reporting-issues">Reporting issues</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../ChangeLog.html">Changelog</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-2-0">Version 3.2.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#general">General</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-1-4">Version 3.1.4</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-1-3">Version 3.1.3</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-1-2">Version 3.1.2</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-1-1">Version 3.1.1</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-1-0">Version 3.1.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-0-0">Version 3.0.0</a></li>
+</ul>
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+</ul>
+
+        </div>
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+<div class="section" id="general-general-parameters">
+<h1>[general]: General parameters<a class="headerlink" href="#general-general-parameters" title="Permalink to this headline"></a></h1>
+<p>Frequently used parameters that apply to the whole simulation.</p>
+<div class="intag admonition">
+<p class="first admonition-title">afm_order</p>
+<blockquote>
+<div><strong>type</strong> = bool;  <strong>default</strong>  = False</div></blockquote>
+<p class="last">copy self energies instead of solving explicitly for afm order</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">beta</p>
+<blockquote>
+<div><strong>type</strong> = float;  <strong>default</strong>  = None</div></blockquote>
+<p class="last">inverse temperature. If set, solid_dmft stores all Greens functions etc on an imaginary-frequency grid
+and also n_iw and n_tau have to be specified.
+If not set, it uses a real-frequency grid and eta, n_w and w_range have to be set</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">block_threshold</p>
+<blockquote>
+<div><strong>type</strong> = float;  <strong>default</strong>  = 1e-5</div></blockquote>
+<p class="last">threshold for finding block structures in the input data (off-diag yes or no)</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">broy_max_it</p>
+<blockquote>
+<div><strong>type</strong> = int;  <strong>default</strong>  = -1</div></blockquote>
+<p class="last">maximum number of iteration to be considered for broyden mixing.
+Only used if general.g0_mix_type=’broyden’.
+1 corresponds to simple linear mixing</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">calc_energies</p>
+<blockquote>
+<div><strong>type</strong> = bool;  <strong>default</strong>  = False</div></blockquote>
+<p class="last">Calculate the energies explicitly within the dmft loop and write them to the observables file.
+Not compatible with ‘ftps’ solver</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">calc_mu_method</p>
+<blockquote>
+<div><strong>type</strong> = string;  <strong>default</strong>  = ‘dichotomy’</div></blockquote>
+<p>optimization method used for finding the chemical potential:</p>
+<ul class="last simple">
+<li>‘dichotomy’: usual method from TRIQS, should always converge but may be slow</li>
+<li>‘newton’: scipy Newton root finder, much faster but might be unstable</li>
+<li>‘brent’: scipy hyperbolic Brent root finder preconditioned with dichotomy to find edge, a compromise between speed and stability</li>
+</ul>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">csc</p>
+<blockquote>
+<div><strong>type</strong> = bool;  <strong>default</strong>  = False</div></blockquote>
+<p class="last">are we doing a CSC calculation?</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">dc</p>
+<blockquote>
+<div><strong>type</strong> = bool;  <strong>default</strong>  = True</div></blockquote>
+<p class="last">is the double-counting correction on?</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">dc_dmft</p>
+<blockquote>
+<div><strong>type</strong> = bool;  <strong>default</strong>  = None</div></blockquote>
+<p>Calculate the double-counting (DC) correction from DMFT or DFT occupations.
+Needs to be set if general.dc = True.</p>
+<ul class="last simple">
+<li>True: DC with DMFT occupation in each iteration</li>
+<li>False: DC with DFT occupations after each DFT cycle</li>
+</ul>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">dc_type</p>
+<blockquote>
+<div><strong>type</strong> = int or list of int;  <strong>default</strong>  = None</div></blockquote>
+<p>Type of double counting correction considered.
+Can be a list per impurity to have different types for different impurities.
+Needs to be set if general.dc = True.</p>
+<ul class="last simple">
+<li>0: FLL</li>
+<li>1: Held formula, needs to be used with slater-kanamori h_int_type=2</li>
+<li>2: AMF</li>
+<li>3: FLL for eg orbitals only with U,J for Kanamori</li>
+</ul>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">enforce_off_diag</p>
+<blockquote>
+<div><strong>type</strong> = bool or list of bool;  <strong>default</strong>  = True</div></blockquote>
+<p class="last">only if False, the block structure can be reduced to ignore off-diagonal elements
+if they are below the general.block_threshold of the block structure finder</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">eta</p>
+<blockquote>
+<div><strong>type</strong> = float;  <strong>default</strong>  = None</div></blockquote>
+<p class="last">broadening of Green’s function. Used when Green’s functions are stored on real-frequency grid,
+i.e., general.beta is None, and for the real-frequency solvers</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">fixed_mu_value</p>
+<blockquote>
+<div><strong>type</strong> = float;  <strong>default</strong>  = None</div></blockquote>
+<p class="last">If given, the chemical potential remains fixed in calculations</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">g0_conv_crit</p>
+<blockquote>
+<div><strong>type</strong> = float;  <strong>default</strong>  = -1.0</div></blockquote>
+<p class="last">stop the calculation if sum_w 1/(w^0.6) ||G0-G0_prev|| is smaller than threshold</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">g0_mix</p>
+<blockquote>
+<div><strong>type</strong> = float;  <strong>default</strong>  = 1.0</div></blockquote>
+<p class="last">Mixing the weiss field G0 with previous iteration G0 for better convergency. 1.0 means no mixing.
+Setting g0_mix to 0.0 with linear mixing can be used for statistic sampling when
+restarting a calculation</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">g0_mix_type</p>
+<blockquote>
+<div><strong>type</strong> = string;  <strong>default</strong>  = ‘linear’</div></blockquote>
+<p class="last">which type of mixing is used. Possible values are:
+linear: linear mixing
+broyden: broyden mixing</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">gimp_conv_crit</p>
+<blockquote>
+<div><strong>type</strong> = float;  <strong>default</strong>  = -1.0</div></blockquote>
+<p class="last">stop the calculation if  sum_w 1/(w^0.6) ||Gimp-Gloc|| is smaller than threshold</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">h_field</p>
+<blockquote>
+<div><strong>type</strong> = float;  <strong>default</strong>  = 0.0</div></blockquote>
+<p class="last">magnetic field</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">h_field_it</p>
+<blockquote>
+<div><strong>type</strong> = int;  <strong>default</strong>  = 0</div></blockquote>
+<p class="last">number of iterations the magnetic field is kept on</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">h_int_basis</p>
+<blockquote>
+<div><strong>type</strong> = string;  <strong>default</strong>  = ‘triqs’</div></blockquote>
+<p>cubic basis convention to compute the interaction U matrix</p>
+<ul class="last simple">
+<li>‘triqs’</li>
+<li>‘vasp’ (equivalent to ‘triqs’)</li>
+<li>‘wien2k’</li>
+<li>‘wannier90’</li>
+<li>‘qe’ (equivalent to ‘wannier90’)</li>
+</ul>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">h_int_type</p>
+<blockquote>
+<div><strong>type</strong> = string;  <strong>mandatory</strong></div></blockquote>
+<p>interaction type:</p>
+<ul class="simple">
+<li>density_density: used for full d-shell or eg- or t2g-subset</li>
+<li>kanamori: only physical for the t2g or the eg subset</li>
+<li>full_slater: used for full d-shell or eg- or t2g-subset</li>
+<li>ntot: U/2 (Ntot^2 - Ntot) interaction</li>
+<li>simple_intra: density-density like but only intra orbital with given U value (no rotations applied)</li>
+<li>crpa: use the cRPA matrix as interaction Hamiltonian</li>
+<li>crpa_density_density: use the density-density terms of the cRPA matrix</li>
+<li>dynamic: use dynamic U from h5 archive</li>
+</ul>
+<p class="last">Needs to be stored as Matsubara Gf under dynamic_U/U_iw in the input h5</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">h5_save_freq</p>
+<blockquote>
+<div><strong>type</strong> = int;  <strong>default</strong>  = 5</div></blockquote>
+<p class="last">how often is the output saved to the h5 archive</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">J</p>
+<blockquote>
+<div><strong>type</strong> = float or list of float;  <strong>mandatory</strong></div></blockquote>
+<p class="last">J interaction value. If it is a float, the same J is assumed for all impurities,
+otherwise as a list a different J can be specified per impurity.</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">jobname</p>
+<blockquote>
+<div><strong>type</strong> = str;  <strong>default</strong>  = ‘dmft_dir’</div></blockquote>
+<p class="last">the output directory for one-shot calculations</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">load_sigma</p>
+<blockquote>
+<div><strong>type</strong> = bool;  <strong>default</strong>  = False</div></blockquote>
+<p class="last">load a old sigma from h5 file</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">load_sigma_iter</p>
+<blockquote>
+<div><strong>type</strong> = int;  <strong>default</strong>  = -1</div></blockquote>
+<p class="last">load the sigma from a specific iteration if wanted.
+If it is -1, loads from the last iteration.</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">magmom</p>
+<blockquote>
+<div><strong>type</strong> = list of float;  <strong>default</strong>  = None</div></blockquote>
+<p class="last">Initialize magnetic moments if magnetic is on. length must be #imps.
+List composed of energetic shifts written in electronvolts.
+This will initialize the spin blocks of the sigma with a diagonal shift
+With -shift for the up block, and +shift for the down block
+(positive shift favours the up spin component, not compatible with spin-orbit coupling)</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">magnetic</p>
+<blockquote>
+<div><strong>type</strong> = bool;  <strong>default</strong>  = False</div></blockquote>
+<p class="last">are we doing a magnetic calculations? If yes put magnetic to True.
+Not implemented for CSC calculations</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">mu_gap_gb2_threshold</p>
+<blockquote>
+<div><strong>type</strong> = float;  <strong>default</strong>  = None</div></blockquote>
+<p class="last">Threshold of the absolute of the lattice GF at tau=beta/2 for use
+of MaxEnt’s lattice spectral function to put the chemical potential
+into the middle of the gap. Does not work if system completely full
+or empty, mu mixing is not applied to it. Recommended value 0.01.</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">mu_gap_occ_deviation</p>
+<blockquote>
+<div><strong>type</strong> = float;  <strong>default</strong>  = None</div></blockquote>
+<p class="last">Only used if mu_gap_gb2_threshold != None. Sets additional criterion
+for finding the middle of the gap through occupation deviation to
+avoid getting stuck in an insulating state with wrong occupation.</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">mu_initial_guess</p>
+<blockquote>
+<div><strong>type</strong> = float;  <strong>default</strong>  = None</div></blockquote>
+<p class="last">The chemical potential of the DFT calculation.
+If not given, mu will be calculated from the DFT bands</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">mu_mix_const</p>
+<blockquote>
+<div><strong>type</strong> = float;  <strong>default</strong>  = 1.0</div></blockquote>
+<p class="last">Constant term of the mixing of the chemical potential. See mu_mix_per_occupation_offset.</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">mu_mix_per_occupation_offset</p>
+<blockquote>
+<div><strong>type</strong> = float;  <strong>default</strong>  = 0.0</div></blockquote>
+<p>Mu mixing proportional to the occupation offset.
+Mixing between the dichotomy result and the previous mui,</p>
+<p>mu_next = factor * mu_dichotomy + (1-factor) * mu_previous, with
+factor = mu_mix_per_occupation_offset * abs(n - n_target) + mu_mix_const.</p>
+<p class="last">The program ensures that 0 &lt;= factor &lt;= 1.
+mu_mix_const = 1.0 and mu_mix_per_occupation_offset = 0.0 means no mixing.</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">mu_update_freq</p>
+<blockquote>
+<div><strong>type</strong> = int;  <strong>default</strong>  = 1</div></blockquote>
+<p class="last">The chemical potential will be updated every # iteration</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">n_iter_dmft</p>
+<blockquote>
+<div><strong>type</strong> = int;  <strong>mandatory</strong></div></blockquote>
+<p class="last">number of iterations per dmft cycle after first cycle</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">n_iter_dmft_first</p>
+<blockquote>
+<div><strong>type</strong> = int;  <strong>default</strong>  = 10</div></blockquote>
+<p class="last">number of iterations in first dmft cycle to converge dmft solution</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">n_iter_dmft_per</p>
+<blockquote>
+<div><strong>type</strong> = int;  <strong>default</strong>  = 2</div></blockquote>
+<p class="last">number of iterations per dmft step in CSC calculations</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">n_iw</p>
+<blockquote>
+<div><strong>type</strong> = int;  <strong>default</strong>  = 1025</div></blockquote>
+<p class="last">number of Matsubara frequencies for the imaginary-frequency grid</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">n_tau</p>
+<blockquote>
+<div><strong>type</strong> = int;  <strong>default</strong>  = 10001</div></blockquote>
+<p class="last">number of imaginary time points for the imaginary-time grid</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">n_w</p>
+<blockquote>
+<div><strong>type</strong> = int;  <strong>default</strong>  = 5001</div></blockquote>
+<p class="last">number of real frequency points for the real-frequency grid</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">noise_level_initial_sigma</p>
+<blockquote>
+<div><strong>type</strong> = float;  <strong>default</strong>  = 0.0</div></blockquote>
+<p class="last">spread of Gaussian noise applied to the initial Sigma</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">occ_conv_crit</p>
+<blockquote>
+<div><strong>type</strong> = float;  <strong>default</strong>  = -1.0</div></blockquote>
+<p class="last">stop the calculation if a certain threshold for the imp occ change is reached</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">path_to_sigma</p>
+<blockquote>
+<div><strong>type</strong> = str;  <strong>default</strong>  = None</div></blockquote>
+<p class="last">path to h5 file from which the sigma should be loaded.
+Needed if load_sigma is true</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">prec_mu</p>
+<blockquote>
+<div><strong>type</strong> = float;  <strong>default</strong>  = 1e-4</div></blockquote>
+<p class="last">general precision for determining the chemical potential at any time calc_mu is called</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">ratio_F4_F2</p>
+<blockquote>
+<div><strong>type</strong> = float or list of float;  <strong>default</strong>  = None</div></blockquote>
+<p class="last">Ratio between the Slater integrals  F_4 and F_2. Only used for the
+interaction Hamiltonians ‘density_density’ and ‘full_slater’ and
+only for d-shell impurities;  <strong>default</strong>  is 0.63.</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">sampling_h5_save_freq</p>
+<blockquote>
+<div><strong>type</strong> = int;  <strong>default</strong>  = 5</div></blockquote>
+<p class="last">overwrites h5_save_freq when sampling has started</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">sampling_iterations</p>
+<blockquote>
+<div><strong>type</strong> = int;  <strong>default</strong>  = 0</div></blockquote>
+<p class="last">for how many iterations should the solution sampled after the CSC loop is converged</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">seedname</p>
+<blockquote>
+<div><strong>type</strong> = str;  <strong>mandatory</strong></div></blockquote>
+<p class="last">seedname for h5 archive with DMFT input and output</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">set_rot</p>
+<blockquote>
+<div><strong>type</strong> = string;  <strong>default</strong>  = None</div></blockquote>
+<p>Local orbital rotations added by solid_dmft</p>
+<ul class="last simple">
+<li>None: keep the rotations stored in the h5 archive</li>
+<li>‘den’ use the DFT occupations density_mat_dft for diagonalization</li>
+<li>‘hloc’: use the DFT local Hamiltonian hloc_dft for diagonalization</li>
+</ul>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">sigma_conv_crit</p>
+<blockquote>
+<div><strong>type</strong> = float;  <strong>default</strong>  = -1.0</div></blockquote>
+<p class="last">stop the calculation if sum_w 1/(w^0.6) ||Sigma-Sigma_prev|| is smaller than threshold</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">sigma_mix</p>
+<blockquote>
+<div><strong>type</strong> = float;  <strong>default</strong>  = 1.0</div></blockquote>
+<p class="last">careful: Sigma mixing can break orbital symmetries, use G0 mixing.
+mixing sigma with previous iteration sigma for better convergency. 1.0 means no mixing</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">store_solver</p>
+<blockquote>
+<div><strong>type</strong> = bool;  <strong>default</strong>  = False</div></blockquote>
+<p class="last">whether to store the whole solver object under DMFT_input in h5 archive</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">U</p>
+<blockquote>
+<div><strong>type</strong> = float or list of float;  <strong>mandatory</strong></div></blockquote>
+<p class="last">U interaction value. If it is a float, the same U is assumed for all impurities,
+otherwise as a list a different U can be specified per impurity.</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">U_prime</p>
+<blockquote>
+<div><strong>type</strong> = float or list of floats;  <strong>default</strong>  = None</div></blockquote>
+<p class="last">U prime interaction value.
+Only used for impurities where general.h_int_type is kanamori.
+If it is a float, the same U prime is assumed for all impurities,
+otherwise as a list a different U prime can be specified per impurity.
+For None;  <strong>default</strong>  of U prime = U-2J is used.</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">w_range</p>
+<blockquote>
+<div><strong>type</strong> = list of int;  <strong>default</strong>  = [-10, 10]</div></blockquote>
+<p class="last">Minimal and maximal range of the real-frequency grid</p>
+</div>
+</div>
+
+
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+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#code-structure">Code structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#dft-interface-notes">DFT interface notes</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/w90_interface.html">Wannier90 interface</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/w90_interface.html#orbital-order-in-the-w90-converter">orbital order in the W90 converter</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/vasp_csc.html">Interface to VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#general-remarks">General remarks</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#file-description">file description</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#workflow">Workflow:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#important-flags">important flags:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#version-and-compilation">version and compilation:</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="reference internal" href="../../documentation.html#input-output">Input/Output</a><ul class="current">
+<li class="toctree-l3 current"><a class="current reference internal" href="#">Input</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="general.html">[general]: General parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="solver.html">[solver]: solver specific parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dft.html">[dft]: DFT related inputs</a></li>
+<li class="toctree-l4"><a class="reference internal" href="advanced.html">[advanced]: Advanced inputs</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../DMFT_output/results.html">Output / results</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../DMFT_output/results.html#group-structure">Group structure</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../DMFT_output/results.html#subgroups">Subgroups</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#further-details-for-running">Further details for running</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/docker.html">Docker</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#building-the-docker-image">Building the docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#pulling-a-docker-image">Pulling a docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_locally.html">Run on your machine</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#module-reference-manual">Module reference manual</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/csc_flow.html">csc_flow</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/dft_managers.html">dft_managers</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.mpi_helpers.html">dft_managers.mpi_helpers</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.qe_manager.html">dft_managers.qe_manager</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.vasp_manager.html">dft_managers.vasp_manager</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/dmft_cycle.html">dmft_cycle</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/dmft_tools.html">dmft_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.afm_mapping.html">dmft_tools.afm_mapping</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.convergence.html">dmft_tools.convergence</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.formatter.html">dmft_tools.formatter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.greens_functions_mixer.html">dmft_tools.greens_functions_mixer</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.initial_self_energies.html">dmft_tools.initial_self_energies</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.interaction_hamiltonian.html">dmft_tools.interaction_hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.legendre_filter.html">dmft_tools.legendre_filter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.manipulate_chemical_potential.html">dmft_tools.manipulate_chemical_potential</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.matheval.html">dmft_tools.matheval</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.observables.html">dmft_tools.observables</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.results_to_archive.html">dmft_tools.results_to_archive</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.solver.html">dmft_tools.solver</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/io_tools.html">io_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.dict_to_h5.html">io_tools.dict_to_h5</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.postproc_toml_dict.html">io_tools.postproc_toml_dict</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.verify_input_params.html">io_tools.verify_input_params</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_gf_imp.html">postprocessing.maxent_gf_imp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_gf_latt.html">postprocessing.maxent_gf_latt</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_sigma.html">postprocessing.maxent_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../issues.html">Support &amp; contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../issues.html#seeking-help">Seeking help</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../issues.html#improving-solid-dmft">Improving solid_dmft</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../issues.html#reporting-issues">Reporting issues</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../ChangeLog.html">Changelog</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-2-0">Version 3.2.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#general">General</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#fix">fix</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#build">build</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#feat">feat</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#doc">doc</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#test">test</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-1-5">Version 3.1.5</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-1-4">Version 3.1.4</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-1-3">Version 3.1.3</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-1-2">Version 3.1.2</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-1-1">Version 3.1.1</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-1-0">Version 3.1.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-0-0">Version 3.0.0</a></li>
+</ul>
+</li>
+</ul>
+
+        </div>
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu"  style="background: #7E588A" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="../../index.html">solid_dmft</a>
+      </nav>
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+        <div class="rst-content">
+          <div role="navigation" aria-label="Page navigation">
+  <ul class="wy-breadcrumbs">
+      <li><a href="../../index.html" class="icon icon-home"></a> &raquo;</li>
+          <li><a href="../../documentation.html">Documentation</a> &raquo;</li>
+      <li>Input</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../../_sources/input_output/DMFT_input/input.rst.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  
+<style>
+/* CSS overrides for sphinx_rtd_theme */
+
+/* 24px margin */
+.nbinput.nblast.container,
+.nboutput.nblast.container {
+    margin-bottom: 19px;  /* padding has already 5px */
+}
+
+/* ... except between code cells! */
+.nblast.container + .nbinput.container {
+    margin-top: -19px;
+}
+
+.admonition > p:before {
+    margin-right: 4px;  /* make room for the exclamation icon */
+}
+
+/* Fix math alignment, see https://github.com/rtfd/sphinx_rtd_theme/pull/686 */
+.math {
+    text-align: unset;
+}
+</style>
+<div class="section" id="input">
+<h1>Input<a class="headerlink" href="#input" title="Permalink to this headline"></a></h1>
+<p>The aim of this section is to provide a comprehensive listing of all the input flags available for the <cite>dmft_config.ini</cite> input file. We begin by listing the possible sections and follow with the input parameters.</p>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="general.html">[general]: General parameters</a></li>
+<li class="toctree-l1"><a class="reference internal" href="solver.html">[solver]: solver specific parameters</a></li>
+<li class="toctree-l1"><a class="reference internal" href="dft.html">[dft]: DFT related inputs</a></li>
+<li class="toctree-l1"><a class="reference internal" href="advanced.html">[advanced]: Advanced inputs</a></li>
+</ul>
+</div>
+<p>Below an exhaustive list containing all the parameters marked by section.</p>
+<p><strong>[general]</strong></p>
+<p>afm_order; beta; block_threshold; broy_max_it; calc_energies; calc_mu_method; csc; dc; dc_dmft; dc_type; enforce_off_diag; eta; fixed_mu_value; g0_conv_crit; g0_mix; g0_mix_type; gimp_conv_crit; h_field; h_field_it; h_int_basis; h_int_type; h5_save_freq; J; jobname; load_sigma; load_sigma_iter; magmom; magnetic; mu_gap_gb2_threshold; mu_gap_occ_deviation; mu_initial_guess; mu_mix_const; mu_mix_per_occupation_offset; mu_update_freq; n_iter_dmft; n_iter_dmft_first; n_iter_dmft_per; n_iw; n_tau; n_w; noise_level_initial_sigma; occ_conv_crit; path_to_sigma; prec_mu; ratio_F4_F2; sampling_h5_save_freq; sampling_iterations; seedname; set_rot; sigma_conv_crit; sigma_mix; store_solver; U; U_prime; w_range;</p>
+<p><strong>[solver]</strong></p>
+<p>type; idx_impurities; delta_interface; fit_max_moment; fit_max_n; fit_max_w; fit_min_n; fit_min_w; imag_threshold; legendre_fit; length_cycle; loc_n_max; loc_n_min; max_time; measure_chi_insertions; measure_chi; measure_density_matrix; measure_G_l; measure_pert_order; move_double; move_shift; n_cycles_tot; n_l; n_warmup_cycles; off_diag_threshold; perform_tail_fit; random_seed; length_cycle; max_time; measure_pert_order; move_double; n_cycles_tot; n_warmup_cycles; random_seed; improved_estimator; legendre_fit; length_cycle; max_time; measure_G_iw; measure_G_l; measure_G_tau; measure_pert_order; n_cycles_tot; n_l; n_warmup_cycles; random_seed; legendre_fit; measure_density_matrix; measure_G_l; measure_G_tau; n_l; bath_fit; calc_me; diag_delta; dmrg_maxm; dmrg_maxmB; dmrg_maxmI; dmrg_maxmIB; dmrg_tw; dt; enforce_gap; ignore_weight; maxm; maxmB; maxmI; maxmIB; n_bath; path_to_gs; ph_symm; refine_factor; state_storage; sweeps; tw; force_real; method; one_shot; tol; with_fock;</p>
+<p><strong>[dft]</strong></p>
+<p>dft_code; dft_exec; mpi_env; n_cores; n_iter; n_iter_first; plo_cfg; projector_type; store_eigenvals; w90_exec; w90_tolerance;</p>
+<p><strong>[advanced]</strong></p>
+<p>dc_factor; dc_fixed_occ; dc_fixed_value; dc_nominal; dc_orb_shift; dc_J; dc_U; map_solver_struct; mapped_solver_struct_degeneracies; pick_solver_struct;</p>
+</div>
+
+
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+<!DOCTYPE html>
+<html class="writer-html5" lang="en" >
+<head>
+  <meta charset="utf-8" />
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+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/w90_interface.html">Wannier90 interface</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/w90_interface.html#orbital-order-in-the-w90-converter">orbital order in the W90 converter</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/vasp_csc.html">Interface to VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#general-remarks">General remarks</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#file-description">file description</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#workflow">Workflow:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#important-flags">important flags:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#version-and-compilation">version and compilation:</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="reference internal" href="../../documentation.html#input-output">Input/Output</a><ul class="current">
+<li class="toctree-l3 current"><a class="reference internal" href="input.html">Input</a><ul class="current">
+<li class="toctree-l4"><a class="reference internal" href="general.html">[general]: General parameters</a></li>
+<li class="toctree-l4 current"><a class="current reference internal" href="#">[solver]: solver specific parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="dft.html">[dft]: DFT related inputs</a></li>
+<li class="toctree-l4"><a class="reference internal" href="advanced.html">[advanced]: Advanced inputs</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../DMFT_output/results.html">Output / results</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../DMFT_output/results.html#group-structure">Group structure</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../DMFT_output/results.html#subgroups">Subgroups</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#further-details-for-running">Further details for running</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/docker.html">Docker</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#building-the-docker-image">Building the docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#pulling-a-docker-image">Pulling a docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_locally.html">Run on your machine</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#module-reference-manual">Module reference manual</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/csc_flow.html">csc_flow</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/dft_managers.html">dft_managers</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.mpi_helpers.html">dft_managers.mpi_helpers</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.qe_manager.html">dft_managers.qe_manager</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.vasp_manager.html">dft_managers.vasp_manager</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/dmft_cycle.html">dmft_cycle</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/dmft_tools.html">dmft_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.afm_mapping.html">dmft_tools.afm_mapping</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.convergence.html">dmft_tools.convergence</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.formatter.html">dmft_tools.formatter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.greens_functions_mixer.html">dmft_tools.greens_functions_mixer</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.initial_self_energies.html">dmft_tools.initial_self_energies</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.interaction_hamiltonian.html">dmft_tools.interaction_hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.legendre_filter.html">dmft_tools.legendre_filter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.manipulate_chemical_potential.html">dmft_tools.manipulate_chemical_potential</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.matheval.html">dmft_tools.matheval</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.observables.html">dmft_tools.observables</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.results_to_archive.html">dmft_tools.results_to_archive</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.solver.html">dmft_tools.solver</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/io_tools.html">io_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.dict_to_h5.html">io_tools.dict_to_h5</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.postproc_toml_dict.html">io_tools.postproc_toml_dict</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.verify_input_params.html">io_tools.verify_input_params</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_gf_imp.html">postprocessing.maxent_gf_imp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_gf_latt.html">postprocessing.maxent_gf_latt</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_sigma.html">postprocessing.maxent_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../issues.html">Support &amp; contribute</a><ul>
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+<li class="toctree-l3"><a class="reference internal" href="../../issues.html#reporting-issues">Reporting issues</a></li>
+</ul>
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+<div class="section" id="solver-solver-specific-parameters">
+<h1>[solver]: solver specific parameters<a class="headerlink" href="#solver-solver-specific-parameters" title="Permalink to this headline"></a></h1>
+<p>Here are the parameters that are uniquely dependent on the solver chosen. Some parameters are used within solid_dmft and some are passed directly into the triqs solver.
+To see which parameters were passed to the solver for a given calculation, look at the triqs_solver_params in DMFT_input/solver in the h5 archive.
+Solver-specific parameters are listed in the respective sections.</p>
+<div class="intag admonition">
+<p class="first admonition-title">type</p>
+<blockquote>
+<div><strong>type</strong> = str;  <strong>mandatory</strong></div></blockquote>
+<p>type of solver chosen for the calculation, currently supports:</p>
+<ul class="last simple">
+<li>‘cthyb’</li>
+<li>‘ctint’</li>
+<li>‘ctseg’</li>
+<li>‘hubbardI’</li>
+<li>‘ftps’</li>
+<li>‘hartree’</li>
+</ul>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">idx_impurities</p>
+<blockquote>
+<div><strong>type</strong> = list of int;  <strong>default</strong>  = None</div></blockquote>
+<p class="last">list of impurities this solver is supposed to solve</p>
+</div>
+<div class="section" id="cthyb">
+<h2>cthyb<a class="headerlink" href="#cthyb" title="Permalink to this headline"></a></h2>
+<div class="intag admonition">
+<p class="first admonition-title">delta_interface</p>
+<blockquote>
+<div><strong>type</strong> = bool;  <strong>default</strong>  = False</div></blockquote>
+<p class="last">use delta interface in cthyb instead of input G0</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">fit_max_moment</p>
+<blockquote>
+<div><strong>type</strong> = int;  <strong>default</strong>  = None</div></blockquote>
+<p class="last">max moment to be fitted. Only used if solver.perform_tail_fit = True</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">fit_max_n</p>
+<blockquote>
+<div><strong>type</strong> = int;  <strong>default</strong>  = None</div></blockquote>
+<p class="last">number of highest matsubara frequency to fit. Only used if solver.perform_tail_fit = True</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">fit_max_w</p>
+<blockquote>
+<div><strong>type</strong> = float;  <strong>default</strong>  = None</div></blockquote>
+<p class="last">highest matsubara frequency to fit. Only used if solver.perform_tail_fit = True</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">fit_min_n</p>
+<blockquote>
+<div><strong>type</strong> = int;  <strong>default</strong>  = None</div></blockquote>
+<p class="last">number of start matsubara frequency to start with. Only used if solver.perform_tail_fit = True</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">fit_min_w</p>
+<blockquote>
+<div><strong>type</strong> = float;  <strong>default</strong>  = None</div></blockquote>
+<p class="last">start matsubara frequency to start with. Only used if solver.perform_tail_fit = True</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">imag_threshold</p>
+<blockquote>
+<div><strong>type</strong> = float;  <strong>default</strong>  = 1e-14</div></blockquote>
+<p class="last">threshold for imag part of G0_tau. be warned if symmetries are off in projection scheme imag parts can occur in G0_tau</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">legendre_fit</p>
+<blockquote>
+<div><strong>type</strong> = bool;  <strong>default</strong>  = False</div></blockquote>
+<p class="last">filter noise of G(tau) with G_l, cutoff is taken from n_l</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">length_cycle</p>
+<blockquote>
+<div><strong>type</strong> = int;  <strong>mandatory</strong></div></blockquote>
+<p class="last">length of each cycle; number of sweeps before measurement is taken</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">loc_n_max</p>
+<blockquote>
+<div><strong>type</strong> = int;  <strong>default</strong>  = None</div></blockquote>
+<p class="last">Restrict local Hilbert space to states with at most this number of particles</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">loc_n_min</p>
+<blockquote>
+<div><strong>type</strong> = int;  <strong>default</strong>  = None</div></blockquote>
+<p class="last">Restrict local Hilbert space to states with at least this number of particles</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">max_time</p>
+<blockquote>
+<div><strong>type</strong> = int;  <strong>default</strong>  = -1</div></blockquote>
+<p class="last">maximum amount the solver is allowed to spend in each iteration</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">measure_chi_insertions</p>
+<blockquote>
+<div><strong>type</strong> = int;  <strong>default</strong>  = 100</div></blockquote>
+<p class="last">number of insertation for measurement of chi</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">measure_chi</p>
+<blockquote>
+<div><strong>type</strong> = str;  <strong>default</strong>  = None</div></blockquote>
+<p>measure the dynamic suszeptibility of an operator O, chi(O,O(tau))
+triqs.github.io/cthyb/unstable/guide/dynamic_susceptibility_notebook.html
+Possible values for this flag are:</p>
+<ul class="last simple">
+<li>‘SzSz’: spin susceptibility</li>
+<li>‘NN’: occupation susceptibility</li>
+</ul>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">measure_density_matrix</p>
+<blockquote>
+<div><strong>type</strong> = bool;  <strong>default</strong>  = False</div></blockquote>
+<p class="last">measures the impurity density matrix and sets also
+use_norm_as_weight to true</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">measure_G_l</p>
+<blockquote>
+<div><strong>type</strong> = bool;  <strong>default</strong>  = False</div></blockquote>
+<p class="last">measure Legendre Greens function</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">measure_pert_order</p>
+<blockquote>
+<div><strong>type</strong> = bool;  <strong>default</strong>  = False</div></blockquote>
+<p class="last">measure perturbation order histograms: triqs.github.io/cthyb/latest/guide/perturbation_order_notebook.html.
+The result is stored in the h5 archive under ‘DMFT_results’ at every iteration
+in the subgroups ‘pert_order_imp_X’ and ‘pert_order_total_imp_X’</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">move_double</p>
+<blockquote>
+<div><strong>type</strong> = bool;  <strong>default</strong>  = True</div></blockquote>
+<p class="last">double moves in solver</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">move_shift</p>
+<blockquote>
+<div><strong>type</strong> = bool;  <strong>default</strong>  = False</div></blockquote>
+<p class="last">shift moves in solver</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">n_cycles_tot</p>
+<blockquote>
+<div><strong>type</strong> = int;  <strong>mandatory</strong></div></blockquote>
+<p class="last">total number of sweeps</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">n_l</p>
+<blockquote>
+<div><strong>type</strong> = int;  <strong>default</strong>  = None</div></blockquote>
+<p class="last">number of Legendre coefficients.
+Needed if measure_G_l=True or legendre_fit=True</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">n_warmup_cycles</p>
+<blockquote>
+<div><strong>type</strong> = int;  <strong>mandatory</strong></div></blockquote>
+<p class="last">number of warmup cycles before real measurement sets in</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">off_diag_threshold</p>
+<blockquote>
+<div><strong>type</strong> = float;  <strong>default</strong>  = 0.0</div></blockquote>
+<p class="last">threshold for off-diag elements in Hloc0</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">perform_tail_fit</p>
+<blockquote>
+<div><strong>type</strong> = bool;  <strong>default</strong>  = False</div></blockquote>
+<p class="last">tail fitting if legendre is off?</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">random_seed</p>
+<blockquote>
+<div><strong>type</strong> = str;  <strong>default</strong>  = None</div></blockquote>
+<p class="last">if None;  <strong>default</strong>  seed by triqs.
+If specified the int will be used for random seeds. Careful, this will give the same random
+numbers on all mpi ranks.
+You can also pass a string that will convert the keywords it or rank on runtime, e.g.,
+34788 * it + 928374 * rank will convert each iteration the variables it and rank for the random
+seed</p>
+</div>
+</div>
+<div class="section" id="ctint">
+<h2>ctint<a class="headerlink" href="#ctint" title="Permalink to this headline"></a></h2>
+<div class="intag admonition">
+<p class="first admonition-title">length_cycle</p>
+<blockquote>
+<div><strong>type</strong> = int;  <strong>mandatory</strong></div></blockquote>
+<p class="last">length of each cycle; number of sweeps before measurement is taken</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">max_time</p>
+<blockquote>
+<div><strong>type</strong> = int;  <strong>default</strong>  = -1</div></blockquote>
+<p class="last">maximum amount the solver is allowed to spend in each iteration</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">measure_pert_order</p>
+<blockquote>
+<div><strong>type</strong> = bool;  <strong>default</strong>  = False</div></blockquote>
+<p class="last">measure perturbation order histograms: triqs.github.io/cthyb/latest/guide/perturbation_order_notebook.html.
+The result is stored in the h5 archive under ‘DMFT_results’ at every iteration
+in the subgroups ‘pert_order_imp_X’ and ‘pert_order_total_imp_X’</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">move_double</p>
+<blockquote>
+<div><strong>type</strong> = bool;  <strong>default</strong>  = True</div></blockquote>
+<p class="last">double moves in solver</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">n_cycles_tot</p>
+<blockquote>
+<div><strong>type</strong> = int;  <strong>mandatory</strong></div></blockquote>
+<p class="last">total number of sweeps</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">n_warmup_cycles</p>
+<blockquote>
+<div><strong>type</strong> = int;  <strong>mandatory</strong></div></blockquote>
+<p class="last">number of warmup cycles before real measurement sets in</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">random_seed</p>
+<blockquote>
+<div><strong>type</strong> = str;  <strong>default</strong>  = None</div></blockquote>
+<p class="last">if None;  <strong>default</strong>  seed by triqs.
+If specified the int will be used for random seeds. Careful, this will give the same random
+numbers on all mpi ranks.
+You can also pass a string that will convert the keywords it or rank on runtime, e.g.,
+34788 * it + 928374 * rank will convert each iteration the variables it and rank for the random
+seed</p>
+</div>
+</div>
+<div class="section" id="ctseg">
+<h2>ctseg<a class="headerlink" href="#ctseg" title="Permalink to this headline"></a></h2>
+<div class="intag admonition">
+<p class="first admonition-title">improved_estimator</p>
+<blockquote>
+<div><strong>type</strong> = bool;  <strong>default</strong>  = False</div></blockquote>
+<p class="last">measure improved estimators
+Sigma_iw will automatically be calculated via
+<a class="reference external" href="http://dx.doi.org/10.1103/PhysRevB.85.205106" rel="noopener noreferrer" target="_blank">http://dx.doi.org/10.1103/PhysRevB.85.205106</a></p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">legendre_fit</p>
+<blockquote>
+<div><strong>type</strong> = bool;  <strong>default</strong>  = False</div></blockquote>
+<p class="last">filter noise of G(tau) with G_l, cutoff is taken from n_l</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">length_cycle</p>
+<blockquote>
+<div><strong>type</strong> = int;  <strong>mandatory</strong></div></blockquote>
+<p class="last">length of each cycle; number of sweeps before measurement is taken</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">max_time</p>
+<blockquote>
+<div><strong>type</strong> = int;  <strong>default</strong>  = -1</div></blockquote>
+<p class="last">maximum amount the solver is allowed to spend in each iteration</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">measure_G_iw</p>
+<blockquote>
+<div><strong>type</strong> = bool;  <strong>default</strong>  = False</div></blockquote>
+<p class="last">should the solver measure G(iw)?</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">measure_G_l</p>
+<blockquote>
+<div><strong>type</strong> = bool;  <strong>default</strong>  = False</div></blockquote>
+<p class="last">measure Legendre Greens function</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">measure_G_tau</p>
+<blockquote>
+<div><strong>type</strong> = bool;  <strong>default</strong>  = True</div></blockquote>
+<p class="last">should the solver measure G(tau)?</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">measure_pert_order</p>
+<blockquote>
+<div><strong>type</strong> = bool;  <strong>default</strong>  = False</div></blockquote>
+<p class="last">measure perturbation order histograms: triqs.github.io/cthyb/latest/guide/perturbation_order_notebook.html.
+The result is stored in the h5 archive under ‘DMFT_results’ at every iteration
+in the subgroups ‘pert_order_imp_X’ and ‘pert_order_total_imp_X’</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">n_cycles_tot</p>
+<blockquote>
+<div><strong>type</strong> = int;  <strong>mandatory</strong></div></blockquote>
+<p class="last">total number of sweeps</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">n_l</p>
+<blockquote>
+<div><strong>type</strong> = int;  <strong>default</strong>  = None</div></blockquote>
+<p class="last">number of Legendre coefficients.
+Needed if measure_G_l=True or legendre_fit=True</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">n_warmup_cycles</p>
+<blockquote>
+<div><strong>type</strong> = int;  <strong>mandatory</strong></div></blockquote>
+<p class="last">number of warmup cycles before real measurement sets in</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">random_seed</p>
+<blockquote>
+<div><strong>type</strong> = str;  <strong>default</strong>  = None</div></blockquote>
+<p class="last">if None;  <strong>default</strong>  seed by triqs.
+If specified the int will be used for random seeds. Careful, this will give the same random
+numbers on all mpi ranks.
+You can also pass a string that will convert the keywords it or rank on runtime, e.g.,
+34788 * it + 928374 * rank will convert each iteration the variables it and rank for the random
+seed</p>
+</div>
+</div>
+<div class="section" id="hubbardi">
+<h2>hubbardI<a class="headerlink" href="#hubbardi" title="Permalink to this headline"></a></h2>
+<div class="intag admonition">
+<p class="first admonition-title">legendre_fit</p>
+<blockquote>
+<div><strong>type</strong> = bool;  <strong>default</strong>  = False</div></blockquote>
+<p class="last">filter noise of G(tau) with G_l, cutoff is taken from n_l</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">measure_density_matrix</p>
+<blockquote>
+<div><strong>type</strong> = bool;  <strong>default</strong>  = False</div></blockquote>
+<p class="last">measures the impurity density matrix and sets also
+use_norm_as_weight to true</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">measure_G_l</p>
+<blockquote>
+<div><strong>type</strong> = bool;  <strong>default</strong>  = False</div></blockquote>
+<p class="last">measure Legendre Greens function</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">measure_G_tau</p>
+<blockquote>
+<div><strong>type</strong> = bool;  <strong>default</strong>  = True</div></blockquote>
+<p class="last">should the solver measure G(tau)?</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">n_l</p>
+<blockquote>
+<div><strong>type</strong> = int;  <strong>default</strong>  = None</div></blockquote>
+<p class="last">number of Legendre coefficients.
+needed if measure_G_l=True or legendre_fit=True</p>
+</div>
+</div>
+<div class="section" id="ftps-parameters">
+<h2>ftps parameters<a class="headerlink" href="#ftps-parameters" title="Permalink to this headline"></a></h2>
+<div class="intag admonition">
+<p class="first admonition-title">bath_fit</p>
+<blockquote>
+<div><strong>type</strong> = bool;  <strong>mandatory</strong></div></blockquote>
+<p class="last">DiscretizeBath vs BathFitter</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">calc_me</p>
+<blockquote>
+<div><strong>type</strong> = bool;  <strong>default</strong>  = True</div></blockquote>
+<p class="last">calculate only symmetry-inequivalent spins/orbitals, symmetrized afterwards</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">diag_delta</p>
+<blockquote>
+<div><strong>type</strong> = bool;  <strong>default</strong>  = False</div></blockquote>
+<p class="last">option to remove off-diagonal terms in the hybridization function
+available for ftps</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">dmrg_maxm</p>
+<blockquote>
+<div><strong>type</strong> = int;  <strong>default</strong>  = 100</div></blockquote>
+<p class="last">TODO: add description</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">dmrg_maxmB</p>
+<blockquote>
+<div><strong>type</strong> = int;  <strong>default</strong>  = 100</div></blockquote>
+<p class="last">maximal bath-bath bond dimensions</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">dmrg_maxmI</p>
+<blockquote>
+<div><strong>type</strong> = int;  <strong>default</strong>  = 100</div></blockquote>
+<p class="last">maximal imp-imp bond dimensions</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">dmrg_maxmIB</p>
+<blockquote>
+<div><strong>type</strong> = int;  <strong>default</strong>  = 100</div></blockquote>
+<p class="last">maximal imp-bath bond dimensions</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">dmrg_tw</p>
+<blockquote>
+<div><strong>type</strong> = float;  <strong>default</strong>  1e-9</div></blockquote>
+<p class="last">truncated weight for every link</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">dt</p>
+<blockquote>
+<div><strong>type</strong> = float;  <strong>mandatory</strong></div></blockquote>
+<p class="last">time step</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">enforce_gap</p>
+<blockquote>
+<div><strong>type</strong> = list of float;  <strong>default</strong>  = None</div></blockquote>
+<p class="last">enforce gap in DiscretizeBath between interval</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">ignore_weight</p>
+<blockquote>
+<div><strong>type</strong> = float;  <strong>default</strong>  = 0.0</div></blockquote>
+<p class="last">ignore weight of peaks for bath fitter</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">maxm</p>
+<blockquote>
+<div><strong>type</strong> = int;  <strong>default</strong>  = 100</div></blockquote>
+<p class="last">TODO: write description</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">maxmB</p>
+<blockquote>
+<div><strong>type</strong> = int;  <strong>default</strong>  = 100</div></blockquote>
+<p class="last">maximal bath-bath bond dimensions</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">maxmI</p>
+<blockquote>
+<div><strong>type</strong> = int;  <strong>default</strong>  = 100</div></blockquote>
+<p class="last">maximal imp-imp bond dimensions</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">maxmIB</p>
+<blockquote>
+<div><strong>type</strong> = int;  <strong>default</strong>  = 100</div></blockquote>
+<p class="last">maximal imp-bath bond dimensions</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">n_bath</p>
+<blockquote>
+<div><strong>type</strong> = int;  <strong>default</strong>  = 0</div></blockquote>
+<p class="last">number of bath sites</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">path_to_gs</p>
+<blockquote>
+<div><strong>type</strong> = string;  <strong>default</strong>  = None</div></blockquote>
+<p class="last">location of GS if already present. Use ‘postprocess’ to skip solver and go directly to post-processing
+of previously terminated time-evolved state</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">ph_symm</p>
+<blockquote>
+<div><strong>type</strong> = bool;  <strong>default</strong>  = False</div></blockquote>
+<p class="last">particle-hole symmetric problem</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">refine_factor</p>
+<blockquote>
+<div><strong>type</strong> = int;  <strong>default</strong>  = 1</div></blockquote>
+<p class="last">rerun ftps cycle with increased accuracy</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">state_storage</p>
+<blockquote>
+<div><strong>type</strong> = string;  <strong>default</strong>  = ‘./’</div></blockquote>
+<p class="last">location of large MPS states</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">sweeps</p>
+<blockquote>
+<div><strong>type</strong> = int;  <strong>default</strong>  = 10</div></blockquote>
+<p class="last">Number of DMRG sweeps</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">tw</p>
+<blockquote>
+<div><strong>type</strong> = float;  <strong>default</strong>  1e-9</div></blockquote>
+<p class="last">truncated weight for every link</p>
+</div>
+</div>
+<div class="section" id="hartree">
+<h2>hartree<a class="headerlink" href="#hartree" title="Permalink to this headline"></a></h2>
+<div class="intag admonition">
+<p class="first admonition-title">force_real</p>
+<blockquote>
+<div><strong>type</strong> = bool;  <strong>default</strong>  = True</div></blockquote>
+<p class="last">force the self energy from Hartree fock to be real</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">method</p>
+<blockquote>
+<div><strong>type</strong> = str;  <strong>default</strong>  = “krylov”</div></blockquote>
+<p class="last">method for root finder. Only used if one_shot=False, see scipy.optimize.root for options.</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">one_shot</p>
+<blockquote>
+<div><strong>type</strong> = bool;  <strong>default</strong>  = False</div></blockquote>
+<p class="last">Perform a one-shot or self-consitent root finding in each DMFT step of the Hartree solver.</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">tol</p>
+<blockquote>
+<div><strong>type</strong> = float;  <strong>default</strong>  = 1e-5</div></blockquote>
+<p class="last">tolerance for root finder if one_shot=False.</p>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">with_fock</p>
+<blockquote>
+<div><strong>type</strong> = bool;  <strong>default</strong>  = False</div></blockquote>
+<p class="last">include Fock exchange terms in the self-energy</p>
+</div>
+</div>
+</div>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
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+    <p>&#169; Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.</p>
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+<head>
+  <meta charset="utf-8" />
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+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_gf_latt.html">postprocessing.maxent_gf_latt</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_sigma.html">postprocessing.maxent_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
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+<li class="toctree-l3"><a class="reference internal" href="../../_ref/util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
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+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
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+<div class="section" id="iterations">
+<h1>Iterations<a class="headerlink" href="#iterations" title="Permalink to this headline"></a></h1>
+<p>List of the main outputs for solid_dmft for every iteration.</p>
+<div class="admonition warning">
+<p class="first admonition-title">Warning</p>
+<p class="last">According to the symmetries found by the solver, the resulting indexing of the triqs.Gf objects might vary.
+In order to retrieve the indices call the Gf.indices method.</p>
+</div>
+<p>Legend:</p>
+<ul class="simple">
+<li>iiter = iteration number: range(0, n_dmft_iter)</li>
+<li>ish = shell number: range(0, n_shells)</li>
+<li>icrsh = correlated shell number: range(0, n_corr_shells)</li>
+<li>iineq = inequivalent correlated shell number: range(0, n_inequiv_shells)</li>
+<li>iorb = orbital number: range(0, n_orbitals)</li>
+<li>sp = spin label</li>
+<li>ikpt = k-point label, the order is the same as given in the wannier90 input: range(0, n_kpt)</li>
+<li>iband = band label before downfolding, n_bands = number of bands included in the disentanglement window during the wannierization: range(0, n_bands)</li>
+</ul>
+<div class="section" id="observables">
+<h2>[observables]<a class="headerlink" href="#observables" title="Permalink to this headline"></a></h2>
+<div class="intag type float admonition">
+<p class="first admonition-title">chemical_potential_pre:</p>
+<blockquote class="last">
+<div>Chemical potential before the solver iteration.</div></blockquote>
+</div>
+<div class="intag type float admonition">
+<p class="first admonition-title">chemical_potential_post:</p>
+<blockquote class="last">
+<div>Chemical potential after the solver iteration.</div></blockquote>
+</div>
+<div class="intag type arr-float admonition">
+<p class="first admonition-title">DC_energ:</p>
+<blockquote class="last">
+<div><p><strong>indices=</strong> [iorb]</p>
+<p>Double counting correction.</p>
+</div></blockquote>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">DC_pot:</p>
+<blockquote class="last">
+<div><p><strong>type=</strong> arr(float);</p>
+<p><strong>indices=</strong> [iiter]</p>
+<p>Double counting potential.**what exactly is the indexing here?**</p>
+</div></blockquote>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">Delta_time_{iimp}:</p>
+<blockquote class="last">
+<div><p><strong>type=</strong> triqs.gf.block_gf.BlockGf</p>
+<p>Imaginary time hybridization function.</p>
+</div></blockquote>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">G0_freq_{iimp}:</p>
+<blockquote class="last">
+<div><p><strong>type=</strong> triqs.gf.block_gf.BlockGf</p>
+<p>Imaginary frequency Weiss field.</p>
+</div></blockquote>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">G0_time_orig_{iimp}:</p>
+<blockquote class="last">
+<div><p><strong>type=</strong> triqs.gf.block_gf.BlockGf</p>
+<p>??</p>
+</div></blockquote>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">G_imp_freq_{iimp}:</p>
+<blockquote class="last">
+<div><p><strong>type=</strong> triqs.gf.block_gf.BlockGf</p>
+<p>Imaginary frequency impurity green function.</p>
+</div></blockquote>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">G_imp_l_{iimp}:</p>
+<blockquote class="last">
+<div><p><strong>type=</strong> triqs.gf.block_gf.BlockGf</p>
+<p>Legendre representation of the impurity green function.</p>
+</div></blockquote>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">G_imp_time_{iimp}:</p>
+<blockquote class="last">
+<div><p><strong>type=</strong> triqs.gf.block_gf.BlockGf</p>
+<p>Imaginary time representation of the impurity green function.</p>
+</div></blockquote>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">Sigma_freq_{iimp}:</p>
+<blockquote class="last">
+<div><p><strong>type=</strong> triqs.gf.block_gf.BlockGf</p>
+<p>Imaginary frequency self-energy obtained from the Dyson equation.</p>
+</div></blockquote>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">deltaN:</p>
+<blockquote class="last">
+<div><p><strong>type=</strong> dict(arr(float))</p>
+<p><strong>indices=</strong> [ispin][ikpt][iband, iband]</p>
+<p>Correction to the DFT occupation of a particular band:</p>
+</div></blockquote>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">deltaN_trace:</p>
+<blockquote class="last">
+<div><p><strong>type=</strong> dict</p>
+<p><strong>indices=</strong> [ispin]</p>
+<p>Total sum of the charge correction for an impurity.</p>
+</div></blockquote>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">dens_mat_pre:</p>
+<blockquote class="last">
+<div><p><strong>type=</strong> arr(dict)</p>
+<p><strong>indices=</strong> [iimp][<em>same as block structure Gf</em>]</p>
+<p>Density matrix before the solver iteration.</p>
+</div></blockquote>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">dens_mat_post:</p>
+<blockquote class="last">
+<div><p><strong>type=</strong> arr(dict)</p>
+<p><strong>indices=</strong> [ispin][iimp]</p>
+<p>Density matrix after the solver iteration.</p>
+</div></blockquote>
+</div>
+</div>
+</div>
+
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+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
+</ul>
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+<li class="toctree-l3"><a class="reference internal" href="../../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#workflow">Workflow:</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
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+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
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+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
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+<div class="section" id="observables-convergence-obs">
+<h1>Observables/convergence_obs<a class="headerlink" href="#observables-convergence-obs" title="Permalink to this headline"></a></h1>
+<p>List of the single-particle observables obtained in a single DMFT iteration</p>
+<p>Legend:</p>
+<ul class="simple">
+<li>iiter = iteration number: range(0, n_dmft_iter)</li>
+<li>iimp = impurity number: range(0, n_imp)</li>
+<li>iorb = orbital number: range(0, n_orbitals)</li>
+<li>ispin = spin label, ‘up’ or ‘down’ in collinear calculations</li>
+</ul>
+<div class="section" id="observables">
+<h2>[observables]<a class="headerlink" href="#observables" title="Permalink to this headline"></a></h2>
+<div class="intag admonition">
+<p class="first admonition-title">iteration:</p>
+<blockquote class="last">
+<div><p><strong>type=</strong> arr(int);</p>
+<p><strong>indices=</strong> [iiter]</p>
+<p>Number of the iteration.</p>
+</div></blockquote>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">mu:</p>
+<blockquote class="last">
+<div><p><strong>type=</strong> arr(float);</p>
+<p><strong>indices=</strong> [iiter]</p>
+<p>Chemical potential fed to the solver at the present iteration (pre-dichotomy adjustment).</p>
+</div></blockquote>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">orb_gb2:</p>
+<blockquote class="last">
+<div><p><strong>type=</strong> arr(dict)</p>
+<p><strong>indices=</strong> [iimp][ispin][iiter, iorb]</p>
+<p>Orbital resolved G(beta/2), proxy for projected density of states at the Fermi level. Low value of orb_gb2 correlate with the presence of a gap.</p>
+</div></blockquote>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">imp_gb2:</p>
+<blockquote class="last">
+<div><p><strong>type=</strong> arr(dict)</p>
+<p><strong>indices=</strong> [iimp][ispin][iiter]</p>
+<p>Site G(beta/2), proxy for total density of states at the Fermi level. Low values correlate with the presence of a gap.</p>
+</div></blockquote>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">orb_Z:</p>
+<blockquote class="last">
+<div><p><strong>type=</strong> arr(dict)</p>
+<p><strong>indices=</strong> [iimp][ispin][iiter, iorb]</p>
+<p>Orbital resolved quasiparticle weight (eff_mass/renormalized_mass). As obtained by linearizing the self-energy around <span class="math notranslate nohighlight">\(\omega = 0\)</span></p>
+<div class="math notranslate nohighlight">
+\[\begin{split}Z = \bigg( 1- \frac{\partial Re[\Sigma]}{\partial \omega} \bigg|_{\omega \rightarrow 0} \bigg)^{-1} \\\end{split}\]</div>
+</div></blockquote>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">orb_occ:</p>
+<blockquote class="last">
+<div><p><strong>type=</strong> arr(dict)</p>
+<p><strong>indices=</strong> [iimp][ispin][iiter, iorb]</p>
+<p>Orbital resolved mean site occupation.</p>
+</div></blockquote>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">imp_occ:</p>
+<blockquote class="last">
+<div><p><strong>type=</strong> arr(dict)</p>
+<p><strong>indices=</strong> [iimp][ispin][iiter]</p>
+<p>Total mean site occupation.</p>
+</div></blockquote>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">E_tot:</p>
+<blockquote class="last">
+<div><p><strong>type=</strong> arr(float)</p>
+<p><strong>indices=</strong> [iiter]</p>
+<p>Total energy, computed as:</p>
+<div class="math notranslate nohighlight">
+\[E_{tot} = E_{DFT} + E_{corr} + E_{int} -E_{DC}\]</div>
+</div></blockquote>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">E_dft:</p>
+<blockquote class="last">
+<div><p><strong>type=</strong> arr(float)</p>
+<p><strong>indices=</strong> [iiter]</p>
+<p><span class="math notranslate nohighlight">\(E_{DFT}\)</span> in the total energy expression. System energy as computed by the DFT code at every csc iteration.</p>
+</div></blockquote>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">E_bandcorr:</p>
+<blockquote class="last">
+<div><p><strong>type=</strong> arr(float)</p>
+<p><strong>indices=</strong> [iiter]</p>
+<p><span class="math notranslate nohighlight">\(E_{corr}\)</span> in the total energy expression. DMFT correction to the kinetic energy.</p>
+</div></blockquote>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">E_corr_en:</p>
+<blockquote class="last">
+<div><p><strong>type=</strong> arr(float)</p>
+<p><strong>indices=</strong> [iiter]</p>
+<p>Sum of the E_DC and E_int_imp terms.</p>
+</div></blockquote>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">E_int_imp:</p>
+<blockquote class="last">
+<div><p><strong>type=</strong> arr(float)</p>
+<p><strong>indices=</strong> [iiter]</p>
+<p><span class="math notranslate nohighlight">\(E_{int}\)</span> in the total energy expression. Energy contribution from the electronic interactions within the single impurity.</p>
+</div></blockquote>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">E_DC:</p>
+<blockquote class="last">
+<div><p><strong>type=</strong> arr(float)</p>
+<p><strong>indices=</strong> [iiter]</p>
+<p><span class="math notranslate nohighlight">\(E_{DC}\)</span> in the total energy expression. Double counting energy contribution.</p>
+</div></blockquote>
+</div>
+</div>
+<div class="section" id="convergence-obs">
+<h2>[convergence_obs]<a class="headerlink" href="#convergence-obs" title="Permalink to this headline"></a></h2>
+<div class="intag admonition">
+<p class="first admonition-title">iteration:</p>
+<blockquote class="last">
+<div><p><strong>type=</strong> arr(int);</p>
+<p><strong>indices=</strong> [iiter]</p>
+<p>Number of the iteration.</p>
+</div></blockquote>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">d_mu:</p>
+<blockquote class="last">
+<div><p><strong>type=</strong> arr(float)</p>
+<p><strong>indices=</strong> [iiter]</p>
+<p>Chemical potential stepwise difference.</p>
+</div></blockquote>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">d_orb_occ:</p>
+<blockquote class="last">
+<div><p><strong>type=</strong> arr(dict)</p>
+<p><strong>indices=</strong> [iimp][ispin][iiter,iorb]</p>
+<p>Orbital occupation stepwise difference.</p>
+</div></blockquote>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">d_imp_occ:</p>
+<blockquote class="last">
+<div><p><strong>type=</strong> arr(dict)</p>
+<p><strong>indices=</strong> [iimp][ispin][iiter]</p>
+<p>Impurity occupation stepwise difference.</p>
+</div></blockquote>
+</div>
+<div class="intag admonition">
+<p class="first admonition-title">d_Etot:</p>
+<blockquote class="last">
+<div><p><strong>type=</strong> arr(float)</p>
+<p><strong>indices=</strong> [iiter]</p>
+<p>Total energy stepwise difference.</p>
+</div></blockquote>
+</div>
+</div>
+</div>
+
+
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+<head>
+  <meta charset="utf-8" />
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+</ul>
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+</li>
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+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
+</li>
+</ul>
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+<li class="toctree-l1"><a class="reference internal" href="../../issues.html">Support &amp; contribute</a><ul>
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+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-1-3">Version 3.1.3</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-1-2">Version 3.1.2</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-1-1">Version 3.1.1</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-1-0">Version 3.1.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-0-0">Version 3.0.0</a></li>
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+<div class="section" id="output-results">
+<h1>Output / results<a class="headerlink" href="#output-results" title="Permalink to this headline"></a></h1>
+<p>The <em>DMFT_results</em> group contains the output of the DMFT iterations. The subgroups contained here fall under two main categories:</p>
+<ul class="simple">
+<li><strong>Iterations</strong>: relevant quantities for the DMFT solutions, such as Weiss field, Green function, extracted self-energy, etc.
+Normally these are solver dependent.</li>
+<li><strong>Observables</strong>: Single-particles quantities that can be measured with the aid of the green function. Includes chemical potential, estimate of the quasiparticle weight, impurity occupation, total energy, energy contributions, etc. The convergence_obs subgroup lists the stepwise difference in the observables’ value as the calculation progresses and can be used as a proxy for convergence.</li>
+</ul>
+<div class="section" id="group-structure">
+<h2>Group structure<a class="headerlink" href="#group-structure" title="Permalink to this headline"></a></h2>
+<a class="reference internal image-reference" href="../../_images/group_structure.png"><img alt="../../_images/group_structure.png" class="align-center" src="../../_images/group_structure.png" style="width: 100%;" /></a>
+</div>
+<div class="section" id="subgroups">
+<h2>Subgroups<a class="headerlink" href="#subgroups" title="Permalink to this headline"></a></h2>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="iterations.html">Iterations</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="#manual-installation-via-cmake">Manual installation via CMake</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="#version-compatibility">Version compatibility</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#custom-cmake-options">Custom CMake options</a></li>
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+</li>
+<li class="toctree-l3"><a class="reference internal" href="md_notes/vasp_csc.html">Interface to VASP</a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="cRPA_VASP/README.html#file-description">file description</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cRPA_VASP/README.html#workflow">Workflow:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cRPA_VASP/README.html#important-flags">important flags:</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
+</li>
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+<li class="toctree-l2"><a class="reference internal" href="documentation.html#module-reference-manual">Module reference manual</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="_ref/csc_flow.html">csc_flow</a></li>
+<li class="toctree-l3"><a class="reference internal" href="_ref/dft_managers.html">dft_managers</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dft_managers.mpi_helpers.html">dft_managers.mpi_helpers</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dft_managers.qe_manager.html">dft_managers.qe_manager</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dft_managers.vasp_manager.html">dft_managers.vasp_manager</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="_ref/dmft_cycle.html">dmft_cycle</a></li>
+<li class="toctree-l3"><a class="reference internal" href="_ref/dmft_tools.html">dmft_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.afm_mapping.html">dmft_tools.afm_mapping</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.convergence.html">dmft_tools.convergence</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.formatter.html">dmft_tools.formatter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.greens_functions_mixer.html">dmft_tools.greens_functions_mixer</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.initial_self_energies.html">dmft_tools.initial_self_energies</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.interaction_hamiltonian.html">dmft_tools.interaction_hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.legendre_filter.html">dmft_tools.legendre_filter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.manipulate_chemical_potential.html">dmft_tools.manipulate_chemical_potential</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.matheval.html">dmft_tools.matheval</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.observables.html">dmft_tools.observables</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.results_to_archive.html">dmft_tools.results_to_archive</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.solver.html">dmft_tools.solver</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="_ref/io_tools.html">io_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="_ref/io_tools.dict_to_h5.html">io_tools.dict_to_h5</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/io_tools.postproc_toml_dict.html">io_tools.postproc_toml_dict</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/io_tools.verify_input_params.html">io_tools.verify_input_params</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="_ref/postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="_ref/postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/postprocessing.eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/postprocessing.maxent_gf_imp.html">postprocessing.maxent_gf_imp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/postprocessing.maxent_gf_latt.html">postprocessing.maxent_gf_latt</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/postprocessing.maxent_sigma.html">postprocessing.maxent_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="_ref/util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="_ref/util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="issues.html">Support &amp; contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="issues.html#seeking-help">Seeking help</a></li>
+<li class="toctree-l2"><a class="reference internal" href="issues.html#improving-solid-dmft">Improving solid_dmft</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="issues.html#reporting-issues">Reporting issues</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="ChangeLog.html">Changelog</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#version-3-2-0">Version 3.2.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#general">General</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#fix">fix</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#build">build</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#feat">feat</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#doc">doc</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#test">test</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#version-3-1-5">Version 3.1.5</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#version-3-1-4">Version 3.1.4</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#version-3-1-3">Version 3.1.3</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#version-3-1-2">Version 3.1.2</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#version-3-1-1">Version 3.1.1</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#version-3-1-0">Version 3.1.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#version-3-0-0">Version 3.0.0</a></li>
+</ul>
+</li>
+</ul>
+
+        </div>
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+            <a href="_sources/install.rst.txt" rel="nofollow"> View page source</a>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
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+<style>
+/* CSS overrides for sphinx_rtd_theme */
+
+/* 24px margin */
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+.nboutput.nblast.container {
+    margin-bottom: 19px;  /* padding has already 5px */
+}
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+.nblast.container + .nbinput.container {
+    margin-top: -19px;
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+    margin-right: 4px;  /* make room for the exclamation icon */
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+/* Fix math alignment, see https://github.com/rtfd/sphinx_rtd_theme/pull/686 */
+.math {
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+</style>
+<div class="section" id="installation">
+<span id="id1"></span><h1>Installation<a class="headerlink" href="#installation" title="Permalink to this headline"></a></h1>
+<div class="section" id="prerequisites">
+<h2>Prerequisites<a class="headerlink" href="#prerequisites" title="Permalink to this headline"></a></h2>
+<ol class="arabic">
+<li><p class="first">The <a class="reference external" href="https://triqs.github.io/triqs/latest/index.html#welcome" rel="noopener noreferrer" target="_blank" title="(in TRIQS v3.2.1)"><span class="xref std std-ref">TRIQS</span></a> library, see <a class="reference external" href="https://triqs.github.io/triqs/latest/install.html" rel="noopener noreferrer" target="_blank">TRIQS installation instruction</a>.
+In the following, we assume that <a class="reference external" href="https://triqs.github.io/triqs/latest/index.html#welcome" rel="noopener noreferrer" target="_blank" title="(in TRIQS v3.2.1)"><span class="xref std std-ref">TRIQS</span></a>, <a class="reference external" href="https://triqs.github.io/dft_tools" rel="noopener noreferrer" target="_blank">triqs/dft_tools</a>, and at least one of the impurity solvers <a class="reference external" href="https://triqs.github.io/triqs/latest/applications.html" rel="noopener noreferrer" target="_blank">available in TRIQS</a>, e.g. cthyb, HubbardI, ctseg, FTPS, or ctint is installed in the directory <code class="docutils literal notranslate"><span class="pre">path_to_triqs</span></code>.</p>
+</li>
+<li><p class="first">Make sure to install besides the triqs requirements also the python packages:</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ pip3 install --user scipy argparse pytest
+</pre></div>
+</div>
+</li>
+<li><p class="first">To build the documentation the following extra python packages are needed:</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ pip3 install --user sphinx sphinx-autobuild pandoc nbsphinx linkify-it-py sphinx_rtd_theme myst-parser
+</pre></div>
+</div>
+</li>
+</ol>
+</div>
+<div class="section" id="installation-via-pip">
+<h2>Installation via pip<a class="headerlink" href="#installation-via-pip" title="Permalink to this headline"></a></h2>
+<p>You can install the latest solid_dmft release simply via pip (PyPi):
+<code class="docutils literal notranslate"><span class="pre">`</span>
+<span class="pre">pip</span> <span class="pre">install</span> <span class="pre">solid_dmft</span>
+<span class="pre">`</span></code>
+However, please make sure that you have a valid TRIQS and TRIQS/DFTTools installation matching the version of solid_dmft. Furthermore, you need at least one of the supported DMFT impurity solvers installed to use solid_dmft.</p>
+</div>
+<div class="section" id="manual-installation-via-cmake">
+<h2>Manual installation via CMake<a class="headerlink" href="#manual-installation-via-cmake" title="Permalink to this headline"></a></h2>
+<p>We provide hereafter the build instructions in the form of a documented bash script. Please change the variable INSTALL_PREFIX to point to your TRIQS installation directory:</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nv">INSTALL_PREFIX</span><span class="o">=</span>/path/to/triqs
+<span class="c1"># source the triqsvars.sh file from your TRIQS installation to load the TRIQS environment</span>
+<span class="nb">source</span> <span class="k">$(</span>INSTALL_PREFIX<span class="k">)</span>/share/triqs/triqsvars.sh
+
+<span class="c1"># clone the flatironinstitute/solid_dmft repository from GitHub</span>
+git clone https://github.com/flatironinstitute/solid_dmft solid_dmft.src
+
+<span class="c1"># checkout the branch of solid_dmft matching your triqs version.</span>
+<span class="c1"># For example if you use the 3.1.x branch of triqs, dfttools. and cthyb</span>
+git checkout <span class="m">3</span>.1.x
+
+<span class="c1"># Create and move to a new directory where you will compile the code</span>
+mkdir solid_dmft.build <span class="o">&amp;&amp;</span> <span class="nb">cd</span> solid_dmft.build
+
+<span class="c1"># In the build directory call cmake, including any additional custom CMake options, see below</span>
+cmake ../solid_dmft.src
+
+<span class="c1"># Compile the code, run the tests, and install the application</span>
+make <span class="nb">test</span>
+make install
+</pre></div>
+</div>
+<p>This installs solid_dmft into your TRIQS installation folder.</p>
+<p>To build <code class="docutils literal notranslate"><span class="pre">solid_dmft</span></code> with documentation you should run:</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ cmake path/to/solid_dmft.src -DBuild_Documentation<span class="o">=</span>ON
+$ make
+$ sphinx-autobuild path/to/solid_dmft.src/doc ./doc/html -c ./doc/
+</pre></div>
+</div>
+<p>The last line will automatically search for changes in your src dir, rebuild the documentation,
+and serve it locally as under <cite>127.0.0.1:8000</cite>.</p>
+</div>
+<div class="section" id="docker-files-images">
+<h2>Docker files &amp; images<a class="headerlink" href="#docker-files-images" title="Permalink to this headline"></a></h2>
+<p>We <a class="reference external" href="https://github.com/TRIQS/solid_dmft/tree/3.1.x/Docker" rel="noopener noreferrer" target="_blank">provide docker files</a> to build solid_dmft inside a docker container with all dependencies and instructions on how to integrate the connected DFT codes as well. Additionally, we host a most recent unstable version of the docker image used for the github CI <a class="reference external" href="https://hub.docker.com/r/materialstheory/solid_dmft_ci" rel="noopener noreferrer" target="_blank">on dockerhub</a>. To use this version, which includes the cthyb solver, the hubbardI solver, dfttools, and the maxent package, pull the following image:</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ docker pull materialstheory/solid_dmft_ci
+</pre></div>
+</div>
+</div>
+<div class="section" id="version-compatibility">
+<h2>Version compatibility<a class="headerlink" href="#version-compatibility" title="Permalink to this headline"></a></h2>
+<p>Keep in mind that the version of <code class="docutils literal notranslate"><span class="pre">solid_dmft</span></code> must be compatible with your TRIQS library version,
+see <a class="reference external" href="https://triqs.github.io/triqs/latest/versions.html#versions" rel="noopener noreferrer" target="_blank" title="(in TRIQS v3.2.1)"><span class="xref std std-ref">TRIQS website</span></a>.
+In particular the Major Version numbers have to be the same.
+To use a particular version, go into the directory with the sources, and look at all available branches:</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ <span class="nb">cd</span> solid_dmft.src <span class="o">&amp;&amp;</span> git branch -vv
+</pre></div>
+</div>
+<p>Checkout the version of the code that you want:</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ git checkout <span class="m">3</span>.1.x
+</pre></div>
+</div>
+<p>and follow steps 3 to 6 above to compile the code.</p>
+</div>
+<div class="section" id="custom-cmake-options">
+<h2>Custom CMake options<a class="headerlink" href="#custom-cmake-options" title="Permalink to this headline"></a></h2>
+<p>The compilation of <code class="docutils literal notranslate"><span class="pre">solid_dmft</span></code> can be configured using CMake-options:</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>cmake ../solid_dmft.src -DOPTION1<span class="o">=</span>value1 -DOPTION2<span class="o">=</span>value2 ...
+</pre></div>
+</div>
+<table border="1" class="docutils align-default">
+<colgroup>
+<col width="58%" />
+<col width="42%" />
+</colgroup>
+<thead valign="bottom">
+<tr class="row-odd"><th class="head">Options</th>
+<th class="head">Syntax</th>
+</tr>
+</thead>
+<tbody valign="top">
+<tr class="row-even"><td>Specify an installation path other than path_to_triqs</td>
+<td>-DCMAKE_INSTALL_PREFIX=path_to_solid_dmft</td>
+</tr>
+<tr class="row-odd"><td>Build in Debugging Mode</td>
+<td>-DCMAKE_BUILD_TYPE=Debug</td>
+</tr>
+<tr class="row-even"><td>Disable testing (not recommended)</td>
+<td>-DBuild_Tests=OFF</td>
+</tr>
+<tr class="row-odd"><td>Build the documentation</td>
+<td>-DBuild_Documentation=ON</td>
+</tr>
+</tbody>
+</table>
+</div>
+</div>
+
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+           </div>
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+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
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diff --git a/issues.html b/issues.html
new file mode 100644
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+++ b/issues.html
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+<!DOCTYPE html>
+<html class="writer-html5" lang="en" >
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Support &amp; contribute &mdash; solid_dmft  documentation</title>
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+      <link rel="stylesheet" href="_static/css/custom.css" type="text/css" />
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+    <script src="_static/js/theme.js"></script>
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+    <link rel="search" title="Search" href="search.html" />
+    <link rel="next" title="Changelog" href="ChangeLog.html" />
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+<body class="wy-body-for-nav"> 
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+            <img src="_static/logo_no_text.png" class="logo" alt="Logo"/>
+          </a>
+              <div class="version">
+                3.2.0
+              </div>
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+        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+              <ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="install.html">Installation</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="install.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="install.html#installation-via-pip">Installation via pip</a></li>
+<li class="toctree-l2"><a class="reference internal" href="install.html#manual-installation-via-cmake">Manual installation via CMake</a></li>
+<li class="toctree-l2"><a class="reference internal" href="install.html#docker-files-images">Docker files &amp; images</a></li>
+<li class="toctree-l2"><a class="reference internal" href="install.html#version-compatibility">Version compatibility</a></li>
+<li class="toctree-l2"><a class="reference internal" href="install.html#custom-cmake-options">Custom CMake options</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="documentation.html">Documentation</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="documentation.html#code-structure">Code structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="documentation.html#dft-interface-notes">DFT interface notes</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="md_notes/w90_interface.html">Wannier90 interface</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/w90_interface.html#orbital-order-in-the-w90-converter">orbital order in the W90 converter</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="md_notes/vasp_csc.html">Interface to VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/vasp_csc.html#general-remarks">General remarks</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="cRPA_VASP/README.html#file-description">file description</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cRPA_VASP/README.html#workflow">Workflow:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cRPA_VASP/README.html#important-flags">important flags:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cRPA_VASP/README.html#version-and-compilation">version and compilation:</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="documentation.html#input-output">Input/Output</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="input_output/DMFT_input/input.html">Input</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="input_output/DMFT_input/general.html">[general]: General parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="input_output/DMFT_input/solver.html">[solver]: solver specific parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="input_output/DMFT_input/dft.html">[dft]: DFT related inputs</a></li>
+<li class="toctree-l4"><a class="reference internal" href="input_output/DMFT_input/advanced.html">[advanced]: Advanced inputs</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="input_output/DMFT_output/results.html">Output / results</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="input_output/DMFT_output/results.html#group-structure">Group structure</a></li>
+<li class="toctree-l4"><a class="reference internal" href="input_output/DMFT_output/results.html#subgroups">Subgroups</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="documentation.html#further-details-for-running">Further details for running</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="md_notes/docker.html">Docker</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/docker.html#building-the-docker-image">Building the docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/docker.html#pulling-a-docker-image">Pulling a docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="md_notes/run_locally.html">Run on your machine</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="documentation.html#module-reference-manual">Module reference manual</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="_ref/csc_flow.html">csc_flow</a></li>
+<li class="toctree-l3"><a class="reference internal" href="_ref/dft_managers.html">dft_managers</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dft_managers.mpi_helpers.html">dft_managers.mpi_helpers</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dft_managers.qe_manager.html">dft_managers.qe_manager</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dft_managers.vasp_manager.html">dft_managers.vasp_manager</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="_ref/dmft_cycle.html">dmft_cycle</a></li>
+<li class="toctree-l3"><a class="reference internal" href="_ref/dmft_tools.html">dmft_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.afm_mapping.html">dmft_tools.afm_mapping</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.convergence.html">dmft_tools.convergence</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.formatter.html">dmft_tools.formatter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.greens_functions_mixer.html">dmft_tools.greens_functions_mixer</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.initial_self_energies.html">dmft_tools.initial_self_energies</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.interaction_hamiltonian.html">dmft_tools.interaction_hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.legendre_filter.html">dmft_tools.legendre_filter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.manipulate_chemical_potential.html">dmft_tools.manipulate_chemical_potential</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.matheval.html">dmft_tools.matheval</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.observables.html">dmft_tools.observables</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.results_to_archive.html">dmft_tools.results_to_archive</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.solver.html">dmft_tools.solver</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="_ref/io_tools.html">io_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="_ref/io_tools.dict_to_h5.html">io_tools.dict_to_h5</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/io_tools.postproc_toml_dict.html">io_tools.postproc_toml_dict</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/io_tools.verify_input_params.html">io_tools.verify_input_params</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="_ref/postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="_ref/postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/postprocessing.eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/postprocessing.maxent_gf_imp.html">postprocessing.maxent_gf_imp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/postprocessing.maxent_gf_latt.html">postprocessing.maxent_gf_latt</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/postprocessing.maxent_sigma.html">postprocessing.maxent_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="_ref/util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="_ref/util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
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+</ul>
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+<li class="toctree-l3"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
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+<span id="issues"></span><h1>Support &amp; contribute<a class="headerlink" href="#support-contribute" title="Permalink to this headline"></a></h1>
+<div class="section" id="seeking-help">
+<h2>Seeking help<a class="headerlink" href="#seeking-help" title="Permalink to this headline"></a></h2>
+<p>If you have any questions please ask them on the solid_dmft github discussion page:
+<a class="reference external" href="https://github.com/flatironinstitute/solid_dmft/discussions" rel="noopener noreferrer" target="_blank">https://github.com/flatironinstitute/solid_dmft/discussions</a>. However, note
+that solid_dmft is targeted at experienced users of DMFT, and we can only provide
+technial support for the code itself not for theory questions about the utilized methods.</p>
+<p>Also make sure to ask only questions relevant for solid_dmft. For questions
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+application.</p>
+<p>Take also a look at the <a class="reference internal" href="tutorials.html#tutorials"><span class="std std-ref">Tutorials</span></a> section of the documentation for examples, and
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+<div class="section" id="improving-solid-dmft">
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+<p>Please post suggestions for new features on the <a class="reference external" href="https://github.com/flatironinstitute/solid_dmft/discussions" rel="noopener noreferrer" target="_blank">github discussion page</a> or create
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+<h3>Reporting issues<a class="headerlink" href="#reporting-issues" title="Permalink to this headline"></a></h3>
+<p>Please report all problems and bugs directly at the github issue page
+<a class="reference external" href="https://github.com/flatironinstitute/solid_dmft/issues" rel="noopener noreferrer" target="_blank">https://github.com/flatironinstitute/solid_dmft/issues</a>.  In order to make
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diff --git a/md_notes/docker.html b/md_notes/docker.html
new file mode 100644
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--- /dev/null
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+<html class="writer-html5" lang="en" >
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
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+      <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
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+<li class="toctree-l3"><a class="reference internal" href="w90_interface.html">Wannier90 interface</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="w90_interface.html#orbital-order-in-the-w90-converter">orbital order in the W90 converter</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="vasp_csc.html">Interface to VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="vasp_csc.html#general-remarks">General remarks</a></li>
+<li class="toctree-l4"><a class="reference internal" href="vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#file-description">file description</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#workflow">Workflow:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#important-flags">important flags:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#version-and-compilation">version and compilation:</a></li>
+</ul>
+</li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#input-output">Input/Output</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../input_output/DMFT_input/input.html">Input</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/general.html">[general]: General parameters</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/dft.html">[dft]: DFT related inputs</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/advanced.html">[advanced]: Advanced inputs</a></li>
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+</li>
+<li class="toctree-l3"><a class="reference internal" href="../input_output/DMFT_output/results.html">Output / results</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_output/results.html#group-structure">Group structure</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_output/results.html#subgroups">Subgroups</a></li>
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+</li>
+<li class="toctree-l2 current"><a class="reference internal" href="../documentation.html#further-details-for-running">Further details for running</a><ul class="current">
+<li class="toctree-l3 current"><a class="current reference internal" href="#">Docker</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#building-the-docker-image">Building the docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#pulling-a-docker-image">Pulling a docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
+</ul>
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+<li class="toctree-l4"><a class="reference internal" href="run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
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+<li class="toctree-l4"><a class="reference internal" href="../_ref/dft_managers.mpi_helpers.html">dft_managers.mpi_helpers</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/dft_managers.qe_manager.html">dft_managers.qe_manager</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/dft_managers.vasp_manager.html">dft_managers.vasp_manager</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../_ref/dmft_cycle.html">dmft_cycle</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../_ref/dmft_tools.results_to_archive.html">dmft_tools.results_to_archive</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/dmft_tools.solver.html">dmft_tools.solver</a></li>
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+</ul>
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+<li class="toctree-l3"><a class="reference internal" href="../_ref/postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/postprocessing.eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/postprocessing.maxent_gf_imp.html">postprocessing.maxent_gf_imp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/postprocessing.maxent_gf_latt.html">postprocessing.maxent_gf_latt</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../_ref/postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../_ref/util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../issues.html">Support &amp; contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../issues.html#seeking-help">Seeking help</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../issues.html#improving-solid-dmft">Improving solid_dmft</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../issues.html#reporting-issues">Reporting issues</a></li>
+</ul>
+</li>
+</ul>
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+<li class="toctree-l1"><a class="reference internal" href="../ChangeLog.html">Changelog</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-2-0">Version 3.2.0</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#test">test</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-5">Version 3.1.5</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-4">Version 3.1.4</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-3">Version 3.1.3</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-2">Version 3.1.2</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-1">Version 3.1.1</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-0">Version 3.1.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-0-0">Version 3.0.0</a></li>
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+          <li><a href="../documentation.html">Documentation</a> &raquo;</li>
+      <li>Docker</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../_sources/md_notes/docker.md.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  
+<style>
+/* CSS overrides for sphinx_rtd_theme */
+
+/* 24px margin */
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+.math {
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+</style>
+<div class="tex2jax_ignore mathjax_ignore section" id="docker">
+<h1>Docker<a class="headerlink" href="#docker" title="Permalink to this headline"></a></h1>
+<p>There are Dockerfiles for images based on Ubuntu 20 (“focal”) with OpenMPI (for non-Cray clusters) or MPICH (for Cray clusters like Daint), IntelMKL, VASP, wannier90 2.1, triqs 3.x.x, and Triqs MaxEnt included.</p>
+<div class="section" id="building-the-docker-image">
+<h2>Building the docker image<a class="headerlink" href="#building-the-docker-image" title="Permalink to this headline"></a></h2>
+<p>The Dockerfile is built with this command, where <code class="docutils literal notranslate"><span class="pre">&lt;version</span> <span class="pre">name&gt;</span></code> could be <code class="docutils literal notranslate"><span class="pre">3.0.0</span></code>:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">docker</span> <span class="n">build</span> <span class="o">-</span><span class="n">t</span> <span class="n">triqs_mpich</span><span class="p">:</span><span class="o">&lt;</span><span class="n">version</span> <span class="n">name</span><span class="o">&gt;</span> <span class="o">-</span><span class="n">f</span> <span class="n">mpich_dockerfile</span> <span class="o">./</span>
+<span class="n">docker</span> <span class="n">build</span> <span class="o">-</span><span class="n">t</span> <span class="n">triqs_openmpi</span><span class="p">:</span><span class="o">&lt;</span><span class="n">version</span> <span class="n">name</span><span class="o">&gt;</span> <span class="o">-</span><span class="n">f</span> <span class="n">openmpi_dockerfile</span> <span class="o">./</span>
+</pre></div>
+</div>
+<p>Note that you need a working, modified vasp version as archive (csc_vasp.tar.gz) in this directory to make the CSC calculation work.</p>
+</div>
+<div class="section" id="pulling-a-docker-image">
+<h2>Pulling a docker image<a class="headerlink" href="#pulling-a-docker-image" title="Permalink to this headline"></a></h2>
+<p>Alternatively, you can pull an already-compiled image from the ETH gitlab container registry.
+First <a class="reference external" href="https://gitlab.ethz.ch/help/user/packages/container_registry/index#authenticating-to-the-gitlab-container-registry" rel="noopener noreferrer" target="_blank">log in with a personal access token</a>.
+This token you can save into a file and then log in into the registry with</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">cat</span> <span class="o">&lt;</span><span class="n">path</span> <span class="n">to</span> <span class="n">token</span><span class="o">&gt;</span> <span class="o">|</span> <span class="n">docker</span> <span class="n">login</span> <span class="n">registry</span><span class="o">.</span><span class="n">ethz</span><span class="o">.</span><span class="n">ch</span> <span class="o">-</span><span class="n">u</span> <span class="o">&lt;</span><span class="n">username</span> <span class="n">gitlab</span><span class="o">&gt;</span> <span class="o">--</span><span class="n">password</span><span class="o">-</span><span class="n">stdin</span>
+</pre></div>
+</div>
+<p>and then run</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">docker</span> <span class="n">pull</span> <span class="n">registry</span><span class="o">.</span><span class="n">ethz</span><span class="o">.</span><span class="n">ch</span><span class="o">/</span><span class="n">d</span><span class="o">-</span><span class="n">matl</span><span class="o">-</span><span class="n">theory</span><span class="o">/</span><span class="n">uni</span><span class="o">-</span><span class="n">dmft</span><span class="o">/&lt;</span><span class="n">image</span> <span class="n">name</span><span class="o">&gt;</span><span class="p">:</span><span class="o">&lt;</span><span class="n">version</span> <span class="n">name</span><span class="o">&gt;</span>
+</pre></div>
+</div>
+<p>Just make sure that the version is the one that you want to have, it might not yet contain recent changes or bug fixes. Alternatively, there is the <a class="reference external" href="https://hub.docker.com/r/flatironinstitute/triqs/" rel="noopener noreferrer" target="_blank">official triqs docker image</a>, which however is not optimized for use on Daint.</p>
+</div>
+<div class="section" id="getting-docker-images-onto-cscs-daint">
+<h2>Getting docker images onto CSCS daint<a class="headerlink" href="#getting-docker-images-onto-cscs-daint" title="Permalink to this headline"></a></h2>
+<p>First, you load the desired docker images with <a class="reference external" href="https://user.cscs.ch/tools/containers/sarus/" rel="noopener noreferrer" target="_blank">sarus on daint</a>.
+Then there are two ways of getting the image on daint:</p>
+<p>(1) For gitlab images (don’t forget that you need the personal access token) or other, public image this can be done via:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">sarus</span> <span class="n">pull</span> <span class="n">registry</span><span class="o">.</span><span class="n">ethz</span><span class="o">.</span><span class="n">ch</span><span class="o">/</span><span class="n">d</span><span class="o">-</span><span class="n">matl</span><span class="o">-</span><span class="n">theory</span><span class="o">/</span><span class="n">uni</span><span class="o">-</span><span class="n">dmft</span><span class="o">/&lt;</span><span class="n">image</span> <span class="n">name</span><span class="o">&gt;</span><span class="p">:</span><span class="o">&lt;</span><span class="n">version</span> <span class="n">name</span><span class="o">&gt;</span>
+<span class="n">sarus</span> <span class="n">pull</span> <span class="n">materialstheory</span><span class="o">/</span><span class="n">triqs</span>
+</pre></div>
+</div>
+<p>Pulling from the gitlab didn’t work on daint when I tried, which leaves you with the second option.</p>
+<p>(2) If you wish to use your locally saved docker image, you first have to save it</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">docker</span> <span class="n">save</span> <span class="o">--</span><span class="n">output</span><span class="o">=</span><span class="n">docker</span><span class="o">-</span><span class="n">triqs</span><span class="o">.</span><span class="n">tar</span> <span class="o">&lt;</span><span class="n">image</span> <span class="n">name</span><span class="o">&gt;</span><span class="p">:</span><span class="o">&lt;</span><span class="n">version</span> <span class="n">name</span><span class="o">&gt;</span>
+</pre></div>
+</div>
+<p>and then upload the tar to daint and then load it via:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">sarus</span> <span class="n">load</span> <span class="n">docker</span><span class="o">-</span><span class="n">triqs</span><span class="o">.</span><span class="n">tar</span> <span class="o">&lt;</span><span class="n">image</span> <span class="n">name</span><span class="o">&gt;</span><span class="p">:</span><span class="o">&lt;</span><span class="n">version</span> <span class="n">name</span><span class="o">&gt;</span>
+</pre></div>
+</div>
+<p>then you can run it as shown in the example files.</p>
+<div class="section" id="running-a-docker-container">
+<h3>Running a docker container<a class="headerlink" href="#running-a-docker-container" title="Permalink to this headline"></a></h3>
+<p>You can start a docker container with either of these commands</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>docker run --rm -it -u $(id -u) -v ~$PWD:/work &lt;image name&gt;:&lt;version name&gt; bash
+docker run --rm -it --shm-size=4g -e USER_ID=`id -u` -e GROUP_ID=`id -g` -p 8378:8378 -v $PWD:/work &lt;image name&gt;:&lt;version name&gt; bash
+</pre></div>
+</div>
+<p>where the second command adds some important flags.</p>
+<ul class="simple">
+<li>The -e flags will translate your current user and group id into the container and make sure writing permissions are correct for the mounted volumes.</li>
+<li>The option –shm-size, which increases shared memory size.
+This is hard coded in Docker to 64m and is often not sufficient and will produce SIBUS 7 errors when starting programs with mpirun! (see also <a class="reference external" href="https://github.com/moby/moby/issues/2606" rel="noopener noreferrer" target="_blank">https://github.com/moby/moby/issues/2606</a>).</li>
+<li>The ‘-v’ flags mounts a host directory as the docker directory given after the colon.
+This way docker can permanently save data; otherwise, it will restart with clean directories each time.
+Make sure you mount all the directories you need (where you save your data, where your uni-dmft directory is, …)!</li>
+<li>All the flags are explained in ‘docker run –help’.</li>
+</ul>
+<p>Inside the docker, you can normally execute program. To run uni-dmft, for example, use</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">mpirun</span> <span class="o">-</span><span class="n">n</span> <span class="mi">4</span> <span class="n">python</span> <span class="o">&lt;</span><span class="n">path</span> <span class="n">to</span> <span class="n">uni</span><span class="o">-</span><span class="n">dmft</span><span class="o">&gt;/</span><span class="n">run_dmft</span><span class="o">.</span><span class="n">py</span>
+</pre></div>
+</div>
+<p>To start a jupyter-lab server from the current path, use</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">jupyter</span><span class="o">.</span><span class="n">sh</span>
+</pre></div>
+</div>
+<p>All these commands you can execute directly by just adding them to the <code class="docutils literal notranslate"><span class="pre">docker</span> <span class="pre">run</span> <span class="pre">...</span> <span class="pre">bash</span></code> command with the <code class="docutils literal notranslate"><span class="pre">-c</span></code> flag, e.g.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>docker run --rm -it --shm-size=4g -e USER_ID=`id -u` -e GROUP_ID=`id -g` -p 8378:8378 -v $PWD:/work &lt;image name&gt;:&lt;version name&gt; bash -c &#39;cd /work &amp;&amp; mpirun -n 4 python &lt;path to uni-dmft&gt;/run_dmft.py&#39;
+</pre></div>
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+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../documentation.html#input-output">Input/Output</a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_input/general.html">[general]: General parameters</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../input_output/DMFT_output/results.html#group-structure">Group structure</a></li>
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+<li class="toctree-l2 current"><a class="reference internal" href="../documentation.html#further-details-for-running">Further details for running</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="docker.html">Docker</a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3 current"><a class="current reference internal" href="#">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#csc-calculations-on-daint">CSC calculations on daint</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../_ref/dmft_tools.formatter.html">dmft_tools.formatter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/dmft_tools.greens_functions_mixer.html">dmft_tools.greens_functions_mixer</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/dmft_tools.initial_self_energies.html">dmft_tools.initial_self_energies</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/dmft_tools.interaction_hamiltonian.html">dmft_tools.interaction_hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/dmft_tools.legendre_filter.html">dmft_tools.legendre_filter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/dmft_tools.manipulate_chemical_potential.html">dmft_tools.manipulate_chemical_potential</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/dmft_tools.matheval.html">dmft_tools.matheval</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/dmft_tools.observables.html">dmft_tools.observables</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/dmft_tools.results_to_archive.html">dmft_tools.results_to_archive</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/dmft_tools.solver.html">dmft_tools.solver</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../_ref/io_tools.html">io_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/io_tools.dict_to_h5.html">io_tools.dict_to_h5</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/io_tools.postproc_toml_dict.html">io_tools.postproc_toml_dict</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/io_tools.verify_input_params.html">io_tools.verify_input_params</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../_ref/postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/postprocessing.eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/postprocessing.maxent_gf_imp.html">postprocessing.maxent_gf_imp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/postprocessing.maxent_gf_latt.html">postprocessing.maxent_gf_latt</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/postprocessing.maxent_sigma.html">postprocessing.maxent_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../_ref/util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../issues.html">Support &amp; contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../issues.html#seeking-help">Seeking help</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../issues.html#improving-solid-dmft">Improving solid_dmft</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../issues.html#reporting-issues">Reporting issues</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../ChangeLog.html">Changelog</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-2-0">Version 3.2.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#general">General</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#fix">fix</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#build">build</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#test">test</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-5">Version 3.1.5</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-4">Version 3.1.4</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-3">Version 3.1.3</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-2">Version 3.1.2</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-1">Version 3.1.1</a></li>
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+<div class="tex2jax_ignore mathjax_ignore section" id="running-solid-dmft-on-a-cluster">
+<h1>Running solid_dmft on a cluster<a class="headerlink" href="#running-solid-dmft-on-a-cluster" title="Permalink to this headline"></a></h1>
+<div class="section" id="running-on-cscs-daint">
+<h2>Running on CSCS daint<a class="headerlink" href="#running-on-cscs-daint" title="Permalink to this headline"></a></h2>
+<p>in some directories one can also find example job files to run everything on
+daint. Note, that one has to first load the desired docker images with sarus
+on daint: <a class="reference external" href="https://user.cscs.ch/tools/containers/sarus/" rel="noopener noreferrer" target="_blank">https://user.cscs.ch/tools/containers/sarus/</a>, see the <a class="reference external" href="http://README.md" rel="noopener noreferrer" target="_blank">README.md</a> in the <code class="docutils literal notranslate"><span class="pre">/Docker</span></code> folder.</p>
+</div>
+<div class="section" id="one-shot-job-on-daint">
+<h2>one shot job on daint<a class="headerlink" href="#one-shot-job-on-daint" title="Permalink to this headline"></a></h2>
+<p>one shot is quite straight forward. Just get the newest version of these
+scripts, go to a working directory and then create job file that looks like
+this:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>#!/bin/bash
+#SBATCH --job-name=&quot;svo-test&quot;
+#SBATCH --time=1:00:00
+#SBATCH --nodes=2
+#SBATCH --ntasks-per-node=36
+#SBATCH --account=eth3
+#SBATCH --ntasks-per-core=1
+#SBATCH --constraint=mc
+#SBATCH --partition=normal
+#SBATCH --output=out.%j
+#SBATCH --error=err.%j
+
+#======START=====
+
+srun sarus run --mpi --mount=type=bind,source=$SCRATCH,destination=$SCRATCH --mount=type=bind,source=/apps,destination=/apps load/library/triqs-2.1-vasp bash -c &quot;cd $PWD ; python /apps/ethz/eth3/dmatl-theory-git/solid_dmft/solid_dmft.py&quot;
+</pre></div>
+</div>
+<p>thats it. This line automatically runs the docker image and executes the
+<code class="docutils literal notranslate"><span class="pre">solid_dmft.py</span></code> script. Unfortunately the new sarus container enginge does not mounts automatically user directories. Therefore, one needs to specify with <code class="docutils literal notranslate"><span class="pre">--mount</span></code> to mount the scratch and apps folder manually. Then, one executes in the container bash to first go into the current dir and then executes python and the dmft script.</p>
+</div>
+<div class="section" id="csc-calculations-on-daint">
+<h2>CSC calculations on daint<a class="headerlink" href="#csc-calculations-on-daint" title="Permalink to this headline"></a></h2>
+<p>CSC calculations need the parameter <code class="docutils literal notranslate"><span class="pre">csc</span> <span class="pre">=</span> <span class="pre">True</span></code> and the mandatory parameters from the group <code class="docutils literal notranslate"><span class="pre">dft</span></code>.
+Then, solid_dmft automatically starts VASP on as many cores as specified.
+Note that VASP runs on cores that are already used by solid_dmft.
+This minimizes the time that cores are idle while not harming the performance because these two processes are never active at the same time.</p>
+<p>For the latest version in the Dockerfile_MPICH, we need the sarus version &gt;= 1.3.2, which can be loaded from the daint modules as <code class="docutils literal notranslate"><span class="pre">sarus/1.3.2</span></code> but isn’t the default version.
+The reason for this is that only from this sarus version on, having more than one version of libgfortran in the docker image is supported, which comes from Vasp requiring the use of gfortran7 and everything else using gfortran9.</p>
+<p>A slurm job script should look like this:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>#!/bin/bash
+#SBATCH --job-name=&quot;svo-csc-test&quot;
+#SBATCH --time=4:00:00
+#SBATCH --nodes=4
+#SBATCH --ntasks-per-node=36
+#SBATCH --account=eth3
+#SBATCH --ntasks-per-core=1
+#SBATCH --constraint=mc
+#SBATCH --partition=normal
+#SBATCH --output=out.%j
+#SBATCH --error=err.%j
+
+# path to solid_dmft.py script
+SCRIPTDIR=/apps/ethz/eth3/dmatl-theory-git/solid_dmft/solid_dmft.py
+# Sarus image that is utilized
+IMAGE=load/library/triqs_mpich
+
+srun --cpu-bind=none --mpi=pmi2 sarus run --mount=type=bind,source=/apps,destination=/apps --mount=type=bind,source=$SCRATCH,destination=$SCRATCH --workdir=$PWD $IMAGE python3 $SCRIPTDIR
+</pre></div>
+</div>
+<p>Note that here the mpi option is given to the <code class="docutils literal notranslate"><span class="pre">srun</span></code> command and not the sarus command, as for one-shot calculations.
+This is important for the python to be able to start VASP.</p>
+<p>In general I found 1 node for Vasp is in most cases enough, which means that we set <code class="docutils literal notranslate"><span class="pre">n_cores</span></code> in the dmft_config.ini to 36 here.
+Using more than one node results in a lot of MPI communication, which in turn slows down the calculation significantly.
+For a 80 atom unit cell 2 nodes are useful, but for a 20 atom unit cell not at all!</p>
+</div>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-4">Version 3.1.4</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-3">Version 3.1.3</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-2">Version 3.1.2</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-1">Version 3.1.1</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-1-0">Version 3.1.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-0-0">Version 3.0.0</a></li>
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+<div class="tex2jax_ignore mathjax_ignore section" id="run-on-your-machine">
+<h1>Run on your machine<a class="headerlink" href="#run-on-your-machine" title="Permalink to this headline"></a></h1>
+<div class="section" id="csc-calculations-locally">
+<h2>CSC calculations locally<a class="headerlink" href="#csc-calculations-locally" title="Permalink to this headline"></a></h2>
+<p>Here one needs a special docker image with vasp included. This can be done by
+building the Dockerfile in <code class="docutils literal notranslate"><span class="pre">/Docker</span></code>.
+Then start this docker image as done above and go to the directory with all
+necessary input files (start with <code class="docutils literal notranslate"><span class="pre">svo-csc</span></code> example). You need a pre-converged
+CHGCAR and preferably a WAVECAR, a set of INCAR, POSCAR, KPOINTS and POTCAR
+files, the PLO cfg file <code class="docutils literal notranslate"><span class="pre">plo.cfg</span></code> and the usual DMFT input file
+<code class="docutils literal notranslate"><span class="pre">dmft_config.ini</span></code>, which specifies the number of ranks for the DFT code and the DFT code executable in the <code class="docutils literal notranslate"><span class="pre">[dft]</span></code> section.</p>
+<p>The whole machinery is started by calling <code class="docutils literal notranslate"><span class="pre">solid_dmft.py</span></code> as for one-shot calculations. Importantly the flag <code class="docutils literal notranslate"><span class="pre">csc</span> <span class="pre">=</span> <span class="pre">True</span></code> has to be set in the general section in the config file. Then:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">mpirun</span> <span class="o">-</span><span class="n">n</span> <span class="mi">12</span> <span class="o">/</span><span class="n">work</span><span class="o">/</span><span class="n">solid_dmft</span><span class="o">.</span><span class="n">py</span>
+</pre></div>
+</div>
+<p>The programm will then run the <code class="docutils literal notranslate"><span class="pre">csc_flow_control</span></code> routine, which starts VASP accordingly by spawning a new child process. After VASP is finished it will run the converter, run the dmft_cycle, and then VASP again until the given
+limit of DMFT iterations is reached. This should also work on most HPC systems (tested on slurm with OpenMPI), as the the child mpirun call is performed without the slurm environment variables. This tricks slrum into starting more ranks than it has available. Note, that maybe a slight adaption of the environment variables is needed to make sure VASP is found on the second node. The variables are stored <code class="docutils literal notranslate"><span class="pre">args</span></code> in the function <code class="docutils literal notranslate"><span class="pre">start_vasp_from_master_node</span></code> of the module <code class="docutils literal notranslate"><span class="pre">csc_flow.py</span></code></p>
+<p>One remark regarding the number of iterations per DFT cycle. Since VASP uses a
+block Davidson scheme for minimizing the energy functional not all eigenvalues
+of the Hamiltonian are updated simultaneously therefore one has to make several
+iterations before the changes from DMFT in the charge density are completely
+considered. The default value are <strong>6</strong> DFT iterations, which is very
+conservative, and can be changed by changing the config parameter <code class="docutils literal notranslate"><span class="pre">n_iter</span></code> in the <code class="docutils literal notranslate"><span class="pre">[dft]</span></code> section. In general one should use <code class="docutils literal notranslate"><span class="pre">IALGO=90</span></code> in VASP, which performs an exact diagonalization rather than a partial diagonalization scheme, but this is very slow for larger systems.</p>
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+<li class="toctree-l4"><a class="reference internal" href="#general-remarks">General remarks</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#file-description">file description</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#workflow">Workflow:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#important-flags">important flags:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
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+</ul>
+</li>
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+</ul>
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+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
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+<div class="tex2jax_ignore mathjax_ignore section" id="interface-to-vasp">
+<h1>Interface to VASP<a class="headerlink" href="#interface-to-vasp" title="Permalink to this headline"></a></h1>
+<div class="section" id="general-remarks">
+<h2>General remarks<a class="headerlink" href="#general-remarks" title="Permalink to this headline"></a></h2>
+<p>One can use the official Vasp 5.4.4 patch 1 version with a few modifications:</p>
+<ul class="simple">
+<li>there is a bug in <code class="docutils literal notranslate"><span class="pre">fileio.F</span></code> around line 1710 where the code tries print out something like “reading the density matrix from Gamma”, but this should be done only by the master node. Adding a <code class="docutils literal notranslate"><span class="pre">IF</span> <span class="pre">(IO%IU0&gt;=0)</span> <span class="pre">THEN</span> <span class="pre">...</span> <span class="pre">ENDIF</span></code> around it fixes this</li>
+<li>in the current version of the dft_tools interface the file <code class="docutils literal notranslate"><span class="pre">LOCPROJ</span></code> should contain the fermi energy in the header. Therefore  one should replace the following line in <code class="docutils literal notranslate"><span class="pre">locproj.F</span></code>:</li>
+</ul>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">WRITE</span><span class="p">(</span><span class="mi">99</span><span class="p">,</span><span class="s1">&#39;(4I6,&quot;  # of spin, # of k-points, # of bands, # of proj&quot; )&#39;</span><span class="p">)</span> <span class="n">NS</span><span class="p">,</span><span class="n">NK</span><span class="p">,</span><span class="n">NB</span><span class="p">,</span><span class="n">NF</span>
+</pre></div>
+</div>
+<p>by</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">WRITE</span><span class="p">(</span><span class="mi">99</span><span class="p">,</span><span class="s1">&#39;(4I6,F12.7,&quot;  # of spin, # of k-points, # of bands, # of proj, Efermi&quot; )&#39;</span><span class="p">)</span> <span class="n">W</span><span class="o">%</span><span class="n">WDES</span><span class="o">%</span><span class="n">NCDIJ</span><span class="p">,</span><span class="n">NK</span><span class="p">,</span><span class="n">NB</span><span class="p">,</span><span class="n">NF</span><span class="p">,</span><span class="n">EFERMI</span>
+</pre></div>
+</div>
+<p>and add the variable <code class="docutils literal notranslate"><span class="pre">EFERMI</span></code> accordingly in the function call.</p>
+<ul class="simple">
+<li>Vasp gets sometimes stuck and does not write the <code class="docutils literal notranslate"><span class="pre">OSZICAR</span></code> file correctly due to a stuck buffer. Adding a flush to the buffer to have a correctly written <code class="docutils literal notranslate"><span class="pre">OSZICAR</span></code> to extract the DFT energy helps, by adding in <code class="docutils literal notranslate"><span class="pre">electron.F</span></code> around line 580 after</li>
+</ul>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">CALL</span> <span class="n">STOP_TIMING</span><span class="p">(</span><span class="s2">&quot;G&quot;</span><span class="p">,</span><span class="n">IO</span><span class="o">%</span><span class="n">IU6</span><span class="p">,</span><span class="s2">&quot;DOS&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>two lines:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">flush</span><span class="p">(</span><span class="mi">17</span><span class="p">)</span>
+<span class="nb">print</span> <span class="o">*</span><span class="p">,</span> <span class="s1">&#39; &#39;</span>
+</pre></div>
+</div>
+<ul class="simple">
+<li>this one is <strong>essential</strong> for the current version of the DMFT code. Vasp spends a very long time in the function <code class="docutils literal notranslate"><span class="pre">LPRJ_LDApU</span></code> and this function is not needed! It is used for some basic checks and a manual LDA+U implementation. Removing the call to this function in <code class="docutils literal notranslate"><span class="pre">electron.F</span></code> in line 644 speeds up the calculation by up to 30%! If this is not done, Vasp will create a GAMMA file each iteration which needs to be removed manually to not overwrite the DMFT GAMMA file!</li>
+<li>make sure that mixing in VASP stays turned on. Don’t set IMIX or the DFT steps won’t converge!</li>
+</ul>
+</div>
+<div class="section" id="locproj-bug-for-individual-projections">
+<h2>LOCPROJ bug for individual projections:<a class="headerlink" href="#locproj-bug-for-individual-projections" title="Permalink to this headline"></a></h2>
+<p>Example use of LOCPROJ for t2g manifold of SrVO3 (the order of the orbitals seems to be mixed up… this example leads to x^2 -y^2, z^2, yz… )
+In the current version there is some mix up in the mapping between selected orbitals in the INCAR and actual selected in the LOCPROJ. This is
+what the software does (left side is INCAR, right side is resulting in the LOCPROJ)</p>
+<ul class="simple">
+<li>xy -&gt; x2-y2</li>
+<li>yz -&gt; z2</li>
+<li>xz -&gt; yz</li>
+<li>x2-y2 -&gt; xz</li>
+<li>z2 -&gt; xy</li>
+</ul>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">LOCPROJ</span> <span class="o">=</span> <span class="mi">2</span> <span class="p">:</span> <span class="n">dxz</span> <span class="p">:</span> <span class="n">Pr</span> <span class="mi">1</span>
+<span class="n">LOCPROJ</span> <span class="o">=</span> <span class="mi">2</span> <span class="p">:</span> <span class="n">dx2</span><span class="o">-</span><span class="n">y2</span> <span class="p">:</span> <span class="n">Pr</span> <span class="mi">1</span>
+<span class="n">LOCPROJ</span> <span class="o">=</span> <span class="mi">2</span> <span class="p">:</span> <span class="n">dz2</span> <span class="p">:</span> <span class="n">Pr</span> <span class="mi">1</span>
+</pre></div>
+</div>
+<p>However, if the complete d manifold is chosen, the usual VASP order (xy, yz, z2, xz, x2-y2) is obtained in the LOCPROJ. This is done as shown below</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">LOCPROJ</span> <span class="o">=</span> <span class="mi">2</span> <span class="p">:</span> <span class="n">d</span> <span class="p">:</span> <span class="n">Pr</span> <span class="mi">1</span>
+</pre></div>
+</div>
+</div>
+<div class="section" id="convergence-of-projectors-with-vasp">
+<h2>convergence of projectors with Vasp<a class="headerlink" href="#convergence-of-projectors-with-vasp" title="Permalink to this headline"></a></h2>
+<p>for a good convergence of the projectors it is important to convergence the wavefunctions to high accuracy. Otherwise this often leads to off-diagonal elements in the the local Green’s function. To check convergence pay attention to the rms and rms© values in the Vasp output. The former specifies the convergence of the KS wavefunction and the latter is difference of the input and out charge density. Note, this does not necessarily coincide with good convergence of the total energy in DFT! Here an example of two calculations for the same system, both converged down to <code class="docutils literal notranslate"><span class="pre">EDIFF=</span> <span class="pre">1E-10</span></code> and Vasp stopped. First run:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>       <span class="n">N</span>       <span class="n">E</span>                     <span class="n">dE</span>             <span class="n">d</span> <span class="n">eps</span>       <span class="n">ncg</span>     <span class="n">rms</span>          <span class="n">rms</span><span class="p">(</span><span class="n">c</span><span class="p">)</span>
+<span class="o">...</span>
+<span class="n">DAV</span><span class="p">:</span>  <span class="mi">25</span>    <span class="o">-</span><span class="mf">0.394708006287E+02</span>   <span class="o">-</span><span class="mf">0.65893E-09</span>   <span class="o">-</span><span class="mf">0.11730E-10</span> <span class="mi">134994</span>   <span class="mf">0.197E-06</span>  <span class="mf">0.992E-05</span>
+<span class="o">...</span>
+</pre></div>
+</div>
+<p>second run with different smearing:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">...</span>
+<span class="n">DAV</span><span class="p">:</span>  <span class="mi">31</span>    <span class="o">-</span><span class="mf">0.394760088659E+02</span>    <span class="mf">0.39472E-09</span>    <span class="mf">0.35516E-13</span> <span class="mi">132366</span>   <span class="mf">0.110E-10</span>  <span class="mf">0.245E-10</span>
+<span class="o">...</span>
+</pre></div>
+</div>
+<p>The total energy is lower as well. But more importantly the second calculation produces well converged projectors preserving symmetries way better, with less off-diagonal elements in Gloc, making it way easier for the solver. Always pay attention to rms.</p>
+</div>
+<div class="section" id="enabling-csc-calculations-with-wannier90-projectors">
+<h2>Enabling CSC calculations with Wannier90 projectors<a class="headerlink" href="#enabling-csc-calculations-with-wannier90-projectors" title="Permalink to this headline"></a></h2>
+<p>You basically only need to add two things to have W90 run in Vasp’s CSC mode, all in <code class="docutils literal notranslate"><span class="pre">electron.F</span></code>:</p>
+<ul class="simple">
+<li>the line <code class="docutils literal notranslate"><span class="pre">USE</span> <span class="pre">mlwf</span></code> at the top of the <code class="docutils literal notranslate"><span class="pre">SUBROUTINE</span> <span class="pre">ELMIN</span></code> together with all the other <code class="docutils literal notranslate"><span class="pre">USE</span> <span class="pre">...</span></code> statements.</li>
+<li>right below where you removed the call to <code class="docutils literal notranslate"><span class="pre">LPRJ_LDApU</span></code> (see above, around line 650), there is the line <code class="docutils literal notranslate"><span class="pre">CALL</span> <span class="pre">LPRJ_DEALLOC_COVL</span></code>. Just add the following block right below, inside the same “if” as the <code class="docutils literal notranslate"><span class="pre">CALL</span> <span class="pre">LPRJ_DEALLOC_COVL</span></code>:</li>
+</ul>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">IF</span> <span class="p">(</span><span class="n">WANNIER90</span><span class="p">())</span> <span class="n">THEN</span>
+   <span class="n">CALL</span> <span class="n">KPAR_SYNC_ALL</span><span class="p">(</span><span class="n">WDES</span><span class="p">,</span><span class="n">W</span><span class="p">)</span>
+   <span class="n">CALL</span> <span class="n">MLWF_WANNIER90</span><span class="p">(</span><span class="n">WDES</span><span class="p">,</span><span class="n">W</span><span class="p">,</span><span class="n">P</span><span class="p">,</span><span class="n">CQIJ</span><span class="p">,</span><span class="n">T_INFO</span><span class="p">,</span><span class="n">LATT_CUR</span><span class="p">,</span><span class="n">INFO</span><span class="p">,</span><span class="n">IO</span><span class="p">)</span>
+<span class="n">ENDIF</span>
+</pre></div>
+</div>
+<p>Then, the only problem you’ll have is the order of compilation in the <code class="docutils literal notranslate"><span class="pre">.objects</span></code> file. It has to change because now electron.F references mlwf. For that move the entries <code class="docutils literal notranslate"><span class="pre">twoelectron4o.o</span></code> and <code class="docutils literal notranslate"><span class="pre">mlwf.o</span></code> (in this order) up right behind <code class="docutils literal notranslate"><span class="pre">linear_optics.o</span></code>. Then, move the lines from <code class="docutils literal notranslate"><span class="pre">electron.o</span></code> to <code class="docutils literal notranslate"><span class="pre">stm.o</span></code> behind the new position of <code class="docutils literal notranslate"><span class="pre">mlwf.o</span></code>.</p>
+<p>Remarks:</p>
+<ul class="simple">
+<li>W90-CSC requires Wannier90 v3, in v2 the tag write_u_matrices does not work correctly. Until now, linking W90 v3 to Vasp with the <code class="docutils literal notranslate"><span class="pre">DVASP2WANNIER90v2</span></code> has worked without any problems even though it is not officially supported</li>
+<li>symmetries in Vasp should remain turned on, otherwise the determination of rotation matrices in dft_tools’ wannier converter will most likely fail</li>
+</ul>
+</div>
+<div class="section" id="speeding-up-by-not-writing-projectors-at-every-step">
+<h2>Speeding up by not writing projectors at every step<a class="headerlink" href="#speeding-up-by-not-writing-projectors-at-every-step" title="Permalink to this headline"></a></h2>
+<p>This is very important for CSC calculations with W90 but also speeds up the PLO-based ones.</p>
+<p>Writing the Wannier projectors is a time consuming step (and to a lesser extent, the PLO projectors) and basically needs only to be done in the DFT iteration right before a DMFT iteration. Therefore, solid_dmft writes the file <code class="docutils literal notranslate"><span class="pre">vasp.suppress_projs</span></code> that tells Vasp when <strong>not</strong> to compute/write the projectors. This requires two small changes in <code class="docutils literal notranslate"><span class="pre">electron.F</span></code> in the Vasp source code:</p>
+<ul class="simple">
+<li>adding the definition of a logical variable where all other variables are defined for <code class="docutils literal notranslate"><span class="pre">SUBROUTINE</span> <span class="pre">ELMIN</span></code>, e.g. around line 150, by inserting <code class="docutils literal notranslate"><span class="pre">LOGICAL</span> <span class="pre">::</span> <span class="pre">LSUPPRESS_PROJS_EXISTS</span></code></li>
+<li>go to the place where you removed the call to <code class="docutils literal notranslate"><span class="pre">LPRJ_LDApU</span></code> (see above, around line 650). This is inside a <code class="docutils literal notranslate"><span class="pre">IF</span> <span class="pre">(MOD(INFO%ICHARG,10)==5)</span> <span class="pre">THEN</span> <span class="pre">...</span> <span class="pre">ENDIF</span></code> block. This whole block has to be disabled when the file <code class="docutils literal notranslate"><span class="pre">vasp.suppress_projs</span></code> exists. So, right under this block’s “IF”, add the lines</li>
+</ul>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">INQUIRE</span><span class="p">(</span><span class="n">FILE</span><span class="o">=</span><span class="s1">&#39;vasp.suppress_projs&#39;</span><span class="p">,</span><span class="o">&amp;</span>
+        <span class="n">EXIST</span><span class="o">=</span><span class="n">LSUPPRESS_PROJS_EXISTS</span><span class="p">)</span>
+
+<span class="n">IF</span> <span class="p">(</span><span class="o">.</span><span class="n">NOT</span><span class="o">.</span> <span class="n">LSUPPRESS_PROJS_EXISTS</span><span class="p">)</span> <span class="n">THEN</span>
+</pre></div>
+</div>
+<p>and right before this block’s “ENDIF”, add another <code class="docutils literal notranslate"><span class="pre">ENDIF</span></code>.</p>
+</div>
+</div>
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+<li class="toctree-l4"><a class="reference internal" href="vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../_ref/postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/postprocessing.eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../_ref/postprocessing.maxent_gf_latt.html">postprocessing.maxent_gf_latt</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../_ref/postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../_ref/util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../_ref/util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../ChangeLog.html#version-3-0-0">Version 3.0.0</a></li>
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+<div class="tex2jax_ignore mathjax_ignore section" id="wannier90-interface">
+<h1>Wannier90 interface<a class="headerlink" href="#wannier90-interface" title="Permalink to this headline"></a></h1>
+<div class="section" id="orbital-order-in-the-w90-converter">
+<h2>orbital order in the W90 converter<a class="headerlink" href="#orbital-order-in-the-w90-converter" title="Permalink to this headline"></a></h2>
+<p>Some interaction Hamiltonians are sensitive to the order of orbitals (i.e. density-density or Slater Hamiltonian), others are invariant under rotations in orbital space (i.e. the Kanamori Hamiltonian).
+For the former class and W90-based DMFT calculations, we need to be careful because the order of W90 (z^2, xz, yz, x^2-y^2, xy) is different from the order expected by TRIQS (xy, yz, z^2, xz, x^2-y^2).
+Therefore, we need to specify the order of orbitals in the projections block (example for Pbnm or P21/n cell, full d shell):</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">begin</span> <span class="n">projections</span>
+<span class="c1"># site 0</span>
+<span class="n">f</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span><span class="mf">0.0</span><span class="p">,</span><span class="mf">0.0</span><span class="p">:</span><span class="n">dxy</span>
+<span class="n">f</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span><span class="mf">0.0</span><span class="p">,</span><span class="mf">0.0</span><span class="p">:</span><span class="n">dyz</span>
+<span class="n">f</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span><span class="mf">0.0</span><span class="p">,</span><span class="mf">0.0</span><span class="p">:</span><span class="n">dz2</span>
+<span class="n">f</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span><span class="mf">0.0</span><span class="p">,</span><span class="mf">0.0</span><span class="p">:</span><span class="n">dxz</span>
+<span class="n">f</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span><span class="mf">0.0</span><span class="p">,</span><span class="mf">0.0</span><span class="p">:</span><span class="n">dx2</span><span class="o">-</span><span class="n">y2</span>
+<span class="c1"># site 1</span>
+<span class="n">f</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span><span class="mf">0.0</span><span class="p">,</span><span class="mf">0.5</span><span class="p">:</span><span class="n">dxy</span>
+<span class="n">f</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span><span class="mf">0.0</span><span class="p">,</span><span class="mf">0.5</span><span class="p">:</span><span class="n">dyz</span>
+<span class="n">f</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span><span class="mf">0.0</span><span class="p">,</span><span class="mf">0.5</span><span class="p">:</span><span class="n">dz2</span>
+<span class="n">f</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span><span class="mf">0.0</span><span class="p">,</span><span class="mf">0.5</span><span class="p">:</span><span class="n">dxz</span>
+<span class="n">f</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span><span class="mf">0.0</span><span class="p">,</span><span class="mf">0.5</span><span class="p">:</span><span class="n">dx2</span><span class="o">-</span><span class="n">y2</span>
+<span class="c1"># site 2</span>
+<span class="n">f</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span><span class="mf">0.5</span><span class="p">,</span><span class="mf">0.0</span><span class="p">:</span><span class="n">dxy</span>
+<span class="n">f</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span><span class="mf">0.5</span><span class="p">,</span><span class="mf">0.0</span><span class="p">:</span><span class="n">dyz</span>
+<span class="n">f</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span><span class="mf">0.5</span><span class="p">,</span><span class="mf">0.0</span><span class="p">:</span><span class="n">dz2</span>
+<span class="n">f</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span><span class="mf">0.5</span><span class="p">,</span><span class="mf">0.0</span><span class="p">:</span><span class="n">dxz</span>
+<span class="n">f</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span><span class="mf">0.5</span><span class="p">,</span><span class="mf">0.0</span><span class="p">:</span><span class="n">dx2</span><span class="o">-</span><span class="n">y2</span>
+<span class="c1"># site 3</span>
+<span class="n">f</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span><span class="mf">0.5</span><span class="p">,</span><span class="mf">0.5</span><span class="p">:</span><span class="n">dxy</span>
+<span class="n">f</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span><span class="mf">0.5</span><span class="p">,</span><span class="mf">0.5</span><span class="p">:</span><span class="n">dyz</span>
+<span class="n">f</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span><span class="mf">0.5</span><span class="p">,</span><span class="mf">0.5</span><span class="p">:</span><span class="n">dz2</span>
+<span class="n">f</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span><span class="mf">0.5</span><span class="p">,</span><span class="mf">0.5</span><span class="p">:</span><span class="n">dxz</span>
+<span class="n">f</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span><span class="mf">0.5</span><span class="p">,</span><span class="mf">0.5</span><span class="p">:</span><span class="n">dx2</span><span class="o">-</span><span class="n">y2</span>
+<span class="n">end</span> <span class="n">projections</span>
+</pre></div>
+</div>
+<p>Warning: simply using <code class="docutils literal notranslate"><span class="pre">Fe:dxy,dyz,dz2,dxz,dx2-y2</span></code> does not work, VASP/W90 brings the d orbitals back to W90 standard order.</p>
+<p>The 45-degree rotation for the sqrt2 x sqrt2 x 2 cell can be ignored because the interaction Hamiltonian is invariant under swapping x^2-y^2 and xy.</p>
+</div>
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new file mode 100644
index 00000000..7aa362e1
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+  <meta charset="utf-8" />
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+  <title>Python Module Index &mdash; solid_dmft  documentation</title>
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+<li class="toctree-l4"><a class="reference internal" href="cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="md_notes/run_locally.html">Run on your machine</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.observables.html">dmft_tools.observables</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.solver.html">dmft_tools.solver</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="_ref/io_tools.dict_to_h5.html">io_tools.dict_to_h5</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="_ref/io_tools.verify_input_params.html">io_tools.verify_input_params</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="_ref/postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="_ref/postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="_ref/postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="_ref/util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
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+</li>
+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="issues.html">Support &amp; contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="issues.html#seeking-help">Seeking help</a></li>
+<li class="toctree-l2"><a class="reference internal" href="issues.html#improving-solid-dmft">Improving solid_dmft</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="issues.html#reporting-issues">Reporting issues</a></li>
+</ul>
+</li>
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+<li class="toctree-l1"><a class="reference internal" href="ChangeLog.html">Changelog</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#version-3-2-0">Version 3.2.0</a></li>
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+      </nav>
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+      <div class="wy-nav-content">
+        <div class="rst-content">
+          <div role="navigation" aria-label="Page navigation">
+  <ul class="wy-breadcrumbs">
+      <li><a href="index.html" class="icon icon-home"></a> &raquo;</li>
+      <li>Python Module Index</li>
+      <li class="wy-breadcrumbs-aside">
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+
+   <h1>Python Module Index</h1>
+
+   <div class="modindex-jumpbox">
+   <a href="#cap-c"><strong>c</strong></a> | 
+   <a href="#cap-d"><strong>d</strong></a> | 
+   <a href="#cap-i"><strong>i</strong></a> | 
+   <a href="#cap-p"><strong>p</strong></a> | 
+   <a href="#cap-s"><strong>s</strong></a> | 
+   <a href="#cap-u"><strong>u</strong></a>
+   </div>
+
+   <table class="indextable modindextable">
+     <tr class="pcap"><td></td><td>&#160;</td><td></td></tr>
+     <tr class="cap" id="cap-c"><td></td><td>
+       <strong>c</strong></td><td></td></tr>
+     <tr>
+       <td></td>
+       <td>
+       <a href="_ref/csc_flow.html#module-csc_flow"><code class="xref">csc_flow</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="pcap"><td></td><td>&#160;</td><td></td></tr>
+     <tr class="cap" id="cap-d"><td></td><td>
+       <strong>d</strong></td><td></td></tr>
+     <tr>
+       <td><img src="_static/minus.png" class="toggler"
+              id="toggle-1" style="display: none" alt="-" /></td>
+       <td>
+       <a href="_ref/dft_managers.html#module-dft_managers"><code class="xref">dft_managers</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="_ref/dft_managers.mpi_helpers.html#module-dft_managers.mpi_helpers"><code class="xref">dft_managers.mpi_helpers</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="_ref/dft_managers.qe_manager.html#module-dft_managers.qe_manager"><code class="xref">dft_managers.qe_manager</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="_ref/dft_managers.vasp_manager.html#module-dft_managers.vasp_manager"><code class="xref">dft_managers.vasp_manager</code></a></td><td>
+       <em></em></td></tr>
+     <tr>
+       <td></td>
+       <td>
+       <a href="_ref/dmft_cycle.html#module-dmft_cycle"><code class="xref">dmft_cycle</code></a></td><td>
+       <em></em></td></tr>
+     <tr>
+       <td><img src="_static/minus.png" class="toggler"
+              id="toggle-2" style="display: none" alt="-" /></td>
+       <td>
+       <a href="_ref/dmft_tools.html#module-dmft_tools"><code class="xref">dmft_tools</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-2">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="_ref/dmft_tools.afm_mapping.html#module-dmft_tools.afm_mapping"><code class="xref">dmft_tools.afm_mapping</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-2">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="_ref/dmft_tools.convergence.html#module-dmft_tools.convergence"><code class="xref">dmft_tools.convergence</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-2">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="_ref/dmft_tools.formatter.html#module-dmft_tools.formatter"><code class="xref">dmft_tools.formatter</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-2">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="_ref/dmft_tools.greens_functions_mixer.html#module-dmft_tools.greens_functions_mixer"><code class="xref">dmft_tools.greens_functions_mixer</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-2">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="_ref/dmft_tools.initial_self_energies.html#module-dmft_tools.initial_self_energies"><code class="xref">dmft_tools.initial_self_energies</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-2">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="_ref/dmft_tools.interaction_hamiltonian.html#module-dmft_tools.interaction_hamiltonian"><code class="xref">dmft_tools.interaction_hamiltonian</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-2">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="_ref/dmft_tools.legendre_filter.html#module-dmft_tools.legendre_filter"><code class="xref">dmft_tools.legendre_filter</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-2">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="_ref/dmft_tools.manipulate_chemical_potential.html#module-dmft_tools.manipulate_chemical_potential"><code class="xref">dmft_tools.manipulate_chemical_potential</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-2">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="_ref/dmft_tools.matheval.html#module-dmft_tools.matheval"><code class="xref">dmft_tools.matheval</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-2">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="_ref/dmft_tools.observables.html#module-dmft_tools.observables"><code class="xref">dmft_tools.observables</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-2">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="_ref/dmft_tools.results_to_archive.html#module-dmft_tools.results_to_archive"><code class="xref">dmft_tools.results_to_archive</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-2">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="_ref/dmft_tools.solver.html#module-dmft_tools.solver"><code class="xref">dmft_tools.solver</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="pcap"><td></td><td>&#160;</td><td></td></tr>
+     <tr class="cap" id="cap-i"><td></td><td>
+       <strong>i</strong></td><td></td></tr>
+     <tr>
+       <td><img src="_static/minus.png" class="toggler"
+              id="toggle-3" style="display: none" alt="-" /></td>
+       <td>
+       <a href="_ref/io_tools.html#module-io_tools"><code class="xref">io_tools</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-3">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="_ref/io_tools.dict_to_h5.html#module-io_tools.dict_to_h5"><code class="xref">io_tools.dict_to_h5</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-3">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="_ref/io_tools.postproc_toml_dict.html#module-io_tools.postproc_toml_dict"><code class="xref">io_tools.postproc_toml_dict</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-3">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="_ref/io_tools.verify_input_params.html#module-io_tools.verify_input_params"><code class="xref">io_tools.verify_input_params</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="pcap"><td></td><td>&#160;</td><td></td></tr>
+     <tr class="cap" id="cap-p"><td></td><td>
+       <strong>p</strong></td><td></td></tr>
+     <tr>
+       <td><img src="_static/minus.png" class="toggler"
+              id="toggle-4" style="display: none" alt="-" /></td>
+       <td>
+       <a href="_ref/postprocessing.html#module-postprocessing"><code class="xref">postprocessing</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-4">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="_ref/postprocessing.eval_U_cRPA_RESPACK.html#module-postprocessing.eval_U_cRPA_RESPACK"><code class="xref">postprocessing.eval_U_cRPA_RESPACK</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-4">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="_ref/postprocessing.eval_U_cRPA_Vasp.html#module-postprocessing.eval_U_cRPA_Vasp"><code class="xref">postprocessing.eval_U_cRPA_Vasp</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-4">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="_ref/postprocessing.maxent_gf_imp.html#module-postprocessing.maxent_gf_imp"><code class="xref">postprocessing.maxent_gf_imp</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-4">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="_ref/postprocessing.maxent_gf_latt.html#module-postprocessing.maxent_gf_latt"><code class="xref">postprocessing.maxent_gf_latt</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-4">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="_ref/postprocessing.maxent_sigma.html#module-postprocessing.maxent_sigma"><code class="xref">postprocessing.maxent_sigma</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-4">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="_ref/postprocessing.pade_sigma.html#module-postprocessing.pade_sigma"><code class="xref">postprocessing.pade_sigma</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-4">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="_ref/postprocessing.plot_correlated_bands.html#module-postprocessing.plot_correlated_bands"><code class="xref">postprocessing.plot_correlated_bands</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="pcap"><td></td><td>&#160;</td><td></td></tr>
+     <tr class="cap" id="cap-s"><td></td><td>
+       <strong>s</strong></td><td></td></tr>
+     <tr>
+       <td></td>
+       <td>
+       <a href="index.html#module-solid_dmft"><code class="xref">solid_dmft</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="pcap"><td></td><td>&#160;</td><td></td></tr>
+     <tr class="cap" id="cap-u"><td></td><td>
+       <strong>u</strong></td><td></td></tr>
+     <tr>
+       <td><img src="_static/minus.png" class="toggler"
+              id="toggle-5" style="display: none" alt="-" /></td>
+       <td>
+       <a href="_ref/util.html#module-util"><code class="xref">util</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-5">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="_ref/util.symmetrize_gamma_file.html#module-util.symmetrize_gamma_file"><code class="xref">util.symmetrize_gamma_file</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-5">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="_ref/util.write_kslice_to_h5.html#module-util.write_kslice_to_h5"><code class="xref">util.write_kslice_to_h5</code></a></td><td>
+       <em></em></td></tr>
+   </table>
+
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
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+    <p>&#169; Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.</p>
+  </div>
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diff --git a/search.html b/search.html
new file mode 100644
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+<!DOCTYPE html>
+<html class="writer-html5" lang="en" >
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Search &mdash; solid_dmft  documentation</title>
+      <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
+      <link rel="stylesheet" href="_static/css/custom.css" type="text/css" />
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+    <script src="_static/js/theme.js"></script>
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+    <script src="_static/language_data.js"></script>
+    <link rel="index" title="Index" href="genindex.html" />
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+            <a href="index.html" class="icon icon-home"> solid_dmft
+            <img src="_static/logo_no_text.png" class="logo" alt="Logo"/>
+          </a>
+              <div class="version">
+                3.2.0
+              </div>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="#" method="get">
+    <input type="text" name="q" placeholder="Search docs" />
+    <input type="hidden" name="check_keywords" value="yes" />
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+  </form>
+</div>
+        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+              <ul>
+<li class="toctree-l1"><a class="reference internal" href="install.html">Installation</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="install.html#prerequisites">Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="install.html#installation-via-pip">Installation via pip</a></li>
+<li class="toctree-l2"><a class="reference internal" href="install.html#manual-installation-via-cmake">Manual installation via CMake</a></li>
+<li class="toctree-l2"><a class="reference internal" href="install.html#docker-files-images">Docker files &amp; images</a></li>
+<li class="toctree-l2"><a class="reference internal" href="install.html#version-compatibility">Version compatibility</a></li>
+<li class="toctree-l2"><a class="reference internal" href="install.html#custom-cmake-options">Custom CMake options</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="documentation.html">Documentation</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="documentation.html#code-structure">Code structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="documentation.html#dft-interface-notes">DFT interface notes</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="md_notes/w90_interface.html">Wannier90 interface</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/w90_interface.html#orbital-order-in-the-w90-converter">orbital order in the W90 converter</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="md_notes/vasp_csc.html">Interface to VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/vasp_csc.html#general-remarks">General remarks</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="cRPA_VASP/README.html#file-description">file description</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cRPA_VASP/README.html#workflow">Workflow:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cRPA_VASP/README.html#important-flags">important flags:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cRPA_VASP/README.html#version-and-compilation">version and compilation:</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="documentation.html#input-output">Input/Output</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="input_output/DMFT_input/input.html">Input</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="input_output/DMFT_input/general.html">[general]: General parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="input_output/DMFT_input/solver.html">[solver]: solver specific parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="input_output/DMFT_input/dft.html">[dft]: DFT related inputs</a></li>
+<li class="toctree-l4"><a class="reference internal" href="input_output/DMFT_input/advanced.html">[advanced]: Advanced inputs</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="input_output/DMFT_output/results.html">Output / results</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="input_output/DMFT_output/results.html#group-structure">Group structure</a></li>
+<li class="toctree-l4"><a class="reference internal" href="input_output/DMFT_output/results.html#subgroups">Subgroups</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="documentation.html#further-details-for-running">Further details for running</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="md_notes/docker.html">Docker</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/docker.html#building-the-docker-image">Building the docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/docker.html#pulling-a-docker-image">Pulling a docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="md_notes/run_locally.html">Run on your machine</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="documentation.html#module-reference-manual">Module reference manual</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="_ref/csc_flow.html">csc_flow</a></li>
+<li class="toctree-l3"><a class="reference internal" href="_ref/dft_managers.html">dft_managers</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dft_managers.mpi_helpers.html">dft_managers.mpi_helpers</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dft_managers.qe_manager.html">dft_managers.qe_manager</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dft_managers.vasp_manager.html">dft_managers.vasp_manager</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="_ref/dmft_cycle.html">dmft_cycle</a></li>
+<li class="toctree-l3"><a class="reference internal" href="_ref/dmft_tools.html">dmft_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.afm_mapping.html">dmft_tools.afm_mapping</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.convergence.html">dmft_tools.convergence</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.formatter.html">dmft_tools.formatter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.greens_functions_mixer.html">dmft_tools.greens_functions_mixer</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.initial_self_energies.html">dmft_tools.initial_self_energies</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.interaction_hamiltonian.html">dmft_tools.interaction_hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.legendre_filter.html">dmft_tools.legendre_filter</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.manipulate_chemical_potential.html">dmft_tools.manipulate_chemical_potential</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.matheval.html">dmft_tools.matheval</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.observables.html">dmft_tools.observables</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.results_to_archive.html">dmft_tools.results_to_archive</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.solver.html">dmft_tools.solver</a></li>
+</ul>
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+<li class="toctree-l3"><a class="reference internal" href="_ref/io_tools.html">io_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="_ref/io_tools.dict_to_h5.html">io_tools.dict_to_h5</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/io_tools.postproc_toml_dict.html">io_tools.postproc_toml_dict</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/io_tools.verify_input_params.html">io_tools.verify_input_params</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="_ref/postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="_ref/postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/postprocessing.eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/postprocessing.maxent_gf_imp.html">postprocessing.maxent_gf_imp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/postprocessing.maxent_gf_latt.html">postprocessing.maxent_gf_latt</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/postprocessing.maxent_sigma.html">postprocessing.maxent_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="_ref/util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="_ref/util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="issues.html">Support &amp; contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="issues.html#seeking-help">Seeking help</a></li>
+<li class="toctree-l2"><a class="reference internal" href="issues.html#improving-solid-dmft">Improving solid_dmft</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="issues.html#reporting-issues">Reporting issues</a></li>
+</ul>
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ft_tools.matheval.MathExpr.allowed_nodes","dmft_tools.matheval.MathExpr.functions","dmft_tools.observables","dmft_tools.results_to_archive","dmft_tools.solver","dmft_tools.solver.SolverStructure","dmft_tools.solver.SolverStructure.__init__","dmft_tools.solver.SolverStructure.solve","io_tools","io_tools.dict_to_h5","io_tools.postproc_toml_dict","io_tools.verify_input_params","postprocessing","postprocessing.eval_U_cRPA_RESPACK","postprocessing.eval_U_cRPA_RESPACK.respack_data","postprocessing.eval_U_cRPA_RESPACK.respack_data.__init__","postprocessing.eval_U_cRPA_Vasp","postprocessing.maxent_gf_imp","postprocessing.maxent_gf_latt","postprocessing.maxent_sigma","postprocessing.pade_sigma","postprocessing.plot_correlated_bands","util","util.symmetrize_gamma_file","util.write_kslice_to_h5","How to do cRPA calculations with VASP","Documentation","solid_dmft","[advanced]: Advanced inputs","[dft]: DFT related inputs","[general]: General parameters","Input","[solver]: solver specific parameters","Iterations","Observables/convergence_obs","Output / results","Installation","Support &amp; contribute","Docker","Running solid_dmft on a cluster","Run on your machine","Interface to VASP","Wannier90 interface","Tutorials","3. CSC with QE/W90 and HubbardI: total energy in Ce2O3","4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2","2. CSC with VASP PLOs: charge order in PrNiO3","1. OS with QE/W90 and cthyb: SrVO3 MIT","5. Plotting the spectral 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+<li class="toctree-l4"><a class="reference internal" href="md_notes/w90_interface.html#orbital-order-in-the-w90-converter">orbital order in the W90 converter</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
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+</li>
+<li class="toctree-l3"><a class="reference internal" href="cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="cRPA_VASP/README.html#file-description">file description</a></li>
+<li class="toctree-l4"><a class="reference internal" href="cRPA_VASP/README.html#workflow">Workflow:</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="documentation.html#input-output">Input/Output</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="input_output/DMFT_input/input.html">Input</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="input_output/DMFT_input/general.html">[general]: General parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="input_output/DMFT_input/solver.html">[solver]: solver specific parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="input_output/DMFT_input/dft.html">[dft]: DFT related inputs</a></li>
+<li class="toctree-l4"><a class="reference internal" href="input_output/DMFT_input/advanced.html">[advanced]: Advanced inputs</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="documentation.html#further-details-for-running">Further details for running</a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
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+</li>
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+<li class="toctree-l2"><a class="reference internal" href="documentation.html#module-reference-manual">Module reference manual</a><ul>
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+</li>
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+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.matheval.html">dmft_tools.matheval</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.observables.html">dmft_tools.observables</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.results_to_archive.html">dmft_tools.results_to_archive</a></li>
+<li class="toctree-l4"><a class="reference internal" href="_ref/dmft_tools.solver.html">dmft_tools.solver</a></li>
+</ul>
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+<li class="toctree-l3"><a class="reference internal" href="_ref/io_tools.html">io_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="_ref/io_tools.dict_to_h5.html">io_tools.dict_to_h5</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="_ref/io_tools.verify_input_params.html">io_tools.verify_input_params</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="_ref/postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="_ref/postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="_ref/postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
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+<li class="toctree-l4"><a class="reference internal" href="_ref/util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
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+</ul>
+</li>
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+</li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="issues.html">Support &amp; contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="issues.html#seeking-help">Seeking help</a></li>
+<li class="toctree-l2"><a class="reference internal" href="issues.html#improving-solid-dmft">Improving solid_dmft</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="issues.html#reporting-issues">Reporting issues</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="ChangeLog.html">Changelog</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#version-3-2-0">Version 3.2.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#general">General</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#fix">fix</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#build">build</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#feat">feat</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#doc">doc</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#test">test</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#version-3-1-5">Version 3.1.5</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#version-3-1-4">Version 3.1.4</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#version-3-1-3">Version 3.1.3</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#version-3-1-2">Version 3.1.2</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#version-3-1-1">Version 3.1.1</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#version-3-1-0">Version 3.1.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ChangeLog.html#version-3-0-0">Version 3.0.0</a></li>
+</ul>
+</li>
+</ul>
+
+        </div>
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu"  style="background: #7E588A" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="index.html">solid_dmft</a>
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+
+      <div class="wy-nav-content">
+        <div class="rst-content">
+          <div role="navigation" aria-label="Page navigation">
+  <ul class="wy-breadcrumbs">
+      <li><a href="index.html" class="icon icon-home"></a> &raquo;</li>
+      <li>Tutorials</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="_sources/tutorials.rst.txt" rel="nofollow"> View page source</a>
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+  </ul>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  
+<style>
+/* CSS overrides for sphinx_rtd_theme */
+
+/* 24px margin */
+.nbinput.nblast.container,
+.nboutput.nblast.container {
+    margin-bottom: 19px;  /* padding has already 5px */
+}
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+/* ... except between code cells! */
+.nblast.container + .nbinput.container {
+    margin-top: -19px;
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+</style>
+<div class="section" id="tutorials">
+<span id="id1"></span><h1>Tutorials<a class="headerlink" href="#tutorials" title="Permalink to this headline"></a></h1>
+<p>These tutorials provide an overview about typical workflows to perform DFT+DMFT calculations with solid_dmft. The tutorials are sorted by complexity and introduce one after another more available features.</p>
+<div class="admonition note">
+<p class="first admonition-title">Note</p>
+<p class="last">The tutorials are run with the 3.1.x branch of triqs. Please use the 3.1.x branch for triqs and all applications to reproduce the results shown here.</p>
+</div>
+<p>Short description of the tutorials linked below:</p>
+<ol class="arabic simple">
+<li>Typical one-shot (OS) DMFT calculation based on prepared hdf5 archive for SrVO3</li>
+<li>Full charge self-consistent (CSC) DFT+DMFT calculation using the PLO formalism with Vasp for PrNiO3</li>
+<li>Full CSC DFT+DMFT calculation using w90 in combination with Quantum Espresso utilizing the lighter HubbardI solver</li>
+<li>OS magnetic DMFT calculation for NdNiO2 in a large energy window for 5 d orbitals</li>
+<li>Postprocessing: plot the spectral function after a DFT+DMFT calculation</li>
+</ol>
+<hr class="docutils" />
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="tutorials/NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
+</li>
+</ul>
+</div>
+</div>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="_ref/util.write_kslice_to_h5.html" class="btn btn-neutral float-left" title="util.write_kslice_to_h5" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="tutorials/SVO_os_qe/tutorial.html" class="btn btn-neutral float-right" title="1. OS with QE/W90 and cthyb: SrVO3 MIT" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+    </div>
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+  <div role="contentinfo">
+    <p>&#169; Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.</p>
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\ No newline at end of file
diff --git a/tutorials/Ce2O3_csc_w90/dft_input/ce2o3.inp b/tutorials/Ce2O3_csc_w90/dft_input/ce2o3.inp
new file mode 100644
index 00000000..8628e633
--- /dev/null
+++ b/tutorials/Ce2O3_csc_w90/dft_input/ce2o3.inp
@@ -0,0 +1,5 @@
+ 0   4 4 3
+ 2.
+  2
+ 0  0  3  7  0  0
+ 1  0  3  7  0  0
diff --git a/tutorials/Ce2O3_csc_w90/dft_input/ce2o3.mod_scf.in b/tutorials/Ce2O3_csc_w90/dft_input/ce2o3.mod_scf.in
new file mode 100644
index 00000000..caf85adc
--- /dev/null
+++ b/tutorials/Ce2O3_csc_w90/dft_input/ce2o3.mod_scf.in
@@ -0,0 +1,100 @@
+&control
+   calculation        = 'scf',
+   restart_mode       = 'restart',
+   wf_collect         = .false.,
+   prefix             = 'ce2o3_soliddmft', 
+   tstress            = .true., 
+   tprnfor            = .true., 
+   pseudo_dir         = 'pseudo/',
+   outdir             = 'QE_tmp/',
+/ 
+&system 
+   ibrav              = 0,
+   celldm(1)          = 7.3510340956,
+   nat                = 5,
+   ntyp               = 2,
+   ecutwfc            = 70.0,
+   ecutrho            = 840.0,
+   occupations        = 'smearing',
+   degauss            = 0.01,
+   smearing           = 'm-p',
+   nbnd               = 50
+   dmft               = .true.
+   dmft_prefix        = 'ce2o3',
+   nosym              = .true.
+/ 
+&electrons 
+   electron_maxstep   = 1,
+   conv_thr           = 1.0d-8,
+   mixing_beta        = 0.3,
+   mixing_mode        = 'local-TF'
+   startingpot        = 'file',
+   startingwfc        = 'file',
+/
+
+ATOMIC_SPECIES 
+   Ce	140.116		  Ce.pbe-spdfn-rrkjus_psl.1.0.0.UPF
+   O    15.9994       o_pbe_v1.2.uspp.F.UPF
+
+CELL_PARAMETERS {alat}
+     1.0000000000   0.0000000000   0.0000000000
+    -0.5000000000   0.8660254038   0.0000000000
+     0.0000000000   0.0000000000   1.5574077247
+
+ATOMIC_POSITIONS (crystal)
+Ce      0.66666700000000      0.33333300000000      0.75412000000000
+Ce      0.33333300000000      0.66666700000000      0.24588000000000
+ O      0.66666700000000      0.33333300000000      0.35741800000000
+ O      0.33333300000000      0.66666700000000      0.64258200000000
+ O      0.00000000000000      0.00000000000000      0.00000000000000
+
+K_POINTS crystal
+48
+  0.00000000  0.00000000  0.00000000  2.083333e-02
+  0.00000000  0.00000000  0.33333333  2.083333e-02
+  0.00000000  0.00000000  0.66666667  2.083333e-02
+  0.00000000  0.25000000  0.00000000  2.083333e-02
+  0.00000000  0.25000000  0.33333333  2.083333e-02
+  0.00000000  0.25000000  0.66666667  2.083333e-02
+  0.00000000  0.50000000  0.00000000  2.083333e-02
+  0.00000000  0.50000000  0.33333333  2.083333e-02
+  0.00000000  0.50000000  0.66666667  2.083333e-02
+  0.00000000  0.75000000  0.00000000  2.083333e-02
+  0.00000000  0.75000000  0.33333333  2.083333e-02
+  0.00000000  0.75000000  0.66666667  2.083333e-02
+  0.25000000  0.00000000  0.00000000  2.083333e-02
+  0.25000000  0.00000000  0.33333333  2.083333e-02
+  0.25000000  0.00000000  0.66666667  2.083333e-02
+  0.25000000  0.25000000  0.00000000  2.083333e-02
+  0.25000000  0.25000000  0.33333333  2.083333e-02
+  0.25000000  0.25000000  0.66666667  2.083333e-02
+  0.25000000  0.50000000  0.00000000  2.083333e-02
+  0.25000000  0.50000000  0.33333333  2.083333e-02
+  0.25000000  0.50000000  0.66666667  2.083333e-02
+  0.25000000  0.75000000  0.00000000  2.083333e-02
+  0.25000000  0.75000000  0.33333333  2.083333e-02
+  0.25000000  0.75000000  0.66666667  2.083333e-02
+  0.50000000  0.00000000  0.00000000  2.083333e-02
+  0.50000000  0.00000000  0.33333333  2.083333e-02
+  0.50000000  0.00000000  0.66666667  2.083333e-02
+  0.50000000  0.25000000  0.00000000  2.083333e-02
+  0.50000000  0.25000000  0.33333333  2.083333e-02
+  0.50000000  0.25000000  0.66666667  2.083333e-02
+  0.50000000  0.50000000  0.00000000  2.083333e-02
+  0.50000000  0.50000000  0.33333333  2.083333e-02
+  0.50000000  0.50000000  0.66666667  2.083333e-02
+  0.50000000  0.75000000  0.00000000  2.083333e-02
+  0.50000000  0.75000000  0.33333333  2.083333e-02
+  0.50000000  0.75000000  0.66666667  2.083333e-02
+  0.75000000  0.00000000  0.00000000  2.083333e-02
+  0.75000000  0.00000000  0.33333333  2.083333e-02
+  0.75000000  0.00000000  0.66666667  2.083333e-02
+  0.75000000  0.25000000  0.00000000  2.083333e-02
+  0.75000000  0.25000000  0.33333333  2.083333e-02
+  0.75000000  0.25000000  0.66666667  2.083333e-02
+  0.75000000  0.50000000  0.00000000  2.083333e-02
+  0.75000000  0.50000000  0.33333333  2.083333e-02
+  0.75000000  0.50000000  0.66666667  2.083333e-02
+  0.75000000  0.75000000  0.00000000  2.083333e-02
+  0.75000000  0.75000000  0.33333333  2.083333e-02
+  0.75000000  0.75000000  0.66666667  2.083333e-02
diff --git a/tutorials/Ce2O3_csc_w90/dft_input/ce2o3.nscf.in b/tutorials/Ce2O3_csc_w90/dft_input/ce2o3.nscf.in
new file mode 100644
index 00000000..579094ab
--- /dev/null
+++ b/tutorials/Ce2O3_csc_w90/dft_input/ce2o3.nscf.in
@@ -0,0 +1,97 @@
+&control
+   calculation        = 'nscf',
+   wf_collect         = .true.,
+   prefix             = 'ce2o3_soliddmft', 
+   tstress            = .true., 
+   tprnfor            = .true., 
+   verbosity          = 'high',
+   pseudo_dir         = 'pseudo/',
+   outdir             = 'QE_tmp/',
+/ 
+&system 
+   ibrav              = 0,
+   celldm(1)          = 7.3510340956,
+   nat                = 5,
+   ntyp               = 2,
+   ecutwfc            = 70.0,
+   ecutrho            = 840.0,
+   occupations        = 'smearing',
+   degauss            = 0.01,
+   smearing           = 'm-p',
+   nbnd               = 50,
+   dmft               = .true.,
+   nosym              = .true.
+/ 
+&electrons 
+   conv_thr           = 1.0d-6,
+   mixing_beta        = 0.7,
+   mixing_mode        = 'local-TF'
+   startingpot        = 'file',
+/
+
+ATOMIC_SPECIES 
+   Ce	140.116		  Ce.pbe-spdfn-rrkjus_psl.1.0.0.UPF
+   O    15.9994       o_pbe_v1.2.uspp.F.UPF
+
+CELL_PARAMETERS {alat}
+     1.0000000000   0.0000000000   0.0000000000
+    -0.5000000000   0.8660254038   0.0000000000
+     0.0000000000   0.0000000000   1.5574077247
+
+ATOMIC_POSITIONS (crystal)
+Ce      0.66666700000000      0.33333300000000      0.75412000000000
+Ce      0.33333300000000      0.66666700000000      0.24588000000000
+ O      0.66666700000000      0.33333300000000      0.35741800000000
+ O      0.33333300000000      0.66666700000000      0.64258200000000
+ O      0.00000000000000      0.00000000000000      0.00000000000000
+
+K_POINTS crystal
+48
+  0.00000000  0.00000000  0.00000000  2.083333e-02
+  0.00000000  0.00000000  0.33333333  2.083333e-02
+  0.00000000  0.00000000  0.66666667  2.083333e-02
+  0.00000000  0.25000000  0.00000000  2.083333e-02
+  0.00000000  0.25000000  0.33333333  2.083333e-02
+  0.00000000  0.25000000  0.66666667  2.083333e-02
+  0.00000000  0.50000000  0.00000000  2.083333e-02
+  0.00000000  0.50000000  0.33333333  2.083333e-02
+  0.00000000  0.50000000  0.66666667  2.083333e-02
+  0.00000000  0.75000000  0.00000000  2.083333e-02
+  0.00000000  0.75000000  0.33333333  2.083333e-02
+  0.00000000  0.75000000  0.66666667  2.083333e-02
+  0.25000000  0.00000000  0.00000000  2.083333e-02
+  0.25000000  0.00000000  0.33333333  2.083333e-02
+  0.25000000  0.00000000  0.66666667  2.083333e-02
+  0.25000000  0.25000000  0.00000000  2.083333e-02
+  0.25000000  0.25000000  0.33333333  2.083333e-02
+  0.25000000  0.25000000  0.66666667  2.083333e-02
+  0.25000000  0.50000000  0.00000000  2.083333e-02
+  0.25000000  0.50000000  0.33333333  2.083333e-02
+  0.25000000  0.50000000  0.66666667  2.083333e-02
+  0.25000000  0.75000000  0.00000000  2.083333e-02
+  0.25000000  0.75000000  0.33333333  2.083333e-02
+  0.25000000  0.75000000  0.66666667  2.083333e-02
+  0.50000000  0.00000000  0.00000000  2.083333e-02
+  0.50000000  0.00000000  0.33333333  2.083333e-02
+  0.50000000  0.00000000  0.66666667  2.083333e-02
+  0.50000000  0.25000000  0.00000000  2.083333e-02
+  0.50000000  0.25000000  0.33333333  2.083333e-02
+  0.50000000  0.25000000  0.66666667  2.083333e-02
+  0.50000000  0.50000000  0.00000000  2.083333e-02
+  0.50000000  0.50000000  0.33333333  2.083333e-02
+  0.50000000  0.50000000  0.66666667  2.083333e-02
+  0.50000000  0.75000000  0.00000000  2.083333e-02
+  0.50000000  0.75000000  0.33333333  2.083333e-02
+  0.50000000  0.75000000  0.66666667  2.083333e-02
+  0.75000000  0.00000000  0.00000000  2.083333e-02
+  0.75000000  0.00000000  0.33333333  2.083333e-02
+  0.75000000  0.00000000  0.66666667  2.083333e-02
+  0.75000000  0.25000000  0.00000000  2.083333e-02
+  0.75000000  0.25000000  0.33333333  2.083333e-02
+  0.75000000  0.25000000  0.66666667  2.083333e-02
+  0.75000000  0.50000000  0.00000000  2.083333e-02
+  0.75000000  0.50000000  0.33333333  2.083333e-02
+  0.75000000  0.50000000  0.66666667  2.083333e-02
+  0.75000000  0.75000000  0.00000000  2.083333e-02
+  0.75000000  0.75000000  0.33333333  2.083333e-02
+  0.75000000  0.75000000  0.66666667  2.083333e-02
diff --git a/tutorials/Ce2O3_csc_w90/dft_input/ce2o3.pw2wan.in b/tutorials/Ce2O3_csc_w90/dft_input/ce2o3.pw2wan.in
new file mode 100644
index 00000000..185d7d0a
--- /dev/null
+++ b/tutorials/Ce2O3_csc_w90/dft_input/ce2o3.pw2wan.in
@@ -0,0 +1,9 @@
+&inputpp 
+   prefix       = 'ce2o3_soliddmft'
+   outdir       = 'QE_tmp/',
+   seedname     = 'ce2o3'
+   write_mmn    = .true.
+   write_amn    = .true.
+   write_spn    = .false.
+   write_unk    = .false.
+/
diff --git a/tutorials/Ce2O3_csc_w90/dft_input/ce2o3.scf.in b/tutorials/Ce2O3_csc_w90/dft_input/ce2o3.scf.in
new file mode 100644
index 00000000..72591ab6
--- /dev/null
+++ b/tutorials/Ce2O3_csc_w90/dft_input/ce2o3.scf.in
@@ -0,0 +1,45 @@
+&control
+   calculation        = 'scf',
+   restart_mode       = 'from_scratch',
+   wf_collect         = .false.,
+   prefix             = 'ce2o3_soliddmft', 
+   tstress            = .true., 
+   tprnfor            = .true., 
+   pseudo_dir         = 'pseudo/',
+   outdir             = 'QE_tmp/',
+/ 
+&system 
+   ibrav              = 0,
+   celldm(1)          = 7.3510340956,
+   nat                = 5,
+   ntyp               = 2,
+   ecutwfc            = 70.0,
+   ecutrho            = 840.0,
+   occupations        = 'smearing',
+   degauss            = 0.01,
+   smearing           = 'm-p',
+/ 
+&electrons 
+   conv_thr           = 1.0d-8,
+   mixing_beta        = 0.7,
+   mixing_mode        = 'local-TF'
+/
+
+ATOMIC_SPECIES 
+   Ce	140.116		  Ce.pbe-spdfn-rrkjus_psl.1.0.0.UPF
+   O    15.9994       o_pbe_v1.2.uspp.F.UPF
+
+CELL_PARAMETERS {alat}
+     1.0000000000   0.0000000000   0.0000000000
+    -0.5000000000   0.8660254038   0.0000000000
+     0.0000000000   0.0000000000   1.5574077247
+
+ATOMIC_POSITIONS crystal
+Ce      0.66666700000000      0.33333300000000      0.75412000000000
+Ce      0.33333300000000      0.66666700000000      0.24588000000000
+ O      0.66666700000000      0.33333300000000      0.35741800000000
+ O      0.33333300000000      0.66666700000000      0.64258200000000
+ O      0.00000000000000      0.00000000000000      0.00000000000000
+
+K_POINTS automatic
+  4 4 3 0 0 0 
diff --git a/tutorials/Ce2O3_csc_w90/dft_input/ce2o3.win b/tutorials/Ce2O3_csc_w90/dft_input/ce2o3.win
new file mode 100644
index 00000000..05f5d8a4
--- /dev/null
+++ b/tutorials/Ce2O3_csc_w90/dft_input/ce2o3.win
@@ -0,0 +1,147 @@
+begin unit_cell_cart
+bohr
+7.3510341   0.0000000  0.0000000
+-3.6755170  6.3661823  0.0000000
+0.0000000   0.0000000  11.4485573
+end unit_cell_cart
+
+begin atoms_frac
+Ce1      0.66666700000000      0.33333300000000      0.75412000000000
+Ce2      0.33333300000000      0.66666700000000      0.24588000000000
+ O      0.66666700000000      0.33333300000000      0.35741800000000
+ O      0.33333300000000      0.66666700000000      0.64258200000000
+ O      0.00000000000000      0.00000000000000      0.00000000000000
+end atoms_frac
+
+! system
+num_wann = 14
+num_bands = 14
+mp_grid 4 4 3
+
+! job control
+exclude_bands : 1-30, 45-50
+iprint = 2
+
+! plotting
+wannier_plot = false
+wannier_plot_supercell = 3
+bands_plot = true
+bands_num_points = 100
+bands_plot_format = gnuplot
+write_hr = true
+write_u_matrices = true
+
+! disentanglement
+!dis_win_min = 11.75
+!dis_win_max = 18.5
+!dis_froz_min = 15.1
+!dis_froz_max = 15.6
+dis_num_iter = 2000
+dis_conv_tol = 1.0E-12
+!dis_mix_ratio = 0.9
+
+! wannierisation
+num_iter = 0
+conv_window = 10
+conv_tol = 1e-12
+
+! pp tools
+kpath = true
+kpath_task = bands
+kpath_bands_colour = spin
+kpath_num_points=500
+begin kpoint_path
+G 0.0 0.0 0.0 M 0.5 0.0 0.0
+M 0.5 0.0 0.0 K 0.33 0.33 0.0
+K 0.33 0.33 0.0 G 0.0 0.0 0.0
+G 0.0 0.0 0.0 A 0.0 0.0 0.5
+A 0.0 0.0 0.5 L 0.5 0.0 0.5
+L 0.5 0.0 0.5 H 0.33 0.33 0.5
+H 0.33 0.33 0.5 A 0.0 0.0 0.5
+end kpoint_path
+
+kslice = true
+kslice_task = fermi_lines
+fermi_energy = 11.3244
+kslice_2dkmesh = 200 200
+kslice_corner =  0.00  0.00  0.00
+kslice_b1 =     -0.25  0.25  0.25
+kslice_b2 =      0.25 -0.25  0.25
+!kslice_fermi_lines_colour = spin
+
+dos = true
+dos_project = 3
+!dos_energy_min = 8.
+!dos_energy_max = 13.
+dos_kmesh = 50
+!dos_adpt_smr = false
+!dos_smr_type = gauss
+!dos_smr_fixed_en_width = 0.05
+
+begin projections
+Ce1:l=3,mr=4:x=1,0,0
+Ce1:l=3,mr=3:x=1,0,0
+Ce1:l=3,mr=5:x=1,0,0
+Ce1:l=3,mr=2:x=1,0,0
+Ce1:l=3,mr=6:x=1,0,0
+Ce1:l=3,mr=1:x=1,0,0
+Ce1:l=3,mr=7:x=1,0,0
+Ce2:l=3,mr=4:x=1,0,0
+Ce2:l=3,mr=3:x=1,0,0
+Ce2:l=3,mr=5:x=1,0,0
+Ce2:l=3,mr=2:x=1,0,0
+Ce2:l=3,mr=6:x=1,0,0
+Ce2:l=3,mr=1:x=1,0,0
+Ce2:l=3,mr=7:x=1,0,0
+end projections
+
+begin kpoints
+  0.00000000  0.00000000  0.00000000
+  0.00000000  0.00000000  0.33333333
+  0.00000000  0.00000000  0.66666667
+  0.00000000  0.25000000  0.00000000
+  0.00000000  0.25000000  0.33333333
+  0.00000000  0.25000000  0.66666667
+  0.00000000  0.50000000  0.00000000
+  0.00000000  0.50000000  0.33333333
+  0.00000000  0.50000000  0.66666667
+  0.00000000  0.75000000  0.00000000
+  0.00000000  0.75000000  0.33333333
+  0.00000000  0.75000000  0.66666667
+  0.25000000  0.00000000  0.00000000
+  0.25000000  0.00000000  0.33333333
+  0.25000000  0.00000000  0.66666667
+  0.25000000  0.25000000  0.00000000
+  0.25000000  0.25000000  0.33333333
+  0.25000000  0.25000000  0.66666667
+  0.25000000  0.50000000  0.00000000
+  0.25000000  0.50000000  0.33333333
+  0.25000000  0.50000000  0.66666667
+  0.25000000  0.75000000  0.00000000
+  0.25000000  0.75000000  0.33333333
+  0.25000000  0.75000000  0.66666667
+  0.50000000  0.00000000  0.00000000
+  0.50000000  0.00000000  0.33333333
+  0.50000000  0.00000000  0.66666667
+  0.50000000  0.25000000  0.00000000
+  0.50000000  0.25000000  0.33333333
+  0.50000000  0.25000000  0.66666667
+  0.50000000  0.50000000  0.00000000
+  0.50000000  0.50000000  0.33333333
+  0.50000000  0.50000000  0.66666667
+  0.50000000  0.75000000  0.00000000
+  0.50000000  0.75000000  0.33333333
+  0.50000000  0.75000000  0.66666667
+  0.75000000  0.00000000  0.00000000
+  0.75000000  0.00000000  0.33333333
+  0.75000000  0.00000000  0.66666667
+  0.75000000  0.25000000  0.00000000
+  0.75000000  0.25000000  0.33333333
+  0.75000000  0.25000000  0.66666667
+  0.75000000  0.50000000  0.00000000
+  0.75000000  0.50000000  0.33333333
+  0.75000000  0.50000000  0.66666667
+  0.75000000  0.75000000  0.00000000
+  0.75000000  0.75000000  0.33333333
+  0.75000000  0.75000000  0.66666667
+end kpoints
diff --git a/tutorials/Ce2O3_csc_w90/dmft_config.toml b/tutorials/Ce2O3_csc_w90/dmft_config.toml
new file mode 100644
index 00000000..23021830
--- /dev/null
+++ b/tutorials/Ce2O3_csc_w90/dmft_config.toml
@@ -0,0 +1,44 @@
+[general]
+seedname = "ce2o3"
+jobname = "b10-U6.46-J0.46"
+csc = true
+
+eta = 0.5
+n_iw = 100
+n_tau = 5001
+
+n_iter_dmft_first = 2
+n_iter_dmft_per = 1
+n_iter_dmft = 5
+
+block_threshold = 1e-03
+
+h_int_type = "density_density"
+U = 6.46
+J = 0.46
+beta = 10
+prec_mu = 0.1
+
+sigma_mix = 1.0
+g0_mix = 1.0
+dc_type = 0
+dc = true
+dc_dmft = true
+calc_energies = true
+
+h5_save_freq = 1
+
+[solver]
+type = "hubbardI"
+n_l = 15
+store_solver = false
+measure_G_l = false
+measure_density_matrix = true
+
+[dft]
+dft_code = "qe"
+n_cores = 10
+mpi_env = "default"
+projector_type = "w90"
+dft_exec = "pw.x"
+w90_tolerance = 1e-1
diff --git a/tutorials/Ce2O3_csc_w90/tutorial.html b/tutorials/Ce2O3_csc_w90/tutorial.html
new file mode 100644
index 00000000..0a818682
--- /dev/null
+++ b/tutorials/Ce2O3_csc_w90/tutorial.html
@@ -0,0 +1,979 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" >
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>3. CSC with QE/W90 and HubbardI: total energy in Ce2O3 &mdash; solid_dmft  documentation</title>
+      <link rel="stylesheet" href="../../_static/pygments.css" type="text/css" />
+      <link rel="stylesheet" href="../../_static/css/custom.css" type="text/css" />
+      <link rel="stylesheet" href="../../_static/plot_directive.css" type="text/css" />
+    <link rel="shortcut icon" href="../../_static/favicon.ico"/>
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+        <script data-url_root="../../" id="documentation_options" src="../../_static/documentation_options.js"></script>
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+        <script defer="defer" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/MathJax.js?config=default"></script>
+        <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+    <script src="../../_static/js/theme.js"></script>
+    <link rel="index" title="Index" href="../../genindex.html" />
+    <link rel="search" title="Search" href="../../search.html" />
+    <link rel="next" title="4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2" href="../NNO_os_plo_mag/tutorial.html" />
+    <link rel="prev" title="2. CSC with VASP PLOs: charge order in PrNiO3" href="../PrNiO3_csc_vasp_plo_cthyb/tutorial.html" /> 
+</head>
+
+<body class="wy-body-for-nav"> 
+  <div class="wy-grid-for-nav">
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+            <a href="../../index.html" class="icon icon-home"> solid_dmft
+            <img src="../../_static/logo_no_text.png" class="logo" alt="Logo"/>
+          </a>
+              <div class="version">
+                3.2.0
+              </div>
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+<li class="toctree-l2"><a class="reference internal" href="../../install.html#manual-installation-via-cmake">Manual installation via CMake</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../install.html#docker-files-images">Docker files &amp; images</a></li>
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+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../documentation.html">Documentation</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#code-structure">Code structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#dft-interface-notes">DFT interface notes</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/w90_interface.html">Wannier90 interface</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/w90_interface.html#orbital-order-in-the-w90-converter">orbital order in the W90 converter</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/vasp_csc.html">Interface to VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#general-remarks">General remarks</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#file-description">file description</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#workflow">Workflow:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#important-flags">important flags:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
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+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#input-output">Input/Output</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../input_output/DMFT_input/input.html">Input</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/general.html">[general]: General parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/solver.html">[solver]: solver specific parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/dft.html">[dft]: DFT related inputs</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/advanced.html">[advanced]: Advanced inputs</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_output/results.html#group-structure">Group structure</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_output/results.html#subgroups">Subgroups</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#further-details-for-running">Further details for running</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/docker.html">Docker</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#building-the-docker-image">Building the docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#pulling-a-docker-image">Pulling a docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_locally.html">Run on your machine</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#module-reference-manual">Module reference manual</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/csc_flow.html">csc_flow</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/dft_managers.html">dft_managers</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.mpi_helpers.html">dft_managers.mpi_helpers</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.vasp_manager.html">dft_managers.vasp_manager</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/dmft_cycle.html">dmft_cycle</a></li>
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+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_gf_latt.html">postprocessing.maxent_gf_latt</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_sigma.html">postprocessing.maxent_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../../tutorials.html">Tutorials</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../issues.html">Support &amp; contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../issues.html#seeking-help">Seeking help</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../issues.html#improving-solid-dmft">Improving solid_dmft</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../issues.html#reporting-issues">Reporting issues</a></li>
+</ul>
+</li>
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+<li class="toctree-l1"><a class="reference internal" href="../../ChangeLog.html">Changelog</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-2-0">Version 3.2.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#general">General</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-1-1">Version 3.1.1</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-1-0">Version 3.1.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-0-0">Version 3.0.0</a></li>
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+.ansi-blue-intense-fg { color: #0065CA; }
+.ansi-blue-intense-bg { background-color: #0065CA; }
+.ansi-magenta-fg { color: #D160C4; }
+.ansi-magenta-bg { background-color: #D160C4; }
+.ansi-magenta-intense-fg { color: #A03196; }
+.ansi-magenta-intense-bg { background-color: #A03196; }
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+.ansi-cyan-bg { background-color: #60C6C8; }
+.ansi-cyan-intense-fg { color: #258F8F; }
+.ansi-cyan-intense-bg { background-color: #258F8F; }
+.ansi-white-fg { color: #C5C1B4; }
+.ansi-white-bg { background-color: #C5C1B4; }
+.ansi-white-intense-fg { color: #A1A6B2; }
+.ansi-white-intense-bg { background-color: #A1A6B2; }
+
+.ansi-default-inverse-fg { color: #FFFFFF; }
+.ansi-default-inverse-bg { background-color: #000000; }
+
+.ansi-bold { font-weight: bold; }
+.ansi-underline { text-decoration: underline; }
+
+
+div.nbinput.container div.input_area div[class*=highlight] > pre,
+div.nboutput.container div.output_area div[class*=highlight] > pre,
+div.nboutput.container div.output_area div[class*=highlight].math,
+div.nboutput.container div.output_area.rendered_html,
+div.nboutput.container div.output_area > div.output_javascript,
+div.nboutput.container div.output_area:not(.rendered_html) > img{
+    padding: 5px;
+    margin: 0;
+}
+
+/* fix copybtn overflow problem in chromium (needed for 'sphinx_copybutton') */
+div.nbinput.container div.input_area > div[class^='highlight'],
+div.nboutput.container div.output_area > div[class^='highlight']{
+    overflow-y: hidden;
+}
+
+/* hide copybtn icon on prompts (needed for 'sphinx_copybutton') */
+.prompt a.copybtn {
+    display: none;
+}
+
+/* Some additional styling taken form the Jupyter notebook CSS */
+div.rendered_html table {
+  border: none;
+  border-collapse: collapse;
+  border-spacing: 0;
+  color: black;
+  font-size: 12px;
+  table-layout: fixed;
+}
+div.rendered_html thead {
+  border-bottom: 1px solid black;
+  vertical-align: bottom;
+}
+div.rendered_html tr,
+div.rendered_html th,
+div.rendered_html td {
+  text-align: right;
+  vertical-align: middle;
+  padding: 0.5em 0.5em;
+  line-height: normal;
+  white-space: normal;
+  max-width: none;
+  border: none;
+}
+div.rendered_html th {
+  font-weight: bold;
+}
+div.rendered_html tbody tr:nth-child(odd) {
+  background: #f5f5f5;
+}
+div.rendered_html tbody tr:hover {
+  background: rgba(66, 165, 245, 0.2);
+}
+
+/* CSS overrides for sphinx_rtd_theme */
+
+/* 24px margin */
+.nbinput.nblast.container,
+.nboutput.nblast.container {
+    margin-bottom: 19px;  /* padding has already 5px */
+}
+
+/* ... except between code cells! */
+.nblast.container + .nbinput.container {
+    margin-top: -19px;
+}
+
+.admonition > p:before {
+    margin-right: 4px;  /* make room for the exclamation icon */
+}
+
+/* Fix math alignment, see https://github.com/rtfd/sphinx_rtd_theme/pull/686 */
+.math {
+    text-align: unset;
+}
+</style>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">matplotlib.ticker</span> <span class="k">as</span> <span class="nn">ticker</span>
+
+<span class="kn">from</span> <span class="nn">triqs.gf</span> <span class="kn">import</span> <span class="o">*</span>
+<span class="kn">from</span> <span class="nn">h5</span> <span class="kn">import</span> <span class="n">HDFArchive</span>
+</pre></div>
+</div>
+</div>
+<div class="section" id="3.-CSC-with-QE/W90-and-HubbardI:-total-energy-in-Ce2O3">
+<h1>3. CSC with QE/W90 and HubbardI: total energy in Ce2O3<a class="headerlink" href="#3.-CSC-with-QE/W90-and-HubbardI:-total-energy-in-Ce2O3" title="Permalink to this headline"></a></h1>
+<p>Disclaimer:</p>
+<ul class="simple">
+<li>These can be heavy calculations. Current parameters won’t give converged solutions, but are simplified to deliver results on 10 cores in 10 minutes.</li>
+<li>The interaction values, results etc. might not be 100% physical and are only for demonstrative purposes!</li>
+</ul>
+<p>The goal of this tutorial is to demonstrate how to perform fully charge self-consistent DFT+DMFT calculations in solid_dmft using <a class="reference external" href="https://www.quantum-espresso.org/" rel="noopener noreferrer" target="_blank">Quantum Espresso</a> (QE) and <a class="reference external" href="http://www.wannier.org/" rel="noopener noreferrer" target="_blank">Wannier90</a> (W90) for the DFT electronic structure using the <a class="reference external" href="https://triqs.github.io/hubbardI/latest/index.html" rel="noopener noreferrer" target="_blank">HubbardI solver</a>.</p>
+<p>We will use Ce<span class="math notranslate nohighlight">\(_2\)</span>O<span class="math notranslate nohighlight">\(_3\)</span> as an example and compute the total energy for the <span class="math notranslate nohighlight">\(s=0\%\)</span> experimental ground state structure. To find the equilibrium structure in DFT+DMFT one then repeats these calculations variing the strain in DFT as was done in Fig. 7 of <a class="reference external" href="https://arxiv.org/abs/2111.10289.pdf" rel="noopener noreferrer" target="_blank">arxiv:2111.10289 (2021)</a>:</p>
+<p><img alt="drawing" class="no-scaled-link" src="../../_images/tenergy_ce2o3.png" style="width: 800px;" /></p>
+<p>In the case of Ce<span class="math notranslate nohighlight">\(_2\)</span>O<span class="math notranslate nohighlight">\(_3\)</span> it turns out that in fact DFT+DMFT predicts the same ground state as is found experimentally, while DFT underestimates, and DFT+DMFT in the one-shot approximation overestimates the lattice parameter, respectively.</p>
+<p>The tutorial will guide you through the following steps:</p>
+<ul class="simple">
+<li>perpare the input for the DFT and DMFT calculations using Quantum Espresso and Wannier90 and TRIQS</li>
+<li>run a charge self-consistent calculation for Ce<span class="math notranslate nohighlight">\(_2\)</span>O<span class="math notranslate nohighlight">\(_3\)</span></li>
+<li>analyse the change in the non-interacting part of the charge density using TRIQS</li>
+<li>analyse the convergence of the total energy and the DMFT self-consistency</li>
+</ul>
+<p>We set <code class="docutils literal notranslate"><span class="pre">path</span></code> variables to the reference files:</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="n">path</span> <span class="o">=</span> <span class="s1">&#39;./ref/&#39;</span>
+</pre></div>
+</div>
+</div>
+<div class="section" id="1.-Input-file-preparation">
+<h2>1. Input file preparation<a class="headerlink" href="#1.-Input-file-preparation" title="Permalink to this headline"></a></h2>
+<p>The primitive cell of Ce<span class="math notranslate nohighlight">\(_2\)</span>O<span class="math notranslate nohighlight">\(_3\)</span> contains 2 Ce atoms with 7 <span class="math notranslate nohighlight">\(f\)</span>-electrons each, so 14 in total. They are relatively flat, so there is no entanglement with any other band. We start from relaxed structure as usual. All files corresponding to this structure should be prepared and stored in a separate directory (<code class="docutils literal notranslate"><span class="pre">save/</span></code> in this case). For details please look at Section III in <a class="reference external" href="https://arxiv.org/abs/2111.10289.pdf" rel="noopener noreferrer" target="_blank">arxiv:2111.10289 (2021)</a>.</p>
+<div class="section" id="DFT-files">
+<h3>DFT files<a class="headerlink" href="#DFT-files" title="Permalink to this headline"></a></h3>
+<p>All input files are of the same kind as usual, unless stated otherwise:</p>
+<p>Quantum Espresso:</p>
+<ol class="arabic">
+<li><p class="first"><a class="reference external" href="./dft_input/ce2o3.scf.in" rel="noopener noreferrer" target="_blank">ce2o3.scf.in</a></p>
+</li>
+<li><p class="first"><a class="reference external" href="./dft_input/ce2o3.nscf.in" rel="noopener noreferrer" target="_blank">ce2o3.nscf.in</a></p>
+<ul class="simple">
+<li>explicit k-mesh</li>
+</ul>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>&amp;system
+    nosym            = .true.
+    dmft             = .true.
+</pre></div>
+</div>
+</li>
+<li><p class="first"><a class="reference external" href="./dft_input/ce2o3.mod_scf.in" rel="noopener noreferrer" target="_blank">ce2o3.mod_scf.in</a>: new!</p>
+<ul class="simple">
+<li>explicit k-mesh</li>
+</ul>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>&amp;system
+    nosym            = .true.
+    dmft             = .true.
+    dmft_prefix      = seedname
+&amp;electrons
+    electron_maxstep = 1
+    mixing_beta      = 0.3
+</pre></div>
+</div>
+</li>
+</ol>
+<p>Optionally:</p>
+<ul class="simple">
+<li><code class="docutils literal notranslate"><span class="pre">seedname.bnd.in</span></code></li>
+<li><code class="docutils literal notranslate"><span class="pre">seedname.bands.in</span></code></li>
+<li><code class="docutils literal notranslate"><span class="pre">seedname.proj.in</span></code></li>
+</ul>
+<p>Wannier90:</p>
+<ol class="arabic">
+<li><p class="first"><a class="reference external" href="./dft_input/ce2o3.win" rel="noopener noreferrer" target="_blank">ce2o3.win</a></p>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>write_u_matrices = .true.
+</pre></div>
+</div>
+</li>
+<li><p class="first"><a class="reference external" href="./dft_input/ce2o3.pw2wan.in" rel="noopener noreferrer" target="_blank">ce2o3.pw2wan.in</a></p>
+</li>
+</ol>
+</div>
+<div class="section" id="DMFT">
+<h3>DMFT<a class="headerlink" href="#DMFT" title="Permalink to this headline"></a></h3>
+<ol class="arabic simple">
+<li>Wannier90Converter: <a class="reference external" href="./dft_input/ce2o3.inp" rel="noopener noreferrer" target="_blank">ce2o3.inp</a></li>
+<li>solid_dmft: <a class="reference external" href="./dmft_config.toml" rel="noopener noreferrer" target="_blank">dmft_config.toml</a></li>
+</ol>
+<p>Here we’ll discuss the most important input flags for solid_dmft:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="o">!</span>cat ./dmft_config.toml
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+[general]
+seedname = &#34;ce2o3&#34;
+jobname = &#34;b10-U6.46-J0.46&#34;
+csc = true
+
+eta = 0.5
+n_iw = 100
+n_tau = 5001
+
+n_iter_dmft_first = 2
+n_iter_dmft_per = 1
+n_iter_dmft = 5
+
+block_threshold = 1e-03
+
+h_int_type = &#34;density_density&#34;
+U = 6.46
+J = 0.46
+beta = 10
+prec_mu = 0.1
+
+sigma_mix = 1.0
+g0_mix = 1.0
+dc_type = 0
+dc = true
+dc_dmft = true
+calc_energies = true
+
+h5_save_freq = 1
+
+[solver]
+type = &#34;hubbardI&#34;
+n_l = 15
+store_solver = false
+measure_G_l = false
+measure_density_matrix = true
+
+[dft]
+dft_code = &#34;qe&#34;
+n_cores = 10
+mpi_env = &#34;default&#34;
+projector_type = &#34;w90&#34;
+dft_exec = &#34;pw.x&#34;
+w90_tolerance = 1.e-1
+</pre></div></div>
+</div>
+<p>Of course you’ll have to switch <code class="docutils literal notranslate"><span class="pre">csc</span></code> on to perform the charge self-consistent calculations. Then we choose the HubbardI Solver, set the number of Legendre polynomials, Matsubara frequencies <span class="math notranslate nohighlight">\(i\omega_n\)</span> and imaginary time grid points <span class="math notranslate nohighlight">\(\tau\)</span>. In this calculation we perform five iterations in total, of which the two first ones are one-shot DMFT iterations, followed by three DFT and three DMFT steps. For the interaction Hamiltonian we use <code class="docutils literal notranslate"><span class="pre">density_density</span></code>. Note that you unlike the
+Kanamori Hamiltonian, this one is not rotationally invariant, so the correct order of the orbitals must be set (inspect the projections card in <code class="docutils literal notranslate"><span class="pre">ce2o3.win</span></code>). We must also use <code class="docutils literal notranslate"><span class="pre">dc_dmft</span></code> and <code class="docutils literal notranslate"><span class="pre">calc_energies</span></code>, since we are interested in total energies. Finally, we will specify some details for the DFT manager, i.e.&nbsp;to use QE, W90 and the tolerance for the mapping of shells. Note that this value should in general be <span class="math notranslate nohighlight">\(1e-6\)</span>, but for demonstration purposes we reduce it here. If <code class="docutils literal notranslate"><span class="pre">dft_exec</span></code>
+is empty, it will assume that <code class="docutils literal notranslate"><span class="pre">pw.x</span></code> and other QE executables are available.</p>
+</div>
+</div>
+<div class="section" id="2.-Running-DFT+DMFT">
+<h2>2. Running DFT+DMFT<a class="headerlink" href="#2.-Running-DFT+DMFT" title="Permalink to this headline"></a></h2>
+<p>Now that everything is set up, copy all files from <code class="docutils literal notranslate"><span class="pre">./dft_input</span></code> and start the calculation:</p>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>cp dft_input/* .
+mpirun solid_dmft &gt; dmft.out &amp;
+</pre></div>
+</div>
+<p>You will note that for each DFT step solid_dmft will append the filenames of the DFT Ouput with a unique identifier <code class="docutils literal notranslate"><span class="pre">_itXY</span></code>, where <code class="docutils literal notranslate"><span class="pre">XY</span></code> is the total iteration number. This allows the user to keep track of the changes within DFT. For the W90 <code class="docutils literal notranslate"><span class="pre">seedname.wout</span></code> and <code class="docutils literal notranslate"><span class="pre">seedname_hr.dat</span></code> files the seedname will be renamed to <code class="docutils literal notranslate"><span class="pre">seedname_itXY</span></code>. If the QE <code class="docutils literal notranslate"><span class="pre">seedname_bands.dat</span></code>, and <code class="docutils literal notranslate"><span class="pre">seedname_bands.proj</span></code> are present, they will be saved, too.</p>
+<p>You can check the output of the calculations while they are running, but since this might take a few minutes, we’ll analyse the results of the reference data in <code class="docutils literal notranslate"><span class="pre">/ref/ce2o3.h5</span></code>. You should check if the current calculation reproduces these results.</p>
+</div>
+<div class="section" id="3.-Non-interacting-Hamiltonian-and-convergence-analysis">
+<h2>3. Non-interacting Hamiltonian and convergence analysis<a class="headerlink" href="#3.-Non-interacting-Hamiltonian-and-convergence-analysis" title="Permalink to this headline"></a></h2>
+<div class="section" id="Tight-binding-Hamiltonian">
+<h3>Tight-binding Hamiltonian<a class="headerlink" href="#Tight-binding-Hamiltonian" title="Permalink to this headline"></a></h3>
+<p>Disclaimer: the bands shown here are only the non-interacting part of the charge density. Only the first iteration corresponds to a physical charge density, namely the Kohn-Sham ground state charge density.</p>
+<p>The first thing to check is whether the DFT Hamiltonian obtained from Wannier90 is correct. For this we use the tools available in <code class="docutils literal notranslate"><span class="pre">triqs.lattice.utils</span></code>. Let us first get the number of iterations and Fermi levels from DFT:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="n">e_fermi_run</span> <span class="o">=</span> <span class="o">!</span>grep <span class="s2">&quot;DFT Fermi energy&quot;</span> triqs.out
+<span class="n">e_fermi_run</span> <span class="o">=</span> <span class="p">[</span><span class="nb">float</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;DFT Fermi energy&#39;</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;eV&#39;</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">e_fermi_run</span><span class="p">]</span>
+<span class="n">n_iter_run</span> <span class="o">=</span> <span class="o">!</span>ls ce2o3_it*_hr.dat
+<span class="n">n_iter_run</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">([</span><span class="nb">int</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;_it&#39;</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;_&#39;</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">n_iter_run</span><span class="p">])</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Fermi levels: </span><span class="si">{</span><span class="n">e_fermi_run</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;iteration counts: </span><span class="si">{</span><span class="n">n_iter_run</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Fermi levels: [14.3557, 14.42, 14.4619, 14.495]
+iteration counts: [1, 3, 4, 5]
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">cm</span>
+<span class="kn">from</span> <span class="nn">triqs.lattice.utils</span> <span class="kn">import</span> <span class="n">TB_from_wannier90</span><span class="p">,</span> <span class="n">k_space_path</span>
+
+<span class="c1"># define a path in BZ</span>
+<span class="n">G</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span> <span class="mf">0.00</span><span class="p">,</span>  <span class="mf">0.00</span><span class="p">,</span>  <span class="mf">0.00</span><span class="p">])</span>
+<span class="n">M</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span> <span class="mf">0.50</span><span class="p">,</span>  <span class="mf">0.00</span><span class="p">,</span>  <span class="mf">0.00</span><span class="p">])</span>
+<span class="n">K</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span> <span class="mf">0.33</span><span class="p">,</span>  <span class="mf">0.33</span><span class="p">,</span>  <span class="mf">0.00</span><span class="p">])</span>
+<span class="n">A</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span> <span class="mf">0.00</span><span class="p">,</span>  <span class="mf">0.00</span><span class="p">,</span>  <span class="mf">0.50</span><span class="p">])</span>
+<span class="n">L</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span> <span class="mf">0.50</span><span class="p">,</span>  <span class="mf">0.00</span><span class="p">,</span>  <span class="mf">0.50</span><span class="p">])</span>
+<span class="n">H</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span> <span class="mf">0.33</span><span class="p">,</span>  <span class="mf">0.33</span><span class="p">,</span>  <span class="mf">0.50</span><span class="p">])</span>
+<span class="n">k_path</span> <span class="o">=</span> <span class="p">[(</span><span class="n">G</span><span class="p">,</span> <span class="n">M</span><span class="p">),</span> <span class="p">(</span><span class="n">M</span><span class="p">,</span> <span class="n">K</span><span class="p">),</span> <span class="p">(</span><span class="n">K</span><span class="p">,</span> <span class="n">G</span><span class="p">),</span> <span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">A</span><span class="p">),</span> <span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">L</span><span class="p">),</span> <span class="p">(</span><span class="n">L</span><span class="p">,</span> <span class="n">H</span><span class="p">),</span> <span class="p">(</span><span class="n">H</span><span class="p">,</span> <span class="n">A</span><span class="p">)]</span>
+<span class="n">n_bnd</span> <span class="o">=</span> <span class="mi">14</span>
+<span class="n">n_k</span> <span class="o">=</span> <span class="mi">20</span>
+
+<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span><span class="mi">2</span><span class="p">),</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">200</span><span class="p">)</span>
+
+<span class="k">for</span> <span class="p">(</span><span class="n">fermi</span><span class="p">,</span> <span class="n">n_iter</span><span class="p">,</span> <span class="n">cycle</span><span class="p">)</span> <span class="ow">in</span> <span class="p">[(</span><span class="n">e_fermi_run</span><span class="p">,</span> <span class="n">n_iter_run</span><span class="p">,</span> <span class="n">cm</span><span class="o">.</span><span class="n">RdYlBu</span><span class="p">)]:</span>
+
+    <span class="n">col_it</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">n_iter</span><span class="p">))</span>
+    <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">it</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">n_iter</span><span class="p">):</span>
+
+        <span class="c1"># compute TB model</span>
+        <span class="n">h_loc_add</span> <span class="o">=</span> <span class="o">-</span> <span class="n">fermi</span><span class="p">[</span><span class="n">ct</span><span class="p">]</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">n_bnd</span><span class="p">)</span> <span class="c1"># to center bands around 0</span>
+        <span class="n">tb</span> <span class="o">=</span> <span class="n">TB_from_wannier90</span><span class="p">(</span><span class="n">path</span><span class="o">=</span><span class="s1">&#39;./&#39;</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="sa">f</span><span class="s1">&#39;ce2o3_it</span><span class="si">{</span><span class="n">it</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">extend_to_spin</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">add_local</span><span class="o">=</span><span class="n">h_loc_add</span><span class="p">)</span>
+
+        <span class="c1"># compute dispersion on specified path</span>
+        <span class="n">k_vec</span><span class="p">,</span> <span class="n">k_1d</span><span class="p">,</span> <span class="n">special_k</span> <span class="o">=</span> <span class="n">k_space_path</span><span class="p">(</span><span class="n">k_path</span><span class="p">,</span> <span class="n">num</span><span class="o">=</span><span class="n">n_k</span><span class="p">,</span> <span class="n">bz</span><span class="o">=</span><span class="n">tb</span><span class="o">.</span><span class="n">bz</span><span class="p">)</span>
+        <span class="n">e_val</span> <span class="o">=</span> <span class="n">tb</span><span class="o">.</span><span class="n">dispersion</span><span class="p">(</span><span class="n">k_vec</span><span class="p">)</span>
+
+        <span class="c1"># plot</span>
+        <span class="k">for</span> <span class="n">band</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_bnd</span><span class="p">):</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">k_1d</span><span class="p">,</span> <span class="n">e_val</span><span class="p">[:,</span><span class="n">band</span><span class="p">]</span><span class="o">.</span><span class="n">real</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="n">cycle</span><span class="p">(</span><span class="n">col_it</span><span class="p">[</span><span class="n">ct</span><span class="p">]),</span> <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s1">&#39;it</span><span class="si">{</span><span class="n">it</span><span class="si">}</span><span class="s1">&#39;</span> <span class="k">if</span> <span class="n">band</span> <span class="o">==</span> <span class="mi">0</span> <span class="k">else</span> <span class="s1">&#39;&#39;</span><span class="p">)</span>
+
+
+<span class="n">ax</span><span class="o">.</span><span class="n">axhline</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span><span class="n">zorder</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span><span class="n">color</span><span class="o">=</span><span class="s1">&#39;gray&#39;</span><span class="p">,</span><span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span><span class="n">ls</span><span class="o">=</span><span class="s1">&#39;--&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="o">-</span><span class="mf">0.2</span><span class="p">,</span><span class="mf">0.8</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="n">zorder</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(</span><span class="n">special_k</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">([</span><span class="sa">r</span><span class="s1">&#39;$\Gamma$&#39;</span><span class="p">,</span> <span class="s1">&#39;M&#39;</span><span class="p">,</span> <span class="s1">&#39;K&#39;</span><span class="p">,</span> <span class="sa">r</span><span class="s1">&#39;$\Gamma$&#39;</span><span class="p">,</span> <span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="s1">&#39;L&#39;</span><span class="p">,</span> <span class="s1">&#39;H&#39;</span><span class="p">,</span> <span class="s1">&#39;A&#39;</span><span class="p">])</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">([</span><span class="n">special_k</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">special_k</span><span class="o">.</span><span class="n">max</span><span class="p">()])</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$\omega$ (eV)&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">fontsize</span><span class="o">=</span><span class="s1">&#39;small&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Warning: could not identify MPI environment!
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+Starting serial run at: 2022-03-25 12:42:36.663824
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;matplotlib.legend.Legend at 0x7f4a21de2160&gt;
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/tutorials_Ce2O3_csc_w90_tutorial_11_3.png" src="../../_images/tutorials_Ce2O3_csc_w90_tutorial_11_3.png" />
+</div>
+</div>
+<p>Note that since this is an isolated set of bands, we don’t have to worry about the disentanglement window here. Pay attention if you do need to use disentanglement though, and make sure that the configuration of Wannier90 works throughout the calculation!</p>
+<p>You see that one of the effects of charge self-consistency is the modificiation of the non-interacting bandstructure. The current results are far from converged, so make sure to carefully go through convergence tests as usual if you want reliable results. The figure below shows the difference to the reference data, which is quite substantial already at the DFT level.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span><span class="mi">2</span><span class="p">),</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">200</span><span class="p">)</span>
+
+<span class="n">e_fermi_ref</span> <span class="o">=</span> <span class="p">[</span><span class="mf">14.7437</span><span class="p">]</span>
+<span class="k">for</span> <span class="p">(</span><span class="n">fermi</span><span class="p">,</span> <span class="n">n_iter</span><span class="p">,</span> <span class="n">path_w90</span><span class="p">,</span> <span class="n">cycle</span><span class="p">,</span> <span class="n">label</span><span class="p">)</span> <span class="ow">in</span> <span class="p">[(</span><span class="n">e_fermi_ref</span><span class="p">,</span> <span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">path</span><span class="p">,</span> <span class="n">cm</span><span class="o">.</span><span class="n">GnBu_r</span><span class="p">,</span> <span class="s1">&#39;reference&#39;</span><span class="p">),</span> <span class="p">(</span><span class="n">e_fermi_run</span><span class="p">,</span> <span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="s1">&#39;./&#39;</span><span class="p">,</span> <span class="n">cm</span><span class="o">.</span><span class="n">RdYlBu</span><span class="p">,</span> <span class="s1">&#39;run&#39;</span><span class="p">)]:</span>
+
+    <span class="n">col_it</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">n_iter</span><span class="p">))</span>
+    <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">it</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">n_iter</span><span class="p">):</span>
+
+        <span class="c1"># compute TB model</span>
+        <span class="n">h_loc_add</span> <span class="o">=</span> <span class="o">-</span> <span class="n">fermi</span><span class="p">[</span><span class="n">ct</span><span class="p">]</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">n_bnd</span><span class="p">)</span> <span class="c1"># to center bands around 0</span>
+        <span class="n">tb</span> <span class="o">=</span> <span class="n">TB_from_wannier90</span><span class="p">(</span><span class="n">path</span><span class="o">=</span><span class="n">path_w90</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="sa">f</span><span class="s1">&#39;ce2o3_it</span><span class="si">{</span><span class="n">it</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">extend_to_spin</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">add_local</span><span class="o">=</span><span class="n">h_loc_add</span><span class="p">)</span>
+
+        <span class="c1"># compute dispersion on specified path</span>
+        <span class="n">k_vec</span><span class="p">,</span> <span class="n">k_1d</span><span class="p">,</span> <span class="n">special_k</span> <span class="o">=</span> <span class="n">k_space_path</span><span class="p">(</span><span class="n">k_path</span><span class="p">,</span> <span class="n">num</span><span class="o">=</span><span class="n">n_k</span><span class="p">,</span> <span class="n">bz</span><span class="o">=</span><span class="n">tb</span><span class="o">.</span><span class="n">bz</span><span class="p">)</span>
+        <span class="n">e_val</span> <span class="o">=</span> <span class="n">tb</span><span class="o">.</span><span class="n">dispersion</span><span class="p">(</span><span class="n">k_vec</span><span class="p">)</span>
+
+        <span class="c1"># plot</span>
+        <span class="k">for</span> <span class="n">band</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_bnd</span><span class="p">):</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">k_1d</span><span class="p">,</span> <span class="n">e_val</span><span class="p">[:,</span><span class="n">band</span><span class="p">]</span><span class="o">.</span><span class="n">real</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="n">cycle</span><span class="p">(</span><span class="n">col_it</span><span class="p">[</span><span class="n">ct</span><span class="p">]),</span> <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s1">&#39;it</span><span class="si">{</span><span class="n">it</span><span class="si">}</span><span class="s1"> - </span><span class="si">{</span><span class="n">label</span><span class="si">}</span><span class="s1">&#39;</span> <span class="k">if</span> <span class="n">band</span> <span class="o">==</span> <span class="mi">0</span> <span class="k">else</span> <span class="s1">&#39;&#39;</span><span class="p">)</span>
+
+
+<span class="n">ax</span><span class="o">.</span><span class="n">axhline</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span><span class="n">zorder</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span><span class="n">color</span><span class="o">=</span><span class="s1">&#39;gray&#39;</span><span class="p">,</span><span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span><span class="n">ls</span><span class="o">=</span><span class="s1">&#39;--&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="o">-</span><span class="mf">0.2</span><span class="p">,</span><span class="mf">0.8</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="n">zorder</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(</span><span class="n">special_k</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">([</span><span class="sa">r</span><span class="s1">&#39;$\Gamma$&#39;</span><span class="p">,</span> <span class="s1">&#39;M&#39;</span><span class="p">,</span> <span class="s1">&#39;K&#39;</span><span class="p">,</span> <span class="sa">r</span><span class="s1">&#39;$\Gamma$&#39;</span><span class="p">,</span> <span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="s1">&#39;L&#39;</span><span class="p">,</span> <span class="s1">&#39;H&#39;</span><span class="p">,</span> <span class="s1">&#39;A&#39;</span><span class="p">])</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">([</span><span class="n">special_k</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">special_k</span><span class="o">.</span><span class="n">max</span><span class="p">()])</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$\omega$ (eV)&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">fontsize</span><span class="o">=</span><span class="s1">&#39;small&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;matplotlib.legend.Legend at 0x7f49a4157c40&gt;
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/tutorials_Ce2O3_csc_w90_tutorial_13_1.png" src="../../_images/tutorials_Ce2O3_csc_w90_tutorial_13_1.png" />
+</div>
+</div>
+</div>
+<div class="section" id="Convergence">
+<h3>Convergence<a class="headerlink" href="#Convergence" title="Permalink to this headline"></a></h3>
+<p>To check the convergence of the impurity Green’s function and total energy you can look into the hdf5 Archive:</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="k">with</span> <span class="n">HDFArchive</span><span class="p">(</span><span class="s1">&#39;./ce2o3.h5&#39;</span><span class="p">,</span><span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">h5</span><span class="p">:</span>
+    <span class="n">observables</span> <span class="o">=</span> <span class="n">h5</span><span class="p">[</span><span class="s1">&#39;DMFT_results&#39;</span><span class="p">][</span><span class="s1">&#39;observables&#39;</span><span class="p">]</span>
+    <span class="n">convergence</span> <span class="o">=</span> <span class="n">h5</span><span class="p">[</span><span class="s1">&#39;DMFT_results&#39;</span><span class="p">][</span><span class="s1">&#39;convergence_obs&#39;</span><span class="p">]</span>
+
+<span class="k">with</span> <span class="n">HDFArchive</span><span class="p">(</span><span class="n">path</span> <span class="o">+</span> <span class="s1">&#39;ce2o3.h5&#39;</span><span class="p">,</span><span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">h5</span><span class="p">:</span>
+    <span class="n">ref_observables</span> <span class="o">=</span> <span class="n">h5</span><span class="p">[</span><span class="s1">&#39;DMFT_results&#39;</span><span class="p">][</span><span class="s1">&#39;observables&#39;</span><span class="p">]</span>
+    <span class="n">ref_convergence</span> <span class="o">=</span> <span class="n">h5</span><span class="p">[</span><span class="s1">&#39;DMFT_results&#39;</span><span class="p">][</span><span class="s1">&#39;convergence_obs&#39;</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">200</span><span class="p">)</span>
+
+<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ref_observables</span><span class="p">[</span><span class="s1">&#39;E_tot&#39;</span><span class="p">]</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">ref_observables</span><span class="p">[</span><span class="s1">&#39;E_tot&#39;</span><span class="p">]),</span> <span class="s1">&#39;x-&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;reference&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">observables</span><span class="p">[</span><span class="s1">&#39;E_tot&#39;</span><span class="p">]</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">observables</span><span class="p">[</span><span class="s1">&#39;E_tot&#39;</span><span class="p">]),</span> <span class="s1">&#39;x-&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;result&#39;</span><span class="p">)</span>
+
+<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ref_convergence</span><span class="p">[</span><span class="s1">&#39;d_G0&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="s1">&#39;x-&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;reference&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">convergence</span><span class="p">[</span><span class="s1">&#39;d_G0&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="s1">&#39;x-&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;result&#39;</span><span class="p">)</span>
+
+<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;total energy (eV)&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;convergence $G_0$&#39;</span><span class="p">)</span>
+
+<span class="k">for</span> <span class="n">it</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">):</span>
+    <span class="n">ax</span><span class="p">[</span><span class="n">it</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;# iteration&#39;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="p">[</span><span class="n">it</span><span class="p">]</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">set_major_locator</span><span class="p">(</span><span class="n">ticker</span><span class="o">.</span><span class="n">MultipleLocator</span><span class="p">(</span><span class="mi">5</span><span class="p">))</span>
+    <span class="n">ax</span><span class="p">[</span><span class="n">it</span><span class="p">]</span><span class="o">.</span><span class="n">grid</span><span class="p">()</span>
+    <span class="n">ax</span><span class="p">[</span><span class="n">it</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">fontsize</span><span class="o">=</span><span class="s1">&#39;small&#39;</span><span class="p">)</span>
+
+<span class="n">fig</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">wspace</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/tutorials_Ce2O3_csc_w90_tutorial_17_0.png" src="../../_images/tutorials_Ce2O3_csc_w90_tutorial_17_0.png" />
+</div>
+</div>
+<p>Note that the total energy jumps quite a bit in the first iteration and is constant for the first two (three) one-shot iterations in this run (the reference data) as expected. Since the HubbardI solver essentially yields DMFT-convergence after one iteration (you may try to confirm this), the total number of iterations necessary to achieve convergence is relatively low.</p>
+<p>This concludes the tutorial. The following is a list of things you can try next:</p>
+<ul class="simple">
+<li>improve the accuracy of the results by tuning the parameters until the results agree with the reference</li>
+<li>try to fnd the equilibrium lattice paramter by repeating the above calculation of the total energy for different cell volumes</li>
+</ul>
+</div>
+</div>
+</div>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="../PrNiO3_csc_vasp_plo_cthyb/tutorial.html" class="btn btn-neutral float-left" title="2. CSC with VASP PLOs: charge order in PrNiO3" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="../NNO_os_plo_mag/tutorial.html" class="btn btn-neutral float-right" title="4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+    </div>
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+  <hr/>
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+  <div role="contentinfo">
+    <p>&#169; Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.</p>
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diff --git a/tutorials/Ce2O3_csc_w90/tutorial.ipynb b/tutorials/Ce2O3_csc_w90/tutorial.ipynb
new file mode 100644
index 00000000..6e154533
--- /dev/null
+++ b/tutorials/Ce2O3_csc_w90/tutorial.ipynb
@@ -0,0 +1,530 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "1cc005bd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.ticker as ticker\n",
+    "\n",
+    "from triqs.gf import *\n",
+    "from h5 import HDFArchive"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f93f161b",
+   "metadata": {},
+   "source": [
+    "# 3. CSC with QE/W90 and HubbardI: total energy in Ce2O3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c1dbd052",
+   "metadata": {},
+   "source": [
+    "Disclaimer:\n",
+    "\n",
+    "* These can be heavy calculations. Current parameters won't give converged solutions, but are simplified to deliver results on 10 cores in 10 minutes.\n",
+    "* The interaction values, results etc. might not be 100% physical and are only for demonstrative purposes!\n",
+    "\n",
+    "The goal of this tutorial is to demonstrate how to perform fully charge self-consistent DFT+DMFT calculations in solid_dmft using [Quantum Espresso](https://www.quantum-espresso.org/) (QE) and [Wannier90](http://www.wannier.org/) (W90) for the DFT electronic structure using the [HubbardI solver](https://triqs.github.io/hubbardI/latest/index.html).\n",
+    "\n",
+    "We will use Ce$_2$O$_3$ as an example and compute the total energy for the $s=0\\%$ experimental ground state structure. To find the equilibrium structure in DFT+DMFT one then repeats these calculations variing the strain in DFT as was done in Fig. 7 of [arxiv:2111.10289 (2021)](https://arxiv.org/abs/2111.10289.pdf):\n",
+    "\n",
+    "<img src=\"./tenergy_ce2o3.png\" alt=\"drawing\" width=\"800\"/>\n",
+    "\n",
+    "In the case of Ce$_2$O$_3$ it turns out that in fact DFT+DMFT predicts the same ground state as is found experimentally, while DFT underestimates, and DFT+DMFT in the one-shot approximation overestimates the lattice parameter, respectively.\n",
+    "\n",
+    "The tutorial will guide you through the following steps: \n",
+    "\n",
+    "* perpare the input for the DFT and DMFT calculations using Quantum Espresso and Wannier90 and TRIQS\n",
+    "* run a charge self-consistent calculation for Ce$_2$O$_3$\n",
+    "* analyse the change in the non-interacting part of the charge density using TRIQS\n",
+    "* analyse the convergence of the total energy and the DMFT self-consistency\n",
+    "\n",
+    "We set `path` variables to the reference files:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "8681be23",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path = './ref/'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "10d286f9",
+   "metadata": {},
+   "source": [
+    "## 1. Input file preparation\n",
+    "\n",
+    "The primitive cell of Ce$_2$O$_3$ contains 2 Ce atoms with 7 $f$-electrons each, so 14 in total. They are relatively flat, so there is no entanglement with any other band.\n",
+    "We start from relaxed structure as usual. All files corresponding to this structure should be prepared and stored in a separate directory (`save/` in this case). For details please look at Section III in [arxiv:2111.10289 (2021)](https://arxiv.org/abs/2111.10289.pdf).\n",
+    "\n",
+    "### DFT files\n",
+    "\n",
+    "All input files are of the same kind as usual, unless stated otherwise:\n",
+    "\n",
+    "Quantum Espresso:\n",
+    "\n",
+    "1. [ce2o3.scf.in](./dft_input/ce2o3.scf.in)\n",
+    "2. [ce2o3.nscf.in](./dft_input/ce2o3.nscf.in)\n",
+    "\n",
+    "    - explicit k-mesh\n",
+    "    ```\n",
+    "    &system\n",
+    "        nosym            = .true.\n",
+    "        dmft             = .true.\n",
+    "    ```\n",
+    "3. [ce2o3.mod_scf.in](./dft_input/ce2o3.mod_scf.in): new!\n",
+    "\n",
+    "    - explicit k-mesh\n",
+    "    ```\n",
+    "    &system\n",
+    "        nosym            = .true.\n",
+    "        dmft             = .true.\n",
+    "        dmft_prefix      = seedname\n",
+    "    &electrons\n",
+    "        electron_maxstep = 1\n",
+    "        mixing_beta      = 0.3\n",
+    "    ```\n",
+    "\n",
+    "Optionally:\n",
+    "\n",
+    "- `seedname.bnd.in`\n",
+    "- `seedname.bands.in`\n",
+    "- `seedname.proj.in`\n",
+    "\n",
+    "Wannier90:\n",
+    "\n",
+    "1. [ce2o3.win](./dft_input/ce2o3.win)\n",
+    "\n",
+    "    ```\n",
+    "        write_u_matrices = .true.\n",
+    "    ```\n",
+    "2. [ce2o3.pw2wan.in](./dft_input/ce2o3.pw2wan.in)\n",
+    "\n",
+    "### DMFT\n",
+    "\n",
+    "1. Wannier90Converter: [ce2o3.inp](./dft_input/ce2o3.inp)\n",
+    "2. solid_dmft: [dmft_config.toml](./dmft_config.toml)\n",
+    "\n",
+    "Here we'll discuss the most important input flags for solid_dmft:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "165c087b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[general]\n",
+      "seedname = \"ce2o3\"\n",
+      "jobname = \"b10-U6.46-J0.46\"\n",
+      "csc = true\n",
+      "\n",
+      "eta = 0.5\n",
+      "n_iw = 100\n",
+      "n_tau = 5001\n",
+      "\n",
+      "n_iter_dmft_first = 2\n",
+      "n_iter_dmft_per = 1\n",
+      "n_iter_dmft = 5\n",
+      "\n",
+      "block_threshold = 1e-03\n",
+      "\n",
+      "h_int_type = \"density_density\"\n",
+      "U = 6.46\n",
+      "J = 0.46\n",
+      "beta = 10\n",
+      "prec_mu = 0.1\n",
+      "\n",
+      "sigma_mix = 1.0\n",
+      "g0_mix = 1.0\n",
+      "dc_type = 0\n",
+      "dc = true\n",
+      "dc_dmft = true\n",
+      "calc_energies = true\n",
+      "\n",
+      "h5_save_freq = 1\n",
+      "\n",
+      "[solver]\n",
+      "type = \"hubbardI\"\n",
+      "n_l = 15\n",
+      "store_solver = false\n",
+      "measure_G_l = false\n",
+      "measure_density_matrix = true\n",
+      "\n",
+      "[dft]\n",
+      "dft_code = \"qe\"\n",
+      "n_cores = 10\n",
+      "mpi_env = \"default\"\n",
+      "projector_type = \"w90\"\n",
+      "dft_exec = \"pw.x\"\n",
+      "w90_tolerance = 1.e-1\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./dmft_config.toml"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f970c47",
+   "metadata": {},
+   "source": [
+    "Of course you'll have to switch `csc` on to perform the charge self-consistent calculations. Then we choose the HubbardI Solver, set the number of Legendre polynomials, Matsubara frequencies $i\\omega_n$ and imaginary time grid points $\\tau$. In this calculation we perform five iterations in total, of which the two first ones are one-shot DMFT iterations, followed by three DFT and three DMFT steps.\n",
+    "For the interaction Hamiltonian we use `density_density`. Note that you unlike the Kanamori Hamiltonian, this one is not rotationally invariant, so the correct order of the orbitals must be set (inspect the projections card in `ce2o3.win`). We must also use `dc_dmft` and `calc_energies`, since we are interested in total energies.\n",
+    "Finally, we will specify some details for the DFT manager, i.e. to use QE, W90 and the tolerance for the mapping of shells. Note that this value should in general be $1e-6$, but for demonstration purposes we reduce it here. If `dft_exec` is empty, it will assume that `pw.x` and other QE executables are available."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "47bb27d5",
+   "metadata": {},
+   "source": [
+    "## 2. Running DFT+DMFT\n",
+    "\n",
+    "Now that everything is set up, copy all files from `./dft_input` and start the calculation:\n",
+    "```\n",
+    "cp dft_input/* .\n",
+    "mpirun solid_dmft > dmft.out &\n",
+    "```\n",
+    "\n",
+    "You will note that for each DFT step solid_dmft will append the filenames of the DFT Ouput with a unique identifier `_itXY`, where `XY` is the total iteration number. This allows the user to keep track of the changes within DFT. For the W90 `seedname.wout` and `seedname_hr.dat` files the seedname will be renamed to `seedname_itXY`. If the QE `seedname_bands.dat`, and `seedname_bands.proj` are present, they will be saved, too.\n",
+    "\n",
+    "You can check the output of the calculations while they are running, but since this might take a few minutes, we'll analyse the results of the reference data in `/ref/ce2o3.h5`. You should check if the current calculation reproduces these results."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c74f73cb",
+   "metadata": {},
+   "source": [
+    "## 3. Non-interacting Hamiltonian and convergence analysis\n",
+    "### Tight-binding Hamiltonian"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7f6d9a1",
+   "metadata": {},
+   "source": [
+    "Disclaimer: the bands shown here are only the non-interacting part of the charge density. Only the first iteration corresponds to a physical charge density, namely the Kohn-Sham ground state charge density.\n",
+    "\n",
+    "The first thing to check is whether the DFT Hamiltonian obtained from Wannier90 is correct. For this we use the tools available in `triqs.lattice.utils`.\n",
+    "Let us first get the number of iterations and Fermi levels from DFT:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "1f204686",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fermi levels: [14.3557, 14.42, 14.4619, 14.495]\n",
+      "iteration counts: [1, 3, 4, 5]\n"
+     ]
+    }
+   ],
+   "source": [
+    "e_fermi_run = !grep \"DFT Fermi energy\" triqs.out\n",
+    "e_fermi_run = [float(x.split('DFT Fermi energy')[1].split('eV')[0]) for x in e_fermi_run]\n",
+    "n_iter_run = !ls ce2o3_it*_hr.dat\n",
+    "n_iter_run = sorted([int(x.split('_it')[-1].split('_')[0]) for x in n_iter_run])\n",
+    "print(f'Fermi levels: {e_fermi_run}')\n",
+    "print(f'iteration counts: {n_iter_run}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "7fa4150b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warning: could not identify MPI environment!\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Starting serial run at: 2022-03-25 12:42:36.663824\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f4a21de2160>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from matplotlib import cm\n",
+    "from triqs.lattice.utils import TB_from_wannier90, k_space_path\n",
+    "\n",
+    "# define a path in BZ\n",
+    "G = np.array([ 0.00,  0.00,  0.00])\n",
+    "M = np.array([ 0.50,  0.00,  0.00])\n",
+    "K = np.array([ 0.33,  0.33,  0.00])\n",
+    "A = np.array([ 0.00,  0.00,  0.50])\n",
+    "L = np.array([ 0.50,  0.00,  0.50])\n",
+    "H = np.array([ 0.33,  0.33,  0.50])\n",
+    "k_path = [(G, M), (M, K), (K, G), (G, A), (A, L), (L, H), (H, A)]\n",
+    "n_bnd = 14\n",
+    "n_k = 20\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 1, figsize=(5,2), dpi=200)\n",
+    "\n",
+    "for (fermi, n_iter, cycle) in [(e_fermi_run, n_iter_run, cm.RdYlBu)]:\n",
+    "\n",
+    "    col_it = np.linspace(0, 1, len(n_iter))\n",
+    "    for ct, it in enumerate(n_iter):\n",
+    "\n",
+    "        # compute TB model\n",
+    "        h_loc_add = - fermi[ct] * np.eye(n_bnd) # to center bands around 0\n",
+    "        tb = TB_from_wannier90(path='./', seed=f'ce2o3_it{it}', extend_to_spin=False, add_local=h_loc_add)\n",
+    "\n",
+    "        # compute dispersion on specified path\n",
+    "        k_vec, k_1d, special_k = k_space_path(k_path, num=n_k, bz=tb.bz)\n",
+    "        e_val = tb.dispersion(k_vec)\n",
+    "\n",
+    "        # plot\n",
+    "        for band in range(n_bnd):\n",
+    "            ax.plot(k_1d, e_val[:,band].real, c=cycle(col_it[ct]), label=f'it{it}' if band == 0 else '')\n",
+    "\n",
+    "    \n",
+    "ax.axhline(y=0,zorder=2,color='gray',alpha=0.5,ls='--')\n",
+    "ax.set_ylim(-0.2,0.8)\n",
+    "ax.grid(zorder=0)\n",
+    "ax.set_xticks(special_k)\n",
+    "ax.set_xticklabels([r'$\\Gamma$', 'M', 'K', r'$\\Gamma$', 'A', 'L', 'H', 'A'])\n",
+    "ax.set_xlim([special_k.min(), special_k.max()])\n",
+    "ax.set_ylabel(r'$\\omega$ (eV)')\n",
+    "ax.legend(fontsize='small')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "45062ca5",
+   "metadata": {},
+   "source": [
+    "Note that since this is an isolated set of bands, we don't have to worry about the disentanglement window here. Pay attention if you do need to use disentanglement though, and make sure that the configuration of Wannier90 works throughout the calculation!\n",
+    "\n",
+    "You see that one of the effects of charge self-consistency is the modificiation of the non-interacting bandstructure. The current results are far from converged, so make sure to carefully go through convergence tests as usual if you want reliable results. The figure below shows the difference to the reference data, which is quite substantial already at the DFT level."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "a3d760e5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f49a4157c40>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 1, figsize=(5,2), dpi=200)\n",
+    "\n",
+    "e_fermi_ref = [14.7437]\n",
+    "for (fermi, n_iter, path_w90, cycle, label) in [(e_fermi_ref, [1], path, cm.GnBu_r, 'reference'), (e_fermi_run, [1], './', cm.RdYlBu, 'run')]:\n",
+    "\n",
+    "    col_it = np.linspace(0, 1, len(n_iter))\n",
+    "    for ct, it in enumerate(n_iter):\n",
+    "\n",
+    "        # compute TB model\n",
+    "        h_loc_add = - fermi[ct] * np.eye(n_bnd) # to center bands around 0\n",
+    "        tb = TB_from_wannier90(path=path_w90, seed=f'ce2o3_it{it}', extend_to_spin=False, add_local=h_loc_add)\n",
+    "\n",
+    "        # compute dispersion on specified path\n",
+    "        k_vec, k_1d, special_k = k_space_path(k_path, num=n_k, bz=tb.bz)\n",
+    "        e_val = tb.dispersion(k_vec)\n",
+    "\n",
+    "        # plot\n",
+    "        for band in range(n_bnd):\n",
+    "            ax.plot(k_1d, e_val[:,band].real, c=cycle(col_it[ct]), label=f'it{it} - {label}' if band == 0 else '')\n",
+    "\n",
+    "    \n",
+    "ax.axhline(y=0,zorder=2,color='gray',alpha=0.5,ls='--')\n",
+    "ax.set_ylim(-0.2,0.8)\n",
+    "ax.grid(zorder=0)\n",
+    "ax.set_xticks(special_k)\n",
+    "ax.set_xticklabels([r'$\\Gamma$', 'M', 'K', r'$\\Gamma$', 'A', 'L', 'H', 'A'])\n",
+    "ax.set_xlim([special_k.min(), special_k.max()])\n",
+    "ax.set_ylabel(r'$\\omega$ (eV)')\n",
+    "ax.legend(fontsize='small')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fcc962ef",
+   "metadata": {},
+   "source": [
+    "### Convergence"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "192ebb20",
+   "metadata": {},
+   "source": [
+    "To check the convergence of the impurity Green's function and total energy you can look into the hdf5 Archive:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "57fbd7ae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with HDFArchive('./ce2o3.h5','r') as h5:\n",
+    "    observables = h5['DMFT_results']['observables']\n",
+    "    convergence = h5['DMFT_results']['convergence_obs']\n",
+    "    \n",
+    "with HDFArchive(path + 'ce2o3.h5','r') as h5:\n",
+    "    ref_observables = h5['DMFT_results']['observables']\n",
+    "    ref_convergence = h5['DMFT_results']['convergence_obs']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "fae94579",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1600x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1,2, figsize=(8, 2), dpi=200)\n",
+    "\n",
+    "ax[0].plot(ref_observables['E_tot']-np.min(ref_observables['E_tot']), 'x-', label='reference')\n",
+    "ax[0].plot(observables['E_tot']-np.min(observables['E_tot']), 'x-', label='result')\n",
+    "\n",
+    "ax[1].plot(ref_convergence['d_G0'][0], 'x-', label='reference')\n",
+    "ax[1].plot(convergence['d_G0'][0], 'x-', label='result')\n",
+    "\n",
+    "ax[0].set_ylabel('total energy (eV)')\n",
+    "ax[1].set_ylabel(r'convergence $G_0$')\n",
+    "\n",
+    "for it in range(2):\n",
+    "    ax[it].set_xlabel('# iteration')\n",
+    "    ax[it].xaxis.set_major_locator(ticker.MultipleLocator(5))\n",
+    "    ax[it].grid()\n",
+    "    ax[it].legend(fontsize='small')\n",
+    "\n",
+    "fig.subplots_adjust(wspace=0.3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4952537b",
+   "metadata": {},
+   "source": [
+    "Note that the total energy jumps quite a bit in the first iteration and is constant for the first two (three) one-shot iterations in this run (the reference data) as expected. Since the HubbardI solver essentially yields DMFT-convergence after one iteration (you may try to confirm this), the total number of iterations necessary to achieve convergence is relatively low."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9afd381d",
+   "metadata": {},
+   "source": [
+    "This concludes the tutorial. The following is a list of things you can try next:\n",
+    "\n",
+    "* improve the accuracy of the results by tuning the parameters until the results agree with the reference    \n",
+    "* try to fnd the equilibrium lattice paramter by repeating the above calculation of the total energy for different cell volumes"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/tutorials/NNO_os_plo_mag/INCAR b/tutorials/NNO_os_plo_mag/INCAR
new file mode 100644
index 00000000..5244a9c5
--- /dev/null
+++ b/tutorials/NNO_os_plo_mag/INCAR
@@ -0,0 +1,29 @@
+System  = NdNiO2
+
+EDIFF = 1e-10
+ENCUT = 450
+
+NELMIN = 30
+
+ISMEAR = 0
+SIGMA = 0.1
+PREC = Accurate
+
+# the energy window to optimize projector channels
+EMIN = -5
+EMAX = 15
+
+NBANDS = 64
+LMAXMIX = 6
+NEDOS = 3001
+
+# switch off all symmetries
+ISYM = -1
+
+# project to Ni d
+LORBIT = 14
+LOCPROJ = 3 4 : d : Pr 
+
+# write WAVECAR, CHGCAR
+LWAVE = .FALSE.        
+LCHARG = .FALSE.
diff --git a/tutorials/NNO_os_plo_mag/KPOINTS b/tutorials/NNO_os_plo_mag/KPOINTS
new file mode 100644
index 00000000..3bdc16ba
--- /dev/null
+++ b/tutorials/NNO_os_plo_mag/KPOINTS
@@ -0,0 +1,4 @@
+Automatically generated mesh
+       0
+Gamma
+ 9 9 5
diff --git a/tutorials/NNO_os_plo_mag/POSCAR b/tutorials/NNO_os_plo_mag/POSCAR
new file mode 100644
index 00000000..a737904e
--- /dev/null
+++ b/tutorials/NNO_os_plo_mag/POSCAR
@@ -0,0 +1,16 @@
+NdNiO2 SC
+1.0
+        5.5437150002         0.0000000000         0.0000000000
+        0.0000000000         5.5437150002         0.0000000000
+        0.0000000000         0.0000000000         3.3099985123
+   Nd   Ni    O
+    2    2    4
+Direct
+     0.000000000         0.500000000         0.500000000
+     0.500000000         0.000000000         0.500000000
+     0.000000000         0.000000000         0.000000000
+     0.500000000         0.500000000         0.000000000
+     0.250000000         0.250000000         0.000000000
+     0.750000000         0.750000000         0.000000000
+     0.750000000         0.250000000         0.000000000
+     0.250000000         0.750000000         0.000000000
diff --git a/tutorials/NNO_os_plo_mag/config.toml b/tutorials/NNO_os_plo_mag/config.toml
new file mode 100644
index 00000000..e2c9f594
--- /dev/null
+++ b/tutorials/NNO_os_plo_mag/config.toml
@@ -0,0 +1,47 @@
+[general]
+seedname = "nno"
+jobname = "NNO_lowT"
+
+enforce_off_diag = false
+block_threshold = 0.001
+
+n_iw = 2001
+n_tau = 20001
+
+prec_mu = 0.001
+
+h_int_type = "density_density"
+U = 8.0
+J = 1.0
+
+# temperature ~290 K
+beta = 40
+
+magnetic = true
+magmom = [-0.3, 0.3]
+afm_order = true
+
+n_iter_dmft = 14
+
+g0_mix = 0.9
+
+dc_type = 0
+dc = true
+dc_dmft = false
+
+load_sigma = false
+path_to_sigma = "pre_AFM.h5"
+
+[solver]
+type = "cthyb"
+length_cycle = 2000
+n_warmup_cycles = 5e+3
+n_cycles_tot = 1e+7
+imag_threshold = 1e-5
+
+perform_tail_fit = true
+fit_max_moment = 6
+fit_min_w = 10
+fit_max_w = 16
+measure_density_matrix = true
+
diff --git a/tutorials/NNO_os_plo_mag/plo.cfg b/tutorials/NNO_os_plo_mag/plo.cfg
new file mode 100644
index 00000000..239325a3
--- /dev/null
+++ b/tutorials/NNO_os_plo_mag/plo.cfg
@@ -0,0 +1,17 @@
+[General]
+BASENAME = nno
+
+[Group 1]
+SHELLS = 1
+NORMALIZE = True
+NORMION = False
+EWINDOW = -10 10
+
+[Shell 1]
+LSHELL = 2
+IONS = 3 4
+TRANSFORM = 0.0  0.0  0.0  0.0  1.0
+            0.0  1.0  0.0  0.0  0.0
+            0.0  0.0  1.0  0.0  0.0
+            0.0  0.0  0.0  1.0  0.0
+            1.0  0.0  0.0  0.0  0.0
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+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
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+<li class="toctree-l1 current"><a class="reference internal" href="../../tutorials.html">Tutorials</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
+</li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-2-0">Version 3.2.0</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-0-0">Version 3.0.0</a></li>
+</ul>
+</li>
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+
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+      <li>4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</li>
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+            <a href="../../_sources/tutorials/NNO_os_plo_mag/tutorial.ipynb.txt" rel="nofollow"> View page source</a>
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+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="n">np</span><span class="o">.</span><span class="n">set_printoptions</span><span class="p">(</span><span class="n">precision</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span><span class="n">suppress</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="kn">from</span> <span class="nn">triqs.plot.mpl_interface</span> <span class="kn">import</span> <span class="n">plt</span><span class="p">,</span><span class="n">oplot</span>
+
+<span class="kn">from</span> <span class="nn">h5</span> <span class="kn">import</span> <span class="n">HDFArchive</span>
+
+<span class="kn">from</span> <span class="nn">triqs_dft_tools.converters.vasp</span> <span class="kn">import</span> <span class="n">VaspConverter</span>
+<span class="kn">import</span> <span class="nn">triqs_dft_tools.converters.plovasp.converter</span> <span class="k">as</span> <span class="nn">plo_converter</span>
+
+<span class="kn">import</span> <span class="nn">pymatgen.io.vasp.outputs</span> <span class="k">as</span> <span class="nn">vio</span>
+<span class="kn">from</span> <span class="nn">pymatgen.electronic_structure.dos</span> <span class="kn">import</span> <span class="n">CompleteDos</span>
+<span class="kn">from</span> <span class="nn">pymatgen.electronic_structure.core</span> <span class="kn">import</span> <span class="n">Spin</span><span class="p">,</span> <span class="n">Orbital</span><span class="p">,</span> <span class="n">OrbitalType</span>
+
+<span class="kn">import</span> <span class="nn">warnings</span>
+<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s2">&quot;ignore&quot;</span><span class="p">)</span> <span class="c1">#ignore some matplotlib warnings</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Warning: could not identify MPI environment!
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+Starting serial run at: 2023-11-24 09:49:44.156139
+</pre></div></div>
+</div>
+<div class="section" id="4.-OS-with-VASP/PLOs-and-cthyb:-AFM-state-of-NdNiO2">
+<h1>4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2<a class="headerlink" href="#4.-OS-with-VASP/PLOs-and-cthyb:-AFM-state-of-NdNiO2" title="Permalink to this headline"></a></h1>
+<p>In this tutorial we will take a look at a magnetic DMFT calculation for NdNiO2 in the antiferromagnetic phase. NdNiO2 shows a clear AFM phase at lower temperatures in DFT+DMFT calculation. The calculations will be performed for a large energy window with all Ni-<span class="math notranslate nohighlight">\(d\)</span> orbitals treated as interacting with a density-density type interaction.</p>
+<p>Disclaimer: the interaction values, results etc. might not be 100% physical and are only for demonstrative purposes!</p>
+<p>This tutorial will guide you through the following steps:</p>
+<ul class="simple">
+<li>run a non-magnetic Vasp calculation for NdNiO2 with a two atom supercell allowing magnetic order</li>
+<li>create projectors in a large energy window for all Ni-<span class="math notranslate nohighlight">\(d\)</span> orbitals and all O-<span class="math notranslate nohighlight">\(p\)</span> orbitals</li>
+<li>create the hdf5 input via the Vasp converter for solid_dmft</li>
+<li>run a AFM DMFT one-shot calculation</li>
+<li>take a look at the output and analyse the multiplets of the Ni-d states</li>
+</ul>
+<p>Warning: the DMFT calculations here are very heavy requiring ~2500 core hours for the DMFT job.</p>
+<p>We set a <code class="docutils literal notranslate"><span class="pre">path</span></code> variable here to the reference files, which should be changed when doing the actual calculations do the work directory:</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="n">path</span> <span class="o">=</span> <span class="s1">&#39;./ref/&#39;</span>
+</pre></div>
+</div>
+</div>
+<div class="section" id="1.-Run-DFT">
+<h2>1. Run DFT<a class="headerlink" href="#1.-Run-DFT" title="Permalink to this headline"></a></h2>
+<p>We start by running Vasp to create the raw projectors. The <a class="reference external" href="INCAR" rel="noopener noreferrer" target="_blank">INCAR</a>, <a class="reference external" href="POSCAR" rel="noopener noreferrer" target="_blank">POSCAR</a>, and <a class="reference external" href="KPOINTS" rel="noopener noreferrer" target="_blank">KPOINTS</a> file are kept relatively simple. For the POTCAR the <code class="docutils literal notranslate"><span class="pre">PBE</span> <span class="pre">Nd_3</span></code>, <code class="docutils literal notranslate"><span class="pre">PBE</span> <span class="pre">Ni_pv</span></code> and <code class="docutils literal notranslate"><span class="pre">PBE</span> <span class="pre">O</span></code> pseudo potentials are used. Here we make sure that the Kohn-Sham eigenstates are well converged (rms), by performing a few extra SCF steps by setting <code class="docutils literal notranslate"><span class="pre">NELMIN=30</span></code>. Then, the INCAR flag <code class="docutils literal notranslate"><span class="pre">LOCPROJ</span> <span class="pre">=</span> <span class="pre">LOCPROJ</span> <span class="pre">=</span> <span class="pre">3</span> <span class="pre">4</span> <span class="pre">:</span> <span class="pre">d</span> <span class="pre">:</span> <span class="pre">Pr</span></code> instructs Vasp to create projectors for the Ni-<span class="math notranslate nohighlight">\(d\)</span>
+shell of the two Ni sties. More information can be found on the <a class="reference external" href="https://triqs.github.io/dft_tools/unstable/guide/conv_vasp.html" rel="noopener noreferrer" target="_blank">DFTTools webpage of the Vasp converter</a>.</p>
+<p>Next, run Vasp with</p>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>mpirun vasp_std 1&gt;out.vasp 2&gt;err.vasp &amp;
+</pre></div>
+</div>
+<p>and monitor the output. After Vasp is finished the result should look like this:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="o">!</span>tail -n <span class="m">10</span> ref/out.vasp
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+DAV:  25    -0.569483098581E+02   -0.31832E-09    0.42131E-12 29952   0.148E-06    0.488E-07
+DAV:  26    -0.569483098574E+02    0.75124E-09    0.25243E-12 30528   0.511E-07    0.226E-07
+DAV:  27    -0.569483098574E+02   -0.12733E-10    0.17328E-12 28448   0.285E-07    0.826E-08
+DAV:  28    -0.569483098578E+02   -0.41837E-09    0.17366E-12 29536   0.151E-07    0.370E-08
+DAV:  29    -0.569483098576E+02    0.22192E-09    0.19300E-12 29280   0.689E-08    0.124E-08
+DAV:  30    -0.569483098572E+02    0.38563E-09    0.27026E-12 28576   0.388E-08    0.598E-09
+DAV:  31    -0.569483098573E+02   -0.92768E-10    0.34212E-12 29024   0.218E-08
+ LOCPROJ mode
+ Computing AMN (projections onto localized orbitals)
+   1 F= -.56948310E+02 E0= -.56941742E+02  d E =-.131358E-01
+</pre></div></div>
+</div>
+<p>let us take a look at the density of states from Vasp:</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="n">vasprun</span> <span class="o">=</span> <span class="n">vio</span><span class="o">.</span><span class="n">Vasprun</span><span class="p">(</span><span class="n">path</span><span class="o">+</span><span class="s1">&#39;/vasprun.xml&#39;</span><span class="p">)</span>
+<span class="n">dos</span> <span class="o">=</span> <span class="n">vasprun</span><span class="o">.</span><span class="n">complete_dos</span>
+<span class="n">Ni_spd_dos</span> <span class="o">=</span> <span class="n">dos</span><span class="o">.</span><span class="n">get_element_spd_dos</span><span class="p">(</span><span class="s2">&quot;Ni&quot;</span><span class="p">)</span>
+<span class="n">O_spd_dos</span> <span class="o">=</span> <span class="n">dos</span><span class="o">.</span><span class="n">get_element_spd_dos</span><span class="p">(</span><span class="s2">&quot;O&quot;</span><span class="p">)</span>
+<span class="n">Nd_spd_dos</span> <span class="o">=</span> <span class="n">dos</span><span class="o">.</span><span class="n">get_element_spd_dos</span><span class="p">(</span><span class="s2">&quot;Nd&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="n">dpi</span><span class="o">=</span><span class="mi">150</span><span class="p">,</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">7</span><span class="p">,</span><span class="mi">4</span><span class="p">))</span>
+
+<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">vasprun</span><span class="o">.</span><span class="n">tdos</span><span class="o">.</span><span class="n">energies</span> <span class="o">-</span> <span class="n">vasprun</span><span class="o">.</span><span class="n">efermi</span> <span class="p">,</span> <span class="n">vasprun</span><span class="o">.</span><span class="n">tdos</span><span class="o">.</span><span class="n">densities</span><span class="p">[</span><span class="n">Spin</span><span class="o">.</span><span class="n">up</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="sa">r</span><span class="s1">&#39;total DOS&#39;</span><span class="p">,</span> <span class="n">lw</span> <span class="o">=</span> <span class="mi">2</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">vasprun</span><span class="o">.</span><span class="n">tdos</span><span class="o">.</span><span class="n">energies</span> <span class="o">-</span> <span class="n">vasprun</span><span class="o">.</span><span class="n">efermi</span> <span class="p">,</span> <span class="n">Ni_spd_dos</span><span class="p">[</span><span class="n">OrbitalType</span><span class="o">.</span><span class="n">d</span><span class="p">]</span><span class="o">.</span><span class="n">densities</span><span class="p">[</span><span class="n">Spin</span><span class="o">.</span><span class="n">up</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="sa">r</span><span class="s1">&#39;Ni-d&#39;</span><span class="p">,</span> <span class="n">lw</span> <span class="o">=</span> <span class="mi">2</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">vasprun</span><span class="o">.</span><span class="n">tdos</span><span class="o">.</span><span class="n">energies</span> <span class="o">-</span> <span class="n">vasprun</span><span class="o">.</span><span class="n">efermi</span> <span class="p">,</span> <span class="n">O_spd_dos</span><span class="p">[</span><span class="n">OrbitalType</span><span class="o">.</span><span class="n">p</span><span class="p">]</span><span class="o">.</span><span class="n">densities</span><span class="p">[</span><span class="n">Spin</span><span class="o">.</span><span class="n">up</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="sa">r</span><span class="s1">&#39;O-p&#39;</span><span class="p">,</span> <span class="n">lw</span> <span class="o">=</span> <span class="mi">2</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">vasprun</span><span class="o">.</span><span class="n">tdos</span><span class="o">.</span><span class="n">energies</span> <span class="o">-</span> <span class="n">vasprun</span><span class="o">.</span><span class="n">efermi</span> <span class="p">,</span> <span class="n">Nd_spd_dos</span><span class="p">[</span><span class="n">OrbitalType</span><span class="o">.</span><span class="n">d</span><span class="p">]</span><span class="o">.</span><span class="n">densities</span><span class="p">[</span><span class="n">Spin</span><span class="o">.</span><span class="n">up</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="sa">r</span><span class="s1">&#39;Nd-d&#39;</span><span class="p">,</span> <span class="n">lw</span> <span class="o">=</span> <span class="mi">2</span><span class="p">)</span>
+
+<span class="n">ax</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Energy relative to Fermi energy (eV)&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;DOS (1/eV)&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="o">-</span><span class="mi">9</span><span class="p">,</span><span class="mf">8.5</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">20</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/tutorials_NNO_os_plo_mag_tutorial_8_0.png" src="../../_images/tutorials_NNO_os_plo_mag_tutorial_8_0.png" />
+</div>
+</div>
+<p>We see that the Ni-<span class="math notranslate nohighlight">\(d\)</span> states are entangled / hybridizing with O-<span class="math notranslate nohighlight">\(p\)</span> states and Nd-<span class="math notranslate nohighlight">\(d\)</span> states in the energy range between -9 and 9 eV. Hence, we orthonormalize our projectors considering all states in this energy window to create well localized real-space states. These projectors will be indeed quite similar to the internal DFT+<span class="math notranslate nohighlight">\(U\)</span> projectors used in VASP due to the large energy window.</p>
+</div>
+<div class="section" id="2.-Creating-the-hdf5-archive-/-DMFT-input">
+<h2>2. Creating the hdf5 archive / DMFT input<a class="headerlink" href="#2.-Creating-the-hdf5-archive-/-DMFT-input" title="Permalink to this headline"></a></h2>
+<p>Next we run the <a class="reference external" href="https://triqs.github.io/dft_tools/unstable/guide/conv_vasp.html" rel="noopener noreferrer" target="_blank">Vasp converter</a> to create an input h5 archive for solid_dmft. The <a class="reference external" href="plo.cfg" rel="noopener noreferrer" target="_blank">plo.cfg</a> looks like this:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="o">!</span>cat plo.cfg
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+[General]
+BASENAME = nno
+
+[Group 1]
+SHELLS = 1
+NORMALIZE = True
+NORMION = False
+EWINDOW = -10 10
+
+[Shell 1]
+LSHELL = 2
+IONS = 3 4
+TRANSFORM = 0.0  0.0  0.0  0.0  1.0
+            0.0  1.0  0.0  0.0  0.0
+            0.0  0.0  1.0  0.0  0.0
+            0.0  0.0  0.0  1.0  0.0
+            1.0  0.0  0.0  0.0  0.0
+</pre></div></div>
+</div>
+<p>we create <span class="math notranslate nohighlight">\(d\)</span> like projectors within a large energy window from -10 to 10 eV for very localized states for both Ni sites. Important: the sites are markes as non equivalent, so that we can later have different spin orientations on them. The flag <code class="docutils literal notranslate"><span class="pre">TRANSFORM</span></code> swaps the <span class="math notranslate nohighlight">\(d_{xy}\)</span> and <span class="math notranslate nohighlight">\(d_{x^2 - y^2}\)</span> orbitals, since the orientation in the unit cell of the oxygen bonds is rotated by 45 degreee. Vasp always performs projections in a global cartesian coordinate frame, so one has to
+rotate the orbitals manually with the octahedra orientation.</p>
+<p>Let’s run the converter:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="c1"># Generate and store PLOs</span>
+<span class="n">plo_converter</span><span class="o">.</span><span class="n">generate_and_output_as_text</span><span class="p">(</span><span class="s1">&#39;plo.cfg&#39;</span><span class="p">,</span> <span class="n">vasp_dir</span><span class="o">=</span><span class="n">path</span><span class="p">)</span>
+
+<span class="c1"># run the archive creat routine</span>
+<span class="n">conv</span> <span class="o">=</span> <span class="n">VaspConverter</span><span class="p">(</span><span class="s1">&#39;nno&#39;</span><span class="p">)</span>
+<span class="n">conv</span><span class="o">.</span><span class="n">convert_dft_input</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Read parameters: LOCPROJ
+0  -&gt;  {&#39;label&#39;: &#39;dxy&#39;, &#39;isite&#39;: 3, &#39;l&#39;: 2, &#39;m&#39;: 0}
+1  -&gt;  {&#39;label&#39;: &#39;dyz&#39;, &#39;isite&#39;: 3, &#39;l&#39;: 2, &#39;m&#39;: 1}
+2  -&gt;  {&#39;label&#39;: &#39;dz2&#39;, &#39;isite&#39;: 3, &#39;l&#39;: 2, &#39;m&#39;: 2}
+3  -&gt;  {&#39;label&#39;: &#39;dxz&#39;, &#39;isite&#39;: 3, &#39;l&#39;: 2, &#39;m&#39;: 3}
+4  -&gt;  {&#39;label&#39;: &#39;dx2-y2&#39;, &#39;isite&#39;: 3, &#39;l&#39;: 2, &#39;m&#39;: 4}
+5  -&gt;  {&#39;label&#39;: &#39;dxy&#39;, &#39;isite&#39;: 4, &#39;l&#39;: 2, &#39;m&#39;: 0}
+6  -&gt;  {&#39;label&#39;: &#39;dyz&#39;, &#39;isite&#39;: 4, &#39;l&#39;: 2, &#39;m&#39;: 1}
+7  -&gt;  {&#39;label&#39;: &#39;dz2&#39;, &#39;isite&#39;: 4, &#39;l&#39;: 2, &#39;m&#39;: 2}
+8  -&gt;  {&#39;label&#39;: &#39;dxz&#39;, &#39;isite&#39;: 4, &#39;l&#39;: 2, &#39;m&#39;: 3}
+9  -&gt;  {&#39;label&#39;: &#39;dx2-y2&#39;, &#39;isite&#39;: 4, &#39;l&#39;: 2, &#39;m&#39;: 4}
+  Found POSCAR, title line: NdNiO2 SC
+  Total number of ions: 8
+  Number of types: 3
+  Number of ions for each type: [2, 2, 4]
+
+    Total number of k-points: 405
+  No tetrahedron data found in IBZKPT. Skipping...
+[WARNING]: Error reading from EIGENVAL, trying LOCPROJ...
+[WARNING]: Error reading Efermi from DOSCAR, trying LOCPROJ...
+eigvals from LOCPROJ
+
+  Unorthonormalized density matrices and overlaps:
+  Spin: 1
+  Site: 3
+  Density matrix                                                  Overlap
+   1.1544881   0.0000000  -0.0000000   0.0000000  -0.0000000       0.9626619  -0.0000000   0.0000000   0.0000002  -0.0000000
+   0.0000000   1.7591058  -0.0000000   0.0000000  -0.0000000      -0.0000000   0.9464342  -0.0000000   0.0000000  -0.0000000
+  -0.0000000  -0.0000000   1.5114185   0.0000000  -0.0000000       0.0000000  -0.0000000   0.9548582  -0.0000000   0.0000000
+   0.0000000   0.0000000   0.0000000   1.7591058  -0.0000000       0.0000002   0.0000000  -0.0000000   0.9464339   0.0000000
+  -0.0000000  -0.0000000  -0.0000000  -0.0000000   1.8114830      -0.0000000  -0.0000000   0.0000000   0.0000000   0.9495307
+  Site: 4
+  Density matrix                                                  Overlap
+   1.1544881  -0.0000000   0.0000000   0.0000000   0.0000000       0.9626621   0.0000000  -0.0000000  -0.0000001  -0.0000000
+  -0.0000000   1.7591058  -0.0000000  -0.0000000   0.0000000       0.0000000   0.9464343  -0.0000000  -0.0000000   0.0000000
+   0.0000000  -0.0000000   1.5114185  -0.0000000  -0.0000000      -0.0000000  -0.0000000   0.9548582   0.0000000   0.0000000
+   0.0000000  -0.0000000  -0.0000000   1.7591058   0.0000000      -0.0000001  -0.0000000   0.0000000   0.9464344   0.0000000
+   0.0000000   0.0000000  -0.0000000   0.0000000   1.8114830      -0.0000000   0.0000000   0.0000000   0.0000000   0.9495307
+
+  Generating 1 shell...
+
+    Shell         : 1
+    Orbital l     : 2
+    Number of ions: 2
+    Dimension     : 5
+    Correlated    : True
+    Ion sort      : [3, 4]
+Density matrix:
+  Shell 1
+Site diag : True
+    Site 1
+     1.9468082    -0.0000000    -0.0000000     0.0000000    -0.0000000
+    -0.0000000     1.8880488    -0.0000000     0.0000000     0.0000000
+    -0.0000000    -0.0000000     1.5912192     0.0000000     0.0000000
+     0.0000000     0.0000000     0.0000000     1.8880488     0.0000000
+    -0.0000000     0.0000000     0.0000000     0.0000000     1.1979419
+      trace:  8.512066911392091
+    Site 2
+     1.9468082     0.0000000    -0.0000000    -0.0000000    -0.0000000
+     0.0000000     1.8880488    -0.0000000    -0.0000000    -0.0000000
+    -0.0000000    -0.0000000     1.5912192    -0.0000000    -0.0000000
+    -0.0000000    -0.0000000    -0.0000000     1.8880488    -0.0000000
+    -0.0000000    -0.0000000    -0.0000000    -0.0000000     1.1979419
+      trace:  8.512066911289741
+
+  Impurity density: 17.024133822681833
+
+Overlap:
+  Site 1
+[[ 1. -0. -0. -0. -0.]
+ [-0.  1. -0. -0. -0.]
+ [-0. -0.  1. -0. -0.]
+ [-0. -0. -0.  1.  0.]
+ [-0. -0. -0.  0.  1.]]
+
+Local Hamiltonian:
+  Shell 1
+    Site 1 (real | complex part)
+    -1.5179223     0.0000000     0.0000000    -0.0000000     0.0000000 |    -0.0000000    -0.0000000    -0.0000000    -0.0000000    -0.0000000
+     0.0000000    -1.2888643     0.0000000    -0.0000000    -0.0000000 |     0.0000000     0.0000000    -0.0000000    -0.0000000    -0.0000000
+     0.0000000     0.0000000    -0.9927644    -0.0000000    -0.0000000 |     0.0000000     0.0000000    -0.0000000     0.0000000     0.0000000
+    -0.0000000    -0.0000000    -0.0000000    -1.2888643     0.0000000 |     0.0000000     0.0000000    -0.0000000     0.0000000     0.0000000
+     0.0000000    -0.0000000    -0.0000000     0.0000000    -1.0828254 |     0.0000000     0.0000000    -0.0000000    -0.0000000     0.0000000
+    Site 2 (real | complex part)
+    -1.5179223    -0.0000000    -0.0000000    -0.0000000    -0.0000000 |     0.0000000     0.0000000    -0.0000000    -0.0000000    -0.0000000
+    -0.0000000    -1.2888643     0.0000000    -0.0000000     0.0000000 |    -0.0000000    -0.0000000     0.0000000     0.0000000    -0.0000000
+    -0.0000000     0.0000000    -0.9927644     0.0000000     0.0000000 |     0.0000000    -0.0000000     0.0000000    -0.0000000    -0.0000000
+    -0.0000000    -0.0000000     0.0000000    -1.2888643     0.0000000 |     0.0000000    -0.0000000     0.0000000    -0.0000000     0.0000000
+    -0.0000000     0.0000000     0.0000000     0.0000000    -1.0828254 |     0.0000000     0.0000000     0.0000000    -0.0000000     0.0000000
+  Storing ctrl-file...
+  Storing PLO-group file &#39;nno.pg1&#39;...
+  Density within window: 42.00000000005771
+Reading input from nno.ctrl...
+{
+    &#34;ngroups&#34;: 1,
+    &#34;nk&#34;: 405,
+    &#34;ns&#34;: 1,
+    &#34;kvec1&#34;: [
+        0.1803844533789928,
+        0.0,
+        0.0
+    ],
+    &#34;kvec2&#34;: [
+        0.0,
+        0.1803844533789928,
+        0.0
+    ],
+    &#34;kvec3&#34;: [
+        0.0,
+        0.0,
+        0.30211493941280826
+    ],
+    &#34;nc_flag&#34;: 0
+}
+
+  No. of inequivalent shells: 2
+</pre></div></div>
+</div>
+<p>We can here cross check the quality of our projectors by making sure that there are not imaginary elements in both the local Hamiltonian and the density matrix. Furthermore, we see that the occupation of the Ni-<span class="math notranslate nohighlight">\(d\)</span> shell is roughly 8.5 electrons which is a bit different from the nominal charge of <span class="math notranslate nohighlight">\(d^9\)</span> for the system due to the large hybridization with the other states. For mor physical insights into the systems and a discussion on the appropriate choice of projectors see this
+research article <a class="reference external" href="https://doi.org/10.1103/PhysRevB.103.195101" rel="noopener noreferrer" target="_blank">PRB 103 195101 2021</a></p>
+</div>
+<div class="section" id="3.-Running-the-AFM-calculation">
+<h2>3. Running the AFM calculation<a class="headerlink" href="#3.-Running-the-AFM-calculation" title="Permalink to this headline"></a></h2>
+<p>now we run the calculation at around 290 K, which should be below the ordering temperature of NdNiO2 in DMFT. The config file <a class="reference external" href="config.toml" rel="noopener noreferrer" target="_blank">config.toml</a> for solid_dmft looks like this:</p>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>[general]
+seedname = &quot;nno&quot;
+jobname = &quot;NNO_lowT&quot;
+
+enforce_off_diag = false
+block_threshold = 0.001
+
+
+n_iw = 2001
+n_tau = 20001
+
+prec_mu = 0.001
+
+h_int_type = &quot;density_density&quot;
+U = 8.0
+J = 1.0
+
+# temperature ~290 K
+beta = 40
+
+magnetic = true
+magmom = -0.3, 0.3
+afm_order = true
+
+n_iter_dmft = 14
+
+g0_mix = 0.9
+
+dc_type = 0
+dc = true
+dc_dmft = false
+
+[solver]
+type = &quot;cthyb&quot;
+length_cycle = 2000
+n_warmup_cycles = 5e+3
+n_cycles_tot = 1e+7
+imag_threshold = 1e-5
+
+perform_tail_fit = true
+fit_max_moment = 6
+fit_min_w = 10
+fit_max_w = 16
+measure_density_matrix = true
+</pre></div>
+</div>
+<p>Let’s go through some special options we set in the config file:</p>
+<ul class="simple">
+<li>we changed <code class="docutils literal notranslate"><span class="pre">n_iw=2000</span></code> because the large energy window of the calculation requires more Matsubara frequencies</li>
+<li><code class="docutils literal notranslate"><span class="pre">h_int_type</span></code> is set to <code class="docutils literal notranslate"><span class="pre">density_density</span></code> to reduce complexity of the problem</li>
+<li><code class="docutils literal notranslate"><span class="pre">beta=40</span></code> here we set the temperature to ~290K</li>
+<li><code class="docutils literal notranslate"><span class="pre">magnetic=true</span></code> lift spin degeneracy</li>
+<li><code class="docutils literal notranslate"><span class="pre">magmom</span></code> here we specify the magnetic order. Here, we say that both Ni sites have the same spin, which should average to 0 at this high temperature. The magnetic moment is specified as an potential in eV splitting up / down channel of the initial self-energy</li>
+<li><code class="docutils literal notranslate"><span class="pre">afm_order=true</span></code> tells solid_dmft to not solve impurities with the same <code class="docutils literal notranslate"><span class="pre">magmom</span></code> but rather copy the self-energy and if necessary flip the spin accordingly</li>
+<li><code class="docutils literal notranslate"><span class="pre">length_cycle=2000</span></code> is the length between two Green’s function measurements in cthyb. This number has to be choosen carefully to give an autocorrelation time ~1 for all orbitals</li>
+<li><code class="docutils literal notranslate"><span class="pre">perform_tail_fit=true</span></code> : here we use tail fitting to get good high frequency self-energy behavior</li>
+<li><code class="docutils literal notranslate"><span class="pre">measure_density_matrix</span> <span class="pre">=</span> <span class="pre">true</span></code> measures the impurity many-body density matrix in the Fock basis for a multiplet analysis</li>
+</ul>
+<p>By setting the flag magmom to a small value with a flipped sign on both sites we tell solid_dmft that both sites are related by flipping the down and up channel. Now we run solid_dmft simply by executing <code class="docutils literal notranslate"><span class="pre">mpirun</span> <span class="pre">solid_dmft</span> <span class="pre">config.toml</span></code>.</p>
+<p>Caution: this is a very heavy job, which should be submitted on a cluster.</p>
+<p>In the beginning of the calculation we find the following lines:</p>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>AFM calculation selected, mapping self energies as follows:
+imp  [copy sigma, source imp, switch up/down]
+---------------------------------------------
+0: [False, 0, False]
+1: [True, 0, True]
+</pre></div>
+</div>
+<p>this tells us that solid_dmft detected correctly how we want to orientate the spin moments. This also reflects itself during the iterations when the second impurity problem is not solved, but instead all properties of the first impurity are copied and the spin channels are flipped:</p>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>...
+copying the self-energy for shell 1 from shell 0
+inverting spin channels: False
+...
+</pre></div>
+</div>
+<p>After the calculation is running or is finished we can take a look at the results:</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="k">with</span> <span class="n">HDFArchive</span><span class="p">(</span><span class="n">path</span><span class="o">+</span><span class="s1">&#39;/nno.h5&#39;</span><span class="p">,</span><span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">ar</span><span class="p">:</span>
+    <span class="n">Sigma_iw</span> <span class="o">=</span> <span class="n">ar</span><span class="p">[</span><span class="s1">&#39;DMFT_results/last_iter/Sigma_freq_0&#39;</span><span class="p">]</span>
+    <span class="n">obs</span> <span class="o">=</span> <span class="n">ar</span><span class="p">[</span><span class="s1">&#39;DMFT_results/observables&#39;</span><span class="p">]</span>
+    <span class="n">conv_obs</span> <span class="o">=</span> <span class="n">ar</span><span class="p">[</span><span class="s1">&#39;DMFT_results/convergence_obs&#39;</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">150</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">7</span><span class="p">,</span><span class="mi">8</span><span class="p">),</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">fig</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">hspace</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
+<span class="c1"># imp occupation</span>
+<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">obs</span><span class="p">[</span><span class="s1">&#39;iteration&#39;</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">obs</span><span class="p">[</span><span class="s1">&#39;imp_occ&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="s1">&#39;up&#39;</span><span class="p">])</span><span class="o">+</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">obs</span><span class="p">[</span><span class="s1">&#39;imp_occ&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="s1">&#39;down&#39;</span><span class="p">]),</span> <span class="s1">&#39;-o&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">r</span><span class="s1">&#39;Ni$_0$&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">obs</span><span class="p">[</span><span class="s1">&#39;iteration&#39;</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">obs</span><span class="p">[</span><span class="s1">&#39;imp_occ&#39;</span><span class="p">][</span><span class="mi">1</span><span class="p">][</span><span class="s1">&#39;up&#39;</span><span class="p">])</span><span class="o">+</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">obs</span><span class="p">[</span><span class="s1">&#39;imp_occ&#39;</span><span class="p">][</span><span class="mi">1</span><span class="p">][</span><span class="s1">&#39;down&#39;</span><span class="p">]),</span> <span class="s1">&#39;-o&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">r</span><span class="s1">&#39;Ni$_1$&#39;</span><span class="p">)</span>
+
+<span class="c1"># imp magnetization</span>
+<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">obs</span><span class="p">[</span><span class="s1">&#39;iteration&#39;</span><span class="p">],</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">obs</span><span class="p">[</span><span class="s1">&#39;imp_occ&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="s1">&#39;up&#39;</span><span class="p">])</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">obs</span><span class="p">[</span><span class="s1">&#39;imp_occ&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="s1">&#39;down&#39;</span><span class="p">])),</span> <span class="s1">&#39;-o&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">r</span><span class="s1">&#39;Ni$_0$&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">obs</span><span class="p">[</span><span class="s1">&#39;iteration&#39;</span><span class="p">],</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">obs</span><span class="p">[</span><span class="s1">&#39;imp_occ&#39;</span><span class="p">][</span><span class="mi">1</span><span class="p">][</span><span class="s1">&#39;up&#39;</span><span class="p">])</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">obs</span><span class="p">[</span><span class="s1">&#39;imp_occ&#39;</span><span class="p">][</span><span class="mi">1</span><span class="p">][</span><span class="s1">&#39;down&#39;</span><span class="p">])),</span> <span class="s1">&#39;-o&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">r</span><span class="s1">&#39;Ni$_1$&#39;</span><span class="p">)</span>
+
+<span class="c1"># dxy, dyz, dz2, dxz, dx2-y2 orbital magnetization</span>
+<span class="n">ax</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">obs</span><span class="p">[</span><span class="s1">&#39;iteration&#39;</span><span class="p">],</span> <span class="nb">abs</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">obs</span><span class="p">[</span><span class="s1">&#39;orb_occ&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="s1">&#39;up&#39;</span><span class="p">])[:,</span><span class="mi">4</span><span class="p">]</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">obs</span><span class="p">[</span><span class="s1">&#39;orb_occ&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="s1">&#39;down&#39;</span><span class="p">])[:,</span><span class="mi">4</span><span class="p">]),</span> <span class="s1">&#39;-o&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">r</span><span class="s1">&#39;$d_{x^2-y^2}$&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">obs</span><span class="p">[</span><span class="s1">&#39;iteration&#39;</span><span class="p">],</span> <span class="nb">abs</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">obs</span><span class="p">[</span><span class="s1">&#39;orb_occ&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="s1">&#39;up&#39;</span><span class="p">])[:,</span><span class="mi">2</span><span class="p">]</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">obs</span><span class="p">[</span><span class="s1">&#39;orb_occ&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="s1">&#39;down&#39;</span><span class="p">])[:,</span><span class="mi">2</span><span class="p">]),</span> <span class="s1">&#39;-o&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">r</span><span class="s1">&#39;$d_{z^2}$&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">obs</span><span class="p">[</span><span class="s1">&#39;iteration&#39;</span><span class="p">],</span> <span class="nb">abs</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">obs</span><span class="p">[</span><span class="s1">&#39;orb_occ&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="s1">&#39;up&#39;</span><span class="p">])[:,</span><span class="mi">0</span><span class="p">]</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">obs</span><span class="p">[</span><span class="s1">&#39;orb_occ&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="s1">&#39;down&#39;</span><span class="p">])[:,</span><span class="mi">0</span><span class="p">]),</span> <span class="s1">&#39;-o&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">r</span><span class="s1">&#39;$d_</span><span class="si">{xy}</span><span class="s1">$&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">obs</span><span class="p">[</span><span class="s1">&#39;iteration&#39;</span><span class="p">],</span> <span class="nb">abs</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">obs</span><span class="p">[</span><span class="s1">&#39;orb_occ&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="s1">&#39;up&#39;</span><span class="p">])[:,</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">obs</span><span class="p">[</span><span class="s1">&#39;orb_occ&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="s1">&#39;down&#39;</span><span class="p">])[:,</span><span class="mi">1</span><span class="p">]),</span> <span class="s1">&#39;-o&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">r</span><span class="s1">&#39;$d_{yz/xz}$&#39;</span><span class="p">)</span>
+
+<span class="n">ax</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span><span class="o">.</span><span class="n">semilogy</span><span class="p">(</span><span class="n">conv_obs</span><span class="p">[</span><span class="s1">&#39;d_Gimp&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="s1">&#39;-o&#39;</span><span class="p">)</span>
+
+<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Imp. occupation&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;magnetization $\mu_B$&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;magnetization $\mu_B$&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="nb">len</span><span class="p">(</span><span class="n">obs</span><span class="p">[</span><span class="s1">&#39;iteration&#39;</span><span class="p">])))</span>
+<span class="n">ax</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Iterations&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="mf">8.4</span><span class="p">,</span><span class="mf">8.6</span><span class="p">)</span>
+<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">();</span><span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">();</span><span class="n">ax</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+
+<span class="n">ax</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;|G$_</span><span class="si">{imp}</span><span class="s1">$-G$_</span><span class="si">{loc}</span><span class="s1">$|&#39;</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/tutorials_NNO_os_plo_mag_tutorial_18_0.png" src="../../_images/tutorials_NNO_os_plo_mag_tutorial_18_0.png" />
+</div>
+</div>
+<p>Let’s take a look at the self-energy of the two Ni <span class="math notranslate nohighlight">\(e_g\)</span> orbitals:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="n">dpi</span><span class="o">=</span><span class="mi">150</span><span class="p">)</span>
+
+<span class="n">ax</span><span class="o">.</span><span class="n">oplot</span><span class="p">(</span><span class="n">Sigma_iw</span><span class="p">[</span><span class="s1">&#39;up_2&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">imag</span><span class="p">,</span> <span class="s1">&#39;-&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;C0&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">r</span><span class="s1">&#39;up $d_{z^2}$&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">oplot</span><span class="p">(</span><span class="n">Sigma_iw</span><span class="p">[</span><span class="s1">&#39;up_4&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">imag</span><span class="p">,</span> <span class="s1">&#39;-&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;C1&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">r</span><span class="s1">&#39;up $d_{x^2-y^2}$&#39;</span><span class="p">)</span>
+
+<span class="n">ax</span><span class="o">.</span><span class="n">oplot</span><span class="p">(</span><span class="n">Sigma_iw</span><span class="p">[</span><span class="s1">&#39;down_2&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">imag</span><span class="p">,</span> <span class="s1">&#39;--&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;C0&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">r</span><span class="s1">&#39;down $d_{z^2}$&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">oplot</span><span class="p">(</span><span class="n">Sigma_iw</span><span class="p">[</span><span class="s1">&#39;down_4&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">imag</span><span class="p">,</span> <span class="s1">&#39;--&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;C1&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">r</span><span class="s1">&#39;down $d_{x^2-y^2}$&#39;</span><span class="p">)</span>
+
+<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s2">&quot;$Im \Sigma (i \omega)$&quot;</span><span class="p">)</span>
+
+<span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">40</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="o">-</span><span class="mf">1.8</span><span class="p">,</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/tutorials_NNO_os_plo_mag_tutorial_20_0.png" src="../../_images/tutorials_NNO_os_plo_mag_tutorial_20_0.png" />
+</div>
+</div>
+<p>We can clearly see that a <span class="math notranslate nohighlight">\(\omega_n=8\)</span> the self-energy is replaced by the tail-fit as specified in the input config file. This cut is rather early, but ensures convergence. For higher sampling rates this has to be changed. We can also nicely observe a splitting of the spin channels indicating a magnetic solution, but we still have a metallic solution with both self-energies approaching 0 for small omega walues. However, the QMC noise is still rather high, especially in the
+<span class="math notranslate nohighlight">\(d_{x^2-y^2}\)</span> orbital.</p>
+</div>
+<div class="section" id="5.-Multiplet-analysis">
+<h2>5. Multiplet analysis<a class="headerlink" href="#5.-Multiplet-analysis" title="Permalink to this headline"></a></h2>
+<p>We follow now the triqs/cthyb tutorial on the <a class="reference external" href="https://triqs.github.io/cthyb/unstable/guide/multiplet_analysis_notebook.html" rel="noopener noreferrer" target="_blank">multiplet analysis</a> to analyze the multiplets of the Ni-d orbitals:</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="n">pd</span><span class="o">.</span><span class="n">set_option</span><span class="p">(</span><span class="s1">&#39;display.width&#39;</span><span class="p">,</span> <span class="mi">130</span><span class="p">)</span>
+
+<span class="kn">from</span> <span class="nn">triqs.operators.util</span> <span class="kn">import</span> <span class="n">make_operator_real</span>
+<span class="kn">from</span> <span class="nn">triqs.operators.util.observables</span> <span class="kn">import</span> <span class="n">S_op</span>
+<span class="kn">from</span> <span class="nn">triqs.atom_diag</span> <span class="kn">import</span> <span class="n">quantum_number_eigenvalues</span>
+<span class="kn">from</span> <span class="nn">triqs.operators</span> <span class="kn">import</span> <span class="n">n</span>
+</pre></div>
+</div>
+</div>
+<p>first we have to load the measured density matrix and the local Hamiltonian of the impurity problem from the h5 archive, which we stored by setting <code class="docutils literal notranslate"><span class="pre">measure_density_matrix=true</span></code> in the config file:</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="k">with</span> <span class="n">HDFArchive</span><span class="p">(</span><span class="n">path</span><span class="o">+</span><span class="s1">&#39;/nno.h5&#39;</span><span class="p">,</span><span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">ar</span><span class="p">:</span>
+    <span class="n">rho</span> <span class="o">=</span> <span class="n">ar</span><span class="p">[</span><span class="s1">&#39;DMFT_results/last_iter/full_dens_mat_0&#39;</span><span class="p">]</span>
+    <span class="n">h_loc</span> <span class="o">=</span> <span class="n">ar</span><span class="p">[</span><span class="s1">&#39;DMFT_results/last_iter/h_loc_diag_0&#39;</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<p><code class="docutils literal notranslate"><span class="pre">rho</span></code> is just a list of arrays containing the weights of each of the impurity eigenstates (many body states), and <code class="docutils literal notranslate"><span class="pre">h_loc</span></code> is a:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="nb">print</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">h_loc</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;class &#39;triqs.atom_diag.atom_diag.AtomDiagReal&#39;&gt;
+</pre></div></div>
+</div>
+<p>containing the local Hamiltonian of the impurity including eigenstates, eigenvalues etc.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="n">res</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="c1"># get fundamental operators from atom_diag object</span>
+<span class="n">occ_operators</span> <span class="o">=</span> <span class="p">[</span><span class="n">n</span><span class="p">(</span><span class="o">*</span><span class="n">op</span><span class="p">)</span> <span class="k">for</span> <span class="n">op</span> <span class="ow">in</span> <span class="n">h_loc</span><span class="o">.</span><span class="n">fops</span><span class="p">]</span>
+
+<span class="c1"># construct total occupation operator from list</span>
+<span class="n">N_op</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">occ_operators</span><span class="p">)</span>
+
+<span class="c1"># create Sz operator and get eigenvalues</span>
+<span class="n">Sz</span><span class="o">=</span><span class="n">S_op</span><span class="p">(</span><span class="s1">&#39;z&#39;</span><span class="p">,</span> <span class="n">spin_names</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;up&#39;</span><span class="p">,</span><span class="s1">&#39;down&#39;</span><span class="p">],</span> <span class="n">n_orb</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">off_diag</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="n">Sz</span> <span class="o">=</span> <span class="n">make_operator_real</span><span class="p">(</span><span class="n">Sz</span><span class="p">)</span>
+<span class="n">Sz_states</span> <span class="o">=</span> <span class="n">quantum_number_eigenvalues</span><span class="p">(</span><span class="n">Sz</span><span class="p">,</span> <span class="n">h_loc</span><span class="p">)</span>
+
+<span class="c1"># get particle numbers from h_loc_diag</span>
+<span class="n">particle_numbers</span> <span class="o">=</span> <span class="n">quantum_number_eigenvalues</span><span class="p">(</span><span class="n">N_op</span><span class="p">,</span> <span class="n">h_loc</span><span class="p">)</span>
+<span class="n">N_max</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="nb">max</span><span class="p">,</span> <span class="n">particle_numbers</span><span class="p">)))</span>
+
+<span class="k">for</span> <span class="n">sub</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="n">h_loc</span><span class="o">.</span><span class="n">n_subspaces</span><span class="p">):</span>
+
+    <span class="c1"># first get Fock space spanning the subspace</span>
+    <span class="n">fs_states</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">ind</span><span class="p">,</span> <span class="n">fs</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">h_loc</span><span class="o">.</span><span class="n">fock_states</span><span class="p">[</span><span class="n">sub</span><span class="p">]):</span>
+        <span class="n">state</span> <span class="o">=</span> <span class="nb">bin</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">fs</span><span class="p">))[</span><span class="mi">2</span><span class="p">:]</span><span class="o">.</span><span class="n">rjust</span><span class="p">(</span><span class="n">N_max</span><span class="p">,</span> <span class="s1">&#39;0&#39;</span><span class="p">)</span>
+        <span class="n">fs_states</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s2">&quot;|&quot;</span><span class="o">+</span><span class="n">state</span><span class="o">+</span><span class="s2">&quot;&gt;&quot;</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">ind</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">h_loc</span><span class="o">.</span><span class="n">get_subspace_dim</span><span class="p">(</span><span class="n">sub</span><span class="p">)):</span>
+
+        <span class="c1"># get particle number</span>
+        <span class="n">particle_number</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">particle_numbers</span><span class="p">[</span><span class="n">sub</span><span class="p">][</span><span class="n">ind</span><span class="p">])</span>
+        <span class="k">if</span> <span class="nb">abs</span><span class="p">(</span><span class="n">particle_number</span><span class="o">-</span><span class="n">particle_numbers</span><span class="p">[</span><span class="n">sub</span><span class="p">][</span><span class="n">ind</span><span class="p">])</span> <span class="o">&gt;</span> <span class="mf">1e-8</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;round error for particle number to large!&#39;</span><span class="p">,</span>
+                             <span class="n">particle_numbers</span><span class="p">[</span><span class="n">sub</span><span class="p">][</span><span class="n">ind</span><span class="p">])</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">particle_number</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">particle_number</span><span class="p">)</span>
+        <span class="n">eng</span><span class="o">=</span><span class="n">h_loc</span><span class="o">.</span><span class="n">energies</span><span class="p">[</span><span class="n">sub</span><span class="p">][</span><span class="n">ind</span><span class="p">]</span>
+
+        <span class="c1"># construct eigenvector in Fock state basis:</span>
+        <span class="n">ev_state</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span>
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">elem</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">h_loc</span><span class="o">.</span><span class="n">unitary_matrices</span><span class="p">[</span><span class="n">sub</span><span class="p">][:,</span><span class="n">ind</span><span class="p">]):</span>
+            <span class="n">ev_state</span> <span class="o">+=</span> <span class="s1">&#39; </span><span class="si">{:+1.4f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">elem</span><span class="p">)</span><span class="o">+</span><span class="n">fs_states</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+
+        <span class="c1"># get spin state</span>
+        <span class="n">ms</span><span class="o">=</span><span class="n">Sz_states</span><span class="p">[</span><span class="n">sub</span><span class="p">][</span><span class="n">ind</span><span class="p">]</span>
+
+        <span class="c1"># add to dict which becomes later the pandas data frame</span>
+        <span class="n">res</span><span class="o">.</span><span class="n">append</span><span class="p">({</span><span class="s2">&quot;Sub#&quot;</span> <span class="p">:</span> <span class="n">sub</span><span class="p">,</span>
+                    <span class="s2">&quot;EV#&quot;</span> <span class="p">:</span> <span class="n">ind</span><span class="p">,</span>
+                    <span class="s2">&quot;N&quot;</span> <span class="p">:</span> <span class="n">particle_number</span><span class="p">,</span>
+                    <span class="s2">&quot;energy&quot;</span> <span class="p">:</span> <span class="n">eng</span><span class="p">,</span>
+                    <span class="s2">&quot;prob&quot;</span><span class="p">:</span> <span class="n">rho</span><span class="p">[</span><span class="n">sub</span><span class="p">][</span><span class="n">ind</span><span class="p">,</span><span class="n">ind</span><span class="p">],</span>
+                    <span class="s2">&quot;m_s&quot;</span><span class="p">:</span> <span class="nb">round</span><span class="p">(</span><span class="n">ms</span><span class="p">,</span><span class="mi">1</span><span class="p">),</span>
+                    <span class="s2">&quot;|m_s|&quot;</span><span class="p">:</span> <span class="nb">abs</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">ms</span><span class="p">,</span><span class="mi">1</span><span class="p">)),</span>
+                    <span class="s2">&quot;state&quot;</span><span class="p">:</span> <span class="n">ev_state</span><span class="p">})</span>
+<span class="c1"># panda data frame from res</span>
+<span class="n">res</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">res</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">res</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="nb">print</span><span class="p">(</span><span class="n">res</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="s1">&#39;prob&#39;</span><span class="p">,</span> <span class="n">ascending</span><span class="o">=</span><span class="kc">False</span><span class="p">)[:</span><span class="mi">10</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+    Sub#  EV#   N        energy      prob  m_s  |m_s|                 state
+4      4    0   9  3.640517e-01  0.310283 -0.5    0.5   +1.0000|0111111111&gt;
+0      0    0   8  0.000000e+00  0.125113 -1.0    1.0   +1.0000|0101111111&gt;
+5      5    0   9  3.640517e-01  0.083760  0.5    0.5   +1.0000|1111101111&gt;
+20    20    0   8  8.851884e-01  0.074717  0.0    0.0   +1.0000|0111111011&gt;
+2      2    0   9  2.739907e-01  0.044306 -0.5    0.5   +1.0000|1101111111&gt;
+55    55    0  10  7.125334e+00  0.038609  0.0    0.0   +1.0000|1111111111&gt;
+3      3    0   9  2.739907e-01  0.035831  0.5    0.5   +1.0000|1111111011&gt;
+51    51    0   8  2.745626e+00  0.033932  0.0    0.0   +1.0000|0111101111&gt;
+1      1    0   8  4.903654e-09  0.031693  1.0    1.0   +1.0000|1111101011&gt;
+21    21    0   8  8.851884e-01  0.019748  0.0    0.0   +1.0000|1101101111&gt;
+</pre></div></div>
+</div>
+<p>This table shows the eigenstates of the impurity with the highest weight / occurence probability. Each row shows the state of the system, where the 1/0 indicates if an orbital is occupied. The orbitals are ordered as given in the projectors (dxy, dyz, dz2, dxz, dx2-y2) from right to left, first one spin-channel, then the other. Additionally each row shows the particle sector of the state, the energy, and the <code class="docutils literal notranslate"><span class="pre">m_s</span></code> quantum number.</p>
+<p>It can be seen, that the state with the highest weight is a state with one hole (N=9 electrons) in the <span class="math notranslate nohighlight">\(d_{x^2-y^2, up}\)</span> orbital carrying a spin of <code class="docutils literal notranslate"><span class="pre">0.5</span></code>. The second state in the list is a state with two holes (N=8). One in the <span class="math notranslate nohighlight">\(d_{x^2-y^2, up}\)</span> and one in the <span class="math notranslate nohighlight">\(d_{z^2, up}\)</span> giving a magnetic moment of 1. This is because the impurity occupation is somewhere between 8 and 9. We can also create a nice state histogram from this:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="c1"># split into ms occupations</span>
+<span class="n">fig</span><span class="p">,</span> <span class="p">(</span><span class="n">ax1</span><span class="p">)</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">150</span><span class="p">)</span>
+
+<span class="n">spin_occ_five</span> <span class="o">=</span> <span class="n">res</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">&#39;N&#39;</span><span class="p">,</span> <span class="s1">&#39;|m_s|&#39;</span><span class="p">])</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
+<span class="n">pivot_df</span> <span class="o">=</span> <span class="n">spin_occ_five</span><span class="o">.</span><span class="n">pivot_table</span><span class="p">(</span><span class="n">index</span><span class="o">=</span><span class="s1">&#39;N&#39;</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="s1">&#39;|m_s|&#39;</span><span class="p">,</span> <span class="n">values</span><span class="o">=</span><span class="s1">&#39;prob&#39;</span><span class="p">)</span>
+<span class="n">pivot_df</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">stacked</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">rot</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">ax1</span><span class="p">)</span>
+
+<span class="n">ax1</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;prob amplitude&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/tutorials_NNO_os_plo_mag_tutorial_33_0.png" src="../../_images/tutorials_NNO_os_plo_mag_tutorial_33_0.png" />
+</div>
+</div>
+<p>This concludes the tutorial. This you can try next:</p>
+<ul class="simple">
+<li>try to find the transition temperature of the system by increasing the temperature in DMFT</li>
+<li>improve the accuracy of the resulting self-energy by restarting the dmft calculation with more n_cycles_tot</li>
+</ul>
+</div>
+</div>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="../Ce2O3_csc_w90/tutorial.html" class="btn btn-neutral float-left" title="3. CSC with QE/W90 and HubbardI: total energy in Ce2O3" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="../correlated_bandstructure/plot_correlated_bands.html" class="btn btn-neutral float-right" title="5. Plotting the spectral function" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+    </div>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.</p>
+  </div>
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
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+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
+
+</footer>
+        </div>
+      </div>
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+      jQuery(function () {
+          SphinxRtdTheme.Navigation.enable(true);
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\ No newline at end of file
diff --git a/tutorials/NNO_os_plo_mag/tutorial.ipynb b/tutorials/NNO_os_plo_mag/tutorial.ipynb
new file mode 100644
index 00000000..1d3cd1f9
--- /dev/null
+++ b/tutorials/NNO_os_plo_mag/tutorial.ipynb
@@ -0,0 +1,846 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "40ad917b-d7a1-4950-8593-abb9b4934e8a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warning: could not identify MPI environment!\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Starting serial run at: 2023-11-24 09:49:44.156139\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "np.set_printoptions(precision=6,suppress=True)\n",
+    "from triqs.plot.mpl_interface import plt,oplot\n",
+    "\n",
+    "from h5 import HDFArchive\n",
+    "\n",
+    "from triqs_dft_tools.converters.vasp import VaspConverter \n",
+    "import triqs_dft_tools.converters.plovasp.converter as plo_converter\n",
+    "\n",
+    "import pymatgen.io.vasp.outputs as vio\n",
+    "from pymatgen.electronic_structure.dos import CompleteDos\n",
+    "from pymatgen.electronic_structure.core import Spin, Orbital, OrbitalType\n",
+    "\n",
+    "import warnings \n",
+    "warnings.filterwarnings(\"ignore\") #ignore some matplotlib warnings"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c24d5aa3-8bf2-471e-868d-32a0d4bb99f7",
+   "metadata": {},
+   "source": [
+    "# 4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aed6468d-cb0b-4eee-93b4-665f4f80ac2d",
+   "metadata": {},
+   "source": [
+    "In this tutorial we will take a look at a magnetic DMFT calculation for NdNiO2 in the antiferromagnetic phase. NdNiO2 shows a clear AFM phase at lower temperatures in DFT+DMFT calculation. The calculations will be performed for a large energy window with all Ni-$d$ orbitals treated as interacting with a density-density type interaction. \n",
+    "\n",
+    "Disclaimer: the interaction values, results etc. might not be 100% physical and are only for demonstrative purposes!\n",
+    "\n",
+    "This tutorial will guide you through the following steps: \n",
+    "\n",
+    "* run a non-magnetic Vasp calculation for NdNiO2 with a two atom supercell allowing magnetic order\n",
+    "* create projectors in a large energy window for all Ni-$d$ orbitals and all O-$p$ orbitals\n",
+    "* create the hdf5 input via the Vasp converter for solid_dmft\n",
+    "* run a AFM DMFT one-shot calculation\n",
+    "* take a look at the output and analyse the multiplets of the Ni-d states\n",
+    "\n",
+    "Warning: the DMFT calculations here are very heavy requiring ~2500 core hours for the DMFT job.\n",
+    "\n",
+    "We set a `path` variable here to the reference files, which should be changed when doing the actual calculations do the work directory:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "6dde4dcd-c06a-45e0-9c06-ca11be265713",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path = './ref/'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1356ed1-b4a6-48d2-8058-863b9e70a0be",
+   "metadata": {},
+   "source": [
+    "## 1. Run DFT \n",
+    "\n",
+    "We start by running Vasp to create the raw projectors. The [INCAR](INCAR), [POSCAR](POSCAR), and [KPOINTS](KPOINTS) file are kept relatively simple. For the POTCAR the `PBE Nd_3`, `PBE Ni_pv` and `PBE O` pseudo potentials are used. Here we make sure that the Kohn-Sham eigenstates are well converged (rms), by performing a few extra SCF steps by setting `NELMIN=30`. Then, the INCAR flag `LOCPROJ = LOCPROJ = 3 4 : d : Pr` instructs Vasp to create projectors for the Ni-$d$ shell of the two Ni sties. More information can be found on the [DFTTools webpage of the Vasp converter](https://triqs.github.io/dft_tools/unstable/guide/conv_vasp.html).\n",
+    "\n",
+    "Next, run Vasp with \n",
+    "```\n",
+    "mpirun vasp_std 1>out.vasp 2>err.vasp &\n",
+    "```\n",
+    "and monitor the output. After Vasp is finished the result should look like this: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "bf1b811e-af03-4714-a644-ad7a7b57c42b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DAV:  25    -0.569483098581E+02   -0.31832E-09    0.42131E-12 29952   0.148E-06    0.488E-07\n",
+      "DAV:  26    -0.569483098574E+02    0.75124E-09    0.25243E-12 30528   0.511E-07    0.226E-07\n",
+      "DAV:  27    -0.569483098574E+02   -0.12733E-10    0.17328E-12 28448   0.285E-07    0.826E-08\n",
+      "DAV:  28    -0.569483098578E+02   -0.41837E-09    0.17366E-12 29536   0.151E-07    0.370E-08\n",
+      "DAV:  29    -0.569483098576E+02    0.22192E-09    0.19300E-12 29280   0.689E-08    0.124E-08\n",
+      "DAV:  30    -0.569483098572E+02    0.38563E-09    0.27026E-12 28576   0.388E-08    0.598E-09\n",
+      "DAV:  31    -0.569483098573E+02   -0.92768E-10    0.34212E-12 29024   0.218E-08\n",
+      " LOCPROJ mode\n",
+      " Computing AMN (projections onto localized orbitals)\n",
+      "   1 F= -.56948310E+02 E0= -.56941742E+02  d E =-.131358E-01\n"
+     ]
+    }
+   ],
+   "source": [
+    "!tail -n 10 ref/out.vasp"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3364605b-c105-4ad8-9350-6569b506df07",
+   "metadata": {},
+   "source": [
+    "let us take a look at the density of states from Vasp:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "3529d644-40f5-4b6b-98f0-2d3a6acdb524",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vasprun = vio.Vasprun(path+'/vasprun.xml')\n",
+    "dos = vasprun.complete_dos\n",
+    "Ni_spd_dos = dos.get_element_spd_dos(\"Ni\")\n",
+    "O_spd_dos = dos.get_element_spd_dos(\"O\")\n",
+    "Nd_spd_dos = dos.get_element_spd_dos(\"Nd\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "5fec0ad8-7ab4-4a02-bd72-b679f6ce6ed4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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j+O6773D33Xf7ePXaYlBKRERERNQKdDrJ52ZBHV2nTp0AoDHoEh09ehQAkJGR4fE57etP33zzTYwZM0bx3KFDh3y5TL989tlnANA4ygYALrnkEuj1emzfvh27d+9WBKzOjpdlGWeddVZjhrU5UVFROOecc3Ds2DHFOtTWxkZHRERERETUosLDGzKt9pmaIvuol08++cTp8/bt9v08Oae9oVDnzp1Vz33xxReeXLZmFixYgN9++w1RUVGYOnVq4/akpKTG7r/3338/bDab0+OLi4sxa9YsAPB6bWhOTg6ApsC/LWBQSkRERERELSo5ORlhYWHIycmB1WpVPX/DDTcgLS0Na9aswTvvvKN4bt68efj999+RlZWFa6+9tnG7Pcjat2+f09fs3bs3AOCdd95RlOmuWbMGL730kt9fkycqKirw5JNPYsqUKQCA+fPnN3bQtXvhhRfQpUsXfP/997jlllsU3XmBhq/v0ksvxcmTJzF27FjcfPPNjc+Vl5dj6NChWLp0KSwWi+I4i8WCp59+Gtu2bUNkZKRiPmxrY/kuERERERG1qPDwcFx55ZVYvnw5Bg4ciDPPPBPh4eE4//zzcfvtt8NgMOCTTz7B1Vdfjb/85S9455130Lt3b+zduxd//PEHDAYDPv30U+j1TeXP3bp1w4ABA7B582acc8456NevH0JCQjB27FiMHTsW//jHP/DBBx/gjTfewKpVqzBgwAAcP34ca9euxf/93/9h9uzZmn6Nzz//fOO81OrqauTl5eGPP/6A2WxGbGws5s+fj8mTJ6uOS0hIwKpVq3DVVVfh888/x1dffYULLrgASUlJOHr0KDZs2ACbzYZx48bh008/VR2/adMmXHfddYiNjcWQIUOQnp6O8vJybNu2Dfn5+QgJCcGbb76J9PR0Tb9efzAoJSIiIiKiFvfee+/hgQcewA8//IBPP/0UVqsV9fX1uP322wEAI0eOxO+//45nnnkGP//8M/78808kJydj0qRJePTRR9GnTx/VORcvXox//vOfWLNmDbZs2QKbzYasrCyMHTsWvXv3xu+//46ZM2di48aN+Oqrr9CnTx+8/fbbuOuuuzQPSr/77jsADU2GYmJikJSUhDFjxmDkyJGYNGkSYmNjXR7bvXt3bN++He+99x6++OILbNmyBZWVlUhOTsbYsWNx++23Y+zYsarj4uLisG7dOnz77bdYtWoVDh06hN9++w0hISHo0qULRo8ejb///e8YMGCApl+rvyS5JVtMkVf69esHANi1a1crXwkREQUjo9GI6OhoAA13+cWB80TUPJvN1lhO2qdPH+h0XD1H7YM337v+xi38qSAiIiIiIqJWw6CUiIiIiIiIWg2DUiIiIiIiImo1DEqJiIiIiIio1TAoJSIiIiIiolbDoJSIiIiIiIhaDYNSIiIiIiIiajUMSomIiIiIiKjVMCglIiIiIiKiVsOglIiIiIiIiFoNg1IiIiIiIiJqNQxKiYiIiIiIqNUwKCUiIiIiIqJWw6CUiIiIiIhanCRJkCQJCQkJKC8vd7rPE088AUmS8Pzzzzvd/sEHH2h2PSNGjIAkSThy5Ihm5yTPtNugdMuWLXj++edx3XXXITMzE5IkISIiwuX+9m96d38uueQSj19/ypQpbs/11ltvafFlEhERERF1aOXl5XjllVda+zKoFYW29gX4atasWVi2bJnH+992220un1uxYgWKi4txwQUXeH0dV1xxBdLT01Xb+/Tp4/W5iIiIiIiCiU6nQ2hoKObOnYv77rsPCQkJHh33t7/9DTfddBMyMjICfIXUEtptUDps2DAMHDgQZ599Ns4++2yngaEjV6n98vJyfPbZZwCASZMmeX0dDz30EEaMGOH1cUREREREwS4sLAx33nkn3njjDbz88suYNWuWR8clJycjOTk5wFdHLaXdlu/OnDkTTz75JMaMGYO0tDSfz7Nw4UKYTCace+656NWrl4ZXSEREREREzXnkkUeg1+vx6quvorS01KNj/FlT+s4776B///6IiIhAZmYm/v73v6OiosLr85B22m2mVCsLFiwAAEyePLmVr4SIiIiIgorNBtR6FoS1OZGJgE6b/FZmZibuuusuzJ8/H3PmzMEzzzyjyXmdeeCBBzBnzhzo9XpccskliIqKwieffILffvsNer0+YK9L7gV1UJqbm4s1a9YgLCwMN954o0/nWLJkCRYvXgyr1Yru3bvj6quvRnZ2tsZXSkREREQdTm0p8FKP1r4K3/wzBzBoVz778MMP47333sNrr72GGTNmICkpSbNz261btw5z5sxBYmIifv31V/Tr1w8AUFJSgksuuQQbNmzQ/DXJM0EdlH7yySeQZRmjRo3y+Rv/tddeUzyeOXMm7rnnHrz66qsIDfXsn9f+AyHKyclBjx7t9BcVEREREZGHOnXqhLvvvhvz5s3D7Nmz8dxzz2n+GvbpGP/3f/+n+PydlJSEl156CVdccYXmr0meabdrSrXgT+nu4MGD8dZbb2H//v2oqanBoUOH8PrrryM+Ph5vvPEG/vnPf2p9uUREREREHdZDDz2EiIgIzJ8/H8XFxZqff+3atQCAG264QfXc5ZdfjsTERM1fkzwTtJnSrVu3Yvfu3YiPj8fVV1/t9fHTp09XPO7evTvuvfdeXHjhhRgyZEhj6UHnzp2bPdeuXbucbneVQSUiIiIi6mgyMjIwbdo0zJ07Fy+99BJeeOEFr45/7733GgNPu+TkZMyePRsAcOLECUiS5PLzeZcuXTxutETaCtqg1J4lnTBhgqaLms844wyMHTsWixYtwo8//ojbb79ds3MTERERUQcSmdiwNrM9igxMVnHmzJl4++238frrr+OBBx7w6ti1a9fiww8/VGzr2rVrY1BKbVdQBqVWq9Wv2aTNsY+Wyc/P1/zcRERERNRB6HSaNgvqCNLT03HPPffg5ZdfxosvvgiDweDxsR988IHbETEZGRk4cuQIjh07hp49e6qez83N9eWSSQNBuab0p59+Qn5+Prp27YoLLrhA8/OXlZUBAKKjozU/NxERERFRRzZz5kxERUXhjTfeQEFBgWbnHT58OABg4cKFqud++OEHlu62oqAMSu2lu5MmTYIkSZqe22QyYcWKFQCAIUOGaHpuIiIiIqKOLjU1Fffeey9qampU5bj++Mtf/gIAePnll7Fnz57G7aWlpXjwwQc1ex3yXtAFpTU1NVi6dCmA5kt3N23ahOzsbIwcOVKxfd++fVi2bBmsVqtie1FREW666SYcO3YMAwcOxHnnnaftxRMRERERBYEHH3wQBoMBtbW1mp1z+PDhuO+++1BcXIzBgwdjzJgxmDBhQuPSu3PPPVez1yLvtNs1pStWrMCsWbMU28xms+Kb6bHHHsPo0aMV+3z55Zeorq7G2WefjezsbLevUVNTg3379qGurk6xPT8/H+PGjUNSUhKys7ORmZmJwsJCbNmyBVVVVcjKysIXX3yheRaWiIiIiCgYpKSk4K9//StefPFFTc/78ssvo0+fPpg/fz5++OEHJCUl4YYbbsBzzz2HcePGafpa5Ll2G5QWFRVh48aNim2yLCu2FRUVqY5zLN31Ve/evXHfffdhw4YNyMnJwaZNm6DX69G7d29cffXVmD59OhISEnw+PxERERFRRyfLstvnX3jhBZdjYZ544gk88cQTXr+mJEmYNm0apk2bpnpu1apVXp+PtCHJzX03UKuxzyl1NceUiIgokIxGY2PTvurqaq+6YBJRA5vNhn379gEA+vTpA50u6FbPUTvlzfeuv3FLu82UEhERtRfrcorx2aZjyEyIxNTh3ZEUrd18bCIiovaOQSkREVEA/X6kFBPf2wh7XdLaA8VYfM95CA9ltoSIiAgIwu67RERELem1nw/CcaHMjuMV+HbXyda7ICIiojaGQSkREVGAlFSb8NvBYtX2n/ZoNwyeiIiovWNQSkREFCCbj5bBalP3E/wzr6IVroaIiKhtYlBKREQUILtOVDrdfrTEiDqLtYWvhoiIqG1iUEpERBQgu084z4jaZCCnqLqFr4aIiKhtYlBKREQUIPsKqlw+d6CAQSkRERHAoJSIiCggLFYbTpTXuXw+r6ymBa+GiIio7WJQSkREFAD55XVOmxzZHXcTsBIREQUTBqVEREQBkFvqPhOaX1HbQldCRETUtjEoJSIiCoBjzZTn5jNTSkREBIBBKRERUUCImdLYiFDF4xPMlBIREQFgUEpERBQQx4SgdOhpSYrHVXX1MJrqW/KSiIjapOLiYjz22GMYPHgw4uPjERUVhZ49e+Luu+/Gzp07W/vyqAUwKCUiIgqAY2XKTOigzvGqfUqN5ha6GiKitunHH39Er1698PTTT+P48eO46KKLMGbMGISFheHdd9/FoEGD8Pzzz7f2ZVKAhTa/CxEREXmrsFK5ZrRPWgzCQ3QwW22N20qMZnROjGrpSyMiahN+//13jB49GhaLBc899xweeOABhIY2hScrV67EpEmT8PDDDyMqKgr/+Mc/WvFqKZCYKSUiItKYzSajqMqk2JYWG4FEQ7hiW0m1ch8iomAhyzJuu+02mM1mPPXUU3jooYcUASkAXHXVVfjyyy8hSRJmzpyJo0ePttLVUqAxU0pERKSx0hoz6oUZpamxeiRFh+OkQwa1hOW7REHNJttQbipv7cvwSbw+HjrJ9/zWN998gz179iAzMxMzZ850ud+FF16ICRMm4IsvvsDrr7+OF1980aPzjxgxAqtXr8bhw4exdu1avPLKK9izZw+io6MxatQoPPvss8jMzPT5+klbDEqJiIg0ViCU7uokIMkQ7iRTyqCUKJiVm8px0ecXtfZl+GT1jauRGJHo8/ErV64EAEyYMAFhYWFu973lllvwxRdf4JtvvvE4KLWbPXs23njjDVxwwQW45pprsGHDBnz00Uf4+eefsX79emRlZfn8NZB2WL5LRESkscJKZVluUrQeoSE6JEfrFdtLjSzfJaLgtG3bNgDAkCFDmt3Xvs/u3bthsVi8ep23334bX3/9NVavXo3//e9/2L9/PyZOnIi8vDyuUW1DGJQSERFpTMyUpsU2BKPMlBIRNSgpKQEApKamNrtvSkoKAMBms6G0tNSr17nhhhtw1VVXNT4OCwvDq6++CoPBgGXLluH48eNenY8Cg0EpERGRxgrFJkcxEQCApGghKOWaUiIKUrIsK/7ryb4AIEmSV69z0003qbYlJSXhsssug81mw7p167w6HwUG15QSERFpTMyUpp7KlCYblOW7JSzfJQpq8fp4rL5xdWtfhk/i9fF+HZ+cnIx9+/ahsLCw2X2LiooANASkCQkJKC4uxgMPPKDab+rUqRg+fLhiW9euXZ2es1u3bgCAEydOeHnlFAgMSomIiDRWIKwpTT2VKRXLd0tZvksU1HSSzq9mQe3ZwIED8dtvv2HLli2YPHmy2323bNkCAOjXrx/CwsJQXV2NDz/8ULXfiBEjVEGpK55kaKnlsHyXiIhIY2IGNDnm1JpSlu8SEQEARo0aBQBYtGhRs82LPv30UwDAlVdeCaAhyynLsurPlClTVMe6mm2am5sLAOjUqZOvXwJpiEEpERGRxsqEYDPpVIY0PlI59sBUb0Odxdpi10VE1FZcddVV6NOnD44fP44XXnjB5X6//vorFi1ahPDwcPz1r3/1+nU+//xz1bbS0lJ8//33kCQJw4YN8/qcpD0GpURERBoTM6D2st24SPUsvopa78YbEBF1BDqdDh988AHCwsLw+OOP44UXXoDVqrxJ980332DcuHGQZRnPP/984zpQb3zxxRf47rvvGh/X19fj/vvvh9FoxNixYzmntI3gmlIiIiINWaw2VNXVK7Y1F5SmxUa0yLUREbUl5557Lr766ivcdNNNeOihh/Dyyy9j2LBh0Ov12LFjB/bs2QOdTodZs2bh/vvv9+k17r77bowaNQoXXnghOnXqhA0bNuDw4cPo1KkT5s2bp/FXRL5ippSIiEhDYuku0BSUhoboEK1X3g9mppSIgtmVV16JAwcO4F//+hcyMjLw888/46uvvkJdXR2mTp2KP/74A48++qjP53/ggQfw3//+FxUVFVi6dCkqKysxefJkbNy4EV26dNHwKyF/MFNKRESkodIaZVAqScq1pHGRYag2NWVSK2oYlBJRcEtJScHTTz+Np59+OiDnv+2223DbbbcF5NykDWZKiYiINCSOeYmLDENoSNPbbaxQwlvOTCkREQU5BqVEREQaEjOl4mxSsQMvy3eJiCjYMSglIiLSUKnYeTdKGZSKzY4YlBIRUbDjmlIiIiINqYJSg/ugtJJBKRGR5latWtXal0BeYKaUiIhIQ2JQmhQtBKVRwprSGnW3XiIiomDCoJSIiEhDJUJQmsDyXSIiIrcYlBIREWlInFPaXPkug1IiIgp2DEqJiIg01Gz5LoNSIiIiBQalREREGhKDUpbvEhERuceglIiISCOyLKO8RhlkelK+K8tywK+NiIiorWq3QemWLVvw/PPP47rrrkNmZiYkSUJERITL/Z944glIkuTyz0MPPeT1NdhsNsydOxf9+/dHZGQkUlJSMGHCBOzevdufL42IiNqpOosNZqtNsS0+UhmUxgvddy1WGbUWa8CvjYiIqK1qt3NKZ82ahWXLlnl93Pnnn4+ePXuqtg8ZMsSr88iyjBtvvBGLFi1CfHw8Ro8ejeLiYixevBgrVqzAL7/8gqFDh3p9fURE1H45K8WNjVS+1YqZUvtxUeHt9i2ZiIjIL+32HXDYsGEYOHAgzj77bJx99tlIT0/36LipU6diypQpfr/+f//7XyxatAi9evXCmjVrkJaWBgBYvHgxrr/+ekycOBF79+5FaGi7/ScmIiIvVdapg9KYiDDVY0kCHCt2y2ssyIiLDPTlERERtUnttnx35syZePLJJzFmzJjGgLAlzZkzBwDw4osvKl5//PjxGDt2LHJycnzK5BIRUfslZkpj9KEI0UmKbSE6CdF65Q1LNjsiomBkX0aXkJCA8vJyp/vYl+A9//zzHp/3yJEjkCQJI0aM0OZCHa7jgw8+0Oyc1KTdBqWt6fDhw9i9ezciIyMxevRo1fPXX389AGD58uUtfWlERNSKKoXgMtZJqS6gLuEVjyMiCibl5eV45ZVXWvsyqBUFXW3pzz//jG3btqGurg5ZWVkYNWqU1+tJt2/fDgA444wzEBam/sBx5plnKvYjIqLgIGY83QWleWW1Lo8jIgoWOp0OoaGhmDt3Lu677z4kJCS09iVRKwi6TOnHH3+MV199FW+//TYee+wxnHXWWbj++utRXV3t8Tlyc3MBAFlZWU6ft2+370dERMFBDC7jIp3f+42N4KxSIiIACAsLw9SpU1FZWYmXX365tS+HWknQBKU9e/bE7NmzsWvXLlRXV+PYsWP45JNPkJmZicWLF2Py5Mken8sewEZFRTl93mAwKPZrTr9+/Zz+ycnJ8fiaiIio9VXW1isei8Gnnap8t67e6X5ERMHgkUcegV6vx6uvvorS0lKPjysuLsZf/vIXpKenIyoqCoMHD8ZHH33k17WsXr0aI0aMQHR0NJKSknDttddi7969fp2Tmhc05buTJk1SPDYYDLjllltw8cUXo3///vjyyy+xbt06nHfeec2eyz7kXJKkZvYkIqJgos6Uck0pEbkm22ywumjw09aFxMdD0mmT38rMzMRdd92F+fPnY86cOXjmmWeaPaakpATnn38+9u/fj6ysLIwdOxYnT57E7bffjmnTpvl0HcuWLcP48eNhtVpx3nnnoUuXLti0aROGDh2Kq6++2qdzkmeCJih1JSMjA7fffjtmz56N7777zqOgNCYmBgBgNBqdPm/fHh0d7dE17Nq1y+n2fv36eXQ8ERG1DeJIGJdrSqNYvktEgLW8HAfOO7+1L8Mnvdb9htDERM3O9/DDD+O9997Da6+9hhkzZiApKcnt/o888gj279+Pa665Bp9//jn0ej0A4JtvvsHYsWO9fv2qqipMnToVVqsVn376KW6++WYAQH19PaZOnYoPP/zQ+y+KPBY05bvu9OrVCwCQn5/v0f5dunQBAOTl5Tl93r7dvh8REQUHTzOlsREcCUNE5KhTp064++67UVVVhdmzZ7vdt7q6Gh9//DFCQ0Mxb968xoAUAEaNGoUJEyZ4/foLFy5EcXExLrvsssaAFABCQ0PxyiuveJxsIt8wKAVQVlYGwPPM5sCBAwEAO3fuhMWi/iCxdetWAMCAAQM0ukIiImoPVN13I5wXJLF8l4hI7aGHHkJERATmz5+P4uJil/tt3boVtbW1GDp0qNMkkGNQ6am1a9cCAG644QbVcwkJCbj88su9Pid5LuiDUlmWsXTpUgDweDRM9+7d0bdvX9TW1mLFihWq5xctWgQAGDNmjHYXSkREbZ4YXIplunZiWS8zpUREDcvqpk2bhurqarz00ksu9ztx4gQA11WJzravXbsWU6ZMUf2xB7++nJO0ExRrSouLi7Fy5UrceOONivR+dXU1HnjgAWzcuBHp6em49tprFcdt2rQJt956KzIzM/HTTz8pnpsxYwbuuusuPPjggzjvvPOQmpoKAFiyZAm++uordO/eHePGjQv410ZERG2HGJR62n2XQSlRcAqJj0evdb+19mX4JCQ+PiDnnTlzJt5++228/vrreOCBB5zu40vT0YMHDzpdF/rEE08gOTmZjUxbWbsNSlesWIFZs2YptpnNZpx77rmNjx977DGMHj0a1dXVuO222/D3v/8dffv2RZcuXVBeXo6tW7eipKQE8fHxWLRokWrES01NDfbt24e6ujrV699xxx1YuXIlli5diuzsbIwcORLFxcVYvXo1IiIisGDBAoSFOf8wQkREHZM42sXlmlIGpUQEQNLpNG0W1BGkp6fjnnvuwcsvv4wXX3yxcdSio06dOgEAjh496vQcubm5qm32zKgrvpyTtNNuy3eLioqwcePGxj9Aw10Tx21FRUUAgKSkJMycORODBw9GXl4evvrqK/z2229IT0/H//3f/2Hnzp04/3zvOp/pdDosXLgQc+bMQadOnfD1119jx44duPbaa7F582aPuvgSEVHHUW+1odokzCn1cCSMqd6GOos1YNdGRNSezJw5E1FRUXjjjTdQUFCgen7IkCGIiIjAxo0bcezYMdXzn332mdevOXz4cAANDY9E5eXl+P77770+J3mu3QalU6ZMgSzLbv/Y74bExMTg+eefx6pVq5CXl4e6ujoYjUbs3LkTs2fPRmZmptPXGDFiBGRZxpEjR5w+HxISghkzZmDnzp2ora1FcXExFi9ezFEuRERBqErIkgKezykF1ONkiIiCVWpqKu69917U1NQ4LbmNjo7GxIkTUV9fj+nTp8NkMjU+9/333+OLL77w+jUnTJiAxMRE1fFWqxX/93//h+rqat++GPJIuw1KiYiI2hJnJbiuR8I4CUpZwktE1OjBBx+EwWBAbW2t0+eff/559OzZE0uXLkWvXr1w880345JLLsGoUaNw1113ef16sbGxeOedd6DT6XDjjTdi+PDhuOWWW9CnTx8sWrQIEydO9PdLIjcYlBIREWlAzHSGhUjQhzp/mw0P1SEyLESxjetKiYiapKSk4K9//avL55OTk/Hbb79h6tSpMJlM+PLLL1FSUoJ3330XDz74oE+vOX78ePzwww+44IIL8Mcff+Cbb77B6aefjvXr16Nnz56+finkAUm2t5qiNsdeBrxr165WvhIiImrOupxi3PLuxsbHCVFh+ONx13Ptzn32J5ysbGqk998pZ+Pi7NSAXqO3jEZj4wzv6upqpw1HiMg9m82Gffv2AQD69OkDnY45IWofvPne9Tdu4U8FERGRBowmZaMig959g3uOhSEiImrAoJSIiEgDNWZlo6NoBqVEREQeYVBKRESkAXEcTFR4iIs9G8RGKoNWBqVERBSsGJQSERFpwCgEpc2V74ozTNl9l4iIghWDUiIiIg2Ia0pZvktEROQZBqVEREQaEDOlUeEMSomIiDzBoJSIiEgDRlWjo2bWlEYI5bt1DEqJOiJJkhr/brPZWvFKiLxjtTZVADl+HwcCg1IiIiIN+D8Spt7FnkTUnkmShPDwcAANs3+J2ovKykoAgF6vD3hQ6v4dk4iIiDzittGRzQqUHgKiU4GIOADqoJSNjog6rpiYGJSUlKCgoAAAYDAYoNMxN0RtjyzLMJlMqKqqQmlpKQAgISEh4K/LoJSIiEgD4kgYg30kTOlhYMF1DUFpeDQw7g3g9GsQF8U1pUTBIikpCUajEXV1dThx4kRrXw6Rx+Lj4xEXFxfw1+EtGiIiIg3UmF2U7y7/R0NACgDmamDpPUB1kWpNabWpHvVWrjcj6ohCQkLQpUsXJCUlNZbyErVVISEhiI2NRWZmJtLT01skq89MKRERkQaclu+W5ACHf1XuaDECOxchru/tqnNU1dUjwcAPrEQdUUhICFJTU5GamgpZliHLcmtfEpGKJEkBXz/qDINSIiIiDajKd/WhwK6lznc+vAZxZ96t2lxRa2FQShQEWuuDP1FbxfJdIiIiDYjlu9H6ECB3vfOdj/6GiFAgPET5Nsx1pUREFIwYlBIREflJlmXVnNKoMB2Q97vzA+rKIZUfRWyksmCJs0qJiCgYMSglIiLyU43ZCnF5WHztMaCuwvVBxQcRq5pVyqCUiIiCD4NSIiIiP4lZUgCIrjrk/qCSA6pZpQxKiYgoGDEoJSIi8pPRZFVti6g46P6gYgalREREAINSIiIiv4njYEJ1EkJLD7g/qDRHNau0sladcSUiIuroGJQSERH5ydmMUql4v3KnzCHKxxXHmSklIiICg1IiIiK/iWtKDWE6oFjIlPa4RPm48jjiIoTuuwxKiYgoCDEoJSIi8lO1sKY0K7waMFUqdzrtYuXj+jqkhhoVm5gpJSKiYMSglIiIyE81Qvlut9AS5Q66MCDrLEBSvu2moVjxmHNKiYgoGDEoJSIi8lO1EJR21hUpd4jLAkL1QHS6YnOyTRmUMlNKRETBiEEpERGRn8SRMJ0gBKXxnRv+G5uh3GwtVTxmUEpERMGIQSkREZGfaoRGR+k2MVPapeG/hlTF5ph6ZVBaWWuBLMuaXx8REVFbxqCUiIjIT2L5brK1QLlD/KmgNDpFsTnSrAxKbbL6XERERB0dg1IiIiI/iXNKE+vFoPRU+a6QKY0wCw2RwBJeIiIKPgxKiYiI/GQ0O64plRFnOqncIe5UUBqtDEpDa4qhk5S7MiglIqJgw6CUiIjIT46Z0lgYEW6rVe4Ql9XwXyEolYyFiI0MU2yrrGX5LhERBRcGpURERH5yDEpTpAr1DjGnuu4K5buoLkScEJSW15i1vjwiIqI2jUEpERGRnxzLd1OlcuWTEXFAWETD34VMKcxVSItUdtstq2H5LhERBRcGpURERH5yzJSmokz5ZHR6098Nyu67ANBVb1Q8LmOmlIiIggyDUiIiIj85jnFRZUpj0pr+HhEHhIQrns4Mq1I8LjMyKCUiouDCoJSIiMgPsiyjxqF8V7WmNNohKJUk1brS9NBqxeNSZkqJiCjIMCglIiLyg6neBqutaV1oqiSW76YpH0clKB6mhCiDUmZKiYgo2DAoJSIi8oNj6S4ApKJcuUNMuvJxVJLiYaKkXFNaykZHREQUZBiUEhER+aHGZFU8Vq0pjRaC0shExcM4uVLxmJlSIiIKNu02KN2yZQuef/55XHfddcjMzIQkSYiIiHC6r81mw5o1a/Dggw9i6NChSE1NhV6vR48ePTBt2jQcPnzY69efMmUKJEly+eett97y90skIqJ2QMyUprhrdASoMqXRNiEo5ZpSIiIKMqGtfQG+mjVrFpYtW+bRvocOHcKFF14IAMjMzMR5550HnU6HTZs24e2338ann36KlStXYvjw4V5fxxVXXIH09HTV9j59+nh9LiIian+M5qagVA8z4qQa5Q6qNaXKoDSqvlzxuKquHharDWEh7fa+MRERkVfabVA6bNgwDBw4EGeffTbOPvtsp4GhnSRJuOKKK/DII480BqcAYDKZMG3aNHzwwQeYOHEiDh48iLCwMK+u46GHHsKIESN8/TKIiKidc5xRqsqSAk6CUmX5rt4idOtFQ7Y0NcZ59Q8REVFH026D0pkzZ3q8b48ePfDtt9+qtuv1erz55ptYunQpcnNzsW7dOlx00UVaXiYREXVwRoc1paomR6ERDbNJHQmZ0tA6oVsvgDKjhUEpEREFjaCvDYqIiEDv3r0BACdOnGjlqyEiovbGMVOaJCnXh8KQ2jCb1FGkciSMVFuCuEhllQ7XlRIRUTBpt5lSrVitVhw9ehQA3JYAu7JkyRIsXrwYVqsV3bt3x9VXX43s7GytL5OIiNooxzWlCVKV8kmhVLdhmzJTippSJESGoqK2aRQMO/ASEVEwCfqg9LPPPkNhYSFSUlJw3nnneX38a6+9png8c+ZM3HPPPXj11VcRGurZP2+/fv2cbs/JyUGPHj28viYiImo5jpnSBFQrnxQDUEAdqMpWZEZZcKS0aVMpM6VERBREgrp899ixY7jvvvsAAE899RT0er3Hxw4ePBhvvfUW9u/fj5qaGhw6dAivv/464uPj8cYbb+Cf//xngK6aiIjakmqHNaU+ZUoBdNbXKR4zU0pERMEkaDOlRqMR1157LYqLizFu3DhMmzbNq+OnT5+ueNy9e3fce++9uPDCCzFkyBC89tprmDFjBjp37tzsuXbt2uV0u6sMKhERtR01Zi8zpWFRQIgesJoaN3UKrwEQ2/i41GhRH0dERNRBBWWm1GKxYPz48diyZQuGDx+OTz/9VLNzn3HGGRg7diysVit+/PFHzc5LRERtU7XJzZrSSCeZUklSBatpYcrZpuUs3yUioiASdEGpzWbDpEmT8N1332HgwIFYvnw5IiMjNX2NXr16AQDy8/M1PS8REbU9RndBqbPyXSfbk3XK47imlIiIgomm5btHjhzBmjVrsH37dhQVFaGiogJxcXFISUnBoEGDcMEFF6Br165avqTX7r33XnzxxRfo3bs3vv/+e8THx2v+GmVlDTPnoqOjNT83ERG1LTVmhzWlqvJdz4LSRCiDUq4pJSKiYOJ3UFpWVoYPP/wQ7777Lvbu3QsAkGVZtZ90ak5b3759cdddd+HWW29FQkKCar9AeuSRR/D222+jS5cu+OGHH5Camqr5a5hMJqxYsQIAMGTIEM3PT0REbYtj+W68JASlzsp3AVX5bqwQzDJTSkREwcTnoLSmpgYvvvgi5syZA6PRiMjISAwfPhznnHMOsrOzkZiYiNjYWFRUVKCsrAx79uzBpk2bsHnzZtx///149NFH8cADD+CBBx6AwWDQ8mty6uWXX8Zzzz2H9PR0/Pjjj+jSpUuzx2zatAm33norMjMz8dNPPzVu37dvH/bu3YsxY8YgJCSkcXtRURHuvvtuHDt2DAMHDvRpxAwREbUv9vJdCTYkQCzfddLoCFAFqzG2CsXjcjY6IiKiIOJzUNqjRw8UFBTgiiuuwKRJkzBu3DiPgkuj0YglS5ZgwYIFePLJJ/H222/jxIkTXr/+ihUrMGvWLMU2s9mMc889t/HxY489htGjR2Pbtm144IEHADR0yX3mmWecnnPq1KkYPnx44+Oamhrs27cPdXXKVv35+fkYN24ckpKSkJ2djczMTBQWFmLLli2oqqpCVlYWvvjii8bsMBERdVzGUyNhYlCDEEmoFHJZvqsMVqPqlUFplake5nobwkODrvUDEREFIZ+D0qFDh+Lf//43Bg8e7NVxBoMBkydPxuTJk7F161ZVYOmpoqIibNy4UbFNlmXFtqKiIgBAeXl5Y0nx+vXrsX79eqfnHDFihCIodaV379647777sGHDBuTk5GDTpk3Q6/Xo3bs3rr76akyfPr3FS5OJiKh1GE+NhEkUmxwBbsp3ldv15nLVLuU1ZqTGRvh7eURERG2ez0Hpl19+6feLn3nmmVi6dKlPx06ZMgVTpkzxaN8RI0Y4Xefq63GdOnXCK6+84vX5iIio47GX76qaHIVGAuFRzg8SMqWhpjJIEuD4llNiZFBKRETBwee6oOrq6uZ3IiIi6sDM9TZYrA2RpKrJkavSXUCVQZVqSxEXGabYVsZmR0REFCR8DkrT0tIwceJErFy5EjabTctrIiIiahccZ5SKY13cBqXiczUlSBSC0vIaNjsiIqLg4HNQWltbi//973+4+uqr0alTJ9x3333YvHmzltdGRB2Mud6Gz3/PxTu/5qCcWSDqAJTjYISg1NV6UkDdlddWj05R9YpNpZxVSkREQcLnoPTIkSN45pln0LdvXxQWFmLevHkYOnQosrOz8eyzz+LIkSMaXiYRtXeyLGPqR5sxc/EOPLtyL0bPW8vAlNq9GrO18e8J3pTvOnkuS1+reFzGoJSIiIKEz0Fply5d8PDDD2Pnzp34448/MGPGDHTq1An79+/HY489hh49euCiiy7Cu+++i/Lycg0vmYjao98OluDX/UWNj4+X1+Kj9Udb8YqI/OeYKY1BjfLJiDjXB4ZHAyHhik0Z4crjS3nThoiIgoQmA9AGDhyI2bNnIzc3Fz/++CNuu+02xMTEYM2aNZg2bRoyMjJw/fXX48svv4TFwjUyRMFo9f5C1baf9qq3EbUnjmtKYyRlphP6WNcHSpKqvDc91Kh4zEwpEREFC02nckuShEsuuQTvv/8+CgoK8Pnnn2PMmDGQZRlLlizB+PHjkZ6ejnvuuUfLlyWidmBPvnqG4978StRb2SiN2q8as7tMqZugFFCtK00OUZb/lrLRERERBQlNg1JHer0eEyZMwLJly5Cfn4/XX38diYmJKCsrwzvvvBOolyWiNupgoXqMlKnehiMlRid7E7UP1aamNaWxkhCU6t2U7wKqdaWJEjOlREQUnEID/QJ//vknPvnkE/zvf/9DaWkpACAkJCTQL0tEbUi91YbCqjqnz+06UYmeqTEtfEVE2lCU70Io3202U6oMSuPkSsVjziklIqJgEZCg9NixY/j000/xySefYNeuXZDlhsHiAwYMwOTJkzFx4sRAvCwRtVHF1WbYZOfPHS5mppTaL6O78l13a0oBVflutE0ISpkpJSKiIKFZUFpRUYGFCxdiwYIFWLt2LWRZhizL6NSpE2655RZMnjwZ/fv31+rliKgdKah0niUFgLyyWpfPEbV1ykZH/q0pjaovV57bbEWdxYqIMFYXERFRx+ZXUGo2m/H111/jk08+wcqVK2E2myHLMqKjo3Httddi8uTJGDlyJCRJ0up6iagdOuk2KK1x+RxRW2dsXFMqI1os320uUyp039Wby1W7lNdYkB7HoJSIiDo2n4PSu+66C4sXL0ZFRQVkWUZISAguu+wyTJ48Gddeey2ioqK0vE4iaseYKaWOyp4pjYIJoZLQSVrfzFppIVMaaiqDToKi1L3UaEZ6XIQWl0pERNRm+RyU/uc//wHQMKN08uTJuOWWW5Cenq7ZhRFRx1FYaXL5XH5FHeqtNoSGBKwZOFHA2NeUqtaTAkCEd913pZpSxEeFo9RhLSmbHRERUTDwOSj95z//icmTJ+OMM87Q8nqIqAMqr3X9wdpqk3Gysg5ZCayuoPbHPhJGtZ4U8CBTqgxKUVuKhMhQBqVERBR0fA5KX3jhBS2vg4g6sIraerfP55XVMiildqnmVPlurJgpDYsCQsLcHyyU78JqRkakFTkOm9iBl4iIgoHm9XLffPMNxo0bh8zMTOj1etx5552K52bMmIETJ05o/bJE1IaVN5PtOVbKZkfUPlWfCkpjJC+bHAGqRkcA0DlCeZ5So8XnayMiImovNA1K7733XowZMwZfffUVqqurYbFYGmeUAkB8fDzmzp2Lzz77TMuXJaI2rqLW/Qfr/ArXjZCI2rIa86nyXTFT2tw4GKChvFenzKZmhCuDUpbvEhFRMNAsKH3//ffx1ltv4ZxzzsG2bdtQUVGh2mfYsGHIzMzE8uXLtXpZInKw83gF/r1sJ+b/fAC1ZmvzB7SQ8hplUJoSo1c8PlHODrzUPtm770b7kimVJNW60rRQo+JxKct3iYgoCPg1p9TR22+/jcTERHz99ddISkpyuV/Pnj1x6NAhrV6WiE7Zd7IKE95aj1pLQzC6/lAJFtw5tE3MCRbLd0/PiMXqqqLGx8cZlFI71Vi+60umFGhYV1pd0PgwJaQaQGrjY2ZKiYgoGGiWKd21axeGDRvmNiAFgPT0dBQWFmr1skR0yqs/7W8MSAHgt4Ml2HCotBWvqIHVJqOyTtno6PROyg/szJRSe1RvtcFU3zCbVNV915NMKaBqdpSAKsVjBqVERBQMNAtKdTodbDZbs/udOHECBoNBq5clIjQEfr/uL1ZtX7W/9W8AVTpZT9o3QwxK6xTrz4naA6NDiXwMhBsrnmZKIxMUD+NkIShloyMiIgoCmgWl2dnZ2Lx5M2pqXHfRLCkpwbZt2zBgwACtXpaIAOQUVTeWETraeVy9trulVdY5CUrTlfMbay1W1bpTorbO6PAzF6tRpjRarlQ85ppSIiIKBpoFpRMnTkRRURH++te/or5e/eFYlmX84x//QHV1NSZPnqzVyxIRgMPFRqfbdx6vbPUMpNGkbLikk4CuSQaIS11PVLCEl9qXGnPTe516TWmcZycRgtIoS7nica3FijpL22laRkREFAiaBaX33nsvhg8fjg8//BB9+/bF3/72NwDAn3/+iQceeAB9+/bF//73P1x88cW47bbbtHpZIgJwtMR5UFpRa2n1JkKOH9wBICo8FOGhOqTFRCi2nyjnWBhqX6pNbsp3Pc6UKrvv6i3q6gZnJfBEREQdiWZBaVhYGL799ltMmzYNubm5eOONNwAAW7duxcsvv4ycnBzceeedWL58OXQ6TcejEgW9IyWuy+Zz3TzXEmqE0TRR4SEAgE7xYlDKTCm1L47lu6pGR95033UQWqduTuasBJ6IiKgj0WwkDABERUXhjTfewJNPPonVq1fjyJEjsFqtyMrKwsUXX4xOnTpp+XJEdIqrTCkA5JbW4LwWvBaR66A0Eltzyxu3Myil9qaqzk35rqeZ0khlplSqLUVUeIji56aCmVIiIurgfA5K161bh2HDhjmdgZiSkoLrr7/erwsjIs/luyl9PVra2plSdfkuAGTGRyq2t3aZMZG3ApEpRU0J4iJCFUFpZa26TwMREVFH4nMd7fDhw5GWlobbb78dS5YsQXV1tZbXRUReKKo2uXwut9WDUteZUkfMlFJ709TxWvZ9TalBCEqtZqRHKDOjLN8lIqKOzuegdNasWejRowc++ugjTJgwASkpKRg1ahTeeOMN5ObmanmNRORGncWqKCMU5bV6UKq8tkgXQWleGYNSal/sQWkEzAiThA65nmZKDamqTV3ClTd5Wb5LREQdnc9B6b/+9S+sX78e+fn5eOedd3D55ZdjzZo1+Nvf/obu3btj0KBBePzxx7Fp0yYtr5eIBMVusqRA65fFiplSw6ny3c6JyqC0sMrkdNYqUVtl/35VrScFAL2HI2HCo4DwaMWmTmFVisfsvktERB2d321wU1NTceedd2LZsmUoKSnBV199halTp6K4uBhPP/00hg0bhoyMDNx9991Yvnw5amuZDSHSUnG1udnna82tN+fQVflutyQDdMKS9ENFXAZA7Uf1qQqFWHE9KeB5phQADCmKh2khlYrHzJQSEVFHp+lsFr1ejzFjxuDtt99GXl4efv/9d/zrX/9CRkYG3nvvPYwbNw5JSUm4+uqr8e6776KoqEjLlycKSsVVykxpXGSYap/WzJaqGh3pG4LSiLAQdE6MUjyXw6CU2pGmTKnw8xUaCYSofw5dilaW8KZIyqCUjY6IiKijC+jA0CFDhuCpp57C1q1bkZubi9deew0XXnghfvzxR0ybNg1vvvlmIF+eKCiITY66JEYhIUr5gbh1g1IxU9rU9LtHirJs8WAhg1JqPxqDUl8779oJmdIkuVzxmJlSIiLq6AIalDrKysrCvffei2+//RbFxcVYuHAhhg0b1lIvT9RhiZnSlBg9MhOEcSut2ESoxuS8fBcAeqYqg9KcQtfzVonaGnv5rs8zSu2ETGm8EJSy+y4REXV0Ps8p9YfBYMB1113XGi9N1OGIjY6So8MRFiJh5/GmEsDj5a3XgbfG4joo7ZFiUDy3r6AKVpuMX/cXoaLWgstOT4NB3yq/poiaZTTbM6XiOJgY704kdOCNqS9TPGamlIiIOroW/7T3/vvvIy8vD48//nhLvzRRhyQ2OkqO1iNaryzfbc1xKzVCR13H8t3TM5QdSg8XG3H1a2uxO78hoM6Ii8DHd56DnqlefsgnagEuM6Xelu9GK8t3DZZSxWNmSomIqKNrsfJdu3fffRdPPvlkS78sUYclZkqTottY+a6L7rsA0DcjBtFCJtQekAJAfkUd/rV0Z2AvkMhHVa7WlHpbvitkSiPMJYrHFTUMSomIqGNr8aCUiLQllvYlRIUhSwxK21L3XYegNDREhyFdE9wev/FwKY6WcK0ptT3GU0FprN+ZUmVQGl5XrHhcZaqHzSZ7fX1ERETthc/lu4WFhT4dZ7Hwji+RlsSgNC4yDGmxEYptBZV1sFhtCAtp+ftQ7rrvAsDoARlYvd/9eKhfDxRjcpLB7T5ELclqkxu/t9VrSuOcHOGG0H1XV1+LKNShBg0/x7LcEJg6G/dERETUEfgclKanp0OSJK+Pk2XZp+OIyDlnQamYKbXJwMmKOtVc0JZQ66Z8FwDGDuyE2d/tQ6HQRdjR7hMVAbk2Il9VO6yV9n9NaapqU7JUgVy56eZSZa2FQSkREXVYfqdNunTp4tWf8PBwLa4bW7ZswfPPP4/rrrsOmZmZkCQJERERzR730Ucf4ZxzzkF0dDQSExNx1VVXYd26dT5dg81mw9y5c9G/f39ERkYiJSUFEyZMwO7du306H5G3LFabKhMZFxmGuMgwGITg71hZy3fglWW5sUOpnZgpjQgLwWs3D0ZKjB4AEB6iQ7qQ6d2dXxXYCyXyktFdUOrtmtLwaCBUeSMpTVepeMwOvERE1JH5nCnt3r07jhw5grVr1yIzM9Pj44YNG4ZNmzb5+rKNZs2ahWXLlnl1zIwZM/DKK68gMjISl19+Oerq6vDDDz/g+++/x8KFC3Httdd6fC5ZlnHjjTdi0aJFiI+Px+jRo1FcXIzFixdjxYoV+OWXXzB06FBvvywirzj7oBoXGQZJkpCZEIn9BdWN21uj2ZGp3gZxKZyYKQWAoaclYdUDI3C0pAYpMXrsPVmJyf9p+j2xN78SVpuMEB2rLKhtUGRKxUZH3mZKJamhA295buOmzuFV+N3hR7aSQSkREXVgPmdKzznnHADA5s2bNbsYbwwbNgyPP/44li9fjpMnTza7/88//4xXXnkFSUlJ2L59O7788kt8++23+PXXXxESEoLbb78dZWVlzZ7H7r///S8WLVqEXr16Ye/evVi0aBFWrVqFhQsXora2FhMnTkR9fX3zJyLyg7OgNPZUiV9mfOs3OxKzuAAQpVcHpQBg0Ifi9E6xSInRo0+6cgSMqd6Ggsq6gFwjkS+U5bvimlIvg1JA1YG3U6iyOqDaxPcTIiLquHwOSocOHQpZlrFx40avjpNlbToIzpw5E08++STGjBmDtLS0ZvefM2cOAODRRx9Fr169GrcPGzYM06ZNQ0VFBd5//32PX99+vhdffFHx+uPHj8fYsWORk5PjdSaXyFtiUBoRpkNEWEPQl5WgXD/aGplSsfMuoC7fdSYlWo+IMOWvp9actUokss8oBTTIlAKqdaVpIcqgVCyDJyIi6kh8DkqvuuoqTJ8+XRHgeeLNN9/Ezz//7OvL+qSurg4//fQTAOD6669XPW/ftnz5co/Od/jwYezevRuRkZEYPXq03+cj8pWzJkd2qlmlbSRTGhnmPFPqSJIkJ5nell8TS+RKU+ZS9n9NKaDqwJsqKZt7VZvUP0tEREQdhc9rSnv37o1XXnnF6+MGDx7s60v6bO/evTCZTEhJSUFWVpbq+TPPPBMA8Oeff3p0vu3btwMAzjjjDISFqbsh2s9n348oUMR1ZoqgVAjqWiPTKAalEWE6j9eFZiZEIaeoaT5pXikzpdR22INSPSwIl4SAMcLLkTCAKlOaJAaldcyUEhFRx+VzUNqe5OY2NI9wFpACgMFgQHx8PMrKylBVVYWYmBin+3l6Pvt2+37N6devn9PtOTk56NGjh0fnoODkTaY0v6IWNpsMXQs2CxLLdz0p3bUTx9q0RqaXyBV7kBgrZkkBTdaUJtjKFY+NXFNKREQdmN8jYdqD6uqGDqRRUa5nNBoMBsW+/pzPm3MR+aOixnVQmiVkSi1W2e0s0ECoMbmfUeqOmOk9yUZH1IbYM6XRkpObJT6tKVWW78YJQSkbHRERUUfmc6Z07969yM7O9vsCtDqPO/bmSpLkOkPkTQMmT87njV27djnd7iqDSmQnZkpjHYLS5Gg9wkN1MNfbGrcdL69Belzz83y1UmPxPShNjlbONC41mjW5JiIt2EvnVetJQ/RAqN77E0YrG/bF1JcqHjMoJSKijsznTOkZZ5yBiRMnYufOnT4dv23bNtx0003o37+/r5fgMXs5rtFodLlPTU3DB4vo6Gi/z2ff7sm5iPzhrnxXp1M3C2rpdaU1Jt/LdxMNyg/2JdUMSqntsP/sadJ5F1AFpXpbDSLRVB3A8l0iIurIfA5KH3vsMSxfvhwDBw7EmWeeiTlz5mDz5s2wWJwP+DaZTNiwYQOee+459O/fH0OGDMHKlSvx+OOP+3zxnurSpQsAIC8vz+nzRqMR5eXliI+Pb3Y9qSfns2+370cUKO6CUqD1mx2JjY68yZQmCZnS4mqTZiOliPxVWWfPlGowoxRQNToCgGSHZkfMlBIRUUfmc/nuv//9b9xzzz145pln8NFHH+Gf//wnJElCWFgYunXrhoSEBMTExKCyshKlpaU4evQo6uvrIcsy4uLiMH36dDz88MNISUlp/sX81KdPH+j1ehQVFSEvL0/VoGjr1q0AgAEDBnh0voEDBwIAdu7cCYvFourA6+35iHzVXFDaOVEZlOYUtew6Z38aHSULmVJTvQ1GsxXR+qDoz0ZtXGVtw/e2ZpnS8GggLAqwNJ0vBRU4hoYMKoNSIiLqyPxqdJSamopXX30VJ06cwH//+19cd911SExMxP79+7Fx40b8+OOP2LRpEw4ePIikpCRcf/31+OCDD5Cfn4+XX365RQJSAIiMjMQll1wCAFi0aJHqefu2MWPGeHS+7t27o2/fvqitrcWKFSv8Ph+Rr5oLSvukKTP/u09UBvyaHGmZKQWAkuqWbdRE5Io9U6rqvutrplSSVNnSFKm88e8s3yUioo5Mk+67kZGRuO2227Bw4UKcOHECBQUF2LFjB9auXYsdO3agoKAAJ06cwOeff45bb70VEREt12jFbsaMGQCAp59+GgcOHGjcvn79erz99tuIjY3FnXfeqThm06ZNyM7OxsiRI12e78EHH0RhYWHj9iVLluCrr75C9+7dMW7cuAB8JURNmgtK+2Uq5yUeKKxGndB8KJD8CUqjwkMQEab8FVXMdaXURmi+phRQrStNcSzf5ZxSIiLqwAJSB5eSkhLwLOiKFSswa9YsxTaz2Yxzzz238fFjjz2G0aNHAwAuvfRSTJ8+Ha+++ioGDRqEyy67DGazGT/88ANsNhs++eQTJCYmKs5XU1ODffv2oa5OPYrijjvuwMqVK7F06dLGwLW4uBirV69GREQEFixYoCrrJdJac0Fp34xYSBJgX4pptcnYX1CFAVnxLXJ9/pTvSpKEJINeMZ+UHXiprWjqviuuKY1zsreHDMr3TcdMKct3iYioI2u3i7OKioqwceNGxTZZlhXbioqKFM/PnTsXgwYNwvz58/HDDz8gLCwMI0eOxKOPPorhw4d79fo6nQ4LFy7Eq6++ivfffx9ff/01DAYDrr32Wjz11FMc50IBZ7HaVJnI+ChlUBqtD0W3JAMOFzd1it51orIFg1LfM6VAQwmvY1DK8l1qC2w2GVWngkTVSBgtM6Uob/y70WyFLMuajSIjIiJqS9ptUDplyhRMmTIloMeNGDHCbbfPkJAQzJgxo7GUl6gliVlSQDmn1O70TrGKoPTPvArcfE5AL62RKijVexmUGpTrSkuYKaU2oNpc31h9oCrf9XVNKeC2fNdqk1FnsSHSyxs7RERE7YEma0qJqOU5C0rF8l0AGJilLCf8M688UJekoirfDfM2U6rswFtUxUwptb5Kh589VaZU3/xYMZdUjY4qFI9ZwktERB0Vg1KidkoMSiPCdNCHqoM+sVR378mqFmt2pM6UeleckShkSp0F4kQtzfH7MEYS1pRqWL6bLASl7MBLREQdFYNSonaquSZHdv0z46BzWIZmtcnY1UKjYfxdUyp+TZUMSqkNsM8oBZxlSrULSlNRDqBpCQkzpURE1FExKCVqp8QAzVVQatCHomdqtGLb9mPlgboshRrhQ7TBi+67ABAbodyfmVJqC+wzSgGtR8Ioy3fDpXrEomk9OINSIiLqqBiUErVTnmZKAWCgUMLbUutKa4QyYW+btIiNmxyDAaLW4nhDKFbLkTBCUAoo15WyfJeIiDqqgAelBw8exIYNG5CbmxvolyIKKhU1ngelAzrHKx5vz6twvqPG/C3fFYNSZkqpLbB/H4bDAr0kfE/6kykN1QMR8YpNqZxVSkREQcDnoLSgoABffPEF1q1b5/T53377DdnZ2ejTpw/OP/98dO/eHWeffTZ27tzp88USURMxQHM2DsZukJApPVxsDPgH3HqrDeZ6m2JblJflu+o1pfxQTq2vss7FjFLAvzWlgJNZpU03kBiUEhFRR+VzUPrxxx/j5ptvxr59+1TPHThwAFdeeSUOHDgAWZaRmJgIANiyZQtGjhyJ4uJi36+YiAB4V77bKy0akqTcdsRhdmkgiKW7gA+Z0gjl11RrsaoCXaKWZi/fVa0nBfzLlAJOxsKUN/6d5btERNRR+RyUrl69GhEREbjhhhtUzz3xxBMwGo3o0qULduzYgaKiIpSUlGDs2LEoLi7GvHnz/LpoIvIuKI0IC0GnuEjFtiMlAQ5KTeqg1NtGR86+Jq4rpdZm/x6MEdeT6sKA0Aj/Tu5mVmm1k58pIiKijsDnoHTv3r0YMmQIDAaDYrvFYsGXX34JSZIwe/Zs9OvXDwAQHx+PDz74AAaDAd9++61/V01EKBeC0ng3QSkAdEuOUjwOeKbUrM7qeN/oSB3EciwMtTaXmdKIWKhKErwllu86ZEprnfxMERERdQQ+B6WFhYXo0qWLavvmzZtRW1uLyMhIjBkzRvFcfHw8zjnnHBw4cMDXlyWiU1QjYaLcB6Xdk5U3kA4XOyk91JDY5CgsREJ4qHe/cvShIYgIUx7DZkfU2spONRmLFdeUCk2KfCJmSlHe+HejmZlSIiLqmHwOSk0mEyorK1XbN2zYAAA488wzodfrVc+npaWhpiawH4aJgoE35bsA0CVRmSk9WVnrYk9tiEFpZJh3WVI7cV2pvckMUWspM5oBALGSUG0Q4cc4GDshU5osNb3P1jIoJSKiDsrnoDQrKwvbt2+HzaZsOvLTTz9BkiQMGzbM6XEVFRVITk729WWJ6BRvg9K0WOVat/yKOs2vyZFYvmvQe7ee1E78upgppdZWWnMqKFVlSrUISl03OnJWEk9ERNQR+ByUXnzxxcjLy8Ozzz7buG3Dhg347rvvAEBVumv3xx9/ICsry9eXJSIAFqtNlYlsLijNEBodnayogyzLml+bnSpT6uV6Ujtx1A3XlFJrqrfaGm+MOF1T6i8hU5qISujQcPNX/JkiIiLqKHwOSv/5z39Cr9fj3//+N7p164azzjoLF110EaxWK8455xxccMEFqmM2bNiA/Px8nHvuuX5dNFGwc5YtdDenFAAy4pSZ0hqzNaClsOIHaG/HwdgxU0ptSXmtBfZ7OYHJlCqD0hBJRhIaSnhZvktERB2Vz0Fp7969sXjxYiQlJSE3Nxdbt26FxWJB37598b///c/pMXPnzgUAXHnllb6+LBHBeWDWXKY0NVa9xvtkAEt4xVLDKC/HwdjFRiiP40gYak329aRAgNaURiUBkvKt2V7Cy0ZHRETUUfn2KfGUUaNGITc3F2vXrkVRURGysrJw/vnnQ6dzHutOnDgRN998M0aOHOnPyxIFPTEojQjTQR/qPhOpDw1BkiEcJQ4fqk9W1qFPekxArjFQmVKW71JrKnUMSgORKdWFAFHJgLGwcVOqVI7dMkfCEBFRx+VXUAoAERERuPTSSz3a9+qrr/b35YgIQEWNOKM03KPjkqP1iqC01GjS9Loc1ZiERke+ZkpVQSk/mFPrKatxzJQGYCQM0NDsyCEoTUAVAK4pJSKijsvvoFRktVpRWloKSZKQkJCAkBDfsiNE5Fp5rVnxOL6ZGaV2SdHhQEHT45Jqs+ud/aRVoyNVppTlu9SKSo1N33+xCED5LgBEJigeJkoNQSnXlBKRp2RZxn/WHsaSrceRHheBf199OromGZo/kKiV+Lym1NGxY8fw0EMPYeDAgYiIiEB6ejrS0tKg1+sxcOBAPPTQQ8jNzdXipYgIQLmYKfU4KFWuKy0OZFBqUX6ANvjafTeCjY6o7XCsLlBnSjUKSqOSFA/jpWoAgNFcH9CO2UTUcXy57TieXrEHu/Mr8fPeQkz6z0aY6nlji9ouv4PS+fPnIzs7Gy+99BJ27NgBq9UKWZYhyzJsNht27NiBl156CX369MG8efO0uGaioFfmY/lukkG5X0uW70ZqVL7LoJRakzJTGqigNFHx0F6+a5MBU73N2RFERAoLNiiTQcdKa/Hj7kIXexO1Pr+C0hdeeAHTp09HXV0dJkyYgC+//BLHjh1DXV0damtrkZubi6VLl2L8+PEwm824//778dxzz2l17URBq6LGt/Ld5GhlUNqS5bu+NjoSu+9WBXCMDVFz7GtKdbAhRqpVPqlZ+a4QlJ4q3wVYwktEzTPVW7Ejr0K1/df9Ra1wNUSe8XlN6a5du/Doo48iISEBS5cudTqXNCsrC1lZWbjmmmvw66+/Yty4cXj88cdx9dVX44wzzvDrwomCWXmtWL7rWaY00SCU7xrbQVAqZEqr6iyQZRmSJPl8bUS+snffjRazpEDAyncTUN349xqLFQni/kREDvbmV8FsVVdV7DiuDlSJ2gqfM6Xz5s2DzWbDggULnAakogsvvBCffPIJrFYr5s+f7+vLEhH8WVMqZkoDWL6r0ZzSGCFTarHKqLOwhJFahz1TqlpPCgSufFeRKWWlABG5t/OE8+Bzf0EV6iystqC2yeeg9Oeff0Z2djauvPJKj48ZNWoU+vbtix9//NHXlyUiAOVi+W6kZ0FporCmVBwtoyUxU2rQa9PoCGjIlhK1BnvJe5yYKZVCgLAobV5EzJRKTZlSo4kfKInIvWOltU6319tkHCiodvocUWvzOSg9ceIE+vfv7/Vx/fv3R35+vq8vS0RwVr7rWVAqjlepMtXDagtMN0/VSJgw34LS6Ah1hpVjYag1yLLcWL4bKzkZB6NVSbm4phTVABp+TjmrlIiac6LceVAKAIdLjC6fI2pNPgel4eHhMJm8L/0zmUwIC/PsAzQROacu3/VsTakYlAJAZYC62Yrluwa9b+W7YSE61XrUSjY7olZQbapH7anSt4B13gWAKOWqUb1kQRQa3m9rLfzeJyL3jrsJSo8UMyiltsnnoLR79+5Yv349bDbP13bZbDasX78ep512mq8vSxT0rDZZlSn0NVMKBG7EiipT6mOjI0C9rjRQgTSRO4VVjjNKnWRKtSKU7wJNY2GYKSWi5hwvcxOUMlNKbZTPQeno0aNRVFSEOXPmeHzM7NmzUVRUhDFjxvj6skRBr7LWAlmouPV0TmlEWAjCQ5U/9oEISm02uTGjZGfwsdERoF5XyrEw1BoKKusa/x7QTKk+FtApf17iT60rZVBKRO5YrDYUVNW5fP5oiZMmbURtgM9B6X333YfY2Fg8/PDDeO6552C1un6jtFqtePbZZ/Hwww8jLi4O06dP9/VliYKeuJ4U8DxTCqizpYEISuvqrarA2deRMICTTCnXlFIrKHLIlMZJQrMQLYNSSVKtK0081YG3xsQbMkTkWqnRrHr/dXSUmVJqo3xOXSQlJWHhwoUYM2YMHn30Ubz55puYMGEChgwZgtTUVMiyjMLCQmzZsgWLFi3C8ePHERoais8++wxJSerSJCLyjNh5NyJMhwgvmgjFRYYpPlw7C3L95Syb40/5rnpWKT+YU8srrGz6uXGcHQpANcbFb1GJgLFQ9Xo1HOdARG4UNzPqrbjajKo6C2KcdLYnak2+19MBuPTSS7FmzRrcdttt2Lt3L+bOnavaRz51u6ZPnz744IMPMHToUH9ekijoqTrveli6a9cSmdIaJ2Mr/CnfFd88uaaUWoNj+W6CmCmN1DgoFTvwnsqU1rJ8l4jcsI+tsouJCEW1qV6RPT1aUoMzMjWs7iDSgF9BKQCcffbZ2L17N7755husXLkS27ZtQ0lJCWRZRnJyMgYOHIhRo0bhqquugqRVu3yiICbOFvWmdBdQB6WBCPBqhA6hktSQ0fVVrFC+y0wptQbHRkfxpxoPNQpEptSBPSjlmlIicqfEqMyUdoqLRLWpXtGRl0EptUV+B6V2o0aNwqhRo7Q6HRG5UCaU7/oblAYiU2o0qWeU+nNTSpUp5ZpSagWFVS2YKRWC0niw0RERNU/MlCZFhyPREK4MSku5rpTaHt9TF0TUKsqMQlDqb/luTSDWlGozo9QuNpKZUmp9jmtK48WgVOtMqdA4KUZq6Jgp/mwRETkqET4jJBrC0TUpSrEtlx14qQ3SLFO6bt06/PLLL9izZw/KysogSRISExNx+umn4+KLL+ZaUiKNFAtvOMkx3gWlYtOgwGRKlR+co/0MSrmmlFqbLMuKTIOq0ZHWmVIhKLWPoGGmlIjcKREaHSVH65EWG6HYxrEw1Bb5HZT++eefuOOOO/DHH38AaGpsZGcv2TvnnHPwn//8B6effrq/L0kU1MQ3nESD3qvjW6J8t1oo3zXofe+8C3BNKbW+4mozTPU2AIAeZkRJQodLzTOl8YqHsacypWx0RETulAnVTwlR6kwpx8JQW+RXUPr777/jkksugdFohMFgwKhRozBo0CAkJydDlmUUFxdj27Zt+Pbbb7Fx40YMGzYMq1atwuDBg7W6fqKgUypmSqP9K98NxEgYMVPqT+ddAIjlmlJqZXllTZmFODj5QBfwTGnDa7J8l4jcESuJ4iJD0SVRGZTmV9bBVG+FPtS/G8ZEWvL5k6LVasXEiRNhNBpx5513Ys6cOYiNjXW6b2VlJWbMmIH3338ft9xyC3bt2gWdjstZiXyhamLgZaZUnXUMRKZU2/Jdriml1naszKF0VxI670ICIuO1fUExKLVnSi02bV+HiDqUSuH9MTYyTJUplWXgWGkteqZGt+SlEbnlc2S4bNkyHDx4EDfeeCPeffddlwEpAMTGxuK9997DhAkTsH//fixfvtzXlyUKeuJg7ESDf2tKAxHgqTKlGq8prTbVw2qTXexN5L1dJyow4a116P3oNxj3+m/4M69c8bxjplTVeTciDtBpnHEQGx3BXr7LGzJE5JqYKY2NCENMRJjqs0IuO/BSG+NzULp8+XLodDo8++yzHh/z3HPPAWgIaInIe+Z6m+ouqLfluzFOMqXiWnB/aR2UiuW7AFDNbClp5HCxETe9vQG/HymDud6GbcfKcdM7G3CgoCkjmueQKY0XmxxpvZ4UcJIprYUONtRauKaUiFxTle+eGhunXlfKZkfUtvgclG7ZsgV9+vRB9+7dPT7mtNNOQ3Z2NrZs2eLry/ps1apVkCSp2T9PPfWUR+fr1q2b2/Ps3bs3wF8RBSNxPSkAJEV7W76rDPBsMmDUuHmK2Ogo2s9GR2IgDXBdKWnnyeW7UCXcSKkxW/H4sl2NN2zy3JXvar2eFAD06uqjaNSw+y4RuWS1yarfZfb3/K6JDEqpbfM5fZGfn4/hw4d7fVzv3r2xdu1aX1/WZ+np6bjtttucPme1WrFgwQIAwAUXXODVeV2dMy4uzul2In+UGJWluzoJiI9UZxHdcZZ1rKqz+L3u05GYKY3ys9FRVHgIQnSSomSXQSlp4UBBFVbtK3L63PpDJfgzrwIDO8fjcHFTdlQ1DqYFMqVAw7rSk+YY7V+LqA0xmurxwMLt+HV/EbomGTDv5sFc++ghZxVE9p4MXZIMiu2Hilm+S22Lz58UKyoqfAq8YmNjUVlZ6evL+iw7OxsffPCB0+e++eYbLFiwAJ07d8ZFF13k1XldnZMoEMQmR4mGcOh0klfniHaWdaytR4aG91GMZm0bHUmShJiIUJQ7tLqvrGX5Lvlv4ZY8t89/9vsx9EqLVpbvimtKA5EpDTcAUgggN2VG41CDPJsMi9WGsBA2C6SOafb3+/DNzpMAgN35lbj748344f6LEOLle10wcnaz1n4jupcQ2O/Jb/nP4kTu+PyuVl9f71MHXZ1Oh/r6tvVh0p4lnThxIrsCU5smZkq97bwLACE6CYZwZTmt1h14xe67/q4pBdQZ3kB0DabgYrPJWPrHcbf7fLXtOHYer4TjsmtV+W4gMqWS5GRdqX0sDEt4qWOqt9qwSLhRdKjIiM1HSlvpitoXce54iE5C1Kn3+36dlEsCiqpMKKyqa7FrI2pO0EdgRqOxsfHSpEmTWvlqiNwrrFQGpckx3jU5sgt0B151oyP/O5OK60rFhk9E3tp5ogJFVcqfqY/uOAeOCRmj2Yr5vxxU7NMpvFbxOCCZUsDJrNKGNWB1bHZEHdTu/Eqn70cbDzMo9YSYKY2NCIUkNfxC65ZkaAxQ7XadYLaU2g6/0hcffvghPvzwQ62upVUsWbIERqMRgwcPRr9+/bw+/qWXXkJOTg70ej369euHa6+9FikpKQG4UiLgZKXyrmZabIRP54mJCEV+RdNjrddnGlWNjpgpJd/YbDKOl9ciJUaPiDBtx66sFtaSnpZswIW9U3B+z2SsOVDcuP3X/cr9MkKrAMdvcUOSptfViJlSCjL7ToozgN1vJyVxWYvjDWidTkLfjFhsOVrWuG33iUpc3Ce1xa6PyB2/Pin6OkbCftemLbCX7k6ePNmn4x988EHF4/vvvx/z5s3DnXfe6fE5XAXDOTk56NGjh0/XRR2TmCn1PShVBnhaZx0DUb6rypRyTWmHd6y0BlP+uwk5RUYkR4fjhfEDMLJvmmbnXy0Emxf2briheO3gTEVQKkpGhXKDIUAf6lxkSms4q5Q6qINF1U637znJjJ4n1JlS5Xv96UJQ6vh3otbmc/muzWbz+Y/V2jbu8p48eRI//fQTQkJCcPPNN3t17NixY7FkyRIcPXoUNTU12LlzJ2bMmAGTyYSpU6fiyy+/DMxFU1ATM6XpPgalsU5mlWpFlmVV+a4mmVJVyTEzpR2ZLMv426dbkVPUkB0srjbjr59uxYny2maO9Eyt2Yptx8oV2y7q0xCUXtEvHZEus7IyouuFUsLolglKYySW71LHllPovCPskWIjv+89IM4otXfetTurW4Li8YZDJTDX2wJ+XUSe0G4GRDv06aefwmq14sorr0R6erpXx86bN0/xuF+/fpgzZw769OmDv/zlL5g5cybGjRvn0bl27drldLsv5cTUsRWoyne9b3QEOMmUaph1NNXbUG9TVlEEJFPKoLRD23G8AtvzlBnJOosNr/9yEM9c29/v82/PK1d8n+ok4JxuDWtDDfpQXN4vDcu2nVAdl6a3QGdVVizAEKAlGxHKxiRNmVJ+OKeO6VCx80ypTW6YFczRMO6pglLhvX54z2RIEhobt9WYrVhzoEjTChQiXwV1oyN/S3edmTp1KlJTU7F//34cPnxYs/MSybKsYflu4DKlYpYU0KbRkfjmyvLdjk1cx2m3eGseKmr8/34Vy9b6ZsQqbp5MHNrV6XETsp3cCApYpjRe8TBWYlBKHZcsy8gvd90N9rhGVRIdmbgUJ06oMEqK1mNQ53jFtv9tyg30ZRF5JGiD0j179uCPP/5AdHS0xxlNT+h0usZ1oPn5+Zqdl6isxgKzVVlm42tQGsjuu2KTI0Cb8l1VIG0KfKa0ss6CtQeKUVjJtvktbcfxCqfb6yw2LN6ah1KjGe/8moPHvtyJ9TklXp9/qxCUDumqLGs7p3siruynrKAJD9Hhpr5Cx+vw6IaZooGgWlPaUNrIMkbqiKpM9ah1872dV1bTglfTPqnLd8NU+4w/M0vx+Mc9hfgjl2tLqfUFbVD68ccfAwCuu+46REVFaXrusrKGH+7oaJaZkHZOVigDI0kCUmJ8Ld8NXCmseK4QneRmfZ7nxDfXQGdKtx0rx/nP/4xJ/9mIEbNXYcnWvOYPIs3syHMelALAqz8dwNj5a/Hsyr34eMNR3PzuBqzc4flNQFmWsSXXfVAKAC9NGIDrh2RBH6pDt6QovHfbWcgKE8oLA1W6CzjpvstMKXVcYiWQ6HgZM6XNcTYSRnTNoE6Ij1K+nz7/zV6fm5cSaSUog1JZlvHpp58C0LZ0F2hYH7pv3z5ERUUhOztb03NTcBPXkyZH6xEW4tuPsLimVMtMqbs5af4IZHMmUb3Vhvs++6Px36XGbMVDi3ewfKyFlNeYcaLCdXa6otaCPOED6hNf7YLF6lnDjkPFRpQLJcBndlEHpTERYZg9YSD2PT0KvzwwoqE7r1EoKw5U6S7gsvtuLYNS6oCaq0gRf+ZJzd1IGLuYiDD8dURPxbaNh0uxap/zJRNELSUog9I1a9bg6NGj6NSpEy655BKX+82fPx/Z2dl4+OGHFdu/++47bNmyRbX/n3/+iQkTJkCWZUydOhXh4eGqfYh8JZYuZcT5VroLBDbA86R8yBeqNaUaj7Fx9MPuAhwpUf57m602LNnCbGlLyC1V/tvrJKBbkvuKlsIqEzYc8qyMV1xPmhqjR1ZCpNtjGm+sVBcqnwhkpjRcWW1jQMOHcncljkTtVWGV+0yp2H2e1JobCWM3eVhXdBI+Q7y5Kidg10XkiaAMSu0NjiZOnAidzvU/QXFxMfbt26daG7p+/XqcddZZ6NatG0aOHImbbroJQ4cOxZAhQ7Bnzx5cdNFFeO655wL6NVDwOSoESV0SfS87D2TToAohKBUbLfhKnd21BKzc6Mc9hU63rz3oenYlaedYqTIj0ik+ElPO69bscb8f8Wxd1J955YrHZ3ZJ8DybbxS+NwKZKdXHKF9Kavh34ZxS6ojEaiBRcTNBKzU/EsYuIiwE91/WW7Ft05FSHCysCti1ETUn6IJSk8mERYsWAQAmTZrk0zmuuOIK3HHHHYiNjcX27duxePFiHDx4EMOHD8e7776Ln376SfN1qkRHhexR12YyR+4EsvtuoIJS8c3VYpVRZ9F+vposy/jNRfD5R245SydbgJgp7ZwQhZvO6YLTM2JdHNFADDZd2X2iUvF4QOc4F3s6UXVS+Tg6gKMU9MpMaTQaPrTXmjlXkDqeAmFNaWa8snqhuUwqqSuIXGVKAeCaQZlIjlb2pfj6TzbopNYTdHNK9Xo9SktLm98RwBNPPIEnnnhCtX3YsGEYNmyYxldG5N6xUg0zpUKgaDRbUW+1IdTHNaqOxKBUq/JdMVMKNATTkeH+N1FydLy81mWZmNlqw76CKlVLfdLWsTL193pEWAgWTB2Kp7/ejU1HStEtyYAzMuPw1uqmkrPtx8ohy7LbrKfVJmNPvjIb0Fywq1B5XPk4NtPzY70VrsyU6iULwlCPWgszpdTxFFYpf++ekRmrWMdfbapHjbkeUeFB99HVI/VWG6pNza8ptQsP1WHswE54/7em8YXf7SrAfZf2dnkMUSAFXaaUqD2SZVmVPeqS6PsYCjFTCkD1ZuYrVaMFN3dqveHsmrXsGmy376T78qUDBSxvCjSxy6Z9vWeiIRwv3zgIa2deggVTh+Kmszsr9iursaCsmRmmR0qMqjWZ/Tp5kSmtPKF8HNvJ82O9JZTvAg3rSpmtp45I7L57hpOfyyJmS11y1rCwufffK/opKz325Fei1GjW9LqIPMWglKgdKKo2qcZAdPGjfNfZG5VWHXgDVb4bFqJTjZYJRLOjvc0EpQcLq90+T/4TP3i6mseblRCJUJ0yK3q0xOj23LuE0t2UGL3no5UsdUCN0EwpkJlSvXqsWLRUx5Ew1CEVVyt/7rslG1S/8xmUuubsJq2rNaV2Z3ZNUP0bbz7iWTUhkdYYlBK1A7lCk6PwEB3SXXxQ90RUeAhChA/zYjDpq0AFpYD6DVZs6qCF/c1kQpt7nvwnfjh1FTSGhuiQKXTNFSsKROJ60n6dvCjdrTqh3hbITGmYAYDy5zQatey+Sx1SufD7PNEQrvrZ57pS18QqpbCQ5meEh4XocGbXeMW23xmUUithUErUDuwTAqGuSVGqoNIbkiQhWi82OwpMprS5O7XeCOR8VTuxfFdcP9pc0EP+sdlklAjlY2IzDkfi2mrxBo5o14kKxWPv1pMKQWl4DBDhxfHe0umcjoVh+S51NDabjPIa5c99fFQYUoWglJlS15yNg/Gkq/hZXRMVjzd52MWcSGsMSonagb1CY5Zsbz5IuyAGi1p14C0xKj80JBm0m9crzlfVek1pvdWGnCJlee6lfZUjP46X1wZsFA01ZEusNuW/b3KM6+8hsQu12CTJkSzLTjKlbXQ9qZ3YgZflu9QBVdXVQ/ixR0KUs0wpZ5W64uuM8LO7KYPSPfmVqLeywze1PAalRO3A3pPKD9LZ6eoGKN6K0QuzSjXKOpZUe57l8lagM6X5FXWwWJWfjC7qrQxK6yw2NoIIIGeZkCSD6++hjDhl+a44VsJRcbVZlYU93ZvyXVXn3ZYISoVZpahFHct3qYMpq1H/TmWm1DvqTKlnVUriEgZzvQ2Hi92vzScKBAalRG2cud6GHceVJYd9MzQISgMwq7TGXK/K4iRpGJSKd361XlOaJ3R9NYSHoG9GjKpU2nFMAWlLXE8aHxWG8FDXb1ViE6QCF+N8AKiy4PpQnXejlSrylI8D2eTITizflWqZKaUORwxKw0MbGtulCj/fXFPqmqrzvYeZ0gRDuKpHxe78Shd7EwUOg1KiNm7niQrUWZSlNIM7J/h9XvENS4uso5glBYCkaO3Kd9WBtLaZUrH0MyshCqFOmkqJI0tIO2JQ2lym3ZtMihiUdk82eLc2u/Sw8nFCV8+P9ZWQKY1BLWrMnFNKHUu5MMopIaphPWRKNDOlnnK2ptRT4o3u5rrQEwUCg1KiNm7dwWLF495p0UjQYJ2mGOBpkXUUA4rwUB1i9No1OhLfZMVujf4SM6X2+Zid4pVB6Uk32Tjyj/ihU/xQKkqNVT5fYjTD4mI9VE6hsiStR6p65IpbpYeUjxNP8+54XwhBqQF1qptURO2dmClNiGp4j0uJdd9992iJEeNe/w19Hv0Gd3zwOyqamVPckfnTZFDsU7GHmVJqBQxKidowWZbx9Z/5im3DTkvS5NxigBeITGmyIdyj7n+eSjQor7lM47WdeapMaUNQmhrDErKWUix+DzUzQ1T8fwO4zqaImdIeKaeCUlkGNv8XeKU/8EwnYPFUoLpIebDVApTnKrcldHd7bZpwUr5rttrYiIQ6lDIhmIyPavhdL96UKqk2NTZCs9lk/OXjLdh2rBymeht+3luIR5ftbJkLboNUjY68yJSKfSoOFHAeN7U8BqVEbdh3u06qymhG9c/Q5NxiE4Qqk/93mIuETKmW60kBIFFoeKN1wyExU9r51HpDsQMkS8gCR12+674qICEqDGEhyhsfrm4aqINSQ8Nf1swBvr4PqMgFLEZgx0Lgg6sAk8P+FccAWVjLmdgCQamT8l0AnFVKHYo4DsaeKRUrIWxyU4f3zUfLVO+PK3fkq84VLMRmhZ6uKQUcbtCdcqKCDdWo5TEoJWqjft1fhOmfbVNsy4yPVLVv95XYyVZskuALca2l2ITGX2KmVOugVLx+e6aUA9xbjri2LDHKfVAqSZI6k+2kvLrWbFU1qOqREg0U7AJ+eVZ94uL9wM9PNz0WS3cj4oEobX4W3dKLmdKGr42zSqkjEct340/93CcZ9BCXfRee6rC9VljaAgBWm4yNh0sDc5FtnK8jYYCG9fWOZJkzuanlMSglaoM2HCrB1I82w1SvLNH7x8ie3jVmcSMQ3XfF8tfOiZEu9vSNs0ypVjNDrTZZtVY0M74hU8qxBC1H1azDgw9Wntw0OFxshPitclqKAfj1JXUG1G7LB0DNqQ+4YpOjlsiSAqry3ehTmVJ24KWORCzfTThVvhuik1QVN/bfv1uPljk9lziLOFj4OhIGAAz6UNX73KEijoWhlsWglKiNKa8x495PtsIsBKSj+2dgwpDOmr1OILrvHhPLXxO8GLfhgSShwZPZaoNRow/nJcamtUp2aadKx9Tlu2x0FCji3f44D4JS8cOUs0ypOHevU1wEokwlwO6vXJ+4vhbY/H7D34v2Kp9riSZHAKBXNiCJlli+Sx2Pq/JdAKru5ycr62Czyfgj13lQGqxNetSNjjzPlALqbOmREgal1LIYlBK1Ma/9fFBVlnrNoE549aZB0GmUJQWcdN/VIFN6rFTMlGoblDrrOlzqZAyNL+wlYXY6qWlNrFgeWmI0s9FMgKhL0Jq/2y+uOyty8j1xotzJeuGdi5RZ0jAD0OsK5YFbPgRstoYyX8WLnt7sdWlCLN9FQ8DNTCl1JGVG542OAPUykJMVdcgrq3V5Q3LPyeAMSlVzSr1odASog9LDzJRSC2NQStSG1Jjr8fnvxxTbRvRJwcs3DEJoiLY/rqo1pX5mSivrLKqySfuaTK0YwkMQHqr8d7A3vfCXsyZN9lJpMVMqy9qvZ6UG4t1+TzKl4ixTsVkSANV60syESODPL5Q79b0aGDFTuKBc4Miv6qA0vX+z16UJodGRvXyXTUioI3GbKY1T/nwXVNbhYJHrOZrHy2pdjoXqqMz1NlX1hCe/Ox2pglJmSqmFMSglakO+/jMf1aam4FAnAbOuOUOzdaSOxPUm5nqbXx90/zxWoXgcHqJrWLOnIUmSVCW8YoMMXxUJmVLHktBEQ7jq/wGbHWnP4qQc25MPVuKaM0+C0j6RVUD+NuVOA28EOp0JpPZTbv/pKcAkZF/Szmj2ujThZCQMwEwpdSyqNaUOTe3SYtTlu+5GltjkhmxqMHFW6eTNnFIA6CJUNomN/4gCjUEpURuydOtxxeOLeqdoXgJrJ2ZKAd/Xldaarfhpb4Fi2+mdYqEPDfHpfO4kCN1YxdmovioU1ok6BqUhOnUwLO5P/nP2/edJCVpKdPPfE+IHrEHmrcodIuKAbhcCkgQMnigcvEX5ODoNiO3U7HVpwsVImBqz/2vAidqCOotVleWLd/g9nxanLt89UOh+jqY43qujE5c9AN6X72YKlU0nK+u4TIVaFINSojaiss6C348oW9nfcJZ2jY1E4ppSwPsOvDXmejy85E8MfPJ7/Pe3I4rnBneJ9+PqXEuKDkymVMx8iutIxRLeYo2CYWoilu4CnjXr8CRTeqJC+SH1tIp1yh1OuxgIOfUzMeBGQOcmy9D1/IbgtSUIQWmEZEEo6lm+Sx2Gs597d42OCqtMHgSlwTXORPw31IfqEBHm3U3hzHhlUGq1yShgRRC1IAalRG3EbweKUe/Q/TU8RIcLe6cE7PUiwtTrM73NlN7/+Tb8b9MxmJ3cTb24T6pf1+dKopCxLNFobafY6EgMQsV1i1plaKmJrx+sxP83NWarIpNoNNUr5p+GwIrEk0JQ2vPSpr8bkoHeV7p+wW7Dm70mzQjlu0BDsyOW71JHId5YlCRl2X66kCktNZqxRxj7EhaivEkUdJlS4b3b2867QMN7a0SY8jMBS3ipJTEoJWojVu0rUjweeloiDHrv1oR4S1xX6k0H3t+PlOK7XQVOnzs9IxbDeyartu8q2YVpP0zDLStuwfKc5d5d7Cli+a5m3XfF8l2ho6uYoXWWjSP/+Dr8PTla3ZXZ8aaB2Hl3gHQIIWZhjahjUAoAgyc7f7GQcOD0cR5dlyb0zoNSjoShjkLsvBsbEaZYwy923wWguhF6kXADt8DJWKiOzJdRWiJJktBJyJYeLw+ujDO1LgalRG2EWLorvskGgriu1JtM6YINR51uz06PwZuTzlSNrzlpPIm7vr8Lv534DTuKd+CRtY9g2cFlXl9zoBodqct3m8uUMijVmi+ddwEgWh+qyvo7dlPOE4LSiyIOKk+Qkg3EZii39bxU3fAIaAhIDUkeXZcmwtTNwgxSHWqZKaUOQt15V/lzHxsRCkO464qJGH0oBmTFK7YFWyM61YxSJ8tzPCGW8J4oD67gnloXg1KiNqC42oRDxcr260O7B/6Dr/jG5emaUovVhp/3Fiq2TRzaBT/930VY8Y8L0DVJ/UH6832fo8qsbOP/1va3YJO9a6SQKDa10aB8V5ZlFFWJ5bvKu/NiMKxV2TA1ETP1nn6wkiQJKW7Kq8VM6bCw/coTdD1PfdKQUGD8e0BkYtO2+C7AZU95dE2aCQkFQpUfFFm+Sx2J2Hk3TqiGkSTJ6XuKXa+0aNVNxGBrRCf+7vQlUwqox7gFWxk0ta7A1gYSkUe2HC1TPI4KD0HfjBgXe2tHNau01rNM6dajZaqs6t8v6aVa++No7fG1qm151XnYUrAFZ6ef7dHrAkBiALrvVtbVw1SvDI6by5Sy0ZH2fM2UAg3l1Y5jXxzLqx3XRUmwoV/9HuXBXYY5P2na6cC9G4D93zQEhn1GARGxHl+TZvTRQH3T12CQalm+Sx1Gea37TCnQMENzd36lajsA9E6LUS23KKgM8kypj0GpmCkVR2kRBRIzpURtwGahdHdwl3iEhgT+x1PswOtppnTbsXLF474ZsW4D0pLaEuwt3ev0udXHVnv0mnZiA6KiKhNkWXaxt2eKnNxVF1+Ha0oDz5+g1F15teMHqx7SCUTbhA+3Xc51feKYNGDIlIYZpq0RkAJAuDJLFA2W71LHUS7OKI1SrxHvmuR6NFqvtBhVt/SSahOsNv/eF9oT8Yayr5lScSzM8SDrYkyti0EpkYdKjWasyykOyJ3DzUKmdEjXRBd7akucYyZ28HNle1654vGZzYx/cRWQAsDGkxs9ek07selFrcWKKpN/MxvFzruxEaGqrq9i0FNqNMMWRB96WoL4wcqbu/1is6NiF+W7Z+mE0t3YrIay3LYsXFk1EYU6zimlDkNcU+osoOqW7Lp8t4+TTKlNDq51/6omcV7OKLXLjFcG/8fLa/2+6UvkKZbvEnlg4eZjePTLnTDV26CTgPsu7Y1/jOylybktVht2HVdmbs7qmqDJuZsjZko97b67/ViF4vFAocmE6FDFIZfP7S3di9K6UiRGeBaIixlMACisrPP5TRhw0uTISbdHMVNqtcmoqLUgwaC+q0++8aeDpDirtMhF+e4ASfhe7HyOF1fYSoQOvAapDiUWDrWnjsGTTKmr9xidBAzsHIeo8FDopIZg1K6wyuT0d3lHpNWa0k7xyn+vOosNFbUWxDv5f0KkNWZKiZqx72QVHlqyo3HNoU0GXv5hP77dma/J+Q8WVqva2w/IitPk3M3xpftuUZVJlS0e2Dne7THuglIA2HRyU7OvaxcRFoJ4Yc2Rv+uHVONgnAS+SQb1thKj89e1WG3YX1CFihrPR+yQs0ZH/pfv1lttOOkwHqKf7ojywE6DvbvI1qAq361FLTOl1EGIQan4+x0AeqVGO218dkZmHGJOjZARfwcEU7Mj9Tgt33JOYhk0EHzrc6n1MCglasbbv+Y4XZvy9Io9mqxZ2S0MAc+Mj2yxu5LiG5cna0r3nVR20I0MC0HPVPUsRUeHKw67fX7zyc3Nvq6jdOHu98kK/z58iOW7zoLS8FCd6kNRUZW62dHBwipcPHsVLn/lVwx97kcs2pLn17UFE//WlDov3z1ZWdeYPQlFPbKlXOWBGQO9v9CWFq78+YqSOKeUOg6x0ZGzoFSnkzD0NHVH+gt6Nc3DFkt4xd/rgVJUZcLuE5WtuoZVPRLGt0xpeKhO1Wn+ZJDNfKXWw6CUyI0acz2+/tN5RjSvrBY/7inw+zV2CUFpv04t10zFl+67h0uUo2t6pBoUg86dOVF9QvF4QMoAxePfT/7e7Os6EkuyCvy8I15U3Xz5LuAkGydkSm02GX/79I/GNvp1FhtmLv4TBwuVgTw5508HSVeZUsc5e72k49BLwvd4hvJ7sU0SynejORKGOhBxJIyrm7J3Du+ueK8xhIdg4tCujY/ThCxfS8wqXb79BM57/idcNW8NRs9boxot1lLEfhD2G3o2oxE1v/8Oc26us8OcEvs2FPh505fIUwxKidzYeKgU5nrXa7dWuAhYvbHrhHJ95uktGpQKmVJT85nSw0XKoLSbm/lxAGC1WVFYo5xpOva0sYrHhyoOoaS2pNnXtksTZ9L5W77rQaYUUK8rFcfRbDxcir1CJtlqk/Hx+qN+XV+w8KcETQxKy2ossFhtOF7e1D3yDJ2QsY/vCkS2zPptvwiZUgO771IHIcuyaplDvIubUeeeloT5Nw/GWV0TMLR7Ij684xx0chhhosqUBrh8t6rOgkeW7IDF2pAh3XuyCvN+OhDQ13RGlmWnN/TMecdx6JpxODr5VuRcfgWK337Ho/OlqcbrMCillsGglMiNtQeL3T7/y75CWKy+NxyRZVk1e61fp5ZZTwqoS3w8WVN6RMiUnuamKyIAlNSVwCorP0CP6DwCUaHKLn+bCzwv4VXdyfXzTVP88OKsmRLgfuwIAHy366TT477afsKv75NgIMuyy7v9nhBvGAANHZIdM6X9pCPKHdpD6S6gDkpZvksdRI3Zquqp4KzRkd2o/hlYdM95+Pwvw3BWN2VzvBQxUxrg8t31OSWqzu9f/3mixbuy15itqtLhuMgwFM6ZDUte0/KRorlzUbdvv3i4ijjejeW71FIYlBK5sSNPmcW8+Rzl6Iiqunr8LswY9UZeWa0qEGzZ8l1xTWl9s+3fDxcLmdJmgtICo7LEOVQXipSoFAxOUzaYsZfwyrKMVcdWYdb6WViwewFq69UjeLS+kyuWebkKSsXAp0jIlP66v8jpcWU1FlWZNilVm+pVH6y8WReVEBUOsYq8uNrUWEoNAH0kYX1veyjdBdTdd1HL8l3qEMpr1dU5cU7WlHpCrHApCHAp7fpD6uqeshoLDhVXB/R11a+p7m0QDQuqf/5FuVGWUbFkcbPnU9/0ZaMjahkMSolckGUZe04qA4lL+6aif6Yyk7nuoOdlpyIxwIuJCEVGXMu1sBczUVab7PbDrsVqw7FS5TDtZoPSGmVQmhaVBp2kw9lpZyu2b8zfCFmW8dhvj+HvP/8dX+z/Ai/8/gLu+PYOmKzu13z686ZZZ7Gqbgw460AIqDvwOmZKK2osOCT8/3S00ckHGGribEauNx9OQ3QSEg3qZkeOM0p76JRrm5HS17uLbC1OMqXmelurNlYh0kKZURlQhegkp112PSEGU4UBzvBtzS13uv1AQQsHpUZlYB+qkxCyZRNkk/p90bhufbPn07oSichTDEqJXDhers5i9s2IxXCHbn8AsOVomc+vcVQohe2ebIAkuW8apCUxUwq4L+HNK6tFvfBBuLnyXWdBKQCck66cD3mk8gieXP8kluUsU2zfWbITC3YvUJ5D/PBRVefzgG9nJV7i2iQ7scNricMHqj+Pl7t9nd+P+P59EgzEdWU6CYgO9+7DqVheXewwvigWRqRK5cIBvb2+zlbhZE0pAJbwUrvnrOO2r++BYlf2wipTwEppZVnG4SLnwafYDDDQxIZ7CYZw1O3c5XRf04EDqC9yXtFjp+puz6CUWgiDUiIX9uYrG9bEnspiDumibIyy7Vg56n1cL3ikRJl17NpM06DmLN6/GDd/fTPu+fEe7C7Z3ez+0Xr1h35xVqSjI0ImMD4qrNnxNWL5rj0o7ZfcDxmGDMVziw84Ly1aenCpIugU3zQtVhmlRnUJkyfE9aQRYTrEOPl3AdyvKd1xvELcXWFPPst33RG/72IiwqBrpquzSBWUVpsaM6WnSUJTMl0okNjd+wttDary3Ybv2RrOKqV2Tiw9dTYOxlPisg6rTVbcONRSWY3FaXUHoH6fDDTx3zAxKhym/a7Xjtbu3On2fOJN2eJqk8+fcYi8waCUyAUxiOibEQtJknBmV2VQWmuxYk++byM/xExpt6QoF3s274t9X+CJ9U9gZ8lOrD2+FlO+nYIjFUfcHhMaooMhPESxTeyA6kgsN+7eTJYUAE7WKJv/pBkaglKdpMOY08Y0ezwAHK08igPlTV0Nk6PV6wfzfWxbL64nTYuNcHmnPkkV9DR9GMgpVP7bDBG+T46X16qyAtTEnxmlduKa35yi6sZy9J6648qdE7oDIb5/AG5R4cqfM4N0auSQmR8UqX0r97DzrieSovWq8WSOpad1Fite/mE/Rrz0Cyb/ZyMOFPg+qkt8L3R0pLjG5XOBUCqU7yYa3Aelpr173Z5PvOkry+qxaUSBwKCUyAVxfWCf9BgADb/wxZLVbcd8K80UM6XNjVdxxWw14/Vtryu21dbX4u0/32722AQn6/BcUQWlHlyvOA4mNSq18e/X9LwGOsmzX0Ob8jc1/j00RKda9+lrUCqul3E1DgZQBz3VpnrUnSqhFJtbXHZ6GsJClB+Q9p3kvFJXtAhKxUzpHw5rvnpI4nrSPl6fv9WExygeGtDwAbHGwkwptW/lQpbPXefd5oToJKREux4L8+iXOzHvpwM4UlKDNQeKcdM7G3yeKyreUHZ0rKxlg1JxXW5qmA2W48dd7A3U7d3n9nyJhnDVexebHVFLYFBK5ILY0MextHZAlrLZ0W4fMqVWm4xcMShN9i1TurlgM0rr1F2Avz38Lcrryt0eK3aadXdHVBwH01yTIwCq60qObFqT2zW2Kyb3naw6pm9iX4zqNkqxbUfxDsXjTvHKoNSxoY03xEypqyZHAJBsUAesJUYzZFnGoSL1TYweKcqyS5bwuubPjFI78abBgcKmGwWqoDS5l9fnbzVC+W6UZIIONs4qpXZPzJT62nnXTizhPVnR8Pv9UFE1Fm1Rdt8uMZrxn7XC7GIPubsJWhTAtazOlAqBfabZ/VKS5jKlkiSp3gdP+njTl8gbDEqJXBDvdnZOaBrS3TdDObZFnDXqifyKWtV8Nl/XlK47vs7p9nq5HutOOH/OTryzXOzmzrEYeHlSvisGpQkRyrLW6UOm4+bsmxEqhSJECsGV3a7EO5e9g7PSz1LsJwalGQ5D0wHgRIVvQakqU+qiyRHQECiJd5CLq0woMZpVmb4eydE4Xfg+2XuSQakrYlCqRabUkToobSdNjgBVoyOgYV0pg1Jq78SRMP5kSgFnndkbfr+v+DPf2e74dOPRxmoXb7jr7Ftvk1WBYiCVCtVNGTXux9SZc3NhM7pf9yrOKmUHXmoJDEqJnKizWFXlKp0Tm7KYpwuzRPedrPR6PMNRIUsarQ9FksG3N+RNJze5fG7N8TVuj032MFNaZ7GqAr/mgtJ6Wz0qTMq7tokRyoHnYbowPDL0EWyauAnrbl6Hly56CfER8Tgj+QzFfseqjsFoaXoj7SS8aeaX+/amKZZvucuUSpKkHgtjNKmC9fBQHTITItE7XVl2mVPUsg0w2hOxaUjjjFJTFbB7GZC3GbC5X0Mp3mCxk2BDZ0lZRo6knj5fa4sLV/+cRaGOs0qp3RPLd/1ZUwqoM6X28t3vdxc42x2VdfVYta/Q6XPuNFfO2pJBnBgAJ1Urg1J9nz5AiEPvCFmG6eBBt+cU15UyKKWWEFRB6YgRIyBJkss/3377rVfnKy8vx3333YeuXbtCr9eja9eumD59OsrLywPzBVCLOe6kFNQxKBUzpXUWm9vGB86IpbBdk6J8aoVfb6vHwXLXbzBrj6+F1eb6w6v4Qd7VGptjpTUQp640V75bbipXbRODUruwkDBEhTX9G/eI76Fab3q4oqnUKiNOmSnN9zFTKo6EET/UiMQS0eJqMw4JowG6JxkQopNUa4Rbuitje+J0TenxLcAr/YAvbgXeGwl8MRmwum4WJf6/sUtFOcIl4Wcgvqvf19xi9DGqTdFSLUfCUJtls8nIKapGlZtu7oCTRkc+3pi1UwdTJhhN9dh1wnVJ67JtJ1w+50pBlfsgzdmosUAR15TGVRYrHoef1h3hXZW/70wHDsAdsWLI37EwNpuMn/cW4H+bcvk+SC75NqG4nRs/fjyio9XlUJmZmR6fo6SkBMOGDcOBAwdw2mmnYdy4cdi1axfmzZuHlStXYsOGDUhKStLysqkFietJE6LCFONTkqP1SI3RK9Yj7smvRM9U9feVK2Km1NcmR0crj8Jic/3GX24qx96yveiX1M/p8+Ka0mIXmVKx8VNKjN7pSBlHzta5xuvj3R5jpw/RIys6C7lVuU3XUHGoMYOqXlPqY6OjKrHRketMKaDuwFtSbUapMCfutJSG/5diJrmwquEDkqGZf7dgJAalsREhwJf3AnUOHyb3fg38/DRw2ZNOz+Hq/12mpPyQhhA9YEjx63pbVEhYwzVbm77PWL5LbVVhVR3u+nAztudVQJKAKed1w2OjT3c64kk1EsbPTKlYvnuyog5/5lXAXSHTT3sLUVlnaarO8EBzQWdLZhbFf0NDmXIOaVinToBNhvnQocZtzQWlWmZKq+osuPODzdh0pOHzQKhOwksTBuDawVk+n5M6pqDKlNrNnj0bH3zwgepP//79PT7H/fffjwMHDuC6667Dvn378Pnnn2Pnzp34+9//joMHD2LGjBkB/Aoo0I6VKbNujllSO7GE19t1peLdwq4+joPZV6rspJcalYpusd0U2zbmb3R5vKrRkYtMqXi9nqwnLatTdiWO08chVOd5QHZa/GmKxznlOY1/FzOlBZV1XpdQm+qtqjv17taUAg3jaBwVV6vLd+1BqbP/p2KGnBqIa0r7Vm8Aipw05NjwBlB+zOk5UmP0iAhTv61lScKw+PjOgK6dvf2pxsLUMVNKbY7NJuPuj7Zge17DzSRZBv772xF8uinX6f6qTKnfjY6UwVRhVR225irfhzonRip6A5jrbfhup3J0mTuyLKvmW4vr2cUGeoFis8koE/4NwyvLFY/D0tKg76Vs7FbnZmQMoF5T6k+jo38v29UYkAINa24fXrIDeS3cpZjavnb2rtw2nDx5Ep988gnCwsLwxhtvIDS06UP2Sy+9hJSUFHzyyScoKHC+hoHavuNCUJqVEKnaR9Xs6ISXQalqRqlvmdL9Zco3lz4JfTA0Y6hi24YTG1weL76ZFlWZIIt1uvBtHIyqyZE+wcWeznWP6654fKyqKRjJEDKl9TbZZZbXFWd3u9OayZSK/14l1SZVFvm05IaMeURYiGrta0vPsGsvxEzpaaUu1kJbzcDq550+pXNSMg0AWWKmNK6zT9fYqoQOvAauKaU2aMWOfGw7Vq7a/tbqHNQLjf2sThoCiWv2vSVm+Iqrzdh4WPk+dHGfVFzUO1Wx7avtnpfwltVYYLEq3yP7ZihL7EuNLdPoqMRoVt2MDSlXfr2hycmqoNR0wP2aUjG493UkzM7jFVjyh3o8TZ3FhrdW5zg5goIZg1IffPPNN7DZbLjwwguRlpameE6v1+Pqq6+G1WrFN99800pXSP4Ss4XpseqgVOys6k2m1GaT1eW7HmQenRGD0t4JvXFuxrmKbVsLt8Jkdf6mIs7lNNXbUG1Szz8Ug1JfxsG4Wk/qSla0srwnr6qppX+yQa/qhOvtWBjxbnZ4qK7ZUSRiM6r8ijrkCuXePRzKuMV/J2ZKnROD0tRi19l9bPsUKHJ+p99ZUKoq343v4vX1tTrVrNJa1Jo5p5Talo/XH3W6Pa+sFhsOKd8PymrMqj4FyTH+rSnNdHID+df9ykqJM7sk4JpBnRTbfjtYjGXbjuP5b/birdU5bm9wiqWskgT0SQtMUNrQdLHO6Y1iZ9eikwCUlii2hSQnQ99bGZRai4tRX+q6S2+GcDO12lTf7PpgZz5Yd8Tlc19tO+FT52PquIIyKP3Pf/6De++9F3/7298wb9485OY6LytxZfv27QCAM8880+nz9u32/aj9ETvQiiWugLp8t6jK5LZNvKOCqjqY6pV3jbv5WL7rmD0EGhoEnZ1+NiQ0BWwmqwnbC51/Pzobo+HsrqgqU+pD+a6/Qenx6qY7rjqdpCoxcjc7zpkioQQrLVbfbLMpcU3ppiOlqjvV9vJdQB2Uuhu6HswqHT7wJKMCUdXOP9wCAGQb8OO/ofpEC+c3S5yW77Y3qvJdE8t3qU3JK6tRlGmKfhG63IqBnyQBiX6OhInWh7odDQUAg7vE49K+aTCEN3WktcnA9M+24a3VOXj+m724cu6vLhvyiDczkwzhqmUf4jpPX6zckY+znv4RQ5/9Cde9uQ4lTgJl8QZ6pwgJtmpl473Q5BSEd+kCKVz5b+suWypmSgHv319rzVaXo3iAhs7H63NKXD5PwScog9Knn34ab775Jl5//XVMnz4dPXv2xKxZszw+3h7EZmU5X6Rt3+5psNuvXz+nf3JyWNrQWsRf9M6C0u5JBsWbGgDs8rCEVyzhjAwLcfoazbHarMirVg4E7xzTGXH6OJyedLpi+28nfnN6jsjwECQI63jEtR7VpnrVG7EvM0q9DUozY5TNxyrNlag0N/0bi+tKvc2UHheaIzVXuguoGyyJcVFqjF7RMEMs/XbW2TnYmeqtqLM03aTpoxN+d4ZGAsPvV27btxJ48zzgp1lAbdPNj0Gd41XnV2VK49phplRVvlvL8l1qU5Zvdx2AAOqMZYkwXzMhKhyhIf5/LHV3gzfJEI4uiVGIDA/BFf3SXe5XXG3Gg4v+dJqhVM22jolAojgqrNq/oLSwqg7/98X2xqqlP3LL8eTy3c1ey2mh6sA1NCUZUkgIwnv2UGx31+woIizEaVWQN37ZV6i4caaT1P9vVgvfExTcgioovfDCC/Hxxx8jJycHNTU12LdvH5555hmEhobi8ccfx6uvvurReapP3YWKinL+i89gMCj2o/ZHzKA5Cxh1OkmVLd153HXbeUditszXcTAFNQWotylL+DrHNGSBxBLe7458B5vsfM5jF6GRk1iOelho5CNJnjVmEjOlCRHerSnNMGQoMr4AcLyqKVuqmlXq5Zum2GXZ2dphUecE91+32IE5M14MnDnvTVRZq/wezpaERkap2cD59wGRwk2Nwt3AmtnA2xcBlQ0fiIf1SIKyyafcQcp3ld9X0Wx0RG3Mz3uVfTSyhTnNB4uqFRURYqbU1zndoq5u+h2c2TWh8b327otOQ7ibIHjTkVJsPlqm2i5WRKXG6pFoUN7Y9TdT+tW2E6qf7xU78lX/ZmJVU1cob3pKej10p6ZNRKjWlTbTgVfV7Mi7G6pilvS8HskYf6YymfPbQeF3MwW1oApKn3rqKUyaNAmnnXYaIiMj0bt3bzzyyCP48ssvAQD//ve/UVvb/A+d/c6ZqyDCVe2/K7t27XL6p0ePHs0fTJqrt9pQIqwHEWd52vXrFKd4vNPNLDRHRzQaByOW7kaGRjZmIy/vdrniuePVx/FH4R9OzyN2F84Vri9HmMOZlRCJiDBlltgZVaMjL4PS8JBwpEYpG1I4lvBmCAGft7NK8zzosizKiItAiJPRBnZiUOosU2rzsktwRyeuJ+0jBqVp/YDIeGDcGwCc/NuXHwW++juAhvmm5/dMbnwqCZWIlIQPiO2yfFf5fRXFkTDUhlTUWrA1t1yx7f7LeivW/csysDOv6T2yWMgmupoz7C3H5ROiIV2b3oOy02Px6k2DEBXu+r1syVZ1kx6xaigtJgIJQtlxqdHs9WdBRyt3qLPOVpuMdUK5qzjSrJOsfE8LTU5u/KyqbnbkPigV15V6c0PVZpOx5oAyC3pV/wwM75Ws2HagsFr1+5+CV1AFpa5cfvnlOOuss1BRUYENG1x3KbWLiWm4+2c0Ol9vUFPT8IHe2SxUavsa3kyU21yV1p6RKQSlxz0r31VlSpO1WU/aOaZz4xtQ38S+OC1OOVLlw10fOj2PmPU8KmQQDwlBaY8Uz763xaA0KcL72b2Z0coSXsegVMyUiuW4zRHLlJvLggJAaIhO1eHRkfhvkxmvPKe53oZio2+dDDsq8UNJjxChc3lK34b/9hkF3PQpEJOhPsnBH4D8PwEA/7769Mby8l4RQqZDF+r8+LaO5bvUhq07WKxYWx8eqsOFvVJUXeq3K4JS5e/B5taCemqwkxJ+O8egFABG9c/AbzMvwad3DcWiacMw47Leiud/3lugCi7Fktm0WL2qa7Cp3ubzz6ep3oodLqqu1ucoM4tiB/kUi7ietCkIdBaUugucxeUx3oyF2V9Yhco6ZQXMxdkp6NcpDuGhytBju5NuzRScGJSe0uvUD2t+vvs1EQDQpUtD6VdeXp7T5+3b7ftR+yLeBQ3RSUh0UVZ0RqbyDfd4eS3KPOi6p+pkq1Gm1F66CzRk8q/peY3i+V+O/YJ1J9apziOW74plrTkuRp40p8zkX/kuAGTFuG521EnMlHqxXlOWZXX5bmLz5btAw5w7V8RMaWqMukuwOHIo2FUKXR07S8qGKEjo1vT37KuA+3YAExcBkcL30x8fAwB6psbgm+kX4If7L8SH1wrrxmIzAV3zWf42h+W71Iat2qfMig3tnojI8BAMzIpXbHcMQMTGPVoFpQM6x8NZMUtUeAj6CzeSASDBEI7zeiTjrG6JGD1AecOqoNKker8WS2ZTYiOQIJTvAr534N2bX6UaOWO3+YjyPVWcl5pkUgalIY5BaW9lwG2rqkL9SdfzWVWNBD1s5AgAvwtjeLISIpERF4nwUJ3q/8EfQoadgheD0lPKyhp+0D3Jbg4cOBAAsHXrVqfP27cPGDBAo6ujliR23k0yhLss1+yZEg29cNevuWZHsuxkHIyPQanjiBRAGZQCwA29b0C8Pl6x7ekNT6OuXvnmoirfLa1R3EHNKRQypanNX6/FZkGFSXm319tGR0DDulJHJ41Nb6LindyiahPM9c7XzYrKaiwwCneyPcmUAg1lX66ImQGdTlJdJ5sdKVU6ZErDYUEyhOymuAY0JAzodRlwzl+U2w983/jXiLAQ9EqLgb5aKL9rj+tJAVX3XZbvUlshy7KqYc1FvVMAAAOyXC9xEct3kzUq343WhzoNPi8/Pa3ZZSenJRtUY9LWH1KWzIqNENNi9IjWh6puPvoalP7ppjfFoWKjYoyKmLWNqVUe65gpDU1Pb1xfale3d6/L1xLLd71ZU7pJCJ7P6db03i9msrfmqtftUnBiUAqgqKgIa9Y0DGp3NebF0ZVXXgmdToc1a9agsFB5R99kMmH58uXQ6XQYNWpUQK6XAsuTzrt2oSE6ZKvKk8qbPb+Y4eimYfmuo+jwaPx98N9Vx7y7413FNrExRI3Z2ng32GaTVXeKPcmUigEp4Fum1F1Q6qwTrvgm7YqYJQ3RSao3YVf6dXIelHZPNjjNqovNjpgpVXIs382QSqCDkCVwFUj2vVr5uOwIUCJ0LS8XOvm216BUr2waEy3VoYZzSqkN2F9QjZPC790RfRqCUnGJS15ZbePPu5gpFcdt+WPSuV1V2yY62SaSJAlDT1MuM3FsYGizyarsZFpsBCRJXVFV6mOzo4MFVS6fs9pkHChouElcZ7GqKruiql0HpZIkISI7W/F87VbnfSYAJ5lSD5fHyLKsypSe3b0pKB3UJV7x3I7jFX6tv6WOI2iC0g0bNuCXX35RfeMfOXIE1157LYxGI8aOHasY8zJ//nxkZ2fj4YcfVhyTkZGBm2++GWazGffeey/q65s+GDz44IMoKirCLbfcgvR01+3Gqe3yJigFgEHCneC1B9x3kxObHOlDdR6NIhHJsqzKlIqlrgBwfe/rMTDl/9k77/A2qqyNv6MuWe69O7ZTbKf33huQQCD0HghLX1hKaLvA0r+lLL1sgAChhEBCIBAS0nuvThw7dtx7t2WrS/P9IRfdO6Nmyy2e3/PwkLkzGo1taeaee97znhHE2OqM1Wg2tQeakX4KqOUS4pjWB3FJvY7TUzXJiZFEK3Q9KQBO1tYd6KC0rLldYu+vlEJJrXy768BLB9pRAQq32xHQE61WRlEP21bohu5CppSkQdselHJ6iir8bSZHfISnAepwcqzoMHVyyjTJvw+aHAEc+a4PdEQbHQGBnmL3BXJxPjpA2VZbnxSq5mQQM8tsaiJaBust+S4ALB0dg3tnJEIiYqCWS/D8olSMS3BPqTOMKsuxVz/VaY0caW1rj1KO2ZGDtjBWK4uaJgPMFv7vb66D/qitnG/5/RXXaTn+F/LGemJbEkoaCynHjCG2tcePO3yfKErhozGYodG7NiUqrtNxFinsf/e0pLu22Sg8EwUA9KOgNDMzE7Nnz0Z0dDRmzpyJG2+8EVOnTkVKSgr279+PtLQ0rFxJZo+qq6uRlZXFW2f67rvvIikpCevWrcOQIUNw4403YtiwYXj//feRlJSE//73v931owl4GU5Q6uJBOXVgKLF9rKDWaQYjn6cdjMiJm6sjGgwN0JjIFVU6UwoAIkaE5yc9DzHTHrzpzDpsK9jWfoyI4WT/WiVEF6hVW1+5xK2eqnRQGiAPgEQkcXC0YyLU5OJOvaEeWpMtsGcYBpFUttRdB97WB3srg8J8HRzJZVC4L2/7gpmDw3iO5msLIzyA7bHPlMbSQamzzCbDALHjybHSU+R2PRWU9kXnXYBHvmsQMqUCvQJaujt9UGib4Z5MIkIydW89X9YIs8XKyTi6q1RxB5GIwTOXpeDCK5fh1PPzcNfUAW6/dijlqp9ZroGpJYCkA2mGaQ+mafdgvrYw2RUazHlnN8a8sg2z3t6FMzzKKrpchiaj5dlFt24LUcvA1pCL4vaZUgBQjSWDUn16Oqx6/oVcOlMKuKdEOkJlSYN8ZMRCdkygEgFUb/T0YseSZYH+Q78JSidMmID7778fkZGRyMjIwLp163D27FmMHDkSb7/9No4ePYqwMP4JJR8hISE4evQoHn74YRiNRvzyyy9oaGjAQw89hCNHjiCEuhEI9B08zZROTAyCxC6oNFlYHMipcXg8t0epd0yOJIyEk1VsZVDgIEyJnkKM7SzaSWzTtT/pLQ9L2gUwJdLPrZ6qne1R2kqEiqs4KNfaSXj96YDPvUxpBhWU0rWgzhCLGNw2iZSCRQcoMT81nPd4OiilW9H0d+yDUk6mNMCF5C6SVAGg7HT7v1n2EpLv0kZHOsHoSKDHaTaYcTSPvNe3SndbSaXurRlljajQGEB3xqKN67yBSMS4rYBphe4/bjRbkdMSKNItWIJ9ZJC2nJ+vLYw9LMvi4R9Otql0imp1uHf1caJGVGs0o5RS+1w2lHwGtj67aG+KuEAlzNXOg1LlqFGAqP33wZpMDrOlCqkYgVTw6M7z9Wg+GZSOtesNC9gWk+maX2d1tAL9h34TlKakpODjjz/G8ePHUVlZCZPJhPr6ehw8eBCPPfYYlEruzfDFF18Ey7L46quveM8ZGBiI999/H4WFhTAYDCgsLMQHH3yAoCDPzVwEeg+eBqW+CilGx5EB10/HixwczdejtGP1pMVNpHQ3wifCaSZyXvw8Yvt4xXFY2Xb50DBKUnOm2FbnQa9gDovhl67S0JnSjpgcAYBKquLIfsub7M2OPM+UWq0sx5CKDkotVgs+T/8c1228Djf/cTP2FO8h9j84KxnLpw5ATKAS4xOC8OWd4xyaaHBcgj2w1u8P2AelUQy1oOMqiIwcRW6XnwGsLZM8fT1gpOqz+qx8l8w2+UAPvckq9LwV6FEOXqyB0U6GKhExmJxE1mSmRNKZUg3HNEcuEXECoJ4iQCXj9JdufV5UUPdu+2wip6aUCkpPFzcgs5y8H5U16LHlXPvzjC4rYRhb2xp7sso1vIaJyT4MWCP5nuIQcoFArFZDkZZGjGm2bYMjIvzpZ5fr5ysdlI4fwH3200HpWSEoFUA/CkoFBNyFdt8Nc6Pec9EI8qHxV0YFjhfwO8rlV3snU+rKeZdmXMQ4YrveUI/c+ty27RFUsFnTbMS50kbOCiadUXWEt4JSwHldaSRHGus64Dtb2sCZMNA/14enPsR7J95DZm0m0qvT8dD2h4h2OlKxCP9clIp9T83G2vsmYXCEY/kvLTFu0JkE6aUd9XZBaSjqyZ2+Lmrz6UypSQtUtzSFp7OkYGwtYfoiPPJdBlYhWyrQo9DS3THxgfBVkMElnSnNqtCgqJYMbiL9FW4pcLoLzjWX24JSekExwq/9+UMHpTXUM2bzWf72K3+mt49frKK8DvyVHLfaBp0J5Y16jnw3ScLtfy0J4fYG9507l9jWbNsG1spf30qrfOi/G01Nk4HzM/DV8tLP29ZFcIH+jRCUCghQeJopBYAlo6LhI2vPkrEssPzro8im6jFZlutk2+F2ME2uTY7siVZHI8KHkgHVZrT9Oy5Ixem/+e2hAs7vg89qnw9vBqX0ddsHpVFuZkotVhZvbcnCjDd34soP9xP7EkN8iLY4xZpifHXuK+IYFizeO/FeRy6ft1ZKyJa2Y98SJpShVsxpIyMadSg30Kw4a/s/XU/qFwVIvNN2otuh5LsihoUSRiEoFehR9uWQctHpg0I5x9AqFKPZir2UISDdNsuq06H50CEYixyrjrqSIdQiY2uGs5yTKW2fH9BBKd2z/Egef1nPnuyqNtOj3CqynjQx1AcxgUqOEWFmmYbjTxEH8tkn8vWFSMF99vjOI1VTlqpqNB84yHtttJKLfk+ao1QrGJVMzOtWTyuzGnQmlwGvwKWPEJQKCNihNZrRZCAzWO4EpX4KKR6cnUyM1WlNuPnzw0SQVN6oh5bqLehOz08+3HHepRkSRNrBX6i90PZvhmEwL4UM/tYcJScEfgqJ20F0ra5nMqWOgr3XNp3HhztzOJInAJhB1UD9fOFnmK3cTGZGTQayarPcvu5WVDIJ/JVk9sBde/3+gL18N4QOSn24k1wOYankdmXLYgudKe2r0l2AI98FbA68Qq9SgZ6iuE7LWWSdPpD7fQ30kXEW5v48SxpI2i+I6rMuIPeqJSi8cxkuzl+Ami9XefGq3WNwBJ0ptQWlZZTRj30w7Uy+a7GyOF/G3+pFa7Qgq2UBm84yJoWqwTAMJ0g+WVTPUV1FWclnG11P2oo8cQDkgwcTY3VrfuA9Np4KSvmen/bQ0t3RcYG8Nb1R/gqOWeCZknqn5xa49BGCUgEBO+isIOBeUAoA90xLxNRk8iFQpTHg6XXpbbKUi5XkQ0QlEyPCr2OOg5ygVO06KB0cSD6IsurIAGueA6OeVqYPCnXbKbjO4B2jI8BFr1JqslPbbCSMIwAgp7IJX+7P4z23iAFumdBupsOyLDbnb3Z4LTuKdrh93fZ0pPa1v9AalIphQRCoiZurTCkAhFNBaUVLUFqXT44HJnTo+noFMu5ikA+jFzKlAj3GfipLGqiSOuzhTGdL6cXZxJYWMizLovTpp2EqbFlQYllUvvkm9JmZXrpq96DLMSo1BtQ2G7k1pX6Oa0rt5bt51c1Ov6ut5T60826ra+0Qqi73t1MlhFEUwwDhZnJ+4SgoBYCAG64ntpt27ISpnCsvpsuL8muancps6aDUURsehmE4/hSCA6+AEJQKCNhBB6VKqZiQ5TpDKhbhs9vGYDTVq3L3hSocyrXdqHMqyQl36yqop5gsJsKBFnAvUzo4iAxKc+pziO3xA4I4NST2zElx36Haq/JdtWP5Lp0pBbgtV9afKOb0c2vl6cuGIDmsXRqZ25CLkqYSh9dyvNxxXzdncINSIVMK2KR8rRPUIDRCxFB/KLUbnzlHmdJLKSiVyAHKyMwHXOWFgEB3QUtwJyeHOFy0pGs0aQaE2IIffXo6DOfPkztZFvXr13f8QjtAQrAKMgk5Rc4sb+QsJtobHQX7kAvYDTpTWysZurUazfGCOlitLHKrqaC05dk0hMrccg0TfSCqI5+5Yp560lb8r7wSjMouC2q1on7tT5zjaGWURm9GvZa/V2mzwcwxEByX4Hgxmi4Fop3+BfofQlAqIGAHXz2pJ0Gjj1yCVXeORxiVXV2512YoREtz7IMhTyhrLiOccwH3gtIBfmSvtmpddVvPT8DW6uRuB/3cAlVSLEhzYTpjR1caHZU3l7f9/Gq5BH4KcrJOB3yb0kmpWLifHCsWDsavD07B36YnEfuOVzgPOk9VnYLJ4rqBOA1XZixkSgFSuhtGS3cZEaByPLFqfyEVlNYXAAYNT1Dqor1Mb4ZhABnVFgZ6wTBLoEewWlkcuEjWSNJKIXtctdxKbAlKm/bs5d2v2bqtW41wJGIRBlLP55OF9WjUk983Z+67QHuvUley1+MFdSht0EFvIp/ryS0ZZNrBmCYl0penHYzj0gexWg3/xYuJsfpffgFrIRe5ogIURMs7wHFd6YnCOljs0rcSEYNRcZ4FpYKbeP9GCEoFBOygnXfdle7a46+S4uE5A4mx3ReqUKnR42IVvzTHU2jprp/MD34y1302+QJX2jDp1onxvKva985IgkrmuOWMPSaLCRqqFYc3g1KT1UQEvXTLFftMaXmDnrOqvPL2sXhgZjJGUK6GAHCs4hixPT1mOrFtsBiQ25ALT+EaMgmZUsBFPakqBBC5oVQIGQQw1HGV523BqT19OVMKAHJyYqpi9BypuoBAd5BR1shxMHcWlDqS9QKATCxCXEvtYvP+/bzHmMvK2iW93QSdndydVcU5xl6+y9fSpvV3RDvl0s+e4jpdm6KqFV+5pG0OkhblD5mTfqtj44Nc9iilCaQkvOayMjQfPESeQyzitMehF9dbOZpHXv/QaH8onSjNhlNmRxq9mQh4i2q1WHusCAcv1gjOvP0EISgVELCDzpTSGU93uWZUNFR2N2OLlcWGkyUcaUtSaMcypZ4677aikCgQpiTlkEUa0sxIJhHhq2XjMG1gCBgGkIoZ3Dk5AfdMS3T7+uh6UqBzNaUhyhBOD9ayJjsJr5OA7whV4+KrkCAtit9BmGVZTqZ0VuwsTlCcXZ/t/sW3wOn3JhgdAaCCUtDOu27KxaUKIJjMeOPiTsBM/Y77elBK1ZWqoRPkuwI9Au26Gx+sIhzMaeKDVZyFuVaGxfhDLhGDNZuhP3fO4Tl0p0937GI7CG0uRD9L/JVS+Ni54krEIgRQgWltky0oLaKC0rlDwjilQT9T/c2TwtrLexRSMUbyLKK2MiHR86BUkZoKeUoKMdawfh3nuIHhdJ/ZRs4xANd5l68/qT3hfnLOwv/BXFv2/aOdOZjx5k6s+PkMblp5CH9bfVxYgOsHCEGpgIAdHWkHw4ePXIKFQ0mp6ztbLxATcACcQn936YjJUduxVABb1Mi13A/zU2D13RNw8l/zcPxf8/DilWkQu2lwBHCluyJGBH9Zx37W1teHq0jDG+cOvO2Z0tNF9cS+sfGBDn+WSm0lKrWVxNjo8NEYGEhmvrPrPA9K6QlZqSDfBQA06NqzLaFMPbnT3aAU4Ep4s/4gt8VyQO2+/LxXQsl3fRi94L4r0CPQJkfOsqSAzdhmioNjxsTbFiwNublgjUbeYwBAd+qUZxfZSZz1ngaAhBCu0smR2RGdKU0I8eFkS+lMKb1oPTmZv5QhRC3HkAg/mKvJTK4k1PnfBAACrrmG2NZs3wGrlrxWWjmVUcoNSo1mK04WkUGpI5OjVhiGwaRE8mfacb4Sv54qwZtbsggjp60ZFfjPZs+d7wX6FkJQKiBgRyUdlKo7FpQCwLWjyeCPrhUJUcucmgo5o6OZUgCI9SXbYtCZUnsCVDL4KbiSJFfQQWmAPABid2SYTnDWFoYT8NllIVut/Fuh+6PZQ7sRqyQqJPglYGBA54NSOnDW6Lnth/ojztvBeBCUhqeR22VUViUgDhD18Uce1avUB4L7rkD3ozdZcISSak4b6DoAunJkFO9462s5Bkf0+2Z2b1BCZ0ppBgRzM8N0m5PaZiPMFitKKPO9uCAVRlGmiDQDw8nv+9LRMeCzuFg6Jhoi1gpLDfk3kYS6bqflt+gKQGzXY91gQPNBsmcpLb3OKGvkyGnTSxo4c5yx8a7VUbR54vbMSjy2lj8j/s3BfE7GWeDSoo8/oQUEvIu3MqUAMDEx2GnQOSImoEPOu0DHepS24klQ2lG8aXLUirO2MHTjdftMaWY5uarrbKJB9yAdHDQYIkbEzZR2RL7L0/qnXMiWosHOyTGUDko9ypSmON8fOtj5/r4AnSkV3HcFeoBTRfUwmNsDEBEDTEp0HZROTQ7hZAeHRPhiclJLUJpDusHTi0iGixe7tbYw1FfOWyfairuZ0rIGPWEABNiC0tFOTIAAYDilpIoNUuHyYeRzUCUT4/ZJCbDU1gJWMih0Jd8FAElgIFRjxhBjmh1k27NUKiht0Jk4QfbBi2TmfHC4LwJ5jJ9oZgwKBS1con9XrZitLH465v35ikDvQQhKBQTs8GZQKhIxWDo62uH+mYNdr2LywbIsJ5D0RL5LB6XO2p90lFpdFwSlaseZ0sgAqqa0JVNa3WRAdRMpB3MmyaIzpa19XemgtLy5HM0mfrMHRyhlYs4ERzA7Ahp07dniDteUAlz5Lme/i6C1L8CR7+oE+a5At3OMqq1MifSDv5PgrRWGYfDxLaMxLiEQIgaYMCAIX945rq2cwlhMLrb6zp1LbFsbGmCu4poNdRUMwzh9XgxwIyitbTZwpLu+cgkCVFKnNaKAzSiI5vVrhuGa0dGQiUVIDPXBqjvHITpAyaknhUgEcZB7z1317FnEdtPOXYQLb3SAkuNwf7KwnthurQVtZVKSG67psKmxpg10fy607kSJYHp0CSMEpQICLVitLKq94L5rz3VjYzmrgK3MSQnn3+GCRmMjmkyki68nmVI6uKvQVnj9Jk9nSjtjctSKc/kuJY01mKHRmzjSXblExOm7Zg9fphQA4v3iwYD8Q9LZanfgZHQFsyPUO60p9eA7EjgAkDiRw18KQakg3xXoBdCGNq5qB+2JDlDip/smI+fVy/HjvZMI53RTEXlPVU0YD0ZJfqeNdDa1i3FkigdwW5oA3KC0rtnECUpjg1RgGAbBarnD/q0Dw9S8pTN+CineuX4ksl5ZiO2PzcCElppMOlgXBweBEbtXMuM7ezaxbamtJQynGIbBaEqKax+EGswWHKM+ExMT3QtKAeD+mUm848lhavx83yRirKReh+zKJt7jBfo+QlAqINBCvc4EMyUb6WxQGhukwg3jYjnjs4eEcdqYuEteQx6xLWEkiPBx38CFNgwyWAyoN9R36FocUaUjH5AhStcyIlc4k+9G8Lg6ljXokUkFpYPCfR2aHBktRhRqyJYDrZlSuViOMBWZtaOPdQfaJVgwO3JVU+qBmkAkAsKGON4fluZ4X1+Bct8VjI4EuhuLlcWJAs9cVvkQ8dyHTVSmVBYXB3ki6fpuyLno8Xt1BkcGToEqqYNMKTlnqOHJlMbZuRTPTeVfeJvnYLwVhmGI8h9zlfs9SmlkcXGQDSD7k2uPHiW2J1OZzz0XqtoWs/fnVBNyboYBJia6/5mYmBiM5VR/9BC1HJ/cMhpj4gM5ZVC7skgzQoFLByEoFRBogZbuArYbY2d5emEKYRQQF6TCv6/s+ASZDkpj/WIhFblvRhSqCuVk/Sq0FR2+Hj7o89EBXUegg9JafS30LS0/FFIxx2CitF6HTMq63pkUq7CxEFaWrMkZ4N/+oIzziyP2daQWl5YZlwvyXdS31JRKYEYQQ62Ae5IpBYC4SfzjqmBbL9O+joz8/KqFmlKBbiazvBEayqDNHUMbV1iammCpryfGpNExkCcnE2OcutMuZkJiEG9/0NFxgbyeEHxGR5yg1M4gacnIKF7zIrp21BV0ptQd5117VBPGE9vNR44Q21OTySC3uE6HUy3O9n+mlxP7RscFIkDlup7UnueuSMGnt47G9WNj8PfZyfjzkWkYGO4LhmEwfRD53rt4+sUKXBoIQamAQAuVGjJACPKRQeqkWbW7+Kuk2PDgFHx66xi8d+NIbHpkmtN+bq7IbcglthP93e8fCgBSkRShSvImb5919AZVWvKh4Y2glC8bTJgdBXAdeLMqyEypM5Oj/MZ8YjtUGQq1XQ0fXYtb2NiRTCm54lsqBKVtLROCwdP7zpOaUgBInsM/Hj+l7zvvAhz5rgp66EyCg3NfRKM34adjRfjtdGmfynbTMs34YBXCeEzcPIXOkgKANDoK8oFUUHqxezOlKpkEC4Zynz2LR/A7CXNrSo0cx1j7539iqBp3TSGzhNeMjuatJ3UGt0epZ54VqnHjiG3d8RNgze33lpRIX05mePXBAuhNFmw9Ty5CX8bz+3IFwzBYODQS/7l2BB6bP5hQqdH+G8cL6mCyWOlTCFwCSFwfIiDQP+CYHHkhS9qKVCzi9C3tKJ0NSgEg3Ccclbp2CUxFs3czpXSvT1oy3BFUUhX85f5oMLRLPMuay5DgnwDAVqt0tqQ9sMmrbsIFTlDKX78DcIPS1vO2QgeljmpKjRYjPj39KXYX74afzA+PjH4EI8NGAuDKd8vqBflubbPte8eR7jJiQOmhLDB+CiD3BwzUuQYt6MQV9iI48l3B6KgvUqUxYMlH+9scTFMj/bD2vklQy3v/lOwoZXI0Nr7zJnYANyiVhIdDJJdDlkTWGxpycsCybIed6zvCigWDcSi3pm2OMDU5xGFQSqurapqNnO9oHLUo/c8rUpAS6YcDOdUYHuOPWyfGe3yN3Exp54JSa1MT9OczoRw2FIAtaLx6VDTe2Xqh7ZgNp0rAol3t0oq35jqtTKDk4QazFRmljRwnZ4G+zyWwdCwg4B286bzbleTWk0GpvcTUXeisY7nWe5lSrUnLMWLyRqYUcF5XSjca336+ktM3zZl8N78hn9hO8EsgtuN8Sfmuo5rSfx/8N1amr8SFugs4VnEMd225CycrT9qun8qUCvJdoLbFHZljcuQT6nl2U6oEpv2DHAtKAoZe2/EL7E1Q7ruCfLdv8taWLKKlRkZZIz7e2b2y1I7AsiwnKB2X0HnpLgAYKZMjaYzNvE8+kHQ+724HXsCW2fzzkWn4v6XD8NHNownHYBra34BlgWbqOxpPBaUMw+DaMTF454aRuHPKAEg6oNDiZko9k+9Kw8K4daXHjxHbt06Mh1Labp5kZYFfTpLu/VOTQxAT2HElGB8BKhmSQskFueNUXbPApYEQlAoItNAXglK9Wc9p4ZIY0IFMKZW59KZ8l68+1VtBKR1M2zvw0kFpbjXZsiXUV+70b0pnSuP9yNVqOlNa3lwOo4VsN3Oy8iR+u/gbMWaymvDSwZdgZa2cTGmrS3B/RW+ytE3YuD1KO9YyCZMfAea9DMROAIYuBW7/FZB2Xl7YK5CTiyoqRg+94L7bp6htNmL9Sa7K4ufjxbA66M/YW6hoNKCikXxOjvXAedcZHJOjlqBUGhXl1IGXtVpRt+ZHFD/8MKre/wBWXdeoT0LUctwwLg5XDI+ETOJ46hyokjrdLxYxiA7smMmhM8zVnaspBQDVWLJfqf5MOrEd5CPDQ7NJOTXNfTP4nXQ7yxiqbvl4oRCUXooIQamAQAuVfSAoLWgsAAty4jLAr/OZUm8aHdHSXV+ZL5TOWnV4gLO2MElhavpwgrQox9JdgBuU0hloOihlwaK4iZxI/ZD5A++5c+pzsLtot0OX4P5KndauHQynR2kHJd8iETDl78DdfwHXfgkEcN2v+yyUfFfIlPY99mZXwWThBp+VGgPSSxp4XtF7OFdKXp+vXIJEHgfajmAsJo3jWjOljEjE48DbHpRWvPoayl98EZqt21D98ccovHMZUQvZ3TAMw1l8tCc6QOkVrwoajvuuh/JdAFAMHUZs686e5Rzzt+mJDh2JrxkdjakDO++0zwcdlNIO0AKXBkJQKiDQAm10FNYLg9KL9aTJQ5RPFFRSz6UyXZkp7Yp60lacBqWhzidHjvrBAUCdvo6oVQW48l21TI0gBZkVsK8r1Zl12FW0y+F7bMzdyOsS3J+D0pqm9qCU2w7GO9n1SwoZ3adUB61BMDrqS+zNrnayr3e7ip4rJc3IUqL8eFu7dARTMakAksa2997mOvDanoOG7GzUffcdsU93+jQ0f/3llWvqKOFOjJ/ig70rbQUAS1MzWC1ppuSpfBcAlMPJoNRUWAhzHRn8ScUirLx9LG6w68HOMMANY2Px2tXk673J6DgyKC1r0KNMaKl2ydH7q+oFBLqJvpApzazNJLaTAjomleFkSpsrvGYeQWddaaffzuCsptRXIUW4n5wjL2sl1UmmlM6SSkQSRKm5RhaxvrGo1bfXVNk78J6uOg2d2fFDcnfRbujMOkT4K9ocZ4H+bXZUa/d74NSUeuq82x+g3HfFDAvW1H8/P32Rk05kh6eL+1am1JX6xF1YlnUo3wXAdeBtyZTWfrOa93x1P6yB3+WXe+XaOoKzTGlXBKXmCu6isiTM8/unPDkZjFwO1tD+DNWfPQv1tGnEcUqZGP937XA8Pn8QCmu1iA5UcvwSvE1SqBq+cgnRjuhsSWOXv69A9yJkSgUEWqikghlnq509RUZtBrGdEpzSofPQQanRakSdwTtyGLp/J19w11Ho6y5pKoHZ2v6QGhTu2MgoLcqxxT5tchTnGweJiLtmR5sdFTQWtP37ZMVJzrFipt0Uwmg14kTFCaEtjB32QWkI3RJGCEq5yLgSdcbUzHMgP2uOFGLhu3sw5Y0d+GTXRbBs765hvNQwWawoqNE63H+qqL5X/03oTKmze6onmKuqiEAIAKSx7bJ7GU+vUtZshmbrVt7zaY8fh0Wj4d3XHcQ4qRmND/KO3NkeU2kZsS0ODIRI6XmwxkilUKSmEmO69HQHRwNhfgqMTQjqlsBQJGKQFk0ugvR2ubuA5whBqYAAAK3RjCZKBtfb5Lssy+J8zXliLDUo1cHRzglRhkDEkF9/b7WFoYPSOL84B0d6Dl3nabaaifcb58B0I8JPgQQnK9SuTI5aoX8WIiitJIPSmbEzMSyElDMdKD2AKKqfank/liDZZ4w58t2O1pReyvAEpWJzs1uBzJZz5Xh6fToyyzUoqdfh/zZn4qsD+V1wkQKOKKhphtmJmVGVxsAx3OstNGhNKK4j71XeypTSWVJGJiNqImn5rrWxEY1//AFLfT3/Ca1WaI8e9cq1dYSBYY4XRxNdlJl0BFNZKbEtjYx0cKRrFC0tYFrRp3PrSnuKodQiyDkhKPUqDVoTNp4uxdH82h5bHBOCUgEBcLOkALzSENyblDSVoNFIrlSnBncsKJWIJAhRkjUn3qortZe0AlyDoM7gL/fn1HXa922dlBTM+7qpA0OcSpPtg0uA26PU0XhrMGu2mnG66jSxb1TYKEyOmkyMHSg9wDE76s81pa09SgEHLWEESKRKsNRikg/0nNZHNBYri5d/z+CMv7stu1e7P9c0GfD6n+ex9JMDeP3P832+J2tOJdkqK0QtJ1ps8B3Dh95kwQ9HCvHSxgx8tT+Ps6DaFZwrIwMAmUSEZBfmcu5CB6XS6Ggwdu2gpFFREKnIRcXK995zek7t0WNO93clzn4vQ6O9k122x1RGZkolUR0PSpVDqaD0/HkHR3Y/w2LI352QKfUexwvqMPOtnXj4h5O47tODeOKnMz3iBi4EpQICACoaycDARybudY3M06tJGU2APIAjZ/WECJX3e5UaLAaO0REtee0sdB2tfd/WkbEBvLXAVwxz/pDmBKWUyVErtNNxWXMZdGYdLtRdgNZMyvJGho3EpKhJxFhOfQ4C1eTEuj8Hpa1GRzKYEMBQMlQhU8qFYcBKyUyLD3RoNjoPSg7n1XCyXADQoDPhr3Pec972JiaLFXesOoLPdufieEEdPtudi8fWnurpy+oUdMA5MEzNyZxluwhKq5sMWPLRfjyzPh1f7s/DixszcPl7e1Fc51gW7A0yKOnu4HBfr7nIGumg1K6eFLA58CqoYMlMSVbpnsb6DO4iTHeRFKoG3xpoqK+8S8qC6N+FNLLjJTOKFLIkyFxeDnNtrYOjuxdaLl6pMaCysf8+P72F2WLFEz+dRp22fYFy3YlibDxT6uRVXYMQlAoIgGty1NuypABwrJxc+R0eOrxTxkThPuSk3xvy3Zz6HE7LGm9mSgEg0Z9sD2CfKZWKRXh64RBi/7SBIZg52HHWzWK1cLK7juS7fD9LYWMhR7ob7xePEGUI0oLTIBVJiX16Jp/YLqvX9eo6sq6k9XsXTNeTAkJNqQMYyuxIzeih0TsPSjell3VoX0/yw5FCnC0hPxd/ni3H6aL6nrkgL0AHpclhagyksmrOMqUsy+IfP55CZjlZL1lYq8X9356A2eI8Y94ZuPWk3pHuAoCpiDI5io3hHKMcMcLpOUIfeYTY1p8/32P3VaVMjORQbrZ0eBdkSQHAVE4uKHdGvisbMACMnFzY1Wf0jmxpYogPfGSksoD+XAp4zh/pZcir5noTfL43r9uvRQhKBQTQN5x3j1aQNTLjwsd16nyctjBeyJRm1pDuwHG+cR1qWeMMOiil2+QsHRODL+8ci5snxOHpy4Zg5e1jnQbv5dpyGK1GYsxRUKqSqjjZ6bzGPE5QOipsFABAKpYiJYhcea42kdfbbLSg0UVQcanSqlDg1JOKJIAioPsvqA/AyMl6NRX0aNQ5l+AeuFjjcN/B3BqYujCY6Qgsyzqsd914uvtX771FThU3KKWlns6C0q0ZFQ5byqSXNGDtsWLefd6gq5x3AT75Lk9QOmqUw9fLEhLgv3gRMWZtbISppMTBK7qe2UO4i2oL0jqubHIGLd+VdkK+y0gkkA8ZTIzpz/dc1tkekYjhuOgLEt7O82c6/9wvvaQB+TzBalciBKUCAuj9PUqrddXIayBXrcZFdC4opYOrsqbOZ0zO15IrqkOChjg4suMkBpBBaX5jPqwsOamePSQcr109DPfNSIKCqtmiKWggpbtqqRrBCv7aVIAr7c1vyOc477YGpQCQFpJGvl8zd9W5vJ9KeFvb93DrScM4cjyBFmSUfNdFprRKY0BuleOJhdZo6XUTuzPFDQ6veev53ik3doXVyuJiJfkz8QalVfxBKcuy+HBnjtP3WLk3t0vqwPQmCy5Sfw9nLbY8xZV8FwB8Jk0Eo+BXMPnOmwdJZCTEgWQvS/25ngum7p46ACHq9p7UKZF+uHKk95zoW2EtFpjpoLQTmVKAK+E1eKGu1HDxIirffhsV//cfNO3e3eEsNi3hPdvL7l09QbPBjF9PlWDz2XIYzJ7V3RvNVuzLcdw72dm+rkB46gsIoPe3g9lZtJPY9pH6YHDQYAdHu0e0OprYLmvufFB6vOI4sd3RljXOSPIna0p1Zh1KmzqePaGddxP8EpxmVuks6oHSA6jUkXW09kEp7cB7ruYsgu0mKwBQ2g8deE0WK2pajI64zruCyZFDKAdeNXROzYqO5ZP1YD4yMaeO8VCu40xqT7Ajs9LhvoIaLdFKqK9Q2qCDzkROGJNCuUFplcaABi3373m8oA5nqD6mS0eTwVtedTO2O/nddZTMcg0sdsEuwwBDIrzUo9RohJmSn/LJd0VKJdTTp3NPIBIh4IbrwTAMp51JT9aVhvkpsOHBKXh83iD884oUrL13ossF0o5gKisHayI/L9LoaAdHu4cihf49di4orf9lA3KvWoKalZ+jdtUqFN17Hypefa1DgemwaCEotae22YjL3tuLR9acwn3fHsfNKw9Db3I/MD2WX+vUKO3ARSEoFRDodnp7pnRL/hZie2r0VN4+mp4QqSZXUyu0FTBZO+7EWdJUgpx6ciW/s9lcPkKUIfCTkROirNqsDp+PNjmK9+eX7rZCt6WhpbuB8kAimzo0hDToqNZVIyyADELL6vtfprS6yYDWOUkoqImFj1BP6hA5+dlXQ4dGJ0HpESooHZMQhKnJpPP2odzeYWTSiqvV+TPF9d1zIV6EzjSq5RKE+8kRH+wDiYhcBMup4vbYXHeClKLGB6vwf0uHYUgEKedef8L7El5aujsgxAc+XjICNJWWAlRwwpcpBYDge5ZzFBT+Vy+BrOV4RSqV4csky0m6m5hAFR6eMxDLpyXCVyF1/YIOYMzPJ7ZFvr4QBztW+rgD/Xs0FhTA0tQxGaf2xAmU/fOfgJkMfOq+/RaNv/3m8flo9+LSBj3qtX1vkcpbvL89G4W17SZnxwvq8M3BfLdff9DFgiS9ENbVCEGpgAC4mdIwv94TlFY0V+BoOVlPuiBhQafPG+VDSomsrJXjnOsKk8WEny78hMd2PYZrf7uW2BcgD8DQ4KEOXtlxGIbh1GmeqznX4fNxglIH9aStDAwY6HT/yLCRRKY13i8evlJy4qj2JX/P/bFXqb1kmSPf9RWcdx2iICdlfozWqXyXNgIZGx+ICQPISeuJgjoiE9aTNOpNOOXCzCi9mydK3oCuFU0KU4NhGEjFIiSE+Dg9Vm+y4HfKCfP6sbGQiEW4fVICMb49s9LpIkVH4Jocec+wx1hMBtsif3+I/fizsMphwxD97n8hiYgAJBL4L1mCiH/+s22/fDBZLqLvpqDUqtXCVFraI8ZKxoJ8YluW4Fzp4w7yQYMAsV1Wl2VhyPL8d8maTCh/4QXAwp+5q3rvfbBGzwLKxFAfyCjX5/Nl3EWc/oDOaMG649xFqJ95xhxB32unDyJVSsV1um4N+oWgVKDfw7IsSuvJoKA3yXe/OvcVUTOplCgxNXpqp88bIA+AUqIkxjyRwTabmnHnljvx0sGXsLVgK5pM5ERqQcICiEXelysB3P6sGbUdl2nxyXddvTcDxw/9MeFjiG0RI8LAQDKQFStIuVppP6wprbBbCAqjg1J11xiCXBLQQSm0Do2OWJbFeR7X1HEDyNq7JoMZmeW9w8Xy4MUaIkCWiUW4YSzpeu2o7rI3w3HetXNnpR14syvIYw/l1hALDwwDLBllk2heNjQCUnH7/chotmKrl9v8dKnzbnERsS1zIT31mz8fA3ftxJDTpxD1xusQKdufYYoUMig1V1R0eTuTxq1bkT1jJnJmz0HelVdxTIe6GmM+uagqS0jo9DlFcjnkSWSZTEckvPU//wxDtuM6aFNpKTQ7d3l0TqlYhIHh5PflfFnvuHd1N0fza6Hhkd5eqGhCmRsL3VYrywlKrxsTA5mEDA270+FYCEoF+j0NOhOaqabsMQHedYztKIfLDuOHzB+IscWJiznBZEdgGAaRPqSE15O60pcOvoQzVWd498lEMtw19K5OXZ8z6KD0fE3H7P8NFgMnEHeVKVXL1BwHYHvo3qQAOEGpUUy+pzsPkEsNe8k8J1Mq9Ch1jDKA2PRjmh26NxfX6TiTltQoP4T5KpAQTN7jjub1Dgnv3uwqYnvcgEAMjSaDIGcOtb2VizztYFrhBKXUsXSN7ajYAEQH2J4BgT4yTB8Y6vT4zmC2WJFZ1pVBKWVyFOteCzFGzF3wlCUkcMyQulLCa6qsROmKp2DV2DJ1huxslP3zX132fnzQ8l1ZgvPnl7vQZkd6D82OWLMZNZ9/QYzJU1OgGEZ6LNSvX+fxtaVEkp+/3rKg1t0cK6hzuM+RS7c9eTXNHJXN2IRApFAlAd1ZtysEpQL9HrqpPMMAEf49myllWRY/ZP6A+7bdBwvbHjBLRVLcOfROr71PlJqU8JY0uWehf6ryFDblbeLdJxVJ8dKUlzjn9iZ0UFqrr0WF1vPswMX6i5y+qq6CUoCbDW0lTBnGK+8dFDiI2G6wkH1RS+r6X1BaZi/fpWtKhR6ljuHJlDqS79Ir3IEqKSJaVCBjE4KIfUedTHC6k33UZGpqciiSqJ6PuVXNXeIy25XwtYNp+3c4OQnMrmiXI7Isywky56SQizbzUsntPReqvNbmJ7e6GQYzeS6vyncLyUypNKbjJj2MWGyTntqhP991QWnDhl/B6sh7d/P+/TAWFDh4hfcx5pGu/LL4BK+cl64r9TQo1WzdymnJE77iKQTedBMxpj1w0ON6VbqOur/Kd084uWefcyOQpHs+h/vJEemvRBpVtytkSgUEupESWrrrq+DIF7oTo8WIFXtW4LXDr8FsJSeb9wy/B7G+7q0kuwNdV+puW5jVGauJbZVEhUdHP4rXp72OLUu34IrEK7x2jXzE+sZy6jQ7UldKGyTF+sbCR+rj4Oh25iXM4x2/PPFy3noeOlNaYygG0P63LarTwWjuXb0iu5p2cwaWK9/1FeS7DuHUlDY7rCHMoDJcqVF+bZ/PcQmkhPdYfm2Ha+JYlkVWuQZFdoYbHaGoVov8GvIc0waGIInKJOpMFpQ19h3Je22zkeMYbB+UDqLkiKUN+jZH5ezKJs7CKd0Dcxa1rTGYcSzfO4sMGdSENNJfgSAfmYOjPcdYSC7QyeI7l+lTDKZ6bHagFtJdmnbt4h1vPnCgy97THotGw8k0ywYkeOXctJOxITsbVg/qPxt+JU2MFCOGQzVhPHznzwMk7SZZrMmE5gP7Pbq2VCpTeqFCA3Mv67XcHVyocByMuxNIZpaTrx8WHQCAG/Rf7MZyCSEoFej30Fmq6MDOS2M7itlqxlN7nsLm/M2cfXPi5uBvw/7m1fejHXhLm13XlNbqa7GjaAcx9sDIB3D3sLuxKHERQlVd386DYRhOu5mz1Wc9Ps+FugvENp3RdMTY8LGcxQEJI8H1g67nPT45IJnYtrAWiOTtMkWLlUVhbfc2qe5pCluCDx/ooWJIozEhU+oE3kypg6CUmpik2LXxGEdlSisaDZzgxx1K6nW47L29WPDuHkz7z04s//oYdEbPeuW1QkvOgnxkSI30Q5ivHEqqnUZxJwLgBq0JPxwpxJojhV43BeKDlhvLxCLE2j1nBoT4gDLgbXPrpbOkUf4KzqQx3E/BkdTuzPKOhJd23vWmdJdlWW5QGte5oFRO1ZUauihTamlqgu7UKd59zYcOd8l70nCyl1Ip5AOdG/G5i5yS78JshiE7263Xmuvq0LRvHzEWeONNYBgGYrUaqnFjiX1NHtaVDqGCUoPZivya/vX81BktqNQYHO4/X9boUk2SRQWlrfeVZEqZcrGqqduUKUJQKtDvoTOlrbU6fHS1u94HJz/AtsJtnPG7ht6Ft2e87XXjIDpT6o7R0Z95fxIZXLlYjiXJS7x6Xe5A9/90VN/qDDooHRzoXu9XiUiCN6e/iUC5LdskE8nwwuQXEOvHn8X2lflyftcB/qQVe05l/3qoFrRMIjj1pIBQU+oMD9x3aQOQVLuAYkCID4KpjJenPemMZiuWf32MWHHfdr4Cr23qWF/DXVQgNSU5BCIRA4ZhOIuF9H3bXc6XNWLOO7vwzPp0PL0+HXPe3t22QNJV0EHpgBAfSOwcROUSMRKCSYVGq4R3x3nydzJrSBivGoPOnu65UMU5piPQGZdUL0p3LdXVYLXk714WH9epcyqGUG1hcnNhNTievHcUw4VswMqfndOdPMk77ilWrRaVb7+D/FtuRdmLL8JUQZao0H1Y5QOTIZJ5J4stVqshjSP/FgY3JbyaLX8RLWAYuRy+8+a2bfvOmkUc37xvn0dzqyAfGcKpDgn9TcJbVOf8ntVsdK0moYPSwa1BKaVM0ZusHb7feooQlAr0e9zJlJ6pOoM7/rwDI1ePxIwfZ+Cd4+9Ab/aufOxY+TGsOruKGFNJVPhg9gf4x5h/dImTbbQvWb9T1lTmslfpzqKdxPbc+Lnwl3tvouIuw0OHE9tnq8/CYnU/Q8OyLLLqSPnuoCD3MqUAkBaShs1LN+PLBV9i89LNLgNzWsLr708GAN0pkelp6rXGNnOeMNSTO2W+gMy1hLrfwsmUNkOj4066G7QmzkTCPihlGAYTk8jWMNtaAiCr1eZIXtfsXK63/kQxr/Plt4cLkFft2SKL0WzFfqo/6Uy79gT0YmFHsrp6kwX3rj6O6qb2n6tKY8Dzv3musvAEjvMuNenjG8upbEKD1oTjhaQMd04Kv4pgBtXKIbNcw+m/7Sksy3ap8y6dJWVkMkjCO7cgJR80yGYM0YrFYgsgWzebmmFp7HyNnOGiY1dZc2UlzHWdk0+zLIvihx5CzcqV0B0/jvo1P6Lg9tthaWr/LOnPkUGpMi2tU+9JwzE7ctOBt/H334lt9axZEKvbP9/q6dOJ/eaqKuJv5A5DIsjPYX9z4KUX0kLUcqip3sF5VY7vwQ1aE8qpoLU1KA31lcNXQZ6ru+YnQlAq0O+hV5zoyc/xiuO4c/OdOFF5AlbWilp9LVadXYV7t94Lo8U7/ZtMFhNeOPACYbojFUnx4ZwPMTN2plfeg48B/gOIbTNrRlFjkYOjgSZjE45XHCfG5sXx11d2NXRQqjVrcbHhotuvz2vMQ4OBlKYNCRri4Gh+VFIVxkWMc0uyzG0LQ65696egtMDugRrKCCZHHkEFpWKGhUXP/ezQ9aQysYhjGDSHyq7tvlCFNUcKMeOtnZj8xg6Menkr7vuW/L63wrIsvjqQ72Af8NMxx/cRPo7l13Jc0GcMbv9exdCZ0g4EpV8fyCcazbeyK6uKI1P1JrTJEV0jC4DT5iK7sgm7s6uI9jhyiQiTEkN432NEbABnUkoH+Z5SUq9DA9VuyKtBaQEZlErjYsGIOjctFat9IKMyfLrTp2E1GlH+8iu4MGECLoyfgPwbboSBMgnyBGOO46AUgMdBFk3T7t1oPnCQGDMVFKLq/ffbtvXp6cR+ug60s3TEgddUVgbtsWPEmP8i0mNCGh8PKdX6p3m/Z3WlXAfe/pUpLaDuY/HBKgyg+h3nVTueU2RR9ahSMdP2eoZhOM+K7nI87zdBqVarxYYNG3D33Xdj+PDh8PPzg4+PD0aMGIGXXnoJTU2e/cITWhoUO/ovs5uaNgt0DquVRS61mmT/xdab9Xh679O82cMTlSfw9rG3vXIdP2T+gEIN+YD++6i/Y1zEOK+c3xF+Mj+EKMlJTm5DrsPjD5YdJKS7UpEUE6Mmdtn1OSNEGYJoNflg80TCe6LiBLEdpgrjSGy9SVIA2fdNB9KZkO5NeClj/0AV2sF4iIKrSmD0DRz5Gx2UDgxXQ0o1nZ89JIxoRG80W/H0+nQU1bYHfLuz+GWgh3JrnU4Efz1V6pEkj66BHBHjjxB1u0Svs/Jds8XqMIgGgC1e7u1pj7N2MK0MDKMdRRuxi6onnZQUDKWMXzEjFYswicp8773QuaCUzpL6K6VOy1s8xVhI9djsZD1pK8rRo4lt7eFDKHvun6j77jvAYlv40J0+jcJld3U4o2nIcb4AasjKcrrfFXXfrOYdr//pZ1jq62GqqOS0g1EMHcb7mo6iSCODXH1mJliLczVS4ybSlV/k6wsfKjPKMAx8pkwhxpqpGlRXpERyvy/9iUKqhjY+iBuU5jpRq2RRbXSSQsnnA32PEjKlXub777/H1VdfjS+//BJWqxULFy7EtGnTkJeXhxdeeAHjxo1DZaXnxgB33HEH73/+/t0vZxTwnNIGHXQm8iZr/2Vcm7UW5c3lDl//feb3OF/TsfqpVjRGDT478xkxNjx0OG5Lva1T53UXuuems6B0T/EeYnts+Fi33Gq7iuEhZLbUk6D0QCnpkDgmbAxvrZa3oFvFNJorAVG77DKrXNNvHHjtJ+lc510hKHWKzBcsyM+pGlo06si6UtrkiHasBIAAlQyXD+uY0/HXTgI8wBY0OpsU2cOyLDafI++zMwaTWVyufNezOtC/MiqINkQ02zK6JihtNpg5ATRtJAKQ0mrA1jJp/Uly4YquG6WZNpBcYNyXU90pHwQ+6a4375EmjslR5+pJW/GZOIHY1mzdhsaNGznHmcvLicyjJxioTCkjlRLbdMDoCZbGRjQfOcK7j9XpUPfTT9AePkSMi3x9OW1cOgudKWV1Opftbhp+/4PY9l0wn7fOlQ5KtceOwap3X25OZ0rLGvSo13pHudYXoBUfsTxBqbMSCjpTOpgyT+MEpd3kedFvglKZTIb7778fFy5cwNmzZ7F27Vps3rwZWVlZGDVqFDIzM/Hoo496fN6vvvqK97/IyEjXLxbocWhJgq9cgjBf2+o8y7JYl002dlaIFRAz5Er1Byc/6NQ1rLuwDo1G8uG/YtyKLqkh5cPdoNTKWrG3eC8xNiN2RpddlzvQEl53g1KNUcMJsLs645vgn8D57Ihk7RNho8WK7Mr+IUGyz+KF0jWlQqbUOSIRoCAnZH5oRk0zWVdKZw7oSVwrD85KdrsF1pE8mzlXcZ0Wf2WQQeTzi1I55iPumu2cLKonsrMAMJ/qvRkTqCK2S+v1HjlCfrU/3+n+8+WNLp14d2ZV4vlfz+KD7dmobnLPPIdW4jAMkBjKXchLDlVz6rhoZg12HpROTSaD0kqNARc6ocDI6ELnXQAw5lOZ0k6aHLWimjiJrCt1Qv1PP8NU6trgzx6LRgMzZTqknjOH2KbrZT2h+cABwiiIpv6HNWj86y9iTDV+PBixd+cMkpAQSELJ0hRndaWGnByOGZL/okW8x/pMmmi7l7XAGo3QHuMvFeBjQIgPofIA+pfZEZ98l76vOA1KHZgctUIvnNElCF1FvwlKb7/9dnz88ccYSNllR0ZG4qOPPgIArF+/HkYP+jAJ9A0a9Sb8dKwIv50uhZ7KitJBaVKYum0lOKMmgxOgfTz3Yzw48kFibG/JXuTUOa8vcYTJasJ3md8RY3Pj5mJE6IgOna8jJAaQQenFen5ZUmZtJmr0pGPs9OjpvMd2F3RQerHhIifAb4VlWfx28Tcs27wMk3+YDIOlfVIpYSSYEzeH93XeQi6WI86PnHSFBpHSsbNuNLy+FLAPmCKYWnKnEJS6hOFx4LXvg2k0cxc4UqP8gPoi4OgXQOYfgMmWlRgY7os3rx1OTPD8FBI8uWAw/Kgg6ZXfz8NksWL1wQLYx4M+MjGuGxuDaQPJCay7QekGKiOYGOLDCYDomlKjxYoqNwPDsyUNOJJPfs7+c+1wSMXtgQvLcpvJ2/O/PRexbNVRfHOwAG9vvYBF7+9zK1ubU0X+HWIDVVBIucGDSMRgVFwgZ7yVgWFqxAapHO4HbBN1OqO8N7vjLrxniumg1H0FGGs0ouG331D57rto2LgRLBVksWYzDBfJZ40sIaHD12qPNDzMFvS4g9mM2q+/9uj8dJYUEgnHvIcvKGVZFjVffInsadORPWs26tau5T2/9shRYltEKe9MpaVo2radGPOdNdO9i/cQeSpdV5rh4Eig4Q8ySyoJDYVqHH8JktjPD8rh5PPbEwmvVCzi1GH3FwmvxcqimFrEiw9WcRy8i2q1vOqr1r7S9gwOJ4NSuu6dr9dyV9BvglJnjBhhCwAMBgNqampcHC3QlzhTXI9Zb+7Ckz+fwd9/OIl5/92NXLsVn4vUKra9ZIHuFRqtjsaY8DG4JeWWtlYgrazJWtOh69tZuJMjD75z6J0dOldHoXtoZtdlQ2viTrb2l5BGBPF+8Q5boHQXQ4KGQCoiZVN8/UpZlsWrh1/Fc/uew7GKY5z9s+JmdYuDMP27Dggg7zfp/SAorWs2Es6pUQx1z/WP6eYr6oNQQak/mlFjN2G4WNUEk4XMIg7THgE+HAv88Riw5mbgy/mArh4AcNXIaGx/fAbeuX4E3rpuBPaumI0HZyXj8flki6Tsyia8u+0Cvj1EZriuGxsLX4UU0ykH2KP5dYRRDx9NBjPWnyCD0sUjojgy0VC1nJMZcdeBl5Yah/rKsWRkNCfIOlFQz/v6jNJGvPEn6RNR3qjHYz+edimPdcd5txXaQdeeq0a6rndnGIYj4aV7v7pLRaOe0wdxeIx790hzVRXyrr8BpSueQs2nn6H0yRUoXH4PrLr2v5exoAAs1apFPsQzozlnBN5+O+94wE03IvDWW4mxupY6TXehg1JZfDzkyaRngKmkBKyJzLzXff89Kt98E+aqKpjLylD+/Ato/PNPzvm1p8iWMsF33w2FM2ddsZiTqfUWtHmS7gR/uxuWZdFISXf9Lr/cafaWU1fqodkR7cCbWd4/gtKKRj2MFjLYjA3iBqVWlr/2vrxR3+Z+3wqdKY0NVHLut91RVyoEpQByc23ZMKlUiqCgIBdHk7z55pu477778Mgjj+B///sfqqq80xtMoPPoTRbct/o4MVkrqtURzd1pMxB7x7GDpaTz3eUDLoeIEUElVeHaQdcS+zZe3MgbyLnirwJSgjM8dHi3ZkkBIC04jZCVWlgLztWc4xy3r4RcxZwSNYVzTHcjE8uQEkyu5PJJePeV7MOPWT/ynkMuluPhUQ93yfXR0HWlIjkpATvuYFJ8KUFmrFhE0plSP9K8SoAHJbkoFsA0EavYdD3pIH8rfP54ALBvY1V2Gtj5attmbJAK14yOwbVjYuCvsi303DIhjjNZ+WjnRcIlV8QAt0+yGdRMHEA+P5sMZpfZi7VHi9BkaJ8giRjg2jHchQmRiEFUgIIYcydTWa814tfTpDzz1gnxkElEGBkbQIxnVfBf6+d7c8EXWx/Jr8WOTOdeFHRGgs7u2OOovlcsYrBklHvfi6lUUHo4rwYGs/utslqhs6S+cgln0ssHa7Gg5B+PwUCZPWoPHULF//1f27ae2i8JC4Mk0HGm2FN8Z85E4C23EGM+kycj/KmnEHTnnYBdsMRqtaj9/nu3z22kTI7kSUmQxVMmTRYLTCXtiy1WoxE1n5LeEQBQ9d77YO36nVqbm2HIJE2SVKNHIfC2W+mXtuE7d65Xf3fke5OmUbrTp3kDeN3JUzAVkY7bfg6ku634TCXnEIbsbJgq3Pd24Zod9Q/5bgHVDkYpFSNULYe/SopAFblIn1/DlfDSBnVquYSjsJCIRUgIIZUZtGFbVyAEpQDee+89AMDChQshl8tdHE2yYsUKfPbZZ3j//fdx7733IiEhAV988YVH50hLS+P97+JF5+5uAs5Ze6wIpTzGFrnVzfhwZzb0JgunZqZ1JbhWX8vpYTk1emrbv68ffD1ETPvXR2vWcjKrrjBajJwazauTr/boHN5AJVVhUCDZn/N01WliW2PUcIK9KdE9H5QC7pkdfXTqI97XBimC8M7MdzitcbqK5EAyU6qxkg/xzPLGS96s4XBuexDqj2aoGEqCKWRKXaMiXVaDmEYiKKUDwQdU2wEdFfwDwLFVgMaxkZtELMILi52bp1w1MhqJLYt5YX4KjtnG4Tye921Bozfho51k1mlOSrhDmSrtwFta79oY5ZeTJYSETSpmcNMEm8JjCBVw87kJ600W/JFe5vD8zhx9Ae5EOSXCcV1mpL8Sc1O48vXrx8ZyamodMSUphCin1JusOF7AdZht1Jvw6e6LeGztKaw+mM8JXNOL64ntodH+EIlc12k2bNjAaQnSSv2aH6HPugAAnMBLPmQw30s6Rfg/n0P0B+8j+J57EP3B+4hd+T+IFArIYqLhd/nlxLF1q79122iHzpTKk5Mh9veHmJLZ2kt4m/fvh5knaWHMz0fzwfYFcN2ZM4BdkAqJBIqhQ+G/aBHkA5M5rwfDIPiee9y67o6gGjcOjMJuMchqRRNPRrPhl/XEtiw+HoqhzvumKocNg8iX/A7aZ0uN+fmo37ABTXv2wMpTWkebt2VVaGC2cOWqlxqFtWSgGRekalOWxFELRwU8daUXqPvcoHA1r4EZX+/krqbfB6WbNm3CF198AalUipdfftnt11155ZVYv349CgoKoNVqcfbsWTz22GMwGAxYvnw5NmzY0HUXLeAW644XO9y3cm8eNqWXERI3hrH1egOAI2Wk851KosKw0Ha79QifCE49JW2K5IpDZYegNbeveDFgurQnqTNGho0ktul2KUfKjsDMkq1gxoaP7Y5LcwmdWT5TfYaQ1GXWZnIyv7ek3IKvF36Nbddtw/SY7quLpeW7DcZaKOTt8hqWdT6J7+uwLIstdi6rHOkuGMCv69ryXDKoyGxYIDSoabLLlNoFpQysmKXdwn8eqwk4u55/Xwuj4hyrh+QSER6dS2b/xyeQx7eaI/Hx6e6LhJIFAP42PdHB0VwH3lIXbWFYlsWPR8mFnwVpEQjztU2y6SxwfnUzx3cgvaQBBieu2Huzq1HAk40AbIEfLZ+j35Pm5SVpSLIzLBkRG4CnL3Nf1hroI8OwaDI4oiW85Q16LHp/H974MxPrT5TgX7+ew80rD0NrbL/HnynhX7B1htVgQNUHHzo9puYzW7aQzpQqBns/KGUYBn7z5iHs8cfgN28eISUNvvsu4lhLXR3qf3bvGU7XwrZKd6UJZLbU3sjJkewVADRbt7b9W3uSPE6RmgqRQgFGIkHUm29C5EcGYqGPPgqli+CvM4gUCqgmjCevdzN5P7HqdGjcRMqQ/a++2qVTMyORwGciWfvb+McfsBqNKPvXv3Bx4WUoe/oZFP3tXuQuvAy6c+RznDZvM5qtbjt+92Vo59244PYFq4RgcvEqv4arJuGaHPEvlNG9SgX5bhdz/vx53HrrrWBZFm+++WZbbak7vP/++7j66qsRFxcHpVKJtLQ0vP322/j4448BAE899ZTb5zp37hzvf0lJSa5fLMBLRaMep4sd1+cZzVY8tpbMBg4O921rPn6yknwwjAkfw6ldvGbgNcT2maozyK5zv2H2jsIdxPbIsJGcnqHdxaiwUcT2obJDaDK234D2lJBOtaPDR0MldW/lvquhzY4aDA1Ez9fNeWQGO1wVjifHPonR4aM5f9OuJtY3FjIRaY8/JI6ctB7KvXTq2uu1Rhy8WIMjebWo1OixP6eGmKRH0kGpOhwQd+/fpE9CZUqDGQ1qW9x3WZYlMqWjmBz4G5y4i55zHpQ6Qixi8NZ1IxBPrcyPH0AHpbW8dZdlDTp8vjePGJubEo5xCY6D4CgqKHXVq/RMcQMn+3nDuPY6+EGUuYeV5WYDjlCLRImhPvBXkp/R7w/zO63SGQmJiNuUnibSX4lNj0zDt3dPwNp7J2HdfZM47+cK2oV3n11QyrIsHv3xJGdie7ygrq1u1mplOaZPw9wISuu++x7mcjLzrqTmVY1//glDbh50dPCV4jwj720UQ4bAZ9o0YqzitddQcOcylP7zn2g+dIj3dRaNhvMzypJti410n1X7TKnu1CmH19K0fUebhFd3kjxONWokcc2JG35B0B23w/+qqxDz0YcIufdvDs/rLXxnzSK2NTt2EDLbht9/h7XZLhgUieC/5Cr3zj2XrIVt3r8fF8aOQ/1PPxPjptJSFN51NyGJDvSRIdKflPTTpQuXIrR8N95OWULfj+nvOcDTDsZBSQF9r+oOB95+G5QWFxdj4cKFqKurw2OPPYZHHnnEK+ddvnw5wsLCcOHCBeTl5bl+gUCXsP08WZfgq5C01T05Yqad3f75WtLWnM4kAsC0mGkIVZLGFOuz3ZvgWawW7CzaSYx1tfurM6ZET4FE1O60abKa2q7PZDVheyHp9DctmnyY9ySRPpGcYN5ewru/lJQaXZF4Rbe126GRiCQct+PIEFJad/Bi3wxKrVYW9VojCmu0+PVUCe7+6ijGvrINN608hOs/O4jxr27HrV8cJl4z3Jd6yPkL9aRuQQWlgYymLeNY3qhHnbbdYGW62EWbpJLjgI4r7+Tj+cWpmJcajtsmxmP9/ZOxeAQ3q00HpXVaE6/s6+2/LhAZSLGIcZkRpINSV5nSnym1THSAElOS2u8VPnIJ4iipMJ1FOEa59k5LDsHS0aTE/KfjxZwMKwCcp86VGOrjVvsduUSMqQNDMH5AECRiz6dptAvy2dIG1LV8PnZlVeFQLr8a49tDBcirbkZmuYb4DAHAiJgAp+9paWxsy4K2oho/HrH/+wwiH7uJMsui9KmnYG0iPxPKsd2vvAm+Zzk5YLVCe+gQGn5eh8I7l6Hijf/jLKhwnHfFYshbXIPpPqvGQlumlDWboTvLNeBrxVxVBX16OlirlRO8KkeRC8bSqCiEP/MMov7vDfh2kbkRjd8VV4BR2n33LBbUfmkrU2NNJk6trM/kyZBGuNf/2HfBAk72l3XQBcPa0ICy518gxmgJb39w4PUsU0pmjs0WK7Kp+7GjTCkt3y2u0/He57xJvwxKq6urMW/ePBQWFmLZsmV46623vHZukUjUluEsK3NchyLQtezLIWs3Zg4Ow+PzByPYh9vEuZUFabZaHitrRWYtKS1KCeKu4kpEEixJXkKMbczdSLQaccSZ6jOo1ZMTg1mxsxwc3fX4yfwwOWoyMfbzhZ/BsiyOlh1Fg4HMOs+Nn9udl+cUhmE4daWtNbFV2irO37I75bp80BJeqYpcQMks16DGzVYXvYEmgxkv/nYOI1/6CyNf2orpb+7EI2tOYXtmJcwunFfnRlN9IQWTI/fwoWpKoWmrKaUnZdPFlGnZuHsAiV12gbUC+e61YrhpfBxW3j4WLy8Z2lbqQBMTqEQUlb2gJek5lU1Yd4IMGG8cF+vUmRYAYuhMqRP3XauVlIoDttpMui6SltPaZxGsVhbHqHrMcQOCcPMEMviobTbiz7Pc5/0J6rX0BLqrGB0fAKVd2xmWBfZfrIbVyuL/Nmc6fJ2VBT7Yno39OaTcNz5Y5bIdTfWnn8HSQD4nwh5/DGJ/fwTceAMxrk9PJ7ZlCQmQhjnvwdoV+IwfD/8lSxzur/3qK9Sv/YkYM9JtbOLjwchs8woZLd8tsAWl+qwssDrysyqmjIk0O3fCcOECrBpyIUM5cqTLn6OrEfv6wn/RFcRY7TerUbv6W1T8500iewkAQXfc4fa5RQoFgu7gd0rmo3n/fiKLnUq1jqLNKy9FOEEpkSklv6dFtVrCAT2/htsmxlFJAd33lGWd9z71Bv0uKNVoNLjsssuQmZmJa665BitXrnSpe/eUujrbg0itdv6AFeg6aGv/aQND4K+U4skF/HUrw6L925wYCxoLoDOTDxDa4bUV2piowdCATbmbXF7f9gIy85gckMzpYdndLExYSGyfqDyBg2UH8XM2KaMZHjIc0ereFTzQEt70atukh86SqqVqzrHdDR2U1puKoJKRmduDfUTCqzNacMvKQ/jqQD7HYt4VQ6P9kKqkMnT+PdtiqM/gxOjI3lhHDS1GMFRJwaCFQBzVwzF3l9cujWEYXgmvPR/syIZ9AkolE+MRqjaVDzpTqjGY0ag38R57pqSB09Jk0YhIznF0fz57uW9WhQYa6nM9Nj4IyWFqTE4i/wbfHuJKeI8VkD/3GCfSZG8il4gxIZF8rx3nK7HxTClHzhzuR5o7/nq6FN8dJlv+TE5yXlZiLCpC3erVxJjvvHlt0t2g228HpI4lyD7Te055E/nSv21Bs4N5YOWbb8LS2B7oGLK5Jket0JlSU7GtLQyd/ZTGx8H/GnLu0LRrN7RHSYMoaWys2xnHriZ4+XIw9n9DlkXFq69y/u7KkSM5rrouz33XXZAPGsQZl0RGIn71N5BQCxbVH7YbF9J1pRmljS7bNPVlGnQm1FMqBnvJLi3fNVlYQlFygZLuhqjlCHKQrFHJuK68XW121K+CUoPBgKuuugrHjh3DggUL8MMPP0DspIdSRzh37hyysrKgUqkwxIs9twTcp7Reh/JG0kVvdEtT8uvGxnImE2IRg+cXp7YtTpyvIaW7Ycowh7WesX6xmBAxgRj78NSHaDY5Xk1iWRY7ish60p6U7rayMGEhInzIB+C9W+/F1oKtxNhlAy7rzstyCzrQvFB7ATqzjtNbdWLkxG6vI6UZGEhOvi825GDcAHLVnM5U9Fbe3XbBae12K3JKsjgqLgBf3jEOotpc8sCg7nFB7vNQRkcBaEaNRgeLlcVZO4Oa8aJMiGG3Ki6SAvGTgMSZ5Pm8GJQCwPgB5D3Wvq40p7IJv1EtWpZNSWgzH3JGZAD3GEcS3r+oLGliqA9vPSedJbCvA6Wlu7FBSkS0ZIFvnUhmxY4X1OGUXR1mRaMeRVSD+7HxXdO2g4/plIR3/ckSPL2OzFAOClfjj79PIxbFLFaWY44yJZn8e9rDsiwqXn2N7MkplSLsicfbN8PD4X/lYofn8HfROqQrYWQyRL74IpK3bUXUm28i8PbbiP3WpibUr13btq2/QLkG2welLTLeNiwWGIuKuXWiI0dx6jQNmZlo2LiRPK4HJM2OkMXHI/SRvzs/SCJB+HPPeZzoESmViFv1JfyuuAKQSsEoFPC7cjEGrPsZqnHjEPLAA8Tx2mPH2lycafVBTbMRVZq+ozTylCIqSypiSAO4YB9ZmzdKK/Y1qPSiFO1ATkNnS7va7KjfBKUWiwU33XQTdu7ciWnTpmH9+vWQyRxLOQHgww8/xJAhQ/DMM88Q41u2bMHx48c5x585cwbXXXcdWJbF8uXLXZ5foGs4WVhPbPsrpUhsaVMgFjH4/I6xeGBmEpJCfTAxMQjf3DWeMNeg60kdZUlbuT2NlJ5Uaivx0PaHUKnl77eVXZ+NIg3pCDk7brbT9+gOpGIp/jbcuWmCUqLElclXdtMVuU9acBrRosfMmnG2+iwOlB4gjrNv69NTJAWQBmaNxkaMjCdvxftzen+mtLrJgFX783n3qWRizBocireuG4H0F+fj/EsLsfvJmfhq2Tj8/vBUrL9/MsJ85UAtVXcf5Nh5VcAOKlMqYlj4WDWo1OiJ+99UEVXHFjsBkPlwg9KaHKCBlOB1BjpTWm4XoL2/ncySquUSLJ/q3t9dLhEj1JfM7DmS8G47T/YAnp/Kn3GiJ2XljXo0tGQijuZT0l2758S81HDbZ9iO1zadbwu+acMyX7mEY6zUlSweEQUZVY+qo+rBnlwwBCFqOW5z4regkIowfVCow/0Nv2xA065dxFjQzTdz+naG3Hsv2VqkBeXo0VAMG8YZ726k0dHwX7wIEc8+C98FC4h9td+sBms2g2VZp61sxP7+EAeT301j7kWeOtGRUI4cCRHVQkZ/hqz/7k1BKQAE3X23U6ltxPP/gnLY0A6dWxIcjOi338KQM6cx5NRJRP/nP5AE2b5vAddcDXEIuRBXt+YHADbpqg+lNDp3CUt4aZOjSH8lUafOMAxHwltg10KGNl9z5Qbe3W1h+k1Q+uGHH+KXX34BAISEhOCBBx7AnXfeyfmvuro9Q1FdXY2srCxObejBgwcxduxYJCQkYM6cObjxxhsxYcIEjBkzBufPn8eMGTPw+uuvd+vPJ9DOiUJyIjEyNoCoI1LJJFixcAi2Pz4Ta/42CVMop0I6UzokyHnGe1r0NEyIJLOlxyqOYdEvi/B5+uewsqR+n3bdjfSJ5K1Z9ZimKmD9vcCH44AfbgKqLnh8imuSr8G4iHEO99+Zdif8ZN1TF+UJfL1Wv834Fo1G8uHUG3qrRvpEQiUhHxoRofXEdmGtlrMi2ttYe6wIRruecFIxg+/vmYBz/16Ac/9egFXLxuPaMTHwVUghEjGID/bBzMFhGBrtb1tJ19YAVK0yggXHcbdQcWWgQYwGxwvqCJXIFDoobQ1GI4YDCspNNZ/smdwZkkJ9OPX7h/NqkFOpwcYzZJb0zskJCHRS60/jjtlRlcaACxXk5GleKn/NYkKIDyd4yyy3SQCPUplS+6BUKhbh7qlkZv9IXm2bo/Dms2SmdmxCIMRu9Pn0FqG+cl4jqlYmJgZhbortd3LPtEQopPzTwcuHRcJPwa8uMVVUoIKa64hDQhBy/32cY2VxcYh8+WVA1P4+Il9fRL70b6+XUHWW4OV3E9vmyko07d0Lc1UVLHXk/EJBKeLkieQCS/ORIzAVk/XTypEjwUgkUE937m9At2LpaRiGQfgzzyDmow+hHD3a9rdkGCiGD0fs558j8PrrvfIenDGZDAHXLiXGGn/9DVatFiIRgyH9yOyogOpRSgegfGP2gSzXedd5UEqrS2gjOG8jcX3IpUGd3Y2kNTjl48UXX0RIiPP6iQULFqCoqAhHjx7F6dOn0dDQAD8/P0ydOhW33HILli1b5nVZsID7nKSC0lbprjuwLIuM2gxizFWmlGEYvDDxBdyy6RbUGdrfW2fW4b0T7yG7LhuvT3u9LZNHO9nOjpvd+YeyxQx8ew1Q3rLSWn0BKDkBPHCQdwLrCLFIjDemvYE7/rwDxU3kg3R4yHDcPexuB6/seYaHDCdMjWiJ9MDAgRx5ck8gYkRIDkjGmer2VXE9U4Ign+i2ukAAOHCxGjcE9WydsTP+OEMu1l05Itpl7RkBLd0VSQG/GP5jBUgkckDuBxjaJ19B0OD30+1/k1DUYbCI6tXcGpSKxEDCNCDz9/Z9eXuAETd65fJa60r/tAvMdl2owu4LVdws6TTPJNsxAUqiXUkxT1B6mOqNqpZLHLrHSsUiJIb6ELK2CxUaRAcqUdZAloGMSyCfJXdMTsA3BwuI1jSvbjqP8kY9J1O7cGj333vun5mEjWdKOcYmEhGD5xeltT13QtRyPDx7IN7cQmYBZRIRHppF1sC3wrIsyv/9EseYJ/LfL0IcEMD7Gv/FiyAfmAzN9u1gjSYEXHcdZDG9y58AAJTDhkExYjj0p9vv0Q3r14ORkFNmkUoFaQx5z5IlJkJ79Kjd637hvEY+0FbC4Tt7FhopyW4r8tQUyGJ65/3Qd84c+M6ZA9ZoBAtA1A2qwMDrr0fN/1YCLa1zrFotNDt2wn/RFUiN9MNxO1OxS7ktDL1YTbuHA9y60vwWcyK9ycJx43WVKaVrdi9WNUFntEAp65oYp99kSl988UWwLOvyvwS7moDW13z11VfEuSZNmoQvvvgCZ86cQXV1NUwmE2pqarBz504sX75cCEh7EIPZgrMl5A1pVFyA268vaSqBxkg+ZFODUl2+LtYvFp/M/QTBCm7tzaa8TfjwpK2heF5DHscN1iv1pGd+bA9IW2kqB3a84vGpwlRh+P6K77F04FL4Sn0RpAjCDYNvwGfzPoNcLHd9gh7ClYHR9Oiedd21h64rPV+TgUlUrXNvlvAW12lxjnrwXzvGwwlUDeliicAEQNxv1kk7D7XYFMI0YLNdHeUUEeW6K/cDouzaSwygvg95ewAvGoTQCpQ/zpThd2ohY9mUBASoPJvQRlF1paX1es4xdFulcQmBTlur0BLezHINjlHS3UCVlJM1UEjFeO2aYRyPnC/25cFkaf9dSsUM5jmQD3clyWFqvHHNMNgnaMUiBv+5djjHtfS+GUm4elR7gCgVM3j96mFIdNBXtWn7djTtIBf+/K5c7LJNiWLIEIQ++CDC/vForwxIWwm4hszMaXbuQvM+0qVaPmgQGBH5uZInkZlSuvWNYvhwMC1zRPXs2RwX3lb8Lut93g00jEzWLQEpYGuF4zOZ7BDQ+LttUY1jdnQpZ0op+W4cT6aUbgvT+prsiibiFs8wwEAHPUpbSYn0Je5vVtamJOkq+k1QKtA/OFfaSEgKGQYY6UFQSteTBsgD3M6upYWkYcNVG7B04FKivhEAVqavxJ7iPdicv5kYD1YEY3TYaLevzyFHV/KPn/oe0NV7fLpARSBenPwiDtx8ALtv2I1/Tvwn1LLe7SbtMijt4VYw9gwLIWuoTlWdIvonArZMaW91ETxATfoDVVJOFsklNZQrrFBP6hm+pDQzkiGlplPFlHQ3YRoZ9NNBaUMRUOe93tqXD4uExIlc1Vch4chf3YF2g+ST79L1nBMTHRv1ANw+fVnlGo50d2xCEK+iZcagUDw2l+scas91Y2MdOlx2NdeMjsHGh6fib9MTcd+MJGx8aCquGc1dQBKLGPz3hpH47aEp+OSW0dizYhaWOlhoYs1mVL79Dvn6oCCEU/4bfRm/Ky4na2DNZtR+/Q1xjCKVq6KSDXB+H1OOHNH2b5Fczts+Rezvj8AbvaNauJTwo9rSNO3bB3NdHWeBJa+62aErd1+HDkrjg3w4x8RRYwW1zWBZliPdjQtSQSVzvhCskknaPFlaoRekvYkQlApcUtAmR8mhaof1MHzQ9aQpQSkeSWsDFAF4cfKL+HD2hxAzZMb8qT1P4dPTnxJjCxIWQCzqZGa9oQQoPcm/z6wDzq3v3Pn7CAl+CQ5b1YSrwjEidATvvp6Avpay5jIMjiZNSKqbjLhY1bU9wTrK4Vxywj4pKdhpJoqXcipoCnU+sReg8KODUvtAjHVcT9pK6BDAhzKwySadtjtDkI8M81t6P/Px4Kxkj7OkALemlDY6qtToOd8bV0EpnSnNKtdw2tg4W3R5aHYynpjP//kNUcvx6BzX7W66krQofzx7eQqevmwIZwJPMzwmAJcNi0Skv9LhMY1//AFjHrmAEf7MM5A4yPr1RcRqNfwowyMa1Tiu/4LCzviI9zWjRhHbwcvvhnp2u9EhI5Ui4uWXIPbtPlOsvoLv3Llg5HZqLbMZms2bkRLpS9SFsyxwipoLXgoYzVaUNZD3Oz75bkIIOaY3WVGpMSCLynC6a7yWFkX6D5wrde2431GEoFTgkoI2OfKknhTw3HnXEdNipuHJcU8SY02mJo7p0eWJl3fo/AQ525zvz/yj8+/RB2AYBtcOupZ339JBSzsf/HuRxIBE+ErJB0KVKYvTM9C+dq43cSSfzERNGOB80s9LOdmeAhG9Z9GgT+BPLsDYB6VJTCknc8oJShkGSKKklhm/evECgUfmDOI190kOU+POyQkdOicdlFZo9DDZqWPoBRO1XII0F4HYkEjyu6gxmJFNuUyOddJjlGEYPDR7IL69ewIG2cnhUiL98N3yCQjzc93upq/Asiw3Y5iWBr8rvPAs62X4L73G6X6+oFQSGgpptANZslgM5ZgxxBAjkSDmow8R8/FHiHz1FST+vhF+8+d3+JovZcRqNdSzyVY6jVv+glwixtBo8jtOzwUvBUrqdbBS4ik++W64r4LThi2/utnjdjCtcH63BfVuva4jCEGpwCXFyQIqKI0PcPu1LMsio8YzkyNn3DzkZsyLn+dw//DQ4Rge4lxy6hZlp5zvz90N6C/dGgt7rh98PUKVZPYnWh2NW1Nu7aEr4kfEiDA8jPzbHy4/zDFjOV1c330X5SZlDTpO/0W6BYhLmqsBDenCioiebwvRp6BMoeyDUE4rGN8oIIQnW5dKtXcq2G/LYBccBM6stZmldUJCPjjCF68sGUrUNEYHKLHy9rFQSDu2SBQTSAalLAuU2xkS0SZHrupJASDCT4EIJ4GjUirGUCpbwMfUgSHY8uh07HtqFg4+Mxub/j7VpZFIX0N38hT0GeRzMuSB+zm1lZcCqnHjII3nN5tTjhgBiQNTTOXIkbzjitRUiNXcMhiGYeA7ezYCli7ltNIRIPG7nFz80B49CktDAycBcbzg0gtKaZMif6UU/kquEtDmdk8Gq/k1zZxFbroW1xFj4snne1aFBvVao4OjO8eldxcR6LeUN+hRSrkljvIgU1qlq0Ktnlxld8fkyBEMw+Dfk/+NGDV/Xc6jox/1jhV+GWVwNO1xm5NpK1YTcJF0/L1U8ZP54dN5n2J02Gj4Sn0xKmwUVs5bCV9Z75sYToyYSGzvLd6LEbHkxLc3ZkrPFJPSHV+FxKWtPAfalEuiAIL5XT4FHOAkUzpdRP1+E2eC48YD2DKlSmpB4dMpwKqFwPp7gJWzgNVXd+oybxofh98fnoYn5g/C/y0dhi3/mI4BIdw6KHfxV0qhopwf7R0pj+aRk9EJLqS7gO1e7WwBc/yAIKIXoKtzxQSqEOmv7HWtTrxBw8bfiG1pTAzUM2f2zMV0MQzDOGxzEnAdvyoHANQz+P0LfOc5XqQWcA/1lCmkhNdiQdOePRgdT871ThXVw0qnFfs4eVRZgrP7aALlwPvXuQo06s3E2IjYALfed1i0P6dlFG0EBwDrjhc77BvtLkJQKnDJQLeC8ZVLkOzAOZAPup7UR+qDGN/OWbL7ynzxzsx3EKQgJ37/GPMPp/1A3cZqASool834KcCAaeSYF2vFejuDAgfh68u+xoGbD+Cby75BrF9sT18SL9NiyL9RhbYCYUHkZzijrBF6quF9T0ObHAyN8if6ALtFyQlyOyxVcN71FKqmNBx1EMMCGUyYJCIzWUh24IgqVQDjXLR5KjrsfL/FBKT/DGx+Bjj8P0BbyzkkNcoPD80eiBvGxUEt79zfmWEYzoTrYpVNaluvNXLMPMY5kd3aM97JcVOTPWh1dAnDms3QbPmLGAu4dmmbm+ylSOCtt0KRlkaMyQcNgt+VVzp4BeA7fz7E/lRmXSqF/5KruuIS+xUilQo+kyYRY5odOziZUo3ezLkX9HVyq8mSgsRQx0EpXTu+PbOS2A5RyxHl715ZgUwiwkgqgKUVKQCwM6sSWmPn5itCUCpwybAvp5rYHhkX4NFkme5POiRoCMdFtyOkBKdg/ZXr8fT4p3Hv8Hvx46IfcdfQuzp9XgBAdbbNzMieyBHAoIXkWPZfbf29BHoHif6JiPIhA4ti02EioWWysL2uEXgGZXLgql6Pl/y95HbM2E5cUT+Fku+KGRZhqMcY0QWoGIPdHgZIJOuwCCY/DPi7uXBDS3mbqoDP5wLr7gYOfQz8+STw0QSuiZWXSQojFxtbjY3o1XuFVIRh0a5ltwAwP82xy3pP9BjtjTQfPgxLLbnoQMspLzVEcjnivvgcgTffBEVaGgJuvAFxX37htBWKSKFA+HPPEuqEsEcfhTQsrDsu+ZKHritt3rMXYUoRR9q/N7uqOy+ry8mrJjOltCuuPa7KDUbGBnik5BhP+UZsO19JdAewWllOK66OIASlApcELMti9wXyBuTp6jannjSo4/WkNMHKYNyScgseGvUQUoM7LgnmQMsgfaMAnxAgeS453lzluvZUoFthGAYzY2cSY1sL/0QStfp58LjewgAAifZJREFUtqTrnO46Ap0pTYv2MCg16YFCKvuWMI3/WAHH+IQAVN/gQYo6PBBTQB4XNRLwcSJhVfgDN6+19Yl1xdmf2/9tNgDfX8+9rzRXAj/cCBi6LktBK2ByWkyJjlBtXEbFBrotu40KUPLWRk9NDkEsj8Nlf0SzeQuxrRg6FLI4/prLSwlxQAAinn8eA9b9jMgXX3RYS2qP/5VXYsD6dQh//l+I/+F7BN/tpYVoAfjOmkUE/NbmZmgPH8G0gaSfxJ4L1fRL+zRc+a5jJeCwGFdBqXuLda3MTSEXVPKqm9vuu4CtzrSmufN1pkJQKnBJkFfdjGJKyz59UKiDo/mhg1KvBo9dRdlpcjuyxTwnOIlbo9ePJLx9hUWJi4jtIk0RYiIqiLGMst4jQaptNqKMqtum7eJdcnEHld1ngISpnb+4/gbDcALJrxf7Y5qIuickzYZLwlOBh44Bt/8KXPcV8Gg68OhZbr3pln8CjS0GVZufAUpPcE4FwNbzdO87/Pu8QDInU9oSlNJtXDw04Hr6siGQitsnu3KJCCsWOm/x0V9gWRZN+/cRY36XLXRwtAAAKFJSEHTzzZw2MAKdQxIaCuVw0ihQs2M7ZgwiFwuO5NdC10k5aW9BZ7RwPFOc1ZSG+yk4Zkf2TB3o2fx4WLQ/xwxu45mytn/vz/HOAoAQlApcEmw8XUZsh/nK3ba7BoAqbRUqtaTmPi0kzcHRvQhOUGrXVmMgZSufTdYCCfQ8Q0OGIsEvgRhrlh8gtnuTfJfuT6aQipxKiHg5s4bcjp8MqDx07xWwEUapOS78xW21Q7d9cYRYajNESrsaCIgDAmKBxe+Rxxgagd8eBk6sBo594fx8R1Z2PltacgL47jrg7RRg7R1AfREAICmM/MyVNehR0ajnqAqc1YnyMTouEL8+OBXXjYnBVSOjsPbeSRhOOWL3V4x5+TCXks9Z9YwZPXQ1Av0d9Rzyvta0cxcmJQUTLaiMZisO5XZeUtobaF14s8eVYdx0B4FnkI8Mw90sa2iFYRgsoPpO/3CkEEazrSxsb7YQlAoIAAD0JgvWHC0kxuanhXukl6ezpCqJihMs9DpYlivfjbBbPaSD0pLjtlYcAr0GhmGwJHkJMZarPQCI2ldEs8o1sPQSF8GzJWSAPCTCz2W7DYLK88D5jeRY6pLOX1h/JYxSc2RRPYmVQUDshI6fP/VKII3q1ZizDfjtIXJMogSu+xqwr8E3aoBT33f8vYuOAqsusy2maUqBjA3AV1cA+kYMCPEBbRewck8uzHbfE5lY5FFLsFZSo/zw5nUj8N6No9x2p+wPNO/fT2xLwsMhS0rqoasR6O/4UnWl5vJyyAvzMDougBjflE4upPRV6AXh+GAVlDLnBmOOauGvHBHluTkhgBvGkVL9Ko0B604Uo7bZ6LVMqWB3KNDn0OhN+DO9HOklDahuMuBsaQNHUnjdGM8cV/n6k3rD5KhLqS8E9FS9YaRdUBo/GZD6AKbWOgTWNqEccWO3XaKAa65KvgofnvwQZtZm12606iH1OwVTva1ljM5kQUFNMxI9cJLuKugHY5vJkdloW/SovQg0VQBNlUBjCVCZafucimWAMhDQ1gCsneGWTA0M52+5IOAGdKaUJmVR512NF7wC4Gvnxyx6B0hbAqT/BGT+3j5+bBUw4V7P39NssJknmcn7OuoLgL1vQz7v3xgc4UeoCD7fl0ccOn5AEFQyYYrjLeig1GfKlEuy5Y1A30CWlARpTAxMxcVtY007d2FB2jwctTM823yuHK9cPRRySdc7RB8vqMWnu3PRpDfj6lHRuG5sjNe+Ixm0l4MbBoOTk4IxJj6Q6Nkql4iwfNqADl1DapQfxicEEbX7/9mciQsVGmJBsDP08lm3gICtlqWu2YgThXV48bdzmPDadqxYdwarDxXgz7PlKKola0knJgZhuIsib5pzNWRblbTgPiDdpbOkigDSRVMit8nx7BEkvL2OEGUIZsSSMjhl8DFiO7O8d9SVch+M/kDmH8C7w2y9LX99ENj+EnD4U1tGtCYbsBhsWbOGQrsFkhYm3AsoA7rvB7jUcJUFTfFCCwqli17Pkx8GRt5s+/f4v5H7qs5zW1a5w7EvbQEo775VgEmPMS6yoDMHe1YzJeAY1mhE85EjxJjPlMk9dDUCAjaVES0fb9q9G1cMjyTGNHpztxgeHc6twQ2fHcLWjAoczK3BinVn8MGOHK+dn2Mw6IaXA8MweP+mURgUblvQDveT4/2bRiEmsOPGbQ/PIb1K6rQmrNqf3+Hz0QjLiAK9Er3Jgq8O5GPz2XJcqNC43ftILhHh+UVpHq1OsSyLM1VkgNc3TI6ooDRyOOFIBwAYNJ+U9OVsByxmoSdkL2PpwKXYXri9bZuVFUMkL4HVEA3AVld6+bBIRy/vFpoNZuTVkEHlZON+YM39ADqwSho+FJj+pHcuro/AsiyOVRzD8YrjsLAWDA4cjGkx0yCnXHTdxjccCB8GVKRz9/nFcBelOovcF2BbapsYMTDtMWDms+37E6YBftG2LHkr6T8B4R4s8lkttvYyjjA0ABc2Y0z8WHx7qNDhYTMHC+03vIX21CmwWm37AMPAZ7IQlAr0LOqZM1D33Xdt27rTpxFj1XOyeb+dLsW81HC+U3gFq5XF0+vTOdnC/267gLkp4ZyeoR05P+0t4e45owOU2PzIdJQ16hGilnU6YzxtYCguGxqBP8+Wd+o8jhBmpgK9jsIaLe786ghyKftrV/gqJHj/xlEe3wAu1l9EnYHsbzcybKRH5+gRnNWTtpI8j9zW19t6RCY56Vso0O1MjpqMCJ8IlDe33+ilAUdhqGgPSnua82WNRJvKMFEj4vc/hQ4FpEmzgWtWAlKl62MvEap11VixZwWOlh8lxv1kflg6cCluGnITItUdWHgYfBl/UDr+Hu8vPj14BKg8ZpPXxk2ymSHZIxIBQ5cCB95vH0tfB8x+3rbPHS5stkm+ifNKAaupfTt7K2bMuxwSEcMrGxsR489x6BXoOM37SfM1RWoqJIEuMugCAl2Mavx4MEolWF2LWs5qRfPevVg8YjgRlG7LqIDWaO4yOf/u7CpOD1HAZvvx4c5sfHzLmE6d/3x5I5qpxIwn/cFFIgbRAd571r6yZCjOljZwVIoA4K/s3O9YkO8K9Cpqmgy49YvDLgNSsYjB3JRwPDw7Gc9cNgT/vWEE9jw5C7OGeL46frSCnCRG+kQiWh3t8Xm6HU6mdAT3GP9oIHIkOWbfa1CgVyAWiXF18tXEmNT/FMDY+n6d7wVtYWhn0yfUW8HQNc2BCbaFkJG3AFMeBRa/D9y1xfbfDd8BS78A7j8I3PaLrc9mP0Fj1OCuLXdxAlIAaDQ2YtW5Vbhs/WX4x85/YE/xHpitZvdPPv4eW69Re0IGAxPu6+RV86AMAFKvstUB0wFpK8OuI7cbCoHiI/zH8nH4U3I7ciQw/xVyLG83glRSh22/bp5w6ffO7E746kkFBHoakVwOn0mTiLGmXbtx2bBIwghNZ7Jg+/lKdBW/n3ZspvTn2XKin2dHoE2EEoJVCPNVODi66wlWy7Hmb5M4RnCLR0QhtJPXJWRKBXoNLMvi2V/SUVir5d0vYoBIfyXmp4Xj7qkDOqWLt4eeKI4NH+uV83YpzdU2R0p7+DKlgC1zYd/gPmMjcMU7tppTgV7DkuQl+PT0p2BbMo+MWA+JXzrMDWNQUq9Dg9YEf5W0x64v3c551w9NuNK8iTwgdYmtx6VgfkLAsixeOPAC8hrynB5nYS3YVrgN2wq3IUwVhodGPoQlyUtclyKow2xB/oYHgKosmwpi0X8BaQ9NWiKG2YLi6qz2sTNrgbiJrl9bnQPk7SHHJtzHXXBrKALq8vHo3IHYfaGKcKdOi/LD0tEx/Oe3WoGjK201q41ltmua+4Jn8mIvYWlqgvbYMUiCgqAYNqzXmgaZ6+qgP0fWBQvSXYHegnrGDDTt2NG23bRvH6IUYkxJDiHalGxKL8PiEVFef3+WZbEvp8rJfmDV/jy8evWwDr/Hvhyyrc2U5J5f0I0OUGLDA5NxorAehbXNSIvyx6BwX6S94vq1zhAypQK9hr8yKrDlXAUxNihcjS/uGIu9K2Yh65XLsP/p2XhhcZrXAlKjxYgDpaQ0aWxEHwhK6f6kEiUQMpD/2KFUSwdDg82FV6BXEaWOwuRocrIn9TvZ9u+MHpbw2mdKrxAfhsJqJ91hRMCc54WAlIffLv6GrQVbibEgRRCGhzpYRAJQqa3E8weex7/2/wsWqxv19NFjgAcOAc/X2gLUwIROXnUnYBhutvTsOpvk1xUnKJdfZZDt/hWWAqioiVj+XgyPCcBHN4+Gv9K2WDM+IQif3TaGv00Ry9qMuP5cAVRl2u6D2VuAL+YD5Wc9+AE7T/OhQ8iZOQvF992P/OtvQMnf/w6rwY3fTw+gPXQI9rp9RqmEcvSoHrwiAYF21DNJsyNrYyN0J0/iCsqDYUdmJZoMHihQ3CSnsgkVjeR3dz5Vv7ruRDHqmo0dOn+TwYwjeWRQOrUXBKWAzUhpTHwgrh4Vg0Hhvl45pxCUCvQKzBYr/m9zJjEW5CPDt3dPwJyUcMQGqSD1pB9iCwaLAV+e/RLLNi/D3Vvuxtfnvobers3AwdKDaLZzBGXAYFr0tI7/IN0FXU8angaIHBSw+8cAcdTKdvpPXXNdAp2ClvCKffIAke2B15NBqc5oQXZlu4T4KjG5kIPBlwPBQs9CGq1Ji3dPvEuM+cv98cMVP+C7y7/DxiUbcdOQm6CU8Nf7/HrxV7xx5A333oxh3K/b7GroNj/6eiDrT+evMRuAU9+RYyNvtik6GAZImEruKzkOwNaL79Tz85D58kKsvW+S4wXLQx8Dp3n6phqbgPV/sxksdQOm0lIUP/x3WJvaJX2ardtQ8drr3fL+ntJ8gPyuq8aNhUgm66GrERAgkYaHQ55CtsZq2r0bC9IiILHT8BrMVmw/X0G/vNPQ0tpIfwVeumoo8d56kxXfH3FsyuaMzWfLoTe1t1ETixhMSgru2MX2AXrJE0ygv/PT8WJOHekLi1MR5tdxCVqzqRnLNi/Df4//F8cqjuFI+RG8dewt3PTHTShrstUAbMojJYgjw0YiVNUHWgnQmdJIx1kXAMCwa8ntrM2AoefrFAVIpkZPhUTUXlXBMBZIfLIBcHuEdifnyxvRqpAMQx3GM+QCEobf0P0X1Qf4PP1zVOvIScu/J/8bUWqbjCzBPwHPTngW26/bjn9N/BeGBg/lnGNN1hrsKd7DGe/VBMbbnHjtoQNOmszfbX1s7RlzZ/u/oymzkNJ2FQHDMFBInbhK1lwEtr7geH/lOeDcL86vz0tUf/IprBruvbf+p59gyPFeCwlvwLIsmuh6UkG6K9DLUM+YTmw37d6NQB8ZR+b6xxnHtZ8d5VRRPbE9OSkEEf4KTmuabw7mQ2/ybOGLZVl8QfVfnjEoFAGqS3dRSAhKBXocndGCd7ddIMaGx/jjyk7q/1859ArSq7mulDn1Obhl0y3YnL8Zf+WTfTvnxs3t1Ht2G3RQ6qietJXUJYBdsAOzDsjc5PDwLsGkA/a/D3yzBFhzC3B2PSELEwB8pD4YE05OvsU+tto8ukdod2Iv3Z0hPg0RY/d3k/sBA+f3wFX1bsqayvBNxjfE2JToKZgTN4dzrK/MF9cPvh4/LPoB/535X0hFZO3wq4dehcli4ryuVzPiJnI7eytQdYH/WAA4/hW5nTCNLEmIoiSjFRmASQ+32PYi6d7rzvt3Aea6OtRv2MC/02pF3Q9ruvwaPMGYnw9zKTmRVwsmRwK9DN+ZM4ltQ3YOjMUlWEQFhrsuVEGj9+59NJ0yABwZFwAAuHvqAGK8otGAz/fmenTuHZmVHOf968Y4qJe/RBCCUoEeZ9WBPI4m/6mFQzpl/JBdl43fc393uL9KV4Undz8JM9teY6AQK7A4aXGH37Pb0NYCtdTNLWqk89f4BNvacNjTnRJeQxPw1RXA1n8BuTttWZGflwG/3AeYO1ZrcakyPZpc9ZWoswCwyK5s8nil1VukF7c/eGeKTpE7k2b1nKlOL+b9k+/DYGm/r4kZMZ4c67ov69z4uXhuwnPEWGlzKX7J6Z5MntdIvYpyBWaBff/lP7b0FNfgyD5LCnDNjqwmW4bTFUVHgfO/kWMTHwCu/ZIcy9/LbUXjZTRbtwImx5Piht9/d7u21KLRoG7tWlT8503UfLkKxuIS1y9qQZ+ZiZrPP0f5K6+i6v0PoEvnr6lt2r2b2JaEhUGWnOz2+wgIdAeKYcMgploUNe3ehfmpEZCK2+eRRrMVWzO8J+FtMpiRS7WCGdrSqmV4TAAmUzLbD3fmoLCG38iThmVZfLCDVE4khfpgflpEJ6649yMEpQI9Sl2zEZ/sukiMTRsY0ml3sS/OfkFsixgRghRBTl+zdNBSBCr6QO81O9kaAEAsB8K5sj8OtPnIxR02F9/uYMszbTVgBGfWAOvuBizeNyDoq0yLIWWPImkjRPIKWKwssis6Zy3fUVpXg8WwYJqImsAm9xF1QTdyvuY8Z1HsukHXISnAvbrbawZeg1FhZGZw1dlVsLJWB6/ohcjVwNi7ybHT3wNFPO1h9rxJbquCgSGLyDGFHxBMmbnR90I+DrxHbisDgRkrgMFXcFvppHdtu6zGTWRdrSKNdP21NjRAs821CV3D738gZ85clD//Amq//BKV//kPLi5YgPJXX4NVx+0dCABWgwH1GzYg7/obkLfkalS+9Tbqvv0W1R9/jPzrrkPp089wXqv5izTo8pk6tde6BAvwozVpUa+vB3sJq5IYsRjq6eRzs2n3bvirpJg2kCzH+vUU1bWgE2SUkr27xSIGKZHt/UOfvTyF8P7Tm6x45pczbv0tDl6s4UiDH5yVDLHo0v7+CUGpQI/y4c4caPRkQPLUwiGdOmeRpgh/5pEP//uG34fflvyG8RHjeV8TrY7GQyMf6tT7dhulJ8jtyOGA2I1WIYMvt7n0tsJagIwNXr00XirOASdWO95//jfg1wds7RoEkOCXwOmT2yrh7Ym60ka9CRcqbDVwo5hs+DHUSq8QlBKwLIu3j79NjKmlatw/8n63z8EwDOd+VNxUjEOlh7xyjd3GhPsAqQ859tMyMiNZeMimnLBn0oP82XdawlviIiitywcy/yDHpj5mC0ylCiCNNBZDNhmEeRNTZSW0hw8TY8F/+xtUk8hWOY0bHSt8AKD2m9UofeIJWBspOb/FgrrVq5G39Foi82nIzkbF//0HOTNnoezpZ6A/Q5nktdCwYQOKHnigLVNrqqiE7iT5+/WdP8/ptQn0Hoo0RXhw+4OY9MMkTPtxGq769Spsyd/S05fVZagpCa/20GFYtVpOGdi+nGpUN3nH6Zru3T0wTE3Utg+N9setE+KJY/bn1OCnY8Uuz/3JbjJZExuk7HRJW19ACEoFeoz04gZ8dSCfGLtqZBSGRvvzv8BN6IyCUqLEzSk3w1/uj0/nfYo70+6EhGmvr0wJSsHK+Suhlqk79b7dRgkVlEaNdu91cjUw+DJyrIszAwBasiAuVgbP/Aj88Q+hxhS2gGRKFFm3JVG3mh11f13p8fy6NpOjGWLa9Xko4HfpPyg9YV/JPhwuI4OPu4fd7VKpQTMuYhwGBQ4ixtZeWNvp6+tWfMOBmU+RY43FwKfTbPXleXuBdfeQ+xUBwDhqrBU6KLXvv8zHkZWAfXZZ5kvKggdfTh5fdBjQ1Ts/Z1UW8O21wCvhwDtpwO433VJ6aLb8RdzfRCoV1DOmw//Kq4jjmvbtg7mujvccTfv2o+IN527Mxtxc5F9/PfJvvgUXL7scuYuvRO2qVbA4OKc92oOHUPbMM2CtVtT//BN5vWq1YHLURyhsLMStm27FnuI9bXOhvIY8PLH7Cbx44EWirOBSwWfKFEDcHhCyRiMaN2/BvNRwKO0CRYuVxcbT7dnSZoMZlY36DmWS6aB0GM/cdcXCwYj0JxfYXvkjA5Uax/XwGaWNRI9VAPjb9CT+VleXGJf+TyjQ4xjNVqw5UohH1pzEY2tP4fO9udiZWYn7vj1OND2Xihk8Pm9wp96rUluJDTkbiLEbBt8Af7ntZiEVSfH42Mex/frt+GzuZ/hx0Y9Ys2gNYn1jO/W+3QbLcmWwtCulM2gJb+FBoNF7chYOzTXAeWrlf+YzwJ1/ABIqE3L8K1sPwb5m6NIF0P1Kxco8gDHgTEn3Z0oP59W2/ZtTT5rMNe3pz1isFrxz/B1iLFwVjltTbvX4XAzD4PpBZGuVXUW7UKOr4X9Bb2XiA0AsmQ2Evt5WX/71IqCBquOc/oRNqssHHZRWngeMDmq0LGbgDBXEj76dPHfCNFv5QyusxVbz7ojqHOCLeUDOVsCstwXYO18B1t/jUunRuIk0llPPnQORQgHfeXPByO2uwWyGZvNmzuuNhYUoefxxzvsoRgwnXw8ALAvdiRMw5pHOnfZIIiLgt2gRRD5kJrtx05+ofPtt1K0hTZd8588XWsH0AViWxfMHnketvpZ3/7rsdbj9z9tRpCnq5iuzZW/fPPombv/zdtzz1z346NRHqNRWeuXcYj8/+Ewhn5v1P/0EH7kE89PIvqHfHCyA1mjGvzeew5hXtmL8a9sx481d2JftWTkTbXI0LIYblPoqpHj1arK8qlFvxsc7L3KObWXdCTKTGqKWXfIGR60IQalAl1LXbMTSTw7g6fXp+PVUKdafKMErf5zHsq+OoqSerF95aNZAxAU76DHnJt+c+wYmO5dFqUiK21Nv5xwXpAjC5OjJSA1OhYjpQ1+DunygiSrU9yQoTZ4DyKkb57kNnb0qx6SvJV0vxXJgwr22noM3fAdQLqM48j/gs+lAIZlp6m9MiJhAZPMZkQVin1xklDZ0u9nRwVxbEBSCBgwT5ZM7kwU5nz0/X/gZOfWkOcXDox6Ggl6AcZMrEq8gephaWAu2FbiuOexViKXAjd8BIW4sOMZOsAWxjogYBtjfr1kLUMFv0oO8XUAzNeEdu4zclqmABMpNNtvB79dqBX65F9DzLAydW8+ti7XDVFrKkcL6XXY5UF8EseYi1DPIergGSsJr1WpR/NDDsDaQ7x3y0EMY8OOPGPDLek59qiNUkyYi+oP3kbxtK6LfehNx33zNCUxrv/gSlipygh50m+cLKwLdz++5v+N4BY9/gx0ZNRm4fP3lmPnjTExdMxUTv5+Iq3+9Gu8ce6etXZ43YVkWn57+FIt/WYxvMr7BycqTOFR2qG3s5ws/e6XmNeBasvWd7uRJGLKzcS0V0OVVNyP1+S1YtT+/rQdoYa0Wd311FKepOk5HaI1mXKwiPR4cqfxmDwnHVSNJRdHaY0Wo13JNHq1WltO65qbxcc5bXl1C9KHZuEBfw2plcd+3xzmrSXyMiA3A/TPdMwFxRLWumiNvW5K8pG/0HXUXehVfFQIEJbr/eokcGHIFOdZV/flYlltLmrLYVs8FAAPnAtd9BTDUzbYyA/hyvs2Zt6mqa66tl6OWqTEijHQblfhcgMnCuv3Q9AaVjfq295suotoQydS2IEIAgE2l8e6Jd4mxwYGDsShxEf8L3EAtU2NmzExibEtBH6wL8wkBlm8F0q5xfExYGnD9akDkZPIlV3ODW0dmR6d/JLejRpMtZlqh2xnlbOMvI8jcCJQcc3xte/4DlDmo16SCTJGPEuozTwDvDgU+mw5/PVlGoTtxAsZiW7aEZVmUPvccDBfIdjq+8+cj5EFbAC9PTETCmh8Q8veHIVJzy1DEoSEIvmc5EjdtQvyqVfCbNw+MxLbopUxLQ/S7/wVEjqeDqokToUhJcfyzC/QKGgwNeOvYW8RYlE8Unhr3FLG41UqNvgYNhgY0m5qRU5+DVedWYfGGxVh1dhUsVu8sfurNejy550l8dOojWFjuObVmLf598N94ZOcjqNO7lpg7w3fWLIhDSJPMmi9XYWpyCK+0lsZoseLZX9LdCpAzStt7dwM2k6PUSAcKDwBPLhhMmBRpjRbe2tLjhXUobySlvYv7QS1pK0JQKtBlfH+kkJD+OSIp1Adf3DEWMknnPo7/O/M/6Mzt2VcRI8KyocucvKIPcpEKShNnOp1M8EKbexQfAeq7QMpTdopo2WA1M6jJDUfB7Xeg4PY7UP3JJ7DEzACu+R83MAWA0z8An07tt1lTbl2pbVJ6rKBzD25P2Hq+PSs/U0wFpYkzAYkg52vljSNvoMlErpyvGLcCYmdBlhssSFhAbB8rP4YqbR9crFH4A9etAu7eCgy91ragBgC+kcCkh4C7NttqUF1BS3hLT3GPMTRxzZOG38B/Pjrb31RuM2drQXv0KIofeRR59z+PsqP+MDY5+HtazTbDNqr8gGVZNPxGtqTxC68GU5fdtq0O10IsI2W5jb/brr/m88+h+ZOU88oHJiPq9dcIJ1xGKkXoAw8geddOxHz0IcJWrEDEiy8g4aefMHDXLoQ9/jjkiWTvxLb3nzYN4f98jncfI5UiwsE+gd7FByc/4Mh2n53wLG5NvRXfX/49EvwSXJ7DYDHgnePv4B+7/gGtyb32JY4obSrFHZvvcMtgaWfRTly54Uq8fextbM7fjIyaDDQZPXObZ6RSBFy7lBhr+O03mEpK8cgcngUpHs6VNmJnlmtJMZ1soU2OaGICVbh8GNk3lZbpAuBkSQeFqzEo3Nfl9VwqCEGpgMe0ygue+yUdr206z2nuCwB6kwUfUj2Wgn1kuGZ0NOJbJLohahnunjoAGx6cghC1nHMOTzhQcgBrMskamMWJi/tOrag7mI1AHtk3DkmzPD9P4kybmYg9XeHCe/Lbtn+a9SLk74xE5Ze/QHvkCLRHjqDqvfeRu/hK6JgUWxYlYhj3HE3lwNeLbe1r+hlTosmgVCSrASOtwaHc7qsp/P207QEpghXTROnkTsF1t42NFzdiawHp3HpV0lUYH8nv9u0JU2OmQiVpL2tgwXLeq08ROx649gtgxUXghXrg8UxgwauO60hpOEEpT6Y083fAfkLNiIGhDrK0wUlAAOmQiRybhLdu7VoU3HY7NFu2QF9hRP1FH+RtCYWuWgpcsxKYTvWdLU8HDrxPDOnPZcB4kawf808gS1cYMeAbR47Vr/0JjX/+iap3yN6uIj8/xHz4IUdy24pYrYbvnDkIvmsZAm+8EcphQ8GIXS+MBN18M8JWrAAk7WUDIh8fRL/3LuRCb9Jez7nqc1ibRSrFZsXOwozYGQCA5MBk/HDFD1iSvIQoDXHEzqKdWP7XcmiMGo+vhWVZbC/Yjht+vwEZNRnEPhEjwvWDrudVkNQb6vHVua/w5O4nccPvN2DSD5Mw48cZWP7XcmzK3QSz1bWhWNDtt4NR2JVLWCyo/ugjzE0Nx+2T4jnHJ4Zwv0c/HnW9SE8Hpe4YdN40jpyPZpZrkGFnXmixsvgjnQxKrxjWf7KkAOD6kykgYIfRbMUD353ANrsMyud7c/HSVUNx68T2L/zaY0UcCcJ7N47C1IG21XGrlYXIzX5LZqsZZc1lKGosQoGmAFm1WThfe75tRdBqtaJSR65sSUVSPDjywQ79jL2WnK3ceqbEDgSlEhmQsogIGnF2PTD54c5dnz0mPZD+E4AWb6YDgTDwxFLm8nIU3HYbYj74AOp7dtlqSne9DhjsFjosBuCHm4A7NtomtP2EIUFDEKQIIla+JeoLOJwbimaDGT7yrr19Z5Vr2upJRzI5CGSoVeuBQj0pAGTVZuHlQy8TY4HyQDwx9gmvnF8ulmNW3Cz8kdve2mRL/hbcnHKzV87fo3Sk5yUdlFZn2TKjcjvZ6hlKups0G1CHOb6G5LnAMbve1jnboA+9DOUvvcw53GoSoWhfCBL/NQ2S1CW2ljOVdhPvXf8HDFkMhNqck+u++454vdTHDGUot5bMP16H+pz2CbKptBQl/3iMc63Rb70JWTx3cu0Ngu9aBr+FC9B88CAgEkM9YzokQZ65Rgt0P2arGS8degmsncu9QqzA0+OfJo5Ty9R4ecrLWDFuBbLrsmG0GiFhJLCyVmzJ34KfLvxEnCO9Oh1/3/F3fDrvU8jFrhMHFqsFu4p24euMr3GykrtYpJaq8faMt9uM/C4bcBn+tf9fDk2ZAKBWX4vDZYdxuOwwPjn9CV6c/CLGhDv20ZAEBSHwhhtQ+/XXbWMNGzYg8NZb8O8r05Aa6Yc/0sugM1owJyUcd05OwC8nS/DsL+2LrtvPV6KmyYBgJ8mSM8WunXdpJiYGI8pfgdKG9rnxLyeLkRqVCgA4ml+LKg3pjHzFcDK7SsNaLNCnp8NcXw9FSiqk4Q7uc30EIVMq4BFv/5VFBKQAYGWBf/16tk3yoDdZOM5ikxKD2wJSAE4DUpZlcajsEF488CIW/7IY474dh8vXX457t92L1w6/hnXZ65BRk4Hy5nKUN5dzAlIAeGjUQ4hUO/8y9zlOk5lgxE0C/KP5j3UFXdtVesJmouQtsv5oC6Drsn2grXR8c2d1OhTdfz8at24HJj0APHTMls21x6wHfrwN0FTwnuNSRMSIMDmKcuH1uQCjxYp9OZ65BHaEj3a2Kx1miU+RO0NTAP/+4QbojGJNMe7fdj9RNgAAz058FgG0GqETLExYSGyfqDyBiub+810giBhKyv1Zqy1D2YqmHMjdRb5mxI3Oz0ll/dn8Qyh77jnAzJ+ZsegZlD3/b7BiKXDlh6T5ksUA/PYwYLXCWFyCho0bidf6J+hssbhEYaupfzQd8I+FMsQIZYjzVh2hj/0D6unTnf8snUQaFYWApUsRcPWSTgek9fp6VOuqiRZtAt7n63NfczKS9464F1Fq/iybr8wXo8NHY2LkRIyNGIvxkePxr0n/wufzP4efjFQsHKs4hhW7VzitMdWatFiTuQZXbrgSj+56lDcgTfBLwHeXf0c4y0+PmY6fF/+M2bGz3fo58xvzsWzzMnye/rnTus/g5XdDpLIzzWRZVL7xfwCAG8fHYfXdE/Dz/ZNx/8wkKGViLBoRCbld+ZjZymJTumPDpyYD1+RoOI/zLo1IxGDJKHLO9uup0rYuFL+fITshDInwRXKY41aF+sxM5F55FfJvvAnF992PnFmzUP7Kq2BNfbeDgRCUCrjNqaJ6/G9vLu8+lgX+8eMplDfo8f3hQk6W9JG57un5ixqLcNeWu3DPX/dgXfY65Dfmw8y6lmzYMyduDu5Mu9Oj1zhEWwuc+gHY91/g/EbA3EP9vWpzuU3gh1/Pf6w7DJgOKKkJhzddeE98AwCwGBlUnSXrIcRBQZDTphkmE0oeewzV/1sJqzwQuGWdre7MnqZy4Kc7+lXLGDoolaguAjDjTycPTG/wx5ky/NbWy43F5SKqrnegIN09UnYEt/15G6p0ZH3ndYOu4wSRnWVy1GSopeTkxJ06rd6O/vx5lL/6GooeeBDlL70Ezc6dYB0Egm1IlUBYKjlWate7Of0nqjepmtuPlGbANMIJvO6CFPqz55y8AGjavRv1a38CYsZwHYOLDoHd+y7Kn3+eCGwZsRWBA5ttG0s+AZu6BHp1GHDjd2DEEoSPbgQj4p9s+19zDYKXL4ferHdLxthTWKwW/JL9C67acBWm/TgNs9bOwuy1s/G/M//r1dfdVzlTdQYfn/qYGEv0T8QdqXd4fK7xkeOx+vLVCFYEE+M7inbg9SOvcwJBk9WE1RmrMX/dfLx6+FUUaqj2Ti3Mjp2N76/4HokBXFPGUFUo3pv9HtZfuR73DLsHEyMnIsonCgz4ExcsWLx34j28eexNh4GpJDQUwX/7GzGmPXoUmm38ztp+Cinmp0UQY7SM1p704gbCC00qZpDixOTInmtGk0FppcaA/TnVMFus+DO9nNjnzODIkJuLgjvuJEsDrFbUffstSh5/AqyLFlW9FUG+K+A2b/+VxWtK2Eq91oS7vz6K4joyazApMRgTE4MdvKqd7QXb8cy+ZzhZB3dRSpS4I+0O3Dv83s63eWFZ4PCnwI5XAPtie99I4PK3bPLX7sJqBTY9aWt/0IpUBaQu6fg5xVKbE+6JdokLzv4MTH204+dspTy9LVNRk6mG1Uj+LaLfeQeq0aNQ+syzaPzDLtC2WlH1zjuo+fRTyAcNgjw5CYrmUVDLTkOqbLnBFh4EdrwMzHup89fZB6CDUkZshFiVj01nZfhnk6HTtdh8bEovwz9+PNW2PYQpQpKIekCnXOX19+0rWKwW/C/9f/j09KecDNDosNFYMW6F199TJpZhdtxs/Hax3TDnj7w/cHsat91VX4C1WlH133dR8/nnhNNt3fc/QBIRgYDrr0PANdcAjAiGnGwYc/PAGvSQhIdDNW4cpFEjgQq77Gj+fmDSgwDLwnLoW5jqbVMbmdoM0YjFttYvzpD7AnETgfy9MDWLUXWGnGDKfM2Im1mNvL9CYTG0Z2kr/u//4DNxAmSznrPVsdblw9gkhqZEgYY/P4ahgWx5FZCkhURhRdaUB/BF7SHs+eE/aDY1I1wVjnnD5mB5+lZETapD6eEAsOb2+6bk1qX4fJYEO3+ajWpdNSSMBEOChmBR0iJcM/AaXmfVnqBaV42n9z6Nw2XkIlaNvgYfnPwAB0sP4oPZH0Atc5z96WmqtFU4XnkcVqsVsb6xSA1OdWhWVqOrweqM1ThYdhBmqxnjI8Zj2dBlCFN5R0JZ3lyO33N/R5GmCBE+EZgdOxuDg9rdpwsbC/HIzkdgtLbLwUWMCC9NeQlSsZTvlC5J9E/Ep/M+xbLNywjjth+zfkSQIgj3j7gfALC3ZC/eOvYW8hoc98KN8InAP0b/A5cNuIww5eJjYOBADAxsT14YLUaUNJXgbPVZrM5YjfO154njV2esRpgyDHcOvZP3fEF33oG6tT/CXNr+7Kp88y2oZ8zg7be7aHgkNp5uz1QezqtFpUaPMF9uO6/TxfXE9pAIP7dbtiSH+WJYtD9Rk/rLyRJYWRY1zaSs/4ph/Go/1mhE8cN/57SIakXz11+o+eILhNxzj1vX1JtgWG80BxLoEtJa+o6dO+d8xbY7OJpfi+s+PUiMPblgMM6WNODPs+UOXmXj5/smYWyCcxnQ9+e/xxtH3iDqGWh8pb6I84tDgn8CUoJSMMB/QFvwqZQokRqc6p2HM8sCf66w1Tc6YvoKYNazHauJchdDE1B4CDi6ErhANVOf/DAw/5XOnT93F/ANFVws3w7EjO3cedfeDmT8CrNOhJzfw8Ba2idXvvPmIeYDmxEIa7Gg/JVXUP/DGkdnsiECApOaETa8ESJpy+fjph+Bwd7NRvVWrt94PfFANtWPhr7setwyIQ6vXs1jDtVBWJbFqv35ePmPDGLx6VnJd/ibxG7xwD/WJjnsys++m7BWKwxZWbA2NUE+eDDEfm6a5XQQR5NuwDap+mrhVxz5m7fYX7If9227jxj79apfebMP3qS5uRnqljYjTU1N8HFgsOMJ5a+9hrpvVrs+kA+Ggc+IJAT6HoY60gDWCugaAtAc9xCadu+AISff7lgW6gkjEfLYs1AOHw4AMFlMOF55HBfrL0IlUWFi5ERbqceBD8Bu/icKdwVzyg3iZlfDJ8yIplI5ivaQC6yy+HiE//M56PdvgubXtdDX8TtSSxQWDLi8EquGTcZHplLe9hgBLIPnqqowp1qPphIF9CyDX6aPx0plgUPFULAiGI+PfRyLEhe5nPh3JYfLDuOpPU+hRu/ciG1CxAR8PPdjyMS9w7nbylqRU5+D3UW7sbNoJ9KrSUO3CJ8ILB+6HEsHLYVE1J7HOVZ+DE/sfoLz8/rL/fHh7A8xMmyk0/fVm/WQiWUOF9DXZq3Ff47+BwYLqc6aEzcHy4ctR6OxEc/tew7VOrKUY1naMjw2lqpF7gBHy4/i3q33En3fAWBM+BiYLCacqeZvfwQAKUEpuD3tdixIWAAp3Yu8A1isFqw6twrvn3ifmCMyYPDZvM8wKWoS7+sa/vgDpY+Ttf1hTzyO4OXLOcfqTRaMeXkrmo3t38uXrkrD7ZMSOMc+8N1xbLLLanr6LP5iXx5e/r1dbq2UijExMQg7s9pVN8Oi/bHx4am8r6/5/HNUvvW28zeRSJDw4xoo3exfrDVpcbziOCq0FQiQB2Bs+NgOlaB0Nm4RgtJeTG8KSm/5/BD257TffEN95djz5CwYzVZc9t4eonDbnvmp4fjf7Y6DHIvVgrePv43VGdwJSpRPFG4achNGhY9CvG88/OX+3fPQ3fs2sN2NTNzIW4HF79qyjt5CW2trhZK5CSg6ZGszQKMOBx483N7vs6NYzMB7I4BGO1vyoUuBa7/s+DlztgHf2izZy4/5o87OuAMiERI3/gZ5Uns/WpZlUf3BB6j++BOXp5aqzYidXgO5n8X2s9+7Fwi4hNyVHbDq7Cq8c/ydtm3WKkVT9rOAVYkbx8ViQVoE0qL8EOor7/D3Q2e04LkN6Vh/ooQYV8CAEz5/h8pi58A4+e/AfK4BTHfTtG8/yl9+CaYCm2SMkckQdPddCH3oIbfcRj2BZVlsL9yOVw+/ypkEAjan5DemvuHVOlIas9WMeT/PI95/+bDleGT0I132noD3g9KG3/9A6RPeMYGCiAVYAKyLzz3DwP/apTh8VTI+zv8WzVVlmHaWxdRzVsRWAxJGBFlQMFBfAYuBDBL8BzQjakJLRiIgDvlVl0O37jeeN3Hy9mIWUTNr8H9jB+A3kWsl0ESdDqP0Rqz1VaNG4t5neUr0FLww8YVu91KwWC1Ymb4Sn5z+xO3a0SuTrsQrU15xeL/KbcjFrqJd0Bg1SApIwuzY2VBJXWS73cRoMWJH0Q6crjyNjJoMZNVlodnU7PJ1yQHJeG7CcxgVNgrfnv8W/z3+X96FBcBWs/nVwq8wKHAQMW62mvH9+e+x9sJaFDQWQCFWYGr0VCwfthxpIbb5Hsuy+OjUR/jszGce/2wTIyfik7mfEMFzZ9icvxlP7n7S9YEtTI+ZjttTb8f4iPFdMlfbeHEjnt33LDEWqgzFz1f+jCAFN/HBsiwKbroZulOn2sYYmQwDfllPzENaeXTNSWw41Z4tHT8gCGvvJQNelmUx5Y0dxJz3P9cOx/Vj3Z+LVGkMmPj69rZaUj4cBcSmigrkXnY5rNp2d/G8cODnqSI8vs5K1GTKkpMwYN06iOSOFVVGixHfnv8W3xz7H6BpglECaJSARCzFjYNvxMOjHvbouycEpZcwvSUoPZZfi2upLOkLi1OxbIqt59nh3Brc8vlhmKkvWKBKij/+Pg1RAfzZy7KmMvz74L+xv3Q/Z9+VSVfiuQnPee1B5DbpPwPr7uaOBw4A6nhkKslzgeu+Jp0fO4C1MhdNnz8L3bFDMDbYHB7FMiukPhYoAk1QBhshVVvAiCXAreuBxBmder829v0X2Pai3QAD3LeXvz2LMwwaW83rphWAoQGGRjFy/wwjJov+S69B1Kuv8r5cd/o0Kt/5L7SHnfckFcstiJtZA0WgGYgeY3PklXU+cwPAJpPO3QFc+AtoLAHkfkDcBFugLu+5PmHVumrM+2kekSkxVM+GsWo+cVywjwxDIn0xJMIPaVF+GB7jjwEhaqJhN8uyKKnXIaeyCSX1OtRrTajSGLA1owIl9dzJ8geJh7G49D1y8OETtjYaPQTLsqj+8CNUf/wx+OoJ/K++GpGvveqVSZHJYsLRiqP46uxXOFh2kLNfzIjx8KiHsWzoss6XDLjBm0ffxDcZ37RtR/pEYvPSzV363t4MSk3l5ci98ipYG+3ctSUSBFy9BPrMLOjT0x2/2As0y4GyICChApC4ET9JVGYkLqyCWGb7nP008XZ8WH4Ez33eiFg3vcakPmYET67HM6NisF/ctTWVKokKj455FDcMvqFbPo8X6y/i+QPP40wVN2sWZjbj+epaqFgWT4SFoJZaKFoxbgVuS72NGNOZdXj98OvYkLOByIgFK4LxzIRnOD17Adv9IK8hD+XN5QhSBmFQ4CCHP/vm/M14+9jbKG92ru5yhlKidKvEKFwVjjWL1iBEaTN41Jq0eHLPk9hTvIf3+MWJi3FL6i34/vz3hEzfXVKCUrBy/kr4y10b7njCmsw1ePUw/3O7lbTgNDw9/mmX2WFv8Hn653jvBPlMmhkzE+/Pfp/3nq87fRr5N95EPCtkyUmIfPllGHNzoT+XAdZshnLECBxNHIt7fmy/BzEMcOiZOQj3a5fwFtZoMf1Nsl/81n9Mx0APe4ku//oYxzS07frEIhx5bg4CVFw1QckTT7b1MAYAK4Dn7hDjYhSDG3ZbsPQA+UwMWrYM4U9xS0qMxcW4+N1K5Oz6DeFlevja5ZXqVcDRQQx+Hy+CX/IQfDTnI4T7uNFDGkJQeknTW4LS2744jL3Z7U/gMF859qyYRWjod2RW4PG1p1GntUk9YgKV+OSWMUiL9sVfBX/hj9w/kF2XDStrhUqigkwsQ1ZdFu/K6gMjH8B9w+/rfilS4SFbT0yLva6fAZZ+bgtMjn5uk/XS1xw5ErjlJ8ctB5xgqS5F9QsPon7PeVhNzn9ekYyFMi0FiokzoRw2DMphwyAJDfX4PQm0tcC7w8i62bhJwJ1/AA7qaADYbvDFx4CsTUDeHluvwJZVY5YFivYEobms/UbOyGRI2rIZ0kjnq/jmqiroMzJgyLkIXXo6mnbuBGsg5UsiqRWx02qhCjParvX61YC6E78Hq9X2c+x6g6xTa0XhD0z9BzDpIe9mxT3gid1PEMY2rFUKbd7DsBqdf+ZUMjGGRvkjOVyNZoMZR/NqHaoaaJ6dHoJ70m8Ao6trH0yaA9y2vkM/gzew6nQoe+6faNy0yelxES+/hMDrruvQe2hNWuwo2oFdRbuwv2Q/UVdlT5gqDG9OfxOjw0d36H06QmZtJq7bSP5cX8z/wiv9UB3hTlDKsiwaDA3QW/QIVYby1uCxVisK774b2oOHiPHI119HwNVLAAD1p49j74f/ROzBfMhb4je9FCgOAZrlDAaWsVB1k9ccI2IRN6sGqpb2LZ+FReFDH1sGKqCJxfPfWxDjSKmqlsM/xgTf0CoYByjxaEI8TpnqiEMYMLhn+D2YFj0N32R847L3rJRlcWuDBoubmlEpEWO1ny/2q/gXfEeHjcaLk1/EAP8Bnv3QblKtq8Znpz/DTxd+4s0WTtPq8GpVDQJbzFYyZFIsiwyHVtQeLIogwqfzPm2TXlY0V+DvOx5GBlU7aM8Ng2/AU+OeglQsBcuy2FuyF++eeBfZddltx4SrwvHAyAewJHlJW3Bqspjw1rG38H3m9279fEqJEv5yf7eD1zHhY8CyLE5UniDGh4cMx5cLv4TGqMGD2x/kOOS6y5SoKThXcw71hnre/eMixuG9We/BV9Y1i6fbC7fj5YMvc6TKkT6ReHDkg1ictLhbFkEAm9z6ge0PYH8Jmcx4evzTuCXlFt7XVLz+BtEixhGS2Fj8Pe1mnFe0zydeXJyKO6e0f49+PFqIp9a1zxNC1DIcfW4uGIaBpakZupMnYK6pgTQiEsphQx32Ez5eUIuln3AXOgHg5glxeI1HDqw9ehQFt5E+AttHMPjsctv9Vmxh8erXFiTaxboswyD+66/gM972jDBXV6P6k09R++MaMGbHjsoAYBIDa6aLcHRmBD5buBKJ/q5LRYSg9BKmNwSlu7Iqceeqo8TY84tScddU7sNOazTjTHEDrCyL8QlBaDDW4fHdj+N4xXG33ksikuDFSS/iquQeMFGpPA98dQWgpWYZ818FJj/Uvp25Cfj5Llh0emgrZTA1SyASs1BEKSFfeC+YtCuBkEHOAzrY6ik1/3sBFSt/hlnb8eBbEhkJRUoKJKGhEAcFQhIQAJG/P2RxcVCkpTmVbbSx+z/ATmoldMJ9wMI3uHWDJj1wcjVw4AOgvoD3dLUXfFBxglytDb5nOcIef9yTHw0AYCwo+P/27ju+ifp/4PjrkibpLp20tEBLGQWk7L0KKEsUZAiIWsSBIiCIoPgDBTciOBC3oCgggogCKl9FhghlCLJnh1BKgdK9Mz6/P0IiIemi0Ej5PB+PPAr3ubu8c7kk977P4uzkpyk8YvtjrqgFoZ3S8QotAo9A6DkDmt9nnoPVEZMRzv4FZ3ZC7gXzjQdFZW4enbDFPNdhWWo2g4EL7OdJrAIJmQnc8+M9NjdxTAZPis7fhSG7GddzIHUPrZp3Bjfgjn0T4Z9ttoVjNpgHhKlihrQ0cn7byKVFi9CfdjzC45UUNzfqrV2LNqz8UyZlFWWx6NAiVhxfUWZzvq6hXXm1y6v4ulayCX0FCSEY/ONgTmX+O11PTO0YFvRccMOes7SkNDErkW+Pf8v6hPVkFJmTLjcXNzrX6szIqJG0DW5rvbmY/uWXnH/9DZt9ew8YQOhbcwE4kXGC5/54jpMZJ3ExCPyzwaCGdG/zhRWArljQ6ajgjr0m6l+ZLygCV189HsFFeAQX4e5fjKnJMI6k1sGwaDmuFRyw28XdQK0OmXgEmRPSxT5ezPezfa91xYK7d5roeFTglwPpXnCkjsKOKIWjdRTa1upAbc9abDyz2XpsLNxc3Hiz25vE1I4BzO/rhn828OauN+1GcwaIyctnWnoGta+6iPzd3Y1X/H256GLfXFOr0vJEiyeIbRp7Xfr1gbl10xeHv+C7k9/Z9XUEUAvBhIxMHsrK+fcbSa0FYzEb3d2YVNP25qG3xpMP7/gYvbGYKb9P4FIJN4CudJtfE/pHDmDzmc3sSt1V4notg1oyu9NstGotz259lv0X95e63xCPELqHdadHnR60rdkWjVrD7tTdvLn7TY6lHytxu8ebP87j0Y+jN+l57NfH7KZDaeTbiMyiTM7nV3wKJ5WiYkaHGQxrOIyc4hxWHF/BiuMrrMlyuHc49ze+n6ENh5Y4GNP1kq/PZ0PSBk5mnkSr0tIiqAWda3W+5gGVKiOtII0hPw6xncNb5cLX/b62NoO+kqmwkMShQyk+FW9XdrV8Tx/Gd3iCc57mGu624b6sfPzfAQef+mYfP1zRxHdAdAgLRrTg0uefc+mjjzHlXfHbodHg3rw57p064tGxI27NmqFc8VmdvOJvvt9n22XGx03D/yZ3s6mdBfPgRgmDB9u8hjwdPDVWTbaHQiPfRjQLbEbcnyt5Y7ER7RVfFaaaAYTPeYv8XTu5tPgLREHFBhP9s7HCsiH+LOj7kcPjeyWZlFZjzk5KM/KKuev9bTaj6fp7ann1PliX+APHM47jqnalVc1W3NvwXpuT9VTGKcb/Pp6zuWcd7dqOn6sfb8e8XaW1DlZJf8K3D9gnpG0fMY+0e0ViZioqIu2NmaSv/BFhsE3YXNyMeNYqxKsuuDeujSowHPwiwK8e+EVebvKoULzjB1Lf/pi8f+wnUL+eFI0Gt9at8ezWDc/u3dDWq+e49rk4Dz7oAJm2F/ui8WCKm01An5aL4UIqxoO/YTy+FVNeHiaDylqzq9aZcHE1odKaKM52ITPeHa4Yzl0dEEDkT+uveRAaY24uyU+MI3/3brsy34a5BN6WY25e5xEErWOhVey/fU2zU+DvpbBnsblJbmUpauj6NHSbCi7Xf+Tb0rwS9worjq+wWy6Ka1J4sQeG7Ggqm5y2C/djfoc8wrY9b5+oNxkI9y5xvOENoE9JIWP5cnL/2EbRsRIuCtVqgp55Bk1oLc5OtO1b6dG9G7U/+qjMFhcFhgKWHl3KokPmGo3SBLgFMK3tNPqG93XaoDJLjy7ljV22yV15BjwSQnAk/QjbkrdxNvcsHhoPmgc1p3OtzqXWsFydlOrcdGw+s5lvjn/jcMCnK7UIbMGElhNodkFH0v0PwBXz57kEB1PvhzXk6ARLjixh8eHFDqcMGdxgMA80foA3dr9h83ye+YJHM70Zee4EWi8Das0VlzJqLVuHvM+UvW/hnlHAA7+b6HzU9lJH0ekQ3dvznv9eUjUF+OQLvPMh0wPuqu3DsBTz7+7X3t7M8a9hF1f3sO5kFGU4bLpaGh+dDx/0+oDowGi7sjx9HqtOrGJP6h7yDHnUdvGm//kk2p/6w/H4AkC2SmG+ny/feTnuQtIsoBlzus2htte1979PyEpg0cFFrE9YX+KAS3X1el6+eImWRZbfNcV8s7DLZDj5P1g9lg/dBB/41ijXc7qbTNQv1nPA9cZ8z7YMakmnWp1o7NeYJh61CMxJg4vHIOecuTWUdy0IaoIxpCXfxq/mo/0f2SRBkT6RPN/+edoFtTTf1M5JJUOlMPLvuZzNK33KLh+dDzM6zOBszlk+O/iZw9YY7i7uvNb1NXrV6WWzXAjBxYKLqBU1fq5+Th3cypn+SP6DcRttp2IK9Qxlaf+l+LvZz/agT0nhn/sfQJ+SYld2tVM+oUzuPgHD5b65cdN7Eezjit5oou2rv5GZ/+/32Gt3NabbqgXk/PxLSbuzUnl64tWrF/5jx6KrF0F+sYGnvvmbX4+Yb1gEeGr5+IHWtK5r3z/24vsLSXv/fZtli+5Q8UsbFbU8arFiwApquNbgw78/5PQn7/Pg79d3SpjDdeCDe7144873S22ZI5PSaswZSanBaOKf9HyOncthwe8nOZZ6xUWaKp+WrX/mVK59cgDmO/bjmo8jMSuRl+JeKtfgAe4u7twdeTdPtnjyhg4SYkcI89x2exabk5arm+TWvwNGfgPqf+9qFSUmcvbpKRQdLbl5kYXiYsLdX4+LhwGNuxGNuxFFLchLdSUryc3xwByKwL2eP27d+qGuGYYxPYOikycpOHAA46XSRzQsD5eQEHT166OrF4E2IgJtuPmvS1AgypldiEX9KMpQyEt1JfecjoI0LcJU+R+8sA8W4tWzfJNjl8RUWMjZp6eQ+/vvdmUqrQn/Rrn4Nsiz9v3Cv4F5cvoLh+3f2zII/4YYat2O8Z8jiMQ/UdQCjYfR9qI3qAkM+sC+1tRkMidzmafBUAjuAeb+ua6VH5G1wFBA7M+xdkPjWwTqwmjmPoK8jKYcOptlN1fwlbxcXQj39yDAU4uvu5ZAbx13+aXQ9OSHKKccNCP0CoGxW6+piXpFGTIyuPTJp2QsXYooLvnGjcrDg9B33saza1cAzr3wIpnffmuzTujb8/Hu18/h9rnFuXx74lu+OvKVw8GLrtTUvykD6g1gSMMh5R7hW5hMFB46RO4ff1B06hSmrGwUnQ6XoCB0kfVwa9Ua1yaNUVQVu5GQr8/njlV3kF38b7/M3nV7My+m5NEYD148yPy/5rPn/B67Mq1KS486Pbir3l10Cu1krVXTG/VkFGVwJu0MbeqaB6ubtmEqR5N3Yrh0iRp5ghp5UCMPfPIENXLBowgueUF8iMLJWgpnA6DeOZjxnYJHrm0y89fzd7GtVja7z+22mdLCwt3FnVmdZtEvwvz+5enzuG/9fSRk/TtXtoLCR2lZdMrJtNl2XcfRzDy/1SaBCswU9E8LZUTY3XhHNMCjUyfUnp7sSd3Do78+apMQqxU18xrFcuzSET66aNvcGOD59s8zMmokYL4B+9XRr1gbv9ZupNKrBbkF8fEdH1Pft36p69nJT4ekbZCRBAUZ5u4WRbnm75qz5pZIca46ZgX4c1ZjX2vqofEgtkksjf0b809WEkfP7eJ4+lHOFWfhqXalkU8k3evfRc86Pa19IAGOXjrKp/s/5Lczm0scGV9nMhGblcMjWdm4WS4nNR4w+BPbqdMuHMX09RCedi1io0fpY0XU0etZcP4iEXoD6zzdecnfj8IyPic6VBRR9ne9VqVlRugd3FMkzMnkxWOQW0otpmsNaNgXQ9OBHPIOIl2fTbBHMI0LC1H2fQWHvze/J5eddHXngZCa5OG4aWSoZygf3v6htWl1WkEaH/z9AesS1lFgKMBFcSGmdgxPtXqKcJ/wMl/PrWz+X/NZfGixzbKGvg35vPfnDq8nDZcucfHd98j++WdMOTmoAwNwb90G/enTdq2xVtaPYdFt5vPX0jrQUcvB31Rx6FevqlDcikZDzf97nhrDh6MoCv9cyuNsRgHNa9fAQ2f/+c35fRPJTz5p0y82IRimx6pxcdGypN8Sbgu4DTDftHh1x8s0fnE5Tc6UHkexGjY1V/Do0oX7ej+Da1AwxvR00pctI+Orr+3GbEjxg7dGuPJQn+kMaTDEpnbelJfHhY2/0P7BWDxD68qktLwKCwt5/fXXWb58OadPn8bPz4++ffvy0ksvERYWVqF9ZWZmMmvWLL7//ntSU1MJDg5m0KBBzJ49mxo1alQ61qpKSjPzi1n1VzI/H0rlcEoWhXr7L3ZFk4Zfva8oVlW8CQqYh1Uf13wc/m7+5BbnUmgsJMg9iFZBrW78YEZCmAcpSvnbPIdm6gHzv/NLuBANa2fuN3fF4DZZP/5I6qzZNiOeXU8ekZ4EvzIXbcsYuzIhBIZz5yg4cJDCQwcv/z103WJReXigCQ3FcC4ZY871fX0B48cTOP7J67IvYTBwbuYLZH3/vcNylcY8Mb1fwzxcXCuYiBohrzCS7NzG5B5Iwngp3W4dtdaIq78e94Bi3IOKcQsworQaCeHdIP8SImkbxhPbKb6Qh6FQBQJULgIXd9A2bo6qxVCIvrdSoybnFOcwdctUh4ODWYxqPIopbaaQmWfi0NksDiRncTo9H0+dmvpBnnSI8KO+5iJK2gnzRW5GEpzZZb5J44ibLzzw/Q1vtmwqKCD9q6+59OmnmHJKr7F0a96ckDdeRxfxbzcCY3Y28XfeifHiv59rdUAAkevXofbxQQjBiYwTxJ2LY3vKdv46/5fDJohgbjbXMaQjt9e9nW5h3co976AhI4O8bX+S+8dW8rb9iTHd/jy6kktgIF69e+Pdtw9urVqVe9Tg9/a+x6cHP7Vd1uM9etTpYbMsrSCNd/56hx/ifyh5Z0IQnAEeheDu4oaLlw+p6lwuqHPxLIDQZCPfvmGuqf6jaUP8DeVPoovV4GK0r7//vqPC8piSX2sD3wbM6z7Prk/ksfRj3Lf+Ppvkz9vFg6VZRsIvnEC4+/N1dD/ePG8/mEyX0C7M6z7P4e/ND6d+YMafM8r1mqa2mepwfti0gjSWHV3GiuMrbG4YWPSq04sZHWbYJH3Xxek4+OU5SNlHvqLwvq8PX3t7WZs8V4QKaOEVQQOvuhxLP8b+wpL7VLoIwV25eTyRkUWI8YoErEZdGP41hNjXBJN1lvylQ3hKlUacm+ObO53zC5ijCsan0yRwqwH7V3Ds+BqmBPlzWmPfVLSmwcBT6ZncmZfPDjdXXgkMIrmEUytM5cq81PM0yXM8v2OZXH0gOBqykh0PfHjZAZ2WSUGBXLxq5OS2wW15s9ubDs8By00gb603ri72c2NK9vQmPWN+GcPfF/+2WR7mGcb09tPpEtrFYV9XIQSiqAiVq/k4G3NzSRoxwqZprAmF57o8zsGASFrVqcHqcZ15+tu/bUaofzxjHwO3LLXbvyYsDH1qKhhKH9isxr33EjxzBoqD8xpA6PWkf72UC/Pn27QyMSnwfKyahBDF5gaZhdFkZOaacfR8ayuhDn6CTApsaabwXVcNj94xneGNhtvVuOf+sY3kp55CXHWdme0GX/VUEd82hH41e9AiEXy2H8Zl10FUxQbuSkxAV7++TErLo7CwkF69erF9+3ZCQkLo2rUrSUlJ7Nq1i8DAQHbs2EGkg2GiHbl06RIdO3bk5MmT1KtXjzZt2nD48GEOHz5M/fr1iYuLw9/fvglBRdzopNRoEizd+Q9zNxwnp7DkD4/a8xjuod+AqnwDpFytWUAz3uv53vX/MS6N0QDJu8wjwh7/GdLL7ksAQGRP88A5l0fTNebkkPrSy2SvXWu/rkqFW3Q0hvNn0Z+z7wdUHi4eUHPCQ3jFTq1QMxxhNFKckEDBgYMUJ5/BmJ6BMSMDY1YWhrQ0ihMSHI5MWlUUrZbASZPwe2j0dW1eJIQgY+kyLsydazcAkvW51QLvuvl41ylE62FACDAUqDEUqjBqayFcgzGZVAiDEaE3oc9TyDtxEVNexfpZqFxMuPrqUetMGItVFGW5YCwq4WpIEei8DbgFGXFv3Qr3QePQtOjleN0yCCH4/czvfPD3B5zIOOFwnTY12zC3+1zzZ85QbL4RczoOTu8w96vNK+f5GtIChnwOARWs2akAY2YmWT/+yKVPP8NwseS4CrTwdz2Fzc0Uitvdxl3176ZbaDdCvUKtFx7Zv2zg7KRJtvu/ozM/PdCA305v5FwZzeoA+ob3ZXzL8dT1rlvmuoa0NAr+/pv8ffso2PMXBQcPmmvLr4E6MADvO3rj3a9vmQlqZmEmA9YMIKvo34trD40Hb3R9g86hnYnPjGdd/DpWnlhJvsH2okIxCYKyIOqMoFmS+eFbSqOWfJOJNifN59meBg1xr2DN7tUO1lV4dYQKk8r+e0GlqBjddDTjWoxDp3bcbPPLw1/y1p63bJb5u/ozssFQ/krbz45z9jWbd9W7i9mdZ5fat/Ldve/y2cHPSo39qVZP8Ugz+zkOr5Svz+fPlD85ePEg2cXmWrWY2jFE+UWVul2lmIywdwlsnA0FGezXafm/QH/+KeFitzJ0JhNDcvKIzc6m1tUDpUQNgLsXgHsp85IXZKL/YTyfpG5liY+XdfCjGkYjDxVriO30f6hvGwpXnmfn9lP8vxf4Pm0Pm93dSHVRE2ww0icvn755+bhe8VuXryh8UsObFd4+5F7ehVbAPTk5PJWeiVcV/S5mqVSs9PLkkIc37jWb0a3Zg/SO6PtvkpR7AdITzN/Faq15tHfPIPAKvn4jyt8CzuedJ/aXWIddxQLcAuge1p3e4b1pF9yu1KlyCo8dI2nYvYgrkr/zbr6M6zmFfI0rC0a2ZMq3+yk2mr/fW1w4wWtxn6Fc8X2v6HSEfbAQz86dMeXlkb9nD3nbd5C3YwdFJxz/Vnt07kzoO2+j9vq3EsRUWEjmd9+R/vkih82Nl8ao+KGjin7h/ZjTbY7Da6xCQyFT142j3rdxdD0s8CgyDxq3u4HC6s4qDHWCmdNtDq1rti75mBw9yumxYzFeKP/1rUxKK+CFF17g5ZdfpmPHjvzvf/+z9pOZP38+U6ZMoVu3bmzZsqVc+3rwwQf56quvGDx4MCtWrMDlcufliRMnsmDBAh588EG+LMdoX6W5kUnpgeRMZqw5xIHkq+8YmlDU+SguuSiadDTef6Pxse8z4+/qz9jmY8kuymbp0aV2AzlY3B15NzM7zLxxd/5Mxn+bMhVkwLn95mZOJ36BgtJrKWy4uEHMs9DpKVCpMKSnk/X991z67HOMGfavTVs/ktB583FtZJ6HrCghgdzffyfn1w0UHDhcZkKouCj43tmFgP+bi9r7+g7hDuZkOn/XLnK3bCV361YMqdc+BL5aa8TF3YSLzoTKLwB1eEtUQRGoPD1BCAyXLmFMT8eYk4PKzQ3XxlH4DB6MtoItDyqiKCGRi++8Q87//nfDnuNGc/FS4xrVELcOvdA1boI2PBxt7bAS75pezSRMbD6zmY/2f+SwSW+giyfzVMG0TIgDfQVrwD0Cods0aPtwmYN2lRpjXh6GixfRX7iA4eJF8+OC5a95WfGZMzZ3ga9kUMHGFgrbmqo4FQJGtf2Pr5uLG7U8alHLsxahHrWIeX8Hfn8l2Kzze7TC8u4qsjyv2l4INAZwK4bONVoRGzGMCE0IpoICTPkFmAryEdZ/F2DKz8eYnk7x6dMUnz6NMa2c84JUkDowAO/efUqtQV19cjUvbn+x9P0YBY2SzYlnWBqEpAtCMkFTgZlJrmdSeqQ2vDFMTaHuqv74igt9I/ryaLNHy+wbaxImpmyewm+nfyvXc97f+H6mtp1a5uigJmFi6pap/O8f++8UlaLiuXbP2dVI/Ofkp5sT07++JF+B1/19WVNCX9OK8jSZGJmdw6isHPyvvPGiqCCiO3R+CiJ7lLyDq539i6Jj64jPOIla50O9+v3RNOxj02XGTtI2+Hs5xP8OOaX3DSxS4KRGiwDq6fV4lHWpq9KAf/1/p7u6FA8Xy+6qY+VZE/LSrKPQ2/EIMtce6wsg7STkXSh5X1ov84jyGg/z2AVuNaDmbVC7PUR0K98UdEY9XDgCZ/eamygX5YJdy5DLn0ONq/k73/oI+Pff7v6V+g2oCsk5yYz+ZXSpg0n56nzpVbcXd0feTYvAFg4TuUufL+LC3Lk2y34Pa8Xc1iNtxhapnXOe+Vvfx1NveyO71ltv4dbvDoqMRagVtU2rDP2FC+ZuKV9/bfe8ugYNqPXWXFTu7mSvX0/6V1+X2GVrU7TCR/1V1PWJ4JsB3+ChKfkGhsFk4NMDn/L1oSUo2TnkuIHaRcvdkXczufXkck0fpE9N5czYxyk6Xo7BIJFJabnp9XqCgoLIzMxk7969tGxp2xStefPmHDhwgD179tC6dcl3DgBSU1MJDQ1FrVZz5swZatb8d/6eoqIiateuTXp6OmfPnrUpq6gbkZSezy7krQ3HWbU3GaEUoXZLQu2egNo1FZU2DUWTgaKUfqc/yi+KBT0XEOwRDJj7+iw9upQlR5ZY79xH+EQwNnos/SP6l11TZtSb7xrmpJoHGypIN/8tzLrqkW3+W5RtHpynOA+umC9MmMBkUDDpVRj1ivnfBhXCoCCEeYh/RQFUoCgCRQWKTyjUj8EU3gd9eh5FCYkU7N1LwaFDJTa98BkymOAZM1CV0PzIcOkSeXFx6JPPok89hyHlHPpz5xCF+bgE18SzWwzed92NpuaN758H5pq1opMnKTp+guLERIqTEilKTKI4MRFRaF/7rQkLw6NLZzzbtcQ9WKAmx/yjGNbWfBf3P6Tw+HHSPvjQnJxe768ylco8GEoFR6qrFLUabViYOUGNMPf5dW0cha5hQ2tTo6sJIVhzag2vxL1M8VV92tRCMDI7h8czs/EpTw2eRxB0nghtxiA07ojCQmuCJgryMVxKx3DhPIYLF9Cfv2BOLC9cwJSbg9AbEIbLD6MBkZdfqSbm25oorOim4rxvxWra/bME8z8z4uagO2q2GxRpQGMErR60hvLNV1kZ2nr18OjcGU1wMKbCAvTJZynYu5fifxyPXn01SxNfr149cY1ujtrTfBEihGD6tumsT1hvs75PrqBFgqBVvKB5YuWnUCkpKVX7+uISEIA6wB+XgEBc/P1ReXhQFB9Pwf79GM79Wytd7ObCli41WNNBQaXV4qnxpJZnLcJ9wokOiKZzaOmDLdnFpM9n9C+jS+xfbTGh5QQebfZouVtr6E16Pj3wKUuPLrU2wY0OiGZS60m0DW5b7vic7uxfsP4ZSNnLca2G5V5e7HfVkqtS4Wc00rhYT2PFjQiPWlzIP88WkccWdzcKHNxw8DcYGZWTxwjfaLxC25gHAHL1MXdvcfM196+/Dn3mK6woB7LPQW4qnD8Mfy8ztwgpD0VlnmM8tA0ENoKgxuYBCa8eSTYrGY6ug8OrzS1MruZTG1o9CNHDwbcu5F2CuIXm0emNJfeHrxS1Fup2hoZ9oHY783e2SW++KX/xOJw7YH7/Uw+YxzWoNMWcmF6drNr9P8Bc06v1NA/KZSgEQ5HtX0Vljt9FZ/6rqM014oranPgqalC5XP53xb73U/NSeWbLM2WOsgzmUYt71OlBI99GeGo8cXNxw0vrhafaneLx0yneYzuC8g/1uvDpbXdhVKlpmpbAzF1f4lNs27zk5N3RfNXVxPH049b+1+4u7gS5BxHkHkSgeyDB7sE0+vsSdd/9AaW4YkOCm4C17RWW9VAR7FmLz/t8Xu7BywoNhdYRpBv6NjQny0KYP0M558w3U/QF5pvXlr+Kyvz5dvfHqPLk3JyPydm4qdTnMSrQ90wCPnVlUlqmTZs20bNnTyIjIzl16pRd+csvv8wLL7zAiy++yKxZs0rd1+LFixkzZgy9evXit9/s79Y+/PDDLFq0iMWLFzN69OhrjtmSlO7dsxuDSY8wCAxCjzAYMJqMGE1GTEYDRqPJ/NdkxHT5YTQaEUYDeoOR9NwC4tPS2Xc2mSMXkzBpz4PuAormEmohUJnMF2Yq0+X+PyZQC/Nddp0edHpw1YNbEUQrAcSoa6EqKMSUV4AwgcpVi8rdFaF1IVdtwkWjwVMoiMJ8REE+oqgIoddj0hsQxQaE3nB58BkjikkPxkIUxXwaCqGAMH9ehMnyb8X8iVQuf0+pBJjAeHkEWJP+8t8K9HW6FiovL0Jmz8K7f/8b+jxVRZhMGFJTKUpMxJB6HnUNH3T166OpU+emG9GvKCGBS598Sta6dWX24yiNW8uWePXpjWf37mjr1kVRqTAVFqJPOUfR8WPk791H/s6dJTbFAfMgBi4hISgaDcasTIxplR+kCpUKXZ1QXBvVR9eoAWofL1SKHsWQi7iUhOnMQc5eSOJXFw+UIgXPQvAoALdiYU7ADOBlELgahLWPn6JSQO2ColYjVC6g1iBUWiguhoJCKKyiCSGv8neEwvIYFWl1vJnYciL96/XndPZpNiRtYF3CujIHJQJofdLE1O9MqJzw66a4uuLRoQMe3bri2a1biS0GiuLjyd6wgZwN/yv3XWhUKnSNGuHesgXaiHoIbw+WnVxB4pmDhKUJGp8RhJdSAVMqnQ4hTHYXSwUuLrQ+fAiAlB9+IKBNG1wCA8us0defP09xfDwqT090jRqVb3qqCsgpzuGZLc+wPWW7XZm/qz+zO82me+3u17Rvo8lISl4Kbi5uVdvt5HoSwtxc//D3cP4IFOeAVy1zItPgDnPNm6KY1zt/mMKjP7I94Wd2F52nAIGP2o2WvlF0jhqCpmFfcyL6XyYEnNoIW990nEACeIeZR2hvMQp8yj9dFACZZy5PKXbe3Ly2VivzMXTUciDtFPw81Vyje8tRoIRBsSq2G5V5sEIXV9C4Xf7rbq7VtSzTuJlbuWlcQa3DKATri87yeX4CCcayB9p0JCBLMM/BTc00d3eydB5EZtg3ZY1rpPD2Papy9+NucFYwbZURn3Lesz1RC5b2UHO0jkKU1o93fdtTy2CwrbQpslTa5JiTfp23uUbdcnxc3ABhrvDJvwS5F6Ecg5FeKeecJ5eOe1NwRcO7Yg2cqK1wrJEaJVLDoleP4OYXKZPSsrzzzjtMnjyZYcOG8e1VIzQCrF+/ngEDBjBo0CC+L2EgFYtJkybx7rvvMnXqVN5880278oULFzJ+/HgmTZrE22+/fc0xN23alKJTp1gbUfaEtdKN5dmjB8Ez/g9NaAV/yKQqpb9wgazvviN38xYKjhyxNgtVeXuba3V8fVF5eqDS6lB0OhSt1tzkuEljPLp0QVPOlg2GixfJ37cPffJZjDnZqD090dSpg2uDBmjCwmzmIjPm5lF04jgFcX+Qv3k9+cdPYyq6uZL+G61YDXvrK6xvq+J4bYW7I+/m6dZP2w3tbzAZiDsXx4/xPxKXElditwGAjkdNPLHeVOE5KitK5e6Oa/No3Fq0wL11G9zbtqlwAlaUkEjOhl/I/mVD+RPUSlL7+ODerh0enTri0amT9WaUqbgYU1YWxuxsVO7uFHl64nV5Oqer5yl1NiEEW5O38nPSz5zJOYOP1oeY2jHcWe/OUpu1SdWYEJC41dzHNu24uVluzabmKa0ie1ZdU1Qh4OSvsOUN6wjJdlxczc1+henfUZWl6yLJxYXfPdz4n4c7hyv4fdz2hImnV5tQlyM7OhkCs0epKdZU7Dc9KEPw3EojYaXcs/47QmFNR4UjdRR8TCYeyM5hTGY2VT8zrC1jkYK+QI2igNbLwJU9I5p+kAuBUdeclJbSeL96OX15wvWSRti1LD9djonZr+e+4N8a0asdO3YMtRDclZjgsFy6wRQFtacnah8flK1boHf5+htL/yGWwVTycs2Pf5KcGg4AQoUoyETkZWEqKkYYFYRJcea4VDecUMCosn2YVIr138UuIPaAbp+OALcA1risYQ1rytyvURgpNhZjMBkwmAzoTXqMl/t0rVG58LNJjWtOsXkEwfI0X1YUFEVlPm8Ulbk2WaUyL7/8F5UKRaP591FUCLt2mR98UrkDBQiTEVNuLqacXEzF16e2WqXTobi5odJqUbRacy1nbg78vtH8KIXpiuPWpk0bVJUc6OhG28IWZjPb2WFI/ykXgNKbHt5QxiBzs0iTwVwDqHL5twmrdeqYGiC8zeuYDObxMoTA3CxMf7lZZQEVroVUa8y1ZCqXkpvEmvs+mZ/TZDT/u6S+sTeVTAD0QK5KRY5Khb4ctZkngTVF4JdT+rEu1MKlbAVRetf+Ep/jbgHe+eZBiFSXv2ZNChToINdVQZ8DrvsFXiYTRpOJ94D3Kv5UVSo+3YSmoHy5jyO3TFKam2u+A+Xu7nj6EcvdX8t6VbWv0iiKgs7dHV2dOpXajzPEx5tHuy3vaMZS5clj7sANboZ8TcdcUVA8fVE8fe2mypDKduUxVyvqsucNvckqzBSNBrWvL2rfa5866HpSqVToLtc0/NcT0upEfp9XvRt2zNXaywloGSx9LsuzbjVRFee5BvC9/KiQMhpO6YBKN2p3EJQbUMr41ZV2I4+5puB0pVrT3DJJqaWVckl95SrSivl67gtu/DykzlBVc6xK/5LHvOrJY1715DGvevKYVz15zKuePOZVTx7zqvdfPua3zG1Pr8tzAOXlOe7Ym395lEjLNDFVtS9JkiRJkiRJkqRb2S2TlNa53AQ2OTnZYblleZ1yNJW9nvuSJEmSJEmSJEm6ld0ySWnz5s0B2Lt3r8Nyy/Lo6Ogq3ZckSZIkSZIkSdKt7JZJSjt37oyPjw/x8fHs27fPrnzVqlUADBgwoMx99e3bF5VKxR9//MGFC7aTwhUVFbF27VpUKhX9+vW7PsFLkiRJkiRJkiRVU7dMUqrVahk/fjwA48ePt+kPOn/+fA4cOECXLl1o27atdfn7779PVFQU06dPt9lXSEgII0eOpLi4mHHjxmEwGKxl06ZN4+LFi9x3330EBwff4FclSZIkSZIkSZJ0c1NERYeKvYkVFhYSExPDzp07CQkJoWvXrvzzzz/s3LkTf39/4uLiqF+/vnX9WbNmMXv2bGJjY/niiy9s9pWWlkaHDh2Ij48nMjKSNm3acPjwYQ4dOkRkZCRxcXEEBARU8SuUJEmSJEmSJEm6udwyNaUArq6ubNq0iZkzZ+Lu7s6aNWtISkoiNjaWffv22SSkZQkICGD37t1MmDCB4uJivv/+e7Kyshg/fjy7du2SCakkSZIkSZIkSVI53FI1pZIkSZIkSZIkSdJ/yy1VUypJkiRJkiRJkiT9t8ikVJIkSZIkSZIkSXIamZRKkiRJkiRJkiRJTiOTUkmSJEmSJEmSJMlpZFIqSZIkSZIkSZIkOY1MSiVJkiRJkiRJkiSnkUmpVGUKCwt57bXXaN68OR4eHri6utKgQQMmTpxIamqqs8Or1latWkXv3r0JCAjA1dWVOnXqMHjwYLZt2+bs0Kq9l156CUVRUBSFb775xtnhVDvHjh1jzpw59OrVizp16qDT6QgODmbw4MH88ccfzg7vplZYWMiLL75Iw4YNcXV1pVatWowZM4bk5GRnh1bt5Ofns2bNGh5++GGio6Px9vbGw8OD5s2b89JLL5Gbm+vsEKu99PR0goKCUBSFqKgoZ4dT7aWmpjJ58mQaNmyIm5sbfn5+tG7dmmnTpjk7tGopLi6OIUOGEBwcjEajwc/Pj169erFq1Spnh2Yl5ymVqkRhYSHdu3dn165d+Pn50bFjR7RaLbt27eLs2bMEBwezY8cOwsPDnR1qtWI0GnnwwQdZtmwZHh4edOnShRo1anD69Gn++usvZs6cyYwZM5wdZrV1/PhxmjdvTnFxMUIIli9fzogRI5wdVrUSFhbG2bNn8fb2pn379vj6+nLkyBEOHTqEoijMnz+fSZMmOTvMm05hYSG9evVi+/bthISE0LVrV5KSkti1axeBgYHs2LGDyMhIZ4dZbXz22Wc8+uijADRt2pQmTZqQnZ3N9u3bycnJISoqii1bthAUFOTkSKuv0aNHs2TJEoQQNGrUiGPHjjk7pGprx44d9O/fn8zMTJo0acJtt91GTk4OR44cITk5GYPB4OwQq5WVK1cyYsQITCYTbdq0ITIykpSUFP78809MJhPPPvssb7zxhrPDBCFJVeDdd98VgGjfvr3IysqyLi8sLBTDhg0TgHjwwQedGGH1NG3aNAGI/v37i0uXLtmUpaenixMnTjgpsurPZDKJbt26iZo1a4qBAwcKQCxfvtzZYVU7d9xxh1i2bJkoKiqyWf7RRx8JQKjVanH48GEnRXfzmjlzpgBEx44dRU5OjnX5vHnzBCC6devmxOiqny+//FI88cQTdt/JKSkpomXLlgIQI0eOdFJ01d9vv/0mAPHYY48JQDRq1MjZIVVbZ8+eFTVq1BBubm5i9erVduU7d+50QlTVl16vF4GBgQIQ33zzjU3Z9u3bhaurq1AURZw6dcpJEf5LJqVSlRgyZIjDD4QQQuzbt08AonHjxk6IrPo6ceKEUKvVok6dOiIvL8/Z4dxyPvnkEwGIr7/+WsTGxsqk1Al69+4tADFr1ixnh3JTKS4uFjVq1BCA2Lt3r115dHS0AMSePXucEN2tZ/v27QIQOp3O7uaLVHn5+fmifv36okmTJuLEiRMyKb3BHnjgAQGIBQsWODuUW8LBgwcFIKKiohyWW26ar1ixooojsyf7lEpVQqfTlbmOn59fFURy6/jss88wGo08/vjjuLu7OzucW0pqairTpk2jV69ejBo1ytnh3LKaN28OQEpKipMjubls27aNzMxMIiMjadmypV350KFDAVi7dm1Vh3ZLspzHRUVFXLp0ycnRVD+zZ88mPj6eDz/8EI1G4+xwqrWMjAy+/fZbfHx8eOSRR5wdzi2hPNff8N+4BpdJqVQl7rjjDgDeeecdsrOzrcuLi4t57bXXAIiNjXVKbNXVxo0bAfOxT0xM5LXXXmPs2LFMnz6d3377zcnRVW8TJ06koKCADz/80Nmh3NISEhIACA4OdnIkN5f9+/cD0KpVK4flluWW9aQby3IeWwYnka6fAwcOMG/ePB566CG6devm7HCqvT///JOioiK6dOmCRqNh1apVTJo0iSeffJIFCxZw/vx5Z4dY7dSrV4969epx7Ngxvv32W5uyHTt2sGHDBiIiIv4T57+LswOQbg0PPPAAP/30EytXriQiIoJOnTqh0WjYtWsXOTk5vPrqq9ZBHqTr4/DhwwDs3LmTKVOmUFRUZC174403uP322/nuu+/w9vZ2VojV0rp161i5ciWzZ8+mQYMGzg7nlhUfH8+6desAuPvuu50czc3l9OnTgHkQKUcsyy3rSTfWu+++C0Dfvn3LXeshlc1kMvHoo49So0YN3nzzTWeHc0uwXJfUrFmTrl27smPHDpvy6dOns3jxYoYNG+aM8KoltVrNF198wV133cXw4cOZO3cukZGRnDt3jm3bttGuXTu++uortFqts0OVNaVS1VCr1SxfvpxnnnmG9PR01q1bx/fff8/Zs2dp0aIFXbp0cXaI1UphYSGFhYUATJo0ie7du3PgwAGys7P59ddfiYiI4LfffuOxxx5zcqTVS25uLuPGjaNhw4Y8++yzzg7nlmUwGBg9ejRFRUUMHz6c1q1bOzukm4pl+pGSmv17eHjYrCfdOD/99BOff/45Go2Gl19+2dnhVCsLFixg165dzJ07F39/f2eHc0vIyMgAYMmSJRw4cIDPP/+cixcvkpiYyNNPP01eXh73338/Bw4ccHKk1UvXrl3ZsmULERER7NmzhxUrVrB161Y8PDy4/fbbqVWrlrNDBGRNqVROQ4cO5dChQxXaZsmSJbRr1w4wfxHdc8897N69m3fffZchQ4bg7u7O1q1bmTBhAr169WLlypUMGjToBkR/c6rMMTcajdZloaGhrF271noX7Pbbb+eHH36gRYsWfPvtt7z88suyRu+yyp7nzz//PGfOnGHjxo2yRqOcKnvMHZkwYQLbtm2jXr16fPDBB5UN8ZYjLs8UpyhKqeXSjXX06FHuv/9+hBDMnTvX2rdUqrwzZ84wY8YMunfvzujRo50dzi3Dcm1iMBhYuHAhY8aMASAgIIB58+Zx+vRpVq1axZtvvsnXX3/tzFCrleXLl/PQQw/RoUMHvvnmG5o2bUpKSgpvvfUWr7zyChs3bmTLli1O71Mtk1KpXJKSkjh+/HiFtsnPz7f+e/LkyWzZsoV33nmHiRMnWpcPHDiQ0NBQ2rdvz1NPPcWAAQNwcZGnJVTumHt4eKBSqTCZTNx///12zTKaNWtGmzZt2LVrF1u2bJFJ6WWVOea7du1i4cKFPPDAA/Ts2fNGhFctVfa75WovvfQSH330ETVr1mTDhg2yD9418PLyAiAvL89hueX4e3p6VllMt5rk5GT69u1LRkYGTz/9NE899ZSzQ6pWxo0bR3Fxsez3X8Us3y0qlcrhOCJjxoxh1apVbN68uYojq75OnjxJbGwsNWvWZP369daWLg0aNODjjz/m3LlzrF27lsWLFzu99Zy8+pfKZc+ePde8rdFoZPny5cC/ozZeqU2bNkRERBAfH09CQgINGza85ueqTipzzAHq1q1LYmIidevWdVgeHh7Orl27uHDhQqWepzqpzDH/6aefMJlMHDx4kJiYGJsyyyTsloRp6NChjB8/vjKhVhuVPc+vtHDhQl588UV8fHz45ZdfqF+//nXb962kTp06gDkxcsSy3LKedH2lpaVxxx13cPr0aR566CHeeustZ4dU7axbt44aNWrwxBNP2Cy3dHs5ffq09Xt83bp18gbMdRIeHg6YB59z1JrIUi6vS66fb775Br1eT9++fa0J6ZXuvfde1q5dy+bNm2VSKlV/Fy5coLi4GKDEQXUsy9PT06ssruquZcuWJCYmlnhMLVMLyB/b6+vvv/8usezo0aMcPXqUFi1aVFk8t4qlS5cyYcIE3N3dWb9+vTzGlWBpJrp3716H5Zbl0dHRVRbTrSInJ4d+/fpx7NgxBg8ezKefflpiM2qpcjIzM9myZYvDsoKCAmuZwWCoyrCqNcsUUxkZGQgh7M5teV1y/VluIt4M199yoCPphvPz87M2H3VUK5KdnW1tvldSrZ5UcZYRRzdt2mRXlpOTY72wLGnaB6liZs2ahRDC4cPSTGn58uUIIXjnnXecG2w189NPPzF69Gg0Gg3ff/89nTt3dnZIN7XOnTvj4+NDfHw8+/btsytftWoVAAMGDKjq0Kq1oqIiBg4cyJ49e+jTpw/Lly9HrVY7O6xqqaTv6sTERAAaNWpkXVajRg3nBluNNGvWjIiICAoKCti5c6dduaXZrrwuuX4sU6KV1Cpp9+7dwL+11M4kk1LphtPpdPTt2xeAp59+mnPnzlnLCgsLGTduHPn5+XTu3JmQkBBnhVntjBgxgvDwcDZs2MCXX35pXW4wGHjqqafIyMjgtttukxfw0k3tzz//tHYLWLFiBb1793ZyRDc/rVZrbV4+fvx4m76l8+fP58CBA3Tp0oW2bds6K8Rqx2g0MnLkSDZt2kTXrl1ZvXr1f2KKBkm63iwj00+cOJG0tDTr8r/++ot58+YB8Pjjjzsltupo4MCBAGzdutWuD3VcXBxvv/024Lh7XVVThBxGT6oC8fHxdO7cmfPnz+Pl5UXHjh1xc3Nj9+7dpKSk4Ofnx5YtW7jtttucHWq1EhcXx+23305eXh6tWrUiPDycvXv3kpSUhL+/P5s2baJZs2bODrPaGz16NF9++SXLly9nxIgRzg6nWvH19SUzM7PUyb+7dOnCI488UsWR3dwKCwuJiYlh586dhISE0LVrV/755x927tyJv78/cXFxss/udfTuu+8yadIkAO65554Sm9q99dZbBAQEVGFkt5akpCQiIiJo1KiRdSwA6foymUyMGDGClStX4ufnR6dOncjNzWX79u0UFxfz6KOP8sknnzg7zGpl6tSp1r7pTZs2pUmTJqSkpLBjxw5MJhOPPfYYH3/8sZOjBIQkVZHU1FQxefJkERUVJVxdXYVOpxP169cXTz75pDhz5oyzw6u2Tpw4IUaNGiVq1qwpNBqNCAsLE48++qj4559/nB3aLSM2NlYAYvny5c4OpdoBynzExsY6O8ybUn5+vpg5c6aIjIwUWq1W1KxZU8TGxorTp087O7Rq58UXXyzXuZyYmOjsUKu1xMREAYhGjRo5O5RqzWg0ioULF4qWLVsKd3d34eHhITp16iSWLFni7NCqrdWrV4vevXsLf39/4eLiInx9fUWPHj3E0qVLnR2alawplSRJkiRJkiRJkpxG9imVJEmSJEmSJEmSnEYmpZIkSZIkSZIkSZLTyKRUkiRJkiRJkiRJchqZlEqSJEmSJEmSJElOI5NSSZIkSZIkSZIkyWlkUipJkiRJkiRJkiQ5jUxKJUmSJEmSJEmSJKeRSakkSZIkSZIkSZLkNDIplSRJkiRJkiRJkpxGJqWSJEmSJEmSJEmS08ikVJIkSZIkSZIkSXIamZRKkiRdpihKmY/Ro0c7O8xbRkxMDIqikJSUdMOfS1EUwsPDb/jz/JeFh4eXeu7fbMenKs8f6V89e/akbt26FBcXX9P2GRkZuLq6olKpOH36dJnrjxkzBkVR+L//+z8A9u3bh6IozJ0795qeX5Ik53BxdgCSJEn/NbGxsSWWdenSpQojka6HzZs306NHD2JjY/niiy+cHU65zZo1i9mzZ7N48eIqvRkyZMgQPD097ZYHBARUWQzSzWn9+vVs2rSJDz/8EK1We0378PX15c4772T16tUsW7aM5557rsR1CwsLWb16NQD3338/AC1btuTuu+/mtdde4+GHH8bPz++a4pAkqWrJpFSSJOkqN1PiIl0fR48eRaPRODuM/4S33nrrpqsVdWTJkiXk5+cTGhrq7FBuGc8//zxBQUGMGTOmUvt54IEHWL16NV9//XWpSenatWvJysqidevWNG7c2Lp8+vTp/Pjjj8yZM4c5c+ZUKhZJkqqGbL4rSZIk3fKioqKIjIx0dhjSdVSnTh2ioqLkzYYq8ueff3LgwAFGjBhxzbWkFv3798ff35/Dhw+zf//+EtdbunQpYE5ir9ShQwfq16/PokWLrrkZsSRJVUsmpZIkSZVg6WtnNBp58803adiwITqdjtq1a/Pss89SVFTkcLvc3FxeeuklmjVrhru7O97e3nTv3p01a9bYrZuUlISiKMTExJCdnc2UKVOIiIhAo9EwadIk63r79u2jX79++Pj44OPjQ58+fdi9ezdffPEFiqIwa9Ys67q33XYbiqJw4sQJh/ElJSWhUqlo0KABQogyj8OV/feWLVtGhw4d8PLyokaNGtZ1hBB8+eWXdOvWjRo1auDm5kZ0dDRvvfUWer2+zOew+OOPPxg/fjzR0dH4+vri5uZGVFQUzz33HJmZmTbrjh49mh49egDw5Zdf2vSRvPJ4XN1n8rvvvkNRFEaMGFFiHOPGjUNRFD799FOb5RV5b0sSHh7O7NmzAXjooYds4t68ebPNul999RVdunTB29sbd3d3oqOjef311yksLCz3812LpKQkxo4dS3h4ODqdjsDAQIYOHcqBAwfs1r3yHDxx4gQjRoygZs2aqFQq1qxZY3OO5+Xl8fTTT1O7dm3c3Nxo1aoVa9eute5r5cqVtGvXDg8PD2rWrMnEiRMpKCiwe85r7VO6bds27rnnHoKCgtDpdISHhzNx4kQuXrxot+7o0aOt78nWrVvp2bMnXl5eeHt7c+edd3LkyJESn2ft2rX06dMHf39/XF1dadiwITNnziQ3N7fU11LS5ys3N5dnnnnGetyaNGnCe++9hxDC7vyeO3euTT9MR3r06IGiKGzbtq1cx+2zzz4DYNSoUSWuc/DgQUaNGkVoaCg6nY5atWrx0EMP2b1HWq2We++9F4Cvv/7a4b7S09P5+eefUavVDj+nI0eOJC0tje+//75c8UuS5GRCkiRJEkIIAYiKfi0Com7dumL48OHCw8ND9OjRQwwYMED4+PgIQIwaNcpum9TUVNGkSRMBiNDQUHH33XeL22+/XXh4eAhAvP766zbrJyYmCkC0a9dOtGjRQvj6+opBgwaJwYMHi1mzZgkhhPjzzz+Fm5ubAETLli3FiBEjRHR0tNBqtWLs2LECEC+++KJ1n++9954AxNSpUx2+rhkzZghAvPHGG+U6Dt27dxeAeOyxx4RKpRJdu3YVI0aMEJ07dxZCCGE0GsWwYcMEILy9vUWvXr3EwIEDRXBwsABE//79hdFodLjPxMREm+Xt27cXOp1OtG7dWgwePFjceeedIiQkRACiadOmIicnx7rup59+Kvr06SMAERkZKWJjY62P77//3rqe5X20KCwsFD4+PsLNzc1mfxZ6vV4EBAQIrVYr0tPTrcsr+t6WZMqUKaJ58+YCEJ07d7aJ++jRo9b1HnvsMQEIV1dX0b9/fzF06FAREBAgANGxY0eRn59frucTQoi6des6PN6O/PHHH8Lb29t6zIcOHSo6duwoFEURbm5u4vfff7dZf/HixQIQI0aMEN7e3iIiIkIMHz5c9O7dW6xbt856jnfs2FG0b99eBAQEiAEDBoiYmBihUqmEWq0Wv/76q5g/f75wcXERHTt2FIMGDRL+/v4CEPfdd59djCWdP6V59913haIoQq1Wi44dO4qhQ4eKqKgoAYiIiAiRkpJis35sbKwAxNNPPy3UarVo3ry5GDJkiGjYsKEAhL+/vzh37pzd8zz99NPW961bt25i8ODB1uPfunVrkZub6/C1lPT5KigoEO3atROACAwMFEOHDhV9+/YVWq1WTJw40e78vnjxotDpdCIkJETo9Xq7+E6ePCkURRFRUVHlPnZBQUHCw8PD7nNssWrVKqHVaq2vcejQoaJly5bW43To0CGb9bdv3279HDna50cffSQA0a9fP4fPt3HjRgGIBx54oNyvQZIk55FJqSRJ0mXXmpQConHjxjYXvwkJCcLX11cA4tSpUzbb9OvXTwBi2rRpori42Lo8Pj5eREZGCrVaLfbv329dbrlgt1y0Z2Rk2OzPaDRaL4LffPNNm7KXXnrJuu2VSWlmZqZwd3cXQUFBNjEIIYTBYBChoaHCxcVFpKamlus4WC6aXV1dxebNm+3K58yZIwBxxx13iAsXLliX5+bmirvuuksA4v3333e4z6uTivXr19skgkKYk0hLgjZ79mybsk2bNglAxMbGlhj/1RftQggxZswYAYglS5bYrb9+/XoBiEGDBtksr+h7W5oXX3xRAGLx4sUOy1etWmW9aD958qR1eVZWlujSpUupNx0cKW9SmpWVJYKDg4VGoxErV660Kfv111+FVqsVoaGhoqioyLrckpQCYvz48cJgMNhsd+U5HhMTY/P+WratX7++8PPzE1u3brWWnT17VgQFBQlAxMfH2+yzoknpjh07hEqlEnXr1rV5j0wmk/VzNHToUJttLEmpSqUSy5Ytsy43GAxiyJAhAhAzZ8602WbFihXWm0dXxlZcXGw9h5955hmHr6Wkz9fLL79s/X7IysqyLt+/f7/1e+jq8/u+++4TgFizZo3d/p599lkBiHnz5pV8wK5w9OhRAYhu3bo5LE9ISBDu7u7Cx8dHbNmyxabsyy+/FIBo27at3Xb169cXgNi4caNdmeUcX7p0qcPnzM7OFiqVSoSHh5frNUiS5FwyKZUkSbrMclFc2uPK2rUrt/ntt9/s9jdhwgS7pGLfvn0CEJ06dRImk8lumzVr1ghATJgwwbrsygv23bt3223z66+/CkBERUXZ7dNgMIiIiAi7pFQIIR566CEBiFWrVtksX7t2rQDE4MGDSzpUdiwXzU8++aRdmaVW0cvLS1y8eNGuPDU1Veh0OtGsWTOH+yxvUpGfny9cXFxEq1atbJZfa1JqqWnp06eP3fqjRo0SgE1Sdi3vbWnKSkq7desmAPH555/blR04cEAoiiK8vLxsksPSWJLSkh779u0TQgjx9ttvC0BMnz7d4X4mTZokAPHdd99Zl1kSy8DAQJGXl2e3jeUcV6vVNgm2EOabLoGBgQIQL7zwgt22kydPdnicKnr+DBw4UABiw4YNdmUmk0m0bNlSqFQqm3PYkpTef//9dtv89ddfAhDdu3e3WW6pAT927JjdNgUFBSI4OFjUqFHDpnawtM+XEEKEhYUJQOzYscOu7IUXXnB4fm/ZskUAYsCAATbL9Xq9CA4OFlqt1uHn1RFLov3oo486LH/qqacEID7++GOH5YMGDRKA+Ouvv2yWz5o1SwBizJgxNsuTkpKEoijC09PT4flkERoaKgCbRF2SpP8m2adUkiTpKrGxsSU+6tSpY7e+RqMhJibGbnnDhg0BOHfunHXZr7/+CsDAgQNRFMVuG8uUM7t377YrCwkJoU2bNnbLt2/fDsDQoUPt9qlWqxk8eLDD1/n4448D2PWJtPz/0Ucfdbhdae6++267Zfv27SMtLY0uXbo4nFakZs2aNGjQgEOHDjnsG+jI2bNn+eijj5g0aRJjxoxh9OjRPPHEE2i1Wk6ePFnhuB2JiYkhNDSU3377jQsXLliX5+fn88MPP+Dt7c2AAQOsyyvz3laUXq8nLi4ORVG477777MqbNWtGdHQ0OTk5pQ4U48iQIUMcnvuWqTUsr3PQoEEOty/tdd5+++24u7uX+Nzh4eHUr1/fZplKpaJu3boA3HHHHXbbWAaouvJzVlEmk4mNGzfi5eVFr1697MoVRaFz586YTCb++usvu/LevXvbLXP0+b9w4QL79++ncePGNGrUyG4bV1dX2rRpQ2ZmpsPz2NHn6/Tp0yQnJxMWFkaHDh3syocNG2a3DKBbt240adKEn3/+mbNnz1qXr127ltTUVO65555yTwNk+Xz4+vo6LL/ys+FISeeMZQCj7777zqaP9NKlSxFCMHjw4FLPJ8s566g/sCRJ/y1yShhJkqSrVHRKmJCQENRqtd1yy1yPVw52ZBnQ49lnn+XZZ58tcZ9paWl2yxwlxAApKSkA1K5d22F5Sdu1a9eOli1b8uuvv/LPP/9Qt25dzp07x08//USdOnUcXmiXxdFzWV7zzz//7DBZu1J6enqZU3jMnz+f6dOn3/BRNVUqFSNGjGDevHmsWLGCCRMmAPDjjz+Sm5vLQw89hKurq3X9yry3FXXp0iWKi4sJDg62ieFK4eHh7N+/33p+lFdZU8JYXmf79u1L3U9FzmGLkt57Dw+PEsstZSUNKlYely5dsg4w5OJS+qWRo9cVFhZmt8zR5/+ff/4BzFMQlfVZSEtLs0tcHR2/a/38Azz22GNMmjSJRYsWMXPmTODabkplZWUB4OXl5bDccs4EBweXup+rj229evXo1KkT27dvZ926dQwdOhQoedTdq3l7e9vEJ0nSf5dMSiVJkiqprIvLKxmNRgC6du1KvXr1SlzPUQ1FSclHWXGIUkbPHTt2LI8//jiLFi1i9uzZLF68GIPBwMMPP4xKVfHGNI5itLzmBg0a0KlTp1K31+l0pZbHxcUxZcoUfHx8+OSTT4iJiSE4ONi6Xa1atSpVY3a1UaNGMW/ePJYtW2ZNSpctW2Ytu1Jl3ttrVZ5zryLnZ3lYXuewYcNKraVylLRe6zlc3vJrZXlNXl5eJbYssLDU2l5LXJbnCQkJKfOmj7+/v92y0o7ftRyb2NhYpk+fzqJFi5gxYwbJycls2LCBevXq0bNnz3Lvx8fHB4Ds7GyH5UajEUVRePDBB0vdT9OmTe2WPfDAA2zfvp2vv/6aoUOHsm/fPo4cOUKtWrXKjNGSjFrikyTpv0smpZIkSVXIUqMydOhQJk6ceF32GRISApib8Tly5syZErcdNWoUU6dOtdaUfP7556hUKsaMGXNdYoN/X/Ntt91W4Vroq1mmd3jllVeIjY21KSsoKCA1NbVS+79ay5Ytady4MXFxcSQkJODr68uGDRsICQmxTjVjcSPe25L4+/uj1WpJTU2loKAANzc3u3UstXKW8+N6CQsL4/jx48yYMYPo6Ojrum9nCQgIQKfTodFoKn2OlsZyjgQHB1+35ynr81/ScoAaNWowfPhwvvjiC3799Vd27NiByWTikUceqVCSGxQUBJhbOjgSFhZGfHw87733nrX2sryGDx/OU089xc8//0xGRoa1lvS+++4r88ZZRkYGAIGBgRV6TkmSqp7sUypJklSFbr/9doAKzVlZFkvt43fffWdXK2oymUqdp8/T05P77ruP5ORkpk6dSkJCAv369XPYHPFatW3bFh8fHzZt2lRiTUp5WS4yHTVVXLlypcNaYa1WC4DBYLim57TUiC5btoyVK1dSXFzMyJEj7S6Ir/d7W1rcGo2GDh06IIRg+fLlduWHDh1i//79eHl50bx58+sSj8WNOIedzcXFhZiYGNLT09m6desNe56wsDAaNWrEgQMHSExMvC77rFu3LrVq1SI5OZmdO3fala9atarU7ceOHQvAxx9/zKJFi3BxcWH06NEVisFyjh07dsxheWXOGV9fX+68806Ki4v55ptvrOf7/fffX+p22dnZpKSkEBERUeFEWJKkqieTUkmSpCrUoUMHevXqxaZNm5g8ebK1H5uFyWTif//7X7knrAfo2bMn9evX5+jRo7z99ts2ZW+88QYJCQmlbm8Z8Oidd94Brm2Ao9LodDqeeeYZMjMzGTJkiLUG70oHDhxgxYoVZe7LMnjM559/jl6vty4/cuRIif04a9WqBcDx48evJXzrQEJLly4tsekuXP/3tqy4Lc2JX3zxRZv3OCcnh/HjxyOEYOzYsdbk9noZO3YsgYGBvPbaayxevNjuRkBeXh5LliwhOTn5uj7vjfb888+jUqmIjY11+B6lpKSwcOHCSj/PjBkzMBqNDBkyhEOHDtmVx8fHs2jRogrt05JYTpkyhZycHOvyQ4cOsWDBglK37dChA82bN2f16tWcPn2aAQMGVLh2vVGjRgQFBbF3716HN1GmTJmCm5sbkydPZu3atXbl6enpfPDBByUOdGbpOzpz5kxSUlJo1qxZmTdbdu/ejRCCrl27Vui1SJLkJM4b+FeSJOm/hctTX8TGxpb4uHrOQRxMtWBhmQbj6qlYUlNTRXR0tACEn5+f6Nmzpxg+fLjo0qWLdeqLt99+27q+ZbqMq6eWuNIff/whXF1dBSBatWolRo4cKZo3by60Wq149NFHBSBeffXVErdv166dAERISIjQ6/VlHSo7ZU2/YTQaxciRIwUgdDqd6Nixoxg+fLjo1auXdcqagQMHlrnPtLQ0ERwcLAAREREh7r33XnH77bcLjUYjhg0bZp3W5GqW4922bVsxevRo8fDDD4sffvjBWl7a+yiEEJ06dbKeH1FRUSWuV9H3tjRnz54Vrq6uQq1Wi759+4oxY8aIhx9+2GYqEcu8lm5ubuLOO+8Uw4YNsz5Phw4dSp0u42rlnadUCCG2bdsm/Pz8rMftzjvvFIMHDxZt2rQRHh4eNlPICFHyZ8GirHO8tPOrpH1XdEoYIYRYsGCBUKvVAhDR0dFiyJAh4s477xS33XabUKvVwsfHx2Z9y5QwmzZtcri/ks6radOmWafAadOmjRg2bJjo06ePiIqKEoBo3rx5hV5Lfn6+aNOmjXXanWHDhol+/foJnU4nxo8fLwDRoEGDEl/3Bx98YD2/169fX8oRKtno0aMFILZt2+aw/LvvvhNubm4CEI0aNRKDBg0SAwcOFC1atBBarVYAdnMwWxQVFVnPN0DMmTOnzHhmzJghAPHNN99c0+uRJKlqyaRUkiTpMssFT2mPqy8WryUpFcJ8ETl//nzRvn174eXlJXQ6nQgPDxe9e/cWCxcutJkfsDxJqRBC7NmzR/Tp00d4eXkJLy8v0atXL7Fjxw7xyiuvCEB89NFHJW47ffp0AYjnn3++1OcoSXkTgFWrVom+ffuKgIAAodFoREhIiOjQoYOYNWuW3byNJe3zzJkz4r777hOhoaHC1dVVNG7cWLz++uvCYDCUmJSePHlSDBo0SPj7+wuVSmX3vpSVlC5cuNB6Drz00kulvsaKvLdl2bBhg+jcubPw9PS0Pv/VCdCSJUtEp06dhKenp3B1dRVNmzYVr776qsjPzy/38whRsaRUCHPSPGXKFBEVFSXc3NyEp6enaNiwoRg+fLhYsWKFzfyoN0tSKoT5czRq1ChRu3ZtodFohJ+fn4iOjhZPPvmk2Lx5s82615qUCmGeB/eee+4RwcHBQqPRiKCgINGqVSsxdepUu/k6y/NasrKyxOTJk0VoaKjQarWiUaNGYt68eeLMmTPWmxQlOX78uABEWFiYMBgMJa5Xmj///FMAYty4cSWuc+LECTF27FhRr149odPphI+Pj2jcuLF46KGHxLp16xzO72vx+OOPC0CoVCpx5syZMuOJjIwUAQEB5Z6nV5Ik51KEKGVYRkmSJOmm169fP3755Rfi4uIcjogqhCAqKoqTJ09y6tSpUkeOlSTp5rJixQpGjBjB448/zocffuhwnddee43/+7//48UXX2TWrFnX/FwtW7YkOTmZ5OTkMkfSvpF27NhBp06dmDZtGnPmzHFaHJIklZ/sUypJklQNpKen2/XVFEKwYMECfvnlF+rXr0+7du0cbrtq1SpOnDhB//79ZUIqSTepv//+G5PJZLPs4MGDTJs2Dfi3b/TVsrOzef/999FqtTz22GOViuHVV18lLS2Nzz//vFL7qaw33niDGjVqWF+7JEn/fbKmVJIkqRqIi4ujU6dOREdHU69ePYxGI4cOHSIhIQE3Nzd++uknYmJibLZ55JFHyMzMZN26dRgMBnbt2kWrVq2c8wIkSaqUqKgosrOzadasGb6+viQlJbFnzx6MRqPDWtLFixezZcsWtm7dSmJiIpMnT2b+/PmVjqNnz56cOnWKU6dOXfdBtspj3759tGrVijlz5sikVJJuIjIplSRJqgYuXLjArFmz2LRpEykpKRQUFBAUFET37t157rnnaNasmd02iqLg4uJCw4YNefnllxk8eLATIpck6XpYuHAh33zzDSdOnCAjIwN3d3eio6N5+OGH7eb0BRg9ejRffvklgYGBjBgxgrlz5zq1ya0kSbc2mZRKkiRJkiRJkiRJTiP7lEqSJEmSJEmSJElOI5NSSZIkSZIkSZIkyWlkUipJkiRJkiRJkiQ5jUxKJUmSJEmSJEmSJKeRSakkSZIkSZIkSZLkNDIplSRJkiRJkiRJkpxGJqWSJEmSJEmSJEmS08ikVJIkSZIkSZIkSXIamZRKkiRJkiRJkiRJTiOTUkmSJEmSJEmSJMlpZFIqSZIkSZIkSZIkOY1MSiVJkiRJkiRJkiSnkUmpJEmSJEmSJEmS5DT/D/U0LNbB8mAeAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1050x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1,dpi=150,figsize=(7,4))\n",
+    "\n",
+    "ax.plot(vasprun.tdos.energies - vasprun.efermi , vasprun.tdos.densities[Spin.up], label=r'total DOS', lw = 2) \n",
+    "ax.plot(vasprun.tdos.energies - vasprun.efermi , Ni_spd_dos[OrbitalType.d].densities[Spin.up], label=r'Ni-d', lw = 2) \n",
+    "ax.plot(vasprun.tdos.energies - vasprun.efermi , O_spd_dos[OrbitalType.p].densities[Spin.up], label=r'O-p', lw = 2)\n",
+    "ax.plot(vasprun.tdos.energies - vasprun.efermi , Nd_spd_dos[OrbitalType.d].densities[Spin.up], label=r'Nd-d', lw = 2)\n",
+    "\n",
+    "ax.axvline(0, c='k', lw=1)\n",
+    "ax.set_xlabel('Energy relative to Fermi energy (eV)')\n",
+    "ax.set_ylabel('DOS (1/eV)')\n",
+    "ax.set_xlim(-9,8.5)\n",
+    "ax.set_ylim(0,20)\n",
+    "ax.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d42627e-84c4-4386-92bd-f1193e9fd8fd",
+   "metadata": {},
+   "source": [
+    "We see that the Ni-$d$ states are entangled / hybridizing with O-$p$ states and Nd-$d$ states in the energy range between -9 and 9 eV. Hence, we orthonormalize our projectors considering all states in this energy window to create well localized real-space states. These projectors will be indeed quite similar to the internal DFT+$U$ projectors used in VASP due to the large energy window. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "19285c12-c23a-4739-b5b1-56aa724bfb7f",
+   "metadata": {},
+   "source": [
+    "## 2. Creating the hdf5 archive / DMFT input\n",
+    "\n",
+    "Next we run the [Vasp converter](https://triqs.github.io/dft_tools/unstable/guide/conv_vasp.html) to create an input h5 archive for solid_dmft. The [plo.cfg](plo.cfg) looks like this: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "825c6168-97a7-4d2d-9699-b1d1e9af95dd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[General]\n",
+      "BASENAME = nno\n",
+      "\n",
+      "[Group 1]\n",
+      "SHELLS = 1\n",
+      "NORMALIZE = True\n",
+      "NORMION = False\n",
+      "EWINDOW = -10 10\n",
+      "\n",
+      "[Shell 1]\n",
+      "LSHELL = 2\n",
+      "IONS = 3 4\n",
+      "TRANSFORM = 0.0  0.0  0.0  0.0  1.0\n",
+      "            0.0  1.0  0.0  0.0  0.0\n",
+      "            0.0  0.0  1.0  0.0  0.0\n",
+      "            0.0  0.0  0.0  1.0  0.0\n",
+      "            1.0  0.0  0.0  0.0  0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat plo.cfg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2c3f2892-bb0a-4b8d-99af-76cff53b194b",
+   "metadata": {},
+   "source": [
+    "we create $d$ like projectors within a large energy window from -10 to 10 eV for very localized states for both Ni sites. Important: the sites are markes as non equivalent, so that we can later have different spin orientations on them. The flag `TRANSFORM` swaps the $d_{xy}$ and $d_{x^2 - y^2}$ orbitals, since the orientation in the unit cell of the oxygen bonds is rotated by 45 degreee. Vasp always performs projections in a global cartesian coordinate frame, so one has to rotate the orbitals manually with the octahedra orientation. \n",
+    "\n",
+    "Let's run the converter:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "8c687309-93f0-48b0-8862-85eca6c572e5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Read parameters: LOCPROJ\n",
+      "0  ->  {'label': 'dxy', 'isite': 3, 'l': 2, 'm': 0}\n",
+      "1  ->  {'label': 'dyz', 'isite': 3, 'l': 2, 'm': 1}\n",
+      "2  ->  {'label': 'dz2', 'isite': 3, 'l': 2, 'm': 2}\n",
+      "3  ->  {'label': 'dxz', 'isite': 3, 'l': 2, 'm': 3}\n",
+      "4  ->  {'label': 'dx2-y2', 'isite': 3, 'l': 2, 'm': 4}\n",
+      "5  ->  {'label': 'dxy', 'isite': 4, 'l': 2, 'm': 0}\n",
+      "6  ->  {'label': 'dyz', 'isite': 4, 'l': 2, 'm': 1}\n",
+      "7  ->  {'label': 'dz2', 'isite': 4, 'l': 2, 'm': 2}\n",
+      "8  ->  {'label': 'dxz', 'isite': 4, 'l': 2, 'm': 3}\n",
+      "9  ->  {'label': 'dx2-y2', 'isite': 4, 'l': 2, 'm': 4}\n",
+      "  Found POSCAR, title line: NdNiO2 SC\n",
+      "  Total number of ions: 8\n",
+      "  Number of types: 3\n",
+      "  Number of ions for each type: [2, 2, 4]\n",
+      "\n",
+      "    Total number of k-points: 405\n",
+      "  No tetrahedron data found in IBZKPT. Skipping...\n",
+      "[WARNING]: Error reading from EIGENVAL, trying LOCPROJ...\n",
+      "[WARNING]: Error reading Efermi from DOSCAR, trying LOCPROJ...\n",
+      "eigvals from LOCPROJ\n",
+      "\n",
+      "  Unorthonormalized density matrices and overlaps:\n",
+      "  Spin: 1\n",
+      "  Site: 3\n",
+      "  Density matrix                                                  Overlap\n",
+      "   1.1544881   0.0000000  -0.0000000   0.0000000  -0.0000000       0.9626619  -0.0000000   0.0000000   0.0000002  -0.0000000\n",
+      "   0.0000000   1.7591058  -0.0000000   0.0000000  -0.0000000      -0.0000000   0.9464342  -0.0000000   0.0000000  -0.0000000\n",
+      "  -0.0000000  -0.0000000   1.5114185   0.0000000  -0.0000000       0.0000000  -0.0000000   0.9548582  -0.0000000   0.0000000\n",
+      "   0.0000000   0.0000000   0.0000000   1.7591058  -0.0000000       0.0000002   0.0000000  -0.0000000   0.9464339   0.0000000\n",
+      "  -0.0000000  -0.0000000  -0.0000000  -0.0000000   1.8114830      -0.0000000  -0.0000000   0.0000000   0.0000000   0.9495307\n",
+      "  Site: 4\n",
+      "  Density matrix                                                  Overlap\n",
+      "   1.1544881  -0.0000000   0.0000000   0.0000000   0.0000000       0.9626621   0.0000000  -0.0000000  -0.0000001  -0.0000000\n",
+      "  -0.0000000   1.7591058  -0.0000000  -0.0000000   0.0000000       0.0000000   0.9464343  -0.0000000  -0.0000000   0.0000000\n",
+      "   0.0000000  -0.0000000   1.5114185  -0.0000000  -0.0000000      -0.0000000  -0.0000000   0.9548582   0.0000000   0.0000000\n",
+      "   0.0000000  -0.0000000  -0.0000000   1.7591058   0.0000000      -0.0000001  -0.0000000   0.0000000   0.9464344   0.0000000\n",
+      "   0.0000000   0.0000000  -0.0000000   0.0000000   1.8114830      -0.0000000   0.0000000   0.0000000   0.0000000   0.9495307\n",
+      "\n",
+      "  Generating 1 shell...\n",
+      "\n",
+      "    Shell         : 1\n",
+      "    Orbital l     : 2\n",
+      "    Number of ions: 2\n",
+      "    Dimension     : 5\n",
+      "    Correlated    : True\n",
+      "    Ion sort      : [3, 4]\n",
+      "Density matrix:\n",
+      "  Shell 1\n",
+      "Site diag : True\n",
+      "    Site 1\n",
+      "     1.9468082    -0.0000000    -0.0000000     0.0000000    -0.0000000\n",
+      "    -0.0000000     1.8880488    -0.0000000     0.0000000     0.0000000\n",
+      "    -0.0000000    -0.0000000     1.5912192     0.0000000     0.0000000\n",
+      "     0.0000000     0.0000000     0.0000000     1.8880488     0.0000000\n",
+      "    -0.0000000     0.0000000     0.0000000     0.0000000     1.1979419\n",
+      "      trace:  8.512066911392091\n",
+      "    Site 2\n",
+      "     1.9468082     0.0000000    -0.0000000    -0.0000000    -0.0000000\n",
+      "     0.0000000     1.8880488    -0.0000000    -0.0000000    -0.0000000\n",
+      "    -0.0000000    -0.0000000     1.5912192    -0.0000000    -0.0000000\n",
+      "    -0.0000000    -0.0000000    -0.0000000     1.8880488    -0.0000000\n",
+      "    -0.0000000    -0.0000000    -0.0000000    -0.0000000     1.1979419\n",
+      "      trace:  8.512066911289741\n",
+      "\n",
+      "  Impurity density: 17.024133822681833\n",
+      "\n",
+      "Overlap:\n",
+      "  Site 1\n",
+      "[[ 1. -0. -0. -0. -0.]\n",
+      " [-0.  1. -0. -0. -0.]\n",
+      " [-0. -0.  1. -0. -0.]\n",
+      " [-0. -0. -0.  1.  0.]\n",
+      " [-0. -0. -0.  0.  1.]]\n",
+      "\n",
+      "Local Hamiltonian:\n",
+      "  Shell 1\n",
+      "    Site 1 (real | complex part)\n",
+      "    -1.5179223     0.0000000     0.0000000    -0.0000000     0.0000000 |    -0.0000000    -0.0000000    -0.0000000    -0.0000000    -0.0000000\n",
+      "     0.0000000    -1.2888643     0.0000000    -0.0000000    -0.0000000 |     0.0000000     0.0000000    -0.0000000    -0.0000000    -0.0000000\n",
+      "     0.0000000     0.0000000    -0.9927644    -0.0000000    -0.0000000 |     0.0000000     0.0000000    -0.0000000     0.0000000     0.0000000\n",
+      "    -0.0000000    -0.0000000    -0.0000000    -1.2888643     0.0000000 |     0.0000000     0.0000000    -0.0000000     0.0000000     0.0000000\n",
+      "     0.0000000    -0.0000000    -0.0000000     0.0000000    -1.0828254 |     0.0000000     0.0000000    -0.0000000    -0.0000000     0.0000000\n",
+      "    Site 2 (real | complex part)\n",
+      "    -1.5179223    -0.0000000    -0.0000000    -0.0000000    -0.0000000 |     0.0000000     0.0000000    -0.0000000    -0.0000000    -0.0000000\n",
+      "    -0.0000000    -1.2888643     0.0000000    -0.0000000     0.0000000 |    -0.0000000    -0.0000000     0.0000000     0.0000000    -0.0000000\n",
+      "    -0.0000000     0.0000000    -0.9927644     0.0000000     0.0000000 |     0.0000000    -0.0000000     0.0000000    -0.0000000    -0.0000000\n",
+      "    -0.0000000    -0.0000000     0.0000000    -1.2888643     0.0000000 |     0.0000000    -0.0000000     0.0000000    -0.0000000     0.0000000\n",
+      "    -0.0000000     0.0000000     0.0000000     0.0000000    -1.0828254 |     0.0000000     0.0000000     0.0000000    -0.0000000     0.0000000\n",
+      "  Storing ctrl-file...\n",
+      "  Storing PLO-group file 'nno.pg1'...\n",
+      "  Density within window: 42.00000000005771\n",
+      "Reading input from nno.ctrl...\n",
+      "{\n",
+      "    \"ngroups\": 1,\n",
+      "    \"nk\": 405,\n",
+      "    \"ns\": 1,\n",
+      "    \"kvec1\": [\n",
+      "        0.1803844533789928,\n",
+      "        0.0,\n",
+      "        0.0\n",
+      "    ],\n",
+      "    \"kvec2\": [\n",
+      "        0.0,\n",
+      "        0.1803844533789928,\n",
+      "        0.0\n",
+      "    ],\n",
+      "    \"kvec3\": [\n",
+      "        0.0,\n",
+      "        0.0,\n",
+      "        0.30211493941280826\n",
+      "    ],\n",
+      "    \"nc_flag\": 0\n",
+      "}\n",
+      "\n",
+      "  No. of inequivalent shells: 2\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Generate and store PLOs\n",
+    "plo_converter.generate_and_output_as_text('plo.cfg', vasp_dir=path)\n",
+    "\n",
+    "# run the archive creat routine\n",
+    "conv = VaspConverter('nno')\n",
+    "conv.convert_dft_input()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bee3bf4f-0b75-445c-b3d3-7402f778fff4",
+   "metadata": {},
+   "source": [
+    "We can here cross check the quality of our projectors by making sure that there are not imaginary elements in both the local Hamiltonian and the density matrix. Furthermore, we see that the occupation of the Ni-$d$ shell is roughly 8.5 electrons which is a bit different from the nominal charge of $d^9$ for the system due to the large hybridization with the other states. For mor physical insights into the systems and a discussion on the appropriate choice of projectors see this research article [PRB 103 195101 2021](https://doi.org/10.1103/PhysRevB.103.195101)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18739e80-3c9e-4bea-9e0b-677421ec99aa",
+   "metadata": {},
+   "source": [
+    "## 3. Running the AFM calculation\n",
+    "\n",
+    "now we run the calculation at around 290 K, which should be below the ordering temperature of NdNiO2 in DMFT. The config file [config.toml](config.toml) for solid_dmft looks like this: \n",
+    "\n",
+    "    [general]\n",
+    "    seedname = \"nno\"\n",
+    "    jobname = \"NNO_lowT\"\n",
+    "\n",
+    "    enforce_off_diag = false\n",
+    "    block_threshold = 0.001\n",
+    "\n",
+    "    \n",
+    "    n_iw = 2001\n",
+    "    n_tau = 20001\n",
+    "\n",
+    "    prec_mu = 0.001\n",
+    "\n",
+    "    h_int_type = \"density_density\"\n",
+    "    U = 8.0\n",
+    "    J = 1.0\n",
+    "\n",
+    "    # temperature ~290 K\n",
+    "    beta = 40\n",
+    "\n",
+    "    magnetic = true\n",
+    "    magmom = -0.3, 0.3\n",
+    "    afm_order = true\n",
+    "\n",
+    "    n_iter_dmft = 14\n",
+    "\n",
+    "    g0_mix = 0.9\n",
+    "\n",
+    "    dc_type = 0\n",
+    "    dc = true\n",
+    "    dc_dmft = false\n",
+    "\n",
+    "    [solver]\n",
+    "    type = \"cthyb\"\n",
+    "    length_cycle = 2000\n",
+    "    n_warmup_cycles = 5e+3\n",
+    "    n_cycles_tot = 1e+7\n",
+    "    imag_threshold = 1e-5\n",
+    "\n",
+    "    perform_tail_fit = true\n",
+    "    fit_max_moment = 6\n",
+    "    fit_min_w = 10\n",
+    "    fit_max_w = 16\n",
+    "    measure_density_matrix = true"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "26910f2d-fd3d-4d72-adc5-99e79f72452d",
+   "metadata": {},
+   "source": [
+    "Let's go through some special options we set in the config file: \n",
+    "\n",
+    "* we changed `n_iw=2000` because the large energy window of the calculation requires more Matsubara frequencies\n",
+    "* `h_int_type` is set to `density_density` to reduce complexity of the problem\n",
+    "* `beta=40` here we set the temperature to ~290K\n",
+    "* `magnetic=true` lift spin degeneracy\n",
+    "* `magmom` here we specify the magnetic order. Here, we say that both Ni sites have the same spin, which should average to 0 at this high temperature. The magnetic moment is specified as an potential in eV splitting up / down channel of the initial self-energy\n",
+    "* `afm_order=true` tells solid_dmft to not solve impurities with the same `magmom` but rather copy the self-energy and if necessary flip the spin accordingly\n",
+    "* `length_cycle=2000` is the length between two Green's function measurements in cthyb. This number has to be choosen carefully to give an autocorrelation time ~1 for all orbitals\n",
+    "*  `perform_tail_fit=true` : here we use tail fitting to get good high frequency self-energy behavior\n",
+    "* `measure_density_matrix = true ` measures the impurity many-body density matrix in the Fock basis for a multiplet analysis\n",
+    "\n",
+    "By setting the flag magmom to a small value with a flipped sign on both sites we tell solid_dmft that both sites are related by flipping the down and up channel. Now we run solid_dmft simply by executing `mpirun solid_dmft config.toml`. \n",
+    "\n",
+    "Caution: this is a very heavy job, which should be submitted on a cluster. \n",
+    "\n",
+    "In the beginning of the calculation we find the following lines:\n",
+    "\n",
+    "    AFM calculation selected, mapping self energies as follows:\n",
+    "    imp  [copy sigma, source imp, switch up/down]\n",
+    "    ---------------------------------------------\n",
+    "    0: [False, 0, False]\n",
+    "    1: [True, 0, True]\n",
+    "\n",
+    "this tells us that solid_dmft detected correctly how we want to orientate the spin moments. This also reflects itself during the iterations when the second impurity problem is not solved, but instead all properties of the first impurity are copied and the spin channels are flipped: \n",
+    "\n",
+    "    ...\n",
+    "    copying the self-energy for shell 1 from shell 0\n",
+    "    inverting spin channels: False\n",
+    "    ...\n",
+    "\n",
+    "After the calculation is running or is finished we can take a look at the results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "f42d62cc-f8b4-4fc7-af76-cdf7ba13e8ea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with HDFArchive(path+'/nno.h5','r') as ar:\n",
+    "    Sigma_iw = ar['DMFT_results/last_iter/Sigma_freq_0']\n",
+    "    obs = ar['DMFT_results/observables']\n",
+    "    conv_obs = ar['DMFT_results/convergence_obs']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "65dba97b-a64c-4d88-b7cc-3607605a9aa3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1050x1200 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(nrows=4, dpi=150, figsize=(7,8), sharex=True)\n",
+    "fig.subplots_adjust(hspace=0.1)\n",
+    "# imp occupation\n",
+    "ax[0].plot(obs['iteration'], np.array(obs['imp_occ'][0]['up'])+np.array(obs['imp_occ'][0]['down']), '-o', label=r'Ni$_0$')\n",
+    "ax[0].plot(obs['iteration'], np.array(obs['imp_occ'][1]['up'])+np.array(obs['imp_occ'][1]['down']), '-o', label=r'Ni$_1$')\n",
+    "\n",
+    "# imp magnetization\n",
+    "ax[1].plot(obs['iteration'], (np.array(obs['imp_occ'][0]['up'])-np.array(obs['imp_occ'][0]['down'])), '-o', label=r'Ni$_0$')\n",
+    "ax[1].plot(obs['iteration'], (np.array(obs['imp_occ'][1]['up'])-np.array(obs['imp_occ'][1]['down'])), '-o', label=r'Ni$_1$')\n",
+    "\n",
+    "# dxy, dyz, dz2, dxz, dx2-y2 orbital magnetization\n",
+    "ax[2].plot(obs['iteration'], abs(np.array(obs['orb_occ'][0]['up'])[:,4]-np.array(obs['orb_occ'][0]['down'])[:,4]), '-o', label=r'$d_{x^2-y^2}$')\n",
+    "ax[2].plot(obs['iteration'], abs(np.array(obs['orb_occ'][0]['up'])[:,2]-np.array(obs['orb_occ'][0]['down'])[:,2]), '-o', label=r'$d_{z^2}$')\n",
+    "ax[2].plot(obs['iteration'], abs(np.array(obs['orb_occ'][0]['up'])[:,0]-np.array(obs['orb_occ'][0]['down'])[:,0]), '-o', label=r'$d_{xy}$')\n",
+    "ax[2].plot(obs['iteration'], abs(np.array(obs['orb_occ'][0]['up'])[:,1]-np.array(obs['orb_occ'][0]['down'])[:,1]), '-o', label=r'$d_{yz/xz}$')\n",
+    "\n",
+    "ax[3].semilogy(conv_obs['d_Gimp'][0], '-o')\n",
+    "\n",
+    "ax[0].set_ylabel('Imp. occupation')\n",
+    "ax[1].set_ylabel(r'magnetization $\\mu_B$')\n",
+    "ax[2].set_ylabel(r'magnetization $\\mu_B$')\n",
+    "ax[-1].set_xticks(range(0,len(obs['iteration'])))\n",
+    "ax[-1].set_xlabel('Iterations')\n",
+    "ax[0].set_ylim(8.4,8.6)\n",
+    "ax[0].legend();ax[1].legend();ax[2].legend()\n",
+    "\n",
+    "ax[3].set_ylabel(r'|G$_{imp}$-G$_{loc}$|')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5d0d0238-d573-4e18-9785-79408d6ac73d",
+   "metadata": {},
+   "source": [
+    "Let's take a look at the self-energy of the two Ni $e_g$ orbitals:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "daf0c1d8-a1fe-413d-a7b2-2eed78258e9f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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yJVqtTLwKIYQQQpgynU7H3/72N6qqqpg7dy6+vr76r5deeqm3wxPdSGbmTMGVPXMAg32c2H++CIC6Ri0ZxdUM8nTorciEEEIIIUQ3U6lUlJeX93YYohdIMmcKdM3J3BC/5mQOICm3QpI5IYQQQohuNnDgQKQUhehpsszSFFzYrf82vEU1S4DkHPmURgghhBBCCFMkyZwpqGluShnu3XoW7qQUQRFCCCGEEMIkSTJnCq4UQAEI9XJA1eLU6ZxyKYIihBBCCCGECZJkzhS0WJ9tZ2VBgKut/nF1vYZLJTW9EZUQQgghhBCiG0kyZwpazMwBRPg4tXp8KrusB4MRQgghhBBC9ARJ5kzBVcncYN/WRVBOZ0sRFCGEEEIIIUyNJHOmwCCZaz0zd1oqWgohhBBCCGFyJJkzBVf1NJkQ6sFrt8XqHydJERQhhBBCCCFMjiRzJqH1zJyzrSW3RPugulLWsrpew4Wi6l6ISwghhBBCCNFdJJkzBTrDWTd7awtCPZt7ziXJUkshhBBCCCFMiiRzpsBIMgcQE+Cs//6UFEERQgghhBDCpEgyZwqsHY0ejvZrLoRyOqesh4IRQgghhBBC9ARJ5lo4ePAgc+fOxc3NDQcHB0aPHs1HH33U6fscO3aMv/zlL0yaNAk/Pz+sra0ZMGAAK1as4NSpU10fuN8wg0PJueW8vu2c/nFSTjkaKYIihBBCCCGEyZBk7ooNGzYQHx/P1q1bGTp0KHPmzOHcuXOsXLmSJ554osP3aWxsZOTIkTz33HOkpKQwbNgwFi5ciLW1NZ9++ikjR45k3bp1XRu8TmNwyN/Flqq6Rv3j2gYtFwqruvZ5hRBCCCFEr1q+fDkqlYojR470diiiF0gyB5SWlnLvvfei0WhYt24du3fvZt26daSkpBAaGsobb7zBrl27Ony/MWPGsHnzZvLz8/n2229Zu3YtaWlpPP300zQ0NHDfffdRVFTUdS9AqzU45GJnha+zTatjJ2XfnBBCCCGESTl+/Djm5ubExMT0diiiF0gyB6xevZry8nIWLVrEkiVL9Me9vb155ZVXAHj99dc7dC8LCwsSEhKYN28eZmbN//eamZnx17/+lcjISCorK/n222+77gUYmZkDiPRpvZfuZFZZ1z2nEEIIIYToVWVlZaSnpxMZGYmtrW1vhyN6gSRzwObNmwFYtmyZwbl58+ZhY2PD9u3bUavVN/Q8KpVK/6lJbm7uDd2rldpy0DQaHI70dWr1OFGSOSGEEEIIk3H8+HEAhg0zrJ8gbg6SzIG+KMnw4cMNzllZWREdHY1arSY1NfWGn+vChQsA+Pj43PC99ArPQF2FweHBVyVzZ/MqUDcYn8UTQgghhBCK3bt3o1KpWLlypdHzK1euRKVSsXv3bv2xjIwMVCoVU6ZMoaKigl//+tcMGDAAGxsbBg8ezBtvvIHWyNaYjti6dSszZszAyckJd3d3Vq5cSWlpKceOHQOMv4cVNweL3g6gt1VUVFBWVgZAQECA0TEBAQEcPXqUzMxMYmNjr/u59u/fz7Fjx7CysmLOnDkdvi4qKsro8fT0dEKa8jWtYZI2+Kpllo1aHcm5FYwIcu3wcwshhBBCiI6rq6tj2rRppKenM23aNOrr69mxYwdPPPEEp06d4oMPPujU/f785z/z/PPPY21tzdSpU7G0tGTDhg0kJSUxcOBAoOMzc2lpaTzwwAMUFRVhbm7OL37xCx555JHOvsQb1lfiMAU3fTJXVdVc4dHOzs7oGHt7e4OxnVVRUcF9990HwOOPP46vr+9138soreEyy2APe6wszKhvbP4UKDGrTJI5IYQQQohukpCQwNChQzl37hweHh6A8gF8fHw8H374IbfeeisLFy7s0L0++eQTnn/+eUaMGMHXX3+tn3i4ePEiI0eO1C+zjIuL69D9rK2t+de//kV0dDRVVVWMGDGCKVOmtDlx0F36ShymwCSSuWXLlpGUlNSpa9asWcPo0aPR6a7de60jY9qj0Wi48847OXfuHKNHj+b555/v1PXJyclGj0dFRUFhivLASDJnYW5GuLcDSTnNSzBl35wQQgghOkynA3U/q4Zt4wwqVa+G8Nprr+kTOYCQkBCeeeYZHnroIf75z392KJkrLy/n0UcfxcnJiU2bNrWaCAgODubBBx/k5ZdfJjg4GBcXlw7FFRQUpP/ewcGB8PBwMjMzezyJ6itxmAKTSOYyMjI6vZ+tpqYGAEdHx1bHnJyc2hzr4OBwXfH97Gc/49tvvyUiIoJvv/0WKyur67pPu4wkcwBDfJ1aJXNS0VIIIYQQHaYuh5eDrj2uL3nqEti69NrTu7m5MXPmTIPjd955Jw899BAHDx5Ep9OhukbC+c4771BaWsrTTz9tdEVXaGgocP375dLT0zl27Bhjx469ruu7Sl+Jo78yiQIoR48eRafTdeprypQpADg5OeHs7AxAdna20fs3HQ8MDOx0bL/73e94//33GTBgANu2bWv1KU2XMrJnDpRkrqXMkhqKq+q6JwYhhBBCiJtcy1mnlpycnHBxcaGqqoqKCsPCdVdrqrZ+xx13GD3ftP3neipZlpeXs3TpUt5++21cXXtv+01fiaM/M4lk7kY1FTVpWnfcUkNDA0lJSVhbWxMREdGp+/7973/ntddew8vLi23btjFgwIAuideoNmbmJoZ58OTsCJxsLPXHTmaXdV8cQgghhBAm7nqrUnZm605iYiLW1tZtLj08evQo0Plkrq6ujsWLF3PPPfe06q/c0/pKHP2dSSyzvFHz5s1j7969rFu3jhUrVrQ6t3nzZtRqNXPnzsXGxqbD93z33Xf54x//iIuLC99//32nE8FO0zYYPRzq5UiolyPHM8vYfjYfgMSscqZFendvPEIIIYTo/2yclWWL/YmN8w3fomlLTFvF77Kystq8NjMz0+jxiooKysvLsbe3N7qtp6WGhgYqKytxdnY2uhyzoKCAr776CmidzG3bto1Zs2axf/9+JkyYAMDGjRu5/fbbWbt2LXPnzuXOO+9kzJgxPP744+3GcCMGDx7M/PnzefXVV/XH6uvriY6O5q677uKZZ57pkThuBjIzBzzwwAM4OTnxzTff6H8xQPlFefLJJwF44oknDK6LjIwkMjKSnJycVsfXrVvHQw89hIODA999912HKwzdkDZm5prEDWj+wyZFUIQQQgjRISqVsv+sP311QfGTpj1qaWlpBueKi4uNruZqeX779u0Gxz///HMAxo8ff839cpaWlri6ulJeXk5+fr7B+ddee43a2lq8vb1b7aebOXMms2fP5g9/+AMAe/bs4Sc/+QmrV69m/vz5bNmyhQ0bNrB161bi4uKIi4tj48aN7cZyPSZMmEBCQoJBzI2NjTz11FM9FsfNQGbmUDaqvv/++yxfvpxly5YxefJkPDw82L59O2VlZTz66KNMnz7d4LqmoisNDc2zYgUFBdx1111otVqCg4N55513eOeddwyuXbx4MYsXL+66F9HGnrkmcQOa1yGfzCrr0MZbIYQQQoibUXBwMIGBgZw+fZpvvvmGRYsWAVBdXc2DDz54zT1vv/vd79i+fTvu7u6A0krgr3/9KwAPP/xwh2IYPnw4O3bs4Nlnn+U///mP/n3be++9x6pVq/Rjrvbqq68SFxfH3/72N15++WVeeeUV7rrrLkBZjdbRJaIlJSWUlJS0O8bJyQkvLy+D4xMmTOCzzz6jsbERCwsLsrKyePHFF/nss8+wsbHpVByifZLMXbF06VL27t3LCy+8QEJCAvX19QwePJhHHnmEe++9t8P3qampob6+HoDTp09z+vRpo+MGDhzYxclc+zNzQ1vMzJXXNnCxqJpBntdXnVMIIYQQwtT95S9/4b777mPp0qXEx8fj4ODAkSNHcHJyYuHChW3OJI0dO5b6+nrCwsJaNQ2vqalhxYoVHX7/9+yzz7Jz507effddDhw4QFRUFElJSZw5c4YxY8Zw+PBho/vlYmJiuOOOO3j66ad5/vnnr7sZ91tvvcVzzz3X7ph77rmHDz/80OD4hAkTqK2t5dSpUwwfPpzHH3+c+Ph4fVIsuo4kcy1MmDCBLVu2dHi8sU2sAwcOvOG+dJ1m7QjuoW2ezi6tYdUPaViZm1GvUT4FOZ5ZJsmcEEIIIUQb7r33XlQqFatWreLAgQO4urqyYMECXnrpJX7zm9+0eZ21tTVbt27lj3/8I19//TVFRUX6vnCPPfZYh58/Pj6etWvX8txzz5GSkkJubi7Dhw/npZdeYu/evW0mcydPnuS7777D0tIST0/P63npgJJM/ulPf2p3jJmZ8R1b4eHheHh4kJCQQFFREZs2bep0T2jRMSpdj2ceoqs0NQ1P/k0gPHWxzXEFlWpGv7ij1bGfjB7A35cM7e4QhRBCCNFHabVa/ZaRiIiINt+Yi47JyMggODiYyZMns3v37l6J4fz580ycOJH7778fCwsL3nnnHc6fP3/dvZJvxKJFi7Czs+PEiRPceuut/P3vf+/xGLpCZ39PmqqPJicnd3tsIAVQTIOu/f1yXo42eDpatzp27FJpd0YkhBBCCCF6UE5ODjNnzmTx4sW8+OKL/Pa3v0Wj0bSqKNmTJkyYwBdffEF1dfU1Z/jE9ZNkzhR0YAPp1c3D0/KrKK8x3s5ACCGEEEL0H8XFxcyaNYuRI0fyr3/9CwBHR0eefvppVq1aRV5eXo/HNHz4cHQ6HatWrcLe3r7Hn/9mIcmcKbjGzBxAlJ9hP5PjmTI7J4QQQgjR37m7u5OcnMzatWtbLQN87LHHqKqqatW+oKd89NFHTJ06leXLl/f4c99MpACKKWhUQ9aPMGBUm0Oi/AwbaB69VMLUSMNyskIIIYQQonN6pQheH6PRaCguLmb9+vVs2LCBEydO9HZIJk+SOVOg00J5ZrvJ3BAjM3NHM2RmTgghhBBCdI09e/YwY8YMBg0axNq1awkLC+vtkEyeJHOmQtN+n7kgNzscrC2oqmsedzK7jAaNFktzWW0rhBBCCCFuzLRp06QZeA+Td/GmQtt+MRMzM5VBERR1g5YzuRXdGZUQQgghhBCim0gyZyo0165MGRNgbN+cLLUUQgghhBCiP5JkzlR0IJkbaiSZO3appDuiEUIIIYQQQnQzSeZMhab+mkOGBrgwIsiVaS0qWB67VHrTV14SQgghhBCiP5JkzlR0YGYu2MOe9Q+N55VlQ/XH8ivqyC6t7c7IhBBCCCGEEN1AkjlTcY0CKC15OFgT7GGvf3xM9s0JIYQQQgjR70gyZyo6sMyypeGBrvrvj8q+OSGEEEIIIfodSeZMxTX6zF1t5MAWyZw0DxdCCCGEEKLfkWTOVJhbdmhYo0bL5lO5nMhsTuBS8yupVHd8maYQQgghhBCi91n0dgCii4y8r0PDzM1U/PGr01Som2fydDo4kVlGfLhnd0UnhBBCCCGE6GIyM2cqdJoODVOpVNI8XAghhBBCCBMgyZyp0HYsmQOI8XcxOCbNw4UQQgghhOhfJJkzFR2cmQOI8TecmTuRWUajRtuVEQkhhBBCCCG6kSRzpqK64zNrQ40ss6yp15ByubIrIxJCCCGEEEJ0I0nmTMX+1zo8NMDVFhc7w+qX0jxcCCGEEOLaVq5ciUqlYvfu3b0dSq9Yvnw5KpWKI0eO9HYoNz1J5kxFY12Hh6pUKmIDXAyOSxEUIYQQQghxLcePH8fc3JyYmJjeDuWmJ8mcqehEMgcQN8DF4NixDCmCIoQQQggh2lZWVkZ6ejqRkZHY2tr2djg3PUnmTEUXJHO55WqyS2u6KCAhhBBCCGFqjh8/DsCwYcN6ORIBksyZDk19p4bHGknmAA6eL+6CYIQQQggh+r/169czevRobG1t8fb25u677yY3N7fdaw4dOsSiRYvw9PTE2tqagQMH8vDDD7e6Tq1WY2NjQ3BwsMH18+fPR6VSMXXqVINz0dHRWFhYUFFRoT+WkZGBSqViypQp1NbW8vvf/56goCCsra0JDQ3l5ZdfRqfTdfq1b926lRkzZuDk5IS7uzsrV66ktLSUY8eOATB8+PBO31N0PUnmTIWmczNzbvZWBLnbEehmR5C7nf74gfSiro5MCCGEEKLfefvtt1m2bBnHjx9n/PjxTJkyhe3btzN27FiKi41/+P3JJ58wadIkNm3aREREBEuWLMHa2pp///vfDB8+nJSUFABsbGwYM2YMGRkZZGRk6K/XaDTs378fUJJCtVqtP1dUVMSZM2eIi4vDycnJ4Lnr6+uZNWsW7777LoMHD2bq1Knk5OTw+9//nmeeeaZTr/3Pf/4zt9xyC/v372fChAlMmDCBDRs2MHPmTA4fPgx0fGYuLS2N+Ph4hgwZQkxMDP/85z87FYtonyRzpqKxczNzAFt/Hc/eJ6fy1JxI/bGD6cXX9emNEEIIIYSpyMjI4Le//S3W1tZs376dHTt28MUXX3D+/HkiIyPZvHmzwTVZWVn87Gc/Q6VSsXHjRvbv38/nn3/O2bNneeyxx8jPz+fuu+/Wj58yZQpAq4qYJ06coLy8nKioKOrq6khISNCf2717NzqdTn/d1Q4dOoRKpSItLY2tW7eydetW9u3bh4WFBW+88QZVVVUdeu2ffPIJzz//PCNGjOD8+fNs2bKFjRs3kpiYyMWLF/nqq68AiIuL69D9rK2t+de//sWZM2c4dOgQb731FsnJyR26VlybJHOmopPLLAFsrcwBGDfIHZVKOVZYWcf5go79sgshhBDi5qBu0FBe29DhL2MfDFeoO359bb3G4Pr6Rm2716gbDK+5Xu+//z51dXXcfffdrZInOzs7/vGPf6BqeuPUwurVq6mtreUnP/kJ8+fP1x83MzPjpZdews/Pjx9//FGfoE2ePBlonczt2bMHgGeffbbNc20lc2ZmZqxevRoPDw/9sZEjR3LLLbdQU1PD0aNHr/m6y8vLefTRR3FycmLTpk0EBATozwUHB/Pggw+i0+kIDg7GxcXlmvcDCAoKIjo6GgAHBwfCw8PJzMzs0LXi2ix6OwDRRa4jmWviam/FEF8nknOV9dcHzhcR5u3YVZEJIYQQop/79+503txxrsPjT/55Fs62rXvaTnhpJ5Xqxg5dv3R4AKuWx7Y69k1iDr9bd6rNa349PYzHZ4Z3OMb2NC11XL58ucG5iIgIhg0bpi8E0mTfvn0A3HXXXQbXWFtbc9ttt/Hmm2+yb98+xo4dy/jx47G2tm6VsO3evRsXFxeWLVtGQECAwTkzMzMmTpxoNOaBAwcSHm74+puO5eXltf+igXfeeYfS0lKefvppfH19Dc6HhoYC179fLj09nWPHjjF27Njrul4Ykpk5U3EDyRzAhNDmT3EOpEsRFCGEEELcvJqKlQQGBho9b+x40zUDBw40ek3T8aZxNjY2jB49mkuXLpGRkYFWq2X//v3Ex8djZmbG5MmTSUhIQK1WU1RURHJyMnFxcW3OiLWcRWvJwcEBgLq6a9dXaFo+escddxg937RU83oqWZaXl7N06VLefvttXF1dO329ME6SOVOhabiuy2rqGzlysYTymuZkMOFCMY0abVdFJoQQQgjRrzQtEzW2nPJarnVNy/Mtl1omJiZSVlamX0Y5ZcoU/b65vXv3trtf7npjvVpiYiLW1tZERUUZPd+0VLOzyVxdXR2LFy/mnnvuYcmSJTccp2gmyyxNxdDbr+uyl7aksObQJQBUgA6oVDeSlFthtBedEEIIIW4+D00J4b6JhmX02+JkY/gW88Dvp9HRGmtW5obzDYvi/JkV5dPmNdYWXTdH4efnR1paGpcuXSIsLMzgvLE9X35+fqSmpnLx4kWjyx0vXVLeb7VcvjhlyhReeOEFdu/eTUlJif5Yy/9tea4p+esODQ0NVFZW4uzsbDQxLCgo0Bc/uTqZe/fdd/n5z39ucE1MTAyJiYnceeedjBkzhscff7x7gr+JycycqQifc12XxQa46L+3tmz+cThwXloUCCGEEEJhY2mOs61lh7+MJQNONh2/vqlIW0tWFmbtXmNjaXjN9Wral7Z27VqDc2lpaSQmJhocnzRpEgCffvqpwbn6+nr9vZrGAYwfPx4rKyt2797N7t27cXV1JTZW2SsYGhqq3zfXtF8uPj7+hl9bWywtLXF1daW8vJz8/HyD86+99hq1tbV4e3sb7Ke78847ycvL0399/PHH2NjY8NRTT7FlyxY2bNjA1q1biYuLIy4ujo0bN3bb67jZSDJnKnTXV8EpLtBF/726oXlp5UHpNyeEEEKIm9S9996LlZUVa9as0Rc2AaitreXXv/41Wq3hdpT7778fW1tbPv/8c7799lv9ca1Wyx//+EdycnIYNWpUq+Iftra2jBo1ikuXLrFt2zb9frkmkydP5tChQyQlJREbG9vhCpLXq6mwybPPPtuqIul7773HqlWrWo1pycHBAR8fH3x8fDhy5AgPP/wwH3/8MXfddRfz5s1Dq9WSmJio/1q4cGG3vo6biSRzpkJ7fclcsLu9QbUpgKMZpV1a4lcIIYQQor8YNGgQL7/8Mmq1mqlTpzJjxgzuuOMOQkNDSUpKatV6oElgYCDvvvsuOp2OBQsWMGnSJO68806GDBnCqlWr8Pb2Zs2aNQbXNS2nVKvVBnvipkyZQn19PTqdrluXWDZ59tlnUalUvPvuu8TExHD77bcTFRXFAw88wKhRo4D298v973//Y8WKFXz++ecsW7as2+MVksyZDt31FSwxM1MxrMXsnIWZsiyirlHL8UulXRGZEEIIIUS/89hjj/Hll18SFxfH/v372bFjB1OmTCEhIQF3d3ej16xYsYK9e/cyf/58zp49y7p166itreWhhx7i2LFjREZGGlzTMoEzlsy1da47xMfHs3btWmJiYkhLS2Pbtm34+vqyceNG/fLQtpK59957j5///Od8/fXXzJs3r9tjFQqVzlhXR9EvREVFQWEKyQ87QNgsWPwfsDf+x6U9/9hxjlXb0gBwtbOktEapjPnI1BB+N9vwj44QQggh+j+tVktqaiqg9E5rubxPiM548803ee655/j2228ZN25cb4fTpTr7e9JUCTQ5ObnbYwOZmTMd536AmuvrDzc8qLnXR01989LKA+el35wQQgghhGjbyy+/zJ/+9Cc+/fRTgoODuXz5MpcvX6a8vLy3Q7spSDJnShrV13VZ7AAXrqyupK6xebnmqewyKtTX179OCCGEEEKYNp1Ox9/+9jeqqqqYO3cuvr6++q+XXnqpt8O7KUifOVPSWHddlzlYWxDh48TZvAoA7KzMqanXoNXB4QslzBzi3ZVRCiGEEEIIE6BSqWQGrpfJzJwp0VxfMgcwIshF/72bvZX+e2lRIIQQQgghRN8kyZwpuc5llgDDA5V9c442Fvg62+iPH5R9c0IIIYQQQvRJsszSlFznMkuA6ZHe/PB4PKGeDlwqqWHqa7sBSM2vpLCyDk9H6y4KUgghhBBCCNEVZGauhYMHDzJ37lzc3NxwcHBg9OjRfPTRR11y7/vuuw+VSoVKpSIhIaFL7mngBmbmnO0sCfd2xMxMxUB3O/xazs7JUkshhBBCCCH6HEnmrtiwYQPx8fFs3bqVoUOHMmfOHM6dO8fKlSt54oknbujeu3bt4oMPPkClUnVRtG1orO+S26hUKsaHeugfy1JLIYQQQggh+h5J5oDS0lLuvfdeNBoN69atY/fu3axbt46UlBRCQ0N544032LVr13XdW61W8/Of/5yoqKjub6J4AzNzVxsf0tx8fP/5IqS3vBBCCCGEEH2LJHPA6tWrKS8vZ9GiRSxZskR/3Nvbm1deeQWA119//bru/de//pXz58/zn//8B0tLyy6Jt003sGeuiVar43xBJSXVzbN8OWW1pBdW3fC9hRBCCCGEEF1HCqAAmzdvBmDZsmUG5+bNm4eNjQ3bt29HrVZjY2NjMKYtSUlJvPrqq9x3331MnDixy+Jt0w20JgBo1GgZ+/edFFUp9xnkYc+FomoAdqYUEOrleMMhCiGEEEIIIbqGzMwBp06dAmD48OEG56ysrIiOjkatVpOamtrhe2q1Wh588EGcnZ31s3vdasrTMOHXN3QLC3Mzgtzt9I/9XJoT110phTd0byGEEEIIIUTXuumTuYqKCsrKygAICAgwOqbpeGZmZofv+89//pOEhARWrVqFm5vbDcd5TV1UW2V0cHOsdY3N++R+zCihQt3QNU8ihBBCCCGEuGE3/TLLqqrmvWB2dnZGx9jb2xuMbU92djZPP/00U6ZM4e67777hGKOiooweT09PJ8TpygOt5oafB2D0QDf+TToA5wsqcbG1oKy2kUatjgPnirglxrdLnkcIIYQQQghxY0wimVu2bBlJSUmdumbNmjWMHj26Q1UaO1vJ8ZFHHqGuro5///vfnbruhui6JpkbMdAVlQp0OiitaWBahCc7U5UlljtTCiSZE0IIIYQQoo8wiWQuIyOjU/vZAGpqagBwdHRsdczJyanNsQ4ODte87/r169m4cSPPPPMMkZGRnYqpLcnJyUaPR0VFQWGK8kCrAa0WzG5s5ayTjSWDfZw4k1cBgLujtf7c7rRCtFodZmbd3C9PCCGEEEIIcU0mkcwdPXr0uq91cnLC2dmZ8vJysrOzGTJkiMGY7OxsAAIDA695v02bNgGwbds29u7d2+pcYmIiAA8//DBOTk788pe/NFpB87rsfwPKMmHZezd8q9HBbvpkrlLdiJkKtDoorKwjObeCmADnG34OIYQQQgghxI256QugAMTGxgJw/Phxg3MNDQ0kJSVhbW1NREREh++ZkJDAnj17Wn2Vl5cDcOLECfbs2aNPEruG7oZbEzRpWQQlMbOMYQNc9I93pRZ0yXMIIYQQQvRXK1euRKVSsXv37t4OpVcsX74clUrFkSNHejuUm54kcyi95ADWrVtncG7z5s2o1WqmT5/eoR5zH374ITqdzujX5MmTATh06BA6nY7HHnusS19HVzQNBxg1sDmZu1yhZniQq/7xzhRJ5oQQQgghbmbHjx/H3NycmJiY3g7lpifJHPDAAw/g5OTEN998w1dffaU/XlBQwJNPPgnAE088YXBdZGQkkZGR5OTk9Fis7WpUd8ltPB2tGeRpr3/saGOp//5kdhnFVV2TNAohhBBCiP6lrKyM9PR0IiMjsbW17e1wbnqSzAFubm68//77mJmZsWzZMqZOncptt91GREQE58+f59FHH2X69OkG16WmppKamkpDQx/pv9ZFM3OgtCgAsDRXYaYCHydlVlKngz1p0kBcCCGEEOJm1LQtadiwYb0ciQBJ5vSWLl3K3r17mT17NomJiXz33XeEhITw/vvv8+abb/Z2eB3TRTNzAHePG8jnD47l9F9m88tpYUyN9NSfk6WWQgghhLgZrF+/ntGjR2Nra4u3tzd33303ubm57V5z6NAhFi1ahKenJ9bW1gwcOJCHH3641XVqtRobGxuCg4MNrp8/fz4qlYqpU6canIuOjsbCwoKKigr9sYyMDFQqFVOmTKG2tpbf//73BAUFYW1tTWhoKC+//HKn22wBbN26lRkzZuDk5IS7uzsrV66ktLSUY8eOATB8+PBO31N0PZOoZtlVJkyYwJYtWzo8vrO/GN2+SbaxvstuNcSvdYuGKRFefH4kC4C9aYU0arRYmMtnAUIIIYQwTW+//Ta/+tWvMDc3Z/LkyXh4eLB9+3bGjh2rL553tU8++YSVK1ei1WoZP348AwYM4Pjx4/z73//mq6++Yvfu3URGRmJjY8OYMWPYu3cvGRkZDBw4EACNRsP+/fsBJSlsSvoAioqKOHPmDMOHDzfaSqu+vp5Zs2aRnJzM6NGjGTx4MHv27OH3v/89lZWVvPDCCx1+7X/+8595/vnnsba2ZurUqVhaWrJhwwaSkpL0sXZmZi4tLY0HHniAoqIizM3N+cUvfsEjjzzS4eu7Sl+JoyvJu3FT0oUzc1ebGOqBpbnSX65C3cjxzLJuey4hhBBCiN6UkZHBb3/7W6ytrdm+fTs7duzgiy++4Pz580RGRrJ582aDa7KysvjZz36GSqVi48aN7N+/n88//5yzZ8/y2GOPkZ+fz913360fP2XKFKD1h/0nTpygvLycqKgo6urqSEhI0J/bvXs3Op1Of93VDh06hEqlIi0tja1bt7J161b27duHhYUFb7zxBlVVVR167Z988gnPP/88I0aM4Pz582zZsoWNGzeSmJjIxYsX9fUl4uLiOnQ/AGtra/71r39x5swZDh06xFtvvdVmH+Xu1Ffi6EqSzJmSLtwzdzV7awvGBLvrH0uLAiGEEOIm0qCG2rKOfxlbvaQu7/j19TWG1zfWt39NQ9d9qP3+++9TV1fH3Xff3Sp5srOz4x//+AcqlcrgmtWrV1NbW8tPfvIT5s+frz9uZmbGSy+9hJ+fHz/++KM+QWuqct4ymduzZw8Azz77bJvn2krmzMzMWL16NR4eHvpjI0eO5JZbbqGmpqZDfZnLy8t59NFHcXJyYtOmTQQEBOjPBQcH8+CDD6LT6QgODsbFxeWa92sSFBREdHQ0AA4ODoSHh5OZmdnh67tKX4mjK8kyS1PSTTNzBZVqEjPLmBrpxf7zRQDsSingqTmR3fJ8QgghhOhj9r8Be17q+PinLoGtS+tjb8RAXXnHro+9E279d+tjp9fCNw+3fc3k38PUP3Q8xnY0LXVcvny5wbmIiAiGDRtm0J943759ANx1110G11hbW3Pbbbfx5ptvsm/fPsaOHcv48eOxtrZulbDt3r0bFxcXli1bRkBAgME5MzMzJk6caDTmgQMHEh4ebnC86VheXl77Lxp45513KC0t5emnn8bX19fgfGhoKHBj++XS09M5duwYY8eOve57dIW+EseNkpk5U9LFM3MV6gamr9rN6Bd38LOPjxHdYh9dyuVKcstqu/T5hBBCCCH6gqZiJYGBgUbPGzvedE3TnrKrNR1vGmdjY8Po0aO5dOkSGRkZaLVa9u/fT3x8PGZmZkyePJmEhATUajVFRUUkJycTFxfX5oxYy1m0lhwcHACoq7v2+8Sm5aN33HGH0fNNSzWvt5JleXk5S5cu5e2338bV1fXaF3STvhJHV5BkzlTYusGo+7v0lk42ltRrtPrHeeVqBrrb6R/LUkshhBBCmKKmInfGllNey7WuaXm+5VLLxMREysrK9Msop0yZot83t3fv3nb3y11vrFdLTEzE2tqaqKgoo+eblmpeTzJXV1fH4sWLueeee1iyZMkNxXkj+kocXUWWWZoKOzeY9dcuv+3YYHeySrIBSLhQzJQILz48mAEoSy3vGhPU5c8phBBCiD5m4uMw9qGOj7dxNjz2+Gnje+mMMbcyPBZzG0TOa/saC5uO3bsD/Pz8SEtL49KlS4SFhRmcN7bPys/Pj9TUVC5evGh0ueOlS5cAWi1fnDJlCi+88AK7d++mpKREf6zl/7Y815T8dYeGhgYqKytxdnY2mhgWFBToi59cncxt27aNWbNmsX//fiZMmADAxo0buf3221m7di1z587lzjvvZMyYMTz++OPd9hoGDx7M/PnzefXVV/XH6uvriY6O5q677uKZZ57pkTh6kszMmYpuKn4ydlBz0ZOEC8VMH+ylf7zvXBE19Y3d8rxCCCGE6EMsbZQ9cB39MjZLZOPc8eut7Ayvt7Bq/xrLrkvmmvalrV271uBcWloaiYmJBscnTZoEwKeffmpwrr6+Xn+vpnEA48ePx8rKit27d7N7925cXV31bQ9CQ0P1++aa9svFx8ff8Gtri6WlJa6urpSXl5Ofn29w/rXXXqO2thZvb2+D/XQzZ85k9uzZ/OEPyp7FPXv28JOf/ITVq1czf/58tmzZwoYNG9i6dStxcXHExcWxcePGLn8NEyZMaFUBtCnuxsZGnnrqqR6LoyfJzJyp6K5kLqQ5mcsoriHIzQ5Hawsq6xqpa9SyN62IOdE+3fLcQgghhBC94d577+WVV15hzZo1rFixQp+A1dbW8utf/xqtVmtwzf3338+rr77K559/zu233868ecosolar5Y9//CM5OTmMGjWqVcENW1tbRo0axYEDB8jPz2f27NmYmTXPtUyePJm1a9fS0NDQ7n65rjJ8+HB27NjBs88+y3/+8x/9DN17773HqlWr9GOMefXVV4mLi+Nvf/sbL7/8Mq+88oq+GMy8efOM/n9mTElJiX4msi1OTk54eXkZHJ8wYQKfffYZjY2NWFhYkJWVxYsvvshnn32GjY1Np+LoL2RmzlR0UzLn72JLoFvzp2PHr1S1bPLDmcvd8rxCCCGEEL1l0KBBvPzyy6jVaqZOncqMGTO44447CA0NJSkpqVXrgSaBgYG8++676HQ6FixYwKRJk7jzzjsZMmQIq1atwtvbmzVr1hhc17ScUq1WG+yJmzJlCvX19eh0um5dYtnk2WefRaVS8e677xITE8Ptt99OVFQUDzzwAKNGjQLa3i8XExPDHXfcwdNPP81vf/vb627G/dZbbxEWFtbu15NPPmn02gkTJlBbW8upU6cAePzxx4mPj2fRokXXFUt/IMmcqaivhoNvQ21pl9967CA3/feH0ouZFeWtf7zjbAGNGtP6hEMIIYQQ4rHHHuPLL78kLi6O/fv3s2PHDqZMmUJCQgLu7u5Gr1mxYgV79+5l/vz5nD17lnXr1lFbW8tDDz3EsWPHiIw0bOvUMoEzlsy1da47xMfHs3btWmJiYkhLS2Pbtm34+vqyceNG/exkW8ncyZMn+e6777C0tMTT0/O6Y3j22WdpaGho9+v99983em14eDgeHh4kJCTwww8/sGnTJt56663rjqU/UOl0Hd2JKvqaqKgoKEwh+WGH5oMPHwavru3/9tXxbJ748iQAA9xs+e7RSYz463Z9pcvPHhzD+BCP9m4hhBBCiD5Gq9WSmpoKKL3TWi7vE6Izzp8/z8SJE7n//vuxsLDgnXfe4fz58/q2CD1p0aJF2NnZceLECW699Vb+/ve/39D9Ovt70lQJNDk5+Yaet6Pkt9bUdEPj8JaJWlZJLaXVDUwIbf5E6odkw02yQgghhBDC9OXk5DBz5kwWL17Miy++yG9/+1s0Gk2ripI9acKECXzxxRdUV1fzpz/9qVdi6EmSzJmabtg75+NsQ4invf7xgfQiZkU1Fz3ZdiYfmeAVQgghhLi5FBcXM2vWLEaOHMm//vUvABwdHXn66adZtWoVeXl5PR7T8OHD0el0rFq1Cnt7+2tf0M9JMmdqumFmDmBiaPPs3MWiaqYP9tJXHc4pqyU5t6JbnlcIIYQQQvRN7u7uJCcns3bt2lbLDx977DGqqqoMWhj0hI8++oipU6eyfPnyHn/u3iCtCUxNN1W1vG3kAEYOdGN8iDvuDtYADA905dglpeDKD8mXifY30iBUCCGEEEKIbqTRaCguLmb9+vVs2LCBEydO9HZIPUZm5vq9q5pydtPMXLS/Mwti/fSJHMCsIc1VLb+XfXNCCCGEEKIX7NmzBx8fH1atWsXatWsJCwvr7ZB6jMzM9Xeqq5I5TX2PPfXsKB/+viUFgNT8StILqwjx7PmqRUIIIYQQ4uY1bdo0k2sG3lEyM9fv9czMnDEDPewZ4uukf7w1SRqICyGEEEII0VMkmevvVFf9J+yBZK66rpFdqQWoGzTMjWmuavnd6Z6vWCSEEEIIIcTNSpK5/u7qZZbdVAAFQKfTsfKDI8Q9/wP3fvAjxzNLmRPdXKUoObeCzOKabnt+IYQQQgghRDNJ5vq9nltmqVKp0OmgQaP0lDtwvohQLwfCvZv3yW1Jktk5IYQQQggheoIkc/1dy5m5W16B0T/r1qdr2W9u/7ki5WlbzM59J/vmhBBCCCGE6BGSzPV3LffMWTuBtWO3Pt3EsOZk7lROOSXV9cyNaU7mTmaVkVNW260xCCGEEEIIISSZMwEtZuZ6oPhJpI8jXo5KrzmdDvadKyTc24FBHvb6MVukEIoQQgjR56larO65Wcu6C3EtLX83VFfXqugDJJnr71r+UHVj8ZPmp1MxOdxT/3hPWiEqlYpbWlS13HQyt9vjEEIIIcSNUalUWFlZAVBdXd3L0QjRNzX9blhZWfXJZE6ahvd3KhWgFCTpqR5zkyM8WXssG4C9aUVotToWxvrzz13pAJzMLudCYRWDpIG4EEII0ac5OjpSXFxMfn4+APb29piZyWf9Qmi1Wqqrq/W/G46O3buV6XpJMtfvtUjm9r0OpRmw4P+69RknhnpgpgKtDoqq6jiTV0G0vzODfZ04m1cBwNeJuTwxM7xb4xBCCCHEjXF3d6e6uhq1Wk1urqysEcIYGxsb3N3dezsMo+Sjl/6uZQGUunKoKuj2p3SxsyJugIv+8Z60QgBuHeanP/b1iRx0Ol23xyKEEEKI62dubk5gYCDu7u76JZdCCIWVlRXu7u4EBgZibm7e2+EYJTNz/d3Va3c13b9vDmByuBfHM8sAJZl7ZGooC2P9+fuWFHQ6yCyp4XhmGSOCXHskHiGEEEJcH3Nzc7y8vPDy8kKn08mHsUKg7Cnti3vkribJXL93ddPwHkrmIjx5Y3saAIWVdTRotPg42zA+xJ0D54sBZXZOkjkhhBCi/+gvb2CFEApZZtnfXf0Ht4eKoMT4O/P8oih2/mYyO38zGUtz5UdpUZy/fszmU7k0aKTUsRBCCCGEEN1Bkrn+TnXVf8IeSubMzVTcPW4ggzwdWn2CNyfaB2sLJabSmgb2pBb2SDxCCCGEEELcbCSZ6/d6Z5llW5xsLJkxxFv/eO2xrF6MRgghhBBCCNMlyVx/Z7DMsneTOYBlIwL03+84W0BBRc/MFgohhBBCCHEzkWSuv+sDyZxGq+PHjBL2XmlREB/mib+LrRKOVqdvMC6EEEIIIYToOpLM9Xu9s2euya7UAka9uJ3b/nOIv29JAZT9dLeNbJ6d++LHLLRaKXMshBBCCCFEV5Jkrr/r5Zm5IDc7SqrrATibV0F2aQ0Ay0cOwOxKaJklNRy6UNyjcQkhhBBCCGHqJJnr71omc1YOEDm3R59+kKcDgzzt9Y93nC0AwM/FlikRXvrjnx/J7NG4hBBCCCGE6G6V6gYSLhSzet8Ffv2/E1wqru7R55em4f1ei2TO3hOWvd/jEcwc7M07hRcA2H42n3vGDwTgjlED2JmiJHffJ1+muKoOdwfrHo9PCCGEEEKIG1WpbiApp4KknHJO55STlFPOxeJqdC12E9U39uzWIknm+ruWM3O9VMlyxhBv3tmrJHMJF4qpUDfgZGPJtEgvvBytKaiso0GjY8OJHB6YNKhXYhRCCCGEEKKjaus1nMkr52RWOaeyyziVXc6Fop6ddesISeb6u5ZNw3u4+EmT4YGuuNpZUlrTQINGx960QuYP9cPC3IylIwL49+50AL5JzJVkTgghhBBC9CkNGi2plys5mV3GqaxyTmaXca6gCk0HC/g5WlsQ5e9EjL8z76y16eZoW5Nkrt/r/Zk5czMV0yK9WX9caUGw/Uw+84f6AXDrMH99Mnc6p5zzBVWEejn0SpxCCCGEEOLmptHquFBYxclsZcbtZHY5Z/MqqG/Uduh6RxsLov2ciQlwJtrfmRh/Z4Lc7DC7Uvnvsyd7Nr2SZK6/a7nMsqEWkr6C0Blg49SjYcwc4qVP5namFNCg0WJpbka4tyNDfJ04k1cBwDeJOfxmVkSPxiaEEEIIIW4+Op2OrJJaZcbtSuKWnFNOdb2mQ9fbWpoT468kbkMDnBka4NIqcesLJJnr71ous0QL6+6Fhw6BzZAeDWNSmCdW5mbUa7RUqBs5mlHKuBB3ABYP82uRzOXyxMxwVFe3VBBCCCGEEOIGFFSoScxS9redyinndHYZpTUNHbrW0lzFYF8nfdIWG+BCqJcD5n0ocTNGkrl+z8gPWC/snbO3tmBciDt70goB2HE2X5/MLYz15+9bUtDplJ5zxzPLGBHk2uMxCiGEEEII06Bu0HA6p5zEzDJOZJWSmFlGbnnH3gObqSDMy1FJ3Aa4EBvgTISPI9YW5t0cddeTZK6FgwcP8sILL5CQkEB9fT1DhgzhkUce4Z577rmu+2m1Wt577z0++ugjkpOTUavV+Pr6Mm7cOP74xz8SFRV140Ebm+HqxaqWe9IKcba1xMK8ecbQx9mGscHu+sbh3yTmSDInhBBCCCE6RKfTcbGomhOZZSRmKclbSl4ljR0sUDLQ3Y6hAS4MDXAmdoALUX5O2FmZRhpkGq+iC2zYsIHbbrsNrVZLfHw8Hh4e7Nixg5UrV3Ly5Elef/31Tt2vpqaGBQsWsHPnTlxdXZk4cSI2NjZcvHiRL774gltuuaUbk7neqWo5N9qHMC8HRga5tkrmQFlq2ZTMbT6VxzPzh2BpLj3rhRBCCCFEa2U19UrSdiV5S8wqo7y2Y8slfZ1tWi2VjPF3xtnOspsj7j2SzAGlpaXce++9aDQa1q9fz5IlSwDIz89n4sSJvPHGGyxYsICpU6d2+J733nsvO3fu5L777uMf//gHdnZ2+nN5eXk0NHTsB/LaVFe+Wnwy0Uszc+4O1m02BZ8T7csz3yRT36ilpLqevWmFTB/s3cMRCiGEEEKIvqRBoyUlr5LErFJ98tbRfm62luYMDXAmLtCFYQNcGRbogrdTz7YG6G2SzAGrV6+mvLycRYsW6RM5AG9vb1555RWWLFnC66+/3uFkbufOnXz55ZeMGjWK//73v5iZtZ6B8vX17dL4sbCBxtrmx700M9ceZ1tLZgz24rvTlwH48miWJHNCCCGEEDcRnU5HXrn6StKmJG+nc8qp62BbgDAvB+IGuOiTt3BvB4PVYDcbSeaAzZs3A7Bs2TKDc/PmzcPGxobt27ejVquxsbl2tv/OO+8A8Pjjjxskct3Cwrp1Mqep7/7nvA7LRw7QJ3M7zhZQUKnGy/Hm+vRECCGEEOJmUd+o5UxeBcculXL8UinHLpVyuaJjkw5u9lYMG+BC3AAXhgW6MnSAM042prtc8npJMgecOnUKgOHDhxucs7KyIjo6mqNHj5KamkpsbOw177dz504AZsyYQVJSEmvXruXy5cv4+Phwyy23MHbs2K59ARZXJUR9YGYuo6ia7WfzCfVyYEqEF6C0L/BztiG3XE2jVsdXx3P4xeSQXo5UCCGEEEJ0hZLqeiVpy1QSt5NZZR2adbM0VzHEz5lhA1wYdmXWbYCbrbSy6oCbPpmrqKigrKwMgICAAKNjAgICOHr0KJmZmddM5vLz8ykqKsLV1ZX33nuPp59+Gq22+Yf4+eefZ8WKFbz//vtYWnbs04W2CqWkp6cTEhKizMy11Et75pr83/Y0/m/7OQCmRXrpkzlzMxW3jRzAmzuUc1/8mMXP4wfJL6oQQgghRD+j1eo4X1jFsSszbscvlXZ4r1uAqy3DAl2VmbdAF4b4OmFj2f/aAvQFN30yV1VVpf++ZZGSluzt7Q3GtqW0tBSAyspK/vCHP/DTn/6UP/3pT3h5ebFjxw5+8Ytf8Mknn+Dv789LL73UBa+APjczN3qgm/77fecKqVA36KfFbxsZwFs7z6HTwcWiao5cLGHMIPfeClUIIYQQQnRAVV0jJ7PKmpO3zFIq1Y3XvM7K3IyYAGdGBLkyPNCV4UEuJrXN5nK5mhOZpVdaJpSRW1aLn4ttjz2/SSRzy5YtIykpqVPXrFmzhtGjR6PTXbs/RUfGNNFoNAA0NjYybtw41qxZoz+3dOlSbGxsmD9/Pm+99RZ//OMfcXJyuuY9k5OTjR7Xz9hdPTPX0MvJXLAbbvZWlFTX06DRsf1MPkuGK7OeAa52TArzZO+V5uJf/JglyZwQQgghRB+TW1bLjxklHM1QkreUyxV0pK2bh4M1I4NcleQtyJVof6d+2YzbmPpGLcm55RzPLON4ZiknLpUaNCqvrdf0aEwmkcxlZGSQmpraqWtqamoAcHR0bHXMWHLVNNbBweGa9215v/vuu8/g/Lx58/D29iY/P58jR44wY8aMTsVtVMtkbsofYPjdN37PG2BhbsbsKB8+P5IJwHenL+uTOYA7Rg3QJ3Pfns7jzwuiTLr/hxBCCCFEX9a0ZPLIxRKOZpTwY0YpOWW117zOTAURPk6MCHJhRJArIwLdTHKv21fHs/n0cCanc8qpv8YewA72Me8yJpHMHT169LqvdXJywtnZmfLycrKzsxkyZIjBmOzsbAACAwOveT8/Pz+srKyor68nKCjI6JigoCDy8/MpKCi47rhbabnM0skfHHu/5P/cmOZkbu+5QirVDTheWWo5Y7C3fuaurlHLVyeyuXdCcG+GK4QQQghx06hv1JKUW86PF0uU2bdLpZTVXLsHsqO1BcOCXBkRqMy8xQ5w1r+/6+8aNFrO5FZgYa4iys+51bniqnqOXSpt81o/ZxtlD2CgC3/7queWWIKJJHM3KjY2lr1793L8+HGDZK6hoYGkpCSsra2JiIi45r0sLCyIjo7m+PHjlJSUGB1TXFwMdGymr0Nazsz1gUqWAGMHueNiZ0lZTQP1jVp2phSwKM4fACsLM24bEcA7ey8A8EnCJVaOH2hyn+IIIYQQQvQFVXWNHL9UytGMEo5klJCYVYa64dpVJgPd7Bg50JWRQW6MCHIl1MsBczPTeL9WWFnH8Uxl79/xS6Wcylb63c0f6svbd7aucD88yEX/vZWFGTH+zgwPdLmyB9C1VaPyN3q4kIskcyhLH/fu3cu6detYsWJFq3ObN29GrVYzd+7cDvWYA1i4cCHHjx9n165d3H777a3OZWRkkJGRAcCwYcO6JP5WM3O9XMmyiaW5GbOH+PDF0SwAvj2Vp0/mAO4cE6hP5tILqzl0oZjxIR69EqsQQgghhCkprKzTJ25HM0pJzi2/5vI/lQoifZwYPdCVUcFujAxyw8fZNAqVNGi0pORVNidvmaVklRhfRnois8zgWJSfM8/MH8LwQBeG+PWtPYCSzAEPPPAAL774It988w1fffUVS5YsAaCgoIAnn3wSgCeeeMLgusjISAB27NiBv39zovLwww/z+uuv88EHH7B06VJmzpwJKNUwH3roITQaDfPmzWPAgAFd8wJazsxp+kYyB3BLTHMytzutkKq6RhyslR+5IHd7Jod7sufK3rlPEzIlmRNCCCGEuA7ZpTUkXCjhyMVifswo5WIHWgRYWZgRF+DCqGBXRg5UZt5MtSn3vR/8yP7zRdccZ2VuhpeTNTX1jdhZNadJNpbm3D+xb24JkmQOcHNz4/3332f58uUsW7aMyZMn4+Hhwfbt2ykrK+PRRx9l+vTpBtc1FV1paGi9xtjT05MPP/yQ5cuXM2fOHMaOHYuXlxcJCQlcvnyZ4OBg3nnnna57AS1n5n5cDTnH4Y5Pu+7+12l8iAdONhZUqBv1Sy0Xxvrpz/90bJA+mfs++TIFFWq8nEzjEyAhhBBCiO6g0+nIKqkl4WIxCReKOXyhpEPFSpxsLBg50I2RA10ZPdCNmADnPjXDdCPyymv5MUNZRjpjsDfx4Z6tzg8NcDaazPk42TA8SFkuOSywf1belGTuiqVLl7J3715eeOEFEhISqK+vZ/DgwTzyyCPce++9nb7frbfeysGDB3nxxRfZv38/R48eZcCAAfzmN7/hD3/4A+7uXViOv+XMXEUuqPrGf1YrCzNmRfmw7phSQGbTydxWydzUSC/8XWzJKaulUavjfz9m8ej0sN4KVwghhBCiz9HpdGQU13D4wpXk7WIJeeXXrpHg62zDqIFujLqybDLcyxEzE9jvptXqSCuo1CdvR6+qvKkCg2Ru1EA3LM0vEOXnrO91NzzQtUf7wXWXvvGuv4+YMGECW7Zs6fD4a/WfGzVqFF9//fUNRtUBVzcNryvv/ufsoAWxfnx1PJsJoR7Mi/Ftdc7cTMWdYwJ59XtlhvOzw5k8PCUEC3Oz3ghVCCGEEKLX6XQ60gurOXyxmIQLJRy+UExB5bW30QR72DN2kNuVBM6NAFfTaRFw5ErVzR8zSjh2qf1m5T9mGFadnBDqwem/zMamh4uT9ARJ5kyBuVXrx3WVoNMpO1l72YQQdxL+OB0vR+PLJ5ePHMD/bU+jQaPjcoWa7WcLmBPt08NRCiGEEEL0Dp1Ox7mCqiszbyUcvlhCUdW1k7cQT3vGDnJnzCB3xgS7taqoaGqe3nCacwVV7Y4Z4GbLqCA3Rge7GZyzsjDdiQJJ5kzB1TNzOi3UV4N1F7U+uAEW5mZtJnIAno7WzIn2ZdPJXAA+PXxJkjkhhBBCmCydTsf5gioOpjcvmyyprr/mdeHeDkryFuzO6GA3PB2tr3lNf1BQoebwxRKOXCzh8MVi/rIwyqAo3siBbq2SOTMVDPFzYmSQMgs5cqCrSSez7ZFkzhRYGPllrqvsE8lcR6wYE6hP5vadK+JiUTXBHva9HJUQQgghxI1rKlhyML2Ig+nFHEwv7tDMW6SPI2MHueuXTro7mEbyll1aoyRuF5TWCVdX3jx8ocQgmZsQ6k5mSbU+eYsLdNFXSL/Zyf8LpuDqmTmAugrA1/B4H6DV6lptwB0d7Ea4twNp+conLp8mXOJP84e0dbkQQgghRJ92uVzNoQtFHDyvJG/XqjapUsEQXyfGBCvJ2+hgN1zsrNq9pr9o0GhZfyz7yszbtStvHrlYYnBs/lA/5g/1MzJaSDJnCtqametD1A0adqUU8E1iLiXV9Xz5i3H6cyqVihVjg3j2m2QA1h7L5rezI0xyk6oQQgghTE9JdT0JF4r1s28XCtvv89aUvI0b5M64EHdGDnTD2bb/93jTanWoVLQqvGJhpuLV71Mpbmcpqb+LLWOClSR27KAurPh+E5BkzhS0OTPXd1wqruGhT4/rH1+9lPLWYf68tCWFmnoN5bUNbDqZy20ju6ipuhBCCCFEF6pUN3DkYol+2eTZvGu/7wr1cmB8iDvjQ5R9b672/X/mTaPVcTavgoQLxfqKk58+MJYhfk76MSqVitHBbmxJuqw/Fuxhz+iBboy5MgsZ4GrXG+GbBEnmTIGxmTl130rmInwcifB2JDVfmTHccDybJ2ZF6M872liyeJg/nx3OBOCThEuSzAkhhBCiT1A3aDiaUaqfeTudU45G236LqgFutowf5MH4UHfGDXLHywQKdGi1OlLzKzl0JYk9crGYiqvaBBy+WNwqmQO4JcYXdwcrffGWm7VYSXeQZM4UGJ2Z61vLLAFuHa7MvgGsP57DYzPCW+2dWzEmSJ/Mncwu52hGCSMHGpaXFUIIIYToTlqtjjN5Few/X8T+c0X8mFFCXaO23Ws8Ha2ZEOLO+BAPxoW4M8DNNGabauobWX8sm0NXWidcq/LmkYsl3DshuNWxhbF+LIyVPW/dQZI5U9ByZs7cGgJGgV3fS4KWDPPn1e9T0Wh15JTVcuhCMRNCm6sVDfFzYnSwm37j63/2pLNakjkhhBBC9IDcslr2nyti3/kiDp4vanePF4CLnSXjBinLJseFeBDiad/vm3TrdDqD12CmUvHXb89S30Yya26mIsbfmTGD3Bgb7M6Iga49Eaq4QpI5U9AymXPwhnu/7b1Y2uHlZMPkcE92phQAsO5YdqtkDuChySH6ZG772QJSL1cS4ePY47EKIYQQwrRVqhs4lF6sn327UNR+0RI7K3NGB7sx4crM2xBfp1YrjPqrrJIaZdbtSt+7DY9MaLUM0sbSnBGBrhy6UAwoPd6i/Z0ZN8idsSHujBroJm0CepH8P28KWi6zbFT3XhwdsGxEgD6Z25KUx3OLonCyaa7eNCXCk0gfR1IuK8tE/7MnnTduj+uNUIUQQghhQho0Wk5mlbHvXBH7zxeRmFXW7r43MxUMDXBhUpgHE0M9GBboipWFWQ9G3D3yyms5lF6sfF0oJru0dauAQ+nFLB7m3+rYrcP8GezrxLgQZc+bKVTeNBWSzJmCljNzmms3oexN0wd74WJnSVlNA+oGLd+eyuMnowP151UqFQ9NCeHX/0sEYOPJXJ6YGW4y686FEEII0TN0Oh0XiqqVpZPniki4UExVXWO71wx0t2NimAcTQz0ZN8gdZ7v+n7SoGzTsSSvkwHklib1W24SD6UUGydzyUVKUrq+SZM4UtJqZ69vJnLWFOYti/fjo0CVAWWrZMpkDmBfjy2s/pJJVUotGq2P1vgs8tyi6N8IVQgghRD9SXtPAgfQi9qQWsu9cIbnl7a9YcrGzZEKIx5UEzsMkPzyuqmvk5x8fa3fMIA97xoYoVTelz1v/IsmcKWg5M9eohoz9SiGUAaN6L6Z23DZygD6ZO3aplPTCKkI8HfTnLczN+Fl8CM98nQTA/37M4pFpoXg5ShlbIYQQQjTTanWczilnT1ohe9IKOZFZSnsdA6zMzRg50JWJYR5MCvUkyq//73vTanWkXK7kwPkiDqYX8c+7hmNn1fwW38PBmsG+Tq164Q1ws9U3LB83yAMfZ3mP1V9JMmcKrm5N8OE88B8JD+7onXiuIcrPqdW+uH1pha2SOYDbRgTw5vZzFFXVUdeoZfW+i/xx7uDeCFcIIYQQfUhBpZp9aUXsSVNm30prGtodH+njqOx7C/Nk9EA3bK3MeyjS7pNdWnNl2WSxQeXNIxdLmBLh1Wr8vBgfQjztmRjqwQQTnYHsE+qrlS8r+x57SknmTIG5leGxur7VNLwllUrFfRODSS+oYtmIAMK8DatV2lia8/P4Qbz43VkAPj50iZ/FD8LDwUiDdCGEEEKYrAaNlmOXStmTVsjetEKSc9t/j+PhYMWkME/iw5W9b56O/f+9Q3lNAwfTlT1vB84XkVFc0+bYA+eLDJK5X04L6+4Qb071NZB9BC7ug4x9kHMMysvAM7LHQpBkzhT0k6bhLS0fee2NtHeNDeQ/e9Iprq6ntkHD6n0X+f0tPffLIYQQQojekVVSo0/eDqa3X7jE3EzFiEBXJkd4Mjnc02RaBjTJKqkh/tVd6NpZPmqmgpgAFyaFejBziHfPBXezaVBD9o9K4nZxH+QcBU37/Qi7myRzpsDCyCdO6r47M9dRdlYWPBg/iJe2pACw5lAGP4sfhJu9kZlIIYQQQvRbtfUaEi4Ws/fK3rdrVVz0d7ElPlxJ3saHurdqc9RfXSquZv/5In4yKrBVMhrgaouPkw15VxVzGeRhz4QryyZNpfJmn6NphLxEuLALLuyBrCPXrhyv6tn2FZLMmQIzczCzBG2LNeMN1aDVKOf6sZ+ODeKdPemU1jRQU6/hvf0X+N1smZ0TQggh+ruskhp2phSwK7WAQ+nF1DVq2xxrZWHG2EHuTA73ZHK4ByGeDqhU/Xv2raqukUPpSgK791whl64snYzxd2ZogIt+nEqlYkKoB7tTC/TJ24RQD/xdbHspchOm00FhipK4XdyjFBW81tYlKwcIHAfBk2DgJFj7056J9QpJ5kyFhQ3UX7UBuK4CbF17J55O0Gp1HL5Ygk6nY3yoR6tz9tYWPDBpEK9+nwrAhwcyWDk+2CTWvwshhBA3kwaNlh8zStiVUsDOlALSrzH7FuJpz+RwL+LDPRgT7N7vC5dotTrO5FXol48ezyylQWO4dnJvWmGrZA7gLwujsLca2u8T2D6pLLM5ebu4F6ry2x9vaQ+BY2HgRAiOB984MO+9lEqSOVNhYQ31V+2Tq6vs88nc3rRCnv76NFkltcQGOPPNLycajLl7XBCr912gtKaB6noNb+5I44XFMb0QrRBCCCE6o6BSze7UQnalFLD/XBGV7ex9c7C2YHyIO5MjPIkP8zSZiosl1fX8dfMZ9p0rpKiq/f1VYV4OONsZbidxsJa37F2muvhK4rZHSeJKL7Y/3swSBoyG4MkwaDL4jwDzvrOkVX4yTIWxIij9YN+cp6M1WSW1AJzMLiflcgWRPk6txjjaWPKraWE8v/kMAJ8fyWLl+GBCvRwM7ieEEEKI3qPV6jiVU87OlAJ2pxZwKru83fGhXg5Mi/RiSoQnI4PcsLLo2f1GXa1Bo8XSvPVrcLC24Ifky1TXawzGO9taMjHUg/hwDyaFeeInSye7Xl0VZB6CC7uVBO7y6WtcoALfoc3JW+C4Hm010FmSzJkKY0VQ+nhFS4DBvk7E+DtzOkf5Y/9pQiZ/XRxtMG7F2CA+OpTBpeIaNFodL29N4b93j+zpcIUQQghxlfLaBvadK2RnSgF7Ugtb9Ty7mpWFGeND3Jka4cW0SC+TmH3LK6/Vzz4eOF/E1sfiW70uKwszxoV4sP1sPmYqiBvgQny4J/HhnsQGuGBuQpU3+wRNI+Qeh/SdSgKX/SNo254RBsAtREncgicrSyft3Hok1K4gyZypMJrM9f2ZOYAVYwN5ar3yKclXx7N56pZIg+UEVhZmPDk7kkc+Ow7AtjP5HL5QzJhB7j0erxBCCHEz0+l0nCuoYueVvW/HLpWi0bZdN9/fxZapkZ5MjfBifIhHv9/71qjRciKrjF0pBexKLeRsXuv3W3vPFXLXmKBWx+6fGMytw/yZGOohVSe7Q+klJXlL3wEX9kJd+zPCOPg0J2+DJoNzQM/E2Q0kmTMV/XRmDmBhrD8vfHuWSnUj1fUaNpzI4adjgwzGzY3xYVigCycyywD425YUvn54vGwGFkIIIbpZfaOWIxdL2H42n+1n88kurW1zrLmZihFBrvrZt3Dv/l95sriqjj1pyuzj3rRCKtRtz/QcOF9kkMyNC5EPn7tUXaVSafL8DiWJK0lvf7y1s1KwpCmB84yAfv4z2USSOVPRcs/cqAcgfA74DO29eDrB1sqc20YM4P0DygbUTxMusWJMoMEffpVKxdNzB7PsP4cAOJlVxu60QqZGePV4zEIIIYSpK6upZ3dqIdvP5rMntbDd4iVu9lZMCfdkaqQX8WGeJjX7VFXXyJi/7aCxndnHAW62TIvwYkqkF+Nk1VDX02qVfm/pO5WvrMPtL500s1QqTg6aAoOmgm9sr1ac7E6m+apuRi1n5nxiIGxm78VyHe4aG6hP5lIuV3L0UimjBhquVx450I0pEZ7sTi0E4M3t55gS7tnvP/ETQggh+oKMomr97NuPGe0vn4zxd2ZqhJLADTWBvV/ltQ3sP1fE5AjPVts9HKwtiBvgwtFLpfpjluYqxgS7M+XK6x/kYS/vRbpaeY7SrDt9J6TvgtqS9se7h0HodAiZBkETwPrmKJQnyZypaDkz13iNzvR9UIinAxNC3TlwvhiAjw9dMprMAfx6epg+mUvMKmPvuSImh3v2WKxCCCGEqdBodZzILGXb2Xy2n8lvt/ebtYUZE0M9mD7Ym+mDvfB2MlJJux/R6XSk5VexK7X13r//rBjBnGifVmOnRnqRXVrL1EhPpkR4MSHUQ9oFdLX6Grh0sHn2rfBs++NtnJWZt5DpEDIVXAJ7JMy+Rn4KTUXLmblGde/FcQN+OjZIn8xtScqjoGIwXkb+oRgW6Ep8uCd705SE7v+2pxEf5iGfiAkhhBAdUF3XyL5zhWw7U8Cu1AJK2qk+6eFgxfRIb2YM8WZCqDt2Vv37rWNdo4aECyXsOJvPjrMF5JQZ7v3bnVpgkMw9MCmYh6eEyHuNrqTTQfF5OPcDnNumJHKadiYkVOYQMKp59s1vGJj172I6XaF//0aKZi1n5hra3pTcl80Y7I2Pkw2XK9Q0aHR8eDCDJ+dEGh376+lh+mTuRGYZ+84VES+zc0IIIYRReeW1bD9bwPYz+RxKL6Zeo21zbKSPI9MHezFjsDexAS6Y9fPlkxqtjq9P5LD9bD570wqN9ntr4m5vhZOt4X4/awtJGrpEfQ1k7FOSt3M/QNml9se7Drwy8zYNgicps3F9mbpc6WvXg0s8JZkzFdaOzd+fXgfZR8E7CmY+13sxdZKFuRn3ThjIG9vTWD5yALePGtDm2BFBrkwK82DfuSIAXt6awvgQdyzM+3ezUSGEEKIr6HQ6UvMr+SE5n++TL5Oc23a7IgszFWMHuesTOFPo/daSmQre3HGOzJIao+eHBjgzNcJL2fvn79zvk9c+RaeD4nQ4v01J4DL2tz/7ZuWo9HkLnaYkcG6Dei7W69FYr/Sxu7BL6WmXcwwqKsDT+GREd5BkzlRYOzV/X3xO+eqHyy1XjA1i2YgA3B2MtFq4ymMzwvXJXHJuBe8fuMjP4kO6O0QhhBCiT9JodRzPLOWH5Mv8cCafS8XGkxcAZ1tLpkZ4Mn2wN5MjPHGy6d/VJxs1Wo5eKmX7mXxuHe5PlF/zDI5KpWL6YC8+OJABgI2lGZPCPJkxWEngvBz7996/Pqe+5krbgCsJXOnF9sd7x0DYDAidCQNGg3k/+FlM/hpOfAKXDkBD279nPUGSOVPRcmauifoaDRP7IHtrC+w7uKF4RJAry0YEsO5YNgCvb0tjdpQPQe723RmiEEII0WeoGzQcSi/m++TLbD+bT1FV2/vfBrrbMWOwsv9tZJBrv1/NUqFuYE9qITvO5rMrtZDy2gYArC3NWiVzAPOH+qJu0DJziNK43MZSlk12qeJ0JXE7f2X2rb0JBWsnpXBJ2EwInQFOfj0W5nXRasHsqt+VwhTltRqj6tmfLUnmTIWNk+GxftI0/Eb8ad5gdqcWUFRVj7pByx83nOaT+8fIBmUhhBAmq0LdwK6UAn44k8/ulIJ294DFDnBh1hBvZkd5E+LZ/5t3ZxbXsP1sPjtS8jl8ocRo77ftZwr43ezWy9xGBLkxIsh4lWxxHRrrIfMQpH0PaVuv3bTbK0pJ3sJmwoAxfXv2rbEeshKuNCTfoRRdmf9G6zGDpsLuvyvfW9hA4LgrlTWnwto7ezRcSeZMhbWxZK7t9fH9hVar42B6MeND3I2uYXexs+IvC6P45WcnADhwvpi1x7JZPrLt/XZCCCFEf1NQoeaHM/n8cCafQ+lFNGiM939r2v82O0qZgfN1tu3hSLvHv3af5+sTOaTlV7U5xsrcjHEh7swY4o1Wq5O9b12tuliZjUrdorQOaO99ppUjDJoMYbOU2Tdn/56Ls7N0Oii50Jy8XdwHDS1adKjLlTEtPwjxHwGTfqsUZRkwFix7b6muJHOmwmgy179n5tYezeKdvRc4X1DFv+8azi0xvkbHzYvx5evBuWw/mw/Ay1tSmBPt0+/X/wshhLi5XSis4oczSgGTE5llbY6ztTRnSoQns6K8mRbhjbOd6f37l5xbYTSRc7O3YlqkFzMGezExzFN6v3UlnQ4Kziozb2nfQ/YR0LVdBRWvIUriFjZLmX2zsOq5WDtLXQ4X9ypJ6fkd7VfVLMtUkj33FnUZzC1g+jPdH2cHyE+8qTC2Z05TrzQQt7h2MZG+aMfZAs4XKH+4X9+WxqwoH8yNfMqmUqn46+IoDpwvorZBQ3F1Pf/YcY6n5w3p6ZCFEEKI66bT6UjKqWBrch4/JOdzrqDtWShXO0tmDPZmdpQPE8P6/x4wdYOGvWmFfJ+cz/gQd5aOCGh1ftYQb749lQdAmJcD0wd7M3OIF3EDXI2+NxDXqbFOaR3QtHyyLLPtsebWyuxb+BwlgXPpR6uidvwVfvxv2+ctbGHgBKUtQuj0Pl1VU5I5U2FszxyAugIc+mf/tcdnhvP9mcvodHCuoIpNJ3NZPMz4NL2vsy2PTA3htR/SAPjgQAZ3jA4kxLPn+nwIIYQQnaXV6jiRVcbWpDy2JF0mu7TtXrH+LrbMjvJhVpRpFDApr2lgZ2o+3yflsyetkNoGZe9ffoXaIJmbGunF03MHM3OINwM9pNBZl6oqUHq+pW6B9F2tlxhezcEHwmdDxC1KCwGrPvzfoiJPmXmzsoOoW1ufC51umMx5DVHaIYROh8Dxvbp0sjMkmTMVxmbmQFnP3E+TuQgfRxbG+vFNYi4A/7c9jXlDfbFs4x+vByYN4vMjWeSU1dKo1fHit2d5f+WongxZCCGEuCaNVsfRjBK2JF1ma9JlLle0Xfkv0sdRn8AN8XXq9wVMLper2XbmMt8n55NwodhoAZOEC8WU1zS0Wi7qZGPJg/F9d3akX2laPpn6rZLA5Rxrf7zfMGX2LXwO+Ma23jvWl2gaIeeokpie+wEun1aO+w03TOYGTlQS04ETlAQuZFrfr6rZBknmTIWxPXPQ74ug/Hp6GJtP5aHR6sgoruGr49ncPirQ6FgbS3OenjeYhz89DsDOlAJ2pRYwNcKrJ0MWQgghDDRotBy+UMJ3SXn8kHy5zRYCKhWMCHRldpQPs6N8CHQ3jQbe3yTm8MGBDBKzytoc42BtwZQIT2ZH+WBt2b9nHfscrQayDkPKt8pXe73fLO2Uao3hs5UvR5+ei7OzqgqVoiXnflD2vqnLDMfknlCKt9i7Nx+zdoTfpPTdxLQTJJkzFW3OzPXvIiiDPB1YOtyfL48qveTe2nGeJcMD2pyduyXah9HBbhy5WALAs98k8f1j8dhZyY+6EEKInlXXqOHg+WK2JOXxw5l8ymoajI4zU8GYYHduiVESOG+n/rG8qzPyK9RGEzl3eytmDlH2/o0Pdcfaon/v/etT6mvgwi5I+Q7StkBNcdtjnQIg4srs28BJfX+JYfIGOPgPyDkOGK/sCoBLkLJs0ljfOxNI5ECSOdNhbql8ktLUhd7RB+w8wKz//yd+dHoYG07k0KDRkVNWy8bEXIO19E1UKhV/WRDFgrf3o9HqyCqp5fUf0vjTfCmGIoQQovupGzTsSStka9Jltp/Jp7Ku0eg4CzMV40M9uCXah1lDvHF36J/FyprodDpOZpez5XQe1pbmPDEzvNX52VE+/O27FAAGuNkye4gPs6N9GB4oBUy6VHWxUrgk5Vtlv1hj23sw8RsGEfOU/W/eUX03uTHWtLuuyvjyUDNLZelk2Czlyz20776uLtL/3+mLZtaOzcncbR9B4NjejaeLBLjasTjOn7XHlNm5/+xJ59Zh/m32jxni58TP4wfxr91KA8v3D1xk3lBfhgW69ljMQgghbh7VdY3sSi1gS9JldqUUUNNGE28rCzPiwzyYE+3LzMH9v4WAUryllO9OX2bL6Txyy5XZD1c7Sx6dFtqqQEuQuz1/WTCE0cHuDPZ17Pd7//qUkotK8pb6ndLIu632AWYWyqxb5DyImNt3e7/pdJCf3Lz3zc4d7vi09ZjQGc3fO/opzcjDZytFWdparWaiJJkzJdZOUKX0Wuvvyyuv9vPJg1h3PFtf2XJHSgEzh3i3Of7R6WFsTbrMhaJqtDr4/frTbPrVRKwsZA2+EEKIG1ehbmDH2Xy2nL7MnrRC6hqNv4G2sTRjaoQXc6J9mBbphWM/74HasnjLlqQ88ivqDMaU1jSQcKGEiWEerY6vnBDcU2GaNp0O8hKv7H/7DgqS2x5r5agkOpHzlATI1qWnouycuiq4sPtKArcNKnObz1nYQoO69dJPJ19Y8Cb4j+zbs4o9QJK5Fg4ePMgLL7xAQkIC9fX1DBkyhEceeYR77rmn0/cqLS3lpZde4ptvvuHSJaUR4aBBg7j11lt58skncXJqo2DJjWj5SYS6vOvv34tCvRyZNcSb75OVZPXdventJnM2lua8tHQoy985BEBqfiX/3p3Or2eE9Ui8QgghTE95bQPbz+Tz7ek89p0rpEFjfK+OvZU50wd7c0u0D5MjPE1i3/b5gio+OpjB1uTLFFYaJnCgvJ8ePdCNuTG+RPreXLMj3U6rVQqYnN2kfJW30//N0VdZOhk5T5mJ66v9hsuylCWhqVuU3nYa40WBaKyFS/tbz8YBjFjZ7SH2B/3/r0sX2bBhA7fddhtarZb4+Hg8PDzYsWMHK1eu5OTJk7z++usdvldhYSHjxo0jPT0dPz8/5syZQ2NjI4cOHeLFF19k/fr1HDp0CBcXl659ES17zZnYzBzALyaHsDu1kOUjB/DgpGuXJx4d7MaKsYF8kqD8wXt71zluifEh3Fv+gRFCCNExleoGtp/N59tTeexNK6JeY3wGzsnGgplDfLgl2jSaeF+tsLKOjxMuGRw3U8G4EHduifZlVpQ3Xo59vHBGf6JpVJKYMxshZXPz6itjPCOvLJ+cp+yFu3qPWV/z42r49jftDFCB//Are99mgu+wHgutv+m2ZK6hoYHU1FQKCwspLy/H2dkZT09PIiIisLTsW0sMSktLuffee9FoNKxfv54lS5YAkJ+fz8SJE3njjTdYsGABU6dO7dD9/v73v5Oens6tt97K559/jrW18olIZWUlc+fOZf/+/bzxxhs899xzXftCWs7MVeRA3ilAp/QEMQHDAl05/MfpuNhZdfiap+ZEsuNsAXnlaho0Op5af4p1vxgvm62FEEK0qaqukR1n89l8Ko89aYXUt7GE0s3eitlR3syJ9mXcIPd+v5S/vlHLwfQijl8q5YlZEa3OjQ52w93eiuLqen3xlrnRPsw0geItfUpjHVzYA2e/UZZQ1pa0MVAFA8YoCVzkPHAP6dEwO6y+BkougE906+MDxhiOtXFWZt/CZkHI9H7bJ7mndWkyV1hYyIcffsi3337LkSNHqKsznIa3sbFh9OjRzJs3j3vuuQdPz97/D7V69WrKy8tZtGiRPpED8Pb25pVXXmHJkiW8/vrrHU7m9u7dC8BTTz2lT+QAHB0d+c1vfsP+/fv58ccfu/ZFAFg7twjiVeXLfyQ8uKPrn6uXdCaRA3C0seTFW6O578OjAJzILOOjgxncN1HW7QshhGhWXdfIjpQCvj2Vy67UthM4d3sr5kT7MC/Gl9HBbq2KfPRHDRot+88XsflkHtvOXKZCrVTfXDzMn0GeDvpx5mYqHpsRhrWlObOGeHf632PRjvoaOL8dzm6EtO/b7hGsMleaXQ9ZCJHz+27/t4o8Zflk2lZlH5ytKzxxtvW+Nu9opR2ChbWyJDTiFhgwFsxl0WBndcn/Y+fOnePZZ59lw4YN1Ncr6109PDwYMWIEbm5uODk5UV5eTmlpKSkpKezZs4c9e/bwpz/9iSVLlvD8888TGhraFaFcl82bNwOwbNkyg3Pz5s3DxsaG7du3o1arsbG59vKBlglcW9zc3Dof6DWf2MjywerCrn+efmZapDeL4vz4JlHZTPvq96nMHOLNADfTaMQqhBDi+tTUN7IzpYBvT+WxM6WgzSImrnaWzIn2Zf5QX8aYQAKn0epIuFDMppO5bE2+bLT/3ZakyzwytfV7s5+OG9hDEd4E1BVKsY8z3yiJXFM18quZWykNvAcvUGbg7Lrh/eON0ung8ilI3ar0s8s90fp8ZZ5SsMWvxVJJlQp+sa9vvp5+5oaTuV/96le8++67aDQapk6dyp133smUKVMIDm575uPChQvs2rWLzz77jC+//JL169fzs5/9jH/84x83Gs51OXXqFADDhw83OGdlZUV0dDRHjx4lNTWV2NhrL1mcOXMmBw8e5OWXXzZYZvnaa68BXFdRlWuyMVJUxYSTOa1Wx3dJeQS42hE3wKXdsc/OH8K+c0WUVNdT26DhuU1nWH3PyJ4JVAghRJ9RW69hV6qSwO1IyUfdYDyBc7GzZE6UD/OG+jJ2kDuW/TyB02p1HL1UyuZTuXx3Oo+iKuPFJqwtzJgS4Um0v7PR8+IG1JYp7QPOfKP0gGur4IeFLYTNgMGLIHyWsvywr9E0wKUDzRU1K7LbHmvlqLRP8Ltq35skcl3ihpO59957j4ceeognn3wSPz+/Dl0zaNAgBg0axP33309OTg6vvPIKq1ev7pVkrqKigrKyMgACAow3og4ICODo0aNkZmZ2KJn77W9/y65du9iwYQODBg1izJgxNDY2cvDgQczNzXnvvfeYOXNmV74MhbGZuYYaqK8GK/uuf75elHChmL9sTCblciVjB7nxv5+Na3e8u4M1z8wfzONfnARg+9l8dqUUMDXSqyfCFUII0YvUDRp2pxaw+VQeO84WUNtgvA+ck40Fs68kcBNCPfp9AtdSeW0Dd/43gUatYQVOKwszpkV4MW+oL9MivbC3lqVuXUZdoVRrTN4A6TvaTuCsHJU+aUMWKvvG+vr7tvRd8NltbZ93CVKWTobPgaAJYCHLcrvLDf+2XrhwAR+f61+z6+/vz5tvvskf/vCHGw3lulRVVem/t7MzvuzO3t7eYGx7HBwc2Lp1Kw8++CCffvopGzZs0J9buHAhI0aM6FSMUVFRRo+np6cTEtJiw6t1G+0Oqgv7/h+FTtLqdKRcVip2Jlwo4WB6EeNDPNq9ZnGcP58fyeLIRWUz8XObkhkf6o61hWlVHBNCCKEkcHvTCvn2dB7bz+RT3UYjb0cbC2YN8WH+lQSuvxcx0el0nMmrINjDvlVLBFd7KyaGebA7VVmxY2muIj7Mk/mxvswY7N3v+9/1KXWVyt635A1KzzSN8VYO2Loq1SeHLIRBU/pmC4HqIqUR+eAFrY8Hx4OVA9Q3vTdWQcAoiJijNCT3jLype7/1pBtO5m4kkeuq+yxbtoykpKROXbNmzRpGjx6NTme8R0xLHRnTUmZmJvPmzSMvL481a9YwZ84cALZs2cLjjz/OxIkT2bZtG2PHju3Ufa+p5cycyhx0V/7hqi4C14Fd+1y9bHyIB2MHuZFwQUnMXv8hjXG/cEfVzh8OlUrFcwujmP+P/Wi0OjKKa1i976LBngAhhBD9U1Mxj02JufxwJp+qukaj4xytLZg5xJt5Q32ZGOZhEh/qncuvZNOpPDafyuVCYTVv3hHHojj/VmMWx/mj0epYMNSP2VE+ONtJAtdl6quvJHBfKQlco9r4ODt3GLwQhixSipmY98H/BqUZV5ZPfqskcjqtUsDEqcUKPEsbJcGrLVX28oXfItUne0m3zaOXlJRw+fJltFot3t7e3Vq1MiMjg9TU1E5dU1OjbDR1dHRsdcxYM++msQ4ODgbnjLnnnntISkri66+/ZtGiRfrjd999Nw4ODixdupQnnniCgwcPduh+ycnJRo8bzNi13DOnMmuRzJnmvrknZkbom4IfvVTKvnNFxIe3/3M22NeJn44N4sODGQD8Y+c5FsX5EeAqxVCEEKI/0mh1HLlYwqZTuWw5nUepkWIeoDTyVhI4PyaZSB+4jKJqNp/KZfOpPP1qlSabTuYZJnPD/Fk8rPUxcQPqa+D8Nkj6SknkGmuNj7N1VRK4qFuVJt59rWKjTgeXT19J4DZDvpEJkpRvYfSDrY8t/rfMvvUBXf7TdOutt3Lw4EGKiopaHffz82PGjBncddddzJgxo42rr8/Ro0ev+1onJyecnZ0pLy8nOzubIUOGGIzJzlY2dQYGBl7zfllZWezevRtra2sWLFhgcH7RokVYW1uTkJDQ4eqYHdbWMsuqgq57jj5kdLAbk8I82HdO+VlbtS2NSWEe7c7OATw+M5zNp3IpqqpH3aDlqfWn+Pi+MZhJ7zkhhOgXdDodiVllbDqpzEQVVBpfxmZnZc6MwcoM3ORwT5NI4C6Xq9l0MpeNJ3M5nVPe5rhKdQNarU7+betqDWql+mTyV0r1xoZq4+NsnJWZq6hbIXhy35uB02ogM0FJ3lI2Q1lm22Md26iJIYlcn9Dlydw333wDgLm5OU5OTmi1WsrLy8nJyeGjjz5izZo1jBs3jjVr1jBo0KCufvrrEhsby969ezl+/LhBMtfQ0EBSUhLW1tZERES0cYdmTYmfvb09ZmaG6+7Nzc2xs7Ojrq6OsrKyLlumCrRO5nQt9gaY6MwcKIlZUzJ3MquMnSkFTB/s3e41zraWPD2vuRjKgfPFfJxwiXvGD+zucIUQQlwn3ZW90ptO5rLpVC5ZJcZnQZqKeSyM82NqhBe2Vv0/gWvyu7UnWXc8m7Z2f0T7OzF/qB/zYnyl/U5X0jQoBT9Or1WKmdRXGh9n7awsOYy69coeuD5c9KO6ED6cB7Txw+QZ2dyQ3G+4JG59WJcnc+vWrWP48OEEBgbqk5na2lpOnjzJ999/z5o1azh48CDx8fEcOXKkwxUwu9O8efPYu3cv69atY8WKFa3Obd68GbVazdy5czs0i9aUnJWUlHDx4kWDFg3p6emUlpZib2+Ph0f7BTs6reUyS12LUsvVRYZjTcTwQFemRniy68qG7te3pTEt0uuas3OL4/zZmnSZ75PzAfj7lrNMDPMgxLNjS2mFEEL0jItF1UoCdzKXcwXGC5GZm6mYFObBgqF+zIoy3WIevs42BolcuLcDC4b6MT/Wj2AP0yp21qu0Wsg6rCRwyRugtsT4OCtHiJyrJHAh0/peEZPGeri4R1neadnifayjDwwYA1kJzccCRl9J4OaDh9QT6C9Uus5W97hBGo2GZ555hpdeeon77ruP1atX9+TTG1VSUkJwcDAVFRWsX7+eJUuWAFBQUMCECRM4f/4827dvZ/r06a2ui4yMBGDHjh34+zevQY+NjeXUqVPMnDmTtWvX4uys9AcpKytj2bJl7Nixg7vuuotPPvnkhuJu2jOn31NXVwl/N9JeIXoZLHvvhp6rLzudXc6Ct/frH/9nxQjmRF97xrO4qo7Z/7dX32sndoAL638xrt83gxVCiP4ut6yWzady2XQyr82lhCoVjAl2Y0GsH7dE++Jm34dnQTqoqZn3N4k5BLja8ej0sFbnzxdUMeP1PQxws2VRrD8LYv2I8DHSlkhcH51O2S92eh0krYfyLOPjLO2VsvvRSyBkeuskqS9oqFX62J35RlkKWlcOd3yuJJ0tHfmv0vdu8AKlAqVjF64Wu4kZvD/vZj2ezDUZN24cGRkZ5OXl9cbTG1i/fj3Lly9Hp9MxefJkPDw82L59O2VlZTz66KO8+eabBtc0zf5cvHiRgQMH6o8fPnyYGTNmUFVVhYeHB2PGjAEgISGB4uJiBg4cyIEDB254VtLgh0Wng+dc0U+ZT35KmSb3CAOfmBt6rr7uZ2uO8sMZZZYt0seR7x6d1KF9Aj8kX+ZnHx/TP/7NzHB+ddU/nkIIIbpfUVUdW07nsfFkLj9mlLY5LnaACwtjlaWEPs597E30ddDpdCTlVPBNYg6bTuWSX6Hs//N1tuHAU9MM/i1Lyiknys/pmitQRCeUXISkdUoSV5hifIy5tdIHLmYZhM0CS9uejfFa6quVKppnvoFzP7RoGXBF7E/g1v/0Tmw3mZ5O5nqtnI67u3un2wl0p6VLl7J3715eeOEFEhISqK+vZ/DgwTzyyCPce++9nbrXmDFjSExM5OWXX2bHjh1s374dMzMzgoODefDBB/nd736Hm1s3dL1XqZR9c3VXPsWMmAt+cV3/PH3Q4zPD9clcemEVybkVxAQ4X/O6WVE+LBsRwLpjyl7HN3ecY2qkF9H+175WCCHEjSmvbeD75MtsOpnLgfNFGOlnDSgf0i2I9WPBUD8C3U1jL9il4mq+Sczl68QcLhQaFtHIK1dz+GIJ40LcWx2Xf5+6SFUBJH+tLKPMPmJ8jMpMKV4ScxsMnq8UNelL1BVKFc2z38C57W1X07SwVVpWCZPU7clcYmIiL730EuHh4Xh6eqLRaDh06BDfffcdU6dO7e6n75QJEyawZcuWDo9vb1IzJCSEd999tyvC6hybFslcXRsbdE3QYF8nFsb6YWdlziNTQzu18fvZBUM4lF5MTlktjVodj3+RyKZfTTSJqmdCCNHX1NQ3sv1sAZtO5rIntZB6jdbouCB3OxbG+rEg1o9wb9NYSlhcVcemk7l8nZhLYlaZ0TEWZiriwz1ZFOdH7IA+ljz0d+oKpXLj6bVwYU/rYnEtBYxSErghi8Gx/aJqveq/U6H4vPFzVg7KDOKQRRA2E6xkP6Wp6vZkrqqqii+//BJoXpYYHR3NHXfcwd///vfufvqbT8vG4XUVvRdHL3jzjrjrWnbiZGPJq7cN5c7/HgbgXEEVq35I5el5hm0qhBBCdF5do4Y9qYVsOpXH9jP51DYYfxPt42TDglhfFsT6EePvbHJLCY9dKuUvm84YPTcyyJVFw/yZF2Ma+//6jMZ6Zdnh6S+v9IJro5m3RwQMvQ2il4Jb36i2rldXBdoGpV9dS2GzWydz1s7KXr4hC5ViLH1tKajoFt2ezE2cOJG8vDwOHDjA5s2bWb9+PTU1NSxfvpygoKDufvqbT8v2BDfRzBxwQ//ojw/x4L4Jwbx/4CIAq/dfZP5QP2IHuHRRdEIIcXNp1Gg5dKGYjYm5bE2+TKW60eg4N3sr5sb4sDDWn5FBribRF02j1XHGyHL/yRGeONlYUHHl/4twbwcWxfmzMNZPWgl0JZ0Oco7Dyc+VQiZtVaJ0CoCYpcosnHd03yq/X1+jJKHJX0HaDzD+VzDt6dZjhixUXmPkPGUGLnhy326HILpFjxdAqaio4De/+Q0ffPAB69atY/HixT359CbF6AbLT5bB+W3K98FTwN5D6SUy/w1wD+nxGPsTdYOGeW/tI/3K3oXRA9344udjTe6TYSGE6C5arY5jmaVsTMzlu9N5FFfXGx3naG3B7GgfFsT6MSHE3WSqCKflV/LV8Ry+PpFDQaWaQ3+YjrdT6yItr/+QSp1Gy6JYfwb7Osq/MV2pLBNOfQEn/9f28kNbV6WNQMxtMGAsGOkJ3Gvaa0juHga//LF1wqnVKq2ozHutBIYwot8XQBk5ciRvvfUW48ePN3reycmJ//73vyQnJ/P8889LMtfVWvaayz3evNSyIuemSuZ0Oh3fJ+fz2ZFM/nv3CKwtrr3/zcbSnOcWRrPiPWW55ZGMEr5Pzu9QmwMhhLhZNVVj3HQql80nc8ktN76MzcbSjBmDvVkQ68fkcE+T2ZdcVFXHxsRcvjqRTVJO6+0N3yTm8LP41v/2PjEroifDM33qCqWC46kvIGOf8TEWNsrsVczyK73g+tDsVWO90kYg+StI+a7thuTVBVCZB04tKqGbmQF9KBkVvaLLk7njx48zadIkZs6cyW9+8xtmzpxpdJyLiwt79uzp6qcXLffMWdg0J3PVhb0TTy/QaHX85N0EjmQoyyo+Scjk/onB17hKMTHMo1UT8pe2nGVapBdWFvLHUgghWjqXX6k08z6Vx8Uiw2qMAJbmKiaHe7Ig1o8Zg72xtzaNGQR1g4btZ/P56ngOe9IK0Rgpw2lhptK3GRBdTNMIF3YrSwxTNre9Dy5oIsTeoSxH7GuVKAEOvwu7XgC18V6KzQ3Jl/S9JFT0GV3+V/XAgQP84he/4IcffmDbtm34+voyY8YMhg8fTkBAAFqtlp07d/L999/Lnrnu0HLPnHmLX/rqop6PpZeYm6kY7OuoT+b+ues8y0cG4Ghj2aHr/zh3MHvPFaHR6sgoruGThEvc18FkUAghTFlWSQ0bT+ay6WQuKZeNzyCYqZR9yAtifZkT5YuzXcf+9vYHGq2OP32dxOZTuW3uARwa4MySYUpDb3cH6x6O0MRdPq0soTy9FqryjY9xD1USuJjl4NrH32fauhomcpb2EDFHSeBCZ/S9huSiz+nyZG7cuHEkJibyySef8NJLL3H27FnWrFnDxx9/rB/TtE3vl7/8ZVc/vWiZzJm1WMJSVdDzsfSiX04LY+2xbGrqNZRU17N630UenxneoWvDvB25Y9QAPj2cCcBbO8+xdHiASb0hEUKIjsqvUPPtKaWZd1vl9EGpxrgg1o+5Mb54OppmEmNupuJ8QaVBIufrbMPiYf4sGeZPmIm0UegzKi8rydvJ/0F+G/2JbV2VKpSxPwH/EX2nkIlOp8R86kulKN2C/2t9PmKOsooKFYTPUhK4sFlgJcVwRMd1y3oHlUrFT3/6U376059y+PBhvv/+exITE8nKyqKhoYHAwEDuuusubr/99u54+ptbyz1zLf+Y3UTLLAE8Ha15YGIwb+1UNkCv3neBn44LwqODn5I+PjOcbxJzqaprpKymgX/sPMef5kurAiHEzaG0up4tSUoz74SLxbRVKi3a34kFQ/2YH+uHv4vplEEvq6ln86k8Bvs6MSKodTn4JcMD+DGjFDsrc26J9mXJcH/GDnLH3ASqcPYZjfWQtgVOfKIUBNEZ6UVoZgnhs5UELmxW31qCWHIRktbB6XVQmKIcM7OA6c+CnVvzOGtH+OkG8BkK1g69E6vo97p98fqYMWMYM2ZMdz+NaNJyz1zLf31vomWWTR6MH8THCZcorWmgul7D2zvP85eFUR261sPBmoemhPDq96kAfHQog5+OCyLIXZpuCiFMU1VdIz8kKwncvnNFNBrZBwYQ4mnPwlh/5sf6EuJpOm9AGzVa9qQVsu5YNjvOFlCv0bIw1s8gmZsb44uNpRmzo3ywszKNPYB9xuUkJYE79UXb7QQCRinLKKOWtE6MeltVISRvUGYRs48Yntc2wpmvYeR9rY8HGS8YKERH3fBfoUcffZTRo0ezbNkybGxkXW+va7nMUttiGchNNjMH4GhjySNTQ3nh27MAfHr4EvdPDO5wL5/7Jwbz2eFMcspqadDoeHlrCv+6a0R3hiyEED1K3aBhV0oBG0/msjOlgLpGIzMgQICrLQti/Vgw1M/kyumn5Vey7lg2Xx3PoaiqdcGS75MvU6luaLXn2tnWkluHBfR0mKartlSZwTrxCeQlGh/jHAixt8PQO8AjtEfDa1ddFZzdpCRwF3aDTmN8XMAoZQ9f5IIeDU/cHG44mXv77bdRqVSMHj2a8PBwZs+eTVxcHEOHDiUuLo7IyEjMzU2j/HC/0HJmrrFFf5+bMJkDWDE2iPf3XyS3XE2DRscb29J4/fa4Dl1rY2nO72ZH8NgXiQB8d/oyRzNKGDmwD30SKIQQndSg0bL/XBGbTubyw5l8quqMF/LwdLRmXowvC+P8GDbAxaQSuPKaBjaezGHdsWxOZhuvJOjhYMWiOH/q20hwxQ3QapTkJ/FTOLsZNEaqflrYKI2wh61QqlL2pX5wTSpy4OtfGD/nEQFDb4PoZeAmRdRE97nhZG7Tpk38+OOPODsrJV+3bdvGtm3b9H/0raysGDJkCHFxccTGxuq/XFxcbvSphTEt98w11jZ/fxMuswQlIXtsZjhPrjsFwIbEHH42eRCRPk7XuFKxMNaP9w9c5NSVf+z/uvkM6x8abzINboUQNweNVsfhi8VsOpnHlqQ8ymoajI5ztrVkbowPC4b6McZE94F9+WMWf/omyWiSZmmuYuYQb5aNCCA+zFP+1ne1kouQ+JnyVZFtfIz/CCWBi17ad9oJaLXK0smA0a2TSs8IZb/bZeU9Bk7+StxDl4N3dN8pxCJMmkqna2tb8/W5dOkSiYmJJCYmcvLkSRITE8nIyGh+wis/2AMGDCAuLo6vv/66K5/+pmK0w3xxOvxj+JUHZkCLf6yevgyWprNBvaM0Wh1z/m8v5wqqAJgx2IvV94zq8PVHLpaw/J1D+scrxw/s8N47IYToLTqdjsSsMjaezOXbU3kUVBrveWZvZc6sKB8WxPoyMdTT5PtqnswqY9E/D7Q6Fu3vxG0jBrAw1g9X+z5USMMU1NfA2Y3KMsq2mnrbeSj74IatAK/BPRtfe4rTlSqap/4HZZmw8jsYOKH1mKMfQN5JiLkNAsf1zRlE0aOMvj/vRl2+czcoKIigoCAWLVqkP1ZRUaFP7JqSvOTkZDZt2tTVTy9a7plDqyRv9p7KV331TZnMmZup+O3sCH7+8TEszFT4ONvQqNF2+BPX0cFuLBnmz1cncgD48GAGYd4O3DWmj/evEULcdHQ6HSmXm5p555JVUmt0nJWFGdMivFgY58fUCC9srUxrO0SFuoFvT+Xx48USVi2PbbVEdGiAM+HeDhRX1bN4mD/LRgQw2LdjqzVEB+l0kHMMTnwMp9dDvZGehCpzpRpl3F3K/5r3kfY/NSVKIZOT/zMsZHLyc8NkbuS9PRebEEb0SBkmJycnJk2axKRJk/THNBoNKSkpPfH0Nxfrq/rb/OoEOPn2Tix9yKwh3vxqWihLhwcw0KPzFSlfvDWGcwVVnM5Rllv++ZtkBnk4MC7EvatDFUKITrtYVK0kcCdz9asQrmZhpmJimAcLhvoxK8q7VVEPU6DV6jiYXsy6Y1lsTb6MukFZmXLP+IHEDnDRj1OpVKy+exS+LjZYyjLKrlVbpvRUO/5R2z3hPMKVGbihd4Cjd4+G16bGeji/TUnW0r4HTb3hGJWZ0itOiD6m12rqmpub66chRReytAFzq+Y/RHWVgCRzKpWK38yKuO7rba3M+e/dI1n49n4KKuto1Or41ecn2PfkVJP7RFsI0T9kldTw3ek8Np/K03/QdDWVCsYEu7Eg1o9bon1xM8ElhJeKq1l3LJv1x7LJLVcbnF97LKtVMgcQ6C5NmbuMTgdZR+DYh8qMVqOR2WArR4heoiRxAaP61l6yHc8rSyXbaoXgNUTpZRdzm3w4LvqkG07mUlJSiIyMvOFAuuo+AmV2rqZY+b6uondjMSE+zja8e/dIlr9ziPpGLUVVdaw7ns1Px8pySyFEz8gpq+W7U3lsPp3HyayyNsfFDXBhQawf82J88XE2vbZBVXWNfHc6j3VHszmSYfxNuIudJYvj/LltpLQR6Ba1pfD/7N13WFTX1sDh3wy9dwQURVHBCjbsxiQm0dhiYnoxMb2Ynpt+b25y86XdJDe9N1M1RZOYYoy9V7BXBAQEAekdZub7YwMzwwwIwgxtvc/Dwzlnn3Nmgwizzt57rd2LVBCXfdD6OT3HwfAbYOAscG6ndVpLsi0DOY8gVUog5ioIGdK+gk8h6mlxMDd48GCuvPJKHn/8cQYPHtzs6xMSEnjxxRf58ccfqaqynl1LNJOLtzGYK7f+tFaoKTknC8ro4df0J7Sx4b5cPSqcLzanAPDx+uNcE9ezU2Z8E0K0Dyfzy/h9bwa/7c0g/kR+g+dFh3jV1YLrzCNPFdU6Jr28mtwSy6lwWg1Mjgrm8hE9OG9AMC6OMnOiVRkMcGIz7PxCFcCuthwJxc0fYq+B4fMgqL/du2hVRZGqB+fiBQPq1XobehXsWqhKIURdrEbhIs8DBykILzqGFv+kPv3007z66qt89913xMTEcO2113LOOecQExODk5PlfPyKigri4+NZvXo133zzDQcOHMDDw4N//vOfLe2KqGW6bq68EIqzVBYmfTX0HNN2/WonDAYDa49k8/Kfhyksr2LVQ5Oblb3tlol9+HJLCnoDpJwu5a/9mUwbIlMvhBCtJ7OgvC6A25mS1+B5kUEeTB8axoyhofTv5tXgeZ2Ji6MDE/oG8svuk3XH+gZ7cvmIHswZ1p1g7843EtnmSnPVerKdn0POEevnREyEETeqYMnRxZ69s85ggJRNKovmgaVQVQqhMZbBXM+xMOcDiJrWfkohCNEMrVKaICsri+eff56FCxdSUFCARqPBycmJiIgI/Pz88PLyorCwkNzcXFJSUqiursZgMODj48NNN93E448/TlBQUGt8PV1Kg6lPP59hTP877AaIX6i2w4bBbWvs18F26mR+GZNeXk21Xv3oPz1jIDdPaF5Bz7u/2cVvezIANVq35K5xnaqgrhDC/k4VqgDu970ZbE9uOIDrE+jB9KGhTB8aSlQ3r075u6dap2ftkWwWbU/lokEhXDbCfKrkpmM53P7VTmbFhHH5yHBievh0yu9DmzIYIGWjCuAO/Gw9KYh7oHEULrCv3btoVUE67P4G4r+GvCTL9ru2tK/yB6LT6ZClCYKDg3njjTd48cUXWbx4McuWLWPjxo0cOWL59CYkJISJEycyffp0rrjiClxd5Qlaq3PzNW6bThPIS7F7V9qjMF83rooL56stJwB4c+VRLhveHV/3picGuH1Sn7pgLiE1nx0peYyK8LdJf4UQnVdWYTl/7MtUafRTcmno8WpEgDszhoZx8ZBQBoR2zgAOIDmnhMU7UvlxVxqnClVdvNMllRbB3Jg+AWx/cgquTjKNstWVnFbB0M7P4fQx6+f0maxG4aKmg2M7SKpTVQ6Hf1ejcImrACv/kVx9VEFvR3nfKTqXVp0Q7Obmxrx585g3bx4A2dnZZGVlUVBQgI+PD8HBwTICZw8eJt9jvUnR8LJcNW+8fvmCLuj+Kf35Of4kRRXVFJRV8cbKo/xrZtOzqw7t4cuYPv5sOa4WTb+16hhf3DSq077BEkK0npP5ZSzfn8kf+zLZntxwANfT350ZNSNwA0O9O+3vl7JKHX/sy2DR9lS2JlkmM9mZksexrGL6BnvWHdNqNbhqJZBrNQYDpO2A7R+rjJQ6KwXmPYJh2LUqoYl/H/v3sSFJ62HRdVCeb6VRA5HnqiyaUdNVxm8hOhmbru4MCgqS4K0tmAZzVWXmpQryT0A3KQkR6OnC3ef15cU/VK3DLzencN2YXkQGeZ7hSqPbJ0XWBXPrjmTz8vLDPDpVMrIKISwl5ZTw575M/tyXwe60hhNThfu7MX2IWgM3KKzzBnAGg4G96QUs2p7KL7tPUlRebXGOk4OGKQO6ccWocHqfRX1Q0QSVJbD3e9j+CWTusXKCRiUDGXGjWlPWXgp7mwoeqL4OU34REHudykbpG94m3RLCXiRVT2dkGsyV5oBPOOQmqv28FAnmatw4LoKvtqSQlldGtd7AC78f5ON5o5p8/Tn9g5jYL5D1R3MAeG9NImE+rlw/NsJGPRZCdBQGg4GDGUX8uT+T5fsyOXyq4WLD3X3d6kbghnTvGmu/vt2WyhNL9lpt6xfsyZWjwpkzrDsBnu0gkUZnlH0EdnwCCd9ChZWHCx7BMPx6NQrnF2H37lnQVavpk3u/h5lvgLNJtlaPABVoHvsbBs5Wo3A9x4FWCsKLrqHFwdz8+fOZMGEC8+fPb9Z1L730EsuXL2fVqlUt7YKozyPQuF2SDX69jMFcvqybq+Xq5MDj0wZw9ze7APj7YBZrj2RzTv+mjSZrtRreuXY4V7y/mUOZ6o3av37ZT5CXK1MHh9is30KI9kmvN5CQls/yfZn8uT+TlNOlDZ4bEeDO1MGhTB0c0iWTd0wZEMzTP2vQ1SSi8nB2YGZMGFeMCmdYuG+X+37Yha4KDv2mplLWJkmrr9cEGHUzRM9oH2vhTieqdXC7v4UitU6dvlMg5krz8y76P5j9Drh627+PQrSxFgdzn3/+OUCzg7lDhw6xdu3alr68sMZ0ZK4kB7qPMO7nn7B/f9qxi4eEMLKXHztqUn8/tXQvf91/Dm7OTVuL4e3qxGc3jWLOO5vILCxHb4B7vtnFG1cNY/pQKVcgRGdXrdOzLTmX5fsyWb7/FJmFVupu1YgO8WLq4BCmDQ6lfzfPTh+wpOeX8cOONFYeOsX3d4w1q/kW7O3KuVFB5JdWccWocKYPCcXDRSYL2URBOuz6QtWGK860bHf2UtMRR93cPrI8VleomnC7voCkdZbt8V9aBnMylVJ0YTb9zZmbm4u/v2T4szuzYC4bfHsa9yWjpRmNRsOzswcz8+0N6PQGUnPL+N/KIzw+rel/0EJ93Ph8/iguf38zReXVVOsNLPh2F2VVMcytl4FNCNHxFZVXse5IDn8fPMWqQ1kUlFU1eG5suC9TB4cwdVAIEV1g3VdFtY6/D2SxaEcq649m1yV3+ftAlsUDrneuHS5FvW3FYICktWoU7tDvYNBZntNtsArghlzePhKjZR9RAVzCNyphW31aRzWdctj19u+bEO2YzYK5//73vzz99NM88cQTPPnkk2hl7rL9mGWzrDLfl2mWFgaGeXPrxD68v1ZNRf1hRxoLzuuHZzOeEkeHePP1LaO54dNt5JdWoTfAw9/vpqxKx/Vjetmq60IIO0nPL2PlwVOsOHCKLcdPU6WznoJSq4G43v5MHRTCRYNDCPVxs3NP28bhzCIWbU9lSXwaeaWWwe3iHakWwZwEcjZQUQx7voNtH0H2Ict2B2cYeIkK4sJHQ3sZHf7xFrUezprAKLV2L+Yq82UkQgjABsGcTqfjjjvu4NNPP8VgMPDMM8+wfPlyvvrqKyIiIlr75YQ1rr6gcTA+iTN94pZ/Qj2xay+/wNuJ+87vx+97M4gN9+XpGQObFcjVGtrDl+9uG8N1H28jp1ildX566T7KK3XcOqkdpXEWQpyRwWBgX3ohKw6e4u8DpziQUdjguU4OGsb3DWTa4BCmDOjWZZJ2FJVX8evuDBbtSGV3ar7Vc3oFuHPFyHCZpWBrucdh28dqfZm1hCY+PWHkTWpUy7MdZhkPqFdw3NENBs2BEfPaV9ApRDvUqsFcbm4uc+fOZc2aNfTr149XX32Vp556ik2bNhETE8Obb75ZV4NO2JBWq55eFZ9S+w4mbywqCqEsD9xl+qspN2cHfr1nAj7uLUu7HB3izeLbx3Dtx1vJKFBrZ57//SDFFdXcc15fnBxkhFqI9qqkoprNiadZfTiLlQezGl3/5uvuxHlRwUwZ2I2J/QLxcm2HKdttyGAwMOvtjSTllFi0uTppuXhwKFeMCmd0b/9OvzawzRgMKsPjtg/hyHKsFsruOwXiblOf27ouX2UJ7PtJPWgecaN527DrYO1LEDxIBXBDLgc337bopRAdTqsFc0eOHGHGjBkcO3aM8847jx9++AFfX18uuugiHn/8cV5//XXmz5/PsmXL+PDDD1vrZUVDPIKMwZyuEqa+qEoU+PZsH3Pj26GWBnK1+gR5svj2sVz78VZO5Kpsdm+sPMp7axLpE+TBgFBvokK8iArxYkh3HwIbeIpfrdOz4VgO3bxdGRBqzNC1KTGHn+NPUl6tw9lBi5uzA+MiA5gyoBuOEiwK0WQGg4FjWcWsOZzNmiNZbE/Ko1Knb/D8iAB3LhjYjSkDujGil1+X/v+m0WiYNjiEd9ck1h0b2sOHK0aGMys2DO8uFtzaVUUR7P4Otn4Ap49atjt7qeLeo26FwL6W7fZ2MkGthdvzPVQWqbIHsdea16zz6QF3b4eASBmFE6KZNAaDwfrE/ybSarXExMRw4sQJ8vLyuP3223n77bdxcDB/ArRy5UrmzZtHRkYGoaGhBAcHs3v3bnQ6K4tyRZMMGqTqxe3fv9+yceFsOL5GbU9/Tc2PF812LKuI8io9g7v7NPvazIJyrv14C4nZlk+ua2k1MLFfEFeMDGfKwOC6NSS5JZXc8dVOtiWpReDjIgO4bkwvlsan89eBU1bv1d3XjevH9uKa0T3ljZQQDSipqGbjsRzWHMlm7eFs0vPLGjxXo4ERPf2YUhPARQZ5dKlRpmqdnjWHs1m0I5Uh3X249/x+Zu3JOSVc8u5GLontzhUjwxkYJmnhbep0oloLl/C1mmVTX0A/GH27WlvW1g9tK4rVGridn0NGgmX7lV/BgJn27pUQdtHo+3MbaJVgTqPRoNVqefXVV7n33nsbPDcvL49bb72Vn376qe4PogRzZ6/RHxbTxcSTn4DJj9qxZ53D3wdOcf+iBLxcHfnt3on4ezS/5k5OcQWP/biH1Yez6+opNcTX3YlLYrszoW8gzy47UDeq11zdvF14eW5Mk+vlCdGZGQwGjpwqZu2RLNYczmZ7cm6DyUsAvFwdmdgvkMlRwZwXHdzgyHlnlpRTwuIdqfy4M42sIrX+N9THlQ2PnoeD1jyYrdLpZfq4Len1airl1vfh2AorJ2ig34UqiOtzbtsXys46CNs/USOHlUWW7S7eagrl6DsgqL/9+yeEHXTIYM7b25tFixZx0UUXNemaTz/9lPvuu4/S0lIJ5lqg0R+WPx+HLe+q7VG3wvT/2rFnHd/J/DLOeWV13Zu+86OD+XjeyLN+Kl9RrSMxq4TDpwo5lFnEoYwiDmQUkl3zRqm5hvf05dyoYKp0ehJzSli+L5PqesHitaN78uT0Abg7S+0m0bWk55ex8VgOG4/lsCnx9Bn/nw0K8+ac/kFMjgpmeE/fLjl9sqxSxx/7Mli0PZWtSVbSwgNf3hzHxH7ykMguKktUoewt71ufSunirZKZxN0C/u0gwVb+CVhyB6RstN7eI06tkxt0CTh3/hIdomuzdzDX4nd5en3D6wsaMn/+/GYXGRfNZJq+tyS77frRQYX5uvHo1Gj+89tBAFYeyuKTDUncMvHs/mi6ODowMMzbbBqSXm9ga1Iu3+9M5fe9GZRXmf9f0mjg0anRxPTw5d01x1h/NIfuvm78Y2oUs2LCzALLzIJyvticzCfrk+rW/Hy99QT7Txby7a1jmlwEXYiOKK+kks3HT9cFcMmnGx/V9nZ1ZGL/ICb3D+Kc/kEEe7vaqaftS23Gzu+2n+CXhJMUVVRbnOPkoOGCgd24clRPxkVKWnibKzypEprs+AzK8y3bA6Ng9G0w9Cpw8bR79xrk2c2yFIKLN8RcrYK4bgPbpFtCdAUtHpkTbafRyH/XQvhlgdruNQFmvAZrX1Z15hxc4Kbf7NjTjslgMHDzFztYdSgLUG9qfrxzHEN7+Lb6axWVV7FsTwbf70hl14l8vFwdefXyGC4cFFJ3TkFpFV6ujmi1DY8OHsos5IFFuzlokkb9woHdeO+6ERbTo4ToqLKLKtienMu2pFy2JuVyKLOQxv6SaTQwOMynZvQtiNjwrjn6Vt/fB05xy8IdVtv6d/PkipHhzBnWvcuUWmhTJ+Nh87uw/yfQ1w+qNdB/as1UysltmyBEr4fElRAaa1ni4O9nYMPrEDIURt0CQ+bKKJzokjrcNEvRdhr9YTn8B3x7ldoOjILLPoYPJqp9Rzd4MkMyRjVBbkklF7+xvi5FeXdfN765dTS9Amz3ByqrqBw3J4ezTnVeWa3n6aX7WLQjte7Y/PG9+edMeTIqOqa0vFK2JeXWfRy3kg6/vt6BHozvG8D4yEDGRgbg6978Na+dicFgsJgmXl6lY/T/raSgTBX59nB2YGZMGFeMCmdYuG+XSvbSJvQ6OPy7CuJObLJsd/JQKftH366yPLalkhxVw27Hp+qh8HlPw6SHzc8pPKk+uo+Q9xeiS+tw0yzr0+v1vPvuu/z4449kZGQQHBzM4MGDiY2NJTY2lqFDh+Lq2jWntNiVh8kTs5Js8Otl3K8uU8c8g+3frw7G38OZN66K5eqPtqA3qLU4c9/fzML5cWblAlpTsFfL/n84O2p5fs5gcoorWFkzqvjpxiR6B7pz/diIVuihELZTXqVj/8kC4k/kE5+aT3xKHicLGq73VivIy4XxkQGM6xvI+L6BdPd1s0Nv27+MgjJ+2JHGkvh0Ft0+liAv4yibq5MDc4Z1Z296AVeOCmf6kFA8XGSNrc1VFKnAaOv7kJds2e4TrmrDDb+hbWutGQyQulUlNDmwVJU5qrXzc5jwgHntOu8w9SGEsKtW/639zDPP8Pzzz1M74HfkyBE2bNhQ94RPq9XSr18/YmNjGTZsGI888khrd0GA+Zq5slz1hM/V1zgHPy9ZgrkmGt0ngH/NHMS/flFPWLKLKrjig818euMoRkW0z+Lrjg5a3rpmGFd+sIW96QWAKl4+OSqYcH/3Nu6dEIrBYCApp4SE1HwSUvOJP5HPwYxCi2Q+1oT6uBLX2199RPjTN9hTRpJqVFbrWXXoFN9tT2XdkWxqv50/7Urj9nPMR3iemj5AppzaS16KWg+3a6H10gI9RsGYu2DALHBow6C6ohj2LFJBXJaVkQWNFkJjoCwfPALs3j0hhLlWn2YZERFBRkYGX3/9NVOnTiU/P5+9e/eSkJBAQkICu3fv5tixY+j1ejQajWSzbIFGh3ErS+D/TJ6QPXQEvrsa0neq/dnvqOkbosm+35HKYz/trSsx4OSg4f3rRnD+gG5t3LOGZRWVc/EbG8gpVtn8LhjYjY9uGNnGvRJdUWW1nmNZxRzIKORgRiEHThZyIKOwborfmfQO9CAuwr8ugOvh5ybBWz3HsopYtD2Vn3alc7qk0qK9X7AnKx48pw161sWl7YBNb8LBX8FQL2mcxgEGzoIxd0P4qLbpX63cJBVsxn9lPdj0DIER82D4PPDpbv/+CdFBdPhplrm5uUydOpW5c+cC4OnpSY8ePZg2bVrdOaWlpezZs4fdu3e39suLWs4e4OQOVTVZ3Uqy1dq52mAu+3Db9a2DunxkOD5uTtzzbTyV1XqCvVwZ3tOvrbvVqGAvV56cHs0Di9T/tRUHTrHy4Kl2HYCKjs1gMJBVVEFiVjGHMlUJjgMnCzmaVdRofTdTbk4ODOnhw7BwX2LDfRnRy6/LZpw8k5KKan7bk8GiHansTMmzek64vxtXjAhn7sgedu5dF6bXw5E/VRB3YrNlu4sPjLgB4m4H33D798+a42uMJY1M9Z4EI2+G6OngcHZruYUQttPqwdyQIUPO+LTU3d2dMWPGMGbMmNZ+eWHKI1DVfgEVzAVFGdskmDsrFw4K4ZtbRvPA4gTeu3YEfmdRSNzeLontznfbjLWjnvl1P+P7BuLqJOUKxNmrqNaRcrqUxKxiErOLOZ5dQmJ2MYnZJRRbSXHfmL7BnsSG+zKspwreorp5ydS/Jrrlix1sPn7a4rizo5Zpg0O4cmQ4Y/oENJoFV7SiqnLY8x1sett6fTi/3jDmToi9Bly87N+/Wroqy8Bs6BUqI2V5viorEHstjJwvxb2FaOdaPZi7++67ueOOO8jJySEwUGrStCmPYJNgLsc8mMuRYO5sjYzwZ9VDk3Gy8mbzZH4ZYe0s8YJGo+G5SwZz8RvrqdYbSM0t47UVR3ji4gFt3TXRjpVV6kjPL1MfeWWk55dyMr+8ZruMjIIymrC0zYyTg4a+wV4MDFU1F2s/+7jJ0/6msJaRcmZMmFkwNzDUmytHhXNJbHd83OX7ajelubDjE9j6IZRkWbaHj4FxCyBqmnnSEHvLOaqmUu5fCvdsAzeT2SXOHjD5MdA6qvpw7amOnRCiQa0ezF1zzTX8/vvvzJ49m59++olu3WQ6V5upn9Gy+3Djfl4KVJWBU/sKPDoKa4Fcck4JM97awPQhofxr1kDcndtPVrj+3byYP6E3H647DsCH647TL9iTy0e2k+k97ZxOb6CkspqSCvVRXKGjslqtfam/7NhBq8HZUas+HLS4ODng7KD2XWqO2XOUxGAwUFGtp6SimtJKHSWV1eSVVJFbUkluSQU5xZU125WcLqngdHElOcUV5JU2bS1bQwI9nYkM8jQL2voFe+HsKCNuzVFZrWflwVN8vzMNP3dnXr0ixqx9Zkwob6w8wgUDu3HVqJ4M7u7TRj3tovKSVWmB+C+NyxrqaGDADBh3L4THtUXvFL0ejq2ArR+oGnG1dn0J4+81P3fMnfbtmxCixWzybvPf//43U6dOZciQIdxyyy3MnDmTYcOGSUkCezPNaFmSDX4RqmC4rgIwqCd0oUPbqnedSmW1ngXfxlNcUa3WrpzI4+1rhhEdYpvyBWfjvvP7sepQFseyigF4/Ke9deuQ/tqfSbXOwB2TI+kd2PWKvFZU6zh6qphjWeojJbeUzIIyMgrKOV1cSVlV6yZqcjQJ+JwctDhpNTg6aHF00OCkrfnsoMXJQYOjyb5Wo8FgMKAzGNDpDehrP+upO1ZZraesSlcXvJVWVjd7BK05X0evAHcigzyJDPakT6AHkcGeRAZ6yqhQC+1LL+CHnWn8nJBeF1i7Omn516yBeJvUoPRydWLTY+fjINMo7etkPGx8U6Xsr5/UxNFVTVEce3fb1ocrL4D4r2H7R5B73LJ97/eWwZwQosNp9WDuzz//5NJLL6WiogKDwcCLL77ISy+9hFarJSoqimHDhtWVJYiNjcXfv+1Tu5eUlPDTTz+xbds2tm7dyu7du6msrOSFF17gscceO+v7Llu2jFdeeYWEhAQMBkNdKYYZM2a0Yu8bUX9kTusAgf3g1D51LPuwBHOtpKCsyuzN1LGsYma/vZGnZwzk2tE920XWPQ8XRz67cRRz3t1ITnEl1XoD8z7dZnbO0oR07j2/H7dN6mN19LGzqKjWsfV4LhuO5bAzJY+9aQVU6vRnvrCVVOsNVFfqKK1s39l8HbUaQnxc6e7rRnc/N/W5ZruHnzs9/Nw69c+JvZ0uruDnhJN8vzONgxmW2QTLq/T8tf8Uc0eYJzKRQM5ODAY4ukIlNUleb9nu5q/qw8Xdav4w1d6yD6uplAnfQlWJZXvQABh9Gwy90v59E0K0ulYP5h5//HHKy8u5+OKLmTp1KgUFBezevZuEhAQOHjzIgQMH+OabbwC1lqe6unkL5W3h6NGj3HDDDa16zzfffJP77rsPR0dHpkyZgouLC3/99RczZ87kjTfe4N577fA0zCyYy1Gfw+PA2VOtn/Ptafs+dBFBXi58f8dYXv3rCO+vTQSgolrPU0v3sfFYDi9eNrRdrAsK93fn43mjuOrDzZRXWQYvFdV6Xll+mJ9rgrppg0M7zRvFap2eVYey+DnhJGuPZDc7SUctZwctHi4OuDg6UBujm36HqvVqWmNltZ5Knb6ulEV74eqkxdfNGX8PZwI8nQnwcMbfw8VkWx0P9XGjm7drp/n3b6+qdHrWHs7m+52prDqUZTXjp0tNMpPLR4Yzto/U9bI7XRXs+wk2/g+yDli2+/WGcfdAzDXg3MZ1PH+9TxX0rk+jhaiLYfTtEDER2sEDRiFE62j1YO7w4cPExMSwbNkyi7bi4uK6wC4+Pr7dlCbw8vLi5ptvJi4ujlGjRvHjjz/y/PPPn/X9jhw5wkMPPYSLiwurV69m7NixdcfHjRvHQw89xLRp0+jXr19rfQnW1R+ZA5jxum1fswtzctDy2LRoxkUG8ODiBHKKVZ2nP/ZlsietgA+uH9Eu1rPEhvvyvyuHcfc3u9DpDTg7apnUL5DdaQVkF6l6dEdOFXPPN/H0CjjMrRP7MHdED7Psl7XrxNrDiOOZZBdV8OWWFBZvTyWzsLzB85wdtUQGedK3Zrpgd183QnxcCfJywdPFEU8XRzxcHJu95qt26mNltZ4Knc64XfO5Wq+nSmegWmegSq+nWmegWqcCwWqdwaRdj84ADhrQajVoNRoctBocNBq0Wg0OWtBqNDg7aHF3ccTD2QE3Zwc8nB1xd3HA3dkRNycHCc7amQMnC7ll4Q6rbcN7+nL5yHCmDw01m1op7KSyFBK+ViNxtcnETHUfqaYpRs9o26QmpoIHme+7+qracCNvBr9ebdIlIYRttXowFxISwoAB1rPkeXp6Mn78eMaPH9/aL9sikZGRfPzxx3X7P//8c4vu98Ybb1BdXc3dd99dF8gB9O/fnyeffJIHH3yQN998k7feeqtFr3NG9dfMCbuY1D+I3++byIOLdrPhmBoRTc8v45qPtrDw5tHEhvu2bQeBqYNDWH7/RNLyyhgZ4Y+niyMFZVW89OchvtlqfNOScrqUp5bu439/H+Gm8b1xd3ZgxYFTbEvKpX83Lz64fgTh/m38JLoB+aWVvL/2OF9sSra65s3VScuEvkGMjQxgRC8/BoZ62yQ5h4NWg1tNYAXyhrwryy+txMfNyewhyNAePvQL9uRozVrWYC8XLh3eg7kjetA3WLIJtomyfLXObMv7UJpj2R51sUpq0nNM241wFWVCxh7of6H58dhrYPV/wCdcTfkccnnbjxYKIWyq1YO5OXPm8Msvv7T2bTuU2lHJ2sLppi6//HIefPBBfv31VzsEc1amWQq7CPZyZeH8ON5bm8hrK46g0xsoLK/muo+38vlNoxgZ0fZrRfsGe9E32FjnyMfNif+bM4TrRvfi/bWJLNtzsi5xRk5xJa8sNy9ncSCjkKs+3MLXt4wmwNOZt1cd47e9GYzpE8DTMwa22bRSnd7A55uS+d+KIxTVm0rp5KDhwkEhzIntzoR+UmtP2F5FtY5VB7P4KT6dNYez+P6OcWYPdDQaDdeM7sn25FwuHxHOxH6BUmOvrRRlwuZ3YMdnUFlk3qZ1VGvMxt9nXubH3jJ2q+yZ+35UiVYePACuJom2XDzh9vVqGUUHmDkhhGi5Vg/m/vnPf/LTTz/xwgsv8Pjjj7f27du9/Px8TpxQIxvDhg2zaO/RoweBgYGkpKRQUFCAj48Np92ZBnNVJVBZourICLvQajXcfW5f+gV7cvc3u6jSGajU6Vs9M2JrGxjmzZtXD+ORi6L4aP1xFm1PpaLaenKQ9PwyrvhgMwaom6L5w840thw/zdvXDLf7KGRidjH/+GEPO1PyzI4HeDhz88TeXDEynEBPF7v2SXQ9BoOBHSl5/LQrnd/2nKSw3PhQYcmuNIv/FzeN781N43vbuZeizulENZUy4RvQVZq3ObnD8HkqM6VvG5Vy0evhyJ+w5V3zxCuVVaokwti7zc+X6ZRCdCmtHsxddtllDBs2jKeeeoqDBw/y5JNPEhXVhk+x7Kw2kPPz88PDw3rg1KNHD3Jycjhx4gRDhgyxXWfc6y2UL8lRwdzpRMjcA9lHoO/50GOk7foguHBQCB9eP5IF38bz5tWxTOwXdOaL2oFwf3eenT2Ye8/vxxebkvltTwYOWg3nDQjGzcmB//19FICsmiDOVFpeGXPf28TlI3swuncAo3r7071eMXWDwUBRRXWrrQX6ZusJnvl1f139NwBvV0duPyeSG8dF4OHSfur+ic7peHYxS+LTWRKfTlpemdVzthzPtVr8W7SBjN2w4X/Wywu4+qpkIXG3g0cbJZ2pLFEB5pb3IDfRst0rDFy8LI8LIbqUVn93s2rVqrrtr776iq+//pq+ffsycuRIhg0bVleSICCgc2bkKi5W6x7c3Rueo14b5NWeeyaDBg2yejwxMZHIyEZq2Dg4qlTJZblqvyRHPbFb8U84VJOgRqOVYM4Ozo0OZsOj5+Lr7tzWXWm2QE8XHrowiocuNH8o083blSeW7KW2ZraLo5YLBnbj970Z6A0qq+O321L5dlsqABP7BfLPGQPpG+zJn/syef73g5zML2P++N48OX3AWb+5rdLpeW7ZARZuTjE7PismjGdmDcLfo+N9z0XHsmj7Cb7Zlsru1Hyr7W5ODlw0qBtzhvdgfGSABHJtyWCAlI2w4XU49rdlu1cojL1HJQ1pq0CpIB22faCyUpYXWLaHDVN9HDgbHGQdrhBdXasHc0lJSSQkJJCQkFCXufLo0aMcPXqUb7/9tu6PWPfu3Rk2bFiLk42AWpu2b9++Zl2zcOFC4uLiWvza9TUly1/tOXbhEWQM5opPqc9BUcZgLvuQ/frSxVkL5HR6A1lF5YT6uFm5on27Oq4nPm5OvLvmGP27efHgBf3p4efOtqRc7v023iJz5PqjOUx9Yz0DQr3Yl26sofXxhiQqdXr+PWtQs9/kFpRWcdc3O9l47HTdsUBPF56fM5iLBoW07AsUook2J562COQ0GhgfGcicYd25aHAInjIy3LYMBji2Eta9AqlbLNv9I2HC/WpdnGMbTsU2GGDhLDh9zPy4RgvR02HM3W2beEUI0e60+l+XXr160atXL2bPnl13rLCwsC6wqw3y9u/fb7V8wdlITk7m8OHDZz7RRGlpaau8dn1eXupJXkmJlUKd9V7b07Npmcr2799v9XhDI3ZmvEMhp+Z7U5CmPgdFG9uzm/d9E62nSqfnocW72Zp0msW3j6VXQMdbz3jxkFAuHhJqdiyutz8rHpzEH3sz2Zacy/bkXFJOq595nd5gFsjVWrg5Ba1Gw79mDmxyQFdQVsV1n2xlb7rxyfXQHj58eP1IQnxcW/BVCWGpSqdnw7EcHDQaJvU3nyo9Z3gPliacBCA6xIs5w7ozO7a7/By2B3o9HP5dBXEZCZbtoTEw4UEYMLN9lBfQaFQZgeU1OQecPWHY9WrKp7+sqxRCWLLLo0Jvb28mTpzIxIkT647pdDoOHWqdUaEdO6zX6GkLPXuqQtx5eXmUlJRYXTeXlpZmdq5N+UUYt/NrpqEF9jceO30U9Lr28UesCzEYDCz4Jp4/92cCcM1HW/n+jrGE+Xa8ETprvFyduGJUOFeMCsdgMLDiwCn+89tBTuQaH6JM7BdIYXl13YjG55uSqdTpeXbWoDNm8yuuqObGz7aZBXIzY8J4Ze5QyVApWo1Ob2BbUi6/7jnJH3szyCutYlhPX4tgbnxkAAvO68u0waEMDPNu4G7CrvQ62L8E1r9qvdB3xESY+CD0ObdtRrmqK2Dv91BVBnG3mrcNu07Vt4u5GoZfD65tX59UCNF+tTiYe+GFF4iJiSEmJobu3bs3+ToHB4emjSx1ML6+vvTs2ZMTJ04QHx/PhAkTzNrT0tLIycmhZ8+ets1kWdchk6xWecnqc2B/QAMYoLoccpMgsK/t+yLqaDQaJkcF1QVz6fllXPvxVj67cRQRgR1vhK4xGo0qBzCpfxBfbUlh14k8Zg4NY+rgEArLq7n+k63sSVNB2TdbT5CWV8Y71wzDq4HEKKWV1cz/bDvxJ/Lrjs0f35unZ5z9ujshahkMBnanFfBLwkl+23uSU4XmCX7iT+STmltqVl/R0UFrsaZUtBFdFexZDBtes5yqCNDvQpj4MPQcbf++gapht/MzVcOuOBNcfCDmKvP1ea7ecMcGmUophGiSFgdzTz75ZN0bKH9//7rArvZj0KBBODp2rbUC06dP57333uOHH36wCOa+//57AGbMmGGfzpiOzNUGc87uENgPco6o/RObJZhrA1fF9aSsSse/f1VPjZNySpj+5nqeu2Qwlw7v0ca9a32uTg7cMrGP2TEfNye+nD+aeZ9tI6FmhG7dkWwuf38zC+fHEextPk3NYDDw0OLdbEvOrTt2/ZheEsiJFjEYDBw+VcSvu0/y6+4MsxFkU86OWs6NCqK8nZcX6ZKqKyD+K5WdsuCEZfuAmSqIC4u1d8+UgjSVlXLnF+Y17CoKYNdCy/IC8vtMCNFELY6y7rrrLvbs2cOePXs4ffo0q1atYtWqVXVvrBwdHRkwYEBdcBcbG0tMTEynyGYZHa3Wnq1cudJsVPK+++7jww8/5P333+eqq65izJgxABw9epTnn38eBwcH7r33Xvt00iyYS1GLqzUa6DXeGMylbFRTOYTd3TS+N2VVOl7+U61dLKnU8eDi3aw6lMWC8/oRFdL50077uDvx3W1jeHBxAr/vVSOVhzKL+PeyA7xzzXCzc99bm8gf+zLr9q8aFX5WiVOEMPVzwknuX5Rgtc1Bq2FC30BmxoRx4aBurVZKQ7SSylKV9XHTm1CUYd6m0cLgy9SauG4D26R7ZO6DTW/Bvh9AX23e5uCiRuX6XdQ2fRNCdAoaQyumVkxKSmL37t11HwkJCSQnJxtfzOQNV2hoKLGxsa2WBKWl5syZQ0aG+kOQlpZGeno64eHhhIWFAaq/S5YsMbum9utJSkoiIiLCrO3111/nwQcfxNHRkQsuuABnZ2f++usvysrKeO2113jggQda3OfaaaoNJUgBoDQXXjZZNP3IcVUzZ+8P8OPN6phPT3hgb4v7I87e73szeOzHPWbFhQHG9gngtkl9ODc6uI16Zj96vYEX/zzEh+uOA+Co1bDp8fMI9lKjc+uOZHPjZ9vQ1/zGOqd/EJ/eOAoHrQRyomn0egNJp0uIDDJPPpVTXEHc83/X/WxpNBAX4c/MmDAuHhIq5S3ao/JC2PEJbHobSnPM27SOMPQqmPBA28w6MRggaR1sfAMSV1q2u/rCqFtUUhPPzv+7XYiupknvz1tRqwZz1hQVFZkFd7WZLMvKytBoNOh07WO6SkREBCkpKQ229+rVyywwhcaDOYBff/2VV155hfj4eABiY2N55JFHmDVrVqv0uUk/LAYDvNgTKmoyCN6yCnqMgKJMeNVkjcd9e1QNOtFm0vPLuP+7eLYn55kdD/F2ZfXDk3Fz7vyJPXR6AxNeWkVGgSpr8MhFUdx9bl/S8kqZ8dYG8kurAAj3d+PXeyZ0yLp9wr6qdXq2JuXy575Mlu/PpLC8ivinL7T4/3TVh5spq9Izc2goM4aGSSbK9qq8UNVg2/Q2lOebtzk4q8yP4+9r279nuip4IxYK08yP+/SEsXepPro0LZu1EKLjsXcwZ/PFbF5eXkyYMMFs7Zher+fIkSPs3r3b1i/fZPUDtaY4Uxw8c+ZMZs6ceZY9aiUajfqjllkz8paXpII5rxAI6GtcIJ6yUYK5Ntbd141vbx3Dkvh0Pt+UzP6TKgD/v0sHd4lADtSUtitHhfO/v48C8N32E9xxTiT/+GFPXSDn6qTlg+tGSiAnGlRepWPjsRyW789kxYFT5NX87NRaeySLqYPNS2p8flOcZEJtzxoL4hzdYOR8GLdAleNpaw5OMOZO+OtJtR8yVAWYAy8Bh66VQ0AIYXtt8ltFq9USHR1dt+ZM2JhfhDGYyzcZfew1XgVzAf3apFvCkqODlstHhjN3RA92puSxPTmP86K7WZxXWa3H2bHx9P0d1RUjw3lz5VH0BkjNLeOhxQlsSjQWBX/+kiGS/l1YyCosZ+WhLFYePMWGYzmUV+mtnueg1ZCYbVkHVAK5dqqxIM7ZU6X1H3M3eAZZvdymSnNh6wcq+2T9BCYj5qnkYnG3Qu9zJKGJEMJm5BFRV2AtoyXApIfh3CfUKJ1oVzQaDSMj/BkZ4W/RlllQzqXvbuTu8/py9aieaDvZmrEwXzfOjQpm5aEsgLpizABTBnTj0uFNL4EiuobU3FImvry6wXZnBy0T+gUydXAIFwzohp+sgWv/Gg3ivNR6s7F3g7vl70ibKzyp+rXzc6gqUWvghs8znzrp4gVXfW3/vgkhuhwJ5roCa7XmAHztULRctCqDwcCTS/ZysqCcJ5fs49fdJ3nx0qGdrjbd1XE964K5Wl4ujvznksGSubILKyitYmNiDhcM7IaTSWH5Hn5uRAS4k3zaWFLAw9mBSf2DmDo4hHOjgyULZUfRnoO404mw4XXY/R3oTabulufDri8sR+eEEMIOJJjrCvxMslmaBnOiw0nLK2NHijFBypbjuUx9Yx0PXRDF/Am9O01mx8lRQYR4u5JZWF537InpAyQpRRdTrdOzOy2fdUdyWHc0m92p+egNsPj2scT1Nr6Z12g0nBfdjeX7M5kyIJjzB3RjdB9/XBxl6mSH0Z6DuIzdKojbvxSot1bezR9G3wExV9u/X0IIgQRzXYPpNMuCdJVpy0GeUndE4f7urHhwEv9cup8/96t6a+VVep7//SC/7c3g5blD6d+t49emc3TQclWcMRHKmD7+XDUqvI17JWzNYDCQfLqUzYmnWX80m43HcizKdQCsP5ptFswB/GNqlBSP74jaaxBnMEDKJtjwGhz727LduzuMvUetjXPuXDMjhBAdiwRzXYFvOKABDGDQQUEa+JuM1lWVQeo2yNyjsoGJdi3Yy5X3rx/B73sz+OfP+8gprgQgITWf6W+uZ2ZMGOdGBTOpXxA+7h03aL9rcl/yS6sorqjm8WnR8ia9EyuuqObJJXvZcvw0pworGj030NMZR61l8h9JYNLBtNcgrpauCn64CYpPmR8P6Avj74ehV4KjrL0UQrQ9Cea6AkcX8A6DwnS1n5dsDOZKcuC1AaBTAQGDLgUfSTDREVw8JJSxfQJ4btkBfopX/7ZVOgM/7Urnp13pBHm5sO2J8ztsEOTsqOWZWYPauhuiFen1BhKzi4kM8jRL3OPh7MCGozmcLqm0uMbJQcPIXv5M6h/ExH6BDAz17nRJf7qU9h7E1XJ0Vv1Y8U+1HxoDEx6EATNBKw8OhBDthwRzXYVfhDGYMy1P4BGoAr3atXQpG2HoFfbunThLfh7OvHZlLDNjwnhiyd66YtsAE/oGWgRyX25O5r01iVwyrDvzxkXQzVvWoAnbySmuIOFEPgmp+cSn5rEntYCiimp+v3eiWXkJjUbD6D7+/L5XTR3uF+zJuMgAJvUPYkyfADxc5E9Vh1dZAts+go3/g7I887a2DOKqyiHha1U6YOR887aR8yGlprxA5HlSXkAI0S7JX8iuwi9CBWpgmQSl1wTjseT1Esx1QOdGB7PyoXP4+2AWaw5nse5INpOjLOsuZRdVcLKgnHfXJPLR+uPMjAnjqlE9GdnLDwOw5nAWP+xMw0Gr4R8XRdMzwN3+X4zokArLqzicWcSetAISUvNJSM0jNbfM6rlbk05b1AqcNzaCGUPDiOvtT6Cniz26LOyhqlyl8F//KpSYZ6ht0yCuohh2fAqb31ZTKd381dRJ0/VvLl5wzXf27ZcQQjSTBHNdRUPlCQAiJkDCV2o7eaPduiRal7uzI7NiwpgVE4Zeb0BnMFick2Myjc10SmYPPze0Gg0nco2p3U8VlrP49rEddpqmsA293mAxzTHldAnnvLKmSddrNVgN8kb3CWiN7on2QlcF8V/BuleMs0JqOXuqDJBtEcSVF8C2D2Hzu1CWazxelgu7FsKYO+3bHyGEaCEJ5rqKhgqHA0SMN27nJkJhBniH2qNXwka0Wg1aLIOwe87tSy9/dz7flGw2JTMtz/zNtYezAy9eNlQCuS6sqLyK5JxSjucUk5RTQnJOCceyi8ksKGfbE1PMAroefu64Omkpr9Jb3CfYy4XYcF9ie/oSG+7L0B6+eMq0yc5Lr4M9i2Hti5Z/axzd1JTF8feDh52D99Jc2Pq++igvMG9zcIHh10PUNPv2SQghWoH8Re0qzIK5FPM2357qI/+E2k/ZCEPm2q1rwn7CfN24/ZxI5k/oze97M/hhZxobjuVQO4in1YCTg5aHLowiMsizbTsr7GbXiTz+2JvByfxy0vLLSM8rrcuSak16fhnh/sYpuA5aDVHdvDieXUJ0qJcK3sL9GNbTl1AfV3ko0BXo9XBgKax5AXKOmLc5OMOIm2Dig+AVYt9+FWerqZTbP4bKYvM2Jw8YdbMqMeDVzb79EkKIViLBXFdhGsyV5aonk64+xmMRE9UicFDr5iSY69ScHLTMju3O7NjuZBaU89eBTKp1Bi4c1I0eftbXyRWUVuHm7ICzo2Va+Fo6vaFDFS7PLqrgaFYRx7NLSM0tJbuoguziCvJKKymr1FFepaeiWkdFlZ7yah37/n2RRSHq819dQ0mFDlcnLa5ODrg4OeDqqK377OrkUNcW08OXy0b0MLs+NbeU+NR8k3ON57s4auu+33qDqsMW7OVq8W+w60QeReXVVFTpKK3UUVheRWFZFUXl1TXb1ZwuqeB0cSUT+wXxz5kDza4/klnER+uTmvx9O5hRaBbMASycPxpvN0cJ3LoagwEO/wGrn4dT+8zbNA4w7DqY9EhNiRw70+vho/Og4IT5cRdvtVZv9J32HyEUQohWJsFcV+EZrKa4VNdMp8tLVqmWa/UabxLMybq5riTEx5UbxkY0ek5BWRXXfrKF0godM4aGEhnsiZerIzo9lFZWE38iny3HT3Mos4ixfQJ46bKhHSJ5ymsrDvPtttQmn19epbcI5rKLKqwWtrbmktgqi2Bue3IuDy7e3eQ+LL9/ElEh5oXhH1yUQPLp0gauMNfT3/Lfpbufm9Vz3ZwciAj0oHegO70DPYgI8CA6xJt+3SxHbTtyTUNxFgwGSFwFq/4DJ3fVa9SoZCLn/AMCItukewBotTDyJlj5b7Xv5gdj7lZTPd18265fQgjRiiSY6yo0GvVHtfbJadZB82AuYoJx+/RRKMq0/3QY0S6VVFRz02fb2JdeCMCbq441ev7OE3mUV+vs0bUzMhgMxKfms2RXOudGB3FetPlUqj6BzZtKWlGtA8yDlmq9ZaKZhlgrbG1tnVljDFi+Xv0AszE5xZZFufsEeXJ1XDjdfd0Iq/mICPCgm7eLjLQJS8kbVRB3YpNl28BLYPLjEBxt3z6dToTcJOg3xfz4qFtg97cQe62aUuniZf16IYTooCSY60pChhqDuZMJEHOVsc2vF/iEQ0HNKEXyBplqKQA1JbM59egevrA//bu17Rum8iodP+1K56stKRzIUEFoYXmVRTDXO9CDQE8X+gR5EBHgToi3K0HergR4OOPm5ICLkxYXR+O0R393Z4vX+vbWMZRX6Siv1lNR87m8SkdFlY6Kmu3yKvU5tqevxfW+7k7E9PBR55hM6ay9rj4rSUrp4edGpU6Pi6MWN2cHvF2d8HZzwsvVEW9X9dnfw5lATxfCfC3/Lbv7uvHCpUOb+N0VXVbaDhXEHV9t2dZ/Gpz7BITa+eco+7Aqe7D3e3APgPv2gLPJ6LOrN9y9TWrECSE6LQnmupKwWNj9jdrOsDKtK2KCeoLpHggVRXbtmmi/nB21vHvtcNYeySb+RD7HsotJyi6hUqfHQaPBQauhd5AHY/sEoDcYuHZ0L4t7bDqWQ7i/u8U6q9am0xv4cWcar604QmZhuVnb6kNZVOn0ODkY15udPyCYHU9NqX+bZokJ923R9RcPCeXiIdazxxoMBqp0BrQaVVhbg/X3pJ/cOKpFfRCiUVkHVRB3aJllW59z4bynoMdI+/Ypc58qe3DgZ6gdrS7Jhl1fWJYXkEBOCNGJSTDXlYTGGrcz96jF4VqTRAoTHlApo4Oi5I+fMKPRaJgcFczkqOBmX1tepeOh73dTVqXjw+tHEtfbNnWlthw/zdNL93E0q9iibXRvfy4b3gN9vWGt9j6FUKPR4OzYvvsoOrH8E7D6BdjzHRjqjRL3HKuCONMp+vaQuRfWvgQHf7Vs8+8DXlJWRwjRtUgw15WEDAaNVv1RriyG08cgqL+xPSiq7fomOi3TmnbXfbyVV6+IYWZMWKvdv7xKx2srjvDR+uNmUxCdHbRcOSqc68f2avNpn0J0KMXZaurijk9AV69ERWgsnP80RJ5v34d+jQVxgVEw6WEYdCk4yNsaIUTXIr/1uhJnDwjsD9mH1H7GbvNgTggbCPVRqfQrq/VU6vQs+DaerKIKbp7Qu8X3Tsop4Y4vd3L4lHFasEYDc2K78+CF/RsssyCEsKK8UNVk2/yOZU22gH4qiBswy/4zN36+B+K/tDzebbAqezBglvksEyGE6EIkmOtqQmNMgrkEGHp5m3ZHdH6zY7vT3deNWxfuIK+0CoDnlh0gxNuV6UNbNiXKy9WRsipj5szIIA9euyK2xevYhOhSqsrVKNy6/6o6pKa8u8PkxyDmmrYb9fKttw6322A451GIniFBnBCiy5Pfgl2N6bo5a0lQQK2ly9gD2z+2njpPiGYaGeHPT3eNJ9zfWM/swcUJ7DqR16L7Bnq68PlNo/D3cObGcRH8du9ECeSEaCpdNez6Et4aAcufMA/k3PzgwudhwS4YfoP9ArnyQstjo28DV18VxF3xJdy+HgbKaJwQQgBoDAZ5t95RDRo0CID9+/c3/aLkjfD5xWrbxRseTTH/g1hRBG/EQOlptb9gV9sWfRWdSmJ2MZe+u4mCMjVCF+jpzJK7xrc4y2VBWRU+blK0WogmMRjU2rNVz0HOEfM2Jw8YezeMuwdcfezXp8x9ak1c+k71d8epXgmNnKPgHykBnBCi3Tur9+ctIL8VuxrTGkAVhZCXZN7u4gUeQcb9pHX26ZfoEiKDPHn/uhE4Oag1NznFldzw6TayisrPcKWyKTGHgpqpmqYkkBOiiY6vhY/Ph8XXmwdyWicYfQfclwDnPWm/QC5zHyy6Ht4fDwd/gcJ02LXQ8rzAfhLICSGEFfKbsatx8YKAvsb9jATLc3qfY9xOWmvzLomuZWxkgFmB6qScEq77eCvFFdWNXrfl+Glu/Gw7V3ywmcyCpgV/Qoga6btg4WxYOEuNftXRQMzVsGAnTHsJPJtffuSs1A/iTB39yz59EEKITkCCua7IdN3cyQTL9j6mwdw6tYZOiFY0d0QP/jHVWArjgoHd8HB2aPD8I6eKuPWLHVRW6zl8qoj5n29Hr5cZ4kKcUfYRWHwDfHQuHF9j3hZ1Mdy5Cea8D369rF7e6hoL4mrXxF2z2D59EUKITkCyWXZFoTGw7we1bS0JSq/xxnp0pachaz+EDLFvH0Wnd9fkvpRV6nB1cuDuc/tatJdXqbbSymru+noXRTUjdy6OWv49exBarRTTFqJBBemw5gVI+Nqy4Hev8TDlGQiPs19/Tu2HNS9aBnAg2SmFEKIFJJjrisJijdsZu9VieNO6QW6+avTu5C61f3ytBHPCJh660Hqh+p0pedz51U7evHoYS+PTOZZlrHn11tXDGBXhb68uCtGxlBfAhtdhy3tQXW86csgQOP8Z6Gvngt8AB362PhInQZwQQrSIBHNdUYhJEpTyfJUExb+P+Tl9zjEGc0lrVWYzIexgZ0ou8z7dTnFFNTd8so1KnXFU4fZz+nDhoJA27J0Q7VR1Jez4VGWErF8rzq83nPcUDLq07YKmMXeqALOiEIIHqdp1EsQJIUSLSTDXFbn5qhTPuYlqP22HZTDX+xz1dBfUOovCk+AdZs9eii6qolpPdc06TdNALjbcl4cbGMkTossyGGD/Elj5b8hLNm/zCFZB0/AbwMFOGV/zkmHbR3D+v8DR2XjczQ8ufE59jp4pQZwQQrQS+W3aVYWPNm6nbrNs7zUevGqCN10lbPifXbolxLjIQD6ZNwoXR+OvJy8XR966ehhODvIrS4g6yRtVmYEfbjIP5Jw8YPITcG88jLrZPoFcQRr8ep8qQL75bYj/0vKcETfCwNkSyAkhRCuS36hdVfgo43aalWDO0RkmPGDc3/k5FGXavFtCAIzvqwI6X3cnXBy1vHpFTIsLiwvRaWQdgm+ugs8vNi8zoHGAkTerIG7yo+Diafu+FGXC7/+AN4epvxP6mhIj6/4LVVJCRAghbE2mWXZVPUyymGXug8oScPYwP2f4DbD+VdU2+jZwdLFvH0WXNqFfIJseOw+DATxc5FeVEBRlwur/U6Ne9TNURs9QUxuD+tunLyU5air+9o8tE6349IRz/gFa+X8rhBC2Jr9pu6rgAeDsBZVFYNCpgrK9J5qf4+QKV36pioy7S/ZAYX/uzvIrSggqimDTW+qjqtS8rccouOA56DXWPn0pzVX92PoBVJWYt3mFwaSHYdj15uvlhBBC2Iy8U+qqtA7QY4SxiGzqVstgDuxbh0gIIYSRrgp2faHqs5Vkm7f591G14gbMsl+ZgcRVsHieykhpyiMYJj4II25SDwGFEELYjQRzXVn4aGMwl7a9TbsihBCihsEAh5bB38/A6WPmbe6BKkPliBvtl6GyVkiM+fRON3+YcD+MuhWcZU2rEEK0BQnmujLTdXOp2yyLh1uj10FhOvj2tG3fhBCiK0rdBn89DalbzI87usHYu2H8feDqbft+VJWB1gkcTN4meATA6Dtg+0cwboHadvGyfV+EEEI0SIK5rqzHCON2WS6cToTAvtbPrSiCXV/Ctg9Ao4V7dkp6aSGEaC05x1StuIO/mB/XaCH2Wjj3CfvU+qyugJ1fwPr/wnlPw/DrzdvH36cCOTdf2/dFCCHEGUkw15W5+UFQNGQfUvtp2xoO5kpz4a8njVNsjq2A/hfZp59CCNFZFWfD2pdg52fGtP61+l2k1sV1G2j7fuiqIP4rVVKgME0dW/syDL3SPJmJPUYFhRBCNJkMrXR1PUzqzVkrHl7LrxdEXWzc3/q+7fokhBCdXWUJrH0F3oxV0xZNA7nQWJj3K1y72PaBnF4HCd/C2yNh2f3GQA6g+BScjLft6wshhGgRGZnr6sJHq5pF0HgwB2p9xKFlajtxFWQfhqAo2/ZPCCE6E101JHyt6sUVZ5q3+faC8/8Jgy61/TR2vR4OLIU1L0DOEfM2rZNKsDLxIfAOtW0/hBBCtIgEc12daemBrANQlt/wWoiICRA8CLL2q/1tH8L0V23dQyGE6PgMBjj6F6z4F2QfNG9z9VVFtkfdAo4utu/HkT9h1fNwaq95m8YBhl0Lkx6RJFdCCNFByDTLri6gn0p1DYABjq9u+FyNBkbfZtw/9Jt6YyCEEKJh6bvgi5nwzRXmgZyDi0oocl+CylRp60AOoCQHvr+pXiCnUWvjFuyAWW9JICeEEB2IBHNdnVYLfacY94+uaPz86BnG7aIMy+k5QgghlNwk+GE+fHQuJK83adBAzNWwYCdc8KxKRmUvnkEQd6txf+BsuGsLXPqhKkQuhBCiQ5FplgL6XQB7vlPbR1eotRQNrdfwCISQIZBZ81T3+BpZNyeEEKZKc2HdK7DtI9BXmbdFngdT/g2hQ23fj5MJUFEIvSeZH5/wAOQlqzVxYbG274cQQgibkZE5oKSkhC+//JIFCxYQFxeHi4sLGo2GF1988azut3PnTp555hkmTpxIWFgYLi4uhIeHc91117Fnz55W7n0r6Hu+qmUEUJIFGQmNn99nsnH7+BobdUoIITqYqjLY8Dq8EQtb3jUP5LoNget+guuX2D6QyzoIi66HD8+BX+9TZQdMufvDlV9KICeEEJ2AjMwBR48e5YYbbmiVe1VXVzNy5EgAAgMDiYuLw93dnfj4eL7++msWL17MN998w9y5c1vl9VqFm5/Kanlis9o/ugK6D2/4/D6TYdNbajt5g8rO5iA/SkKILkqvgz2LYdV/zFP7A3j3gPOegqFXgNbBtv04nahq1u1ZDNSsZ849DgnfwIh5tn1tIYQQbUJG5gAvLy9uvvlmPvjgA3bt2sWTTz7ZovuNHj2aZcuWcerUKX777Te+//57jhw5wpNPPklVVRXz588nJyenlXrfSvpdYNw++lfj5/YcCw41RWQrSyDnsO36JYQQ7dmxlfDBObD0DvNAzsVHTadcsANir7ZtIFeQBr/cC2+Pgj2LqAvkQE2L9+tlu9cWQgjRpmQ4BYiMjOTjjz+u2//555/P+l6Ojo5s2bLF4rhWq+W5557jxx9/5NChQ/z222/Mm9eOnpT2uxBWPqu203eqjGcegdbPdfZQi/Z9e0HEeHD1sV8/hRCiPcjYAyv+aZkBWOsEcbfBpIfVdEZbKjoFG16DHZ+CrtK8LTAKzn0CBsyyfc06IYQQbUaCOTvSaDQMGTKEQ4cOcfLkybbujrlug8ErDIpOAgY49jfEXNXw+WPutFvXhBCi3chPVdMp64+AAQyeC+c/DX4Rtu/H6hdg05tQVWp+3LcXTH7cPtM6hRBCtDkJ5uzs+PHjAISEhLRxT+rRaNRUy11fqP2jfzUezAkhRFdSlq9Gwba8D7oK87aIiWq2QmNrjVtbeb55IOcVBuc8AsOuBwcn+/VDCCFEm5Jgzo42bNjAzp07cXZ2ZurUqU2+btCgQVaPJyYmEhkZ2VrdU1Mt64K5v6GiCFy8Wu/+QgjR0VRXwPaPVamBsjzztqABKojrd4F6IGZPEx6EXQvByV2VGBg5H5xc7dsHIYQQbU6COTspLCxk/vz5ADzwwAOEhoa2cY+s6DMZnDygqgQqCmDbh+pNwpmU5UHhSehmPegUQogOR6+H/T/Byn9D/gnzNq9QtR4t9lrbTmWsroCdX8ChX+H6peav5dUNrlkEYcPBxdN2fRBCCNGudYpgbu7cuezbt69Z1yxcuJC4uDgb9cicTqfjmmuu4ejRo8TFxfHss8826/r9+/dbPd7QiN1Zc/GE0berqUQAG9+EUbc0nOAkbQf88Q84GQ/ugfDAfnB0bt0+CSGEvSWtg7+etqy56ewFE+6DMXepRFC2oquG3d/A2pehIFUd2/sDxFxpfl79YuBCCCG6nE4RzCUnJ3P4cPPS45eWlp75pFZy22238dtvvxEVFcVvv/2Gs3M7DnjGLYBtH0FlkVqTseV9mPyo9XP9ekPmPjDoVbHxg7/AkHZUP08IIZrj1AH4+xk4utz8uNZRTWOc9A/wDLLd6+v1sO9HWPN/qj6cqY3/U0lN7D2dUwghRLvWKYK5HTt2tHUXGvTII4/w6aefEh4ezooVKwgMbCDdf3vh7q8yVa57We1vfgdG36YKi9fnEQCDL1NPkEFNy5RgTgjR0RSehNX/Bwlfq4dTpgbOhvP/BQGtuD65PoMBDi1Tfcg6YN6mdYIRN6pSBxLICSGEqKdTBHPt1QsvvMB///tfgoODWbFiBeHh4W3dpaYZexds/UCtm6sogM3vwnkNFFKPu9UYzKVuhYzdEBpjv74KIcTZKi+ADf+DLe9BdZl5W/gYuPA5CLfhdHyDQRUdX/Wc5ZROjRZir1GjgVL0WwghRAOkkqiNfPjhhzzxxBP4+vqyfPlyoqKi2rpLTefmB+PuMe5v+xB0VdbP7T4cuo80Ofcj2/ZNCCFaqrpSBXBvxKo1wqaBXEBfuPJrmP+nbQM5UHXivr6sXiCnUfXq7t4Os9+RQE4IIUSjJJhrgejoaKKjo0lPTzc7/sMPP3DnnXfi6enJ77//TmxsbNt0sCVG3w4OLmq7PB9SNjZ8btxtxu2930Nprk27JoQQZ0WvV4lE3hkFfz4GZSa/qzyCYfprcNcWGDDDPlMaB881/p4FiJoOd26EuZ9AYF/bv74QQogOT2MwGAxt3Yn2YM6cOWRkZACQlpZGeno64eHhhIWFARAaGsqSJUvMrtHU/LFPSkoiIiICgKysLMLDw6msrGTIkCEMH269iOwll1zCJZdc0qI+12azbCjbZYt9fYUxEcCoW2H6f62fV10Brw2E0hy1P+UZmPCAbfokhBBnI2kdrPinyr5ryskDxt8LY++xbYr/zH0QPBC09Z6h/vkEZB+E856C7iNs9/pCCCHswubvz+uRNXM14uPjSUlJMTuWmppKaqpKC92rV9OmupSWllJZWQnA3r172bt3r9XzIiIiWhzM2dyAGcZg7tBvMO1lyzciAI4uaoH++ppgb9PbKviT2kdCiLZ2aj+s+BccW2F+XOOgfm9Nfgw8g233+pl7Yc2LKsHJ3E9V0ihTFzwLDvKnWAghxNmRvyA1kpOTm32NtUHNiIgIq8c7pKiLQXOfyu5WdFI90e7RwJPj0bfDlnehqlSN0G3/SEbnhBBtpyCtJkPlN0C938kDZqkMlbacynjqAKx9EQ78bDy25kUYeIl58W8J5IQQQrSArJkTDfMIVBndah1a1vC5nsEqs2WtjW9AZYnt+iaEENaU5auRuLdGqFIDpoFcz7Fw8wq48kvbBXLZh+GH+fDeOPNADsDRFYpP2eZ1hRBCdEnySFA0bsAMOLFJbR9aBlP+1fC54+6D7Z9AyFA49wlw9rBPH4UQoroCtn8M616BsjzztsD+ai1v1MW2S2yScwzWvqSSQNUfCew2GCY/DtHTpVacEEKIViXBnGhc9AxY/oTazjkC2UcgqL/1cz0CVCY2317yhkUIYR96Pez7EVY9C/knzNs8u6kgatj1tpvOWHgSVv0Hdn9rWXA8KFq9/oBZ1tcbCyGEEC0kwZxonF8vCBmiFvEDHPoVgh5q5PwIu3RLCCE4vkZlqMzYbX7c2RPG3wdj77b9DAF9NexZbB7IBfRTiVUGzTFfHyeEEEK0MgnmxJlFzzAGc3t/hAkPNm/kraoMnNxs0zchRNeTuVeti0tcaX5c6wgj58Okf4BnkH364tsTRsxTUzz9+8A5j8GQuRLECSGEsAuZ9yHObOAlxu2s/XBkedOvTVoPb8RCciNFx4UQoilyj8OPt8D7EywDuYGXwN3b4OJXbBPIFZ6E3x+BQ79btk18CGa/C3dvh5grJZATQghhNzIyJ84sOBr6T4Ujf6r9dS9D/4vOPDoX/zX8sgAMOlh8g1pP5xVi+/4KITqXokyV2GTn52pao6le41Wtth4jbfPa+amw8X+wayHoKtWDqf5TzdfAeYfBsGtt8/pCCCFEI2RkTjTNpH8Yt9N3Wj4VtyYo2viEujRHTUMSQoimKsuHlc/Cm8PU7w/TQC5oAFz9Hdz4m20Cubxk+OVe42vrKtXxrP2Nl2kRQggh7EiCOdE0PUZA5PnG/bUvw5mKo/cYYR4EHv7DNn0TQnQuVWWqVuUbMbD+VagqNbb59oQ5H6iR/qhprZ8593QiLL0L3hwOu74AfZWxzSsMpr0C/S5s3dcUQgghzpJMsxRNd86jxhG51K2QtA76nNP4NYPmwOr/qO1T+9TTbsl4KYSwRlcNCV/BmhehKMO8zT0QzvkHjLgRHF1a/7Wzj8D6/6o6cfVLDPiEw4QHYNh1tnltIYQQ4ixJMCearudo6H0OJK1V+xteO3MwF9hXFezNOaL2D/8BY+60bT+FEB2LXg8Hf1b12k4fM29z9oLx96rfGy5etnn90lyVVEVXYX7ctxdMehiGXgWOzrZ5bSGEEKIFZJqlaJ5Jjxi3j6+B7MNnvibqYuP2od9avUtCiA7KYIBjK+GjyfD9jeaBnIMLjL0H7tutRuRsFcgBuPtDzFXGff9IuOQ9WLATht8ggZwQQoh2S4I50TwRE6DbYOP+to/OfE30dON2yib1FFwI0bWlboMvZsJXl5oX/dZo1XTGBTvhoufBI6B1Xzd9F5zYYnl84oMQPAgu/UiVOIi9BhycWve1hRBCiFYmwZxoHo0G4m4z7u/+FsoLG7+m+0jwCFbbBh0cXWG7/gkh2reT8fD15fDJBZC83rxtwEy4awvMfgd8w1vvNQ0GOL4WFs6Gj86F3x62TODkF6GSqgy9AhxkBYIQQoiOQYI50XxDLgdXH7VdWawCusZotRA11bh/WKZaCtHlnNoP310LH06Go3+Zt0VMhFtWwZVfQVBU672mXg8HfoGPzoOFs9TUcIBTe61n123tzJhCCCGEjcnjR9F8zu4w7HrY/Lba3/YhjLrVvIhufVHTVdFdUGtkqiskK5wQXUH2EVjzAuxfAtQbDes+Es57Evqc27qBVHUl7F0MG/4Hp49atocNNz6QEkIIITowCebE2Rl1C2x+BzCopAXHV0Pf8xs+v8850HMsRJ6nEqI4SEIBITq13OOqHuWeRZap/kOGwnlPqXptrRnEVRSr2nCb3oaik5btfc5VJQZ6T5JROCGEEJ2CBHPi7Pj3hv4XwZE/1f6WdxsP5pzcYP6f9umbEKLt5KfCulcg4WvQV5u3BQ2Ac59Qa+NaO5iqKoe3RkBxZr0GDQycDRPuh7BhrfuaQgghRBuTYE6cvdF3GIO5Y3+rjHShMW3bJyFE2yjKhPWvws7PQVdp3hbQFyY/DoPmgNbBNq/v5Ar9LoD4L9W+1glir4Zx96l6l0IIIUQnJMGcOHt9JqvgrTat+IbX4fLPz+5eBoNMexKiIyrJUf/3t38M1eXmbb494ZzHYOiVrZshMnWbWnfbe6L58fH3wYGfYcQ8GHMXeIe13msKIYQQ7ZAEc+LsaTQw8SFYfIPa378Uzj3WtKfgej2UZIFXCOz4FP76J0ROhssXNp5IRQjRPhRnwaY3YfsnUFVq3ubdHSY9DLHXtV7Bbb0ODv6qEi+lbVf1Lu/YYP4QKLAfPHRYJWkSQgghugAJ5kTLRM+EgH41GeMMsPF/MPvths/PTYKNb6jpmW5+KhX5bw9DzzHg1xvK88Hd306dF0I0W1EmbHxTPYSpLjNv8whWD3hG3KimPbaGiiKI/1qty81PMR4/tQ+S1qoZAqYkkBNCCNGFSDAnWkarVdnhfr5L7e/+Tq2N8elu/XyDHnZ+praLMuDHW1Qh8ayD6mm+m599+i2EaJ7Ck+pBzM7PLadTugeoKY6jbm29YCo3SU3d3PUlVBRYtvcaD45urfNaQgghRAclwZxouSGXw+r/g8I00FfBqudgzvvWzw2IhKBoyD6k9k/uUp/LciFqmqybE6K9KUhTa+J2LbRMbOIRBOPuhZHzwcWz5a9lMEDiKtj2UU1ypXp16TQOMOgSGHsPdB/e8tcTQgghOjgJ5kTLOTqrp/J/PKL2d38L0TNgwAzr50ddbAzmavWaoDLdCSHah7wUFcTFf6Ue0pjy7Kb+z4+4qXWnNZblwXfXWI78uXjD8BtUBl3f8NZ7PSGEEKKDk2BOtI6R82HPd5C+U+3/eh+EjwbPIMtzo6fDhteM+xotTHtRRuWEaA9yj6sgLuEbyzpxXqFqWvXwG1TtyJaqn8XW3R8Gz4WEr9R+QF+Iuw1irgZX75a/nhBCCNHJSDAnWoeDI8z5EN6foJIilOaogO6qry2DtLDh4BliLO474kb15H3P9yoYjJ5umXJcCGFbJxNUAqMDP6u1raa8u6sgbtj1LU9sUl2hslLu/BwGXwYjbzJvH32b+v0Rdxv0OVey2wohhBCNkGBOtJ7AvnDhc/D7w2r/8G/w24Mw9UVwdDGep9Wq85beCaGxcN7T6rz9S1S7g5NlMFdeqGpL9RzTOmtzxNmrKoMjyyEoCoIHtHVvREsYDHB8jQrijq+xbPcJh4kPQuy15v+Hz0b2Edj1hRrxK8tVxyoKLYO50Bi4ZlHLXksIIYToIiSYE61r5M1w6Dc4vlrt7/gU0nepYuL+vY3nDb1CPZUH0DpA9xHGYC59l/k9K4rhnTiV/bLPuXDDUlt/FaIxK5+DLe+oZBT37wGfHm3dI9Fcep0agdv4P8jYbdnu3wfG36+mN7akTlxVGRz4RY3Cndhk2Z6xGzL3QsiQs38NIYQQoguTYE60Lq0WLv0Qvr4cMhLUsYwE+HAy3L4O/HqZnOtg3O4+0rh9Ml692axtd3BW6+8OLFVBYmEGeIfa9usQDdvyjvps0MHW9+HC/7Rtf0TTVZVBwtew6S3IS7ZsDxumgrgBM83/fzaHwaCCtN3fqlIl5fmW5zi6qYc5I25Uxb+FEEIIcVYkmBOtzzMY5i+Hv55UdaJAvaHb8p5KdGJNaIwa6THooKpEZbvsNki1OTpD2nbjuSd3gfd0m34JogG6elkNDQbr54n2pSxP/V/c+gGUZFu2R54PE+6HiIktT0S0ZxEsud16W7fBKoAbcjm4+bbsdYQQQgghwZywESdXmP4qeIXAqpqRm0PLYOoL1t8sOrtDt4FqyhWoRCi1wRyomlKF6Wo7bYdKkiLsryDVfH/8/W3SDdFEBemw5V01zbGy2LxNo4VBl6oSA6FDz+7+umqV/MhUvwtB62QsZ+DkXjMKd5P6fyxZa4UQQohWI8GcsK1hN8Cq5wGDCgQyEtRULmu6jzQGc78sUEkXaqd6dR+pMuCBsfyBsL/cJOO2dw/rpSdE28s+DBvfgD2LLWvEObqqrJTj7gG/iObfW1cFSWth7w9wbCXcG2+elMjdH/pfBCU5EHOlKjUgZQWEEEIIm5BgTtiWVze13i11i9o/+GsjwdwI2PmZcX/pnXDJe8YEKbVOxoNeLynL20KeSTBnmtBGtA8ntqqkJod/t2xz9VXp/kffDh6BzbuvrgqOr4UDS1SCo7I8Y9uhZRBzlfn5cz9rWeIUIYQQQjSJBHPC9gbMNAnmlsH5/7R+Xo+R5vt7Fqm1PDFXQlgsoAEMKp356aMqNb6wr1wJ5todvR6O/qWCuBObLdu9e8DYu1Wh7+aU9aiuVCNw+5eqgM1aIhNQWTHrB3MSyAkhhBB2IcGcsL0BM1QyFICcw2oKmLVALLA/eAQZEzSMvkOVMABw8YKgaMg+qPbTd0ow1xZMgzk/CebalK5KTXXc+Ibx/4WpoAFqPdyQuap2Y1PpdfDLvXDoVygvsH6Ogwv0nQJDL4f+086u/0IIIYRoMQnmhO35Rag6UrXr4Q7+aj0Q0zqoaZWb3oKoaSqYM02W0GOEeTAXe43Nuy7qMZ1mufLfKvCe+kLb9acrqiiGXQth8ztQmGbZ3nOcykzZ78KmJRsxGMzP0zpA5m7LQM7BBfpdAAMvUWviZB2cEEII0eYkmBP2MWCWMZg7tAwmPWz9vH4XqA9ruo+A+K/UtiRBsT+DwXxkDiD/RNv0pSsqyVF1/bZ9ZH3KY9R0FcSFxzV+H10VnNgCR5fDkeUqGO87xfyc/lPV/1dHV9U2aI4K4Fy8WuurEUIIIUQrkGBO2MeAmbD6ebV9Ml4FAb49m3cP0yQomfugqlyVQBD2odfBzDdgxT+hOFMdqyxp2z51BblJsPlt9SCjuty8TesEQ6+E8fc2PO1Yr4dT+yB5PSSth5SNat1prSN/WQZzQ65Q05r7T23eOjshhBBC2JUEc8I+gqLBPxJyE9X+in+qjHfNqTkVPBCcPdW0ze7DVd0sCebsx8FRJaPBYCwKXVXapl3q1DL2qKQm+5eAQW/e5uypim+PuQt8ultem3UIktZB8jpI3mCefbK+I3/CtJfM/y8G9VcfQgghhGjXJJgT9qHRQNyt8Odjan//Eoi62JjgpCkcnOCRRAng2pqTu3FbgrnWZTCoIGzj/yBxlWW7R5BaSzrqZnDza/g+310Nuccbbndwgd4T1chbU9fWCSGEEKLdkWBO2E/cbSr5ScpGtf/bw9BzLPiGN/0eEsi1PWeTYK5SgrlWodep/xsb/6emIdfn1xvGLYCYq6EoQ02NTNsO6TvgvKeh7/nm5/eeZB7MabSqvmPERNXWcww4e9j0SxJCCCGE7UkwJ+xH6wBz3of3xqs1OxUF8NOtcMVC8Axu696JppKRudZTXanqKW543TgF2VRgfzWC5uCigr1V/4GyXPNzUrdaBnN9JkP6LhW49Z6kHppI9kkhhBCi05FgTtiXb0+4+BXjmqsTm+HNYWrUYezdZ58tT6+D3x+BnCMw5RnLAuSi5X57GPTV5skzJJg7O5WlEP8lbHzTenkBJ3eV7CTniPpoTNp2y2OD5qgPIYQQQnRq2rbuQHtQUlLCl19+yYIFC4iLi8PFxQWNRsOLL77Yaq8xf/58NBoNGo2GLVu2tNp9O6ShV8KQy437lcWw5gV4bxyctjI6UV9lCRxbCSv+BUf/Vsf2fg87PlEZ+768FLKsFFEWLbPvB9j5Gez70XhMplk2ncEA2Ydh6Z3wSiT88Q/zQE6jVQHYbWvBPdAy6YmpwP4Qcw1Mfw0u/I/t+y6EEEKIdklG5oCjR49yww032Oz+q1ev5rPPPkOj0WAwGGz2Oh2GRgOXvA9hw2HdK8ZpY/kn4LNpcMPPEDyg4ev/fBx2faG2S7Kh3xQ1Ba1WRQF8fTncshK8utnu6+hKyvKsZ0TUV6m6ZQ5O9u9Te1ZeqB4oZO2HUwcgYzdk7rEsLQCgcYBh18L4+yEgUh3rNhAKTqg6b0FREDxIHes2SK19ayz5iRBCCCG6DAnmAC8vL26++Wbi4uIYNWoUP/74I88//3yr3Lu8vJzbb7+dQYMG4ePjw6ZNm1rlvh2egyOMvUu9iV3/mkr8AFB8Cj67GK5fAmGx1q+NmGgM5pLWqXpziavNzylIhc8vVtn/sg+BVyhc/a0qayCar36xcFNVpeDgY7++tHcHl8Gia5t+/nlPwcQHzY+d/y814ubfR601FUIIIYSwQoI5IDIyko8//rhu/+eff261ez/33HMcO3aMdevW8dRTT7XafTsNVx+44N8Q2A9+WaCmlpXlwpdzYMFOcPe3vKb3RON2QSqkbAD/3qowskZrnJ52+pj6ADWqtPkdtV5PNF+eSTAX0Ff9uzm5q4/GpgN2NqW5KuFIyiaVddLVB6762vyc2tG1BmnArxdEng/dR0DEBMtTug1stS4LIYQQovOSYM6G9u3bxyuvvML8+fOZMMHKGzZhNOw6cHKDn25TSTbKcmH9q3CRlRFSrxAIjIKcw2q/IA3u3Ah5KWpq2/HVsPV94/mhsXD5Zyq9u1D0elj1nJraev7TZx6xNB2ZC4q2DGA6q6JTah1myiaVrCfrgHm7q49aC1dbp628EPb9ZP1eLt4w+naV7MdVRjKFEEII0XISzNmIXq/n1ltvxcfHh5dffrmtu9MxDL4M8pJh5bNqf9tHMOZO8OlheW7vScZgLmk9jLhRjXb49YJ+F6jRo9zj6nPwAPAKk8LIpg4sgQ2vqe2SLJj3a+Pnm47M+XfyoPjUfjjwCxxdbr3mm6nyAhUQe4XC9o9h3cuWawvd/GHC/TDqVvMafUIIIYQQLSTBnI288847bNmyhS+++AJ/fytTBYV1o+9UQVxRBugqVJbL2e9Yntd7Emz/SG0nrTMfHdE6QNyt9utzR7TZ5HuatO7M5xeeNG779Gz9/rQnuxaaj+zW5x6oim73HAOhMZC+U41ymhbpBjX6Nm4BjL7j7EtuCCGEEEI0QoI5G0hLS+PJJ59k8uTJrZIlc9CgQVaPJyYmEhl5pvU5HYyzO5zzKCy7X+0nfANjF0BwtPl5ERMADWBQI0vZhy3PEQ1zdG3e+ZUlxu3OMkVQVw3FmZYjv4Pnmgdz3j3Uz1uvsdBznFrfqdGoUbvlT0LKRvPrnT1hzF2qbqKbr82/DCGEEEJ0XZ0imJs7dy779u1r1jULFy4kLi7OJv25++67qaio4L333rPJ/Tu9YdfD5rdV8hKDXk27vPob83Pc/SFkiEr3DpC48szBnK4aTh9tvOxBV+Hg3LzzTYM5Z3eVbr80B6rK1L+Dd1jz+5CxB07ugoGz7ZtqX1cFu7+D9f9Vgelta82n4PYYCQNmQffh0H+qWiNo2l6cDSueht3fmt9Xo4URN8Hkx8EzyD5fixBCCCG6tE4RzCUnJ3P48OFmXVNaaptixz/++CO//PILTz/9NNHRrTNStH//fqvHGxqx6/AcHFW69u9vVPuHf1NJJQZfan5et0HGYG75E2rNnVeI5f1KcuCHmyBtB1RXwGMnwMWzaX0pzgb3ANBqz/rLaZfqj8yZTlO1prLYuO3soTKPpu9Q+5d+DEMvt35dQ4qz4JMLVN2142tVghpb0+thz3ew+gVVw61W+i7oMcK4r9HAlV9aXm8wQMLXajSuPN+8re8FcOFz8qBACCGEEHbVKYK5HTt2tHUX6vz6q0oksWLFCtatM1+LlJCQAMBdd92Ft7c399xzD3PnzrV3FzuGgZdA+BhI3aL2l90P4aPBp7vxnCFzjaMjAX3Bs4EC4W5+cDJB1UMDFYT0mXzmPqz6jypqHjYcbl6hgszOwrHeyFxlcePruly81ChWRbGaRujkZmyrOosHI0f/MhbQPlOSkdaQvhN+/4cxAK2lcYCMePNgzprTibDsAUhaa348eKCqB9f3/NbtrxBCCCFEE3Sid6fty5YtWxpsi49Xb14vueQSO/WmA9JoYM778P4EFWiUF8DSO+D6n42jZJHnq4QpqVvVqEhDI0taBwiPg2N/q/0TW88czFWWqEAO1FTA5PUQeW6rfGntwoz/wYGaeopaRxWkNRbM3bFBfTYY1GdnD2Pb2QRz+mrjtrVspa2lqgxW/Au2fQgYjMe1jhBztSrW7d+n4et1VWrK75oXjcEnqID2/H/CqFukqLcQQggh2owEc63s888/5/PPP7faNnnyZNauXcvmzZsZM2aMfTvWEfn3hmkvw893qf2kdbD5LRh/n9rXaGDai027V/gYk2Bu85nPd3RT68p0lWo/L7lZXW/33P3hqWzLEbozqQ2YnUxS7Juup2uq4mzjtoeN1pdl7IGfboXsQ+bHB86GC549c229rEPw0y2Qudf8eP9pMP2/tg1ChRBCCCGaoJMtBLKv6OhooqOjSU9Pb+uudF6x16hkFLX+/jccX9P8+/Q0CZ7TtqtkKI3RatVUz1oFac1/zfauuYGcKdNgrqqs+deXZBm3PYPPvh8N2fk5fHSeeSAXFA03/AJXLGw8kDMYVHmMD88xD+Q8guHyz+HqbyWQE0IIIUS7ICNzNebMmUNGRgagSgsAvPvuuyxduhSA0NBQlixZYnZNbdKVqqoq+3W0q9FoYOYbKnlJ0Ukw6GDxPLh1FQQ0oyxD9xFqap2+Wk3bzNqvaoQ1xrQ4tmnRbGFe/PpsplmWmI7MBba8P/W5B5pP5Ry3AM57GhxdGr+uLA+W3gWHfzc/PvwGNZpnz6ybQgghhBBnIMFcjfj4eFJSUsyOpaamkpqaCkCvXr3aolsC1JTAq7+BT6dBdZnKJPjNlXDL302v4+XsDiFD1fo3gJRNZw7mTEdvOts0y+aorlTBl7OHWivm4NjyBCim0yxX/Uet2bvg3y3va60BM1TwtfkdtfayKesdM3bDoush3+T3gEcQzH4X+l/Yen0TQgghhGglEszVSE5ObvY1BoPhzCeZWLNmTbNfQ9QIGwZz3jOWKzh9FN4bB2PvgRHzzBNyNCRivDGYO/AzjLnT+nmluWoExs9kZC63DUbmDAaVdMM0cGotn06D/BNq5HPKM9DnXPAIsH5u9iH4YKLadvGGx1PByeT7XdnCkTmA1G3Nv8eZjFsAw69v2mhawjcqW6VpkpO+F8Al79pmGqgQQgghRCuQNXOi4xg0RxVkrlWYDssfh9cHw9G/m3C9SZ26E5sbHm379ip1z01vGo+V5aqMmvaUkQCv9IW/nga97uzvk30Edi8yT1RSdBIK06AgFX68WSWWaYhZwfCaIK7F0yyzzPdbMo21sgR2fGrMtFlLozlzIKfXw9/PwNI7jYGcRqsyVV6zWAI5IYQQQrRrEsyJjuWcR2H8/aB1Mh4ry1VFwQvOkIgmbBgE9DPu7/ne8pzSXJUgpTDNct2UraZa/vmEGin77lo4usJ4fPcitb5v05twfPXZ3bs4Cz6eAktug5/vMR6vn7SkLK/he1gL5loyzVJXZfl6RRlnl0ilqkwF38seUMW8mzNaXlWmfm42vG485h4A1/0EEx/qfIXihRBCCNHpyLsV0bFoNGpt1f171BRLx5qgoqJQFRZv7M28RgMxVxr3T+2zPOfY32DQq23PbhAYZWyzVTCXsRtObIJDy6DwpDpmMMDW94znZB85u3tv+B9U1Iwo7v/JeLz+1Miy/IbvUVls3K4L5lowzbIkx/rxvBTrxxui18NPt6mSFQBb3oG9PzTt2rJ8WDgbDiw1HgseCLet6Vz1BIUQQgjRqcmaOdExeYfBRc9D8AD4+W517OhfsPs7iL264euGXqmCj6FXQnC0ZfuR5cbtfhdAyWnIUVlLbbZurvS0cbs2s6NGA0OugL2L1X5RxtndO3Wr5TGDwXI0rckjc57qc8R4leLfyaP52SjrT7GslZdk/d+kIev/Cwd/Me4PvRIGX9rw+XWvnwNfXmJediDyPLj8C3D1bvrrCyGEEEK0MQnmRMcWey3s+wkSV6r9Px8FfZWqCRbYz7J8gW9PmPIv6/cyGCB5vXG/34VwMkElCvGLOHOR6bNVajJS5W6ShMS/j3G7KPPs7n1qv/m+Xq9S9hvqrcErz2/4HtamWfr2VB9nIzAK7tiogrov5xiPNydYPvQbrH7euB95vso6qXVo/LrCk7DwEmOADqrswPTXwMGpwcuEEEIIIdojCeZEx6bRwKw34Z0xUFmkkpT8ssDYPuN1GDm/afcqSIXiU8b9XuNh4OyGg7/WoNerdXq13E1GubxCjNtnOzI36y346RbjflWJ9WQqjY7MWZlm2RJOrhAyWG2PvBl2fKK2m5oEJfuwml5Zyz8S5n6iSiY0pugUfD4dco8bj41bABc8p36OhBBCCCE6GFkzJzo+nx5w0X+sty1/Uo2sNUXaduO2X2/bFLOurzzffJTMtDyAV6hx+2yDuQEzzPcrS6wnLGl0zZyVkbnW4t/M8g/VlfDjLcYA09kLrv72zFkrS3PV1ErTQG7yExLICSGEEKJDk2BOdA7D56lRuAGz1Iha7dquqlL4/RHriVH0OkjbAUf+UvtpO4xtPUbZvs9gvl5O4wCuvsZ9b9Ng7iynWTq6qvvWqii2njWyohB01dbvYW3NXGsxreV3Mh5ObGm8DMP6/0LmHuP+pR9AUFTD5wNUFMHXcyHrgPHY+f+CyY9KICeEEEKIDk2mWYrOQaNR0ylrp1QmfAtL71DbR/6Eg7/CwFnG85PWw+Lr1fRC317Qb7f5yJy9grmSeuvlTIML05G5ymIoL2x+gg6NRgVgtRktK4sbXldWXmC9cLi1aZalufDrvSqZTFUZzPv1zNMcrTEdmSvNgU8vUqNs0dPh/GfAM8jYfjIe1v3XuD/iRnVeY3RVquRD+k7jsYkPwcQHm99XIYQQQoh2RkbmROcUcxVETDTu//GoedHvgEjjOrH8FMg6qEoE1Oox0ridnwqJq2D7J5BztOHXzE+FA7+o0a+mspbJspZ7IGhNAqSzHZ0znRpZWdJwPbeGkqBYm2ZpMKgAOXGlKqvQnFpzP90GH0yCry5T5QgcXc3by/Ig/itYXW/q7OoXjFNSfXvChQ1Mra1lMKhR2aS1xmNxt8F5Tze9r0IIIYQQ7ZgEc6Jz0mjUtEsHZ7VfdBI+Og/SdqqAbOmd5ud/eA7oKtW2oyt0G2xsW3KHyrr424MqMMhPhU1vw+lE4zlleSpAWXw9/HxX0/vZUCZLUEWrPc8yCcrJBJUU5pd7YejlcNcWuH+fClJdfWDwXIi62Pya2uC2LM98xHDolWpa4sSHIXyMOubsbn5tc4K5UwdU4Hzsb/U9H3a99fNMC6iDSnIy4kZAozJXung1/jpbP4Cdnxn3B18GU1+SqZVCCCGE6DRkmqXovAL7qQBkzf+p/dPH4JMLQKNV5QtM1QZyERMhKBocnY1t/hGQskFtH18LK59Vo3w7PoG7t6mU9geXQVlNVsoDP6vROZcmrC+rP82yPq8QNXrnFWLZ58akboXsg+qj+0i44FljW1CUCoxATVs06NXURp8eKtj99EL1PZq3DHqOhqip6sOUoyugAWrWIpqO3p1JSbZx2yMIck2CYo3WWLS9MF0lLKkt0eDiBTPfUMXiA/s1/hpH/4bljxv3u4+E2e+oAFkIIYQQopOQYE50bpMeVun4N74JGNQ0vfo11kydPgbX/Wh+zDRJR22R6qFXwejbjbXJ6qfVT9umClGfiVcI9Byrgjprdezm/QpObs0fTTItFh4+uuHzJj1svv/huaoOHcBvD8GdG6xfp9GAk7v63kLDUzfr0+vNgznPYPMRztpArtbyJ1W2SlNnCuTyU+HHm4338u4BV32jvo9CCCGEEJ2IPKYWnZvWQY1K3fS7SnRSyycc5n6mAhJTRRmwZ5H5MWtB1p7vzNPcnzpg3u7UxBT+w66D+X/Cgh1wwb8t253dz25aYOo243bPRoK5+qpMRthO7W38XNOplk2dZlmWZx5Mu/o0Xjri8O9qOmZT6apUIFe7/s/JHa75Dry6Nf0eQgghhBAdhARzomvoNQ7u3AQXPg/TXoG7t8LgSyFiguW5G/5nnh7fNONirWHXw5C5xv1Mk8Dn6kXNC6DOhsEAecmQut38uF4PK59TBdBrNTYyV9+UZ4zbpglkrDENhJs6zdJ0VM7BGUpOUzdVE43xuKmvLoOq8qbdf/Xz5qOS01+DkCFNu1YIIYQQooORaZai63DxhHH3mB+LPB+O/mV+LDdRTaccNEft+9UL5gL7w7SXjPuluVCYZty3ZfBwfC1s/J/KrgkQ0E+N6oGa6rj0Tti/xHh++GhI+EaNNlaWwPAbVOKT7ENq2qFvLwgxSfbi5m/crh3d+utpNcLp7AEjbjJm3TQN5po6zbIky7hdf71cQCRc/jksvsF81BPg1Si4f48ayWvIsb9hw+vG/ZhrIPbqpvVLCCGEEKIDkmBOdG19z7d+fP1rMPASNcXRzQ8CoyDnsEr8MfczY4r+qnJV/6yWewB4h7VuH3VVqixB6Wk17bA2kAM4fVSNbrn7w6LrzKckBkXDpR/Btg9V8AZQnKVKCqx9Ue37R6oRxrI8CB6oAqpapXlqBHDzO8apkYMuNQZzZzPNsrixYK6vCoQ9QyyDufC4xgO5snz42SRQD+wPF7/StD4JIYQQQnRQEsyJri2gr/XjmXtUsfGoaSqgu3Yx7FkM/S6EboNg2YMqyUnWQZXmv1bIEPM1bgaDCr7yU9RIkVO9mmqH/1SBmHuAGiWrX3g7+wi8U1PA3MHZ+pTJtG0qMDIN5CLPU6Ncrj716swVmycCyU2EtTWjjFEXQ48njG1leVBdYb7GzfRe1qZZVpaqpDC1iWHqM83e6RGkEs7Uqv238DIpx9B9pEp4cuHz1u9X66+njKUbHJxVwN2UbKJCCCGEEB2YBHOia9NoYOqL8Odj4OINocMguabI9N/PQN8LVIDlFwHn/MN4Xdp24zq5Pd8Zj3cbrI6nbFbBUMEJVQAbIG0HXPKu8dzKUvj2SuP+Q4fNAxlQ2R5r6SohZZPl15C6Vd2rVo9RcM1iY0DlbBLUVBab77v5GevLHf7dfJ1cVYmxrZZpAFd/muXKZ2H9q4AGPLupIHXcveZTHU2nWdbPZFk7KugVajwWGgMzXrP8mk0lroL4L4375zxqPnVUCCGEEKKTkmBOiDF3Qt8paqSoIBXenwgY1NTE3d+odWb1hcWq0bu6/eGQdUCtM3u/JqmKac00UGvXJjxgTK1vWjAcrNeZc/WpKQFQE6xZK6twYqt5kDTsevORMbORuRLz9W3e3c0DNtPabGCeSKX+vXy6q/WEzh4qscn6V2saDFCcqT6W3qGmQI6+XdV4qz/N0nQ00b82mDMtlJ5p+fWaqiiGX+4z7ocMgfH3NXy+EEIIIUQnItkshQAVYLn5qmAg5irj8dX/Zz7qVSs01rjdfQTcthoeT4cL/wPammck9WumYTAJeDCfcujqY31qokZjOVpX38ldcOtquGIhDJwNA2eZt7t4Gbcris3Xt51pfZ9p2QBHN5UIpdaM1+G+BLh9HRxd3vA9lj+mCpFnH4bgASrpTMgQ8A6F4lPG8+qmWZqMzNVOnWzI6v9To5+gvu+z3214iqcQQgghRCcjwZwQ9Z37JDi4qO2iDNjwmkpCYios1riduU+1OziqaYseQQ3fe/ciY3KP0lzjcffAhq8xDW5qdR9h7GN1OeQcUYHcFQtVH0w1OjJ3hmDOdGTOuYHaeTs/My/NMOsty3PStsPn09X6wut/gjs2QPgYY7uTu/HrbOrIXPYR2PaBcX/8/RA6tOHzhRBCCCE6GQnmhKjPN1xNC6y17hX4vzD4YJIKxkCtjdPWjADpKozZInVVUF7QyM31KlMmmE+ztDbFspa1YC5kKHQfbtw/saXh682CuSLzkTmvBoK5qS/BVd+Ab0/r96lVclrVtas19CroNR40DlbOzVbTLvU1I5amyU/8I9U0TDD/eotPGc+vb/kToK9W2749YdIj1s8TQgghhOikJJgTwpqJD4Krr3FfVwkZu1Udt4I0cHRRUwZrnUxQnw/+apmm3yPYfH/3t2r6ollmx8ZG5qxMswweYJ7Z0rRQdn3OJtMsLUbmrASKXqEw5g6Inq5KMdTdx0p2yDX/Z6xH5+wFFzyrEpmYJi3xMQkIE1fBlpokMNaSnwB4dTNuG3SWawsBjq6AYyuM+xf+xzJTqBBCCCFEJyfBnBDWuPnB3E9VNkWtSZ4ggw4O/6G2TadaHvhZlSHY/rHxmHuAusdV36ggx6u7Oq6vhl/vN08GUlmiAhSDwbIv1kbmgqKhp8k0xdSt1q8FURbkyQAAJmNJREFUK9MsTYJNN3/zgA3MyzXUlhyofx+AiiJVrqHW5MeMgVjwIOPxkhyImm7c//sZFRiblSUwCeZcvMwDx/rr5nRValSuVq8JMKDeOkEhhBBCiC5AgjkhGtL3fJXc44mTMOIm4/HaYC7EZH3WsRUqsEnZaDx29SIYfBmEj1IZFieblDZIXAmHlhn3k9bC13NV4e/60zStjZ4FD4Qeccb9iiLzZCJb3oNPp6k6dqZBWHU5VBQa953cVEkGU7XZNkGVMqhlWiQcVDFx03sNu86kf9Emr1mqpkDWjhCGj4bcJMuC4aYaWze341O1RhAADUx9wby2nxBCCCFEFyHBnBBn4uiigrJayetV8NStXi2z5A3G7ZCh0GOkefuwG2DgJcZ902Cm1qFl8OFklVSlVv2ROfcA8AwCjwBj2YSeY4zTOU8nqrp5JzbBT7eqkcVeE6D/VPV1lJsEYPmpUHba/P4BJsFcdYVxu/40S9Pg0dFVZQOt5eKliqDXKjoJV38DaCBlA6x72XrBcGtfs+nIXFWZeUbQ4TdI0hMhhBBCdFlSZ06Ipug5Rq2hK89X6+cSV0P0DBUkpWxQtd32/2Q8P+5Wy9EirRbmfKCmHaZsoEG5x+GTC9WoYGBfyzVz1/5g3J71Fpz7lCrAXft6iauM7RWFatTwpt+Mx07tVyUKji6HP/4B+nq160pz4Od7VP25wP7wzzxVQLz+eYUnjdumUzWrylVfug2G/BTja57zD1XM/NjfMGgOfDbVeE1TR+Z2fGoMIh1d4dwnEEIIIYToqmRkToimcHCCfhcY94/8qYKzG5fBAwdUiv/aKYmuPioFf0Nir7HMIjn6Dpj8BFATkFWVwOaaFP9eYTDpH6qu29XfqRptpry6mQeOyevN23d+Yb7fbZDq6/pXVSbO+jQOEP+lGiVM2aS+Thcv85E3gLwU47ZpbbdNb8F/guHIH8Zjp/arz/0vhItfNi9y7uoL7v71viYrwVxlKWz4n/H4yPlnrsEnhBBCCNGJycicEE3Vfyrs/V5tH1muRqq0DmqEaPM7xvNG32G5vgzg+FpYfL1aE+cRDH3OVQlG+k1RUzC9Q1VQ8/vD6vy9P8JFL6h7nfdk0/qo15tP9wRI36GmbYbUTAutKIJf7zO2O3upkgW1PE2ySZaZ1MIzex2dWvdWxyT5Su06OtOi6VkHjNu6KvNyBhETLO9vNs2yJpjb+ZkxCHR0VXXlhBBCCCG6MBmZE6Kp+k4xZrYszYG0HWp74+smo3K+MPZu69d3G2TMDlmSBePvhVtWwDmPGpOcxF5jTBRSWQQHf2leH7MPQulpy+O7TEbnVj5rLAaudYL5f4Bfb2O7aQKW0gaCuezDoCs37ptOwawosjz/9DFjSYSdn8Ppo2pbo1VF2uszDSiLMqyMyt1sXsJACCGEEKILkmBOiKZy84Ve44z7R/6AwgzY9pHx2IT71TRLazwCod+Fxv3d31me4+wBgy817sd/1bw+Jq0zbmtM/nsnfK3KJ6z+P9j2ofH4xIfUtM0Qk2QupolNSnOsF+0+uct8v9Kk3IFpMFdbPNygVwFgeQGsecHYPux66DbQ8v71R+Z2fm4yKuemsoMKIYQQQnRxEswJ0Rz9pxm3t7yvEpVU14xQeQRD3G2NXx9zlXH74K/WR7GGXW/cTl6vEqI0VZLJerkRN4JTTVmCyhJYfAOsfcnY7uQBEx5Q28EmAVV+qvk9n/WD/+sBCd8aj6XXC+Z0FcbRuYoGpmxmHYANrxtHDp08Gk5gYroWriQLtr5n3B85X0blhBBCCCGQYE6I5okyycBYXQYFJ4z7kx62LKxdX/+pxpG7qlIV0NXXY6QqCl4r/uum9U2vM8+SGXUxDLms4fMNOnCqyUIZPMB4/PQx6hKx1KosUusDa9UfmQNjMXLTYM63p3F701uw8U3j/vj7Gk5gYnrcoIf8mu+zxgHG3mX9GiGEEEKILkaCOSGaw7+PSnBSn19vNRJ2JvVr1i29E9a9Yn6ORmNegDvhG8uyANZk7jWud9M4qHIKI+djEZjVMq0bZzoyl5tofapoSTYUpKvac6Z18GpVWgnm/E3W4mUdUAEkqGmU4+5p+Gtx9gAXK30YOAt8ejR8nRBCCCFEFyLBnBDNNe0l+EcS3PQHTH8Npvwb5v2qArWmiLnafH/P95bnDL3SmGyl6CQcWHrm+5qul+s+XJUTCBsGl39unuCklpNJxk3/PioZCqiAq36BcIDlT8AbQ2tq01VZtteNzJkUJQ/sb3medw9VYuFMo5jWRu3GyKicEEIIIUQtCeaEOBvu/ioZyqibVdIT3/CmX9tjlPl+dbnlOZ7BED3duP/X06rQd2NM68v1nmTcHnQJ9D3f8nwnN+O2g5N54OXgbP019NUqGUktn3CY+SZc+jG4B6hjpiNzoTHGoBSg7wVwx3oIi238awHLdXHdR1h+74QQQgghujAJ5oSwN40GLnjWuN9Q0pTz/2UMqgrTYf1/G76nrhpSNhv3Iyaat1sbBTMN5sB83Zxp3bjG9J8KI+bB0MvB1RsMBss1c9Nehu4j4cLn4ZrFlgXCG2Ka0RLUqJymgSmjQgghhBBdkBQNF6ItjK1ZL6arqlnXZkVAJIxbAOtfVfub3oaYayDIytTFrP3Gwt9aRwgfbd5eW7vOlFO9wuamwZyuEtz8oCzPeMzBRSVnSdloPNZjpPk9qivMp2C6eKnRy1E3W/8aG2M6zdIrFAbObv49hBBCCCE6MRmZE6ItaB1UNsdJD4Oze8PnTXxITWUEFST98Yga/aqvtoA5qLpx9e/ZpJE5kyQoWkfLtX1T/qXWBp77JLh4Q484GHhJvZsaYPLjKlgdfkPDNfeaot9Fxu1z/qGmggohhBBCiDoyMidEe+bsAVNfgEU12S2Pr1FlAbqPMD8vfadxu3u90bLa+1gca2RkLj8Ftrxr3j7yZhWEnvMPFWSaliqo5eQGkx9r8MtplojxcMcGlSWz5+gzny+EEEII0cVIMCdEexc9A0JjISNB7Z+Mtwzm0rYbt+tPfQRwsZKdsv40S99e6lhtVkpTWkfzbJ21gdzBXyFxFZTkQL8L1GhcawoZ0rr3E0IIIYToRGSapRDtnUajSg3Uyjpo3l6WDzlHjPvWMj5aKzVQf5qlVmterNzseg/ryUdSt8KOT+HgL+ZTPYUQQgghhM3JyJwQHYHperb6wdzJXcZtNz9VM66+7iPg2h9hz3dw7G+VZdK3l/XXqb2fxkEFhgad9ambAB5Bxu3S0037WoQQQgghRKuQYE6IjsB0PVvWAZUEpXakLM10vdwI6yNoHoHQb4r6aMzw6yHhKxXIGXSQukVlwrzlb+vnuwcat0ty1BTQI8tVFkv/SIia2rSvTwghhBBCNJtMswRKSkr48ssvWbBgAXFxcbi4uKDRaHjxxRdbdF+9Xs9HH33EhAkT8PPzw83NjT59+nDttdeyf//+Vuq96BKCTIK5sjwoPmXcN10v19AIWlP1HAMPHoQbfzMeqyyC6krr53uYBnPZaqrlmhdg+ROw/aOW9UUIIYQQQjRKRuaAo0ePcsMNrZu4obS0lJkzZ7Jq1Sr8/PyYMGECrq6uJCUlsWjRIqZNm8agQYNa9TVFJ+YRAJ7djEFc1gFVh81ggHSTtWrW1ss1l3eYKj1gqiwPvLpZ6ZdJMFeaY14w3MVKbTshhBBCCNFqJJgDvLy8uPnmm4mLi2PUqFH8+OOPPP/88y2650033cSqVauYP38+b731Fu7uxsyBGRkZVFVVNXK1EFYEDzAJ5g5C5HmQl2y+Vs00UUpLOHuAg7MqHg5Qlms9mDOdZlleYN4XCeaEEEIIIWxKgjkgMjKSjz/+uG7/559/btH9Vq1axeLFixk1ahQfffQRWq35bNbQ0NAW3V90UcEDVZ05UCNzYF5fzj8S3P2tX2swwNeXQ9JaFaBd9Q1ETGi4qLdGYwzkAA79Zr5ur5bpyByo4LJW/dE9IYQQQgjRqmTNnA188MEHADzwwAMWgZwQZ80sCUpNRkvTcgDW6svV0mggZZMxQPvuGmNg2BT1C4jXcvYwr1dnFszJyJwQQgghhC3JyJwNrFq1CoApU6awb98+vv/+ezIzMwkJCWHatGmMGTOmjXsoOiSz8gSHQK+vVyz8DOvlnD2gqsS4X79oeGOqKxpucw+EghNqOzfJeFyCOSGEEEIIm5JgrpWdOnWKnJwc/Pz8+OSTT3jyySfR6/V17c8++yzXXXcdn376KU5OTk26Z0OJUhITE4mMjGyVfosOICjKuF1VogK5jN3GY91HNH69iyeUZBn3mxPMObo23OYRYAzmTINFCeaEEEIIIWxK5gC2sry8PACKiop4/PHHufbaazl8+DB5eXn88MMPBAYG8tVXX/H000+3cU9Fh+PipYp911p2P+hrEul4hULIkMavd/Yw33dya/z8i14wbl/SwDRLAI9gtfYuoK9lf4UQQgghhM10ipG5uXPnsm/fvmZds3DhQuLi4lq9LzqdDoDq6mrGjh3LwoUL69ouu+wyXF1dmTFjBm+++SZPPPEE3t5nThLRUE06KW3QBQUPhPyaUbDaJCgA4xaAwxlGep09zffPNDI36mZw9VaJTPpe0PB5V38HtWtD3xwOuYlqW4I5IYQQQgib6hTBXHJyMocPH27WNaWlpTbpi5eX8Q3s/PnzLdqnT59Ot27dOHXqFNu2bWPKlCk26YfopIIHwJE/zY+5+cOIG898bf2RuDONzDm6wLDrznxf0yQ/ZnXmJJulEEIIIYQtdYpgbseOHWc+yU7CwsJwdnamsrKSXr16WT2nV69enDp1iqysLKvtQjTINAlKrTF3WU6htEZb7797c9bMNZUUDRdCCCGEsJtOEcy1J46OjgwePJhdu3aRm5tr9ZzTp1VhZU9PT6vtQjSofq03F2+Iu7Vp12rqLZF1tkEw92iyCugqCsGnR+vfXwghhBBC1JEEKDYwa9YsAFavXm3RlpycTHJyMgDDhg2zZ7dEZxDYHzQOxv1Rt4Cbb9OurR/MOZ5hmmVTGQxQXgi5xyHnCHgGQUCkmqYphBBCCCFsRoK5FoiOjiY6Opr09HSz43fddRfe3t589tlnrFixou54cXExd955JzqdjunTpxMeHm7vLouOztEF+k9V257dYOzdTb+2fjDXWgXtT8bDi+Hw5jD4Ykbr3FMIIYQQQpyRTLOsMWfOHDIyMgBIS0sD4N1332Xp0qUAhIaGsmTJErNrapOuVFVVmR0PCgri888/54orrmDq1KmMGTOG4OBgtmzZQmZmJr179+aDDz6w8VckOq25n8Lx1RAyFDwCm36dm59t+mPah/IC0FWdObOmEEIIIYRoMQnmasTHx5OSkmJ2LDU1ldTUVIAGk5k0ZM6cOWzatInnn3+eDRs2sGPHDsLDw3nooYd4/PHHCQgIaLW+iy7GyRWipjX/ugv/A9EzoKoUMLRef9zrBZSlp8ErpPXuL4QQQgghrNIYDIZWfFcn7Km2zlxDdeiEsJvnw6CqxLgfeT5c/1Pb9UcIIYQQog3Y+/25rJkTQrScR72R5vL8NumGEEIIIURXIsGcEKLl6k+1lBpzQgghhBA2J8GcEKLlPILM9yWYE0IIIYSwOQnmhBAtVz+rpot32/RDCCGEEKILkWBOCNFy7vXWzMnInBBCCCGEzUkwJ4RoOZlmKYQQQghhdxLMCSFazmKapQRzQgghhBC2JsGcEKLluo8w35dgTgghhBDC5iSYE0K0XFCUeUAnCVCEEEIIIWxOgjkhROuoKDJuy8icEEIIIYTNObZ1B4QQncTgy6AgVQV1PuFt3RshhBBCiE5PgjkhROuY/Fhb90AIIYQQokuRaZZCCCGEEEII0QFJMCeEEEIIIYQQHZAEc0IIIYQQQgjRAUkwJ4QQQgghhBAdkARzQgghhBBCCNEBSTAnhBBCCCGEEB2QBHNCCCGEEEII0QFJMCeEEEIIIYQQHZAEc0IIIYQQQgjRAUkwJ4QQQgghhBAdkARzQgghhBBCCNEBSTAnhBBCCCGEEB2QBHNCCCGEEEII0QFJMCeEEEIIIYQQHZAEc0IIIYQQQgjRAUkwJ4QQQgghhBAdkARzQgghhBBCCNEBSTAnhBBCCCGEEB2QBHNCCCGEEEII0QFJMCeEEEIIIYQQHZAEc0IIIYQQQgjRAUkwJ4QQQgghhBAdkARzQgghhBBCCNEBSTAnhBBCCCGEEB2QBHNCCCGEEEII0QFJMCeEEEIIIYQQHZAEc0IIIYQQQgjRAUkwJ4QQQgghhBAdkARzQgghhBBCCNEBSTAnhBBCCCGEEB2QBHNCCCGEEEII0QFJMCeEEEIIIYQQHZAEc0BJSQlffvklCxYsIC4uDhcXFzQaDS+++OJZ3zMvL49HH32U6Oho3NzccHNzY9CgQTz11FMUFha2Yu+FEEIIIYQQXZFjW3egPTh69Cg33HBDq90vOzubsWPHkpiYSFhYGFOnTqW6uprNmzfz/PPP8+OPP7J582Z8fX1b7TWFEEIIIYQQXYuMzAFeXl7cfPPNfPDBB+zatYsnn3yyRfd74YUXSExMZM6cORw/fpwlS5bw66+/kpSUxIQJEzh06BCvv/56K/VeCCGEEEII0RXJyBwQGRnJxx9/XLf/888/t+h+69atA+DRRx/FxcWl7riXlxcPPfQQGzZsYPv27S16DSGEEEIIIUTXJiNzNmAawDXE39/fDj0RQgghhBBCdFYSzNnABRdcAMBLL71ERUVF3fGioiL++9//AjBv3rw26ZsQQgghhBCic5Bpljbw8MMPs3r1apYsWUKfPn0YPXo01dXVbNq0CQcHBz755JO6gE8IIYQQQgghzoYEczbg6enJn3/+ya233srXX3/NkiVL6tpmzZrFiBEjmnW/QYMGWT1+6NAhnJycGmwXQgghhBBC2E9iYiJOTk52e71OEczNnTuXffv2NeuahQsXEhcXZ5P+nDhxgunTp5ORkcHChQuZOnUqAH/88QcPPPAAEyZMYMWKFYwZM6ZFr6PX66mqqmqNLosmSkxMBFTSHGEf8j23P/me2598z+1Pvuf2J99z+5Pvuf1VVVVRXV1tt9frFMFccnIyhw8fbtY1paWlNuqNWg+3b98+li5dyuzZs+uO33DDDXh6enLZZZfx4IMPsmnTpibdb//+/VaP147INdQuWp98z+1Pvuf2J99z+5Pvuf3J99z+5Htuf/I9tz97z5jrFMHcjh072roLdVJTU1mzZg0uLi7MnDnTon327Nm4uLiwZcsWysvLcXV1bYNeCiGEEEIIITo6yWbZytLS0gDw8PBAq7X89jo4OODu7o7BYCA/P9/OvRNCCCGEEEJ0FhLMtbKQkBAAcnNzSUpKsmhPTEwkLy8PDw8PAgMD7d09IYQQQgghRCchwVwLREdHEx0dTXp6et2x3r17M3ToUABuv/12CgoK6try8/O5/fbbAbjkkktwdOwUs1yFEEIIIYQQbUCiiRpz5swhIyMDME6VfPfdd1m6dCkAoaGhZiUGgLqkK/UzSn744YdMmTKFFStW0LdvX0aPHg3Ali1bOH36NBEREbz88su2/HKEEEIIIYQQnZzGYDAY2roT7UFERAQpKSkNtvfq1Yvk5GSzYxqNBoCkpCQiIiLM2hITE3nppZdYuXIl6enpaLVaevfuzaxZs3jkkUfw9/dv7S9BCCGEEEII0YVIMCeEEEIIIYQQHZCsmRNCCCGEEEKIDkiCOSGEEEIIIYTogCSYE0IIIYQQQogOSII5IYQQQgghhOiAJJgTQgghhBBCiA5IgjkhhBBCCCGE6IAkmOtgysvL+de//kX//v1xdXUlLCyM+fPn1xU6F61v8uTJaDSaBj/+/PPPtu5ih7Rz505efPFFLr30Urp3745Go8HV1fWM1y1cuJC4uDg8PT3x9/fn4osvZtOmTXboccfX3O/5M8880+jP/mOPPWbH3nc8paWlLF26lJtvvpmhQ4fi7e2Nh4cHMTExPPvssxQXFzd4rfycn52z+Z7Lz3nLvfbaa1x66aX069cPHx8fXFxc6NWrF/PmzWP//v0NXic/52evud9z+TlvXbm5uQQHB6PRaIiOjm70XFv/nDu22p2EzZWXl3P++eezadMmQkNDmT17NsnJyXz22WcsW7aMzZs3ExkZ2dbd7LQuu+wyPD09LY537/7/7d19TJXlH8fxjyCSaEAgKqhsCsGKogmtmQS6Mh+yMh9WtmrKysq2yqlbWwWuYeUSZtRvudWcIokucdZ82NRkPqKUglr41BRFpiwgQAQBiev3R+PkCeTIw7nxxvdr4w+u67rd93z3cfrlnPtmWA9UY38pKSn66aefOnTNwoULtWLFCvXv318TJ05UfX29du3apZ07d2rjxo2aPn26m6rtHTrTc0mKi4tTeHh4q/XY2NjuKKvXysrK0rx58yRJUVFRmjx5sq5evarc3FwtWbJE69ev1969ezV48GCn68h553W25xI574rPPvtMtbW1io6O1sMPPyxJKiws1Nq1a7Vhwwb9+OOPmjJlitM15LxrOtNziZx3l4ULF6q8vPy2zrk95wa2kZSUZCSZxx9/3NTU1DjW09LSjCSTkJDQg9X1XuPGjTOSTFFRUU+X0qssW7bMJCcnmy1btpjS0lIjyXh7e9/y/O7du40kExgYaM6ePetYz83NNf369TN+fn7mr7/+sqJ02+poz5csWWIkmdWrV1tXZC+SkZFh5s+f75RXY4y5fPmyGT16tJFkXn75Zac9ct41nek5Oe+6AwcOmOvXr7da/+abb4wkExISYpqamhzr5LzrOtpzct59fv75ZyPJvPnmm0aSiYyMbPOcVTlnmLOJxsZG4+/vbySZ/Pz8VvvR0dFGkjly5EgPVNe7McxZw9Vg8cwzzxhJZsWKFa323nvvPSPJpKamurHC3odhrufk5uY6+t/Q0OBYJ+fuc6uek3P3Cg8PN5JMYWGhY42cu1dbPSfn3aOurs6Eh4ebBx980Jw9e7bdYc6qnHPPnE0cOHBAVVVVCgsL0+jRo1vtz5o1S5K0ZcsWq0sD3K6+vl67d++W9G/Wb0b+YTePPPKIJKmhoUEVFRWSyLm7tdVzuJ+np6ckqV+/fpLIuRX+23N0n08++UTnzp3TypUr5eXldctzVuace+Zs4vjx45KkmJiYNvdb1lvOofutWrVKFRUV8vDwUEREhF544QWFhob2dFl3hdOnT6uhoUFBQUEaPnx4q/2W/J84ccLq0u4KOTk5OnbsmOrr6zV8+HBNmTKF+yu66Pz585IkLy8vBQQESCLn7tZWz29Gzrvf2rVrdebMGUVERGjUqFGSyLm7tdXzm5Hzzjtx4oTS0tKUmJiohIQEXbhw4ZZnrcw5w5xNFBcXS1Kbgbh5veUcut/SpUudvl+8eLGSkpKUlJTUQxXdPVzlf8CAAfL391dlZaVqamp07733Wller5eZmen0fVJSkmbOnKk1a9a0+VAguJaeni5Jmjx5sry9vSWRc3drq+c3I+ddt3z5chUWFqq2tlanTp1SYWGhQkJClJWVJQ+Pfz4MRs671+30/GbkvHOam5s1b948+fv764svvnB53sqc8zFLm2h5nLKPj0+b+wMGDHA6h+6TkJCgzMxMnTt3TnV1dTpz5ow+/fRT9e3bV8nJyY7/IMB9XOVf4u+AO4SHhys1NVWFhYW6du2aLl26pHXr1mnYsGHatGmTXnvttZ4u0Za2b9+uVatWycvLSykpKY51cu4+t+q5RM67044dO5SRkaHs7GwVFhZqxIgRysrKcnrnh5x3r9vpuUTOu+rrr7/WL7/8ouXLlyswMNDleUtz3uW77mCJN954w0gyH3/8cZv7LTdhRkREWFzZ3WvHjh1GkvHz8zN1dXU9XY7tqZ2HcXz//fdGknniiSdueX1ISIiRZC5fvuyuEnud9nrensuXL5vAwEAjyRw8eNANlfVeJ0+eNPfdd5+RZL788kunPXLuHu31vD3kvPMqKyvNvn37zIQJE4wks3TpUsceOXeP9nreHnLuWnFxsRk4cKAZN26c03pRUdEtH4BiZc55Z84mWt5+ra2tbXO/rq5OkniL3EITJ07Uo48+qurqah0+fLiny+nVXOVf4u+AlYKDg5WYmCjpn58K4/aUlJRo8uTJqqys1MKFC/X+++877ZPz7ueq5+0h553n7++v+Ph4bd++XbGxsUpKStKvv/4qiZy7S3s9bw85d+2dd95RY2OjVq5cedvXWJlzhjmbaHnQRklJSZv7Les8kMNa999/vyTpypUrPVxJ7+Yq/7W1taqqqpK/vz/3V1iE7HdMeXm5nn76aRUXFysxMVGpqamtzpDz7nU7PXeFnHeNl5eXXnrpJRljHE/tI+fu1VbPXSHn7du6dat8fHw0f/58jR8/3vE1e/ZsSf/cH9ey1vKRSStzzgNQbKLlkcr5+flt7resR0dHW1YTpMrKSkn89NDdIiMj5e3trbKyMpWUlLS6oZj8W4/s376amhpNmTJFp0+f1owZM/Tdd9+pT58+rc6R8+5zuz13hZx33aBBgyRJZWVlksi5Ff7bc1fIuWtVVVXau3dvm3vXr1937DU1NUmyNue8M2cTcXFx8vPz07lz51RQUNBqPzs7W5L07LPPWl3aXausrEz79++XdOtfGYHu0b9/fz355JOS/s36zci/tYwx2rx5syTxSGsXGhoaNG3aNB05ckSTJk3S+vXrHb8D6r/IeffoSM/bQ867R8t/csPCwiSRcyv8t+ftIeeuGWPa/CoqKpL0z+DWsubv7y/J4px36Y47WOqjjz4ykszYsWPNtWvXHOtpaWkub7JE5xw6dMjk5OSY5uZmp/WioiITFxdnJJnnn3++h6rrXeTiYRy7du0ykkxgYKA5e/asYz03N9d4e3sbX19fU1FRYUWpvUZ7PS8rKzMZGRmmvr7eab2mpsa89dZbRpIZOnSoqa2ttaJUW2pqajLTp083kkx8fPxt9Yqcd01He07Ou27fvn1mw4YN5saNG07rjY2N5quvvjIeHh6mf//+pri42LFHzrumoz0n5+7R3gNQjLEu532MMabrIyGsUF9fr/HjxysvL0/BwcGKj4/XxYsXlZeXp8DAQB0+fFjh4eE9XWavsmbNGiUmJio4OFgREREaOnSoSkpKdPToUdXX1ysqKko5OTkaPHhwT5dqO9u2bXN6RHheXp769Omjxx57zLGWlJSkqVOnOr5fsGCB0tPT5ePjo6efflqNjY3atWuXmpub9cMPP2jmzJmWvga76UjPL1y4oJEjR8rX11cPPPCAQkNDVVVVpfz8fFVUVMjf319bt25VXFxcT7wUW0hPT9eCBQskSdOnT5evr2+b51JTUx0fi5LIeVd0tOfkvOta/p0cNGiQYmNjFRgYqPLycv3222+6cuWK7rnnHmVkZOjFF190uo6cd15He07O3aOlr5GRkTp9+nSbZyzJeZfHQViqrq7OJCUlmbCwMNOvXz8zZMgQM2fOHKefeKH7nDx50syfP9/ExMSYoKAg07dvX+Pn52fGjBlj0tLS+JUEXbB69Wojqd2v1atXt3ldbGys8fHxMX5+fmbSpElm//791r8AG+pIz69evWo++OADM27cODNs2DDj7e1tfHx8TFRUlFm0aJEpKSnp2RdjA0uWLHHZb0mmqKio1bXkvHM62nNy3nXnz583H374oYmLizPBwcHGy8vLDBgwwERFRZl3333X/PHHH7e8lpx3Tkd7Ts7dw9U7cy3cnXPemQMAAAAAG+IBKAAAAABgQwxzAAAAAGBDDHMAAAAAYEMMcwAAAABgQwxzAAAAAGBDDHMAAAAAYEMMcwAAAABgQwxzAAAAAGBDDHMAAAAAYEMMcwAAAABgQwxzAAAAAGBDDHMAAAAAYEMMcwAAAABgQwxzAAC40aFDh+Th4aGYmBg1Nzff8tymTZvUp08fTZs2zcLqAAB2xjAHAICbNDc36+2335YxRunp6fLwuPU/uzExMZKk3Nxcq8oDANgcwxwAAG6ybt06nThxQhMmTFB8fHy7Z0NDQ+Xp6any8nKVl5dbVCEAwM4Y5gAAcJNly5ZJkhYtWuTyrKenp3x9fSVJpaWlbq0LANA7MMwBAOAGhw8f1smTJxUcHKyJEyc67e3fv18FBQWtrmm5p669e+sAAGjBMAcAgBvs2LFDkjRhwgSne+VKS0uVkJCgzz//3Ol8XV2dqqurJUlDhgyxrlAAgG0xzAEA4AbHjh2T9O+DTVocPHhQkhQREeG0fvz4cUlSSEgIwxwA4LYwzAEA4AaXLl2SJA0fPtxpfdu2bZKkoKAgp/WdO3dKkp566inHWm1trTw9PbVmzRrNmTNHAQEBCggIUEpKijtLBwDYBMMcAABucOPGDUnO979VV1crOztbkvT333871puampSRkSFJevXVVx3rx44dU3Nzs1asWKHnnntOR44c0eLFi5WcnKzz589b8TIAAHcwhjkAANwgNDRUkpSTk+NYW7x4sYKCgvTQQw/p0KFDjvXk5GQVFRVpzJgxTg9LKSgokIeHhzIzMzVr1iyNGjVKr7/+uiTpzz//tOiVAADuVH17ugAAAHqjV155RVu3btW3336rixcvqqqqSnl5ecrOztbGjRu1YcMGTZ06VRUVFcrLy9PQoUOVmZnp9Gfk5+dr7Nixio6Odqy1vCMXFhZm6esBANx5GOYAAHCD2bNnq6SkRP/73/+0Z88eRUZGauPGjZoxY4ZiYmJUWlqqPXv2aODAgZo7d65SUlJa3V9XUFCgSZMmOa0dPXpUI0aMaHXPHQDg7tPHGGN6uggAAOCssbFRAwcOVFZWlmbNmuVYnzt3rqqrq7V58+YerA4AcCfgnjkAAO5Av//+u27cuKHY2Fin9aNHj7ZaAwDcnRjmAAC4AxUUFCggIEAjR450rF2/fl2nTp1imAMASOJjlgAAAABgS7wzBwAAAAA2xDAHAAAAADbEMAcAAAAANsQwBwAAAAA2xDAHAAAAADbEMAcAAAAANsQwBwAAAAA2xDAHAAAAADbEMAcAAAAANsQwBwAAAAA2xDAHAAAAADbEMAcAAAAANsQwBwAAAAA2xDAHAAAAADbEMAcAAAAANsQwBwAAAAA2xDAHAAAAADb0fyCvjlT/pwbbAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 960x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1,dpi=150)\n",
+    "\n",
+    "ax.oplot(Sigma_iw['up_2'].imag, '-', color='C0', label=r'up $d_{z^2}$')\n",
+    "ax.oplot(Sigma_iw['up_4'].imag, '-', color='C1', label=r'up $d_{x^2-y^2}$')\n",
+    "\n",
+    "ax.oplot(Sigma_iw['down_2'].imag, '--', color='C0', label=r'down $d_{z^2}$')\n",
+    "ax.oplot(Sigma_iw['down_4'].imag, '--', color='C1', label=r'down $d_{x^2-y^2}$')\n",
+    "\n",
+    "ax.set_ylabel(r\"$Im \\Sigma (i \\omega)$\")\n",
+    "\n",
+    "ax.set_xlim(0,40)\n",
+    "ax.set_ylim(-1.8,0)\n",
+    "ax.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2a07a928-e69f-4ad1-91ea-0386024ed5de",
+   "metadata": {},
+   "source": [
+    "We can clearly see that a $\\omega_n=8$ the self-energy is replaced by the tail-fit as specified in the input config file. This cut is rather early, but ensures convergence. For higher sampling rates this has to be changed. We can also nicely observe a splitting of the spin channels indicating a magnetic solution, but we still have a metallic solution with both self-energies approaching 0 for small omega walues. However, the QMC noise is still rather high, especially in the $d_{x^2-y^2}$ orbital. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b22265a-4138-4d9c-8315-917320f27cb3",
+   "metadata": {},
+   "source": [
+    "## 5. Multiplet analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d3c2f507-757a-4880-b9dc-1f254c78c512",
+   "metadata": {},
+   "source": [
+    "We follow now the triqs/cthyb tutorial on the [multiplet analysis](https://triqs.github.io/cthyb/unstable/guide/multiplet_analysis_notebook.html) to analyze the multiplets of the Ni-d orbitals: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "c00e89e4-cf2e-4fca-84b1-11cb42072217",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "pd.set_option('display.width', 130)\n",
+    "\n",
+    "from triqs.operators.util import make_operator_real\n",
+    "from triqs.operators.util.observables import S_op\n",
+    "from triqs.atom_diag import quantum_number_eigenvalues\n",
+    "from triqs.operators import n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe674d6b-dae6-4497-82f5-6b8004afb275",
+   "metadata": {},
+   "source": [
+    "first we have to load the measured density matrix and the local Hamiltonian of the impurity problem from the h5 archive, which we stored by setting `measure_density_matrix=true` in the config file: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "786a549c-9306-4099-a4f0-3f19d2bdbb36",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with HDFArchive(path+'/nno.h5','r') as ar:\n",
+    "    rho = ar['DMFT_results/last_iter/full_dens_mat_0'] \n",
+    "    h_loc = ar['DMFT_results/last_iter/h_loc_diag_0']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "585625be-0888-460e-879b-2a60215a69bb",
+   "metadata": {},
+   "source": [
+    "`rho` is just a list of arrays containing the weights of each of the impurity eigenstates (many body states), and `h_loc` is a: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "efeafafa-502b-4acd-8e76-4f7eab6eb9c3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'triqs.atom_diag.atom_diag.AtomDiagReal'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(type(h_loc))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "72450efb-b8b8-4169-9c01-6fb6259a3178",
+   "metadata": {},
+   "source": [
+    "containing the local Hamiltonian of the impurity including eigenstates, eigenvalues etc."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "d767053a-f785-44d1-8a82-eafc5c8b9911",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res = [] \n",
+    "# get fundamental operators from atom_diag object\n",
+    "occ_operators = [n(*op) for op in h_loc.fops]\n",
+    "\n",
+    "# construct total occupation operator from list\n",
+    "N_op = sum(occ_operators)\n",
+    "\n",
+    "# create Sz operator and get eigenvalues\n",
+    "Sz=S_op('z', spin_names=['up','down'], n_orb=5, off_diag=False)\n",
+    "Sz = make_operator_real(Sz)\n",
+    "Sz_states = quantum_number_eigenvalues(Sz, h_loc)\n",
+    "\n",
+    "# get particle numbers from h_loc_diag\n",
+    "particle_numbers = quantum_number_eigenvalues(N_op, h_loc)\n",
+    "N_max = int(max(map(max, particle_numbers)))\n",
+    "\n",
+    "for sub in range(0,h_loc.n_subspaces):\n",
+    "\n",
+    "    # first get Fock space spanning the subspace\n",
+    "    fs_states = []\n",
+    "    for ind, fs in enumerate(h_loc.fock_states[sub]):\n",
+    "        state = bin(int(fs))[2:].rjust(N_max, '0')\n",
+    "        fs_states.append(\"|\"+state+\">\")\n",
+    "\n",
+    "    for ind in range(h_loc.get_subspace_dim(sub)):\n",
+    "\n",
+    "        # get particle number\n",
+    "        particle_number = round(particle_numbers[sub][ind])\n",
+    "        if abs(particle_number-particle_numbers[sub][ind]) > 1e-8:\n",
+    "            raise ValueError('round error for particle number to large!',\n",
+    "                             particle_numbers[sub][ind])\n",
+    "        else:\n",
+    "            particle_number = int(particle_number)\n",
+    "        eng=h_loc.energies[sub][ind]\n",
+    "\n",
+    "        # construct eigenvector in Fock state basis:\n",
+    "        ev_state = ''\n",
+    "        for i, elem in enumerate(h_loc.unitary_matrices[sub][:,ind]):\n",
+    "            ev_state += ' {:+1.4f}'.format(elem)+fs_states[i]\n",
+    "\n",
+    "        # get spin state\n",
+    "        ms=Sz_states[sub][ind]\n",
+    "\n",
+    "        # add to dict which becomes later the pandas data frame\n",
+    "        res.append({\"Sub#\" : sub,\n",
+    "                    \"EV#\" : ind,\n",
+    "                    \"N\" : particle_number,\n",
+    "                    \"energy\" : eng,\n",
+    "                    \"prob\": rho[sub][ind,ind],\n",
+    "                    \"m_s\": round(ms,1),\n",
+    "                    \"|m_s|\": abs(round(ms,1)),\n",
+    "                    \"state\": ev_state})\n",
+    "# panda data frame from res\n",
+    "res = pd.DataFrame(res, columns=res[0].keys())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "54f249f9-15b8-4b1c-bebb-7b63952e875e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    Sub#  EV#   N        energy      prob  m_s  |m_s|                 state\n",
+      "4      4    0   9  3.640517e-01  0.310283 -0.5    0.5   +1.0000|0111111111>\n",
+      "0      0    0   8  0.000000e+00  0.125113 -1.0    1.0   +1.0000|0101111111>\n",
+      "5      5    0   9  3.640517e-01  0.083760  0.5    0.5   +1.0000|1111101111>\n",
+      "20    20    0   8  8.851884e-01  0.074717  0.0    0.0   +1.0000|0111111011>\n",
+      "2      2    0   9  2.739907e-01  0.044306 -0.5    0.5   +1.0000|1101111111>\n",
+      "55    55    0  10  7.125334e+00  0.038609  0.0    0.0   +1.0000|1111111111>\n",
+      "3      3    0   9  2.739907e-01  0.035831  0.5    0.5   +1.0000|1111111011>\n",
+      "51    51    0   8  2.745626e+00  0.033932  0.0    0.0   +1.0000|0111101111>\n",
+      "1      1    0   8  4.903654e-09  0.031693  1.0    1.0   +1.0000|1111101011>\n",
+      "21    21    0   8  8.851884e-01  0.019748  0.0    0.0   +1.0000|1101101111>\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(res.sort_values('prob', ascending=False)[:10])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2af9aa9e-481b-48fb-952e-0d53080236c3",
+   "metadata": {},
+   "source": [
+    "This table shows the eigenstates of the impurity with the highest weight / occurence probability. Each row shows the state of the system, where the 1/0 indicates if an orbital is occupied. The orbitals are ordered as given in the projectors (dxy, dyz, dz2, dxz, dx2-y2) from right to left, first one spin-channel, then the other. Additionally each row shows the particle sector of the state, the energy, and the `m_s` quantum number.\n",
+    "\n",
+    "It can be seen, that the state with the highest weight is a state with one hole (N=9 electrons) in the $d_{x^2-y^2, up}$ orbital carrying a spin of `0.5`. The second state in the list is a state with two holes (N=8). One in the $d_{x^2-y^2, up}$ and one in the $d_{z^2, up}$ giving a magnetic moment of 1. This is because the impurity occupation is somewhere between 8 and 9. We can also create a nice state histogram from this: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "52d1d26d-587f-4b4d-a46a-f71850423b7d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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5uRo0aJCioqLUqlUrBQcH6/Tp04qMjNTWrVsVHR0tPz+/Cs0ZEhJSaruXlwP8/QIAADg4wggcxoIFCxQVFaXevXvriy++UMOGDSVJS5cu1QsvvKDQ0FDt2bOnQnOuXbu2GioFAABAeXCbFhzCtWvXtGLFCknSqlWrioKIJIWHhyswMFB79+7V4cOHrSoRAAAAFUQYgUPYv3+/MjIy5Ofnp+7du5foHz16tCRpy5YtNV0aAAAAKonbtOAQjh49KkkKCgoqtd/ebh9XXosWLVJiYqJcXV0VEBCgkSNHysfHp2rFAgAAoFwII3AIKSkpkqQ2bdqU2m9vt48rr+nTpxf7ftq0aVq+fLkmTJhQiSoBAABQEdymBYeQlZUlSXJ3dy+138PDo9i4m/nlL3+pjz/+WMnJycrJydGxY8cUHh6uvLw8TZw4UZs3bzalbgAAAJSNKyNwCIZhSJJsNtsN+8tr+fLlxb4PCAjQkiVL1LVrV02ePFkzZszQiBEjKlUrAAAAyocrI3AInp6ekqTs7OxS+3NyciSp2FO2KmPixIlq3ry5Tp48qaSkpCrNBQAAgBsjjMAhtGvXTpLKfNO6vd0+rrKcnJyKXpx4/vz5Ks0FAACAGyOMwCF069ZNkhQbG1tqv709MDCwymulp6dLqvpVFgAAANwYYQQOoU+fPvLy8lJiYqKOHDlSon/Tpk2SpOHDh1dpnfj4eCUkJMjd3V3+/v5VmgsAAAA3RhiBQ3BxcVFYWJgkKSwsrNjekaVLlyouLk59+/ZVz549i9pXrlwpf39/zZw5s9hc27dvL/VN7XFxcXr88cdlGIYmTpwoFxeXajoaAAAASDxNCw5k1qxZ2rFjh6KiotS5c2f169dPycnJiomJUbNmzRQZGVls/MWLF5WQkFBi70d0dLTmzp2r9u3by8/PTz4+PkpKSlJsbKwKCgrUv39/LVy4sCYPDQAAoE4ijDiqiMtWV1Dj3NzctHv3bi1cuFAbNmzQ5s2b1aRJE4WEhGj+/Plq27ZtueYZMmSIzpw5o4MHD+ro0aO6fPmyGjVqpL59++qJJ57Q+PHjVa9evWo+GgAAANiMir6gAZUSEBAg6ac9CWUpLCxUQkKCJKlr165ycuIuulsdPzMAAEwU4WXh2nXvF71mKc95blk4cwIAAABgCcIIAAAAAEsQRgAAAABYgjACAAAAwBKEEQAAAACWIIwAAAAAsATvGQEAALhF3LnuTkvX/zbkW0vXR93DlREAAAAAliCMAAAAALCEQ4eR3NxczZkzR126dJGbm5t8fX0VGhqqs2fPVmneU6dOqUGDBrLZbBo6dKhJ1QIAAAD4OYcNI7m5uRo0aJDmzZunrKwsBQcHq23btoqMjFRQUJASExMrPffkyZOVl5dnYrUAAAAA/pvDhpEFCxYoKipKvXv31smTJ7Vx40bFxMRoyZIlSk1NVWhoaKXmfeedd7R7925NmjTJ5IoBAAAA/JxDhpFr165pxYoVkqRVq1apYcOGRX3h4eEKDAzU3r17dfjw4QrN++OPP+r//b//pwcffFBjxowxtWYAAAAAxTlkGNm/f78yMjLk5+en7t27l+gfPXq0JGnLli0Vmve5557T1atX9frrr5tSJ8xz+PBhvfrqqxo1apRat24tm80mNze3Ss+XkZGh559/Xu3bt5erq6vat2+vqVOnKiMjw7yiAQAAcEMO+Z6Ro0ePSpKCgoJK7be328eVx2effaaNGzdq3rx5uu2226q8Cb66Wf0c8vIw81nl8+fP1z/+8Q9T5kpLS1Pv3r116tQpderUSSNGjFB8fLyWL1+uzz77TAcOHFCzZs1MWQsAAABlc8gwkpKSIklq06ZNqf32dvu4m8nOztaUKVPUtWtXzZgxo0q1BQQElNqemJgoPz+/Ks1dl/Xu3VvdunVTz5491bNnT7Vs2bLSc02bNk2nTp3SqFGjtHHjRtWv/9N/Bs8995xWrFih8PBwrVu3zqzSAQAAUAaHDCNZWVmSJHd391L7PTw8io27mVmzZik5OVm7du2Si4uLOUXCVFUNiXYXLlzQ+vXr5ezsrNWrVxcFEUlatGiRPvjgA61fv15//vOf1aJFC1PWBAAAQOkcMowYhiFJstlsN+wvj0OHDmnFihV66qmnNHDgwCrXFh8fX2p7WVdMULO2bdumwsJCDRw4sETYcHV11aOPPqo1a9Zo27ZtGjdunDVFAgAA1BEOuYHd09NT0k+3V5UmJydHkoo9Zas0BQUFmjRpkry8vLR48WJzi8QtqTr2GwEAAKByHPLKSLt27SSpzE3m9nb7uLKcPXtW33zzjVq2bKnHH3+8WJ/9qUpff/21BgwYoIYNG2rr1q1VrBxWM3u/EQAAACrPIcNIt27dJEmxsbGl9tvbAwMDyzXfhQsXdOHChVL70tPTtWfPHnl5eVWiUtxqzN5vBAAAgMpzyNu0+vTpIy8vLyUmJurIkSMl+jdt2iRJGj58+A3n6dChgwzDKPVr9+7dkqQhQ4bIMAzeP1FLmLnfCAAAAFXjkGHExcVFYWFhkqSwsLBie0eWLl2quLg49e3bVz179ixqX7lypfz9/TVz5swarxe3DrP2GwEAAKDqHPI2Lemnx/Hu2LFDUVFR6ty5s/r166fk5GTFxMSoWbNmioyMLDb+4sWLSkhI0Pnz5y2qGLcCs/YbAQAAoOoc8sqIJLm5uWn37t2aPXu23N3dtXnzZp0+fVohISE6cuSIbrvtNqtLxC3I7P1GAAAAqDybwU3yNcL+npGy3kMiSYWFhUpISJAkde3aVU5OZWfFO9fdaW6B1eDbkG+rbW6bzSZXV1fl5uZW6HPnz59XmzZtVL9+fZ05c0bNmzcv6svLy1Pbtm2Vlpamf//73+V6y3tFfmYAANyM1f++V+e/3eUSYeEDgyIuW7e2gyvPeW5ZOHNCrVTWHqFWrVppzJgxys/P15QpU1RQUFDUN336dKWmpmrs2LHlCiIAAACoGofdM4K65dNPP9X8+fOLteXn56tXr15F38+ePVuPPPKIpBvvEfrLX/6iAwcO6KOPPpK/v7/uvvtuxcfH69ixY/Lz89Nrr71WvQcDAAAASYQROIjU1FTFxMQUazMMo1hbampqueby9vbWwYMHNWfOHG3evFmffPKJWrRoobCwMM2dO1dNmzY1tXYAAACUjj0jNcTsPSO4NfAzAwCYiT0j7BlxROwZAQAAAOBwCCMAAAAALEEYAQAAAGAJwggAAAAASxBGAAAAAFiCMAIAAADAEoQRAAAAAJYgjAAAAACwBGEEAAAAgCUIIwAAAAAsQRgBAAAAYAnCCAAAAABLEEYAAAAAWIIwAgAAAMAS9a0uAJVz3P8XVpdwU784cdy0uQ4fPqwvv/xSX3/9tWJiYnTu3Dm5uroqNze3wnN16NBBycnJZfYfP35c/v7+VSkXAAAA5UAYgUOYP3++/vGPf5g6Z0hISKntXl5epq4DAACA0hFG4BB69+6tbt26qWfPnurZs6datmxZ5TnXrl1b9cIAAABQaYQROIQZM2ZYXQIAAABMxgZ2AAAAAJbgygjqrEWLFikxMVGurq4KCAjQyJEj5ePjY3VZAAAAdQZhBHXW9OnTi30/bdo0LV++XBMmTLCoIgAAgLqF27RQ5/zyl7/Uxx9/rOTkZOXk5OjYsWMKDw9XXl6eJk6cqM2bN1tdIgAAQJ3AlRHUOcuXLy/2fUBAgJYsWaKuXbtq8uTJmjFjhkaMGGFNcQAAAHUIV0aA/zVx4kQ1b95cJ0+eVFJSktXlAAAA1HqEEeB/OTk5yc/PT5J0/vx5i6sBAACo/QgjwM+kp6dLkho2bGhxJQAAALWf6WFk27ZtGjFihFq3bi1XV9diTybatm2bwsPDde7cObOXBaosPj5eCQkJcnd3l7+/v9XlAAAA1HqmhpEpU6Zo+PDh+uc//6msrCxdu3ZNhmEU9Tdu3Fh/+ctf9MEHH5i5LFDCypUr5e/vr5kzZxZr3759uw4fPlxifFxcnB5//HEZhqGJEyfKxcWlpkoFAACos0wLI2vWrNEbb7yhe+65R998840uX75cYkzv3r3VunVrbdmyxaxlUUd8+umn6tWrV9GXJOXn5xdr+/TTT4vGX7x4UQkJCSX2fkRHR+vuu+9Whw4dNGjQIP3617/Wvffeqx49euj48ePq37+/Fi5cWKPHBgAAUFeZ9mjfN998U02bNtXWrVvVrFmzMsfddttt+uGHH8xats76xYnjVpdQo1JTUxUTE1OszTCMYm2pqak3nWfIkCE6c+aMDh48qKNHj+ry5ctq1KiR+vbtqyeeeELjx49XvXr1TK8fAAAAJZkWRuLj4zVw4MAbBhFJatmypQ4cOGDWsqgjxo0bp3HjxpV7fEREhCIiIkq09+7dW7179zavMAAAAFSaabdpOTk5qbCw8Kbjzp07Jw8PD7OWBQAAAOCgTAsj/v7+OnTokHJycsock5aWpm+++UaBgYFmLQsAAADAQZkWRp544gmlpqbqmWeeUUFBQYl+wzD03HPPKSsrS7/5zW/MWhYAAACAgzJtz8iUKVP00Ucfad26ddq/f7+GDBki6adHpv7+97/X1q1bdfLkST3wwAMKCQkxa1kAAAAADsq0KyPOzs76/PPP9bvf/U4pKSlavXq1JCk2NlZLly5VYmKiJkyYoC1btsjJiRe/AwAAAHWdaVdGJMnd3V2rV6/W3LlztWfPHp0+fVrXr19XmzZtNHDgQPn6+pq5HAAAAAAHZmoYsfPx8dHo0aOrY2oAAAAAtQT3SwEAAACwRKWvjMybN6/Si9psNs2ePbvSnwcAAADg+CodRiIiImSz2WQYRrF2m81W9Gd733+3EUYAAAAAVDqMREZGlmiLiorS22+/rXbt2mn06NFq166dJCklJUUfffSRkpOTNWnSJN13332VrxgAAABArVDpMPLf7wo5cOCAJk+erNmzZ+ull15SvXr1ivX/6U9/0vz58/Xqq69q3LhxlV0WAAAAQC1h2gb2l156SV27dtXcuXNLBBFJqlevniIiItS1a1fNmTPHrGUBAAAAOCjTwsjXX3+tO+6446bj7rjjDn399ddmLYs6IicnR5s3b9aECRMUGBioRo0aycPDQ926ddO8efOUlZVV4TkzMjL0/PPPq3379nJ1dVX79u01depUZWRkmH8AAAAAKMHU94ycOHHClDG4uVW/22V1CTf1zBsPmDbXhg0bNGnSJElSQECAhg4dqitXrigqKkpz5szR+++/rz179qh58+blmi8tLU29e/fWqVOn1KlTJ40YMULx8fFavny5PvvsMx04cEDNmjUzrX4AAACUZNqVkb59++qbb77RokWLyhyzePFiHTlyRH379jVrWdQRLi4uevrpp3Xy5EkdO3ZMf//73/X5558rISFB3bt314kTJ/T888+Xe75p06bp1KlTGjVqlBISErRx40YdO3ZMzz77rL7//nuFh4dX38EAAABAkmQz/vvZvJUUFxen++67T1evXlXXrl2LnqZls9mUnJysjz76SCdOnFCDBg0UFRWlwMBAM5Z1GAEBAZKk+Pj4MscUFhYqISFBktS1a1c5OZWdFevalZEbiY6O1n333SdXV1dduXJFLi4uNxx/4cIFtW7dWvXq1dOZM2fUokWLor68vDy1bdtWly5d0r///e9ifaWpyM8MAICbuXPdnZau/23It5aurwgvC9e+bN3aDq4857llMe02rcDAQO3cuVPjxo3TiRMn9PLLLxe9X8Sed7p06aK1a9fWuSCC6tWtWzdJPwWJtLQ0tWrV6objt23bpsLCQg0cOLBE2HB1ddWjjz6qNWvWaNu2bTz5DQAAoBqZumfk3nvv1XfffaevvvpK+/fv17lz52QYhnx9fdW3b18NHDiw2AsQATP88MMPkiRnZ2c1bdr0puOPHj0qSQoKCiq1PygoSGvWrCkaBwAAgOphahiRfnrb+sCBAzVw4ECzpwZKtWzZMknS0KFD5erqetPxKSkpkqQ2bdqU2m9vt48DAABA9eAGdzi0zz77TO+8846cnZ01f/78cn3G/hhgd3f3Uvs9PDyKjQMAAED1MO3KyLvvvluh8U899ZRZS6OOOn78uJ588kkZhqFFixYV7R25GfseprJuGTTpmQ4AAAC4CdPCyLhx48q1H8QwDNlsNsIIquTs2bMaOnSo0tPTFR4erqlTp5b7s56enpKk7OzsUvtzcnIkSQ0bNqx6oQAAACiTaWHkpZdeKjWMFBYW6syZM9qzZ4+SkpI0btw4tW/f3qxlUQddvHhRgwcPVkpKisaPH6/FixdX6PPt2rWT9FOgKY293T4OAAAA1cO0MBIREXHD/mvXrun555/Xpk2bdPDgQbOWRR2TmZmpYcOG6cSJExo1apTefvvtCj+hzX47V2xsbKn99nYeQQ0AAFC9amwDu7Ozs5YtW6YGDRroxRdfrKllUYvk5eUpODhYhw4d0pAhQ/T++++rXr16FZ5n6NChcnJy0r59+/Tjjz+WWGPLli1ycnLSsGHDzCodAAAApajRp2nVr19fPXr00JdfflmTy6IWuH79usaMGaPdu3erX79++vjjj2/6pvWVK1fK399fM2fOLNbeqlUrjRkzRvn5+ZoyZYoKCgqK+qZPn67U1FSNHTtWLVu2rJZjAQAAwE9Mf8/IzVy4cKHMjcNAWVauXKlPPvlEkuTt7a0pU6aUOm7x4sXy9vaW9NPekoSEBJ0/f77EuL/85S86cOCAPvroI/n7++vuu+9WfHy8jh07Jj8/P7322mvVdzAAAACQVINhpLCwUKtWrVJ0dLTuueeemlq21nrmjQesLqFGpaenF/3ZHkpKExERURRGbsTb21sHDx7UnDlztHnzZn3yySdq0aKFwsLCNHfu3HK9yR0AAABVYzNMeqnCAw+UfXKclZWlpKQkXbp0STabTVu2bKlz9+MHBARIkuLj48scU1hYqISEBElS165d5eTEOylvdfzMAABmunPdnZau/23It5aurwgvC9e+bN3aDq4857llMe3KyFdffXXDfmdnZ/Xt21cvvfSSBg0aZNayAAAAAByUaWEkKSmpzD4XFxd5e3vL2dnZrOUAAAAAODjTwggvMgQAAABQEabd4B4aGqo1a9bcdNzatWsVGhpq1rIAAAAAHJRpYWTt2rXav3//Tcf961//0rp168xaFgAAAICDqvFH/+Tn51fqrdmlyc3N1Zw5c9SlSxe5ubnJ19dXoaGhOnv2bLnnKCgoUEREhB555BF16tRJnp6ecnNzU+fOnfXMM88oJSXFlFoBAAAAFFejYcQwDMXGxsrHx6fKc+Xm5mrQoEGaN2+esrKyFBwcrLZt2yoyMlJBQUFKTEws9zxz587V3r171apVKw0dOlRDhgxRfn6+Vq9ercDAQMXGxla5XgAAAADFVWkD+3+/W+Tzzz8v830jBQUFSkxM1IULF/Sb3/ymKstKkhYsWKCoqCj17t1bX3zxhRo2bChJWrp0qV544QWFhoZqz549N53Hzc1N+/fv17333qv69f/vr+P69euaPXu2Fi5cqClTpujAgQNVrhkAAADA/6nSSw9//oI3m82mm03l7OysoUOH6p133inXW7LLcu3aNTVv3lwZGRmKjY1V9+7di/V369ZNcXFxOnTokHr06FHpdQoKCuTp6anc3FxlZWXJw8Oj0nPx0sPaiZ8ZAMBMvPSQlx46Isteemh/t4hhGOrUqZNGjx6tRYsWlTrWzHeN7N+/XxkZGfLz8ysRRCRp9OjRiouL05YtW6oURmw2m5ycnOTk5FTsqgkAAACAqqvSGfbP3y0yZ84cde/evUbeN3L06FFJUlBQUKn99nb7uMowDEOvvvqqcnJy9OCDD8rV1bXScwEAAAAoybRf98+ZM8esqW7K/oSrNm3alNpvb6/ok7BmzJih//znP7py5Yri4uKUmJgof39/vfXWW+Wew36Z6r8lJibKz8+vQvUAAAAAtZlD3nuUlZUlSXJ3dy+13763wz6uvD766KNiT+G64447tH79enXs2LGSlQIAAAAoS6XDiH0vxXfffacuXbpU6N0hNptNBQUFlV26aKO8zWa7YX9Fff/995Kkixcv6vDhw/rjH/+oHj166K9//atCQkLKNUdZG3fKumICAAAA1FWVDiPt2rWTzWYr2pDetm3bMsOB2Tw9PSVJ2dnZpfbn5ORIUtHjfivK29tbQ4YMUa9evRQYGKinn35aDzzwgNq2bVu5gqvBkv8ZbnUJN/XCxq2mzZWTk6MvvvhCW7Zs0cGDB3X69Gldv35dt912mx577DGFh4dX6OfdoUMHJScnl9l//Phx+fv7m1E6AAAAylDpMHL69Okbfl+d2rVrJ0llvmnd3m4fV1leXl4aPny4Vq9erS+//FKhoaFVmg+Vt2HDBk2aNEnST1eZhg4dqitXrigqKkpz5szR+++/rz179qh58+YVmresK15eXhY+WhAAAKCOcMg9I926dZOkMt+Mbm8PDAys8lr296GkpqZWeS5UnouLi55++mlNmzZNnTt3Lmo/f/68HnnkER05ckTPP/+8NmzYUKF5165da3KlAAAAKC+HfENbnz595OXlpcTERB05cqRE/6ZNmyRJw4dX/VYm+1vceRKWtZ566imtXr26WBCRpFatWmnVqlWSpI8//lj5+flWlAcAAIBKqPSVkYo+Nve/VeUWKhcXF4WFhemVV15RWFiYvvjii6InaC1dulRxcXHq27evevbsWfSZlStXauXKlRo5cqQWLlxY1P7Pf/6z6M3wP9/zkpOTo1deeUV79uxRy5YtNXTo0ErXi+plv1KWl5entLQ0tWrVyuKKAAAAUB6VDiMdOnSo9Ib1qj5NS5JmzZqlHTt2KCoqSp07d1a/fv2UnJysmJgYNWvWTJGRkcXGX7x4UQkJCTp//nyx9tjYWM2dO1e+vr7q3r27vLy8dOHCBX3zzTe6dOmSvLy89Pe//73Sm+FR/X744QdJkrOzs5o2bVqhzy5atEiJiYlydXVVQECARo4cKR8fn+ooEwAAAP+l0mHk/vvvr7GnZ5XGzc1Nu3fv1sKFC7VhwwZt3rxZTZo0UUhIiObPn1/uJ1+NGjVKmZmZ2rdvnw4ePKhLly6pQYMGuu222zR58mQ9++yz/Kb9Frds2TJJ0tChQ+Xq6lqhz06fPr3Y99OmTdPy5cs1YcIE0+oDAABA6WxGZV/KgQqxv2ekrPeQSFJhYaESEhIkSV27dpWTU9lbeurao33L8tlnn2n48OGqX7++Dh48WHTL1s0899xzGjhwoHr06CEfHx/98MMPWrNmjZYtW6br16/rk08+0YgRI246T0V+ZgAA3Myd6+60dP1vQ761dH1FWPg0y4jL1q3t4MpznlsWh3yaFiD99C6QJ598UoZhaNGiReUOIpK0fPnyYt8HBARoyZIl6tq1qyZPnqwZM2aUK4wAAACg8qr117iZmZnKzMysziVQR509e1ZDhw5Venq6wsPDNXXqVFPmnThxopo3b66TJ08qKSnJlDkBAABQOtPDyNatWzVs2DB5eXmpcePGaty4sRo1aqRhw4Zpy5YtZi+HOujixYsaPHiwUlJSNH78eC1evNi0uZ2cnIoe4/zfDzsAAACAuUwLI4ZhaMKECQoODtb27duVmZkpLy8vNWrUSFlZWdq+fbtGjBihcePGiW0qqKzMzEwNGzZMJ06c0KhRo/T222+b/iCF9PR0SeIJagAAANXMtDCybNkyRUZGqlWrVnr99dd1+fJlXbp0Senp6bp8+bJef/11tWrVSu+9917R04+AisjLy1NwcLAOHTqkIUOG6P3331e9evVMXSM+Pl4JCQlyd3eXv7+/qXMDAACgONPCyFtvvSV3d3ft27dPkydPlqenZ1Gfp6enJk+erH379qlBgwZ66623zFoWdcT169c1ZswY7d69W/369dPHH38sFxeXG35m5cqV8vf318yZM4u1b9++XYcPHy4xPi4uTo8//rgMw9DEiRNvOj8AAACqxrSnaSUlJemhhx5Sx44dyxzTsWNHDRo0SF988YVZy6KOWLlypT755BNJkre3t6ZMmVLquMWLF8vb21tS2S+6jI6O1ty5c9W+fXv5+fnJx8dHSUlJio2NVUFBgfr376+FCxdW7wEBAADAvDDi4+NTrt8ku7i4FJ0sovJq4h0etxL7Pg5JRaGkNBERETf939eQIUN05swZHTx4UEePHtXly5fVqFEj9e3bV0888YTGjx9v+u1fAAAAKMm0MDJy5Ej97W9/U3p6upo0aVLqmEuXLmnXrl0aO3asWcuijoiIiFBERIQpn+ndu7d69+5tTmEAAACoNNP2jLz88svq1KmTHnjgAe3atatE/65duzR48GB16tRJCxYsMGtZAAAAAA7KtCsjwcHBcnFx0eHDhzV48GA1bdpU7du3lySlpKQoLS1NktSrVy8FBwcX+6zNZtPOnTvNKgUAAACAAzAtjHz11VdFfzYMQ2lpaUUB5Oeio6NLtJn9nggAAAAAtz5Tn6YFAAAAAOVlWhix35IFAAAAAOVh2gZ2AAAAAKgI066M/FxKSorOnz+vvLy8Msfcf//91bG0Q/v53pnr16/LyYmseKu7fv160Z/Z+wQAAFAxpoaRd955R6+88oqSk5NvOvbnJ3H4ic1mk6urq/Ly8nTlyhU1a9bM6pJwE1euXJEkubq6EkYAAAAqyLQw8vrrryssLEyGYah79+7y8/OTh4eHWdPXGU2aNNGFCxf0448/qqCgQJ6enpzo3mIMw1BeXp4yMzN16dIlSSrzRZ8AAAAom2lhZOnSpXJxcdGnn36qBx54wKxp6xwvLy/l5uYqIyNDly5dKjrZxa2rcePG8vLysroMAAAAh2NaGDl79qwGDhxIEKkiJycntWzZUh4eHsrMzFR2dja3tN2C6tWrJw8PD3l6esrT05MrVwAAAJVgWhhp166dDMMwa7o6zWazqVGjRmrUqJGkn24L4u/21mGz2QgfAAAAJjAtjISEhOjPf/6zzp8/r1atWpk1LcTJLwAAAGon054dO2PGDA0YMEBDhw7V3r17zZoWAAAAQC1l2pWRevXq6c0339SgQYM0cOBAubi4qGXLlqX+Rt9msykxMdGspQEAAAA4INPCyPHjxzVw4EClpqYWPfq0PO8bAQAAAFA3mXab1gsvvKAff/xRISEhiouLU1ZWlgoLC8v8AgAAAFC3mXZl5F//+pcCAwO1Zs0as6YEAAAAUIuZdmXExcVFnTt3Nms6AAAAALWcaWFk4MCB+uabb8yaDgAAAEAtZ1oYefXVV5Wenq7p06ezJwQAAADATZm2Z+Rvf/ubhg8friVLlmjz5s0aMGCAWrduXeajfWfPnm3W0gAAAAAckGlhJCIiQjabTYZh6Pvvv9f3339f5ljCCAAAAADTwkhkZKRZUwEAAACoA0wLIyEhIWZNBQAAAKAOMG0DOwAAAABUBGEEAAAAgCVMu01LkgzD0Pr16/WPf/xDp06dUmZmpgzDKDHOZrMpMTHRzKUBAAAAOBjTwkh+fr4eeeQR7dq1q9QAIqnoaVsAAAAAYNptWkuWLNHOnTs1fPhwnTp1Sr/5zW9ks9mUl5en48ePKyIiQh4eHvp//+//8VJEAAAAAOZdGdm4caOaNm2qDRs2yMPDQ05OP+UcZ2dnde3aVS+99JIGDhyogQMHqmvXrgoNDTVraQAAAAAOyLQrI99//73uueceeXh4/DTx/4aR69evF43p16+f+vTpo9WrV5u1LAAAAAAHZVoYqVevnho1alT0vT2UpKamFhvXunVrJSQkmLUsAAAAAAdlWhhp3bq1UlJSir6/7bbbJEkHDhwoNi4uLk4NGzY0a1kAAAAADsq0MNKrVy/Fx8fr6tWrkqSHH35YkjR16lRt27ZN3377rZ599lkdP35c9957r1nLAgAAAHBQpoWRxx57TO7u7vryyy8l/XRl5Pnnn9eZM2c0fPhw3XXXXVq1apXc3d31pz/9yaxlAQAAADgo056m9cgjj+j8+fPF2pYsWaKePXtq8+bNSk9PV5cuXfTcc8+pc+fOZi0LAAAAwEGZ+gb20vz617/Wr3/96+peBgAAAICDMe02LQAAAACoCMIIAAAAAEsQRgAAAABYgjACAAAAwBKEEQAAAACWIIwAAAAAsARhBAAAAIAlqu09I2lpaTp37pwkqVWrVvL29q6upQAAAAA4IFOvjBiGoZUrV+oXv/iFmjdvrrvuukt33XWXWrRooV/84hdasWKFCgsLzVwSAAAAgIMy7cpIXl6eHn30Ue3cuVOGYahJkyZq3769DMNQSkqKEhIS9Pzzz+uf//yntm7dKldXV7OWBgAAAOCATLsysmDBAu3YsUMBAQHatm2b0tLSFBsbqyNHjigtLU3btm3THXfcoV27dmnBggVmLQsAAADAQZkWRv72t7+pcePG2r17t4YMGVKif8iQIdq5c6e8vLz03nvvmbUsAAAAAAdlWhg5d+6cBg0apGbNmpU5xtvbWw888IDOnz9v1rIAAAAAHJRpYaR169bKz8+/6bhr167J19fXrGUBAAAAOCjTwsgTTzyhnTt3Kjk5ucwxycnJ2rlzp8aOHWvWsgAAAAAclGlhZNasWRo0aJDuv/9+rVmzRtnZ2UV92dnZWrNmjfr3769BgwbppZdeMmtZAAAAAA6q0o/27dSpU4k2wzB09uxZTZo0SZMmTVKTJk0kSenp6UVjbDab/P39lZiYWNmlAQAAANQClQ4jp0+fvumYS5culWi70W1cAAAAAOqOSocR3qQOAAAAoCpM2zMCAAAAABVBGAEAAABgCdPDyLFjxzRlyhTdeeedatasmby9vXXnnXfqmWee0bFjx8xeDgAAAICDMjWMLFu2TEFBQXrzzTcVHx+v9PR0Xbp0SfHx8Xr99dcVFBSkZcuWmbZebm6u5syZoy5dusjNzU2+vr4KDQ3V2bNnyz1HRkaGNmzYoLFjx+r222+Xh4eHPD09de+992rZsmW6du2aafUCAAAA+D+mhZEvv/xS06ZNk4uLi6ZNm6YjR44oPT1dGRkZ+uabb/TCCy/I1dVV4eHh2rlzZ5XXy83N1aBBgzRv3jxlZWUpODhYbdu2VWRkpIKCgsr96ODFixfriSee0MaNG+Xu7q5HH31U99xzj44eParnn39eDzzwgHJycqpcLwAAAIDiTAsjS5cuVf369fXFF19o8eLF6tatm7y8vNSoUSMFBgZq0aJF+uKLL+Tk5KQlS5ZUeb0FCxYoKipKvXv31smTJ7Vx40bFxMRoyZIlSk1NVWhoaLnmadiwof7whz8oJSVFhw4d0gcffKCdO3fq22+/Vbt27bR//369/PLLVa4XAAAAQHE2wzAMMyZq1qyZgoKC9OWXX95w3ODBgxUbG6u0tLRKr3Xt2jU1b95cGRkZio2NVffu3Yv1d+vWTXFxcTp06JB69OhR6XXef/99jR07Vh06dFBSUlKl55GkgIAASVJ8fHyV5gEAALXXnevutHT9b0O+tXR9RXhZuPZl69Z2cFU5zzXtykhOTo58fHxuOs7Hx6fKtz3t379fGRkZ8vPzKxFEJGn06NGSpC1btlRpnW7dukmSzp07V6V5AAAAAJRkWhhp27atoqOjdf369TLHFBQUKDo6Wm3btq3SWkePHpUkBQUFldpvb7ePq6wffvhBktSyZcsqzQMAAACgJNPCSHBwsJKTkzVx4kRduXKlRP+VK1c0adIkpaSkaMSIEVVaKyUlRZLUpk2bUvvt7fZxlWV/8ldwcHCV5gEAAABQUn2zJpo5c6Y+/vhjvfvuu9q8ebMefvhhdejQQTabTUlJSfr000915coVderUSTNnzqzSWllZWZIkd3f3Uvs9PDyKjauMN954Qzt27FDjxo314osvlvtz9nvm/ltiYqL8/PwqXQ8AAABQ25gWRpo2baq9e/fqd7/7nT799FO9//77JcY88sgjevPNN9WkSZMqrWXfc2+z2W7YX1l79uzR1KlTZbPZtGbNGvn6+lZpPgAAAAAlmRZGJKl169basmWLkpKStH///qKN376+vurbt686duxoyjqenp6SpOzs7FL77RvkGzZsWOG54+LiNGLECOXn52v58uUaOXJkhT5f1lMEyrpiAgAAANRVpoWRoKAg+fn56cMPP1THjh1NCx6ladeunSSV+aZ1e7t9XHklJiZqyJAhysjIUEREhJ599tmqFQoAAACgTKZtYE9ISJCzs7NZ092Q/ZG7sbGxpfbb2wMDA8s957lz5zR48GBduHBBU6dO1Zw5c6peKAAAAIAymRZGOnfuXKUXGVZEnz595OXlpcTERB05cqRE/6ZNmyRJw4cPL9d86enpGjJkiJKSkjR+/Hi99tprptYLAAAAoCTTwsiECRO0Z88enThxwqwpy+Ti4qKwsDBJUlhYWLG9I0uXLlVcXJz69u2rnj17FrWvXLlS/v7+JZ7klZOTo4cffljHjh3Tr371K7399ttlbowHAAAAYB7T9ow8++yzio+PV//+/fXiiy/q0UcfVbt27eTi4mLWEsXMmjVLO3bsUFRUlDp37qx+/fopOTlZMTExatasmSIjI4uNv3jxohISEnT+/Pli7X/84x914MAB1atXT/Xr19eECRNKXW/t2rXVchwAAABAXWVaGKlXr56knx6r+/vf/16///3vyxxrs9lUUFBQpfXc3Ny0e/duLVy4UBs2bNDmzZvVpEkThYSEaP78+eV+y3t6erok6fr169qwYUOZ4wgjAAAAgLlsRlVfyvG/7C84LK+kpCQzlnUY9kf7lvXoXwAAgDvX3Wnp+t+GfGvp+orwsnDty9at7eCqcp5r2pWR06dPmzUVAAAAgDrA1JceAgAAoPIyj79qdQlAjTLtaVqlyczMVGZmZnUuAQAAAMBBmR5Gtm7dqmHDhsnLy0uNGzdW48aN1ahRIw0bNkxbtmwxezkAAAAADsq0MGIYhiZMmKDg4GBt375dmZmZ8vLyUqNGjZSVlaXt27drxIgRGjdunEzaMw8AAADAgZkWRpYtW6bIyEi1atVKr7/+ui5fvqxLly4pPT1dly9f1uuvv65WrVrpvffe07Jly8xaFgAAAICDMi2MvPXWW3J3d9e+ffs0efJkeXp6FvV5enpq8uTJ2rdvnxo0aKC33nrLrGUBAAAAOCjTwkhSUpIGDRqkjh07ljmmY8eOGjRoUJ17xwgAAACAkkwLIz4+PnJxcbnpOBcXF3l7e5u1LAAAAAAHZVoYGTlypHbt2qX09PQyx1y6dEm7du3SiBEjzFoWAAAAgIMyLYy8/PLL6tSpkx544AHt2rWrRP+uXbs0ePBgderUSQsWLDBrWQAAAAAOyrQ3sAcHB8vFxUWHDx/W4MGD1bRpU7Vv316SlJKSorS0NElSr169FBwcXOyzNptNO3fuNKsUAAAAAA7AtDDy1VdfFf3ZMAylpaUVBZCfi46OLtFms9nMKgMAAACAgzAtjPCELAAAAAAVYVoYsd+SBQAAAADlYdoGdgAAAACoCMIIAAAAAEsQRgAAAABYgjACAAAAwBKEEQAAAACWIIwAAAAAsARhBAAAAIAlCCMAAAAALEEYAQAAAGAJwggAAAAASxBGAAAAAFiCMAIAAADAEoQRAAAAAJYgjAAAAACwBGEEAAAAgCUIIwAAAAAsQRgBAAAAYAnCCAAAAABLEEYAAAAAWIIwAgAAAMAShBEAAAAAliCMAAAAALAEYQQAAACAJQgjAAAAACxBGAEAAABgCcIIAAAAAEsQRgAAAABYgjACAAAAwBKEEQAAAACWIIwAAAAAsARhBAAAAIAlCCMAAAAALEEYAQAAAGAJwggAAAAASxBGAAAAAFiCMAIAAADAEoQRAAAAAJYgjAAAAACwBGEEAAAAgCUIIwAAAAAsQRgBAAAAYAnCCAAAAABLEEYAAAAAWIIwAgAAAMAShBEAAAAAliCMAAAAALAEYQQAAACAJQgjAAAAACxBGAEAAABgCcIIAAAAAEsQRgAAAABYgjACAAAAwBKEEQAAAACWIIwAAAAAsARhBAAAAIAlHDqM5Obmas6cOerSpYvc3Nzk6+ur0NBQnT17tkLz7NmzR3PnztUjjzwiHx8f2Ww2+fv7V1PVAAAAACSpvtUFVFZubq4GDRqkqKgotWrVSsHBwTp9+rQiIyO1detWRUdHy8/Pr1xzTZ06VUePHq3migEAAAD8nMNeGVmwYIGioqLUu3dvnTx5Uhs3blRMTIyWLFmi1NRUhYaGlnuuhx56SK+88oq++OILxcbGVmPVAAAAAOwc8srItWvXtGLFCknSqlWr1LBhw6K+8PBwrVu3Tnv37tXhw4fVo0ePm8735z//uejPp0+fNr1eAAAAACU55JWR/fv3KyMjQ35+furevXuJ/tGjR0uStmzZUtOlAQAAACgnhwwj9v0dQUFBpfbb29kHAgAAANy6HPI2rZSUFElSmzZtSu23t9vH1aSAgIBS2xMTE8u9oR4AAACoCxzyykhWVpYkyd3dvdR+Dw+PYuMAAAAA3Hoc8sqIYRiSJJvNdsN+K8THx5faXtYVEwAAAKCucsgrI56enpKk7OzsUvtzcnIkqdhTtgAAAADcWhwyjLRr106SynzTur3dPg4AAADArcchw0i3bt0kqcwXFNrbAwMDa6wmAAAAABXjkGGkT58+8vLyUmJioo4cOVKif9OmTZKk4cOH13RpAAAAAMrJIcOIi4uLwsLCJElhYWHF9o4sXbpUcXFx6tu3r3r27FnUvnLlSvn7+2vmzJk1Xi8AAACAkhzyaVqSNGvWLO3YsUNRUVHq3Lmz+vXrp+TkZMXExKhZs2aKjIwsNv7ixYtKSEjQ+fPnS8z117/+VX/9618lSXl5eZKk5ORk9erVq2jM6tWry3zJIgAAAICKc9gw4ubmpt27d2vhwoXasGGDNm/erCZNmigkJETz589X27Ztyz3X2bNnFRMTU6wtNze3WNuVK1dMqx0AAACAZDOsfClHHWJ/z0hZ7yEBAADo8OKnlq5/+tVHLF1fEV4Wrn3ZurUdXFXOcx1yzwgAAAAAx0cYAQAAAGAJwggAAAAASxBGAAAAAFiCMAIAAADAEoQRAAAAAJYgjAAAAACwBGEEAAAAgCUIIwAAAAAsQRgBAAAAYAnCCAAAAABLEEYAAAAAWIIwAgAAAMAShBEAAAAAliCMAAAAALAEYQQAAACAJQgjAAAAACxBGAEAAABgCcIIAAAAAEsQRgAAAABYgjACAAAAwBKEEQAAAACWIIwAAAAAsARhBAAAAIAlCCMAAAAALEEYAQAAAGAJwggAAAAASxBGAAAAAFiCMAIAAADAEoQRAAAAAJYgjAAAAACwBGEEAAAAgCUIIwAAAAAsQRgBAAAAYAnCCAAAAABLEEYAAAAAWIIwAgAAAMAShBEAAAAAliCMAAAAALAEYQQAAACAJQgjAAAAACxR3+oCAAAAfu64/y8sW/sXJ45btjZQF3FlBAAAAIAlCCMAAAAALMFtWgAAAIDFOrz4qaXrn371EUvW5coIAAAAAEsQRgAAAABYgjACAAAAwBLsGQEAALeUX/z6nNUlAKghXBkBAAAAYAnCCAAAAABLEEYAAAAAWIIwAgAAAMAShBEAAAAAliCMAAAAALAEYQQAAACAJQgjAAAAACxBGAEAAABgCcIIAAAAAEsQRgAAAABYgjACAAAAwBKEEQAAAACWIIwAAAAAsARhBAAAAIAlCCMAAAAALEEYAQAAAGAJwggAAAAASxBGAAAAAFiCMAIAAADAEoQRAAAAAJZw6DCSm5urOXPmqEuXLnJzc5Ovr69CQ0N19uzZCs+VkZGh559/Xu3bt5erq6vat2+vqVOnKiMjw/zCAQAAADhuGMnNzdWgQYM0b948ZWVlKTg4WG3btlVkZKSCgoKUmJhY7rnS0tJ0zz33aNmyZapfv75GjBghT09PLV++XD179lRaWlo1HgkAAABQNzlsGFmwYIGioqLUu3dvnTx5Uhs3blRMTIyWLFmi1NRUhYaGlnuuadOm6dSpUxo1apQSEhK0ceNGHTt2TM8++6y+//57hYeHV+ORAAAAAHWTQ4aRa9euacWKFZKkVatWqWHDhkV94eHhCgwM1N69e3X48OGbznXhwgWtX79ezs7OWr16terXr1/Ut2jRIvn4+Gj9+vX6z3/+Y/6BAAAAAHWYQ4aR/fv3KyMjQ35+furevXuJ/tGjR0uStmzZctO5tm3bpsLCQt1///1q0aJFsT5XV1c9+uijun79urZt22ZO8QAAAAAkOWgYOXr0qCQpKCio1H57u31cTc0FAAAAoPzq33zIrSclJUWS1KZNm1L77e32cTU1lyQFBASU2n7ixAk5OzuX2Q8AgF1iRvkfwlId/Br7Wbq+UrOsW/tDa/+dPvcfC49dUsCWhjcfVJ342VumKj/7xMREOTs7V+qzDhlGsrJ++mG5u7uX2u/h4VFsXE3NdSM2m63SPyQz2J8u5udn8T8wFqjLxy7V7ePn2OvmsUuOf/xVCQOOfuySJB//Sn/U0Y+/c4uqnRBKjnvskir9s68Nx+7IP3tnZ+eic+aKcsgwYhiGpJ9O8G/UX9NzSVJ8fHyFxtcU+xWZW7W+6lSXj12q28fPsdfNY5fq9vHX5WOX6vbxc+x189glxz5+h9wz4unpKUnKzs4utT8nJ0eSij1lqybmAgAAAFB+DhlG2rVrJ0llvmnd3m4fV1NzAQAAACg/hwwj3bp1kyTFxsaW2m9vDwwMrNG5AAAAAJSfQ4aRPn36yMvLS4mJiTpy5EiJ/k2bNkmShg8fftO5hg4dKicnJ+3bt08//vhjsb68vDxt2bJFTk5OGjZsmDnFAwAAAJDkoGHExcVFYWFhkqSwsLBi+z2WLl2quLg49e3bVz179ixqX7lypfz9/TVz5sxic7Vq1UpjxoxRfn6+pkyZooKCgqK+6dOnKzU1VWPHjlXLli2r+agAAACAusVmVPRxUbeI3NxcDRgwQDExMWrVqpX69eun5ORkxcTEqFmzZjpw4IBuu+22ovERERGaO3euQkJCtHbt2mJzXbx4Ub169VJiYqL8/Px09913Kz4+XseOHZOfn58OHDggb2/vGj5CAAAAoHZzyCsjkuTm5qbdu3dr9uzZcnd31+bNm3X69GmFhIToyJEjxYLIzXh7e+vgwYN69tlnlZ+fr08++USXL19WWFiYvv76a4IIAAAAUA0c9soIAAAAAMfmsFdGAAAAADg2wggAAAAASxBGAAAAAFiCMAIAAADAEoQRAAAAAJYgjAAAAACwBGGkFsvNzdWcOXPUpUsXubm5ydfXV6GhoTp79qzVpVW7w4cP69VXX9WoUaPUunVr2Ww2ubm5WV1WtcvJydHmzZs1YcIEBQYGqlGjRvLw8FC3bt00b948ZWVlWV1itVu6dKlGjRqlzp07y8vLS66urmrfvr1CQkIUHx9vdXk16tKlS2revLlsNpv8/f2tLqfaDRgwQDabrcyvzz//3OoSq92FCxc0bdo0denSRQ0aNFDTpk3Vo0cPTZ8+3erSqs1XX311w5+7/WvevHlWl1ptDhw4oMcee0wtW7aUs7OzmjZtqkGDBmnTpk1Wl1btDhw4oODgYHl7e8vNzU1dunTRrFmzlJOTY3Vppqjs+cy7776re+65Rw0bNlTTpk318MMPKyoqqgYqrjjeM1JL5ebmatCgQYqKiip6Q/3p06f19ddfy8fHR9HR0fLz87O6zGozYsQI/eMf/yjW5urqqtzcXIsqqhl//etfNWnSJElSQECAbr/9dl25ckVRUVHKzMyUv7+/9uzZo+bNm1tcafXx9vZWdna2AgMD1bp1a0lSfHy8Tp48KRcXF23evFnDhg2zuMqaMW7cOL377rsyDENdu3bViRMnrC6pWg0YMEB79uzRY489poYNG5bof+GFF3TnnXdaUFnNiI6O1sMPP6yMjAzdfvvtuuOOO5SZmanvvvtOZ8+eVUFBgdUlVosTJ07o1VdfLbXv+vXr+tvf/iZJ2rVrlwYOHFiTpdWIDz/8UL/+9a9VWFiou+++W35+fjp37pz+9a9/qbCwUDNmzCjz78fRrV+/XiEhIbp+/bp69Oihdu3a6dChQzpz5oy6deumffv2ydPT0+oyq6Qy5zPh4eF67bXX1KBBAz300EPKzc3Vzp07ZRiGPvzwQ40cObK6y64YA7XS7NmzDUlG7969jczMzKL2JUuWGJKM+++/38Lqqt+rr75qvPTSS8aWLVuMCxcuGJIMV1dXq8uqduvWrTOefvpp4+TJk8Xaz507Z3Tv3t2QZIwZM8ai6mrG/v37jatXr5ZoX716tSHJ8PX1NQoKCiyorGbt2LHDkGT89re/NSQZXbt2tbqkate/f39DkpGUlGR1KTXu3//+t9G4cWOjQYMGxscff1yiPyYmxoKqrPfZZ58Zkoy2bdsa169ft7oc0127ds3w8fExJBkffPBBsb6oqCjDzc3NsNlsxvfff29RhdXnzJkzhpubmyHJWLNmTVF7bm6u8fjjjxuSjN/97ncWVmiOip7P7Ny505BkNGvWrNi5QFRUlOHi4mJ4eXkZly5dqonSy40wUgvl5+cbjRs3NiQZsbGxJfoDAwMNScahQ4csqM4adSWM3EhUVFTR30NeXp7V5VjitttuMyQZ8fHxVpdSrXJycozbbrvNuP32242TJ08SRuqA3/zmN4YkY8WKFVaXcksZO3asIcl48cUXrS6lWnz77beGJMPf37/U/uDgYEOSsXHjxhqurPrNnz/fkGQMHjy4RN+PP/5ouLu7G87OzsbFixctqK763Ox85uGHHzYkGa+99lqJvueee86QZCxevLgaK6w49ozUQvv371dGRob8/PzUvXv3Ev2jR4+WJG3ZsqWmS4OFunXrJknKy8tTWlqaxdVYo169epIkFxcXiyupXnPnzlViYqJef/11OTs7W10Oqll6err+/ve/y8vLSxMnTrS6nFtGdnZ20e0tTz75pMXVVA9XV9dyjWvatGk1V1LzDh8+LOmn2zP/m4+Pj26//XZdu3ZNn332WQ1XZh377VjS/53r/dytev5X3+oCYL6jR49KkoKCgkrtt7fbx6Fu+OGHHySpaHNjXfPuu+8qISFBXbp0UadOnawup9rExcVpyZIlGj9+vO6//36dPn3a6pJq3DvvvKO0tDQ5OTmpS5cuGjFihNq1a2d1WdXmX//6l/Ly8vTggw/K2dlZmzZt0v79+3Xt2jX5+/vrV7/6lVq0aGF1mTXu448/VnZ2trp3766AgACry6kWnTp1UqdOnXTixAn9/e9/169+9auivujoaG3fvl0dO3bU/fffb2GV1SM7O1uS1KRJk1L77f/OHT16VL/5zW9qrC4rnThxQnl5efLx8VGbNm1K9NvP/+Li4mq6tBsijNRCKSkpklTq/xB/3m4fh7ph2bJlkqShQ4eW+7dpjmzRokWKj49Xdna2jh8/rvj4ePn6+mrDhg1ycqqdF4ULCws1adIkNW7cWH/+85+tLscyL7/8crHvf//732v27NmaPXu2RRVVL/tT4lq0aKF+/fopOjq6WP/MmTMVGRmpxx9/3IryLGPfuF6bT0Tr1auntWvX6tFHH9X//M//aNGiRfLz89P58+e1f/9+3XPPPXrvvfdq5dVgHx8fSVJycnKp/fb2uvQLmZud/3l4eKhx48ZKT09XZmbmLbO5v3b+i1zH2R/f6u7uXmq/h4dHsXGo/T777DO98847cnZ21vz5860up0Zs375d69at06ZNmxQfH6+2bdtqw4YN6tGjh9WlVZsVK1bo66+/1qJFi9SsWTOry6lx999/v9577z0lJiYqJydHCQkJeuWVV1S/fn299NJLRYG8tklPT5f009W/uLg4vfPOO0pNTVVSUpLCw8OVnZ2tJ5988pb7bWh1unDhgnbu3Kl69eppzJgxVpdTrfr166c9e/aoY8eOOnTokDZu3Ki9e/fKw8NDDz74oHx9fa0usVr0799fkvT+++8rPz+/WN+BAweUkJAgScrMzKzx2qxys/M/6dY8BySM1ELG/z6t2Waz3bAfdcPx48f15JNPyjAMLVq0qGjvSG23Y8cOGYah9PR07d27V127dtWAAQP0yiuvWF1atThz5oxmzZql/v37a9y4cVaXY4l58+bpySefVKdOndSgQQN16dJFf/jDH7R582ZJ0pw5c3T16lVri6wG169flyQVFBRo6dKlCg0Nlbe3tzp06KAlS5Zo9OjRys/Pr1NXyzZs2KDr169r8ODBatmypdXlVKv3339f9957r9q1a6eYmBhlZWXp5MmTGjNmjF5++WU9+OCDunbtmtVlmu6JJ55Qu3btlJKSouDgYMXHxyszM1Off/65Hn/8cdWv/9PNP7X1Snhpbnb+9/Mxt5K68xOqQ+yX3ez3U/43+4uASnsOP2qXs2fPaujQoUpPT1d4eLimTp1qdUk1rnHjxurXr58+++wz9ejRQ7Nnz9bBgwetLst0U6ZMUX5+vl5//XWrS7nlPPTQQ7r77rt1+fJlHThwwOpyTGf//3wnJyeFhISU6A8NDZX008sB64q6cIuWJJ06dUohISHy8fHRp59+qnvuuUceHh7q3Lmz3nzzTT366KOKjo5WZGSk1aWazsPDQ1u3blW7du30+eef64477lCjRo00bNgwOTk5KTw8XFLZe0pqo5ud/0m35jkgYaQWsm/ULOtN6/b22ryhE9LFixc1ePBgpaSkaPz48Vq8eLHVJVnK2dlZ//M//yPDMG65J4mYYevWrXJ3d9fTTz+tAQMGFH39+te/lvTTvcT2tlvp8nxN6dy5syTp/PnzFldivg4dOkiSWrZsWep+MHv/jz/+WINVWef48eM6cuSIGjZsqBEjRlhdTrX64IMPdO3aNQ0dOrTo9pufs29or61B9M4779SJEye0bt06Pfvss3r66af1xhtv6NixY0VjauvDC0pzs/O/7OxsZWRkqHHjxrfMfhGJDey1kv02nNjY2FL77e2BgYE1VhNqVmZmpoYNG6YTJ05o1KhRevvtt2942bau8Pb2liSlpqZaXEn1yMjI0J49e0rtu3r1alFfbX0T943Y91XcSr8NNIv9Ee7p6ekyDKPEf+v2R3nXxmMvzXvvvSdJGjVq1A3vna8N7CedjRo1KrXf3n7p0qUaq6mmNWjQQE899ZSeeuqpYu07duyQVPqjf2urrl27ytXVVampqTp79myJjey36vkfV0ZqoT59+sjLy0uJiYk6cuRIif5NmzZJkoYPH17TpaEG5OXlKTg4WIcOHdKQIUP0/vvvF71fo66zn4z7+flZXIn5jJ9eYlviKykpSdJP/0jZ2xo3bmxtsTUsNTVV+/btk1T2I88d2Z133qmOHTvq6tWriomJKdFv/614bTz2/2YYhjZs2CCp9t+iJaloP8yhQ4dK7bffkmq/OlZX7NmzR7GxsQoICFCfPn2sLqfGNGjQQA888ICk/zvX+7lb9vyvxl+ziBrxxz/+0ZBk3HfffUZWVlZR+5IlSwxJRt++fS2sruapjryBvaCgwBg5cqQhyejXr5+RnZ1tdUk1au/evcYHH3xgXLt2rVh7fn6+sXz5csPJyclo0KCBkZKSYlGFNS8pKalOvIE9Ojra2LVrl1FYWFisPSkpyejTp48hyfjlL39pUXXV74033jAkGT179jRSU1OL2g8dOmQ0btzYkGR8+OGHFlZYM/bs2WNIMnx9fY3r169bXU61O3z4sCHJkGSsXr26WF90dLTh4eFhSDK+/PJLiyqsXkeOHCnx//eHDx82fH19DZvNZuzatcuiyqrPzc5nvvzyS0OS0axZM+PkyZNF7VFRUYarq6vRqFEjIy0trSZKLTdu06qlZs2apR07digqKkqdO3dWv379lJycrJiYGDVr1qxWbmb7uU8//bTEI2zz8/PVq1evou9nz56tRx55pKZLq1YrV67UJ598IumnW5KmTJlS6rjFixcX3bJUmyQmJmr8+PHy9vZWjx491KxZM128eFHffvutzp8/Lzc3N61du1Zt27a1ulSY7MSJExo/frxatWqlLl26qGXLljp79qwOHz6s3NxcBQQE6O2337a6zGozadIk7dy5Ux9++KG6du2q++67T1lZWYqKilJ+fr4mTZpU6huZaxv7xvUnnniiTjxFKSgoSL///e+1ePFiTZkyRatWrdLtt9+uc+fOKTo6WoWFhfrtb3+rBx980OpSq8Xzzz+v7777TnfddZe8vb11+vRpxcTEyMnJSW+++aYGDhxodYlVVtHzmQcffFBTp07VsmXLdNddd2nw4MHKz8/Xl19+qcLCQq1fv/7We/Gx1WkI1ScnJ8eYPXu24efnZ7i4uBgtWrQwQkJC6sRvhSMjI4t+W1TWV2RkpNVlmm7OnDk3PW5JRlJSktWlVosffvjB+MMf/mD06dPHaNWqleHs7Gx4eHgYAQEBxrPPPmucOnXK6hJrXF25MvLdd98ZTz/9tBEUFGT4+PgY9evXN7y8vIxevXoZS5YsMXJycqwusdpdv37dWLVqldG9e3fD3d3d8PDwMO677z7j3Xfftbq0GpGbm2s0adLEkGQcPXrU6nJq1Mcff2w89NBDRrNmzYz69esbTZo0MQYOHGisX7/e6tKq1dtvv23079/f8PHxMZydnQ1fX19j7NixxpEjR6wuzTSVPZ+JjIw0evToYbi7uxteXl7GkCFDjH379tX8AZSDzTBuwQcOAwAAAKj1av81TAAAAAC3JMIIAAAAAEsQRgAAAABYgjACAAAAwBKEEQAAAACWIIwAAAAAsARhBAAAAIAlCCMAAAAALEEYAQAAAGAJwggAAAAASxBGAAAAAFiCMAIAcGg2m002m01NmjRRRkZGqWMiIiJks9n06quv1mxxAIAbIowAAGqFjIwMvfbaa1aXAQCoAMIIAMDhOTk5ycXFRX/5y1+Unp5udTkAgHIijAAAHJ6zs7MmTpyoK1euaOnSpVaXAwAoJ8IIAKBW+MMf/iBXV1ctW7ZMly5dsrocAEA5EEYAALVC69atNWnSJGVmZmrJkiVWlwMAKAfCCACg1pg5c6bc3Ny0YsUKpaWlWV0OAOAmCCMAgFrD19dXv/3tb5WZmanFixdbXQ4A4CYIIwCAWuXFF1+Um5ubVq5cqYsXL1pdDgDgBggjAIBapVWrVvrd736nrKwsLVq0yOpyAAA3QBgBANQ6M2bMUIMGDbRq1SqlpqZaXQ4AoAyEEQBArdOyZUs9/fTTys7O1p///GerywEAlIEwAgColWbMmCF3d3etXr1a//nPf6wuBwBQCsIIAKBWat68uaZMmaKcnBytW7fO6nIAAKUgjAAAaq3p06fLw8NDV69etboUAEApCCMAgFrLx8dHzzzzjNVlAADKYDMMw7C6CAAAAAB1D1dGAAAAAFiCMAIAAADAEoQRAAAAAJYgjAAAAACwBGEEAAAAgCUIIwAAAAAsQRgBAAAAYAnCCAAAAABLEEYAAAAAWIIwAgAAAMAShBEAAAAAliCMAAAAALAEYQQAAACAJQgjAAAAACxBGAEAAABgCcIIAAAAAEsQRgAAAABY4v8DeAtWBguUaZ4AAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 900x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# split into ms occupations\n",
+    "fig, (ax1) = plt.subplots(1,1,figsize=(6,4), dpi=150)\n",
+    "\n",
+    "spin_occ_five = res.groupby(['N', '|m_s|']).sum()\n",
+    "pivot_df = spin_occ_five.pivot_table(index='N', columns='|m_s|', values='prob')\n",
+    "pivot_df.plot.bar(stacked = True, rot=0, ax = ax1)\n",
+    "\n",
+    "ax1.set_ylabel(r'prob amplitude')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f5111521-4e2b-4bce-8270-654883a31cd6",
+   "metadata": {},
+   "source": [
+    "This concludes the tutorial. This you can try next:\n",
+    "\n",
+    "* try to find the transition temperature of the system by increasing the temperature in DMFT\n",
+    "* improve the accuracy of the resulting self-energy by restarting the dmft calculation with more n_cycles_tot "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/tutorials/PrNiO3_csc_vasp_plo_cthyb/1_dft_scf/INCAR b/tutorials/PrNiO3_csc_vasp_plo_cthyb/1_dft_scf/INCAR
new file mode 100644
index 00000000..a82aba30
--- /dev/null
+++ b/tutorials/PrNiO3_csc_vasp_plo_cthyb/1_dft_scf/INCAR
@@ -0,0 +1,16 @@
+SYSTEM = PrNiO3
+ICHARG = 1
+
+EDIFF = 1E-10
+ISYM = -1
+NELMIN = 10
+
+ISMEAR = -5
+
+LMAXMIX = 6
+KPAR=2
+LORBIT = 14
+
+LWAVE = TRUE
+LCHARG = TRUE
+
diff --git a/tutorials/PrNiO3_csc_vasp_plo_cthyb/1_dft_scf/KPOINTS b/tutorials/PrNiO3_csc_vasp_plo_cthyb/1_dft_scf/KPOINTS
new file mode 100644
index 00000000..3bca6a83
--- /dev/null
+++ b/tutorials/PrNiO3_csc_vasp_plo_cthyb/1_dft_scf/KPOINTS
@@ -0,0 +1,5 @@
+Automatic kpoint scheme
+0
+M
+6 6 4
+0 0 0
diff --git a/tutorials/PrNiO3_csc_vasp_plo_cthyb/1_dft_scf/POSCAR b/tutorials/PrNiO3_csc_vasp_plo_cthyb/1_dft_scf/POSCAR
new file mode 100644
index 00000000..8603ee76
--- /dev/null
+++ b/tutorials/PrNiO3_csc_vasp_plo_cthyb/1_dft_scf/POSCAR
@@ -0,0 +1,28 @@
+PrNiO3
+1.0000000000000000
+     5.4402908379016592   -0.0000000000000001    0.0000000000000000
+     0.0000000000000003    5.4197165967367118    0.0000000000000000
+     0.0000000000000000    0.0000000000000000    7.6865924864886876
+   Pr   Ni   O 
+     4     4    12
+Direct
+  0.9925004811478786  0.0360095115625612  0.2500000000000000
+  0.0074995188521214  0.9639904884374388  0.7500000000000000
+  0.4925004811478786  0.4639904884374388  0.7500000000000000
+  0.5074995188521214  0.5360095115625612  0.2500000000000000
+  0.5000000000000000  0.0000000000000000  0.0000000000000000
+  0.0000000000000000  0.5000000000000000  0.0000000000000000
+  0.5000000000000000  0.0000000000000000  0.5000000000000000
+  0.0000000000000000  0.5000000000000000  0.5000000000000000
+0.7244596839579465   0.2829997850927632   0.0379419462321593
+0.7176953261668324   0.2762354273016492   0.4620580507678369
+0.2755403160420465   0.7170002449072392   0.9620580797678429
+0.2823046738331605   0.7237646026983533   0.5379419202321571
+0.2176953261668395   0.2237645726983508   0.9620580797678429
+0.2244596839579535   0.2170002149072368   0.5379419202321571
+0.7823046738331676   0.7762353973016467   0.0379419462321593
+0.7755403160420535   0.7829997550927608   0.4620580507678369
+0.5682970898526349   0.0063067516834749   0.7466178211044430
+0.0682971188526338   0.4936932483165251   0.2466178211044430
+0.9317029101473651   0.5063067816834774   0.7533821788955570
+0.4317028811473662   0.9936932183165226   0.2533821788955570
diff --git a/tutorials/PrNiO3_csc_vasp_plo_cthyb/2_dmft_csc/dmft_config.toml b/tutorials/PrNiO3_csc_vasp_plo_cthyb/2_dmft_csc/dmft_config.toml
new file mode 100644
index 00000000..42227590
--- /dev/null
+++ b/tutorials/PrNiO3_csc_vasp_plo_cthyb/2_dmft_csc/dmft_config.toml
@@ -0,0 +1,48 @@
+[general]
+seedname = "vasp"
+set_rot = "hloc"
+
+csc = true
+
+
+prec_mu = 0.001
+
+h_int_type = "kanamori"
+U = 2.50
+J = 0.50
+beta = 40
+
+g0_mix = 0.95
+n_iter_dmft_first = 4
+n_iter_dmft_per = 2
+n_iter_dmft = 26
+h5_save_freq = 5
+
+dc = true
+dc_type = 1
+dc_dmft = true
+
+calc_energies = true
+
+[solver]
+type = "cthyb"
+n_l = 33
+length_cycle = 1000
+n_warmup_cycles = 10000
+n_cycles_tot = 2e+6
+imag_threshold = 1e-5
+legendre_fit = true
+measure_density_matrix = true
+measure_pert_order = true
+
+[dft]
+n_iter = 4
+# as of openmpi ver 4.0.7 there is a problem running with more than one core
+# use OMP_NUM_THREADS instead
+n_cores = 1
+dft_code = "vasp"
+dft_exec = "vasp_std"
+mpi_env = "default"
+plo_cfg = "plo.cfg"
+projector_type = "plo"
+store_eigenvals = true
diff --git a/tutorials/PrNiO3_csc_vasp_plo_cthyb/2_dmft_csc/plo.cfg b/tutorials/PrNiO3_csc_vasp_plo_cthyb/2_dmft_csc/plo.cfg
new file mode 100644
index 00000000..deea85c0
--- /dev/null
+++ b/tutorials/PrNiO3_csc_vasp_plo_cthyb/2_dmft_csc/plo.cfg
@@ -0,0 +1,16 @@
+
+[General]
+DOSMESH = -1.5 2.5 3001
+
+[Group 1]
+SHELLS = 1
+NORMALIZE = True
+# fermi = 4.9951850
+EWINDOW = -0.4 2.5
+# band window 88 96
+BANDS = 88 96
+
+[Shell 1]
+LSHELL = 2
+IONS = [5 8] [6 7]
+TRANSFILE = rotations.dat
diff --git a/tutorials/PrNiO3_csc_vasp_plo_cthyb/2_dmft_csc/rotations.dat b/tutorials/PrNiO3_csc_vasp_plo_cthyb/2_dmft_csc/rotations.dat
new file mode 100644
index 00000000..a6e9087f
--- /dev/null
+++ b/tutorials/PrNiO3_csc_vasp_plo_cthyb/2_dmft_csc/rotations.dat
@@ -0,0 +1,16 @@
+# site 0
+0.934903328236 0.158756134373 0.24446845897 -0.0832730502507 -0.184534626354
+0.241735350757 0.0675036811638 -0.923852038988 0.280885007209 -0.0678844312449
+
+# site 1
+0.886420310595 -0.155063632081 -0.374797078314 -0.130782073983 0.18065852369
+0.373255694678 -0.0970829976258 0.874072833311 0.277991821882 0.0998614389929
+
+# site 2
+0.920463359629 -0.164443872384 0.281060719559 0.102020995995 -0.190530849766
+0.278741259742 -0.079548854677 -0.908551705266 -0.289951275964 -0.0802330746182
+
+# site 3
+0.89603649378 0.14856834824 -0.36187736207 0.117578327229 0.173971720414
+0.360062242083 0.0880637654722 0.884495740853 -0.268172655037 0.0913819814928
+
diff --git a/tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html b/tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html
new file mode 100644
index 00000000..2ee7beaa
--- /dev/null
+++ b/tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html
@@ -0,0 +1,932 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" >
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>2. CSC with VASP PLOs: charge order in PrNiO3 &mdash; solid_dmft  documentation</title>
+      <link rel="stylesheet" href="../../_static/pygments.css" type="text/css" />
+      <link rel="stylesheet" href="../../_static/css/custom.css" type="text/css" />
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+    <link rel="index" title="Index" href="../../genindex.html" />
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+    <link rel="next" title="3. CSC with QE/W90 and HubbardI: total energy in Ce2O3" href="../Ce2O3_csc_w90/tutorial.html" />
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+</head>
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+</ul>
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+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#workflow">Workflow:</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../issues.html">Support &amp; contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../issues.html#seeking-help">Seeking help</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../issues.html#improving-solid-dmft">Improving solid_dmft</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../issues.html#reporting-issues">Reporting issues</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../ChangeLog.html">Changelog</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-2-0">Version 3.2.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#general">General</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-1-3">Version 3.1.3</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-1-2">Version 3.1.2</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-1-1">Version 3.1.1</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-1-0">Version 3.1.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-0-0">Version 3.0.0</a></li>
+</ul>
+</li>
+</ul>
+
+        </div>
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu"  style="background: #7E588A" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="../../index.html">solid_dmft</a>
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+      <li>2. CSC with VASP PLOs: charge order in PrNiO3</li>
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+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+
+<span class="kn">from</span> <span class="nn">h5</span> <span class="kn">import</span> <span class="n">HDFArchive</span>
+<span class="kn">from</span> <span class="nn">triqs.gf</span> <span class="kn">import</span> <span class="n">BlockGf</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">&#39;figure.figsize&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">&#39;figure.dpi&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">150</span>
+</pre></div>
+</div>
+</div>
+<p>Disclaimer: charge self-consistent (CSC) calculations are heavy. The current parameters won’t give well converged solution but are tuned down to give results in roughly 150 core hours.</p>
+<div class="section" id="2.-CSC-with-VASP-PLOs:-charge-order-in-PrNiO3">
+<h1>2. CSC with VASP PLOs: charge order in PrNiO3<a class="headerlink" href="#2.-CSC-with-VASP-PLOs:-charge-order-in-PrNiO3" title="Permalink to this headline"></a></h1>
+<p>Set the variable <code class="docutils literal notranslate"><span class="pre">read_from_ref</span></code> below to False if you want to plot your own calculated results. Otherwise, the provided reference files are used.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="c1"># Reads files from reference? Otherwise, uses your simulation results</span>
+<span class="n">read_from_ref</span> <span class="o">=</span> <span class="kc">True</span>
+<span class="n">path_mod</span> <span class="o">=</span> <span class="s1">&#39;/ref&#39;</span> <span class="k">if</span> <span class="n">read_from_ref</span> <span class="k">else</span> <span class="s1">&#39;&#39;</span>
+</pre></div>
+</div>
+</div>
+<p>PrNiO3 is a perovskite that exhibits a metal-insulator transition coupled to a breathing distortion and charge disproportionation, see <a class="reference external" href="https://doi.org/10.1038/s41535-019-0145-4" rel="noopener noreferrer" target="_blank">here</a>. In this tutorial, we will run DMFT calculation on the low-temperature insulating state. We will do this in a CSC way, where the correlated orbitals are defined by <a class="reference external" href="https://doi.org/10.1088/1361-648x/aae80a" rel="noopener noreferrer" target="_blank">projected localized orbitals (PLOs)</a> calculated with VASP.</p>
+<div class="section" id="1.-Running-the-initial-scf-DFT-calculation">
+<h2>1. Running the initial scf DFT calculation<a class="headerlink" href="#1.-Running-the-initial-scf-DFT-calculation" title="Permalink to this headline"></a></h2>
+<p>(~ 2 core hours)</p>
+<p>To get started, we run a self-consistent field (scf) DFT calculation:</p>
+<ul class="simple">
+<li>Go into folder <code class="docutils literal notranslate"><span class="pre">1_dft_scf</span></code></li>
+<li>Insert the POTCAR as concatenation of the files <code class="docutils literal notranslate"><span class="pre">PAW_PBE</span> <span class="pre">Pr_3</span></code>, <code class="docutils literal notranslate"><span class="pre">PAW_PBE</span> <span class="pre">Ni_pv</span></code> and <code class="docutils literal notranslate"><span class="pre">PAW_PBE</span> <span class="pre">O</span></code> distributed with VASP</li>
+<li>Goal: get a well-converged charge density (CHGCAR) and understand where the correlated bands are (DOSCAR and potentially PROCAR and band structure)</li>
+</ul>
+<p>Other input files are:</p>
+<ul class="simple">
+<li><a class="reference external" href="1_dft_scf/INCAR" rel="noopener noreferrer" target="_blank">INCAR</a>: using a large number of steps for good convergence. Compared to the DMFT calculation, it is relatively cheap and it is good to have a well converged starting point for DMFT.</li>
+<li><a class="reference external" href="1_dft_scf/POSCAR" rel="noopener noreferrer" target="_blank">POSCAR</a>: PrNiO3 close to the experimental low-temperature structure (P21/n symmetry)</li>
+<li><a class="reference external" href="1_dft_scf/KPOINTS" rel="noopener noreferrer" target="_blank">KPOINTS</a>: approximately unidistant grid of 6 x 6 x 4</li>
+</ul>
+<p>Then run Vasp with the command <code class="docutils literal notranslate"><span class="pre">mpirun</span> <span class="pre">-n</span> <span class="pre">8</span> <span class="pre">vasp_std</span></code>.</p>
+<p>The main output here is:</p>
+<ul class="simple">
+<li>CHGCAR: the converged charge density to start the DMFT calculation from</li>
+<li>DOSCAR: to identify the energy range of the correlated subspace. (A partial DOS and band structure can be very helpful to identify the correlated subspace as well. The partial DOS can be obtained by uncommenting the LORBIT parameter in the INCAR but then the below functions to plot the DOS need to be adapted.)</li>
+</ul>
+<p>We now plot the DFT DOS and discuss the correlated subspace.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="n">dft_energy</span><span class="p">,</span> <span class="n">dft_dos</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">loadtxt</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;1_dft_scf</span><span class="si">{</span><span class="n">path_mod</span><span class="si">}</span><span class="s1">/DOSCAR&#39;</span><span class="p">,</span>
+                                 <span class="n">skiprows</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span> <span class="n">unpack</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">usecols</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="n">fermi_energy</span> <span class="o">=</span> <span class="mf">5.012206</span> <span class="c1"># can be read from DOSCAR header or OUTCAR</span>
+
+<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">dft_energy</span><span class="o">-</span><span class="n">fermi_energy</span><span class="p">,</span> <span class="n">dft_dos</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">axhline</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Energy relative to Fermi energy (eV)&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;DOS (1/eV)&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="o">-</span><span class="mi">8</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">50</span><span class="p">);</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/tutorials_PrNiO3_csc_vasp_plo_cthyb_tutorial_6_0.png" src="../../_images/tutorials_PrNiO3_csc_vasp_plo_cthyb_tutorial_6_0.png" />
+</div>
+</div>
+<p>The DOS contains (you can check this with the partial DOS):</p>
+<ul class="simple">
+<li>Ni-eg bands in the range -0.4 to 2.5 eV with a small gap at around 0.6 eV</li>
+<li>mainly Ni-t2g bands between -1.5 and -0.5 eV</li>
+<li>mainly O-p bands between -7 and -1.5 eV The Ni-d and O-p orbitals are hybridized, with an overlap in the DOS betwen Ni-t2g and O-p.</li>
+</ul>
+<p>DFT does not describe the system correctly in predicting a metallic state. In a simplified picture, the paramagnetism in DMFT will be able to split the correlated bands and push the Fermi energy into the gap of the eg orbitals, as we will see below.</p>
+<p>We will use the Ni-eg range to construct our correlated subspace.</p>
+<p>Note: with the coarse k-point mesh used in the tutorial the DOS will look much worse. We show here the DOS with converged number of kpoints for illustration.</p>
+</div>
+<div class="section" id="2.-Running-the-CSC-DMFT-calculations">
+<h2>2. Running the CSC DMFT calculations<a class="headerlink" href="#2.-Running-the-CSC-DMFT-calculations" title="Permalink to this headline"></a></h2>
+<p>(~ 150 core hours)</p>
+<p>We now run the DMFT calculation. In CSC calculations, the corrected charge density from DMFT is fed back into the DFT calculation to re-calculate the Kohn-Sham energies and projectors onto correlated orbitals.</p>
+<p>With VASP, the procedure works as described <a class="reference external" href="https://triqs.github.io/dft_tools/latest/guide/dftdmft_selfcons.html#vasp-plovasp" rel="noopener noreferrer" target="_blank">here</a>, where the GAMMA file written by DMFT contains the charge density <em>correction</em>. In the VASP-CSC implementation, we first converge a non-scf DFT calculation based on the CHGCAR from before, then run DMFT on the results. The VASP process stays alive but idle during the DMFT calculation. Then, when we want to update the DFT-derived quantities energies, we need to
+run multiple DFT steps in between the DMFT steps because the density correction is fed into VASP iteratively through mixing to ensure stability.</p>
+<div class="section" id="Input-files-for-CSC-DMFT-calculations">
+<h3>Input files for CSC DMFT calculations<a class="headerlink" href="#Input-files-for-CSC-DMFT-calculations" title="Permalink to this headline"></a></h3>
+<p>We first take a look into the input file <a class="reference external" href="2_dmft_csc/dmft_config.toml" rel="noopener noreferrer" target="_blank">dmft_config.toml</a> and discuss some parameters. Please make sure you understand the role of the other parameters as well, as documented in the <a class="reference external" href="https://triqs.github.io/solid_dmft/_ref/read_config.html" rel="noopener noreferrer" target="_blank">reference manual of the read_config.py</a> on the solid_dmft website. This is a selection of parameters from the dmft_config.toml:</p>
+<p>Group [general]:</p>
+<ul class="simple">
+<li><code class="docutils literal notranslate"><span class="pre">set_rot</span> <span class="pre">=</span> <span class="pre">&quot;hloc&quot;</span></code>: rotates the local impurity problem into a basis where the local Hamiltonian is diagonal</li>
+<li><code class="docutils literal notranslate"><span class="pre">n_l</span> <span class="pre">=</span> <span class="pre">35</span></code>: the number of Legendre coefficients to measure the imaginary-time Green’s function in. Too few resulting in a “bumpy” Matsubara self-energy, too many include simulation noise. See also <a class="reference external" href="https://doi.org/10.1103/PhysRevB.84.075145" rel="noopener noreferrer" target="_blank">https://doi.org/10.1103/PhysRevB.84.075145</a>.</li>
+<li><code class="docutils literal notranslate"><span class="pre">dc_dmft</span> <span class="pre">=</span> <span class="pre">true</span></code>: using the DMFT occupations for the double counting is mandatory in CSC calculations. The DFT occupations are not well defined after the first density correction anymore</li>
+</ul>
+<p>Group [solver]:</p>
+<ul class="simple">
+<li><code class="docutils literal notranslate"><span class="pre">legendre_fit</span> <span class="pre">=</span> <span class="pre">true</span></code>: turns on measuring the Green’s function in Legendre coefficients</li>
+</ul>
+<p>Group [dft]:</p>
+<ul class="simple">
+<li><code class="docutils literal notranslate"><span class="pre">n_iter</span> <span class="pre">=</span> <span class="pre">4</span></code>: number of DFT iterations between the DMFT occupations. Should be large enough for the density correction to be fully mixed into the DFT calculation</li>
+<li><code class="docutils literal notranslate"><span class="pre">n_cores</span> <span class="pre">=</span> <span class="pre">32</span></code>: number of cores that DFT is run on. Check how many cores achieve the optimal DFT performance</li>
+<li><code class="docutils literal notranslate"><span class="pre">dft_code</span> <span class="pre">=</span> <span class="pre">&quot;vasp&quot;</span></code>: we are running VASP</li>
+<li><code class="docutils literal notranslate"><span class="pre">dft_exec</span> <span class="pre">=</span> <span class="pre">&quot;vasp_std&quot;</span></code>: the executable is vasp_std and its path is in the ROOT variable in our docker setup</li>
+<li><code class="docutils literal notranslate"><span class="pre">mpi_env</span> <span class="pre">=</span> <span class="pre">&quot;default&quot;</span></code>: sets the mpi environment</li>
+<li><code class="docutils literal notranslate"><span class="pre">plo_cfg</span> <span class="pre">=</span> <span class="pre">&quot;plo.cfg&quot;</span></code>: the name of the config file for constructing the PLOs</li>
+<li><code class="docutils literal notranslate"><span class="pre">projector_type</span> <span class="pre">=</span> <span class="pre">&quot;plo&quot;</span></code>: chooses PLO projectors</li>
+</ul>
+<p>The <a class="reference external" href="2_dmft_csc/plo.cfg" rel="noopener noreferrer" target="_blank">plo.cfg</a> file is described <a class="reference external" href="https://triqs.github.io/dft_tools/latest/guide/conv_vasp.html" rel="noopener noreferrer" target="_blank">here</a>. The <a class="reference external" href="2_dmft_csc/rotations.dat" rel="noopener noreferrer" target="_blank">rotations.dat</a> file is generated by diagonalizing the local d-shell density matrix and identifying the least occupied eigenstates as eg states. This we have limited k-point resolution wie also specify the band indices that describe our target space (isolated set of correlated states).</p>
+</div>
+<div class="section" id="Starting-the-calculations">
+<h3>Starting the calculations<a class="headerlink" href="#Starting-the-calculations" title="Permalink to this headline"></a></h3>
+<p>Now we can start the calculations:</p>
+<ul class="simple">
+<li>Go into the folder <code class="docutils literal notranslate"><span class="pre">2_dmft_csc</span></code></li>
+<li>Link relevant files like CHGCAR, KPOINTS, POSCAR, POTCAR from previous directory by running <code class="docutils literal notranslate"><span class="pre">./2_link_files.sh</span></code></li>
+<li>Run with <code class="docutils literal notranslate"><span class="pre">mpirun</span> <span class="pre">-n</span> <span class="pre">32</span> <span class="pre">python3</span> <span class="pre">solid_dmft</span></code></li>
+</ul>
+</div>
+<div class="section" id="Analyzing-the-projectors">
+<h3>Analyzing the projectors<a class="headerlink" href="#Analyzing-the-projectors" title="Permalink to this headline"></a></h3>
+<p>Now we plot the DOS of the PLOs we are using to make sure that our correlated subspace works out as expected. You can speed up the calculation of the PLOs by removing the calculation of the DOS from the plo.cfg file.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="n">energies</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="n">doss</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="k">for</span> <span class="n">imp</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">4</span><span class="p">):</span>
+    <span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">loadtxt</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;2_dmft_csc</span><span class="si">{</span><span class="n">path_mod</span><span class="si">}</span><span class="s1">/pdos_0_</span><span class="si">{</span><span class="n">imp</span><span class="si">}</span><span class="s1">.dat&#39;</span><span class="p">,</span> <span class="n">unpack</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="n">energies</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+    <span class="n">doss</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="mi">1</span><span class="p">:])</span>
+
+<span class="n">energies</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">energies</span><span class="p">)</span>
+<span class="n">doss</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">doss</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
+
+<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">dft_energy</span><span class="o">-</span><span class="n">fermi_energy</span><span class="p">,</span> <span class="n">dft_dos</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Initial DFT total&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">energies</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">doss</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;PLO from CSC&#39;</span><span class="p">)</span>
+<span class="c1">#for energy, dos in zip(energies, doss):</span>
+<span class="c1">#    ax.plot(energy, dos.T)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">axhline</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="o">-</span><span class="mi">8</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Energy relative to Fermi energy (eV)&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;DOS (1/eV)&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+<span class="k">pass</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/tutorials_PrNiO3_csc_vasp_plo_cthyb_tutorial_10_0.png" src="../../_images/tutorials_PrNiO3_csc_vasp_plo_cthyb_tutorial_10_0.png" />
+</div>
+</div>
+<p>This plot shows the original DFT charge density and the PLO-DOS after applying the DMFT charge corrections. It proves that we are capturing indeed capturing the eg bands with the projectors, where the partial DOS differs a bit because of the changes from the charge self-consistency. Note that this quantity in the CSC DMFT formalism does not have any real meaning, it mainly serves as a check of the method. The correct quantity to analyze are the lattice or impurity Green’s functions.</p>
+</div>
+</div>
+<div class="section" id="3.-Plotting-the-results:-observables">
+<h2>3. Plotting the results: observables<a class="headerlink" href="#3.-Plotting-the-results:-observables" title="Permalink to this headline"></a></h2>
+<p>We first read in the pre-computed observables from the h5 archive and print their names:</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="k">with</span> <span class="n">HDFArchive</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;2_dmft_csc</span><span class="si">{</span><span class="n">path_mod</span><span class="si">}</span><span class="s1">/vasp.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">archive</span><span class="p">:</span>
+    <span class="n">observables</span> <span class="o">=</span> <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_results/observables&#39;</span><span class="p">]</span>
+    <span class="n">conv_obs</span> <span class="o">=</span> <span class="n">archive</span><span class="p">[</span><span class="s1">&#39;DMFT_results/convergence_obs&#39;</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="n">observables</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+dict_keys([&#39;E_DC&#39;, &#39;E_bandcorr&#39;, &#39;E_corr_en&#39;, &#39;E_dft&#39;, &#39;E_int&#39;, &#39;E_tot&#39;, &#39;imp_gb2&#39;, &#39;imp_occ&#39;, &#39;iteration&#39;, &#39;mu&#39;, &#39;orb_Z&#39;, &#39;orb_gb2&#39;, &#39;orb_occ&#39;])
+</pre></div></div>
+</div>
+<p>We will now use this to plot the occupation per impurity <code class="docutils literal notranslate"><span class="pre">imp_occ</span></code> (to see if there is charge disproportionation), the impurity Green’s function at <span class="math notranslate nohighlight">\(\tau=\beta/2\)</span> <code class="docutils literal notranslate"><span class="pre">imp_gb2</span></code> (to see if the system becomes insulating), the total energy <code class="docutils literal notranslate"><span class="pre">E_tot</span></code>, the DFT energy <code class="docutils literal notranslate"><span class="pre">E_dft</span></code>, and DMFT self-consistency condition over the iterations:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">7</span><span class="p">,</span><span class="mi">7</span><span class="p">))</span>
+
+<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">):</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">observables</span><span class="p">[</span><span class="s1">&#39;imp_occ&#39;</span><span class="p">][</span><span class="n">i</span><span class="p">][</span><span class="s1">&#39;up&#39;</span><span class="p">])</span><span class="o">+</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">observables</span><span class="p">[</span><span class="s1">&#39;imp_occ&#39;</span><span class="p">][</span><span class="n">i</span><span class="p">][</span><span class="s1">&#39;down&#39;</span><span class="p">]),</span> <span class="s1">&#39;.-&#39;</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="sa">f</span><span class="s1">&#39;C</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">,</span>
+                <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s1">&#39;Impurity </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">observables</span><span class="p">[</span><span class="s1">&#39;imp_gb2&#39;</span><span class="p">][</span><span class="n">i</span><span class="p">][</span><span class="s1">&#39;up&#39;</span><span class="p">])</span><span class="o">+</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">observables</span><span class="p">[</span><span class="s1">&#39;imp_gb2&#39;</span><span class="p">][</span><span class="n">i</span><span class="p">][</span><span class="s1">&#39;down&#39;</span><span class="p">]),</span> <span class="s1">&#39;.-&#39;</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="sa">f</span><span class="s1">&#39;C</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+
+    <span class="n">axes</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span><span class="o">.</span><span class="n">semilogy</span><span class="p">(</span><span class="n">conv_obs</span><span class="p">[</span><span class="s1">&#39;d_Gimp&#39;</span><span class="p">][</span><span class="n">i</span><span class="p">],</span> <span class="s1">&#39;.-&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="sa">f</span><span class="s1">&#39;C</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s1">&#39;Impurity </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+
+<span class="c1"># Not impurity-dependent</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">observables</span><span class="p">[</span><span class="s1">&#39;E_tot&#39;</span><span class="p">],</span> <span class="s1">&#39;.-&#39;</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Total energy&#39;</span><span class="p">)</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">observables</span><span class="p">[</span><span class="s1">&#39;E_dft&#39;</span><span class="p">],</span> <span class="s1">&#39;x--&#39;</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;DFT energy&#39;</span><span class="p">)</span>
+
+
+<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Imp. occupation</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$G(\beta/2)$&#39;</span><span class="p">)</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Energy&#39;</span><span class="p">)</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;|G$_</span><span class="si">{imp}</span><span class="s1">$-G$_</span><span class="si">{loc}</span><span class="s1">$|&#39;</span><span class="p">)</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+
+<span class="n">axes</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Iterations&#39;</span><span class="p">)</span>
+<span class="n">fig</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">hspace</span><span class="o">=</span><span class="mf">.08</span><span class="p">)</span>
+<span class="k">pass</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/tutorials_PrNiO3_csc_vasp_plo_cthyb_tutorial_15_0.png" src="../../_images/tutorials_PrNiO3_csc_vasp_plo_cthyb_tutorial_15_0.png" />
+</div>
+</div>
+<p>These plots show:</p>
+<ul class="simple">
+<li>The occupation converges towards a disproportionated 1.6+0.4 electrons state</li>
+<li>Both sites become insulating, which we can deduce from <span class="math notranslate nohighlight">\(G(\beta/2)\)</span> from its relation to the spectral function at the Fermi energy <span class="math notranslate nohighlight">\(A(\omega = 0) \approx -(\beta/\pi) G(\beta/2)\)</span></li>
+<li>convergence is only setting in at around 20 DMFT iterations, which can be also seen from the column <code class="docutils literal notranslate"><span class="pre">rms(c)</span></code> in the Vasp OSZICAR file, and more DMFT iterations should be done ideally</li>
+</ul>
+<p>Therefore, we can conclude that we managed to capture the desired paramagnetic, insulating state that PrNiO3 shows in the experiments.</p>
+</div>
+<div class="section" id="4.-Plotting-the-results:-the-Legendre-Green’s-function">
+<h2>4. Plotting the results: the Legendre Green’s function<a class="headerlink" href="#4.-Plotting-the-results:-the-Legendre-Green’s-function" title="Permalink to this headline"></a></h2>
+<p>We now take a look at the imaginary-time Green’s function expressed in Legendre coefficients <span class="math notranslate nohighlight">\(G_l\)</span>. This is the main solver output (if we are measuring it) and also saved in the h5 archive.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="n">legendre_gf</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="k">with</span> <span class="n">HDFArchive</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;2_dmft_csc</span><span class="si">{</span><span class="n">path_mod</span><span class="si">}</span><span class="s1">/vasp.h5&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">archive</span><span class="p">:</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">):</span>
+        <span class="n">legendre_gf</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">archive</span><span class="p">[</span><span class="sa">f</span><span class="s1">&#39;DMFT_results/last_iter/Gimp_l_</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
+
+<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">legendre_coefficients_per_imp</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">legendre_gf</span><span class="p">):</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">legendre_coefficients_per_imp</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Only blocks up_0 and down_0 supported&#39;</span><span class="p">)</span>
+
+    <span class="n">data</span> <span class="o">=</span> <span class="p">(</span><span class="n">legendre_coefficients_per_imp</span><span class="p">[</span><span class="s1">&#39;up_0&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">data</span> <span class="o">+</span> <span class="n">legendre_coefficients_per_imp</span><span class="p">[</span><span class="s1">&#39;down_0&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="p">)</span><span class="o">.</span><span class="n">T</span>
+
+    <span class="n">l_max</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
+
+    <span class="n">ax</span><span class="o">.</span><span class="n">semilogy</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">l_max</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">trace</span><span class="p">(</span><span class="n">data</span><span class="p">[:,</span> <span class="p">:,</span> <span class="p">::</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">real</span><span class="p">,</span> <span class="n">axis1</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">axis2</span><span class="o">=</span><span class="mi">1</span><span class="p">)),</span> <span class="s1">&#39;x-&#39;</span><span class="p">,</span>
+                <span class="n">c</span><span class="o">=</span><span class="sa">f</span><span class="s1">&#39;C</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s1">&#39;Imp. </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">, even indices&#39;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">semilogy</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">l_max</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">trace</span><span class="p">(</span><span class="n">data</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">1</span><span class="p">::</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">real</span><span class="p">,</span> <span class="n">axis1</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">axis2</span><span class="o">=</span><span class="mi">1</span><span class="p">)),</span> <span class="s1">&#39;.:&#39;</span><span class="p">,</span>
+                <span class="n">c</span><span class="o">=</span><span class="sa">f</span><span class="s1">&#39;C</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s1">&#39;Imp. </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">, odd indices&#39;</span><span class="p">)</span>
+
+<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+
+<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Legendre coefficient $G_l$ (eV$^{-1}$)&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;Index $l$&#39;</span><span class="p">)</span>
+<span class="k">pass</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/tutorials_PrNiO3_csc_vasp_plo_cthyb_tutorial_18_0.png" src="../../_images/tutorials_PrNiO3_csc_vasp_plo_cthyb_tutorial_18_0.png" />
+</div>
+</div>
+<p>The choice of the correct <code class="docutils literal notranslate"><span class="pre">n_l</span></code>, i.e., the Legendre cutoff is important. If it is too small, we are ignoring potential information about the Green’s function. If it is too large, the noise filtering is not efficient. This can be seen by first running a few iterations with large <code class="docutils literal notranslate"><span class="pre">n_l</span></code>, e.g., 50. Then, the coefficients will first decay exponentially as in the plot above and then at higher <span class="math notranslate nohighlight">\(l\)</span> starting showing noisy behavior. For more information about the Legendre coefficients, take a
+look <a class="reference external" href="https://doi.org/10.1103/PhysRevB.84.075145" rel="noopener noreferrer" target="_blank">here</a>.</p>
+<p>The noise itself should reduce with sqrt(<code class="docutils literal notranslate"><span class="pre">n_cycles_tot</span></code>) for QMC calculations but the prefactor always depends on material and its Hamiltonian, the electron filling, etc. But if you increase <code class="docutils literal notranslate"><span class="pre">n_cycles_tot</span></code>, make sure to test if you can include more Legendre coefficients.</p>
+</div>
+<div class="section" id="5.-Next-steps-to-try">
+<h2>5. Next steps to try<a class="headerlink" href="#5.-Next-steps-to-try" title="Permalink to this headline"></a></h2>
+<p>Here are some suggestions on how continue on this type of DMFT calculations:</p>
+<ul class="simple">
+<li>change U and J and try to see if you can reach a metallic state. What does the occupation look like?</li>
+<li>try for better convergence: change <code class="docutils literal notranslate"><span class="pre">n_cycles_tot</span></code>, <code class="docutils literal notranslate"><span class="pre">n_iter_dmft</span></code> and <code class="docutils literal notranslate"><span class="pre">n_l</span></code></li>
+<li>play around with the other parameters in the dmft_config.toml</li>
+<li>analyze other quantities or have a look at the spectral functions from analytical continuation</li>
+<li>try other ways to construct the correlated orbitals in CSC, e.g., with Wannier90</li>
+<li>apply this to the material of your choice!</li>
+</ul>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="../SVO_os_qe/tutorial.html" class="btn btn-neutral float-left" title="1. OS with QE/W90 and cthyb: SrVO3 MIT" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="../Ce2O3_csc_w90/tutorial.html" class="btn btn-neutral float-right" title="3. CSC with QE/W90 and HubbardI: total energy in Ce2O3" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+    </div>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.</p>
+  </div>
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+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
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+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
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+</footer>
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diff --git a/tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.ipynb b/tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.ipynb
new file mode 100644
index 00000000..f53ec5dc
--- /dev/null
+++ b/tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.ipynb
@@ -0,0 +1,471 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a661f418-c4f0-435e-8db9-ff074ad58b49",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from h5 import HDFArchive\n",
+    "from triqs.gf import BlockGf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "55c5a91d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.rcParams['figure.figsize'] = (8, 4)\n",
+    "plt.rcParams['figure.dpi'] = 150"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0275b487",
+   "metadata": {},
+   "source": [
+    "Disclaimer: charge self-consistent (CSC) calculations are heavy. The current parameters won't give well converged solution but are tuned down to give results in roughly 150 core hours.\n",
+    "\n",
+    "# 2. CSC with VASP PLOs: charge order in PrNiO3\n",
+    "\n",
+    "Set the variable `read_from_ref` below to False if you want to plot your own calculated results. Otherwise, the provided reference files are used."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "1e21a834-d629-43a6-8c93-6f7e786aeca0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Reads files from reference? Otherwise, uses your simulation results\n",
+    "read_from_ref = True\n",
+    "path_mod = '/ref' if read_from_ref else ''"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "13dd34fd",
+   "metadata": {},
+   "source": [
+    "PrNiO3 is a perovskite that exhibits a metal-insulator transition coupled to a breathing distortion and charge disproportionation, see [here](https://doi.org/10.1038/s41535-019-0145-4).\n",
+    "In this tutorial, we will run DMFT calculation on the low-temperature insulating state. We will do this in a CSC way, where the correlated orbitals are defined by [projected localized orbitals (PLOs)](https://doi.org/10.1088/1361-648x/aae80a) calculated with VASP.\n",
+    "\n",
+    "## 1. Running the initial scf DFT calculation \n",
+    "\n",
+    "(~ 2 core hours)\n",
+    "\n",
+    "To get started, we run a self-consistent field (scf) DFT calculation:\n",
+    "\n",
+    "* Go into folder `1_dft_scf`\n",
+    "* Insert the POTCAR as concatenation of the files `PAW_PBE Pr_3`, `PAW_PBE Ni_pv` and `PAW_PBE O` distributed with VASP\n",
+    "* Goal: get a well-converged charge density (CHGCAR) and understand where the correlated bands are (DOSCAR and potentially PROCAR and band structure)\n",
+    "\n",
+    "Other input files are:\n",
+    "\n",
+    "* [INCAR](1_dft_scf/INCAR): using a large number of steps for good convergence. Compared to the DMFT calculation, it is relatively cheap and it is good to have a well converged starting point for DMFT.\n",
+    "* [POSCAR](1_dft_scf/POSCAR): PrNiO3 close to the experimental low-temperature structure (P21/n symmetry)\n",
+    "* [KPOINTS](1_dft_scf/KPOINTS): approximately unidistant grid of 6 x 6 x 4\n",
+    "\n",
+    "Then run Vasp with the command `mpirun -n 8 vasp_std`.\n",
+    "\n",
+    "The main output here is:\n",
+    "\n",
+    "* CHGCAR: the converged charge density to start the DMFT calculation from\n",
+    "* DOSCAR: to identify the energy range of the correlated subspace. (A partial DOS and band structure can be very helpful to identify the correlated subspace as well. The partial DOS can be obtained by uncommenting the LORBIT parameter in the INCAR but then the below functions to plot the DOS need to be adapted.)\n",
+    "\n",
+    "We now plot the DFT DOS and discuss the correlated subspace."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f4a4fc12",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dft_energy, dft_dos = np.loadtxt(f'1_dft_scf{path_mod}/DOSCAR',\n",
+    "                                 skiprows=6, unpack=True, usecols=(0, 1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "f1c5c3ca",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fermi_energy = 5.012206 # can be read from DOSCAR header or OUTCAR\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(dft_energy-fermi_energy, dft_dos)\n",
+    "ax.axhline(0, c='k')\n",
+    "ax.axvline(0, c='k')\n",
+    "ax.set_xlabel('Energy relative to Fermi energy (eV)')\n",
+    "ax.set_ylabel('DOS (1/eV)')\n",
+    "ax.set_xlim(-8, 5)\n",
+    "ax.set_ylim(0, 50);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "892a15f1",
+   "metadata": {},
+   "source": [
+    "The DOS contains (you can check this with the partial DOS):\n",
+    "\n",
+    "* Ni-eg bands in the range -0.4 to 2.5 eV with a small gap at around 0.6 eV\n",
+    "* mainly Ni-t2g bands between -1.5 and -0.5 eV\n",
+    "* mainly O-p bands between -7 and -1.5 eV\n",
+    "The Ni-d and O-p orbitals are hybridized, with an overlap in the DOS betwen Ni-t2g and O-p.\n",
+    "\n",
+    "DFT does not describe the system correctly in predicting a metallic state. In a simplified picture, the paramagnetism in DMFT will be able to split the correlated bands and push the Fermi energy into the gap of the eg orbitals, as we will see below.\n",
+    "\n",
+    "We will use the Ni-eg range to construct our correlated subspace.\n",
+    "\n",
+    "Note: with the coarse k-point mesh used in the tutorial the DOS will look much worse. We show here the DOS with converged number of kpoints for illustration."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "afb54167",
+   "metadata": {},
+   "source": [
+    "## 2. Running the CSC DMFT calculations\n",
+    "\n",
+    "(~ 150 core hours)\n",
+    "\n",
+    "We now run the DMFT calculation. In CSC calculations, the corrected charge density from DMFT is fed back into the DFT calculation to re-calculate the Kohn-Sham energies and projectors onto correlated orbitals.\n",
+    "\n",
+    "With VASP, the procedure works as described [here](https://triqs.github.io/dft_tools/latest/guide/dftdmft_selfcons.html#vasp-plovasp), where the GAMMA file written by DMFT contains the charge density *correction*. In the VASP-CSC implementation, we first converge a non-scf DFT calculation based on the CHGCAR from before, then run DMFT on the results. The VASP process stays alive but idle during the DMFT calculation. Then, when we want to update the DFT-derived quantities energies, we need to run multiple DFT steps in between the DMFT steps because the density correction is fed into VASP iteratively through mixing to ensure stability. \n",
+    "\n",
+    "### Input files for CSC DMFT calculations\n",
+    "\n",
+    "We first take a look into the input file [dmft_config.toml](2_dmft_csc/dmft_config.toml) and discuss some parameters. Please make sure you understand the role of the other parameters as well, as documented in the [reference manual of the read_config.py](https://triqs.github.io/solid_dmft/_ref/read_config.html) on the solid_dmft website. This is a selection of parameters from the dmft_config.toml:\n",
+    "\n",
+    "Group [general]:\n",
+    "\n",
+    "* `set_rot = \"hloc\"`: rotates the local impurity problem into a basis where the local Hamiltonian is diagonal\n",
+    "* `n_l = 35`: the number of Legendre coefficients to measure the imaginary-time Green's function in. Too few resulting in a \"bumpy\" Matsubara self-energy, too many include simulation noise. See also https://doi.org/10.1103/PhysRevB.84.075145.\n",
+    "* `dc_dmft = true`: using the DMFT occupations for the double counting is mandatory in CSC calculations. The DFT occupations are not well defined after the first density correction anymore\n",
+    "\n",
+    "Group [solver]:\n",
+    "\n",
+    "* `legendre_fit = true`: turns on measuring the Green's function in Legendre coefficients\n",
+    "\n",
+    "Group [dft]:\n",
+    "\n",
+    "* `n_iter = 4`: number of DFT iterations between the DMFT occupations. Should be large enough for the density correction to be fully mixed into the DFT calculation\n",
+    "* `n_cores = 32`: number of cores that DFT is run on. Check how many cores achieve the optimal DFT performance\n",
+    "* `dft_code = \"vasp\"`: we are running VASP\n",
+    "* `dft_exec = \"vasp_std\"`: the executable is vasp_std and its path is in the ROOT variable in our docker setup  \n",
+    "* `mpi_env = \"default\"`: sets the mpi environment\n",
+    "* `plo_cfg = \"plo.cfg\"`: the name of the config file for constructing the PLOs\n",
+    "* `projector_type = \"plo\"`: chooses PLO projectors\n",
+    "\n",
+    "The [plo.cfg](2_dmft_csc/plo.cfg) file is described [here](https://triqs.github.io/dft_tools/latest/guide/conv_vasp.html). The [rotations.dat](2_dmft_csc/rotations.dat) file is generated by diagonalizing the local d-shell density matrix and identifying the least occupied eigenstates as eg states. This we have limited k-point resolution wie also specify the band indices that describe our target space (isolated set of correlated states).\n",
+    "\n",
+    "### Starting the calculations\n",
+    "\n",
+    "Now we can start the calculations:\n",
+    "\n",
+    "* Go into the folder `2_dmft_csc`\n",
+    "* Link relevant files like CHGCAR, KPOINTS, POSCAR, POTCAR from previous directory by running `./2_link_files.sh`\n",
+    "* Run with `mpirun -n 32 python3 solid_dmft`\n",
+    "\n",
+    "### Analyzing the projectors\n",
+    "\n",
+    "Now we plot the DOS of the PLOs we are using to make sure that our correlated subspace works out as expected. You can speed up the calculation of the PLOs by removing the calculation of the DOS from the plo.cfg file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "2bba493e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "energies = []\n",
+    "doss = []\n",
+    "for imp in range(4):\n",
+    "    data = np.loadtxt(f'2_dmft_csc{path_mod}/pdos_0_{imp}.dat', unpack=True)\n",
+    "    energies.append(data[0])\n",
+    "    doss.append(data[1:])\n",
+    "    \n",
+    "energies = np.array(energies)\n",
+    "doss = np.array(doss)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "4ffe8e91",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "ax.plot(dft_energy-fermi_energy, dft_dos, label='Initial DFT total')\n",
+    "ax.plot(energies[0], np.sum(doss, axis=(0, 1)), label='PLO from CSC')\n",
+    "#for energy, dos in zip(energies, doss):\n",
+    "#    ax.plot(energy, dos.T)\n",
+    "ax.axhline(0, c='k')\n",
+    "ax.axvline(0, c='k')\n",
+    "ax.set_xlim(-8, 5)\n",
+    "ax.set_ylim(0,)\n",
+    "ax.set_xlabel('Energy relative to Fermi energy (eV)')\n",
+    "ax.set_ylabel('DOS (1/eV)')\n",
+    "ax.legend()\n",
+    "pass"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2a2a3293-3ef7-4457-942d-8a6bdcaabe29",
+   "metadata": {},
+   "source": [
+    "This plot shows the original DFT charge density and the PLO-DOS after applying the DMFT charge corrections. It proves that we are capturing indeed capturing the eg bands with the projectors, where the partial DOS differs a bit because of the changes from the charge self-consistency. Note that this quantity in the CSC DMFT formalism does not have any real meaning, it mainly serves as a check of the method. The correct quantity to analyze are the lattice or impurity Green's functions.\n",
+    "\n",
+    "## 3. Plotting the results: observables\n",
+    "\n",
+    "We first read in the pre-computed observables from the h5 archive and print their names:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d353c296-868a-45b5-bda6-2481d4df74ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with HDFArchive(f'2_dmft_csc{path_mod}/vasp.h5', 'r') as archive:\n",
+    "    observables = archive['DMFT_results/observables']\n",
+    "    conv_obs = archive['DMFT_results/convergence_obs']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ad578719-aa61-4560-baba-f01a4f28b726",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['E_DC', 'E_bandcorr', 'E_corr_en', 'E_dft', 'E_int', 'E_tot', 'imp_gb2', 'imp_occ', 'iteration', 'mu', 'orb_Z', 'orb_gb2', 'orb_occ'])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "observables.keys()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd150f57-3a8c-418a-a088-470180c86d87",
+   "metadata": {},
+   "source": [
+    "We will now use this to plot the occupation per impurity `imp_occ` (to see if there is charge disproportionation), the impurity Green's function at $\\tau=\\beta/2$ `imp_gb2` (to see if the system becomes insulating), the total energy `E_tot`, the DFT energy `E_dft`, and DMFT self-consistency condition over the iterations:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "41e955de-7a19-4e1f-bf27-f6973a8855d1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x1400 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(nrows=4, sharex=True, dpi=200,figsize=(7,7))\n",
+    "\n",
+    "for i in range(2):\n",
+    "    axes[0].plot(np.array(observables['imp_occ'][i]['up'])+np.array(observables['imp_occ'][i]['down']), '.-', c=f'C{i}',\n",
+    "                label=f'Impurity {i}')\n",
+    "    axes[1].plot(np.array(observables['imp_gb2'][i]['up'])+np.array(observables['imp_gb2'][i]['down']), '.-', c=f'C{i}')\n",
+    "    \n",
+    "    axes[3].semilogy(conv_obs['d_Gimp'][i], '.-', color=f'C{i}', label=f'Impurity {i}')\n",
+    "    \n",
+    "# Not impurity-dependent\n",
+    "axes[2].plot(observables['E_tot'], '.-', c='k', label='Total energy')\n",
+    "axes[2].plot(observables['E_dft'], 'x--', c='k', label='DFT energy')\n",
+    "\n",
+    "\n",
+    "axes[0].set_ylabel('Imp. occupation\\n')\n",
+    "axes[0].set_ylim(0, 2)\n",
+    "axes[0].legend()\n",
+    "axes[1].set_ylabel(r'$G(\\beta/2)$')\n",
+    "axes[2].set_ylabel('Energy')\n",
+    "axes[2].legend()\n",
+    "axes[3].set_ylabel(r'|G$_{imp}$-G$_{loc}$|')\n",
+    "axes[3].legend()\n",
+    "\n",
+    "axes[-1].set_xlabel('Iterations')\n",
+    "fig.subplots_adjust(hspace=.08)\n",
+    "pass"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "599730cc-8214-48cd-80a6-14676f2e23c0",
+   "metadata": {},
+   "source": [
+    "These plots show:\n",
+    "\n",
+    "* The occupation converges towards a disproportionated 1.6+0.4 electrons state\n",
+    "* Both sites become insulating, which we can deduce from $G(\\beta/2)$ from its relation to the spectral function at the Fermi energy $A(\\omega = 0) \\approx -(\\beta/\\pi) G(\\beta/2)$\n",
+    "* convergence is only setting in at around 20 DMFT iterations, which can be also seen from the column `rms(c)` in the Vasp OSZICAR file, and more DMFT iterations should be done ideally\n",
+    "\n",
+    "Therefore, we can conclude that we managed to capture the desired paramagnetic, insulating state that PrNiO3 shows in the experiments.\n",
+    "\n",
+    "## 4. Plotting the results: the Legendre Green's function\n",
+    "\n",
+    "We now take a look at the imaginary-time Green's function expressed in Legendre coefficients $G_l$. This is the main solver output (if we are measuring it) and also saved in the h5 archive."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "91f19160-3f34-4738-a9fa-8fe9c4289b0c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "legendre_gf = []\n",
+    "with HDFArchive(f'2_dmft_csc{path_mod}/vasp.h5') as archive:\n",
+    "    for i in range(2):\n",
+    "        legendre_gf.append(archive[f'DMFT_results/last_iter/Gimp_l_{i}'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "50176755-edbb-41ed-9656-5c648a08a6c0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "for i, legendre_coefficients_per_imp in enumerate(legendre_gf):\n",
+    "    if len(legendre_coefficients_per_imp) != 2:\n",
+    "        raise ValueError('Only blocks up_0 and down_0 supported')\n",
+    "\n",
+    "    data = (legendre_coefficients_per_imp['up_0'].data + legendre_coefficients_per_imp['down_0'].data).T\n",
+    "\n",
+    "    l_max = data.shape[2]\n",
+    "\n",
+    "    ax.semilogy(np.arange(0, l_max, 2), np.abs(np.trace(data[:, :, ::2].real, axis1=0, axis2=1)), 'x-',\n",
+    "                c=f'C{i}', label=f'Imp. {i}, even indices')\n",
+    "    ax.semilogy(np.arange(1, l_max, 2), np.abs(np.trace(data[:, :, 1::2].real, axis1=0, axis2=1)), '.:',\n",
+    "                c=f'C{i}', label=f'Imp. {i}, odd indices')\n",
+    "\n",
+    "ax.legend()\n",
+    "\n",
+    "ax.set_ylabel('Legendre coefficient $G_l$ (eV$^{-1}$)')\n",
+    "ax.set_xlabel(r'Index $l$')\n",
+    "pass"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8308345c-3f72-476c-8f58-583f9aeb1ccf",
+   "metadata": {},
+   "source": [
+    "The choice of the correct `n_l`, i.e., the Legendre cutoff is important. If it is too small, we are ignoring potential information about the Green's function. If it is too large, the noise filtering is not efficient. This can be seen by first running a few iterations with large `n_l`, e.g., 50. Then, the coefficients will first decay exponentially as in the plot above and then at higher $l$ starting showing noisy behavior. For more information about the Legendre coefficients, take a look [here](https://doi.org/10.1103/PhysRevB.84.075145).\n",
+    "\n",
+    "The noise itself should reduce with sqrt(`n_cycles_tot`) for QMC calculations but the prefactor always depends on material and its Hamiltonian, the electron filling, etc. But if you increase `n_cycles_tot`, make sure to test if you can include more Legendre coefficients.\n",
+    "\n",
+    "## 5. Next steps to try\n",
+    "\n",
+    "Here are some suggestions on how continue on this type of DMFT calculations:\n",
+    "\n",
+    "* change U and J and try to see if you can reach a metallic state. What does the occupation look like?\n",
+    "* try for better convergence: change `n_cycles_tot`, `n_iter_dmft` and `n_l`\n",
+    "* play around with the other parameters in the dmft_config.toml\n",
+    "* analyze other quantities or have a look at the spectral functions from analytical continuation\n",
+    "* try other ways to construct the correlated orbitals in CSC, e.g., with Wannier90\n",
+    "* apply this to the material of your choice!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "550fa534-c187-49d7-96e4-0848f53dd854",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/tutorials/SVO_os_qe/tutorial.html b/tutorials/SVO_os_qe/tutorial.html
new file mode 100644
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+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#workflow">Workflow:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#important-flags">important flags:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#version-and-compilation">version and compilation:</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#input-output">Input/Output</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../input_output/DMFT_input/input.html">Input</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/general.html">[general]: General parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/solver.html">[solver]: solver specific parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/dft.html">[dft]: DFT related inputs</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/advanced.html">[advanced]: Advanced inputs</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../input_output/DMFT_output/results.html">Output / results</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_output/results.html#group-structure">Group structure</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_output/results.html#subgroups">Subgroups</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#further-details-for-running">Further details for running</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/docker.html">Docker</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#building-the-docker-image">Building the docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#pulling-a-docker-image">Pulling a docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_locally.html">Run on your machine</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#module-reference-manual">Module reference manual</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/csc_flow.html">csc_flow</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dft_managers.vasp_manager.html">dft_managers.vasp_manager</a></li>
+</ul>
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+<li class="toctree-l3"><a class="reference internal" href="../../_ref/dmft_cycle.html">dmft_cycle</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/dmft_tools.html">dmft_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.afm_mapping.html">dmft_tools.afm_mapping</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.results_to_archive.html">dmft_tools.results_to_archive</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/dmft_tools.solver.html">dmft_tools.solver</a></li>
+</ul>
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+<li class="toctree-l3"><a class="reference internal" href="../../_ref/io_tools.html">io_tools</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.dict_to_h5.html">io_tools.dict_to_h5</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.postproc_toml_dict.html">io_tools.postproc_toml_dict</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/io_tools.verify_input_params.html">io_tools.verify_input_params</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_Vasp.html">postprocessing.eval_U_cRPA_Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_gf_imp.html">postprocessing.maxent_gf_imp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_gf_latt.html">postprocessing.maxent_gf_latt</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.maxent_sigma.html">postprocessing.maxent_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../../tutorials.html">Tutorials</a><ul class="current">
+<li class="toctree-l2 current"><a class="current reference internal" href="#">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../correlated_bandstructure/plot_correlated_bands.html">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../correlated_bandstructure/plot_correlated_bands.html#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../correlated_bandstructure/plot_correlated_bands.html#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../correlated_bandstructure/plot_correlated_bands.html#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../correlated_bandstructure/plot_correlated_bands.html#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../correlated_bandstructure/plot_correlated_bands.html#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../correlated_bandstructure/plot_correlated_bands.html#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../correlated_bandstructure/plot_correlated_bands.html#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../issues.html">Support &amp; contribute</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../issues.html#seeking-help">Seeking help</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../issues.html#improving-solid-dmft">Improving solid_dmft</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../issues.html#reporting-issues">Reporting issues</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../ChangeLog.html">Changelog</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-2-0">Version 3.2.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#general">General</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#fix">fix</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#build">build</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#doc">doc</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#test">test</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-1-5">Version 3.1.5</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-1-4">Version 3.1.4</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-1-3">Version 3.1.3</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-1-2">Version 3.1.2</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-1-1">Version 3.1.1</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-1-0">Version 3.1.0</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-0-0">Version 3.0.0</a></li>
+</ul>
+</li>
+</ul>
+
+        </div>
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu"  style="background: #7E588A" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="../../index.html">solid_dmft</a>
+      </nav>
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+      <div class="wy-nav-content">
+        <div class="rst-content">
+          <div role="navigation" aria-label="Page navigation">
+  <ul class="wy-breadcrumbs">
+      <li><a href="../../index.html" class="icon icon-home"></a> &raquo;</li>
+          <li><a href="../../tutorials.html">Tutorials</a> &raquo;</li>
+      <li>1. OS with QE/W90 and cthyb: SrVO3 MIT</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../../_sources/tutorials/SVO_os_qe/tutorial.ipynb.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  
+<style>
+/* CSS overrides for sphinx_rtd_theme */
+
+/* 24px margin */
+.nbinput.nblast.container,
+.nboutput.nblast.container {
+    margin-bottom: 19px;  /* padding has already 5px */
+}
+
+/* ... except between code cells! */
+.nblast.container + .nbinput.container {
+    margin-top: -19px;
+}
+
+.admonition > p:before {
+    margin-right: 4px;  /* make room for the exclamation icon */
+}
+
+/* Fix math alignment, see https://github.com/rtfd/sphinx_rtd_theme/pull/686 */
+.math {
+    text-align: unset;
+}
+</style>
+<p><em>Disclaimer:</em></p>
+<p>Heavy calculations (~ 800 core hours): Current tutorial is best performed on an HPC facility.</p>
+<div class="section" id="1.-OS-with-QE/W90-and-cthyb:-SrVO3-MIT">
+<h1>1. OS with QE/W90 and cthyb: SrVO3 MIT<a class="headerlink" href="#1.-OS-with-QE/W90-and-cthyb:-SrVO3-MIT" title="Permalink to this headline"></a></h1>
+<p>Hello and welcome to the first part of the tutorial for solid_dmft. Here we will guide to set up and run your first DMFT calculations.</p>
+<p>To begin your DMFT journey we will immediately start a DMFT run on strontium vanadate (SVO). SVO is a member of a family of material known as complex perovskite oxides with 1 electron occupying the t2g manifold. Below, we show the band structure of the frontier (anti-bonding) t2g bands for SVO.</p>
+<p><img alt="svobands" src="../../_images/bnd_structure.png" /></p>
+<p>In these materials, the electrons sitting on the transition metal ions (V in this case) are fairly localized, and the fully delocalized picture of DFT is insufficient to describe their physics. DMFT accounts for the electron-electron interaction by providing a fully interacting many body correction to the DFT non-interacting problem.</p>
+<p>If you want to generate the h5 archive <code class="docutils literal notranslate"><span class="pre">svo.h5</span></code> yourself, all the necessary files are in the <code class="docutils literal notranslate"><span class="pre">./quantum_espresso_files/</span></code> folder. We used quantum espresso in this tutorial.</p>
+<hr class="docutils" />
+<div class="section" id="1.-Starting-out-with-DMFT">
+<h2>1. Starting out with DMFT<a class="headerlink" href="#1.-Starting-out-with-DMFT" title="Permalink to this headline"></a></h2>
+<p>To start your first calculation run:</p>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>mpirun solid_dmft
+</pre></div>
+</div>
+<p>Once the calculation is finished, inspect the <code class="docutils literal notranslate"><span class="pre">/out/</span></code> folder: our file of interest for the moment will be <code class="docutils literal notranslate"><span class="pre">observables_imp0.dat</span></code>, open the file:</p>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>it |        mu |            G(beta/2) per orbital |               orbital occs up+down |impurity occ
+ 0 |  12.29775 | -0.10489   -0.10489     -0.10489 |  0.33366      0.33366      0.33366 |     1.00097
+ 1 |  12.29775 | -0.09467   -0.09488     -0.09529 |  0.36155      0.35073      0.36169 |     1.07397
+ 2 |  12.31989 | -0.08451   -0.08363     -0.08463 |  0.33581      0.34048      0.34488 |     1.02117
+ 3 |  12.29775 | -0.08282   -0.08296     -0.08254 |  0.32738      0.34572      0.34479 |     1.01789
+ 4 |  12.28973 | -0.08617   -0.08595     -0.08620 |  0.33546      0.33757      0.33192 |     1.00494
+ 5 |  12.28825 | -0.08410   -0.08458     -0.08510 |  0.33582      0.33402      0.33759 |     1.00743
+ 6 |  12.28486 | -0.08474   -0.08549     -0.08618 |  0.32276      0.33028      0.32760 |     0.98063
+ 7 |  12.29097 | -0.08172   -0.08220     -0.08118 |  0.32072      0.33046      0.33529 |     0.98647
+ 8 |  12.29497 | -0.08318   -0.08254     -0.08332 |  0.34075      0.32957      0.33089 |     1.00120
+</pre></div>
+</div>
+<p>The meaning of the column names is the following:</p>
+<ul>
+<li><p class="first"><strong>it</strong>: number of the DMFT iteration</p>
+</li>
+<li><p class="first"><strong>mu</strong>: value of the chemical potential</p>
+</li>
+<li><p class="first"><strong>G(beta/2) per orbital</strong>: Green’s function evaluated at <span class="math notranslate nohighlight">\(\tau=\beta/2\)</span>, this value is proportional to the projected density of states at the fermi level, the first objective of this tutorial would be to try and drive this value to 0</p>
+</li>
+<li><p class="first"><strong>orbital occs up+down:</strong> occupations of the various states in the manifold</p>
+</li>
+<li><p class="first rubric" id="impurity-occ-number-of-electrons-in-each-site"><strong>impurity occ</strong>: number of electrons in each site</p>
+</li>
+</ul>
+</div>
+<div class="section" id="2.-Looking-at-the-Metal-Insulator-Transition">
+<h2>2. Looking at the Metal-Insulator Transition<a class="headerlink" href="#2.-Looking-at-the-Metal-Insulator-Transition" title="Permalink to this headline"></a></h2>
+<p>In the following steps we will try to drive the system towards a Mott-insulating state.</p>
+<p>Inspect the script <code class="docutils literal notranslate"><span class="pre">run_MIT_coarse.sh</span></code>, we iterate the same type of calculation that was performed in the last step for a series of value of U {2-10} and J {0.0-1.0}.</p>
+<p>Run the script, sit back and have a long coffee break, this is going to take a while (about 6 hours on 30 cores).</p>
+<p>Once the run is finished run</p>
+<p><code class="docutils literal notranslate"><span class="pre">python3</span> <span class="pre">./collect_results_coarse.py</span></code></p>
+<p>The script will produce a heatmap image of the value of G(beta/2) for each pair of U and J. The darker area corresponds to an insulating state.</p>
+<p><img alt="coarsegrid" src="../../_images/MIT_coarse.jpg" /></p>
+<p>Do you notice anything strange? (hint: look at the bottom right corner and check the output file <code class="docutils literal notranslate"><span class="pre">observables_imp0.dat</span></code> for U = 2 J=1.0. )</p>
+<p>We have seen that for 1 electron per system U and J are competing against each other: larger J favor the metallic state. The coulomb integral U wants to repel neighbouring electrons while J would like to bring electrons together on one site,. When the latter component dominates the resulting phase is known as a charge disproportionated state which is also insulating. What is happening in the bottom right corner is that the J favors here charge disproportionation but the unit cell has a single
+site, therefore the system has trouble converging and oscillates between a high occupation and a low occupation state.</p>
+</div>
+<div class="section" id="3.-Refining-the-diagram">
+<h2>3. Refining the diagram<a class="headerlink" href="#3.-Refining-the-diagram" title="Permalink to this headline"></a></h2>
+<p>In order to get better resolution in terms of the diagram you can run the script <code class="docutils literal notranslate"><span class="pre">run_MIT_fine.sh</span></code> and plot the result with</p>
+<p><code class="docutils literal notranslate"><span class="pre">python3</span> <span class="pre">./collect_results_fine.py</span></code></p>
+<p>The result is also visible here:</p>
+<p><img alt="finegrid" src="../../_images/MIT_fine.jpg" /></p>
+</div>
+<div class="section" id="4.-Plotting-the-spectral-function">
+<h2>4. Plotting the spectral function<a class="headerlink" href="#4.-Plotting-the-spectral-function" title="Permalink to this headline"></a></h2>
+<p>The spectral function in DMFT represents the local density of states of the impurity site. In order to plot it we need to use one of the scripts that implements the maximum entropy method ( <a class="reference external" href="https://triqs.github.io/maxent/latest/" rel="noopener noreferrer" target="_blank">Maxent</a> ), while in the folder run (be aware that you need to substitute <code class="docutils literal notranslate"><span class="pre">/path_to_solid_dmft/</span></code> with the path where you have installed solid_dmft) :</p>
+<p><code class="docutils literal notranslate"><span class="pre">mpirun</span> <span class="pre">-n</span> <span class="pre">30</span> <span class="pre">python3</span> <span class="pre">/path_to_solid_dmft/python/solid_dmft/postprocessing/maxent_gf_imp.py</span> <span class="pre">./J0.0/U4/out/svo.h5</span></code></p>
+<p>and plot the result by running in the docker container:</p>
+<p><code class="docutils literal notranslate"><span class="pre">python3</span> <span class="pre">read_spectral_function.py</span></code></p>
+<p><img alt="Afunc" src="../../_images/A_func_J=0.0_U=4.jpg" /></p>
+<p>Take care to edit the values of J and U in the python file. What is happing to the spectral function (density of states) as one cranks U up?</p>
+</div>
+<div class="section" id="5-Visualizing-the-MIT">
+<h2>5 Visualizing the MIT<a class="headerlink" href="#5-Visualizing-the-MIT" title="Permalink to this headline"></a></h2>
+<p>We will now plot the spectral function at different U values for J = 0.0 eV:</p>
+<p>Run the script <code class="docutils literal notranslate"><span class="pre">run_maxent_scan.sh</span></code>.</p>
+<p>Then collect the data:</p>
+<p><code class="docutils literal notranslate"><span class="pre">python3</span> <span class="pre">read_spectral_function_transition.py</span></code></p>
+<p><img alt="MIT" src="../../_images/A_func_transition.jpg" /></p>
+</div>
+</div>
+
+
+           </div>
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diff --git a/tutorials/SVO_os_qe/tutorial.ipynb b/tutorials/SVO_os_qe/tutorial.ipynb
new file mode 100644
index 00000000..ce1a444d
--- /dev/null
+++ b/tutorials/SVO_os_qe/tutorial.ipynb
@@ -0,0 +1,194 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "df9acc44",
+   "metadata": {},
+   "source": [
+    "*Disclaimer:*\n",
+    "\n",
+    "Heavy calculations (~ 800 core hours): Current tutorial is best performed on an HPC facility.\n",
+    "\n",
+    "# 1. OS with QE/W90 and cthyb: SrVO3 MIT"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bbec1073",
+   "metadata": {},
+   "source": [
+    "Hello and welcome to the first part of the tutorial for solid_dmft. Here we will guide to set up and run your first DMFT calculations. \n",
+    "\n",
+    "To begin your DMFT journey we will immediately start a DMFT run on strontium vanadate (SVO). SVO is a member of a family of material known as complex perovskite oxides with 1 electron occupying the t2g manifold. Below, we show the band structure of the frontier (anti-bonding) t2g bands for SVO.\n",
+    "\n",
+    "![svobands](./ref/bnd_structure.png \"SVO band structure\")\n",
+    "\n",
+    "In these materials, the electrons sitting on the transition metal ions (V in this case) are fairly localized, and the fully delocalized picture of DFT is insufficient to describe their physics. DMFT accounts for the electron-electron interaction by providing a fully interacting many body correction to the DFT non-interacting problem.\n",
+    "\n",
+    "If you want to generate the h5 archive `svo.h5` yourself, all the necessary files are in the `./quantum_espresso_files/` folder. We used quantum espresso in this tutorial.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5aa9cda6",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## 1. Starting out with DMFT\n",
+    "\n",
+    "\n",
+    "To start your first calculation run:\n",
+    "\n",
+    "```\n",
+    "mpirun solid_dmft\n",
+    "\n",
+    "```\n",
+    "\n",
+    "Once the calculation is finished, inspect the `/out/` folder: our file of interest for the moment will be `observables_imp0.dat`, open the file:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2bc83f5f",
+   "metadata": {},
+   "source": [
+    "```\n",
+    " it |        mu |            G(beta/2) per orbital |               orbital occs up+down |impurity occ\n",
+    "  0 |  12.29775 | -0.10489   -0.10489     -0.10489 |  0.33366      0.33366      0.33366 |     1.00097\n",
+    "  1 |  12.29775 | -0.09467   -0.09488     -0.09529 |  0.36155      0.35073      0.36169 |     1.07397\n",
+    "  2 |  12.31989 | -0.08451   -0.08363     -0.08463 |  0.33581      0.34048      0.34488 |     1.02117\n",
+    "  3 |  12.29775 | -0.08282   -0.08296     -0.08254 |  0.32738      0.34572      0.34479 |     1.01789\n",
+    "  4 |  12.28973 | -0.08617   -0.08595     -0.08620 |  0.33546      0.33757      0.33192 |     1.00494\n",
+    "  5 |  12.28825 | -0.08410   -0.08458     -0.08510 |  0.33582      0.33402      0.33759 |     1.00743\n",
+    "  6 |  12.28486 | -0.08474   -0.08549     -0.08618 |  0.32276      0.33028      0.32760 |     0.98063\n",
+    "  7 |  12.29097 | -0.08172   -0.08220     -0.08118 |  0.32072      0.33046      0.33529 |     0.98647\n",
+    "  8 |  12.29497 | -0.08318   -0.08254     -0.08332 |  0.34075      0.32957      0.33089 |     1.00120\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b50e7e1",
+   "metadata": {},
+   "source": [
+    "The meaning of the column names is the following:\n",
+    "\n",
+    "* **it**: number of the DMFT iteration\n",
+    "* **mu**: value of the chemical potential\n",
+    "* **G(beta/2) per orbital**: Green's function evaluated at $\\tau=\\beta/2$, this value is proportional to the projected density of states at the fermi level, the first objective of this tutorial would be to try and drive this value to 0\n",
+    "* **orbital occs up+down:** occupations of the various states in the manifold\n",
+    "* **impurity occ**: number of electrons in each site\n",
+    "---\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b5182921",
+   "metadata": {},
+   "source": [
+    "## 2. Looking at the Metal-Insulator Transition\n",
+    "\n",
+    "In the following steps we will try to drive the system towards a Mott-insulating state. \n",
+    "\n",
+    "Inspect the script `run_MIT_coarse.sh`, we iterate the same type of calculation that was performed in the last step for a series of value of U {2-10} and J {0.0-1.0}. \n",
+    "\n",
+    "Run the script, sit back and have a long coffee break, this is going to take a while (about 6 hours on 30 cores).\n",
+    "\n",
+    "Once the run is finished run \n",
+    "\n",
+    "`python3 ./collect_results_coarse.py`\n",
+    "\n",
+    "The script will produce a heatmap image of the value of G(beta/2) for each pair of U and J. The darker area corresponds to an insulating state.\n",
+    "\n",
+    "![coarsegrid](./ref/MIT_coarse.jpg \"Coarser grid\")\n",
+    "\n",
+    "Do you notice anything strange? (hint: look at the bottom right corner and check the output file `observables_imp0.dat` for U = 2 J=1.0. )\n",
+    "\n",
+    "We have seen that for 1 electron per system U and J are competing against each other: larger J favor the metallic state. The coulomb integral U wants to repel neighbouring electrons while J would like to bring electrons together on one site,. When the latter component dominates the resulting phase is known as a charge disproportionated state which is also insulating. What is happening in the bottom right corner is that the J favors here charge disproportionation but the unit cell has a single site, therefore the system has trouble converging and oscillates between a high occupation and a low occupation state."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b6bdb73",
+   "metadata": {},
+   "source": [
+    "## 3. Refining the diagram\n",
+    "\n",
+    "In order to get better resolution in terms of the diagram you can run the script `run_MIT_fine.sh` and plot the result with \n",
+    "\n",
+    "`python3 ./collect_results_fine.py`\n",
+    "\n",
+    "The result is also visible here:\n",
+    "\n",
+    "![finegrid](./ref/MIT_fine.jpg \"Finer grid\")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dfabf20a",
+   "metadata": {},
+   "source": [
+    "## 4. Plotting the spectral function\n",
+    "\n",
+    "The spectral function in DMFT represents the local density of states of the impurity site.\n",
+    "In order to plot it we need to use one of the scripts that implements the maximum entropy method ( [Maxent](https://triqs.github.io/maxent/latest/) ), while in the folder run (be aware that you need to substitute `/path_to_solid_dmft/` with the path where you have installed solid_dmft) :\n",
+    "\n",
+    "`mpirun -n 30 python3 /path_to_solid_dmft/python/solid_dmft/postprocessing/maxent_gf_imp.py ./J0.0/U4/out/svo.h5`\n",
+    "\n",
+    "and plot the result by running in the docker container:\n",
+    "\n",
+    "`python3 read_spectral_function.py`\n",
+    "\n",
+    "\n",
+    "![Afunc](./ref/A_func_J=0.0_U=4.jpg \"Afunc\")\n",
+    "\n",
+    "\n",
+    "Take care to edit the values of J and U in the python file. What is happing to the spectral function (density of states) as one cranks U up?\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "566941ec",
+   "metadata": {},
+   "source": [
+    "## 5 Visualizing the MIT\n",
+    "\n",
+    "We will now plot the spectral function at different U values for J = 0.0 eV:\n",
+    "\n",
+    "Run the script `run_maxent_scan.sh`.\n",
+    "\n",
+    "Then collect the data:\n",
+    "\n",
+    "`python3 read_spectral_function_transition.py`\n",
+    "\n",
+    "![MIT](./ref/A_func_transition.jpg \"MIT\")\n",
+    "\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/tutorials/correlated_bandstructure/plot_correlated_bands.html b/tutorials/correlated_bandstructure/plot_correlated_bands.html
new file mode 100644
index 00000000..df5cbb5a
--- /dev/null
+++ b/tutorials/correlated_bandstructure/plot_correlated_bands.html
@@ -0,0 +1,869 @@
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+<head>
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+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/w90_interface.html">Wannier90 interface</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/w90_interface.html#orbital-order-in-the-w90-converter">orbital order in the W90 converter</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/vasp_csc.html">Interface to VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#general-remarks">General remarks</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#locproj-bug-for-individual-projections">LOCPROJ bug for individual projections:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#convergence-of-projectors-with-vasp">convergence of projectors with Vasp</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#enabling-csc-calculations-with-wannier90-projectors">Enabling CSC calculations with Wannier90 projectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/vasp_csc.html#speeding-up-by-not-writing-projectors-at-every-step">Speeding up by not writing projectors at every step</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../cRPA_VASP/README.html">How to do cRPA calculations with VASP</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#file-description">file description</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#workflow">Workflow:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#important-flags">important flags:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#convergency-tests">convergency tests:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#parameterization-of-u-and-j-from-crpa-calculations">Parameterization of U and J from cRPA calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#general-sidemarks">general sidemarks:</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../cRPA_VASP/README.html#version-and-compilation">version and compilation:</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#input-output">Input/Output</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../input_output/DMFT_input/input.html">Input</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/general.html">[general]: General parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/solver.html">[solver]: solver specific parameters</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/dft.html">[dft]: DFT related inputs</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_input/advanced.html">[advanced]: Advanced inputs</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_output/results.html#group-structure">Group structure</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../input_output/DMFT_output/results.html#subgroups">Subgroups</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#further-details-for-running">Further details for running</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/docker.html">Docker</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#building-the-docker-image">Building the docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#pulling-a-docker-image">Pulling a docker image</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/docker.html#getting-docker-images-onto-cscs-daint">Getting docker images onto CSCS daint</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_locally.html">Run on your machine</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_locally.html#csc-calculations-locally">CSC calculations locally</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../md_notes/run_cluster.html">Running solid_dmft on a cluster</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#running-on-cscs-daint">Running on CSCS daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#one-shot-job-on-daint">one shot job on daint</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../md_notes/run_cluster.html#csc-calculations-on-daint">CSC calculations on daint</a></li>
+</ul>
+</li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="../../documentation.html#module-reference-manual">Module reference manual</a><ul>
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+<li class="toctree-l3"><a class="reference internal" href="../../_ref/postprocessing.html">postprocessing</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.eval_U_cRPA_RESPACK.html">postprocessing.eval_U_cRPA_RESPACK</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.pade_sigma.html">postprocessing.pade_sigma</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/postprocessing.plot_correlated_bands.html">postprocessing.plot_correlated_bands</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../../_ref/util.html">util</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.symmetrize_gamma_file.html">util.symmetrize_gamma_file</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../../_ref/util.write_kslice_to_h5.html">util.write_kslice_to_h5</a></li>
+</ul>
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+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../../tutorials.html">Tutorials</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../SVO_os_qe/tutorial.html">1. OS with QE/W90 and cthyb: SrVO3 MIT</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../SVO_os_qe/tutorial.html#1.-Starting-out-with-DMFT">1. Starting out with DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../SVO_os_qe/tutorial.html#2.-Looking-at-the-Metal-Insulator-Transition">2. Looking at the Metal-Insulator Transition</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../SVO_os_qe/tutorial.html#3.-Refining-the-diagram">3. Refining the diagram</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../SVO_os_qe/tutorial.html#4.-Plotting-the-spectral-function">4. Plotting the spectral function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../SVO_os_qe/tutorial.html#5-Visualizing-the-MIT">5 Visualizing the MIT</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../PrNiO3_csc_vasp_plo_cthyb/tutorial.html">2. CSC with VASP PLOs: charge order in PrNiO3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../PrNiO3_csc_vasp_plo_cthyb/tutorial.html#1.-Running-the-initial-scf-DFT-calculation">1. Running the initial scf DFT calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../PrNiO3_csc_vasp_plo_cthyb/tutorial.html#2.-Running-the-CSC-DMFT-calculations">2. Running the CSC DMFT calculations</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Input-files-for-CSC-DMFT-calculations">Input files for CSC DMFT calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Starting-the-calculations">Starting the calculations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../PrNiO3_csc_vasp_plo_cthyb/tutorial.html#Analyzing-the-projectors">Analyzing the projectors</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../PrNiO3_csc_vasp_plo_cthyb/tutorial.html#3.-Plotting-the-results:-observables">3. Plotting the results: observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../PrNiO3_csc_vasp_plo_cthyb/tutorial.html#4.-Plotting-the-results:-the-Legendre-Green’s-function">4. Plotting the results: the Legendre Green’s function</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../PrNiO3_csc_vasp_plo_cthyb/tutorial.html#5.-Next-steps-to-try">5. Next steps to try</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../Ce2O3_csc_w90/tutorial.html">3. CSC with QE/W90 and HubbardI: total energy in Ce2O3</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../Ce2O3_csc_w90/tutorial.html#1.-Input-file-preparation">1. Input file preparation</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../Ce2O3_csc_w90/tutorial.html#DFT-files">DFT files</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../Ce2O3_csc_w90/tutorial.html#DMFT">DMFT</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../Ce2O3_csc_w90/tutorial.html#2.-Running-DFT+DMFT">2. Running DFT+DMFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../Ce2O3_csc_w90/tutorial.html#3.-Non-interacting-Hamiltonian-and-convergence-analysis">3. Non-interacting Hamiltonian and convergence analysis</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="../Ce2O3_csc_w90/tutorial.html#Tight-binding-Hamiltonian">Tight-binding Hamiltonian</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../Ce2O3_csc_w90/tutorial.html#Convergence">Convergence</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../NNO_os_plo_mag/tutorial.html">4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../NNO_os_plo_mag/tutorial.html#1.-Run-DFT">1. Run DFT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../NNO_os_plo_mag/tutorial.html#2.-Creating-the-hdf5-archive-/-DMFT-input">2. Creating the hdf5 archive / DMFT input</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../NNO_os_plo_mag/tutorial.html#3.-Running-the-AFM-calculation">3. Running the AFM calculation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../NNO_os_plo_mag/tutorial.html#5.-Multiplet-analysis">5. Multiplet analysis</a></li>
+</ul>
+</li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">5. Plotting the spectral function</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#1.-Configuration">1. Configuration</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#Basic-options">Basic options</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#Wannier90">Wannier90</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#BZ-configuration">BZ configuration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#Self-energy">Self-energy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#Plotting-options">Plotting options</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#2.-Run-and-Plotting">2. Run and Plotting</a></li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="../../issues.html#seeking-help">Seeking help</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../issues.html#improving-solid-dmft">Improving solid_dmft</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../issues.html#reporting-issues">Reporting issues</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../../ChangeLog.html#version-3-2-0">Version 3.2.0</a></li>
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+  background: rgba(66, 165, 245, 0.2);
+}
+
+/* CSS overrides for sphinx_rtd_theme */
+
+/* 24px margin */
+.nbinput.nblast.container,
+.nboutput.nblast.container {
+    margin-bottom: 19px;  /* padding has already 5px */
+}
+
+/* ... except between code cells! */
+.nblast.container + .nbinput.container {
+    margin-top: -19px;
+}
+
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+    margin-right: 4px;  /* make room for the exclamation icon */
+}
+
+/* Fix math alignment, see https://github.com/rtfd/sphinx_rtd_theme/pull/686 */
+.math {
+    text-align: unset;
+}
+</style>
+<div class="section" id="5.-Plotting-the-spectral-function">
+<h1>5. Plotting the spectral function<a class="headerlink" href="#5.-Plotting-the-spectral-function" title="Permalink to this headline"></a></h1>
+<p>In this tutorial we go through the steps to plot tight-binding bands from a Wannier90 Hamiltonian and spectralfunctions with analytically continued (real-frequency) self-energies obtained from DMFT.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="o">%</span><span class="k">matplotlib</span> inline
+<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">display</span>
+<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">Image</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">importlib</span><span class="o">,</span> <span class="nn">sys</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">cm</span>
+<span class="kn">from</span> <span class="nn">timeit</span> <span class="kn">import</span> <span class="n">default_timer</span> <span class="k">as</span> <span class="n">timer</span>
+
+<span class="kn">from</span> <span class="nn">ase.io.espresso</span> <span class="kn">import</span> <span class="n">read_espresso_in</span>
+
+<span class="kn">from</span> <span class="nn">h5</span> <span class="kn">import</span> <span class="n">HDFArchive</span>
+<span class="kn">from</span> <span class="nn">solid_dmft.postprocessing</span> <span class="kn">import</span> <span class="n">plot_correlated_bands</span> <span class="k">as</span> <span class="n">pcb</span>
+</pre></div>
+</div>
+</div>
+<div class="section" id="1.-Configuration">
+<h2>1. Configuration<a class="headerlink" href="#1.-Configuration" title="Permalink to this headline"></a></h2>
+<p>The script makes use of the <code class="docutils literal notranslate"><span class="pre">triqs.lattice.utils</span></code> class, which allows to set up a tight-binding model based on a Wannier90 Hamiltonian. Additionally, you may upload a self-energy in the usual <code class="docutils literal notranslate"><span class="pre">solid_dmft</span></code> format to compute correlated spectral properties. Currently, the following options are implemented:</p>
+<ol><li><p>bandstructure</p>
+</li><li><p>Fermi slice</p>
+</li></ol><div class="section" id="Basic-options">
+<h3>Basic options<a class="headerlink" href="#Basic-options" title="Permalink to this headline"></a></h3>
+<div class="line-block">
+<div class="line">We start with configuring these options. For this example we try a tight-binding bandstructure including the correlated bands (<code class="docutils literal notranslate"><span class="pre">kslice</span> <span class="pre">=</span> <span class="pre">False</span></code>, <code class="docutils literal notranslate"><span class="pre">'tb':</span> <span class="pre">True</span></code>, <code class="docutils literal notranslate"><span class="pre">'alatt':</span> <span class="pre">True</span></code>), but feel free to come back here to explore. Alternatively to an intensity plot of the correlated bands (<code class="docutils literal notranslate"><span class="pre">qp_bands</span></code>), you can compute the correlated quasiparticle bands assuming a Fermi liquid regime.</div>
+<div class="line">The options for <span class="math notranslate nohighlight">\(\Sigma(\omega)\)</span> are <code class="docutils literal notranslate"><span class="pre">calc</span></code> or <code class="docutils literal notranslate"><span class="pre">model</span></code>, which performs a Fermi liquid linearization in the low-frequency regime. The latter will be reworked, so better stick with <code class="docutils literal notranslate"><span class="pre">calc</span></code> for now.</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="n">kslice</span> <span class="o">=</span> <span class="kc">False</span>
+
+<span class="n">bands_config</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;tb&#39;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span> <span class="s1">&#39;alatt&#39;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span> <span class="s1">&#39;qp_bands&#39;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span> <span class="s1">&#39;sigma&#39;</span><span class="p">:</span> <span class="s1">&#39;calc&#39;</span><span class="p">}</span>
+<span class="n">kslice_config</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;tb&#39;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span> <span class="s1">&#39;alatt&#39;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span> <span class="s1">&#39;qp_bands&#39;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span> <span class="s1">&#39;sigma&#39;</span><span class="p">:</span> <span class="s1">&#39;calc&#39;</span><span class="p">}</span>
+<span class="n">config</span> <span class="o">=</span> <span class="n">kslice_config</span> <span class="k">if</span> <span class="n">kslice</span> <span class="k">else</span> <span class="n">bands_config</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="section" id="Wannier90">
+<h3>Wannier90<a class="headerlink" href="#Wannier90" title="Permalink to this headline"></a></h3>
+<p>Next we will set up the Wannier90 Input. Provide the path, seedname, chemical potential and orbital order used in Wannier90. You may add a spin-component, and any other local Hamiltonian. For <code class="docutils literal notranslate"><span class="pre">t2g</span></code> models the orbital order can be changed (to <code class="docutils literal notranslate"><span class="pre">orbital_order_to</span></code>) and a local spin-orbit coupling term can be added (<code class="docutils literal notranslate"><span class="pre">add_lambda</span></code>). The spectral properties can be viewed projected on a specific orbital.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="n">w90_path</span> <span class="o">=</span> <span class="s1">&#39;./&#39;</span>
+<span class="n">w90_dict</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;w90_seed&#39;</span><span class="p">:</span> <span class="s1">&#39;svo&#39;</span><span class="p">,</span> <span class="s1">&#39;w90_path&#39;</span><span class="p">:</span> <span class="n">w90_path</span><span class="p">,</span> <span class="s1">&#39;mu_tb&#39;</span><span class="p">:</span> <span class="mf">12.3958</span><span class="p">,</span> <span class="s1">&#39;n_orb&#39;</span><span class="p">:</span> <span class="mi">3</span><span class="p">,</span>
+            <span class="s1">&#39;orbital_order_w90&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;dxz&#39;</span><span class="p">,</span> <span class="s1">&#39;dyz&#39;</span><span class="p">,</span> <span class="s1">&#39;dxy&#39;</span><span class="p">],</span> <span class="s1">&#39;add_spin&#39;</span><span class="p">:</span> <span class="kc">False</span><span class="p">}</span>
+
+<span class="n">orbital_order_to</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;dxy&#39;</span><span class="p">,</span> <span class="s1">&#39;dxz&#39;</span><span class="p">,</span> <span class="s1">&#39;dyz&#39;</span><span class="p">]</span>
+<span class="n">proj_on_orb</span> <span class="o">=</span> <span class="kc">None</span> <span class="c1"># or &#39;dxy&#39; etc</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="section" id="BZ-configuration">
+<h3>BZ configuration<a class="headerlink" href="#BZ-configuration" title="Permalink to this headline"></a></h3>
+<div class="section" id="Optional:-ASE-Brillouin-Zone">
+<h4>Optional: ASE Brillouin Zone<a class="headerlink" href="#Optional:-ASE-Brillouin-Zone" title="Permalink to this headline"></a></h4>
+<p>It might be helpful to have a brief look at the Brillouin Zone by loading an input file of your favorite DFT code (Quantum Espresso in this case). ASE will write out the special <span class="math notranslate nohighlight">\(k\)</span>-points, which we can use to configure the BZ path. Alternatively, you can of course define the dictionary <code class="docutils literal notranslate"><span class="pre">kpts_dict</span></code> yourself. Careful, it might not define <span class="math notranslate nohighlight">\(Z\)</span>, which is needed and added below.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="n">scf_in</span> <span class="o">=</span> <span class="s1">&#39;./svo.scf.in&#39;</span>
+
+<span class="c1"># read scf file</span>
+<span class="n">atoms</span> <span class="o">=</span> <span class="n">read_espresso_in</span><span class="p">(</span><span class="n">scf_in</span><span class="p">)</span>
+<span class="c1"># set up cell and path</span>
+<span class="n">lat</span> <span class="o">=</span> <span class="n">atoms</span><span class="o">.</span><span class="n">cell</span><span class="o">.</span><span class="n">get_bravais_lattice</span><span class="p">()</span>
+<span class="n">path</span> <span class="o">=</span> <span class="n">atoms</span><span class="o">.</span><span class="n">cell</span><span class="o">.</span><span class="n">bandpath</span><span class="p">(</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">npoints</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span>
+<span class="n">kpts_dict</span> <span class="o">=</span> <span class="n">path</span><span class="o">.</span><span class="n">todict</span><span class="p">()[</span><span class="s1">&#39;special_points&#39;</span><span class="p">]</span>
+
+<span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">kpts_dict</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span>
+<span class="n">lat</span><span class="o">.</span><span class="n">plot_bz</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+G [0. 0. 0.]
+M [0.5 0.5 0. ]
+R [0.5 0.5 0.5]
+X [0.  0.5 0. ]
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;Axes3DSubplot:&gt;
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/tutorials_correlated_bandstructure_plot_correlated_bands_14_2.png" src="../../_images/tutorials_correlated_bandstructure_plot_correlated_bands_14_2.png" />
+</div>
+</div>
+<hr class="docutils" />
+<p>Depending on whether you select <code class="docutils literal notranslate"><span class="pre">kslice=True</span></code> or <code class="docutils literal notranslate"><span class="pre">False</span></code>, a corresponding <code class="docutils literal notranslate"><span class="pre">tb_config</span></code> needs to be provided containing information about the <span class="math notranslate nohighlight">\(k\)</span>-points, resolution (<code class="docutils literal notranslate"><span class="pre">n_k</span></code>) or <code class="docutils literal notranslate"><span class="pre">kz</span></code>-plane in the case of the Fermi slice. Here we just import the <span class="math notranslate nohighlight">\(k\)</span>-point dictionary provided by ASE above and add the <span class="math notranslate nohighlight">\(Z\)</span>-point. If you are unhappy with the resolution of the final plot, come back here and crank up <code class="docutils literal notranslate"><span class="pre">n_k</span></code>. For the kslice, the first letter corresponds to the upper left corner
+of the plotted Brillouin zone, followed by the lower left corner and the lower right one (<span class="math notranslate nohighlight">\(Y\)</span>, <span class="math notranslate nohighlight">\(\Gamma\)</span>, and <span class="math notranslate nohighlight">\(X\)</span> in this case).</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="c1"># band specs</span>
+<span class="n">tb_bands</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;bands_path&#39;</span><span class="p">:</span> <span class="p">[(</span><span class="s1">&#39;R&#39;</span><span class="p">,</span> <span class="s1">&#39;G&#39;</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;G&#39;</span><span class="p">,</span> <span class="s1">&#39;X&#39;</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;X&#39;</span><span class="p">,</span> <span class="s1">&#39;M&#39;</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;M&#39;</span><span class="p">,</span> <span class="s1">&#39;G&#39;</span><span class="p">)],</span> <span class="s1">&#39;Z&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mf">0.5</span><span class="p">]),</span> <span class="s1">&#39;n_k&#39;</span><span class="p">:</span> <span class="mi">50</span><span class="p">}</span>
+<span class="n">tb_bands</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">kpts_dict</span><span class="p">)</span>
+
+<span class="c1"># kslice specs</span>
+<span class="n">tb_kslice</span> <span class="o">=</span> <span class="p">{</span><span class="n">key</span><span class="p">:</span> <span class="n">tb_bands</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="n">tb_bands</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span> <span class="k">if</span> <span class="n">key</span><span class="o">.</span><span class="n">isupper</span><span class="p">()}</span>
+<span class="n">kslice_update</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;bands_path&#39;</span><span class="p">:</span> <span class="p">[(</span><span class="s1">&#39;Y&#39;</span><span class="p">,</span> <span class="s1">&#39;G&#39;</span><span class="p">),(</span><span class="s1">&#39;G&#39;</span><span class="p">,</span> <span class="s1">&#39;X&#39;</span><span class="p">)],</span> <span class="s1">&#39;Y&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.5</span><span class="p">,</span><span class="mf">0.0</span><span class="p">,</span><span class="mi">0</span><span class="p">]),</span> <span class="s1">&#39;n_k&#39;</span><span class="p">:</span> <span class="mi">50</span><span class="p">,</span> <span class="s1">&#39;kz&#39;</span><span class="p">:</span> <span class="mf">0.0</span><span class="p">}</span>
+<span class="n">tb_kslice</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">kslice_update</span><span class="p">)</span>
+
+<span class="n">tb_config</span> <span class="o">=</span> <span class="n">tb_kslice</span> <span class="k">if</span> <span class="n">kslice</span> <span class="k">else</span> <span class="n">tb_bands</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="section" id="Self-energy">
+<h3>Self-energy<a class="headerlink" href="#Self-energy" title="Permalink to this headline"></a></h3>
+<p>Here we provide the info needed from the h5Archive, like the self-energy, iteration count, spin and block component and the frequency mesh used for the interpolation. The values for the mesh of course depend on the quantity of interest. For a kslice the resolution around <span class="math notranslate nohighlight">\(\omega=0\)</span> is crucial and we need only a small energy window, while for a bandstructure we are also interested in high energy features.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="n">freq_mesh_kslice</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;window&#39;</span><span class="p">:</span> <span class="p">[</span><span class="o">-</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">],</span> <span class="s1">&#39;n_w&#39;</span><span class="p">:</span> <span class="nb">int</span><span class="p">(</span><span class="mf">1e6</span><span class="p">)}</span>
+<span class="n">freq_mesh_bands</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;window&#39;</span><span class="p">:</span> <span class="p">[</span><span class="o">-</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="s1">&#39;n_w&#39;</span><span class="p">:</span> <span class="nb">int</span><span class="p">(</span><span class="mf">1e3</span><span class="p">)}</span>
+<span class="n">freq_mesh</span> <span class="o">=</span> <span class="n">freq_mesh_kslice</span> <span class="k">if</span> <span class="n">kslice</span> <span class="k">else</span> <span class="n">freq_mesh_bands</span>
+
+<span class="n">dmft_path</span> <span class="o">=</span> <span class="s1">&#39;./svo_example.h5&#39;</span>
+
+<span class="n">proj_on_orb</span> <span class="o">=</span> <span class="n">orbital_order_to</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">proj_on_orb</span><span class="p">)</span> <span class="k">if</span> <span class="n">proj_on_orb</span> <span class="k">else</span> <span class="kc">None</span>
+<span class="n">sigma_dict</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;dmft_path&#39;</span><span class="p">:</span> <span class="n">dmft_path</span><span class="p">,</span> <span class="s1">&#39;it&#39;</span><span class="p">:</span> <span class="s1">&#39;last_iter&#39;</span><span class="p">,</span> <span class="s1">&#39;orbital_order_dmft&#39;</span><span class="p">:</span> <span class="n">orbital_order_to</span><span class="p">,</span> <span class="s1">&#39;spin&#39;</span><span class="p">:</span> <span class="s1">&#39;up&#39;</span><span class="p">,</span>
+              <span class="s1">&#39;block&#39;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="s1">&#39;eta&#39;</span><span class="p">:</span> <span class="mf">0.0</span><span class="p">,</span> <span class="s1">&#39;w_mesh&#39;</span><span class="p">:</span> <span class="n">freq_mesh</span><span class="p">,</span> <span class="s1">&#39;linearize&#39;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span> <span class="s1">&#39;proj_on_orb&#39;</span> <span class="p">:</span> <span class="n">proj_on_orb</span><span class="p">}</span>
+</pre></div>
+</div>
+</div>
+<p><strong>Optional</strong>: for completeness and as a sanity check we quickly take a look at the self-energy. Make sure you provide a physical one!</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="k">with</span> <span class="n">HDFArchive</span><span class="p">(</span><span class="n">dmft_path</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">h5</span><span class="p">:</span>
+    <span class="n">sigma_freq</span> <span class="o">=</span> <span class="n">h5</span><span class="p">[</span><span class="s1">&#39;DMFT_results&#39;</span><span class="p">][</span><span class="s1">&#39;last_iter&#39;</span><span class="p">][</span><span class="s1">&#39;Sigma_freq_0&#39;</span><span class="p">]</span>
+
+<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">2</span><span class="p">),</span> <span class="n">squeeze</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">200</span><span class="p">)</span>
+
+<span class="n">orb</span> <span class="o">=</span> <span class="mi">0</span>
+<span class="n">sp</span> <span class="o">=</span> <span class="s1">&#39;up_0&#39;</span>
+<span class="n">freq_mesh</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">w</span><span class="o">.</span><span class="n">value</span> <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">sigma_freq</span><span class="p">[</span><span class="n">sp</span><span class="p">][</span><span class="n">orb</span><span class="p">,</span><span class="n">orb</span><span class="p">]</span><span class="o">.</span><span class="n">mesh</span><span class="p">])</span>
+
+<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">freq_mesh</span><span class="p">,</span> <span class="n">sigma_freq</span><span class="p">[</span><span class="n">sp</span><span class="p">][</span><span class="n">orb</span><span class="p">,</span><span class="n">orb</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">real</span><span class="p">)</span>
+<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">freq_mesh</span><span class="p">,</span> <span class="o">-</span><span class="n">sigma_freq</span><span class="p">[</span><span class="n">sp</span><span class="p">][</span><span class="n">orb</span><span class="p">,</span><span class="n">orb</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">imag</span><span class="p">)</span>
+
+<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;Re$\Sigma(\omega)$&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;Im$\Sigma(\omega)$&#39;</span><span class="p">)</span>
+<span class="k">for</span> <span class="n">ct</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">):</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="n">ct</span><span class="p">]</span><span class="o">.</span><span class="n">grid</span><span class="p">()</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="n">ct</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="n">ct</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$\omega$ (eV)&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/tutorials_correlated_bandstructure_plot_correlated_bands_22_0.png" src="../../_images/tutorials_correlated_bandstructure_plot_correlated_bands_22_0.png" />
+</div>
+</div>
+</div>
+<div class="section" id="Plotting-options">
+<h3>Plotting options<a class="headerlink" href="#Plotting-options" title="Permalink to this headline"></a></h3>
+<p>Finally, you can choose colormaps for each of the functionalities from any of the available on matplotlib colormaps. <code class="docutils literal notranslate"><span class="pre">vmin</span></code> determines the scaling of the logarithmically scaled colorplots. The corresponding tight-binding bands will have the maximum value of the colormap. By the way, colormaps can be reversed by appending <code class="docutils literal notranslate"><span class="pre">_r</span></code> to the identifier.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="n">plot_config</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;colorscheme_alatt&#39;</span><span class="p">:</span> <span class="s1">&#39;coolwarm&#39;</span><span class="p">,</span> <span class="s1">&#39;colorscheme_bands&#39;</span><span class="p">:</span> <span class="s1">&#39;coolwarm&#39;</span><span class="p">,</span> <span class="s1">&#39;colorscheme_kslice&#39;</span><span class="p">:</span> <span class="s1">&#39;PuBuGn&#39;</span><span class="p">,</span>
+               <span class="s1">&#39;colorscheme_qpbands&#39;</span><span class="p">:</span> <span class="s1">&#39;Greens&#39;</span><span class="p">,</span> <span class="s1">&#39;vmin&#39;</span><span class="p">:</span> <span class="mf">0.0</span><span class="p">}</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="section" id="2.-Run-and-Plotting">
+<h2>2. Run and Plotting<a class="headerlink" href="#2.-Run-and-Plotting" title="Permalink to this headline"></a></h2>
+<p>Now that everything is set up we may hit run. Caution, if you use a lot of <span class="math notranslate nohighlight">\(k\)</span>-points, this may take a while! In the current example, it should be done within a second.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="n">start_time</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
+
+<span class="n">tb_data</span><span class="p">,</span> <span class="n">alatt_k_w</span><span class="p">,</span> <span class="n">freq_dict</span> <span class="o">=</span> <span class="n">pcb</span><span class="o">.</span><span class="n">get_dmft_bands</span><span class="p">(</span><span class="n">fermi_slice</span><span class="o">=</span><span class="n">kslice</span><span class="p">,</span> <span class="n">with_sigma</span><span class="o">=</span><span class="n">bands_config</span><span class="p">[</span><span class="s1">&#39;sigma&#39;</span><span class="p">],</span> <span class="n">add_mu_tb</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+                                                   <span class="n">orbital_order_to</span><span class="o">=</span><span class="n">orbital_order_to</span><span class="p">,</span> <span class="n">qp_bands</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;qp_bands&#39;</span><span class="p">],</span>
+                                                   <span class="o">**</span><span class="n">w90_dict</span><span class="p">,</span> <span class="o">**</span><span class="n">tb_config</span><span class="p">,</span> <span class="o">**</span><span class="n">sigma_dict</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Run took </span><span class="si">{0:.3f}</span><span class="s1"> s&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">timer</span><span class="p">()</span> <span class="o">-</span> <span class="n">start_time</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Warning: could not identify MPI environment!
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+Starting serial run at: 2022-08-01 11:33:20.627842
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+H(R=0):
+     12.9769  0.0000  0.0000
+      0.0000 12.9769  0.0000
+      0.0000  0.0000 12.9769
+Setting Sigma from ./svo_example.h5
+Adding mu_tb to DMFT μ; assuming DMFT was run with subtracted dft μ.
+μ=12.2143 eV set for calculating A(k,ω)
+Run took 0.588 s
+</pre></div></div>
+</div>
+<p>That’s it. Now you can look at the output:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="k">if</span> <span class="n">kslice</span><span class="p">:</span>
+    <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">),</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">200</span><span class="p">)</span>
+
+    <span class="n">pcb</span><span class="o">.</span><span class="n">plot_kslice</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span><span class="p">,</span> <span class="n">alatt_k_w</span><span class="p">,</span> <span class="n">tb_data</span><span class="p">,</span> <span class="n">freq_dict</span><span class="p">,</span> <span class="n">w90_dict</span><span class="p">[</span><span class="s1">&#39;n_orb&#39;</span><span class="p">],</span> <span class="n">tb_config</span><span class="p">,</span>
+                    <span class="n">tb</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;tb&#39;</span><span class="p">],</span> <span class="n">alatt</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;alatt&#39;</span><span class="p">],</span> <span class="n">quarter</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="o">**</span><span class="n">plot_config</span><span class="p">)</span>
+
+<span class="k">else</span><span class="p">:</span>
+    <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span><span class="mi">3</span><span class="p">),</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">200</span><span class="p">)</span>
+
+    <span class="n">pcb</span><span class="o">.</span><span class="n">plot_bands</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span><span class="p">,</span> <span class="n">alatt_k_w</span><span class="p">,</span> <span class="n">tb_data</span><span class="p">,</span> <span class="n">freq_dict</span><span class="p">,</span> <span class="n">w90_dict</span><span class="p">[</span><span class="s1">&#39;n_orb&#39;</span><span class="p">],</span> <span class="n">dft_mu</span><span class="o">=</span><span class="mf">0.</span><span class="p">,</span>
+                   <span class="n">tb</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;tb&#39;</span><span class="p">],</span> <span class="n">alatt</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;alatt&#39;</span><span class="p">],</span> <span class="n">qp_bands</span><span class="o">=</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;qp_bands&#39;</span><span class="p">],</span> <span class="o">**</span><span class="n">plot_config</span><span class="p">)</span>
+
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="o">-</span><span class="mf">1.25</span><span class="p">,</span><span class="mf">1.75</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/tutorials_correlated_bandstructure_plot_correlated_bands_30_0.png" src="../../_images/tutorials_correlated_bandstructure_plot_correlated_bands_30_0.png" />
+</div>
+</div>
+<hr class="docutils" />
+</div>
+</div>
+
+
+           </div>
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+    <p>&#169; Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.</p>
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+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
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diff --git a/tutorials/correlated_bandstructure/plot_correlated_bands.ipynb b/tutorials/correlated_bandstructure/plot_correlated_bands.ipynb
new file mode 100644
index 00000000..691622bc
--- /dev/null
+++ b/tutorials/correlated_bandstructure/plot_correlated_bands.ipynb
@@ -0,0 +1,480 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "50bbc308",
+   "metadata": {},
+   "source": [
+    "# 5. Plotting the spectral function"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e8d5feac",
+   "metadata": {},
+   "source": [
+    "In this tutorial we go through the steps to plot tight-binding bands from a Wannier90 Hamiltonian and spectralfunctions with analytically continued (real-frequency) self-energies obtained from DMFT."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "0d69c4d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "from IPython.display import display\n",
+    "from IPython.display import Image\n",
+    "import numpy as np\n",
+    "import importlib, sys\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib import cm\n",
+    "from timeit import default_timer as timer\n",
+    "\n",
+    "from ase.io.espresso import read_espresso_in\n",
+    "\n",
+    "from h5 import HDFArchive\n",
+    "from solid_dmft.postprocessing import plot_correlated_bands as pcb"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c3ce4f44",
+   "metadata": {},
+   "source": [
+    "## 1. Configuration"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "42a860c4",
+   "metadata": {},
+   "source": [
+    "The script makes use of the `triqs.lattice.utils` class, which allows to set up a tight-binding model based on a Wannier90 Hamiltonian. Additionally, you may upload a self-energy in the usual `solid_dmft` format to compute correlated spectral properties.\n",
+    "Currently, the following options are implemented:\n",
+    "<ol>\n",
+    "<li>bandstructure</li>\n",
+    "<li>Fermi slice</li>\n",
+    "</ol>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b8d962f9",
+   "metadata": {},
+   "source": [
+    "### Basic options"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b652e03a",
+   "metadata": {},
+   "source": [
+    "We start with configuring these options. For this example we try a tight-binding bandstructure including the correlated bands (`kslice = False`, `'tb': True`, `'alatt': True`), but feel free to come back here to explore. Alternatively to an intensity plot of the correlated bands (`qp_bands`), you can compute the correlated quasiparticle bands assuming a Fermi liquid regime.\\\n",
+    "The options for $\\Sigma(\\omega)$ are `calc` or `model`, which performs a Fermi liquid linearization in the low-frequency regime. The latter will be reworked, so better stick with `calc` for now."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b8f73a48",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kslice = False\n",
+    "\n",
+    "bands_config = {'tb': True, 'alatt': True, 'qp_bands': False, 'sigma': 'calc'}\n",
+    "kslice_config = {'tb': True, 'alatt': True, 'qp_bands': False, 'sigma': 'calc'}\n",
+    "config = kslice_config if kslice else bands_config"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3c6ece97",
+   "metadata": {},
+   "source": [
+    "### Wannier90"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6d0ce79b",
+   "metadata": {},
+   "source": [
+    "Next we will set up the Wannier90 Input. Provide the path, seedname, chemical potential and orbital order used in Wannier90. You may add a spin-component, and any other local Hamiltonian. For `t2g` models the orbital order can be changed (to `orbital_order_to`) and a local spin-orbit coupling term can be added (`add_lambda`). The spectral properties can be viewed projected on a specific orbital."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "27a94d47",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "w90_path = './'\n",
+    "w90_dict = {'w90_seed': 'svo', 'w90_path': w90_path, 'mu_tb': 12.3958, 'n_orb': 3,\n",
+    "            'orbital_order_w90': ['dxz', 'dyz', 'dxy'], 'add_spin': False}\n",
+    "\n",
+    "orbital_order_to = ['dxy', 'dxz', 'dyz']\n",
+    "proj_on_orb = None # or 'dxy' etc"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "57f41c87",
+   "metadata": {},
+   "source": [
+    "### BZ configuration"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f23d7e3a",
+   "metadata": {},
+   "source": [
+    "#### Optional: ASE Brillouin Zone"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fc7b2fac",
+   "metadata": {},
+   "source": [
+    "It might be helpful to have a brief look at the Brillouin Zone by loading an input file of your favorite DFT code (Quantum Espresso in this case). ASE will write out the special $k$-points, which we can use to configure the BZ path. Alternatively, you can of course define the dictionary `kpts_dict` yourself. Careful, it might not define $Z$, which is needed and added below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "c6e46f88",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "G [0. 0. 0.]\n",
+      "M [0.5 0.5 0. ]\n",
+      "R [0.5 0.5 0.5]\n",
+      "X [0.  0.5 0. ]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes3DSubplot:>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "scf_in = './svo.scf.in'\n",
+    "\n",
+    "# read scf file\n",
+    "atoms = read_espresso_in(scf_in)\n",
+    "# set up cell and path\n",
+    "lat = atoms.cell.get_bravais_lattice()\n",
+    "path = atoms.cell.bandpath('', npoints=100)\n",
+    "kpts_dict = path.todict()['special_points']\n",
+    "\n",
+    "for key, value in kpts_dict.items():\n",
+    "    print(key, value)\n",
+    "lat.plot_bz()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "31956a53",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e47c2a48",
+   "metadata": {},
+   "source": [
+    "Depending on whether you select `kslice=True` or `False`, a corresponding `tb_config` needs to be provided containing information about the $k$-points, resolution (`n_k`) or `kz`-plane in the case of the Fermi slice. Here we just import the $k$-point dictionary provided by ASE above and add the $Z$-point. If you are unhappy with the resolution of the final plot, come back here and crank up `n_k`. For the kslice, the first letter corresponds to the upper left corner of the plotted Brillouin zone, followed by the lower left corner and the lower right one ($Y$, $\\Gamma$, and $X$ in this case)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "68c0f047",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# band specs\n",
+    "tb_bands = {'bands_path': [('R', 'G'), ('G', 'X'), ('X', 'M'), ('M', 'G')], 'Z': np.array([0,0,0.5]), 'n_k': 50}\n",
+    "tb_bands.update(kpts_dict)\n",
+    "\n",
+    "# kslice specs\n",
+    "tb_kslice = {key: tb_bands[key] for key in list(tb_bands.keys()) if key.isupper()}\n",
+    "kslice_update = {'bands_path': [('Y', 'G'),('G', 'X')], 'Y': np.array([0.5,0.0,0]), 'n_k': 50, 'kz': 0.0}\n",
+    "tb_kslice.update(kslice_update)\n",
+    "\n",
+    "tb_config = tb_kslice if kslice else tb_bands"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bf58de16",
+   "metadata": {},
+   "source": [
+    "### Self-energy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "67e42361",
+   "metadata": {},
+   "source": [
+    "Here we provide the info needed from the h5Archive, like the self-energy, iteration count, spin and block component and the frequency mesh used for the interpolation. The values for the mesh of course depend on the quantity of interest. For a kslice the resolution around $\\omega=0$ is crucial and we need only a small energy window, while for a bandstructure we are also interested in high energy features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "70fd0787",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "freq_mesh_kslice = {'window': [-0.5, 0.5], 'n_w': int(1e6)}\n",
+    "freq_mesh_bands = {'window': [-5, 5], 'n_w': int(1e3)}\n",
+    "freq_mesh = freq_mesh_kslice if kslice else freq_mesh_bands\n",
+    "\n",
+    "dmft_path = './svo_example.h5'\n",
+    "\n",
+    "proj_on_orb = orbital_order_to.index(proj_on_orb) if proj_on_orb else None\n",
+    "sigma_dict = {'dmft_path': dmft_path, 'it': 'last_iter', 'orbital_order_dmft': orbital_order_to, 'spin': 'up',\n",
+    "              'block': 0, 'eta': 0.0, 'w_mesh': freq_mesh, 'linearize': False, 'proj_on_orb' : proj_on_orb}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e314f15",
+   "metadata": {},
+   "source": [
+    "__Optional__: for completeness and as a sanity check we quickly take a look at the self-energy. Make sure you provide a physical one!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e7cb04b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 2000x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "with HDFArchive(dmft_path, 'r') as h5:\n",
+    "    sigma_freq = h5['DMFT_results']['last_iter']['Sigma_freq_0']\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 2, figsize=(10,2), squeeze=False, dpi=200)\n",
+    "\n",
+    "orb = 0\n",
+    "sp = 'up_0'\n",
+    "freq_mesh = np.array([w.value for w in sigma_freq[sp][orb,orb].mesh])\n",
+    "\n",
+    "ax[0,0].plot(freq_mesh, sigma_freq[sp][orb,orb].data.real)\n",
+    "ax[0,1].plot(freq_mesh, -sigma_freq[sp][orb,orb].data.imag)\n",
+    "\n",
+    "ax[0,0].set_ylabel(r'Re$\\Sigma(\\omega)$')\n",
+    "ax[0,1].set_ylabel(r'Im$\\Sigma(\\omega)$')\n",
+    "for ct in range(2):\n",
+    "    ax[0,ct].grid()\n",
+    "    ax[0,ct].set_xlim(-2, 2)\n",
+    "    ax[0,ct].set_xlabel(r'$\\omega$ (eV)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c249dc9",
+   "metadata": {},
+   "source": [
+    "### Plotting options"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93d1db24",
+   "metadata": {},
+   "source": [
+    "Finally, you can choose colormaps for each of the functionalities from any of the available on <a href=\"https://matplotlib.org/stable/tutorials/colors/colormaps.html\">matplotlib colormaps</a>. `vmin` determines the scaling of the logarithmically scaled colorplots. The corresponding tight-binding bands will have the maximum value of the colormap. By the way, colormaps can be reversed by appending `_r` to the identifier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a2d79e6d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_config = {'colorscheme_alatt': 'coolwarm', 'colorscheme_bands': 'coolwarm', 'colorscheme_kslice': 'PuBuGn',\n",
+    "               'colorscheme_qpbands': 'Greens', 'vmin': 0.0}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b2e5a0e",
+   "metadata": {},
+   "source": [
+    "## 2. Run and Plotting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "89a67dd6",
+   "metadata": {},
+   "source": [
+    "Now that everything is set up we may hit run. Caution, if you use a lot of $k$-points, this may take a while! In the current example, it should be done within a second."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "7e875f21",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warning: could not identify MPI environment!\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Starting serial run at: 2022-08-01 11:33:20.627842\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "H(R=0):\n",
+      "     12.9769  0.0000  0.0000\n",
+      "      0.0000 12.9769  0.0000\n",
+      "      0.0000  0.0000 12.9769\n",
+      "Setting Sigma from ./svo_example.h5\n",
+      "Adding mu_tb to DMFT μ; assuming DMFT was run with subtracted dft μ.\n",
+      "μ=12.2143 eV set for calculating A(k,ω)\n",
+      "Run took 0.588 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "start_time = timer()\n",
+    "\n",
+    "tb_data, alatt_k_w, freq_dict = pcb.get_dmft_bands(fermi_slice=kslice, with_sigma=bands_config['sigma'], add_mu_tb=True,\n",
+    "                                                   orbital_order_to=orbital_order_to, qp_bands=config['qp_bands'],\n",
+    "                                                   **w90_dict, **tb_config, **sigma_dict)\n",
+    "\n",
+    "print('Run took {0:.3f} s'.format(timer() - start_time))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7780b5d",
+   "metadata": {},
+   "source": [
+    "That's it. Now you can look at the output:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "1936db33",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1200x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "if kslice:\n",
+    "    fig, ax = plt.subplots(1, figsize=(3,3), dpi=200)\n",
+    "\n",
+    "    pcb.plot_kslice(fig, ax, alatt_k_w, tb_data, freq_dict, w90_dict['n_orb'], tb_config,\n",
+    "                    tb=config['tb'], alatt=config['alatt'], quarter=0, **plot_config)\n",
+    "\n",
+    "else:\n",
+    "    fig, ax = plt.subplots(1, figsize=(6,3), dpi=200)\n",
+    "\n",
+    "    pcb.plot_bands(fig, ax, alatt_k_w, tb_data, freq_dict, w90_dict['n_orb'], dft_mu=0.,\n",
+    "                   tb=config['tb'], alatt=config['alatt'], qp_bands=config['qp_bands'], **plot_config)\n",
+    "\n",
+    "    ax.set_ylim(-1.25,1.75)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "186cf322",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}