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differ diff --git a/.doctrees/nbsphinx/tutorials/Ce2O3_csc_w90/tutorial.ipynb b/.doctrees/nbsphinx/tutorials/Ce2O3_csc_w90/tutorial.ipynb new file mode 100644 index 00000000..65a50578 --- /dev/null +++ b/.doctrees/nbsphinx/tutorials/Ce2O3_csc_w90/tutorial.ipynb @@ -0,0 +1,531 @@ +{ + "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", + "\"drawing\"\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.ini](./dmft_config.ini)\n", + "\n", + "Here we'll discuss the most important input flags for solid_dmft:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "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", + "#dft_mu = 0.\n", + "solver_type = hubbardI\n", + "n_l = 15\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", + "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 = \n", + "w90_exec = wannier90.x\n", + "w90_tolerance = 1.e-1\n" + ] + } + ], + "source": [ + "!cat ./dmft_config.ini" + ] + }, + { + "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": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "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": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "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": [ + "
" + ] + }, + "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 (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": "3.10.12" + } + }, + "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..ce0ad298 --- /dev/null +++ b/.doctrees/nbsphinx/tutorials/NNO_os_plo_mag/tutorial.ipynb @@ -0,0 +1,845 @@ +{ + "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: 2022-03-10 17:08:16.981449\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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b+eEPf8gFF1wAwHXXXTfYQTfsqquuYu7cuTz22GOcffbZnu68EPr63v/+97N161ZOPfVUPvGJTwy+1tTUxKGHHsq9995Lb2+vZ7/e3l6uvPJKXnvtNYqKijzzw441le+KiIiIiMioys/P5/jjj+cf//gH++67LwcccAD5+fkcfvjhfOpTn6KkpITbbruNU045hc997nP84Q9/YPfdd2fFihW8+uqrlJSUcPvtt1NQMFT+vNNOO7HPPvvw8ssvc8ghh7Bo0SJycnI49dRTOfXUU/nqV7/KLbfcwvXXX89TTz3FPvvsw6ZNm1iyZAnf+ta3+OUvf5nWr/FnP/vZ4HypbW1tbNy4kVdffZWenh7Ky8u57rrrOO+883z7VVVV8dRTT3HiiSdyxx138MADD3DkkUdSXV3NunXreP755wkGg5x22mncfvvtvv1ffPFFPvrRj1JeXs6BBx7I9OnTaWpq4rXXXmPLli3k5ORwww03MH369LR+valQUCoiIiIiIqPu5ptv5uKLL+bxxx/n9ttvp7+/n76+Pj71qU8B8L73vY+XXnqJn/zkJzz55JO88cYb1NTUcO655/K9732PBQsW+I55zz338O1vf5vFixfzyiuvEAwGmT17Nqeeeiq77747L730EpdeeikvvPACDzzwAAsWLOD3v/89F110UdqD0kcffRQINRkqKyujurqak08+mfe9732ce+65lJeXx9x355135vXXX+fmm2/mzjvv5JVXXqGlpYWamhpOPfVUPvWpT3Hqqaf69quoqODZZ5/lkUce4amnnuLdd9/lmWeeIScnh7lz53LSSSfxla98hX322SetX2uqzGh2mJLEGGOWLly4cOHSpUvH+lJERCQLtbe3U1paCoSe8rsTzovIyILB4GA56YIFCwgENHpOJoZEfnYXLVrEsmXLlllrFyVzLv1WiIiIiIiIyJhRUCoiIiIiIiJjRkGpiIiIiIiIjBkFpSIiIiIiIjJmFJSKiIiIiIjImFFQKiIiIiIiImNGQamIiIiIiIiMGQWlIiIiIiIiMmYUlIqIiIiIiMiYUVAqIiIiIiIiY0ZBqYiIiIiIiIwZBaUiIiIiIiIyZhSUioiIiIiIyJiZsEGpMeZAY8xlxpi/G2M2GWOsMaZrmO1tHP+eTOD8t4xwrM+n5ysVEREREZl8jDEYY6iqqqKpqSnqNldccQXGGH72s59FXX/LLbek7XqOOeYYjDGsXbs2bceU+OSO9QWk4PvAhxPY/tZhXjsJqAEWJ3EdjwJbo6xfmcSxRERERESySlNTE9dccw0//OEPx/pSZIxM5KD0OeB14KWBf9ECw0HW2guirTfGVAJnDSz+JYnr+Jm19qkk9hMRERERyWqBQIDc3FyuvfZavv71r1NVVRXXfl/+8pc566yzmDFjRoavUEbDhC3ftdZeZa39H2vtg9babSkc6gygAHjeWrsqTZcnIiIiIiIjyMvL48ILL6SlpYWrr7467v1qamrYY489qKioyODVyWiZsEFpGp078PHPY3oVIiIiIiJZ6Dvf+Q4FBQX8+te/pqGhIa59UhlT+oc//IG9996bwsJCZs2axVe+8hWam5sTPo6kz0Qu302ZMWYucCTQC9yR5GE+aow5HcgB1gD/sNauSNMlioiIiMhkFQxCZ3xB2LhTNAUC6clvzZo1i4suuojrrruOX/3qV/zkJz9Jy3Gjufjii/nVr35FQUEBxx13HMXFxdx2220888wzFBQUZOy8MrysDkqBcwADPGytrU/yGF9xlq8yxtwAfM1a2xfPAYwxS2O8tGuS1yQiIiIi411nA/xigt7ufXs1lNSk7XCXX345N998M7/97W/55je/SXV1ddqOHfbss8/yq1/9iilTpvCf//yHRYsWAVBfX89xxx3H888/n/ZzSnyyvXw3ldLdV4HPA7sDxcAuwJeAJuCLwC/ScH0iIiIiIpPezJkz+exnP0trayu//OUvM3KOG2+8EYBvfetbgwEpQHV1Nb/4hW7dx1LWBqXGmAOAhYSCyH8kur+19tfW2t9ba1dZazuttWustdcDRwE9wFeMMXPiPNaiaP+A1Ylel4iIiIjIRHTZZZdRWFjIddddR11dXdqPv2TJEgDOPPNM32sf/OAHmTJlStrPKfHJ5vLdcJb0Lmttd7oOaq19yxjzAPAx4P3AH9N1bBERERGRyWrGjBl8/vOf59prr+UXv/gFV111VUL733zzzYOBZ1hNTc1g5nXz5s0YY5gzJ3reaO7cuXE3WpL0ysqg1BiTQ2pzk44kPLWMJk4SERERkeiKpoTGZk5ERZnJKl566aX8/ve/53e/+x0XX3xxQvsuWbKEW2+91bNu3rx5GSsHlvTJyqAUeB+hgHEdsDgDxw/P+tuWgWOLiIiIyGQQCKS1WdBkMH36dL7whS9w9dVX8/Of/5ySkpK4973llluGnSJmxowZrF27lg0bNjB//nzf6+vXr0/mkiUNsnVMabh09y/WWpvOAxtjCoCTBhZfSeexRUREREQmu0svvZTi4mKuv/56tm3blrbjHnHEEQDcddddvtcef/xxle6OoawLSo0xxcBHBhaHLd01xhxijFlhjHnCWb/AGPPhgTLgyPW1wN+AOcDrwLPpu3IRERERkclv6tSpfPGLX6Sjo8NXjpuKz33ucwBcffXVLF++fHB9Q0MDl1xySdrOI4mbsEGpMeYkY8zz4X8Dq/Mj1xljToqy62lAKfCStXbFCKcpBhbgny90BnAfsM0Ys8QYc4cx5t+EuuWeBmwEzkx3FlZEREREJBtccskllJSU0NnZmbZjHnHEEXz961+nrq6O/fffn5NPPpkzzjiD3XbbDYD3vOc9aTuXJGbCBqVALXBoxD8A46yrjbLfYOluCud+G7iWUEOjXQllXg8aWP4hsI+19u0Uji8iIiIikrVqa2v50pe+lPbjXn311dxwww3Mnz+fxx9/nGeeeYYzzzyTf//73xQUFKT9fBIfo2Te+GWMWbpw4cKFS5cuHetLERGRFHX29FOYF8AYM9aXErf29nZKS0sBaGtrS6jhiIiEBINBVq5cCcCCBQsIBCZyTkiySSI/u4sWLWLZsmXLrLWLkjlXtnbfFRERGRV9/UEu//ub3PfaJqqK87nq9H04do+pY31ZIiIi44Ye1YiIiGTQH59Zy12vbKS337K9tZsv3PYKW5rTN0ZKRERkolNQKiIikiHWWv7ywjrPuq7eIH//76YxuiIREZHxR0GpiIhIhizf0sq6+g7f+iWr6sbgakRERMYnBaUiIiIZ8vrGpqjrV2xtQY0GRUREQhSUioiIZMhbm5qjrm/s6GVHW/coX42IiMj4pKBUREQkQ97a3BLztbe3to3ilYiIiIxfCkpFREQyZM2O2IHn6mFeExERySYKSkVERDKgqaOHlq6+mK9v1rQwIiIigIJSERGRjFjf4O+6G2lzU9coXYmIiMj4pqBUREQkA0YOSpUpFRERAQWlIiIiGTFSULpFQamIiAigoFRERCQjNjhB6T6zKzzLW1u66OsPjuYliYiIjEsKSkVERDLAzZQevNMUz3LQhuYrFRERyXYKSkVERDJgi9PIaO9ZFRjj3aa+vXsUr0hEZHyqq6vj+9//Pvvvvz+VlZUUFxczf/58PvvZz/LWW2+N9eXJKFBQKiIikmbWWra2eIPSmZVFVBXne9bVt/WM5mWJiIw7//rXv9htt9248sor2bRpE0cffTQnn3wyeXl53HTTTey333787Gc/G+vLlAzLHesLEBERmWzauvvo6On3rJteXkh1ST4N7UOBaF2bMqUikr1eeuklTjrpJHp7e/npT3/KxRdfTG7uUHjy0EMPce6553L55ZdTXFzMV7/61TG8WskkZUpFRETSbFuLP9icWl5AdakypSIiEKooOf/88+np6eFHP/oRl112mScgBTjxxBO57777MMZw6aWXsm7dujG6Wsk0ZUpFRETSbLtTultRlEdhXg7VJQWe9RpTKpLdgjZIU3fTWF9GUioLKgmY5PNbDz/8MMuXL2fWrFlceumlMbc76qijOOOMM7jzzjv53e9+x89//vO4jn/MMcfw9NNPs2bNGpYsWcI111zD8uXLKS0t5YQTTuD//b//x6xZs5K+fkkvBaUiIiJptq3VG5ROKw8Fo8qUikikpu4mjr7j6LG+jKQ8/fGnmVI4ZeQNY3jooYcAOOOMM8jLyxt227PPPps777yThx9+OO6gNOyXv/wl119/PUceeSQf/vCHef755/nTn/7Ek08+yXPPPcfs2bOT/hokfVS+KyIikmZbm70Z0GnlhQC+TGmdglIRyVKvvfYaAAceeOCI24a3WbZsGb29iU2l9fvf/54HH3yQp59+mr/+9a+8/fbbnHPOOWzcuFFjVMcRBaUiIiJptq3FzZQOBKVuplTluyKSperr6wGYOnXqiNvW1tYCEAwGaWhoSOg8Z555JieeeOLgcl5eHr/+9a8pKSnh/vvvZ9OmTQkdTzJDQamIiEiabY9Rvluj8l0RESDU6CjyYzzbAhh3wucRnHXWWb511dXVfOADHyAYDPLss88mdDzJDI0pFRERSbMdrd4MaG1peEyp0+hIU8KIZLXKgkqe/vjTY30ZSaksqExp/5qaGlauXMn27dtH3HbHjh1AKCCtqqqirq6Oiy++2LfdhRdeyBFHHOFZN2/evKjH3GmnnQDYvHlzglcumaCgVEREJM3q270Z0HAwWlXsbebR3tNPT1+Q/FwVLolko4AJpNQsaCLbd999eeaZZ3jllVc477zzht32lVdeAWDRokXk5eXR1tbGrbfe6tvumGOO8QWlscSToZXRo3dBERGRNGtwg9KSUNluRVG+b9vmzsSadoiITAYnnHACAHffffeIzYtuv/12AI4//ngglOW01vr+XXDBBb59Y81tun79egBmzpyZ7JcgaaSgVEREJI36+oM0dXhvsKaUhoNS/7QHCkpFJBudeOKJLFiwgE2bNnHVVVfF3O4///kPd999N/n5+XzpS19K+Dx33HGHb11DQwOPPfYYxhgOO+ywhI8p6aegVEREJI0aO/xB5pSBTGl+boDi/BzPa82danYkItknEAhwyy23kJeXxw9+8AOuuuoq+vv7Pds8/PDDnHbaaVhr+dnPfjY4DjQRd955J48++ujgcl9fH9/4xjdob2/n1FNP1Tyl44TGlIqIiKSRW7oLUFU8VLZbUZRHR8/QjZebVRURyRbvec97eOCBBzjrrLO47LLLuPrqqznssMMoKCjgzTffZPny5QQCAX784x/zjW98I6lzfPazn+WEE07gqKOOYubMmTz//POsWbOGmTNn8pvf/CbNX5EkS5lSERGRNHKD0oqiPPJyAp7lSCrfFZFsdvzxx7Nq1Sq++93vMmPGDJ588kkeeOABurq6uPDCC3n11Vf53ve+l/TxL774Yv74xz/S3NzMvffeS0tLC+eddx4vvPACc+fOTeNXIqlQplRERCSNYjU5Cqt0OvAqUyoi2a62tpYrr7ySK6+8MiPHP//88zn//PMzcmxJD2VKRURE0qih3Tv36BQnKFWmVERExEtBqYiISBq5c5S6QWmlMy2MglIREcl2CkpFRETSyFe+W+pkSn3lu+q+KyIi2U1jSkVERNJopEypyndFRDLvqaeeGutLkAQoUyoiIpJGDW1uUFrgWXaD0iYFpSIikuUUlIqIiKRRot13lSkVEZFsp6BUREQkjRJudKQpYUREJMspKBUREUkTa62vcdFIY0qbOnux1mb82kRERMarCRuUGmMONMZcZoz5uzFmkzHGGmO6htn+ioFtYv37WRLXEDDGfN0Y86YxptMYs8MYc5cxZmFqX52IiExEHT399AW9AaYbhLrlu/1BS3tPf8avTUREZLyayN13vw98OIn9ngHeibL+lUQOYowxwB3Ax4Am4J9ADXA6cJIx5lhr7QtJXJ+IiExQ0caHljtBqbsMoWlhSgsm8luyiIhI8ibyO+BzwOvASwP/tsa5383W2lvScP5PEQpIVwFHWmu3ARhjTgfuBm4zxuxhre1Lw7lERGQCcINSY6DMCTbLCnIJGIhMqDZ19DK7ajSuUEREZPyZsEGptfaqyOVQ4nJUfWvg4yXhgBTAWnuPMeYB4FRCmdx7RvvCRERkbLhBaXlhHoGA9/0pEDCUF+XRFNHgqEUdeEVEJItN2DGlY8kYszOwEOgkVLbrunvg4ymjdlEiIjLm3KDUHU8aa72mhRERkWyWjUHpccaYa40xNxpjvmeMOTCJY+w78PEta220O4n/OtuJiEgW8GVKi6IXJCkoFREJVToaY6iqqqKpqSnqNldccQXGGH72s/h7kq5duxZjDMccc0x6LjTiOm655Za0HVOGTNjy3RSc5yz/2BhzD3CBtbYtzmPMHfi4McbrG53thmWMWRrjpV3jvB4RERkH3DJcZUpFREbW1NTENddcww9/+MOxvhQZI9mUKX0HuBhYBJQCc4BzgE2EOub+OYFjlQ587IjxeruznYiIZIF4y3fdDrwKSkUkWwUCAfLz87n22mtpbGwc68uRMZI1Qam19i/W2l9Za5dZa9uttRuttbcDBwP1wGnGmPfGebhw14q0zHZurV0U7R+wOh3HFxGR0aExpSIiicnLy+PCCy+kpaWFq6++eqwvR8ZI1gSlsVhrtwB/HFj8UJy7tQ58LInxenh9vOXAIiIyCbjlu9HmJAUFpSIikb7zne9QUFDAr3/9axoaGuLer66ujs997nNMnz6d4uJi9t9/f/70pz+ldC1PP/00xxxzDKWlpVRXV/ORj3yEFStWpHRMGVk2jimNZtXAxxlxbr9+4OPsGK/PdrYTEZEsoEypiCTCBoP0x2jwM97lVFZiAunJb82aNYuLLrqI6667jl/96lf85Cc/GXGf+vp6Dj/8cN5++21mz57NqaeeytatW/nUpz7F5z//+aSu4/777+f000+nv7+f9773vcydO5cXX3yRQw89lFNO0aQamaSgNCQ8ZXm8mc3XBz7uZYzJi9KB94CBj2+kfGUiIjJhxBuUVjrrNU+pSHbqb2pi1XsPH+vLSMpuzz5D7pQpaTve5Zdfzs0338xvf/tbvvnNb1JdXT3s9t/5znd4++23+fCHP8wdd9xBQUEBAA8//DCnnnpqwudvbW3lwgsvpL+/n9tvv51PfOITAPT19XHhhRdy6623Jv5FSdyyvnzXGGOAjwwsvhLPPtbaNcByoAg4KcomHxv4+GDKFygiIhOGb0qYQmVKRUTiMXPmTD772c/S2trKL3/5y2G3bWtr489//jO5ubn85je/GQxIAU444QTOOOOMhM9/1113UVdXxwc+8IHBgBQgNzeXa665htJS9S/NpKwISo0xNcaYTxpjCpz1pcANwKHAVuBe5/VDjDErjDFPRDlseCT2z40xUyP2+ShwKrAGuC99X4WIiIx3zZ19nmWV74qIxO+yyy6jsLCQ6667jrq6upjb/fe//6Wzs5NDDz2UuXP9MzBGBpXxWrJkCQBnnnmm77Wqqio++MEPJnxMid+ELd81xpwEfN9ZnW+MeT5i+cfW2n8SmprlVuC3xpjlhMZ6VhIqs60GmoCPWWvdKV6KgQVAYZRL+D/gREJZ1nDgWgMcDXQB50Yp6xURkUnKWhv3PKVuA6SWrj6stYSKd0REstOMGTP4/Oc/z7XXXssvfvELrrrqqqjbbd68GSBqQBpr/ZIlS7j55pt963/5y19SU1OT1DElfSZsUArUEspwRjLOutqBj/XAVcB7gPnAfkA/oWzmLcA11tpNiZzcWhs0xpwBfA34NHAyoflJ7wV+YK1dmsjxRERkYuvqDdLTH/SsizdT2h+0tHX3URaj3FdEJqecykp2e/aZsb6MpORUVmbkuJdeeim///3v+d3vfsfFF18cdRtrQ7MyJvIg75133ok6LvSKK66gpqYmqWNK+kzYoNRaewuhgDKebVuBy5I4x1MMzUka7fV+QmW8mlRJRCTLRSvBjRmUFvvXN3f2KigVyTImEEhrs6DJYPr06XzhC1/g6quv5uc//zklJf4ZGGfOnAnAunXroh5j/Xr/BBgXXHABF1xwQczzJnNMSZ+sGFMqIiKSadGC0ljzlJbm5xJwHnlqXKmISMill15KcXEx119/Pdu2bfO9fuCBB1JYWMgLL7zAhg0bfK//7W9/S/icRxxxBBBqeORqamriscceS/iYEj8FpSIiImnQ2uUNKkvyc8hxI88BgYDxBawKSkVEQqZOncoXv/hFOjo6opbclpaWcs4559DX18fXvvY1uru7B1977LHHuPPOOxM+5xlnnMGUKVN8+/f39/Otb32LtrZ4Z46UZCgoFRERSYP2nn7PcknB8CNk3NJezVUqIjLkkksuoaSkhM7Ozqiv/+xnP2P+/Pnce++97LbbbnziE5/guOOO44QTTuCiiy5K+Hzl5eX84Q9/IBAI8PGPf5wjjjiCs88+mwULFnD33XdzzjnnpPolyTAUlIqIiKRBe7d3OpjSBINSZUpFRIbU1tbypS99KebrNTU1PPPMM1x44YV0d3dz3333UV9fz0033cQll1yS1DlPP/10Hn/8cY488kheffVVHn74YRYuXMhzzz3H/Pnzk/1SJA4TttGRiIjIeNLmBKWJZkoVlIpItgl3vI3lqquuijktDITKfG+66SZuuummhI8dy3HHHcdxxx3nW3/FFVdwxRVXJHVMGZkypSIiImngZkpLCnKG3d4dU9rUoaBURESyk4JSERGRNEi0fLdSmVIRERFAQamIiEhatHWn1uhIQamIiGQrBaUiIiJp4C/fVVAqIiISDwWlIiIiaZBq911NCSMiItlKQamIiEga+Lrv5itTKiIiEg8FpSIiImnQ3hOj+25vFzx0Cfxmf7jtDGh4F1BQKpItjDGDnweDwTG8EpHE9PcP9UqI/DnOBM1TKiIikgZuo6PB8t3Hvw8v/iH0ecO7UL8avvicb0qYlq4+rLUZf+MXkdFljCE/P5+enh7a29upqKgY60sSiUtLSwsABQUFCkpFREQmgqiNjjob4ZVbvRs2rIbl/6Bi1ome1f1BS1t3H2WF3mBVRCa+srIy6uvr2bZtGwAlJSUEAipYlPHHWkt3dzetra00NDQAUFVVlfHzKigVERFJgw5fUJoDS++D/m7/xqufpGK3D/tWN3f2KigVmYSqq6tpb2+nq6uLzZs3j/XliMStsrJyVLL7ekQjIiKSBlEbHa35T/SN1yymND+XgFMNpXGlIpNTTk4Oc+fOpbq6mvz8/LG+HJFh5eTkUF5ezqxZs5g+ffqoZPWVKRUREUmRtZb2Hu+Y0pKCXNjwYvQdmtcTaNtKeVEeTR1DgaiCUpHJKycnh6lTpzJ16lSstVhrx/qSRHyMMWPS20BBqYiISIq6+4L0B703mBW9O6BlY+yd6t6mwglKNVepSHYYqxt/kfFK5bsiIiIpckt3AcpaVw2/U93bVGpaGBEREQWlIiIiqXI77wIUN78z/E51q3zTwigoFRGRbKSgVEREJEVupjRgIK9hhExp/TtUKCgVERFRUCoiIpKq9m5/kyNT97Z3o6kLvcvNGxWUioiIoKBUREQkZW75bml+DuxY4d1o1+O8y80bqSj09hts7vSXAYuIiEx2CkpFRERS5JbvzsjvhM5G70bz3+9d7m2nNq/Ls0qZUhERyUYKSkVERFLkZkp3zq3zbhDIhbnvAbxTQMzAu52CUhERyUYKSkVERFLkZkrnBnZ4NyifBXlFUDbds7q2f7tnWfOUiohINlJQKiIikqKOHm+jo5k4QWnl3NDHshne1cEGz7IypSIiko0UlIqIiKTILd+dYb0ZUCrnhT6WTvOsLu3zjjtt7uzFWpv26xMRERnPFJSKiIikyC3fre3f5t0gnCktrfWsLu6p9yz3B63vWCIiIpOdglIREZEUuZnSKb1bvRsMBqXeTGlht9MQCZXwiohI9lFQKiIikqK27sgxpZbK7i3eDWIEpbmddeQEvB15FZSKiEi2UVAqIiKSoshMaQXt5AW9849SMTv0scRbvmvatlFemOtZp6BURESyjYJSERGRFLX3DAWl00yjf4PwVDBOppT2HVQW53tWNXUoKBURkeyioFRERCRFkc2Jppom74tFUyC3IPR56VTvaz1tzCj0jkdt7OjJwBWKiIiMXwpKRUREUhRZvjsVJ1MazpKCPygF5hW2e5Yb2xWUiohIdlFQKiIikqL2iEZHvkxpZMlufinkFXtenp3X6lmuV1AqIiJZRkGpiIhICqy1w48pjcyUGgMlNZ6Xp+UqUyoiItlNQamIiEgKOnv7sXZoudbNlEYGpQDF1Z7FmkCbZ7lBjY5ERCTLKCgVERFJQWSTI4hWvjt8UDrFeMt3lSkVEZFso6BUREQkBZHjSQGm0uTdoMyZBsYJSstti2e5QUGpiIhkmQkblBpjDjTGXGaM+bsxZpMxxhpjumJsGzDGHGmM+bkx5gVjzHZjTLcxZrUx5kZjzM5JnP+WgXPG+vf51L9KEREZ79o9mVIbZUzpDO+yE5SW9jd7lhWUiohItskd6wtIwfeBD8e57S7AfwY+3wQ8CwSBQ4DPAWcbY0601i5J4joeBbZGWb8yiWOJiMgEE1m+W0YnRcYJKkvdTOkU72Jfk2e5s7efzp5+ivJz0nmZIiIi49ZEDkqfA14HXhr4Fy0wDLOEgsf/Z60NB6cYYwqAG4ELgNuMMfOttYl2mPiZtfapBPcREZFJwjNHqZslhREbHeX3NPl2aezooSi/KB2XJyIiMu5N2KDUWntV5LIxZrhtVwPHR1nfbYz5AvARYC7wXuDp9F6piIhMZm2eoLTJ+2JBBeQ5waUTlOZ0NZATMPQHh1r4NrT3MLNSQamIiGSHCTumNF2stV3A2wOLM8fyWkREZOKJbHQ0BW8nXXdOUsAXlJqOeqqK8z3rNK5URESyyYTNlKaLMSYHmDewOFwJcCwfNcacDuQAa4B/WGtXpOv6RERkfIss360yiQeldDVTW26oi5iutLFDQamIiGSPrA9KgbOAqcAOQg2QEvUVZ/kqY8wNwNestX3RdnAZY5bGeGnXJK5HRERGUWT5bpWbKXUDUICiKb5Vcwq7WR6xrEypiIhkk6wu3zXGzAGuHVj8gbW2O4HdXwU+D+wOFBPq8PsloAn4IvCLtF2oiIiMW5GZ0iluprTYH4BGWzensMOz3KigVEREskjWZkqNMSXAvUANcJ+19sZE9rfW/tpZtQa43hjzH+AV4CvGmKuttRviONaiGNe4FFiYyHWJiMjoau8Zpnw3SlaUnLxQA6TuoflJp+d1AEONjRpUvisiIlkkrUGpMWYn4EhgX6AWqACaCZXGvgYsttauS+c5k2GMyQPuAQ4ElgBnp+vY1tq3jDEPAB8D3g/8MV3HFhGR8WfYRkfRynchlC2NCEqn5bYDQ9uqfFdERLJJykGpMaYKOB+4CNgjvDrKpnZg++XATcCfrLVRJnTLLGNMAPgL8CFC85yeYq3tTPNpVg18nJHm44qIyDgTWb5badq8L8YMSquhcc3gYrWzn4JSERHJJkkHpcaYYuAS4FtACdBJKOv4IrACaABaCGVLq4A9gUOAg4BrgCuNMb8EfmmtbU/ha0jU9cCZhKaB+aC1tikD56ga+Ng27FYiIjLhtQ07pnSYoDRCFS2e5cb23rRcm4iIyESQSqZ0NTANeJRQ5vG+eILLgbGcHwXOBf4H+ByjND+oMeb/DZxvPfABa+32DJyjADhpYPGVdB9fRETGF8+Y0rjLd73ry603KNWYUhERySapdN99ATjQWnuCtfa2eLOd1tp2a+2frbUfIpQ1fSGFa4ibMeabwOWE5iJ9v7V2fRz7HGKMWWGMecJZv8AY8+GBOU4j19cCfwPmECoNTmaKGRERmUDCY0oL6KHEOE3chxtTGqGk382U9mCtTds1ioiIjGdJZ0qttaelenJr7X+BjySzrzHmJOD7zup8Y8zzEcs/ttb+0xizH/DLgXVrgO8aE23YKzdba5dELBcDC4BCZ7sZwH1AvTFmBbCJ0FynBwJlwEbgTKs7ChGRSS9cvlsZbcRGtClhwBesFvU1eZb7gpaWrj4qivLScYkiIiLjWipjSkuttWM5ZrIWONRZZ5x1tQMfKxlqvnTYwL9oniI0LnYkbxOa3/Q9wK6Exsp2D6z/B/DrsWjiJCIioy/c6Mg3ntQEoLAi+k5OUJrf7X/LaGjvUVAqIiJZIZUxpduMMfcBtwGPWGuD6bmk+FhrbwFuiXPbp4jeETip/ay1m4FvJHo8ERGZXIJBS0dPqHzXP0dpFQRyouyFLygNdDZQmBegq3forbSxo4edKUnr9YqIiIxHqYwpLQI+QSgzuNkYc60x5qD0XJaIiMj4F9nkKO45SqO91tFAdUmBZ1WjpoUREZEskUpQuhPwXWA5ofGUXwVeGGgM9B1jzE6pX56ITDYN7T1saU731MAiYyPc5AiiZEoTCUp7Wqkt9q6qV1AqIiJZIumg1Fq73lr7U2vtXsD+wNXAZmB34MfAamPM08aYi4wxlWm5WhGZ0P703FoO++kTHPbTJ7n8728QDKoXmExskXOUVrmNjopiNDmCqAHrnMIuz7IypSIiki1SyZQOsta+bq29GJgLvB+4FWgFjgRuBLYYY+42xpxmjFHXBpEstLaunR/9YxndfaExc399cQMPvrlljK9KJDXtEUFphXFmRiuqjL1jUSVuy4LZ+R2eZc1VKiIi2SItQWmYDXnSWvtpYBrwceBBQu+8HwXuAbYaY25I53lFZPx76K0t9DmZ0fte3TRGVyOSHpFjSsvwBpUxO+9CqAFSUZVn1fQ8b1CrTKmIiGSLtAalkay13dbau6y1HyY0r+eXgAagCvhsps4rIuPTGxuafete29CEpvOViSxyTGmZcYLSgvLhd3ZKeKfmeIPSBgWlIiKSJTIWlIYZY/YBLgMuB8IDbPpj7yEik9HKba2+dQ3tPWxr6R6DqxFJj8jyXX+mNLGgtDrg/R1RUCoiItkilXlKYzLGzAHOBs4BFjE0cOYN4M+E5jYVkSxhrWVTU/SOu8u2NDO9onCUr0gkPSIbHZWnmCmtdKaUaezoTenaREREJoq0BaXGmArgDOBc4AhCgagh1JH3duDP1to303U+EZk4Gjt66RlocORaubWN4/aYNspXJJIew2dKhxlTClDs7c5b1t/iWVamVEREskVKQakxJh84mVBG9EQgn1Ag2gbcSygr+oTVoDGRrLa1uSvmaxsbO2K+JjLeeYJS41QDJFi+W9Lf5Flu7uylrz9Ibk7GR9qIiIiMqaSDUmPMTcDpQAWhQLQfeJxQIHqvtVZ3miICwLaW2EHphsboZb0iE0HbYKMj68+UJli+W9jb5NuksaOX2rKC5C9QRERkAkglU/qZgY+vEwpEb7fWbk39kkRkstkyXKa0Qc+vZOIKZ0oL6KXA9HlfHLF81xuU5nU3+jZp7OhRUCoiIpNeKkHpLwiNE30rXRcjIpPTcJnSjY2dBIOWQMDE3EZkvGobmKe03M2SQsJBqelooLwwl5auoeBW40pFRCQbJD1QxVp7qQJSEYlHU0fsG+ue/iA72jQtjExM4Uypb45SSLh8l456ppTke1Y1KigVEZEskPbuCcaYE4wx9xljNhljuo0x/+u8drUxZma6zysi41dT5/BTW2xQCa9MUINBqZspzS2E3Pwoe0Rwuu/S18m0Ym+X6noFpSIikgXSGpQaY64HHgROBUqBPIbmKAVoAr4OnJXO84rI+NY0wnyLG9SBVyaocKMj3xylI5Xugj9TCswr9Db+UqZURESyQdqCUmPMp4HPAy8C+1lrfe/I1trngE3AKek6r4iMf8OV7wJsboo95lRkPOvoiZEpHal0F0KBq8nxrJqZ5z1Owwi/OyIiIpNBOjOlnwMagJOttW8Ms907wC5pPK+IDHh7Wys/fWg5Ny9+l+6+/pF3GCVu+a47bm6jpoWRCWpoTGmCc5QCGOPLlk53glJlSkVEJBuk0n3XtQj4t7W2foTttgLvSeN5RQR4Z3sbH73+WdoGbpKfeaeO/7vgYIwZ+662bvnuopnlLF5VN7i8qUlBqUxM4d+3ctq9L8STKYVQUNq+fXCxJqcNqB1c1phSERHJBunMlAbjPN5McN+9RSRV1/zr7cEbZIB/r9zBS2v98x6Otv6gpaXLG5QunOG9Yd+soFQmoL7+IF29ocZEvu678YwpBV+mtNq0epYbVb4rIiJZIJ1B6QrgIGNMcawNjDHVwH7AcOW9IpKg/qDl6ZU7fOufWLFtDK7Gq7WrF2u96xbO9Aalmxo7se5GIuNce89QiXwZSZTvgq8Db4V1gtL24ZuEiYiITAbpDEpvI1Rz9DtjjK8s2IRqCH9DqCvvn9N4XpGst3pHmydLGvbWpuYxuBqv5ijTwbiZ0s7e/hE79IqMN+0Rv3O+7ruJlO9GKA16f2cbVL4rIiJZIJ1B6fXAEuB8YLkx5rqB9fsYY34JLAc+AfwbuDWN5xXJeu/uaIu6/q1NLWOegWzv9jZcChjYqaaEgDPUVeNKZaLxBKVu990ky3eL+7xBaWdvP12946dpmYiISCakLSi11vYCxwM3AnOBLw68dADwTWBX4H+BU6y1wagHEZGkrK2PPs9nc2fvmHe2DU+ZEVaSn0teToDp5YWe9QpKZaKJrE5I15jSgh7/OPCWKNUGIiIik0k6u+9ire0AvmiM+R/gaGAnIAfYSKgz7+Z0nk9EQtbVx+4dtq6+gzlTYg71zrjIcXcARfmheRlnVhaxuXloftJNmhZGJpjIKoCk5ikFX1Ca29Xg26S5s5epzkMcERGRySTpoNQY817gORulNtBauwO4O5ULE5H4rakbJihtaOcIakbxarw63UxpQejPzqyqIl5eN5QVUgdemWjauocymL6gNO5GR96g1HQ0UJKf43mYE21ctoiIyGSSSvnuEmCbMeaPxpiPGmNK03VRIpKYrREZR9f6GKW9o8UdU1ockSmNpPJdmWhau9LR6MjbfZeOOioKvc+LFZSKiMhkl0pQ+n1gNfBJ4C5ghzHmYWPMF40xc9NydSISlx2t3TFfWztMae9ocMeUhoPSWU5QqkypTDRDY0otpb4pYeIcU1pS610O9jGz0Pv7rKBUREQmu6SDUmvtT6y1hwEzgM8CjwFHAtcBa4wxrxljfmSMOSQ9lyoi0XT09PnGbUba0DDWjY7cTOlA+a4TlG7QmFKZYMLdd4vpJtc4/fviLd8tnepbNTff201bQamIiEx2KXfftdZut9b+r7X2w0A1cCpwM1ADfA94zhizxRjzB2PMKcaYouGOJyKJqWsdfh7DjY1jXL7rBKUlBaFM6bxqb/OlhvYeGjUno0wgrQNBqW88KcRfvptb4Muqzsxr8Sy3dPrnIBYREZlM0jlPKdbabmvtg9baz1lrZwMHAz8BtgAXAvcB9caYfxhjLjLG1A5zOBGJw4622ONJAVq6+mjpGrtMS0e394a6KC+UKZ07pZj8HO+foHdizLcqMh61DYwp9Y0nhfiDUoASb7Z0ek6rZ1mZUhERmezSGpS6rLWvWGt/YK09gNDcpV8B/gO8n9B8pl/I5PlFsoE7nnR6eSHGeLcZy+lWOnqjZ0pzcwLsVOPNlr6zXUGpTBxtsTKl+aWQk0Bz+9JpnsVa0+RZVlAqIiKTXUaD0kjW2o3W2uuttccTKu09A3hutM4vMlm5QenMykKmlhV41m0cy6C02210NHSzvtvUMs9rq7YpKJWJI2amNJEsKUCpt2io2jZ7lhWUiojIZJf0PKWpsNa2A38fi3OLTDY72rzjMGvLCjDGsK1lKFgdy3Gl7pjScPddgF2nemeSUvmuTCQxx5TG2+QozMmUVgQbPcstCkpFRGSSG7VMaZgx5tPGmB+M9nlFJis3U1pbVsDsKm8/sbHMlHYOE5TOd4LSpZua2dDQwXfvfZOv/PVVXlzTMCrXKJKMcPfdlDOlzrQwZX3en/uxHBMuIiIyGsYiU3oRcAjwozE4t8ik4wtKSwupKPIGgmM5prTdmae0pGDoz86+s71dR+vbezjy5/8eXP7H65u57uz9OXmfmZm9SJEkxBxTGu8cpWFOprS41xuUqnxXREQmu1HPlIpIejV2eMt3p5TmM7vK20BoY9PYle92dMfOlM6dUsz08sJh9//pQyvoD9qMXJtIKsJjSsvcTGnC5bve7ruF3XWeZQWlIiIy2SWdKTXG+Gf8jk9esucUEb8mJyitLMqjosj7azamjY56Yzc6Msbw3vnV/P2/m2Luv6mpk1fWNXLIzlMydo0iyQiPKS13M6Uplu/mddVjCGIHnht39PTT2x8kL0fPkUVEZHJKpXx3K5BM+sIkuZ+IRNHc6Q36KovzmFXpHVPa1NFLa1cvZYWj/0zIzZSWRGRKAc46eO6wQSnAS2sbFJTKuNLd109PXxBIR6bUW75rgn1U0kYjQ8dp7uylprTA3VNERGRSSMeY0vUJbj8DZUtF0sJaS3OnN1NaUZTHTCcohVDGcY/po/+r544pLXKC0oN3quLw+dU88059zGMs3dwc8zWRsdAe8bClDKcSIdExpU6mFKDGtNBoFZSKiEh2SKUWaM3AxyOstTvH+w94NQ3XjTHmQGPMZcaYvxtjNhljrDGmK479PmmMedEY02aMaTDGPGSMeW+S1xAwxnzdGPOmMabTGLPDGHOXMWZhMscTSVRnbz+9/d7Cg8qifArzcvxzlTaMfglvf9DS1Rv0rItsdAShEt7rzz6Qsw6ew661JRy1ey0XHrGzZ5ulm1syfq0iiQiPJ4UomdJEy3dz86GoyrNqZq73Z17TwoiIyGSWSqb0RWAn4CBg+Nq7zPg+8OFEdjDGXA18A+gEHgMKgQ8AHzTGnGGtvTeBYxngDuBjQBPwT6AGOB04yRhzrLX2hUSuTyRR0RqghMeTzq4qYntEZ95NTaMflHb29vvWFTuZUoCK4jx+dvo+g8tvbWrm5iVrBpfX1XfQ3t3nC2hFxkq48y5EGVOaaKYUoGQqdA7NTzo3vw0ifr1buvqi7CQiIjI5pJIpfYHQ+NBDE9zPpHDOSM8RmlbmFGD6iCc15jhCAWk9sK+19jRr7fHAUUA/8EdjTNVwx3B8ilBAugrYw1r7MWvtMcAZQBFwmzFGd9CSUU0d3qDUGCgrDP3YzXI78DaOfgfejh7/jXRko6NY3PlLYWyCapFYIoPSlDOl4OvAO8PJlLZ3KygVEZHJK5Wg9CHg14SCskR8ATguhfMCYK29ylr7P9baB6212+LY5VsDH6+01g5es7X2OeBGoAL4dAKXED7eJZHnt9beAzwA7EqCmVyRRLmZ0vLCPAKB0HOf2VXecaVj0YHXbXIE0TOlrsK8HN/4ubEIqkViaese+t1LeZ5S8AWl0wLecdRtypSKiMgklnRQaq1921r7DWvtHxPc71Vr7dPJnjcZxphC4H0Di3dH2SS87pQ4j7czsJBQGfA/Uz2eSLLcTGnkVDDjISh1mxzlBAwFufH92RkP1y8SS+tAkGgIUorTziDR7rvg68BbY7yZ0jZlSkVEZBLLlknP9gAKgB3W2o1RXv/vwMd9orwWzb4DH9+y1kbrPvFfZzuRjHCbn1QWRwalY1++29njzZQW5+UQGo49MgWlMp6Fg8QyOgkYZ5azZMp3nQ681bYp6vlEREQmo2wZ8zh34GO0gBRrbbsxpgmoMsaUWWtbUzlexPq5MV73MMYsjfHSrvHsL9mrKcp0MGFuUNfY0TvqzYLa3aC0YOTS3TA3qN6koFTGkXA5ra90F5LMlHrLd6uCjZ5lBaUiIjKZJZ0pNcbskY4LSNdxRhDumjJcqqjd2TaV4yVyLJGk+caURgSls2LMVTqaOpwb6ZI4mhyFTSv3jind0dYdY0uR0dfSFfrdKzPu75SB/LLED+iU75b3KygVEZHskUr57lvGmNuMMXsls7MxZj9jzN+AN1O4hrhPN/DRxrFNuo4XN2vtomj/gNXpOL5MXm5QWhkRlI6HZkEdTqa0KI4mR2HVzrXXKyiVcaSlM0amtKAcAkm8tTpBaUlfIwGG5vhVoyMREZnMUglKf0yokc/rxpj/GmO+ZYw5yBiTF21jY0yBMeY9xpjLjTFvAq8AJxKa1iXTwuW4JcNsE64VbEvD8cLr4zmWSNKGa3QEYz8u050SJpFMaU1Jvme5rq0nxpYioy/8QKjctHtfSKZ0F3xBaYAg1Qw1O1KmVEREJrOkB5dZa39ojLkB+C7wSeAXhDKHvcaYtUAjoeCtHJgCzBs4nwGaCU0n81Nr7Y5UvoA4rR/4ODvai8aYEqASaIpjPOmIx4tYvz7G6yJp4cuUFvuD0tc2NA0ur60b3UxpKmNKa8q8mdLmzl56+oLkx9m9VySTwr97ZTgPepJpcgRQUgMmAHYoO1prmthhKwEFpSIiMrml1PHEWrsd+Jox5jLgTOBk4HBg9yibbwUWE5pC5U5rbVeUbTJlJdAN1BpjZkfpwHvAwMc34jze6wMf9zLG5EXpwJvo8USS4galbqZ011rvsOblW7zTTGSaW74bzxylYdVOphSgsaOHaeWFKV+XSKqGxpSmYY5SgEBOqANv29C021NNE8sGBomofFdERCaztKQcrLWd1tpbrbVnWGtnAtOAvYEjBj5Os9bOtNZ+3Fr7p1EOSLHWdgJPDix+LMom4XUPxnm8NcByoAg4KdXjiSRrpKB00Uxv1uatzc1Ym5ah0HFxGx0VJ1C+W1mcT8AZ6V2ncaUyTgyW77pjSpMt3wVfB95a0zT4uTvnr4iIyGSSkTo4a+0Oa+1Sa+2zAx9Ho0R3JFcPfPyeMWa38EpjzGHA54AW4H8jdzDGHGKMWWGMeWKY4/3cGDM1Yp+PAqcCa4D70nf5In7+oNSbXdxrljdr09rVx4aG0RtX6pbvliSQKc0JGKZoXKmMU4ONjtxMabLluwCl0z2LtTQPfq5MqYiITGYTdnCWMeYkY8zz4X8Dq/Mj1xljBrOY1tp/ERrHWg28Zoy5zxjzEPAfIA/4tLW2wTlNMbCA6POF/h9wL7AbsMIYc5cx5t/A3UAXcG6Usl6RtAkG7YiZ0hkVhb7AbunmZkZLp5PdKUogUwr4ugerA6+MB9ZaWmJmSpMs3wVfs6PITGmrxpSKiMgkNmGDUqAWODTiH4SaKEWuq43cwVr7deBThEpvPwC8F3gCONpae08iJ7fWBoEzgG8BmwmNp92bUKB6kLX22WS+KJF4tXb34Vbiuo2OjDG+Et7XN45eUJpKphSgutQbUNcrUyrjQFdvkJ7+UEMi/5jSzJTv9vQF6ekLIiIiMhml1OhoLFlrbwFuyeR+1tqnGGb+UmttP6Ey3qtjbSOSKc0d/kS8mykF2Gd2BYtX1Q0uvx7RjTfT3ClhigsS+5NTXeLNlNa1K1MqYy/c5AiiZEpTKd8t85bvTo0ISgHau/vIz/U3ABMREZnoJnKmVCSruaW7uQETtbvtvrMrPctvbmqmPzg6zY5S6b4L/kxpgzKlMg5E/u5lNFNKk2dZ08KIiMhkpaBUZIKKNkepMf7E/n5zKz3Lbd19rN7RlslLG9TRnVpQ6mZ+3a9ZZCx4glJ3ntK0jin1ltorKBURkclKQanIBNXU6c0alkcp3QWYWlbIrMoiz7rX1jdl6rI83GksShJsdKSgVMajluEypQXpC0pLTRfFDM2gpqBUREQmKwWlIhOUL1MaIygF2HeO90b5tY1Nmbgkn84Uy3cVlMp4FPlzmN55Sqf5VkU2O1JQKiIik1XGg1JjzHxjzHuMMXMzfS6RbNLUMfx0MJH2m1PpWR6rTGmijY7cr6lFQamMA+Gfw1z6KDZO861UGh0VlEJeiWdV5LhSzVUqIiKTVdJBqTFmmjHmTGPMe2O8frgxZgWwEngGWGOMeckYs1ey5xSRIW6ANlxQ6jY7Wrmtle6+/ugbp0l/0NLV653CItEpYdyS5BbdlMs40NwZ+jksdceTQmpjSgHKvNnSqcqUiohIFkglU3oe8FdggfuCMWY34BFgN0JTqjQMvHQg8IQxpiaF84oI/kxpZXHsqSIWzfLeKPcHLevrO2JsnR6dvf6gtyjF8t227j76+jVXo4ytcPmubzwppFa+C8M2O2pXUCoiIpNUKkHp0UAXcGeU164ASoD1wN7W2lqgGngAqAG+msJ5RQT/+MpYjY4ASgtymVrmnfPz3br2jFxXWEeUG+hUGx2BsqUy9sLzlPrGk5ocyCtO7eClbqa0cfDzVv3si4jIJJVKULoH8Iq11nNna4zJA04DLHCxtXYpgLW2CbgAaAeOT+G8IkJijY4Adq7xjlVbk+GgtL3HnyktLkgtUwpqdiRjL/wzWB5tjtIo0zIlxM2UMpQpjVZ9ICIiMhmkEpROJZQJdR0EFAGdwIORLwwEpi8SKusVkRQ0JTCmFGCXWico3ZHhTKnT5CgnYMjPSexPTmFeDvm53n3U7EjGWlNHaDomf+fdytQPXjrVsxjZfdf9nRIREZksUglKC4Bog2feM/Dxv9ba7iivbwNSrG8SETc4qywePiidO8UblG5ujtKkJY06okwHY5LIImlaGBlv6tsHglLjPNhJtckRQNl0z2Jko6OObmVKRURkckolKN0I7GuMcY/xPkKlu8/F2K8CqEvhvCLCULYmbKRM6YyKQs/y5qbMBqVuU5ZEx5OGlRd691NQKmOtMRyUkoGgdJhGR+6DHhERkckilaD038Bs4DvhFcaY9wAfGlh8MNpOwP6EAloRSVJvf9A3ZjPRoHRLcxfW2rRfW1hnlExpMpQplfGkP2gHS+d9Y0qLKlM/gVO+W00zAUIdp915f0VERCaLVILSXwDdwA+NMWuNMS8DTwM5wIvW2sXuDgNB6wzg+RTOK5L1oo2rrBihfHdGRZFnuaOnP6OdbN2gOdEmR2EKSmU8aeroIfwsxz+mNB2ZUm/5bo6xVNMC+B/0iIiITBZJB6XW2reB04F6YC5wAJAHLAc+EWO3rw98fCTZ84qIv8kRjJwpnVpe4Fu3tbkrbdfkcpuyFCdZvut+XeHpOETGQkP7UNl8RsaUltSAMyom3OwoWkdrERGRySCVTCnW2ocJBaQfBM4hNHfp3tbatTF2uQ34CPBEKucVyXZutrAwL0BB7vCZyMK8HKpL8j3rtrZkMijNTPmuuu/KWKqPDEozkSkN5EBJrWdVuNlRp8p3RURkkkoudRHBWtsF/CvObf+R6vlExN/kqKo4P8aWXrVlBZ6b6vq2aA2y06MjXY2OVL4r40hjxO9PhS9TWpmek5RMhbZtg4tTBsp31ehIREQmq5SDUpcxJgeYQqgDb6O1Vu+iImnW1JHYHKVh1aXe4LW+rSfGlqnzjSlVoyOZBIbPlFam5yTFVZ7FKtMKKCgVkfgFg5arHl3Bvf/dxPSKQv7fR/Zmr1lpqOYQyZCUynfDjDFzjDE/M8a8DnQBWwnNR9ptjHl94LW56TiXiECjE5TGmymtKfWOK61rz2CmNE1BqZspbelUCaOMncZMjykFKK72LFaZNiA0TjuTHbNFZPL4ywvr+P3T77K9tZs3NjZzwR9f8k3VJjKepByUGmO+DKwAvg3sTaj7rhn4FxhY921gpTHmq6meT0SilO+WxJkpLfEGpZnMlPoaHRWkp9GRMqUyljI+phR8QekUQpnSoIXuvmB6ziEik9qdL2/wLNe1dfPIW1vH6GpERpZSUGqMuRT4NVAI3AWcBswZWC4i1ATpI8A9QD5wjTHm8lTOKSLRynfjy5S65bt1GRxT2t7tzZSWJJspLfQGpa3qvitjKNx9N4d+So3TKCxjmdLWwc9VwisiI+ns6Wf5llbf+iXv1I3B1YjEJ+kxpcaYRcCVQCPwkWjzkgIbB/7db4w5CrgP+JEx5h/W2reSPbdItmv0NTqKL1NaM4pjSjt7vZnSoqQbHXn3a+kKlTAaY5K+NpFkhX/3ymn3v1hUmZ6TuJnSiKC0vbuPKSXxPYQSkez05qZm+oP+Uv83NzWPwdWIxCeVTOlXB/Y/N0ZA6mGt/Q+haWNygC+ncF6RrOeWsMY7ptRfvjvxMqX9QatskYyZ8IOcctPhfzFdmdKiKZ7FStoGP+/s1c++iAxv2ebowefqHW20aVypjFOpBKXHASustY/Eu8PAvKbLgfencF6RrOdmSivizJRWORmWpgyOz0zXmFI3KAVoUQmvjJGhTKkTlObkQ25hek5S7A1Kp6h8V0QSsLGxM+p6a2Hl1pZRvhqR+KQSlM4E3kxivzeBGSmcVyTrNbYnlyl1mwZ19PTTk6HGKb7uu3nJZUpLC/3BrDrwyliw1kZkSqN03k1XSbk7ppRWQrOs+ef/FRFxxQpKAdbURanyEBkHUglKe4CCEbfyKwCU5hBJgVu+WxlnpjTafKaZ6mbrC0oLkgtKcwKGMifLqmZHMhaaOnrp6Q89xMnYHKXgC0pzTXDwfMqUishINjXFDkrX1kUZDy8yDqQSlK4BDjPGxH2MgW0PA95N4bwiWa2nL+gbExJvo6PRDErd+dBKkmx0BFHmKlVQKmNgW+tQt92KTM1RCr7yXYDKgblK23uUKRWR4Q0XlK5RUCrjVCpB6T+BWuBbCexz8cA+D6ZwXpGsFi2IrIyzfDc/N0Cx03CouTP9HXj7g9Y3n6J73kSUOSW8Kt+VsbCtZagxmK/7bmF5+k6UV+wbnxqeq7RTmVIRGUZnT//g1FXRKCiV8SqVoPRaoAX4qTHmcmNMzDtOY0yOMeY7wE+BZkJzm4pIEpo6/G820TKgsbjbZiJT6jY5guQbHYG/2ZEypTIWtrUMZUqrTJv3xSJ/djNpxsScq7RdQamIDGOk+cfX1rdjrX+6GJGxlvRdorW23hhzBqGs55XAF4wxdwGvANsBA0wFDgQ+BswC+oCzrLX1qV64SLZq7PAGZGUFueTlxP98qaIojy3NQzfXTR2ZCEr9N87JTgkDUeYqzWDXYJFYtkcEpZHTtABRS25TUjwFWjYNLg5lSlUlICKxjRSUdvT0s6O1m6nlaeoWLpImyacuAGvtv4wxRwK3AnsAX4+yWbgd4UrgAmvtC6mcUyTbuZnSeKeDGdx+VDKl/qC0KJWg1Jcp1Y25jL7trUM3e5HTtADpzZRGOV7lwPnU6EhEhhPuEB42vbyQxo4ez5CatfUdCkpl3EkpKAWw1r4ELDTGnACcCOwHVBMKRuuA14GHgYes6gVEUuZmNuOdDibM7dSbiaDUbXKUGzDkJ5DNdbmNjtR9V8ZCZPlupVu+65Tbpsw5XrWCUhGJQ327N1NaW1ZAWWEuq7YP/c1aW9/OITun+UGaSIpSDkrDrLUPEwo+RSSDGp1MabzTwYS5mdLRKN8tys/BpDCHY7kaHck4ENnoqAonU5qJ8t0IFQONlaKN1xYRCatzMqXVpfnkBgKeoHR9veYqlfEnbUGpiIwOt6tedUlimVI3KM3E+Ew3U+rOM5qoMjU6knHA2+jILd+tSu/JnClmygemoFGjIxEZjjumtLqkwDdt3Np6deCV8SdtQakx5r3AscCeQBVggQZgGfBvjSUVSY8d7htOaUFC+7vTxzRlICh151EtSTEoVaMjGWvdff1sHQxKLZXulDDpLt91g1JCmQ1NCSMiw3HHlNaU5jN7SrFn3TplSmUcSjkoNcbsA/wfsH94lbOJHdjuReAz1tplqZ5TJJu5bzjVpYllSt3xmZkYU5r2oFSNjmSMbW7qItwVoYxO8owTHKa7fLew0rNYbkI3kW4VgohIJPc9vaokn52qvUFpeFqYVIbViKRbSneKxpiDgSeBEqCd0JjS1wg1ODJADaHGR8cDhwLPGWOOsda+msp5RbKZ28SgpiSxTKl/TGnsSbaT5d44l6acKc18ybHIcDY2DmUWfKW7kP7uu75MaSgz29WrTKmIxOYGpeWFecybUuJZ19rVR1NHL1UJDv8RyaSk7xSNMTnAbYQC0v8FvmWtbYmxbTlwNfBp4HZjzCJrbTDatiIyvJQzpU7ToNYMZB3dTGnKQWmh2323T095Ja2stTy3up7XNzazaGY5R+5W4/n52tDQOfi5r8lRTgHke2/6UhZjTKm674rIcNyeC+VFucysLCQ3YOgLDk2Csba+XUGpjCup3Cl+GJgP3GGtvWi4DQeC1QuNMWXAx4BTgPtTOLdIVrLWRhkvklim1Jd1zEDTIDdTmu4xpT39Qbr7ghTmJT/3qUhYMGj59t1vcM9/Nw6uO2XfmVz78f3ICYQC0w2eTKk7HcwUSPcDkqhjSq2CUhEZltudvqIoj9ycAHOmFLOmbmgs/Lr6Dvafm+YGbSIpSH7iwFBgGQS+k8A+lw98/HAK502KMeYYY4yN498P4jze2hGOs0emvybJPi1dffT0e4sMEs+UeoPSrt4gPX3pLVzwZ0pTCx7d7rugEl5Jn1ufW+sJSAH+8fpmblr87uDyhoaIoNTNlKa7dBd8QWmB6aOAXjpVvisiMVhrfe+N4ff8eVHGlYqMJ6mkLw4EVlpr18S7g7X2XWPMioF9R9tW4NYYr+UA5w58vjjB48Y6ZnOCxxEZUb3TeRdC7d4T4ZbvArR29SbcxXc4bd3eG+dUM6VlUa65pauXqeWFKR1XpKOnj2v/tSrqa799YhVnHzqX8sI8b1AaLVOabk6jIwiNK23t0c+8TH4vr23gP6vq2Km6mI/sP0tDNeLU3Rf0PbgO95HYqboE2DG4fm2dglIZX1K5U5wBLEliv7eBI1I4b1KstSuAC6K9Zow5gVBQugF4OsHjRj2mSCbUO3OUluTnUJSfWBbSLd+FUAY2nUFpust383ICFOfneEoXmzvVhVRS9/CbW2N2oG7v6ee+Vzdx3nvm8U7ExPOVbqOjjASlFb5VFaadHb1VBIOWQEA36TI5PbliG5++5eXB5Tc3NfM/pywawyuaOKL9LSsfDEq9mdKV29p824qMpVTKdytILhvYApSncN5MCGdJb1MDJhnP3ExpMoFkQW6A/Bzvr35rmseVuuW70TKdifI3O1L5rqTujpc3DPv67S+sZ2NjJ+0RD0SmjEb5bk4u5Jd6VoXnKu3qUwmvTF6/fPRtz/Ktz65lS3NnjK0lUrRhLeH334UzvQ+6Vm1rVTdvGVdSCUpzCY0pTVSQNMyPmi7GmBKGxrj+ZSyvRWQkO1rdoDTxznnGGF+Q6DZGSFWb09G3JD8NQanT7EhzlUqqdrR28+KaBs+6Txwy17O8Ymsr9766ybOuNtcpeyuuzsj1qQOvZJutzV0s2+KdyCFo4ckV28foiiYWN1Nakp9D3sBD6IUzvfmgvqDl7W1RprcSGSOpBKWTxUcJTWvzqrV2aaI7G2O+bYy50Rjza2PMZ40xtem/RJGQrS1dnuVpZcmNL3NLeNOddWzvSW/5LvibHanRkaTq6bd3eJZLC3L5n1MWMquyyLP+6se9mZtZeR2e5YyU70KMDrzQqaBUJqk3NjZFXb90c9QZB8Xhnw5m6H2ztCCXXWq8U1e9tUnfVxk/Ur1TPN8Yc35armTshEt3/5zk/j93lq8xxnzVWvu/8R7AGBMrGN41yWuSSWprszdTOr0iyaDUzZSmOyhN8zylkPlrlvGnrz/Idf9+h6ff3sHCGeV88wO7p3Xs81MrvdmXw+dXU5iXw0cPmMVvn3wn5n7Tc5oh8sevdFrarsnDlykNBaXKlMpk9c6O6OMcl29R8BSPaNPBRFo4s5x3IxocvbGxibMP9VaHiIyVVDOlJsl/44IxZjrwPqAf+GuCuz9AKMs6DygG9gKuBgqAm40xp6XvSkVCtjmZ0mSDUn/WMc3lu75GR6nPJ+qbX1WNjia9K/+5nGv/tYpX1zdx2wvrOesPz9OdpvGUwaBlyTt1nnXHLJgKwEf2nzXsvuX9Td4VpVPTck0+TgfeCsLlu/rZl8npnRjNd1ZubSUYtKN8NROPW77r9mLYb06lZ3nxqjqs1fdVxoekg1JrbSCFf+NlxvuzCU0H87i1dmsiO1prv2qtvddau95a22mtXWqt/RbwxYFNrkrgWIui/QNWJ3JNMvm55bvTk5wSxR2fmc7y3b7+IF293uHmmWh0pEzp5LatpYs/PbfWs27V9jb+9uLwjYni9c6ONpo6vD9DR+0eGn2xS20p+8+tjLpfPr3k9zo9/koyFZRGH1OquUplsoqVKe3o6fe9/4mfb45S52Hukbt5R5htaupkxVaNK5XxIdvHlKZauhvNzcB2YHdjzM5pPK4I25qdMaVJBqVlBW6Al77MS3u3/4Y5HWNK/YG0skWT2ePLthEtMfL7p1fT1596k/SX1zZ6lmdWFHrGkp5x4Jyo+52wU5RnqhnLlGpMqWSXyPmAXRsb1YF3JL5MqfO+ufu0UmY4FVZ3vbwx49clEo+sDUqNMXsC+wNtwH3pOu7AlDLhDOeMdB1XpK27j1anLDbpMaVuJ9s0Ng1qi1JaOJEbHalkbGy8tqEp6vrNzV08sWI71lqWbm7mkbe20tyR+M/Cy+u8XXcP3MnbrOhjB872zesH8PGFzpjWQB4UVSV8/rio+65kke6+fhqH+V3e2Bg7YJUQX6Mj533TGMOH9/MOT/jri+t9Q4NExkLWBqXAeQMf/26tTfdfuvAdimYmlrTZ2ux/00i6fNdXCpu+rKM7HQykaUqYUS7frWvr5uybnmeX7zzEqdctYZm6P46qNzfGngb7/5as4fN/eYWTfrOEz//lFY771VOsTLAE7ZV13kzpwTt5A8v83ADXnX0AU8uGgtBPHb4Th011fr5Lp4LJUKsEZUoli2xv6R72dWVKR+ZmSt1GRwCfOGQOgYg/WZ29/VzjdBgXGQtZGZQaYwyh8aSQ3tJdjDGLgAVAB7AinceW7OY+ySwvzKUoP7nh2b55StMY4LlvimUFueQEUr9pz2R2N5pv3PEaz66uB+CNjc2c/8cXfQ2cJDM6e/pZtT12kPnCmgYeXbptcLm+vYeL73o97oYd21u7WFfvfRZ54Dx/tnOvWRU8/e1jufXTh/Do14/if05ZhGl35kvMVOkuQFGlZ7HCqNGRTF7bW4fP1ilTOjK3AaA7phRgXnUJZx7kHZ5w58sbNGepjLmsDEqBIwl1zd0MPBlrI2PMl40xK4wxP3XWf8gYc2CU7fcB7iLUYfhma21Pei9bstkm5ynxjIqiGFuOzN/JNnNBabQ3xWRkMrvremVdA4tXeTuz7mjt5t5XN2XsnDJkQ2OHbzxpYd7wb1dvbmqOey7DV5zxpKUFuewxvTzqtkX5ORy9ey0LppeFVrR55zbNWJMjgIIyz2Ipob8Bnb2pj6kVGW/cKc9cW6JUC4mX+4A5WqYU4Jsf2J2ivKGH2kELv35iVUavTWQk2RqUhhsc3TYwBjSWGkJZT3ds6GHAy8aYtcaYJ4wxfzPGvAC8AuwJPA1cnu6Lluy2rqHdszw3yni3eLkBXjqbBsVTPpQMN7hNZ8dg1z9e3xJ1/dMrd0RdL+m13sliTi0r4FOHj9w37hlnipdY3PGq+8+tjD+b37bNu5zJTKkblJrQTXmnMqUyCY00rnGk8l6JNiVM9KEzU8sLuehI79/UR9/aynaNLZUxlHVBqTGmAPjYwOJfkjzMo8D/AS3AvsDpwHxgCXAR8L4MjFOVLLfWuVGfNyX5oHQ0y3fTFZS619zVG0zbnJWuxauiB5/Pv1ufls6vMrz1TgfOuVOK+fThO1NVPPzPUqzmSK63NnvHq+47uzL+i2t1HlhkMijNj54pVaMjmYy2OeW77u/7SOW94q96Gu799zNH7uKpQOkLWh58I/oDWZHRkHVBqbW221o7xVprrLVvjLDtFQPbXeCsf85a+xlr7T7W2hprbZ61ttpae6y19mZrre4YJO3c7NG8VDKlzhtVW3df2rrMNnd4q9bTlikt9B8nE9PC7GjtZvWO9qivtXX3sWq7+pdlmhuUzplSTG1ZAX/+zKHsUlsChG5YD9nZ2zE3nqA01LXXW+a716zopbtRtWz2LpfPir5dOjiZ0mLTTQ79dGieUpmE3Ezo3s7DosaOXnr69FAwlmDQ+jr0Dzd8pqIojxP39hYCPvLW1oxcm0g8si4oFZmIrLWsrfcGSvOqS5I+nvtGZW30qVySMVqZUshMs6MVW4cfl6hmEJnnNjSZUxUaP73XrAoe/8bRvPjd9/Hc5e/jytP28my3pblrxKz/pqZOmpxpJxbNrIixdRQtzrjiUQxKAUroUvddmZTcDvN7R3lYtKNNJbyxtHb34fZ6G6mnw4l7eYPSl9c1qKGfjBkFpSITQFNHry8rmEqmNJMBni8oHaHkMl6FeTkU5Hr/ZGWi2dFIU4us2qZMaabtaPXeeM6oHGrqlRMwTC0rpDAvh3nVxb7ZWNyKAtdbm7wPHSqK8phdFWfTsL4eaHO671ZkMigt9a0qpVPdd2VScgPO3aaWke/8zdeYx9iivYeP9FD48Pk15OUM/RENWv90WSKjRUGpyATgZklzAoaZlcl33y3Nz/XdzKerFDZTmVLIbNfgsOVbhg9KlSnNPDcorS0tiLpdQW4OM50u1O5UL65lznjSRTPLMfHOM9q6BXBTERkMSvOjBKWmU913ZVJqcoZ+TCnJ9/3ub29VpjQW9703J2AoGWHauKL8HPZxyqRfXFOf7ksTiYuCUpEJwA2E5k4pJi8n+V/fQMBQVpCZeT8zGpQ6Gd5MjClduc2bSVs4w1tCNlLQI6mx1voyJrVl0YNS8FcMuF2qXW8540kXzUxhPGluERT55zdNm0COLzAtpVPdd2XSsdbS6JTVVxXnM7VcQWm83KEL5YW5cT1wc8fmv7imIa3XJRIvBaUiE4Cbvdtjun+sWaLKMjTvZ327/2l3uvivOb2Z0v6g9ZXnvn/hNM/yxsYOrDtwR9KmubOX3n7v97dm2KDUO7Z6pPLdpU6mdK9ZKYwnrZiFr+Qg3dyg1HSq+65MOi1dffQ7zfaqSvKY6vzu71D5bkzug+V45wg/ZCdvUPrmpmbf/4XIaFBQKjIBLN/ize7sOSOB7E4MmZr3s77NG5TWxCi9TEamy3e3tnTR7XR3PHZBrWe5vafflw2W9KmL0sikpjT2g41ZlYWe5a3D3LQ2tPewzenwmVimdBSbHIW5c5XSqUZHMum4pbswkCkt8/5+K1MaW0un03k3Ssf6aNwHc129Qd+QIZHRoKBUZJzr7Q/y5iZvdictQak7V2kaAq2u3n5f577qYQKKRPmuOc2Z0g3OVCRFeTnsNauCnIA3G7axsTOt55Uh7k1nRVEeBbmxx0VNLXduWlti37Sucsrg83MC7JRIF+vmjd7lMQhKS0yXMqUy6bilu/k5AYrzc3yZUgWlsSU7dKa2rMA3RGLZ5uG70ItkgoJSkXHurU3NvpvQA+ZWpnzcTJTvug1qINOZ0vSOrXOD0jlTisjLCTDdCXzc7SR9fE2OhindBZjmBqWtsTOl7vyzO9eUkJvI2OyGd73LVTvFv2+ynA68ZXTSqXlKZZJpdIZ9VJXkYYyJMqY0+u+3hlREGVNa5O+yH4v7oNutzhIZDQpKRca5JavqPMu7TS2lOg2BnvuGlY7yXXc8aX5OwJfdTIVbjtSU5jJaNwM6pyrURGeW0+l4S7PGNWVKvJ13w6Y5N611bT309kfvTvvOdu944flTne621kJwmM629au9y1N2Gfba0qLAe7Oo8l2ZjBqd8t2q4lCFjVu+65bfv7ujjZN/u5jdvvsw5/3vC77gNpuk0mTQbeinoFTGgoJSkXHMWss/3vB2/HzvrtVpObYb4KUj61jnBBQ1pfnxT7cRhykl3mtO9w3IhkZvBjQ8f2WtE/hoAvfMqXPHJI+UKXVuWiF6xh7gnR3eoHTXcFBqLTz3O/jFfPhxNfz1bGh2xo/290LTeu+66lEISp1GRyWmk57+IH0xAm+Ricgt360cmN/arZSob+sebMITDFo+/5dXeGtTC31By+JVdXz3vjdH54LHIV+jozjHlALsOcM7TOBtzcctY0BBqcg4du+rm3xvDiftMzMtx87E+Ew3WEtHRjfSlBLnBiXNQenGBidTOiWUKfWNaxpm3KKkxg0oh2tyBKGb13ynBHdbjGZHq2NlSp+8Eh79DnTUgQ3Cyn/CH0+AzqahjZvWg3UylKOSKfXeLJYR+hntUAmvTCLR5igFfOW7QRsKTAGeX1Pve3985K2tNGRpttQdghNv913wV41sbu6kS39jZJQpKBUZp/7x+mYuufsNz7o5U4o4aF565kX0d99NPVO60ck0zqjwZ7FSUe1ML9PQnt7g0J8pDQel8Y9blNQ0dzo3p8XDB6XGGF82JVozlPbuPjY1eR867FpbAptegcW/8h+4aR3863+Glt3S3aIpmZ2jNMztvmtCX4NKeGUycQPJyoHf++qSApw+c4O/38+84x3aAqGg9YV36zNzkeOcW76bSFDqNnyzFnXglVGnoFRkHHp06Va+9rdX6XPmCvvmB3Yn4L5DJ6ksA5nSDU6mce5ApjFd3DlPG9p70tbgoq8/6Muwhct3fXPlqQNkxvjGRRWPfGPljivdHiVT+q7T5MgY2LW2FJ7+ORDjZ+jV26BlS+hzt8nRaGRJwdfoqDScKVVQKpNIk1O+WzXwe58T8D90Cv+dfnltY9RjLcvS8ZD+8t34+zmUFOT6Gvqt2aGgVEaXglKRcWZrcxffvOM13LmrzzxoNqftl74pKPxjSlMPStf7utdmNijt7be0dqenA299e4/vex7u7OrvAKmgNFOSadbhduCNNlfpGuep/6zKIgrbN8Hbj8Q+cLAXXvxD6PPty7yvVe864nWlhdPoqMSEvjZlSmUyidXoCPAFS1tbuugPWl7f2BT1WNk6nUkqjY4Adqn1ZkvfrVNQKqNLQanIOPOrx1bS7txwnn/YPH720X3S2jRoNMp3050pjTbnaUNbesYPueNEcwJmsFzYLd9taO+hp0+NZjIhmRI0N5NS1+r/mdjUGCWL//od3o0KK2DvM73rXv1zqMnRtre866ftNeJ1pUWMMaWdvemdDklkLLnlu56g1BkGsrW5i3X17XT1Rv8bnK2dY1MNSneucYJSZUpllCkoFRlHGtt7uP91b7fdD+83kytOXZS2st2waOW7qZTC1rd1+zqnzplSFGPr5BTn51KY5/2zVZ+mcaVu6W5tacHg9zzaXJl16sCbEW4X6HhurNy5cKP932xq8j4wmVVZBG84Qelep8NRF3vXte+AFf+EbU6mdPooBaX5bvlu6OtQ+a5MJr7y3YhO675MaXPXsN1ht7R00d2XXb8fXb39dDsPShMZUwr+oFRjSmW0KSgVGUfu+e9GTwYuPyfA905amNYMaZhbvtvbb31vaol4dX2TZ7k4P8fXPCEdqt0OvOnKlDoluZElu1XFeeTlmGG3l9T19AXpdDo+pi0odTKliwp3QP0q70b7fgJqF8CcQ73rH/0O9Hn3Z9reI15XWjiZ0nD5roJSmSystcOX71Z4H25ubeli1bbWYY4Hm5uyqxldtJ4QiWZK3comt/JJJNMUlIqMIw84WdLj95oeNUuXDtGeoiYzrtRayyvrGrj2ibc96/eZXUFuTvr/xERrdpQObkfdyJJdYwy1pSM305HUuOVnEG9Q6v2ZcDP2gK/z7n5dL3k3KJ0Osw8Ofb7/ed7XWpw5S6t2gtLaEa8rLdzuu6j7rkwunVGyfN6g1Pu3d2tzF29vH34ezQ0N2RVQRXvvTjQoDXebD9ve2p11GWcZWwpKRcaJ7S1dvLGx2bPuzIPmZOx8bvkuJN6Bd0NDByf+Zgmn3/Acb23yjuM5YG5mpstwg9J0zVW6rSV2phSijFtMU4ZWhiQdlLrdkdu6PaXo1lo2OpnSnRqe8R5kt/eHWvICLPqIr2zWu/ORI15T2jhBaYHpI59eX0ZZZKJq7PD/3nsbHTmZ0ubhM6Xgn95rsnP/dhbn55CX4EPhWVXe77O1sCXLMs4ythSUiowT/1653bNcVpDLITtPydj58nICFOXleNa5k28Ppz9ouehPL8dsKnH8XtOjru/u76ajN/kbBrfZUboypTt8mVJvoOOWiNZrTGnaJXtj5Waxe/qCnq7MTR29nnLXIrqo2PaC9yC7fXDo84LSUGAayy7HjHhNaRMlOC6hU+W7Mmk0On/DcwLG89DUbXTU2t3Hiq3eoNR9WOk+hJrsUm1yFN7HfVjtVpiIZJKCUpFx4um3d3iWj9q9lvzczP6Klhc5zY4SKN998I3NvhuDsA8snMY+syt96+9++26OvuNoDr39UL7/zPfp7k88sKvOWPmu91rcaUbcYFiNjtLP/fmL98YqWol75Fyy7o3VwYG3McGIn5tArj/QPOD86CcrrIAFJ8Z1XWlRECUoNV109qj7rkwO7njSyqI8T2M/t9FRNMcs8JbTu43rJju3QZzbMyJesyq92VKNK5XRpKBUZByw1vLimgbPuqN3z/yYtTJ3rtIEMqV3v7IxyvFyOePA2Vx95r6+197Y8QY/fO6HtPeGOvrd9859XP/a9QleMUxxGx2lrXw3sUypynfTL9mn/YV5OZQWeB+w1EUEpW7W5Niid7wHmHlAKNiMNOfg6MHnQZ+B/PROdTSsPH+zsFK6lCmVScMt360s9v7eF+Xn+B5GRppaVsDu07xl7u4UX5NdOjKl4B9X6jaIE8kkBaUi48Da+g5fkJPJ0t2wcqdUpzXOMaXNnb08t7res+43n9ifN6/4EL84Y19fsAtwz6p7fOvuWnkXnW5X0xH4M6Wp33z0B63v++/OTRpPh1dJTTJzlIYN1+zIzZQeEljh3Xnee6Mf9MO/G2p+BLDgJDj60rivKS0CAV8Jr8p3ZTJpGqbzbthONbE7uS+YXsY0pweA27huskvlb2ek2VVuplRBqYwef6cTERl1LzlZ0tqyAuZVZz4b475xuSVAsTz/bj19waFGMvm5AY7bY2rM7a21LNm0xLe+tbeVf6//NyfuEn85pDt2qK419YxlfXs3/UHvHK3uTY7bTEdBafr5bqwSKEGrKS1gbf1QqVnk/0/k0/58etm9b6V353mHRz9o8RT4zONQtwpyC6BqXtzXk1b5pdAz1G201HSp+65MGo3tbqY0SlBaXcIr6xqj7r/b1DLfQ0S3cd1kl75MqROUakypjCJlSkXGgRfXeoPSQ3aakpG5SV3+8t34MqWvbWjyLB80r8pXPhlpbctatndsj/raM5ufibo+Frcr7o62boJOQJkot9QrYKDayYzWuMGwynfTLpUbq+Ey2ZuahoLVfcxq8mzkeQzMOST2gY2B2t3HLiAF37jSEjrVfVcmDf8cpf7f+51rYj+kXTC91PcQsbmzl64s+h1xx+O7/SLi5Y4pVfmujCYFpSJx6A9a7nllIxff9Tq/f3p12t/sXnaC0oN3ysx0Kq5ky3dfd4LS/eZUDrv9qsZVMV97bvNznuk7RuI2vegPWupSLOHd4TQ5qi4tICfgfSjgZkqbO3vpcebWk9SkFJSWxW5EFVm+e1DAO58u0/aCosr4L3IsuOW7RmNKZfJwf++roowfnT819hRNe84oZ2qUZkju3/XJLFNjSrc0d9LXr/c5GR0q3xUZgbWWr/71Vf755pbBdf98cwt/veg9lAyTHYxXc2evp+wQ4KCdMj+eFKJkSuMo3+0PWt98qvuOEJSubl4d87UdnTtY3bSa+VXzRzw3DAWMkeW225q7feVbiXCbHLlP3cGfiYNQ5193ugJJXjozpTsiyrojx0XtFVjj3XHOwYx7zlylpXSyuVfdd2Vy8HXfjZIpPXBe9PfE0oJcFs4oJydgKMwL0NU7FEBta+lizpRRbEo2htIVlM6o9L6fBW2oKkjvczIalCkVGcEDr2/2BKQAb2xs5lePvR1jj8Qs2+yd5zMvx/g6CWaKb0qYODKl7+5oo63be0O8/whB6btN7w77+gtbXxj29Ug5AeOblzLV9v/udDDRAtzKojxf9jTWuNInlm/jf+5/i/9bsiarSshS5b+xiv+hT6zy3fbuPpoiunvubZygdOb+CV7lGHAypcV0K1Mqk4av+26RP1NaW1bAzlGaHR28UxW5OQGMMb5pvNy/65NZuoLSKcX55OV43+e2Ztn0OjJ2FJSKjODmxWuirv/z82vZnoY/1ks3e7OOC6aXZXx+0jC3kUxrHFPCvLO9zbM8tawgaulUpDXN0b+HYS9ueXHE80aa5jy1TfVN0+3U6E4HAxAIGF+TpR1RgtI/PrOGz9z6Mrc+t44fPbiMC2992ddESaLzzVMaJWMSS6ygNLJ0t4I25gWcsc0z9kvsIseCM6a01HSq0ZFMGv7uu9F/749d4G+md8JeMwY/d/9uj8ZcpV29/fzmiVVcds8bPPtOXcbPF4v73p1sUBoIGN9D2a3NGlcqo0NBqcgw1td38Oam5qiv9fZb7n11U8rnWOpkShfNqIixZfqVOWNK3aAgmnfr2j3Lu9bGHusTtrVjq2f5lF1O8Sy/vO1lgjb+cSvTy9N78+F2aowVZLuBT73T7KixvYerHvFON7LknToefGNzSteXLVJ52l8bZUyptdbTqMNXuptTAFP3TPxCR5tvSpguNTqSSaOx3S3fjT4n6ReP3ZVdaoeypQfMreQjB8waXHb/bo9GpvRrf3uVqx9/m7+9tIGzb36Bf6+M3tAv02JNCdP8jwfZ8OUvs/VHP6avvj7arj5uqe7WZmVKZXQoKBUZxtOrdgz7+v2vpR5svOUEvXvNKk/5mPFyp4SJJ1O6xglKd66NPX8cQGdfJ83d3q/xlF29QWlLTwsrG5xpOobhlmmlv3zXnymFaHNhevd7bNlWz5imsDte2pDS9WWLdI4p7eoN0t7T75nSwFe6O20R5CSXURhVbvddo3lKZXLoD1panPedqpLov5M1pQXc/fn3ctXpe/OrM/blb589jLycodvYab5pYTIbTL2zvY1Hl27zrLvhqdj9EzKlrz/oG1JTUZRH8/33s/nb36btX0/QePvtrL/gUwR7Ru4a7zYT3Jpl0+vI2FFQKjKMF971Pll0G+As29KSUoe/zp5+Vu/wlsMumjV6mVK3fDeeMaVuULrLMJOaA2xt3+pbt2/tvswtm+tZ9+JWbwlvQ1dDzOypG5Sm+qa5o2Xk8l2IUiLq/N8/8pb/awV4dnW95jUdQW9/0BdopRKUQuj/JzJTuiiw1rvBRBhPCpDvNjrSPKUyObgPogCqYmRKITRP9ccPnsvpB872DXNxpwtzp/pKt2eilOu+vLbBFyBmmhvUQ+hvZ93v/+BZ171qFS3/fGjE46X7oa9IvBSUigxj2RZvae2Xj53vm49z8QjZ1OG8s72NyOGGAQN7Th/FTKlTvtvR00/vCO3ffZnSEYLSbR3eJ8ll+WUU5xVz8HRv19NwULq8fjkff/DjHH3H0Rx1x1E8suYR3zHdJ7nbUigvCgatL1PqvimHuZnS+oiys2DQ8vLa6JO7A7y4piHmaxL95tTN5A+npCCX4vwcz7q6tm7PmNLdjFNuP32vxC5yrOR7f8dK6KSjpy+hqZRExiO38y5E774bD/ehcaaDqedW+8thg9Zf/ZRpDVGmRCvesp6ed/0NBlv/9a8RjzdD5bsyRhSUisTQ0dPnC8D2mV3JYbtWe9b95+3kg9I19d7jz6oqosi5sc6kaDf9bcOU8DZ19NDgjP8ZKSh1M6XTS6YDcMj0QzzrX9zyIisbVvKZxz7DsvplADR3N3Pp4kt5q+4t7zGcN81trcm/aTZ29NDnNCJyn7iHVcdopgOwekcbrcM8IX/+3fjG82SraEFpos06ojU72tgYmm4ph352Nt4u2tTukdhFjhVfo6Mugha6NU+uTHBuk6Pi/BwKcpN7D/RX0GQ2mFq+tSXqeve+IdMa2r1/O8sKcul9MXrzwI4XXsD2DZ/JdRsJKlMqo0VBqUgMK7e2Yp0s5oLpZRy1e61nu5fXxc6OjWSt8+a1U/XwAd5IWntaeWTNIzy/5fm4sihu+S4MX8LrNjnKDZgR54HzBaXFoaD0sJmHkWuGMrVd/V187B8fo7Wn1bN90Aa56Y2bPOvcJ+JNHb1JT73iZkmNiV4KCtHmwhza97UNTcOe5/URXs92bpOtwrxAwjenbiZ7R1vPYPnubLODAuPcjNUsSPxCx0KURkeAphuSCa+x3Z0OJvkx3m5Q2trVR0dPZkppe/qCbGjoiPra6Ael3vewqpJ8ulauiLptsK2NruXLhz2ef0xpl6oyZFQoKBWJYfkWb3C0c00JhXk5HLxTlWf9xsbOpMcLukHpSFnH4axvWc9HH/go3/7Pt7nosYv4ypNfoTc4/BjRwrwAuc7cmy2dsd/E1+zwXu/cKcWeRhPRuEHptJJpAFQVVnHErCOG3Tds8abFtPUMjb2NVl6bbImR+xS4uiQ/5tfkb3Q09JR/5Vbvz0uZU+a9YmsrfSOURmezdMyz5z402NLUOfjQYb5buls0BUq8VQ/jVoF3TGmJCQXaanYkE51bvhur82483GAK/J3VtzR3cuuza3lyxTaCKUzVtb6hg1i7v7tjdIPSeqd6aUpJPt0rY8+j3rUssaC0o6c/6rhVkXRTUCoSw0qnNGfPGaGxnrtNLaPEKbF9bX1TUudwy3fnpZAp/dHzP/IEgE9vfJo7Vtwx7D7GGN/4nYYoY3zCEh1PCv4xpeFMKcCp808dcX+A3mAvz2x+ZnC5rDDPF/RtTnIuNbcZhjtHWyQ36Glo7x68sXEbVp2630zPcndfkLX1o3uzMpGkJSh1GlS9vrFp8PP5xumUXTtBsqTgy5SWDmRKFZTKROf+3sfqvBuPkoJc3/tC5MPKx5Zu5ehfPMX/PLCUT9/yMt+487WkM4DuA2XPa6P8d96dUmdKcS7dq1bF3L5rxfBBabThKyrhldGgoFQkhnVOac78qaEbw5yAYe/Z3g65I5VuxjxHvfccO9cMXwob8zgt63hhywu+9bcuu5W+4PBPOIcrSXUlE5TWd3rHUk4tHpoA/dg5x7Jb1W6+fXIDuZTlebNDr+943bM8s7LIs7y5KT2ZUrc0OJL7vQraoSf9q52n44fsPMXXxXeZk32XIW75bjoypf9d1zT4uS9TWrN7wscfM+6UMHQCVh14ZcJLZ6YUYo+H7Ojp47K/v0lPxDjs+1/bzD/fdMaZx2l9jNJdSK3xXjLcTOmc/jZsV+xr6F4evbQ3rDAvhyrnYbWaHcloUFAqEoP7pjM3YuzkfnO8JbyRGZl4NXf2+poGJTumdPHGxVHXb23fyhs73hh239qy+INSd0zpLrWlMbYcUt/lDUqri4ZKJnMDufz0iJ9Snj/UcTg/kM+Vh1/JeYvO8+znfh0zK703H1uaksuUuk2SYnXeBagu9d8w1bX10NXbz4ZG78/LrrWlLJzp7aS8fEv0xhiSnkxprfP/0xkx5nJ+YPJkSnOMpZCejI2XExktjR1OpjTJzrth0cZDAjy+bJvv/RbgxqdXJ5UtHa65Xmt3H+2jOC2Mmymd2dU07PZdb7+N7R/+gdb0Cu9D30w3jRIBBaUiUQWDlo2N3iAnMijd18mUvrmpOeE3Nrf8JyeOpkGxRJa2up7e+PSw+8YblAaDljV13hLVkTKl1loau7yNoKoKvAH9gikLuP+0+7nk4Ev4+gFf5/7T7uekXU5i35p9Pdstr1/uyfrOcDOl6SrfHSYozcsJ+Mqd69u6WVvfjvvfv3NNyWDJd9jbW5UpjcUNSj2doYPxjcWN1aAKYBe3fHeiNDkC35hSCJXwdqjRkUxwbvfd4eYojUesOTYffCN6RvStTS2+qd/iMdIcqKNZ7upmSqe2eR8E51R471dsRwc969cPe8zp7vQ6ypTKKMiqoNQY85Qxxg7z7/gEj1dpjLnWGLPOGNM98PHXxpjKDH0JMkq2t3Z7ynzAG5TuNcv7R76po9czH2I83HEns6uKRmwaFI21lqV1S2O+/p+N/xl2f19QGqNp09aWLrp6vd+TXWqHD0rbett8zZamFE3xbVdTVMN5C8/jM3t/htllswHYs3pPzzY9wR42tm4cXJ7lBKWbki3f9c1RGjuwgVAjpEg72rp5Z7s3WJ9ZUUhJQS67OplkdwyxDImaKe1qhr9/Fn4yDa7dB964c9hjuGNKw8ppp8I45XZTdk7pekdVvr8iodh00aXyXZng3O67yVRIRIo2V2kwaHk+ypyiYfe9uinma7GMFHSOZmbRzQBXNdd5lgv325ecam9Tt+63Y485Bf+0a+n4erp6+2nq6FEnX4kpq4LSCPcAt0b5F/dfJmNMNfAi8DWgD7gPaAW+Crw08LpMUG7pbkFuwBO8za4q8mXMEp0w2x2fmWzp7vaO7TR2x56W5p2md3wdcCPV+saURn/zca+3OD/HN2bS1dDV4FvnZkpjqSqsYkqhN4Bd3bx68PN0le9ud8eUDtPoCKLNhdnD6u3e782uA+OP3UzyhoYOdeCNwZcpLcyDB78Jb9wB/T3QtA7+fhGseCjmMdyf5bBZps5ZY6BidqqXPHryisB4365L6VSjIxmXgkHLn55by4m/Xsx5//sCS1a5v39D3DGlqWZKfcFUcxertg8/h/T9r22mP8FOvCMFaSNlUtPJLd8tbfLOnZ4/axYFu3t7N3S/Hbs7L8TOOCfDWsvNi9/lkJ/8i/1+9DinXvcM7zqNAUUge4PSi621F0T592YCx7gG2A34O7DAWvtxa+1ewG+B+cDVGbhuGSXu/GNzphRjzNDUKcYY9prpL+FNhH+O0uRKd1c2rvQsF+UWUZbvLfd7bvNzMfePt3zXHU+6c02J53sSjRuUFuUWUZwX/9e5a+WunuXVTUNB6YwKt9FRZ8JPYINB65undLgxpeDPxtW1dfs674YzpG5Q2ttvE86oZws3KJ3Xvw7eutu/4T+/Bb3Rv4czKgsJRPmRnG28N2mUzYDc4R+ojCvGgPM7XaLyXRmnfvzPZfzg/qUs29LC4lV1nP/HF3llnf8BJaS3+y5EC6a6eWWEucS3t3bz/LuxM6lR93GCzrwc7x+e0cqUWmt95buFLd7vde6MGRTu7m3sNlJQ6o7N3ZJC+e6fnlvHlf9cPjitzJubmvnMrS9rnmXxydagNCXGmOnAOUAv8EVrbeQjuG8DO4BzjDHTxuL6JHVu05q5UcZ6uiW8b21KbFzKWqfz7k5JzlH6dqP3zWWPKXvwnhnv8ax7bkvsoNSdAiVWUOrOURpPk6OGTu+bo5v5HMmuFd6gdE3zmsHP3fLd9p7+YedYjaa+vcf3hHyk8t0ap3y3PmpQGvq/rCrO85WjucG9hDQ7/3d71j8WfcPWzfDiH6K+VJCbw6yqIt96X1BaOTepaxxTbgde00mnGh3JOPPWpmb++Mxaz7r+oOUXj670bWutpb7Nmc6kJLWHRW4wtb21i5fXet+HTt13JoucJnT3JlDC29bdR5uTeV3kPKSO1lQpE1q7++h2hhrlNjlBaW0tBYkGpVEyzsnY0drNzx/xd/tdU9fOrc+uTeqYMnkpKE3OCYS+d/+x1nomYbTWdgP/AHIGtpMJyJ1we0aFP3u2ty8oTazZkTumNNmgdEWD9w/+gqoFHDbzMM+6F7a8QNBGLxt1M6UtXX1Rn2Am2uQIonTeLUysqn1uuTd42NQ2dOMwrbwQN1GbaLMjtyQpYKB6mGY5EH0KHXey9HCm1Bjj+z4NN79dNnOnhJlZ75/iaNDiq6EzevZj5xr/w5LZbvnuRAxKo8xVqvJdGW9+/593o65//t0GVjhzf7d09dHjDGeoidLhPBHuVGG9/dY37cuB86r4yP6zPOseeWsrO1q7eWltAytHaEjnDvkA2HOGt5LBDbYzJdq1mEYnKK3xB6U969cT7Iz9ful+H+vbe5LKbP75+XW0x/g79dcX12t8qXhka1D6GWPM9caY64wxXzXGJHqHEm4L+t8Yr//X2U4mGHdcpZtNBNhrlvdJa317T9wlLk0dPTQ5rfB3TnJM6drmtZ7l3ap282VKG7oaWNUYvbGBG5RC9GypO6Z0lziCUl/n3cL4xpOGzSr13jhsah0KSvNzA74xhJsTLI11v87asgJyotV/RnDLd1/f2OyZegS8WWQFpfGJLOMroZPyxmFGU3Q1hQLTKHaOUgY/OTOlXb6fO5Gx1NLVy2NLY/cveGL5ds9yXZSmesN10I5HTWk+Jfk5nnVuJvHAeVWcsu9MT6l/W3cfB//kX5xx43N86Nr/cM7Nz/uyoWHuQ+uywlxmV3n/7jS0pz6mdNW2Vs6+6XmO/sW/+enDy+mN0o/AvZYpBQGCjd733dzaWgrmz8fzFNdaut9ZTSxupjR0rsSypf1By99ejN3ld219R9JzvMvklK1B6feALwBfAn4NvGOM+X4C+4fvaDbGeH2js92wjDFLo/0Ddh1xZ8kId5xhtMBt7pRiygtzPevibXbkBni5AcPsKGWHI7HWsr7V+0d/Xvk85pTNYXapt5HLs5ufjXqM8sJcivK8b+LudDg9fUE2OOviyZS6Y0oTLd8Nd+IN2965na6+oTdG92luokGpm1mN9vDB5b5Zu2VaZQW5nhJgt/Tb/d4K9PUHPTeAC8wGTGRm3+TAbh/y7vTcdfDAV2Dlw54pY9wyOpgkQakvU9pJpzKlMo488uZWXwAY6ckVTlDqvM+WFuRS6LwXJcoYM2zVUVFeDgumlzGtvJDD59fE3O6Zd+r54QPRu9q7wdm08kKmOMM6Ui3f7ejp4/z/e5FnV9ezrr6D3z/9Lr/+l//BsltWOz+vB3d+stzaGgJFReTNneNZ3/22v6Q6rLwwj9IC7/3N5gQ73L+4psF3L+V6auWOYV+X7JJtQel/gPMIBXvFwALgu4S65/7IGPO1OI8TvjvoiPF6u7OdTDBuBi1al1ljTJRxpfEFpW7p7pwpxeQmMR1MfVc9nX3OfKploRtut4T30bWPRj2GMcYXOK1v8F7fuvp239jLnUeYDgbSEJSW+jukbm4bmm/S7cC7OcFxLxsavN+7eB4MzBlhm/nTSj0NoNwxjmp05BdugBG2R2CDd4Oa3eD4n0Ig4ibJBuG/f4K/ngW3fQx6Qn+OD9/Nf6M5GYPSEpXvyjjzqJMlzXfe097c2Ex339DPbJ1T4ppq6W7YcA9M95tTOTj12vmH7TTsce56ZaOvXwBEC0oLfEGp23woUY+8tdX3fvbHZ9bQ4Ywj3+ZUdc0zzntgbi45lZUACTc7coctbUlweMw/3/TODb3H9DI+c4R3Kq6n31ZQKkOyKii11v7AWvsXa+271tpOa+3b1tr/B5w2sMkPjTHxpKvCd5yxiuGHr//zX9eiaP+A2LUVkjHBoPUHpTGa37hBabwdeNfUeZ9nzEuy8+6GVu/Ne14gj6nFUwF439z3eV5bWr/U07020lzn/OucJkzuG/O08oLQlB0jSDUoLc4r9u2zsW2oQGFmlA68iXC7LEdraOVyy7Rc850GUG6gu7Ex8S7Bk53bgXMP45R8TVsE1bvCMZdFP8DqJ+Cx7wKhBlgLpg2N74o6R+lEDEqjNjpSUCrjQ1dvP886c4F+/5SFnorRnv4gyzYPjSt1y3dTLd0NG25oyUE7DQ0hef/CaXzuqF2GPdY9r/gL4tyS2Wllhb75q91pWhJ1/2ubfevae/p5YY33PdXtAjzbOlVY1dWYQOhWv2D3BZ7XukYKSp1KpEQ68FprfeXap+w7k6N3r/Wse2tTs7rwyqCsCkpjsdY+BrwMVADvGWFzCM1HChDrL1/4rlUTMU1AjR099DlZwWjluxClA+/m+DrwrnObHCU5ntQNSmeXzSYnECp/OnTGodQWed8A/rL8L1GPM88JxtY1uEFp9EY+I/EFpUWJBaXgz5ZubI0ISlMs33W7LM+OIygtzMuJ+fMAsNs07/dmjhPEtnX3+YKwbOd+P3YLODdkU/cMfTziW3Dkt4j63O/lP0LjOgAuP3EP8nNDb28Tfo7SsKiNjtR9V8aHF9c0eMY4GwMn7z3D917xesQYwkwFpfvMroz52gHzvH0NLj9xT2678FC+/v7duOT4BXxgoXfShEfe8o+RdbOTU8sLqXKC0vae/qSDrf6g5SWnY3DYM86cr2757vQ+73tabs1Q5Yi/A2/0PhNhM51MaSLvrxsbO31B7AcXTuOAeVWesbx9QZvwdHoyeSkoHRL+7ZwRx7bhx/ix7mxmO9vJBLIjgeYLbgfeHa3dcTUDcJvdxDM+M5r1Ld4fsTllQ2NGcgO5nLzLyZ7X7111L8vrl/uO42Zq17uZ0u3R5+EciS8oLUg8KHWbHXkypW75boJjXtYnkSmF4Ut450/1fm+mV/jnztS4Ui83KJ0bcEq6pgwMrw8E4H0/gC+/DMdc7i3nxcJ/bwXgmAVTefwbR/Hz0/fhhpOdct6JNkdpWJRMqcp3Zbz490pvVmy/OZVUleSz35xKz/rXhgtKy9JTvusGnmG5AcMBc/2vHT6/hq+/f3e+eMx8vnzsfM9r79a1+8pW3Y6308sLfJlSSH5c6Tvb22L+bruZUjdAru72dg72BqW7eV7rr6+nrz72/KzuXOCJZErd65xSks/8qaWUFuSyYLq3SeRI88hK9lBQOiT8lyqe7ObrAx8PiPF6eP0bKV2RjAm3HGZKSf7gGBTXvCnFvmYAb24c/qmftdbX6CjZ6WDcTGl4PGnYuQvPpSBn6Aa83/ZzxXNX0B/0vuHNdTK1biY31jycw+kP9tPU3eRZl0ymdGbpTM/y1vahJ9dupnRrS5dv7GsszZ29vg7I8Qalu08ri/nanjO8b7h5OQHf3HkKSr0ig9Ic+pmOk92smuddrpkfKuU96NPe9SsfHvx0XnUJZx48h51ynJuuiVi6C5Dv/ZkroUtlbzJuPO00rDl2QWgYyb5OUPpGxPvjjlZ3TGl6HhaFAyDX0bvX+uaNdu01q8LXwPDZd7x/Q7ZGaXRUXpjn69yebFD6+jAdaVdua/V04XXvV8o7vfcfuVOHqqXy587FFHrfi7pW+OcQDZvhe+gb//vWi2u837NDdpoy2GvhgLmVntf+q6BUBigoBYwxtcCRA4uxpnmJ9AgQBI40xkx1jlUAnDLw+sNR9pVxLp4mR2GBgPFNwv3W5uGD0saOXl9jl2Sng4lWvhtpavFUPrPXZzzrltUv47blt3nWueW7LV191A88xbbW+st3o7zhu5p7mn1zoyY6phRgRom3eMHb6MgblPYHLdtb43ua644nNcafeY3FLdsevJ6KQt/TZVCzo5FEBqUzTAM5OB08K52gNGzvM7zL25dB8ybvuianYGWiBqVuplSNjmScWFffzrvOg9ZjFoSCoX2cv5Vr6ttpH+i0Xd+emfJdgDMP8heyffzgOVG29MoJGN6zi3c+7Tc2Ng1+bq31jSmdWl5IIGCoKk5Ps6OV22LPk9rTFxycF7unL+jL4pa0ee8/ciIypSYnh4LdvNnSzldfi3kut9GRG4wP50UnU3roLkPv/W62+vWI769kt6wJSo0x7zHGHGsi22KG1u8E3EtofOgD1tqNEa992Rizwhjz08h9rLVbgL8C+cD1xpjIx2o/B2qB2621sSfsknErnulgIrklvCNlSt0saV6OiTsYco2UKQX4zN6fYecKb8e7G9+40TO1yuyqIgpyvX8OwuNjt7d2++Zri6d8t6HTPyamqiCxeUoBZpR6g9It7UMToVeX5A+OHQyLt4TX/X+YXl5IQW580xG4/+dh+8coG3ObI21sjNW4Ozu1RASlc4y3DJC8EiiuJqqZB0BhpXfdume8y5MlKHW77xoFpTI+uNN61JTms9fA1EwLppd5hi9YCyu2hoIuN8uXzqD0/PfuxAcjxod+8rB5vvGisbjZ3cheEc2dvfQ4096EpwBLpNlRb38wZsO7aB1/Iy3bErrH2NTUiVsYVNDa5FmOLN8FKD7wQM9yx8svxzyP+4C1qaM3ruZq21q6WOsMATpk56Gg1P3+bmuJb9iTTH5ZE5QCewBPApuMMU8ZY/5mjFkCLAcOB5YCFzn71BCaNibaONOvE+qOezqwYuB4bwJfHVj/jYx8FZJxbqZtxKB0tjdAef7det+bViR3PGmy08G09LT4ymMjx5SG5efkc8VhV3jWtfa08tTGpwaXc3MCvozvGwMlROEbiLDi/BxfOWo07njS8vxy8nJG7tjrcjOlDV0NgwG1MSbpZgzLt3ibUu02TEmua88Z5ZQ5JV4AR+9WG2XrUEfYSJtUvusRmSn1Td9SNc876XuknFyY6/Sm2/yad7lpnXd5ogalBW6jo05PYxmRseKOJz1696kEBiLRwrwc30PMZVta6A9aX+Yt2Yez0RTk5vD78w7k6W8fw3OXH8ePPrwXJtbfEYf7Xrhsc8vgsBA3SwpD81tXlXjf36JlSjc0dHDqdUvY7bsPc8KvF7MqSlZ0xKB0IEh2h9lUFudBvXfoQ26t9z2p+OCDPMudr72G7YkePEf7/3Dn9o7GzZKWFeayR8Q40l1qSihzhj0NV7Is2SObgtIXgBuALcBCQsHkXsBrwLeAg62122Pu7bDW1gEHA78llDH9CKHuvdcBhwy8LhOQv3x3+DfK9+7qfRLZ3tPP8+/Gbh6Qqc67ARPwNQUKO2DaARw641DPun+t+5dn2e1Y+PpAxvcN581ij+llgzccw2noTm06mDB3TCkMP6403qB0mROULnTGgg4nPzfAxw/yPgCoKc3nxH2i90mLNi2MDGnqGLop8mVKY5Xuhs3Yz7u85TXn4JMlU+qMKTXqvitjr6u3n+ecqWDCpbth/iCvme2t/vH/0YY+pMIYw7zqkoSP6w7P6Ozt592BQNENpKdEVOtUl3gfYDc45cnWWr58+38Hx9Wu2NrKhX962TN3a1dvv+/94X17eEaJsXQwKHWmlptSTF+dE5TWeP8vig7wtkKxXV10xCjhLc7P9Y3B3RJHJZIblB680xTPeNtAwD/H+xsjVJhJdsiaoNRau9xa+0Vr7YHW2qnW2jxrbaW19jBr7dXWWt9dorX2CmutsdZeEOOYjdbar1pr51prCwY+fsVaG72Xt0wIbvnucGNKIZRJdctR7nh5Q/SNgTXOG0nSQWmL9xwzSmYMm4k8fqfjPcsvbX3JUz607xzvm8TrG5uw1g4Gp2HDtduP5JbvJhuUluSVUJ7vvanZ3D40rjSZDoHWWt5y2tAvdG6cgjbIrUtv5RMPfoLzHz6fZzc963n9Wx9cwGn7zaQwL8AutSX8/rwDfU2vwtzAOdFJyCe7yEypbwoXt8mRa+b+3uUtr0O4kVdnE3Q5NzsTNSiNMqa0qzdIMM7GXiKZ8Ny79XRHVAYFDBzlVIy4f1uXbW7xDbPIzwlE7WA7FmpKC3zjKcO9IrY6f7sjq4amONfvNjpaurnF9366rr6Dfy0behC3pq4dt6r35H29DzuXb2nBWusLSueXGmyX9/sa2egIILeqioI99/Ssa338cWJxvw/xPPR9wW1ytLP/vX+fKPcbIlkTlIrEy82UjlS+C/Dhfb3ZvIff3MKr66N3lPNPBxNfx1dX5NQo4G9y5Dp0ujdT2tDVwJrmNYPL+zrB5o7WblZsbfW9WbjBayxu+W51UYxxgXFwS3i3tA2NK53llBjF00Ro6eYW6tq8Nwz7OmXYv3vtd/zy5V/yVv1b/Hf7f/nCE1/ghS0vDL5elJ/DtWftz/IfHc+T3zqGA+fFDrrdMqjGOMfmZIvILsi1OEFk2QizdM3cz7vc0wb174Q+b3YfDk3QOUrBP6aULsDS1aefIxk7T63wVjYcMLeKimLvw9GFM7x/W1dsbfWNq59RWRhXBc5oWTTTe83Lt4TKbN2HnpF/292gtN55j3nozS1E8883hx6yvuNMvzazopAD53rfWxo7etnW0u2rutotx19anFvtf98t+8D7Pcutjz2GDUYfcuRW+bjTqLka2nt4e5v3a4galM6q9Cy/uak55hhbyR4KSkUciXTfDTv9gNkU5Q01yQla+PQtL7HSGY+Zyelgoo0njTS7bDbTir2NHpbWLx38fOeaEt/Yx1ueWev7fsSdKXXnKE0yUwrDNzuKt3y3rz/ITx9ezuE/e5KTf7vE89pO1cXMi8hYb2zdyP+99X+ebYI2yDWvXOM7bjzjlKKVj8UzNidbRGZKa02T98XSEZqTlE33B65b3wx9dEt3J+ocpeDLlAaMpZhuNTuSMbV4lbeywS3dBX+mtLsv6NvPzcgF29tpW7yY7nffTdOVJmbPGd5y+XAPArd8dXrEdVeXDp8pdctaw55euYO+gWlefNOvTS1ldlWRrwpn+ZYW1jhB6VzjfU8JlJYSKPK/95R/6EOe5b7t22l/9rmo1zbPqeRa65zT9dJa79dYlJcz2PQq0j7OQ+Cmjl42NOg9MdspKBWJ0NHT5+s0G0+mtKI4jy8du6tnXWNHL2ff9Lxn6pGtLV1JdbKNxjcdTOnwGSBjDAurF3rWrWhY4Xn9g4u8AYBbhlxemBv39DXpDEpnlngz0ZFB6QxfaWz08t0fP7iM3z/9btRM6rHOmJ07376TvqB/vN7S+qWsbFgZ93WHlRQkNzYnW3iDUidTWjqVEU1b5F3eviz0sXGtd/1ELd0F35hSgBI6lXGXMbOhocM3FczRu/t/X6eU5PuCzn++4c0aRs4R3bl0KatPOYUNF32Wd088ibobf5/Gq45PZGMeGGr4t8UZUxr5wHG48t3+oB0cC+pq7+kfPL5v+rXaUgIBw4Lp3t//V9c3+qquZvV7l90mR2EFu+5KwYIFnnWNt98edVv3oblbMuxyA+8D5lX6OuRDKAPrfr9UwisKSkUiuC3qITQHWTw+e9SuHD7fWypT397DJXe/MViW4pbmlOTn+N6s47Wx1Vu+O1KmFGDBFO8b0cpGb4D1wYXTh93/6AVT4y6xSmum1C3fbY9dvtvQ3uO7UX97Wyu3Pud0YR2QEzCcc+jQuMWgDfLwmthTDD+x/om4rzuSb2yOMqWDwuW7ufQxBacb5UiZUvAHpdsGKgDcoLRqp6Sub1wo8D+8KtW0MDKGlrzjzXZWFef5mhqFuY3k3M7Ruww8nLXBIFsuu5y+zUN/43dcey2dby1lNO3hZEp3tHZT19btG1Ma+XfdbXQU2X13TV3bsN2yXx7IMLr3CLvWhoLC/8/eWYfHUa1//Dur2Wzc3ZukSZPU3Y1SSqFAizsUh/vD4WKXi8PF3R1KaQv1Une3pKkkjbvLus7vj22SPWdmLdlI2/08T5+nc2Z2dpLszpz3vO/7/dKZ27+OVRN2MAIGCDeSASNtB2NN4HXXEtvKbdtgqKriHJcQTLYXlTaq7JbZcvxJE/nbdhiG4WRLcz1B6UWPJyj14MGKBiUZlHpLhDbFa2gkIgG+vHkkRiWQXpV7i5uw41ypUiHVa5ES5uO0TL01epOeUKAFnAtK0wPTie3ClkJie3RiELFiTTNzsBNZq3P0ZvlutdK20BHADfj+PFzJOaaDZy5NR0pY14T/bOtZzu/WmgO1BxxeLx8csSNPphQAoDOaOidrQVBAwFATHqeC0iHkdkdQ2lxCjgeRfr3nFUIJICDvRXJoPAq8HvqNnYWkfdPEQaE2Fy1tBasdJJ7LyGmOHYOusJCzv2358m5eZfdICJZzvLvP1Co4lTj2ynfbNAYYzpXl0tZqNIfKWmAys50qvx2khFmC0cFUUE/3diaEyCFoJQWGRKG2g1K/y+dDILfKgprNaPl9Cec4WohRoTNyypI792kNyK8mK134+kk7sKX47+HixROUevBgBZ0pdaZ01xq5VIRvbhvF8c78ckcRAOAsT79Id6hSVoEFOXl3JHQEAIn+5KS8WdsMlaGr5EcoYHD35CTe14b4SHBJpv1MKnFuN6nvAtzy3TpVHUznFFadKY2lS8Vig2T499zBWP3QRNw1ifx5D9YetHstuQ250Jm4GXVH0GJHzlrXXOjY7SdlhIC3E5+bMLIsHW0VFtXdCylTyjAcsSMfRuvxKvXQL5jMLHZRfaGTBtkOgui+UpqOjKByxw7e/e0bbYvx9AZCnpLZQ6UtUGjJRSDrRVE+9eCWcwGco7LXQ6UtqGxRE0rGADoXTB1ZlmVG+cNYTy4S2CrfBQChjxz+CxYQY60rVoA1GIixqAAZJJSPuq2+0sNlLUT2ViIUYCjlTGANLS54oqqNYxPk4eLCE5R68GBFg4IMZpwROaLx8xLjXzNTibE9RU2obFFzSnNSuhmU0qW7AdIA+PL0nNFE+0aDAbmSTfemXj8qFsPjAjivfXBaCrysxJzsoTfpoTCQK8NBMvdlSo2sEY2argmRPbGjimY1p4/085tG4O7JSRyvNAA4XHeY2J4ZN5P4nRnMBhS1Frn+M1AZXU/5roV2e0GpPBQQOPGZC0nlZBFRewJopUq2A8/jTCkASCmvUk9PqYd+IreyFe1UgGYvKOW713YgFQkQF2QJSlW7dvMeY2pohL6khHdfb5FOBaVbz3Ct7K0tYQK8JaALnzpU3supoHR0Avk8rG3XcsSf/GVihJzLvmZE+XEyt+T5AjkepUI75bsAt4TX1NgI5fbt5DkEDGKDyGcXPY/pgC7dzYn1tztnoDOlar2JOPf+4ia8t7EASw9VQG/suwUJD/2HJyj14MEKrkdp9/o95w+Ngq9X1ySZZYGlhypxkhI6GBTmOJDkw1Xl3Q6kQiki5GS2s7ydVCgVCQX49rZRmJcdCalIAB+pCA/PGIRbxyc4fX106S4ABHt13xImyCsIEgG5Cm2vr9Q6CN1PPSgDvcUYHMG/6syyLCconRI7hfP7LWgpcP7iz0FnSp3xU70YIOxguiNyBAAiCRBC9kvj7CbARJWZnc+ZUoDXFsbTU+qhP6ADqEFhPrytFB3EBHpz+hM7yIm1iOGY9XpoT5/mPQYANEePdu9iuwldMnusopXYDpZLIJN0BV1CAYNAb36xo7JmMrs4NT0Ufl7kQtpSSljQur1HKhJiJNUaZM3YpGAYG6hMaYjtTCkASFNSIBs6lBhrXfon5zg6Y0zPYzqgg1J7pbuApRKNrirbWdgAs5nFM8tzce2X+/DB5kI88Wcurv5sD9rUBhtn8nCh4AlKPXiwgg5KXS3f7cBLLMR8yrv0g82FHOVdunzFWTjKu06U7nZAB1jlinLOMQHeEnx8w3DkvjQbR56fhUdnpbrU+0oHpSJG5FQm1xYCRsAJpgkFXn9agbcrKKXFE0YnBtnse6pWVXOufUT4CAwKHESMdSco5Vxjq8bjywbao7SV3OlMP2kH4VQJ7+nV5LbY2/kgd6BCiR3JGa0nU+qhX6D7SScNsh8A2TtmZLwl2NIVFgJG2z3S6mPHnL9AN0Ar8NIk8ti50SW8TSrLnILOlCYEyzEsjgwy6Z7KFEqZf7KN3190gAwpYT7coNRO+W4HAQuvIbaVu3fD1EZeB+3ZeoInKNUaTBz13NE2RI6smZBCZnM3n6rHlzuL8dsBco6TV9WGF1eecHg+D+c3nqDUgwcraE/O7galALBwpP3sZZS/l9PKvjSVSrJ815EdjDV0UEoHuNZIRUJeOXdH0IFdoFcgBEzPbjf2xI645btdWcjTNWQZsT2f1dNN5Cq9v9QfMT4xSA0ky7G7lSmlglKV3sQpf7sYsW8H40pQSinwNlJ/o8AEcGrrzjd4M6Wez5CHvkWhNeBIeSsxNinVfqkoAFw1PJp3fMZgy/dce/Kk3dfrzrh+3+0JdPkuDZ/HOG1z0qTUQ2c0caxk4oK8MSLeduYT4GYoFwyPhohnQfXaUbGA0QhTSwsx7kxQ6jdnDhiJ1TUbjZy+Xrof+FRNO6f383BZCwymrjEBA4c/H9D1t+9gb3ET3ljHny3/61h1p1+shwsTT1DqwYMV7sqUApYsaGq47Z7RYU7csG3RHTuYDuL8SK9GunzXHbhTebcDe16ltkSEWJbFqVryIWZvonGq+RR5bFA6GIbhBqXNrk+Owv2lnJjII3YEtPbUo7QDWoGXJjTd/v7zASktdKSBxuDptfLQt3QoxXYgEQowxkGpJgAMiwvkKLiPTw7u1DDQFxUT+wS+5L1ad/Zsn4odBcolRM8oDV+mNMSHnDM0q/SobNGALoqJD/bG8Dj7c4CcWDJDGebrhRvHkM/vEB8pbh4bD2MTqbwL2Fff7UAgl0M+fjwxpthE2p7RyslqvYkjdrSniCznzor2d8q5YNKgEJcWvn874P75ioeBgyco9eDBCncIHXXAMAwW2cmWzkjvXikhy7I9CkrpY60zju7Cncq7HdjzKuVkStsspbHVbVqOWmK6HRXDM82kb2uHhQ5dvtuia0GbzjX5eqlIyJmw1HjEjuyr77oSlNIKvDR0ee/5iIQWOtJC48mUeuhj6N7BobEB8JY4Z5327rVDsXBEDJJC5LhuVCy+vGVkZ2uIvpKs2vGdNYvYZjUaGKrd/7yyB+1Xak2HYrA1nEypSs8p3Q2SS+DrJUZOrD9s2X4LBQynbBYAnpuXgcdmpWJQmA9mDg7DH/eMRaBcwindhUgEYUCAzWu3xnfmDGJbtXMnzLquBfowXy/OAn2Hr2oHu8+SQfH4FMcBMWBRz790iPOq/iuPV8No8izEXah4glIPHs5hNJkJs2ug+0JHHVw1PAYyHvU5kYDB9G4GpQ2aBmhNZPDsSlAa4U0+AOrV9Z32Ku7CWhkXsJTv9hRXyne1BjNa1Aacpkp9fL1EHGEFa063kGVDaUEW8Zxon2iIGHLSVdZOKbs6Af3e1R6vUrSpu75zIehBptQvCvAKsL3fUdB6PkD3lELjETry0OccpILSUYnO39/9vMR4e2EOtjw+FW9cnU1k0wwV5GKrbNhQCPzJwExXwPUw7U2y7agG5/DYnXDLd3Uoo7KKHV7gvl5ijEzgX7DNiuZXrhULBXhoxiBsfHQKvr51FJLO9Z1yRY5CwAicm+L7TJ1KtDaY1WpojhwhjqHVgq2D0DaNgaPdMCHZuaAUAB6YlsJbljw+ORjbHp9KjLWqDZzeVQ8XDp6g1IOHczSp9JwSm56U7wKWB9R9U5M544tGxSLAm+tp5gwlbaQsvpfQC6HejntHOqAFg4ysEU1abulPT6hXk9L5Yd49F5ihy3drVbWd/w/3lXJWnKtbNRzD8sERfjYFm5R6JXFOoCsoFQlEiPYl+6H4BKIcYU+Q6WKl1W6m1IWeUoaxX8IbkeXahQ1EeHxK1R6fUg99CJ+gzSgbgZUrsCwLQwWZKZXExkGakkKM6c6e7fF7ucKkVNviQnxqwx0WLh00q/Qobybv8x1BKQDMzuC/x7mSPQTgkkcpjSgkBF5DyHunav9+YpsWJNpe0NBp07LpZB3Hn9SZftIOUsN98dqCLCIwzYkNwEfXD0NCiJxrzXO6gT6FhwsET1DqwcM56tvJflKhgOE1w3aV+6YmY9HILiGi6elheHbu4G6fr7iN7LtJ9E90SUQoWBYMEeXpSAdjPaVOXUdsh3u7EFzYgC7fVRqUaNdbMqEioQDhfty+UloUYbCdUqzS9lJiW8AIkOCX0LnNEYhqty0QZYtITu+rJ1PaYZngBR38GCpIdyUoBYDESfzjAfFAQBz/vvMJTqbUo77roW85VtHaLUEbR5haWmBWk2WuktgYSAdRQWlh32ZKh8UGwF8m5ozbsjsJknN7SsspO5h4K2ucBcOiOdVUIgGDeZR6vyO6o7xrjXzMaGJbvf8AsT0ljTxfm8bQ6du6Nq+G2Dc5NYSwynGGRaNi8c//TcbLV2Tiy5tHYNm94xB8rt1lahq5qM3nF+vhwsATlHrwcI4GJRkghPhIbFqHuIJYKMBb1+Rg7zPTse3xqfjm1pFOCQDYoqi1iNhODuBmYu0hYAScINHdQWlvZErD5eFgQP49apS2+0orW7iZUnv9pHQGOtonGhJh16JEnC8lEGUnU2pmzahSVqFFS6ohRnNUgj2Z0o6gNIQWOQJct3AZNIt/3Fawer5B9ZT6MBqP+u55yr7iJtz/y2E89WcuShpVjl8wQKBLdzOi/ODrxQ3aXIXOkkIkgigiAtIUsp+/rzOlIqEA143mtsdcN4q/ZSaYypQ2KHUoo3pKrTOlwT5SvLpgCMTCrmfbvy8bzHlWOKKnQan3mDHEtiYvD2ZV1+cyOkDWad3TwRfbi1DXrsUOyh5obha5gOwsSaE+uGVcAmZnRkAk7ApPplDZ6lM17VBoPZ6lFyLdnxl78HCBQWdKe1q6S2PPWNwV6Eypq0EpYCnhrVJWdW5biwb1FJZleyUolQglCJGFoEHT9QCsUdV0ltjGBspwuKwrCDxd247iBiVxDnvKu3RQap0lBZxXLS5vL8cjWx/B2dazEDEi3JV9F+7PuR8Mw/CU73oypU1KS1AaRnuUirwAqX2fQA6Rw4CgJKCZ/I4g86ruX+BAgi9T6lHfPe84XNaMm77eD+O5msdVudVY+eBEpITZVmsfKBygBG7cUboLAHqqn1QcHQVGKIR0EBmU6ouKwJpMYISuZeJ6wiMzBiG/qh27zjZ2bo9J4vfgpOcNCq0RCi35HIoPJgWSrhoeg+wYfxwoaUF2jD+G2OljtUWPg9LhwwGRqMsn1miE+shR+Eya2HnMolGxOGT1jD1S3ooxr5FKvRKRADNtlCR3l2FxARALmc4MvZkFjpa3YrKN0moP5y+eTKkHD+eg7WB6KnLUW9CZ0kT/RJfPQfeVujNT2qprhd5MCkbR79dd7Ikd0RO6Nbk1RJ8Lw1h6V2xBl+/Sv1dnMqUGs6EzIAUs/bqfH/8cP538yXL9VPlubZsWZsrv7WKCZdnOTCmvHYyrvqICAXDp2wBjNWFNnwckT+/hlQ4QOD6lGo/67nnI62tPdwakgMVi472NfevB2R2MJjOOlJHVH7QATncxVJD3U0mMJRNJl++yej305dx7r1mnA0uLQrgJb4kIP905GtufmIpDz83E/81KtXmsPQuZDhKsync7SAnzxQ1j4roVkAI9D0oFcjlkVF+p5igpdnTl0GjEBNpfXL96eDT83JA5t8ZLLOT8Xg5Rn0MPFwaeoNSDh3M0cIJS92ZK3UGLtoXjAZrs341MKaXAS/eA9gQ6S8qAQbCMf1XZVex5ldJBqYrqtUsMkUNup2yakyn1TyC26Uxpq66VYwuz8uzKzoDUms+Pf442XRuiqEypnkfx+WJCqTNCf07ev0ciR9YMmgks3gpMfRa44lNg4Q+uB7cDFY5PqdajvnueUVin4J1QbzpVN+BLsYsbVZz7qi31WFfhZEpjLToMoqAgCIPJ54d1X6mxoQFlt96GMzlDUTznUqgp1Vh3wTAM4oPlHFsvGrlUBD8v288ZmVjo9iosoOdBKQDIRgwntjXHc4ltiUiA/15pW0zOSyzAfVNSbO7vCXRG/nBZs40jPZzPeIJSDx7OUU95lPbGg6On0KW7YoEYMb4xNo62TW9mSukAN0QWArHAPSun9rxKHZW+8Xm+dWAymzjluIl+ZKY0Sh4FIUOWjNF+sSvOruA9v8KgwN9n/0aor5QjfX8x95U2WwXk3ExpD0rAInOAqU8Bw24EhBdQlwonU+oROjrf2HaGXzlUZzRjR0Ej776Bwokq8jsa5c/1r+wufMq7HdhS4GWNRlTc/wDU55Ri9WVlqFh8D4yN/ft7tNeqEx/sbVMBvruwZjOMTaSCfreC0qxsYluTlwfWTLYHTEsLw9OXpvO+/j/zMxHHkwV2B7SY1tHyVo9f6QWIJyj14OEcdVRPaZgTZTh9TWELqTwY7xfPUdJ1hr4MSt3RT9oBXb5rLXQUHyzn9TrrIDPKdn9itaqaU3JMl++KhWJOUGztVVqrqsXxhuM232N18WoIBQxHJfhitoWxzhL3yKP0YoEKSr0ZHbS6izfTfj7S0ZfIx8HSgZ39OVFFqplndrPUlA99JX+mFACnr7QjU6rYtAnavDxin1mpRMvvS9x2Xd0hwo4XdgLVT+oOTM3NXb2g5xCFOu8T2oEshwxKze3t0Jdx/bjvnZKMb28bidEJQQjxkWJ4XAC+u20Urh3VewrntMiSWm9CQZ3SxtEezlcuoCVkDx56Rn07mSkNH4CZ0pNNJ4nt9CD+FUtH0EFpo6YRBpMBYmHPM5rWgSLgHjuYDujy3WpVV0+pWChAfLA3ihr4lSztBaV06a6vxBdBXtyytDi/OFQquyZPZYquB/ahukN2r/1U8ynUKGsQFeCFKqvs6MVsC9OstM6UtpI7e5IpvVCR8lQDGNTcMRvojWYcLW9Bu9aIKamhkIg869J9zUnKpsqaYxWtfXch3eBENblwNMRO9YkrmPV6GGvJhVFJbJe6LUfs6FymtH3NGt7ztS5fhpAH7nd7RtJZIu0EpfEh7s8kGmoooUKhsFuZUlFEBIShITA1dC2caHNzIU3k6lZMTw/H9PS+u0cH+0gRGyRDhZXn64mqNmTYea57OP/wPJE8eABgNrMcoSM6ozUQyG/KJ7YzgzO7dR4648eCRb3GPd5f1tlDgOvv2ROifaKJ7UZNIxT6LtuXLBsr9wIGyI4OsHne0rZSYjvRP5F3QsMRO7Iq+T1SR/YyTYqeBH8peT27q3fzKPBezJnSru8cr9CRBxIJNygVGpyzE2nXGnDN53tw7Zf7cPePhzDj3W0X9WevP2hTGzjaBdacqGqDYYCWJJrNLE5WkwH1kGj3BASGqiqAEikSx1hnSqny3ZJSmFpbodyxk/d8xuoa6EtL3XJt3SEp1HY2ND7I/ZlSQzUZlIrDw7ulTswwDGTZOcQY3Vfan9DP99yq1v65EA+9hico9eABQLNaT6ghAgMvKNUatRzl3YzgjG6dy0/iB5mIDI7cVcJboSB7g2iBoJ4Q7xfP6eu07rMdn8xfspQdEwB/b9tZYFp5l7aD6RynxI+sM6x0UDoqYhTGRo4lxvZU7+Eo8F7MmdImlZ1MqdwTlHKQctWjJWaVU71Vb68/g9zKrsC/olmDF/7Ot/OKgYFKZ8TR8pYBG6y5wtkGhd39OqMZpU56lrIsC52x7/qJy5rVUOrIEtHuKsXSGKjSXWFAAIS+XZ91uqcURiOavvkWrM52gK/au9ct19YdBoXZVnlPj7S9r7sYqquJbXFUlI0jHSPLJkt4tfkD5x6RRS0s51XZrjrw4BomM4uvdxZj/se7cPePh3C2vn9Koz1BqQcP4HqUMgwQQplg9zenm0/DxHZNQgSMoNvluwzDcMpq3RGUsizbq5lSsVDMCXKLW62C0hR+ld+pafZLmRzZwdgaL20vBcuyaNW2oqiNXDAYHj4cE6ImEGP7avYhyp8sC6++iLNVXeW7LEI5PaWe8l0OQjFYIfn5kUMLtcF+cNKk1OG3A1wbjY0n63DKTjlpf/Pn4UqMeGUjFny6B6Nf3YTddvoxzwcKqR64lDAfhPuRf89CJyaDa/NqMPntrUh7bj3GvLYJSw7yeya7E1rkKMRH6jaFetriRRxLPjOEfn5E5hQAmr76yu45tcdt9/f3NrZE94QCBhmR7i83NdSQQakoKtLGkY7xyiSrr7RnzoA1DgxVaDpTeqqm/YJYrBoIvLn+NF5Zcwq5lW3YeLIOV3+2p18qaTxBqQcPAOoo5d1guRQi4cD6ehypJzNxSf5J8BZ3vz+lN8SOGjQNUBvJHrd4v/gen9ca2gLH2oIlJtAbUyhDbYlIgEUj7QfGdPmurUwprcirMqjQoGng/G28hF7ICMrgZEoVegWEUlIlseYizpR2lMz7QQ0pYyB3esp3eWFpBV5GC6XW/qRxTV4NpxKkg9W51bzj/U1RgxLPLM+F1mCZdLaoDXjw1yNQaA0OXjlwoQPOQWE+nKwaHbjS/H2sCvf/cqSzt66uXYenluXhu90ldl/XUzj9pNF+buvZNFB2MJJYrqK8LCeHM2YNHUxp+jHDFx0gg1zCLZ9NDfeFl9j1slpHuDNT6pUxmNhmtVroS3r3s+UsdLm43mhGQZ396gMPjqloVuPbXeTfuE1jwBfbi228ovcYWLNuDx76CY7Ikd/AEzk6UHuA2B4RPqJH5+uNoPR082li21vkzXmfnpIcQAaldIbytauykBpumbhLRQK8szAHUQG2JfqVeiUaNKRNg62gNFwezil7Lm0r5ZTuZodmQywUI0IegVAZGSS3mcnrrVdoL1pp+7pz3ztO6S7gCUptwNBepdCg3UGgtumU7X7x9Sfcp7ztTj7YVAiDiQykW9QGrBug1+sMfEEpnVUrrLc9ya5v1+LZ5Xm8+15fexpFDb1XcpdPlUq6S+QIAPSVZMuHOIa7iGg3KBWJEPrIw+Q5i0tgVjsvAuZOBAIGoxO5QnljeMbcgZHuKY3sflAqCgqCKJLMtPZngG9NgLcEcUHkQjydwffgOr8dKOddtFx6qKLPLcc8QakHD+DawQy0flKD2cDbs9gTeiMo5VMHFjDuvc3QQal1+S5gWaVe+/AkbH18Ko48Pwvzc+w/oOlyYwYMYv34M6sCRsAJWEvaSjiZ0uHhFhNyhmGQFZJF7KvRniG2zSxQZ0f85EKmKyilJhZSf0BseyHhYoah+krl0EJhJ1NqMJlxyI7VSFGDqvPvMFBoUxuwPp//frQ6t4Z3/HygiApKU8J9kRrufKb0s+1FUNmYJOpNZny0uZB3X09hWZY3U+ouOJnSOO79Vz5hvM3XyyeMh/fo0YC1uI/ZDO3p0zZf09tcM4L8GRgGWDjSdU9xZ3BnphQAvDJJrQrtyZM2jnQNY1MTjA0NYFn+qg1n4IgdVXqC0p6y2caipUpvwoE+tqnyBKUePMCSrbLGXb0y7uJY/TFojGR9f0+DUnv2Kt0lr5FcxR8cPNjGkd0nyT+J2K5R1UBFKZCKhAIkhsghlzp2vaL7SaN8oiAV2v7700HpqeZTONV0ihgbHja88//ZoaRwREFrPqSUFUd168XXV8qyLGo7glK0kjs9WVLbUOW7PowG7RrbmdITVW1QO1jt3lfcZHd/X7PpVB30Rv7qgQMlTedlZYFSZySsoIBz5bvh5N+zuFHJ+/M1q/T4db/93tFVuTWc93AHVa0atKrJz1immzKlLMvCUOE4UypNToYkKYkzDgABV14JgZcXpMnkgqX2xAm3XGN3mJsVgcWTkyARCiATC/H6giy3/c6sMSmVMLW2EmPi6B4GpRnuDUqNTU2ouP8BFE6YiMJJk1E873JoTnQv+5oVQ/4OL/ZMKcuy+Hx7EUa/ugmT3tqCFUcrHb/IisoWNc7YKYHeUdBgc19v4AlKPXgAN1MaNsAypRtKNxDbaYFpvD6arhDlQwWlyuoerWBqjVocqCFLjIeEDOn2+WyR4J/Ayb4WtnQ/Q8BR3qUUdmlosaO/i/6Gke3KVAkZIXJCu0rN6KC0sKUQUQFkX9HFGJS2a42d/YIeOxgXcLF8d38JudKdEuaDGenk73dv0cAKSrfbmQhpDWa7k6iBCp0lFTBAYogcKaHk39NgYlHWzC07/etoFXRWgbpIwGD1QxMhs+pRNJlZ/OYgcO0OJ6jSXT8vEWIC3VPJYGpp4ZTZ8vWUAkDA1VdzxiTx8fCdMQMAj0jPqf7LlDIMg2fnDkbef2bj5MuX4LrR7lOht4bT7ykQcEShXIUOSnUnT4E1d28hyKRUovy226HcsqVzTF9UhPI774SeWoxwBq7YkeKiFjtalVuDN9adRr1Ch4pmDR794zgOlzmf3dx2xn7QeaispaeX6BKeoNSDBwzsnlKj2YiNZRuJsdkJs3t8XjooVRqUaNe7rsSp1Cuxp2oPXtv/GrSmrt+jgBFw1GfdgVQo5WRLaf9WV+B4lPrxK+927qeCUqOZLJ0cHDSYEKDKDM4kgmgja4R/IPkgqGkbWOWTfYF1yWgY3VPqUd61jRc5KfNl1HbLd4+VtxLbYxKDMC6ZVKmmA9f+xGxmscuByu6xita+uRg3QveTxgV5w0ssRKBcghAfSoGXJ+heepjMgFyWHYkh0f6cktC/jlX1aHGRj5Oc0l1/N4ocUYGJSARRBL8OQeDNN0E2vKsKReDtjag33wAjsSjlew0m1ei1p8gKlv5AKhK67XfFBx2UimNiIJD0zDmADkrNajX0ZWU2jrZP/dvvQFfIXTQ2t7Wh/q23XD4f3cusN5lR3OCcjdKFBsuy+HxbETUGfO6CQNERKugMlpOfnb5WOPYEpR48gBsUhPsOnEzpupJ1aNaSk8Y5CXN6fN4I7whOxrFa6VoJ75LTSzB96XTcs+kerDi7gtg3NHQoAr0Ce3ydfGQGkyvidC+rKzjrUWrrvWmGhQ8jtr3F3pxzSr3JHo6aizBTWmv1nQtlqNVYX/eKY11QUEGpP1R2y3fza8iAIic2AGOTyKC0pFHFaWHoL/Kr29Fs5V8LAMPiAojtgWxjYwva9y/FSnV3ECV2VED1lZ6tV3B+5oXnehYXUr2LlS0aHHZzduNENSVy5CZ/UgDQl1Olu9FRYIT8CrUCiQTxP/2IqHfeQdgTTyDxrxWQDR3aud9rMNkuojt7Fma9Hr2JsakJNS++hLLbb0fT9993O6PYXfSlpcS2JDGhx+cUh4VBFEoK9GnzXX/GavLy0PrHHzb3KzZu4g1Y7eHvLUY0JVx4subiLOEtrFfiJM+9cGdhA7QObMI6OEot8N06PoHY1hvNDhXB3YknKPVw0aM1mDqtKTqIdlNpUk9p07Xhw6MfEmM5oTkcr87uIBaKEeZNlvG5EpT+dfYvvLL/FU6vawe3Zd7Wk8uzS2YIGRjmN3YvU2pmzRyhI0fluzG+MQiU2g62aRsYAEgNTCXfV0T+nqsvwkxprXWmlNNT6smU2sQrgNj0Y9Rot5EpbdMYOq1DOsiM8sPgSD/4UP3WB0v6tkzLFjsKySqCpBA5pqeR96mi+vMvM3KWUtW17iVNDaeDUvJYWj053E/ame0eEu2H5FA5sX+DDZGo7kL37WVGuVHkiFLelfD0k1rDCIXwn3cZgu+8A5I48jkoTad8u41G6M+eRW/BGgwov/tutC5ZAvXefah/4000fvpZr70fHzoqUypNSHDLed3RV9r48SeW1J0dWv/80+XzDo4kxcFO1Zx/5fzuwFaFi9Zgdqr6pUWlR0kjeS+dnh7GKc3vy75dT1Dq4aKHr3RyIASlFe0VuHPDnRxV3DuG3OG296DFjqqUVU69rlnbjDcPvGlz/5SYKZgaO7Unl2YXOltZ3FYMtcF1+f8aVQ0nqHaUKWUYhtMn2oFEIMHI8JGccTooVbHkROxi7CmtIzKl1EPPkym1DZUp9YPapncnnV0TCxkMCvOFUMBgRDy5sHKwj1UWbUH3k05ODeXYppztReuT3oLPDqbz/5QCL51V3XSyjtieOTgcQoGlJJRhGFyWRVp42FLT7A717VrOom2vZkpt9JM6g9DXF+JYMqjtzRLe9n/+ge4kef6mb7+FSdF3QZK+pJTYliTabz9xlp4q8GpPn4Zy+3ZiLPTRRxF8993EWNvatS5nlwdHkosiJ6vPv8oJd2BPVd2Zagm6DcJLLEBahC+nbzfPE5R68NB3VLaQwYyflwh+XuJ+uhoL60vXY9HqRTjTQtqHjIkYg2mx09z2PhyxIycVeH/M/xFKAzlxivGJwfCw4Xhk+CN4d+q7vdpHkxqYChHTlelhweJUs+uTD9pX1U/ix8ke8zE9bjrv+Pjo8UQ/aQdpQWnEdqO+FEDXCnJZk9rtfWADnQqr710YXb7rETqyjSyA2PRnVGjX8GdK86nJ2qAwX0jOKT/TPooDoa9UoTVwepwmp4ZwgtIGhQ5tavverAMJrcGEckq8yPpnom1hihtUnQq8zSo9jpSTv5OZg8lKghnUdnGjCsVuCtzpz5BcIkRisNzG0a5D9ypK4uJ7dD66hFd7sveCUsXGTZwxVq2Gau/eXntP4r0MBk5PqSTBTUEpT6bUlWdU6x9LiW1hYCCCbroR/gsWEOOmhkaXVZIzqKD0VE37Rff8BIA8O3Y4dB84H/l0r3iUP8RCAWfRia7c6E08QamHi56qFjJLFRPIDSr6kqUFS/HE9ic4QV+ILAT/nfBftwZ7fAq8jjCYDJz+0bmJc7Hu6nX44dIfcFfWXZAIeya04AgvkRdSAlOIsRONrsv/n2kmg/70oHSnfr8z4mbw2sZcm3Yt7/F0plRjUoERtXZuK3VGopz1YqCsyTJJF8OIIIaaQPt4MqU24WRKVTbVd+lJh3XZJR2Unq5tR5ud3lRbGExmvLPhDCa9tQWXvLcDfxx0XVGzg71FTYSJu1jIYExiMOKD5Z2ZwQ7oIM9V6tq1HIG73qK4QcWpYkwOtV2+qzeZUXru+7H1dD2sfe1lYiFHqCor2h+hlI3ZltPuyZbSpXsZUX4QCNz3DNKXk2rBkoQeBqUZVFDaS16lrMEA1a5dvPvU+/b1ynvS6IqKwOrILLY0LdXG0a7BETtqb4eh0jm7EdZgQPvatcRY4PXXQ+DtDWlSIsfaR2GlzOsMdKa0SaXnZPMvdExmlljYpaEVs/k4VUsGmxnnng90jztdudGbeIJSDxc9lZygtP9Kd7eUb8F/9/6XM57sn4zvLvkOkT6RPK/qPtE+0cS2M0Hp9srtHOGlxdmL3XpdzkCX8B5vOO7yOeiglM5o2sJf6o+Hhj1EjE2NnWpTbTjcOxy+EjIb4u1DThr78sY/EOgISjkepYCnfNcedFDKqGz2lNJlbdZBaXaMf2fWFLC0fh3sRrb03yvy8PHWs6ho1uBMnQJPLsvFH4e6F5huo0p3R8YHQS4VQSISIIKy6aIrXJxFoTVg8Y+HMOa1zRj92mbc/8thp0VBuksh1U8aHSAjPJQDvCWcoLJDgXfzabJ0d+KgEHiJSSEggYDh9N1uPeOmoJSzsOG+0l2TUglTI6m0TPeJugrdV6o71X07E3voSkpgVvLfs9UHD7rtfVQHDqDx88+h2LwZrIn8nGrzSS0FcXQ0RIHuERcURUZCSJ3LWbEj5a5dHO9U/yuv6Py/7wyy0ki5dZtL1xYX5A25hPwO8An+XMhUt2pgMNnODte2a9Gisi/ydYYKStMjLM8HujKlSaXniM/1Fp6g1MNFDz254esnbde347Pjn2HxP4vx1I6nsKdqj9uvo1ZVi+d3Pw8W5I1mfvJ8/HrZrw4FeLoDnSmtUFQ4LIP5p+wfYnt42HAkByTbOLr3sPYCBYDj9cddLuGhy6PTg9JtHMnl1sxb8cG0D7AodRGeH/s83p/6vs0sK8MwnGxpcBDpDXkxBaVag6kzM8yxgxFKAFnvqDZfENBCR1BDoeZOGHRGE+czlWEVUEhFQgyLJc9FB0CO2HO2EX8c4mZPXll90uXyWpZlsZXK7k1J61IApRcL6cVEZ9/jwV+P4h+rHs21ebV4e8MZO6/qOfTfYRCVGQX4xI6U0BvN2FFABm0zB/OXtk+jvGcPlrRAo+95sE1nXNzaT0rbjDAMpyfUVbwGc+1MDFQ21h3YU43VFZfArOt55q7hw49QfsutaHj/A1Q+8CCq/vV/RIBNB6VeQ9znC84wTLfFjtpXrSK2ZUOHEosNPlOnEvt1Z87AUOf8IopAwCCdp4T3YoKuFJGKBBBRFQzFjbYF4bQGE0fkKC3CsnAeF+QNiZAMD/tqfuIJSj1c9HAzpWT5bp2qDotWLcKnxz7F3pq9WFuyFvdsugfvHn7XbdfAsixe3PMixyd0cfZivDrxVd4+RXdAi/qojWrUqW1PTA1mA3ZVkSVLlyVd1huX5pChYUOJ7XpNPUcUyh6NmkaOsFNaoHOZ0g6mx03H8+Oex6K0RRAK+G0MOhgUMIjYlsjI33PReSje0l2sH6ihfB6lvdiPfN5DZUqljBE6LXfyUVCrJEphAa5q5QwqwPknvw7FDUo89sfxznLcb3ba9rz7elcJ73i71oi/jjknmtbB6VoFR3RuhlWgRd+Xu5Mp3ZBfyxFSAoAf95YSFkXuhrZUoMvjLGPk36agXoGDpc1Q6sgsOB18djA+JZgocdabzNhX0sR7rLO0qPSookTYhkS7UXmXCkrFkZEQSHvmES4KC4UwmCxv1uTlAQDa129AycJFKL7iStT/712Y1d0vAdfZU/U1maAr7Jnqr/bMGTR++ikxpti4ES2//965rTlBBaWZ9u3KXKU7QalJqYRiM1mO6zf/cmJblpMDgS/5eVft3u3StdH3sotN7Ki0ibznJ4f6IC6YvEfa6ys/W6+EiXo+dASlIqEAiSFyzvF9wUUTlDIM480wzJUMw3zDMEwuwzDtDMOoGIY5zjDMCwzDcJ8S9s9XyjAMa+ef8ykXD/0KHQzEB3V9sc2sGY9vf5xXlfa7E99hWcEyt1zDtopt2FNNZl8vSbgEDw590C3nt0W4dzjkYvLmU9RaZONo4Fj9MSj0ZMnHlJgpvXJtjkjwS4C/lJygu1LCe6juELEtF8t7NeM7KJAMSvVCslSa9ia8kCm1WqHlZEo9djD2oTKlAMBouaIWtHdfQrA3fCkBt9kZZJl0k0qP6f/bjmVHKjvLcd/5p4D3MooblHb7Fv887Fz/WQf0uWICZUQZWU8zpSzL4pOt/Pc2g4nFuhM1Lp3PFWi1YLo8DuBmT8/UKjgqujkx/giz4aHt5yXGcMrPdQdPAO4KtMiRVCRASqhLUyW7cESOethPClgyfLJsUh1dtXcfmr7/HlX/+he0eXnQnTmDpq++QsXie8B208fUkb+m7kzPelmbv/2Od7zp8y/A6vUwKZWcTKlsiJuDUh4FXkfVSIqNm8g+V5EIfpdeShzDiESQjxtHjKl27XTp2jIiyWf/RZcpbSIXVOKDvZEUQn437WVKT1Olu3FB3oRNGEfx3BOUup0bAKwAcAcsP/d6ADsBJAL4D4CDDMN0R/LxBxv/Lk433/OMJqUOLVSZmfWXcWPZRhxrOGbz9W8ceAN1KtdK3mgMZgPeOfQOMRbmHYbnxz7fqwq2gOUBnuxPBmL2gtJtFduI7cFBgxEu758ggmEYZIeQkw9XgtJ91aQYxbCwYRAJRDaO7jl0+W6bsRpAVxbkVHU7zOaLQ0Gw2BOUdh8vbvmkl1kJtZ7MqNEBRQaPt2RCiBxjKMEjZ/l+T6nd/XlVbahXOJ99/Ify1pyeHkbc/3oalB4sbbFrbfBPfs/u47bQG83EIgwApFBZUaCrn6uDogYlJ9s8Pd3+d2PSoFBie2dho40jnYPuJ02P9INI6L5po76MLKsVx/c8KAUA+TjSK7pt+XLUv/kW5zj1oUNo+v6Hbr2H3kEmVFdsu8LAEWa9HopNXGVfADDW16Nt7VqoDxwErHpMGbEYsmHDuv2efNCZUlNzM4y19quR2letJLZ9Jk7k7XOVT5pIbKt27+H0zNqDzpSWNKrcUq5+vlBGBaVxwd5IovyK7WVKz9SSz4eOLGkHdFBK98X3FhdTUKoH8BmAVJZlh7Asu4hl2TkA0gAcBZAO4H1XT8qy7G02/vXesqsHt0F7x0lEAsRaZUp/PfWr3ddrTVp8nvt5j65hfcl6lCvIh/OjIx7lZAF7i6QAUgmvuM32w3R7Jek71ptepM7A6St1Mig1mAzYXL6ZGBsdMdpt18VHSgCpFmxmTRBIuzIZCp2RU5JzoWK9qh0Kyg7G1xOU2kUoAishJx9+UKNJSWZ86KDUlkDNvVOdrw4oOTfJaVMbsJTqJb1rYiKx0g4AOwucC4pKGlU4Ttkb0DYnfOW7rvSQf22nDBmw+PrpjPYntWVNKvy6vxwbT9ZBb3ROPKesScUpo+bLlA6J9uMIT9HiInS5Nc3kVDIoPVuv7JEHMq28m8mzsNETOJlSdwWl48dzB218Vpq++AKmdteybGatFvoKUsxLPp7M/OlLqX5ZF1AfOAizyvazoOXHn6DcSpbIyoYOhUDmXpFGcWwsp8zWXgmvoa4eqn37iTF/qnS3A5+JZFBqamtzyRomLcKX6PIws8CZPrQu6W/ouUJCsBxJIXRQ6nymdLCDoNSTKXUzLMv+yLLs/SzLFlLjNQAeOLd5FcMwvetl4aHPMZtZHC1vQX51G2cSQ3/RkkK6rAcq2itwpP4Isf/tyW/jqkFXEWN/Ff7V7Wwpy7L4Pv97YiwrJAtzE+d263zdgc6Unm3lXwEuay9DWTv5oJ0S2z+lux3khJFB6anmU9CZbAtM5Dbk4pV9r2DOsjlo1bUS+2bFz+qNS+zER+KDSDmpnhzgT/Z89aVJdX9iHTBFcDxKPcq7DqFKeP0ZFZqsAhizmeWUs2VE+gEGLVCyE2gs7JykT0sLw10Tud6Gw6hSUAB4de0psCyL3w+WQ2OlWCsUMLhjYiImpJC9fLSari1WHCUzgiE+EkygbE/oTKlKb0Krk2JKZU0qbDxF3qMfnZVKTGr1JrNdG4X1J2ox670deHZFHu7+8RCu/XIvFDaseKyhFz7D/aTwl3F9sKUiIbLtiAiF+0kdBoVZ0f4I8CbP3ZMS3lxqoWCIi8q7mrwTaF+/gbf/kmVZTgmsu4JSSXIyR4XXFmaVCi2//ubS+fXFxQCl6OszjVSU1ZeV8r5WuX07yu+4A+V3L4bqwAHeY1R77Qspak+eROvSP8n3nzLZwVW7Dq/YkR0F3va1a4nfi0Auh880fl91cWQkJCnk3EO5k99ihw9viYjT93ix9JWyLMsROooP8kYSVVpf1qTm9I12QAelaVSlBh2U1rRpOf3tvcFFE5Q6oCO9IgUQbO9AD+cXlS1qzPtoFxZ8ugeXfbgLN3y1H03KrqCFq4rYtVq0pmQNsS9AGoAZcTPw4NAH4SXs6usxskb8UfBHt65vf+1+FLSQPVt3Zd3V62W71tB+n6ebT8Ng4k62aIGjEFkIBgcN5hzXl2SFZEHAdN3GjGYjTjbxPzR/zP8RN669EUvOLEG9huzVGh42HDG+Mb16rQC3r5RW4LVnhn2hoNIZibKiSIYSY/Hv/b/D+Q5DB6VQEfe10iYV1FQpW46oDPhwKPDDPODjkcAfNwNGSyD778sG47Mbh+OmsXG4YUwclt03Divun4B7JpNVFLvPNmHpoUp8tZMUOLp0SASiAmSYkkpm8vYWNTrMZuqNZvx2gKwUmZcdxSkTjfT34niVOlvC++PeMiJR5isV4Y6JiUgLJ7MDh8v4LXEqW9T4vyXHiOzo0fJWPL08z+F7c0WOuKW7HUxICbG5b152lMPnglDAYCJ1jh2F3QtKW1R6zsQ3O8a5oNSsVqPiwQdRunAhqv71LxTPuxw1L75EKMcaqqo4lipeTgaSjmAYBoHXX8+7Tz5+HHxnzybGmn/8EWaN8xllOsgWR0dz/FEN5RUcK5r2jRtRcc+9UO3ZC9XOnSi//Q5e+xjNkaPEdvDixRBHR3OOs8Z3zhynr98V6KBUc+yYzWPbqNJd31mz7GZvfSZOIrZVO13rK6X9Si+WvtJGpZ5zf48L9uYE6XqTGTVt3M91k1KHBsrXNZ0qh04MkYO2Iy7qg2ypJyi10PHkNQBwyaiNYZgnGIb5nGGYDxiGWcwwTKjjV3noC4wmM+764RDhX7W3uAmLfzoMo8nysKBvYtYiDnQQNidhDsRCMUK9Q3F5MlmS8mfBn9CbXBdMWFtMGkwn+CX0eUlsVkgWsa0z6XC6mSvSsLOKfGBMiJpABIT9gVws55TFHq/nlvDmNuTi7UNv2zzPI8Mfcfu18UEr8Iq9yOD4cDmVNbwAOVzWAuvF22hPUOo6sgBiM4BREplS2rMvSs4gaPWdgMKqq+TUKmCXRUGcYRhcmhWJV67MwmsLsjAi3tJn+vCMQYjwJ9VQn1yWi0YlOaG541ymlc6UNir1KLJTQgYAq3OrOROka0ZwPwMiIder1J55fAdag4kjunTtqFj4SEUYFkf2utnKtHy9s4TIDHewJrfGZiDbwWmqd4uvdLeDedm2faj5fid8TKb6SncVNtrMltgjl6rakIoEnL4zPliWRfW//w3lJrI9onXJEjR+0qUmqz11itgv9PeHKMJ9VRIB11wNb6q3VBwbi6i33kLIvfcQ46bmZrQuW+70uWllXemgQZwsL6vXw1jT9X1jTSY0vPM/8kQmE+refodYuDHrdJwyVu9RIxF44402r8d79GhIYnrnvinLIauR1IcO8aoWawsKoDtJ/k1tle52QPeVavLyOP6m9siggtKLxau0jCrdlQgFiPSXIcRHwvFvLW3k/q1of1KpSICEYDKg9RILERdEtkz0RQmvJyi10DEjXc+yrKvmUm8BuAfAwwC+AFDKMMydrpyAYZh8vn8A+t788QJiVW41p0QBsEyKv99TCoPJzClP6iiPUugVONFIPhisg8Ub0m8g9jVrm7GlguzxcITRbMTWiq3E2DWp1/R5oOcv9UeSP5kROVpPrtRqjVocqiXVaifGkA+U/sKZvtIPj3zI+1oBI8DTo5/G8PDhvXJtNHSmVMmSfUm5lW19UiLTn+wr7gpCfaCGH0M9ND1BqWO8yeAviGknekrpftL7/HYCrTw9bns+AjS2F0LkUhGevdR+NcTk1FAMPxfcxQV5cwJH6783jdZgwrsbyUqR0YlBNr0waQ/pKicypauOV6NNQ1Z+3DTWEkBkUNkBvueFwWTGMjtKwl9TWWMa+pz0RNqaQeG+GBHPFYWZlRHOyQrZYlIqmSlt1xpxvLKVc5zOaMKKo5V4a/1pbMiv5Yis5VaQr8mM8oPYCZEj5ZYtUKxbz7uv6auvYKiylGrrTpPesNLBg91aIcQIhYj9/HOEPPgg5JMnIeSBB5D451KIQkLglZEBOdXT2PTtN2ANzpWD02XH0kEpEAYHQyCnMlVWPbPqgwe5vqwAtLm5RPZRm59PXgfDQDZ0KAKvuxaiMP6e4uC773bquruDfPw4QNTVK87q9VDt28c5ro0K6kWhofAeM8buub1HjgTjZXW/MJuh2ru3c9PY0gLl7t3QnjrFW3FBf5dO1VwcYoG0yFFMkAxCAQOGYZBAZUv5dCpOUfek1HBfThUKwNNX2ge2dRd9UMowzFwAd8KSJX3ehZeuBHAVgHgA3gCGAHgXlhLgrxmGudK9V+rBVX47UGFz3/ubCrGjoIGz+j383ITgQO0BmNiufWKBmAhcUgJTMCJ8BPHaP8+QPR6OOFJ3hNPXOCNuhkvncBfDwkjVPrqX9mDtQaJXU8AIMC6SFHboL/iCUusHWGlbKfbXkuILk2Mm4/mxz2PD1Rtw42DbK9Duhs7qtujrIRZ3KZSazCwOlrpUrHFewbIsNliprEYyPD+rn/0yNQ8A5GTgEQwFmlVd308y48fiUi1ZkdGJXgnk/2X3rWZm2BaeEjDAE7O7vH0ZhsHYJFLNd3+J7c/zD3tKOSW4d09KsnE0EBNABaVOiPj8vJ8sDZ40KKRz4pZOTWqLGpQcAaP86nYo7CwU/XOyDnXt/CrDaj1XvIwuk6N5dcEQoi800t8LL8133uoj0l+GVMpehu4rbVXrcc1ne/F/S47j021FuOenw1j802HiZ6eFp7JjAhy+N2s0op7OBlrv1+vR8PEnALiZUq801zyinUEglSL0wQcQ9+WXCH3oQQj9uxY7gheTgZyxugZtq9fQp+CFLt+VDhoEhmE42VIyKCUXda1RbPin8/+aI+SzVzpoEIS+vhB4eyPytdcAMdkzHLBwIXwm9d4CsdDPD97DyUVbxYYNxDar16NtJVm663/llWCE9r27BVIpvEePIsba164FazKh/v33cXbyFFTceRdKFlyFkquvhr60lDiW7rFW600XhVhgGU8/aQd0xpNW/gYcK+92QKuE060IvcFFHZQyDDMYwM8AGABPsCzrtJ8Ey7IPsyy7gmXZcpZlNSzL5rMs+xiA+88d8qYL58rk+wfAtjeHB7s0KXU4ZGdyr9QZcecP5EMiKUSOILlF5+pw3WFi3/Cw4ZCJyAnRNanXENv7a/dzhIDssamclHwfHDS4T/oa+aCD0j3Ve6Axdk34dlTuIPbnhOb0mTqwI+igtEHTgBpVV9nUupJ1xP5AaSDem/oeFqUtQoS8b0V1kvyTIGJIhdLUWPKhsa+oZ4b3AwmN3oQTVW04VdMOlc6II+WtRDlnNEOps8pDATG/D6MHK7zJoJTOlFqXsWUypQjRlNo+1wn7Xsv2slfPzh2MLKrPcEwSmcXdX9zEm+VoUurw8RZycj86IQgz7SjM0plSRz2l+dVtOE5l/DqypIAlQ2CNwcSiuJGceB2kgupwPylk4q7JtsnM4lcq8O2goE5J9LIKGPs9pYDFGmbTo1Pw3ysy8ebVWVj/yGREB9juy+ODLuGlrWEeX3qcI6q26VQd3t9kyVqzLMvJrubEOr7ft/65DPoSMnNsHQgCQNuqVdBXVkFzmHzG0j2ZvY33qFGQDR1KjNW+9BIqH/kX6t58C5o8fiVYs0oFQyWZOZemWBYbaZ9V6yBKc5SsPrJGsXFj53dETfWTykZ0BYQ+Eycg4eef4DN1KmTDhiHsiScQ8dKLNs/rLmixovZ162Fs6aqwaN/wD0wtZMWF/1ULnDq373RSIEqxcROKLplj8WO1yhjrTp5C2S23wtjY9VkO9ZUixIfUJr0YSnjp8t14q0A0IYQsuS1tcly+m24zKCUXt4o8mdLeg2GYGFi8SgMBvMuy7AduOvXXAOoBpDIMw5U09NAnbD5dT/StycRCu/06ADAlretBTovlDAvn+n/Nip/FCcyWFdqf4HVgZs0cS5L+ypICwMToiUTZsMao6QxETWYTNpZtJI6fFE0KFPQn8X7xCJAGEGPH6o91/p/uhZ2bNBcSYf+IbIuFYiT4JxBj0WHk5HDPeRyUag0m1Ldr8U9+LR7+7ShGvLIR8z7ahUs/2InMFzfg6s9IVckMb2oC4SnddQ5O+a6is6e0XqElejSnChystZbvBXTOWSncPTkRmVF+mJYWis9vGoG7eLKatO9pvULHOzF6f1MhJwP53Dz7JZx0cOYoU7ryWDWxHeHnhRnpXUGvv0zMOSc9YaMzvdPTw3HlMDKb/9uBchhMXIsYukc1IUQOmcR+9ggAQnykuHlcAq4dFQd/b65SryMmUdYwxypaO0uY95xtxKZT9Xwvw1c7i1HZokZhvZLT5zs0lltWbI1ZpULDJx8TY16ZmUhas5os0TQaUfPsszC1kfc975Ej7Z7f3TAMg+DFi4kxVqeDYsMGNH/3HUoXLkTDx59wFlQ4/qMCASRJlu8B7bPaYQvDmkzQHLf9PTRUVUF3rkSVzpTSWUpZTg5iP/8MCb/9iuA773CYjXQH/lfMB2OVoWX1ejSf83dljUY0fvopcbxs5AhIE52b/vrNmwfGmwyk6KC/A2N9PWpeeqlzm2EYZFCK0HTrwoUIXb4bH+xt9X/75bsmM8uxzqE9kjugg9KyJpVD26yeclEGpQzDhADYCCAOwHcAHnfXuVmWNaMrw2k/CvLQa2ynypUmp4bgxcsz4eslsvEKYE6mJWtmZs0coZ+MoAzO8VKhFPOT5xNjf5/9m1e5luZE4wnUq8mJwcz4mQ5f11sEy4I5Pp1LC5YCAA7VHUKTlgyUets+xRUYhkF2aDYx1tFX2qRpstsb3B/QYkdSb/JzcKK6Da1q10Wz+gu13ojX157CyFc2If359Rj92mYs/ukwVh6v5igE0syKocoiPUGpc1Dlu0FQoOlc+S4dCE0RURmfYTcDAqtAx2wESp2zYnh0VhrWPDwJ390+GnOG8FcZJIbIEepLiiPRfaWljSr8SinuLhgW7bBElNtTalvoiGVZrM4l7cKvGh7NUfWlS9dO1XRN2Mw85fRjEoNw81gy+KhX6Iiy9A4Ol5HZI3v9pO5kTGIQpFaepyYziz1nG2E2s3h9HVfErgODicVHm89yMqvRATIkBHvbeJWFxq++gqmBfF3YE49DFBKCgGvIqiI1ZYUijolxqC7bG/hMmwofKlNnTePHH6Ptr7+JMVrkSBIbC8G5oNtW+a7u7FmO7yhDqdIqtmyF/uxZjtCPbFjf6B3YQxQUBN9LSXXfpq+/RsuSP9Dw/vuc7HjQrbc6fW6hjw8Cr7/O6eOVmzZDbZVlp0t4LwZbGI4djNV3k1bgLadsYcqb1dAayAU0Wy0FdFBqZi2e0r3JRReUMgzjC2AdgHQAywHczbriwO0cHUuKfeM264HDEWoyMDUtDKG+Ujw6K5X3+PQIX4xKsKzwl7eXQ2Ugv3gZwdygFOCW8DZrm7GudB3vsdbQpbsJfgkcsaG+hvZG3V+zHwdqDuDPArJXdnDQYE62r7+xJXa0p3oPWHR9vb1F3hge1r8PeVrsqN1UAS9x162YZc+fbKnWYMKNX+/HFzuKOYqsjkgOlSNbTons+Me68eouYOwIHVlnCryhxTCGFBJC+jwgjlQlRZFrIm32sPSVckt4rflwcyExUZKKBHjiEsc9hXRWs11rtOkVeryyjZNJnZcdxTmOLl2zVsstrFdyRJJGJQYhI8oPoxLIzOGPe7itG4coZd6OZ0xv4yUWYjSVsd58uh6r82o4ZbvW9x4AWHakEr/uJ3+WiSkhdjPY+soqNH/7HTEmnzwJ8rGWz1nw7bcBdjJ6PpP7p/KGYRhEv/2WXTuVujfegMnKuobjrTqoSyeAE5RWVoI1GDjZT3FsLAIXLSLGlFu2cHxLRVGRkMQMjB77kMWLCcEjmEyoffFFNH39DXGcV0YGfGe6tsAect99EMfHccaFgYGI/vADCIPIz3LDB13ChfRCz4WeKW3XGtCsIhetrbOj8dTiEW0Lc5oqbw7xkSDEh1xE7MBHKkKkP9lO09sKvBdVUMowjBTA3wBGAtgA4HqWZd2ai2YYJhNAGgA1ANtLkh56jepWDWraSOGJkecEjG4eG4/hlCk8wwAvzMuA4Jz6GF26G+wVjDBv/j6nJP8kjuDRh0c+hFJv+4vLsiw2l5GluzPjZ/apNykfc5PmIkxG/px3/nMn1peSSop08DoQoIPSM81noDVqsbOSLN0dEzmm30p3O6DFjs62FmIUNYHcdZbqtRygvLexAEfLW11+XVq4L76/fTSELVQpXFD/LsycN1CZ0kAo0aTQwGRmccIq6BgjOAUxrLLRAhGQMAFIpgzti0gV8J5Cl/DuL2nuLIM8W6/EX8eqiP23jk9AlBN9k3zH2CrhXZdHZkmTQuQYzJMRoMWOrMt3D5SQwXR0gKwzML55XAKx70BpM9G/Wt+u5ZTZjUywXwLrTqZQJbx/Hq7EU3/mEmNJoXJse3waEZgazSzHxmfiINv+qSzLou6VV8DqrSbKQiHCn3qqc1McHQ3/eZfZPIff5fatQ3oTgVyOmPffQ+LffyH8uefgf9VVxH5zWxtalyzp3NadIad10kFdi4yShATy5EYj9BWVUFP9pLJhQzkZWu3Jk2hfuYoYk48iq5f6E2lKCkLuucf+QQIBwp/7t8tzGaGPD+J/+AHyKZM7z+MzbRoSly+D3+zZCLnvPuJ49YED0BZYFtvoTGmjUod6G8JjFwLl1D2FYYAYqwqSUB8pxxbG+j5Eq4HbKt3tgKPA6wlK3QPDMEIAvwGYBmAngKtYlrVbI8cwzIMMw5xmGOZ1avwShmFG8ByfDWApLMJJXzs6v4fe4Qjl9ejnJULyOf9RkVCA7+8YjdsnJCA6QIacGH98fctIjLcyHKeD0ozgDLs32dsybyO269R1uH/z/ahSVvEeX9haiHIFWbrWn6W7HUiFUizOXmz3GJlIhgWDnBMw6EuyQrKInlgja0ReYx52V+8mjpsU0/+9sJxMqb4dwxPIW/GuwoEflDYqdfhudynvPrGQwZjEILx8RSYOPTcTp/87B2senohPbhiOJYvHYs3DExEbKAOaKC03T1DqHJTQkYBh4WNWoK5dS9z/JgnyyNfFjgGkvkASFZQ2FQLtZP9lT6AVeGvatKhotgSPH2wuJPr9vSVC3DPZub+7l1jIWdW3ZQuzg/oOXZoVwXsfpzOlNW3azvL5A6Xks8Q6+zgnM4JTpvzKmpOdlhR7qeywr1TkcALoTubnREEsJH9eWm3+yUvSEeHvxSlHtsZLLCD0Fmjali+Hcts2Yizw+ushTSYd7YLvvReMhLsgKMvJ4QgO9QdeaWkIuulGRL32KnxmkvoOzT/8CNZgAMuy0FJWNl7pXQJNosBACIPJKgF9cRE0R48RY97DhsF7xHAIKBEouu+UVqbtb0IeuB8B115rc3/4s89yemCdRRwRgbgvvkDa0SNIO3YUsZ99CnGkpQMu4NpFnN9ry2+/AbCozXpTQVj+BSx2RC90RfnLIBV1/fwMw3D6Sq1LbmnfZEfew3RQWugJSt3GgwA6ZtONAD5lGOZ7nn/WT/sQWLKedG/oOACHGIYpZRhmM8MwvzMMsx/AYQCDAWwH8Ezv/jgebEH38QyLC+zMggKAn5cYL16eid1PT8ffD07EjMGk5cHJZm5Qao8pMVMwJoL04zpafxSXr7gcHx75EAYzWf5FZ0kj5ZG8Pasu01YF/H4j8L/BwA+XA7V5jl9DcU3qNRgVYftBeGvmrQNGddcab7E3p1fzx/wf0a4nb8ATo/rfWzXKJwreIrLEJjyE/MyWN6s5K6IDjd8PlENvJe4iFjL44Y7ROPzcTJx8eQ6W3DMOt4xLQIiPFF5iITKj/HFZdiTGJAVb+vpUjYCOmjwEe6yZncKbWwYaxLTjYGkz6tq7yqg5QWlHMBqZA3hR3+Pi7W67vORQH44q5r7iJpypVWB1Lhn83j4hAcE2ysf44PSV8mRKm5Q6nKImplPT+KtdEkPkkFB9pqdrFWBZlpMptQ5KJSIB7pxIirkcLG3Bx1stPYdrqUztqMQgXi/A3iLMzwuX53DLlTsYkxiESzItz77Fk5M5ZbwdzM2KhJ8Xv9iSoaoKda++RowJQ0IQ+uADnGOliYmI/O/LgKDrfQS+voh85b/9XiVEE0IJIBnr66HcsQPG+gaYmsmSbK/B6cS2NIlcYFHtPwBDBWlPJxs+HIxIBJ+OzKANvMeMtbu/r2EEAkT+5yVEv/8evLKzLWk6ANKMwYj5/DME3dRzezWBTAYBtXghkEgQsJBslWr/eyXMKhUEAobj4Xsh95XSwkV0uS7A7Su1VuulhdwcBaW0Wjj9endzMQWl1nUzCwDcauOfD/elHDYA+BZAO4AcAFcDSAGwC8DdAGawLDuwZ5QXMEeockI+M3JbmFkzTjWR/mmOglKGYfDi+BcR5EVOFA1mA77K+wqPbn0URnNXCd0/Zf8Qx82Im9Hzh7JRB/y0ADi9GlBUAyU7gB/mA4o6l04jFAjx5qQ3EefL7e/ICc3BXVl39ew6exG6hHdb5TZiOzUwFZE+/a89JmAESAkkS3i1TCVnEj/QS3hpEZkrhkZjSmoogn2kEAudeLQ0U1lSocTTU+osIikgJSdiQVBg1fGuv0kEmjBIQFVrJJ8rGRQIgQSqaqB4m9suj2EYTk/jltP1eHfjGcIixVcqsutLygfHq5QnU0r3ZMslQgyNDeA9n1goQDKVDThTq0BFs4YI8AFuT+ht4xOI0jkAeHdjAR789Qg25JP33g4hvb7kgWkphOBRB2Ihg5fmZ3Y+d0J9pXh4xiDOcVKRAA9P544DlrLdmhdehFlNTnUiX34ZwoAA3tf4X3EFEv9cipAHH0Tw4sVIXL6MKH8dKMiysznZ29Y/lnJKdwXe3hDHkOJskhRyYa11GanIL/Dx6bSQ8Z1uW3HfKyNjwPST0vjNmYPEP5ZYsppHjyBp+XL4Tp3aq+8ZeO21RF+yWa2GYpNFm4PuK72Qg1J6sZovKKXHShotr1HrjRyP08EOqjcyqPLoogYl1Hrbvs095aIJSlmWfYllWcaJf6U8r7mNOtdelmXvZFk2m2XZEJZlxSzLBrMsO41l2a/d3afqwXm0BhPyKSEHV4LSCkUFlAayPMFRUAoAsb6x+GLWF7y9p9sqt+GNA28AAApaCnC2lVTvc4sVzNGfgEayrAiaZmDTSy6fKtQ7FL/M/QXXpl2LIK8ghMnCcNPgm/DFrC8gFTqf0ehrcsJy7O6fEjOlj67EMamBpODWiaYTGJ9MlmTuOksqSA8kyppUnN6URSNdDCibyO8BAhMswZIH56DEjkKYNmw61RUITRJSWVKvACBqaNd20lRyf8l2wI2af7RX5vr8Wk6gduekRAR4u9bjzfEq5cmU7qYWdEYnBtldKBnMI3a0n8qSBsslSA4lMxBeYiHeujobdAKUXrCRigSYnUlW5PQFyaE+eHthDkRWFygWMnhnYQ4nu3Tv5GQsGtkVYElEArx1TTYSqKxLB+2rV0O1m2yP8L/6KvhOn8Z7fAdeGRkIffABhD36f5DEDtxFKDozp9y5E4otpCCYND0djID8XEmTyKCUpYJ2WU5Op42Lz/RpEIbw9+v6zZvXrevuSwReXhDIHPeCuwNxZCTkEycQY23nenDpvtL8anIOeCFR1mzbo7QD+jvbkSnl9U0Ot5+HS4/wJSo8WBacKhR3ctEEpR4uDnIr22C0algSMECOjRVyPugsaZBXEMK9nZtMpAel468r/sKNg2+ESEBazyw5swSrilZhXQmpzBvmHYbh4W5Qgz34Lf/4iT8BpevBTYBXAJ4b+xy2X7sdmxdtxlOjn4JczD85GSjQmVKa/raCsSY7hLSwOVZ/DBNSyCBj99kmQqF0ILH7LDlhD/GRuLT4AwBoJFUsEeQp3XUJPzKLEsmQf5OJAsoKJnEyGfTTQamiBmiklHp7wKVDIjllsdYEeotxB1X+6gwcr1KeTCldZTAhxbZQD8C1RDhVo+BYwYxKCOKtaBmfEoJn5w7mjFtz/eg4l4NvdzE/JwrrHpmEh6en4JEZg7Dukcm4Yig3AycQMHjrmhys/9ckfHvbSOx6ahrvcQBg1ulQ/957xJgoLAzhTz/dKz9Df+A3Zw4EcqtnntmM1t+XEMd4ZWZyXidNsX8fkw3r8jwXSCQIvvNOzjHCkBAEXruIM36x4385acGn2rsXhvp6ZFJepaVNao5C7YUCx6M0iJspTaAC1bJmNcxmFmeoftKEYDm8xPYXgr3EQqSEkoHriSpPUOrBg1PQIkdpEX7wkdr2JqWhRY4GB9s3c6fxlfji6dFP4+vZX0MiICch/971b3yd9zUxNjdxLiHQ0y2aioD6fP59Jj2Q90fPzn+eEOcbh0R//klurG8shoQM6eMrss2wsGHEdoOmAYOiyIdom8aAgrre7d/oLrTv5ISUENf75WpJJVCEpfMf54EffzJgiLYKShmYMZHuJ+0o3e0gOAXwpXoOC0il7R5dnrcY87Jtl8v/a2aqzV5Fe3CCUipTWt6kRiUVqNpTjwUszwlrztQqOEJFdDmyNXdNSsLLV2TyfgeiA2S8pbF9yaBwXzw6Ow3/NyuVI1xCkx7hh+np4Qjz9bJ5TOuyZTBWk9ng8Oefg9DXfn/a+YRALoffZbYVgwF+ISJpuv37mPdw8t4fdOst8L/yyq739fZG1BtvkAGxBwCA74zpEHhbBWFmM9pXr0F6pC+nJ/ooNRe8ENAaTKillIXjeMp3E0IoWxijGTXtWsKDGbDtT0qTGU3eH3Mrey8T7QlKPVxQ0CJHI+IDXHo9R3m3mwJEI8JH4KXxLxFj1n6ZHcxLckOJjiOPwdNrev4e5wEMw+DaNH5lwEWpi3oe/LuReL94Tg9ylfYUpz/tmJXFxECBZVlOUDomMdjG0TZPAtRQQWmk/Uy3Bwp/spfNOlOawZQjmKEWNGgbGIYBBs0ix04sd+cV4pGZg3h7GnNiA3DDGG7fujPQ5bsNCh20VqqyfGW3aeH2J190T5rGYOpUC+7AXlAKALeMS8Dy+8ZjbFIQGMby652YEoLfF49FkLx/bajcCWs2o+WHH4kx2cgRLntTng/QJbwEDAP5KG5QKgoMhCSRf3GUEYuJTClgEQ+KeuN1xP/8E2I+/ghJ69fBhypT9WBBIJPBd/ZsYkyxYQPEQgFyYgKIcXoueCFQ2aLmdFjwle/y2cKUNqo4Zbdp4c6pgWdFk5now5T/sjsZOLM0Dx56CMuynNWx4XHOlxSyLMtR3s0M5pbnOMvlyZdjYepCm/vHRY5DWpBjw3iHVB8jt2l13PK9FqXTi4CrB12NBL8EYiw1MBXXD76+fy7IBgzDYGjoUGJsd/VujhjLsW54gPY2ZU1q1CtIARjaAsQhihpATX0mI7L5j/XAj53y3UkCKuAPSrL07NJkXklu1xwDji8BDn4NbHwROPIToOt+tj4+WI4Prx8GmVWJWGaUH768eYRzYlg80EEpAMKXen8JOWEak8RfdmtNqK8UsUG2e+N8vUScHkw+cmID8Pviccj/zyU49fIc/HzXGMTylNedz6h27YK+rIwYC33wwQGnoOsOvIYMgTSN/xktHzfWpqATHXh2jufk2OzB9B45Er4zZ0Icxq8S7cGC32WkT7rm+HEY6us57SMXYlBaTPkHh/hIeCsB+WxhihqUyKP0VuheXFvQAm+lTWrU8XjB6o1mzpireIJSDxcM5c1qNCrJEkhX+twqFZVQ6MkJmDMiR/Z4avRTGBzE7TcSC8R4bORjPTp3JzWktxkm/guw7v9kzcAZspf1QsVL5IWvZn+Fy5Iuw6DAQZibOBdfz/56QAo0jY8aT2zvrtqNnFjyIXG0YuA9WHOpB1uIj4QjQe8QOksq8QUCXe8vvKihlIqty3enCql7Au1L2kHCZK7i8YrFwJrHgN3vAysfBD63b1vhiEsyI7D18al4Z2EOfrhjNP5+YALC/WyXhjrCz0sMPy9yIlZupSh5gApKRyc4t2AyMt72cROSXStP95aIHPZqna90iMt0IE1Lg/eYMTaOPr9hGAaBNmxOAhbZ9uv0nckvXuh76Ry3XNfFjHzMGAh8yBJ05ZatnLne8cpWGEw9D5IGEsWNZFCaFGK7FD+JEmX7+1g11HpSg3VoXIBT7zs40g++1D2Xvs8CwFc7iznX6CqeoNTDBcNByug8xEeCOBdWqfObyb7MQGkgIuQ9k/GXCqV4f9r7SAnosgCRiWR4beJr7smSGrRAAynOhLixQAr1UCzc0PP3Ok+IkEfgjUlvYPn85Xhz8psI9HJRgKePmBRDWnK06lrh6096OBbWK6HQkj63/Q2tbj0k2t/1LEnlAXI7IovwL/TgBFRPaSjTBgkM8IEaIxhKsIgu0+1AKALGcT0lCZS19ve31wDrnwW+uQRYfg9QeZhzSIS/F64ZEYMpqaEWj9oekkQJb5w9Z+he06YhAlQAGO1kafm4ZNvHTUq135N6sWBWqzkKtAELF16QWdIOAq6+Gj7TyEUd+fjx8J1t4zsFwGfyZIijyH5tgY8P/OfPt/EKD87CSCTwmUIq6Ss2beJUxWkNZuRWtvbhlfU+xQ2kMwQdeFpDiz/RmePYIBlCnPSHFgoYTraUVjgHLIGqydQzcUbPLMDDBcOOAlJldnhcoEsPS04/aXCGWx62UT5R+GPeH/hs5md4beJrWHfVOsxJdNOKaf1JwEx5RkVkAanU+Yu2WrxMPQwYonyiiMUKAChU7YRYSMqv5/WiqEB3OEHJ7Q+hHn5OUbKD3I67MDMtvYofVxk1gmnGeEE+xIzVirhAzPUktWbknUAUf7khB/oe0lQEfDEZ2PcJULEPyP0d+GYWcPJv587XTWixnrP1lgoXevXez0uEdAfm8B3MzggnrFM6EAsZXDqk//2NBwKKrVtJixOBAH5zLum/C+oDGIEAMR+8j/Bnn4Xf5Zcj/JmnEfPpJxwrGOI1IhEiX38dgnPCT4xEgshXXrmghKD6EzoTrdq/H35mHad3fNuZgWur1h1K6EypnaCU7gOlGRrr2mI93aKz6VQd4Q5gNJndUjLtCUo9XBCYzCx2FJI3oClpoTaO5udkIzcodRdioRgToyfi8uTLESxzURTGHnTpbnAKIPU9lxmxmmDplUAZ6Snnof+ZGU+Kg/xTth7pkWR2nw4C+xOWZTly8EMcPPw4qJuBqiPkWGLPSkQvSrz8LWXPVsyK1OCJ5EryuPhxgNSO4qpIAtyw9NxC1rl7hiyIW9YLANte7/q/phX47TpAVU8ew5qAFfcCLWTfoTsZxAlKLRmEfcVU6W5iEAROlt0GeEt4vUTnZkVeUEJFPUHxz0ZiWz5uHEQ2fDYvJBiJBEG33Izot99C0K23QuDluPxcPmY0kv/ZgLgff0DKls0XfPDel8gnTQIjtlLuNhig3LGDM+e70IJSuqc00U75rqOgdLiTpbsdzMogqwYblXrC7SK3qg1KnZF+mct4glIPFwTHKlrQqibLHKemOS8YYGbNyG8iy3d7InLUZ9BBaYeCqU8YED2C3FfwT99ckwenmZ9ElnO16loRFEr6d56s7j1PMFepbNGgTUN+z4ZEOyeW0Mnp1ZbApQORFxA71g1Xd5HBMEAImWl/fhQwqH0feVyKE6qoPqHADUuAxwuAh44AjxcC9+8DQil7i/1fAGc3AyYjsPQ2276mBjWw6SWnfxS7aNuAunzA2KUXQGdKLabwLA6UOG/jwsezcwcTJW2R/l54+lKPVREAsEYjVHv2EGN+nh5Ju4gCAyEfPfqiCNz7EqGPD7zHjyPGFJs2YWoqGZTmVbWhUXlhVIi1qQ1oorxX7WVK/b3FdgPTKamuJW0SQ+ScqpNlh7sWQLecqqdf0i08QamHC4JlR6qI7UFhPhw/O3uUt5dDaSDr9TNDzuOgFOCW8BasA0dP3EO/EusXi+Fhw4mxZgFZ2nqyZuAEpflU1jbAW+zS9wwsCxz+nhwbNAuQXFgKpX1GGFXNcfQXoK2CHEux3fvGwScMCE629JpKfYBrvgVokbBldwE/XwUUb7V/rpN/9SxbyrLA7g+At1OAz8YD/0vtFGwbFEZOjto0BuRVtaGIyiS4alUUE+iNbU9MxStXDsEL8zKw9uFJiPR34fN9AaPJzYNZQQoByid5Khw89A++M6gS3h07MSLaB96UFcrW0+4Jlvqb07XkPEAkYBxqptgKPBND5Jy+fGeYm0W2Maw4WoXmc4HyZjf9nj1BqYfzGpZlUVinIFZsAGBedpSNV/BzoukEsR0iC0G4N7eUa0BhMlgyCNYQQSnp54WWUqCRzMJ56H+uTr2a2C5VHwMj7sr4FDWoCB/G/oRTuhtlR+TIZADqT1sy9EVbLdZFh74BqighnCF2vAA92IcOSuup+0FQMhDGVf92mvBMYPpz5JimGSjZTo7Jw4B7d5PlxKwZOPBl99/74NfAxhcA07nsgKYF+ONWoLEQ0YEywmYGAD7YRN7b/LxETlseWOMjFeGmsfG4Y2IiAj1lu52odu0ktqWpqRCHe+xLPPQPvpT4lFmlgvHoEUxIIbPSq3Jr+vKyoDOa0KrWOz7QRU5QFVOp4b4ObbWuHBYFvsfzDaO75xF93ahYQvNCZzTjw82FKKhTcDxQuwvX4MaDhwGM3mjG+vxaLD9SiRNVbWhS6TnJP6GAwcKRMfwnsEF+I7d0d8ArCjacBkxUaYp1UBqRDfhGWjwhOyhYD4Sm9s31eXCK2fGz8eaBN9Gu77qpSwIPQldvyXSbzCwK6hTIpszB+wPa52xItL8lo1WyAzizFmguBpR1gLLB0mtIi3DRBMQB6fN68YovcMIdVHNkLgDvrMQVRt0J4F+29wulwPW/ARFDgGE3Afs/69p3/Hdg5kuAUGzz5by01wCb/sMdN+mAjS9CeP2vyI7xJzxJ6ZX6KWlhblH69WBBuYvUJJBPnNhPV+LBAyAKDYVXdja0uV32Yoqt2zDvslux8WRd59jus41oUOgQ6tu7tnBmM4u3/zmDL3cUw2RmMS0tFO8uGuq2hS2u6r3jBbeUMF9cNyoWvx3oqp6JDpDhhjHdC0rD/LxweXYUlh/tqkz8cW8pfjtQ3q3z8eG5Y3sYsLAsi9JGFTbk1+K73SV48e8TmPzWVjz821FsO9OARiU3IAWA60fHIsqVkkIAJxrJTOl5WbobEA/IrBTVGAZIpcQVCi4ea5jzBS+RF+Ynk72l0sDDALoCOnetQvYEi8gR+WDMivQCli8GfpwP7P8cKPzH8rlUVDsOSIFzAYtnbbTbxI7hltdaM+Sqnr+HvaBWIAIW/QDEjLRsj76b3K9utGTJXWXH2wDlGd1JwTqgvQYjE+yrR05Pd61nyoNtjC0t0OblEWM+kzxBqYf+xWcqaQ2j3LoVMweHEVUUJjOLtXm9ny39bHsRPttW1KlIu/VMAx749QjMZve0THFU750UGPzP/CG4c2IikkPluHJoFH5fPBZyafefuf+amQqpqCt0NLOWjKm78ASlHgYke842Ys77OzH1nW2456fD+M+qk/hhbxlq27V2X5cUIscTs10TptAYNRyRoyHBQ1y+5j7HXj9pB3Rfaflei/qphwHFNalkCSsrUEDk2+U/OxDEjuradRyhhUln3wXy/ujeCUfeAWS6IWg6jyhuK8YLu1/AvBXzMGfZHDyy5RFsKN0Ag6mbXrQSbyB+PP++2LGOM6mukjwDnQq9kTnArauAtEu79gcnAzGjyNfkLnHtPdTNwLFfbe9nzUD+crv9omIhg6mpntJSd6Has4fQI2BkMshGjLDzCg8eeh+6hNdQWQlRRRlmZZCtVyuPk/7f7qZNY8AnW89yxvcUNbnlvdV6Y6fCeAe0D6ktJCIBnp+Xgc2PTcX71w1DrIM+VEfEBXvj4RmDbO7vqd24Jyj1MOBYcrAcN36zH2fqbKyU8yBggJmDw/H74rHw93atVCy3IRcGc9ekUMAIMDRsqEvn6BecCUoTp1jUTTtgTcCplb17XR5cJjkgmSN4JA7Y3/n/gSB2RGdJJ0qL4HfiB8cvlIda/jHnVq+DkoHL3rX8G+gl8m5kQ+kGLFy5ECvOrkBZexmqlFXYUrEFj29/HLOXzcZnxz5Dg7obFgbZi/jHJz/eswvm47qfgWergKfLgXt28AfEWdT1nF4D6Jy/l+Pwd4BR07UtEAFJU8ljirZiXHIwAm3c62dnRnj6Qd2IaucuYls+ejQEEs/v10P/Ik1PhyiSFN9RbN2KK4aSmiKHy1pQ0axGb/HX0Sqo9fy6Dx9tKST8PLvD/pJmWJ9CKGAwOLL/PG/vm5KMq4ZzfbIBIMzXsV2SPTx1Ux4GFDsLG/DM8jyHIrGjEgJx/eg4pEf4wUcqQqBcDF8vF/uWznGg9gCxnRGUAV/JADe5NpuAWrKcCpFDucdJvC3qpqdWdY3lLgVG3NabV+ehG1yTeg2O1Hf5d4p8zoIRN4E1BONUjQJmM+u052JvkEsFpU9IVwDWiVORDBh3PxCYYBG+8QkFAhMB73O2HGYzYDYAot7t7RmIHK47jKd2PAUTyz9xadQ04tPjn+KL3C8wMXoirky5EtNip0EoEPIeT5C1CDj6M+lDPOzmc17FvYDEtg0BAEvJ8Pqnu2x/jBpLYJpzneNzm4zAwW/IscwFQNpcoHhb11jZHohZI64eHoOvd5UQhzMMcPekJPvvUb7X0msfO9ryefVgE5Zlodrt6Sf1MPBgGAY+U6eg9bffO8eUW7dh0u13IsBbTNgErs6twX1Tk3vlOjadqrO5r6hBhY0nazFnSKTNYxyxs6CR2B4eFwBvSf+FbwIBg7evycHgCD98u7sENW1aJIXI8eScdDy2qmfX5QlKPQwY2jQGPLE0F/SiUoC3GCmhPogKkCE2SIYZg8MxPM5+P5Er7Ksmff1GRYyyceQAoumsxQvQmshs/mOzFpFBadkuoK0S8HdNDMpD7zIrfhbeOPAGIXgkDjgIfcMcKHVGlDerkRDiICDoRawzpYOZMuToj5AHTH8OGP+g7RMIBIDg4gtImzRNeGL7EzYDUmtMrAnbK7dje+V2DAkegjcmv4F4v3j7LxKKgJuWAYe+AxrPWPpMs69109V3A3mIxRu10Kp//divzgWlhf8A7aS9F8beZ+mXt8agAqoO4+GZI7HpVB1Km7ruhbePT8TQ2AD+8zcUAH/cbBGJAwAwwLgHgNmv9GnWnjWZ0PLrb1Bu2wZhcBBC7rkH0uTemTD3FF1BIYwNZAZfPnFCP12NBw8kvlOnEkGp5tgxCBRtuHRIJCHAs+p4da8EpVqDiRBc4+OrnSU9Ckp3nSW/fxNT+r9fXihgcPfkJNw1KREsi84F88d6eF5P+a6HAcOHmws5PaP3TEnC4edm4c/7xuPD64fhiUvSXQ5I9SY98hrykN+YT5TpAkCtqha5jbnE2JjIMd37AfoSunTXN8riMcjHoNmAlOo/yPuzd67LQ7fhEzwS++UBsKzS9GcJL8uyyK3sCkoXCMlyPsjDzqm0eqB57/B7aNCQk4rLki7D82Of55RsW3Oi6QRuWXcLzrZwe5U4iM9lqS//ABh6A+BMhrU3oUuKS3Y451l66FtyO3qE5Z88BIjIIveV7Yaflxh/PTABd0xIxGXZkXjz6iw8d5kNC5z2auD7y6wCUgBggb0fA7vfd3xtboJlWdQ8/wLqXn0Vqt270b5yFUoWLoLm+HHHL+4HVLvI77o4OhqShIT+uRgPHii8x44FI7MStjSbody+HZfnkEHgyZp2Tl+mO9hf0gy9ldCPUMDgv1eQvfyHy1pwpLylW+cvrFOgoI687kmpITaO7nsYhnFrBZcnKPUwIKhoVuPHvaXE2KRBIXh6TjqEPfjAb6/YjkuWXYIb1t6A69Zchzl/zsE/pf907rf+PwD4SnwxOmJ0t9+vz3Cmn7QDsReQQQY7yFvq/mvy0GMuT76c2BZImsBILKU7dE9nX1LbrkWj0mI/JIAZVwjJcj4Mu8kSGHkgONF4An8X/U2MjY8aj1cnvIpFaYvww6U/YOnlS7EgZQFkIu7vr1nbjAe3PAil3v2TqV4l/TLAy3ohjAWO/2b/NS2lwNlN5NjIO7r+H0f1r1YfBQAEeEvwwuUZ+OSG4bh2VBz/BMlsBv68w2JTxMe2NyxBax+g2PAP2pYvJ8ZYtRrVTz8D1tBNwategYqMtgAAbZlJREFURLWb6iedMGHg26V5uGgQSKWQjyfvDcpt2zEmMZhjA7OqFwSP9hU3EdtDYwNw3eg4RPqTvZVf7yzu1vmt7VwAINRXimwnlXfPRzxBqYcBwdsbzsBg6qrbFQsZvHplVo8efnuq9uDhrQ+jUdNVj1+vqcdj2x/Dp8c+hclswtICMjibHjsdYlc99foDV4JSgJu5qDsB1OXzH9ubGLRA+X6g9oRlouiBYHDQYITJyIy3yMeiwpvfjwq81lnSYUwhwplW8oD+LBcdoJhZM14/8DoxJhfL8erEV4le0fSgdLw84WVsXbQVL49/GQl+CcRrqpRV+N/h//XFJbsPsQwYQipK48iPgD2V4cPfo6MqAIClusNanTmayipXUeXj9jj+q6WP1BZGLbDvU+fP101YlkXjJ5/w7tOXlKBtzZpevwZXMGs0UB86TIx5Snc9DDR8p00ltlU7d0JgNOCyLDJbuiq3GqwjwRIXyaskF4vHJAZBLBTg9gkJxPi6E7UuLyw3KnX49QBZYbJgWPQF7b984f5kHs4bcitbObLZt4xLQFxw96WrdSYdnt/zPMwsf+Dz2fHPMP638ShtLyXG5ybN7fZ79hlmE1B9jBxzFJTGT7SU+FqT200rj+5yYhnwvzTg29nA5xOAz8Zxf46LHIZhMDl2MjHWEZSeqGpz+wPVWawfptOEx8idYRlAmGs2TBcD60rWIbeBbA24J/sehMj4S6/kYjkWDFqA3+f9juwQsj98eeFyFLd2b6W93xh2E7ndXmW7bUCvBo78RI4Nvd4i1NZBFBWUKqqBdif8B3UKYPPL5FhAHNeO6PgS+0GzG9AePw5dYaHN/a1/uFbBwrIsTG1tYI1OeALTr9XrYWxshFmjsXmM+uBBsHorNTOhEPKxY11+Lw8eehP5ZPKZaVapoD50CJfnkHOe4gaVWxd3LW0trcRYdkwAAOC60XHwsfIDZVngxZX5LvmWfrWzGFoDWRp80xgHGgPnOZ6g1EO/wrIsXl1zihjz9RLhwWkpPTrvX4V/oV5to1TrHGojKRSUEpCCcZHjevS+fUJjAddYns4i0AgEQBaVucj7s++ylQX/AMvuArStXWMNp4HvLiVVNT1gSgxpCC70LgMEGjSp9Khr1/XLNVlnSqcJjpE7B83u24s5D9AatXj/yPvEWJxvHG4cfKPD18rFcrw15S2inNfMmvHZ8c/cfZm9S/RwII66n259zRKA0hz+DlCTCpMYcTu5HZwCSP3IsWonsqWHvgWUlDrmZe8BU58hx1T13PJhN9O2Zq3d/ZojR6ArKnJ4HpNShfr33kfh+AkoGDMWZ0aOQvVTT0FfZr9vlzUa0b5xI8oXL8aZUaNROHESzowYifI77oT29GnO8YqNG4ltWXY2hH5+nOM8eOhPxGFh8Moie84VW7dheFwAogPItgh3epaWN6vRriUXhLJjLKW1fl5i3DuFVAE/XNaCn/Y50VsPoEWlx097uVnSniRrzgc8QamHfmXL6XqOctkD01J65DFnMBvwzQnSVmBk+Eg8PfppCBjbH/nHRz5+fvTKVJHlVPCLAXwjHL+OLuFtrwTK97jvumyhVwGrHrGY3tMY1MCv1wGlu7j7LlLGRI6BRND1+WcYM0Ryi9hNfnXf95WazSyOn1sNDkczMgXUQ9UTlHL46eRPqFXVEmOPj3wcEqFz97Von2jcNJjMNG4s2+hwoW3AMfH/yO22cmDDMyA8v3RKYPcH5HHJ07nZd4GAWxHiqITXqAf2fU6OpcwEBs0EQlOBGEpp3Vql3M2wRiPa160jxoLvuQfC4GBirO2vv+yex1BVhdLrrkXTF1/A1GIRT2G1WrT9vRLFV1yJ5p9/AUstNhqqq9Hw0cc4O30Gqh56GKodO8Hqzi1wmc1Q7dmDkoWLiOtjDQYoNpJBuu/MGa78yB76mQM1B/DKvlfw713/xp8Ff0Jn6p9Fzb7AZ9pUYlu5dSsAYD7lWfr3saoe+4Z2kEuV7ob4SIhe0rsmJSEuiAwi31x/2inP1O/2lBLepwIGPU7WnA94glIP/YZGb8IrVJY0OkCG28Yn9Oi8q4tWo0ZFlnXdl3Mfbhx8Iz6d8SmvB+m9OfdiQvR50itTeYjcjhnh3OvCh1hKLa3JXeKea7LH0Z8tpXa2MGqAXxYB5ftsH3MRIRPJMDJiJDEm9DkDADhR1fd9pWfqFJ1+b1OEVC+z1N/i9eihk0ZNI77O+5oYGx0xGlNjp7p0nlszbyWypSbWhGUFy9xxiX3HoNmW1gFrDn8PrHwQUDdbgtP1T3EzmXQWswO6IsRRpjR/BffeY33uIVeT+wr/cVw9wrJA4UZg4wuWYLqtyv7x51Dt3w9TI5kN9r/iCvhfcQUx1rZqNSeo7MDU3o7yO++C/ix/NpXValH3yisov+12tK9di+ZffkH5HXfg7IyZaPzkExjr7SxqGAyoevwJKM5N5pU7d8HU2koc4nvJJQ5+Sg8DAZPZhP/s/Q/u/OdOLDmzBCuLVuI/e/+DK/+6EvmN/aAl0Qf4TptGbBsqK6HNP4kFw6KJ8bp2HfYWkeJE3SWP6hHNivYnEhteYiHeuIrM4Kr1Jjy1LNduK45SZ8QPe0qJsfk5Uf1qCddXeIJSD32CSmfE/uImHChphlpvBMuyeGllPkoaVcRxj1+SCi9x9+0MTGYTZ0I4LGxYp/fohOgJWDF/BW7NuBXDw4Zjeux0fDz9Yzww9IFuv2efQ2dKo50MShkGyFpIjp3825JN6C1Y9pyAiRVx44GMK8kxgwr4+RpuwH2RMjGanMiL5AUAWJzoh0zpfit1QU7pbvI04HwQButDPjn2CdEawIDpVhWGv9QflyVdRowtLVgKo9n1/sF+g2GAee8BQsqf9ujPwHuZwKdjLf+3JnmG7YUOuq+0+iiZdaU5/B25HTcOiLFa8KGz/KoGoOaY7fOZjMDSW4FfrrEEpBtfAD4eZWlPcED7alLEyCszE9KkRPhfQSqjG2troT5wkPN61mRC1WOPQ19a6vC91AcOoOrRx1D331eg2rPX/u/ImnPvocnPR/OPP5LXm5UFSYzH2/p84Ku8r/BnAbd/u1JZiZvX3YzfT//eb/oEJW0lWFW0ChvLNnKqSXqCND0d4igyK9r6xx9IDfdFRiRZct7hX3qqph3PLM/DXT8cwhfbi6DRO/aStobuJ806109qzfiUENwwJo4Y21PUhL+O2V7M+utoFdo0ZH/7/RdBlhQARI4P8eCh+7Asi5/2leGt9Weg1FkmU2Ihg6gAGcqayBKGYXEBuCInmu80TrO+dD3KFeXE2OLsxcSEMFwejsdHPd6j9+k3dEquam70SP5j+chaCGz+T9e2tg0o2gKkzXHP9dFUHgLqT5JjU54EEiYCrIksl9MrgB8uB6Y9C4y596IOdiZETwCs5qUCcTsE0locLZeCZdk+LTPfV2wprxfDiImCE+ROT+kuwZnmM1heSNp9XJFyBQYH2/DOdMC1adcSk8sGTQMO1B7A+Kjxdl41wAhNBRZ8DvxJ9Yga1JRnKACxHLjsHdvnojOlmhagpQQISuIe21zMVdwdex+5HZxs6VVtsvKCLfzHdo/+lv9aFvKIn0MF/HEzcOtqIHYU78vMOh2nP9PvMsuCg1daGqRpadCdOdO5r23lSsjHkn7Z9e++C9XOncSYJDkZES+8AMWGDWj59Vf+a+bBKycbgddeB/mY0Wj88iu0LumqmGHVapRefQ3nNYHXX+/0+T30H6Vtpfgy90ub+w1mA17d/yo2l29GVkgWjGYjDGYDonyiMCF6ApL8eb5LbqC4rRhvHXwLu6u67MQYMJiTMAdPjn7SpgCcszAMA/+rrkLjxx93jrWvXo2wJ5/EVcOjcXJNV5XRmrwa+PyZi+VHKztdHzadqsOavBr8vngsvCWOQyOzmeVULtmyannm0nRsO12P6jZt59inW4twRU40r4UV3fc6c3AYUsO5FX4XIp5MqYde5ZtdJXjh7/zOgBQADCaWE5BKRQK8fU1Oj0x4TWYTvsr9ihjLDM7EhKjzpCzXGcp2W4K5DgQix8q71gTEArGUeuKJXiwJpLOkAfFA4hRLwHn1t0DqpeR+gxr45zngi8lA8fbeu64BTqJfIqJ9yAUakc8ZNCr1KG1y3I/iLnRGE3adtZQcjhScgS9DKXWmzOyzaxnomMwmvLTnJULxWyaS4aFhD3X7nOlB6cgIJkvu15es7/b5+o0hVwFXfwOI7Yh0MAJg/of8AWYH/rGANzV5tdVXSquLy4K49xsAGESVpBZs4D9fYyGw92P+fUYtsPwuy6IhD8qt22BWWu1jGPgFVwDfzwM+mwD/BPI7rdiwgVDFbVu5Es3ffEscIwwORtzXX0E+ZjQiXngecd9+A1GEbW0Bga8vAq69FonLlyFxyRIEXLUA4uhoRLz0IidbSyMMCYHfvMvsHuOh/2FZFq/sewUGM5lli/HhZrj31ezDV3lf4bv87/DzqZ/x1sG3cMVfV+CBzQ+gQlHBOb4nbKvYhhvW3EAEpADAgsW60nVY8PcCbCrruchYwDVXW3rPz2FWq9G27E8sHBkLXykZaC45VEHYEAKWHlFaeNMWJU0qYl4LAFkx/EGpr5cYL83PJMYK65XYVsAtp69p0+BgKamzsnBkrFPXdCHgCUo99Bonqtrwxjquoh+NgAHev3YoUsJ8evR+q4pXoaiN7LWhs6TnPUVbyO3YMYDUxd8b3Ud1Zi2/ImZP0bYD+WTWSB93FVr+/BMtvy+BrqIKWPQDf2BTfxL4cT6w4t7eubYBDsMwnBJeodySSaEfWL3JnqKmzgfvVLp0N2oY4BveZ9cy0Pn51M840URmkm/PvB1h3mE2XuEccxNJm6pN5ZugN/ViyX1vkXUNcN8eIGsRwFAtGrIgYNFPXIVwGobh6Ss9yj2OZYHjv3HfX8QjNJVKZfurjwKqrt5Ps06H9g3/oOn1x6GqEdiuhG0ptZTz8kCLF3mHGyE++j+gdCdQdwJ+0oOw9mg1q1RQbLHc6zV5eah57nnyhGIxYj76EOLILh9G+fjxSF63FuHPPQf5+HEQR0dDOigF/ldcgaj/vYNBO3cg8j8vwSuDXORgGAaR//0vvMeQmVlrQh98AAJJ98UHPfQNq4tXY3/tfmLsloxbsHLBStyeebuNV5HsqNyBq1dejbXF9pWincHMmvFV7ld4eMvDUBlUNo9r1bXi/7b9H57e+TTym/K73aIgjoiAD9Vb2vT1N/AVmHGrk1olvx+sQGmj7WvtgPYnDfeTItzPy8bRwMzB4UgNJ+dqSw5yg/81uTXEPcbXS4SpaaEOr+dCwVO+66FbtGkMyKtsg0wixNDYAAh5MpyvrjkFowOVs2C5BK9flYXZmU6ox9qhUdOI9w+/T4ylBqa6LC4y4Dm7mdxOnsZ/nD0yrrCIi3RkdPRKoHADkLmg59dnTd5SS+YTljli4wk/NP6xlBASCVi4EOGPfw3Bzpct1g00x38D6k4AN/8FyHtW3nO+MTF6Ipac6Sqrs1jDaLG/uBmL+mjldH1eV8/PdI8VjE2KW4vx8VEyi5bgl4A7su7o8bkvSbgE7xzqKmlV6BXYU73n/Ly3BSUCV38FzHndoritbgR8I4GkqYDESRGPqOGWEtsO+DKlFfstQaI1Odfxny9uHCDxsdwHAQCs5T6bcy10xcWouOdeGCo6Jo8hkIdrET2xBcJJ91v6+yusBNoOfWO5vyZ12ToZGxuhpMpu/ePIsj+xtxnycD1UdV29t23LlsF72DBU3H8/6RUKIOKF5+E9nFtiLJDJEHTTjQi6ybH1kDWMRIKYDz9A2W23Q3eKzBT5zb0UAYsW2Xilh4FCm66NuE8AQIQ8Ag8MfQBigRiPjnwUw8KG4d+7/w0FbSlHoTFq8NTOp1CuKMc92fd0a2G/TdeGf+/6N7ZXciuevEXeYBiGE6iuKV6DNcVrIGJEiPSJRKxvLGJ9Y5EamIpLEi6Bv5Q/E2lN8J13Qrm5a55kbGhA65IluPfaG7A2rwbFDgJOk5nFL/vL8O/LMuwexxU5CrB7vEDA4LbxiXh2RV7n2JbT9WhW6RFk5TaxiirdvSQzAlJR93VWzjc8QakHl/njYAX+syofqnNN4YPCfPDxDcORFtFV876nqBF7i0mFswemJeO+qSk4Wt6CimYNQn2lmJgSApnEuS+cmTWjTlWHgpYCnGo+hWZtc+f4zsqdaNKS7/ev4f+yawFz3lGbBzRRxuvJ3ZDo9w0HEiYBJVYPixPL3B+UHvmh87+N+T5ozPcBQKpKti5dCk3+CcR+9jnEOdcDax61/JzW1OZZek1vWwN4B7n3GgcwoyNGQywQd5ZidVjDbC/wg9nM9qjU3Rla1frO3pYYph6DBJQwgycoBQCoDWo8uu1RaE1aYvw/4/8DKS3w0w0i5BEYHjYcR+q7gq+1JWvPz6C0A3kIkHll915LZ0prjgNmEyCweo4co/orQ1K5IkkdiKSWloIzVkJEhf/AlHgpyu+4E8ZaUoxFVeeFyp3BiHvsXjCsAfh8YufiGwBg5UOWjPC5CpbW5SsAU1fLBSM0wzeW/KwAgF+CmghKVXv24ux07v098MYbEbhwIWe8pwj9/ZHw269o/u47KHfsBIQC+M2di8BrrwUjuICeoxco7x1+r3NO1MEzo5+Bt1XJ/LS4aVgTtgZrS9biVNMp6M16iBgRzDBjT9UetOhaiNd/cuwTCBgBFmcvdvo62vXtWFawDN+c+AZtOq4w37ykeXhh3AtQG9R4df+r2Fi2kXOMkTWiQlFBlBH/79D/cF36dbgr6y5eB4UOvIcPg/e4sVDv7VosavjkUyRffjl+vHM0Hv7tKI6UtwIAkkLl+O8VQ7DtTD2+2lnSefzyI1V4ck46xELbn/vjFa3EdpaNflJrLsuOxH9W5UNntMyDDCYWK49V4bYJiQCAsiYVjlMZ2Mtzojjn4YM1my+I76knKPXgEuvyavDkslxirLBeieu/2oeVD05ATKA3WJbFexsLiGMi/b3w0PRB8BILMWmQ41IEtUGNXVW7cLT+KCoUFShXlKNKUQW92bmytckxkznlj+c9x38nt/1igMih3TvXkKvJoLTgH0u5rZebjNGrj1omiwDUDRI0nrB9Xt3JUyi97jrEfv45vBZvtwSz/7xgET7qoP6kRfXyphWA8OK4bXmLvTEifAT21XQ9XIXyM2isHYJjla0YHhfYq+//094yaAyWyTRHddc7xPYk/yJCb9LjX1v/xWkbuC7tOgwPd9/vZ07iHCIo3VaxDWqDmphwXjTQnzuDCmg4A4Sfy2wYNED+X+QxOddbSn9tMWgWGZQWbUb92+9wAtIO1PUSNP6+GqH33w/MfAlY92TXztYyi5jc3Ldh1mjQ/P33xGt9Y7QQis9VEKVeCkRkATvfgV+sFvXHTTBpbS/Seo8di/Cnn7L9c/QQgZcXQu67DyH33ef4YA8Dhj3Ve7CskNSGmBY7DdPjpnOODfQKxI2DuZl0pV6Jdw69wznPR0c/go/YBzcMvsHuNZS3l+OXU79gxdkV0Bg1nP0CRoBHhj+C2zNvB8MwkIlk+N+U/2Fl0Uq8tv81QrGcD7VRjW9PfIv1Jevx5uQ3MTRsqM1jQx96CGVWQam5rQ2NH3+CmOefw/L7J6C2TQutwYT4YEvGNsLfiwhKm1R6bDvTgFkZ/O0pBpOZo4SfE+s4KPWXiTE7M4LIhi470hWU0lnSILkEE5JJH2NrzGo1Gj76GG0rVsDU1gavzEyEPvwQfCZPdngtA5XzP6z20GfUt2vxFBWQdtCs0uPenw9DZzRhR2EjDpaSK24PTEtxyurFYDbg67yvMfPPmXhs+2P4+dTP2F65HSVtJU4HpOHe4fjvhP+6r5e0rcpSIqZpcXxsb6FtB479Qo7lXEs09bvE4MstIkkdmHSW3lJ3cdBiy8OyQN1Rx4GusaYGZTfcAOXuPcDIO4DF24AAUkYdJTts9mxdqHCsYXzOAGCxId99Uvp8nK1X4NNtXYHWpYID5AEpM7v/2btAaNY24+EtD2NvDanwmhaYhsdGPubW95oVP4uo+tAYNdhRucOt79EfmHU6KLZsRfPPv0CxaRNM7U748PqEWgSPrLH2Kz2zFiAyNAyQ7aD8dNAsYlNVpkLr0qV2X9L4yadQHz0KjLrbYnNlzYEvgeLtaPz0U5iayexVUNq58sFhNwHX/wZM/zcw500IRCzCsmyXVUqSkhDzwftgxANflbyivQJvHngTi1YtwlUrr8LTO5/G8Ybjjl/owWWatc14cc+LxJhMJMOzY5516Tw+Eh+8NP4lPD36ac6+Nw68YVNg7UzzGTy05SHMWzEPv57+lTcgDfIKwhezvsAdQ+4g5mUMw+CKlCuw5qo1uDvrbl5BJppqVTXu3HAntpRvsXmM9/Dh8L2UdBRo+f13aAssyZIIfy8khMg7ryU51Acj4slFXjpAJH7mWgW0BrLqK4fHDoaPa0aQP2NeVRsK6hTn3rOG2Dc3KwIiG9las0qFsttuR/N331n8hFkW2hMnULH4HjT//Avva84HLo6Ugwe38OGWQrRrbTegn6hqx+trT2MfVbYbHSBzqgeuor0Cj21/DKeanVM/4yM9KB3vTn0XQV5uKPOsPgase6qrZ4gRAulzgdmvAoHxPT+/K+x8hxsUZ9vokXIG7yBL6W+hldJk3p+2+65cQdUE5FomdO3lXtA2kwIZoY8+Ct+ZM1Fx370wlHXZ95hVKlTcvRjy8ePglZUNadRj8Gp6AxJBTVeSY98nQMwIrljTBcrE6IlEn5DFGqYOyw574bFZaZCI3B8YljSqcNt3BzuzpKFowVgB9Z1Mn8vzyouHg7UH8fSOp1GvIdUT/aX+eHfqu/AS2Ra86A4hshCMjhhNZM1XF6/GnMResnLqA5Q7dqDmhReJbCQjFsP3kksQeO0iyEaOBMMwMGs00JeVgdVqIQoPhygiAkzUUKDNSiSkbK8lyAOAo9SELHEy4O9gsusfA4RlAPUnYTYyqD0YQOxmRGZEjmpD9b4AgD13MzKZUP3U00hcvhzCKz4GPpsAGDVgWUDbIkbro3eh9TQ5xZJHaiELMkCfOBl/p03Crq3/QpO2CbG+sZiZMgFT2d1QN4nRVkz210pSB+Hss9fii+OvokpRBZlIhozgDFyWdBnSgtIc/Kb7DqPZiK/yvsKXx7+Eke2aKxS2FGJt8Voszl6MB4Y+MKDFB01mE+rUdRAyQgTLgiES2J4mm1kz9lTvwf6a/TCYDRgVPgpTY6dCKHBPD6CZNSO3IRcVigpEyCMwNHQoxFZWaRqjBv/a+i+O3+ejIx5FhLx7Oh0dWdQ3DrzROcaCxTO7noGf1K/TjqpR04iPj36M5YXLwcK2dsjs+Nl4ctSTCJfbFsULkYXg4eEP4+HhD6NN14by9vLO8t0TjSewo2oHoWquN+vx+PbH8cWsLzo96GnCH38cyi1bwep0lgGTCXWvv464b7/l/fxdOTQKh8u65lgbT9ZBrTfy2sMcp/xJ44O9ESh3TghsYkoIwnylqFfoOseWHa7ElcOicaaOXJSab8MikWVZVD/9DLS5/EmiuldfhSQhAT4Tzz/nCU9Q6sEpKprV+P0AqRQ2JTUUFS1qFDd0NY5/v6eU89pHZgxyOHk+3nAcD21+iNPTwIeP2AdpQWlI8EvozB7IRDKMCB+ByTGT7T5EnKZwE/D79YC1ymWHr2bRNuDan7onMuQq6maLAfzuD8jx9HkW/7+eMORqMigt2gy0lnMzlK6y+33ApIPZBDTkkllSSXw8gm+/DYxYjITff0fl/Q9Ac5RUz1Tt2WsxfAcAMJD4hyEkQwG/OI0lOF35MBCe1fOf/zwgyT8JkfJI1Ki6VlBFfsfQ2BCBn/aV4c6JiW59v8Nlzbjrh0NoUXdZCswX7oGAsZp0SHwHVD8pazKBNZn6RB3UaDbi8+Of48vcLzkTMblYji9mfoE4vx5+f2wwN3EuEZTuqtqFJk0TgmW2y7sGKm2r16D6iSdAS9myBgPaV69G++rVluBTJIKhqoo4ThwXh4CR0fA3CSCWWSaqplObofbdDNX2TVBvyIVBbZmQS/2M8L0kHgHt7RD6dd2L2vXtqFRUwlvkjTi/OMtzZNAsoP4k6o/7Qq8gnyFh2Qr4x2ugVwiJVgRDeTmqn3gCUW+9CW30HVAs/wmKKi8Y1TzPIAGLsJx27I8YhBflOlTtf7Vz1/GG41gNIDs2Bi+I6xAVpkdbmQwsK0D15Al4I6MexWfeIE63v3Y/vsv/DpOiJ+GxkY8hOSDZtT+Cm6lV1eLJHU/iaD2PGjIsgc0XuV9AZ9Lh0RGPDqjAVGvUYlfVLqwvXY8dlTs6s30ykQyTYybjziF3cryGGzWNeHLHkzhY22Uo/cupX5Admo33pr7XY9XtvIY8vLDnBZxt7fLQDfIKwh1D7sDC1IVQG9V4bNtjnN/3mIgxWJTWM2GqGwffCIVegU+OfdI5ZjQb8cDmB3Bb5m3Qm/RYWrCUNysKAEJGiJnxM3FLxi3IDs126b39pf7ICs1CVmhW51hZexle2vMSDtUd6hwzmA14ZMsjWDJvCWL9uEkPcXQ0gu+8E42ffto5pt67D4pNm+A3axbn+EuzIvHiynx0aHNqDCZsOV2Pedncns5j53pSO3A2SwoAQgGDBcOi8cWO4s6xFUerOheBO4jw88LIeP4WHeXmzRzfYwKWRfWTTyJp5d8QhTgvENmma0O9uh4B0gCEeveP4i/D2tQ399DfMAyTn5GRkZGfn9/fl4LHlx7Hn4crO7clIgG2PzEVbRoD5n+8G3qjmfd16RG+WPPwJF513g7WlazD87ufh86kI8YZMBgdORpDQ4cizi+uU4kt2Cu4dx9olYcswjoGOz0OQonFdy/Dvr+by7AsUL7PUoJWvBWoPQHQq5ACMXDvLiAsvWfvpVMA/0u3Up0EMO5B4JJXbb/GEQ0FFuEPkw5Np+WoP0b2WUR/9CHxQDDrdKh55hm0r13n8NQ+0RpEjW219GOFpgN3b3FesfM85s0Db+LnUz93bpuNPlAVPgOpyOJ9NnNwOEJ9eyaow7Isft5XhpdXnyS82xiYsdv7CUSZrcqKsq8DrvqiR+/nDvSVVWh47z0oNm8Gq9VCmp6O0Icfhu/03lksKmsvwwu7XyB6OzsI9grGe9Pew7CwYb3y3oCl52vaH9MIQaUnRz2JmzNu7rX3BACVSgUfH4toj1KphFzes++cJi8PpdffABi7Z/vQCcNCFmQAawa0reKuDCYPwsBAhD32KOqnZeHDYx9hV+VOpFSaMDHfjKQWCWLkUQgLCoW5cAdUNWSWWxasR/yMRjACgBX5oixvPDTH82y8k23Ch7dh43hfvO7nBRPL/7wEADHL4u7WNmTr9Pja3w+HZI6z7iKBCHdn3Y27su6CRNj31i07KnfguV3PObWoDACPj3wct2beanO/zqTDsfpjUOgVSA5IRoJfgluf+RWKChxvOI6C5gIUtBTgaP1Rhz2N85Pn4/9G/B9CZCE4WHsQT+54Eo2aRt5jk/yT8P2c7xHoxQ0qjtQdwR8Ff6CgpQB+Ej9Mip6EhWkL4SfpWuzYUr4FT+54kjMn6sBH7AOj2cgRV4uQR+C3y35DiKznSvUsy+L1A6/jt9O/OT74HL5iX1w16CrcMPgGRPk4J9DjLEazES/vfRkrzq4gxjODM/HTpT8RGeQOzGo1iuZeRlRjCENDkLRyJUSB3L/NTV/v7/TlBoA5mRH4/OYRnONmvbsdhfVd86YX5mXgDhcWiM/UKnDJ+/bbLx6YlownLuHO8cwaDYouuwzG6q5nstILOBHPYOwZcq7oM2UKYj7/zOF3Z3/Nfnx67FPi2ZYSkIK7s+7GpYmXuvTdy8zMxMmTJ0+yLJvp+GgunqB0ADNQgtLSRhVmvLsdJit7l7smJuK5eRZhiZ/2luL5v7nXKBQw+H3xWIxK4C+l1Zv0+PjYx/juxHecfUn+SXhlwivEalmf0FAAfHsJoHHCC5IRAPPeB0bYfrg6jckA/foPoVn/M/TVDTAbGQglZoi9TZAGGiD1M6KzpWz2q8D4B3v+ngCw9knggFWAIfYGHjoC+EXafo0t6vKBJTcDzUUwagUoWhMGs6ErQy4bPhzxv/zMucGxZjOavvoaTV99RRrM8yD1NyB2ShPE3mZgyDXAVV+5t7exveZcxrgC8PIH4sZYhFX6cVX/bMtZLFhJKiNraxbA0NrlKxjmK0VahC8GR/ohLdwXGVF+GBTmw9uPwrIsGhQ6VLZq0KY2oEGhw5q8GmwvaOAce3fICfxb+Ro5ePs6IH4859i+RLF5M6qffgZmBbcHL/yZpxF0qxu+k+do1DTip5M/4eeTP/P2tY+PGo9XJ77qlomgI57e+TTWFHcJ8qQHpWPp5fZ7H3uKO4NSs0qFkquuhr6sjBiXxMfDUF0N1mCw8Ur3UBLOoCAayC5hEelE/MQIWSRe0gCpnyWAPjbyRnzcUIH73j8Lb/54geckLMKGtuPHCT74toeLR45I8k/Ci+NedKvIlj3a9e1468Bb+Lvob84+Icvi+nYFvFgW3/n7wWR1DxUwAnw0/SNMjiEFWViWxe9nfsdnxz4jAtwR4SPw3JjnkBKYwnsdBrMBbbo2+Ih97JbOn2k+g/ePvI9dVbtc/VEBWKoh0gLTcLT+qN2SVQDIDsnGN5d803k9LMvi42Mf48vcLznH+kn8cG/Ovbgu7Tr8UfAH3jr4FlGu6gy+El98d8l3bi3nNplNeGbnM1hXan/RWCaS4Y4hd+CWjFt6VXzNZDbhse2PYXM5aY13a8ateHzU47yvaVu9BtWPk/vkkych5oMPoDtbBO3pU4DRCFlODv5W+eCpZV0LThKRAIefmwlfr66At1Gpw8hXNhHnW3bfeE5PqiPmf7wLuZTSrjXbn5iK+GDuvbb+vffR9AW5KPzBfAF2ZzB4YpkZowrJz2XESy8i8DpuW5ZZq0Xt5rXYseZLMMXlCGlj4aMF9GKgMpjBwVQGO4YwmJ46F/+d+F+nleQ9QekFzEAJSh/94xiWH+myg5CJhdj51DSE+Fg+pCzL4n//FODjrV1lJiIBg9euysKikbEoaCnAupJ1ONtyFmaYIRPJIBFIsK9mHxo03InwhOgJ+N+U/0Eu7uMsWFuVJSBtI8uUkbkAuPRtYO1jwEnuwxcTHwWmPQvwrNQ5gjWZ0Pb5f9H82x/QNdr+LjIiM7wCDZDlDIPXnDsgyxoCcVxcz1ePm4qAj0d2eZYClmDvmm8cv1bbDhSstwgQle0GmrvKUWoO+qO1iPz7Jfz+G2RDh9o8nVmthmLrVmhPnoSuoBDa06dgauCuRItkJsROaYJXgBEYehMw7z1A1MPsQFMRsOtd4PgSwExNjMOzgOnPAWn9179367pbiVVMs9EH6uJHwJpsS+NLRQKkR/phSJQf0iN80a414lBpMw6VtUBhpze8g5mD/PGl4kEIWrtUCRGRBdyzs9+CdJZl0fTFF2h4/wPbBzEMYr/+Cj4Tut9Pc7blLLZUbMH2yu3Ia8jjnYCKGBEeGv4Qbsu8rc+sp/ZU78E9G+8hxv68/M9e7St0Z1Ba8/zzaF36JzEWeNNNCP/3szC1tqJ1xQpU/PwNvKrJRUEzAKMIkPQwueoqEaNaEZhsyaBt9PXHk6FBMLImpFWwePYPE2R2tPfEPkb4xmjhk6jGa1kJWCnkHjwsbBgmx0zGisIVKFeU85yFJFurw1yVCg1CIVb4+qBZyN+7uCh1Ef414l92rTN6gtqgxh9n/sB3+d9xbEgAINpgxFsNjcjWWX7mlT5y/DuULDP3Ecvx89xfOsuONUYNXtz9os0ASCQQ4elRT2NR2qLO515pWyk+O/4ZNpdvhs6kg4gRYUzkGNw39D7khOZ0vpZlWSwrXIbX97/utGCiKwRIAyBgBJzfxZyEOXhz8pswmA14fvfzWFfiuCKID1+xLxQG2yJYIbIQfDHrC6QGur+lxWQ24b3D7+GHkz9w9gkYAa5IvgIPDnuwx+XKzqI2qHHt6mtR2l5KjH8y4xPOIgdg+dtX3HMPVDtIr2AIBIRnOgBIx0/AwqDZaBJ13ePeXZSDq4Z39aSvOl6Nh37rKpn2lghx7IXZkIgEYFkWxpoaGJuaIY4IhyjUdgns6txqPPgrf6n7tLRQfHf7aM64rqQExfOvAKwW707EM3j5egHAMPBVs/jf1yYEWFmxslIJklf8BWmSJZPLGgxoXbYcNR9/CKbRfvKlyRf4co4A7Ljh+HjGx075xHqC0guYgRCUnqhqw/yPd8EqSYp7piThmUsHc47NrWzFjoIGmFlgfk4UIgNFeH3/6xyJcXssSl2Ep8c8DbGgjxUGFbXAD/OBxjPkePIM4PrfLUGP2QSsfRw49C1YFtC3i2BQCSEQsZCmDoJw5uNA6iVOl5Rqti1D7cv/hbba2SV3EoG/P2SZmZAOTocoJBSioEAIg4IgDAyy3BCd7SVY+TDhKQrAEoSPseFN1lwM7P8SOPozadtyDkWVFJU7yQmI32WXIfp/73COtQfLslBs3Ija51+AqY1cURSIzYid1AzvML0lUJrzBpDgwAJIUWdR6VTWA2AtwlVGLVC0FShYRwbmfGRcYfm9+NoWbOgt9tfsx13/3EWMmbQR0NZcA7PWsWKhq9w7OR5Paj6AIO8PcsfV3wBZ17j9/RzBGo1QHzqMxi8+J/znbCEMCUHy6lUQBgQ4/R56kx4rClfgz8I/cbr5tN1jo32i8ebkN4mJb19gMpswe9ls1Ku7BJYWpCzAyxNe7rX3tBeUqg1qrC5ejTXFa1DSVgKtSYs43zhMjJ6Ia1KvQYxv12ez/Z9/UPXwI8S5ZTk5luoJkQiNmkY8v/t57KrciUHVQFQTC6MQqA5iUB0M6MRASjUw47gZE06y8KLWjhgBC1mIHvJwHWQherBhQ1FVEwPzDsefFw4Mi/Ch7Z1KuZu8ZXgiPAxGq8WJ6EYWt24yI7uE7bQxKA0D9qcJcCJTjqHDpiMWIqxsPIoCJTfg7BD9ETAC6Ew6fHL0E3yf/z3vAkik0YjHmlowW61Bx3KQmmHwRYAffqCykB2EycLw7JhnMSO+G17WNlDqlfjt9G/48eSPaNW18h4zVaXGK41N8DeTP8eX/n74KCiAGIuRR+HbOd/DyBrx6Mb7cVpR6vAaLk2Yg3nJl2N7xXYsL1xOCCpZsyBlAR4f9ThEjAiv7HsFq4pX2T2vgBFgRPgIzEmY0+kB/PfZv/HNiW+gMqhsvm5C1AS8Pul1aI1a3LL+Fo7o0Iy4GWhQNyC3kV+UxhELUhbghXEvIL8pH1/lfoUdlTs6PyNCRohLEy/FYyMf6/VKjVNNp7DkzBIUtBRAIpQgJzQHV6ZciUR/9+oaOMPp5tO4cc2NxAKDn8QPSy9fyls2bKivR8n8KywqtQ5oCI3BA6PuhuLcPI4OEJ9ZnovfrPRVpqaF4vvbR0OxaRPq338f+rNdqvXimBjIx42F99ixkI8dC1Fw17zIbGax8Iu9hLgSYKkwXPXgRGREkXocLMui/NbboD7QpYRvFABP3ClEVQgDf6k/ouRRkB7Mx7N/kHMZJi0ZKd//BNWuXWj48CMYKqjEiwOWTmRw+LIUfDH7S4cCWp6g9AKmv4NSrcGEaz7fgxNVXTL93hIhvl0chW3VaztvTsPDhmN+8nziw9qoacQjWx5x+kYsYkR4evTTuDb9Wrf/HA5pPAv8upDI9AEAokcAt6zsNEEHLKWmba/egca/d8OgtBKzYFjIgvXwidbDNyMMkkFpYIKTgIB4IDDBotYbEAcIJTCd3Ir6N/6D1kMNAHov4ySOjoZ8/DjIJ06CfPw4CH1trJoraoGPR1M2CgAmPwHz6IdhqK2HsakJpjN7YMpdC3PFSZj0DMwmBowAEIjMEEpYCCVmGFRCNJ70BWuykn2XyZC8bi3EEd1TA9RXVqJi8T3QF5N/H+accEjgIJWltDkyx2InM+Sarr+ZXgWcXGkJoMu6V7JF4BVg6bkdemOfZwsf2vwQtlVu44wbVYnQN0+GSZmGnrp8hfpK8b8Zvphc8BpQTL1XzGjgzn/67Oc2KVVoX70Kqt27odp/AGYbdiF+c+dCFBqC5h9+JMb9r74KUa867o82mU1YWbQSnx3/jBCU4kPICHHj4Btx/9D7+76S4xwfHf2IKAEUCURYd9U6p9Q2GzWN2FezD1WKKsjFcmSHZiMzONOuWihfUFrYUoglZ5ZgdfFqmxN2ISPE3MS5uDfnXkQ0m1FyzUKiPF/g7Y3Ev1aAiY7EquJVeP/w+7w9iYODBuOKlCvw66lfO7OJMh2L9AoWNzWKMbK9DlI/I2TBBghEXfOZ/Gu+wH0nPkXsqSbctsmMmCbOqaGPCcWq+Ga0y1j4qVn4aoB2OYPpsUZMYC3BxWZvGR6nAlLAYj2mNCiBdiUClZasgsbL8XdDyAjx4rgXsWDQAs6+/KZ8/JD/Aw7WHoRCr0CCJBCXNtfhhrpyyGzM1U5LxHgpJAj5Uv7yuksSLsHzY593KsthixZtC349/St+OfULFDwLkQAgN5vxRFMLrlKqup5qg+cDkx4FTq8Fu+NtPBUahHU+5PfGmxHDzBqh5QnGfcxmKHvQntHxHeX7jPpL/ZETmoO0wDSkBqZipHcMQlRN5yqlGMAnHIgYgkaRCO8ffp9ToiwTyXB/zv24JfMWCDStgLIOhbpm3LzzMbtBLGD5DNyccTOqlFXYWGZbsOa+nPtwX859REVUraoW+Y35EDAC5ITluMdp4Dzkt9O/4bX9ZGvJkOAh+HbOt5CJZJzj1UeOoPyuu8Gq7fcOA8ChsDS8MO5OsIwAIgGDQ8/NRIC3BGYzi4lvbkF1W1cv73Nz0zH/0Eo0fckty6aRpqbCd9YsBN50I0SBgahq1eDmr/ejuNHyeREJGLxxdTbHNgYAWpb8gdoXSeufv8cw+GW6EHKxHD9f+jMS/RPx5I4nEfvlOlxyxL2x3Y5MBiuuicSnl36FpIAkm8d5gtILmP4KShVaAwrqFHh3YwF2n7V6kjMGjB6xC6dUGzivEQvEuHrQ1bgr6y5UKivx5I4nidV8ewwPG46nRj+FjOAMd/0IztFWBRz5EdjzIVfUKHgQcMcGQN61smVsakL1M89wy0B4EMuN8A7TQ+xtgtjbBJG3CQIhC1WdF5oLvWHW8z9ohTIGXpkZEEWnwNjSDF3hWRhr7E+UnUIkgiwrC9KUFEgSEyFJSLD8i42x+N6dWA78eTuMWgFUtVKoaqVQN0hgUPVcyTj82WcRdEvPxFiMLS28Sr0AIPEzIHiwEv7xGktwKpZbekGFUktZsc4J70NrRF5gEybBVFEAtrkSjJCFUGomY7HEyZZ+4mAexUt1s2ViY9QBPmEWT0U3WAQ0a5tx/errUa3i908LEMUh2ngzKmvDUNnCr4pojUQkQLBcAn+ZGKG+UlweZ8CVqj8gyfuNVJ0GLL/Tu7f0XFzLCcx6PVp//x2Nn3/B8XgkYBiEPfWkpX+UZVFx772c72bcd99CPm4c78sNZgO2lG/BZ8c+Q1FbEe8xHXiLvDErfhbuzLrTpcyAobYWqt27oTtbBFN7GxiJBKLQUEiTkiEbNgzicNdL3ho1jZizbA4hgrIodRGeH/e8zddUKirx2fHPsLp4NadXLVQWirmJczEveR7SAtOICbCZNaOmuQYxIZZJ0lu73sKhxgOoqTiFkHYgpJ1FSDsQpGDhqwYMIqDRj8HpGOBsFAOdhEFEmwCv/CmGXz05US9/aP7/t3fe8XEU1wP/vjt1ybLl3rDlbowx2BgwJdhUBwIEQu8lhST8IIRAIKEnQIDQAoTQMR0CoRfTTW+2cbfBvXfLVpeuvN8fs2edTnfSqVhny+/7+exnpZnd2bezs3vzZt68x5cjs/hw2YdsiR0Q8zhvt/O4aORFpPvTWVW6itPeOq2WiWS6pPHUypXsVlXb2uSrPX/BJWVztjqv8YeUvRYo+63I5YAOo+jUfyjtDj6YrBEjeGPRG1z1+VW1zs/2Z/N498P4YfNC/l4+j2BMnZ0y5BSu3OdKguEgHyz7gKfnPM3sjQ3/Tmf5s7h97O2M3WVsg8duJRyCZV+5QaKiJVCx2Q22VZfApiVQXUIQeDa/HfcVtKcijhLXPbc7v9/j9wzpOISlxUuZu246P66ZwprKTbRLz2FQ590Z1/+njOkxppajpLVla3li9hO89ON/qUjgdAdgbHkFV23YRI9QlAfRg6+Cgy6vGcT6YSKVL53HuV3aJVSgI+SFw9y6bgP7VFZxX0F7nmjfcKzrxnBmz7FcnDOQ7I2LYN1ctyVQtumyK+x2HMsLx/BFxWo2VRXRPbc74/IH03HumzD7VVhfEzLri867cGE7H6EEa05z03O5feztW2NQT1s3jdsn314rjmthfiGX7315XHNUw6GqXPbJZby39L1a6WN6jOHeQ+6Nu7a4YvZs1vztb1ROr5ksyejbl1BZGaENtZcKPbzb0bw8aBwAt/xid07dpw/fLdnESQ/Ujks9sfMC9JEHGiW7v317etzyD9odfDAV1c7L78rN5Ywb0pXB3epOHlRMm8bSs89Bq2t+l9e1hz/9yk9VhnD3uLu3WkQEQgEumfg7jr/pC3ol4RplXm/YsFtPjhz7awp69CVUVETRC/+l/Jtv4h77wKkduOqnt9ZdDx4OUzJ1MruPP5xlm4pNKU0WEckC/gKcBvQBNgETgWtVdUV958YpqwNwPXA80B1YA7wKXKeqm1tA1lZRSisDId6dvYZ3Zq5h5sotrNxct0MracV0KHyWYPqSJl0jOy2bkwefTEFWAWWBMiqCFXTL6cYBvQ5gUMGgZt5BElQUwZqZzpvtmpmwejqsS1CvHfvDOW/Uim1X+vkXrLryyjofrpYioz10veB08s66sk5w9OCGDVTMmkXlzFlUzJpJ5cxZ9XfWG4PfT0bv3qT36kVwyRyqVm1umXI98n/2M3re/s8W8ZwYrqxk5Z8uo/TDD+Pmp+UE6TS0jA79y2vNmCSDKlRs6UBpcBSlSwNUL1lWy+mKLy1MRn6QzPZBsgqqyelSTWanNOSAC6HP/q59Lf0cXTiJwIrlBMv9qIIvTUnvmI1/yIHIiJNgyFGQ3vQYlkuLl3LxRxezaMuiuPl+8XPBiAs4aeA5zFtdzqxVW5i1cgvLNpWTm5HGgK657NuvE3t189G9ehm+LctcR3flFJj/HoTjmML50uHkJ7d5bFINBtny2uus//d9tTwLxiOtWzd63HRTrThsgTVrWPSzowmX1Sg/6bvsQv/XX8OX7UbOt1Rt4avVX/Ht6m/5aNlHbKyMM33m0TW7K4f2PZRxvccxuvvopLyahqurqZgyhdLPPqfss8+omj+/3uMzd92V/CMOp90RR5A5IPmQHjd+fSMv/PDC1v8F4bHxjzG6++hax5UFynh4xsM8OedJArFrpaPIrVDyy6FHXk+y8wvYRBnrtJjSis30WBbkzRtcp/vNvQfTp9RHWhI+WEICawqgyxbIqB3tgM92E+49xpdw1r1zdmduOuAm9u9V26HWFyu/4Lcf/LZWWtf0djy3fCVdKzaD+Hhn95/x17LZBGPa8vBOw7n/sPvjekT997R/88D05DqWJw8+mavGXFVrHbGqMnXdVJ6a8xQfLfsorgnuwA4DufnAm+uEFmkWwWqY9gx8fDOUrWNlmp+/d+rIFzl1Z4qSIVfSOLDjcAYUDObHDbOYtHlunRniaMaVlfObzcXsHtVZJqMdHP8A7Hp03RNWTmXd8yfzy/w0lmTEX6LTvzrAXcVB+u95NmQXwMyX+HDLD1zdpVPcWdOMsHJmcTEnlJTxcU42/+nYkTKJL3OepHH9phLGb67ryyIpOg2CHiPcgPaKbxMu+ZiYm8M1nTtRGRN1oFdeL/518L/qrAFXVRYXL2Z16Wo6ZnVkcMHgFot12pYprS7l1LdOZWlxbcdpwzoN49ox17Jb5/h6UWDlSoIbN5LeqxdpnToRWLmSJaeeRnB9TbsIiJ9Lxl7Mog69OGBgJ5751RiufnUmT39dY4p/cukPnPfBw02Wv/OFF9L5/xLH7VVVit98izXXX1/rdw3gHyf5+H6gj/OGn8ele11aK688UM5fHj+dM+7/gXa1HTRv5cee8NxYHyN+egaX7315rSVzqkrRU0+x9pZb66y7XdceHh7vI7j3cI7sfTjDV/rJ/mI6oUlfklZUwjGLF7GwutqU0mTwFNIPgf2B1cBnQCGwD7Ae2E9V6x8yrymrE/AVMAhYBEwGdvO2BcAYVU3c40nuGttUKVVV3pixmpvemsPa4sSjoP7sJeT0fhbSGjnj5NErrxf3HHLPNlmEnxBVWDcHfnjHOeNZMZk6oVXi0XUYnPUKtHOmcOGqKtbdcQdFTz4V93B/x46Ei7egwVDc/IYQv9L55/vS8ap78eUmNxocWUxfMXMWlbNmEVi5guCmIkKbNhEs2kRoUxGEmiZPS9LhlFPofs3VSFrLhUPWUIi1t9xK0VPxnweAPzNE+8IK8vtUkNEuiCqEq32Eqn2EwrmEMnrgzFzDhKrCVBXnUDK/lNCW+k2uYvGlh8nu7GbDQ1U+qkvTqC5Jq2W6HH1sVkGA7G4+svc9iOzjLyZtwJ6Nu3mP8kA5z857lufmPse6ivjWCKO6juLWg251Jp2qsHEBLP/G276F9fWvmdxKux5wwiMNr9dtBuHKSkrefZcNDzxI9eLF9R67uBt8sXsaHHsE44b8lNHdRtdSMoqee441N9ReX5l+0rF8ddpwJi2fxJS1Uwhp/e/GqK6juGDEBezbY98GO4bh8nIqZs2iYtp0KqZMoezbb9GKhmep45ExcAD5R4yn3fjxZA4eVO9AzurS1Rz76rG1QkIUZBbwr0P+xciuI1lZupJ3Fr/D03Oejqt451Yoey5SRi5Uhi13s51x7w+oDIcZPf9HACYPGkxOM71dL+0CV5/tRvjjcWifQ7luv+viKo8Ad0y+gwmzJ9RK653bk3N2OYyppct5Z8XHdc4Z02MMdx98d0KTa1Xlr5//lTcXvVmv7CcOPpFrxlxTr2Or5cXLmbhkIjM3zKS4utjNqvUex2F9D2uZGNrxqCyGT2+Dr/+DhoO8nZvDzZ0KKE7gCKm5HFZWzm82b2HX6piBju67w0lPxLcgiVC0hOIXzuDG0Opaprx+VY4rr+bPQ84k58A/QqY3Y6QKs19h/cd/4zEt4tOcbNb40+geCnJEWTmnFJfSPer3brXfzz87FfBBTk6tKEH7l1dw9cYidmluKKIkWZSexuPt85mVk0dO+z4cNPBYzhp2Vo132mCVc7BXVeyU29yuzuv9ThDmrCWZXzSfcyeeS3F13Y/YwA4DGdt7LIf3PZxhnYbV+00t++orlp3/y1oxkZe168rFYy+hOj2DJ8/fhwuemkJ5tWtrQzYt5Y6vHsQfiBqQEaH7tdfQ4eSTqZo/n7Kvvqbs66+o+G4y4QRmw/lHHUWPm2/Cl1UzWK3BIMUT32Xjo49SNXdunXPe2Ut4/Ag/+3TfhwcPfzDud2VT5SYue/YsDn9xMXsurrmnBT3gfwf4mDM0h6v3u4ZjByQOa1gyaRIr/3hp3N+0ynTwhesOOJpS2ghE5G/ANThl8ghVLfXSLwXuAD5V1aTsakTkSeAs4GXgFFW32l5E7gEuAp5U1WbFJdiWSun8tSVc+9psvloUT28OI2ml+DLWk5Y/nYwOk0Fqj5Zkp2VzypBTKKku4fWFrycchd+3+77cNva2bbfuQdU5q6kud2siV89wJps/TIQtDXszrMXoX8IRN0JGDqHSUorffIsNDz0Yd9bG36UzPW+8kdyDDkKrqij/5htKPniP0kmTCK5Pbhaz3V796Hbj3aT3a1llXcNhquYvoOzLLyn77DPKv/uu2aEWfOlh/Jlh/LkZ+Lv1wd9zMJLXDgJBwuVlhLYUO2dE4TBZw4bR4ZSTyRm17cISlE+Zwpq/30jVvCSVq+2QjE4ZZI8cRdZ+h5E5YCAZ/fuR1qVL0rPKgVCAtxa/xYRZE+Kan+b7s/lbZiGHLp4C5Y2c4fdnwj6/hnFX1nQOm4AGgwQ3biS4bj3B9fG36oULE/5gA0wZIHy1qzCrr7Apv27ddMzqSJ92feiT34euWV3Y76a3aDe3ttHLW3sLLx7oozzemj9VMgMwPHsAvxl4FiPzdkUrKwhXuE29fbi8gnBFOaFNRVQvX0Zg2XKqly/fJgNAGYWFtBs/nvzxR5C5665x28Tjsx7nzil31knPTsveGtQ+s1rZdbnSZ71zHNS9SOlRJBSUJv+7X96CSumKTvC30/1szqt7P6O6juKCPS5gvx771fsOBEIBfvP+b5i8dnJS1xxfOJ6bD7y5wZnu6lA1F7x/QcJyz9vtPC7Z65JW87TcJNb/CBOvhIUfssbv5+ounfgmiTinyeBX5ajScs7fUszA2N+TToNcmLI9zwR/Eop3OARzXmPV3FeZWbIUf2Yee/Y9lM6jzoWcBH2FUABm/BemPwdLv4QGBpdWpfmZnpmJAkOrq+kfSEIZTc9xllLicz4momN4N5eO/aHHnq6/snGBU0jj3UN6DuR0dvWQngNpmZDdAbrtBrvsC7uMSc7bvCpsXuaswtbMdMpvKOCuKb7amy8Ncjs7xTi3s7t+bifI6QQZeSkNiZYMszfO5tfv/rpeD8U9c3tyeN/DOWbAMQm9la+74w42PvxIrbS3Csdw3x4n1KqDbmUbuevTeymoqt0+ul55BQXnnE1IQ/jwbR3U1ECAihkz2PjY43EtvbL2GEGv22/Hl51N8TsT2fTEEwRWxDfc/L6/cNuJPjrldeOFY16o18FVWaCMW769hU+nv07nohAb8mFTvnBgrwO5Yu8rKGxfmPDcCJXz5rHst78ltGZtg8eCKaVJIyLpwDqgAzBKVb+PyZ8OjABGq+qUBsrqDqwEQsAuqro2Ki8TWA50BHpF5zVB5hZXSjeUVnHPh/N55ptlhMJBfJlr8Gcvx5+9HF/WCnzpmxF//d5gY2c+15St4eEZD/Pygpe3mkwVZBZw7vBzOWfYOQ2boYTDrtNcsgbKN7oYoRVFbu1M5Rb3Ma3c4kaDI/9XlUBVKQTKtprQqIKGhFBACAeEcNCHBoVwUFDcN0VEweec5IgAWXnIgIMI9z+G6vJ0AsuWUTFtGuVTv0cr49s95B70E3r+4x+1PKlFUFWq5s2j7MuvCKxcQWD1GgJr1hBctZJwVRXpXTuRu//+dDj1DLKGbvv1eeBmc8q/+47KH3+keskSqhcvoXrJkoQmwL6cHHL22YfcPQeR06WajLwAvvZd3DrNHntuNz9QGg5T+sknbHzo4bhrTXdEfO3yyOjfn8x+/ckY0J/M/v3J6N+fjL59kQTKQDAc5JGZj/DA9AfizgKeUlzCxUWbyQ8n8a1Py4bR58H+F0N+DzQQ2KqchcvL3Wz8unVOoVy3joD3d7ikFA0G0WAAAkE0FCJcUeHaWBN/Y+bsAs+O8/Nj78a1t54blX8+GiI9pirCwKZ8qEqD9JALL5IRhKxq8G3Dn0Fffj65++5LWvfuaGUFgZUrqZgxs8GYvBHSd9mF/PFHkDduHFnDh28dUQ+Gg1z00UV1Yi4WlCijFiijFyi7L9Fmh1FJpJRKejppPXqQ3r07ad27kdapM+HKCipnzqJy7txaynrIJ3w2MoPHDwpSle0nNy2X3u160699P4Z2HMrhfQ+v5am3IYoqizjz7TMbDKNy6pBTuXKfK5M2hSwPlHPbd7fx2oLXtnp17ZXXiz+M+gNH9jsyaflSiir88DZM/Au6eSmfZmfxQn47ZmRmUOrzURAKM7S6mqGBEP0y2rO+uphPM/18n5mJxvm2Z4bDHFdaxnnVGfTqe6CznsjqAHldnBLTaSB0HtS6vwvBKqc0Fq9y3tTXzXZ+EYpXNnxuhJ4jnWVU58FO4eu6K+T3qrmPcNiZ6M5+Fea+Hr/sjHYw4mS39RwFG36EL++FGS+QlGVWU8jIcz4NBh4Gu+zjnkE44PpM63/wlidNc8poRRLBeBvCl+7MqHM6Qm4X5wCqXXfnLyGvW9S+G2R3rBs3XNX10epssenq6j4ty22NHPyaXzSfSyddWidUTDyGFAzh4D4HM7hgMLnpueSk5ZCXnkeeZFF23kVUz6092P3fQQfzxLAjCYuPwUXLuOabCXSurD0zu+KQYTz5s2xmb5pDIBxAEDpld6JrTle65nSlW043umZ3YfAHC+j66FtIMr/HMXy6m/DgUT5ycwt46PCHkl4KsK583daQcsM7Da/51laXQ+kaKNvo/KoEK90+UOmeRVYHyOlIsNLPimv+ScW06Ykv4vHTZYtYVmFKaYOIyMHAR8BCVa0TgVlErgH+Btygqtc3UNZ5wGPAh6p6WJz8R4HzgfNUdUIzZJ49bNiwYVMnf0cwHECDSlADaDBIKBwiFA4RDgUJhcJuHw4R9rZQKISGggSCITaVVrBw4yamr1zO7HXLCadtQDPXQeZ60jSILwz+mM0XhrSwkhGArABkBlznbYgWcET6LqRXVBMuq3Dr5rIy8WVnEkj3USTV+DPS6SwZ+Cor0IpywtVVaHUADQTQ6iDhQAg0hBCGcDUSqsR1GQEFVfH2oGHZmiYAnjKpilM8Az7CASEUdPta9jotjGRm0vWyyyg484wWWSOZakJbtjgldckSAmvW4m/fnsxBA8keMaLOutbtnfLJk9nw8MOUffJps8rJHDSIvEMOIfeA/cno2xdfbi7h4mKqFi2masF8qubOo3zq1AZdqvtycpCMDEIlJS0ykyZZmWQNLCRryCAy+vbB374dPl8QLdsEm5aiq2ezdMMi3srIJRASsqrde5tV5fa5VUrXyjDtqpV0TwkTAUnzI/508Keh/kzn5Kk6gFZUQmUVtJKpWzSLurm1LouG5PO7kb/ngJ4HsLxkOW8vfpv3l75f79rICD+ZGeaiNxsXfL7FECFr993JO/BAcn9yINm7717HhD0S4qbkvfcoef/9WmuZ6iUtjayhQ8nec08y+hUSzMvm8XlPsGb1AnptVHZdpvRv8jBofMpVGf2jC5W1fMITdNpzD9J79sTfqVPCgZKIWXNg1Sr8eXlk77UXaQUFBMNB/OJvke/nhooNXPzRxczcMLNOXm56LlfsfQXHDTyuSdcqqS5hweYFZKdlM6jDoB1zfV+wyimnc16DtXPcrF+7Hk6RGXAI9N3fmYqGgrDqezb88AYfLf2AyZVrqRDIlzRG5vbmiMLx5A8+yilu2/PvXigAM1+Ez+6EjQnWcmcXOM/pe50Hnet0AxMTDsOq793yh9K1rt56jqypw1hWTXPh41Z816Rb2WERv3PqF61sNhV/hhskTc+C9GznbC89GzJy3N8ZOW4m2ZdGpMNYqSGeq1rBhMrlbNKmWYf13Kjc+liIzJifvrV5eWzOzGXQxrV1fNxPLxRuOdlHyJ/c+7HHwjB/fDVMTpIhc1cVwHPjfHwz1EfvtHbc22l/Boal7mRN5RY3YZOW5SycMvNq6jAt2z2PyMRP2Xp3fJKowualHdg4L4fA5tp5C7vDjCF+ygel89Sdc1m2usqU0oYQkUuAu4AXVfXkOPk/A94EXlXVur7aax97N/AH4J+q+uc4+RcC9wF3q+ofmyHz7AEZGcPe6JfY/bLROmSP3ose111H5qBWcMpkNJmqRYvY8sqrFL/7LoFltWdRJCMDf4cO+NvnQ3q6C1Wank5G795k7TaMdoceSkZhYVLXCaxdS8WUKVTOnUu4vAJfbi7pvXuRUVhIZr9++Dt3RkTQYJDA6tVU/fgjFV9/QsXXH1OxeD0a3I47dykgDMzuK7wzWpg8SBjf76dcuc+VdUyTiiqLeHPRm3y+8nOmrp1aa01lLIdMC/PL98J1ZkxbHL+frCFDyN5zD7JH7UXuAfuTVhB/PWQ8NBymYto0St59j+L33msZb9tJIJmZ5O63H3ljDyJn9GjS+/QBnEIZMVnWQIDqgo607+5i88bGKU01wXCQtxe/zcTFE1lespz2me0Zt8s4jht43DaP29hmCQVcD9Sfvn0roYkIh9zM5tSnYMN8Z07cbTcYdhwMPbpZjuYaJ0fYzZh+fqebQU1EZr6r70Tef41GEwJmZmYwKSeb93NzWNbIQfYDZ4X5vzfCSQVYW9oFrj3Tn1Q4qGh2Wa9c8WKIrvXohfN7wGv7+fhukJCFclJJKRcWbSEnxXpbdYmfQLkfEcjsEMCfUSPPbveXMmd92JTShhCRO4E/Anep6qVx8vcApgFTVXWvBsp6Gedx9w+qek+c/J/jvPC+rKonJCFbIvvcoekivj472KxVm0EEX04O/vbt8eXkpFoao7Goot4spfj9208HKxxGSzcRLi1GAyE0DGHPIqAtE/K5Leyr+TvkE0I+F0ok5IMMfwadszvHjTMXi6IEw0ECoQDV4WqC4SAhdRYkAH6fn6yQn6ySKqioTN6MWAQRH/gExId4e3ziZgbFh6SnIenpbsvMbNG2pZWVhMvKCJWWNnstOOC+Y5mZSEaG2yJypyendITDYeZ5a7eHDh2Kr5mOjgxjpyNiFhkOAeIUfn+GWy8aWaOsYef9PBxy+2iz1nAAAhWunMYifjdz5vf6kdHvfOSbqAoajLp+iLb0g1QtQqlPKPH5CCYZGz63kgbX3lelw8Z8IdzEz79PoX2ZklMFEYfRKlCRAWVZQlU6ZKiSHw7TLpyckpxqFm4KUxWiRFWbFMdpG7mC2y7J8/aJPGqUxRzXWmXVRzigWrawurp+W8Htk4j7vaS8GW+3VFVBUQusy2gd2kad71hYnbcgVVRRQoMzBlbnjaWqfj8BSTAAYN68eVbnrYe189anDdZ5GGiBwa1tx3ZZ51W4eJH1Uk1Nb7+JxPXVXgWRn8GoP1uSbVnnu5BYN2qQnUkpjYxlJBr6aMxYR0uWRVOnubdnIrO/bfHetleszlsfq/PWx+q89bE6b32szlsfq/PWx+q89dme63xHmA1uKSKDDYkWxETsM5NxidiSZRmGYRiGYRiGYey07ExKacTrSSK/871jjmutsgzDMAzDMAzDMHZadialNBJgZ1SC/Ej6jFYuyzAMwzAMwzAMY6dlZ1JKvwC2AANEZGSc/BO9/ZtJlDURt3r8JyLSNTpDRDKBY7z8d5ourmEYhmEYhmEYRttnp1FKVbUaFzsU4D4R2boeVEQuBUYAn6vqd1Hp/yci80TkHzFlrQaeAzKA+0Uk2mHUbUAX4FlVXbNt7sYwDMMwDMMwDKNtsNPEKQUQkSxgErAvsBr4DOjr/b8RGKOqC6KOvx64DnhCVc+NKasz8DXOtfJCYDKwGzDc+3+Mqm7YpjdkGIZhGIZhGIaxg7PTzJQCqGolcDDwd1wcneOAQuAJYGS0QppEWRuAvYF7cTOmxwPtcbOx+5hCahiGYRiGYRiG0TA71UypYRiGYRiGYRiGsX2xU82UGoZhGIZhGIZhGNsXppQahmEYhmEYhmEYKcOUUsMwDMMwDMMwDCNlmFJqGIZhGIZhGIZhpAxTSg3DMAzDMAzDMIyUYUqpYRiGYRiGYRiGkTJMKTVaDRHJEpG/ish0ESkTkUoRmS8i94hI91TL15YRkRNF5D0R2eDV+zIReVlEDky1bG0dEblWRNTbTk21PG0NERkqIleIyIdeu64SkTVe+/5JquXbkfG+2TeIyI/ed2OViDwmIr1TLVtbQ0RyROQ4EXlURGaISLH3Oznd+4bkpVrGto6IdBSRdd63el6q5WnriEh3EbnL+75UiMgmEZkiIrelWra2iIiMEZH/eb+PAa++PxSRE1MtWwSLU2q0CiKSBXwC7ANsAr4Cqr3/ewFrgP1UdUmqZGyLiIgfeBI4HSgDPgc2A32AvYC/q+qNKROwjSMiQ4DpQAYgwGmq+nxqpWpbiMgK3DekGPgGKAKGAcMBBS5V1btTJuAOivfN/hDYH1gNfAYU4r7Z63Hf64UpE7CNISK/Ah72/p0NzAHycfXfDpgHjFXVdamRsO0jIhOAs3Hf6h9UdWhqJWq7iMh+wNtAB1xbn4Vr58OA3qqaljrp2h4ichLwPG4ycjKwEOgJHOCl3aqqV6ZOQoc9dKO1+A2uM/MNcISqFgOISCbwFHAScANwTsokbJvcjFNI3wbOUtVNkQwRKQA6p0qwto6ICPAQbhDga+DnKRWo7TIHuBz4n6pWRxJF5ALgAeB2EXlPVeekSsAdlL/iFKKvcN/sUgARuRS4A3gMGJs68doc1cB/gLtUdX4kUUR6AG8BI4G7cd9zo4URkUNx/Y+HcP0VYxshIj1xfZJM4Beq+kpM/j4pEayNIiJpwL9xyuepqvpCVN5+wEfAn0Xk4VQPNNpMqdEqiMhLwAnEvBBe3p7A98BcVR2WAvHaJCIyCJgLrAR2VdXyFIu0UyEiv8Z1cM4EDsd1eGymtBURkXeBI4DrVfWGVMuzoyAi6cA63CzGKFX9PiZ/OjACGK2qU1pfwp0Lr+P4JVAF5EcPvhjNR0SygRm4gYHjgB+xmdJthog8CZwFXKSq96VanraOiAwHZgLzVHXXOPmv4gbNT1HV/7ayeLWwNaVGa1GVxDGbGj7EaAS/AvzAA6aQti7eGunbgA9V9ZlUy7MTM93b90ypFDseB+IU0oWxCqnHS97+mFaTaOcm0o4zgU6pFKSNch0wAPgdEEixLG0az0LrZGAL8EiKxdlZSKb/DdtBH9yUUqO1eN/bXyIi+ZFEEcnAmYkBPNHqUrVtDvX274tIP8/J1IMi8g8ROSylkrV97gGycZ0cI3X09/ZrUirFjsce3n5qgvypMccZ25ZIOw6wHXQc2xIiMgL4E/C4qn6aanl2Ag7ADa58DgQ8J4x3i8i/ReQiEemWYvnaIou8baiInByd4VlhjAcWAylv/7am1GgtngKOwq0dXSwiX+J+YPfBLW6/SlUfrud8o/Hs5u33xa0By4zKu1JEPgBOiKzvNVoGETka186vi14bZrQuIjIAONr79/VUyrID0sfbr0iQvyLmOGPb8gdvP1FVk531MBpARHw451KbgT+nVpqdhki/ZC3Oedp+Mfn/EJHzVPXF1hWr7aKqIRE5F3gDeEFELsc5OuqBs4r5FudzJOXLAmym1GgVVDUEnAbcDnTEdRaPx3nNnIYbNTNaCM9zZpb37904z8cjcN4cD8eNih2GW/NotBBe2Ib7cWuSbk2xODstnmOHCbiBmBds3WOjiYQfSWT2XxZznLGNEJGjgF/iBnGvSbE4bY2LcAPjl6vqxlQLs5NQ4O3PxvVJfgl0AfoBdwK5wNPeDLbRQqjqZzjHdIuB0cApwEG4b/kHwKrUSVeDzZQaSeE5KhreyNPOVtVvvfMLgFeAvXGjvv/DdXgOAu4FPhSRk1T11RYTegenmXXuj0pbCRwTNQr2gYj8HDcYcLKIXGMzeo7mtnOct+NdgENtRiM5WqDO43EvbgR4EfD7psq2EyPePpEnREmQbrQgIrIr8DSuvi9X1ekNnGIkiYjsAtwIfKKqE1Iszs5EpG+SBlyoqo95/28A/iQifYATcTPXZ6ZAvjaJiJwGPI6LBHAqLuxUT+Ay4GrgUBEZq6opXVNtSqmRLIXAkEaekxP19124UZpLVPWeqPTXRGQlLlTMv0TkTVUNNkvStkMhTaxzVS0TkTDOGuLpWLMMVZ0pIpNxo8RjAVNKHYU0sc49N/YXAk+p6kctLFdbppDmfVtqISLXAr/FmYeNjw6DZCRNibfPTZAfqf/SVpBlp0REegMTcTNLd6rqv1IsUlvjflz8aFv337pEvi1h4vsReQynlI5rLYHaOl4khidwv4k/U9WIpct84AIv7NQxwHmk2HrOlFIjKVR1dFPPFRE/znQXarw2Rpc9WUQW47zf9ceZPu70NKfOPZbiTGKWJshfglNKuzbzOm2GZtb5UbhBgN1FZFJMXiS0wLUi8lvgJXOF72iBdr4VEbkQF+94C/BTVV3QUmXvZCzz9r0T5PeOOc5oQUSkM845YB/c7MZlqZWoTXI0bi3pf1xI6a1Elr30ifqOHx2J02s0myXefk0Ca6JIvvVLWo5TgXTcmvSyOPn/xSml4zCl1NgJ6IobkQRI5FQnkt5x24uz0/A9TilNVKeR0AL2Y9uy7FlP3q7eNq1VJNmJEJEzcGa75bjR4GmplWiHJmImOipBfiR9RivIslMhIu2Ad3ADWS8Dv1YLKL+t6ICzFIpHdlSe9ZVbjkiIqQIRkTht2/olLU9kEHG773+boyOjNdiEC0oNboF1LbwQMRHzvUSzekbjiXgcPTg2w+v4RDqWicI+GI1AVa9XVYm3UWOmdJqXdkkKRW1zeM5gJuCcwRyvql+kVqIdni9ws80DRGRknPwTvf2brSdS20dEMoHXcL+T7+K+F6HUStU2qedb3c875Ieo9M0pFLVNoaozcc52snGRAWIZ5+2tX9JyREKiJbJK2tvbL9n2otSPKaXGNscz0Zjo/XunZ78ObPUSez9ujdIXqro6BSK2VZ7HfWTGi8g5kUTPM+m/cGuVZuE6oIaxQyIiB1CzLOAUVX0vlfK0Bbw16BHz8vtEZOvaUhG5FOc183NV/S4V8rVFvGUuz+EGET8DfrE9hGgwjG1AxDP9PZ6pOgAishcuZizAA60uVdvlNW9/kIjUWkMtImOAP3r/1lle19qIWYUYrYEXM/ALoBtuoftXQAVuhKYnbjZ1rKrOSpmQbRDvg/MBzmHJVJySOgrnXGYjcLA3cmlsQ0RkAnAObubj+RSL06YQkSKcGV59wb8/V9VHWk2oNoA3YDgJN5uxGqco9fX+3wiMsTW7LYeI/AEXvgucp/pEpnaXqeqGVhFqJ0RECnHfkh9UdWgDhxtNwIsP+zwunvcm4EtceKn9cUu9HlbV36ROwraHiPyTmrXps4E5uL73frgJyodU9YIUibcVs5M3WgVVXSgiewBXAEfiQsEIsBz4N3CLqiYK1G40EVX92jO/uw4Xl3R3nAe2h4EbVdUclRg7Oh28fT9qTO/iYUppI1DVShE5GPgLcDpwHFCEM0W/RlWXp1C8tkhB1N/H13Pc9bjwGYaxQ6KqYRE5FTfo9SvgEFz4qcnAA6r6VArFa5Oo6uUi8iXOM/1euCVzJbgY9o+o6rOplC+CzZQahmEYhmEYhmEYKcPWlBqGYRiGYRiGYRgpw5RSwzAMwzAMwzAMI2WYUmoYhmEYhmEYhmGkDFNKDcMwDMMwDMMwjJRhSqlhGIZhGIZhGIaRMkwpNQzDMAzDMAzDMFKGKaWGYRiGYRiGYRhGyjCl1DAMwzAMwzAMw0gZppQahmEYhmEYhmEYKcOUUsMwDMMwDMMwDCNlmFJqGIZhGIZhGIZhpAxTSg3DMDxERJPYJqRazp0FEZnk1XlhK1xLRWTJtr7O9oyILGmg7S9JtYyNoTXbj1GDiHwkIktFJKOJ5xeISKWIhEWkTxLHP+Y955u8/0d6/1/elOsbhpEa0lItgGEYxnbIE/Xkfd5qUhgtgoiMAz4GnlDVc1MqTCMQkeuB64DzVHVCK176f0BpnPQNrSiDsQMiIj8DDgZ+p6rVTSlDVYtE5C3gF8DpwC31XC/LOw7gae/870XkdeCvIvKoqm5qihyGYbQuppQahmHEsCMpLkaLsSsQSLUQ2wmXqeqSVAvRApwN5AArUy3ITsTNwDrgsWaW8xRO2TyTepRS4BigPTBFVedGpf8DOBa4wtsMw9jOMfNdwzAMY6dHVeep6sJUy2G0HKq6zHuuNtjQCojIAcAI4PmmzpJG8TawEdhNRPao57gzvP1T0Ymq+jWwADi/qWbEhmG0LqaUGoZhNIPIWjsR8YvIn0XkRxGpEpHlInKriGQmOC9PRK4VkZkiUi4ixSLyiYgcF+fYQu86k0QkX0TuEJHFIhIQkbujjhspIu+IyBZve1dE9haRc73zr486dpaXNjiBfIXemq75IiJJ1MPW9XsicrqIfC0iJSKyOeoYEZFzRORTEdksIhUiMkNELhOR9IauEVXOT0TkPu/cIq+ceSJyi4h0iDl2As50F+CcmDWS0fVRa82kiJzgpT1fjxz3e8f8OiY96WdbT9lLcKa7AI/HyD0u5tizRORz7zrlXr38xTNt3GZ4z/pBr/1Xich6EXlJREbEOXZrGxSRwSLyvIis9drYcTFtPFdE7vTeoQoRmSoix0SVdZKIfCsiZV4Z94hIdpxrNmlNqYgcKCKviMg6776WeNfoEufYCZFnIiIHiVtPWeI9i7dEZFg91znGe0c3iltD+aOI/F1E8uq7l3rerzwRuT2q3uaIyMXeexfbvi+XqHWYCeT72DvmwCSr7lfe/pl6ytxdRJ4RkZVe3a4Skcdjn5Gn1P7X+/fMBGV1BI4EQkC89/Q5oDNwfJLyG4aRSlTVNttss802VQB1n8VGn7ME1ykqBT4C3gA2e3lPxzmnGzDby18BvAa8752vwJUxxxd66d8A3wObgFdwa/+u847ZHyj3jpuK65BNB6qAB7z066PKvMhLuy3Bff3dy78iyXqY5B3/IK6T+Kknw+devg/XyVRgC/AB8Cqw2kt7C/AlKLMwJv1roBKY7NXBm8Aq79hZQF7Usb8CJnp5C4AJUdtxsc8x6v9M7xmWR5cXlZ8GrPfqt6Cpz7ae+rwdmOad83mM3EOjjnvQO6bCq8MXPbkU+BLIbkRbXhKvvhMce6D3HCN1/qJ3vbBXZwfHHH+ud+xz3nmLcO/Mu8DPqGnjX3rPdz3uPfrYa09B4DDgjzgz6y9x78AG77xn6mmTDd5P1DkXe/cQ9K7xIjDXK2cR0CPm+Ale3h3eOdOAl4AfvPQNQPc417kj6rl9gmvHkfqfDOQ28v3Kwn0fFGc++yLwDq59/ou67bsz7h1aBaTFkW+gVw9zG1F3a3Ht3Jcg/wRPnsg9voj7VkXqabeY4/ej5j2qUyZwgZf/doLrHeLlP5nsPdhmm22p21IugG222Wbb9rLRdKVUgTnRnV+gH055VGBAzDlve+m3AulR6f1xilMQGBGVXhh1nS+BDjHl+aI6wZfH5F0Tde71UentgTKvI5kec47f6wgGgG5J1kOk01wBjI2T/2cv/z2gS1R6LvC6l3dhgjILY9KPIkoR9NIyqVHQro3JG+elT2jgOS6JSXvUSz8rzvFHeXmvNOfZNlCn13tlnZsg/4SoTvvAqPR84DPqGXRIUN6SePUd57h83GBCNXBiTN5hOMVjBZARlX5uVDu8F/DHnBfdxj+mtqIfOXc+zqTzJ1F5Pb02rED/ZNpPPfc1BqfwLaH2+yfUvEcvxpwzwUsPAafFvEMveXl/iznnZGoGj6K/GelRbfifjXy/rqbm+5AflT6Cmu9QbPt+xkv/eZzybvHyLk2y7oZ6x3+SIL8f7nuzGTgoJu9s79xv45w338s7JE5epI2fnuCa7bznsjjZd8A222xL3ZZyAWyzzTbbtpctqlNc33ZcgnMOjVPePcQoFcCeXtoXgMQ55+de/j1RaYVR1xkd55zDvLy5sWV6neNFxCilXt5jXvoJMelHe+n/a0TdRTrN98XJi8wqFgOd4+R3w83azEhQZmGSMmTjFOkpMenjaJpSGplpmRjn+Ke9vBOj0hr9bBu4n+tj209M/ide/vlx8nbHzXQVE6UcNnC9JQ20/T294y7x/r85QTl3efm/iEo7l5pZvJw450TaeJAoBdvL83nnKXBDnHPvjFdPTWg/r3rHHxEnT3BKZCi6DVOjlD4V55xRXt6kmPRpXvqQOOdk4RT+IqJmB6nn/fLyl3v5Y+Lk3ZCgfR/kpb8Rk57myVBFnPc1wfUjivZDCfLv9vJ/kyD/FS9/VEz6dV76ozHpfb32XRKvPUUdt8I7Pz+Z+7DNNttSt9maUsMwjLo8Uc+2LM7xAVynMZYfvX2PqLTDvf1rqqpxzomEnNk7Tt5qVZ0cJ31/b/9SbJmqGgJejnMOOLNegF/HpEf+fzjBefXxepy0kThzwc9VtU5YEVVdi5sRGR5vbWA8RKSXiPxWRO4WF6dwAvAf3OzdoCbIHY9JOM+th4lI16hr5+AUzGKc6XCE5jzbRiFuDe4YXIf72dh8VZ0JzMDNFu3RyOL/R/y2HwmtEbnPVxOcX999fqCq5fVce4mqLohOUNUwsNT79/0450QcVPWIk5cUIuIDDsUpOR/G5nvP8wucgrxXnCLei5NW5/332tEeOLPYH+JcJ2KW3oH47bjO+yUulmdvYIU6Bz+xvBgnDVX9FGfhcaSI9IrKOgbojrMCSDYMUOT9KEqQv/XdSJCfqM1EHBidELNG+gzcQMHLDbSnSJutsx7YMIztCwsJYxiGEYM2PiTMak/5iyUS6zHa2VGht79VRG6tp8zOcdLiKcTgTBjBzZbEI+55qvqtiHwPHC4ifVV1qYj0wJmmLiN+R7sh4l2r0NsfKSLxlLVoOtJACA8RuRQX8mGbetVU1bA4R0d/Ak7BmZ2CCzWRBzzuKRERCr19U55tY+mEu/81MTJEswSnAPVMkJ+IhkLCFHr7b6R+H1iNacMREj37snryI3lxnYolSSfcMwUINuG+VsQmqGqpV060XH29/a5JvAudcWb50cSrvya9/x4P4WYxz8etI4emDUq19/YlCfILvf2axtStqi4SkS9xA29H40yiIYHX3TgUx8hnGMZ2iimlhmEYzaehzmU0fm//Gc6sNhHxZigSKR8NyVFfL/BB3Izp+ThTufNwvw2PejNUjSWejJF7no9b81YfVfVlisgYnJOYLcBvcLOZa1S1ystfRTNmzOLwDE4pPZ0apfT0qLxomvNsm0oyba8x7TMZIvf5Is6pUSK+iZPW1DacbH5TidxTCYktCyIsjZOWrFyR66ym4UGfjXHS6qu/ptTNE7gBnvNF5EbcjOt4XPv9qBHlbPH2+Qny/Z58TzZQzuw4aU/hlNIzgZdEZCQwDOekqSEZI8rolnqPMgwj5ZhSahiG0bpEZlReUtV7WqjM1d6+T4L8Xeo59xngn7hO6d+BX+LWaj3WQrJBzT3PasIsdCyR8A5Xq+oT0Rme6W/3ZpZfC1X9XkTmAmNEpD/OPHE8rs4/jjl8WzzbRGzEmSp3F5FsVa2Ic0xkVm51nLzmsAIYAtyoqjNauOxUsQE3IBJogTZaH5E2sqYFr9PQ+58oHVXdLCIv4Nb8Ho7zeOsDHklggp6Idd6+Y4L8FcAA4GJVLU5wTCJewHkQPlJECqiZJX02iYGzAm+/vpHXNAyjlbE1pYZhGK3LB97+uBYsMzL7eILE2MZ5a+USxulT1VLcmsTeOOW0P/COqtYxR2wG3+FmKg4WkUQzKckS6WTGM1U8ifizwtXevqkDsZEZ0dO9a2QAz8XpELf0s00ot6oGcKFTBDgtNl9EhuNMd0twoYFakm3RhlOKqgZxs+4dReSgbXidFTiT3BEi0q+FylyKmzXsLSL7xjnkxAaKeNDbX4CzmAjiHDg1hkgbG5ogv8ltRlWLcOGOMoBTqWnvT9d3nvet6YnzvttYRdgwjFbGlFLDMIxWxHNE8iFOQbtLRPKi80XEJyJHNCJgPTgTtgXArrg4jtFciVM06yPi8OgSb98UB0cJ8Uxrb8c5b/mfiPSNPUZERojIKUkUF3Ee80vP2U/k/GG4MCzxWOXthyQtdG0ijoTOILHp7rZ4tg3JHTEnvsGbxY1cpx1wH05hfVBVq+Od3AwexM08/VVEzoszEJIrImeLSO8Wvu625maclcAT8Z6RiPQUkQtb4Do34sxZ/+cNHsReZ4CInN/IMiOK5R3e84+UNRwXkzghXrudDvwCN6v6pqo2anbdc9q0DhglIvEGf+7AhbO5S0SOic0UkY4i8vt6HJ1F1o7+HadozlTVhgZb9sa9A58lcw+GYaQWM981DMOIwfPkmohlqnptMy9xBm492SXA2SIyDdfJ74VTQLrglMvPE5xfC1UNich5OM+kd4jIGbjZmGE4RfVhnPOSuMqJqk4TkW+BfXCmgG819cbq4WZPntOAH0RkKs4BS2ec0twP55nzhQbKeRy3xvMYr5zvcCaDY3HeYPehxmwVAFVdIiIzgNHefc7GhfZ4XVXjeQuuhaoujnK2MhSYp6pTExzeks/2Pdwawj96ysUqamJY/qCqL4nIQ7i1tbNE5CPcGs9x3nW+xq0TblFUtUhEjsd5gn0MuE5EZuHMX/vg2lwuzutyS864b1NU9VMR+QPO8c9nXpuZjwvT0hd3X6XAv5t5nadFZHdc7N5pnrOxxbj1mH1xbWw6jTOh/yfunTgAWCgik3COmw7Bvf//R4L33+NB4H7v76YOSr2NMwPeF+epeCuqOl9EzsTNbr4uIj/ghbDC3fMw3EzoszjlNZa3cJ50O3n/1ztL6jEuSi7DMLZzbKbUMAyjLufUsx3b3MK9EChjgEtxnd69cWZtvYHvgQtJrtMVXebnwIHAu7hQEkfj1smNpcYxSzzHKREiYTAe90wZWxRVDavq6ThTwo89GX+B64yuxcXkvCKJcjbi6utZXCf2WJzCdy1xzFijOAGntPYHzsatnR3ViFuInhmtE4IlSr4We7aqugoXeuZr3LM935O7R9QxF+Du53vcsz4GN2N1FXBIA+EymoyqfoGLhRqZATsEOAKnWL2J81Y8Z1tce1uiqvfhlKpncKbix+LWWYZxFgU/b6HrXIELQfM6rm0ch1Piy/HWeDeyvAqvvLtwyufPcW39r9RYECTz/q/AfUOaQkSZPT1epqq+jDMpfxBIB47EKY6ZuPo+mgQOibzZ/v96/4ap5x2M4jTcN/CVpKQ3DCOlSOPWsRuGYRg7GiLyDvBTYIyq1vGI6plfzsMpigNVtT7PsYZh7EB4ZvHPAw+o6u8SHPNX4CbgBlW9vhnX+h6nZPeOeMROBSKyH26t/W3eAIBhGNs5NlNqGIbRBvDWZPWNSRMRuQinkC4Avk1w+onAYOBtU0gNY8dERPb0HJtFp+0O3Ob9G3d20XMIFDHvfaiZYlyFM8n/ZTPLaS5XApupuXfDMLZzbKbUMAyjDeDF7/wSmIGLMegHhuNM+CqAo1R1Usw5j+CcDx2N8zGwTz1rJQ3D2I4RkXk48+mZuNBFhcBo3Legziyptw59LHAQbk33Xap6aQvI8REwEGd10dJOtpK5/khgKnCFqppSahg7CKaUGoZhtAFEpCtuXebBOO+U2bi1hZ8At6jqzDjnKC78w4/ANd6aL8MwdkA8z8Cn4qweCnDrU2cAj8bG9PWOn4BbJ78eZ957eSpNbg3D2LkxpdQwDMMwDMMwDMNIGbam1DAMwzAMwzAMw0gZppQahmEYhmEYhmEYKcOUUsMwDMMwDMMwDCNlmFJqGIZhGIZhGIZhpAxTSg3DMAzDMAzDMIyUYUqpYRiGYRiGYRiGkTJMKTUMwzAMwzAMwzBShimlhmEYhmEYhmEYRsowpdQwDMMwDMMwDMNIGaaUGoZhGIZhGIZhGCnDlFLDMAzDMAzDMAwjZZhSahiGYRiGYRiGYaQMU0oNwzAMwzAMwzCMlPH/uMY81b9mmA4AAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "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:\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 from 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 : [1, 2]\n", + "Density matrix:\n", + " Shell 1\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.ini](config.ini) for solid_dmft looks like this: \n", + "\n", + " [general]\n", + " seedname = nno\n", + " jobname = NNO_AFM\n", + "\n", + " block_threshold = 0.001\n", + "\n", + " solver_type = cthyb\n", + " n_iw = 8001\n", + " n_tau = 50001\n", + "\n", + " prec_mu = 0.001\n", + "\n", + " h_int_type = density_density\n", + " U = 8.0\n", + " J = 1.0\n", + "\n", + " beta = 40\n", + "\n", + " magnetic = True\n", + " magmom = -0.001, 0.001\n", + " afm_order = True\n", + "\n", + " n_iter_dmft = 12\n", + "\n", + " g0_mix = 0.9 \n", + "\n", + " dc_type = 0\n", + " dc = True\n", + " dc_dmft = False\n", + "\n", + " [solver]\n", + " length_cycle = 150\n", + " n_warmup_cycles = 10000\n", + " n_cycles_tot = 2e+8\n", + " imag_threshold = 1e-5\n", + "\n", + " perform_tail_fit = True\n", + " fit_max_moment = 6\n", + " fit_min_w = 8\n", + " fit_max_w = 14\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=8001` 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 in units of the DC potential. Hence, small values should be chosen. \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=150` 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.ini`. \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']" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "65dba97b-a64c-4d88-b7cc-3607605a9aa3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(nrows=3, dpi=150, figsize=(7,6), 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'], (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'], (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'], (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'], (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[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", + "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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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "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(-1.5,0)\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": [ + "\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'], orb_names=[0, 1, 2, 3, 4], 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", + "5 5 0 9 0.562426 0.341965 0.5 0.5 +1.0000|1111101111>\n", + "0 0 0 8 0.000000 0.160698 1.0 1.0 +1.0000|1111101011>\n", + "3 3 0 9 0.472365 0.057380 0.5 0.5 +1.0000|1111111011>\n", + "25 25 0 8 1.147767 0.048976 0.0 0.0 +1.0000|1101101111>\n", + "40 40 0 8 1.629801 0.041522 1.0 1.0 +1.0000|1111101110>\n", + "55 55 0 10 7.522083 0.038053 0.0 0.0 +1.0000|1111111111>\n", + "4 4 0 9 0.562426 0.028791 -0.5 0.5 +1.0000|0111111111>\n", + "50 50 0 8 2.745626 0.024248 0.0 0.0 +1.0000|0111101111>\n", + "8 8 0 8 0.637905 0.021395 1.0 1.0 +1.0000|1111100111>\n", + "11 11 0 9 0.734944 0.021376 -0.5 0.5 +1.0000|1101111111>\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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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "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 (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/PrNiO3_csc_vasp_plo_cthyb/tutorial.ipynb b/.doctrees/nbsphinx/tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.ipynb new file mode 100644 index 00000000..678f59fe --- /dev/null +++ b/.doctrees/nbsphinx/tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.ipynb @@ -0,0 +1,463 @@ +{ + "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": [ + "
" + ] + }, + "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.ini](2_dmft_csc/dmft_config.ini) 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.ini:\n", + "\n", + "Group [general]:\n", + "\n", + "* `set_rot = hloc`: rotates the local impurity problem into a basis where the local Hamiltonian is diagonal\n", + "* `plo_cfg = plo.cfg`: the name of the config file for constructing the PLOs (see below)\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", + "* `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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VVVi8eHF4D5qIiIiIiIgohsRlUKCiogLvvfceMjMzcdttt3ndvqGhAatWrQIATJo0ye1x533Lly8P7YESEREREVHcEARB9p9Op0NWVhaGDx+OhQsXQhRF2fYPPfQQBEHAQw895PNrWK1WLFiwACNHjkSrVq1gNBrRrl07XH311fjss88CPva3334bgwYNQkpKCgRBQKdOnQLeVywSRRHvvfcerr76apxxxhlISkpCeno6+vTpgzvuuMMtUxwA7HY7Xn31VYwYMQLZ2dlITExEmzZtMGjQIPzxj3/0+PMuLy/Ho48+iqFDhyI/Px8GgwF5eXm48MIL8fTTT6O0tDSc325ExWX5wA8//ACz2YwxY8bAYDDggw8+wPfffw+r1YpevXrh2muvRevWrV3b79q1C2azGfn5+Wjfvr3b/gYOHAgA2LZtW8S+ByIiIiIiik1TpkwB0LioLC4uxg8//IDvv/8eq1atwttvvx3wfg8ePIjx48fjt99+Q3JyMoYNG4bc3FwcPHgQH3/8MT766CNcffXVeOONN5CUlOTzfn/66SfcdNNNSEpKwkUXXYSsrCzk5eUFfJyx5uTJk7jyyitRVFQEvV6PQYMG4fzzz4fFYsGOHTvw8ssv4+WXX8YjjzyCBx54AABgsVhw2WWX4euvv4Zer8e5556LM844Aw0NDdi6dSvmz5+PVatWYcKECW6v9+mnn+KWW25BVVUVsrKycO655yInJwfl5eXYsGEDvvvuO/z73//G+vXr0adPn0j/OEIuLoMCO3bsAAC0bt0aw4cPR1FRkezx++67D//73/9wzTXXAAAOHToEAKoBAQBITU1FVlYWKioqUFNTg/T0dI+vr/WLLy4uRteuXf36XoiIiIiIKLYsWbJEdnvlypUYP3483nnnHUyePFl1IelNVVUVCgsLceDAAVx33XVYsGCBrJ/Zrl27cP311+PDDz+Ew+HARx995PO+ly9fDofDgRdffBG33nqr38cWy2pra1FYWIhdu3bh0ksvxfz589GhQwfZNps2bcLf//53FBcXu+578cUX8fXXX6NTp0745ptv3NZpP//8Mz7//HO31/vqq69w5ZVXQqfTYc6cOfjzn/8Mg8HgetxiseCNN97A/fff32yyBeK2fABoHC+4bds2LFq0CKWlpdi/fz9mzZoFk8mEm266yXXlv7a2FgCQkpKiuc/U1FTZtkRERERERAAwduxY3HzzzQCAjz/+OKB9/OMf/8CBAwdw0UUX4a233nJrcN6rVy988803KCgowLJly/Duu+/6vO8jR44AALp06RLQscWy++67D7t27cKYMWPwySefuAUEAGDw4MH45ptv8Ic//MF1nzOoMnv2bNULt2effTZmz54tu6+urg633HILHA4HFi5ciFmzZskCAgCQmJiIW2+9FZs3b242JRpxGRSw2+0AAJvNhueeew633nor8vLy0KlTJ8yZMweTJk2CxWLB008/DQCu2h9BEDT3qawP8mTHjh2q/zFLgIiIiIioeTr77LMBAIcPH/b7ueXl5XjttdcAAPPmzYNOp74My8vLw4MPPgigqVG6J0uWLIEgCPjf//4HABg5cqSrH4Iz22Hq1KkQBAFr1qzBV199hZEjRyIrKwuCIKCyshJA47rqxRdfxKBBg5CWloa0tDQMGTIECxYscK29pAoLCyEIAg4cOIB3330X55xzDlJSUtCuXTv8/e9/h8ViAdCYSX3DDTegVatWSElJwahRo/wq2T516hQWLVoEAHjhhReg1+s1t9XpdBg6dKjrtvMqfn5+vs+vt3TpUpSUlODcc891lZBoadeuHYMC0eRM79fpdKq/LGfKzJo1a2Tbm0wmzX3W1dUBANLS0kJ5qERERERE1AzU1NQAAIxGo9/P/fbbb9HQ0IABAwagd+/eHre9/vrrIQgCfvrpJ5SXl3vctlu3bpgyZYrr4uTFF1+MKVOmYMqUKejWrZts27feegvjxo2DyWTCuHHjcM4550AQBNjtdlxxxRWYOXMm9u3bhzFjxmDMmDHYtWsX7rzzTlxzzTVwOByqr//888/jpptugsFgwMUXXwyLxYJnnnkGt99+O/bu3YvzzjsPGzduxPDhw9GtWzd8++23GDlyJE6ePOnzz62+vh5nn32215+bkrN0fNGiRbDZbD49x1lOcOONN/r1WvEuLnsKOCMyBQUFqm9K5+MlJSUA4EoxcabVKJlMJlRWViIrK8trPwEiIiIiomZNFIGGqmgfhf+SMgEPmcHBEEXR1am+X79+fj9/69atAIBBgwZ53TY7OxtdunRBcXExtm7ditGjR2tuO2zYMAwbNgxTp05FcXEx7r33XhQWFqpu++qrr+Kdd97BddddJ7t/zpw5+OKLL9C3b1988803aNWqFQDg+PHjGDlyJJYtW4aXX34Zd955p9s+Fy1ahNWrV2P48OEAgBMnTmDAgAF4/fXX8dNPP+GWW27BM888A51OB1EUMXXqVCxduhTz58/Hww8/7PVn8fPPPwNoagzvj9tuuw3ffvstPv30U3Tt2hVXXnklzj//fJx//vmaveaCeb14FpdBAWfqTkVFBURRdCsLcEbUnFf9e/bsCaPRiNLSUhw5csTtj2DLli0AAnuDExERERE1Kw1VwFMdo30U/vvHQSA5K6S7tNvt+P333/H444+jqKgIRqMR06ZN83s/zvWJc8HtTX5+PoqLi1FWVub3a2m59NJL3QICQGNaPtBY1iA9vjZt2uCZZ57B5ZdfjhdeeEE1KHD33Xe7AgJA40XbyZMn47nnnoPFYsFTTz3lKpUQBAF//etfsXTpUqxdu9anY3b+3PwpAXC68cYbcfToUfzrX//CoUOH8Pzzz+P5558HAPTu3Rt/+tOf8Ic//EFWkhDM68WzuCwf6Nu3Lzp37oz6+nr8+OOPbo87ywacEZ7k5GSMGjUKAPDBBx+4be+8L5AuokRERERE1Lw46/ITEhLQo0cPLFmyBOnp6Xj77bcD6iPm7F/max8zX3qi+evyyy93u+/QoUM4dOgQCgoKXOslqQkTJiArKwu7d+9W7bQ/duxYt/uczQ4LCwuRkCC/Bu382R0/ftynY/an75uae+65B4cOHcKCBQtw7bXXujLKd+7ciT/+8Y+YNGmSZmlESxKXQQGgsXsnAMycOVMWQdu8ebOrKceMGTNc98+aNQsA8Nhjj2Hv3r2u+4uKivDKK68gIyMD06dPj8ShExERERFRDHPW5U+bNg133XUXFi5ciIMHD+LKK68MaH95eXkAmsqbvXEuwHNzcwN6PTVqXfuPHTsGAJoN8wRBQMeOHWXbSrVr187tPudUN0+Pmc1mn47Z+XMLZvRfXl4eZsyYgXfffRf79+/H7t27cfvttwNonCTx9ttvu7Z1/ryby6hBX8Vl+QAA3H777Vi1ahXef/999OzZE+effz5qa2uxfv16WCwW3H777Zg0aZJr+zFjxuCuu+7C888/jwEDBmDs2LGwWCxYuXIlHA4H3nzzTeTk5ETxOyIiIiIiigFJmY2p+PEmKTNku3J27g+V/v37A2i8gOnNqVOnsH//ftnzQiEpKUnzMV8yEtS28fS8UGQ5DBgwAEBTuXco9OjRA//9739x6tQpfPjhh/j8888xefJk1+sdPXoUW7ZswbBhw0L2mrEuboMCOp0O77zzDgoLC7Fw4UKsXr0agiBg8ODBmDFjhmuOqNS8efMwYMAAvPTSS1i5ciUMBgNGjx6N2bNnt6hfOhERERGRJkEIeW1+Szdq1CgYjUb88ssv+O2333DmmWdqbvvOO+9AFEUMHjzYdaU8XNq2bQsAriCEmkOHDgFo7DEQaaNGjUJSUhJ+/vln7Nq1C7169QrZvgsLC/Hhhx/Kss4vvfRSfP7553j77bcxc+bMkL1WrIvb8gGgMTBw5513YsuWLTCZTKitrcUPP/ygGhBwmjp1KjZt2uSaOPDll18yIEBERERERGGTm5uLW265BUBjcz6tOvaysjI8+uijAJrKn8OpQ4cO6NChA06cOIHVq1e7Pf7555+joqICPXv2jErzvZycHNe4+T//+c+w2+2a24qiiA0bNshue1JcXAygKTACALfccgvy8/OxYcMGvPbaax6ff+zYMRw4cMDbtxAX4jooQEREREREFA+eeuopdOjQAV9//TVuvPFGVFRUyB7fvXs3xowZgxMnTuDyyy/HDTfcEJHj+vOf/wygMVghraU/ceIE7rnnHtk20fDkk0+ie/fu+OabbzBx4kQcPnzYbZtffvkFF110EV5++WXXfZdffjlefPFFVFZWum2/YsUK17ZXXXWV6/7U1FQsWbIEOp0Ot912G+bOnQur1Sp7rs1mw9KlSzFo0KBmExSI2/IBIiIiIiKiWLBw4UJ8+eWXqo+lp6dj5cqVyM7Oxpo1azB+/Hi8++67+PTTTzF8+HDk5ubi4MGD2LBhAxwOByZOnIi33norYsd+9913Y/Xq1VixYgW6d++OUaNGQRRFrFq1CjU1NZg4cSLuuOOOiB2PUnp6OtauXYuJEyfis88+w4oVKzB48GB06tQJFosFO3fuxK5duwA0NpV3Onz4MGbOnIm//vWvOPvss9GpUydYrVbs2rULO3fuBADcdtttblMZxo8fjw8++ABTpkzBrFmz8Mgjj+C8885DTk4OysvL8eOPP6KyshJZWVk+j5iMdQwKEBERERERBeHo0aM4evSo6mOZmU0NEDt37oxffvkFCxcuxHvvvYfNmzejuroaeXl5uPzyyzFt2jTV0YHhpNfr8emnn2L+/PlYsmQJvvrqKwDAmWeeiWnTpuEPf/gDdLroJpi3adMGRUVFeO+99/Duu+/ip59+ws8//wyDwYCOHTvijjvuwPTp0zFo0CDXcz744AN8/vnnWLlyJfbs2YPffvsNFosFrVq1wpVXXolp06bhsssuU329K6+8EsOHD8d//vMfrFixAhs3bkR1dTUyMzPRr18/XHbZZbj11lubTaN6QQx2+CO59OnTBwCwY8eOKB8JERERxYtO934uu33gyUujdCS+qaysRHZ2tut2RUUFsrKyondA5DeHw4Hdu3cDAHr27Bn1BR8RufPnfRrsOpSfAEREREQxpMJkifYhEBFRC8KgABEREVEMefTz36J9CERE1IIwKEBEREQUQz7aol6XTEREFA4MChARERERERG1UAwKEBEREUVRp9yUaB8CERG1YAwKEBEREUWRTidE+xCIiKgFY1CAiIiIKIp0AoMCFFmC5G+O08mJYpP0vSmE+d8JBgWIiIiIooiJAhRpgiBAr9cDAMxmc5SPhojUON+ber2eQQEiIiKi5oyZAhQNKSmNvSxqamqifCREpMb53kxNTQ37ayWE/RWIiIiISBODAhQNGRkZqKmpwalTp5CQkICMjAxX9gARRY/dbkd1dTVOnToFAEhPTw/7azIoQERERBRFOuZtUhSkp6cjMzMTVVVVKCkpQUlJSbQPiYgUsrKyGBQgIiIiau6YKUDRIAgCCgoKkJycjIqKCvYWIIohRqMR2dnZyMzMDHs/AYBBASIiIqKoYlCAokWn0yE7OxvZ2dkQRZGTCIhigCAIEQkESDEoQERERBRFeo4foBgQjYUIEcUGVrERERERERERtVAMChARERFFEVO2iYgomhgUICIiIooihgSIiCiaGBQgIiIiIiIiaqEYFCAiIiKKIlYPEBFRNDEoQERERBRFjAkQEVE0MShAREREFE1MFSAioihiUICIiIgoihgSICKiaGJQgIiIiIiIiKiFYlCAiIiIKIpYPUBERNHEoAARERFRFIksICAioihiUICIiIgoipgpQERE0cSgABEREVEUMShARETRxKAAERERERERUQvFoAARERFRFDFRgIiIoolBASIiIqIoElk/QEREUcSgABEREREREVELxaAAERERURQxUYCIiKKJQQEiIiIiIiKiFopBASIiIqIoEtlqkIiIoohBASIiIqIoUpYPpCbqo3MgRETUIjEoQERERBRFyjyBbq3To3IcRETUMjEoQERERBRL2HmQiIgiiEEBIiIioigSFUEAhgSIiCiSGBQgIiIiiiJlEICJAkREFEkMChARERFFk6i8yagAERFFDoMCRERERFHETAEiIoomBgWIiIiIYgiDAkREFEkMChARERFFERsNEhFRNDEoQERERBRF7uUDDAsQEVHkMChAREREFEXKGABjAkREFEkMChARERFFkXLaAKcPEBFRJDEoQERERBRDmClARESRFLdBgcLCQgiCoPnfl19+qfq8pUuXYsiQIUhLS0NOTg7Gjx+P9evXR/joiYiIiBq5lQ9E5zCIiKiFSoj2AQTr6quvRlpamtv97dq1c7tv1qxZmDt3LpKTk3HRRRehoaEBK1euxNdff433338fV155ZSQOmYiIiMjFvacAwwJERBQ5cR8UePbZZ9GpUyev261evRpz585Fbm4uioqK0L17dwBAUVERCgsLMW3aNBQWFiI7OzvMR0xERESkjSEBIiKKpLgtH/DXnDlzAACzZ892BQQAYOjQoZgxYwaqqqqwePHiaB0eERERtVBumQGMChARUQS1iKBAQ0MDVq1aBQCYNGmS2+PO+5YvXx7R4yIiIiJSYkyAiIgiKe7LBxYtWoTy8nLodDr06NEDEydORIcOHWTb7Nq1C2azGfn5+Wjfvr3bPgYOHAgA2LZtW0SOmYiIiMhJGQRwsKcAERFFUNwHBR577DHZ7b/97W944IEH8MADD7juO3ToEACoBgQAIDU1FVlZWaioqEBNTQ3S09M9vmafPn1U7y8uLkbXrl39OXwiIiJq4dwbDUbnOIiIqGWK2/KBESNG4PXXX0dxcTHq6uqwe/du/Pvf/0ZCQgIefPBBPP/8865ta2trAQApKSma+0tNTZVtS0RERBQJoiJXQHmbiIgonOI2U+CRRx6R3e7Rowfuv/9+DB48GBdffDH+9a9/4f/+7/+QnJzsauAjCILm/vwZ/7Njxw7V+7UyCIiIiIh8xUwBIiKKpLjNFNBy0UUXYfDgwaiqqsKGDRsAwFUOYDKZNJ9XV1cHAEhLSwv/QRIRERGdxvIBIiKKpmYXFADgGjl4/PhxAHA1Hjxy5Ijq9iaTCZWVlcjKyvLaT4CIiIgolBgDICKiaGqWQYGKigoATVf9e/bsCaPRiNLSUtXAwJYtWwAA/fr1i9xBEhEREUEtU4BhAiIiipxmFxQoLS3FunXrADSNGkxOTsaoUaMAAB988IHbc5z3TZgwIUJHSUREROSkbDRIREQUOXEZFNiwYQO+/fZbt0j6gQMHcOWVV8JkMuHyyy+XjSCcNWsWgMYRhnv37nXdX1RUhFdeeQUZGRmYPn16ZL4BIiIiIg0OZgoQEVEExeX0gV27dmHatGlo06YNevTogYKCAhw5cgSbN29GQ0MD+vTpg1dffVX2nDFjxuCuu+7C888/jwEDBmDs2LGwWCxYuXIlHA4H3nzzTeTk5ETpOyIiIqKWio0GiYgomuIyKHDuuefijjvuwI8//ojffvsNP/zwA1JTUzFgwABcc801uOOOO5CcnOz2vHnz5mHAgAF46aWXsHLlShgMBowePRqzZ8/GsGHDovCdEBERUUunjAEwJkBERJEUl0GB3r17Y/78+QE9d+rUqZg6dWpoD4iIiIgoQMpySGYKEBFRJMVlTwEiIiKi5sI9BsCoABERRQ6DAkRERERR5HAwU4CIiKKHQQEiIiKiKHIoGw1G5zCIiKiFYlCAiIiIKIrsbpkCDAsQEVHkMChAREREFEV2RRBAmTlAREQUTgwKEBEREUURMwWIiCiaGBQgIiIiiiK3oECUjoOIiFomBgWIiIiIokQ5eQAAowJERBRRDAoQERERRYmynwDAmAAREUUWgwJEREREEXaovA6fbD0Kk9nm9hh7ChARUSQlRPsAiIiIiFoSk9mGS19ch5oGG4Z1y3N7nCEBIiKKJGYKEBEREUXQp78cQ01DY4bA9/vK3B5nogAREUUSgwJEREREXtgdIl797nfM/vhXnKxuCGpfZqvd4+MicwWIiCiCWD5ARERE5MWqnSfx7y92AgCOVzZg0dRzwvZaagMJiIiIwoWZAkREREReLFhb7Pp61a6SoPYlCILnDRgUICKiCGJQgIiIiMgLZZ3/5oOnwvdajAoQEVEEMShARERE5IVdkdP/2/GasL0WGw0SEVEkMShARERE5IXJbJPddoSx8J8xASIiiiQGBYiIiIi8qFUEBcQwXs4P576JiIiUGBQgIiIi8kKZKWBjpgARETUTDAoQEREReWGy2GW3gwkKeB0+wKgAERFFEIMCRERERF50yk2R3XaEeeXOEgIiIooUBgWIiIiIvLiwR77sdjBrdi+JAkHvn4iIyB8MChARERF5YVes0sN9JZ8xASIiihQGBYiIiIi8sDuUQYHwvh7LB4iIKFIYFCCiuHTKZMGgR1ei072fY9uRymgfDhE1cza7fJEexuEDAJgpQEREkcOgABHFpbvf3YpykwUAcPlLP0T5aIiouXPLFAjzsp2JAkREFCkMChBRXFq7pzTah0BELYhyBGH4MwUYFSAioshgUICIiIjIC/eeAsGMH/A+f4CZAkREFCkMChARERF5YXM4ZLe5aCciouaCQQEiIiIiL5SZAo4wRwXCvX8iIiInBgWIiIiIvFD2FAhmye69eICZCEREFDkMChBRs6C8ikdEFEqRzhTgJxoREUUKgwJE1CxYbA7vGxERBchmVyzTwz19gKkCREQUIQwKEFHcUTtZttgZFCCi8GGmABERNVcMChBR3LEqr9gBsDIoQERhpJw+EO6KJSYKEBFRpDAoQERxRy0AwKAAEYWTMlMgmEW74FOnwcD3T0RE5A8GBYgo7qj1D2BPASIKJ+X0gfCXDzAqQEREkcGgABHFHbWsADODAkQURpGecMKBKkREFCkMChBR3LGqnC2bzLYoHAkRtRQRzxRgUwEiIooQBgWIKO7YVRoN1lnsUTgSImopQtlTwBcMCRARUaQwKEBEcUfZBRxgpgARhZf79IFwZwqEdfdEREQuDAoQUdxRq+1VpvYSEYWSMkMpmI8cAd7HD7DRIBERRQqDAkQUd+wql9A4kpCIwsk98Mj6ASIiah4YFCCiuGNT6SlgVbmPiChUlBlKb288jG1HKgPal+A9UYAxASIiihgGBYgo7qiVDzBTgIjCSa1E6dpXisLWz4Q9BYiIKFISQrmzAwcOYN26dfjll19QWlqKqqoqZGZmIj8/HwMGDMDw4cPRsWPHUL4kEbVAaifnNgYFiCiM1IKRDVYHVu8qwWX924b89dhTgIiIIiXooEBFRQVee+01vPrqq9i1axcA9dm6wulcud69e+P222/HLbfcguzs7GBfnohaILWu3xaWDxBRGKlNPQkn9k4lIqJICTgoUFdXh6effhpz5syByWRCcnIyhg0bhiFDhqBXr17IyclBRkYGqqqqUFFRgZ07d2Ljxo3YtGkT7r77bsyePRt/+9vf8Le//Q2pqamh/J6IqJlT6ynATAEiCie1TIFwUrvAQkREFA4BBwW6du2KkydP4uKLL8ZNN92EiRMn+rS4N5lM+Oijj/DGG2/g4YcfxiuvvIJjx44FehhE1AKpnZyrTSQgIgoVrbGnvjQNDAQ/0oiIKFICbjR47rnnYvPmzVixYgUmT57s89X+1NRU3Hzzzfjqq6+wadMmnHvuuYEeAhG1UGppvA7m2hJRmDgcYkgX6WGKIxAREQUk4KDAxx9/jLPPPjuoFx84cCCWLVsW1D4A4NSpU2jVqhUEQUCvXr08brt06VIMGTIEaWlpyMnJwfjx47F+/fqgj4GIIketpwCrB4goXLSyBEJJr5OHCpgpQEREkRJwUKC2tjaUxxGUWbNmoayszKftpkyZgu3bt2PMmDEYMmQIVq5ciREjRoQkOEFEkaHWU8Ae4SZgRNRyRKKfQH6aUVaKwOkDREQUKQEHBVq3bo3Jkyfjiy++gCOKJ+OrVq3Ca6+9httvv93jdqtXr8bcuXORm5uLX375BR9//DG+/PJLfPfdd9Dr9Zg2bRoqKioidNREFAz2FCCiSAr15AG1PgQ5qYmysgJ+pBERUaQEHBSor6/H22+/jcsuuwxt27bFX/7yF2zatCmUx+bTMcyYMQNnnnkm/va3v3ncds6cOQCA2bNno3v37q77hw4dihkzZqCqqgqLFy8O6/ESUWiopfKyfICIwiXUmQIGvfvpV05qInSSaAFjAkREFCkBBwUOHDiAf//73+jduzdKSkrwwgsv4Nxzz0WvXr3w+OOP48CBAyE8THUPP/wwiouLsWDBAhgMBs3tGhoasGrVKgDApEmT3B533rd8+fLwHCgRhZR6TwFGBYgoPELdUyDJoHe7LyvFIMsgUPucIyIiCoeAgwIdOnTAfffdh+3bt+Pnn3/GrFmz0LZtW+zZswcPPPAAunbtigsvvBCvvvoqKisrQ3jIjbZt24Y5c+Zg2rRpGDFihMdtd+3aBbPZjPz8fLRv397t8YEDB7r2SUSxT72nQBQOhIhaBE+ZAkKIZgmkJOpl+2JMgIiIIiXgoIBU//798eyzz+LQoUP45ptvMGXKFKSnp2PdunWYMWMG2rRpg0mTJuHjjz+G1WoN+vUcDgduv/12ZGVl4emnn/a6/aFDhwBANSAANI5JzMrKQkVFBWpqarzur0+fPqr/FRcX+/eNEFFA1E7QeVWNiMIlEp8vxgS9YlYhP9OIiCgyQhIUcBIEAaNGjcLixYtx8uRJvPvuu5gwYQJEUcRHH32Eq6++GgUFBbjjjjuCep0XX3wRGzduxDPPPIPc3Fyv2zsnJaSkpGhuk5qaKtuWiGKXWipvqBuBERE5ecwUCCBRQC3GkJigY6NBIiKKioRw7dhoNOKaa67BNddcg4qKCrzzzjt48MEHUV5ejv/+979YsGBBQPs9fPgwZs+ejQsvvBBTp0716Tni6X9ZBQ//cot+/Ou7Y8cO1fv79Onj8z6IKHBqkwZYPkBE4eLpFCGQxbvauEFjgk4xkpCIiCgywhYUcNq2bRvefPNNvP322zh16hQAQK93b7DjqzvvvBMWi8WvoEJ6ejoAwGQyaW5TV1cHAEhLSwv42IgoMuwqEQBHBOaIE1HLFInygcZMAfYUICKiyAtLUODw4cN466238Oabb2LHjh2uq/D9+vXDzTffjMmTJwe8788++wxZWVluJQgNDQ0AGvsHFBYWurZNS0tDhw4dAABHjhxR3afJZEJlZSWysrJcAQQiil3q5QM8gyai8PD08RKq8gFjgl6RKcDPNCIiioyQBQWqqqrw/vvv44033sD3338PURQhiiLatm2LG2+8ETfffDP69u0bkteqrKzE2rVrVR+rr693PWaz2QAAPXv2hNFoRGlpKY4cOeLWcHDLli0AGoMWRBT72GiQiCIp1J8vantjTwEiIoqWoBoNWiwWWQPBP/zhD/juu++QkpKCm266CV9//TUOHz6Mp59+OmQBAWewQfnf/v37ATQGAJz3ZWVlAQCSk5MxatQoAMAHH3zgtk/nfRMmTAjJMRJReKn3FOAZNBGFRyTKk9pnJ0MnsHyAiIgiL+BMgdtvvx0ffvghqqqqIIoi9Ho9xo4di5tvvhlXXnmlx07/0TBr1iysWLECjz32GC699FJ0794dAFBUVIRXXnkFGRkZmD59epSPkoh8YbczKEBEkRPqjxdlc+N+7TMxpndr2UhCZj8REVGkBBwUWLRoEQCgf//+uPnmm3HjjTeioKAgZAcWamPGjMFdd92F559/HgMGDMDYsWNhsViwcuVKOBwOvPnmm8jJyYn2YRKRD9T6BzAoQETh4mmBHkBLAZl2Wcn48I7zodcJQe+LiIgoEAEHBe655x7cfPPNOOuss0J5PGE1b948DBgwAC+99BJWrlwJg8GA0aNHY/bs2Rg2bFi0D4+IfKQWAFArKSAiCoVwXrVvlWGEQd9YzSmwfICIiKIg4KDAU089FcrjCFqnTp3c0vHUTJ06FVOnTg3/ARFR2LCnABGFm/OcQhAEONynoAa576avpdkBnD5ARETREFSjQTUrVqzAxIkT0a5dOxiNRlmd/ooVKzBr1iwcO3Ys1C9LRC2IaqYAgwJEFCJFxeXo99DXKHx2DU5UNYQ1U0CaHSBtNMiPNCIiipSQBgXuvPNOTJgwAZ9++ilqa2thtVplV++zsrIwb948vPPOO6F8WSJqYWwqjQbZlIuIQmXywg2oMdtwsLwOT3+1K+SfL1r707HRIBERRUHIggKLFy/Gyy+/jCFDhmDr1q2oqqpy22bo0KFo164dli9fHqqXJaIWyK6Sy6sWKCAiCoT0Kv2G4nKPV+2FALoDSvenl+xAL4kKMPuJiIgiJeCeAkqvvPIKcnJy8NlnnyE3N1dzu27duuH3338P1csSUQuk2lOAV9WIKAz0esGnnkX++O1YtetrneTyjDRAwKAAERFFSsgyBXbs2IGhQ4d6DAgAQEFBAUpKSkL1skTUAqmdLDt4Ak1EYaAXhJAu0A+V12HxD/ub9i/JDtBJvuZnGhERRUrIggI6nQ4OH9rzHjt2DKmpqaF6WSJqgdRKBWw8gSaiMNAJQkib/j36+W9u+3eSlQ8w+4mIiCIkZEGBXr16YdOmTairq9Pcpry8HFu3bkW/fv1C9bJE1AKpZgrwBJqIwkCvC235QHmtWXZbFhRg+QAREUVByIICkydPRmlpKf74xz/CZrO5PS6KImbOnIna2lrcfPPNoXpZImqBVHsK8ASaiMJAr/OWKeBfp0GzTZ5VKZ04ICsfYKCTiIgiJGSNBu+88058+OGHeO211/D999/j4osvBgBs27YNf/vb3/DZZ59hz549GDVqFKZMmRKqlyWiFkitVIBBASIKB71O8DuVv7LOAptDRF6a0e0xZVBAWjIgzxTw80CJiIgCFLJMAYPBgC+//BIzZszAoUOHMH/+fADAli1b8Nxzz6G4uBjTp0/H8uXLodOF7GWJqAWyq/QUYFCAiMKhMVPA98+XTQdO4fwnV2PoE6vw3Z5St8fNNrvstiCoNxrkZxoREUVKyDIFACAlJQXz58/Hww8/jLVr1+LAgQOw2+1o3749Ro4cibZt24by5YiohVLNFGCqLRGFgU7wr6fA41/sRJ2lceH/5o8HMaJHvuxxizJTQNZosOl+lg8QEVGkBBwUWL9+PYYOHSqLcDvl5+dj0qRJQR0YEZEWtZNlXlUjonDQ6wT4MFzJZcuhStfXX+046fa4W08BSSCAjQaJiCgaAs7jHzZsGFq3bo1p06bho48+Qm1tbSiPi4hIE3sKEFGk6AXfewpU1Vm9bqPMFNBplA8wU4CIiCIl4KDAo48+iq5du2Lp0qW45pprkJ+fj3HjxmH+/Pk4dOhQKI+RiEjGrnLZznE6KNBgtaO6wfuJORGRL/wZSfjZr8e8buM+faApEJAgCQrYVHqnEBERhUPAQYF//vOfKCoqwvHjx/Hf//4XF110EdatW4c//elP6Ny5MwYMGIAHH3wQGzduDOXxEhGpZgXYHCJKahowes5aDHxkJZb/4v3knIjC4/Ntx3Hjqxvw4eYj0T6UoHkbSSitokxPMrg9rvy8Ut6WTh+QBgjYJ4WIiCIl6DEArVq1wvTp0/HJJ5+gvLwcn376KW677TaUlZXhsccew9ChQ9GmTRv83//9H5YvX476+vpQHDcRtWBqQQGHKOLFVftwtLIeNoeIP7/9cxSOjIgsNgf++NYWrC8ux1/f/wU1cZ6548/0gXSje6um2gabx+dIgwrSAIGDJVFERBQhIZ0NaDQaMWHCBLzyyis4cuQIfvrpJ/zzn/9EmzZtsHDhQkycOBG5ubm47LLL8Oqrr6K01H1UDxGRN1o9BXYcq4rC0RCRlDIIcKyyIUpHEhpeMwUkX1vs7qVN3sqZEiUjB6RBAWYKEBFRpIQ0KKA0aNAgPPLII9iyZQsOHTqEF198ESNGjMA333yDGTNmYMGCBeF8eSJqprTKB9SmoRBRZCnfhyLie3GrEwSPV+2lj1gDCAoYJEEBafkAMwWIiChSAh5J6K/27dvjzjvvxJ133gmTyYSvvvoK6enpkXp5IooRB8tN+HDLUYzp3Qr92mcFtA/V8gGHCIYEiKJP+T6MtwveyqaCBr3v5QNqQYEaL+UD0uwAWaYAgwJERBQhEQsKSKWmpuKqq66KxksTUZRN+99P+L3MhIXrfseWB8YiyaD3ex9qJ8t2UQQTBYiiT/k+jLegQL3VLrudakzwWD7w9Y6TGNO7NfQ6QXViQHV9Y6aAKIpYsLbY42vLGw36cdBERERBCGv5gJrFixfjkUceifTLElEMEEURv5eZAAB1Fjs2H6wIaD9aPQUE5goQRZ3yfRhv5QMmsyIokKj3mCnw4ZYjeGT5DgDqARBnpsDqXSV4+svdbo/LGw02fc3yASIiipSIBwVeffVVPPzww5F+WSKKAcoT5kCX8KqZAg4x8B0SUdjEW6aAsgTAoNd5XaC/VnQQAFSDBz/sKwMA/Pe731WfK30KGw0SEVE0RDwoQEQtl691ud6ojyRkTIAoFsRbZoCS2tH7etFebTvnQr/B5t5vAJD3MJCVDzBTgIiIIiTgngIlJSUBPc9qje95xUQUuFCd42qdLPPCGlH0Kd+H8fa+VDYaFOFbQFMURdXtzKeDAWZFrwIn6bQGaaYAyweIiChSAg4KFBQUBDT+SxQ5NoyopQpVpoDNoX7FzWxTP+kmoshRvsvjLXNA7WNKGShQ02B1qG5nOR0UsGhkCqQam5qtSoMCar1TiIiIwiHo6QMdOnTwa/vjx48zW4CohVKeL79WdACDOmXDmODfBAKtTAGThUEBomhzu9Ie52tbUfQty8lksalu5+xRoBUUHdWrtetrveSiSaiCqERERN4EHBTo3LkzDhw4gO+//x7t2rXz+XlDhw7Fxo0bA31ZIopjypPcr3acxMc/H8V15/gXXNQMCpg9zwMnovBzzxSIL27lDxB9qu+vt9hVF/KW00EBtSzJKUM7YmCHLNdtWaNBZgoQEVGEBNxocMiQIQCATZs2hexgiKh5Uzth/seHv/q9H620WgYFiKIv3i9wK8sdGjMFvH9TWpkCzp4CaoWTtw3vIgsW6Dh9gIiIoiDgoMC5554LURTx448/+vU8X+ryiKh5CnejQZYPEEWf+6I6fv7dF0URK387qXK/9+eazHaPPQXUogLK5AFZ+QAzBYiIKEICLh8YP348Dh48iO7du/v1vAULFqC6ujrQlyWiOBaqxYHNrr4fZbDgzjc3Y8Pvp/DAhN648uz2IXltIvLCLf0+fny27Tge+3yn7D6tqQJKdRabavDA4iFTQKeICsjLB7wfLxERUSgEHBTo0aMH5s6d6/fzzj777EBfkojiXCgufNnsDleNrjdf/HoCAHD3u7/4FBSw2BxITAg4gYqIoNJTII6iAn9++2e3+0TIU/lH9szHt7tL3bYzmf3vKaAMCujYaJCIiKKAZ79EFDFqaf9GPxfhdRqzvoMhiiKmL/kJ/R7+Cq8XHQj5/olaEve1bHwvbh2iKPue0pIM6NYqzW07u0NUDXx6yhRwKx+QfByy0SAREUUKgwJEFDFq5QNJBv/GEdaHoW/A9/vKsGpXCRqsDjzwyY6Q75+oJVFr1BfPRFFe368X1Bf4do0yA1dQwIeeAmw0SERE0RBwUGDXrl0hOYBQ7YeIYp/aha+qeqtrjrcvpBMG9DoB/7ikV9DHdfhUvew2G3wRBc59pF98EyH/7FKm/Ds5HKJq4NP5+SaohBLcegqw0SAREUVBwEGBs846C5MnT8b27dsDev7WrVtx/fXXo2/fvoEeAhHFGa0a2Tc3HPR5H3WSTIEUgx7DuuUFfVwpifJshVoLRxsSBSqeewqoUY4kFARB9aq/VvmAc4SqaqaA4ra00aDW6FUiIqJQCzgo8MADD2D58uXo378/Bg4ciDlz5mDTpk2wWq2q25vNZmzYsAFPPPEE+vbti0GDBuGLL77Agw8+GPDBE1F80QoKPLT8N5/3US/pKZBi1MOQoH7Vzh/Kk3XXCDEi8pvyank8jSRUJy8L0Gl85GiVD9g8ZEJ5mj7ATAEiIoqUgKcP/Otf/8Idd9yBf//731i6dCnuueceCIIAg8GATp06ITs7G+np6aiursapU6dw8OBB2Gw2iKKIzMxM3HXXXbjvvvuQn58fyu+HiGJYKNYGZmvTCXZigg4Gfehbo5gZFCAKWLMrH1BkCuh1gmopQGP5gPvzra5MAffnKO9KYE8BIiKKgoCDAgDQqlUrPP/883jyySfx3nvv4bPPPsMPP/yAPXv2uG1bUFCA4cOH49JLL8W1116LpKSkYF6aiOJQKEZs2RxNC3aDTofEEAQFrHb5cZnDMOGAqKWK97WtQ5SXBagt7oHGdH+1rAjnFAG1x5T70uuaPs9YPkBERJESVFDAKTk5GVOmTMGUKVMAAKWlpSgpKUFVVRUyMzPRqlUrZgQQkdviOxDSMV0JesHnTAFRFDVP5s02u+I2MwWIAuWWKRDnUQFlpoBOUO8PoAweONlPBwvUgqLKUgRppoCnsgMiIqJQCklQQCk/P59BACJyozV3W6tGV4306plep4NB79uTHWLjKDE10pIEgEEBomAoRxLG+wVvEfL6fq3pA42NBtW/WatdVL3yn5ooPw1LkHxIaX1eEhERhVroi3GJiDRojR50iNqPKdkk2QYJOgGGBN8+xqRlB0oWxWuzfIAocMp1cSjKhqKpMVOg6bZeI4qpNX0AaPz8UWscqFPsK4HTB4iIKAoYFCCiiPF0kisdNeh5H00LeL1O8LmnQFmtRfMxZgoQhU/cBwUU0wcEQb2vgENU7ykANH72+bLIl/UUCEG5FRERkS8YFCCiiJHWyGanGGSP1fsYFJCm1Br86Cnw6ne/az7GngJEoaNcysZ5TAAQ5d+DThBUy5bsDu0AiM0u+jRiUJ4pwM8hIiKKDAYFiChipI0GUxITZCfAJovNp33IewoImqm8Skcr6zUfs9iUmQIsHyAKlPJqebxnCjhEURaM1AlAu6xk1e00ywfsDp9GDLKnABERRQODAkQUMUcq6lxfG/QCkhP1rtuBZAok6Hz/CPN0Pq7MDODJOFHglO+e+WuK4/o9JUI5fUBAQab7WGWbXdT8nLE5RLefgVo8U8+eAkREFAUMChBRRJhtdryweq/rdmZKoqzzts89BezyngK+8jQWTZkZwFpeosAp32qbD1bgm50no3MwIaBsNKjTCW5TAwDArjF2EGj8TFEGBRJVmqQmsKcAERFFAYMCRBQRG/efwuFTTSn81w5ujxRJpkAg5QPO8oNJg9p7fZ7W6fUXvx7He5uOKF6DtbxEgXN/t/3vh/1ROI7QECEPKuoE9QW9w6HdaNDqcLhd+VfrhyItH2CmABERRUrYgwL79u3Dhg0bcOjQoXC/FBHFsN9LTa6vu+SnYvK5HYMvHzh9Ap1k8P5RpnayfvhUHe58c4vb/TwZJwqc2rpYgO9ZPbFGVGQA6FQmDwDOTAH1fag1GlSbnCLts2JncJJi2dEtwL5vmkEnUSICgggKnDx5Eu+99x7Wr1+v+vgPP/yAXr16oWfPnrjgggvQuXNnnHPOOdi+fXvAB0tE8evwqaZ+Aud2zgGAwMoHVHoKJBv0Wpu7qJ22fLn9hOq28Vz/TBRtze3dI6JxsoCTIAg4I0el0aBDu3zAqtJoUC1TQNZTgOUDFKsObwReHQW8cTWwYUG0j4aIQiDgoMDrr7+OG264Abt373Z7bO/evbjkkkuwd+9eiKKInJzGBcDmzZsxevRolJWVBX7ERBSXquqtrq9zU40AoMgU8K18wK6YPgAASb4EBdSuXmpcvLTyZJwoYP681+KCKM800gsCLuvXFr0K0mWb2R3amQJWu8Mt2Kg2jUDWU4DBSYpVn/4ZrvDfV/dF9VCIKDQCDgqsXbsWSUlJuPbaa90ee+ihh2AymdChQwf8+uuvKC0tRXl5OS6//HKUlZXhhRdeCOqgiSj+1JqbFv3pSY0ZAvKeAr5lClgll+wS/AgK5KYm+rR/gGm7RMEQm1mugMOtfABI0OvwxczhGN+3wHW/XdTuKWCxuQcFLjqztdt2HElIcaG+ItpHQEQhFnBQYNeuXRg0aBBSU1Nl91utVnz88ccQBAHPPvss+vTpAwDIysrCkiVLkJqaii+//DK4owbw3HPP4aqrrkL37t2RmZkJo9GIjh07YsqUKdixY4fm85YuXYohQ4YgLS0NOTk5GD9+vGYJBBGFjjQokOYKCvhfPqDWU8Co0vRLacjpkgVfMFOAKHBq6+IDZSb3O+OE2vQB5/+3yWwqI/BUPmC2OWT7SE3U464x3d22k/YUkAZAiYiIwingoEBJSQk6dOjgdv+mTZtQX1+P5ORkTJgwQfZYVlYWhgwZgr1797o9z1+PP/44VqxYgZycHIwePRqXXnopkpKSsHTpUgwcOBArVqxwe86sWbMwZcoUbN++HWPGjMGQIUOwcuVKjBgxAsuWLQv6mIhIW02DJChgdM8U8LV8wCYrH2j8CPOpfEDlPkGrYRiv0BEFTG1hrNfHb/2ACFGW6i/92JAu4m0OUbPnWoNVHvRcOetCtEpPcttOr2OmABERRZ77oF0fmc1mVFdXu92/YcMGAMDAgQNhNBrdHm/dujXq6urc7vfXJ598gkGDBiEpSf6P6oIFC3DnnXfitttuw6FDh6DXNy4WVq9ejblz5yI3NxdFRUXo3r0xQl9UVITCwkJMmzYNhYWFyM7ODvrYiMhdTUNTT4FgygfsKiMJDT4sONQWKlrPYi0vUeDUFrNaHfvjgSgqRxI2fS86ySLe4WH6QL0iKCANJkhJmw/yc4hiV/y+n4lIXcCZAu3bt8cvv/wCh6L2dtWqVRAEAUOHDlV9XlVVFfLy8gJ9WZcLLrjALSAAAHfccQe6deuGY8eOyZogzpkzBwAwe/ZsV0AAAIYOHYoZM2agqqoKixcvDvq4iEidvKeAAYC8fMDXkYTSjtzOq2paV/yl/Dm/tjFtlyhgakGBeL7qfaSiHtJTHb3k8yZBMS1Aq6eA8vNNpxEUYKYAERFFQ8BBgZEjR+LIkSN4/PHHXfdt2LABX331FQC4lQ44/fzzz2jfvn2gL+sTZ3ZAYmJjY7GGhgasWrUKADBp0iS37Z33LV++PKzHRdRSORwiTpksrttq5QN1PpcPNJ2dOzMEfLkKqXayrvU0nowTBU4tK8cRx++p345XeygfkF/Z1+opoMwU0Gt8+LCnAMWH+H0/E5G6gIMC99xzD4xGI/71r3+hU6dOGDx4MC688ELY7XYMGTIEw4cPd3vOhg0bcPz4cZx33nlBHbQnS5cuxe7du9GjRw906dIFQGNTRLPZjPz8fNWAxMCBAwEA27ZtC9txEbVUp0wWdLn/C1nzPmdQIFkWFPAxU0Clp4AviYyqY9J8eA0i8o9NpVGn2vi9eFItGakqDUJKpwVY7Q6fywe0eiwkSMoHGJwkIqJICbinQI8ePfDhhx9iypQpOHToEA4dOgQA6N27N95++23V58ybNw8AcMkllwT6sm6eeeYZ7NixAyaTCTt37sSOHTvQtm1bvPXWW9CdXjA4j00rQyE1NRVZWVmoqKhATU0N0tPTVbdzck5UUCouLkbXrl2D+G6Imp97P3QPtjl7CiRLGgRW1lndtlNjt7v3FNCphDffvv08LPr+d3yzswSARk8Bjat1LB8gCpxaACDeF7jSoKU081/az8RmF5Gg0fO0zuxbTwH3xoWiT+VRRJHFv0mi5ibgoAAAjBs3DocOHcL333+P0tJStG/fHhdccIFrMa40efJk3HDDDRg9enQwLyvz1VdfuUoDAOCMM87A66+/jkGDBrnuq62tBQCkpKRo7ic1NRWVlZWora31GhQgIt99/dtJt/ucmQLS+tlfj1b5tD95psDpngIqJyhJBp3sZPql1fswvm8btM5w70Xi6TWIyD9qAYB4f09JG6VKP7fk5QMOzfMfaU8VAEjUq2+nVwQL7A5Rlo1AREQUDkEFBQAgKSkJY8aM8Wnbyy67LNiXc/PNN98AACorK/Hrr7/ikUceQWFhIR577DH885//BNBUS+wp2q7VHEjNjh07VO/XyiAgasku7JGPtXtKZfc5U2R/L62V3e/LVTFpT4EEV6NB9+0EQZBd0Ss3WXDfR79i8dRzJNtovIZK+jMR+UYtKJCf5j6NKJ6YbU2fO9LPKIOsfECEXqf+2SENCugEeZmAlDKDwObQzj4gIiIKlYB7Cmix2+0oLS1FWVkZ7HbfaoRDISsrC8OHD8cXX3yBQYMG4YEHHsBPP/0EAK4r/yaTSfP5zjGJaWlp4T9YohakV4E880Z6heySs9rIHrP6sBiXXnF0nlirNRrUCe73r95VIrvNngJEoadWqjPurIIoHEnoSD8T5D0F5JkCWmUStQ1NQYHEBO1TL2WwIN7LLqiZYkkLUbMTkqDA4cOHce+996J///5ISkpCQUEBWrduDaPRiP79++Pee+911fWHm8FgwHXXXQdRFF3TBDp06AAAOHLkiOpzTCYTKisrkZWVxdIBohBTpsNKr6y1ypBfPbQ5vNfyq/UUUM0UgCCb+e0Puw/HQUTq1DJttEbwxQtpnxHpt6JXjCTUCihKMwW0SgeU+3Puk4iIKNyCDgq89NJL6NWrF5555hn8+uuvsNvtEMXG5jgOhwO//vornnnmGfTs2RMvvPBCKI7Zq7y8PABAaWljynLPnj1hNBpRWlqqGhjYsmULAKBfv34ROT6ilkR5tV46ccCgqL/1N1PAeQKtlikgCECqMbC8WyuvzhEFTHUkYZy/pSrq1KcPyBoNOkTZlX3p4r9GGhTwUA/gXj7AACXFoDifJkJE7oIKCjz11FO466670NDQgGuuuQYff/wxDh8+jIaGBtTX1+PQoUNYtmwZrr76algsFtx999144oknQnXsmtauXQsArkkAycnJGDVqFADggw8+cNveed+ECRPCfmxELY2yE3mSZOKAsoGWL13/pVfxnc9XuwgpCEBqonvbFOlrSOuEZa/Bq3NEAVO7Wu5P355Yp9NqNGiXlw9IA6C1kkaFRo/lA+6NBomIiMIt4KDAjh07MHv2bGRnZ2PNmjV45513cPnll6Ndu3ZITEyE0WhE+/btccUVV+C9997Dt99+i8zMTDz44IPYvn17UAe9bt06vPvuu7DZ5N18rVYrXnzxRbz++utITk7Gdddd53ps1qxZAIDHHnsMe/fudd1fVFSEV155BRkZGZg+fXpQx0VE7pQL/ZRED0EBH06AZT0FXCfkaj0FBKQY3YMCFsnxtMlM9voaROQftYWsWvZAvNIaSWi1yzMFpJ91JslIQo89BRTZU/wsopjEngJEzU7AQYEXXngBDocDb7zxBoYPH+51+xEjRuDNN9+E3W7HSy+9FOjLAgCKi4tx/fXXo02bNrjkkkswefJkXHzxxejYsSNmzpyJxMRELFmyBGeccYbrOWPGjMFdd92F8vJyDBgwABMnTsT48eMxYsQIWK1WLF68GDk5OUEdFxG5U57UJhs8lQ94zxSwqfQU0MoUSFMpH3h5TbH312DKLlHAmmP5gJSs0aBOu9Gg9LPO154Cys8y9hQgIqJICHgk4erVq9GrVy9ccsklPj9n3Lhx6N27t2uMYKAuvPBC3H///Vi7di22bduGsrIyJCYmolOnTpg0aRJmzpyJbt26uT1v3rx5GDBgAF566SWsXLkSBoMBo0ePxuzZszFs2LCgjomI1JXUmGW3O+Smur7W6RrHBjrPo305Abar9BRQG2MoQECqSqbAC6v34c6R3ZBk0EOE+usxZZcocGrv4+aUKSD9uJFmO9nsoqxcKkkrKOAhU0AQBBj0gqu/CgOUREQUCQEHBY4dO4bLLrvM7+f17dvXNRUgUJ07d8a///3vgJ47depUTJ06NajXJyLfHD5Vh8+3HZfd99exPWS3DXqdq7bflxNgm489BXQCkJ2SqLoPs9WBJINe8+qlLxkLRKROLQDQjGICsgkB0gknVkWmgLR8QMpTUMC5f2dQgAFKik0sHyBqbgIuH0hMTITZbPa+oYLZbIbBYAj0ZYkojrzx40HZ7RuGdECnvFTZfdKTaostsEwBtekDCXod8tONbvcDcJ3PaDU/44k4UeDU6uAdzeg9JS8fkGcKWCTNS9V6mgCeywca9ykJNLB8gIiIIiDgoEDnzp1RVFQEhx+pbQ6HA0VFRejSpUugL0tEceRQeZ3s9tkdsty2kaXf+vB5YpX1FDj9EaZy0SJBJyAvTSMocJrW1Us29yIKnFoAIF7eUt6u4gPyzKQEvXwBL51okp6kERTwIVPAiQFKik38uyRqbgIOClx66aUoLS3FnDlzfH7Os88+i9LSUo7+I2ohslPl6fvdW6W5bePvVTG7bPqAdqaAXiegXVYyMlROzJ2ba/UUYHMvosDF8/QBo5er+IC8h4lBEdQ0W5umDKh99gDegwLKfRIREYVbwEGBv/zlL8jIyMB9992HJ554Ana7XXNbu92Oxx9/HPfddx8yMzNx1113BfqyRBRHlOuAAWdkuW0jOwH2ZfqA5CRZ76GngF4nIDFBh6XTz9Xcl9b5NjMFiAKn9v7RKtWJNb5kCui1pg/YRTRIMgUyktRLJf3JFOBnEcUm9hQgam4CbjSYm5uL999/HxMmTMDs2bOxYMECXHPNNRg0aBBatWoFURRRUlKCzZs344MPPsDRo0eRkJCAd955B7m5uaH8HogoRkkXAn8c2VV1SoC8fCCwTAFB5QTFeWKtFogQT5+3a129tPPqHFHA4nkkoS+HKZ2kKg1qWu0O2feuVT7gLRtBGWggIiIKt4CDAgAwZswYrFu3DlOmTMGuXbswb948t22ci4KePXtiyZIlOPdc7at2RNS8yJoCqgQEAMAgKx/wJVPAvaeAWqZAgtqdpznLBrSCEDwRJwqcaqPBOMgUOHyqDqdMFq/bSYOb0p4CFrsDYtPkQbfyKSejQX0qQdM+2VOAiIgiK6igAACcc845+O2337BixQp88cUX2Lp1K8rLyyGKIvLy8tC/f3+MGzcO48ePV71KSETNl3Rmt9b7Xznn2xvpNs7nqu1b7ykocHoX0sWLfDY4T8SJAmW2ugf34uEtdflL3/u0ndb0AWXcQ2skqtaoQid5+QCzligG8XyeqNkJOijgNG7cOIwbNy5UuyOiZkB6kqy1SJelyvpwAmxTGUmodn4i3a+S86qlXZKZYEzQw2pvvMznS28DokgQRRG/Hq1Cx5xUZKbExzjfBpt7j6F46ClQUWf1aTvpR5nBQymAVlAg2VumgM6/QCkREVGwAm40SETkjTT1VevCvSHB3+kDTQt2b9MHtDhfRRpgSDJIgxM8EafYMHflHlz+0g+48NlvUeXjojXa1DMFms97StZoUK8V7BSQnaoexEn2minAzyKKcc3o/UxEjUKWKbB+/Xp8++232LlzJyoqKiAIAnJycnDmmWdi5MiR7CVA1AJJFwI6jUW6wc9UWV8zBXwpH5AGLYwJTSfqrOOlWPHC6n0AgMo6K+at2oN/XdYnykfkXYPVPVMgmm+pkpoGVNfb0E1lJGogZCMJNTKSUhL1SE1UP8VK8pIpYGBPASIiirCggwLbtm3Drbfeip9//hmAe4qg8x/PIUOGYNGiRTjzzDODfUkiihOyoIAPPQV8yxSQ9gHQbjToISbg+pySBhiMBv8aHhJF2o6j1dE+BJ/UmG1u90UrU2B/mQmXvfg9as02PHV1X1x3Toeg9yn9bNHKFEhPMiDNqH6KxZ4CFPfYU4Co2QkqKPDTTz9h1KhRMJlMSE1Nxbhx4zBgwADk5eVBFEWUlZVh69at+PLLL/Hjjz9i6NChWLNmDc4+++xQHT8RxTCfpg/o/Ru/Jd2mKVNAvu8EneCxsalzD9LjS2KmAMW4Y1X10T4En1TXu5c5RCvb+N+f/4ba00GKf3z4q2ZQwJ+eB9KsJ62gQFaKAakaQQH2FCAiolgTcFDAbrdj8uTJMJlMmD59OubMmYOMjAzVbaurqzFr1iwsXrwYN954I3bs2AGdhyZgRNQ8SNfWmuUD0pFeKg3KlGwqPQWUe/ZUOgBIpg9IMgISE1jHS7FDFEW3lPvjVQ3RORg/rdtb5nZftDIFDpbX+bSdP4cnnz6gfi6TnZKIxASdbKqJk7fyAek+GaAkIqJICDgo8Mknn2Dfvn247rrr8Oqrr3rcNiMjAwsXLkRNTQ0++OADLF++HFdccUWgL01EccLhQ6NBaSqtyeI5KOBwyBdKeo1GgwleggLOBYrZ1hQUkKb6MihA0VRrtuGG/27AsUp5ZkA8LBC1jjFah+5rlrM/hyf9eNHrBAiCe1Ah6/SkiFRjAioVDSK9NRqUlVSxfICIiCIg4Mv1y5cvh06nw+OPP+7zc5544gkAjQEFImr+pFcHta7eZyQ3deiubvDcXd2uOPNu6ikg37dWVoKTcy/ShmipRnn5QDyMUKPmackP+/Hr0SqUmyxuj92/7FdUqaTnB+LL7cdx22ubsPK3kyHZHwBU1LkfMyCfGhKL/MlkUH7eqDUbTE86HRRQaTboT0+BeAgEUUvEngJEzU3AQYHNmzejZ8+e6Ny5s8/P6dKlC3r16oXNmzcH+rJEFEekV+K1ggLpSU0nzTUN7g3KpJQnyFrTB7xlCjgX/PWyoID85J3ZAhQt64vLNR9768dDWLju96Bfo85iw4w3tuCbnSdx+9JNMPtQuuML5VVxJ1+aiEZTUEEBlb4CzoW/NNjo5L2ngH99Vogij3+XRM1NwEGB48ePo0ePHn4/r0ePHjh27FigL0tEcUIURew5Weu6nZ9mVN0uI0mSKeDlCqhyKkCCRlBA76VnifP8v8GqXj4A8AodRd7hU3XYuP+U1wXqi6fHFAZD2Z+gSmMx7696jRIgW4xP9PAnMUj5eZOskg3gXPirNRv03lOAmQJERBRZAfcUqKqqQmZmpt/Py8jIQHV1fIxVIqLA2R0iymrNrttd8lNVt8tQyRSw2BzYfaIG3VunyU6gNTMF4LmnQMfcFFnDMecCQBpkSElkpgBFz4EyE0bNWRO12vtQsdg1ggJR+saUnw1a/AkKKLOe0ox6lNXKt3H2DVAbS+i1fIA9BSjmsXyAqLkJOFPAZrMFNEFAp9PBZvOcIkxE8U+ZLpyZnKi6XbokU6DmdE+BP7+9BZe99D0m/ucHWbNC5cLC1VNA8VGkPGkf27u17LZ4OvVRGhRQpvTG+pVNal4e/ey3qAYEQvXSFpv6niy22H4/BVM+oJYN4MoU8JBFoEWWKcDyASIiigDOBSSisLAoFtWJevWPG2lPgap6K0qqG/DVjsbGZ7tO1ODgqaYr/FqZAsqTdGVQYEZhV9lthytToGl/yYny42OmAEXSkYp67xudpnb12V/K63yh6qupfN87RS1TIAzTB5QfZapBgdPZAClqPQW8TR/QcTwqERFFVlBnFq+99hpee+21UB0LETUjyvp/Q4L62XlOalMGwSmTBaWSkgNAXqOs3KdecJYPyCnLB/LSjEjQCa4TbGejQU+ZAqzlpViVmKCD3SHiiS924kB5He4b3wtd89P82ofg62rZT1oZAbGeeeNPpkCCIjUpVWWR7/w8UQZwBAEwJni+HiP9/LKxfICIiCIgqKBAoCO7wnUyQkSxQ9k126CRKZCf3tSAsKLOivJa+UgzaVd06T71OsE1elD5maI26aAxm+B0UEBlf0ZFUEAZgCAKJ9GPa9V6nYBPfzmKhd/vB9DYoPCru0eE69D8ohUUiPXpA6Ifb/cExbSBNEkJlFNyonqjwWSD3us5kLSnADMFiIgoEgIOCjgYvSYiD7QmBShJgwIAcEhSLgDIxxpKr5pJ96fcter4Q8ldzoCmNNXZmKCDTmgqLWCmAMWqBJ2Ad3867Lq9+2RNFI9GTrvRYGyfM/gTlFEGOKXNUp1yT2dAKbMIvE0eAAADewoQEVGEsacAEYWFdMGdqNdpXh0zJuiRmdx0pe1whTwo8MO+MtfX0quN0hNz5b6VV/IAeYmB2vQBg14nSwuO9Sub1Lz42/3emOB9cenX64eo1aB2+UBsv5+UMcC/jtUeuawMcGYku2cKOIOdykwBX/pB6NlTgIiIIoxBASIKC+mCW22RLiXNFlA2XJPOZJcuLKT7dMsUUAlASJsRqpUPGPQ6WYYBMwUokuos6lfY1SToBCR6qUv3JlxFfBbJe0raXFSrAWGkOTTe18pyyNuGd9HchyFBmSkgDwoIAtAmMxmAe1AgXSWrQClBz54CFONYBkzU7DAoQERhYbWpX9VX08pDUEC2T83yAe89BaSbOFQaDRr0guxknD0FKJIq6yzeNzrNLopem9VFizRTQNp5P1qZAsosohdW71XdThkr8LTmMSgaDWYkyxf6rdKNrqBNliKLwKegAIOTREQUYbF5VkFEcc+iSM33RJYpoOgpICXLFNBp71M1KCD5WhQbrwz+XmaSHaN0oWWO8bnq1HxYbA6Y/MgUsNtFn2rTo0EaFEhNbFoAx8oV73nfqAcFlJkCnoICysynTMXCv11Wsuvr3LRE2WPpKk0J3fYvnT4Q42UXRETUPDAoQERhYZX1FPCcaigdS1hu0r5iatMoSdApggDKzAHlfaIIfLTlqOzxxqBA00JLOvWAKJz8yRIAGuvMlZkCgU4Danp+UE93kQUFJJkCsd6jQ3l0OkHAXaO7q26r7CmQlSxf+J/dIbvpsRRFUIA9BYiIKAYxKEBEYSGr1/eS6uzrVU+rQ6PRoGI7taAAFOUDf33/F9nDCXoBSQZJpoA1Nq5sUvNXWW+V3e7eKs3j9naH6NZo0N/MlnCVBEunD0jr6bUaEEaDWl8BhzJTAMDtI7rg3nG93LZVliRkpciv/vdrn+n6WplFoAxgqkngSEIiIoowBgWIKCyUnf09SfTweLbkhFuWKeChp4BaZYG3U3GDjpkCFB3/+2G/7PZXfxnhcXubQ4TRIP8jr/ej/EBNqJae0owAaQO+eqtds8lfpNU02NzuU6bp63UC0owJmHFhV/Ro7TlIowwKdMhJcX2tDAr48jOQ9xSInWAKERE1XwwKEFFY+NNTwFMndemC3yqbPtD0HIOiPEHtPFp6dU95VRAActISZZkCDcwUoAh5e+Nh2W2dTnCbby9lVykfqLcGGRQIUf2ANCNAuVhuiEKgTS0YWFnvXq7haYSqtzGCbTKTXYGA9KQE9CxIdz1m0OuQIWku2CU/1esxS3uixHrZBRERNQ8MChBRWCg7+3viqZO6NC1a2qxMuk9leYJdZYEjzdqtrLO6Pd4mI0lWxtAQ5CKLKBjJidoLUZvDAUGx3PVnpKGaUPUUkL5flVfJgz3GUNl6uBKHT9XhP9/uw69HqgB4/rzy1hxQrxPw/oyhePzKvvj0T8OQovjd3Ty0IwCgc14qbj6vk9fj4/QBin0cSUjU3HjveENEFAB/ygeUs7ylpItz+fSBppMSZfmBWoqu9MrfwXKT2+M6ncDpAxQzUhSZAh1zU3CwvHEyh90hugW+/C0fUAYBwtNoUP6+jsYCV613wqvrfocAAb8ercIzX+3GDUM64KIzW7seT1B8nvgyRrB1RhJuPLeD6mP3XNwLt17QGVkpiaqTUZSkr8+eAkREFAnMFCCisLDapE0BPZ8I92mbofmYzSG6eglYZdMHmj6+lEEBtUwB6RFs+P2U7LG/X9ITAGIqU6CkugGbD1aELK2b4otyQf36ree6vrY5RLe/izqLe528J8q/KjFEXQWkafjJigai0VjgqgUith+txq9Hq1y33954CM9+vdt1WxnEHN+3jetrT59VnuSmGX0KCADsKUBERJHHoAARhYU/PQX6tM1Ep9wUzced+7I51AMNyo7e3jIFDlfUub7u3z4TM0Z0BSAfH3asst7jMYdTea0ZY55bi6sXrMczX+32/gRqNmZc2Pi3ePN5HV33jeyZD73k710U3WvN6/wMYimDCqHLFGg6jiSD9wyecJN+DjmpLex3HKt2fa0coTrurAL84cIu6Nc+E/+4xH0aQaixpwAREUUagwJEFBb+lA/odQIeueIszcedTf/k0we096maKSA5zy+vbWo0NqZ3a1dQoX12ctM2Jv9mx4fSf9f9jurTHdLnrymO2nFQZBRkJLm+Ht27FQDgmsHtccOQM3BBt1zcP7637OoxAJjM8swAv8sHvNwOlHQRm6jXyRa40cgUUBuFePhUncqWTZIVpRuCIOC+cb3x6Z+GYUSP/JAenxrpSEL2FCAiokhgTwEiCgvpybinRoJOw7vnISVRr9qMzDke0KrRU0BJ5eKgrHygrNbs+jonrSk7QFo7rDa2LFJKq83eN6JmQzo5wJlyb9Dr8MRV/Vz3l9bI/yakf8OA/038lHEztYkcgZC+7xMT9NDrBNfCNhoLXLWgQLWX97ayQWKkSQOe7ClARESRwEwBIgoLs59BAUEQkC1J35ft63SmgHRRkeChT4FamrJ0tKH02KSvKR09VmuOXlBg+bZjUXttijxpUECZcu+kDILtK6mV78PPngLK3IBwNBpMTNBBL0T3qrda+YA3GVEPCkiyKwI4fiIiIn8xKEBEYWGW1BYbE7Rnrksp03adnPPNrQ71RoNKagEDtS7kjcfWtB/p6LHaKGYKsI645bA7RNlCOsmg/h7QK/6mK+rk5S3BjvsLVaaA2S4PCkR7vJ5apoA3aR6moUSCniMJKdZxIiFRs8OgABGFhax8QOPqp5JWSYDZ1VNA0mjQQ/mAWg8Dra0TJUGBWMgUKKluiMrrUnRIg2eAe8d+J+V7QxkE+PVolayPhzfKGMBFc78LycQNWaaAXpA1AWVQwDcGjiQkIqIIY1CAiMLCLFsc+PZRo9WQ0Lkvm8ZIQgDonJfq+npgh2y3fQgaqQLSY5P2FKhusPpwxL55ZW0xLnhyNeav2ed120NemqBR86JsEKiVLaMcZ6cMCny27Ti6/3OFz4tgtaXmS6u9/316Y/WUKRCF8ZqBLKqV4yAjTc/yASIiijAGBYgoLJxX9wE/MgU0+gQ4r2BaNUYSAsCMC7sgQSegfXYybh/R2W0fWuUDnjIFlGPbAlFS3YAnVuzC0cp6PP3lbhyp8Lzor6gLXTCCYl+94up8kkapjadpG1I/7i/3aTu1P+2Xvg0+KCDPFNArMgXiY4Eb7aBAQpQnNhARUcvD6QNEFBaB9BQwaCx8nJkCVpv2SMLrzumAy/q3RbJBr5oV4EtQQJopIIqNV2ODXSAcr5KXA6zZXYqbJDPolZS14tS8SVP2ExN0skW0lIdqGZkTVb6Vn4ghG0Iop2w0KO8pEJaX9CjNmOB3KVCa0bfPq3CRZkGxpwAREUUCMwWIKCykXb99mT4AaGcKOAMM3vaZkpigWSag86F8IC1JHgAIRV8BZZ336l0lHrevZFCgRam3NP19aPUTABrLXzyN4XSq9DHTRC1TQCtw5g+LonxAmgpfb7Vjx7GqkGTg+GrSoPZ+PyfNGN3pA3pmChARUYQxU4CIwkJWPuBjUEBrUdJwel/Kq5D+8KXRoDFBj0S9zrWwqWmwoXWGXy/jRlnjvXpXCURR1AxesHygZWmQZNR4CgoAjUEzb4tEX0fwqa3LM0Mwis+i6CUiXeBOWbwRAHDNoPZ45pr+Qb+WL7SmOXiSGu1MAfYUICKiCGOmABGFhUkyN12reZqSTWMUnytTIJiggFamgGI/0myBmhA0GzSrnNQfKNfuK6DMFIh2J3QKL2mjQW/vk9YZSV7353ujQff3Wqt0o0/P9fj6skwBwa1BIgC8v/kIHBG6Ah5ImUS033PMFKDYx5mERM0NgwJEFBbSNObM5ESfnqM1K92ZKWAOKiigfr9yMkKoxxJaVRZpntKnK0zyQISyoSI1L9JGg94yaqQTNrTYfGzmp/YnqPexmaH2PkW3RoN6jTdeVX3sZsREu9GggT0FiIgowhgUIKKwkC6ofb3ypnVVzJkpEMiYQydfygcAebPB2obggwJq6dyeUryVjQa5KGjepI0GvWUK+BIUsGpk2/gi2FR15WsrewpIhSLg5pMAfhwxlSkQxO+TiIjIVwwKEFFY2D2MD/TlOVLO/gSyRoN+1gprlQ8YPGQK1IRg4aKWzq2cMS+lbBQXhdHuFEGyoICXv+nxfdvIbl/cp7XbNj6XD6j8XSmbYvpLGezyFBSQTicJp0DePrlpvmU2hYt8JCF7ChARUfgxKEBEYSG9apjg41V9raucDa6eApJUaz8zBbQatyszDtJlPQVCP30AABo8BAXcMgUYFWjWZD0FvAQFzumUg8VTB2N49zzcMrQj/jKmh9s2PpcPqCyXg8kyANwDEsqRhFLSqQvh9OP+U35tLwhAgQ+9G8JJOoXFISJi/ReIiKjlYgcrIgoLu2Rx4ssoNeVzpMwhmT7gfgwGveA2F17WUyAU5QMqV26ldeRA40m/TidAFEW3TAGtPgvUPNRLpnT40il/VK/WGNWrMUOgtMbs9vgbGw5BLwh48LI+mlfpAfVMgWCvSiv/1g169UaDgHzqQjj9crjSr+0Nep1mVlGkJCh6O9gcIhJ9/AwlIiIKBDMFiCgsbLJMAd9OaJUnw07Oq+3KGej+UDvPV+tLkJ7UNJat1hyC6QNeggIvrNqLfg9/jSe+2Il6q90tBZvZw82b9G/B3/F50qwWqdeKDuKzbcc8Plct1BRs/boyK0Y5klCq3kO2TDT5m4EUDsqfGfuKEBFRuEX/X78A1NXV4eOPP8b06dPRr18/ZGRkIDU1Ff3798cjjzyC2tpazecuXboUQ4YMQVpaGnJycjB+/HisX78+gkdP1DJYZZkCvn3UnN81V/V+52LFbA2i0aBKVEAtsCAdSRiS6QMqCy1nT4EGqx3PrdyDWrMNr3z3O3Yer3HblpkCzVuDLCjg39+0pyDCh1uOenzu0vUH3O7z1ADTF8pGoIIgaAbvGqyxERTIUARW/A02hoMys4p9BSjmRDmbhohCL/r/+gXgrbfewpVXXonFixfD4XDgkksuwfDhw7F//37861//wjnnnIOSkhK3582aNQtTpkzB9u3bMWbMGAwZMgQrV67EiBEjsGzZsih8J0TNl/Tqlq+ZAhPPboeURD2SDXqc3SHLdb8zwBBUpoDKfcomg4C8fKA6TOUDzgWRcmFUXOIe0GRQoHnZV1KLu9/diiU/7IcoiqizNP2NhXIUXnaKQfMxh0PERz+7Bw2CzRRQK+/JTFY/DmUJTTj4coVd+R6PhbWOQfHZ5mvzSCIiokDFZU+BxMRE3HHHHbj77rvRvXt31/3Hjx/HpZdeip9//hl/+ctf8NZbb7keW716NebOnYvc3FwUFRW5nldUVITCwkJMmzYNhYWFyM7Ojvj3Q9TciKIou0Ju8DFT4Kx2mfjx/tEAgNfWH8DPhyoBNC1WpCfH3ma6K6kFJowqV2ZDP5LQffHjTJ1WXpktN1nctnWIjT/PaNc5U2j84fVNKC41YdnPR9HvjCzUmZv+PlK8jCT0h9Z4TwDYfdI9I6XxOaGbPuAMCmSlqHfyl2b9hIu392+yQR+R4IS/khSfbWolSERERKEUl5kCt9xyC+bPny8LCABAmzZt8J///AcA8NFHH8FiaTrBnjNnDgBg9uzZsucNHToUM2bMQFVVFRYvXhyBoydq/pTrEb2PmQJAY01/epJBNrHAuVgJptGgWm2zek+BpqBAZX3wPQU8lQ8oF0bf7Dypug8mCzQfxaUm19fLthyFSZopkOh/nH5Ej3zV+z0tiO94Y7Pq/Va7CDGIPzbp+9M5hjQripkCNYqeIFcMaCu7nZOaiOHd82T3TT2/U7gPy6sEvXxqQ6yUWhARUfMVl0EBT/r37w8AMJvNKC8vBwA0NDRg1apVAIBJkya5Pcd53/LlyyN0lETNm7LhmCGAztnSk2KrSqaAv0EBtWwFY4L7ldlW6U3jyEqqG/x6DTVqqb/OK6rKK4CbD1ao7oNjCZsnm8PhChABQIrR/0yB+8b1Ur1fqx/GnpM1OFBe57p9ab82imNy/1vbdaIaz361GzuPV3s8FqtKpkC2RqZARIICisDIbcO6yG5npxrwwIQzXbfPyEnGHYXdwn5cvpBmQjVEIKuCiIhatrgsH/Dk999/BwAYDAbk5OQAAHbt2gWz2Yz8/Hy0b9/e7TkDBw4EAGzbti1yB0rUjClreT2NRtMirfe3qSyi/S0fUM0UUNlHa8mM8pIaM+wOMaDjd1JL/bXaHPji1+O4880tPu2DfQWaJ6tdlAUFAskU6N0mA3Ou6Y+/vv+L7H6TRlBg5W/ybJQLuubh823HXbdtdhHS/oV2h4gbX/0Rp0wWvPnjQWyaPVbz/WCxuTcCzdTobRCJq9/SwEirdKOsiSjQGLDo0Todux+7BHaHiJQAfv7hkmTQw+RsSBqh8Y1ERNRyxc6/gCHy/PPPAwAuueQSGI1GAMChQ4cAQDUgAACpqanIyspCRUUFampqkJ6e7vE1+vTpo3p/cXExunbtGuihEzUbyoZlCQGM+ZL2ALA5GtOaZTXLev+uqqr1FFALChRkNgUF7A4R5bVmtJIECvylzJpw3udrQADgWMLmymZ3yBbvgfYUUPvbVl4l13JelxzZbavDgWQ0HcexynqcOt3roqLOimOV9TgjJ0V1X7LpA6ezcKKZKSAtoUhLSnD7+TqDMGoZQ9EmnSwRif4LRETUsjWr8oEvvvgCixYtgsFgwKOPPuq63zmiMCVF/UQGaAwMSLclosBZFatYgx89BVzPkaT7W2wOt6Z8/pYPKMd8AerZBmnGBNkEghNBlhColQ+U1bo3FPRk08FTQR0DxSabQ54pEOiVarUpGjUN6v0wlGUFyoBXMBMI1BoNak1BiMRCV9qvIc3oHhTwdwRkJEmboDJTgIiIwq3ZZArs3LkTN910E0RRxDPPPOPqLQDA1TjJU/duf5or7dixQ/V+rQwCopYmFOUDykwB5eLa//IBtZ4C6vtonWFEbWnjguJEVQP6qScZ+UQtKPD5r8dVttR286KNOPDkpYEfBMUkm10+kjCQngKAesCr1myDwyFCp3jsSEW96+u7Rnd3e65yAoHyn0bl/qTk5QON22WnamQKWMK/0FVOdlAGXbQmI8SCpARppgCDAhRrOA2HqLmJ3TC5H44cOYJLLrkEFRUVmDVrFu666y7Z485yAJPJpPZ0AEBdXWPjpbS0tPAdKFEL4d5oMJDyAXlPAeXiOhSZAlr7kJYQnAwyU0CtfMCbQDIrKP7YHA6YzMH1FADUywccovxKudPhU01NBs/ISXEPCigyBZT9LDz9Zao1As1PN6puG4mr38rJDsrgZOe81LAfQ6CkWQxsNEhEROEW90GBsrIyjB07FocOHcK0adPw7LPPum3ToUMHAI3BAzUmkwmVlZXIysry2k+AiLyTZgoIgueri1oMiukDPx2Qp9D7HRRQWThp1RJLmw0GUz7wzW8nsWpXid/P06rDpualusEmq63P1Bjf541WFpxaX4GjlU2ZAu2zk90WysGUD8imD5wO6qUb1QMdEckUkE12aDwO58jBdGOC24jCWCL9bDKzfICIiMIsrssHampqMG7cOOzatQtXXXUVXn31VdWTo549e8JoNKK0tBRHjhxxazi4ZUtjw69+/fpF5LiJmjurZGERSJYAoMgUcDgw4w15Yz61K/8e96eWKaDRALFAGhSoMvv1Ok47j1fjtqWbAnpubpoRJTWBvS7Fj2OSBbogAHlpgQWDKuvUe1SoBQWkjQ2zUxIhCAIMesH1nlX2A1GGCHQeyvCki1dn0E4QBCQZdG5XuxtUympCTfq9pp7uJ/DPS3vj4j4F6NYqLbbLB5gpQEREERS3mQJmsxlXXHEFNm3ahIsvvhhvv/029BrdyJOTkzFq1CgAwAcffOD2uPO+CRMmhO+AiVoQaV2y2hV6X8h6CqhcvfTUI0SNWk+BcJYPLPv5aEDPAxrHp1HzJw0K5KUZA5rSAQDdW6lnuFWrNBuUpvg7y1Sk2QLKfiD+jMM8fKrp+0mVZAhkJbsvvhsinSlwujTDoNdhaNdczbKGWCGdPhCJ8Y1ERNSyxWVQwG6344YbbsC3336L4cOH46OPPkJioueI/6xZswAAjz32GPbu3eu6v6ioCK+88goyMjIwffr0sB43UUshXcQH0mQQkGcYKK9eBrQ/1fIBrUaDwZcPHKmo876RBgYFWgbp+rt1RuC/87PaZWLK0I7oki+vkVdOIHA4RNgkL+qcWiB7r9k9Nxr0FCQoq23Kbuma39SfR60sIhIjCWWZAgE2cYwWeVCAmQJERBRecVk+8NJLL2HZsmUAgLy8PNx5552q2z377LPIy8sDAIwZMwZ33XUXnn/+eQwYMABjx46FxWLBypUr4XA48OabbyInJ0d1P0TkH7WFh7+8ZQr4Sy04oZkpIAkK7CsJbExpMCfyWnPgqflqnZ7kfSMPHr7iLADAlfN/wM+HKgEA1fXy8gHlWE9nUMzze823zIHNByuwYvsJ1+1cydSBTJWxhJGokw/FuMdokZYPsKcAERGFW3z9K3laRUWF62tncEDNQw895AoKAMC8efMwYMAAvPTSS1i5ciUMBgNGjx6N2bNnY9iwYWE9ZqKWxC65sh9wpoBe3mgwWP5MH2iTKV+gfbD5CCYN8m8uYaCN1IwJOlwxoC2eW7knoOdT7PI0+rZVEJkCUulJTQtwZaaA21QQvTMoIO/fIaU8ZLVvYX+ZCZNeXi+7T5od4KznlzJHoqeAJX4zBaSNBpkpQDHHz/I9Iop9cRkUeOihh/DQQw8F9NypU6di6tSpIT0eIpKTNxoMsKeATnuhEtD+VDIWtIIC+elGdM1PRXFp4xjTv73/CwQAV/sRGCg3+dco8LrBZ2DyeR2Qn25Em8xkv55L8cFTKUr77NBkh6QnNf2zXq1oNKgc62lwZgrotDMF7IoogFpQ4MVVe93ul2YHqE35MEdgoVtnjt9MAaO00SAzBYiIKMzisqcAEcU26cIi0OZpoS4f8Gf6gCAIuHtsD9l99330K8prfV/o+zs9wGjQoV/7LAYEmrHjVdpBgQ4hKhnJkGQKKBsN1kpq7AUBSDldty57rykbDSrW7qLbPAL1Ras0U+Cqge3cHo9ESrwsU0AlWyGWJSWw0SAREUUOgwJEFHKy6QMBlw9oNz8LhFoZg1ajQQC4tG8bnNkmw3XbYnfg/c1HfHoti82Byjr3zu+eeDoWah7KPASKOuaGKijQdEW80iT/G5SOKExLTIDu9HvCU6NBZQ8Bh0p8Ti17QDrub+yZrfF/I7rI+gxEonxA1lPAGF+ZAtJGg5H4WRERUcvGs1AiCjl5pkCg5QPaVy+D3Z+TpyaIgiDgv7cMkt335Ipd+MCHwID0iqzUjed2cH19docs2WNqKdbUvJR6yDTp1ipN8zF/tM9uyjQpLpU3yZQFBSTBA72n8oEARxRKMwUEQcD943vjwzvOd90XkZ4C5jjOFJA2GmSmABERhRmDAkQUctJFvF4X2MeMdMGuXJgEQq2MwdtkhPbZKZg/eaDsvidX7PTYMA4A6izqQYG/jO6O8X0LcNXZ7fDw5X1kjyV7WbR4e02KbVa7A9/tKVV97IoBbUNW896zoCm7ZffJGtnfjTRYJe09IG806LmnwEmVvgjKP80EnaC6CJde/bY7xJBkAHkSz9MH2GiQiIgiKb7+lSSiuCAtHzAEming4Xl92mZoPqbFn5GEUsrFelmtBVX1Vll6tFKdxuSBVhlJmD+5MfvAvRO855+TzSEG/LOk6HI4RExasB6/HKly3TdlaEdkpybC7hBx+4guIXutngXprq9rGmwoqTGj9ekRm9JpBNIpBQZZTwHl9AH5iv9Pb/2MLQ+MlW+j6DOQlWKAoNKdXFkiY7Y5Ah5Z6o0oirKeAilxnCnAngJERBRuzBQgopCTlQ+EYPoAAGRLupnfP753APvzr3zAKdngvpgoN1k8PkcrKKB87XFnFQBorAO/YoB7MzapUGRLUHRsPlQhCwgAwBk5KfjLmB7460U9Zc0Bg5WZbEB+etN4w30lTSUE0kyBNEmNvcfpA4qL1KdMFrcmgco/zYxk9e9H2lEfCG9afL3VLstgSI2zngLSgKUlzBkVRP5jgJqouWFQgIhCTpqCrFzc+0o5GcAkGS/mLdVejVr5gC/N/TrkpEAZT6hQCQrYHSJmvv0zLpq7Fmt2l/h0TM9e0x8v3HA2PvnTMNfVXKcbhnSQ3Q5FXwVfrN51EiOe/hZ/fGsLbFyMhMTRinq3+8I5ZaJrfqrra2lQQNpTQFY+4KHRoFowqrjEJLutLB/I0ggKKN/T4ewrIP28AORBkHggb7Qavvf+l9uPY9hTqzHr3a1wMPBIRNRiMShARCEnXUwG3GhQ8Tzp1bJAOvUHminQNisZ91zcS3afWqbA+5sO49NfjmHPyVrM+2avT8eUakzA5f3bonNeqttjf7+4p+y2PYwLA6lbl2zCoVN1+HzbcXz+6/GIvGZzV6oydaBVhlFly9Domt/UtFAaFKiuVy8fkL7XfGkseLTSPcghlakRFEjQ62Tvw3AGBaR9PXSCPB0/HshKOsIYnJvxxhYcqajHRz8fxVqNnhdERNT8xde/kkQUF2SZAgHWDHsKJgQSFAi0pwAA3FHYFed1yXHdPqUSFPjWS3aA8sq/N8pUa2WtdyT8dOBUxF+zOSozuQcF8tLCFxTo0bqpr8CekzWur6slPQWkC3fpe9TqQ1Cgsk7596/sKaDdb0P63lWWIYRSrWzyQIJqj4NYJs3eiFSW0C9HKiPyOkREFHsYFCCikJNlCgTYU8DgoewgkPF9ak36/Gncl5vatIhTCwoIHmosB3bIwvRhnX1+LcA9iBGNngIceBAap2rd/15y07QXzsHq3lo9U6BKkimQkdyUTm/QaV+VVvu7q1f0AlD+nWhlCgCAUdKjwxzGrvqyyQPG+GoyCMiDopYIjG8E+H4nImrJ4qvIjojigrynQGBBAZ2H5wWWKeD/SEKp7NSmhY5aUEDL6F6tsGjqOT5v76TsxRCpq4VSLDEODbW/l/Qw1rh3b9WUKVBusqCs1oy8NCOq65uunksX7tIAVIXJgskLN2BfSS2entRfNVNAWa+v3MJjUECWKRC+xa4sUyDO+gkA8s+maGQJERFRy8JMASIKOXn5QOjTdn1N+5dSC04kqUwW0JLjJVNASyBNEQG4NTeMzvQBRgW8EUURFpvDY5O2MsXfS6+C9LCms+elJcrKE9YXl6Oq3oof95e77pMu3KUL0JfX/o4f9pXjZLUZC9bsc5s+AAD1knp9wL3EICvF16BA+MoH6iSBi9TEOA8KRKifCBERtVwMChBRyNlDMH3Ak0DKB9SCE/40H8uRLHQq3GqqtQU6H10QBPmoOJYPxJzVu07izAe/Qo/ZKzByzhqVWvtGpxQ9BeZdPyCsxyUIAkb2zHfdnvn2z+j/8NeyLvYdc5qaW0rLaKQNPTf8fko1U8DbyE3PmQKRKR8wyTIF4rB8QPLeV06ECNTK307i5kU/4pOtR0OyP2rB4qxHBxF5x6AAEYWcNQQ9BTwJVaaAP8GF7NSmGnC1kYRaUoK4SilN67ZHIYWYQQFtNQ1W3PXOVld9/cHyOny4RX2xJe0p8PnMYehVkBH247vy7HYeH+/aqiko4Ol9oJYBYVIEBZSbeMoUkAbiGsKYKWCyyBsNxptQjyS0O0TcvnQT1u0tw13vbFXNduLbnYio5WJQgIhCTpopoNb1PxgGvRDQPtV6CviVKSAJCpyKQKYAgOhnCnCZoOn7vWWoaZCn0W8/WuW2XYPVLltESxtWhtPQrrkeH5cGqzyVuNhVIkPK8gFRsU3sZQrEX1BAmtkUip4CdYrf2W/HqoPeJxERNR8MChBRyEmvbAU6klBLoEEG9fIBPzIFJGPWDp+qx/Eqz7PanYIJCuhlXeHZaDBWWGwOvPLd72737zjmHhRQXpGVNqwMJ0EQMOea/qqPXX/OGbLbyvGXUmqd75WZAsp+F5nJHkYSGiLTaFB6jPEYFJBOX7HaRbfAi78aFAGYBmv4sjSIiCj+MChARCEnTXUPdfmA8uTWV8rj0OsEv6YPSDMFAGDoE6uxetdJr89LDiJ1WRpQiU6jQVKqabDi5kU/YuvhSrfH9pXUol6xYK5uaBoDmJKoD6gfRqBG9mqlev/M0d1lt5M9BMcOnapzu0/5PUqvygP+TB8I38L0RFWD62tP5QyxShnEDPb9rwwCqGWAsF6IiKjlYlCAiEJOnikQGw2JlBkGSX72JZBmCjjdumST62utvkshyxSIYqPBqjor1uwucVsMtjQ7j1djzHNr8eP+U677BnXMRuLp4I1DBHaekKdl10pKDNIifMU6JzURbTKTXLdTE/XY9eglaJuVLNvOU1Bgh0qauTIVXVlO43P5QBgzBXadqHF93aN1WtheJ1yUActg3//KAAwzBYiISIpBASIKOfn0gdgICihPsv0pHQAa6649LZ60ruSFqqdAsFcKX1q9F6OeXYP3Nx32+Tmi2Ji2fNWCHzD1fz/h1iU/BXUM8cxqd2DWe7/gZHXTJIH22clYeusQ9G2f6bpvh6KvQI3kKnpaUuTT2P80qpvr6+nDOqv+3Xt6L6z8zT0bRjl9QJn94KkRqCxTIEw9Bax2B/aVNAUFerYOf2PHUDMogqmWICcQKJsVmswMChARUZP4K7QjopgnvaoV6p4CgVIGBYwBTDDISU3E0Ur1XgL1GlfeQjV9IJBmYzUNVsxfUwyHKOKVtY018Pd8sA2TBrWH4MNIKRGNV4qLS00AgKLfy2G1O/wqu4h3NQ1WzF25F+9vOixb4LfJTMKrtwxGqjEBfdpmYPPBCgDA9qPamQLpUahtn3xuR5zfNQ9ltWYM6pCtuo2nYJcaZVBgSOcc7Cup9em5xghMH9hfZnItgvU6QTZpIV4oPzeD7SmiDCoqsz2I/BMbwX4iCh0GBYgo5GxhHkkYCGUQwN9MAaCxSZxWUKC63qp6fzQzBf7x4TZ88esJt/utdhGJCT4EBUTRLe24pZUdP//NXiz+Yb/svqsHtsez1/RzBVbObNN0JXr3yRrZtrVRzhQAgM55qeicp70w9tRoUI10Qbn1cCXe+vGQ6/YfR3b1+FxpVkG4UtilpQNd8lIj2schVJSfm7YgMwWU5QfKwA6Rf1rYPwRELUDLudxDRBEjLx+IjY8ZZVDAU4qzFrW+Ak5ltepjCj2Ne/NGerXQ35riBqtdNSAAuDeK06L2ip7GFO45WYOHPt2B7/aU+rT/eLDw+/1u903o10aWadGtVVPNenFpraxTfDR7CvhKmSmg9t7469gerq+lC8rrXimSbac2+lP2WonSoEB4ygd2S/o69GoTf6UDgHtmkzXI8iG7ItOIQQEiIpKKjbN1ImpWrI7YazSoPMmWXk30lXICgZMoiiitMas+FqpMAX/Th49UuHeNd9IqdVBqXNsKKvepu33pJixZfwC3LN6ICpN6kKQ56NNWvtCUBgVqGmyyvwVZTwFjbHbBVwaurhzQThZEmzW2B64Y0M51u95qh+P0e1zZLNBbZlBSJDIFjje9t3sVpIflNcJNrxMg/VFag2zKqEw0qGf5AAUlNv5dJ6LQYVCAiEJOelVK2fU/WoJZnDtpZQrUmm2ajcBSQ9RTQHmlz5vKOvVyBsCPoIDkf133eQgKHCxvCkR8t7f5ZAtI6XUCctOMsvuyUhKRl9b0tyGtr5f1FIhS+YA3ylKavPREPD2pHzrnpeLy/m3xfyO6yAIHoqjdD8Db+z1J2lMgAuUDPVvHZ1AAUGYKBVs+IH++SSVTgAnhREQtV2yeoRBRXJONJIyRoIByIZercdXf4z40nlPdoH3VLajygSBGElZp9DgAfG8yJqpEABw+NhXwdbt4k5OaqLrw7ZqfhrLaxlGFu0/W4PxueQCAWnPT7yFWywcKMpJkt8tqLLjn4nay7ADl77POYldtount/S4vHwh9UKCmwSrr+9EzTjMFAMCgE+DMt1FOD/CXsidJSx8vSkREcswUIKKQC9VIwnnXDQjB0agLpKyhtWLx5FTrISgQTIaCPohGg56CAr4uxtTW9VpHoTy+ZhoTQKLG5IUzJSUFvx5pHEtYa7bhvU1HXPdHq9GgN+2zk2W3i34vd9smSdGsr05jpJ3XTAHJfnzNWPHHHkmjxzRjgtv3Fk+kmQLWEDcaNLF8gIiIJGLzDIWI4lqoRhIO1BihFgqBjNVTpn87F0DSq8FKysWUP6RNGv3tKeApKFBv8W2B8fmvx92CGloZAMpFSyDTEuKB1kK2f/ss19e/HKkEAMz5erdsm1jNFBAEAWN6t8Y3O08CAP48qpvbNjqdgGSD3vX911nVF5XegoCykYRhaDS4U9JPoGdBuk+jN2OVQRYUCO795PBh+kBzDeRRGMTx+4qI1MXmGQoRxbVQjSQMZEKArwIJCihHtzkXvlrlAymJeuiC+P7DlSngzxXa9zcfkd0WNdZxyp4KzXWBMaRTjur9/dpnur7+vcyEmgYr/vfDAdk2sdpTAACevLovHv8iAenGBEzo11Z1m1SjJCigkX7uLQgonXQQjvKB3SfkQYF4lijJZgp1poCvJUREqprrBzxRCxa7ZyhEFLdClSmgFhR44YazA96flLK5mi8S9e7P+XL7cc2reME2N5SWOESjp4AarZGEbpkMzeRCkiDIz38Le+arbtcpNxXpSQmoabBBFIGXVu9z2yaYppPhlpdmxHPXDvC4jbQfgFb5gNfpA2EOChw81dTssrtkKkQ8kn7+BRsUUAYVNUcSOuzAsZ+BvB5AUnyOcyQiIv+xpwARhVw4MwW65KUGvD+pJIP/H39q38o9H2xDjUamQDBNBoHgpg+c8jASMJjFmFZsQrloaSYxAYw7q8D1dWqiHpMGtVfdTqcTZNkCr3z3u9s2OWn+N7eMJdKghlZgyfv0AWlQIPTlA1V1TX/3WiNE44U0m8kSxEjC0hoz7nn/F9l9WkEdfHwHsHA08N9CwM5sAiKiloJBASIKOVmjwQAa+jkZVYICoSplVNu3N+lJ7nPmaxpsqG5QvyqfYgjuynAw0wc8BQVMGguCw5KrrFp87SnQXAiS8MafRnX3mPnST9JXQI1Wo8p4IQ1yaZWgeCvLkZYPhKPRYKUkQyYz2f39Gk8MIWo0+PSXu9xGEKoFdUSIwLZ3G2+cKgaKVwf8mtTMsacAUbPDoAARhVyoRhIm6AS3cw9diE5GkgMoH+jTNgNXDWzndv9xyQg0qRRjKDMF/AsKlNc2BQWeuKqv7LHnVu7BvpIa5VPw8tpir/vVKiVVllA0l5JTaRDE25/yiO7qpQUA0C4rGW3iPCggLYfRCix565uQJGs0GPqggLRsJislzjMFJIFLSxCNBpV9QQAfAzKW2oBfk4iI4guDAkQxbvPBCvywr0x1Znyski5g9brAP2YEQXBbXIbqxxBITwGdTsBz1w7ArkcvkS3YD2pcYQ+227xs+kAQmQKd81Jlae+1ZhvGPPedrCmb3SHi01+Oed2v1t+hTXEl0+pnuUOskgcFPEcFzuuSgwu65cruu7x/W1wzqD1evPHsoJpOxoIUH8oH1LJppKTvO7PN4dYVPxgOh4jqZpQpYAxB+cB3e0pV77faRbd9LvpeUfKiCy6oSURE8YNBAaIYtm5vKa5esB6TF/6I9ze5X+2JVdIFYTDlA2qCSTnulJvi+vrqgeq14b5IMuiRLbkKeahcPSgQbLf5QDMFRFGUBQXy0hKRn2502+7NHw+6vl5fXKbZG0FK6zCU0wdCPZLwtfUHMPzp1fj357+FdL/eSL8Nb0kqgiBg8dRz0LtNBhL1Otw9pgdeuOFsPHNN/7CO14wUaaaAVqO6jGRvmQLyhaY5iFp5pVqLTfb7iveggCEh+OkDtyzeqPlYveJ3aLMqyqAEniKSlvgOcBKRu9hthUxEuPvdra6v//7hNlx7zhnROxg/yHoKhPjqaDCd8+dPHoQ5X+9Gn7YZGN27VVDHkZNqQFmtGUA4MwUkPQX8SB8urTXLFumtMpJUAxTSTIFvd6lfUVTydfpAsHPVpUxmG/79xU5YbA68um4/LjmrDQZ1jMwiW/QjUwAAjAl6rLhreDgPKWrkmQJa5QPeMgXkC80Gqz3ohpxOFYo+GhkxPALSF6HqKaDFpPgsNUDx2cqgAGmKn8xFIvJNfP+LSdTMldVqN4uLZTZZT4HQnlj2bZfpfSMNZ7bNwKKp54TkOKSZAlpXxdOMwV2pDHT6gLRhYFaKARlJBnTISXHbrrKu6crgpoOn3F5b7fvydfqAv9MSPDle1SBLdd55vDpsQYHjVfXISDIg9XRAR/r9xnn2f9CkmQL1muUD/mUK1FvtCNVvcufxatfX7bOTgxqHGgsSgywf8FaaoQzsGKAI9AgsHyAiaini+19MIopJthCWDygXYrHSPMyXcWdpQV6plP7s/Okp8NvxpgyATrmNIxzHntka3RRz20tPZzpU1Vux/WiV6/6P/3gBNt4/GjcM6eC2b62eAsrMgFBmCpTWmGW3T1Y3hGzfUi+t3ouhT6zGBU+tdgVWZJkCLTwqIGs0qJEpkJbo+W/eoNfJMmBC2WzwlyNNf8P9vUyCiAfyRoP+BwU2Hazw+Lgy6ypBGRTw0FMgmBGJREQUexgUIIph8Tr1J5TlA2cFkRkQTr4EBdJD2Ghw/ppinxfD2w5Xur7u177x52dM0OP16UNwXpcc12MVdRbY7A4UFZe5rojnpiaiX7tM5KYZcWbbDLd9a08fUO8psPN4NY5UeB916ElJjfz7PlYZnqDA/344AKAxg+LNHw9h88EKfLu7qaxCiNc3ZIhIyweU9ehOvgROpNkCDdbQLS63Hal0fd23fWx+bvhDmilgtfkfZNu4v9zj47UNnssHfjpYpZlt8J9v9/l9PNSctOzPQqLmiEEBohBbWnQAf3xrC3Ycq/K+cTNlDWH5gHTM2xUD2ga1r1CKRKaAcub7o5/51mTvV8lV/36SK6ZtMpOxWFI+IYqNUwrW7ilz3Tese55rYXehyog9h2amgHxxZ3OIeGPDQYx7fh2GP/0t1mp0QfeFMlPgmMYIyGCU15pRLqlJf3ltMa5esF62TQtPFFBkCriXD9w2rLNP+5EGBYJpHColiiK2HZH+3cd/UMCgD67R4CFJGVFemhGDFSU3ZYoeDAZB/rt4fvU+LN+mPpHkfz/s9/t4iIgodjEoQBRCO45V4cFPduDzbccxZfFPQe/Pl8ZmsUiWKRBk+cAdhV1x9cD2mDigLe4f3zvYQwuZvDT3bv5K2SnB9RSQdh8HgM+2Hff6nDqLDXtONpUP9FcsjlISE5AqWdyV1JhlY8uGSwIBHXLd+xBoZwrIHzBb7Zj98XbXcz7XWFz4QhkUKDeZNbYM3J6T3meyp3pJjW/ukr1MH5hyfief9iNtNmgOUVDgQHmdbHpGML1HYkVikOUD0oyamaO74YM7zkdhz6b3t/J9pcwUECDihVV7/X5dIiKKPy37DIcoxL7afsL1tbMzfTDiMyQgv6oVbPlAqjEBc67tH+whhVwrlRF/QGOwoKzWjDRjAs7plKO6ja8SA2iUdvhUvasUIDFBhy75aW7b5KUbYTo9RnHHsSoclVx5H949T7btRWe2xte/nXTd9jVToMTt6n7gKf/KfVXUWTW2DNy+khqPjycm6DC0a27IXzeepCrKB5T9JXyNYcrKB2yhCQpISwe65Kd6nYIQDwxBNhqU9gxwjmeUBjNPVdXg8YSFyBcq8YjtZreeAno4ZNkGRC5xesGCiLQxKEAUQv40g2vOpJkC+maac52vEhRI1Ouw7M7z8dWOEzi/ax5yfcgm8ERZPuCLckkwKj/NqPrzz0sz4uDpoIC0OVt2igGtM5Jk2z50eR9ZUMBkblo4iKKI0hozMpINsuaSAGBWLPaCOYdUXtGsrLNAFMWQ1fiv21uKZ77arfn4nYVdMb5vG7efTUujLB9Qftz5+vtIlpYPWELTU2BbM2syCCh6CgSQKWCWBBKMp7MOpEGBjkeW49qE1QCA1kIF5qb8GZDE7nRwwKDXhfS9Rs2EVsoYEcUtBgWIQkhrNF2g4vE8TBRFWXAkkIVtPGiV7r5AzE834oycFNw2vEtIXkPtZ2e22WFM0O4KXioJCuSlqfc9aJPZdOw//t7UjExt0ds2KxlZKQbX+MKfD1egb/tMiKKI+z76Fe/8dBjts5MxXVFPblY0kFPe9oey0aDVLsJksSMtyEaOQGNA4OZFGzUff3pSP1w7+IygX6c5kI8kVMkU8HE/0vKBUE0fkDUZbAalA4CifCCATAF5UKDxdycNZp5T9pHr6366/eiYaVAEBUSkWsrR8PUjSO40BOg5zu9jICKi+NA8z9aJoiSQTAFRFPGfb/fhr+/9IpsvDwBCHBYQKAMjzTVToFWG0a3xXEeVGvxgOFN+pZQdw5XKa5uah2llKvQqSHd9XVxqcn1dkKl+Jfyqs9u7vv7x91MAgC2HKvDOT4cBAEcq6mVd+gH5ggQANh445fG4tVhsDtXSgzqz55+DksMhqo5T/GSre6+DedcNwD0X98SiKYMZEJBIlQRhas02KH+avvZACXX5gN0hYvvRatft/mc0j6CAIehMgaafbaIrU6ApUHjSni7bfmA7eamRHnY8a3gZyUXPAW9fD5QX+30MREQUHxgUIAqhQDIFvtpxEs98tRsfbjmCf3y4Tf5gHK6nlYGRYBsNxqokgx7dW8lPqru1cq/fD4ZakKHWy2JY2oQvV2NCQu827qMGAaCfxhVWaS392j2lMNvsmP+tfIFwXDERoKreve7/S0nPDV+9vuGg6ves1uhOy7YjlRj42EoMfWI19peZZI/9dqzabfsLuuXhjyO7YXTv1n4fb3MmzcwwmW1un3eB9BTQGm3oj30lta4pBnqdgDPbNMeggP//tkizc5zlA/mSQOFJa7Js+wl95D0zXkmchwv1kn+Tflro+vISnXZ2DbUA8ZjGSEQeMShAFEKBBAUWff+76+v1xfK50vH4z65bUCDIkYSxbNKg9rLbY88M7SJSLShQE4JMgb7tMlUzOK7RuCo+rFueK+W71mzDy2t+x6pdJbJtTlTLr+Yfr3K/un/fR9tg8uMK//JfjmmOYfQnKPCH1zejss6KE9UN+OeyX133i6Ioa7LopFV20dJJR2w6RGDTgQrZ476XDzQFBZQZJYGQlg50b5Umm5IQz6QjCQOZPiB9jlr5QIUoD2LqXr/c8w7Fpv09nfiq38dDRESxq/merRNFgVr5wCmTBcWl2uPOLB6uAMVjMN5ubxmZAgBw67DOmHZBJ7TLSsZdo7tjWLc870/yQ5vMZLf7qhs8d94vkwQFtBa3rTKSMHNUd7f7z8hRL39ITtTjwh5No8zmfrPHbRtlsEJt+kZFnRVFisCXJ54yC/yZby8NUEgDb2W1FreMhuHd89hUTUN6kryHw21L5WNXff25JUlq5UORKdAcmwwCTVf3gQB7CkgyBZId1cBb16HL8qvRWzgIAKiCn5lNjtD0fyAiotjDoABRCNkVHdiHP70aAx9didFz1uLNHw+qPsfm4QpQPPYUsCp+BsGOJIxlep2Af13WBz/cOwp3j+0R8sWkXie4jQisrvejfMDDFe+7xnTHNZJMh79f0tPjfkf2bOXxcV8ps2E8kWYf3FHYFR0kQYtQLCZPKrIbCjKScNdo92AJNVI2uGxQNI/09c9feiU/FI0Gtx1tCgr0bd88SgeA4HoKiKIo6ymQs/s9YM+X0B/5EQ8nvQUAsIh+NuoUGRQgp+b77zpRS8WgAFEIbD9ahXs/3Ib3Nh2R3X/4VFNq8j+XbVd9rqeTPX+uhsYKZQlFcy4fiIQnr+4nu+0tU0BWPpDqeSTioxPPwtTzO2HK0I646byOHre9sGd+SDJX1heXAWhc1K/49Tj2ldRobiudOjC0S66s+710BnugpL0K2mQmYcP9ozG4U07Q+22pAikfCLbRoMXmwE5JX4jmlCkQTFDA5hBlIyPTDqx0fT1EbCyhEdxaRXohhmZ8JMWv9cVl+HZ3CUR//3aIKOZxJCFRkERRxIw3NuNIhXttsi+0GkipXQmNh3nRypPX5pwpEAntspIxpndrfLPzJACgWqWBn1R5rW+ZAkDj4uyhy/v4dBxtMpNxZ2FX/Od0g0GDXsANQzpgaZF6BoyWXSdq8J9v9+GZr3YDaOyK/sb0czGks3wxLooiTlY3fS+tM5JkV5hDETCT9jdIDcF4w5YuJdG3n6G8fCC4hebuEzWu2vlEvQ49C9K9PCN+BDOSUNmrQW86LrudBDN0yoVdz0uB478A1fLgtgvLB1q0VTtPYvprmwAAv+RZ0XxycogIYFCAKGjFpSafAgJai2Otkz1lajPQeKInvcoWi6SZAoIA6BgUCJp0NKGnoEC9xQ6TJJiUp9FoMFD3XNwLNwzpgOp6G9plJWP3yRqfggJ5aUbodXAt8p0BAaDx7//h5Tvw6Z+GyZofVtVbZe+N1hlGRaZA8AuUWgYFQibZoPe5wV9SYugyBbYdrXR93btNumwhHe+kmQKees+oUf67Ihjk/UnyhCroBMW/PZe/CKTkAIIA62tXwrB/tfxxlZGe1HLc8eYW19cnqxuQ2XzeakSEOC4f2Lx5M5588klcddVVaNeuHQRBQFKS+oxtqaVLl2LIkCFIS0tDTk4Oxo8fj/Xr10fgiKm5+nG/bzXSXfJTVe/XSgtVu9+fzu3RIs18MLB0ICRks8Wr3Rv4OUn7CQBAdkrou+i3z07BmW0zkJliQI7GyEMlvQ4Y0T1f8/Edx6rx1sZDsvuk32digg6ZyQYkG8IXFEgzxnawLVb8YUQX1fvbZ7s3xdSSJOlNYA4y42Pb4aZ+Av2aUekAACQmNAXJ/C0fMCuCLYIi9f+aHgZZ+YCl+6VAaq6rMURCrsrv2dlTgMGBFkkaaBLZU4Co2YnbM/ZHH30U9913H5YtW4Zjx4759JxZs2ZhypQp2L59O8aMGYMhQ4Zg5cqVGDFiBJYtWxbmI6bmasPvp3zaTi0jwOEQUVKjvsiTdpF3CsVCKNykmQJqY+/If9IF1+GKOs3tpP0EMpMNYb9qmqsRFLh9eGeMO6vAdbtvuyxc1KdAtk2iXoczcpq+r+e+3u3ql7D4+/24eN53rscKMpIgCAKSJenpoWhQVyuZmJDqY+p7S3fPxeoNKTvlqQc91UiznYItA2muTQaB4HoKSCcP6HUCBJs88+zWASlIk2RsJKZlyx4X1IICzvIBu/u/TUREFN/i9ixo6NCh6N+/P8455xycc845KCgo8Lj96tWrMXfuXOTm5qKoqAjduzd2mC4qKkJhYSGmTZuGwsJCZGdne9wPkZQoivjxd98yBdSCAu/8dFhz+00H3IMN3prMxQKbZPpAcx5HGEnts5u67nsqVZEGmLz1EwiFzGQD9DrBrblkskGP+ZMHYs2eUhyrrMeEfm2RkqjHOZ2y8dOBCuSlJeLxK/tiQIcsjHxmDUwWOyrqrFiwphgX9ynAI5/9Jttf26zGLLAUQ/CNBqUtOUolP6+89NCWWjRXCXod2mUl42il/O+wsx9BgeTEpsWucoKBP+otduw52dSosjk1GQQaA2dOwfQUMCboAEVQIN1WgesGtwM2nr5DUAQQc9QyBU7v06odmKTwaLDa8cra3yEIwP+N6BLzZYREFH/iNijwj3/8w6/t58yZAwCYPXu2KyAANAYXZsyYgRdeeAGLFy/GX//615AeJzVvJ6obNK/0K6nVhN6/7FfN7X86WOF23y+Hq9CnbWxfDbNJvk82GQwN6RX1Y5X1sDtE1SyM/WW1rq+l4/vCRacTkJlswCmT/Mphgl4HQRDcxhi+94ehqDHbkJaY4Oo18X8jumLuN3sANGYILFhT7PY6bbMav//kEPQU0EmiAtKRhwUZ3svPqFGqSqlFp1w/MgUk5QPBjJb87Xi1KyCVbNCjW6u0gPcViwwJQWQKSMoHElWCAqgtQZpB+hmi+DzJ7uy+U2f5gEUlKMCSgrD673e/uz4ndQLwp1EcnUpEoRW35QP+aGhowKpVqwAAkyZNcnvced/y5csjelwU/45VujcD1GLxs6HWwXKT233f7i7xax/RYJNcNU7Qt4iPmLBrl9W0wLc5RNliVmp/WdPfTJe8yCyQpE0QnbQyRARBQEaSQdZ88vYRnZF/+iq9smO6UzuVoECgi0npkZ1kUCAgalMGumr0TFETqpGEvx6pdH19VruMZleuFNJMAaviM8NUAtRJstySFMHmrA7uO3VmCpTtdn9s46t+HR/557mVe1xfP/v1Hg9bEhEFpkWcse/atQtmsxn5+flo37692+MDBw4EAGzbti3Sh0Zx7kRV04lWFy/psxY/rvSIoojjVe4Lv+/3lrk1kIo1Nsn3yUyB0EhO1MuaDR7VKCGQlhZ0zA1/pgAAZKgEBaRXgr1JSUzAYxPPgqdJm85MgYykptdSm87hC+nrSN9jrTMZFPCVs5xDyp+r9NKggDmI8oHtx6pdX/dtlxXwfmKVbCShn5kCsskd+hrArshoqzwElEoW99mdFC+eArMxT36fs6fA4Z/cXk/c+F+OLCQiimMtIihw6FBjV2u1gAAApKamIisrCxUVFaipqVHdRqpPnz6q/xUXu6e9UvN2vKppEeat0ZavV3pOmSw4ZbKobl9vtWPvyVqVZ8UOuyxTgEGBUJEuvk0a9fTVksZ5WSnui/VwUMsU8HU0ndPFfQrw2MSzoBVD6prfuODs1aZpBv2vR6shBpCyLJzOFRBFESWSCQfMFPBdj9bpstt5aYnI9WP8ZZKh6dQjmEaD2yVNBs9qlxHwfmKVvNGgf3/rzuDxZP03+LR+qvsGh38CSiS9O1r3cdvElnGG/A5npsCRjW7bCqYS4NhWv46RiIhiR4sICtTWNi6iUlK0r5ylpqbKtiXyhTRToCAzCSN6aI9dc4hwa8imZuCjKzHosW9ct5MNerSVXMWsaYjtsYRWaVCAIwlDJsWH1PkaSSNK6VX1cGql0qAvOYAmWJPP7Ygv/zICN54rT1tO0Anod7qrfN92TSnOZbVmj+MZNZ0OPJwyWWRXXxkU8J2yoZ+yd4Q3svKBAIMCDVY79pY0/Xt9VrvY7rUSCIMkqGp3iD79++HkzMD4t2GxxgZVgOX0z0/QA63OdNskIbeT7LbVZgMcDtVMAQDACWZbthQcSUjU/LSIM3bn1STBQ36qP1ecduzYofpf165dgz5Wii/HJSnMbTKS8PTV/Txu729dKAD0aZuBVGNTDa/JHNtBAbtDPgqLQkO60NYKCkhH7KUlRaaPbK+CdLf7Au2M3aN1Ou4e00N23zmdclz7y0szygJk2yQ15b5y/knulnStT0nUIyM5bvvuRtzw7nmYfG4HdMpNwdgzW+NvGmMKtcjKBwL4TASAXSdqXIvkJIPOa/lWPEpU9GTxp9mg2eZAFrxnPgIAOpwHJLlnWiQqggLHK01Axf7GgIKKkr2bfD4+IiKKLS0iKJCe3njSajK5N25zqqtr7Kablta8uhdTeCkzBQoyk3DVwHaa20uDAr5e9bn2nDPkQYEAR7FFipXTB8LCl9nu0iyS9AgFBXq3cV9M+Fs+IJWSqJeVEdwytKPsceksemn6uK8ECHA4RMz7Zq/rvoEdsj0GjUkuQa/Dv6/sizX3jMSrtwxGaz+zLIySWnm7Q/S7sz4g/933bpPRLJuaSnsKAP71FTDb7HjB8JJvG3cbrXq3kC1/7+0vqQFqT2q/5vEdPh8fERHFlub3r6iKDh0a01GPHDmi+rjJZEJlZSWysrJcAQQiX0iDAm0yG5uhpap05nYy25sWc74s7tONCZjQrw3SJEGB2pjPFGBPgXBI9pJybbU7ZMGC9AiVD5zZJsOtSWAg5QNOqcYETB/WGcYEHaZd0AmXnFUge1xaQrAtkKCAALzz02Fs3H/Kdd9N53X08AwKNWUmSSDZAjuOSfoJxPiY1kAZlJkCfvycLDYHRui1R97KdBurfr9iAkGmYAJ+Wqi5m+zafRxNSEQUp1pEUKBnz54wGo0oLS1VDQxs2bIFANCvn+fUbyIph0OUjzQ7ndacojLD20l6Fb3Wh94AF59VgJTEBNlc8FgvH7Cxp0BYeBvHp/y7kAaSwik7NRHnd82V3RdMUAAA/nnpmdj92Dj867I+blfw+0rq2bcfrXKVfjVY7Xhu5R4sWFPssUynzmLHs183dV0f37fALfBA4WVUXAE3+9lXYNuRSry98bDrdnNsMgioBAX8aDaoGmjpfjFw61dAiuT9mlYAFPRV34liIsEAXTGw/UPN10xzeM4kICKi2NUiztiTk5MxatQoAMAHH3zg9rjzvgkTJkT0uCi+lZnMsgWwMyjgKVNAuljxpWHguNOLleyUpnF05bUWv481kjiSMDykC22TSlBA+fcUqaAAAEwcIC+ZCWd9vrzZoAXHTmfrPLx8B15YtRdPfbkLn/5yzOM+Tpka30MpiXo8OMG96zqFl1tQwI8r4A6HiBv+u0F236CO2SE5rlhjUGRa+dOTxu1n2v0iYPJ7jf0DxjwMV8fNwn9Acx5opvrEJk/qj273+zlERBR9LSIoAACzZs0CADz22GPYu7eplrSoqAivvPIKMjIyMH369GgdHsWhncebmjilGxNci7AUD/XU0pO6WrNVczunYd0b50S3Oz2nHZDPoo9FNpYPhIV09F9VvfvfTmWddPJAQkSbPI7///buOz6KaosD+G+2pveE9EIgCS0JvUPoVUCIgoJSlGJDBFHxgbT3UCwIKnZBUEAUBKUoAtJ7CYReUoAktJBeN7t73x/Lzu5kS3o2yZ7v55PPZ3fuzOzd3Ukyc+bcc1v5oJGTZhaCUE97BLianumlqtzsZQh00+3/yM10KFVqwZ3jownp5drXK72a8ME8UnskYpEgYFiRGQhuZRQYBMUaezTMWkAcxwmKDVaopkDp4WkDP9A9bvMcMD0OeO0s0G6S6Z1Iyj/NpFZG0rkKb0OM42ttFOXADw8t2xlCSINXb8st79ixA4sXLxYsUygU6NSpE/983rx5GDJkCACgb9++eP3117FixQpER0ejX79+UCgU2L17N9RqNdatWwc3N7dafQ+k/nrvj4tYe+wW/1z/wsLcVHAVyRRYPKIl5BJNgMHfTT8oUFDh/tYmpYqGD9QEV3tdtkhGvuFUfBkFugwSN711a4O9XIJfpnTGwesP0bd5I4hqOCDRI8wDPx+/DQD498oDgxkQyjP7QYCbLV7oFlIj/SNlk0tEUD6+uK9IpsDltBzB87cGhtf48WZJUjEHbQykIgUZVYpSwWNZqdkZ3Mp57L90DPiqM/90pXIYDqtbIbtYBeBdg9X9TiwGHERAh8mAvIHUaEo7B2x9GXBsBMSuBmxdauylGGP4cNc1fLU/AQCwIMYFE84+jSM2eZiheBlb1d1q7LUrgipHENLw1NugwMOHD3HixAnBMsaYYNnDh8LI6vLlyxEdHY0vvvgCu3fvhlQqRZ8+fTB37lx061Y3/tCSuu9iarYgIAAIgwJdmriX3oSn0Cs0WFZQIMxLd/fLz0V3Z7SuZwrQlIQ1w00QFDAcQpKpt8y1loMCABDiYY+QWpoWrneEFx8UOHwzHTHhnoL28hx2c4c0r/TUiaTq5FIxf8e/QkGBu7oCgxwHvNSzYU8FLJWIoI0KVGT4gLg4U7jAtpI3PRo1B57ZiHv/LMOWzGB8onwCaoigZnmmt9m7EDi3Hnj5GCCunYKnNWrDM0BuGvDgEnBkOdB3QY291PmUbD4gAAAuR/4LiDWf9XLZl9haROeqhJCaUW+DAhMmTMCECRNqbTtCtM7ezjRY5qMXFPB3tcOY9gH45dQdg/UUSr1Cg2UUDNS/oPZ31WUKPMpXoFChqtK0bzVJvxhW6TGxpPLcywgK6C9zs6v9oEBt6hLqAVupGIUlKuQVK/HraeHvWnGJ8OLJ1U6KTL3hFc90CMSAFlRc0JJs9OoKVKTQYMID3dTCM/qENfipJPWHD1QkU0CiFxQoFtlBLqnC34TwgfAOH4ipagaX03cw5/dyzGrw6AZw/xLgG135160rcvVqlFzaWqNBAf1ZNQCgKZdaY69FCCH6KLeXkApKN1LoLyrARfD8vSea483+YVg4rIUgYKA/JlR/9oEOwYZ3cfRTYhs52QjG4KZm1d0hBPpTElKmQPVxLStToMCymQK1yUYqxuBWPvzzs7ezBO3X7+cKnuvXuVgxJhpLnmxZo/0jZZPrT7FZgTvgyY90QYHGnrWTmWJJ+jMQXL6bgzm/X8C2MgppAgCn0N3JV0iqJ41fJOLwTIdAvNKrnNkZJXX3/1SlsYpPn1kRpYvzlpS6d+eJrBp9fUKI9aKgACEVVHo8t6NcgqGtfAXL7GQSvNq7KcZ3CRbUGFAo1bj9qACnkzOQW6S7c+nvZov1L3YU7EOsdwdMLOLg46ILLtzJqLtDCEr0hg9IxPQnprroZwpkFZYIgi9AqUyBBh4UAICn2pmujH4+JRsXUzV33I7cTBcM1Ql2t2/wd5frA3klMgWUKjWS0nVBgdoarmJJMr3P6b0/LmHDydt4bUNcmbVlOIXuc1JKbM2sWXGzB0Tg4OxeZa7HSuru/6lKq+GgQF6x8HdBUSoo8LLkjxp9fUKI9aIzdkIqqPRd2h8mtIeznelxk/onddfv56L/8gOI/foYPvv3Jr/cUS4xuIAufZc92F13AnzzgZnxnBamEhQapIuv6qJ/oc8YkFUgPA71Zx9wMXM8NhQdQ9wQ5G56loN1JzR1P5bvuc4vC/GwR0u9KQ2J5ehnCpS3psD1+3n8uhIRh1DPhjnrgD5TQ7D2XL5vdjtO7y69SlL9s4G4lAo8rrcZY7DO7uNnq/11La6GgwL5pYYVdhBdEzwfID4FMco/3IYQQsqLggKElGHXpXvo+sG/mPbTGTzMLcbOC/f4tqWjWqFDiPkCTvondR/tuoaiEsOTCgcbCVxLXcgFlrrg0a+wvmLvDdRVgikJafaBauMglwiOpdLBKWuqKQBopmsb3znYZPvWuDRk5Ctw7k4Wv2zJk61oSEsdoZ8pUN4pCePu6MbJN/NxqrN1VaqTflBZX1nHsUipCwqoqzlTwJirfiOxXinMHuh/879AXgObSk9dzgvyzGTg/C9AoWENIsYYbj8qMHrclw4KlObLZSDB5jlg7XCgIKN8fSGEkHKgM3ZCzHiUV4yZG88hNasQf1+6hw5L9gja3e3LnsfZ1EmdPge5FE28HPBcpyCEetpjxZhog6kNG+vdFcsrVgqGH9QlSv3hA3QBVm04jhNkCzwqFRSwppoCWk+3DxDU7PBzsYWzreb3prBEhXd/v8AXvhSLOLQJcrFEN4kRdnoX9LM3xSM9z3CazdJOJekugloHutREt+ocqYkhWOIyAq5i/aCAtOaHWfh6eeJd5WQkqRsJG059X+OvXavKkylwYROwIgrYMhXYMs2g+Z3NF9Djo30YvOKQQWBAf3YhJ5jJCEzcr5km0WLofzshDQ0FBQgx49tDify0WYAmbVtfeU5MZZKy72Y52EjAcRwWj2iJvbNiMDzaz2Cdvs2EJ1vxKdkG69QFgkwBmn2gWnk46IJQj/LMZApYSVDAQS7BqgntEeJhD1c7KRaPaIHR7QP49r8v6bJ6Wvo6QV6O30VSO+xlwrHS7++8anZ9xhgO33zEP+/U2PTUrw2J6aCA+e0kKt3FJZNW//CB0kIeD287pm4hbDi/HlDXbMp9rWJlZApc3wVsfkHv+d9ARiL/VKFUY+Pj2VIS0/NxQi/QxRjDqWTd8xYi4dTHhq/1V/n7TQghZaCgACEmMMaw0ci0glorxkTD3aEcmQLlKLbnKC97dlBPR7kgCBFnZGrEukBJNQVqjH5Q4GFuEf+YMSbMFLCC4QNazXycsO/NGMS91x+9IxrhpZ6hRlOrJ3YNsUDviCl2pVL/N59NMbv+1Xu5fDYBxwFdQq0jKCA3kWkmKqNYplQvUwC1kCkQ4aMZ3rZE+SzWK3vrGrJuA9d21Pjr15qyMgWOrDBc9ser/MMChXB4QKHe83s5RXiQq8uYacEl84+LmIk6MYkHzPeHEELKiYIChJiQW6wUFG/TahPogr9ndDd6N98YUyd1+hzKERQAgB5NPfnHcaWmYasrhFMS0p+Y6qQfFNCfGjOroIRPkwcAz3IEqxoqV3sZPhvTWrDs6Xb+GB7ta2ILYgn2Rv7mmastcOiGbmx6pJ8zXKwk8GUqU6CsLCypWpcpwMlqPigQ6GaHpl4OyIMd3lW+iN2qNrrGXe8ClZmJ4FECsGqQ5iezjLvmtcVc1sO1v4FbRwyX3zoCZCQB0Axr0ieXiIHzG4EVUSjaOU/Q1lwvU2CHWjg7EW/7jHJ1mxBCylK+KxFCrMyey/exaPtlwbJ/3ugBF1spvJxsTGxlnKnq0focbMr3qxitlylwPiULjLE6N71aiUp30lSe907Kz9NRd7H/7aFEvDkgHABwN1uXNWAjFcHJ1rr/tA+J9EGwRzf8ffEeBrTwphkH6iB7ueFQjhv38+BiJ8Wne64jp1CJGX2b8t/dn+fT+PV6hHkabNtQmfobalPGUBipSpcpwMlrfpYGjuOwakJ7rD2WjO8OJWGpcgxiROch5VSabIHtbwDDPgfE5ZwZpSAD+HkUkKm5mMbWl4GmfYGsO0DnVwD30Jp7M+aYyhR4eB3YMNr0dn+9DYz9FQUKYVBAxAHYMgUAEJL5HcK4JrjOAtAr3BMRSbpMxcOqVugvOgNHrlRwJaRHZd4FIYQYsO4zR0KMWLb7Oj4rVd2/c2N3hDVyNLGFeeUrNFi+X8Uofxf+cXqeAmnZRfBzqfnK0hUhzBSgoEB1ytErLqlQqvmg0P0cXVDA28mmzgWKLKGFrzNa+FIwoK4ylilw8MZD/Hz8Fh/kOpn0CFte6Yr03GJcTM3h1xsWZT1ZH6YyBcr6FZepi/hcUJG85msKAECAmx3+M6Q5nG2l+PgfYLVqIKZIHg8dOL8ByE4BRn4LOJXj+/v3v7qAAADcOqz5AYBLvwPjtwPeLav/TZTFVE2BQx8Ln/eeB4jEwJ4Fmuc3dgHFuShUCIMKouIcwfMI7jauswBE+Tqgya1Ufvk1FoAk5o1ILkmwPuSVOy8hhJDSKLeXED0Z+Qp88a8uICAWcXihWwi+fb5tpfdZnqCAYzkzBdzsZQhw0wUBzutNt1ZXCAsN0p+Y6lQ6vTqnSDMe9Z5eUKBRBTNZCLEEY8HMj3ZdE2S95BQp8fwPJ/Hahjh+WftgVzStZIC2PjJVHFP/72xpjDHYMN3nKK7lC8dXezdFTLgnPlWOwil1mK4h+RCwshNwbCVQUmR848JM4PBy4PQPpl+gMBNYNQC4/k+19rtcjGUKMAYkHdQ9dwkCus8CurwuXC/poEGmgCw/VfBcDM3+W9mlQwbN33clE+Em80MCMxJMKcoxXNZQpd8ATn6nyTwhhFQ7OmMnRM+hGw+hf661Y3o3zBvaHI425Ux5NEImLsfsA+XMFACE2QJ1MiigoikJa8rMfmGC59pig/f0LqS8nSkoQOo+/b9j5qRmFQqKr80eEFFDPaqb3OyN/+9RmQkKKFRq2EIvKGBT8zUFSvtwVCQ83FwxTvEutqq66BqKszU1Br5oB5xdCxRmaS6qU05rhgh8EgHsmV/2CyjygN9fNB1cqCEqlRKvrD+LCynZ+HL/TbT77x58teUfIPeubqXJ/2pSOUQiILi7bvmf01GSc1+wP2n+XcFzCacJGjRV6WYsSGI+UECKG2ojdYyKLDMLEavtKQnz04HvegM73wS+76c5bggh1YqCAoToSUrP5x8PjfRBhLdTlfcplVRfTQEAaKU3Pvra/dxK9akm0ZSENcffVZgGrL1YEgQFKFOA1APBHvaY3ruJ0UKsDnIJpvZobLB8dLsAdAhxq43u1Rn6dUT0mQsKFJWoYc/pAikSm9rPrPByssEXz7RBCSfDjJJX8HbJZBSK9IIT2XeAP18DlgYBC12A7/sA59YBSr2LfBtnHOn+E+aVTOAX3WJ6U/MWZQN3z5e7T7+euoPpG+JwMbXyF9JMrcaO+LsYvvIwPvz7GtLzipF5ZotuBc8IwN5D93zQUkDy+G9yQToC4j4R7E+enyZ4LgKDiAN8cuL5ZfFM87uw01ixwWLLZApwqOVpJq/v0r3XvHvAlT9r9/UJsQIUFCBET1qWroiPn2v1jNWXl5FCLxOLKjR/eqinrmhU4sN8M2taBk1JWLOi/HVBoYfaoEAOZQqQ+mdm/3BcXTwQ03oKi8bN6NsU7wyKwOLhLWAj1fz9fCLKFwuGtbBENy3Kw8RMIuaCAsUlKthCFxSQ2lpmuEVUgAsmd28MgMNGVS90L/gYf9kNAxOVkXln5w50fR2p4w7j5cM2+EnVH/2KP8STxQvRs/hTnFU30a1beiy/Ccnp+Xhrczz+PJ+GcT+cqPR7knCai2Htx9+US8G70g26FYK6Cjdo1ALo8x7/1P/WFjhAVwRSXiDMFBBDjbBGjpCmneSXnX48BCOZ+WCF8knh/qspUyCnqAQf77qGsd8fx+u/xGHf1Qeau/FFOcjIV+Cr/Qn8um25a2gmMj1dc414JKzzhNQztfv6hFgBKjRIiJ5Labqoe+m7spVVVk2BimQJAEColy4ocCezAMVKVYWCCjVNkClAUxJWO09HGwCaE8EHOZoT/9KFBgmpLziOw/Q+TXD1Xg4upmZjWs9QvNhdc2f0uc7BeLKNP1RqBmfbyg/hqs8qnSmgN3xAalv7wwe03ugXhpPJGYi7nYV0OOOljDFoIu2HLwL2ITzrILiCdN3K/h2gaj8ZJ226YueVTGz65jI/hd8N5s+vdkTdEm1ENzVP7l3QDD8oo/Liv1cf8I+NTTVcWVPE24ULgrsartRhCnDwY6AwAyKmRBfRJfyjbg8AsDEICqjQr1EucO0Cv+yMXl2GT5WxKGYyvCXdqFlQDTUFcopKEPvVUVy/nwcA8MNDPHXpFUB8CQCQwfzwl2IaPOGG96Q/4Qnx8Sq/ZoWd/Un4/N7F2u8DIQ0cBQUIeexedpEgKNC5cfWkqZYVFChvkUEt/ZNExoD84roWFNCrKUDDB6qd/vf/MK8YajVD8iNdxkh1ZbgQUlvsZBL8OLGD0baK1FtpiExmCjAzQQGlCu6cLijAySxXmNFGKsaGyZ0w5/cL2BKnKap3s8QdAxNj0dJnAma1LUALLgnnCtzxR05THPkjHVkF58zu829VB7wm2ap5knsXeJQAeDQxXLGkCMhJBdwaQ23m86qKUJEw/X/DoyYYrWYQ6WfJiaVA4xjNrAkAeojiTQYFFkt/BK79yD8vcQvD9TR/vTU4QZBAMNyikub/cYkPCDghD+tkSxAs0tU+aMKlYrXsQ2QyRzQp9X4BAA7eVe6DWSe/A/SDRwDw4AqgVmvqNhBCqgX9NhHy2L5rujsJgW52gjT9qpCVMXzApYJ3wErvT6GsnbF9hQoVztzKENyVLo0xhuv3dHUOrPXuXk3ycJDxjx/lKZD8KB9FJbpjoIlXzc9JTgipHZXJFFAo1bDTGz4AWe1MSWiKjVSMZU9HYfnoaLjZ6/5+Xbybh4m71ejwTxCmHHbAjvi7BnfxbaVizBvaHCff7YO/Z3TH2kkdcIkF4z5z0a10eQsMFGQAn7cFPm8D/PVWuYMC2QUlWLnvJv6+eK9c69txCv7xZlV3zPkrBeNXnxRMHwsACO3NPxwkPgkZSsBBDY9Hp83uX9rlJaBUUb8S6N0EUBajKu5kFGDrOd0MCB822iMICGi5c7nGAwKAZoz/t72A+5er1JfSSlRq/Lr5V6h2vm2kMR/IulWtr0eItaOgACGP7bygi9j3jvCqtrnepWVkCjjbycy2G+yv1N332ggKnE7OQNel/2LUV8fQ+f29WPbPNTAjJ1nnU7KR9rjoHccBXUI9DNYhVeOqd7xk5BfjVHIG/7yJlwPsZNZ9Z5WQhsTVTgZjpVnMBQVKlErY6l2sQmr57CGO4zCitR/2zOyJkW2MVNHXIxOL0CvcE0tHtcKJ//TBC91C4OVkgwhvJ/QI88TgVt7YqtJL0//3v4BKKdxJ/K9ATorm8clvIS7JK1c/3//rCj7adQ3Tfj6D44mPjK7zSq9QnHuvH/Y844wITjc93pfKYQCAQzfSMf+PS8KNmg3lCw66c7l4TvwPRokPme9MxFCg9fNYMSZasLgYesF2lQJV8dvpO9D+Kw9zl2FAyV6+7WvlE9is6m50u7GKOcIFaWeBvQur1JfS/joWh5j42RBDM4SEOXgD+vUormyr1tcjxNpRUIAQAI/yinE0QXcCMKhl9aXDVXemAMdxgiEJCpXKzNpVdyejABN/PIWMfM3Jh5oBn/17Ez+fMJwr+Ofjush9uyBXk3e5SOW5O+gHBRR4e7Nu7GlHK6vMTkhDJxZxRocQmAsKqEtP0ye1bKaAPjd7GZY9HY0/X+2KZzoEwF6muevdyEmOF7qF4MuxbXB6Xl+sntgBo9sHwsnIdMBdQj1wQV1qdoorfwifp18XPHUqKN/c9r+c0hXQm7fV+Lj1QS194GInQ5MUXYZCiW87MHddWv+WuFTczdYVLoatKxD1DP90tuRXfCz9xngnWsYCQz8Fnv4JEEswPNoP+9+M4ZsV1RQUUKsZfo/TZQnMDr4JrkBzHlTEpPhS+QRWKocbbHdBHYzT6nCksFJB/8QDVc5c4CkVaHl4Ory4LACAgolxpceXQPSzunWOfQGoa/b8hxBrQkEBQqDJEtCeZDVykqN9cPVdXJVVU6AyKfb6MxrkFinBGMPB6w8xfUMc5m69YJi6WEmMMfxn60XkFikN2hZtu4TrelMipmUV4s/zuvTCcZ2CqqUPREg/U+B8irDytLVN10aINQhyN7yoNxcUUCkKhAskda/4aKS/C94fGYnTc/th76yeOPpOH8wb2hyDW/kYDQTo69TYHUUolWF34htAP3stJ1XQLFZpAiVylP8iukBh/IKzha8ToMgH4n/jl0nbjcdnz7QWrNf5/X+R8FCXocB6z0UmNNMc23CG/6PvN44Fpp8DYn8A2k0SjJfXn1WmuoYPnEzOQEqmJnDBcUCPnB18W6JXX+TAAYnMF4fFwnofN1rMQDFkGFj8AWYppun1pRBIOVXp/ujL/eNNNC7SBWUWKCdgf0EQ0H2WbqW8+0DauWp5PUIIBQUIAQD8ejqFf/xEpK+wSFAVGZuHW5+LXcWDAvrF5H46fgv/23EFz686iT/Pp+Hn47fxyrqzUJs5aSyvredScfD6Q/75u4Mj4PX47n+JiuH7Q4kAgKISFWZvOs8PZfB0lGNQS58qvz4xpD8mt7SOIe612BNCSG0IdjecPUBpLlNAUShcUAeGD5hiKxMj1NMB4gr8zw31tIetTan3dOcEcHGz7nneA0EzpyzG99KPcEH+AiaLt5fr/2OxUgWlynB4HsdxwP73geLHQVmZI9DiSbT0c0aEt7Co47LduoyFy9lSTC1+HTlM2PcHrm1w7437aPT8D4BbiNG+6E/vq2B65wxMVem75T8cTuIfjwwshvyObjhDs6HT8e+snjj3Xj90e/NXoOM0wCUImPQPnnx6Ajwc5MiDHTare+CSTVvdTn8cAmx/QzNbwP1LhsM6yuHS7jVwvLCGf75RGYP1qt64kJINuAYBgZ11Kyf+W+H9E0KMo6AAsXoPc4txIVV3x/WpdgHVun9pGcMHKpMpoN/H38+m4nu9f+6AZkzjprMppTerkISHeZi3VTcusmeYJyZ3b4w5gyP4ZX+cS8OdjAKMX3USR27qhl/M7h9eZoYEqRxzQQH9u0mEkIYh2MMwKGDuolatlymghFhT/b4B4TgOvr7+hg07ZgJZj9P/S4SBkci0jegrjoOMU+E/0vVmZ2/Qpz+skJdyGjj6he5563GAXBMMWD2xPTroZRruiL+Lj3ZdhVKlxq5L93GSNcNIxUJsV3VELrNFgdwTXqM/K/Nvt37QRDB8AKjUEIJTyRnYfVlXUHCm/U5do2cEuMDOaOzpABc7mWbow6ClwIx4ILAjOI5DdIALv/q23KbCnZ9eBfz5KvBVF2CJL/BtDLBlGnDoE+Dyn8CDq4DSeJ+3nbwCu8NL+Ofx6hC8p5wAgEO8NjOucS/dBgn7K/zeCSHGUUUqYvVO6xVq83G2QVij6q3eXhPDB8Z3DsL2+DTE3c4yuc6/Vx7g6UoGOPKLlZj60xnkFWui/A5yCf47oiU4jsPgVj74344rSM9ToFipRvcP9wm2HRLpg9i2Rk7YSLUwFRQY2KKGp4UihFhERTMFmEJXU6AY8gZ5opfn1gInbkego+iqbmFRNrBxHPDiHqBEOIQiNOOg4LlKzSAtcyZfDnuu3EcUs4Mzp7e/VQMAPP783ZsCvf/DN/k422L95I7ou+wAkh9ptlm5LwFnbmUi8aFm6tibzB/LXf8Drm8YBrb0htFKkqV7wnEQizio1AyK0t+osrhC2SAZ+Qq8tj6Ofz7CPRW+Sb/rVug4TTOewIypPRvjwPUHKFEx/KqKwTPifxEkemC4oqoYSIvT/AjekBhwDdbc+Zc7AnYeSEi7j46ph/k6AgBwvcNiFB/WnEOlZhUiPa8YHqG9gP2PAwd3TmiGcsgMf0cIIRVDt/KI1TuRpAsKtA92q7ZZB7TkEvNnHubu/JoiEYuwfHQ0n8oPaKZuGtcpkH8uKHJUQfP+uIibD3RjIT9+KgoBbppxrXKJGJO6GU9xHNXGHytGR1fr8AsiZCMVI8DN8ATw+S5Uw4GQhijYw7CmgLkp9lhJPv9YwVX8/0t9oGLAGMVc9C7+GG+WTNU13D0H3PgHUJqeOhfQTHdXFo4D/r36AIUoVehR/TglnhMBTyznswS0JGIRvn2+neDv9PHEDDzI1Y3/XzisBYZE+lRo2IR2XUFNAf3+lINazTDr13O493hqYR9RFj5SfwSOPR6C4BqiyXwoQ/tgN+yZ2ROzB4QjrHEwBqo+wZDi/2FOyQvYoOyFy+oglDAz5z5MBWQkAAn/Apf/AE7/gNC07YKAwKMWEzBqyFBBoc2v9icAvm0AudPjN1SiydwghFRZQwwgE1Ih+lO61UShNkcb879mlU35DnK3xz9v9MC+aw9QVKJGTLgnbj0qwM/HNVWWtVMDVoRKzfDWpnj8flZXpOnlmFDN3Qw9k7s3xoFrDwUBlQldgvHe0OYUEKgFkf4uuJMhDPqEelZvhgshpG6oaKYA9GYfKOYa5pAiRxsJGERIZL5IVPngackhdOAuaxrPrTcYPlBaVkEJHMsoaPjw8UW8WG4igNB7LhDczWhTWCNH7JrRA+9sviAowAsAgW52aBvkava1jZGIOCgAlJQ+da/A8IHvDiVi3zVNnSAplNji+S2k2Y/v8IskwJNfl3u4SZC7PV7p1QSv9GqC7MIS7L58Hzsv3MWCm+koVqghhwJNuRQ0E91GY+4uQrk0hHJpCOQeQMqZroOggAR57V+H++B5AMfhqXb+mmAAgE1nUvDOoAhI/doCiY+zFFNOAo17lvszIIQYR0EBYtVyi0pw+W4O/7wmpnQrKyjg41z5IlAudjI82VqXql+i1J0opucVQ6FUl2tsP2MMH/x1Fd8cTBQsbxfkipn9wgzWl4pFWPtCB/x66g4SHuajWxMP9G3eqNLvg1RMpxA37Ii/yz93kEsEWSOEkIbDXm74P8RsoTy91PkSUcPMFBjfJRjfHdLW0uHwZclQdJA9Dgpc3V7m9o/yivnsNy1mIvtCBCNBAb92QNcZZl/DTibBijHRaOnnhI93XYdCpUaPME+8N7Q5bMoeu2BAmymgLJ0pUEZQICNfgZNJj+BkK8VHu67xy7/22gzv7HO6FQd+AAR2qnC/AM0wyNi2/oht64+iEhVOJ2fi4I2HOHjdHb/dE04fKYESgdwDhHJpaMRlwhGFcOeyoYAUqfDClElTENQ4nF9/Ws9QfH8oESUqhuzCEpxOzkTngA66oMCd6pnxgBBrR0EBYtWS0wv4WYzsHldBrm5OZdQMcK3E7AOmeDvbgOM0MzMxppkm0FiRqtI+//emQUAAAFY80xoSE4US5RIxnuscXNUuk0ro3awR5v2hKwIZ6GZX7cNeCCF1l7kpCZlepkAJ1zCDhf6udvhlSics2nYZl+/m4JC6FR4wF0H6uTkZuQUAhHfrS1TGP1Nx6aBAYGdg5LeAqOwLe47jMKVHKJ5qG4CswhKElOP/sSm6GQg4qEUyiNSPgwEq01MQq9QMo746iqR0zZASN+TgKfEBtJWnoE+ObrYBRI8F2r9Y6b7ps5GK0a2pB7o19cC7g5vhQU4RDt1Ix6EbD3HoRjoe5UOT4cF8BdvJxCK8P7IVghoLaxI520rRtYkH9j/OcNh37QE6h+lNk5hySnPCQ/8DCakSCgoQq5amN+7e18W2RlLfHWQS/kK9NGdbabVezMkkIng72eDu46EDSen5ZQYFNpy8LZg2Sev759vBz6XuTmVlzfxcbNE+2BWnkjMBAKOosCMhDdoHI1vhnd8v8M/NDR/glLr/ayWihjl8AAA6NXbHzte7490tF7D+xG1sUXXFVMmOcm2bkZtvsKxYaTylXZApMPUQ4BNZ4b662svgWon6QfrEIl2AnomkAB8UMJ0pkPwonw8IAMCH0m/QVxwHQZzDJwoY8kmNXVR7OdlgVFt/jGrrD7Wa4eq9XJxKzsD5lCxkFZSgUKFCuLcjnuscZPLGTM8wTz4ocDQhHejVTtdYmAE8SgA8mtRI/wmxFhQUIFbtbpbu5MmnhqZzE4k4OMglyC0yLAYU6ln9FXMDXO34oMDEH0/h4OxeCHQ3LFQFAP9cuof/bLkgWLbkyVbo08wLjZwa7slkQ/Dp6Ggs230dfi62GN+ZigwS0pA93S4APx5NxtV7uQDMFxrk9IrslYgaZqaAvoXDWsDf1Ra7jw7B+OJ/YMOZvnOulZFrWHNAoTReO0DG6S0XWe60WaJ304Lp98NMUCCrQL+NCWdrAACJLTDy+wrNXlAVIhGH5r5OaO7rVKHtujbx4B9fSstBFrODi2cE8PDx+0k9TUEBQqqIZh8gVu2uXjE+3yqM7S+Lk4mCRkMifY0ur4pQL2Gkfeu5VKPr/Xv1Pl7bEAftDadGTnIcfrsXnu0YSAGBesDf1Q7Lno7GrP7hJod4EEIaBpGIQ89wT/652UwBvSJ7SisICkjFIrwc0wS/zXkW77m8j72q1tii6ooRxYtMbpOZV2CwrNhEUEDM6X3W5RgyUFP0ZypQi/TOKVTCGw6MMaw6nIS5Wy8gPiUbAIMtiuCOHDhypYIhsasAT8O6QXVNUy8HeDhoMi0YA44nPtLMQqBVespDQkiFUaYAsWqp+pkCLjV3IexkKxW8lpb+FILVZXL3EGw4eZt/nlkgvItQqFDhP1svCGYYcLSRYM2kDvB3NZ5RQAghxLL07xSbKzQoVukXGrSeIWAcx+GJoSPw3A+aYLsMpjMGsvMM/x+bDAro59pzlgsKSMS6779IZAftYISj21fjZrQnnusUBI7j8NfFe1i0XVN00R6F2C5bjDDuDs7btAe0syJKbIHZNwymU6yrOI5D51APbHs8k8PRhEcY6N0KOP94hUc3Ldc5QhoIur1ErFaxUiWYUs/YtE/VpZGT4d2aIa18IJdU/wlGY08HvNZbl0a3+/J9XErL5p9/tOuaICBgJxPjh/HtEeFdsXQ+QgghtUesN+bbXKaASG/2AYXYugK93Zp4IMrfGQCggBQPmIvR9XIKDDMFjA0f6NusETimV2tAZLnT5kh/F/7xL/m6u+Qd763HH3/+jl3HzkH9z3xk/70YdtBkQY4W70dLUTJknArti4/rduYVUW8CAlqdGutmhzqdnAk469XSyUkzsgUhpCIoKECs1ta4VH4eYplEhG5NPcrYovJ8jRTs04/6V7ewRrp/9imZhXjyy6P4cv9NfHMgAauOJPFtIg74bVpndKiBqRgJIYRUH/1Cc+YyBSRKXWE5pcS6ggIcx+GlGF1QPI0Z/9+WYzRTQFho0NVOig9HtQQHvc/agpkCbw8Mh9PjKY6XFQ9HotobgGZ4w2b5Qgz8Jwaio8vxTP46LJauQgR3GzMkm43vzKX6sxRrWrsg3Xd59V4OChwCdI3pNwC9WTcIIRVHQQFilVRqhq/2J/DPR7Xxh4dDzY29NFbFvyYnzxka6YPpfZryxYQVSjU+/Psa3v9LWGToh/Ht0cLXuQZ7QgghpDrolw4pMRsU0KspYGWZAgDQt5kX//gucze6Tk5B2YUGf53aGW62pYIAFiw06O9qh4+figIAFEGON0umQcGMBylGiQ/jb/k7cOIMMyIAAM4BxpfXYU29HOD4OCiiZsC5Im9A8njYp7oEuH/Rgr0jpP6joACxSjsv3EXyI80/SxEHTOvZuEZfz9/VSFCgBufU5TgOM/uF4adJHU0GO6b3aYoeYZ5G2wghhNQtUr2ogFJlfPw7AEj0agpYW6YAAEjEIvw6tTOcbaV4wBnPAMwvLIKqVGBFv6aAv6stmjZyBNSlpim0YKFBAOjfwhuLR7QEAJxlYZhdMhVqZv5cQmWsvfnwmuhejRKJOLQJdOWfn03J00ynqJV6xgK9IqThoKAAsToqNcPKfbqiNE9E+SKoBusJAMJ0fq2azBTQ6tbUA3tn9sSELsGCysW9wj0xs1+YYBkhhJC6Sz8oUGImKCDVCwqorDAoAAAdQtxwbE5vjO7b2Wi7iKnwKL9YsEw/U0AuefxZs1JBAQsOH9B6rlMQlj0dhfBGjsgPH4X4Ht8gngvDZXUQflT2hwq6PqpFUhxr/SEKJx8BbN0AqR3wxGdAQAcLvoPK05/KMDE9H/Brp2tMOW2BHhHScNDsA8TqbDpzh5/rGQBeigmt8dcM9XSARMQJi0PV0vW4s50UC4a1wKu9m+BSWg6KS1SUIUAIIfWMVKILCihUZoYPCDIFajbgXZfZySSAbwujbRKo8CCnGF6OulmH9GsKyLRFgOtYpoDWyDb+GNlGW2ivHQq6j8LJpAx0draFuOQKcHo1IJZA1P5FdNPeTX/zOqAsqncFBvUFu+uCXIkP84FmetMSUqYAIVVCQQFiVXKLSvDRrmv88+HRvrVSdV8mESHU0wHX7uuCEVxtRQUe83CQoycFAwghpF6S6RWnNTd8QKrSGy8vs96gAAAgoJPRxRKo8CC3CICupk5xuTIF6maCrZ1MgphwbS2FDsYzAcRSzU89Fq53vnY5LQfFjVqDHyCZkQAUZAB2VDiZkMqom3/dCKkhi7ZdRnqeAgBgIxXh7YERtfbaET71NzpPCCHEsiozfEBi41Cjfarz5A5Aq6cNFkugwv0c4fABo0GBOpopYK1a+DrBVqr5DhQqNc7nuQB2esUk0+Is0zFCGgAKChCrUKJS44t/b+C3Myn8spdjmhidKrCmhHsLgwI1WGeQEEJIA6MfFDA3fECu1gUFpLYUjMaIL4GnfxIsknIq3M8RTmGnX1NApg0KlJSq3i+pvXMGYkgqFqF1oAv//NStTMCvrW4FGkJASKVRUIA0aPdzivDjkSQMXnEIH/9znV8eFeBSK7UE9LWkqf8IIYRUkiBTQGk6U8BGrbvYldnV/PC4Ok8sBZoPA1xDdIvKzBR4nBGQ91C3gswBENOoW0trF6wbHnA6OUMYFKBig4RUGv11Iw3OvewirDtxC/9efYBLaTkG7Z6OcqwYHS04waoN0XrRbQBITs+v1dcnhBBSf8kkuvQyk8MH1CrIobvYldtTUIAn0p3ySqHCAzOZAvzwgfsXdCt4NK3R7pHyaR+sm5bw9K1MqLq20823cOcEoFYDIrrnSUhFUVCANBgqNcP3hxKxfM8NFJaoDNo5DhjTPhBvDQiHq72s1vvnZCMs8BN3J6vW+0AIIaR+KldNgaJswVMbe5ca7FE9o1dkTw4FUnOFQYEivfMGPihwN163gndkjXaPlE/rQFeIOEDNgNwiJW7KWyKcEwFMDRRlAQ+vAI2MzzpBCDGNggKkQUjLKsTMX8/heGKGQVuAmy0Gt/LBk639amWmAXMcbSTILVICAFr50XACQggh5SMMCpioKaAXFFAzDraOrsbXs0bOAcCDywCAMC4Fxx4VQKVmEIs0GRgFCiW/qr388enxPb2ggA8FBeoCB7kEzX2dcDFVkwl65I4C4d6RwN1zmhVuHaWgACGVQPk1pN7bd+0BBq04JAgIuNvL8PbACOyZ2QMHZ/fCnEHNLB4QAIBfpnSCVMxBJhbhrQHhlu4OIYSQekJYaNB4poCyIJN/nAtbONjWflZcneXbmn8YKUpCTpESF1J1QZR8hS5TwE4uBlRK4N5F3fbeUbXSTVK2rqEe/OPDN9OBoK66xltHLNAjQuo/yhQg9RZjDD8cTsKSnVeg1rtpMqSVD5Y82QrOdnVvPt4Wvs44M68fihQqeDnZWLo7hBBC6glZOYYPFGY/gna+gWxmDxcbOs3j+bXhH0aKEgAA+689QHSACwCgoFgvU0Am0WQVKAs1C0QSwLtVrXWVmNe9qSe+OZgIADiW8Agl7TtBenylpvHWUYAxmuKJkAqiTAFSLzHG8MFfV/HfHbqAgJ1MjI+fisIXz7aukwEBLScbKQUECCGEVIhUv9CgidkHCnLS+cc5cICjnIICPL1MgUZcFryQif3XdLMLCDIFZGIgVa+SfaOWgJT+b9cV7YJd+boPhSUqnEMzXWPefSAj0UI9I6T+oqAAqXce5RVj5q/n+SgxAPi52GLTtC6IbesPjqLDhBBCGpjy1BQoytUNoysQ2dP/Q30OXoCTP/80UpSI8ylZyMhXABDWFHCQS4AUvTnv/dvVWjdJ2WykYnRs7M4/33dHBXjqBQaSD1ugV4TUbxQUIPVGfrESX/x7AzEf7ceWuFR+eTMfJ2x9pSua+1q+ZgAhhBBSE2SlagowZhgYUOXc5R/nStwM2q2ebzT/MFKUAMaAQzc02QJZBSV8m4ONRJgp4EdBgbqmR1NdXYGDNx4CwXp1BRL2WqBHhNRvFBQgdV5KZgE++ecaeny4Dx//cx25euP+2gS64JfJneDpKLdgDwkhhJCapZ8pAABKtZFsgdz7/MM8mYdhu7XTu+PfS3QOALD/2kMoVWokp+fzbX62SuDhNaPbkbqhR5gn//hiag4e+fTQNSbsA1QlRrYihJhCg81InZNfrMTRhEc4fOMhDt1MR+LDfIN1nGwkeL1vGMZ3DoJETLEtQgghDZtULBwKUKJSGwQKxPm6oECRjVet9KteCR8M7FkAAGglSkYIdxcHr8sQn5rN1xSQiUVorr4B4HHQxcYZcAu1TH+JSU29HBDiYY+kx8GcbTlNMEEsB1TFQHEOcPs4ENLdwr0kpP6wuqupoqIizJ8/H2FhYbCxsYGvry8mTZqElJQUS3fN6l27l4uZv55D60W7MXntaaw5dssgICCXiDCtZygOvdUbL3QLoYAAIYQQqyCVCP/flSgNMwVsC3XDB0psKShgwDMcaKSbReAJ0TE8ylfg7U3x/LLoABfIU47qtvHvAIjoXKOu4TgOg1p688+3XckWBgFu7LJArwipv6zqr1xRURH69OmDRYsWIS8vD8OHD0dAQABWr16NNm3aICEhwdJdtEoqNcPKfTcx5LND+P1sqtH5l0M87PHOoAgcm9MH7wyKqNOzCxBCCCHVTVYqCG7wv5IxOBfe0bU7BtZGt+qfliP5h8PERwEw3HiQxy/r1NhNWKiO7jbXWYNb+fCPz9zKRIZvT13jle2aqQkJIeViVcMHlixZgqNHj6Jz5874559/4ODgAABYtmwZZs2ahUmTJuHAgQMW7qV1ScsqxBsbz+FEUoZguZONBN2aeqB7U090a+KBADc7C/WQEEIIsbzSQwVKSgcF8h9Cri7knzL3JrXRrfqn5Uhg70IAQBNRGppzt3CZBfPN3YPsgON6Mw8EU1Cgrmrh64RQT3skPM4qXZ8ThVe1jZlJmroQXhEW6x8h9YnVZAqUlJTg888/BwCsXLmSDwgAwMyZMxEZGYmDBw/izJkzpnZBqtmO+LsYtOKQICDg52KLFWOicWZeP3w5ti2e6RBIAQFCCCFWTyziINIrK2AQFHiky3bMZA7w8KThA0a5BgP+7fmnY+UH+cfeTjZoo44H1I+L1MmdAO/IWu4gKS+O4zCuUxD//Pv4YjCPcN0KaXEW6BUh9ZPVBAUOHz6MrKwshIaGonXr1gbtsbGxAIBt27bVdteszo37uZj602m8sv4ssgt11WGHRPpg5+vdMTzaz+COCCGEEGLt9P83lqhKpUZnJPIPk5k3fJxta6tb9U/0WP7hsw7nMLZjINoGueLzZ6IhPvGVbr3GMYDYqpJq652RbfwheRwtyyoowX1bvaKQhz8FFAUW6hkh9YvV/KU7f/48AKBNmzZG27XLtetVllqtRlZWVpX20dBcSMlGfEoW7mQUID41GxdTswXttjIx5gyKwPBoP7DifGQVW6ijhBBCLKMgE4j7SfNYcK3Lyn7MamJ5Ne6juvrIGGaXpEChUoMDcOvHdbgnFYN73N7i/p/8FpdUbohGUY2dj2RnZ5t9Xud5dgCKHn+uRXcxO/w3wB/A3/9D1q1DuvWajQXonK7Oa+rC4VJaLgDg5ettsIrboWlIuYr0D7oiyb0HGMeZ2YPGlUYjUCx1rMmultukrlRMm1SMWq2GqApFUa0mKHD79m0AgL+/v9F27XLteua0aNHC6PKrV69CrVbD1dW1kr20XhM/snQPCCGEkIZiD/CxX629WnBwcK29Vs1YbHzx0kG12w1SZXcAbBEsOff4pzxWVG9nquBtS3eA1Ev6w+MrymqCAnl5msqydnbGx6fb29sL1qsMbYQmIoKKmpCK0c58ERpKcyGTiqPjh1QWHTuksujYIZVFxw6pLDp2TLt9+zZ/PVsZVhMUYI9T8TgT6UOsAtOWXLp0yehybQaBqXZCTKFjh1QFHT+ksujYIZVFxw6pLDp2SGXRsVNzrGawiqOjZoxQfn6+0faCAk0hkqqkXRBCCCGEEEIIIfWJ1QQFAgMDAQApKSlG27XLtesRQgghhBBCCCENndUEBaKiogAAZ8+eNdquXR4ZSfPREkIIIYQQQgixDlYTFOjatSucnZ2RkJCAuLg4g/ZNmzYBAIYOHVrbXSOEEEIIIYQQQizCaoICMpkMr776KgDg1VdfFdQWWLZsGeLj49GtWze0b9/eUl0khBBCCCGEEEJqFccqUna/nisqKkJMTAxOnDgBHx8fdO/eHbdu3cKJEyfg7u6O48ePo0mTJpbuJiGEEEIIIYQQUiusKigAAIWFhXj//fexfv163LlzB66urhg4cCAWL16MgIAAS3ePEEIIIYQQQgipNVYXFCCEEEIIIYQQQoiG1dQUIIQQQgghhBBCiBAFBQghhBBCCCGEECtFQQFCCCGEEEIIIcRKUVCAEEIIIYQQQgixUhQUIIQQQgghhBBCrBQFBQghhBBCCCGEECtFQYEaVlRUhCVLliAqKgr29vawsbFB06ZNMX36dNy7d8/S3SP1wKZNm9C/f394eHjAxsYGgYGBGDlyJA4fPmzprpF6YtGiReA4DhzH4ZdffrF0d0gddfXqVSxduhR9+vRBYGAg5HI5vL29MXLkSBw6dMjS3SN1QFFREebPn4+wsDDY2NjA19cXkyZNQkpKiqW7RuqwgoICbN26FS+88AIiIyPh5OQEe3t7REVFYdGiRcjLy7N0F0k9kZGRAS8vL3Ach4iICEt3p0HhGGPM0p1oqIqKitCzZ0+cPHkSbm5u6Ny5M2QyGU6ePInU1FR4e3vj2LFjCA4OtnRXSR2kUqnw/PPPY/369bC3t0e3bt3g4uKC27dv48yZM5g3bx7mzp1r6W6SOu7atWuIioqCQqEAYwwbNmzAmDFjLN0tUgf5+/sjNTUVTk5O6NixI1xdXXH58mVcvHgRHMdh2bJlmDFjhqW7SSykqKgIffr0wdGjR+Hj44Pu3bsjOTkZJ0+ehKenJ44dO4bQ0FBLd5PUQd9//z0mT54MAGjRogWaN2+OnJwcHD16FLm5uYiIiMCBAwfg5eVl4Z6Sum7ChAlYu3YtGGMIDw/H1atXLd2lhoORGrNixQoGgHXs2JFlZ2fzy4uKithTTz3FALDnn3/egj0kddlbb73FALDBgwezR48eCdoyMjLY9evXLdQzUl+o1WrWo0cP1qhRIzZ8+HAGgG3YsMHS3SJ1VL9+/dj69etZcXGxYPnXX3/NADCxWMwuXbpkod4RS5s3bx4DwDp37sxyc3P55Z988gkDwHr06GHB3pG6bM2aNeyll14yOG9JS0tjrVu3ZgDYM888Y6Hekfpiz549DACbMmUKA8DCw8Mt3aUGhTIFalBsbCw2b96MX375BaNHjxa0nTt3Dq1bt0azZs1w+fJlC/WQ1FU3btxAs2bN4OfnhytXrsDOzs7SXSL10HfffYcpU6bg559/xu7du7FmzRrKFCCVMmDAAPzzzz9YsGAB5s+fb+nukFpWUlICLy8vZGVl4ezZs2jdurWgPSoqCvHx8Th9+jTatm1roV6S+ujYsWPo0qUL5HI5cnJyIJPJLN0lUgcVFhYiMjISMpkMW7duRVhYGGUKVDOqKVCD5HJ5meu4ubnVQk9IffP9999DpVJh2rRpFBAglXLv3j289dZb6NOnD8aOHWvp7pB6LioqCgCQlpZm4Z4QSzh8+DCysrIQGhpqEBAANDdBAGDbtm213TVSz2n/thQXF+PRo0cW7g2pqxYuXIiEhAR89dVXkEqllu5Og0RBgRrUr18/AMDy5cuRk5PDL1coFFiyZAkAYPz48RbpG6nb9u7dC0BzDCUlJWHJkiWYOnUq5syZgz179li4d6Q+mD59OgoLC/HVV19ZuiukAUhMTAQAeHt7W7gnxBLOnz8PAGjTpo3Rdu1y7XqElJf2b4tUKqUbZcSo+Ph4fPLJJ5g4cSJ69Ohh6e40WBJLd6Ahe+6557Bz50789ttvCAkJQZcuXSCVSnHy5Enk5ubif//7H194hRB9ly5dAgCcOHECs2bNQnFxMd/2wQcfoG/fvti8eTOcnJws1UVSh23fvh2//fYbFi5ciKZNm1q6O6SeS0hIwPbt2wEAw4YNs3BviCXcvn0bgKYYpTHa5dr1CCmvFStWAAAGDhxYrgxbYl3UajUmT54MFxcXfPjhh5buToNGmQI1SCwWY8OGDXjzzTeRkZGB7du3Y8uWLUhNTUV0dDS6detm6S6SOqioqAhFRUUAgBkzZqBnz56Ij49HTk4Odu/ejZCQEOzZswdTpkyxcE9JXZSXl4eXX34ZYWFhePvtty3dHVLPKZVKTJgwAcXFxRg9ejSNF7dS2injTA1ns7e3F6xHSHns3LkTP/zwA6RSKRYvXmzp7pA66PPPP8fJkyfx0Ucfwd3d3dLdadAoU8CM2NhYXLx4sULbrF27Fh06dAAAZGZm4sknn8SpU6ewYsUKjBo1CnZ2djh48CBee+019OnTB7/99htGjBhRA70nllSVY0elUvHL/Pz8sG3bNr7wTt++ffHHH38gOjoav/76KxYvXkx3ghuYqv7deffdd3Hnzh3s3buX7rpYmaoeO8a89tprOHz4MBo3bowvv/yyql0k9ZS2JjXHcWbbCSmvK1euYNy4cWCM4aOPPuJrCxCidefOHcydOxc9e/bEhAkTLN2dBo+CAmYkJyfj2rVrFdqmoKCAf/zGG2/gwIEDWL58OaZPn84vHz58OPz8/NCxY0e8/vrrGDp0KCQS+ioakqocO/b29hCJRFCr1Rg3bpxBJd5WrVqhXbt2OHnyJA4cOEBBgQamKsfOyZMnsXLlSjz33HPo3bt3TXSP1GFV/Z9V2qJFi/D111+jUaNG2LVrF433tWKOjo4AgPz8fKPt2uPIwcGh1vpE6q+UlBQMHDgQmZmZmDlzJl5//XVLd4nUQS+//DIUCgXVRqoldCVqxunTpyu9rUqlwoYNGwDoqvLqa9euHUJCQpCQkIDExESEhYVV+rVI3VOVYwcAgoKCkJSUhKCgIKPtwcHBOHnyJB48eFCl1yF1T1WOnZ07d0KtVuPChQuIiYkRtGmn7dFe6MXGxuLVV1+tSldJHVPVvzv6Vq5cifnz58PZ2Rl///03mjRpUm37JvVPYGAgAM3FnDHa5dr1CDElPT0d/fr1w+3btzFx4kR8/PHHlu4SqaO2b98OFxcXvPTSS4Ll2iG2t2/f5s91tm/fTkHJKqKgQA158OABFAoFAJgsBqddnpGRUWv9IvVD69atkZSUZPLY0E7bQ38AiTHnzp0z2XblyhVcuXIF0dHRtdYfUr+sW7cOr732Guzs7LBjxw46Vgif2n327Fmj7drlkZGRtdYnUv/k5uZi0KBBuHr1KkaOHInvvvvO5JAUQgAgKysLBw4cMNpWWFjItymVytrsVoNEhQZriJubG5/2bezuTU5ODp/maepuMLFe2grf+/btM2jLzc3lT8BMTQ9FrNOCBQvAGDP6o53+dMOGDWCMYfny5ZbtLKmTdu7ciQkTJkAqlWLLli3o2rWrpbtE6oCuXbvC2dkZCQkJiIuLM2jftGkTAGDo0KG13TVSTxQXF2P48OE4ffo0BgwYgA0bNkAsFlu6W6QOM3U+k5SUBAAIDw/nl7m4uFi2sw0ABQVqiFwux8CBAwEAM2fOxN27d/m2oqIivPzyyygoKEDXrl3h4+NjqW6SOmrMmDEIDg7Grl27sGbNGn65UqnE66+/jszMTLRs2ZJO2Akh1ebIkSP8cLeNGzeif//+Fu4RqStkMhk/3OjVV18V1BZYtmwZ4uPj0a1bN7Rv395SXSR1mEqlwjPPPIN9+/ahe/fu+P333w3qJRFCLItjVDK2xiQkJKBr1664f/8+HB0d0blzZ9ja2uLUqVNIS0uDm5sbDhw4gJYtW1q6q6QOOn78OPr27Yv8/Hy0adMGwcHBOHv2LJKTk+Hu7o59+/ahVatWlu4mqScmTJiANWvWYMOGDRgzZoylu0PqIFdXV2RlZSEkJAQ9evQwuk63bt3w4osv1nLPSF1QVFSEmJgYnDhxAj4+PujevTtu3bqFEydOwN3dHcePH6faE8SoFStWYMaMGQCAJ5980uSw2o8//hgeHh612DNSHyUnJyMkJATh4eF8vSRSdVRToAaFhobi/PnzWLp0Kf766y8cPHgQjDEEBATglVdewTvvvAN/f39Ld5PUUZ06dUJcXBwWLlyIPXv24MKFC2jUqBEmT56MuXPnUkEnQki1ysrKAgAkJSXx6ZnGUFDAOtnY2GDfvn14//33sX79emzduhWurq4YP348Fi9ejICAAEt3kdRRmZmZ/OMtW7aYXG/BggUUFCDEQihTgBBCCCGEEEIIsVJUU4AQQgghhBBCCLFSFBQghBBCCCGEEEKsFAUFCCGEEEIIIYQQK0VBAUIIIYQQQgghxEpRUIAQQgghhBBCCLFSFBQghBBCCCGEEEKsFAUFCCGEEEIIIYQQK0VBAUIIIYQQQgghxEpRUIAQQgghhBBCCLFSFBQghBBCCCGEEEKsFAUFCCGEEEIIIYQQK0VBAUIIqeM4jivzZ8KECZbuptWIiYkBx3FITk6u8dfiOA7BwcE1/jp1WXBwsNljv759PrV5/BCd3r17IygoCAqFolLbZ2ZmwsbGBiKRCLdv3y5z/UmTJoHjOPznP/8BAMTFxYHjOHz00UeVen1CCKlJEkt3gBBCSPmMHz/eZFu3bt1qsSekOuzfvx+9evXC+PHj8eOPP1q6O+W2YMECLFy4EKtXr67VYNSoUaPg4OBgsNzDw6PW+kDqpx07dmDfvn346quvIJPJKrUPV1dXDBkyBL///jvWr1+Pd955x+S6RUVF+P333wEA48aNAwC0bt0aw4YNw5IlS/DCCy/Azc2tUv0ghJCaQEEBQgipJ+rThSOpHleuXIFUKrV0N+qEjz/+uN5lBRizdu1aFBQUwM/Pz9JdsRrvvvsuvLy8MGnSpCrt57nnnsPvv/+On3/+2WxQYNu2bcjOzkbbtm3RrFkzfvmcOXPw559/YunSpVi6dGmV+kIIIdWJhg8QQgghdVRERARCQ0Mt3Q1SjQIDAxEREUHBnlpy5MgRxMfHY8yYMZXOEtAaPHgw3N3dcenSJZw/f97keuvWrQOgCSLo69SpE5o0aYJVq1ZVehgDIYTUBAoKEEJIA6Qda61SqfDhhx8iLCwMcrkcAQEBePvtt1FcXGx0u7y8PCxatAitWrWCnZ0dnJyc0LNnT2zdutVg3eTkZHAch5iYGOTk5GDWrFkICQmBVCrFjBkz+PXi4uIwaNAgODs7w9nZGQMGDMCpU6fw448/guM4LFiwgF+3ZcuW4DgO169fN9q/5ORkiEQiNG3aFIyxMj8H/fHb69evR6dOneDo6AgXFxd+HcYY1qxZgx49esDFxQW2traIjIzExx9/jJKSkjJfQ+vQoUN49dVXERkZCVdXV9ja2iIiIgLvvPMOsrKyBOtOmDABvXr1AgCsWbNGMEZe//MoPWZ+8+bN4DgOY8aMMdmPl19+GRzH4bvvvhMsr8h3a0pwcDAWLlwIAJg4caKg3/v37xes+9NPP6Fbt25wcnKCnZ0dIiMj8f7776OoqKjcr1cZycnJmDp1KoKDgyGXy+Hp6YnY2FjEx8cbrKt/DF6/fh1jxoxBo0aNIBKJsHXrVsExnp+fj5kzZyIgIAC2trZo06YNtm3bxu/rt99+Q4cOHWBvb49GjRph+vTpKCwsNHjNytYUOHz4MJ588kl4eXlBLpcjODgY06dPx8OHDw3WnTBhAv+dHDx4EL1794ajoyOcnJwwZMgQXL582eTrbNu2DQMGDIC7uztsbGwQFhaGefPmIS8vz+x7MfX7lZeXhzfffJP/3Jo3b47PPvsMjDGD4/ujjz4SjMM3plevXuA4DocPHy7X5/b9998DAMaOHWtynQsXLmDs2LHw8/ODXC6Hr68vJk6caPAdyWQyPP300wCAn3/+2ei+MjIy8Ndff0EsFhv9PX3mmWeQnp6OLVu2lKv/hBBSKxghhJA6DQCr6J9rACwoKIiNHj2a2dvbs169erGhQ4cyZ2dnBoCNHTvWYJt79+6x5s2bMwDMz8+PDRs2jPXt25fZ29szAOz9998XrJ+UlMQAsA4dOrDo6Gjm6urKRowYwUaOHMkWLFjAGGPsyJEjzNbWlgFgrVu3ZmPGjGGRkZFMJpOxqVOnMgBs/vz5/D4/++wzBoDNnj3b6PuaO3cuA8A++OCDcn0OPXv2ZADYlClTmEgkYt27d2djxoxhXbt2ZYwxplKp2FNPPcUAMCcnJ9anTx82fPhw5u3tzQCwwYMHM5VKZXSfSUlJguUdO3ZkcrmctW3blo0cOZINGTKE+fj4MACsRYsWLDc3l1/3u+++YwMGDGAAWGhoKBs/fjz/s2XLFn497feoVVRUxJydnZmtra1gf1olJSXMw8ODyWQylpGRwS+v6HdryqxZs1hUVBQDwLp27Sro95UrV/j1pkyZwgAwGxsbNnjwYBYbG8s8PDwYANa5c2dWUFBQrtdjjLGgoCCjn7cxhw4dYk5OTvxnHhsbyzp37sw4jmO2trbs33//Fay/evVqBoCNGTOGOTk5sZCQEDZ69GjWv39/tn37dv4Y79y5M+vYsSPz8PBgQ4cOZTExMUwkEjGxWMx2797Nli1bxiQSCevcuTMbMWIEc3d3ZwDYs88+a9BHU8ePOStWrGAcxzGxWMw6d+7MYmNjWUREBAPAQkJCWFpammD98ePHMwBs5syZTCwWs6ioKDZq1CgWFhbGADB3d3d29+5dg9eZOXMm/7316NGDjRw5kv/827Zty/Ly8oy+F1O/X4WFhaxDhw4MAPP09GSxsbFs4MCBTCaTsenTpxsc3w8fPmRyuZz5+PiwkpISg/7duHGDcRzHIiIiyv3ZeXl5MXt7e4PfY61NmzYxmUzGv8fY2FjWunVr/nO6ePGiYP2jR4/yv0fG9vn1118zAGzQoEFGX2/v3r0MAHvuuefK/R4IIaSmUVCAEELquMoGBQCwZs2aCS4+EhMTmaurKwPAbt68Kdhm0KBBDAB76623mEKh4JcnJCSw0NBQJhaL2fnz5/nl2gsm7UVTZmamYH8qlYq/CPnwww8FbYsWLeK31Q8KZGVlMTs7O+bl5SXoA2OMKZVK5ufnxyQSCbt37165PgftRYuNjQ3bv3+/QfvSpUsZANavXz/24MEDfnleXh574oknGAD2xRdfGN1n6Yu6HTt2CC7EGdNcxGsvkBcuXCho27dvHwPAxo8fb7L/pS+aGGNs0qRJDABbu3atwfo7duxgANiIESMEyyv63Zozf/58BoCtXr3aaPumTZv4i6YbN27wy7Ozs1m3bt3MBn2MKW9QIDs7m3l7ezOpVMp+++03Qdvu3buZTCZjfn5+rLi4mF+uDQoAYK+++ipTKpWC7fSP8ZiYGMH3q922SZMmzM3NjR08eJBvS01NZV5eXgwAS0hIEOyzokGBY8eOMZFIxIKCggTfkVqt5n+PYmNjBdtogwIikYitX7+eX65UKtmoUaMYADZv3jzBNhs3buSDd/p9UygU/DH85ptvGn0vpn6/Fi9ezP99yM7O5pefP3+e/ztU+vh+9tlnGQC2detWg/29/fbbDAD75JNPTH9geq5cucIAsB49ehhtT0xMZHZ2dszZ2ZkdOHBA0LZmzRoGgLVv395guyZNmjAAbO/evQZt2mN83bp1Rl8zJyeHiUQiFhwcXK73QAghtYGCAoQQUsdpL0rM/ejfXdbfZs+ePQb7e+211wwu6uLi4hgA1qVLF6ZWqw222bp1KwPAXnvtNX6Z/gXTqVOnDLbZvXs3A8AiIiIM9qlUKllISIhBUIAxxiZOnMgAsE2bNgmWb9u2jQFgI0eONPVRGdBetLzyyisGbdq76o6Ojuzhw4cG7ffu3WNyuZy1atXK6D7Le1FXUFDAJBIJa9OmjWB5ZYMC2juNAwYMMFh/7NixDIDgorgy3605ZQUFevTowQCwH374waAtPj6ecRzHHB0dBRfn5miDAqZ+4uLiGGOMffrppwwAmzNnjtH9zJgxgwFgmzdv5pdpL+w9PT1Zfn6+wTbaY1wsFgsCHIxpgl6enp4MAHvvvfcMtn3jjTeMfk4VPX6GDx/OALBdu3YZtKnVata6dWsmEokEx7A2KDBu3DiDbc6cOcMAsJ49ewqWazNArl69arBNYWEh8/b2Zi4uLoK74+Z+vxhjzN/fnwFgx44dM2h77733jB7fBw4cYADY0KFDBctLSkqYt7c3k8lkRn9fjdEGOiZPnmy0/fXXX2cA2DfffGO0fcSIEQwAO3PmjGD5ggULGAA2adIkwfLk5GTGcRxzcHAwejxp+fn5MQCCQAkhhFgS1RQghJB6Yvz48SZ/AgMDDdaXSqWIiYkxWB4WFgYAuHv3Lr9s9+7dAIDhw4eD4ziDbbRTHp46dcqgzcfHB+3atTNYfvToUQBAbGyswT7FYjFGjhxp9H1OmzYNAAzGxGufT5482eh25gwbNsxgWVxcHNLT09GtWzej09o1atQITZs2xcWLF42ODTcmNTUVX3/9NWbMmIFJkyZhwoQJeOmllyCTyXDjxo0K99uYmJgY+Pn5Yc+ePXjw4AG/vKCgAH/88QecnJwwdOhQfnlVvtuKKikpwfHjx8FxHJ599lmD9latWiEyMhK5ublmC7UZM2rUKKPHvnZqN+37HDFihNHtzb3Pvn37ws7OzuRrBwcHo0mTJoJlIpEIQUFBAIB+/foZbKMtEKn/e1ZRarUae/fuhaOjI/r06WPQznEcunbtCrVajTNnzhi09+/f32CZsd//Bw8e4Pz582jWrBnCw8MNtrGxsUG7du2QlZVl9Dg29vt1+/ZtpKSkwN/fH506dTJof+qppwyWAUCPHj3QvHlz/PXXX0hNTeWXb9u2Dffu3cOTTz5Z7mkotb8frq6uRtv1fzeMMXXMaAsIbt68WVAjY926dWCMYeTIkWaPJ+0xa6weBCGEWAJNSUgIIfVERack9PHxgVgsNliunetdv9igtqDW22+/jbffftvkPtPT0w2WGQtIAEBaWhoAICAgwGi7qe06dOiA1q1bY/fu3bh16xaCgoJw9+5d7Ny5E4GBgUYvdMpi7LW07/mvv/4yerGsLyMjo8wp5JYtW4Y5c+bUeFVxkUiEMWPG4JNPPsHGjRvx2muvAQD+/PNP5OXlYeLEibCxseHXr8p3W1GPHj2CQqGAt7e3oA/6goODcf78ef74KK+ypiTUvs+OHTua3U9FjmEtU9+9vb29yXZtm6minuXx6NEjvsCfRGL+lM3Y+/L39zdYZuz3/9atWwA0U2CW9buQnp5uEDgw9vlV9vcfAKZMmYIZM2Zg1apVmDdvHoDKBQWzs7MBAI6OjkbbtceMt7e32f2U/mwbN26MLl264OjRo9i+fTtiY2MBmJ51oDQnJydB/wghxNIoKEAIIQ1UWSf3+lQqFQCge/fuaNy4scn1jN2hM3XxV1Y/mJnZA6ZOnYpp06Zh1apVWLhwIVavXg2lUokXXngBIlHFk9yM9VH7nps2bYouXbqY3V4ul5ttP378OGbNmgVnZ2d8++23iImJgbe3N7+dr69vle4YlzZ27Fh88sknWL9+PR8UWL9+Pd+mryrfbWWV59iryPFZHtr3+dRTT5m9S2ssaFDZY7i87ZWlfU+Ojo4mM2u0tFkLlemX9nV8fHzKDLq5u7sbLDP3+VXmsxk/fjzmzJmDVatWYe7cuUhJScGuXbvQuHFj9O7du9z7cXZ2BgDk5OQYbVepVOA4Ds8//7zZ/bRo0cJg2XPPPYejR4/i559/RmxsLOLi4nD58mX4+vqW2UdtMEDbP0IIsTQKChBCCOHvKMbGxmL69OnVsk8fHx8AmjRiY+7cuWNy27Fjx2L27Nn8ncIffvgBIpEIkyZNqpa+Abr33LJlywpnYZSmnV7sv//9L8aPHy9oKywsxL1796q0/9Jat26NZs2a4fjx40hMTISrqyt27doFHx8ffqpDrZr4bk1xd3eHTCbDvXv3UFhYCFtbW4N1tHeltcdHdfH398e1a9cwd+5cREZGVuu+LcXDwwNyuRxSqbTKx6g52mPE29u72l6nrN9/U8sBwMXFBaNHj8aPP/6I3bt349ixY1Cr1XjxxRcrFGTw8vICoMn0Mcbf3x8JCQn47LPP+Lv35TV69Gi8/vrr+Ouvv5CZmclnCTz77LNlBi4zMzMBAJ6enhV6TUIIqSlUU4AQQgj69u0LABWas74s2rvvmzdvNsgKUKvVZufpdnBwwLPPPouUlBTMnj0biYmJGDRokNF06Mpq3749nJ2dsW/fPpN3EstLe5JvLFX6t99+M5oVIZPJAABKpbJSr6nNCFi/fj1+++03KBQKPPPMMwYXJNX93Zrrt1QqRadOncAYw4YNGwzaL168iPPnz8PR0RFRUVHV0h+tmjiGLU0ikSAmJgYZGRk4ePBgjb2Ov78/wsPDER8fj6SkpGrZZ1BQEHx9fZGSkoITJ04YtG/atMns9lOnTgUAfPPNN1i1ahUkEgkmTJhQoT5oj7GrV68aba/KMePq6oohQ4ZAoVDgl19+4Y/3cePGmd0uJycHaWlpCAkJqXAgghBCagoFBQghhKBTp07o06cP9u3bhzfeeIMfx6ylVqvxzz//4PDhw+XeZ+/evdGkSRNcuXIFn376qaDtgw8+QGJiotnttQUHly9fDqByBQbNkcvlePPNN5GVlYVRo0bxd7D1xcfHY+PGjWXuS1u87YcffkBJSQm//PLlyybH8fv6+gIArl27Vpnu84X81q1bZ3LoAFD9321Z/dYOZ5g/f77gO87NzcWrr74KxhimTp3KBxeqy9SpU+Hp6YklS5Zg9erVBoGY/Px8rF27FikpKdX6ujXt3XffhUgkwvjx441+R2lpaVi5cmWVX2fu3LlQqVQYNWoULl68aNCekJCAVatWVWif2gv7WbNmITc3l19+8eJFfP7552a37dSpE6KiovD777/j9u3bGDp0aIWzS8LDw+Hl5YWzZ88aDWLNmjULtra2eOONN7Bt2zaD9oyMDHz55ZcmC41qawfMmzcPaWlpaNWqVZnBrlOnToExhu7du1fovRBCSE2i4QOEEFJPmLtLFhgYiEWLFlVp/+vWrUP//v2xfPlyrF27FtHR0fD09ERqaiquXbuGhw8f4tNPP+UrcpdFLBZj9erV6NevH2bNmoV169YhPDwcly9fxpUrVzB58mR89913Ji8Oo6Oj0aFDB5w8eRI+Pj4YMmRIld6fMe+++y4uX76MDRs2IDw8HG3atEFgYCDS09ORmJiIpKQkDB8+HKNHjza7n4kTJ+KTTz7Btm3bEB4ejvbt2yMjIwMHDhzAiBEjcPLkSYOgQ3BwMCIjI3H69Gl06NABLVq0gFgsxrBhw4xWcy8tJCSEL3Z29epVREREoE2bNkbXrc7vtn///rCxscGnn36KixcvwtfXFxzHYfbs2QgPD0dsbCymTJmCb7/9Fi1btkTv3r1hZ2eH/fv34+HDh+jUqRMWLlxY5utUlKurK7Zs2YJhw4Zh0qRJWLhwIVq2bAm5XI7bt2/jypUryM/PR1xcXLVmnNS0Hj16YMWKFZgxYwa6d++OyMhING3aFEVFRbh16xauXLkCBwcHvPLKK1V6nXHjxuHChQv48MMPER0djdatWyMkJAQ5OTm4desWrl69iqioqAoN4Zk9eza2bduGI0eOIDQ0FDExMcjLy8O///6LyZMn44svvjAbHJo6dSpefvllAJUPCg4ePBg//vgjTpw4ga5duwramjZtip9//hnjxo3DsGHDEB4ejmbNmoExhlu3buHy5ctQKBR49tlnjQ6FGTJkCNzc3PDo0SMAZWcJAMD+/fv5fhFCSJ1hsckQCSGElAvMzNGu/YmKijLYpvT831raudnnz59v0FZQUMCWLVvGOnbsyBwdHZlcLmfBwcGsf//+bOXKlYL5wbVzuJee77y006dPswEDBjBHR0fm6OjI+vTpw44dO8b++9//MgDs66+/NrntnDlzGAD27rvvmn0NU8o7J/ymTZvYwIEDmYeHB5NKpczHx4d16tSJLViwwGDedlP7vHPnDnv22WeZn58fs7GxYc2aNWPvv/8+UyqVLCgoiBn7l3vjxg02YsQI5u7uzkQikcH3Yu57ZIyxlStX8sfAokWLzL7Hiny3Zdm1axfr2rUrc3Bw4F9/3759gnXWrl3LunTpwhwcHJiNjQ1r0aIF+9///scKCgrK/TqMMf6zK+s71EpNTWWzZs1iERERzNbWljk4OLCwsDA2evRotnHjRlZcXMyva+53gbGyj3Fzx5epfZf3mCzt9OnTbOzYsSwgIIBJpVLm5ubGIiMj2SuvvML2798vWHf8+PFGvxMtc8fV3r172ZNPPsm8vb2ZVCplXl5erE2bNmz27NnszJkzFX4v2dnZ7I033mB+fn5MJpOx8PBw9sknn7A7d+4wAKxTp04mt7127RoDwPz9/ZlSqTS5njlHjhxhANjLL79scp3r16+zqVOnssaNGzO5XM6cnZ1Zs2bN2MSJE9n27duZWq02ue20adMYACYSididO3fK7E9oaCjz8PAQHIeEEGJpHGNmyj8TQgghNWTQoEH4+++/cfz4caMV4RljiIiIwI0bN3Dz5k2zlfMJIfXLxo0bMWbMGEybNg1fffWV0XWWLFmC//znP5g/fz4WLFhQ6ddq3bo1UlJSkJKSUuZMIjXp2LFj6NKlC9566y0sXbrUYv0ghJDSqKYAIYSQGpORkWGQNs8Yw+eff46///4bTZo0QYcOHYxuu2nTJly/fh2DBw+mgAAh9dS5c+egVqsFyy5cuIC33noLgK42Rmk5OTn88IIpU6ZUqQ//+9//kJ6ejh9++KFK+6mqDz74AC4uLvx7J4SQuoIyBQghhNSY48ePo0uXLoiMjETjxo2hUqlw8eJFJCYmwtbWFjt37kRMTIxgmxdffBFZWVnYvn07lEolTp48aXKsPCGkbouIiEBOTg5atWoFV1dXJCcn4/Tp01CpVEazBFavXo0DBw7g4MGDSEpKwhtvvIFly5ZVuR+9e/fGzZs3cfPmzWovclkecXFxaNOmDZYuXUpBAUJInUNBAUIIITXmwYMHWLBgAfbt24e0tDQUFhbCy8sLPXv2xDvvvINWrVoZbMNxHCQSCcLCwrB48WKMHDnSAj0nhFSHlStX4pdffsH169eRmZkJOzs7REZG4oUXXsD48eMN1p8wYQLWrFkDT09PjBkzBh999JFFU/4JIcQaUFCAEEIIIYQQQgixUlRTgBBCCCGEEEIIsVIUFCCEEEIIIYQQQqwUBQUIIYQQQgghhBArRUEBQgghhBBCCCHESlFQgBBCCCGEEEIIsVIUFCCEEEIIIYQQQqwUBQUIIYQQQgghhBArRUEBQgghhBBCCCHESlFQgBBCCCGEEEIIsVIUFCCEEEIIIYQQQqwUBQUIIYQQQgghhBArRUEBQgghhBBCCCHESlFQgBBCCCGEEEIIsVL/B9WmHgU+JXPjAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "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": [ + "
" + ] + }, + "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": [ + "
" + ] + }, + "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.ini\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!" + ] + } + ], + "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..3f4628f1 --- /dev/null +++ b/.doctrees/nbsphinx/tutorials/SVO_os_qe/tutorial.ipynb @@ -0,0 +1,194 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "3fa75e8f", + "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": "5c1d7b71", + "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": "6d5345b9", + "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": "999bb518", + "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": "5ec433e7", + "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": "5e8e2fce", + "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": "b5fe3a3a", + "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": "62bce864", + "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": "349ae5d9", + "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", + "
    \n", + "
  1. bandstructure
  2. \n", + "
  3. Fermi slice
  4. \n", + "
" + ] + }, + { + "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": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "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": [ + "
" + ] + }, + "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 matplotlib colormaps. `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", 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[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..73056ba4 --- /dev/null +++ b/ChangeLog.html @@ -0,0 +1,599 @@ + + + + + + + Changelog — solid_dmft documentation + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

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

  • +
+
+
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
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+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for csc_flow

+################################################################################
+#
+# solid_dmft - A versatile python wrapper to perform DFT+DMFT calculations
+#              utilizing the TRIQS software library
+#
+# Copyright (C) 2018-2020, ETH Zurich
+# Copyright (C) 2021, The Simons Foundation
+#      authors: A. Hampel, M. Merkel, and S. Beck
+#
+# solid_dmft is free software: you can redistribute it and/or modify it under the
+# terms of the GNU General Public License as published by the Free Software
+# Foundation, either version 3 of the License, or (at your option) any later
+# version.
+#
+# solid_dmft is distributed in the hope that it will be useful, but WITHOUT ANY
+# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
+# PARTICULAR PURPOSE. See the GNU General Public License for more details.
+
+# You should have received a copy of the GNU General Public License along with
+# solid_dmft (in the file COPYING.txt in this directory). If not, see
+# <http://www.gnu.org/licenses/>.
+#
+################################################################################
+"""
+contains the charge self-consistency flow control functions
+"""
+
+from timeit import default_timer as timer
+import subprocess
+import shlex
+import os
+import numpy as np
+
+# triqs
+from h5 import HDFArchive
+import triqs.utility.mpi as mpi
+
+from triqs_dft_tools.converters.wannier90 import Wannier90Converter
+from triqs_dft_tools.converters.vasp import VaspConverter
+from triqs_dft_tools.converters.plovasp.vaspio import VaspData
+import triqs_dft_tools.converters.plovasp.converter as plo_converter
+
+from solid_dmft.dmft_cycle import dmft_cycle
+from solid_dmft.dft_managers import vasp_manager as vasp
+from solid_dmft.dft_managers import qe_manager as qe
+
+def _run_plo_converter(general_params):
+    if not mpi.is_master_node():
+        return
+
+    # Checks for plo file for projectors
+    if not os.path.exists(general_params['plo_cfg']):
+        print('*** Input PLO config file not found! '
+              + 'I was looking for {} ***'.format(general_params['plo_cfg']))
+        mpi.MPI.COMM_WORLD.Abort(1)
+
+    # Runs plo converter
+    plo_converter.generate_and_output_as_text(general_params['plo_cfg'], vasp_dir='./')
+    # Writes new H(k) to h5 archive
+    converter = VaspConverter(filename=general_params['seedname'])
+    converter.convert_dft_input()
+
+def _run_wannier90(general_params, dft_params):
+    if not mpi.is_master_node():
+        return
+
+    if not os.path.exists(general_params['seedname'] + '.win'):
+        print('*** Wannier input file not found! '
+              + 'I was looking for {0}.win ***'.format(general_params['seedname']))
+        mpi.MPI.COMM_WORLD.Abort(1)
+
+    # Runs wannier90 twice:
+    # First preprocessing to write nnkp file, then normal run
+    command = shlex.split(dft_params['w90_exec'])
+    subprocess.check_call(command + ['-pp', general_params['seedname']], shell=False)
+    subprocess.check_call(command + [general_params['seedname']], shell=False)
+
+def _run_w90converter(seedname, tolerance):
+    if (not os.path.exists(seedname + '.win')
+        or not os.path.exists(seedname + '.inp')):
+        print('*** Wannier input/converter config file not found! '
+              + 'I was looking for {0}.win and {0}.inp ***'.format(seedname))
+        mpi.MPI.COMM_WORLD.Abort(1)
+
+    #TODO: choose rot_mat_type with general_params['set_rot']
+    converter = Wannier90Converter(seedname, rot_mat_type='hloc_diag', bloch_basis=True, w90zero=tolerance)
+    converter.convert_dft_input()
+    mpi.barrier()
+
+    # Checks if creating of rot_mat succeeded
+    if mpi.is_master_node():
+        with HDFArchive(seedname+'.h5', 'r') as archive:
+            assert archive['dft_input']['use_rotations'], 'Creation of rot_mat failed in W90 converter'
+    mpi.barrier()
+
+def _full_qe_run(seedname, dft_params, mode):
+    assert mode in ('initial', 'restart', 'update')
+
+    # runs a full iteration of DFT
+    qe_wrapper = lambda calc_type: qe.run(dft_params['n_cores'], calc_type, dft_params['dft_exec'],
+                                          dft_params['mpi_env'], seedname)
+
+    # Initially run an scf calculation
+    if mode == 'initial':
+        qe_wrapper('scf')
+    # For charge update, use mode scf
+    elif mode == 'update':
+        qe_wrapper('mod_scf')
+
+    # Rest is executed regardless of mode
+    # Optionally does bnd, bands, proj if files are present
+    for nscf in ['bnd', 'bands', 'proj']:
+        if os.path.isfile(f'{seedname}.{nscf}.in'):
+            qe_wrapper(nscf)
+
+    # nscf
+    qe_wrapper('nscf')
+    # w90 parts
+    qe_wrapper('win_pp')
+    qe_wrapper('pw2wan')
+    qe_wrapper('win')
+    _run_w90converter(seedname, dft_params['w90_tolerance'])
+
+
+def _store_dft_eigvals(path_to_h5, iteration, projector_type):
+    """
+    save the eigenvalues from LOCPROJ/wannier90 file to h5 archive
+    """
+    with HDFArchive(path_to_h5, 'a') as archive:
+        if 'dft_eigvals' not in archive:
+            archive.create_group('dft_eigvals')
+
+        if projector_type == 'plo':
+            vasp_data = VaspData('./')
+            eigenvals = np.array(vasp_data.plocar.eigs[:, :, 0]) - vasp_data.plocar.efermi
+        elif projector_type == 'w90':
+            with open('LOCPROJ') as locproj_file:
+                fermi_energy = float(locproj_file.readline().split()[4])
+            n_k = archive['dft_input']['n_k']
+            num_ks_bands = archive['dft_input']['n_orbitals'][0, 0]
+            eigenvals = np.loadtxt('wannier90.eig', usecols=2)
+            eigenvals = eigenvals.reshape((n_k, num_ks_bands)) - fermi_energy
+
+        archive['dft_eigvals']['it_'+str(iteration)] = eigenvals
+
+def _full_vasp_run(general_params, dft_params, initial_run, n_iter_dft=1, sum_k=None):
+    """
+    Performs a complete DFT cycle in Vasp and the correct converter. If
+    initial_run, Vasp is starting and performing a normal scf calculation
+    followed by a converter run. Otherwise, it performs n_iter_dft runs of DFT,
+    generating the projectors with the converter, and recalculating the charge
+    density correction with the new projectors.
+
+    Parameters
+    ----------
+    general_params : dict
+        general parameters as a dict
+    dft_params : dict
+        dft parameters as a dict
+    initial_run : bool
+        True when VASP is called for the first time. initial_run = True requires
+        n_iter_dft = 1.
+    n_iter_dft : int, optional
+        Number of DFT iterations to perform. The default is 1.
+    sum_k : SumkDFT, optional
+        The SumkDFT object required to recalculate the charge-density correction
+        if n_iter_dft > 1. The default is None.
+
+    Returns
+    -------
+    vasp_process_id : int
+        The process ID of the forked VASP process.
+    irred_indices : np.array
+        Integer indices of kpts in the irreducible Brillouin zone. Only needed
+        for Wannier projectors, which are normally run with symmetries.
+    """
+
+
+    if initial_run:
+        assert n_iter_dft == 1
+    else:
+        assert n_iter_dft == 1 or sum_k is not None, 'Sumk object needed to run multiple DFT iterations'
+
+    for i in range(n_iter_dft):
+        if initial_run:
+            vasp_process_id = vasp.run_initial_scf(dft_params['n_cores'], dft_params['dft_exec'],
+                                                   dft_params['mpi_env'])
+        else:
+            vasp_process_id = None
+            vasp.run_charge_update()
+
+        if dft_params['projector_type'] == 'plo':
+            _run_plo_converter(general_params)
+            irred_indices = None
+        elif dft_params['projector_type'] == 'w90':
+            _run_wannier90(general_params, dft_params)
+            mpi.barrier()
+            _run_w90converter(general_params['seedname'], dft_params['w90_tolerance'])
+            mpi.barrier()
+            kpts = None
+            if mpi.is_master_node():
+                with HDFArchive(general_params['seedname']+'.h5', 'r') as archive:
+                    kpts = archive['dft_input/kpts']
+            irred_indices = vasp.read_irred_kpoints(kpts)
+
+        # No need for recalculation of density correction if we run DMFT next
+        if i == n_iter_dft - 1:
+            break
+
+        # Recalculates the density correction
+        # Reads in new projectors and hopping and updates chemical potential
+        # rot_mat is not updated since it's more closely related to the local problem than DFT
+        # New fermi weights are directly read in calc_density_correction
+        mpi.barrier()
+        if mpi.is_master_node():
+            with HDFArchive(general_params['seedname']+'.h5', 'r') as archive:
+                sum_k.proj_mat = archive['dft_input/proj_mat']
+                sum_k.hopping = archive['dft_input/hopping']
+        sum_k.proj_mat = mpi.bcast(sum_k.proj_mat)
+        sum_k.hopping = mpi.bcast(sum_k.hopping)
+        sum_k.calc_mu(precision=general_params['prec_mu'])
+
+        # Writes out GAMMA file
+        sum_k.calc_density_correction(dm_type='vasp',  kpts_to_write=irred_indices)
+
+    return vasp_process_id, irred_indices
+
+
+# Main CSC flow method
+
+[docs] +def csc_flow_control(general_params, solver_params, dft_params, advanced_params): + """ + 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. + + Parameters + ---------- + general_params : dict + general parameters as a dict + solver_params : dict + solver parameters as a dict + dft_params : dict + dft parameters as a dict + advanced_params : dict + advanced parameters as a dict + """ + + # Removes legacy file vasp.suppress_projs if present + vasp.remove_legacy_projections_suppressed() + + # if GAMMA file already exists, load it by doing extra DFT iterations + if dft_params['dft_code'] == 'vasp' and os.path.exists('GAMMA'): + # TODO: implement + raise NotImplementedError('GAMMA file found but restarting from updated ' + + 'charge density not yet implemented for Vasp.') + + # Reads in iteration offset if restarting + iteration_offset = 0 + if mpi.is_master_node() and os.path.isfile(general_params['seedname']+'.h5'): + with HDFArchive(general_params['seedname']+'.h5', 'r') as archive: + if 'DMFT_results' in archive and 'iteration_count' in archive['DMFT_results']: + iteration_offset = archive['DMFT_results']['iteration_count'] + iteration_offset = mpi.bcast(iteration_offset) + + iter_dmft = iteration_offset+1 + + # Runs DFT once and converter + mpi.barrier() + irred_indices = None + start_time_dft = timer() + mpi.report(' solid_dmft: Running {}...'.format(dft_params['dft_code'].upper())) + + if dft_params['dft_code'] == 'qe': + if iteration_offset == 0: + _full_qe_run(general_params['seedname'], dft_params, 'initial') + else: + _full_qe_run(general_params['seedname'], dft_params, 'restart') + elif dft_params['dft_code'] == 'vasp': + vasp_process_id, irred_indices = _full_vasp_run(general_params, dft_params, True) + + mpi.barrier() + end_time_dft = timer() + mpi.report(' solid_dmft: DFT cycle took {:10.3f} seconds'.format(end_time_dft-start_time_dft)) + + # Now that everything is ready, starts DFT+DMFT loop + while True: + dft_energy = None + if mpi.is_master_node(): + # Writes eigenvals to archive if requested + if dft_params['store_eigenvals']: + if dft_params['dft_code'] == 'qe': + # TODO: implement + raise NotImplementedError('store_eigenvals not yet compatible with dft_code = qe') + _store_dft_eigvals(path_to_h5=general_params['seedname']+'.h5', + iteration=iter_dmft, + projector_type=dft_params['projector_type']) + + # Reads the DFT energy + if dft_params['dft_code'] == 'vasp': + dft_energy = vasp.read_dft_energy() + elif dft_params['dft_code'] == 'qe': + dft_energy = qe.read_dft_energy(general_params['seedname'], iter_dmft) + dft_energy = mpi.bcast(dft_energy) + + mpi.report('', '#'*80, 'Calling dmft_cycle') + + if mpi.is_master_node(): + start_time_dmft = timer() + + # Determines number of DMFT steps + if iter_dmft == 1: + iter_one_shot = general_params['n_iter_dmft_first'] + elif iteration_offset > 0 and iter_dmft == iteration_offset + 1: + iter_one_shot = general_params['n_iter_dmft_per'] - (iter_dmft - 1 + - general_params['n_iter_dmft_first'])%general_params['n_iter_dmft_per'] + else: + iter_one_shot = general_params['n_iter_dmft_per'] + # Maximum total number of iterations is n_iter_dmft+iteration_offset + iter_one_shot = min(iter_one_shot, + general_params['n_iter_dmft'] + iteration_offset - iter_dmft + 1) + + ############################################################ + # run the dmft_cycle + is_converged, sum_k = dmft_cycle(general_params, solver_params, advanced_params, + dft_params, iter_one_shot, irred_indices, dft_energy) + ############################################################ + + iter_dmft += iter_one_shot + + if mpi.is_master_node(): + end_time_dmft = timer() + print('\n' + '='*80) + print('DMFT cycle took {:10.3f} seconds'.format(end_time_dmft-start_time_dmft)) + print('='*80 + '\n') + + # If all steps are executed or calculation is converged, finish DFT+DMFT loop + if is_converged or iter_dmft > general_params['n_iter_dmft'] + iteration_offset: + break + + # Restarts DFT + mpi.barrier() + start_time_dft = timer() + mpi.report(' solid_dmft: Running {}...'.format(dft_params['dft_code'].upper())) + + # Runs DFT and converter + if dft_params['dft_code'] == 'qe': + _full_qe_run(general_params['seedname'], dft_params, 'update') + elif dft_params['dft_code'] == 'vasp': + # Determines number of DFT steps + if iter_dmft == general_params['n_iter_dmft_first'] + 1: + n_iter_dft = dft_params['n_iter_first'] + else: + n_iter_dft = dft_params['n_iter'] + _, irred_indices = _full_vasp_run(general_params, dft_params, False, n_iter_dft, sum_k) + + mpi.barrier() + end_time_dft = timer() + mpi.report(' solid_dmft: DFT cycle took {:10.3f} seconds'.format(end_time_dft-start_time_dft)) + + # Kills background VASP process for clean end + if mpi.is_master_node() and dft_params['dft_code'] == 'vasp': + print(' solid_dmft: Stopping VASP\n', flush=True) + vasp.kill(vasp_process_id)
+ +
+ +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ 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..eacd3182 --- /dev/null +++ b/_modules/dft_managers/mpi_helpers.html @@ -0,0 +1,490 @@ + + + + + + dft_managers.mpi_helpers — solid_dmft documentation + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for dft_managers.mpi_helpers

+
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+################################################################################
+#
+# solid_dmft - A versatile python wrapper to perform DFT+DMFT calculations
+#              utilizing the TRIQS software library
+#
+# Copyright (C) 2018-2020, ETH Zurich
+# Copyright (C) 2021, The Simons Foundation
+#      authors: A. Hampel, M. Merkel, and S. Beck
+#
+# solid_dmft is free software: you can redistribute it and/or modify it under the
+# terms of the GNU General Public License as published by the Free Software
+# Foundation, either version 3 of the License, or (at your option) any later
+# version.
+#
+# solid_dmft is distributed in the hope that it will be useful, but WITHOUT ANY
+# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
+# PARTICULAR PURPOSE. See the GNU General Public License for more details.
+
+# You should have received a copy of the GNU General Public License along with
+# solid_dmft (in the file COPYING.txt in this directory). If not, see
+# <http://www.gnu.org/licenses/>.
+#
+################################################################################
+
+"""
+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.
+"""
+
+import os
+import socket
+from collections import defaultdict
+import shlex
+import time
+
+import triqs.utility.mpi as mpi
+
+
+
+[docs] +def create_hostfile(number_cores, cluster_name): + """ + 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. + + Parameters + ---------- + number_cores: int, the number of cores that vasp runs on + cluster_name: string, the name of the server + + Returns + ------- + string: name of the hostfile if not run locally and if called by master node + """ + + if cluster_name == 'default': + return None + + hostnames = mpi.world.gather(socket.gethostname(), root=0) + if mpi.is_master_node(): + # create hostfile based on first number_cores ranks + hosts = defaultdict(int) + for hostname in hostnames[:number_cores]: + hosts[hostname] += 1 + + mask_hostfile = {'openmpi': '{} slots={}', # OpenMPI format + 'openmpi-intra': '{} slots={}', # OpenMPI format + 'mpich': '{}:{}', # MPICH format + }[cluster_name] + + hostfile = 'dft.hostfile' + with open(hostfile, 'w') as file: + file.write('\n'.join(mask_hostfile.format(*i) for i in hosts.items())) + return hostfile + + return None
+ + + +
+[docs] +def find_path_to_mpi_command(env_vars, mpi_exe): + """ + Finds the complete path for the mpi executable by scanning the directories + of $PATH. + + Parameters + ---------- + env_vars: dict of string, environment variables containing PATH + mpi_exe: string, mpi command + + Returns + ------- + string: absolute path to mpi command + """ + + for path_directory in env_vars.get('PATH').split(os.pathsep): + if path_directory: + potential_path = os.path.join(path_directory, mpi_exe) + if os.access(potential_path, os.F_OK | os.X_OK): + return potential_path + + return None
+ + + +
+[docs] +def get_mpi_arguments(mpi_profile, mpi_exe, number_cores, dft_exe, hostfile): + """ + 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. + + Parameters + ---------- + cluster_name: string, name of the cluster so that settings can be tailored to it + mpi_exe: string, mpi command + number_cores: int, the number of cores that vasp runs on + dft_exe: string, the command to start the DFT code + hostfile: string, name of the hostfile + + Returns + ------- + list of string: arguments to start mpi with + """ + + if mpi_profile == 'default': + return [mpi_exe, '-np', str(number_cores)] + shlex.split(dft_exe) + + # For the second node, mpirun starts DFT by using ssh + # Therefore we need to handover the env variables with -x + if mpi_profile == 'openmpi': + return [mpi_exe, '-hostfile', hostfile, '-np', str(number_cores), + '-mca', 'mtl', '^psm,psm2,ofi', + '-mca', 'btl', '^vader,openib,usnix', + '-x', 'LD_LIBRARY_PATH', + '-x', 'PATH', '-x', 'OMP_NUM_THREADS'] + shlex.split(dft_exe) + # Run mpi with intra-node communication among ranks (on a single node). + if mpi_profile == 'openmpi-intra': + return [mpi_exe, '-np', str(number_cores), + '--mca', 'pml', 'ob1', '--mca', 'btl', 'self,vader', + '-x', 'LD_LIBRARY_PATH', + '-x', 'PATH', '-x', 'OMP_NUM_THREADS'] + shlex.split(dft_exe) + + if mpi_profile == 'mpich': + return [mpi_exe, '-launcher', 'ssh', '-hostfile', hostfile, + '-np', str(number_cores), '-envlist', 'PATH'] + shlex.split(dft_exe) + + return None
+ + + +
+[docs] +def poll_barrier(comm, poll_interval=0.1): + """ + Use asynchronous synchronization, otherwise mpi.barrier uses up all the CPU time during + the run of subprocess. + + Parameters + ---------- + comm: MPI communicator + poll_interval: float, time step for pinging the status of the sleeping ranks + """ + + req = comm.Ibarrier() + while not req.Test(): + time.sleep(poll_interval)
+ +
+ +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/dft_managers/qe_manager.html b/_modules/dft_managers/qe_manager.html new file mode 100644 index 00000000..f80461e4 --- /dev/null +++ b/_modules/dft_managers/qe_manager.html @@ -0,0 +1,509 @@ + + + + + + dft_managers.qe_manager — solid_dmft documentation + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for dft_managers.qe_manager

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+################################################################################
+#
+# solid_dmft - A versatile python wrapper to perform DFT+DMFT calculations
+#              utilizing the TRIQS software library
+#
+# Copyright (C) 2018-2020, ETH Zurich
+# Copyright (C) 2021, The Simons Foundation
+#      authors: A. Hampel, M. Merkel, and S. Beck
+#
+# solid_dmft is free software: you can redistribute it and/or modify it under the
+# terms of the GNU General Public License as published by the Free Software
+# Foundation, either version 3 of the License, or (at your option) any later
+# version.
+#
+# solid_dmft is distributed in the hope that it will be useful, but WITHOUT ANY
+# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
+# PARTICULAR PURPOSE. See the GNU General Public License for more details.
+
+# You should have received a copy of the GNU General Public License along with
+# solid_dmft (in the file COPYING.txt in this directory). If not, see
+# <http://www.gnu.org/licenses/>.
+#
+################################################################################
+
+"""
+Contains the function to run a QuantumEspresso iteration. Needed for CSC calculations.
+"""
+
+import os
+import subprocess
+import sys
+
+import triqs.utility.mpi as mpi
+
+from solid_dmft.dft_managers import mpi_helpers
+
+
+def _start_with_piping(mpi_exe, mpi_arguments, qe_file_ext, env_vars, seedname):
+    """
+    Handles the piping of the output when starting QE.
+
+    Parameters
+    ----------
+    mpi_exe: string, mpi command
+    mpi_arguments: list of string, arguments to start mpi with
+    qe_file_ext : string, file name for QE
+    env_vars: dict of string, environment variables containing PATH
+    seedname: string, QE input file
+
+    Returns
+    -------
+    int: id of the VASP child process
+    """
+
+    if qe_file_ext in ['scf', 'nscf', 'pw2wan', 'mod_scf', 'bnd', 'bands', 'proj']:
+
+        inp = open(f'{seedname}.{qe_file_ext}.in', 'r')
+        out = open(f'{seedname}.{qe_file_ext}.out', 'w')
+        err = open(f'{seedname}.{qe_file_ext}.err', 'w')
+
+        print('  solid_dmft: Starting {} calculation...'.format(qe_file_ext))
+
+        # start subprocess
+        qe_result = subprocess.run(mpi_arguments, stdin=inp, env=env_vars, capture_output=True,
+                                   text=True, shell=False)
+
+        # write output and error file
+        output = qe_result.stdout
+        error = qe_result.stderr
+        out.writelines(output)
+        err.writelines(error)
+
+        if qe_result.returncode != 0:
+            mpi.report('QE calculation failed. Exiting programm.')
+            sys.exit(1)
+
+    elif 'win' in qe_file_ext:
+        print('  solid_dmft: Starting Wannier90 {}...'.format(qe_file_ext))
+        # don't need any piping for Wannier90
+        subprocess.check_call(mpi_arguments + [seedname], env=env_vars, shell=False)
+
+
+
+[docs] +def run(number_cores, qe_file_ext, qe_exec, mpi_profile, seedname): + """ + 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. + + Parameters + ---------- + number_cores: int, the number of cores that vasp runs on + qe_file_ext: string, qe executable + qe_exec: string, path to qe executables + mpi_profile: string, name of the cluster so that settings can be tailored to it + """ + + # get MPI env + hostfile = mpi_helpers.create_hostfile(number_cores, mpi_profile) + qe_exec_path = qe_exec.strip(qe_exec.rsplit('/')[-1]) + qe_exec = qe_exec_path + + if mpi.is_master_node(): + # clean environment + env_vars = {} + for var_name in ['PATH', 'LD_LIBRARY_PATH', 'SHELL', 'PWD', 'HOME', + 'OMP_NUM_THREADS', 'OMPI_MCA_btl_vader_single_copy_mechanism']: + var = os.getenv(var_name) + if var: + env_vars[var_name] = var + + # assuming that mpirun points to the correct mpi env + mpi_exe = mpi_helpers.find_path_to_mpi_command(env_vars, 'mpirun') + + if qe_file_ext in ['scf', 'nscf', 'mod_scf', 'bnd']: + qe_exec += f'pw.x -nk {number_cores}' + elif qe_file_ext in ['pw2wan']: + qe_exec += 'pw2wannier90.x -nk 1 -pd .true.' + elif qe_file_ext in ['bands']: + qe_exec += f'bands.x -nk {number_cores}' + elif qe_file_ext in ['proj']: + qe_exec += f'projwfc.x -nk {number_cores}' + elif qe_file_ext in ['win_pp']: + qe_exec += 'wannier90.x -pp' + elif qe_file_ext in ['win']: + qe_exec += 'wannier90.x' + + arguments = mpi_helpers.get_mpi_arguments(mpi_profile, mpi_exe, number_cores, qe_exec, hostfile) + _start_with_piping(mpi_exe, arguments, qe_file_ext, env_vars, seedname) + + mpi_helpers.poll_barrier(mpi.MPI.COMM_WORLD)
+ + + +
+[docs] +def read_dft_energy(seedname, iter_dmft): + """ + Reads DFT energy from quantum espresso's out files + + 1. At the first iteration, the DFT energy is read from the scf file. + + 2. 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. + + """ + dft_energy = 0.0 + RYDBERG = 13.605693123 # eV + + if iter_dmft == 1: + with open(f'{seedname}.scf.out', 'r') as file: + dft_output = file.readlines() + for line in dft_output: + if '!' in line: + print("\nReading total energy from the scf calculation \n") + dft_energy = float(line.split()[-2]) * RYDBERG + print(f"The DFT energy is: {dft_energy} eV") + break + if line =="": + raise EOFError("Did not find scf total energy") + else: + with open(f'{seedname}.mod_scf.out', 'r') as file: + dft_output = file.readlines() + for line in dft_output: + #if 'eband, Ef (eV)' in line: + if "(sum(wg*et))" in line: + print("\nReading band energy from the mod_scf calculation \n") + #band_energy = float(line.split()) + band_energy_modscf = float(line.split()[-2])*RYDBERG + print(f"The mod_scf band energy is: {band_energy_modscf} eV") + if 'total energy' in line: + print("\nReading total energy from the mod_scf calculation \n") + dft_energy = float(line.split()[-2]) * RYDBERG + print(f"The uncorrected DFT energy is: {dft_energy} eV") + dft_energy -= band_energy_modscf + print(f"The DFT energy without kinetic part is: {dft_energy} eV") + + with open(f'{seedname}.nscf.out', 'r') as file: + dft_output = file.readlines() + for line in dft_output: + if 'The nscf band energy' in line: + print("\nReading band energy from the nscf calculation\n") + band_energy_nscf = float(line.split()[-2]) * RYDBERG + dft_energy += band_energy_nscf + print(f"The nscf band energy is: {band_energy_nscf} eV") + print(f"The corrected DFT energy is: {dft_energy} eV") + break + return dft_energy
+ +
+ +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/dft_managers/vasp_manager.html b/_modules/dft_managers/vasp_manager.html new file mode 100644 index 00000000..f1ee149b --- /dev/null +++ b/_modules/dft_managers/vasp_manager.html @@ -0,0 +1,577 @@ + + + + + + dft_managers.vasp_manager — solid_dmft documentation + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for dft_managers.vasp_manager

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+################################################################################
+#
+# solid_dmft - A versatile python wrapper to perform DFT+DMFT calculations
+#              utilizing the TRIQS software library
+#
+# Copyright (C) 2018-2020, ETH Zurich
+# Copyright (C) 2021, The Simons Foundation
+#      authors: A. Hampel, M. Merkel, and S. Beck
+#
+# solid_dmft is free software: you can redistribute it and/or modify it under the
+# terms of the GNU General Public License as published by the Free Software
+# Foundation, either version 3 of the License, or (at your option) any later
+# version.
+#
+# solid_dmft is distributed in the hope that it will be useful, but WITHOUT ANY
+# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
+# PARTICULAR PURPOSE. See the GNU General Public License for more details.
+
+# You should have received a copy of the GNU General Public License along with
+# solid_dmft (in the file COPYING.txt in this directory). If not, see
+# <http://www.gnu.org/licenses/>.
+#
+################################################################################
+
+"""
+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.
+
+This functionality is contained in the simpler public functions.
+"""
+
+import os
+import signal
+import time
+import numpy as np
+
+import triqs.utility.mpi as mpi
+
+from solid_dmft.dft_managers import mpi_helpers
+
+
+def _fork_and_start_vasp(mpi_exe, arguments, env_vars):
+    """
+    Forks a process from the master process that then calls mpi to start vasp.
+    The child process running VASP never leaves this function whereas the main
+    process returns the child's process id and continues. We use explicitly
+    os.fork as os.execve here because subprocess does not return, and as VASP
+    needs to keep running throughout the whole DFT+DMFT calculation that is
+    not a viable solution here.
+
+    Parameters
+    ----------
+    mpi_exe: string, mpi command
+    arguments: list of string, arguments to start mpi with
+    env_vars: dict of string, environment variables containing PATH
+
+    Returns
+    -------
+    int: id of the VASP child process
+    """
+
+    # fork process
+    vasp_process_id = os.fork()
+    if vasp_process_id == 0:
+        # close file descriptors, if rank0 had open files
+        for fd in range(3, 256):
+            try:
+                os.close(fd)
+            except OSError:
+                pass
+        print('\n Starting VASP now\n')
+        os.execve(mpi_exe, arguments, env_vars)
+        print('\n VASP exec failed\n')
+        os._exit(127)
+
+    return vasp_process_id
+
+
+def _is_lock_file_present():
+    """
+    Checks if the lock file 'vasp.lock' is there, i.e. if VASP is still working.
+    """
+
+    res_bool = False
+    if mpi.is_master_node():
+        res_bool = os.path.isfile('./vasp.lock')
+    res_bool = mpi.bcast(res_bool)
+    return res_bool
+
+
+
+[docs] +def remove_legacy_projections_suppressed(): + """ Removes legacy file vasp.suppress_projs if present. """ + if mpi.is_master_node(): + if os.path.isfile('./vasp.suppress_projs'): + print(' solid_dmft: Removing legacy file vasp.suppress_projs', flush=True) + os.remove('./vasp.suppress_projs') + mpi.barrier()
+ + + +
+[docs] +def run_initial_scf(number_cores, vasp_command, cluster_name): + """ + 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. + + Parameters + ---------- + number_cores: int, the number of cores that vasp runs on + vasp_command: string, the command to start vasp + cluster_name: string, name of the cluster so that settings can be tailored to it + """ + + # Removes STOPCAR + if mpi.is_master_node() and os.path.isfile('STOPCAR'): + os.remove('STOPCAR') + mpi.barrier() + + # get MPI env + vasp_process_id = 0 + + hostfile = mpi_helpers.create_hostfile(number_cores, cluster_name) + + if mpi.is_master_node(): + # clean environment + env_vars = {} + for var_name in ['PATH', 'LD_LIBRARY_PATH', 'SHELL', 'PWD', 'HOME', + 'OMP_NUM_THREADS', 'OMPI_MCA_btl_vader_single_copy_mechanism']: + var = os.getenv(var_name) + if var: + env_vars[var_name] = var + + # assuming that mpirun points to the correct mpi env + mpi_exe = mpi_helpers.find_path_to_mpi_command(env_vars, 'mpirun') + + arguments = mpi_helpers.get_mpi_arguments(cluster_name, mpi_exe, number_cores, vasp_command, hostfile) + vasp_process_id = _fork_and_start_vasp(mpi_exe, arguments, env_vars) + + mpi_helpers.poll_barrier(mpi.MPI.COMM_WORLD) + vasp_process_id = mpi.bcast(vasp_process_id) + + # Waits for VASP to start + while not _is_lock_file_present(): + time.sleep(1) + mpi.barrier() + + # Waits for VASP to finish + while _is_lock_file_present(): + time.sleep(1) + mpi.barrier() + + return vasp_process_id
+ + + +
+[docs] +def run_charge_update(): + """ + 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. + """ + if mpi.is_master_node(): + open('./vasp.lock', 'a').close() + mpi.barrier() + + # Waits for VASP to finish + while _is_lock_file_present(): + time.sleep(1) + mpi.barrier()
+ + + +
+[docs] +def read_dft_energy(): + """ + Reads DFT energy from the last line of Vasp's OSZICAR. + """ + with open('OSZICAR', 'r') as file: + nextline = file.readline() + while nextline.strip(): + line = nextline + nextline = file.readline() + dft_energy = float(line.split()[2]) + + return dft_energy
+ + + +
+[docs] +def read_irred_kpoints(kpts): + """ Reads the indices of the irreducible k-points from the OUTCAR. """ + + def read_outcar(file): + has_started_reading = False + for line in file: + if 'IBZKPT_HF' in line: + has_started_reading = True + continue + + if not has_started_reading: + continue + + if 't-inv' in line: + yield line + continue + + if '-'*10 in line: + break + + irred_indices = None + if mpi.is_master_node(): + with open('OUTCAR', 'r') as file: + outcar_data_raw = np.loadtxt(read_outcar(file), usecols=[0, 1, 2, 4]) + outcar_kpoints = outcar_data_raw[:, :3] + outcar_indices = (outcar_data_raw[:, 3]-.5).astype(int) + assert np.allclose(outcar_kpoints, kpts) + + symmetry_mapping = np.full(outcar_kpoints.shape[0], -1, dtype=int) + + for i, (kpt_outcar, outcar_index) in enumerate(zip(outcar_kpoints, outcar_indices)): + for j, kpt in enumerate(kpts): + if np.allclose(kpt_outcar, kpt): + # Symmetry-irreducible k points + if i == outcar_index: + symmetry_mapping[j] = outcar_index + # Symmetry-reducible + else: + symmetry_mapping[j] = outcar_index + break + + # Asserts that loop left through break, i.e. a pair was found + assert np.allclose(kpt_outcar, kpt) + + irreds, irred_indices = np.unique(symmetry_mapping, return_index=True) + assert np.all(np.diff(irreds) == 1) + assert np.all(symmetry_mapping >= 0) + + return mpi.bcast(irred_indices)
+ + + +
+[docs] +def kill(vasp_process_id): + """ Kills the VASP process. """ + # TODO: if we kill the process in the next step, does it make a difference if we write the STOPCAR + with open('STOPCAR', 'wt') as f_stop: + f_stop.write('LABORT = .TRUE.\n') + os.kill(vasp_process_id, signal.SIGTERM) + mpi.MPI.COMM_WORLD.Abort(1)
+ +
+ +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/dmft_cycle.html b/_modules/dmft_cycle.html new file mode 100644 index 00000000..9060b7a4 --- /dev/null +++ b/_modules/dmft_cycle.html @@ -0,0 +1,1100 @@ + + + + + + dmft_cycle — solid_dmft documentation + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for dmft_cycle

+################################################################################
+#
+# solid_dmft - A versatile python wrapper to perform DFT+DMFT calculations
+#              utilizing the TRIQS software library
+#
+# Copyright (C) 2018-2020, ETH Zurich
+# Copyright (C) 2021, The Simons Foundation
+#      authors: A. Hampel, M. Merkel, and S. Beck
+#
+# solid_dmft is free software: you can redistribute it and/or modify it under the
+# terms of the GNU General Public License as published by the Free Software
+# Foundation, either version 3 of the License, or (at your option) any later
+# version.
+#
+# solid_dmft is distributed in the hope that it will be useful, but WITHOUT ANY
+# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
+# PARTICULAR PURPOSE. See the GNU General Public License for more details.
+
+# You should have received a copy of the GNU General Public License along with
+# solid_dmft (in the file COPYING.txt in this directory). If not, see
+# <http://www.gnu.org/licenses/>.
+#
+################################################################################
+"""
+main DMFT cycle, DMFT step, and helper functions
+"""
+
+
+# system
+import os
+from copy import deepcopy
+from timeit import default_timer as timer
+import numpy as np
+
+# triqs
+from triqs.operators.util.observables import S_op, N_op
+from triqs.version import git_hash as triqs_hash
+from triqs.version import version as triqs_version
+from h5 import HDFArchive
+import triqs.utility.mpi as mpi
+from triqs.gf import Gf, make_hermitian, MeshReFreq, MeshImFreq
+from triqs.gf.tools import inverse
+from triqs_dft_tools.sumk_dft import SumkDFT
+
+# own modules
+from solid_dmft.version import solid_dmft_hash
+from solid_dmft.version import version as solid_dmft_version
+from solid_dmft.dmft_tools.observables import (calc_dft_kin_en, add_dmft_observables, calc_bandcorr_man, write_obs,
+                                         add_dft_values_as_zeroth_iteration, write_header_to_file, prep_observables)
+from solid_dmft.dmft_tools.solver import SolverStructure
+from solid_dmft.dmft_tools import convergence
+from solid_dmft.dmft_tools import formatter
+from solid_dmft.dmft_tools import interaction_hamiltonian
+from solid_dmft.dmft_tools import results_to_archive
+from solid_dmft.dmft_tools import afm_mapping
+from solid_dmft.dmft_tools import manipulate_chemical_potential as manipulate_mu
+from solid_dmft.dmft_tools import initial_self_energies as initial_sigma
+from solid_dmft.dmft_tools import greens_functions_mixer as gf_mixer
+
+
+def _determine_block_structure(sum_k, general_params, advanced_params):
+    """
+    Determines block structrure and degenerate deg_shells
+    computes first DFT density matrix to determine block structure and changes
+    the density matrix according to needs i.e. magnetic calculations, or keep
+    off-diag elements
+
+    Parameters
+    ----------
+    sum_k : SumK Object instances
+
+    Returns
+    -------
+    sum_k : SumK Object instances
+        updated sum_k Object
+    """
+    mpi.report('\n *** determination of block structure ***')
+
+    # this returns a list of dicts (one entry for each corr shell)
+    # the dict contains one entry for up and one for down
+    # each entry is a square complex numpy matrix with dim=corr_shell['dim']
+    zero_Sigma = [sum_k.block_structure.create_gf(ish=iineq, gf_function=Gf, mesh=sum_k.mesh)
+                  for iineq in range(sum_k.n_inequiv_shells)]
+    sum_k.put_Sigma(zero_Sigma)
+    if general_params['solver_type'] in ['ftps']:
+        G_loc_all = sum_k.extract_G_loc(broadening=general_params['eta'], transform_to_solver_blocks=False)
+        dens_mat = [G_loc_all[iineq].density() for iineq in range(sum_k.n_inequiv_shells)]
+    else:
+        dens_mat = sum_k.density_matrix(method='using_gf')
+
+    original_dens_mat = deepcopy(dens_mat)
+
+    # for certain systems it is needed to keep off diag elements
+    # this enforces to use the full corr subspace matrix
+    solver_struct_ftps = None
+    if general_params['enforce_off_diag'] or general_params['solver_type'] in ['ftps', 'hartree']:
+        if general_params['solver_type'] in ['ftps']:
+            # first round to determine real blockstructure
+            mock_sumk = deepcopy(sum_k)
+            mock_sumk.analyse_block_structure(dm=dens_mat, threshold=general_params['block_threshold'])
+            solver_struct_ftps = [None] * sum_k.n_inequiv_shells
+            for icrsh in range(sum_k.n_inequiv_shells):
+                solver_struct_ftps[icrsh] = mock_sumk.deg_shells[icrsh]
+            mpi.report('Block structure written to "solver_struct_ftps":')
+            mpi.report(solver_struct_ftps)
+
+        mpi.report('enforcing off-diagonal elements in block structure finder')
+        for dens_mat_per_imp in dens_mat:
+            for dens_mat_per_block in dens_mat_per_imp.values():
+                dens_mat_per_block += 2 * general_params['block_threshold']
+
+    if not general_params['enforce_off_diag'] and general_params['block_suppress_orbital_symm']:
+        mpi.report('removing orbital symmetries in block structure finder')
+        for dens_mat_per_imp in dens_mat:
+            for dens_mat_per_block in dens_mat_per_imp.values():
+                dens_mat_per_block += 2*np.diag(np.arange(dens_mat_per_block.shape[0]))
+
+    mpi.report('using 1-particle density matrix and Hloc (atomic levels) to '
+               'determine the block structure')
+    sum_k.analyse_block_structure(dm=dens_mat, threshold=general_params['block_threshold'])
+
+    if advanced_params['pick_solver_struct'] != 'none':
+        mpi.report('selecting subset of orbital space for gf_struct_solver from input:')
+        mpi.report(advanced_params['pick_solver_struct'])
+        sum_k.block_structure.pick_gf_struct_solver(advanced_params['pick_solver_struct'])
+
+    # Applies the manual mapping to each inequivalent shell
+    if advanced_params['map_solver_struct'] != 'none':
+        sum_k.block_structure.map_gf_struct_solver(advanced_params['map_solver_struct'])
+        if advanced_params['mapped_solver_struct_degeneracies'] != 'none':
+            sum_k.block_structure.deg_shells = advanced_params['mapped_solver_struct_degeneracies']
+
+    # if we want to do a magnetic calculation we need to lift up/down degeneracy
+    if general_params['magnetic'] and sum_k.SO == 0:
+        mpi.report('magnetic calculation: removing the spin degeneracy from the block structure')
+
+        for icrsh, deg_shells_site in enumerate(sum_k.block_structure.deg_shells):
+            deg_shell_mag = []
+            # find degenerate orbitals that do not simply connect different spin channels
+            for deg_orbs in deg_shells_site:
+                for spin in ['up', 'down']:
+                    # create a list of all up / down orbitals
+                    deg = [orb for orb in deg_orbs if spin in orb]
+                    # if longer than one than we have two deg orbitals also in a magnetic calculation
+                    if len(deg) > 1:
+                        deg_shell_mag.append(deg)
+            sum_k.block_structure.deg_shells[icrsh] = deg_shell_mag
+
+    # for SOC we remove all degeneracies
+    elif general_params['magnetic'] and sum_k.SO == 1:
+        sum_k.block_structure.deg_shells = [[] for icrsh in range(len(sum_k.block_structure.deg_shells))]
+
+    return sum_k, original_dens_mat, solver_struct_ftps
+
+
+def _calculate_rotation_matrix(general_params, sum_k):
+    """
+    Applies rotation matrix to make the DMFT calculations easier for the solver.
+    Possible are rotations diagonalizing either the local Hamiltonian or the
+    density. Diagonalizing the density has not proven really helpful but
+    diagonalizing the local Hamiltonian has.
+    Note that the interaction Hamiltonian has to be rotated if it is not fully
+    orbital-gauge invariant (only the Kanamori fulfills that).
+    """
+
+    # Extracts new rotation matrices from density_mat or local Hamiltonian
+    if general_params['set_rot'] == 'hloc':
+        q_diag = sum_k.eff_atomic_levels()
+    elif general_params['set_rot'] == 'den':
+        q_diag = sum_k.density_matrix(method='using_gf')
+    else:
+        raise ValueError('Parameter set_rot set to wrong value.')
+
+    chnl = sum_k.spin_block_names[sum_k.SO][0]
+
+    rot_mat = []
+    for icrsh in range(sum_k.n_corr_shells):
+        ish = sum_k.corr_to_inequiv[icrsh]
+        eigvec = np.array(np.linalg.eigh(np.real(q_diag[ish][chnl]))[1], dtype=complex)
+        if sum_k.use_rotations:
+            rot_mat.append( np.dot(sum_k.rot_mat[icrsh], eigvec) )
+        else:
+            rot_mat.append( eigvec )
+
+    sum_k.rot_mat = rot_mat
+    # in case sum_k.use_rotations == False before:
+    sum_k.use_rotations = True
+    # sum_k.eff_atomic_levels() needs to be recomputed if rot_mat were changed
+    if hasattr(sum_k, "Hsumk"): delattr(sum_k, "Hsumk")
+    mpi.report('Updating rotation matrices using dft {} eigenbasis to maximise sign'.format(general_params['set_rot']))
+
+    # Prints matrices
+    mpi.report('\nNew rotation matrices')
+    formatter.print_rotation_matrix(sum_k)
+
+    return sum_k
+
+
+def _chi_setup(sum_k, general_params, solver_params):
+    """
+
+    Parameters
+    ----------
+    sum_k : SumkDFT object
+        Sumk object with the information about the correct block structure
+    general_paramters: general params dict
+    solver_params: solver params dict
+
+    Returns
+    -------
+    solver_params :  dict
+        solver_paramters for the QMC solver
+    Op_list : list of one-particle operators to measure per impurity
+    """
+
+    if general_params['measure_chi'] == 'SzSz':
+        mpi.report('\nSetting up Chi(S_z(tau),S_z(0)) measurement')
+    elif general_params['measure_chi'] == 'NN':
+        mpi.report('\nSetting up Chi(n(tau),n(0)) measurement')
+
+    Op_list = [None] * sum_k.n_inequiv_shells
+
+    for icrsh in range(sum_k.n_inequiv_shells):
+        n_orb = sum_k.corr_shells[icrsh]['dim']
+        orb_names = list(range(n_orb))
+
+        if general_params['measure_chi'] == 'SzSz':
+            Op_list[icrsh] = S_op('z',
+                                  spin_names=sum_k.spin_block_names[sum_k.SO],
+                                  n_orb=n_orb,
+                                  map_operator_structure=sum_k.sumk_to_solver[icrsh])
+        elif general_params['measure_chi'] == 'NN':
+            Op_list[icrsh] = N_op(spin_names=sum_k.spin_block_names[sum_k.SO],
+                                  n_orb=n_orb,
+                                  map_operator_structure=sum_k.sumk_to_solver[icrsh])
+
+    solver_params['measure_O_tau_min_ins'] = general_params['measure_chi_insertions']
+
+    return solver_params, Op_list
+
+
+
+[docs] +def dmft_cycle(general_params, solver_params, advanced_params, dft_params, + n_iter, dft_irred_kpt_indices=None, dft_energy=None): + """ + main dmft cycle that works for one shot and CSC equally + + Parameters + ---------- + general_params : dict + general parameters as a dict + solver_params : dict + solver parameters as a dict + advanced_params : dict + advanced parameters as a dict + observables : dict + current observable array for calculation + n_iter : int + number of iterations to be executed + dft_irred_kpt_indices: iterable of int + If given, writes density correction for csc calculations only for + irreducible kpoints + + Returns + --------- + observables : dict + updated observable array for calculation + """ + + # create Sumk object + # TODO: use_dft_blocks=True yields inconsistent number of blocks! + + # first we have to determine the mesh + if general_params['solver_type'] in ['ftps']: + sumk_mesh = MeshReFreq(window=general_params['w_range'], + n_w=general_params['n_w']) + else: + sumk_mesh = MeshImFreq(beta=general_params['beta'], + S='Fermion', + n_iw=general_params['n_iw']) + + sum_k = SumkDFT(hdf_file=general_params['jobname']+'/'+general_params['seedname']+'.h5', + mesh=sumk_mesh, use_dft_blocks=False, h_field=general_params['h_field']) + + iteration_offset = 0 + + # determine chemical potential for bare DFT sum_k object + if mpi.is_master_node(): + archive = HDFArchive(general_params['jobname']+'/'+general_params['seedname']+'.h5', 'a') + if 'DMFT_results' not in archive: + archive.create_group('DMFT_results') + if 'last_iter' not in archive['DMFT_results']: + archive['DMFT_results'].create_group('last_iter') + if 'DMFT_input' not in archive: + archive.create_group('DMFT_input') + archive['DMFT_input']['program'] = 'solid_dmft' + archive['DMFT_input'].create_group('solver') + archive['DMFT_input'].create_group('version') + archive['DMFT_input']['version']['triqs_hash'] = triqs_hash + archive['DMFT_input']['version']['triqs_version'] = triqs_version + archive['DMFT_input']['version']['solid_dmft_hash'] = solid_dmft_hash + archive['DMFT_input']['version']['solid_dmft_version'] = solid_dmft_version + + if 'iteration_count' in archive['DMFT_results']: + iteration_offset = archive['DMFT_results/iteration_count'] + sum_k.chemical_potential = archive['DMFT_results/last_iter/chemical_potential_post'] + print(f'RESTARTING DMFT RUN at iteration {iteration_offset+1} using last self-energy') + else: + print('INITIAL DMFT RUN') + print('#'*80, '\n') + else: + archive = None + + iteration_offset = mpi.bcast(iteration_offset) + sum_k.chemical_potential = mpi.bcast(sum_k.chemical_potential) + + # Incompatabilities for SO coupling + if sum_k.SO == 1: + if not general_params['csc'] and general_params['magnetic'] and general_params['afm_order']: + raise ValueError('AFM order not supported with SO coupling') + + # need to set sigma immediately here, otherwise mesh in unclear for sumK + # Initializes empty Sigma for calculation of DFT density even if block structure changes later + zero_Sigma = [sum_k.block_structure.create_gf(ish=iineq, gf_function=Gf, mesh=sum_k.mesh) + for iineq in range(sum_k.n_inequiv_shells)] + sum_k.put_Sigma(zero_Sigma) + + # Initializes chemical potential with mu_initial_guess if this is the first iteration + if general_params['mu_initial_guess'] != 'none' and iteration_offset == 0: + sum_k.chemical_potential = general_params['mu_initial_guess'] + mpi.report('\ninitial chemical potential set to {:.3f} eV\n'.format(sum_k.chemical_potential)) + + if general_params['solver_type'] in ['ftps']: + dft_mu = sum_k.calc_mu(precision=general_params['prec_mu'], + broadening=general_params['eta']) + else: + dft_mu = sum_k.calc_mu(precision=general_params['prec_mu'], method=general_params['calc_mu_method']) + + # calculate E_kin_dft for one shot calculations + if not general_params['csc'] and general_params['calc_energies']: + E_kin_dft = calc_dft_kin_en(general_params, sum_k, dft_mu) + else: + E_kin_dft = None + + # check for previous broyden data oterhwise initialize it: + if mpi.is_master_node() and general_params['g0_mix_type'] == 'broyden': + if not 'broyler' in archive['DMFT_results']: + archive['DMFT_results']['broyler'] = [{'mu' : [],'V': [], 'dV': [], 'F': [], 'dF': []} + for _ in range(sum_k.n_inequiv_shells)] + + # Generates a rotation matrix to change the basis + if general_params['set_rot'] != 'none': + # calculate new rotation matrices + sum_k = _calculate_rotation_matrix(general_params, sum_k) + # Saves rotation matrix to h5 archive: + if mpi.is_master_node() and iteration_offset == 0: + archive['DMFT_input']['rot_mat'] = sum_k.rot_mat + mpi.barrier() + + # determine block structure for solver + det_blocks = None + # load previous block_structure if possible + if mpi.is_master_node(): + det_blocks = 'block_structure' not in archive['DMFT_input'] + det_blocks = mpi.bcast(det_blocks) + + # Previous rot_mat only not None if the rot_mat changed from load_sigma or previous run + previous_rot_mat = None + solver_struct_ftps = None + # determine block structure for GF and Hyb function + if det_blocks and not general_params['load_sigma']: + sum_k, dm, solver_struct_ftps = _determine_block_structure(sum_k, general_params, advanced_params) + # if load sigma we need to load everything from this h5 archive + elif general_params['load_sigma']: + #loading block_struc and rot_mat and deg_shells + if mpi.is_master_node(): + with HDFArchive(general_params['path_to_sigma'], 'r') as old_calc: + sum_k.block_structure = old_calc['DMFT_input/block_structure'] + sum_k.deg_shells = old_calc['DMFT_input/deg_shells'] + previous_rot_mat = old_calc['DMFT_input/rot_mat'] + if general_params['solver_type'] in ['ftps']: + solver_struct_ftps = old_calc['DMFT_input/solver_struct_ftps'] + + if not all(np.allclose(x, y) for x, y in zip(sum_k.rot_mat, previous_rot_mat)): + print('WARNING: rot_mat in current run is different from loaded_sigma run.') + else: + previous_rot_mat = None + + sum_k.block_structure = mpi.bcast(sum_k.block_structure) + sum_k.deg_shells = mpi.bcast(sum_k.deg_shells) + previous_rot_mat = mpi.bcast(previous_rot_mat) + solver_struct_ftps = mpi.bcast(solver_struct_ftps) + + # In a magnetic calculation, no shells are degenerate + if general_params['magnetic'] and sum_k.SO == 0: + sum_k.deg_shells = [[] for _ in range(sum_k.n_inequiv_shells)] + dm = None + else: + # Master node checks if rot_mat stayed the same + if mpi.is_master_node(): + sum_k.block_structure = archive['DMFT_input']['block_structure'] + sum_k.deg_shells = archive['DMFT_input/deg_shells'] + previous_rot_mat = archive['DMFT_input']['rot_mat'] + if not all(np.allclose(x, y) for x, y in zip(sum_k.rot_mat, previous_rot_mat)): + print('WARNING: rot_mat in current step is different from previous step.') + archive['DMFT_input']['rot_mat'] = sum_k.rot_mat + else: + previous_rot_mat = None + if general_params['solver_type'] in ['ftps']: + solver_struct_ftps = archive['DMFT_input/solver_struct_ftps'] + + sum_k.block_structure = mpi.bcast(sum_k.block_structure) + sum_k.deg_shells = mpi.bcast(sum_k.deg_shells) + previous_rot_mat = mpi.bcast(previous_rot_mat) + solver_struct_ftps = mpi.bcast(solver_struct_ftps) + dm = None + + # Compatibility with h5 archives from the triqs2 version + # Sumk doesn't hold corr_to_inequiv anymore, which is in block_structure now + if sum_k.block_structure.corr_to_inequiv is None: + if mpi.is_master_node(): + sum_k.block_structure.corr_to_inequiv = archive['dft_input/corr_to_inequiv'] + sum_k.block_structure = mpi.bcast(sum_k.block_structure) + + # Determination of shell_multiplicity + shell_multiplicity = [sum_k.corr_to_inequiv.count(icrsh) for icrsh in range(sum_k.n_inequiv_shells)] + + # Initializes new empty Sigma with new blockstructure for calculation of DFT density + zero_Sigma = [sum_k.block_structure.create_gf(ish=iineq, gf_function=Gf, mesh=sum_k.mesh) + for iineq in range(sum_k.n_inequiv_shells)] + sum_k.put_Sigma(zero_Sigma) + + # print block structure and DFT input quantitites! + formatter.print_block_sym(sum_k, dm, general_params) + + # extract free lattice greens function + if general_params['solver_type'] in ['ftps']: + G_loc_all_dft = sum_k.extract_G_loc(broadening=general_params['eta'], with_Sigma=False, mu=dft_mu) + else: + G_loc_all_dft = sum_k.extract_G_loc(with_Sigma=False, mu=dft_mu) + density_mat_dft = [G_loc_all_dft[iineq].density() for iineq in range(sum_k.n_inequiv_shells)] + + for iineq in range(sum_k.n_inequiv_shells): + density_shell_dft = G_loc_all_dft[iineq].total_density() + mpi.report('total density for imp {} from DFT: {:10.6f}'.format(iineq, np.real(density_shell_dft))) + + if general_params['magnetic']: + sum_k.SP = 1 + + if general_params['afm_order']: + general_params = afm_mapping.determine(general_params, archive, sum_k.n_inequiv_shells) + + # Constructs interaction Hamiltonian and writes it to the h5 archive + h_int = interaction_hamiltonian.construct(sum_k, general_params, advanced_params) + if mpi.is_master_node(): + archive['DMFT_input']['h_int'] = h_int + + # If new calculation, writes input parameters and sum_k <-> solver mapping to archive + if iteration_offset == 0: + if mpi.is_master_node(): + archive['DMFT_input']['general_params'] = general_params + archive['DMFT_input']['solver_params'] = solver_params + archive['DMFT_input']['advanced_params'] = advanced_params + + archive['DMFT_input']['block_structure'] = sum_k.block_structure + archive['DMFT_input']['deg_shells'] = sum_k.deg_shells + archive['DMFT_input']['shell_multiplicity'] = shell_multiplicity + if general_params['solver_type'] in ['ftps']: + archive['DMFT_input']['solver_struct_ftps'] = solver_struct_ftps + + solvers = [None] * sum_k.n_inequiv_shells + for icrsh in range(sum_k.n_inequiv_shells): + # Construct the Solver instances + solvers[icrsh] = SolverStructure(general_params, solver_params, advanced_params, + sum_k, icrsh, h_int[icrsh], + iteration_offset, solver_struct_ftps) + + # store solver hash to archive + if mpi.is_master_node(): + if 'version' not in archive['DMFT_input']: + archive['DMFT_input'].create_group('version') + archive['DMFT_input']['version']['solver_name'] = general_params['solver_type'] + archive['DMFT_input']['version']['solver_hash'] = solvers[0].git_hash + archive['DMFT_input']['version']['solver_version'] = solvers[0].version + + # Determines initial Sigma and DC + sum_k, solvers = initial_sigma.determine_dc_and_initial_sigma(general_params, advanced_params, sum_k, + archive, iteration_offset, density_mat_dft, solvers) + + sum_k = manipulate_mu.set_initial_mu(general_params, sum_k, iteration_offset, archive, shell_multiplicity) + + + # setup of measurement of chi(SzSz(tau) if requested + if general_params['measure_chi'] != 'none': + solver_params, Op_list = _chi_setup(sum_k, general_params, solver_params) + else: + Op_list = None + + mpi.report('\n {} DMFT cycles requested. Starting with iteration {}.\n'.format(n_iter, iteration_offset+1)) + + # Prepares observable and conv dicts + observables = None + conv_obs = None + if mpi.is_master_node(): + observables = prep_observables(archive, sum_k) + conv_obs = convergence.prep_conv_obs(archive, sum_k) + observables = mpi.bcast(observables) + conv_obs = mpi.bcast(conv_obs) + + if mpi.is_master_node() and iteration_offset == 0: + write_header_to_file(general_params, sum_k) + observables = add_dft_values_as_zeroth_iteration(observables, general_params, dft_mu, dft_energy, sum_k, + G_loc_all_dft, density_mat_dft, shell_multiplicity) + write_obs(observables, sum_k, general_params) + # write convergence file + convergence.prep_conv_file(general_params, sum_k) + + # The infamous DMFT self consistency cycle + is_converged = False + for it in range(iteration_offset + 1, iteration_offset + n_iter + 1): + + # remove h_field when number of iterations is reached + if sum_k.h_field != 0.0 and general_params['h_field_it'] != 0 and it > general_params['h_field_it']: + mpi.report('\nRemoving magnetic field now.\n') + sum_k.h_field = 0.0 + # enforce recomputation of eff_atomic_levels + delattr(sum_k, 'Hsumk') + + mpi.report('#'*80) + mpi.report('Running iteration: {} / {}'.format(it, iteration_offset + n_iter)) + (sum_k, solvers, + observables, is_converged) = _dmft_step(sum_k, solvers, it, general_params, + solver_params, advanced_params, dft_params, + h_int, archive, shell_multiplicity, E_kin_dft, + observables, conv_obs, Op_list, dft_irred_kpt_indices, dft_energy, + is_converged, is_sampling=False) + + if is_converged: + break + + if is_converged: + mpi.report('*** Required convergence reached ***') + else: + mpi.report('** All requested iterations finished ***') + mpi.report('#'*80) + + # Starts the sampling dmft iterations if requested + if is_converged and general_params['sampling_iterations'] > 0: + mpi.report('*** Sampling now for {} iterations ***'.format(general_params['sampling_iterations'])) + iteration_offset = it + + for it in range(iteration_offset + 1, + iteration_offset + 1 + general_params['sampling_iterations']): + mpi.report('#'*80) + mpi.report('Running iteration: {} / {}'.format(it, iteration_offset+general_params['sampling_iterations'])) + sum_k, solvers, observables, _ = _dmft_step(sum_k, solvers, it, general_params, + solver_params, advanced_params, dft_params, + h_int, archive, shell_multiplicity, E_kin_dft, + observables, conv_obs, Op_list, dft_irred_kpt_indices, dft_energy, + is_converged=True, is_sampling=True) + + mpi.report('** Sampling finished ***') + mpi.report('#'*80) + + mpi.barrier() + + # close the h5 archive + if mpi.is_master_node(): + del archive + + return is_converged, sum_k
+ + + +def _dmft_step(sum_k, solvers, it, general_params, + solver_params, advanced_params, dft_params, + h_int, archive, shell_multiplicity, E_kin_dft, + observables, conv_obs, Op_list, dft_irred_kpt_indices, dft_energy, + is_converged, is_sampling): + """ + Contains the actual dmft steps when all the preparation is done + """ + + # init local density matrices for observables + density_tot = 0.0 + density_shell = np.zeros(sum_k.n_inequiv_shells) + density_mat = [None] * sum_k.n_inequiv_shells + density_mat_unsym = [None] * sum_k.n_inequiv_shells + density_shell_pre = np.zeros(sum_k.n_inequiv_shells) + density_mat_pre = [None] * sum_k.n_inequiv_shells + + mpi.barrier() + + if sum_k.SO: + printed = ((np.real, 'real'), (np.imag, 'imaginary')) + else: + printed = ((np.real, 'real'), ) + + # Extracts G local + if general_params['solver_type'] in ['ftps']: + G_loc_all = sum_k.extract_G_loc(broadening=general_params['eta']) + else: + G_loc_all = sum_k.extract_G_loc() + + # Copies Sigma and G0 before Solver run for mixing later + Sigma_freq_previous = [solvers[iineq].Sigma_freq.copy() for iineq in range(sum_k.n_inequiv_shells)] + G0_freq_previous = [solvers[iineq].G0_freq.copy() for iineq in range(sum_k.n_inequiv_shells)] + if general_params['dc'] and general_params['dc_type'] == 4: + cpa_G_loc = gf_mixer.init_cpa(sum_k, solvers, general_params) + + # looping over inequiv shells and solving for each site seperately + for icrsh in range(sum_k.n_inequiv_shells): + # copy the block of G_loc into the corresponding instance of the impurity solver + # TODO: why do we set solvers.G_freq? Isn't that simply an output of the solver? + solvers[icrsh].G_freq << G_loc_all[icrsh] + + density_shell_pre[icrsh] = np.real(solvers[icrsh].G_freq.total_density()) + mpi.report('\n *** Correlated Shell type #{:3d} : '.format(icrsh) + + 'Estimated total charge of impurity problem = {:.6f}'.format(density_shell_pre[icrsh])) + density_mat_pre[icrsh] = solvers[icrsh].G_freq.density() + mpi.report('Estimated density matrix:') + for key, value in sorted(density_mat_pre[icrsh].items()): + for func, name in printed: + mpi.report('{}, {} part'.format(key, name)) + mpi.report(func(value)) + + # dyson equation to extract G0_freq, using Hermitian symmetry + solvers[icrsh].G0_freq << inverse(solvers[icrsh].Sigma_freq + inverse(solvers[icrsh].G_freq)) + + # dyson equation to extract cpa_G0_freq + if general_params['dc'] and general_params['dc_type'] == 4: + cpa_G0_freq[icrsh] << inverse(solvers[icrsh].Sigma_freq + inverse(cpa_G_loc[icrsh])) + + # mixing of G0 if wanted from the second iteration on + if it > 1: + solvers[icrsh] = gf_mixer.mix_g0(solvers[icrsh], general_params, icrsh, archive, + G0_freq_previous[icrsh], it, sum_k.deg_shells[icrsh]) + + if general_params['solver_type'] in ['cthyb', 'ctint', 'hubbardI', 'inchworm']: + solvers[icrsh].G0_freq << make_hermitian(solvers[icrsh].G0_freq) + sum_k.symm_deg_gf(solvers[icrsh].G0_freq, ish=icrsh) + + # store solver to h5 archive + if general_params['store_solver'] and mpi.is_master_node(): + archive['DMFT_input/solver'].create_group('it_'+str(it)) + archive['DMFT_input/solver/it_'+str(it)]['S_'+str(icrsh)] = solvers[icrsh].triqs_solver + + # store DMFT input directly in last_iter + if mpi.is_master_node(): + archive['DMFT_results/last_iter']['G0_freq_{}'.format(icrsh)] = solvers[icrsh].G0_freq + + # setup of measurement of chi(SzSz(tau) if requested + if general_params['measure_chi'] != 'none': + solvers[icrsh].solver_params['measure_O_tau'] = (Op_list[icrsh], Op_list[icrsh]) + + if (general_params['magnetic'] and general_params['afm_order'] and general_params['afm_mapping'][icrsh][0]): + # If we do a AFM calculation we can use the init magnetic moments to + # copy the self energy instead of solving it explicitly + solvers = afm_mapping.apply(general_params, solver_params, icrsh, sum_k.gf_struct_solver[icrsh], solvers) + else: + # Solve the impurity problem for this shell + mpi.report('\nSolving the impurity problem for shell {} ...'.format(icrsh)) + mpi.barrier() + start_time = timer() + solvers[icrsh].solve(it=it) + mpi.barrier() + mpi.report('Actual time for solver: {:.2f} s'.format(timer() - start_time)) + + # some printout of the obtained density matrices and some basic checks from the unsymmetrized solver output + density_shell[icrsh] = np.real(solvers[icrsh].G_freq_unsym.total_density()) + density_tot += density_shell[icrsh]*shell_multiplicity[icrsh] + density_mat_unsym[icrsh] = solvers[icrsh].G_freq_unsym.density() + density_mat[icrsh] = solvers[icrsh].G_freq.density() + formatter.print_local_density(density_shell[icrsh], density_shell_pre[icrsh], + density_mat_unsym[icrsh], sum_k.SO) + + # update solver in h5 archive + if general_params['store_solver'] and mpi.is_master_node(): + archive['DMFT_input/solver/it_'+str(it)]['S_'+str(icrsh)] = solvers[icrsh].triqs_solver + + # add to cpa_G_time + if general_params['dc'] and general_params['dc_type'] == 4: + cpa_G_time << cpa_G_time + general_params['cpa_x'][icrsh] * solvers[icrsh].G_time + + # Done with loop over impurities + + if mpi.is_master_node(): + # Done. Now do post-processing: + print('\n *** Post-processing the solver output ***') + print('Total charge of all correlated shells : {:.6f}\n'.format(density_tot)) + + # if CPA average Sigma over impurities before mixing + if general_params['dc'] and general_params['dc_type'] == 4: + solvers = gf_mixer.mix_cpa(cpa_G0_freq, sum_k.n_inequiv_shells, solvers) + solvers = gf_mixer.mix_sigma(general_params, sum_k.n_inequiv_shells, solvers, Sigma_freq_previous) + + # calculate new DC + # for the hartree solver the DC potential will be formally set to zero as it is already present in the Sigma + if general_params['dc'] and general_params['dc_dmft']: + sum_k = initial_sigma.calculate_double_counting(sum_k, density_mat, + general_params, advanced_params) + + #The hartree solver computes the DC energy internally, set it in sum_k + if general_params['solver_type'] == 'hartree': + for icrsh in range(sum_k.n_inequiv_shells): + sum_k.dc_energ[icrsh] = solvers[icrsh].DC_energy + + # doing the dmft loop and set new sigma into sumk + sum_k.put_Sigma([solvers[icrsh].Sigma_freq for icrsh in range(sum_k.n_inequiv_shells)]) + + # saving previous mu for writing to observables file + previous_mu = sum_k.chemical_potential + sum_k = manipulate_mu.update_mu(general_params, sum_k, it, archive) + + # if we do a CSC calculation we need always an updated GAMMA file + E_bandcorr = 0.0 + deltaN = None + dens = None + if general_params['csc']: + # handling the density correction for fcsc calculations + assert dft_irred_kpt_indices is None or dft_params['dft_code'] == 'vasp' + deltaN, dens, E_bandcorr = sum_k.calc_density_correction(dm_type=dft_params['dft_code'], + kpts_to_write=dft_irred_kpt_indices) + elif general_params['calc_energies']: + # for a one shot calculation we are using our own method + E_bandcorr = calc_bandcorr_man(general_params, sum_k, E_kin_dft) + + # Writes results to h5 archive + results_to_archive.write(archive, sum_k, general_params, solver_params, solvers, it, + is_sampling, previous_mu, density_mat_pre, density_mat, deltaN, dens) + + mpi.barrier() + + # calculate observables and write them to file + if mpi.is_master_node(): + print('\n *** calculation of observables ***') + observables = add_dmft_observables(observables, + general_params, + solver_params, + dft_energy, + it, + solvers, + h_int, + previous_mu, + sum_k, + density_mat, + shell_multiplicity, + E_bandcorr) + + write_obs(observables, sum_k, general_params) + + # write the new observable array to h5 archive + archive['DMFT_results']['observables'] = observables + + # Computes convergence quantities and writes them to file + if mpi.is_master_node(): + conv_obs = convergence.calc_convergence_quantities(sum_k, general_params, conv_obs, observables, + solvers, G0_freq_previous, G_loc_all, Sigma_freq_previous) + convergence.write_conv(conv_obs, sum_k, general_params) + archive['DMFT_results']['convergence_obs'] = conv_obs + conv_obs = mpi.bcast(conv_obs) + + mpi.report('*** iteration finished ***') + + # Checks for convergence + is_now_converged = convergence.check_convergence(sum_k.n_inequiv_shells, general_params, conv_obs) + if is_now_converged is None: + is_converged = False + else: + # if convergency criteria was already reached don't overwrite it! + is_converged = is_converged or is_now_converged + + # Final prints + formatter.print_summary_observables(observables, sum_k.n_inequiv_shells, + sum_k.spin_block_names[sum_k.SO]) + if general_params['calc_energies']: + formatter.print_summary_energetics(observables) + if general_params['magnetic'] and sum_k.SO == 0: + # if a magnetic calculation is done print out a summary of up/down occ + formatter.print_summary_magnetic_occ(observables, sum_k.n_inequiv_shells) + formatter.print_summary_convergence(conv_obs, general_params, sum_k.n_inequiv_shells) + + return sum_k, solvers, observables, is_converged +
+ +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/dmft_tools/afm_mapping.html b/_modules/dmft_tools/afm_mapping.html new file mode 100644 index 00000000..43e95caa --- /dev/null +++ b/_modules/dmft_tools/afm_mapping.html @@ -0,0 +1,445 @@ + + + + + + dmft_tools.afm_mapping — solid_dmft documentation + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for dmft_tools.afm_mapping

+# -*- coding: utf-8 -*-
+################################################################################
+#
+# solid_dmft - A versatile python wrapper to perform DFT+DMFT calculations
+#              utilizing the TRIQS software library
+#
+# Copyright (C) 2018-2020, ETH Zurich
+# Copyright (C) 2021, The Simons Foundation
+#      authors: A. Hampel, M. Merkel, and S. Beck
+#
+# solid_dmft is free software: you can redistribute it and/or modify it under the
+# terms of the GNU General Public License as published by the Free Software
+# Foundation, either version 3 of the License, or (at your option) any later
+# version.
+#
+# solid_dmft is distributed in the hope that it will be useful, but WITHOUT ANY
+# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
+# PARTICULAR PURPOSE. See the GNU General Public License for more details.
+
+# You should have received a copy of the GNU General Public License along with
+# solid_dmft (in the file COPYING.txt in this directory). If not, see
+# <http://www.gnu.org/licenses/>.
+#
+################################################################################
+
+import numpy as np
+import triqs.utility.mpi as mpi
+
+
+[docs] +def determine(general_params, archive, n_inequiv_shells): + """ + 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. + """ + + afm_mapping = None + if mpi.is_master_node(): + # Reads mapping from h5 archive if it exists already from a previous run + if 'afm_mapping' in archive['DMFT_input']: + afm_mapping = archive['DMFT_input']['afm_mapping'] + elif len(general_params['magmom']) == n_inequiv_shells: + # find equal or opposite spin imps, where we use the magmom array to + # identity those with equal numbers or opposite + # [copy Yes/False, from where, switch up/down channel] + afm_mapping = [None] * n_inequiv_shells + abs_moms = np.abs(general_params['magmom']) + + for icrsh in range(n_inequiv_shells): + # if the moment was seen before ... + previous_occurences = np.nonzero(np.isclose(abs_moms[:icrsh], abs_moms[icrsh]))[0] + if previous_occurences.size > 0: + # find the source imp to copy from + source = np.min(previous_occurences) + # determine if we need to switch up and down channel + switch = np.isclose(general_params['magmom'][icrsh], -general_params['magmom'][source]) + + afm_mapping[icrsh] = [True, source, switch] + else: + afm_mapping[icrsh] = [False, icrsh, False] + + + print('AFM calculation selected, mapping self energies as follows:') + print('imp [copy sigma, source imp, switch up/down]') + print('---------------------------------------------') + for i, elem in enumerate(afm_mapping): + print('{}: {}'.format(i, elem)) + print('') + + archive['DMFT_input']['afm_mapping'] = afm_mapping + + # if anything did not work set afm_order false + else: + print('WARNING: couldn\'t determine afm mapping. No mapping used.') + general_params['afm_order'] = False + + general_params['afm_order'] = mpi.bcast(general_params['afm_order']) + if general_params['afm_order']: + general_params['afm_mapping'] = mpi.bcast(afm_mapping) + + return general_params
+ + + +def apply(general_params, solver_params, icrsh, gf_struct_solver, solvers): + imp_source = general_params['afm_mapping'][icrsh][1] + invert_spin = general_params['afm_mapping'][icrsh][2] + mpi.report('\ncopying the self-energy for shell {} from shell {}'.format(icrsh, imp_source)) + mpi.report('inverting spin channels: '+str(invert_spin)) + + if invert_spin: + for spin_channel in gf_struct_solver.keys(): + if 'up' in spin_channel: + target_channel = spin_channel.replace('up', 'down') + else: + target_channel = spin_channel.replace('down', 'up') + + solvers[icrsh].Sigma_freq[spin_channel] << solvers[imp_source].Sigma_freq[target_channel] + solvers[icrsh].G_freq[spin_channel] << solvers[imp_source].G_freq[target_channel] + solvers[icrsh].G_freq_unsym[spin_channel] << solvers[imp_source].G_freq_unsym[target_channel] + solvers[icrsh].G0_freq[spin_channel] << solvers[imp_source].G0_freq[target_channel] + solvers[icrsh].G_time[spin_channel] << solvers[imp_source].G_time[target_channel] + + if solver_params['measure_pert_order']: + if not hasattr(solvers[icrsh], 'perturbation_order'): + solvers[icrsh].perturbation_order = {} + solvers[icrsh].perturbation_order[spin_channel] = solvers[imp_source].perturbation_order[target_channel] + solvers[icrsh].perturbation_order_total = solvers[imp_source].perturbation_order_total + + else: + solvers[icrsh].Sigma_freq << solvers[imp_source].Sigma_freq + solvers[icrsh].G_freq << solvers[imp_source].G_freq + solvers[icrsh].G_freq_unsym << solvers[imp_source].G_freq_unsym + solvers[icrsh].G0_freq << solvers[imp_source].G0_freq + solvers[icrsh].G_time << solvers[imp_source].G_time + + if solver_params['measure_pert_order']: + solvers[icrsh].perturbation_order = solvers[imp_source].perturbation_order + solvers[icrsh].perturbation_order_total = solvers[imp_source].perturbation_order_total + + if solver_params['measure_density_matrix']: + solvers[icrsh].density_matrix = solvers[imp_source].density_matrix + solvers[icrsh].h_loc_diagonalization = solvers[imp_source].h_loc_diagonalization + + if general_params['measure_chi'] != 'none': + solvers[icrsh].O_time = solvers[imp_source].O_time + + return solvers +
+ +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/dmft_tools/convergence.html b/_modules/dmft_tools/convergence.html new file mode 100644 index 00000000..7e796ca3 --- /dev/null +++ b/_modules/dmft_tools/convergence.html @@ -0,0 +1,687 @@ + + + + + + dmft_tools.convergence — solid_dmft documentation + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for dmft_tools.convergence

+################################################################################
+#
+# solid_dmft - A versatile python wrapper to perform DFT+DMFT calculations
+#              utilizing the TRIQS software library
+#
+# Copyright (C) 2018-2020, ETH Zurich
+# Copyright (C) 2021, The Simons Foundation
+#      authors: A. Hampel, M. Merkel, and S. Beck
+#
+# solid_dmft is free software: you can redistribute it and/or modify it under the
+# terms of the GNU General Public License as published by the Free Software
+# Foundation, either version 3 of the License, or (at your option) any later
+# version.
+#
+# solid_dmft is distributed in the hope that it will be useful, but WITHOUT ANY
+# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
+# PARTICULAR PURPOSE. See the GNU General Public License for more details.
+
+# You should have received a copy of the GNU General Public License along with
+# solid_dmft (in the file COPYING.txt in this directory). If not, see
+# <http://www.gnu.org/licenses/>.
+#
+################################################################################
+'''
+contain helper functions to check convergence
+'''
+# system
+import os.path
+import numpy as np
+
+# triqs
+from triqs.gf import MeshImFreq, MeshImTime, MeshReFreq, BlockGf
+from solid_dmft.dmft_tools import solver
+
+def _generate_header(general_params, sum_k):
+    """
+    Generates the headers that are used in write_header_to_file.
+    Returns a dict with {file_name: header_string}
+    """
+
+    n_orb = solver.get_n_orbitals(sum_k)
+
+    header_energy_mask = '| {:>11} '
+    header_energy = header_energy_mask.format('δE_tot')
+
+    headers = {}
+    for iineq in range(sum_k.n_inequiv_shells):
+        number_spaces = max(13*n_orb[iineq]['up']-1, 21)
+        header_basic_mask = '{{:>3}} | {{:>11}} | {{:>{0}}} | {{:>11}} | {{:>11}} | {{:>11}} | {{:>11}} '.format(number_spaces)
+
+        file_name = 'conv_imp{}.dat'.format(iineq)
+        headers[file_name] = header_basic_mask.format('it', 'δμ','δocc orb','δimp occ','δGimp', 'δG0','δΣ')
+
+        if general_params['calc_energies']:
+            headers[file_name] += header_energy
+
+
+    return headers
+
+
+[docs] +def write_conv(conv_obs, sum_k, general_params): + """ + writes the last entries of the conv arrays to the files + + Parameters + ---------- + conv_obs : list of dicts + convergence observable arrays/dicts + + sum_k : SumK Object instances + + general_params : dict + + __Returns:__ + + nothing + + """ + + n_orb = solver.get_n_orbitals(sum_k) + + for icrsh in range(sum_k.n_inequiv_shells): + line = '{:3d} | '.format(conv_obs['iteration'][-1]) + line += '{:10.5e} | '.format(conv_obs['d_mu'][-1]) + + # Adds spaces for header to fit in properly + if n_orb[icrsh]['up'] == 1: + line += ' '*11 + # Adds up the spin channels + for iorb in range(n_orb[icrsh]['up']): + line += '{:10.5e} '.format(conv_obs['d_orb_occ'][icrsh][-1][iorb]) + line = line[:-3] + ' | ' + + # imp occupation change + line += '{:10.5e} | '.format(conv_obs['d_imp_occ'][icrsh][-1]) + # Gimp change + line += '{:10.5e} | '.format(conv_obs['d_Gimp'][icrsh][-1]) + # G0 change + line += '{:10.5e} | '.format(conv_obs['d_G0'][icrsh][-1]) + # Σ change + line += '{:10.5e}'.format(conv_obs['d_Sigma'][icrsh][-1]) + + if general_params['calc_energies']: + line += ' | {:10.5e}'.format(conv_obs['d_Etot'][-1]) + + file_name = '{}/conv_imp{}.dat'.format(general_params['jobname'], icrsh) + with open(file_name, 'a') as obs_file: + obs_file.write(line + '\n')
+ + +
+[docs] +def max_G_diff(G1, G2, norm_temp = True): + """ + 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. + + 1/ (2* sqrt(beta)) sqrt( sum_n sum_ij [abs(G1 - G2)_ij(w_n)]^2 ) + + this is only done for MeshImFreq Gf objects, for all other + meshes the weights are set to 1 + + Parameters + ---------- + G1 : Gf or BlockGf to compare + + G2 : Gf or BlockGf to compare + + norm_temp: bool, default = True + divide by an additional sqrt(beta) to account for temperature scaling + only correct for uniformly distributed error. + + __Returns:__ + + diff : float + difference between the two Gfs + """ + + if isinstance(G1, BlockGf): + diff = 0.0 + for block, gf in G1: + diff += max_G_diff(G1[block], G2[block], norm_temp) + return diff + + assert G1.mesh == G2.mesh, 'mesh of two input Gfs does not match' + assert G1.target_shape == G2.target_shape, 'can only compare Gfs with same shape' + + # subtract largest real value to make sure that G1-G2 falls off to 0 + if type(G1.mesh) is MeshImFreq: + offset = np.diag(np.diag(G1.data[-1,:,:].real - G2.data[-1,:,:].real)) + else: + offset = 0.0 + + # calculate norm over all axis but the first one which are frequencies + norm_grid = abs(np.linalg.norm(G1.data - G2.data - offset, axis=tuple(range(1, G1.data.ndim)))) + # now calculate Frobenius norm over grid points + norm = np.linalg.norm(norm_grid, axis=0) + + if type(G1.mesh) is MeshImFreq: + norm = np.linalg.norm(norm_grid, axis=0) / np.sqrt(G1.mesh.beta) + elif type(G1.mesh) is MeshImTime: + norm = np.linalg.norm(norm_grid, axis=0) * np.sqrt(G1.mesh.beta/len(G1.mesh)) + elif type(G1.mesh) is MeshReFreq: + norm = np.linalg.norm(norm_grid, axis=0) / np.sqrt(len(G1.mesh)) + else: + raise ValueError('MeshReTime is not implemented') + + if type(G1.mesh) in (MeshImFreq, MeshImTime) and norm_temp: + norm = norm / np.sqrt(G1.mesh.beta) + + return norm
+ + +
+[docs] +def prep_conv_obs(h5_archive, sum_k): + """ + prepares the conv arrays and files for the DMFT calculation + + Parameters + ---------- + h5_archive: hdf archive instance + hdf archive for calculation + sum_k : SumK Object instances + + __Returns:__ + conv_obs : dict + conv array for calculation + """ + + # determine number of impurities + n_inequiv_shells = h5_archive['dft_input']['n_inequiv_shells'] + + # check for previous iterations + conv_prev = [] + if 'convergence_obs' in h5_archive['DMFT_results']: + conv_prev = h5_archive['DMFT_results']['convergence_obs'] + + # prepare observable dicts + if len(conv_prev) > 0: + conv_obs = conv_prev + else: + conv_obs = dict() + conv_obs['iteration'] = [] + conv_obs['d_mu'] = [] + conv_obs['d_Etot'] = [] + conv_obs['d_orb_occ'] = [[] for i in range(n_inequiv_shells)] + conv_obs['d_imp_occ'] = [[] for i in range(n_inequiv_shells)] + + conv_obs['d_Gimp'] = [[] for i in range(n_inequiv_shells)] + conv_obs['d_G0'] = [[] for i in range(n_inequiv_shells)] + conv_obs['d_Sigma'] = [[] for i in range(n_inequiv_shells)] + + return conv_obs
+ + +
+[docs] +def prep_conv_file(general_params, sum_k): + """ + Writes the header to the conv files + + Parameters + ---------- + general_params : dict + general parameters as a dict + n_inequiv_shells : int + number of impurities for calculations + + + __Returns:__ + nothing + """ + + headers = _generate_header(general_params, sum_k) + + for file_name, header in headers.items(): + path = os.path.join(general_params['jobname'], file_name) + with open(path, 'w') as conv_file: + conv_file.write(header + '\n')
+ + + +
+[docs] +def calc_convergence_quantities(sum_k, general_params, conv_obs, observables, + solvers, G0_old, G_loc_all, Sigma_freq_previous): + """ + Calculations convergence quantities, i.e. the difference in observables + between the last and second to last iteration. + + Parameters + ---------- + sum_k : SumK Object instances + + general_params : dict + general parameters as a dict + + conv_obs : list of dicts + convergence observable arrays + + observables : list of dicts + observable arrays + + solvers : solver objects + + G0_old : list of block Gf object + last G0_freq + + G_loc_all : list of block Gf objects + G_loc extracted from before imp solver + + Sigma_freq_previous : list of block Gf objects + previous impurity sigma to compare with + + Returns + ------- + conv_obs : list of dicts + updated convergence observable arrays + + """ + + conv_obs['iteration'].append(observables['iteration'][-1]) + conv_obs['d_mu'].append(abs(observables['mu'][-1] - observables['mu'][-2] )) + for icrsh in range(sum_k.n_inequiv_shells): + if not sum_k.corr_shells[icrsh]['SO']: + # difference in imp occupation + conv_obs['d_imp_occ'][icrsh].append(abs((observables['imp_occ'][icrsh]['up'][-1]+ + observables['imp_occ'][icrsh]['down'][-1])- + (observables['imp_occ'][icrsh]['up'][-2]+ + observables['imp_occ'][icrsh]['down'][-2]))) + # difference in orb occ spin absolute + conv_obs['d_orb_occ'][icrsh].append(abs(observables['orb_occ'][icrsh]['up'][-1]- + observables['orb_occ'][icrsh]['up'][-2])+ + abs(observables['orb_occ'][icrsh]['down'][-1]- + observables['orb_occ'][icrsh]['down'][-2])) + else: + conv_obs['d_imp_occ'][icrsh].append(abs(observables['imp_occ'][icrsh]['ud'][-1]- + observables['imp_occ'][icrsh]['ud'][-2])) + conv_obs['d_orb_occ'][icrsh].append(abs(observables['orb_occ'][icrsh]['ud'][-1]+ + observables['imp_occ'][icrsh]['ud'][-2])) + + conv_obs['d_Gimp'][icrsh].append(max_G_diff(solvers[icrsh].G_freq, G_loc_all[icrsh])) + conv_obs['d_G0'][icrsh].append(max_G_diff(solvers[icrsh].G0_freq, G0_old[icrsh])) + conv_obs['d_Sigma'][icrsh].append(max_G_diff(solvers[icrsh].Sigma_freq, Sigma_freq_previous[icrsh])) + + if general_params['calc_energies']: + conv_obs['d_Etot'].append(abs(observables['E_tot'][-1]-observables['E_tot'][-2])) + + return conv_obs
+ + + +
+[docs] +def check_convergence(n_inequiv_shells, general_params, conv_obs): + """ + check last iteration for convergence + + Parameters + ---------- + n_inequiv_shells : int + Number of inequivalent shells as saved in SumkDFT object + + general_params : dict + general parameters as a dict + + conv_obs : list of dicts + convergence observable arrays + + Returns + ------- + is_converged : bool + true if desired accuracy is reached. None if no convergence criterion + is set + + """ + + # If no convergence criterion is set, convergence is undefined and returns None + if (general_params['occ_conv_crit'] <= 0.0 and general_params['gimp_conv_crit'] <= 0.0 + and general_params['g0_conv_crit'] <= 0.0 and general_params['sigma_conv_crit'] <= 0.0): + return None + + # Checks convergence criteria + for icrsh in range(n_inequiv_shells): + # Checks imp occ + if (conv_obs['d_imp_occ'][icrsh][-1] > general_params['occ_conv_crit'] + and general_params['occ_conv_crit'] > 0.0): + return False + + # Checks Gimp + if (conv_obs['d_Gimp'][icrsh][-1] > general_params['gimp_conv_crit'] + and general_params['gimp_conv_crit'] > 0.0): + return False + + # Checks G0 + if (conv_obs['d_G0'][icrsh][-1] > general_params['g0_conv_crit'] + and general_params['g0_conv_crit'] > 0.0): + return False + + # Checks Sigma + if (conv_obs['d_Sigma'][icrsh][-1] > general_params['sigma_conv_crit'] + and general_params['sigma_conv_crit'] > 0.0): + return False + + return True
+ +
+ +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/dmft_tools/formatter.html b/_modules/dmft_tools/formatter.html new file mode 100644 index 00000000..403413d6 --- /dev/null +++ b/_modules/dmft_tools/formatter.html @@ -0,0 +1,532 @@ + + + + + + dmft_tools.formatter — solid_dmft documentation + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for dmft_tools.formatter

+# -*- coding: utf-8 -*-
+################################################################################
+#
+# solid_dmft - A versatile python wrapper to perform DFT+DMFT calculations
+#              utilizing the TRIQS software library
+#
+# Copyright (C) 2018-2020, ETH Zurich
+# Copyright (C) 2021, The Simons Foundation
+#      authors: A. Hampel, M. Merkel, and S. Beck
+#
+# solid_dmft is free software: you can redistribute it and/or modify it under the
+# terms of the GNU General Public License as published by the Free Software
+# Foundation, either version 3 of the License, or (at your option) any later
+# version.
+#
+# solid_dmft is distributed in the hope that it will be useful, but WITHOUT ANY
+# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
+# PARTICULAR PURPOSE. See the GNU General Public License for more details.
+
+# You should have received a copy of the GNU General Public License along with
+# solid_dmft (in the file COPYING.txt in this directory). If not, see
+# <http://www.gnu.org/licenses/>.
+#
+################################################################################
+"""
+Contains formatters for things that need to be printed in DMFT calculations.
+"""
+
+import numpy as np
+import triqs.utility.mpi as mpi
+
+
+
+
+
+
+
+
+
+def print_local_density(density, density_pre, density_mat, spin_orbit=False):
+    if not mpi.is_master_node():
+        return
+
+    if spin_orbit:
+        printed = ((np.real, 'real'), (np.imag, 'imaginary'))
+    else:
+        printed = ((np.real, 'real'), )
+
+    print('\nTotal charge of impurity problem: {:7.5f}'.format(density))
+    print('Total charge convergency of impurity problem: {:7.5f}'.format(density-density_pre))
+    print('\nDensity matrix:')
+    for key, value in sorted(density_mat.items()):
+        for func, name in printed:
+            print('{}, {} part'.format(key, name))
+            print(func(value))
+        eigenvalues = np.linalg.eigvalsh(value)
+        print('eigenvalues: {}'.format(eigenvalues))
+        # check for large off-diagonal elements and write out a warning
+        if np.max(np.abs(value - np.diag(np.diag(value)))) >= 0.1:
+            print('\n!!! WARNING !!!')
+            print('!!! large off diagonal elements in density matrix detected! I hope you know what you are doing !!!')
+            print('!!! WARNING !!!\n')
+
+
+def print_summary_energetics(observables):
+    if not mpi.is_master_node():
+        return
+
+    print('\n' + '='*60)
+    print('summary of energetics:')
+    print('total energy: ', observables['E_tot'][-1])
+    print('DFT energy: ', observables['E_dft'][-1])
+    print('correlation energy: ', observables['E_corr_en'][-1])
+    print('DFT band correction: ', observables['E_bandcorr'][-1])
+    print('='*60 + '\n')
+
+
+def print_summary_observables(observables, n_inequiv_shells, spin_block_names):
+    if not mpi.is_master_node():
+        return
+
+    print('='*60)
+    print('summary of impurity observables:')
+    for icrsh in range(n_inequiv_shells):
+        total_occ = np.sum([observables['imp_occ'][icrsh][spin][-1] for spin in spin_block_names])
+        print('total occupany of impurity {}: {:7.4f}'.format(icrsh, total_occ))
+    for icrsh in range(n_inequiv_shells):
+        total_gb2 = np.sum([observables['imp_gb2'][icrsh][spin][-1] for spin in spin_block_names])
+        print('G(beta/2) occ of impurity {}: {:8.4f}'.format(icrsh, total_gb2))
+    for icrsh in range(n_inequiv_shells):
+        print('Z (simple estimate) of impurity {} per orb:'.format(icrsh))
+        for spin in spin_block_names:
+            Z_spin = observables['orb_Z'][icrsh][spin][-1]
+            print('{:>5}: '.format(spin) + ' '.join("{:6.3f}".format(Z_orb) for Z_orb in Z_spin))
+    print('='*60 + '\n')
+
+
+def print_summary_magnetic_occ(observables, n_inequiv_shells):
+    if not mpi.is_master_node():
+        return
+
+    occ = {'up': 0.0, 'down': 0.0}
+    print('\n' + '='*60)
+    print('\n *** summary of magnetic occupations: ***')
+    for icrsh in range(n_inequiv_shells):
+        for spin in ['up', 'down']:
+            temp = observables['imp_occ'][icrsh][spin][-1]
+            print('imp '+str(icrsh)+' spin '+spin+': {:6.4f}'.format(temp))
+            occ[spin] += temp
+
+    print('total spin up   occ: '+'{:6.4f}'.format(occ['up']))
+    print('total spin down occ: '+'{:6.4f}'.format(occ['down']))
+    print('='*60 + '\n')
+
+
+def print_summary_convergence(conv_obs, general_params, n_inequiv_shells):
+    if not mpi.is_master_node():
+        return
+
+    print('='*60)
+    print('convergence:')
+    print('δμ:      {:.4e}'.format(conv_obs['d_mu'][-1]))
+    # if calc energies calc /print also the diff in Etot
+    if general_params['calc_energies']:
+        print('δE_tot:  {:.4e}'.format(conv_obs['d_Etot'][-1]))
+        print("---")
+    for icrsh in range(n_inequiv_shells):
+        print('Impurity '+str(icrsh)+':')
+        print('δn imp : {:.4e}'.format(conv_obs['d_imp_occ'][icrsh][-1]))
+        print('δn orb : '+'  '.join("{:.4e}".format(orb) for orb in conv_obs['d_orb_occ'][icrsh][-1]))
+        print('δ Gimp : {:.4e}'.format(conv_obs['d_Gimp'][icrsh][-1]))
+        print('δ G0   : {:.4e}'.format(conv_obs['d_G0'][icrsh][-1]))
+        print('δ Σ    : {:.4e}'.format(conv_obs['d_Sigma'][icrsh][-1]))
+
+    print('='*60)
+    print('\n')
+
+ +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/dmft_tools/initial_self_energies.html b/_modules/dmft_tools/initial_self_energies.html new file mode 100644 index 00000000..97a3cc1e --- /dev/null +++ b/_modules/dmft_tools/initial_self_energies.html @@ -0,0 +1,812 @@ + + + + + + dmft_tools.initial_self_energies — solid_dmft documentation + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
  • + + +
  • +
  • +
+
+
+
+
+ +

Source code for dmft_tools.initial_self_energies

+# -*- coding: utf-8 -*-
+################################################################################
+#
+# solid_dmft - A versatile python wrapper to perform DFT+DMFT calculations
+#              utilizing the TRIQS software library
+#
+# Copyright (C) 2018-2020, ETH Zurich
+# Copyright (C) 2021, The Simons Foundation
+#      authors: A. Hampel, M. Merkel, and S. Beck
+#
+# solid_dmft is free software: you can redistribute it and/or modify it under the
+# terms of the GNU General Public License as published by the Free Software
+# Foundation, either version 3 of the License, or (at your option) any later
+# version.
+#
+# solid_dmft is distributed in the hope that it will be useful, but WITHOUT ANY
+# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
+# PARTICULAR PURPOSE. See the GNU General Public License for more details.
+
+# You should have received a copy of the GNU General Public License along with
+# solid_dmft (in the file COPYING.txt in this directory). If not, see
+# <http://www.gnu.org/licenses/>.
+#
+################################################################################
+
+"""
+Contains all functions related to determining the double counting and the
+initial self-energy.
+"""
+
+# system
+from copy import deepcopy
+import numpy as np
+
+# triqs
+from h5 import HDFArchive
+import triqs.utility.mpi as mpi
+from triqs.gf import BlockGf, Gf
+
+
+
+[docs] +def calculate_double_counting(sum_k, density_matrix, general_params, advanced_params): + """ + Calculates the double counting, including all manipulations from advanced_params. + + Parameters + ---------- + sum_k : SumkDFT object + density_matrix : list of gf_struct_solver like + List of density matrices for all inequivalent shells + general_params : dict + general parameters as a dict + advanced_params : dict + advanced parameters as a dict + + Returns + -------- + sum_k : SumKDFT object + The SumKDFT object containing the updated double counting + """ + + mpi.report('\n*** DC determination ***') + + # copy the density matrix to not change it + density_matrix_DC = deepcopy(density_matrix) + + if general_params['solver_type'] == 'hartree': + mpi.report('\nSOLID_DMFT: Hartree solver detected, zeroing out the DC correction. This gets computed at the solver level') + for icrsh in range(sum_k.n_inequiv_shells): + sum_k.calc_dc(density_matrix_DC[icrsh], orb=icrsh, + use_dc_value=0.0) + return sum_k + + # Sets the DC and exits the function if advanced_params['dc_fixed_value'] is specified + if advanced_params['dc_fixed_value'] != 'none': + for icrsh in range(sum_k.n_inequiv_shells): + sum_k.calc_dc(density_matrix_DC[icrsh], orb=icrsh, + use_dc_value=advanced_params['dc_fixed_value']) + return sum_k + + # use DC for CPA + if general_params['dc'] and general_params['dc_type'] == 4: + zetas = general_params['cpa_zeta'] + for icrsh in range(sum_k.n_inequiv_shells): + sum_k.calc_dc(density_matrix_DC[icrsh], orb=icrsh, use_dc_value=zetas[icrsh]) + + return sum_k + + + if advanced_params['dc_fixed_occ'] != 'none': + mpi.report('Fixing occupation for DC potential to provided value') + + assert sum_k.n_inequiv_shells == len(advanced_params['dc_fixed_occ']), "give exactly one occupation per correlated shell" + for icrsh in range(sum_k.n_inequiv_shells): + mpi.report('fixing occupation for impurity '+str(icrsh)+' to n='+str(advanced_params['dc_fixed_occ'][icrsh])) + n_orb = sum_k.corr_shells[icrsh]['dim'] + # we need to handover a matrix to calc_dc so calc occ per orb per spin channel + orb_occ = advanced_params['dc_fixed_occ'][icrsh]/(n_orb*2) + # setting occ of each diag orb element to calc value + for inner in density_matrix_DC[icrsh].values(): + np.fill_diagonal(inner, orb_occ+0.0j) + + # The regular way: calculates the DC based on U, J and the dc_type + for icrsh in range(sum_k.n_inequiv_shells): + if general_params['dc_type'] == 3: + # this is FLL for eg orbitals only as done in Seth PRB 96 205139 2017 eq 10 + # this setting for U and J is reasonable as it is in the spirit of F0 and Javg + # for the 5 orb case + mpi.report('Doing FLL DC for eg orbitals only with Uavg=U-J and Javg=2*J') + Uavg = advanced_params['dc_U'][icrsh] - advanced_params['dc_J'][icrsh] + Javg = 2*advanced_params['dc_J'][icrsh] + sum_k.calc_dc(density_matrix_DC[icrsh], U_interact=Uavg, J_hund=Javg, + orb=icrsh, use_dc_formula=0) + else: + sum_k.calc_dc(density_matrix_DC[icrsh], U_interact=advanced_params['dc_U'][icrsh], + J_hund=advanced_params['dc_J'][icrsh], orb=icrsh, + use_dc_formula=general_params['dc_type']) + + # for the fixed DC according to https://doi.org/10.1103/PhysRevB.90.075136 + # dc_imp is calculated with fixed occ but dc_energ is calculated with given n + if advanced_params['dc_nominal']: + mpi.report('\ncalculating DC energy with fixed DC potential from above\n' + + ' for the original density matrix doi.org/10.1103/PhysRevB.90.075136\n' + + ' aka nominal DC') + dc_imp = deepcopy(sum_k.dc_imp) + dc_new_en = deepcopy(sum_k.dc_energ) + for ish in range(sum_k.n_corr_shells): + n_DC = 0.0 + for value in density_matrix[sum_k.corr_to_inequiv[ish]].values(): + n_DC += np.trace(value.real) + + # calculate new DC_energ as n*V_DC + # average over blocks in case blocks have different imp + dc_new_en[ish] = 0.0 + for spin, dc_per_spin in dc_imp[ish].items(): + # assuming that the DC potential is the same for all orbitals + # dc_per_spin is a list for each block containing on the diag + # elements the DC potential for the self-energy correction + dc_new_en[ish] += n_DC * dc_per_spin[0][0] + dc_new_en[ish] = dc_new_en[ish] / len(dc_imp[ish]) + sum_k.set_dc(dc_imp, dc_new_en) + + # Print new DC values + mpi.report('\nFixed occ, new DC values:') + for icrsh, (dc_per_shell, energy_per_shell) in enumerate(zip(dc_imp, dc_new_en)): + for spin, dc_per_spin in dc_per_shell.items(): + mpi.report('DC for shell {} and block {} = {}'.format(icrsh, spin, dc_per_spin[0][0])) + mpi.report('DC energy for shell {} = {}'.format(icrsh, energy_per_shell)) + + # Rescales DC if advanced_params['dc_factor'] is given + if advanced_params['dc_factor'] != 'none': + rescaled_dc_imp = [{spin: advanced_params['dc_factor'] * dc_per_spin + for spin, dc_per_spin in dc_per_shell.items()} + for dc_per_shell in sum_k.dc_imp] + rescaled_dc_energy = [advanced_params['dc_factor'] * energy_per_shell + for energy_per_shell in sum_k.dc_energ] + sum_k.set_dc(rescaled_dc_imp, rescaled_dc_energy) + + # Print new DC values + mpi.report('\nRescaled DC, new DC values:') + for icrsh, (dc_per_shell, energy_per_shell) in enumerate(zip(rescaled_dc_imp, rescaled_dc_energy)): + for spin, dc_per_spin in dc_per_shell.items(): + mpi.report('DC for shell {} and block {} = {}'.format(icrsh, spin, dc_per_spin[0][0])) + mpi.report('DC energy for shell {} = {}'.format(icrsh, energy_per_shell)) + + return sum_k
+ + + +def _load_sigma_from_h5(h5_archive, iteration): + """ + Reads impurity self-energy for all impurities from file and returns them as a list + + Parameters + ---------- + h5_archive : HDFArchive + HDFArchive to read from + iteration : int + at which iteration will sigma be loaded + + Returns + -------- + self_energies : list of green functions + + dc_imp : numpy array + DC potentials + dc_energy : numpy array + DC energies per impurity + density_matrix : numpy arrays + Density matrix from the previous self-energy + """ + + internal_path = 'DMFT_results/' + internal_path += 'last_iter' if iteration == -1 else 'it_{}'.format(iteration) + + n_inequiv_shells = h5_archive['dft_input']['n_inequiv_shells'] + + # Loads previous self-energies and DC + self_energies = [h5_archive[internal_path]['Sigma_freq_{}'.format(iineq)] + for iineq in range(n_inequiv_shells)] + last_g0 = [h5_archive[internal_path]['G0_freq_{}'.format(iineq)] + for iineq in range(n_inequiv_shells)] + dc_imp = h5_archive[internal_path]['DC_pot'] + dc_energy = h5_archive[internal_path]['DC_energ'] + + # Loads density_matrix to recalculate DC if dc_dmft + density_matrix = h5_archive[internal_path]['dens_mat_post'] + + print('Loaded Sigma_imp0...imp{} '.format(n_inequiv_shells-1) + + ('at last it ' if iteration == -1 else 'at it {} '.format(iteration))) + + return self_energies, dc_imp, dc_energy, last_g0, density_matrix + + +def _sumk_sigma_to_solver_struct(sum_k, start_sigma): + """ + Extracts the local Sigma. Copied from SumkDFT.extract_G_loc, version 2.1.x. + + Parameters + ---------- + sum_k : SumkDFT object + Sumk object with the information about the correct block structure + start_sigma : list of BlockGf (Green's function) objects + List of Sigmas in sum_k block structure that are to be converted. + + Returns + ------- + Sigma_inequiv : list of BlockGf (Green's function) objects + List of Sigmas that can be used to initialize the solver + """ + + Sigma_local = [start_sigma[icrsh].copy() for icrsh in range(sum_k.n_corr_shells)] + Sigma_inequiv = [BlockGf(name_block_generator=[(block, Gf(mesh=Sigma_local[0].mesh, target_shape=(dim, dim))) + for block, dim in sum_k.gf_struct_solver[ish].items()], + make_copies=False) for ish in range(sum_k.n_inequiv_shells)] + + # G_loc is rotated to the local coordinate system + if sum_k.use_rotations: + for icrsh in range(sum_k.n_corr_shells): + for bname, gf in Sigma_local[icrsh]: + Sigma_local[icrsh][bname] << sum_k.rotloc( + icrsh, gf, direction='toLocal') + + # transform to CTQMC blocks + for ish in range(sum_k.n_inequiv_shells): + for block, dim in sum_k.gf_struct_solver[ish].items(): + for ind1 in range(dim): + for ind2 in range(dim): + block_sumk, ind1_sumk = sum_k.solver_to_sumk[ish][(block, ind1)] + block_sumk, ind2_sumk = sum_k.solver_to_sumk[ish][(block, ind2)] + Sigma_inequiv[ish][block][ind1, ind2] << Sigma_local[ + sum_k.inequiv_to_corr[ish]][block_sumk][ind1_sumk, ind2_sumk] + + # return only the inequivalent shells + return Sigma_inequiv + + +def _set_loaded_sigma(sum_k, loaded_sigma, loaded_dc_imp, general_params): + """ + Adjusts for the Hartree shift when loading a self energy Sigma_freq from a + previous calculation that was run with a different U, J or double counting. + + Parameters + ---------- + sum_k : SumkDFT object + Sumk object with the information about the correct block structure + loaded_sigma : list of BlockGf (Green's function) objects + List of Sigmas loaded from the previous calculation + loaded_dc_imp : list of dicts + List of dicts containing the loaded DC. Used to adjust the Hartree shift. + general_params : dict + general parameters as a dict + + Raises + ------ + ValueError + Raised if the block structure between the loaded and the Sumk DC_imp + does not agree. + + Returns + ------- + start_sigma : list of BlockGf (Green's function) objects + List of Sigmas, loaded Sigma adjusted for the new Hartree term + + """ + # Compares loaded and new double counting + if len(loaded_dc_imp) != len(sum_k.dc_imp): + raise ValueError('Loaded double counting has a different number of ' + + 'correlated shells than current calculation.') + + has_double_counting_changed = False + for loaded_dc_shell, calc_dc_shell in zip(loaded_dc_imp, sum_k.dc_imp): + if sorted(loaded_dc_shell.keys()) != sorted(calc_dc_shell.keys()): + raise ValueError('Loaded double counting has a different block ' + + 'structure than current calculation.') + + for channel in loaded_dc_shell.keys(): + if not np.allclose(loaded_dc_shell[channel], calc_dc_shell[channel], + atol=1e-4, rtol=0): + has_double_counting_changed = True + break + + # Sets initial Sigma + start_sigma = loaded_sigma + + if not has_double_counting_changed: + print('DC remained the same. Using loaded Sigma as initial Sigma.') + return start_sigma + + # Uses the SumkDFT add_dc routine to correctly substract the DC shift + sum_k.put_Sigma(start_sigma) + calculated_dc_imp = sum_k.dc_imp + sum_k.dc_imp = [{channel: np.array(loaded_dc_shell[channel]) - np.array(calc_dc_shell[channel]) + for channel in loaded_dc_shell} + for calc_dc_shell, loaded_dc_shell in zip(sum_k.dc_imp, loaded_dc_imp)] + start_sigma = sum_k.add_dc() + start_sigma = _sumk_sigma_to_solver_struct(sum_k, start_sigma) + + # Prints information on correction of Hartree shift + first_block = sorted(key for key, _ in loaded_sigma[0])[0] + print('DC changed, initial Sigma is the loaded Sigma with corrected Hartree shift:') + print(' Sigma for imp0, block "{}", orbital 0 '.format(first_block) + + 'shifted from {:.3f} eV '.format(loaded_sigma[0][first_block].data[0, 0, 0].real) + + 'to {:.3f} eV'.format(start_sigma[0][first_block].data[0, 0, 0].real)) + + # Cleans up + sum_k.dc_imp = calculated_dc_imp + [sigma_freq.zero() for sigma_freq in sum_k.Sigma_imp] + + return start_sigma + + +
+[docs] +def determine_dc_and_initial_sigma(general_params, advanced_params, sum_k, + archive, iteration_offset, density_mat_dft, solvers): + """ + 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. + + * 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. + + * 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. + + * New calculation, without DC: Sigma is initialized as 0, + starting the calculation from the DFT Green's function. + + Parameters + ---------- + general_params : dict + general parameters as a dict + advanced_params : dict + advanced parameters as a dict + sum_k : SumkDFT object + Sumk object with the information about the correct block structure + archive : HDFArchive + the archive of the current calculation + iteration_offset : int + the iterations done before this calculation + density_mat_dft : numpy array + DFT density matrix + solvers : list + list of Solver instances + + Returns + ------- + sum_k : SumkDFT object + the SumkDFT object, updated by the initial Sigma and the DC + solvers : list + list of Solver instances, updated by the initial Sigma + + """ + start_sigma = None + last_g0 = None + if mpi.is_master_node(): + # Resumes previous calculation + if iteration_offset > 0: + print('\nFrom previous calculation:', end=' ') + start_sigma, sum_k.dc_imp, sum_k.dc_energ, last_g0, _ = _load_sigma_from_h5(archive, -1) + if general_params['csc'] and not general_params['dc_dmft']: + sum_k = calculate_double_counting(sum_k, density_mat_dft, general_params, advanced_params) + # Loads Sigma from different calculation + elif general_params['load_sigma']: + print('\nFrom {}:'.format(general_params['path_to_sigma']), end=' ') + with HDFArchive(general_params['path_to_sigma'], 'r') as sigma_archive: + (loaded_sigma, loaded_dc_imp, _, + _, loaded_density_matrix) = _load_sigma_from_h5(sigma_archive, general_params['load_sigma_iter']) + + # Recalculate double counting in case U, J or DC formula changed + if general_params['dc']: + if general_params['dc_dmft']: + sum_k = calculate_double_counting(sum_k, loaded_density_matrix, + general_params, advanced_params) + else: + sum_k = calculate_double_counting(sum_k, density_mat_dft, + general_params, advanced_params) + + start_sigma = _set_loaded_sigma(sum_k, loaded_sigma, loaded_dc_imp, general_params) + + # Sets DC as Sigma because no initial Sigma given + elif general_params['dc']: + sum_k = calculate_double_counting(sum_k, density_mat_dft, general_params, advanced_params) + + # initialize Sigma from sum_k + if general_params['solver_type'] in ['ftps']: + start_sigma = [sum_k.block_structure.create_gf(ish=iineq, gf_function=Gf, space='solver', + mesh=sum_k.mesh) + for iineq in range(sum_k.n_inequiv_shells)] + else: + start_sigma = [sum_k.block_structure.create_gf(ish=iineq, space='solver', mesh=sum_k.mesh) + for iineq in range(sum_k.n_inequiv_shells)] + for icrsh in range(sum_k.n_inequiv_shells): + dc_value = sum_k.dc_imp[sum_k.inequiv_to_corr[icrsh]][sum_k.spin_block_names[sum_k.SO][0]][0, 0] + + if (general_params['magnetic'] and general_params['magmom'] and sum_k.SO == 0): + # if we are doing a magnetic calculation and initial magnetic moments + # are set, manipulate the initial sigma accordingly + fac = general_params['magmom'][icrsh] + + # init self energy according to factors in magmoms + # if magmom positive the up channel will be favored + for spin_channel in sum_k.gf_struct_solver[icrsh].keys(): + if 'up' in spin_channel: + start_sigma[icrsh][spin_channel] << -fac + dc_value + else: + start_sigma[icrsh][spin_channel] << fac + dc_value + else: + start_sigma[icrsh] << dc_value + # Sets Sigma to zero because neither initial Sigma nor DC given + + elif (not general_params['dc'] and general_params['magnetic']): + start_sigma = [sum_k.block_structure.create_gf(ish=iineq, gf_function=Gf, space='solver', mesh=sum_k.mesh) + for iineq in range(sum_k.n_inequiv_shells)] + for icrsh in range(sum_k.n_inequiv_shells): + if (general_params['magnetic'] and general_params['magmom'] and sum_k.SO == 0): + mpi.report(f'\n*** Adding magnetic bias to initial sigma for impurity {icrsh} ***') + # if we are doing a magnetic calculation and initial magnetic moments + # are set, manipulate the initial sigma accordingly + fac = general_params['magmom'][icrsh] + + # if magmom positive the up channel will be favored + for spin_channel in sum_k.gf_struct_solver[icrsh].keys(): + if 'up' in spin_channel: + start_sigma[icrsh][spin_channel] << -fac + else: + start_sigma[icrsh][spin_channel] << fac + else: + if general_params['solver_type'] in ['ftps']: + start_sigma = [sum_k.block_structure.create_gf(ish=iineq, gf_function=Gf, space='solver', + mesh=sum_k.mesh) + for iineq in range(sum_k.n_inequiv_shells)] + else: + start_sigma = [sum_k.block_structure.create_gf(ish=iineq, space='solver', mesh=sum_k.mesh) + for iineq in range(sum_k.n_inequiv_shells)] + + # Adds random, frequency-independent noise in zeroth iteration to break symmetries + if not np.isclose(general_params['noise_level_initial_sigma'], 0) and iteration_offset == 0: + if mpi.is_master_node(): + for start_sigma_per_imp in start_sigma: + for _, block in start_sigma_per_imp: + noise = np.random.normal(scale=general_params['noise_level_initial_sigma'], + size=block.data.shape[1:]) + # Makes the noise hermitian + noise = np.broadcast_to(.5 * (noise + noise.T), block.data.shape) + block += Gf(indices=block.indices, mesh=block.mesh, data=noise) + + # bcast everything to other nodes + sum_k.dc_imp = mpi.bcast(sum_k.dc_imp) + sum_k.dc_energ = mpi.bcast(sum_k.dc_energ) + start_sigma = mpi.bcast(start_sigma) + last_g0 = mpi.bcast(last_g0) + # Loads everything now to the solver + for icrsh in range(sum_k.n_inequiv_shells): + solvers[icrsh].Sigma_freq = start_sigma[icrsh] + if last_g0: + solvers[icrsh].G0_freq = last_g0[icrsh] + + # Updates the sum_k object with the Matsubara self-energy + sum_k.put_Sigma([solvers[icrsh].Sigma_freq for icrsh in range(sum_k.n_inequiv_shells)]) + + # load sigma as first guess in the hartree solver if applicable + if general_params['solver_type'] == 'hartree': + # TODO: + # should this be moved to before the solve() call? Having it only here means there is a mismatch + # between the mixing at the level of the solver and the sumk (solver mixes always 100%) + for icrsh in range(sum_k.n_inequiv_shells): + mpi.report(f"SOLID_DMFT: setting first guess hartree solver for impurity {icrsh}") + solvers[icrsh].triqs_solver.reinitialize_sigma(start_sigma[icrsh]) + + return sum_k, solvers
+ +
+ +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
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+ + + + \ No newline at end of file diff --git a/_modules/dmft_tools/interaction_hamiltonian.html b/_modules/dmft_tools/interaction_hamiltonian.html new file mode 100644 index 00000000..1e1c3548 --- /dev/null +++ b/_modules/dmft_tools/interaction_hamiltonian.html @@ -0,0 +1,875 @@ + + + + + + dmft_tools.interaction_hamiltonian — solid_dmft documentation + + + + + + + + + + + + + + + + + + +
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+ +

Source code for dmft_tools.interaction_hamiltonian

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+################################################################################
+#
+# solid_dmft - A versatile python wrapper to perform DFT+DMFT calculations
+#              utilizing the TRIQS software library
+#
+# Copyright (C) 2018-2020, ETH Zurich
+# Copyright (C) 2021, The Simons Foundation
+#      authors: A. Hampel, M. Merkel, and S. Beck
+#
+# solid_dmft is free software: you can redistribute it and/or modify it under the
+# terms of the GNU General Public License as published by the Free Software
+# Foundation, either version 3 of the License, or (at your option) any later
+# version.
+#
+# solid_dmft is distributed in the hope that it will be useful, but WITHOUT ANY
+# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
+# PARTICULAR PURPOSE. See the GNU General Public License for more details.
+
+# You should have received a copy of the GNU General Public License along with
+# solid_dmft (in the file COPYING.txt in this directory). If not, see
+# <http://www.gnu.org/licenses/>.
+#
+################################################################################
+"""
+Contains all functions related to constructing the interaction Hamiltonian.
+"""
+
+# system
+import os
+import numpy as np
+from itertools import product
+
+# triqs
+from h5 import HDFArchive
+import triqs.utility.mpi as mpi
+from triqs.operators import util, n, c, c_dag, Operator
+from solid_dmft.dmft_tools import solver
+try:
+    import forktps as ftps
+except ImportError:
+    pass
+
+
+def _extract_U_J_list(param_name, n_inequiv_shells, general_params):
+    """
+    Checks if param_name ('U', 'U_prime', or 'J') are a single value or
+    different per inequivalent shell. If just a single value is given,
+    this value is applied to each shell.
+    """
+
+    if not isinstance(param_name, str):
+        formatted_param = ['none' if p == 'none' else '{:.2f}'.format(p)
+                           for p in general_params[param_name]]
+    else:
+        formatted_param = general_params[param_name]
+
+    if len(general_params[param_name]) == 1:
+        mpi.report('Assuming {} = '.format(param_name)
+                   + '{} for all correlated shells'.format(formatted_param[0]))
+        general_params[param_name] *= n_inequiv_shells
+    elif len(general_params[param_name]) == n_inequiv_shells:
+        mpi.report('{} list for correlated shells: {}'.format(param_name, formatted_param))
+    else:
+        raise IndexError('Property list {} '.format(general_params[param_name])
+                         + 'must have length 1 or n_inequiv_shells')
+
+    return general_params
+
+
+def _load_crpa_interaction_matrix(sum_k, filename='UIJKL'):
+    """
+    Loads VASP cRPA data to use as an interaction Hamiltonian.
+    """
+    def _round_to_int(data):
+        return (np.array(data) + .5).astype(int)
+
+    # Loads data from VASP cRPA file
+    print('Loading cRPA matrix from file: '+str(filename))
+    data = np.loadtxt(filename, unpack=True)
+    u_matrix_four_indices = np.zeros(_round_to_int(np.max(data[:4], axis=1)), dtype=complex)
+    for entry in data.T:
+        # VASP switches the order of the indices, ijkl -> ikjl
+        i, k, j, l = _round_to_int(entry[:4])-1
+        u_matrix_four_indices[i, j, k, l] = entry[4] + 1j * entry[5]
+
+    # Slices up the four index U-matrix, separating shells
+    u_matrix_four_indices_per_shell = [None] * sum_k.n_inequiv_shells
+    first_index_shell = 0
+    for ish in range(sum_k.n_corr_shells):
+        icrsh = sum_k.corr_to_inequiv[ish]
+        n_orb = solver.get_n_orbitals(sum_k)[icrsh]['up']
+        u_matrix_temp = u_matrix_four_indices[first_index_shell:first_index_shell+n_orb,
+                                              first_index_shell:first_index_shell+n_orb,
+                                              first_index_shell:first_index_shell+n_orb,
+                                              first_index_shell:first_index_shell+n_orb]
+        # I think for now we should stick with real interactions make real
+        u_matrix_temp.imag = 0.0
+
+        if ish == icrsh:
+            u_matrix_four_indices_per_shell[icrsh] = u_matrix_temp
+        elif not np.allclose(u_matrix_four_indices_per_shell[icrsh], u_matrix_temp, atol=1e-6, rtol=0):
+            # TODO: for some reason, some entries in the matrices differ by a sign. Check that
+            # mpi.report(np.allclose(np.abs(u_matrix_four_indices_per_shell[icrsh]), np.abs(u_matrix_temp),
+            # atol=1e-6, rtol=0))
+            mpi.report('Warning: cRPA matrix for impurity {} '.format(icrsh)
+                       + 'differs for shells {} and {}'.format(sum_k.inequiv_to_corr[icrsh], ish))
+
+        first_index_shell += n_orb
+
+    if not np.allclose(u_matrix_four_indices.shape, first_index_shell):
+        print('Warning: different number of orbitals in cRPA matrix than in calculation.')
+
+    return u_matrix_four_indices_per_shell
+
+
+# def _adapt_U_2index_for_SO(Umat, Upmat):
+#     """
+#     Changes the two-index U matrices such that for a system consisting of a
+#     single block 'ud' with the entries (1, up), (1, down), (2, up), (2, down),
+#     ... the matrices are consistent with the case without spin-orbit coupling.
+
+#     Parameters
+#     ----------
+#     Umat : numpy array
+#         The two-index interaction matrix for parallel spins without SO.
+#     Upmat : numpy array
+#         The two-index interaction matrix for antiparallel spins without SO.
+
+#     Returns
+#     -------
+#     Umat_SO : numpy array
+#         The two-index interaction matrix for parallel spins. Because in SO all
+#         entries have nominal spin 'ud', this matrix now contains the original
+#         Umat and Upmat.
+#     Upmat_SO : numpy array
+#         The two-index interaction matrix for antiparallel spins. Unused because
+#         in SO, all spins have the same nominal spin 'ud'.
+#     """
+
+#     Umat_SO = np.zeros(np.array(Umat.shape)*2, dtype=Umat.dtype)
+#     Umat_SO[::2, ::2] = Umat_SO[1::2, 1::2] = Umat
+#     Umat_SO[::2, 1::2] = Umat_SO[1::2, ::2] = Upmat
+#     Upmat_SO = None
+
+#     return Umat_SO, Upmat_SO
+
+
+def _adapt_U_4index_for_SO(Umat_full):
+    """
+    Changes the four-index U matrix such that for a system consisting of a
+    single block 'ud' with the entries (1, up), (1, down), (2, up), (2, down),
+    ... the matrix is consistent with the case without spin-orbit coupling.
+    This can be derived directly from the definition of the Slater Hamiltonian.
+
+    Parameters
+    ----------
+    Umat_full : numpy array
+       The four-index interaction matrix without SO.
+
+    Returns
+    -------
+    Umat_full_SO : numpy array
+        The four-index interaction matrix with SO. For a matrix U_ijkl, the
+        indices i, k correspond to spin sigma, and indices j, l to sigma'.
+    """
+
+    Umat_full_SO = np.zeros(np.array(Umat_full.shape)*2, dtype=Umat_full.dtype)
+    for spin, spin_prime in ((0, 0), (0, 1), (1, 0), (1, 1)):
+        Umat_full_SO[spin::2, spin_prime::2, spin::2, spin_prime::2] = Umat_full
+
+    return Umat_full_SO
+
+
+def _construct_kanamori(sum_k, general_params, icrsh):
+    """
+    Constructs the Kanamori interaction Hamiltonian. Only Kanamori does not
+    need the full four-index matrix. Therefore, we can construct it directly
+    from the parameters U and J.
+    """
+
+    n_orb = solver.get_n_orbitals(sum_k)[icrsh]['up']
+    if sum_k.SO == 1:
+        assert n_orb % 2 == 0
+        n_orb = n_orb // 2
+
+    if n_orb not in (2, 3):
+        mpi.report('warning: are you sure you want to use the kanamori hamiltonian '
+                   + 'outside the t2g or eg manifold?')
+
+    # check if Uprime has been specified manually
+    if general_params['U_prime'][icrsh] == 'U-2J':
+        U_prime = general_params['U'][icrsh] - 2.0 * general_params['J'][icrsh]
+    else:
+        U_prime = general_params['U_prime'][icrsh]
+
+    if general_params['solver_type'] == 'ftps':
+        # 1-band modell requires J and U' equals zero
+        if n_orb == 1:
+            up, j = 0.0, 0.0
+        else:
+            up = U_prime
+            j = general_params['J'][icrsh]
+        h_int = ftps.solver_core.HInt(u=general_params['U'][icrsh], j=j, up=up, dd=False)
+    elif sum_k.SO == 0:
+        # Constructs U matrix
+        Umat, Upmat = util.U_matrix_kanamori(n_orb=n_orb, U_int=general_params['U'][icrsh],
+                                             J_hund=general_params['J'][icrsh],
+                                             Up_int=U_prime)
+
+        h_int = util.h_int_kanamori(sum_k.spin_block_names[sum_k.SO], n_orb,
+                                    map_operator_structure=sum_k.sumk_to_solver[icrsh],
+                                    U=Umat, Uprime=Upmat, J_hund=general_params['J'][icrsh],
+                                    H_dump=os.path.join(general_params['jobname'], 'H.txt'))
+    else:
+        h_int = _construct_kanamori_soc(general_params['U'][icrsh], general_params['J'][icrsh],
+                                        n_orb, sum_k.sumk_to_solver[icrsh],
+                                        os.path.join(general_params['jobname'], 'H.txt'))
+    return h_int
+
+
+def _construct_kanamori_soc(U_int, J_hund, n_orb, map_operator_structure, H_dump=None):
+    r"""
+    Adapted from triqs.operators.util.hamiltonians.h_int_kanamori. Assumes
+    that spin_names == ['ud'] and that map_operator_structure is given.
+    """
+
+    orb_names = list(range(n_orb))
+
+    if H_dump:
+        H_dump_file = open(H_dump, 'w')
+        H_dump_file.write("Kanamori Hamiltonian:" + '\n')
+
+    H = Operator()
+    mkind = util.op_struct.get_mkind(None, map_operator_structure)
+
+    s = 'ud'
+
+    # density terms:
+    # TODO: reformulate in terms of Umat and Upmat for consistency with triqs?
+    if H_dump:
+        H_dump_file.write("Density-density terms:" + '\n')
+    for a1, a2 in product(orb_names, orb_names):
+        if a1 == a2:  # same spin and orbital
+            continue
+
+        if a1 // 2 == a2 // 2:  # same orbital (, different spins)
+            U_val = U_int
+        elif a1 % 2 != a2 % 2:  # different spins (, different orbitals)
+            U_val = U_int - 2*J_hund
+        else:  # same spins (, different orbitals)
+            U_val = U_int - 3*J_hund
+
+        H_term = 0.5 * U_val * n(*mkind(s, a1)) * n(*mkind(s, a2))
+        H += H_term
+
+        # Dump terms of H
+        if H_dump and not H_term.is_zero():
+            H_dump_file.write('%s' % (mkind(s, a1), ) + '\t')
+            H_dump_file.write('%s' % (mkind(s, a2), ) + '\t')
+            H_dump_file.write(str(U_val) + '\n')
+
+    # spin-flip terms:
+    if H_dump:
+        H_dump_file.write("Spin-flip terms:" + '\n')
+    for a1, a2, a3, a4 in product(orb_names, orb_names, orb_names, orb_names):
+        if a1 == a2 or a1 == a3 or a1 == a4 or a2 == a3 or a2 == a4 or a3 == a4:
+            continue
+
+        if not (a1//2 == a2//2 and a3//2 == a4//2 and a1//2 != a3//2 and a1 % 2 != a3 % 2):
+            continue
+
+        H_term = -0.5 * J_hund * c_dag(*mkind(s, a1)) * c(*mkind(s, a2)) * c_dag(*mkind(s, a3)) * c(*mkind(s, a4))
+        H += H_term
+
+        # Dump terms of H
+        if H_dump and not H_term.is_zero():
+            H_dump_file.write('%s' % (mkind(s, a1), ) + '\t')
+            H_dump_file.write('%s' % (mkind(s, a2), ) + '\t')
+            H_dump_file.write('%s' % (mkind(s, a3), ) + '\t')
+            H_dump_file.write('%s' % (mkind(s, a4), ) + '\t')
+            H_dump_file.write(str(-J_hund) + '\n')
+
+    # pair-hopping terms:
+    if H_dump:
+        H_dump_file.write("Pair-hopping terms:" + '\n')
+    for a1, a2, a3, a4 in product(orb_names, orb_names, orb_names, orb_names):
+        if a1 == a2 or a1 == a3 or a1 == a4 or a2 == a3 or a2 == a4 or a3 == a4:
+            continue
+
+        if not (a1//2 == a2//2 and a3//2 == a4//2 and a1//2 != a3//2 and a1 % 2 != a3 % 2):
+            continue
+
+        H_term = 0.5 * J_hund * c_dag(*mkind(s, a1)) * c_dag(*mkind(s, a2)) * c(*mkind(s, a4)) * c(*mkind(s, a3))
+        H += H_term
+
+        # Dump terms of H
+        if H_dump and not H_term.is_zero():
+            H_dump_file.write('%s' % (mkind(s, a1), ) + '\t')
+            H_dump_file.write('%s' % (mkind(s, a2), ) + '\t')
+            H_dump_file.write('%s' % (mkind(s, a3), ) + '\t')
+            H_dump_file.write('%s' % (mkind(s, a4), ) + '\t')
+            H_dump_file.write(str(-J_hund) + '\n')
+
+    return H
+
+
+def _construct_dynamic(sum_k, general_params, icrsh):
+    """
+    Constructs the interaction Hamiltonian for a frequency-dependent interaction.
+    Works only without spin-orbit coupling and only for one orbital.
+    """
+
+    mpi.report('###### Dynamic U calculation ######, load parameters from input archive.')
+    U_onsite = None
+    if mpi.is_master_node():
+        with HDFArchive(general_params['jobname']+'/'+general_params['seedname']+'.h5', 'r') as archive:
+            U_onsite = archive['dynamic_U']['U_scr']
+    U_onsite = mpi.bcast(U_onsite)
+
+    n_orb = solver.get_n_orbitals(sum_k)[icrsh]['up']
+    if sum_k.SO == 1:
+        raise ValueError('dynamic U not implemented for SO!=0')
+    if n_orb > 1:
+        raise ValueError('dynamic U not implemented for more than one orbital')
+
+    mpi.report('onsite interaction value for imp {}: {:.3f}'.format(icrsh, U_onsite[icrsh]))
+    h_int = util.h_int_density(sum_k.spin_block_names[sum_k.SO], n_orb,
+                               map_operator_structure=sum_k.sumk_to_solver[icrsh],
+                               U=np.array([[0]]), Uprime=np.array([[U_onsite[icrsh]]]), H_dump=os.path.join(general_params['jobname'], 'H.txt'))
+
+    return h_int
+
+
+def _generate_four_index_u_matrix(sum_k, general_params, icrsh):
+    """
+    Generates the four-index interaction matrix per impurity with the interaction
+    parameters U and J (and ratio_F4_F2 for the d shell).
+    """
+
+    # ish points to the shell representative of the current group
+    ish = sum_k.inequiv_to_corr[icrsh]
+    n_orb = solver.get_n_orbitals(sum_k)[icrsh]['up']
+    if sum_k.SO == 1:
+        assert n_orb % 2 == 0
+        n_orb = n_orb // 2
+
+    if sum_k.corr_shells[ish]['l'] != 2:
+        slater_integrals = util.U_J_to_radial_integrals(l=sum_k.corr_shells[ish]['l'],
+                                                        U_int=general_params['U'][icrsh],
+                                                        J_hund=general_params['J'][icrsh])
+    else:
+        # Implements parameter R=F4/F2. For R=0.63 equivalent to util.U_J_to_radial_integrals
+        U = general_params['U'][icrsh]
+        J = general_params['J'][icrsh]
+        R = general_params['ratio_F4_F2'][icrsh]
+        R = 0.63 if R == 'none' else R
+        slater_integrals = np.array([U, 14*J/(1+R), 14*J*R/(1+R)])
+
+    mpi.report('\nImpurity {}: The corresponding slater integrals are'.format(icrsh))
+    formatted_slater_integrals = [y for x in list(zip([2*x for x in range(len(slater_integrals))], slater_integrals)) for y in x]
+    mpi.report(('F{:d} = {:.2f}, '*len(slater_integrals)).format(*formatted_slater_integrals))
+
+    # Constructs U matrix
+    # construct full spherical symmetric U matrix and transform to cubic basis
+    # the order for the cubic orbitals is given by the convention. The TRIQS
+    # convention is as follows ("xy","yz","z^2","xz","x^2-y^2")
+    # this is consistent with the order of orbitals in the VASP interface
+    # but not necessarily with wannier90, qe, and wien2k! 
+    # This is also true for the f-shell.
+    Umat_full = util.U_matrix_slater(l=sum_k.corr_shells[ish]['l'],
+                              radial_integrals=slater_integrals, basis='spherical')
+    Umat_full = util.transform_U_matrix(Umat_full,
+                                        util.spherical_to_cubic(l=sum_k.corr_shells[ish]['l'],
+                                                                convention=general_params['h_int_basis'])
+                                        )
+
+    if n_orb == 2:
+        Umat_full = util.eg_submatrix(Umat_full)
+        mpi.report('Using eg subspace of interaction Hamiltonian')
+    elif n_orb == 3:
+        Umat_full = util.t2g_submatrix(Umat_full)
+        mpi.report('Using t2g subspace of interaction Hamiltonian')
+    elif n_orb not in (5, 7):
+        raise ValueError('Calculations for d shell only support 2, 3 or 5 orbitals'
+                         + 'and for the f shell only 7 orbitals')
+
+    return Umat_full
+
+
+def _rotate_four_index_matrix(sum_k, general_params, Umat_full, icrsh):
+    """ Rotates the four index matrix into the local frame. """
+
+    ish = sum_k.inequiv_to_corr[icrsh]
+    # Transposes rotation matrix here because TRIQS has a slightly different definition
+    Umat_full_rotated = util.transform_U_matrix(Umat_full, sum_k.rot_mat[ish].T)
+
+    if general_params['h_int_type'][icrsh] in ('density_density', 'crpa_density_density'):
+        if not np.allclose(Umat_full_rotated, Umat_full):
+            mpi.report('WARNING: applying a rotation matrix changes the dens-dens Hamiltonian.\n'
+                       + 'This changes the definition of the ignored spin flip and pair hopping.')
+    elif general_params['h_int_type'][icrsh] in ('full_slater', 'crpa'):
+        if not np.allclose(Umat_full_rotated, Umat_full):
+            mpi.report('WARNING: applying a rotation matrix changes the interaction Hamiltonian.\n'
+                       + 'Please ensure that the rotation is correct!')
+
+    return Umat_full_rotated
+
+
+def _construct_density_density(sum_k, general_params, Umat_full_rotated, icrsh):
+    """
+    Constructs the density-density Slater-Hamiltonian from the four-index
+    interaction matrix.
+    """
+
+    # Constructs Hamiltonian from Umat_full_rotated
+    n_orb = solver.get_n_orbitals(sum_k)[icrsh]['up']
+
+    Umat, Upmat = util.reduce_4index_to_2index(Umat_full_rotated)
+    h_int = util.h_int_density(sum_k.spin_block_names[sum_k.SO], n_orb,
+                               map_operator_structure=sum_k.sumk_to_solver[icrsh],
+                               U=Umat, Uprime=Upmat, H_dump=os.path.join(general_params['jobname'], 'H.txt'))
+
+    return h_int
+
+
+def _construct_slater(sum_k, general_params, Umat_full_rotated, icrsh):
+    """
+    Constructs the full Slater-Hamiltonian from the four-index interaction
+    matrix.
+    """
+
+    n_orb = solver.get_n_orbitals(sum_k)[icrsh]['up']
+
+    h_int = util.h_int_slater(sum_k.spin_block_names[sum_k.SO], n_orb,
+                              map_operator_structure=sum_k.sumk_to_solver[icrsh],
+                              U_matrix=Umat_full_rotated,
+                              H_dump=os.path.join(general_params['jobname'], 'H.txt'))
+
+    return h_int
+
+
+
+[docs] +def construct(sum_k, general_params, advanced_params): + """ + 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. + + 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, https://cms.mpi.univie.ac.at/wiki/index.php/LDAUTYPE ) for all + other Hamiltonians. + + 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 + """ + + # Extracts U and J + mpi.report('*** interaction parameters ***') + + general_params = _extract_U_J_list('h_int_type', sum_k.n_inequiv_shells, general_params) + for param_name in ('U', 'J'): + general_params = _extract_U_J_list(param_name, sum_k.n_inequiv_shells, general_params) + if 'kanamori' in general_params['h_int_type']: + general_params = _extract_U_J_list('U_prime', sum_k.n_inequiv_shells, general_params) + for param_name in ('dc_U', 'dc_J'): + advanced_params = _extract_U_J_list(param_name, sum_k.n_inequiv_shells, advanced_params) + + # Extracts ratio_F4_F2 if any shell uses a solver supporting it + if 'density_density' in general_params['h_int_type'] or 'full_slater' in general_params['h_int_type']: + general_params = _extract_U_J_list('ratio_F4_F2', sum_k.n_inequiv_shells, general_params) + + # Constructs the interaction Hamiltonian. Needs to come after setting sum_k.rot_mat + mpi.report('\nConstructing the interaction Hamiltonians') + h_int = [None] * sum_k.n_inequiv_shells + for icrsh in range(sum_k.n_inequiv_shells): + mpi.report('\nImpurity {}: constructing a {} type interaction Hamiltonian'.format(icrsh, general_params['h_int_type'][icrsh])) + + # Kanamori + if general_params['h_int_type'][icrsh] == 'kanamori': + h_int[icrsh] = _construct_kanamori(sum_k, general_params, icrsh) + continue + + # for density density or full slater get full four-index U matrix + if general_params['h_int_type'][icrsh] in ('density_density', 'full_slater'): + mpi.report('\nNote: The input parameters U and J here are orbital-averaged parameters.') + mpi.report('Note: The order of the orbitals is important. See also the doc string of this method.') + # Checks that all entries are l == 2 or R == 'none' + if (sum_k.corr_shells[sum_k.inequiv_to_corr[icrsh]]['l'] != 2 + and general_params['ratio_F4_F2'][icrsh] != 'none'): + raise ValueError('Ratio F4/F2 only implemented for d-shells ' + + 'but set in impurity {}'.format(icrsh)) + + if general_params['h_int_type'][icrsh] == 'density_density' and general_params['solver_type'] == 'ftps': + # TODO: implement + raise NotImplementedError('\nNote: Density-density not implemented for ftps.') + + Umat_full = _generate_four_index_u_matrix(sum_k, general_params, icrsh) + + if sum_k.SO == 1: + Umat_full = [_adapt_U_4index_for_SO(Umat_full_per_imp) + for Umat_full_per_imp in Umat_full] + + # Rotates the interaction matrix + Umat_full_rotated = _rotate_four_index_matrix(sum_k, general_params, Umat_full, icrsh) + + # construct slater / density density from U tensor + if general_params['h_int_type'][icrsh] == 'full_slater': + h_int[icrsh] = _construct_slater(sum_k, general_params, Umat_full_rotated, icrsh) + else: + h_int[icrsh] = _construct_density_density(sum_k, general_params, Umat_full_rotated, icrsh) + + continue + + # simple total impurity occupation interation: U/2 (Ntot^2 - Ntot) + if general_params['h_int_type'][icrsh] == 'ntot': + n_tot_op = Operator() + for block, n_orb in sum_k.gf_struct_solver[icrsh].items(): + n_tot_op += sum(n(block, orb) for orb in range(n_orb)) + h_int[icrsh] = general_params['U'][icrsh]/2.0 * (n_tot_op*n_tot_op - n_tot_op) + continue + + # read from file options + if general_params['h_int_type'][icrsh] in ('crpa', 'crpa_density_density'): + Umat_full = _load_crpa_interaction_matrix(sum_k, icrsh) + + if sum_k.SO == 1: + Umat_full = [_adapt_U_4index_for_SO(Umat_full_per_imp) + for Umat_full_per_imp in Umat_full] + + # Rotates the interaction matrix + Umat_full_rotated = _rotate_four_index_matrix(sum_k, general_params, Umat_full, icrsh) + + # construct slater / density density from U tensor + if general_params['h_int_type'][icrsh] == 'crpa': + h_int[icrsh] = _construct_slater(sum_k, general_params, Umat_full_rotated, icrsh) + else: + h_int[icrsh] = _construct_density_density(sum_k, general_params, Umat_full_rotated, icrsh) + continue + + # dynamic interaction from file + if general_params['h_int_type'][icrsh] == 'dynamic': + h_int[icrsh] = _construct_dynamic(sum_k, general_params, icrsh) + continue + + raise NotImplementedError('Error when constructing the interaction Hamiltonian.') + + return h_int
+ +
+ +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/dmft_tools/legendre_filter.html b/_modules/dmft_tools/legendre_filter.html new file mode 100644 index 00000000..b02cd0f8 --- /dev/null +++ b/_modules/dmft_tools/legendre_filter.html @@ -0,0 +1,381 @@ + + + + + + dmft_tools.legendre_filter — solid_dmft documentation + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for dmft_tools.legendre_filter

+################################################################################
+#
+# solid_dmft - A versatile python wrapper to perform DFT+DMFT calculations
+#              utilizing the TRIQS software library
+#
+# Copyright (C) 2018-2020, ETH Zurich
+# Copyright (C) 2021, The Simons Foundation
+#      authors: A. Hampel, M. Merkel, and S. Beck
+#
+# solid_dmft is free software: you can redistribute it and/or modify it under the
+# terms of the GNU General Public License as published by the Free Software
+# Foundation, either version 3 of the License, or (at your option) any later
+# version.
+#
+# solid_dmft is distributed in the hope that it will be useful, but WITHOUT ANY
+# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
+# PARTICULAR PURPOSE. See the GNU General Public License for more details.
+
+# You should have received a copy of the GNU General Public License along with
+# solid_dmft (in the file COPYING.txt in this directory). If not, see
+# <http://www.gnu.org/licenses/>.
+#
+################################################################################
+import numpy as np
+
+# triqs
+from triqs.gf import BlockGf
+from triqs.gf.tools import fit_legendre
+
+
+
+[docs] +def apply(G_tau, order=100, G_l_cut=1e-19): + """ Filter binned imaginary time Green's function + using a Legendre filter of given order and coefficient threshold. + + Parameters + ---------- + G_tau : TRIQS imaginary time Block Green's function + auto : determines automatically the cut-off nl + order : int + Legendre expansion order in the filter + G_l_cut : float + Legendre coefficient cut-off + + Returns + ------- + G_l : TRIQS Legendre Block Green's function + Fitted Green's function on a Legendre mesh + """ + + l_g_l = [] + + for _, g in G_tau: + + g_l = fit_legendre(g, order=order) + g_l.data[:] *= (np.abs(g_l.data) > G_l_cut) + g_l.enforce_discontinuity(np.identity(g.target_shape[0])) + + l_g_l.append(g_l) + + G_l = BlockGf(name_list=list(G_tau.indices), block_list=l_g_l) + + return G_l
+ +
+ +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/dmft_tools/manipulate_chemical_potential.html b/_modules/dmft_tools/manipulate_chemical_potential.html new file mode 100644 index 00000000..75ecdc60 --- /dev/null +++ b/_modules/dmft_tools/manipulate_chemical_potential.html @@ -0,0 +1,754 @@ + + + + + + dmft_tools.manipulate_chemical_potential — solid_dmft documentation + + + + + + + + + + + + + + + + + + +
+ + +
+ +
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+ +

Source code for dmft_tools.manipulate_chemical_potential

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+################################################################################
+#
+# solid_dmft - A versatile python wrapper to perform DFT+DMFT calculations
+#              utilizing the TRIQS software library
+#
+# Copyright (C) 2018-2020, ETH Zurich
+# Copyright (C) 2021, The Simons Foundation
+#      authors: A. Hampel, M. Merkel, and S. Beck
+#
+# solid_dmft is free software: you can redistribute it and/or modify it under the
+# terms of the GNU General Public License as published by the Free Software
+# Foundation, either version 3 of the License, or (at your option) any later
+# version.
+#
+# solid_dmft is distributed in the hope that it will be useful, but WITHOUT ANY
+# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
+# PARTICULAR PURPOSE. See the GNU General Public License for more details.
+
+# You should have received a copy of the GNU General Public License along with
+# solid_dmft (in the file COPYING.txt in this directory). If not, see
+# <http://www.gnu.org/licenses/>.
+#
+################################################################################
+"""
+Contains all the functions related to setting the chemical potential in the
+next iteration.
+"""
+
+import numpy as np
+
+import triqs.utility.mpi as mpi
+from triqs.gf import BlockGf, GfImFreq, GfImTime, Fourier
+try:
+    if mpi.is_master_node():
+        from solid_dmft.postprocessing import maxent_gf_latt
+    imported_maxent = True
+except ImportError:
+    imported_maxent = False
+
+def _mix_chemical_potential(general_params, density_tot, density_required,
+                            previous_mu, predicted_mu):
+    """
+    Mixes the previous chemical potential and the predicted potential with linear
+    mixing:
+    new_mu = factor * predicted_mu + (1-factor) * previous_mu, with
+    factor = mu_mix_per_occupation_offset * |density_tot - density_required| + mu_mix_const
+    under the constrain of 0 <= factor <= 1.
+
+    Parameters
+    ----------
+    general_params : dict
+        general parameters as a dict
+    density_tot : float
+        total occupation of the correlated system
+    density_required : float
+        required density for the impurity problem
+    previous_mu : float
+        the chemical potential from the previous iteration
+    predicted_mu : float
+        the chemical potential predicted by methods like the SumkDFT dichotomy
+
+    Returns
+    -------
+    new_mu : float
+        the chemical potential that results from the mixing
+
+    """
+    mu_mixing = general_params['mu_mix_per_occupation_offset'] * abs(density_tot - density_required)
+    mu_mixing += general_params['mu_mix_const']
+    mu_mixing = max(min(mu_mixing, 1), 0)
+    new_mu = mu_mixing * predicted_mu + (1-mu_mixing) * previous_mu
+
+    mpi.report('Mixing dichotomy mu with previous iteration by factor {:.3f}'.format(mu_mixing))
+    mpi.report('New chemical potential: {:.3f}'.format(new_mu))
+    return new_mu
+
+
+
+def _initialize_lattice_gf(sum_k, general_params):
+    """
+    Creates lattice Green's function (GF) that is averaged over orbitals,
+    blocks and spins. Returns lattice GF as input for an analytical
+    continuation as well as G_lattice(tau=beta/2) (proxy for the spectral
+    weight) and G_lattice(beta) (proxy for the total occupation).
+
+    Parameters
+    ----------
+    sum_k : SumkDFT object
+        Sumk object to generate the lattice GF from.
+    general_params : dict
+        general parameters as dict.
+
+    Returns
+    -------
+    gf_lattice_iw : triqs.gf.BlockGf
+        trace of the lattice GF over all blocks, orbitals and spins in
+        Matsubara frequency.
+    g_betahalf : complex
+        the Fourier transform of gf_lattice_iw evaluated at tau=beta/2.
+    occupation : complex
+        the total density from gf_lattice_iw
+    """
+
+    # Initializes lattice GF to zero for each process
+    mesh = sum_k.Sigma_imp[0].mesh
+    trace_gf_latt = GfImFreq(mesh=mesh, data=np.zeros((len(mesh), 1, 1), dtype=complex))
+    occupation = 0
+
+    # Takes trace over orbitals and spins
+    ikarray = np.arange(sum_k.n_k)
+    for ik in mpi.slice_array(ikarray):
+        gf_latt = sum_k.lattice_gf(ik)*sum_k.bz_weights[ik]
+        trace_gf_latt.data[:] += np.trace(sum(g.data for _, g in gf_latt), axis1=1, axis2=2).reshape(-1, 1, 1)
+        occupation += gf_latt.total_density()
+
+    trace_gf_latt << mpi.all_reduce(trace_gf_latt)
+    occupation = mpi.all_reduce(occupation)
+
+    # Lattice GF as BlockGf, required for compatibility with MaxEnt functions
+    gf_lattice_iw = BlockGf(name_list=['total'], block_list=[trace_gf_latt])
+
+    # Fourier transforms the lattice GF
+    gf_tau = GfImTime(beta=general_params['beta'], n_points=general_params['n_tau'], indices=[0])
+    gf_tau << Fourier(gf_lattice_iw['total'])
+
+    tau_mesh = np.array([float(m) for m in gf_tau.mesh])
+    middle_index = np.argmin(np.abs(tau_mesh-general_params['beta']/2))
+    samp = 10
+
+    # Extracts G_latt(tau) at beta/2
+    g_betahalf = np.mean(gf_tau.data[middle_index-samp:middle_index+samp, 0, 0])
+    mpi.report('Lattice Gf: occupation = {:.5f}'.format(occupation))
+    mpi.report('            G(beta/2)  = {:.5f}'.format(g_betahalf))
+
+    return gf_lattice_iw, g_betahalf, occupation
+
+def _determine_band_edge(mesh, spectral_function, spectral_func_threshold, valence_band, edge_threshold=.2):
+    """
+    Finds the band edge of a spectral function. This is done in two steps:
+    starting from the Fermi energy, looks for the first peak
+    (>spectral_func_threshold and local maximum on discrete grid). Then moves
+    back towards Fermi energy until the spectral function is smaller than the
+    fraction edge_threshold of the peak value.
+
+    Parameters
+    ----------
+    mesh : numpy.ndarray of float
+        the real frequencies grid.
+    spectral_function : numpy.ndarray of float
+        the values of the spectral function on the grid.
+    spectral_func_threshold : float
+        Threshold for spectral function to cross before looking for peaks.
+    valence_band : bool
+        Determines if looking for valence band (i.e. the upper band edge) or
+        the conduction band (i.e. the lower band edge).
+    edge_threshold : float
+        Fraction of the peak value that defines the band edge value.
+
+    Returns
+    -------
+    float
+        The frequency value of the band edge.
+    """
+    # Determines direction to move away from Fermi energy to find band edge
+    direction = 1 if valence_band else -1
+
+    # Starts closest to the Fermi energy
+    starting_index = np.argmin(np.abs(mesh))
+    print('Starting at index {} with A(omega={:.3f})={:.3f}'.format(starting_index, mesh[starting_index], spectral_function[starting_index]))
+    assert spectral_function[starting_index] < spectral_func_threshold
+
+    # Finds peak
+    peak_index = None
+    for i in range(starting_index+direction, mesh.shape[0] if valence_band else -1, direction):
+        # If A(omega) low, go further
+        if spectral_function[i] < spectral_func_threshold:
+            continue
+
+        # If spectral function still increasing, go further
+        if spectral_function[i-direction] < spectral_function[i]:
+            continue
+
+        peak_index = i-direction
+        break
+
+    assert peak_index is not None, 'Band peak not found. Check frequency range of MaxEnt'
+    print('Peak at index {} with A(omega={:.3f})={:.3f}'.format(peak_index, mesh[peak_index], spectral_function[peak_index]))
+
+    # Finds band edge
+    edge_index = starting_index
+    for i in range(peak_index-direction, starting_index-direction, -direction):
+        # If above ratio edge_threshold of peak height, go further back to starting index
+        if spectral_function[i] > edge_threshold * spectral_function[peak_index]:
+            continue
+
+        edge_index = i
+        break
+
+    print('Band edge at index {} with A(omega={:.3f})={:.3f}'.format(edge_index, mesh[edge_index], spectral_function[edge_index]))
+    return mesh[edge_index]
+
+def _set_mu_to_gap_middle_with_maxent(general_params, sum_k, gf_lattice_iw, archive=None):
+    """
+    Bundles running maxent on the total lattice GF, analyzing the spectral
+    function and determining the new chemical potential.
+
+    Parameters
+    ----------
+    general_params : dict
+        general parameters as dict.
+    sum_k : SumkDFT object
+        SumkDFT object needed for original chemical potential and frequency
+        range of MaxEnt continuation.
+    gf_lattice_iw : BlockGf
+        trace of the lattice GF over all blocks, orbitals and spins in
+        Matsubara frequency.
+    archive : HDFArchive, optional
+        If given, writes spectral function (i.e. MaxEnt result) to archive.
+
+    Returns
+    -------
+    float
+        new chemical potential located in the middle of the gap from MaxEnt.
+        None if not master node or if something went wrong.
+    """
+
+
+    if not mpi.is_master_node():
+        return None
+
+    if not imported_maxent:
+        mpi.report('WARNING: cannot find gap with MaxEnt, MaxEnt not found')
+        return None
+
+    # Runs MaxEnt using the Chi2Curvature analyzer
+    maxent_results, mesh = maxent_gf_latt._run_maxent(gf_lattice_iw, sum_k, .02, None, None, 200, 30)
+    mesh = np.array(mesh)
+    spectral_function = maxent_results['total'].get_A_out('Chi2CurvatureAnalyzer')
+
+    # Writes spectral function to archive
+    if archive is not None:
+        unpacked_results = maxent_gf_latt._unpack_maxent_results(maxent_results, mesh)
+        archive['DMFT_results/last_iter']['Alatt_w'] = unpacked_results
+
+    # Checks if spectral function at Fermi energy below threshold
+    spectral_func_threshold = general_params['beta']/np.pi * general_params['mu_gap_gb2_threshold']
+    if spectral_function[np.argmin(np.abs(mesh))] > spectral_func_threshold:
+        mpi.report('WARNING: cannot find gap with MaxEnt, spectral function not gapped at Fermi energy')
+        return None
+
+    # Determines band edges for conduction and valence band
+    edge_threshold = 0.2
+    conduction_edge = _determine_band_edge(mesh, spectral_function, spectral_func_threshold, False, edge_threshold)
+    valence_edge = _determine_band_edge(mesh, spectral_function, spectral_func_threshold, True, edge_threshold)
+
+    return sum_k.chemical_potential + (valence_edge + conduction_edge) / 2
+
+
+[docs] +def set_initial_mu(general_params, sum_k, iteration_offset, archive, shell_multiplicity): + """ + Handles the different ways of setting the initial chemical potential mu: + * Chemical potential set to fixed value: uses this value + + * New calculation: determines mu from dichotomy method + + * Resuming calculation and chemical potential not updated this iteration: + loads calculation before previous iteration. + + * Resuming calculation and chemical potential is updated: + 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. + + + Parameters + ---------- + general_params : dict + general parameters as dict. + sum_k : SumkDFT object + contains system information necessary to determine the initial mu. + iteration_offset : int + the number of iterations executed in previous calculations. + archive : HDFArchive + needed to potentially load previous results and write MaxEnt results to. + shell_multiplicity : iterable of ints + number of equivalent shells per impurity. + + Returns + ------- + sum_k : SumkDFT object + the altered SumkDFT object with the initial mu set correctly. + """ + + # Uses fixed_mu_value as chemical potential if parameter is given + if general_params['fixed_mu_value'] != 'none': + sum_k.set_mu(general_params['fixed_mu_value']) + mpi.report('+++ Keeping the chemical potential fixed at {:.3f} eV +++'.format(general_params['fixed_mu_value'])) + return sum_k + + # In first iteration, determines mu and returns + if iteration_offset == 0: + if general_params['dc'] and general_params['dc_type'] == 4: + return sum_k + if general_params['solver_type'] in ['ftps']: + sum_k.calc_mu(precision=general_params['prec_mu'], broadening=general_params['eta']) + else: + sum_k.calc_mu(precision=general_params['prec_mu'], method=general_params['calc_mu_method']) + return sum_k + + # If continuing calculation and not updating mu, loads sold value + if iteration_offset % general_params['mu_update_freq'] != 0: + if mpi.is_master_node(): + sum_k.chemical_potential = archive['DMFT_results/last_iter/chemical_potential_pre'] + sum_k.chemical_potential = mpi.bcast(sum_k.chemical_potential) + mpi.report('Chemical potential not updated this step, ' + + 'reusing loaded one of {:.3f} eV'.format(sum_k.chemical_potential)) + return sum_k + + # If continuing calculation and updating mu, reads in occupation and + # chemical_potential_pre from the last run + previous_mu = None + if mpi.is_master_node(): + previous_mu = archive['DMFT_results/last_iter/chemical_potential_pre'] + previous_mu = mpi.bcast(previous_mu) + + # Runs maxent if spectral weight too low and occupation is close to desired one + # TODO: which solvers work? + if general_params['solver_type'] in ['cthyb', 'ctint'] and general_params['mu_gap_gb2_threshold'] != 'none': + sum_k.chemical_potential = previous_mu + gf_lattice_iw, g_betahalf, occupation = _initialize_lattice_gf(sum_k, general_params) + fulfills_occupation_crit = (general_params['mu_gap_occ_deviation'] == 'none' + or np.abs(occupation - sum_k.density_required) < general_params['mu_gap_occ_deviation']) + + if -np.real(g_betahalf) < general_params['mu_gap_gb2_threshold'] and fulfills_occupation_crit: + new_mu = _set_mu_to_gap_middle_with_maxent(general_params, sum_k, gf_lattice_iw, archive) + new_mu = mpi.bcast(new_mu) + if new_mu is not None: + sum_k.chemical_potential = new_mu + mpi.report('New chemical potential in the gap: {:.3f} eV'.format(new_mu)) + return sum_k + # Calculates occupation for mu mixing below + elif np.isclose(general_params['mu_mix_per_occupation_offset'], 0): + occupation = 0 # The occupation does not matter in this case + else: + _, _, occupation = _initialize_lattice_gf(sum_k, general_params) + + # If system not gapped, gets chemical potential from dichotomy method + if general_params['solver_type'] in ['ftps']: + sum_k.calc_mu(precision=general_params['prec_mu'], broadening=general_params['eta']) + else: + sum_k.calc_mu(precision=general_params['prec_mu'], method=general_params['calc_mu_method']) + + # Applies mu mixing to dichotomy result + sum_k.chemical_potential = _mix_chemical_potential(general_params, occupation, + sum_k.density_required, + previous_mu, sum_k.chemical_potential) + + return sum_k
+ + +
+[docs] +def update_mu(general_params, sum_k, it, archive): + """ + Handles the different ways of updating the chemical potential mu: + * Chemical potential set to fixed value: uses this value + + * Chemical potential not updated this iteration: nothing happens. + + * Chemical potential is updated: 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. + + Parameters + ---------- + general_params : dict + general parameters as dict. + sum_k : SumkDFT object + contains system information necessary to update mu. + it : int + the number of the current iteration. + archive : HDFArchive + needed to potentially write MaxEnt results to. + + Returns + ------- + sum_k : SumkDFT object + the altered SumkDFT object with the updated mu. + """ + + # Uses fixed_mu_value as chemical potential if parameter is given + if general_params['fixed_mu_value'] != 'none': + sum_k.set_mu(general_params['fixed_mu_value']) + mpi.report('+++ Keeping the chemical potential fixed at {:.3f} eV +++'.format(general_params['fixed_mu_value'])) + return sum_k + + # If mu won't be updated this step, don't update it... + if it % general_params['mu_update_freq'] != 0: + mpi.report('Chemical potential not updated this step, ' + + 'reusing previous one of {:.3f} eV'.format(sum_k.chemical_potential)) + return sum_k + + # Runs maxent if spectral weight too low and occupation is close to desired one + # TODO: which solvers work? + if general_params['solver_type'] in ['cthyb', 'ctint','ctseg'] and general_params['mu_gap_gb2_threshold'] != 'none': + gf_lattice_iw, g_betahalf, occupation = _initialize_lattice_gf(sum_k, general_params) + fulfills_occupation_crit = (general_params['mu_gap_occ_deviation'] == 'none' + or np.abs(occupation - sum_k.density_required) < general_params['mu_gap_occ_deviation']) + + if -np.real(g_betahalf) < general_params['mu_gap_gb2_threshold'] and fulfills_occupation_crit: + new_mu = _set_mu_to_gap_middle_with_maxent(general_params, sum_k, gf_lattice_iw, archive) + new_mu = mpi.bcast(new_mu) + if new_mu is not None: + sum_k.chemical_potential = new_mu + mpi.report('New chemical potential in the gap: {:.3f} eV'.format(new_mu)) + return sum_k + # Calculates occupation for mu mixing below + elif np.isclose(general_params['mu_mix_per_occupation_offset'], 0): + occupation = 0 # The occupation does not matter in this case + else: + _, _, occupation = _initialize_lattice_gf(sum_k, general_params) + + # If system not gapped, gets chemical potential from dichotomy method + previous_mu = sum_k.chemical_potential + if general_params['solver_type'] in ['ftps']: + sum_k.calc_mu(precision=general_params['prec_mu'], broadening=general_params['eta']) + else: + sum_k.calc_mu(precision=general_params['prec_mu'], method=general_params['calc_mu_method']) + + # Applies mu mixing to dichotomy result + sum_k.chemical_potential = _mix_chemical_potential(general_params, occupation, + sum_k.density_required, + previous_mu, sum_k.chemical_potential) + mpi.barrier() + return sum_k
+ +
+ +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/dmft_tools/matheval.html b/_modules/dmft_tools/matheval.html new file mode 100644 index 00000000..9f12e34d --- /dev/null +++ b/_modules/dmft_tools/matheval.html @@ -0,0 +1,381 @@ + + + + + + dmft_tools.matheval — solid_dmft documentation + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for dmft_tools.matheval

+# https://stackoverflow.com/a/30516254
+
+import ast
+import math
+
+
+
+[docs] +class MathExpr(object): + allowed_nodes = ( + ast.Module, + ast.Expr, + ast.Load, + ast.Expression, + ast.Add, + ast.Sub, + ast.UnaryOp, + ast.Num, + ast.BinOp, + ast.Mult, + ast.Div, + ast.Pow, + ast.BitOr, + ast.BitAnd, + ast.BitXor, + ast.USub, + ast.UAdd, + ast.FloorDiv, + ast.Mod, + ast.LShift, + ast.RShift, + ast.Invert, + ast.Call, + ast.Name, + ) + + functions = { + "abs": abs, + "complex": complex, + "min": min, + "max": max, + "pow": pow, + "round": round, + } + functions.update( + {key: value for (key, value) in vars(math).items() if not key.startswith("_")} + ) + +
+[docs] + def __init__(self, expr): + if any(elem in expr for elem in "\n#"): + raise ValueError(expr) + + node = ast.parse(expr.strip(), mode="eval") + for curr in ast.walk(node): + if not isinstance(curr, self.allowed_nodes): + raise ValueError(curr) + + self.code = compile(node, "<string>", "eval")
+ + + def __call__(self, **kwargs): + return eval(self.code, {"__builtins__": None}, {**self.functions, **kwargs})
+ +
+ +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/dmft_tools/observables.html b/_modules/dmft_tools/observables.html new file mode 100644 index 00000000..fa367506 --- /dev/null +++ b/_modules/dmft_tools/observables.html @@ -0,0 +1,961 @@ + + + + + + dmft_tools.observables — solid_dmft documentation + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for dmft_tools.observables

+################################################################################
+#
+# solid_dmft - A versatile python wrapper to perform DFT+DMFT calculations
+#              utilizing the TRIQS software library
+#
+# Copyright (C) 2018-2020, ETH Zurich
+# Copyright (C) 2021, The Simons Foundation
+#      authors: A. Hampel, M. Merkel, and S. Beck
+#
+# solid_dmft is free software: you can redistribute it and/or modify it under the
+# terms of the GNU General Public License as published by the Free Software
+# Foundation, either version 3 of the License, or (at your option) any later
+# version.
+#
+# solid_dmft is distributed in the hope that it will be useful, but WITHOUT ANY
+# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
+# PARTICULAR PURPOSE. See the GNU General Public License for more details.
+
+# You should have received a copy of the GNU General Public License along with
+# solid_dmft (in the file COPYING.txt in this directory). If not, see
+# <http://www.gnu.org/licenses/>.
+#
+################################################################################
+"""
+Contains all functions related to the observables.
+"""
+
+# system
+import os.path
+import numpy as np
+
+# triqs
+import triqs.utility.mpi as mpi
+from triqs.gf import Gf, MeshImTime
+from triqs.atom_diag import trace_rho_op
+from triqs.gf.descriptors import Fourier
+from solid_dmft.dmft_tools import solver
+
+
+[docs] +def prep_observables(h5_archive, sum_k): + """ + prepares the observable arrays and files for the DMFT calculation + + Parameters + ---------- + h5_archive: hdf archive instance + hdf archive for calculation + sum_k : SumK Object instances + + Returns + ------- + observables : dict + observable array for calculation + """ + + # determine number of impurities + n_inequiv_shells = h5_archive['dft_input']['n_inequiv_shells'] + + # check for previous iterations + obs_prev = [] + if 'observables' in h5_archive['DMFT_results']: + obs_prev = h5_archive['DMFT_results']['observables'] + + # prepare observable dicts + if len(obs_prev) > 0: + observables = obs_prev + else: + observables = dict() + observables['iteration'] = [] + observables['mu'] = [] + observables['E_tot'] = [] + observables['E_bandcorr'] = [] + observables['E_int'] = [[] for _ in range(n_inequiv_shells)] + observables['E_corr_en'] = [] + observables['E_dft'] = [] + observables['E_DC'] = [[] for _ in range(n_inequiv_shells)] + observables['orb_gb2'] = [{spin: [] for spin in sum_k.spin_block_names[sum_k.SO]} + for _ in range(n_inequiv_shells)] + observables['imp_gb2'] = [{spin: [] for spin in sum_k.spin_block_names[sum_k.SO]} + for _ in range(n_inequiv_shells)] + observables['orb_occ'] = [{spin: [] for spin in sum_k.spin_block_names[sum_k.SO]} + for _ in range(n_inequiv_shells)] + observables['orb_Z'] = [{spin: [] for spin in sum_k.spin_block_names[sum_k.SO]} + for _ in range(n_inequiv_shells)] + observables['imp_occ'] = [{spin: [] for spin in sum_k.spin_block_names[sum_k.SO]} + for _ in range(n_inequiv_shells)] + + return observables
+ + +def _generate_header(general_params, sum_k): + """ + Generates the headers that are used in write_header_to_file. + Returns a dict with {file_name: header_string} + """ + n_orb = solver.get_n_orbitals(sum_k) + + header_energy_mask = ' | {:>10} | {:>10} {:>10} {:>10} {:>10}' + header_energy = header_energy_mask.format('E_tot', 'E_DFT', 'E_bandcorr', 'E_int_imp', 'E_DC') + + headers = {} + for iineq in range(sum_k.n_inequiv_shells): + number_spaces = max(10*n_orb[iineq]['up'] + 3*(n_orb[iineq]['up']-1), 21) + header_basic_mask = '{{:>3}} | {{:>10}} | {{:>{0}}} | {{:>{0}}} | {{:>17}}'.format(number_spaces) + + # If magnetic calculation is done create two obs files per imp + if general_params['magnetic'] and sum_k.SO == 0: + for spin in ('up', 'down'): + file_name = 'observables_imp{}_{}.dat'.format(iineq, spin) + headers[file_name] = header_basic_mask.format('it', 'mu', 'G(beta/2) per orbital', + 'orbital occs '+spin, 'impurity occ '+spin) + + if general_params['calc_energies']: + headers[file_name] += header_energy + else: + file_name = 'observables_imp{}.dat'.format(iineq) + headers[file_name] = header_basic_mask.format('it', 'mu', 'G(beta/2) per orbital', + 'orbital occs up+down', 'impurity occ') + + if general_params['calc_energies']: + headers[file_name] += header_energy + + return headers + + +
+[docs] +def write_header_to_file(general_params, sum_k): + """ + Writes the header to the observable files + + Parameters + ---------- + general_params : dict + general parameters as a dict + n_inequiv_shells : int + number of impurities for calculations + + + Returns + ------- + nothing + """ + + headers = _generate_header(general_params, sum_k) + + for file_name, header in headers.items(): + path = os.path.join(general_params['jobname'], file_name) + with open(path, 'w') as obs_file: + obs_file.write(header + '\n')
+ + + +
+[docs] +def add_dft_values_as_zeroth_iteration(observables, general_params, dft_mu, dft_energy, + sum_k, G_loc_all_dft, density_mat_dft, shell_multiplicity): + """ + Calculates the DFT observables that should be written as the zeroth iteration. + + Parameters + ---------- + observables : observable arrays/dicts + + general_params : general parameters as a dict + + dft_mu : dft chemical potential + + sum_k : SumK Object instances + + G_loc_all_dft : Gloc from DFT for G(beta/2) + + density_mat_dft : occupations from DFT + + shell_multiplicity : degeneracy of impurities + + Returns + ------- + + observables: list of dicts + """ + dft_energy = 0.0 if dft_energy is None else dft_energy + observables['iteration'].append(0) + observables['mu'].append(float(dft_mu)) + + if general_params['calc_energies']: + observables['E_bandcorr'].append(0.0) + observables['E_corr_en'].append(0.0) + observables['E_dft'].append(dft_energy) + else: + observables['E_bandcorr'].append('none') + observables['E_corr_en'].append('none') + observables['E_dft'].append('none') + + for iineq in range(sum_k.n_inequiv_shells): + if general_params['calc_energies']: + observables['E_int'][iineq].append(0.0) + double_counting_energy = shell_multiplicity[iineq]*sum_k.dc_energ[sum_k.inequiv_to_corr[iineq]] if general_params['dc'] else 0.0 + observables['E_DC'][iineq].append(double_counting_energy) + else: + observables['E_int'][iineq].append('none') + observables['E_DC'][iineq].append('none') + + # Collect all occupation and G(beta/2) for spin up and down separately + for spin in sum_k.spin_block_names[sum_k.SO]: + g_beta_half_per_impurity = 0.0 + g_beta_half_per_orbital = [] + occupation_per_impurity = 0.0 + occupation_per_orbital = [] + Z_per_orbital = [] + + # iterate over all spin channels and add the to up or down + # we need only the keys of the blocks, but sorted! Otherwise + # orbitals will be mixed up_0 to down_0 etc. + for spin_channel in sorted(sum_k.gf_struct_solver[iineq].keys()): + if not spin in spin_channel: + continue + + if general_params['solver_type'] in ['ftps']: + freq_mesh = np.array([w.value for w in G_loc_all_dft[iineq][spin_channel].mesh]) + fermi_idx = abs(freq_mesh).argmin() + gb2_averaged = G_loc_all_dft[iineq][spin_channel].data[fermi_idx].imag + + # Z is not defined without Sigma, adding 1 + Z_per_orbital.extend( [1.0] * G_loc_all_dft[iineq][spin_channel].target_shape[0] ) + else: + # G(beta/2) + mesh = MeshImTime(beta=general_params['beta'], S="Fermion", + n_max=general_params['n_tau']) + G_time = Gf(mesh=mesh, indices=G_loc_all_dft[iineq][spin_channel].indices) + G_time << Fourier(G_loc_all_dft[iineq][spin_channel]) + + # since G(tau) has always 10001 values we are sampling +-10 values + # hard coded around beta/2, for beta=40 this corresponds to approx +-0.05 + mesh_mid = len(G_time.data) // 2 + samp = 10 + gg = G_time.data[mesh_mid-samp:mesh_mid+samp] + gb2_averaged = np.mean(np.real(gg), axis=0) + + # Z is not defined without Sigma, adding 1 + Z_per_orbital.extend( [1.0] * G_time.target_shape[0] ) + + g_beta_half_per_orbital.extend(np.diag(gb2_averaged)) + g_beta_half_per_impurity += np.trace(gb2_averaged) + + # occupation per orbital + den_mat = np.real(density_mat_dft[iineq][spin_channel]) + occupation_per_orbital.extend(np.diag(den_mat)) + occupation_per_impurity += np.trace(den_mat) + + # adding those values to the observable object + observables['orb_gb2'][iineq][spin].append(np.array(g_beta_half_per_orbital)) + observables['imp_gb2'][iineq][spin].append(g_beta_half_per_impurity) + observables['orb_occ'][iineq][spin].append(np.array(occupation_per_orbital)) + observables['imp_occ'][iineq][spin].append(occupation_per_impurity) + observables['orb_Z'][iineq][spin].append(np.array(Z_per_orbital)) + + # for it 0 we just subtract E_DC from E_DFT + if general_params['calc_energies']: + observables['E_tot'].append(dft_energy - sum([dc_per_imp[0] for dc_per_imp in observables['E_DC']])) + else: + observables['E_tot'].append('none') + + return observables
+ + + +
+[docs] +def add_dmft_observables(observables, general_params, solver_params, dft_energy, it, solvers, h_int, + previous_mu, sum_k, density_mat, shell_multiplicity, E_bandcorr): + """ + 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 + + Parameters + ---------- + observables : observable arrays/dicts + + general_params : general parameters as a dict + + solver_params : solver parameters as a dict + + it : iteration counter + + solvers : Solver instances + + h_int : interaction hamiltonian + + previous_mu : dmft chemical potential for which the calculation was just done + + sum_k : SumK Object instances + + density_mat : DMFT occupations + + shell_multiplicity : degeneracy of impurities + + E_bandcorr : E_kin_dmft - E_kin_dft, either calculated man or from sum_k method if CSC + + Returns + ------- + observables: list of dicts + """ + + # init energy values + E_corr_en = 0.0 + # Read energy from OSZICAR + dft_energy = 0.0 if dft_energy is None else dft_energy + + # now the normal output from each iteration + observables['iteration'].append(it) + observables['mu'].append(float(previous_mu)) + observables['E_bandcorr'].append(E_bandcorr) + observables['E_dft'].append(dft_energy) + + # if density matrix was measured store result in observables + if general_params['solver_type'] in ['cthyb', 'hubbardI'] and solver_params["measure_density_matrix"]: + mpi.report("\nextracting the impurity density matrix") + # Extract accumulated density matrix + density_matrix = [solvers[icrsh].density_matrix for icrsh in range(sum_k.n_inequiv_shells)] + # Object containing eigensystem of the local Hamiltonian + diag_local_ham = [solvers[icrsh].h_loc_diagonalization for icrsh in range(sum_k.n_inequiv_shells)] + + if general_params['calc_energies']: + # dmft interaction energy with E_int = 0.5 * Tr[Sigma * G] + if (general_params['solver_type'] in ['cthyb', 'hubbardI'] + and solver_params["measure_density_matrix"]): + E_int = [trace_rho_op(density_matrix[icrsh], h_int[icrsh], diag_local_ham[icrsh]) + for icrsh in range(sum_k.n_inequiv_shells)] + elif general_params['solver_type'] == 'hartree': + E_int = [solvers[icrsh].interaction_energy for icrsh in range(sum_k.n_inequiv_shells)] + else: + warning = ( "!-------------------------------------------------------------------------------------------!\n" + "! WARNING: calculating interaction energy using Migdal formula !\n" + "! consider turning on measure density matrix to use the more stable trace_rho_op function !\n" + "!-------------------------------------------------------------------------------------------!" ) + print(warning) + # calc energy for given S and G + E_int = [0.5 * np.real((solvers[icrsh].G_freq * solvers[icrsh].Sigma_freq).total_density()) + for icrsh in range(sum_k.n_inequiv_shells)] + + for icrsh in range(sum_k.n_inequiv_shells): + observables['E_int'][icrsh].append(shell_multiplicity[icrsh]*E_int[icrsh].real) + E_corr_en += shell_multiplicity[icrsh] * (E_int[icrsh].real - sum_k.dc_energ[sum_k.inequiv_to_corr[icrsh]]) + + + observables['E_corr_en'].append(E_corr_en) + + # calc total energy + E_tot = dft_energy + E_bandcorr + E_corr_en + observables['E_tot'].append(E_tot) + + for icrsh in range(sum_k.n_inequiv_shells): + if general_params['dc']: + observables['E_DC'][icrsh].append(shell_multiplicity[icrsh]*sum_k.dc_energ[sum_k.inequiv_to_corr[icrsh]]) + else: + observables['E_DC'][icrsh].append(0.0) + + if general_params['solver_type'] not in ['ftps']: + if solvers[icrsh].G_time: + G_time = solvers[icrsh].G_time + else: + G_time = solvers[icrsh].G_time_orig + + # Collect all occupation and G(beta/2) for spin up and down separately + for spin in sum_k.spin_block_names[sum_k.SO]: + g_beta_half_per_impurity = 0.0 + g_beta_half_per_orbital = [] + occupation_per_impurity = 0.0 + occupation_per_orbital = [] + Z_per_orbital = [] + + # iterate over all spin channels and add the to up or down + for spin_channel in sorted(sum_k.gf_struct_solver[icrsh].keys()): + if not spin in spin_channel: + continue + if general_params['solver_type'] in ['ftps']: + freq_mesh = np.array([w.value for w in solvers[icrsh].G_freq[spin_channel].mesh]) + fermi_idx = abs(freq_mesh).argmin() + gb2_averaged = solvers[icrsh].G_freq[spin_channel].data[fermi_idx].imag + + # Z is not defined without Sigma, adding 1 + Z_per_orbital.extend( [1.0] * solvers[icrsh].G_freq[spin_channel].target_shape[0] ) + else: + # G(beta/2) + # since G(tau) has always 10001 values we are sampling +-10 values + # hard coded around beta/2, for beta=40 this corresponds to approx +-0.05 + mesh_mid = len(G_time[spin_channel].data) // 2 + samp = 10 + gg = G_time[spin_channel].data[mesh_mid-samp:mesh_mid+samp] + gb2_averaged = np.mean(np.real(gg), axis=0) + + # get Z + Z_per_orbital.extend( calc_Z(Sigma= solvers[icrsh].Sigma_freq[spin_channel]) ) + + g_beta_half_per_orbital.extend(np.diag(gb2_averaged)) + g_beta_half_per_impurity += np.trace(gb2_averaged) + + # occupation per orbital and impurity + den_mat = np.real(density_mat[icrsh][spin_channel]) + occupation_per_orbital.extend(np.diag(den_mat)) + occupation_per_impurity += np.trace(den_mat) + + # adding those values to the observable object + observables['orb_gb2'][icrsh][spin].append(np.array(g_beta_half_per_orbital)) + observables['imp_gb2'][icrsh][spin].append(g_beta_half_per_impurity) + observables['orb_occ'][icrsh][spin].append(np.array(occupation_per_orbital)) + observables['imp_occ'][icrsh][spin].append(occupation_per_impurity) + observables['orb_Z'][icrsh][spin].append(np.array(Z_per_orbital)) + + return observables
+ + +
+[docs] +def write_obs(observables, sum_k, general_params): + """ + writes the last entries of the observable arrays to the files + + Parameters + ---------- + observables : list of dicts + observable arrays/dicts + + sum_k : SumK Object instances + + general_params : dict + + Returns + ------- + nothing + + """ + + n_orb = solver.get_n_orbitals(sum_k) + + for icrsh in range(sum_k.n_inequiv_shells): + if general_params['magnetic'] and sum_k.SO == 0: + for spin in ('up', 'down'): + line = '{:3d} | '.format(observables['iteration'][-1]) + line += '{:10.5f} | '.format(observables['mu'][-1]) + + if n_orb[icrsh][spin] == 1: + line += ' '*11 + for item in observables['orb_gb2'][icrsh][spin][-1]: + line += '{:10.5f} '.format(item) + line = line[:-3] + ' | ' + + if n_orb[icrsh][spin] == 1: + line += ' '*11 + for item in observables['orb_occ'][icrsh][spin][-1]: + line += '{:10.5f} '.format(item) + line = line[:-3] + ' | ' + + line += '{:17.5f}'.format(observables['imp_occ'][icrsh][spin][-1]) + + if general_params['calc_energies']: + line += ' | {:10.5f}'.format(observables['E_tot'][-1]) + line += ' | {:10.5f}'.format(observables['E_dft'][-1]) + line += ' {:10.5f}'.format(observables['E_bandcorr'][-1]) + line += ' {:10.5f}'.format(observables['E_int'][icrsh][-1]) + line += ' {:10.5f}'.format(observables['E_DC'][icrsh][-1]) + + file_name = '{}/observables_imp{}_{}.dat'.format(general_params['jobname'], icrsh, spin) + with open(file_name, 'a') as obs_file: + obs_file.write(line + '\n') + else: + line = '{:3d} | '.format(observables['iteration'][-1]) + line += '{:10.5f} | '.format(observables['mu'][-1]) + + # Adds spaces for header to fit in properly + if n_orb[icrsh]['up'] == 1: + line += ' '*11 + # Adds up the spin channels + for iorb in range(n_orb[icrsh]['up']): + val = np.sum([observables['orb_gb2'][icrsh][spin][-1][iorb] for spin in sum_k.spin_block_names[sum_k.SO]]) + line += '{:10.5f} '.format(val) + line = line[:-3] + ' | ' + + # Adds spaces for header to fit in properly + if n_orb[icrsh]['up'] == 1: + line += ' '*11 + # Adds up the spin channels + for iorb in range(n_orb[icrsh]['up']): + val = np.sum([observables['orb_occ'][icrsh][spin][-1][iorb] for spin in sum_k.spin_block_names[sum_k.SO]]) + line += '{:10.5f} '.format(val) + line = line[:-3] + ' | ' + + # Adds up the spin channels + val = np.sum([observables['imp_occ'][icrsh][spin][-1] for spin in sum_k.spin_block_names[sum_k.SO]]) + line += '{:17.5f}'.format(val) + + if general_params['calc_energies']: + line += ' | {:10.5f}'.format(observables['E_tot'][-1]) + line += ' | {:10.5f}'.format(observables['E_dft'][-1]) + line += ' {:10.5f}'.format(observables['E_bandcorr'][-1]) + line += ' {:10.5f}'.format(observables['E_int'][icrsh][-1]) + line += ' {:10.5f}'.format(observables['E_DC'][icrsh][-1]) + + file_name = '{}/observables_imp{}.dat'.format(general_params['jobname'], icrsh) + with open(file_name, 'a') as obs_file: + obs_file.write(line + '\n')
+ + + +
+[docs] +def calc_dft_kin_en(general_params, sum_k, dft_mu): + """ + Calculates the kinetic energy from DFT for target states + + Parameters + ---------- + general_params : dict + general parameters as a dict + + sum_k : SumK Object instances + + dft_mu: float + DFT fermi energy + + + Returns + ------- + E_kin_dft: float + kinetic energy from DFT + + """ + + H_ks = sum_k.hopping + num_kpts = sum_k.n_k + E_kin = 0.0 + ikarray = np.array(list(range(sum_k.n_k))) + for ik in mpi.slice_array(ikarray): + nb = int(sum_k.n_orbitals[ik]) + # calculate lattice greens function need here to set sigma other n_iw is assumend to be 1025! + # TODO: implement here version for FTPS! + G_freq_lat = sum_k.lattice_gf(ik, with_Sigma=True, mu=dft_mu).copy() + # # calculate G(beta) via the function density, which is the same as fourier trafo G(w) and taking G(b) + G_freq_lat_beta = G_freq_lat.density() + for spin in sum_k.spin_block_names[sum_k.SO]: + E_kin += np.trace(np.dot(H_ks[ik, 0, :nb, :nb], G_freq_lat_beta[spin][:, :])) + E_kin = np.real(E_kin) + # collect data and put into E_kin_dft + E_kin_dft = mpi.all_reduce(E_kin) + mpi.barrier() + # E_kin should be divided by the number of k-points + E_kin_dft = E_kin_dft/num_kpts + + mpi.report(f'Kinetic energy contribution dft part: {E_kin_dft:.8f}') + + return E_kin_dft
+ + +
+[docs] +def calc_bandcorr_man(general_params, sum_k, E_kin_dft): + """ + Calculates the correlated kinetic energy from DMFT for target states + and then determines the band correction energy + + Parameters + ---------- + general_params : dict + general parameters as a dict + + sum_k : SumK Object instances + + E_kin_dft: float + kinetic energy from DFT + + + Returns + ------- + E_bandcorr: float + band energy correction E_kin_dmft - E_kin_dft + + """ + E_kin_dmft = 0.0j + E_kin = 0.0j + H_ks = sum_k.hopping + num_kpts = sum_k.n_k + + # kinetic energy from dmft lattice Greens functions + ikarray = np.array(list(range(sum_k.n_k))) + for ik in mpi.slice_array(ikarray): + nb = int(sum_k.n_orbitals[ik]) + # calculate lattice greens function + G_freq_lat = sum_k.lattice_gf(ik, with_Sigma=True, with_dc=True).copy() + # calculate G(beta) via the function density, which is the same as fourier trafo G(w) and taking G(b) + G_freq_lat_beta = G_freq_lat.density() + for spin in sum_k.spin_block_names[sum_k.SO]: + E_kin += np.trace(np.dot(H_ks[ik, 0, :nb, :nb], G_freq_lat_beta[spin][:, :])) + E_kin = np.real(E_kin) + + # collect data and put into E_kin_dmft + E_kin_dmft = mpi.all_reduce(E_kin) + mpi.barrier() + # E_kin should be divided by the number of k-points + E_kin_dmft = E_kin_dmft/num_kpts + + if mpi.is_master_node(): + print('Kinetic energy contribution dmft part: '+str(E_kin_dmft)) + + E_bandcorr = E_kin_dmft - E_kin_dft + + return E_bandcorr
+ + +
+[docs] +def calc_Z(Sigma): + """ + 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 + + Parameters + ---------- + Sigma: Gf on MeshImFreq + self-energy on Matsubara mesh + + + Returns + ------- + orb_Z: 1d numpy array + list of Z values per orbital in Sigma + + """ + orb_Z = [] + + iw = [np.imag(n) for n in Sigma.mesh] + # find smallest iw_n + n_iw0 = int(0.5*len(iw)) + + for orb in range(0,Sigma.target_shape[0]): + Im_S_iw = Sigma[orb,orb].data.imag + # simple extraction from linear fit to first two Matsubara freq of Sigma + # assuming Fermi liquid like self energy + Z = 1/(1 - (Im_S_iw[n_iw0+1]-Im_S_iw[n_iw0]) / (iw[n_iw0+1]-iw[n_iw0]) ) + orb_Z.append(abs(Z)) + + return np.array(orb_Z)
+ +
+ +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/dmft_tools/results_to_archive.html b/_modules/dmft_tools/results_to_archive.html new file mode 100644 index 00000000..83866c86 --- /dev/null +++ b/_modules/dmft_tools/results_to_archive.html @@ -0,0 +1,463 @@ + + + + + + dmft_tools.results_to_archive — solid_dmft documentation + + + + + + + + + + + + + + + + + + +
+ + +
+ +
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+ +

Source code for dmft_tools.results_to_archive

+# -*- coding: utf-8 -*-
+################################################################################
+#
+# solid_dmft - A versatile python wrapper to perform DFT+DMFT calculations
+#              utilizing the TRIQS software library
+#
+# Copyright (C) 2018-2020, ETH Zurich
+# Copyright (C) 2021, The Simons Foundation
+#      authors: A. Hampel, M. Merkel, and S. Beck
+#
+# solid_dmft is free software: you can redistribute it and/or modify it under the
+# terms of the GNU General Public License as published by the Free Software
+# Foundation, either version 3 of the License, or (at your option) any later
+# version.
+#
+# solid_dmft is distributed in the hope that it will be useful, but WITHOUT ANY
+# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
+# PARTICULAR PURPOSE. See the GNU General Public License for more details.
+
+# You should have received a copy of the GNU General Public License along with
+# solid_dmft (in the file COPYING.txt in this directory). If not, see
+# <http://www.gnu.org/licenses/>.
+#
+################################################################################
+
+import os
+import triqs.utility.mpi as mpi
+
+
+def _compile_information(sum_k, general_params, solver_params, solvers,
+                         previous_mu, density_mat_pre, density_mat, deltaN, dens):
+    """ Collects all results in a dictonary. """
+
+    write_to_h5 = {'chemical_potential_post': sum_k.chemical_potential,
+                   'chemical_potential_pre': previous_mu,
+                   'DC_pot': sum_k.dc_imp,
+                   'DC_energ': sum_k.dc_energ,
+                   'dens_mat_pre': density_mat_pre,
+                   'dens_mat_post': density_mat,
+                  }
+
+    if deltaN is not None:
+        write_to_h5['deltaN'] = deltaN
+    if dens is not None:
+        write_to_h5['deltaN_trace'] = dens
+
+    for icrsh in range(sum_k.n_inequiv_shells):
+        if general_params['solver_type'] in ['cthyb', 'hubbardI']:
+            write_to_h5['Delta_time_{}'.format(icrsh)] = solvers[icrsh].Delta_time
+
+            # Write the full density matrix to last_iter only - it is large
+            if solver_params['measure_density_matrix']:
+                write_to_h5['full_dens_mat_{}'.format(icrsh)] = solvers[icrsh].density_matrix
+                write_to_h5['h_loc_diag_{}'.format(icrsh)] = solvers[icrsh].h_loc_diagonalization
+
+        elif general_params['solver_type'] in ['ftps']:
+            write_to_h5['Delta_freq_{}'.format(icrsh)] = solvers[icrsh].Delta_freq
+
+        write_to_h5['Gimp_time_{}'.format(icrsh)] = solvers[icrsh].G_time
+        write_to_h5['G0_freq_{}'.format(icrsh)] = solvers[icrsh].G0_freq
+        write_to_h5['Gimp_freq_{}'.format(icrsh)] = solvers[icrsh].G_freq
+        write_to_h5['Sigma_freq_{}'.format(icrsh)] = solvers[icrsh].Sigma_freq
+
+        if general_params['solver_type'] == 'cthyb':
+            if solver_params['measure_pert_order']:
+                write_to_h5['pert_order_imp_{}'.format(icrsh)] = solvers[icrsh].perturbation_order
+                write_to_h5['pert_order_total_imp_{}'.format(icrsh)] = solvers[icrsh].perturbation_order_total
+
+            if general_params['measure_chi'] != 'none':
+                write_to_h5['O_{}_time_{}'.format(general_params['measure_chi'], icrsh)] = solvers[icrsh].O_time
+
+            # if legendre was set, that we have both now!
+            if (solver_params['measure_G_l']
+                or not solver_params['perform_tail_fit'] and general_params['legendre_fit']):
+                write_to_h5['G_time_orig_{}'.format(icrsh)] = solvers[icrsh].G_time_orig
+                write_to_h5['Gimp_l_{}'.format(icrsh)] = solvers[icrsh].G_l
+
+        if general_params['solver_type'] == 'ctint' and solver_params['measure_histogram']:
+            write_to_h5['pert_order_imp_{}'.format(icrsh)] = solvers[icrsh].perturbation_order
+
+        if general_params['solver_type'] == 'hubbardI':
+            write_to_h5['G0_Refreq_{}'.format(icrsh)] = solvers[icrsh].G0_Refreq
+            write_to_h5['Gimp_Refreq_{}'.format(icrsh)] = solvers[icrsh].G_Refreq
+            write_to_h5['Sigma_Refreq_{}'.format(icrsh)] = solvers[icrsh].Sigma_Refreq
+
+            if solver_params['measure_G_l']:
+                write_to_h5['Gimp_l_{}'.format(icrsh)] = solvers[icrsh].G_l
+
+        if general_params['solver_type'] == 'hartree':
+            write_to_h5['Sigma_Refreq_{}'.format(icrsh)] = solvers[icrsh].Sigma_Refreq
+
+        if general_params['solver_type'] == 'ctseg':
+            # if legendre was set, that we have both now!
+            if (solver_params['measure_gl'] or general_params['legendre_fit']):
+                write_to_h5['G_time_orig_{}'.format(icrsh)] = solvers[icrsh].G_time_orig
+                write_to_h5['Gimp_l_{}'.format(icrsh)] = solvers[icrsh].G_l
+            if solver_params['measure_ft']:
+                write_to_h5['F_freq_{}'.format(icrsh)] = solvers[icrsh].F_freq
+                write_to_h5['F_time_{}'.format(icrsh)] = solvers[icrsh].F_time
+
+    return write_to_h5
+
+
+[docs] +def write(archive, sum_k, general_params, solver_params, solvers, it, is_sampling, + previous_mu, density_mat_pre, density_mat, deltaN=None, dens=None): + """ + Collects and writes results to archive. + """ + + if not mpi.is_master_node(): + return + + write_to_h5 = _compile_information(sum_k, general_params, solver_params, solvers, + previous_mu, density_mat_pre, density_mat, deltaN, dens) + + # Saves the results to last_iter + archive['DMFT_results']['iteration_count'] = it + for key, value in write_to_h5.items(): + archive['DMFT_results/last_iter'][key] = value + + # Permanently saves to h5 archive every h5_save_freq iterations + if ((not is_sampling and it % general_params['h5_save_freq'] == 0) + or (is_sampling and it % general_params['sampling_h5_save_freq'] == 0)): + + archive['DMFT_results'].create_group('it_{}'.format(it)) + for key, value in write_to_h5.items(): + # Full density matrix only written to last_iter - it is large + if 'full_dens_mat_' not in key and 'h_loc_diag_' not in key: + archive['DMFT_results/it_{}'.format(it)][key] = value + + # Saves CSC input + if general_params['csc']: + for dft_var in ['dft_update', 'dft_input', 'dft_misc_input']: + if dft_var in archive: + archive['DMFT_results/it_{}'.format(it)].create_group(dft_var) + for key, value in archive[dft_var].items(): + archive['DMFT_results/it_{}'.format(it)][dft_var][key] = value + for band_elem in ['_bands.dat', '_bands.dat.gnu', '_bands.projwfc_up', '_band.dat']: + if os.path.isfile('./{}{}'.format(general_params['seedname'], band_elem)): + os.rename('./{}{}'.format(general_params['seedname'], band_elem), + './{}{}_it{}'.format(general_params['seedname'], band_elem, it)) + for w90_elem in ['_hr.dat', '.wout']: + if os.path.isfile('./{}{}'.format(general_params['seedname'], w90_elem)): + os.rename('./{}{}'.format(general_params['seedname'], w90_elem), + './{}_it{}{}'.format(general_params['seedname'], it, w90_elem))
+ +
+ +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/dmft_tools/solver.html b/_modules/dmft_tools/solver.html new file mode 100644 index 00000000..ebfa0e89 --- /dev/null +++ b/_modules/dmft_tools/solver.html @@ -0,0 +1,1503 @@ + + + + + + dmft_tools.solver — solid_dmft documentation + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for dmft_tools.solver

+################################################################################
+#
+# solid_dmft - A versatile python wrapper to perform DFT+DMFT calculations
+#              utilizing the TRIQS software library
+#
+# Copyright (C) 2018-2020, ETH Zurich
+# Copyright (C) 2021, The Simons Foundation
+#      authors: A. Hampel, M. Merkel, and S. Beck
+#
+# solid_dmft is free software: you can redistribute it and/or modify it under the
+# terms of the GNU General Public License as published by the Free Software
+# Foundation, either version 3 of the License, or (at your option) any later
+# version.
+#
+# solid_dmft is distributed in the hope that it will be useful, but WITHOUT ANY
+# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
+# PARTICULAR PURPOSE. See the GNU General Public License for more details.
+
+# You should have received a copy of the GNU General Public License along with
+# solid_dmft (in the file COPYING.txt in this directory). If not, see
+# <http://www.gnu.org/licenses/>.
+#
+################################################################################
+import numpy as np
+from itertools import product
+
+from triqs.gf import MeshImTime, MeshReTime, MeshReFreq, MeshLegendre, Gf, BlockGf, make_hermitian, Omega, iOmega_n, make_gf_from_fourier, fit_hermitian_tail
+from triqs.gf.tools import inverse, make_zero_tail
+from triqs.gf.descriptors import Fourier
+from triqs.operators import c_dag, c, Operator
+import triqs.utility.mpi as mpi
+from h5 import HDFArchive
+
+from . import legendre_filter
+from .matheval import MathExpr
+
+
+[docs] +def get_n_orbitals(sum_k): + """ + determines the number of orbitals within the + solver block structure. + + Parameters + ---------- + sum_k : dft_tools sumk object + + Returns + ------- + n_orb : dict of int + number of orbitals for up / down as dict for SOC calculation + without up / down block up holds the number of orbitals + """ + n_orbitals = [{'up': 0, 'down': 0} for i in range(sum_k.n_inequiv_shells)] + for icrsh in range(sum_k.n_inequiv_shells): + for block, n_orb in sum_k.gf_struct_solver[icrsh].items(): + if 'down' in block: + n_orbitals[icrsh]['down'] += sum_k.gf_struct_solver[icrsh][block] + else: + n_orbitals[icrsh]['up'] += sum_k.gf_struct_solver[icrsh][block] + + return n_orbitals
+ + +def _gf_fit_tail_fraction(Gf, fraction=0.4, replace=None, known_moments=None): + """ + fits the tail of Gf object by making a polynomial + fit of the Gf on the given fraction of the Gf mesh + and replacing that part of the Gf by the fit + + 0.4 fits the last 40% of the Gf and replaces the + part with the tail + + Parameters + ---------- + Gf : BlockGf (Green's function) object + fraction: float, optional default 0.4 + fraction of the Gf to fit + replace: float, optional default fraction + fraction of the Gf to replace + known_moments: np.array + known moments as numpy array + Returns + ------- + Gf_fit : BlockGf (Green's function) object + fitted Gf + """ + + Gf_fit = Gf.copy() + # if no replace factor is given use the same fraction + if not replace: + replace = fraction + + for i, bl in enumerate(Gf_fit.indices): + Gf_fit[bl].mesh.set_tail_fit_parameters(tail_fraction=fraction) + if known_moments: + tail = Gf_fit[bl].fit_hermitian_tail(known_moments[i]) + else: + tail = Gf_fit[bl].fit_hermitian_tail() + nmax_frac = int(len(Gf_fit[bl].mesh)/2 * (1-replace)) + Gf_fit[bl].replace_by_tail(tail[0],n_min=nmax_frac) + + return Gf_fit + +
+[docs] +class SolverStructure: + + r''' + Handles all solid_dmft solver objects and contains TRIQS solver instance. + + Attributes + ---------- + + Methods + ------- + solve(self, **kwargs) + solve impurity problem + ''' + +
+[docs] + def __init__(self, general_params, solver_params, advanced_params, sum_k, icrsh, h_int, iteration_offset, solver_struct_ftps): + r''' + Initialisation of the solver instance with h_int for impurity "icrsh" based on soliDMFT parameters. + + Parameters + ---------- + general_paramuters: dict + general parameters as dict + solver_params: dict + solver-specific parameters as dict + sum_k: triqs.dft_tools.sumk object + SumkDFT instance + icrsh: int + correlated shell index + h_int: triqs.operator object + interaction Hamiltonian of correlated shell + iteration_offset: int + number of iterations this run is based on + ''' + + self.general_params = general_params + self.solver_params = solver_params + self.advanced_params = advanced_params + self.sum_k = sum_k + self.icrsh = icrsh + self.h_int = h_int + self.iteration_offset = iteration_offset + self.solver_struct_ftps = solver_struct_ftps + if solver_params.get("random_seed") is None: + self.random_seed_generator = None + else: + self.random_seed_generator = MathExpr(solver_params["random_seed"]) + + # initialize solver object, options are cthyb + if self.general_params['solver_type'] == 'cthyb': + from triqs_cthyb.version import triqs_cthyb_hash, version + + # sets up necessary GF objects on ImFreq + self._init_ImFreq_objects() + # sets up solver + self.triqs_solver = self._create_cthyb_solver() + self.git_hash = triqs_cthyb_hash + self.version = version + + elif self.general_params['solver_type'] == 'ctint': + from triqs_ctint.version import triqs_ctint_hash, version + + # sets up necessary GF objects on ImFreq + self._init_ImFreq_objects() + # sets up solver + self.triqs_solver = self._create_ctint_solver() + self.solver_params['measure_histogram'] = self.solver_params.pop('measure_pert_order') + self.solver_params['use_double_insertion'] = self.solver_params.pop('move_double') + self.git_hash = triqs_ctint_hash + self.version = version + + elif self.general_params['solver_type'] == 'hubbardI': + from triqs_hubbardI.version import triqs_hubbardI_hash, version + + # sets up necessary GF objects on ImFreq + self._init_ImFreq_objects() + self._init_ReFreq_hubbardI() + # sets up solver + self.triqs_solver = self._create_hubbardI_solver() + self.git_hash = triqs_hubbardI_hash + self.version = version + + elif self.general_params['solver_type'] == 'hartree': + from triqs_hartree_fock.version import triqs_hartree_fock_hash, version + + # sets up necessary GF objects on ImFreq + self._init_ImFreq_objects() + self._init_ReFreq_hartree() + # sets up solver + self.triqs_solver = self._create_hartree_solver() + self.git_hash = triqs_hartree_fock_hash + self.version = version + + elif self.general_params['solver_type'] == 'ftps': + from forktps.version import forktps_hash, version + + # additional parameters + self.bathfit_adjusted = self.iteration_offset != 0 + self.path_to_gs_accepted = bool(self.solver_params['path_to_gs']) + self.convert_ftps = {'up': 'up', 'down': 'dn', 'ud': 'ud', 'ud_0': 'ud_0', 'ud_1': 'ud_1'} + self.gf_struct = self.sum_k.gf_struct_solver_list[self.icrsh] + for ct, block in enumerate(self.gf_struct): + spin = block[0].split('_')[0] if not self.sum_k.corr_shells[self.icrsh]['SO'] else block[0] + # FTPS solver does not know a more complicated gf_struct list of indices, so make sure the order is correct! + indices = block[1] if not self.sum_k.corr_shells[self.icrsh]['SO'] else list(range(3)) + self.gf_struct[ct] = (self.convert_ftps[spin], indices) + # sets up necessary GF objects on ReFreq + self._init_ReFreq_objects() + self.bathfit_adjusted = self.iteration_offset != 0 + self.path_to_gs_accepted = bool(self.solver_params['path_to_gs']) + # sets up solver + self.triqs_solver, self.sector_params, self.dmrg_params, self.tevo_params, self.calc_me, self.calc_mapping = self._create_ftps_solver() + self.git_hash = forktps_hash + self.version = version + + elif self.general_params['solver_type'] == 'inchworm': + + # sets up necessary GF objects on ImFreq + self._init_ImFreq_objects() + # sets up solver + self.triqs_solver = self._create_inchworm_solver() + # self.git_hash = inchworm_hash + + elif self.general_params['solver_type'] == 'ctseg': + from triqs_ctseg.version import triqs_ctseg_hash, version + + # sets up necessary GF objects on ImFreq + self._init_ImFreq_objects() + # sets up solver + self.triqs_solver = self._create_ctseg_solver() + self.git_hash = triqs_ctseg_hash + self.version = version
+ + + # ******************************************************************** + # initialize Freq and Time objects + # ******************************************************************** + + def _init_ImFreq_objects(self): + r''' + Initialize all ImFreq objects + ''' + + # create all ImFreq instances + self.n_iw = self.general_params['n_iw'] + self.G_freq = self.sum_k.block_structure.create_gf(ish=self.icrsh, gf_function=Gf, space='solver', + mesh=self.sum_k.mesh) + # copy + self.Sigma_freq = self.G_freq.copy() + self.G0_freq = self.G_freq.copy() + self.G_freq_unsym = self.G_freq.copy() + self.Delta_freq = self.G_freq.copy() + + # create all ImTime instances + self.n_tau = self.general_params['n_tau'] + self.G_time = self.sum_k.block_structure.create_gf(ish=self.icrsh, gf_function=Gf, space='solver', + mesh=MeshImTime(beta=self.general_params['beta'], + S='Fermion', n_tau=self.n_tau) + ) + # copy + self.Delta_time = self.G_time.copy() + + # create all Legendre instances + if (self.general_params['solver_type'] == 'cthyb' and self.solver_params['measure_G_l'] + or self.general_params['solver_type'] == 'cthyb' and self.general_params['legendre_fit'] + or self.general_params['solver_type'] == 'ctseg' and self.solver_params['measure_gl'] + or self.general_params['solver_type'] == 'ctseg' and self.general_params['legendre_fit'] + or self.general_params['solver_type'] == 'hubbardI' and self.solver_params['measure_G_l']): + + self.n_l = self.general_params['n_l'] + self.G_l = self.sum_k.block_structure.create_gf(ish=self.icrsh, gf_function=Gf, space='solver', + mesh=MeshLegendre(beta=self.general_params['beta'], + max_n=self.n_l, S='Fermion') + ) + # move original G_freq to G_freq_orig + self.G_time_orig = self.G_time.copy() + + if self.general_params['solver_type'] in ['cthyb', 'hubbardI'] and self.solver_params['measure_density_matrix']: + self.density_matrix = None + self.h_loc_diagonalization = None + + if self.general_params['solver_type'] in ['cthyb'] and self.general_params['measure_chi'] != 'none': + self.O_time = None + + def _init_ReFreq_objects(self): + r''' + Initialize all ReFreq objects + ''' + + # create all ReFreq instances + self.n_w = self.general_params['n_w'] + self.G_freq = self.sum_k.block_structure.create_gf(ish=self.icrsh, gf_function=Gf, space='solver', + mesh=self.sum_k.mesh) + # copy + self.Sigma_freq = self.G_freq.copy() + self.G0_freq = self.G_freq.copy() + self.Delta_freq = self.G_freq.copy() + self.G_freq_unsym = self.G_freq.copy() + + # create another Delta_freq for the solver, which uses different spin indices + n_orb = self.sum_k.corr_shells[self.icrsh]['dim'] + n_orb = n_orb//2 if self.sum_k.corr_shells[self.icrsh]['SO'] else n_orb + gf = Gf(target_shape = (n_orb, n_orb), mesh=MeshReFreq(n_w=self.n_w, window=self.general_params['w_range'])) + + self.Delta_freq_solver = BlockGf(name_list =tuple([block[0] for block in self.gf_struct]), block_list = (gf, gf), make_copies = True) + + # create all ReTime instances + # FIXME: dummy G_time, since time_steps will be recalculated during run + #time_steps = int(2 * self.solver_params['time_steps'] * self.solver_params['refine_factor']) if self.solver_params['n_bath'] != 0 else int(2 * self.solver_params['time_steps']) + time_steps = int(2 * 1 * self.solver_params['refine_factor']) if self.solver_params['n_bath'] != 0 else int(2 * 1) + self.G_time = self.sum_k.block_structure.create_gf(ish=self.icrsh, gf_function=Gf, space='solver', + mesh=MeshReTime(n_t=time_steps+1, + window=[0,time_steps*self.solver_params['dt']]) + ) + + def _init_ReFreq_hubbardI(self): + r''' + Initialize all ReFreq objects + ''' + + # create all ReFreq instances + self.n_w = self.general_params['n_w'] + self.G_Refreq = self.sum_k.block_structure.create_gf(ish=self.icrsh, gf_function=Gf, space='solver', + mesh=MeshReFreq(n_w=self.n_w, window=self.general_params['w_range']) + ) + # copy + self.Sigma_Refreq = self.G_Refreq.copy() + self.G0_Refreq = self.G_Refreq.copy() + + def _init_ReFreq_hartree(self): + r''' + Initialize all ReFreq objects + ''' + + # create all ReFreq instances + self.n_w = self.general_params['n_w'] + self.Sigma_Refreq = self.sum_k.block_structure.create_gf(ish=self.icrsh, gf_function=Gf, space='solver', + mesh=MeshReFreq(n_w=self.n_w, window=self.general_params['w_range']) + ) + + # ******************************************************************** + # solver-specific solve() command + # ******************************************************************** + +
+[docs] + def solve(self, **kwargs): + r''' + solve impurity problem with current solver + ''' + + if self.random_seed_generator is None: + random_seed = {} + else: + random_seed = { "random_seed": int(self.random_seed_generator(it=kwargs["it"], rank=mpi.rank)) } + + if self.general_params['solver_type'] == 'cthyb': + + if self.general_params['cthyb_delta_interface']: + mpi.report('\n Using the delta interface for cthyb passing Delta(tau) and Hloc0 directly.') + # prepare solver input + sumk_eal = self.sum_k.eff_atomic_levels()[self.icrsh] + solver_eal = self.sum_k.block_structure.convert_matrix(sumk_eal, space_from='sumk', ish_from=self.sum_k.inequiv_to_corr[self.icrsh]) + # fill Delta_time from Delta_freq sum_k to solver + for name, g0 in self.G0_freq: + self.Delta_freq[name] << iOmega_n - inverse(g0) - solver_eal[name] + known_moments = make_zero_tail(self.Delta_freq[name], 1) + tail, err = fit_hermitian_tail(self.Delta_freq[name], known_moments) + # without SOC delta_tau needs to be real + if not self.sum_k.SO == 1: + self.triqs_solver.Delta_tau[name] << make_gf_from_fourier(self.Delta_freq[name], self.triqs_solver.Delta_tau.mesh, tail).real + else: + self.triqs_solver.Delta_tau[name] << make_gf_from_fourier(self.Delta_freq[name], self.triqs_solver.Delta_tau.mesh, tail) + + + # Make non-interacting operator for Hloc0 + Hloc_0 = Operator() + for spin, spin_block in solver_eal.items(): + for o1 in range(spin_block.shape[0]): + for o2 in range(spin_block.shape[1]): + # check if off-diag element is larger than threshold + if o1 != o2 and abs(spin_block[o1,o2]) < self.solver_params['off_diag_threshold']: + continue + else: + # TODO: adapt for SOC calculations, which should keep the imag part + Hloc_0 += spin_block[o1,o2].real/2 * (c_dag(spin,o1) * c(spin,o2) + c_dag(spin,o2) * c(spin,o1)) + self.solver_params['h_loc0'] = Hloc_0 + else: + # fill G0_freq from sum_k to solver + self.triqs_solver.G0_iw << self.G0_freq + + # update solver in h5 archive one last time for debugging if solve command crashes + if self.general_params['store_solver'] and mpi.is_master_node(): + with HDFArchive(self.general_params['jobname']+'/'+self.general_params['seedname']+'.h5', 'a') as archive: + if not 'it_-1' in archive['DMFT_input/solver']: + archive['DMFT_input/solver'].create_group('it_-1') + archive['DMFT_input/solver/it_-1'][f'S_{self.icrsh}'] = self.triqs_solver + archive['DMFT_input/solver/it_-1'][f'solve_params_{self.icrsh}'] = self.solver_params + archive['DMFT_input/solver/it_-1']['mpi_size'] = mpi.size + + # Solve the impurity problem for icrsh shell + # ************************************* + self.triqs_solver.solve(h_int=self.h_int, **{ **self.solver_params, **random_seed }) + # ************************************* + + # call postprocessing + self._cthyb_postprocessing() + + elif self.general_params['solver_type'] == 'ctint': + # fill G0_freq from sum_k to solver + self.triqs_solver.G0_iw << self.G0_freq + + if self.general_params['h_int_type'] == 'dynamic': + for b1, b2 in product(self.sum_k.gf_struct_solver_dict[self.icrsh].keys(), repeat=2): + self.triqs_solver.D0_iw[b1,b2] << self.U_iw[self.icrsh] + + # Solve the impurity problem for icrsh shell + # ************************************* + self.triqs_solver.solve(h_int=self.h_int, **{ **self.solver_params, **random_seed }) + # ************************************* + + # call postprocessing + self._ctint_postprocessing() + + elif self.general_params['solver_type'] == 'hubbardI': + # fill G0_freq from sum_k to solver + self.triqs_solver.G0_iw << self.G0_freq + + # Solve the impurity problem for icrsh shell + # ************************************* + # this is done on every node due to very slow bcast of the AtomDiag object as of now + self.triqs_solver.solve(h_int=self.h_int, calc_gtau=self.solver_params['measure_G_tau'], + calc_gw=True, calc_gl=self.solver_params['measure_G_l'], + calc_dm=self.solver_params['measure_density_matrix']) + # if density matrix is measured, get this too. Needs to be done here, + # because solver property 'dm' is not initialized/broadcastable + if self.solver_params['measure_density_matrix']: + self.density_matrix = self.triqs_solver.dm + self.h_loc_diagonalization = self.triqs_solver.ad + # ************************************* + + # call postprocessing + self._hubbardI_postprocessing() + + elif self.general_params['solver_type'] == 'hartree': + # fill G0_freq from sum_k to solver + self.triqs_solver.G0_iw << self.G0_freq + + # Solve the impurity problem for icrsh shell + # ************************************* + # this is done on every node due to very slow bcast of the AtomDiag object as of now + self.triqs_solver.solve(h_int=self.h_int, with_fock=self.solver_params['with_fock'], + one_shot=self.solver_params['one_shot'], + method=self.solver_params['method'], tol=self.solver_params['tol']) + + # call postprocessing + self._hartree_postprocessing() + + elif self.general_params['solver_type'] == 'ftps': + import forktps as ftps + from forktps.DiscreteBath import DiscretizeBath, TimeStepEstimation + from forktps.BathFitting import BathFitter + from forktps.Helpers import MakeGFstruct + # from . import OffDiagFitter as off_fitter + + def make_positive_definite(G): + # ensure that Delta is positive definite + for name, gf in G: + for orb, w in product(range(gf.target_shape[0]), gf.mesh): + if gf[orb,orb][w].imag > 0.0: + gf[orb,orb][w] = gf[orb,orb][w].real + 0.0j + return G + + # create h_loc solver object + h_loc = ftps.solver_core.Hloc(MakeGFstruct(self.Delta_freq_solver), SO=bool(self.sum_k.corr_shells[self.icrsh]['SO'])) + # need eff_atomic_levels + sumk_eal = self.sum_k.eff_atomic_levels()[self.icrsh] + + # fill Delta_time from Delta_freq sum_k to solver + for name, g0 in self.G0_freq: + spin = name.split('_')[0] if not self.sum_k.corr_shells[self.icrsh]['SO'] else name + ftps_name = self.convert_ftps[spin] + solver_eal = self.sum_k.block_structure.convert_matrix(sumk_eal, space_from='sumk', ish_from=self.sum_k.inequiv_to_corr[self.icrsh])[name] + self.Delta_freq[name] << Omega + 1j * self.general_params['eta'] - inverse(g0) - solver_eal + # solver Delta is symmetrized by just using 'up_0' channel + self.Delta_freq_solver[ftps_name] << Omega + 1j * self.general_params['eta'] - inverse(g0) - solver_eal + + # ensure that Delta is positive definite + self.Delta_freq_solver = make_positive_definite(self.Delta_freq_solver) + + # remove off-diagonal terms + if self.general_params['diag_delta']: + for name, delta in self.Delta_freq_solver: + for i_orb, j_orb in product(range(delta.target_shape[0]),range(delta.target_shape[1])): + if i_orb != j_orb: + delta[i_orb,j_orb] << 0.0 + 0.0j + + # option to increase bath sites, but run with previous eta to get increased accuracy + if self.solver_params['n_bath'] != 0 and self.solver_params['refine_factor'] != 1: + if not self.bathfit_adjusted or self.bathfit_adjusted and self.iteration_offset > 0: + mpi.report('Rescaling "n_bath" with a factor of {}'.format(self.solver_params['refine_factor'])) + self.solver_params['n_bath'] = int(self.solver_params['refine_factor']*self.solver_params['n_bath']) + + if self.solver_params['bath_fit']: + + # bathfitter + # FIXME: this is temporary, since off-diagonal Bathfitter is not yet integrated in FTPS + if self.sum_k.corr_shells[self.icrsh]['SO']: + fitter = off_fitter.OffDiagBathFitter(Nb=self.solver_params['n_bath']) if (self.solver_params['refine_factor'] != 1 and self.solver_params['n_bath'] != 0) else off_fitter.OffDiagBathFitter(Nb=None) + Delta_discrete = fitter.FitBath(Delta=self.Delta_freq_solver, eta=self.general_params['eta'], ignoreWeight=self.solver_params['ignore_weight'], + SO=bool(self.sum_k.corr_shells[self.icrsh]['SO'])) + else: + fitter = BathFitter(Nb=self.solver_params['n_bath']) if self.solver_params['n_bath'] != 0 else BathFitter(Nb=None) + Delta_discrete = fitter.FitBath(Delta=self.Delta_freq_solver, eta=self.general_params['eta'], ignoreWeight=self.solver_params['ignore_weight']) + else: + # discretizebath + gap_interval = self.solver_params['enforce_gap'] if self.solver_params['enforce_gap'] != 'none' else None + Delta_discrete = DiscretizeBath(Delta=self.Delta_freq_solver, Nb=self.solver_params['n_bath'], gap=gap_interval, + SO=bool(self.sum_k.corr_shells[self.icrsh]['SO'])) + + # should be done only once after the first iteration + if self.solver_params['n_bath'] != 0 and self.solver_params['refine_factor'] != 1: + if not self.bathfit_adjusted or self.bathfit_adjusted and self.iteration_offset > 0: + mpi.report('Rescaling "1/eta" with a factor of {}'.format(self.solver_params['refine_factor'])) + # rescaling eta + self.general_params['eta'] /= self.solver_params['refine_factor'] + + if not self.bathfit_adjusted: + self.bathfit_adjusted = True + + self.triqs_solver.b = Delta_discrete + # calculate time_steps + time_steps = TimeStepEstimation(self.triqs_solver.b, eta=self.general_params['eta'], dt=self.solver_params['dt']) + mpi.report('TimeStepEstimation returned {} with given bath, "eta" = {} and "dt" = {}'.format(time_steps, self.general_params['eta'], + self.solver_params['dt'])) + # need to update tevo_params and G_time + self.tevo_params.time_steps = time_steps + self.G_time = self.sum_k.block_structure.create_gf(ish=self.icrsh, gf_function=Gf, space='solver', + mesh=MeshReTime(n_t=2*time_steps+1, + window=[0,2*time_steps*self.solver_params['dt']]) + ) + + + # fill Hloc FTPS object + # get hloc_dft from effective atomic levels + for name, gf in self.Delta_freq: + solver_eal = self.sum_k.block_structure.convert_matrix(sumk_eal, space_from='sumk', ish_from=self.sum_k.inequiv_to_corr[self.icrsh])[name] + if not self.sum_k.corr_shells[self.icrsh]['SO']: + name = self.convert_ftps[name.split('_')[0]] + solver_eal = solver_eal.real + # remove off-diagonal terms + if self.general_params['diag_delta']: + solver_eal = np.diag(np.diag(solver_eal)) + h_loc.Fill(name, solver_eal) + + # fill solver h_loc + self.triqs_solver.e0 = h_loc + + # FIXME: unfortunately, in the current implementation the solver initializations aren't included yet in dmft_cycle, + # so for debugging it is done here again + # store solver to h5 archive + if self.general_params['store_solver'] and mpi.is_master_node(): + archive = HDFArchive(self.general_params['jobname']+'/'+self.general_params['seedname']+'.h5', 'a') + archive['DMFT_input/solver'].create_group('it_-1') + archive['DMFT_input/solver/it_-1']['Delta'] = self.Delta_freq_solver + archive['DMFT_input/solver/it_-1']['S_'+str(self.icrsh)] = self.triqs_solver + + # Solve the impurity problem for icrsh shell + # ************************************* + path_to_gs = self.solver_params['path_to_gs'] if self.solver_params['path_to_gs'] != 'none' and self.path_to_gs_accepted else None + # fix to make sure this is only done in iteration 1 + if self.path_to_gs_accepted: + self.path_to_gs_accepted = False + if path_to_gs != 'postprocess': + self.triqs_solver.solve(h_int=self.h_int, params_GS=self.dmrg_params, params_partSector=self.sector_params, + tevo=self.tevo_params, eta=self.general_params['eta'], calc_me = self.calc_me, + state_storage=self.solver_params['state_storage'],path_to_gs=path_to_gs) + else: + self.triqs_solver.post_process(h_int=self.h_int, params_GS=self.dmrg_params, params_partSector=self.dmrg_params, + tevo=self.tevo_params, eta=self.general_params['eta'], calc_me = self.calc_me, + state_storage=self.solver_params['state_storage']) + # ************************************* + + # call postprocessing + self._ftps_postprocessing() + + elif self.general_params['solver_type'] == 'inchworm': + # fill Delta_time from Delta_freq sum_k to solver + self.triqs_solver.Delta_tau << make_gf_from_fourier(self.Delta_freq).real + + # Solve the impurity problem for icrsh shell + # ************************************* + self.triqs_solver.solve(h_int=self.h_int, **{ **self.solver_params, **random_seed }) + # ************************************* + + # call postprocessing + self._inchworm_postprocessing() + + if self.general_params['solver_type'] == 'ctseg': + # fill G0_freq from sum_k to solver + self.triqs_solver.G0_iw << self.G0_freq + + + if self.general_params['h_int_type'] == 'dynamic': + for b1, b2 in product(self.sum_k.gf_struct_solver_dict[self.icrsh].keys(), repeat=2): + self.triqs_solver.D0_iw[b1+"|"+b2] << self.U_iw[self.icrsh] + + # Solve the impurity problem for icrsh shell + # ************************************* + self.triqs_solver.solve(h_int=self.h_int, **{ **self.solver_params, **random_seed }) + # ************************************* + + # call postprocessing + self._ctseg_postprocessing() + + return
+ + + # ******************************************************************** + # create solvers objects + # ******************************************************************** + + def _create_cthyb_solver(self): + r''' + Initialize cthyb solver instance + ''' + from triqs_cthyb.solver import Solver as cthyb_solver + + gf_struct = self.sum_k.gf_struct_solver_list[self.icrsh] + # Construct the triqs_solver instances + if self.solver_params['measure_G_l']: + triqs_solver = cthyb_solver(beta=self.general_params['beta'], gf_struct=gf_struct, + n_iw=self.general_params['n_iw'], n_tau=self.general_params['n_tau'], + n_l=self.general_params['n_l'], delta_interface=self.general_params['cthyb_delta_interface']) + else: + triqs_solver = cthyb_solver(beta=self.general_params['beta'], gf_struct=gf_struct, + n_iw=self.general_params['n_iw'], n_tau=self.general_params['n_tau'], + delta_interface=self.general_params['cthyb_delta_interface']) + + return triqs_solver + + def _create_ctint_solver(self): + r''' + Initialize ctint solver instance + ''' + from triqs_ctint import Solver as ctint_solver + + gf_struct = self.sum_k.gf_struct_solver_list[self.icrsh] + + if self.general_params['h_int_type'] == 'dynamic': + self.U_iw = None + if mpi.is_master_node(): + with HDFArchive(self.general_params['jobname']+'/'+self.general_params['seedname']+'.h5', 'r') as archive: + self.U_iw = archive['dynamic_U']['U_iw'] + self.U_iw = mpi.bcast(self.U_iw) + n_iw_dyn = self.U_iw[self.icrsh].mesh.last_index()+1 + # Construct the triqs_solver instances + triqs_solver = ctint_solver(beta=self.general_params['beta'], gf_struct=gf_struct, + n_iw=self.general_params['n_iw'], n_tau=self.general_params['n_tau'], use_D=True, use_Jperp=False, + n_iw_dynamical_interactions=n_iw_dyn,n_tau_dynamical_interactions=(int(n_iw_dyn*2.5))) + else: + # Construct the triqs_solver instances + triqs_solver = ctint_solver(beta=self.general_params['beta'], gf_struct=gf_struct, + n_iw=self.general_params['n_iw'], n_tau=self.general_params['n_tau'], use_D=False, use_Jperp=False) + + return triqs_solver + + def _create_hubbardI_solver(self): + r''' + Initialize hubbardI solver instance + ''' + from triqs_hubbardI import Solver as hubbardI_solver + + gf_struct = self.sum_k.gf_struct_solver_list[self.icrsh] + # Construct the triqs_solver instances + if self.solver_params['measure_G_l']: + triqs_solver = hubbardI_solver(beta=self.general_params['beta'], gf_struct=gf_struct, + n_iw=self.general_params['n_iw'], n_tau=self.general_params['n_tau'], + n_l=self.general_params['n_l'], n_w=self.general_params['n_w'], + w_min=self.general_params['w_range'][0], w_max=self.general_params['w_range'][1], + idelta=self.general_params['eta']) + else: + triqs_solver = hubbardI_solver(beta=self.general_params['beta'], gf_struct=gf_struct, + n_iw=self.general_params['n_iw'], n_tau=self.general_params['n_tau'], + n_w=self.general_params['n_w'], idelta=self.general_params['eta'], + w_min=self.general_params['w_range'][0], w_max=self.general_params['w_range'][1]) + + return triqs_solver + + def _make_spin_equal(self, Sigma): + + # if not SOC than average up and down + if not self.general_params['magnetic'] and not self.sum_k.SO == 1: + Sigma['up_0'] = 0.5*(Sigma['up_0'] + Sigma['down_0']) + Sigma['down_0'] = Sigma['up_0'] + + return Sigma + + def _create_hartree_solver(self): + r''' + Initialize hartree_fock solver instance + ''' + from triqs_hartree_fock import ImpuritySolver as hartree_solver + + gf_struct = self.sum_k.gf_struct_solver_list[self.icrsh] + + # Construct the triqs_solver instances + # Always initialize the solver with dc_U and dc_J equal to U and J and let the _interface_hartree_dc function + # take care of changing the parameters + triqs_solver = hartree_solver(beta=self.general_params['beta'], gf_struct=gf_struct, + n_iw=self.general_params['n_iw'], force_real=self.solver_params['force_real'], + symmetries=[self._make_spin_equal], + dc_U= self.general_params['U'][self.icrsh], + dc_J= self.general_params['J'][self.icrsh] + ) + + def _interface_hartree_dc(hartree_instance, general_params, advanced_params, icrsh): + """ Modifies in-place class attributes to infercace with options in solid_dmft + for the moment supports only DC-relevant parameters + + Parameters + ---------- + general_params : dict + solid_dmft general parameter dictionary + advanced_params : dict + solid_dmft advanced parameter dictionary + icrsh : int + correlated shell number + """ + for key in ['dc', 'dc_type']: + if key in general_params and general_params[key] != 'none': + setattr(hartree_instance, key, general_params[key]) + + for key in ['dc_factor', 'dc_fixed_value']: + if key in advanced_params and advanced_params[key] != 'none': + setattr(hartree_instance, key, advanced_params[key]) + + #list valued keys + for key in ['dc_U', 'dc_J', 'dc_fixed_occ']: + if key in advanced_params and advanced_params[key] != 'none': + setattr(hartree_instance, key, advanced_params[key][icrsh]) + + # Handle special cases + if 'dc_dmft' in general_params: + if general_params['dc_dmft'] == False: + mpi.report('HARTREE SOLVER: Warning dft occupation in the DC calculations are meaningless for the hartree solver, reverting to dmft occupations') + + if hartree_instance.dc_type == 0 and not self.general_params['magnetic']: + mpi.report(f"HARTREE SOLVER: Detected dc_type = {hartree_instance.dc_type}, changing to 'cFLL'") + hartree_instance.dc_type = 'cFLL' + elif hartree_instance.dc_type == 0 and self.general_params['magnetic']: + mpi.report(f"HARTREE SOLVER: Detected dc_type = {hartree_instance.dc_type}, changing to 'sFLL'") + hartree_instance.dc_type = 'sFLL' + elif hartree_instance.dc_type == 1: + mpi.report(f"HARTREE SOLVER: Detected dc_type = {hartree_instance.dc_type}, changing to 'cHeld'") + hartree_instance.dc_type = 'cHeld' + elif hartree_instance.dc_type == 2 and not self.general_params['magnetic']: + mpi.report(f"HARTREE SOLVER: Detected dc_type = {hartree_instance.dc_type}, changing to 'cAMF'") + hartree_instance.dc_type = 'cAMF' + elif hartree_instance.dc_type == 2 and self.general_params['magnetic']: + mpi.report(f"HARTREE SOLVER: Detected dc_type = {hartree_instance.dc_type}, changing to 'sAMF'") + hartree_instance.dc_type = 'sAMF' + + # Give dc information to the solver in order to customize DC calculation + _interface_hartree_dc(triqs_solver, self.general_params, self.advanced_params, self.icrsh) + + return triqs_solver + + def _create_inchworm_solver(self): + r''' + Initialize inchworm solver instance + ''' + + return triqs_solver + + def _create_ctseg_solver(self): + r''' + Initialize cthyb solver instance + ''' + from triqs_ctseg import Solver as ctseg_solver + + if self.general_params['h_int_type'] == 'dynamic': + self.U_iw = None + if mpi.is_master_node(): + with HDFArchive(self.general_params['jobname']+'/'+self.general_params['seedname']+'.h5', 'r') as archive: + self.U_iw = archive['dynamic_U']['U_iw'] + self.U_iw = mpi.bcast(self.U_iw) + n_w_b_nn = self.U_iw[self.icrsh].mesh.last_index()+1 + else: + n_w_b_nn = 1001 + + gf_struct = self.sum_k.gf_struct_solver_list[self.icrsh] + # Construct the triqs_solver instances + if self.solver_params['measure_gl']: + triqs_solver = ctseg_solver(beta=self.general_params['beta'], gf_struct=gf_struct, + n_iw=self.general_params['n_iw'], n_tau=self.general_params['n_tau'], + n_legendre_g=self.general_params['n_l'], n_w_b_nn = n_w_b_nn, n_tau_k=int(n_w_b_nn*2.5), n_tau_jperp=int(n_w_b_nn*2.5)) + else: + triqs_solver = ctseg_solver(beta=self.general_params['beta'], gf_struct=gf_struct, + n_iw=self.general_params['n_iw'], n_tau=self.general_params['n_tau'], + n_w_b_nn = n_w_b_nn, n_tau_k=int(n_w_b_nn*2.5), n_tau_jperp=int(n_w_b_nn*2.5)) + + return triqs_solver + + def _create_ftps_solver(self): + r''' + Initialize ftps solver instance + ''' + import forktps as ftps + + # convert self.solver_struct_ftps to mapping and solver-friendly list + if not self.sum_k.corr_shells[self.icrsh]['SO']: + # mapping dictionary + calc_mapping = {self.solver_struct_ftps[self.icrsh][deg_shell][0]: + self.solver_struct_ftps[self.icrsh][deg_shell][1:] for deg_shell in range(len(self.solver_struct_ftps[self.icrsh]))} + # make solver-friendly list from mapping keys + calc_me = [[item.split('_')[0], int(item.split('_')[1])] for item in calc_mapping.keys()] + # replace 'down' with 'dn' + calc_me = [[item[0].replace('down','dn'),item[1]] for item in calc_me] + else: + # for SOC we just end up calculating everything for now + # TODO: perhaps skip down channel + calc_mapping = None + calc_me = [[f'ud_{i}',j] for i,j in product(range(2), range(3))] + + # create solver + triqs_solver = ftps.Solver(gf_struct=self.gf_struct, nw=self.general_params['n_w'], + wmin=self.general_params['w_range'][0], wmax=self.general_params['w_range'][1]) + + + # create partSector params + sector_params = ftps.solver.DMRGParams(maxmI=50, maxmIB=50, maxmB=50, tw=1e-10, nmax=5, sweeps=5) + + # for now prep_imagTevo, prep_method and nmax hard-coded + # create DMRG params + dmrg_params = ftps.solver.DMRGParams(maxmI=self.solver_params['dmrg_maxmI'], maxmIB=self.solver_params['dmrg_maxmIB'], + maxmB=self.solver_params['dmrg_maxmB'], tw=self.solver_params['dmrg_tw'], + prep_imagTevo=True, prep_method='TEBD', sweeps=self.solver_params['sweeps'], nmax=2, + prep_time_steps=5, napph=2 + ) + + # create TEVO params + tevo_params = ftps.solver.TevoParams(dt=self.solver_params['dt'], time_steps=1, #dummy, will be updated during the run + maxmI=self.solver_params['maxmI'], maxmIB=self.solver_params['maxmIB'], + maxmB=self.solver_params['maxmB'], tw=self.solver_params['tw']) + + return triqs_solver, sector_params, dmrg_params, tevo_params, calc_me, calc_mapping + + # ******************************************************************** + # post-processing of solver output + # ******************************************************************** + + def _cthyb_postprocessing(self): + r''' + Organize G_freq, G_time, Sigma_freq and G_l from cthyb solver + ''' + + def set_Gs_from_G_l(): + + # create new G_freq and G_time + for i, g in self.G_l: + g.enforce_discontinuity(np.identity(g.target_shape[0])) + # set G_freq from Legendre and Fouriertransform to get G_time + self.G_freq[i].set_from_legendre(g) + self.G_time[i].set_from_legendre(g) + + # Symmetrize + self.G_freq << make_hermitian(self.G_freq) + self.G_freq_unsym << self.G_freq + self.sum_k.symm_deg_gf(self.G_freq, ish=self.icrsh) + self.sum_k.symm_deg_gf(self.G_time, ish=self.icrsh) + # Dyson equation to get Sigma_freq + self.Sigma_freq << inverse(self.G0_freq) - inverse(self.G_freq) + + return + + # get Delta_time from solver + self.Delta_time << self.triqs_solver.Delta_tau + + # if measured in Legendre basis, get G_l from solver too + if self.solver_params['measure_G_l']: + # store original G_time into G_time_orig + self.G_time_orig << self.triqs_solver.G_tau + self.G_l << self.triqs_solver.G_l + # get G_time, G_freq, Sigma_freq from G_l + set_Gs_from_G_l() + + else: + self.G_freq << make_hermitian(self.triqs_solver.G_iw) + self.G_freq_unsym << self.G_freq + self.sum_k.symm_deg_gf(self.G_freq, ish=self.icrsh) + # set G_time + self.G_time << self.triqs_solver.G_tau + self.sum_k.symm_deg_gf(self.G_time, ish=self.icrsh) + + if self.general_params['legendre_fit']: + self.G_time_orig << self.triqs_solver.G_tau + # run the filter + self.G_l << legendre_filter.apply(self.G_time, self.general_params['n_l']) + # get G_time, G_freq, Sigma_freq from G_l + set_Gs_from_G_l() + elif self.solver_params['perform_tail_fit'] and not self.general_params['legendre_fit']: + # if tailfit has been used replace Sigma with the tail fitted Sigma from cthyb + self.Sigma_freq << self.triqs_solver.Sigma_iw + self.sum_k.symm_deg_gf(self.Sigma_freq, ish=self.icrsh) + else: + # obtain Sigma via dyson from symmetrized G_freq + self.Sigma_freq << inverse(self.G0_freq) - inverse(self.G_freq) + + # if density matrix is measured, get this too + if self.solver_params['measure_density_matrix']: + self.density_matrix = self.triqs_solver.density_matrix + self.h_loc_diagonalization = self.triqs_solver.h_loc_diagonalization + + if self.solver_params['measure_pert_order']: + self.perturbation_order = self.triqs_solver.perturbation_order + self.perturbation_order_total = self.triqs_solver.perturbation_order_total + + if self.general_params['measure_chi'] != 'none': + self.O_time = self.triqs_solver.O_tau + + return + + def _ctint_postprocessing(self): + r''' + Organize G_freq, G_time, Sigma_freq and G_l from cthyb solver + ''' + #TODO + + # def set_Gs_from_G_l(): + + # # create new G_freq and G_time + # for i, g in self.G_l: + # g.enforce_discontinuity(np.identity(g.target_shape[0])) + # # set G_freq from Legendre and Fouriertransform to get G_time + # self.G_freq[i].set_from_legendre(g) + # self.G_time[i] << Fourier(self.G_freq[i]) + # # Symmetrize + # self.G_freq << make_hermitian(self.G_freq) + # # Dyson equation to get Sigma_freq + # self.Sigma_freq << inverse(self.G0_freq) - inverse(self.G_freq) + + # return + + self.G_freq << make_hermitian(self.triqs_solver.G_iw) + self.G_freq_unsym << self.G_freq + self.sum_k.symm_deg_gf(self.G_freq, ish=self.icrsh) + self.Sigma_freq << inverse(self.G0_freq) - inverse(self.G_freq) + self.G_time << Fourier(self.G_freq) + + # TODO: probably not needed/sensible + # if self.general_params['legendre_fit']: + # self.G_freq_orig << self.triqs_solver.G_iw + # # run the filter + # self.G_l << legendre_filter.apply(self.G_time, self.general_params['n_l']) + # # get G_time, G_freq, Sigma_freq from G_l + # set_Gs_from_G_l() + + if self.solver_params['measure_histogram']: + self.perturbation_order = self.triqs_solver.histogram + + return + + def _hubbardI_postprocessing(self): + r''' + Organize G_freq, G_time, Sigma_freq and G_l from hubbardI solver + ''' + + # get everything from solver + self.Sigma_freq << self.triqs_solver.Sigma_iw + self.G0_freq << self.triqs_solver.G0_iw + self.G0_Refreq << self.triqs_solver.G0_w + self.G_freq << make_hermitian(self.triqs_solver.G_iw) + self.G_freq_unsym << self.triqs_solver.G_iw + self.sum_k.symm_deg_gf(self.G_freq, ish=self.icrsh) + self.G_freq << self.G_freq + self.G_Refreq << self.triqs_solver.G_w + self.Sigma_Refreq << self.triqs_solver.Sigma_w + + # if measured in Legendre basis, get G_l from solver too + if self.solver_params['measure_G_l']: + self.G_l << self.triqs_solver.G_l + + if self.solver_params['measure_G_tau']: + self.G_time << self.triqs_solver.G_tau + + return + + def _hartree_postprocessing(self): + r''' + Organize G_freq, G_time, Sigma_freq and G_l from hartree solver + ''' + + # get everything from solver + self.G0_freq << self.triqs_solver.G0_iw + self.G_freq_unsym << self.triqs_solver.G_iw + self.sum_k.symm_deg_gf(self.G_freq, ish=self.icrsh) + self.G_freq << self.G_freq + for bl, gf in self.Sigma_freq: + self.Sigma_freq[bl] << self.triqs_solver.Sigma_HF[bl] + self.Sigma_Refreq[bl] << self.triqs_solver.Sigma_HF[bl] + self.G_time << Fourier(self.G_freq) + self.interaction_energy = self.triqs_solver.interaction_energy() + self.DC_energy = self.triqs_solver.DC_energy() + + return + + def _inchworm_postprocessing(self): + r''' + Organize G_freq, G_time, Sigma_freq and G_l from inchworm solver + ''' + + return + + def _ftps_postprocessing(self): + r''' + Organize G_freq, G_time, Sigma_freq and G_l from ftps solver + ''' + from forktps.DiscreteBath import SigmaDyson + + # symmetrization of reduced solver G + def symmetrize_opt(G_in, soc): + G = G_in.copy() + if soc: + def swap_2(): + for i in range(2): + G['ud_1'][i,2] = -G['ud_1'][i,2] + G['ud_1'][2,i] = -G['ud_1'][2,i] + swap_2() + G['ud_0'] = 0.5*(G['ud_0'] + G['ud_1']) + G['ud_1'] = G['ud_0'] + for name , g in G: + g[1,1] = 0.5*(g[1,1]+g[2,2]) + g[2,2] = g[1,1] + swap_2() + else: + switch = lambda spin: 'dn' if spin == 'down' else 'up' + for key, mapto in self.calc_mapping.items(): + spin, block = key.split('_') + for deg_item in mapto: + map_spin, map_block = deg_item.split('_') + mpi.report(f'mapping {spin}-{block} to {map_spin}-{map_block}...') + G[switch(map_spin)].data[:,int(map_block),int(map_block)] = G[switch(spin)].data[:,int(block),int(block)] + # particle-hole symmetry: enforce mirror/point symmetry of G(w) + if self.solver_params['ph_symm']: + for block, gf in G: + gf.data.real = 0.5 * ( gf.data[::1].real - gf.data[::-1].real ) + gf.data.imag = 0.5 * ( gf.data[::1].imag + gf.data[::-1].imag ) + return G + + def symmetrize(G): + return symmetrize_opt(G, soc=self.sum_k.corr_shells[self.icrsh]['SO']) + + def make_positive_definite(G): + # ensure that Delta is positive definite + for name, gf in G: + for orb, w in product(range(gf.target_shape[0]), gf.mesh): + if gf[orb,orb][w].imag > 0.0: + gf[orb,orb][w] = gf[orb,orb][w].real + 0.0j + return G + + G_w = symmetrize(self.triqs_solver.G_w) + if not self.sum_k.corr_shells[self.icrsh]['SO']: + G_w = make_positive_definite(G_w) + + # calculate Sigma_freq via Dyson + # do not use Dyson equation directly, as G0 might have wrong eta + Sigma_w_symm = SigmaDyson(Gret=self.triqs_solver.G_ret, bath=self.triqs_solver.b, + hloc=self.triqs_solver.e0, mesh=self.Delta_freq_solver.mesh, + eta=self.general_params['eta'], symmG=symmetrize) + + # convert everything to solver objects + for block, gf in G_w: + if not self.sum_k.corr_shells[self.icrsh]['SO']: + reverse_convert = dict(map(reversed, self.convert_ftps.items())) + sumk_name = reverse_convert[block.split('_')[0]] + '_0' + else: + sumk_name = block + self.G_freq[sumk_name] << gf + # in FTPS the unsym result is not calculated. Symmetries are used by construction + self.G_freq_unsym[sumk_name] << gf + self.Sigma_freq[sumk_name] << Sigma_w_symm[block] + self.G_time[sumk_name] << self.triqs_solver.G_ret[block] + + return + + + def _ctseg_postprocessing(self): + r''' + Organize G_freq, G_time, Sigma_freq and G_l from cthyb solver + ''' + + def set_Gs_from_G_l(): + + if self.solver_params['measure_ft'] and mpi.is_master_node(): + print('\n !!!!WARNING!!!! \n you enabled both improved estimators and legendre based filtering / sampling. Sigma will be overwritten by legendre result. \n !!!!WARNING!!!!\n') + + # create new G_freq and G_time + for i, g in self.G_l: + g.enforce_discontinuity(np.identity(g.target_shape[0])) + # set G_freq from Legendre and Fouriertransform to get G_time + self.G_freq[i].set_from_legendre(g) + self.G_time[i].set_from_legendre(g) + # Symmetrize + self.G_freq << make_hermitian(self.G_freq) + self.G_freq_unsym << self.G_freq + self.sum_k.symm_deg_gf(self.G_freq, ish=self.icrsh) + self.sum_k.symm_deg_gf(self.G_time, ish=self.icrsh) + # Dyson equation to get Sigma_freq + self.Sigma_freq << inverse(self.G0_freq) - inverse(self.G_freq) + + return + + # first print average sign + if mpi.is_master_node(): + print('\nAverage sign: {}'.format(self.triqs_solver.average_sign)) + # get Delta_time from solver + self.Delta_time << self.triqs_solver.Delta_tau + + self.G_time << self.triqs_solver.G_tau + self.sum_k.symm_deg_gf(self.G_time, ish=self.icrsh) + + if self.solver_params['measure_gw']: + self.G_freq << self.triqs_solver.G_iw + else: + if mpi.is_master_node(): + # create empty moment container (list of np.arrays) + Gf_known_moments = make_zero_tail(self.G_freq,n_moments=2) + for i, bl in enumerate(self.G_freq.indices): + # 0 moment is 0, dont touch it, but first moment is 1 for the Gf + Gf_known_moments[i][1] = np.eye(self.G_freq[bl].target_shape[0]) + self.G_freq[bl] << Fourier(self.G_time[bl], Gf_known_moments[i]) + self.G_freq << mpi.bcast(self.G_freq) + + self.G_freq << make_hermitian(self.G_freq) + self.G_freq_unsym << self.G_freq + self.sum_k.symm_deg_gf(self.G_freq, ish=self.icrsh) + + # if measured in Legendre basis, get G_l from solver too + if self.solver_params['measure_gl']: + # store original G_time into G_time_orig + self.G_time_orig << self.triqs_solver.G_tau + self.G_l << self.triqs_solver.G_l + # get G_time, G_freq, Sigma_freq from G_l + set_Gs_from_G_l() + elif self.general_params['legendre_fit']: + self.G_time_orig << self.triqs_solver.G_tau + self.G_l << legendre_filter.apply(self.G_time, self.general_params['n_l']) + # get G_time, G_freq, Sigma_freq from G_l + set_Gs_from_G_l() + # if improved estimators are turned on calc Sigma from F_tau, otherwise: + elif self.solver_params['measure_ft']: + self.F_freq = self.G_freq.copy() + self.F_freq << 0.0 + self.F_time = self.G_time.copy() + self.F_time << self.triqs_solver.F_tau + F_known_moments = make_zero_tail(self.F_freq, n_moments=1) + if mpi.is_master_node(): + for i, bl in enumerate(self.F_freq.indices): + self.F_freq[bl] << Fourier(self.triqs_solver.F_tau[bl], F_known_moments[i]) + # fit tail of improved estimator and G_freq + self.F_freq << _gf_fit_tail_fraction(self.F_freq, fraction=0.9, replace=0.5, known_moments=F_known_moments) + self.G_freq << _gf_fit_tail_fraction(self.G_freq ,fraction=0.9, replace=0.5, known_moments=Gf_known_moments) + + self.F_freq << mpi.bcast(self.F_freq) + self.G_freq << mpi.bcast(self.G_freq) + for block, fw in self.F_freq: + for iw in fw.mesh: + self.Sigma_freq[block][iw] = self.F_freq[block][iw] / self.G_freq[block][iw] + + else: + mpi.report('\n!!!! WARNING !!!! tail of solver output not handled! Turn on either measure_ft, legendre_fit, or measure_gl\n') + self.Sigma_freq << inverse(self.G0_freq) - inverse(self.G_freq) + + + if self.solver_params['measure_hist']: + self.perturbation_order = self.triqs_solver.histogram + + return
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© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

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+ + + + \ No newline at end of file diff --git a/_modules/index.html b/_modules/index.html new file mode 100644 index 00000000..762758c2 --- /dev/null +++ b/_modules/index.html @@ -0,0 +1,341 @@ + + + + + + Overview: module code — solid_dmft documentation + + + + + + + + + + + + + + + + + + +
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+ + + + \ No newline at end of file diff --git a/_modules/postprocessing/eval_U_cRPA_RESPACK.html b/_modules/postprocessing/eval_U_cRPA_RESPACK.html new file mode 100644 index 00000000..f3bc5a76 --- /dev/null +++ b/_modules/postprocessing/eval_U_cRPA_RESPACK.html @@ -0,0 +1,631 @@ + + + + + + postprocessing.eval_U_cRPA_RESPACK — solid_dmft documentation + + + + + + + + + + + + + + + + + + +
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Source code for postprocessing.eval_U_cRPA_RESPACK

+#!@TRIQS_PYTHON_EXECUTABLE@
+
+import numpy as np
+from itertools import product
+
+
+
+[docs] +class respack_data: + ''' + respack data class + ''' + +
+[docs] + def __init__(self, path, seed): + self.path = path + self.seed = seed + self.freq = None + self.n_orb = None + # Uijij + self.U_R = None + # Vijij + self.V_R = None + # Uijji = Uiijj + self.J_R = None + # Vijji = Viijj + self.X_R = None + + # full Uijkl reconstructed + self.Uijkl = None + self.Vijkl = None + + # freq dependent direction Coulomb + self.Uij_w = None + self.Jij_w = None + self.w_mesh = None
+
+ + + +def _read_R_file(file): + ''' + read respack Wmat, Jmat, Vmat, Xmat file format + + Parameters: + ----------- + file: string + string to file + + Returns: + -------- + U_R: dict of np.ndarray + keys are tuples of 3d integers representing the R vectors + values are n_orb x n_orb np.ndarray complex + n_orb: int + number of orbitals + ''' + # read X_R from file + with open(file, 'r') as fd: + # eliminate header + fd.readline() + fd.readline() + fd.readline() + + # get number of R vectors + r_vec_max = [abs(int(i)) for i in fd.readline().strip().split()] + assert len(r_vec_max) == 3 + + # determine number of orbitals + while True: + line = fd.readline().strip().split() + if not line: + break + else: + n_orb = int(line[0]) + + # open again and read whole file + U_R = {} + with open(file, 'r') as fd: + # eliminate header + fd.readline() + fd.readline() + fd.readline() + + for x, y, z in product(range(-r_vec_max[0], r_vec_max[0]+1), + range(-r_vec_max[1], r_vec_max[1]+1), + range(-r_vec_max[2], r_vec_max[2]+1) + ): + fd.readline() # remove rvec line + U_R[tuple([x, y, z])] = np.zeros((n_orb, n_orb), dtype=complex) + for i, j in product(range(n_orb), range(n_orb)): + line = fd.readline().strip().split() + U_R[tuple([x, y, z])][i, j] = float(line[2])+1j*float(line[3]) + fd.readline() # remove empty line before next block + + return U_R, n_orb + + +def _read_freq_int(path, n_orb, U_or_J, w_or_iw='w'): + ''' + read frequency dependent files from disk + + Parameters: + ----------- + path: string + path to respack calculations + n_orb: int + number of orbitals + U_or_J: string + pass either U or J for reading U or reading J + w_or_iw: string, optional default='w' + read either real frequency axis or Matsubara axis results + + Returns: + -------- + Uij_w: np.ndarray, dtype=complex shape=(n_w, n_orb, n_orb) + direct freq dependent Coulomb integrals between orbitals + w_mesh: np.ndarry, shape=(n_w) + frequency mesh of Uij_w tensor + ''' + + assert U_or_J == 'U' or U_or_J == 'J' + + if U_or_J == 'U': + file = path+'/dir-intW/dat.UvsE.' + else: + file = path+'/dir-intJ/dat.JvsE.' + + # read first w_mesh + if w_or_iw == 'w': + w_mesh = np.loadtxt(file+'001-001')[:, 0] + else: + w_mesh = np.loadtxt(file+'001-001')[:, 1] + + Uij_w = np.zeros((w_mesh.shape[0], n_orb, n_orb), dtype=complex) + + for i in range(0, n_orb): + for j in range(i, n_orb): # only the upper triangle part is calculated by RESPACK + temp_u = np.loadtxt(file+str(i+1).zfill(3)+'-'+str(j+1).zfill(3)) + Uij_w[:, i, j] = temp_u[:, 2] + 1j*temp_u[:, 3] + if not i == j: # set the lower triangle with the complex conj + Uij_w[:, j, i] = temp_u[:, 2] + - 1j*temp_u[:, 3] + + return Uij_w, w_mesh + + +
+[docs] +def read_interaction(seed, path='./'): + ''' + Parameters: + ----------- + seed: string + seed of QE / w90 file names + path: string + path to respack calculations + + Returns: + -------- + res: respack data class + ''' + + res = respack_data(seed, path) + + # read w3d.out file for frequency info + with open(path+'/'+seed+'.w3d.out', 'r') as w3d: + lines = w3d.readlines() + for line in lines: + if 'CALC_IFREQ=' in line: + res.freq = int(line.replace(' ', '').strip().split('=')[1].split(',')[0]) + break + + # read R dependent matrices and IFREQ + res.U_R, res.n_orb = _read_R_file(file=path+'/dir-intW/dat.Wmat') + res.V_R, _ = _read_R_file(file=path+'/dir-intW/dat.Vmat') + res.J_R, _ = _read_R_file(file=path+'/dir-intJ/dat.Jmat') + res.X_R, _ = _read_R_file(file=path+'/dir-intJ/dat.Xmat') + + # create R=0 matrices for user convenience + res.Uijij = res.U_R[(0, 0, 0)] + res.Uijji = res.J_R[(0, 0, 0)] + res.Vijij = res.V_R[(0, 0, 0)] + res.Vijji = res.X_R[(0, 0, 0)] + + # reconstruct full Uijkl tensor assuming Uijji == Uiijj + res.Uijkl = construct_Uijkl(res.U_R[(0, 0, 0)], res.J_R[(0, 0, 0)]) + res.Vijkl = construct_Uijkl(res.V_R[(0, 0, 0)], res.X_R[(0, 0, 0)]) + + # read freq dependent results + res.Uij_w, res.w_mesh = _read_freq_int(path, res.n_orb, U_or_J='U') + res.Jij_w, res.w_mesh = _read_freq_int(path, res.n_orb, U_or_J='J') + + return res
+ + + +
+[docs] +def construct_Uijkl(Uijij, Uiijj): + ''' + construct full 4 index Uijkl tensor from respack data + assuming Uijji = Uiijj + + ---------- + Uijij: np.ndarray + Uijij matrix + Uiijj: np.ndarray + Uiijj matrix + + ------- + uijkl : numpy array + uijkl Coulomb tensor + + ''' + + n_orb = Uijij.shape[0] + orb_range = range(0, n_orb) + Uijkl = np.zeros((n_orb, n_orb, n_orb, n_orb), dtype=complex) + + for i, j, k, l in product(orb_range, orb_range, orb_range, orb_range): + if i == j == k == l: # Uiiii + Uijkl[i, j, k, l] = Uijij[i, j] + elif i == k and j == l: # Uijij + Uijkl[i, j, k, l] = Uijij[i, j] + elif i == l and j == k: # Uijji + Uijkl[i, j, k, l] = Uiijj[i, j] + elif i == j and k == l: # Uiijj + Uijkl[i, j, k, l] = Uiijj[i, k] + return Uijkl
+ + + +
+[docs] +def fit_slater_fulld(u_ijij_crpa, u_ijji_crpa, U_init, J_init, fixed_F4_F2=True): + ''' + finds best Slater parameters U, J for given Uijij and Uijji matrices + using the triqs U_matrix operator routine assumes F4/F2=0.625 + + Parameters: + ----------- + u_ijij_crpa: np.ndarray of shape (5, 5) + Uijij matrix + u_ijji_crpa: np.ndarray of shape (5, 5) + Uijji matrix + U_init: float + inital value of U for optimization + J_init: float + inital value of J for optimization + fixed_F4_F2: bool, optional default=True + fix F4/F2 ratio to 0.625 + Returns: + -------- + U_int: float + averaged U value + J_hund: float + averaged J value + ''' + + from triqs.operators.util.U_matrix import U_matrix_slater, spherical_to_cubic, reduce_4index_to_2index, transform_U_matrix + from scipy.optimize import minimize + + # input checks + assert u_ijij_crpa.shape == (5, 5), 'fit slater only implemented for full d shell (5 orbitals)' + assert u_ijji_crpa.shape == (5, 5), 'fit slater only implemented for full d shell (5 orbitals)' + + def minimizer(parameters): + U_int, J_hund = parameters + Umat_full = U_matrix_slater(l=2, U_int=U_int, J_hund=J_hund, basis='spherical') + Umat_full = transform_U_matrix(Umat_full, T) + + Umat, u_ijij_slater = reduce_4index_to_2index(Umat_full) + u_iijj_slater = u_ijij_slater - Umat + return np.sum((u_ijji_crpa - u_iijj_slater)**2 + (u_ijij_crpa - u_ijij_slater)**2) + + def minimizer_radial(parameters): + F0, F2, F4 = parameters + Umat_full = U_matrix_slater(l=2, radial_integrals=[F0, F2, F4], basis='spherical') + Umat_full = transform_U_matrix(Umat_full, T) + + Umat, u_ijij_slater = reduce_4index_to_2index(Umat_full) + u_iijj_slater = u_ijij_slater - Umat + return np.sum((u_ijji_crpa - u_iijj_slater)**2 + (u_ijij_crpa - u_ijij_slater)**2) + + # transformation matrix from spherical to w90 basis + T = spherical_to_cubic(l=2, convention='wannier90') + + if fixed_F4_F2: + result = minimize(minimizer, (U_init, J_init)) + + U_int, J_hund = result.x + print('Final results from fit: U = {:.3f} eV, J = {:.3f} eV'.format(U_int, J_hund)) + print('optimize error', result.fun) + else: + # start with 0.63 as guess + F0 = U_init + F2 = J_init * 14.0 / (1.0 + 0.63) + F4 = 0.630 * F2 + + initial_guess = (F0, F2, F4) + + print('Initial guess: F0 = {0[0]:.3f} eV, F2 = {0[1]:.3f} eV, F4 = {0[2]:.3f} eV'.format(initial_guess)) + + result = minimize(minimizer_radial, initial_guess) + F0, F2, F4 = result.x + print('Final results from fit: F0 = {:.3f} eV, F2 = {:.3f} eV, F4 = {:.3f} eV'.format(F0, F2, F4)) + print('(F2+F4)/14 = {:.3f} eV'.format((F2+F4)/14)) + print('F4/F2 = {:.3f} eV'.format(F4/F2)) + print('optimize error', result.fun) + U_int = F0 + J_hund = (F2+F4)/14 + + return U_int, J_hund
+ +
+ +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
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+ + + + \ No newline at end of file diff --git a/_modules/postprocessing/eval_U_cRPA_Vasp.html b/_modules/postprocessing/eval_U_cRPA_Vasp.html new file mode 100644 index 00000000..a9fa4f40 --- /dev/null +++ b/_modules/postprocessing/eval_U_cRPA_Vasp.html @@ -0,0 +1,861 @@ + + + + + + postprocessing.eval_U_cRPA_Vasp — solid_dmft documentation + + + + + + + + + + + + + + + + + + +
+ + +
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+ +

Source code for postprocessing.eval_U_cRPA_Vasp

+#!@TRIQS_PYTHON_EXECUTABLE@
+
+import numpy as np
+import collections
+from itertools import product
+
+'''
+python functions for reading Uijkl from a VASP cRPA run and the evaluating the matrix
+elements for different basis sets.
+
+Copyright (C) 2020, A. Hampel and M. Merkel from Materials Theory Group
+at ETH Zurich
+'''
+
+
+
+[docs] +def read_uijkl(path_to_uijkl, n_sites, n_orb): + ''' + reads the VASP UIJKL files or the vijkl file if wanted + + Parameters + ---------- + path_to_uijkl : string + path to Uijkl like file + n_sites: int + number of different atoms (Wannier centers) + n_orb : int + number of orbitals per atom + + Returns + ------- + uijkl : numpy array + uijkl Coulomb tensor + + ''' + dim = n_sites*n_orb + uijkl = np.zeros((dim, dim, dim, dim), dtype=complex) + data = np.loadtxt(path_to_uijkl) + + for line in range(0, len(data[:, 0])): + i = int(data[line, 0])-1 + j = int(data[line, 1])-1 + k = int(data[line, 2])-1 + l = int(data[line, 3])-1 + uijkl[i, j, k, l] = data[line, 4]+1j*data[line, 5] + + return uijkl
+ + + +
+[docs] +def construct_U_kan(n_orb, U, J, Up=None, Jc=None): + ''' + construct Kanamori Uijkl tensor for given U, J, Up, and Jc + + Parameters + ---------- + n_orb : int + number of orbitals + U : float + U value for elements Uiiii + J : float + Hunds coupling J for tensor elements Uijji + Up : float, optional, default=U-2J + inter orbital exchange term Uijij + Jc : float, optional, default=J + Uiijj term, is the same as J for real valued wave functions + + Returns + ------- + uijkl : numpy array + uijkl Coulomb tensor + + ''' + + orb_range = range(0, n_orb) + U_kan = np.zeros((n_orb, n_orb, n_orb, n_orb)) + + if not Up: + Up = U-2*J + if not Jc: + Jc = J + + for i, j, k, l in product(orb_range, orb_range, orb_range, orb_range): + if i == j == k == l: # Uiiii + U_kan[i, j, k, l] = U + elif i == k and j == l: # Uijij + U_kan[i, j, k, l] = Up + elif i == l and j == k: # Uijji + U_kan[i, j, k, l] = J + elif i == j and k == l: # Uiijj + U_kan[i, j, k, l] = Jc + return U_kan
+ + + +
+[docs] +def red_to_2ind(uijkl, n_sites, n_orb, out=False): + ''' + 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--> U_ikjl + + Parameters + ---------- + uijkl : numpy array + 4d numpy array of Coulomb tensor + n_sites: int + number of different atoms (Wannier centers) + n_orb : int + number of orbitals per atom + out : bool + verbose mode + + Returns + ------- + Uij_anti : numpy array + red 2 index matrix U_mm'mm' + Uiijj : numpy array + red 2 index matrix U_iijj + Uijji : numpy array + red 2 index matrix Uijji + Uij_par : numpy array + red 2 index matrix U_mm\'mm\' - U_mm\'m\'m + ''' + dim = n_sites*n_orb + + # create 2 index matrix + Uij_anti = np.zeros((dim, dim)) + Uij_par = np.zeros((dim, dim)) + Uiijj = np.zeros((dim, dim)) + Uijji = np.zeros((dim, dim)) + + for i in range(0, dim): + for j in range(0, dim): + # the indices in VASP are switched: U_ijkl ---VASP--> U_ikjl + Uij_anti[i, j] = uijkl[i, i, j, j] + Uij_par[i, j] = uijkl[i, i, j, j]-uijkl[i, j, j, i] + Uiijj[i, j] = uijkl[i, j, i, j] + Uijji[i, j] = uijkl[i, j, j, i] + + np.set_printoptions(precision=3, suppress=True) + + if out: + print('reduced U anti-parallel = U_mm\'\^oo\' = U_mm\'mm\' matrix : \n', Uij_anti) + print('reduced U parallel = U_mm\'\^oo = U_mm\'mm\' - U_mm\'m\'m matrix : \n', Uij_par) + print('reduced Uijji : \n', Uijji) + print('reduced Uiijj : \n', Uiijj) + + return Uij_anti, Uiijj, Uijji, Uij_par
+ + + +
+[docs] +def calc_kan_params(uijkl, n_sites, n_orb, out=False): + ''' + 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 + + Parameters + ---------- + uijkl : numpy array + 4d numpy array of Coulomb tensor + n_sites: int + number of different atoms (Wannier centers) + n_orb : int + number of orbitals per atom + out : bool + verbose mode + + Returns + ------- + int_params : direct + kanamori parameters + ''' + + int_params = collections.OrderedDict() + + # calculate intra-orbital U + U = 0.0 + for i in range(0, n_orb): + U += uijkl[i, i, i, i] + U = U/(n_orb) + int_params['U'] = U + + # calculate the U' + Uprime = 0.0 + for i in range(0, n_orb): + for j in range(0, n_orb): + if i != j: + Uprime += uijkl[i, i, j, j] + Uprime = Uprime / (n_orb*(n_orb-1)) + int_params['Uprime'] = Uprime + + # calculate J + J = 0.0 + for i in range(0, n_orb): + for j in range(0, n_orb): + if i != j: + J += uijkl[i, j, i, j] + J = J / (n_orb*(n_orb-1)) + int_params['J'] = J + + if out: + print('U= ', "{:.4f}".format(U)) + print('U\'= ', "{:.4f}".format(Uprime)) + print('J= ', "{:.4f}".format(J)) + + return int_params
+ + + +
+[docs] +def fit_kanamori(uijkl, n_orb, switch_jk=False, fit_2=True, fit_3=False, fit_4=True): + ''' + Fit Kanamori Hamiltonian with scipy to 2,3, and / or 4 parameters + + Parameters + ----------- + uijkl: np.array (n_orb x n_orb x n_orb x n_orb) + input four index tensor + n_orb: int + number of orbitals + switch_jk: bool, default=False + flip two inner indices in input U tensor (for Vasp) + fit_2: bool, default=True + fit two parameter form + fit_3: bool, default=False + fit three parameter form (U,Up,J=Jc) + fit_4: bool, default=True + fit four parameter form + + Returns + ------- + Uijkl_fit: np.array (n_orb x n_orb x n_orb x n_orb) + fitted Uijkl tensor + ''' + from scipy.optimize import minimize + + def minimizer_2params(parameters): + U, J = parameters + + Uijkl_fit = construct_U_kan(n_orb, U, J) + + return np.sum((uijkl - Uijkl_fit)**2) + + def minimizer_3params(parameters): + U, J, Up = parameters + + Uijkl_fit = construct_U_kan(n_orb, U, J, Up) + + return np.sum((uijkl - Uijkl_fit)**2) + + def minimizer_4params(parameters): + U, J, Up, Jc = parameters + + Uijkl_fit = construct_U_kan(n_orb, U, J, Up, Jc) + + return np.sum((uijkl - Uijkl_fit)**2) + + # check if J = JC (Hunds exchange and pair hopping have same amplitude) + # true for real values wave functions + if np.max(uijkl.imag) > 0.0: + print(f"Largest imaginary part of Uijkl: {np.max(uijkl.imag)}. Kanamori Hint assumed to be real valued. Neglecting imag part") + uijkl = uijkl.real + + if switch_jk: + uijkl = np.moveaxis(uijkl, 1, 2) + + # fit U, J + if fit_2: + initial_guess = (4, 1) + result = minimize(minimizer_2params, initial_guess) + U, J = result.x + Uijkl_fit = construct_U_kan(n_orb, U, J) + print('Result 2 parameter fit: \nU = {:.4f} eV, J = {:.4f} eV'.format(U, J)) + print(f'optimize error {result.fun:.3e}') + max_ind = np.unravel_index(np.argmax(np.abs(Uijkl_fit-uijkl), axis=None), Uijkl_fit.shape) + print(f'U max diff: U{max_ind}= {np.abs(Uijkl_fit-uijkl)[max_ind]:.4e}') + + print('\n-------------------------\n') + + # fit U, J, Up + if fit_3: + initial_guess = (4, 1, 2) + result = minimize(minimizer_3params, initial_guess) + U, J, Up = result.x + Uijkl_fit = construct_U_kan(n_orb, U, J, Up) + print('Result 3 parameter fit: \nU = {:.4f} eV, U\' = {:.4f} eV J = {:.4f} eV'.format(U, Up, J)) + print(f'optimize error {result.fun:.3e}') + max_ind = np.unravel_index(np.argmax(np.abs(Uijkl_fit-uijkl), axis=None), Uijkl_fit.shape) + print(f'U max diff: U{max_ind}= {np.abs(Uijkl_fit-uijkl)[max_ind]:.4e}') + print(f'U=U\'-2J deviation: {U-Up-2*J:.4f}') + + print('\n-------------------------\n') + + if fit_4: + # fit U, J, Up, Jc + initial_guess = (4, 1, 2, 1) + result = minimize(minimizer_4params, initial_guess) + U, J, Up, Jc = result.x + Uijkl_fit = construct_U_kan(n_orb, U, Up, J, Jc) + print('Result 4 parameter fit: \nU = {:.4f} eV, U\' = {:.4f} eV, J = {:.4f} eV, Jc = {:.4f} eV'.format(U, Up, J, Jc)) + print(f'optimize error {result.fun:.3e}') + print(f'U max diff: U{max_ind}= {np.abs(Uijkl_fit-uijkl)[max_ind]:.4e}') + max_ind = np.unravel_index(np.argmax(np.abs(Uijkl_fit-uijkl), axis=None), Uijkl_fit.shape) + print(f'U=U\'-2J deviation: {U-Up-2*J:.4f}') + print(f'J=Jc deviation: {J-Jc:.4f}') + + return Uijkl_fit
+ + + +
+[docs] +def calc_u_avg_fulld(uijkl, n_sites, n_orb, out=False): + ''' + 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) + + Returns F0=U, and J=(F2+F4)/14 + + Parameters + ---------- + uijkl : numpy array + 4d numpy array of Coulomb tensor + n_sites: int + number of different atoms (Wannier centers) + n_orb : int + number of orbitals per atom + out : bool + verbose mode + + Returns + ------- + int_params : direct + Slater parameters + ''' + + int_params = collections.OrderedDict() + Uij_anti, Uiijj, Uijji, Uij_par = red_to_2ind(uijkl, n_sites, n_orb, out=out) + # 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) + # here we assume cubic harmonics (real harmonics) as basis functions in the order + # dz2 dxz dyz dx2-y2 dxy + # triqs basis: basis ordered as (xy,yz,z^2,xz,x^2-y^2) + + # calculate J + J_cubic = 0.0 + for i in range(0, n_orb): + for j in range(0, n_orb): + if i != j: + J_cubic += Uijji[i, j] + J_cubic = J_cubic/(20.0) + # 20 for 2l(2l+1) + int_params['J_cubic'] = J_cubic + + # conversion from cubic to spherical: + J = 7.0 * J_cubic / 5.0 + + int_params['J'] = J + + # calculate intra-orbital U + U_0 = 0.0 + for i in range(0, n_orb): + U_0 += Uij_anti[i, i] + U_0 = U_0 / float(n_orb) + int_params['U_0'] = U_0 + + # now conversion from cubic to spherical + U = U_0 - (8.0*J_cubic/5.0) + + int_params['U'] = U + + if out: + print('cubic U_0= ', "{:.4f}".format(U_0)) + print('cubic J_cubic= ', "{:.4f}".format(J_cubic)) + print('spherical F0=U= ', "{:.4f}".format(U)) + print('spherical J=(F2+f4)/14 = ', "{:.4f}".format(J)) + + return int_params
+ + + +
+[docs] +def calculate_interaction_from_averaging(uijkl, n_sites, n_orb, out=False): + ''' + 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. + + Returns F0=U, and J=(F2+F4)/14 + + Parameters + ---------- + uijkl : numpy array + 4d numpy array of Coulomb tensor + n_sites: int + number of different atoms (Wannier centers) + n_orb : int + number of orbitals per atom + out : bool + verbose mode + + Returns + ------- + U, J: tuple + Slater parameters + ''' + + l = 2 + + Uij_anti, Uiijj, Uijji, Uij_par = red_to_2ind(uijkl, n_sites, n_orb, out=out) + + # Calculates Slater-averaged parameters directly + U = [None] * n_sites + J = [None] * n_sites + for impurity in range(n_sites): + u_ijij_imp = Uij_anti[impurity*n_orb:(impurity+1)*n_orb, impurity*n_orb:(impurity+1)*n_orb] + U[impurity] = np.mean(u_ijij_imp) + + u_iijj_imp = Uiijj[impurity*n_orb:(impurity+1)*n_orb, impurity*n_orb:(impurity+1)*n_orb] + J[impurity] = np.sum(u_iijj_imp) / (2*l*(2*l+1)) - U[impurity] / (2*l) + U = np.mean(U) + J = np.mean(J) + + if out: + print('spherical F0=U= ', "{:.4f}".format(U)) + print('spherical J=(F2+f4)/14 = ', "{:.4f}".format(J)) + + return U, J
+ + + +
+[docs] +def fit_slater_fulld(uijkl, n_sites, U_init, J_init, fixed_F4_F2=True): + ''' + finds best Slater parameters U, J for given Uijkl tensor + using the triqs U_matrix operator routine + assumes F4/F2=0.625 + ''' + + from triqs.operators.util.U_matrix import U_matrix_slater, reduce_4index_to_2index + from scipy.optimize import minimize + # transform U matrix orbital basis ijkl to nmop, note the last two indices need to be switched in the T matrices + + def transformU(U_matrix, T): + return np.einsum("im,jn,ijkl,lo,kp->mnpo", np.conj(T), np.conj(T), U_matrix, T, T) + + def minimizer(parameters): + U_int, J_hund = parameters + Umat_full = U_matrix_slater(l=2, U_int=U_int, J_hund=J_hund, basis='cubic') + Umat_full = transformU(Umat_full, rot_def_to_w90) + + Umat, Upmat = reduce_4index_to_2index(Umat_full) + u_iijj_crpa = Uiijj[:5, :5] + u_iijj_slater = Upmat - Umat + u_ijij_crpa = Uij_anti[:5, :5] + u_ijij_slater = Upmat + return np.sum((u_iijj_crpa - u_iijj_slater)**2 + (u_ijij_crpa - u_ijij_slater)**2) + + def minimizer_radial(parameters): + F0, F2, F4 = parameters + Umat_full = U_matrix_slater(l=2, radial_integrals=[F0, F2, F4], basis='cubic') + Umat_full = transformU(Umat_full, rot_def_to_w90) + + Umat, Upmat = reduce_4index_to_2index(Umat_full) + u_iijj_crpa = Uiijj[:5, :5] + u_iijj_slater = Upmat - Umat + u_ijij_crpa = Uij_anti[:5, :5] + u_ijij_slater = Upmat + return np.sum((u_iijj_crpa - u_iijj_slater)**2 + (u_ijij_crpa - u_ijij_slater)**2) + + # rot triqs d basis to w90 default basis! + # check your order of orbitals assuming: + # dz2, dxz, dyz, dx2-y2, dxy + rot_def_to_w90 = np.array([[0, 0, 0, 0, 1], + [0, 0, 1, 0, 0], + [1, 0, 0, 0, 0], + [0, 1, 0, 0, 0], + [0, 0, 0, 1, 0]]) + + Uij_anti, Uiijj, Uijji, Uij_par = red_to_2ind(uijkl, n_sites, n_orb=5, out=False) + + if fixed_F4_F2: + result = minimize(minimizer, (U_init, J_init)) + + U_int, J_hund = result.x + print('Final results from fit: U = {:.3f} eV, J = {:.3f} eV'.format(U_int, J_hund)) + print('optimize error', result.fun) + else: + # start with 0.63 as guess + F0 = U_init + F2 = J_init * 14.0 / (1.0 + 0.63) + F4 = 0.630 * F2 + + initial_guess = (F0, F2, F4) + + print('Initial guess: F0 = {0[0]:.3f} eV, F2 = {0[1]:.3f} eV, F4 = {0[2]:.3f} eV'.format(initial_guess)) + + result = minimize(minimizer_radial, initial_guess) + F0, F2, F4 = result.x + print('Final results from fit: F0 = {:.3f} eV, F2 = {:.3f} eV, F4 = {:.3f} eV'.format(F0, F2, F4)) + print('(F2+F4)/14 = {:.3f} eV'.format((F2+F4)/14)) + print('F4/F2 = {:.3f} eV'.format(F4/F2)) + print('optimize error', result.fun) + U_int = F0 + J_hund = (F2+F4)/14 + + return U_int, J_hund
+ + +# example for a five orbital model +# uijkl=read_uijkl('UIJKL',1,5) +# calc_u_avg_fulld(uijkl,n_sites=1,n_orb=5,out=True) +# print('now via fitting with fixed F4/F2=0.63 ratio') +# fit_slater_fulld(uijkl,1,3,1, fixed_F4_F2 = True) +# print('now directly fitting F0,F2,F4') +# fit_slater_fulld(uijkl,1,3,1, fixed_F4_F2 = False) + +# calculate_interaction_from_averaging(uijkl, 1, 5, out=True) + + +# example for 3 orbital kanamori +# uijkl=read_uijkl('UIJKL',1,3) +# calc_kan_params(uijkl,1,3,out=True) +
+ +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/postprocessing/maxent_gf_imp.html b/_modules/postprocessing/maxent_gf_imp.html new file mode 100644 index 00000000..bb34f58d --- /dev/null +++ b/_modules/postprocessing/maxent_gf_imp.html @@ -0,0 +1,615 @@ + + + + + + postprocessing.maxent_gf_imp — solid_dmft documentation + + + + + + + + + + + + + + + + + + +
+ + +
+ +
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+ +
+
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+ +

Source code for postprocessing.maxent_gf_imp

+################################################################################
+#
+# TRIQS: a Toolbox for Research in Interacting Quantum Systems
+#
+# Copyright (C) 2016-2018, N. Wentzell
+# Copyright (C) 2018-2019, Simons Foundation
+#   author: N. Wentzell
+#
+# TRIQS is free software: you can redistribute it and/or modify it under the
+# terms of the GNU General Public License as published by the Free Software
+# Foundation, either version 3 of the License, or (at your option) any later
+# version.
+#
+# TRIQS is distributed in the hope that it will be useful, but WITHOUT ANY
+# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+# FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
+# details.
+#
+# You should have received a copy of the GNU General Public License along with
+# TRIQS. If not, see <http://www.gnu.org/licenses/>.
+#
+################################################################################
+"""
+Analytic continuation of the impurity Green's function to the impurity spectral
+function using maxent.
+
+Reads G_imp(i omega) from the h5 archive and writes A_imp(omega) back. See
+the docstring of main() for more information.
+
+Not mpi parallelized.
+
+Author: Maximilian Merkel, Materials Theory Group, ETH Zurich, 2020 - 2022
+"""
+
+import sys
+import time
+import numpy as np
+
+from triqs_maxent.elementwise_maxent import PoormanMaxEnt
+from triqs_maxent.omega_meshes import HyperbolicOmegaMesh
+from triqs_maxent.alpha_meshes import LogAlphaMesh
+from triqs_maxent.logtaker import VerbosityFlags
+from h5 import HDFArchive
+from triqs.utility import mpi
+from triqs.gf import BlockGf
+
+
+def _read_h5(external_path, iteration):
+    """
+    Reads the h5 archive to get the impurity Green's functions.
+
+    Parameters
+    ----------
+    external_path : string
+        path to h5 archive
+    iteration : int
+        The iteration that is being read from, None corresponds to 'last_iter'
+
+    Returns
+    -------
+    gf_imp_tau : list
+        Impurity Green's function as block Green's function for each impurity
+    """
+
+    """Reads the block Green's function G(tau) from h5 archive."""
+
+    h5_internal_path = 'DMFT_results/' + ('last_iter' if iteration is None
+                                          else f'it_{iteration}')
+
+    with HDFArchive(external_path, 'r') as archive:
+        impurity_paths = [key for key in archive[h5_internal_path].keys()
+                          if 'Gimp_time_' in key and 'orig' not in key]
+        # Sorts impurity paths by their indices, not sure if necessary
+        impurity_indices = [int(s[s.rfind('_')+1:]) for s in impurity_paths]
+        impurity_paths = [impurity_paths[i] for i in np.argsort(impurity_indices)]
+
+        gf_imp_tau = [archive[h5_internal_path][p] for p in impurity_paths]
+    return gf_imp_tau
+
+
+def _sum_greens_functions(block_gf, sum_spins):
+    """
+    Sums over spin channels if sum_spins. It combines "up" and "down" into one
+    block "total", or for SOC, simply renames the blocks ud into "total".
+    """
+
+    if not sum_spins:
+        return block_gf
+
+    for ind in block_gf.indices:
+        if ind.startswith('up_'):
+            assert ind.replace('up', 'down') in block_gf.indices
+        elif ind.startswith('down_'):
+            assert ind.replace('down', 'up') in block_gf.indices
+        elif not ind.startswith('ud_'):
+            raise ValueError(f'Block {ind} in G(tau) has unknown spin type. '
+                             + 'Check G(tau) or turn off sum_spins.')
+
+    summed_gf_imp = {}
+
+    for block_name, block in sorted(block_gf):
+        if block_name.startswith('up_'):
+            new_block_name = block_name.replace('up', 'total')
+            opp_spin_block_name = block_name.replace('up', 'down')
+            summed_gf_imp[new_block_name] = block + block_gf[opp_spin_block_name]
+        elif block_name.startswith('ud_'):
+            summed_gf_imp[block_name.replace('ud', 'total')] = block
+
+    return BlockGf(name_list=summed_gf_imp.keys(), block_list=summed_gf_imp.values())
+
+
+def _run_maxent(gf_imp_list, maxent_error, n_points_maxent, n_points_alpha,
+                omega_min, omega_max, analyzer='LineFitAnalyzer'):
+    """
+    Runs maxent to get the spectral functions from the list of block GFs.
+    """
+
+    omega_mesh = HyperbolicOmegaMesh(omega_min=omega_min, omega_max=omega_max,
+                                     n_points=n_points_maxent)
+
+    mpi.report(f'Continuing impurities with blocks: ')
+    imps_blocks = []
+    for i, block_gf in enumerate(gf_imp_list):
+        blocks = list(block_gf.indices)
+        mpi.report('- Imp {}: {}'.format(i, blocks))
+        for block in blocks:
+            imps_blocks.append((i, block))
+    mpi.report('-'*50)
+    imps_blocks_indices = np.arange(len(imps_blocks))
+
+    # Initialize collective results
+    data_linefit = [0] * len(imps_blocks)
+    data_chi2 =  [0] * len(imps_blocks)
+
+    mpi.barrier()
+    for i in mpi.slice_array(imps_blocks_indices):
+        imp, block = imps_blocks[i]
+        print(f"\nRank {mpi.rank}: solving impurity {imp+1} block '{block}'")
+
+        solver = PoormanMaxEnt(use_complex=True)
+        solver.set_G_tau(gf_imp_list[imp][block])
+        solver.set_error(maxent_error)
+        solver.omega = omega_mesh
+        solver.alpha_mesh = LogAlphaMesh(alpha_min=1e-6, alpha_max=1e2,
+                                         n_points=n_points_alpha)
+        # silence output
+        solver.maxent_diagonal.logtaker.verbose = VerbosityFlags.Quiet
+        solver.maxent_offdiagonal.logtaker.verbose = VerbosityFlags.Quiet
+        results = solver.run()
+
+        n_orb = gf_imp_list[imp][block].target_shape[0]
+        opt_alpha = np.zeros((n_orb, n_orb, 2), dtype=int)
+        opt_alpha[:, :, :] = -1  # set results to -1 to distinguish them from 0
+        for i_orb in range(n_orb):
+            for j_orb in range(n_orb):
+                for l_com in range(2):  # loop over complex numbers
+                    if results.analyzer_results[i_orb][j_orb][l_com] == {}:
+                        continue
+                    opt_alpha[i_orb, j_orb,
+                              l_com] = results.analyzer_results[i_orb][j_orb][l_com][analyzer]['alpha_index']
+
+        print(
+            f"Optimal alphas , Imp {imp+1} block '{block}': \n--- Real part ---\n", opt_alpha[:, :, 0])
+        if np.any(opt_alpha[:, :, 1] != -1):
+            print('Imp {i+1} block {block} Imag part ---\n', opt_alpha[:, :, 1])
+        if np.any(opt_alpha[:, :, 0] == -1):
+            print('(a -1 indicates that maxent did not run for this block due to symmetry)')
+
+        # store unpacked data in flatted list / maxent res object not bcastable
+        data_linefit[i] = results.get_A_out('LineFitAnalyzer')
+        data_chi2[i] = results.get_A_out('Chi2CurvatureAnalyzer')
+
+    # slow barrier to reduce CPU load of waiting ranks
+    mpi.barrier(1000)
+    # Synchronizes information between ranks
+    for i in imps_blocks_indices:
+        data_linefit[i] = mpi.all_reduce(data_linefit[i])
+        data_chi2[i] = mpi.all_reduce(data_chi2[i])
+
+    # final result list
+    unpacked_results = [{'mesh': np.array(omega_mesh),
+                         'Aimp_w_line_fit': {},
+                         'Aimp_w_chi2_curvature': {}
+                         } for _ in range(len(gf_imp_list))]
+
+    for i in imps_blocks_indices:
+        imp, block = imps_blocks[i]
+        unpacked_results[imp]['Aimp_w_line_fit'][block] = data_linefit[i]
+        unpacked_results[imp]['Aimp_w_chi2_curvature'][block] = data_chi2[i]
+
+    return unpacked_results
+
+
+def _write_spectral_function_to_h5(unpacked_results, external_path, iteration):
+    """ Writes the mesh and the maxent result for each analyzer to h5 archive. """
+
+    h5_internal_path = 'DMFT_results/' + ('last_iter' if iteration is None
+                                          else f'it_{iteration}')
+
+    with HDFArchive(external_path, 'a') as archive:
+        for i, res in enumerate(unpacked_results):
+            archive[h5_internal_path][f'Aimp_maxent_{i}'] = res
+
+
+
+[docs] +def main(external_path, iteration=None, sum_spins=False, maxent_error=0.02, + n_points_maxent=200, n_points_alpha=50, omega_min=-20, omega_max=20): + """ + 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. + + Parameters + ---------- + external_path : string + Path to the h5 archive to read from and write to. + iteration : int/string + Iteration to read from and write to. Defaults to last_iter. + sum_spins : bool + Whether to sum over the spins or continue the impurity GF + for the up and down spin separately, for example for magnetized results. + maxent_error : float + The error that is used for the analyzers. + n_points_maxent : int + Number of omega points on the hyperbolic mesh used in the continuation. + n_points_alpha : int + Number of points that the MaxEnt alpha parameter is varied on logarithmically. + omega_min : float + Lower end of range where the GF is being continued. Range has to comprise + all features of the impurity GF for correct normalization. + omega_max : float + Upper end of range where the GF is being continued. See omega_min. + + Returns + ------- + maxent_results : list + The omega mesh and impurity spectral function from two different analyzers + in a dict for each impurity + """ + + gf_imp_tau = [] + if mpi.is_master_node(): + start_time = time.time() + + gf_imp_tau = _read_h5(external_path, iteration) + for i, gf in enumerate(gf_imp_tau): + gf_imp_tau[i] = _sum_greens_functions(gf, sum_spins) + gf_imp_tau = mpi.bcast(gf_imp_tau) + + maxent_results = _run_maxent(gf_imp_tau, maxent_error, n_points_maxent, + n_points_alpha, omega_min, omega_max) + + if mpi.is_master_node(): + _write_spectral_function_to_h5(maxent_results, external_path, iteration) + + total_time = time.time() - start_time + mpi.report('-'*80, 'DONE') + mpi.report(f'Total run time: {total_time:.0f} s.') + + return maxent_results
+ + + +def _strtobool(val): + """Convert a string representation of truth to true (1) or false (0). + True values are 'y', 'yes', 't', 'true', 'on', and '1'; false values + are 'n', 'no', 'f', 'false', 'off', and '0'. Raises ValueError if + 'val' is anything else. + Copied from distutils.util in python 3.10. + """ + val = val.lower() + if val in ('y', 'yes', 't', 'true', 'on', '1'): + return 1 + elif val in ('n', 'no', 'f', 'false', 'off', '0'): + return 0 + else: + raise ValueError("invalid truth value {!r}".format(val)) + + +if __name__ == '__main__': + # Casts input parameters + if len(sys.argv) > 2: + if sys.argv[2].lower() == 'none': + sys.argv[2] = None + if len(sys.argv) > 3: + sys.argv[3] = _strtobool(sys.argv[3]) + if len(sys.argv) > 4: + sys.argv[4] = float(sys.argv[4]) + if len(sys.argv) > 5: + sys.argv[5] = int(sys.argv[5]) + if len(sys.argv) > 6: + sys.argv[6] = int(sys.argv[6]) + if len(sys.argv) > 7: + sys.argv[7] = float(sys.argv[7]) + if len(sys.argv) > 8: + sys.argv[8] = float(sys.argv[8]) + + main(*sys.argv[1:]) +
+ +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/postprocessing/maxent_gf_latt.html b/_modules/postprocessing/maxent_gf_latt.html new file mode 100644 index 00000000..a88b1a0e --- /dev/null +++ b/_modules/postprocessing/maxent_gf_latt.html @@ -0,0 +1,628 @@ + + + + + + postprocessing.maxent_gf_latt — solid_dmft documentation + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
  • + + +
  • +
  • +
+
+
+
+
+ +

Source code for postprocessing.maxent_gf_latt

+################################################################################
+#
+# TRIQS: a Toolbox for Research in Interacting Quantum Systems
+#
+# Copyright (C) 2016-2018, N. Wentzell
+# Copyright (C) 2018-2019, Simons Foundation
+#   author: N. Wentzell
+#
+# TRIQS is free software: you can redistribute it and/or modify it under the
+# terms of the GNU General Public License as published by the Free Software
+# Foundation, either version 3 of the License, or (at your option) any later
+# version.
+#
+# TRIQS is distributed in the hope that it will be useful, but WITHOUT ANY
+# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+# FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
+# details.
+#
+# You should have received a copy of the GNU General Public License along with
+# TRIQS. If not, see <http://www.gnu.org/licenses/>.
+#
+################################################################################
+"""
+Analytic continuation of the lattice Green's function to the lattice spectral
+function using maxent.
+
+Reads G_latt(i omega) from the h5 archive and writes A_latt(omega) back. See
+the docstring of main() for more information.
+
+mpi parallelized for the generation of the imaginary-frequency lattice GF over
+k points.
+
+Author: Maximilian Merkel, Materials Theory Group, ETH Zurich, 2020 - 2022
+"""
+
+import sys
+import time
+import numpy as np
+
+from triqs_maxent.tau_maxent import TauMaxEnt
+from triqs_maxent.omega_meshes import HyperbolicOmegaMesh
+from triqs_maxent.alpha_meshes import LogAlphaMesh
+from triqs_dft_tools.sumk_dft import SumkDFT
+from h5 import HDFArchive
+from triqs.utility import mpi
+from triqs.gf import Gf, BlockGf
+
+
+def _read_h5(external_path, iteration):
+    """
+    Reads the h5 archive to get the Matsubara self energy, the double-counting potential,
+    the chemical potential and the block structure.
+
+    Parameters
+    ----------
+    external_path : string
+        path to h5 archive
+    iteration : int
+        The iteration that is being read from, None corresponds to 'last_iter'
+
+    Returns
+    -------
+    sigma_iw : list
+        Self energy as block Green's function for each impurity
+    chemical_potential : float
+        The chemical potential of the problem. Should be approximately real
+    dc_potential : list
+        Double counting for each impurity
+    block_structure : triqs_dft_tools.BlockStructure
+        Block structure mapping from the DMFT calculation
+
+    """
+
+    h5_internal_path = 'DMFT_results/' + ('last_iter' if iteration is None
+                                          else f'it_{iteration}')
+
+    with HDFArchive(external_path, 'r') as archive:
+        impurity_paths = [key for key in archive[h5_internal_path].keys() if 'Sigma_freq_' in key]
+        # Sorts impurity paths by their indices, not sure if necessary
+        impurity_indices = [int(s[s.rfind('_')+1:]) for s in impurity_paths]
+        impurity_paths = [impurity_paths[i] for i in np.argsort(impurity_indices)]
+
+        sigma_iw = [archive[h5_internal_path][p] for p in impurity_paths]
+
+        block_structure = archive['DMFT_input']['block_structure']
+        # Fix for archives from triqs 2 when corr_to_inequiv was in SumkDFT, not in BlockStructure
+        if block_structure.corr_to_inequiv is None:
+            block_structure.corr_to_inequiv = archive['dft_input/corr_to_inequiv']
+
+        dc_potential = archive[h5_internal_path]['DC_pot']
+
+        if 'chemical_potential_post' in archive[h5_internal_path]:
+            chemical_potential = archive[h5_internal_path]['chemical_potential_post']
+        else:
+            # Old name for chemical_potential_post
+            chemical_potential = archive[h5_internal_path]['chemical_potential']
+
+    return sigma_iw, chemical_potential, dc_potential, block_structure
+
+
+def _generate_lattice_gf(sum_k, sum_spins):
+    """
+    Generates the lattice GF from the SumkDFT object. If sum_spins, it
+    has one block "total". Otherwise, the block names are the spins.
+    """
+    # Initializes lattice GF to zero for each process
+    spin_blocks = ['total'] if sum_spins else sum_k.spin_block_names[sum_k.SO]
+    mesh = sum_k.Sigma_imp[0].mesh
+    trace_gf_latt = {key: Gf(mesh=mesh, data=np.zeros((len(mesh), 1, 1), dtype=complex))
+                     for key in spin_blocks}
+
+    # Takes trace over orbitals (and spins). Individual entries do not make sense
+    # because the KS Hamiltonian ususally has the bands sorted by energy
+    for ik in mpi.slice_array(np.arange(sum_k.n_k)):
+        gf_latt = sum_k.lattice_gf(ik) * sum_k.bz_weights[ik]
+        if sum_spins:
+            trace_gf_latt['total'].data[:] += np.trace(sum(g.data for _, g in gf_latt),
+                                                       axis1=1, axis2=2).reshape(-1, 1, 1)
+        else:
+            for s in spin_blocks:
+                trace_gf_latt[s].data[:] += np.trace(gf_latt[s].data,
+                                                     axis1=1, axis2=2).reshape(-1, 1, 1)
+
+    for s in spin_blocks:
+        trace_gf_latt[s] << mpi.all_reduce(trace_gf_latt[s])
+
+    # Lattice GF as BlockGf, required for compatibility with MaxEnt functions
+    gf_lattice_iw = BlockGf(name_list=trace_gf_latt.keys(),
+                            block_list=trace_gf_latt.values())
+    return gf_lattice_iw
+
+
+def _run_maxent(gf_lattice_iw, sum_k, error, omega_min, omega_max,
+                n_points_maxent, n_points_alpha, analyzer='LineFitAnalyzer'):
+    """
+    Runs maxent to get the spectral function from the block GF.
+    """
+
+    # Automatic determination of energy range from hopping matrix
+    if omega_max is None:
+        num_ks_orbitals = sum_k.hopping.shape[2]
+        hopping_diagonal = sum_k.hopping[:, :, np.arange(num_ks_orbitals), np.arange(num_ks_orbitals)]
+        hopping_min = np.min(hopping_diagonal)
+        hopping_max = np.max(hopping_diagonal)
+        omega_min = min(-20, hopping_min - sum_k.chemical_potential)
+        omega_max = max(20, hopping_max - sum_k.chemical_potential)
+        mpi.report('Set omega range to {:.3f}...{:.3f} eV'.format(omega_min, omega_max))
+
+    omega_mesh = HyperbolicOmegaMesh(omega_min=omega_min, omega_max=omega_max,
+                                     n_points=n_points_maxent)
+
+    # Prints information on the blocks found
+    mpi.report('Found blocks {}'.format(list(gf_lattice_iw.indices)))
+
+    # Initializes and runs the maxent solver
+    # TODO: parallelization over blocks
+    results = {}
+    for block, gf in gf_lattice_iw:
+        mpi.report('-'*80, f'Running MaxEnt on block "{block}" now', '-'*80)
+        solver = TauMaxEnt()
+        solver.set_G_iw(gf)
+        solver.set_error(error)
+        solver.omega = omega_mesh
+        solver.alpha_mesh = LogAlphaMesh(alpha_min=1e-6, alpha_max=1e2, n_points=n_points_alpha)
+        results[block] = solver.run()
+
+        opt_alpha = results[block].analyzer_results[analyzer]['alpha_index']
+        mpi.report(f'Optimal alpha, block "{block}" from {analyzer}: {opt_alpha}')
+
+    return results, omega_mesh
+
+
+def _unpack_maxent_results(results, omega_mesh):
+    """
+    Converts maxent result to dict with mesh and spectral function from each
+    analyzer.
+    """
+    data_linefit = {}
+    data_chi2 = {}
+    for key, result in results.items():
+        data_linefit[key] = result.get_A_out('LineFitAnalyzer')
+        data_chi2[key] = result.get_A_out('Chi2CurvatureAnalyzer')
+
+    data = {'mesh': np.array(omega_mesh), 'Alatt_w_line_fit': data_linefit,
+            'Alatt_w_chi2_curvature': data_chi2}
+    return data
+
+
+def _write_spectral_function_to_h5(unpacked_results, external_path, iteration):
+    """ Writes the mesh and the maxent result for each analyzer to h5 archive. """
+
+    h5_internal_path = 'DMFT_results/' + ('last_iter' if iteration is None
+                                          else f'it_{iteration}')
+
+    with HDFArchive(external_path, 'a') as archive:
+        archive[h5_internal_path]['Alatt_maxent'] = unpacked_results
+
+
+
+[docs] +def main(external_path, iteration=None, sum_spins=False, maxent_error=.02, + n_points_maxent=200, n_points_alpha=50, omega_min=None, omega_max=None): + """ + 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. + + Parameters + ---------- + external_path: string + Path to the h5 archive to read from and write to. + iteration: int/string + Iteration to read from and write to. Defaults to last_iter. + sum_spins: bool + Whether to sum over the spins or continue the lattice GF + for the up and down spin separately, for example for magnetized results. + maxent_error : float + The error that is used for the analyzers. + n_points_maxent : int + Number of omega points on the hyperbolic mesh used in the continuation. + n_points_alpha : int + Number of points that the MaxEnt alpha parameter is varied on logarithmically. + omega_min : float + 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. + omega_max : float + Upper end of range where the GF is being continued. See omega_min. + + Returns + ------- + unpacked_results : dict + The omega mesh and lattice spectral function from two different analyzers + """ + + if (omega_max is None and omega_min is not None + or omega_max is not None and omega_min is None): + raise ValueError('Both or neither of omega_max and omega_min have to be None') + + start_time = time.time() + + # Sets up the SumkDFT object + h5_content = None + if mpi.is_master_node(): + h5_content = _read_h5(external_path, iteration) + sigma_iw, chemical_potential, dc_potential, block_structure = mpi.bcast(h5_content) + sum_k = SumkDFT(external_path, mesh=sigma_iw[0].mesh, use_dft_blocks=False) + sum_k.block_structure = block_structure + sum_k.put_Sigma(sigma_iw) + sum_k.set_mu(chemical_potential) + sum_k.set_dc(dc_potential, None) + + # Generates the lattice GF + gf_lattice_iw = _generate_lattice_gf(sum_k, sum_spins) + mpi.report('Generated the lattice GF.') + + # Runs MaxEnt + unpacked_results = None + if mpi.is_master_node(): + maxent_results, omega_mesh = _run_maxent(gf_lattice_iw, sum_k, maxent_error, + omega_min, omega_max, n_points_maxent, + n_points_alpha) + unpacked_results = _unpack_maxent_results(maxent_results, omega_mesh) + _write_spectral_function_to_h5(unpacked_results, external_path, iteration) + unpacked_results = mpi.bcast(unpacked_results) + + total_time = time.time() - start_time + mpi.report('-'*80, 'DONE') + mpi.report(f'Total run time: {total_time:.0f} s.') + + return unpacked_results
+ + + +def _strtobool(val): + """Convert a string representation of truth to true (1) or false (0). + True values are 'y', 'yes', 't', 'true', 'on', and '1'; false values + are 'n', 'no', 'f', 'false', 'off', and '0'. Raises ValueError if + 'val' is anything else. + Copied from distutils.util in python 3.10. + """ + val = val.lower() + if val in ('y', 'yes', 't', 'true', 'on', '1'): + return 1 + elif val in ('n', 'no', 'f', 'false', 'off', '0'): + return 0 + else: + raise ValueError("invalid truth value {!r}".format(val)) + + +if __name__ == '__main__': + # Casts input parameters + if len(sys.argv) > 2: + if sys.argv[2].lower() == 'none': + sys.argv[2] = None + if len(sys.argv) > 3: + sys.argv[3] = _strtobool(sys.argv[3]) + if len(sys.argv) > 4: + sys.argv[4] = float(sys.argv[4]) + if len(sys.argv) > 5: + sys.argv[5] = int(sys.argv[5]) + if len(sys.argv) > 6: + sys.argv[6] = int(sys.argv[6]) + if len(sys.argv) > 7: + sys.argv[7] = float(sys.argv[7]) + if len(sys.argv) > 8: + sys.argv[8] = float(sys.argv[8]) + + main(*sys.argv[1:]) +
+ +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/postprocessing/maxent_sigma.html b/_modules/postprocessing/maxent_sigma.html new file mode 100644 index 00000000..30a05458 --- /dev/null +++ b/_modules/postprocessing/maxent_sigma.html @@ -0,0 +1,697 @@ + + + + + + postprocessing.maxent_sigma — solid_dmft documentation + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for postprocessing.maxent_sigma

+################################################################################
+#
+# TRIQS: a Toolbox for Research in Interacting Quantum Systems
+#
+# Copyright (C) 2016-2018, N. Wentzell
+# Copyright (C) 2018-2019, Simons Foundation
+#   author: N. Wentzell
+#
+# TRIQS is free software: you can redistribute it and/or modify it under the
+# terms of the GNU General Public License as published by the Free Software
+# Foundation, either version 3 of the License, or (at your option) any later
+# version.
+#
+# TRIQS is distributed in the hope that it will be useful, but WITHOUT ANY
+# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+# FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
+# details.
+#
+# You should have received a copy of the GNU General Public License along with
+# TRIQS. If not, see <http://www.gnu.org/licenses/>.
+#
+################################################################################
+"""
+Analytic continuation of the self-energy using maxent on an auxiliary Green's
+function.
+
+Reads Sigma(i omega) from the h5 archive and writes Sigma(omega) back. See
+the docstring of main() for more information.
+
+mpi parallelized for the maxent routine over all blocks and for the continuator
+extraction over omega points.
+
+Author: Maximilian Merkel, Materials Theory Group, ETH Zurich, 2020 - 2022
+
+Warnings:
+    * When using this on self-energies with SOC, please check that the formalism
+      is correct, in particular the Kramers-Kronig relation.
+"""
+
+import time
+import sys
+import numpy as np
+
+from triqs.utility import mpi
+from triqs_maxent.sigma_continuator import InversionSigmaContinuator, DirectSigmaContinuator
+from triqs_maxent.elementwise_maxent import PoormanMaxEnt
+from triqs_maxent.omega_meshes import HyperbolicOmegaMesh
+from triqs_maxent.alpha_meshes import LogAlphaMesh
+from triqs_maxent.logtaker import VerbosityFlags
+from h5 import HDFArchive
+
+
+def _read_h5(external_path, iteration):
+    """
+    Reads the h5 archive to get the Matsubara self energy, the double-counting potential
+    and the chemical potential.
+
+    Parameters:
+    -----------
+    external_path : string
+        path to h5 archive
+    iteration : int
+        The iteration that is being read from, None corresponds to 'last_iter'
+
+    Returns:
+    --------
+    sigma_iw : list
+        Self energy as block Green's function for each impurity
+    dc_potential : list
+        Double counting for each impurity
+    chemical_potential : float
+        The chemical potential of the problem. Should be approximately real
+    chemical_potential_zero : float
+        The chemical potential at 0 iteration. Should be approximately real
+    """
+
+    h5_internal_path = 'DMFT_results/' + ('last_iter' if iteration is None
+                                          else f'it_{iteration}')
+
+    with HDFArchive(external_path, 'r') as archive:
+        impurity_paths = [key for key in archive[h5_internal_path].keys() if 'Sigma_freq_' in key]
+        # Sorts impurity paths by their indices, not sure if necessary
+        impurity_indices = [int(s[s.rfind('_')+1:]) for s in impurity_paths]
+        impurity_paths = [impurity_paths[i] for i in np.argsort(impurity_indices)]
+        sigma_iw = [archive[h5_internal_path][p] for p in impurity_paths]
+
+        inequiv_to_corr = archive['dft_input']['inequiv_to_corr']
+        dc_potential = [archive[h5_internal_path]['DC_pot'][icrsh]
+                        for icrsh in inequiv_to_corr]
+
+        if 'chemical_potential_post' in archive[h5_internal_path]:
+            chemical_potential = archive[h5_internal_path]['chemical_potential_post']
+        else:
+            # Old name for chemical_potential_post
+            chemical_potential = archive[h5_internal_path]['chemical_potential']
+        chemical_potential_zero = archive['DMFT_results/observables']['mu'][0]
+
+    return sigma_iw, dc_potential, chemical_potential, chemical_potential_zero
+
+
+def _create_sigma_continuator(sigma_iw, dc_potential, chemical_potential, chemical_potential_zero, continuator_type):
+    """
+    Initializes the inversion and direct sigma continuator. Returns a list of
+    continuators. Types of supported auxiliary Green's functions:
+    * 'inversion_dc': inversion continuator, constant C = dc_potential for the impurity
+    * 'inversion_sigmainf': inversion continuator, constant C = Sigma(i infinity) + chemical potential
+    * 'direct': direct continuator
+    """
+    n_inequiv_shells = len(sigma_iw)
+
+    if continuator_type == 'inversion_dc':
+        shifts = [None] * n_inequiv_shells
+        for iineq in range(n_inequiv_shells):
+            for dc_block in dc_potential[iineq].values():
+                # Reads first element from matrix for shift
+                if shifts[iineq] is None:
+                    shifts[iineq] = dc_block[0, 0]
+                # Checks that matrix for up and down is unit matrix * shift
+                if not np.allclose(dc_block, np.eye(dc_block.shape[0])*shifts[iineq]):
+                    raise NotImplementedError('Only scalar dc per impurity supported')
+
+        continuators = [InversionSigmaContinuator(sigma_imp, shift)
+                        for sigma_imp, shift in zip(sigma_iw, shifts)]
+    elif continuator_type == 'inversion_sigmainf':
+        shifts = [{key: sigma_block.data[-1].real + (chemical_potential - chemical_potential_zero)
+                   for key, sigma_block in sigma_imp} for sigma_imp in sigma_iw]
+        continuators = [InversionSigmaContinuator(sigma_imp, shift)
+                        for sigma_imp, shift in zip(sigma_iw, shifts)]
+    elif continuator_type == 'direct':
+        for sigma_imp in sigma_iw:
+            for _, sigma_block in sigma_imp:
+                if sigma_block.data.shape[1] > 1:
+                    # TODO: implement making input diagonal if it is not
+                    raise NotImplementedError('Continuing only diagonal elements of non-diagonal '
+                                              'matrix not implemented yet')
+        continuators = [DirectSigmaContinuator(sigma_imp) for sigma_imp in sigma_iw]
+    else:
+        raise NotImplementedError
+
+    mpi.report(f'Created sigma continuator of type "{continuator_type}"')
+
+    return continuators
+
+
+def _run_maxent(continuators, error, omega_min, omega_max, n_points_maxent,
+                n_points_alpha, analyzer):
+    """
+    Uses maxent to continue the auxiliary Green's function obtained from the
+    continuator. The range for alpha is set to 1e-6 to 1e2.
+    Returns the real-frequency auxiliary Green's function
+    """
+
+    # Finds blocks of impurities and prints summary
+    mpi.report('Continuing impurities with blocks:')
+    imps_blocks = []
+    for i, continuator in enumerate(continuators):
+        blocks = list(continuator.Gaux_iw.indices)
+        mpi.report('- Imp {}: {}'.format(i, blocks))
+        for block in blocks:
+            imps_blocks.append((i, block))
+
+    # Initializes arrays to save results in
+    spectral_funcs = [np.zeros(1)] * len(imps_blocks)
+    opt_alphas = [np.zeros(1, dtype=int)] * len(imps_blocks)
+    omega_mesh = HyperbolicOmegaMesh(omega_min=omega_min, omega_max=omega_max, n_points=n_points_maxent)
+
+    # Runs MaxEnt while parallelizing over impurities and blocks
+    imps_blocks_indices = np.arange(len(imps_blocks))
+    for i in mpi.slice_array(imps_blocks_indices):
+        imp, block = imps_blocks[i]
+        g_aux_block = continuators[imp].Gaux_iw[block]
+        solver = PoormanMaxEnt(use_complex=True)
+        solver.set_G_iw(g_aux_block)
+        solver.set_error(error)
+        solver.omega = omega_mesh
+        solver.alpha_mesh = LogAlphaMesh(alpha_min=1e-6, alpha_max=1e2, n_points=n_points_alpha)
+        # Turns off MaxEnt output, it's far too messy in the parallel mode
+        # For some reason, MaxEnt still prints "appending"
+        solver.maxent_diagonal.logtaker.verbose = VerbosityFlags.Quiet
+        solver.maxent_offdiagonal.logtaker.verbose = VerbosityFlags.Quiet
+        result = solver.run()
+
+        spectral_funcs[i] = result.get_A_out(analyzer)
+
+        opt_alphas[i] = np.full(g_aux_block.data.shape[1:] + (2, ), -1, dtype=int)
+        for j in range(opt_alphas[i].shape[0]):
+            for k in range(j+1):
+                for l in range(2): # loop over complex numbers
+                    if result.analyzer_results[k][j][l] == {}:
+                        continue
+                    opt_alphas[i][k, j, l] = result.analyzer_results[k][j][l][analyzer]['alpha_index']
+
+    # Synchronizes information between ranks
+    for i in imps_blocks_indices:
+        spectral_funcs[i] = mpi.all_reduce(spectral_funcs[i])
+        opt_alphas[i] = mpi.all_reduce(opt_alphas[i])
+
+    for i, block_index in enumerate(imps_blocks):
+        mpi.report(f'Optimal alphas, block {block_index}:')
+        mpi.report('--- Real part ---', opt_alphas[i][:, :, 0])
+        if np.any(opt_alphas[i][:, :, 1] != -1):
+            mpi.report('--- Imag part ---', opt_alphas[i][:, :, 1])
+
+    # Sorts results into original order of impurities and blocks
+    # and adds information from Hermitian conjugate of off-diagonal elements
+    sorted_spectral_funcs = [{} for _ in range(len(continuators))]
+    for (imp, block), val in zip(imps_blocks, spectral_funcs):
+        for i in range(val.shape[0]):
+            for j in range(i):
+                val[i, j] = val[j, i].conj()
+        if not np.allclose(val.imag, 0):
+            mpi.report('The result is complex. This might be correct but comes '
+                       + 'without guarantuee of formal correctness.')
+        sorted_spectral_funcs[imp][block] = val
+
+    return sorted_spectral_funcs, omega_mesh
+
+
+def _get_sigma_omega_from_aux(continuators, aux_spectral_funcs, aux_omega_mesh,
+                              omega_min, omega_max, n_points_interp, n_points_final):
+    """ Extracts the real-frequency self energy from the auxiliary Green's function. """
+    for cont_imp, spec_imp in zip(continuators, aux_spectral_funcs):
+        cont_imp.set_Gaux_w_from_Aaux_w(spec_imp, aux_omega_mesh, np_interp_A=n_points_interp,
+                                        np_omega=n_points_final, w_min=omega_min, w_max=omega_max)
+
+    g_aux_w = [continuator.Gaux_w for continuator in continuators]
+    sigma_w = [continuator.S_w for continuator in continuators]
+    return g_aux_w, sigma_w
+
+
+def _write_sigma_omega_to_h5(g_aux_w, sigma_w, external_path, iteration):
+    """ Writes real-frequency self energy to h5 archive. """
+    h5_internal_path = 'DMFT_results/' + ('last_iter' if iteration is None
+                                          else f'it_{iteration}')
+
+    with HDFArchive(external_path, 'a') as archive:
+        for i, (g_aux_imp, sigma_imp) in enumerate(zip(g_aux_w, sigma_w)):
+            archive[h5_internal_path][f'Sigma_maxent_{i}'] = sigma_imp
+            archive[h5_internal_path][f'G_aux_for_Sigma_maxent_{i}'] = g_aux_imp
+
+
+
+[docs] +def main(external_path, iteration=None, continuator_type='inversion_sigmainf', maxent_error=.02, + omega_min=-12., omega_max=12., n_points_maxent=400, n_points_alpha=50, + analyzer='LineFitAnalyzer', n_points_interp=2000, n_points_final=1000): + """ + 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. + + Parameters + ---------- + external_path : string + Path to the h5 archive to read from and write to + iteration : int/string + Iteration to read from and write to. Default to last_iter + continuator_type : string + Type of continuator to use, one of 'inversion_sigmainf', 'inversion_dc', 'direct' + maxent_error : float + The error that is used for the analyzers. + omega_min : float + 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. + omega_max : float + Upper end of range where Sigma is being continued. See omega_min. + n_points_maxent : int + Number of omega points on the hyperbolic mesh used in analytically + continuing the auxiliary GF + n_points_alpha : int + Number of points that the MaxEnt alpha parameter is varied on logarithmically + analyzer : string + Analyzer used int MaxEnt, one of 'LineFitAnalyzer', 'Chi2CurvatureAnalyzer', + 'ClassicAnalyzer', 'EntropyAnalyzer', 'BryanAnalyzer' + n_points_interp : int + Number of points where auxiliary GF is interpolated to integrate over + it for the Kramers-Kronig relation + n_points_final : int + 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 + + Returns + ------- + sigma_w : list of triqs.gf.BlockGf + Sigma(omega) per inequivalent shell + g_aux_w : list of triqs.gf.BlockGf + G_aux(omega) per inequivalent shell + + Raises + ------ + NotImplementedError + -- 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. + """ + # Checks on input parameters + if continuator_type not in ('inversion_sigmainf', 'inversion_dc', 'direct'): + raise NotImplementedError('Unsupported type of continuator chosen') + + if analyzer not in ('LineFitAnalyzer', 'Chi2CurvatureAnalyzer', 'ClassicAnalyzer', + 'EntropyAnalyzer', 'BryanAnalyzer'): + raise NotImplementedError('Unsupported type of analyzer chosen') + + assert omega_min < omega_max + + # Reads in data and initializes continuator object + start_time = time.time() + continuators = None + if mpi.is_master_node(): + sigma_iw, dc_potential, chemical_potential, chemical_potential_zero = _read_h5(external_path, iteration) + mpi.report('Finished reading h5 archive. Found {} impurities.'.format(len(sigma_iw))) + continuators = _create_sigma_continuator(sigma_iw, dc_potential, + chemical_potential, chemical_potential_zero, continuator_type) + continuators = mpi.bcast(continuators) + init_end_time = time.time() + + # Runs MaxEnt + mpi.report('Starting run of maxent now.') + aux_spectral_funcs, aux_omega_mesh = _run_maxent(continuators, maxent_error, + omega_min, omega_max, + n_points_maxent, n_points_alpha, + analyzer) + maxent_end_time = time.time() + + # Extracts Sigma(omega) + mpi.report(f'Extracting Σ(ω) now with {mpi.size} process(es).') + g_aux_w, sigma_w = _get_sigma_omega_from_aux(continuators, aux_spectral_funcs, + aux_omega_mesh, omega_min, omega_max, + n_points_interp, n_points_final) + extract_end_time = time.time() + + # Writes results into h5 archive + mpi.report('Writing results to h5 archive now.') + if mpi.is_master_node(): + _write_sigma_omega_to_h5(g_aux_w, sigma_w, external_path, iteration) + mpi.report('Finished writing Σ(ω) to archive.') + + all_end_time = time.time() + + # Prints timing summary + run_time_report = '\n{:<8} | {:<10}\n'.format('Task', 'Duration (s)') + length_table = len(run_time_report) - 2 + run_time_report += '-'*length_table + '\n' + run_time_report += '{:<8} | {:10.4f}\n'.format('Reading', init_end_time - start_time) + run_time_report += '{:<8} | {:10.4f}\n'.format('MaxEnt', maxent_end_time - init_end_time) + run_time_report += '{:<8} | {:10.4f}\n'.format('Extract.', extract_end_time - maxent_end_time) + run_time_report += '{:<8} | {:10.4f}\n'.format('Writing', all_end_time - extract_end_time) + run_time_report += '-'*length_table + '\n' + run_time_report += '{:<8} | {:10.4f}\n'.format('Total', all_end_time - start_time) + + mpi.report(run_time_report) + return sigma_w, g_aux_w
+ + + +if __name__ == '__main__': + # Casts input parameters + if len(sys.argv) > 2: + if sys.argv[2].lower() == 'none': + sys.argv[2] = None + if len(sys.argv) > 4: + sys.argv[4] = float(sys.argv[4]) + if len(sys.argv) > 5: + sys.argv[5] = float(sys.argv[5]) + if len(sys.argv) > 6: + sys.argv[6] = float(sys.argv[6]) + if len(sys.argv) > 7: + sys.argv[7] = int(sys.argv[7]) + if len(sys.argv) > 8: + sys.argv[8] = int(sys.argv[8]) + if len(sys.argv) > 10: + sys.argv[10] = int(sys.argv[10]) + if len(sys.argv) > 11: + sys.argv[11] = int(sys.argv[11]) + + main(*sys.argv[1:]) +
+ +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/postprocessing/plot_correlated_bands.html b/_modules/postprocessing/plot_correlated_bands.html new file mode 100644 index 00000000..f9a653c1 --- /dev/null +++ b/_modules/postprocessing/plot_correlated_bands.html @@ -0,0 +1,1218 @@ + + + + + + postprocessing.plot_correlated_bands — solid_dmft documentation + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
  • + + +
  • +
  • +
+
+
+
+
+ +

Source code for postprocessing.plot_correlated_bands

+#!/usr/bin/env python3
+################################################################################
+#
+# TRIQS: a Toolbox for Research in Interacting Quantum Systems
+#
+# Copyright (C) 2016-2018, N. Wentzell
+# Copyright (C) 2018-2019, Simons Foundation
+#   author: N. Wentzell
+#
+# TRIQS is free software: you can redistribute it and/or modify it under the
+# terms of the GNU General Public License as published by the Free Software
+# Foundation, either version 3 of the License, or (at your option) any later
+# version.
+#
+# TRIQS is distributed in the hope that it will be useful, but WITHOUT ANY
+# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+# FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
+# details.
+#
+# You should have received a copy of the GNU General Public License along with
+# TRIQS. If not, see <http://www.gnu.org/licenses/>.
+#
+################################################################################
+
+"""
+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.
+
+Written by Sophie Beck, 2021-2022
+
+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)
+"""
+
+import matplotlib.pyplot as plt
+from matplotlib.ticker import MaxNLocator
+from matplotlib.colors import Normalize
+from matplotlib import cm
+from scipy.optimize import brentq
+from scipy.interpolate import interp1d
+from scipy.signal import argrelextrema
+import numpy as np
+import itertools
+import skimage.measure
+from warnings import warn
+
+from h5 import HDFArchive
+from triqs.gf import BlockGf, MeshReFreq, Gf
+from triqs.lattice.utils import TB_from_wannier90, k_space_path
+from triqs_dft_tools.sumk_dft import SumkDFT
+
+
+def lambda_matrix_w90_t2g(add_lambda):
+
+    lambda_x, lambda_y, lambda_z = add_lambda
+
+    lambda_matrix = np.zeros((6, 6), dtype=complex)
+    lambda_matrix[0, 1] = +1j*lambda_z/2.0
+    lambda_matrix[0, 5] = -1j*lambda_x/2.0
+    lambda_matrix[1, 5] =    -lambda_y/2.0
+    lambda_matrix[2, 3] = +1j*lambda_x/2.0
+    lambda_matrix[2, 4] =    +lambda_y/2.0
+    lambda_matrix[3, 4] = -1j*lambda_z/2.0
+    lambda_matrix += np.transpose(np.conjugate(lambda_matrix))
+
+    return lambda_matrix
+
+
+def change_basis(n_orb, orbital_order_to, orbital_order_from):
+
+    change_of_basis = np.eye(n_orb)
+    for ct, orb in enumerate(orbital_order_to):
+        orb_idx = orbital_order_from.index(orb)
+        change_of_basis[orb_idx, :] = np.roll(np.eye(n_orb, 1), ct)[:, 0]
+
+    return change_of_basis
+
+
+def print_matrix(matrix, n_orb, text):
+
+    print('{}:'.format(text))
+
+    if np.any(matrix.imag > 1e-4):
+        fmt = '{:16.4f}' * n_orb
+    else:
+        fmt = '{:8.4f}' * n_orb
+        matrix = matrix.real
+
+    for row in matrix:
+        print((' '*4 + fmt).format(*row))
+
+
+def _sigma_from_dmft(n_orb, orbital_order, with_sigma, spin, orbital_order_dmft=None, **specs):
+
+    if orbital_order_dmft is None:
+        orbital_order_dmft = orbital_order
+
+    if with_sigma == 'calc':
+        print('Setting Sigma from {}'.format(specs['dmft_path']))
+
+        sigma_imp_list = []
+        dc_imp_list = []
+        with HDFArchive(specs['dmft_path'], 'r') as ar:
+            for icrsh in range(ar['dft_input']['n_inequiv_shells']):
+                try:
+                    sigma = ar['DMFT_results'][specs['it']][f'Sigma_freq_{icrsh}']
+                    assert isinstance(sigma.mesh, MeshReFreq), 'Imported Greens function must be real frequency'
+                except(KeyError, AssertionError):
+                    try:
+                        sigma = ar['DMFT_results'][specs['it']][f'Sigma_maxent_{icrsh}']
+                    except KeyError:
+                        try:
+                            sigma = ar['DMFT_results'][specs['it']][f'Sigma_Refreq_{icrsh}']
+                        except KeyError:
+                            raise KeyError('Provide either "Sigma_freq_0" in real frequency, "Sigma_Refreq_0" or "Sigma_maxent_0".')
+                sigma_imp_list.append(sigma)
+
+            for ish in range(ar['dft_input']['n_corr_shells']):
+                dc_imp_list.append(ar['DMFT_results'][specs['it']]['DC_pot'][ish])
+
+            mu_dmft = ar['DMFT_results'][specs['it']]['chemical_potential_post']
+
+            sum_k = SumkDFT(specs['dmft_path'], mesh=sigma.mesh)
+            sum_k.block_structure = ar['DMFT_input/block_structure']
+            sum_k.deg_shells = ar['DMFT_input/deg_shells']
+            sum_k.set_mu(mu_dmft)
+            # set Sigma and DC into sum_k
+            sum_k.dc_imp = dc_imp_list
+            sum_k.set_Sigma(sigma_imp_list)
+
+            # use add_dc function to rotate to sumk block structure and subtract the DC
+            sigma_sumk = sum_k.add_dc()
+
+            assert np.allclose(sum_k.proj_mat[0], sum_k.proj_mat[-1]), 'upfolding works only when proj_mat is the same for all kpoints (wannier mode)'
+
+            # now upfold with proj_mat to band basis, this only works for the
+            # case where proj_mat is equal for all k points (wannier mode)
+            sigma = Gf(mesh=sigma.mesh, target_shape=[n_orb, n_orb])
+            for ish in range(ar['dft_input']['n_corr_shells']):
+                sigma += sum_k.upfold(ik=0, ish=ish,
+                                      bname=spin, gf_to_upfold=sigma_sumk[ish][spin],
+                                      gf_inp=sigma)
+
+        # already subtracted
+        dc = 0.0
+
+    else:
+        print('Setting Sigma from memory')
+
+        sigma = with_sigma[spin]
+        dc = specs['dc'][0][spin][0, 0]
+        mu_dmft = specs['mu_dmft']
+
+    SOC = (spin == 'ud')
+    w_mesh_dmft = np.linspace(sigma.mesh.w_min, sigma.mesh.w_max, len(sigma.mesh))
+    assert sigma.target_shape[0] == n_orb, f'Number of Wannier orbitals: {n_orb} and self-energy target_shape {sigma.target_shape} does not match'
+
+    sigma_mat = sigma.data.real - np.eye(n_orb) * dc + 1j * sigma.data.imag
+
+    # rotate sigma from orbital_order_dmft to orbital_order
+    change_of_basis = change_basis(n_orb, orbital_order, orbital_order_dmft)
+    sigma_mat = np.einsum('ij, kjl -> kil', np.linalg.inv(change_of_basis), np.einsum('ijk, kl -> ijl', sigma_mat, change_of_basis))
+
+    # set up mesh
+    if 'w_mesh' in specs:
+        freq_dict = specs['w_mesh']
+        w_mesh = np.linspace(*freq_dict['window'], freq_dict['n_w'])
+        freq_dict.update({'w_mesh': w_mesh})
+    else:
+        w_mesh = w_mesh_dmft
+        freq_dict = {'w_mesh': w_mesh_dmft, 'n_w': len(sigma.mesh), 'window': [sigma.mesh.w_min, sigma.mesh.w_max]}
+
+    sigma_interpolated = np.zeros((n_orb, n_orb, freq_dict['n_w']), dtype=complex)
+
+    # interpolate sigma
+    def interpolate_sigma(w_mesh, w_mesh_dmft, orb1, orb2): return np.interp(w_mesh, w_mesh_dmft, sigma_mat[:, orb1, orb2])
+
+    for ct1, ct2 in itertools.product(range(n_orb), range(n_orb)):
+        if ct1 != ct2 and not SOC:
+            continue
+        sigma_interpolated[ct1, ct2] = interpolate_sigma(w_mesh, w_mesh_dmft, ct1, ct2)
+
+    return sigma_interpolated, mu_dmft, freq_dict
+
+
+def sigma_FL(n_orb, orbital_order, Sigma_0, Sigma_Z, freq_dict, eta=0.0, mu_dmft=None):
+
+    print('Setting Re[Sigma] with Fermi liquid approximation')
+
+    if np.any(Sigma_0) and mu_dmft == None:
+        raise ValueError('Sigma_0 does not preserve electron count. Please provide "mu_dmft".')
+    elif not np.any(Sigma_0) and mu_dmft == None:
+        mu_dmft = 0.
+
+    eta = eta * 1j
+
+    # set up mesh
+    w_mesh = np.linspace(*freq_dict['window'], freq_dict['n_w'])
+    freq_dict.update({'w_mesh': w_mesh})
+
+    # setting up sigma
+    sigma_array = np.zeros((n_orb, n_orb, freq_dict['n_w']), dtype=complex)
+    def approximate_sigma(orb): return (1-1/Sigma_Z[orb]) * freq_dict['w_mesh'] + Sigma_0[orb] - mu_dmft
+    for ct, orb in enumerate(orbital_order):
+        sigma_array[ct, ct] = approximate_sigma(ct) + 1j * eta
+
+    return sigma_array, freq_dict
+
+
+def _calc_alatt(n_orb, mu, eta, e_mat, sigma, qp_bands=False, e_vecs=None,
+                proj_nuk=None, trace=True, **freq_dict):
+    '''
+    calculate slice of lattice spectral function for given TB dispersion / e_mat and self-energy
+
+    Parameters
+    ----------
+    n_orb : int
+          number of Wannier orbitals
+    proj_nuk : optinal, 2D numpy array (n_orb, n_k)
+          projections to be applied on A(k,w) in band basis. Only works when band_basis=True
+
+    Returns
+    -------
+    alatt_k_w : numpy array, either (n_k, n_w) or if trace=False (n_k, n_w, n_orb)
+            Lattice Green's function on specified k-path / mesh
+
+    '''
+
+    # adjust to system size
+    def upscale(quantity, n_orb): return quantity * np.identity(n_orb)
+    mu = upscale(mu, n_orb)
+    eta = upscale(eta, n_orb)
+    if isinstance(e_vecs, np.ndarray):
+        sigma_rot = np.zeros(sigma.shape, dtype=complex)
+
+    w_vec = np.array([upscale(freq_dict['w_mesh'][w], n_orb) for w in range(freq_dict['n_w'])])
+    n_k = e_mat.shape[2]
+
+    if not qp_bands:
+        if trace:
+            alatt_k_w = np.zeros((n_k, freq_dict['n_w']))
+        else:
+            alatt_k_w = np.zeros((n_k, freq_dict['n_w'], n_orb))
+
+        def invert_and_trace(w, eta, mu, e_mat, sigma, trace, proj=None):
+            # inversion is automatically vectorized over first axis of 3D array (omega first index now)
+            Glatt = np.linalg.inv(w + eta[None, ...] + mu[None, ...] - e_mat[None, ...] - sigma.transpose(2, 0, 1))
+            A_w_nu = -1.0/np.pi * np.diagonal(Glatt, axis1=1, axis2=2).imag
+            if isinstance(proj, np.ndarray):
+                A_w_nu = A_w_nu * proj[None, :]
+            if trace:
+                return np.sum(A_w_nu, axis=1)
+            else:
+                return A_w_nu
+
+        for ik in range(n_k):
+            # if evecs are given transform sigma into band basis
+            if isinstance(e_vecs, np.ndarray):
+                sigma_rot = np.einsum('ij,jkw->ikw', e_vecs[:, :, ik].conjugate().transpose(), np.einsum('ijw,jk->ikw', sigma, e_vecs[:, :, ik]))
+                if isinstance(proj_nuk, np.ndarray):
+                    alatt_k_w[ik, :] = invert_and_trace(w_vec, eta, mu, e_mat[:, :, ik], sigma_rot, trace, proj_nuk[:, ik])
+                else:
+                    alatt_k_w[ik, :] = invert_and_trace(w_vec, eta, mu, e_mat[:, :, ik], sigma_rot, trace)
+            else:
+                alatt_k_w[ik, :] = invert_and_trace(w_vec, eta, mu, e_mat[:, :, ik], sigma, trace)
+
+    else:
+        alatt_k_w = np.zeros((n_k, n_orb))
+        kslice = np.zeros((freq_dict['n_w'], n_orb))
+        def kslice_interp(orb): return interp1d(freq_dict['w_mesh'], kslice[:, orb])
+
+        for ik in range(n_k):
+            for iw, w in enumerate(freq_dict['w_mesh']):
+                np.fill_diagonal(sigma[:, :, iw], np.diag(sigma[:, :, iw]).real)
+                #sigma[:,:,iw] = sigma[:,:,iw].real
+                kslice[iw], _ = np.linalg.eigh(upscale(w, n_orb) + eta + mu - e_mat[:, :, ik] - sigma[:, :, iw])
+
+            for orb in range(n_orb):
+                w_min, w_max = freq_dict['window']
+                try:
+                    x0 = brentq(kslice_interp(orb), w_min, w_max)
+                    w_bin = int((x0 - w_min) / ((w_max - w_min) / freq_dict['n_w']))
+                    alatt_k_w[ik, orb] = freq_dict['w_mesh'][w_bin]
+                except ValueError:
+                    pass
+
+    return alatt_k_w
+
+
+def _calc_kslice(n_orb, mu, eta, e_mat, sigma, qp_bands, e_vecs=None, proj_nuk=None, **freq_dict):
+    '''
+    calculate lattice spectral function for given TB dispersion / e_mat and self-energy
+
+    Parameters
+    ----------
+    n_orb : int
+          number of Wannier orbitals
+    proj_nuk : optinal, 2D numpy array (n_orb, n_k)
+          projections to be applied on A(k,w) in band basis. Only works when band_basis=True
+
+    Returns
+    -------
+    alatt_k_w : numpy array, either (n_k, n_w) or if trace=False (n_k, n_w, n_orb)
+            Lattice Green's function on specified k-path / mesh
+
+    '''
+
+    # adjust to system size
+    def upscale(quantity, n_orb): return quantity * np.identity(n_orb)
+    mu = upscale(mu, n_orb)
+    eta = upscale(eta, n_orb)
+
+    iw0 = np.where(np.sign(freq_dict['w_mesh']) == True)[0][0]-1
+    print_matrix(sigma[:, :, iw0], n_orb, 'Zero-frequency Sigma')
+
+    if isinstance(e_vecs, np.ndarray):
+        sigma_rot = np.zeros(sigma.shape, dtype=complex)
+
+    n_kx, n_ky = e_mat.shape[2:4]
+
+    if not qp_bands:
+        alatt_k_w = np.zeros((n_kx, n_ky))
+
+        def invert_and_trace(w, eta, mu, e_mat, sigma, proj=None):
+            # inversion is automatically vectorized over first axis of 3D array (omega first index now)
+            Glatt = np.linalg.inv(w + eta + mu - e_mat - sigma)
+            A_nu = -1.0/np.pi * np.diagonal(Glatt).imag
+            if isinstance(proj, np.ndarray):
+                A_nu = A_nu * proj
+            return np.sum(A_nu)
+
+        for ikx, iky in itertools.product(range(n_kx), range(n_ky)):
+            if isinstance(e_vecs, np.ndarray):
+                sigma_rot = np.einsum('ij,jk->ik',
+                                      e_vecs[:, :, ikx, iky].conjugate().transpose(),
+                                      np.einsum('ij,jk->ik', sigma[:, :, iw0], e_vecs[:, :, ikx, iky]))
+            else:
+                sigma_rot = sigma[:, :, iw0]
+
+            if isinstance(proj_nuk, np.ndarray):
+                alatt_k_w[ikx, iky] = invert_and_trace(upscale(freq_dict['w_mesh'][iw0], n_orb), eta, mu,
+                                                       e_mat[:, :, ikx, iky], sigma_rot, proj_nuk[:, ikx, iky])
+            else:
+                alatt_k_w[ikx, iky] = invert_and_trace(upscale(freq_dict['w_mesh'][iw0], n_orb), eta, mu, e_mat[:, :, ikx, iky], sigma_rot)
+
+    else:
+        assert n_kx == n_ky, 'Not implemented for N_kx != N_ky'
+
+        def search_for_extrema(data):
+            # return None for no extrema, [] if ends of interval are the only extrema,
+            # list of indices if local extrema are present
+            answer = np.all(data > 0) or np.all(data < 0)
+            if answer:
+                return
+            else:
+                roots = []
+                roots.append(list(argrelextrema(data, np.greater)[0]))
+                roots.append(list(argrelextrema(data, np.less)[0]))
+                roots = sorted([item for sublist in roots for item in sublist])
+            return roots
+
+        alatt_k_w = np.zeros((n_kx, n_ky, n_orb))
+        # go through grid horizontally, then vertically
+        for it in range(2):
+            kslice = np.zeros((n_kx, n_ky, n_orb))
+
+            for ik1 in range(n_kx):
+                e_temp = e_mat[:, :, :, ik1] if it == 0 else e_mat[:, :, ik1, :]
+                for ik2 in range(n_kx):
+                    e_val, _ = np.linalg.eigh(eta + mu - e_temp[:, :, ik2] - sigma[:, :, iw0])
+                    k1, k2 = [ik2, ik1] if it == 0 else [ik1, ik2]
+                    kslice[k1, k2] = e_val
+
+                for orb in range(n_orb):
+                    temp_kslice = kslice[:,ik1,orb] if it == 0 else kslice[ik1,:,orb]
+                    roots = search_for_extrema(temp_kslice)
+                    # iterate through sections between extrema
+                    if roots is not None:
+                        idx_1 = 0
+                        for root_ct in range(len(roots) + 1):
+                            idx_2 = roots[root_ct] if root_ct < len(roots) else n_kx
+                            root_section = temp_kslice[idx_1:idx_2+1]
+                            try:
+                                x0 = brentq(interp1d(np.linspace(idx_1, idx_2, len(root_section)), root_section), idx_1, idx_2)
+                                k1, k2 = [int(np.floor(x0)), ik1] if it == 0 else [ik1, int(np.floor(x0))]
+                                alatt_k_w[k1, k2, orb] += 1
+                            except(ValueError):
+                                pass
+                            idx_1 = idx_2
+
+        alatt_k_w[np.where(alatt_k_w > 1)] = 1
+
+    return alatt_k_w
+
+
+def _get_tb_bands(e_mat, proj_on_orb=[None], **specs):
+    '''
+    calculate eigenvalues and eigenvectors for given list of e_mat on kmesh
+
+    Parameters
+    ----------
+    e_mat : numpy array of shape (n_orb, n_orb, nk) or (n_orb, n_orb, nk, nk)
+
+    Returns
+    -------
+    e_val : numpy array of shape (n_orb, n_orb, nk) or (n_orb, n_orb, nk, nk)
+        eigenvalues as matrix
+    e_vec : numpy array of shape (n_orb, n_orb, nk) or (n_orb, n_orb, nk, nk)
+        eigenvectors as matrix
+    '''
+
+    e_val = np.zeros((e_mat.shape), dtype=complex)
+    e_vec = np.zeros((e_mat.shape), dtype=complex)
+    n_orb = e_mat.shape[0]
+
+    for ikx in range(e_mat.shape[2]):
+        # if we have a 2d kmesh e_mat is dim=4
+        if len(e_mat.shape) == 4:
+            for iky in range(e_mat.shape[3]):
+                e_val[range(n_orb), range(n_orb), ikx, iky], e_vec[:, :, ikx, iky] = np.linalg.eigh(e_mat[:, :, ikx, iky])
+        else:
+            e_val[range(n_orb), range(n_orb), ikx], e_vec[:, :, ikx] = np.linalg.eigh(e_mat[:, :, ikx])
+
+    if proj_on_orb[0] is not None:
+        print(f'calculating projection on orbitals {proj_on_orb}')
+        total_proj = np.zeros(np.shape(e_vec[0]))
+        for band in range(n_orb):
+            for orb in proj_on_orb:
+                total_proj[band] += np.real(e_vec[orb, band] * e_vec[orb, band].conjugate())
+    else:
+        total_proj = None
+
+    return e_val, e_vec, total_proj
+
+
+def get_tb_kslice(tb, mu_tb, **specs):
+
+    w90_paths = list(map(lambda section: (np.array(specs[section[0]]), np.array(specs[section[1]])), specs['bands_path']))
+    upper_left = np.diff(w90_paths[0][::-1], axis=0)[0]
+    lower_right = np.diff(w90_paths[1], axis=0)[0]
+    Z = np.array(specs['Z'])
+
+    # calculate FS at the mu_tb value
+    FS_kx_ky, band_char = get_kx_ky_FS(lower_right, upper_left, Z, tb, N_kxy=specs['n_k'], kz=specs['kz'], fermi=mu_tb)
+
+    return FS_kx_ky, band_char
+
+
+def _fract_ind_to_val(x, ind):
+    ind[ind == len(x)-1] = len(x)-1-1e-6
+    int_ind = [int(indi) for indi in ind]
+    int_ind_p1 = [int(indi)+1 for indi in ind]
+    return x[int_ind] + (x[int_ind_p1] - x[int_ind])*(np.array(ind)-np.array(int_ind))
+
+
+def get_kx_ky_FS(lower_right, upper_left, Z, tb, select=None, N_kxy=10, kz=0.0, fermi=0.0):
+
+    # create mesh
+    kx = np.linspace(0, 0.5, N_kxy)
+    ky = np.linspace(0, 0.5, N_kxy)
+
+    if select is None:
+        select = np.array(range(tb.n_orbitals))
+
+    # go in horizontal arrays from bottom to top
+    E_FS = np.zeros((tb.n_orbitals, N_kxy, N_kxy))
+    for kyi in range(N_kxy):
+        path_FS = [(upper_left/(N_kxy-1)*kyi + kz*Z, lower_right+upper_left/(N_kxy-1)*kyi+kz*Z)]
+        k_vec, dst, tks = k_space_path(path_FS, num=N_kxy)
+        E_FS[:, :, kyi] = tb.dispersion(k_vec).transpose() - fermi
+
+    contours = {}
+    FS_kx_ky = {}
+    FS_kx_ky_prim = {}
+    band_char = {}
+    # contour for each sheet
+    for sheet in range(tb.n_orbitals):
+        contours[sheet] = skimage.measure.find_contours(E_FS[sheet, :, :], 0.0)
+
+    sheet_ct = 0
+    for sheet in contours.keys():
+        for sec_per_sheet in range(np.shape(contours[sheet])[0]):
+            # once on 2D cubic mesh
+            FS_kx_ky[sheet_ct] = np.vstack([_fract_ind_to_val(kx, contours[sheet][sec_per_sheet][:, 0]),
+                                            _fract_ind_to_val(ky, contours[sheet][sec_per_sheet][:, 1]),
+                                            kz*np.ones(len(contours[sheet][sec_per_sheet][:, 0]))]).T.reshape(-1, 3)
+            # repeat on actual mesh for computing the weights
+            ks_skimage = contours[sheet][sec_per_sheet]/(N_kxy-1)
+            FS_kx_ky_prim[sheet_ct] = (+ np.einsum('i,j->ij', ks_skimage[:, 0], lower_right)
+                                       + np.einsum('i,j->ij', ks_skimage[:, 1], upper_left)
+                                       + np.einsum('i,j->ij', kz * np.ones(ks_skimage.shape[0]), Z))
+            band_char[sheet_ct] = {}
+            # compute the weight aka band character
+            for ct_k, k_on_sheet in enumerate(FS_kx_ky_prim[sheet_ct]):
+                E_mat = tb.fourier(k_on_sheet)
+                e_val, e_vec = np.linalg.eigh(E_mat[select[:, np.newaxis], select])
+                orb_on_FS = np.argmin(np.abs(e_val))
+
+                band_char[sheet_ct][ct_k] = [np.round(np.real(e_vec[orb, orb_on_FS]*np.conjugate(e_vec[orb, orb_on_FS])), 4) for orb in range(len(select))]
+            sheet_ct += 1
+
+    return FS_kx_ky, band_char
+
+
+def _setup_plot_bands(ax, special_k, k_points_labels, freq_dict):
+
+    ax.axhline(y=0, c='gray', ls='--', lw=0.8, zorder=0)
+    ax.set_ylabel(r'$\omega - \mu$ (eV)')
+#     ax.set_ylim(*freq_dict['window'])
+    for ik in special_k:
+        ax.axvline(x=ik, linewidth=0.7, color='k', zorder=0.5)
+    ax.set_xticks(special_k)
+    ax.set_xlim(special_k[0], special_k[-1])
+    k_points_labels = [r'$\Gamma$' if k == 'G' else k for k in k_points_labels]
+    ax.set_xticklabels(k_points_labels)
+
+
+def setup_plot_kslice(ax):
+
+    ax.set_aspect(1)
+    # ax.set_xlim(0,1)
+    # ax.set_ylim(0,1)
+    ax.xaxis.set_major_locator(MaxNLocator(integer=True))
+    ax.yaxis.set_major_locator(MaxNLocator(integer=True))
+    ax.set_xlabel(r'$k_x\pi/a$')
+    ax.set_ylabel(r'$k_y\pi/b$')
+
+
+def plot_bands(fig, ax, alatt_k_w, tb_data, freq_dict, n_orb, tb=True, alatt=False, qp_bands=False, **plot_dict):
+
+    assert tb_data['special_k'] is not None, 'a regular k point mesh has been used, please call plot_dos'
+
+    proj_on_orb = tb_data['proj_on_orb']
+    total_proj = tb_data['proj_nuk']
+
+    if alatt:
+        if alatt_k_w is None:
+            raise ValueError('A(k,w) unknown. Specify "with_sigma = True"')
+        if qp_bands:
+            for orb in range(n_orb):
+                ax.scatter(tb_data['k_mesh'], alatt_k_w[:, orb].T, c=np.array([eval('cm.'+plot_dict['colorscheme_qpbands'])(1.0)]), zorder=2., s=1.)
+        else:
+            kw_x, kw_y = np.meshgrid(tb_data['k_mesh'], freq_dict['w_mesh'])
+
+            vmax = plot_dict['vmax'] if 'vmax' in plot_dict else np.max(alatt_k_w)
+            vmin = plot_dict['vmin'] if 'vmin' in plot_dict else 0.0
+
+            graph = ax.pcolormesh(kw_x, kw_y, alatt_k_w.T, cmap=plot_dict['colorscheme_alatt'],
+                                  norm=Normalize(vmin=vmin, vmax=vmax), shading='gouraud')
+            colorbar = plt.colorbar(graph)
+            colorbar.set_label(r'$A(k, \omega)$')
+
+    if tb:
+        # if projection is requested, _get_tb_bands() ran already
+        if proj_on_orb[0] is not None:
+            eps_nuk = tb_data['e_mat']
+            evec_nuk = tb_data['e_vecs']
+        else:
+            eps_nuk, evec_nuk, _ = _get_tb_bands(**tb_data)
+        for band in range(n_orb):
+            if not proj_on_orb[0] is not None:
+                if isinstance(plot_dict['colorscheme_bands'], str):
+                    color = eval('cm.'+plot_dict['colorscheme_bands'])(1.0)
+                else:
+                    color = plot_dict['colorscheme_bands']
+                ax.plot(tb_data['k_mesh'], eps_nuk[band, band].real - tb_data['mu_tb'], c=color, label=r'tight-binding', zorder=1., lw=1)
+            else:
+                color = eval('cm.'+plot_dict['colorscheme_bands'])(total_proj[band])
+                ax.scatter(tb_data['k_mesh'], eps_nuk[band, band].real - tb_data['mu_tb'], c=color, s=1, label=r'tight-binding', zorder=1.)
+
+    _setup_plot_bands(ax, tb_data['special_k'], tb_data['k_points_labels'], freq_dict)
+
+
+def plot_dos(fig, ax, alatt_k_w, tb_data, freq_dict, tb=False, alatt=True, label=None, color=None):
+
+    assert tb == False, 'plotting TB DOS is not supported yet.'
+
+    assert len(alatt_k_w.shape) == 2, 'input Akw should only have a k and omega index'
+
+    if not label:
+        label = ''
+
+    if not color:
+        ax.plot(freq_dict['w_mesh'], np.sum(alatt_k_w, axis=0)/alatt_k_w.shape[0], label=label)
+    else:
+        ax.plot(freq_dict['w_mesh'], np.sum(alatt_k_w, axis=0) /
+                alatt_k_w.shape[0], label=label, color=color)
+
+    ax.axvline(x=0, c='gray', ls='--', zorder=0)
+    ax.set_xlabel(r'$\omega - \mu$ (eV)')
+    ax.set_ylabel(r'A($\omega$)')
+
+    ax.set_xlim(*freq_dict['window'])
+
+    return
+
+def plot_kslice(fig, ax, alatt_k_w, tb_data, freq_dict, n_orb, tb_dict, tb=True, alatt=False, quarter=0, **plot_dict):
+
+    proj_on_orb = tb_data['proj_on_orb']
+    if quarter:
+        assert isinstance(quarter, int) or all(isinstance(x, int) for x in quarter), 'quarter should be'\
+            f'an integer or list of integers, but is {type(quarter)}.'
+
+    if isinstance(quarter, int):
+        quarter = [quarter]
+
+    sign = [1, -1]
+    quarters = np.array([sign, sign])
+    four_quarters = list(itertools.product(*quarters))
+    used_quarters = [four_quarters[x] for x in quarter]
+
+    vmax = plot_dict['vmax'] if 'vmax' in plot_dict else np.max(alatt_k_w)
+    vmin = plot_dict['vmin'] if 'vmin' in plot_dict else 0.0
+
+    if alatt:
+        if alatt_k_w is None:
+            raise ValueError('A(k,w) unknown. Specify "with_sigma = True"')
+        n_kx, n_ky = tb_data['e_mat'].shape[2:4]
+        kx, ky = np.meshgrid(range(n_kx), range(n_ky))
+        for (qx, qy) in used_quarters:
+            if len(alatt_k_w.shape) > 2:
+                for orb in range(n_orb):
+                    ax.contour(qx * kx/(n_kx-1), qy * ky/(n_ky-1), alatt_k_w[:, :, orb].T,
+                               colors=np.array([eval('cm.'+plot_dict['colorscheme_qpbands'])(0.7)]), levels=1, zorder=2)
+            else:
+                graph = ax.pcolormesh(qx * kx/(n_kx-1), qy * ky/(n_ky-1), alatt_k_w.T,
+                                      cmap=plot_dict['colorscheme_kslice'],
+                                      norm=Normalize(vmin=vmin, vmax=vmax),
+                                      shading='gouraud')
+                #colorbar = plt.colorbar(graph)
+                #colorbar.set_label(r'$A(k, 0$)')
+
+    if tb:
+        FS_kx_ky, band_char = get_tb_kslice(tb_data['tb'], tb_data['mu_tb'], **tb_dict)
+        for sheet in FS_kx_ky.keys():
+            for k_on_sheet in range(FS_kx_ky[sheet].shape[0]):
+                if not proj_on_orb[0] is not None:
+                    if isinstance(plot_dict['colorscheme_bands'], str):
+                        color = eval('cm.'+plot_dict['colorscheme_bands'])(1.0)
+                    else:
+                        color = plot_dict['colorscheme_bands']
+                else:
+                    total_proj = 0
+                    for orb in proj_on_orb:
+                        total_proj += band_char[sheet][k_on_sheet][orb]
+                    color = eval('cm.'+plot_dict['colorscheme_bands'])(total_proj)
+                for (qx, qy) in used_quarters:
+                    ax.plot(2*qx * FS_kx_ky[sheet][k_on_sheet:k_on_sheet+2, 0], 2*qy * FS_kx_ky[sheet][k_on_sheet:k_on_sheet+2, 1], '-',
+                            solid_capstyle='round', c=color, zorder=1., label=plot_dict['label'] if 'label' in plot_dict else '')
+
+    setup_plot_kslice(ax)
+
+    return ax
+
+
+
+[docs] +def get_dmft_bands(n_orb, w90_path, w90_seed, mu_tb, add_spin=False, add_lambda=None, add_local=None, + with_sigma=None, fermi_slice=False, qp_bands=False, orbital_order_to=None, + add_mu_tb=False, band_basis=False, proj_on_orb=None, trace=True, eta=0.0, + mu_shift=0.0, proj_nuk=None, **specs): + ''' + 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. + + Parameters + ---------- + n_orb : int + Number of Wannier orbitals in seed_hr.dat + w90_path : string + Path to w90 files + w90_seed : string + Seed of wannier90 calculation, i.e. seed_hr.dat and seed.wout + add_spin : bool, default=False + Extend w90 Hamiltonian by spin indices + add_lambda : float, default=None + Add SOC term with strength add_lambda (works only for t2g shells) + add_local : numpy array, default=None + Add local term of dimension (n_orb x n_orb) + with_sigma : str, or BlockGf, default=None + 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 + add_mu_tb : bool, default=False + Add the TB specified chemical potential to the lattice Green function + set to True if DMFT calculation was performed with DFT fermi subtracted. + proj_on_orb : int or list of int, default=None + orbital projections to be made for the spectral function and TB bands + the integer refer to the orbitals read + trace : bool, default=True + 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 + eta : float, default=0.0 + Broadening of spectral function, finitie shift on imaginary axis + if with_sigma=None it has to be provided !=0.0 + mu_shift : float, default=0.0 + Manual extra shift when calculating the spectral function + proj_nuk : numpy array, default [None] + 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. + + Returns + ------- + tb_data : dict + tight binding dict containing the kpoint mesh, dispersion / emat, and eigenvectors + + alatt_k_w : numpy array (float) of dim n_k x n_w ( x n_orb if trace=False) + lattice spectral function data on the kpoint mesh defined in tb_data and frequency + mesh defined in freq_dict + + freq_dict : dict + frequency mesh information on which alatt_k_w is evaluated + ''' + + # set default ordering + if 'orbital_order_w90' in specs: + orbital_order_w90 = specs['orbital_order_w90'] + else: + orbital_order_w90 = list(range(n_orb)) + + if orbital_order_to is None: + orbital_order_to = orbital_order_w90 + + # checks + assert len(set(orbital_order_to)) == len(orbital_order_to), 'Please provide a unique identifier for each orbital.' + + assert set(orbital_order_w90) == set(orbital_order_to), f'Identifiers of orbital_order_to and orbital_order_w90'\ + f'do not match! orbital_order_to is {orbital_order_to}, but orbital_order_w90 is {orbital_order_w90}.' + + assert with_sigma or eta != 0.0, 'if no Sigma is provided eta has to be different from 0.0' + + # proj_on_orb + assert isinstance(proj_on_orb, (int, type(None))) or all(isinstance(x, (int, type(None))) for x in proj_on_orb), 'proj_on_orb should be '\ + f'an integer or list of integers, but is {type(specs["proj_on_orb"])}.' + + if isinstance(proj_on_orb, (int, type(None))): + proj_on_orb = [proj_on_orb] + else: + proj_on_orb = proj_on_orb + + # if projection is requested we have to use band_basis + if proj_on_orb[0] is not None: + band_basis = True + + # if proj_nuk is given we need to use the band_basis + if isinstance(proj_nuk, np.ndarray) and not band_basis: + band_basis = True + + # set up Wannier Hamiltonian + n_orb = 2 * n_orb if add_spin else n_orb + change_of_basis = change_basis(n_orb, orbital_order_to, orbital_order_w90) + H_add_loc = np.zeros((n_orb, n_orb), dtype=complex) + if not isinstance(add_local, type(None)): + assert np.shape(add_local) == (n_orb, n_orb), 'add_local must have dimension (n_orb, n_orb), but has '\ + f'dimension {np.shape(add_local)}' + H_add_loc += add_local + if add_spin and add_lambda: + H_add_loc += lambda_matrix_w90_t2g(add_lambda) + eta = eta * 1j + + tb = TB_from_wannier90(path=w90_path, seed=w90_seed, extend_to_spin=add_spin, add_local=H_add_loc) + # print local H(R) + h_of_r = np.einsum('ij, jk -> ik', np.linalg.inv(change_of_basis), np.einsum('ij, jk -> ik', tb.hoppings[(0, 0, 0)], change_of_basis)) + if n_orb <= 12: + print_matrix(h_of_r, n_orb, 'H(R=0)') + + # kmesh prep + if ('bands_path' in specs and 'kmesh' in specs) or ('bands_path' not in specs and 'kmesh' not in specs): + raise ValueError('choose either a bands_path or kmesh!') + elif 'bands_path' in specs: + w90_paths = list(map(lambda section: ( + np.array(specs[section[0]]), np.array(specs[section[1]])), specs['bands_path'])) + k_points_labels = [k[0] for k in specs['bands_path']] + [specs['bands_path'][-1][1]] + n_k = specs['n_k'] + k_vec, k_1d, special_k = k_space_path(w90_paths, bz=tb.bz, num=n_k) + elif 'kmesh' in specs: + assert 'reg' in specs['kmesh'], 'only regular kmesh is implemented' + + special_k = k_points_labels = None + + # read kmesh size + if 'n_k' in specs: + k_dim = specs['n_k'] + elif 'k_dim' in specs: + k_dim = specs['k_dim'] + else: + raise ValueError('please specify either n_k or k_dim') + + # create regular kmesh + if isinstance(k_dim, int): + k_spacing = np.linspace(0, 1, k_dim, endpoint=False) + k_vec = np.array(np.meshgrid(k_spacing, k_spacing, k_spacing)).T.reshape(-1, 3) + n_k = k_dim**3 + k_1d = (k_dim, k_dim, k_dim) + elif all(isinstance(x, int) for x in k_dim) and len(k_dim) == 3: + k_x = np.linspace(0, 1, k_dim[0], endpoint=False) + k_y = np.linspace(0, 1, k_dim[1], endpoint=False) + k_z = np.linspace(0, 1, k_dim[2], endpoint=False) + k_vec = np.array(np.meshgrid(k_x, k_y, k_z)).T.reshape(-1, 3) + n_k = k_dim[0]*k_dim[1]*k_dim[2] + k_1d = k_dim + else: + raise ValueError( + 'k_dim / n_k needs to be either an int or a list / tuple of int length 3') + + # calculate tight-binding eigenvalues for non slices + if not fermi_slice: + # Fourier trafo on input grid / path + e_mat = tb.fourier(k_vec).transpose(1, 2, 0) + e_mat = np.einsum('ij, jkl -> ikl', np.linalg.inv(change_of_basis), + np.einsum('ijk, jm -> imk', e_mat, change_of_basis)) + else: + assert 'Z' in specs, 'Please provide Z point coordinate in tb_data_dict as input coordinate' + Z = np.array(specs['Z']) + + k_vec = np.zeros((n_k*n_k, 3)) + e_mat = np.zeros((n_orb, n_orb, n_k, n_k), dtype=complex) + + upper_left = np.diff(w90_paths[0][::-1], axis=0)[0] + lower_right = np.diff(w90_paths[1], axis=0)[0] + for ik_y in range(n_k): + path_along_x = [(upper_left/(n_k-1)*ik_y + specs['kz']*Z, lower_right+upper_left/(n_k-1)*ik_y+specs['kz']*Z)] + k_vec[ik_y*n_k:ik_y*n_k+n_k, :], k_1d, special_k = k_space_path(path_along_x, bz=tb.bz, num=n_k) + e_mat[:, :, :, ik_y] = tb.fourier(k_vec[ik_y*n_k:ik_y*n_k+n_k, :]).transpose(1, 2, 0) + #if add_spin: + # e_mat = e_mat[2:5, 2:5] + e_mat = np.einsum('ij, jklm -> iklm', np.linalg.inv(change_of_basis), np.einsum('ijkl, jm -> imkl', e_mat, change_of_basis)) + + if band_basis: + e_mat, e_vecs, orb_proj = _get_tb_bands(e_mat, proj_on_orb) + else: + e_vecs = total_proj = orb_proj = None + + # now we merge proj_nuk and orb_proj (has reverse shape) + if isinstance(proj_nuk, np.ndarray) and isinstance(orb_proj, np.ndarray): + proj_nuk = proj_nuk * orb_proj + elif not isinstance(proj_nuk, np.ndarray) and isinstance(orb_proj, np.ndarray): + proj_nuk = orb_proj + + # dmft output + if with_sigma: + sigma_types = ['calc', 'model'] + if isinstance(with_sigma, str): + if with_sigma not in sigma_types: + raise ValueError('Invalid sigma type. Expected one of: {}'.format(sigma_types)) + elif not isinstance(with_sigma, BlockGf): + raise ValueError('Invalid sigma type. Expected BlockGf.') + + # get sigma + if with_sigma == 'model': + mu_dmft = None if 'mu_dmft' not in specs else specs['mu_dmft'] + delta_sigma, freq_dict = sigma_FL(n_orb, orbital_order_to, specs['Sigma_0'], specs['Sigma_Z'], specs['w_mesh'], eta=eta, mu_dmft=mu_dmft) + mu = mu_tb + mu_shift + # else is from dmft or memory: + else: + delta_sigma, mu_dmft, freq_dict = _sigma_from_dmft(n_orb, orbital_order_to, with_sigma, **specs) + mu = mu_dmft + mu_shift + + if add_mu_tb: + print('Adding mu_tb to DMFT μ; assuming DMFT was run with subtracted dft μ.') + mu += mu_tb + + print('μ={:2.4f} eV set for calculating A(k,ω)'.format(mu)) + + assert n_orb == delta_sigma.shape[0] and n_orb == delta_sigma.shape[ + 1], f'Number of orbitals n_orb={n_orb} and shape of sigma: {delta_sigma.shape} does not match' + if isinstance(proj_nuk, np.ndarray): + assert n_orb == proj_nuk.shape[0], f'Number of orbitals n_orb={n_orb} does not match shape of proj_nuk: {proj_nuk.shape[0]}' + if not fermi_slice: + assert proj_nuk.shape[-1] == e_vecs.shape[ + 2], f'Number of kpoints in proj_nuk : {proj_nuk.shape[-1]} does not match number of kpoints in e_vecs: {e_vecs.shape[2]}' + else: + assert proj_nuk.shape == tuple([n_orb, e_vecs.shape[2], e_vecs.shape[3]] + ), f'shape of projectors {proj_nuk.shape} does not match expected shape of [{n_orb},{e_vecs.shape[2]},{e_vecs.shape[3]}]' + + # calculate alatt + if not fermi_slice: + alatt_k_w = _calc_alatt(n_orb, mu, eta, e_mat, delta_sigma, qp_bands, e_vecs=e_vecs, + trace=trace, proj_nuk=proj_nuk, **freq_dict) + else: + alatt_k_w = _calc_kslice(n_orb, mu, eta, e_mat, delta_sigma, qp_bands, e_vecs=e_vecs, + proj_nuk=proj_nuk, **freq_dict) + else: + freq_dict = {} + freq_dict['w_mesh'] = None + freq_dict['window'] = None + alatt_k_w = None + + tb_data = {'k_mesh': k_1d, 'special_k': special_k, 'k_points': k_vec, + 'k_points_labels': k_points_labels, 'e_mat': e_mat, + 'e_vecs': e_vecs, 'tb': tb, 'mu_tb': mu_tb, + 'proj_on_orb': proj_on_orb, 'proj_nuk': proj_nuk} + + return tb_data, alatt_k_w, freq_dict
+ +
+ +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/read_config.html b/_modules/read_config.html new file mode 100644 index 00000000..37284017 --- /dev/null +++ b/_modules/read_config.html @@ -0,0 +1,1605 @@ + + + + + + read_config — solid_dmft documentation + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for read_config

+################################################################################
+#
+# solid_dmft - A versatile python wrapper to perform DFT+DMFT calculations
+#              utilizing the TRIQS software library
+#
+# Copyright (C) 2018-2020, ETH Zurich
+# Copyright (C) 2021, The Simons Foundation
+#      authors: A. Hampel, M. Merkel, and S. Beck
+#
+# solid_dmft is free software: you can redistribute it and/or modify it under the
+# terms of the GNU General Public License as published by the Free Software
+# Foundation, either version 3 of the License, or (at your option) any later
+# version.
+#
+# solid_dmft is distributed in the hope that it will be useful, but WITHOUT ANY
+# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
+# PARTICULAR PURPOSE. See the GNU General Public License for more details.
+
+# You should have received a copy of the GNU General Public License along with
+# solid_dmft (in the file COPYING.txt in this directory). If not, see
+# <http://www.gnu.org/licenses/>.
+#
+################################################################################
+"""
+Provides the read_config function to read the config file
+
+Reads the config file (default dmft_config.ini) with python's configparser
+module. It consists of at least the section 'general', and optionally of the
+sections 'solver', 'dft' and 'advanced'.
+Comments are in general possible with with the delimiters ';' or
+'#'. However, this is only possible in the beginning of a line not within
+the line! For default values, the string 'none' is used. NoneType cannot be
+saved in an h5 archive (in the framework that we are using).
+
+List of all parameters, sorted by sections:
+
+---XXX---start
+List of all parameters, sorted by sections:
+
+[  general  ]
+-------------
+
+seedname : str
+            seedname for h5 archive with DMFT input and output
+jobname : str, optional, default='dmft_dir'
+            the output directory for one-shot calculations
+csc : bool, optional, default=False
+            are we doing a CSC calculation?
+plo_cfg : str, optional, default='plo.cfg'
+            config file for PLOs for the converter
+h_int_type : string
+            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
+            * 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
+h_int_basis : string
+              cubic basis convention to compute the interaction U matrix
+              * 'triqs'
+              * 'vasp' (equivalent to 'triqs')
+              * 'wien2k'
+              * 'wannier90'
+              * 'qe' (equivalent to 'wannier90')
+U :  float or comma separated list of floats
+            U values for impurities if only one value is given, the same U is assumed for all impurities
+U_prime :  float or comma separated list of floats
+            U prime values for impurities if only one value is given, the same U prime is assumed for all impurities
+            only used if h_int_type is kanamori
+J :  float or comma separated list of floats
+            J values for impurities if only one value is given, the same J is assumed for all impurities
+ratio_F4_F2 : float or comma separated list of floats, optional, 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, where the default is 0.63.
+beta : float, only used if solver ImFreq
+            inverse temperature for Greens function etc
+n_iter_dmft_first : int, optional, default= 10
+            number of iterations in first dmft cycle to converge dmft solution
+n_iter_dmft_per : int, optional, default= 2
+            number of iterations per dmft step in CSC calculations
+n_iter_dmft : int
+            number of iterations per dmft cycle after first cycle
+dc_type : int
+            Type of double counting correction considered:
+            * 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
+dc_dmft : bool
+           Whether to use DMFT or DFT occupations:
+
+           * DC with DMFT occupation in each iteration -> True
+           * DC with DFT occupations after each DFT cycle -> False
+cpa_zeta : float or comma separated list of floats
+            shift of local levels per impurity in CPA
+cpa_x : float or comma separated list of floats
+            probability distribution for summing G(tau) in CPA
+solver_type : str
+            type of solver chosen for the calculation, currently supports:
+
+            * 'cthyb'
+            * 'ctint'
+            * 'ftps'
+            * 'hubbardI'
+            * 'hartree'
+            * 'ctseg'
+
+n_iw : int, optional, default=1025
+            number of Matsubara frequencies
+n_tau : int, optional, default=10001
+            number of imaginary time points
+n_l : int, needed if measure_G_l=True or legendre_fit=True
+            number of Legendre coefficients
+n_w : int, optional, default=5001
+            number of real frequency points
+w_range : tuple, optional, default=(-10, 10)
+            w_min and w_max, example: w_range = -10, 10
+eta : float, only used if solver ReFreq
+            broadening of Green's function
+diag_delta : bool, optional, default=False
+            option to remove off-diagonal terms in the hybridization function
+
+
+h5_save_freq : int, optional, default=5
+            how often is the output saved to the h5 archive
+magnetic : bool, optional, default=False
+            are we doing a magnetic calculations? If yes put magnetic to True.
+            Not implemented for CSC calculations
+magmom : list of float seperated by comma, optional default=[]
+            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)
+enforce_off_diag : bool, optional, default=False
+            enforce off diagonal elements in block structure finder
+h_field : float, optional, default=0.0
+            magnetic field
+h_field_it : int, optional, default=0
+            number of iterations the magnetic field is kept on
+sigma_mix : float, optional, 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
+g0_mix : float, optional, 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
+g0_mix_type : string, optional, default='linear'
+            which type of mixing is used. Possible values are:
+            linear: linear mixing
+            broyden: broyden mixing
+broy_max_it : int, optional, default=1
+            maximum number of iteration to be considered for broyden mixing
+            1 corresponds to simple linear mixing
+dc : bool, optional, default=True
+            dc correction on yes or no?
+calc_energies : bool, optional, default=False, not compatible with 'ftps' solver
+            calc energies explicitly within the dmft loop
+block_threshold : float, optional, default=1e-05
+            threshold for finding block structures in the input data (off-diag yes or no)
+block_suppress_orbital_symm : bool, optional, default=False
+            should blocks be checked if symmetry-equiv. between orbitals?
+            Does not affect spin symmetries.
+load_sigma : bool, optional, default=False
+            load a old sigma from h5 file
+path_to_sigma : str, needed if load_sigma is true
+            path to h5 file from which the sigma should be loaded
+load_sigma_iter : int, optional, default= last iteration
+            load the sigma from a specific iteration if wanted
+noise_level_initial_sigma : float, optional, default=0.0
+            spread of Gaussian noise applied to the initial Sigma
+occ_conv_crit : float, optional, default= -1
+            stop the calculation if a certain threshold for the imp occ change is reached
+gimp_conv_crit : float, optional, default= -1
+            stop the calculation if  sum_w 1/(w^0.6) ||Gimp-Gloc|| is smaller than threshold
+g0_conv_crit : float, optional, default= -1
+            stop the calculation if sum_w 1/(w^0.6) ||G0-G0_prev|| is smaller than threshold
+sigma_conv_crit : float, optional, default= -1
+            stop the calculation if sum_w 1/(w^0.6) ||Sigma-Sigma_prev|| is smaller than threshold
+sampling_iterations : int, optional, default= 0
+            for how many iterations should the solution sampled after the CSC loop is converged
+sampling_h5_save_freq : int, optional, default= 5
+            overwrites h5_save_freq when sampling has started
+calc_mu_method : string, optional, 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
+prec_mu : float
+            general precision for determining the chemical potential at any time calc_mu is called
+fixed_mu_value : float, optional, default= 'none'
+            If given, the chemical potential remains fixed in calculations
+mu_update_freq : int, optional, default= 1
+            The chemical potential will be updated every # iteration
+mu_initial_guess : float, optional, default= 'none'
+            The chemical potential of the DFT calculation.
+            If not given, mu will be calculated from the DFT bands
+mu_mix_const : float, optional, default= 1.0
+            Constant term of the mixing of the chemical potential. See mu_mix_per_occupation_offset.
+mu_mix_per_occupation_offset : float, optional, 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.
+afm_order : bool, optional, default=False
+            copy self energies instead of solving explicitly for afm order
+set_rot : string, optional, default='none'
+            use density_mat_dft to diagonalize occupations = 'den'
+            use hloc_dft to diagonalize occupations = 'hloc'
+measure_chi_SzSz : bool, optional, default=False
+            measure the dynamic spin suszeptibility chi(sz,sz(tau))
+            triqs.github.io/cthyb/unstable/guide/dynamic_susceptibility_notebook.html
+measure_chi_insertions : int, optional, default=100
+            number of insertation for measurement of chi
+mu_gap_gb2_threshold : float, optional, 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.
+mu_gap_occ_deviation : float, optional, 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.
+
+[  solver  ]
+------------
+store_solver : bool, optional default= False
+            store the whole solver object under DMFT_input in h5 archive
+
+cthyb parameters
+================
+length_cycle : int
+            length of each cycle; number of sweeps before measurement is taken
+n_warmup_cycles : int
+            number of warmup cycles before real measurement sets in
+n_cycles_tot : int
+            total number of sweeps
+measure_G_l : bool
+            measure Legendre Greens function
+measure_G_tau : bool,optional, default=True
+            should the solver measure G(tau)?
+measure_G_iw : bool,optional, default=False
+            should the solver measure G(iw)?
+measure_density_matrix : bool, optional, default=False
+            measures the impurity density matrix and sets also
+            use_norm_as_weight to true
+measure_pert_order : bool, optional, 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'
+max_time : int, optional, default=-1
+            maximum amount the solver is allowed to spend in each iteration
+imag_threshold : float, optional, default= 10e-15
+            threshold for imag part of G0_tau. be warned if symmetries are off in projection scheme imag parts can occur in G0_tau
+off_diag_threshold : float, optional
+            threshold for off-diag elements in Hloc0
+delta_interface : bool, optional, default=False
+            use new delta interface in cthyb instead of input G0
+move_double : bool, optional, default=True
+            double moves in solver
+perform_tail_fit : bool, optional, default=False
+            tail fitting if legendre is off?
+fit_max_moment : int, optional
+            max moment to be fitted
+fit_min_n : int, optional
+            number of start matsubara frequency to start with
+fit_max_n : int, optional
+            number of highest matsubara frequency to fit
+fit_min_w : float, optional
+            start matsubara frequency to start with
+fit_max_w : float, optional
+            highest matsubara frequency to fit
+random_seed : str, optional default 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
+legendre_fit : bool, optional default= False
+            filter noise of G(tau) with G_l, cutoff is taken from n_l
+loc_n_min : int, optional
+            Restrict local Hilbert space to states with at least this number of particles
+loc_n_max : int, optional
+            Restrict local Hilbert space to states with at most this number of particles
+
+ftps parameters
+===============
+n_bath :     int
+            number of bath sites
+bath_fit :   bool, default=False
+            DiscretizeBath vs BathFitter
+refine_factor : int, optional, default=1
+            rerun ftps cycle with increased accuracy
+ph_symm :    bool, optional, default=False
+            particle-hole symmetric problem
+calc_me :    bool, optional, default=True
+            calculate only symmetry-inequivalent spins/orbitals, symmetrized afterwards
+enforce_gap : list of floats, optional, default='none'
+            enforce gap in DiscretizeBath between interval
+ignore_weight : float, optional, default=0.0
+            ignore weight of peaks for bath fitter
+dt :         float
+            time step
+state_storage : string, default= './'
+            location of large MPS states
+path_to_gs : 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
+sweeps :     int, optional, default= 10
+            Number of DMRG sweeps
+maxmI :      int, optional, default= 100
+            maximal imp-imp bond dimensions
+maxmIB :     int, optional, default= 100
+            maximal imp-bath bond dimensions
+maxmB :      int, optional, default= 100
+            maximal bath-bath bond dimensions
+tw :         float, default 1E-9
+            truncated weight for every link
+dmrg_maxmI : int, optional, default= 100
+            maximal imp-imp bond dimensions
+dmrg_maxmIB : int, optional, default= 100
+            maximal imp-bath bond dimensions
+dmrg_maxmB : int, optional, default= 100
+            maximal bath-bath bond dimensions
+dmrg_tw :    float, default 1E-9
+            truncated weight for every link
+
+ctseg parameters
+================
+measure_hist : bool, optional, default=False
+               measure perturbation_order histograms
+improved_estimator  : bool, optional, default=False
+              measure improved estimators
+              Sigma_iw will automatically be calculated via
+              http://dx.doi.org/10.1103/PhysRevB.85.205106
+
+hartree parameters
+================
+with_fock : bool, optional, default=False
+        include Fock exchange terms in the self-energy
+force_real : bool, optional, default=True
+        force the self energy from Hartree fock to be real
+one_shot : bool, optional, default=True
+        Perform a one-shot or self-consitent root finding in each DMFT step of the Hartree solver.
+method : bool, optional, default=True
+        method for root finder. Only used if one_shot=False, see scipy.optimize.root for options.
+tol : float, optional, default=1e-5
+        tolerance for root finder if one_shot=False.
+
+[  dft  ]
+---------
+dft_code : string
+            Choose the DFT code interface, for now Quantum Espresso and Vasp are available.
+
+            Possible values:
+
+            * 'vasp'
+            * 'qe'
+n_cores : int
+            number of cores for the DFT code (VASP)
+n_iter : int, optional, default= 6
+            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
+n_iter_first : int, optional, 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.
+dft_exec :  string, default= 'vasp_std'
+            command for the DFT executable
+store_eigenvals : bool, optional, default= False
+            stores the dft eigenvals from LOCPROJ (projector_type=plo) or
+            wannier90.eig (projector_type=w90) file in h5 archive
+mpi_env : string, default= 'local'
+            selection for mpi env for DFT / VASP in default this will only call VASP as mpirun -np n_cores_dft dft_exec
+projector_type : string, optional, default= 'w90'
+            plo: uses VASP's PLO formalism, requires LOCPROJ in the INCAR
+            w90: uses Wannier90 (for VASP and QuantumEspresso)
+w90_exec :  string, default='wannier90.x'
+            the command to start a single-core wannier run
+w90_tolerance :  float, default=1e-6
+            threshold for mapping of shells and checks of the Hamiltonian
+
+[  advanced  ]
+--------------
+dc_factor : float, optional, default= 'none' (corresponds to 1)
+            If given, scales the dc energy by multiplying with this factor, usually < 1
+dc_fixed_value : float, optional, 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
+dc_fixed_occ : list of float, optional, 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!
+dc_U :  float or comma seperated list of floats, optional, default= general_params['U']
+            U values for DC determination if only one value is given, the same U is assumed for all impurities
+dc_J :  float or comma seperated list of floats, optional, default= general_params['J']
+            J values for DC determination if only one value is given, the same J is assumed for all impurities
+map_solver_struct : list of dict, optional, default=no additional mapping
+            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
+mapped_solver_struct_degeneracies : list, optional, 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.
+pick_solver_struct : list of dict, optional, default=no additional picking
+            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.
+            Not listed blocks / orbitals will be not treated in impurity solver.
+            Keeps degenerate shells.
+
+
+---XXX---end
+
+"""
+
+from configparser import ConfigParser
+import triqs.utility.mpi as mpi
+import numpy as np
+
+# Workaround to get the default configparser boolean converter
+BOOL_PARSER = lambda b: ConfigParser()._convert_to_boolean(b)
+
+# TODO: it might be nicer to not have optional parameters at all and instead use
+#       explicit default values
+
+# Dictionary for the parameters. Contains the four sections general, dft, solver
+# and advanced. Inside, all parameters are listed with their properties:
+#   - converter: converter applied on the string value of the parameter
+#   - valid for: a criterion for validity. If not fulfilled, the program crashes.
+#                Always of form lambda x, params: ..., with x being the current parameter
+#   - used: determines if parameter is used (and if not given, set to default value)
+#           or unused and ignored. If 'used' and no default given, the program crashes.
+#           If 'used' and default=None, this is an optional parameter
+#   - default: default value for parameter. Can be a function of params but can only
+#              use values that have NO default value. If it is None but 'used'
+#              is True, the parameter becomes an optional parameter
+PROPERTIES_PARAMS = {'general': {'seedname': {'used': True},
+
+                                 'h_int_type': {'valid for': lambda x, _: all(hint in ('density_density',
+                                                                                       'kanamori',
+                                                                                       'full_slater',
+                                                                                       'crpa',
+                                                                                       'crpa_density_density',
+                                                                                       'dynamic',
+                                                                                       'ntot') for hint in x),
+                                                'converter': lambda s: list(map(str, s.replace(" ", "").split(','))),
+                                                'used': True},
+
+                                 'U': {'converter': lambda s: list(map(float, s.split(','))), 'used': True},
+
+                                 'U_prime': {'converter': lambda s: list(map(float, s.split(','))),
+                                             'default': ['U-2J'],
+                                             'valid for': lambda x, params: all(r == 'U-2J' or hint in ('kanamori')
+                                                                                    for r, hint in zip(x, params['general']['h_int_type'])),
+                                             'used': True},
+
+                                 'J': {'converter': lambda s: list(map(float, s.split(','))), 'used': True},
+
+                                 'ratio_F4_F2': {'converter': lambda s: list(map(float, s.split(','))),
+                                                 'default': ['none'],
+                                                 'valid for': lambda x, params: all(r == 'none' or hint in ('density_density','full_slater')
+                                                                                    for r, hint in zip(x, params['general']['h_int_type'])),
+                                                 'used': True},
+
+                                 'beta': {'converter': float, 'valid for': lambda x, _: x > 0,
+                                          'used': lambda params: params['general']['solver_type'] in ['cthyb', 'ctint', 'inchworm', 'hubbardI','ctseg','hartree']},
+
+                                 'n_iter_dmft': {'converter': int, 'valid for': lambda x, _: x >= 0, 'used': True},
+
+                                 'dc': {'converter': BOOL_PARSER, 'used': True, 'default': True},
+
+                                 'dc_type': {'converter': int, 'valid for': lambda x, _: x in (0, 1, 2, 3, 4),
+                                             'used': lambda params: params['general']['dc']},
+
+                                 'prec_mu': {'converter': float, 'valid for': lambda x, _: x > 0, 'used': True},
+
+                                 'dc_dmft': {'converter': BOOL_PARSER,
+                                             'used': lambda params: params['general']['dc']},
+
+                                 'cpa_zeta': {'converter': lambda s: list(map(float, s.split(','))),
+                                              'used': lambda params: params['general']['dc'] and params['general']['dc_type'] == 4},
+
+                                 'cpa_x': {'converter': lambda s: list(map(float, s.split(','))),
+                                           'used': lambda params: params['general']['dc'] and params['general']['dc_type'] == 4},
+
+                                 'solver_type': {'valid for': lambda x, _: x in ['cthyb', 'ctint', 'ftps', 'hubbardI','ctseg', 'hartree'],
+                                                 'used': True},
+                                 
+
+                                 'n_l': {'converter': int, 'valid for': lambda x, _: x > 0,
+                                         'used': lambda params: params['general']['solver_type'] in ['cthyb', 'inchworm', 'hubbardI', 'ctseg']
+                                         and (params['solver']['measure_G_l'] or params['solver']['legendre_fit'])},
+
+                                 'n_iw': {'converter': int, 'valid for': lambda x, _: x > 0,
+                                          'used': lambda params: params['general']['solver_type'] in ['cthyb', 'ctint', 'inchworm', 'hubbardI','ctseg','hartree'], 'default': 1025},
+
+                                 'n_tau': {'converter': int, 'valid for': lambda x, _: x > 0,
+                                           'used': lambda params: params['general']['solver_type'] in ['cthyb', 'ctint', 'inchworm', 'hubbardI','ctseg','hartree'], 'default': 10001},
+
+                                 'n_w': {'converter': int, 'valid for': lambda x, _: x > 0,
+                                         'used': lambda params: params['general']['solver_type'] in ['ftps', 'hubbardI', 'hartree'], 'default': 5001},
+
+                                 'w_range': {'converter': lambda s: tuple(map(float, s.split(','))),
+                                             'valid for': lambda x, _: x[0] < x[1],
+                                             'used': lambda params: params['general']['solver_type'] in ['ftps', 'hubbardI', 'hartree'], 'default': (-10, 10)},
+
+                                 'eta': {'converter': float, 'valid for': lambda x, _: x >= 0,
+                                         'used': lambda params: params['general']['solver_type'] in ['ftps', 'hubbardI', 'hartree']},
+
+                                 'diag_delta': {'converter': BOOL_PARSER, 'used': True, 'default': False},
+
+                                 'csc': {'converter': BOOL_PARSER,
+                                         'used': True, 'default': False},
+
+                                 'n_iter_dmft_first': {'converter': int, 'valid for': lambda x, _: x > 0,
+                                                       'used': lambda params: params['general']['csc'], 'default': 10},
+
+                                 'n_iter_dmft_per': {'converter': int, 'valid for': lambda x, _: x > 0,
+                                                     'used': lambda params: params['general']['csc'], 'default': 2},
+
+                                 'plo_cfg': {'used': lambda params: (params['general']['csc']
+                                                                     and params['dft']['projector_type'] == 'plo'),
+                                             'default': 'plo.cfg'},
+
+                                 'jobname': {'used': lambda params: not params['general']['csc'], 'default': lambda params: 'dmft_dir'},
+
+                                 'h5_save_freq': {'converter': int, 'valid for': lambda x, _: x > 0,
+                                                  'used': True, 'default': 5},
+
+                                 'magnetic': {'converter': BOOL_PARSER,
+                                              'used': True, 'default': False},
+
+                                 'magmom': {'converter': lambda s: list(map(float, s.split(','))),
+                                            'used': lambda params: params['general']['magnetic'],
+                                            'default': []},
+
+                                 'h_field': {'converter': float, 'used': True, 'default': 0.0},
+
+                                 'h_field_it': {'converter': int, 'used': True, 'default': 0},
+
+                                 'afm_order': {'converter': BOOL_PARSER,
+                                               'used': lambda params: params['general']['magnetic'],
+                                               'default': False},
+
+                                 'sigma_mix': {'converter': float,
+                                               'valid for': lambda x, params: x >= 0 and (np.isclose(params['general']['g0_mix'], 1)
+                                                                                         or np.isclose(x, 1)),
+                                               'used': True, 'default': 1.0},
+
+                                 'g0_mix': {'converter': float, 'valid for': lambda x, _: x >= 0,
+                                               'used': True, 'default': 1.0},
+
+                                 'g0_mix_type': {'valid for': lambda x, _: x in ('linear', 'broyden'),
+                                                'used': True, 'default': 'linear'},
+
+                                 'broy_max_it': {'converter': int, 'valid for': lambda x, _: x >= 1 or x==-1 ,
+                                                'used': lambda params: params['general']['g0_mix_type'] == 'broyden', 'default': -1},
+
+                                 'calc_energies': {'converter': BOOL_PARSER, 'used': True, 'default': False},
+
+                                 'block_threshold': {'converter': float, 'valid for': lambda x, _: x > 0,
+                                                     'used': True, 'default': 1e-5},
+
+                                 'block_suppress_orbital_symm': {'converter': BOOL_PARSER,
+                                                                 'used': lambda params: not params['general']['enforce_off_diag'], 'default': False},
+
+                                 'enforce_off_diag': {'converter': BOOL_PARSER, 'used': True, 'default': False},
+
+                                 'load_sigma': {'converter': BOOL_PARSER, 'used': True, 'default': False},
+
+                                 'path_to_sigma': {'used': lambda params: params['general']['load_sigma']},
+
+                                 'load_sigma_iter': {'converter': int,
+                                                     'used': lambda params: params['general']['load_sigma'], 'default': -1},
+
+                                 'noise_level_initial_sigma': {'converter': float,
+                                                               'valid for': lambda x, _: x > 0 or np.isclose(x, 0),
+                                                               'used': True, 'default': 0.},
+
+                                 # TODO: change default to 'none'
+                                 'occ_conv_crit': {'converter': float, 'used': True, 'default': -1},
+                                 'gimp_conv_crit': {'converter': float, 'used': True, 'default': -1},
+                                 'g0_conv_crit': {'converter': float, 'used': True, 'default': -1},
+                                 'sigma_conv_crit': {'converter': float, 'used': True, 'default': -1},
+
+                                 'sampling_iterations': {'converter': int, 'valid for': lambda x, _: x >= 0,
+                                                         'used': lambda params: params['general']['occ_conv_crit'] > 0 or
+                                                                                params['general']['gimp_conv_crit'] > 0 or
+                                                                                params['general']['g0_conv_crit'] > 0,
+                                                         'default': 0},
+
+                                 'sampling_h5_save_freq': {'converter': int, 'valid for': lambda x, _: x > 0,
+                                                           'used': lambda params: (params['general']['occ_conv_crit'] > 0
+                                                                                   and params['general']['sampling_iterations'] > 0),
+                                                           'default': 5},
+
+                                 'fixed_mu_value': {'converter': float, 'used': True, 'default': 'none'},
+
+                                 'calc_mu_method': {'valid for': lambda x, _: x in ['dichotomy', 'newton', 'brent'],
+                                                 'used': True,
+                                                 'default': 'dichotomy',
+                                                 },
+
+                                 'mu_update_freq': {'converter': int, 'valid for': lambda x, _: x > 0,
+                                                    'used': lambda params: params['general']['fixed_mu_value'] == 'none',
+                                                    'default': 1},
+
+                                 'mu_initial_guess': {'converter': float, 'used': True, 'default': 'none'},
+
+                                 'mu_mix_const': {'converter': float,
+                                                  'valid for': lambda x, _: x > 0 or np.isclose(x, 0),
+                                                  'used': lambda params: params['general']['fixed_mu_value'] == 'none',
+                                                  'default': 1.},
+
+                                 'mu_mix_per_occupation_offset': {'converter': float,
+                                                                  'valid for': lambda x, _: x > 0 or np.isclose(x, 0),
+                                                                  'used': lambda params: params['general']['fixed_mu_value'] == 'none',
+                                                                  'default': 0.},
+
+                                 'set_rot': {'valid for': lambda x, _: x in ('none', 'den', 'hloc'),
+                                             'used': True, 'default': 'none'},
+
+                                 'measure_chi': {'valid for': lambda x, _: x in ('SzSz', 'NN', 'none'), 'used': True, 'default': 'none'},
+
+                                 'measure_chi_insertions': {'converter': int, 'used': True, 'default': 100},
+
+                                 # TODO: used for which solvers? Generalize to real freq. solvers without maxent?
+                                 'mu_gap_gb2_threshold': {'converter': float,
+                                                          'valid for': lambda x, _: x == 'none' or x > 0 or np.isclose(x, 0),
+                                                          'used': lambda params: params['general']['solver_type'] in ['cthyb', 'ctint','ctseg'],
+                                                          'default': 'none'},
+
+                                 'mu_gap_occ_deviation': {'converter': float,
+                                                          'valid for': lambda x, _: x == 'none' or x > 0 or np.isclose(x, 0),
+                                                          'used': lambda params: (params['general']['solver_type'] in ['cthyb', 'ctint','ctseg']
+                                                                                  and params['general']['mu_gap_gb2_threshold'] != 'none'),
+                                                          'default': 'none'},
+
+                                 'h_int_basis' : {'valid for': lambda x, _: x in ('triqs', 'wien2k', 'wannier90', 'qe', 'vasp'),
+                                            'used': True, 'default' : 'triqs'}
+
+                                },
+                     'dft': {'dft_code': {'used': lambda params: params['general']['csc'],
+                                          'valid for': lambda x, _: x in ('vasp', 'qe')},
+
+                             'n_cores': {'converter': int, 'valid for': lambda x, _: x > 0,
+                                         'used': lambda params: params['general']['csc']},
+
+                             'n_iter': {'converter': int, 'valid for': lambda x, _: x > 0,
+                                        'used': lambda params: params['general']['csc'] and params['dft']['dft_code'] == 'vasp',
+                                        'default': 4},
+
+                             'n_iter_first': {'converter': int, 'valid for': lambda x, _: x > 0,
+                                              'used': lambda params: params['general']['csc'] and params['dft']['dft_code'] == 'vasp',
+                                              'default': lambda params: params['dft']['n_iter']},
+
+                             'dft_exec': {'used': lambda params: params['general']['csc'], 'default': 'vasp_std'},
+
+                             'store_eigenvals': {'converter': BOOL_PARSER,
+                                                 'used': lambda params: params['general']['csc'],
+                                                 'default': False},
+
+                             'mpi_env': {'valid for': lambda x, _: x in ('default', 'openmpi', 'openmpi-intra', 'mpich'),
+                                         'used': lambda params: params['general']['csc'], 'default': 'default'},
+
+                             'projector_type': {'valid for': lambda x, params: x == 'w90' or x == 'plo' and params['dft']['dft_code'] == 'vasp',
+                                                'used': lambda params: params['general']['csc'], 'default': 'w90'},
+
+                             'w90_exec': {'used': lambda params: (params['general']['csc']
+                                                                  and params['dft']['dft_code'] == 'vasp' and params['dft']['projector_type'] == 'w90'),
+                                                'default': 'wannier90.x'},
+
+                             'w90_tolerance': {'converter': lambda s: float(s),
+                                                'used': lambda params: params['general']['csc'] and params['dft']['projector_type'] == 'w90',
+                                                'default': 1e-6},
+                            },
+                     'solver': {
+                                #
+                                'store_solver': {'converter': BOOL_PARSER, 'used': True, 'default': False},
+                                #
+                                # cthyb parameters
+                                #
+                                'length_cycle': {'converter': int, 'valid for': lambda x, _: x > 0,
+                                                 'used': lambda params: params['general']['solver_type'] in ['cthyb', 'ctint', 'ctseg']},
+
+                                'n_warmup_cycles': {'converter': lambda s: int(float(s)),
+                                                    'valid for': lambda x, _: x > 0,
+                                                    'used': lambda params: params['general']['solver_type'] in ['cthyb', 'ctint', 'ctseg']},
+
+                                'n_cycles_tot': {'converter': lambda s: int(float(s)),
+                                                 'valid for': lambda x, _: x >= 0,
+                                                 'used': lambda params: params['general']['solver_type'] in ['cthyb', 'ctint', 'ctseg']},
+
+                                'max_time': {'converter': int, 'valid for': lambda x, _: x >= 0,
+                                             'default': None,
+                                             'used': lambda params: params['general']['solver_type'] in ['cthyb', 'ctint', 'ctseg']},
+
+                                'imag_threshold': {'converter': float, 'default': None,
+                                                   'used': lambda params: params['general']['solver_type'] in ['cthyb']},
+
+                                'off_diag_threshold': {'converter': float, 'default': 0.0,
+                                                   'used': lambda params: params['general']['solver_type'] in ['cthyb']},
+
+                                'delta_interface': {'converter': BOOL_PARSER, 'default': False,
+                                                  'used': lambda params: params['general']['solver_type'] in ['cthyb']},
+
+                                'measure_G_tau': {'converter': BOOL_PARSER, 'default': True,
+                                                  'used': lambda params: params['general']['solver_type'] in ['hubbardI', 'ctseg']},
+
+                                'measure_G_iw': {'converter': BOOL_PARSER, 'default': False,
+                                                  'used': lambda params: params['general']['solver_type'] in ['ctseg']},
+
+                                'measure_G_l': {'converter': BOOL_PARSER, 'default': False,
+                                                'used': lambda params: params['general']['solver_type'] in ['cthyb', 'hubbardI', 'ctseg']},
+
+                                'measure_density_matrix': {'converter': BOOL_PARSER, 'default': False,
+                                                           'used': lambda params: params['general']['solver_type'] in ['cthyb', 'hubbardI']},
+
+                                'move_double': {'converter': BOOL_PARSER, 'default': True,
+                                                'used': lambda params: params['general']['solver_type'] in ['cthyb', 'ctint']},
+
+                                'measure_pert_order': {'converter': BOOL_PARSER, 'default': False,
+                                                       'used': lambda params: params['general']['solver_type'] in ['cthyb', 'ctint', 'ctseg']},
+
+                                'move_shift': {'converter': BOOL_PARSER, 'default': False,
+                                                'used': lambda params: params['general']['solver_type'] in ['cthyb']},
+
+                                'random_seed': {'converter': str, 'default': None,
+                                                'used': lambda params: params['general']['solver_type'] in ['cthyb', 'ctint', 'ctseg']},
+
+                                'perform_tail_fit': {'converter': BOOL_PARSER,
+                                                     'used': lambda params: params['general']['solver_type'] in ['cthyb']
+                                                             and not params['solver']['measure_G_l'],
+                                                     'default': False},
+
+                                'fit_max_moment': {'converter': int, 'valid for': lambda x, _: x >= 0,
+                                                   'used': lambda params: 'perform_tail_fit' in params['solver']
+                                                           and params['solver']['perform_tail_fit']
+                                                           and params['general']['solver_type'] in ['cthyb'],
+                                                   'default': None},
+
+                                'fit_min_n': {'converter': int, 'valid for': lambda x, _: x >= 0,
+                                              'used': lambda params: 'perform_tail_fit' in params['solver']
+                                                      and params['solver']['perform_tail_fit']
+                                                      and params['general']['solver_type'] in ['cthyb'],
+                                              'default': None},
+
+                                'fit_max_n': {'converter': int, 'valid for': lambda x, params: x >= params['solver']['fit_min_n'],
+                                              'used': lambda params: 'perform_tail_fit' in params['solver']
+                                                      and params['solver']['perform_tail_fit']
+                                                      and params['general']['solver_type'] in ['cthyb'],
+                                              'default': None},
+
+                                'fit_min_w': {'converter': float, 'valid for': lambda x, _: x >= 0,
+                                              'used': lambda params: 'perform_tail_fit' in params['solver']
+                                                      and params['solver']['perform_tail_fit']
+                                                      and params['general']['solver_type'] in ['cthyb'],
+                                              'default': None},
+
+                                'fit_max_w': {'converter': float, 'valid for': lambda x, params: x >= params['solver']['fit_min_w'],
+                                              'used': lambda params: 'perform_tail_fit' in params['solver']
+                                                      and params['solver']['perform_tail_fit']
+                                                      and params['general']['solver_type'] in ['cthyb'],
+                                              'default': None},
+
+                                'legendre_fit': {'converter': BOOL_PARSER,
+                                                 'used': lambda params: params['general']['solver_type'] in ['cthyb','ctseg'],
+                                                 'default': False},
+
+                                'loc_n_min': {'converter': int, 'valid for': lambda x, _: x >= 0,
+                                              'used': lambda params: params['general']['solver_type'] in ['cthyb'],
+                                              'default': None},
+
+                                'loc_n_max': {'converter': int, 'valid for': lambda x, _: x >= 0,
+                                              'used': lambda params: params['general']['solver_type'] in ['cthyb'],
+                                              'default': None},
+
+
+                                #
+                                # extra ctseg params
+                                #
+                                'improved_estimator': {'converter': BOOL_PARSER,
+                                                 'used': lambda params: params['general']['solver_type'] in ['ctseg'],
+                                                 'default': False},
+
+                                #
+                                # extra hartree params
+                                #
+                                'with_fock': {'converter': BOOL_PARSER,
+                                                 'used': lambda params: params['general']['solver_type'] in ['hartree'],
+                                                 'default': False},
+                                'one_shot': {'converter': BOOL_PARSER,
+                                                 'used': lambda params: params['general']['solver_type'] in ['hartree'],
+                                                 'default': False},
+                                'force_real': {'converter': BOOL_PARSER,
+                                                 'used': lambda params: params['general']['solver_type'] in ['hartree'],
+                                                 'default': True},
+                                'method': {'valid for': lambda x, _: x in ['krylov', 'broyden1', 'broyden2', 'hybr', 'linearmixing'],
+                                                 'used': lambda params: params['general']['solver_type'] in ['hartree'],
+                                                 'default': 'krylov'},
+                                'tol': {'converter': float, 'valid for': lambda x, _: x >= 0,
+                                              'used': lambda params: params['general']['solver_type'] in ['hartree'],
+                                              'default': 1e-5},
+
+                                #
+                                # ftps parameters
+                                #
+                                'n_bath': {'converter': int, 'valid for': lambda x, _: x >= 0,
+                                           'used': lambda params: params['general']['solver_type'] in ['ftps'],
+                                           'default': 0},
+                                'bath_fit': {'converter': BOOL_PARSER,
+                                             'used': lambda params: params['general']['solver_type'] in ['ftps']},
+                                'refine_factor': {'converter': int, 'valid for': lambda x, _: x >= 0,
+                                                  'used': lambda params: params['general']['solver_type'] in ['ftps'],
+                                                  'default': 1},
+                                'ph_symm': {'converter': BOOL_PARSER, 'default': False,
+                                            'used': lambda params: params['general']['solver_type'] in ['ftps']},
+                                'calc_me': {'converter': BOOL_PARSER, 'default': True,
+                                            'used': lambda params: params['general']['solver_type'] in ['ftps']},
+                                'enforce_gap': {'converter': lambda s: [float(eval(x)) for x in s.split(',')],
+                                                'used': lambda params: params['general']['solver_type'] in ['ftps'],
+                                                'default': 'none'},
+                                'ignore_weight': {'converter': float, 'valid for': lambda x, _: x >= 0,
+                                                  'used': lambda params: params['general']['solver_type'] in ['ftps'],
+                                                  'default': 0.0},
+                                'dt': {'converter': float, 'valid for': lambda x, _: x >= 0,
+                                       'used': lambda params: params['general']['solver_type'] in ['ftps']},
+                                'state_storage': {'used': lambda params: params['general']['solver_type'] in ['ftps'],
+                                                  'default': './'},
+                                'path_to_gs': {'used': lambda params: params['general']['solver_type'] in ['ftps'],
+                                               'default': 'none'},
+                                'sweeps': {'converter': int, 'valid for': lambda x, _: x >= 0,
+                                           'used': lambda params: params['general']['solver_type'] in ['ftps'],
+                                           'default': 10},
+                                'maxm': {'converter': int, 'valid for': lambda x, _: x > 0, 'default': 100,
+                                         'used': lambda params: params['general']['solver_type'] in ['ftps']},
+                                'maxmI': {'converter': int, 'valid for': lambda x, _: x > 0, 'default': 100,
+                                          'used': lambda params: params['general']['solver_type'] in ['ftps']},
+                                'maxmIB': {'converter': int, 'valid for': lambda x, _: x > 0, 'default': 100,
+                                           'used': lambda params: params['general']['solver_type'] in ['ftps']},
+                                'maxmB': {'converter': int, 'valid for': lambda x, _: x > 0, 'default': 100,
+                                          'used': lambda params: params['general']['solver_type'] in ['ftps']},
+                                'tw': {'converter': float, 'valid for': lambda x, _: x > 0, 'default': 1e-9,
+                                       'used': lambda params: params['general']['solver_type'] in ['ftps']},
+                                'dmrg_maxm': {'converter': int, 'valid for': lambda x, _: x > 0, 'default': 100,
+                                              'used': lambda params: params['general']['solver_type'] in ['ftps']},
+                                'dmrg_maxmI': {'converter': int, 'valid for': lambda x, _: x > 0, 'default': 100,
+                                               'used': lambda params: params['general']['solver_type'] in ['ftps']},
+                                'dmrg_maxmIB': {'converter': int, 'valid for': lambda x, _: x > 0, 'default': 100,
+                                                'used': lambda params: params['general']['solver_type'] in ['ftps']},
+                                'dmrg_maxmB': {'converter': int, 'valid for': lambda x, _: x > 0, 'default': 100,
+                                               'used': lambda params: params['general']['solver_type'] in ['ftps']},
+                                'dmrg_tw': {'converter': float, 'valid for': lambda x, _: x > 0, 'default': 1e-9,
+                                            'used': lambda params: params['general']['solver_type'] in ['ftps']},
+                               },
+                     'advanced': {'dc_factor': {'converter': float, 'used': True, 'default': 'none'},
+
+                                  'dc_fixed_value': {'converter': float, 'used': True, 'default': 'none'},
+
+                                  'dc_fixed_occ': {'converter': lambda s: list(map(float, s.split(','))),
+                                                   'used': True, 'default': 'none'},
+
+                                  'dc_nominal': {'converter': BOOL_PARSER, 'used': True, 'default': False},
+
+                                  'dc_U': {'converter': lambda s: list(map(float, s.split(','))),
+                                           'used': True, 'default': lambda params: params['general']['U']},
+
+                                  'dc_J': {'converter': lambda s: list(map(float, s.split(','))),
+                                           'used': True, 'default': lambda params: params['general']['J']},
+
+                                  'map_solver_struct': {'converter': eval,
+                                                        'valid for': lambda x, _: x =='none' or isinstance(x, dict) or isinstance(x, list),
+                                                        'used': True, 'default': 'none'},
+
+                                  'mapped_solver_struct_degeneracies': {'converter': eval,
+                                            'valid for': lambda x, _: x=='none' or isinstance(x, list),
+                                            'used': lambda params: params['advanced']['map_solver_struct'] != 'none',
+                                            'default': 'none'},
+                                  'pick_solver_struct': {'converter': eval,
+                                                        'valid for': lambda x, _: x =='none' or isinstance(x, dict) or isinstance(x, list),
+                                                        'used': True, 'default': 'none'},
+                                 }
+                    }
+
+
+# -------------------------- config section cleanup --------------------------
+def _config_find_default_section_entries(config):
+    """
+    Returns all items in the default section.
+
+    Parameters
+    ----------
+    config : ConfigParser
+        A configparser instance that has read the config file already.
+
+    Returns
+    -------
+    list
+        All entries in the default section.
+    """
+    return list(config['DEFAULT'].keys())
+
+def _config_add_empty_sections(config):
+    """
+    Adds empty sections if no parameters in the whole section were given.
+
+    Parameters
+    ----------
+    config : ConfigParser
+        A configparser instance that has read the config file already.
+
+    Returns
+    -------
+    config : ConfigParser
+        The config parser with all required sections.
+    """
+    for section_name in PROPERTIES_PARAMS:
+        if section_name not in config:
+            config.add_section(section_name)
+
+    return config
+
+def _config_remove_unused_sections(config):
+    """
+    Removes sections that are not supported by this program.
+
+    Parameters
+    ----------
+    config : ConfigParser
+        A configparser instance that has read the config file already.
+
+    Returns
+    -------
+    config : ConfigParser
+        The config parser without unnecessary sections.
+    unused_sections : list
+        All sections that are not supported.
+    """
+    unused_sections = []
+    for section_name in list(config.keys()):
+        if section_name != 'DEFAULT' and section_name not in PROPERTIES_PARAMS.keys():
+            unused_sections.append(section_name)
+            config.remove_section(section_name)
+
+    return config, unused_sections
+
+# -------------------------- parameter reading --------------------------
+def _convert_params(config):
+    """
+    Applies the converter given in the PROPERTIES_PARAMS to the config. If no
+    converter is given, a default string conversion is used.
+
+    Parameters
+    ----------
+    config : ConfigParser
+        A configparser instance that has passed through the above clean-up
+        methods.
+
+    Returns
+    -------
+    parameters : dict
+        Contains dicts for each section. These dicts contain all parameter
+        names and their respective value that are in the configparser and in
+        the PROPERTIES_PARAMS.
+    """
+    parameters = {name: {} for name in PROPERTIES_PARAMS}
+
+    for section_name, section_params in parameters.items():
+        for param_name, param_props in PROPERTIES_PARAMS[section_name].items():
+            if param_name not in config[section_name]:
+                continue
+
+            # Uses converter for parameters
+            if 'converter' in param_props:
+                section_params[param_name] = param_props['converter'](str(config[section_name][param_name]))
+            else:
+                section_params[param_name] = str(config[section_name][param_name])
+
+    return parameters
+
+
+def _find_nonexistent_params(config):
+    """
+    Returns all parameters that are in the config but not in the
+    PROPERTIES_PARAMS and are therefore not recognized by the program.
+
+    Parameters
+    ----------
+    config : ConfigParser
+        A configparser instance that has passed through the above clean-up
+        methods.
+
+    Returns
+    -------
+    nonexistent_params : dict
+        Contains a list for each section, which contains all unused parameter
+        names.
+    """
+    nonexistent_params = {section_name: [] for section_name in PROPERTIES_PARAMS}
+
+    for section_name, section_params in PROPERTIES_PARAMS.items():
+        for param_name in config[section_name]:
+            if param_name not in (key.lower() for key in section_params):
+                nonexistent_params[section_name].append(param_name)
+
+    return nonexistent_params
+
+
+def _apply_default_values(parameters):
+    """
+    Applies default values to all parameters that were not given in the config.
+
+    Parameters
+    ----------
+    parameters : dict
+        Contains dicts for each section, which contain the parameter names and
+        their values as read from the config file.
+
+    Returns
+    -------
+    parameters : dict
+        The parameters dict including the default values.
+    default_values_used : dict
+        Contains a list for each section, which contains all parameters that
+        were set to their default values. Used to find out later which
+        unnecessary parameters were actually given in the config file.
+    """
+    default_values_used = {section_name: [] for section_name in PROPERTIES_PARAMS}
+
+    for section_name, section_params in PROPERTIES_PARAMS.items():
+        for param_name, param_props in section_params.items():
+            if 'default' not in param_props:
+                continue
+
+            if param_name in parameters[section_name]:
+                continue
+
+            default_values_used[section_name].append(param_name)
+            if callable(param_props['default']):
+                parameters[section_name][param_name] = param_props['default'](parameters)
+            else:
+                parameters[section_name][param_name] = param_props['default']
+
+    return parameters, default_values_used
+
+
+def _check_if_params_used(parameters, default_values_used):
+    """
+    Checks if the parameters in the config file are used or unnecessary.
+
+    Parameters
+    ----------
+    parameters : dict
+        Contains dicts for each section, which contain the parameter names and
+        their values as read from the config file or otherwise set to default.
+    default_values_used : dict
+        Contains a list for each section, which contains all parameters that
+        were set to their default values.
+
+    Returns
+    -------
+    parameters : dict
+        The parameters dict where all unnecessary parameters were removed.
+    unnecessary_params : dict
+        Contains a list for each section, which contains all parameters that
+        were given in the config file but are unnecessary.
+    missing_required_params : dict
+        Contains a list for each section, which contains all parameters that
+        are required, have no default and are missing from the config file.
+    """
+    unnecessary_params = {section_name: [] for section_name in PROPERTIES_PARAMS}
+    missing_required_params = {section_name: [] for section_name in PROPERTIES_PARAMS}
+
+    for section_name, section_params in PROPERTIES_PARAMS.items():
+        for param_name, param_props in section_params.items():
+            # 'used' could be bool or function returning bool
+            if callable(param_props['used']):
+                required = param_props['used'](parameters)
+            else:
+                required = param_props['used']
+
+            if required:
+                if param_name not in parameters[section_name]:
+                    missing_required_params[section_name].append(param_name)
+                elif parameters[section_name][param_name] is None:
+                    del parameters[section_name][param_name]
+                continue
+
+            if param_name in parameters[section_name]:
+                del parameters[section_name][param_name]
+
+                if param_name not in default_values_used[section_name]:
+                    unnecessary_params[section_name].append(param_name)
+
+    return parameters, unnecessary_params, missing_required_params
+
+def _checks_validity_criterion(parameters):
+    """
+    Checks the validity criterion from the PROPERTIES_PARAMS.
+
+    Parameters
+    ----------
+    parameters : dict
+        Contains dicts for each section, which contain the parameter names and
+        their values as read from the config file, set to default if required
+        or removed if they are unnecessary.
+
+    Returns
+    -------
+    invalid_params : dict
+        Contains a list for each section, which contains all parameters that
+        do not fulfill their validity criterion.
+    """
+    invalid_params = {section_name: [] for section_name in PROPERTIES_PARAMS}
+
+    for section_name, section_params in PROPERTIES_PARAMS.items():
+        for name, value in parameters[section_name].items():
+            if 'valid for' not in section_params[name]:
+                continue
+
+            condition = section_params[name]['valid for']
+            if not condition(value, parameters):
+                invalid_params[section_name].append(name)
+
+    return invalid_params
+
+
+
+[docs] +def read_config(config_file): + """ + Reads in the config file, checks its sections and parameters and returns + the parameters sorted by their categories. + + Parameters + ---------- + config_file : string + File name of the config file usable for configparser. + + Raises + ------ + ValueError + Required parameters are missing or parameters do not fulfill their + validity criterion. + + Returns + ------- + general_params : dict + + solver_params : dict + + dft_params : dict + + advanced_params : dict + """ + config = ConfigParser() + config.read(config_file) + + # Checks if default section is empty + config_default_entries = _config_find_default_section_entries(config) + if config_default_entries: + print('Warning: the following parameters are not in any section and will be ignored:') + print(', '.join(config_default_entries)) + + # Adds empty sections if they don't exist + config = _config_add_empty_sections(config) + + # Removes unused sections and prints a warning + config, unused_sections = _config_remove_unused_sections(config) + if unused_sections: + print('Warning: ignoring parameters in following unexpected sections:') + print(', '.join(unused_sections)) + + # Reads and converts all valid parameters + parameters = _convert_params(config) + + # Checks for unused parameters given in the config file + nonexistent_params = _find_nonexistent_params(config) + if any(nonexistent_params.values()): + print('Warning: the following parameters are not supported:') + for section_name, section_params in nonexistent_params.items(): + if section_params: + print('- Section "{}": '.format(section_name) + ', '.join(section_params)) + + # Applies default values + parameters, default_values_used = _apply_default_values(parameters) + + # Prints warning if unnecessary parameters are given + parameters, unnecessary_params, missing_required_params = _check_if_params_used(parameters, default_values_used) + if any(unnecessary_params.values()): + print('Warning: the following parameters are given but not used in this calculation:') + for section_name, section_params in unnecessary_params.items(): + if section_params: + print('- Section "{}": '.format(section_name) + ', '.join(section_params)) + + # Raises error if required parameters are not given + if any(missing_required_params.values()): + required_error_string = '' + for section_name, section_params in missing_required_params.items(): + if section_params: + required_error_string += '\n- Section "{}": '.format(section_name) + ', '.join(section_params) + raise ValueError('The following parameters are required but not given:' + + required_error_string) + + # Raises error if parameters invalid + invalid_params = _checks_validity_criterion(parameters) + if any(invalid_params.values()): + invalid_error_string = '' + for section_name, section_params in invalid_params.items(): + if section_params: + invalid_error_string += '\n- Section "{}": '.format(section_name) + ', '.join(section_params) + raise ValueError('The following parameters are not valid:' + + invalid_error_string) + + # warning if sigma mixing is used, remove in future versions + if parameters['general']['sigma_mix'] < 1.0: + if parameters['general']['g0_mix'] < 1.0: + raise ValueError('You shall not use Sigma and G0 mixing together!') + + # Workarounds for some parameters + + # make sure that pick_solver_struct and map_solver_struct are a list of dict + if isinstance(parameters['advanced']['pick_solver_struct'], dict): + parameters['advanced']['pick_solver_struct'] = [parameters['advanced']['pick_solver_struct']] + + if isinstance(parameters['advanced']['map_solver_struct'], dict): + parameters['advanced']['map_solver_struct'] = [parameters['advanced']['map_solver_struct']] + + if parameters['general']['solver_type'] in ['cthyb', 'ctint', 'ctseg']: + parameters['solver']['n_cycles'] = parameters['solver']['n_cycles_tot'] // mpi.size + del parameters['solver']['n_cycles_tot'] + + if parameters['general']['solver_type'] in ['cthyb']: + parameters['general']['cthyb_delta_interface'] = parameters['solver']['delta_interface'] + del parameters['solver']['delta_interface'] + + if parameters['general']['solver_type'] in ['ctseg']: + # some parameters have different names for ctseg + parameters['solver']['measure_gt'] = parameters['solver']['measure_G_tau'] + del parameters['solver']['measure_G_tau'] + + parameters['solver']['measure_gw'] = parameters['solver']['measure_G_iw'] + del parameters['solver']['measure_G_iw'] + + # make sure measure_gw is true if improved estimators are used + if parameters['solver']['improved_estimator']: + parameters['solver']['measure_gt'] = True + parameters['solver']['measure_ft'] = True + else: + parameters['solver']['measure_ft'] = False + del parameters['solver']['improved_estimator'] + + parameters['solver']['measure_gl'] = parameters['solver']['measure_G_l'] + del parameters['solver']['measure_G_l'] + + parameters['solver']['measure_hist'] = parameters['solver']['measure_pert_order'] + del parameters['solver']['measure_pert_order'] + + if parameters['general']['solver_type'] in ['cthyb'] and parameters['solver']['measure_density_matrix']: + # also required to measure the density matrix + parameters['solver']['use_norm_as_weight'] = True + + if parameters['general']['solver_type'] in ['ftps'] and parameters['general']['calc_energies']: + raise ValueError('"calc_energies" is not valid for solver_type = "ftps"') + + if parameters['general']['dc'] and parameters['general']['dc_type'] == 4: + assert sum(parameters['general']['cpa_x']) == 1., 'Probability distribution for CPA must equal 1.' + + # little workaround since #leg coefficients is not directly a solver parameter + if 'legendre_fit' in parameters['solver']: + parameters['general']['legendre_fit'] = parameters['solver']['legendre_fit'] + del parameters['solver']['legendre_fit'] + + parameters['general']['store_solver'] = parameters['solver']['store_solver'] + del parameters['solver']['store_solver'] + + return parameters['general'], parameters['solver'], parameters['dft'], parameters['advanced']
+ +
+ +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/util/update_dmft_config.html b/_modules/util/update_dmft_config.html new file mode 100644 index 00000000..df2810d1 --- /dev/null +++ b/_modules/util/update_dmft_config.html @@ -0,0 +1,468 @@ + + + + + + util.update_dmft_config — solid_dmft documentation + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for util.update_dmft_config

+# -*- coding: utf-8 -*-
+################################################################################
+#
+# TRIQS: a Toolbox for Research in Interacting Quantum Systems
+#
+# Copyright (C) 2016-2018, N. Wentzell
+# Copyright (C) 2018-2019, Simons Foundation
+#   author: N. Wentzell
+#
+# TRIQS is free software: you can redistribute it and/or modify it under the
+# terms of the GNU General Public License as published by the Free Software
+# Foundation, either version 3 of the License, or (at your option) any later
+# version.
+#
+# TRIQS is distributed in the hope that it will be useful, but WITHOUT ANY
+# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+# FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
+# details.
+#
+# You should have received a copy of the GNU General Public License along with
+# TRIQS. If not, see <http://www.gnu.org/licenses/>.
+#
+################################################################################
+"""
+Updates the config file (usually dmft_config.ini) for continuing old calculations.
+"""
+
+import os.path
+import shutil
+import sys
+from configparser import ConfigParser
+
+def _backup_old_file(path):
+    """ Copies file to same folder with the prefix "backup_". """
+    directory, filename = os.path.split(path)
+    shutil.copy2(path, os.path.join(directory, 'backup_'+filename))
+
+def _load_config_file(path):
+    """ Loads file with the configparser module. Returns ConfigParser object. """
+    config = ConfigParser()
+    config.read(path)
+    return config
+
+def _update_section_names(config):
+    """
+    Applies the mapping between legacy names of sections and new names. The
+    mapping is saved in LEGACY_SECTION_NAME_MAPPING.
+
+    Parameters
+    ----------
+    config : ConfigParser
+        A configparser instance that has read the config file already.
+
+    Returns
+    -------
+    config : ConfigParser
+        The configparser with correct section names.
+    """
+
+    LEGACY_SECTION_NAME_MAPPING = {'solver': 'solver_parameters', 'advanced': 'advanced_parameters'}
+
+    for new_name, legacy_name in LEGACY_SECTION_NAME_MAPPING.items():
+        # Only new name in there, everything is okay
+        if legacy_name not in config.keys():
+            continue
+
+        # Only legacy name exists, creates updated section
+        if new_name not in config.keys():
+            config.add_section(new_name)
+
+        # Transfers parameters in legacy section to updated section
+        for param_name, param_value in config[legacy_name].items():
+            config.set(new_name, param_name, param_value)
+        config.remove_section(legacy_name)
+
+    return config
+
+def _update_params(config):
+    """
+    Updates parameter names/values and adds new required parameters.
+
+    Parameters
+    ----------
+    config : ConfigParser
+        The config parser with correct section names.
+
+    Returns
+    -------
+    config : ConfigParser
+        The completely updated config parser.
+    """
+
+    # Updates h_int_type
+    if config['general']['h_int_type'] in ('1', '2', '3'):
+        config['general']['h_int_type'] = {'1': 'density_density',
+                                           '2': 'kanamori',
+                                           '3': 'full_slater'
+                                          }[config['general']['h_int_type']]
+
+    # ---new params
+    # Updates solver_type - if not existent, uses previous default cthyb
+    if 'solver_type' not in config['general']:
+        config['general']['solver_type'] = 'cthyb'
+
+    if 'dft_code' not in config['dft']:
+        config['dft']['dft_code'] = 'vasp'
+
+    # ---updated params
+    # Updates legendre coefficients
+    if 'n_LegCoeff' in config['solver']:
+        config['general']['n_l'] = config['solver']['n_LegCoeff']
+        del config['solver']['n_LegCoeff']
+
+    # Updates dft_executable
+    if 'executable' in config['dft']:
+        config['dft']['dft_exec'] = config['dft']['executable']
+        del config['dft']['executable']
+
+    # Updates dft_executable
+    if 'wannier90_exec' in config['dft']:
+        config['dft']['w90_exec'] = config['dft']['wannier90_exec']
+        del config['dft']['wannier90_exec']
+
+    return config
+
+def _write_config_file(config, path):
+    """ Writes config parser content to a file. """
+    with open(path, 'w') as file:
+        config.write(file)
+
+
+[docs] +def main(path='dmft_config.ini'): + """ Combines methods in the full work flow for updating the config file. """ + if not os.path.isfile(path): + raise ValueError('File {} does not exist.'.format(path)) + + _backup_old_file(path) + + config = _load_config_file(path) + config = _update_section_names(config) + config = _update_params(config) + _write_config_file(config, path)
+ + +if __name__ == '__main__': + if len(sys.argv) == 1: + main() + elif len(sys.argv) == 2: + main(sys.argv[1]) + else: + raise TypeError('Maximally one argument supported: the config file name') +
+ +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/util/update_results_h5.html b/_modules/util/update_results_h5.html new file mode 100644 index 00000000..a5d73b5a --- /dev/null +++ b/_modules/util/update_results_h5.html @@ -0,0 +1,411 @@ + + + + + + util.update_results_h5 — solid_dmft documentation + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for util.update_results_h5

+# -*- coding: utf-8 -*-
+################################################################################
+#
+# TRIQS: a Toolbox for Research in Interacting Quantum Systems
+#
+# Copyright (C) 2016-2018, N. Wentzell
+# Copyright (C) 2018-2019, Simons Foundation
+#   author: N. Wentzell
+#
+# TRIQS is free software: you can redistribute it and/or modify it under the
+# terms of the GNU General Public License as published by the Free Software
+# Foundation, either version 3 of the License, or (at your option) any later
+# version.
+#
+# TRIQS is distributed in the hope that it will be useful, but WITHOUT ANY
+# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+# FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
+# details.
+#
+# You should have received a copy of the GNU General Public License along with
+# TRIQS. If not, see <http://www.gnu.org/licenses/>.
+#
+################################################################################
+"""
+Updates the h5 archives with soliDMFT results for continuing old calculations.
+"""
+import os.path
+import shutil
+import sys
+import numpy as np
+from h5 import HDFArchive
+# Import needed for transfering Green's functions in _update_results_per_iteration
+from triqs.gf import BlockGf
+
+def _backup_old_file(path):
+    """ Copies file to same folder with the prefix "backup_". """
+    directory, filename = os.path.split(path)
+    shutil.copy2(path, os.path.join(directory, 'backup_'+filename))
+
+def _update_results_per_iteration(archive):
+    iterations = [key for key in archive['DMFT_results'] if key.startswith('it_')]
+    iterations.append('last_iter')
+
+    for it in iterations:
+        keys = list(archive['DMFT_results'][it].keys())
+        for key in keys:
+            if '_iw' in key:
+                new_key = key.replace('_iw_', '_freq_').replace('_iw', '_freq_')
+                archive['DMFT_results'][it][new_key] = archive['DMFT_results'][it][key]
+                del archive['DMFT_results'][it][key]
+            elif '_tau' in key:
+                new_key = key.replace('_tau_', '_time_').replace('_tau', '_time_')
+                archive['DMFT_results'][it][new_key] = archive['DMFT_results'][it][key]
+                del archive['DMFT_results'][it][key]
+            # Updates chemical potential to _post
+            elif key == 'chemical_potential':
+                it_number = int(it[3:]) if it.startswith('it_') else -1
+                archive['DMFT_results'][it]['chemical_potential_post'] = archive['DMFT_results'][it]['chemical_potential']
+                archive['DMFT_results'][it]['chemical_potential_pre'] = archive['DMFT_results']['observables']['mu'][it_number]
+                del archive['DMFT_results'][it]['chemical_potential']
+
+def _update_observables(archive):
+    if 'orb_Z' not in archive['DMFT_results']['observables']:
+        orb_gb2 = archive['DMFT_results']['observables']['orb_gb2']
+        orb_Z = []
+
+        for i in range(len(orb_gb2)):
+            orb_Z.append({})
+            for spin in orb_gb2[i]:
+                orb_Z[i][spin] = [np.full(np.shape(z_per_it), 'none')
+                                  for z_per_it in orb_gb2[i][spin]]
+
+        archive['DMFT_results']['observables']['orb_Z'] = orb_Z
+
+
+[docs] +def main(path='dmft_config.ini'): + """ Combines methods in the full work flow for updating the h5 archive. """ + if not os.path.isfile(path): + raise ValueError('File {} does not exist.'.format(path)) + + _backup_old_file(path) + + with HDFArchive(path, 'a') as archive: + _update_results_per_iteration(archive) + _update_observables(archive)
+ + +if __name__ == '__main__': + if len(sys.argv) == 1: + main() + elif len(sys.argv) == 2: + main(sys.argv[1]) + else: + raise TypeError('Maximally one argument supported: the archive file name') +
+ +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/util/write_kslice_to_h5.html b/_modules/util/write_kslice_to_h5.html new file mode 100644 index 00000000..48c5b905 --- /dev/null +++ b/_modules/util/write_kslice_to_h5.html @@ -0,0 +1,439 @@ + + + + + + util.write_kslice_to_h5 — solid_dmft documentation + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for util.write_kslice_to_h5

+#!/usr/bin/env python3
+################################################################################
+#
+# TRIQS: a Toolbox for Research in Interacting Quantum Systems
+#
+# Copyright (C) 2016-2018, N. Wentzell
+# Copyright (C) 2018-2019, Simons Foundation
+#   author: N. Wentzell
+#
+# TRIQS is free software: you can redistribute it and/or modify it under the
+# terms of the GNU General Public License as published by the Free Software
+# Foundation, either version 3 of the License, or (at your option) any later
+# version.
+#
+# TRIQS is distributed in the hope that it will be useful, but WITHOUT ANY
+# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+# FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
+# details.
+#
+# You should have received a copy of the GNU General Public License along with
+# TRIQS. If not, see <http://www.gnu.org/licenses/>.
+#
+################################################################################
+
+"""
+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.
+
+This only works for k independent projectors as from a TB model or from Wannier90.
+
+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.
+
+Adapted from "write_bands_to_h5.py" by Sophie Beck, 2021
+"""
+
+import sys
+import numpy as np
+from h5 import HDFArchive
+
+
+def _read_bands(seedname):
+    """ Reads the -kslice-bands.dat and the -kslice-coord.dat file. """
+
+    print('Reading {0}-kslice-bands.dat and {0}-kslice-coord.dat'.format(seedname))
+
+    kpoints = np.loadtxt('{}-kslice-coord.dat'.format(seedname), skiprows=0, usecols=(0, 1))
+    # to avoid issues with scientific notation of decimals
+    kpoints = np.around(kpoints, decimals=10)
+    band_energies = np.loadtxt('{}-kslice-bands.dat'.format(seedname), skiprows=0, usecols=(0))
+
+    # reshape to band indices
+    sub_bands = band_energies.size//len(kpoints)
+    band_energies = band_energies.reshape((len(kpoints), sub_bands))
+
+    # blow up to mimic using projectors
+    band_energies = np.array([np.diag(e) for e in band_energies], dtype=complex)
+    # add dummy spin components
+    band_energies = band_energies.reshape((kpoints.shape[0], 1, band_energies.shape[1], band_energies.shape[1]))
+    kpoints = np.append(kpoints, np.zeros((kpoints.shape[0], 1)),axis=1)
+
+    return kpoints, band_energies
+
+
+def _read_h5_dft_input_proj_mat(archive_name):
+    """
+    Reads the projection matrix from the h5. In the following,
+    it is assumed to be k independent.
+    """
+    with HDFArchive(archive_name, 'r') as archive:
+        return archive['dft_input/proj_mat']
+
+
+def _write_dft_bands_input_to_h5(archive_name, data):
+    """Writes all the information back to the h5 archive. data is a dict. """
+    with HDFArchive(archive_name, 'a') as archive:
+        if 'dft_bands_input' in archive:
+            del archive['dft_bands_input']
+        archive.create_group('dft_bands_input')
+        for key in data:
+            archive['dft_bands_input'][key] = data[key]
+    print('Written results to {}'.format(archive_name))
+
+
+
+[docs] +def main(seedname, filename_archive=None): + """ + Executes the program on the band data from the files <seedname>_bands.dat and + <seedname>_bands.kpt. If no seedname_archive is specified, <seedname>.h5 is used. + """ + + if filename_archive is None: + filename_archive = seedname + '.h5' + print('Using the archive "{}"'.format(filename_archive)) + + kpoints, band_energies = _read_bands(seedname) + dft_proj_mat = _read_h5_dft_input_proj_mat(filename_archive) + + data = {'n_k': kpoints.shape[0], + 'n_orbitals': np.ones((kpoints.shape[0], 1), dtype=int) * 3, # The 1 in here only works for SO == 0 + 'proj_mat': np.broadcast_to(dft_proj_mat[0], + (kpoints.shape[0], ) + dft_proj_mat.shape[1:]), + 'hopping': band_energies, + # Quantities are not used for unprojected spaghetti + 'n_parproj': 'none', + 'proj_mat_all': 'none', + # Quantity that SumkDFTTools does not need but that is nice for plots + 'kpoints': kpoints} + + _write_dft_bands_input_to_h5(filename_archive, data)
+ + +if __name__ == '__main__': + if len(sys.argv) == 2: + main(sys.argv[1]) + elif len(sys.argv) == 3: + main(sys.argv[1], sys.argv[2]) + else: + print('Please give a seedname (and optionally an archive to write to). Exiting.') + sys.exit(2) +
+ +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_ref/csc_flow.html b/_ref/csc_flow.html new file mode 100644 index 00000000..27a64c86 --- /dev/null +++ b/_ref/csc_flow.html @@ -0,0 +1,378 @@ + + + + + + + csc_flow — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

csc_flow

+

contains the charge self-consistency flow control functions

+
+
+csc_flow.csc_flow_control(general_params, solver_params, dft_params, advanced_params)[source]
+

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.

+
+
Parameters:
+

general_params : dict

+
+

general parameters as a dict

+
+

solver_params : dict

+
+

solver parameters as a dict

+
+

dft_params : dict

+
+

dft parameters as a dict

+
+

advanced_params : dict

+
+

advanced parameters as a dict

+
+
+
+
+ +
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_ref/dft_managers.html b/_ref/dft_managers.html new file mode 100644 index 00000000..58675f9c --- /dev/null +++ b/_ref/dft_managers.html @@ -0,0 +1,364 @@ + + + + + + + dft_managers — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

dft_managers

+

DFT code driver modules

+

Modules

+ + + + + + + + + + + + +

dft_managers.mpi_helpers

Contains the handling of the QE process.

dft_managers.qe_manager

Contains the function to run a QuantumEspresso iteration.

dft_managers.vasp_manager

Contains the handling of the VASP process.

+
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_ref/dft_managers.mpi_helpers.html b/_ref/dft_managers.mpi_helpers.html new file mode 100644 index 00000000..37fac871 --- /dev/null +++ b/_ref/dft_managers.mpi_helpers.html @@ -0,0 +1,418 @@ + + + + + + + dft_managers.mpi_helpers — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

dft_managers.mpi_helpers

+

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.

+
+
+dft_managers.mpi_helpers.create_hostfile(number_cores, cluster_name)[source]
+

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.

+
+
Parameters:
+

number_cores: int, the number of cores that vasp runs on :

+

cluster_name: string, the name of the server :

+
+
Returns:
+

string: name of the hostfile if not run locally and if called by master node :

+
+
+
+ +
+
+dft_managers.mpi_helpers.find_path_to_mpi_command(env_vars, mpi_exe)[source]
+

Finds the complete path for the mpi executable by scanning the directories +of $PATH.

+
+
Parameters:
+

env_vars: dict of string, environment variables containing PATH :

+

mpi_exe: string, mpi command :

+
+
Returns:
+

string: absolute path to mpi command :

+
+
+
+ +
+
+dft_managers.mpi_helpers.get_mpi_arguments(mpi_profile, mpi_exe, number_cores, dft_exe, hostfile)[source]
+

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.

+
+
Parameters:
+

cluster_name: string, name of the cluster so that settings can be tailored to it :

+

mpi_exe: string, mpi command :

+

number_cores: int, the number of cores that vasp runs on :

+

dft_exe: string, the command to start the DFT code :

+

hostfile: string, name of the hostfile :

+
+
Returns:
+

list of string: arguments to start mpi with :

+
+
+
+ +
+
+dft_managers.mpi_helpers.poll_barrier(comm, poll_interval=0.1)[source]
+

Use asynchronous synchronization, otherwise mpi.barrier uses up all the CPU time during +the run of subprocess.

+
+
Parameters:
+

comm: MPI communicator :

+

poll_interval: float, time step for pinging the status of the sleeping ranks :

+
+
+
+ +
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_ref/dft_managers.qe_manager.html b/_ref/dft_managers.qe_manager.html new file mode 100644 index 00000000..009f4dc2 --- /dev/null +++ b/_ref/dft_managers.qe_manager.html @@ -0,0 +1,379 @@ + + + + + + + dft_managers.qe_manager — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

dft_managers.qe_manager

+

Contains the function to run a QuantumEspresso iteration. Needed for CSC calculations.

+
+
+dft_managers.qe_manager.read_dft_energy(seedname, iter_dmft)[source]
+

Reads DFT energy from quantum espresso’s out files

+
    +
  1. At the first iteration, the DFT energy is read from the scf file.

  2. +
  3. 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.

  4. +
+
+ +
+
+dft_managers.qe_manager.run(number_cores, qe_file_ext, qe_exec, mpi_profile, seedname)[source]
+

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.

+
+
Parameters:
+

number_cores: int, the number of cores that vasp runs on :

+

qe_file_ext: string, qe executable :

+

qe_exec: string, path to qe executables :

+

mpi_profile: string, name of the cluster so that settings can be tailored to it :

+
+
+
+ +
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_ref/dft_managers.vasp_manager.html b/_ref/dft_managers.vasp_manager.html new file mode 100644 index 00000000..b75841b8 --- /dev/null +++ b/_ref/dft_managers.vasp_manager.html @@ -0,0 +1,400 @@ + + + + + + + dft_managers.vasp_manager — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

dft_managers.vasp_manager

+

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.

+

This functionality is contained in the simpler public functions.

+
+
+dft_managers.vasp_manager.kill(vasp_process_id)[source]
+

Kills the VASP process.

+
+ +
+
+dft_managers.vasp_manager.read_dft_energy()[source]
+

Reads DFT energy from the last line of Vasp’s OSZICAR.

+
+ +
+
+dft_managers.vasp_manager.read_irred_kpoints(kpts)[source]
+

Reads the indices of the irreducible k-points from the OUTCAR.

+
+ +
+
+dft_managers.vasp_manager.remove_legacy_projections_suppressed()[source]
+

Removes legacy file vasp.suppress_projs if present.

+
+ +
+
+dft_managers.vasp_manager.run_charge_update()[source]
+

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.

+
+ +
+
+dft_managers.vasp_manager.run_initial_scf(number_cores, vasp_command, cluster_name)[source]
+

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.

+
+
Parameters:
+

number_cores: int, the number of cores that vasp runs on :

+

vasp_command: string, the command to start vasp :

+

cluster_name: string, name of the cluster so that settings can be tailored to it :

+
+
+
+ +
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_ref/dmft_cycle.html b/_ref/dmft_cycle.html new file mode 100644 index 00000000..03f2c014 --- /dev/null +++ b/_ref/dmft_cycle.html @@ -0,0 +1,391 @@ + + + + + + + dmft_cycle — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

dmft_cycle

+

main DMFT cycle, DMFT step, and helper functions

+
+
+dmft_cycle.dmft_cycle(general_params, solver_params, advanced_params, dft_params, n_iter, dft_irred_kpt_indices=None, dft_energy=None)[source]
+

main dmft cycle that works for one shot and CSC equally

+
+
Parameters:
+

general_params : dict

+
+

general parameters as a dict

+
+

solver_params : dict

+
+

solver parameters as a dict

+
+

advanced_params : dict

+
+

advanced parameters as a dict

+
+

observables : dict

+
+

current observable array for calculation

+
+

n_iter : int

+
+

number of iterations to be executed

+
+

dft_irred_kpt_indices: iterable of int :

+
+

If given, writes density correction for csc calculations only for +irreducible kpoints

+
+
+
Returns:
+

observables : dict

+
+

updated observable array for calculation

+
+
+
+
+ +
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_ref/dmft_tools.afm_mapping.html b/_ref/dmft_tools.afm_mapping.html new file mode 100644 index 00000000..0cdb5c00 --- /dev/null +++ b/_ref/dmft_tools.afm_mapping.html @@ -0,0 +1,358 @@ + + + + + + + dmft_tools.afm_mapping — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

dmft_tools.afm_mapping

+
+
+dmft_tools.afm_mapping.determine(general_params, archive, n_inequiv_shells)[source]
+

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.

+
+ +
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_ref/dmft_tools.convergence.html b/_ref/dmft_tools.convergence.html new file mode 100644 index 00000000..70d0349c --- /dev/null +++ b/_ref/dmft_tools.convergence.html @@ -0,0 +1,510 @@ + + + + + + + dmft_tools.convergence — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

dmft_tools.convergence

+

contain helper functions to check convergence

+
+
+dmft_tools.convergence.calc_convergence_quantities(sum_k, general_params, conv_obs, observables, solvers, G0_old, G_loc_all, Sigma_freq_previous)[source]
+

Calculations convergence quantities, i.e. the difference in observables +between the last and second to last iteration.

+
+
Parameters:
+

sum_k : SumK Object instances

+

general_params : dict

+
+

general parameters as a dict

+
+

conv_obs : list of dicts

+
+

convergence observable arrays

+
+

observables : list of dicts

+
+

observable arrays

+
+

solvers : solver objects

+

G0_old : list of block Gf object

+
+

last G0_freq

+
+

G_loc_all : list of block Gf objects

+
+

G_loc extracted from before imp solver

+
+

Sigma_freq_previous : list of block Gf objects

+
+

previous impurity sigma to compare with

+
+
+
Returns:
+

conv_obs : list of dicts

+
+

updated convergence observable arrays

+
+
+
+
+ +
+
+dmft_tools.convergence.check_convergence(n_inequiv_shells, general_params, conv_obs)[source]
+

check last iteration for convergence

+
+
Parameters:
+

n_inequiv_shells : int

+
+

Number of inequivalent shells as saved in SumkDFT object

+
+

general_params : dict

+
+

general parameters as a dict

+
+

conv_obs : list of dicts

+
+

convergence observable arrays

+
+
+
Returns:
+

is_converged : bool

+
+

true if desired accuracy is reached. None if no convergence criterion +is set

+
+
+
+
+ +
+
+dmft_tools.convergence.max_G_diff(G1, G2, norm_temp=True)[source]
+

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.

+

1/ (2* sqrt(beta)) sqrt( sum_n sum_ij [abs(G1 - G2)_ij(w_n)]^2 )

+

this is only done for MeshImFreq Gf objects, for all other +meshes the weights are set to 1

+
+
Parameters:
+

G1 : Gf or BlockGf to compare

+

G2 : Gf or BlockGf to compare

+

norm_temp: bool, default = True :

+
+

divide by an additional sqrt(beta) to account for temperature scaling +only correct for uniformly distributed error.

+
+

__Returns:__ :

+

diff : float

+
+

difference between the two Gfs

+
+
+
+
+ +
+
+dmft_tools.convergence.prep_conv_file(general_params, sum_k)[source]
+

Writes the header to the conv files

+
+
Parameters:
+

general_params : dict

+
+

general parameters as a dict

+
+

n_inequiv_shells : int

+
+

number of impurities for calculations

+
+

__Returns:__ :

+

nothing :

+
+
+
+ +
+
+dmft_tools.convergence.prep_conv_obs(h5_archive, sum_k)[source]
+

prepares the conv arrays and files for the DMFT calculation

+
+
Parameters:
+

h5_archive: hdf archive instance :

+
+

hdf archive for calculation

+
+

sum_k : SumK Object instances

+

__Returns:__ :

+

conv_obs : dict

+
+

conv array for calculation

+
+
+
+
+ +
+
+dmft_tools.convergence.write_conv(conv_obs, sum_k, general_params)[source]
+

writes the last entries of the conv arrays to the files

+
+
Parameters:
+

conv_obs : list of dicts

+
+

convergence observable arrays/dicts

+
+

sum_k : SumK Object instances

+

general_params : dict

+

__Returns:__ :

+

nothing :

+
+
+
+ +
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_ref/dmft_tools.formatter.html b/_ref/dmft_tools.formatter.html new file mode 100644 index 00000000..f586fb06 --- /dev/null +++ b/_ref/dmft_tools.formatter.html @@ -0,0 +1,364 @@ + + + + + + + dmft_tools.formatter — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

dmft_tools.formatter

+

Contains formatters for things that need to be printed in DMFT calculations.

+
+
+dmft_tools.formatter.print_block_sym(sum_k, dm, general_params)[source]
+

Prints a summary of block structure finder, determination of +shell_multiplicity, local Hamiltonian, DFT density matrix.

+
+ +
+
+dmft_tools.formatter.print_rotation_matrix(sum_k)[source]
+

Prints the rotation matrix, real and imaginary part separately.

+
+ +
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_ref/dmft_tools.greens_functions_mixer.html b/_ref/dmft_tools.greens_functions_mixer.html new file mode 100644 index 00000000..7d9a4755 --- /dev/null +++ b/_ref/dmft_tools.greens_functions_mixer.html @@ -0,0 +1,350 @@ + + + + + + + dmft_tools.greens_functions_mixer — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

dmft_tools.greens_functions_mixer

+
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_ref/dmft_tools.html b/_ref/dmft_tools.html new file mode 100644 index 00000000..96c7fe1c --- /dev/null +++ b/_ref/dmft_tools.html @@ -0,0 +1,391 @@ + + + + + + + dmft_tools — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

dmft_tools

+

DMFT routine helper functions used during solid_dmft run

+

Modules

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

dmft_tools.afm_mapping

dmft_tools.convergence

contain helper functions to check convergence

dmft_tools.formatter

Contains formatters for things that need to be printed in DMFT calculations.

dmft_tools.greens_functions_mixer

dmft_tools.initial_self_energies

Contains all functions related to determining the double counting and the initial self-energy.

dmft_tools.interaction_hamiltonian

Contains all functions related to constructing the interaction Hamiltonian.

dmft_tools.legendre_filter

dmft_tools.manipulate_chemical_potential

Contains all the functions related to setting the chemical potential in the next iteration.

dmft_tools.matheval

dmft_tools.observables

Contains all functions related to the observables.

dmft_tools.results_to_archive

dmft_tools.solver

+
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_ref/dmft_tools.initial_self_energies.html b/_ref/dmft_tools.initial_self_energies.html new file mode 100644 index 00000000..6abaed25 --- /dev/null +++ b/_ref/dmft_tools.initial_self_energies.html @@ -0,0 +1,440 @@ + + + + + + + dmft_tools.initial_self_energies — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

dmft_tools.initial_self_energies

+

Contains all functions related to determining the double counting and the +initial self-energy.

+
+
+dmft_tools.initial_self_energies.calculate_double_counting(sum_k, density_matrix, general_params, advanced_params)[source]
+

Calculates the double counting, including all manipulations from advanced_params.

+
+
Parameters:
+

sum_k : SumkDFT object

+

density_matrix : list of gf_struct_solver like

+
+

List of density matrices for all inequivalent shells

+
+

general_params : dict

+
+

general parameters as a dict

+
+

advanced_params : dict

+
+

advanced parameters as a dict

+
+
+
Returns:
+

sum_k : SumKDFT object

+
+

The SumKDFT object containing the updated double counting

+
+
+
+
+ +
+
+dmft_tools.initial_self_energies.determine_dc_and_initial_sigma(general_params, advanced_params, sum_k, archive, iteration_offset, density_mat_dft, solvers)[source]
+

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.

+
    +
  • 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.

  • +
  • 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.

  • +
  • New calculation, without DC: Sigma is initialized as 0, +starting the calculation from the DFT Green’s function.

  • +
+
+
Parameters:
+

general_params : dict

+
+

general parameters as a dict

+
+

advanced_params : dict

+
+

advanced parameters as a dict

+
+

sum_k : SumkDFT object

+
+

Sumk object with the information about the correct block structure

+
+

archive : HDFArchive

+
+

the archive of the current calculation

+
+

iteration_offset : int

+
+

the iterations done before this calculation

+
+

density_mat_dft : numpy array

+
+

DFT density matrix

+
+

solvers : list

+
+

list of Solver instances

+
+
+
Returns:
+

sum_k : SumkDFT object

+
+

the SumkDFT object, updated by the initial Sigma and the DC

+
+

solvers : list

+
+

list of Solver instances, updated by the initial Sigma

+
+
+
+
+ +
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_ref/dmft_tools.interaction_hamiltonian.html b/_ref/dmft_tools.interaction_hamiltonian.html new file mode 100644 index 00000000..d4d95dde --- /dev/null +++ b/_ref/dmft_tools.interaction_hamiltonian.html @@ -0,0 +1,372 @@ + + + + + + + dmft_tools.interaction_hamiltonian — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

dmft_tools.interaction_hamiltonian

+

Contains all functions related to constructing the interaction Hamiltonian.

+
+
+dmft_tools.interaction_hamiltonian.construct(sum_k, general_params, advanced_params)[source]
+

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.

+

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, https://cms.mpi.univie.ac.at/wiki/index.php/LDAUTYPE ) for all +other Hamiltonians.

+

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

+
+ +
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_ref/dmft_tools.legendre_filter.html b/_ref/dmft_tools.legendre_filter.html new file mode 100644 index 00000000..a05ca258 --- /dev/null +++ b/_ref/dmft_tools.legendre_filter.html @@ -0,0 +1,377 @@ + + + + + + + dmft_tools.legendre_filter — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

dmft_tools.legendre_filter

+
+
+dmft_tools.legendre_filter.apply(G_tau, order=100, G_l_cut=1e-19)[source]
+

Filter binned imaginary time Green’s function +using a Legendre filter of given order and coefficient threshold.

+
+
Parameters:
+

G_tau : TRIQS imaginary time Block Green’s function

+

auto : determines automatically the cut-off nl

+

order : int

+
+

Legendre expansion order in the filter

+
+

G_l_cut : float

+
+

Legendre coefficient cut-off

+
+
+
Returns:
+

G_l : TRIQS Legendre Block Green’s function

+
+

Fitted Green’s function on a Legendre mesh

+
+
+
+
+ +
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_ref/dmft_tools.manipulate_chemical_potential.html b/_ref/dmft_tools.manipulate_chemical_potential.html new file mode 100644 index 00000000..9445c020 --- /dev/null +++ b/_ref/dmft_tools.manipulate_chemical_potential.html @@ -0,0 +1,445 @@ + + + + + + + dmft_tools.manipulate_chemical_potential — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

dmft_tools.manipulate_chemical_potential

+

Contains all the functions related to setting the chemical potential in the +next iteration.

+
+
+dmft_tools.manipulate_chemical_potential.set_initial_mu(general_params, sum_k, iteration_offset, archive, shell_multiplicity)[source]
+

Handles the different ways of setting the initial chemical potential mu: +* Chemical potential set to fixed value: uses this value

+
    +
  • New calculation: determines mu from dichotomy method

  • +
  • +
    Resuming calculation and chemical potential not updated this iteration:

    loads calculation before previous iteration.

    +
    +
    +
  • +
  • +
    Resuming calculation and chemical potential is updated:

    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.

    +
    +
    +
  • +
+
+
Parameters:
+

general_params : dict

+
+

general parameters as dict.

+
+

sum_k : SumkDFT object

+
+

contains system information necessary to determine the initial mu.

+
+

iteration_offset : int

+
+

the number of iterations executed in previous calculations.

+
+

archive : HDFArchive

+
+

needed to potentially load previous results and write MaxEnt results to.

+
+

shell_multiplicity : iterable of ints

+
+

number of equivalent shells per impurity.

+
+
+
Returns:
+

sum_k : SumkDFT object

+
+

the altered SumkDFT object with the initial mu set correctly.

+
+
+
+
+ +
+
+dmft_tools.manipulate_chemical_potential.update_mu(general_params, sum_k, it, archive)[source]
+

Handles the different ways of updating the chemical potential mu: +* Chemical potential set to fixed value: uses this value

+
    +
  • Chemical potential not updated this iteration: nothing happens.

  • +
  • +
    Chemical potential is updated: 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.

    +
    +
    +
  • +
+
+
Parameters:
+

general_params : dict

+
+

general parameters as dict.

+
+

sum_k : SumkDFT object

+
+

contains system information necessary to update mu.

+
+

it : int

+
+

the number of the current iteration.

+
+

archive : HDFArchive

+
+

needed to potentially write MaxEnt results to.

+
+
+
Returns:
+

sum_k : SumkDFT object

+
+

the altered SumkDFT object with the updated mu.

+
+
+
+
+ +
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_ref/dmft_tools.matheval.MathExpr.__init__.html b/_ref/dmft_tools.matheval.MathExpr.__init__.html new file mode 100644 index 00000000..948b1d77 --- /dev/null +++ b/_ref/dmft_tools.matheval.MathExpr.__init__.html @@ -0,0 +1,357 @@ + + + + + + + dmft_tools.matheval.MathExpr.__init__ — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

dmft_tools.matheval.MathExpr.__init__

+
+
+MathExpr.__init__(expr)[source]
+
+ +
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_ref/dmft_tools.matheval.MathExpr.allowed_nodes.html b/_ref/dmft_tools.matheval.MathExpr.allowed_nodes.html new file mode 100644 index 00000000..50f4c4c5 --- /dev/null +++ b/_ref/dmft_tools.matheval.MathExpr.allowed_nodes.html @@ -0,0 +1,357 @@ + + + + + + + dmft_tools.matheval.MathExpr.allowed_nodes — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

dmft_tools.matheval.MathExpr.allowed_nodes

+
+
+MathExpr.allowed_nodes = (<class 'ast.Module'>, <class 'ast.Expr'>, <class 'ast.Load'>, <class 'ast.Expression'>, <class 'ast.Add'>, <class 'ast.Sub'>, <class 'ast.UnaryOp'>, <class 'ast.Num'>, <class 'ast.BinOp'>, <class 'ast.Mult'>, <class 'ast.Div'>, <class 'ast.Pow'>, <class 'ast.BitOr'>, <class 'ast.BitAnd'>, <class 'ast.BitXor'>, <class 'ast.USub'>, <class 'ast.UAdd'>, <class 'ast.FloorDiv'>, <class 'ast.Mod'>, <class 'ast.LShift'>, <class 'ast.RShift'>, <class 'ast.Invert'>, <class 'ast.Call'>, <class 'ast.Name'>)
+
+ +
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_ref/dmft_tools.matheval.MathExpr.functions.html b/_ref/dmft_tools.matheval.MathExpr.functions.html new file mode 100644 index 00000000..1a746443 --- /dev/null +++ b/_ref/dmft_tools.matheval.MathExpr.functions.html @@ -0,0 +1,357 @@ + + + + + + + dmft_tools.matheval.MathExpr.functions — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

dmft_tools.matheval.MathExpr.functions

+
+
+MathExpr.functions = {'abs': <built-in function abs>, 'acos': <built-in function acos>, 'acosh': <built-in function acosh>, 'asin': <built-in function asin>, 'asinh': <built-in function asinh>, 'atan': <built-in function atan>, 'atan2': <built-in function atan2>, 'atanh': <built-in function atanh>, 'ceil': <built-in function ceil>, 'comb': <built-in function comb>, 'complex': <class 'complex'>, 'copysign': <built-in function copysign>, 'cos': <built-in function cos>, 'cosh': <built-in function cosh>, 'degrees': <built-in function degrees>, 'dist': <built-in function dist>, 'e': 2.718281828459045, 'erf': <built-in function erf>, 'erfc': <built-in function erfc>, 'exp': <built-in function exp>, 'expm1': <built-in function expm1>, 'fabs': <built-in function fabs>, 'factorial': <built-in function factorial>, 'floor': <built-in function floor>, 'fmod': <built-in function fmod>, 'frexp': <built-in function frexp>, 'fsum': <built-in function fsum>, 'gamma': <built-in function gamma>, 'gcd': <built-in function gcd>, 'hypot': <built-in function hypot>, 'inf': inf, 'isclose': <built-in function isclose>, 'isfinite': <built-in function isfinite>, 'isinf': <built-in function isinf>, 'isnan': <built-in function isnan>, 'isqrt': <built-in function isqrt>, 'lcm': <built-in function lcm>, 'ldexp': <built-in function ldexp>, 'lgamma': <built-in function lgamma>, 'log': <built-in function log>, 'log10': <built-in function log10>, 'log1p': <built-in function log1p>, 'log2': <built-in function log2>, 'max': <built-in function max>, 'min': <built-in function min>, 'modf': <built-in function modf>, 'nan': nan, 'nextafter': <built-in function nextafter>, 'perm': <built-in function perm>, 'pi': 3.141592653589793, 'pow': <built-in function pow>, 'prod': <built-in function prod>, 'radians': <built-in function radians>, 'remainder': <built-in function remainder>, 'round': <built-in function round>, 'sin': <built-in function sin>, 'sinh': <built-in function sinh>, 'sqrt': <built-in function sqrt>, 'tan': <built-in function tan>, 'tanh': <built-in function tanh>, 'tau': 6.283185307179586, 'trunc': <built-in function trunc>, 'ulp': <built-in function ulp>}
+
+ +
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_ref/dmft_tools.matheval.MathExpr.html b/_ref/dmft_tools.matheval.MathExpr.html new file mode 100644 index 00000000..2e149666 --- /dev/null +++ b/_ref/dmft_tools.matheval.MathExpr.html @@ -0,0 +1,380 @@ + + + + + + + dmft_tools.matheval.MathExpr — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

dmft_tools.matheval.MathExpr

+
+
+class dmft_tools.matheval.MathExpr(expr)[source]
+

Bases: object

+
+
+__init__(expr)[source]
+
+ +
+ + + + + + + +

__init__(expr)

+

Attributes

+ + + + + + + + + +

allowed_nodes

functions

+
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_ref/dmft_tools.matheval.html b/_ref/dmft_tools.matheval.html new file mode 100644 index 00000000..db125270 --- /dev/null +++ b/_ref/dmft_tools.matheval.html @@ -0,0 +1,358 @@ + + + + + + + dmft_tools.matheval — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

dmft_tools.matheval

+

Classes

+ + + + + + +

MathExpr(expr)

+
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_ref/dmft_tools.observables.html b/_ref/dmft_tools.observables.html new file mode 100644 index 00000000..3dfd1bfc --- /dev/null +++ b/_ref/dmft_tools.observables.html @@ -0,0 +1,530 @@ + + + + + + + dmft_tools.observables — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

dmft_tools.observables

+

Contains all functions related to the observables.

+
+
+dmft_tools.observables.add_dft_values_as_zeroth_iteration(observables, general_params, dft_mu, dft_energy, sum_k, G_loc_all_dft, density_mat_dft, shell_multiplicity)[source]
+

Calculates the DFT observables that should be written as the zeroth iteration.

+
+
Parameters:
+

observables : observable arrays/dicts

+

general_params : general parameters as a dict

+

dft_mu : dft chemical potential

+

sum_k : SumK Object instances

+

G_loc_all_dft : Gloc from DFT for G(beta/2)

+

density_mat_dft : occupations from DFT

+

shell_multiplicity : degeneracy of impurities

+
+
Returns:
+

observables: list of dicts :

+
+
+
+ +
+
+dmft_tools.observables.add_dmft_observables(observables, general_params, solver_params, dft_energy, it, solvers, h_int, previous_mu, sum_k, density_mat, shell_multiplicity, E_bandcorr)[source]
+

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

+
+
Parameters:
+

observables : observable arrays/dicts

+

general_params : general parameters as a dict

+

solver_params : solver parameters as a dict

+

it : iteration counter

+

solvers : Solver instances

+

h_int : interaction hamiltonian

+

previous_mu : dmft chemical potential for which the calculation was just done

+

sum_k : SumK Object instances

+

density_mat : DMFT occupations

+

shell_multiplicity : degeneracy of impurities

+

E_bandcorr : E_kin_dmft - E_kin_dft, either calculated man or from sum_k method if CSC

+
+
Returns:
+

observables: list of dicts :

+
+
+
+ +
+
+dmft_tools.observables.calc_Z(Sigma)[source]
+

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

+
+
Parameters:
+

Sigma: Gf on MeshImFreq :

+
+

self-energy on Matsubara mesh

+
+
+
Returns:
+

orb_Z: 1d numpy array :

+
+

list of Z values per orbital in Sigma

+
+
+
+
+ +
+
+dmft_tools.observables.calc_bandcorr_man(general_params, sum_k, E_kin_dft)[source]
+

Calculates the correlated kinetic energy from DMFT for target states +and then determines the band correction energy

+
+
Parameters:
+

general_params : dict

+
+

general parameters as a dict

+
+

sum_k : SumK Object instances

+

E_kin_dft: float :

+
+

kinetic energy from DFT

+
+
+
Returns:
+

E_bandcorr: float :

+
+

band energy correction E_kin_dmft - E_kin_dft

+
+
+
+
+ +
+
+dmft_tools.observables.calc_dft_kin_en(general_params, sum_k, dft_mu)[source]
+

Calculates the kinetic energy from DFT for target states

+
+
Parameters:
+

general_params : dict

+
+

general parameters as a dict

+
+

sum_k : SumK Object instances

+

dft_mu: float :

+
+

DFT fermi energy

+
+
+
Returns:
+

E_kin_dft: float :

+
+

kinetic energy from DFT

+
+
+
+
+ +
+
+dmft_tools.observables.prep_observables(h5_archive, sum_k)[source]
+

prepares the observable arrays and files for the DMFT calculation

+
+
Parameters:
+

h5_archive: hdf archive instance :

+
+

hdf archive for calculation

+
+

sum_k : SumK Object instances

+
+
Returns:
+

observables : dict

+
+

observable array for calculation

+
+
+
+
+ +
+
+dmft_tools.observables.write_header_to_file(general_params, sum_k)[source]
+

Writes the header to the observable files

+
+
Parameters:
+

general_params : dict

+
+

general parameters as a dict

+
+

n_inequiv_shells : int

+
+

number of impurities for calculations

+
+
+
Returns:
+

nothing :

+
+
+
+ +
+
+dmft_tools.observables.write_obs(observables, sum_k, general_params)[source]
+

writes the last entries of the observable arrays to the files

+
+
Parameters:
+

observables : list of dicts

+
+

observable arrays/dicts

+
+

sum_k : SumK Object instances

+

general_params : dict

+
+
Returns:
+

nothing :

+
+
+
+ +
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_ref/dmft_tools.results_to_archive.html b/_ref/dmft_tools.results_to_archive.html new file mode 100644 index 00000000..f4946b00 --- /dev/null +++ b/_ref/dmft_tools.results_to_archive.html @@ -0,0 +1,356 @@ + + + + + + + dmft_tools.results_to_archive — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

dmft_tools.results_to_archive

+
+
+dmft_tools.results_to_archive.write(archive, sum_k, general_params, solver_params, solvers, it, is_sampling, previous_mu, density_mat_pre, density_mat, deltaN=None, dens=None)[source]
+

Collects and writes results to archive.

+
+ +
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_ref/dmft_tools.solver.SolverStructure.__init__.html b/_ref/dmft_tools.solver.SolverStructure.__init__.html new file mode 100644 index 00000000..99597ec7 --- /dev/null +++ b/_ref/dmft_tools.solver.SolverStructure.__init__.html @@ -0,0 +1,386 @@ + + + + + + + dmft_tools.solver.SolverStructure.__init__ — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

dmft_tools.solver.SolverStructure.__init__

+
+
+SolverStructure.__init__(general_params, solver_params, advanced_params, sum_k, icrsh, h_int, iteration_offset, solver_struct_ftps)[source]
+

Initialisation of the solver instance with h_int for impurity “icrsh” based on soliDMFT parameters.

+
+
Parameters:
+

general_paramuters: dict :

+
+

general parameters as dict

+
+

solver_params: dict :

+
+

solver-specific parameters as dict

+
+

sum_k: triqs.dft_tools.sumk object :

+
+

SumkDFT instance

+
+

icrsh: int :

+
+

correlated shell index

+
+

h_int: triqs.operator object :

+
+

interaction Hamiltonian of correlated shell

+
+

iteration_offset: int :

+
+

number of iterations this run is based on

+
+
+
+
+ +
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_ref/dmft_tools.solver.SolverStructure.html b/_ref/dmft_tools.solver.SolverStructure.html new file mode 100644 index 00000000..50e5a347 --- /dev/null +++ b/_ref/dmft_tools.solver.SolverStructure.html @@ -0,0 +1,416 @@ + + + + + + + dmft_tools.solver.SolverStructure — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

dmft_tools.solver.SolverStructure

+
+
+class dmft_tools.solver.SolverStructure(general_params, solver_params, advanced_params, sum_k, icrsh, h_int, iteration_offset, solver_struct_ftps)[source]
+

Bases: object

+

Handles all solid_dmft solver objects and contains TRIQS solver instance.

+
+
Methods:
+

solve(self, **kwargs) :

+
+

solve impurity problem

+
+
+
+
+
+__init__(general_params, solver_params, advanced_params, sum_k, icrsh, h_int, iteration_offset, solver_struct_ftps)[source]
+

Initialisation of the solver instance with h_int for impurity “icrsh” based on soliDMFT parameters.

+
+
Parameters:
+

general_paramuters: dict :

+
+

general parameters as dict

+
+

solver_params: dict :

+
+

solver-specific parameters as dict

+
+

sum_k: triqs.dft_tools.sumk object :

+
+

SumkDFT instance

+
+

icrsh: int :

+
+

correlated shell index

+
+

h_int: triqs.operator object :

+
+

interaction Hamiltonian of correlated shell

+
+

iteration_offset: int :

+
+

number of iterations this run is based on

+
+
+
+
+ +
+
+solve(**kwargs)[source]
+

solve impurity problem with current solver

+
+ +
+ + + + + + + + + + +

__init__(general_params, solver_params, ...)

Initialisation of the solver instance with h_int for impurity "icrsh" based on soliDMFT parameters.

solve(**kwargs)

solve impurity problem with current solver

+
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_ref/dmft_tools.solver.SolverStructure.solve.html b/_ref/dmft_tools.solver.SolverStructure.solve.html new file mode 100644 index 00000000..19990c5e --- /dev/null +++ b/_ref/dmft_tools.solver.SolverStructure.solve.html @@ -0,0 +1,358 @@ + + + + + + + dmft_tools.solver.SolverStructure.solve — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

dmft_tools.solver.SolverStructure.solve

+
+
+SolverStructure.solve(**kwargs)[source]
+

solve impurity problem with current solver

+
+ +
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_ref/dmft_tools.solver.html b/_ref/dmft_tools.solver.html new file mode 100644 index 00000000..70339dae --- /dev/null +++ b/_ref/dmft_tools.solver.html @@ -0,0 +1,397 @@ + + + + + + + dmft_tools.solver — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

dmft_tools.solver

+
+
+class dmft_tools.solver.SolverStructure(general_params, solver_params, advanced_params, sum_k, icrsh, h_int, iteration_offset, solver_struct_ftps)[source]
+

Handles all solid_dmft solver objects and contains TRIQS solver instance.

+
+
Methods:
+

solve(self, **kwargs) :

+
+

solve impurity problem

+
+
+
+
+
+solve(**kwargs)[source]
+

solve impurity problem with current solver

+
+ +
+ +
+
+dmft_tools.solver.get_n_orbitals(sum_k)[source]
+

determines the number of orbitals within the +solver block structure.

+
+
Parameters:
+

sum_k : dft_tools sumk object

+
+
Returns:
+

n_orb : dict of int

+
+

number of orbitals for up / down as dict for SOC calculation +without up / down block up holds the number of orbitals

+
+
+
+
+ +

Classes

+ + + + + + +

SolverStructure(general_params, ...)

Handles all solid_dmft solver objects and contains TRIQS solver instance.

+
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_ref/postprocessing.eval_U_cRPA_RESPACK.html b/_ref/postprocessing.eval_U_cRPA_RESPACK.html new file mode 100644 index 00000000..e9573fe2 --- /dev/null +++ b/_ref/postprocessing.eval_U_cRPA_RESPACK.html @@ -0,0 +1,387 @@ + + + + + + + postprocessing.eval_U_cRPA_RESPACK — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

postprocessing.eval_U_cRPA_RESPACK

+
+
+postprocessing.eval_U_cRPA_RESPACK.construct_Uijkl(Uijij, Uiijj)[source]
+

construct full 4 index Uijkl tensor from respack data +assuming Uijji = Uiijj

+
+
Uiijj: np.ndarray

Uiijj matrix

+
+
+
+ +
+
+postprocessing.eval_U_cRPA_RESPACK.fit_slater_fulld(u_ijij_crpa, u_ijji_crpa, U_init, J_init, fixed_F4_F2=True)[source]
+

finds best Slater parameters U, J for given Uijij and Uijji matrices +using the triqs U_matrix operator routine assumes F4/F2=0.625

+
+ +
+
+postprocessing.eval_U_cRPA_RESPACK.read_interaction(seed, path='./')[source]
+
+ +
+
+class postprocessing.eval_U_cRPA_RESPACK.respack_data(path, seed)[source]
+

respack data class

+
+ +

Classes

+ + + + + + +

respack_data(path, seed)

respack data class

+
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_ref/postprocessing.eval_U_cRPA_RESPACK.respack_data.__init__.html b/_ref/postprocessing.eval_U_cRPA_RESPACK.respack_data.__init__.html new file mode 100644 index 00000000..6fb39dbf --- /dev/null +++ b/_ref/postprocessing.eval_U_cRPA_RESPACK.respack_data.__init__.html @@ -0,0 +1,357 @@ + + + + + + + postprocessing.eval_U_cRPA_RESPACK.respack_data.__init__ — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

postprocessing.eval_U_cRPA_RESPACK.respack_data.__init__

+
+
+respack_data.__init__(path, seed)[source]
+
+ +
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_ref/postprocessing.eval_U_cRPA_RESPACK.respack_data.html b/_ref/postprocessing.eval_U_cRPA_RESPACK.respack_data.html new file mode 100644 index 00000000..dc0b1839 --- /dev/null +++ b/_ref/postprocessing.eval_U_cRPA_RESPACK.respack_data.html @@ -0,0 +1,370 @@ + + + + + + + postprocessing.eval_U_cRPA_RESPACK.respack_data — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

postprocessing.eval_U_cRPA_RESPACK.respack_data

+
+
+class postprocessing.eval_U_cRPA_RESPACK.respack_data(path, seed)[source]
+

Bases: object

+

respack data class

+
+
+__init__(path, seed)[source]
+
+ +
+ + + + + + + +

__init__(path, seed)

+
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_ref/postprocessing.eval_U_cRPA_Vasp.html b/_ref/postprocessing.eval_U_cRPA_Vasp.html new file mode 100644 index 00000000..07cf7a41 --- /dev/null +++ b/_ref/postprocessing.eval_U_cRPA_Vasp.html @@ -0,0 +1,622 @@ + + + + + + + postprocessing.eval_U_cRPA_Vasp — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

postprocessing.eval_U_cRPA_Vasp

+
+
+postprocessing.eval_U_cRPA_Vasp.calc_kan_params(uijkl, n_sites, n_orb, out=False)[source]
+

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

+
+
Parameters:
+

uijkl : numpy array

+
+

4d numpy array of Coulomb tensor

+
+

n_sites: int :

+
+

number of different atoms (Wannier centers)

+
+

n_orb : int

+
+

number of orbitals per atom

+
+

out : bool

+
+

verbose mode

+
+
+
Returns:
+

int_params : direct

+
+

kanamori parameters

+
+
+
+
+ +
+
+postprocessing.eval_U_cRPA_Vasp.calc_u_avg_fulld(uijkl, n_sites, n_orb, out=False)[source]
+

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)

+

Returns F0=U, and J=(F2+F4)/14

+
+
Parameters:
+

uijkl : numpy array

+
+

4d numpy array of Coulomb tensor

+
+

n_sites: int :

+
+

number of different atoms (Wannier centers)

+
+

n_orb : int

+
+

number of orbitals per atom

+
+

out : bool

+
+

verbose mode

+
+
+
Returns:
+

int_params : direct

+
+

Slater parameters

+
+
+
+
+ +
+
+postprocessing.eval_U_cRPA_Vasp.calculate_interaction_from_averaging(uijkl, n_sites, n_orb, out=False)[source]
+

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.

+

Returns F0=U, and J=(F2+F4)/14

+
+
Parameters:
+

uijkl : numpy array

+
+

4d numpy array of Coulomb tensor

+
+

n_sites: int :

+
+

number of different atoms (Wannier centers)

+
+

n_orb : int

+
+

number of orbitals per atom

+
+

out : bool

+
+

verbose mode

+
+
+
Returns:
+

U, J: tuple :

+
+

Slater parameters

+
+
+
+
+ +
+
+postprocessing.eval_U_cRPA_Vasp.construct_U_kan(n_orb, U, J, Up=None, Jc=None)[source]
+

construct Kanamori Uijkl tensor for given U, J, Up, and Jc

+
+
Parameters:
+

n_orb : int

+
+

number of orbitals

+
+

U : float

+
+

U value for elements Uiiii

+
+

J : float

+
+

Hunds coupling J for tensor elements Uijji

+
+

Up : float, optional, default=U-2J

+
+

inter orbital exchange term Uijij

+
+

Jc : float, optional, default=J

+
+

Uiijj term, is the same as J for real valued wave functions

+
+
+
Returns:
+

uijkl : numpy array

+
+

uijkl Coulomb tensor

+
+
+
+
+ +
+
+postprocessing.eval_U_cRPA_Vasp.fit_kanamori(uijkl, n_orb, switch_jk=False, fit_2=True, fit_3=False, fit_4=True)[source]
+

Fit Kanamori Hamiltonian with scipy to 2,3, and / or 4 parameters

+
+
Parameters:
+

uijkl: np.array (n_orb x n_orb x n_orb x n_orb) :

+
+

input four index tensor

+
+

n_orb: int :

+
+

number of orbitals

+
+

switch_jk: bool, default=False :

+
+

flip two inner indices in input U tensor (for Vasp)

+
+

fit_2: bool, default=True :

+
+

fit two parameter form

+
+

fit_3: bool, default=False :

+
+

fit three parameter form (U,Up,J=Jc)

+
+

fit_4: bool, default=True :

+
+

fit four parameter form

+
+
+
Returns:
+

Uijkl_fit: np.array (n_orb x n_orb x n_orb x n_orb) :

+
+

fitted Uijkl tensor

+
+
+
+
+ +
+
+postprocessing.eval_U_cRPA_Vasp.fit_slater_fulld(uijkl, n_sites, U_init, J_init, fixed_F4_F2=True)[source]
+

finds best Slater parameters U, J for given Uijkl tensor +using the triqs U_matrix operator routine +assumes F4/F2=0.625

+
+ +
+
+postprocessing.eval_U_cRPA_Vasp.read_uijkl(path_to_uijkl, n_sites, n_orb)[source]
+

reads the VASP UIJKL files or the vijkl file if wanted

+
+
Parameters:
+

path_to_uijkl : string

+
+

path to Uijkl like file

+
+

n_sites: int :

+
+

number of different atoms (Wannier centers)

+
+

n_orb : int

+
+

number of orbitals per atom

+
+
+
Returns:
+

uijkl : numpy array

+
+

uijkl Coulomb tensor

+
+
+
+
+ +
+
+postprocessing.eval_U_cRPA_Vasp.red_to_2ind(uijkl, n_sites, n_orb, out=False)[source]
+

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–> U_ikjl

+
+
Parameters:
+

uijkl : numpy array

+
+

4d numpy array of Coulomb tensor

+
+

n_sites: int :

+
+

number of different atoms (Wannier centers)

+
+

n_orb : int

+
+

number of orbitals per atom

+
+

out : bool

+
+

verbose mode

+
+
+
Returns:
+

Uij_anti : numpy array

+
+

red 2 index matrix U_mm’mm’

+
+

Uiijj : numpy array

+
+

red 2 index matrix U_iijj

+
+

Uijji : numpy array

+
+

red 2 index matrix Uijji

+
+

Uij_par : numpy array

+
+

red 2 index matrix U_mm’mm’ - U_mm’m’m

+
+
+
+
+ +
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_ref/postprocessing.html b/_ref/postprocessing.html new file mode 100644 index 00000000..048a118c --- /dev/null +++ b/_ref/postprocessing.html @@ -0,0 +1,373 @@ + + + + + + + postprocessing — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

postprocessing

+

Postprocessing tools

+

Modules

+ + + + + + + + + + + + + + + + + + + + + +

postprocessing.eval_U_cRPA_RESPACK

postprocessing.eval_U_cRPA_Vasp

postprocessing.maxent_gf_imp

Analytic continuation of the impurity Green's function to the impurity spectral function using maxent.

postprocessing.maxent_gf_latt

Analytic continuation of the lattice Green's function to the lattice spectral function using maxent.

postprocessing.maxent_sigma

Analytic continuation of the self-energy using maxent on an auxiliary Green's function.

postprocessing.plot_correlated_bands

Reads DMFT_ouput observables such as real-frequency Sigma and a Wannier90 TB Hamiltonian to compute spectral properties.

+
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_ref/postprocessing.maxent_gf_imp.html b/_ref/postprocessing.maxent_gf_imp.html new file mode 100644 index 00000000..30ff5bc3 --- /dev/null +++ b/_ref/postprocessing.maxent_gf_imp.html @@ -0,0 +1,409 @@ + + + + + + + postprocessing.maxent_gf_imp — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

postprocessing.maxent_gf_imp

+

Analytic continuation of the impurity Green’s function to the impurity spectral +function using maxent.

+

Reads G_imp(i omega) from the h5 archive and writes A_imp(omega) back. See +the docstring of main() for more information.

+

Not mpi parallelized.

+

Author: Maximilian Merkel, Materials Theory Group, ETH Zurich, 2020 - 2022

+
+
+postprocessing.maxent_gf_imp.main(external_path, iteration=None, sum_spins=False, maxent_error=0.02, n_points_maxent=200, n_points_alpha=50, omega_min=-20, omega_max=20)[source]
+

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.

+
+
Parameters:
+

external_path : string

+
+

Path to the h5 archive to read from and write to.

+
+

iteration : int/string

+
+

Iteration to read from and write to. Defaults to last_iter.

+
+

sum_spins : bool

+
+

Whether to sum over the spins or continue the impurity GF +for the up and down spin separately, for example for magnetized results.

+
+

maxent_error : float

+
+

The error that is used for the analyzers.

+
+

n_points_maxent : int

+
+

Number of omega points on the hyperbolic mesh used in the continuation.

+
+

n_points_alpha : int

+
+

Number of points that the MaxEnt alpha parameter is varied on logarithmically.

+
+

omega_min : float

+
+

Lower end of range where the GF is being continued. Range has to comprise +all features of the impurity GF for correct normalization.

+
+

omega_max : float

+
+

Upper end of range where the GF is being continued. See omega_min.

+
+
+
Returns:
+

maxent_results : list

+
+

The omega mesh and impurity spectral function from two different analyzers +in a dict for each impurity

+
+
+
+
+ +
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_ref/postprocessing.maxent_gf_latt.html b/_ref/postprocessing.maxent_gf_latt.html new file mode 100644 index 00000000..850d7809 --- /dev/null +++ b/_ref/postprocessing.maxent_gf_latt.html @@ -0,0 +1,413 @@ + + + + + + + postprocessing.maxent_gf_latt — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

postprocessing.maxent_gf_latt

+

Analytic continuation of the lattice Green’s function to the lattice spectral +function using maxent.

+

Reads G_latt(i omega) from the h5 archive and writes A_latt(omega) back. See +the docstring of main() for more information.

+

mpi parallelized for the generation of the imaginary-frequency lattice GF over +k points.

+

Author: Maximilian Merkel, Materials Theory Group, ETH Zurich, 2020 - 2022

+
+
+postprocessing.maxent_gf_latt.main(external_path, iteration=None, sum_spins=False, maxent_error=0.02, n_points_maxent=200, n_points_alpha=50, omega_min=None, omega_max=None)[source]
+

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.

+
+
Parameters:
+

external_path: string :

+
+

Path to the h5 archive to read from and write to.

+
+

iteration: int/string :

+
+

Iteration to read from and write to. Defaults to last_iter.

+
+

sum_spins: bool :

+
+

Whether to sum over the spins or continue the lattice GF +for the up and down spin separately, for example for magnetized results.

+
+

maxent_error : float

+
+

The error that is used for the analyzers.

+
+

n_points_maxent : int

+
+

Number of omega points on the hyperbolic mesh used in the continuation.

+
+

n_points_alpha : int

+
+

Number of points that the MaxEnt alpha parameter is varied on logarithmically.

+
+

omega_min : float

+
+

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.

+
+

omega_max : float

+
+

Upper end of range where the GF is being continued. See omega_min.

+
+
+
Returns:
+

unpacked_results : dict

+
+

The omega mesh and lattice spectral function from two different analyzers

+
+
+
+
+ +
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_ref/postprocessing.maxent_sigma.html b/_ref/postprocessing.maxent_sigma.html new file mode 100644 index 00000000..e09abf17 --- /dev/null +++ b/_ref/postprocessing.maxent_sigma.html @@ -0,0 +1,448 @@ + + + + + + + postprocessing.maxent_sigma — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

postprocessing.maxent_sigma

+

Analytic continuation of the self-energy using maxent on an auxiliary Green’s +function.

+

Reads Sigma(i omega) from the h5 archive and writes Sigma(omega) back. See +the docstring of main() for more information.

+

mpi parallelized for the maxent routine over all blocks and for the continuator +extraction over omega points.

+

Author: Maximilian Merkel, Materials Theory Group, ETH Zurich, 2020 - 2022

+
+
Warnings:
    +
  • When using this on self-energies with SOC, please check that the formalism +is correct, in particular the Kramers-Kronig relation.

  • +
+
+
+
+
+postprocessing.maxent_sigma.main(external_path, iteration=None, continuator_type='inversion_sigmainf', maxent_error=0.02, omega_min=-12.0, omega_max=12.0, n_points_maxent=400, n_points_alpha=50, analyzer='LineFitAnalyzer', n_points_interp=2000, n_points_final=1000)[source]
+

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.

+
+
Parameters:
+

external_path : string

+
+

Path to the h5 archive to read from and write to

+
+

iteration : int/string

+
+

Iteration to read from and write to. Default to last_iter

+
+

continuator_type : string

+
+

Type of continuator to use, one of ‘inversion_sigmainf’, ‘inversion_dc’, ‘direct’

+
+

maxent_error : float

+
+

The error that is used for the analyzers.

+
+

omega_min : float

+
+

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.

+
+

omega_max : float

+
+

Upper end of range where Sigma is being continued. See omega_min.

+
+

n_points_maxent : int

+
+

Number of omega points on the hyperbolic mesh used in analytically +continuing the auxiliary GF

+
+

n_points_alpha : int

+
+

Number of points that the MaxEnt alpha parameter is varied on logarithmically

+
+

analyzer : string

+
+

Analyzer used int MaxEnt, one of ‘LineFitAnalyzer’, ‘Chi2CurvatureAnalyzer’, +‘ClassicAnalyzer’, ‘EntropyAnalyzer’, ‘BryanAnalyzer’

+
+

n_points_interp : int

+
+

Number of points where auxiliary GF is interpolated to integrate over +it for the Kramers-Kronig relation

+
+

n_points_final : int

+
+

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

+
+
+
Returns:
+

sigma_w : list of triqs.gf.BlockGf

+
+

Sigma(omega) per inequivalent shell

+
+

g_aux_w : list of triqs.gf.BlockGf

+
+

G_aux(omega) per inequivalent shell

+
+
+
Raises:
+

NotImplementedError :

+
+

– 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.

+
+
+
+
+ +
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_ref/postprocessing.plot_correlated_bands.html b/_ref/postprocessing.plot_correlated_bands.html new file mode 100644 index 00000000..6b51e34a --- /dev/null +++ b/_ref/postprocessing.plot_correlated_bands.html @@ -0,0 +1,447 @@ + + + + + + + postprocessing.plot_correlated_bands — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

postprocessing.plot_correlated_bands

+

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.

+

Written by Sophie Beck, 2021-2022

+

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)

+
+
+postprocessing.plot_correlated_bands.get_dmft_bands(n_orb, w90_path, w90_seed, mu_tb, add_spin=False, add_lambda=None, add_local=None, with_sigma=None, fermi_slice=False, qp_bands=False, orbital_order_to=None, add_mu_tb=False, band_basis=False, proj_on_orb=None, trace=True, eta=0.0, mu_shift=0.0, proj_nuk=None, **specs)[source]
+

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.

+
+
Parameters:
+

n_orb : int

+
+

Number of Wannier orbitals in seed_hr.dat

+
+

w90_path : string

+
+

Path to w90 files

+
+

w90_seed : string

+
+

Seed of wannier90 calculation, i.e. seed_hr.dat and seed.wout

+
+

add_spin : bool, default=False

+
+

Extend w90 Hamiltonian by spin indices

+
+

add_lambda : float, default=None

+
+

Add SOC term with strength add_lambda (works only for t2g shells)

+
+

add_local : numpy array, default=None

+
+

Add local term of dimension (n_orb x n_orb)

+
+

with_sigma : str, or BlockGf, default=None

+
+

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

+
+

add_mu_tb : bool, default=False

+
+

Add the TB specified chemical potential to the lattice Green function +set to True if DMFT calculation was performed with DFT fermi subtracted.

+
+

proj_on_orb : int or list of int, default=None

+
+

orbital projections to be made for the spectral function and TB bands +the integer refer to the orbitals read

+
+

trace : bool, default=True

+
+

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

+
+

eta : float, default=0.0

+
+

Broadening of spectral function, finitie shift on imaginary axis +if with_sigma=None it has to be provided !=0.0

+
+

mu_shift : float, default=0.0

+
+

Manual extra shift when calculating the spectral function

+
+

proj_nuk : numpy array, default [None]

+
+

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.

+
+
+
Returns:
+

tb_data : dict

+
+

tight binding dict containing the kpoint mesh, dispersion / emat, and eigenvectors

+
+

alatt_k_w : numpy array (float) of dim n_k x n_w ( x n_orb if trace=False)

+
+

lattice spectral function data on the kpoint mesh defined in tb_data and frequency +mesh defined in freq_dict

+
+

freq_dict : dict

+
+

frequency mesh information on which alatt_k_w is evaluated

+
+
+
+
+ +
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_ref/read_config.html b/_ref/read_config.html new file mode 100644 index 00000000..5913b442 --- /dev/null +++ b/_ref/read_config.html @@ -0,0 +1,783 @@ + + + + + + + read_config — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

read_config

+

Provides the read_config function to read the config file

+

Reads the config file (default dmft_config.ini) with python’s configparser +module. It consists of at least the section ‘general’, and optionally of the +sections ‘solver’, ‘dft’ and ‘advanced’. +Comments are in general possible with with the delimiters ‘;’ or +‘#’. However, this is only possible in the beginning of a line not within +the line! For default values, the string ‘none’ is used. NoneType cannot be +saved in an h5 archive (in the framework that we are using).

+

List of all parameters, sorted by sections:

+

—XXX—start +List of all parameters, sorted by sections:

+
+

[ general ]

+
+
seednamestr

seedname for h5 archive with DMFT input and output

+
+
jobnamestr, optional, default=’dmft_dir’

the output directory for one-shot calculations

+
+
cscbool, optional, default=False

are we doing a CSC calculation?

+
+
plo_cfgstr, optional, default=’plo.cfg’

config file for PLOs for the converter

+
+
h_int_typestring

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

  • +
  • 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

+
+
h_int_basisstring

cubic basis convention to compute the interaction U matrix +* ‘triqs’ +* ‘vasp’ (equivalent to ‘triqs’) +* ‘wien2k’ +* ‘wannier90’ +* ‘qe’ (equivalent to ‘wannier90’)

+
+
Ufloat or comma separated list of floats

U values for impurities if only one value is given, the same U is assumed for all impurities

+
+
U_primefloat or comma separated list of floats

U prime values for impurities if only one value is given, the same U prime is assumed for all impurities +only used if h_int_type is kanamori

+
+
Jfloat or comma separated list of floats

J values for impurities if only one value is given, the same J is assumed for all impurities

+
+
ratio_F4_F2float or comma separated list of floats, optional, 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, where the default is 0.63.

+
+
betafloat, only used if solver ImFreq

inverse temperature for Greens function etc

+
+
n_iter_dmft_firstint, optional, default= 10

number of iterations in first dmft cycle to converge dmft solution

+
+
n_iter_dmft_perint, optional, default= 2

number of iterations per dmft step in CSC calculations

+
+
n_iter_dmftint

number of iterations per dmft cycle after first cycle

+
+
dc_typeint

Type of double counting correction considered: +* 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

+
+
dc_dmftbool

Whether to use DMFT or DFT occupations:

+
    +
  • DC with DMFT occupation in each iteration -> True

  • +
  • DC with DFT occupations after each DFT cycle -> False

  • +
+
+
cpa_zetafloat or comma separated list of floats

shift of local levels per impurity in CPA

+
+
cpa_xfloat or comma separated list of floats

probability distribution for summing G(tau) in CPA

+
+
solver_typestr

type of solver chosen for the calculation, currently supports:

+
    +
  • ‘cthyb’

  • +
  • ‘ctint’

  • +
  • ‘ftps’

  • +
  • ‘hubbardI’

  • +
  • ‘hartree’

  • +
  • ‘ctseg’

  • +
+
+
n_iwint, optional, default=1025

number of Matsubara frequencies

+
+
n_tauint, optional, default=10001

number of imaginary time points

+
+
n_lint, needed if measure_G_l=True or legendre_fit=True

number of Legendre coefficients

+
+
n_wint, optional, default=5001

number of real frequency points

+
+
w_rangetuple, optional, default=(-10, 10)

w_min and w_max, example: w_range = -10, 10

+
+
etafloat, only used if solver ReFreq

broadening of Green’s function

+
+
diag_deltabool, optional, default=False

option to remove off-diagonal terms in the hybridization function

+
+
h5_save_freqint, optional, default=5

how often is the output saved to the h5 archive

+
+
magneticbool, optional, default=False

are we doing a magnetic calculations? If yes put magnetic to True. +Not implemented for CSC calculations

+
+
magmomlist of float seperated by comma, optional default=[]

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)

+
+
enforce_off_diagbool, optional, default=False

enforce off diagonal elements in block structure finder

+
+
h_fieldfloat, optional, default=0.0

magnetic field

+
+
h_field_itint, optional, default=0

number of iterations the magnetic field is kept on

+
+
sigma_mixfloat, optional, 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

+
+
g0_mixfloat, optional, 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

+
+
g0_mix_typestring, optional, default=’linear’

which type of mixing is used. Possible values are: +linear: linear mixing +broyden: broyden mixing

+
+
broy_max_itint, optional, default=1

maximum number of iteration to be considered for broyden mixing +1 corresponds to simple linear mixing

+
+
dcbool, optional, default=True

dc correction on yes or no?

+
+
calc_energiesbool, optional, default=False, not compatible with ‘ftps’ solver

calc energies explicitly within the dmft loop

+
+
block_thresholdfloat, optional, default=1e-05

threshold for finding block structures in the input data (off-diag yes or no)

+
+
block_suppress_orbital_symmbool, optional, default=False

should blocks be checked if symmetry-equiv. between orbitals? +Does not affect spin symmetries.

+
+
load_sigmabool, optional, default=False

load a old sigma from h5 file

+
+
path_to_sigmastr, needed if load_sigma is true

path to h5 file from which the sigma should be loaded

+
+
load_sigma_iterint, optional, default= last iteration

load the sigma from a specific iteration if wanted

+
+
noise_level_initial_sigmafloat, optional, default=0.0

spread of Gaussian noise applied to the initial Sigma

+
+
occ_conv_critfloat, optional, default= -1

stop the calculation if a certain threshold for the imp occ change is reached

+
+
gimp_conv_critfloat, optional, default= -1

stop the calculation if sum_w 1/(w^0.6) ||Gimp-Gloc|| is smaller than threshold

+
+
g0_conv_critfloat, optional, default= -1

stop the calculation if sum_w 1/(w^0.6) ||G0-G0_prev|| is smaller than threshold

+
+
sigma_conv_critfloat, optional, default= -1

stop the calculation if sum_w 1/(w^0.6) ||Sigma-Sigma_prev|| is smaller than threshold

+
+
sampling_iterationsint, optional, default= 0

for how many iterations should the solution sampled after the CSC loop is converged

+
+
sampling_h5_save_freqint, optional, default= 5

overwrites h5_save_freq when sampling has started

+
+
calc_mu_methodstring, optional, 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

  • +
+
+
prec_mufloat

general precision for determining the chemical potential at any time calc_mu is called

+
+
fixed_mu_valuefloat, optional, default= ‘none’

If given, the chemical potential remains fixed in calculations

+
+
mu_update_freqint, optional, default= 1

The chemical potential will be updated every # iteration

+
+
mu_initial_guessfloat, optional, default= ‘none’

The chemical potential of the DFT calculation. +If not given, mu will be calculated from the DFT bands

+
+
mu_mix_constfloat, optional, default= 1.0

Constant term of the mixing of the chemical potential. See mu_mix_per_occupation_offset.

+
+
mu_mix_per_occupation_offsetfloat, optional, 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.

+
+
afm_orderbool, optional, default=False

copy self energies instead of solving explicitly for afm order

+
+
set_rotstring, optional, default=’none’

use density_mat_dft to diagonalize occupations = ‘den’ +use hloc_dft to diagonalize occupations = ‘hloc’

+
+
measure_chi_SzSzbool, optional, default=False

measure the dynamic spin suszeptibility chi(sz,sz(tau)) +triqs.github.io/cthyb/unstable/guide/dynamic_susceptibility_notebook.html

+
+
measure_chi_insertionsint, optional, default=100

number of insertation for measurement of chi

+
+
mu_gap_gb2_thresholdfloat, optional, 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.

+
+
mu_gap_occ_deviationfloat, optional, 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.

+
+
+
+
+

[ solver ]

+
+
store_solverbool, optional default= False

store the whole solver object under DMFT_input in h5 archive

+
+
+
+

cthyb parameters

+
+
length_cycleint

length of each cycle; number of sweeps before measurement is taken

+
+
n_warmup_cyclesint

number of warmup cycles before real measurement sets in

+
+
n_cycles_totint

total number of sweeps

+
+
measure_G_lbool

measure Legendre Greens function

+
+
measure_G_taubool,optional, default=True

should the solver measure G(tau)?

+
+
measure_G_iwbool,optional, default=False

should the solver measure G(iw)?

+
+
measure_density_matrixbool, optional, default=False

measures the impurity density matrix and sets also +use_norm_as_weight to true

+
+
measure_pert_orderbool, optional, 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’

+
+
max_timeint, optional, default=-1

maximum amount the solver is allowed to spend in each iteration

+
+
imag_thresholdfloat, optional, default= 10e-15

threshold for imag part of G0_tau. be warned if symmetries are off in projection scheme imag parts can occur in G0_tau

+
+
off_diag_thresholdfloat, optional

threshold for off-diag elements in Hloc0

+
+
delta_interfacebool, optional, default=False

use new delta interface in cthyb instead of input G0

+
+
move_doublebool, optional, default=True

double moves in solver

+
+
perform_tail_fitbool, optional, default=False

tail fitting if legendre is off?

+
+
fit_max_momentint, optional

max moment to be fitted

+
+
fit_min_nint, optional

number of start matsubara frequency to start with

+
+
fit_max_nint, optional

number of highest matsubara frequency to fit

+
+
fit_min_wfloat, optional

start matsubara frequency to start with

+
+
fit_max_wfloat, optional

highest matsubara frequency to fit

+
+
random_seedstr, optional default 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

+
+
legendre_fitbool, optional default= False

filter noise of G(tau) with G_l, cutoff is taken from n_l

+
+
loc_n_minint, optional

Restrict local Hilbert space to states with at least this number of particles

+
+
loc_n_maxint, optional

Restrict local Hilbert space to states with at most this number of particles

+
+
+
+
+

ftps parameters

+
+
n_bathint

number of bath sites

+
+
bath_fitbool, default=False

DiscretizeBath vs BathFitter

+
+
refine_factorint, optional, default=1

rerun ftps cycle with increased accuracy

+
+
ph_symmbool, optional, default=False

particle-hole symmetric problem

+
+
calc_mebool, optional, default=True

calculate only symmetry-inequivalent spins/orbitals, symmetrized afterwards

+
+
enforce_gaplist of floats, optional, default=’none’

enforce gap in DiscretizeBath between interval

+
+
ignore_weightfloat, optional, default=0.0

ignore weight of peaks for bath fitter

+
+
dtfloat

time step

+
+
state_storagestring, default= ‘./’

location of large MPS states

+
+
path_to_gsstring, 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

+
+
sweepsint, optional, default= 10

Number of DMRG sweeps

+
+
maxmIint, optional, default= 100

maximal imp-imp bond dimensions

+
+
maxmIBint, optional, default= 100

maximal imp-bath bond dimensions

+
+
maxmBint, optional, default= 100

maximal bath-bath bond dimensions

+
+
twfloat, default 1E-9

truncated weight for every link

+
+
dmrg_maxmIint, optional, default= 100

maximal imp-imp bond dimensions

+
+
dmrg_maxmIBint, optional, default= 100

maximal imp-bath bond dimensions

+
+
dmrg_maxmBint, optional, default= 100

maximal bath-bath bond dimensions

+
+
dmrg_twfloat, default 1E-9

truncated weight for every link

+
+
+
+
+

ctseg parameters

+
+
measure_histbool, optional, default=False

measure perturbation_order histograms

+
+
improved_estimatorbool, optional, default=False

measure improved estimators +Sigma_iw will automatically be calculated via +http://dx.doi.org/10.1103/PhysRevB.85.205106

+
+
+
+
+

hartree parameters

+
+
with_fockbool, optional, default=False

include Fock exchange terms in the self-energy

+
+
force_realbool, optional, default=True

force the self energy from Hartree fock to be real

+
+
one_shotbool, optional, default=True

Perform a one-shot or self-consitent root finding in each DMFT step of the Hartree solver.

+
+
methodbool, optional, default=True

method for root finder. Only used if one_shot=False, see scipy.optimize.root for options.

+
+
tolfloat, optional, default=1e-5

tolerance for root finder if one_shot=False.

+
+
+
+
+
+

[ dft ]

+
+
dft_codestring

Choose the DFT code interface, for now Quantum Espresso and Vasp are available.

+

Possible values:

+
    +
  • ‘vasp’

  • +
  • ‘qe’

  • +
+
+
n_coresint

number of cores for the DFT code (VASP)

+
+
n_iterint, optional, default= 6

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

+
+
n_iter_firstint, optional, 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.

+
+
dft_execstring, default= ‘vasp_std’

command for the DFT executable

+
+
store_eigenvalsbool, optional, default= False

stores the dft eigenvals from LOCPROJ (projector_type=plo) or +wannier90.eig (projector_type=w90) file in h5 archive

+
+
mpi_envstring, default= ‘local’

selection for mpi env for DFT / VASP in default this will only call VASP as mpirun -np n_cores_dft dft_exec

+
+
projector_typestring, optional, default= ‘w90’

plo: uses VASP’s PLO formalism, requires LOCPROJ in the INCAR +w90: uses Wannier90 (for VASP and QuantumEspresso)

+
+
w90_execstring, default=’wannier90.x’

the command to start a single-core wannier run

+
+
w90_tolerancefloat, default=1e-6

threshold for mapping of shells and checks of the Hamiltonian

+
+
+
+
+

[ advanced ]

+
+
dc_factorfloat, optional, default= ‘none’ (corresponds to 1)

If given, scales the dc energy by multiplying with this factor, usually < 1

+
+
dc_fixed_valuefloat, optional, 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

+
+
dc_fixed_occlist of float, optional, 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!

+
+
dc_Ufloat or comma seperated list of floats, optional, default= general_params[‘U’]

U values for DC determination if only one value is given, the same U is assumed for all impurities

+
+
dc_Jfloat or comma seperated list of floats, optional, default= general_params[‘J’]

J values for DC determination if only one value is given, the same J is assumed for all impurities

+
+
map_solver_structlist of dict, optional, default=no additional mapping

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

+
+
mapped_solver_struct_degeneracieslist, optional, 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.

+
+
pick_solver_structlist of dict, optional, default=no additional picking

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. +Not listed blocks / orbitals will be not treated in impurity solver. +Keeps degenerate shells.

+
+
+

—XXX—end

+
+
+
+read_config.read_config(config_file)[source]
+

Reads in the config file, checks its sections and parameters and returns +the parameters sorted by their categories.

+
+
Parameters:
+

config_file : string

+
+

File name of the config file usable for configparser.

+
+
+
Returns:
+

general_params : dict

+

solver_params : dict

+

dft_params : dict

+

advanced_params : dict

+
+
Raises:
+

ValueError :

+
+

Required parameters are missing or parameters do not fulfill their +validity criterion.

+
+
+
+
+ +
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_ref/util.html b/_ref/util.html new file mode 100644 index 00000000..556d48ec --- /dev/null +++ b/_ref/util.html @@ -0,0 +1,367 @@ + + + + + + + util — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

util

+

external helper functions, not used by any DMFT routine

+

Modules

+ + + + + + + + + + + + + + + +

util.symmetrize_gamma_file

util.update_dmft_config

Updates the config file (usually dmft_config.ini) for continuing old calculations.

util.update_results_h5

Updates the h5 archives with soliDMFT results for continuing old calculations.

util.write_kslice_to_h5

Reads the -kslice-bands.dat and the -kslice-coord.dat file (as Wannier90 writes them).

+
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_ref/util.symmetrize_gamma_file.html b/_ref/util.symmetrize_gamma_file.html new file mode 100644 index 00000000..4a40a497 --- /dev/null +++ b/_ref/util.symmetrize_gamma_file.html @@ -0,0 +1,350 @@ + + + + + + + util.symmetrize_gamma_file — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

util.symmetrize_gamma_file

+
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_ref/util.update_dmft_config.html b/_ref/util.update_dmft_config.html new file mode 100644 index 00000000..1a4b39bb --- /dev/null +++ b/_ref/util.update_dmft_config.html @@ -0,0 +1,357 @@ + + + + + + + util.update_dmft_config — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

util.update_dmft_config

+

Updates the config file (usually dmft_config.ini) for continuing old calculations.

+
+
+util.update_dmft_config.main(path='dmft_config.ini')[source]
+

Combines methods in the full work flow for updating the config file.

+
+ +
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_ref/util.update_results_h5.html b/_ref/util.update_results_h5.html new file mode 100644 index 00000000..c0f92c15 --- /dev/null +++ b/_ref/util.update_results_h5.html @@ -0,0 +1,357 @@ + + + + + + + util.update_results_h5 — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

util.update_results_h5

+

Updates the h5 archives with soliDMFT results for continuing old calculations.

+
+
+util.update_results_h5.main(path='dmft_config.ini')[source]
+

Combines methods in the full work flow for updating the h5 archive.

+
+ +
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_ref/util.write_kslice_to_h5.html b/_ref/util.write_kslice_to_h5.html new file mode 100644 index 00000000..176187b7 --- /dev/null +++ b/_ref/util.write_kslice_to_h5.html @@ -0,0 +1,365 @@ + + + + + + + util.write_kslice_to_h5 — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

util.write_kslice_to_h5

+

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.

+

This only works for k independent projectors as from a TB model or from Wannier90.

+

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.

+

Adapted from “write_bands_to_h5.py” by Sophie Beck, 2021

+
+
+util.write_kslice_to_h5.main(seedname, filename_archive=None)[source]
+

Executes the program on the band data from the files <seedname>_bands.dat and +<seedname>_bands.kpt. If no seedname_archive is specified, <seedname>.h5 is used.

+
+ +
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ 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/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.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..7a64c487 --- /dev/null +++ b/_sources/_ref/postprocessing.rst.txt @@ -0,0 +1,36 @@ +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.plot_correlated_bands + + diff --git a/_sources/_ref/read_config.rst.txt b/_sources/_ref/read_config.rst.txt new file mode 100644 index 00000000..fd15b885 --- /dev/null +++ b/_sources/_ref/read_config.rst.txt @@ -0,0 +1,21 @@ +read\_config +============ + +.. automodule:: read_config + :members: + + + + + + + + + + + + + + + + diff --git a/_sources/_ref/util.rst.txt b/_sources/_ref/util.rst.txt new file mode 100644 index 00000000..19f1a3b4 --- /dev/null +++ b/_sources/_ref/util.rst.txt @@ -0,0 +1,34 @@ +util +==== + +.. automodule:: util + :members: + + + + + + + + + + + + + + + +.. rubric:: Modules + +.. autosummary:: + :toctree: + :template: autosummary_module_template.rst + :recursive: + + + util.symmetrize_gamma_file + util.update_dmft_config + util.update_results_h5 + 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.update_dmft_config.rst.txt b/_sources/_ref/util.update_dmft_config.rst.txt new file mode 100644 index 00000000..132b9225 --- /dev/null +++ b/_sources/_ref/util.update_dmft_config.rst.txt @@ -0,0 +1,21 @@ +util.update\_dmft\_config +========================= + +.. automodule:: util.update_dmft_config + :members: + + + + + + + + + + + + + + + + diff --git a/_sources/_ref/util.update_results_h5.rst.txt b/_sources/_ref/util.update_results_h5.rst.txt new file mode 100644 index 00000000..82d73487 --- /dev/null +++ b/_sources/_ref/util.update_results_h5.rst.txt @@ -0,0 +1,21 @@ +util.update\_results\_h5 +======================== + +.. automodule:: util.update_results_h5 + :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 index 00000000..d53d95f2 --- /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 + postprocessing + read_config + util + + + + diff --git a/_sources/index.rst.txt b/_sources/index.rst.txt new file mode 100644 index 00000000..e520f1aa --- /dev/null +++ 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 `. + 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 `_, and the DFT code interface +`TRIQS/DFTTools `_. Works with triqs >3.x.x. +solid_dmft takes advantage of various +`impurity solvers available `_ +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 `_ in the JOSS journal. If you are using this code for your research, please cite the paper using this `bib file `_. + +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..db32dc31 --- /dev/null +++ b/_sources/input_output/DMFT_input/advanced.rst.txt @@ -0,0 +1,71 @@ +[advanced]: Advanced inputs +--------------------------- + +Advanced parameters, do not modify default value unless you know what you are doing + + + + + +.. admonition:: dc_factor + :class: intag + + **type=** float; **optional**; **default=** 'none' (corresponds to 1) + + If given, scales the dc energy by multiplying with this factor, usually < 1 + +.. admonition:: dc_fixed_value + :class: intag + + **type=** float; **optional**; **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_fixed_occ + :class: intag + + **type=** list of float; **optional**; **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_U + :class: intag + + **type=** float or comma seperated list of floats; **optional**; **default=** general_params['U'] + + U values for DC determination if only one value is given, the same U is assumed for all impurities + +.. admonition:: dc_J + :class: intag + + **type=** float or comma seperated list of floats; **optional**; **default=** general_params['J'] + + J values for DC determination if only one value is given, the same J is assumed for all impurities + +.. admonition:: map_solver_struct + :class: intag + + **type=** list of dict; **optional**; **default=** no additional mapping + + 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; **optional**; **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; **optional**; **default=** no additional picking + + 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. + Not listed blocks / orbitals will be not treated in impurity solver. 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..73c852ed --- /dev/null +++ b/_sources/input_output/DMFT_input/dft.rst.txt @@ -0,0 +1,91 @@ + +[dft]: DFT related inputs +------------------------- + +List of parameters that relate to the DFT calculation, useful mostly when doing CSC. + + + + + + +.. 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:: n_cores + :class: intag + + **type=** int + + number of cores for the DFT code (VASP) + +.. admonition:: n_iter + :class: intag + + **type=** int; **optional**; **default=** 6 + + 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; **optional**; **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:: dft_exec + :class: intag + + **type=** string; **default=** 'vasp_std' + + command for the DFT executable + +.. admonition:: store_eigenvals + :class: intag + + **type=** bool; **optional**; **default=** False + + stores the dft eigenvals from LOCPROJ (projector_type=plo) or + wannier90.eig (projector_type=w90) file in h5 archive + +.. admonition:: mpi_env + :class: intag + + **type=** string; **default=** 'local' + + selection for mpi env for DFT / VASP in default this will only call VASP as mpirun -np n_cores_dft dft_exec + +.. admonition:: projector_type + :class: intag + + **type=** string; **optional**; **default=** 'w90' + + plo: uses VASP's PLO formalism, requires LOCPROJ in the INCAR + w90: uses Wannier90 (for VASP and QuantumEspresso) + +.. 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..d67d8597 --- /dev/null +++ b/_sources/input_output/DMFT_input/general.rst.txt @@ -0,0 +1,515 @@ +[general]: General parameters +----------------------------- + +Includes the majority of the parameters + + + + + + +.. admonition:: seedname + :class: intag + + **type=** str + + seedname for h5 archive with DMFT input and output + +.. admonition:: jobname + :class: intag + + **type=** str; **optional**; **default=** 'dmft_dir' + + the output directory for one-shot calculations + +.. admonition:: csc + :class: intag + + **type=** bool; **optional**; **default=** False + + are we doing a CSC calculation? + +.. admonition:: plo_cfg + :class: intag + + **type=** str; **optional**; **default=** 'plo.cfg' + + config file for PLOs for the converter + +.. admonition:: h_int_type + :class: intag + + **type=** string + + 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 + * 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:: h_int_basis + :class: intag + + **type=** string + + cubic basis convention to compute the interaction U matrix + * 'triqs' + * 'vasp' (equivalent to 'triqs') + * 'wien2k' + * 'wannier90' + * 'qe' (equivalent to 'wannier90') + +.. admonition:: U + :class: intag + + **type=** float or comma separated list of floats + + U values for impurities if only one value is given, the same U is assumed for all impurities + +.. admonition:: U_prime + :class: intag + + **type=** float or comma separated list of floats + + U prime values for impurities if only one value is given, the same U prime is assumed for all impurities + only used if h_int_type is kanamori + +.. admonition:: J + :class: intag + + **type=** float or comma separated list of floats + + J values for impurities if only one value is given, the same J is assumed for all impurities + +.. admonition:: ratio_F4_F2 + :class: intag + + **type=** float or comma separated list of floats; **optional**; **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, where the default is 0.63. + +.. admonition:: beta + :class: intag + + **type=** float, only used if solver ImFreq + + inverse temperature for Greens function etc + +.. admonition:: n_iter_dmft_first + :class: intag + + **type=** int; **optional**; **default=** 10 + + number of iterations in first dmft cycle to converge dmft solution + +.. admonition:: n_iter_dmft_per + :class: intag + + **type=** int; **optional**; **default=** 2 + + number of iterations per dmft step in CSC calculations + +.. admonition:: n_iter_dmft + :class: intag + + **type=** int + + number of iterations per dmft cycle after first cycle + +.. admonition:: dc_type + :class: intag + + **type=** int + + Type of double counting correction considered: + * 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:: dc_dmft + :class: intag + + **type=** bool + + Whether to use DMFT or DFT occupations: + + * DC with DMFT occupation in each iteration -> True + * DC with DFT occupations after each DFT cycle -> False + +.. admonition:: cpa_zeta + :class: intag + + **type=** float or comma separated list of floats + + shift of local levels per impurity in CPA + +.. admonition:: cpa_x + :class: intag + + **type=** float or comma separated list of floats + + probability distribution for summing G(tau) in CPA + +.. admonition:: solver_type + :class: intag + + **type=** str + + type of solver chosen for the calculation, currently supports: + + * 'cthyb' + * 'ctint' + * 'ftps' + * 'hubbardI' + * 'hartree' + * 'ctseg' + + +.. admonition:: n_iw + :class: intag + + **type=** int; **optional**; **default=** 1025 + + number of Matsubara frequencies + +.. admonition:: n_tau + :class: intag + + **type=** int; **optional**; **default=** 10001 + + number of imaginary time points + +.. admonition:: n_l + :class: intag + + **type=** int, needed if measure_G_l=True or legendre_fit=True + + number of Legendre coefficients + +.. admonition:: n_w + :class: intag + + **type=** int; **optional**; **default=** 5001 + + number of real frequency points + +.. admonition:: w_range + :class: intag + + **type=** tuple; **optional**; **default=** (-10, 10) + + w_min and w_max, example: w_range = -10, 10 + +.. admonition:: eta + :class: intag + + **type=** float, only used if solver ReFreq + + broadening of Green's function + +.. admonition:: diag_delta + :class: intag + + **type=** bool; **optional**; **default=** False + + option to remove off-diagonal terms in the hybridization function + + + +.. admonition:: h5_save_freq + :class: intag + + **type=** int; **optional**; **default=** 5 + + how often is the output saved to the h5 archive + +.. admonition:: magnetic + :class: intag + + **type=** bool; **optional**; **default=** False + + are we doing a magnetic calculations? If yes put magnetic to True. + Not implemented for CSC calculations + +.. admonition:: magmom + :class: intag + + **type=** list of float seperated by comma; **optional** default=[] + + 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:: enforce_off_diag + :class: intag + + **type=** bool; **optional**; **default=** False + + enforce off diagonal elements in block structure finder + +.. admonition:: h_field + :class: intag + + **type=** float; **optional**; **default=** 0.0 + + magnetic field + +.. admonition:: h_field_it + :class: intag + + **type=** int; **optional**; **default=** 0 + + number of iterations the magnetic field is kept on + +.. admonition:: sigma_mix + :class: intag + + **type=** float; **optional**; **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:: g0_mix + :class: intag + + **type=** float; **optional**; **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; **optional**; **default=** 'linear' + + which type of mixing is used. Possible values are: + linear: linear mixing + broyden: broyden mixing + +.. admonition:: broy_max_it + :class: intag + + **type=** int; **optional**; **default=** 1 + + maximum number of iteration to be considered for broyden mixing + 1 corresponds to simple linear mixing + +.. admonition:: dc + :class: intag + + **type=** bool; **optional**; **default=** True + + dc correction on yes or no? + +.. admonition:: calc_energies + :class: intag + + **type=** bool; **optional**; **default=** False, not compatible with 'ftps' solver + + calc energies explicitly within the dmft loop + +.. admonition:: block_threshold + :class: intag + + **type=** float; **optional**; **default=** 1e-05 + + threshold for finding block structures in the input data (off-diag yes or no) + +.. admonition:: block_suppress_orbital_symm + :class: intag + + **type=** bool; **optional**; **default=** False + + should blocks be checked if symmetry-equiv. between orbitals? + Does not affect spin symmetries. + +.. admonition:: load_sigma + :class: intag + + **type=** bool; **optional**; **default=** False + + load a old sigma from h5 file + +.. admonition:: path_to_sigma + :class: intag + + **type=** str, needed if load_sigma is true + + path to h5 file from which the sigma should be loaded + +.. admonition:: load_sigma_iter + :class: intag + + **type=** int; **optional**; **default=** last iteration + + load the sigma from a specific iteration if wanted + +.. admonition:: noise_level_initial_sigma + :class: intag + + **type=** float; **optional**; **default=** 0.0 + + spread of Gaussian noise applied to the initial Sigma + +.. admonition:: occ_conv_crit + :class: intag + + **type=** float; **optional**; **default=** -1 + + stop the calculation if a certain threshold for the imp occ change is reached + +.. admonition:: gimp_conv_crit + :class: intag + + **type=** float; **optional**; **default=** -1 + + stop the calculation if sum_w 1/(w^0.6) ||Gimp-Gloc|| is smaller than threshold + +.. admonition:: g0_conv_crit + :class: intag + + **type=** float; **optional**; **default=** -1 + + stop the calculation if sum_w 1/(w^0.6) ||G0-G0_prev|| is smaller than threshold + +.. admonition:: sigma_conv_crit + :class: intag + + **type=** float; **optional**; **default=** -1 + + stop the calculation if sum_w 1/(w^0.6) ||Sigma-Sigma_prev|| is smaller than threshold + +.. admonition:: sampling_iterations + :class: intag + + **type=** int; **optional**; **default=** 0 + + for how many iterations should the solution sampled after the CSC loop is converged + +.. admonition:: sampling_h5_save_freq + :class: intag + + **type=** int; **optional**; **default=** 5 + + overwrites h5_save_freq when sampling has started + +.. admonition:: calc_mu_method + :class: intag + + **type=** string; **optional**, 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:: prec_mu + :class: intag + + **type=** float + + general precision for determining the chemical potential at any time calc_mu is called + +.. admonition:: fixed_mu_value + :class: intag + + **type=** float; **optional**; **default=** 'none' + + If given, the chemical potential remains fixed in calculations + +.. admonition:: mu_update_freq + :class: intag + + **type=** int; **optional**; **default=** 1 + + The chemical potential will be updated every # iteration + +.. admonition:: mu_initial_guess + :class: intag + + **type=** float; **optional**; **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; **optional**; **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; **optional**; **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:: afm_order + :class: intag + + **type=** bool; **optional**; **default=** False + + copy self energies instead of solving explicitly for afm order + +.. admonition:: set_rot + :class: intag + + **type=** string; **optional**; **default=** 'none' + + use density_mat_dft to diagonalize occupations = 'den' + use hloc_dft to diagonalize occupations = 'hloc' + +.. admonition:: measure_chi_SzSz + :class: intag + + **type=** bool; **optional**; **default=** False + + measure the dynamic spin suszeptibility chi(sz,sz(tau)) + triqs.github.io/cthyb/unstable/guide/dynamic_susceptibility_notebook.html + +.. admonition:: measure_chi_insertions + :class: intag + + **type=** int; **optional**; **default=** 100 + + number of insertation for measurement of chi + +.. admonition:: mu_gap_gb2_threshold + :class: intag + + **type=** float; **optional**; **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; **optional**; **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. 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..f17795ab --- /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]** + +seedname; jobname; csc; plo_cfg; h_int_type; h_int_basis; U; U_prime; J; ratio_F4_F2; beta; n_iter_dmft_first; n_iter_dmft_per; n_iter_dmft; dc_type; dc_dmft; cpa_zeta; cpa_x; solver_type; n_iw; n_tau; n_l; n_w; w_range; eta; diag_delta; h5_save_freq; magnetic; magmom; enforce_off_diag; h_field; h_field_it; sigma_mix; g0_mix; g0_mix_type; broy_max_it; dc; calc_energies; block_threshold; block_suppress_orbital_symm; load_sigma; path_to_sigma; load_sigma_iter; noise_level_initial_sigma; occ_conv_crit; gimp_conv_crit; g0_conv_crit; sigma_conv_crit; sampling_iterations; sampling_h5_save_freq; calc_mu_method; prec_mu; fixed_mu_value; mu_update_freq; mu_initial_guess; mu_mix_const; mu_mix_per_occupation_offset; afm_order; set_rot; measure_chi_SzSz; measure_chi_insertions; mu_gap_gb2_threshold; mu_gap_occ_deviation; + +**[solver]** + + +store_solver; length_cycle; n_warmup_cycles; n_cycles_tot; measure_G_l; measure_G_tau; measure_G_iw; measure_density_matrix; measure_pert_order; max_time; imag_threshold; off_diag_threshold; delta_interface; move_double; perform_tail_fit; fit_max_moment; fit_min_n; fit_max_n; fit_min_w; fit_max_w; random_seed; legendre_fit; loc_n_min; loc_n_max; n_bath; bath_fit; refine_factor; ph_symm; calc_me; enforce_gap; ignore_weight; dt; state_storage; path_to_gs; sweeps; maxmI; maxmIB; maxmB; tw; dmrg_maxmI; dmrg_maxmIB; dmrg_maxmB; dmrg_tw; measure_hist; improved_estimator;; with_fock; force_real; one_shot; method; tol; + +**[dft]** + +dft_code; n_cores; n_iter; n_iter_first; dft_exec; store_eigenvals; mpi_env; projector_type; w90_exec; w90_tolerance; + +**[advanced]** + +dc_factor; dc_fixed_value; dc_fixed_occ; dc_U; dc_J; 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..46493e03 --- /dev/null +++ b/_sources/input_output/DMFT_input/solver.rst.txt @@ -0,0 +1,384 @@ +[solver]: solver specific parameters +------------------------------------ + +Here are the parameters that are uniquely dependent on the solver chosen. Below a list of the supported solvers: + + + + + + + + +.. admonition:: store_solver + :class: intag + + **type=** bool; **optional** default= False + + store the whole solver object under DMFT_input in h5 archive + +cthyb parameters +================ + +.. admonition:: length_cycle + :class: intag + + **type=** int + + length of each cycle; number of sweeps before measurement is taken + +.. admonition:: n_warmup_cycles + :class: intag + + **type=** int + + number of warmup cycles before real measurement sets in + +.. admonition:: n_cycles_tot + :class: intag + + **type=** int + + total number of sweeps + +.. admonition:: measure_G_l + :class: intag + + **type=** bool + + measure Legendre Greens function + +.. admonition:: measure_G_tau + :class: intag + + **type=** bool; **optional**; **default=** True + + should the solver measure G(tau)? + +.. admonition:: measure_G_iw + :class: intag + + **type=** bool; **optional**; **default=** False + + should the solver measure G(iw)? + +.. admonition:: measure_density_matrix + :class: intag + + **type=** bool; **optional**; **default=** False + + measures the impurity density matrix and sets also + use_norm_as_weight to true + +.. admonition:: measure_pert_order + :class: intag + + **type=** bool; **optional**; **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:: max_time + :class: intag + + **type=** int; **optional**; **default=** -1 + + maximum amount the solver is allowed to spend in each iteration + +.. admonition:: imag_threshold + :class: intag + + **type=** float; **optional**; **default=** 10e-15 + + threshold for imag part of G0_tau. be warned if symmetries are off in projection scheme imag parts can occur in G0_tau + +.. admonition:: off_diag_threshold + :class: intag + + **type=** float; **optional** + + threshold for off-diag elements in Hloc0 + +.. admonition:: delta_interface + :class: intag + + **type=** bool; **optional**; **default=** False + + use new delta interface in cthyb instead of input G0 + +.. admonition:: move_double + :class: intag + + **type=** bool; **optional**; **default=** True + + double moves in solver + +.. admonition:: perform_tail_fit + :class: intag + + **type=** bool; **optional**; **default=** False + + tail fitting if legendre is off? + +.. admonition:: fit_max_moment + :class: intag + + **type=** int; **optional** + + max moment to be fitted + +.. admonition:: fit_min_n + :class: intag + + **type=** int; **optional** + + number of start matsubara frequency to start with + +.. admonition:: fit_max_n + :class: intag + + **type=** int; **optional** + + number of highest matsubara frequency to fit + +.. admonition:: fit_min_w + :class: intag + + **type=** float; **optional** + + start matsubara frequency to start with + +.. admonition:: fit_max_w + :class: intag + + **type=** float; **optional** + + highest matsubara frequency to fit + +.. admonition:: random_seed + :class: intag + + **type=** str; **optional** default 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 + +.. admonition:: legendre_fit + :class: intag + + **type=** bool; **optional** default= False + + filter noise of G(tau) with G_l, cutoff is taken from n_l + +.. admonition:: loc_n_min + :class: intag + + **type=** int; **optional** + + Restrict local Hilbert space to states with at least this number of particles + +.. admonition:: loc_n_max + :class: intag + + **type=** int; **optional** + + Restrict local Hilbert space to states with at most this number of particles + +ftps parameters +=============== + +.. admonition:: n_bath + :class: intag + + **type=** int + + number of bath sites + +.. admonition:: bath_fit + :class: intag + + **type=** bool; **default=** False + + DiscretizeBath vs BathFitter + +.. admonition:: refine_factor + :class: intag + + **type=** int; **optional**; **default=** 1 + + rerun ftps cycle with increased accuracy + +.. admonition:: ph_symm + :class: intag + + **type=** bool; **optional**; **default=** False + + particle-hole symmetric problem + +.. admonition:: calc_me + :class: intag + + **type=** bool; **optional**; **default=** True + + calculate only symmetry-inequivalent spins/orbitals, symmetrized afterwards + +.. admonition:: enforce_gap + :class: intag + + **type=** list of floats; **optional**; **default=** 'none' + + enforce gap in DiscretizeBath between interval + +.. admonition:: ignore_weight + :class: intag + + **type=** float; **optional**; **default=** 0.0 + + ignore weight of peaks for bath fitter + +.. admonition:: dt + :class: intag + + **type=** float + + time step + +.. admonition:: state_storage + :class: intag + + **type=** string; **default=** './' + + location of large MPS states + +.. 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:: sweeps + :class: intag + + **type=** int; **optional**; **default=** 10 + + Number of DMRG sweeps + +.. admonition:: maxmI + :class: intag + + **type=** int; **optional**; **default=** 100 + + maximal imp-imp bond dimensions + +.. admonition:: maxmIB + :class: intag + + **type=** int; **optional**; **default=** 100 + + maximal imp-bath bond dimensions + +.. admonition:: maxmB + :class: intag + + **type=** int; **optional**; **default=** 100 + + maximal bath-bath bond dimensions + +.. admonition:: tw + :class: intag + + **type=** float, default 1E-9 + + truncated weight for every link + +.. admonition:: dmrg_maxmI + :class: intag + + **type=** int; **optional**; **default=** 100 + + maximal imp-imp bond dimensions + +.. admonition:: dmrg_maxmIB + :class: intag + + **type=** int; **optional**; **default=** 100 + + maximal imp-bath bond dimensions + +.. admonition:: dmrg_maxmB + :class: intag + + **type=** int; **optional**; **default=** 100 + + maximal bath-bath bond dimensions + +.. admonition:: dmrg_tw + :class: intag + + **type=** float, default 1E-9 + + truncated weight for every link + +ctseg parameters +================ + +.. admonition:: measure_hist + :class: intag + + **type=** bool; **optional**; **default=** False + + measure perturbation_order histograms + +.. admonition:: improved_estimator + :class: intag + + **type=** bool; **optional**; **default=** False + + measure improved estimators + Sigma_iw will automatically be calculated via + http://dx.doi.org/10.1103/PhysRevB.85.205106 + +hartree parameters +================ + +.. admonition:: with_fock + :class: intag + + **type=** bool; **optional**; **default=** False + + include Fock exchange terms in the self-energy + +.. admonition:: force_real + :class: intag + + **type=** bool; **optional**; **default=** True + + force the self energy from Hartree fock to be real + +.. admonition:: one_shot + :class: intag + + **type=** bool; **optional**; **default=** True + + Perform a one-shot or self-consitent root finding in each DMFT step of the Hartree solver. + +.. admonition:: method + :class: intag + + **type=** bool; **optional**; **default=** True + + method for root finder. Only used if one_shot=False, see scipy.optimize.root for options. + +.. admonition:: tol + :class: intag + + **type=** float; **optional**; **default=** 1e-5 + + tolerance for root finder if one_shot=False. 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 ` library, see `TRIQS installation instruction `_. + In the following, we assume that :ref:`TRIQS `, `triqs/dft_tools `_, and at least one of the impurity solvers `available in TRIQS `_, 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 `_ 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 `_. 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 `. +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: +``_. 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 `_ for even more +tutorials. + + +Improving solid_dmft +==================== + +Please post suggestions for new features on the `github discussion page +`_ 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 +``_. 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 `_ 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 `` could be `3.0.0`: +``` +docker build -t triqs_mpich: -f mpich_dockerfile ./ +docker build -t triqs_openmpi: -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 | docker login registry.ethz.ch -u --password-stdin +``` +and then run +``` +docker pull registry.ethz.ch/d-matl-theory/uni-dmft/: +``` +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/: +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 : +``` +and then upload the tar to daint and then load it via: +``` +sarus load docker-triqs.tar : +``` +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 : bash +docker run --rm -it --shm-size=4g -e USER_ID=`id -u` -e GROUP_ID=`id -g` -p 8378:8378 -v $PWD:/work : 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 /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 $PWD:/work : bash -c 'cd /work && mpirun -n 4 python /run_dmft.py' +``` diff --git a/_sources/md_notes/run_cluster.md.txt b/_sources/md_notes/run_cluster.md.txt new file mode 100644 index 00000000..f17a69bf --- /dev/null +++ b/_sources/md_notes/run_cluster.md.txt @@ -0,0 +1,70 @@ +# Running solid_dmft on a cluster + +## Running on CSCS daint + +in some directories one can also find example job files to run everything on +daint. Note, that one has to first load the desired docker images with sarus +on daint: https://user.cscs.ch/tools/containers/sarus/, see the README.md in the `/Docker` folder. + +## one shot job on daint + +one shot is quite straight forward. Just get the newest version of these +scripts, go to a working directory and then create job file that looks like +this: +``` +#!/bin/bash +#SBATCH --job-name="svo-test" +#SBATCH --time=1:00:00 +#SBATCH --nodes=2 +#SBATCH --ntasks-per-node=36 +#SBATCH --account=eth3 +#SBATCH --ntasks-per-core=1 +#SBATCH --constraint=mc +#SBATCH --partition=normal +#SBATCH --output=out.%j +#SBATCH --error=err.%j + +#======START===== + +srun sarus run --mpi --mount=type=bind,source=$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..65a50578 --- /dev/null +++ b/_sources/tutorials/Ce2O3_csc_w90/tutorial.ipynb.txt @@ -0,0 +1,531 @@ +{ + "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", + "\"drawing\"\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.ini](./dmft_config.ini)\n", + "\n", + "Here we'll discuss the most important input flags for solid_dmft:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "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", + "#dft_mu = 0.\n", + "solver_type = hubbardI\n", + "n_l = 15\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", + "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 = \n", + "w90_exec = wannier90.x\n", + "w90_tolerance = 1.e-1\n" + ] + } + ], + "source": [ + "!cat ./dmft_config.ini" + ] + }, + { + "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": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "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": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "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": [ + "
" + ] + }, + "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 (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": "3.10.12" + } + }, + "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..ce0ad298 --- /dev/null +++ b/_sources/tutorials/NNO_os_plo_mag/tutorial.ipynb.txt @@ -0,0 +1,845 @@ +{ + "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: 2022-03-10 17:08:16.981449\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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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "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:\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 from 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 : [1, 2]\n", + "Density matrix:\n", + " Shell 1\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.ini](config.ini) for solid_dmft looks like this: \n", + "\n", + " [general]\n", + " seedname = nno\n", + " jobname = NNO_AFM\n", + "\n", + " block_threshold = 0.001\n", + "\n", + " solver_type = cthyb\n", + " n_iw = 8001\n", + " n_tau = 50001\n", + "\n", + " prec_mu = 0.001\n", + "\n", + " h_int_type = density_density\n", + " U = 8.0\n", + " J = 1.0\n", + "\n", + " beta = 40\n", + "\n", + " magnetic = True\n", + " magmom = -0.001, 0.001\n", + " afm_order = True\n", + "\n", + " n_iter_dmft = 12\n", + "\n", + " g0_mix = 0.9 \n", + "\n", + " dc_type = 0\n", + " dc = True\n", + " dc_dmft = False\n", + "\n", + " [solver]\n", + " length_cycle = 150\n", + " n_warmup_cycles = 10000\n", + " n_cycles_tot = 2e+8\n", + " imag_threshold = 1e-5\n", + "\n", + " perform_tail_fit = True\n", + " fit_max_moment = 6\n", + " fit_min_w = 8\n", + " fit_max_w = 14\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=8001` 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 in units of the DC potential. Hence, small values should be chosen. \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=150` 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.ini`. \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']" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "65dba97b-a64c-4d88-b7cc-3607605a9aa3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(nrows=3, dpi=150, figsize=(7,6), 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'], (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'], (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'], (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'], (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[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", + "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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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "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(-1.5,0)\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": [ + "\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'], orb_names=[0, 1, 2, 3, 4], 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", + "5 5 0 9 0.562426 0.341965 0.5 0.5 +1.0000|1111101111>\n", + "0 0 0 8 0.000000 0.160698 1.0 1.0 +1.0000|1111101011>\n", + "3 3 0 9 0.472365 0.057380 0.5 0.5 +1.0000|1111111011>\n", + "25 25 0 8 1.147767 0.048976 0.0 0.0 +1.0000|1101101111>\n", + "40 40 0 8 1.629801 0.041522 1.0 1.0 +1.0000|1111101110>\n", + "55 55 0 10 7.522083 0.038053 0.0 0.0 +1.0000|1111111111>\n", + "4 4 0 9 0.562426 0.028791 -0.5 0.5 +1.0000|0111111111>\n", + "50 50 0 8 2.745626 0.024248 0.0 0.0 +1.0000|0111101111>\n", + "8 8 0 8 0.637905 0.021395 1.0 1.0 +1.0000|1111100111>\n", + "11 11 0 9 0.734944 0.021376 -0.5 0.5 +1.0000|1101111111>\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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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "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 (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/_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..678f59fe --- /dev/null +++ b/_sources/tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.ipynb.txt @@ -0,0 +1,463 @@ +{ + "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": [ + "
" + ] + }, + "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.ini](2_dmft_csc/dmft_config.ini) 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.ini:\n", + "\n", + "Group [general]:\n", + "\n", + "* `set_rot = hloc`: rotates the local impurity problem into a basis where the local Hamiltonian is diagonal\n", + "* `plo_cfg = plo.cfg`: the name of the config file for constructing the PLOs (see below)\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", + "* `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": [ + "
" + ] + }, + "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": [ + "
" + ] + }, + "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": [ + "
" + ] + }, + "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.ini\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!" + ] + } + ], + "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", + "
    \n", + "
  1. bandstructure
  2. \n", + "
  3. Fermi slice
  4. \n", + "
" + ] + }, + { + "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": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "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": [ + "
" + ] + }, + "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 matplotlib colormaps. `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": [ + "
" + ] + }, + "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/_sphinx_javascript_frameworks_compat.js b/_static/_sphinx_javascript_frameworks_compat.js new file mode 100644 index 00000000..81415803 --- /dev/null +++ b/_static/_sphinx_javascript_frameworks_compat.js @@ -0,0 +1,123 @@ +/* Compatability shim for jQuery and underscores.js. + * + * Copyright Sphinx contributors + * Released under the two clause BSD licence + */ + +/** + * 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; +} diff --git a/_static/basic.css b/_static/basic.css new file mode 100644 index 00000000..30fee9d0 --- /dev/null +++ b/_static/basic.css @@ -0,0 +1,925 @@ +/* + * basic.css + * ~~~~~~~~~ + * + * Sphinx stylesheet -- basic theme. + * + * :copyright: Copyright 2007-2023 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; +} + +div.sphinxsidebar form { + margin-top: 10px; +} + +div.sphinxsidebar input { + border: 1px solid #98dbcc; + font-family: sans-serif; + font-size: 1em; +} + +div.sphinxsidebar #searchbox form.search { + overflow: hidden; +} + +div.sphinxsidebar #searchbox input[type="text"] { + float: left; + width: 80%; + padding: 0.25em; + box-sizing: border-box; +} + +div.sphinxsidebar #searchbox input[type="submit"] { + float: left; + width: 20%; + border-left: none; + padding: 0.25em; + box-sizing: border-box; +} + + +img { + border: 0; + max-width: 100%; +} + +/* -- search page ----------------------------------------------------------- */ + +ul.search { + margin: 10px 0 0 20px; + padding: 0; +} + +ul.search li { + padding: 5px 0 5px 20px; + background-image: url(file.png); + background-repeat: no-repeat; + background-position: 0 7px; +} + +ul.search li a { + font-weight: bold; +} + +ul.search li p.context { + color: #888; + margin: 2px 0 0 30px; + text-align: left; +} + +ul.keywordmatches li.goodmatch a { + font-weight: bold; +} + +/* -- index page ------------------------------------------------------------ */ + +table.contentstable { + width: 90%; + margin-left: auto; + margin-right: auto; +} + +table.contentstable p.biglink { + line-height: 150%; +} + +a.biglink { + 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: 360px; + 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:visited { + color: #551A8B; +} + +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; +} + +nav.contents, +aside.topic, +div.admonition, div.topic, blockquote { + clear: left; +} + +/* -- topics ---------------------------------------------------------------- */ + +nav.contents, +aside.topic, +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, +nav.contents > :last-child, +aside.topic > :last-child, +div.topic > :last-child, +div.admonition > :last-child { + margin-bottom: 0; +} + +div.sidebar::after, +aside.sidebar::after, +nav.contents::after, +aside.topic::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; +} + +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; +} + +ol.simple ol p, +ol.simple ul p, +ul.simple ol p, +ul.simple ul p { + margin-top: 0; +} + +ol.simple > li:not(:first-child) > p, +ul.simple > li:not(:first-child) > p { + margin-top: 0; +} + +ol.simple p, +ul.simple p { + margin-bottom: 0; +} + +aside.footnote > span, +div.citation > span { + float: left; +} +aside.footnote > span:last-of-type, +div.citation > span:last-of-type { + padding-right: 0.5em; +} +aside.footnote > p { + margin-left: 2em; +} +div.citation > p { + margin-left: 4em; +} +aside.footnote > p:last-of-type, +div.citation > p:last-of-type { + margin-bottom: 0em; +} +aside.footnote > p:last-of-type:after, +div.citation > p:last-of-type: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 > 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; +} + +.sig dd { + margin-top: 0px; + margin-bottom: 0px; +} + +.sig dl { + margin-top: 0px; + margin-bottom: 0px; +} + +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; +} + +.translated { + background-color: rgba(207, 255, 207, 0.2) +} + +.untranslated { + background-color: rgba(255, 207, 207, 0.2) +} + +/* -- 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; + white-space: nowrap; +} + +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; 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+ +/** + * Simple result scoring code. + */ +if (typeof Scorer === "undefined") { + var Scorer = { + // Implement the following function to further tweak the score for each result + // The function takes a result array [docname, title, anchor, descr, score, filename] + // and returns the new score. + /* + score: result => { + const [docname, title, anchor, descr, score, filename] = result + return score + }, + */ + + // 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, + }; +} + +const _removeChildren = (element) => { + while (element && element.lastChild) element.removeChild(element.lastChild); +}; + +/** + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping + */ +const _escapeRegExp = (string) => + string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string + +const _displayItem = (item, searchTerms, highlightTerms) => { + const docBuilder = DOCUMENTATION_OPTIONS.BUILDER; + const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX; + const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX; + const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY; + const contentRoot = document.documentElement.dataset.content_root; + + const [docName, title, anchor, descr, score, _filename] = item; + + let listItem = document.createElement("li"); + let requestUrl; + let linkUrl; + if (docBuilder === "dirhtml") { + // dirhtml builder + let dirname = docName + "/"; + if (dirname.match(/\/index\/$/)) + dirname = dirname.substring(0, dirname.length - 6); + else if (dirname === "index/") dirname = ""; + requestUrl = contentRoot + dirname; + linkUrl = requestUrl; + } else { + // normal html builders + requestUrl = contentRoot + docName + docFileSuffix; + linkUrl = docName + docLinkSuffix; + } + let linkEl = listItem.appendChild(document.createElement("a")); + linkEl.href = linkUrl + anchor; + linkEl.dataset.score = score; + linkEl.innerHTML = title; + if (descr) { + listItem.appendChild(document.createElement("span")).innerHTML = + " (" + descr + ")"; + // highlight search terms in the description + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + } + else if (showSearchSummary) + fetch(requestUrl) + .then((responseData) => responseData.text()) + .then((data) => { + if (data) + listItem.appendChild( + Search.makeSearchSummary(data, searchTerms) + ); + // highlight search terms in the summary + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + }); + Search.output.appendChild(listItem); +}; +const _finishSearch = (resultCount) => { + Search.stopPulse(); + Search.title.innerText = _("Search Results"); + if (!resultCount) + Search.status.innerText = Documentation.gettext( + "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.innerText = _( + `Search finished, found ${resultCount} page(s) matching the search query.` + ); +}; +const _displayNextItem = ( + results, + resultCount, + searchTerms, + highlightTerms, +) => { + // results left, load the summary and display it + // this is intended to be dynamic (don't sub resultsCount) + if (results.length) { + _displayItem(results.pop(), searchTerms, highlightTerms); + setTimeout( + () => _displayNextItem(results, resultCount, searchTerms, highlightTerms), + 5 + ); + } + // search finished, update title and status message + else _finishSearch(resultCount); +}; + +/** + * Default splitQuery function. Can be overridden in ``sphinx.search`` with a + * custom function per language. + * + * The regular expression works by splitting the string on consecutive characters + * that are not Unicode letters, numbers, underscores, or emoji characters. + * This is the same as ``\W+`` in Python, preserving the surrogate pair area. + */ +if (typeof splitQuery === "undefined") { + var splitQuery = (query) => query + .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu) + .filter(term => term) // remove remaining empty strings +} + +/** + * Search Module + */ +const Search = { + _index: null, + _queued_query: null, + _pulse_status: -1, + + htmlToText: (htmlString) => { + const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html'); + htmlElement.querySelectorAll(".headerlink").forEach((el) => { el.remove() }); + const docContent = htmlElement.querySelector('[role="main"]'); + if (docContent !== undefined) return docContent.textContent; + console.warn( + "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template." + ); + return ""; + }, + + init: () => { + const query = new URLSearchParams(window.location.search).get("q"); + document + .querySelectorAll('input[name="q"]') + .forEach((el) => (el.value = query)); + if (query) Search.performSearch(query); + }, + + loadIndex: (url) => + (document.body.appendChild(document.createElement("script")).src = url), + + setIndex: (index) => { + Search._index = index; + if (Search._queued_query !== null) { + const query = Search._queued_query; + Search._queued_query = null; + Search.query(query); + } + }, + + hasIndex: () => Search._index !== null, + + deferQuery: (query) => (Search._queued_query = query), + + stopPulse: () => (Search._pulse_status = -1), + + startPulse: () => { + if (Search._pulse_status >= 0) return; + + const pulse = () => { + Search._pulse_status = (Search._pulse_status + 1) % 4; + Search.dots.innerText = ".".repeat(Search._pulse_status); + if (Search._pulse_status >= 0) window.setTimeout(pulse, 500); + }; + pulse(); + }, + + /** + * perform a search for something (or wait until index is loaded) + */ + performSearch: (query) => { + // create the required interface elements + const searchText = document.createElement("h2"); + searchText.textContent = _("Searching"); + const searchSummary = document.createElement("p"); + searchSummary.classList.add("search-summary"); + searchSummary.innerText = ""; + const searchList = document.createElement("ul"); + searchList.classList.add("search"); + + const out = document.getElementById("search-results"); + Search.title = out.appendChild(searchText); + Search.dots = Search.title.appendChild(document.createElement("span")); + Search.status = out.appendChild(searchSummary); + Search.output = out.appendChild(searchList); + + const searchProgress = document.getElementById("search-progress"); + // Some themes don't use the search progress node + if (searchProgress) { + searchProgress.innerText = _("Preparing search..."); + } + Search.startPulse(); + + // index already loaded, the browser was quick! + if (Search.hasIndex()) Search.query(query); + else Search.deferQuery(query); + }, + + /** + * execute search (requires search index to be loaded) + */ + query: (query) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + const allTitles = Search._index.alltitles; + const indexEntries = Search._index.indexentries; + + // stem the search terms and add them to the correct list + const stemmer = new Stemmer(); + const searchTerms = new Set(); + const excludedTerms = new Set(); + const highlightTerms = new Set(); + const objectTerms = new Set(splitQuery(query.toLowerCase().trim())); + splitQuery(query.trim()).forEach((queryTerm) => { + const queryTermLower = queryTerm.toLowerCase(); + + // maybe skip this "word" + // stopwords array is from language_data.js + if ( + stopwords.indexOf(queryTermLower) !== -1 || + queryTerm.match(/^\d+$/) + ) + return; + + // stem the word + let word = stemmer.stemWord(queryTermLower); + // select the correct list + if (word[0] === "-") excludedTerms.add(word.substr(1)); + else { + searchTerms.add(word); + highlightTerms.add(queryTermLower); + } + }); + + if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js + localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" ")) + } + + // console.debug("SEARCH: searching for:"); + // console.info("required: ", [...searchTerms]); + // console.info("excluded: ", [...excludedTerms]); + + // array of [docname, title, anchor, descr, score, filename] + let results = []; + _removeChildren(document.getElementById("search-progress")); + + const queryLower = query.toLowerCase(); + for (const [title, foundTitles] of Object.entries(allTitles)) { + if (title.toLowerCase().includes(queryLower) && (queryLower.length >= title.length/2)) { + for (const [file, id] of foundTitles) { + let score = Math.round(100 * queryLower.length / title.length) + results.push([ + docNames[file], + titles[file] !== title ? `${titles[file]} > ${title}` : title, + id !== null ? "#" + id : "", + null, + score, + filenames[file], + ]); + } + } + } + + // search for explicit entries in index directives + for (const [entry, foundEntries] of Object.entries(indexEntries)) { + if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) { + for (const [file, id] of foundEntries) { + let score = Math.round(100 * queryLower.length / entry.length) + results.push([ + docNames[file], + titles[file], + id ? "#" + id : "", + null, + score, + filenames[file], + ]); + } + } + } + + // lookup as object + objectTerms.forEach((term) => + results.push(...Search.performObjectSearch(term, objectTerms)) + ); + + // lookup as search terms in fulltext + results.push(...Search.performTermsSearch(searchTerms, excludedTerms)); + + // let the scorer override scores with a custom scoring function + if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item))); + + // 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((a, b) => { + const leftScore = a[4]; + const rightScore = b[4]; + if (leftScore === rightScore) { + // same score: sort alphabetically + const leftTitle = a[1].toLowerCase(); + const rightTitle = b[1].toLowerCase(); + if (leftTitle === rightTitle) return 0; + return leftTitle > rightTitle ? -1 : 1; // inverted is intentional + } + return leftScore > rightScore ? 1 : -1; + }); + + // remove duplicate search results + // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept + let seen = new Set(); + results = results.reverse().reduce((acc, result) => { + let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(','); + if (!seen.has(resultStr)) { + acc.push(result); + seen.add(resultStr); + } + return acc; + }, []); + + results = results.reverse(); + + // for debugging + //Search.lastresults = results.slice(); // a copy + // console.info("search results:", Search.lastresults); + + // print the results + _displayNextItem(results, results.length, searchTerms, highlightTerms); + }, + + /** + * search for object names + */ + performObjectSearch: (object, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const objects = Search._index.objects; + const objNames = Search._index.objnames; + const titles = Search._index.titles; + + const results = []; + + const objectSearchCallback = (prefix, match) => { + const name = match[4] + const fullname = (prefix ? prefix + "." : "") + name; + const fullnameLower = fullname.toLowerCase(); + if (fullnameLower.indexOf(object) < 0) return; + + let score = 0; + const 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.slice(-1)[0] === object) + score += Scorer.objNameMatch; + else if (parts.slice(-1)[0].indexOf(object) > -1) + score += Scorer.objPartialMatch; // matches in last name + + const objName = objNames[match[1]][2]; + const title = titles[match[0]]; + + // If more than one term searched for, we require other words to be + // found in the name/title/description + const otherTerms = new Set(objectTerms); + otherTerms.delete(object); + if (otherTerms.size > 0) { + const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase(); + if ( + [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0) + ) + return; + } + + let anchor = match[3]; + if (anchor === "") anchor = fullname; + else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname; + + const descr = objName + _(", in ") + title; + + // 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]], + ]); + }; + Object.keys(objects).forEach((prefix) => + objects[prefix].forEach((array) => + objectSearchCallback(prefix, array) + ) + ); + return results; + }, + + /** + * search for full-text terms in the index + */ + performTermsSearch: (searchTerms, excludedTerms) => { + // prepare search + const terms = Search._index.terms; + const titleTerms = Search._index.titleterms; + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + + const scoreMap = new Map(); + const fileMap = new Map(); + + // perform the search on the required terms + searchTerms.forEach((word) => { + const files = []; + const arr = [ + { files: terms[word], score: Scorer.term }, + { files: titleTerms[word], score: Scorer.title }, + ]; + // add support for partial matches + if (word.length > 2) { + const escapedWord = _escapeRegExp(word); + Object.keys(terms).forEach((term) => { + if (term.match(escapedWord) && !terms[word]) + arr.push({ files: terms[term], score: Scorer.partialTerm }); + }); + Object.keys(titleTerms).forEach((term) => { + if (term.match(escapedWord) && !titleTerms[word]) + arr.push({ files: titleTerms[word], score: Scorer.partialTitle }); + }); + } + + // no match but word was a required one + if (arr.every((record) => record.files === undefined)) return; + + // found search word in contents + arr.forEach((record) => { + if (record.files === undefined) return; + + let recordFiles = record.files; + if (recordFiles.length === undefined) recordFiles = [recordFiles]; + files.push(...recordFiles); + + // set score for the word in each file + recordFiles.forEach((file) => { + if (!scoreMap.has(file)) scoreMap.set(file, {}); + scoreMap.get(file)[word] = record.score; + }); + }); + + // create the mapping + files.forEach((file) => { + if (fileMap.has(file) && fileMap.get(file).indexOf(word) === -1) + fileMap.get(file).push(word); + else fileMap.set(file, [word]); + }); + }); + + // now check if the files don't contain excluded terms + const results = []; + for (const [file, wordList] of fileMap) { + // check if all requirements are matched + + // as search terms with length < 3 are discarded + const filteredTermCount = [...searchTerms].filter( + (term) => term.length > 2 + ).length; + if ( + wordList.length !== searchTerms.size && + wordList.length !== filteredTermCount + ) + continue; + + // ensure that none of the excluded terms is in the search result + if ( + [...excludedTerms].some( + (term) => + terms[term] === file || + titleTerms[term] === file || + (terms[term] || []).includes(file) || + (titleTerms[term] || []).includes(file) + ) + ) + break; + + // select one (max) score for the file. + const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w])); + // add result to the result list + 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. + */ + makeSearchSummary: (htmlText, keywords) => { + const text = Search.htmlToText(htmlText); + if (text === "") return null; + + const textLower = text.toLowerCase(); + const actualStartPosition = [...keywords] + .map((k) => textLower.indexOf(k.toLowerCase())) + .filter((i) => i > -1) + .slice(-1)[0]; + const startWithContext = Math.max(actualStartPosition - 120, 0); + + const top = startWithContext === 0 ? "" : "..."; + const tail = startWithContext + 240 < text.length ? "..." : ""; + + let summary = document.createElement("p"); + summary.classList.add("context"); + summary.textContent = top + text.substr(startWithContext, 240).trim() + tail; + + return summary; + }, +}; + +_ready(Search.init); diff --git a/_static/sphinx_highlight.js b/_static/sphinx_highlight.js new file mode 100644 index 00000000..8a96c69a --- /dev/null +++ b/_static/sphinx_highlight.js @@ -0,0 +1,154 @@ +/* Highlighting utilities for Sphinx HTML documentation. */ +"use strict"; + +const SPHINX_HIGHLIGHT_ENABLED = true + +/** + * highlight a given string on a node by wrapping it in + * span elements with the given class name. + */ +const _highlight = (node, addItems, text, className) => { + if (node.nodeType === Node.TEXT_NODE) { + const val = node.nodeValue; + const parent = node.parentNode; + const pos = val.toLowerCase().indexOf(text); + if ( + pos >= 0 && + !parent.classList.contains(className) && + !parent.classList.contains("nohighlight") + ) { + let span; + + const closestNode = parent.closest("body, svg, foreignObject"); + const isInSVG = closestNode && closestNode.matches("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.classList.add(className); + } + + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + const rest = document.createTextNode(val.substr(pos + text.length)); + parent.insertBefore( + span, + parent.insertBefore( + rest, + node.nextSibling + ) + ); + node.nodeValue = val.substr(0, pos); + /* There may be more occurrences of search term in this node. So call this + * function recursively on the remaining fragment. + */ + _highlight(rest, addItems, text, className); + + if (isInSVG) { + const rect = document.createElementNS( + "http://www.w3.org/2000/svg", + "rect" + ); + const bbox = parent.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: parent, target: rect }); + } + } + } else if (node.matches && !node.matches("button, select, textarea")) { + node.childNodes.forEach((el) => _highlight(el, addItems, text, className)); + } +}; +const _highlightText = (thisNode, text, className) => { + let addItems = []; + _highlight(thisNode, addItems, text, className); + addItems.forEach((obj) => + obj.parent.insertAdjacentElement("beforebegin", obj.target) + ); +}; + +/** + * Small JavaScript module for the documentation. + */ +const SphinxHighlight = { + + /** + * highlight the search words provided in localstorage in the text + */ + highlightSearchWords: () => { + if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight + + // get and clear terms from localstorage + const url = new URL(window.location); + const highlight = + localStorage.getItem("sphinx_highlight_terms") + || url.searchParams.get("highlight") + || ""; + localStorage.removeItem("sphinx_highlight_terms") + url.searchParams.delete("highlight"); + window.history.replaceState({}, "", url); + + // get individual terms from highlight string + const terms = highlight.toLowerCase().split(/\s+/).filter(x => x); + if (terms.length === 0) return; // nothing to do + + // There should never be more than one element matching "div.body" + const divBody = document.querySelectorAll("div.body"); + const body = divBody.length ? divBody[0] : document.querySelector("body"); + window.setTimeout(() => { + terms.forEach((term) => _highlightText(body, term, "highlighted")); + }, 10); + + const searchBox = document.getElementById("searchbox"); + if (searchBox === null) return; + searchBox.appendChild( + document + .createRange() + .createContextualFragment( + '" + ) + ); + }, + + /** + * helper function to hide the search marks again + */ + hideSearchWords: () => { + document + .querySelectorAll("#searchbox .highlight-link") + .forEach((el) => el.remove()); + document + .querySelectorAll("span.highlighted") + .forEach((el) => el.classList.remove("highlighted")); + localStorage.removeItem("sphinx_highlight_terms") + }, + + initEscapeListener: () => { + // only install a listener if it is really needed + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return; + if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) { + SphinxHighlight.hideSearchWords(); + event.preventDefault(); + } + }); + }, +}; + +_ready(() => { + /* Do not call highlightSearchWords() when we are on the search page. + * It will highlight words from the *previous* search query. + */ + if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords(); + SphinxHighlight.initEscapeListener(); +}); diff --git a/_static/triqs_logo/Icon/JPG/Triqs_Icon_RGB_Black.jpg b/_static/triqs_logo/Icon/JPG/Triqs_Icon_RGB_Black.jpg new file mode 100644 index 00000000..a25819aa Binary files /dev/null and b/_static/triqs_logo/Icon/JPG/Triqs_Icon_RGB_Black.jpg differ diff --git a/_static/triqs_logo/Icon/JPG/Triqs_Icon_RGB_Full.jpg b/_static/triqs_logo/Icon/JPG/Triqs_Icon_RGB_Full.jpg new file mode 100644 index 00000000..8fac42ac Binary files /dev/null and b/_static/triqs_logo/Icon/JPG/Triqs_Icon_RGB_Full.jpg differ diff --git a/_static/triqs_logo/Icon/SVG/Triqs_Icon_RGB_Black.svg b/_static/triqs_logo/Icon/SVG/Triqs_Icon_RGB_Black.svg new file mode 100644 index 00000000..ff598488 --- /dev/null +++ b/_static/triqs_logo/Icon/SVG/Triqs_Icon_RGB_Black.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/_static/triqs_logo/Icon/SVG/Triqs_Icon_RGB_Full.svg b/_static/triqs_logo/Icon/SVG/Triqs_Icon_RGB_Full.svg new file mode 100644 index 00000000..76d6707d --- /dev/null +++ b/_static/triqs_logo/Icon/SVG/Triqs_Icon_RGB_Full.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/_static/triqs_logo/Icon/SVG/Triqs_Icon_RGB_White.svg b/_static/triqs_logo/Icon/SVG/Triqs_Icon_RGB_White.svg new file mode 100644 index 00000000..4fbd963d --- /dev/null +++ b/_static/triqs_logo/Icon/SVG/Triqs_Icon_RGB_White.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/_static/triqs_logo/Logo/JPG/Triqs_Logo_RGB_Black.jpg b/_static/triqs_logo/Logo/JPG/Triqs_Logo_RGB_Black.jpg new file mode 100644 index 00000000..a9206e03 Binary files /dev/null and b/_static/triqs_logo/Logo/JPG/Triqs_Logo_RGB_Black.jpg differ diff --git a/_static/triqs_logo/Logo/JPG/Triqs_Logo_RGB_Full.jpg b/_static/triqs_logo/Logo/JPG/Triqs_Logo_RGB_Full.jpg new file mode 100644 index 00000000..58e6a815 Binary files /dev/null and b/_static/triqs_logo/Logo/JPG/Triqs_Logo_RGB_Full.jpg differ diff --git a/_static/triqs_logo/Logo/SVG/Triqs_Logo_RGB_Black.svg b/_static/triqs_logo/Logo/SVG/Triqs_Logo_RGB_Black.svg new file mode 100644 index 00000000..27ff0f46 --- /dev/null +++ b/_static/triqs_logo/Logo/SVG/Triqs_Logo_RGB_Black.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/_static/triqs_logo/Logo/SVG/Triqs_Logo_RGB_Full.svg b/_static/triqs_logo/Logo/SVG/Triqs_Logo_RGB_Full.svg new file mode 100644 index 00000000..149bdb6c --- /dev/null +++ b/_static/triqs_logo/Logo/SVG/Triqs_Logo_RGB_Full.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/_static/triqs_logo/Logo/SVG/Triqs_Logo_RGB_White.svg b/_static/triqs_logo/Logo/SVG/Triqs_Logo_RGB_White.svg new file mode 100644 index 00000000..eb273f97 --- /dev/null +++ b/_static/triqs_logo/Logo/SVG/Triqs_Logo_RGB_White.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/_static/triqs_logo/triqs_favicon.ico b/_static/triqs_logo/triqs_favicon.ico new file mode 100644 index 00000000..9e20d735 Binary files /dev/null and b/_static/triqs_logo/triqs_favicon.ico differ diff --git a/_static/workflow.png b/_static/workflow.png new file mode 100644 index 00000000..e934d6f1 Binary files /dev/null and b/_static/workflow.png differ diff --git a/cRPA_VASP/README.html b/cRPA_VASP/README.html new file mode 100644 index 00000000..858a025c --- /dev/null +++ b/cRPA_VASP/README.html @@ -0,0 +1,493 @@ + + + + + + + How to do cRPA calculations with VASP — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

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. +
  3. 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. +
    3. 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

      • +
      +
    4. +
    +
  4. +
  5. 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

  6. +
  7. ALGO=CRPA to make vasp calculate U matrices (bare, screened etc. )

    +
      +
    1. omegamax=0 (default) for frequency depend U matrix

    2. +
    3. NCRPA_BANDS for selecting bands in a non-disentagled workflow (vasp.at/wiki/index.php/NCRPA_BANDS)

    4. +
    5. 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.

    6. +
    7. 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

      • +
      +
    8. +
    +
  8. +
+
+
+

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:

    + +
  • +
  • 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

+
+
+

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

  • +
+
+
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/documentation.html b/documentation.html new file mode 100644 index 00000000..d37cd508 --- /dev/null +++ b/documentation.html @@ -0,0 +1,411 @@ + + + + + + + Documentation — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

Documentation

+
+

Code structure

+_images/code_structure.png +

more details in the reference manual below.

+

To get started with the code after a successful Installation, take a look at the Tutorials section. Here we provide further special information and a reference manual for all available functions.

+
+
+

DFT interface notes

+ +
+
+

Input/Output

+ +
+
+

Further details for running

+ +
+
+

Module reference manual

+ + + + + + + + + + + + + + + + + + + + + + + + +

csc_flow

contains the charge self-consistency flow control functions

dft_managers

DFT code driver modules

dmft_cycle

main DMFT cycle, DMFT step, and helper functions

dmft_tools

DMFT routine helper functions used during solid_dmft run

postprocessing

Postprocessing tools

read_config

Provides the read_config function to read the config file

util

external helper functions, not used by any DMFT routine

+
+
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/genindex.html b/genindex.html new file mode 100644 index 00000000..2e75ff50 --- /dev/null +++ b/genindex.html @@ -0,0 +1,872 @@ + + + + + + Index — solid_dmft documentation + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
  • + +
  • +
  • +
+
+
+
+
+ + +

Index

+ +
+ _ + | A + | C + | D + | F + | G + | K + | M + | P + | R + | S + | U + | W + +
+

_

+ + +
+ +

A

+ + + +
+ +

C

+ + + +
+ +

D

+ + + +
    +
  • + 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 + +
  • +
+ +

F

+ + + +
+ +

G

+ + + +
+ +

K

+ + +
+ +

M

+ + +
+ +

P

+ + + +
+ +

R

+ + + +
+ +

S

+ + + +
+ +

U

+ + + +
    +
  • + util.update_dmft_config + +
  • +
  • + util.update_results_h5 + +
  • +
  • + util.write_kslice_to_h5 + +
  • +
+ +

W

+ + + +
+ + + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/index.html b/index.html new file mode 100644 index 00000000..558f8b1c --- /dev/null +++ b/index.html @@ -0,0 +1,376 @@ + + + + + + + solid_dmft — solid_dmft documentation + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

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, and the DFT code interface +TRIQS/DFTTools. Works with triqs >3.x.x. +solid_dmft takes advantage of various +impurity solvers available +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 Installation.

+

Learn how to use solid_dmft in the Documentation and the Tutorials.

+

For more technical information about the implementation check also the solid_dmft publication in the JOSS journal. If you are using this code for your research, please cite the paper using this bib file.

+
+

Workflow of DFT+DMFT calculations with solid_dmft

+_images/workflow.png +
+
+
+_images/flatiron.png +_images/eth_logo_kurz_pos.png +
+
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/input_output/DMFT_input/advanced.html b/input_output/DMFT_input/advanced.html new file mode 100644 index 00000000..25c959ef --- /dev/null +++ b/input_output/DMFT_input/advanced.html @@ -0,0 +1,414 @@ + + + + + + + [advanced]: Advanced inputs — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

[advanced]: Advanced inputs

+

Advanced parameters, do not modify default value unless you know what you are doing

+
+

dc_factor

+
+

type= float; optional; default= ‘none’ (corresponds to 1)

+

If given, scales the dc energy by multiplying with this factor, usually < 1

+
+
+
+

dc_fixed_value

+
+

type= float; optional; 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

+
+
+
+

dc_fixed_occ

+
+

type= list of float; optional; 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!

+
+
+
+

dc_U

+
+

type= float or comma seperated list of floats; optional; default= general_params[‘U’]

+

U values for DC determination if only one value is given, the same U is assumed for all impurities

+
+
+
+

dc_J

+
+

type= float or comma seperated list of floats; optional; default= general_params[‘J’]

+

J values for DC determination if only one value is given, the same J is assumed for all impurities

+
+
+
+

map_solver_struct

+
+

type= list of dict; optional; default= no additional mapping

+

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

+
+
+
+

mapped_solver_struct_degeneracies

+
+

type= list; optional; 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.

+
+
+
+

pick_solver_struct

+
+

type= list of dict; optional; default= no additional picking

+

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. +Not listed blocks / orbitals will be not treated in impurity solver.

+
+
+
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/input_output/DMFT_input/dft.html b/input_output/DMFT_input/dft.html new file mode 100644 index 00000000..fc486d01 --- /dev/null +++ b/input_output/DMFT_input/dft.html @@ -0,0 +1,432 @@ + + + + + + + [dft]: DFT related inputs — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + + + + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/input_output/DMFT_input/general.html b/input_output/DMFT_input/general.html new file mode 100644 index 00000000..27555a03 --- /dev/null +++ b/input_output/DMFT_input/general.html @@ -0,0 +1,859 @@ + + + + + + + [general]: General parameters — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

[general]: General parameters

+

Includes the majority of the parameters

+
+

seedname

+
+

type= str

+

seedname for h5 archive with DMFT input and output

+
+
+
+

jobname

+
+

type= str; optional; default= ‘dmft_dir’

+

the output directory for one-shot calculations

+
+
+
+

csc

+
+

type= bool; optional; default= False

+

are we doing a CSC calculation?

+
+
+
+

plo_cfg

+
+

type= str; optional; default= ‘plo.cfg’

+

config file for PLOs for the converter

+
+
+
+

h_int_type

+
+

type= string

+

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

  • +
  • 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

+
+
+
+

h_int_basis

+
+

type= string

+
+

cubic basis convention to compute the interaction U matrix +* ‘triqs’ +* ‘vasp’ (equivalent to ‘triqs’) +* ‘wien2k’ +* ‘wannier90’ +* ‘qe’ (equivalent to ‘wannier90’)

+
+
+
+
+

U

+
+

type= float or comma separated list of floats

+

U values for impurities if only one value is given, the same U is assumed for all impurities

+
+
+
+

U_prime

+
+

type= float or comma separated list of floats

+

U prime values for impurities if only one value is given, the same U prime is assumed for all impurities +only used if h_int_type is kanamori

+
+
+
+

J

+
+

type= float or comma separated list of floats

+

J values for impurities if only one value is given, the same J is assumed for all impurities

+
+
+
+

ratio_F4_F2

+
+

type= float or comma separated list of floats; optional; 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, where the default is 0.63.

+
+
+
+

beta

+
+

type= float, only used if solver ImFreq

+

inverse temperature for Greens function etc

+
+
+
+

n_iter_dmft_first

+
+

type= int; optional; default= 10

+

number of iterations in first dmft cycle to converge dmft solution

+
+
+
+

n_iter_dmft_per

+
+

type= int; optional; default= 2

+

number of iterations per dmft step in CSC calculations

+
+
+
+

n_iter_dmft

+
+

type= int

+

number of iterations per dmft cycle after first cycle

+
+
+
+

dc_type

+
+

type= int

+

Type of double counting correction considered: +* 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

+
+
+
+

dc_dmft

+
+
+

type= bool

+
+

Whether to use DMFT or DFT occupations:

+
    +
  • DC with DMFT occupation in each iteration -> True

  • +
  • DC with DFT occupations after each DFT cycle -> False

  • +
+
+
+
+

cpa_zeta

+
+

type= float or comma separated list of floats

+

shift of local levels per impurity in CPA

+
+
+
+

cpa_x

+
+

type= float or comma separated list of floats

+

probability distribution for summing G(tau) in CPA

+
+
+
+

solver_type

+
+

type= str

+

type of solver chosen for the calculation, currently supports:

+
    +
  • ‘cthyb’

  • +
  • ‘ctint’

  • +
  • ‘ftps’

  • +
  • ‘hubbardI’

  • +
  • ‘hartree’

  • +
  • ‘ctseg’

  • +
+
+
+
+

n_iw

+
+

type= int; optional; default= 1025

+

number of Matsubara frequencies

+
+
+
+

n_tau

+
+

type= int; optional; default= 10001

+

number of imaginary time points

+
+
+
+

n_l

+
+

type= int, needed if measure_G_l=True or legendre_fit=True

+

number of Legendre coefficients

+
+
+
+

n_w

+
+

type= int; optional; default= 5001

+

number of real frequency points

+
+
+
+

w_range

+
+

type= tuple; optional; default= (-10, 10)

+

w_min and w_max, example: w_range = -10, 10

+
+
+
+

eta

+
+

type= float, only used if solver ReFreq

+

broadening of Green’s function

+
+
+
+

diag_delta

+
+

type= bool; optional; default= False

+

option to remove off-diagonal terms in the hybridization function

+
+
+
+

h5_save_freq

+
+

type= int; optional; default= 5

+

how often is the output saved to the h5 archive

+
+
+
+

magnetic

+
+

type= bool; optional; default= False

+

are we doing a magnetic calculations? If yes put magnetic to True. +Not implemented for CSC calculations

+
+
+
+

magmom

+
+

type= list of float seperated by comma; optional default=[]

+

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)

+
+
+
+

enforce_off_diag

+
+

type= bool; optional; default= False

+

enforce off diagonal elements in block structure finder

+
+
+
+

h_field

+
+

type= float; optional; default= 0.0

+

magnetic field

+
+
+
+

h_field_it

+
+

type= int; optional; default= 0

+

number of iterations the magnetic field is kept on

+
+
+
+

sigma_mix

+
+

type= float; optional; 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

+
+
+
+

g0_mix

+
+

type= float; optional; 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

+
+
+
+

g0_mix_type

+
+

type= string; optional; default= ‘linear’

+

which type of mixing is used. Possible values are: +linear: linear mixing +broyden: broyden mixing

+
+
+
+

broy_max_it

+
+

type= int; optional; default= 1

+

maximum number of iteration to be considered for broyden mixing +1 corresponds to simple linear mixing

+
+
+
+

dc

+
+

type= bool; optional; default= True

+

dc correction on yes or no?

+
+
+
+

calc_energies

+
+

type= bool; optional; default= False, not compatible with ‘ftps’ solver

+

calc energies explicitly within the dmft loop

+
+
+
+

block_threshold

+
+

type= float; optional; default= 1e-05

+

threshold for finding block structures in the input data (off-diag yes or no)

+
+
+
+

block_suppress_orbital_symm

+
+

type= bool; optional; default= False

+

should blocks be checked if symmetry-equiv. between orbitals? +Does not affect spin symmetries.

+
+
+
+

load_sigma

+
+

type= bool; optional; default= False

+

load a old sigma from h5 file

+
+
+
+

path_to_sigma

+
+

type= str, needed if load_sigma is true

+

path to h5 file from which the sigma should be loaded

+
+
+
+

load_sigma_iter

+
+

type= int; optional; default= last iteration

+

load the sigma from a specific iteration if wanted

+
+
+
+

noise_level_initial_sigma

+
+

type= float; optional; default= 0.0

+

spread of Gaussian noise applied to the initial Sigma

+
+
+
+

occ_conv_crit

+
+

type= float; optional; default= -1

+

stop the calculation if a certain threshold for the imp occ change is reached

+
+
+
+

gimp_conv_crit

+
+

type= float; optional; default= -1

+

stop the calculation if sum_w 1/(w^0.6) ||Gimp-Gloc|| is smaller than threshold

+
+
+
+

g0_conv_crit

+
+

type= float; optional; default= -1

+

stop the calculation if sum_w 1/(w^0.6) ||G0-G0_prev|| is smaller than threshold

+
+
+
+

sigma_conv_crit

+
+

type= float; optional; default= -1

+

stop the calculation if sum_w 1/(w^0.6) ||Sigma-Sigma_prev|| is smaller than threshold

+
+
+
+

sampling_iterations

+
+

type= int; optional; default= 0

+

for how many iterations should the solution sampled after the CSC loop is converged

+
+
+
+

sampling_h5_save_freq

+
+

type= int; optional; default= 5

+

overwrites h5_save_freq when sampling has started

+
+
+
+

calc_mu_method

+
+

type= string; optional, 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

  • +
+
+
+
+

prec_mu

+
+

type= float

+

general precision for determining the chemical potential at any time calc_mu is called

+
+
+
+

fixed_mu_value

+
+

type= float; optional; default= ‘none’

+

If given, the chemical potential remains fixed in calculations

+
+
+
+

mu_update_freq

+
+

type= int; optional; default= 1

+

The chemical potential will be updated every # iteration

+
+
+
+

mu_initial_guess

+
+

type= float; optional; default= ‘none’

+

The chemical potential of the DFT calculation. +If not given, mu will be calculated from the DFT bands

+
+
+
+

mu_mix_const

+
+

type= float; optional; default= 1.0

+

Constant term of the mixing of the chemical potential. See mu_mix_per_occupation_offset.

+
+
+
+

mu_mix_per_occupation_offset

+
+

type= float; optional; 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.

+
+
+
+

afm_order

+
+

type= bool; optional; default= False

+

copy self energies instead of solving explicitly for afm order

+
+
+
+

set_rot

+
+

type= string; optional; default= ‘none’

+

use density_mat_dft to diagonalize occupations = ‘den’ +use hloc_dft to diagonalize occupations = ‘hloc’

+
+
+
+

measure_chi_SzSz

+
+

type= bool; optional; default= False

+

measure the dynamic spin suszeptibility chi(sz,sz(tau)) +triqs.github.io/cthyb/unstable/guide/dynamic_susceptibility_notebook.html

+
+
+
+

measure_chi_insertions

+
+

type= int; optional; default= 100

+

number of insertation for measurement of chi

+
+
+
+

mu_gap_gb2_threshold

+
+

type= float; optional; 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.

+
+
+
+

mu_gap_occ_deviation

+
+

type= float; optional; 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.

+
+
+
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/input_output/DMFT_input/input.html b/input_output/DMFT_input/input.html new file mode 100644 index 00000000..e24b09a0 --- /dev/null +++ b/input_output/DMFT_input/input.html @@ -0,0 +1,367 @@ + + + + + + + Input — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

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.

+ +

Below an exhaustive list containing all the parameters marked by section.

+

[general]

+

seedname; jobname; csc; plo_cfg; h_int_type; h_int_basis; U; U_prime; J; ratio_F4_F2; beta; n_iter_dmft_first; n_iter_dmft_per; n_iter_dmft; dc_type; dc_dmft; cpa_zeta; cpa_x; solver_type; n_iw; n_tau; n_l; n_w; w_range; eta; diag_delta; h5_save_freq; magnetic; magmom; enforce_off_diag; h_field; h_field_it; sigma_mix; g0_mix; g0_mix_type; broy_max_it; dc; calc_energies; block_threshold; block_suppress_orbital_symm; load_sigma; path_to_sigma; load_sigma_iter; noise_level_initial_sigma; occ_conv_crit; gimp_conv_crit; g0_conv_crit; sigma_conv_crit; sampling_iterations; sampling_h5_save_freq; calc_mu_method; prec_mu; fixed_mu_value; mu_update_freq; mu_initial_guess; mu_mix_const; mu_mix_per_occupation_offset; afm_order; set_rot; measure_chi_SzSz; measure_chi_insertions; mu_gap_gb2_threshold; mu_gap_occ_deviation;

+

[solver]

+

store_solver; length_cycle; n_warmup_cycles; n_cycles_tot; measure_G_l; measure_G_tau; measure_G_iw; measure_density_matrix; measure_pert_order; max_time; imag_threshold; off_diag_threshold; delta_interface; move_double; perform_tail_fit; fit_max_moment; fit_min_n; fit_max_n; fit_min_w; fit_max_w; random_seed; legendre_fit; loc_n_min; loc_n_max; n_bath; bath_fit; refine_factor; ph_symm; calc_me; enforce_gap; ignore_weight; dt; state_storage; path_to_gs; sweeps; maxmI; maxmIB; maxmB; tw; dmrg_maxmI; dmrg_maxmIB; dmrg_maxmB; dmrg_tw; measure_hist; improved_estimator;; with_fock; force_real; one_shot; method; tol;

+

[dft]

+

dft_code; n_cores; n_iter; n_iter_first; dft_exec; store_eigenvals; mpi_env; projector_type; w90_exec; w90_tolerance;

+

[advanced]

+

dc_factor; dc_fixed_value; dc_fixed_occ; dc_U; dc_J; map_solver_struct; mapped_solver_struct_degeneracies; pick_solver_struct;

+
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/input_output/DMFT_input/solver.html b/input_output/DMFT_input/solver.html new file mode 100644 index 00000000..80a5121d --- /dev/null +++ b/input_output/DMFT_input/solver.html @@ -0,0 +1,727 @@ + + + + + + + [solver]: solver specific parameters — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

[solver]: solver specific parameters

+

Here are the parameters that are uniquely dependent on the solver chosen. Below a list of the supported solvers:

+
+

store_solver

+
+

type= bool; optional default= False

+

store the whole solver object under DMFT_input in h5 archive

+
+
+
+

cthyb parameters

+
+

length_cycle

+
+

type= int

+

length of each cycle; number of sweeps before measurement is taken

+
+
+
+

n_warmup_cycles

+
+

type= int

+

number of warmup cycles before real measurement sets in

+
+
+
+

n_cycles_tot

+
+

type= int

+

total number of sweeps

+
+
+
+

measure_G_l

+
+

type= bool

+

measure Legendre Greens function

+
+
+
+

measure_G_tau

+
+

type= bool; optional; default= True

+

should the solver measure G(tau)?

+
+
+
+

measure_G_iw

+
+

type= bool; optional; default= False

+

should the solver measure G(iw)?

+
+
+
+

measure_density_matrix

+
+

type= bool; optional; default= False

+

measures the impurity density matrix and sets also +use_norm_as_weight to true

+
+
+
+

measure_pert_order

+
+

type= bool; optional; 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’

+
+
+
+

max_time

+
+

type= int; optional; default= -1

+

maximum amount the solver is allowed to spend in each iteration

+
+
+
+

imag_threshold

+
+

type= float; optional; default= 10e-15

+

threshold for imag part of G0_tau. be warned if symmetries are off in projection scheme imag parts can occur in G0_tau

+
+
+
+

off_diag_threshold

+
+

type= float; optional

+

threshold for off-diag elements in Hloc0

+
+
+
+

delta_interface

+
+

type= bool; optional; default= False

+

use new delta interface in cthyb instead of input G0

+
+
+
+

move_double

+
+

type= bool; optional; default= True

+

double moves in solver

+
+
+
+

perform_tail_fit

+
+

type= bool; optional; default= False

+

tail fitting if legendre is off?

+
+
+
+

fit_max_moment

+
+

type= int; optional

+

max moment to be fitted

+
+
+
+

fit_min_n

+
+

type= int; optional

+

number of start matsubara frequency to start with

+
+
+
+

fit_max_n

+
+

type= int; optional

+

number of highest matsubara frequency to fit

+
+
+
+

fit_min_w

+
+

type= float; optional

+

start matsubara frequency to start with

+
+
+
+

fit_max_w

+
+

type= float; optional

+

highest matsubara frequency to fit

+
+
+
+

random_seed

+
+

type= str; optional default 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

+
+
+
+

legendre_fit

+
+

type= bool; optional default= False

+

filter noise of G(tau) with G_l, cutoff is taken from n_l

+
+
+
+

loc_n_min

+
+

type= int; optional

+

Restrict local Hilbert space to states with at least this number of particles

+
+
+
+

loc_n_max

+
+

type= int; optional

+

Restrict local Hilbert space to states with at most this number of particles

+
+
+
+
+

ftps parameters

+
+

n_bath

+
+

type= int

+

number of bath sites

+
+
+
+

bath_fit

+
+

type= bool; default= False

+

DiscretizeBath vs BathFitter

+
+
+
+

refine_factor

+
+

type= int; optional; default= 1

+

rerun ftps cycle with increased accuracy

+
+
+
+

ph_symm

+
+

type= bool; optional; default= False

+

particle-hole symmetric problem

+
+
+
+

calc_me

+
+

type= bool; optional; default= True

+

calculate only symmetry-inequivalent spins/orbitals, symmetrized afterwards

+
+
+
+

enforce_gap

+
+

type= list of floats; optional; default= ‘none’

+

enforce gap in DiscretizeBath between interval

+
+
+
+

ignore_weight

+
+

type= float; optional; default= 0.0

+

ignore weight of peaks for bath fitter

+
+
+
+

dt

+
+

type= float

+

time step

+
+
+
+

state_storage

+
+

type= string; default= ‘./’

+

location of large MPS states

+
+
+
+

path_to_gs

+
+

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

+
+
+
+

sweeps

+
+

type= int; optional; default= 10

+

Number of DMRG sweeps

+
+
+
+

maxmI

+
+

type= int; optional; default= 100

+

maximal imp-imp bond dimensions

+
+
+
+

maxmIB

+
+

type= int; optional; default= 100

+

maximal imp-bath bond dimensions

+
+
+
+

maxmB

+
+

type= int; optional; default= 100

+

maximal bath-bath bond dimensions

+
+
+
+

tw

+
+

type= float, default 1E-9

+

truncated weight for every link

+
+
+
+

dmrg_maxmI

+
+

type= int; optional; default= 100

+

maximal imp-imp bond dimensions

+
+
+
+

dmrg_maxmIB

+
+

type= int; optional; default= 100

+

maximal imp-bath bond dimensions

+
+
+
+

dmrg_maxmB

+
+

type= int; optional; default= 100

+

maximal bath-bath bond dimensions

+
+
+
+

dmrg_tw

+
+

type= float, default 1E-9

+

truncated weight for every link

+
+
+
+
+

ctseg parameters

+
+

measure_hist

+
+

type= bool; optional; default= False

+
+

measure perturbation_order histograms

+
+
+
+
+

improved_estimator

+
+

type= bool; optional; default= False

+
+

measure improved estimators +Sigma_iw will automatically be calculated via +http://dx.doi.org/10.1103/PhysRevB.85.205106

+
+
+
+
+
+

hartree parameters

+
+

with_fock

+
+

type= bool; optional; default= False

+
+

include Fock exchange terms in the self-energy

+
+
+

force_real

+
+

type= bool; optional; default= True

+
+

force the self energy from Hartree fock to be real

+
+
+

one_shot

+
+

type= bool; optional; default= True

+
+

Perform a one-shot or self-consitent root finding in each DMFT step of the Hartree solver.

+
+
+

method

+
+

type= bool; optional; default= True

+
+

method for root finder. Only used if one_shot=False, see scipy.optimize.root for options.

+
+
+

tol

+
+

type= float; optional; default= 1e-5

+
+

tolerance for root finder if one_shot=False.

+
+
+
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/input_output/DMFT_output/iterations.html b/input_output/DMFT_output/iterations.html new file mode 100644 index 00000000..37912c4c --- /dev/null +++ b/input_output/DMFT_output/iterations.html @@ -0,0 +1,478 @@ + + + + + + + Iterations — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

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]

+
+

chemical_potential_pre:

+
+

Chemical potential before the solver iteration.

+
+
+
+

chemical_potential_post:

+
+

Chemical potential after the solver iteration.

+
+
+
+

DC_energ:

+
+

indices= [iorb]

+

Double counting correction.

+
+
+
+

DC_pot:

+
+

type= arr(float);

+

indices= [iiter]

+

Double counting potential.**what exactly is the indexing here?**

+
+
+
+

Delta_time_{iimp}:

+
+

type= triqs.gf.block_gf.BlockGf

+

Imaginary time hybridization function.

+
+
+
+

G0_freq_{iimp}:

+
+

type= triqs.gf.block_gf.BlockGf

+

Imaginary frequency Weiss field.

+
+
+
+

G0_time_orig_{iimp}:

+
+

type= triqs.gf.block_gf.BlockGf

+

??

+
+
+
+

G_imp_freq_{iimp}:

+
+

type= triqs.gf.block_gf.BlockGf

+

Imaginary frequency impurity green function.

+
+
+
+

G_imp_l_{iimp}:

+
+

type= triqs.gf.block_gf.BlockGf

+

Legendre representation of the impurity green function.

+
+
+
+

G_imp_time_{iimp}:

+
+

type= triqs.gf.block_gf.BlockGf

+

Imaginary time representation of the impurity green function.

+
+
+
+

Sigma_freq_{iimp}:

+
+

type= triqs.gf.block_gf.BlockGf

+

Imaginary frequency self-energy obtained from the Dyson equation.

+
+
+
+

deltaN:

+
+

type= dict(arr(float))

+

indices= [ispin][ikpt][iband, iband]

+

Correction to the DFT occupation of a particular band:

+
+
+
+

deltaN_trace:

+
+

type= dict

+

indices= [ispin]

+

Total sum of the charge correction for an impurity.

+
+
+
+

dens_mat_pre:

+
+

type= arr(dict)

+

indices= [iimp][same as block structure Gf]

+

Density matrix before the solver iteration.

+
+
+
+

dens_mat_post:

+
+

type= arr(dict)

+

indices= [ispin][iimp]

+

Density matrix after the solver iteration.

+
+
+
+
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/input_output/DMFT_output/observables.html b/input_output/DMFT_output/observables.html new file mode 100644 index 00000000..0d338f84 --- /dev/null +++ b/input_output/DMFT_output/observables.html @@ -0,0 +1,514 @@ + + + + + + + Observables/convergence_obs — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

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]

+
+

iteration:

+
+

type= arr(int);

+

indices= [iiter]

+

Number of the iteration.

+
+
+
+

mu:

+
+

type= arr(float);

+

indices= [iiter]

+

Chemical potential fed to the solver at the present iteration (pre-dichotomy adjustment).

+
+
+
+

orb_gb2:

+
+

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.

+
+
+
+

imp_gb2:

+
+

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.

+
+
+
+

orb_Z:

+
+

type= arr(dict)

+

indices= [iimp][ispin][iiter, iorb]

+

Orbital resolved quasiparticle weight (eff_mass/renormalized_mass). As obtained by linearizing the self-energy around \(\omega = 0\)

+
+\[\begin{split}Z = \bigg( 1- \frac{\partial Re[\Sigma]}{\partial \omega} \bigg|_{\omega \rightarrow 0} \bigg)^{-1} \\\end{split}\]
+
+
+
+

orb_occ:

+
+

type= arr(dict)

+

indices= [iimp][ispin][iiter, iorb]

+

Orbital resolved mean site occupation.

+
+
+
+

imp_occ:

+
+

type= arr(dict)

+

indices= [iimp][ispin][iiter]

+

Total mean site occupation.

+
+
+
+

E_tot:

+
+

type= arr(float)

+

indices= [iiter]

+

Total energy, computed as:

+
+\[E_{tot} = E_{DFT} + E_{corr} + E_{int} -E_{DC}\]
+
+
+
+

E_dft:

+
+

type= arr(float)

+

indices= [iiter]

+

\(E_{DFT}\) in the total energy expression. System energy as computed by the DFT code at every csc iteration.

+
+
+
+

E_bandcorr:

+
+

type= arr(float)

+

indices= [iiter]

+

\(E_{corr}\) in the total energy expression. DMFT correction to the kinetic energy.

+
+
+
+

E_corr_en:

+
+

type= arr(float)

+

indices= [iiter]

+

Sum of the E_DC and E_int_imp terms.

+
+
+
+

E_int_imp:

+
+

type= arr(float)

+

indices= [iiter]

+

\(E_{int}\) in the total energy expression. Energy contribution from the electronic interactions within the single impurity.

+
+
+
+

E_DC:

+
+

type= arr(float)

+

indices= [iiter]

+

\(E_{DC}\) in the total energy expression. Double counting energy contribution.

+
+
+
+
+

[convergence_obs]

+
+

iteration:

+
+

type= arr(int);

+

indices= [iiter]

+

Number of the iteration.

+
+
+
+

d_mu:

+
+

type= arr(float)

+

indices= [iiter]

+

Chemical potential stepwise difference.

+
+
+
+

d_orb_occ:

+
+

type= arr(dict)

+

indices= [iimp][ispin][iiter,iorb]

+

Orbital occupation stepwise difference.

+
+
+
+

d_imp_occ:

+
+

type= arr(dict)

+

indices= [iimp][ispin][iiter]

+

Impurity occupation stepwise difference.

+
+
+
+

d_Etot:

+
+

type= arr(float)

+

indices= [iiter]

+

Total energy stepwise difference.

+
+
+
+
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/input_output/DMFT_output/results.html b/input_output/DMFT_output/results.html new file mode 100644 index 00000000..d9ecdc13 --- /dev/null +++ b/input_output/DMFT_output/results.html @@ -0,0 +1,368 @@ + + + + + + + Output / results — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

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

+../../_images/group_structure.png +
+
+

Subgroups

+ +
+
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/install.html b/install.html new file mode 100644 index 00000000..904ea28f --- /dev/null +++ b/install.html @@ -0,0 +1,458 @@ + + + + + + + Installation — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

Installation

+
+

Prerequisites

+
    +
  1. The TRIQS library, see TRIQS installation instruction. +In the following, we assume that TRIQS, triqs/dft_tools, and at least one of the impurity solvers available in TRIQS, e.g. cthyb, HubbardI, ctseg, FTPS, or ctint is installed in the directory path_to_triqs.

  2. +
  3. Make sure to install besides the triqs requirements also the python packages:

    +
    $ pip3 install --user scipy argparse pytest
    +
    +
    +
  4. +
  5. 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
    +
    +
    +
  6. +
+
+
+

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 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. 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 TRIQS website. +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

+
+
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/issues.html b/issues.html new file mode 100644 index 00000000..88a893b1 --- /dev/null +++ b/issues.html @@ -0,0 +1,386 @@ + + + + + + + Support & contribute — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

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 Tutorials section of the documentation for examples, and +the official TRIQS tutorial page for even more +tutorials.

+
+
+

Improving solid_dmft

+

Please post suggestions for new features on the github discussion page 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:

+
    +
  1. In all cases specify which version of the application you are using. You can +find the version number in the file CMakeLists.txt at the root of the +application sources.

  2. +
  3. 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 file referenced in the issue.

  4. +
  5. If you are experiencing a problem during the execution of the application, provide +a script which allows to quickly reproduce the problem.

  6. +
+

Thanks!

+
+
+
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/md_notes/docker.html b/md_notes/docker.html new file mode 100644 index 00000000..3cb11201 --- /dev/null +++ b/md_notes/docker.html @@ -0,0 +1,423 @@ + + + + + + + Docker — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

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. +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, 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. +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'
+
+
+
+
+
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/md_notes/run_cluster.html b/md_notes/run_cluster.html new file mode 100644 index 00000000..7fd719d2 --- /dev/null +++ b/md_notes/run_cluster.html @@ -0,0 +1,415 @@ + + + + + + + Running solid_dmft on a cluster — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

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!

+
+
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/md_notes/run_locally.html b/md_notes/run_locally.html new file mode 100644 index 00000000..e85c7ab1 --- /dev/null +++ b/md_notes/run_locally.html @@ -0,0 +1,371 @@ + + + + + + + Run on your machine — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

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.

+
+
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/md_notes/vasp_csc.html b/md_notes/vasp_csc.html new file mode 100644 index 00000000..06d81bdc --- /dev/null +++ b/md_notes/vasp_csc.html @@ -0,0 +1,455 @@ + + + + + + + Interface to VASP — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

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© 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.

+
+
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/md_notes/w90_interface.html b/md_notes/w90_interface.html new file mode 100644 index 00000000..3b54d01b --- /dev/null +++ b/md_notes/w90_interface.html @@ -0,0 +1,385 @@ + + + + + + + Wannier90 interface — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

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.

+
+
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/objects.inv b/objects.inv new file mode 100644 index 00000000..9c1da28e Binary files /dev/null and b/objects.inv differ diff --git a/py-modindex.html b/py-modindex.html new file mode 100644 index 00000000..0808ccb1 --- /dev/null +++ b/py-modindex.html @@ -0,0 +1,518 @@ + + + + + + Python Module Index — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
  • + +
  • +
  • +
+
+
+
+
+ + +

Python Module Index

+ +
+ c | + d | + p | + r | + s | + u +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
 
+ c
+ csc_flow +
 
+ d
+ dft_managers +
    + dft_managers.mpi_helpers +
    + dft_managers.qe_manager +
    + dft_managers.vasp_manager +
+ dmft_cycle +
+ dmft_tools +
    + 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 +
 
+ p
+ postprocessing +
    + postprocessing.eval_U_cRPA_RESPACK +
    + postprocessing.eval_U_cRPA_Vasp +
    + postprocessing.maxent_gf_imp +
    + postprocessing.maxent_gf_latt +
    + postprocessing.maxent_sigma +
    + postprocessing.plot_correlated_bands +
 
+ r
+ read_config +
 
+ s
+ solid_dmft +
 
+ u
+ util +
    + util.symmetrize_gamma_file +
    + util.update_dmft_config +
    + util.update_results_h5 +
    + util.write_kslice_to_h5 +
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/search.html b/search.html new file mode 100644 index 00000000..e1a3c98f --- /dev/null +++ b/search.html @@ -0,0 +1,335 @@ + + + + + + Search — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
  • + +
  • +
  • +
+
+
+
+
+ + + + +
+ +
+ +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + + + + + + \ No newline at end of file diff --git a/searchindex.js b/searchindex.js new file mode 100644 index 00000000..700dec54 --- /dev/null +++ b/searchindex.js @@ -0,0 +1 @@ +Search.setIndex({"docnames": ["ChangeLog", "_ref/csc_flow", "_ref/dft_managers", "_ref/dft_managers.mpi_helpers", "_ref/dft_managers.qe_manager", "_ref/dft_managers.vasp_manager", "_ref/dmft_cycle", "_ref/dmft_tools", "_ref/dmft_tools.afm_mapping", "_ref/dmft_tools.convergence", "_ref/dmft_tools.formatter", "_ref/dmft_tools.greens_functions_mixer", "_ref/dmft_tools.initial_self_energies", "_ref/dmft_tools.interaction_hamiltonian", "_ref/dmft_tools.legendre_filter", "_ref/dmft_tools.manipulate_chemical_potential", "_ref/dmft_tools.matheval", "_ref/dmft_tools.matheval.MathExpr", "_ref/dmft_tools.matheval.MathExpr.__init__", "_ref/dmft_tools.matheval.MathExpr.allowed_nodes", "_ref/dmft_tools.matheval.MathExpr.functions", "_ref/dmft_tools.observables", "_ref/dmft_tools.results_to_archive", 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"poll_barrier() (in module dft_managers.mpi_helpers)": [[3, "dft_managers.mpi_helpers.poll_barrier"]], "dft_managers.qe_manager": [[4, "module-dft_managers.qe_manager"]], "read_dft_energy() (in module dft_managers.qe_manager)": [[4, "dft_managers.qe_manager.read_dft_energy"]], "run() (in module dft_managers.qe_manager)": [[4, "dft_managers.qe_manager.run"]], "dft_managers.vasp_manager": [[5, "module-dft_managers.vasp_manager"]], "kill() (in module dft_managers.vasp_manager)": [[5, "dft_managers.vasp_manager.kill"]], "read_dft_energy() (in module dft_managers.vasp_manager)": [[5, "dft_managers.vasp_manager.read_dft_energy"]], "read_irred_kpoints() (in module dft_managers.vasp_manager)": [[5, "dft_managers.vasp_manager.read_irred_kpoints"]], "remove_legacy_projections_suppressed() (in module dft_managers.vasp_manager)": [[5, "dft_managers.vasp_manager.remove_legacy_projections_suppressed"]], "run_charge_update() (in module dft_managers.vasp_manager)": [[5, "dft_managers.vasp_manager.run_charge_update"]], "run_initial_scf() (in module dft_managers.vasp_manager)": [[5, "dft_managers.vasp_manager.run_initial_scf"]], "dmft_cycle": [[6, "module-dmft_cycle"]], "dmft_cycle() (in module dmft_cycle)": [[6, "dmft_cycle.dmft_cycle"]], "dmft_tools": [[7, "module-dmft_tools"]], "determine() (in module dmft_tools.afm_mapping)": [[8, "dmft_tools.afm_mapping.determine"]], "dmft_tools.afm_mapping": [[8, "module-dmft_tools.afm_mapping"]], "calc_convergence_quantities() (in module dmft_tools.convergence)": [[9, "dmft_tools.convergence.calc_convergence_quantities"]], "check_convergence() (in module dmft_tools.convergence)": [[9, "dmft_tools.convergence.check_convergence"]], "dmft_tools.convergence": [[9, "module-dmft_tools.convergence"]], "max_g_diff() (in module dmft_tools.convergence)": [[9, "dmft_tools.convergence.max_G_diff"]], "prep_conv_file() (in module dmft_tools.convergence)": [[9, "dmft_tools.convergence.prep_conv_file"]], "prep_conv_obs() (in module dmft_tools.convergence)": [[9, "dmft_tools.convergence.prep_conv_obs"]], "write_conv() (in module dmft_tools.convergence)": [[9, "dmft_tools.convergence.write_conv"]], "dmft_tools.formatter": [[10, "module-dmft_tools.formatter"]], "print_block_sym() (in module dmft_tools.formatter)": [[10, "dmft_tools.formatter.print_block_sym"]], "print_rotation_matrix() (in module dmft_tools.formatter)": [[10, "dmft_tools.formatter.print_rotation_matrix"]], "dmft_tools.greens_functions_mixer": [[11, "module-dmft_tools.greens_functions_mixer"]], "calculate_double_counting() (in module dmft_tools.initial_self_energies)": [[12, "dmft_tools.initial_self_energies.calculate_double_counting"]], "determine_dc_and_initial_sigma() (in module dmft_tools.initial_self_energies)": [[12, "dmft_tools.initial_self_energies.determine_dc_and_initial_sigma"]], "dmft_tools.initial_self_energies": [[12, "module-dmft_tools.initial_self_energies"]], "construct() (in module dmft_tools.interaction_hamiltonian)": [[13, "dmft_tools.interaction_hamiltonian.construct"]], "dmft_tools.interaction_hamiltonian": [[13, "module-dmft_tools.interaction_hamiltonian"]], "apply() (in module dmft_tools.legendre_filter)": [[14, "dmft_tools.legendre_filter.apply"]], "dmft_tools.legendre_filter": [[14, "module-dmft_tools.legendre_filter"]], "dmft_tools.manipulate_chemical_potential": [[15, "module-dmft_tools.manipulate_chemical_potential"]], "set_initial_mu() (in module dmft_tools.manipulate_chemical_potential)": [[15, "dmft_tools.manipulate_chemical_potential.set_initial_mu"]], "update_mu() (in module dmft_tools.manipulate_chemical_potential)": [[15, "dmft_tools.manipulate_chemical_potential.update_mu"]], "dmft_tools.matheval": [[16, "module-dmft_tools.matheval"]], "__init__() (dmft_tools.matheval.mathexpr method)": [[18, "dmft_tools.matheval.MathExpr.__init__"]], "allowed_nodes (dmft_tools.matheval.mathexpr attribute)": [[19, "dmft_tools.matheval.MathExpr.allowed_nodes"]], "functions (dmft_tools.matheval.mathexpr attribute)": [[20, "dmft_tools.matheval.MathExpr.functions"]], "add_dft_values_as_zeroth_iteration() (in module dmft_tools.observables)": [[21, "dmft_tools.observables.add_dft_values_as_zeroth_iteration"]], "add_dmft_observables() (in module dmft_tools.observables)": [[21, "dmft_tools.observables.add_dmft_observables"]], "calc_z() (in module dmft_tools.observables)": [[21, "dmft_tools.observables.calc_Z"]], "calc_bandcorr_man() (in module dmft_tools.observables)": [[21, "dmft_tools.observables.calc_bandcorr_man"]], "calc_dft_kin_en() (in module dmft_tools.observables)": [[21, "dmft_tools.observables.calc_dft_kin_en"]], "dmft_tools.observables": [[21, "module-dmft_tools.observables"]], "prep_observables() (in module dmft_tools.observables)": [[21, "dmft_tools.observables.prep_observables"]], "write_header_to_file() (in module dmft_tools.observables)": [[21, "dmft_tools.observables.write_header_to_file"]], "write_obs() (in module dmft_tools.observables)": [[21, "dmft_tools.observables.write_obs"]], "dmft_tools.results_to_archive": [[22, "module-dmft_tools.results_to_archive"]], "write() (in module dmft_tools.results_to_archive)": [[22, "dmft_tools.results_to_archive.write"]], "solverstructure (class in dmft_tools.solver)": [[23, "dmft_tools.solver.SolverStructure"]], "dmft_tools.solver": [[23, "module-dmft_tools.solver"]], "get_n_orbitals() (in module dmft_tools.solver)": [[23, "dmft_tools.solver.get_n_orbitals"]], "solve() (dmft_tools.solver.solverstructure method)": [[23, "dmft_tools.solver.SolverStructure.solve"], [26, "dmft_tools.solver.SolverStructure.solve"]], "__init__() (dmft_tools.solver.solverstructure method)": [[25, "dmft_tools.solver.SolverStructure.__init__"]], "postprocessing": [[27, "module-postprocessing"]], "construct_uijkl() (in module postprocessing.eval_u_crpa_respack)": [[28, "postprocessing.eval_U_cRPA_RESPACK.construct_Uijkl"]], "fit_slater_fulld() (in module postprocessing.eval_u_crpa_respack)": [[28, "postprocessing.eval_U_cRPA_RESPACK.fit_slater_fulld"]], "postprocessing.eval_u_crpa_respack": [[28, "module-postprocessing.eval_U_cRPA_RESPACK"]], "read_interaction() (in module postprocessing.eval_u_crpa_respack)": [[28, "postprocessing.eval_U_cRPA_RESPACK.read_interaction"]], "respack_data (class in postprocessing.eval_u_crpa_respack)": [[28, "postprocessing.eval_U_cRPA_RESPACK.respack_data"]], "__init__() (postprocessing.eval_u_crpa_respack.respack_data method)": [[30, "postprocessing.eval_U_cRPA_RESPACK.respack_data.__init__"]], "calc_kan_params() (in module postprocessing.eval_u_crpa_vasp)": [[31, "postprocessing.eval_U_cRPA_Vasp.calc_kan_params"]], "calc_u_avg_fulld() (in module postprocessing.eval_u_crpa_vasp)": [[31, "postprocessing.eval_U_cRPA_Vasp.calc_u_avg_fulld"]], "calculate_interaction_from_averaging() (in module postprocessing.eval_u_crpa_vasp)": [[31, "postprocessing.eval_U_cRPA_Vasp.calculate_interaction_from_averaging"]], "construct_u_kan() (in module postprocessing.eval_u_crpa_vasp)": [[31, "postprocessing.eval_U_cRPA_Vasp.construct_U_kan"]], "fit_kanamori() (in module postprocessing.eval_u_crpa_vasp)": [[31, "postprocessing.eval_U_cRPA_Vasp.fit_kanamori"]], "fit_slater_fulld() (in module postprocessing.eval_u_crpa_vasp)": [[31, "postprocessing.eval_U_cRPA_Vasp.fit_slater_fulld"]], "postprocessing.eval_u_crpa_vasp": [[31, "module-postprocessing.eval_U_cRPA_Vasp"]], "read_uijkl() (in module postprocessing.eval_u_crpa_vasp)": [[31, "postprocessing.eval_U_cRPA_Vasp.read_uijkl"]], "red_to_2ind() (in module postprocessing.eval_u_crpa_vasp)": [[31, "postprocessing.eval_U_cRPA_Vasp.red_to_2ind"]], "main() (in module postprocessing.maxent_gf_imp)": [[32, "postprocessing.maxent_gf_imp.main"]], "postprocessing.maxent_gf_imp": [[32, "module-postprocessing.maxent_gf_imp"]], "main() (in module postprocessing.maxent_gf_latt)": [[33, "postprocessing.maxent_gf_latt.main"]], "postprocessing.maxent_gf_latt": [[33, "module-postprocessing.maxent_gf_latt"]], "main() (in module postprocessing.maxent_sigma)": [[34, "postprocessing.maxent_sigma.main"]], "postprocessing.maxent_sigma": [[34, "module-postprocessing.maxent_sigma"]], "get_dmft_bands() (in module postprocessing.plot_correlated_bands)": [[35, "postprocessing.plot_correlated_bands.get_dmft_bands"]], "postprocessing.plot_correlated_bands": [[35, "module-postprocessing.plot_correlated_bands"]], "read_config": [[36, "module-read_config"]], "read_config() (in module read_config)": [[36, "read_config.read_config"]], "util": [[37, "module-util"]], "util.symmetrize_gamma_file": [[38, "module-util.symmetrize_gamma_file"]], "main() (in module util.update_dmft_config)": [[39, "util.update_dmft_config.main"]], "util.update_dmft_config": [[39, "module-util.update_dmft_config"]], "main() (in module util.update_results_h5)": [[40, "util.update_results_h5.main"]], "util.update_results_h5": [[40, "module-util.update_results_h5"]], "main() (in module util.write_kslice_to_h5)": [[41, "util.write_kslice_to_h5.main"]], "util.write_kslice_to_h5": [[41, "module-util.write_kslice_to_h5"]], "solid_dmft": [[44, "index-0"], [44, "module-solid_dmft"]]}}) \ No newline at end of file diff --git a/tutorials.html b/tutorials.html new file mode 100644 index 00000000..8d87a9c7 --- /dev/null +++ b/tutorials.html @@ -0,0 +1,400 @@ + + + + + + + Tutorials — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

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. +
  3. Full charge self-consistent (CSC) DFT+DMFT calculation using the PLO formalism with Vasp for PrNiO3

  4. +
  5. Full CSC DFT+DMFT calculation using w90 in combination with Quantum Espresso utilizing the lighter HubbardI solver

  6. +
  7. OS magnetic DMFT calculation for NdNiO2 in a large energy window for 5 d orbitals

  8. +
  9. Postprocessing: plot the spectral function after a DFT+DMFT calculation

  10. +
+
+ +
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ 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 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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.ini b/tutorials/Ce2O3_csc_w90/dmft_config.ini new file mode 100644 index 00000000..7bb8c2c8 --- /dev/null +++ b/tutorials/Ce2O3_csc_w90/dmft_config.ini @@ -0,0 +1,44 @@ +[general] +seedname = ce2o3 +jobname = b10-U6.46-J0.46 +csc = True +#dft_mu = 0. +solver_type = hubbardI +n_l = 15 +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] +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 = 1.e-1 diff --git a/tutorials/Ce2O3_csc_w90/tutorial.html b/tutorials/Ce2O3_csc_w90/tutorial.html new file mode 100644 index 00000000..f58976c5 --- /dev/null +++ b/tutorials/Ce2O3_csc_w90/tutorial.html @@ -0,0 +1,996 @@ + + + + + + + 3. CSC with QE/W90 and HubbardI: total energy in Ce2O3 — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+
[1]:
+
+
+
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.ticker as ticker
+
+from triqs.gf import *
+from h5 import HDFArchive
+
+
+
+
+

3. CSC with QE/W90 and HubbardI: total energy in Ce2O3

+

Disclaimer:

+
    +
  • These can be heavy calculations. Current parameters won’t give converged solutions, but are simplified to deliver results on 10 cores in 10 minutes.

  • +
  • The interaction values, results etc. might not be 100% physical and are only for demonstrative purposes!

  • +
+

The goal of this tutorial is to demonstrate how to perform fully charge self-consistent DFT+DMFT calculations in solid_dmft using Quantum Espresso (QE) and Wannier90 (W90) for the DFT electronic structure using the HubbardI solver.

+

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):

+

drawing

+

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.

+

The tutorial will guide you through the following steps:

+
    +
  • perpare the input for the DFT and DMFT calculations using Quantum Espresso and Wannier90 and TRIQS

  • +
  • run a charge self-consistent calculation for Ce\(_2\)O\(_3\)

  • +
  • analyse the change in the non-interacting part of the charge density using TRIQS

  • +
  • analyse the convergence of the total energy and the DMFT self-consistency

  • +
+

We set path variables to the reference files:

+
+
[2]:
+
+
+
+path = './ref/'
+
+
+
+
+

1. Input file preparation

+

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. 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).

+
+

DFT files

+

All input files are of the same kind as usual, unless stated otherwise:

+

Quantum Espresso:

+
    +
  1. ce2o3.scf.in

  2. +
  3. ce2o3.nscf.in

    +
      +
    • explicit k-mesh

    • +
    +
    &system
    +    nosym            = .true.
    +    dmft             = .true.
    +
    +
    +
  4. +
  5. ce2o3.mod_scf.in: new!

    +
      +
    • explicit k-mesh

    • +
    +
    &system
    +    nosym            = .true.
    +    dmft             = .true.
    +    dmft_prefix      = seedname
    +&electrons
    +    electron_maxstep = 1
    +    mixing_beta      = 0.3
    +
    +
    +
  6. +
+

Optionally:

+
    +
  • seedname.bnd.in

  • +
  • seedname.bands.in

  • +
  • seedname.proj.in

  • +
+

Wannier90:

+
    +
  1. ce2o3.win

    +
    write_u_matrices = .true.
    +
    +
    +
  2. +
  3. ce2o3.pw2wan.in

  4. +
+
+
+

DMFT

+
    +
  1. Wannier90Converter: ce2o3.inp

  2. +
  3. solid_dmft: dmft_config.ini

  4. +
+

Here we’ll discuss the most important input flags for solid_dmft:

+
+
[3]:
+
+
+
+!cat ./dmft_config.ini
+
+
+
+
+
+
+
+
+[general]
+seedname = ce2o3
+jobname = b10-U6.46-J0.46
+csc = True
+#dft_mu = 0.
+solver_type = hubbardI
+n_l = 15
+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]
+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 =
+w90_exec = wannier90.x
+w90_tolerance = 1.e-1
+
+
+

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. 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. 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.

+
+
+
+

2. Running DFT+DMFT

+

Now that everything is set up, copy all files from ./dft_input and start the calculation:

+
cp dft_input/* .
+mpirun solid_dmft > dmft.out &
+
+
+

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.

+

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.

+
+
+

3. Non-interacting Hamiltonian and convergence analysis

+
+

Tight-binding Hamiltonian

+

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.

+

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. Let us first get the number of iterations and Fermi levels from DFT:

+
+
[4]:
+
+
+
+e_fermi_run = !grep "DFT Fermi energy" triqs.out
+e_fermi_run = [float(x.split('DFT Fermi energy')[1].split('eV')[0]) for x in e_fermi_run]
+n_iter_run = !ls ce2o3_it*_hr.dat
+n_iter_run = sorted([int(x.split('_it')[-1].split('_')[0]) for x in n_iter_run])
+print(f'Fermi levels: {e_fermi_run}')
+print(f'iteration counts: {n_iter_run}')
+
+
+
+
+
+
+
+
+Fermi levels: [14.3557, 14.42, 14.4619, 14.495]
+iteration counts: [1, 3, 4, 5]
+
+
+
+
[5]:
+
+
+
+from matplotlib import cm
+from triqs.lattice.utils import TB_from_wannier90, k_space_path
+
+# define a path in BZ
+G = np.array([ 0.00,  0.00,  0.00])
+M = np.array([ 0.50,  0.00,  0.00])
+K = np.array([ 0.33,  0.33,  0.00])
+A = np.array([ 0.00,  0.00,  0.50])
+L = np.array([ 0.50,  0.00,  0.50])
+H = np.array([ 0.33,  0.33,  0.50])
+k_path = [(G, M), (M, K), (K, G), (G, A), (A, L), (L, H), (H, A)]
+n_bnd = 14
+n_k = 20
+
+fig, ax = plt.subplots(1, 1, figsize=(5,2), dpi=200)
+
+for (fermi, n_iter, cycle) in [(e_fermi_run, n_iter_run, cm.RdYlBu)]:
+
+    col_it = np.linspace(0, 1, len(n_iter))
+    for ct, it in enumerate(n_iter):
+
+        # compute TB model
+        h_loc_add = - fermi[ct] * np.eye(n_bnd) # to center bands around 0
+        tb = TB_from_wannier90(path='./', seed=f'ce2o3_it{it}', extend_to_spin=False, add_local=h_loc_add)
+
+        # compute dispersion on specified path
+        k_vec, k_1d, special_k = k_space_path(k_path, num=n_k, bz=tb.bz)
+        e_val = tb.dispersion(k_vec)
+
+        # plot
+        for band in range(n_bnd):
+            ax.plot(k_1d, e_val[:,band].real, c=cycle(col_it[ct]), label=f'it{it}' if band == 0 else '')
+
+
+ax.axhline(y=0,zorder=2,color='gray',alpha=0.5,ls='--')
+ax.set_ylim(-0.2,0.8)
+ax.grid(zorder=0)
+ax.set_xticks(special_k)
+ax.set_xticklabels([r'$\Gamma$', 'M', 'K', r'$\Gamma$', 'A', 'L', 'H', 'A'])
+ax.set_xlim([special_k.min(), special_k.max()])
+ax.set_ylabel(r'$\omega$ (eV)')
+ax.legend(fontsize='small')
+
+
+
+
+
+
+
+
+Warning: could not identify MPI environment!
+
+
+
+
+
+
+
+Starting serial run at: 2022-03-25 12:42:36.663824
+
+
+
+
[5]:
+
+
+
+
+<matplotlib.legend.Legend at 0x7f4a21de2160>
+
+
+
+
+
+
+../../_images/tutorials_Ce2O3_csc_w90_tutorial_11_3.png +
+
+

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!

+

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.

+
+
[6]:
+
+
+
+fig, ax = plt.subplots(1, 1, figsize=(5,2), dpi=200)
+
+e_fermi_ref = [14.7437]
+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')]:
+
+    col_it = np.linspace(0, 1, len(n_iter))
+    for ct, it in enumerate(n_iter):
+
+        # compute TB model
+        h_loc_add = - fermi[ct] * np.eye(n_bnd) # to center bands around 0
+        tb = TB_from_wannier90(path=path_w90, seed=f'ce2o3_it{it}', extend_to_spin=False, add_local=h_loc_add)
+
+        # compute dispersion on specified path
+        k_vec, k_1d, special_k = k_space_path(k_path, num=n_k, bz=tb.bz)
+        e_val = tb.dispersion(k_vec)
+
+        # plot
+        for band in range(n_bnd):
+            ax.plot(k_1d, e_val[:,band].real, c=cycle(col_it[ct]), label=f'it{it} - {label}' if band == 0 else '')
+
+
+ax.axhline(y=0,zorder=2,color='gray',alpha=0.5,ls='--')
+ax.set_ylim(-0.2,0.8)
+ax.grid(zorder=0)
+ax.set_xticks(special_k)
+ax.set_xticklabels([r'$\Gamma$', 'M', 'K', r'$\Gamma$', 'A', 'L', 'H', 'A'])
+ax.set_xlim([special_k.min(), special_k.max()])
+ax.set_ylabel(r'$\omega$ (eV)')
+ax.legend(fontsize='small')
+
+
+
+
+
[6]:
+
+
+
+
+<matplotlib.legend.Legend at 0x7f49a4157c40>
+
+
+
+
+
+
+../../_images/tutorials_Ce2O3_csc_w90_tutorial_13_1.png +
+
+
+
+

Convergence

+

To check the convergence of the impurity Green’s function and total energy you can look into the hdf5 Archive:

+
+
[7]:
+
+
+
+with HDFArchive('./ce2o3.h5','r') as h5:
+    observables = h5['DMFT_results']['observables']
+    convergence = h5['DMFT_results']['convergence_obs']
+
+with HDFArchive(path + 'ce2o3.h5','r') as h5:
+    ref_observables = h5['DMFT_results']['observables']
+    ref_convergence = h5['DMFT_results']['convergence_obs']
+
+
+
+
+
[8]:
+
+
+
+fig, ax = plt.subplots(1,2, figsize=(8, 2), dpi=200)
+
+ax[0].plot(ref_observables['E_tot']-np.min(ref_observables['E_tot']), 'x-', label='reference')
+ax[0].plot(observables['E_tot']-np.min(observables['E_tot']), 'x-', label='result')
+
+ax[1].plot(ref_convergence['d_G0'][0], 'x-', label='reference')
+ax[1].plot(convergence['d_G0'][0], 'x-', label='result')
+
+ax[0].set_ylabel('total energy (eV)')
+ax[1].set_ylabel(r'convergence $G_0$')
+
+for it in range(2):
+    ax[it].set_xlabel('# iteration')
+    ax[it].xaxis.set_major_locator(ticker.MultipleLocator(5))
+    ax[it].grid()
+    ax[it].legend(fontsize='small')
+
+fig.subplots_adjust(wspace=0.3)
+
+
+
+
+
+
+
+../../_images/tutorials_Ce2O3_csc_w90_tutorial_17_0.png +
+
+

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.

+

This concludes the tutorial. The following is a list of things you can try next:

+
    +
  • improve the accuracy of the results by tuning the parameters until the results agree with the reference

  • +
  • try to fnd the equilibrium lattice paramter by repeating the above calculation of the total energy for different cell volumes

  • +
+
+
+
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/tutorials/Ce2O3_csc_w90/tutorial.ipynb b/tutorials/Ce2O3_csc_w90/tutorial.ipynb new file mode 100644 index 00000000..65a50578 --- /dev/null +++ b/tutorials/Ce2O3_csc_w90/tutorial.ipynb @@ -0,0 +1,531 @@ +{ + "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", + "\"drawing\"\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.ini](./dmft_config.ini)\n", + "\n", + "Here we'll discuss the most important input flags for solid_dmft:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "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", + "#dft_mu = 0.\n", + "solver_type = hubbardI\n", + "n_l = 15\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", + "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 = \n", + "w90_exec = wannier90.x\n", + "w90_tolerance = 1.e-1\n" + ] + } + ], + "source": [ + "!cat ./dmft_config.ini" + ] + }, + { + "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": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "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": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "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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Iblu92nuOF84OPQiGj0vfOEVERKRbUDArIhmRzoW5wjpTA3bCLv1ZtHoL/3P7ayxdU+1LyYHiggBlxQVR5QC8YLWlJEB5SQFvf76BZz9Y1XxcZBmAmoYgk8YO63K3t4d1xVAznmwMNdMdOvoRaqa7j7EjduCyiaOTrksd2cdlE0e3+/+m7vA5iOSE4ePg8Cvh1RujQ1nAlbYRzEJ0OHv4lQplRUQi+PG7X3cQDAa57bbbuP/++1m6dCl1dXX06tWLTZs2ZXpokkYKZkXEd34szBUWL3hYuXEbYyrK+Pt/vuS9LzZFtZ+9aG3C40nUkWP6c+weFaGQ1ZvBGhnEFuZ3vALq3bOXRoWyqbpNPxtub9+efhRqdhympTt09CPU9KOPKUfvklBd6Xh9JBJmdofPQSRnHPFzGDmhVbjqYmfM1lVB/TYo7OFtHzpFM2VFRGL4+btfV3fttddyzTXXEAgE2H333enVqxdlZWWZHpakmYJZEfGVnwtzba5pYMFXm8kLwG6Dyvhk1RYAHnlrxfZ8CQAEDEb07cmuFWXsMrCM5eu38vT7XzW/H7no1KxP1zJ2RF8mjR3Wqb7SdZt+um97VqiZPWFaukPHdJ/frz46G0omelx3+BxEckaccLXVjFmArWugcES7x4mI5Co/f/fr6pxz3HXXXQA8/vjjnHbaaRkekfhFwayI+CpdC3Nt3FrPR19t5sMvN7Pgyyo++mozy9dvS8mYB5QVsWtFGWNCIeyYinJGDyxtXvTq7tlLo0LZVC46la7b9BVq5laoCekPHdN9fr/6SLfu8DmI5KzCMhqtkHzXslAmW1ZDnxEZG5KISDZL96LM3cnatWtZt24dAMcff3yGRyN+UjArIr7b3oW5Tt9vCK8sXMOCL70g9qMvq/hyU+tZnskqLcpnl4Gl7FpRzpiKMnatKGPXgWX06VnY5jHpXHQqnbfpK9TMvVBTRES2kxl1Bb3Ir48oexRZZ1ZERFrZ3t/9cmVh0si7FktKSjI4EvFbx4UNRUTS4JIJo7jiuDHN2ze98Cl3z14a1ebu2Uui/mHeuX9P/vzGMsZe/zLnPzifm19axIsLVm93KHva1wcz94oj+HDqMfz90kO44Zt78Z2DR3DQyL6dDmWT+Trj6eyM1ti+5i/b0Gb7KUfvwhMXj0v6YueSCaN44uJxCdd6UqgpIiLdRV1Br+gdCmZFRDqU2O9+XSOUHTlyJH369GHu3Ll8+OGHnHXWWQwaNIi8vDymTp3a3O7FF1/k1FNPZdCgQRQWFtK/f39OOukkZs2aFXW+ZcuWYWaMGDGieZ+ZNT+mT58e1f6LL75gypQp7LbbbvTs2ZPS0lL2228/br75Zmpra1uNN3x+MwPgmWeeYeLEifTt2xczY/bs2c1tGxoauPfee5kwYQJ9+/alqKiI4cOHc8EFF7BkyZK4348JEyY0j3PdunX84Ac/YNiwYRQVFTFs2DAuu+yydhcva2hoYNq0aUycOJF+/fpRVFTE0KFDOeaYY7jnnnuoq6trdUyy34NspxmzIpIx8f56unRtNYN6FfPcB6v4bN3WqPZL1m5tdY6OFOYF2LWijIDBf1dubt4fWQP2qf98yc4DyrZ70axULjrl14xWhZoiIiKJq83vHb1DwayIdFPBoGPjtvqOGybozP2HsK2+kTtneQHfTS98yrb6RiYfPILpbyxr3g/wwyN35sz9h7C+unUo11l9ehQSCFjKzjdv3jzOOOMMzIzdd9+d3r17Y2YEg0Euvvhipk2bBkDfvn3Zc889WbFiBc8++yzPPvssN9xwA1deeSUAxcXFHHLIIdTV1fHOO+8AcMghhzT3M3DgwObX//73vznzzDOprq6muLiYkSNHUldXx/vvv89//vMfnnzySV566SXKy8vjjvl3v/sdl19+Of369WPUqFFRM3PXrVvHiSeeyFtvvYWZMXjwYIYNG8bixYt54IEHeOyxx5pD3XhWrlzJvvvuy+rVq9ljjz0oKCjg888/54477uCNN97gjTfeoKCgIOqY1atXc+KJJzZ/3YMHD2bkyJGsWrWKmTNnMmPGDI477rio0Hp7vwfZSMGsiMQ1f9mGToVvyR538j478t6Kjbz0sfeLzZPvrky6z7Ci/AC7DSpnz8Hl7DW4F3vs2ItdBpZx/9zP44aona0B60d9VtBCPiIiItlGM2ZFJFds3FbPfte9nNY+7py1JCqQ7Wj/9nj3l0fRt7QoZee76aabOPfcc7njjjsoKysDvHIE119/PdOmTWPEiBHce++9HH300c3HPPbYY1x44YVcddVVHHjggRxxxBFUVFQwd+5cli1bxk477QTA3LlzW/W3cOFCzjjjDLZt28a1117Lz372s+ZgdenSpZxzzjm8+eab/OhHP2o1yzbsl7/8JXfeeSeXXnopgUAA5xz19V74ftZZZ/HWW29xxBFHcPfdd7PrrrsCUF9fz7XXXsv111/PpEmTWLhwIX379m117muvvZZjjz2WBx54gP79+wPw6quvcsIJJ/DOO+/w0EMPccEFFzS3d85x+umn88477zBq1CgeeughDj744Ob3165dy/Tp0+nZs2dKvwfZSKUMRKSVW2cs4ox75iV0y32ku2cv5Yx75nHrjEVttmkKOt5dvpGbX1zI/9z+GgffOKs5lE1GSUEe+w/vw+SDR/C70/fmhR8fxoJrjuXp/z2E607Zi0ljh7Hn4F5thrLQ+TID4dmssefrSGR/icxmDffVGQplRUREUq+2VTC7JjMDERGRjBozZgz33ntvcygLXjB74403UlBQwD/+8Y+oUBZg0qRJ/PrXv8Y5x0033ZRUf1dffTVbt27lZz/7Gb/61a+iZruOGjWKJ554gp49e/Lwww/z5Zdfxj3H9773PX7wgx8QCHhRoJlRVFTECy+8wMyZMxk5ciTPPPNMcygLUFhYyHXXXcfJJ5/M+vXrm2cCx+rTpw+PPPJIcygLcPjhhzeHsc8++2xU+2eeeYa5c+fSo0cPZsyYERXKAvTv35//+7//izpfKr4H2UgzZkUkyvxlG7h95mIguVmkkbNPb5+5OGqmZ1VtA3MWrWXWp2uYvXAtG7Ymd0tMaVE+e+xYzp6DezXPht2pXyl5HdyKkmgNWEh+gS7NZhUREck9KmUgIiLghax5eXlR+55//nm2bdvG+PHj2WeffeIed+qppzJlyhTmzJlDU1NTq3PEU19fzz//+U8ALr744rhthgwZwtixY5k9ezavvvoqZ599dqs2kydPjnvsk08+CcA555wTFTTHjvuZZ55h1qxZzWUYIn3rW9+KWz7goIMO4o477mhVo/bvf/874M3UDc8Ubk+qvgfZSMGsiEQZO2IHrjhuTFJBZbwAtG/PQu577TNmfrKG+cs20Bh0nRrP9w8byZX/MybpekDprgELms0qIiKSa1qVMtiiYFZEJBftskvrhZD/+9//ArBo0SIOPfTQuMc55/1eXFNTw/r16xkwYECHfS1evJiamhrMjPPOO6/NdosWeXeurlwZvzzgbrvtFnd/eNyPPfYYM2fOjNsmvIBXW+cePXp03P3hGrnV1dVR+xcsWADAuHHj4h4XK1Xfg2ykYFZEWmkrqJx84I6t2sYGoPsP78Nj81dE7etIcX6A2sZg83bkwlz3vvYZfXoWJrUwl181YEVERCS3tApmt66BYBACqhAnIt1Lnx6FvPvLo1J+3tiFvnqVFLC5pqF5+4dH7szkg0ekvN8+PQpTer7I2qdh4fCysrKSysrKDs+xbdu2hPoKn9c5x+uvv97p88Ybc+T5Fy5cyMKFC1N67nDZhHAgHVZVVQV4JRASkarvQTZSMCsiccULKhsaGhge0ebmFxfyh1eib0l4Z/nGhM6/y8BSjhgzgC21jTz61orm/du7MBe01IC9febipGvAhvtLtAasiIiI5I5WpQyCjVCzEXq2XghFRKQrCwQspYtlgTepJzKUjfe7352zltCjMD+piTnZorS0FICLLrqIe+65J+XnLSoqora2NmXnjT3/X//6V84666yUnz+ecNmDcODakXR/DzJJf9oVkTbFLo71+5lLeepz48WVxhG3vt4qlG1PYV6A8bv055pv7MFrlx/BS1MOp3dJYdxQNl7fiS7MFTbl6F144uJxSf+DfsmEUTxx8TimHN361hQRERHJbXUFrevnUd3xrCgRkVzXXqm57f3dL1vstddeAHzwwQcpPe8uu+xCUVERdXV1zbfqp1K6xt2ePffcE4A33ngjofbp/h5kkoJZEWnXJRNGMeWolnoxcyrzeP6LPCqr6jo8dkBZEWeNHcq95+7He//vaB767gF85+ARDN2hR8ILc23PP9CqASsiIiKp5CyfuvyYhVG0AJiISLv8+N0vG5x44omUlJQwb968hG63T1RJSQknnHACADfffHPKzht2xhlnAPDggw+yYcOGlJ8/ntNOOw3w6touW7asw/bp/h5kkq/BrJnlmdl+ZnaymV1oZleY2Y/M7FtmdpSZDfRzPCLSvmXrtnLtvz7mvrmfJ3zM14b0YspRu/CvHxzKmz+fyI2n7c0xe1TQs6ilckqyC3N19X+gRUREpPtoVc6gek1GxiEi0hXk0u9+AwYM4KqrrgLg5JNP5vHHH6epqSmqTWVlJffccw/XX399Uue+7rrrKC0tZdq0aVx22WWsW7cu6v36+npefPFFzjzzzFZ9duSkk07iqKOOorKykokTJ/L222+3avPJJ59w9dVX8/TTTyd17vb6POyww9i6dSvHHnss8+bNi3p/3bp13Hzzzaxdu7Z5Xzq/B5mU9hqzZjYamARMAA4CSjpovxR4DXgOeNY5V5/uMYpIi2DQ8eritTz0xjJmL1pLTI3uVnoW5nHY6P4cOWYAE8b0Z0BZcbvttTCXiIiIdGV1Bb2g9ouWHVtUykBEJJ5c/N3vF7/4BRs2bODWW29l0qRJlJeXM3r0aAKBAKtWrWLlypUAfPvb307qvLvtthvPPPMMZ555JnfccQd33XUXu+yyC7169WLTpk0sXbqUhgZvEbVHH3006XE//vjjnHbaabzyyisceOCBDBo0iKFDh9LQ0MCyZcvYuNFbS2batGlJnzseM+OJJ57ghBNO4N133+Xggw9m6NChVFRUUFlZyZdffkkwGOT000+nf//+vnwPMiVtwayZnQZcBhwS3pXgoTsDo4DJwGYzux+40zm3ot2jRHLI/GUbOvUPU3vHVdU28MQ7K/nLvGUsW5/4CoYXTxjFD48c3XHDEC3MJSIiIl1ZXX6v6B2aMSsiElcu/u5nZvz+97/nzDPP5O677+a1115jwYIFFBYWMnjwYE455RROPPFEvvGNbyR97iOPPJJPPvmEu+66i+eee45Fixbx2WefUVFRwYEHHsiRRx7JKaecQn5+8lFfnz59ePnll3niiSf4y1/+wrvvvst7771Hr169GDZsGKeccgonn3wyxxxzTNLnbsvAgQN54403uO+++/jb3/7Ghx9+yOrVqxk4cCBHHXUUp512GjvuuGPUMen8HmRKykdqZqcCvwZ2oyWMrQXeB94G3gXWABuAjXgzaHcA+gC7AGOBA4ChQG/gJ8APzewB4BrnnIo4SU67dcaipP9hg5ZbSC6bODpqYatFq7fw0Lxl/P0/X7KtPrHp/j3yHdsavf+8b3lpEfmBQFJjmXL0Lhw6ul/S/8BeMmFUl/prqYiISKaZ2QC8a+sD8K6zxwJ9Q2//2Tk3OUX9TAWuTrD5Ec652anoNxNqC3pH71CNWRGRNnW33/0+++wzqqqqOmx30EEHcdBBByV83hEjRuA6ul0V6N+/P1OnTmXq1KkpPS9AIBBg0qRJTJo0KaH2ALNnz273/QkTJrTbf2FhIZdeeimXXnppwn0m+z3IdikNZs1sFnA4XiBbCzwPPAI8l2xJAjPbGTgb+BawK3ARcLaZneOcezaV4xbpKuYv28DtMxcDLbd2JBKIRtb1uX3mYsaN3IFNNY08NG8Zbyxd3+6xRfkB6hqDzdsnDWviqMGO5T125fczlyY9ljAtzCUiIuILpYYpVlsQO2NW32IRkfbodz+RtqV6xuwEYB1wK3CXc67jPyO0wTm3BLgWuNbMDgV+BRwNfB1QMCs5aeyIHbjiuDFR9Xag/UA0ttj64bv056dPfMCXm2ra7WvXgWWM6NeTFxe01E37ycRRDN+2EIDvHTqcgoKCpMYiIiIiGfUF8AmQuvsQ49urg/cTX1U0C7UuZaBgVkRERDon1cHsFXiBbOIFKhPgnJsLHGtmY4F+qTy3SFcTrxh65P5IsaFsXsB4ddHaVu0i3z92j4GcN24E763YyE0vLGx+74rjxjD5wB156aWWfcmMRUREJFeZ2SjgfLw7y0YD4WRvM7AYmI1XVmBJGrq/FpgPzHfOrTazEaQ5GHXOfZTO82eaShmIiIhIqqQ0mHXO/S6V54tz/vnpPL9IV5FIIPqHWYu5+aVFUcc1BePXdunbs5BvHTCMsw8cxo69S0KBbnQoe8mEUdTUtJ5lq3BWRESkbWZ2JXANUBDatQ74MvS6HG+h3EOAy83saufcjans3zmXaN1XSVBdbCmD2s3QUAMFJZkZkIiIiHRZXWeZMhGJ0lYgetrXBzPl8fd5fUn7tWMB9h7Si++MG8EJew+iuCAP8OrYRs6yTWSRsXhjycZC7SIiIn4ys28BvwEW4C2O+5JzblNMm97AsXhlu643s2XOub/5PFRJQm1sKQOA6jXQZ7j/gxEREZEuLeXBrJn9E3gI+GeyC36JSHLiBaKRoWo8BXnGiXvvyHnjhrPvsD6t3h87Ygcumzia22cuTiiUjTeWyyaOVigrIiICPwY+Aw5yzm2N1yAU1D5mZs8D7wNTAAWzWawxrwcuvxhrrG3ZqWBWREREOiEdM2ZPBE4ANpvZ48BfnHOvp6EfEQEuPnwkH325mec+XNVuu4HlRXz7wOF864Bh9C8rarftlKN34dDR/ZIOVy+ZMEozZUVERFrsAfyxrVA2knNui5n9Hbgk/cNKLzObgbdgbxmwCfgYeAH4k3Nu43acd0gHTSrCL2pqauKWYNoetbW14YEQ7NGPvKqVze/VbVhBsN+eKe1Psk/zz0DMa8kd3e1nIBgM4pxrfi0di/w+6XvWfTjnCAaDHV47pPraAtJXysCA3sD3gO+Z2ed4s2gfds59lqY+RXLO2i11XPWPD5nxcduLThwwYgfOO3g4x+5RQUFeIOFzdzZcVSgrIiLSrJ6Whb4SUR46pqs7KuJ1f7xFzw4HrjCzyc65Zzp53i8SbThnzhz69UvfmsGbG4uIvOL5dP6rLFumKnG5ZM6cOZkegmRYd/gZ6Nu3LyUlJZgZVVVVmR5Ol1NdXZ3pIUgKNDY2Nv9B99NP278Ded26dSnvP/GUJnE74y1wsAQvoDVgJ+BqYLGZvWZmF5pZMhepIhLjuQ9Wccytr7Ybyk4+eASPXzyOE/feMalQVkRERFLiTeAsM9u7o4Zm9jXgW8C8tI8qfT7Eq6V7ErAfcBDwHeCl0Pu9gafM7H8yMroUqi3oHbVd1Lg5MwMRERGRLi3lf9YNzYi9BrjGzA4CzgUmQfMflQ8OPe4ws2fxZtL+2znXlOqxiHRHG7fW8//+uYB//feruO8X5BkNTd7tKNPfWMbA8uKE68SKiIhISl0NvAa8aWaPADOAxUA4xesFjAaOAc7GmzRxdQbGmQq3Oeemxtn/FvCQmV0E3APkAfeZ2c7OuWTvBxzawfsVwHyA8ePHM2RIR5UPklNbW9s8Q26HYbvBh+82v7fzwFKGH3NMSvuT7BP5MzB+/HiKi4szPCLxW3f7GVixYgVNTU3k5+dTXl6e6eF0CcFgsHmmbGlpKYGAJkB1dWvXrqWkpITS0lL222+/dtuuXLmy3fc7I6332zjn3sS7EP0xcDxeSHsCUAQUA6eFHuvM7FG8erT/SeeYRLqymZ+s5sq/f8jaLXVx3z9138HcOmkf7p69NGpBMEDhrIiIiM+cc/PN7DjgPuAC4LttNDW8RcIudM6949f4Uim0iFl77//JzPYHLgR2BL4JPJJkH+3+NmRmza9LSkooKSlJ5vRJyes1KGo7v2Y9+WnsT7JPcXFxWn/GJPt1h5+BQCDQXCdVAWPyAoGAvm/dhJkRCAQ6/G86Hf/N+1IIyTnXADwDPGNmvYEz8ULaQ0JN+gM/An5kZp8AfwYecc7FnxIokmOqahv49b8+5ol32/595IdH7sxPj9kVaAlhFc6KiIhklnNutpntChwJTMCbIRsu6bUZbwbtq8DMHLiD7E94wSx4NWeTCmazies5MHpHddulpURERETa4nuF+tBf0+8F7jWznfAC2m/jXaQC7AbcCPzGzF4B/uyc67IXbSLba+7idVz+5H/5anPbq35ecdyuXDJh56h9CmdFRESyQyhwnRF65LKPI14PztgoUsCVDojeoWBWREREOiGjc66dc5875651zu0KjAPuBjbg3c6Vh7ei60MZHKJIxmyta+RXT3/EOfe/1UEoO6ZVKBt2yYRRXHHcmObtm174lLtnL035WEVERHKJmeWZWWGmx9EFWcdNugbXs3/0juo1ELodWERERCRRWVMMwzn3lnPuf/Fq0C4AXIaHJBkyf9kGX4/LRvOXbeD4O17jL28ub7edF8q2PwM2Xjjbnb5XIiIiGfAjoNrM3jezM2LfNLNSM9vHzHy/Oy3L7R7xukuXLGtVyiDYALWbMjIWERER6bqyIpg1sx3N7P/M7ANgHtEXbfrTcw65dcYizrhnXtKzOu+evZQz7pnHrTMWddjWj+C3s328vmQd1z/3MWf+aR7L129r9f7Bo/py/iEjgMRC2bDIcPayiaMZO2KHTo1PREQk14XC1ivwSoI1Av+M02w48B+88PZdM7vPzC41s3Fm1sPH4WabiyJev5qxUaRCz36t922p9H8cIiIiSZo8eTJmxtSpUzM9FCEDNWbDzKwncBpejdkJtITE4VucPgX+EnpIDpi/bAO3z1wMJFcP9e7ZS5vb3z5zMYeO7tdm8HjrjEXcPnNxUqFmZB+XTRzNlKN3abdtZ/v4f898xEPz4s+QLSnI4+fHj+GcA4cTCBjH7zUo6XD1kgmj2H9EH4WyIiIi2+cYYADe3V2XOefq2mlbCOwTepwf2hc0s8XAfODC0CK5Wc3MJgMPhjavcc5NjXl/L6DGObeknXNcBFwQ2qwE/pH6kfoorxBKdoCaiD/GV6+Ggbu3fYyISK5aPg+Gj/PvOOmUZcuWMX36dHr37s2Pf/zjTA8nZ/gazJqZ4V3MngucApSE3wo9rwcew1vwa76fY5PMGztiB644bkxSi1VFhrLgzSJtK3j0I/jtTB/1jUEmP/g2byxdH/f9/Yb34eYzvsZO/Xo27+tsuKpQVkREZLudEHqe55x7vYO2DvgCGBaxLw8YA+wKzAWmpXyEEczsUCCyGH3kVM+dQ6FrM+fc9E50sx9wX2jh3n8DH+Jd1+fjfa3nAEeH2jYBFznntnain+xSVhETzK7J3FhERLLVKzfAqzfCUVPh0CmJHzf3Vnh5Khx+JRzx83SNTiIsW7aMa665huHDhyuY9ZEvwayZ7YMXxn4LCBdkCoexDcDzwJ+B57rCrAFJn3CImUg4Gy+UbS8ETXfw25k+PllVxXkPvM3aLa0n2xTmB/jZMbtwwaEjyQt0m7UyREREurr98ALXfyXS2Dk3wsx60zJzdh/gUGAkMIU0B7PAhcB32njvkNAj0vRO9hNeuPeodtqsBy5wzsUr/9D1lA6ANR+3bFerlIGISJTl87xQFryQFRILZ8OhLHjHj5ygmbPSbaUtmDWzwcC38QLZ8D09kenSfOAh4K/OuYyuRGRmw/AWcTgBb0ZDHbAEeBz4o3OudbHPzvd1FN6sgUOBQXi1yVYDHwAzgb8456pT1V9XlEg4m2wom8y5/eijsSnIn+Z8xi0vLSQYZ5m7vQb34vdnfo3RA8s67E9ERER8tVPo+b+JHuCc2wTMDj0ws53xynbtamZ7OOcWpHaIvnser0zBOGBfvIkYffGu/Tfgfa9eAKY756oyNciUK41ZAEwzZkVEog0f582UDYesiYSzkaEseMcrlJVuLOWLf5nZd8zsZWA5cANeKGuhxxfAjcBuzrkDnXN3ZUEoewJeKPpTvFutegB9gLHA74D/mNnIFPTTx8yeBmbgzVoYFeqrHBiNV2/3j0TfapazIherAi/cDC8I1tnANJFzh6WzjyVrqjntnnn87sXWoWx+wPjJ0bvw90sPVigrIiKSnXqFnjudwoVqsc4JbZ643SNqv6/JzjlL9NHGOaZHtJka5/01zrkHnHPfc87t75wb6pzr4Zwrcc4Nds4d75y7o1uFshAnmF2dmXGIiGSzQ6d44WrYy1O98DWeeKFsMuUPfDBy5Ej69OnD3Llz+fDDDznrrLMYNGgQeXl5UYtpvfjii5x66qkMGjSIwsJC+vfvz0knncSsWbPaPPcTTzzBMcccQ//+/SkoKGCHHXZgzJgxnHfeebz44otRbadOnYqZMXny5DbPZ2aYGcuWLevw65owYQJHHHEEAMuXL28+NplzSOekY8bsg3i3d4Uv7LYCf8ebHTvLORdnfmBmmNnX8GbF9gCq8YLkV/Bq354FfA+v/tdzZja2szNZzawXXiC7X2jXc8Df8Gbl5uGt3DsWOL3TX0w3FG/m6e9nLKShqeVHaJ+hvflqUw1XP/MRgYCRZ0ZewPsfR14A8iz8Orzf21eQZxw5ZgCzPl3TfO7/frGJI8cMYPbCNTz/UcutaCfvsyMj+vbgxQWVBELnNfP6CpgRCBDabwTMex0w47DR/VhdNYLpbyxr7mP+svW8vmQ9dY3BVl/vrgPLuOXMr7Hn4F6t3hMREZGsUQ8UAD07aBNu15YX8RbA3a+dNpLNYoPZLSplICISVzhcbW/mbBcIZSPNmzePM844AzNj9913p3fv3pgZwWCQiy++mGnTvEpFffv2Zc8992TFihU8++yzPPvss9xwww1ceeWVUef7xS9+wW9+8xsABgwYwNe+9jW2bdvGypUr+ctf/kJVVRXHHnts2r6evfbai/Xr1/PRRx9RVFTE/vvvH/V+cXFx2vrOdekqZeDwAs4/A0+lshRAit2GF8o2Asc45+ZFvDcrtGLub/Fm0v4EuLaT/dyJd9HdCJzjnHss5v3XgUfN7Cd4Qa2EXDJhFJtr6rnn1c8AokJZgPe/2MT7X2xKSV8vLKjkhQWtL6ifef8rnnn/q5T0MevTta32Gd7XedlRoynK18cvIiKS5dbjhbKD22rgnFtsZqW0lPOK54PQ814pHJv4SaUMRKQ7CwajFzjcXvueC/XbYM5vve2Xp3rbB14Eb/2pZT/A+Mu99lvXpa7/kh0gkLqbxm+66SbOPfdc7rjjDsrKvLtda2pquP7665k2bRojRozg3nvv5eijj24+5rHHHuPCCy/kqquu4sADD2yeobpu3Tpuuukm8vPzefTRRzn99NMxa7mJ5c0332Tp0ui7fFPtzjvvZPbs2RxxxBFUVFQwd+7ctPYnLdIRzF4FPOycW5mGc6eMmY3Fm6UAcH9MKBt2C3A+sBvwYzO7IdnFyUIr4Z4b2rwuTijbLDSbuDGZ8+eCIX16ZHoIabNDjwLumzyWrw/rk+mhiIiISGI+wFuT4Gi8O6Dics410hK+xhNO8fqnbmjiqzKVMhCRbqxmA/wu8XJ+nTLnt9GBbEf7t8f/LYWe/VJ2ujFjxnDvvfdSUNByc0xNTQ033ngjBQUF/OMf/2CfffaJOmbSpEmsWrWKKVOmcNNNNzUHs0uWLKGpqYl99tmHM844o1VfBx10EAcddFDKxi7ZJeU1Zp1zN2Z7KBtySsTrB+M1cM4F8UowgFd3dkIn+vlB6LkaL+iVJD33QWpmq2abovwAr185UaGsiIhI1zIb74aXb5rZ9hSED9c1UlH5rip2xmztJmiozchQRETEX5MmTSIvL/qO1+eff55t27Yxbty4VqFs2KmnngrAnDlzaGpqAmD48OEALFq0iP/85z/pG7RkpXSVMmiTmQXwAs5xQAVeKYFfOudWRbQpDI2tyTlXl6ahHBZ63gq82067VyNeH4pXKzYhoa/j5NDmv8M1as0sH+/2NwdUOufqEz1nrrl79lLmfdb27RNfH9ab3XcspykIwaCjyTmCzoVe0/I66AiGtr3Xrvn1yo01rNxY0+rc/UsL6dOzsPm4YOgcTUGHc+G+CO13of1EjCHUn/P2x6prDDL9jWVJLSomIiIiGfcX4Hq8BVxvAy7o5HkqQs/ZWvJLOlI6oPW+rWug9zD/xyIiIr7aZZddWu3773//C3gB66GHHhr3uPCySzU1Naxfv54BAwYwaNAgzj33XP7yl7+w//77N5c5OPjggxk/fjzl5eXp+0Ik43wNZs3sBOAOYETMW7cAqyK2LwD+AFSb2Y7Oua1pGM5uoecloVvN2vJpnGMS9TUgXCF5nplV4C0wdgYtC0bUmtkreGUO3kjy/ACY2ZAOmoQv/KmpqaGmpnUIuT1qa2vjvt5e0+Yu5/czW9dRGb9zX+YsWQ/Af1ZsYsLovnzv0OGd7yMi+O1Vks/mGu/HYW11PeceOLTT547qI+LriOzjphc+paGhYbv78Eu6PmvJLvqcc4c+69zgx+ec6muLbOacW2dmf8Rbf2CymS1wzv2+E6cK/8bWPW8NygXFvSGvCJoi5pFUK5gVEckFPXu2XgN006ZNAFRWVlJZ2fGCkNu2tfxt9v7772fPPfdk2rRpvPnmm7z55psAFBYWcsYZZ3DzzTdTUVHR1qmkC/MtmDWzC4E/4d36BbAO6Ic3azTW/cB1QG/gVODhFI+lONQ3QLtlF5xzG81sK16QOjTJriIXfCgGPozoN3L//wDHmtlPnXO3JdkHwBeJNpwzZw79+qWurkq886fCy18a/1oRfyGsnWwNvYbR/P7vZy5l8eJFHDU43o9S4n2cNKyJowY3Ru3v7Ln97CNTUvVZS3bT55w79FnnhnR9zuvWpXBxjq7hl8DxeAvE/s7MhgI/d84llHyHSiB8F+86+PW0jVLSy8wrZ7B5Rcu+LR3/Ii4i0iWU7ODVZU212IW+int7pWDCxl/uLQiWaiU7pP6cMUpLSwG46KKLuOeee5I6tqCggMsvv5zLL7+cFStWMHfuXGbOnMkTTzzBI488wscff8xbb73VXNM2vDiYi3d7LrB1azrmN0o6pLzGbDxmtjNwV2hzFrC7cy7OvT+e0K39T+GFuMekYUiRtbyqE2gf/okuTbKfyP/yr8YLZZ8F9scLZAcClwJVeJ/F783sf5Lso9uJDTPzLPp/NL0KHUcNdpw0rKl5379W5PHyl0ai4gemXj/be24/+xARERH/hQLYY4AleNerPwI+NrNzzayovWPNrCfeomHhAqWPpHOskmax5Qy0AJiIdBeBgLdYViof7/0lOpQ9aipcudx7DpvzW69dqvsOpD/+2muvvQD44IP21v7s2LBhwzj77LO5//77ee+99ygqKuK9997jrbfeam4TnrG7enX8f3cWL16cdL/hsFf85deM2R8DBcBHwPEJ1lR9DbgQ2CcN4ymOeJ3IWML3J5Uk2U/k3PYi4F/AKaFFxcBbjfduM/sQr5ZtAPitmb3g2vqzR3wdzeStAOYDjB8/niFDOqp8kJza2trmGTjjx4+nuLi4gyPaNm3ucv61ouWvcpcePoI/vrosqs03jjqMwb1LOAYYHVEm4F8r8hg9elSHZQFi+/jJxNbHdPbcfvaRCan8rCV76XPOHfqsc4Mfn/PKlV1h3dfUcs6tNLPDgZl4M2eHA9OB28zs33gzYT8B1gMNeNdj44HvATvizZZ92Tn3auuzS5dRFnNbafWazIxDRCTbzb0VXp7asn3UVDh0ivc6/Bx+P/wc3t9FnHjiiZSUlDBv3jxef/11DjnkkO0+56hRo6ioqGD58uV89VVL9aPRo0cD8P7771NfX09hYWHUcXfffXfSfZWUeJFXLpWoygZ+BbMT8S4+b0tioatwqpWOIk2Rt5kVttmqRXjmQ7I/nbG3s/1fRCjbzDk318z+DpwO7Bl6fJhoJ865dn8bivyrR0lJSfN/bOlQXFzc6fPPX7YhqhbrFceN4Ygx/VsFs8P696Yw3/tr14+OHkNBQQE3veCVAv79zKWMGz2AsSPi36Zw9+ylrfpoa/GteOcuKCjocLEuP/rIBtvzWUvXoc85d+izzg3p+pxz9WfHObfKzPbDK23wU7zryj7At0KPthiwCPh22gcp6dVqxqxKGYiItNJeKBvWDcLZAQMGcNVVV/GrX/2Kk08+mT/+8Y+cdtpp5OW13ElbWVnJ008/zfr16/nFL34BwMsvv8xzzz3Hd77zHfbZZ5/mtk1NTdx3330sX74cM+PrX/9683tHHHEEPXr0YPXq1Vx55ZXcfPPNBAIBgsEgf/rTn3jooYeSHv/IkSMBWLNmDQsWLGCPPfbo5HdCkuFLKQNaZnS+n8Qx4fIBPVI7FAC2RLxOpDxBeOZrImUP2urnc+fcwnbavhjxemyS/XQLY0fswGUTvb/6hMPMVZujs+1+pUXNoWzYJRNGccVxYwC4bOLoNkPZ+cs2NAegkX20J/Lc4C3WNX/Zhjbb+9GHiIiIZA/nXI1z7hfAXnjrItTiBa9tPRzwKHCQcy7nivN2O6UDo7c1Y1ZEJFoioWzYoVOiyxq8PNU7vgv5xS9+wZQpU1i/fj2TJk1ihx12YP/99+eAAw5g6NChDBo0iEsuuYRPPvmk+Zjq6mpuu+029t13X/r06cPXv/519t9/fwYOHMjFF18MwDXXXMPOO+/cfEx5eTnXXXcdALfeeiv9+/dn7NixVFRU8IMf/IC77rqLZPXr149jjz0WgP3224/999+fCRMmMGHChIQWM5PO8WvGbPi2/GQKVvQPPVeleCw452rNLLz4WLv39ZtZH1qC2YQX2YrTvqN7/CLbtll/t7ubcvQuHDq6X3O4WhkTzA7qFf/2y0smjGL/EX3aDGWhJfi9febihALTyHODF5i2F/z61YeIiIhkH+fcYuA8M7sEOAI4EBiNN4O2CVgLvAs865z7LGMDldRqFcyqxqyISLPl8xIPZcPizZwdehAMH5f68aWBmfH73/+eM888k7vvvpvXXnuNBQsWUFhYyODBgznllFM48cQT+cY3vtF8zGGHHcZdd93FzJkz+eCDD1iyZAk1NTUMGDCAb37zm1x66aVMnDixVV9Tpkyhf//+3HbbbSxYsIDFixez//778/Of/5yJEydywQUXJD3+hx9+mF/+8pe88MILfPDBBzQ0NABeaSxJD7+C2a+AnYFd8C5IE3F46HlZOgaEV/PrMGBnM8t3zjW20W5MzDHJWBDxOq/NVq3fb2ssOSEylIydMVvRRjAbe1xbYoPfRCUS/PrZh4iIiGQn59xWvMVen830WMQHscHsFgWzIiLNho+Dw6+EV29MLJQNiwxnD78yq0LZzz77jKqqjucPHnTQQRx00EEJnbNv375ceumlXHrppUmP55xzzuGcc86J+15bSxdNnz6d6dOnx32vX79+3HPPPUmPQzrPr2B2Dt6MgbOBv3bU2Mz6ARfhzbSdlaYxzcULZnsC+wFvtdHu8IjXryfTgXNuuZmtwKuT29HUycj3v0ymn+6scnN0Wd+2Zswmo7PBZzLH+dGHiIiIiGRYvBmzzoFWthYR8Rzxcxg5Iflw9dApXWqmrEhn+VVj9t7Q8/Fmdn57Dc1sCPA8XpmBpohjU+3piNdxx2RmAeC80OYm4JVO9PNU6HmgmR3cTrtvRrx+rRP9dEvJzJgVEREREfFVWUwwG2yAmo2ZGYuISLbqbLiqUFZygC/BrHNuPnAPXo3Z+8zsCTM7M6LJ3mY2yczuBxbizWB1wC3OuSVpGtPbtASgF5hZvP/ifwrsFnp9u3OuIfJNM5tsZi70mNpGV7fhLQIBcIeZ9YxtYGbnABNCm8855zqqR5szEq0xKyIiIiLiu579W+9TnVkRERFJkF+lDAB+iFc24Fy82aHfpGVRsEci2oXv+5kOXJXmMV2GV56gBHjJzH6DNyu2BDgL+H6o3SLgls504JxbYWb/D/gtXuD8tpn9FvgI6IX3fbg41LwKSLDoSm6IDWYryksyNBIRERERkRj5RVDSJ3qWbPVqGLBb28eIiIiIhPgWzDrnmoDvmNk/gZ8DX2+j6cfAdc65v/kwpvfMbBLwMFAO/CZOs0XACc65LdvRz+/MbAfgCmB3vNA51hrglNCKvgJsqW1gS130OmiaMSsiIiIiWaW0IiaYXZO5sYiIiEiX4ueMWQCcc08BT5nZjsD+wAAgD1gPvOecW+rzeP5lZnvjzZ49ARgC1ANLgCeAPzjntqWgn5+HQulL8BYdG4RX4mAR8E/gTufc5u3tpztZXVXbap9qzIqIiIhIVikdAGs/adneUpm5sYiIiEiX4nswG+ac+wovkMw459xy4CehRzLHTSf+7Ne22s8D5iXTRy6LXfirT48CigvyMjQaEREREZE4SmMWAFONWREREUmQL4t/iXRGbDBb0Uv1ZUVEREQky5TFBrMqZSAiIiKJUTArWSt24S/VlxURERGRrNNqxqxKGYiIiEhiUhrMmtn/mVmPVJ4z5vwHmNnx6Tq/ZJfWM2YVzIqIiIhIlmkVzGrGrIh0DXl5XqnApqYmgsFghkcj4r9gMEhTUxPQ8t+D31I9Y/Ym4PNQQFuaqpOa2cFm9m+8+qz7p+q8kt0qN9dEbQ8qVzArIiIi2c3MAmZ2pJn9wszuNLP7zWxQTJtCM+thZkWZGqekkGrMikgXVVzs/Y7tnKO6ujrDoxHxX3V1Nc45AEpKMlM+M9XB7GygP3AjUGlmj5rZCWaWdOxsZiPN7Jdm9gnwGnAssAV4J5UDluylGbMiIiLSlZjZCcBiYAZwLXApMBnoE9P0Arzr2jVm1tPPMUoaxAazNRuhsS4zYxERSUJ5eXnz68rKSqqqqjRzVnJCMBikqqqKysqW8kNlZWUZGUt+Kk/mnDvSzL4JXAeMASaFHlvN7D/AfOA9YA2wMfQoAXbAu2DdBRgLHAAMC53WgHrgfuAa55zuDcoRlVWxNWa1+JeIiIhkJzO7EPgT3rUrwDqgH+DiNL8f73q5N3Aq8LAPQ5R0KR3Qel/1Gug91P+xiIgkoWfPnpSUlFBTU0NTUxNffvklZpaxW7q7isbGRgDWrl2b4ZFIZzU1NTXPlAVvtmzPnpn5W3lKg1kA59zfzewfwOnAj4FxQCkwHjgswdOEL2g3AQ8AtzvnvkjtSCWb1dQ3sWlbQ9Q+zZgVERGRbGRmOwN3hTZnAT9wzn1qZnGnHTnn6s3sKeBC4BgUzHZtJX0grxCa6lv2KZgVkS7AzBg2bBgrVqygpsYrJeicaw4epTXnXPP3qqSkBDPr4AjJdiUlJQwbNixjn2XKg1kA58XOTwBPmNmuwJnAEcCBeDNk2/MZXumC54B/Oed0H1AOip0tCwpmRUREJGv9GCgAPgKOd87Vt98c8K53LwT2Sd+wxBdmXjmDzRHzSKor224vIpJFAoEAw4cPZ+vWrWzZsqV59qzEFwwGm4PZ0tJSAoFUVwgVP+Tl5VFSUkJZWRk9e/bMaMCelmA2knNuIfBr4Ndmlg/sCwzBq0W7A1ALrA09PnTOrUr3mCT7rYpZ+KusOJ/SorT/uIqIiIh0xkS8kgW3JRjKAiwNPQ9rt5V0DaUDYoJZLQAmIl2HmVFaWkppacrWcO+2ampq+PTTTwHYb7/9MrZglHQfviZdzrlGvDqz8/3sV7qeytiFv8o1W1ZERESyVvie9feTOGZr6LlHaociGVFaEb1drWUxREREpGOacy1ZaVVsMKsyBiIiIpK9wqtHJHMfXP/Qc1WKxyKZELsA2BaVMhAREZGOKZiVrBQ7Y3aQglkRERHJXl+FnndJ4pjDQ8/LUjsUyYjSgdHbmjErIiIiCVAwK1mp9YxZ1W0RERGRrDUHb7bs2Yk0NrN+wEV4M21npXFc4pey2GBWNWZFRESkYwpmJSutrtKMWREREeky7g09H29m57fX0MyGAM8D/YCmiGOlK2s1Y1bBrIiIiHRMwaxkJdWYFRERka7COTcfuAdv1ux9ZvaEmZ0Z0WRvM5tkZvcDC4H98GbL3uKcW+L/iCXl4gWzzsVvKyIiIhKSn+kBiMSqbwyyrrouap9mzIqIiEiW+yHQEzgX+GboEU7mHoloF14gbDpwlV+DkzSLDWab6qF2E5T0ychwREREpGvQjFnJOrFlDAAGlavGrIiIiGQv51yTc+47wBnAe3gBbLzHx8DZzrnvOqcpld1G6YDW+7aonIGIiIi0TzNmJetUxgSzJQV5lJfoR1VERESyn3PuKeApM9sR2B8YAOQB64H3nHNLMzk+SZP8Iiju7c2SDateDQPGZGpEIiIi0gUo7ZKsE1tfdlCvYsysjdYiIiIi2cc59xXwz0yPQ3xUVhETzK7J2FBERESka1ApA8k6lZtrora18JeIiIiIZL3YcgbVlZkZh4iIiHQZvsyYNbODnHNv+tGXdH2xM2YVzIqIiEg2M7MyYEpo817nXLuJnJkNAr4X2vydc66mvfbSRcQuAFatGrMiIiLSPr9mzL5hZgvM7KdmFqcyvkiLyjilDERERESy2CnAVODbHYWyIZXAt4GrgZPSNyzxVatgVqUMREREpH1+ljIYA/wW+MLM/m5mJ5mZSilIK61nzJZkaCQiIiIiCfkm4IDHE2nsnHPA3wADzkjjuMRPscHsFpUyEBERkfb5FYxeBryPd/FZAJwMPA2sNLMbzGwXn8YhXUCrGbPlmjErIiIiWW1M6PmNJI6ZF3rePcVjkUzRjFkRERFJki/BrHPuTufcfsA+wJ3AeryQtgK4HPjEzOaa2flm1tOPMUl2amwKsmaLasyKiIhIlzIk9LwqiWPC0ykHp3gskillqjErIiIiyfG1lIBz7gPn3GV4F6CnA88BQbyQdhxwH7DKzO4zs0P8HJtkh7XVdQRd9D7VmBUREZEsFww990jimHBbXxbjFR/Ezpit2QCN9ZkZi4iIiHQJGanx6pxrcM793Tl3EjAU+DmwEC+gLQXOB+aY2Sdm9n9mNrCd00k3EltftjAvwA49CzM0GhEREZGEhGfK7p/EMeG2KkTaXcQGswBbVc5ARERE2pbxxbecc5XOuZucc7sDh+DNmq3GC2l3BW7EWzDsaTM7LoNDFR/E1pet6FWMmWVoNCIiIiIJeQ3v2vVSMyvoqHGozaV4C4bNTfPYxC8lfSAQ8/GrnIGIiIi0I+PBbIxCoAjIw7tQBe8iNx84CXjOzN4zs4MyND5Js9gZs6ovKyIiIl3Ag6Hn0cCjZtZmSYPQe38FwovfPthWW+lizFrPmt2iYFZERETalvGaVmY2DPgOMBkYEd4NNAH/Bv4C7BVqMxT4GjDbzA53zr3l93glvSo310Rtq76siIiIZDvn3Btm9jfgLOCbwIFmNg2Yg1fmwAE7AuOBC/EWC3PAk865VzMzakmL0gFQtbJlWzNmRUREpB0ZCWbNrBjvovV84Ai8IDZ8v/pnwAPAg865cL2uJ8xsKt7F7m1AP+Ba4Fj/Ri1+0IxZERER6aK+i3eNehTeQrdT22gXvuadgTfxQLqTsoro7WrVmBUREZG2+RrMmtmBeGHsJKA8vBuoA54G7nPOzYx3rHMuiHdrWAB4CNgv7QMW38XWmB1UrmBWREREsp9zrtbMjgV+BPwML5yN5wvgd8BdzjnXRhvpqkoHRG9Xa203ERERaZsvwayZ/R9eILtreFfoeQHeYl9/cc5tSPB080PPfVI3QskWrWfMlmRoJCIiIiLJCQWtt5vZHcA+wL54s2gB1gH/Af6rQLYbi60xqxmzIiIi0g6/ZszehFdHy4CtwGN4s2Pf7MS5GlM5MMkewaBjdVXMjFmVMhAREZEuJhS8vhd6SC5pFcyqxqyIiIi0zc9SBu/gzY79q3OuurMncc4tBQIpG5VkjXVb62gMRk8gUTArIiIiIl1GbDC7RcGsiIiItM2vYPZrzrkPfepLuqjY+rL5AaNvaVGGRiMiIiIikqR4M2adA7P47UVERCSn+RLMKpSVRMTWlx1YXkxeQBexIiIi0rWYWRmwE1AG5HXU3jk3J+2DEn+UxQSzTXVQuxlKemdkOCIiIpLd/CxlINKu2BmzFSpjICIiIl2ImX0PuBTYO4nDHGm8JjezAcABocfY0KNv6O0/O+cmp6HPs/AW/t0bb8HeSuA14K5OrjHRdfQc0Hpf9WoFsyIiIhKXL8GsmY3vxGEOqAU2A8ucc/WpHZVkm9gZswpmRUREpCswszzgKeCk8K4MDieWb0VOzawYeAI4Meat4aHH2WY21Tn3a7/G5LuCYiju5c2SDateDf13zdyYREREJGv5NWN2Nl7Q2lmNZvY+MB24zznXkIIxSZap3FwTtT2oXMGsiIiIdAkXA98IvV4NPAi8C2wAgpkaVBxfAJ8Ax6Tp/PfTEsq+AtwOfAXsBVwFjAKuNbNVzrn70jSGzCutiAlm12RuLCIiIpLV/CxlsD0zBwrwbrvaH7jEzE50zq1IzbAkW2jGrIiIiHRR54WePwYOc85tzORgYlwLzAfmO+dWm9kI4PNUd2JmhwNnhzb/BZzqnGsKbc83s3/ihdXDgN+a2ZPOuU2pHkdWKB0A6xa2bG+pzNxYREREJKv5FcwegReu/ho4EO8v508A7wBrQ2364wWvZwA7Am8BVwMlwJ7ApNDznsDzZraPc67Rp/GLDyqrooPZQb1KMjQSERERkaTshnd32K+zLJTFOXe1T11dHnpuAi6NCGXD41hnZlcAf8WrO3sBcItPY/NXacwCYNW+VZMQERGRLibgRyfOuVeBH+EtOnAnMMo5N8U594hz7qXQ4xHn3BS8W5zuwgtwf+yce8Y5d71zbm/g+tApd8NbUEC6CeecZsyKiIhIV7ew4ybdj5mVAhNDmzOccyvbaPp3oCr0+ptpH1imtApmVcpARERE4vMlmDWz8/HqTT3vnLvMOVfXVlvnXJ1z7ofA88CxZvb9iPd+BbyKVxah+17M5aCN2xqob4wuwTZIwayIiIh0DYtDzztkdBSZcwBQFHr9aluNQov5vhk+xswK0j2wjCiLDWZVykBERETi8yWYBb6Ld3vXvUkc8ye8APY7Mfunh56/tv3DkmyxKmbhr4BB/7KiNlqLiIiIZJW/4V23nthRw25qt4jXn3bQNvx+PjA6PcPJMM2YFRERkQT5VWM2fLHW1m1N8YTbjonZ/0noOVdnJHRLlTFlDPqXFVGQ59ffDURERES2yx3At/EWqf2Hc+61TA/IZ0MjXnd0vf9FzHEfJ9qJmQ3poElF+EVNTQ01NTXttU1abW1t3NexAgW9iZxe4LZUUpvisUhmJPozIN2XfgZEPwO5LdXXFuBfMBu+J30o8F6Cx4QvvGKnTTaEnnV10420qi9brjIGIiIi0jU45+rM7Bi8GqozzOwO4FHgU+dcLvzWVhbxurqDtlsjXpcm2c8XHTfxzJkzh379+iV5+sTNmTOnzffKalZyZMS21WxgxgvP4wJ+/eolfmjvZ0Byg34GRD8DuWfdunUpP6dfUxI/Cz1/L4ljLgo9L43Zv2Poee12jUiyyuoqLfwlIiIiXZOZNQGrgHFAIfBT4F1gq5k1dfBozOTYUyTywq2+g7aRa02UpGEsGVdX0KvVvqLGqjgtRUREJNf59WfbJ4E9gePN7Dbg8lDx/1bMrBD4HXA8Xl3aJ2KaHBJ6XpKeoUomxM6YHdSrW16ni4iISPdkHWx3d5EXcoUdtI28Gy7ZO+CGdvB+BTAfYPz48QwZ0lHlg+TU1tY2z44aP348xcVtTCRwQdyCy7BgS+Z++H5jcIP2Sel4xH8J/wxIt6WfAdHPQG5buTKZCq2J8SuYvRk4FxgF/BA4zcwex5tJEK6GPwDYHziDllmxS4FbwicxszzgbLzA9kVfRi6+iK0xqxmzIiIi0oVck+kBZNiWiNcdlSfoGfG6o7IHUZxz7f42ZNaSh5eUlFBSkr4/9BcXF7d//tKBUPVlS/uGTZDG8Yj/OvwZkG5PPwOin4Hck47P25dg1jm3zcyOBJ4H9gAGAz9uo3n4iuoj4ATnXORf0ocAD4ZeP5mGoUqGrNocPWFikIJZERER6SKcc7kezEYGpkOAd9ppGznrNeGasV1O6YCoYJbq1Zkbi4iIiGQt35a9d859AXwdr+bWQrwANt5jUajNfqFjIs+x3Dl3TejxJSliZsPM7GYz+8TMtprZBjN728x+ZmY9UtVPTJ+DzGyTmbnQY3Y6+ukKnHNa/EtERESk6/o44vWYDtqG32+kO5cmKx0YvV29Jn47ERERyWm+Lg3qnGsAbgVuNbMd8erO9gm9vRFYkMrANRFmdgLwCBBZpb8HMDb0uNDMjnfOfRbv+O1wZ0yfOWtLXSPb6pui9qnGrIiIiEiXMR9v0a9C4HDgxniNQmtJHBQ+pq01J7qF2GB2S2VmxiEiIiJZzZdg1sz+X+jlW865FwGcc18BX/nRf1vM7GvA43hBbDVwA/AK3gqxZwHfA3YFnjOzsc65pOpgtdPvScBpePV1B6TinF1ZbH1ZgAHlRXFaioiIiGQ3MwsAE4BxeItR9QB+6ZxbFdGmEO86vMk5V5eJcaaSc26Lmc0E/gc4ysyGtFEP9ptAeej1P3wbYCa0mjGrUgYiIiLSml+lDKYCVxO9Cms2uA3vYrkROMY59xvn3Dzn3Czn3PeBy0PtxgA/SUWHZlYK3BXa/FkqztnVxZYx6NuzkOKCvAyNRkRERKRzQndiLQZmANcClwKTablDLOwCvAWz1phZT7KcmU2OKL81tY1mN4ee84G7Qov2Rp6jH3BTaHMTcF86xpo1SmPmXqiUgYiIiMThVzC7PvS8wqf+OmRmY/FmMwDc75ybF6fZLcAnodc/NrOCFHT9G7xFD15xzv0lBefr8ipjFv6q0MJfIiIi0sWY2YXAP4Gd8NZNWE/Lorax7scLJ0uBU9M8rkNDwepkM5sMnB7x9s6R74Xe7xTn3Czgb6HNbwAzzOwbZra/mZ0PvAkMC71/pXNuY2f76hLKKqK3q1XKQERERFrzq8bsEuAAvNu5ssUpEa8fjNfAORc0s4fwShz0wQtyZ3S2QzM7APhfvBpcl3T2PN1N7IzZQQpmRUREpAsxs51puSNqFvAD59ynZhaM1945V29mTwEXAscAD6dxeBcC32njvUNCj0jTt6Ov7+KVKjgeOCL0iBQEfu2c+9N29NE1xFv8yzmwtrJ6ERERyUV+zZh9DG/GwJk+9ZeIw0LPW4F322n3asTrQzvbmZnlA/fifc9vcs4t7Oy5upvYGrOaMSsiIiJdzI+BAmABcLxz7tMEjnkt9LxPmsbkO+dcjXPuBODbeJMZ1uBNSPgCeBQ41Dk3NXMj9FFsKYPGWqirysxYREREJGv5NWP2j3h/qT/PzF51zv3Zp37bs1voeYlzrrGddpEX1ru12apjPwO+BizFK2eQMmY2pIMmzTOVa2pqqKmpaa9t0mpra+O+TtSXG7dGbffrkZ/yMUpqbO9nLV2DPufcoc86N/jxOevfbSYCDrjNOVef4DFLQ8/D2m21nZxzk/Hq3G7POaaTxExa59yjeEFs7oqdMQuwZTUU9/J/LCIiIpK1/ApmK/Buo7ofeMDMvo13sfYBsBFoau9g51xKa9OaWTHQL7QZb8XYyL43mtlWoCdebdjO9DcS+H+hzUudc6n+reiLRBvOmTOHfv36ddywk+bMmZP0MUu+zCOyBNvaFYt56aVFKRyVpENnPmvpevQ55w591rkhXZ/zunXr0nLeLiR8jfh+EseE/zLdI7VDkaxQUAJFvaBuc8u+6tXQf5fMjUlERESyjl/B7DK8WQTgJXATQ49EOFI/zrKI19UJtA8Hs6Wd7O9PQAnwmHPupU6eo9vaFDOvpHdhZsYhIiIi0kmR17mJ6h961v3t3VXpgNbBrIiIiEgEv4JZiL5QzXTV+8giponcblYXei5JtiMzOw84Cu+ie0qyxyeoo5m8FcB8gPHjxzNkSEeVD5JTW1vbPANn/PjxFBcnXiN2a30jNfOiZ++ccOQh7NRXk0ey0fZ81tJ16HPOHfqsc4Mfn/PKle3egJQLvgJ2Bnah/bULIh0eel6WjgFJFiirgPWLW7YVzIqIiEgMv4LZ833qJ1GRpQQSmZ9ZFHpOqoCamfUDbglt/sI5tyqZ4xPlnGv3tyGLWP21pKSEkpKk8+WEFRcXJ3X+r6pbT1geMaAXJYV+/s1AOiPZz1q6Jn3OuUOfdW5I1+esnx3mAKOBs4G/dtQ4dI14Ed5M21npHZpkTOwCYApmRUREJIYv6VeWLPYVaUvE60TKE/QMPSdS9iDS7/Fq2b6DtwCaxKjcHF1ut1dJAT0UyoqIiEjXci9wAXC8mZ3vnHuwrYahRVv/jneN2Bg6Vrqj2AXAqtdkZhwiIiKStXIyAXPO1ZrZOrwL4nbv6zezPrQEswkvsmVmOwLnhjZnAWdGzlyNY4CZnRV6/blz7q1E++rKVsUEs4N66TbarLF8Hgwf599xIiIiXZRzbr6Z3QNcDNxnZscDT0Q02dvM9gKOAc7CK6vlgFucc0t8H7D4IzaY3VKZmXGIiIhI1srJYDbkE+AwYGczy3fONbbRbkzMMYmKLJFweQLtd6Pl1rc/AzkRzFZujq4OUaFgNjHpDk1fuQFevRGOmgqHJlEaee6t8PJUOPxKOOLnyY9PRESk6/oh3h/zzwW+GXqEFwV7JKJd+C/104Gr/BqcZIBmzIqIiEgHAn53aGYBMzvSzH5hZnea2f1mNiimTaGZ9TCzorbOkwJzQ889gf3aaXd4xOvX0zec3KQZs53wyg3w4HFeCJqMubd6x71yQ/vtls/zQlnwQtZE+wmHsuAdv3xecuMTERHpwpxzTc657wBnAO/hBbDxHh8DZzvnvuucc22dT7oB1ZgVERGRDvgazJrZCcBiYAZwLXApMBnoE9P0Arw6sGvMrCfp8XTE67iLk5lZADgvtLkJeCXRkzvnljnnrKNHxCGvRuyfnNyX0nXF1pitKO8mi4d0NpTs6Dg/QtPh47yZsmGJ9BN5fvCOVzkDERHJQc65p5xz++OVyzoF+D5wCXAmMNo5t6dz7m8ZHKL4pawienvbOmhqyMxYREREJCv5Fsya2YXAP4Gd8GYLrKflVq5Y9+MFoaXAqekYj3PubeC10OYFZhYvRfopXokBgNudc1FXUmY22cxc6DE1HePs7rrljNl0zmj1KzQ9dEri/cQ7fzLlD0RERLoh59xXzrl/Oufuc879yTn3pHNuaabHJT6KLWUAsHWt/+MQERGRrOVLMGtmOwN3hTZnAbs75wa01d45Vw88hRfcHpPGoV0G1ODV2n3JzH5uZgeZ2RFm9ifgt6F2i4Bb0jiOnFVZFTNjtqsHs37MaPUrNI3TT/6bd6bu/CIiIiLdWckOEIhZ0kPlDERERCSCX4t//RgoAD4Cjg8Frx15DbgQ2Cddg3LOvWdmk4CHgXLgN3GaLQJOcM5tSdc4clVtQxMbtkb/KHT5GbPhGa3hsDL83F5Y2dkZrZHnj9fP9oamwSCMvRBqt8Bc7+8SBa9ez759DmFt+Z4UPP88fBhxJ6ZCWREREZEWgQD0HABbvmrZt0XBrIiIiLTwK5idiLcq7W0JhrIA4Vu9hqVnSB7n3L/MbG+82bMn4NUDqweWAE8Af3DObUvnGHLV6pjZsuDTjNnl8zpX/zTR4xIJTcO2d0ZrbD/OwX6T4fXb4fXbWtrudQb06Adv3g111VC/Beq2hF5Xe6/rq73t8Ov66rjdDtv4OsM2xqyDp1BWRERymJl15nrVAbXA5iSuj6WrKY0JZjVjVkRERCL4FcwODT2/n8QxW0PPPVI7lNacc8uBn4QeyRw3HZi+nX23VWe324utL1talE9ZcUF6g9NXbvBKBSQbJIYD1MOvhCN+3nH7VM9ord8KWyqheo13QR9+bFkNfXeG9Uu8djOv8R6xPnzCe6SDQlkREZHPt+dgM1sJvAlMd879OzVDkqwQW2e2ek1mxiEiIiJZya9g1oWekwkh+4eeq1I8FskSlZvj1JdNZ3AaW/8VEusjtv7ryAnbP3M2NpTd5xzo2R9eu8W7YI8NYduYvZpxJX0UyoqIiCR3jRvPULy7tk43s5eBs5xzG7d/WJJxZbHBbGVmxiEiIiJZya9g9itgZ2AX4N0Ejzk89LwsHQOSzIudMXt48ZL0Bqd+1X8Na2qA3b4B6xbD+4+09Dnz1+Caotu+/7D3yGaW13rcNRu975HCWRERyW3nh54vBg7EK1HwIvAOsDb0Xn9gf+BYoAh4G7gXb52DPYGTgX7AUcDTtFwLS1fWasasShmIiIhIC7+C2TnAaOBs4K8dNTazfsBFeDNtZ6V3aJIplZtrorar+u8Pe09Nb3Ca6vqvjfWwaQVsWAobPvMe60OvN61oHWRC/H3pklcIPfpCYSkUlYaey7xH874y77m9fe88CLOubT5tfV5PCptC1UaSCdFFRES6Iefcn83sHuAA4BngIudc3HvWzWwAXiB7ErDAOXdhaP8PgXuA84BDzWySc+4xX74ASR+VMhAREZF2+BXM3gtcABxvZuc75x5sq6GZDQH+jjdjoDF0rHRDsTNmB/Uq9mfhrM7Ufx33A+i/G8z7Y3QIu2kFuGDHfaZKQQ/vAr+swltMorQC1i+GpRF/vyjuBbWbvddN9XDgRdsXms69NSqUXbDjmSwZeCLHly+k4NXrvZ0KZ0VEJIeZ2SnA94E3gG8651xbbZ1za8zsVGAucL6ZveSce9w5V2tm38WbPbsvcBagYLariw1mt6iUgYiIiLTwJZh1zs0PzSK4GLjPzI4HIlci2tvM9gKOwbsILcabLXuLc26JH2MU/1VWxdaYLfFepHrhrFjOwYEXQ0MNvHpTSx+bV8LOR3mzQxe/GHGAwbw/eI+0MK++bOlArw5Z6cCW0LV0QCiEDe0vKo0+dO6t8PafWrbD34fI78/2hKYx3+eGw3/BkqpdAWg86IcUFBSkph8REZGu7VK8a9fb2wtlw5xzzsxuwwtevw88HtofNLNpwN14ZQ+kq4s3Y9Y5sJxd/1dEREQi+DVjFuCHQE/gXOCboUf4wvWRiHbhq5TpwFV+DU78F3fGbFi8cHbdEhh2IHz6b1j0fEvboQfChs/hqe9BYw001EJjrRe8NtRE7AtvR/fbbP593qOVDn+/al9+idd3WEEPaNjWsn3kr2D8T5M/b3vhdDIzj5M4f+N+F8NLL7XsS0U/IiIiXd/eoedkJhSE2+4Vs/+D0HPf7RqRZIfSAdHbjTVQtwWKyzMzHhEREckqvgWzzrkm4Dtm9k/g58DX22j6MXCdc+5vfo1N/FffGGRddV3UvorIYBZah35tLZD1xVveI5N69IUdRoYeo1peL36xZVYuxJ/ROutaCAS2OzRtdfz2hKZtnb+mpnVbhbMiIiLhlK1/EseE25bF7A//Y9uwXSOS7BA7Yxa8BcAUzIqIiAj+zpgFwDn3FPCUme2Id4vWACAPWA+855xb6veYxH9rttQSe6PfoNhgFrxw76v/wsf/8Gdg7enRD/pGhK7Nj52gpE/r9nNvjR/KQnpC03g608/yecmXiYjXz9CD2l+ITUREpPv4AtgZ+DbwUgdtw84NPa+I2R+eYrk2BeOSTCvsAUXlUFfVsq96NfQbnbkxiYiISNbwPZgNc859BfwzU/1LZq2OqS9bXBCgV0lB/MY9M3Qn36gjYd9zWgLY4l6JH5uuGa1+hKbDx8HhV8KrNyZXuzeyn8OvVCgrIiK55J/AT4FzzOw959xt7TU2syl4Ia6j9fXwgaHn5akepGRI6YDWwayIiIgIGQxmJbe1ri9bgrW1CMLCF+Lv7z8GKvaGgmKvjmvzc+iRXxzxOvb90Ot3H4ye1VrSB2o2eq+XzoKdxsOepyX3xaVzRqtfoekRP4eRE5IPVw+dopmyIiKSi24EvoNXF/YWM/s28BDwLrAm1GYA3t1i59JS0mtt6NhIZ+EFtjPSPGbxS2kFrI8oP1y9pu22IiIiklMUzEpGVMYEswPLi+I3fO33ULUy/ntrP4WvndX5WqZtlRqIDFaTrZfqx4xWv0LTzoarCmVFRCTHOOfWm9nRwAvAQLzgta31FMBb7LYSOM45t6F5p9lI4O3Q46n0jVh8FbsA2JbKzIxDREREso7vwayZfQ04DBiJt9hBXgeHOOfcBWkfmPgq3ozZVubeCjOvab1//OUw57fe684uNNXerNbtqf/q14xWhaYiIiJZxTn3XzPbDbgGOA9oqwbSZrzZtFOdcxtjzvEZcH5aByr+i10ATDNmRUREJMS3YNbMdgUeAA5K5jC8W7kUzHYzsTNmK2IX/ooNTsPKB8ORv/AWUujsrNZ01X8NUxkAERGRnOSc2wRcZmaXA/sBewLhFUI3AguAd5xzdZkZoWREWWwwqxqzIiIi4vElmDWzwcAcoB9e2ApQjXeBGvRjDJJdVm2uidoeFBnMthXKAgz6mvfc2eA0nfVfI2lGq4iISM4ws/NCLxc6594KBa9vhB6S61rNmFUwKyIiIh6/Zsz+AuiPN/v1PuBm59win/qWLNRqxmx5KJiNDU57j4BNy1q2w8EsJB+c+lH/VURERHLRdLzr3G8Bb2V2KJJ1YmvMKpgVERGRkIBP/RyHd7H6kHPu+wplc1tT0LF6S/QdfIN6lbQOTideDTUbog+ODGbBC06Pijjm5aneeeIJ13+F5Ou/hvtIpP6riIiI5JrNoefFGR2FZKfSiujtreugqTEzYxEREZGs4lcwu2Po+SGf+pMstq66jqagi9pX0au4dXC6xylQVxV9cMXerU+YTHB6xM/h/BeSXyzs0CnecUf8PLnjREREJBd8Hnru024ryU2xpQxwsHVtRoYiIiIi2cWvUgYbgQHAJp/6kyy2KqaMQUGe0bdnobcRuXDWgn9EH9ijH5TvSFzJLJyl+q8iIiKSWv8A9gFOAmZldiiSdXr0BcsD19Syr3o1lA/K3JhEREQkK/g1Y/ad0PMuPvUnWawyZuGvgeXFBALWsiMcgK76b/SBg74GZrRJwamIiIhkxu3AcuASMzsy04ORLBMIxKkzuyYzYxEREZGs4lcwewdgwPd96k+yWOyM2UG9ittoGCeYFREREckyzrkq4GjgU+BFM7vXzCaY2Q5m7f1VWXJGq2C2MjPjEBERkaziSzDrnJsB/BY4wszuNrMCP/qV7FQZE8xW9Cpp3cg5WPVB9D4FsyIiIpKFzKwJWAjsBeQBFwAzgbVAo5k1tfPQKlC5ILbObPXqzIxDREREsoovNWbN7DzgY+ANvFmzJ5nZk3izCrZ1dLxzTouGdSMJzZit+gq2rYveNyjOwl8iIiIimRc7K1azZCVaq2BWpQxERETEv8W/pgMuYnsQ8MMEj3WAgtlupNWM2fI4wWxsGYOiXtBnpzSOSkRERKTTrsn0ACTLxQazW1TKQERERPwLZkEzByRkVVX04l9xZ8y2qi+7d/sLf4mIiIhkiHNOway0TzNmRUREJA6/gllNdRQAgkHH6s11UfsqEgpmVV9WRERERLqoMtWYFRERkdZ8CWadc8v96Eey34Zt9dQ3BaP2DYq3+FelFv4SERERkW4i3uJfzumOMBERkRznZykDkVb1ZfMCRv+youhG1Wuh6svofRVa+EtERESyn5kFgAnAOKAC6AH80jm3KqJNId51eJNzri7eeaSbKR0Qvd2wDeqroagsM+MRERGRrKBgVny1KiaYHVBWRF4gZqZAZUwZg/wS6Dc6zSMTERER2T5mdgJwBzAi5q1bgFUR2xcAfwCqzWxH59xWf0YoGRM7Yxa8OrMKZkVERHJawO8OzWxnM7vWzGaY2YdmtsTMdo5ps6eZHW9mh/s9Pkmvys3RC38lVF+2Yi8I5KVxVCIiIiLbx8wuBP6Jt7aCAetpe/Hb+4FNQClwqh/jkwwr7AmFMSHslsrMjEVERESyhm/BrJkFzOx3wCfAL4CJwB54F6+FMc2HAs8CM8xssF9jlPSLnTE7SAt/iYiISBcXmmRwV2hzFrC7c25AW+2dc/XAU3jB7THpH6FkhdhyBloATEREJOf5OWP2T8BPgDzgK+DJtho65/4NfBZqe7ovoxNfxNaYrSiPs/DXKi38JSIiIl3Kj4ECYAFwvHPu0wSOeS30vE+axiTZpqwiert6TWbGISIiIlnDl2DWzCbg1dIC+A0wwjl3ZgeHPYE3i+CI9I1M/NbhjNmaTbDx8+h9g7Twl4iIiGS1iYADbgvNhk3E0tDzsPQMSbJOqxmzKmUgIiKS6/xa/Ovi0PPzzrlfJnjM26HnPdIwHsmQyqqYGbOxwWzlh9HbgQLov1uaRyUiIiKyXYaGnt9P4pjwgl89UjsUyVqxC4BpxqyIiEjO86uUwTi8WQT3J3HMytBzRbutpMtwzrEqZvGvVjNmY+vLDtwd8mNLEIuIiIhkFRd6bmuxr3j6h56rUjwWyVatglnVmBUREcl1fgWz4ft2Pm+3VbTG0HNBisciGbK5poHahmDUvlYzZrXwl4iIiHQ9X4Wed0nimMNDz8tSOxTJWrHB7BYFsyIiIrnOr2A2PE0ymVu1wvW2NqZ4LJIhsfVlzWBAWWwpAy38JSIiIl3OHLzZsmcn0tjM+gEX4c20nZXGcUk20YxZERERieFXMBueKbtvEsecGHr+OMVjkQypjAlm+5UWUZgf8SNYvxXWLYo+qELBrIiIiGS9e0PPx5vZ+e01NLMhwPNAP6Ap4ljp7spigtlt6yDYlJmxiIiISFbwK5h9CW8WwffNrMM+zWw/4Fy8WQQvpHls4pPYGbOt6suuXgAuotSBBWCg1n4TERGR7Oacmw/cg3e9e5+ZPWFmZ0Y02dvMJpnZ/cBCYD+869xbnHNL/B+xZETsjFkXhK1rMzMWERERyQp+BbN/wCtnsBcwzczarBtrZqfhhbGFeIshaBZBN1EZs/BXRXkH9WX77QqFWqhYREREuoQfAn/BC2e/CfyVlkXBHgEeBSYDJaE2fwau8n2Ukjk9+noTDyKpnIGIiEhO8yWYdc59CfwI7yJ0MvCZmf0xoskFZna3mS0GHgf64l3Ift85t9mPMUr6dThjdtX70duqLysiIiJdhHOuyTn3HeAM4D286954j4+Bs51z33XOubbOJ91QIA96DojeV70mM2MRERGRrJDvV0fOufvNzAF3AINpWfAA4MehZws91wEXO+ee8Gt8kn6VVdHBbEWvkugGq7Twl4iIiHRtzrmngKfMbEdgf2AAkAesB95zzi3N5Pgkw0oHQHVly/aWyrbbioiISLfnWzAL4Jx7wMxewgtivwHsHNPkS+CfwO+cc8v8HJukX7szZhvrYM0n0QcM2tuHUYmIiIiknnPuK7zrWpEWsXVmVcpAREQkp/kazAI451YCPwN+ZmblRMwicM6t83s84p/Vm2NnzEYEs2s+gWBD9AEVe/kwKhERERERn5TFBrMqZSAiIpLLfA9mIznnqvAW+JJubkttA1vqGqP2Rc2YjV34a4eRUNzLh5GJiIiIbB8z+1/gMU0ykA61mjGrUgYiIiK5zJfFv0RWx9SXBRhY3k4wq/qyIiIi0nXcCXxlZs+Z2dlm1iPTA4rHzIaZ2c1m9omZbTWzDWb2tpn9bHvHbGZTzcwl+JiQmq+oC2oVzGrGrIiISC7L6IxZyR2x9WV36FlIcUFey45KLfwlIiIiXVo+cFzosc3MngEeAV5yzjVldGSAmZ0QGk/kLUk9gLGhx4Vmdrxz7rNMjC9nqMasiIiIRFAwK76IDWajZss2NULlR9EHVGjhLxEREekyDgTOBiYBFUBP4Fuhxzozewx41Dn3ZiYGZ2ZfAx7HC2KrgRuAV4AS4Czge8CuwHNmNtY5V72dXXa0UMDn23n+ris2mN2iYFZERCSXKZgVX1TGBLNR9WXXL4bGmugDNGNWREREugjn3Hxgvpn9FDgS+DZwKlAO9Af+F/hfM1sGPAz81Tn3qY9DvA0vlG0EjnHOzYt4b5aZLQZ+C4wBfgJcuz2dOec+6rhVjiodEL3dsBXqqqGoNDPjERERkYxSjVnxReyM2Yr2Fv4qHwI9+/kwKhEREZHUcc4FnXMvO+fOBwYCZwLPAA2AATsBvwQWmNk7ZvZjMxuUzjGZ2VhgQmjz/phQNuwW4JPQ6x+bWUE6x5TTYmfMgsoZiIiI5DAFs+KLys3RM2IHaeEvERER6cacc3XOuSedc6filTf4PjAbcHgh7dfxAtHlaR7KKRGvH4zXwDkXBB4KbfahJciVVCsqhcKY2bEKZkVERHKWglnxRfszZrXwl4iIiHRfzrlNzrn7nHNHAsOBK4BNeAFtXnvHpsBhoeetwLvttHs14vWh6RuOtCpnoGBWREQkZymYBcxsmJndbGafmNlWM9tgZm+b2c/MrMd2nrvczM4ys2lm9h8z22Rm9Wa21sxmh/ronaIvJWtVVsXWmC3xXgSDUBkbzGrhLxEREel+zGxP4Ad4NWd7+dTtbqHnJc65xnbaRda83a3NVgkwsxlmtj50zbsmdM17pZn12Z7zdhulFdHb1WsyMw4RERHJuJxf/MvMTgAeIfriuAcwNvS40MyOd8591olz/w/wD6Aoztv9gMNDj5+Z2becc68k20dXUFPfxKZtDVH7mmfMbvwc6qqiD9CMWREREekmzGwY8C28BcH2CO8OPW/Dq0Gbrr6L8a45AVa219Y5t9HMtgI9gaHb2fVREa/703LNe4WZTXbOdeprNrMhHTRpTjxramqoqalpr23Samtr475OVkFJ36hfwho2rqQxxWOV9EjVz4B0XfoZEP0M5LZUX1tAjgezZvY14HG8ILYauAF4BSgBzgK+B+wKPGdmY51z1Ul20RcvlA0CM4AXgP/i3bo2BO8CfRLe4hDPmtkhzrn3t++ryj6xs2UhIpiNrS/bsz+UpXUNDBEREZG0MrMdgDPwrvUOxgtiw2FsE/Ay8DDwtHNuaxqHUhbxOpHr2HAwW9pRwzZ8CDwNvA18BRTgXUt/GzgG6A08ZWYnOef+3Ynzf5Fowzlz5tCvX/oWk50zZ06nj91rQy0jI7a/WvQe79e9tP2DEl9tz8+AdA/6GRD9DOSedevWpfycOR3MArfhhbKNwDExq9TOMrPFwG+BMcBPgGuTPH8D8CfgN865FTHvvQf8y8xeB+4IjeMWYGKyX0S2WxWz8FdZcT6lRaEfvXgLf5khIiIi0pWYWQlwMnA2cCwt19nhC5u38e7S+ptzbq1Pw4oo6k99Au3rQs8lnejrNufc1Dj73wIeMrOLgHvwaureZ2Y7O+dycppobX7vqO3ixs2ZGYiIiIhkXEqDWTOblcrzhTjnXMrDSjMbS8uKs/fHhLJhtwDn49XZ+rGZ3eCca4jTLi7n3GPAYx20udPMzgP2ByaYWV/n3PpE++gKKjfH1peN+B2hVX1ZlTEQERGRrsXMHgJOwZttCi1h7GLgUeAR59ySDAwt8iKsMIH24fJbSQemzrlNHbz/JzPbH7gQ2BH4Jl5QnYyOSixUAPMBxo8fz5AhHVU+SE5tbW3z7Kjx48dTXFzcwRHx5X2wHlY90bzdvzjIMccck5IxSnql6mdAui79DIh+BnLbypXtVobqlFTPmJ0AOFouRrdH+DwuBeeK55SI1w/GHYBzwdCF9g1AH7yvb0YaxjIbL5gNADsB3SqYXRUTzFaEF/5yrvWM2Qot/CUiIiJdzjkRr9fg/WH+Yefc/AyNJ2xLxOtEyhOEg+Vky3cl6k94wSx4NWeTCmadc+3+NmQRd12VlJRQUtKZib+JKS4u7vz5d4jOlwPb1qZ1rJIe2/UzIN2CfgZEPwO5Jx2fd6qD2TmkL0hNtcNCz1uBd9tp92rE60NJTzAbuThYMA3nz6hWM2bLQ39RqvoStsVk0JoxKyIiIl3PVrwFXx8BZjjnsuJ6zjlXa2br8BYAa3f6qJn1oSWYTbiWa5I+jng9OE19ZL/SgdHbW9dCsAkCeZkZj4iIiGRMSoNZ59yEVJ4vzXYLPS9xzjW20+7TOMek2uGh50YgE7e5pVXrGbNtLPxV1Av6jPBnUCIiIiKpMyCL66V+gjchYWczy2/nundMzDHpoIUEoHUw64KwdR2UDYzfXkRERLqtnFz8y8yK8WYOALR7S5RzbqOZhVeo7aiuVWfGcgIQvn//RedcVSfO0VEBrYrwi5qaGmpqUvt7Q21tbdzXYV9t2ha13bdHHjU1NeSveIeCiP1NA/ekPs7xkj06+qyle9Dn3KKpqYmtW7dSU1NDfX09znWVm0IS09TURN++fQH4/PPPycvTbK3uKNnPORAIUFJSQmlpKUVFRe22DUv1tUVXk8WhLMBcvGC2J7Af3mJc8Rwe8fr1NI1l94jXX6Wpj+zXsx9YwAtkw6pXK5gVERHJQTkZzAJlEa8TqaEVDmYTqc2VMDPbAbgrtNkE/KqTp0r4drM5c+bQr1+/jht2UrgIdqQV6/KInCDx1eIFvLTuIw5Y+gqDItp9XlvOgpdeStvYJLXifdbS/eTy55yfn0+vXr3Iy8sjEAiQl5cXVb+wuwjXScr1YK27S+Zzds6xfv16nHNUVVUl9AeadevWbfcYuxMzK8NbN6AM6PAvHs65dP7P9mng56HX5xMnmDWzAHBeaHMT8EqaxnJRxOtX22zV3QXyoGd/L4wNq16TufGIiIhIxuRqMBu5bF59Au3rQs8pq/JrZnl4dciGh3Zd55x7L1XnzxaNQahuiA4yehV5M8561yyL2r+5ZDgiItkgEAjQp08f8vLyKCkpIRAINO/vjuGsSJhzjmAwiHOOuro6ysvLaWxspLGxvapPEmZm3wMupeVuqEQ40nhN7px728xew5s1e4GZ/dk5Ny+m2U9pKdl1u3OuIfJNM5tMy2K51zjnpsa8vxdQ45xrsySXmV0EXBDarMSryZu7SgfEBLOVmRuLiIiIZEzGgtlQMNkHL+xs97dc59yKFHcfOfWjMIH24fv4Ujmd6I/AcaHXzwG/3o5zdVRioQKYDzB+/HiGDOmo8kFyamtrm2fVjR8/nuLiltx75cYaeCv62v+bxx1BedMmSt7bGLV/j6POZvd+u6R0bJJa7X3W0n3oc4aNGzeyceNGzIySkhIGDBhAcXFxtwtlg8Eg1dXejSOlpaXNAbR0L8l+zk1NTaxevZqqqiqccwwdOrS5FEJbVq5stzJUtxe6rn0KOCm8K4PDiecyvPIEJcBLZvYbvFmxJcBZwPdD7RYBt3Ti/PsB95nZK8C/gQ+B9Xi/a4wBzgGODrVtAi5yzm3t3JfSTZQOxPs2hUSGtCIiIpIzfA1mzawf8EPgFLwaU4n8BpiOWQRbIl4nUp4gvEJtImUPOmRmN9ByATwXOMM519TZ8znn2v1tKDJIKCkpab6dMR2Ki4ujzr+xMjrL7lGYx4DeZdiSt6MPLOhB8eA9tRptFxL7WUv3lKufc2VlZXN4NWTIEAoKCjo4ousLBAIKZnNAIp9zIBBg0KBBbNmyBTOjvr6+w/8P5OL/J2JcDHwj9Ho13uzSd4ENQLCtg/zinHvPzCYBDwPlwG/iNFsEnOCc2xLnvUTkAUeFHm1ZD1zgnPtnJ/voPkorordVykBERCQn+RbMmtnBwN+B/mR4FoFzrtbM1uEtANbu9FEz60NLMJtwLdd2zncFcGVo8z/AiVm+YMR2WbU5+kur6BWacbbq/eiGFXsplBWRrBG+bTsvLy8nQlmRWHl5eeTn59PY2EhTU6f/dpxLwvVZPwYOc85tbK9xJjjn/mVme+PNnj0B7xq4HlgCPAH8wTm3rZ1TtOd5vDIF44B9gYFAX7xr/g3Af4EXgOmdWei2WyodEL29RaUMREREcpEvwayZ9QWewbtAqwbuw1tYYCrejNgL8coa7A+cjFcD9nXg/jQO6xO8Wls7m1m+c66t4mljYo7pNDO7FLgx4lzHOuc2b885s13l5ugFQwb1Ct0SXflBdMNBX/NpRCIiietupQtEJG12w7um/XU2hrJhzrnlwE9Cj2SOmw5Mb+f9NcADoYckonRg9LZmzIqIiOQkv2bM/gAvlK0DxjnnFpjZHnjBLM658GICmFkF8ChwODDPOXdFmsY0Fy+Y7YlXF6vVCrUhh0e8fr2znZnZucAfQpufAUc557r9EsarYoLZivLQrY6r/hvdsCKZNTJEREREstLCTA9Auoiy2GBWNWZFRERykV/F5P4HbxbBA865Be01dM5V4t1etRT4mZkdmaYxPR3x+vx4DcwsQMutaZvwFklImpl9E6/WmAErgYnOua86c66uJu6M2ZqNsHFZdEPNmBUREZGua3HoeYeMjkK6jlYzZhXMioiI5CK/gtmdQ88vR+xz4RehlWxb3vBqrt6KF2RenI4BOefeBl4LbV5gZuPiNPsp3q1pALc75xoi3zSzyWbmQo+p8foxs2OAv+ItiLAGb6bsshR8CV3CqqqYGbO9iqHyw+hGeYXQfwwiIiIiXdTf8K5bT8z0QKSLiA1m66uhLiXrDIuIiEgX4lcpg/LQ8/KIfZGJXRnejNRI74SeD0zTmMBb/OB1oAR4ycx+gzcrtgQ4C/h+qN0i4JZkT25mBwH/AAqBBmAKUGBme7Zz2Ern3KZk+8pWlTGLfw3qVdy6jMGA3SG/0MdRiYiIiKTUHcC3gUvM7B/Oudc6OkByXGwwC7B1DRSV+j8WERERyRi/ZsyG//wbGQRviHg9Is4xoVWiGBDnvZRwzr0HTAKqgFLgN8A8YBbRoewJzrktnejiOKBH6HUB8AjwYQePUzrRT1ZqaAqyZktd1L6KXsWwSgt/iYiEzV+2oeNGKTyuKwoGg/z+979njz32oLi4GDOjd+/emR6WSDPnXB1wDPAuMMPMfmtm+5hZcQeHSq4qKoWCntH7tqicgYiISK7xK5hdEnoeFt4RmhVaGdo8Is4xB4eet6ZvWOCc+xewN17phEXANrzZu+8AVwD7OueWtHkCadPaLXU4F71vUK+S1jNmB2nhLxHJTbfOWMQZ98zj7tlLkzru7tlLOeOeedw6Y1GaRpZdrr32Wn7605/y6aefMnr0aA455BDGjYtXgUgkM8ysCVgFjMO7U+qneCHtVjNr6uDRmMmxSwaVxsw/UZ1ZERGRnONXKYO3gP2AscCTEftfACYDl5vZc865RQBmdgBwOV4d2vnpHpxzbjnwk9AjmeOmA9PbeX8qMLXzI+vaVsUs/FWYH6BPfj2siwkSBu3j36BERLLE/GUbuH2mt17QTS98CsAlE0Z1eNzds5c2t7995mIOHd2PsSO673pDzjnuuusuAB5//HFOO+20DI9IJC7rYFuktbIK2Ph5y3b1msyNRURERDLCr2D2ReB/gW/izUIN+z1wDl65go/M7L94t/7vgrdYlgNu92mMkmKVMcHsoF7F2OoFRKz7BpYHA/fwd2AiIllg7IgduOK4Mc0hayLhbGQoC3DFcWO6dSgLsHbtWtatWwfA8ccfn+HRiLTpmkwPQLqgVjNmK+O3ExERkW7Lz2D2ISDPzHZyzn0O4Jz7yMwuAe4OjWW/mOOmOude8GmMkmKrYhb+qiiPs/BX/12hoMTHUYmIZI9wCJtIOBsvlE1khm1XV1PT8m9JSYn+vZDs5JxTMCvJi10ATKUMREREco4vNWadcw3OucnOuXPDoWzEe/cDewE3Ac/ilTe4EzjAOfdrP8Yn6RFvxiyVsfVltfCXiOS2SyaM4orjxjRv3/TCp61qznaFUHbEiBGYGbNnz+bDDz/krLPOYtCgQeTl5TF16tTmdi+++CKnnnoqgwYNorCwkP79+3PSSScxa9asqPMtW7YMM2PEiBHN+8ys+TF9+vSo9l988QVTpkxht912o2fPnpSWlrLffvtx8803U1sb/e9R5PnNvDvOn3nmGSZOnEjfvn2bv46whoYG7r33XiZMmEDfvn0pKipi+PDhXHDBBSxZEr8M/YQJE5rHuW7dOn7wgx8wbNgwioqKGDZsGJdddhmbNm1q8/vZ0NDAtGnTmDhxIv369aOoqIihQ4dyzDHHcM8991BXV9fqmLa+B7fcckvc74GIZFirYFalDERERHKNXzNm2+WcWwj8PNPjkNSqrIr+JbCiVwksjwlmK7Twl4h0LcGgY+O2+pSe88z9h7CtvpE7Z3kh300vfMq2+kYmHzyC6W8sa94P8MMjd+bM/Yewvrp1MJesYDBInnMELHXlMF977TV+85vfYGbsvvvu9O7dGzMjGAxy8cUXM23aNAD69u3LnnvuyYoVK3j22Wd59tlnueGGG7jyyisBKC4u5pBDDqGuro533nkHgEMOOaS5n4EDWwKNf//735x55plUV1dTXFzMyJEjqaur4/333+c///kPTz75JC+99BLl5eVxx/y73/2Oyy+/nH79+jFq1Kiombnr1q3jxBNP5K233sLMGDx4MMOGDWPx4sU88MADPPbYY82hbjwrV65k3333ZfXq1eyxxx4UFBTw+eefc8cdd/DGG2/wxhtvUFBQEHXM6tWrOfHEE5u/7sGDBzNy5EhWrVrFzJkzmTFjBscdd1xUaN3R9+Dxxx/nqaeeavN7ICIZEBvMblEpAxERkVyTFcGsdE+xM2YHlwVgzSfRjTRjVkS6mI3b6tnvupfT3s+ds5ZEBbId7e+sWT86gB16FHTcMEHXXHMN5513HrfffjtlZWWAV47g+uuvZ9q0aYwYMYJ7772Xo48+uvmYxx57jAsvvJCrrrqKAw88kCOOOIKKigrmzp3LsmXL2GmnnQCYO3duq/4WLlzIGWecwbZt27j22mv52c9+1hysLl26lHPOOYc333yTH/3oR61m2Yb98pe/5M477+TSSy8lEAjgnKO+3gvfzzrrLN566y2OOOII7r77bnbddVcA6uvrufbaa7n++uuZNGkSCxcupG/fvq3Ofe2113LsscfywAMP0L9/fwBeffVVTjjhBN555x0eeughLrjggub2zjlOP/103nnnHUaNGsVDDz3EwQcf3Pz+2rVrmT59Oj179kz6e3DllVfy8MMPd/AJSqqY2UBgTyBcCHoD8JFzTveri0czZkVERHKeL6UMzCxoZo1mtnsSx4wKH5fOsUn6rIoJZndmBQRjPs6KvXwckYiIpNsee+zBtGnTmkNZ8ILZG2+8kYKCAv7xj39EhbIAkyZN4te//jXOOW666aak+rv66qvZunUrP/vZz/jVr34VNdt11KhRPPHEE/Ts2ZOHH36YL7/8Mu45vve97/GDH/yAQMC7LDIzioqKeOGFF5g5cyYjR47kmWeeaQ5lAQoLC7nuuus4+eSTWb9+ffNM4Fh9+vThkUceaQ5lAQ4//PDmMPbZZ5+Nav/MM88wd+5cevTowYwZM6JCWYD+/fvzf//3f1Hn6+h78Nhjj9GzZ08ef/zxNr8HkhrmucjMPgS+Al4C/hZ6vAR8ZWYfmtn3zVI4VV26prKYYHbrWgg2ZWYsIiIikhG+BLMhnb341EVrFxQMOlbHlDIYWrs4utEOo6BYt1SKiHQn5557Lnl5eVH7nn/+ebZt28a4cePYZ5994h536qmnAjBnzhyamhILJurr6/nnP/8JwMUXXxy3zZAhQxg7dixNTU28+uqrcdtMnjw57v4nn3wSgHPOOScqaI437tgauWHf+ta34pYPOOiggwBa1aj9+9//DngzdcMzhduT6Pdg3333bfd7INvPzPoArwF/BHbHu4aN99gdb+HbOWbWOyODlewQO2PWNcG29ZkZi4iIiGRENpcyCAeyLqOjkE5Zt7WOxmD0R7fDlk+jG6mMgYhIt7Pbbru12vff/3r1xRctWsShhx4a9zjnvH8zampqWL9+PQMGDOiwr8WLF1NTU4OZcd5557XZbtGiRYBX7zXRMUeO+7HHHmPmzJlx24QX8Grr3KNHj467P1wjt7q6Omr/ggULABg3blzc42Il+j1YuHAhgGbMpklo9uszQHiK83rgceAtoBLvunYgcABwJtAv1PYZ4HC/xytZokc/vB+NiGvm6tVQ2vH//0RERKR7yOZgNlyobWtGRyGdEltfNj9glKz7KLrRIC38JSJdT58ehbz7y6PScu7Yhb56lRSwuaahefuHR+7M5INHpKy/YDBIXlNtxw2TEFn7NCwcXlZWVlJZ2fHiNtu2bUuor/B5nXO8/vrrnT5vvDFHnn/hwoXNwWaqzh0umxAOpMOqqqoArwRCIlL1PZDtdjZwKF7C9ihwqXNuS5x2D5nZlcBdwLnAoWb2LefcX/0bqmSNvHzo2R+2RtSWrV4NqNSXiIhIrvA7mE1o9quZ9QR+GNpcmr7hSLrE1pfdsawAWx0bzGrGrIh0PYGA0be0KOXnvXv20qhQ9orjxnDJhFHcPXspN73g3XFw56wl9CjM55IJo1LSZzAYpKqqLiXnak9paSkAF110Effcc0/Kz1tUVERtbWoD5sjz//Wvf+Wss85K+fnjCZc9CAeuHUnke+B9zlVR55eUOzv0/Kpz7tz2GjrnqoHvmNkwvNmy5wAKZnNV6cDoYHaL1oYTERHJJWmpMWtmn0U+It56Kfa9OI8vgU3At/CC3H+lY4ySXrEzZr9euhYaY35hrFAwKyICRIWv0BLKAlwyYRRXHDem+b2bXviUu2d3rb9Z7rWXN/vrgw8+SOl5d9llF4qKiqirq2suV5BK6Rp3e/bcc08A3njjjYTap/t7IAn7Ot516x+SOObO0PO+qR+OdBmxZQuqFcyKiIjkknQt/jUi5gFeAaXBcd6LfQwC8kLt3wJ+m6YxShrFzpjdt2B5dINeQ6FnX0REcl17oWxYVw9nTzzxREpKSpg3b15Ct9snqqSkhBNOOAGAm2++OWXnDTvjjDMAePDBB9mwYUPKzx/PaaedBnh1bZctW9Zh+3R/DyRhO4SeP0/imHDbHdptJd1bWUX0dvWa+O1ERESkW0pXMPvnmAd4swieifNe5GM6cA9wNXA0cIhzTjVmu6DKzTVR22OCMb+nqIyBiEhCoWxYVw5nBwwYwFVXXQXAySefzOOPP05TU1NUm8rKSu655x6uv/76pM593XXXUVpayrRp07jssstYt25d1Pv19fW8+OKLnHnmma367MhJJ53EUUcdRWVlJRMnTuTtt99u1eaTTz7h6quv5umnn07q3O31edhhh7F161aOPfZY5s2bF/X+unXruPnmm1m7dm3zvkS+BzNnzuT8889P+nsgCdscet4xiWPCbatSPBbpSlrNmO24DreIiIh0H2mpMeucOz9y28y+E3r5C+fcx+noU7JL7IzZYfWLoxtUaOEvEclt85dtSDiUDQu/Hz7uphc+Zf8RfRg7Ivsn3P3iF79gw4YN3HrrrUyaNIny8nJGjx5NIBBg1apVrFy5EoBvf/vbSZ13t91245lnnuHMM8/kjjvu4K677mKXXXahV69ebNq0iaVLl9LQ4C2g9uijjyY97scff5zTTjuNV155hQMPPJBBgwYxdOhQGhoaWLZsGRs3bgRg2rRpSZ87HjPjiSee4IQTTuDdd9/l4IMPZujQoVRUVFBZWcmXX35JMBjk9NNP///t3Xl8VNX9//H3hySsAdnC4gLIoihYNxCwyFIVbV2qVsWvVarWunXxS9sv+tWfit3U1orW+hWl7rV1aSu17oKCosFiXVoXFpcgKDuGLSGQ5Pz+uHcyS2YmM8nMnWTm9Xw85jF3Ofeec5kZ5pPPnHuOysrK0v43iJ1sDBnznrzxYs+X9HSKx1wQcSwKVWnf6HV6zAIAUFCy1WM21vWSfiaJSKNArN0aTsya6tV7e8xs1vSYBVDgRg/qqcuPHiYptaRsSGTP2cuPHtYmkrKSl3C85ZZbVF5ermnTpqlXr156//33tWzZMnXt2lWnnHKK/vCHP2jWrFlpn/trX/taQ8/VQw89VJ9//rnefvttVVdXa8yYMbr22mv11ltvqbg4/d+je/TooXnz5umRRx7RCSecIOec3n77ba1atUoDBgzQ+eefr7lz56adUE6mb9++ev3113XHHXfoqKOO0rZt2/Tuu+9Kko455hjddddd2nPP6I6ZTf0bzJgxQwsXLmzWvwFS8hd5w3CdamYzzcwSFTTPTEmnyruj7PFgmohWqVFiljFmAQAoJIFE586564OoB62Dcy6qx+xAW6eS2pgRKUjMAoCmH7ufxg/rnXZy9dJJQ1pdT9lUxkOVpLFjx2rs2LEpn3fQoEEp9fIsKyvTzJkzNXPmzIyeV5LatWunqVOnaurUqSmVl6QFCxYk3T9p0qSk9bdv316XXXaZLrvsspTrTPRvUF9fr61buVs+y+ZI+oGk4ZKukfQtM7tf3nwJ6+QlYPtJGiPpO5JG+Mct9Y9FoYpNzG4jMQsAQCHJWbcJvyfBYIUnPNgs6VPnXH2u2oTM+LJqt3bVhl/GkVYRXaBLn8YTHQBAgWpucrU1JWWBQuec221mX5f0kqR9JR2o5BPYmqRPJH3dOVcbQBPRWsUmZndtk3btkNp3yU17AABAoIIayqCBmR1nZv+QN9HBckmL/cdySVvM7EkzmxJ0u5A5a2Im/hrZriK6QP+DpcR3+AEAALQ5zrmVkr4i6bfyJgOzBI8tkm6WdIhz7rPctBatRte+jbcxziwAAAUjsB6zZtZe0gOSzgxtilOsi6QTJJ1gZo9KOs85tyugJiJD1sZM/HVoyWfeDXwh/Zn4CwAA5B/n3A5J/2NmV0s6XNJIRd8d9p6kfxHfokH7Uqmks7S7Krxt+zqp5765axMAAAhMkEMZ/EneJAcmqVbSi/LG3Vrrb+sr6QhJx0oqkTTVb9+Z8U6G1mtNVGLW6QB9Gl2A8WUBAEAe8xOv5f4DSMxMKu0jfVkR3sYEYAAAFIxAErNmdoKk0+T1m3xZ0gX+7V7xyg6QdK+kr8mbOOEbzrlngmgnMiOyx+ye2qRuLmbCERKzAAAAgKe0X0xilqEMAAAoFEGNMXue//yupOMTJWUlyR9r6+uS3vE3nZ/VliHjInvMjmwX01u24x5S94EBtwgAACB7zKyrmV3rP5qc4dTM+keU7xREG9GKlfaJXt+2NjftAAAAgQsqMTtWXm/Z3zrndjdV2C9zs7whDsZmuW3IsLVbw5N/jWgXk4Nn4i8AAJB/TpE0U9K3nXOpZNXWSvq2pOsknZS9ZqFNKI2ZAIyhDAAAKBhBJWbL/OcP0jhmqf/cO8NtQZZF9pgdYTE9Zvsx8RcAAMg7oSG7HkulsHPOSXpEXieEM7LYLrQFXWMTswxlAABAoQgqMbvDf+6VxjGhGWyrkpZCq+KcixpjdmS7iugC/Q8JtD0AAAABGO4/v57GMaGJwQ7McFvQ1jTqMctQBgAAFIqgErPL/OepaRxzVsyxaAO21dSqaledJKlMlepnX0YXYOIvAACQf/b2n9ekcUwo+7ZXhtuCtqZRYpYeswAAFIqgErNPyrtV63wzO6+pwn6Z8+XdEjY3mw1DZq3dWtOwPCK2t2xJF6nXkGAbBAAAkH31/nPnNI4JlS3OcFvQ1sRLzNbXxy8LAADySlCJ2dvl9SAwSfeY2dNm9i0z29vMSvzH3v62ZyTd45f9QtLvA2ojMmBdRGL2QKuI3tnvIKldUbANAgAAyL5QT9lRaRwTKst964UuNjHr6qSqTblpCwAACFQgiVnn3A5JJ0qqlJdwPV7e5AgrJe30Hyv9bcf5Zb6UdKJzjjFm25DIHrONx5dl4i8AAJCXXpUXv15mZiVNFfbLXCbv7rBFWW4bWrsuZfLePhG2r8tJUwAAQLCC6jEr59zbkg6S9Fd5t3tZgke9pL9I+opz7t2g2ofMWLc1YuIv+zR6J+PLAgCA/HSf/zxM0p/MLOGQBv6+P0vaL+ZYFKqiYqlL7+htJGYBACgIgY5p5Zz7QtIZZtZP0mRJIyX19HdvlvSepAXOuXQmTkArEuox203bNaDdhuidJGYBAEAecs69bmaPyJu89jRJY8xsjqRX5A1z4CTtKWmCpAvlTRbmJP3FObcwN61Gq1LaV9oRETuTmAUAoCDkZLIB59xaeT0FkGdCY8yOaLcyekdRe6lseA5aBABAas477zw98MADuu666zRz5sxcNwdtzwWSeks6RtJekmYmKBe6Z/1FSd/JfrPQJpT2kSJzsSRmAQAoCIEMZWBmE/xHpzSO6Rg6LpttQ2aFesyOiJ34q+8IqajJIdcAoPCsLA/2OKStoqJCM2fO1K233prrpqAVc87tlDdXwnR5E9gmGrZrlaQfSTrePwaQSvtFr29fn5t2AACAQAU1xuwCSS9J2jeNY/aKOA5tRKjH7Mh2MePL9mPiLwBo5OUbpPuOlxbNSu+4RbO8416+ITvtQpSKigpdf/31JGbRJOe5TdIASYfLG7bgSv9xoaTDJA1yzv3eOedy11K0OqV9ote3rc1NOwAAQKCCHMrAmi6S0eMQsJ110raaWknSyNges4wvCwDRVpZLC2/0lufN9J7HT2/6uEWzwuUX3igNniQNHJeFBgJoLj/p+rb/AJpW2jd6nR6zAAAUhKB6zDZHqG11OW0FUrZll/fcWTs12GLmb+t/SODtAYBWbeA46ZiZ4fV5M5vuORuZlJW840nKAkDb1zU2McsYswAAFILWnJgd5D9vyWUjkLrKGq9z8wG2Uu0s4u48K5L6HpijVgFAKzZ+eurJ2XhJ2VR62AZk0KBBMjMtWLBA//nPf3TWWWepf//+KioqippI6/nnn9epp56q/v37q3379iorK9NJJ52kl15KPHLR448/rilTpqisrEwlJSXq2bOnhg8frmnTpun555+PKjtz5kyZmc4777yE5zMzmZkqKiqavK5JkyZp8uTJkqSVK1c2HJvOOQCgSY16zJKYBQCgEGRlKAMzG5BgV38z297E4R0kDZH0c0lO0vuZbBuyJ9RjdkS7iugdZcOlkpTnfQOAwhJKroaSrvGGNWjlSdlIr776qn71q1/JzHTggQeqe/fuMjPV19frkksu0Zw5cyRJvXr10siRI/XZZ5/pqaee0lNPPaUbbrhBV155ZdT5rr76av3qV7+SJPXp00cHH3ywqqqqtHr1aj300EPaunWrjjvuuKxdz0EHHaRNmzbpvffeU4cOHTRq1Kio/R07dsxa3QAKSGxitmartKtKat85N+0BAACByNYYs5/G2WaSXmjGuR5sYVsQkEo/Mdt4fFkm/gKQR+rrperNmT3noed6f4C/8mtvfd5Mb33MxdIbd4W3S9KEGV75HRtbXm99veSKJcvcDTTXX3+9pk2bpttuu01du3aVJFVXV+uXv/yl5syZo0GDBunuu+/Wscce23DMo48+qgsvvFBXXXWVxowZ09BDdePGjbrppptUXFysP/3pTzr99NNlFh56fvHixfr4448z1vZ4br/9di1YsECTJ09Wv379tGjRoqzWB6B1WFKxWaMH9QzsuEaJWUnasV5qPyj9cwEAgDYjW4nZRBN2pTOR105Jv3PO3ZuB9iAAlbu8l3dkbI9ZJv4CkE+qN0u/GZL9el75dXRCtqntzdBOkl30llznXhk5nySNGDFCc+bMUVFRUcO26upq3XjjjSopKdETTzyhQw45JOqYqVOnas2aNZo+fbpuuummhsTsRx99pLq6Oh1yyCE644wzGtU1duxYjR07NmNtBwBJmvXict02f4WuOH64Lp2U+v/3dy74WDc9t1SXHz1M04/dL71KO3SVijtJtdXhbdvWST0GpXceAADQpmQrMXt+zPp98oYluEbS50mOc/ISsmskve2ca2rYA7QilbukDtqlYbY6egeJWQAoGOeee25UUlaSnnnmGVVVVWnChAmNkrIhp556qqZPn65XXnlFdXV1Kioq0sCBAyVJy5cv11tvvaXDDjss280HUOCWVGzWbfNXSJJuem6pJKWUnA0lZSXptvkrNH5Y7/R6zppJpX2kypXhbYwzCwBA3stKYtY590Dkupnd5y/Odc59kI06kXuVNab9bLVKrC56R7+DctMgAEDgDjjggEbb3n33XUlegnX8+PFxj3POmzSyurpamzZtUp8+fdS/f3+de+65euihhzRq1KiGYQ6OPPJITZgwQd26dcvehQAoSKMH9dQVxw9vSLKmkpyNTMpK0hXHD2/ecAZd+5GYBQCgwGSrx2ysyf5zvLFnkScqd0lHxQ5j0Guod2sWAKAgdOnSpdG2yspKSdLatWu1du3aJs9RVVXVsHzPPfdo5MiRmjNnjhYvXqzFixdLktq3b68zzjhDN998s/r165eZxgOAwknY2OTseWP2bFQ2XlI2neEPopT2iV4nMQsAQN4LJDHrnFsYRD3Ind310o5a08jimNx7Pyb+ApBnOvWU/idLE07FTvTVsbu0szK8PmGGNyFYhtTX18vVZj8UKC0tlSRdfPHFmj17dlrHlpSUaMaMGZoxY4Y+++wzLVq0SPPnz9fjjz+uhx9+WB988IHeeOMNlZSUSFLD5GChHrixduzY0YIrAVAo4iVnd+/erQHOG3VAynBSVmo8ARiJWQAA8l5QPWaR57bs8p6Z+AtA3mvXTurSO/PnXTQrOil7zExp/HRv+7yZ3rZXfi217+xtz4T6emnr1sycK4mDDvKGtPn3v//dovMMGDBAZ599ts4++2xdddVVGjFihN5++2298cYbDUMkhHrsrlsXP6GxYsWKtOsNJXsBFJbY5Owt8z9Wt5IifWvfeq1cVKFb5n/SULbFSVlJKo3p/b99fcvOBwAAWr12uW4A8kNljVSsWh1gn0XvIDELAE2LTL5K4aSs5D0fE7Fv3kyvfBty4oknqlOnTiovL9drr72WkXMOGTKkYQiDL774omH7sGHDJEnvvPOOdu3a1ei4O++8M+26OnXqJMkb/xZAYbl00hBdcfzwhvWtu033LS/KfFJWajyUwbamh34BAABtG4lZZETlLtMQ+0IdbHf0DhKzAJBcsqRsSBtPzvbp00dXXXWVJOmb3/ymHnvsMdXVRU8UuXbtWs2ePVu//OUvG7bNmzdP06dP1zvvvBNVtq6uTnfddZdWrlwpM9Nhhx3WsG/y5Mnq3Lmz1q1bpyuvvFL19fWSvGEb7rzzTj344INpt3/w4MGSpPXr1+v9999P+3gAbdulk4bou+P3jbtvYK/OOnJIr8xU1GgoA3rMAgCQ70jMIiMqd0kjrCJ64x4DpM7NmJEWAApFKknZkDaenL366qs1ffp0bdq0SVOnTlXPnj01atQoHXHEEdpnn33Uv39/XXrppfrwww8bjtm+fbtuvfVWHXrooerRo4cOO+wwjRo1Sn379tUll1wiSbr++us1dOjQhmO6deumX/ziF5KkWbNmqaysTKNHj1a/fv30gx/8QHfccUfabe/du7eOO+44SdLhhx+uUaNGadKkSZo0aVJKk5kBaPuK28Uf0mTlpip9847XdOkf/6WP1m9vWSVdYxKzO9Z7Q84AAIC8RWIWGVG5y+KML8vEXwCQ0Mry1JOyIfGSsyvLM9+2LDAz3XLLLSovL9e0adPUq1cvvf/++1q2bJm6du2qU045RX/4wx80a1Y42XzUUUfpjjvu0GmnnabevXvro48+0rvvvqsOHTrotNNO07x583TNNdc0qmv69Ol66KGHdPjhh6uqqkorVqzQV77yFb3wwgu64IILmtX+P/7xj7r44ovVr18//fvf/9bChQu1cOFC7dy5s9n/JgDaju6dS5Luf/a9tZoya6Gu+Mu/9UVlM4c9ie0xW18rVW9u3rkAAECbwORfyIjKGmkEE38BQOoGjpMmXiktvDG1pGxIqNy8md7xA8dlq4VpqaioSKnc2LFjNXbs2JTK9urVS5dddpkuu+yytNtzzjnn6Jxzzom7zzkXd/v999+v+++/P+6+3r17a/bs2Wm3A0Dbd+eCj3XTc8sa1tu3c9pV37gHbb2THn1zlZ5453N9Z9xAXTZpqHp0aZ/85CvLw/+PdymTZJIi/o/avi7+hJORxwEAgDaLHrPIiC01rvFQBiRmASC5yf8rnf9c6knZkPHTveMm/2922gUAkBRKyi5tWD9pQJ1+M6ZOP5ocf8xZSdpVW685r36qCb9+WbfPX6EdNbXxC758g3Tf8eFhaYpKpM4x49VuX9f4uEWzvONeviHdy8mu5t7Bke5xQdUDAEAASMxKMrMBZnazmX1oZjvMbLOZ/dPMfmpmnTNYz1lm9ryZrTGznWZWYWYPmVlqXYdasT12r1OpxdzOSWIWAJrW3B5P9JQCkCZi3vTEJmV/fPQQHbOX15v10gn76orjhyc9fltNrX774nJN/M3LeuD1Cu2qjRgvdmW5d8eEFD1meOxwBttiErORY5MvvDH1ZGO2k5mxSeZUpZtkDqoeAAACUvCJWTM7QdK/Jf1E0nBJnSX1kDRa0m8kvWVmg1tYR0cz+4ekP0uaIqmfpA6SBko6R9JrZtZ4kLxW6MM3nm+0bVddvQbWVURtq+3cR+raL+lxAAAACAYxb3pik7JXHD9c3xs/MKrMpZOGNErOxpsjbOP2Xbruyfd19C0LNPftz1Vf77wf1+JN6FjaJ/rgyB6z8SaMTOVHumwnMxMlmVM5fzpJ5qDq8S2paN74vukel0/15NO1UE/zjsuna6Ge5h2XT9cSlIJOzJrZwZIek7SHpO2SrpZ0pKSjJc3xi+0v6WkzK21BVfdIOtFfflnSKZKOkPRdSR/Lex1+ZmYXtqCOrCu/56c64NkzVf5gdDy9YVuNRrZbGbXN9TsofNyD13jH3fPTQNoJAACAMGLe9MRLyl46aUjcsrHJ2XonHdi/a9yyqzZX678ffUff+N2remnpOs2q/oZu3H1WuMC8mY0n+9q+3nuOScreuPsszao+oemLCSKZOXCcXh34g/B6KvXEXM+rA3/QdJI5qHokzXpxuc6YXa47F3zcZNlIdy74WGfMLtesF5enVD6f6smna6Ge5tWTT9dCPc2rJ5+uJUgFnZiVdKu83gK1kqY4537lnCt3zr3knLtI0gy/3HBJP25OBWY2UdLZ/uo/JB3rnPu7c26Jc+5eSWMlfebv/7WZdW/epWTXh288r3GrvLh93Ce/i0rOrttaoxH2aVT54r0OleQlZcd98jvvuFVz6DkLAAAQvFtFzJuSJRWbU07KhsQmZz9Ys02/Pv0gTd6/LG75pWu36YL739Rt8z/S7LqTo5Oza96NLrx9bdyk7Oy6k3Xb/BVN9vxZ4vZvnPxNM5l54+6ztMTtn7iOis06d9mRqdcT5/znLjuy6WsJsJ7b5q+QJN303NKU//CPTOin9NrkUT35dC3U07x68ulaqKd59eTTtQStYBOzZjZa0iR/9R7nXLyfgX8r6UN/+b/NrKQZVYUC3TpJlznn6iJ3Ouc2SrrCX+0hr0dBq3PAmONUPvhHDeuRydm1W3ZqZLuKqPLW/+CopKwklQ/+kQ4Yc1wg7QUAAAAxb7pGD+qpy48eJim1pGxIZHL28qOH6cxRA3Tf+Ufo0YvG6vCBPZIe2yg5G+nTV6OSizftnqrZdSc3tG/0oJ5NXs8ex85oUTJzj2NnJK1n9KCeuuL44Y2vI149CZLMqV5LkPWEpPKHf7xe1oVUTz5dC/U0r558uhbqaV49+XQtQSvOdQNy6JSI5fviFXDO1ZvZg5JukBdATpL0YqoV+LeCHe2vvuicW52g6N8kbZXUTdJp8oLjVmfctJ+r/EGFe8B+8juVPyjt6HW0etj2qLJvlc/XuFX3N6yXD/6Rxk37eZDNBQAAADFv2qYfu5/GD+ud9h9tl04aolGDekQdN2ZwL/3lknGa/+F6/eb5ZVq2blvcY0PJ1itLHonesWN91OpPix/TBcXPyUr7qveqfaQn+nrj0pb28SYOi3zu2F0y06WThuhOzdCNL0acP5S0HD89fPIESdlUktOhMjc9p+jrmDdTqtkmHXqu9Oa90uvhTht31Z6g5+tH64YJHfVfI+ulDaFbS53/5BqtX3qA1HN7ke577RCV1Vbqu8XPhevZtlY65GzpnYelN+6Kuo5QUjadRLt3PUujns8bs2ejsukMfZFqPfGOb+315NO1JKuH90BhXEuyengPFMa1BKmQE7NH+c87JP0rSbmFEcvjlUaQKm9MrQ5xzhPFObfLzBbLmyThCDMrcc7tTqOewMRLzu78/L2oMjVqr8NIygIAALQGxLzN0NyeNPGOMzMdc2BfTR7eR0+++7l++8Jyrf6yulG52XUnax9br28Xv5Tw/EXmVKYt0o4t0sdNjJFX1F7q4iVtLy3tqw8GdNRrqw/UV4s+8PbPmylVbZYOmyYtmROVzHyydpzGjNxPkzvNk179u7S7Wtpd5T9HLlc1LF+6u0rTum1XXc0O1TlTkfkJ1Vd/6z1iXFz8tC4uflr6p7xHiqZKmtohzo43ZnuPCM1JyobE+8N/9+7dipwCLhN/8KeSYGgr9eTTtSSqh/dA7uvIdT28B3JfR5D1BMFcwy+RhcXMNkjqLeld59whScr1kBQafOJx59yZadTxfUm/91dPdc7NTVL2NkmhsQJGOOc+SLWeFNqxt6RVkrRq1SrtvffeLT5n7DAFCcuRlM0r1dXVeuGFFyRJU6ZMUadOnXLcImQDr7O0YsUK1dbWqri4WMOGDct1c7Kmvr5eW7dulSR169ZN7doV7AhHea25r3M6n4PVq1drn332Ca3uk6THJAJGzJs5mfp+rKmt05/f+Ey3v/SRNu3YFbWvg3bpww7nq50V5t9omRRKyk7ar0xjh/RKWC7Rn8PO76n7xiebtHD5xobtw7vXa2g3p8qSMi36ODxG4cT9emvM4MT1NCW2non79dbYwb21+JNNWrh8Q8P2SfuV6cihXj0m854t+lzmb7CGdTWsL/poo+Z9GO6JfeyBfTRhWJleWbFBL37QeHujk4fOmeA6zKRXlm/Q8++va9h23Ii+mrBfWaPtUw70tjd6CWJeFJdg16srNkRdyzEH9NH4ob0bXWNoe3PFnm9kj3rtv4fT5vZ99PLyTVmr5+iI65kfZ7uU+P0bKbbIax9t1EtLw+f72vA++urQ3o22h+oJv4+i328N74E477fY1/r4Ef00cf8yLVy2Qc+9vzZq+4T94o/H7bU9/gWGrvuV5Rv0wgfR76mj/PfaixHbj/Xfa4krSv4PGfv5GNmjXsO7O21u31cvLQt/bpv7uZESf3Ym7tdHC5dv0PMx/24T9y+LOl9klaaolUZteHnpej3zXvh83ziov742vI9eWrpez/xnTXj7yH6aPLyPpMbvo9gNsa/VgmUb9Ox70W2etH+ZFsR5D0xKMCZ7KhKdL3Z7ppKy2Yh5CzIxa2YdJYV+pn7aOXdiE+W3S+oiabFzrulpPMPH3ajwWFqjnXNvJin7U0m/8VePd86lPEuWH4Qm00/SEklavnx5xoLUtx75ub668o6E+18b+H0ddtY1Cfej7dm5c6deeeUVSdKECRPUsWPHHLcI2cDr7H3h7t69W2amYcOG5W3Csr6+Xtu3e0PRlJaW5u11FrrmvM7OOa1YsUL19fUqLi6ODEDjWr16tfbbb7/QKonZVoKYN7OJ2Ux/P+6oqdUDi1fp3tc/045d3pC8lxQ92Xg4A0n1TmqX7C96RNnuOmpkzb25bgYAoBX48dFD9L3xA5sumIJsxLyFOpRB14jl7QlLhe2QF6SWZrGeHRHL6dazKtWCr7zyinr3bv6veFF6jtHfN27UN3c82mjX37tMlXqOaehVgPwT+sME+a1QX+euXbuqS5cu6tKli9avX6/OnTvnuklZF0rcIb+l+jrX1NSotrZWO3bsUFVVlT788MOk5Tdu3Jh0P3KGmDdLMvX9OFTSVV+RXvy8nQ7c8JRmFIeTsl+60oZ5HNqZdPvuU/Rs/RHqbVtUpi0qs0qVmf+sLd52q1R325Ggtrav3nnZ6fDosyaTCw+b4Cu1nbqk6MmGsXsBAIXppAF1Gli1TC+8sCwj58tGzFuoidnIn7d3JSwVVuM/p3u/Ujr11EQst5n7hosHfVV6Pzoxu9V1lvY7IUctAoCWq6mpUadOnVRbW6svv/xSktSpU6eGW7iAfFZbW6stW7aovr5e9fX12rUrlVAJrRQxbxtQWiL9T6cnNaL4sYZtodvwI3vQ/rBkrnbs7thksrG9dqu3n7j1krVbVKZwEre3bdEQfaGe7Rrn0Le5jtrk9lC12munOqjatVe1OsRdr3Ydwvucv80vc2LRYp1fHO4MXem6RCWMf7P7TN1dd2JUgjX0HLst2c2/sT2MI5PZoe0kZwGgMHUocjpmr9Y/SkChJmZ3Riy3T6F8aHj5xqP0Z66eyCHs060n+f2FEbd1TZgwIaO3db3z0JWNtnWzKvXe/AbDGOQhbnEvDLzO3m3ca9as0c6dO2Vmqqys1JYtW1RUVJRXyVnnnOrqvNtn8+3aEJbO6+ycU21tbUPZnj176qCDDlJRUVHSOlavZuSCVoqYN4Oy9f1YvPh2lbzdOCkrhZOKoSTjlSWPaMSeXTW/53+p3vlj+jk1LDvnDZXotKecv32DnDZElPn6lkc1ekv4Nv8vXRf18JOmXW2nnt7jbD3V1RtiOPF/F/F3tDPpzK2PauqWcFI2XpL5f0oe04DepXq629SE/y5Njbh3wtZHddbWcFI2Xj1XljyiQb066+luU5NcS/LxUkM+21ytTzZWNSozuHdnDeiZud8YEtUzqFdn7dOjk0L9hUP/Pg2JbH9DeD36+Nj9X1Tu1OrKnYq1V/eO6r9Hx6hjUhVbfO3WGn2xpXEde+7RUf26dUj+msTsTDSWpiSt2bJTq75sXM8+PTppr+6Zi2M/r9ypVXEm7msL9cT7t/68cqc+25y8nkTvq9j3nWL2h8qsS/Ae6N+tg/p26+C3rXHj4r01kr1f1m6t0edNvJ8zYc2WnSnX05JhQxNdz557dFTfbh2izp1oDOZ47Ygtu2F7jdZva/x7ap+u7dWna+NZFmNfglT+dtiwrUZrttY02t6/Wwf16RZvJsfmWb+1cT01daaVnffP2DAGUnZi3kJNzG6LWE7lFqou/nO693mmU0+XiOW06mlqTIvID0unTp0yNpFP+YPXaNwX9zesV7pSdfd/of7qyjtU/ngRE3/lsY4dOxbkpFCFppBf53333VefffaZqqvDAWsouZUvnHMN10eP4PyV7usc2t+uXTsNHDgwpeRTof4/0QYQ82ZJxr4fF82SFv6yYTUyKdu9c4kqq3Y3Ss6etGGOTjp4L2n89ObVNy+clL1x91l6pMO3dFbNXxvOf9bWe3XWEQMydv7ZdSere+cSza6Kvo6pW+7V1NHZr6dF1+O7c8HHWrA8PLN352Knqlrv/fbJxiqdMWpARiaVia0n9B6QpIpNVZo6OnP1RM5UHlnP55U7dc7YQS2uJ1kdX2zZqXPHtbyOpupZ9WW1zh4zMCv1RL4HsllPUNeTjXqS1bFma42mHblvm3k/x6sn8j2QzXpy8dlZv22Xzv/q4Lx4D9wy/2OVlJRkpB4pOzFvQc7y4ZzbKSk0METSn9L9GWpDAWTK41r5IoPHpn6yj+wBkG49gSt/8BqN++R3Det/7zJVCw/7P7028PsN28Z98juVP0ivWQBtUygptc8++6h79+7q0KGDiouL8+pRVFSk6upqVVdXq6ioKOft4dE6XueOHTuqrKxMQ4cOLcge8/mEmLeVWzRLmjezYTUyKXvF8cP1zrVTdMXxwyV5PWdv3H1W+Nh5M73jW1jfHsfO0DvXTtEex87Iyvln150cdS3Zuo6s1OOL/YP/pAF1umF0nX58dPiP/JueW6o7F3zcrPMnqif2PdCW6smna4lXD++B1lFHLuvhPdA66giynmwq1B6zkvShpKMkDTWzYudcbYJyw2OOSccHCc6TrJ5aSR+lWU+gYpOyrw38vtRzjCTpsLOu8XrK+vu95KzoOQugTTIzlZaWqrQ03flp2obq6motXeoFMocffji9HvMUr3PBI+ZtjVaWJ03Khnr2hJ5vem5po56zmjdT2mesNHBc0/UlSMpG1nOnZujGF2POL6XW0zRJsrTxtajxdbS2enyxf/D/+OghGljlTSDzvfEDVVJS0rA/9NycXlnxEgvx3gNtoZ58upZ49fAeaB115LIe3gOto44g68m2guwx61vkP3eRdHiSchMjll9Ls44lCk+AMDFRITNrL2ls6BjnXKudZSM2KVs++EeNxpIdN+3nKh/8o/A6PWcBAAByhZi3NRo4Tpp4paTESdmQSycNid9zduKVGUnKRtbTrJ6zCZLMya4lbo/WleWtox5fvD/4Y8cpjHxtpOb1ykqWWGhr9eTTtSSqh/dA7uvIdT28B3JfR5D1BKGQE7NzI5bPj1fAzNpJmuavVkp6OZ0KnHPbJM33V48xs0S3dp0mqZu//EQ6dQQpXlI2UU9YkrMAAACtwtyIZWLeVuROO1On11ybNCkbEpucPb3mWt1pZzZdSZxkZrykbGQ9cZOzyZKZA8dpycCLGs6fKFkaey2RSdMlAy9qOskcVD1K7Q/+2HpC0vnDP5/qyadroR7eA9TDeyBIBZuYdc79U9Kr/up3zSzeN/RPJB3gL9/mnNsdudPMzjMz5z9mJqjqZv+5WNIdZhY1rbGZ9ZZ0k79aKekPaV1IQD584/mUk7Ih8ZKzH77xfJIjAAAAkEnEvK3TkorNuum5pXrTeX8sJvujMiTyj8s33XDd9NxSLanYnLwet79urT1NUtNJ2ch6IpOzt9aepiVu/6TXcsaySQ1J5nSuJZRkPmPZpKavJcB6Uv2DP7aekJRemzyqJ5+uhXqaV08+XQv1NK+efLqWoBVsYtZ3uaRqeQHkC2b2v2Y21swmm9ldkn7tl1su6bfNqcA595KkR/zVkyW9aGYnm9koMztf0mJJA/z9VzrnvmzuxWTTAWOOU/k+35OUWlI2JDI5W77P93TAmOOy1kYAAADERczbyowe1FOXHz1MUmp/VIZE/nF5+dHDNHpQzybrcRP/V6fXXJtSUjaynj2OnaHTa66Vm/i/SesJXcubbnizruVNNzzlawmyHin7r02+1JNP10I9zasnn66FeppXTz5dS+CccwX9kHSSpC2SXILHMklDExx7XkS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+ "text/plain": [ + "
" + ] + }, + "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 (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": "3.10.12" + } + }, + "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.ini b/tutorials/NNO_os_plo_mag/config.ini new file mode 100644 index 00000000..75db03d1 --- /dev/null +++ b/tutorials/NNO_os_plo_mag/config.ini @@ -0,0 +1,47 @@ +[general] +seedname = nno +jobname = NNO_lowT + +enforce_off_diag = False +block_threshold = 0.001 + +solver_type = cthyb +n_iw = 8001 +n_tau = 50001 + +prec_mu = 0.001 + +h_int_type = density_density +U = 8.0 +J = 1.0 + +# temperature ~290 K +beta = 40 + +magnetic = True +magmom = -0.001, 0.001 +afm_order = True + +n_iter_dmft = 15 + +g0_mix = 0.9 + +dc_type = 0 +dc = True +dc_dmft = False + +load_sigma = False +path_to_sigma = pre_AFM.h5 + +[solver] +length_cycle = 150 +n_warmup_cycles = 10000 +n_cycles_tot = 2e+8 +imag_threshold = 1e-5 + +perform_tail_fit = True +fit_max_moment = 6 +fit_min_w = 8 +fit_max_w = 14 +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 diff --git a/tutorials/NNO_os_plo_mag/tutorial.html b/tutorials/NNO_os_plo_mag/tutorial.html new file mode 100644 index 00000000..1b9300f8 --- /dev/null +++ b/tutorials/NNO_os_plo_mag/tutorial.html @@ -0,0 +1,1245 @@ + + + + + + + 4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2 — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+
[1]:
+
+
+
+import numpy as np
+np.set_printoptions(precision=6,suppress=True)
+from triqs.plot.mpl_interface import plt,oplot
+
+from h5 import HDFArchive
+
+from triqs_dft_tools.converters.vasp import VaspConverter
+import triqs_dft_tools.converters.plovasp.converter as plo_converter
+
+import pymatgen.io.vasp.outputs as vio
+from pymatgen.electronic_structure.dos import CompleteDos
+from pymatgen.electronic_structure.core import Spin, Orbital, OrbitalType
+
+import warnings
+warnings.filterwarnings("ignore") #ignore some matplotlib warnings
+
+
+
+
+
+
+
+
+Warning: could not identify MPI environment!
+
+
+
+
+
+
+
+Starting serial run at: 2022-03-10 17:08:16.981449
+
+
+
+

4. OS with VASP/PLOs and cthyb: AFM state of NdNiO2

+

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.

+

Disclaimer: the interaction values, results etc. might not be 100% physical and are only for demonstrative purposes!

+

This tutorial will guide you through the following steps:

+
    +
  • run a non-magnetic Vasp calculation for NdNiO2 with a two atom supercell allowing magnetic order

  • +
  • create projectors in a large energy window for all Ni-\(d\) orbitals and all O-\(p\) orbitals

  • +
  • create the hdf5 input via the Vasp converter for solid_dmft

  • +
  • run a AFM DMFT one-shot calculation

  • +
  • take a look at the output and analyse the multiplets of the Ni-d states

  • +
+

Warning: the DMFT calculations here are very heavy requiring ~2500 core hours for the DMFT job.

+

We set a path variable here to the reference files, which should be changed when doing the actual calculations do the work directory:

+
+
[2]:
+
+
+
+path = './ref/'
+
+
+
+
+

1. Run DFT

+

We start by running Vasp to create the raw projectors. The INCAR, POSCAR, and 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.

+

Next, run Vasp with

+
mpirun vasp_std 1>out.vasp 2>err.vasp &
+
+
+

and monitor the output. After Vasp is finished the result should look like this:

+
+
[3]:
+
+
+
+!tail -n 10 ref/out.vasp
+
+
+
+
+
+
+
+
+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
+
+
+

let us take a look at the density of states from Vasp:

+
+
[4]:
+
+
+
+vasprun = vio.Vasprun(path+'/vasprun.xml')
+dos = vasprun.complete_dos
+Ni_spd_dos = dos.get_element_spd_dos("Ni")
+O_spd_dos = dos.get_element_spd_dos("O")
+Nd_spd_dos = dos.get_element_spd_dos("Nd")
+
+
+
+
+
[5]:
+
+
+
+fig, ax = plt.subplots(1,dpi=150,figsize=(7,4))
+
+ax.plot(vasprun.tdos.energies - vasprun.efermi , vasprun.tdos.densities[Spin.up], label=r'total DOS', lw = 2)
+ax.plot(vasprun.tdos.energies - vasprun.efermi , Ni_spd_dos[OrbitalType.d].densities[Spin.up], label=r'Ni-d', lw = 2)
+ax.plot(vasprun.tdos.energies - vasprun.efermi , O_spd_dos[OrbitalType.p].densities[Spin.up], label=r'O-p', lw = 2)
+ax.plot(vasprun.tdos.energies - vasprun.efermi , Nd_spd_dos[OrbitalType.d].densities[Spin.up], label=r'Nd-d', lw = 2)
+
+ax.axvline(0, c='k', lw=1)
+ax.set_xlabel('Energy relative to Fermi energy (eV)')
+ax.set_ylabel('DOS (1/eV)')
+ax.set_xlim(-9,8.5)
+ax.set_ylim(0,20)
+ax.legend()
+plt.show()
+
+
+
+
+
+
+
+../../_images/tutorials_NNO_os_plo_mag_tutorial_8_0.png +
+
+

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.

+
+
+

2. Creating the hdf5 archive / DMFT input

+

Next we run the Vasp converter to create an input h5 archive for solid_dmft. The plo.cfg looks like this:

+
+
[6]:
+
+
+
+!cat plo.cfg
+
+
+
+
+
+
+
+
+[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
+
+
+

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.

+

Let’s run the converter:

+
+
[7]:
+
+
+
+# Generate and store PLOs
+plo_converter.generate_and_output_as_text('plo.cfg', vasp_dir=path)
+
+# run the archive creat routine
+conv = VaspConverter('nno')
+conv.convert_dft_input()
+
+
+
+
+
+
+
+
+Read parameters:
+0  ->  {'label': 'dxy', 'isite': 3, 'l': 2, 'm': 0}
+1  ->  {'label': 'dyz', 'isite': 3, 'l': 2, 'm': 1}
+2  ->  {'label': 'dz2', 'isite': 3, 'l': 2, 'm': 2}
+3  ->  {'label': 'dxz', 'isite': 3, 'l': 2, 'm': 3}
+4  ->  {'label': 'dx2-y2', 'isite': 3, 'l': 2, 'm': 4}
+5  ->  {'label': 'dxy', 'isite': 4, 'l': 2, 'm': 0}
+6  ->  {'label': 'dyz', 'isite': 4, 'l': 2, 'm': 1}
+7  ->  {'label': 'dz2', 'isite': 4, 'l': 2, 'm': 2}
+8  ->  {'label': 'dxz', 'isite': 4, 'l': 2, 'm': 3}
+9  ->  {'label': 'dx2-y2', 'isite': 4, 'l': 2, 'm': 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 from 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      : [1, 2]
+Density matrix:
+  Shell 1
+    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 'nno.pg1'...
+  Density within window: 42.00000000005771
+Reading input from nno.ctrl...
+{
+    "ngroups": 1,
+    "nk": 405,
+    "ns": 1,
+    "kvec1": [
+        0.1803844533789928,
+        0.0,
+        0.0
+    ],
+    "kvec2": [
+        0.0,
+        0.1803844533789928,
+        0.0
+    ],
+    "kvec3": [
+        0.0,
+        0.0,
+        0.30211493941280826
+    ],
+    "nc_flag": 0
+}
+
+  No. of inequivalent shells: 2
+
+
+

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

+
+
+

3. Running the AFM calculation

+

now we run the calculation at around 290 K, which should be below the ordering temperature of NdNiO2 in DMFT. The config file config.ini for solid_dmft looks like this:

+
[general]
+seedname = nno
+jobname = NNO_AFM
+
+block_threshold = 0.001
+
+solver_type = cthyb
+n_iw = 8001
+n_tau = 50001
+
+prec_mu = 0.001
+
+h_int_type = density_density
+U = 8.0
+J = 1.0
+
+beta = 40
+
+magnetic = True
+magmom = -0.001, 0.001
+afm_order = True
+
+n_iter_dmft = 12
+
+g0_mix = 0.9
+
+dc_type = 0
+dc = True
+dc_dmft = False
+
+[solver]
+length_cycle = 150
+n_warmup_cycles = 10000
+n_cycles_tot = 2e+8
+imag_threshold = 1e-5
+
+perform_tail_fit = True
+fit_max_moment = 6
+fit_min_w = 8
+fit_max_w = 14
+measure_density_matrix = True
+
+
+

Let’s go through some special options we set in the config file:

+
    +
  • we changed n_iw=8001 because the large energy window of the calculation requires more Matsubara frequencies

  • +
  • h_int_type is set to density_density to reduce complexity of the problem

  • +
  • beta=40 here we set the temperature to ~290K

  • +
  • magnetic=True lift spin degeneracy

  • +
  • 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 in units of the DC potential. Hence, small values should be chosen.

  • +
  • 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

  • +
  • length_cycle=150 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

  • +
  • perform_tail_fit=True : here we use tail fitting to get good high frequency self-energy behavior

  • +
  • measure_density_matrix = True measures the impurity many-body density matrix in the Fock basis for a multiplet analysis

  • +
+

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.ini.

+

Caution: this is a very heavy job, which should be submitted on a cluster.

+

In the beginning of the calculation we find the following lines:

+
AFM calculation selected, mapping self energies as follows:
+imp  [copy sigma, source imp, switch up/down]
+---------------------------------------------
+0: [False, 0, False]
+1: [True, 0, True]
+
+
+

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:

+
...
+copying the self-energy for shell 1 from shell 0
+inverting spin channels: False
+...
+
+
+

After the calculation is running or is finished we can take a look at the results:

+
+
[8]:
+
+
+
+with HDFArchive(path+'/nno.h5','r') as ar:
+    Sigma_iw = ar['DMFT_results/last_iter/Sigma_freq_0']
+    obs = ar['DMFT_results/observables']
+
+
+
+
+
[9]:
+
+
+
+fig, ax = plt.subplots(nrows=3, dpi=150, figsize=(7,6), sharex=True)
+fig.subplots_adjust(hspace=0.1)
+# imp occupation
+ax[0].plot(obs['iteration'], np.array(obs['imp_occ'][0]['up'])+np.array(obs['imp_occ'][0]['down']), '-o', label=r'Ni$_0$')
+ax[0].plot(obs['iteration'], np.array(obs['imp_occ'][1]['up'])+np.array(obs['imp_occ'][1]['down']), '-o', label=r'Ni$_1$')
+
+# imp magnetization
+ax[1].plot(obs['iteration'], (np.array(obs['imp_occ'][0]['up'])-np.array(obs['imp_occ'][0]['down'])), '-o', label=r'Ni$_0$')
+ax[1].plot(obs['iteration'], (np.array(obs['imp_occ'][1]['up'])-np.array(obs['imp_occ'][1]['down'])), '-o', label=r'Ni$_1$')
+
+# dxy, dyz, dz2, dxz, dx2-y2 orbital magnetization
+ax[2].plot(obs['iteration'], (np.array(obs['orb_occ'][0]['up'])[:,4]-np.array(obs['orb_occ'][0]['down'])[:,4]), '-o', label=r'$d_{x^2-y^2}$')
+ax[2].plot(obs['iteration'], (np.array(obs['orb_occ'][0]['up'])[:,2]-np.array(obs['orb_occ'][0]['down'])[:,2]), '-o', label=r'$d_{z^2}$')
+ax[2].plot(obs['iteration'], (np.array(obs['orb_occ'][0]['up'])[:,0]-np.array(obs['orb_occ'][0]['down'])[:,0]), '-o', label=r'$d_{xy}$')
+ax[2].plot(obs['iteration'], (np.array(obs['orb_occ'][0]['up'])[:,1]-np.array(obs['orb_occ'][0]['down'])[:,1]), '-o', label=r'$d_{yz/xz}$')
+
+ax[0].set_ylabel('Imp. occupation')
+ax[1].set_ylabel(r'magnetization $\mu_B$')
+ax[2].set_ylabel(r'magnetization $\mu_B$')
+ax[-1].set_xticks(range(0,len(obs['iteration'])))
+ax[-1].set_xlabel('Iterations')
+ax[0].set_ylim(8.4,8.6)
+ax[0].legend();ax[1].legend();ax[2].legend()
+
+plt.show()
+
+
+
+
+
+
+
+../../_images/tutorials_NNO_os_plo_mag_tutorial_18_0.png +
+
+

Let’s take a look at the self-energy of the two Ni \(e_g\) orbitals:

+
+
[10]:
+
+
+
+fig, ax = plt.subplots(1,dpi=150)
+
+ax.oplot(Sigma_iw['up_2'].imag, '-', color='C0', label=r'up $d_{z^2}$')
+ax.oplot(Sigma_iw['up_4'].imag, '-', color='C1', label=r'up $d_{x^2-y^2}$')
+
+ax.oplot(Sigma_iw['down_2'].imag, '--', color='C0', label=r'down $d_{z^2}$')
+ax.oplot(Sigma_iw['down_4'].imag, '--', color='C1', label=r'down $d_{x^2-y^2}$')
+
+ax.set_ylabel(r"$Im \Sigma (i \omega)$")
+
+ax.set_xlim(0,40)
+ax.set_ylim(-1.5,0)
+ax.legend()
+plt.show()
+
+
+
+
+
+
+
+../../_images/tutorials_NNO_os_plo_mag_tutorial_20_0.png +
+
+

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.

+
+
+

5. Multiplet analysis

+

We follow now the triqs/cthyb tutorial on the multiplet analysis to analyze the multiplets of the Ni-d orbitals:

+
+
[11]:
+
+
+
+import pandas as pd
+pd.set_option('display.width', 130)
+
+from triqs.operators.util import make_operator_real
+from triqs.operators.util.observables import S_op
+from triqs.atom_diag import quantum_number_eigenvalues
+from triqs.operators import n
+
+
+
+

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:

+
+
[12]:
+
+
+
+with HDFArchive(path+'nno.h5','r') as ar:
+    rho = ar['DMFT_results/last_iter/full_dens_mat_0']
+    h_loc = ar['DMFT_results/last_iter/h_loc_diag_0']
+
+
+
+

rho is just a list of arrays containing the weights of each of the impurity eigenstates (many body states), and h_loc is a:

+
+
[13]:
+
+
+
+print(type(h_loc))
+
+
+
+
+
+
+
+
+<class 'triqs.atom_diag.atom_diag.AtomDiagReal'>
+
+
+

containing the local Hamiltonian of the impurity including eigenstates, eigenvalues etc.

+
+
[14]:
+
+
+
+res = []
+# get fundamental operators from atom_diag object
+occ_operators = [n(*op) for op in h_loc.fops]
+
+# construct total occupation operator from list
+N_op = sum(occ_operators)
+
+# create Sz operator and get eigenvalues
+Sz=S_op('z', spin_names=['up','down'], orb_names=[0, 1, 2, 3, 4], off_diag=False)
+Sz = make_operator_real(Sz)
+Sz_states = quantum_number_eigenvalues(Sz, h_loc)
+
+# get particle numbers from h_loc_diag
+particle_numbers = quantum_number_eigenvalues(N_op, h_loc)
+N_max = int(max(map(max, particle_numbers)))
+
+for sub in range(0,h_loc.n_subspaces):
+
+    # first get Fock space spanning the subspace
+    fs_states = []
+    for ind, fs in enumerate(h_loc.fock_states[sub]):
+        state = bin(int(fs))[2:].rjust(N_max, '0')
+        fs_states.append("|"+state+">")
+
+    for ind in range(h_loc.get_subspace_dim(sub)):
+
+        # get particle number
+        particle_number = round(particle_numbers[sub][ind])
+        if abs(particle_number-particle_numbers[sub][ind]) > 1e-8:
+            raise ValueError('round error for particle number to large!',
+                             particle_numbers[sub][ind])
+        else:
+            particle_number = int(particle_number)
+        eng=h_loc.energies[sub][ind]
+
+        # construct eigenvector in Fock state basis:
+        ev_state = ''
+        for i, elem in enumerate(h_loc.unitary_matrices[sub][:,ind]):
+            ev_state += ' {:+1.4f}'.format(elem)+fs_states[i]
+
+        # get spin state
+        ms=Sz_states[sub][ind]
+
+        # add to dict which becomes later the pandas data frame
+        res.append({"Sub#" : sub,
+                    "EV#" : ind,
+                    "N" : particle_number,
+                    "energy" : eng,
+                    "prob": rho[sub][ind,ind],
+                    "m_s": round(ms,1),
+                    "|m_s|": abs(round(ms,1)),
+                    "state": ev_state})
+# panda data frame from res
+res = pd.DataFrame(res, columns=res[0].keys())
+
+
+
+
+
[15]:
+
+
+
+print(res.sort_values('prob', ascending=False)[:10])
+
+
+
+
+
+
+
+
+    Sub#  EV#   N    energy      prob  m_s  |m_s|                 state
+5      5    0   9  0.562426  0.341965  0.5    0.5   +1.0000|1111101111>
+0      0    0   8  0.000000  0.160698  1.0    1.0   +1.0000|1111101011>
+3      3    0   9  0.472365  0.057380  0.5    0.5   +1.0000|1111111011>
+25    25    0   8  1.147767  0.048976  0.0    0.0   +1.0000|1101101111>
+40    40    0   8  1.629801  0.041522  1.0    1.0   +1.0000|1111101110>
+55    55    0  10  7.522083  0.038053  0.0    0.0   +1.0000|1111111111>
+4      4    0   9  0.562426  0.028791 -0.5    0.5   +1.0000|0111111111>
+50    50    0   8  2.745626  0.024248  0.0    0.0   +1.0000|0111101111>
+8      8    0   8  0.637905  0.021395  1.0    1.0   +1.0000|1111100111>
+11    11    0   9  0.734944  0.021376 -0.5    0.5   +1.0000|1101111111>
+
+
+

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.

+

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:

+
+
[16]:
+
+
+
+# split into ms occupations
+fig, (ax1) = plt.subplots(1,1,figsize=(6,4), dpi=150)
+
+spin_occ_five = res.groupby(['N', '|m_s|']).sum()
+pivot_df = spin_occ_five.pivot_table(index='N', columns='|m_s|', values='prob')
+pivot_df.plot.bar(stacked = True, rot=0, ax = ax1)
+
+ax1.set_ylabel(r'prob amplitude')
+plt.show()
+
+
+
+
+
+
+
+../../_images/tutorials_NNO_os_plo_mag_tutorial_33_0.png +
+
+

This concludes the tutorial. This you can try next:

+
    +
  • try to find the transition temperature of the system by increasing the temperature in DMFT

  • +
  • improve the accuracy of the resulting self-energy by restarting the dmft calculation with more n_cycles_tot

  • +
+
+
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ 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..ce0ad298 --- /dev/null +++ b/tutorials/NNO_os_plo_mag/tutorial.ipynb @@ -0,0 +1,845 @@ +{ + "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: 2022-03-10 17:08:16.981449\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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b+eEPf8gFF1wAwHXXXTfYQTfsqquuYu7cuTz22GOcffbZnu68EPr63v/+97N161ZOPfVUPvGJTwy+1tTUxKGHHsq9995Lb2+vZ7/e3l6uvPJKXnvtNYqKijzzw441le+KiIiIiMioys/P5/jjj+cf//gH++67LwcccAD5+fkcfvjhfOpTn6KkpITbbruNU045hc997nP84Q9/YPfdd2fFihW8+uqrlJSUcPvtt1NQMFT+vNNOO7HPPvvw8ssvc8ghh7Bo0SJycnI49dRTOfXUU/nqV7/KLbfcwvXXX89TTz3FPvvsw6ZNm1iyZAnf+ta3+OUvf5nWr/FnP/vZ4HypbW1tbNy4kVdffZWenh7Ky8u57rrrOO+883z7VVVV8dRTT3HiiSdyxx138MADD3DkkUdSXV3NunXreP755wkGg5x22mncfvvtvv1ffPFFPvrRj1JeXs6BBx7I9OnTaWpq4rXXXmPLli3k5ORwww03MH369LR+valQUCoiIiIiIqPu5ptv5uKLL+bxxx/n9ttvp7+/n76+Pj71qU8B8L73vY+XXnqJn/zkJzz55JO88cYb1NTUcO655/K9732PBQsW+I55zz338O1vf5vFixfzyiuvEAwGmT17Nqeeeiq77747L730EpdeeikvvPACDzzwAAsWLOD3v/89F110UdqD0kcffRQINRkqKyujurqak08+mfe9732ce+65lJeXx9x355135vXXX+fmm2/mzjvv5JVXXqGlpYWamhpOPfVUPvWpT3Hqqaf69quoqODZZ5/lkUce4amnnuLdd9/lmWeeIScnh7lz53LSSSfxla98hX322SetX2uqzGh2mJLEGGOWLly4cOHSpUvH+lJERCQLtbe3U1paCoSe8rsTzovIyILB4GA56YIFCwgENHpOJoZEfnYXLVrEsmXLlllrFyVzLv1WiIiIiIiIyJhRUCoiIiIiIiJjRkGpiIiIiIiIjBkFpSIiIiIiIjJmFJSKiIiIiIjImFFQKiIiIiIiImNGQamIiIiIiIiMGQWlIiIiIiIiMmYUlIqIiIiIiMiYUVAqIiIiIiIiY0ZBqYiIiIiIiIwZBaUiIiIiIiIyZhSUioiIiIiIyJiZsEGpMeZAY8xlxpi/G2M2GWOsMaZrmO1tHP+eTOD8t4xwrM+n5ysVEREREZl8jDEYY6iqqqKpqSnqNldccQXGGH72s59FXX/LLbek7XqOOeYYjDGsXbs2bceU+OSO9QWk4PvAhxPY/tZhXjsJqAEWJ3EdjwJbo6xfmcSxRERERESySlNTE9dccw0//OEPx/pSZIxM5KD0OeB14KWBf9ECw0HW2guirTfGVAJnDSz+JYnr+Jm19qkk9hMRERERyWqBQIDc3FyuvfZavv71r1NVVRXXfl/+8pc566yzmDFjRoavUEbDhC3ftdZeZa39H2vtg9babSkc6gygAHjeWrsqTZcnIiIiIiIjyMvL48ILL6SlpYWrr7467v1qamrYY489qKioyODVyWiZsEFpGp078PHPY3oVIiIiIiJZ6Dvf+Q4FBQX8+te/pqGhIa59UhlT+oc//IG9996bwsJCZs2axVe+8hWam5sTPo6kz0Qu302ZMWYucCTQC9yR5GE+aow5HcgB1gD/sNauSNMlioiIiMhkFQxCZ3xB2LhTNAUC6clvzZo1i4suuojrrruOX/3qV/zkJz9Jy3Gjufjii/nVr35FQUEBxx13HMXFxdx2220888wzFBQUZOy8MrysDkqBcwADPGytrU/yGF9xlq8yxtwAfM1a2xfPAYwxS2O8tGuS1yQiIiIi411nA/xigt7ufXs1lNSk7XCXX345N998M7/97W/55je/SXV1ddqOHfbss8/yq1/9iilTpvCf//yHRYsWAVBfX89xxx3H888/n/ZzSnyyvXw3ldLdV4HPA7sDxcAuwJeAJuCLwC/ScH0iIiIiIpPezJkz+exnP0trayu//OUvM3KOG2+8EYBvfetbgwEpQHV1Nb/4hW7dx1LWBqXGmAOAhYSCyH8kur+19tfW2t9ba1dZazuttWustdcDRwE9wFeMMXPiPNaiaP+A1Ylel4iIiIjIRHTZZZdRWFjIddddR11dXdqPv2TJEgDOPPNM32sf/OAHmTJlStrPKfHJ5vLdcJb0Lmttd7oOaq19yxjzAPAx4P3AH9N1bBERERGRyWrGjBl8/vOf59prr+UXv/gFV111VUL733zzzYOBZ1hNTc1g5nXz5s0YY5gzJ3reaO7cuXE3WpL0ysqg1BiTQ2pzk44kPLWMJk4SERERkeiKpoTGZk5ERZnJKl566aX8/ve/53e/+x0XX3xxQvsuWbKEW2+91bNu3rx5GSsHlvTJyqAUeB+hgHEdsDgDxw/P+tuWgWOLiIiIyGQQCKS1WdBkMH36dL7whS9w9dVX8/Of/5ySkpK4973llluGnSJmxowZrF27lg0bNjB//nzf6+vXr0/mkiUNsnVMabh09y/WWpvOAxtjCoCTBhZfSeexRUREREQmu0svvZTi4mKuv/56tm3blrbjHnHEEQDcddddvtcef/xxle6OoawLSo0xxcBHBhaHLd01xhxijFlhjHnCWb/AGPPhgTLgyPW1wN+AOcDrwLPpu3IRERERkclv6tSpfPGLX6Sjo8NXjpuKz33ucwBcffXVLF++fHB9Q0MDl1xySdrOI4mbsEGpMeYkY8zz4X8Dq/Mj1xljToqy62lAKfCStXbFCKcpBhbgny90BnAfsM0Ys8QYc4cx5t+EuuWeBmwEzkx3FlZEREREJBtccskllJSU0NnZmbZjHnHEEXz961+nrq6O/fffn5NPPpkzzjiD3XbbDYD3vOc9aTuXJGbCBqVALXBoxD8A46yrjbLfYOluCud+G7iWUEOjXQllXg8aWP4hsI+19u0Uji8iIiIikrVqa2v50pe+lPbjXn311dxwww3Mnz+fxx9/nGeeeYYzzzyTf//73xQUFKT9fBIfo2Te+GWMWbpw4cKFS5cuHetLERGRFHX29FOYF8AYM9aXErf29nZKS0sBaGtrS6jhiIiEBINBVq5cCcCCBQsIBCZyTkiySSI/u4sWLWLZsmXLrLWLkjlXtnbfFRERGRV9/UEu//ub3PfaJqqK87nq9H04do+pY31ZIiIi44Ye1YiIiGTQH59Zy12vbKS337K9tZsv3PYKW5rTN0ZKRERkolNQKiIikiHWWv7ywjrPuq7eIH//76YxuiIREZHxR0GpiIhIhizf0sq6+g7f+iWr6sbgakRERMYnBaUiIiIZ8vrGpqjrV2xtQY0GRUREQhSUioiIZMhbm5qjrm/s6GVHW/coX42IiMj4pKBUREQkQ97a3BLztbe3to3ilYiIiIxfCkpFREQyZM2O2IHn6mFeExERySYKSkVERDKgqaOHlq6+mK9v1rQwIiIigIJSERGRjFjf4O+6G2lzU9coXYmIiMj4pqBUREQkA0YOSpUpFRERAQWlIiIiGTFSULpFQamIiAigoFRERCQjNjhB6T6zKzzLW1u66OsPjuYliYiIjEsKSkVERDLAzZQevNMUz3LQhuYrFRERyXYKSkVERDJgi9PIaO9ZFRjj3aa+vXsUr0hEZHyqq6vj+9//Pvvvvz+VlZUUFxczf/58PvvZz/LWW2+N9eXJKFBQKiIikmbWWra2eIPSmZVFVBXne9bVt/WM5mWJiIw7//rXv9htt9248sor2bRpE0cffTQnn3wyeXl53HTTTey333787Gc/G+vLlAzLHesLEBERmWzauvvo6On3rJteXkh1ST4N7UOBaF2bMqUikr1eeuklTjrpJHp7e/npT3/KxRdfTG7uUHjy0EMPce6553L55ZdTXFzMV7/61TG8WskkZUpFRETSbFuLP9icWl5AdakypSIiEKooOf/88+np6eFHP/oRl112mScgBTjxxBO57777MMZw6aWXsm7dujG6Wsk0ZUpFRETSbLtTultRlEdhXg7VJQWe9RpTKpLdgjZIU3fTWF9GUioLKgmY5PNbDz/8MMuXL2fWrFlceumlMbc76qijOOOMM7jzzjv53e9+x89//vO4jn/MMcfw9NNPs2bNGpYsWcI111zD8uXLKS0t5YQTTuD//b//x6xZs5K+fkkvBaUiIiJptq3VG5ROKw8Fo8qUikikpu4mjr7j6LG+jKQ8/fGnmVI4ZeQNY3jooYcAOOOMM8jLyxt227PPPps777yThx9+OO6gNOyXv/wl119/PUceeSQf/vCHef755/nTn/7Ek08+yXPPPcfs2bOT/hokfVS+KyIikmZbm70Z0GnlhQC+TGmdglIRyVKvvfYaAAceeOCI24a3WbZsGb29iU2l9fvf/54HH3yQp59+mr/+9a+8/fbbnHPOOWzcuFFjVMcRBaUiIiJptq3FzZQOBKVuplTluyKSperr6wGYOnXqiNvW1tYCEAwGaWhoSOg8Z555JieeeOLgcl5eHr/+9a8pKSnh/vvvZ9OmTQkdTzJDQamIiEiabY9Rvluj8l0RESDU6CjyYzzbAhh3wucRnHXWWb511dXVfOADHyAYDPLss88mdDzJDI0pFRERSbMdrd4MaG1peEyp0+hIU8KIZLXKgkqe/vjTY30ZSaksqExp/5qaGlauXMn27dtH3HbHjh1AKCCtqqqirq6Oiy++2LfdhRdeyBFHHOFZN2/evKjH3GmnnQDYvHlzglcumaCgVEREJM3q270Z0HAwWlXsbebR3tNPT1+Q/FwVLolko4AJpNQsaCLbd999eeaZZ3jllVc477zzht32lVdeAWDRokXk5eXR1tbGrbfe6tvumGOO8QWlscSToZXRo3dBERGRNGtwg9KSUNluRVG+b9vmzsSadoiITAYnnHACAHffffeIzYtuv/12AI4//ngglOW01vr+XXDBBb59Y81tun79egBmzpyZ7JcgaaSgVEREJI36+oM0dXhvsKaUhoNS/7QHCkpFJBudeOKJLFiwgE2bNnHVVVfF3O4///kPd999N/n5+XzpS19K+Dx33HGHb11DQwOPPfYYxhgOO+ywhI8p6aegVEREJI0aO/xB5pSBTGl+boDi/BzPa82danYkItknEAhwyy23kJeXxw9+8AOuuuoq+vv7Pds8/PDDnHbaaVhr+dnPfjY4DjQRd955J48++ujgcl9fH9/4xjdob2/n1FNP1Tyl44TGlIqIiKSRW7oLUFU8VLZbUZRHR8/QjZebVRURyRbvec97eOCBBzjrrLO47LLLuPrqqznssMMoKCjgzTffZPny5QQCAX784x/zjW98I6lzfPazn+WEE07gqKOOYubMmTz//POsWbOGmTNn8pvf/CbNX5EkS5lSERGRNHKD0oqiPPJyAp7lSCrfFZFsdvzxx7Nq1Sq++93vMmPGDJ588kkeeOABurq6uPDCC3n11Vf53ve+l/TxL774Yv74xz/S3NzMvffeS0tLC+eddx4vvPACc+fOTeNXIqlQplRERCSNYjU5Cqt0OvAqUyoi2a62tpYrr7ySK6+8MiPHP//88zn//PMzcmxJD2VKRURE0qih3Tv36BQnKFWmVERExEtBqYiISBq5c5S6QWmlMy2MglIREcl2CkpFRETSyFe+W+pkSn3lu+q+KyIi2U1jSkVERNJopEypyndFRDLvqaeeGutLkAQoUyoiIpJGDW1uUFrgWXaD0iYFpSIikuUUlIqIiKRRot13lSkVEZFsp6BUREQkjRJudKQpYUREJMspKBUREUkTa62vcdFIY0qbOnux1mb82kRERMarCRuUGmMONMZcZoz5uzFmkzHGGmO6htn+ioFtYv37WRLXEDDGfN0Y86YxptMYs8MYc5cxZmFqX52IiExEHT399AW9AaYbhLrlu/1BS3tPf8avTUREZLyayN13vw98OIn9ngHeibL+lUQOYowxwB3Ax4Am4J9ADXA6cJIx5lhr7QtJXJ+IiExQ0caHljtBqbsMoWlhSgsm8luyiIhI8ibyO+BzwOvASwP/tsa5383W2lvScP5PEQpIVwFHWmu3ARhjTgfuBm4zxuxhre1Lw7lERGQCcINSY6DMCTbLCnIJGIhMqDZ19DK7ajSuUEREZPyZsEGptfaqyOVQ4nJUfWvg4yXhgBTAWnuPMeYB4FRCmdx7RvvCRERkbLhBaXlhHoGA9/0pEDCUF+XRFNHgqEUdeEVEJItN2DGlY8kYszOwEOgkVLbrunvg4ymjdlEiIjLm3KDUHU8aa72mhRERkWyWjUHpccaYa40xNxpjvmeMOTCJY+w78PEta220O4n/OtuJiEgW8GVKi6IXJCkoFREJVToaY6iqqqKpqSnqNldccQXGGH72s/h7kq5duxZjDMccc0x6LjTiOm655Za0HVOGTNjy3RSc5yz/2BhzD3CBtbYtzmPMHfi4McbrG53thmWMWRrjpV3jvB4RERkH3DJcZUpFREbW1NTENddcww9/+MOxvhQZI9mUKX0HuBhYBJQCc4BzgE2EOub+OYFjlQ587IjxeruznYiIZIF4y3fdDrwKSkUkWwUCAfLz87n22mtpbGwc68uRMZI1Qam19i/W2l9Za5dZa9uttRuttbcDBwP1wGnGmPfGebhw14q0zHZurV0U7R+wOh3HFxGR0aExpSIiicnLy+PCCy+kpaWFq6++eqwvR8ZI1gSlsVhrtwB/HFj8UJy7tQ58LInxenh9vOXAIiIyCbjlu9HmJAUFpSIikb7zne9QUFDAr3/9axoaGuLer66ujs997nNMnz6d4uJi9t9/f/70pz+ldC1PP/00xxxzDKWlpVRXV/ORj3yEFStWpHRMGVk2jimNZtXAxxlxbr9+4OPsGK/PdrYTEZEsoEypiCTCBoP0x2jwM97lVFZiAunJb82aNYuLLrqI6667jl/96lf85Cc/GXGf+vp6Dj/8cN5++21mz57NqaeeytatW/nUpz7F5z//+aSu4/777+f000+nv7+f9773vcydO5cXX3yRQw89lFNO0aQamaSgNCQ8ZXm8mc3XBz7uZYzJi9KB94CBj2+kfGUiIjJhxBuUVjrrNU+pSHbqb2pi1XsPH+vLSMpuzz5D7pQpaTve5Zdfzs0338xvf/tbvvnNb1JdXT3s9t/5znd4++23+fCHP8wdd9xBQUEBAA8//DCnnnpqwudvbW3lwgsvpL+/n9tvv51PfOITAPT19XHhhRdy6623Jv5FSdyyvnzXGGOAjwwsvhLPPtbaNcByoAg4KcomHxv4+GDKFygiIhOGb0qYQmVKRUTiMXPmTD772c/S2trKL3/5y2G3bWtr489//jO5ubn85je/GQxIAU444QTOOOOMhM9/1113UVdXxwc+8IHBgBQgNzeXa665htJS9S/NpKwISo0xNcaYTxpjCpz1pcANwKHAVuBe5/VDjDErjDFPRDlseCT2z40xUyP2+ShwKrAGuC99X4WIiIx3zZ19nmWV74qIxO+yyy6jsLCQ6667jrq6upjb/fe//6Wzs5NDDz2UuXP9MzBGBpXxWrJkCQBnnnmm77Wqqio++MEPJnxMid+ELd81xpwEfN9ZnW+MeT5i+cfW2n8SmprlVuC3xpjlhMZ6VhIqs60GmoCPWWvdKV6KgQVAYZRL+D/gREJZ1nDgWgMcDXQB50Yp6xURkUnKWhv3PKVuA6SWrj6stYSKd0REstOMGTP4/Oc/z7XXXssvfvELrrrqqqjbbd68GSBqQBpr/ZIlS7j55pt963/5y19SU1OT1DElfSZsUArUEspwRjLOutqBj/XAVcB7gPnAfkA/oWzmLcA11tpNiZzcWhs0xpwBfA34NHAyoflJ7wV+YK1dmsjxRERkYuvqDdLTH/SsizdT2h+0tHX3URaj3FdEJqecykp2e/aZsb6MpORUVmbkuJdeeim///3v+d3vfsfFF18cdRtrQ7MyJvIg75133ok6LvSKK66gpqYmqWNK+kzYoNRaewuhgDKebVuBy5I4x1MMzUka7fV+QmW8mlRJRCTLRSvBjRmUFvvXN3f2KigVyTImEEhrs6DJYPr06XzhC1/g6quv5uc//zklJf4ZGGfOnAnAunXroh5j/Xr/BBgXXHABF1xwQczzJnNMSZ+sGFMqIiKSadGC0ljzlJbm5xJwHnlqXKmISMill15KcXEx119/Pdu2bfO9fuCBB1JYWMgLL7zAhg0bfK//7W9/S/icRxxxBBBqeORqamriscceS/iYEj8FpSIiImnQ2uUNKkvyc8hxI88BgYDxBawKSkVEQqZOncoXv/hFOjo6opbclpaWcs4559DX18fXvvY1uru7B1977LHHuPPOOxM+5xlnnMGUKVN8+/f39/Otb32LtrZ4Z46UZCgoFRERSYP2nn7PcknB8CNk3NJezVUqIjLkkksuoaSkhM7Ozqiv/+xnP2P+/Pnce++97LbbbnziE5/guOOO44QTTuCiiy5K+Hzl5eX84Q9/IBAI8PGPf5wjjjiCs88+mwULFnD33XdzzjnnpPolyTAUlIqIiKRBe7d3OpjSBINSZUpFRIbU1tbypS99KebrNTU1PPPMM1x44YV0d3dz3333UV9fz0033cQll1yS1DlPP/10Hn/8cY488kheffVVHn74YRYuXMhzzz3H/Pnzk/1SJA4TttGRiIjIeNLmBKWJZkoVlIpItgl3vI3lqquuijktDITKfG+66SZuuummhI8dy3HHHcdxxx3nW3/FFVdwxRVXJHVMGZkypSIiImngZkpLCnKG3d4dU9rUoaBURESyk4JSERGRNEi0fLdSmVIRERFAQamIiEhatHWn1uhIQamIiGQrBaUiIiJp4C/fVVAqIiISDwWlIiIiaZBq911NCSMiItlKQamIiEga+Lrv5itTKiIiEg8FpSIiImnQ3hOj+25vFzx0Cfxmf7jtDGh4F1BQKpItjDGDnweDwTG8EpHE9PcP9UqI/DnOBM1TKiIikgZuo6PB8t3Hvw8v/iH0ecO7UL8avvicb0qYlq4+rLUZf+MXkdFljCE/P5+enh7a29upqKgY60sSiUtLSwsABQUFCkpFREQmgqiNjjob4ZVbvRs2rIbl/6Bi1ome1f1BS1t3H2WF3mBVRCa+srIy6uvr2bZtGwAlJSUEAipYlPHHWkt3dzetra00NDQAUFVVlfHzKigVERFJgw5fUJoDS++D/m7/xqufpGK3D/tWN3f2KigVmYSqq6tpb2+nq6uLzZs3j/XliMStsrJyVLL7ekQjIiKSBlEbHa35T/SN1yymND+XgFMNpXGlIpNTTk4Oc+fOpbq6mvz8/LG+HJFh5eTkUF5ezqxZs5g+ffqoZPWVKRUREUmRtZb2Hu+Y0pKCXNjwYvQdmtcTaNtKeVEeTR1DgaiCUpHJKycnh6lTpzJ16lSstVhrx/qSRHyMMWPS20BBqYiISIq6+4L0B703mBW9O6BlY+yd6t6mwglKNVepSHYYqxt/kfFK5bsiIiIpckt3AcpaVw2/U93bVGpaGBEREQWlIiIiqXI77wIUN78z/E51q3zTwigoFRGRbKSgVEREJEVupjRgIK9hhExp/TtUKCgVERFRUCoiIpKq9m5/kyNT97Z3o6kLvcvNGxWUioiIoKBUREQkZW75bml+DuxY4d1o1+O8y80bqSj09hts7vSXAYuIiEx2CkpFRERS5JbvzsjvhM5G70bz3+9d7m2nNq/Ls0qZUhERyUYKSkVERFLkZkp3zq3zbhDIhbnvAbxTQMzAu52CUhERyUYKSkVERFLkZkrnBnZ4NyifBXlFUDbds7q2f7tnWfOUiohINlJQKiIikqKOHm+jo5k4QWnl3NDHshne1cEGz7IypSIiko0UlIqIiKTILd+dYb0ZUCrnhT6WTvOsLu3zjjtt7uzFWpv26xMRERnPFJSKiIikyC3fre3f5t0gnCktrfWsLu6p9yz3B63vWCIiIpOdglIREZEUuZnSKb1bvRsMBqXeTGlht9MQCZXwiohI9lFQKiIikqK27sgxpZbK7i3eDWIEpbmddeQEvB15FZSKiEi2UVAqIiKSoshMaQXt5AW9849SMTv0scRbvmvatlFemOtZp6BURESyjYJSERGRFLX3DAWl00yjf4PwVDBOppT2HVQW53tWNXUoKBURkeyioFRERCRFkc2Jppom74tFUyC3IPR56VTvaz1tzCj0jkdt7OjJwBWKiIiMXwpKRUREUhRZvjsVJ1MazpKCPygF5hW2e5Yb2xWUiohIdlFQKiIikqL2iEZHvkxpZMlufinkFXtenp3X6lmuV1AqIiJZRkGpiIhICqy1w48pjcyUGgMlNZ6Xp+UqUyoiItlNQamIiEgKOnv7sXZoudbNlEYGpQDF1Z7FmkCbZ7lBjY5ERCTLKCgVERFJQWSTI4hWvjt8UDrFeMt3lSkVEZFso6BUREQkBZHjSQGm0uTdoMyZBsYJSstti2e5QUGpiIhkmQkblBpjDjTGXGaM+bsxZpMxxhpjumJsGzDGHGmM+bkx5gVjzHZjTLcxZrUx5kZjzM5JnP+WgXPG+vf51L9KEREZ79o9mVIbZUzpDO+yE5SW9jd7lhWUiohItskd6wtIwfeBD8e57S7AfwY+3wQ8CwSBQ4DPAWcbY0601i5J4joeBbZGWb8yiWOJiMgEE1m+W0YnRcYJKkvdTOkU72Jfk2e5s7efzp5+ivJz0nmZIiIi49ZEDkqfA14HXhr4Fy0wDLOEgsf/Z60NB6cYYwqAG4ELgNuMMfOttYl2mPiZtfapBPcREZFJwjNHqZslhREbHeX3NPl2aezooSi/KB2XJyIiMu5N2KDUWntV5LIxZrhtVwPHR1nfbYz5AvARYC7wXuDp9F6piIhMZm2eoLTJ+2JBBeQ5waUTlOZ0NZATMPQHh1r4NrT3MLNSQamIiGSHCTumNF2stV3A2wOLM8fyWkREZOKJbHQ0BW8nXXdOUsAXlJqOeqqK8z3rNK5URESyyYTNlKaLMSYHmDewOFwJcCwfNcacDuQAa4B/WGtXpOv6RERkfIss360yiQeldDVTW26oi5iutLFDQamIiGSPrA9KgbOAqcAOQg2QEvUVZ/kqY8wNwNestX3RdnAZY5bGeGnXJK5HRERGUWT5bpWbKXUDUICiKb5Vcwq7WR6xrEypiIhkk6wu3zXGzAGuHVj8gbW2O4HdXwU+D+wOFBPq8PsloAn4IvCLtF2oiIiMW5GZ0iluprTYH4BGWzensMOz3KigVEREskjWZkqNMSXAvUANcJ+19sZE9rfW/tpZtQa43hjzH+AV4CvGmKuttRviONaiGNe4FFiYyHWJiMjoau8Zpnw3SlaUnLxQA6TuoflJp+d1AEONjRpUvisiIlkkrUGpMWYn4EhgX6AWqACaCZXGvgYsttauS+c5k2GMyQPuAQ4ElgBnp+vY1tq3jDEPAB8D3g/8MV3HFhGR8WfYRkfRynchlC2NCEqn5bYDQ9uqfFdERLJJykGpMaYKOB+4CNgjvDrKpnZg++XATcCfrLVRJnTLLGNMAPgL8CFC85yeYq3tTPNpVg18nJHm44qIyDgTWb5badq8L8YMSquhcc3gYrWzn4JSERHJJkkHpcaYYuAS4FtACdBJKOv4IrACaABaCGVLq4A9gUOAg4BrgCuNMb8EfmmtbU/ha0jU9cCZhKaB+aC1tikD56ga+Ng27FYiIjLhtQ07pnSYoDRCFS2e5cb23rRcm4iIyESQSqZ0NTANeJRQ5vG+eILLgbGcHwXOBf4H+ByjND+oMeb/DZxvPfABa+32DJyjADhpYPGVdB9fRETGF8+Y0rjLd73ry603KNWYUhERySapdN99ATjQWnuCtfa2eLOd1tp2a+2frbUfIpQ1fSGFa4ibMeabwOWE5iJ9v7V2fRz7HGKMWWGMecJZv8AY8+GBOU4j19cCfwPmECoNTmaKGRERmUDCY0oL6KHEOE3chxtTGqGk382U9mCtTds1ioiIjGdJZ0qttaelenJr7X+BjySzrzHmJOD7zup8Y8zzEcs/ttb+0xizH/DLgXVrgO8aE23YKzdba5dELBcDC4BCZ7sZwH1AvTFmBbCJ0FynBwJlwEbgTKs7ChGRSS9cvlsZbcRGtClhwBesFvU1eZb7gpaWrj4qivLScYkiIiLjWipjSkuttWM5ZrIWONRZZ5x1tQMfKxlqvnTYwL9oniI0LnYkbxOa3/Q9wK6Exsp2D6z/B/DrsWjiJCIioy/c6Mg3ntQEoLAi+k5OUJrf7X/LaGjvUVAqIiJZIZUxpduMMfcBtwGPWGuD6bmk+FhrbwFuiXPbp4jeETip/ay1m4FvJHo8ERGZXIJBS0dPqHzXP0dpFQRyouyFLygNdDZQmBegq3forbSxo4edKUnr9YqIiIxHqYwpLQI+QSgzuNkYc60x5qD0XJaIiMj4F9nkKO45SqO91tFAdUmBZ1WjpoUREZEskUpQuhPwXWA5ofGUXwVeGGgM9B1jzE6pX56ITDYN7T1saU731MAiYyPc5AiiZEoTCUp7Wqkt9q6qV1AqIiJZIumg1Fq73lr7U2vtXsD+wNXAZmB34MfAamPM08aYi4wxlWm5WhGZ0P703FoO++kTHPbTJ7n8728QDKoXmExskXOUVrmNjopiNDmCqAHrnMIuz7IypSIiki1SyZQOsta+bq29GJgLvB+4FWgFjgRuBLYYY+42xpxmjFHXBpEstLaunR/9YxndfaExc399cQMPvrlljK9KJDXtEUFphXFmRiuqjL1jUSVuy4LZ+R2eZc1VKiIi2SItQWmYDXnSWvtpYBrwceBBQu+8HwXuAbYaY25I53lFZPx76K0t9DmZ0fte3TRGVyOSHpFjSsvwBpUxO+9CqAFSUZVn1fQ8b1CrTKmIiGSLtAalkay13dbau6y1HyY0r+eXgAagCvhsps4rIuPTGxuafete29CEpvOViSxyTGmZcYLSgvLhd3ZKeKfmeIPSBgWlIiKSJTIWlIYZY/YBLgMuB8IDbPpj7yEik9HKba2+dQ3tPWxr6R6DqxFJj8jyXX+mNLGgtDrg/R1RUCoiItkilXlKYzLGzAHOBs4BFjE0cOYN4M+E5jYVkSxhrWVTU/SOu8u2NDO9onCUr0gkPSIbHZWnmCmtdKaUaezoTenaREREJoq0BaXGmArgDOBc4AhCgagh1JH3duDP1to303U+EZk4Gjt66RlocORaubWN4/aYNspXJJIew2dKhxlTClDs7c5b1t/iWVamVEREskVKQakxJh84mVBG9EQgn1Ag2gbcSygr+oTVoDGRrLa1uSvmaxsbO2K+JjLeeYJS41QDJFi+W9Lf5Flu7uylrz9Ibk7GR9qIiIiMqaSDUmPMTcDpQAWhQLQfeJxQIHqvtVZ3miICwLaW2EHphsboZb0iE0HbYKMj68+UJli+W9jb5NuksaOX2rKC5C9QRERkAkglU/qZgY+vEwpEb7fWbk39kkRkstkyXKa0Qc+vZOIKZ0oL6KXA9HlfHLF81xuU5nU3+jZp7OhRUCoiIpNeKkHpLwiNE30rXRcjIpPTcJnSjY2dBIOWQMDE3EZkvGobmKe03M2SQsJBqelooLwwl5auoeBW40pFRCQbJD1QxVp7qQJSEYlHU0fsG+ue/iA72jQtjExM4Uypb45SSLh8l456ppTke1Y1KigVEZEskPbuCcaYE4wx9xljNhljuo0x/+u8drUxZma6zysi41dT5/BTW2xQCa9MUINBqZspzS2E3Pwoe0Rwuu/S18m0Ym+X6noFpSIikgXSGpQaY64HHgROBUqBPIbmKAVoAr4OnJXO84rI+NY0wnyLG9SBVyaocKMj3xylI5Xugj9TCswr9Db+UqZURESyQdqCUmPMp4HPAy8C+1lrfe/I1trngE3AKek6r4iMf8OV7wJsboo95lRkPOvoiZEpHal0F0KBq8nxrJqZ5z1Owwi/OyIiIpNBOjOlnwMagJOttW8Ms907wC5pPK+IDHh7Wys/fWg5Ny9+l+6+/pF3GCVu+a47bm6jpoWRCWpoTGmCc5QCGOPLlk53glJlSkVEJBuk0n3XtQj4t7W2foTttgLvSeN5RQR4Z3sbH73+WdoGbpKfeaeO/7vgYIwZ+662bvnuopnlLF5VN7i8qUlBqUxM4d+3ctq9L8STKYVQUNq+fXCxJqcNqB1c1phSERHJBunMlAbjPN5McN+9RSRV1/zr7cEbZIB/r9zBS2v98x6Otv6gpaXLG5QunOG9Yd+soFQmoL7+IF29ocZEvu678YwpBV+mtNq0epYbVb4rIiJZIJ1B6QrgIGNMcawNjDHVwH7AcOW9IpKg/qDl6ZU7fOufWLFtDK7Gq7WrF2u96xbO9Aalmxo7se5GIuNce89QiXwZSZTvgq8Db4V1gtL24ZuEiYiITAbpDEpvI1Rz9DtjjK8s2IRqCH9DqCvvn9N4XpGst3pHmydLGvbWpuYxuBqv5ijTwbiZ0s7e/hE79IqMN+0Rv3O+7ruJlO9GKA16f2cbVL4rIiJZIJ1B6fXAEuB8YLkx5rqB9fsYY34JLAc+AfwbuDWN5xXJeu/uaIu6/q1NLWOegWzv9jZcChjYqaaEgDPUVeNKZaLxBKVu990ky3eL+7xBaWdvP12946dpmYiISCakLSi11vYCxwM3AnOBLw68dADwTWBX4H+BU6y1wagHEZGkrK2PPs9nc2fvmHe2DU+ZEVaSn0teToDp5YWe9QpKZaKJrE5I15jSgh7/OPCWKNUGIiIik0k6u+9ire0AvmiM+R/gaGAnIAfYSKgz7+Z0nk9EQtbVx+4dtq6+gzlTYg71zrjIcXcARfmheRlnVhaxuXloftJNmhZGJpjIKoCk5ikFX1Ca29Xg26S5s5epzkMcERGRySTpoNQY817gORulNtBauwO4O5ULE5H4rakbJihtaOcIakbxarw63UxpQejPzqyqIl5eN5QVUgdemWjauocymL6gNO5GR96g1HQ0UJKf43mYE21ctoiIyGSSSvnuEmCbMeaPxpiPGmNK03VRIpKYrREZR9f6GKW9o8UdU1ockSmNpPJdmWhau9LR6MjbfZeOOioKvc+LFZSKiMhkl0pQ+n1gNfBJ4C5ghzHmYWPMF40xc9NydSISlx2t3TFfWztMae9ocMeUhoPSWU5QqkypTDRDY0otpb4pYeIcU1pS610O9jGz0Pv7rKBUREQmu6SDUmvtT6y1hwEzgM8CjwFHAtcBa4wxrxljfmSMOSQ9lyoi0XT09PnGbUba0DDWjY7cTOlA+a4TlG7QmFKZYMLdd4vpJtc4/fviLd8tnepbNTff201bQamIiEx2KXfftdZut9b+r7X2w0A1cCpwM1ADfA94zhizxRjzB2PMKcaYouGOJyKJqWsdfh7DjY1jXL7rBKUlBaFM6bxqb/OlhvYeGjUno0wgrQNBqW88KcRfvptb4Muqzsxr8Sy3dPrnIBYREZlM0jlPKdbabmvtg9baz1lrZwMHAz8BtgAXAvcB9caYfxhjLjLG1A5zOBGJw4622ONJAVq6+mjpGrtMS0e394a6KC+UKZ07pZj8HO+foHdizLcqMh61DYwp9Y0nhfiDUoASb7Z0ek6rZ1mZUhERmezSGpS6rLWvWGt/YK09gNDcpV8B/gO8n9B8pl/I5PlFsoE7nnR6eSHGeLcZy+lWOnqjZ0pzcwLsVOPNlr6zXUGpTBxtsTKl+aWQk0Bz+9JpnsVa0+RZVlAqIiKTXUaD0kjW2o3W2uuttccTKu09A3hutM4vMlm5QenMykKmlhV41m0cy6C02210NHSzvtvUMs9rq7YpKJWJI2amNJEsKUCpt2io2jZ7lhWUiojIZJf0PKWpsNa2A38fi3OLTDY72rzjMGvLCjDGsK1lKFgdy3Gl7pjScPddgF2nemeSUvmuTCQxx5TG2+QozMmUVgQbPcstCkpFRGSSG7VMaZgx5tPGmB+M9nlFJis3U1pbVsDsKm8/sbHMlHYOE5TOd4LSpZua2dDQwXfvfZOv/PVVXlzTMCrXKJKMcPfdlDOlzrQwZX3en/uxHBMuIiIyGsYiU3oRcAjwozE4t8ik4wtKSwupKPIGgmM5prTdmae0pGDoz86+s71dR+vbezjy5/8eXP7H65u57uz9OXmfmZm9SJEkxBxTGu8cpWFOprS41xuUqnxXREQmu1HPlIpIejV2eMt3p5TmM7vK20BoY9PYle92dMfOlM6dUsz08sJh9//pQyvoD9qMXJtIKsJjSsvcTGnC5bve7ruF3XWeZQWlIiIy2SWdKTXG+Gf8jk9esucUEb8mJyitLMqjosj7azamjY56Yzc6Msbw3vnV/P2/m2Luv6mpk1fWNXLIzlMydo0iyQiPKS13M6Uplu/mddVjCGIHnht39PTT2x8kL0fPkUVEZHJKpXx3K5BM+sIkuZ+IRNHc6Q36KovzmFXpHVPa1NFLa1cvZYWj/0zIzZSWRGRKAc46eO6wQSnAS2sbFJTKuNLd109PXxBIR6bUW75rgn1U0kYjQ8dp7uylprTA3VNERGRSSMeY0vUJbj8DZUtF0sJaS3OnN1NaUZTHTCcohVDGcY/po/+r544pLXKC0oN3quLw+dU88059zGMs3dwc8zWRsdAe8bClDKcSIdExpU6mFKDGtNBoFZSKiEh2SKUWaM3AxyOstTvH+w94NQ3XjTHmQGPMZcaYvxtjNhljrDGmK479PmmMedEY02aMaTDGPGSMeW+S1xAwxnzdGPOmMabTGLPDGHOXMWZhMscTSVRnbz+9/d7Cg8qifArzcvxzlTaMfglvf9DS1Rv0rItsdAShEt7rzz6Qsw6ew661JRy1ey0XHrGzZ5ulm1syfq0iiQiPJ4UomdJEy3dz86GoyrNqZq73Z17TwoiIyGSWSqb0RWAn4CBg+Nq7zPg+8OFEdjDGXA18A+gEHgMKgQ8AHzTGnGGtvTeBYxngDuBjQBPwT6AGOB04yRhzrLX2hUSuTyRR0RqghMeTzq4qYntEZ95NTaMflHb29vvWFTuZUoCK4jx+dvo+g8tvbWrm5iVrBpfX1XfQ3t3nC2hFxkq48y5EGVOaaKYUoGQqdA7NTzo3vw0ifr1buvqi7CQiIjI5pJIpfYHQ+NBDE9zPpHDOSM8RmlbmFGD6iCc15jhCAWk9sK+19jRr7fHAUUA/8EdjTNVwx3B8ilBAugrYw1r7MWvtMcAZQBFwmzFGd9CSUU0d3qDUGCgrDP3YzXI78DaOfgfejh7/jXRko6NY3PlLYWyCapFYIoPSlDOl4OvAO8PJlLZ3KygVEZHJK5Wg9CHg14SCskR8ATguhfMCYK29ylr7P9baB6212+LY5VsDH6+01g5es7X2OeBGoAL4dAKXED7eJZHnt9beAzwA7EqCmVyRRLmZ0vLCPAKB0HOf2VXecaVj0YHXbXIE0TOlrsK8HN/4ubEIqkViaese+t1LeZ5S8AWl0wLecdRtypSKiMgklnRQaq1921r7DWvtHxPc71Vr7dPJnjcZxphC4H0Di3dH2SS87pQ4j7czsJBQGfA/Uz2eSLLcTGnkVDDjISh1mxzlBAwFufH92RkP1y8SS+tAkGgIUorTziDR7rvg68BbY7yZ0jZlSkVEZBLLlknP9gAKgB3W2o1RXv/vwMd9orwWzb4DH9+y1kbrPvFfZzuRjHCbn1QWRwalY1++29njzZQW5+UQGo49MgWlMp6Fg8QyOgkYZ5azZMp3nQ681bYp6vlEREQmo2wZ8zh34GO0gBRrbbsxpgmoMsaUWWtbUzlexPq5MV73MMYsjfHSrvHsL9mrKcp0MGFuUNfY0TvqzYLa3aC0YOTS3TA3qN6koFTGkXA5ra90F5LMlHrLd6uCjZ5lBaUiIjKZJZ0pNcbskY4LSNdxRhDumjJcqqjd2TaV4yVyLJGk+caURgSls2LMVTqaOpwb6ZI4mhyFTSv3jind0dYdY0uR0dfSFfrdKzPu75SB/LLED+iU75b3KygVEZHskUr57lvGmNuMMXsls7MxZj9jzN+AN1O4hrhPN/DRxrFNuo4XN2vtomj/gNXpOL5MXm5QWhkRlI6HZkEdTqa0KI4mR2HVzrXXKyiVcaSlM0amtKAcAkm8tTpBaUlfIwGG5vhVoyMREZnMUglKf0yokc/rxpj/GmO+ZYw5yBiTF21jY0yBMeY9xpjLjTFvAq8AJxKa1iXTwuW4JcNsE64VbEvD8cLr4zmWSNKGa3QEYz8u050SJpFMaU1Jvme5rq0nxpYioy/8QKjctHtfSKZ0F3xBaYAg1Qw1O1KmVEREJrOkB5dZa39ojLkB+C7wSeAXhDKHvcaYtUAjoeCtHJgCzBs4nwGaCU0n81Nr7Y5UvoA4rR/4ODvai8aYEqASaIpjPOmIx4tYvz7G6yJp4cuUFvuD0tc2NA0ur60b3UxpKmNKa8q8mdLmzl56+oLkx9m9VySTwr97ZTgPepJpcgRQUgMmAHYoO1prmthhKwEFpSIiMrml1PHEWrsd+Jox5jLgTOBk4HBg9yibbwUWE5pC5U5rbVeUbTJlJdAN1BpjZkfpwHvAwMc34jze6wMf9zLG5EXpwJvo8USS4galbqZ011rvsOblW7zTTGSaW74bzxylYdVOphSgsaOHaeWFKV+XSKqGxpSmYY5SgEBOqANv29C021NNE8sGBomofFdERCaztKQcrLWd1tpbrbVnWGtnAtOAvYEjBj5Os9bOtNZ+3Fr7p1EOSLHWdgJPDix+LMom4XUPxnm8NcByoAg4KdXjiSRrpKB00Uxv1uatzc1Ym5ah0HFxGx0VJ1C+W1mcT8AZ6V2ncaUyTgyW77pjSpMt3wVfB95a0zT4uTvnr4iIyGSSkTo4a+0Oa+1Sa+2zAx9Ho0R3JFcPfPyeMWa38EpjzGHA54AW4H8jdzDGHGKMWWGMeWKY4/3cGDM1Yp+PAqcCa4D70nf5In7+oNSbXdxrljdr09rVx4aG0RtX6pbvliSQKc0JGKZoXKmMU4ONjtxMabLluwCl0z2LtTQPfq5MqYiITGYTdnCWMeYkY8zz4X8Dq/Mj1xljBrOY1tp/ERrHWg28Zoy5zxjzEPAfIA/4tLW2wTlNMbCA6POF/h9wL7AbsMIYc5cx5t/A3UAXcG6Usl6RtAkG7YiZ0hkVhb7AbunmZkZLp5PdKUogUwr4ugerA6+MB9ZaWmJmSpMs3wVfs6PITGmrxpSKiMgkNmGDUqAWODTiH4SaKEWuq43cwVr7deBThEpvPwC8F3gCONpae08iJ7fWBoEzgG8BmwmNp92bUKB6kLX22WS+KJF4tXb34Vbiuo2OjDG+Et7XN45eUJpKphSgutQbUNcrUyrjQFdvkJ7+UEMi/5jSzJTv9vQF6ekLIiIiMhml1OhoLFlrbwFuyeR+1tqnGGb+UmttP6Ey3qtjbSOSKc0d/kS8mykF2Gd2BYtX1Q0uvx7RjTfT3ClhigsS+5NTXeLNlNa1K1MqYy/c5AiiZEpTKd8t85bvTo0ISgHau/vIz/U3ABMREZnoJnKmVCSruaW7uQETtbvtvrMrPctvbmqmPzg6zY5S6b4L/kxpgzKlMg5E/u5lNFNKk2dZ08KIiMhkpaBUZIKKNkepMf7E/n5zKz3Lbd19rN7RlslLG9TRnVpQ6mZ+3a9ZZCx4glJ3ntK0jin1ltorKBURkclKQanIBNXU6c0alkcp3QWYWlbIrMoiz7rX1jdl6rI83GksShJsdKSgVMajluEypQXpC0pLTRfFDM2gpqBUREQmKwWlIhOUL1MaIygF2HeO90b5tY1Nmbgkn84Uy3cVlMp4FPlzmN55Sqf5VkU2O1JQKiIik1XGg1JjzHxjzHuMMXMzfS6RbNLUMfx0MJH2m1PpWR6rTGmijY7cr6lFQamMA+Gfw1z6KDZO861UGh0VlEJeiWdV5LhSzVUqIiKTVdJBqTFmmjHmTGPMe2O8frgxZgWwEngGWGOMeckYs1ey5xSRIW6ANlxQ6jY7Wrmtle6+/ugbp0l/0NLV653CItEpYdyS5BbdlMs40NwZ+jksdceTQmpjSgHKvNnSqcqUiohIFkglU3oe8FdggfuCMWY34BFgN0JTqjQMvHQg8IQxpiaF84oI/kxpZXHsqSIWzfLeKPcHLevrO2JsnR6dvf6gtyjF8t227j76+jVXo4ytcPmubzwppFa+C8M2O2pXUCoiIpNUKkHp0UAXcGeU164ASoD1wN7W2lqgGngAqAG+msJ5RQT/+MpYjY4ASgtymVrmnfPz3br2jFxXWEeUG+hUGx2BsqUy9sLzlPrGk5ocyCtO7eClbqa0cfDzVv3si4jIJJVKULoH8Iq11nNna4zJA04DLHCxtXYpgLW2CbgAaAeOT+G8IkJijY4Adq7xjlVbk+GgtL3HnyktLkgtUwpqdiRjL/wzWB5tjtIo0zIlxM2UMpQpjVZ9ICIiMhmkEpROJZQJdR0EFAGdwIORLwwEpi8SKusVkRQ0JTCmFGCXWico3ZHhTKnT5CgnYMjPSexPTmFeDvm53n3U7EjGWlNHaDomf+fdytQPXjrVsxjZfdf9nRIREZksUglKC4Bog2feM/Dxv9ba7iivbwNSrG8SETc4qywePiidO8UblG5ujtKkJY06okwHY5LIImlaGBlv6tsHglLjPNhJtckRQNl0z2Jko6OObmVKRURkckolKN0I7GuMcY/xPkKlu8/F2K8CqEvhvCLCULYmbKRM6YyKQs/y5qbMBqVuU5ZEx5OGlRd691NQKmOtMRyUkoGgdJhGR+6DHhERkckilaD038Bs4DvhFcaY9wAfGlh8MNpOwP6EAloRSVJvf9A3ZjPRoHRLcxfW2rRfW1hnlExpMpQplfGkP2gHS+d9Y0qLKlM/gVO+W00zAUIdp915f0VERCaLVILSXwDdwA+NMWuNMS8DTwM5wIvW2sXuDgNB6wzg+RTOK5L1oo2rrBihfHdGRZFnuaOnP6OdbN2gOdEmR2EKSmU8aeroIfwsxz+mNB2ZUm/5bo6xVNMC+B/0iIiITBZJB6XW2reB04F6YC5wAJAHLAc+EWO3rw98fCTZ84qIv8kRjJwpnVpe4Fu3tbkrbdfkcpuyFCdZvut+XeHpOETGQkP7UNl8RsaUltSAMyom3OwoWkdrERGRySCVTCnW2ocJBaQfBM4hNHfp3tbatTF2uQ34CPBEKucVyXZutrAwL0BB7vCZyMK8HKpL8j3rtrZkMijNTPmuuu/KWKqPDEozkSkN5EBJrWdVuNlRp8p3RURkkkoudRHBWtsF/CvObf+R6vlExN/kqKo4P8aWXrVlBZ6b6vq2aA2y06MjXY2OVL4r40hjxO9PhS9TWpmek5RMhbZtg4tTBsp31ehIREQmq5SDUpcxJgeYQqgDb6O1Vu+iImnW1JHYHKVh1aXe4LW+rSfGlqnzjSlVoyOZBIbPlFam5yTFVZ7FKtMKKCgVkfgFg5arHl3Bvf/dxPSKQv7fR/Zmr1lpqOYQyZCUynfDjDFzjDE/M8a8DnQBWwnNR9ptjHl94LW56TiXiECjE5TGmymtKfWOK61rz2CmNE1BqZspbelUCaOMncZMjykFKK72LFaZNiA0TjuTHbNFZPL4ywvr+P3T77K9tZs3NjZzwR9f8k3VJjKepByUGmO+DKwAvg3sTaj7rhn4FxhY921gpTHmq6meT0SilO+WxJkpLfEGpZnMlPoaHRWkp9GRMqUyljI+phR8QekUQpnSoIXuvmB6ziEik9qdL2/wLNe1dfPIW1vH6GpERpZSUGqMuRT4NVAI3AWcBswZWC4i1ATpI8A9QD5wjTHm8lTOKSLRynfjy5S65bt1GRxT2t7tzZSWJJspLfQGpa3qvitjKNx9N4d+So3TKCxjmdLWwc9VwisiI+ns6Wf5llbf+iXv1I3B1YjEJ+kxpcaYRcCVQCPwkWjzkgIbB/7db4w5CrgP+JEx5h/W2reSPbdItmv0NTqKL1NaM4pjSjt7vZnSoqQbHXn3a+kKlTAaY5K+NpFkhX/3ymn3v1hUmZ6TuJnSiKC0vbuPKSXxPYQSkez05qZm+oP+Uv83NzWPwdWIxCeVTOlXB/Y/N0ZA6mGt/Q+haWNygC+ncF6RrOeWsMY7ptRfvjvxMqX9QatskYyZ8IOcctPhfzFdmdKiKZ7FStoGP+/s1c++iAxv2ebowefqHW20aVypjFOpBKXHASustY/Eu8PAvKbLgfencF6RrOdmSivizJRWORmWpgyOz0zXmFI3KAVoUQmvjJGhTKkTlObkQ25hek5S7A1Kp6h8V0QSsLGxM+p6a2Hl1pZRvhqR+KQSlM4E3kxivzeBGSmcVyTrNbYnlyl1mwZ19PTTk6HGKb7uu3nJZUpLC/3BrDrwyliw1kZkSqN03k1XSbk7ppRWQrOs+ef/FRFxxQpKAdbURanyEBkHUglKe4CCEbfyKwCU5hBJgVu+WxlnpjTafKaZ6mbrC0oLkgtKcwKGMifLqmZHMhaaOnrp6Q89xMnYHKXgC0pzTXDwfMqUishINjXFDkrX1kUZDy8yDqQSlK4BDjPGxH2MgW0PA95N4bwiWa2nL+gbExJvo6PRDErd+dBKkmx0BFHmKlVQKmNgW+tQt92KTM1RCr7yXYDKgblK23uUKRWR4Q0XlK5RUCrjVCpB6T+BWuBbCexz8cA+D6ZwXpGsFi2IrIyzfDc/N0Cx03CouTP9HXj7g9Y3n6J73kSUOSW8Kt+VsbCtZagxmK/7bmF5+k6UV+wbnxqeq7RTmVIRGUZnT//g1FXRKCiV8SqVoPRaoAX4qTHmcmNMzDtOY0yOMeY7wE+BZkJzm4pIEpo6/G820TKgsbjbZiJT6jY5guQbHYG/2ZEypTIWtrUMZUqrTJv3xSJ/djNpxsScq7RdQamIDGOk+cfX1rdjrX+6GJGxlvRdorW23hhzBqGs55XAF4wxdwGvANsBA0wFDgQ+BswC+oCzrLX1qV64SLZq7PAGZGUFueTlxP98qaIojy3NQzfXTR2ZCEr9N87JTgkDUeYqzWDXYJFYtkcEpZHTtABRS25TUjwFWjYNLg5lSlUlICKxjRSUdvT0s6O1m6nlaeoWLpImyacuAGvtv4wxRwK3AnsAX4+yWbgd4UrgAmvtC6mcUyTbuZnSeKeDGdx+VDKl/qC0KJWg1Jcp1Y25jL7trUM3e5HTtADpzZRGOV7lwPnU6EhEhhPuEB42vbyQxo4ez5CatfUdCkpl3EkpKAWw1r4ELDTGnACcCOwHVBMKRuuA14GHgYes6gVEUuZmNuOdDibM7dSbiaDUbXKUGzDkJ5DNdbmNjtR9V8ZCZPlupVu+65Tbpsw5XrWCUhGJQ327N1NaW1ZAWWEuq7YP/c1aW9/OITun+UGaSIpSDkrDrLUPEwo+RSSDGp1MabzTwYS5mdLRKN8tys/BpDCHY7kaHck4ENnoqAonU5qJ8t0IFQONlaKN1xYRCatzMqXVpfnkBgKeoHR9veYqlfEnbUGpiIwOt6tedUlimVI3KM3E+Ew3U+rOM5qoMjU6knHA2+jILd+tSu/JnClmygemoFGjIxEZjjumtLqkwDdt3Np6deCV8SdtQakx5r3AscCeQBVggQZgGfBvjSUVSY8d7htOaUFC+7vTxzRlICh151EtSTEoVaMjGWvdff1sHQxKLZXulDDpLt91g1JCmQ1NCSMiw3HHlNaU5jN7SrFn3TplSmUcSjkoNcbsA/wfsH94lbOJHdjuReAz1tplqZ5TJJu5bzjVpYllSt3xmZkYU5r2oFSNjmSMbW7qItwVoYxO8owTHKa7fLew0rNYbkI3kW4VgohIJPc9vaokn52qvUFpeFqYVIbViKRbSneKxpiDgSeBEqCd0JjS1wg1ODJADaHGR8cDhwLPGWOOsda+msp5RbKZ28SgpiSxTKl/TGnsSbaT5d44l6acKc18ybHIcDY2DmUWfKW7kP7uu75MaSgz29WrTKmIxOYGpeWFecybUuJZ19rVR1NHL1UJDv8RyaSk7xSNMTnAbYQC0v8FvmWtbYmxbTlwNfBp4HZjzCJrbTDatiIyvJQzpU7ToNYMZB3dTGnKQWmh2323T095Ja2stTy3up7XNzazaGY5R+5W4/n52tDQOfi5r8lRTgHke2/6UhZjTKm674rIcNyeC+VFucysLCQ3YOgLDk2Csba+XUGpjCup3Cl+GJgP3GGtvWi4DQeC1QuNMWXAx4BTgPtTOLdIVrLWRhkvklim1Jd1zEDTIDdTmu4xpT39Qbr7ghTmJT/3qUhYMGj59t1vcM9/Nw6uO2XfmVz78f3ICYQC0w2eTKk7HcwUSPcDkqhjSq2CUhEZltudvqIoj9ycAHOmFLOmbmgs/Lr6Dvafm+YGbSIpSH7iwFBgGQS+k8A+lw98/HAK502KMeYYY4yN498P4jze2hGOs0emvybJPi1dffT0e4sMEs+UeoPSrt4gPX3pLVzwZ0pTCx7d7rugEl5Jn1ufW+sJSAH+8fpmblr87uDyhoaIoNTNlKa7dBd8QWmB6aOAXjpVvisiMVhrfe+N4ff8eVHGlYqMJ6mkLw4EVlpr18S7g7X2XWPMioF9R9tW4NYYr+UA5w58vjjB48Y6ZnOCxxEZUb3TeRdC7d4T4ZbvArR29SbcxXc4bd3eG+dUM6VlUa65pauXqeWFKR1XpKOnj2v/tSrqa799YhVnHzqX8sI8b1AaLVOabk6jIwiNK23t0c+8TH4vr23gP6vq2Km6mI/sP0tDNeLU3Rf0PbgO95HYqboE2DG4fm2dglIZX1K5U5wBLEliv7eBI1I4b1KstSuAC6K9Zow5gVBQugF4OsHjRj2mSCbUO3OUluTnUJSfWBbSLd+FUAY2nUFpust383ICFOfneEoXmzvVhVRS9/CbW2N2oG7v6ee+Vzdx3nvm8U7ExPOVbqOjjASlFb5VFaadHb1VBIOWQEA36TI5PbliG5++5eXB5Tc3NfM/pywawyuaOKL9LSsfDEq9mdKV29p824qMpVTKdytILhvYApSncN5MCGdJb1MDJhnP3ExpMoFkQW6A/Bzvr35rmseVuuW70TKdifI3O1L5rqTujpc3DPv67S+sZ2NjJ+0RD0SmjEb5bk4u5Jd6VoXnKu3qUwmvTF6/fPRtz/Ktz65lS3NnjK0lUrRhLeH334UzvQ+6Vm1rVTdvGVdSCUpzCY0pTVSQNMyPmi7GmBKGxrj+ZSyvRWQkO1rdoDTxznnGGF+Q6DZGSFWb09G3JD8NQanT7EhzlUqqdrR28+KaBs+6Txwy17O8Ymsr9766ybOuNtcpeyuuzsj1qQOvZJutzV0s2+KdyCFo4ckV28foiiYWN1Nakp9D3sBD6IUzvfmgvqDl7W1RprcSGSOpBKWTxUcJTWvzqrV2aaI7G2O+bYy50Rjza2PMZ40xtem/RJGQrS1dnuVpZcmNL3NLeNOddWzvSW/5LvibHanRkaTq6bd3eJZLC3L5n1MWMquyyLP+6se9mZtZeR2e5YyU70KMDrzQqaBUJqk3NjZFXb90c9QZB8Xhnw5m6H2ztCCXXWq8U1e9tUnfVxk/Ur1TPN8Yc35armTshEt3/5zk/j93lq8xxnzVWvu/8R7AGBMrGN41yWuSSWprszdTOr0iyaDUzZSmOyhN8zylkPlrlvGnrz/Idf9+h6ff3sHCGeV88wO7p3Xs81MrvdmXw+dXU5iXw0cPmMVvn3wn5n7Tc5oh8sevdFrarsnDlykNBaXKlMpk9c6O6OMcl29R8BSPaNPBRFo4s5x3IxocvbGxibMP9VaHiIyVVDOlJsl/44IxZjrwPqAf+GuCuz9AKMs6DygG9gKuBgqAm40xp6XvSkVCtjmZ0mSDUn/WMc3lu75GR6nPJ+qbX1WNjia9K/+5nGv/tYpX1zdx2wvrOesPz9OdpvGUwaBlyTt1nnXHLJgKwEf2nzXsvuX9Td4VpVPTck0+TgfeCsLlu/rZl8npnRjNd1ZubSUYtKN8NROPW77r9mLYb06lZ3nxqjqs1fdVxoekg1JrbSCFf+NlxvuzCU0H87i1dmsiO1prv2qtvddau95a22mtXWqt/RbwxYFNrkrgWIui/QNWJ3JNMvm55bvTk5wSxR2fmc7y3b7+IF293uHmmWh0pEzp5LatpYs/PbfWs27V9jb+9uLwjYni9c6ONpo6vD9DR+0eGn2xS20p+8+tjLpfPr3k9zo9/koyFZRGH1OquUplsoqVKe3o6fe9/4mfb45S52Hukbt5R5htaupkxVaNK5XxIdvHlKZauhvNzcB2YHdjzM5pPK4I25qdMaVJBqVlBW6Al77MS3u3/4Y5HWNK/YG0skWT2ePLthEtMfL7p1fT1596k/SX1zZ6lmdWFHrGkp5x4Jyo+52wU5RnqhnLlGpMqWSXyPmAXRsb1YF3JL5MqfO+ufu0UmY4FVZ3vbwx49clEo+sDUqNMXsC+wNtwH3pOu7AlDLhDOeMdB1XpK27j1anLDbpMaVuJ9s0Ng1qi1JaOJEbHalkbGy8tqEp6vrNzV08sWI71lqWbm7mkbe20tyR+M/Cy+u8XXcP3MnbrOhjB872zesH8PGFzpjWQB4UVSV8/rio+65kke6+fhqH+V3e2Bg7YJUQX6Mj533TGMOH9/MOT/jri+t9Q4NExkLWBqXAeQMf/26tTfdfuvAdimYmlrTZ2ux/00i6fNdXCpu+rKM7HQykaUqYUS7frWvr5uybnmeX7zzEqdctYZm6P46qNzfGngb7/5as4fN/eYWTfrOEz//lFY771VOsTLAE7ZV13kzpwTt5A8v83ADXnX0AU8uGgtBPHb4Th011fr5Lp4LJUKsEZUoli2xv6R72dWVKR+ZmSt1GRwCfOGQOgYg/WZ29/VzjdBgXGQtZGZQaYwyh8aSQ3tJdjDGLgAVAB7AinceW7OY+ySwvzKUoP7nh2b55StMY4LlvimUFueQEUr9pz2R2N5pv3PEaz66uB+CNjc2c/8cXfQ2cJDM6e/pZtT12kPnCmgYeXbptcLm+vYeL73o97oYd21u7WFfvfRZ54Dx/tnOvWRU8/e1jufXTh/Do14/if05ZhGl35kvMVOkuQFGlZ7HCqNGRTF7bW4fP1ilTOjK3AaA7phRgXnUJZx7kHZ5w58sbNGepjLmsDEqBIwl1zd0MPBlrI2PMl40xK4wxP3XWf8gYc2CU7fcB7iLUYfhma21Pei9bstkm5ynxjIqiGFuOzN/JNnNBabQ3xWRkMrvremVdA4tXeTuz7mjt5t5XN2XsnDJkQ2OHbzxpYd7wb1dvbmqOey7DV5zxpKUFuewxvTzqtkX5ORy9ey0LppeFVrR55zbNWJMjgIIyz2Ipob8Bnb2pj6kVGW/cKc9cW6JUC4mX+4A5WqYU4Jsf2J2ivKGH2kELv35iVUavTWQk2RqUhhsc3TYwBjSWGkJZT3ds6GHAy8aYtcaYJ4wxfzPGvAC8AuwJPA1cnu6Lluy2rqHdszw3yni3eLkBXjqbBsVTPpQMN7hNZ8dg1z9e3xJ1/dMrd0RdL+m13sliTi0r4FOHj9w37hlnipdY3PGq+8+tjD+b37bNu5zJTKkblJrQTXmnMqUyCY00rnGk8l6JNiVM9KEzU8sLuehI79/UR9/aynaNLZUxlHVBqTGmAPjYwOJfkjzMo8D/AS3AvsDpwHxgCXAR8L4MjFOVLLfWuVGfNyX5oHQ0y3fTFZS619zVG0zbnJWuxauiB5/Pv1ufls6vMrz1TgfOuVOK+fThO1NVPPzPUqzmSK63NnvHq+47uzL+i2t1HlhkMijNj54pVaMjmYy2OeW77u/7SOW94q96Gu799zNH7uKpQOkLWh58I/oDWZHRkHVBqbW221o7xVprrLVvjLDtFQPbXeCsf85a+xlr7T7W2hprbZ61ttpae6y19mZrre4YJO3c7NG8VDKlzhtVW3df2rrMNnd4q9bTlikt9B8nE9PC7GjtZvWO9qivtXX3sWq7+pdlmhuUzplSTG1ZAX/+zKHsUlsChG5YD9nZ2zE3nqA01LXXW+a716zopbtRtWz2LpfPir5dOjiZ0mLTTQ79dGieUpmE3Ezo3s7DosaOXnr69FAwlmDQ+jr0Dzd8pqIojxP39hYCPvLW1oxcm0g8si4oFZmIrLWsrfcGSvOqS5I+nvtGZW30qVySMVqZUshMs6MVW4cfl6hmEJnnNjSZUxUaP73XrAoe/8bRvPjd9/Hc5e/jytP28my3pblrxKz/pqZOmpxpJxbNrIixdRQtzrjiUQxKAUroUvddmZTcDvN7R3lYtKNNJbyxtHb34fZ6G6mnw4l7eYPSl9c1qKGfjBkFpSITQFNHry8rmEqmNJMBni8oHaHkMl6FeTkU5Hr/ZGWi2dFIU4us2qZMaabtaPXeeM6oHGrqlRMwTC0rpDAvh3nVxb7ZWNyKAtdbm7wPHSqK8phdFWfTsL4eaHO671ZkMigt9a0qpVPdd2VScgPO3aaWke/8zdeYx9iivYeP9FD48Pk15OUM/RENWv90WSKjRUGpyATgZklzAoaZlcl33y3Nz/XdzKerFDZTmVLIbNfgsOVbhg9KlSnNPDcorS0tiLpdQW4OM50u1O5UL65lznjSRTPLMfHOM9q6BXBTERkMSvOjBKWmU913ZVJqcoZ+TCnJ9/3ub29VpjQW9703J2AoGWHauKL8HPZxyqRfXFOf7ksTiYuCUpEJwA2E5k4pJi8n+V/fQMBQVpCZeT8zGpQ6Gd5MjClduc2bSVs4w1tCNlLQI6mx1voyJrVl0YNS8FcMuF2qXW8540kXzUxhPGluERT55zdNm0COLzAtpVPdd2XSsdbS6JTVVxXnM7VcQWm83KEL5YW5cT1wc8fmv7imIa3XJRIvBaUiE4Cbvdtjun+sWaLKMjTvZ327/2l3uvivOb2Z0v6g9ZXnvn/hNM/yxsYOrDtwR9KmubOX3n7v97dm2KDUO7Z6pPLdpU6mdK9ZKYwnrZiFr+Qg3dyg1HSq+65MOi1dffQ7zfaqSvKY6vzu71D5bkzug+V45wg/ZCdvUPrmpmbf/4XIaFBQKjIBLN/ize7sOSOB7E4MmZr3s77NG5TWxCi9TEamy3e3tnTR7XR3PHZBrWe5vafflw2W9KmL0sikpjT2g41ZlYWe5a3D3LQ2tPewzenwmVimdBSbHIW5c5XSqUZHMum4pbswkCkt8/5+K1MaW0un03k3Ssf6aNwHc129Qd+QIZHRoKBUZJzr7Q/y5iZvdictQak7V2kaAq2u3n5f577qYQKKRPmuOc2Z0g3OVCRFeTnsNauCnIA3G7axsTOt55Uh7k1nRVEeBbmxx0VNLXduWlti37Sucsrg83MC7JRIF+vmjd7lMQhKS0yXMqUy6bilu/k5AYrzc3yZUgWlsSU7dKa2rMA3RGLZ5uG70ItkgoJSkXHurU3NvpvQA+ZWpnzcTJTvug1qINOZ0vSOrXOD0jlTisjLCTDdCXzc7SR9fE2OhindBZjmBqWtsTOl7vyzO9eUkJvI2OyGd73LVTvFv2+ynA68ZXTSqXlKZZJpdIZ9VJXkYYyJMqY0+u+3hlREGVNa5O+yH4v7oNutzhIZDQpKRca5JavqPMu7TS2lOg2BnvuGlY7yXXc8aX5OwJfdTIVbjtSU5jJaNwM6pyrURGeW0+l4S7PGNWVKvJ13w6Y5N611bT309kfvTvvOdu944flTne621kJwmM629au9y1N2Gfba0qLAe7Oo8l2ZjBqd8t2q4lCFjVu+65bfv7ujjZN/u5jdvvsw5/3vC77gNpuk0mTQbeinoFTGgoJSkXHMWss/3vB2/HzvrtVpObYb4KUj61jnBBQ1pfnxT7cRhykl3mtO9w3IhkZvBjQ8f2WtE/hoAvfMqXPHJI+UKXVuWiF6xh7gnR3eoHTXcFBqLTz3O/jFfPhxNfz1bGh2xo/290LTeu+66lEISp1GRyWmk57+IH0xAm+Ricgt360cmN/arZSob+sebMITDFo+/5dXeGtTC31By+JVdXz3vjdH54LHIV+jozjHlALsOcM7TOBtzcctY0BBqcg4du+rm3xvDiftMzMtx87E+Ew3WEtHRjfSlBLnBiXNQenGBidTOiWUKfWNaxpm3KKkxg0oh2tyBKGb13ynBHdbjGZHq2NlSp+8Eh79DnTUgQ3Cyn/CH0+AzqahjZvWg3UylKOSKfXeLJYR+hntUAmvTCLR5igFfOW7QRsKTAGeX1Pve3985K2tNGRpttQdghNv913wV41sbu6kS39jZJQpKBUZp/7x+mYuufsNz7o5U4o4aF565kX0d99NPVO60ck0zqjwZ7FSUe1ML9PQnt7g0J8pDQel8Y9blNQ0dzo3p8XDB6XGGF82JVozlPbuPjY1eR867FpbAptegcW/8h+4aR3863+Glt3S3aIpmZ2jNMztvmtCX4NKeGUycQPJyoHf++qSApw+c4O/38+84x3aAqGg9YV36zNzkeOcW76bSFDqNnyzFnXglVGnoFRkHHp06Va+9rdX6XPmCvvmB3Yn4L5DJ6ksA5nSDU6mce5ApjFd3DlPG9p70tbgoq8/6Muwhct3fXPlqQNkxvjGRRWPfGPljivdHiVT+q7T5MgY2LW2FJ7+ORDjZ+jV26BlS+hzt8nRaGRJwdfoqDScKVVQKpNIk1O+WzXwe58T8D90Cv+dfnltY9RjLcvS8ZD+8t34+zmUFOT6Gvqt2aGgVEaXglKRcWZrcxffvOM13LmrzzxoNqftl74pKPxjSlMPStf7utdmNijt7be0dqenA299e4/vex7u7OrvAKmgNFOSadbhduCNNlfpGuep/6zKIgrbN8Hbj8Q+cLAXXvxD6PPty7yvVe864nWlhdPoqMSEvjZlSmUyidXoCPAFS1tbuugPWl7f2BT1WNk6nUkqjY4Adqn1ZkvfrVNQKqNLQanIOPOrx1bS7txwnn/YPH720X3S2jRoNMp3050pjTbnaUNbesYPueNEcwJmsFzYLd9taO+hp0+NZjIhmRI0N5NS1+r/mdjUGCWL//od3o0KK2DvM73rXv1zqMnRtre866ftNeJ1pUWMMaWdvemdDklkLLnlu56g1BkGsrW5i3X17XT1Rv8bnK2dY1MNSneucYJSZUpllCkoFRlHGtt7uP91b7fdD+83kytOXZS2st2waOW7qZTC1rd1+zqnzplSFGPr5BTn51KY5/2zVZ+mcaVu6W5tacHg9zzaXJl16sCbEW4X6HhurNy5cKP932xq8j4wmVVZBG84Qelep8NRF3vXte+AFf+EbU6mdPooBaX5bvlu6OtQ+a5MJr7y3YhO675MaXPXsN1ht7R00d2XXb8fXb39dDsPShMZUwr+oFRjSmW0KSgVGUfu+e9GTwYuPyfA905amNYMaZhbvtvbb31vaol4dX2TZ7k4P8fXPCEdqt0OvOnKlDoluZElu1XFeeTlmGG3l9T19AXpdDo+pi0odTKliwp3QP0q70b7fgJqF8CcQ73rH/0O9Hn3Z9reI15XWjiZ0nD5roJSmSystcOX71Z4H25ubeli1bbWYY4Hm5uyqxldtJ4QiWZK3comt/JJJNMUlIqMIw84WdLj95oeNUuXDtGeoiYzrtRayyvrGrj2ibc96/eZXUFuTvr/xERrdpQObkfdyJJdYwy1pSM305HUuOVnEG9Q6v2ZcDP2gK/z7n5dL3k3KJ0Osw8Ofb7/ed7XWpw5S6t2gtLaEa8rLdzuu6j7rkwunVGyfN6g1Pu3d2tzF29vH34ezQ0N2RVQRXvvTjQoDXebD9ve2p11GWcZWwpKRcaJ7S1dvLGx2bPuzIPmZOx8bvkuJN6Bd0NDByf+Zgmn3/Acb23yjuM5YG5mpstwg9J0zVW6rSV2phSijFtMU4ZWhiQdlLrdkdu6PaXo1lo2OpnSnRqe8R5kt/eHWvICLPqIr2zWu/ORI15T2jhBaYHpI59eX0ZZZKJq7PD/3nsbHTmZ0ubhM6Xgn95rsnP/dhbn55CX4EPhWVXe77O1sCXLMs4ythSUiowT/1653bNcVpDLITtPydj58nICFOXleNa5k28Ppz9ouehPL8dsKnH8XtOjru/u76ajN/kbBrfZUboypTt8mVJvoOOWiNZrTGnaJXtj5Waxe/qCnq7MTR29nnLXIrqo2PaC9yC7fXDo84LSUGAayy7HjHhNaRMlOC6hU+W7Mmk0On/DcwLG89DUbXTU2t3Hiq3eoNR9WOk+hJrsUm1yFN7HfVjtVpiIZJKCUpFx4um3d3iWj9q9lvzczP6Klhc5zY4SKN998I3NvhuDsA8snMY+syt96+9++26OvuNoDr39UL7/zPfp7k88sKvOWPmu91rcaUbcYFiNjtLP/fmL98YqWol75Fyy7o3VwYG3McGIn5tArj/QPOD86CcrrIAFJ8Z1XWlRECUoNV109qj7rkwO7njSyqI8T2M/t9FRNMcs8JbTu43rJju3QZzbMyJesyq92VKNK5XRpKBUZByw1vLimgbPuqN3z/yYtTJ3rtIEMqV3v7IxyvFyOePA2Vx95r6+197Y8QY/fO6HtPeGOvrd9859XP/a9QleMUxxGx2lrXw3sUypynfTL9mn/YV5OZQWeB+w1EUEpW7W5Niid7wHmHlAKNiMNOfg6MHnQZ+B/PROdTSsPH+zsFK6lCmVScMt360s9v7eF+Xn+B5GRppaVsDu07xl7u4UX5NdOjKl4B9X6jaIE8kkBaUi48Da+g5fkJPJ0t2wcqdUpzXOMaXNnb08t7res+43n9ifN6/4EL84Y19fsAtwz6p7fOvuWnkXnW5X0xH4M6Wp33z0B63v++/OTRpPh1dJTTJzlIYN1+zIzZQeEljh3Xnee6Mf9MO/G2p+BLDgJDj60rivKS0CAV8Jr8p3ZTJpGqbzbthONbE7uS+YXsY0pweA27huskvlb2ek2VVuplRBqYwef6cTERl1LzlZ0tqyAuZVZz4b475xuSVAsTz/bj19waFGMvm5AY7bY2rM7a21LNm0xLe+tbeVf6//NyfuEn85pDt2qK419YxlfXs3/UHvHK3uTY7bTEdBafr5bqwSKEGrKS1gbf1QqVnk/0/k0/58etm9b6V353mHRz9o8RT4zONQtwpyC6BqXtzXk1b5pdAz1G201HSp+65MGo3tbqY0SlBaXcIr6xqj7r/b1DLfQ0S3cd1kl75MqROUakypjCJlSkXGgRfXeoPSQ3aakpG5SV3+8t34MqWvbWjyLB80r8pXPhlpbctatndsj/raM5ufibo+Frcr7o62boJOQJkot9QrYKDayYzWuMGwynfTLpUbq+Ey2ZuahoLVfcxq8mzkeQzMOST2gY2B2t3HLiAF37jSEjrVfVcmDf8cpf7f+51rYj+kXTC91PcQsbmzl64s+h1xx+O7/SLi5Y4pVfmujCYFpSJx6A9a7nllIxff9Tq/f3p12t/sXnaC0oN3ysx0Kq5ky3dfd4LS/eZUDrv9qsZVMV97bvNznuk7RuI2vegPWupSLOHd4TQ5qi4tICfgfSjgZkqbO3vpcebWk9SkFJSWxW5EFVm+e1DAO58u0/aCosr4L3IsuOW7RmNKZfJwf++roowfnT819hRNe84oZ2qUZkju3/XJLFNjSrc0d9LXr/c5GR0q3xUZgbWWr/71Vf755pbBdf98cwt/veg9lAyTHYxXc2evp+wQ4KCdMj+eFKJkSuMo3+0PWt98qvuOEJSubl4d87UdnTtY3bSa+VXzRzw3DAWMkeW225q7feVbiXCbHLlP3cGfiYNQ5193ugJJXjozpTsiyrojx0XtFVjj3XHOwYx7zlylpXSyuVfdd2Vy8HXfjZIpPXBe9PfE0oJcFs4oJydgKMwL0NU7FEBta+lizpRRbEo2htIVlM6o9L6fBW2oKkjvczIalCkVGcEDr2/2BKQAb2xs5lePvR1jj8Qs2+yd5zMvx/g6CWaKb0qYODKl7+5oo63be0O8/whB6btN7w77+gtbXxj29Ug5AeOblzLV9v/udDDRAtzKojxf9jTWuNInlm/jf+5/i/9bsiarSshS5b+xiv+hT6zy3fbuPpoiunvubZygdOb+CV7lGHAypcV0K1Mqk4av+26RP1NaW1bAzlGaHR28UxW5OQGMMb5pvNy/65NZuoLSKcX55OV43+e2Ztn0OjJ2FJSKjODmxWuirv/z82vZnoY/1ks3e7OOC6aXZXx+0jC3kUxrHFPCvLO9zbM8tawgaulUpDXN0b+HYS9ueXHE80aa5jy1TfVN0+3U6E4HAxAIGF+TpR1RgtI/PrOGz9z6Mrc+t44fPbiMC2992ddESaLzzVMaJWMSS6ygNLJ0t4I25gWcsc0z9kvsIseCM6a01HSq0ZFMGv7uu9F/749d4G+md8JeMwY/d/9uj8ZcpV29/fzmiVVcds8bPPtOXcbPF4v73p1sUBoIGN9D2a3NGlcqo0NBqcgw1td38Oam5qiv9fZb7n11U8rnWOpkShfNqIixZfqVOWNK3aAgmnfr2j3Lu9bGHusTtrVjq2f5lF1O8Sy/vO1lgjb+cSvTy9N78+F2aowVZLuBT73T7KixvYerHvFON7LknToefGNzSteXLVJ52l8bZUyptdbTqMNXuptTAFP3TPxCR5tvSpguNTqSSaOx3S3fjT4n6ReP3ZVdaoeypQfMreQjB8waXHb/bo9GpvRrf3uVqx9/m7+9tIGzb36Bf6+M3tAv02JNCdP8jwfZ8OUvs/VHP6avvj7arj5uqe7WZmVKZXQoKBUZxtOrdgz7+v2vpR5svOUEvXvNKk/5mPFyp4SJJ1O6xglKd66NPX8cQGdfJ83d3q/xlF29QWlLTwsrG5xpOobhlmmlv3zXnymFaHNhevd7bNlWz5imsDte2pDS9WWLdI4p7eoN0t7T75nSwFe6O20R5CSXURhVbvddo3lKZXLoD1panPedqpLov5M1pQXc/fn3ctXpe/OrM/blb589jLycodvYab5pYTIbTL2zvY1Hl27zrLvhqdj9EzKlrz/oG1JTUZRH8/33s/nb36btX0/QePvtrL/gUwR7Ru4a7zYT3Jpl0+vI2FFQKjKMF971Pll0G+As29KSUoe/zp5+Vu/wlsMumjV6mVK3fDeeMaVuULrLMJOaA2xt3+pbt2/tvswtm+tZ9+JWbwlvQ1dDzOypG5Sm+qa5o2Xk8l2IUiLq/N8/8pb/awV4dnW95jUdQW9/0BdopRKUQuj/JzJTuiiw1rvBRBhPCpDvNjrSPKUyObgPogCqYmRKITRP9ccPnsvpB872DXNxpwtzp/pKt2eilOu+vLbBFyBmmhvUQ+hvZ93v/+BZ171qFS3/fGjE46X7oa9IvBSUigxj2RZvae2Xj53vm49z8QjZ1OG8s72NyOGGAQN7Th/FTKlTvtvR00/vCO3ffZnSEYLSbR3eJ8ll+WUU5xVz8HRv19NwULq8fjkff/DjHH3H0Rx1x1E8suYR3zHdJ7nbUigvCgatL1PqvimHuZnS+oiys2DQ8vLa6JO7A7y4piHmaxL95tTN5A+npCCX4vwcz7q6tm7PmNLdjFNuP32vxC5yrOR7f8dK6KSjpy+hqZRExiO38y5E774bD/ehcaaDqedW+8thg9Zf/ZRpDVGmRCvesp6ed/0NBlv/9a8RjzdD5bsyRhSUisTQ0dPnC8D2mV3JYbtWe9b95+3kg9I19d7jz6oqosi5sc6kaDf9bcOU8DZ19NDgjP8ZKSh1M6XTS6YDcMj0QzzrX9zyIisbVvKZxz7DsvplADR3N3Pp4kt5q+4t7zGcN81trcm/aTZ29NDnNCJyn7iHVcdopgOwekcbrcM8IX/+3fjG82SraEFpos06ojU72tgYmm4ph352Nt4u2tTukdhFjhVfo6Mugha6NU+uTHBuk6Pi/BwKcpN7D/RX0GQ2mFq+tSXqeve+IdMa2r1/O8sKcul9MXrzwI4XXsD2DZ/JdRsJKlMqo0VBqUgMK7e2Yp0s5oLpZRy1e61nu5fXxc6OjWSt8+a1U/XwAd5IWntaeWTNIzy/5fm4sihu+S4MX8LrNjnKDZgR54HzBaXFoaD0sJmHkWuGMrVd/V187B8fo7Wn1bN90Aa56Y2bPOvcJ+JNHb1JT73iZkmNiV4KCtHmwhza97UNTcOe5/URXs92bpOtwrxAwjenbiZ7R1vPYPnubLODAuPcjNUsSPxCx0KURkeAphuSCa+x3Z0OJvkx3m5Q2trVR0dPZkppe/qCbGjoiPra6Ael3vewqpJ8ulauiLptsK2NruXLhz2ef0xpl6oyZFQoKBWJYfkWb3C0c00JhXk5HLxTlWf9xsbOpMcLukHpSFnH4axvWc9HH/go3/7Pt7nosYv4ypNfoTc4/BjRwrwAuc7cmy2dsd/E1+zwXu/cKcWeRhPRuEHptJJpAFQVVnHErCOG3Tds8abFtPUMjb2NVl6bbImR+xS4uiQ/5tfkb3Q09JR/5Vbvz0uZU+a9YmsrfSOURmezdMyz5z402NLUOfjQYb5buls0BUq8VQ/jVoF3TGmJCQXaanYkE51bvhur82483GAK/J3VtzR3cuuza3lyxTaCKUzVtb6hg1i7v7tjdIPSeqd6aUpJPt0rY8+j3rUssaC0o6c/6rhVkXRTUCoSw0qnNGfPGaGxnrtNLaPEKbF9bX1TUudwy3fnpZAp/dHzP/IEgE9vfJo7Vtwx7D7GGN/4nYYoY3zCEh1PCv4xpeFMKcCp808dcX+A3mAvz2x+ZnC5rDDPF/RtTnIuNbcZhjtHWyQ36Glo7x68sXEbVp2630zPcndfkLX1o3uzMpGkJSh1GlS9vrFp8PP5xumUXTtBsqTgy5SWDmRKFZTKROf+3sfqvBuPkoJc3/tC5MPKx5Zu5ehfPMX/PLCUT9/yMt+487WkM4DuA2XPa6P8d96dUmdKcS7dq1bF3L5rxfBBabThKyrhldGgoFQkhnVOac78qaEbw5yAYe/Z3g65I5VuxjxHvfccO9cMXwob8zgt63hhywu+9bcuu5W+4PBPOIcrSXUlE5TWd3rHUk4tHpoA/dg5x7Jb1W6+fXIDuZTlebNDr+943bM8s7LIs7y5KT2ZUrc0OJL7vQraoSf9q52n44fsPMXXxXeZk32XIW75bjoypf9d1zT4uS9TWrN7wscfM+6UMHQCVh14ZcJLZ6YUYo+H7Ojp47K/v0lPxDjs+1/bzD/fdMaZx2l9jNJdSK3xXjLcTOmc/jZsV+xr6F4evbQ3rDAvhyrnYbWaHcloUFAqEoP7pjM3YuzkfnO8JbyRGZl4NXf2+poGJTumdPHGxVHXb23fyhs73hh239qy+INSd0zpLrWlMbYcUt/lDUqri4ZKJnMDufz0iJ9Snj/UcTg/kM+Vh1/JeYvO8+znfh0zK703H1uaksuUuk2SYnXeBagu9d8w1bX10NXbz4ZG78/LrrWlLJzp7aS8fEv0xhiSnkxprfP/0xkx5nJ+YPJkSnOMpZCejI2XExktjR1OpjTJzrth0cZDAjy+bJvv/RbgxqdXJ5UtHa65Xmt3H+2jOC2Mmymd2dU07PZdb7+N7R/+gdb0Cu9D30w3jRIBBaUiUQWDlo2N3iAnMijd18mUvrmpOeE3Nrf8JyeOpkGxRJa2up7e+PSw+8YblAaDljV13hLVkTKl1loau7yNoKoKvAH9gikLuP+0+7nk4Ev4+gFf5/7T7uekXU5i35p9Pdstr1/uyfrOcDOl6SrfHSYozcsJ+Mqd69u6WVvfjvvfv3NNyWDJd9jbW5UpjcUNSj2doYPxjcWN1aAKYBe3fHeiNDkC35hSCJXwdqjRkUxwbvfd4eYojUesOTYffCN6RvStTS2+qd/iMdIcqKNZ7upmSqe2eR8E51R471dsRwc969cPe8zp7vQ6ypTKKMiqoNQY85Qxxg7z7/gEj1dpjLnWGLPOGNM98PHXxpjKDH0JMkq2t3Z7ynzAG5TuNcv7R76po9czH2I83HEns6uKRmwaFI21lqV1S2O+/p+N/xl2f19QGqNp09aWLrp6vd+TXWqHD0rbett8zZamFE3xbVdTVMN5C8/jM3t/htllswHYs3pPzzY9wR42tm4cXJ7lBKWbki3f9c1RGjuwgVAjpEg72rp5Z7s3WJ9ZUUhJQS67OplkdwyxDImaKe1qhr9/Fn4yDa7dB964c9hjuGNKw8ppp8I45XZTdk7pekdVvr8iodh00aXyXZng3O67yVRIRIo2V2kwaHk+ypyiYfe9uinma7GMFHSOZmbRzQBXNdd5lgv325ecam9Tt+63Y485Bf+0a+n4erp6+2nq6FEnX4kpq4LSCPcAt0b5F/dfJmNMNfAi8DWgD7gPaAW+Crw08LpMUG7pbkFuwBO8za4q8mXMEp0w2x2fmWzp7vaO7TR2x56W5p2md3wdcCPV+saURn/zca+3OD/HN2bS1dDV4FvnZkpjqSqsYkqhN4Bd3bx68PN0le9ud8eUDtPoCKLNhdnD6u3e782uA+OP3UzyhoYOdeCNwZcpLcyDB78Jb9wB/T3QtA7+fhGseCjmMdyf5bBZps5ZY6BidqqXPHryisB4365L6VSjIxmXgkHLn55by4m/Xsx5//sCS1a5v39D3DGlqWZKfcFUcxertg8/h/T9r22mP8FOvCMFaSNlUtPJLd8tbfLOnZ4/axYFu3t7N3S/Hbs7L8TOOCfDWsvNi9/lkJ/8i/1+9DinXvcM7zqNAUUge4PSi621F0T592YCx7gG2A34O7DAWvtxa+1ewG+B+cDVGbhuGSXu/GNzphRjzNDUKcYY9prpL+FNhH+O0uRKd1c2rvQsF+UWUZbvLfd7bvNzMfePt3zXHU+6c02J53sSjRuUFuUWUZwX/9e5a+WunuXVTUNB6YwKt9FRZ8JPYINB65undLgxpeDPxtW1dfs674YzpG5Q2ttvE86oZws3KJ3Xvw7eutu/4T+/Bb3Rv4czKgsJRPmRnG28N2mUzYDc4R+ojCvGgPM7XaLyXRmnfvzPZfzg/qUs29LC4lV1nP/HF3llnf8BJaS3+y5EC6a6eWWEucS3t3bz/LuxM6lR93GCzrwc7x+e0cqUWmt95buFLd7vde6MGRTu7m3sNlJQ6o7N3ZJC+e6fnlvHlf9cPjitzJubmvnMrS9rnmXxydagNCXGmOnAOUAv8EVrbeQjuG8DO4BzjDHTxuL6JHVu05q5UcZ6uiW8b21KbFzKWqfz7k5JzlH6dqP3zWWPKXvwnhnv8ax7bkvsoNSdAiVWUOrOURpPk6OGTu+bo5v5HMmuFd6gdE3zmsHP3fLd9p7+YedYjaa+vcf3hHyk8t0ap3y3PmpQGvq/rCrO85WjucG9hDQ7/3d71j8WfcPWzfDiH6K+VJCbw6yqIt96X1BaOTepaxxTbgde00mnGh3JOPPWpmb++Mxaz7r+oOUXj670bWutpb7Nmc6kJLWHRW4wtb21i5fXet+HTt13JoucJnT3JlDC29bdR5uTeV3kPKSO1lQpE1q7++h2hhrlNjlBaW0tBYkGpVEyzsnY0drNzx/xd/tdU9fOrc+uTeqYMnkpKE3OCYS+d/+x1nomYbTWdgP/AHIGtpMJyJ1we0aFP3u2ty8oTazZkTumNNmgdEWD9w/+gqoFHDbzMM+6F7a8QNBGLxt1M6UtXX1Rn2Am2uQIonTeLUysqn1uuTd42NQ2dOMwrbwQN1GbaLMjtyQpYKB6mGY5EH0KHXey9HCm1Bjj+z4NN79dNnOnhJlZ75/iaNDiq6EzevZj5xr/w5LZbvnuRAxKo8xVqvJdGW9+/593o65//t0GVjhzf7d09dHjDGeoidLhPBHuVGG9/dY37cuB86r4yP6zPOseeWsrO1q7eWltAytHaEjnDvkA2HOGt5LBDbYzJdq1mEYnKK3xB6U969cT7Iz9ful+H+vbe5LKbP75+XW0x/g79dcX12t8qXhka1D6GWPM9caY64wxXzXGJHqHEm4L+t8Yr//X2U4mGHdcpZtNBNhrlvdJa317T9wlLk0dPTQ5rfB3TnJM6drmtZ7l3ap282VKG7oaWNUYvbGBG5RC9GypO6Z0lziCUl/n3cL4xpOGzSr13jhsah0KSvNzA74xhJsTLI11v87asgJyotV/RnDLd1/f2OyZegS8WWQFpfGJLOMroZPyxmFGU3Q1hQLTKHaOUgY/OTOlXb6fO5Gx1NLVy2NLY/cveGL5ds9yXZSmesN10I5HTWk+Jfk5nnVuJvHAeVWcsu9MT6l/W3cfB//kX5xx43N86Nr/cM7Nz/uyoWHuQ+uywlxmV3n/7jS0pz6mdNW2Vs6+6XmO/sW/+enDy+mN0o/AvZYpBQGCjd733dzaWgrmz8fzFNdaut9ZTSxupjR0rsSypf1By99ejN3ld219R9JzvMvklK1B6feALwBfAn4NvGOM+X4C+4fvaDbGeH2js92wjDFLo/0Ddh1xZ8kId5xhtMBt7pRiygtzPevibXbkBni5AcPsKGWHI7HWsr7V+0d/Xvk85pTNYXapt5HLs5ufjXqM8sJcivK8b+LudDg9fUE2OOviyZS6Y0oTLd8Nd+IN2965na6+oTdG92luokGpm1mN9vDB5b5Zu2VaZQW5nhJgt/Tb/d4K9PUHPTeAC8wGTGRm3+TAbh/y7vTcdfDAV2Dlw54pY9wyOpgkQakvU9pJpzKlMo488uZWXwAY6ckVTlDqvM+WFuRS6LwXJcoYM2zVUVFeDgumlzGtvJDD59fE3O6Zd+r54QPRu9q7wdm08kKmOMM6Ui3f7ejp4/z/e5FnV9ezrr6D3z/9Lr/+l//BsltWOz+vB3d+stzaGgJFReTNneNZ3/22v6Q6rLwwj9IC7/3N5gQ73L+4psF3L+V6auWOYV+X7JJtQel/gPMIBXvFwALgu4S65/7IGPO1OI8TvjvoiPF6u7OdTDBuBi1al1ljTJRxpfEFpW7p7pwpxeQmMR1MfVc9nX3OfKploRtut4T30bWPRj2GMcYXOK1v8F7fuvp239jLnUeYDgbSEJSW+jukbm4bmm/S7cC7OcFxLxsavN+7eB4MzBlhm/nTSj0NoNwxjmp05BdugBG2R2CDd4Oa3eD4n0Ig4ibJBuG/f4K/ngW3fQx6Qn+OD9/Nf6M5GYPSEpXvyjjzqJMlzXfe097c2Ex339DPbJ1T4ppq6W7YcA9M95tTOTj12vmH7TTsce56ZaOvXwBEC0oLfEGp23woUY+8tdX3fvbHZ9bQ4Ywj3+ZUdc0zzntgbi45lZUACTc7coctbUlweMw/3/TODb3H9DI+c4R3Kq6n31ZQKkOyKii11v7AWvsXa+271tpOa+3b1tr/B5w2sMkPjTHxpKvCd5yxiuGHr//zX9eiaP+A2LUVkjHBoPUHpTGa37hBabwdeNfUeZ9nzEuy8+6GVu/Ne14gj6nFUwF439z3eV5bWr/U07020lzn/OucJkzuG/O08oLQlB0jSDUoLc4r9u2zsW2oQGFmlA68iXC7LEdraOVyy7Rc850GUG6gu7Ex8S7Bk53bgXMP45R8TVsE1bvCMZdFP8DqJ+Cx7wKhBlgLpg2N74o6R+lEDEqjNjpSUCrjQ1dvP886c4F+/5SFnorRnv4gyzYPjSt1y3dTLd0NG25oyUE7DQ0hef/CaXzuqF2GPdY9r/gL4tyS2Wllhb75q91pWhJ1/2ubfevae/p5YY33PdXtAjzbOlVY1dWYQOhWv2D3BZ7XukYKSp1KpEQ68FprfeXap+w7k6N3r/Wse2tTs7rwyqCsCkpjsdY+BrwMVADvGWFzCM1HChDrL1/4rlUTMU1AjR099DlZwWjluxClA+/m+DrwrnObHCU5ntQNSmeXzSYnECp/OnTGodQWed8A/rL8L1GPM88JxtY1uEFp9EY+I/EFpUWJBaXgz5ZubI0ISlMs33W7LM+OIygtzMuJ+fMAsNs07/dmjhPEtnX3+YKwbOd+P3YLODdkU/cMfTziW3Dkt4j63O/lP0LjOgAuP3EP8nNDb28Tfo7SsKiNjtR9V8aHF9c0eMY4GwMn7z3D917xesQYwkwFpfvMroz52gHzvH0NLj9xT2678FC+/v7duOT4BXxgoXfShEfe8o+RdbOTU8sLqXKC0vae/qSDrf6g5SWnY3DYM86cr2757vQ+73tabs1Q5Yi/A2/0PhNhM51MaSLvrxsbO31B7AcXTuOAeVWesbx9QZvwdHoyeSkoHRL+7ZwRx7bhx/ix7mxmO9vJBLIjgeYLbgfeHa3dcTUDcJvdxDM+M5r1Ld4fsTllQ2NGcgO5nLzLyZ7X7111L8vrl/uO42Zq17uZ0u3R5+EciS8oLUg8KHWbHXkypW75boJjXtYnkSmF4Ut450/1fm+mV/jnztS4Ui83KJ0bcEq6pgwMrw8E4H0/gC+/DMdc7i3nxcJ/bwXgmAVTefwbR/Hz0/fhhpOdct6JNkdpWJRMqcp3Zbz490pvVmy/OZVUleSz35xKz/rXhgtKy9JTvusGnmG5AcMBc/2vHT6/hq+/f3e+eMx8vnzsfM9r79a1+8pW3Y6308sLfJlSSH5c6Tvb22L+bruZUjdAru72dg72BqW7eV7rr6+nrz72/KzuXOCJZErd65xSks/8qaWUFuSyYLq3SeRI88hK9lBQOiT8lyqe7ObrAx8PiPF6eP0bKV2RjAm3HGZKSf7gGBTXvCnFvmYAb24c/qmftdbX6CjZ6WDcTGl4PGnYuQvPpSBn6Aa83/ZzxXNX0B/0vuHNdTK1biY31jycw+kP9tPU3eRZl0ymdGbpTM/y1vahJ9dupnRrS5dv7GsszZ29vg7I8Qalu08ri/nanjO8b7h5OQHf3HkKSr0ig9Ic+pmOk92smuddrpkfKuU96NPe9SsfHvx0XnUJZx48h51ynJuuiVi6C5Dv/ZkroUtlbzJuPO00rDl2QWgYyb5OUPpGxPvjjlZ3TGl6HhaFAyDX0bvX+uaNdu01q8LXwPDZd7x/Q7ZGaXRUXpjn69yebFD6+jAdaVdua/V04XXvV8o7vfcfuVOHqqXy587FFHrfi7pW+OcQDZvhe+gb//vWi2u837NDdpoy2GvhgLmVntf+q6BUBigoBYwxtcCRA4uxpnmJ9AgQBI40xkx1jlUAnDLw+sNR9pVxLp4mR2GBgPFNwv3W5uGD0saOXl9jl2Sng4lWvhtpavFUPrPXZzzrltUv47blt3nWueW7LV191A88xbbW+st3o7zhu5p7mn1zoyY6phRgRom3eMHb6MgblPYHLdtb43ua644nNcafeY3FLdsevJ6KQt/TZVCzo5FEBqUzTAM5OB08K52gNGzvM7zL25dB8ybvuianYGWiBqVuplSNjmScWFffzrvOg9ZjFoSCoX2cv5Vr6ttpH+i0Xd+emfJdgDMP8heyffzgOVG29MoJGN6zi3c+7Tc2Ng1+bq31jSmdWl5IIGCoKk5Ps6OV22LPk9rTFxycF7unL+jL4pa0ee8/ciIypSYnh4LdvNnSzldfi3kut9GRG4wP50UnU3roLkPv/W62+vWI769kt6wJSo0x7zHGHGsi22KG1u8E3EtofOgD1tqNEa992Rizwhjz08h9rLVbgL8C+cD1xpjIx2o/B2qB2621sSfsknErnulgIrklvCNlSt0saV6OiTsYco2UKQX4zN6fYecKb8e7G9+40TO1yuyqIgpyvX8OwuNjt7d2++Zri6d8t6HTPyamqiCxeUoBZpR6g9It7UMToVeX5A+OHQyLt4TX/X+YXl5IQW580xG4/+dh+8coG3ObI21sjNW4Ozu1RASlc4y3DJC8EiiuJqqZB0BhpXfdume8y5MlKHW77xoFpTI+uNN61JTms9fA1EwLppd5hi9YCyu2hoIuN8uXzqD0/PfuxAcjxod+8rB5vvGisbjZ3cheEc2dvfQ4096EpwBLpNlRb38wZsO7aB1/Iy3bErrH2NTUiVsYVNDa5FmOLN8FKD7wQM9yx8svxzyP+4C1qaM3ruZq21q6WOsMATpk56Gg1P3+bmuJb9iTTH5ZE5QCewBPApuMMU8ZY/5mjFkCLAcOB5YCFzn71BCaNibaONOvE+qOezqwYuB4bwJfHVj/jYx8FZJxbqZtxKB0tjdAef7det+bViR3PGmy08G09LT4ymMjx5SG5efkc8VhV3jWtfa08tTGpwaXc3MCvozvGwMlROEbiLDi/BxfOWo07njS8vxy8nJG7tjrcjOlDV0NgwG1MSbpZgzLt3ibUu02TEmua88Z5ZQ5JV4AR+9WG2XrUEfYSJtUvusRmSn1Td9SNc876XuknFyY6/Sm2/yad7lpnXd5ogalBW6jo05PYxmRseKOJz1696kEBiLRwrwc30PMZVta6A9aX+Yt2Yez0RTk5vD78w7k6W8fw3OXH8ePPrwXJtbfEYf7Xrhsc8vgsBA3SwpD81tXlXjf36JlSjc0dHDqdUvY7bsPc8KvF7MqSlZ0xKB0IEh2h9lUFudBvXfoQ26t9z2p+OCDPMudr72G7YkePEf7/3Dn9o7GzZKWFeayR8Q40l1qSihzhj0NV7Is2SObgtIXgBuALcBCQsHkXsBrwLeAg62122Pu7bDW1gEHA78llDH9CKHuvdcBhwy8LhOQv3x3+DfK9+7qfRLZ3tPP8+/Gbh6Qqc67ARPwNQUKO2DaARw641DPun+t+5dn2e1Y+PpAxvcN581ij+llgzccw2noTm06mDB3TCkMP6403qB0mROULnTGgg4nPzfAxw/yPgCoKc3nxH2i90mLNi2MDGnqGLop8mVKY5Xuhs3Yz7u85TXn4JMlU+qMKTXqvitjr6u3n+ecqWDCpbth/iCvme2t/vH/0YY+pMIYw7zqkoSP6w7P6Ozt592BQNENpKdEVOtUl3gfYDc45cnWWr58+38Hx9Wu2NrKhX962TN3a1dvv+/94X17eEaJsXQwKHWmlptSTF+dE5TWeP8vig7wtkKxXV10xCjhLc7P9Y3B3RJHJZIblB680xTPeNtAwD/H+xsjVJhJdsiaoNRau9xa+0Vr7YHW2qnW2jxrbaW19jBr7dXWWt9dorX2CmutsdZeEOOYjdbar1pr51prCwY+fsVaG72Xt0wIbvnucGNKIZRJdctR7nh5Q/SNgTXOG0nSQWmL9xwzSmYMm4k8fqfjPcsvbX3JUz607xzvm8TrG5uw1g4Gp2HDtduP5JbvJhuUluSVUJ7vvanZ3D40rjSZDoHWWt5y2tAvdG6cgjbIrUtv5RMPfoLzHz6fZzc963n9Wx9cwGn7zaQwL8AutSX8/rwDfU2vwtzAOdFJyCe7yEypbwoXt8mRa+b+3uUtr0O4kVdnE3Q5NzsTNSiNMqa0qzdIMM7GXiKZ8Ny79XRHVAYFDBzlVIy4f1uXbW7xDbPIzwlE7WA7FmpKC3zjKcO9IrY6f7sjq4amONfvNjpaurnF9366rr6Dfy0behC3pq4dt6r35H29DzuXb2nBWusLSueXGmyX9/sa2egIILeqioI99/Ssa338cWJxvw/xPPR9wW1ytLP/vX+fKPcbIlkTlIrEy82UjlS+C/Dhfb3ZvIff3MKr66N3lPNPBxNfx1dX5NQo4G9y5Dp0ujdT2tDVwJrmNYPL+zrB5o7WblZsbfW9WbjBayxu+W51UYxxgXFwS3i3tA2NK53llBjF00Ro6eYW6tq8Nwz7OmXYv3vtd/zy5V/yVv1b/Hf7f/nCE1/ghS0vDL5elJ/DtWftz/IfHc+T3zqGA+fFDrrdMqjGOMfmZIvILsi1OEFk2QizdM3cz7vc0wb174Q+b3YfDk3QOUrBP6aULsDS1aefIxk7T63wVjYcMLeKimLvw9GFM7x/W1dsbfWNq59RWRhXBc5oWTTTe83Lt4TKbN2HnpF/292gtN55j3nozS1E8883hx6yvuNMvzazopAD53rfWxo7etnW0u2rutotx19anFvtf98t+8D7Pcutjz2GDUYfcuRW+bjTqLka2nt4e5v3a4galM6q9Cy/uak55hhbyR4KSkUciXTfDTv9gNkU5Q01yQla+PQtL7HSGY+Zyelgoo0njTS7bDbTir2NHpbWLx38fOeaEt/Yx1ueWev7fsSdKXXnKE0yUwrDNzuKt3y3rz/ITx9ezuE/e5KTf7vE89pO1cXMi8hYb2zdyP+99X+ebYI2yDWvXOM7bjzjlKKVj8UzNidbRGZKa02T98XSEZqTlE33B65b3wx9dEt3J+ocpeDLlAaMpZhuNTuSMbV4lbeywS3dBX+mtLsv6NvPzcgF29tpW7yY7nffTdOVJmbPGd5y+XAPArd8dXrEdVeXDp8pdctaw55euYO+gWlefNOvTS1ldlWRrwpn+ZYW1jhB6VzjfU8JlJYSKPK/95R/6EOe5b7t22l/9rmo1zbPqeRa65zT9dJa79dYlJcz2PQq0j7OQ+Cmjl42NOg9MdspKBWJ0NHT5+s0G0+mtKI4jy8du6tnXWNHL2ff9Lxn6pGtLV1JdbKNxjcdTOnwGSBjDAurF3rWrWhY4Xn9g4u8AYBbhlxemBv39DXpDEpnlngz0ZFB6QxfaWz08t0fP7iM3z/9btRM6rHOmJ07376TvqB/vN7S+qWsbFgZ93WHlRQkNzYnW3iDUidTWjqVEU1b5F3eviz0sXGtd/1ELd0F35hSgBI6lXGXMbOhocM3FczRu/t/X6eU5PuCzn++4c0aRs4R3bl0KatPOYUNF32Wd088ibobf5/Gq45PZGMeGGr4t8UZUxr5wHG48t3+oB0cC+pq7+kfPL5v+rXaUgIBw4Lp3t//V9c3+qquZvV7l90mR2EFu+5KwYIFnnWNt98edVv3oblbMuxyA+8D5lX6OuRDKAPrfr9UwisKSkUiuC3qITQHWTw+e9SuHD7fWypT397DJXe/MViW4pbmlOTn+N6s47Wx1Vu+O1KmFGDBFO8b0cpGb4D1wYXTh93/6AVT4y6xSmum1C3fbY9dvtvQ3uO7UX97Wyu3Pud0YR2QEzCcc+jQuMWgDfLwmthTDD+x/om4rzuSb2yOMqWDwuW7ufQxBacb5UiZUvAHpdsGKgDcoLRqp6Sub1wo8D+8KtW0MDKGlrzjzXZWFef5mhqFuY3k3M7Ruww8nLXBIFsuu5y+zUN/43dcey2dby1lNO3hZEp3tHZT19btG1Ma+XfdbXQU2X13TV3bsN2yXx7IMLr3CLvWhoLC/8/eWYfHUa1//Dur2Wzc3ZukSZPU3Y1SSqFAizsUh/vD4WKXi8PF3R1KaQv1Une3pKkkjbvLus7vj22SPWdmLdlI2/08T5+nc2Z2dpLszpz3vO/7/dKZ27+OVRN2MAIGCDeSASNtB2NN4HXXEtvKbdtgqKriHJcQTLYXlTaq7JbZcvxJE/nbdhiG4WRLcz1B6UWPJyj14MGKBiUZlHpLhDbFa2gkIgG+vHkkRiWQXpV7i5uw41ypUiHVa5ES5uO0TL01epOeUKAFnAtK0wPTie3ClkJie3RiELFiTTNzsBNZq3P0ZvlutdK20BHADfj+PFzJOaaDZy5NR0pY14T/bOtZzu/WmgO1BxxeLx8csSNPphQAoDOaOidrQVBAwFATHqeC0iHkdkdQ2lxCjgeRfr3nFUIJICDvRXJoPAq8HvqNnYWkfdPEQaE2Fy1tBasdJJ7LyGmOHYOusJCzv2358m5eZfdICJZzvLvP1Co4lTj2ynfbNAYYzpXl0tZqNIfKWmAys50qvx2khFmC0cFUUE/3diaEyCFoJQWGRKG2g1K/y+dDILfKgprNaPl9Cec4WohRoTNyypI792kNyK8mK134+kk7sKX47+HixROUevBgBZ0pdaZ01xq5VIRvbhvF8c78ckcRAOAsT79Id6hSVoEFOXl3JHQEAIn+5KS8WdsMlaGr5EcoYHD35CTe14b4SHBJpv1MKnFuN6nvAtzy3TpVHUznFFadKY2lS8Vig2T499zBWP3QRNw1ifx5D9YetHstuQ250Jm4GXVH0GJHzlrXXOjY7SdlhIC3E5+bMLIsHW0VFtXdCylTyjAcsSMfRuvxKvXQL5jMLHZRfaGTBtkOgui+UpqOjKByxw7e/e0bbYvx9AZCnpLZQ6UtUGjJRSDrRVE+9eCWcwGco7LXQ6UtqGxRE0rGADoXTB1ZlmVG+cNYTy4S2CrfBQChjxz+CxYQY60rVoA1GIixqAAZJJSPuq2+0sNlLUT2ViIUYCjlTGANLS54oqqNYxPk4eLCE5R68GBFg4IMZpwROaLx8xLjXzNTibE9RU2obFFzSnNSuhmU0qW7AdIA+PL0nNFE+0aDAbmSTfemXj8qFsPjAjivfXBaCrysxJzsoTfpoTCQK8NBMvdlSo2sEY2argmRPbGjimY1p4/085tG4O7JSRyvNAA4XHeY2J4ZN5P4nRnMBhS1Frn+M1AZXU/5roV2e0GpPBQQOPGZC0nlZBFRewJopUq2A8/jTCkASCmvUk9PqYd+IreyFe1UgGYvKOW713YgFQkQF2QJSlW7dvMeY2pohL6khHdfb5FOBaVbz3Ct7K0tYQK8JaALnzpU3supoHR0Avk8rG3XcsSf/GVihJzLvmZE+XEyt+T5AjkepUI75bsAt4TX1NgI5fbt5DkEDGKDyGcXPY/pgC7dzYn1tztnoDOlar2JOPf+4ia8t7EASw9VQG/suwUJD/2HJyj14MEKrkdp9/o95w+Ngq9X1ySZZYGlhypxkhI6GBTmOJDkw1Xl3Q6kQiki5GS2s7ydVCgVCQX49rZRmJcdCalIAB+pCA/PGIRbxyc4fX106S4ABHt13xImyCsIEgG5Cm2vr9Q6CN1PPSgDvcUYHMG/6syyLCconRI7hfP7LWgpcP7iz0FnSp3xU70YIOxguiNyBAAiCRBC9kvj7CbARJWZnc+ZUoDXFsbTU+qhP6ADqEFhPrytFB3EBHpz+hM7yIm1iOGY9XpoT5/mPQYANEePdu9iuwldMnusopXYDpZLIJN0BV1CAYNAb36xo7JmMrs4NT0Ufl7kQtpSSljQur1HKhJiJNUaZM3YpGAYG6hMaYjtTCkASFNSIBs6lBhrXfon5zg6Y0zPYzqgg1J7pbuApRKNrirbWdgAs5nFM8tzce2X+/DB5kI88Wcurv5sD9rUBhtn8nCh4AlKPXiwgg5KXS3f7cBLLMR8yrv0g82FHOVdunzFWTjKu06U7nZAB1jlinLOMQHeEnx8w3DkvjQbR56fhUdnpbrU+0oHpSJG5FQm1xYCRsAJpgkFXn9agbcrKKXFE0YnBtnse6pWVXOufUT4CAwKHESMdSco5Vxjq8bjywbao7SV3OlMP2kH4VQJ7+nV5LbY2/kgd6BCiR3JGa0nU+qhX6D7SScNsh8A2TtmZLwl2NIVFgJG2z3S6mPHnL9AN0Ar8NIk8ti50SW8TSrLnILOlCYEyzEsjgwy6Z7KFEqZf7KN3190gAwpYT7coNRO+W4HAQuvIbaVu3fD1EZeB+3ZeoInKNUaTBz13NE2RI6smZBCZnM3n6rHlzuL8dsBco6TV9WGF1eecHg+D+c3nqDUgwcraE/O7galALBwpP3sZZS/l9PKvjSVSrJ815EdjDV0UEoHuNZIRUJeOXdH0IFdoFcgBEzPbjf2xI645btdWcjTNWQZsT2f1dNN5Cq9v9QfMT4xSA0ky7G7lSmlglKV3sQpf7sYsW8H40pQSinwNlJ/o8AEcGrrzjd4M6Wez5CHvkWhNeBIeSsxNinVfqkoAFw1PJp3fMZgy/dce/Kk3dfrzrh+3+0JdPkuDZ/HOG1z0qTUQ2c0caxk4oK8MSLeduYT4GYoFwyPhohnQfXaUbGA0QhTSwsx7kxQ6jdnDhiJ1TUbjZy+Xrof+FRNO6f383BZCwymrjEBA4c/H9D1t+9gb3ET3ljHny3/61h1p1+shwsTT1DqwYMV7sqUApYsaGq47Z7RYU7csG3RHTuYDuL8SK9GunzXHbhTebcDe16ltkSEWJbFqVryIWZvonGq+RR5bFA6GIbhBqXNrk+Owv2lnJjII3YEtPbUo7QDWoGXJjTd/v7zASktdKSBxuDptfLQt3QoxXYgEQowxkGpJgAMiwvkKLiPTw7u1DDQFxUT+wS+5L1ad/Zsn4odBcolRM8oDV+mNMSHnDM0q/SobNGALoqJD/bG8Dj7c4CcWDJDGebrhRvHkM/vEB8pbh4bD2MTqbwL2Fff7UAgl0M+fjwxpthE2p7RyslqvYkjdrSniCznzor2d8q5YNKgEJcWvn874P75ioeBgyco9eDBCncIHXXAMAwW2cmWzkjvXikhy7I9CkrpY60zju7Cncq7HdjzKuVkStsspbHVbVqOWmK6HRXDM82kb2uHhQ5dvtuia0GbzjX5eqlIyJmw1HjEjuyr77oSlNIKvDR0ee/5iIQWOtJC48mUeuhj6N7BobEB8JY4Z5327rVDsXBEDJJC5LhuVCy+vGVkZ2uIvpKs2vGdNYvYZjUaGKrd/7yyB+1Xak2HYrA1nEypSs8p3Q2SS+DrJUZOrD9s2X4LBQynbBYAnpuXgcdmpWJQmA9mDg7DH/eMRaBcwindhUgEYUCAzWu3xnfmDGJbtXMnzLquBfowXy/OAn2Hr2oHu8+SQfH4FMcBMWBRz790iPOq/iuPV8No8izEXah4glIPHs5hNJkJs2ug+0JHHVw1PAYyHvU5kYDB9G4GpQ2aBmhNZPDsSlAa4U0+AOrV9Z32Ku7CWhkXsJTv9hRXyne1BjNa1Aacpkp9fL1EHGEFa063kGVDaUEW8Zxon2iIGHLSVdZOKbs6Af3e1R6vUrSpu75zIehBptQvCvAKsL3fUdB6PkD3lELjETry0OccpILSUYnO39/9vMR4e2EOtjw+FW9cnU1k0wwV5GKrbNhQCPzJwExXwPUw7U2y7agG5/DYnXDLd3Uoo7KKHV7gvl5ijEzgX7DNiuZXrhULBXhoxiBsfHQKvr51FJLO9Z1yRY5CwAicm+L7TJ1KtDaY1WpojhwhjqHVgq2D0DaNgaPdMCHZuaAUAB6YlsJbljw+ORjbHp9KjLWqDZzeVQ8XDp6g1IOHczSp9JwSm56U7wKWB9R9U5M544tGxSLAm+tp5gwlbaQsvpfQC6HejntHOqAFg4ysEU1abulPT6hXk9L5Yd49F5ihy3drVbWd/w/3lXJWnKtbNRzD8sERfjYFm5R6JXFOoCsoFQlEiPYl+6H4BKIcYU+Q6WKl1W6m1IWeUoaxX8IbkeXahQ1EeHxK1R6fUg99CJ+gzSgbgZUrsCwLQwWZKZXExkGakkKM6c6e7fF7ucKkVNviQnxqwx0WLh00q/Qobybv8x1BKQDMzuC/x7mSPQTgkkcpjSgkBF5DyHunav9+YpsWJNpe0NBp07LpZB3Hn9SZftIOUsN98dqCLCIwzYkNwEfXD0NCiJxrzXO6gT6FhwsET1DqwcM56tvJflKhgOE1w3aV+6YmY9HILiGi6elheHbu4G6fr7iN7LtJ9E90SUQoWBYMEeXpSAdjPaVOXUdsh3u7EFzYgC7fVRqUaNdbMqEioQDhfty+UloUYbCdUqzS9lJiW8AIkOCX0LnNEYhqty0QZYtITu+rJ1PaYZngBR38GCpIdyUoBYDESfzjAfFAQBz/vvMJTqbUo77roW85VtHaLUEbR5haWmBWk2WuktgYSAdRQWlh32ZKh8UGwF8m5ozbsjsJknN7SsspO5h4K2ucBcOiOdVUIgGDeZR6vyO6o7xrjXzMaGJbvf8AsT0ljTxfm8bQ6du6Nq+G2Dc5NYSwynGGRaNi8c//TcbLV2Tiy5tHYNm94xB8rt1lahq5qM3nF+vhwsATlHrwcI4GJRkghPhIbFqHuIJYKMBb1+Rg7zPTse3xqfjm1pFOCQDYoqi1iNhODuBmYu0hYAScINHdQWlvZErD5eFgQP49apS2+0orW7iZUnv9pHQGOtonGhJh16JEnC8lEGUnU2pmzahSVqFFS6ohRnNUgj2Z0o6gNIQWOQJct3AZNIt/3Fawer5B9ZT6MBqP+u55yr7iJtz/y2E89WcuShpVjl8wQKBLdzOi/ODrxQ3aXIXOkkIkgigiAtIUsp+/rzOlIqEA143mtsdcN4q/ZSaYypQ2KHUoo3pKrTOlwT5SvLpgCMTCrmfbvy8bzHlWOKKnQan3mDHEtiYvD2ZV1+cyOkDWad3TwRfbi1DXrsUOyh5obha5gOwsSaE+uGVcAmZnRkAk7ApPplDZ6lM17VBoPZ6lFyLdnxl78HCBQWdKe1q6S2PPWNwV6Eypq0EpYCnhrVJWdW5biwb1FJZleyUolQglCJGFoEHT9QCsUdV0ltjGBspwuKwrCDxd247iBiVxDnvKu3RQap0lBZxXLS5vL8cjWx/B2dazEDEi3JV9F+7PuR8Mw/CU73oypU1KS1AaRnuUirwAqX2fQA6Rw4CgJKCZ/I4g86ruX+BAgi9T6lHfPe84XNaMm77eD+O5msdVudVY+eBEpITZVmsfKBygBG7cUboLAHqqn1QcHQVGKIR0EBmU6ouKwJpMYISuZeJ6wiMzBiG/qh27zjZ2bo9J4vfgpOcNCq0RCi35HIoPJgWSrhoeg+wYfxwoaUF2jD+G2OljtUWPg9LhwwGRqMsn1miE+shR+Eya2HnMolGxOGT1jD1S3ooxr5FKvRKRADNtlCR3l2FxARALmc4MvZkFjpa3YrKN0moP5y+eTKkHD+eg7WB6KnLUW9CZ0kT/RJfPQfeVujNT2qprhd5MCkbR79dd7Ikd0RO6Nbk1RJ8Lw1h6V2xBl+/Sv1dnMqUGs6EzIAUs/bqfH/8cP538yXL9VPlubZsWZsrv7WKCZdnOTCmvHYyrvqICAXDp2wBjNWFNnwckT+/hlQ4QOD6lGo/67nnI62tPdwakgMVi472NfevB2R2MJjOOlJHVH7QATncxVJD3U0mMJRNJl++yej305dx7r1mnA0uLQrgJb4kIP905GtufmIpDz83E/81KtXmsPQuZDhKsync7SAnzxQ1j4roVkAI9D0oFcjlkVF+p5igpdnTl0GjEBNpfXL96eDT83JA5t8ZLLOT8Xg5Rn0MPFwaeoNSDh3M0cIJS92ZK3UGLtoXjAZrs341MKaXAS/eA9gQ6S8qAQbCMf1XZVex5ldJBqYrqtUsMkUNup2yakyn1TyC26Uxpq66VYwuz8uzKzoDUms+Pf442XRuiqEypnkfx+WJCqTNCf07ev0ciR9YMmgks3gpMfRa44lNg4Q+uB7cDFY5PqdajvnueUVin4J1QbzpVN+BLsYsbVZz7qi31WFfhZEpjLToMoqAgCIPJ54d1X6mxoQFlt96GMzlDUTznUqgp1Vh3wTAM4oPlHFsvGrlUBD8v288ZmVjo9iosoOdBKQDIRgwntjXHc4ltiUiA/15pW0zOSyzAfVNSbO7vCXRG/nBZs40jPZzPeIJSDx7OUU95lPbGg6On0KW7YoEYMb4xNo62TW9mSukAN0QWArHAPSun9rxKHZW+8Xm+dWAymzjluIl+ZKY0Sh4FIUOWjNF+sSvOruA9v8KgwN9n/0aor5QjfX8x95U2WwXk3ExpD0rAInOAqU8Bw24EhBdQlwonU+oROjrf2HaGXzlUZzRjR0Ej776Bwokq8jsa5c/1r+wufMq7HdhS4GWNRlTc/wDU55Ri9WVlqFh8D4yN/ft7tNeqEx/sbVMBvruwZjOMTaSCfreC0qxsYluTlwfWTLYHTEsLw9OXpvO+/j/zMxHHkwV2B7SY1tHyVo9f6QWIJyj14OEcdVRPaZgTZTh9TWELqTwY7xfPUdJ1hr4MSt3RT9oBXb5rLXQUHyzn9TrrIDPKdn9itaqaU3JMl++KhWJOUGztVVqrqsXxhuM232N18WoIBQxHJfhitoWxzhL3yKP0YoEKSr0ZHbS6izfTfj7S0ZfIx8HSgZ39OVFFqplndrPUlA99JX+mFACnr7QjU6rYtAnavDxin1mpRMvvS9x2Xd0hwo4XdgLVT+oOTM3NXb2g5xCFOu8T2oEshwxKze3t0Jdx/bjvnZKMb28bidEJQQjxkWJ4XAC+u20Urh3VewrntMiSWm9CQZ3SxtEezlcuoCVkDx56Rn07mSkNH4CZ0pNNJ4nt9CD+FUtH0EFpo6YRBpMBYmHPM5rWgSLgHjuYDujy3WpVV0+pWChAfLA3ihr4lSztBaV06a6vxBdBXtyytDi/OFQquyZPZYquB/ahukN2r/1U8ynUKGsQFeCFKqvs6MVsC9OstM6UtpI7e5IpvVCR8lQDGNTcMRvojWYcLW9Bu9aIKamhkIg869J9zUnKpsqaYxWtfXch3eBENblwNMRO9YkrmPV6GGvJhVFJbJe6LUfs6FymtH3NGt7ztS5fhpAH7nd7RtJZIu0EpfEh7s8kGmoooUKhsFuZUlFEBIShITA1dC2caHNzIU3k6lZMTw/H9PS+u0cH+0gRGyRDhZXn64mqNmTYea57OP/wPJE8eABgNrMcoSM6ozUQyG/KJ7YzgzO7dR4648eCRb3GPd5f1tlDgOvv2ROifaKJ7UZNIxT6LtuXLBsr9wIGyI4OsHne0rZSYjvRP5F3QsMRO7Iq+T1SR/YyTYqeBH8peT27q3fzKPBezJnSru8cr9CRBxIJNygVGpyzE2nXGnDN53tw7Zf7cPePhzDj3W0X9WevP2hTGzjaBdacqGqDYYCWJJrNLE5WkwH1kGj3BASGqiqAEikSx1hnSqny3ZJSmFpbodyxk/d8xuoa6EtL3XJt3SEp1HY2ND7I/ZlSQzUZlIrDw7ulTswwDGTZOcQY3Vfan9DP99yq1v65EA+9hico9eABQLNaT6ghAgMvKNUatRzl3YzgjG6dy0/iB5mIDI7cVcJboSB7g2iBoJ4Q7xfP6eu07rMdn8xfspQdEwB/b9tZYFp5l7aD6RynxI+sM6x0UDoqYhTGRo4lxvZU7+Eo8F7MmdImlZ1MqdwTlHKQctWjJWaVU71Vb68/g9zKrsC/olmDF/7Ot/OKgYFKZ8TR8pYBG6y5wtkGhd39OqMZpU56lrIsC52x7/qJy5rVUOrIEtHuKsXSGKjSXWFAAIS+XZ91uqcURiOavvkWrM52gK/au9ct19YdBoXZVnlPj7S9r7sYqquJbXFUlI0jHSPLJkt4tfkD5x6RRS0s51XZrjrw4BomM4uvdxZj/se7cPePh3C2vn9Koz1BqQcP4HqUMgwQQplg9zenm0/DxHZNQgSMoNvluwzDcMpq3RGUsizbq5lSsVDMCXKLW62C0hR+ld+pafZLmRzZwdgaL20vBcuyaNW2oqiNXDAYHj4cE6ImEGP7avYhyp8sC6++iLNVXeW7LEI5PaWe8l0OQjFYIfn5kUMLtcF+cNKk1OG3A1wbjY0n63DKTjlpf/Pn4UqMeGUjFny6B6Nf3YTddvoxzwcKqR64lDAfhPuRf89CJyaDa/NqMPntrUh7bj3GvLYJSw7yeya7E1rkKMRH6jaFetriRRxLPjOEfn5E5hQAmr76yu45tcdt9/f3NrZE94QCBhmR7i83NdSQQakoKtLGkY7xyiSrr7RnzoA1DgxVaDpTeqqm/YJYrBoIvLn+NF5Zcwq5lW3YeLIOV3+2p18qaTxBqQcPAOoo5d1guRQi4cD6ehypJzNxSf5J8BZ3vz+lN8SOGjQNUBvJHrd4v/gen9ca2gLH2oIlJtAbUyhDbYlIgEUj7QfGdPmurUwprcirMqjQoGng/G28hF7ICMrgZEoVegWEUlIlseYizpR2lMz7QQ0pYyB3esp3eWFpBV5GC6XW/qRxTV4NpxKkg9W51bzj/U1RgxLPLM+F1mCZdLaoDXjw1yNQaA0OXjlwoQPOQWE+nKwaHbjS/H2sCvf/cqSzt66uXYenluXhu90ldl/XUzj9pNF+buvZNFB2MJJYrqK8LCeHM2YNHUxp+jHDFx0gg1zCLZ9NDfeFl9j1slpHuDNT6pUxmNhmtVroS3r3s+UsdLm43mhGQZ396gMPjqloVuPbXeTfuE1jwBfbi228ovcYWLNuDx76CY7Ikd/AEzk6UHuA2B4RPqJH5+uNoPR082li21vkzXmfnpIcQAaldIbytauykBpumbhLRQK8szAHUQG2JfqVeiUaNKRNg62gNFwezil7Lm0r5ZTuZodmQywUI0IegVAZGSS3mcnrrVdoL1pp+7pz3ztO6S7gCUptwNBepdCg3UGgtumU7X7x9Sfcp7ztTj7YVAiDiQykW9QGrBug1+sMfEEpnVUrrLc9ya5v1+LZ5Xm8+15fexpFDb1XcpdPlUq6S+QIAPSVZMuHOIa7iGg3KBWJEPrIw+Q5i0tgVjsvAuZOBAIGoxO5QnljeMbcgZHuKY3sflAqCgqCKJLMtPZngG9NgLcEcUHkQjydwffgOr8dKOddtFx6qKLPLcc8QakHD+DawQy0flKD2cDbs9gTeiMo5VMHFjDuvc3QQal1+S5gWaVe+/AkbH18Ko48Pwvzc+w/oOlyYwYMYv34M6sCRsAJWEvaSjiZ0uHhFhNyhmGQFZJF7KvRniG2zSxQZ0f85EKmKyilJhZSf0BseyHhYoah+krl0EJhJ1NqMJlxyI7VSFGDqvPvMFBoUxuwPp//frQ6t4Z3/HygiApKU8J9kRrufKb0s+1FUNmYJOpNZny0uZB3X09hWZY3U+ouOJnSOO79Vz5hvM3XyyeMh/fo0YC1uI/ZDO3p0zZf09tcM4L8GRgGWDjSdU9xZ3BnphQAvDJJrQrtyZM2jnQNY1MTjA0NYFn+qg1n4IgdVXqC0p6y2caipUpvwoE+tqnyBKUePMCSrbLGXb0y7uJY/TFojGR9f0+DUnv2Kt0lr5FcxR8cPNjGkd0nyT+J2K5R1UBFKZCKhAIkhsghlzp2vaL7SaN8oiAV2v7700HpqeZTONV0ihgbHja88//ZoaRwREFrPqSUFUd168XXV8qyLGo7glK0kjs9WVLbUOW7PowG7RrbmdITVW1QO1jt3lfcZHd/X7PpVB30Rv7qgQMlTedlZYFSZySsoIBz5bvh5N+zuFHJ+/M1q/T4db/93tFVuTWc93AHVa0atKrJz1immzKlLMvCUOE4UypNToYkKYkzDgABV14JgZcXpMnkgqX2xAm3XGN3mJsVgcWTkyARCiATC/H6giy3/c6sMSmVMLW2EmPi6B4GpRnuDUqNTU2ouP8BFE6YiMJJk1E873JoTnQv+5oVQ/4OL/ZMKcuy+Hx7EUa/ugmT3tqCFUcrHb/IisoWNc7YKYHeUdBgc19v4AlKPXgAN1MaNsAypRtKNxDbaYFpvD6arhDlQwWlyuoerWBqjVocqCFLjIeEDOn2+WyR4J/Ayb4WtnQ/Q8BR3qUUdmlosaO/i/6Gke3KVAkZIXJCu0rN6KC0sKUQUQFkX9HFGJS2a42d/YIeOxgXcLF8d38JudKdEuaDGenk73dv0cAKSrfbmQhpDWa7k6iBCp0lFTBAYogcKaHk39NgYlHWzC07/etoFXRWgbpIwGD1QxMhs+pRNJlZ/OYgcO0OJ6jSXT8vEWIC3VPJYGpp4ZTZ8vWUAkDA1VdzxiTx8fCdMQMAj0jPqf7LlDIMg2fnDkbef2bj5MuX4LrR7lOht4bT7ykQcEShXIUOSnUnT4E1d28hyKRUovy226HcsqVzTF9UhPI774SeWoxwBq7YkeKiFjtalVuDN9adRr1Ch4pmDR794zgOlzmf3dx2xn7QeaispaeX6BKeoNSDBwzsnlKj2YiNZRuJsdkJs3t8XjooVRqUaNe7rsSp1Cuxp2oPXtv/GrSmrt+jgBFw1GfdgVQo5WRLaf9WV+B4lPrxK+927qeCUqOZLJ0cHDSYEKDKDM4kgmgja4R/IPkgqGkbWOWTfYF1yWgY3VPqUd61jRc5KfNl1HbLd4+VtxLbYxKDMC6ZVKmmA9f+xGxmscuByu6xita+uRg3QveTxgV5w0ssRKBcghAfSoGXJ+heepjMgFyWHYkh0f6cktC/jlX1aHGRj5Oc0l1/N4ocUYGJSARRBL8OQeDNN0E2vKsKReDtjag33wAjsSjlew0m1ei1p8gKlv5AKhK67XfFBx2UimNiIJD0zDmADkrNajX0ZWU2jrZP/dvvQFfIXTQ2t7Wh/q23XD4f3cusN5lR3OCcjdKFBsuy+HxbETUGfO6CQNERKugMlpOfnb5WOPYEpR48gBsUhPsOnEzpupJ1aNaSk8Y5CXN6fN4I7whOxrFa6VoJ75LTSzB96XTcs+kerDi7gtg3NHQoAr0Ce3ydfGQGkyvidC+rKzjrUWrrvWmGhQ8jtr3F3pxzSr3JHo6aizBTWmv1nQtlqNVYX/eKY11QUEGpP1R2y3fza8iAIic2AGOTyKC0pFHFaWHoL/Kr29Fs5V8LAMPiAojtgWxjYwva9y/FSnV3ECV2VED1lZ6tV3B+5oXnehYXUr2LlS0aHHZzduNENSVy5CZ/UgDQl1Olu9FRYIT8CrUCiQTxP/2IqHfeQdgTTyDxrxWQDR3aud9rMNkuojt7Fma9Hr2JsakJNS++hLLbb0fT9993O6PYXfSlpcS2JDGhx+cUh4VBFEoK9GnzXX/GavLy0PrHHzb3KzZu4g1Y7eHvLUY0JVx4subiLOEtrFfiJM+9cGdhA7QObMI6OEot8N06PoHY1hvNDhXB3YknKPVw0aM1mDqtKTqIdlNpUk9p07Xhw6MfEmM5oTkcr87uIBaKEeZNlvG5EpT+dfYvvLL/FU6vawe3Zd7Wk8uzS2YIGRjmN3YvU2pmzRyhI0fluzG+MQiU2g62aRsYAEgNTCXfV0T+nqsvwkxprXWmlNNT6smU2sQrgNj0Y9Rot5EpbdMYOq1DOsiM8sPgSD/4UP3WB0v6tkzLFjsKySqCpBA5pqeR96mi+vMvM3KWUtW17iVNDaeDUvJYWj053E/ame0eEu2H5FA5sX+DDZGo7kL37WVGuVHkiFLelfD0k1rDCIXwn3cZgu+8A5I48jkoTad8u41G6M+eRW/BGgwov/tutC5ZAvXefah/4000fvpZr70fHzoqUypNSHDLed3RV9r48SeW1J0dWv/80+XzDo4kxcFO1Zx/5fzuwFaFi9Zgdqr6pUWlR0kjeS+dnh7GKc3vy75dT1Dq4aKHr3RyIASlFe0VuHPDnRxV3DuG3OG296DFjqqUVU69rlnbjDcPvGlz/5SYKZgaO7Unl2YXOltZ3FYMtcF1+f8aVQ0nqHaUKWUYhtMn2oFEIMHI8JGccTooVbHkROxi7CmtIzKl1EPPkym1DZUp9YPapncnnV0TCxkMCvOFUMBgRDy5sHKwj1UWbUH3k05ODeXYppztReuT3oLPDqbz/5QCL51V3XSyjtieOTgcQoGlJJRhGFyWRVp42FLT7A717VrOom2vZkpt9JM6g9DXF+JYMqjtzRLe9n/+ge4kef6mb7+FSdF3QZK+pJTYliTabz9xlp4q8GpPn4Zy+3ZiLPTRRxF8993EWNvatS5nlwdHkosiJ6vPv8oJd2BPVd2Zagm6DcJLLEBahC+nbzfPE5R68NB3VLaQwYyflwh+XuJ+uhoL60vXY9HqRTjTQtqHjIkYg2mx09z2PhyxIycVeH/M/xFKAzlxivGJwfCw4Xhk+CN4d+q7vdpHkxqYChHTlelhweJUs+uTD9pX1U/ix8ke8zE9bjrv+Pjo8UQ/aQdpQWnEdqO+FEDXCnJZk9rtfWADnQqr710YXb7rETqyjSyA2PRnVGjX8GdK86nJ2qAwX0jOKT/TPooDoa9UoTVwepwmp4ZwgtIGhQ5tavverAMJrcGEckq8yPpnom1hihtUnQq8zSo9jpSTv5OZg8lKghnUdnGjCsVuCtzpz5BcIkRisNzG0a5D9ypK4uJ7dD66hFd7sveCUsXGTZwxVq2Gau/eXntP4r0MBk5PqSTBTUEpT6bUlWdU6x9LiW1hYCCCbroR/gsWEOOmhkaXVZIzqKD0VE37Rff8BIA8O3Y4dB84H/l0r3iUP8RCAWfRia7c6E08QamHi56qFjJLFRPIDSr6kqUFS/HE9ic4QV+ILAT/nfBftwZ7fAq8jjCYDJz+0bmJc7Hu6nX44dIfcFfWXZAIeya04AgvkRdSAlOIsRONrsv/n2kmg/70oHSnfr8z4mbw2sZcm3Yt7/F0plRjUoERtXZuK3VGopz1YqCsyTJJF8OIIIaaQPt4MqU24WRKVTbVd+lJh3XZJR2Unq5tR5ud3lRbGExmvLPhDCa9tQWXvLcDfxx0XVGzg71FTYSJu1jIYExiMOKD5Z2ZwQ7oIM9V6tq1HIG73qK4QcWpYkwOtV2+qzeZUXru+7H1dD2sfe1lYiFHqCor2h+hlI3ZltPuyZbSpXsZUX4QCNz3DNKXk2rBkoQeBqUZVFDaS16lrMEA1a5dvPvU+/b1ynvS6IqKwOrILLY0LdXG0a7BETtqb4eh0jm7EdZgQPvatcRY4PXXQ+DtDWlSIsfaR2GlzOsMdKa0SaXnZPMvdExmlljYpaEVs/k4VUsGmxnnng90jztdudGbeIJSDxc9lZygtP9Kd7eUb8F/9/6XM57sn4zvLvkOkT6RPK/qPtE+0cS2M0Hp9srtHOGlxdmL3XpdzkCX8B5vOO7yOeiglM5o2sJf6o+Hhj1EjE2NnWpTbTjcOxy+EjIb4u1DThr78sY/EOgISjkepYCnfNcedFDKqGz2lNJlbdZBaXaMf2fWFLC0fh3sRrb03yvy8PHWs6ho1uBMnQJPLsvFH4e6F5huo0p3R8YHQS4VQSISIIKy6aIrXJxFoTVg8Y+HMOa1zRj92mbc/8thp0VBuksh1U8aHSAjPJQDvCWcoLJDgXfzabJ0d+KgEHiJSSEggYDh9N1uPeOmoJSzsOG+0l2TUglTI6m0TPeJugrdV6o71X07E3voSkpgVvLfs9UHD7rtfVQHDqDx88+h2LwZrIn8nGrzSS0FcXQ0RIHuERcURUZCSJ3LWbEj5a5dHO9U/yuv6Py/7wyy0ki5dZtL1xYX5A25hPwO8An+XMhUt2pgMNnODte2a9Gisi/ydYYKStMjLM8HujKlSaXniM/1Fp6g1MNFDz254esnbde347Pjn2HxP4vx1I6nsKdqj9uvo1ZVi+d3Pw8W5I1mfvJ8/HrZrw4FeLoDnSmtUFQ4LIP5p+wfYnt42HAkByTbOLr3sPYCBYDj9cddLuGhy6PTg9JtHMnl1sxb8cG0D7AodRGeH/s83p/6vs0sK8MwnGxpcBDpDXkxBaVag6kzM8yxgxFKAFnvqDZfENBCR1BDoeZOGHRGE+czlWEVUEhFQgyLJc9FB0CO2HO2EX8c4mZPXll90uXyWpZlsZXK7k1J61IApRcL6cVEZ9/jwV+P4h+rHs21ebV4e8MZO6/qOfTfYRCVGQX4xI6U0BvN2FFABm0zB/OXtk+jvGcPlrRAo+95sE1nXNzaT0rbjDAMpyfUVbwGc+1MDFQ21h3YU43VFZfArOt55q7hw49QfsutaHj/A1Q+8CCq/vV/RIBNB6VeQ9znC84wTLfFjtpXrSK2ZUOHEosNPlOnEvt1Z87AUOf8IopAwCCdp4T3YoKuFJGKBBBRFQzFjbYF4bQGE0fkKC3CsnAeF+QNiZAMD/tqfuIJSj1c9HAzpWT5bp2qDotWLcKnxz7F3pq9WFuyFvdsugfvHn7XbdfAsixe3PMixyd0cfZivDrxVd4+RXdAi/qojWrUqW1PTA1mA3ZVkSVLlyVd1huX5pChYUOJ7XpNPUcUyh6NmkaOsFNaoHOZ0g6mx03H8+Oex6K0RRAK+G0MOhgUMIjYlsjI33PReSje0l2sH6ihfB6lvdiPfN5DZUqljBE6LXfyUVCrJEphAa5q5QwqwPknvw7FDUo89sfxznLcb3ba9rz7elcJ73i71oi/jjknmtbB6VoFR3RuhlWgRd+Xu5Mp3ZBfyxFSAoAf95YSFkXuhrZUoMvjLGPk36agXoGDpc1Q6sgsOB18djA+JZgocdabzNhX0sR7rLO0qPSookTYhkS7UXmXCkrFkZEQSHvmES4KC4UwmCxv1uTlAQDa129AycJFKL7iStT/712Y1d0vAdfZU/U1maAr7Jnqr/bMGTR++ikxpti4ES2//965rTlBBaWZ9u3KXKU7QalJqYRiM1mO6zf/cmJblpMDgS/5eVft3u3StdH3sotN7Ki0ibznJ4f6IC6YvEfa6ys/W6+EiXo+dASlIqEAiSFyzvF9wUUTlDIM480wzJUMw3zDMEwuwzDtDMOoGIY5zjDMCwzDcJ8S9s9XyjAMa+ef8ykXD/0KHQzEB3V9sc2sGY9vf5xXlfa7E99hWcEyt1zDtopt2FNNZl8vSbgEDw590C3nt0W4dzjkYvLmU9RaZONo4Fj9MSj0ZMnHlJgpvXJtjkjwS4C/lJygu1LCe6juELEtF8t7NeM7KJAMSvVCslSa9ia8kCm1WqHlZEo9djD2oTKlAMBouaIWtHdfQrA3fCkBt9kZZJl0k0qP6f/bjmVHKjvLcd/5p4D3MooblHb7Fv887Fz/WQf0uWICZUQZWU8zpSzL4pOt/Pc2g4nFuhM1Lp3PFWi1YLo8DuBmT8/UKjgqujkx/giz4aHt5yXGcMrPdQdPAO4KtMiRVCRASqhLUyW7cESOethPClgyfLJsUh1dtXcfmr7/HlX/+he0eXnQnTmDpq++QsXie8B208fUkb+m7kzPelmbv/2Od7zp8y/A6vUwKZWcTKlsiJuDUh4FXkfVSIqNm8g+V5EIfpdeShzDiESQjxtHjKl27XTp2jIiyWf/RZcpbSIXVOKDvZEUQn437WVKT1Olu3FB3oRNGEfx3BOUup0bAKwAcAcsP/d6ADsBJAL4D4CDDMN0R/LxBxv/Lk433/OMJqUOLVSZmfWXcWPZRhxrOGbz9W8ceAN1KtdK3mgMZgPeOfQOMRbmHYbnxz7fqwq2gOUBnuxPBmL2gtJtFduI7cFBgxEu758ggmEYZIeQkw9XgtJ91aQYxbCwYRAJRDaO7jl0+W6bsRpAVxbkVHU7zOaLQ0Gw2BOUdh8vbvmkl1kJtZ7MqNEBRQaPt2RCiBxjKMEjZ/l+T6nd/XlVbahXOJ99/Ify1pyeHkbc/3oalB4sbbFrbfBPfs/u47bQG83EIgwApFBZUaCrn6uDogYlJ9s8Pd3+d2PSoFBie2dho40jnYPuJ02P9INI6L5po76MLKsVx/c8KAUA+TjSK7pt+XLUv/kW5zj1oUNo+v6Hbr2H3kEmVFdsu8LAEWa9HopNXGVfADDW16Nt7VqoDxwErHpMGbEYsmHDuv2efNCZUlNzM4y19quR2letJLZ9Jk7k7XOVT5pIbKt27+H0zNqDzpSWNKrcUq5+vlBGBaVxwd5IovyK7WVKz9SSz4eOLGkHdFBK98X3FhdTUKoH8BmAVJZlh7Asu4hl2TkA0gAcBZAO4H1XT8qy7G02/vXesqsHt0F7x0lEAsRaZUp/PfWr3ddrTVp8nvt5j65hfcl6lCvIh/OjIx7lZAF7i6QAUgmvuM32w3R7Jek71ptepM7A6St1Mig1mAzYXL6ZGBsdMdpt18VHSgCpFmxmTRBIuzIZCp2RU5JzoWK9qh0Kyg7G1xOU2kUoAishJx9+UKNJSWZ86KDUlkDNvVOdrw4oOTfJaVMbsJTqJb1rYiKx0g4AOwucC4pKGlU4Ttkb0DYnfOW7rvSQf22nDBmw+PrpjPYntWVNKvy6vxwbT9ZBb3ROPKesScUpo+bLlA6J9uMIT9HiInS5Nc3kVDIoPVuv7JEHMq28m8mzsNETOJlSdwWl48dzB218Vpq++AKmdteybGatFvoKUsxLPp7M/OlLqX5ZF1AfOAizyvazoOXHn6DcSpbIyoYOhUDmXpFGcWwsp8zWXgmvoa4eqn37iTF/qnS3A5+JZFBqamtzyRomLcKX6PIws8CZPrQu6W/ouUJCsBxJIXRQ6nymdLCDoNSTKXUzLMv+yLLs/SzLFlLjNQAeOLd5FcMwvetl4aHPMZtZHC1vQX51G2cSQ3/RkkK6rAcq2itwpP4Isf/tyW/jqkFXEWN/Ff7V7Wwpy7L4Pv97YiwrJAtzE+d263zdgc6Unm3lXwEuay9DWTv5oJ0S2z+lux3khJFB6anmU9CZbAtM5Dbk4pV9r2DOsjlo1bUS+2bFz+qNS+zER+KDSDmpnhzgT/Z89aVJdX9iHTBFcDxKPcq7DqFKeP0ZFZqsAhizmeWUs2VE+gEGLVCyE2gs7JykT0sLw10Tud6Gw6hSUAB4de0psCyL3w+WQ2OlWCsUMLhjYiImpJC9fLSari1WHCUzgiE+EkygbE/oTKlKb0Krk2JKZU0qbDxF3qMfnZVKTGr1JrNdG4X1J2ox670deHZFHu7+8RCu/XIvFDaseKyhFz7D/aTwl3F9sKUiIbLtiAiF+0kdBoVZ0f4I8CbP3ZMS3lxqoWCIi8q7mrwTaF+/gbf/kmVZTgmsu4JSSXIyR4XXFmaVCi2//ubS+fXFxQCl6OszjVSU1ZeV8r5WuX07yu+4A+V3L4bqwAHeY1R77Qspak+eROvSP8n3nzLZwVW7Dq/YkR0F3va1a4nfi0Auh880fl91cWQkJCnk3EO5k99ihw9viYjT93ix9JWyLMsROooP8kYSVVpf1qTm9I12QAelaVSlBh2U1rRpOf3tvcFFE5Q6oCO9IgUQbO9AD+cXlS1qzPtoFxZ8ugeXfbgLN3y1H03KrqCFq4rYtVq0pmQNsS9AGoAZcTPw4NAH4SXs6usxskb8UfBHt65vf+1+FLSQPVt3Zd3V62W71tB+n6ebT8Ng4k62aIGjEFkIBgcN5hzXl2SFZEHAdN3GjGYjTjbxPzR/zP8RN669EUvOLEG9huzVGh42HDG+Mb16rQC3r5RW4LVnhn2hoNIZibKiSIYSY/Hv/b/D+Q5DB6VQEfe10iYV1FQpW46oDPhwKPDDPODjkcAfNwNGSyD778sG47Mbh+OmsXG4YUwclt03Divun4B7JpNVFLvPNmHpoUp8tZMUOLp0SASiAmSYkkpm8vYWNTrMZuqNZvx2gKwUmZcdxSkTjfT34niVOlvC++PeMiJR5isV4Y6JiUgLJ7MDh8v4LXEqW9T4vyXHiOzo0fJWPL08z+F7c0WOuKW7HUxICbG5b152lMPnglDAYCJ1jh2F3QtKW1R6zsQ3O8a5oNSsVqPiwQdRunAhqv71LxTPuxw1L75EKMcaqqo4lipeTgaSjmAYBoHXX8+7Tz5+HHxnzybGmn/8EWaN8xllOsgWR0dz/FEN5RUcK5r2jRtRcc+9UO3ZC9XOnSi//Q5e+xjNkaPEdvDixRBHR3OOs8Z3zhynr98V6KBUc+yYzWPbqNJd31mz7GZvfSZOIrZVO13rK6X9Si+WvtJGpZ5zf48L9uYE6XqTGTVt3M91k1KHBsrXNZ0qh04MkYO2Iy7qg2ypJyi10PHkNQBwyaiNYZgnGIb5nGGYDxiGWcwwTKjjV3noC4wmM+764RDhX7W3uAmLfzoMo8nysKBvYtYiDnQQNidhDsRCMUK9Q3F5MlmS8mfBn9CbXBdMWFtMGkwn+CX0eUlsVkgWsa0z6XC6mSvSsLOKfGBMiJpABIT9gVws55TFHq/nlvDmNuTi7UNv2zzPI8Mfcfu18UEr8Iq9yOD4cDmVNbwAOVzWAuvF22hPUOo6sgBiM4BREplS2rMvSs4gaPWdgMKqq+TUKmCXRUGcYRhcmhWJV67MwmsLsjAi3tJn+vCMQYjwJ9VQn1yWi0YlOaG541ymlc6UNir1KLJTQgYAq3OrOROka0ZwPwMiIder1J55fAdag4kjunTtqFj4SEUYFkf2utnKtHy9s4TIDHewJrfGZiDbwWmqd4uvdLeDedm2faj5fid8TKb6SncVNtrMltgjl6rakIoEnL4zPliWRfW//w3lJrI9onXJEjR+0qUmqz11itgv9PeHKMJ9VRIB11wNb6q3VBwbi6i33kLIvfcQ46bmZrQuW+70uWllXemgQZwsL6vXw1jT9X1jTSY0vPM/8kQmE+refodYuDHrdJwyVu9RIxF44402r8d79GhIYnrnvinLIauR1IcO8aoWawsKoDtJ/k1tle52QPeVavLyOP6m9siggtKLxau0jCrdlQgFiPSXIcRHwvFvLW3k/q1of1KpSICEYDKg9RILERdEtkz0RQmvJyi10DEjXc+yrKvmUm8BuAfAwwC+AFDKMMydrpyAYZh8vn8A+t788QJiVW41p0QBsEyKv99TCoPJzClP6iiPUugVONFIPhisg8Ub0m8g9jVrm7GlguzxcITRbMTWiq3E2DWp1/R5oOcv9UeSP5kROVpPrtRqjVocqiXVaifGkA+U/sKZvtIPj3zI+1oBI8DTo5/G8PDhvXJtNHSmVMmSfUm5lW19UiLTn+wr7gpCfaCGH0M9ND1BqWO8yeAviGknekrpftL7/HYCrTw9bns+AjS2F0LkUhGevdR+NcTk1FAMPxfcxQV5cwJH6783jdZgwrsbyUqR0YlBNr0waQ/pKicypauOV6NNQ1Z+3DTWEkBkUNkBvueFwWTGMjtKwl9TWWMa+pz0RNqaQeG+GBHPFYWZlRHOyQrZYlIqmSlt1xpxvLKVc5zOaMKKo5V4a/1pbMiv5Yis5VaQr8mM8oPYCZEj5ZYtUKxbz7uv6auvYKiylGrrTpPesNLBg91aIcQIhYj9/HOEPPgg5JMnIeSBB5D451KIQkLglZEBOdXT2PTtN2ANzpWD02XH0kEpEAYHQyCnMlVWPbPqgwe5vqwAtLm5RPZRm59PXgfDQDZ0KAKvuxaiMP6e4uC773bquruDfPw4QNTVK87q9VDt28c5ro0K6kWhofAeM8buub1HjgTjZXW/MJuh2ru3c9PY0gLl7t3QnjrFW3FBf5dO1VwcYoG0yFFMkAxCAQOGYZBAZUv5dCpOUfek1HBfThUKwNNX2ge2dRd9UMowzFwAd8KSJX3ehZeuBHAVgHgA3gCGAHgXlhLgrxmGudK9V+rBVX47UGFz3/ubCrGjoIGz+j383ITgQO0BmNiufWKBmAhcUgJTMCJ8BPHaP8+QPR6OOFJ3hNPXOCNuhkvncBfDwkjVPrqX9mDtQaJXU8AIMC6SFHboL/iCUusHWGlbKfbXkuILk2Mm4/mxz2PD1Rtw42DbK9Duhs7qtujrIRZ3KZSazCwOlrpUrHFewbIsNliprEYyPD+rn/0yNQ8A5GTgEQwFmlVd308y48fiUi1ZkdGJXgnk/2X3rWZm2BaeEjDAE7O7vH0ZhsHYJFLNd3+J7c/zD3tKOSW4d09KsnE0EBNABaVOiPj8vJ8sDZ40KKRz4pZOTWqLGpQcAaP86nYo7CwU/XOyDnXt/CrDaj1XvIwuk6N5dcEQoi800t8LL8133uoj0l+GVMpehu4rbVXrcc1ne/F/S47j021FuOenw1j802HiZ6eFp7JjAhy+N2s0op7OBlrv1+vR8PEnALiZUq801zyinUEglSL0wQcQ9+WXCH3oQQj9uxY7gheTgZyxugZtq9fQp+CFLt+VDhoEhmE42VIyKCUXda1RbPin8/+aI+SzVzpoEIS+vhB4eyPytdcAMdkzHLBwIXwm9d4CsdDPD97DyUVbxYYNxDar16NtJVm663/llWCE9r27BVIpvEePIsba164FazKh/v33cXbyFFTceRdKFlyFkquvhr60lDiW7rFW600XhVhgGU8/aQd0xpNW/gYcK+92QKuE060IvcFFHZQyDDMYwM8AGABPsCzrtJ8Ey7IPsyy7gmXZcpZlNSzL5rMs+xiA+88d8qYL58rk+wfAtjeHB7s0KXU4ZGdyr9QZcecP5EMiKUSOILlF5+pw3WFi3/Cw4ZCJyAnRNanXENv7a/dzhIDssamclHwfHDS4T/oa+aCD0j3Ve6Axdk34dlTuIPbnhOb0mTqwI+igtEHTgBpVV9nUupJ1xP5AaSDem/oeFqUtQoS8b0V1kvyTIGJIhdLUWPKhsa+oZ4b3AwmN3oQTVW04VdMOlc6II+WtRDlnNEOps8pDATG/D6MHK7zJoJTOlFqXsWUypQjRlNo+1wn7Xsv2slfPzh2MLKrPcEwSmcXdX9zEm+VoUurw8RZycj86IQgz7SjM0plSRz2l+dVtOE5l/DqypIAlQ2CNwcSiuJGceB2kgupwPylk4q7JtsnM4lcq8O2goE5J9LIKGPs9pYDFGmbTo1Pw3ysy8ebVWVj/yGREB9juy+ODLuGlrWEeX3qcI6q26VQd3t9kyVqzLMvJrubEOr7ft/65DPoSMnNsHQgCQNuqVdBXVkFzmHzG0j2ZvY33qFGQDR1KjNW+9BIqH/kX6t58C5o8fiVYs0oFQyWZOZemWBYbaZ9V6yBKc5SsPrJGsXFj53dETfWTykZ0BYQ+Eycg4eef4DN1KmTDhiHsiScQ8dKLNs/rLmixovZ162Fs6aqwaN/wD0wtZMWF/1ULnDq373RSIEqxcROKLplj8WO1yhjrTp5C2S23wtjY9VkO9ZUixIfUJr0YSnjp8t14q0A0IYQsuS1tcly+m24zKCUXt4o8mdLeg2GYGFi8SgMBvMuy7AduOvXXAOoBpDIMw5U09NAnbD5dT/StycRCu/06ADAlretBTovlDAvn+n/Nip/FCcyWFdqf4HVgZs0cS5L+ypICwMToiUTZsMao6QxETWYTNpZtJI6fFE0KFPQn8X7xCJAGEGPH6o91/p/uhZ2bNBcSYf+IbIuFYiT4JxBj0WHk5HDPeRyUag0m1Ldr8U9+LR7+7ShGvLIR8z7ahUs/2InMFzfg6s9IVckMb2oC4SnddQ5O+a6is6e0XqElejSnChystZbvBXTOWSncPTkRmVF+mJYWis9vGoG7eLKatO9pvULHOzF6f1MhJwP53Dz7JZx0cOYoU7ryWDWxHeHnhRnpXUGvv0zMOSc9YaMzvdPTw3HlMDKb/9uBchhMXIsYukc1IUQOmcR+9ggAQnykuHlcAq4dFQd/b65SryMmUdYwxypaO0uY95xtxKZT9Xwvw1c7i1HZokZhvZLT5zs0lltWbI1ZpULDJx8TY16ZmUhas5os0TQaUfPsszC1kfc975Ej7Z7f3TAMg+DFi4kxVqeDYsMGNH/3HUoXLkTDx59wFlQ4/qMCASRJlu8B7bPaYQvDmkzQHLf9PTRUVUF3rkSVzpTSWUpZTg5iP/8MCb/9iuA773CYjXQH/lfMB2OVoWX1ejSf83dljUY0fvopcbxs5AhIE52b/vrNmwfGmwyk6KC/A2N9PWpeeqlzm2EYZFCK0HTrwoUIXb4bH+xt9X/75bsmM8uxzqE9kjugg9KyJpVD26yeclEGpQzDhADYCCAOwHcAHnfXuVmWNaMrw2k/CvLQa2ynypUmp4bgxcsz4eslsvEKYE6mJWtmZs0coZ+MoAzO8VKhFPOT5xNjf5/9m1e5luZE4wnUq8mJwcz4mQ5f11sEy4I5Pp1LC5YCAA7VHUKTlgyUets+xRUYhkF2aDYx1tFX2qRpstsb3B/QYkdSb/JzcKK6Da1q10Wz+gu13ojX157CyFc2If359Rj92mYs/ukwVh6v5igE0syKocoiPUGpc1Dlu0FQoOlc+S4dCE0RURmfYTcDAqtAx2wESp2zYnh0VhrWPDwJ390+GnOG8FcZJIbIEepLiiPRfaWljSr8SinuLhgW7bBElNtTalvoiGVZrM4l7cKvGh7NUfWlS9dO1XRN2Mw85fRjEoNw81gy+KhX6Iiy9A4Ol5HZI3v9pO5kTGIQpFaepyYziz1nG2E2s3h9HVfErgODicVHm89yMqvRATIkBHvbeJWFxq++gqmBfF3YE49DFBKCgGvIqiI1ZYUijolxqC7bG/hMmwofKlNnTePHH6Ptr7+JMVrkSBIbC8G5oNtW+a7u7FmO7yhDqdIqtmyF/uxZjtCPbFjf6B3YQxQUBN9LSXXfpq+/RsuSP9Dw/vuc7HjQrbc6fW6hjw8Cr7/O6eOVmzZDbZVlp0t4LwZbGI4djNV3k1bgLadsYcqb1dAayAU0Wy0FdFBqZi2e0r3JRReUMgzjC2AdgHQAywHczbriwO0cHUuKfeM264HDEWoyMDUtDKG+Ujw6K5X3+PQIX4xKsKzwl7eXQ2Ugv3gZwdygFOCW8DZrm7GudB3vsdbQpbsJfgkcsaG+hvZG3V+zHwdqDuDPArJXdnDQYE62r7+xJXa0p3oPWHR9vb1F3hge1r8PeVrsqN1UAS9x162YZc+fbKnWYMKNX+/HFzuKOYqsjkgOlSNbTons+Me68eouYOwIHVlnCryhxTCGFBJC+jwgjlQlRZFrIm32sPSVckt4rflwcyExUZKKBHjiEsc9hXRWs11rtOkVeryyjZNJnZcdxTmOLl2zVsstrFdyRJJGJQYhI8oPoxLIzOGPe7itG4coZd6OZ0xv4yUWYjSVsd58uh6r82o4ZbvW9x4AWHakEr/uJ3+WiSkhdjPY+soqNH/7HTEmnzwJ8rGWz1nw7bcBdjJ6PpP7p/KGYRhEv/2WXTuVujfegMnKuobjrTqoSyeAE5RWVoI1GDjZT3FsLAIXLSLGlFu2cHxLRVGRkMQMjB77kMWLCcEjmEyoffFFNH39DXGcV0YGfGe6tsAect99EMfHccaFgYGI/vADCIPIz3LDB13ChfRCz4WeKW3XGtCsIhetrbOj8dTiEW0Lc5oqbw7xkSDEh1xE7MBHKkKkP9lO09sKvBdVUMowjBTA3wBGAtgA4HqWZd2ai2YYJhNAGgA1ANtLkh56jepWDWraSOGJkecEjG4eG4/hlCk8wwAvzMuA4Jz6GF26G+wVjDBv/j6nJP8kjuDRh0c+hFJv+4vLsiw2l5GluzPjZ/apNykfc5PmIkxG/px3/nMn1peSSop08DoQoIPSM81noDVqsbOSLN0dEzmm30p3O6DFjs62FmIUNYHcdZbqtRygvLexAEfLW11+XVq4L76/fTSELVQpXFD/LsycN1CZ0kAo0aTQwGRmccIq6BgjOAUxrLLRAhGQMAFIpgzti0gV8J5Cl/DuL2nuLIM8W6/EX8eqiP23jk9AlBN9k3zH2CrhXZdHZkmTQuQYzJMRoMWOrMt3D5SQwXR0gKwzML55XAKx70BpM9G/Wt+u5ZTZjUywXwLrTqZQJbx/Hq7EU3/mEmNJoXJse3waEZgazSzHxmfiINv+qSzLou6VV8DqrSbKQiHCn3qqc1McHQ3/eZfZPIff5fatQ3oTgVyOmPffQ+LffyH8uefgf9VVxH5zWxtalyzp3NadIad10kFdi4yShATy5EYj9BWVUFP9pLJhQzkZWu3Jk2hfuYoYk48iq5f6E2lKCkLuucf+QQIBwp/7t8tzGaGPD+J/+AHyKZM7z+MzbRoSly+D3+zZCLnvPuJ49YED0BZYFtvoTGmjUod6G8JjFwLl1D2FYYAYqwqSUB8pxxbG+j5Eq4HbKt3tgKPA6wlK3QPDMEIAvwGYBmAngKtYlrVbI8cwzIMMw5xmGOZ1avwShmFG8ByfDWApLMJJXzs6v4fe4Qjl9ejnJULyOf9RkVCA7+8YjdsnJCA6QIacGH98fctIjLcyHKeD0ozgDLs32dsybyO269R1uH/z/ahSVvEeX9haiHIFWbrWn6W7HUiFUizOXmz3GJlIhgWDnBMw6EuyQrKInlgja0ReYx52V+8mjpsU0/+9sJxMqb4dwxPIW/GuwoEflDYqdfhudynvPrGQwZjEILx8RSYOPTcTp/87B2senohPbhiOJYvHYs3DExEbKAOaKC03T1DqHJTQkYBh4WNWoK5dS9z/JgnyyNfFjgGkvkASFZQ2FQLtZP9lT6AVeGvatKhotgSPH2wuJPr9vSVC3DPZub+7l1jIWdW3ZQuzg/oOXZoVwXsfpzOlNW3azvL5A6Xks8Q6+zgnM4JTpvzKmpOdlhR7qeywr1TkcALoTubnREEsJH9eWm3+yUvSEeHvxSlHtsZLLCD0Fmjali+Hcts2Yizw+ushTSYd7YLvvReMhLsgKMvJ4QgO9QdeaWkIuulGRL32KnxmkvoOzT/8CNZgAMuy0FJWNl7pXQJNosBACIPJKgF9cRE0R48RY97DhsF7xHAIKBEouu+UVqbtb0IeuB8B115rc3/4s89yemCdRRwRgbgvvkDa0SNIO3YUsZ99CnGkpQMu4NpFnN9ry2+/AbCozXpTQVj+BSx2RC90RfnLIBV1/fwMw3D6Sq1LbmnfZEfew3RQWugJSt3GgwA6ZtONAD5lGOZ7nn/WT/sQWLKedG/oOACHGIYpZRhmM8MwvzMMsx/AYQCDAWwH8Ezv/jgebEH38QyLC+zMggKAn5cYL16eid1PT8ffD07EjMGk5cHJZm5Qao8pMVMwJoL04zpafxSXr7gcHx75EAYzWf5FZ0kj5ZG8Pasu01YF/H4j8L/BwA+XA7V5jl9DcU3qNRgVYftBeGvmrQNGddcab7E3p1fzx/wf0a4nb8ATo/rfWzXKJwreIrLEJjyE/MyWN6s5K6IDjd8PlENvJe4iFjL44Y7ROPzcTJx8eQ6W3DMOt4xLQIiPFF5iITKj/HFZdiTGJAVb+vpUjYCOmjwEe6yZncKbWwYaxLTjYGkz6tq7yqg5QWlHMBqZA3hR3+Pi7W67vORQH44q5r7iJpypVWB1Lhn83j4hAcE2ysf44PSV8mRKm5Q6nKImplPT+KtdEkPkkFB9pqdrFWBZlpMptQ5KJSIB7pxIirkcLG3Bx1stPYdrqUztqMQgXi/A3iLMzwuX53DLlTsYkxiESzItz77Fk5M5ZbwdzM2KhJ8Xv9iSoaoKda++RowJQ0IQ+uADnGOliYmI/O/LgKDrfQS+voh85b/9XiVEE0IJIBnr66HcsQPG+gaYmsmSbK/B6cS2NIlcYFHtPwBDBWlPJxs+HIxIBJ+OzKANvMeMtbu/r2EEAkT+5yVEv/8evLKzLWk6ANKMwYj5/DME3dRzezWBTAYBtXghkEgQsJBslWr/eyXMKhUEAobj4Xsh95XSwkV0uS7A7Su1VuulhdwcBaW0Wjj9endzMQWl1nUzCwDcauOfD/elHDYA+BZAO4AcAFcDSAGwC8DdAGawLDuwZ5QXMEeockI+M3JbmFkzTjWR/mmOglKGYfDi+BcR5EVOFA1mA77K+wqPbn0URnNXCd0/Zf8Qx82Im9Hzh7JRB/y0ADi9GlBUAyU7gB/mA4o6l04jFAjx5qQ3EefL7e/ICc3BXVl39ew6exG6hHdb5TZiOzUwFZE+/a89JmAESAkkS3i1TCVnEj/QS3hpEZkrhkZjSmoogn2kEAudeLQ0U1lSocTTU+osIikgJSdiQVBg1fGuv0kEmjBIQFVrJJ8rGRQIgQSqaqB4m9suj2EYTk/jltP1eHfjGcIixVcqsutLygfHq5QnU0r3ZMslQgyNDeA9n1goQDKVDThTq0BFs4YI8AFuT+ht4xOI0jkAeHdjAR789Qg25JP33g4hvb7kgWkphOBRB2Ihg5fmZ3Y+d0J9pXh4xiDOcVKRAA9P544DlrLdmhdehFlNTnUiX34ZwoAA3tf4X3EFEv9cipAHH0Tw4sVIXL6MKH8dKMiysznZ29Y/lnJKdwXe3hDHkOJskhRyYa11GanIL/Dx6bSQ8Z1uW3HfKyNjwPST0vjNmYPEP5ZYsppHjyBp+XL4Tp3aq+8ZeO21RF+yWa2GYpNFm4PuK72Qg1J6sZovKKXHShotr1HrjRyP08EOqjcyqPLoogYl1Hrbvs095aIJSlmWfYllWcaJf6U8r7mNOtdelmXvZFk2m2XZEJZlxSzLBrMsO41l2a/d3afqwXm0BhPyKSEHV4LSCkUFlAayPMFRUAoAsb6x+GLWF7y9p9sqt+GNA28AAApaCnC2lVTvc4sVzNGfgEayrAiaZmDTSy6fKtQ7FL/M/QXXpl2LIK8ghMnCcNPgm/DFrC8gFTqf0ehrcsJy7O6fEjOlj67EMamBpODWiaYTGJ9MlmTuOksqSA8kyppUnN6URSNdDCibyO8BAhMswZIH56DEjkKYNmw61RUITRJSWVKvACBqaNd20lRyf8l2wI2af7RX5vr8Wk6gduekRAR4u9bjzfEq5cmU7qYWdEYnBtldKBnMI3a0n8qSBsslSA4lMxBeYiHeujobdAKUXrCRigSYnUlW5PQFyaE+eHthDkRWFygWMnhnYQ4nu3Tv5GQsGtkVYElEArx1TTYSqKxLB+2rV0O1m2yP8L/6KvhOn8Z7fAdeGRkIffABhD36f5DEDtxFKDozp9y5E4otpCCYND0djID8XEmTyKCUpYJ2WU5Op42Lz/RpEIbw9+v6zZvXrevuSwReXhDIHPeCuwNxZCTkEycQY23nenDpvtL8anIOeCFR1mzbo7QD+jvbkSnl9U0Ot5+HS4/wJSo8WBacKhR3ctEEpR4uDnIr22C0algSMECOjRVyPugsaZBXEMK9nZtMpAel468r/sKNg2+ESEBazyw5swSrilZhXQmpzBvmHYbh4W5Qgz34Lf/4iT8BpevBTYBXAJ4b+xy2X7sdmxdtxlOjn4JczD85GSjQmVKa/raCsSY7hLSwOVZ/DBNSyCBj99kmQqF0ILH7LDlhD/GRuLT4AwBoJFUsEeQp3XUJPzKLEsmQf5OJAsoKJnEyGfTTQamiBmiklHp7wKVDIjllsdYEeotxB1X+6gwcr1KeTCldZTAhxbZQD8C1RDhVo+BYwYxKCOKtaBmfEoJn5w7mjFtz/eg4l4NvdzE/JwrrHpmEh6en4JEZg7Dukcm4Yig3AycQMHjrmhys/9ckfHvbSOx6ahrvcQBg1ulQ/957xJgoLAzhTz/dKz9Df+A3Zw4EcqtnntmM1t+XEMd4ZWZyXidNsX8fkw3r8jwXSCQIvvNOzjHCkBAEXruIM36x4385acGn2rsXhvp6ZFJepaVNao5C7YUCx6M0iJspTaAC1bJmNcxmFmeoftKEYDm8xPYXgr3EQqSEkoHriSpPUOrBg1PQIkdpEX7wkdr2JqWhRY4GB9s3c6fxlfji6dFP4+vZX0MiICch/971b3yd9zUxNjdxLiHQ0y2aioD6fP59Jj2Q90fPzn+eEOcbh0R//klurG8shoQM6eMrss2wsGHEdoOmAYOiyIdom8aAgrre7d/oLrTv5ISUENf75WpJJVCEpfMf54EffzJgiLYKShmYMZHuJ+0o3e0gOAXwpXoOC0il7R5dnrcY87Jtl8v/a2aqzV5Fe3CCUipTWt6kRiUVqNpTjwUszwlrztQqOEJFdDmyNXdNSsLLV2TyfgeiA2S8pbF9yaBwXzw6Ow3/NyuVI1xCkx7hh+np4Qjz9bJ5TOuyZTBWk9ng8Oefg9DXfn/a+YRALoffZbYVgwF+ISJpuv37mPdw8t4fdOst8L/yyq739fZG1BtvkAGxBwCA74zpEHhbBWFmM9pXr0F6pC+nJ/ooNRe8ENAaTKillIXjeMp3E0IoWxijGTXtWsKDGbDtT0qTGU3eH3Mrey8T7QlKPVxQ0CJHI+IDXHo9R3m3mwJEI8JH4KXxLxFj1n6ZHcxLckOJjiOPwdNrev4e5wEMw+DaNH5lwEWpi3oe/LuReL94Tg9ylfYUpz/tmJXFxECBZVlOUDomMdjG0TZPAtRQQWmk/Uy3Bwp/spfNOlOawZQjmKEWNGgbGIYBBs0ix04sd+cV4pGZg3h7GnNiA3DDGG7fujPQ5bsNCh20VqqyfGW3aeH2J190T5rGYOpUC+7AXlAKALeMS8Dy+8ZjbFIQGMby652YEoLfF49FkLx/bajcCWs2o+WHH4kx2cgRLntTng/QJbwEDAP5KG5QKgoMhCSRf3GUEYuJTClgEQ+KeuN1xP/8E2I+/ghJ69fBhypT9WBBIJPBd/ZsYkyxYQPEQgFyYgKIcXoueCFQ2aLmdFjwle/y2cKUNqo4Zbdp4c6pgWdFk5now5T/sjsZOLM0Dx56CMuynNWx4XHOlxSyLMtR3s0M5pbnOMvlyZdjYepCm/vHRY5DWpBjw3iHVB8jt2l13PK9FqXTi4CrB12NBL8EYiw1MBXXD76+fy7IBgzDYGjoUGJsd/VujhjLsW54gPY2ZU1q1CtIARjaAsQhihpATX0mI7L5j/XAj53y3UkCKuAPSrL07NJkXklu1xwDji8BDn4NbHwROPIToOt+tj4+WI4Prx8GmVWJWGaUH768eYRzYlg80EEpAMKXen8JOWEak8RfdmtNqK8UsUG2e+N8vUScHkw+cmID8Pviccj/zyU49fIc/HzXGMTylNedz6h27YK+rIwYC33wwQGnoOsOvIYMgTSN/xktHzfWpqATHXh2jufk2OzB9B45Er4zZ0Icxq8S7cGC32WkT7rm+HEY6us57SMXYlBaTPkHh/hIeCsB+WxhihqUyKP0VuheXFvQAm+lTWrU8XjB6o1mzpireIJSDxcM5c1qNCrJEkhX+twqFZVQ6MkJmDMiR/Z4avRTGBzE7TcSC8R4bORjPTp3JzWktxkm/guw7v9kzcAZspf1QsVL5IWvZn+Fy5Iuw6DAQZibOBdfz/56QAo0jY8aT2zvrtqNnFjyIXG0YuA9WHOpB1uIj4QjQe8QOksq8QUCXe8vvKihlIqty3enCql7Au1L2kHCZK7i8YrFwJrHgN3vAysfBD63b1vhiEsyI7D18al4Z2EOfrhjNP5+YALC/WyXhjrCz0sMPy9yIlZupSh5gApKRyc4t2AyMt72cROSXStP95aIHPZqna90iMt0IE1Lg/eYMTaOPr9hGAaBNmxOAhbZ9uv0nckvXuh76Ry3XNfFjHzMGAh8yBJ05ZatnLne8cpWGEw9D5IGEsWNZFCaFGK7FD+JEmX7+1g11HpSg3VoXIBT7zs40g++1D2Xvs8CwFc7iznX6CqeoNTDBcNByug8xEeCOBdWqfObyb7MQGkgIuQ9k/GXCqV4f9r7SAnosgCRiWR4beJr7smSGrRAAynOhLixQAr1UCzc0PP3Ok+IkEfgjUlvYPn85Xhz8psI9HJRgKePmBRDWnK06lrh6096OBbWK6HQkj63/Q2tbj0k2t/1LEnlAXI7IovwL/TgBFRPaSjTBgkM8IEaIxhKsIgu0+1AKALGcT0lCZS19ve31wDrnwW+uQRYfg9QeZhzSIS/F64ZEYMpqaEWj9oekkQJb5w9Z+he06YhAlQAGO1kafm4ZNvHTUq135N6sWBWqzkKtAELF16QWdIOAq6+Gj7TyEUd+fjx8J1t4zsFwGfyZIijyH5tgY8P/OfPt/EKD87CSCTwmUIq6Ss2beJUxWkNZuRWtvbhlfU+xQ2kMwQdeFpDiz/RmePYIBlCnPSHFgoYTraUVjgHLIGqydQzcUbPLMDDBcOOAlJldnhcoEsPS04/aXCGWx62UT5R+GPeH/hs5md4beJrWHfVOsxJdNOKaf1JwEx5RkVkAanU+Yu2WrxMPQwYonyiiMUKAChU7YRYSMqv5/WiqEB3OEHJ7Q+hHn5OUbKD3I67MDMtvYofVxk1gmnGeEE+xIzVirhAzPUktWbknUAUf7khB/oe0lQEfDEZ2PcJULEPyP0d+GYWcPJv587XTWixnrP1lgoXevXez0uEdAfm8B3MzggnrFM6EAsZXDqk//2NBwKKrVtJixOBAH5zLum/C+oDGIEAMR+8j/Bnn4Xf5Zcj/JmnEfPpJxwrGOI1IhEiX38dgnPCT4xEgshXXrmghKD6EzoTrdq/H35mHad3fNuZgWur1h1K6EypnaCU7gOlGRrr2mI93aKz6VQd4Q5gNJndUjLtCUo9XBCYzCx2FJI3oClpoTaO5udkIzcodRdioRgToyfi8uTLESxzURTGHnTpbnAKIPU9lxmxmmDplUAZ6Snnof+ZGU+Kg/xTth7pkWR2nw4C+xOWZTly8EMcPPw4qJuBqiPkWGLPSkQvSrz8LWXPVsyK1OCJ5EryuPhxgNSO4qpIAtyw9NxC1rl7hiyIW9YLANte7/q/phX47TpAVU8ew5qAFfcCLWTfoTsZxAlKLRmEfcVU6W5iEAROlt0GeEt4vUTnZkVeUEJFPUHxz0ZiWz5uHEQ2fDYvJBiJBEG33Izot99C0K23QuDluPxcPmY0kv/ZgLgff0DKls0XfPDel8gnTQIjtlLuNhig3LGDM+e70IJSuqc00U75rqOgdLiTpbsdzMogqwYblXrC7SK3qg1KnZF+mct4glIPFwTHKlrQqibLHKemOS8YYGbNyG8iy3d7InLUZ9BBaYeCqU8YED2C3FfwT99ckwenmZ9ElnO16loRFEr6d56s7j1PMFepbNGgTUN+z4ZEOyeW0Mnp1ZbApQORFxA71g1Xd5HBMEAImWl/fhQwqH0feVyKE6qoPqHADUuAxwuAh44AjxcC9+8DQil7i/1fAGc3AyYjsPQ2276mBjWw6SWnfxS7aNuAunzA2KUXQGdKLabwLA6UOG/jwsezcwcTJW2R/l54+lKPVREAsEYjVHv2EGN+nh5Ju4gCAyEfPfqiCNz7EqGPD7zHjyPGFJs2YWoqGZTmVbWhUXlhVIi1qQ1oorxX7WVK/b3FdgPTKamuJW0SQ+ScqpNlh7sWQLecqqdf0i08QamHC4JlR6qI7UFhPhw/O3uUt5dDaSDr9TNDzuOgFOCW8BasA0dP3EO/EusXi+Fhw4mxZgFZ2nqyZuAEpflU1jbAW+zS9wwsCxz+nhwbNAuQXFgKpX1GGFXNcfQXoK2CHEux3fvGwScMCE629JpKfYBrvgVokbBldwE/XwUUb7V/rpN/9SxbyrLA7g+At1OAz8YD/0vtFGwbFEZOjto0BuRVtaGIyiS4alUUE+iNbU9MxStXDsEL8zKw9uFJiPR34fN9AaPJzYNZQQoByid5Khw89A++M6gS3h07MSLaB96UFcrW0+4Jlvqb07XkPEAkYBxqptgKPBND5Jy+fGeYm0W2Maw4WoXmc4HyZjf9nj1BqYfzGpZlUVinIFZsAGBedpSNV/BzoukEsR0iC0G4N7eUa0BhMlgyCNYQQSnp54WWUqCRzMJ56H+uTr2a2C5VHwMj7sr4FDWoCB/G/oRTuhtlR+TIZADqT1sy9EVbLdZFh74BqighnCF2vAA92IcOSuup+0FQMhDGVf92mvBMYPpz5JimGSjZTo7Jw4B7d5PlxKwZOPBl99/74NfAxhcA07nsgKYF+ONWoLEQ0YEywmYGAD7YRN7b/LxETlseWOMjFeGmsfG4Y2IiAj1lu52odu0ktqWpqRCHe+xLPPQPvpT4lFmlgvHoEUxIIbPSq3Jr+vKyoDOa0KrWOz7QRU5QFVOp4b4ObbWuHBYFvsfzDaO75xF93ahYQvNCZzTjw82FKKhTcDxQuwvX4MaDhwGM3mjG+vxaLD9SiRNVbWhS6TnJP6GAwcKRMfwnsEF+I7d0d8ArCjacBkxUaYp1UBqRDfhGWjwhOyhYD4Sm9s31eXCK2fGz8eaBN9Gu77qpSwIPQldvyXSbzCwK6hTIpszB+wPa52xItL8lo1WyAzizFmguBpR1gLLB0mtIi3DRBMQB6fN68YovcMIdVHNkLgDvrMQVRt0J4F+29wulwPW/ARFDgGE3Afs/69p3/Hdg5kuAUGzz5by01wCb/sMdN+mAjS9CeP2vyI7xJzxJ6ZX6KWlhblH69WBBuYvUJJBPnNhPV+LBAyAKDYVXdja0uV32Yoqt2zDvslux8WRd59jus41oUOgQ6tu7tnBmM4u3/zmDL3cUw2RmMS0tFO8uGuq2hS2u6r3jBbeUMF9cNyoWvx3oqp6JDpDhhjHdC0rD/LxweXYUlh/tqkz8cW8pfjtQ3q3z8eG5Y3sYsLAsi9JGFTbk1+K73SV48e8TmPzWVjz821FsO9OARiU3IAWA60fHIsqVkkIAJxrJTOl5WbobEA/IrBTVGAZIpcQVCi4ea5jzBS+RF+Ynk72l0sDDALoCOnetQvYEi8gR+WDMivQCli8GfpwP7P8cKPzH8rlUVDsOSIFzAYtnbbTbxI7hltdaM+Sqnr+HvaBWIAIW/QDEjLRsj76b3K9utGTJXWXH2wDlGd1JwTqgvQYjE+yrR05Pd61nyoNtjC0t0OblEWM+kzxBqYf+xWcqaQ2j3LoVMweHEVUUJjOLtXm9ny39bHsRPttW1KlIu/VMAx749QjMZve0THFU750UGPzP/CG4c2IikkPluHJoFH5fPBZyafefuf+amQqpqCt0NLOWjKm78ASlHgYke842Ys77OzH1nW2456fD+M+qk/hhbxlq27V2X5cUIscTs10TptAYNRyRoyHBQ1y+5j7HXj9pB3Rfaflei/qphwHFNalkCSsrUEDk2+U/OxDEjuradRyhhUln3wXy/ujeCUfeAWS6IWg6jyhuK8YLu1/AvBXzMGfZHDyy5RFsKN0Ag6mbXrQSbyB+PP++2LGOM6mukjwDnQq9kTnArauAtEu79gcnAzGjyNfkLnHtPdTNwLFfbe9nzUD+crv9omIhg6mpntJSd6Has4fQI2BkMshGjLDzCg8eeh+6hNdQWQlRRRlmZZCtVyuPk/7f7qZNY8AnW89yxvcUNbnlvdV6Y6fCeAe0D6ktJCIBnp+Xgc2PTcX71w1DrIM+VEfEBXvj4RmDbO7vqd24Jyj1MOBYcrAcN36zH2fqbKyU8yBggJmDw/H74rHw93atVCy3IRcGc9ekUMAIMDRsqEvn6BecCUoTp1jUTTtgTcCplb17XR5cJjkgmSN4JA7Y3/n/gSB2RGdJJ0qL4HfiB8cvlIda/jHnVq+DkoHL3rX8G+gl8m5kQ+kGLFy5ECvOrkBZexmqlFXYUrEFj29/HLOXzcZnxz5Dg7obFgbZi/jHJz/eswvm47qfgWergKfLgXt28AfEWdT1nF4D6Jy/l+Pwd4BR07UtEAFJU8ljirZiXHIwAm3c62dnRnj6Qd2IaucuYls+ejQEEs/v10P/Ik1PhyiSFN9RbN2KK4aSmiKHy1pQ0axGb/HX0Sqo9fy6Dx9tKST8PLvD/pJmWJ9CKGAwOLL/PG/vm5KMq4ZzfbIBIMzXsV2SPTx1Ux4GFDsLG/DM8jyHIrGjEgJx/eg4pEf4wUcqQqBcDF8vF/uWznGg9gCxnRGUAV/JADe5NpuAWrKcCpFDucdJvC3qpqdWdY3lLgVG3NabV+ehG1yTeg2O1Hf5d4p8zoIRN4E1BONUjQJmM+u052JvkEsFpU9IVwDWiVORDBh3PxCYYBG+8QkFAhMB73O2HGYzYDYAot7t7RmIHK47jKd2PAUTyz9xadQ04tPjn+KL3C8wMXoirky5EtNip0EoEPIeT5C1CDj6M+lDPOzmc17FvYDEtg0BAEvJ8Pqnu2x/jBpLYJpzneNzm4zAwW/IscwFQNpcoHhb11jZHohZI64eHoOvd5UQhzMMcPekJPvvUb7X0msfO9ryefVgE5Zlodrt6Sf1MPBgGAY+U6eg9bffO8eUW7dh0u13IsBbTNgErs6twX1Tk3vlOjadqrO5r6hBhY0nazFnSKTNYxyxs6CR2B4eFwBvSf+FbwIBg7evycHgCD98u7sENW1aJIXI8eScdDy2qmfX5QlKPQwY2jQGPLE0F/SiUoC3GCmhPogKkCE2SIYZg8MxPM5+P5Er7Ksmff1GRYyyceQAoumsxQvQmshs/mOzFpFBadkuoK0S8HdNDMpD7zIrfhbeOPAGIXgkDjgIfcMcKHVGlDerkRDiICDoRawzpYOZMuToj5AHTH8OGP+g7RMIBIDg4gtImzRNeGL7EzYDUmtMrAnbK7dje+V2DAkegjcmv4F4v3j7LxKKgJuWAYe+AxrPWPpMs69109V3A3mIxRu10Kp//divzgWlhf8A7aS9F8beZ+mXt8agAqoO4+GZI7HpVB1Km7ruhbePT8TQ2AD+8zcUAH/cbBGJAwAwwLgHgNmv9GnWnjWZ0PLrb1Bu2wZhcBBC7rkH0uTemTD3FF1BIYwNZAZfPnFCP12NBw8kvlOnEkGp5tgxCBRtuHRIJCHAs+p4da8EpVqDiRBc4+OrnSU9Ckp3nSW/fxNT+r9fXihgcPfkJNw1KREsi84F88d6eF5P+a6HAcOHmws5PaP3TEnC4edm4c/7xuPD64fhiUvSXQ5I9SY98hrykN+YT5TpAkCtqha5jbnE2JjIMd37AfoSunTXN8riMcjHoNmAlOo/yPuzd67LQ7fhEzwS++UBsKzS9GcJL8uyyK3sCkoXCMlyPsjDzqm0eqB57/B7aNCQk4rLki7D82Of55RsW3Oi6QRuWXcLzrZwe5U4iM9lqS//ABh6A+BMhrU3oUuKS3Y451l66FtyO3qE5Z88BIjIIveV7Yaflxh/PTABd0xIxGXZkXjz6iw8d5kNC5z2auD7y6wCUgBggb0fA7vfd3xtboJlWdQ8/wLqXn0Vqt270b5yFUoWLoLm+HHHL+4HVLvI77o4OhqShIT+uRgPHii8x44FI7MStjSbody+HZfnkEHgyZp2Tl+mO9hf0gy9ldCPUMDgv1eQvfyHy1pwpLylW+cvrFOgoI687kmpITaO7nsYhnFrBZcnKPUwIKhoVuPHvaXE2KRBIXh6TjqEPfjAb6/YjkuWXYIb1t6A69Zchzl/zsE/pf907rf+PwD4SnwxOmJ0t9+vz3Cmn7QDsReQQQY7yFvq/mvy0GMuT76c2BZImsBILKU7dE9nX1LbrkWj0mI/JIAZVwjJcj4Mu8kSGHkgONF4An8X/U2MjY8aj1cnvIpFaYvww6U/YOnlS7EgZQFkIu7vr1nbjAe3PAil3v2TqV4l/TLAy3ohjAWO/2b/NS2lwNlN5NjIO7r+H0f1r1YfBQAEeEvwwuUZ+OSG4bh2VBz/BMlsBv68w2JTxMe2NyxBax+g2PAP2pYvJ8ZYtRrVTz8D1tBNwategYqMtgAAbZlJREFURLWb6iedMGHg26V5uGgQSKWQjyfvDcpt2zEmMZhjA7OqFwSP9hU3EdtDYwNw3eg4RPqTvZVf7yzu1vmt7VwAINRXimwnlXfPRzxBqYcBwdsbzsBg6qrbFQsZvHplVo8efnuq9uDhrQ+jUdNVj1+vqcdj2x/Dp8c+hclswtICMjibHjsdYlc99foDV4JSgJu5qDsB1OXzH9ubGLRA+X6g9oRlouiBYHDQYITJyIy3yMeiwpvfjwq81lnSYUwhwplW8oD+LBcdoJhZM14/8DoxJhfL8erEV4le0fSgdLw84WVsXbQVL49/GQl+CcRrqpRV+N/h//XFJbsPsQwYQipK48iPgD2V4cPfo6MqAIClusNanTmayipXUeXj9jj+q6WP1BZGLbDvU+fP101YlkXjJ5/w7tOXlKBtzZpevwZXMGs0UB86TIx5Snc9DDR8p00ltlU7d0JgNOCyLDJbuiq3GqwjwRIXyaskF4vHJAZBLBTg9gkJxPi6E7UuLyw3KnX49QBZYbJgWPQF7b984f5kHs4bcitbObLZt4xLQFxw96WrdSYdnt/zPMwsf+Dz2fHPMP638ShtLyXG5ybN7fZ79hlmE1B9jBxzFJTGT7SU+FqT200rj+5yYhnwvzTg29nA5xOAz8Zxf46LHIZhMDl2MjHWEZSeqGpz+wPVWawfptOEx8idYRlAmGs2TBcD60rWIbeBbA24J/sehMj4S6/kYjkWDFqA3+f9juwQsj98eeFyFLd2b6W93xh2E7ndXmW7bUCvBo78RI4Nvd4i1NZBFBWUKqqBdif8B3UKYPPL5FhAHNeO6PgS+0GzG9AePw5dYaHN/a1/uFbBwrIsTG1tYI1OeALTr9XrYWxshFmjsXmM+uBBsHorNTOhEPKxY11+Lw8eehP5ZPKZaVapoD50CJfnkHOe4gaVWxd3LW0trcRYdkwAAOC60XHwsfIDZVngxZX5LvmWfrWzGFoDWRp80xgHGgPnOZ6g1EO/wrIsXl1zihjz9RLhwWkpPTrvX4V/oV5to1TrHGojKRSUEpCCcZHjevS+fUJjAddYns4i0AgEQBaVucj7s++ylQX/AMvuArStXWMNp4HvLiVVNT1gSgxpCC70LgMEGjSp9Khr1/XLNVlnSqcJjpE7B83u24s5D9AatXj/yPvEWJxvHG4cfKPD18rFcrw15S2inNfMmvHZ8c/cfZm9S/RwII66n259zRKA0hz+DlCTCpMYcTu5HZwCSP3IsWonsqWHvgWUlDrmZe8BU58hx1T13PJhN9O2Zq3d/ZojR6ArKnJ4HpNShfr33kfh+AkoGDMWZ0aOQvVTT0FfZr9vlzUa0b5xI8oXL8aZUaNROHESzowYifI77oT29GnO8YqNG4ltWXY2hH5+nOM8eOhPxGFh8Moie84VW7dheFwAogPItgh3epaWN6vRriUXhLJjLKW1fl5i3DuFVAE/XNaCn/Y50VsPoEWlx097uVnSniRrzgc8QamHfmXL6XqOctkD01J65DFnMBvwzQnSVmBk+Eg8PfppCBjbH/nHRz5+fvTKVJHlVPCLAXwjHL+OLuFtrwTK97jvumyhVwGrHrGY3tMY1MCv1wGlu7j7LlLGRI6BRND1+WcYM0Ryi9hNfnXf95WazSyOn1sNDkczMgXUQ9UTlHL46eRPqFXVEmOPj3wcEqFz97Von2jcNJjMNG4s2+hwoW3AMfH/yO22cmDDMyA8v3RKYPcH5HHJ07nZd4GAWxHiqITXqAf2fU6OpcwEBs0EQlOBGEpp3Vql3M2wRiPa160jxoLvuQfC4GBirO2vv+yex1BVhdLrrkXTF1/A1GIRT2G1WrT9vRLFV1yJ5p9/AUstNhqqq9Hw0cc4O30Gqh56GKodO8Hqzi1wmc1Q7dmDkoWLiOtjDQYoNpJBuu/MGa78yB76mQM1B/DKvlfw713/xp8Ff0Jn6p9Fzb7AZ9pUYlu5dSsAYD7lWfr3saoe+4Z2kEuV7ob4SIhe0rsmJSEuiAwi31x/2inP1O/2lBLepwIGPU7WnA94glIP/YZGb8IrVJY0OkCG28Yn9Oi8q4tWo0ZFlnXdl3Mfbhx8Iz6d8SmvB+m9OfdiQvR50itTeYjcjhnh3OvCh1hKLa3JXeKea7LH0Z8tpXa2MGqAXxYB5ftsH3MRIRPJMDJiJDEm9DkDADhR1fd9pWfqFJ1+b1OEVC+z1N/i9eihk0ZNI77O+5oYGx0xGlNjp7p0nlszbyWypSbWhGUFy9xxiX3HoNmW1gFrDn8PrHwQUDdbgtP1T3EzmXQWswO6IsRRpjR/BffeY33uIVeT+wr/cVw9wrJA4UZg4wuWYLqtyv7x51Dt3w9TI5kN9r/iCvhfcQUx1rZqNSeo7MDU3o7yO++C/ix/NpXValH3yisov+12tK9di+ZffkH5HXfg7IyZaPzkExjr7SxqGAyoevwJKM5N5pU7d8HU2koc4nvJJQ5+Sg8DAZPZhP/s/Q/u/OdOLDmzBCuLVuI/e/+DK/+6EvmN/aAl0Qf4TptGbBsqK6HNP4kFw6KJ8bp2HfYWkeJE3SWP6hHNivYnEhteYiHeuIrM4Kr1Jjy1LNduK45SZ8QPe0qJsfk5Uf1qCddXeIJSD32CSmfE/uImHChphlpvBMuyeGllPkoaVcRxj1+SCi9x9+0MTGYTZ0I4LGxYp/fohOgJWDF/BW7NuBXDw4Zjeux0fDz9Yzww9IFuv2efQ2dKo50MShkGyFpIjp3825JN6C1Y9pyAiRVx44GMK8kxgwr4+RpuwH2RMjGanMiL5AUAWJzoh0zpfit1QU7pbvI04HwQButDPjn2CdEawIDpVhWGv9QflyVdRowtLVgKo9n1/sF+g2GAee8BQsqf9ujPwHuZwKdjLf+3JnmG7YUOuq+0+iiZdaU5/B25HTcOiLFa8KGz/KoGoOaY7fOZjMDSW4FfrrEEpBtfAD4eZWlPcED7alLEyCszE9KkRPhfQSqjG2troT5wkPN61mRC1WOPQ19a6vC91AcOoOrRx1D331eg2rPX/u/ImnPvocnPR/OPP5LXm5UFSYzH2/p84Ku8r/BnAbd/u1JZiZvX3YzfT//eb/oEJW0lWFW0ChvLNnKqSXqCND0d4igyK9r6xx9IDfdFRiRZct7hX3qqph3PLM/DXT8cwhfbi6DRO/aStobuJ806109qzfiUENwwJo4Y21PUhL+O2V7M+utoFdo0ZH/7/RdBlhQARI4P8eCh+7Asi5/2leGt9Weg1FkmU2Ihg6gAGcqayBKGYXEBuCInmu80TrO+dD3KFeXE2OLsxcSEMFwejsdHPd6j9+k3dEquam70SP5j+chaCGz+T9e2tg0o2gKkzXHP9dFUHgLqT5JjU54EEiYCrIksl9MrgB8uB6Y9C4y596IOdiZETwCs5qUCcTsE0locLZeCZdk+LTPfV2wprxfDiImCE+ROT+kuwZnmM1heSNp9XJFyBQYH2/DOdMC1adcSk8sGTQMO1B7A+Kjxdl41wAhNBRZ8DvxJ9Yga1JRnKACxHLjsHdvnojOlmhagpQQISuIe21zMVdwdex+5HZxs6VVtsvKCLfzHdo/+lv9aFvKIn0MF/HEzcOtqIHYU78vMOh2nP9PvMsuCg1daGqRpadCdOdO5r23lSsjHkn7Z9e++C9XOncSYJDkZES+8AMWGDWj59Vf+a+bBKycbgddeB/mY0Wj88iu0LumqmGHVapRefQ3nNYHXX+/0+T30H6Vtpfgy90ub+w1mA17d/yo2l29GVkgWjGYjDGYDonyiMCF6ApL8eb5LbqC4rRhvHXwLu6u67MQYMJiTMAdPjn7SpgCcszAMA/+rrkLjxx93jrWvXo2wJ5/EVcOjcXJNV5XRmrwa+PyZi+VHKztdHzadqsOavBr8vngsvCWOQyOzmeVULtmyannm0nRsO12P6jZt59inW4twRU40r4UV3fc6c3AYUsO5FX4XIp5MqYde5ZtdJXjh7/zOgBQADCaWE5BKRQK8fU1Oj0x4TWYTvsr9ihjLDM7EhKjzpCzXGcp2W4K5DgQix8q71gTEArGUeuKJXiwJpLOkAfFA4hRLwHn1t0DqpeR+gxr45zngi8lA8fbeu64BTqJfIqJ9yAUakc8ZNCr1KG1y3I/iLnRGE3adtZQcjhScgS9DKXWmzOyzaxnomMwmvLTnJULxWyaS4aFhD3X7nOlB6cgIJkvu15es7/b5+o0hVwFXfwOI7Yh0MAJg/of8AWYH/rGANzV5tdVXSquLy4K49xsAGESVpBZs4D9fYyGw92P+fUYtsPwuy6IhD8qt22BWWu1jGPgFVwDfzwM+mwD/BPI7rdiwgVDFbVu5Es3ffEscIwwORtzXX0E+ZjQiXngecd9+A1GEbW0Bga8vAq69FonLlyFxyRIEXLUA4uhoRLz0IidbSyMMCYHfvMvsHuOh/2FZFq/sewUGM5lli/HhZrj31ezDV3lf4bv87/DzqZ/x1sG3cMVfV+CBzQ+gQlHBOb4nbKvYhhvW3EAEpADAgsW60nVY8PcCbCrruchYwDVXW3rPz2FWq9G27E8sHBkLXykZaC45VEHYEAKWHlFaeNMWJU0qYl4LAFkx/EGpr5cYL83PJMYK65XYVsAtp69p0+BgKamzsnBkrFPXdCHgCUo99Bonqtrwxjquoh+NgAHev3YoUsJ8evR+q4pXoaiN7LWhs6TnPUVbyO3YMYDUxd8b3Ud1Zi2/ImZP0bYD+WTWSB93FVr+/BMtvy+BrqIKWPQDf2BTfxL4cT6w4t7eubYBDsMwnBJeodySSaEfWL3JnqKmzgfvVLp0N2oY4BveZ9cy0Pn51M840URmkm/PvB1h3mE2XuEccxNJm6pN5ZugN/ViyX1vkXUNcN8eIGsRwFAtGrIgYNFPXIVwGobh6Ss9yj2OZYHjv3HfX8QjNJVKZfurjwKqrt5Ps06H9g3/oOn1x6GqEdiuhG0ptZTz8kCLF3mHGyE++j+gdCdQdwJ+0oOw9mg1q1RQbLHc6zV5eah57nnyhGIxYj76EOLILh9G+fjxSF63FuHPPQf5+HEQR0dDOigF/ldcgaj/vYNBO3cg8j8vwSuDXORgGAaR//0vvMeQmVlrQh98AAJJ98UHPfQNq4tXY3/tfmLsloxbsHLBStyeebuNV5HsqNyBq1dejbXF9pWincHMmvFV7ld4eMvDUBlUNo9r1bXi/7b9H57e+TTym/K73aIgjoiAD9Vb2vT1N/AVmHGrk1olvx+sQGmj7WvtgPYnDfeTItzPy8bRwMzB4UgNJ+dqSw5yg/81uTXEPcbXS4SpaaEOr+dCwVO+66FbtGkMyKtsg0wixNDYAAh5MpyvrjkFowOVs2C5BK9flYXZmU6ox9qhUdOI9w+/T4ylBqa6LC4y4Dm7mdxOnsZ/nD0yrrCIi3RkdPRKoHADkLmg59dnTd5SS+YTljli4wk/NP6xlBASCVi4EOGPfw3Bzpct1g00x38D6k4AN/8FyHtW3nO+MTF6Ipac6Sqrs1jDaLG/uBmL+mjldH1eV8/PdI8VjE2KW4vx8VEyi5bgl4A7su7o8bkvSbgE7xzqKmlV6BXYU73n/Ly3BSUCV38FzHndoritbgR8I4GkqYDESRGPqOGWEtsO+DKlFfstQaI1Odfxny9uHCDxsdwHAQCs5T6bcy10xcWouOdeGCo6Jo8hkIdrET2xBcJJ91v6+yusBNoOfWO5vyZ12ToZGxuhpMpu/ePIsj+xtxnycD1UdV29t23LlsF72DBU3H8/6RUKIOKF5+E9nFtiLJDJEHTTjQi6ybH1kDWMRIKYDz9A2W23Q3eKzBT5zb0UAYsW2Xilh4FCm66NuE8AQIQ8Ag8MfQBigRiPjnwUw8KG4d+7/w0FbSlHoTFq8NTOp1CuKMc92fd0a2G/TdeGf+/6N7ZXciuevEXeYBiGE6iuKV6DNcVrIGJEiPSJRKxvLGJ9Y5EamIpLEi6Bv5Q/E2lN8J13Qrm5a55kbGhA65IluPfaG7A2rwbFDgJOk5nFL/vL8O/LMuwexxU5CrB7vEDA4LbxiXh2RV7n2JbT9WhW6RFk5TaxiirdvSQzAlJR93VWzjc8QakHl/njYAX+syofqnNN4YPCfPDxDcORFtFV876nqBF7i0mFswemJeO+qSk4Wt6CimYNQn2lmJgSApnEuS+cmTWjTlWHgpYCnGo+hWZtc+f4zsqdaNKS7/ev4f+yawFz3lGbBzRRxuvJ3ZDo9w0HEiYBJVYPixPL3B+UHvmh87+N+T5ozPcBQKpKti5dCk3+CcR+9jnEOdcDax61/JzW1OZZek1vWwN4B7n3GgcwoyNGQywQd5ZidVjDbC/wg9nM9qjU3Rla1frO3pYYph6DBJQwgycoBQCoDWo8uu1RaE1aYvw/4/8DKS3w0w0i5BEYHjYcR+q7gq+1JWvPz6C0A3kIkHll915LZ0prjgNmEyCweo4co/orQ1K5IkkdiKSWloIzVkJEhf/AlHgpyu+4E8ZaUoxFVeeFyp3BiHvsXjCsAfh8YufiGwBg5UOWjPC5CpbW5SsAU1fLBSM0wzeW/KwAgF+CmghKVXv24ux07v098MYbEbhwIWe8pwj9/ZHw269o/u47KHfsBIQC+M2di8BrrwUjuICeoxco7x1+r3NO1MEzo5+Bt1XJ/LS4aVgTtgZrS9biVNMp6M16iBgRzDBjT9UetOhaiNd/cuwTCBgBFmcvdvo62vXtWFawDN+c+AZtOq4w37ykeXhh3AtQG9R4df+r2Fi2kXOMkTWiQlFBlBH/79D/cF36dbgr6y5eB4UOvIcPg/e4sVDv7VosavjkUyRffjl+vHM0Hv7tKI6UtwIAkkLl+O8VQ7DtTD2+2lnSefzyI1V4ck46xELbn/vjFa3EdpaNflJrLsuOxH9W5UNntMyDDCYWK49V4bYJiQCAsiYVjlMZ2Mtzojjn4YM1my+I76knKPXgEuvyavDkslxirLBeieu/2oeVD05ATKA3WJbFexsLiGMi/b3w0PRB8BILMWmQ41IEtUGNXVW7cLT+KCoUFShXlKNKUQW92bmytckxkznlj+c9x38nt/1igMih3TvXkKvJoLTgH0u5rZebjNGrj1omiwDUDRI0nrB9Xt3JUyi97jrEfv45vBZvtwSz/7xgET7qoP6kRfXyphWA8OK4bXmLvTEifAT21XQ9XIXyM2isHYJjla0YHhfYq+//094yaAyWyTRHddc7xPYk/yJCb9LjX1v/xWkbuC7tOgwPd9/vZ07iHCIo3VaxDWqDmphwXjTQnzuDCmg4A4Sfy2wYNED+X+QxOddbSn9tMWgWGZQWbUb92+9wAtIO1PUSNP6+GqH33w/MfAlY92TXztYyi5jc3Ldh1mjQ/P33xGt9Y7QQis9VEKVeCkRkATvfgV+sFvXHTTBpbS/Seo8di/Cnn7L9c/QQgZcXQu67DyH33ef4YA8Dhj3Ve7CskNSGmBY7DdPjpnOODfQKxI2DuZl0pV6Jdw69wznPR0c/go/YBzcMvsHuNZS3l+OXU79gxdkV0Bg1nP0CRoBHhj+C2zNvB8MwkIlk+N+U/2Fl0Uq8tv81QrGcD7VRjW9PfIv1Jevx5uQ3MTRsqM1jQx96CGVWQam5rQ2NH3+CmOefw/L7J6C2TQutwYT4YEvGNsLfiwhKm1R6bDvTgFkZ/O0pBpOZo4SfE+s4KPWXiTE7M4LIhi470hWU0lnSILkEE5JJH2NrzGo1Gj76GG0rVsDU1gavzEyEPvwQfCZPdngtA5XzP6z20GfUt2vxFBWQdtCs0uPenw9DZzRhR2EjDpaSK24PTEtxyurFYDbg67yvMfPPmXhs+2P4+dTP2F65HSVtJU4HpOHe4fjvhP+6r5e0rcpSIqZpcXxsb6FtB479Qo7lXEs09bvE4MstIkkdmHSW3lJ3cdBiy8OyQN1Rx4GusaYGZTfcAOXuPcDIO4DF24AAUkYdJTts9mxdqHCsYXzOAGCxId99Uvp8nK1X4NNtXYHWpYID5AEpM7v/2btAaNY24+EtD2NvDanwmhaYhsdGPubW95oVP4uo+tAYNdhRucOt79EfmHU6KLZsRfPPv0CxaRNM7U748PqEWgSPrLH2Kz2zFiAyNAyQ7aD8dNAsYlNVpkLr0qV2X9L4yadQHz0KjLrbYnNlzYEvgeLtaPz0U5iayexVUNq58sFhNwHX/wZM/zcw500IRCzCsmyXVUqSkhDzwftgxANflbyivQJvHngTi1YtwlUrr8LTO5/G8Ybjjl/owWWatc14cc+LxJhMJMOzY5516Tw+Eh+8NP4lPD36ac6+Nw68YVNg7UzzGTy05SHMWzEPv57+lTcgDfIKwhezvsAdQ+4g5mUMw+CKlCuw5qo1uDvrbl5BJppqVTXu3HAntpRvsXmM9/Dh8L2UdBRo+f13aAssyZIIfy8khMg7ryU51Acj4slFXjpAJH7mWgW0BrLqK4fHDoaPa0aQP2NeVRsK6hTn3rOG2Dc3KwIiG9las0qFsttuR/N331n8hFkW2hMnULH4HjT//Avva84HLo6Ugwe38OGWQrRrbTegn6hqx+trT2MfVbYbHSBzqgeuor0Cj21/DKeanVM/4yM9KB3vTn0XQV5uKPOsPgase6qrZ4gRAulzgdmvAoHxPT+/K+x8hxsUZ9vokXIG7yBL6W+hldJk3p+2+65cQdUE5FomdO3lXtA2kwIZoY8+Ct+ZM1Fx370wlHXZ95hVKlTcvRjy8ePglZUNadRj8Gp6AxJBTVeSY98nQMwIrljTBcrE6IlEn5DFGqYOyw574bFZaZCI3B8YljSqcNt3BzuzpKFowVgB9Z1Mn8vzyouHg7UH8fSOp1GvIdUT/aX+eHfqu/AS2Ra86A4hshCMjhhNZM1XF6/GnMResnLqA5Q7dqDmhReJbCQjFsP3kksQeO0iyEaOBMMwMGs00JeVgdVqIQoPhygiAkzUUKDNSiSkbK8lyAOAo9SELHEy4O9gsusfA4RlAPUnYTYyqD0YQOxmRGZEjmpD9b4AgD13MzKZUP3U00hcvhzCKz4GPpsAGDVgWUDbIkbro3eh9TQ5xZJHaiELMkCfOBl/p03Crq3/QpO2CbG+sZiZMgFT2d1QN4nRVkz210pSB+Hss9fii+OvokpRBZlIhozgDFyWdBnSgtIc/Kb7DqPZiK/yvsKXx7+Eke2aKxS2FGJt8Voszl6MB4Y+MKDFB01mE+rUdRAyQgTLgiES2J4mm1kz9lTvwf6a/TCYDRgVPgpTY6dCKHBPD6CZNSO3IRcVigpEyCMwNHQoxFZWaRqjBv/a+i+O3+ejIx5FhLx7Oh0dWdQ3DrzROcaCxTO7noGf1K/TjqpR04iPj36M5YXLwcK2dsjs+Nl4ctSTCJfbFsULkYXg4eEP4+HhD6NN14by9vLO8t0TjSewo2oHoWquN+vx+PbH8cWsLzo96GnCH38cyi1bwep0lgGTCXWvv464b7/l/fxdOTQKh8u65lgbT9ZBrTfy2sMcp/xJ44O9ESh3TghsYkoIwnylqFfoOseWHa7ElcOicaaOXJSab8MikWVZVD/9DLS5/EmiuldfhSQhAT4Tzz/nCU9Q6sEpKprV+P0AqRQ2JTUUFS1qFDd0NY5/v6eU89pHZgxyOHk+3nAcD21+iNPTwIeP2AdpQWlI8EvozB7IRDKMCB+ByTGT7T5EnKZwE/D79YC1ymWHr2bRNuDan7onMuQq6maLAfzuD8jx9HkW/7+eMORqMigt2gy0lnMzlK6y+33ApIPZBDTkkllSSXw8gm+/DYxYjITff0fl/Q9Ac5RUz1Tt2WsxfAcAMJD4hyEkQwG/OI0lOF35MBCe1fOf/zwgyT8JkfJI1Ki6VlBFfsfQ2BCBn/aV4c6JiW59v8Nlzbjrh0NoUXdZCswX7oGAsZp0SHwHVD8pazKBNZn6RB3UaDbi8+Of48vcLzkTMblYji9mfoE4vx5+f2wwN3EuEZTuqtqFJk0TgmW2y7sGKm2r16D6iSdAS9myBgPaV69G++rVluBTJIKhqoo4ThwXh4CR0fA3CSCWWSaqplObofbdDNX2TVBvyIVBbZmQS/2M8L0kHgHt7RD6dd2L2vXtqFRUwlvkjTi/OMtzZNAsoP4k6o/7Qq8gnyFh2Qr4x2ugVwiJVgRDeTmqn3gCUW+9CW30HVAs/wmKKi8Y1TzPIAGLsJx27I8YhBflOlTtf7Vz1/GG41gNIDs2Bi+I6xAVpkdbmQwsK0D15Al4I6MexWfeIE63v3Y/vsv/DpOiJ+GxkY8hOSDZtT+Cm6lV1eLJHU/iaD2PGjIsgc0XuV9AZ9Lh0RGPDqjAVGvUYlfVLqwvXY8dlTs6s30ykQyTYybjziF3cryGGzWNeHLHkzhY22Uo/cupX5Admo33pr7XY9XtvIY8vLDnBZxt7fLQDfIKwh1D7sDC1IVQG9V4bNtjnN/3mIgxWJTWM2GqGwffCIVegU+OfdI5ZjQb8cDmB3Bb5m3Qm/RYWrCUNysKAEJGiJnxM3FLxi3IDs126b39pf7ICs1CVmhW51hZexle2vMSDtUd6hwzmA14ZMsjWDJvCWL9uEkPcXQ0gu+8E42ffto5pt67D4pNm+A3axbn+EuzIvHiynx0aHNqDCZsOV2Pedncns5j53pSO3A2SwoAQgGDBcOi8cWO4s6xFUerOheBO4jw88LIeP4WHeXmzRzfYwKWRfWTTyJp5d8QhTgvENmma0O9uh4B0gCEeveP4i/D2tQ399DfMAyTn5GRkZGfn9/fl4LHlx7Hn4crO7clIgG2PzEVbRoD5n+8G3qjmfd16RG+WPPwJF513g7WlazD87ufh86kI8YZMBgdORpDQ4cizi+uU4kt2Cu4dx9olYcswjoGOz0OQonFdy/Dvr+by7AsUL7PUoJWvBWoPQHQq5ACMXDvLiAsvWfvpVMA/0u3Up0EMO5B4JJXbb/GEQ0FFuEPkw5Np+WoP0b2WUR/9CHxQDDrdKh55hm0r13n8NQ+0RpEjW219GOFpgN3b3FesfM85s0Db+LnUz93bpuNPlAVPgOpyOJ9NnNwOEJ9eyaow7Isft5XhpdXnyS82xiYsdv7CUSZrcqKsq8DrvqiR+/nDvSVVWh47z0oNm8Gq9VCmp6O0Icfhu/03lksKmsvwwu7XyB6OzsI9grGe9Pew7CwYb3y3oCl52vaH9MIQaUnRz2JmzNu7rX3BACVSgUfH4toj1KphFzes++cJi8PpdffABi7Z/vQCcNCFmQAawa0reKuDCYPwsBAhD32KOqnZeHDYx9hV+VOpFSaMDHfjKQWCWLkUQgLCoW5cAdUNWSWWxasR/yMRjACgBX5oixvPDTH82y8k23Ch7dh43hfvO7nBRPL/7wEADHL4u7WNmTr9Pja3w+HZI6z7iKBCHdn3Y27su6CRNj31i07KnfguV3PObWoDACPj3wct2beanO/zqTDsfpjUOgVSA5IRoJfgluf+RWKChxvOI6C5gIUtBTgaP1Rhz2N85Pn4/9G/B9CZCE4WHsQT+54Eo2aRt5jk/yT8P2c7xHoxQ0qjtQdwR8Ff6CgpQB+Ej9Mip6EhWkL4SfpWuzYUr4FT+54kjMn6sBH7AOj2cgRV4uQR+C3y35DiKznSvUsy+L1A6/jt9O/OT74HL5iX1w16CrcMPgGRPk4J9DjLEazES/vfRkrzq4gxjODM/HTpT8RGeQOzGo1iuZeRlRjCENDkLRyJUSB3L/NTV/v7/TlBoA5mRH4/OYRnONmvbsdhfVd86YX5mXgDhcWiM/UKnDJ+/bbLx6YlownLuHO8cwaDYouuwzG6q5nstILOBHPYOwZcq7oM2UKYj7/zOF3Z3/Nfnx67FPi2ZYSkIK7s+7GpYmXuvTdy8zMxMmTJ0+yLJvp+GgunqB0ADNQgtLSRhVmvLsdJit7l7smJuK5eRZhiZ/2luL5v7nXKBQw+H3xWIxK4C+l1Zv0+PjYx/juxHecfUn+SXhlwivEalmf0FAAfHsJoHHCC5IRAPPeB0bYfrg6jckA/foPoVn/M/TVDTAbGQglZoi9TZAGGiD1M6KzpWz2q8D4B3v+ngCw9knggFWAIfYGHjoC+EXafo0t6vKBJTcDzUUwagUoWhMGs6ErQy4bPhzxv/zMucGxZjOavvoaTV99RRrM8yD1NyB2ShPE3mZgyDXAVV+5t7exveZcxrgC8PIH4sZYhFX6cVX/bMtZLFhJKiNraxbA0NrlKxjmK0VahC8GR/ohLdwXGVF+GBTmw9uPwrIsGhQ6VLZq0KY2oEGhw5q8GmwvaOAce3fICfxb+Ro5ePs6IH4859i+RLF5M6qffgZmBbcHL/yZpxF0qxu+k+do1DTip5M/4eeTP/P2tY+PGo9XJ77qlomgI57e+TTWFHcJ8qQHpWPp5fZ7H3uKO4NSs0qFkquuhr6sjBiXxMfDUF0N1mCw8Ur3UBLOoCAayC5hEelE/MQIWSRe0gCpnyWAPjbyRnzcUIH73j8Lb/54geckLMKGtuPHCT74toeLR45I8k/Ci+NedKvIlj3a9e1468Bb+Lvob84+Icvi+nYFvFgW3/n7wWR1DxUwAnw0/SNMjiEFWViWxe9nfsdnxz4jAtwR4SPw3JjnkBKYwnsdBrMBbbo2+Ih97JbOn2k+g/ePvI9dVbtc/VEBWKoh0gLTcLT+qN2SVQDIDsnGN5d803k9LMvi42Mf48vcLznH+kn8cG/Ovbgu7Tr8UfAH3jr4FlGu6gy+El98d8l3bi3nNplNeGbnM1hXan/RWCaS4Y4hd+CWjFt6VXzNZDbhse2PYXM5aY13a8ateHzU47yvaVu9BtWPk/vkkych5oMPoDtbBO3pU4DRCFlODv5W+eCpZV0LThKRAIefmwlfr66At1Gpw8hXNhHnW3bfeE5PqiPmf7wLuZTSrjXbn5iK+GDuvbb+vffR9AW5KPzBfAF2ZzB4YpkZowrJz2XESy8i8DpuW5ZZq0Xt5rXYseZLMMXlCGlj4aMF9GKgMpjBwVQGO4YwmJ46F/+d+F+nleQ9QekFzEAJSh/94xiWH+myg5CJhdj51DSE+Fg+pCzL4n//FODjrV1lJiIBg9euysKikbEoaCnAupJ1ONtyFmaYIRPJIBFIsK9mHxo03InwhOgJ+N+U/0Eu7uMsWFuVJSBtI8uUkbkAuPRtYO1jwEnuwxcTHwWmPQvwrNQ5gjWZ0Pb5f9H82x/QNdr+LjIiM7wCDZDlDIPXnDsgyxoCcVxcz1ePm4qAj0d2eZYClmDvmm8cv1bbDhSstwgQle0GmrvKUWoO+qO1iPz7Jfz+G2RDh9o8nVmthmLrVmhPnoSuoBDa06dgauCuRItkJsROaYJXgBEYehMw7z1A1MPsQFMRsOtd4PgSwExNjMOzgOnPAWn9179367pbiVVMs9EH6uJHwJpsS+NLRQKkR/phSJQf0iN80a414lBpMw6VtUBhpze8g5mD/PGl4kEIWrtUCRGRBdyzs9+CdJZl0fTFF2h4/wPbBzEMYr/+Cj4Tut9Pc7blLLZUbMH2yu3Ia8jjnYCKGBEeGv4Qbsu8rc+sp/ZU78E9G+8hxv68/M9e7St0Z1Ba8/zzaF36JzEWeNNNCP/3szC1tqJ1xQpU/PwNvKrJRUEzAKMIkPQwueoqEaNaEZhsyaBt9PXHk6FBMLImpFWwePYPE2R2tPfEPkb4xmjhk6jGa1kJWCnkHjwsbBgmx0zGisIVKFeU85yFJFurw1yVCg1CIVb4+qBZyN+7uCh1Ef414l92rTN6gtqgxh9n/sB3+d9xbEgAINpgxFsNjcjWWX7mlT5y/DuULDP3Ecvx89xfOsuONUYNXtz9os0ASCQQ4elRT2NR2qLO515pWyk+O/4ZNpdvhs6kg4gRYUzkGNw39D7khOZ0vpZlWSwrXIbX97/utGCiKwRIAyBgBJzfxZyEOXhz8pswmA14fvfzWFfiuCKID1+xLxQG2yJYIbIQfDHrC6QGur+lxWQ24b3D7+GHkz9w9gkYAa5IvgIPDnuwx+XKzqI2qHHt6mtR2l5KjH8y4xPOIgdg+dtX3HMPVDtIr2AIBIRnOgBIx0/AwqDZaBJ13ePeXZSDq4Z39aSvOl6Nh37rKpn2lghx7IXZkIgEYFkWxpoaGJuaIY4IhyjUdgns6txqPPgrf6n7tLRQfHf7aM64rqQExfOvAKwW707EM3j5egHAMPBVs/jf1yYEWFmxslIJklf8BWmSJZPLGgxoXbYcNR9/CKbRfvKlyRf4co4A7Ljh+HjGx075xHqC0guYgRCUnqhqw/yPd8EqSYp7piThmUsHc47NrWzFjoIGmFlgfk4UIgNFeH3/6xyJcXssSl2Ep8c8DbGgjxUGFbXAD/OBxjPkePIM4PrfLUGP2QSsfRw49C1YFtC3i2BQCSEQsZCmDoJw5uNA6iVOl5Rqti1D7cv/hbba2SV3EoG/P2SZmZAOTocoJBSioEAIg4IgDAyy3BCd7SVY+TDhKQrAEoSPseFN1lwM7P8SOPozadtyDkWVFJU7yQmI32WXIfp/73COtQfLslBs3Ija51+AqY1cURSIzYid1AzvML0lUJrzBpDgwAJIUWdR6VTWA2AtwlVGLVC0FShYRwbmfGRcYfm9+NoWbOgt9tfsx13/3EWMmbQR0NZcA7PWsWKhq9w7OR5Paj6AIO8PcsfV3wBZ17j9/RzBGo1QHzqMxi8+J/znbCEMCUHy6lUQBgQ4/R56kx4rClfgz8I/cbr5tN1jo32i8ebkN4mJb19gMpswe9ls1Ku7BJYWpCzAyxNe7rX3tBeUqg1qrC5ejTXFa1DSVgKtSYs43zhMjJ6Ia1KvQYxv12ez/Z9/UPXwI8S5ZTk5luoJkQiNmkY8v/t57KrciUHVQFQTC6MQqA5iUB0M6MRASjUw47gZE06y8KLWjhgBC1mIHvJwHWQherBhQ1FVEwPzDsefFw4Mi/Ch7Z1KuZu8ZXgiPAxGq8WJ6EYWt24yI7uE7bQxKA0D9qcJcCJTjqHDpiMWIqxsPIoCJTfg7BD9ETAC6Ew6fHL0E3yf/z3vAkik0YjHmlowW61Bx3KQmmHwRYAffqCykB2EycLw7JhnMSO+G17WNlDqlfjt9G/48eSPaNW18h4zVaXGK41N8DeTP8eX/n74KCiAGIuRR+HbOd/DyBrx6Mb7cVpR6vAaLk2Yg3nJl2N7xXYsL1xOCCpZsyBlAR4f9ThEjAiv7HsFq4pX2T2vgBFgRPgIzEmY0+kB/PfZv/HNiW+gMqhsvm5C1AS8Pul1aI1a3LL+Fo7o0Iy4GWhQNyC3kV+UxhELUhbghXEvIL8pH1/lfoUdlTs6PyNCRohLEy/FYyMf6/VKjVNNp7DkzBIUtBRAIpQgJzQHV6ZciUR/9+oaOMPp5tO4cc2NxAKDn8QPSy9fyls2bKivR8n8KywqtQ5oCI3BA6PuhuLcPI4OEJ9ZnovfrPRVpqaF4vvbR0OxaRPq338f+rNdqvXimBjIx42F99ixkI8dC1Fw17zIbGax8Iu9hLgSYKkwXPXgRGREkXocLMui/NbboD7QpYRvFABP3ClEVQgDf6k/ouRRkB7Mx7N/kHMZJi0ZKd//BNWuXWj48CMYKqjEiwOWTmRw+LIUfDH7S4cCWp6g9AKmv4NSrcGEaz7fgxNVXTL93hIhvl0chW3VaztvTsPDhmN+8nziw9qoacQjWx5x+kYsYkR4evTTuDb9Wrf/HA5pPAv8upDI9AEAokcAt6zsNEEHLKWmba/egca/d8OgtBKzYFjIgvXwidbDNyMMkkFpYIKTgIB4IDDBotYbEAcIJTCd3Ir6N/6D1kMNAHov4ySOjoZ8/DjIJ06CfPw4CH1trJoraoGPR1M2CgAmPwHz6IdhqK2HsakJpjN7YMpdC3PFSZj0DMwmBowAEIjMEEpYCCVmGFRCNJ70BWuykn2XyZC8bi3EEd1TA9RXVqJi8T3QF5N/H+accEjgIJWltDkyx2InM+Sarr+ZXgWcXGkJoMu6V7JF4BVg6bkdemOfZwsf2vwQtlVu44wbVYnQN0+GSZmGnrp8hfpK8b8Zvphc8BpQTL1XzGjgzn/67Oc2KVVoX70Kqt27odp/AGYbdiF+c+dCFBqC5h9+JMb9r74KUa867o82mU1YWbQSnx3/jBCU4kPICHHj4Btx/9D7+76S4xwfHf2IKAEUCURYd9U6p9Q2GzWN2FezD1WKKsjFcmSHZiMzONOuWihfUFrYUoglZ5ZgdfFqmxN2ISPE3MS5uDfnXkQ0m1FyzUKiPF/g7Y3Ev1aAiY7EquJVeP/w+7w9iYODBuOKlCvw66lfO7OJMh2L9AoWNzWKMbK9DlI/I2TBBghEXfOZ/Gu+wH0nPkXsqSbctsmMmCbOqaGPCcWq+Ga0y1j4qVn4aoB2OYPpsUZMYC3BxWZvGR6nAlLAYj2mNCiBdiUClZasgsbL8XdDyAjx4rgXsWDQAs6+/KZ8/JD/Aw7WHoRCr0CCJBCXNtfhhrpyyGzM1U5LxHgpJAj5Uv7yuksSLsHzY593KsthixZtC349/St+OfULFDwLkQAgN5vxRFMLrlKqup5qg+cDkx4FTq8Fu+NtPBUahHU+5PfGmxHDzBqh5QnGfcxmKHvQntHxHeX7jPpL/ZETmoO0wDSkBqZipHcMQlRN5yqlGMAnHIgYgkaRCO8ffp9ToiwTyXB/zv24JfMWCDStgLIOhbpm3LzzMbtBLGD5DNyccTOqlFXYWGZbsOa+nPtwX859REVUraoW+Y35EDAC5ITluMdp4Dzkt9O/4bX9ZGvJkOAh+HbOt5CJZJzj1UeOoPyuu8Gq7fcOA8ChsDS8MO5OsIwAIgGDQ8/NRIC3BGYzi4lvbkF1W1cv73Nz0zH/0Eo0fckty6aRpqbCd9YsBN50I0SBgahq1eDmr/ejuNHyeREJGLxxdTbHNgYAWpb8gdoXSeufv8cw+GW6EHKxHD9f+jMS/RPx5I4nEfvlOlxyxL2x3Y5MBiuuicSnl36FpIAkm8d5gtILmP4KShVaAwrqFHh3YwF2n7V6kjMGjB6xC6dUGzivEQvEuHrQ1bgr6y5UKivx5I4nidV8ewwPG46nRj+FjOAMd/0IztFWBRz5EdjzIVfUKHgQcMcGQN61smVsakL1M89wy0B4EMuN8A7TQ+xtgtjbBJG3CQIhC1WdF5oLvWHW8z9ohTIGXpkZEEWnwNjSDF3hWRhr7E+UnUIkgiwrC9KUFEgSEyFJSLD8i42x+N6dWA78eTuMWgFUtVKoaqVQN0hgUPVcyTj82WcRdEvPxFiMLS28Sr0AIPEzIHiwEv7xGktwKpZbekGFUktZsc4J70NrRF5gEybBVFEAtrkSjJCFUGomY7HEyZZ+4mAexUt1s2ViY9QBPmEWT0U3WAQ0a5tx/errUa3i908LEMUh2ngzKmvDUNnCr4pojUQkQLBcAn+ZGKG+UlweZ8CVqj8gyfuNVJ0GLL/Tu7f0XFzLCcx6PVp//x2Nn3/B8XgkYBiEPfWkpX+UZVFx772c72bcd99CPm4c78sNZgO2lG/BZ8c+Q1FbEe8xHXiLvDErfhbuzLrTpcyAobYWqt27oTtbBFN7GxiJBKLQUEiTkiEbNgzicNdL3ho1jZizbA4hgrIodRGeH/e8zddUKirx2fHPsLp4NadXLVQWirmJczEveR7SAtOICbCZNaOmuQYxIZZJ0lu73sKhxgOoqTiFkHYgpJ1FSDsQpGDhqwYMIqDRj8HpGOBsFAOdhEFEmwCv/CmGXz05US9/aP7/t3fe8XEU1wP/vjt1ybLl3rDlbowx2BgwJdhUBwIEQu8lhST8IIRAIKEnQIDQAoTQMR0CoRfTTW+2cbfBvXfLVpeuvN8fs2edTnfSqVhny+/7+exnpZnd2bezs3vzZt68x5cjs/hw2YdsiR0Q8zhvt/O4aORFpPvTWVW6itPeOq2WiWS6pPHUypXsVlXb2uSrPX/BJWVztjqv8YeUvRYo+63I5YAOo+jUfyjtDj6YrBEjeGPRG1z1+VW1zs/2Z/N498P4YfNC/l4+j2BMnZ0y5BSu3OdKguEgHyz7gKfnPM3sjQ3/Tmf5s7h97O2M3WVsg8duJRyCZV+5QaKiJVCx2Q22VZfApiVQXUIQeDa/HfcVtKcijhLXPbc7v9/j9wzpOISlxUuZu246P66ZwprKTbRLz2FQ590Z1/+njOkxppajpLVla3li9hO89ON/qUjgdAdgbHkFV23YRI9QlAfRg6+Cgy6vGcT6YSKVL53HuV3aJVSgI+SFw9y6bgP7VFZxX0F7nmjfcKzrxnBmz7FcnDOQ7I2LYN1ctyVQtumyK+x2HMsLx/BFxWo2VRXRPbc74/IH03HumzD7VVhfEzLri867cGE7H6EEa05z03O5feztW2NQT1s3jdsn314rjmthfiGX7315XHNUw6GqXPbJZby39L1a6WN6jOHeQ+6Nu7a4YvZs1vztb1ROr5ksyejbl1BZGaENtZcKPbzb0bw8aBwAt/xid07dpw/fLdnESQ/Ujks9sfMC9JEHGiW7v317etzyD9odfDAV1c7L78rN5Ywb0pXB3epOHlRMm8bSs89Bq2t+l9e1hz/9yk9VhnD3uLu3WkQEQgEumfg7jr/pC3ol4RplXm/YsFtPjhz7awp69CVUVETRC/+l/Jtv4h77wKkduOqnt9ZdDx4OUzJ1MruPP5xlm4pNKU0WEckC/gKcBvQBNgETgWtVdUV958YpqwNwPXA80B1YA7wKXKeqm1tA1lZRSisDId6dvYZ3Zq5h5sotrNxct0MracV0KHyWYPqSJl0jOy2bkwefTEFWAWWBMiqCFXTL6cYBvQ5gUMGgZt5BElQUwZqZzpvtmpmwejqsS1CvHfvDOW/Uim1X+vkXrLryyjofrpYioz10veB08s66sk5w9OCGDVTMmkXlzFlUzJpJ5cxZ9XfWG4PfT0bv3qT36kVwyRyqVm1umXI98n/2M3re/s8W8ZwYrqxk5Z8uo/TDD+Pmp+UE6TS0jA79y2vNmCSDKlRs6UBpcBSlSwNUL1lWy+mKLy1MRn6QzPZBsgqqyelSTWanNOSAC6HP/q59Lf0cXTiJwIrlBMv9qIIvTUnvmI1/yIHIiJNgyFGQ3vQYlkuLl3LxRxezaMuiuPl+8XPBiAs4aeA5zFtdzqxVW5i1cgvLNpWTm5HGgK657NuvE3t189G9ehm+LctcR3flFJj/HoTjmML50uHkJ7d5bFINBtny2uus//d9tTwLxiOtWzd63HRTrThsgTVrWPSzowmX1Sg/6bvsQv/XX8OX7UbOt1Rt4avVX/Ht6m/5aNlHbKyMM33m0TW7K4f2PZRxvccxuvvopLyahqurqZgyhdLPPqfss8+omj+/3uMzd92V/CMOp90RR5A5IPmQHjd+fSMv/PDC1v8F4bHxjzG6++hax5UFynh4xsM8OedJArFrpaPIrVDyy6FHXk+y8wvYRBnrtJjSis30WBbkzRtcp/vNvQfTp9RHWhI+WEICawqgyxbIqB3tgM92E+49xpdw1r1zdmduOuAm9u9V26HWFyu/4Lcf/LZWWtf0djy3fCVdKzaD+Hhn95/x17LZBGPa8vBOw7n/sPvjekT997R/88D05DqWJw8+mavGXFVrHbGqMnXdVJ6a8xQfLfsorgnuwA4DufnAm+uEFmkWwWqY9gx8fDOUrWNlmp+/d+rIFzl1Z4qSIVfSOLDjcAYUDObHDbOYtHlunRniaMaVlfObzcXsHtVZJqMdHP8A7Hp03RNWTmXd8yfzy/w0lmTEX6LTvzrAXcVB+u95NmQXwMyX+HDLD1zdpVPcWdOMsHJmcTEnlJTxcU42/+nYkTKJL3OepHH9phLGb67ryyIpOg2CHiPcgPaKbxMu+ZiYm8M1nTtRGRN1oFdeL/518L/qrAFXVRYXL2Z16Wo6ZnVkcMHgFot12pYprS7l1LdOZWlxbcdpwzoN49ox17Jb5/h6UWDlSoIbN5LeqxdpnToRWLmSJaeeRnB9TbsIiJ9Lxl7Mog69OGBgJ5751RiufnUmT39dY4p/cukPnPfBw02Wv/OFF9L5/xLH7VVVit98izXXX1/rdw3gHyf5+H6gj/OGn8ele11aK688UM5fHj+dM+7/gXa1HTRv5cee8NxYHyN+egaX7315rSVzqkrRU0+x9pZb66y7XdceHh7vI7j3cI7sfTjDV/rJ/mI6oUlfklZUwjGLF7GwutqU0mTwFNIPgf2B1cBnQCGwD7Ae2E9V6x8yrymrE/AVMAhYBEwGdvO2BcAYVU3c40nuGttUKVVV3pixmpvemsPa4sSjoP7sJeT0fhbSGjnj5NErrxf3HHLPNlmEnxBVWDcHfnjHOeNZMZk6oVXi0XUYnPUKtHOmcOGqKtbdcQdFTz4V93B/x46Ei7egwVDc/IYQv9L55/vS8ap78eUmNxocWUxfMXMWlbNmEVi5guCmIkKbNhEs2kRoUxGEmiZPS9LhlFPofs3VSFrLhUPWUIi1t9xK0VPxnweAPzNE+8IK8vtUkNEuiCqEq32Eqn2EwrmEMnrgzFzDhKrCVBXnUDK/lNCW+k2uYvGlh8nu7GbDQ1U+qkvTqC5Jq2W6HH1sVkGA7G4+svc9iOzjLyZtwJ6Nu3mP8kA5z857lufmPse6ivjWCKO6juLWg251Jp2qsHEBLP/G276F9fWvmdxKux5wwiMNr9dtBuHKSkrefZcNDzxI9eLF9R67uBt8sXsaHHsE44b8lNHdRtdSMoqee441N9ReX5l+0rF8ddpwJi2fxJS1Uwhp/e/GqK6juGDEBezbY98GO4bh8nIqZs2iYtp0KqZMoezbb9GKhmep45ExcAD5R4yn3fjxZA4eVO9AzurS1Rz76rG1QkIUZBbwr0P+xciuI1lZupJ3Fr/D03Oejqt451Yoey5SRi5Uhi13s51x7w+oDIcZPf9HACYPGkxOM71dL+0CV5/tRvjjcWifQ7luv+viKo8Ad0y+gwmzJ9RK653bk3N2OYyppct5Z8XHdc4Z02MMdx98d0KTa1Xlr5//lTcXvVmv7CcOPpFrxlxTr2Or5cXLmbhkIjM3zKS4utjNqvUex2F9D2uZGNrxqCyGT2+Dr/+DhoO8nZvDzZ0KKE7gCKm5HFZWzm82b2HX6piBju67w0lPxLcgiVC0hOIXzuDG0Opaprx+VY4rr+bPQ84k58A/QqY3Y6QKs19h/cd/4zEt4tOcbNb40+geCnJEWTmnFJfSPer3brXfzz87FfBBTk6tKEH7l1dw9cYidmluKKIkWZSexuPt85mVk0dO+z4cNPBYzhp2Vo132mCVc7BXVeyU29yuzuv9ThDmrCWZXzSfcyeeS3F13Y/YwA4DGdt7LIf3PZxhnYbV+00t++orlp3/y1oxkZe168rFYy+hOj2DJ8/fhwuemkJ5tWtrQzYt5Y6vHsQfiBqQEaH7tdfQ4eSTqZo/n7Kvvqbs66+o+G4y4QRmw/lHHUWPm2/Cl1UzWK3BIMUT32Xjo49SNXdunXPe2Ut4/Ag/+3TfhwcPfzDud2VT5SYue/YsDn9xMXsurrmnBT3gfwf4mDM0h6v3u4ZjByQOa1gyaRIr/3hp3N+0ynTwhesOOJpS2ghE5G/ANThl8ghVLfXSLwXuAD5V1aTsakTkSeAs4GXgFFW32l5E7gEuAp5U1WbFJdiWSun8tSVc+9psvloUT28OI2ml+DLWk5Y/nYwOk0Fqj5Zkp2VzypBTKKku4fWFrycchd+3+77cNva2bbfuQdU5q6kud2siV89wJps/TIQtDXszrMXoX8IRN0JGDqHSUorffIsNDz0Yd9bG36UzPW+8kdyDDkKrqij/5htKPniP0kmTCK5Pbhaz3V796Hbj3aT3a1llXcNhquYvoOzLLyn77DPKv/uu2aEWfOlh/Jlh/LkZ+Lv1wd9zMJLXDgJBwuVlhLYUO2dE4TBZw4bR4ZSTyRm17cISlE+Zwpq/30jVvCSVq+2QjE4ZZI8cRdZ+h5E5YCAZ/fuR1qVL0rPKgVCAtxa/xYRZE+Kan+b7s/lbZiGHLp4C5Y2c4fdnwj6/hnFX1nQOm4AGgwQ3biS4bj3B9fG36oULE/5gA0wZIHy1qzCrr7Apv27ddMzqSJ92feiT34euWV3Y76a3aDe3ttHLW3sLLx7oozzemj9VMgMwPHsAvxl4FiPzdkUrKwhXuE29fbi8gnBFOaFNRVQvX0Zg2XKqly/fJgNAGYWFtBs/nvzxR5C5665x28Tjsx7nzil31knPTsveGtQ+s1rZdbnSZ71zHNS9SOlRJBSUJv+7X96CSumKTvC30/1szqt7P6O6juKCPS5gvx771fsOBEIBfvP+b5i8dnJS1xxfOJ6bD7y5wZnu6lA1F7x/QcJyz9vtPC7Z65JW87TcJNb/CBOvhIUfssbv5+ounfgmiTinyeBX5ajScs7fUszA2N+TToNcmLI9zwR/Eop3OARzXmPV3FeZWbIUf2Yee/Y9lM6jzoWcBH2FUABm/BemPwdLv4QGBpdWpfmZnpmJAkOrq+kfSEIZTc9xllLicz4momN4N5eO/aHHnq6/snGBU0jj3UN6DuR0dvWQngNpmZDdAbrtBrvsC7uMSc7bvCpsXuaswtbMdMpvKOCuKb7amy8Ncjs7xTi3s7t+bifI6QQZeSkNiZYMszfO5tfv/rpeD8U9c3tyeN/DOWbAMQm9la+74w42PvxIrbS3Csdw3x4n1KqDbmUbuevTeymoqt0+ul55BQXnnE1IQ/jwbR3U1ECAihkz2PjY43EtvbL2GEGv22/Hl51N8TsT2fTEEwRWxDfc/L6/cNuJPjrldeOFY16o18FVWaCMW769hU+nv07nohAb8mFTvnBgrwO5Yu8rKGxfmPDcCJXz5rHst78ltGZtg8eCKaVJIyLpwDqgAzBKVb+PyZ8OjABGq+qUBsrqDqwEQsAuqro2Ki8TWA50BHpF5zVB5hZXSjeUVnHPh/N55ptlhMJBfJlr8Gcvx5+9HF/WCnzpmxF//d5gY2c+15St4eEZD/Pygpe3mkwVZBZw7vBzOWfYOQ2boYTDrtNcsgbKN7oYoRVFbu1M5Rb3Ma3c4kaDI/9XlUBVKQTKtprQqIKGhFBACAeEcNCHBoVwUFDcN0VEweec5IgAWXnIgIMI9z+G6vJ0AsuWUTFtGuVTv0cr49s95B70E3r+4x+1PKlFUFWq5s2j7MuvCKxcQWD1GgJr1hBctZJwVRXpXTuRu//+dDj1DLKGbvv1eeBmc8q/+47KH3+keskSqhcvoXrJkoQmwL6cHHL22YfcPQeR06WajLwAvvZd3DrNHntuNz9QGg5T+sknbHzo4bhrTXdEfO3yyOjfn8x+/ckY0J/M/v3J6N+fjL59kQTKQDAc5JGZj/DA9AfizgKeUlzCxUWbyQ8n8a1Py4bR58H+F0N+DzQQ2KqchcvL3Wz8unVOoVy3joD3d7ikFA0G0WAAAkE0FCJcUeHaWBN/Y+bsAs+O8/Nj78a1t54blX8+GiI9pirCwKZ8qEqD9JALL5IRhKxq8G3Dn0Fffj65++5LWvfuaGUFgZUrqZgxs8GYvBHSd9mF/PFHkDduHFnDh28dUQ+Gg1z00UV1Yi4WlCijFiijFyi7L9Fmh1FJpJRKejppPXqQ3r07ad27kdapM+HKCipnzqJy7txaynrIJ3w2MoPHDwpSle0nNy2X3u160699P4Z2HMrhfQ+v5am3IYoqizjz7TMbDKNy6pBTuXKfK5M2hSwPlHPbd7fx2oLXtnp17ZXXiz+M+gNH9jsyaflSiir88DZM/Au6eSmfZmfxQn47ZmRmUOrzURAKM7S6mqGBEP0y2rO+uphPM/18n5mJxvm2Z4bDHFdaxnnVGfTqe6CznsjqAHldnBLTaSB0HtS6vwvBKqc0Fq9y3tTXzXZ+EYpXNnxuhJ4jnWVU58FO4eu6K+T3qrmPcNiZ6M5+Fea+Hr/sjHYw4mS39RwFG36EL++FGS+QlGVWU8jIcz4NBh4Gu+zjnkE44PpM63/wlidNc8poRRLBeBvCl+7MqHM6Qm4X5wCqXXfnLyGvW9S+G2R3rBs3XNX10epssenq6j4ty22NHPyaXzSfSyddWidUTDyGFAzh4D4HM7hgMLnpueSk5ZCXnkeeZFF23kVUz6092P3fQQfzxLAjCYuPwUXLuOabCXSurD0zu+KQYTz5s2xmb5pDIBxAEDpld6JrTle65nSlW043umZ3YfAHC+j66FtIMr/HMXy6m/DgUT5ycwt46PCHkl4KsK583daQcsM7Da/51laXQ+kaKNvo/KoEK90+UOmeRVYHyOlIsNLPimv+ScW06Ykv4vHTZYtYVmFKaYOIyMHAR8BCVa0TgVlErgH+Btygqtc3UNZ5wGPAh6p6WJz8R4HzgfNUdUIzZJ49bNiwYVMnf0cwHECDSlADaDBIKBwiFA4RDgUJhcJuHw4R9rZQKISGggSCITaVVrBw4yamr1zO7HXLCadtQDPXQeZ60jSILwz+mM0XhrSwkhGArABkBlznbYgWcET6LqRXVBMuq3Dr5rIy8WVnEkj3USTV+DPS6SwZ+Cor0IpywtVVaHUADQTQ6iDhQAg0hBCGcDUSqsR1GQEFVfH2oGHZmiYAnjKpilM8Az7CASEUdPta9jotjGRm0vWyyyg484wWWSOZakJbtjgldckSAmvW4m/fnsxBA8keMaLOutbtnfLJk9nw8MOUffJps8rJHDSIvEMOIfeA/cno2xdfbi7h4mKqFi2masF8qubOo3zq1AZdqvtycpCMDEIlJS0ykyZZmWQNLCRryCAy+vbB374dPl8QLdsEm5aiq2ezdMMi3srIJRASsqrde5tV5fa5VUrXyjDtqpV0TwkTAUnzI/508Keh/kzn5Kk6gFZUQmUVtJKpWzSLurm1LouG5PO7kb/ngJ4HsLxkOW8vfpv3l75f79rICD+ZGeaiNxsXfL7FECFr993JO/BAcn9yINm7717HhD0S4qbkvfcoef/9WmuZ6iUtjayhQ8nec08y+hUSzMvm8XlPsGb1AnptVHZdpvRv8jBofMpVGf2jC5W1fMITdNpzD9J79sTfqVPCgZKIWXNg1Sr8eXlk77UXaQUFBMNB/OJvke/nhooNXPzRxczcMLNOXm56LlfsfQXHDTyuSdcqqS5hweYFZKdlM6jDoB1zfV+wyimnc16DtXPcrF+7Hk6RGXAI9N3fmYqGgrDqezb88AYfLf2AyZVrqRDIlzRG5vbmiMLx5A8+yilu2/PvXigAM1+Ez+6EjQnWcmcXOM/pe50Hnet0AxMTDsOq793yh9K1rt56jqypw1hWTXPh41Z816Rb2WERv3PqF61sNhV/hhskTc+C9GznbC89GzJy3N8ZOW4m2ZdGpMNYqSGeq1rBhMrlbNKmWYf13Kjc+liIzJifvrV5eWzOzGXQxrV1fNxPLxRuOdlHyJ/c+7HHwjB/fDVMTpIhc1cVwHPjfHwz1EfvtHbc22l/Boal7mRN5RY3YZOW5SycMvNq6jAt2z2PyMRP2Xp3fJKowualHdg4L4fA5tp5C7vDjCF+ygel89Sdc1m2usqU0oYQkUuAu4AXVfXkOPk/A94EXlXVur7aax97N/AH4J+q+uc4+RcC9wF3q+ofmyHz7AEZGcPe6JfY/bLROmSP3ose111H5qBWcMpkNJmqRYvY8sqrFL/7LoFltWdRJCMDf4cO+NvnQ3q6C1Wank5G795k7TaMdoceSkZhYVLXCaxdS8WUKVTOnUu4vAJfbi7pvXuRUVhIZr9++Dt3RkTQYJDA6tVU/fgjFV9/QsXXH1OxeD0a3I47dykgDMzuK7wzWpg8SBjf76dcuc+VdUyTiiqLeHPRm3y+8nOmrp1aa01lLIdMC/PL98J1ZkxbHL+frCFDyN5zD7JH7UXuAfuTVhB/PWQ8NBymYto0St59j+L33msZb9tJIJmZ5O63H3ljDyJn9GjS+/QBnEIZMVnWQIDqgo607+5i88bGKU01wXCQtxe/zcTFE1lespz2me0Zt8s4jht43DaP29hmCQVcD9Sfvn0roYkIh9zM5tSnYMN8Z07cbTcYdhwMPbpZjuYaJ0fYzZh+fqebQU1EZr6r70Tef41GEwJmZmYwKSeb93NzWNbIQfYDZ4X5vzfCSQVYW9oFrj3Tn1Q4qGh2Wa9c8WKIrvXohfN7wGv7+fhukJCFclJJKRcWbSEnxXpbdYmfQLkfEcjsEMCfUSPPbveXMmd92JTShhCRO4E/Anep6qVx8vcApgFTVXWvBsp6Gedx9w+qek+c/J/jvPC+rKonJCFbIvvcoekivj472KxVm0EEX04O/vbt8eXkpFoao7Goot4spfj9208HKxxGSzcRLi1GAyE0DGHPIqAtE/K5Leyr+TvkE0I+F0ok5IMMfwadszvHjTMXi6IEw0ECoQDV4WqC4SAhdRYkAH6fn6yQn6ySKqioTN6MWAQRH/gExId4e3ziZgbFh6SnIenpbsvMbNG2pZWVhMvKCJWWNnstOOC+Y5mZSEaG2yJypyendITDYeZ5a7eHDh2Kr5mOjgxjpyNiFhkOAeIUfn+GWy8aWaOsYef9PBxy+2iz1nAAAhWunMYifjdz5vf6kdHvfOSbqAoajLp+iLb0g1QtQqlPKPH5CCYZGz63kgbX3lelw8Z8IdzEz79PoX2ZklMFEYfRKlCRAWVZQlU6ZKiSHw7TLpyckpxqFm4KUxWiRFWbFMdpG7mC2y7J8/aJPGqUxRzXWmXVRzigWrawurp+W8Htk4j7vaS8GW+3VFVBUQusy2gd2kad71hYnbcgVVRRQoMzBlbnjaWqfj8BSTAAYN68eVbnrYe189anDdZ5GGiBwa1tx3ZZ51W4eJH1Uk1Nb7+JxPXVXgWRn8GoP1uSbVnnu5BYN2qQnUkpjYxlJBr6aMxYR0uWRVOnubdnIrO/bfHetleszlsfq/PWx+q89bE6b32szlsfq/PWx+q89dme63xHmA1uKSKDDYkWxETsM5NxidiSZRmGYRiGYRiGYey07ExKacTrSSK/871jjmutsgzDMAzDMAzDMHZadialNBJgZ1SC/Ej6jFYuyzAMwzAMwzAMY6dlZ1JKvwC2AANEZGSc/BO9/ZtJlDURt3r8JyLSNTpDRDKBY7z8d5ourmEYhmEYhmEYRttnp1FKVbUaFzsU4D4R2boeVEQuBUYAn6vqd1Hp/yci80TkHzFlrQaeAzKA+0Uk2mHUbUAX4FlVXbNt7sYwDMMwDMMwDKNtsNPEKQUQkSxgErAvsBr4DOjr/b8RGKOqC6KOvx64DnhCVc+NKasz8DXOtfJCYDKwGzDc+3+Mqm7YpjdkGIZhGIZhGIaxg7PTzJQCqGolcDDwd1wcneOAQuAJYGS0QppEWRuAvYF7cTOmxwPtcbOx+5hCahiGYRiGYRiG0TA71UypYRiGYRiGYRiGsX2xU82UGoZhGIZhGIZhGNsXppQahmEYhmEYhmEYKcOUUsMwDMMwDMMwDCNlmFJqGIZhGIZhGIZhpAxTSg3DMAzDMAzDMIyUYUqpYRiGYRiGYRiGkTJMKTVaDRHJEpG/ish0ESkTkUoRmS8i94hI91TL15YRkRNF5D0R2eDV+zIReVlEDky1bG0dEblWRNTbTk21PG0NERkqIleIyIdeu64SkTVe+/5JquXbkfG+2TeIyI/ed2OViDwmIr1TLVtbQ0RyROQ4EXlURGaISLH3Oznd+4bkpVrGto6IdBSRdd63el6q5WnriEh3EbnL+75UiMgmEZkiIrelWra2iIiMEZH/eb+PAa++PxSRE1MtWwSLU2q0CiKSBXwC7ANsAr4Cqr3/ewFrgP1UdUmqZGyLiIgfeBI4HSgDPgc2A32AvYC/q+qNKROwjSMiQ4DpQAYgwGmq+nxqpWpbiMgK3DekGPgGKAKGAcMBBS5V1btTJuAOivfN/hDYH1gNfAYU4r7Z63Hf64UpE7CNISK/Ah72/p0NzAHycfXfDpgHjFXVdamRsO0jIhOAs3Hf6h9UdWhqJWq7iMh+wNtAB1xbn4Vr58OA3qqaljrp2h4ichLwPG4ycjKwEOgJHOCl3aqqV6ZOQoc9dKO1+A2uM/MNcISqFgOISCbwFHAScANwTsokbJvcjFNI3wbOUtVNkQwRKQA6p0qwto6ICPAQbhDga+DnKRWo7TIHuBz4n6pWRxJF5ALgAeB2EXlPVeekSsAdlL/iFKKvcN/sUgARuRS4A3gMGJs68doc1cB/gLtUdX4kUUR6AG8BI4G7cd9zo4URkUNx/Y+HcP0VYxshIj1xfZJM4Beq+kpM/j4pEayNIiJpwL9xyuepqvpCVN5+wEfAn0Xk4VQPNNpMqdEqiMhLwAnEvBBe3p7A98BcVR2WAvHaJCIyCJgLrAR2VdXyFIu0UyEiv8Z1cM4EDsd1eGymtBURkXeBI4DrVfWGVMuzoyAi6cA63CzGKFX9PiZ/OjACGK2qU1pfwp0Lr+P4JVAF5EcPvhjNR0SygRm4gYHjgB+xmdJthog8CZwFXKSq96VanraOiAwHZgLzVHXXOPmv4gbNT1HV/7ayeLWwNaVGa1GVxDGbGj7EaAS/AvzAA6aQti7eGunbgA9V9ZlUy7MTM93b90ypFDseB+IU0oWxCqnHS97+mFaTaOcm0o4zgU6pFKSNch0wAPgdEEixLG0az0LrZGAL8EiKxdlZSKb/DdtBH9yUUqO1eN/bXyIi+ZFEEcnAmYkBPNHqUrVtDvX274tIP8/J1IMi8g8ROSylkrV97gGycZ0cI3X09/ZrUirFjsce3n5qgvypMccZ25ZIOw6wHXQc2xIiMgL4E/C4qn6aanl2Ag7ADa58DgQ8J4x3i8i/ReQiEemWYvnaIou8baiInByd4VlhjAcWAylv/7am1GgtngKOwq0dXSwiX+J+YPfBLW6/SlUfrud8o/Hs5u33xa0By4zKu1JEPgBOiKzvNVoGETka186vi14bZrQuIjIAONr79/VUyrID0sfbr0iQvyLmOGPb8gdvP1FVk531MBpARHw451KbgT+nVpqdhki/ZC3Oedp+Mfn/EJHzVPXF1hWr7aKqIRE5F3gDeEFELsc5OuqBs4r5FudzJOXLAmym1GgVVDUEnAbcDnTEdRaPx3nNnIYbNTNaCM9zZpb37904z8cjcN4cD8eNih2GW/NotBBe2Ib7cWuSbk2xODstnmOHCbiBmBds3WOjiYQfSWT2XxZznLGNEJGjgF/iBnGvSbE4bY2LcAPjl6vqxlQLs5NQ4O3PxvVJfgl0AfoBdwK5wNPeDLbRQqjqZzjHdIuB0cApwEG4b/kHwKrUSVeDzZQaSeE5KhreyNPOVtVvvfMLgFeAvXGjvv/DdXgOAu4FPhSRk1T11RYTegenmXXuj0pbCRwTNQr2gYj8HDcYcLKIXGMzeo7mtnOct+NdgENtRiM5WqDO43EvbgR4EfD7psq2EyPePpEnREmQbrQgIrIr8DSuvi9X1ekNnGIkiYjsAtwIfKKqE1Iszs5EpG+SBlyoqo95/28A/iQifYATcTPXZ6ZAvjaJiJwGPI6LBHAqLuxUT+Ay4GrgUBEZq6opXVNtSqmRLIXAkEaekxP19124UZpLVPWeqPTXRGQlLlTMv0TkTVUNNkvStkMhTaxzVS0TkTDOGuLpWLMMVZ0pIpNxo8RjAVNKHYU0sc49N/YXAk+p6kctLFdbppDmfVtqISLXAr/FmYeNjw6DZCRNibfPTZAfqf/SVpBlp0REegMTcTNLd6rqv1IsUlvjflz8aFv337pEvi1h4vsReQynlI5rLYHaOl4khidwv4k/U9WIpct84AIv7NQxwHmk2HrOlFIjKVR1dFPPFRE/znQXarw2Rpc9WUQW47zf9ceZPu70NKfOPZbiTGKWJshfglNKuzbzOm2GZtb5UbhBgN1FZFJMXiS0wLUi8lvgJXOF72iBdr4VEbkQF+94C/BTVV3QUmXvZCzz9r0T5PeOOc5oQUSkM845YB/c7MZlqZWoTXI0bi3pf1xI6a1Elr30ifqOHx2J02s0myXefk0Ca6JIvvVLWo5TgXTcmvSyOPn/xSml4zCl1NgJ6IobkQRI5FQnkt5x24uz0/A9TilNVKeR0AL2Y9uy7FlP3q7eNq1VJNmJEJEzcGa75bjR4GmplWiHJmImOipBfiR9RivIslMhIu2Ad3ADWS8Dv1YLKL+t6ICzFIpHdlSe9ZVbjkiIqQIRkTht2/olLU9kEHG773+boyOjNdiEC0oNboF1LbwQMRHzvUSzekbjiXgcPTg2w+v4RDqWicI+GI1AVa9XVYm3UWOmdJqXdkkKRW1zeM5gJuCcwRyvql+kVqIdni9ws80DRGRknPwTvf2brSdS20dEMoHXcL+T7+K+F6HUStU2qedb3c875Ieo9M0pFLVNoaozcc52snGRAWIZ5+2tX9JyREKiJbJK2tvbL9n2otSPKaXGNscz0Zjo/XunZ78ObPUSez9ujdIXqro6BSK2VZ7HfWTGi8g5kUTPM+m/cGuVZuE6oIaxQyIiB1CzLOAUVX0vlfK0Bbw16BHz8vtEZOvaUhG5FOc183NV/S4V8rVFvGUuz+EGET8DfrE9hGgwjG1AxDP9PZ6pOgAishcuZizAA60uVdvlNW9/kIjUWkMtImOAP3r/1lle19qIWYUYrYEXM/ALoBtuoftXQAVuhKYnbjZ1rKrOSpmQbRDvg/MBzmHJVJySOgrnXGYjcLA3cmlsQ0RkAnAObubj+RSL06YQkSKcGV59wb8/V9VHWk2oNoA3YDgJN5uxGqco9fX+3wiMsTW7LYeI/AEXvgucp/pEpnaXqeqGVhFqJ0RECnHfkh9UdWgDhxtNwIsP+zwunvcm4EtceKn9cUu9HlbV36ROwraHiPyTmrXps4E5uL73frgJyodU9YIUibcVs5M3WgVVXSgiewBXAEfiQsEIsBz4N3CLqiYK1G40EVX92jO/uw4Xl3R3nAe2h4EbVdUclRg7Oh28fT9qTO/iYUppI1DVShE5GPgLcDpwHFCEM0W/RlWXp1C8tkhB1N/H13Pc9bjwGYaxQ6KqYRE5FTfo9SvgEFz4qcnAA6r6VArFa5Oo6uUi8iXOM/1euCVzJbgY9o+o6rOplC+CzZQahmEYhmEYhmEYKcPWlBqGYRiGYRiGYRgpw5RSwzAMwzAMwzAMI2WYUmoYhmEYhmEYhmGkDFNKDcMwDMMwDMMwjJRhSqlhGIZhGIZhGIaRMkwpNQzDMAzDMAzDMFKGKaWGYRiGYRiGYRhGyjCl1DAMwzAMwzAMw0gZppQahmEYhmEYhmEYKcOUUsMwDMMwDMMwDCNlmFJqGIZhGIZhGIZhpAxTSg3DMDxERJPYJqRazp0FEZnk1XlhK1xLRWTJtr7O9oyILGmg7S9JtYyNoTXbj1GDiHwkIktFJKOJ5xeISKWIhEWkTxLHP+Y955u8/0d6/1/elOsbhpEa0lItgGEYxnbIE/Xkfd5qUhgtgoiMAz4GnlDVc1MqTCMQkeuB64DzVHVCK176f0BpnPQNrSiDsQMiIj8DDgZ+p6rVTSlDVYtE5C3gF8DpwC31XC/LOw7gae/870XkdeCvIvKoqm5qihyGYbQuppQahmHEsCMpLkaLsSsQSLUQ2wmXqeqSVAvRApwN5AArUy3ITsTNwDrgsWaW8xRO2TyTepRS4BigPTBFVedGpf8DOBa4wtsMw9jOMfNdwzAMY6dHVeep6sJUy2G0HKq6zHuuNtjQCojIAcAI4PmmzpJG8TawEdhNRPao57gzvP1T0Ymq+jWwADi/qWbEhmG0LqaUGoZhNIPIWjsR8YvIn0XkRxGpEpHlInKriGQmOC9PRK4VkZkiUi4ixSLyiYgcF+fYQu86k0QkX0TuEJHFIhIQkbujjhspIu+IyBZve1dE9haRc73zr486dpaXNjiBfIXemq75IiJJ1MPW9XsicrqIfC0iJSKyOeoYEZFzRORTEdksIhUiMkNELhOR9IauEVXOT0TkPu/cIq+ceSJyi4h0iDl2As50F+CcmDWS0fVRa82kiJzgpT1fjxz3e8f8OiY96WdbT9lLcKa7AI/HyD0u5tizRORz7zrlXr38xTNt3GZ4z/pBr/1Xich6EXlJREbEOXZrGxSRwSLyvIis9drYcTFtPFdE7vTeoQoRmSoix0SVdZKIfCsiZV4Z94hIdpxrNmlNqYgcKCKviMg6776WeNfoEufYCZFnIiIHiVtPWeI9i7dEZFg91znGe0c3iltD+aOI/F1E8uq7l3rerzwRuT2q3uaIyMXeexfbvi+XqHWYCeT72DvmwCSr7lfe/pl6ytxdRJ4RkZVe3a4Skcdjn5Gn1P7X+/fMBGV1BI4EQkC89/Q5oDNwfJLyG4aRSlTVNttss802VQB1n8VGn7ME1ykqBT4C3gA2e3lPxzmnGzDby18BvAa8752vwJUxxxd66d8A3wObgFdwa/+u847ZHyj3jpuK65BNB6qAB7z066PKvMhLuy3Bff3dy78iyXqY5B3/IK6T+Kknw+devg/XyVRgC/AB8Cqw2kt7C/AlKLMwJv1roBKY7NXBm8Aq79hZQF7Usb8CJnp5C4AJUdtxsc8x6v9M7xmWR5cXlZ8GrPfqt6Cpz7ae+rwdmOad83mM3EOjjnvQO6bCq8MXPbkU+BLIbkRbXhKvvhMce6D3HCN1/qJ3vbBXZwfHHH+ud+xz3nmLcO/Mu8DPqGnjX3rPdz3uPfrYa09B4DDgjzgz6y9x78AG77xn6mmTDd5P1DkXe/cQ9K7xIjDXK2cR0CPm+Ale3h3eOdOAl4AfvPQNQPc417kj6rl9gmvHkfqfDOQ28v3Kwn0fFGc++yLwDq59/ou67bsz7h1aBaTFkW+gVw9zG1F3a3Ht3Jcg/wRPnsg9voj7VkXqabeY4/ej5j2qUyZwgZf/doLrHeLlP5nsPdhmm22p21IugG222Wbb9rLRdKVUgTnRnV+gH055VGBAzDlve+m3AulR6f1xilMQGBGVXhh1nS+BDjHl+aI6wZfH5F0Tde71UentgTKvI5kec47f6wgGgG5J1kOk01wBjI2T/2cv/z2gS1R6LvC6l3dhgjILY9KPIkoR9NIyqVHQro3JG+elT2jgOS6JSXvUSz8rzvFHeXmvNOfZNlCn13tlnZsg/4SoTvvAqPR84DPqGXRIUN6SePUd57h83GBCNXBiTN5hOMVjBZARlX5uVDu8F/DHnBfdxj+mtqIfOXc+zqTzJ1F5Pb02rED/ZNpPPfc1BqfwLaH2+yfUvEcvxpwzwUsPAafFvEMveXl/iznnZGoGj6K/GelRbfifjXy/rqbm+5AflT6Cmu9QbPt+xkv/eZzybvHyLk2y7oZ6x3+SIL8f7nuzGTgoJu9s79xv45w338s7JE5epI2fnuCa7bznsjjZd8A222xL3ZZyAWyzzTbbtpctqlNc33ZcgnMOjVPePcQoFcCeXtoXgMQ55+de/j1RaYVR1xkd55zDvLy5sWV6neNFxCilXt5jXvoJMelHe+n/a0TdRTrN98XJi8wqFgOd4+R3w83azEhQZmGSMmTjFOkpMenjaJpSGplpmRjn+Ke9vBOj0hr9bBu4n+tj209M/ide/vlx8nbHzXQVE6UcNnC9JQ20/T294y7x/r85QTl3efm/iEo7l5pZvJw450TaeJAoBdvL83nnKXBDnHPvjFdPTWg/r3rHHxEnT3BKZCi6DVOjlD4V55xRXt6kmPRpXvqQOOdk4RT+IqJmB6nn/fLyl3v5Y+Lk3ZCgfR/kpb8Rk57myVBFnPc1wfUjivZDCfLv9vJ/kyD/FS9/VEz6dV76ozHpfb32XRKvPUUdt8I7Pz+Z+7DNNttSt9maUsMwjLo8Uc+2LM7xAVynMZYfvX2PqLTDvf1rqqpxzomEnNk7Tt5qVZ0cJ31/b/9SbJmqGgJejnMOOLNegF/HpEf+fzjBefXxepy0kThzwc9VtU5YEVVdi5sRGR5vbWA8RKSXiPxWRO4WF6dwAvAf3OzdoCbIHY9JOM+th4lI16hr5+AUzGKc6XCE5jzbRiFuDe4YXIf72dh8VZ0JzMDNFu3RyOL/R/y2HwmtEbnPVxOcX999fqCq5fVce4mqLohOUNUwsNT79/0450QcVPWIk5cUIuIDDsUpOR/G5nvP8wucgrxXnCLei5NW5/332tEeOLPYH+JcJ2KW3oH47bjO+yUulmdvYIU6Bz+xvBgnDVX9FGfhcaSI9IrKOgbojrMCSDYMUOT9KEqQv/XdSJCfqM1EHBidELNG+gzcQMHLDbSnSJutsx7YMIztCwsJYxiGEYM2PiTMak/5iyUS6zHa2VGht79VRG6tp8zOcdLiKcTgTBjBzZbEI+55qvqtiHwPHC4ifVV1qYj0wJmmLiN+R7sh4l2r0NsfKSLxlLVoOtJACA8RuRQX8mGbetVU1bA4R0d/Ak7BmZ2CCzWRBzzuKRERCr19U55tY+mEu/81MTJEswSnAPVMkJ+IhkLCFHr7b6R+H1iNacMREj37snryI3lxnYolSSfcMwUINuG+VsQmqGqpV060XH29/a5JvAudcWb50cSrvya9/x4P4WYxz8etI4emDUq19/YlCfILvf2axtStqi4SkS9xA29H40yiIYHX3TgUx8hnGMZ2iimlhmEYzaehzmU0fm//Gc6sNhHxZigSKR8NyVFfL/BB3Izp+ThTufNwvw2PejNUjSWejJF7no9b81YfVfVlisgYnJOYLcBvcLOZa1S1ystfRTNmzOLwDE4pPZ0apfT0qLxomvNsm0oyba8x7TMZIvf5Is6pUSK+iZPW1DacbH5TidxTCYktCyIsjZOWrFyR66ym4UGfjXHS6qu/ptTNE7gBnvNF5EbcjOt4XPv9qBHlbPH2+Qny/Z58TzZQzuw4aU/hlNIzgZdEZCQwDOekqSEZI8rolnqPMgwj5ZhSahiG0bpEZlReUtV7WqjM1d6+T4L8Xeo59xngn7hO6d+BX+LWaj3WQrJBzT3PasIsdCyR8A5Xq+oT0Rme6W/3ZpZfC1X9XkTmAmNEpD/OPHE8rs4/jjl8WzzbRGzEmSp3F5FsVa2Ic0xkVm51nLzmsAIYAtyoqjNauOxUsQE3IBJogTZaH5E2sqYFr9PQ+58oHVXdLCIv4Nb8Ho7zeOsDHklggp6Idd6+Y4L8FcAA4GJVLU5wTCJewHkQPlJECqiZJX02iYGzAm+/vpHXNAyjlbE1pYZhGK3LB97+uBYsMzL7eILE2MZ5a+USxulT1VLcmsTeOOW0P/COqtYxR2wG3+FmKg4WkUQzKckS6WTGM1U8ifizwtXevqkDsZEZ0dO9a2QAz8XpELf0s00ot6oGcKFTBDgtNl9EhuNMd0twoYFakm3RhlOKqgZxs+4dReSgbXidFTiT3BEi0q+FylyKmzXsLSL7xjnkxAaKeNDbX4CzmAjiHDg1hkgbG5ogv8ltRlWLcOGOMoBTqWnvT9d3nvet6YnzvttYRdgwjFbGlFLDMIxWxHNE8iFOQbtLRPKi80XEJyJHNCJgPTgTtgXArrg4jtFciVM06yPi8OgSb98UB0cJ8Uxrb8c5b/mfiPSNPUZERojIKUkUF3Ee80vP2U/k/GG4MCzxWOXthyQtdG0ijoTOILHp7rZ4tg3JHTEnvsGbxY1cpx1wH05hfVBVq+Od3AwexM08/VVEzoszEJIrImeLSO8Wvu625maclcAT8Z6RiPQUkQtb4Do34sxZ/+cNHsReZ4CInN/IMiOK5R3e84+UNRwXkzghXrudDvwCN6v6pqo2anbdc9q0DhglIvEGf+7AhbO5S0SOic0UkY4i8vt6HJ1F1o7+HadozlTVhgZb9sa9A58lcw+GYaQWM981DMOIwfPkmohlqnptMy9xBm492SXA2SIyDdfJ74VTQLrglMvPE5xfC1UNich5OM+kd4jIGbjZmGE4RfVhnPOSuMqJqk4TkW+BfXCmgG819cbq4WZPntOAH0RkKs4BS2ec0twP55nzhQbKeRy3xvMYr5zvcCaDY3HeYPehxmwVAFVdIiIzgNHefc7GhfZ4XVXjeQuuhaoujnK2MhSYp6pTExzeks/2Pdwawj96ysUqamJY/qCqL4nIQ7i1tbNE5CPcGs9x3nW+xq0TblFUtUhEjsd5gn0MuE5EZuHMX/vg2lwuzutyS864b1NU9VMR+QPO8c9nXpuZjwvT0hd3X6XAv5t5nadFZHdc7N5pnrOxxbj1mH1xbWw6jTOh/yfunTgAWCgik3COmw7Bvf//R4L33+NB4H7v76YOSr2NMwPeF+epeCuqOl9EzsTNbr4uIj/ghbDC3fMw3EzoszjlNZa3cJ50O3n/1ztL6jEuSi7DMLZzbKbUMAyjLufUsx3b3MK9EChjgEtxnd69cWZtvYHvgQtJrtMVXebnwIHAu7hQEkfj1smNpcYxSzzHKREiYTAe90wZWxRVDavq6ThTwo89GX+B64yuxcXkvCKJcjbi6utZXCf2WJzCdy1xzFijOAGntPYHzsatnR3ViFuInhmtE4IlSr4We7aqugoXeuZr3LM935O7R9QxF+Du53vcsz4GN2N1FXBIA+EymoyqfoGLhRqZATsEOAKnWL2J81Y8Z1tce1uiqvfhlKpncKbix+LWWYZxFgU/b6HrXIELQfM6rm0ch1Piy/HWeDeyvAqvvLtwyufPcW39r9RYECTz/q/AfUOaQkSZPT1epqq+jDMpfxBIB47EKY6ZuPo+mgQOibzZ/v96/4ap5x2M4jTcN/CVpKQ3DCOlSOPWsRuGYRg7GiLyDvBTYIyq1vGI6plfzsMpigNVtT7PsYZh7EB4ZvHPAw+o6u8SHPNX4CbgBlW9vhnX+h6nZPeOeMROBSKyH26t/W3eAIBhGNs5NlNqGIbRBvDWZPWNSRMRuQinkC4Avk1w+onAYOBtU0gNY8dERPb0HJtFp+0O3Ob9G3d20XMIFDHvfaiZYlyFM8n/ZTPLaS5XApupuXfDMLZzbKbUMAyjDeDF7/wSmIGLMegHhuNM+CqAo1R1Usw5j+CcDx2N8zGwTz1rJQ3D2I4RkXk48+mZuNBFhcBo3Legziyptw59LHAQbk33Xap6aQvI8REwEGd10dJOtpK5/khgKnCFqppSahg7CKaUGoZhtAFEpCtuXebBOO+U2bi1hZ8At6jqzDjnKC78w4/ANd6aL8MwdkA8z8Cn4qweCnDrU2cAj8bG9PWOn4BbJ78eZ957eSpNbg3D2LkxpdQwDMMwDMMwDMNIGbam1DAMwzAMwzAMw0gZppQahmEYhmEYhmEYKcOUUsMwDMMwDMMwDCNlmFJqGIZhGIZhGIZhpAxTSg3DMAzDMAzDMIyUYUqpYRiGYRiGYRiGkTJMKTUMwzAMwzAMwzBShimlhmEYhmEYhmEYRsowpdQwDMMwDMMwDMNIGaaUGoZhGIZhGIZhGCnDlFLDMAzDMAzDMAwjZZhSahiGYRiGYRiGYaQMU0oNwzAMwzAMwzCMlPH/uMY81b9mmA4AAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "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:\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 from 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 : [1, 2]\n", + "Density matrix:\n", + " Shell 1\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.ini](config.ini) for solid_dmft looks like this: \n", + "\n", + " [general]\n", + " seedname = nno\n", + " jobname = NNO_AFM\n", + "\n", + " block_threshold = 0.001\n", + "\n", + " solver_type = cthyb\n", + " n_iw = 8001\n", + " n_tau = 50001\n", + "\n", + " prec_mu = 0.001\n", + "\n", + " h_int_type = density_density\n", + " U = 8.0\n", + " J = 1.0\n", + "\n", + " beta = 40\n", + "\n", + " magnetic = True\n", + " magmom = -0.001, 0.001\n", + " afm_order = True\n", + "\n", + " n_iter_dmft = 12\n", + "\n", + " g0_mix = 0.9 \n", + "\n", + " dc_type = 0\n", + " dc = True\n", + " dc_dmft = False\n", + "\n", + " [solver]\n", + " length_cycle = 150\n", + " n_warmup_cycles = 10000\n", + " n_cycles_tot = 2e+8\n", + " imag_threshold = 1e-5\n", + "\n", + " perform_tail_fit = True\n", + " fit_max_moment = 6\n", + " fit_min_w = 8\n", + " fit_max_w = 14\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=8001` 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 in units of the DC potential. Hence, small values should be chosen. \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=150` 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.ini`. \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']" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "65dba97b-a64c-4d88-b7cc-3607605a9aa3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(nrows=3, dpi=150, figsize=(7,6), 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'], (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'], (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'], (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'], (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[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", + "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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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "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(-1.5,0)\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": [ + "\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'], orb_names=[0, 1, 2, 3, 4], 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", + "5 5 0 9 0.562426 0.341965 0.5 0.5 +1.0000|1111101111>\n", + "0 0 0 8 0.000000 0.160698 1.0 1.0 +1.0000|1111101011>\n", + "3 3 0 9 0.472365 0.057380 0.5 0.5 +1.0000|1111111011>\n", + "25 25 0 8 1.147767 0.048976 0.0 0.0 +1.0000|1101101111>\n", + "40 40 0 8 1.629801 0.041522 1.0 1.0 +1.0000|1111101110>\n", + "55 55 0 10 7.522083 0.038053 0.0 0.0 +1.0000|1111111111>\n", + "4 4 0 9 0.562426 0.028791 -0.5 0.5 +1.0000|0111111111>\n", + "50 50 0 8 2.745626 0.024248 0.0 0.0 +1.0000|0111101111>\n", + "8 8 0 8 0.637905 0.021395 1.0 1.0 +1.0000|1111100111>\n", + "11 11 0 9 0.734944 0.021376 -0.5 0.5 +1.0000|1101111111>\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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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "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 (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/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.ini b/tutorials/PrNiO3_csc_vasp_plo_cthyb/2_dmft_csc/dmft_config.ini new file mode 100644 index 00000000..e01b4763 --- /dev/null +++ b/tutorials/PrNiO3_csc_vasp_plo_cthyb/2_dmft_csc/dmft_config.ini @@ -0,0 +1,49 @@ +[general] +seedname = vasp +enforce_off_diag = True +set_rot = hloc + +csc = True +plo_cfg = plo.cfg + +solver_type = cthyb +n_l = 33 + +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] +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 +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..193cc00c --- /dev/null +++ b/tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.html @@ -0,0 +1,941 @@ + + + + + + + 2. CSC with VASP PLOs: charge order in PrNiO3 — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+
[1]:
+
+
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+from h5 import HDFArchive
+from triqs.gf import BlockGf
+
+
+
+
+
[2]:
+
+
+
+plt.rcParams['figure.figsize'] = (8, 4)
+plt.rcParams['figure.dpi'] = 150
+
+
+
+

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.

+
+

2. CSC with VASP PLOs: charge order in PrNiO3

+

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.

+
+
[3]:
+
+
+
+# Reads files from reference? Otherwise, uses your simulation results
+read_from_ref = True
+path_mod = '/ref' if read_from_ref else ''
+
+
+
+

PrNiO3 is a perovskite that exhibits a metal-insulator transition coupled to a breathing distortion and charge disproportionation, see here. 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) calculated with VASP.

+
+

1. Running the initial scf DFT calculation

+

(~ 2 core hours)

+

To get started, we run a self-consistent field (scf) DFT calculation:

+
    +
  • Go into folder 1_dft_scf

  • +
  • Insert the POTCAR as concatenation of the files PAW_PBE Pr_3, PAW_PBE Ni_pv and PAW_PBE O distributed with VASP

  • +
  • Goal: get a well-converged charge density (CHGCAR) and understand where the correlated bands are (DOSCAR and potentially PROCAR and band structure)

  • +
+

Other input files are:

+
    +
  • 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.

  • +
  • POSCAR: PrNiO3 close to the experimental low-temperature structure (P21/n symmetry)

  • +
  • KPOINTS: approximately unidistant grid of 6 x 6 x 4

  • +
+

Then run Vasp with the command mpirun -n 8 vasp_std.

+

The main output here is:

+
    +
  • CHGCAR: the converged charge density to start the DMFT calculation from

  • +
  • 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.)

  • +
+

We now plot the DFT DOS and discuss the correlated subspace.

+
+
[4]:
+
+
+
+dft_energy, dft_dos = np.loadtxt(f'1_dft_scf{path_mod}/DOSCAR',
+                                 skiprows=6, unpack=True, usecols=(0, 1))
+
+
+
+
+
[5]:
+
+
+
+fermi_energy = 5.012206 # can be read from DOSCAR header or OUTCAR
+
+fig, ax = plt.subplots()
+ax.plot(dft_energy-fermi_energy, dft_dos)
+ax.axhline(0, c='k')
+ax.axvline(0, c='k')
+ax.set_xlabel('Energy relative to Fermi energy (eV)')
+ax.set_ylabel('DOS (1/eV)')
+ax.set_xlim(-8, 5)
+ax.set_ylim(0, 50);
+
+
+
+
+
+
+
+../../_images/tutorials_PrNiO3_csc_vasp_plo_cthyb_tutorial_6_0.png +
+
+

The DOS contains (you can check this with the partial DOS):

+
    +
  • Ni-eg bands in the range -0.4 to 2.5 eV with a small gap at around 0.6 eV

  • +
  • mainly Ni-t2g bands between -1.5 and -0.5 eV

  • +
  • 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.

  • +
+

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.

+

We will use the Ni-eg range to construct our correlated subspace.

+

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.

+
+
+

2. Running the CSC DMFT calculations

+

(~ 150 core hours)

+

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.

+

With VASP, the procedure works as described here, 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.

+
+

Input files for CSC DMFT calculations

+

We first take a look into the input file dmft_config.ini 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 on the solid_dmft website. This is a selection of parameters from the dmft_config.ini:

+

Group [general]:

+
    +
  • set_rot = hloc: rotates the local impurity problem into a basis where the local Hamiltonian is diagonal

  • +
  • plo_cfg = plo.cfg: the name of the config file for constructing the PLOs (see below)

  • +
  • 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.

  • +
  • 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

  • +
+

Group [solver]:

+
    +
  • legendre_fit = True: turns on measuring the Green’s function in Legendre coefficients

  • +
+

Group [dft]:

+
    +
  • 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_cores = 32: number of cores that DFT is run on. Check how many cores achieve the optimal DFT performance

  • +
  • dft_code = vasp: we are running VASP

  • +
  • dft_exec = vasp_std: the executable is vasp_std and its path is in the ROOT variable in our docker setup

  • +
  • mpi_env = default: sets the mpi environment

  • +
  • projector_type = plo: chooses PLO projectors

  • +
+

The plo.cfg file is described here. The 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).

+
+
+

Starting the calculations

+

Now we can start the calculations:

+
    +
  • Go into the folder 2_dmft_csc

  • +
  • Link relevant files like CHGCAR, KPOINTS, POSCAR, POTCAR from previous directory by running ./2_link_files.sh

  • +
  • Run with mpirun -n 32 python3 solid_dmft

  • +
+
+
+

Analyzing the projectors

+

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.

+
+
[6]:
+
+
+
+energies = []
+doss = []
+for imp in range(4):
+    data = np.loadtxt(f'2_dmft_csc{path_mod}/pdos_0_{imp}.dat', unpack=True)
+    energies.append(data[0])
+    doss.append(data[1:])
+
+energies = np.array(energies)
+doss = np.array(doss)
+
+
+
+
+
[7]:
+
+
+
+fig, ax = plt.subplots()
+
+ax.plot(dft_energy-fermi_energy, dft_dos, label='Initial DFT total')
+ax.plot(energies[0], np.sum(doss, axis=(0, 1)), label='PLO from CSC')
+#for energy, dos in zip(energies, doss):
+#    ax.plot(energy, dos.T)
+ax.axhline(0, c='k')
+ax.axvline(0, c='k')
+ax.set_xlim(-8, 5)
+ax.set_ylim(0,)
+ax.set_xlabel('Energy relative to Fermi energy (eV)')
+ax.set_ylabel('DOS (1/eV)')
+ax.legend()
+pass
+
+
+
+
+
+
+
+../../_images/tutorials_PrNiO3_csc_vasp_plo_cthyb_tutorial_10_0.png +
+
+

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.

+
+
+
+

3. Plotting the results: observables

+

We first read in the pre-computed observables from the h5 archive and print their names:

+
+
[8]:
+
+
+
+with HDFArchive(f'2_dmft_csc{path_mod}/vasp.h5', 'r') as archive:
+    observables = archive['DMFT_results/observables']
+    conv_obs = archive['DMFT_results/convergence_obs']
+
+
+
+
+
[9]:
+
+
+
+observables.keys()
+
+
+
+
+
[9]:
+
+
+
+
+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'])
+
+
+

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:

+
+
[10]:
+
+
+
+fig, axes = plt.subplots(nrows=4, sharex=True, dpi=200,figsize=(7,7))
+
+for i in range(2):
+    axes[0].plot(np.array(observables['imp_occ'][i]['up'])+np.array(observables['imp_occ'][i]['down']), '.-', c=f'C{i}',
+                label=f'Impurity {i}')
+    axes[1].plot(np.array(observables['imp_gb2'][i]['up'])+np.array(observables['imp_gb2'][i]['down']), '.-', c=f'C{i}')
+
+    axes[3].semilogy(conv_obs['d_Gimp'][i], '.-', color=f'C{i}', label=f'Impurity {i}')
+
+# Not impurity-dependent
+axes[2].plot(observables['E_tot'], '.-', c='k', label='Total energy')
+axes[2].plot(observables['E_dft'], 'x--', c='k', label='DFT energy')
+
+
+axes[0].set_ylabel('Imp. occupation\n')
+axes[0].set_ylim(0, 2)
+axes[0].legend()
+axes[1].set_ylabel(r'$G(\beta/2)$')
+axes[2].set_ylabel('Energy')
+axes[2].legend()
+axes[3].set_ylabel(r'|G$_{imp}$-G$_{loc}$|')
+axes[3].legend()
+
+axes[-1].set_xlabel('Iterations')
+fig.subplots_adjust(hspace=.08)
+pass
+
+
+
+
+
+
+
+../../_images/tutorials_PrNiO3_csc_vasp_plo_cthyb_tutorial_15_0.png +
+
+

These plots show:

+
    +
  • The occupation converges towards a disproportionated 1.6+0.4 electrons state

  • +
  • Both sites become insulating, which we can deduce from \(G(\beta/2)\) from its relation to the spectral function at the Fermi energy \(A(\omega = 0) \approx -(\beta/\pi) G(\beta/2)\)

  • +
  • 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

  • +
+

Therefore, we can conclude that we managed to capture the desired paramagnetic, insulating state that PrNiO3 shows in the experiments.

+
+
+

4. Plotting the results: the Legendre Green’s function

+

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.

+
+
[11]:
+
+
+
+legendre_gf = []
+with HDFArchive(f'2_dmft_csc{path_mod}/vasp.h5') as archive:
+    for i in range(2):
+        legendre_gf.append(archive[f'DMFT_results/last_iter/Gimp_l_{i}'])
+
+
+
+
+
[12]:
+
+
+
+fig, ax = plt.subplots()
+
+for i, legendre_coefficients_per_imp in enumerate(legendre_gf):
+    if len(legendre_coefficients_per_imp) != 2:
+        raise ValueError('Only blocks up_0 and down_0 supported')
+
+    data = (legendre_coefficients_per_imp['up_0'].data + legendre_coefficients_per_imp['down_0'].data).T
+
+    l_max = data.shape[2]
+
+    ax.semilogy(np.arange(0, l_max, 2), np.abs(np.trace(data[:, :, ::2].real, axis1=0, axis2=1)), 'x-',
+                c=f'C{i}', label=f'Imp. {i}, even indices')
+    ax.semilogy(np.arange(1, l_max, 2), np.abs(np.trace(data[:, :, 1::2].real, axis1=0, axis2=1)), '.:',
+                c=f'C{i}', label=f'Imp. {i}, odd indices')
+
+ax.legend()
+
+ax.set_ylabel('Legendre coefficient $G_l$ (eV$^{-1}$)')
+ax.set_xlabel(r'Index $l$')
+pass
+
+
+
+
+
+
+
+../../_images/tutorials_PrNiO3_csc_vasp_plo_cthyb_tutorial_18_0.png +
+
+

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.

+

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.

+
+
+

5. Next steps to try

+

Here are some suggestions on how continue on this type of DMFT calculations:

+
    +
  • change U and J and try to see if you can reach a metallic state. What does the occupation look like?

  • +
  • try for better convergence: change n_cycles_tot, n_iter_dmft and n_l

  • +
  • play around with the other parameters in the dmft_config.ini

  • +
  • analyze other quantities or have a look at the spectral functions from analytical continuation

  • +
  • try other ways to construct the correlated orbitals in CSC, e.g., with Wannier90

  • +
  • apply this to the material of your choice!

  • +
+
+
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file 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..678f59fe --- /dev/null +++ b/tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.ipynb @@ -0,0 +1,463 @@ +{ + "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": [ + "
" + ] + }, + "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.ini](2_dmft_csc/dmft_config.ini) 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.ini:\n", + "\n", + "Group [general]:\n", + "\n", + "* `set_rot = hloc`: rotates the local impurity problem into a basis where the local Hamiltonian is diagonal\n", + "* `plo_cfg = plo.cfg`: the name of the config file for constructing the PLOs (see below)\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", + "* `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": [ + "
" + ] + }, + "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": [ + "
" + ] + }, + "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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" + ] + }, + "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.ini\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!" + ] + } + ], + "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 index 00000000..5d69e534 --- /dev/null +++ b/tutorials/SVO_os_qe/tutorial.html @@ -0,0 +1,425 @@ + + + + + + + 1. OS with QE/W90 and cthyb: SrVO3 MIT — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + + + +
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Disclaimer:

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Heavy calculations (~ 800 core hours): Current tutorial is best performed on an HPC facility.

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1. OS with QE/W90 and cthyb: SrVO3 MIT

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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.

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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.

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svobands

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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.

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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.

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1. Starting out with DMFT

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To start your first calculation run:

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mpirun solid_dmft
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Once the calculation is finished, inspect the /out/ folder: our file of interest for the moment will be observables_imp0.dat, open the file:

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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
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The meaning of the column names is the following:

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  • it: number of the DMFT iteration

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  • mu: value of the chemical potential

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  • 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

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  • orbital occs up+down: occupations of the various states in the manifold

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  • impurity occ: number of electrons in each site

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2. Looking at the Metal-Insulator Transition

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In the following steps we will try to drive the system towards a Mott-insulating state.

+

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}.

+

Run the script, sit back and have a long coffee break, this is going to take a while (about 6 hours on 30 cores).

+

Once the run is finished run

+

python3 ./collect_results_coarse.py

+

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.

+

coarsegrid

+

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. )

+

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.

+
+
+

3. Refining the diagram

+

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

+

python3 ./collect_results_fine.py

+

The result is also visible here:

+

finegrid

+
+
+

4. Plotting the spectral function

+

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 ( Maxent ), 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) :

+

mpirun -n 30 python3 /path_to_solid_dmft/python/solid_dmft/postprocessing/maxent_gf_imp.py ./J0.0/U4/out/svo.h5

+

and plot the result by running in the docker container:

+

python3 read_spectral_function.py

+

Afunc

+

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?

+
+
+

5 Visualizing the MIT

+

We will now plot the spectral function at different U values for J = 0.0 eV:

+

Run the script run_maxent_scan.sh.

+

Then collect the data:

+

python3 read_spectral_function_transition.py

+

MIT

+
+
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/tutorials/SVO_os_qe/tutorial.ipynb b/tutorials/SVO_os_qe/tutorial.ipynb new file mode 100644 index 00000000..3f4628f1 --- /dev/null +++ b/tutorials/SVO_os_qe/tutorial.ipynb @@ -0,0 +1,194 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "3fa75e8f", + "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": "5c1d7b71", + "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": "6d5345b9", + "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": "999bb518", + "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": "5ec433e7", + "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": "5e8e2fce", + "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": "b5fe3a3a", + "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": "62bce864", + "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": "349ae5d9", + "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..a866330b --- /dev/null +++ b/tutorials/correlated_bandstructure/plot_correlated_bands.html @@ -0,0 +1,887 @@ + + + + + + + 5. Plotting the spectral function — solid_dmft documentation + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +
+

5. Plotting the spectral function

+

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.

+
+
[1]:
+
+
+
+%matplotlib inline
+from IPython.display import display
+from IPython.display import Image
+import numpy as np
+import importlib, sys
+import matplotlib.pyplot as plt
+from matplotlib import cm
+from timeit import default_timer as timer
+
+from ase.io.espresso import read_espresso_in
+
+from h5 import HDFArchive
+from solid_dmft.postprocessing import plot_correlated_bands as pcb
+
+
+
+
+

1. Configuration

+

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. Currently, the following options are implemented:

+
  1. bandstructure

    +
  2. Fermi slice

    +
+

Basic options

+
+
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.
+
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.
+
+
+
[2]:
+
+
+
+kslice = False
+
+bands_config = {'tb': True, 'alatt': True, 'qp_bands': False, 'sigma': 'calc'}
+kslice_config = {'tb': True, 'alatt': True, 'qp_bands': False, 'sigma': 'calc'}
+config = kslice_config if kslice else bands_config
+
+
+
+
+
+

Wannier90

+

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.

+
+
[3]:
+
+
+
+w90_path = './'
+w90_dict = {'w90_seed': 'svo', 'w90_path': w90_path, 'mu_tb': 12.3958, 'n_orb': 3,
+            'orbital_order_w90': ['dxz', 'dyz', 'dxy'], 'add_spin': False}
+
+orbital_order_to = ['dxy', 'dxz', 'dyz']
+proj_on_orb = None # or 'dxy' etc
+
+
+
+
+
+

BZ configuration

+
+

Optional: ASE Brillouin Zone

+

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.

+
+
[4]:
+
+
+
+scf_in = './svo.scf.in'
+
+# read scf file
+atoms = read_espresso_in(scf_in)
+# set up cell and path
+lat = atoms.cell.get_bravais_lattice()
+path = atoms.cell.bandpath('', npoints=100)
+kpts_dict = path.todict()['special_points']
+
+for key, value in kpts_dict.items():
+    print(key, value)
+lat.plot_bz()
+
+
+
+
+
+
+
+
+G [0. 0. 0.]
+M [0.5 0.5 0. ]
+R [0.5 0.5 0.5]
+X [0.  0.5 0. ]
+
+
+
+
[4]:
+
+
+
+
+<Axes3DSubplot:>
+
+
+
+
+
+
+../../_images/tutorials_correlated_bandstructure_plot_correlated_bands_14_2.png +
+
+
+

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).

+
+
[5]:
+
+
+
+# band specs
+tb_bands = {'bands_path': [('R', 'G'), ('G', 'X'), ('X', 'M'), ('M', 'G')], 'Z': np.array([0,0,0.5]), 'n_k': 50}
+tb_bands.update(kpts_dict)
+
+# kslice specs
+tb_kslice = {key: tb_bands[key] for key in list(tb_bands.keys()) if key.isupper()}
+kslice_update = {'bands_path': [('Y', 'G'),('G', 'X')], 'Y': np.array([0.5,0.0,0]), 'n_k': 50, 'kz': 0.0}
+tb_kslice.update(kslice_update)
+
+tb_config = tb_kslice if kslice else tb_bands
+
+
+
+
+
+
+

Self-energy

+

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.

+
+
[6]:
+
+
+
+freq_mesh_kslice = {'window': [-0.5, 0.5], 'n_w': int(1e6)}
+freq_mesh_bands = {'window': [-5, 5], 'n_w': int(1e3)}
+freq_mesh = freq_mesh_kslice if kslice else freq_mesh_bands
+
+dmft_path = './svo_example.h5'
+
+proj_on_orb = orbital_order_to.index(proj_on_orb) if proj_on_orb else None
+sigma_dict = {'dmft_path': dmft_path, 'it': 'last_iter', 'orbital_order_dmft': orbital_order_to, 'spin': 'up',
+              'block': 0, 'eta': 0.0, 'w_mesh': freq_mesh, 'linearize': False, 'proj_on_orb' : proj_on_orb}
+
+
+
+

Optional: for completeness and as a sanity check we quickly take a look at the self-energy. Make sure you provide a physical one!

+
+
[7]:
+
+
+
+with HDFArchive(dmft_path, 'r') as h5:
+    sigma_freq = h5['DMFT_results']['last_iter']['Sigma_freq_0']
+
+fig, ax = plt.subplots(1, 2, figsize=(10,2), squeeze=False, dpi=200)
+
+orb = 0
+sp = 'up_0'
+freq_mesh = np.array([w.value for w in sigma_freq[sp][orb,orb].mesh])
+
+ax[0,0].plot(freq_mesh, sigma_freq[sp][orb,orb].data.real)
+ax[0,1].plot(freq_mesh, -sigma_freq[sp][orb,orb].data.imag)
+
+ax[0,0].set_ylabel(r'Re$\Sigma(\omega)$')
+ax[0,1].set_ylabel(r'Im$\Sigma(\omega)$')
+for ct in range(2):
+    ax[0,ct].grid()
+    ax[0,ct].set_xlim(-2, 2)
+    ax[0,ct].set_xlabel(r'$\omega$ (eV)')
+
+
+
+
+
+
+
+../../_images/tutorials_correlated_bandstructure_plot_correlated_bands_22_0.png +
+
+
+
+

Plotting options

+

Finally, you can choose colormaps for each of the functionalities from any of the available on matplotlib colormaps. 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.

+
+
[8]:
+
+
+
+plot_config = {'colorscheme_alatt': 'coolwarm', 'colorscheme_bands': 'coolwarm', 'colorscheme_kslice': 'PuBuGn',
+               'colorscheme_qpbands': 'Greens', 'vmin': 0.0}
+
+
+
+
+
+
+

2. Run and Plotting

+

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.

+
+
[9]:
+
+
+
+start_time = timer()
+
+tb_data, alatt_k_w, freq_dict = pcb.get_dmft_bands(fermi_slice=kslice, with_sigma=bands_config['sigma'], add_mu_tb=True,
+                                                   orbital_order_to=orbital_order_to, qp_bands=config['qp_bands'],
+                                                   **w90_dict, **tb_config, **sigma_dict)
+
+print('Run took {0:.3f} s'.format(timer() - start_time))
+
+
+
+
+
+
+
+
+Warning: could not identify MPI environment!
+
+
+
+
+
+
+
+Starting serial run at: 2022-08-01 11:33:20.627842
+
+
+
+
+
+
+
+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
+
+
+

That’s it. Now you can look at the output:

+
+
[10]:
+
+
+
+if kslice:
+    fig, ax = plt.subplots(1, figsize=(3,3), dpi=200)
+
+    pcb.plot_kslice(fig, ax, alatt_k_w, tb_data, freq_dict, w90_dict['n_orb'], tb_config,
+                    tb=config['tb'], alatt=config['alatt'], quarter=0, **plot_config)
+
+else:
+    fig, ax = plt.subplots(1, figsize=(6,3), dpi=200)
+
+    pcb.plot_bands(fig, ax, alatt_k_w, tb_data, freq_dict, w90_dict['n_orb'], dft_mu=0.,
+                   tb=config['tb'], alatt=config['alatt'], qp_bands=config['qp_bands'], **plot_config)
+
+    ax.set_ylim(-1.25,1.75)
+
+
+
+
+
+
+
+../../_images/tutorials_correlated_bandstructure_plot_correlated_bands_30_0.png +
+
+
+
+
+ + +
+
+
+ +
+ +
+

© Copyright Copyright (C) 2018-2020, ETH Zurich Copyright (C) 2021-2022, The Simons Foundation authors: A. Hampel, M. Merkel, A. Carta, and S. Beck.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file 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", + "
    \n", + "
  1. bandstructure
  2. \n", + "
  3. Fermi slice
  4. \n", + "
" + ] + }, + { + "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": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "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": [ + "
" + ] + }, + "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 matplotlib colormaps. `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": [ + "
" + ] + }, + "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 +}