20240513_fft_ops_test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3ef0fed7-1cdc-4b2a-b5a2-66647d5688a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import sys\n",
    "import scipy\n",
    "sys.path.insert(0, \"/so/home/jsugiyama/git/sotodlib/\")\n",
    "import sotodlib\n",
    "from sotodlib.tod_ops import fft_ops, detrend_tod\n",
    "from sotodlib import core\n",
    "from sotodlib import hwp\n",
    "from sotodlib.io import load_smurf as ls\n",
    "from sotodlib.io.load_smurf import Observations, Files, TuneSets, Tunes\n",
    "from sotodlib.io import g3tsmurf_utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "614df904-08c2-4478-9bbb-eeb68ec71555",
   "metadata": {},
   "outputs": [],
   "source": [
    "ctx_file = '/so/metadata/satp3/contexts/use_this.yaml'\n",
    "ctx = core.Context(ctx_file)\n",
    "obs_id = 'obs_1709601492_satp3_1111111'\n",
    "ws = 'ws0'\n",
    "bandpass = 'f150'\n",
    "meta = ctx.get_meta(obs_id, dets={'wafer_slot': 'ws0', 'wafer.bandpass': 'f150'})\n",
    "aman = ctx.get_obs(meta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6c5f85a0-9715-47eb-b28d-7c8df066a5e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "for idet in range(aman.signal.shape[0]) : \n",
    "    aman.signal[idet] *= aman.det_cal.phase_to_pW[idet]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8a578c7a-7311-4dfa-9456-ee6a9d916026",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.02564496, -0.02735919, -0.02968833, ..., -0.03053415,\n",
       "        -0.02829826, -0.02548477],\n",
       "       [-0.03261045, -0.03135622, -0.03046858, ..., -0.03714111,\n",
       "        -0.03748849, -0.0380674 ],\n",
       "       [-0.03813046, -0.0365559 , -0.03528485, ..., -0.04219781,\n",
       "        -0.04307051, -0.04293773],\n",
       "       ...,\n",
       "       [-0.03441855, -0.03475183, -0.03419667, ..., -0.02635875,\n",
       "        -0.02767313, -0.02995005],\n",
       "       [-0.03448242, -0.03484531, -0.03401093, ..., -0.02638803,\n",
       "        -0.02667837, -0.0278213 ],\n",
       "       [-0.03572106, -0.03510451, -0.03366596, ..., -0.02531236,\n",
       "        -0.02594626, -0.02711093]], dtype=float32)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "detrend_tod(aman)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d12b8af5-ab4f-43fa-b0e8-07ab1ad073c8",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Key conflict: more than one merge target shares keys: {'dsT'}",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[32], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mhwp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdemod_tod\u001b[49m\u001b[43m(\u001b[49m\u001b[43maman\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhwp_angle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maman\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhwp_solution\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhwp_angle\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/git/sotodlib/sotodlib/hwp/hwp.py:554\u001b[0m, in \u001b[0;36mdemod_tod\u001b[0;34m(aman, signal_name, hwp_angle, demod_mode, bpf_cfg, lpf_cfg)\u001b[0m\n\u001b[1;32m    550\u001b[0m demod \u001b[38;5;241m=\u001b[39m tod_ops\u001b[38;5;241m.\u001b[39mfourier_filter(aman, bpf, detrend\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    551\u001b[0m                                signal_name\u001b[38;5;241m=\u001b[39msignal_name) \u001b[38;5;241m*\u001b[39m phasor\n\u001b[1;32m    553\u001b[0m \u001b[38;5;66;03m# dsT\u001b[39;00m\n\u001b[0;32m--> 554\u001b[0m \u001b[43maman\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwrap_new\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdsT\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mfloat32\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mshape\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdets\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43msamps\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    555\u001b[0m aman\u001b[38;5;241m.\u001b[39mdsT \u001b[38;5;241m=\u001b[39m aman[signal_name]\n\u001b[1;32m    556\u001b[0m aman[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdsT\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m tod_ops\u001b[38;5;241m.\u001b[39mfourier_filter(\n\u001b[1;32m    557\u001b[0m     aman, lpf, signal_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdsT\u001b[39m\u001b[38;5;124m'\u001b[39m, detrend\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m)\n",
      "File \u001b[0;32m~/git/sotodlib/sotodlib/core/axisman.py:668\u001b[0m, in \u001b[0;36mAxisManager.wrap_new\u001b[0;34m(self, name, shape, cls, **kwargs)\u001b[0m\n\u001b[1;32m    666\u001b[0m         axis_map\u001b[38;5;241m.\u001b[39mappend((dim, s\u001b[38;5;241m.\u001b[39mcopy()))\n\u001b[1;32m    667\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m(shape\u001b[38;5;241m=\u001b[39mshape_ints, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m--> 668\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwrap\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis_map\u001b[49m\u001b[43m)\u001b[49m[name]\n",
      "File \u001b[0;32m~/git/sotodlib/sotodlib/core/axisman.py:615\u001b[0m, in \u001b[0;36mAxisManager.wrap\u001b[0;34m(self, name, data, axis_map, accept_new, accept_merge)\u001b[0m\n\u001b[1;32m    613\u001b[0m helper\u001b[38;5;241m.\u001b[39m_fields[name] \u001b[38;5;241m=\u001b[39m data\n\u001b[1;32m    614\u001b[0m helper\u001b[38;5;241m.\u001b[39m_assignments[name] \u001b[38;5;241m=\u001b[39m assign\n\u001b[0;32m--> 615\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmerge\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhelper\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/git/sotodlib/sotodlib/core/axisman.py:821\u001b[0m, in \u001b[0;36mAxisManager.merge\u001b[0;34m(self, *amans)\u001b[0m\n\u001b[1;32m    819\u001b[0m     both \u001b[38;5;241m=\u001b[39m fields\u001b[38;5;241m.\u001b[39mintersection(newf)\n\u001b[1;32m    820\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(both):\n\u001b[0;32m--> 821\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mKey conflict: more than one merge target \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    822\u001b[0m                          \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mshares keys: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mboth\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m    823\u001b[0m     fields\u001b[38;5;241m.\u001b[39mupdate(newf)\n\u001b[1;32m    825\u001b[0m \u001b[38;5;66;03m# Get the intersected axis descriptions.\u001b[39;00m\n",
      "\u001b[0;31mValueError\u001b[0m: Key conflict: more than one merge target shares keys: {'dsT'}"
     ]
    }
   ],
   "source": [
    "hwp.demod_tod(aman, hwp_angle=aman.hwp_solution.hwp_angle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "517647b3-b86f-452a-a5ff-cdee43a48d3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "signal=aman.signal\n",
    "freqs, pxx = fft_ops.calc_psd(\n",
    "            aman, signal=signal, timestamps=aman.timestamps, max_samples=2**20, merge=True, nperseg=len(aman.timestamps)\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "accce5a1-a609-42a1-90f5-cadb78e68235",
   "metadata": {},
   "outputs": [],
   "source": [
    "signal=aman.demodQ\n",
    "freqs_demodQ, pxx_demodQ = fft_ops.calc_psd(\n",
    "            aman, signal=signal, timestamps=aman.timestamps, max_samples=2**20, merge=False, nperseg=len(aman.timestamps)\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "262cc3c5-2028-4eb3-8fa8-9800e603f087",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import hwp frequency\n",
    "hwp_freq = np.median(aman['hwp_solution']['hwp_rate_1'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1e12200-8a83-4fd8-88a6-a83a19fc181c",
   "metadata": {
    "tags": []
   },
   "source": [
    "## New function \"calc_masked_psd\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f34ece7-28e7-4495-8943-dbf35b530523",
   "metadata": {},
   "source": [
    "\"calc_masked_psd\" masks hwpss or single peak in the given PSD.\\\n",
    "This enables applying 1/f curve for the PSD with peaks.\\\n",
    "You can adjust the masking range of hwpss and single peak in case of bad weather."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "56220c01-9f2b-405b-9f18-274f22e941a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "f_mask, pxx_mask = fft_ops.calc_masked_psd(aman, hwpss=True, hwp_freq=hwp_freq,peak=True, peak_freq=26, peak_width=(-2, +6), merge=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "97b2c2ba-c186-4a11-bee1-c58d045cf9cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f0984809150>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.loglog(aman.freqs, np.sqrt(aman.Pxx[3]), label=\"Raw PSD\")\n",
    "masked_p = pxx_mask[3]\n",
    "plt.loglog(f_mask, np.sqrt(masked_p), label=\"Masked PSD\")\n",
    "plt.xlabel('Frequency (Hz)')\n",
    "plt.xlim(1E-4, 60)\n",
    "plt.ylabel('signal (pW/$\\sqrt{Hz}$)')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34e21721-a4e2-48fd-8ce4-08e355ced70a",
   "metadata": {
    "tags": []
   },
   "source": [
    "## New function \"calc_binned_psd\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4dbb333-4b17-4326-83f2-6a3ef243e6ca",
   "metadata": {},
   "source": [
    "\"binning_psd\" returns the binned PSD.\\\n",
    "The parameters \"unbinned_mode\" and \"base\" changes the number of first un-binned modes and binning width."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c5254835-ca9d-442e-9403-b03dba678810",
   "metadata": {},
   "outputs": [],
   "source": [
    "f_binned, pxx_binned = fft_ops.calc_binned_psd(aman, f=f_mask, pxx=pxx_mask, unbinned_mode=3, base=1.3, merge=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "993b1a4f-e375-4669-b2e3-2f3d0ad4a95a",
   "metadata": {},
   "outputs": [],
   "source": [
    "f_demod_binned, pxx_demod_binned = fft_ops.calc_binned_psd(aman, f=freqs_demodQ, pxx=pxx_demodQ, unbinned_mode=3, base=1.3, merge=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c90b05cc-36dd-4b0c-9473-497e137ec220",
   "metadata": {},
   "source": [
    "### Binned PSD before demodultion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "4bbc04f3-ec3c-40e3-ad99-14c339c8f137",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0001, 50)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.loglog(f_mask, np.sqrt(pxx_mask[300]), alpha=0.4, label=\"Masked PSD\")\n",
    "plt.scatter(f_binned, np.sqrt(pxx_binned[300]), c='tab:orange', label=\"binning\")\n",
    "plt.legend()\n",
    "plt.xlabel('Frequency (Hz)')\n",
    "plt.ylabel('signal (pW/$\\sqrt{Hz}$)')\n",
    "plt.xlim(1E-4, 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30bfd141-c79d-4e0a-a772-1067fa538cff",
   "metadata": {},
   "source": [
    "### Binned PSD after demodultion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "abaa15cd-02b2-432f-b4cb-1ae9d0b6478c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'signal (pW/$\\\\sqrt{Hz}$)')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.loglog(freqs_demodQ[freqs_demodQ<2], np.sqrt(pxx_demodQ[300][freqs_demodQ<2]), alpha=0.4, label=\"Raw demodQ PSD\")\n",
    "p = pxx_demod_binned[300][f_demod_binned<2]\n",
    "f = f_demod_binned[f_demod_binned<2]\n",
    "plt.scatter(f, np.sqrt(p), c='tab:orange', label=\"binning\")\n",
    "plt.legend()\n",
    "plt.xlabel('Frequency (Hz)')\n",
    "plt.ylabel('signal (pW/$\\sqrt{Hz}$)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce97d6c4-253e-471e-ac4c-c66140d23bf8",
   "metadata": {},
   "source": [
    "## New features of fit_noise_model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2502e7f5-144c-475a-97ae-63ecf996608c",
   "metadata": {},
   "source": [
    "* Minimum frequency for the fit can be decrared\n",
    "* IndexError Exception is added to the Hessian calculation. IndexError occurs when the 1/f fit fails in the case where, for example, the signal is nan.\n",
    "* Binning makes the calculation faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "92d7e3b8-3f6c-4501-9a08-feef48d3e2f7",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707067412_0_194 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707067412_0_274 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707067412_0_380 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707067485_1_038 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707067485_1_042 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707067485_1_202 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707067485_1_326 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707067485_1_454 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707067599_2_005 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707067599_2_112 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707067599_2_197 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707067599_2_304 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707067756_3_099 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707067756_3_300 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707067756_3_472 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707067906_4_096 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707067906_4_114 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707067906_4_163 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707067906_4_489 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707068064_5_070 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707068064_5_090 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707068064_5_134 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707068064_5_291 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707068064_5_345 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707068064_5_358 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707068064_5_454 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707068064_5_486 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707068224_6_000 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707068224_6_136 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707068224_6_208 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707068224_6_442 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707068224_6_489 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707068377_7_026 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707068377_7_050 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707068377_7_124 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707068377_7_133 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707068377_7_146 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707068377_7_166 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707068377_7_186 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707068377_7_204 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707068377_7_217 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707068377_7_268 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707068377_7_282 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707068377_7_284 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707068377_7_325 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707068377_7_486 skipping.\n"
     ]
    }
   ],
   "source": [
    "noise_fit_stats = fft_ops.fit_noise_model(aman, f=f_binned, pxx=pxx_binned, lowf=1, f_min=0.01, f_max=20, fwhite=(5, 20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "53f10065-647e-46cb-892f-9e1a3aa64e1b",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067412_0_012 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067412_0_060 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067412_0_074 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067412_0_076 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067412_0_082 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067412_0_100 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067412_0_124 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067412_0_148 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067412_0_164 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067412_0_188 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707067412_0_194 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067412_0_202 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067412_0_236 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067412_0_258 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067412_0_260 skipping.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/so/home/jsugiyama/git/sotodlib/sotodlib/tod_ops/fft_ops.py:332: RuntimeWarning: invalid value encountered in power\n",
      "  return w * (1 + (fknee / f) ** alpha)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067412_0_266 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707067412_0_274 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067412_0_284 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067412_0_298 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067412_0_306 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067412_0_316 skipping.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/so/home/jsugiyama/git/sotodlib/sotodlib/tod_ops/fft_ops.py:332: RuntimeWarning: invalid value encountered in power\n",
      "  return w * (1 + (fknee / f) ** alpha)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067412_0_340 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067412_0_354 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707067412_0_380 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067412_0_396 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067412_0_404 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067412_0_436 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067412_0_482 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067412_0_498 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707067485_1_038 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707067485_1_042 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707067485_1_202 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707067485_1_326 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707067485_1_454 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067485_1_482 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067485_1_508 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707067599_2_005 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067599_2_032 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067599_2_040 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067599_2_095 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707067599_2_112 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067599_2_160 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067599_2_168 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067599_2_191 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707067599_2_197 skipping.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/so/home/jsugiyama/git/sotodlib/sotodlib/tod_ops/fft_ops.py:332: RuntimeWarning: invalid value encountered in power\n",
      "  return w * (1 + (fknee / f) ** alpha)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067599_2_224 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067599_2_232 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067599_2_256 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067599_2_272 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067599_2_288 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707067599_2_304 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067599_2_320 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067599_2_367 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067599_2_399 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067599_2_495 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067756_3_084 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067756_3_088 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707067756_3_099 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067756_3_115 skipping.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/so/home/jsugiyama/git/sotodlib/sotodlib/tod_ops/fft_ops.py:332: RuntimeWarning: invalid value encountered in power\n",
      "  return w * (1 + (fknee / f) ** alpha)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067756_3_216 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067756_3_277 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067756_3_299 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707067756_3_300 skipping.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/so/home/jsugiyama/git/sotodlib/sotodlib/tod_ops/fft_ops.py:332: RuntimeWarning: invalid value encountered in power\n",
      "  return w * (1 + (fknee / f) ** alpha)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067756_3_364 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067756_3_395 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067756_3_467 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707067756_3_472 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067756_3_485 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067756_3_499 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067756_3_509 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067906_4_042 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067906_4_048 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067906_4_059 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067906_4_061 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067906_4_075 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707067906_4_096 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707067906_4_114 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067906_4_119 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067906_4_138 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707067906_4_163 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067906_4_172 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067906_4_183 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067906_4_203 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067906_4_204 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067906_4_237 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067906_4_335 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067906_4_344 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067906_4_347 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067906_4_368 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067906_4_428 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067906_4_429 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707067906_4_489 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707067906_4_500 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707068064_5_002 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707068064_5_070 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707068064_5_090 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707068064_5_098 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707068064_5_134 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707068064_5_138 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707068064_5_146 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707068064_5_151 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707068064_5_159 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707068064_5_163 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707068064_5_186 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707068064_5_291 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707068064_5_320 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707068064_5_326 skipping.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/so/home/jsugiyama/git/sotodlib/sotodlib/tod_ops/fft_ops.py:332: RuntimeWarning: invalid value encountered in power\n",
      "  return w * (1 + (fknee / f) ** alpha)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707068064_5_345 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707068064_5_355 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707068064_5_358 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707068064_5_378 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707068064_5_422 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707068064_5_443 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707068064_5_454 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707068064_5_486 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707068064_5_507 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707068224_6_000 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707068224_6_136 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707068224_6_176 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707068224_6_208 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707068224_6_442 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707068224_6_447 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707068224_6_489 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707068224_6_503 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707068224_6_505 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707068377_7_004 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707068377_7_026 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707068377_7_050 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707068377_7_056 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707068377_7_076 skipping.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/so/home/jsugiyama/git/sotodlib/sotodlib/tod_ops/fft_ops.py:332: RuntimeWarning: invalid value encountered in power\n",
      "  return w * (1 + (fknee / f) ** alpha)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707068377_7_124 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707068377_7_132 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707068377_7_133 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707068377_7_146 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707068377_7_166 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707068377_7_186 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707068377_7_204 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707068377_7_217 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707068377_7_268 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707068377_7_282 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707068377_7_284 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707068377_7_307 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707068377_7_325 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707068377_7_389 skipping.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/so/home/jsugiyama/git/sotodlib/sotodlib/tod_ops/fft_ops.py:332: RuntimeWarning: invalid value encountered in power\n",
      "  return w * (1 + (fknee / f) ** alpha)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707068377_7_409 skipping.\n",
      "Cannot calculate Hessian for detector sch_ufm_mv5_1707068377_7_426 skipping.\n",
      "Cannot fit 1/f curve for detector sch_ufm_mv5_1707068377_7_486 skipping.\n"
     ]
    }
   ],
   "source": [
    "noise_fit_stats_demodQ = fft_ops.fit_noise_model(aman, f=f_demod_binned, pxx=pxx_demod_binned, lowf=0.1, f_max=2, fwhite=(0.1, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9df5e2db-3f9a-46eb-af1a-99f4f25f4dfa",
   "metadata": {},
   "source": [
    "### f_knee obtained from fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "b49e6d5d-298a-481f-8bc2-02b2414b3426",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'f_knee before demodulation')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Before demodulation\n",
    "f_knee = noise_fit_stats.fit.T[0]\n",
    "plt.figure()\n",
    "plt.hist(f_knee[((f_knee>0)&(f_knee<2))], bins=100)\n",
    "plt.xlabel('f_knee (Hz)')\n",
    "plt.title('f_knee before demodulation')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "30a4c228-11d3-4166-ba2a-afb9d64b9c60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'f_knee after demodulation')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AAHB5bE8GAADW4hoVlCjGGF24cOGyfQICArjLLAAUE6yooERZuXKlypYte9lHQfdzAQDYiWtUUKJkZ2e77o1yKfm7XAAA9iOoAAAAa/HVDwAAsJZ1F9Pm5eXp4MGDCg8P54JHAACKCWOMsrOzFRsb63FvrqthXVA5ePCgxx1NAQBA8XDgwAGPf1X5algXVMLDwyX9dqLly5cv4moAAMCVyMrKUlxcnOtz3FesCyr5X/eUL1+eoAIAQDHj68s2uJgWAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFoEFQAAYK3Aoi7gWosf+bFH297x3YqgEgAA8EdYUQEAANYiqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArOVVUElNTZXD4XB7REdHu44bY5SamqrY2FiFhIQoKSlJW7Zs8XnRAACgdPB6RaVBgwY6dOiQ67F582bXsfT0dE2YMEGTJk1SRkaGoqOj1alTJ2VnZ/u0aAAAUDp4HVQCAwMVHR3telSuXFnSb6spEydO1OjRo9WjRw81bNhQM2fO1JkzZzRnzhyfFw4AAEo+r4PKjh07FBsbq+rVq6t3797avXu3JGnPnj3KzMxUcnKyq6/T6VRiYqLWrFlzyfFycnKUlZXl9gAAAJC8DCotW7bUrFmz9Omnn2ratGnKzMxU69atdfz4cWVmZkqSoqKi3F4TFRXlOlaQcePGKSIiwvWIi4srxGkAAICSyKug0rVrV91999264YYb1LFjR3388W93Ip45c6arj8PhcHuNMcaj7fdGjRqlU6dOuR4HDhzwpiQAAFCCXdX25LCwMN1www3asWOHa/fPxasnR44c8Vhl+T2n06ny5cu7PQAAAKSrDCo5OTnatm2bYmJiVL16dUVHR2vZsmWu47m5uVq5cqVat2591YUCAIDSJ9Cbzn/5y1/UvXt3/elPf9KRI0f00ksvKSsrSwMHDpTD4VBKSorS0tKUkJCghIQEpaWlKTQ0VH369PFX/QAAoATzKqj8+OOPuu+++3Ts2DFVrlxZN998s9atW6dq1apJkkaMGKGzZ89q6NChOnnypFq2bKmlS5cqPDzcL8UDAICSzWGMMUVdxO9lZWUpIiJCp06d8sv1KvEjP/Zo2zu+m8/fBwCA0sRfn9/c6wcAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWOuqgsq4cePkcDiUkpLiajPGKDU1VbGxsQoJCVFSUpK2bNlytXUCAIBSqNBBJSMjQ2+99ZYaNWrk1p6enq4JEyZo0qRJysjIUHR0tDp16qTs7OyrLhYAAJQuhQoqv/zyi/r27atp06apQoUKrnZjjCZOnKjRo0erR48eatiwoWbOnKkzZ85ozpw5PisaAACUDoUKKsOGDVO3bt3UsWNHt/Y9e/YoMzNTycnJrjan06nExEStWbOmwLFycnKUlZXl9gAAAJCkQG9fMG/ePH3zzTfKyMjwOJaZmSlJioqKcmuPiorSvn37Chxv3LhxGjt2rLdlAACAUsCrFZUDBw7oiSee0OzZsxUcHHzJfg6Hw+25McajLd+oUaN06tQp1+PAgQPelAQAAEowr1ZUNmzYoCNHjqhZs2autgsXLuiLL77QpEmTtH37dkm/razExMS4+hw5csRjlSWf0+mU0+ksTO0AAKCE82pFpUOHDtq8ebM2bdrkejRv3lx9+/bVpk2bVKNGDUVHR2vZsmWu1+Tm5mrlypVq3bq1z4sHAAAlm1crKuHh4WrYsKFbW1hYmCpWrOhqT0lJUVpamhISEpSQkKC0tDSFhoaqT58+vqsaAACUCl5fTPtHRowYobNnz2ro0KE6efKkWrZsqaVLlyo8PNzXbwUAAEo4hzHGFHURv5eVlaWIiAidOnVK5cuX9/n48SM/9mjbO76bz98HAIDSxF+f39zrBwAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwlldBZerUqWrUqJHKly+v8uXLq1WrVvrf//1f13FjjFJTUxUbG6uQkBAlJSVpy5YtPi8aAACUDl4FlapVq2r8+PFav3691q9fr1tvvVV33nmnK4ykp6drwoQJmjRpkjIyMhQdHa1OnTopOzvbL8UDAICSzaug0r17d912222qXbu2ateurZdfflnlypXTunXrZIzRxIkTNXr0aPXo0UMNGzbUzJkzdebMGc2ZM8df9QMAgBKs0NeoXLhwQfPmzdPp06fVqlUr7dmzR5mZmUpOTnb1cTqdSkxM1Jo1a3xSLAAAKF0CvX3B5s2b1apVK/36668qV66cFi1apPr167vCSFRUlFv/qKgo7du375Lj5eTkKCcnx/U8KyvL25IAAEAJ5fWKSp06dbRp0yatW7dOjzzyiAYOHKitW7e6jjscDrf+xhiPtt8bN26cIiIiXI+4uDhvSwIAACWU10ElKChItWrVUvPmzTVu3Dg1btxY//jHPxQdHS1JyszMdOt/5MgRj1WW3xs1apROnTrlehw4cMDbkgAAQAl11f+OijFGOTk5ql69uqKjo7Vs2TLXsdzcXK1cuVKtW7e+5OudTqdru3P+AwAAQPLyGpVnn31WXbt2VVxcnLKzszVv3jytWLFCS5YskcPhUEpKitLS0pSQkKCEhASlpaUpNDRUffr08Vf9AACgBPMqqBw+fFj9+/fXoUOHFBERoUaNGmnJkiXq1KmTJGnEiBE6e/ashg4dqpMnT6ply5ZaunSpwsPD/VI8AAAo2RzGGFPURfxeVlaWIiIidOrUKb98DRQ/8mOPtr3ju/n8fQAAKE389fnNvX4AAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArBVY1AWgZIof+bFH297x3YqgEgBAccaKCgAAsBZBBQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKwVWNQFAKVB/MiP3Z7vHd+tiCoBgOKFFRUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGuxPRlF5uItu9KVbdtlqy8AlB6sqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIvtySj2CrvN2V/vz3ZpAPAdVlQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLXT8okdiJAwAlAysqAADAWgQVAABgLa+Cyrhx43TTTTcpPDxcVapU0V133aXt27e79THGKDU1VbGxsQoJCVFSUpK2bNni06IBAEDp4FVQWblypYYNG6Z169Zp2bJlOn/+vJKTk3X69GlXn/T0dE2YMEGTJk1SRkaGoqOj1alTJ2VnZ/u8eAAAULJ5dTHtkiVL3J5Pnz5dVapU0YYNG3TLLbfIGKOJEydq9OjR6tGjhyRp5syZioqK0pw5c/TQQw/5rnIAAFDiXdU1KqdOnZIkRUZGSpL27NmjzMxMJScnu/o4nU4lJiZqzZo1BY6Rk5OjrKwstwcAAIB0FduTjTEaPny42rZtq4YNG0qSMjMzJUlRUVFufaOiorRv374Cxxk3bpzGjh1b2DJQjBR080Cb3pstzABgn0KvqDz66KP69ttvNXfuXI9jDofD7bkxxqMt36hRo3Tq1CnX48CBA4UtCQAAlDCFWlF57LHHtHjxYn3xxReqWrWqqz06OlrSbysrMTExrvYjR454rLLkczqdcjqdhSkDAACUcF6tqBhj9Oijj2rhwoX6/PPPVb16dbfj1atXV3R0tJYtW+Zqy83N1cqVK9W6dWvfVAwAAEoNr1ZUhg0bpjlz5ujDDz9UeHi465qUiIgIhYSEyOFwKCUlRWlpaUpISFBCQoLS0tIUGhqqPn36+OUEAABAyeVVUJk6daokKSkpya19+vTpGjRokCRpxIgROnv2rIYOHaqTJ0+qZcuWWrp0qcLDw31SMAAAKD28CirGmD/s43A4lJqaqtTU1MLWBAAAIIm7J8NyRbmlGQBQ9LgpIQAAsBZBBQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtdieDK8VtGXYV3ceZjsyAOD3WFEBAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAW25OB//LVtmu2WAOA77CiAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1uCkh4IXieMNBX91sEQCKAisqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWYnsycBnFcTvylbj4vNiuDMBWrKgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiL7cnwiZK6jdc2bCsGUNqwogIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFrs+gGKsYJ2W7ETCEBJwooKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC12J4MWIobPQIAKyoAAMBiBBUAAGAtr4PKF198oe7duys2NlYOh0MffPCB23FjjFJTUxUbG6uQkBAlJSVpy5YtvqoXAACUIl4HldOnT6tx48aaNGlSgcfT09M1YcIETZo0SRkZGYqOjlanTp2UnZ191cUCAIDSxeuLabt27aquXbsWeMwYo4kTJ2r06NHq0aOHJGnmzJmKiorSnDlz9NBDD11dtQAAoFTx6TUqe/bsUWZmppKTk11tTqdTiYmJWrNmTYGvycnJUVZWltsDAABA8vH25MzMTElSVFSUW3tUVJT27dtX4GvGjRunsWPH+rIM+BjbZH2POQWAK+OXXT8Oh8PtuTHGoy3fqFGjdOrUKdfjwIED/igJAAAUQz5dUYmOjpb028pKTEyMq/3IkSMeqyz5nE6nnE6nL8sAAAAlhE9XVKpXr67o6GgtW7bM1Zabm6uVK1eqdevWvnwrAABQCni9ovLLL79o586drud79uzRpk2bFBkZqT/96U9KSUlRWlqaEhISlJCQoLS0NIWGhqpPnz4+LRwAAJR8XgeV9evXq3379q7nw4cPlyQNHDhQM2bM0IgRI3T27FkNHTpUJ0+eVMuWLbV06VKFh4f7rmoAAFAqeB1UkpKSZIy55HGHw6HU1FSlpqZeTV0AAADcPRnu2DZbOhX0c987vluRjQMA+bgpIQAAsBZBBQAAWIugAgAArEVQAQAA1iKoAAAAa7Hrp5i6eHcFOyvgb+zoAVAUWFEBAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAW25NLsCvZwsxNCHE1CvP7wzZnAN5gRQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFpsTy5ibNWErYrj1nX+PAElDysqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWYnuyruwuw0WpOG4TRdEpqb8v/tx6bPvfAUBpxooKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC12J5socJsLy2pW1JR/F3J7ybbg73HnKG0YEUFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC12PVTSIW9QVpR7s5hZxBKGn/tKLqSca/lLpvS/GfXnzejRPHAigoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLXYnlyA0rwVELCBP/8M2jZ2QVttfXVj0sJs4/XV1uySsq2Ymz8WPVZUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsxfZkH2JbM1B6FIc/7/6q0VfjFvUW5pKw9biwP4vidK6sqAAAAGv5LahMmTJF1atXV3BwsJo1a6ZVq1b5660AAEAJ5ZegMn/+fKWkpGj06NHauHGj2rVrp65du2r//v3+eDsAAFBC+SWoTJgwQQ888ICGDBmievXqaeLEiYqLi9PUqVP98XYAAKCE8nlQyc3N1YYNG5ScnOzWnpycrDVr1vj67QAAQAnm810/x44d04ULFxQVFeXWHhUVpczMTI/+OTk5ysnJcT0/deqUJCkrK8vXpUmS8nLO+GVcACiMgv6uK6l/T118rgWdZ2H6FNbFY1/Jz8Jfn02FVdjfFX+cR/6Yxhifjuu37ckOh8PtuTHGo02Sxo0bp7Fjx3q0x8XF+as0ALBGxMSiruDauZJz9VWfwijK977W/Hke2dnZioiI8Nl4Pg8qlSpVUkBAgMfqyZEjRzxWWSRp1KhRGj58uOt5Xl6eTpw4oYoVKxYYbK5GVlaW4uLidODAAZUvX96nY+PSmPeiwbwXDeb92mPOi8bF826MUXZ2tmJjY336Pj4PKkFBQWrWrJmWLVumP//5z672ZcuW6c477/To73Q65XQ63dquu+46X5flpnz58vwyFwHmvWgw70WDeb/2mPOi8ft59+VKSj6/fPUzfPhw9e/fX82bN1erVq301ltvaf/+/Xr44Yf98XYAAKCE8ktQuffee3X8+HG98MILOnTokBo2bKhPPvlE1apV88fbAQCAEspvF9MOHTpUQ4cO9dfwheJ0OjVmzBiPr5rgX8x70WDeiwbzfu0x50XjWs27w/h6HxEAAICPcFNCAABgLYIKAACwFkEFAABYi6ACAACsVayDypQpU1S9enUFBwerWbNmWrVq1WX7r1y5Us2aNVNwcLBq1KihN99806PPggULVL9+fTmdTtWvX1+LFi3yV/nFlq/nfdq0aWrXrp0qVKigChUqqGPHjvr666/9eQrFkj9+3/PNmzdPDodDd911l4+rLv78Me8///yzhg0bppiYGAUHB6tevXr65JNP/HUKxZI/5n3ixImqU6eOQkJCFBcXpyeffFK//vqrv06hWPJm3g8dOqQ+ffqoTp06KlOmjFJSUgrsd9Wfq6aYmjdvnilbtqyZNm2a2bp1q3niiSdMWFiY2bdvX4H9d+/ebUJDQ80TTzxhtm7daqZNm2bKli1r3n//fVefNWvWmICAAJOWlma2bdtm0tLSTGBgoFm3bt21Oi3r+WPe+/TpYyZPnmw2btxotm3bZgYPHmwiIiLMjz/+eK1Oy3r+mPd8e/fuNddff71p166dufPOO/18JsWLP+Y9JyfHNG/e3Nx2221m9erVZu/evWbVqlVm06ZN1+q0rOePeZ89e7ZxOp3m3XffNXv27DGffvqpiYmJMSkpKdfqtKzn7bzv2bPHPP7442bmzJmmSZMm5oknnvDo44vP1WIbVFq0aGEefvhht7a6deuakSNHFth/xIgRpm7dum5tDz30kLn55ptdz3v16mW6dOni1qdz586md+/ePqq6+PPHvF/s/PnzJjw83MycOfPqCy4h/DXv58+fN23atDH/8z//YwYOHEhQuYg/5n3q1KmmRo0aJjc31/cFlxD+mPdhw4aZW2+91a3P8OHDTdu2bX1UdfHn7bz/XmJiYoFBxRefq8Xyq5/c3Fxt2LBBycnJbu3Jyclas2ZNga9Zu3atR//OnTtr/fr1Onfu3GX7XGrM0sZf836xM2fO6Ny5c4qMjPRN4cWcP+f9hRdeUOXKlfXAAw/4vvBizl/zvnjxYrVq1UrDhg1TVFSUGjZsqLS0NF24cME/J1LM+Gve27Ztqw0bNri+Vt69e7c++eQTdevWzQ9nUfwUZt6vhC8+V/32L9P607Fjx3ThwgWPuzFHRUV53LU5X2ZmZoH9z58/r2PHjikmJuaSfS41Zmnjr3m/2MiRI3X99derY8eOviu+GPPXvH/55Zd6++23tWnTJn+VXqz5a953796tzz//XH379tUnn3yiHTt2aNiwYTp//ryef/55v51PceGvee/du7eOHj2qtm3byhij8+fP65FHHtHIkSP9di7FSWHm/Ur44nO1WAaVfA6Hw+25Mcaj7Y/6X9zu7ZilkT/mPV96errmzp2rFStWKDg42AfVlhy+nPfs7Gz169dP06ZNU6VKlXxfbAni69/3vLw8ValSRW+99ZYCAgLUrFkzHTx4UK+++ipB5Xd8Pe8rVqzQyy+/rClTpqhly5bauXOnnnjiCcXExOi5557zcfXFlz8+A692zGIZVCpVqqSAgACPRHbkyBGP5JYvOjq6wP6BgYGqWLHiZftcaszSxl/znu9vf/ub0tLS9Nlnn6lRo0a+Lb4Y88e8b9myRXv37lX37t1dx/Py8iRJgYGB2r59u2rWrOnjMyle/PX7HhMTo7JlyyogIMDVp169esrMzFRubq6CgoJ8fCbFi7/m/bnnnlP//v01ZMgQSdINN9yg06dP68EHH9To0aNVpkyxvBLCZwoz71fCF5+rxfInExQUpGbNmmnZsmVu7cuWLVPr1q0LfE2rVq08+i9dulTNmzdX2bJlL9vnUmOWNv6ad0l69dVX9eKLL2rJkiVq3ry574svxvwx73Xr1tXmzZu1adMm1+OOO+5Q+/bttWnTJsXFxfntfIoLf/2+t2nTRjt37nQFQ0n64YcfFBMTU+pDiuS/eT9z5oxHGAkICJD5bVOJD8+geCrMvF8Jn3yuXvFlt5bJ30b19ttvm61bt5qUlBQTFhZm9u7da4wxZuTIkaZ///6u/vnb15588kmzdetW8/bbb3tsX/vyyy9NQECAGT9+vNm2bZsZP34825Mv4o95f+WVV0xQUJB5//33zaFDh1yP7Ozsa35+tvLHvF+MXT+e/DHv+/fvN+XKlTOPPvqo2b59u/noo49MlSpVzEsvvXTNz89W/pj3MWPGmPDwcDN37lyze/dus3TpUlOzZk3Tq1eva35+tvJ23o0xZuPGjWbjxo2mWbNmpk+fPmbjxo1my5YtruO++FwttkHFGGMmT55sqlWrZoKCgsyNN95oVq5c6To2cOBAk5iY6NZ/xYoVpmnTpiYoKMjEx8ebqVOneoz53nvvmTp16piyZcuaunXrmgULFvj7NIodX897tWrVjCSPx5gxY67B2RQf/vh9/z2CSsH8Me9r1qwxLVu2NE6n09SoUcO8/PLL5vz58/4+lWLF1/N+7tw5k5qaamrWrGmCg4NNXFycGTp0qDl58uQ1OJviw9t5L+jv7mrVqrn1udrPVcd/3wgAAMA6xfIaFQAAUDoQVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArEVQAUooY4wefPBBRUZGyuFw/OFdkpOSkpSSknJNaiuMW265RXPmzPHpmJs3b1bVqlV1+vRpn44LwHcIKkAJtWTJEs2YMUMfffSRDh06pIYNGxZ1SYX20UcfKTMzU71793a1xcfHa+LEiR59U1NT1aRJkysa94YbblCLFi302muv+ahSAL5GUAFKqF27dikmJkatW7dWdHS0AgOL5c3SJUmvv/66Bg8e7Jc73A4ePFhTp07VhQsXfD42gKtHUAFKoEGDBumxxx7T/v375XA4FB8f7/UYS5YsUUREhGbNmuUa86677tLf/vY3xcTEqGLFiho2bJjOnTvnek1ubq5GjBih66+/XmFhYWrZsqVWrFjhNu6aNWt0yy23KCQkRHFxcXr88ccv+9XLsWPH9Nlnn+mOO+7w+hwkyeFweDx+Px+dO3fW8ePHtXLlykKND8C/CCpACfSPf/xDL7zwgqpWrapDhw4pIyPDq9fPmzdPvXr10qxZszRgwABX+/Lly7Vr1y4tX75cM2fO1IwZMzRjxgzX8cGDB+vLL7/UvHnz9O233+qee+5Rly5dtGPHDkm/XRPSuXNn9ejRQ99++63mz5+v1atX69FHH71kLatXr1ZoaKjq1avn3ST816FDh1yPnTt3qlatWrrllltcx4OCgtS4cWOtWrWqUOMD8C9uSgiUUBMnTtTEiRO1d+/eK+qflJSkJk2aqHbt2nr22We1aNEitW/f3nV80KBBWrFihXbt2qWAgABJUq9evVSmTBnNmzdPu3btUkJCgn788UfFxsa6XtexY0e1aNFCaWlpGjBggEJCQvTPf/7TdXz16tVKTEzU6dOnFRwcXOB5vPHGG9q1a5dbe3x8vA4dOqSyZcu6tefm5qp+/foeFw8bY3T33Xdr//79WrVqlUJCQlzHevTooYiICE2fPv2K5grAtVN8v7QG4HMLFizQ4cOHtXr1arVo0cLjeIMGDVwhRZJiYmK0efNmSdI333wjY4xq167t9pqcnBxVrFhRkrRhwwbt3LlT7777ruu4MUZ5eXnas2dPgasmZ8+eLTDASNLTTz+tQYMGubW9/vrr+uKLLzz6Pvvss1q7dq0yMjLcQookhYSE6MyZMwW+B4CiRVAB4NKkSRN98803mj59um666SY5HA634xevXjgcDuXl5UmS8vLyFBAQoA0bNriFGUkqV66cq89DDz2kxx9/3OO9//SnPxVYU6VKlXTy5MlLHqtVq5ZbW2RkpEe/2bNn67XXXtOKFStUtWpVj+MnTpxQzZo1C3wPAEWLoALApWbNmvr73/+upKQkBQQEaNKkSVf82qZNm+rChQs6cuSI2rVrV2CfG2+8UVu2bPEIF380bmZmpk6ePKkKFSpc8evyrV27VkOGDNE///lP3XzzzQX2+e6779SzZ0+vxwbgf1xMC8BN7dq1tXz5ci1YsMCrfwCudu3a6tu3rwYMGKCFCxdqz549ysjI0CuvvKJPPvlEkvTMM89o7dq1GjZsmDZt2qQdO3Zo8eLFeuyxxy45btOmTVW5cmV9+eWXXp9LZmam/vznP6t3797q3LmzMjMzlZmZqaNHj7r67N27Vz/99JM6duzo9fgA/I+gAsBDnTp19Pnnn2vu3Ll66qmnrvh106dP14ABA/TUU0+pTp06uuOOO/TVV18pLi5OktSoUSOtXLlSO3bsULt27dS0aVM999xziomJueSYAQEBuv/++92ua7lS33//vQ4fPqyZM2cqJibG9bjppptcfebOnavk5GRVq1bN6/EB+B+7fgBY7/Dhw2rQoIE2bNjg00CRk5OjhIQEzZ07V23atPHZuAB8hxUVANaLiorS22+/rf379/t03H379mn06NGEFMBirKgApcD+/ftVv379Sx7funXrJXfdAEBRIqgApcD58+cv+w+/xcfHF+t7AQEouQgqAADAWlyjAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABY6/8B5w8d4wF4Z+AAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# After demodulation\n",
    "f_knee = noise_fit_stats_demodQ.fit.T[0]\n",
    "plt.figure()\n",
    "plt.hist(f_knee[((f_knee>0)&(f_knee<0.1))], bins=100)\n",
    "plt.xlabel('f_knee (Hz)')\n",
    "plt.title('f_knee after demodulation')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "SO shared + LOCAL",
   "language": "python",
   "name": "so-stack-local"
  },
  "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
}