20240705_fft_ops_PR.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\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "614df904-08c2-4478-9bbb-eeb68ec71555",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:00:44.833535\n"
     ]
    }
   ],
   "source": [
    "start = datetime.datetime.now()\n",
    "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': ws, 'wafer.bandpass': bandpass})\n",
    "aman = ctx.get_obs(meta)\n",
    "for idet in range(aman.dets.count) : \n",
    "    aman.signal[idet] *= aman.det_cal.phase_to_pW[idet]\n",
    "alive = np.where(np.any(np.isnan(aman.signal), axis=1)==False)\n",
    "aman.restrict('dets', aman.dets.vals[alive])\n",
    "detrend_tod(aman)\n",
    "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",
    "        )\n",
    "stop = datetime.datetime.now()\n",
    "print(stop - start)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce97d6c4-253e-471e-ac4c-c66140d23bf8",
   "metadata": {},
   "source": [
    "# New features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2502e7f5-144c-475a-97ae-63ecf996608c",
   "metadata": {},
   "source": [
    "* PSD mask\n",
    "    * Masking HWPSS or vibration signals makes the fitting stable\n",
    "* PSD binning\n",
    "    * Fitting becomes about 25 times faster than without binning\n",
    "    * Knee frequency and white noise level are unbiased\n",
    "    * The standard errors of the fit parameters become smaller\n",
    "* Other changes in the fitting functions\n",
    "    * Fitting parameters can be reduced (white noise or alpha can be fixed)\n",
    "    * 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."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7a8b02f-9cae-4dc8-8cff-66f42881c036",
   "metadata": {},
   "source": [
    "# \"calc_psd_mask\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f34ece7-28e7-4495-8943-dbf35b530523",
   "metadata": {},
   "source": [
    "``calc_psd_mask`` returns Ranges array to mask hwpss or single peak in the given PSD.\\\n",
    "If mode == \"replace\", it will return new mask in aman.PSD_mask.\\\n",
    "If mode == \"add\", new mask range will be added to the existing aman.PSD_mask.\n",
    "You can obtain masked PSD like:\\\n",
    "\"\"\"\\\n",
    "mask = ~aman.PSD_mask.mask()\\\n",
    "f = f[mask]\\\n",
    "pxx = pxx[:, mask]\\\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "13f3672e-afe8-44cb-b75b-779e8946ff61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:00:00.139487\n"
     ]
    },
    {
     "data": {
      "image/png": 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bdQghhBgWSpBIt9C3ZyTsLV0gkUlw8doxeXlW/k1IGSnM+RYKe7tZCqwBADV1FfKy3Id/AgBsLVzA55uBzzeTtzSVVxWrvK9E0ogDJz/B0cu78L8T6ylJIoQQI0EJEuk2wnvGAABu5/0OiaRpi4OswpsAADc7P4W6FuZNLUg19Y/HFxWWPgAAuNh6KdWrqFWd+Px8ZhdySptW264VVeLw6e2d/RiEmBw9DUHqEmnZ5bhwr7T9isToUIJEuo2IvlEQ8i0hktQj4845AEBB6T0AgLdLkEJdK6EdAKC24XGCVFLZNP7I3amHvMzyrwSpqkVX3KPyAmz/YTW+PbERd4uuAAAGBsQCAB5WZaO2jma9EWIq7hRVI6ukFlUNjWyHQnSMEiTSbfD5Zgj0iAAAXH9wBg2iOpTVFgAAenr3U6hrbekAAKhrqALQNI6ooq4IAODtEiCvZyVsqlddVy4vu3n/MqpF5cgpvQ0GDLwde2HM4NmwMGvqtst/dE8fH48QwiKGYTsComvdNkHKzc1FdHQ0goODERYWhoMHD7IdEukCkX3HAgCKq3Jx+cZvkDEyWPCt4Omi2MVma2kPAKhvbGrtyX+UDYlMAj6XDy/Xx2OVbCya6rVsaXpUkSd/zecKMCbySQCAg5UbAKCw5PFWJYQQQgxTt10Hic/nY9OmTQgPD0dxcTEiIiIwadIkWFlZsR0a0SM3Jy+42/VAUeUDXM5sWjjS1d5PqZ6NVdMaRw3ipunBecVNA7TtLd3A5fFa1PurpUlUJS+rqH0IABgVMhPBPQfD7q/1khxtPFBQcU8hgSKE6I++1lci3UO3bUHy8PBAeHg4AMDV1RWOjo4oK6MZRt1BaM9RAACJrGnMgI9rb6U69tZNi8qJJPWQSaV4WNrU6uNs561Qz866aRHJOnFTS5NMKkVVfdN4JB/3PvLkCABcHXwBNCVQGXfOYsePryG3MFNnn4sQQojuGGyClJKSgqlTp8LT0xMcDgeHDx9WqrNt2zb4+/tDKBQiMjISZ86c6dC9UlNTIZPJ4OPj035lYvTCAofKp+cDQIB3mFIdO2tHcMABAwaVNWXyBSI9WgzQBiBfLLKhsQYyqRRFZXmQyCTgcXhwd/RSqOvu0nRuVX0JTmV8g4q6RzhwWrNNdAkhhoehgUcmzWATpNraWvTv3x9bt25VeXz//v1YsWIFXnvtNVy5cgWjRo1CXFwccnJy5HUiIyMRGhqq9FVQUCCvU1paigULFmDHjh1txiMSiVBVVaXwRYwTn2+GXp6RAABLga18xe2WuDwezPkWAIDSyoeorHsEAPByDVSo52DT1IIkY2SoqitHQXEWAMDGwhl8vplCXU8nH/A4PEhkEkhkEgCAlJHS2kiEUFcYMUAGOwYpLi4OcXFxao9v3LgRixcvxpIlSwAAmzZtwvHjx7F9+3asX78eAJCWltbmPUQiEWbMmIE1a9Zg+PDhbdZdv3493nrrLS0/BTFUw/tPRnlNEXp5R6itIxRYoUFSh6yCG5AyUvC5ZnB3VOxia14sskFSh7KKh3hUkQvg8YDslrg8HiwFtqgWlSuUX/3zLEZFTNPBpyKEEKIrBtuC1BaxWIy0tDTExsYqlMfGxuL8+fMaXYNhGCxatAhjxozB/Pnz262/Zs0aVFZWyr9yc3M7FDsxDHbWjnhqwisYFDJWbR0Ls6Y1jnIfNS30aG/pqjBAu5mtZdN4pfsFN+VrJbnYK7dKAYDwrxW6WyqveaRUVltXTeOTCDEiUhl1t5kag21BaktJSQmkUinc3BT/Sndzc0NRUZFG1zh37hz279+PsLAw+fimr7/+Gv369VNZ39zcHObm5p2KmxgXS6EtUAmU1jR1yTrZeKisF+g5AMVVufgz/zLqxE1dr34efVXWtVCRIIkbG5TKfkj+Lwor76Of7yjEjWg/gSeEsKuyvhGOVgK2wyA6ZJQJUjNOqzmcDMMolakzcuRIyGQyfYRFTETzatoMmv4ydHFQPYi/X6/huHjnCGpEFQCalgLw9+rT5jVbEkuUE6TCyvsAgGs5ZxDRZ4zKcVKEEEL0xyi72JydncHj8ZRai4qLi5ValQjpKGsLxWTGw9lfZT07a0e42vrK34f2UD+erfU1AUAiFSmV8TiPu/KyC261GyshhBDdMsoESSAQIDIyEomJiQrliYmJ7Q62JkRT1n+tpg0AXA4XPm4Baus2D/bmcwWI6BOl0TWbiSWKCVJtXTWkjFT+vrDsgWYBE2KkODSNjRggg+1iq6mpwd27d+Xvs7KykJGRAUdHR/j6+iIhIQHz58/HwIEDMWzYMOzYsQM5OTlYunQpi1ETU2L7135sAGAjdFSatt/SoOCxKK8qhrdrIITmlmrr2f21ACUA+TpLUqlYoU55jeLO4KVV+dqGrkQiaURpZTF11RFCiIYMNkFKTU1FTEyM/H1CQgIAYOHChdizZw/i4+NRWlqKt99+G4WFhQgNDcXRo0fh56e8bQQhHeFg6yp/7Wjt2WZdPt8Mk0YuaPeatlaPEyQrczvUiCogbtXFVt0qQaqoewiJpLHNBK09Sak/4ErWSQzrPYWWFCDdBm01QjrDYBOk6OjodlcpXbZsGZYtW9ZFEZHuxsbKXv5a3bR9bdnbPE6QbISOqBFVoLFVC1JNfQUAwNnaCxV1xZDIGlH46AF8PHp1+L5Xsk4CAC7cOUIJEiE6QgtpmzajHINESFfg881gwW/avNjTRfUAbW0JzS3B5zZNBba1bNqnTfrXnnDNauoqAACW5jZwsHIHAOQW38Wj8gLcz73Roftam9t3LGBCiEaoscr0UIJESBtGhc1C/x7RCPIL19k1Lcyakq7mjW4ZMGgQ1cmP14maNr61EFjDybapay+vOBOHUv6L785+ghv3UlVeVyJpxKPyApXHBHyh/HXLexFCdIMak0wPJUiEtCG890hMGPaUTq/pZNPUXefn0Vc+e6e+oVZ+vLa+abFJS6EdPJ2aZs49KLmOir/2gzt3/Uc8Ki/Azp/eQOLF/fLzvju1FbuPrcXVzIvyMrFYeQmB5usTQghRjxIkQrrYzJjnsDjubfh79QGP2zTwulb0OEESNTa18FgIbdDXPxJ8ruLg7Mq6Yuz77UOU1RbJxxYBQE5p03pJF2/+AgD45ewebPlhBbIL/oRU+rgbr66+Wj8fjBADU1ojbr8SIWpQgkRIF+PzzeBk3zS2yIzXNB5J3DJBktQDACwFVrCytEHcoGcVzmfAoEGivpusTlQJALiRex5SRopLN09Awkjkx2uoBYkYGH3NNmuUUscX6ThKkAhhkYDXtL9fvbheXtY8q81C2LRvm6uj+hl0fK7yRFSxtAGVNWXy9+YCC4UWpPqGms4FTQhRQoO0TQ8lSISwiPdXC5KosUWC9FcLktC8aTC3g42L2vMlMglkUqlSeUFxlvw1wzCQyB63INWJa5XqE0IIUUQJEiEsau5iE7VIWiSyphYkS6ENAIDL4ymf2EL9X7PSmpckAIDi8jz56wZxjcJSAmIxzWIjhJD2UIJECIvM/pp+L/qri00iaZS39lgKHyc8Ap5Q+eS/NPyVXAnMLORlt3N/l7+uF1eDaTEJWSpTbnEihE3UPUUMESVIhLCoeX0icWMDrt/9HZ98t1x+zOqvFiQAmBvzL4T3iIGz9ePxSM0LTtaLG5oKWizrW1lfIn/dvK5SM2mL7jZCDEFVA/2fJIaHEiRCWGTGbxqkLWqsx9HLuyBlmlp3+FyBQteap4sfYofNg7Od9+Nz/+qea/hr0LWMkam8R51YcdZa65W7CWHbo2rl9boIYRslSISwyEpoBwAoqcpXKG9OflqLiZyFXu4RmDliOfh/zYDLf3QP3538L+obmxIlS4GtwjmtE6f2utiy8m/jUNKnqKguabMeIYSYMkqQCGGRr3vTBrSPqnMVypun/7dmY2WPGTFLEegbCjNe0wKSv//5C+4X/wHJXy1Drra+bd5TJm27O+NgykZkFqXjl3NfoEFUh/TbZ1DXQDPfCCHdCyVIhLDI1z0IXI7yt6EZX/2gbHkdNUmUq4NPm+dJNByDVFpdgCNnd+O3K1/j8OlPNTqHEEJMBSVIhLBIaG6pMtGRMe3PNDPjq+6Gc3NspwVJw1lsMkaG+8V/AADyyu5odA4hhJgKSpAIYZmTtScAgMd5PCi7UdL+oFU+V3ULkrWFrcryZjJGsQVJ1UKTTfVoOQBC2kIbmZg2SpAIYdmEofPh69QXc6MS5GWN0vYTJHMzNd1wHB4G94qDndBJ5eGW0/wflRdg8/cr8FPKLuQW3cfVzIvyYwwjA6eNFWrSb57Gf394BVn5t9uNlRBCjI3yRk6EkC7l4uCJJ2NXKpQJBVZqaj9mYW6jspzD5SJ64AxE9o3B9p9WKR2XyR7Paku5chhiqQi383/H7fzfFesxMvC4PLVjln77Yy8A4MiFHXhx9sZ24yWEEGNCLUiEGJCpQ/4JZ2svxA1e1G5dSws7leV8blNXHZejeosSKfN4HSRpGzPaGA07EBqlDRrVI4QQY0ItSIQYkL49I9G3Z6RGdW0t7VWWc/6aFcdTs4dbyxak1uORWtNkxhttXUIIMUWUIBFipKzVtCBxuU3jhvh/rZPUWsvB1x0ZiJ16Ixlnb3wvf69pSxMhhBgTSpAIMVI21g4qy/ncpsSIz1edILUcpC1VM4OtLaeufqP1OYQQYmxoDBIhRsrOSvUsNQ637b3RZS02ta1pKNdpTIQQYiqoBYkQIyUQqF4Hicdt+9u6uqEU93NvIDPvD1SLKEEihBBVKEEixIgN7/sEHpY9QEllLiobSgGon73W0ndnP9H6XhJJo9puu6z82ziZ9g1GhE7XeJA5IYQYMupiI8SIjQyfhFljloHDffytrG72WmdJpI1qj/18/lOU1Rbh598/w5/ZV1FdW6GXGAgxJAxDExRMGSVIhJgYdS1IQr5lp65bUJyl9pi4xVpIh89vxZ6jb3XqXoQQwjZKkAgxCY+/lblc1d/WZnyLTt3hpwufoq6hVqO69RLN6hFCiKGiBIkQE6NunBAYmepyDYmlImQXqN53ra092wghxBhRgkSICeByNEtQ5oxOaL9SG/KK76osl6pYcPJM+k/y18cvfKPwnhBCDF23T5Dq6urg5+eHl19+me1QCNErBgz8vfrATqh6/SRN3Mg5q3HdC3eOAAAelRfgjwfJuHDnCCQS9QO9CSHEkHT7BOndd9/FkCFD2A6DkE5qvwWpeUsQS3PVW5RoQiwVaX2OTPZ4ps+lG79B1oHVuwkhpKt16wQpMzMTt2/fxqRJk9gOhZAu08snokvvl377lPz12ZuHcP7a8S69PyGEdITBJkgpKSmYOnUqPD09weFwcPjwYaU627Ztg7+/P4RCISIjI3HmzBmt7vHyyy9j/fr1OoqYEPZwOe1/Kzev2WIptNV3OAqu5Sh+X97NT1N4LxaL8NnhV3Eo6dOuDIsQQtpksAlSbW0t+vfvj61bt6o8vn//fqxYsQKvvfYarly5glGjRiEuLg45OTnyOpGRkQgNDVX6KigowI8//oigoCAEBQV11UcihFXMX7PYVG1F0nIW2sSBz+rsnqrGHHFa/djJuHMGlfUlyCxK19l9CSGkswx2q5G4uDjExcWpPb5x40YsXrwYS5YsAQBs2rQJx48fx/bt2+WtQmlpaWrPv3jxIr799lscPHgQNTU1aGxshK2tLd544w2V9UUiEUSix+MvqqqqOvKxCNETzafZm/GVv+15XDNIZGIAgKOdu8KxmLB4JF3d36GoxBKxUtnDqmz8lLILQ0Imws3JCzX1FR26NiGE6JPBJkhtEYvFSEtLw7///W+F8tjYWJw/f16ja6xfv16eSO3ZswfXr19Xmxw113/rLVodmBgmjobT/AGAy1H+tjdrkSC15GbrB2d7rw7H1ShRPaj7dv7vyH54HX5uobid/7u8/M/sqwjyCwMA/Hrua9wtSMfA3hMgEtcjeuCMDsdBCCHaMtgutraUlJRAKpXCzc1NodzNzQ1FRUV6ueeaNWtQWVkp/8rNzdXLfQjRm7/GIJnxBUqH+LzHZS3XVBIKrBSOaautGWv1klqF5AgAEtO+xrcnPsat+2m4lnMG9ZJanLnxAy5l/oo/szM6HAch+qbF3yjESBhlC1Kz1n81Mwyj1V/SzRYtWtRuHXNzc5ibm2t9bUIMRfM0f56KBImnkCA9/rvJwtymU5vfqupia0utqBK1okrklN5SOlZRVdLhOAghRFtG2YLk7OwMHo+n1FpUXFys1KpESHegzd8FfK5ywmPWIkHitEiQBHyhykHdfC4fPV37t3uvRqnuFoaUMhKdXYuQrsAwDM5mluB6fiXboZAOMMoESSAQIDIyEomJiQrliYmJGD58OEtREcKe1jPDmg3v+4T8dXMLkpmKvdoie48HAPg69QWX+zjb4nA4EKhocYoIGI/ZY5/HwIDYNuNidJggnbnxA46e/Upn1yOks5h2jhdVNSCnrA5X8yhBMkYG28VWU1ODu3cf7/uUlZWFjIwMODo6wtfXFwkJCZg/fz4GDhyIYcOGYceOHcjJycHSpUtZjJoQwzIyfBK4HA4u3v4FcYOfAQDweMrf9h7OvnhhxsewFFrhUXmBvJzL4YHbRhfbmMGzkXrvhNrjjVp2sbXneu5ZTMICnV6TEH2RSNtLoYghM9gEKTU1FTExMfL3CQlNm2wuXLgQe/bsQXx8PEpLS/H222+jsLAQoaGhOHr0KPz8/NgKmRCDNLx/HIaGxsoTHTOe8lg6LocLS6GV/HUzDocLPle5xUnc2KDRvSUy2nuNEGKcDDZBio6Olq/8q86yZcuwbNmyLoqIEMPV3uSElq1AqgZdt2xV4nJa1OVyVdaXaNh1JqV914gRkskYHLtRBCtzPqKCXNgOh7DEYBMkQojm1I1BUkXAV25Bajkwm6MwBomrsn6jRLMWpJ9//0zjuHRNJpW22T1IiDoltSJU1DWioo5aQLuzTg3SbrmyNCHEOKhKGlp2o3FadbGpqq/L2Wn6kHhxPzZ99xJyi+6zHQoxRjR0iEDLBOn48eNYtGgRAgICYGZmBktLS9jY2CAqKgrvvvsuCgoK2r8IIcTgcLiPfxS0HIPEVdN15+7kr/eY2iMWixRet3x/JeskJDIxfkvdy0ZohBAToFEX2+HDh7F69WpUVlZi0qRJeOWVV+Dl5QULCwuUlZXh+vXr+O2337Bu3TosWrQI69atg4sL9dsS0lV4POWB1Nrgc1WPQVJnaOj4Tt2vM46d34ur2acBAE8MfwGB3iHYemglGIbBS7M3g99iGQMpDRInhHSQRgnSe++9h48++giTJ08Gl6vc6DR37lwAQH5+Pj755BN89dVX+Ne//qXbSAkhao0d+CS+O70JIb4dWwdM1fc1oNjd1hJfxVpKXaU5OQKA5IyD8HD2hUTWtIhkZU0pnOwfb7YrldHikoSQjtEoQbp06ZJGF/Py8sKHH37YqYAIIdpzc/LC8zP/0+HzWyY8LbvboCZBMhR8Lh+Z2X/I38sYmWKFdmbCEkKIOlr/9EtJSUFxcbFSeWNjI1JSUnQSFCFEv1xtfdQeUzfuyBBV1Zfitz/UjzOSqT1CCCFt0zpBio6ORv/+/XHhwgWF8rKyMoWFHQkhhmvBxFcxbsB8lcdajkEy9GRJLFVcbkAma9Vi1KpF6WzGUfz2+0F9h0W6CWqgNG0daj9/8sknMXbsWOzZs0ehvL2FHQkhhoHL48Hawkb1MW7Laf6GnSC1xrRKiJhW87XP3zqM9PuJKCrJxcPSfNzNud6V4REjQb/JCNCBhSI5HA7WrFmDUaNGYeHChbh69So2bNggP0YIMQ6B3v3gaR8AWytntkPRm9YJUrPyqmL5IpZPCV+Ft2uPLoyKmIrqBpolacq0bkFqbiWaOXMmUlJS8N133yEuLg4VFRW6jo0QokdcHg9/i1uNaaMXK5a3WijSmMgYGS5ee7x5bstWbVmLbU9arvCddlP9ZrtEf8QS4x8hVt9IW+mYsk799BswYAAuXbqEiooKjB07VlcxEUJY1HLlbO5fPyImRi5iKRrt/O+3d5Fy/Tv5+zpxFfKKHwBQMcPtL3cKU7siNNKKjIZkEAOndYK0cOFCWFhYyN+7u7vj9OnTGDt2LHx9fXUaHCHEMIQFDcfUIf8EAHg79mY5Gu18c/I9JKcegkxm/C0WxLhQDmjctB6DtHv3bqUyc3NzfPnllzoJiBBiOFpuXNu3ZyQ8Xd+HnbUjixF1zKXMX5UGcBOiSxzQGFxTo3GCdPXqVY3qhYWFdTgYQohh4UBx2xFjTI6aPSzPZjsEQogR0ThBCg8PB4fDkQ96bJ6xxjCMvJzD4UAqpUFrhBDDQ9uOEF2jLjTTpnGClJWVJX/NMAxCQ0Nx9OhR+Pn56SUwQgj7NFko0sOuJwor73dBNJ0jlojUHrubcx2BvqEor3yE8qpiWFrY4tLNExjVfxoc7GjjbdI+qYpsqbxOzEIkRFc0TpBaJ0IcDgfe3t6UIBFiytRsYtvSE1FLkXYrCZcyf5WXxUYswIn0r/QZmdYeVeeqPfbDuc1Y5bsDnx99TaG8pDIPz059U9+hEQPTkZah7NJa+DtbKZRJWq/sToyKcS1yQggxODZW9ogeOAMLYx8nEmZ8IYsR6U5JTT7bIZgsU1tXWKoyGaIEyZhRgkQIUUubvdisLWzlr834Wk+QZd2H+/6hUb37uTdQXvlIz9GQ7kp1okXY0KkEibYWIcQ0WfCbugr8vYI1Poej0B33+HWYX5SuwmLdqUvf4buznyh1xRHtdY9p8dp9xvJaMfZfzsXlB2V6iodoQ+M/8wYMGKCQENXX12Pq1KkQCAQK9dLT03UXHSGEFf+c/j4aJWJYWare0LY9LbcrGdF/MrKLb6CyvkRX4XWpY+f3wt3JD5ZCa6Teo21JiP5cL6gEAGQ+rMGgHsa7pIap0DhBmj59ukKCNH36dL0ERAhhn0BgDoHAXKtzLIVWcLb2hoyRwtutp7zc3MwCM6NewOn0Q7A0t8H13LO6DlevrmafxlUVSyhdvHYC6Zm/YdrIZbTZrYlRt8kx6V40TpBefvllWFtb6zMWQoiRe3bqG/LX86JfATg8CATmcBF4YvbY5yGRNOJuYToaJHUsRqkbzXu+Hbu4G0umvcVyNIQQXdN4DJKzszPi4uKwfft2FBQU6DMmQogJ8PHoBR/3ngplfL4ZZkataPM8N1vjWjpExtAClB1hakNYadFI06NxgnTnzh1MmjQJ33//Pfz9/TFo0CCsW7dO4y1ICCEEUD8zLrxHDCYNWoy+vkO6OCLdEItFOJ32Ix6W0tIAhJgCjRMkPz8/vPjii/jtt99QXFyMhIQE3LhxA6NHj4a/vz9eeuklnDp1irYaIYS0qeUA7pYsLewQGjgEMhjnprKJl/bh9z9/wf8S32E7FEKIDnRomr+dnR3mzZuHb7/9FiUlJfjss88gk8nwzDPPwMXFBXv37tV1nISQbkImM64/skSN9QCA/NJMAICUMa74ieHoHksfGI9OLxTJ5/MRGxuLLVu2IDs7GydPnkRQUJAuYiOEmCCOmhak5q43Y0uQ6htrAAAVdY8Xj6ytq8apS9+htKKIrbAMHi2IqBmRRIrcsjp6XizQ+XK3AwYM0PUlCSHdgDEvPFtbV63w/uezO5FTegs3cs7jxdkbWYrKsN1/VMt2CEbh1K1ilNc1oq+HDQb4OrAdTreicQtSY2MjVq1ahcDAQAwePBi7d+9WOP7w4UPweDydB0gIMS0Mo3qMUXPLUkTfmK4MRyf+++O/FN4XlN8D8Lh1iSgTS41zrJk6+mrfKa9rBABklxr/0hjGRuME6d1338VXX32FpUuXIjY2FitXrsQ///lPhTqMkc1zzMrKQkxMDIKDg9GvXz/U1tJfNITom6SdLjRLoVWbxwnRNyP7VUb0ROMutr1792Lnzp2YMmUKAOCZZ55BXFwcnnnmGXzxxRcAjK+JfNGiRXjnnXcwatQolJWVwdxcu5WDCSHaY6SNKsu5av5esxTYok5cpc+Q9Opq5kVIGkUICxoOPt+M7XCIDrWXSOnyV2Kd2LjG5pkCjVuQ8vPzERoaKn8fEBCA5ORkXLhwAfPnzze66f03btyAmZkZRo0aBQBwdHQE3wh3ICfE2NjaOKks53CVf5v0dO0Pf7dQFbUNm0Qmlr8+lvoFfvtjL75NpLFI3Q21RBk3jRMkd3d33Lt3T6HM09MTp06dwuXLl7Fw4UKdBpaSkoKpU6fC09MTHA4Hhw8fVqqzbds2+Pv7QygUIjIyEmfOnNH4+pmZmbC2tsa0adMQERGB9957T4fRE0LUsbN2xMwRy9HPd5RCOQemPYaxoOJe+5UIaUd1QyPO3y1Bea24/cqkUzROkMaMGYNvvvlGqbw5SXrw4IEu40JtbS369++PrVu3qjy+f/9+rFixAq+99hquXLmCUaNGIS4uDjk5OfI6kZGRCA0NVfoqKChAY2Mjzpw5g//+97+4cOECEhMTkZiYqNPPQAhRLdA3FC72PmyHQYjOWAmUE3x9jDpJ+bMED0rrcOwGLSGhbxr3Kb3++uu4ffu2ymNeXl5ISUnBiRMndBZYXFwc4uLi1B7fuHEjFi9ejCVLlgAANm3ahOPHj2P79u1Yv349ACAtLU3t+d7e3hg0aBB8fJp+SE+aNAkZGRkYP368yvoikQgikUj+vqrKeMdEEGKI1G1BQogx4PM6vaygRirrm8bwUfed/mm11ci4cePkM9UYhlEYd+Th4aHzbjZ1xGIx0tLSEBsbq1AeGxuL8+fPa3SNQYMG4eHDhygvL4dMJkNKSgr69u2rtv769ethZ2cn/2pOrAghOsLtml8whOiKSGJcY2+JdrT6ifTJJ59gy5YtAICtW7fik08+0UtQ7SkpKYFUKoWbm5tCuZubG4qKNGt25PP5eO+99zB69GiEhYWhV69e8hl6qqxZswaVlZXyr9zc3E59BkK6O243Xjft/B+/4sLVY2yHQTrpfsnjpWFKakRt1NQfhmHQ0EiJmj5oNW3rxRdfxPjx4xEVFYXvvvsOJ0+e1FdcGmm9rADDMFotNdBeN15L5ubmtAwAIToUFjgUKVcPQixtANA9utjEYhFEjfU4e/MQAGBA79EQmluyHBVprSO9VxV1qpev0EZHvgWS7hSjqFKEuFB3OFgJOh0DeUzjFqS33noL69evh6urK0aOHAl3d3e89957ePvtt/UZn0rOzs7g8XhKrUXFxcVKrUqEEMPE55thdnSC/D1XzR5tpkYiefyL1NiWR9El00+Hu0ZRZVPL1d1HqldtZ2SmtWJ5V9K4BSk6OhoAUFZWBh8fH3h6eiIqKkpfcbVJIBAgMjISiYmJmDFjhrw8MTER06dPZyUmQkgnqUiQeFzT6oY798cRpN17PFv2QeEdhAQMZDEi9pjCGGNDHygtk0qRfmw3eGZCDIj9G9vhGB2N/2SLiopCcHAwLl26hIsXL+L3339HSEiI3pKkmpoaZGRkICMjA0DTtiAZGRnyafwJCQnYuXMnvvjiC9y6dQsrV65ETk4Oli5dqpd4CCG617JbreXrAf5jYWVuh+iIWWyEpTeX7x6HrMVedCUV+SxG071oM06nvsWq1WKJ8bbAVFc8grSuEuLKh2yHYpS0GoP0ww8/4P/+7/9ga2uLN998E99//73Sfmy6kpqaipiYx5tWJiQ0NcUvXLgQe/bsQXx8PEpLS/H222+jsLAQoaGhOHr0KPz8/PQSDyFE99R1q40fGo/xiO/iaLpe88a9YrEIAkH3GuMoNYmun7abkB5Vd93A7bzyOgzq4dhl9+sOtEqQFi9eDN5fM09iY2Mh0+N/8Ojo6HY3v122bBmWLVumtxgIIV2Hw+lYd9oA/7G4ksXuhJGOupVzEfcLr6GkJg/jBsyHi507Dp39LyICx4FhZLj64AxmjV4Od2fTW1bkTpHqMTOGhulEZ6BUpv5ckUSKirpGuNqYyycXdWZcVr3YFBJOw6JVgrR582bw+XwsX74cW7duRWNjo7xlhxBCtMVp0YLU0Vls44fGI/fRbZTUGF93VbWoHNWicgDAb1e+hr2lGxokdTh/+yd5nd8uf4O/xa1mK0SiJ9+nNf1/jfCzRx93W5ajIapoNW3kxRdfxA8//IA//vgD3333HZYvX66vuAgh3QBXxQa1HWFpbqOT67Ctok55rIhUJmEhEtPDxoDqlv+71fWIpGdXdEksANAoFuHetYuoqijtsnsaM41bkN566y1wOBz5NP9JkybJN3h944039BYgIaR70GaaP4/Dg5SRQsDrXuN2iGEx9Flsrd27chqVOddQcvschsz5F9vhGDyjnOZPCDENLZMijhatST1c+6GXdzh8PYL0EZZBYUBjS9hkbElQW+rKC9gOwagY7DR/Qojp4yoMzNa8BYnL4SIsaDjsbZxVHn9i+AvgdXDQt6GpaahkO4Qu0Sg1vkSwvdyJzdyqvrYad88dYjEC46fVGCRV0/wJIUQXdLmStrdbAAYHabaNkKGrE1cpvG+5ErcpuZJTwXYIJuVuaiIkojr5+/ZmhRNlGnex1dXVKax5NGHCBL0ERAjpPrTpVtOGqe3rduz8Xkwc/jTKKx9h16+vw9HKA89OfZPtsHQqp6wOg/31t45PdUMjLAS6bVWsEbU9gJ7N/4Utk6NmHEqStKJxgmRvb48hQ4YgJiYGMTExGD58OG3eSgjRGV0mNVwODzIT+mVwNfs0buaehxnPHDJGhpKafFzNvIiwXkPZDk1nxBIZZDJGZzMbW6tqkMBVx7PpJVLN/48xTMc2oyXs0bhNe9euXejduze++eYbjB07Fg4ODhgzZgzWrVuHs2fPorHRNJt9CSH6w9FRt9roAYpbknC5prfxrUTWiPrGx4srXr17msVo9EPSxsKKhHQ1jX+KzJ8/Hzt37sTdu3eRk5ODTz/9FP7+/ti9ezeioqLg4OBA3W6EEFZ4uvhh0qDFbIfRpZpXXy6vfMRyJISYpg79meXt7Y0FCxZg165dOH78OF599VXweDz89ttvuo6PEGLCFKf5d258CI/7eMRARwZ863KQeFe5dO03fH70NXx/ahvboWhF3YBhU+oWJcZPq61GAOD+/ftISkpCcnIykpOTUVlZieHDh2P16tU05Z8QohWuDqfit/zlyuVpf117SzeU1RbqLB59yy+/i/zyuwCAew8zIJNKIZFKjGLTW5FE9ZT+a/mV3XvDVR2OUWo93olhABmtyq4VjROkhQsXIikpCdXV1RgxYgRGjx6NF154AQMHDpRvYEsIIV1Bxhjfmjn69tmPr6JOXIXnZ2yA0NwSYrHIYJMldQ1FD6saujYQPTOkQdmlD3MhqatqvyKR0zhB+vrrr+Hr64tXX30VY8eOxYABA+R94IQQ0hEcPQ6mZrpZEtW86W1O0Z+wEFhhX/J/EOAWjlljlrEcmbLfbinvOWeoOtPrp/W5uuxhbHXzvBvndXjx7kHjBOnmzZvybrWNGzeioaEBI0eORFRUFKKjoxEREWGSM0cIIfqjy6n9DCPt1PnGOAZJnTNXfwLQ1PVmiKobVHf1SA1kFhtbceSUKa9dpI362mo8uH4eZkIriCqLdRRV96VxgtSnTx/06dMHS5cuBdCUMJ0+fRpJSUnYsGED6uvrMXLkSBw5ckRvwRJCCGnb4fPbYCN0YjuMDqkVSdHQKIXQjN1hG1kltTq5Tnld+8vfeDtYyF93Ni/789IxNJTkdO4iRE7rQdrNgoOD4ejoCAcHBzg4OODbb7/Fr7/+qsvYCCEmTl+DtFVxs/XDw6psnd3PkFU3lLIdQoflV9QjwMWa1Rj0sS2HuivqcqRKQ2WJ7i5GtJvmX1xcjAMHDuC5555D37594eXlhWeeeQa3b9/GypUrcerUKX3FSQgxQV3VLT8qZCZmxyzXybWMbRPcR3/t4F7wKBvfnvgYuYWZrMbT3kDsznYzNZMY4ea3nWUoXZSmQuMWpODgYNy5cwd8Ph+DBg3CrFmzEBMTgxEjRkAoFOozRkII6ZRhYRNVlvO5ZpDImrtBTHPSyd7E9VgxdwsOndmKWlElClLuIiH+v6zEwjAMTt5qe2xMaY24i6LRDKPTkdOmJ+POWVy9l4KpI/4OBzsXtsPRKY0TpOnTpyMmJgYjR46EpaWlPmMihHQTLQdGd3qQtBaz1vhcAeaMfgkAsC/5P527r4ETS0XIyr+NWlElALRICA2TWM0aSZ2neaJDE7Q1dyL9KwDArxe/wlMT/sVyNLqlcYK0fv16pbLmflqa7k8IMTY+Hr1QVJKr9XkcDke307G7wO0Hl9kOAQCQWVzTfiXSYUwbv4vF1fodnyRq1E3XqCHp0J9su3btQmhoKIRCIYRCIUJDQ7Fz505dx0YIMXGarHjN45oplakaRGttYdeqjnYtEeZmpjtU4FrOmXbrnL1yBNt/WI2Hpfl6iyP1Qbnert0eQ+u663ISEdsRGB2tE6TXX38dL730EqZOnYqDBw/i4MGDmDp1KlauXIn/+7//00eMhJBubFT4NNhZOGOA/9g26/X0CUFEz/EY1//pDt1neL9pHTrPVJy//ROqReX47fJevVxfxvIA4nuPdDN1v6P0MTOO6JfW0/y3b9+Ozz//HPPmzZOXTZs2DWFhYXjxxRfxzjvv6DRAQkj34OLopbLcytIG/3ziPQDAlayTbV5j3JA5Wt3TysJG/tra0rbNuh52PVFYeR+BHpG4nf+7VvcxJjKZfsYAqdt/TZXK+kbYWSi3HHadx11VzXlNZU0ZLl1PxMC+Y0xuMLIuPKrWvrva0GmdIEmlUgwcOFCpPDIyEhIJbYRHCNHO89M3QCJrhKXQqsvvbWNlj4mRi8Dl8todJD5n7HLcy7uJ3n79cfug8SdI1+/+jqLSHK2Tyo4qrKzXuO4vVwvx1BDfTt2vrE63XWrfJ21FSU0e7hdm4J8zlMfkEtOjdRfb3/72N2zfvl2pfMeOHXj66Y41bRNCui8rSxvYWet+B3ehuWYJV1jQcIQGDtHgepYICRgIPt8MQr5xz+S9m3MdRy/vQvr9RCSnHlI4VtOgn3FCueWaJ0i6oOs1gUpq8gAAlQa9CKfmE6YyM87g+tmftLr6o/ICnLr0HWrrqrUNzCh1apD2kiVLsGTJEoSGhuLzzz8Hl8tFQkKC/IsQQtgyKHgs/JyDMbzvEzq/9oxRull0ki0/nNssf30pU3EHhOZNb9l2/l4Jqhu6ZkmCopJcpN5IBgDkFT/Ag3vfQCrq3F5mecUPIK6+roPomkjr8yGRqH4ejEyG2uKjqBJrHnNZ5iXUFmaipEhxa5K84ge4fvd3XM28iD2/rFMYtL/72Fqk3juBH8/sUHnN7II/Nb6/MdC6i+369euIiIgAANy7dw8A4OLiAhcXF1y//vg/A039J4Swic83Q/z4FXq5to97T4X3gwIn4PLd43q5FxselRfAxcFTJ9f5+exOhPeKAbh9tDr3QUkdHpTUdbirTSJtuwUpOfUQvF17IdA3FF8lrgPQtHnymRs/QCSpR4OoAK5+HU+EvznZNG7OnWcNvmWPDl8HAOrLzqK0IhkbD+6Gp89L4JrZKBwXVV5CeU06ygFEcXtrdW3pX0mXWCzCziOvo0ZUoXD86IUv8MyU1xXK8sruqLxW7sM/4ecZpNX9m5VWFEEosIKVpU37lbuI1glSUlKSPuIghBBWybRcFsCU7PjxNYX3B5M2YdnMD9s8p7KmDDYWduDyeMgrfoCGhhoE+obKj1fXVuC7pE+aZsZd+RrePbt2lvPNwiq1x1JvJONS5q+4lPkr5gn+LS/PK7kLkaSpK1AsbTq/sx11ElEB+JY9IJLIwOdp12nTIKqDrLECpRXJ8rLa0t9g4z5DoV5J+W8djq8k5w7cvANw/e5FpeQIABrEqteuahDVQWium67miuoS7Pr1DQDAqnmqW6fY0OHNagkhhJiGirpHCu9rRBU4k/4TQgOGqZyxdeLCPmQ8aPpjeeaI5fIuu2ds1spbnrb/tEonsTWI6lBSWQxv1x5andfWukd5xY9bQPYlva91TF8ffR81DeWwdJgEM0t/AICkPgdZ+XXw91LdUlZZ3wgrc8VfuTJxKf744xAqigIR0iMSkrpi8C0Dmq4nacTmH1YoXUcirUJl4beorr+LM/wJaKyxVzhewzTAmqP5ml6ledfw+YFT4FmobrmpE6tONDf/sAIxYfEa36ctOUV3dXIdXdMonV26dClyczWbwrd//37s3aufdTQIIYR0jQt3jmDfyf+grkFx/SCxWCRPjgDF8UzFZQU6j2Pb4TX45uR72H1kHcRi9YsdisUinE77Eem3lRfFZGQy1JWewp2sKwCAP4vSVF4ju/iGUtnl+8orUBdW3ke1qBwPi/aCkcnASEUoKvwKB1M2Ijn1kMK4nZLyU2BkEkikjaisKUOD6PGK05XFP6GusQi38s7iu7OfoKhoH2SNlZA2FGLjwedVxlgrykV1fVNCceHP43hYvF/heJo0W+V5qjAMgz+kOSiXVqGkRvUCoVJGik0HlmPj/mVKx5Ku7ldxhunQqAXJxcUFoaGhGD58OKZNm4aBAwfC09MTQqEQ5eXluHnzJs6ePYtvv/0WXl5e2LHDcJrI2vLxxx9j586dYBgG48aNwyeffEJjpwghCvhcAazM214jyVTViCqw9dBKzBmdIG8ZaZSob5mRSsWoa6jF8Qtf6ywGiaypy+tRdS52Hnkdy2Z+iNKKIly8fgw3cs+rPCe36BbKy4pg77EAHC4X4poMlFWex48Xz2OVv/rfT/WNit1JjEyCP65tQtZdOzVnAKKKczCzeDwmrbnrrqX8B+/j2weP37vZ+uFhlepEpiB3i9p76VqWrAT1aH8gvFjaoNH1Cksf4HDSZxjabxLcnX00D8RAu7c1SpDWrVuHF198Ebt27cKnn36qMBgbAGxsbDBu3Djs3LkTsbGxeglU1x49eoStW7fixo0bMDMzw+jRo3Hx4kUMGzaM7dAIIQZk+ayPO7+RrpE7mLIRy2duwtZDCW2O1TqWtgdQ3TgDWWMluGbqEw1N1IgqIJE04vvTW5S6BVu6U5gKALCuvw8zq0CIau/Jj+0+sk7j+0kaciGW1qCkRv0eciUVp2FVp10XkbrkqKvlMmU6vV7Wo6sAgLyyTLww6yOdXpsNGo9BcnV1xZo1a7BmzRpUVFQgOzsb9fX1cHZ2RkBAgFG2vEgkEjQ0NGXGjY2NcHV1ZTkiQogmunLbBj6//RWd24vH3tINFXUPdRUSK1SNh9EGw+hmyv7PZ3a1mRy1VFF6DC5WL6Cq/vGYI21WfK4sOaZRvVqx/vavM0bqxi0Zmw79WWRvb4/+/ftj6NChCAwM1EtylJKSgqlTp8LT0xMcDgeHDx9WqrNt2zb4+/tDKBQiMjISZ860vyFjMxcXF7z88svw9fWFp6cnxo0bh4CAAB1+AkKIvni7Bur1+gKebjeufXbyGzq9njHiaLGIYVsyi9I1riuSVHTqXg0SQ14U0rAlXf6e7RA6zWDbjWtra9G/f39s3bpV5fH9+/djxYoVeO2113DlyhWMGjUKcXFxyMl5vOhVZGQkQkNDlb4KCgpQXl6OI0eO4MGDB8jPz8f58+eRkpLSVR+PENIBC8a/jujQuRgSMl6v95ky9B9wtHLH5MH/0Mn1NGmFIsSUXL57HBeuatYCZ6gMdpp/XFwc4uLi1B7fuHEjFi9ejCVLlgAANm3ahOPHj2P79u1Yv75pn5y0NDWd4QAOHjyIwMBAODo2bXEwefJkXLx4EaNHj1ZZXyQSQSR6PIOiqso0mhAJMSbuzj7aDf7UQsvd5r3demLJtLf1ch+iOUaqftYaMXxnbvyA4J6D9bKVUFcw2BaktojFYqSlpSkNCI+NjcX586pnNbTm4+OD8+fPo6GhAVKpFMnJyejdW/0KpOvXr4ednZ38y8dHPz+kCSGmj8812L9NDQoj0+2Gs6Trffbzv3Hh6jGF5Q2MhVEmSCUlJZBKpXBzc1Mod3NzQ1FRkUbXGDp0KCZNmoQBAwYgLCwMAQEBmDZtmtr6a9asQWVlpfxL03WhCCGEkO7szI0f8GOKcSz/05JR/xnTenA4wzBaDRh/99138e6772pU19zcHObm5lrFRwghpDOM8m94okJ2yU0UPMpGSXk+woKGsx2ORjRKkAYMGKBx4pGervkMg45ydnYGj8dTai0qLi5WalUihJBmXg6ByC+/i2Afdtc7C/EdiT8eJLMagzHgcI1v+Rii3v9+a2qQkEgaEREcxXI07dMoQXriiSf0HIZ2BAIBIiMjkZiYiBkzHm/al5iYiOnTp7MYGSHEkM0Z8xIeFN5BoHeI0jGmC1fzHT84vtslSIyOpvkT45f98JZCgmSoG0VrlCC9+eab+o5DSU1NDe7efbw6aVZWFjIyMuDo6AhfX18kJCRg/vz5GDhwIIYNG4YdO3YgJycHS5cu7fJYCSHGQSAwR5BfmM6va2vl3G4dS4GtfAE9Lo+n8xgMHQfaL+5JSRVhk8GOQUpNTUVMTIz8fUJCAgBg4cKF2LNnD+Lj41FaWoq3334bhYWFCA0NxdGjR+Hn58dWyISQboTPNYNE1rQ6dESfUXhUkYtrOeoXq3W0dkNdGS0Pog0OjUHqFgx1Kx+tEySpVIqPP/4YBw4cQE5ODsRixWmYZWW62dslOjq63eX7ly1bhmXLlHcYJoSQzuBy2m/heXrcq/jyxFvwdwkDl8dD3Ij5eFB8E9UNtPqyrjDofNcLI5PoIBKiS5lF6XhUXgCBmRDJaT/A0tyG7ZBU0jpBeuutt7Bz504kJCTg9ddfx2uvvYYHDx7g8OHDeOMNWk6fENI9uDl5IWHOfxVXydZiLAWXwzXYsRcmhZGyHQFRYfextXC19UFxleEumaN1u9bevXvx+eef4+WXXwafz8e8efOwc+dOvPHGG7h48aI+YiSEEL2zbbHaL5+n2d+OndlCZHGc5rvKE2KKDDk5AjrQglRUVIR+/foBAKytrVFZWQkAmDJlCl5//XXdRkcIIV3EUmiFp8a+Cj6X1yWDqB3sXPR+D2PXWHuH7RBIN6Z1C5K3tzcKCwsBAIGBgThx4gQA4PLly7SQIiHEqHm79tDbXm9EO401d/Co5AjbYZBuTOsEacaMGTh58iQA4KWXXsLrr7+OXr16YcGCBXj22Wd1HiAhhBDjp82UfUndPTwqPqSj+9I4L2NyNdNwhupo3cX2/vvvy1/Pnj0b3t7eOH/+PAIDA9vcy4wQQghpj6Q+B48efg8ZJDDnO0AkKe/U9WTiRzqKzHhou+2WITmW+gXCeg1lOwwAOlgHaejQoRg61DA+DCGEGBIBzxyeDr0AoN1lS0ydJgtFSuvz8ahoP6SMGJZm7rBzmYzCgl2dum9j/YNOnW+MLkrvw4FjCQeOFRw4lhBwDHbJQ4PWoaf2559/Ijk5GcXFxZDJFJsvaao/IYQ0WT5rU7dcNbsjpA2FePTwW0gZESzMXGDv+TQgrev0dUUNhj1TSh/EkOAhU4WHTNPCpNYwhwPHCo4cK9hyLMA10talrqZ1gvT555/jueeeg7OzM9zd3RWa8TgcDiVIhBDyl44mR45W7iirLWq/oomQiopRUrQPElk9hHwnOHrOB4dnAZkuEiTxQx1EaFz6c31QxtSinKlDDRpQAxFqGBFymTLwwIU9xwIOHCu2w1TrUXkBXBw82Q5D+wTpnXfewbvvvovVq1frIx5CCOn2lkx7G9t/WI1qUefG3xgDmbgUJUV70SirgznfHo4efwOHZ6mz6zfKOp9kGRt7riXs0fQMxYwE5Uwdyv9KmMSQoJSpRSlTy3KU6u0+thar5u1gOwztE6Ty8nLMmTNHH7EQQgjpRmSNFSgp/B8apbUQ8Gzh5PE3cM0Mc9sJYyXg8OHGsYUbbMEwDGohkrcuVTDdL3nUhtbT/OfMmSNf+4gQQohuPTH8BbZD6BKyxmqUFv4PYmk1BDxrOHs8Da6ZPdthmTQOhwNrjhC+XCf059F6X+3RugUpMDAQr7/+Oi5evIh+/frBzExxqf3ly5frLDhCCOlugvzC2A5BL1qugyRrrEVZ4dcQSSpgxrVsajkSOLEYHTE0qTeSMTAkmtUYtE6QduzYAWtra5w+fRqnT59WOMbhcChBIoQQDY0OnY2U69/B27E38sq6x7YajLQO5UX/Q4OkDHyuBZzdnwZP4Mx2WMTAnLr6DdLvJuIf099lLQatE6SsrCx9xEEIId3O0H6xGNg3BlfvXuwWCRIjFaGsYC/qGx+BzxXCxe1J8IRuer0njyOAlBHr9R5EPyrq2F3kU+sxSIQQQnSHzzeDtUU3GJjMSFBe+A3qGx+CxxHA2W0ueBZeer+tUOCq93sQ06R1C1JCQoLKcg6HA6FQiMDAQEyfPh2Ojo6dDo4QQowJo8Fq0aoE+YWjf0E0nGzZX/tFX8offo8GSSl4HDM4u80B38K3S+4rMPdErSivS+5FTIvWCdKVK1eQnp4OqVSK3r17g2EYZGZmgsfjoU+fPti2bRv+9a9/4ezZswgODtZHzIQQYnImDHtKsYBjWg38DZJScMGHs8tMmFn6d9l9BUJfoOpSl92P6FZy6iFED5zByr21/g6cPn06xo0bh4KCAqSlpSE9PR35+fkYP3485s2bh/z8fIwePRorV67UR7yEENKt2QiNd7aXs8s0mFn36tJ7dlVLFdGPS5m/snZvrROk//znP1i3bh1sbW3lZba2tli7di0+/PBDWFpa4o033kBaWppOAyWEEALMjn4RoT4j2Q5Day7OkyCwYaFXgSPo+nsSk6B1glRZWYni4mKl8kePHqGqqmljPHt7e4jFNGuAEEKaMYx245Nc7VQv5Odk64ZJIxfoIqQuZW4bwXYIhGilQ11szz77LA4dOoS8vDzk5+fj0KFDWLx4MZ544gkAwKVLlxAUFKTrWAkhpNuIG74Q4T1i8Ldxr7EdCiHdktaDtD/77DOsXLkSTz75JCQSSdNF+HwsXLgQH3/8MQCgT58+2Llzp24jJYSQbsRSaIXYYfPYDoOQbkvrBMna2hqff/45Pv74Y9y/fx8MwyAgIADW1tbyOuHh4bqMkRBCjN7w0Cn47uwn8HfRfisRAU8IsbQBACBjZOCCp+vwCCGtaJ0gNbO2tkZYmGnuGUQIIbrW0ycEy2dugtDcUutzp49YhoMpG/UQFSFEHY0SpJkzZ2LPnj2wtbXFzJkz26z7ww8/6CQwQggxNR1JjgDAXGCh40gIIe3RKEGys7MDh8ORvyaEEEIIMWUaJUi7d+9W+ZoQQshjHd1qhBBieLSe5l9fX4+6ujr5++zsbGzatAknTpzQaWCEEEIIIWzp0DpIX331FQCgoqICgwcPxoYNGzB9+nRs375d5wESQoixcLCineMJMRVaJ0jp6ekYNWoUAOC7776Du7s7srOz8dVXX2Hz5s06D5AQQozF5BGL2Q5BLswviu0QCDFqWidIdXV1sLGxAQCcOHECM2fOBJfLxdChQ5Gdna3zAHVhxowZcHBwwOzZs5WOHTlyBL1790avXr1ocUtCSKfYWTuir9dQtsMAAMQOeRIzRyxnO4xO4fBpUhBhj9YJUmBgIA4fPozc3FwcP34csbGxAIDi4mKFDWwNyfLly+Xdgi1JJBIkJCTg1KlTSE9PxwcffICysjIWIiSEEPW4nMc/qmUymWbn8HgI9A3VV0hdgsPt8FJ9hHSa1gnSG2+8gZdffhk9evTAkCFDMGzYMABNrUkDBgzQeYC6EBMTI2/1aunSpUsICQmBl5cXbGxsMGnSJBw/fpyFCAkhpPM44MDFxge9PQayHQohRk/rBGn27NnIyclBamoqjh07Ji8fO3asfC82baSkpGDq1Knw9PQEh8PB4cOHleps27YN/v7+EAqFiIyMxJkzZ7S+jyoFBQXw8vKSv/f29kZ+fr5Ork0IIV2hf49o+WsOh4NnpryO6dH/YC8gLZnztf41REiX6FD7pbu7O9zd3RXKBg8e3KEAamtr0b9/fzzzzDOYNWuW0vH9+/djxYoV2LZtG0aMGIHPPvsMcXFxuHnzJnx9fQEAkZGREIlESueeOHECnp6eau/NMMprljQviNmaSCRSuEdVVVW7n40QQvQtyDcCfzxIbreet2Mv5JVl6j8gQkwE6x28cXFxiIuLU3t848aNWLx4MZYsWQIA2LRpE44fP47t27dj/fr1AIC0tLQO3dvLy0uhxSgvLw9DhgxRWXf9+vV46623OnQfQgjRp+bNbJ2svdqo1T1bamgcE+kog/6OEYvFSEtLkw8EbxYbG4vz5893+vqDBw/G9evXkZ+fj+rqahw9ehQTJkxQWXfNmjWorKyUf+Xm5nb6/oQQogtPjnkFIT7D8cSopWyHQojJMOjUuqSkBFKpFG5ubgrlbm5uKCoq0vg6EyZMQHp6Ompra+Ht7Y1Dhw5h0KBB4PP52LBhA2JiYiCTybBq1So4OTmpvIa5uTnMzc079XkIIUQf3J19MHnkIrbD6BDanIUYKoNOkJq1HhfEMIzasUKqtDUzbdq0aZg2bVqHYyOEkK4kY6Rsh0BIt2DQXWzOzs7g8XhKrUXFxcVKrUqEEGIInOw8dH5NDkf5R7WXQyDsLd3g595L5/cjhBh4C5JAIEBkZCQSExMxY8YMeXliYiKmT5/OYmSEEKLa0NBY1NRVoIdnX73e5+mJq/R6fUK6O9YTpJqaGty9e1f+PisrCxkZGXB0dISvry8SEhIwf/58DBw4EMOGDcOOHTuQk5ODpUtpMCIhxPBweTzEDpvHdhgasTCzRn1jDdthEGKQWE+QUlNTERMTI3+fkJAAAFi4cCH27NmD+Ph4lJaW4u2330ZhYSFCQ0Nx9OhR+Pn5sRUyIYSYBC6Xx3YIhBgs1hOk6OholQs2trRs2TIsW7asiyIihBCiD309bHCrsFr+PtjTFneLqQWLGCaDHqRNCCEE4HI1n7Wrjfb+OAWAOaMTdHY/RysBxvV1BQDE9HFBuI+9zq5NiK6x3oJECCHEcNlbq14brqNcbYV4aoivTq9JiD5QCxIhhJiw/j2iIeAJMSZyLtuhqDTE35HtEAhRiVqQCCHEiMg06BZracKwpzBh2FMqjzGMrN3zOXrq3mvm42iJmRFe+CE9v/3KhHQhakEihBDCKqEZzaYjhocSJEIIIWoJzCx0di2uFltEEcI2SpAIIaSbCvEd3m4doZlQZ/fzdtA+2fL0eUHtMWdrb53OsiOkJUqQCCHEiDCy9scNaWLB+NfRr1f7CZIuabPJeDOumb3aY7FDFsDfq08nIiJEPUqQCCHEwHFVbFbbWe7OPhAKLOXvfZ364J9T39f5fQgxVjSLjRBCuikbK3sM6z0FBaX3MSvmefD5Zkp1uDwaQE26J0qQCCGkGxsVMY3tEAySGc8KjdJatsMgLKIuNkIIMXCCFgOledSiI0ez4og+UQsSIYQYOBsrewzvMw3gcCE0t2z/BBPjZD8apRUpbIdBuhlqQSKEECMwcsAUjAyfxHYYbZoX/Qqcrb11fl0Lx9EYGBCr8+sS0hZKkAghhOiEj0cvONt56eXaYwbPhp2Fs0KZJrP7JvVz10s8xPRRgkQIIURjQr5+u/jcbM3VHrO3ctXqWkK+JewtBZ0NiXRTlCARQgjRGI+nvBSAJnq5Wbd5PKq3CzzthRge4IxA17bramp+7GsdPpfP1U0MxHhRgkQIIURjHHRs5tigHo5tHveyt0B0b1dYCHgQ8Dv/qyk6dC4c7Fw6dK6FmQsc3GZ2OgZi3ChBIoQQorG4Ic9CyLeEu12PLr83wzAa1+Vy256kbW/ppvaYg/uT4AqcNL4XMU2UIBFCCJHr7Tm4zeP+Xn2wfM4mhAdGd01AbeB0YAuWIUGTMaz3FPxj+jq1dTzsm7rXOEb4K9KZY8N2CCbD+P71CSGE6M3Ukc8gJiy+3XqhAUPg69RXqVybVp7OsrN20PocL5eAdlcPjw7uAQCwMNNuULgh4HawC5QoowSJEEKIHJfHQ2SfaLjY+MDPObjNek/GrpS/H9wrrivCk3tm4to2F81kIO3wtXlc3SYZLo4TdXo90jUoQSKEkG6iOeFpbzFHLo+HZ6a8jvjxKzS+tr9nSGdC05qLg6f8tbO1ftZeYvC4Nayjg9MBgMNTvXSBl19Ch6+pDo9+resMPUlCCOkmpo/+B6JD5yJ+7Mr2K2toXvQrmBi5CH6eQTq7prapyKyYFxHRczz+Nq79af2+7r06FJO7Xc8OnQcAZlaqk0cOzxJ2VqEdvq4qrjQGSWdoLzZCCOkmhOaWGNxvnE6v6ePRCz4e6pMOe0s3RIfP0ek9W7OzdsS4Ie3fgwMeBAL1C1G2pWVrkrY4XOW2CA+PZzp8vbZvpp/LdkfUgkQIIUQvrMzt8I/p6xDkF6aT68VEzoU53wL9e0R36HxNusm8HAI7dG1t8SyauwUpozFU1IJECCGkTVwOFzJGBkuBLatxuDl54aU5n7AaQ1vshE6obChVKufzKAkyRtSCRAghpE1zRifA27E3Zo5+UavzbIUdW2yRw2I+YWmuOgn0cPQHn6t6mxUh3xH+LmF4Kna1yuOOtB+cUaIEiRBCSJv8PIPw1IR/wdPFT6vzpoxYrKeI2sfIHo8ZcrCOAACE9Jigtv64AfPh5RCIcYOfVHmcw+HhyTGrVB7zCXwBc8a9ABsre9WxaBizLvCt2t7ShWiOutgIIYToDKdF809H90LTNSvXSbBwGIWBoerHF0X0GYWIPqMUygQCV9Q3PgQAMJCB24GVu7uaV/BwVF1JASOVsB2K0TP8f20dmDFjBhwcHDB79myF8tzcXERHRyM4OBhhYWE4ePAgSxESQohpiIqYAStzuw4PpNYXrpn209+tnGP1EIn+adpD6cSx0mscxq5bJEjLly/HV199pVTO5/OxadMm3Lx5E7/99htWrlyJ2tpaFiIkhBDTYGftiOdn/gcThj3FdiidxuVZdPoaHPDQx73r1ibisDmAy8R0iwQpJiYGNjbK/0E9PDwQHh4OAHB1dYWjoyPKysq6ODpCCCFdoTO5g4V5x1tbfBzVb4nSmRW6VV+P6ArrCVJKSgqmTp0KT09PcDgcHD58WKnOtm3b4O/vD6FQiMjISJw5c0bncaSmpkImk8HHx0fn1yaEEKI5N1sh2yHIuTiOh69TXwwN7Vh3m463ddMI37pjsweJItYTpNraWvTv3x9bt25VeXz//v1YsWIFXnvtNVy5cgWjRo1CXFwccnJy5HUiIyMRGhqq9FVQUKBRDKWlpViwYAF27Nihk89ECCGk49xshRjb1xU9nNW3vLTHxbHt/eY0ZW4/BE/GrgSfr3qKf1cYGDBeq/pBw6aAK+j4syNNWJ/FFhcXh7g49btAb9y4EYsXL8aSJUsAAJs2bcLx48exfft2rF+/HgCQlpbW4fuLRCLMmDEDa9aswfDhw9usJxKJ5O+rqqo6fE9CCCFtc7MVorCyQevz5oxOQHnlQ/h79enU/ft42OB2YTUCXa21Om9k8AycvXmoU/duqX+PaHi5BiL1XqJG9S3MrWBlYw/vAeOQ8/tPOoujO2I9QWqLWCxGWloa/v3vfyuUx8bG4vz5852+PsMwWLRoEcaMGYP58+e3WXf9+vV46623tL6HVCpFY2NjR0MkxGiZmZmBx+OxHQbpZvy9+nQ6OQKAAT726OFkBXuL9luO7C0eLwQ5OGQc7uSm4lF1rkb34fCUuxOd7EcjNnwIbmenIyriCTwovKXRtcL8otrcF49ox6ATpJKSEkilUri5uSmUu7m5oaioSOPrTJgwAenp6aitrYW3tzcOHTqEQYMG4dy5c9i/fz/CwsLkY5++/vpr9OvXT+kaa9asQUJCgvx9VVVVm+OVGIZBUVERKioqNI6TEFNjb28Pd3d3mllDjA6Hw4GjlWYrYA/t+XjMD59vhmemvI4P9/1Do3O5HOUEzNyM2+4mwKpMHP60/LUm33POHBuUMjRzWx2DTpCatf6HZhhGqx+4x48fV1k+cuRIyGQyja5hbm4Oc3PNd4FuTo5cXV1haWlJvyBIt8IwDOrq6lBcXAygacYoIabKQqDbllJv+84vL6AJd64dSpgalDI1XXI/Y2PQCZKzszN4PJ5Sa1FxcbFSq5IhkUql8uTIyYlmE5DuycKi6Yd8cXExXF1dqbuNGBxvx17IK8uEh11Prc/1dwlD1qOrCHAL131grQj4+pvVF8h1RalU+wRp0qDFOHp5lx4iMhwGnSAJBAJERkYiMTERM2bMkJcnJiZi+vTpLEbWtuYxR5aWNIuAdG/N3wONjY2UIBHW+DtboVYkgVOrLrMnopbhxr2LCAkYqtF1zM0eJypTRz2LnKK76OnVV2VdZ2tvlNTkoYer8pANbfXw6oMg90hYWzoi/b5mg7X1jcPhgc/lQyIz3S1NWE+QampqcPfuXfn7rKwsZGRkwNHREb6+vkhISMD8+fMxcOBADBs2DDt27EBOTg6WLl3KYtSaoW410t3R9wDpDIbRzTavwwJUt+RbCq0wKGSsxtdxsHNB/x7RMOMJIDS3RJBfmNq68WNX4s6DdAQHDNI6XlWeiPknABhMggQA/q5hyCxKZzsMvWE9QUpNTUVMTIz8ffNA6IULF2LPnj2Ij49HaWkp3n77bRQWFiI0NBRHjx6Fn592u0oTQgghnaXpFipWljaICI7SczTs0vUq4IaG9QQpOjq63b8Sli1bhmXLlnVRRMRUJCcnIyYmBuXl5bC3t2c7HEIIUaaildXRVhdjbE07eekKrK+kTQzHokWLwOFw8P777yuUHz58mLpKCCFED4R2gxXeDwqcgLAg9YsW65oAfPC5ZhCoWI+pu6MEiSgQCoX44IMPUF5eznYohBBisnq6NG1+29/v8RIYfC4fMYNmtXmeh11PCHhCTB/6nE7i4HI4eGHGRrwwc4NOrmdKKEEiCsaNGwd3d3f5Ni7qfP/99wgJCYG5uTl69OiBDRva/uZau3YtwsPD8cUXX8DX1xfW1tZ47rnnIJVK8eGHH8Ld3R2urq549913Fc7buHEj+vXrBysrK/j4+GDZsmWoqXk8JTU7OxtTp06Fg4MDrKysEBISgqNHj6qMob6+HpMnT8bQoUNRVlam4RMhhBDdG+LviMn9PBDqZafVefMn/RsvzNwAgUB3ayUJBOZa7zVna6ld3MaI9TFI3YVEqtmClLrG52mXA/N4PLz33nt46qmnsHz5cnh7K2/4mJaWhrlz52Lt2rWIj4/H+fPnsWzZMjg5OWHRokVqr33v3j38+uuvOHbsGO7du4fZs2cjKysLQUFBOH36NM6fP49nn30WY8eOxdChTdNuuVwuNm/ejB49eiArKwvLli3DqlWrsG3bNgDA888/D7FYjJSUFFhZWeHmzZuwtlbeO6myshJTpkyBUCjEyZMnYWVlpdVzIYR0vZ4u1rhVWA03W80X6TUWHA4HdpZNScmokJm4cOsnTBj4rEbnsrlxLgAM6z0FPh69IGoU4c+iju+FaugoQeoCEqkMB1LzWLn33IHeWidJM2bMQHh4ON58803s2qW8ENjGjRsxduxYvP766wCAoKAg3Lx5E//5z3/aTJBkMhm++OIL2NjYIDg4GDExMbhz5w6OHj0KLpeL3r1744MPPkBycrI8QVqxYoX8fH9/f6xbtw7PPfecPEHKycnBrFmz5NvD9OypvODbw4cPER8fj4CAAOzbtw8CgWbbBxBC2GVnYYbZkd4w45n2GMhhYRMxJGQ8uDpcK6ytYaPBsc/g1qn/gZF0bJ/QURHTAACBvqHAuQ5dwihQFxtR6YMPPsCXX36JmzdvKh27desWRowYoVA2YsQIZGZmQiqVqr1mjx49YGNjI3/v5uaG4OBgcLlchbLm7SkAICkpCePHj4eXlxdsbGywYMEClJaWora2af+g5cuX45133sGIESPw5ptv4urVq0r3HTduHHr27IkDBw5QckSIkRHwud1ikoguk6P22Ng5dtm9jBm1IHUBPo+LuQOVu6q66t4dMXr0aEyYMAGvvvqqUquQqr3wNFnQzcxMsVmYw+GoLGveHy87OxuTJk3C0qVLsW7dOjg6OuLs2bNYvHixfLXyJUuWYMKECfjll19w4sQJrF+/Hhs2bMCLL74ov+bkyZPx/fff4+bNmyo3IiaEEH2bMzoBB1M2sh0G0QIlSF2ko4kKm95//32Eh4cjKChIoTw4OBhnz55VKDt//jyCgoJ0up1EamoqJBIJNmzYIG9lOnDggFI9Hx8fLF26FEuXLsWaNWvw+eefKyRI77//PqytrTF27FgkJycjODhYZzESQogm/L36yF8L+exvQ8VhGOhmnXLTRQkSUatfv354+umnsWXLFoXyf/3rXxg0aBDWrVuH+Ph4XLhwAVu3bpWPC9KVgIAASCQSbNmyBVOnTsW5c+fw6aefKtRZsWIF4uLiEBQUhPLycpw6dQp9+yrvjfTRRx9BKpVizJgxSE5ORp8+fZTqEEJIV/BzDWU7BKIB42vWIF1q3bp1St1nEREROHDgAL799luEhobijTfewNtvv93mAO2OCA8Px8aNG/HBBx8gNDQUe/fuVVp+QCqV4vnnn0ffvn0xceJE9O7dW22i9vHHH2Pu3LkYM2YM/vzzT53GSgghXcnCvJ2ZuC2GQfSf/M9O3WtUyMxOnW+sqAWJyO3Zs0epzM/PDw0NDUrls2bNwqxZbS9o1tLatWuxdu3adu+XnJys8H7lypVYuXKlQtn8+fPlr1u3brWkahubzZs3Y/PmzZoFTQghBsrd2afN4y33SRNaKi99oo1hYRNx5sYPWp3jaOWOstqiTt2XbdSCRAghhHQlhp118brK8D7TMGfMCrbD6DRKkAghhJBuwsKlBwDA3MELAMA1U96DrZ/vqE7dY+SAKbCzNv6lBKiLjRBCCDExfDPVa77ZuPoCAIKGxiH3Tjo8ej4eMD6u/9OobajEiP6TkV+aafRdZJ1FCRIhhBBiYhxdveAQEAlzS1uVx4UWVugVrthSFBEcJX8d7DcMZ28ekr93sfHBo+pc/QRroKiLjRBCCDFBQRHR8OsToZNrhfgN08l1jAklSIQQQghRINNgdwRNDes9RWfX6kqUIBFCCCFEb3p4KC/eawwoQSKEEEK6CXMLm/YrEQA0SJsQQggxeX5DpqG6tBAefkHtVyYAqAWJEL3hcDg4fPgw22EQQgjcfXuh14DR4HA79mvf2d5Do3rmfIsOXd8QUYJE5BYtWoQnnnhCqTw5ORkcDgcVFRWoqamBmZkZ9u/fr1AnPj4eHA4H9+7dUygPCAjAq6++CqBpuxEOhwMOhwMejwcfHx8sWbIEjx49ktdPSkpCTEwMHB0dYWlpiV69emHhwoWQSCTyOp999hn69+8PKysr2NvbY8CAAfjggw90+CQIIYS01NMnBKNDZ+OJ4S+or+PaH7OiVqo9bmwoQSJasba2xsCBA5GUlKRQfvr0afj4+CiU5+Xl4f79+4iJiZGXhYSEoLCwEDk5Odi+fTt+/vlnLFiwAABw48YNxMXFYdCgQUhJScG1a9ewZcsWmJmZQSZrWpp/165dSEhIwPLly/HHH3/g3LlzWLVqFWpqarrg0xNCiOGwEzp16f2G9otFkF+YQll06FwAwOjQ2Zg99nl4u/bo0pj0iRIkorWYmBiFTWVv3bqF+vp6LFu2TKE8KSkJZmZmGDFihLyMz+fD3d0dXl5emDJlCpYvX44TJ06gvr4eiYmJ8PDwwIcffojQ0FAEBARg4sSJ2LlzJwSCplVhf/75Z8ydOxeLFy9GYGAgQkJCMG/ePKxbt05tvM0tYMePH8eAAQNgYWGBMWPGoLi4GL/++iv69u0LW1tbzJs3D3V1dfLzjh07hpEjR8Le3h5OTk6YMmWKQguZWCzGCy+8AA8PDwiFQvTo0QPr169XG8fbb78NNzc3ZGRkaPG0CSFEtfhx/4KnfQAAwM3Wj5UYBvcbh4Q5/8XQfrGs3F+fKEHqKlIJO196EBMTgzt37qCwsBBAUyI0atQojBkzRilBGjJkCCwtLdVey8LCAjKZDBKJBO7u7igsLERKSora+u7u7rh48SKys7O1jnvt2rXYunUrzp8/j9zcXMydOxebNm3CN998g19++QWJiYnYsmWLvH5tbS0SEhJw+fJlnDx5ElwuFzNmzJC3Zm3evBk//fQTDhw4gDt37uB///sfevTooXRfhmHw0ksvYdeuXTh79izCw8O1jp0QQlqzt3HG3+JW45mJa/H0hFWsxcHnm7F2b32iWWxdQSoBrnzNzr0HzAd4mv8zHzlyBNbW1gplUqlU4f2IESNgZmaG5ORkzJs3D8nJyYiKikJERAQqKyuRmZmJXr16ITk5GX/729/U3uv27dvYvn07Bg8eDBsbG8yZMwfHjx9HVFQU3N3dMXToUIwdOxYLFiyArW3TcvlvvvkmZs6ciR49eiAoKAjDhg3DpEmTMHv2bHDbGXz4zjvvyFuzFi9ejDVr1uDevXvo2bMnAGD27NlISkrC6tWrAQCzZs1SOH/Xrl1wdXXFzZs3ERoaipycHPTq1QsjR44Eh8OBn5/yX3ASiQQLFixAamoqzp07B29v7zZjJIQQbbk4eLIdgkmiFiSiICYmBhkZGQpfO3fuVKhjaWmJwYMHy1uLTp8+jejoaPD5fIwYMQLJycnIyclBVlYWxowZo3DutWvXYG1tDQsLCwQHB8PHxwd79+4FAPB4POzevRt5eXn48MMP4enpiXfffVc+bgkAPDw8cOHCBVy7dg3Lly9HY2MjFi5ciIkTJ8pbdtQJC3vcd+7m5gZLS0t5ctRcVlxcLH9/7949PPXUU+jZsydsbW3h7+8PAMjJyQHQNKg9IyMDvXv3lncVtrZy5UpcuHABZ86coeSIEEKMCLUgdQUev6klh617a8HKygqBgYEKZXl5eUr1YmJisH//fty4cQP19fWIiGja7ycqKgpJSUkQCAQQCoUYOnSownm9e/fGTz/9BB6PB09PT5ibmytd28vLC/Pnz8f8+fPxzjvvICgoCJ9++ineeusteZ3Q0FCEhobi+eefx9mzZzFq1CicPn1aYUB4a2Zmj5uBORyOwvvmspZJ1tSpU+Hj44PPP/8cnp6ekMlkCA0NhVgsBgBEREQgKysLv/76K3777TfMnTsX48aNw3fffSe/xvjx47Fv3z4cP34cTz/9tNrYCCHElD0xfBkOn9/GdhhaoQSpq2iZqBi6mJgYvPPOO/jmm28wcuRI8Hg8AE0J0pYtW2Bubo5hw4ZBKBQqnCcQCJQSsLY4ODjAw8MDtbW1ausEBwcDQJt1tFVaWopbt27hs88+w6hRTTtenz17Vqmera0t4uPjER8fj9mzZ2PixIkoKyuDo6MjAGDatGmYOnUqnnrqKfB4PDz55JM6i5EQQvSlX+BQnL91GI5W7p2+lpmZEEKhlQ6i6lqm9VubdJnhw4fD3NwcW7ZswWuvvSYvHzRoECorK/H999/jlVde0eqan332GTIyMjBjxgwEBASgoaEBX331FW7cuCEfPP3cc8/B09MTY8aMgbe3NwoLC/HOO+/AxcUFw4bpbrdpBwcHODk5YceOHfDw8EBOTg7+/e9/K9T5+OOP4eHhgfDwcHC5XBw8eBDu7u6wt7dXqDdjxgx8/fXXmD9/Pvh8PmbPnq2zOAkhxofR4Uaw+mJn7YjlMzdBwFdu5dfUkKDJaBDXwt3ZB4/KC3QYXdegBIl0SHP3WfP4o2ZmZmYYNmwYTp482WZ3lyqDBw/G2bNnsXTpUhQUFMDa2hohISE4fPgwoqKiAADjxo3DF198ge3bt6O0tBTOzs7y+zk56W5NEC6Xi2+//RbLly9HaGgoevfujc2bNyt8Vmtra3zwwQfIzMwEj8fDoEGDcPToUZWDxWfPng2ZTIb58+eDy+Vi5syZOouVEEL0QWiufgayJqIip+soEnZwGGNIZTtpxowZSE5OxtixYxXGhzSrq6tD3759MWfOHHz00UcaXbOqqgp2dnaorKyUz7Bq1tDQgKysLPj7+yt1MRHSndD3AiGPfbjvHwCAIPdIPBHzT5aj6VrllY/w+dGm3gY+VwCJTKzxuavm7dBpLG39/m6pW8xiW758Ob766iu1x999910MGTKkCyMihBBCug8HOxcEuIWjt8dAmHGNY92kbpEgxcTEwMbGRuWxzMxM3L59G5MmTeriqAghhJDuY9aYZZge/Q+2w9AY6wlSSkoKpk6dCk9PT7W7n2/btk3eRB8ZGYkzZ87o7P4vv/xym9tDEEIIIaT7YT1Bqq2tRf/+/bF161aVx/fv348VK1bgtddew5UrVzBq1CjExcXJF+sDgMjISPm6OC2/CgraHjX/448/IigoCEFBQTr9TIQQQggxbqzPYouLi0NcXJza4xs3bsTixYuxZMkSAMCmTZtw/PhxbN++Xd7yk5aW1qF7X7x4Ed9++y0OHjyImpoaNDY2wtbWFm+88YZSXZFIBJFIJH9fVVXVoXsSQgghRDMedj3br6QnrLcgtUUsFiMtLQ2xsYq7BMfGxuL8+fOdvv769euRm5uLBw8e4KOPPsLf//53lclRc107Ozv5l4+PT6fvTwghhHQ31haOGtc168Q6TJ1l0AlSSUkJpFIp3NzcFMrd3NxQVFSk8XUmTJiAOXPm4OjRo/D29sbly5e1jmXNmjWorKyUf+Xm5mp9DUIIIaS7mzby77C3dJG/nzRoMYvRqMd6F5smOByOwnuGYZTK2nL8+PF26yxatKjN4+bm5ir3DSOEEEKI5pzs3fGP6e8i485Z2Fo6oKdPCI5e3sV2WEoMOkFydnYGj8dTai0qLi5WalUihBBCiPEI7z1S/tpSYIs6sWGN7TXoLjaBQIDIyEgkJiYqlCcmJmL48OEsRUW6k+joaKxYsUKn10xOTgaHw0FFRYVOr6tvxho3IYR0BOsJUk1NDTIyMpCRkQEAyMrKQkZGhnwaf0JCAnbu3IkvvvgCt27dwsqVK5GTk4OlS5eyGLVpWrRoEZ544gml8pa/GGtqamBmZob9+/cr1ImPjweHw8G9e/cUygMCAvDqq68CANauXQsOhwMOhwMejwcfHx8sWbIEjx49ktdPSkpCTEwMHB0dYWlpiV69emHhwoWQSCTyOp999hn69+8PKysr2NvbY8CAAfjggw90+CQe++GHH7Bu3Tq9XJsQ0j25OvqyHYLBsRE6sB2CEtYTpNTUVAwYMAADBgwA0JQQDRgwQD6bLD4+Hps2bcLbb7+N8PBwpKSk4OjRo/Dz82Mz7G7L2toaAwcORFJSkkL56dOn4ePjo1Cel5eH+/fvK2xaGxISgsLCQuTk5GD79u34+eefsWDBAgDAjRs3EBcXh0GDBiElJQXXrl3Dli1bYGZmBplMBgDYtWsXEhISsHz5cvzxxx84d+4cVq1ahZqaGrUxL1q0CGvXru3Q53V0dFS7CjshhGhjXsy/MbzvExgaGtt+5W5m2sh/QMBTHufr5RzAQjRNWE+QoqOjwTCM0teePXvkdZYtW4YHDx5AJBIhLS0No0ePZi9ggpiYGCQnJ8vf37p1C/X19Vi2bJlCeVJSEszMzDBixAh5GZ/Ph7u7O7y8vDBlyhQsX74cJ06cQH19PRITE+Hh4YEPP/wQoaGhCAgIwMSJE7Fz504IBAIAwM8//4y5c+di8eLFCAwMREhICObNm9epVp5t27ahV69eEAqFcHNzw+zZs+XHWnex9ejRA++99x6effZZ2NjYwNfXFzt2KG6keP78eYSHh0MoFGLgwIE4fPgwOByOvJVUlfPnz2P06NGwsLCAj48Pli9fjtraWrX1165di/DwcHzxxRfw9fWFtbU1nnvuOUilUnz44Ydwd3eHq6sr3n33XYXzNm7ciH79+sHKygo+Pj5YtmyZQnKZnZ2NqVOnwsHBAVZWVggJCcHRo0dVxlBfX4/Jkydj6NChKCsrUxsrIaSJj3tPjAyfBC6Px3YoBsfBzgUr5m5RKBsZPAMj+k9mKSIDSJC6C4lMwsqXPsTExODOnTsoLCwE0JQIjRo1CmPGjFFKkIYMGQJLS0u117KwsIBMJoNEIoG7uzsKCwuRkpKitr67uzsuXryI7OxsnXyW1NRULF++HG+//Tbu3LmDY8eOtZuAb9iwAQMHDsSVK1ewbNkyPPfcc7h9+zYAoLq6GlOnTkW/fv2Qnp6OdevWYfXq1W1e79q1a5gwYQJmzpyJq1evYv/+/Th79ixeeOGFNs+7d+8efv31Vxw7dgz79u3DF198gcmTJyMvLw+nT5/GBx98gP/7v//DxYsX5edwuVxs3rwZ169fx5dffolTp05h1apV8uPPP/88RCKRvAXvgw8+gLW1tdK9KysrERsbC7FYjJMnT8LRUfN1TQghRBPD+8exmkwa9Cw2UyGRSXDo7iFW7j0jcAb4XM3/mY8cOaL0C1EqlSq8HzFiBMzMzJCcnIx58+YhOTkZUVFRiIiIQGVlJTIzM9GrVy8kJyfjb3/7m9p73b59G9u3b8fgwYNhY2ODOXPm4Pjx44iKioK7uzuGDh2KsWPHYsGCBbC1tQUAvPnmm5g5cyZ69OiBoKAgDBs2DJMmTcLs2bPB5Wqf7+fk5MDKygpTpkyBjY0N/Pz85N296kyaNAnLli0DAKxevRoff/wxkpOT0adPH+zduxccDgeff/45hEIhgoODkZ+fj7///e9qr/ef//wHTz31lLylqlevXti8eTOioqKwfft2CIVClefJZDJ88cUXsLGxQXBwsDxxPXr0KLhcLnr37o0PPvgAycnJGDp0KAAotIb5+/tj3bp1eO6557Bt2zb585g1axb69esHAOjZU3kV24cPHyI+Ph4BAQHYt2+fvHWPEEI6i88VQCITsx0GAGpBIq3ExMTIB803f+3cuVOhjqWlJQYPHixvLTp9+jSio6PB5/MxYsQIJCcnIycnB1lZWRgzZozCudeuXYO1tTUsLCwQHBwMHx8f7N27FwDA4/Gwe/du5OXl4cMPP4Snpyfeffdd+bglAPDw8MCFCxdw7do1LF++HI2NjVi4cCEmTpwoH6e0d+9eWFtby7/27t2L9957T6kMAMaPHw8/Pz/07NkT8+fPx969e1FXV9fmMwoLC5O/5nA4cHd3R3FxMQDgzp07CAsLU0hqBg8e3Ob10tLSsGfPHoX4JkyYAJlMhqysLLXn9ejRQ2F8lJubG4KDgxUSRTc3N3lsQFOr3vjx4+Hl5QUbGxssWLAApaWl8u685cuX45133sGIESPw5ptv4urVq0r3HTduHHr27IkDBw5QckQIMVnUgtQF+Fw+ZgTOYO3e2rCyskJgYKBCWV5enlK9mJgY7N+/Hzdu3EB9fT0iIiIAAFFRUUhKSoJAIIBQKJS3XDTr3bs3fvrpJ/B4PHh6eqpcfNPLywvz58/H/Pnz8c477yAoKAiffvop3nrrLXmd5g2Jn3/+eZw9exajRo3C6dOnERMTg2nTpmHIkCHyuqtXr4aXlxeWL18uL2teR8vGxgbp6elITk7GiRMn8MYbb2Dt2rW4fPky7O3tVT4jMzMzhfccDkeenKlaxJRhGJXXaSaTyfDPf/5TIb5mvr7qZ7uoiqOt2LKzszFp0iQsXboU69atg6OjI86ePYvFixejsbERALBkyRJMmDABv/zyC06cOIH169djw4YNePHFF+XXnDx5Mr7//nvcvHlT3tJECCGmhhKkLqJtomLoYmJi8M477+Cbb77ByJEjwfurnzgqKgpbtmyBubk5hg0bptQ9JBAIlBKwtjg4OMDDw6PNAcvBwcEAIK9jY2Oj0LJiY2MDR0dHtffl8/kYN24cxo0bhzfffBP29vY4deoUZs6cqXGczZq72UQikTz5S01NbfOciIgI3LhxQ6vn0hGpqamQSCTYsGGDvJXpwIEDSvV8fHywdOlSLF26FGvWrMHnn3+ukCC9//77sLa2xtixY5GcnCx//oQQYkqoi410yPDhw2Fubo4tW7YgKipKXj5o0CBUVlbi+++/V5jer4nPPvsMzz33HE6cOIF79+7hxo0bWL16NW7cuIGpU6cCAJ577jmsW7cO586dQ3Z2Ni5evIgFCxbAxcUFw4YN0/pzHDlyBJs3b0ZGRgays7Px1VdfQSaToXfv3lpfCwCeeuopyGQy/OMf/8CtW7dw/PhxfPTRRwCUt8xptnr1aly4cAHPP/88MjIykJmZiZ9++kkhKdGFgIAASCQSbNmyBffv38fXX3+NTz/9VKHOihUrcPz4cWRlZSE9PR2nTp1C3759la710Ucf4emnn8aYMWPkA9QJIaSzensNBAC42bK/lA8lSKRDmrvPqqurER0dLS83MzPDsGHDUF1drXWCNHjwYNTU1GDp0qUICQlBVFQULl68iMOHD8uTsHHjxuHixYuYM2cOgoKCMGvWLAiFQpw8eRJOTk5afw57e3v88MMPGDNmDPr27YtPP/0U+/btQ0hIiNbXAgBbW1v8/PPPyMjIQHh4OF577TX5ml7qBluHhYXh9OnTyMzMxKhRozBgwAC8/vrr8PDw6FAM6oSHh2Pjxo344IMPEBoair1792L9+vUKdaRSKZ5//nn07dsXEydORO/eveUDuFv7+OOPMXfuXIwZMwZ//vmnTmMlhHRPE4Y+jalD/on4cSvZDgUcpr0BEkSlqqoq2NnZobKyUj7DqllDQwOysrLg7++v9pci6T727t2LZ555BpWVlbCwsGA7nC5F3wuEEEPT1u/vlkxrYAwhBuCrr75Cz5494eXlhT/++AOrV6/G3Llzu11yRAghxowSJEJ0rKioCG+88QaKiorg4eGBOXPmKK1oTQghxLBRgkSIjq1atUphdWpCCCHGhwZpE0IIIYS0QgmSHtH4d9Ld0fcAIcRYUYKkB82rGbe3ZQUhpq75e6D1Ct+EEGLoaAySHvB4PNjb28v3wLK0tFS7SCAhpohhGNTV1aG4uBj29vbyldYJIcRYUIKkJ+7u7gCgsFEoId2Nvb29/HuBEEKMCSVIesLhcODh4QFXV1f5RqCEdCdmZmbUckQIMVqUIOkZj8ejXxKEEEKIkaFB2oQQQgghrVCCRAghhBDSCiVIhBBCCCGt0BikDmpeAK+qqorlSAghhBCiqebf2+0tZEsJUgdVV1cDAHx8fFiOhBBCCCHaqq6uhp2dndrjHIb2AugQmUyGgoIC2NjYaLwI5KBBg3D58mWt76Xpee3Va+u4qmOalLV8X1VVBR8fH+Tm5sLW1rbdeDuCnmHn0TPsPHqGnWfKz7Arnp+6mHR5XkefoTblbDxDhmFQXV0NT09PcLnqRxpRC1IHcblceHt7a3UOj8fr0D+0pue1V6+t46qOaVKmqo6tra3efijQM+w8eoadR8+w87rDM9Tn81MXky7P6+gz1KacrWfYVstRMxqk3YWef/55vZ7XXr22jqs6pklZRz9TR9Ez7Dx6hp1Hz7Dz6Bl2nqE+Q23K2X6GbaEuNqIzVVVVsLOzQ2VlpV7/ajJl9Aw7j55h59Ez7Bx6fp1nCM+QWpCIzpibm+PNN9+Eubk526EYLXqGnUfPsPPoGXYOPb/OM4RnSC1IhBBCCCGtUAsSIYQQQkgrlCARQgghhLRCCRIhhBBCSCuUIBFCCCGEtEIJEiGEEEJIK5QgkS5XXV2NQYMGITw8HP369cPnn3/OdkhGJzc3F9HR0QgODkZYWBgOHjzIdkhGacaMGXBwcMDs2bPZDsVoHDlyBL1790avXr2wc+dOtsMxSvT/rnO66ucfTfMnXU4qlUIkEsHS0hJ1dXUIDQ3F5cuX4eTkxHZoRqOwsBAPHz5EeHg4iouLERERgTt37sDKyort0IxKUlISampq8OWXX+K7775jOxyDJ5FIEBwcjKSkJNja2iIiIgK///47HB0d2Q7NqND/u87pqp9/1IJEuhyPx4OlpSUAoKGhAVKpFJSna8fDwwPh4eEAAFdXVzg6OqKsrIzdoIxQTEwMbGxs2A7DaFy6dAkhISHw8vKCjY0NJk2ahOPHj7MdltGh/3ed01U//yhBIkpSUlIwdepUeHp6gsPh4PDhw0p1tm3bBn9/fwiFQkRGRuLMmTNa3aOiogL9+/eHt7c3Vq1aBWdnZx1Fbxi64hk2S01NhUwmg4+PTyejNixd+Qy7i84+04KCAnh5ecnfe3t7Iz8/vytCNxj0/7LzdPkM9fnzjxIkoqS2thb9+/fH1q1bVR7fv38/VqxYgddeew1XrlzBqFGjEBcXh5ycHHmdyMhIhIaGKn0VFBQAAOzt7fHHH38gKysL33zzDR4+fNgln62rdMUzBIDS0lIsWLAAO3bs0Ptn6mpd9Qy7k84+U1UtvRwOR68xGxpd/L/s7nT1DPX+848hpA0AmEOHDimUDR48mFm6dKlCWZ8+fZh///vfHbrH0qVLmQMHDnQ0RIOnr2fY0NDAjBo1ivnqq690EaZB0+f/w6SkJGbWrFmdDdHodOSZnjt3jnniiSfkx5YvX87s3btX77Eaqs78v+yu/+9a6+gz7Iqff9SCRLQiFouRlpaG2NhYhfLY2FicP39eo2s8fPgQVVVVAJp2bE5JSUHv3r11Hquh0sUzZBgGixYtwpgxYzB//nx9hGnQdPEMiSJNnungwYNx/fp15Ofno7q6GkePHsWECRPYCNcg0f/LztPkGXbVzz++3q5MTFJJSQmkUinc3NwUyt3c3FBUVKTRNfLy8rB48WIwDAOGYfDCCy8gLCxMH+EaJF08w3PnzmH//v0ICwuT999//fXX6Nevn67DNUi6eIYAMGHCBKSnp6O2thbe3t44dOgQBg0apOtwjYImz5TP52PDhg2IiYmBTCbDqlWraPZpC5r+v6T/d+pp8gy76ucfJUikQ1qPO2AYRuOxCJGRkcjIyNBDVMalM89w5MiRkMlk+gjLqHTmGQKgGVgqtPdMp02bhmnTpnV1WEalvWdI/+/a19Yz7Kqff9TFRrTi7OwMHo+n9Fd6cXGxUsZPVKNn2Hn0DHWPnmnn0TPsPEN6hpQgEa0IBAJERkYiMTFRoTwxMRHDhw9nKSrjQs+w8+gZ6h49086jZ9h5hvQMqYuNKKmpqcHdu3fl77OyspCRkQFHR0f4+voiISEB8+fPx8CBAzFs2DDs2LEDOTk5WLp0KYtRGxZ6hp1Hz1D36Jl2Hj3DzjOaZ6i3+XHEaCUlJTEAlL4WLlwor/Pf//6X8fPzYwQCARMREcGcPn2avYANED3DzqNnqHv0TDuPnmHnGcszpL3YCCGEEEJaoTFIhBBCCCGtUIJECCGEENIKJUiEEEIIIa1QgkQIIYQQ0golSIQQQgghrVCCRAghhBDSCiVIhBBCCCGtUIJECCGEENIKJUiEENIFxGIxAgMDce7cOdZiePnll7F8+XLW7k+IMaEEiRCitUWLFoHD4Sh9tdxfiSjasWMH/Pz8MGLECIXypKQkTJkyBS4uLhAKhQgICEB8fDxSUlLkdZKTk8HhcFBRUaF03R49emDTpk0axbBq1Srs3r0bWVlZnfkohHQLlCARQjpk4sSJKCwsVPjy9/dXqicWi1mIzvBs2bIFS5YsUSjbtm0bxo4dCycnJ+zfvx+3bt3C119/jeHDh2PlypU6j8HV1RWxsbH49NNPdX5tQkwNJUiEkA4xNzeHu7u7whePx0N0dDReeOEFJCQkwNnZGePHjwcA3Lx5E5MmTYK1tTXc3Nwwf/58lJSUyK9XW1uLBQsWwNraGh4eHtiwYQOio6OxYsUKeR0Oh4PDhw8rxGFvb489e/bI3+fn5yM+Ph4ODg5wcnLC9OnT8eDBA/nxRYsW4YknnsBHH30EDw8PODk54fnnn0djY6O8jkgkwqpVq+Dj4wNzc3P06tULu3btAsMwCAwMxEcffaQQw/Xr18HlcnHv3j2Vzyo9PR13797F5MmT5WU5OTlYsWIFVqxYgS+//BJjxoyBv78/hg8fjpdeegmpqama/lPI7dmzR2XL3tq1a+V1pk2bhn379ml9bUK6G0qQCCE69+WXX4LP5+PcuXP47LPPUFhYiKioKISHhyM1NRXHjh3Dw4cPMXfuXPk5r7zyCpKSknDo0CGcOHECycnJSEtL0+q+dXV1iImJgbW1NVJSUnD27FlYW1tj4sSJCi1ZSUlJuHfvHpKSkvDll19iz549CknWggUL8O2332Lz5s24desWPv30U1hbW4PD4eDZZ5/F7t27Fe77xRdfYNSoUQgICFAZV0pKCoKCgmBraysv+/7779HY2IhVq1apPIfD4Wj12QEgPj5eoUVv37594PP5Ct16gwcPRm5uLrKzs7W+PiHdCkMIIVpauHAhw+PxGCsrK/nX7NmzGYZhmKioKCY8PFyh/uuvv87ExsYqlOXm5jIAmDt37jDV1dWMQCBgvv32W/nx0tJSxsLCgnnppZfkZQCYQ4cOKVzHzs6O2b17N8MwDLNr1y6md+/ejEwmkx8XiUSMhYUFc/z4cXnsfn5+jEQikdeZM2cOEx8fzzAMw9y5c4cBwCQmJqr87AUFBQyPx2N+//13hmEYRiwWMy4uLsyePXvUPq+XXnqJGTNmjELZ0qVLGVtbW4Wy7777TuGZXr16lWEYhklKSmIAKBxr/uJwOMzHH3+sdM+7d+8yTk5OzIcffqhQXllZyQBgkpOT1cZLCGEYPqvZGSHEaMXExGD79u3y91ZWVvLXAwcOVKiblpaGpKQkWFtbK13n3r17qK+vh1gsxrBhw+Tljo6O6N27t1YxpaWl4e7du7CxsVEob2hoUOj+CgkJAY/Hk7/38PDAtWvXAAAZGRng8XiIiopSeQ8PDw9MnjwZX3zxBQYPHowjR46goaEBc+bMURtXfX09hEKhUnnrVqIJEyYgIyMD+fn5iI6OhlQqVTh+5swZpc8WHR2tdN3KykpMmTIFcXFxeOWVVxSOWVhYAGhqbSOEqEcJEiGkQ6ysrBAYGKj2WEsymQxTp07FBx98oFTXw8MDmZmZGt2Tw+GAYRiFspZjh2QyGSIjI7F3716lc11cXOSvzczMlK4rk8kAPE4g2rJkyRLMnz8fH3/8MXbv3o34+HhYWlqqre/s7CxPwJr16tULlZWVKCoqgru7OwDA2toagYGB4PNV/2j29/eHvb29QlnrulKpFPHx8bC1tcXnn3+udI2ysjIAis+DEKKMxiARQvQuIiICN27cQI8ePRAYGKjw1ZxomZmZ4eLFi/JzysvL8eeffypcx8XFBYWFhfL3mZmZCi0hERERyMzMhKurq9J97OzsNIq1X79+kMlkOH36tNo6kyZNgpWVFbZv345ff/0Vzz77bJvXHDBgAG7fvq2Q3M2ePRtmZmYqk8bOWLlyJa5du4ZDhw6pbLW6fv06zMzMEBISotP7EmJqKEEihOjd888/j7KyMsybNw+XLl3C/fv3ceLECTz77LOQSqWwtrbG4sWL8corr+DkyZO4fv06Fi1aBC5X8UfUmDFjsHXrVqSnpyM1NRVLly5VaA16+umn4ezsjOnTp+PMmTPIysrC6dOn8dJLLyEvL0+jWHv06IGFCxfi2WefxeHDh5GVlYXk5GQcOHBAXofH42HRokVYs2YNAgMDFboGVYmJiUFtbS1u3LghL/P19cWGDRvwySefYOHChUhKSsKDBw+Qnp6OzZs3y++jjd27d2Pbtm349NNPweVyUVRUhKKiItTU1MjrnDlzBqNGjdKopYyQ7owSJEKI3nl6euLcuXOQSqWYMGECQkND8dJLL8HOzk6eBP3nP//B6NGjMW3aNIwbNw4jR45EZGSkwnU2bNgAHx8fjB49Gk899RRefvllha4tS0tLpKSkwNfXFzNnzkTfvn3x7LPPor6+XmEGWXu2b9+O2bNnY9myZejTpw/+/ve/o7a2VqHO4sWLIRaL2209AgAnJyfMnDlTqevvxRdfxIkTJ/Do0SPMnj0bvXr1wqRJk5CVlYVjx46hX79+GscMAKdPn4ZUKsW0adPg4eEh/2q5LMG+ffvw97//XavrEtIdcZjWHfqEEGIgoqOjER4ervFK0V3p3LlziI6ORl5eHtzc3Nqtf+3aNYwbN07lIPKu8ssvv+CVV17B1atX1Y5zIoQ0oRYkQgjRgkgkwt27d/H6669j7ty5GiVHQNPYpg8//FBh0cquVltbi927d1NyRIgG6LuEEEK0sG/fPixevBjh4eH4+uuvtTp34cKFeopKMy0X5iSEtI262AghhBBCWqEuNkIIIYSQVihBIoQQQghphRIkQgghhJBWKEEihBBCCGmFEiRCCCGEkFYoQSKEEEIIaYUSJEIIIYSQVihBIoQQQghphRIkQgghhJBW/h/zhK5Z8QCOggAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "start = datetime.datetime.now()\n",
    "plt.figure()\n",
    "plt.loglog(aman.freqs, aman.Pxx[0], alpha=0.4, label=\"No mask\")\n",
    "# create hwpss mask\n",
    "fft_ops.calc_psd_mask(aman, mask_hwpss=True, hwpss_width=((-0.4, 0.6),(-0.2, 0.2)), merge=True)\n",
    "mask = ~aman.psd_mask.mask()\n",
    "plt.loglog(aman.freqs[mask], aman.Pxx[0][mask], alpha=0.4, label=\"HWPSS mask\")\n",
    "\n",
    "# add single peak mask\n",
    "fft_ops.calc_psd_mask(aman, psd_mask=aman.psd_mask.mask(),mask_peak=True, peak_freq=28, peak_width=(-5, +5), merge=True)\n",
    "mask = ~aman.psd_mask.mask()\n",
    "plt.loglog(aman.freqs[mask], aman.Pxx[0][mask], alpha=0.4, label=\"HWPSS+single mask\")\n",
    "plt.xlabel('Frequency (GHz)')\n",
    "plt.ylabel('signal (pW**2/Hz)')\n",
    "plt.legend()\n",
    "stop = datetime.datetime.now()\n",
    "print(stop - start)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d467846-e009-42e4-8d3c-1038469f65d0",
   "metadata": {},
   "source": [
    "# \"fit_noise_model\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9dffe3b-1837-4e3d-885a-12ec5824989b",
   "metadata": {},
   "source": [
    "New feature of fit_noise_model is that\n",
    "* Fit PSD with masks if ``mask``==True and PSD_mask in aman,\n",
    "* Parameters of white noise or alpha can be fixed to the initially estimated value. For example, we experimentally know that fixing alpha is good for fitting demodulated signals.\n",
    "* ``f_min`` has been added to limit the minimum frequency to fit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9c75cec0-696c-4654-82b2-6b1bc9048cb3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/so/home/jsugiyama/git/sotodlib/sotodlib/tod_ops/fft_ops.py:366: RuntimeWarning: invalid value encountered in power\n",
      "  return wn * (1 + (fknee / f) ** alpha)\n",
      "/so/home/jsugiyama/git/sotodlib/sotodlib/tod_ops/fft_ops.py:366: RuntimeWarning: invalid value encountered in power\n",
      "  return wn * (1 + (fknee / f) ** alpha)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:01:52.711395\n"
     ]
    }
   ],
   "source": [
    "start = datetime.datetime.now()\n",
    "noise_fit_stats_fixed_wn = fft_ops.fit_noise_model(aman, \n",
    "                                          f=aman.freqs, \n",
    "                                          pxx=aman.Pxx, \n",
    "                                          lowf=1, \n",
    "                                          f_min=0.001, \n",
    "                                          f_max=20, \n",
    "                                          fwhite=(3, 20), \n",
    "                                          mask=True, \n",
    "                                          fixed_parameter={\"wn\":2E-9})\n",
    "stop = datetime.datetime.now()\n",
    "print(stop - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "28f65af0-1a10-4146-ad97-a1b5d8fedfb5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/so/home/jsugiyama/git/sotodlib/sotodlib/tod_ops/fft_ops.py:366: RuntimeWarning: invalid value encountered in power\n",
      "  return wn * (1 + (fknee / f) ** alpha)\n",
      "/so/home/jsugiyama/git/sotodlib/sotodlib/tod_ops/fft_ops.py:366: RuntimeWarning: invalid value encountered in power\n",
      "  return wn * (1 + (fknee / f) ** alpha)\n",
      "/so/home/jsugiyama/git/sotodlib/sotodlib/tod_ops/fft_ops.py:366: RuntimeWarning: invalid value encountered in power\n",
      "  return wn * (1 + (fknee / f) ** alpha)\n",
      "/so/home/jsugiyama/git/sotodlib/sotodlib/tod_ops/fft_ops.py:366: RuntimeWarning: invalid value encountered in power\n",
      "  return wn * (1 + (fknee / f) ** alpha)\n",
      "/so/home/jsugiyama/git/sotodlib/sotodlib/tod_ops/fft_ops.py:366: RuntimeWarning: invalid value encountered in power\n",
      "  return wn * (1 + (fknee / f) ** alpha)\n",
      "/so/home/jsugiyama/git/sotodlib/sotodlib/tod_ops/fft_ops.py:366: RuntimeWarning: invalid value encountered in power\n",
      "  return wn * (1 + (fknee / f) ** alpha)\n",
      "/so/home/jsugiyama/git/sotodlib/sotodlib/tod_ops/fft_ops.py:366: RuntimeWarning: invalid value encountered in power\n",
      "  return wn * (1 + (fknee / f) ** alpha)\n",
      "/so/home/jsugiyama/git/sotodlib/sotodlib/tod_ops/fft_ops.py:366: RuntimeWarning: invalid value encountered in power\n",
      "  return wn * (1 + (fknee / f) ** alpha)\n",
      "/so/home/jsugiyama/git/sotodlib/sotodlib/tod_ops/fft_ops.py:366: RuntimeWarning: invalid value encountered in power\n",
      "  return wn * (1 + (fknee / f) ** alpha)\n",
      "/so/home/jsugiyama/git/sotodlib/sotodlib/tod_ops/fft_ops.py:366: RuntimeWarning: invalid value encountered in power\n",
      "  return wn * (1 + (fknee / f) ** alpha)\n",
      "/so/home/jsugiyama/git/sotodlib/sotodlib/tod_ops/fft_ops.py:366: RuntimeWarning: invalid value encountered in power\n",
      "  return wn * (1 + (fknee / f) ** alpha)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:02:02.761576\n"
     ]
    }
   ],
   "source": [
    "start = datetime.datetime.now()\n",
    "noise_fit_stats_fixed_alpha = fft_ops.fit_noise_model(aman, \n",
    "                                          f=aman.freqs, \n",
    "                                          pxx=aman.Pxx, \n",
    "                                          lowf=1, \n",
    "                                          f_min=0.001, \n",
    "                                          f_max=20, \n",
    "                                          fwhite=(3, 20), \n",
    "                                          mask=True, \n",
    "                                          fixed_parameter={\"alpha\":3.4})\n",
    "stop = datetime.datetime.now()\n",
    "print(stop - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b1eaff6b-ca8d-42db-8605-e59339c308c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/so/home/jsugiyama/git/sotodlib/sotodlib/tod_ops/fft_ops.py:366: RuntimeWarning: invalid value encountered in power\n",
      "  return wn * (1 + (fknee / f) ** alpha)\n",
      "/so/home/jsugiyama/git/sotodlib/sotodlib/tod_ops/fft_ops.py:366: RuntimeWarning: invalid value encountered in power\n",
      "  return wn * (1 + (fknee / f) ** alpha)\n",
      "/so/home/jsugiyama/git/sotodlib/sotodlib/tod_ops/fft_ops.py:366: RuntimeWarning: invalid value encountered in power\n",
      "  return wn * (1 + (fknee / f) ** alpha)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:04:14.565212\n"
     ]
    }
   ],
   "source": [
    "start = datetime.datetime.now()\n",
    "noise_fit_stats_all_params = fft_ops.fit_noise_model(aman, \n",
    "                                          f=aman.freqs, \n",
    "                                          pxx=aman.Pxx, \n",
    "                                          lowf=1, \n",
    "                                          f_min=0.001, \n",
    "                                          f_max=20, \n",
    "                                          fwhite=(3, 20), \n",
    "                                          mask=True)\n",
    "stop = datetime.datetime.now()\n",
    "print(stop - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "87b3add7-dd09-4c5d-bc08-808dc5fd936a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.17094340e+00, 2.00000000e-09, 3.28081038e+00],\n",
       "       [1.15688262e+00, 2.00000000e-09, 3.29040022e+00],\n",
       "       [1.16994709e+00, 2.00000000e-09, 3.31446268e+00],\n",
       "       ...,\n",
       "       [1.09956407e+00, 2.00000000e-09, 3.38281480e+00],\n",
       "       [1.20806557e+00, 2.00000000e-09, 3.30624163e+00],\n",
       "       [1.29905619e+00, 2.00000000e-09, 3.20806127e+00]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "noise_fit_stats_fixed_wn.fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6a076b72-52d7-4c83-8f1b-b1c5575f2a96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.05796465e+00, 2.34685167e-09, 3.40000000e+00],\n",
       "       [1.06630072e+00, 2.25400590e-09, 3.40000000e+00],\n",
       "       [1.06829360e+00, 2.35432815e-09, 3.40000000e+00],\n",
       "       ...,\n",
       "       [1.03987434e+00, 2.28379744e-09, 3.40000000e+00],\n",
       "       [1.07449828e+00, 2.50817179e-09, 3.40000000e+00],\n",
       "       [1.07608622e+00, 2.70525016e-09, 3.40000000e+00]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "noise_fit_stats_fixed_alpha.fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "eb27f147-9f7f-4109-8fc5-f37faf86fcfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.06868204e+00, 2.34525013e-09, 3.36870993e+00],\n",
       "       [1.08285225e+00, 2.25167783e-09, 3.35218570e+00],\n",
       "       [1.06593998e+00, 2.35473238e-09, 3.40678256e+00],\n",
       "       ...,\n",
       "       [1.02119352e+00, 2.28643190e-09, 3.45534547e+00],\n",
       "       [1.06490260e+00, 2.50959164e-09, 3.42825248e+00],\n",
       "       [1.07476207e+00, 2.70538544e-09, 3.40381974e+00]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "noise_fit_stats_all_params.fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f3830432-621e-49c2-b6aa-ba816b673b20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'fknee (Hz)')"
      ]
     },
     "execution_count": 15,
     "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.hist(noise_fit_stats_all_params.fit.T[0], bins=np.linspace(0.5, 1.6, 50), histtype=u'step', label=\"free params\")\n",
    "plt.hist(noise_fit_stats_fixed_wn.fit.T[0], bins=np.linspace(0.5, 1.6, 50), histtype=u'step', label=\"wn fixed\")\n",
    "plt.hist(noise_fit_stats_fixed_alpha.fit.T[0], bins=np.linspace(0.5, 1.6, 50), histtype=u'step', label=\"alpha fixed\")\n",
    "plt.legend()\n",
    "plt.xlabel(\"fknee (Hz)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9fc3253b-e0fb-49ca-8df2-cffa2672d246",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f7cb0627f10>"
      ]
     },
     "execution_count": 16,
     "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[mask], aman.Pxx[0][mask], alpha=0.4, label=\"HWPSS+single mask\")\n",
    "\n",
    "noise_fit_stats = noise_fit_stats_fixed_wn\n",
    "plt.loglog(aman.freqs, fft_ops.noise_model(aman.freqs, noise_fit_stats.fit[0]), label=\"fit (fixed wn)\")\n",
    "\n",
    "noise_fit_stats = noise_fit_stats_fixed_alpha\n",
    "plt.loglog(aman.freqs, fft_ops.noise_model(aman.freqs, noise_fit_stats.fit[0]), label=\"fit (fixed alpha)\")\n",
    "\n",
    "noise_fit_stats = noise_fit_stats_all_params\n",
    "plt.loglog(aman.freqs, fft_ops.noise_model(aman.freqs, noise_fit_stats.fit[0]),\"--\",  label=\"fit (free params)\")\n",
    "\n",
    "plt.xlabel('Frequency (GHz)')\n",
    "plt.ylabel('signal (pW**2/Hz)')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a67af9ae-02b9-4fff-8a49-199d36bdce6f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f7cb0e1ed40>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Vrl1bzs7Oql27tlatWmXzdg3DUHBwsHx8fOTi4qKgoCAdPnzYqk5SUpJGjBghDw8PlS5dWk899ZR+/fXXYt/2ixcvasSIEapRo4ZcXV1VuXJlvfrqq0pISMh124ty+2+v27Fjx1yf/22J43aFrf27d+9W69atVbp0aZUrV05BQUG6fv36fdH+uLg4vfDCCzKbzSpdurQaNWqkb775JtdtL8ztX7lypTp06CAPDw+ZTCYdOHAg0zru9dgnSTKKoWXLlhklS5Y0PvvsM+PIkSPGa6+9ZpQuXdo4c+ZMlvV/+eUXw9XV1XjttdeMI0eOGJ999plRsmRJ45tvvingyAsnW/fn1q1bDUnGsWPHjNjYWMuUkpJSwJEXPuvWrTPGjx9vfPvtt4YkY9WqVTnW572ZM1v3J+9N5LeifLws6senonw86NChgxEeHm4cOnTIOHDggNG5c2ejcuXKxpUrV7JdpjDt/7uJvzDt/zVr1hhr1641jh07Zhw7dsx46623jJIlSxqHDh3Ksn5h2ve2xl6Y9ntxkh/fPXbt2mU4ODgYISEhxtGjR42QkBDD0dHR2LNnj03bnTZtmuHm5mZ8++23xsGDB43nnnvO8Pb2NhITEy11hgwZYjz44IPGpk2bjH379hmtWrUy6tevn6v3RVFu+8GDB43u3bsba9asMU6ePGn88MMPRkBAgPHss8/esd3Fof0ZzZw50+jYsWOuzp/Fqf27du0yypYta4SGhhqHDh0yjh8/bnz99dfGjRs37ov2t23b1njkkUeMH3/80Th16pQxdepUo0SJEsa+ffuKfPsXL15sTJ482fjss88MScb+/fszxXMvx750xTKh9uijjxpDhgyxKqtZs6YxduzYLOuPGTPGqFmzplXZ4MGDjcceeyzfYixKbN2f6Rdrly5dKoDoiq7cnLB4b+aeLV+geW8ivxSX42VRPz4V9eNBfHy8IcmIjIzMtk5h3v+5ib8w73/DMIzy5csb//73v7OcV5j3vWHkHHth3+9FVX589+jRo4fx5JNPWtXp0KGD0bNnz1xvNy0tzTCbzca0adMs82/cuGG4u7sbn3zyiWEYhvHXX38ZJUuWNJYtW2ap89tvvxklSpQw1q9fX6zbnpUVK1YYTk5Oxs2bN7Otk1FxaP+BAweMSpUqGbGxsTYn1Ip6+5s2bWpMmDAhN03NUlFvf+nSpY3FixdbradChQrZnkNuV1jbn1FMTEyWCbV7PfalK3a3fCYnJys6Olrt27e3Km/fvr127dqV5TK7d+/OVL9Dhw7au3evbt68mW+xFgV3sz/TNWzYUN7e3mrTpo22bt2an2EWW7w38wfvTeSH++14WVyOT4Vx36ffblShQoVs6xTm/Z+b+NMVtv2fmpqqZcuW6erVq2rWrFmWdQrrvs9N7OkK234vyvLru0d2ddLXmZvtxsTEKC4uzqqOs7OzAgMDLXWio6N18+ZNqzo+Pj6qU6fOHc9dRb3tWUlISFDZsmXl6OiYU9NzHcftClv7r127pl69emnOnDkym813bHNGRb398fHx+vHHH+Xp6anmzZvLy8tLgYGB2rFjx33Rfkl6/PHHtXz5cl28eFFpaWlatmyZkpKSFBQUVKTbnxv3cuzLqNgl1P7880+lpqbKy8vLqtzLy0txcXFZLhMXF5dl/ZSUFP3555/5FmtRcDf709vbW59++qm+/fZbrVy5UjVq1FCbNm20bdu2ggi5WOG9mbd4byI/3W/Hy6J+fCqs+94wDI0ePVqPP/646tSpk229wrr/cxt/Ydv/Bw8eVJkyZeTs7KwhQ4Zo1apVql27dpZ1C9u+tyX2wrbfi4P8+u6RXZ30deZmu+n/3qmOk5OTypcvn+v4i0vbb3fhwgVNnTpVgwcPzrbNGRWH9o8aNUrNmzfX008/nas2Z1TU2//LL79IkoKDgzVo0CCtX79ejRo1Ups2bXTixIli335JWr58uVJSUlSxYkU5Oztr8ODBWrVqlR566KEi3f7cuJdjX0Z3Tr0XUSaTyeq1YRiZyu5UP6vy+5Ut+7NGjRqqUaOG5XWzZs107tw5ffDBB2rZsmW+xlkc8d7MO7w3URDup+NlUT4+FdZ9P3z4cP3888+5+oW8MO7/3MZf2PZ/jRo1dODAAf3111/69ttv1a9fP0VGRmabmCpM+96W2Avbfi9O8uO7R27WmVd1bpebOne7/sLY9sTERHXu3Fm1a9fWpEmTso09K0W1/WvWrNGWLVu0f//+bGPNjaLa/rS0NEnS4MGDNWDAAEm3eu/+8MMPWrBggUJDQ7Ntg61x3Kn+7eUF9f6fMGGCLl26pM2bN8vDw0OrV6/WP//5T23fvl1169bNtg22xnGn+reX59dxLTdsXU+x66Hm4eEhBweHTFnF+Pj4TFnMdGazOcv6jo6OqlixYr7FWhTczf7MymOPPZarTD+s8d7Mf7w3kVfut+NlcTw+2XvfjxgxQmvWrNHWrVtVqVKlHOsWxv1vS/xZsef+d3JyUrVq1dSkSROFhoaqfv36+vDDD7OsW9j2vS2xZ8Xe7/uiLr++e2RXJ32dudlu+i18d6qTnJysS5cu5Tr+4tL2dJcvX9aTTz6pMmXKaNWqVSpZsmSO7U5X1Nu/ZcsWnTp1SuXKlZOjo6PlNtdnn302V7f8FfX2e3t7S1KmHx9q1aqls2fP5tBy5TqO2xWm9p86dUpz5szRggUL1KZNG9WvX1+TJk1SkyZN9PHHHxfp9ufGvRz7Mip2CTUnJyc1btxYmzZtsirftGmTmjdvnuUyzZo1y1R/48aNatKkSa4PqMXV3ezPrOzfv99y0ELu8d7Mf7w3kVfut+NlcTw+2WvfG4ah4cOHa+XKldqyZYv8/f3vuExh2v93E39WCtN73zAMJSUlZTmvMO37rOQUe1YK034vivLru0d2ddLXmZvt+vv7y2w2W9VJTk5WZGSkpU7jxo1VsmRJqzqxsbE6dOjQHc9dRb3t0q2eae3bt5eTk5PWrFmjUqVK5djm4tT+sWPH6ueff9aBAwcskyTNmjVL4eHhxb79fn5+8vHx0bFjx6zWc/z4cVWpUqXYt//atWuSpBIlrFNCDg4Olt57RbX9uXEvxz4ruX58QRGS/hjVzz//3Dhy5IgxcuRIo3Tp0sbp06cNwzCMsWPHGi+88IKlfvrjW0eNGmUcOXLE+Pzzz+32+PPCyNb9OWvWLGPVqlXG8ePHjUOHDhljx441JBnffvutvZpQaFy+fNnYv3+/sX//fkOSMXPmTGP//v2WR/zy3rSNrfuT9ybyW1E+Xhb141NRPh688sorhru7uxEREWHExsZapmvXrlnqFOb9fzfxF6b9P27cOGPbtm1GTEyM8fPPPxtvvfWWUaJECWPjxo1Zxl6Y9r2tsRem/V6c5Md3j507dxoODg7GtGnTjKNHjxrTpk0zHB0djT179uR6u4ZhGNOmTTPc3d2NlStXGgcPHjR69epleHt7G4mJiZY6Q4YMMSpVqmRs3rzZ2Ldvn9G6dWujfv36RkpKSrFue2JiotG0aVOjbt26xsmTJ62OX7lpe1Fvf1Zk41M+i3r7Z82aZZQtW9b4+uuvjRMnThgTJkwwSpUqZZw8ebLYtz85OdmoVq2a8cQTTxg//vijcfLkSeODDz4wTCaTsXbt2iLf/gsXLhj79+831q5da0gyli1bZuzfv9+IjY211LmXY1+6YplQMwzD+Pjjj40qVaoYTk5ORqNGjawe3d6vXz8jMDDQqn5ERITRsGFDw8nJyfDz8zPmzZtXwBEXbrbsz/fee8946KGHjFKlShnly5c3Hn/88Vx/KIu79MfV3z7169fPMAzem7aydX/y3kRBKKrHy6J+fCrKx4Os4pZkhIeHW+oU5v1/N/EXpv0/cOBAy2f2gQceMNq0aWNJSBlG4d73tsZemPZ7cZMf3z2+/vpro0aNGkbJkiWNmjVrZpn4zGm7hmEYaWlpxqRJkwyz2Ww4OzsbLVu2NA4ePGhV5/r168bw4cONChUqGC4uLkaXLl2Ms2fPFvu2Z3fekGTExMQU+/ZnxdaE2p3iKArtDw0NNSpVqmS4uroazZo1M7Zv337ftP/48eNG9+7dDU9PT8PV1dWoV6+esXjx4mLR/vDw8Cw/25MmTbLUuddjn2EYhskw/h4FDgAAAAAAAMAdFbsx1AAAAAAAAID8REINAAAAAAAAsAEJNQAAAAAAAMAGJNQAAAAAAAAAG5BQAwAAAAAAAGxAQg0AAAAAAACwAQk1AAAAAAAAwAYk1AAAAAAAAAAbkFBDsWEYhl5++WVVqFBBJpNJBw4cyLF+UFCQRo4cWSCx3Y2WLVtq6dKlebrOgwcPqlKlSrp69WqerhcAkPdOnz6dq/NZRgsXLlS5cuXyLSYAAADcQkINxcb69eu1cOFC/ec//1FsbKzq1Klj75Du2n/+8x/FxcWpZ8+eljI/Pz+FhYVlqhscHKwGDRrkar1169bVo48+qlmzZuVRpAAAAADuVwcPHpTJZMq3CSjMSKih2Dh16pS8vb3VvHlzmc1mOTo62juku/bRRx9pwIABKlEi7z+iAwYM0Lx585Samprn6wYAAABw/4iMjJRhGPk2AYUZCTUUC/3799eIESN09uxZmUwm+fn52byO9evXy93dXYsXL7ass1u3bvrggw/k7e2tihUratiwYbp586ZlmeTkZI0ZM0YPPvigSpcuraZNmyoiIsJqvbt27VLLli3l4uIiX19fvfrqqznecvnnn39q8+bNeuqpp2xug6Qsf9nJuD86dOigCxcuKDIy8q7WDwDIO+vXr9fjjz+ucuXKqWLFiurSpYtOnTqVZd2IiAiZTCatXbtW9evXV6lSpdS0aVMdPHgwU90NGzaoVq1aKlOmjJ588knFxsZa5kVFRaldu3by8PCQu7u7AgMDtW/fvnxrIwCgeLpx44ZcXV3tHQZgNyTUUCx8+OGHmjJliipVqqTY2FhFRUXZtPyyZcvUo0cPLV68WH379rWUb926VadOndLWrVu1aNEiLVy4UAsXLrTMHzBggHbu3Klly5bp559/1j//+U89+eSTOnHihKRbXaA7dOig7t276+eff9by5cu1Y8cODR8+PNtYduzYIVdXV9WqVcu2nfC32NhYy3Ty5ElVq1ZNLVu2tMx3cnJS/fr1tX379rtaPwAg71y9elWjR49WVFSUfvjhB5UoUULPPPOM0tLSsl3mX//6lz744ANFRUXJ09NTTz31lNWPPdeuXdMHH3ygL774Qtu2bdPZs2f1xhtvWOZfvnxZ/fr10/bt27Vnzx4FBASoU6dOunz5cr62FQBQvPzwww9q3bq1vcMA7MZk0I8SxURYWJjCwsJ0+vTpXNUPCgpSgwYNVL16db311ltatWqVWrVqZZnfv39/RURE6NSpU3JwcJAk9ejRQyVKlNCyZct06tQpBQQE6Ndff5WPj49lubZt2+rRRx9VSEiI+vbtKxcXF82fP98yf8eOHQoMDNTVq1dVqlSpLNsxe/bsTD0U/Pz8FBsbq5IlS1qVJycnq3bt2pkGrTYMQ88++6zOnj2r7du3y8XFxTKve/fucnd3V3h4eK72FQCgYJw/f16enp46ePCgypQpI39/f+3fv18NGjRQRESEWrVqpWXLlum5556TJF28eFGVKlXSwoUL1aNHDy1cuFADBgzQyZMn9dBDD0mS5s6dqylTpiguLi7Lbaampqp8+fJaunSpunTpUmBtBQAUbR9//LGGDRtm7zAAu6GHGu5r3377rUaOHKmNGzdaJdPSPfzww5ZkmiR5e3srPj5ekrRv3z4ZhqHq1aurTJkylikyMtKSDIuOjtbChQut5nfo0EFpaWmKiYnJMqbr169nmWiTbvVKOHDggNU0ZMiQLOu+9dZb2r17t1avXm2VTJMkFxcXXbt27c47CACQr06dOqXevXuratWqKlu2rPz9/SVJZ8+ezXaZZs2aWf6uUKGCatSooaNHj1rKXF1dLck0yfrcJUnx8fEaMmSIqlevLnd3d7m7u+vKlSs5bhMAULR88skncnNzU0pKiqXsypUrKlmypJ544gmrutu3b5fJZNLx48etyvv376+xY8dmuX7DMLIc77lly5Z68cUXM5XPnTtXrq6ujOOMYqXojtoO5IEGDRpo3759Cg8P1yOPPJLpSTK39wYzmUyW23DS0tLk4OCg6Ohoq6SbJJUpU8ZSZ/DgwXr11Vczbbty5cpZxuTh4aFLly5lO69atWpWZRUqVMhUb8mSJZo1a5YiIiJUqVKlTPMvXrxo9WULAGAfXbt2la+vrz777DP5+PgoLS1NderUUXJysk3ryXj+yurclfGGhP79++v8+fMKCwtTlSpV5OzsrGbNmtm8TQBA4dWqVStduXJFe/fu1WOPPSbpVuLMbDYrKipK165ds4x/FhERIR8fH1WvXt2yfFpamtauXas1a9Zkuf7o6Gg1btzYqswwDB04cEA9e/bMsn79+vUzfW8CijJ6qOG+9tBDD2nr1q367rvvNGLECJuWbdiwoVJTUxUfH69q1apZTWazWZLUqFEjHT58ONP8atWqycnJKdv1xsXFZZtUu5Pdu3frpZde0vz58y0nz9sdOnRIDRs2vKv1AwDyxoULF3T06FFNmDBBbdq0Ua1atXJ17N+zZ4/l70uXLun48eOqWbNmrre7fft2vfrqq+rUqZMefvhhOTs7688//7yrNgAA7CcoKEjDhw/X8OHDLQ+3mTBhggzDUI0aNeTj42P1wLSIiAg9/fTTeuihh7Rr1y6r8tvv1tm5c6dKlCihpk2bSpK++eYbqzGY9+7dq0ceecRqmRMnTujy5cuZEm1S5gRcSEhIlg9Tmzlz5j3tE6AgkVDDfa969eraunWr5fZPW5br06eP+vbtq5UrVyomJkZRUVF67733tG7dOknSm2++qd27d2vYsGE6cOCATpw4oTVr1uSYvGvYsKEeeOAB7dy50+a2xMXF6ZlnnlHPnj3VoUMHxcXFKS4uTufPn7fUOX36tH777Te1bdvW5vUDAPJO+fLlVbFiRX366ac6efKktmzZotGjR99xuSlTpuiHH37QoUOH1L9/f3l4eKhbt2653m61atX0xRdf6OjRo/rxxx/Vp0+fTEMDAACKhkWLFsnR0VE//vijPvroI82aNUv//ve/Jd1KuG3dutVSd+vWrQoKClJgYKClPDk5Wbt3786UUFuzZo26du2qEiVKKCkpSampqfryyy8t8w3DyHR3T/qdO/Xq1bMqT0pK0uHDh60SaiNGjLB6mNorr7yiKlWqqEePHnmzY4ACQEINkFSjRg1t2bJFX331lV5//fVcLxceHq6+ffvq9ddfV40aNfTUU0/pxx9/lK+vrySpXr16ioyM1IkTJ/TEE0+oYcOGmjhxory9vbNdp4ODgwYOHGh1wsqt//3vf/rjjz+0aNEieXt7W6aMvx599dVXat++vapUqWLz+gEAeSf9ITfR0dGqU6eORo0apffff/+Oy02bNk2vvfaaGjdurNjYWK1ZsybbXs9ZWbBggS5duqSGDRvqhRde0KuvvipPT897aQoAwE58fX01a9Ys1ahRQ3369NGIESM0a9YsSbcSajt37lRKSoouX76s/fv3q2XLlgoMDLT0XNuzZ4+uX7+eZULt6aefliQ5Ozvrueee0+HDh2UYhs6dO5fl8DX79u1TamqqXF1drXqdlSpVSikpKVYJNTc3N5nNZpnNZs2fP1/r1q1TZGRklsPVAIUVT/kECqE//vhDDz/8sKKjo/M08ZWUlKSAgAB99dVXatGiRZ6tFwCQ/9Jvybl06ZLKlStn73AAAHYWFBSkqlWrasGCBZay7777Tv/4xz9048YNxcTEKCAgQLt27dKlS5f0r3/9S4cPH1ZcXJwqV66sS5cuacaMGfr888915swZyzqOHj2qJk2a6M8//7TqwTxq1Cj16tVLR44cUc+ePTM9SK1169YqV66c3n77bavyr7/+WjNnztTly5fl6Gg9jPvkyZMVHh6uyMhIfvBHkUMPNaAQ8vLy0ueff57nT1w7c+aMxo8fTzINAAAAKOaqVaumSpUqaevWrdq6dasCAwMlSWazWf7+/tq5c6e2bt2q1q1bWy23Zs0atWvXLtNwAE899ZS+++473bhxI1MyTZL279+voKD/194dszS2hGEA/tBAmmAr2GpEGwkWxiKSnD8QaxtLg4iNhb/ATgRRf4iijVYpA+JBBFsLbcRCRIsEFLzFsmFDdu9y9rpXDM8Dpzgzw8zhlC8z89WiVCr1PI+PjzEzMyNMY+AI1BhIt7e3USgUfvl8dFD1NywuLvaVtP6vJicno9FofOicAADA5/ixUM3392Kx2K2mmSRJNJvNaDabUavVuuOq1Wqcnp5Gq9XqO+55dHQU9Xq9b62FhYU4OzuLkZGRvr6bm5t4enqK2dnZvr40TfsKFQjTGAS53w+Br2dsbCwuLy//tR8AvpJarRZu6gDgR3d3d7GxsRGNRiPSNI39/f3Y2dnp9idJEmtra/H6+trdoRbxLVBbXV2NTqfTE6g9PDzE+fl5HB4e9q2Vy+Vienr6p8XNLi4uYmhoKEqlUk/729tbXF1dxcrKSrdta2srDg4O4uTkJPL5fNzf30fEt2I9+Xz+T38F/O8EagykXC4XExMTn/0ZAAAAf83y8nK02+2Ym5uL4eHhWF9f7wmvkiSJdrsdU1NTMTo62m2vVqvx8vIS4+Pj3YJqERHHx8dRLpd/Waxmb2/vp/d4pmkaxWIxCoVCT/v19XV0Op3uzrX39/fY3t6O5+fnmJ+f7xnbarWiXC5n/gfwWRQlAAAAgC/m+31lu7u7HzZnvV6PSqUSm5ubHzYnDCp3qAEAAABRqVRiaWnpsz8DvgRHPgEAAAA70yADRz4BAAAAIANHPgEAAAAgA4EaAAAAAGQgUAMAAACADARqAAAAAJCBQA0AAAAAMhCoAQAAAEAGAjUAAAAAyECgBgAAAAAZCNQAAAAAIAOBGgAAAABkIFADAAAAgAwEagAAAACQwT9Eq6lHUXiMIgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15, 5))\n",
    "ax1 = fig.add_subplot(1, 3, 1)\n",
    "ax1.set_title(\"f_knee\")\n",
    "noise_fit_stats = noise_fit_stats_fixed_wn\n",
    "f_knee = noise_fit_stats.fit.T[0]\n",
    "ax1.hist(f_knee[((f_knee>0)&(f_knee<2))], bins=100, label=\"fixed wn\")\n",
    "\n",
    "noise_fit_stats = noise_fit_stats_fixed_alpha\n",
    "f_knee = noise_fit_stats.fit.T[0]\n",
    "ax1.hist(f_knee[((f_knee>0)&(f_knee<2))], bins=100, label=\"fixed alpha\")\n",
    "\n",
    "noise_fit_stats = noise_fit_stats_all_params\n",
    "f_knee = noise_fit_stats.fit.T[0]\n",
    "ax1.hist(f_knee[((f_knee>0)&(f_knee<2))], bins=100, label=\"free params\")\n",
    "ax1.set_xlabel('f_knee (Hz)')\n",
    "ax1.legend()\n",
    "\n",
    "ax2 = fig.add_subplot(1, 3, 2)\n",
    "ax2.set_title(\"alpha\")\n",
    "noise_fit_stats = noise_fit_stats_fixed_wn\n",
    "alpha = noise_fit_stats.fit.T[2]\n",
    "ax2.hist(alpha[((alpha>0)&(alpha<4))], bins=100, label=\"fixed wn\")\n",
    "\n",
    "noise_fit_stats = noise_fit_stats_all_params\n",
    "alpha = noise_fit_stats.fit.T[2]\n",
    "ax2.hist(alpha[((alpha>0)&(alpha<4))], bins=100, label=\"free params\")\n",
    "ax2.set_xlabel('alpha')\n",
    "ax2.legend()\n",
    "\n",
    "ax3 = fig.add_subplot(1, 3, 3)\n",
    "ax3.set_title(\"white noise level\")\n",
    "\n",
    "noise_fit_stats = noise_fit_stats_fixed_alpha\n",
    "wn = noise_fit_stats.fit.T[1]\n",
    "ax3.hist(np.sqrt(wn)[np.sqrt(wn)<0.0001], bins=100, label=\"fixed alpha\")\n",
    "\n",
    "noise_fit_stats = noise_fit_stats_all_params\n",
    "wn = noise_fit_stats.fit.T[1]\n",
    "ax3.hist(np.sqrt(wn)[np.sqrt(wn)<0.0001], bins=100, label=\"free params\")\n",
    "ax3.set_xlabel('pW/$\\sqrt{Hz}$')\n",
    "ax3.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34e21721-a4e2-48fd-8ce4-08e355ced70a",
   "metadata": {
    "tags": []
   },
   "source": [
    "# \"calc_binned_psd\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4dbb333-4b17-4326-83f2-6a3ef243e6ca",
   "metadata": {},
   "source": [
    "``calc_binned_psd`` returns the binned PSD. This function is incorporated to ``fit_binned_noise_model``.\\\n",
    "The parameters ``unbinned_mode`` and ``base`` changes the number of first un-binned modes and binning width, respectively.\\\n",
    "If the number of data points in a bin is less than ``limit_N``, that bin is handled with\n",
    "chi2 distribution and its error is \\<psd>. Otherwise the central limit theorem\n",
    "is applied to the bin and the error is estimated as  std(psd)/sqrt(len(psd))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c5254835-ca9d-442e-9403-b03dba678810",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:00:01.495386\n"
     ]
    }
   ],
   "source": [
    "start = datetime.datetime.now()\n",
    "mask = ~aman.psd_mask.mask()\n",
    "f = aman.freqs[mask]\n",
    "pxx = aman.Pxx[:, mask]\n",
    "f_binned, pxx_binned, bin_size = fft_ops.calc_binned_psd(aman, f=f, pxx=pxx, unbinned_mode=3, base=1.2, merge=False)\n",
    "stop = datetime.datetime.now()\n",
    "print(stop - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c2d026e7-de64-43db-b3cb-deca8df2fb16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "661"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pxx_binned)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d378e96b-d412-4a90-b89b-fe96bedb9402",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(661, 370684)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aman.Pxx.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3dfa7fc9-357a-45e8-865f-081e46d41b49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(661, 69)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pxx_binned.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4bbc04f3-ec3c-40e3-ad99-14c339c8f137",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0001, 50)"
      ]
     },
     "execution_count": 24,
     "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(aman.freqs[mask], np.sqrt(aman.Pxx[0][mask]), alpha=0.4, label=\"HWPSS+single mask\")\n",
    "plt.scatter(f_binned, np.sqrt(pxx_binned[0]), c='tab:orange', label=\"binned\")\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": "7a378447-eecd-4f9a-8513-efe6c43fa424",
   "metadata": {},
   "source": [
    "## \"fit_binned_noise_model\"\n",
    "This function calculates binned PSD and then starts fitting when PSD is handed.\\\n",
    "The calculation time cost becomes much smaller."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5710f3b4-3958-46a8-89cd-a84c59347826",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[8.14088210e-02 2.28070468e-01 8.88465464e-01 2.47808433e+00\n",
      " 2.47808433e+00 1.16312361e+00 1.77316606e+00 2.14831758e+00\n",
      " 3.59296709e-01 9.54714492e-02 9.14030075e-02 3.65557969e-02\n",
      " 2.08907053e-02 3.98094915e-02 2.86360160e-02 1.87569391e-02\n",
      " 1.76434778e-02 1.17468778e-02 2.26486474e-03 3.59308370e-03\n",
      " 1.33922289e-03 1.55595236e-03 8.59019172e-04 3.65239568e-04\n",
      " 1.92224106e-04 2.06169891e-04 5.83372312e-05 4.89313170e-05\n",
      " 3.08396775e-05 1.72491546e-05 1.20703035e-05 4.87735588e-06\n",
      " 3.79398625e-06 2.60634988e-06 1.18623609e-06 6.17150931e-07\n",
      " 3.48346219e-07 2.02906165e-07 1.14217585e-07 5.74623336e-08\n",
      " 3.07446975e-08 1.57545994e-08 7.88529597e-09 4.65413130e-09\n",
      " 3.62504093e-09 2.98526515e-09 2.99071368e-09 2.56142352e-09\n",
      " 2.41889242e-09 2.40575249e-09 2.27845320e-09 2.32052044e-09\n",
      " 2.18938911e-09 2.27976060e-09 2.21616725e-09 2.25077468e-09\n",
      " 2.33879693e-09 2.22930163e-09 2.20868057e-09 2.25302288e-09\n",
      " 2.24789054e-09 2.04295514e-09 1.95012895e-09 1.79861714e-09\n",
      " 1.48506052e-09 8.57910076e-10 3.90045302e-10 1.51992183e-10\n",
      " 7.98127328e-11]\n",
      "[[0.00000000e+00 2.69778611e-04 5.39557221e-04 8.09335832e-04\n",
      "  8.09335832e-04 1.07911444e-03 1.34889305e-03 1.61867166e-03\n",
      "  1.88845027e-03 2.15822889e-03 2.56289680e-03 3.10245402e-03\n",
      "  3.64201124e-03 4.18156846e-03 4.99090430e-03 5.93512943e-03\n",
      "  7.01424388e-03 8.36313693e-03 9.98180859e-03 1.20051482e-02\n",
      "  1.42982664e-02 1.69960525e-02 2.03682851e-02 2.44149643e-02\n",
      "  2.92709793e-02 3.50712194e-02 4.19505740e-02 5.03137109e-02\n",
      "  6.02955195e-02 7.21657783e-02 8.64640447e-02 1.03729876e-01\n",
      "  1.24367940e-01 1.49052682e-01 1.78728330e-01 2.14339106e-01\n",
      "  2.57099016e-01 3.08356952e-01 3.69731586e-01 4.43246257e-01\n",
      "  5.31598752e-01 6.37621746e-01 7.64552583e-01 9.16842608e-01\n",
      "  1.09961762e+00 1.31881274e+00 1.58171199e+00 2.84669671e+00\n",
      "  3.27511233e+00 3.72861018e+00 4.59902418e+00 5.32475533e+00\n",
      "  6.25131807e+00 7.44588965e+00 8.83761721e+00 1.04700042e+01\n",
      "  1.24698224e+01 1.48614152e+01 1.76869084e+01 2.11767719e+01\n",
      "  3.45599365e+01 3.87739308e+01 4.35933908e+01 4.93736673e+01\n",
      "  5.63063032e+01 6.46211497e+01 7.45937858e+01 8.65545554e+01\n",
      "  9.65397363e+01]]\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "too many indices for array: array is 1-dimensional, but 2 were indexed",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[23], line 6\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28mprint\u001b[39m(p)\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28mprint\u001b[39m(f)\n\u001b[0;32m----> 6\u001b[0m wnest \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mmedian(\u001b[43mp\u001b[49m\u001b[43m[\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m>\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mfwhite\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m&\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m<\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mfwhite\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m)\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m1.5\u001b[39m\n",
      "\u001b[0;31mIndexError\u001b[0m: too many indices for array: array is 1-dimensional, but 2 were indexed"
     ]
    }
   ],
   "source": [
    "fwhite=(3, 20)\n",
    "p = pxx_binned[1]\n",
    "f = f_binned\n",
    "print(p)\n",
    "print(f)\n",
    "wnest = np.median(p[((f > fwhite[0]) & (f < fwhite[1]))])*1.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "411cf644-745d-4985-a863-50ccd3e3ff2d",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[8.1408821e-02 2.2807047e-01 8.8846546e-01 ... 2.9207886e-10 1.8257970e-10\n",
      " 5.5733786e-11]\n",
      "[0.00000000e+00 2.69778611e-04 5.39557221e-04 ... 1.00001805e+02\n",
      " 1.00002075e+02 1.00002345e+02]\n"
     ]
    }
   ],
   "source": [
    "fwhite=(3, 20)\n",
    "p = aman.Pxx[:, mask][1]\n",
    "f = aman.freqs[mask]\n",
    "print(p)\n",
    "print(f)\n",
    "wnest = np.median(p[((f > fwhite[0]) & (f < fwhite[1]))])*1.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0a987734-60a9-46f9-87be-d46bb6778685",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/so/home/jsugiyama/git/sotodlib/sotodlib/tod_ops/fft_ops.py:366: RuntimeWarning: invalid value encountered in power\n",
      "  return wn * (1 + (fknee / f) ** alpha)\n",
      "/so/home/jsugiyama/git/sotodlib/sotodlib/tod_ops/fft_ops.py:371: RuntimeWarning: invalid value encountered in log\n",
      "  output = np.sum((np.log(model) + y / model)*bin_size)\n",
      "/so/home/jsugiyama/git/sotodlib/sotodlib/tod_ops/fft_ops.py:371: RuntimeWarning: invalid value encountered in log\n",
      "  output = np.sum((np.log(model) + y / model)*bin_size)\n",
      "/so/home/jsugiyama/git/sotodlib/sotodlib/tod_ops/fft_ops.py:371: RuntimeWarning: invalid value encountered in log\n",
      "  output = np.sum((np.log(model) + y / model)*bin_size)\n",
      "/so/home/jsugiyama/git/sotodlib/sotodlib/tod_ops/fft_ops.py:371: RuntimeWarning: invalid value encountered in log\n",
      "  output = np.sum((np.log(model) + y / model)*bin_size)\n",
      "/so/home/jsugiyama/git/sotodlib/sotodlib/tod_ops/fft_ops.py:366: RuntimeWarning: invalid value encountered in power\n",
      "  return wn * (1 + (fknee / f) ** alpha)\n",
      "/so/home/jsugiyama/git/sotodlib/sotodlib/tod_ops/fft_ops.py:371: RuntimeWarning: invalid value encountered in log\n",
      "  output = np.sum((np.log(model) + y / model)*bin_size)\n",
      "/so/home/jsugiyama/git/sotodlib/sotodlib/tod_ops/fft_ops.py:371: RuntimeWarning: invalid value encountered in log\n",
      "  output = np.sum((np.log(model) + y / model)*bin_size)\n",
      "/so/home/jsugiyama/git/sotodlib/sotodlib/tod_ops/fft_ops.py:371: RuntimeWarning: invalid value encountered in log\n",
      "  output = np.sum((np.log(model) + y / model)*bin_size)\n",
      "/so/home/jsugiyama/git/sotodlib/sotodlib/tod_ops/fft_ops.py:371: RuntimeWarning: invalid value encountered in log\n",
      "  output = np.sum((np.log(model) + y / model)*bin_size)\n",
      "/so/home/jsugiyama/git/sotodlib/sotodlib/tod_ops/fft_ops.py:371: RuntimeWarning: invalid value encountered in log\n",
      "  output = np.sum((np.log(model) + y / model)*bin_size)\n",
      "/so/home/jsugiyama/git/sotodlib/sotodlib/tod_ops/fft_ops.py:371: RuntimeWarning: invalid value encountered in log\n",
      "  output = np.sum((np.log(model) + y / model)*bin_size)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:00:07.964665\n"
     ]
    }
   ],
   "source": [
    "start = datetime.datetime.now()\n",
    "noise_fit_stats_bin = fft_ops.fit_noise_model(aman, \n",
    "                                             f=aman.freqs, \n",
    "                                             pxx=aman.Pxx, \n",
    "                                             lowf=1, \n",
    "                                             f_min=0.001, \n",
    "                                             f_max=20, \n",
    "                                             fwhite=(3, 20), \n",
    "                                             mask=True, \n",
    "                                             binning=True,\n",
    "                                             unbinned_mode=1, base=1.2)\n",
    "stop = datetime.datetime.now()\n",
    "print(stop - start)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a42a6ca-33f0-4606-98d5-933d55062a23",
   "metadata": {},
   "source": [
    "### comparison of fit results between binned and not binned PSD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "fd581e18-1e2d-4daa-ba6f-c9e6b804d681",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/so/home/jsugiyama/git/sotodlib/sotodlib/tod_ops/fft_ops.py:366: RuntimeWarning: divide by zero encountered in divide\n",
      "  return wn * (1 + (fknee / f) ** alpha)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.0001, 50)"
      ]
     },
     "execution_count": 23,
     "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(aman.freqs[mask], np.sqrt(aman.Pxx[0][mask]), alpha=0.4, label=\"HWPSS+single mask\", zorder=0)\n",
    "plt.scatter(f_binned, np.sqrt(pxx_binned[0]), label=\"binned\", zorder=1)\n",
    "noise_fit_stats = noise_fit_stats_all_params\n",
    "plt.loglog(aman.freqs, np.sqrt(fft_ops.noise_model(aman.freqs, noise_fit_stats.fit[0])),  label=\"fit (free params)\")\n",
    "noise_fit_stats = noise_fit_stats_bin\n",
    "plt.loglog(aman.freqs, np.sqrt(fft_ops.noise_model(aman.freqs, noise_fit_stats.fit[0])), label=\"fit (binned)\", ls=\"--\")\n",
    "plt.legend()\n",
    "plt.xlabel('Frequency (Hz)')\n",
    "plt.ylabel('signal (pW/$\\sqrt{Hz}$)')\n",
    "plt.xlim(1E-4, 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "56fbef60-66be-4d17-a315-54f0cdfb3f83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f7c2f9943d0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15, 5))\n",
    "ax1 = fig.add_subplot(1, 3, 1)\n",
    "ax1.set_title(\"f_knee\")\n",
    "noise_fit_stats = noise_fit_stats_all_params\n",
    "f_knee = noise_fit_stats.fit.T[0]\n",
    "ax1.hist(f_knee[((f_knee>0)&(f_knee<2))], bins=100, label=\"free params\")\n",
    "noise_fit_stats = noise_fit_stats_bin\n",
    "f_knee = noise_fit_stats.fit.T[0]\n",
    "ax1.hist(f_knee[((f_knee>0)&(f_knee<2))], bins=100, label=\"free params (binned)\")\n",
    "\n",
    "ax1.set_xlabel('f_knee (Hz)')\n",
    "ax1.legend()\n",
    "\n",
    "ax2 = fig.add_subplot(1, 3, 2)\n",
    "ax2.set_title(\"alpha\")\n",
    "noise_fit_stats = noise_fit_stats_all_params\n",
    "alpha = noise_fit_stats.fit.T[2]\n",
    "ax2.hist(alpha[((alpha>0)&(alpha<4))], bins=100, label=\"free params\")\n",
    "\n",
    "noise_fit_stats = noise_fit_stats_bin\n",
    "alpha = noise_fit_stats.fit.T[2]\n",
    "ax2.hist(alpha[((alpha>0)&(alpha<4))], bins=100, label=\"free params (binned)\")\n",
    "\n",
    "ax2.set_xlabel('alpha')\n",
    "ax2.legend()\n",
    "\n",
    "ax3 = fig.add_subplot(1, 3, 3)\n",
    "ax3.set_title(\"white noise level\")\n",
    "noise_fit_stats = noise_fit_stats_all_params\n",
    "wn = noise_fit_stats.fit.T[1]\n",
    "ax3.hist(np.sqrt(wn)[np.sqrt(wn)<0.0001], bins=100, label=\"free params\")\n",
    "noise_fit_stats = noise_fit_stats_bin\n",
    "wn = noise_fit_stats.fit.T[1]\n",
    "ax3.hist(np.sqrt(wn)[np.sqrt(wn)<0.0001], bins=100, label=\"free params (binned)\")\n",
    "ax3.set_xlabel('pW/$\\sqrt{Hz}$')\n",
    "ax3.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70715807-0857-49e5-8760-1718e9d22225",
   "metadata": {},
   "source": [
    "### comparison of fit parameters std errors between binned and not binned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d04988ec-fab9-41eb-b0c2-ac8abd1d8421",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f7c2f1e33a0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15, 5))\n",
    "ax1 = fig.add_subplot(1, 3, 1)\n",
    "ax1.set_title(\"f_knee fit error\")\n",
    "f_knee = []\n",
    "alpha = []\n",
    "wn = []\n",
    "noise_fit_stats = noise_fit_stats_all_params\n",
    "for i in range(aman.dets.count):\n",
    "    f_knee.append(np.sqrt(noise_fit_stats.cov[i][0][0]))\n",
    "f_knee=np.array(f_knee)\n",
    "ax1.hist(f_knee[((f_knee>0)&(f_knee<0.01))], bins=100, label=\"free params\")\n",
    "noise_fit_stats = noise_fit_stats_bin\n",
    "f_knee = []\n",
    "for i in range(aman.dets.count):\n",
    "    f_knee.append(np.sqrt(noise_fit_stats.cov[i][0][0]))\n",
    "f_knee=np.array(f_knee)\n",
    "ax1.hist(f_knee[((f_knee>0)&(f_knee<0.01))], bins=100, label=\"free params (binned)\")\n",
    "\n",
    "ax1.set_xlabel('f_knee error (Hz)')\n",
    "ax1.legend()\n",
    "\n",
    "ax2 = fig.add_subplot(1, 3, 2)\n",
    "ax2.set_title(\"alpha fit error\")\n",
    "noise_fit_stats = noise_fit_stats_all_params\n",
    "for i in range(aman.dets.count):\n",
    "    alpha.append(np.sqrt(noise_fit_stats.cov[i][2][2]))\n",
    "alpha=np.array(alpha)\n",
    "ax2.hist(alpha[((alpha>0)&(alpha<0.05))], bins=100, label=\"free params\")\n",
    "\n",
    "noise_fit_stats = noise_fit_stats_bin\n",
    "alpha = []\n",
    "for i in range(aman.dets.count):\n",
    "    alpha.append(np.sqrt(noise_fit_stats.cov[i][2][2]))\n",
    "alpha=np.array(alpha)\n",
    "ax2.hist(alpha[((alpha>0)&(alpha<0.05))], bins=100, label=\"free params (binned)\")\n",
    "\n",
    "ax2.set_xlabel('alpha error')\n",
    "ax2.legend()\n",
    "\n",
    "ax3 = fig.add_subplot(1, 3, 3)\n",
    "ax3.set_title(\"white noise fit error\")\n",
    "noise_fit_stats = noise_fit_stats_all_params\n",
    "for i in range(aman.dets.count):\n",
    "    wn.append(np.sqrt(noise_fit_stats.cov[i][1][1]))\n",
    "\n",
    "ax3.hist(np.log10(wn), bins=100, label=\"free params\")\n",
    "wn = []\n",
    "noise_fit_stats = noise_fit_stats_bin\n",
    "for i in range(aman.dets.count):\n",
    "    wn.append(np.sqrt(noise_fit_stats.cov[i][1][1]))\n",
    "\n",
    "ax3.hist(np.log10(wn), bins=100, label=\"free params (binned)\")\n",
    "#ax3.set_xlim(1E-8,1E-1)\n",
    "#ax3.set_xscale('log')\n",
    "ax3.set_xlabel('log10(pW/$\\sqrt{Hz}$)')\n",
    "ax3.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ccd36d8a-86ee-4bdd-bc96-b6b8841e5db4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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
}