{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import h5py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "ca_df = pd.read_csv('ca_df.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "conns = pd.read_csv('Connections.csv')\n",
    "segs = pd.read_csv('L5Segments.csv')\n",
    "segs_degrees = pd.read_csv('SegmentsDegrees.csv').groupby(['Type','Sec ID'])['Degrees'].max().reset_index()\n",
    "segs['segmentID'] = segs.index\n",
    "segs = segs.set_index(['Type','Sec ID']).join(segs_degrees.set_index(['Type','Sec ID'])).reset_index()\n",
    "\n",
    "conns.loc[conns.Type=='dend','Sec ID'] = conns.loc[conns.Type=='dend','Name'].apply(lambda x: int(x.split('dend[')[1].split(']')[0]))\n",
    "conns.loc[conns.Type=='apic','Sec ID'] = conns.loc[conns.Type=='apic','Name'].apply(lambda x: int(x.split('apic[')[1].split(']')[0]))\n",
    "conns.loc[conns.Type=='soma','Sec ID'] = conns.loc[conns.Type=='soma','Name'].apply(lambda x: int(x.split('soma[')[1].split(']')[0]))\n",
    "conns.loc[conns.Type=='axon','Sec ID'] = conns.loc[conns.Type=='axon','Name'].apply(lambda x: int(x.split('axon[')[1].split(']')[0]))\n",
    "\n",
    "conns['X'] = conns['Name'].apply(lambda x: float(x.split('(')[1].split(')')[0]))\n",
    "\n",
    "conns.rename(columns={'Distance':'conns_Distance'},inplace=True)\n",
    "\n",
    "conns['Sec ID'] = conns['Sec ID'].astype(int)\n",
    "conns['X'] = conns['X'].astype(float)\n",
    "\n",
    "segs['Sec ID'] = segs['Sec ID'].astype(int)\n",
    "segs['X'] = segs['X'].astype(float)\n",
    "\n",
    "segs['Elec_distanceQ'] = 'None'\n",
    "\n",
    "segs.loc[segs.Type=='dend','Elec_distanceQ'] = pd.qcut(segs.loc[segs.Type=='dend','Elec_distance'], 10, labels=False)\n",
    "segs.loc[segs.Type=='apic','Elec_distanceQ'] = pd.qcut(segs.loc[segs.Type=='apic','Elec_distance'], 10, labels=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "segs_ca_df = segs.set_index('segmentID').join(ca_df.set_index('segmentID')).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>segmentID</th>\n",
       "      <th>Type</th>\n",
       "      <th>Sec ID</th>\n",
       "      <th>BMTK ID</th>\n",
       "      <th>X</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Coord X</th>\n",
       "      <th>Coord Y</th>\n",
       "      <th>Coord Z</th>\n",
       "      <th>Elec_distance</th>\n",
       "      <th>Degrees</th>\n",
       "      <th>Elec_distanceQ</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>na_lower_bound</th>\n",
       "      <th>ca_lower_bound</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1040</th>\n",
       "      <td>1040</td>\n",
       "      <td>apic</td>\n",
       "      <td>1</td>\n",
       "      <td>86</td>\n",
       "      <td>0.346154</td>\n",
       "      <td>80.254802</td>\n",
       "      <td>-16.295059</td>\n",
       "      <td>67.300051</td>\n",
       "      <td>3.654825</td>\n",
       "      <td>0.922488</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1050</th>\n",
       "      <td>1049</td>\n",
       "      <td>apic</td>\n",
       "      <td>2</td>\n",
       "      <td>87</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>125.707142</td>\n",
       "      <td>-20.165805</td>\n",
       "      <td>110.894753</td>\n",
       "      <td>8.035568</td>\n",
       "      <td>0.834321</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>216229.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1051</th>\n",
       "      <td>1049</td>\n",
       "      <td>apic</td>\n",
       "      <td>2</td>\n",
       "      <td>87</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>125.707142</td>\n",
       "      <td>-20.165805</td>\n",
       "      <td>110.894753</td>\n",
       "      <td>8.035568</td>\n",
       "      <td>0.834321</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1210262.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1053</th>\n",
       "      <td>1050</td>\n",
       "      <td>apic</td>\n",
       "      <td>2</td>\n",
       "      <td>87</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>131.481450</td>\n",
       "      <td>-20.161000</td>\n",
       "      <td>116.505119</td>\n",
       "      <td>6.836384</td>\n",
       "      <td>0.822341</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1210260.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1055</th>\n",
       "      <td>1051</td>\n",
       "      <td>apic</td>\n",
       "      <td>2</td>\n",
       "      <td>87</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>137.255759</td>\n",
       "      <td>-19.478333</td>\n",
       "      <td>121.977183</td>\n",
       "      <td>5.219003</td>\n",
       "      <td>0.810390</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>455555.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329816</th>\n",
       "      <td>2491</td>\n",
       "      <td>apic</td>\n",
       "      <td>105</td>\n",
       "      <td>190</td>\n",
       "      <td>0.921053</td>\n",
       "      <td>167.347659</td>\n",
       "      <td>-94.632744</td>\n",
       "      <td>84.965838</td>\n",
       "      <td>-48.580974</td>\n",
       "      <td>0.051683</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>19.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1400447.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329835</th>\n",
       "      <td>2492</td>\n",
       "      <td>apic</td>\n",
       "      <td>105</td>\n",
       "      <td>190</td>\n",
       "      <td>0.973684</td>\n",
       "      <td>172.278122</td>\n",
       "      <td>-98.815349</td>\n",
       "      <td>86.356414</td>\n",
       "      <td>-49.924340</td>\n",
       "      <td>0.048964</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>17.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1231286.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329848</th>\n",
       "      <td>2501</td>\n",
       "      <td>apic</td>\n",
       "      <td>107</td>\n",
       "      <td>192</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>121.198641</td>\n",
       "      <td>-53.364582</td>\n",
       "      <td>64.412122</td>\n",
       "      <td>-44.010966</td>\n",
       "      <td>0.095414</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>380154.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329865</th>\n",
       "      <td>2510</td>\n",
       "      <td>apic</td>\n",
       "      <td>108</td>\n",
       "      <td>193</td>\n",
       "      <td>0.785714</td>\n",
       "      <td>126.779844</td>\n",
       "      <td>-57.005121</td>\n",
       "      <td>51.834454</td>\n",
       "      <td>-45.517306</td>\n",
       "      <td>0.085551</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>106933.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329870</th>\n",
       "      <td>2511</td>\n",
       "      <td>apic</td>\n",
       "      <td>108</td>\n",
       "      <td>193</td>\n",
       "      <td>0.928571</td>\n",
       "      <td>131.076229</td>\n",
       "      <td>-58.890431</td>\n",
       "      <td>51.344319</td>\n",
       "      <td>-49.313504</td>\n",
       "      <td>0.079254</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>106928.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>110485 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        segmentID  Type  Sec ID  BMTK ID         X    Distance    Coord X  \\\n",
       "1040         1040  apic       1       86  0.346154   80.254802 -16.295059   \n",
       "1050         1049  apic       2       87  0.100000  125.707142 -20.165805   \n",
       "1051         1049  apic       2       87  0.100000  125.707142 -20.165805   \n",
       "1053         1050  apic       2       87  0.300000  131.481450 -20.161000   \n",
       "1055         1051  apic       2       87  0.500000  137.255759 -19.478333   \n",
       "...           ...   ...     ...      ...       ...         ...        ...   \n",
       "329816       2491  apic     105      190  0.921053  167.347659 -94.632744   \n",
       "329835       2492  apic     105      190  0.973684  172.278122 -98.815349   \n",
       "329848       2501  apic     107      192  0.500000  121.198641 -53.364582   \n",
       "329865       2510  apic     108      193  0.785714  126.779844 -57.005121   \n",
       "329870       2511  apic     108      193  0.928571  131.076229 -58.890431   \n",
       "\n",
       "           Coord Y    Coord Z  Elec_distance  Degrees Elec_distanceQ  \\\n",
       "1040     67.300051   3.654825       0.922488        2              9   \n",
       "1050    110.894753   8.035568       0.834321        3              9   \n",
       "1051    110.894753   8.035568       0.834321        3              9   \n",
       "1053    116.505119   6.836384       0.822341        3              9   \n",
       "1055    121.977183   5.219003       0.810390        3              9   \n",
       "...            ...        ...            ...      ...            ...   \n",
       "329816   84.965838 -48.580974       0.051683        3              5   \n",
       "329835   86.356414 -49.924340       0.048964        3              5   \n",
       "329848   64.412122 -44.010966       0.095414        4              7   \n",
       "329865   51.834454 -45.517306       0.085551        4              6   \n",
       "329870   51.344319 -49.313504       0.079254        4              6   \n",
       "\n",
       "        Unnamed: 0  na_lower_bound  ca_lower_bound  \n",
       "1040           0.0             NaN             0.0  \n",
       "1050           1.0             NaN        216229.0  \n",
       "1051           2.0             NaN       1210262.0  \n",
       "1053           1.0             NaN       1210260.0  \n",
       "1055           1.0             NaN        455555.0  \n",
       "...            ...             ...             ...  \n",
       "329816        19.0             NaN       1400447.0  \n",
       "329835        17.0             NaN       1231286.0  \n",
       "329848         0.0             NaN        380154.0  \n",
       "329865         0.0             NaN        106933.0  \n",
       "329870         0.0             NaN        106928.0  \n",
       "\n",
       "[110485 rows x 15 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "segs_ca_df[(segs_ca_df.Type=='apic') & (~pd.isnull(segs_ca_df.ca_lower_bound))].drop_duplicates(subset='ca_lower_bound')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "v = h5py.File('./output/v_report.h5','r')\n",
    "hva = h5py.File('./output/Ca_HVA.ica_report.h5','r')\n",
    "lva = h5py.File('./output/Ca_LVAst.ica_report.h5','r')\n",
    "ih = h5py.File('./output/Ih.ihcn_report.h5','r')\n",
    "\n",
    "nmda = h5py.File('./output/inmda_report.h5','r')\n",
    "\n",
    "na = h5py.File('./output/NaTa_t.gNaTa_t_report.h5','r')\n",
    "spks = h5py.File('./output/spikes.h5','r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ffe93457c88>]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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99tlxjzlR3rro4fmjTfzttUtYXJabtnFNwG234rBZTDfgEEyxGoFmb4DH36pHKc03fqq5h8OXuvojjQDsNgsLS9xcvbSMxWU57Dvv4YGdZ/jBzjNU5btw2a2EI1FauoP4QyNfVd+ytop7blo+aJ/TurmFrJtbyP/5wAqON3Xzh+MtvHm+g5Ic+4hrUBMhz2Wb8jWrH/7xLM8eauTuGy7LCKFSSuHv9uJyusmNEas1a9aMu5/u/XFSNglYXBYtvH3IUlb3m90opRARFi1aRHNzM729vbhcqV90//cdJyly2/nUlVO7p2s2IiKU5DgyPjI33ZhiNQJNXYFB9Z7mFLlYWZXPRzfNYVllLotLc6kudA1eR3kP1HX4eerAJc62+QiFo1j08NmqfBeVBU6y7VZCYW3Hv0KLxJtblD1i4UMRYVllHssq469PJYM8ZxZN3kDK+h+N401e/vWFE9y0upLPvid57s3J0NvbS19fCLcjB1eBtvbX0DD+pCmBCwFU32A1Mjb5Lv63xZz+4mmC9UGi/oHoNhVSBC8Gcc5zUlmpbeRubGxk4cLU/m2OXOri1VNtfOXGy5IS3WYyforc9v6AERMN85M4Aquq8zn+zRuw6BtIx5pGZk5RNn9zTW2KZ5d8nITo7PZNydjRqOKeJ4+Q58rin25eOem9Ucmis7MTAIvTjTOvGNAEY7x4d3sR68B7srgsVP5lZf8m38JthcM2EYtN6NrdhXOek6qqqv6xUy1Wv9p7EWeWhds2zU3pOCaJKcjOwuOf+vXjTMJM4jUCFovgzLJit1lmfL6z1tZWfvaF9/H2T74yJePveKeZ/Rc8fPmGyyh0Z85ifr9YOdyEsFFQUDAhserc2UmkO0IffYScIVb99ypqv1fbn43C2ES88pmV2AptiF2IdEfo3KmNb6yRNTc3J+mdxacnGObpA5d43+rEe/JMUk9htj3pZWnSjYh8R0SOi8ghEXlSRApiXotbdHckZvYvsMmYee655+jt7sR37iAXLlwc/YQkopTi/j+cZl5xNh9cH7fiy5QRK1bdgTBVVVUTE6uXO8EKBzjAyX88SeE1hXGPMzYRF7ynAKz6eUBhoXZ8V1fXBN/J2Hj+SBO+UIT/sdm0qqaSwuwsPNPfDbgDWKmUWg2cBL4CiYvujtaZKVYmABw5cqT/8Ysv70zr2AfrOjl8qYtPX7Uw4yzYATdgDj3BMJWVlRNas3LVunDd7eJu7qbqsqoRjzU2EdfeX4trsRZMUVCgXZR6PKktyvfC0SYq852sn1sw+sEmKUEpxdk92+loaSQcmXxeyalCKfVCTO3BN9CqZEDiorsjklm/DCZTxunTp6mYswCxZ/PHV8efpWEy/GZfPa4sKzevHflHfCrwerUieHanZllVVFRMyBW3+tnVtF7eCkBNTfz8jLGICNWfrWb1s6sByMvLQ0T6xTMV9IYivHKqleuWl2fMmuFs5Pnnn+c/v/V3NP/6H+mc+n2PNqOIrX67a4L9fAp4Tn88loK9wycywYFNZhgNDQ1U1szBo1wcO3p09BOSRCgc5XdvN3DjqgpynZm3RuL3a9sUcnKy6Qn2UV5ePuF1o/p6LXt7dfX4XZ0Wi4X8/PyUitWrp1oJ9EW5bsU0Srs/A3n55ZcBCHfUc66+kZLL5k/ldMJKqY2JXhSRF4lfp+EepdTT+jH3oFXV+KVxWpzjR81BZoqVCaAt3C9ZvZEsfzanTuzp39+Tat4830F3MMyNcWpsZQK9vVoRvNwcfc2qrAy/34/P58PtHt+G7Lq6Omw2GxUVExODgoKClIrVyydayXXY2LygKGVjmIxOrEt+1xt72TS1YjUiSqltI70uIncC7wOuUaq/eM9YCvYOw3QDmqCUorm5mYqKcrKK5+Dt9NDa2pqWsV96pwW7zcIVi4vTMt54McQqz+2mJxCmvLwcmFhUXl1dHVVVVVitE0vAW1hYmNI1q73n2tm0oGjEWksmqaexsZFN77oKxMKbe/ZM9XQmjIjcgFZ/8ANKKX/MS88At4mIQy+waxTdHRHzU2lCMBikt7eX8tISsoq19ZR33nknLWPvPNHCuxYVDyrrnUkYbsC8nGx6ggNi1dLSMu6+6urqmDNnzugHJiCVllVrd5AzrT7TqsoAWlpaWLRwIVnFczh2OL35IJPM/UAusENEDorID0ErugsYRXefJ6bo7kiYYmWCz6dtBC7My8VWpK2nnDp1KuXjtnYHOdvm4/KFmWlVgWZZZWVlkZvtwB+KTGq/02TFqrCwMGVi9eb5DgBTrKYYpRQtLS3UVFZgr1jEmXeOjH5ShqKUWqyUmqOUWqvfPhvz2r1KqUVKqaVKqedG6sfAFCuTfrEqKsjFlleKNStr3EUGJ8L+C5pLa+P8+HuOMgEjF1+2w4YvOHE3oFKK+vr6SVtWqXID7jnbjivLyqrqideWMpk8nZ2dhMNhqirLya6sxdvROqGtEjORSYmViBSJyA4ROaXfx/3VEZE79WNO6QtuRvsGETms72S+T/QV/UT9isjVItKlm5QHReRrk5m/iYYhVnm5ueQ47RRVzEmLZbXvfAd2m2VYFdlMwu/343K5yLHb6AmGKS3VktmO1w24d+9egsEgy5Ytm/BccnNz6enpmfD5I7H/ood1cwvM9aopxvhclZeXUzxPS1T91ltvTbi/L/76IN97MfXf5XQw2U/ml4GXlFK1wEv680GISBHwdWAL2savr8eI2gPAXWgLbLVou5lH6/fVGLPyG5OcvwkDYuV2u8l1ZpFXPjctYnWgrpPV1fk4bOmp+DsRent7yc7Oxq1bVg6Hg4KCgnFbVs8//zwiwgc/+MEJzyUnJ4eenh4GgqqSQygc5WRTj2lVZQDG56qsrIyqRZeBCPv3759wfy+faJnS5NTJZLJidTPwsP74YeCWOMdcD+xQSnUopTxoKThuEJFKIE8ptVsPaXwk5vyx9GuSJIwgArfbTZ7Lhru0htOnTxONpm73vFKKE03dLK9KXRb5ZGC4AXMcVvx9EaJRNaG9Vrt372blypXk509cENxuN5FIhFAouWl4TrV0E4pEM/5/MRswonBLS0spLcont3zehMXKG+jD4+9jXnHiag7TicmKVblSqhFAv49XkS7RbuVq/fHQ9tH6vVxE3haR50RkRaKJichdxq7rcHhqazRlOoZllZ2dTZ4zC0dxNcFgkLq6ulHOnDgNXQF6gmGWlGd2YT/DDZjtsKEU9PZpQRbjcQMqpdi7dy9bt26d1FxycnIAku4KPNqgZenIZHfsbMHImJKfn09hth131eIJuwHr9Lp7c0coPTSdGFWsRORFETkS53bzGMdItFt5IruY3wLmKaXWAN8Hnkp0oFLqIaXURqXURpstM8OiM4VYN2CeKwtLgbZBN5WuwJNNWiHCTBerWDcg0B9kMR7LyuPx4PF4JrVeBakTq2MNXrLtVhYUJ6/qtMnEiP0uFmTbsZUv4tKlSxOLPp1tYqWU2qaUWhnn9jTQrLvz0O/jXW4m2q1cz0Biw9h2EvWrlPIqpXr0x78HskSkZBzv1yQOg9esbKg8TayOHz+esjFPNhtilZOyMZJBrBsQwBcav2V1/vx5AObNm1zl49RZVl0sr8zDYjHzAU41sd/FwuwsIkVapeaJWFd1HdqG9pGKuk4nJusGfAYwovvuBJ6Oc8x24DoRKdQDK64DtuvuvW4R2apHAd4Rc37cfkWkIiZicLM+//ZJvodZzyDLyplFwJ5PeXk5b7zxRsrGPNHcTXmeg4LszKldFY9+N6B9sGXV0dFBX9/YkoxeuHABgPnz509qLkZ6p2SKlVKKYw1eVpjrVRmBz+dDRHC5XBRm27GVaoU2J7Ju1eQNkG23kuecGZ6lyYrVt4FrReQUcK3+HBHZKCI/BlBKdQDfBN7Ub9/Q2wA+B/wYLUX8GQay8sbtF/gwcERE3gbuA25TyQ6NmoUMDbDoCUa46qqrePXVV1M25snm7ox3AcKAGzBHdwNOJItFsi0r4+IiGTR2BfCFItROg//FbKCnpwe3242IUOi2Y3Fks2DhxNatmr0BynIdMyaD/qQkVynVDlwTp30f8Jcxz38K/DTBcSvH0e/9aCk8TJLI0ACLSFSx+fJ38dhjj3Hx4kXmzk1uIb5IVHGquYePb53cj3c6MNyAxpqVPxTuz2LR0tIypgzqFy9exO12U1Q0uewQqXADnm7R+lpcltnu2NlCbILkQr1S85KVq9m/f9+4+2rpDlKW50zq/KYScwegCT6fD4fDgdVq7S/TsWajFrn2+uuvJ328ug4/wXCUpdPgar5frOzamlVPMDLuLBYtLS1UVFRM+go3lWK1qNQUq0wgVqwMF/n8pau4ePEibW1t4+qrxRug3BQrk5lE7Bckz6VZEFULlpKVlcXBgweTPt4JI7iiIvPFylizio0GjLWsxkJbWxslJZOPA0rFmtWZ1h7yXVmU5GT22uFsIZ5lVbVIiyIdjytQKUWzN0h5riP5k5wiTLEyGSxWumUViMDy5cs5dOhQ0sczwtZrM9z1pJQiEAjEDV2HsVtWyRKrVKxZnW7pYXFZzoxZ15juDBYr7QKicK6Wdmk8QRY9wbC2JzDPFCuTGYTf74+xrDSx8gb6WLNmTUrE6kRzNzWFA9ZKphIIaGlqYt2AvmCEnJwcXC7XmMWqtbU1oy2rRaXm/qpMYbCXIwuLQMjqZOHCheOyrDr9WqRqYYZH244HU6xMhlhWmoB4e8OsXr2ahoaGcfvKR+NUc8+0WK8yoiRdLhc2qwWHzYIvFEZExrXXqq2trT8B7mSw2Ww4nc6kiVWnP0RbT8gMrsggYr+LVouQ78rC4w+xYcOGcVlWpliZzEh8Ph/Z2drGQSPAwrCsAN5+++2kjRUKRznT2jMt1quMKsHG3ybHoWVeB8acxcLv99Pb25sUywoGktkmgzOtZiRgpuHz+frdvaCJjcffx4YNGzh37hwdHR0jnD1AZ6+WP7JAX/eaCZhiZTLoai5Xt6y6A2FWrVoFwNGjR5M21vl2H+GomhaWlSFWLpcLALfDhl8Xq7FaVoZVmiyxcrvdSVuzMiMBMw9jn5VBQXYWHp9mWQHsGWOZe49uWZliZTKjiBUrZ5YVh82Ct7ePsrIyiouLOXIkedVKjTRLtRmeZgkGuwEBsu1WeoJa9e2xWlZGFu1MtKzOtvmwWy3UFM6MdDwzgdjvIkCRW7OsrrjiClwuF7///e/H1E+XX7Os8l2mG9BkBjH0C5LnysIb6ENEWLlyZXLFqqkbi0yPq/l4bkBfjBuwpaVl1DIqybaskilWF9r81BS5sJo5ATMCpdSw72JBtp1OfwiXy8U111zDs88+O6Z6Zp2mZWUyE4mNBgTNFegNaD/KK1eu5OjRo0kr+HeiuZv5JW6cWZlbcNEgrhswNOAGjEQio5aZz2ix6vAzb4YkOZ0JBINBotHooO9iYbYWYAFw0003ce7cuTFF6Hr8feQ4bDOq8vPMeScmEyY2wAK0vVbeXu3KbOXKlXi9Xurr6xOdPi5OTpNIQBjuBnQ7rIMCLGD0vVaGWCUjGhC0NatkiJVSiovtPuaZZUEyhtiE0gYF2XYCfVF6QxE+8pGP4HQ6efDBB0ftq7M3RL5r5lhVYIrVrCcajdLb2xvHDThgWQFJcQUG+iJcaPdNm6SpQ92AbrsNn75mNdYsFq2trVgsFgoKCpIyp2QFWLT7QvhCkRlT62gmEE+sjNBzjz9EcXExt912G7/4xS/o6uoasa8uf9+McgGCKVazntiM6wZ5ThvdumW1YoVWjDkZ4eunW3qIKqaNZRXPDegbp2VlbAi2WJLzVUuWWF1o1/7vM6Xk+UwgnlgVuTXBMVyBX/jCF+jp6eHuu++mu7s7YV+dvX0zao8VmGI164n3Bcl1DlhWhYWFLFu2jJdffnnSYxmRgEsrMj+4AoaLVY7Dhi8URik1ZrFqaWlJmgsQtDWrZIjVxQ6tD1OsMgfj/xq7z8pIZmsETKxbt44///M/58EHH+SjH/1owr48/hD5pmVlMpOIa1m5bHgDA4UFr7vuOl555ZX+H++JcqK5G7vVMm3WSYy/jeEGzHZYiSoI9EUpKirCarWOyQ2YTLEyLKvJBrxcaPcjghm2nkGM5gY0+NnPfsH04sUAACAASURBVMY//uM/8txzz3Hu3Lm4fXX5+ygw16xMZhKxtawM8pxZhMJRAn3a+sz1119PIBDgtddem9RYp5p7WFjqnjYRSvEsK9CShFosFkpLS8fkBjTWt5KB2+0mEokQCoVGP3gELrb7qchzTouozNmCETgzSKwMN6Bv8P/7tttuA2Dnzp3D+lFK0dXbN+UBFiLyTRE5JCIHReQFEanS20VE7hOR0/rr68fS3/T41TBJGfGu5mKT2QK8+93vxm6388ILL0xqrBNN06M6sEFvby8igsOhZa522wcKMMLYslikwrKCySezvdDhN4MrMoy4a1bZdkSgtTs46NjLLruMgoICdu/ePayfYDhKOKrImfpy9t9RSq1WSq0Ffgd8TW+/EajVb3cBD4ylM1OsZjnxviDGFZkRvu52u7nyyisnJVbdgT4udfaydBrkBDTw+/04nc7+8hluh1GAcfT8gJ2dnXzyk5/E4/FMukJwLMb/abLrVhfa/eZ6VYYR77tos1oodjtoGSJWFouFLVu2xE2/ZAQBGRdXU4VSyhvz1A0YvuubgUeUxhtAgYhUjtbfpMRKRIpEZIeInNLvCxMcd6d+zCkRuTOmfYOIHNbNwftE/1UQkY+IyFERiYrIxiF9fUU//oSIXD+Z+ZskvpoDaO8ZcD1ce+21HDp0aMIZ2E/peeimm2UV6x4dqGk1esqlv/u7v+PnP/95/3HJIhli1RuK0NYTNC2rDCPedxGgPG+4WIEWqXvy5MlhWVSMz2eSSvDYRGRfzO2u8ZwsIveKSB3wMQYsq2qgLuawer1tRCZrWX0ZeEkpVQu8pD8fOtki4OvAFmAz8PUYUXsAzQw0TMIb9PYjwAeBV4b0tRy4DVihH/sDETGd7pMgXoBFkVsTq44YP/m73vUuAN54440JjXNCL7g4XcLWYaCkvUFsAUZI7AZsamrikUce4fOf/zyvvfYan/rUp5I2p2SIVUOXthZXXega5UiTdJJIrMpyHTR7A8OOX7p0KYFAgIsXLw5qNyz/HEdSfhrDSqmNMbeHYl8UkRdF5Eic280ASql7lFJzgF8Cf22cFmecUSOGJitWNwMP648fBm6Jc8z1wA6lVIdSygPsAG7Qzb48pdRupYU2PWKcr5R6Ryl1IsF4jyqlgkqpc8BpNAE0mSDxAiyK9RLn7TFitXHjRmw2G7t27ZrQOO80eslx2KiZRj+QRkl7A8Ot4gsNuAH9fv+w9aPHH3+cvr4+/uqv/qo/AWmySIpYdWpiVZU/ff4XswGfz4eIDPu8lOc541pWl12mVRA+cWLwT6Xx+UxHcVOl1Dal1Mo4t6eHHPor4EP643pgTsxrNUDDaGNNVqzKlVKN+qQbgXhhT4lMvmr98dD2kRiz+SgidxmmazgcHqXb2ctI4bKxllV2djZr166Nu6A7Fo41eFlWmYtlGiVNHe4GNKoFD1hWMDyLxdNPP82SJUtYvnx50ueUVLEqMMUqkzCS2BprpAZluQ7ae4JEooONj6VLlwJw/PjxQe2GZTXVlbhFpDbm6QcAY6LPAHfoUYFbgS5DR0ZiVLEazcwby5zjtKkR2ifS1/BGpR4yTFebbcqjYjKWeGJlt1nIddoGiRXA5Zdfzt69exmv+EejincavSyvzJv8hNPIUDfgQOj6wJoVDN4Y7PV62blzJzffPNavx/gwNoxORqwudQYQgYp8Z7KmZZIEhmZcNyjNcxJV0N4z2LoqKysjPz9/uGWVIQEWwLd1rTgEXAf8T73998BZNM/Yj4C/Gktno74bpdS2RK+JSLOIVCqlGnW3Xrw43nrg6pjnNcBOvb1mSPtopuCEzEeTxCRyPRS77YPcgKCJ1fe//30OHTrE+vVj2hoBQJ3Hjy8UYdk0E6uhbsBsI3R9hJRLr732Gn19fdxwww2kgmSErjd09lKe65w2+91mC0MLLxqU52pbJ5q9QcryBi4wRISlS5dy8uTJQcf7+wMspnY5Xyn1oQTtCvj8ePub7Kf1GcCI7rsTGOqnBNgOXCcihXpgxXXAdt3s6xaRrXoU4B0Jzh863m0i4hCRBWhBGXsn+R5mNX6/n+zs7GGuhyK3nQ7f4Cu5yy+/HGDc61bHGrQI1uVV00ushroB7TYLdquFnlBiN+Drr7+OzWZjy5YtKZlTstyAVQWmVZVpJLKsDIFq6R4eZDF//nwuXLgwqG0gwGLKLaukMlmx+jZwrYicAq7VnyMiG0XkxwBKqQ7gm8Cb+u0behvA54Afo5mDZ4Dn9PNvFZF64HLgWRHZrvd1FPgNcAx4Hvi8Uioyyfcwq0n0BSlyOwaFrgPMmzePiooK9u4d3/XB0QYvFpleYesw3A0I2tXq0DWrpqam/td37drF2rVr4/5Nk0HyxMpcr8o0En0Xq3R3bUPXcLGaN28eFy9eHBS+bnw+s6feDZhUJiVWSql2pdQ1Sqla/b5Db9+nlPrLmON+qpRarN9+FtO+T48cWaSU+mvdPEQp9aRSqkYp5VBKlSulro855179+KVKqecmM3+T4bWsDIrd9mFrViLCpk2bePPNN8c1xv4LHpZV5k271D5D3YCgF2DU3Sx2u52ysrL+Wl9KKQ4fPszatWtTNieXy4WI4PP5aGlp4frrr+cf/uEfxnx+NKpo6ApQbYpVxpFIrEpyHNitFuo9/mGvzZs3j1AoNMgV3RMKY7dasNtmlpt3Zr0bk3GT0LLKsePxh4YlTN20aRMnTpzA6/UOOyceoXCUA3UeNs1PXhaHdBHvb+O22/rdLAA1NTX9YtXS0kJ7e3t/WZVUICJkZ2fj8/n4j//4D1544QW+9a1vcfDgwTGd3+4LEQpHTcsqA0n0XbRYhOpCF/We4Ymk582bBzDIFegLhqd8vSoVmGI1y0n0BSl22+mLKLy9gyP/Nm7ciFKK/fv3j6n/Iw1dBPqibF4wQ8TKYe3fxwIwZ84c6uq03RRHjx4FSKlYwUDm9V/+8pesX7+erKwsHnnkkTGda4atZy6JvosANYUuLo1RrPzByJSHracCU6xmOX6/f8RF3eYhi7qbNm0CGLMrcM9ZbXlyullW8Soog+YGNELXYbBlZYQQL1u2LKVzc7vdnDx5kjNnznDHHXdwzTXXsH379jGdOyBWZoBFpuHz+QbVsoqlZhyWVU8wPOOCK8AUq1lPoqu5Cl2smoYs6paUlDB//vwxi9WL7zSzsjqPUj38drpglAeJ5wb0D3EDdnZ20tPTw7lz53A4HFRVVaV0bm63u78Y5ubNm3n3u9/NsWPHxpS38ZIuVuaaVeYxkmVVXeCirSfYX7bHIC8vj/z8/EEpl3yhsGlZmcw8EgVYGGIVLyfZWIMsGjp7eeuih2uXVUx+omkmXhoqGFzaHjQ3IEB9fT1nz55l/vz5SSthn4jcXC2q0mazsXbtWq666ipgbFsKGjoDZNutU17ryGQwSqmE+6xgoEhmPOsq1hUN2qb1bLu5ZmUyw+ju7iYvb/j+p7I8YyNifLG6cOECra2tI/b9yz2aa+KD60dNqJxxJEoqmuOwDguwAE2szp07x8KFC1M+N8NyW716NS6Xi7Vr1yIiYwqyMMLWh+6rM5lagsEg0Wg0oVjN0TPkX2gfvmWhsrJyUDSgz3QDmsxEvF5vXLFyZlkpyM6iKYFYwcjrVnUdfn7y2jluXFnR/0WbTsTLRg966Hoo0h8lGStWZ8+eZcGCBSmfW0WFZqlu3boV0FIwLVq0iLfffnvUcxu6zD1WmUiiiyODxaXaWtbpluGZSyoqKmhsHEitp0UDmmJlMoOIRCL4fL5+t9JQKvKcNHUNz/a8YcMGRIR9+/YNe+3IkSN0dnZyz1NHsIrw1ZuSn8w1HST68XA7bISjimBY24RZXa1ZjYcPH6azszMtltV1111Hbm5uf2lzgDVr1oxJrBq7AlTmmcEVmcZoYpWfnUVpriOuWFVWVtLU1NR/AWVaViYzDiO/XDzLCrSIwHgpXnJzc7nsssuGWVY/+9nPWLVqFZetXMPOwxf4u+uXTtur+IRiZR+ced3pdFJaWsorr2il19IhVh/4wAfwer39a1WgidWZM2fo7u5OeF44EqWtJ0i5mcA24xhNrABqy3L6i5jGUlFRQSgUwuPxoJTCF4qY+6xMZhbGxt5EYlWR5xgWDWhgBFkYV3PhcJivfOUrFBUX03zpIu4zL3LH5fNTMu90MFKABTBs3cqwMtPhBozHmjVrgIG9XvFo6wmhlFZ51iSzMC4cE3k5QBOr0y09wzbqG27hpqYmguEokaiacamWwBSrWY0hVom+IOV5Ttp6goQj0WGvbdiwgebm5v68eK+99hrNzc1c/5l/xFGzHDm3B+s0ql01lERrVgV6ra9Of19/2/z58/sfL1q0KPWTi0OiQnyxGOuP5bmmZZVpGBZxon1WAIvLc+kJhoetI1dWVgKaWM3UJLZgitWsxviCJLKsyvU6Om1DEtoCrFu3DoA9e/YQjUZ56qmncDqd7AvPZd3Wqzh+9NCkkq1ONYncMoXZWsi3xz/wNzE2ZpaVlY14ZZxKFixYgM1mG1YuIhYjsrPcXLPKOIzv4kifn+WV2mtHLg1OdWZYVo2NjQO1rEyxMplJjO4G1DcGx4kI3Lp1K8XFxXzsYx/D6XTyve99j8VrLyckdm6/4UqUUsMqmE4nEoqVO7FllZ+fn57JxSErK4uFCxeOKFYthljlm27ATGMsYrWiKh+rRXi7rnNQe3zLylyzMplBjPYFqdRT8sTLSZaVlcWjjz7KDTfcwB133MGqVatwbvowWxYUce0VG4GR108ynURrVoW6GzDWsjLC16e6IvWSJUtGdAM2e4NYLUKx2xSrTGMsa1bOLCuXVeTydv1gscrLy8PpdOqWlZbhIscx8zZ9zzxb0WTMjGZZzSvWrIoLHfHdedu2bWPbNq2Q9Lk2H3/y3Z18bkUFixfPISsra0aI1dASIfmuLETAE2NZGcENX/3qV9M3wTgsWbKEF198kWg0GjeLRpM3QGmOY1qvJc5UxrJmBbBmTgG/e7uBaFRh0f+PIkJpaSltbW30BLXPZY5z5v20m5bVLGa0AIsch42SHAcX2obX0RnKjmNaoMW1y8ux2WwsWLCA8+fPJ22u6caooDz0R99qEfKcWXTGWFaLFy8mEAhw++23p3uag1i6dCmBQKA/se5Qmr0BMxIwQ+nu7kZERi3auW5OAd5AmJMtg7colJaW0traSndAcwPmmmJlMpNobm7GarVSUFCQ8Jj5xdkJLatYdhxr5rKK3P5sFTU1NVy6dClpc003IyUVLczOGmRZATgcUy8CS5YsARJHBLZ4g2ZwRYbS3d1NTk7OqGmwrlhcAsBrpwYnLS4pKaGtrW1ArMwAi8GISJGI7BCRU/p9YYLj7tSPOSUid8a0bxCRwyJyWkTuE/0/JSIfEZGjIhIVkY0xx88XkV4ROajffjiZ+c926urqqK6uxmpNvBg7r9jNhfaRLav2niD7L3i4bnl5f1ts6YzpSKIEv6CFr8daVpmCIVaJgiyauwOmWGUoPT09Y4okrSpwsbDUzeun44tVf4CFaVkN48vAS0qpWuAl/fkgRKQI+DqwBdgMfD1G1B4A7gJq9dsNevsR4IPAK3HGPKOUWqvfPjvJ+c9q6uvr+7OGJ2JecTaNXYFhpQlieel4C1EF160YyK5eXV3NpUuXiEaH79GaDoxuWWWeWFVWVpKTkxNXrAJ9ETr9faYbMEMxLKuxcOXiEvac6xj0nTTWrLoDfVgtgivLjAYcys3Aw/rjh4Fb4hxzPbBDKdWhlPIAO4AbRKQSyFNK7VbaluxHjPOVUu8opRKHNZkkhbq6ujGJFTCidbXjWDNV+U5WVA0EatTU1BAOh2lpaUnOZNNMoqKUoEUEenx9cV+bSkQkYURgi1fL8VhmWlYZSXd395j36F2/ogJ/KMIfjg98t0pKSvB6vXR2+8lx2GZkVv3JilW5UqoRQL8vi3NMNVAX87xeb6vWHw9tH40FInJARP4oIlclOkhE7hKRfSKyLxwOJzps1qKUGpNltaxSE6BjjV1xX+8NRXj1VCvblpcP+oIYCV6nqytwOroBQXMFnjp1ali7UfG5whSrjGQ8YrV1YTGluQ6ePjiwJlxSoq1ltba1zcjsFTAGsRKRF0XkSJzbzWMcI57EqxHaR6IRmKuUWgd8EfiViMSNu1ZKPaSU2qiU2jjV+18ykdbWVoLBYP8eoUQsLHHjzLIM2zVv8NrpNgJ9Ua6NWa8CKC/Xnk9XyypRnS/Q3IC+UIRgOLFrdKqora3l/PnzhEKDxdTMXpHZjHXNCrSI1PetruTlE6109WoWfmlpKQC/23sSR9bMjJsb9V0ppbYppVbGuT0NNOvuPPT7eL9M9UDs5XsN0KC318RpH2kuQaVUu/54P3AGWDLaezAZjlFZdDTLyma1sKwyj0NDNiIa7DjWRK7DxpYFxYPajS/PaAUaM5VEdb4ASnO1dZ/W7uHlU0Cr5fXrNy/GLZSXapYsWUI0GuXs2bOD2pt1N6C5ZpWZjGfNCuDmtdWEwlG2H9W2jBiWVcTfhceXWVa/iPydiCgRKdGfix5Qd1pEDonI+rH0M1kJfgYwovvuBJ6Oc8x24DoRKdQDK64Dtutuw24R2apHAd6R4Px+RKRURKz644VoQRlnRzrHJD5jFSuAzQuKOFjXOSjTOEAkqnjpnRauvqwMu23wR6msTPMIT1fLaiSxMkpsxKui/E6jl+v+/RXufvww1/7bK/zhePOwY1JJbW0tMDwisNkbwG6zmOXsM5TxuAEB1tTkM684m6cOaK7A/IIiAKK9XgJ9mRPUJCJzgGuBizHNNzIQVHcXWqDdqExWrL4NXCsip/QJfVuf4EYR+TGAUqoD+Cbwpn77ht4G8Dngx8BpNCvpOf38W0WkHrgceFZEtuvHvxs4JCJvA48Bn43py2QcGGtJYxGrq5eU0RdRw8JlD1z00O4LDXMBgrYT3+FwzEjLqj9nYpzClN/472O4HVZ++9nLWVqRy2d+sZ8fv3qWSHQ0D3dyGEmsKvKcM3LhfSYwXrESEf5s4xx2nWnneJOXt9s0l3S018uq6qnLURmHfwf+nsFLPDcDjyiNN4ACw0M3EpNazNFdctfEad8H/GXM858CP01w3Mo47U8CT8Zpfxx4fDJzNtGoq6vDbrf3u+tGYsO8Qtx2KztPtHB9THj6C8eaybIKVy8d3oeIUFZWNi0tq2AwSCgUGneC3wvtPnafbedL1y9l0/wifv7JTdz8H6/zT8++QzAc5fN/sjjlcy8qKqKkpGRYkIWZvSJziUQi+P3+cWfsv33zXH648wxf+u0h2r1a/s4rahz84ONj8qqNFZuIxJYEf0gp9dBYThSRDwCXlFJvD7lIShR01zhSfzNzJc5kVIwNwfFyyA3FbrOwbXk5vzvUiD+kuQJD4ShPvFXPe5aUkeeM71oyUsBMN0bLmViQnYXdZhnmBnzpHU2Yb15bBUBxjoPn/udVrJtbwGP70xcVWVtbO8yyavEGzbD1DMXIQzmeNSvQKgB8+0OreafRizcUJS+/gLnZYUpyknpREjYC1fTbIKEaJQDvHuBrcfqcSHCdKVazlbGErcfy8a3z6A6EefqgFgPz2/11tPWE+NjWuQnPKSsrm5FiJSJU5DlpHFJF+UBdJ1X5TmoKB0Lec51Z3LCignNtvrQtfA8NX1dK0eQNmEUXM5TRPm8jcdPqSt68Zxu7v/Jeysu0jcHpJFEAHloswQLgbRE5jxZA95aIVJA46G5ETLGapYxXrDbOK2RVdT7/tuMkvzvUwLefO87mBUVcvSSxG7G0tHRaugHH8uMxrzib822Do/0OXPSwbu7wjGMr9TWEIw3x96olm9raWi5dutR/xd4TDOMPRagw61hlJB6PB4DCwrjZ6kal0G0n15nVn8UiE1BKHVZKlSml5iul5qMJ1HqlVBNaYN4delTgVqDL2K87EqZYzUKi0Sj19fWj7rGKRUT49odWEQhF+OtfHSDbbuXf/mzNiAv2M9UNCNr+s3NtPrTkK9DSHaDe08u6ucOTAq+s0sTq8KX0iJWRI/D06dNAbNi6aVllIp2d2raQkRJKj4Xc3Nz+UiMZzu/RLK/TwI+AvxrLSeZu2VlIa2srfX194xIr0CqVbv9f7+ZgXSdXLCohP3vkMOiysjL8fv+IefYyka4uTVRGEqsFJW56gmFae4KU5To5eFH7wYknVvnZWZTnOTjbmp59V7ERgWvWrOmvEFxmugEzkslaVgZWqzVjc3Hq1pXxWAGfH28fpljNQoyw9fGKFWhZn6sKXKMfyOCNwdNJrMZkWZVqi+Gnm3s0sarrJMsqrKiKHzY8tyibi6Nkr08WixdrUYfGupURtViRb4pVJpIsy8pisRCJZF5WlWRhugFnIePZYzUZpuvG4Pb2dkALA0+EsZflQJ32Q3PgYifLK/NwJsh2PbfIzcWO9IhVTk4O1dXV/RGBhhuwLNdcs8pEZoNllQxMsZqFGNkrJmJZjYfpmnKppaUFq9U6olgVuu0sLsth3/kOvIE+9l/0sHF+4uPnFWfT5B251Eoyqa2t7besmr0Bchw23DM0wel0x7CsJhINGItpWZnMOOrr68nKyhrThuDJYPQ/3Syr5uZmSktLR92DdvnCYnafbeeBnWcIhaPcsjZx0YC5egXlek96rKslS5b0W1Yt3QHKzA3BGYvH4yE3N5fJJtw2LSuTGUd9ff2YNwRPBsMNOB0tKyNr/Eh8fOs8An1RHth5hqtqS1hVkzjNzdwx1AVLJrW1tbS1teHxeLRy9mZwRcbS2dk5aRcgzHzLyvQLzELGu8dqorjdbpxO57QUK0NoR2JpRS7fvGUl+893cPeNl4147DzdskrXupURvn7q1CmauwOsj7P/yyQz6OzsnHRwBZiWlckMpK6uLuXrVaDtzZqOe62am5vHZFkBfGLrPP7fbeuozB85QrLIbcdhs9DQ2ZuMKY6KEb5+4sQJLdWSGVyRsXg8HtOyGgOmWM0yjArB6RArmJ4bg8dqWY0HEaGqwEVD1/CyIqlg4cKF2Gw2Dh46QjAcNTcEZzDJsqwsFotpWZnMHNra2giFQmlxA8L0E6uenh78fv+YLavxUJnvTJtl5XA4WLlyJXv3aQmzS03LKmPxeDxJcwOalpXJjGEyG4InwnQTKyNyMdmWFWgbqhs702NZAWzYsIHDbx9EKWVmr8hgkhlgYVpWJjOGdO2xMphuYtXcrFX2TYVlVZXvpKU7QDiSnh+UDRs20OXpIOJtNWtZZSihUIju7u6kiJVpWZnMKKbCsvL5fPT2psf9NVlSaVlVFriIKmjuHl5hOBVs3LgRgGDTKbOWVYbS1NQEQGXlqIVyR8W0rExmFJcuXcJqtabEcojHdMtiYYhVqtasABrTtG61atUqLFYbqvUsOWb2ioykoUEr41RVVTXpvkzLagREpEhEdojIKf0+ri0rInfqx5wSkTtj2jeIyGEROS0i94leb0JEviMix0XkkIg8KSIFMed8RT/+hIhcP5n5z0aam5spKytL+YZgg+kmVoYbMBXZPYwEwOmKCHQ6nRTNWUS05UxaxjMZP42NWhkn07Iancn+Yn0ZeEkpVQu8pD8fhIgUAV8HtgCbga/HiNoDwF1ArX67QW/fAaxUSq0GTgJf0ftaDtwGrNCP/YGIxM8cahKXVIRlj8R0E6uWlhby8/NxOJK/xmNYVumKCATIrV6Cr+Fkf90tk8wimW5Ac1PwyNwMPKw/fhi4Jc4x1wM7lFIdSikPmhDdICKVQJ5Sarde3+QR43yl1AtKqbB+/htoZY+N8R5VSgWVUufQindtnuR7mFWMZ8NrMphuYtXa2poyMc91ZpHrsKXNDQhgK19MyOfl/PnzaRvTZOw0Njb2b56fLOam4JEpN8oR6/fxvuXVQF3M83q9rVp/PLR9KJ8Cnhulr2GIyF0isk9E9oXD4XiHzEpMy2pk2traKCkpSVn/6dwYrJSir3A+APv370/LmCbjo6mpidLS0kknsQXTskJEXhSRI3FuN49xjHh1z9UI7bFj3wOEgV+O0tfwRqUeUkptVEptTMYHYSaglEq7ZZWfn09WVpYpVjqVBU4au9JjWfUEw0QL5mK1ZbFP3xxsklk0NTUlxQUIM9+yGvVXXCm1LdFrItIsIpVKqUbdrRevFkQ9cHXM8xpgp95eM6S9IabvO4H3AdeoAYd7PTAn0TkmI9PT00Nvb29aLSsRoaSkZFqJ1bp161LWf2W+i8P1XSnrP5aW7iBiy2J+7WXs3bs3LWOajI/GxkYqKiqS0test6xG4RnAiO67E3g6zjHbgetEpFAPrLgO2K67DbtFZKseBXiHcb6I3ADcDXxAKRWbpvoZ4DYRcYjIArSgDPNbOEZSGZY9EtNlY7BSKvVuwHwn7b5QWoowtugVgtes38i+fftm9FX3dKWpqSlpYjXTLavJitW3gWtF5BRwrf4cEdkoIj8GUEp1AN8E3tRv39DbAD4H/BgtUOIMA2tT9wO5wA4ROSgiP9T7Ogr8BjgGPA98Xik1c/87SSaV2RlGYrqIld/vJxAIpNgNqIWvN6Zh3aqlWxtj69YtdHd3c/z48ZSPaTJ2otEozc3NSXMDznTLalKLOUqpduCaOO37gL+Mef5T4KcJjlsZp33xCGPeC9w7wSnPalKZnWEkSktLp8WaSVtbG0DKLSvQNgYvKHGnbBwYsKyuvvJdAOzZs4cVK1akdEyTsdPR0UFfX19SLSvQRDBd+yjTycx7RyYJmUqxmg6WVXt7O5BasTLSHrWkIeVSS3cAZ5aF9auWk5+fb65bZRjGHqtkrlkBM9a6MsVqFtHRoXlfi4uL0zpuaWkpXV1dhEKhtI47XtJhWZXpCWUNF10qafYGKct1YrVa2bx5M3v27En5mCZjJ5kbgmHAspqp61amWM0iOjs7sdvtOJ3pTWpq7LUyxCBTSYdY5TpsOLMstKbJAde6DAAAIABJREFUsjIqBG/ZsoXDhw/j9/tHOcskXRiplkzLamyYYjWLMCqS6ikY08Z02RicDrESEcpynWlyAwb7KwRv3ryZSCRibg7OIJLtBsw0y0pE/reIXNKD5A6KyJ/GvDbuHK+mWM0iklU+e7xMJ7GyWCwp/xuV5jr6gx9SSYs32F8heMuWLQDs3r075eOajI2mpiays7PJzc1NSn8Zaln9u1JqrX77PUw8x6spVrMIU6xGpq2tjeLi4pRHUpXlOlK+ZuUPhekJhvvXyMrKyli6dCmvvPJKSsc1GTvGHqtkeToyzbIagQnleDXFahaRrPLZ42U6iVUqXYAGZbmOlK9ZGZZbeUw5+6uvvppXX311OvyYzQqSmb0CUmZZ2Ywcq/rtrnGe/9d6qaefxlTbGHOO11hMsZpFTJVlVVRUhMViMcVKpyzPiTcQTmkWi2ZvQB9roNTJe97zHrxeLwcPHkzZuCZjJ5l5ASFlllXYyLGq3x6KfXGU3LEPAIuAtUAj8K/GaXHGGbWGjSlWswiPxzMlYmWxWCgrK+tfUM5UDDdgqinN0QQkldZVs953WYxl9Z73vAeAnTt3pmxck7GTzFRLMDVrVkqpbUqplXFuTyulmpVSEaVUFPgRA66+CeV4NcVqlqCUmjLLCrS9JEaobqbS2tqakgrBQynt32uVOrFq0jO7VxYMiFVVVRW1tbWmWGUAgUAAj8eTVLHKtDUrPbm5wa3AEf3xhHK8mrUzZgmBQIBQKDRlYlVVVUVDQ+YmyE9HElsDY+9TawqDLBo6A+Q4bOQ5swa1X3311fzmN78hHA4npYaSycQw8nSmwg2YQdGA/yIia9FcfOeBz4CW41VEjByvYcaY49W0rGYJnZ2dAFMqVplsWXm9XsLhcFosK8M1l0rLqrGrl8r84Zu/r7nmGrq6usz9VlNMsvdYwYAbMFMsK6XUJ5RSq5RSq5VSHzAK9eqv3auUWqSUWqqUem6kfgxMsZolGGI1FdGAoF1BNjc3k6lVm43gj3RYVkVuOxYhpXutGrsC/RneY7nmmmsQEXbs2JGysU1Gx7hwm+GWVVIxxWqWkAmWlVGpOBNJR/YKA6tFKMlJbfh6Q2egP8N7LCUlJaxbt84UqynGsKySWa4n0yyrZGOK1SxhqsXKuILMVFegIVbpcAOCFlKeqo3BwXCEtp4glfnDLSuAbdu2sXv3bnp6elIyvsno1NfXY7VaUxJgYVpWJtMaQ6zy8/OnZPyqqiqAjA2ySKcbEEhpfsDmLq3f2EjAWK699lr6+vrMbBZTSH19PZWVlf3WUDLI0HRLScMUq1mC1+sFpl6sMtWyOnv2LBaLJalXuiNRmkI3YIMetl6VwLK68sorcTqdpitwCqmrq2POnDmjHzgOMi10PdmYYjVDCIVC7Nu3L+EHtaurC4C8vLx0TqufsrIyRCRjLasDBw6wfPnytJVPKctz0NYTJBIddeP+uGnUxaoizpoVgNPp5KqrruKll14ac59KKc6ePWuWGEkSqRAr07IaAREpEpEdInJKv48baiYid+rHnBKRO2PaN4jIYT1V/H2iZ3QUke+IyHE9p9STIlKgt88Xkd6YlPM/nMz8ZwpKKd7//vezadMmPv7xj8c9xuv1YrVayc7OTvPsNGw2G+Xl5RkrVm+99Rbr1q1L23hluQ6iCtp9ybeuGjq1tbCqBG5A0KyrI0eO9LuHR6Kvr48Pf/jDLFq0iOrqag4cOJC0uc5GlFLU19ebltU4maxl9WXgJaVULfCS/nwQIlIEfB3YgpZu4+sxovYAcBfaDuZatHTxADuAlUqp1cBJ4CsxXZ6JSTn/2UnOf0awe/duXnjhBZYsWcKjjz7Ka6+9NuwYr9dLXl5e2mtZxZKpe62amppobGxk/fr1aRuz1NhrlYLw9cauXvJdWWTbE2/6veKKK1BK8cYbb4za31e/+lWeeOIJ/vZv/xaLxcK3vvWtZE531nHq1Cl6e3tZvHhxUvs1LauRuRl4WH/8MHBLnGOuB3YopTqUUh40IbpBT8WRp5TarZRSwCPG+UqpF5RSxoacN9ByR5kk4LHHHsNut/PHP/6RoqIifvCDHww7pqura8pcgAbV1dXU1dWNfmCaMSyFdFpWRp2p1p4UiFVnIO6G4Fi2bNmC1Wrl9ddfH/G4J554gu985zvcddddfPe73+X9738/O3fuRPvKmkyEJ554AoD3ve99Se3XtKxGptzYlazfl8U5JlE6+Gr98dD2oXwKiN3hvEBEDojIH0XkqkQTE5G7jLT2mboRNVls376d9773vVRUVHDbbbfx5JNP9gdUGBiW1VQyf/58zp07l3E/dCdOnABgxYoVaRuzP+VSSiyrAFVxNgTHkpOTw5o1a0YUq/Pnz3PHHXewceNG/vVftYTZ69evp7W1NeOTEmcqSil+8YtfcPnll1NTk9xr8FlvWY2SAn4sJEoHP2qaeBG5By131C/1pkZgrlJqHfBF4FciEvcXWCn1kJHWfibnQPN6vbzzzju8613vAuATn/gEgUCAxx9/fNhx+fn50NcHBw9Cb2/a57pgwQK6u7vp6OhI+9gjcfr0afLz89OScd3AsKxSsdeq3uOnehSxAs0VuGfPHvr6+uK+ft999xEMBnnsscfIyckBYO3atQBmmZEJsmvXLo4dO8Zf/MVfJL3vWW9ZjZQCHmg2Muvq9y1xukiUDr6ewe69QWni9UCM9wEf092E6JUl2/XH+4EzwJKxv92Zx/79+1FKsXmzln1/y5YtLFiwgF//+teDjut3A77yCjz1FDz6KKTZwlmwYAGgXbFnEqdPn2bx4sVpXc9zZlnJd2UlPXy9y9+HNxBmXvHogTRXXHEFfr+ft99+e9hrkUiERx55hFtvvZW5c+f2t69atQqAo0ePJm/Ss4j777+f3NxcPvrRjya971lvWY3CM4AR3Xcn8HScY7YD14lIoR5YcR2wXXcbdovIVj0K8A7jfJH/v70zj4+qOv//+2QhJIGErOwQlrDLGgRkFWStAoJsagXcRerWSrH9/qq11le1LaB1pSoiQgVFdgmEVUUEA0IEBJKwJSwBkhASAlnP74/nTjIJk30yMwn3/XrNa2bO3Llz7tyZ+znPc57nOWoU8EdgrNa6IFZWKRWilHI3HrdGgjJOVPEYajR790pl/d69ewOglGLy5Mls2bKF5OTkgu0K3ICGy4vTp+GXXxza17CwMABOnjzp0M8tC4tYOZqQ+l52Tww+kyJ/l2YB5RMrwKYrcN++fSQnJzN+fNFp6ICAAPz8/Dh9+rQdentrERcXx4oVK3jyyScLLFV7cstbVmXwD2C4UioWGG48RykVoZT6CEBrnQL8DfjJuL1qtAE8BXwExCFWkmVu6h2gPhBVLER9EBCjlDoIfAU8abWvW5Lo6GjatGlDYGBgQduUKVPIy8srmMgFEauGXl5w8SKMGAENG8KOHWAlaNWNxbJyJbHKycnh1KlTThGr0GoUqxaBZYtVs2bNaNGihU2xioyMRCnF8OHDb3otLCzMFKtK8Oabb+Lp6cnzzz9fLfuv7ZZVlSZzDJfcMBvt0cCjVs8/AT4pYbsuNtptXjm01iuBlbZeu1U5cuRIgWvGQvfu3QkPD2f58uU89thjgLgBW1tGXO3aQXAwLFsG770HL74IDkiG9ff3JyAgwKXE6vTp0+Tl5TlNrPadSbXrPi1i1Tyw7DkrkHyrLVu2kJWVhZeXV0F7ZGQkvXr1slkr0RIoY1J+zp49y6effsqjjz5q10rr1piWlYnLkpubS2xsLB06dCjSrpRiypQpbN++naSkJLKzs7lx4wYtrl+HwEAIChLBuusuyMsDB1ZCb926NXFxcQ77vLKw9MUpYuVXl0vpWXaNjkxIzSTQtw71iy26WBLTp0/n4sWLzJs3r6AfKSkp7Nmzh9GjR9t8T8uWLTl16pTLRXW6Mv/85z/Jz8/nxRdfrLbPqO2WlSlWNZgTJ06Qk5Nzk1gBTJ48mfz8fFauXEl6ejqeQMPr16F9e7AEEnTtKvcOFKtOnTpx5MiRIm3Jycm8+OKLvPTSSw6PFHSmWDX0q8uNnHyuZNqOxqsMCSmZNC+HC9DC8OHDGT9+PH/605/o0KED99xzD0OGDCE/P59Ro0bZfE9YWBjp6enlqn5hIknA7733HjNmzChwhVcHtd2yqr0x3bcAx48fB6Bdu5sDIrt06ULHjh1Zvnw5o0aNog3g7ekpYmWhfn3w8QEH5sx06tSJJUuWkJaWhr+/P1prxo8fX1B1Y8OGDXz//fdVzgnTWpOTk0OdOnVK3S4uLg5fX1+7ritUXizh5WevXCfAt/R+lpczKZnc1rT8xYqVUqxYsYLFixezatUqEhISaNiwIWPHjqVv374232MJlDl16pTTFvN0daKjo/n73/+Oj48Phw8fxsvLi9dee61aP9O0rExclrNnzwIUCS22YHEFfvfddxw9epSOgGf9+mBdj0wpCA0FY3kMR2BJvLVYV5GRkXz//fd8+OGHbN68mSNHjjB9+vQq/eHWrFlDixYt8PLyYtSoUaXWI3RG2LqFZgEiVomp9ikOm5uXz9nU6+UKrrDG09OTRx99lA0bNnDgwAGioqJ47bXXCkbqxWnZsiXgeikIrsLx48cZNmwY3333HZs2bSI2NpZly5ZVe0X/2m5ZmWJVgzmfmMhUoNGXX4KNhfSmTJmC1pr3/v1vOgG5HTpA8fVzgoPh8mWH5VxZxMqSp7N48WJCQ0OZMWMGw4cP59///jerV6/m9ddfr9T+lyxZwvjx4wkODmbOnDns2rWLvn37lihYsbGxtGnTpnIHU0UKxco+Cdpnr1wnN18TFuRrl/2VhEWszpw5U62fUxPRWvO73/0OpRQ///wzFy5cICUlhXvuuafaP9u0rExcj/x82LWLzps308vXF/crV2Dz5ps269ChA78ZMwaPbdtwB3wGD755X8HBUs3CQUs/hIWFFbhGcnNz2bx5M6NHjy5w1z3zzDM88MAD/OUvf2HNGltpeyXz888/8/DDDzNs2DB+/PFH3njjDXbu3ElKSgoTJkzgxo2i1SKys7OJj4+nY8eOdju+iuDv7Uk9Lw+7iVVskgxY2oTaP4fHmqCgILy9vU2xssG2bdvYvHkzr7zyCs2bN8fDw6NIlGV1YlpWJq7Hhg0QFcXprCx2t2gBAwdCTAzYqGi+YNo0ugPfAo2NUjlFsIQmO8gV6ObmRrdu3YiOjmbv3r2kpqYWiTpTSrFw4UIiIiKYMmUKO3bsKNd+8/PzmTlzJiEhIaxYsaLgAtGzZ0+WLFnCnj17mDZtGtZ1ImNjY8nLy3OaWCmlaNrA225iFXdJxKptNYuVUooWLVqYYmWD+fPn07BhQ5580vELQpiWlYlrkZ0twtSzJ0uVIrtNG+jfXwIlNm8u6s47fpy28fG0HDGC5C5dbGfNW5Zxv3zZMf0Hbr/9dvbt28e6detwc3O7KfHUx8eHjRs30rp1ayZOnFiunJ6oqCgOHjzIG2+8USRBGuDee+/lP//5D6tXr+aRRx4p+DNb5s06depkpyOrOM0CvDl7xU5idTGD0Ppe+HuXL2y9KrRs2dJMDC5GZmYmUVFRPPDAAw5bxNMa07IycS2OHZNitN26cf78eUkwrFsXBg+GkydhyxbYtg0WLYL//Q8aNWLmunUcjImxvT8/P6hTx6FBFn369OH69eu8+eab9O3b9yZxAXE1rVu3jvz8fMaPH8+1a9dK3ee7775LaGgokydPtvn67NmzefXVV/nss8947rnnSExM5PXXX8fHx4f21hGSDqZZgLfdAixiL2ZUu1VlwbSsbubbb78lOzubkSNHOuXzLWJlWlYmrsHhw+Dnh27enOTkZIItllFEBHToALt2wfffQ24uDBgAM2ZAnTolR7spVRhk4SAsRXfz8/OZMGFCidu1adOGL774gkOHDvHwww+XmIR66tQp1q9fz2OPPVbq/MD//d//8fzzz/Of//yH5s2bExcXx8qVK522ejJA0wBv0m/kkna9arlWWmviL2YQ7kCxSkpKumke8FZm//79AAUrIDgaixuwtlpWZp5VTeLGDYiNhd69uZqeTm5ubuGyFu7uMGUKXLkCvr5iLZWXkBA44bh6wK1bt6Zp06acPXuWSZMmlbrtyJEjef3115k7dy69evVizpw5N23z/vvv4+bmxhNPPFHqvpRS/Otf/6Jt27YcOXKEWbNmOdUFCIUFZxNSMvGvQH5UcS5cvUFGVq7DLCtLRGBCQgLh4eEO+UxXJz4+nkaNGlVLkdry4IqWlVLqd8BsZKmnDVrrOUb7S8AjQB7wjNZ6U1n7MsWqJnHsmJRH6tKloKJ6kTWYlILKJGk2agQHD0J6uiQKVzNKKXbt2kVaWprNHLHizJkzh/379zN37ly6detWxM1y/fp1Pv74Y8aNG0dz6xyyEnBzc2PWrFlV6r89seREnU7OpEsVxCruoiW4ovrPH8iqzyC5fqZYCfHx8U5LgwDXC7BQSt2JrCbfVWudpZQKNdo7AVOBzkATYItSqp3WulST0HQD1iQOHQJ/f2jatECsbM33VBhLCRhbaxTl5tqMMqwqLVu2pKul3FMZKKX45JNP6NKlC1OnTuXo0aMFr73zzjskJyfz3HPP2b2PjqB1iORExV+6OU+uIhSKlWNG9Q0aNACkQLKJ4CpiVdJimk7gKeAfWussAK21Zb3DccAXxvqEJ5FVN24va2emWNUUMjIgPh66dAGlCmro2WV120aNoFkzmeuyzF1pDUePwoIF8OGHErzhRHx9fVm9ejVeXl4MHz6c06dPs3fvXv7f//t/3H333QwcONCp/assPnU8aNrAu8piFXsxA39vT4Lr2adsU1n4+4sVePXqVYd8nqtz48YNzp4961SxsuQq2lmsPJRS0Va3xyvw3nbAQKXUHqXUTqVUb6O9KZBgtV2i0VZ6RyrwwSYOIjU1lS+++IKJEycSGhoqjfv2STJwjx4Att2AVWHcOPjkE3j3XRGv/HwpcGux3A4dKrTAnETr1q3ZtGkTQ4YMoVevXmRlZdG4cWMWLVrk1H5VlTah9exiWYWH1nNY2ShL7UbTshJOnjyJ1tqpYuXpKSkLdharXK11REkvKqW2ALbqSP0Z0ZcAoC/QG1hhLJpr60daZgkd07JyQWbNmsWsWbOYNm2aNGRkSJRfx44FeVF2F6uQEHjqKUkw9vUFb2/4zW9g9mz53Lg4h5VkKo1u3bqxc+dOunXrxogRI9ixY0dhRGQNpU2IL/EXr5GfX/nvN96BYetQaFmZYiXEx8cDzqneb6GaxKpUtNZ3aa272LitQSymr7WwF8gHgo126wnmZkDJBTwNTMvKxbh27RorV8r6ktu2bWP//v30TEyUuaO77irYziJWdq167ecHQ4fe3N62Lfz6q7gIbSzG52i6du3K1q1bnd0Nu9EmpB7Xc/I4l3a9XMvRFyflWjbJ17IdKlZeXl54eXmZbkADy1IztdCyqgqrgaHADqVUO6AOcBlYCyxTSs1DAizCgb1l7azKlpVSKlApFaWUijXubV49lVLTjW1ilVLTrdp7KaV+UUrFKaXeVoYfQyn1N6VUjLGs/WalVBOjXRnbxRmv96zqMbgSu3btIicnhxUrVlC3bl2+fPtt+Pln6NNHFk00SE5Oxt/fHw8PB4w3LKNFF1o0sTZhWdLjQELl1odydHCFBT8/P9OyMoiPj8fPz89+no5K4IJi9QnQWil1CPgCmG5YWYeBFcARIBJ4uqxIQLCPG3AusFVrHQ5sNZ4XQSkVCLwM9EGiPl62ErX3gccRdQ0HLCu+/VNr3VVr3R1YD/zFaB9tte3jxvtrDVFRUXh6ejJmzBjuHT+e9C+/JNfTUypUWJGSkuK4P4a/v1hUVlF4Lsm1axJ+X8Po1MQPb093ok9Vbon72ItyzI4WK39/f1OsDCyRgM5YasaCUgoPDw+ys7Od1gdrtNbZWusHDbdgT631NqvX/q61bqO1bq+13lie/dlDrMYBi43Hi4HxNrYZCURprVO01qlAFDBKKdUY8NNa79ZSnuAzy/u11tb+BV8KJ+DGAZ8ZCv0j0MDYT41Ha82qVau488478fX1ZdaddxKcmclOd3cpqWRFcnKyY0dxXbvC6dMOLctUbnJzpS7ivHnwwQcOqyBvLzzd3ejRogHRp1PgzBmooGst7mIGPnXcaeLvXU09tI2/v7/pBjRwdti6BU9PT1eyrOyKPcSqodb6PIBxH2pjm5JCFZsaj4u3A6CU+rtSKgF4gELLqlJhjzWBmJgY4uPjue+++yA7mzvS08ny9+eNqKibtnW4WPXoIYK5bJnriEFeHuzfD++9Bz/8AJ06yXIn27c7u2cVJqJlAFd+jSN74UdgzFmWl7iLGbQJqYebm2NH9aYbUMjLy+PkyZPVJ1apqfDFF/I7/+gjWL4cvv1Wfv/FuOXFSim1RSl1yMZtXDk/p6RQxVJDGLXWf9ZaNweWIiU7SttX8T4/bskNsF4WwpVZs2YNSinGjRsH332HW3o6zZ98kqitW/nll1+KbHv58mXHRsHVqwf33CN/HCfnXAHi8lu0CNauBS8vePBBmDgRevWSMP/UyrnUnEVEc3+Gxu7hfNp1sWArUIXgxKVrtAmp3gUXbVGb3IC5ubksX76c999/n/PlSILPycnhgw8+YMyYMfTs2ZOcnJzqEavLl+Hjj+U/FxhYWHR62zZYulRWYbDilherMsITkyxuOOP+oo1dlBSqmGg8Lt5enGXAxDL2VbzPC7XWEVrrCEcEIXz66ae88MILpFdhzmTr1q306tWL0Pr1Yc8euO02ps6Zg4+PD/Pnzy+yrcPFCqB9eynpdNHWKXYgqamSE3bhAtx3Hzz+eGEQyKBBUiexhllXEeeOEnT9KgcDw6QhKalc77uRk8fZK9dpHeL4enQhISEklbOfrswPP/xAr169mDp1KrNmzaJdu3Z88MEHJRZOjo6OJiIigqeeeopTp06RagyMIiJKTEeqHLm58NVXMnB59FGYOhUeekjSScaPFwH76KMigU+3vFiVwVrAEt03HbC1vOsmYIRSKsAIrBgBbDLchulKqb5GFOBDlvcrpawLjo0FLLP7a4GHjKjAvkCaxQ3pLBYtWsTMmTOZP38+L7/8cqX2kZmZye7du7nzzjshOlpGTAMGEBgYyMyZM1m6dCkXLlwAJFs+IyODEEeHkXt4SESiM8Xq0iUZaWZmwvTpBRU9CqhfH26/HX75BYz1qlyetDR8ftzF9TbtWBtkLFdSzuU3TiXL0imtgh1vWTVv3pxLly7V2Mrr8fHxjB8/nv79+5OamspXX33FoUOH6NevH0899RTjxo3jotVvPScnh7/+9a/07duX5ORkvv76aw4fPsyZM2fIzMykh5Gwbzd275YB2bhxN6eMdO8O06bJf3FTYQ3YOnXquEyAhb2xh1j9AxiulIoFhhvPUUpFKKU+AtBapwB/A34ybq8abSD1oz5C6kPFA5bIkH8YrsYYRNyeNdq/AU4Y2/8XcGpV0m+++YbHHnuMkSNHMmnSJD7++GMyMipejcASsj5s4ED48UexFBo2BODZZ58lJyeH9957DxCrCnBOMmxIiPPEKjtb1ugCePhhKKlw7cCBUoXj668rHKzgFIyLTf6IEfxwOZe8+n7lFqsTl0SsWjvBDWgpHJyYmFjGlq5Heno6gwcPZvv27bzyyiscOXKEiRMn0rlzZyIjI1mwYAGbN2+mY8eOvPHGG6xatYqIiAheeeUVpk6dyqFDh7j33nsLov+8ve0c3JKRAd99J8v+lLTeWrt2MGyYDOCMeWTTsioFrXWy1nqY1jrcuE8x2qO11o9abfeJ1rqtcVtk1R5tuBTbaK1nG1GBaK0nGu1dtdb3aK3PGu1aa/20sf1tWuvoqh5DZdm5cyeTJk2iW7dufPnll8yePZurV6+ydu3aCu9r+/bteHh4MNDLS+ZjBg0qeC08PJyxY8fy3nvvkZmZWSBWDresAEJDISVFXBSOZuNGcQFOmlR6cnLdujB5slTc2LnTcf2rDPHxYgEOGkSXLmFkZueRWD9YxKocFUNOXhaxCgtynlglJCSUsaUVGRlSg9ISJOCkucWFCxdy9uxZNm7cyMsvv1xkWQ83NzeeffZZ9u3bx+23387cuXOZMGECaWlprFy5ks8//7ygkG+1YVmTrtgq2jdhWbXAOAe1WazMChaV4Ny5c/zpT3/is88+o3379mzYsIH69eszYMAAmjZtyvLly7n//vsrtM9t27YxsHdvfH7+GcLDC3+EBi+88AJr1qzhnXfeKXA3OMWyCg2Vi+jly2K9OIqff5bboEFgrKVUKgEB0Lu3zP316SP9djW0hq1bpa/9+tH7mlxkDio/WqafgLQ0KOOieOLSNRr51cXXy/F/5XKLVV6euLYPH4bERJmDCQiQqijbtsm5adQIGjeWx2FhMu9oi4wMSVMIDi4yoKsoX3/9NT179ix1ocTOnTuzceNGYmJiuHz5MnfccYdjlqu/cUOiXG+7rUghAJs0aSLfVUICtG9fq8XKrA1YAbTWvP3227Rr147//e9//OEPf2D37t00Mi7abm5uTJo0icjISK5cKX81gtTUVKKjo/lt27YSem2j5NGgQYMYO3Ysr776KgcOHACcaFmBY12BFy7Ahg1SSHfIkPK/b9Ag8PGBzz+XCEFX4+RJOHdOVnT28KCxvzdNG3jzQ45hJZXDFXjycoZT5qtAxEopxYnSFu7MyxPX7caNYin07y8BAs8+C889ByNGSNL5qVPiDl2yRM51cbSGmBgptBwTIyJXSRfvlStX2L17N/fcc0+5tu/atStDhw51jFCBDMqys6Fv37K39fQUoTdcsV5eXmRlZVVzB52DKVYV4Pe//z3PPvssQ4YM4ciRI7z55ps3uQOmTJlCdnY2a9bcHGeSk5NTJHrIQmRkJHXy8hjm4yO5Qo1t5ziPWBcxAAAaY0lEQVS/9dZb5OfnF6yW6xSxCgyUkZyjxOrGDXEZeXtL5J9bBX6yPj7wwAPyh163Do4fr75+VobvvpOAkG7dCpoiwgLYnqzRXl4Swl4GJy9fo5UT5qtA5mnCw8OJiYmxvUF2tswbxsXB3XdL1OawYQXFmGnQAO64Q87RCy/AH/4g38XBg2JBWcjMlOjPr7+W35+lwPPBg5Xq98GDB9Fa06dPn0q9v1rJzxdvQMuWJV4HbqJZMxn05Ofj7e3N9evXq7ePTsIUqzKwhK9+/vnnzJ8/n9mzZ7Nu3boScyr69OlDWFgYCxcuLHhvXl4er732GkFBQbRq1YrAwEDuuOMOFi9ezMcff8wrr7zC3f7+NGvYEO68s8S+hIWF8eabbxY8d0odMnd3udg4ImRZa1i1StxhkyZJNfiK0qQJzJolc1yRkTYTKZ1CYqJYVv36SZSlQUTLAJKu5ZDcJEwuxqV8z1dv5JCamUPLwDKK32ZniwsuJkaWerFjjcfu3bsXWPpFSE2VsOojR2TepTxh3fXqSXBMXl6hJZyfL+Hb589LVNwjj0jAQcuWYoHYmNe7du0a77zzDvv377f5MQcNkevevXvZfcrPh7Nnxbq3p8Vy/rzted9ff4UrV+R3UV6aNZNzfPEi3t7eZLpK0r6dMeesSiExMZGBAwcyYsQIPvvsMwYPHsy8efNKrf+llOKll17iiSee4IUXXuD+++/nb3/7G+vWrWPChAmMHj2aCxcusGTJEmbMmAFAhyZNeHnMGNy6dy+zqvnTTz9NVFRU4TpXzqBJE6kTqHXRsHF7kpEhQhUfD6NG3TSHVyHc3eWCuWyZXARvL3NR0upFa9iyRcS3V68iL0WEyfphP7Tuwdhzp8TdZbEkinHxqoSMN/IvxT2ltRz3qVNF23/7W7BDEmv37t1ZsWIFaWlpBcuGcOoUrFghF/oHH6zY5wQHS5Tb7t3Qs6ecrxMnYOzYgrXcAHm8erW4SovNYT799NMsXrwYNzc31qxZw913313k9QMHDhAaGlrgvi+RlBSxyK2T4OvVE/dc//62f/vnzsGBA/K65fuwJj8fduyQ4JKICLE4LWRlye8iKEi+g/JiOf74eHx8fMqV1FwTMcWqFK5evUr79u3573//S5cuXfjyyy8LKhuTny9BBkFBhZPBWsPJkzzSuTPx99/PmwsWsGDBAtzc3PjP22/z9G9/i0pKgpQUXhoyhCNJSfj4+ND69GlUcnK55mOUUjZdjA6lRQsZ1V66VD2BC7GxciHKypI/c7ELeqUID5c5rx07JOS9vC6W6mD/frmg/+Y3Un3DinYN61Pfy4MfL+cwtmNHcV2WMChIuioj/YZ+pYjVr7/KZw0bBp07y3e6ZIkkTbduXeXBRjfDhRkTEyOrNe/ZI3NPFnddZaz/kSPh/ffhv/+Vealu3YoKFYi7fONG+R1aiVVWVhbLly9n6tSpxMbGMnXqVHbv3s1tt91WsM2BAwfo3r17yYPOvDwRyx075L89cqQsn5OaKq7ZLVvkO+3TRwJFMjOlgPKhQ/J9g1hOM2cWuq1zcuCnn0TILC70Awdkzs5Y4ZeoKLGqrN9XHvz8JM3l+PFa7QY0xaoUOnXqRGRkJKmpqdSrV69QqC5flvpt58/L5OaECfKnXLsWDh7EHXgjPJzf//WvRNevT3iHDoSfPStLxBu4AwV/H29v2Yc916aqTsLC5P7IEfuKlcXi2LVL/nzTp9tv/0rBmDHw2Wcy//H002VG2lULZ85IAEHbtjZF2N1N0bNlANGnUmBQc7mgpaTYvOgnGZZViWKltcyLBQXJSN9yARw2TCyGuDgR8SrQs6es0LP7hx8YmJMjFkOHDlJhobIBCUFBknqwfbv81u6552ZRrVNHEsJjYiSQxljReu/evdy4cYOpU6fSu3dvevfuzdixY/npp58IDg4mJyeHw4cP89xzz9n+7DNnYP16EZSOHWH0aBEDC/37w9690rfi7tS6dcWN7+Mj53jfPolIzcwU6zYxUVx2EyfKPhctEg9F167iaty3TwSwMl6Erl0hKoq2N27wrSlWty5FFjg8f16iy0CW7YiOlkrf9erJKHDwYLkIxccT+u23jElNldGmv7+M0Jo3lz+Wm5vsC6TNEetS2YuAALkg7d4tmfT2uuhv2yZCFREh35VlcGAvQkIkmfi992T0P2WKffdfFlevinusQYNSg0V6hwXwr82XSAtsjT/IRc6mWFksK6+bXgPkYmqZ67H+rO7dRcR27BDRLMnC0BqOHZP9dOlSOEgBsT7OnKFRUhJPtWyJ2+LFEsnas6dYwxWxDGzRrl3ZrrC+fWXAtHIlPPYYIKWTAAYOHEhgYCCrVq1i0KBB3HfffaxatYrExESys7MLLMIix/PNNyIY/v5iFdpKxlVKBKVXLxG2a9dksOnrK78vDw/53n79VdIS6tWTAVhamghwp06yH63ld3DwoISob9gg25YyZ13md3H4MD03b+aDa9cqtw8XpwZdIV2A2Fj48kv5cT70kFxAeveWihMJCbKSb9eusm2PHvLYMrfTocPNgtSqleOPwV6MHClhxNu3w733Vn1/Fy5IImTPnuIeq665sIAAGYlv3SoXYetlyDMz5dxW9bMzMmQ/1rlC6eli1WVny2+nFKujV0uxEqIzPRhWp46MuotfXBHLqn5dD3zq2Pgbay1Wjr9/4W/Sgru7uJxXrxbRspWvlJ8vF+9oI+c+OlouiHfeKa63b78tqJrwm+bN2bx7NzcGDKDusGHVd+6KExIiARmbN4v1GRjIyZMnCQ4OJtCwtG6//XY++ugjHnroIYKDg8k3CgT3tQ4Lz86WQURcnFhOgwcXuuZKwsND3Ki2UEoE+7//LYxkfeihohaTUjJo2LlTzsO5c2JxeZUw8CgLw13p8+mnhJsBFrcwWsufdeNGcU/df7+EHIOMhqyWmy+Cu7vME9RGAgJkhPnDD/IHr6q7bscO+aOOGFH9F7t+/cS9tmGDBG9cuyYX4IQEOY5hw6BpU7kgZWSIdRIeXj631qlTIkqBgRJc0KCB7GPxYrGsHnywzO+qR4sG1PV0Y2dcMsOaNBGxskHS1RsluwD37ZPjuftu2wm23bpJ4MK2bTJ/Z+0OvHJF3ITx8ZIDNnCgiPuPP4qXQGsJmujdG5o3x7NfP94eNYpROTmMdvTig507i1gdPgwDB5KQkECLYm60Bx98kM6dO7NmzRr27t1LUFAQrS1Ck58vgmIJ4uhpp4XHAwMln+zECRE1W5Gs/frJ/OXBgzJw7dKlap/ZogVZQUH0zM4mLzcX95rkrSkHteto7M2VKyJSCQkysRoeLu6byo5+ahv9+8v3s22bVISuLOfOiQU6ZEjl5zkqgoeHzIMsW1ZYazA4WHJ+jh4tbLOmSROZQyvt3Gst7sX8fHH7LFxY6CpOSxOhKsd8RF1PdwaFh7D5cBKv9GqC2949EuZc7OIjYmWjP5cvSz/atCk5OEUpuTgnJcnI/vHHxQqLjZVQca3lO7K8f8wYcYsdOyb/Ayv34cBBg6hbty6RkZGMHj26zOOzK/7+4kY/dAgGDuTMmTM200p69Ohhu9BsVJSI8rhxNwdxVBVfX3HxlYSXFzzxhHgVWrSo+iBNKdI6dyZo/XrSd++mwcCBVdufi2GKVWlkZYnlEBwsbq++fR3n4qgJ+PjIBX77drEoQkLElZKWJiN3W6G7xTl3TpI9LSHBjiIsTBJRL18WEWjYUM7t0KFy8UpLE4Hw9hbLaPt2+PRTmXAvSXCOHRMrbMIEEbevvhJr3NtbEl/LUybKYGTnRmw+ksSxOiF0zMuTeSvrOSNkzqpPq8Cib9Qa1qyRYxo/vvTfq4eHuJ4++UQm+9u1k4i1Ro1kPq/4XGSbNjbD0L29vRk6dChr165lwYIFjl/avUsX+Z4vXiQhIUFWLigPP/8s8659+thfqMqLr69dUggK6NSJc0DuqlUy8Ovd2377djKmWJVGaCj8+c8l1ykzEYE5eFAu5EoVJmn+9JOswVNSrklsrLhAjh8X0Zs40TFWlTV160p0ljUeHrYn1hs1EvH55JOiFoc1+/ZJlFeXLhJg8MQTIl4NGsgxVoBhHUPxcFOsT69LR3d3+Z6sxEprzcX0G4QWdwPu3y+egPHjC13VpREaKvMpK1dKlNttt8nxlTVnU4xx48bxzTffcOjQoSJh4g6hSxfYsoXMdevISEsrqFlYKj/+KEnirVqJ67mWEBQSwgrgj56eBG/cKFawM6JeqwGzgkVpKGUKVVl4ecGMGTI6HTxYLtBPPCGT1kuXSoQYiJUSGyvh/f/+t7jazp6V+ZDf/a7kyWpXITwcnn9erKPISEhOLvp6erpYld26FUbCKSUWVgWFCqCBTx0Ghgfz1aGL5LVsKetzWVU8SM3MISdPF3UDpqdL5FlYmM2AjBJp0kTmV+bMkUFDBYUKKKiz55QcQF9fGDSIjH37eAJoVdqcYHa2WGGRkRKa/sADteo/HhQURBoQa5l7+/FHp/bHnphiZVJ1/PzEPTZkiEzWN24sc1jJyRIm/vnn8M9/ingdPiyj2WnT5OI/dKjjLarKUreuzFl6eEj0mHWI8IEDYlWWp4RPOZl2ewuSrmaxq2EHEaKtWwss1wtpRvUKi2V1+rTMkeXkVC6aUqlKiaqFxo0b06dPH+clrA8cyNHu3WkA9DhyRGpKFuf0aYlgtVTinzSpZqWMlANLCbakGzfESt6/X+beawGmWJlUD61aSSZ+SIhcaDt3llHsiy/KBb99+5o5oq1fX6yPS5dg3jyJ8vvmG8kPCwurXMWGEhjaIZQm/nWZH5+DjoiQ+ZWlSyE9naR0IyG4jhGivnix5KU9+miZJbuqi/HjxxMdHc2Zci4caW9+1ZrlQHBeHrz1liT3ZmeLwO/fL9+Rh4fk2o0eXfVcMBfEIlYpKSkyeFTKdhX7GkjtGlaYuBbNmsl8SG2jbVt46imxpmJjJZqrXr2yF8qrIB7ubsweGs6fVv3CtiERDGvYUMK0330XVa854w7/TJtPfwQvI0Vi7FinRqpOnjyZl156iWXLljF37txq+QytNZs2bWLRokUcP36cvn378thjj9GzZ08SEhI45e5OvdmzZf5w3z6JEqxTR9IG2raVgVJNseQrgb+/P+7u7iQnJ0t6yaRJdh1AOROlbVQtrm34+vrqa7U0q9ukdpOTl89d83bi7enO+t8NwONKKmzYwJ4tP7HpbBZzX5xAndtuk3knF4hUHTBgAKmpqRw6dMiuUYE3btxg1apVvPHGGxw8eJDQ0FC6devG999/z/Xr1+natSsxMTE0b9680LI7fVqCf3JzZa6xR49aaU0VJzQ0lAkTJvDBBx9UeV9KqUyttXPWoClGlc6cUipQKRWllIo17m0Wt1NKTTe2iVVKTbdq76WU+kUpFaeUelsZv26l1N+UUjFKqQNKqc1KqSZG+xClVJrRfkAp9Zeq9N/ExNXxdHfjj6M6cPRCOl/8lCCj5IceYsPdM1g9+D7qjBolCcwuIFQAv/3tbzly5EiJy3OUl5ycHJYvX87TTz9N//79CQoK4v777ycrK4tFixaRkJDA5s2bOXfuHPPmzSM0NJT27dszefLkwp20bCnW5oQJEr15CwgVQKtWrYiz4zIwlUUptdzqWn1KKXXA6rWXjOv+MaXUyHLtUGtd6RvwJjDXeDwXeMPGNoHACeM+wHgcYLy2F+gHKGAjMNpo97N6/zPAB8bjIcD6ivbTx8dHm5jUVPLz8/XkD37Q3f+6SV+5lq211vqRT3/SI+fvdHLPbiYlJUV7e3vrhx9+uNL7yMnJ0aNGjdKArl+/vh4wYIB+5pln9KZNm3ReXp4de1s7mTFjhm7cuLFd9gVc01XQCF14Hf838BfjcSfgIOAFtALiAfey9lHVocY4YLHxeDEw3sY2I4EorXWK1joViAJGKaUaG6K02/hSPrO8X2ttvV61L1D7fZUmJiWglOLlezqTdj2Hhd/FA1K9otR1rJxEQEAA06dPZ+nSpVy6dKlS+1iyZAmRkZHMmzePK1eu8N133/HWW28xYsQI3G4R66gqdOrUifPnz9+0IrmzMDxmkwFLaZhxwBda6yyt9UkgDihzkbmqnvmGWuvzAMa9rQSHpkCC1fNEo62p8bh4OwBKqb8rpRKABwBrd18/pdRBpdRGpVSJhfeUUo8rpaKVUtG5tlbkNDGpQXRq4ke/NkFsOSJrISVdvUHD+q4nVgDPPPMMWVlZvPvuu5V6/4IFC+jRowfPPfecKU6VwJKUHW0pQlw1PCzXUeP2eCX2MRBI0lrHGs9L0oRSKfOXoJTaopQ6ZOM2rpwdteVM16W0ywOt/6y1bg4sBWYbzfuBllrrbsB/gNUlfajWeqHWOkJrHeFRy3IpTG5NBrQN4VhSOufTrnM5I6vkpUGcTMeOHRk/fjzz58/n8uXLFXrvr7/+SkxMDDNnznR82aZawqBBg/D29rZXzluu5Tpq3BZav1hOfZhGoVUFZVz7S6JMsdJa36W17mLjtgZIMtx5GPcXbewiEbCuf9IMOGe0N7PRXpxlwESjL1e11hnG428AT6VUcFnHYGJSGxgYLj/1NQfOka+5udSSC/Haa6+RkZHB66+/XqH3LV++HKUU9913XzX1rPbj4+PDmDFj+OKLL8jIyKjWzypDH1BKeQATgOVWbytJE0qlqibHWmA68A/j3paUbwJet4oUHAG8pLVOUUqlK6X6AnuAhxBrCaVUuJXJOBY4arQ3QsxJrZS6HRHbYnVvTExqJ50a+xHg48mKn8SD0tgF56wsdO7cmUceeYS33nqLyZMnF6wfdfbsWV577TVOnTpF48aNady4MZmZmSQnJ5OSksKuXbsYPHgwjRs3dvIR1Gx+//vfs3LlSl5//fUKDxjszF3AUa219ZTPWmCZUmoe0AQIR4LtSqeKER5BwFYg1rgPNNojgI+stnsYmUSLA2ZatUcAh5BokHcozPtaabTHAOuApkb7bOAwEknyI3BHefppRgOa1BZmLd2nW/5xvW75x/U6Numqs7tTKleuXNEtWrTQvr6++sUXX9QxMTG6Xbt22svLS/fq1Us3adJEu7m5aT8/Px0WFqZ79Oih7777bv3tt986u+u1gocfflgrpXRUVFSl90EVowGBT4EnbbT/2bjuH8OIAi/rZiYFm5jUIL6MTuDFr2IAOPq3UdT1dO2SVQkJCcyZM4cVK1aQn59PnTp12LJlCwONtZa01ubcVDVx7do1evfuTYcOHfj6668rtQ9XSgo2xcrEpAaRk5fPvzYdo0eLBozqUnNcZSdOnODDDz9k+PDh3FXSytomdicxMZGGDRvi6elZqfebYuVgTLEyMTExqTiuJFZmEoOJiYmJictjipWJiYmJictjipWJiYmJictjipWJiYmJictjipWJiYmJictjipWJiYmJictjipWJiYmJictjipWJiYmJictzSyQFK6XygetV2IUHcCstinWrHS+Yx3yrYB5zxfDWWruEUXNLiFVVUUpFa60jnN0PR3GrHS+Yx3yrYB5zzcUlFNPExMTExKQ0TLEyMTExMXF5TLEqHwvL3qRWcasdL5jHfKtgHnMNxZyzMjExMTFxeUzLysTExMTE5THFysTExMTE5THFqhSUUqOUUseUUnFKqbnO7o+9UEo1V0ptV0r9qpQ6rJR61mgPVEpFKaVijfsAo10ppd42vocYpVRP5x5B5VBKuSulflZKrTeet1JK7TGOd7lSqo7R7mU8jzNeD3Nmv6uCUqqBUuorpdRR43z3uwXO8/PG7/qQUup/Sqm6te1cK6U+UUpdVEodsmqr8HlVSk03to9VSk13xrGUF1OsSkAp5Q68C4wGOgHTlFKdnNsru5EL/F5r3RHoCzxtHNtcYKvWOhzYajwH+Q7CjdvjwPuO77JdeBb41er5G8B843hTgUeM9keAVK11W2C+sV1N5S0gUmvdAeiGHH+tPc9KqabAM0CE1roL4A5Mpfad60+BUcXaKnRelVKBwMtAH+B24GWLwLkkWmvzZuMG9AM2WT1/CXjJ2f2qpmNdAwwHjgGNjbbGwDHj8YfANKvtC7arKTegGfIHHgqsBxRwGfAofr6BTUA/47GHsZ1y9jFU4pj9gJPF+17Lz3NTIAEINM7demBkbTzXQBhwqLLnFZgGfGjVXmQ7V7uZllXJWH70FhKNtlqF4fboAewBGmqtzwMY96HGZrXhu1gAzAHyjedBwBWttaUMjfUxFRyv8XqasX1NozVwCVhkuD8/Ukr5UovPs9b6LPAv4AxwHjl3+6j95xoqfl5r1Pk2xapklI22WhXnr5SqB6wEntNaXy1tUxttNea7UErdDVzUWu+zbraxqS7HazUJD6An8L7WugdwjULXkC1q/HEbbqxxQCugCeCLuMGKU9vOdWmUdIw16thNsSqZRKC51fNmwDkn9cXuKKU8EaFaqrX+2mhOUko1Nl5vDFw02mv6d9EfGKuUOgV8gbgCFwANlFIexjbWx1RwvMbr/kCKIztsJxKBRK31HuP5V4h41dbzDHAXcFJrfUlrnQN8DdxB7T/XUPHzWqPOtylWJfMTEG5EEdVBJmnXOrlPdkEppYCPgV+11vOsXloLWCKCpiNzWZb2h4yoor5AmsXdUBPQWr+ktW6mtQ5DzuM2rfUDwHbgPmOz4sdr+R7uM7Z32RFnSWitLwAJSqn2RtMw4Ai19DwbnAH6KqV8jN+55Zhr9bk2qOh53QSMUEoFGBbpCKPNNXH2pJkr34AxwHEgHvizs/tjx+MagJj7McAB4zYG8dVvBWKN+0Bje4VERsYDvyCRVk4/jkoe+xBgvfG4NbAXiAO+BLyM9rrG8zjj9dbO7ncVjrc7EG2c69VAQG0/z8BfgaPAIWAJ4FXbzjXwP2ROLgexkB6pzHkFHjaOPQ6Y6ezjKu1mllsyMTExMXF5TDegiYmJiYnLY4qViYmJiYnLY4qViYmJiYnLY4qViYmJiYnLY4qViYmJiYnLY4qViYmJiYnLY4qViYmJiYnL8/8BEPLlEktpjXgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ca_spk_idx = int(np.random.choice(segs_ca_df[(segs_ca_df.Type=='apic') \n",
    "                         & (~pd.isnull(segs_ca_df.ca_lower_bound))].drop_duplicates(subset='ca_lower_bound').index))\n",
    "\n",
    "ca_spk_time = int(segs_ca_df.loc[ca_spk_idx,'ca_lower_bound'])\n",
    "ca_spk_seg = int(segs_ca_df.loc[ca_spk_idx,'segmentID'])\n",
    "\n",
    "\n",
    "plt.plot(hva['report']['biophysical']['data'][ca_spk_time-500:ca_spk_time+500,ca_spk_seg]+\n",
    "         lva['report']['biophysical']['data'][ca_spk_time-500:ca_spk_time+500,ca_spk_seg]+\n",
    "         ih['report']['biophysical']['data'][ca_spk_time-500:ca_spk_time+500,ca_spk_seg])\n",
    "\n",
    "plt.twinx()\n",
    "\n",
    "plt.plot(v['report']['biophysical']['data'][ca_spk_time-500:ca_spk_time+500,ca_spk_seg],color='k')\n",
    "plt.plot(v['report']['biophysical']['data'][ca_spk_time-500:ca_spk_time+500,0],color='r',alpha=0.5)\n",
    "\n",
    "plt.plot(500,v['report']['biophysical']['data'][ca_spk_time,ca_spk_seg],'m*',markersize=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "spktimes = np.sort(np.random.choice(segs_ca_df[(segs_ca_df.Type=='apic') & \n",
    "           (~pd.isnull(segs_ca_df.ca_lower_bound))].drop_duplicates(subset='ca_lower_bound')['ca_lower_bound'],10000)/10)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "nmda_df = pd.read_csv('nmda_df.csv')\n",
    "nmda_df.rename(columns={'seg_id':'segmentID'},inplace=True)\n",
    "segs_nmda_df = segs.set_index('segmentID').join(nmda_df.set_index('segmentID')).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "nmda_dend = np.zeros((10,27,))\n",
    "nmda_apic = np.zeros((10,27,))\n",
    "\n",
    "edges_dend=np.quantile(segs_nmda_df[(segs_nmda_df.mag<-0.1)&\n",
    "                               (segs_nmda_df.Type=='dend')]['Elec_distance'].unique(),np.arange(0,1.1,0.1))\n",
    "edges_apic=np.quantile(segs_nmda_df[(segs_nmda_df.mag<-0.1)&\n",
    "                               (segs_nmda_df.Type=='apic')]['Elec_distance'].unique(),np.arange(0,1.1,0.1))\n",
    "\n",
    "actual_spktimes = []\n",
    "c = 0\n",
    "for i in np.sort(spktimes):\n",
    "    # excludes bursts\n",
    "    if i-c > 10:\n",
    "        for e in np.arange(0,10):\n",
    "            nmda_inds = segs_nmda_df[(segs_nmda_df.mag<-0.1)&\n",
    "                                 (segs_nmda_df.Type=='dend')&\n",
    "                                 (segs_nmda_df.Elec_distance>edges_dend[e])&\n",
    "                                 (segs_nmda_df.Elec_distance<=edges_dend[e+1])]['nmda_lower_bound'].values.astype(int)\n",
    "\n",
    "            x2, _ = np.histogram(nmda_inds/10,bins=np.arange(np.floor(i)-100,np.floor(i)+40,5))\n",
    "            nmda_dend[e] += x2\n",
    "            \n",
    "            nmda_inds = segs_nmda_df[(segs_nmda_df.mag<-0.1)&\n",
    "                                 (segs_nmda_df.Type=='apic')&\n",
    "                                 (segs_nmda_df.Elec_distance>edges_apic[e])&\n",
    "                                 (segs_nmda_df.Elec_distance<=edges_apic[e+1])]['nmda_lower_bound'].values.astype(int)\n",
    "\n",
    "            x2, _ = np.histogram(nmda_inds/10,bins=np.arange(np.floor(i)-100,np.floor(i)+40,5))\n",
    "            nmda_apic[e] += x2\n",
    "        \n",
    "        actual_spktimes.append(i)\n",
    "    c = i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "plt.subplot(2,1,1)\n",
    "plt.title('NMDA spikes - apical')\n",
    "plt.imshow(nmda_apic/len(spktimes))\n",
    "plt.xticks(ticks=np.arange(0,26,4)-0.5,labels=['{}'.format(i) for i in np.arange(-100,40,20)])\n",
    "plt.yticks(ticks=[0,9],labels=['further','closer'])\n",
    "plt.colorbar()\n",
    "plt.xlabel('time (ms)')\n",
    "\n",
    "plt.subplot(2,1,2)\n",
    "plt.title('NMDA spikes - basal')\n",
    "plt.imshow(nmda_dend/len(spktimes))\n",
    "plt.xticks(ticks=np.arange(0,26,4)-0.5,labels=['{}'.format(i) for i in np.arange(-100,40,20)])\n",
    "plt.yticks(ticks=[0,9],labels=['further','closer'])\n",
    "plt.colorbar(label='events per AP')\n",
    "\n",
    "plt.xlabel('time (ms)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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