PRUEBAS MNE - GRABACIONES OPEN BCI (ALEXANDRA IMAICELA - ESTIMULOS 4 SEGUNDOS).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e97ef774",
   "metadata": {},
   "outputs": [],
   "source": [
    "#1 importar liberias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dd9ed0dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import os\n",
    "import pandas as pd\n",
    "import math as mt\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9aacedf2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#LIBRERIAS PARA ANALISIS DE EEG\n",
    "\n",
    "    #PARA GRAFICAS INTERACTIVAS\n",
    "%matplotlib qt\n",
    "\n",
    "\n",
    "    #PARA PROCESAMIENTOS DATOS EEG\n",
    "import mne"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b98dc085",
   "metadata": {},
   "outputs": [],
   "source": [
    "#2 Lectura de los archivos en formato .BDF \n",
    "    #se excluyen las informacion de los canales de aceleracion en los X, Y, Z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "953ec7c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting EDF parameters from C:\\Users\\jarv_\\Documents\\OpenBCI_GUI\\Recordings\\OpenBCI-BDF-2023-01-24_11-03-00.bdf...\n",
      "BDF file detected\n",
      "Channel 'EEG 1' recognized as type EEG (renamed to '1').\n",
      "Channel 'EEG 2' recognized as type EEG (renamed to '2').\n",
      "Channel 'EEG 3' recognized as type EEG (renamed to '3').\n",
      "Channel 'EEG 4' recognized as type EEG (renamed to '4').\n",
      "Channel 'EEG 5' recognized as type EEG (renamed to '5').\n",
      "Channel 'EEG 6' recognized as type EEG (renamed to '6').\n",
      "Channel 'EEG 7' recognized as type EEG (renamed to '7').\n",
      "Channel 'EEG 8' recognized as type EEG (renamed to '8').\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "Reading 0 ... 154999  =      0.000 ...   619.996 secs...\n"
     ]
    }
   ],
   "source": [
    "prueba = mne.io.read_raw_bdf(r\"C:\\Users\\jarv_\\Documents\\OpenBCI_GUI\\Recordings\\OpenBCI-BDF-2023-01-24_11-03-00.bdf\", eog=None, misc=None, units = 'uV' ,infer_types=True,exclude=['Accel X','Accel Y','Accel Z'], preload = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6cfa36d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"table table-hover table-striped table-sm table-responsive small\">\n",
       "    <tr>\n",
       "        <th>Measurement date</th>\n",
       "        \n",
       "        <td>January 24, 2023  11:03:00 GMT</td>\n",
       "        \n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>Experimenter</th>\n",
       "        \n",
       "        <td>Unknown</td>\n",
       "        \n",
       "    </tr>\n",
       "        <th>Participant</th>\n",
       "        \n",
       "        <td>Unknown</td>\n",
       "        \n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>Digitized points</th>\n",
       "        \n",
       "        <td>5 points</td>\n",
       "        \n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>Good channels</th>\n",
       "        <td>2 EEG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>Bad channels</th>\n",
       "        <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>EOG channels</th>\n",
       "        <td>Not available</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>ECG channels</th>\n",
       "        <td>Not available</td>\n",
       "    \n",
       "    <tr>\n",
       "        <th>Sampling frequency</th>\n",
       "        <td>250.00 Hz</td>\n",
       "    </tr>\n",
       "    \n",
       "    \n",
       "    <tr>\n",
       "        <th>Highpass</th>\n",
       "        <td>0.00 Hz</td>\n",
       "    </tr>\n",
       "    \n",
       "    \n",
       "    <tr>\n",
       "        <th>Lowpass</th>\n",
       "        <td>125.00 Hz</td>\n",
       "    </tr>\n",
       "    \n",
       "    \n",
       "    \n",
       "    <tr>\n",
       "        <th>Filenames</th>\n",
       "        <td>OpenBCI-BDF-2023-01-24_11-03-00.bdf</td>\n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        <th>Duration</th>\n",
       "        <td>00:10:20 (HH:MM:SS)</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<RawEDF | OpenBCI-BDF-2023-01-24_11-03-00.bdf, 2 x 155000 (620.0 s), ~2.4 MB, data loaded>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prueba = prueba.pick_channels(['7','8']) #prueba.pick_channels(['1','2','7','8'])\n",
    "\n",
    "ch_names = {'7':'O1','8':'O2'} #{'1':'FP1','2':'FP2','7':'O1','8':'O2'}\n",
    "\n",
    "prueba.rename_channels(ch_names, allow_duplicates=False, verbose=None)\n",
    "\n",
    "montage =  mne.channels.make_standard_montage('standard_1020')\n",
    "prueba.set_montage(montage, match_case = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8f6d5571",
   "metadata": {},
   "outputs": [],
   "source": [
    "prueba.info['line_freq'] = 60."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0d5e7322-7abf-4d05-a181-7c0912ec82f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting up band-stop filter from 59 - 61 Hz\n",
      "\n",
      "IIR filter parameters\n",
      "---------------------\n",
      "Butterworth bandstop zero-phase (two-pass forward and reverse) non-causal filter:\n",
      "- Filter order 16 (effective, after forward-backward)\n",
      "- Cutoffs at 59.35, 60.65 Hz: -6.02, -6.02 dB\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"table table-hover table-striped table-sm table-responsive small\">\n",
       "    <tr>\n",
       "        <th>Measurement date</th>\n",
       "        \n",
       "        <td>January 24, 2023  11:03:00 GMT</td>\n",
       "        \n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>Experimenter</th>\n",
       "        \n",
       "        <td>Unknown</td>\n",
       "        \n",
       "    </tr>\n",
       "        <th>Participant</th>\n",
       "        \n",
       "        <td>Unknown</td>\n",
       "        \n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>Digitized points</th>\n",
       "        \n",
       "        <td>5 points</td>\n",
       "        \n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>Good channels</th>\n",
       "        <td>2 EEG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>Bad channels</th>\n",
       "        <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>EOG channels</th>\n",
       "        <td>Not available</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>ECG channels</th>\n",
       "        <td>Not available</td>\n",
       "    \n",
       "    <tr>\n",
       "        <th>Sampling frequency</th>\n",
       "        <td>250.00 Hz</td>\n",
       "    </tr>\n",
       "    \n",
       "    \n",
       "    <tr>\n",
       "        <th>Highpass</th>\n",
       "        <td>0.00 Hz</td>\n",
       "    </tr>\n",
       "    \n",
       "    \n",
       "    <tr>\n",
       "        <th>Lowpass</th>\n",
       "        <td>125.00 Hz</td>\n",
       "    </tr>\n",
       "    \n",
       "    \n",
       "    \n",
       "    <tr>\n",
       "        <th>Filenames</th>\n",
       "        <td>OpenBCI-BDF-2023-01-24_11-03-00.bdf</td>\n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        <th>Duration</th>\n",
       "        <td>00:10:20 (HH:MM:SS)</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<RawEDF | OpenBCI-BDF-2023-01-24_11-03-00.bdf, 2 x 155000 (620.0 s), ~2.4 MB, data loaded>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prueba.notch_filter([60],  method='iir', verbose=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f6002f3c-55da-469c-8b56-c3cf0e2eaa47",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting up band-stop filter from 49 - 51 Hz\n",
      "\n",
      "IIR filter parameters\n",
      "---------------------\n",
      "Butterworth bandstop zero-phase (two-pass forward and reverse) non-causal filter:\n",
      "- Filter order 16 (effective, after forward-backward)\n",
      "- Cutoffs at 49.38, 50.62 Hz: -6.02, -6.02 dB\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"table table-hover table-striped table-sm table-responsive small\">\n",
       "    <tr>\n",
       "        <th>Measurement date</th>\n",
       "        \n",
       "        <td>January 24, 2023  11:03:00 GMT</td>\n",
       "        \n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>Experimenter</th>\n",
       "        \n",
       "        <td>Unknown</td>\n",
       "        \n",
       "    </tr>\n",
       "        <th>Participant</th>\n",
       "        \n",
       "        <td>Unknown</td>\n",
       "        \n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>Digitized points</th>\n",
       "        \n",
       "        <td>5 points</td>\n",
       "        \n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>Good channels</th>\n",
       "        <td>2 EEG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>Bad channels</th>\n",
       "        <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>EOG channels</th>\n",
       "        <td>Not available</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>ECG channels</th>\n",
       "        <td>Not available</td>\n",
       "    \n",
       "    <tr>\n",
       "        <th>Sampling frequency</th>\n",
       "        <td>250.00 Hz</td>\n",
       "    </tr>\n",
       "    \n",
       "    \n",
       "    <tr>\n",
       "        <th>Highpass</th>\n",
       "        <td>0.00 Hz</td>\n",
       "    </tr>\n",
       "    \n",
       "    \n",
       "    <tr>\n",
       "        <th>Lowpass</th>\n",
       "        <td>125.00 Hz</td>\n",
       "    </tr>\n",
       "    \n",
       "    \n",
       "    \n",
       "    <tr>\n",
       "        <th>Filenames</th>\n",
       "        <td>OpenBCI-BDF-2023-01-24_11-03-00.bdf</td>\n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        <th>Duration</th>\n",
       "        <td>00:10:20 (HH:MM:SS)</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<RawEDF | OpenBCI-BDF-2023-01-24_11-03-00.bdf, 2 x 155000 (620.0 s), ~2.4 MB, data loaded>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prueba.notch_filter([50],  method='iir', verbose=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1f89daff-ad57-4aca-89c8-5451096b9a96",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filtering raw data in 1 contiguous segment\n",
      "Setting up band-pass filter from 5 - 11 Hz\n",
      "\n",
      "IIR filter parameters\n",
      "---------------------\n",
      "Butterworth bandpass zero-phase (two-pass forward and reverse) non-causal filter:\n",
      "- Filter order 16 (effective, after forward-backward)\n",
      "- Cutoffs at 5.00, 11.00 Hz: -6.02, -6.02 dB\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done   2 out of   2 | elapsed:    1.4s remaining:    0.0s\n",
      "[Parallel(n_jobs=4)]: Done   2 out of   2 | elapsed:    1.4s finished\n"
     ]
    }
   ],
   "source": [
    "#filtro pasa banda entre 4 Hz - 20 Hz para quitar el DC offset y restringir la banda de frecuencias\n",
    "\n",
    "prueba = prueba.filter(l_freq=5, h_freq=11 ,picks='eeg', n_jobs=4 , method='iir')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "41c9cd48",
   "metadata": {},
   "outputs": [],
   "source": [
    "#recorte de las seccion donde se realiza el experimento hasta 30 segundos antes del final\n",
    "inicio = 211.624\n",
    "final = inicio+404\n",
    "\n",
    "prueba = prueba.crop(tmin=inicio, tmax=final, include_tmax=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d3183e80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"table table-hover table-striped table-sm table-responsive small\">\n",
       "    <tr>\n",
       "        <th>Measurement date</th>\n",
       "        \n",
       "        <td>January 24, 2023  11:03:00 GMT</td>\n",
       "        \n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>Experimenter</th>\n",
       "        \n",
       "        <td>Unknown</td>\n",
       "        \n",
       "    </tr>\n",
       "        <th>Participant</th>\n",
       "        \n",
       "        <td>Unknown</td>\n",
       "        \n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>Digitized points</th>\n",
       "        \n",
       "        <td>5 points</td>\n",
       "        \n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>Good channels</th>\n",
       "        <td>2 EEG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>Bad channels</th>\n",
       "        <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>EOG channels</th>\n",
       "        <td>Not available</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>ECG channels</th>\n",
       "        <td>Not available</td>\n",
       "    \n",
       "    <tr>\n",
       "        <th>Sampling frequency</th>\n",
       "        <td>250.00 Hz</td>\n",
       "    </tr>\n",
       "    \n",
       "    \n",
       "    <tr>\n",
       "        <th>Highpass</th>\n",
       "        <td>5.00 Hz</td>\n",
       "    </tr>\n",
       "    \n",
       "    \n",
       "    <tr>\n",
       "        <th>Lowpass</th>\n",
       "        <td>11.00 Hz</td>\n",
       "    </tr>\n",
       "    \n",
       "    \n",
       "    \n",
       "    <tr>\n",
       "        <th>Filenames</th>\n",
       "        <td>OpenBCI-BDF-2023-01-24_11-03-00.bdf</td>\n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        <th>Duration</th>\n",
       "        <td>00:06:44 (HH:MM:SS)</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<RawEDF | OpenBCI-BDF-2023-01-24_11-03-00.bdf, 2 x 101001 (404.0 s), ~1.6 MB, data loaded>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prueba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bf8fda1b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using matplotlib as 2D backend.\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<MNEBrowseFigure size 1920x969 with 4 Axes>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Channels marked as bad:\n",
      "none\n"
     ]
    }
   ],
   "source": [
    "prueba.plot(duration=5.0,show_first_samp=True,scalings='auto',title='EEG SUJETO 1 EXP. BLANCO (SIN PROCESAR)',clipping=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08857922",
   "metadata": {},
   "outputs": [],
   "source": [
    "prueba.plot_sensors()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2c9a28d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "eventos_id = {'6.6 Hz': 1,'7.5 Hz': 2,'8.57 Hz':3,'10 Hz':4,'baseline':5}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4d0e31a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "muestra_inicial_recorte=int(inicio*250)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ee5a3829",
   "metadata": {},
   "outputs": [],
   "source": [
    "etiquetas=[5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 2, 1, 3, 4, 5, 4, 3, 2, 1, 5, 1, 4, 2, 3, 5, 1, 3, 4, 2, 5, 4, 1, 2, 3, 5, 4, 1, 2, 3, 5, 2, 1, 4, 3, 5, 3, 1, 4, 2, 5, 4, 1, 2, 3, 5, 2, 4, 3, 1, 5, 2, 3, 1, 4, 5, 4, 1, 2, 3, 5, 3, 2, 1, 4, 5, 2, 4, 1, 3, 5, 2, 1, 4, 3, 5, 3, 4, 1, 2, 5, 4, 1, 3, 2, 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5a2e0f79",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[52906, 0, 5], [53906, 0, 1], [54906, 0, 2], [55906, 0, 3], [56906, 0, 4], [57906, 0, 5], [58906, 0, 1], [59906, 0, 2], [60906, 0, 3], [61906, 0, 4], [62906, 0, 5], [63906, 0, 1], [64906, 0, 2], [65906, 0, 3], [66906, 0, 4], [67906, 0, 5], [68906, 0, 2], [69906, 0, 1], [70906, 0, 3], [71906, 0, 4], [72906, 0, 5], [73906, 0, 4], [74906, 0, 3], [75906, 0, 2], [76906, 0, 1], [77906, 0, 5], [78906, 0, 1], [79906, 0, 4], [80906, 0, 2], [81906, 0, 3], [82906, 0, 5], [83906, 0, 1], [84906, 0, 3], [85906, 0, 4], [86906, 0, 2], [87906, 0, 5], [88906, 0, 4], [89906, 0, 1], [90906, 0, 2], [91906, 0, 3], [92906, 0, 5], [93906, 0, 4], [94906, 0, 1], [95906, 0, 2], [96906, 0, 3], [97906, 0, 5], [98906, 0, 2], [99906, 0, 1], [100906, 0, 4], [101906, 0, 3], [102906, 0, 5], [103906, 0, 3], [104906, 0, 1], [105906, 0, 4], [106906, 0, 2], [107906, 0, 5], [108906, 0, 4], [109906, 0, 1], [110906, 0, 2], [111906, 0, 3], [112906, 0, 5], [113906, 0, 2], [114906, 0, 4], [115906, 0, 3], [116906, 0, 1], [117906, 0, 5], [118906, 0, 2], [119906, 0, 3], [120906, 0, 1], [121906, 0, 4], [122906, 0, 5], [123906, 0, 4], [124906, 0, 1], [125906, 0, 2], [126906, 0, 3], [127906, 0, 5], [128906, 0, 3], [129906, 0, 2], [130906, 0, 1], [131906, 0, 4], [132906, 0, 5], [133906, 0, 2], [134906, 0, 4], [135906, 0, 1], [136906, 0, 3], [137906, 0, 5], [138906, 0, 2], [139906, 0, 1], [140906, 0, 4], [141906, 0, 3], [142906, 0, 5], [143906, 0, 3], [144906, 0, 4], [145906, 0, 1], [146906, 0, 2], [147906, 0, 5], [148906, 0, 4], [149906, 0, 1], [150906, 0, 3], [151906, 0, 2], [152906, 0, 5]]\n"
     ]
    }
   ],
   "source": [
    "tiempo_estimulo = 4\n",
    "\n",
    "eventos = [[0+muestra_inicial_recorte,0,5],[875+muestra_inicial_recorte,0,1],[1750+muestra_inicial_recorte,0,2],[2625+muestra_inicial_recorte,0,3],[3500+muestra_inicial_recorte,0,4],[4375+muestra_inicial_recorte,0,5],[5260+muestra_inicial_recorte,0,2],[6125+muestra_inicial_recorte,0,1],[7000+muestra_inicial_recorte,0,4],[7875+muestra_inicial_recorte,0,3],[8750+muestra_inicial_recorte,0,5],[9625+muestra_inicial_recorte,0,3],[10500+muestra_inicial_recorte,0,4],[11375+muestra_inicial_recorte,0,1],[12250+muestra_inicial_recorte,0,2],[13125+muestra_inicial_recorte,0,5],[14000+muestra_inicial_recorte,0,4],[14875+muestra_inicial_recorte,0,1],[15750+muestra_inicial_recorte,0,2],[16625+muestra_inicial_recorte,0,3],[17500+muestra_inicial_recorte,0,5],[18375+muestra_inicial_recorte,0,3],[19250+muestra_inicial_recorte,0,2],[20125+muestra_inicial_recorte,0,4],[21000+muestra_inicial_recorte,0,1],[21875+muestra_inicial_recorte,0,5],[22750+muestra_inicial_recorte,0,4],[23625+muestra_inicial_recorte,0,1],[24500+muestra_inicial_recorte,0,3],[25375+muestra_inicial_recorte,0,2],[26250+muestra_inicial_recorte,0,5],[27125+muestra_inicial_recorte,0,3],[28000+muestra_inicial_recorte,0,1],[28875+muestra_inicial_recorte,0,2],[29750+muestra_inicial_recorte,0,4],[30625+muestra_inicial_recorte,0,5],[31500+muestra_inicial_recorte,0,2],[32375+muestra_inicial_recorte,0,1],[33250+muestra_inicial_recorte,0,3],[34125+muestra_inicial_recorte,0,4],[35000+muestra_inicial_recorte,0,5],[35875+muestra_inicial_recorte,0,4],[36750+muestra_inicial_recorte,0,2],[37625+muestra_inicial_recorte,0,1],[38500+muestra_inicial_recorte,0,3],[39375+muestra_inicial_recorte,0,5],[40250+muestra_inicial_recorte,0,4],[41125+muestra_inicial_recorte,0,1],[42000+muestra_inicial_recorte,0,3],[42875+muestra_inicial_recorte,0,2],[43750+muestra_inicial_recorte,0,5],[44625+muestra_inicial_recorte,0,1],[45500+muestra_inicial_recorte,0,3],[46375+muestra_inicial_recorte,0,4],[47250+muestra_inicial_recorte,0,2],[48125+muestra_inicial_recorte,0,5],[49000+muestra_inicial_recorte,0,4],[49875+muestra_inicial_recorte,0,1],[50750+muestra_inicial_recorte,0,3],[51625+muestra_inicial_recorte,0,2],[52500+muestra_inicial_recorte,0,5],[53375+muestra_inicial_recorte,0,4],[54250+muestra_inicial_recorte,0,3],[55125+muestra_inicial_recorte,0,2],[56000+muestra_inicial_recorte,0,1],[56875+muestra_inicial_recorte,0,5],[57750+muestra_inicial_recorte,0,2],[58625+muestra_inicial_recorte,0,4],[59500+muestra_inicial_recorte,0,3],[60375+muestra_inicial_recorte,0,1],[61250+muestra_inicial_recorte,0,5],[62125+muestra_inicial_recorte,0,1],[63000+muestra_inicial_recorte,0,3],[63875+muestra_inicial_recorte,0,4],[64750+muestra_inicial_recorte,0,2],[65625+muestra_inicial_recorte,0,5],[66500+muestra_inicial_recorte,0,3],[67375+muestra_inicial_recorte,0,4],[68250+muestra_inicial_recorte,0,1],[69125+muestra_inicial_recorte,0,2],[70000+muestra_inicial_recorte,0,5],[70875+muestra_inicial_recorte,0,4],[71750+muestra_inicial_recorte,0,2],[72625+muestra_inicial_recorte,0,3],[73500+muestra_inicial_recorte,0,1],[74375+muestra_inicial_recorte,0,5],[75250+muestra_inicial_recorte,0,2],[76125+muestra_inicial_recorte,0,4],[77000+muestra_inicial_recorte,0,1],[77875+muestra_inicial_recorte,0,3],[78750+muestra_inicial_recorte,0,5],[79625+muestra_inicial_recorte,0,3],[80500+muestra_inicial_recorte,0,4],[81375+muestra_inicial_recorte,0,1],[82250+muestra_inicial_recorte,0,2],[83125+muestra_inicial_recorte,0,5],[84000+muestra_inicial_recorte,0,4],[84875+muestra_inicial_recorte,0,2],[85750+muestra_inicial_recorte,0,1],[86625+muestra_inicial_recorte,0,3],[87500+muestra_inicial_recorte,0,5]]\n",
    "\n",
    "for indice in range(len(etiquetas)):\n",
    "    eventos[indice][0]=int((tiempo_estimulo*250*indice)+muestra_inicial_recorte)\n",
    "    eventos[indice][2]=etiquetas[indice]\n",
    "        \n",
    "        \n",
    "print(eventos)\n",
    "\n",
    "eventos = np.array(eventos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ef95bca5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 52906      0      5]\n",
      " [ 53906      0      1]\n",
      " [ 54906      0      2]\n",
      " [ 55906      0      3]\n",
      " [ 56906      0      4]\n",
      " [ 57906      0      5]\n",
      " [ 58906      0      1]\n",
      " [ 59906      0      2]\n",
      " [ 60906      0      3]\n",
      " [ 61906      0      4]\n",
      " [ 62906      0      5]\n",
      " [ 63906      0      1]\n",
      " [ 64906      0      2]\n",
      " [ 65906      0      3]\n",
      " [ 66906      0      4]\n",
      " [ 67906      0      5]\n",
      " [ 68906      0      2]\n",
      " [ 69906      0      1]\n",
      " [ 70906      0      3]\n",
      " [ 71906      0      4]\n",
      " [ 72906      0      5]\n",
      " [ 73906      0      4]\n",
      " [ 74906      0      3]\n",
      " [ 75906      0      2]\n",
      " [ 76906      0      1]\n",
      " [ 77906      0      5]\n",
      " [ 78906      0      1]\n",
      " [ 79906      0      4]\n",
      " [ 80906      0      2]\n",
      " [ 81906      0      3]\n",
      " [ 82906      0      5]\n",
      " [ 83906      0      1]\n",
      " [ 84906      0      3]\n",
      " [ 85906      0      4]\n",
      " [ 86906      0      2]\n",
      " [ 87906      0      5]\n",
      " [ 88906      0      4]\n",
      " [ 89906      0      1]\n",
      " [ 90906      0      2]\n",
      " [ 91906      0      3]\n",
      " [ 92906      0      5]\n",
      " [ 93906      0      4]\n",
      " [ 94906      0      1]\n",
      " [ 95906      0      2]\n",
      " [ 96906      0      3]\n",
      " [ 97906      0      5]\n",
      " [ 98906      0      2]\n",
      " [ 99906      0      1]\n",
      " [100906      0      4]\n",
      " [101906      0      3]\n",
      " [102906      0      5]\n",
      " [103906      0      3]\n",
      " [104906      0      1]\n",
      " [105906      0      4]\n",
      " [106906      0      2]\n",
      " [107906      0      5]\n",
      " [108906      0      4]\n",
      " [109906      0      1]\n",
      " [110906      0      2]\n",
      " [111906      0      3]\n",
      " [112906      0      5]\n",
      " [113906      0      2]\n",
      " [114906      0      4]\n",
      " [115906      0      3]\n",
      " [116906      0      1]\n",
      " [117906      0      5]\n",
      " [118906      0      2]\n",
      " [119906      0      3]\n",
      " [120906      0      1]\n",
      " [121906      0      4]\n",
      " [122906      0      5]\n",
      " [123906      0      4]\n",
      " [124906      0      1]\n",
      " [125906      0      2]\n",
      " [126906      0      3]\n",
      " [127906      0      5]\n",
      " [128906      0      3]\n",
      " [129906      0      2]\n",
      " [130906      0      1]\n",
      " [131906      0      4]\n",
      " [132906      0      5]\n",
      " [133906      0      2]\n",
      " [134906      0      4]\n",
      " [135906      0      1]\n",
      " [136906      0      3]\n",
      " [137906      0      5]\n",
      " [138906      0      2]\n",
      " [139906      0      1]\n",
      " [140906      0      4]\n",
      " [141906      0      3]\n",
      " [142906      0      5]\n",
      " [143906      0      3]\n",
      " [144906      0      4]\n",
      " [145906      0      1]\n",
      " [146906      0      2]\n",
      " [147906      0      5]\n",
      " [148906      0      4]\n",
      " [149906      0      1]\n",
      " [150906      0      3]\n",
      " [151906      0      2]\n",
      " [152906      0      5]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(101, 3)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(eventos)\n",
    "eventos.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "87475c6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "101"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(eventos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4da72707",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Not setting metadata\n",
      "101 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n"
     ]
    }
   ],
   "source": [
    "epocas =mne.Epochs(prueba,eventos,eventos_id,tmin=0,reject = dict(   eeg=20e-6     # unit: V (EEG channels)\n",
    "                ), tmax=tiempo_estimulo,baseline=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "87378fbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"table table-hover table-striped table-sm table-responsive small\">\n",
       "    <tr>\n",
       "        <th>Number of events</th>\n",
       "        <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>Events</th>\n",
       "        \n",
       "        <td>10 Hz: 20<br/>6.6 Hz: 20<br/>7.5 Hz: 20<br/>8.57 Hz: 20<br/>baseline: 21</td>\n",
       "        \n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>Time range</th>\n",
       "        <td>0.000 – 4.000 sec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>Baseline</th>\n",
       "        <td>off</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Epochs |  101 events (good & bad), 0 - 4 sec, baseline off, ~10 kB, data not loaded,\n",
       " '6.6 Hz': 20\n",
       " '7.5 Hz': 20\n",
       " '8.57 Hz': 20\n",
       " '10 Hz': 20\n",
       " 'baseline': 21>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "epocas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "784d8e34",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using data from preloaded Raw for 101 events and 1001 original time points ...\n",
      "0 bad epochs dropped\n",
      "Using matplotlib as 2D backend.\n",
      "Using data from preloaded Raw for 20 events and 1001 original time points ...\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<MNEBrowseFigure size 1920x969 with 4 Axes>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropped 0 epochs: \n",
      "The following epochs were marked as bad and are dropped:\n",
      "[]\n",
      "Channels marked as bad:\n",
      "none\n"
     ]
    }
   ],
   "source": [
    "epocas.plot(events=eventos,event_id=eventos_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "a069d8e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<MNEBrowseFigure size 1920x969 with 4 Axes>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prueba.plot(events=eventos, start=5, duration=10, color='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2e84df13",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = mne.viz.plot_events(eventos, sfreq=prueba.info['sfreq'],\n",
    "                          first_samp=prueba.first_samp, event_id=eventos_id)\n",
    "fig.subplots_adjust(right=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cea34550",
   "metadata": {},
   "outputs": [],
   "source": [
    "f6_6 = epocas['6.6 Hz'].average()\n",
    "f7_5 = epocas['7.5 Hz'].average()\n",
    "f8_57 = epocas['8.57 Hz'].average()\n",
    "f10 = epocas['10 Hz'].average()\n",
    "base = epocas['baseline'].average()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bd77d35c",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig1 = f6_6.plot()\n",
    "fig2 = f7_5.plot()\n",
    "fig3 = f8_57.plot()\n",
    "fig4 = f10.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "8da44f7e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No projector specified for this dataset. Please consider the method self.add_proj.\n"
     ]
    },
    {
     "data": {
      "image/png": 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CQvgRI0bwlSpV4t999908nBkpShqNhm/QoAFfs2bNXMseP36c12q1JvMA8J9++qlofseOHXlfX18+OTlZmLdmzRoeAH/gwAGz2+/Xrx/fpk0bvmXLlnydOnXycTaEkOKIUsgIKYGaNm0KuVwumle9enXUqVMHUVFRua7v7OwMDw+PfO/f398fAJCUlCTM++eff3Dt2jXMnTsX9vb2yMjIgFartXmbsbGxGD58OCpUqAA7Ozv4+vqiW7duuaYrEcsKcp0kJibixo0b6NGjh2gbDRo0QK1atbB582aTddRqNSZMmIAJEyagatWqVrf/66+/4tq1a/jiiy/ycEa6WpsOHTrA09MT9vb2qFy5MkaMGJGnbRDrJBIJ/Pz8RH/flrRo0QIsy5rM8/DwEF1jKSkpOHjwIAYPHgwXFxdh/pAhQ+Dk5IQ//vjDZNsnTpzAtm3bsGzZsjwd/+3bt9GrVy/4+PhAoVCgQoUK6N+/P5KTk/O0HULI60MpZISUEjzPIy4uDnXq1CmSbSckJECj0eD27duYMWMGJBIJWrVqJZQ5dOgQAMDOzg5BQUGIjIyEXC5Hjx498OOPP+YaMPXq1QvXr1/H+PHj4e/vj+fPn+PgwYN49OiREDCRgrP1OtGnf9nb25ssc3BwwPXr1xEbGytKEVq2bBlevnyJWbNmYceOHRa3nZqaiunTp+OTTz4RrZ+b58+fo3379vDy8sKMGTPg5uaGBw8eWN0XsU16ejoyMzORnJyMv/76C3///Tf69euXr22lpaUhLS0Nnp6ewrz//vsPGo0GQUFBorJyuRyBgYG4fPmyaL5Wq8X48ePx3nvvoV69ejbvW6VSoUOHDlAqlRg/fjx8fHzw9OlT7NmzB0lJSXB1dc3XORFCXi0KYAgpJTZt2oSnT59i/vz5hb7tuLg4+Pr6Cu8rVKiA8PBwBAQECPNu374NAOjbty/CwsIwc+ZMXL16FQsXLsTjx49x6tQpMAxjdvtJSUk4c+YMvvnmG3z88cfC/JkzZxb6uZR2tl4nZcuWhZubG06fPi2an5CQgBs3bgAAnj59KgQgsbGxWLBgARYvXix6wm7O/PnzYW9vj0mTJuXp2M+cOYOXL1/in3/+Ed0If/7553naDjE1ZcoUrFq1CgDAsix69uyJFStW5Gtby5Ytg0qlEgVA+kb9ht8jer6+vjh58qRo3sqVK/Hw4UPhwYitbty4gfv372Pr1q3o3bu3MH/OnDl52g4h5PWiFDJCSoGbN29i7NixCAkJwdChQwt9+x4eHjh48CB2796N+fPnw9PT06TnIP37xo0b47fffkOvXr0wf/58LFiwAGfOnMHhw4ctbt/e3h5yuRzHjh3Dy5cvC/34iU5erhOWZTFmzBgcPnwYM2fOxO3btxEZGYm+fftCpVIBADIzM4Xy06dPF3qgsubWrVv47rvv8M0338DOzi5Px+/m5gZA17mAWq3O07rEuokTJ+LgwYPYuHEjOnbsCK1WK/ye8+LEiRP47LPP0LdvX7Rp00aYr79WzP3OFQqF6FpKSEjAnDlzMHv2bIsdRViir2E5cOAAMjIy8nz8hJA3xGtug0MIKWIxMTF8lSpVeD8/P/7p06d5Xj8/jfhPnz7NA+B3794tzHv33Xd5APzGjRtFZR8+fMgD4D/77DOr21y6dCnPsiwvk8n45s2b84sWLeJjYmLydC7EsvxcJ0qlkh85ciTPsiwPgAfAt2/fnn///fd5APzly5d5nuf5iIgInmEY/siRI8K6GzZsMNuIPywsjG/ZsqVonq2N+DmO43v16sUD4F1cXPiuXbvy69ev57Oysmw6H2K7du3a8Y0bN+Y5jrN5naioKN7Dw4MPDAzkU1JSRMv03zMnTpwwWa9Pnz68j4+P8P7999836TwiL434J0+ezAPg7e3t+fbt2/MrVqzgk5KSbD4PQsjrRzUwhJRgycnJ6NixI5KSkrB//36UK1fuley3adOm8PX1xaZNm4R5+n2XLVtWVNbb2xsAcq1ZmThxIm7duoWFCxdCoVBg9uzZqFWrlkluPMm7/F4ncrkca9euxbNnz3DixAlER0fjwIEDSE5OBsuyqFatGgBg2rRpaN68OSpXrowHDx7gwYMHiI+PB6BLHXr06BEA4MiRI9i/fz8mTJgglHvw4AE0Gg0yMzPx4MEDpKSkWDwehmGwbds2REREYNy4cXj69ClGjBiBRo0a5TqWCMmb3r1748KFCzaPGfT48WO0b98erq6u2LdvH5ydnUXL9alj5saHiYmJEa7J27dvY/Xq1fjoo4/w7Nkz4RrJysqCWq3GgwcPkJiYaPVYlixZgn///ReffPIJMjMz8dFHH6FOnTp48uSJTedCCHkDvO4IihBSNDIzM/nmzZvzDg4O/JkzZ/K9nfzUwPA8z7u7u/MdO3YU3q9cuZIHwK9bt05U7u7duzwA/osvvsjT9m/dusU7ODjY1N0vsaywrhM9jUbD+/r68iEhIcK8SpUqCTU05iZXV1ee53NqZaxNS5cuzdPxbNq0iQfAr1mzpsDnRnIsW7aMB8CfO3cu17Lx8fF8QEAA7+3tzd+6dctsmaSkJF4qlfJTp04VzVcqlbyTkxM/YsQInud5/ujRo7leIxMmTMjTuehrjI27dSaEvLmoET8hJZBWq0W/fv0QERGBXbt2ISQkxGLZmJgYJCcno2rVqpDJZHnaT3p6OhiGgYODg2j+9u3b8fLlS1FD6m7dumHChAnYsGEDhg0bJnStunbtWgBAu3btLO4nIyMDLMuKBjSsWrUqnJ2dcx0MkVhWFNfJ4sWLERMTg++//16Yt3r1apP2BkeOHMH333+PxYsXC509tGnTBn/++afJNkePHo1KlSrh008/tdrj1MuXL+Hm5ibqDCIwMBAA6DrJp+fPnwu1pHpqtRq//PIL7O3tUbt2bWG+uWskPT0dnTp1wtOnT3H06FFUr17d7H5cXV0RGhqK3377DbNnzxZqaH799VekpaWhT58+AIC6deuavUZmzZqF1NRUfPfdd1a76E5JSYGDgwOk0pzbn3r16oFlWbpGCClGKIAhpASaMmUK/vrrL3Tp0gWJiYn47bffRMsHDx4svJ45cyY2btyI+/fvi7oj1vfcdP36dQC6G4lTp04B0N0sALp0jtDQUPTr1w8BAQFgWRYXL17Eb7/9Bn9/f0yYMEHYno+PDz799FPMmTMHYWFh6N69O65evYo1a9ZgwIABaNy4scXzuXXrFtq2bYu+ffuidu3akEql+PPPPxEXF4f+/fsX7MMqxQp6nfz222/Yvn07WrRoAScnJxw6dAh//PEH3nvvPfTq1UtYt3379ib71o8h0rJlSyHQrVixIipWrGhSduLEiShbtiy6d+9u9Xw2btyIH3/8ET169EDVqlWRmpqKNWvWwMXFBZ06dbLlIyFGxowZg5SUFLRo0QLly5dHbGwsNm3ahJs3b2LJkiVwcnISypq7RgYNGoTz589jxIgRiIqKEo394uTkJPqdfvHFF2jatClatmyJ0aNH48mTJ1iyZAnat2+PsLAwAICnp6fZ60A/Fkxu18iRI0cwbtw49OnTBzVq1IBGo8Gvv/4KiUQiumYJIW+4110FRAgpfC1btrSaYmFo6NChPAD+/v37ovm2rP/ixQt+9OjRfEBAAO/o6MjL5XK+evXq/MSJE/kXL16YHBfHcfz333/P16hRg5fJZLyfnx8/a9YsXqVSWT2f+Ph4fuzYscJ+XF1d+eDgYP6PP/7I/4dECnydnDt3jm/RogXv7u7OKxQKvkGDBvzKlSttathtqRG/ObY24r906RI/YMAAvmLFirydnR3v7e3Nd+7cmb948WKu6xLzfv/9dz40NJQvW7YsL5VKeXd3dz40NJTftWuXSVlz14i19MFKlSqZbOPkyZN806ZNeYVCwXt5efFjx441afBvjq2N+O/du8ePGDGCr1q1Kq9QKHgPDw++devW/KFDh3JdlxDy5mB4nudfQZxECCGEEEIIIQVGvZARQgghhBBCig0KYAghhBBCCCHFBgUwhBBCCCGEkGKDAhhCCCGEEEJKgB9++AH+/v5QKBQIDg7G+fPnrZbfunUrAgICoFAoUK9ePezbt0+0nGEYs9M333wjlPH39zdZ/tVXXxXJ+elRAEMIIYQQQkgxt2XLFkyePBlz587FpUuX0KBBA3To0AHPnz83W/7MmTMYMGAARo4cicuXL6N79+7o3r07rl27JpSJiYkRTevXrwfDMCbdjs+fP19Ubvz48UV6rtQLGSGEEEIIIcVccHAwGjdujBUrVgAAOI6Dn58fxo8fjxkzZpiU79evH9LT07Fnzx5h3ttvv43AwECsXLnS7D66d++O1NRUHD58WJjn7++PiRMnYuLEiYV7QlbQQJaEEEIIIYQUkqysLKhUqkLZFs/zYBhGNM/Ozg52dnaieSqVCpGRkZg5c6Ywj2VZhIaGIiIiwuy2IyIiMHnyZNG8Dh06YOfOnWbLx8XFYe/evdi4caPJsq+++goLFixAxYoVMXDgQEyaNAlSadGFGRTAEEIIIYQQUgiysrJQuXJlxMbGFsr2nJyckJaWJpo3d+5czJs3TzQvPj4eWq0WZcuWFc0vW7Ysbt68aXbbsbGxZstbOvaNGzfC2dkZPXv2FM3/6KOP0LBhQ3h4eODMmTOYOXMmYmJi8O2339pyivlCAQwhhBBCCCGFQKVSITY2Fo8fP4aLi0uBtpWSkgI/Pz+TbRnXvrwq69evx6BBg6BQKETzDWtx6tevD7lcjjFjxmDhwoVFdqwUwBBCCkVmZiZSU1Ph7e39ug+FvMEePXoEPz8/k5QIQvTi4+Nhb28PR0fH130ohOSbi4tLgQOYvGzL09MTEokEcXFxovlxcXHw8fExu46Pj4/N5U+ePIno6Ghs2bIl1+MNDg6GRqPBgwcPULNmzVzL5wf1QkYIKbC7d+8iKCgIVapUsZg7S0o3tVqN8ePHo1KlSnjvvfegVCpf9yGRN9CBAwdQrVo1NGjQADdu3Hjdh0NIsSGXy9GoUSNR43qO43D48GGEhISYXSckJERUHgAOHjxotvy6devQqFEjNGjQINdjuXLlCliWLdIHmlQDQwgpkCNHjqB3794oU6YM2rZtix49emDBggX49NNP6Sk7AQAkJiaiT58+OHHiBMaMGYMNGzbg5s2b2LFjh0n+NSmdeJ7HsmXL8PHHH6Ndu3Z4+vQp3n77bfz+++949913X/fhEZJnU1zrQs4UrJ5AxXN5Kj958mQMHToUQUFBaNKkCZYtW4b09HQMHz4cADBkyBCUL18eCxcuBABMmDABLVu2xJIlS/Duu+9i8+bNuHjxIlavXi3abkpKCrZu3YolS5aY7DMiIgLnzp1D69at4ezsjIiICEyaNAmDBw+Gu7t7Ps88d1QDQwjJF57n8cMPP6B9+/YICgrC+fPn8eeff2Lu3LmYPXs2BgwYgIyMjNd9mOQ1u3HjBpo0aYKrV6/in3/+wcqVK3H8+HHcu3cPjRs3xuXLl1/3IZLXTKlUYuTIkZg8eTKmTJmCvXv34syZM2jdujW6dOmCr7/+GjTiAyluHKUsnAo4OUrzdpver18/LF68GHPmzEFgYCCuXLmC/fv3Cw+KHj16hJiYGKF806ZNER4ejtWrV6NBgwbYtm0bdu7cibp164q2u3nzZvA8jwEDBpjs087ODps3b0bLli1Rp04dfPHFF5g0aZJJEFTYaBwYQkieqVQqfPTRR1i1ahUmTJiAxYsXi7pL3LZtG4YOHYqAgADs3LkTfn5+r/FoyeuyZ88eDBw4EJUqVcKuXbtQpUoVYdmTJ0/QvXt3REVF4eeff0afPn1e45GS1yUuLg49e/bExYsXsWbNGgwZMkRYxnEc5syZgy+++AKDBw/GmjVrTBoPE/KmSUlJgaurK2baVYaigDUwWTyHhcr7SE5OLrT2NCUF1cAQQvLkxYsXaNeuHdavX4+1a9di2bJlJn299+7dG6dPn0Z8fDwaN25ssQ96UjLxPI9Fixaha9euaN26Nc6cOSMKXgCgQoUKOHHiBLp27Yq+ffti7ty54Li8pUuQ4u3y5csICgrCvXv3cPz4cVHwAujGsPj888/x+++/Y9u2bWjZsiWePXv2mo6WkLxxlbFwlUkKOOX9Nv2HH36Av78/FAoFgoODcf78eavlt27dioCAACgUCtSrVw/79u0TLR82bBgYhhFNYWFhojKJiYkYNGgQXFxc4ObmhpEjR5p0/VzYqAaGEGITnuexd+9eDB8+HBkZGWjUqBHSMjIRFxuLlJQUKJVZ4DkOdnYKODg6wNu7LDzLeODWrVt48eIFPvnkE8yaNQsymex1nwopQjExMRg0aBCOHj2K+vXrw9HZBTExsUhMiIdSqYRarYJMJoednR3c3D1QztcHyqxMXL58WUhnqFSp0us+DVKENBoNlixZgtmzZ8Pd3R21a9dGQmIi4mLjkJ6RDmVWFliWhZ2dHZydXeDj6wsnRwdcunQJUqkUa9asQa9evaiNHXkj6WtgvnerBntGUqBtZfJajE+6Y3MNzJYtWzBkyBCsXLkSwcHBWLZsGbZu3Yro6GizDerPnDmDFi1aYOHChejcuTPCw8OxaNEiXLp0SUgjGzZsGOLi4rBhwwZhPTs7O1H7lo4dOyImJgarVq2CWq3G8OHD0bhxY4SHhxfo/K2hAIYQYlFqair27NmDffv24e/9B5AQ/wIA4OzhBbcKVeHo6QNHd084OLtAbqeAVCIBp1FBq8xAVnIiYu/eREZyAuIePwTP85DL5WjZshXefbcTunfvTjeqJQDP84iIiMDu3buxZ+9eXPvvPwCARGYHp7L+gFNZSBzcIVG4gOM4cMoMSBw9oFVlQZuZAk16ApiMF1AmPIJGreuZrGZALXTp/C46d+6M5s2bg2UpWaC4e/r0KXbu3Im9e/fh6NEjyMrKAsMwKOfnD98q1VCmbDm4l/GEk7Mz7OzswADQqlRIT01GckI8nsc8wb1bNxEXo6uB8fDwQPv27dGpUyd06dIFbm5ur/X8CNHTBzAbPWvCgS1YAJPBaTE0PtrmACY4OBiNGzfGihUrAOjSMP38/DB+/HjMmDHDpHy/fv2Qnp6OPXv2CPPefvttBAYGYuXKlQB0AUxSUpLFHkajoqJQu3ZtXLhwAUFBQQCA/fv3o1OnTnjy5AnKlSuX19O2CfVCRggR4Xkex48fx6pVq7Bz1y5kZWbCvWINlA3uBP8yPvB7qxlcvHxhJ2UhYRlhkktZyKUSSFgGdlIW9nIJEh/dRZUaAWC0KpzbvxPK9DT8G3EMU6dNw8SJE9G8eXOMGDEC/fv3p9z2Yubp06dYtWoVNv7yCx49fAi5kxucKjeCX6cJkNi7wqVaE/C87nriOR4cx0OTmQV1RhKkTl7QajjwHAdOo4JGlQmtWomsx1ehSY/H/ZcPseyH1Vi8eDHKlSuPIUP+h/fff58C3mJGpVJh27ZtWLduHY4ePQpWIkHNt5qg2+hJcHJ1Q/OOPWDv4ACWZSBhGLAMIJOwkDAAyzDCazspC4VUApYBkhOe49C+3YiPi8OJIwexefNm2NnZoXPnzhg9ejTatWtHNTPkjWAvl8C+gAGMvhOylJQU0Xw7OzuTASJVKhUiIyMxc+ZMYR7LsggNDbWYxh0RESEahBIAOnToYBKsHDt2DN7e3nB3d0ebNm3w+eefo0yZMsI23NzchOAFAEJDQ8GyLM6dO4cePXrk6ZxtRQEMIQSA7knN1q1b8eWXC/Hvv1fhVq4yKrYfikpvd4Czl68oWAEADccLry1JeREDtmYAFAp7hPYaCBnLoNfw96HJykDE4f04+tdWDB8+HNOmTcP48eMxYcIEaqj4hrt58yYWLFiAP/74A6xUDrc6LVGz1Tg4+tUBD931wHO6oMWkgp/noEx4CKmTl8EsbfYLHvKyAZBqVJCXC4RWq4Hm5SMkxP6Hb5ctx9dff42ePXti9uzZqF+//qs6XZIP6enp+OGHH7B06TLExsagVlAIhny6CEFtOsLJ2QUSlgHL6IKW3LAGZVgG8PQqi0HD3oOEZTDlk1l4EROLPTu3YevmTejQoQNq1aqFGTNmYODAgSZt8wh5lWSOMsglBQtg1Fpd7bNxRzhz587FvHnzRPPi4+Oh1WpNuqYvW7Ysbt68aXb7sbGxZsvHxsYK78PCwtCzZ09UrlwZd+/exSeffIKOHTsiIiICEokEsbGxJulpUqkUHh4eou0UNvrrJoTgwIEDmDTlY0Rdvwav2sEIHPstvAIaQyrR1bJobQhWDOkDHYWjk8kylmXg6OSEDt37oGPPvnj24C62b1yDz7/4AkuXLcOsTz/FuHHjIJfLC/MUSQE9e/YM02fMQPimTVC4eaF8+1HwfKs9WLkDeE4XtDAAOCtZyaxcDrl7eZP5+iBGCGYAMAwDibMvWEdvcBXfAZ7fwF/7j2Dbtm3o3bs3Fi9eTDUybxiNRoPVq1dj7rzPkJT0Ek3CeqJdYBO8825PmwMWjuMhkVguxzAQalhYMPAp54vRY8dj9IfjcC7iNFb/sAJDhw7Fl19+KXQkQTUy5HWwc5HDTlKw22yNVgMAePz4sejhnnHtS1Hq37+/8LpevXqoX78+qlatimPHjqFt27av7DiMUWIxIaXY06dP0aNHD4SFhSE2S4K3JvyAumO+QZmAxib/6Ws50xtTc/MMMQyT/bQVkGTfwBjem0gYoFKVapj82SJsPXYRrTt1xdSpU9GgQQMcP368UM6RFIxWq8WSJUtQo0ZNbNu5B/5dxqLexA3wbdoTUoWjUI6xJcBlWCif3xXeGgYsvDYniOE4rTiYkcggLVsXbO1+kFVpjV17D6BmzZr4/PPPoVarC+EsSUGdO3cODRs1wrhx41A5qDlmhh/CgBkLUdavcoG3LWEYMAAY5Fxj+q8nlgEYABKWRdN3mmPj71tw8NhJ+JYrj+7du6NTp064f/9+gY+BkLySOcggcyzg5KDr9MbFxUU0mQtgPD09IZFIEBcXJ5ofFxcHHx8fs8fo4+OTp/IAUKVKFXh6euLOnTvCNp4/fy4qo9FokJiYaHU7BUUBDCGlVHh4OOrUqYuDx04hYNBs1PtgGVz961pdR8vxuQYthjU1mWk5ebsso6t9AXQ3JKxBOQnDwKdcOXzy5bcI//sYHJzd0KpVK0yaNAlZWVn5OT1SCO7evYvmzVtg6tSpcHurHQI/3gjfkO5gpeLaMVuHOmAYBnZett/Q8tmBjD6YYVgJJJ4BYAJ6QesRgDlz5yI4OBg3btyweZukcKlUKnz66ado2rQpUtTAuJ+2of/MRSjjWwEAkJaUUKj7Y6H7LgFyghg9htEtC3zrLfzx5y78Er4Z169fR/369bFmzRoaDJO8UnbOdlC4FGyyc7a9pkUul6NRo0Y4fPiwMI/jOBw+fBghISFm1wkJCRGVB4CDBw9aLA/oxvBKSEiAr6+vsI2kpCRERkYKZY4cOQKO4xAcHGzz8ecVBTCElDJZWVkYNWoUBg0aBEWVRqg7cS28AtvoGlznEpzkBQvA09cPrFGwos9p1zfU1QUzENJLatSuh7Xb9+LjOZ/jp59+QrNmzfDgwYNCOy5im127diHwrYa4dvch6n3wHap0+whSe/MpgXmhTHgkem8YpIgmrdZsOQBgJHKwvkGQVO+MG7cfoVFQEH7//fc8niEpqKdPn6JVq9ZY9PXXCB02AaOWb4ZfgLh9krdfZXDZv0stz4PjdQ9COJ4X3ucHm10bw0D3PaIPXvQkLIvOXbrg5Nnz6NGzF0aPHo3//e9/SE9Pz98OCckjmaO04DUwjnlLQZs8eTLWrFmDjRs3IioqCh988AHS09MxfPhwAMCQIUNEjfwnTJiA/fv3Y8mSJbh58ybmzZuHixcvYty4cQCAtLQ0TJ06FWfPnsWDBw9w+PBhdOvWDdWqVUOHDh0AALVq1UJYWBhGjRqF8+fP4/Tp0xg3bhz69+9fZD2QARTAEFKqvHjxAs1atMSGjb+gSs8pqNbvE0gVzqInk/oeo7RWnlYa1sKYq5GRsAwSnj2GMj01Z152+phx7YshNjvlTCqRYMiYcfj1r38Q9yIejRo1wqlTp/JzyiSPeJ7HggUL0L17d9hXDkT9CavhWrmeSTmGZfLVtkDuZvt/aMY1MIbzAICxLwNt5Y5Q2ZfDwIEDMX36dBoM8xW5ePEi3mrYCDfu3MPIb39Di4HvQ2Im319u74Doyxesto3irEQxut7JxAGKYfqYMX1Ao+fm4oIVP/6EdRs2YufOnXjnnXfw9OlTm86RkIKQOdhB5ljAySFvbV369euHxYsXY86cOQgMDMSVK1ewf/9+oaH+o0ePEBMTI5TXj721evVqNGjQANu2bcPOnTuFMWAkEgn+/fdfdO3aFTVq1MDIkSPRqFEjnDx5UpTGtmnTJgQEBKBt27bo1KkTmjVrhtWrVxfCp2gZjQNDSClx7949tA1th5j4JFQfNB/OFQMA6J6g629GhdcsI3Rtqu8i2bAXMv2k70pZ14WyrhtluZSFg1wCVdILuLm6ooybK+ykLGQsA5mEgYxlIZMwJrUv+uAF0N2E6G9Ykl++xISR/8O/ly7g999/L7IuGYmuvcv777+PtWvXwr/jCFRqNwTI/h+Cy+5VTF9Lxxn0NKZ7nR38ZpfRl9NqOSEo5jkeKfcvw6FCA2g1HDiNGlpVpq4bZWUWtKpMaDUqXTsYtcpszQygD2JyAhWe58G9uAHu2QUMGNAfGzdupAFTi9CBAwfQs1cvePhVRf/PfoJbGU8AMOmpUMIyYAE8f3gXFapWz/mbZyDqiUyfXirL/s7Rfz/IJCwUElb3/SHRLZeyDCTCd0Z2+xgmpy0MYFAjk/2eyS574/p19OrZAwzD4MCBA6hVq9Yr/+xIyacfB+bquL5wtitYZzSpShUarPjD5nFgShOqgSGkFLh37x6at2iJF2kqBIxaCsfyNfKULmZL2xdjd69ehL2Dg/Cezb5hEbeDyQle9AyDFwBwc/fAus070DbsXfTp0wfbtm3L03EQ22i1WowYMRLr1q9HwKCZqBw2DAzDmDTOz62xPsvkBMEATGppDNvP5DTY5yw26DdfAyOuZWEYBhLvOpD4t8LmLX+gX7/+1Li/iPz999/o0rUrKtRtjMFf/Qwn9zLQGNXIGn5XMAyDx7euF3i/LGNQ82KQPmZajhG1jdEHLwBQp04dHDl6FC4uLmjZsiW1nSJFSuaoKJQpr3744Qf4+/tDoVAgODgY58+ft1p+69atCAgIgEKhQL169bBv3z5hmVqtxvTp01GvXj04OjqiXLlyGDJkCJ49eybahr+/v+7/C4Ppq6++yvOx5wUFMISUcM+ePUOLlq2QrAKqDfkaCndf0XLjJ+nW2BrESFgGFarXAsuyuqer2TcQ+nYuLGN66yFhGSF40WOgu/mQ29nhmx/XoFO3nujfv7/oC5YUHM/z+OCDD/Drb7+i9pDZ8G3S0Ww5ayljDGs5uDGcz7CWc7qNa1ksLbOEdfMHW6kVdu7ahSFDhlA6WSE7duwYunXvjsoN30GPT5ZBprDPNZ0UAPxq1LFp+8ar63ssNOwYRP/SMJ1MX/tiGLywEF+v+lcVypfHkP/9D56enmjTpg31UEaKjNzJHnJnh4JNTvZ52ueWLVswefJkzJ07F5cuXUKDBg3QoUMHk17C9M6cOYMBAwZg5MiRuHz5Mrp3747u3bvj2rVrAICMjAxcunQJs2fPxqVLl7Bjxw5ER0eja9euJtuaP38+YmJihGn8+PF5/9DygFLICCnBUlNT0bRZc9x9FIPqw7+FnZtuAEHW4Om4Pl3MXBqZTGI+dcwwpcxOSB/LSSFzVkhx8+R+NA/rCrvsFBCFlBWlj8kkjCh1TH+DkXNDwohvUqDrmnH8yCE4c+Iojh8/Lhr5l+TfF198gVmzZqHWoBko9/a7AHLaJRimgxmmkXEG862lkenX0Wp0qWQpdy/CoWJDaFQacBoV1FlpQvoYp1FBq1EJ6WOcRmU1dcwSLukBtA+OYdq0qVi0aFFRfGSlzvXr1xHS9B14Vq2NvvN+gkQmF6WKAchO7zKaGAYPrl1CucrV4OzqZjWFTMIwQqopyzBQSPXfHRLdfFa3npQ1nz5mKXUsJ7UM2L17N2rXqgVXF2e0bhsKqVSKM2fOwMPD45V/pqRk0qeQPVgyES72BRuvJSVTCf8py2xOIQsODkbjxo2xYsUKALpeyPz8/DB+/HjMmDHDpHy/fv2Qnp6OPXv2CPPefvttBAYGYuXKlWb3ceHCBTRp0gQPHz5ExYoVAehqYCZOnIiJEyfm4yzzh2pgCCmheJ7HkKFDEX3rDir3mw+5ixf47Hu/3Gpa9Cw15LdWEyPNvpkpW7EKgJwbE/1rwHJNjGHwIpqX/Vomk+G7VetQo1Zt9OjRw+JTJWK7v/76C7NmzULVd0fCL6Sz2TLm0sj0ga6t9L97mUvOiM3malQM08dE820MXoDsmphyjfH1118jPDzc5mMk5iUlJaFz165wKOODbjOXARJd+yL994Dx94E+jUyb3RlIxdqBeHb/rqjnMcOeyHJjeJXpvzIMgxJrwQsMyl25cgWuLi6oVrUKvLy8sGvHNsTHv8DAgQOh1Vqv3SMkr6SOikKZAF1QZDgplUqT/alUKkRGRiI0NFSYx7IsQkNDERERYfYYIyIiROUBoEOHDhbLA0BycjIYhoGbm5to/ldffYUyZcrgrbfewjfffAONRmPrR5UvFMAQUkItXboUO//8ExW6TIG9t3/O03Kje0DDp+nmKmTz0/5FwjJIe5kgClBYg6DFsNtkw9Qx4xsOczck9g4O+Onn35ClVGLgwIGUJlQA9+7dw+D/DYFPgxaoEjYMAEyCTcMghc1nz2OGlPEPLC7jjNLHzLWBsRXrVRusRzWMHPketXUoAJ7nMWz4cMQ9j0eXmUshVejatZkLXjQWvitYlrV63Zj7epGYKW+8DUubNE4dYxkgMTER169dQ8sWzbNPjEPVqlXxy4b1+Oeff7BgwQKLx0dIfkgcHCFxdCrY5KAbLNjPzw+urq7CtHDhQpP9xcfHQ6vVCj2O6ZUtWxaxsbFmjzE2NjZP5bOysjB9+nQMGDBAVCP00UcfYfPmzTh69CjGjBmDL7/8EtOmTcvT55VXeetgmhBSLPz333+YMXMmyr7dC241Q8DxPFiGAc/xwg0px/FgWdObAo7jdfnjRk/XtRwvykU3fG/upkUm11Wd67tNNuxGGYBJ6piePnXMUkNchgF8fMth+aoNGNizM5YvX/5Kq61LCq1Wi6HDhoFROKHukE8hlbDC71HCMNDyPFiWEWrrGJYplHGC7Lwqi7ZjKUgxfZ+3QJVhGLAVQsDf34fBgwfj3Llz1DNZPqxfvx67du5E10++g5uPHwBAy3GQsKzJd4DESo1c6sv4PO9bn2Kme509L/v7Qf9AxLj2hTEorz8anufxx5YtGD16dPYMTvjZpnUrfDpzOhYsWICOHTsW6cB7pHRh7R3A2uetDYvJNnjdI7zHjx+LAgbDLoxfFbVajb59+4Lnefz000+iZZMnTxZe169fH3K5HGPGjMHChQuL7FipBoaQEkar1WLo0GFQeJRH2Rb/y2m/IBrrJfftmLtZtaU2RsIyYDgOCoW92aeogOnTVXOpY0DODYnJ+A9g8E6Llhgx5kPMnDkTt2/fzv2EiMiKFStw+tQp1Bn8CWTZA1RaugG1JVVMl1KWnV5m1BOZoazn94TXlhrqm9bA5K+WjWGl0Pg2xZWrV6ktTD48efIEEydNQr12PVC1SWuLf/u21NB6+1URBrTMK0s1LZYuS+Oa3O3bt6NHjx6669sgeAEAhucw4+PJaPhWIIYOHWo2NYeQ/GDsHMEoCjjZ6WpgXFxcRJO5oMDT0xMSiQRxcXGi+XFxcfDx8TF7jD4+PjaV1wcvDx8+xMGDB3NtjxMcHAyNRlOkg1BTAENICbNq1SpcvnwJZduPE7qsNQxiDAMTS42yDZfnxtzNS2LcM8jsdLm7usa2ht0o68rYkjomSgUxCF7076fPmgtPL29MmDDBbPobMS82NhazZs2Gf4seKFPjLZOxOwDTVDI9fRqZcecPlhivr/DWtY2yNDil4bzCwDp4gvWqi88//7xI/zMtiaZ8/DEYqR2aDpkizMtJHeNE7/Wv9Wlkxu1gGIkEd69dtbgvS981Qk0Lk/PdoP/e0C83+30BXfkbN27Ay9MTPmVz2l4ZBi8AIJVKser773Dnzh0sXbrUps+GkNywjs5gHV0KODnbvD+5XI5GjRrh8OHDwjyO43D48GGEhISYXSckJERUHgAOHjwoKq8PXm7fvo1Dhw6hTJkyuR7LlStXwLIsvL29cy2bXxTAEFKCJCUlYeYnn6JMg/ZwLBdglKojfm2pFsY4ENDyprUulp646m9+ZTI5yvj4WHlCaia/3UzqmK6sOHhhDN47Ojpgzudf4u+//8b+/fvN74yY+OSTT6BhJAjoPlo031IQA8BijUpe8DwPZfxD8TzDASm15sZ9KXgbJ8a7PrSMDFM+/rjA2yotTp06hT+2bMHbgyfAztHZbHfJWoPfTW61MJ7lK8LeydmkIb8tLF125mYbpo6pVCpEnDmDVi1b6GbousnTrZvTownAcahTuxY+GPUePv/8c4v5/4TkBWNnD0bhULDJLm8paJMnT8aaNWuwceNGREVF4YMPPkB6ejqGDx8OABgyZAhmzpwplJ8wYQL279+PJUuW4ObNm5g3bx4uXryIcePGAdAFL71798bFixexadMmaLVaxMbGIjY2FiqVCoCuI4Bly5bh6tWruHfvHjZt2oRJkyZh8ODBcHd3L6RP0xQFMISUIN9++y3SMzLg9c5gs93gAuJUMiCnFsZkng1Bi7l5EpbBvX8vQmGvq/oWukrObv+ivyk2rH2xljpmHLwAEI0t07FzVzR5OwSzZ8+mWhgb3L59Gxt/+QXVOg6F3NFVVPsCmAYxgGktSr5xGth5Vc55a6WWJb+N981hJDLwXg2wY/t2XLlypVC2WZLxPI9PPv0UXv41Ub35u8J8wxRSW8Z/McQwDJ7cNu1Mwfj7yJC5VFMguyMQg7RS41RTXRlg29at6N+/f/ZJWQhe9HgOn06fAomEpXRDUihYh4LWvriAdci962RD/fr1w+LFizFnzhwEBgbiypUr2L9/v9BQ/9GjR4iJiRHKN23aFOHh4Vi9ejUaNGiAbdu2YefOnahbty4A4OnTp/jrr7/w5MkTBAYGwtfXV5jOnDkDQNceZ/PmzWjZsiXq1KmDL774ApMmTcLq1asL6ZM0j8aBIaSESE5ORvkKfrCvFQrf1iOF+YY3n4ZP0XPaKYjTgiRSVjQejETK6gIPozFg9D/148DYyyWwl0vhrJAi9dlDVK9ZA/YyCRRmxoGRSViTAMZc6ljOPHHwAoifvp4+eQJ9u72L3bt3o3Nn810BE53hw4dj61/70Hr+Fkjk4lGejW9OhZ+8QU91BmPCcFrOZCBUS2PBcFoOqvRUZL54BDuvGlBnZUKjyoQmMx1ale41p1ZBq8o0GgOmcHqZ43kO7J1d6NyhDbZv314o2yypTpw4gZYtW6LT9GWo3LiVMN840M0JdllR4Ksfq0X0ncEwiL1/G37VaojGf5GwDGTZA97KJKwwDoydVPfd4SCXQMoCcpaBVKIbJ0b3fWQwDgzEY76wDBAVFYUXz5/reh0zaPdiErwYVUV//tXX+GbZ93j48KFJ70yE2EI/Dkzivyfh4uxUsG2lpsGjfnObx4EpTagGhpASYsOGDcjMzESZoO66di18zo2modx6ksqtHYylsR8MPbtzXXit74FM3/7FJKcdjNkUEVuDF5Zh0LxFSzQKaozvvvvO6rmVdnFxcdgUHo7KbftBrrDPtfbFuBbGOI3M/CCoOQ35jfFaNSQOrhbbO/Am3SgXXhfZDMNC414LO3fupLYwuVi2bBm8K1WDf1BL0XxrqaTWvg/07WCy0tOQnpJsujyX56jGNbT6djCA+doXnudx+tSpnC6TAevBi752hucwdsx7kEgkWLVqldVjIiQ3vMyuUKa8+uGHH+Dv7w+FQoHg4GCcP3/eavmtW7ciICAACoUC9erVw759+8TnwfOYM2cOfH19YW9vj9DQUJOOcxITEzFo0CC4uLjAzc0NI0eORFpaWp6PPS8ogCGkBOB5HitW/AD3gGaQOXmI5gPiUdX1cp6Qm6aRmWsHYwv9DbFfjTq69xYad5uba/IklclpsA+YBi8sI75JHjH6Axw6dAg3b9606VhLo7Vr14JhJfBv1kU0P7cUMku/x9wYt5nhOS0YRiJ6r/9ZVI34DbHuVSGRKUy6ACU5Hj9+jF27dqFmu75COxXz7V9yfmptDDQr1qqPZ/fvWE0bM8Qa3KEwDKN7mAHDeeLy+tqXffv24d13s1PfDFLHAJgPXgx4uLpiYN9e+Omnn4p8ID5SsvEyBXiZfQEnRe47MrBlyxZMnjwZc+fOxaVLl9CgQQN06NDB4qDPZ86cwYABAzBy5EhcvnwZ3bt3R/fu3XHt2jWhzNdff43ly5dj5cqVOHfuHBwdHdGhQwdkZWUJZQYNGoTr16/j4MGD2LNnD06cOJHTbXkRoQCGkBIgIiICd+/egXPd9ibLzAUxlmphjHsoM5SXnPeY+7eF4ELfA5m+/Yv+xthc6pg5hr2U6W5QGFEtjn7q3K0bXN3c8Ouvv1o9ttKK53n8vPEXlGvYGnJH01QEcz2RGb8uKG1GMhihZzxxbYv+dWG2fTHGSGTgXPzxy6+/0sjrFoSHh0Mqk6N6806i+ebav5irkdFP5ga1ZCUSMNlRia0PRfRBicXlBqljAJCRkYGU5GSU8xV3A8vontTo3ph0pcwLEwCM/N8gxMbGmvTOREhe8FJFdhBTgEmatwDm22+/xahRozB8+HDUrl0bK1euhIODA9avX2+2/HfffYewsDBMnToVtWrVwoIFC9CwYUOsWLFCdw48j2XLlmHWrFno1q0b6tevj19++QXPnj3Dzp07AejSNffv34+1a9ciODgYzZo1w/fff4/Nmzfj2bNnBfoMraEAhpASYNOmTbB39YJD+dpCgGIYqJhLJxO3U8jZlmEDflsa8utJsm9MJCwD7wqVrB6vtRG0jVPHjIMX/frG27Czs0PX7j2wadMmasxvRmRkJO7cvoXywR0A5AQs1gIU41oYwzQyfbupvGDl9mDlOb3qWAtUCjN9TMStMmJjYnDixImi2X4x98tvv6FSUCvIszvhMGapEb+lWhjjGpzUxJwBLa09B5GYubTEnXgwJjcwLAP8tWsXunfvrpuRXfvC8OLG+oY/GTPfFW81qI+AmjWwadMmywdISC4KswYmJSVFNJkbr0ilUiEyMhKhoaHCPJZlERoaioiICLPHGBERISoPAB06dBDK379/H7GxsaIyrq6uCA4OFspERETAzc0NQUFBQpnQ0FCwLItz587l89PLHQUwhBRzPM/jz11/wb5KMBjG9E/aXBBjrgbGXG9khutbo7/RlWb/zEh+KTTS1fdApm//or8vsdRw3xzj4AXI6YnMcHq3Szc8fPgQ169ft7it0mrPnj2QOzjDp3aQSdBidhwYG2thDNvBWFqul/XiHsDITIITntOC15rWyBQFxsELEjtH7Nmzp0j3Uxw9fPgQN65dg39wG9OxXCzUwNraFbK+nLdfZdH3jLn1c2pvTdu/mWtbpZeSkgKpTAYHe4XpaL0cZzl4MWgDA54DAx7d3w3Dvn37qKaO5BsntSuUCQD8/Pzg6uoqTAsXLjTZX3x8PLRarUnnE2XLlrXYNXhsbKzV8vqfuZUxHu9FKpXCw8OjSLskpwCGkGLuxo0biHn6BE6VGwHI7h3KYAJMgxhrzJXl8nDDokpPhZObu9UylseHMa19sRS8mNO0WTM4ODiYNEIkwJ69e1G2TjBYidRiGXNBTF5SyAwb8ptjV6aSEKhaSiEznlfYGIYB51gOu3dTAGNs3759kEikKFc32Ozy3BrxaznOJI1MVJ7nkZ6ajPhnj/N0XObauhh+FwhtX/buRdcuBu279LUvnGkNjCh4MSMstA0SEhJw4cKFPB0rIXoqLV8oE6Brm5acnCxMhmO5lFYUwBBSzJ08eRIMK4FjhTpme3cyF8QYdolrKY3M8CdgW866hGWgUavh4OSi6yJV6A4554ZYdONh1PbFXPBiuEy/HpB9E2M02SsUaBwcjJMnT+Z6rKVJeno6rly+DK9ajUTzzQUo5mpghLJGaWTWmFue9fyu6H2RpYnlgnX2xe3bt/DixYvXsv831cmTJ1G2Wm3IHSyP/m2+Eb/1HsgMVahRB1pt7t8lxp10CN8JBpeVYa2tPqXGTi4zH5SIxoExCl6MamDAc2j8VgM4OjrSdwnJN5WGg7KAk0qju0ZdXFxEk52dae9knp6ekEgkiIuLE82Pi4uDj4+PSXkA8PHxsVpe/zO3MsadBGg0GiQmJlrcb2GgAIaQYu7s2bNwLVcVkFjubtFcL2TWyhmytI7xjYn+hvf5o3twcMq5AZIYpIPomXaLar4rZSA77cxM8GJJ4ybBOHv2LLWDMXDx4kVotVp4VqsPwDRwsdSdsrllhvLaDkbhXRWAaQ0LZ9Bwv6hTyACAcdClO5w9e7bI91WcREScRZmq9YT3muxaFI1RbYotXakbMiwnk9vh/vXLpgPqWtiULRWALAMcOHAAYWFhOTMNa18MAhqzwYsZUqkUQW81sNh2gJDcFGYNjC3kcjkaNWok6nyC4zgcPnwYISEhZtcJCQkx6azi4MGDQvnKlSvDx8dHVCYlJQXnzp0TyoSEhCApKQmRkZFCmSNHjoDjOAQHm6/NLQyWcwkIIW+spKQkfPfdd3j27BnOn78Axt1ftNww6NA/Cec4XhjUUlcLk/MEg2MBCXKWMWDAczwYo5a0Wo6HhGWEn+a4eHjCwckZbHbbCEO59ShkKXUMMK2RsZQLX79BIOLj4zF58mRoNBqMHj0a9erVM1u2pNu6dWt2WpAEUrkcLuUq59po3/Bm0/i9JQzLWG+RnU354h7sHX0B6IIWwFIKWRHXzMidILNzwLZt27Bjxw60bdsWgwYNynOnBCXBrVu38OOPP4JlWTx4cB+t3h0BACbpX4bzpEbfAeJrhIPhs1GpQRl9+Yo1daN8a3ne7N+xcbfdpo31cxrw60umJCfD3c3VYkCiC2isBC+Gr7NzIAPr1cHOff/gww8/RJkyZTBhwgR4enqa3z4hRlQ8D5WNQb61beTF5MmTMXToUAQFBaFJkyZYtmwZ0tPTMXz4cADAkCFDUL58eaENzYQJE9CyZUssWbIE7777LjZv3oyLFy9i9erVAHQPFydOnIjPP/8c1atXR+XKlTF79myUK1dO6CyjVq1aCAsLw6hRo7By5Uqo1WqMGzcO/fv3R7ly5Qp0/tZQAENIMTR69Gjs3bsXarUaWq0WZZrpnnKYqy3Rz2NYRhfEwHx6D8/xwnye4wGWya7FYIT3xszVwjy6+R+qVa8uzDMcyNKQuXFfzDFOJ7PWiBcAatSsCUA3GJ+Xlxe2b9+OqKgouLq6Wl2vpPn777/Rt29fuLm5ITU1FS5l/SCV5v6Vbxig6n+/oqAn+/6Ug/h6Y1lGN8/giSHLMNAi5728TGXRvvjX1ECaYRgwds4IDw+Hk5MTfv75Z8jlcvTt2/e1HM/rkpGRgbCwMKSmpiI+Xtc7mFv5ymaDF0MajjcbmFgqaxjoSCQMXjx9iPJVqgllOJ7PSTfVpzAy4po//RgwhrvSv75//z4qV6mSs8C49sVKL2QW080ABFSvjocP12DVqlVwdHTEhQsX8Pfff5fKQJfknVLDQ6YpWACjzOP6/fr1w4sXLzBnzhzExsYiMDAQ+/fvFxrhP3r0CKzBAEtNmzZFeHg4Zs2ahU8++QTVq1fHzp07UbduXaHMtGnTkJ6ejtGjRyMpKQnNmjXD/v37oVDkdPG8adMmjBs3Dm3btgXLsujVqxeWL19eoHPPDaWQEVLM/Pfff9i6dStWrlyJVatWgeM4cMr0XNPDDJfz2T2OGfdIZunhJZCTXmbuibxhmpF/QF2T5cYs3e8Y174YdqMMwCQn3nDSc3fXdSDQvHlzXLp0CUlJSaVy4MLZs2ejdevWePz4MWQyGbIyTEdFttSVcl56IDOZZ+XmTp2sy6M2N2jlq0whAwBVVgYA4N69e+jatStmz579Svb7JtmwYQMeP36MM2fOCAM/MjL7XNbKYdqI33x7GFE5noebpw/UZrqBNcc43dSciIgINLWQIiNsh+dtC14MZGYP1Ldw4UL88ssvOHDgAKWUEZuptFyhTHk1btw4PHz4EEqlEufOnROlcR07dgw///yzqHyfPn0QHR0NpVKJa9euoVMn8RhQDMNg/vz5iI2NRVZWFg4dOoQaNWqIynh4eCA8PBypqalITk7G+vXr4eTklOdjzwuqgSGkmElPTwcAvPXWW1Cr1QAAR79Am9c3TCXT4zleSCPjsp+SctlPTrlcnrAakrAMHkVfQ5XqNbIHrtTN1z/wYRjTGxLDnscsMUwns1ZTw/NAmTKecHZ2RrNmzVChQgV4eXkJn1lpkp6ejpYtW8LJyQkVKlSAyrOqaLmloCWvKWQsy5g0ymYtrCexzxlA01ovZK8C4+wLNzYJ7u7ueOutt0T526VFeno6nJ2dUb16dbRo0QJ/HzgAF9+KwnJLDyuAnFoYc7V0ujFhWKN5Od8jcgcHZKalwE5hfpA+k9RTM7W3+nmGvZCJxn0xqH0RjfWSW/oYIKSQtXhHd+MXHBwMf39/ACiV3yUkf9KUHHh5wb7T0pWvp6OT4oBqYAgpZvTVtllZWUhISAAASJ3KCMvNdKhjsEx8Q2Lc0N24C2WTgSx505Qx4xvhijXrimpK9EGMcTlzY7+Yq30xLJFb5ob+xqZ8+QrIyNA9Yc/KyhJVdZcWCoUCWdlPkLOysuDsXQFA7l0jWxoHRjSZ3FDaFuDynMZoMNXXN8YGY+eCTLpGhGskLS0NTm6eQnqJpcDVcH5uqWaWamTcy5bD47u3AFhpvM+a/u0bjiMF6JYnJyfDxcUFuTL8MjTufcxcWQDlstNuEhIShM+pNF4nJH8yNFpkqAs4aYruOzIxMRGDBg2Ci4sL3NzcMHLkSKSlmdbUG8rKysLYsWNRpkwZODk5oVevXqIeyq5evYoBAwbAz88P9vb2qFWrFr777jvRNo4dO6ZL4zWa8jpmDNXAEFLMGAYwmZmZAAA2e7ArK+1XhbE5eI4XmtmaaxfDi9vgCutY6zbX8KY49sEdVKtR0+o5GN/v6hvvW2LYExlMDw/Gp/3w4QNERUUBoJtTAMjMzISHXJGnMV30LNXCCG1eDNpYGd6NMqz4euQ0anCvaLBKm2g1SE9PA8dxpfYasbe3R1ZWFnieR2ZmJqR2us8gt1o3c+1eTNvDWV5PKpPDxcPL7LYlxt8NuVyyERERaNq0qemCXIKT3NLHAF237ADw5MkTVK2qq8EsjdcJyZ9MtRaMumDfdZkFXN+aQYMGISYmBgcPHoRarcbw4cMxevRohIeHW1xn0qRJ2Lt3L7Zu3QpXV1eMGzcOPXv2xOnTpwEAkZGR8Pb2xm+//QY/Pz+cOXMGo0ePhkQiwbhx40Tbio6OFj18MB4MMzcUwBBSzOj7f8/KyoJKpdLNZCS5/n9sGMSYX86L7h6MG/LbQsIy8Crvl91oXzyiNoDseeJ1TIIZxnJPZID5amMW4iAmMzMT//zzDwDd52Suz/ySzs7OTghgVCoVpDLbv+7NNeS3haXUMQDgtBpInQ1rCnOCGV5r2Abm1aRM8KlPdceVHcCU1muE53mo1WqoVCqwEmmeukaWsIxQC2Ma0HCQsBIA5hvyP7sbjUrVcx50GK4uYcQ9kRl/Z7AG85KSkuDh7maaPpZNaPtiLWXMHJ6DLPtv5o8//hDaEZTG64TkT7pKC05VwACmgOtbEhUVhf379+PChQsICgoCAHz//ffo1KkTFi9ebLb3sOTkZKxbtw7h4eFo06YNAF07ulq1auHs2bN4++23MWLECNE6VapUQUREBHbs2GESwHh7e8PNzS3f50ApZIQUM/rUKHt7e+E/U16rMSlnPM4CYPREXD+YpUE5/Tp5GQ9GT5od7GQkJ4nmC21XjMobj/1inC4mLptdxsr+hW5VGcDZxUVolGxvby98ZqVJRkYG7O11DbLt7Oyg1ahFy03SwnJpyG+pbG4DWuqpkuMArcZqgPJK28C4ZKfUSSRwcHAotdeIRCKBVCo1e40YNszPSyBruL651wBQoXotk++o3B5uFLjzLzOBC5Md9OgnQ2q17nu1f//+cHBwAIBSeZ2Q/CnMFLKUlBTRpLSxEwxLIiIi4ObmJgQvABAaGgqWZXHu3Dmz60RGRkKtViM0NFSYFxAQgIoVK1rt3CI5ORkeHh4m8wMDA+Hr64t27doJNTh5QTUwhBQz+u5Ovby8hBQyTqOriTEdHE733rgWwzglzDCNzHCZLQ34dTe0uvBBlZYKZzfTLypjxoGIeHRtC+PAmKzDWBysspxvOVTP7srZy8tL+MxKk/j4eHh56dJ0FAoFtCrdf3h5GQfG0jxA94Scy+4i2bAhP8Myoq6UhfJ2jmBZe2g58w34XzlWAicnZzAMA09Pz1J7jXh66tq9KBQKaJS6Grvc2r+IalPMjgVjvoywHZ7Hk1s3RF0pW2I8iK2oJ0Lw5ttfGTbet1L7YhywGMvI/n4tV66cMP5LabxOSP6kZqmhlqhzL2hFVpZufT8/P9H8uXPnYt68efnebmxsrEnKllQqhYeHh8W2KLGxsZDL5Sa1JmXLlrW4zpkzZ7Blyxbs3btXmOfr64uVK1ciKCgISqUSa9euRatWrXDu3Dk0bNjQ5nOgAIaQYubFixcAAE9PT+FpoDYrBZyLN7Ke3wUjkUEZ/wAKr8pQvXwCxypv69q8MDltXKylkunpB7QExIGMtaexWq0G9k7OYLNTQIwbe5tj2PuYuTFeTJ/CMqLX5oKYhIQE4YbD09NT+MxKE/3NKaD7DNJTk2xqA5OXtDFrKWPGMmOioagQlHvBV0WTBTd3XbDt5eWFhIQEaLVaSCSS13xgr86LFy9E10hmyktotFyunTIYByWG48KIy+S8Nh78skKNOma3zVoYM8qcxMRElDF4smucPiZiFKxYCl4YngOf/QUZn5gIQPfZ6D+n0vhdQvInPUsLjcQ0OyIvlFm6hzyPHz8WtRexlMo4Y8YMLFq0yOo29e1Di9q1a9fQrVs3zJ07F+3btxfm16xZEzVr5qSPNm3aFHfv3sXSpUvx66+/2rx9CmAIKWbi4+MhkUjg5uYmtIFRp7yAOv0lFF5VIHUqA7syuq5QpU6eyHx6HTIXb8hcvEU3BoaN+XX/MEI7GMOG/DzHg5EYBDLGrWyzSVgG8c8ew69iRZNlOWlk5m9MTHobg/naF1t6u1Kr1UhIiBcG7iqNNTBKpRKpqalCDYyvry+uv0jI83YsDWZpGLQItXYMAz67Rkb3exIHNnIPPzASKXi1riZIn0rGZY8B86prY3h1Jnz9fQDoblB5nsfLly9L1UjrhrV0vr6+0KhVUGWkws7Rhl69slkbyNKwHYyxp3ejUL5KVbPLAH0gY33f9+7dQxX9AJY2tmsBcq950YuNew4A8PHxgUwmg6ura6n7LiH5l6rUQFXQAEapW9/FxcWm3vamTJmCYcOGWS1TpUoV+Pj44Pnz56L5Go0GiYmJ8PHxMbuej48PVCoVkpKSRLUwcXFxJuvcuHEDbdu2xejRozFr1qxcj7tJkyY4depUruUMUQBDSDHz4sULlClTBizLwtvbGwp7B2TGRKHM24PAyuxEbVUYqQIOFeoi6/ldsHaOYBWWB5YSxn3heUigr63hrXYDZNzlrot7GTi6iEe819fC6GMP45QQQ8YDV9qS825cC/Pg/n3wPC/c2Hh6euLGjRu5b6gE0d9k6W/GK1eujIs3DorKWApI9MusjQeT18b9PM8j6/k9ODqXt/0kipicz0TNGro0Q8On66UpgDE838qVKwMA0uKewK5KbQCmYwIZstYTmbnxX/QN+fXvy1erBY1aBZldTq9e5mpszVUWM9ndKT958gSBDRqYBi/G6WM2BizG7t67D5ZlUalSJQCltzaX5E9KphpypmApZKrMvK3v5eUlPJSwJiQkBElJSYiMjESjRo0AAEeOHAHHcaKBLw01atQIMpkMhw8fRq9evQDoehJ79OgRQgwGkr1+/TratGmDoUOH4osvvrDpuK9cuQJfX1+byupRI35Cipn4+HiUKaPrzYllWQTUrAF10lOT4EWP53jYefpD+eKe0CbGlv/PjRvyW2rEb3izcv/6ZUhlcltPBYBhQGM+WslL7QsA3IqOBgDUqlULQOm86dAHMPrrpFatWnj57CG47M4ezDXYz2sXy+Ya8ltr0K/wripcQ9xrbgPD8zy4zCTRNQKUvvQgw++SgIAAAEByzAMApkFtbg35DceEsSW4TU9JhjK7jYkhS6mnulpZ8TyVSgW5ce961vqSh+21LwBw8/ZdVKlSRUjXKY3fJST/0pVqpGUVbEpXFiwAsqRWrVoICwvDqFGjcP78eZw+fRrjxo1D//79hR7Inj59ioCAAJw/fx4A4OrqipEjR2Ly5Mk4evQoIiMjMXz4cISEhODtt98GoEsba926Ndq3b4/JkycjNjYWsbGxor+bZcuWYdeuXbhz5w6uXbuGiRMn4siRIxg7dmyezoECGEKKGX9/f9y9e1e4SQ0MDASrSs6llzAWUqcyyIq9ZbJEPLCg+QEsLTWWNyRhGfjVqKvbG8uYjKb9qly+fAmenp5ClfbZs2eFp8ulRbly5SCVSoXeZOrXrw+NWoWkJ/esrmfc85jxPJPyeegWSp2c+yBlryygUaVBrcxAgwYNAOiuEQDCk/bSwt/fH+fOnQPP83BycoK/f2Uk3rthNQDJSzfL1tZxdi8DjVqXAssypt8XtgTUJg81CrkL7sgr/6JevXoAgNTUVFy7dq3UfZeQ/EvO0CApQ12gKTmjYClo1mzatAkBAQFo27YtOnXqhGbNmmH16tXCcrVajejoaFHPe0uXLkXnzp3Rq1cvtGjRAj4+PtixY4ewfNu2bXjx4gV+++03+Pr6ClPjxo2FMiqVClOmTEG9evXQsmVLXL16FYcOHULbtm3zdPwUwBBSzAwePBg8z+PXX38Fx3F4+vQpUmPuQqu0PoKuzMUXkEhFPZWZC1iMgxdzXSoD5m92H9+6JppvKYjR33joG/Cba/+S3y5T/9y+DVqtFgkJCYiOjsbJkycxcuTI/G2smPLy8kLXrl2xZs0aXfpW9ngwD84ezNdglnq21NRY+p2zdrr8baEHMu3r64GMS3oAIKdL3LVr16Jdu3alLoAZOXIkrly5gsjISCQnJyMrKxNPLh6xeX19YJJbr2XmyrMSiVAjqGd87ejbzBl29GHpe8GkZqUAqWOA7uYt4kIkYmNjodFosGXLFmRkZOTavoAQPaVKA6WygJOq6AIYDw8PhIeHIzU1FcnJyVi/fj2cnHLSzP39/cHzPFq1aiXMUygU+OGHH5CYmIj09HTs2LFD1P5l3rx54HneZHrw4IFQZtq0abhz5w4yMzORkJCAo0ePonXr1nk+fgpgCClmvLy80KtXL3z66ado2LAhDh06BADIio0WyvD6MV6Mbyw4LVRJMdllxIvM1bJYqnnR38RKjW5o/QPq5vl8jMeCKYjMzEw8e/oUKpUKQUFBaN26NcqUKYMePXoUbMPF0JgxY3Dt2jU0atQIXbp0gbOzC5Kf3s11PVt7KtPT33TmVuPGc0X3H3FecVkv4ejoiAEDBqBx48bCaNGlTVhYGCpWrIhOnTqhYcOGSElJQUZyAlTpKVbXy0v7J62FWhFWIkFGSrLp/EKouWUMv7fyGcScv3QFHMfh/PnzaNiwISZOnIiOHTuioplOSggxR63UFspUVBITEzFo0CC4uLjAzc0NI0eORFqa9QehWVlZGDt2LMqUKQMnJyf06tULcXFxojIMw5hMmzdvFpU5duwYGjZsCDs7O1SrVg0///xzno+fAhhCiqEff/wRc+bMga+vL7Zv347yfhWR8fCS2bKGQYydVzVosyzfnIjTyfJ2TBKWQcz923lbyUB+a1wMg6xTJ09AqVRi27ZtaNCgAfr27Yvjx49DoVBY2ULJ1L59e+zYsQPVqlXDhx9+iIkTJ+D5zUvgNLnnVJtLH7M20KU1QjCtUQnXl2Gq2KvugYzneUgyYjFmzBhMnToVFStWxO+//y40Si1NJBIJDh48iCFDhqBWrVrYtWsXeI5D7HXzA9kZslbrYm6Z8Tw7ewfYO9ve25nNClDrYmj/oWPw8vLCX3/9hfLly2PGjBn5uskipZcqSw1lZsEmVVbRtIEBgEGDBuH69es4ePAg9uzZgxMnTuT6IGfSpEnYvXs3tm7diuPHj+PZs2fo2bOnSbkNGzYgJiZGmLp37y4su3//Pt599120bt0aV65cwcSJE/Hee+/hwIEDeTp+hrcluZ0Q8kb76KOPsPrncFQYtBIMKzHbHkbfwDorNgr25Wrr0rRY3XyWZcCwuiclEikLhmUgkbCQSBmwEhYSKQuJhAUrZSGTsLCXS2Avl8BZIYWTQgpXBzmcFVK8fBiNGrXrwV4mgUP2pJCwcJBLcsaGYXN+stD9ZKC7ITYcwFJo3K8/fgsRjuFX2LixH+L0yRO4e/euzY3+S4vLly+jYcOGaDf9e1Ro0DTX8obpPsav9ZNKw0HL8+A4HpyGg1bLged4aLNfcxoeGrUWGrUWac/ugXWuAK0qC+qsNGiVWdCqMrNfZ4LTqKBVZQrdKxcVLi0O2jv7cOzYMbRs2bJI91Uc1W8QiGSFF5p++KUwz1zvYobvDVML9bWyEpaBXMpm/5RAwjKwk7LCvKzkBCQ8voc6QU1hJ2XhIJMIPx1kLBRSCWQsA7mEgYxlIGUBmYTRbZ9hIGGAbVv/QL8+vQFOkzMGDKcx3wOZjY34eYYFz/Oo904omjZrTkELybOUlBS4urqizsStkNg5FGhbWmUGri/rg+TkZJu6UbZVVFQUateujQsXLiAoSDc+1/79+9GpUyc8efJEaMhvKDk5GV5eXggPD0fv3r0BADdv3kStWrUQEREhNORnGAZ//vmnKGgxNH36dOzduxfXrl0T5vXv3x9JSUnYv3+/zedANTCElAADBw6EMjUBmc9y7y7YzrMyMh5ftbjcemcApgxvalISdWONGDbuZg2+ZSw9tLfUA1leZGRlYfeunRg0aBAFL2YEBgaiRs0APDhj+h+E8SCExizVthjPtxhkclqos1MXjeebe12UmOT78C1XDs2bN38l+ytu/jd4EGKunIQqwzSVJC+pY4blzTXoVzg4oYxvBQDmO4Ow5S/Y5PlrIdW+RF75F7fv3sOgQYMKZXukdFIpNYUyAbqgyHBSKpUFOraIiAi4ubkJwQsAhIaGgmVZofMXY5GRkVCr1QgNDRXmBQQEoGLFioiIiBCVHTt2LDw9PdGkSROsX79e9LcaEREh2gYAdOjQwWQbuaEAhpASIDg4GNWr10BG9KFcyzJSBSQO7jZvW58CxBm1qTHXoFtmZycEI+bGuzS+/8lLnJFbG51df+5AcnIy/ve//9m+0VKEYRgMHzYUD84fQlZqksly4yCmII39jfE8B5mnroH86+o6GQB4rRpMygMMHTIELEv//ZkzcOBAcFoNHp75O9eyufU0Zo0yMx3PHz8QPdSwMEauRUX1oGLdr5tRrlw5tGnTpki2T0oHdZYaqsyCTersFDI/Pz+4uroK08KFCwt0bLGxsfD29hbNk0ql8PDwQGys+R4jY2NjIZfLRYNYAkDZsmVF68yfPx9//PEHDh48iF69euHDDz/E999/L9qOfqBpw22kpKQg00zX6pbQNzghJQDDMBg/fhySb5+BJi33kaKV8fcB6DMseLM9jVmab41cbpen8nmlD1j0PZsYzl/104/o0KEDatSoUaTHUJyNHDkSDIBbR/8UzddwvGgcD0O5BTK2dKWszUiBJi3B5uMsKtzLO9BqVPjggw9e96G8scqXL48ePXrg/tGtQjpfbteArcGL4TUmVzigbKUq+T/QfOIZy7c9PMMiIfElwrfvxIcffgiJRPIKj4yUNOqsTKizMgo46W7oHz9+jOTkZGGaOXOm2X3OmDHDbCN6w+nmzZtFet6zZ8/GO++8g7feegvTp0/HtGnT8M033xT6fiiAIaSEGDp0KJycnPDyyl9WBxQEAIWX6Y1DXlPHzEl9mVjgbejp4xPjpBBzNTFHjx3FlcuXMWHChELbf0nk5eWF/w0ejOh/NkOjtP1Jly2sXnMSOeRu5UWzXnVNDM9zkCbeRK+ePaknqVxMnDgRiU8f4Onl48K8/Ax2ClgObjJSk5Hw7LHJ/LzUwtjUhNdKwCLaVna5FWs2gGGYUtkrHSlc6oxkqNMLOGXoeupzcXERTfrBVY1NmTIFUVFRVqcqVarAx8cHz58/F62r0WiQmJgo6hbZkI+PD1QqFZKSkkTz4+LiLK4D6DJEnjx5IqS9+fj4mPRcFhcXBxcXF9jb21v9TA1RAENICeHi4oKPp0xG6o1/oElLsHpDqUx8Ao1Gle99WbqR8arw6m8MeZ7Hws8/R5MmTRAWFvbK91/cfPLJJ8hMfombh7YV+b647Ab+6rR4aDOtd81b1PjE21BnJmPOnDmv9TiKg3feeQet27TB9Z1rCq1TBZNeyBQO8K6Y8yClyMe9NQhkjGth9O8TEl/i+9UbMHbsWHh5eRXxAZGSTp2VDnVWWgGn9Dzt08vLCwEBAVYnuVyOkJAQJCUlITIyUlj3yJEj4DgOwcHBZrfdqFEjyGQyHD58WJgXHR2NR48eISQkxOIxXblyBe7u7kLQFRISItoGABw8eNDqNsyhAIaQEmTChAlwcXZC4vlwALqn4vrJkF0ZP0gkMovbyU9tjIRl8Cj6utlluaWYcEZPUk3eW1n3z51/4sL5c/j888+p8b4NqlSpghEjRuC/XeuRlfLSYjlLv7P8PImX2rtC6lTGahmGLbp0HV6rAvPiX/Tt2w916+Z9rKLS6IvPP8fLx3dw/9SeItl+ekqS2XFgClUu6WL6SW/BN8vAA5g6dWrRHhcpFbRZ6dBkphVo0uYxgLFVrVq1EBYWhlGjRuH8+fM4ffo0xo0bh/79+ws9kD19+hQBAQE4f/48AMDV1RUjR47E5MmTcfToUURGRmL48OEICQkReiDbvXs31q5di2vXruHOnTv46aef8OWXX2L8+PHCvt9//33cu3cP06ZNw82bN/Hjjz/ijz/+wKRJk/J0DhTAEFKCuLq6YtFXC5Fy8ygyY8znuTIsA1VSjNmb/dxSMnJLTatYs47JPGsPcEXjzfG6Rv68heXmpKanY84nM9G1a1e0a9fOemEi+PzzzyFnGVz648dXsj9N+ktw6qxXsi+znv8LKaPFN998/fqOoZgJCQnRjROx/UeLA1uaGy/IVhKpFI4urgU6RhOGAYu54MVKQPPv9RtY9fNvmDt3rknjZkLyQ5WeClV6SgGn1CI7vk2bNiEgIABt27ZFp06d0KxZM6xevVpYrlarER0djYyMDGHe0qVL0blzZ/Tq1QstWrSAj48PduzYISyXyWT44YcfEBISgsDAQKxatQrffvst5s6dK5SpXLky9u7di4MHD6JBgwZYsmQJ1q5diw4dOuTp+GkcGEJKGK1Wi5CQEEQ9iIN3j0VgpTm5sgzLgNOooHx+B47l6wj/n+vHgmElrDAmjG7cF/14MCxYCQupXAKFTDcGjJNCCofsn04KGdwcZPjvyB607txTNKaDQsLCTspCJmGsjgOjH/vFcCwYwHpPZdOmTEb4b7/iv//+Q5Uqr75BcHH2448/YuzYsWg38yf41mksugk117uU8XgwKg0nzFMbjP/CaTlhHBiNmoNGpUXGi6fgJfbgWQdostKhUWVCk5kOrSoTGlVmkY4Dw6W/AHdnH7788gvMmDGjULdd0j179gy1ateGd/1mCB41T3RdmBvkVMIyonFgcsaDkQjjwujHgnn55AFc3Fzh5eUNhVQCexkLhcF4MPZSCaT5GQcme+wX0VgwgOlPA2q1Gs069oRKy+Hy5cuQy+VF96GSEk8/Doy03iAwkoJdS7xWBc1/mwp9HJiSQPq6D4AQUrgkEgk2btyIwMC38PLcJng2Hyks43keyhf3YF+2mjAvt1oVW2k53mwNjCU8D4DR1bqY73KZB8sw4HnzQczhQwexdvUqrFixgoKXfHj//fexbds2nF83H2ELNsHO0bb/HC2NtG4Nr1WBZ+SiOv+iTBnL2a8a0pgzqB/UCB9//HGR76+kKVeuHL5fvhxDhw6Fb2ALVGxs2q1wfmthlJnpkJSxnlZojv57g4dBjTHD2j4GjJmyX3z7Pf67EYVz585R8EIKTcKpHwscdOiCoU2FdERiiYmJGD9+PHbv3g2WZdGrVy989913cHJysrhOVlYWpkyZgs2bN0OpVKJDhw748ccfhW6Rf/75ZwwfPtzsunFxcfD29saxY8fQunVrk+UxMTFWOwMwRilkhJRAtWrVwuLF3+Dl1T1IvX0KAMCpMpHx6DJkLt6iWhkAQq2LnnFQw3NaZCY8y3W/T+/o0ta0Zip2b0XdQFKirpcy44pf49Im741mPHn8GGNGjkSHDh2oS9x8YlkWGzZsAKPKwpmVc8zWfJgbiNAaZdJzcGoznUPo7jYtricEM5wafKbldjl5wfM8+CdnIOGU+PXXXyGV0vO6/Pjf//6H3r174/y6BUiJfVTg7Skz05ES/xz2Ts6Q5iNY4DgOF86fh1KpzFsvZMbpZdnT/iPH8dXSFfjss8/QqFGjPB8PIcXVoEGDcP36dRw8eBB79uzBiRMncu19b9KkSdi9eze2bt2K48eP49mzZ+jZs6ewvF+/foiJiRFNHTp0QMuWLU1SM6Ojo0Xl8pq6Sd/ohJRQ48aNw8mTp7D9z+91N4isFC5VmgjLbexdFADwcP9apD2JRsU2/VChYUsA5m9qy/pXEzXA5zgekACP7t/FwllTYWenwLrNO4RhtjnwkGS/4XgeLBhw0KWR8chpzK+viQGAtLQ0/G9gfzg5OWLTpk00IGEBVKpUCeHhm/Duu+/iyvaVeKvPh8KyvNaypMXcw62t30Lm5IHq/WaJlrFye3CQmASmDCsR1cSo7x0FODUkFULAKNzyejoi3PP/wL28h5//+AM1a9Ys0LZKM4ZhsG7dOjRsFIRTy6ei7adrhNo641oX48FQzdk07yMo01PQdsAouLp75Fqe11e5ZPs9/Dfs2bkTdevVQ8O3AqFSayCX6hrjMywArS4Q5xkGDLJrWxiDn4BQA3Pz9h0M+2AiOnXqaHFcDUJKoqioKOzfvx8XLlxAUFAQAOD7779Hp06dsHjxYqEhv6Hk5GSsW7cO4eHhwiCvGzZsQK1atXD27Fm8/fbbsLe3F3WF/OLFCxw5cgTr1q0z2Z63t7fJoJh5Qf/zE1JCMQyD9evXoV69ukg8tQ72nv753paDTxVwWg0UHj5Wb2yTnpuO4Kvlebh7esHZxRVN3mkuzNdvRt94X8/wPS+U5cHxPJQqFUYM/R/u3rmDXbt2oUw+UlCIWMeOHfHVV1/hv13rcfPQdpMUMVtrYWSOrmBYKRx9TNP5OHUWtJm6HqespY6xLuUAVgbIHPJzKjn7e3kXXEwkZs2ahT59+hRoW0TXRfvePbvBpyfh9PJpgEZltg2M8Wtz770rVoW9kwt8K1eHzKAGRn95aY3+9gHdgw4+u0yDBoHIUmahWbNmqFEzAFFRUeZrVwzfG/7Mfh37PB5dBwxDufLl8dtv9CCEvNlSUlJEk35MlfyKiIiAm5ubELwAQGhoKFiWxblz58yuExkZCbVajdDQUGFeQEAAKlasiIiICLPr/PLLL3BwcEDv3r1NlgUGBsLX1xft2rXD6dOn83wO9BdLSAnm5OSEA3/vQ1kPZzz7ay7UqS8AwKTxvp5hKplhGpl3w3YIHLcCTuV1bWcs3cwqHHNyZzmDMo5Ozli2Phwjxk4Ex+d0iyzqhQzi7pMNgxgeuoa2Y0YOx4ljR7F9+3YEBgba/kEQq6ZOnYpx48bhwsavcO/UXovlzP3e9emCdi5lUP/9b+HXdrCwjGV015fcpQxYO0dhvrkghmElkPo0gLRKaIEavnJJD8A9OoUhQ4Zg/vz5+d4OEatZsyb27P4Lifev4/h306BV6W6gzLV9MWzAbzgPADqP/QSjF2+AMjMDL8088MhN7br1sHPPPrRt1x41a9bEzeho3QLjKmWGBa9vPGcYxDAs4l4koEPP/lBrtNi3b1+BngIT8ir4+fnB1dVVmBYuXFig7cXGxpqkbEmlUnh4eCA21vzfZWxsLORyucnfS9myZS2us27dOgwcOFBUK+Pr64uVK1di+/bt2L59O/z8/NCqVStcunQpT+dAAQwhJVzZsmVx4vgxeDnLEbNrNtQpMQDEAQrDMqJulfU9gLG5pITon9brJ47XzdMHL9rsmhNzbWL0+esceCGQ0Qcx+vdcdm1MZmYm3hs2BPv37cXWrVvRvn37fH0WxDyGYfDdd99hxIgROLvmM9w5+qdJr2N6xr9zPWtjBzESGZSxt0TXEyPRpY+xBmlkupSy/P+3xL28C+7hcfTt2xfr1q2jcYEK2TvvvIM9u3fjRXQkji/7GJwqU1gmZfM2PpB3BX/RAw8gp/ZF3xyLBy8aA8r4a0QikUClymlzxTMswIprYERBDMPi0ZOnaNetN1LS0nH06FFUrPjqB98lJK8eP36M5ORkYbKU8jhjxgwwDGN1unnT/BALhS0iIgJRUVEYOXKkaH7NmjUxZswYNGrUCE2bNsX69evRtGlTLF26NE/bpwCGkFKgUqVKOHXyBCp4ueLJthnIjNU9tdTXuBje6Bk34NcPhGkczJh7Gp+ZltNnvXHQouV0gQkPcYCiJ36dXZYHEhLi0btbFxw9fAg7duxAt27d8nTuxDYsy2L16tUYO3Yszm34Ev9u/wk8x+W5Ib/ZbUvlkHtUAGA5hawgvZLxPA9t3L/QPjyBIUP+h99+o0b7RSU0NBT79u7Fy3vXcGDBaKiS40VtX0xrXljhvWE5ZWY6nj++r3voYfCQgzNzvZlLK+V5XVpZmTJl8CI+wWwNjH6ePoi58u9/aBHWBSqNFseOHUONGjUK/HkQ8iq4uLiIJv2o9samTJmCqKgoq1OVKlXg4+OD58+fi9bVaDRITEy02BOYj48PVCoVkpKSRPPj4uLMrrN27VoEBgba1DlGkyZNcOfOnVzLGaIAhpBSomLFijgbcQYN69fF422fIunfvw3bxprpiczytvQ3Fxqjp/HuPuWFmhgA2bUvpoNZ6tPI+OxARV8LYziQJcfzuBx5Ee1bNsODe3dx7NgxdO7cueAfBLGIZVksX74cixYtwo3dG3By+cdQpaeYHQvGkL72xVyvUPoAWBl/T7i+2OyaFn0jfkZiWAOTt0CG16rBPzoOLiYSc+bMwYYNGyh4KWJt2rTB6VMnIclKxq5PBiHu5iWzKWOW0ssAQOHkDM9y4toPw5pbIPv7wOjBBiCe16ZtKP45eFD3hmFzamGy3+u/yH7Z/AdaduyK8hX8EBERgerVq+f/AyDkDeXl5YWAgACrk1wuR0hICJKSkhAZGSmse+TIEXAch+DgYLPbbtSoEWQyGQ4fPizMi46OxqNHjxASEiIqm5aWhj/++MOk9sWSK1euwNfXN0/nSgEMIaWIh4cHjh87grEffoCYwysRs2cRtJkpJrUrtowNY5xKpJs4PLx1PTt1LCclRJ9Kpm+IC0BUC2McxKg1Gny/dDG6dWwHvwoVcOnSJTRp0sTscZDCxTAMpk2bhr179yL9wXUcnT8Ez6Mumk8j43lRWydrFN66xv2WghTDebamkXHpcZDc3weF6gW2b9+Ozz77jNLGXpEGDRrgyuVLaFS/LvYsGIPIP36CVq22mEZmPD89OQnpybouszneNO3UOJVUnFaqSy3jeEAqk0EikSAtPUMIWAyDmJfJKRj+/jiMGjsBAwYMwIkTJ4QxKwgprWrVqoWwsDCMGjUK58+fx+nTpzFu3Dj0799f6IHs6dOnCAgIwPnz5wEArq6uGDlyJCZPnoyjR48iMjISw4cPR0hICN5++23R9rds2QKNRoPBgweb7HvZsmXYtWsX7ty5g2vXrmHixIk4cuQIxo4dm6dzoACGkFJGLpdj+fLl2LZtG/jnN3F/4zgk/ncYYHhdqphhOhljmjpmjpbjoOF4ePlVhlxhL2r3YnhjYphGZlgLY3iDcvXSJfTo2A5ff7EAU6ZMwfHjx1GhQoVC/xyIdZ06dcLVK5fxVp2aOLVkHP7dtAiZKUkWy+t/z+YCGoZhoE6OFdX4CbUvJm1gcq+B4TVKcM/Og7uzH4F1quPy5UuisQjIq1G2bFkcOnQQc+fMweWd67Hz0//hWdRlUe2L8Wv9T57j4O4tfuKqD170rwF98MILPZEZ0tf4denaDTv+/FM0vgsHBlv/3IlGIc2x78BB/PLLL1i/fr2oMTEhpdmmTZsQEBCAtm3bolOnTmjWrBlWr14tLFer1YiOjkZGRoYwb+nSpejcuTN69eqFFi1awMfHBzt27DDZ9rp169CzZ0+zHWSoVCpMmTIF9erVQ8uWLXH16lUcOnQIbdu2zdPxM7xNI0ERQkqiuLg4TJgwAVu2bIFzhVoo22oYXCs3AMMykEh1bWMkUhashIVEyoJhGcgkLOzlEsilup8OcgmcFFI4KWRwVkhx7chutO7SC3YSFg4yCeykLBRSFjKWhZ1Ulw/PMoCE0f1kGN2TlNinj7F88VfY9vsm1K1bF6tWrTKpliavHsdx+OmnnzDzk0+h5gD/DkNQ/p3ukMjthBoYTsOB43hdWxQNB57jodVy4DS6n1oNh8yXz6FRacDYeUCryoJGlQlNZjq0qkxwGhU0St3PnEltciw8pwWXeAvsi/8gk/D4bN48TJ48GRJJ/tvPkMJx6dIljB4zBpEXLyKgRSe8M3AsPCv4i3ok0//9y6UsnkVfQ7nKVeHs5AyZhIGdhIVMwsJBxsJOKoGDjIVCKoGUBeQsA6mEgYRhIGMZSFjd94eEyX7IwgDHjx1DOZ+yqBVQE2fPRmDO3Hk4eeo0unXrhhUrVtBDEPLKpKSkwNXVFcnJyXBxcXljtlXSUA0MIaVY2bJlsXnzZhw9ehSVyyhw57fpuBc+A8m3z4LnOJNUMtYor91cGln56nVy2kwIvZDpyhvmsOtrYaJv3MAnk8ejTfBbOHbwAJYvX45Lly5R8PKGYFkWY8eOxZ3btzC4fx/c2vkjIhb0x72Dv0GZmizqfcxaWxiZvTP4rJTstlbZNS4Siei1nnFvZLxWBe3z62Dv7AL/9Bz69e6O27duYerUqRS8vCEaNmyI8+fOYc2aNUi8dQkbxnbFvm9nIvbODQA5Dfj13x+Z6alQOOR0rW04DgzH8eC4nLZ2uppafTlelEamv9ZatmyJNWvX4t0uXdG6bTskvkzC33//jZ07d1LwQogZiYmJGDRoEFxcXODm5oaRI0ciLS3N6jqrV69Gq1at4OLiAoZhTBr027rdf//9F82bN4dCoYCfnx++/vrrPB8/BTCEELRq1QpXLkfizz//RFV3GW6Hz0XUipF4fGgjshKe5qk75edPHkKj0QpdIOtvSPQ9DWk5HslJSdj62y8Y2rMTurYOwemjh/D555/j/v37GDduHDXCfgN5e3tjzZo1iL55E/26d8bDv9fj3Ge9ER3+BRKjL4DTaKyuz8rsoM1KttiQHzBKIWMYcKkx4J+cBqK3gXl+Cb27v4vr16/jt99+Q/ny5Yv0fEnesSyL9957Dw/u3cOSxYuRdOcK1n/UG79MGYhzf4UjK+WlUNbRxV2UrqpnqSG//qfQCxl07x88fIivv/4ajRq+hZ9++gnx8fHYvHkz/v33X4SFhRX1KRNSbA0aNAjXr1/HwYMHsWfPHpw4cQKjR4+2uk5GRgbCwsLwySef5Hu7KSkpaN++PSpVqoTIyEh88803mDdvnih9zRaUQkYIMXH27FmsWrUKW7dtR3paKpzKVoJ7rWC4V2sAz2r14ezhCbmUhVzKwkEugb1cAnu5FM4KKaRcFrISnqNazQBd+piEBafKxMOof3H90gVcOHkUVy+eA8/zaNu2LYYPH47evXtDJpO97tMmefD8+XOsXbsWP2/ciNu3bkHu4AyXao3gXDkQThVqQe7hB4AVUsg4LYeMuAeQuFSARqWBVpUppJFxGhXUygxokmOgefkQ2qSHQMpjaFWZqOTvj2FDh2L06NFC41JSPGg0GuzatQvr16/HgQMHwPM8KtUJRO3gluC0WnT832g4OztDwjKQsSykEgYKCSuknTrIJJBLWEhZBjIWkEoYpCTG48rFizh/9gyOHPoHUTduwMHBAd27d8eYMWPQvHlz6siBvFbFIYUsKioKtWvXxoULFxAUFAQA2L9/Pzp16oQnT57k+l177NgxtG7dGi9fvhS1c7Fluz/99BM+/fRTYWBMQDd+zc6dO/M0Rg0FMIQQizIyMrB//37s27cP+/7ej5hnTwEAjq4ecPatBIWHD1w8PGHv7IIa3q6QSRlwWi1ePo8Fw7BIjH+OZw/u4uG9O1Cr1XByckLr1q3RqVMndO3alW5ISwCe53Hp0iXs3r0be/buxZXLl6HVaiGV28HRswIY57JgHdwhtXeFJ5uKylWrITNLjYyMDGSkJiMtORHq1Hgkxz9BemoKOI5DvfoN0KXzu+jSpQuaNGlCN6QlwPPnz7F7927s3bsXh48cQUpyMgDAu1wFlK1UBR7evvDw9IKzszO8newAMNBo1MjMyED88+d48ugB7t25hRfZY1f4+PigQ4cO6NSpEzp16gQnJycreyfk1dEHHY8fPy6UAMbPz89kW3Z2dhbHgrHF+vXrMWXKFLx8mVMrqtFooFAosHXrVvTo0cPq+pYCGFu2O2TIEKSkpGDnzp1CmaNHj6JNmzZITEyEu7u7TedAeRqEEIscHBzQs2dPoYenx48f49y5c7hx4wZu3ryJJ0+eIObmbcSmpMCvRQuoJBJIJBK4SqVQKOSo5FkVLRoH6m5KvL1Rrlw5ODg4wMHBASkpKdBoNLC3t4eDgwPs7e3BFmAUdvJ6MAyDRo0aoVGjRpg3bx7S0tJw4cIF/Pvvv7h58ybu3buH2NjHeHH/ElwqVYJTlgNcWBYSVwnsvN2gUPjAyckJzs7OcHNzg4+PD1xcXGBvbw+pVIqoqCjY29uLJplMRkFNMePt7Y2RI0di5MiR4DgOUVFRuHjxIqKiohAdHY2YmAe4dOUsWJYVglaJRAKZTAYvNydU9G2MsHZt4ePjg9q1a6N69epwc3ODQqGga4G8UeRyOXx8fODn51co23NycjLZ1ty5czFv3rx8bzM2Nhbe3t6ieVKpFB4eHoiNjS3S7cbGxqJy5cqiMvquzWNjYymAIYQUPj8/P7NfyjzPIz4+HhkZGcjMzNQ9XTd4rVbrepRKSUlBSkqK2W0zDAM7OzshwNEHNobv7ezs6GblDaevZWvdurXJsrS0NCQnJyMzM9NkysrKgkajAc/zSE5ORnL2E3pzJBKJSVBjPNGN7ZuLZVnUqVMHderUMVmmHw08OTkZKSkpwk+NQRur58+fC6OIy2QyuLq6wsXFBa6ursJrfWoKIa+aQqHA/fv3oVKpCmV7PM+bfJdZqn2ZMWMGFi1aZHV7UVFRhXJcrxsFMISQAmMYBl5eXhaXq9Vqs4GN4XuO45CVlYWsrCwkJiaa3Y5EIoFCoTAJbAxfU1uaN5eTk5PFVB+e54XrxNqkUqmg1WqRlpZmtccchmGgUChyDXSoF7M3i1Qqhbe3t+gpLs/zyMjIEAJb/ZSWlga1Wo34+HjEx8eLtmNvby8KaFxdXXXtbej3TV4BhUIBhULxyvc7ZcoUDBs2zGqZKlWqwMfHR3gIoKd/eODj45Pv/duyXR8fH8TFxYnK6N/nZd8UwBBCipxMJoNMJrOYD8zzPJRKpdnARv9aqVRCq9UiPT0d6enpVvdlLrAxfE2pam8ehmEgl8shl8vh6upqsZxWq801yMnKygLP88J7a+Ryea5BDqWsvV4Mw8DR0RGOjo6idnNarRapqami2hrjGj7DdBiWZeHk5CSqrXF1dYWDgwP9fkmJ4OXlZfVhol5ISAiSkpIQGRmJRo0aAQCOHDkCjuMQHByc7/3bst2QkBB8+umnUKvVwgPHgwcPombNmjanjwHUiJ8QUkxwHCcENJYCHX2qmjWGqWqWghxKVSveeJ5HVlZWrkGOJpeun/UoZa14UalUooBG/9rS94NUKjVJQXN1dS1QI2lC3nQdO3ZEXFwcVq5cCbVajeHDhyMoKAjh4eEAgKdPn6Jt27b45Zdf0KRJEwC6NiqxsbG4ePEiRo0ahRMnTsDZ2RkVK1aEh4eHTdtNTk5GzZo10b59e0yfPh3Xrl3DiBEjsHTp0ly7cTZEAUwhGzZsGDZu3IiFCxdixowZwvydO3eiR48eZgd4e5UWLlyIHTt24ObNm7C3t0fTpk2xaNEi1KxZU1Tuhx9+wDfffIPY2Fg0aNAA33//vXABE/Kmspaqpr9x1Wq1uW6HZVlR5wLmUtYoVa14y0vKmi0oZe3Npq+RM66tSU1NBcdxZtdRKBQmtTXOzs40ThUpERITEzFu3Djs3r0bLMuiV69eWL58uZDm++DBA1SuXBlHjx5Fq1atAADz5s3DZ599ZrKtDRs2CKlruW0X0A1kOXbsWFy4cAGenp4YP348pk+fnqfjpwCmkA0bNgxbtmyBQqHAvXv3hOqwNyWACQsLQ//+/dG4cWNoNBp88sknuHbtGm7cuAFHR92oyFu2bMGQIUOwcuVKBAcHY9myZdi6dSuio6NNepcgpDjheR4qlcpqWxx9+lFuZDKZxRocBwcHKBQKulktAfKSsmYLSll7s3AcJ3QuYVhjYylNVZ/OZlxb4+TkRL8zQl4hCmAK2bBhw5CQkIA7d+6gS5cu+PrrrwG8OQGMsRcvXsDb2xvHjx9HixYtAADBwcFo3LgxVqxYAUD3Be/n54fx48eLapUIKYn0nQlYaotja6oaAOGJvKVOByhVrWSglLWSR6PRiGpq9JOlGjmJRAJnZ2dRbY0+DY1+T4QUPqoHLQISiQRffvklBg4ciI8++ggVKlQosn19+eWX+PLLL62WuXHjBipWrGh2mb6rUn3uokqlQmRkJGbOnCmUYVkWoaGhiIiIKKSjJuTNxbKsEGBYotForLbF0aeq6XtVMxzUy3hfxqlqxgEPpaq9+RiGEQILS6iXteJFP3aF/v9GIKezEeM0tJSUFGi1WiQlJSEpKUm0HX2nFMZtbOjvmpCCoQCmiPTo0QOBgYGYO3cu1q1bV2T7ef/999G3b1+rZSyNds5xHCZOnIh33nkHdevWBQDEx8dDq9UKgwrplS1bFjdv3iycgyakmNM3+rXWq5qlVDX966ysLHAcZ1OvavqbUXM1OXSTWjxQL2vFnz5oVCgUov8jeZ5Henq6SRpaWloaVCoVXrx4gRcvXoi25eDgYLabZ+ohkRDbUABThBYtWoQ2bdrg448/LrJ9GD8hyouxY8fi2rVrOHXqVCEfFSGlm76nMzs7O4vdQhqnqpkLclQqFdRqNdRqtcUBQAHkOgAopRsVHxKJxOp4OUDeUtZUKhVUKhUNDFqEGIYRfmfly5cX5mu1WtFgnPrgxvDvPiYmRijPsiycnZ1NOg6wt7enz54QIxTAFKEWLVqgQ4cOmDlzpsnAQp07d0ZMTAyUSiVmzpyJQYMGYfr06ahZsyZGjBgBABgxYgS6dOmCHj164Ndff8Xy5cuhUqnQtm1bfPvttwDyn0I2btw47NmzBydOnBCluHl6ekIikZgdZKgggxsRQsRsTVXLbQBQrVYLpVIJpVJpNVXNlgFA6SapeKCUteJBIpHA3d3d5CGGUqk0SUFLSUmBWq0W5j1+/Fgorx9Dy7jGRi6Xv+pTIuSNQY34C9mwYcOQlJSEnTt3AgD+++8/BAYG4uOPP8bXX38tNOJPTEyEh4cH0tPT0bhxY1y+fBn//fcfZs+ejb///hsajQZVq1ZFdHQ07t+/j9mzZ2Pz5s2QSqUYMmQI+vXrh3fffReJiYkWRy3X8/f3F7p95Hke48ePx59//oljx46hevXqJuWDg4PRpEkTfP/99wB0T4orVqyIcePGUSN+Qt4g+lQ1awOA2tpDllQqzXUAULo5LXmol7U3A8/zyMjIMKmtSUtLs9jNs729vdlununvlJQGVANTxOrVq4dBgwZh+fLlovlLly7FX3/9BQB49OgRHj16hKCgINy7dw8vX77E+fPn8c4770ChUODw4cM4e/YsgoKCAAAZGRnCCKd5TSEbO3YswsPDsWvXLjg7OwujFOurqQFg8uTJGDp0KIKCgtCkSRMsW7YM6enpGD58eIE/D0JI4TFMVXNzczNbxjBVzVKgo1KphF6XbElVsxTkUJpR8UMpa28GfffMjo6OonarHMchNTXVpH2N4dhWhhkT+nQ249oaR0fHUvV5kpKPAphXYP78+diyZYvw/ujRozh9+jTOnTsHhUKBoKAgKJVKAED37t2xc+dOnDlzBn369AGg+wIbNWoU5s6dW+Bj+emnnwBAGJRIz3AQon79+uHFixeYM2cOYmNjERgYiP3795s07CeEvPkKmqqmv0nSaDR5SlWzNgAo3UgVL5Sy9vqwLCsEI4b07eKMu3lWq9VITU1Famoqnjx5IpSXSqUWu3kmpDiiFLLXYNeuXfjtt9+wdetWXLlyRUghq1u3Li5evIhPPvkEt2/fRlRUFBQKBa5fv44+ffrg5MmTKFOmDJ4/fw6tVgtfX9/XfSqEkFLAOFXNXE1OZmamzalq1gYALQ03paUZpawVHX1NmXEaWmpqKrRardl17OzszHbzrE87J+RNRQHMa6BUKtGtWzc8evQIderUwePHj7F27VqhK+MqVarg7bffRnh4uLDOpk2b8M0334DjONjZ2eHnn39GnTp1XtcpEEKICMdxUCqVVgcAtTQIoDE7OzurA4CWtvSi0oYGBi1c+u7ajdPQ0tPTzQaKDMOIunnWBzVOTk7UzTN5Y1AAQwgh5JXQp6pZGwDUlptSfUqTtfY4peWpe2mVl5Q1W5TGlDV9uzfjGht9SrsxiURitpvnkhz8kTcXBTCEEELeCPqbUmtj4+Q1Vc1aTU5Juhkl5lHKWt4plUqT2pqUlBSLDxdkMplJpwGurq6QyWSv+MhJaUIBDCGEkGJDn15kLcix9ATZmFwuz3UAUEqZKfkoZS13PM8jPT3dpOOAtLQ0i8Gfg4OD2W6e6W+KFAYKYAghhJQo+qfu1gYAtTVVLbcBQOVyebG6ESX5Qylr5mm1WqGbZ8PgJjMz02x5lmXNdvPs4OBAf0ckTyiAIYQQUqoY3oxaGwDU0gCChiQSSa4DgFKPTqUHpazpqFQqk9qalJQUqNVqs+VlMplJN88uLi7UzTOxiAIYQgghxIhhqpqlQCevqWrWBgCltJrSo7SmrPE8j8zMTLPdPFt6WKBQKMx28/ym10yRokcBDCGEEJIP1lLV9K/zmqqmD2ycnZ1RuXLlV3AW5E30qlLWKleuDLlcXsRnYx3HcUhLSzPbzbM5DMPA0dFRCGh8fX3h7u7+io+avG5Ur10IOI7Ds2fP4Ozs/NqfcBBCCHm1FAoFFAqFyXye54Wuo/VP3A2fvGdlZQmpavqUGz0XFxeUKVPmVZ4GeQPpx2RxcHAwu1yr1QrXkeGkVCqFa02pVAqD0RpeYwDg7u5u9tp9HfQBiZ5Go0FqaqrJpFKpoFKp8PLlSwCAWq0usTUyPM8jNTUV5cqVo1paI1QDUwiePHkCPz+/130YhBBCiiHj2hcHBwdkZWUhOjr6dR8aKQEYhhEGhzWeLl68aHN7nDeFQqEQpZTdvXtXCGZKqsePH6NChQqv+zDeKFQDUwicnZ0B6C4wFxeX13w0hBBCiiuNRoM9e/YAADp37kwdAJBCVxKusZJwDrZISUmBn5+fcJ9JcpTM3/grpk8bc3FxoQCGEEJIvmk0GiFdyMXFpcTemJHXpyRcYyXhHPKCmieYooQ6QgghhBBCSLFBAQwhhBBCCCGk2KAAhhBCCCGEEFJsUABDSD7duPG6j4AQQgghpPShAIaQfHj6FAgKAu7de91HQgghhBBSulAAQ0g+rFsHzJ8P/Pjj6z4SQgghhJDShQKYYuznn3/Gl19+Ca1W+7oPpdSJjAQ++gigceYIIYQQQl6tkt1xdgmlVqsxceJE/Pjjj2AYBqdOncLvv/8OV1fX131opYZGA8jlQDEbwJgQQgghpNijGphiJiEhAR06dMDq1auxevVq7N+/HxEREQgODsbt27df9+GVCkolYGene21vD2Rmvt7jIYQQQggpTSiAKSY0Gg22bNmCmjVrIiIiAu+88w62b9+OJUuWICgoCHFxcWjQoAGWLFmCTLqjLlJ37wLVquleV6sG3Lnzeo+HEEIIIaQ0oQDmDcbzPI4cOYKhQ4fC29sb/fv3R0JCAsqVKweZTAYnJye4uLhAKpWiXLlyUCqV+Pjjj+Hh4YHu3btj27ZtUKvVr/s0Spxbt4AaNXSvq1cHqOKLEEIIIeTVoTYwbyCe57Fjxw589tln+O+//1CjRg2MGzcOjRo1wjvvvANPT0+z66lUKvzzzz+IiorC9u3b0adPH1SoUAEzZ87EqFGjIJPJXvGZlEyPHgG1agGABhUqPMOtWxVf9yERQgghhJQaVAPzhrl9+zbatGmD3r17w9fXF0eOHMHNmzcxf/58dOvWzWLwAgByuRydO3fG1KlTcfbsWVy9ehWtWrXC+PHjERgYiDNnzrzCMym5YmIAX18A2A9v7/fw/Dml7BFCCCGEvCoUwLxBNm3ahMDAQDx69AgHDhzAgQMH0Lp1azAMk6/t1a9fH7/++isuXboEFxcXNGvWDPPnzwfHcYV85KVLTgCzGWXLDkBc3P3XfUiEkAL47z9g/frXfRSEEEJsRQHMG4DnecyePRuDBw9G7969cfXqVbRv377Qtt+gQQOcOnUKc+fOxbx58zBgwAAolcpC235pk5gIeHgAwEt4ejbHixfUzoiQ4iwiApg7F3jx4nUfCSGEEFtQAPOa8TyPadOm4fPPP8eiRYvw888/w8nJqdD3I5FIMHfuXGzfvh27du1C7969qYF/PvE8wDCJAMpAJvOHWk3BICHF2Z07QKdOwNWrr/tICCGE2IICmNfs22+/xeLFi7F8+XJMmzYt3+liturRowd27dqFAwcOYMyYMeBpJMZ8igZQE9QPBiHF3927QLNmwLNnr/tICCGE2KLYBTA//PAD/P39oVAoEBwcjPPnz1stv3XrVgQEBEChUKBevXrYt2+faPmwYcPAMIxoCgsLK8pTEBw9ehTTpk3DtGnTMH78+FeyTwDo0KED1q9fjw0bNmDlypWvbL8lgVoN6DpzuwkgAACyg05qyE9IcaVUAlWqUABDCCHFRbEKYLZs2YLJkydj7ty5uHTpEho0aIAOHTrg+fPnZsufOXMGAwYMwMiRI3H58mV0794d3bt3x7Vr10TlwsLCEBMTI0y///57kZ9LSkoKhg4dihYtWuDLL78s8v0ZGzx4MD788ENMmTIFt2kgE5vFxQHe3kBODQygUMiRmfnodR4WISSfOA5gGKB8eeDp09d9NIQQQmxRrAKYb7/9FqNGjcLw4cNRu3ZtrFy5Eg4ODlhvofuY7777DmFhYZg6dSpq1aqFBQsWoGHDhlixYoWonJ2dHXx8fITJ3d29yM9lwYIFSEhIwIYNGyCRSIp8f+Z888038PHxwUcfffRa9l8cJSQAXl4AcA9AFQCAt7cWz58nvcajIoTkV0oK4OaWDF/fb/HsGfXQSAghxUGxCWBUKhUiIyMRGhoqzGNZFqGhoYiIiDC7TkREhKg8oEufMi5/7NgxeHt7o2bNmvjggw+QkJBg9ViUSiVSUlJEU148efIEy5cvx/T/s3feYY6VZf//JNNbkplkkult+7J9gaWDgC6IKKiIWHhFBAvYUEAs4E/fV3yl2RAElfLakCIqUqSL9F3Y3nenl2SSSab35PfHM2fSzklOyuzM7D6f65prdzLJyZnMKc/3Lt/7+uupq6tL6LXpJD8/n1tvvZWnn36aF198cdb2Yz7h8SgOZCNAPgDFxRn4fIkdA4nxCHDvDG5fIjl68XqhuLiRnJxHGRuLfe2XSCQSydxg3ggYt9vN5OQkDocj7HGHw0FXV5fqa7q6uuI+/5xzzuHBBx/k+eef53//9395+eWXOffcc5mcnNTcl5tvvhmz2Tz9VV1dndDv8tOf/pT8/Hy+/vWvJ/S6meDCCy9k7dq1/OQnP5ntXZkXBC2Ug+YHFks2Pt/gDL7rA8DLM7h9ieToRQiYJuBLQP8s741EIpFI9DBvBMxM8fGPf5wPfvCDrFy5kgsuuIAnnniCt99+m5deeknzNTfccAO9vb3TX62trbrfb2xsjPvuu4/LL7+coqKiNPwGqWEwGPjqV7/K008/TVNT02zvzpynpwesVj8QdIuzWPLw+Waqib8PyAV6Zmj7EsmRRZwEehRCwDiBM4F+pDGjRCKRzH3mjYCx2WxkZGTgdDrDHnc6nZSVlam+pqysLKHnAzQ0NGCz2Thw4IDmc3JycjCZTGFfenn22Wfp6enhM5/5jO7XzDQf/ehHyc/P56GHHprtXZnziAyMD7BPP2axFOHzjc3QO7YD9UARQsxIJBIt3G5YtgyGhvS/xusdp7h4FCgjP7+XkZEZ2z2JRCKRpIl5I2Cys7NZv349zz///PRjfr+f559/nhNPPFH1NSeeeGLY80EICK3ng+hP8Xg8lJeXp2fHI3jqqadoaGjgmGOOmZHtJ0NBQQFnn302Tz311GzvypxH9MB0ApXTjxUXF+HzaZccpkYHUIGwbJZucRJJLB5/HI47Dv78Z/2v8Xr7KS7OAgxYLIP4fDO0cxKJRCJJG/NGwABcc8013HvvvTzwwAPs3r2bL37xiwwODnLZZZcBcOmll3LDDTdMP18pjbrtttvYs2cP3//+99m0aRNXX301AAMDA1x77bW88cYbNDU18fzzz/OhD32IhQsXsnHjxrTvf2trK//+978544wzZnxgZaKcccYZvPHGG+zevVsOt4yByMC0EipgLJZsvN7cGXpHRcA4gO4Zeg+J5MjgmWfg5z+HV1/V/5qenkGKi/MAsFjGZ7ifTSKRSCTpYF4JmIsvvphbb72VG2+8kTVr1rBlyxaefvrp6Ub9lpYWOjs7p59/0kkn8cc//pF77rmH1atX88gjj/D444+zYsUKADIyMti2bRsf/OAHWbx4MZdffjnr16/nlVdeIScnJ637ftddd1FTU8P27dtZtWpVWredDtauXcvo6CjLly+fFniSaLxeKClpAYIZOosFfL6CGXpHRcCUAurzjiQSiWBkRAyk1BgNporXO0JxsehHtFgy8PmkE5lEIpHMdTJnewcS5eqrr9ZcYKs13l900UVcdNFFqs/Py8vjmWeeSefuafLTn/6UBQsWcPDgQbxe72F5z0QwGoWWra2t5a677uK2224jN3emsgrzl/FxyMx0AmumHxMCpnCG3rETIWAmgbdn6D0kkiOHRJPbXu84JSWij1E4CvbOwF5JJBKJJJ3MqwzMfMZgMEz33px//vmzvDfRbNiwAYDTTjuNQCBAZua807aHkW5Cm/hNJujtzSfUWjl9dCCyPXZkBkYi0cbnA2UGcUYGTEzoe53XG6C42AqAxZKP1yutlCUSiWSuIwVMGtm3T/tnOTk5DE1Z46Qzs5GI204ssrOzAZHFMhqNUsDExIUo6RJkZsLkZDaQpj9GGMNAHkLAyB4YiUSLpiZQ5gJXV4Ned/uRkQny8kQZssVSOIOW6OlhfByee26292L+4ffD976HtMmWSI4QpIBJI7fdpv2zgYEBCgtFmdHYWHosdw8dEjfs3jRUPExMhSuPO+44/H4/w8Nz+yY+GwQCSnnKIBDe82IwZAK+GXhXpR6mGDkLRiLRJlTA1NdDY6O+1wUCYwiTDLBYzDNoiZ4ebr0VvvhFiDFrWaLCQw/BY4/B00/P9p5IJJJ0IAVMGvF4tDMibrebioqKqeelp0n0N7+B006DrVtT35ayTytXrgz7XhJkaAjy8yF0iKVCIJDFzAgYBSPgn8HtSyTzm+ZmqKkR/y8vh64uva8MoLSDWiy2GbRETw+vvAKXXgr/+c9s78n84uGH4amn4Pe/n+09kUgk6UAKmDRy7LE+tm+Pzk+Pjo7S19dHQ0MDRqMxbRPvDx7084lPBHjnndS31TgVrly0aBEA3d2yXCkSYaEM6r0uMyFgAvj9Bl55RZY9SCTxcLlgypASm00MtUyU4uICfL65Wz47NgY5OXDOOfDii7O9N/OLkREhcI+kOT9+GdOSHMVIAZNG6ut/zKFDe6IeDwQC5Obm4na7KS8v57k0FTAPDW3jlFOe4913U9/WK6+8gsFgmLaPVnpiJEGEgAmgftpkAel2Lxpiz56FfOpTcM89oJb5kUgkApcLSqda00pL9QsYgyEYHbBYDDPmKLh9u35jAS0OHICFC8XXgQPp2a+ZpLFRCIfZxukMiluD4chY+AcCcMEFIqsk0c/YWGJzoiRzFylg0khDw/s5dKgz6vHc3Fw++tGP8uMf/5iOjo60CJhAoBPow25/mnRUez3yyCMEAgGuvPJK1qxZw/Lly1Pf6BFGTw9YrYOANepn2dkZjI31pfkdfWzevIIvfQm2bAFxus7t8haJZLbweMA6dWomm4HJzYXh4fQHb/7yF7j+evjqV1Pbzu7dsHy5cFubD5mESy+Fv/51tvcC3n0X1q4V/6+p0W/wMJf597+Fhf+jj872nswvHn8cPv5x2Lt3tvdEkipSwKSRuroTOXhQPbRz6623cvnll3PxxRfj8Xhobm5O6b08nnexWsuAXQnPPYhkfHycgwcPcv7553PxxRfzhz/8AUOqGz0C8XigpKSXUAcyhcJCIwMD6Z7g7WXTpsVccAG0tQEUAQNpfg+J5MhgYgKyssT/bTbQUwU7OTmOMeIuGJqRSQeBgMigPv64WDinIjz27IGlS9O1ZzPLtm1QWTk3MgS7d8Mxx4j/L1smvp/vbNnSxqc+9TStrbLEOBH+/Gf41a/mhrCWpIYUMGnEZsvC5TKj1iPhcDi4/fbbueeee8jLy+MPf/hDSu916JCXhoZMIAfwp3QBe+qpp+jp6eG///u/ueuuu2T2RQNRQtZD6AwYhcLCDAYG0u3c5uXgwTIWLVJuUGbSX6YmkRx55ObC6Gj85w0O9pGfH37xDAQygPQ5kSmL5+xsUfLzr38lv62WFqitFf8XA3TTsIMzxDvvwEc/Kkr7ZpuDB2HBAvH/BQvE9/OdHTsOsnLlXVRWDieVbTwa8ftFCdmJJx4ZIvZoRwqYNGIwgMGQBWjXdBUVFfHxj3+cu+++OyU7ZZerD4fDDJRRVDRCfwqz137xi19w3HHHsWrVquQ3kka2bROzDuYaQsCED7FUKCrKpL9fx4opIXwYjZkYjWKx4vXaAf1laoODcPvtMjonkWgxONhPYWFk1jwL8KbtPf7+d/jQh8T/TzgB3nwz+W05nWCfuvzM9YV4YyM0NIiyvnRY/adCa6uYDQRQUQGd0ZXe84wxOjvzKCu7kIaGgxw6NNv7Mz9oahIW6zYbaSm9l8wuUsCkGYMhj0Ag9gCCa665hra2Nu67776k38fjGcVqtQDllJX14XQmt51XX32V5557jm9+85tJ70s6+elP4brrYs/UmS1ECVkX6iVkOQwMpHd+RCDgRSymxGKlsbGaRATMN74BL78ML7yQ1t2SSOYcY2PB8rFEGBwcpKAgslw2dhAqUTZtEsIFRPnXnmifF91MTorBuSDKszo6Ut+/meLQIbFYrK0VFtezid8PGRni/xUVc/tz00MgsB8owmBYQUPDbh0CZhuQooPEEcC2bTA1KQKYL8G922d7B+YsUsCkGYslk97e9pjPWbFiBZ/4xCf43ve+h9erL9LX0QH79gXdUzyeQqzWDKAch6NnSsB0A28BsdIxtwG/BWBycpKvfe1rrF27lo9+9KO69mMm8fnEkLEnnxSTpueaU4xo4m9DvYQsh/7+9DbY9/UNYTKJxZXdDi5XKYmUkDU3w29/K74kkiOZ7u6gA1ko8RYo6gImm0AgfUNjh4eV+VFgNIqvdAyhLC+f25kEn0+YDcy2gJmcZKrP6RGgKWmDh7lEb+9BSkqygOU0NLyjQ8B8Cnhl5ndsjrN9e1DAzB8h+8Zs78CcRQqYNFNamoPLFb979Cc/+Qmjo6N85StfifvcH/5QRNuWLIGzzoKRET9ut2nKcaeCsjInBsMTQBWwAagHtmts7TngL8AYt912G5s3b+bOO+/EGNnJOgs89BBcdpm42axcKQTbXKKnB4qLW1AvIctiYCC9xgdO5zgOh9imwwEuVwl6MzDNzWLhYLMlV77R1AT9/bLf5miksZF5V5LidotjPRSzGfrinC6DgyMUFGSEPZaXZ2RkxJeW/WpvFwulUGprk3PB8vsJMxxIXsAc3shQTc3sChhRPtYL/Aa4DKMxcNgi7xMTcNFFonE8nXR1teNw5AP5NDQ0xzlfdwPFwJPp3Yl5yM6dsGIFwItUVfXSHjvWPAfwIXpfJWrM/qr1CMNuL8Tlir/wq6io4Je//CW///3vuffeezWfd+AA3Hhj8PuXX4bf/GYYj6d06oZdjsPRzurVnweUxhEfcL3K1voQTlbH8e9/P8h3vvMdrr32Wk488USdv93MEqwVf4STTtrF66/P9h6FI1yOtErIDAwM5Kb1/VyuAA6HqBcRGRgzegXMf/4Dp5wi/q/XkUnB64X3v7+P3//+f4Gdie20ZN5z5ZXwhS/M9l4kRnDI7I3A/wOEgIkn3gcGRikoCK89Kyoy0t+fHre/LVuC9r0KixYlN8Ml1CYakhUwbmAZkO5+vXBGR8XATYDa2olZFTCigf91xLGxAjh8A3R27xbH5d/+lt7tOp1uHI5iAEpLR+jujiVKXwSuJb3X8hSabmeRgQEoKtoBXI/D8WLSpfeHj0auu+6K2d6JOYsUMGnGbjfhcunrQP/0pz/NF77wBb70pS/xlIbXZGSjmdEIbvcgHo9t6mZWjsPRSHZ2L0H3s0nUp8K/CZzAzp12Lrzwa5x66qn8z//8j659nWn27RNNlrm5Q8CvOeGEG3njjbk482QU4fwWTmEh9PfnpfWdnM4M7HYxk0IIGBN6S8j27BHzIgA2bIC33tL/vi+9BJ///GM888w3gF8ltM/J0N4uLS3nCuPjotwpLy/1oYuHE68XiosDwNvAJmACk0lPBkZNwGSmTcDs2SNse0NJdghlZ6cQLQoOB0kswG4D1gDPJL4DCdDdrZgN/Jja2lU0N8+eK4sQMG8BxwNnAc+Rna3PpS5VNm8WVRPpNjFwOrNxOMT9xmCoBGI5YB4CFgIZMZ6jRRuiaiOUVxBCcA74YyfAyIgiql8Gvk1Z2fY5L2D8/kM0NVXEf+JRihQwacZuN04tNPXxi1/8gvPOO48LL7yQv6mEadasEY2fRqNo3szMhIsuamR0NH/qZLRTXNzKK698ceoVUx2efF7l3Q7yzjsZnHnmD6mqyuGxxx4jU+kInWX+7//E0DNRp/wpqqpW0dmZjqv+d4GZLxQvKoKBgXQLmOBNSgiYAvRmYEJtQ5cvT6xxePt2PyecsJVAwMro6MxPfPvmN+GWW+Zez9PRiDJnZPFi2L9f32suuig1Z610IPotOoBFiMXVDl0ZmMHBCQoLwwMSRUXZ9PenZ3y82tyWZAVMVxeUlQW/z8xMppdmB3ArkOaUQATBnqR/U1DwRYaHY5sijI/DE0/MzL4cPDjKggVexL3xNODVw9Y/9M47sG7dICUl6XW9cjotIcdCNRDreG0C6oBsErcHvwr4MaDcQPzA94HNwN0Jbmt22bVLCSb8Bzgdh2MQp3MO2p2G0N7upqIiGeF5dCAFTJqx26G7W3/NYmZmJn/5y1/4wAc+wIc//GF+8pOf4A9ZyeXkwKuvwve+B1/+slgoHHPMIYJZgEzM5n4efPAnwIPANxGRkf+Keq+HH36W0067gZqaOp5//lgsFkvyv2iaeest4c0OLwAbMRjOJBBItdOyHfg/YOZHFRcWkvYSMqezEIdDRIeFjXIOegVMf78ooQGxYNK7GAXYubOf5csLWLQIGhuPAboS2u9EGBkRX+99L7z22oy9zTzED/yeJ574B9deO3HYava3boXVq0UP2rZt8Z//8svi2L/11pnft1h4vWCxbANOAU4EXteZgZmgoCD8vC0qykmbJXpnZ7joAKirS67HSK3PJ/Hjwo9Y8M5s6Lm7G2y2USAPOA+IPQzmG98QpdIz0ZNw6JCHhoYlU9+VAL6pnkLt1+zZA3fckY73HqChYTmrVm1lZ9oquCbp6irB4VC+ryY7ezBGRmkEyAUcxPs7hNODKDn/NfDDqcf+CmwEbIhKj7ktAELZvh3EpIg+oBiHoxSnM3126TPB/v3DLFiQ3sDokYQUMGkm2Kug/86SnZ3NQw89xPXXX8/111/P+973Pg6GGPyXlMD3vy9meqxeDeDEYAhGDU2mYXp7DcCngZuBc8K273a7+cxnPsPHPvYYxcUf4IILXsJmS8JzdIZobYWqKjFHRyyWy4DjyMjwpVjG8jgi2pjC5LgpRkYgN9ePiGJFMzMlZJbpm5TRCIFAJskMskzUbWVgYIiiokUsWgT79p2GKD2cGZSmypNPFtHK+cYddyTXkB2fqxgfd3HHHesIBF7h1Vdn4j2iUUoPly/XN+jtP/+BK64QEfQUxlqljMjA7AOWA6uB7TozMH4KCvLDHisqyqOvL30LM0OEt0dOTnLleZE9MMq29YsYH4erIVhkYA4gTGUaiJ0hEAGWW26B++9P/76MjPSQl3dm2GMORyCmgHnsMdF4//bbqb23wdCC0fggNTXPpfE60Y3TWRYiYGpwOLo1fp/Qg8NBYsGoncAxwALACvwOuBO4eurn64D5c9EWDmSjCDEHDkcJTucsXrR0sH9/NgsWFM32bsxZpIBJM6Wl4HLZgMGEXpeRkcGPfvQj/vWvf7F//36WL1/O17/+ddpVQlKBgJPQPoyMDL9q+Y3P5+Pmm29m8eLFPP7449x11zpWr/4zL71UgN+fgYigzD7PPAPnnAPQCtRMPZpDdbUzxYv+buDYFPdO4PVCSckwag38EFpClr5Quctlnh5aJ8hCTwamtzeYfQFF/Oh7T+F0NAgsYPFi2LdvJaKvYGZ4913R5CzeK7Vt6XQkTxtNTcLu+6tfTfeW/w6U8M9/XsOHPlTJl770CA88EKvGPX10dAjHQ73D/nbuFGJn5UrYsWPm908LkYHZD9QClUAHJpMeARNQETDZ9PenXrYxMCACG+lCTcDoEWkKb73Vyr33fmLqWlBEIjOlEkUImEaEYQDk5Y0xPKxeI6o4tZ16auqCIRLxuw4gSgsVKrDbvTH7H15/HW64IbX98fvBYOgDTqOqyk1ra7pEcTtud2lINq4ah6ODLlVt4kVknUAEBhPJvCkCBuAWRCn2Hbz6aj7PPQewHjFfZn6wfz8sWnQQIcggP7+a4eHD0AiVAvv2lbNggSwh00IKmDSTnw/Dw4UklqoN8t73vpfdu3fzne98h/vuu4+6ujouvPBC/vznP9PTI2YTDA76KCiIzASIFerQ0BBPPvkkn/3sZ6mqquKmm27ikksuYd++fdTXn8dZZxlZvhwOHlzF4egN0cOrryqOWZuA46Yfr6930tiYiiBoQQiiPGAolV2cslDuR0SxohElZIXEbqZMjImJjIjhfEb01DA3NQmr1lByckQWKR5eL1itLmABixbB/v0OYK/ufU6Ud98VfV41NdDSkvx2AgFRhvaDH6Rt1+Ly8MNCvExMpLsh+E7guzz9NJx/PjQ0nElHx+HpNlUWySKTHP/5vb2ivPHYY8XAxtlCNPE7EQvzTGBCl43ywEAmBQWRPTDpyaYqYlCNZJrI1QRMaal+h8H778/mkUfWT5UGLgQOxnlF8ggBsw9lsehwTOB0qqeBX3tNZGCzs0UmL5243Z3YbBNAaBpsCXZ7o+bxPTEhMltr1pBS2ZfbPYjNNgwYqK6uoa0tuTVBNB34/XnTgznFKIVWDQHTiigZBCFgEs3ArJj6fw7wHf75z9X8/Ofws5/BwYPLmMljKN2IIbD7CYrZWgKB9PS6zQx+9u+vpKFhtvdj7iIFzIyQQ7ICBiA/P58bb7yRlpYWbr/9dlpbW7nkkkuwWq0sXbqUiy56ml27ruPGG2/kpptu4hvf2MvmzZewdu1azGYz5513Hq+88grXXXcdzc3N3HnnndjtpezeXc3KlSLivWXLWmCWxyNP0dWluOtsJjRjUlc3SlNTKlFCP5CBy7UMcSFPHmHT6kNLwOTnw+BgPiLalx4CgfDak6wsGBuLb7rQ2ak+e0KPQHC5wG7vBMqoqID29izSKcoiaWkRPQGJZInUeOcdeM97ROT0cJkBvP46nH66EN//+U+6troZsWjIo7FRTDKH88jO7josJVrKrJHMzPhlTsEBgbB+/ewKmP5+KCwMVwT6MjC5FBaGn2cm08wLmGSG6IULmLeA66YsdPW9/uDBLD7/ed+UI+FCIIHGuAQRAmY3YiYZOBw5OJ3q1+Bdu5TeBOGspr4QT46DB99hwYJIU52FOBz7NTMwra3iepnq/Jr29gNUVYnrdUVFDR0d6YpytKOUQQkyKSvzaPw+nYBiXZdoCVkzUEcgAG1t8LvfiaHIDz4I//3f8POf1yMczuY+brdy7oQKmGpm8t6WOh1MTuZFBDEloUgBMyPkkI4mSZPJxJe//GU2bdpES0sLDzzwAGeffTY9PZN0dj7P/fffz29/+1ueeMLJ6GgHxx57LL/4xS/YtWsX+/bt48Ybb6R82nezl8bGGurrRWTp3XcXMxcETPhNeQfBlDWUlxfS1eVLcssDQAEvvgjLl1/PxEQ6BIwHLQEjFuBZpE/ARK/mRblIQdxXRtqtgugxamuL/65CwPgAQ8jQPCMzVW4YCAQXwbm5Ymp5Mvzzn/DhDyfuuJYKY2Mis3XyyfBG2oYl/xW4mPZ28TcT5LJsWTO7d+tXZldcAffck659UkdZ6IFYqM/uVOtJjMbQTIoFs7lPRxN/DgURp1Q6MzCRgQSFysrEG9aDJWljwLeAJZSWvqBLwLS1QU1NF8cdVzBVFlVDqkGdWHg8YLO5ENlvcDjMOJ3qQb0DB4KOiatXCyOJdHHwYAsLFkSko6nGbj+omYE5dEgEDozG1IIhbW1tVFZaAMjKWsr4eGpVAAqBQAfhAgYcDp9GyWcnoByEiWZgAkxMGPnYx0Rme2IC/vIXcc1btQr2789Cb2VDICAqA2aLt96C444DMQNo4dSjuRgMk4fNICVRurracDjm6M7NEaSAmRFSy8CoUV1dzaWXXsovf/lLfvjDz/C97+2kpaWFtrY29u79Eqec8g/uvvtevvCFL7Bs2TIMkZ2jdNPSUkFNjbLIK2MuCJgtW2DdOhAL9nFCm+TLykro6ko2QtIINPDLX8JnPtPG22+nFmkRAsaFloABpck+PQO+hoYGyc8PFw0WC/T25qu/IAS1hVN1tV4BM4ndHv47BAJ1CCvO9DIxwVQZxD+Bc6mqmkx6Ebxzp+jDSG82RJuhISG4QNzMt29P15bfAdazaRMcf3zw0dWrx9i6Vd+Ho/SNJTpbZ3RUlPEoxGsQD80wRF1uDjvDBPvnAGowmVp1ZWDUBUwBiVvOhhM8DyeAXYQGJSor9Z2PkYjP+SngAuAySkvfoLs7/iLnwAFYunQ/VVX2qeOjnJl0FxwfD5CVFbx+ORw2urrU+0JF+Z/4/9KlqffCBRnl4MEiFiyINC6oorDwEP0al+rGRqbLdsxmNJ8Xj/b2fqqqlJ7JOgKB9ET7e3u9WCzhJeSlpf243WpqKzQDU4z6fDht/vEPOOEEEQy58kqRmQVxHObnw9BQ9Ew0Nf72NzjpJD39Wj7SYboTyRtviN8DOggKOigoGGEoPboy7bz7bh+rV0sBEwspYGYAgyEbvz+9AiZIAI/HFFELXYzZPBIn2uhhYiKXrCyxSBkbK2AuCBjhDALhtboCh6MkhXKCdqCakRG48MJJnn02fuYiFqKJvxOwx3hWJunKwDidgzgc4Tc8iwV8vkQzMGIRUVWlzy3L5RqgtDR4WbBawetdxUz0wbS1QXV1AOEUdz5lZZuTns2gRKePPVYMj5tp9u4NzvcQC950bLUXKAQy2LRJlGUpLFpUzsGD+q4pr7wCp50m9iteBiKUYCnnb4FvUFIihLsWHR3hmT49JVszxwihCxMhYJri/v7Dw9nTQlRB/D0tiAbo5AkKmMsQNrT/O/2zZDIwQf4CfAwwUlpqpVtHCubQIWhoaMNgyCUjAwKBMma2B3Ic4VwlcDisOJ2xFmPfAr7KggVihlV6eI4DB9ZNZ3eC2DAYtD8z8VmJ/6fyd+roMFJRoXwGGWRnj6elx8fpnMThCD9oS0tzcLvVStSCAmZysgj9ATY/YOCJJ+BjH1N/xvr1sHnzGvSIonvvFX0z994b75k3A9eS7gDw1q1KmWKA0GWvxTKEzzc3BpCNj8PnPgc//an4/p//zOe97z08roHzFSlgZgCzOYve3vRE4qPx4XY7IuYBFGOxDODzab8qEPAgXKwUchHRiNllxw5hoytq/9eH/Sw/38HISLI+ym14vXVYLLB2bTE7dqRmRSgyMK3EysAYDBmkT8AM43CE35DMZvD5TIiIrjZi4N048AHgJOCA7gxMd/cwdnvwOBGlZyuZCQEjIp1diL/7JygvfzspAROaOThcpUy7d4dPWM/P38Tg4EdINMIZzrsoPWA7dijCXlBXV0tTk74yvv/8R2SiTjpJ9OnoRQjfAGKBvJ+ysomYAYTOzn4qKoIzlhIZfpluAoExwoMLZWRmdsUZ9DgOGKKyR0LAmBBzMJJHCLw3ETMz/gS8hiKKkh+k6J/ahhguU1q6hO7u+Iu9gweVc000/rvdZpKxZNfPCDBdA4nDkYHTGb0Y8/nAbB5ABJwmqKnZnVLfSTi/oadnQZTxgVj2aIsppYQMkutVUujuLgy7lpaWDtLdnXrTeFeXGYcj/KAtLCzRmF0kxhIEArBsmZHXX1+k8hw1fAQCxbS3i+y9GitXws6dxxBvHaH0yp13XrzrUQDYAvwAeFjnfsZHKUvOzo7+m1ssk/h8sxZ1CeM//xF9V1u2iOz5/v0FLF+u8eFLAClgZoSSkhy83pnKS3bh8TiiMjAWS39MAePxDIS9xmw24PPN/oCk4KC38AZ+QRmBQLKNj+1s397AqlWQn1+SdG+FgnAhcwKRDaFBRAlZugTMOA5HuFARJWQ24tmfjo5CTs69wEeBR4BvU1amrznW5RrDbg+WqYlSlwUEJzGnD7FQ2AScBVgoK+uiqyvxXpt9+2DJ1Jy6VM0A9NLYSEhk91lWruxlx46bgOtT2OpWYDWBgPI3DP6kuLhe9yRvxRhh2bLEynFE47UHMbfjVByOQzGPmY6OLVRUPAc8BqTHCjsZxIymIcIFjJ34Udw+woM6goICxVEwtdHpQ0NQUPBH4HNTj1yGGDasiAj92xofV8p3tgKrph8vLV1Nd3f8NNuhQwEaGkRfZl0dNDfPXM2fKA0dIlzAgNNZTGTwRRhVbAU+C1xGdvaDaXIi24LfX0nouIFIMjJQFbgDA0LEQioBkQBud3ilhMPh1+wDSoTQ+WAKBkM56rN2xNyTpiZRqv2zn52v811cdHfXR/VShiKyZfGHoh44AIsWKQ6tsa7PB4GlCCfSd3XuZ3yefRbOPhvE+Rw+CdZiMcwZAfPqq3DmmXDXXcKe/9vffgiQFsqxkAJmBiguzqanZ6YOPCcej01FwPTGFDBdXSOUlQVvWuImZiedc0sSJRAQtbQiAhrewC8oJPnm8TZ27Spn+XIQi5TUfk+Rgekn3I4zkkwCgXQJmAkcjvDUtighK0Hf/IYngEsQZXkBMjI8caLRApfLT2lpUKSJDIwVESFNL2Lx8m/gVADKy0vp7NRpqRTCoUOwcGHwezFUNE07qUG4VfVjrF59DFu3rkJEx5NNAQkBI0rrwn9iMGSSyDFsMIgymEQmvgtDjZ2IafZnYLXuiFNCNkJFxU3A34DZEzCih8JH+IwmPQKmF1H2GY4QwdmkWkIm/l67EMM1QQwYfhEQPR+JzC0K9om8BLxn+nGzuZze3vj3moGBcYqKRGCirm5mG6rFceRDzOMR5OXB6KgJYW0fpLUVamq2IwZergO2p9w8L7iNlpZro+zkgxRgs42risjQjJxwYkzm/X2MjuaHlSfa7Vm4XKll9WAUp9M6FfQLpRwhVtR54w0491xlVpkeutm3b/F0YEiN+npobHQQT8Bs3aoM4I537G1CZOPLSWd548MPwwUXgCiZrwn7mcWSic+XPufQVNi0SZRA5+XBnXe6Of309N9zjzSkgJkBxM0pjRPMwnDi8RSrCJiemALG5ZrA4QhGG4WAWUCqUcZIAgG4/HI9ta6KKAC1Bn4FMQAtmT1x0dJimr6BZWZOpmRDOzQE+fmxs0H5+TAykp7Mm9MZiIqyiRKy+ALGYBhGlLop0ccLEYImPqOjE+TmBheCVVXQ3j4z0VohYJoQQhXKyurp6krcNrupSRzPCodjIe10MvX3CQCHWL26jC1bAL6A6CFJaqtAGZs3h/e/KGRmGhgfj32zDS2nS9QGViw8dyEWkkuwWg/EyPp48XqtFBcrzkYBFi2aHQHj84HF0kN4Bib+wgp6p4ShGlmkUkImosxDwBKCQQ9l0O1IwplCn08RMG8CJ0w/Hlxsx17xGwwjKGVntbXKIjIH9ah9aohMXjehGRhBAZFzQ1paAtTUtAH5iOWIkbIyf4pWyh3AOLt21U4FsNSowm7vi3IiE8Mng98nn4FpQvxOQRyOPJzOVCMrnTidVVH3BtH/NRJxTAVQgh5vvQUbNiTyPi727atl8WLtZ+TmwuhoHvHOs2CZuLiuieukGkoVhgERWEg9Faf0U4mAUDNi0G0QiyUHn29mqmWczsSE+Pg4IYL3WeDMGdirIwspYGaAmRYw/f2FEROeTVgs3pgCRiyIg+EgcRNbRLob+f/1Lygv7+Wxx8bjRsGDcy6iG/gVHA5vkjczPy0tBmqmAi5VVZ4UeyMCGAyxT5fCQiP9/emZxOZ0GsPqp0EpIYtduz48DLm53cAZIY+eArxOZqaeQXGjKAsdCHVLMsV832To65vAbA6uFqzWlbjdiS+oIgXMkiUzv5AO2j83AQuorVXEwunAKySe8Qs2l0Y28CtUV0/S1hb7fG1pCWaGcnJISLQLAbMfMbvDhNXqjJGB2U4gYJpa7C0F9ukaHDkTiOxEN+EZmELiNyz3olZCJkgtAyNElQuRWQjlJCBxz22xPRDBi8heknxiXcdFD8IYyudTVyeuvYnb6upDCJgOQjMwwf0MFzCtrX1UV4eWea2kurpLl+GINr8DPhfSuK1GJQ5H9OwUt5uw/tLy8mQFTDOKhbSCw2HC6Ux1Fkw7XV1lKgKmnIKCPgbDjN58gAUQ18gFC6CoaJj+fj3XJhd795bHFDCCHAKB2MdQc3Pw+rxiRSzHxgOA8oYLSceMme99D7797ek9IVrA5OHzpXUKMSCMZM44A26/Xd/zR0ZE5iXIU8C5ad+vIw0pYGaAkhLweouI12ydHE4MhuyIxlMzFos7TgYmA7s9qHpEKreWdAuYxx4b4PLLv8AHPvALXnwx9mI0KGCiG/gV7PahpBa1IByRzFP3+pqablpaUikjGyPUVUeNwsIMBgbSI2BcrgwcjvD6bVFCVkSsDIxYhLYCIR68VAPNU3Xo8d45GKkFUasvopTJTe4OBNTrzAX9hJYNGo21SVmNtrQoEbaXgMtZsmSSven3HJgmPEq7BViLwSBq6sfGMoBlBAK7+fa3RT2zPlwoGYRdu+CYyGpKoKoqg/b22I0ToQ5KCnoj/eLY8aDMmCgp6cfj0XrxNoL9YMcjyj8Se790ESwhCz1fDIAhzsT73qnZTdEYDKllYIQD2UGiBcxxiOtdYni9wjFJWZCGEggUEgjs1nyt+Lv2owiYykrFQMBBut2eQBEw7YQLSsjJyWdkpCnssdZWH9XVIfWfrKe6en9SFtNBXgXOZPNmxaJfjSrs9q6oDEzk8NG8PLG4TJRAoJnIDIzdbsPlSrU2rgO3uyTCxAfAgc3miiiJCzqQTUyIQcgNDW4aG/Us2N0cOGAJK81Vw27Pors7dgYjWGkhrms7d2o9c5JgSWcdqa5N/vMf0V8bNFtpIVrAFODzpX+ddtddYmaOXkv/trbQuV/bgEFC78MSdaSAmQGKi6Gnx0G6y7METqIbE01YLN1xMjCFOBzBEq2aGmhpsZNOASOGVXVSX/9lNm5s4JlnYi94gwLmTaJv9AKbzYDbnWgkdBjIm+6xAaiqGqS1NflOfmEmoO1ABlBUlMHAQHouhiMjAfLywrN4ooSsEH0CJrR42QAUUl4+GtP5SDQKj6GUdAEhwywXkIyA+dOfYOPG6MyPyBR5gRCrrWlHqMREoBgoGUBY1a5m8eLfqAiYXcBaRGQrNYLlY6D0rYBiKwpwLv/85z4yMuDRR/UugA4iPmOx4I609gUoLy+goyN2ViF4TglsNnQ3/3s8AazW4LFltWbR06O+2PH7d2AwKM5+x6IImLIyPSI5vYjshNoiKoDJpJ0VmpjoIyNDq38ki1QyMEEBE+n6tBYx6yexwa2ihOwQaoGewsJ8BgcPaL7W6YSyMg+KQM7KUs7HUiDxnrN4CAHTS+Tywm7Px+n0hT3W39+PyRSqMpZTXb07hQxMD0LkGadMFLSeV4XD0Rp1rIbP0PoPyfZiDA52UFgYOWzShtOZ6lj1Dvz+PKIP2yxKS30RQ01Dh1gK6uv7OXRIzwWpl7GxaIvxSCoqcujoiF9irNyHCwuJyBIpREY96kh19tgdd8C114Y+El3pYbGY026jPDEh+qZWrhTBLj2mFK2tUF09CnwYMVbgV2ndpyMVKWBmAFFCZiN+DXYyuInuFcnGYvHFycCYsYeUiOfmwshIemfBHDjgZ8mSHcCJLFx4Ls3N6oPLFIKLrZ1EN/ALbLaMuBGeaNqZnKwKWXyD3S4sgpNBlGDEFzCFhVk60/N6GCeyVEQsxvKIVcrldvux2fqIPrVXUF7eEVPAuN3KwiMcEVFfiEjxJ8af/iQsfZ+K0A3NzVBb20SooxJARkYhk5Pa0WQ1xP5tRSwOr8JkelSlfPFu4DYUFyj9fAthKxwktCRCmE+IAu/TT4eXX4ZA4DR+9atqvvEN0Tj7yit63ucAsIDBQe1FV0VFMZ2dsaOnkRHkRATF2Ng4OTnBY85iqdR0U/T5+iguVlY3iwBRszcbjfwiA6Mm7IyYzX5NATM4OERhofriy2jMYnIyVQHTQ7TpR3CYYGkp6BjhAigibQ9qAqa0NI/ubm2V2tUFDkdkiR3MlIARZVjRH3pZWSZOZ+Qg3kHCRd4Cqqu3pSBgXgbOoK0t/DyIphK7/VBUBqa9XXndPuA6xFySxHG7e7Faw0vILJYMfL74g4hj04GSIY3EZutTzcCMjQnRClBePq6zjK0PNYOLSMrLjXR2WjR/HszGPoPSp6WeFQ1moAV1pLI2cTrFGifcRW2UyOCvxVKMz5feHs833oATTwR4m1WretixI/5rhIB5HfgQ4h4Ve60hEUgBMwMIAVPCTNwcRkcNZGdH/9nM5sGYAsbtNkWlnQ2GXERUIj28/XYbxx8/grhp52AwGPD7tcWHuFlMTD1fPRJaWpqVRAlZG07n4rCLl82WqTHoS43w5/X2KgukWEMsobAwm4GBdEVzJoi0bM7IAL8/i1gCxuPxYrWqRfmWUl7eGLOfyOUCuz1628LVK/ESsv5+cRO5+GJ44YXwnwnxuhdh2RuktDQPl2uX7vcI9gY8CXwQcRwtxWDoD7l5DgH7EU2R/cRy6wmnGTH/5l5CI4ThDmTDKKUiJ5wg7C9ffDGfNWsOYrFMsGZNrJrvUEQGZtcuNBuPy8ttcaOdQVtygcOhzz5bMIjofxFkZFQzOaku+t3uImw2ZV+MiHN4clYEjM83icWido2wYjINaw7XHBwcpqBA/bpTWJiZUja1o2OIigqt0GseMJyQgBElZNsQIj2c0tKCmMEZkYFpJ/T6ZTCA3z9TGZhxSkujm69ECasFZSE7OTk6FWQKvZ9lUVXlTEHAvA1s0NG0Xo7dfjBGBuYJ4PvAAXJyJhMuI/N4crDZwgVA5LyhZAgEtAVMaWkfbnfo/UcImNDBxiUlBjye+CmBQCBWf1gQMc+oWPPnIhvnA64A/g8QA4D3RLnyNxJ67QkEakklA/PPf8L5OhyjzeYCfL5o86BUePppOPfcfcB3aWh4RJfjnxAwTwIfSeu+HOlIATMDCAFjYiZuDh6PSWUwF5hMQzGnYE9OGqPSzgUFGfT3p8/u+e23ezj22ODqqaYmk5YW7Xpvvx8yMrYTXkYUjs2Wj9ud6EKinZaWBdMN/KBMKtZjyfy/CNvTYKpCzIDpITIdH0lRURYDA6nfpUZHIStrhOhmXWXWjHYZkcfTjc2mNr13KeXlu2JmYMQMmOismXAis5Now+/+/eJmpXbDamwMUF/fReQlqLzcQmen/shbU5OSxduCcM8CMRRzT0jz7UPAxVP/X4fImujhDYSD2xLEDVYQzMCER/Ryc+HjH4cf/xiuuWY3sJtVq4SNaHzEDXznTvX+F4CKiqyY0U5QRGjwe319TwojhNuMVqPuUiVmXJSGBfSXAXtnKQMzQnGx2iLEitk8ECMDM0pBgfoirajIQF9f8tHyjo4+Kiq0pmgvAg5gsyWWgRFzqKK3WVqaQXe3Zq3UVAamldAMjM0GbncZMyNgRigtjTaxEcfiIpTm7K6unZSXR99/8vMzGB5OVjzuBZbw1ltw/PGxnpeF1eqNKq8MZmBeRRguHIvd7tH9d1Jwu82q92mR1Uje6aK3dxyzWT0zYrMZ6O4OvX4LAdPeHuyvsFoz8HjiB9l8PiPFxfGXh0LAlKBlWiIW5m8Cj6NksleuRCUrERQwt90G69aZ+Oc/4zoIaPLMM3DOOaGPDBLZkwSQm2tgdDTZsr49wO1E3o+Fi+RPgF9QU9NJS0v8CpLWVj/V1d2Elm9L4iMFzAwg3H/ySP/NIaApYDIz/TGapZUsRzjCSrmU6ItPN8nYa+7f72fx4mCE8Jhjitm5U30xGmyEfh04UXObNlthwjcPaKOlpTpslobVmk93t57yrleBnyAGQArEorCd0AiRGoWFBvr7Ux8OKt6vB7ULroiKaQsYt7sPq1UtU7SYsrJtcQRMP3Z79GeUrJWyMmDSYFAc1II/a2wcpL4++kZaVlZMV5f+ksGgA9kgwYv/BpYseYd9+5Tf5S/Ax6b+vwSxyNGDUh62ntDG62AG5gCitC7I5z4nnPhsthXAWwkMLOwBSsIsRyOxWBR3Q+3jWAQFgt/rFTCBAAQCI4SL9GoMBrW/hYvu7pqIjK7ogxHD7eK/XzrxekexWNTOFSsmU3+MDEwsAUNK53JHxxjlmlMAFwP7EhpmKX5H9YWryORoD9gVGRgXoZH7igro7CxlJpr4h4dHyc+PvgaJY3ExypDClpb9VFer7fcCjeNODyNAHjt3amcyFTIyJqNsbp1Opq6Bw4jryXrs9paoUrPY9OPxlKo02ouqB78/+aoHp9MSNs8tFBGkC/3cggJGKacrKcmipyf+fbCrq1jzfUIRAqYcrX4xUdK6H1iDKH0f0XAiEwKmsRFee0183XbbBUkbgvT3K5l5hSZEWVrq/PWvcO65o5xzTh/PPbcOMW9NHEhOJ5SWjmM0dgOLqa2to7k5/gLG5RrE4ShJy/4dTUgBM2PkkP6bQx8eT5nqhTHe69TmHQjr18WE9+r0cd999/Kvf/0uoXcQC6AJjEbL9GPLl5eza5e6qgqmtd9Aq4EfwGo14/Ekepi209paGpaByckxMTYWL/KkpNY/APxr+lHRtN1CvAxMYWEig8K0Ee/nQ31oZuwInsczjNUaOX8BIA+HoyvmYtblGoyyboZQK2VR+qKXffuYtuFcvx7eeSf4s6amAerqout8y8sNdHZa0TvAVAiYfsLdmQwsWWJh796diMXSEoJiMBEBswuRWQg2qUNomck+ws0SQjkeYU6BzsF8wjVr/340nX+E4M8mEXcsvT0ww8OQn99P+DFeSfRsCYAW3O5aFQHzdsLWzenA55vAYilS+YkNs9kbIwMzQUGB+qT2VAXM2NgwOTkLNH4aFDD6MzBeLBb1Y01sx4FWaanIwIQvMMXC00zqwzrVGCF6BowiYGpQTAxaW7uorla7pi4gI2OAiYSTMCNALpOT4n6UGbeFI4vI+TkiANBOMBN5LHb73gQFTDNud61qoLG42IjXm7x1tdNpUbFQFthsZrq7Q8tjRVAn1OGquDgbrze+MBECJv7+lJVBV5d2dr6ry09ZWQ9iqbkWeJclS1AxWWkE6rj7brjuOuH+dtxxB9iyJfGS7I6OyN4XEMGmSEONUPQppfvvFwGqxx+/jscey+Suu87g1Vc/A9wHwN/+Bued9zqiGR+qqytobY1/QZyYGCAra6mufZAEkQJmxsgm/RmYLtzuqhipafWTfWzMR1ZW9J9aWCkfAwSbpnt7H+VPf/osP/rR6YyM6L+5uVweHI7wrM2CBTkcOqSutoIN/G6im0uDZGWVMpHwnaydlhZzmIABMwZDvNrfbQhHqRzEqSFuBl1diotP7DtiQQEMDsaxbdGByxW94FAwGjPx+2OVkAWw2dS7VzMzA0xOal+ou7tHsdujF21VVYqAaSARb/5QAXPssWK+icLIyCB5edG1UuKGuAiRno+PEDB7iDQDWLz4XPbt2wF8E7gm9CcoDefxEdFcIWKC50gwy7GX4NyCSKoAMUnZ4dCzUBV/F8XuVJs8QsvZQpmcDK2zfxeY0J2BEe513YTP7sjFZFIrwWrG7a6IEDANKJ+r0RjLOjv9jI+Pk52tdlG0YjL1aGZgBgYMmhkYk0kRMMmacgygvWBKXMAMD/eTl7da9WdiO5VETrlX8PnAbA7PaFRUQEeHkeR/v1gMEz0DRhEwVmA7MEZrawE1NWolMwsoL3fFzBarsw9YzN69omw1PmJ6fbRA34OYbQRQkUQGphmPJ/L8ENjtmbhcybqTDtLV5dAUMFZrmWp/S2gGJiOjgMnJ+KJAZHri75EIWBSgZVjkdHpxOJTg0QbgLbKz1YIcXUxMlPHuu8HSv/PPP8A//pF4Ju7tt9XKB6Oz5UGy0FPW19YmXCXvvLObnJxm8vPXcf/9cNNNH6al5TlgiMceC3DBBbegCJjc3MWM6GqgCh8pINGHFDAzxkwIGCceT5mGgDGhNXemp2eAkpLon4kSsnpCF2dPPeXhE58o5owzBti6Vf8wjd27G0P81gUVFdDeXoHa5yAETB8QL51UQuITecdpbc2McKExEwjEE0IHCS5Ij0OJuoshoPGHOIoMTOoCJpiBicZkMtLXpy2kenvzMJm0VsA2xDwbdVwuP6Wl0ZFsUUIGiVop9/YG0/jr1ikWw0q2rh+13icRGV6A3lkZYgbMNoILDkF9vYnGxgsRdpShStaEvhr0SYJNrJkoGaHwGTCi3l4dw9RXgOpqsZ/aDAH5YXOLtMjNzWF4WH1jHg9TfSnbgauAqygpIcYwyvDXWq1uhEtWEKu1X+X1zbjdkSUyBsSitZXaWmhpGQe+hriRpzRBVgdjqBtsWDGbu2OUkOVqupCJDIyV+MMwoxHHSLQJRxAb0J2QgAkE+jAYohv4ITQDo3WQRQ/hFeeZvvdW5wVE+W844ncfRi0Dk58PQ0NGxDytX9PaujaszDdIA1VVLUk08jcD9Tr6XxQqMZkGph0Lx8eV4EHoeW3Abu9NUMA04XaXqt6nHY58nM7EjylBJ05nraaAycqqY3xcycCMoly/wh3Z8tGT3e7qKtZ8n0gCgRy0BExXVx9l00poFUr/oZq9+QsvGDj77OD19YQTRti8OfFeqK1bYc2ayEdjCZhsAoH4J+JPfwo33ghG468Q11dxnbjnHiOXX/4LvvvdlznjjB3k5KwElPtoNfqqFgaIvIdJ4iMFzAyRkZGRROYgHi14POWaAsZonFCNfIqyouioS00NNDU5CAqYbl55ZRWnnZbDhg1m3npLfwZm9+4eli2zhD2WkQGBgBml5jkUETnfBpwQZ8vJRQnFbJDQRywYjWNxSnlaCC52T0FMVIeurhEcjvhmByIDo16SkgixBIzZDL292s3FgYAhhttNDQbDsOZn4HaD1WqJerysTJlGnYyV8gRwC2bzdnw+IV4OHYL6+lbUxKtwzSpHKTOJx/g4ZGfvIvLin5UFo6M5BAJiITIxAY88ojhy6TGu6CJ8kJhwbAofOKZmTRtKFdBGTQ1xFmNiXsOBA7AoVpUDUF6eSVeXegQ3WJZ5O/B7oB2jUfvvHUpw2GH4wVNSMqQS1W3B7S5WuQ6dBzw21ch/L6K37X+By4k8h7duhS99KbysMFkMhhHU/w42TCZnjBKyHE3L6qCASbzEyuMJqNqRBxGfsdWqf0aPmiuhghCpxWjbzo4Rea6lImACgUP85jfbeeedB4kskxaugG7UBEyQq4GXaG1dEXIuhVJFdfXBJIZZNgM1CQkYu903LU6C509oBgbs9glcrkSmtTfj8Vg0MjBFuFzaQaTLL4cPflBrGGwHTmd5jMxILSIYAqEzTwYHRXBNUEB8AePH4ynSWGeokUMgoJWBmcThUIILZSjmOOEDLScBIw89BB/7WPC1mZllTEyoZchis2ePWgauHa0S8IICA0NDsU/EyUnYtg2OPXYI0Sd79vTPGhrgF7+wsXJlNtdffxdwY8grjWRkTMbMSAvRP0Zk8EgSn3knYO68807q6urIzc1lw4YNvPXWWzGf//DDD7N06VJyc3NZuXIlTz75ZNjPA4EAN954I+Xl5eTl5XH22Wezf//+lPdTTE1Pt6NEE93dpWEuQ0FMmM0jqjdrj2dE9WJUUABDQwUEF6V/5dChldTXw3HH1fPWW/prwHfvhmXLossGjEYTk5Nboh4Xc0D+Q3wBA0ZjIIGSlAkgQ2URb6a4OPasnHABswGlh8HlGsBu12rIDRIsIUutLCNWxkccV1qOQ/G8+2uwWns1I/KTk+NkZkbfHbOymKpH15+BCQ4RfQyxyLmGBQv8HDoEb701yvHHq5ei5ebC6GgheoRSMBtyiEg7ZiCsofx3vxPONP/v/4Eow4pXmtBG+CLsGGAn+/dHioxY9eTHALt0ZGA6gPIQQwJtKioK6OhQX5ELC+UAQnw1AOci5i/ERwiY6O0K16LIm3srPT0F09O1g1wA/IPFi3/Dvn01COe3RQj76oenn+X1wte/Dl/4AnzrW+lo+tfOwJhMXTEzMLEFTAmJ9BspdHT0UFERb8FbTFZWj84+jx4MBu26QqMRAoE81ATMxARkZIwS+fmUlSkCJpf450I4L7/8Kq+8cjE/+tF3iZyR1N0NNlsvWtchcb4eBzzK6GhORJBJIYPqalcSGRgxaV3cW/Q8vwKHwzVdYhnsbRNCSMFuL8HlGkhgP5oZGiogXyXO5HAU4XSqL7t8PvG1eLGWc2EHTmdpjMxIHsEy8mYip84L8jEYJuKIgn56ey1xs8EKxcWZeL3qJ9nIyBi5uUqaTblWBlixItSJrI2xsVo6OiKvfxXU1HjiXDtDeRMQJa/R++5Ha7lrNhvx+WJXWLz5Jpx8MhgMDwD/ReR1f+lSuPjiszAYfkWkzXVJyRA9PdpVJKJSYTBqm5L4zCsB89BDD3HNNddw00038c4777B69Wo2btyISyO/+9prr3HJJZdw+eWX8+6773LBBRdwwQUXsCPEw+8nP/kJP//5z7n77rt58803KSgoYOPGjTrrFrURVsra1pbJ0YTTadEQMGbM5hHVm7XHM4HVqhV1NiCic056ep6nuLgUgwFsthx6evTvf1OTmbq6aBeN6uoi2tqi70Qiy/A6sSyUFUpKBnSVwUxtmeHhapUJwgVYrd44rj9tBGu38xALIz/j471kZx8X951FCVkhiTS6q+F0TuBwqF/wYgvj1qnZPlrUUlYWq7Z8lPgZBX2rCrEgBngaUUq0kdNPf4eXXoK33+7k+OPVmq4VlPKr2Kq1s1NZcIyjNrPgtNPEYMnBQXjoIbjrLiGch4frUPpTtGknvI5/BbAjRMB4EOWNsVgG7NKdgQn2hWlTXm6ms1N91dvVBeXlXQRLJU4B3pia+RF7ux7PGFZr9KK7pCSXnh5fxKNj+P0ZKtPAM4DfsHjxAvbtOy/k8S8jBomKyPMdd8B3vgOrVsE99wgxk6zbkFigj6BeilqCydSuMtRUEE/A9PVZSCYD09HRSUVFvNkSixGzieITCGwmELDEeVY+aiVkYjjtAJHnddBsIfFZMH/7WwHXXVdKQUEZ7e3hAUS3O3b2qbBQLNjEcGDt96iu9tDammgDdwuBQDUGg96ZK5XY7R3TGZjGRmUB7Sc0S2uxVOD1JpKBGZqyu4/G4TDgcqkrgyefFNmXiy4S2eJoOujuNsc08cnImJwSxU1AbUggSSEfk2lQMysp6KW316xbwJSX59DZqZVVGia8hNcBODn2WDHwUdDIv/51Bu97X+RrK1m//oCuLK3BcD/wS/z+D06Vb4biIdzkJRwxYDR2Wd+rr8Jpp/mBRwk6WurDZpvE7dbO8IjKh2TLCo9u5pWAuf3227niiiu47LLLWL58OXfffTf5+fn87nfqjlk/+9nPOOecc7j22mtZtmwZP/zhD1m3bh2//OUvAZF9+elPf8p3v/tdPvShD7Fq1SoefPBBOjo6ePzxx1Pa15ISZZhlakIoHCf9/bkh6eBQTFgs6sMsPZ5JjeGGon7a6fwwcCWvvvp+Tj45eOE1GCAQ0Nd/4vcbyMiIvms0NORw8GD0DgcC/qkbWHwP9tLSgQSGULbR2rpUpbbaQElJP96Y65FxRO+SwgpEY78PtQnYkYgMTAGinjV5+vsnKCxUXwDFKiEbH28lMzOWgKmhvLwthoCZRJQYRCOmJ2egZRQRiYhmBhDZhUrgs5xzzi38/vewbds4ixati7OFtYhUvTbCsWucyOnKCmeeCX/5C9x0E3zlK8KV6OST4d131yDEaiwiMzArCM/AxHIgU2gAGnVmYCp0ZmCMdHSolxqIEph3gFOnHhFZI3Etir1dj6cfqzX62LFaC/F4Ejme66iufg9NTaHXglzgMuBu/H54/XXxtwHx+55xBjzwQAJvEYIoWepH/TqSRWHhAAOquz/BwEBenAyMieQyMN4YM2AU9JtJDA1to6AgXjZffT6UMCDxoj2E105iAsbN7t0LWb48g/e8J4NXX10T9r4iW62dVlJmQrW2EmGyEk5lZR5tbYkGggbp7CxUcaDSfBfKypqmr4cHD8KCBUNEXgONxgUEAsnaOodjtyvDPKPZtEkMwz32WO0MjN+fpxI4CN3+GN3dXYiy7TW43UQIngKKi/viBAT76OsrwqTtzB1GeXkunZ1aS8nIe8pyYNe0MYwIXDTyf/93HJ/4RORrK1i+fBe74s419mMw/Am4j9bWG6mu3hTx8y2oDYBVsFiy8fli/31FWeILiGxyYnNjSkuNdHdrX4A9Hj82mxQwyTBvBMzY2BibN2/m7LODtYdGo5Gzzz6b11+PbiYEeP3118OeD7Bx48bp5zc2NtLV1RX2HLPZzIYNGzS3CTA6OkpfX1/YVyTFxdDTU0Oiw//iYTBo9TiYsVjUZx6IaLj6wnb1ati69UPA1bzyysc59dTgz0QWKX4hcn9/L4WF6tHyhgY4dKiO0JucKP3xoUcUANhsftxun67nCgvlBtWbY3FxIpkcgE/h9988FdGJn43KyAC/P/acFn1MYDCoL4CCGZhoIeHxuGJk2gCqKS8/pCpghoYgP19bJFZWKn0wmegxVRCL6V6UOmwRDa/mmmve4NZbb8FoXKX52qIiGBj4InArojn0LuDFqOcdOAALF3ag5fZktcJnPyt6Vj74QfHYqlWwbdtC4mdgIgWMyD4dOCCOaVEnH0/AVAOt5OURZ5K3KCEL769RJzj5OjplIRarmwieV8KZ0OEIxHUiE2Wm0Ytkq9VCT0/oQnKAeMPWRO9bZNbnk8A/eeWVTk45Ran53gw4+cpX4M9/Di0lG5z6io/XK85rLUR5ldpP+hgcNGkEgxQBU0gyAqa9fYSKiliZTBDH7D4yM0UfVyx8vn0UF8euj8/JgZGR6IWVyHS7URMwIkCVWAamr+8tiovzMBjgxBPhtdfeQ2igob3dR2WldvZp2TJRbhzqUKhGbm4to6OJZ7J37Yo//yWImUWL9qNUjAsBc4Do87oB/WV2w0SWEIUiDBesqBmJKBbq4j6idtx2xNw2QHm5ic7O7Yiy2vowBzJBPkVFA5pZSUEfk5PZOmyolfc0qg7YHRqCvLzIzMxylJ7blSthyxZobXVjMBSolMaVsmTJThXL5XCs1r3AyUAmBw+ewaJFOwk3FXiX2AImH58vdqB5aAgKCu4Croi9MyrYbFm43dopL7d7AKv1MNo2HkHMGwHjdruZnJzEEXGUOxwOurq0PMi7Yj5f+TeRbQLcfPPNmM3m6a9qFSsVsfivJH0OPKK3Q7vUwoTZ3KeRgTFitapH7YWAMQLvZfv2nLAhevX1fpqa4guwPXs6WLpU/QIgBMxKIBhSEjbBrcBpcbcNahOGY9EaNcRSoaRkIEYkeozw7AvAStra1lFZmchE4ExSzcDEatgVAsaq+h4eTy9WaywTgTzKy7tUBUx3NzFLP4JWyrVoux0FEQKmjfDFwLc5//x7WbPmU8S69Agr5XLg04jyIxtwJ5HOZLt2wbJl4Q23kVx8MXzta8EyilWrYPv2ShIvITPg9xuYmAhMORVtQQxni0UWWs6A4YgSssnJ+LMrhP1tJWqBEfGZbyV84OoCHA6PDgEzpmrgUFJSiscTenNtZny8Lu5+Ll4M4a2ERuBefv/7LXz605cDFyKmc3+dzMxLueeeXVxxxQitrd9H9M58ANhJPISAiXVt0Lpg9jI4aIqTgcknuRKyABUVsWdGiTK/g1it8V3ivF4jFkvsqG9wYRy+YBSithO10lDx3pUkImB27mzkmGNE+af4Gy8AXpr+eXv7AJWV2qF7RcDs3CmG3GrTgMGg95oPIqBjmOrF1PsaA/X1XRyaascT/Ts7iL6elKM+D0mNFoaGFpCn0T6amQmTkwVEXn8CAVFWp2RXxIgDIp4Tr8cRysoq6OrahRA6BpWgSB4mU28cATOAPqMTQTCoEk5X1+hUECsUUVYL8MUvClevq68+k+9+V+39MrBafXHPD7t9B37/WYBSBngywjxE4W1iC5gCfD7t67TIYvUj7snxAhPRlJYW0N2tLZA8ngFstnmzFJ9TJPSpTR5Oc/85zA033EBvb+/0V6tKgbsQMA6iBUwA+BzxS1gi2cbk5MoYdcNmLBafRgYmC6tV/U69ejW8+66IrpeWhtcl19fn0NgY33J2375elixRv7AKAVNPqBOZaLLcSawBlqGUlmbiduuNxh2ipaVcNQNTUqJmCavgQdh7hnPw4PUsXJjIhNwMUhEw/f1QWDgCxMrAFKMWwfN4hrHZtB3KAMrLvaoCxuWawG7XvqsFrZT1OZGJYWL7CV8MlAC/JZ5wFQIG4CJEk/BFCAHzo7Dn7dsHixZtIn4mJEhNDTQ3W4gvYPpobDRxwQXw4lTyZ8+etSxbpiyqDqJmHKBOvJWPb8qtLz7iumJDbRbMyEiAvLxRwi/rS3E42uIKmN7eSczm6OJ6q9URcc4cwuNZHneY7vr1Yh5DKMPDNXR2nkNDwz3AP4EfAn8EvkdNzW/4zW9+yH/919c4dOgJhIva92O/CeD1TlBcHCs6biCguvLsZXCwMI6AySOZDExXlwmHI54BirDzttmI25fn89VFTBWPRgiYhUTeb0QGphW1DEx5OXR0qNvca7F9e4CVK8UizmCAzMwixseDQrOtbZTKyjrN1y9aJMTLSy8Rlu2PZgGZmf0JDEX1AiU0NgrzDr1kZmaEzUUxGNQyq0bM5nh9IwrNeDxLpuzMtcgl8v7f1aX08wnWrRP35VB8voK4x0F5uZ2urlyE8x8qGZgMioqGGBiI1SQ0SCICpqICOjtLiLzOdXV1T5mKhFKDYjaxYAF89atwzTUPs2JF7OtfLPFYXHwQYQwhRF99/VJED1AH4ncZJpbDl8WSj9ervRQWc4XeAaJq3HRRWmrC7dZOs3q9QxQXp+5eejSSkICprKzkW9/6Fvv26R0Elz5sNhsZGRk4I+7ETqczxGc8nLKyspjPV/5NZJsAOTk5mEymsK9IRN25lWgB8ydEFOV7mtsHcfH6+99DH3kNj+f0GAsHE2Zzj0YGJpeSEm0LzoEBuPde+MAHwn9WX2+hsTF+udChQyM0NFhUf1ZYCIODJYTa4jY3+6mt7UQ0ysfHZsuNeQEIp4WWFotqBqa4eIieHq0eDg9g4+mnYfv24KOiTEnnWwOpZmBExKyP2BkYC2oCxu2eVC0DCqWsbIiuruhAhMvVj92uHaCorFQyMPqcyJxOKCvbQSLiIriPahavDkRGQyy2lPIko1FPKVcQkYnJQU9m9Pbb4frr4b//Wywyn3vuLE46aT/iRh1A3+VTOFkJgwftZ3V3G+IseoL7HwgUoD7McpRoq9ClOBwH4gqYQGAUozE6a2AyZdHbG3qeHsLtXhhXwJx2WlD4Kfz97/DBDxqIXhwtAm6noeF/eOABC5dfDkNDlQgRH1sse70DFBfHWoyZUc+C9TI4mK8pYHJyYHw8m8QzMH4mJ426y2+s1tgCxmB4C693LXEqyKYETAOR2VGRgWlGzeRARM4TKSELsH17LStXBv9+y5cb2LVrMUrZrM83gcWifT7m5AijitxcVF26gtRTWdk2VbaqBxdgp7kZjdkyWlSTkzNMa6sys0q9t81u78fl0nMPaqK7uyHO+ZFHpICJHL65cmX4fQjGaGsri/u7lZUZ6Oz8PMLSPHIGjKCoaDhOBmaIeJmeUMrLoaurlMiyz66uHsrKIjOH4dfMs8+G00/fgTZZlJb6Y8xLmsRgCKBUTwSNGL4F3IQIhJyn9WIALBZDzNEEwpb538AZMbejhc1WQne3tgLz+cYoLk634dPRQUIC5qqrruKRRx5h2bJlnHrqqdx///0MDaWnuS0e2dnZrF+/nueff376Mb/fz/PPP8+JJ56o+poTTzwx7PkAzz777PTz6+vrKSsrC3tOX18fb775puY29SJ6YIpRfM+D/A34MeKCq31Q33CDcOgJeuG/jst1nIYDGYgMjFtVwExOGsjM1P5TX3WVmM1xwQXhj9fUWGltjX8hO3Qog4aGWCUTOYSWvDQ3d1BbG8uFKhyRgtXrSDOB02lUtZosKTHQ06MV0nPj9VZwyy2i4Vu5wIu6aN27SqoCprUVqqt7iJ2BMQHRqTaPpxCrNfYpnZ/vYHg4OpslBIz2a4MlZPoyMKIkbSfh5Uz6CL5XJB9B2DIT0kzfQ3w3sHAKCzPiRCBhYiKD/ftFnf8tt8AnPwl/+9t6zj//T4j+F72qth5omppCrv0sETnUuUnyETXukfQR7eq3FIdjJzEqYqcYQW1OghBMYiCn4BBud21cAVNdHdqkK/jzn4XDUrzXfeUr8LOfgbBlfirm873eQYqLY12jrFP9NpH0MjKSp2Hjq5BF4hmYNvQGZsCMzTYUV8D4fMviRt5tNujuFj1XoYgMjIvo8lilHLGEyFku2rTQ1FQXZjQhMgUbCfbBTGIwxP79//d/xTkVm0KqqjoTsFIWM5lGR1FxoIxFLatWubjhBiWAp97jZbcbcLniRAEAMeS1Kub5kZ+fzeBg+LYi7zPLlxPRvN5MW9vyuD1ywey1oL09uq8unoAJBBLLwBQUwMCAicjjqKtrgPJytYBaMcHzqhftga8A5Sxc2KdptV5Q0M3AQPBmHxyefDziWrgV+GzM/RdZbe3A3549wyxd2kuizfsKpaUluN3ar/V6J7BY0j1y4+ggIQHzve99jwMHDvD888/T0NDA1VdfTXl5OVdccQVvvvnmTO3jNNdccw333nsvDzzwALt37+aLX/wig4ODXHbZZQBceuml3HDDDdPP/+pXv8rTTz/Nbbfdxp49e/j+97/Ppk2buPrqqwHREP+1r32N//7v/+bvf/8727dv59JLL6WiooILIlfzCSJOiiLCI72TiEWGBWVho8bBg6LU5ctf3sLvf/9b4A5glK4ucwwPeBMWi/bU6Vhs3ChcgLIj7nHl5UV0dMQ/sbq7c7DZtBeRJhP09i5GiQ42N7dTW6uvgR/AZjPhduvxxQyultSMDkpKjHi9WlE0D08/vZJLLxW1uYor0v79StO2XlIrIRMCppvYGZgihDNaOG63Ke7CUvSwqAmYUex27YtsZaVSQlaH1nEbiiiFSyySp1BTo+XadS7wLwCeeALOOkvdPjkedXXQ3KwZCQAm2LWrjrVTZdPr1sGvfgV3311AVtZW4M+IsjZd7wY0RS0sggwC+SGRw/jk5OQxMhK+shOftxsx7TqUchyOfXEzMCJLoRVUCA1AtOB223UcZyKKrFigHjworonxMgkgAinPPw+BwHtQM28IxesdiVN+YSMra1SlFEnMKolltxsIZCCyWvqZmDhARkbsMs4gC7HZOuIMs9yNz1eps4SsjMgMTF+fWLCqITIw+o0KAoEt+P3msDLjdevgnXeOBx4jEOgmEIhnHy0yL7EcyBSqq90JCBgXyfQnQB1f/vKrvPACnHvuJFqzOOz2PFwuPZmqg7jdFTGzqQ5HDi5XeAY9MtMvKhdCn3GItrZFUdmUSCKvM9EuZOJ4iFUONzw8Sl5eojNJcogWMGOUlamd8MFGfvFvLNeFCurr3TSqJZyBoqJ2+vu1PpSvAL8i3j0iOFtNPaB88KCPBQviTBiOQVGRkb4+bVHv8/kxmy1Jb/9oJqnOoTPOOIMHHniArq4ubrvtNnbv3s2JJ57IMcccw+23357ufZzm4osv5tZbb+XGG29kzZo1bNmyhaeffnq6Cb+lpYXOkNqTk046iT/+8Y/cc889rF69mkceeYTHH3+cFSGd6tdddx1f/vKXufLKKznuuOMYGBjg6aefJjexME4U4qTIRpQmKWwl2Ex2LBBp9yd49lk47zw/Z531DV555dOICPOvpiaYa71jEWazK86gxsQQ7knxb0ggxKAWog/m/YjFZ4D29gkqK9fo3g+TyUpfn54LahcDA3WaZSFmcw4+n1Ymx82mTVUcfzxceCH89a/COWp0NF6pQzgGQyZ+f6oCpgstAZOVBePjOUSXtozj8Zh1TE+uQU3AdHePY7dr/6J5eSASN9lENgproW8WQzRVVVpzU4T7WiAwyDPPwMaNOxFWwYlRWwtNTZqRAMDNW2+t5biQ0T8LFsCiRQbg/cALwEk6360eaIwhYDpRhljqzcCUl+dElQGKGTDNRGdgDFitsXq/lFkq2hlOg6GA4MyScdzuLF0C5sorYcqxnh//GKbiRnFRHK5ef70AcaxplzZ6veMUF8c6Qa0UFQ2pRJz1RlQTO4hdrlYcDr2ifQE2W5NmBsZgEAtqr9eos4SsBLU5TVrXZtG7YESvNXpX137KysIXYuLabkH06tyLxZKMiFCnqqqP9na9pcPd9PZW6Lb+DbKckpItHDwIZrMYhKmG3W6OEh3qDOJ258U8P+z2fJzO8N/r4MHoQFlWFiHC+yBtbdVxMzC5ueGOh9FzYMBkGopZztrb69c9AyZIDuHOX9DZaaCsTO06K6yUBbuIL2DaNAWMmPMkBMzoaHQQVg9irWZGK/A4Pt5DdvYpiW94inj3wd5eAyZT3Bu3RIWUrA8KCwv53Oc+x3/+8x/+8Y9/0NXVxbXXXpuufVPl6quvprm5mdHRUd588002bAg2gr/00kvcf//9Yc+/6KKL2Lt3L6Ojo+zYsYP3v//9YT83GAz84Ac/oKuri5GREZ577jkWx/J31ImYXh555L6GsPsDWEdoX0go27bBmjU7yMw8gdzcbAYH/wuwxylnysBsjrZRTnZAXMRWNH8yOtobpwxDucmdhohcP8X4uI2sLP0paoPBqnMezR4OHDhWs2fFaDTh92u5jXjYu7eEJUvE3+5974MvfUnMqEiE/HwDw8OJRW1DEQYHrWiVkAmyiM7AdOHx2HUJmMLCaBcalwvs9th3/+CxZCD+omcCUeqUOLm54mYUya5dcPXV/4/3v3+Aiy+GrCwx6yBRhMNPBdozmly89dZyjj9e7WdfBV5B/6WzDqWETFvAVCSUgamoMETNgunshLKyDtSMKDIyLExOaovOnp5AzEFqRmM+k5MHEJ9XpmpUV43Fi0Uz/CWXiH/XxRv9E8IHPwhPPQXCBELbR9Xr9VNcHKsc1YrJNKASce7FYEg8OxiPjg4PFRV6y0EWYLUe0BQwJlMrcMzUrJvYWxICJjLjD+I8VRdq6r1m2uzYMcjKleHXJWVxNjl5I9u3j7FiRTz3Nf1UVmbT3q43GNRNS0sFter6IwbCzlq4hu1AKyBit9twueLNdBMXyHjnh8NhxOUKv9YKm96IPVsU6uS3n7Y2e1wBE4rW8FpRQqa9qhYCJrGlYU5OJiMj4Rmq7u4cSkvVKjOCTmR6BExDw8Fpp7hIQjMw4t6Z0G4DylDXfLR7wQam9nlmmJwcJzMzfcL/aCIlATM0NMT999/P6aefzgc/+EGsViv/8z//k659O0IoJDgX5A2CzlsNqNexixOxpuavwHmcdJIY/AaiTyVWOVNOzkRUqURv7wRmcyqT4bMJBLRrHJqbW6mriy0uFiyAQ4fygEsJBH6Lfvem4D7oixJuY//+NTHmC5gxGNT3NRBwA9nT5RFXXQUbNsBUdaJuCgqMDA7qy1BMqGgp0TvSQey64CyiMzCdeL1WHSU6tZSXt0ctXHw+MJtj95JYrUyVu8S2BxdCR72nIlkGBsTf5MtftvKHP/yIyy8HYd96cuwXqiBKyGrRrv130dFhi1uuoY9KoI2yMq0eGDHEUgz+1LfFYPN18Fju7JxQsSxVWEqsskYxh0BbdIs5CYcQGWS14Xja/Oxn8P/+H9x2m77nK6xdq7gwHYdWphoUG2VLjC3ZNGxjRQlZPMSxrD8K1NExomOIpcICbLadmgKmpGQ/gcDx9PQIs5VYCDczI6HZqvFxyMoaRau0SitQoMX27RWsWBEd4hZ2yiewdev3Wb06fXawFRVmOjr02pC56OoqTWCIpYKYlSR4Fa3Mqt1egcsVz4VVGAnEFzDhwyy1gozHHCMc2wT76e7WUyIsys/6+tQcyATxemB6ewOYzfoDjABlZZk4neFRgslJo+pwa3H/V/oo9yCMYbSoxG4/iEvjUp2b62NkxAIk2kcYTiCQg5qA6e2dxGQaJvWJIxloB8z8xJvvI1Enqb/Ka6+9xuc+9znKy8u56qqrqKur48UXX2Tfvn1861vfSvc+znMWEyy/EE5XgkLUhrWJIY9gMGwFjuWMM4JuPsPDiZUzgfAYt1p1e1FGUVISwOvVDtMdOuShoSH2QkBkYAAupaXlUWpqkmuGi882tm9fwDGaVUUWzUyOyzUeVvpRWAif/zw6MhrhFBRkMDAQf/bHzp2iUTM0Yaj87UWflPYiyGBQEzAd+P25Mac0C2yUl3dEZQMCgTEMhlhlVWJugxgqJmZYaNHXBybTIOFzVBIjN1cpWRM89hj813/BkiWVlJTsRSxAu0lGJAkr5XIiSx6COIHspEvgwskEJmOUkIkhloEAMSzSwxHN1wsI7Xfo6nJTXq7lZLMUg2FAc6Hk8XhiziGwWs14PJ3AW8AG3G50OaaBOJ4XL068nDAzU2RCh4ePQ8xxUGd8fILs7FirOismk1c1AxMIxL4OiT4EB2r9Zlp0dJipqNCb2XFgNjdp9i0qAsbrjZ+BERn/8MdcLigtHSRWb4g4JrLRXlwp9LBjxyJWRlYoIibHv/66mB6/Zk2czSRAXl4VIyN6FVY3LpclhslNLByITOh2YIXqM0pLc3C54hnPHAAW0t0d+75ht4PTaUcJKnR3o7rfQQEjBJbfb9B1jVi4UJSk7d2rPmunqGiM/n5tUd7bC2ZzYtnJsrJ8urpCA6X+KXcwNTIRx1wTwvAi1nlYgcHQoXHtUtwgxcUlkSx2NNE9PCDaEmpq9Mzyio3BkEUgoN+uXKKPhATMT37yE5YtW8Ypp5zC9u3bueWWW+jq6uKBBx7gtNP0DSU8+liCKIFoRlnQjY3BmWfC//7vR6Ke3dSknIRisOKaNSISmWwpmBAwyZ+AItqrXUB/6NAQDQ2xS48qKoKuUtu3o3oTjEde3hgq5lkRdLF1awGrNIe8C0tVtc+ytTWb6uokCmgjKCzMZHAw/uf9/e97eemlcf785+Agu2AEaRRxQVVH9NlErnriT2meejVlZWqzYMaI5+a1eLEiYBYQy4lMLHC9pCJg6usJc5559FH4yPTpcjVwCsKVLHEKCmBoqBAtAePx9MV1c0uU0tKAhhVoJ35/RUILfHFO1iOil1Nb6fTGsH5fSmGhR7Pu3e3uw2rVXkQI974G4P+A46Yaw/Xvb7KsXQtbt9YR2zRiDDWL4CBWTKYeVQETrwdGGLHUoN9meIiODofuTBoYMBoDmtf23FwfUI7fj47AhEIBysK4owMqK32ozYBRyMiAyUk78X/HrTid1aomMuefD3/5i2hEjzF9IAlqEZa+ehjC5cpJUsB8COEMmoeW+5a4ZuQQOxu3G1jKyAiagyxBZGBcrmqUCgwtq35l6CfsZ3h4edxSbQWl9CzSmlmhsNDIwIB2Nqm3NwOzObEgoxAwwe/9/i4Mhlg7/CHgUmBjnC0LxzLR+xn5s25GR4OBvlQyMEJQRZ8Dzc2t1Nam7hBWWGigvz+mW4ckCRK6S99yyy2cc845bNu2jTfffJMrr7ySopA7WXd3N3/605/SvpPzFdGEtxZ4HXga4aIEzz0H550Hzz+/jvHx8FXF1q2wevUIipVjRoaIRr/ySiIThoN4PMNYrck3wlRUZNHRoZ1vFmVtse9aRqPILkxOJi9gbLYB3O5YwmCUQCCT8XFDjEY+M4WFQxHuLoLWVivV1amH3AsKshkYiF3uNjExxvDwNioqruMb34Bf/EI8vm0brFAPAIZhMmXR1xcZMdUrYJRhlsF9DC6gYl8O9GZgRBmci1QEzJo1sGWL+L8yoyF4qfkA8HeUYW3JoR5xA9i/P8CiRekUMCVkZnpVSwahg66uyoQWfiIDUwVsm36ss3OY8nKt0syF2O1tmmUYYhK0dmpXlA5+Cvg1ilhIT3YqNuIYMBJ70ThJ7F4rK0VF3VECZnx8jKys2H9jIWASGfS4i46O+gQEDIhzNnoxmZ3dz9iYuAck9lnXoDTyixkgwl5YC2FZvpB4v+Pk5FaMRvVAVVGRyMLccEO6j4taDIYhzV6OSFwuYrh0xuIDiGDAzXGep77IDbIVWB333UQGpgzlGqolYAoKFCeyl9i792xVMaLGwoViyO+ePeoZmIyMPPz+eAImsWBeRYWR9vZgAKynp52SklgHw8WIQbX/FWfLwsK9ulrN2GUPfX3BpqBg8DdxDIZs1QxJS4uPmprU+1PEtSTaBGJyclx35l0STUIfXUdHB3fccQePPfYYP/jBD6K+vvnNb3LllVfO1L7OO8RBuxyRgfkLcDYgymEuugg2bmzluefCC6C3bYPVqw8Q2kx46aXi+f8V71wnh6ysybA+GI9nDKs1+btKRUUBHR3aafzGRhO1tZa421mxQqTD9S7SI7HZxnG7Y3lEv8m+fefGGTpppri4F6/KbLrWVpsua894iAxM7Ods2fIKa9YIZ6ezz57k1VdFj8ff/ibMA+JhsWTg84VHt/z+TgwGvQLGSGdnMLLZ00PMJm6FpUvFTTG8hjkaIWBEc3qyrF0bFDC//z186lORz0g61DZFDoGAegZm375sFi/WO8tDDzVE2tsG6aWpqSihyGFJCfT0lBAqYMREei2rz2zs9h5NAeN2j2G1amdRxfvVEnRQPDwE+2AsJD5QUiEPk6kvquZ/cDCXgoLY10Vx/S5Dv4DZgctVrru8TrAEtf4kq3UPbndiEavsbBgdrUU51sQMkGZAe/qhKO+tJ97veOhQOw0N2sP2fvhDYcefXqzY7d2ax20kTqd6KVZ8jMAziIb+WBQSmvWMphGoiyvi8vNheNhEPAEDInjgcv2bnTtPj1EaHc769fDvfwvTE/UFff6Uw506vb15CffACCEcrJvr6nJTVhZrGznAmei1wQ+WoQcxGPbQ3x+8xwwMiLLPZCgqymJgIFpgtLePU1WVejNkcXEGXm/0ed7X58FsjtdbJdEiIQGTlSUOtr/+9a9hX4888gh33HEHv//97/n2t789Izs6HxE3QIDPIybDinSn0ynq8N/73iFeeCH84N2xA4455l1CXS8uuAAefFDPwt9MSclYmGWqyzWJw5G8xK+osNDREcuFLIvc3PgC6cQT4R//EBeZePXcagiXnViL7Jf461/P5YMfjLUVCyUlXlVL2ZaW0gQnOKsjmvhj5/pfe62PU05xAKsxGLZw7bVw+eWi9EpPBKm4WPGtD+LzDWOx6IualZcX0dkZrMdrawtQVRVjmt4U2dlKGr+AWKUdQsC0kIqAWbxYRBEDAXjhBVFymU5stkzcbvWaqoMHC1mwIJ2TkYNRcbVyocZGQ0KRQzFcMgvRKwUigh+IKWAdjkFcLvVeOI9nIuYcp6B5gyA9zobxqapS5gEtIzg3IsjkJBiN8XfGZBqKysD09+fGLYMT1+9S9A963Ibfb0owonoMmZl9UeUxpaW7cLuXJvRZi+GhiwnNwFRV7UMcf+rU10NjYyXxBMymTTaOPTY5V8HkMVBZ6ZmaPxULP2DE44lvdpAKGRmFTExoCRjRjxEI6P3jFxAqYLTcRc84Yxcvv3wGW7fm6BYwOTmwerUo7VM/FguIZU3e21uQsI2yOPYcKL1UXV29lJenKwiUR339aJSVshAwCdiyxcBiycbrjb6ndXSYqKhIvcG+uDhLdfs+nzep9ZBEkNTK9t133w372rZtG93d3Xzxi19kZ9A246gnKGA+DLwXCLc2XLHCzI4d4c1yg4NQULCXSGcOfdEtE1brSNgCXUSlkm+aFwJGfUEeCOhz8gE45xwxnO4Tn0huP2w2I263dmojEHiDF1+sjmN7bKakxK0iYCZobU2XgIGBgdgXvL17c1i2rBw4HXiZ975XDM+86y4QN+PYgjA4eCuIx1OoO9Nmsdjx+YIrpra2fqqq9NWah9sBq6+uhIBxo38ieTQZGeJG/Kc/iYii/h4AfVRW5tLRoV6S2NRUSn19Ot+wGmjBYkG1YTv52m0z4Mbv34b2EEqB3V6I06m+EHe7DVit2jVsoQJmaCh2fX86MRjE393vVxcwPp8fiyVOuhMwmaIH9w0M5MaN1orrdwn6MzB7p2bmJMJybLauqGGWwh62iqEh/cYtwpyiDtFvqQiY3cQKJIjItgNtQwuAoRi24jNLZWUP7e3xItQ9QEmCvUKJU16eS1eXlvviIaCevj50Lv5zAaHMRNN86M/agAuB83nPe/6PRx+9jHfeSaz0+kc/gq98Reun+cQWMPkJCxiLBbxeG4p47uoa1BhimQyV1Ne7VGbBNDE4KFJuqV6XLJYcfL7Iz2QMt9uSsJGPGsXFuXi90ZUsPl9vwtkuSZC0Vd9lZmbyta99jcceeyxdm5z3lJQQVa4k0vri/0ZjHQUFvunyhqDbzEFiWwtqYaakZDDsZuhyGXE4dHb/qVBebqCzUz2s1dPTiM2mb9Gcmyt6fz7+8eT2w2bLwe3WclNr46WXTmX9eiOZMfVULsXFPhUB08PQUKHmAMxEEM5FsQWM6LfJRFjEbgbEvBlxXHiJ10wvbhaRAqZI94XWYKglEAjNwPioqtInRNeuVaarlwLqWRshYGKV++nj29+GO++E669PeVNRVFZm0d6ufsfr6SnSNTFeP6KELNqJbBDIT8o9R5QLnQ/8A5frP3Fn+Njtpbhc6mYcXm8BxcXaf3/RcyP+7/Hot1BOBzU10NKyEjUB4/X2UVwc3zBDZGBCxXaA/v48nRmYEoRDVTyG6O0txmRKtFx3CTZba4SVcj+TkzmAQZeFskJtLbS0VKG4Xvb2gsk0gFZjOihmGcXEskWHd9i/fymLkh9GnjSVldDeHm+ApLAvnmmqq4toadFykvk3cJpui/HcXANDQ5mMjxNxzxoHLgNuBf5Bff3NrF2bycUX63cpBNF/q/38eAIm8QyMKJsLlth1dZFGAVNBfX2ryiyYCQIBcWw3N6fiQAbFxQZ8vshIwX6gIC19XSUlhXi90Z+5zzdAcfFMubIe+aS1fai5uZn65G0gjjiKi4laLO/ZE+oMUsvJJ7/La6+J77ZsUYa99RN7DogWJqzWgTAB4/FkUlKSvItGTo4oE1Pj0KFuGhr0n91GY/JNnqWl+XR3ay1WHuG22z7LNdfE24qBkpJ+lR4Yz5QPfOqIxstY2xogEMicurkUE23R2k1sV6XQycFKRGcYj8eWQKSohkBgeLo8pa1tmKoqfeptwwZ4800QAlu9kb+7O0BpqZ6p1bFZuxZefZU0iwlBZaWB9na1D0x8KOltRq4GWlUEjOgTcjoTbz4uL4eurvcDv6W5eS+1tbHL9ez2SlwutSxbYGpeg/ZrbTamHdREY3hi+5oKy5fD7t11qPVceb29uo6NoiID/f2h144B+vutugRMT4+Z2It7hTdpaXlPEn10eVitnjABYzC8jscjhlklImBEBiaPYE9NIO5xbDJBX18+SjZAjb6+d8nJMc9Ks3FlZZ6OYZbdBAIzPwiwujqD1tZi1DPPQsB0d+sTMCtWwPbt6zhwwB0hDH8LfJzQAOb118MVV6Sy55EUYDBMaJoj9PXlYUpm+UEhgcAuIEB7u5WKinRdRCsoKmqJcFEcRJTCCZqbEyvDjUTcUyPXSbuIl9nWS3FxkWrputc7THGxnAGTLEmNIv75z38e9ZjT6eR3v/sd559/ftjPv6KdxzziKS5WXJuChAuYXE477V0effQzbNwImzcnNq06GjNWa29Evfo4RqMllY0iDpN+Ik/mQ4f6aWioS3Hb+rDZSnC71RfFL73UyapVVl03jpKS/qi/ycSEh8zMpOxroigshIEB7Vz26Oh+srNDL1hZKJbZAjexXINAHFfNzWWIyGM10IbHU5OAgKnC4fg3LtcKHA5oa5ukqkqfSlizBsSs2oWIReUJUc/x+cawWA53vXxiVFbCO+9Ef2ATEwNkZKR7pVYE9E8NsAt9XAyxDAQSF0zV1dDSYqa29rc0N+dQWxt7n+32BlyubSo/6SFeI63RGOx7STXSmSjLlsGWLVmce270/Ca3e0BXw7zJlEdf3xjB39PDwIBNZwYmg9CBodr8mZaW7yU1CdxmM+DxeBEBDTAY/oXTKdysvF5DghkYACPj45NkZEwSL5sLwnikv9+v+Xk88QScd17qVrLJUFlpob093hyzbgYGymfc2rumBt5+exGiRK8u5Cd+xLlcpjsDI8YjnITV2s6KFcoLJoGHgX+lc7dVyKewcJSRkUzy86ODghMTGWQlkRSw2Qpwu1spLe2isbE2qXNBnQrg3YjH9iJm7AmammIP+Y6HqGooROmnAhgZ2UdOzlnJbzQE0QMTLVR8vjGKi+f2vXIuk5SAueOOO1Qfz83N5dlnn+XZZ58FwGAwHPUCJjLav2cPYY3ma9Y08p3viP+/+SZccYWPWEMMY2PCavWyb1/oYxMoN8ZkMRhyCATaMRjCfRwPHZrk9NNnPvIFYLOV4XarNfG3cc89F3DbbfoO5eLiwahISEfHIBUVqQ+rgtAMTHDAViiNjV7q60PvcEsRF2OlwDm27Sko0SI7IoIv+is8nkpWx3fwnCKbxYvb2LdPRP7dbrBa9Y2wzs4WA/P8/gUYjU+qPicQGMFoTL6B/3BQUQHt7aWI8yN47LS391BVFXfgUFKUlYVO1gboYHKyIqm6/WXLhMvQqacuobFRmGTEoqCgkMFBtXOkGYNB/w20qQlOOSWhXU2J5ctFH5ToGRgmtK/K5RrAbo8fvczJMTMyEipCeujvt8XtgVErAVbHB7TS3FyVlLiz2apoa2sieJ3eQV/fyWJPE8jAlJYy5dhVx8GDHSxcaEC4nMVG2FXXc+qp4vvNm8Vcl/POg5NPhgcfPJE//nF2FllWaxkeTzwfZRcu1zFJWijrp7oaHn10OfAG4QLmVZRATiIC5sknl+L3uwjO/n4cOJ9YJX/pIZ+iomGGh9UETPIOHYsX57JvXy6lpVuZmFiclAhSpwL451RwUHEa20MgEFyPNDUZUjJ6sVigHM6nFgAAMrFJREFUvb0c6ELpGevq6qKiIj1mLmItGH3B8fkmqK8/DEO1jlCSCjU2Njbq+joUXbR4VKF2A2xtJaxZPCOjiNraUV5/XTT4m83J9r+AyMC4p8sRRNR0HGWmTLJYrQZ6eqKbPA8dMlFfn6zYSoycHAujo9EX176+ZxkaqqVc3/qbkpL+KAHT2jpGdbXOYQNxEE38hYisSjTt7QMR/SYrEROgFdzoKSETtflKPVIrHo8joWbDxYvb2bdPfJ5+/wgGg34Hg+XLYdeuxWhbKQ+TygyYw4HdDi5XKZEN2k1N/dTVxYv4JkMGZWWTUSVk7e11SZVkBad0C2ty/Q2+4QuWycmDGI3xF6dms+ipaG4mjZHV+JSVMTV0VRkIHMTlGsFuj3/zNxhshGdReujvL44bsVcWTELgamVheoHPAN9P+rOx2ZbgdislXNsRv6sIfiQiYAwG4cwWCKxk9+42li3bgVqGNJITToCXX14BBHjnHbjxRrj4YmHr/t73jvGpT22eUXevWBgMeoZZduN02pO0UNaP6MepRpSLhfJ/wCWAMsQ3/raqq2HPHgetrZMhmYPfAZ9N2/5qU0Bh4RAjI2oKY4xkOwtWrIAdO45hfPwnZGamc6JpBdAx5ZinPLabQCAozlPvgQGfr4LQobkdHTlUVKSntFxLwHi9BoqLk6rXk5DmHhhJOGo9MH5/ZHNdA1/6UiNXXqnMeUlFwJiorOyabrjt7xcNrKn+mSsqjHR0RIciu7qKKSs7DBPtAC1nrhdf7GbjRv0ZJqt1OCqi19oamGqqTx3RxB+chh2J0zmMwxEa1YkUMPEzMOJiayHYXNyKx1OSkIBZssTP3r19UxHDHkLrieNx4onw+utmtGdzjJKKhfLhQLhb5RLpvtTcPDpDC/RyHA5nhIDpoKkpuah9XV3wZu7z6e0TKgTC0rN4vY2UlMS37xFzHkQzf2KDGlNDKa0LBJYRLWD82O16VtY2gv1iIErIzHEFjLCrBlAis5G8jXCL+gZwfEw73FhYrXVTBiXjwN34/cEBrV5vYtbAVVXQ0fF+du/uYOnSV4Fj477m9NPhxRfX43J5+eY3hWX/unVw223wwgvP8KlPpSe4kxzlKNa82rhwuWwzLmBEP2ghInCjBAJaEddBkQ0Q2ez42zIY4He/y+Daa58EPAhRtJzkel8TJZ+ioiGGh9XueUMkmwESAuZCmppOpb4+nRm7ImAgYhbMXkKzi8PD+t361BBVDaUEBcwEHR2labvW5efD0FD00FqfLw+LJT1rj6MRKWBmELNZLC4U+vvVBi01sHr1TrZvV0rLDiL6C5LBhMnkmrZq7egQU9dTpbw8h46OyLIaYaF8OCZyhxOahRnn1VcXcMop+v0Tc3IKGRsLj6a2tGRQXZ2eRjpRQhZLwEzicFhCHllKuMNS/AyMaLwtILioasHtNiUkYBYscLBnzzBbt06wZk2z/hciBIxiPBGJsLPsZ65nYEBMX44UME1NAerqZsIVpgartSnCLreDxsbSpCyUMzJEtH1gQL99aGamiYmJTWGPud1dWK3xs6h1dWJexUxb1aohDAuinchcLiN2XavWcsIFTA/9/UUJDL2rRFjbhtILXA/8FRC1V6Ojwm0xUUQPzApE9L0HWDX9s0R6YEAIj3feqWT79mKWLduDngWx0Qhf+MJWLrkki5/+NHIB/h8gTn3ijJJBQcFIRAN3JN24XOYZFzAghIfffzai3GsS+Cpw0/TPExH4DQ1w5pnHT23jRuBwzdDLx2QaUBUwgcAgSXYWUFsLTU0Wtm37fyF9vukiEJGBGSKRoFs8glUNTVOP7KajY2naBIxYJ2UhxGoQn69wRkxqjhakgJlBRJQ3+P3evaic2A0ID3mF1ErIDIZgo3tjI9TXx/L310dFRREdHeGL/qGhg+Tm6hucmC4Mhiz8/lBB9jo7d67WPeBLYCayjKa1NYeamvQ0qebkwMhILloCpqsrG4cjdFGRTXh5SjtiwaWN0ahkD5QMTBejo7kJ+eDn5KwgL6+HZ57xsnp1YhHW8nKlGT0PUS4WxOmEsrIu5oOAyc7OYnQ0soQsk9ramWhYrsZobI0YTNhLU1Ne0qUPxx8P3/0uuudz2GwmurvDB/F5PEZstvjn8dlnw+9+p3fGRXoR/T6LiJyC7vXm6jSLiIzi99DfX6ir6VssWNUc9+4Gvo7SrzgyIs79ZBCBrsXA5QgXqpA9TaCEDMSx8Ne/wsDA8ZSUPKj7dRddNMLzz/+LVasif7KNUEE1G4hhlrF6M4ZxOrNnvAcGFFvvryL+TmcCFwPBCdPBUQh62Qh8EniAVHtV9VNAUVE/IyPRQmVkZIjc3OT6YIxGYd/861/Dhz6U6j5GkklDw9hUBmaC0CzR8HBGStkXUEpkTQQFzLt0dCzWXZqujxwiRw/09cW3c5doIwXMYSTcgUwhUsA4gWSvxCagd+qmK9KtDQ1qpQ+JUVFhobMz/FA5dMiZVLlEKlgsBnp7W6e/Hx19lqwsW4L2nmYi69nb2wuprEzPysxgAIMhE+0MTDEOR2TaykLQTllvZCmHYAYm/vDLaFZywQVPs337KKeckviNU5RHLie0ZhgU//8O4pXBzQUqKgxRmcXu7gxKS2ei4L8GZcBgEAONjYYkh1jClVfCSy/BVVfpe74IRIQel+M4nVZdNfsLF4oAzKc+lcyepoawUi5AOCGGoy8DLARMUDx6GBjI17VwsNnA7V6JsFQVTE5O8oc/TNLXd970Y3v3wpL4/fKqBH+HMxAzOoJ4vYnZiK9bJ+aKfPKThSQ2SHYRyvyYID2Ia9PsLhMqK4fjWim7XByWDMyyZbB7dw7wJPA8QsCEk3hVwrnAYWwsI5+ioj6GhqIzzb29o5jNelz31PnOd4TgLktnCwwAi6mu3kdrK4hzcfn0T9zugpTLfjMzmZq9pPSivUNHR2Way2WzCe+5HMLvz5oVe/IjBfnRzTC5uSI6B1oCRjSoBUlmMapQAAxSWipqcRsbJ6mv98R9VTwqKjLp6LCEPXbgQC8LFx5ea02bLY/u7qbp7zdvHmD9+kRrhs1kZY0xFtKnPTaWQXZ2+upQAwFtAdPTU6gSUV0B7EjwXYyIhksnyQ1xK+LjH9/Ek09+muzsxEtETjgB3nhjA+HiWxEwXubDpaWmJpuWlkgXnjEMhplYCYlAhdEoSr+EiM7UXTOvRkUFvPsuugewVlUZaG+vIti7tJmOjtW6TQQeeEBkYg43wjQCRNRVWVxNoP86WURBwQDD01q1k/7+fF0lZKJ8bQGhvUPf+lYr+/adyiWXGKdF0ebNJOACGE1AI+g9NpZ4Zueee5IZGLyQaAHzIiLLMLtUVRlpb49dCn14BQyIYy/8nqH1N5x7FFBU1KuagentHcNsTr7naf16+POfU9k3LVaQlbWT8XGATcD66Z90dhaycGE6PnwDosxrBNiLz1eUYDYtztYN2fj9oQKme6qMWZIsc3+VMc9Rml8B9u1DZZpxBsGSJjeQ5GoGUG7o1dXiPZuaJqirG43zmvgIJ6AShLgSHDgwzsKFhyFnH0JNjZmWFtfUd128+uoGTj450UPYTGnpYMTk63STgbqAGSEQyFCJuCiN/D3oLSMQN0s78DyBQHIlHgbDNzEYriKZci/RyL8EbQEz96mry6epKViKMDkJRuM4kVHw9FABtIdcD9oQFtipDc1M5LXivU8Bnp165AXa21foFjAbNkRODT88iJk3ELQch0DgEPr/TgZMpuGQfsQBhoaydJWdiGufYuEs+hh37/bx//5fDaefDg89JJ73r3/Be9+rc3fmJA4i+8HgOSA9czBSobIyl7a2QY2fTgLGlJu49bJqlQgaqJFotmz2yKeoyMfwsFoGZjwlATNzHAPsmDLW2ESoOUV7exFLlqRLPR4P/AVxvTaktce3qCiD/v7ekEdcaRugfbQiBcwMU1XFVNpTNDirR0utiOaurUAKYbwpGhpEtmdkZJzc3NQLLLOzYWyskNBM0YEDVhYtSqREIXUWLLBx4IDi4vEMb755ku76/yAW7PbeqXkJwr0kNzfdtrlaGZhuROlXJCsRteb6+5+EWcBJwK14vRuStDldB3w0mReyahVs3+4gWsBM4HDEsz2dG9TVFdDUFDwhW1qgpqY7xitSwQgEQpx0mhgaWpBQ31KqCAGzEnhs6pH/zECZRPoJDtJcA2wBwOk8iN2uX02VlPRHWdrrWZyUlys2zqKv7dFHR/nYx14AavnqV0W2o6VFmCmkYjUsrrHJvz511D6MRkTmcHaprDTT3q41p6sHsB627EdpKXR3q2db2trEOTb3ycVk6lVt4u/tnZiVPrf4LAO2TdnfuwktuevoKEq6fDMU8Tc9H/gxcEnaj6ni4ky83lAh7iI4wFqSDFLAzDDV1ULAjI7Gil6uA95BLGJTFzDvfS/cdx8UFQ2TfD9NOIFAAcESgwk6OmxpbnCLz8KFWRw4UAoECAT+zuBgeQJOQgpm7HbvtIBpa/NTXZ16mV046gLG73dppIxrEL0ku9DrQCcWVhuB/6Kj46TD/rfIzobx8XwCgfC+jq6uQcrK5sdk4dpaA01NwfNj/35YtKgzxitSJYf6+tEpJ50m9u1bzuLF8V6TPioroa2tCGGn/N/AMnp6MuZF1LikBDyeY1EETEuLk5oa/cdZSckIPT3DJDqoLyhgzgGe4MknOzn/fOESmJMjrIY/9jFhppAKNhsRDnWzQRXBPq2diL6Y2ae83E5np5bajG87n27q68XckUhaW+eLgDFMD7KMpLfXj9l82K1FdZAH5LJ4cSN79pxMqODu68vRNTw0HmKGm+h36+8/O4m1RWxKSrLxeoNmImNjbrKzZ8Lx8uhBCpgZRml+3bJFTN9VZx2irvMNYG2K72ikvHyS3l747Gf3klx/RDRmcw69vU1T3+0hECg87M1n9fVw6NAy4A8cOFDDokXJnPxm7Hb3tIBpaRmgujp2g2jiZBAIRG/T6/VqLBYNiMnOPwM+oOsdRGlLLfDVwz6bQ6Guzkhzc3jY2eUapbQ0lTLIw4do0A72UO3fP86iRa4Yr0iVBTQ0tE1lYHazZ89Sli2bwbeLIDiX6scIq+4bgdRK2A4XopF/AUozfXNzP7W1+lMeJSW59PR0o8emPJRFi0TpL3yY0dHfMjTUSXHxx6Z/vnYtvPGG6AlLBauVqLLW8XEj2Yc1QHsa8MrU//8CJNxIMyNkZ1czPq5VCu1iYsJxWEsb3/te+Oc/ox/fu5fDGpBIBTEHRq2EzI/ZPFeXhZ9g1aofsW3bh6cfUbIk6biGBYMVQozW1KS+zVCKi/Pp6QmmWX2+PiyWw+xJf4QxV4/UI4ZVq2DrVnj9ddE3oM7xwNOI3olUF3+iHO3hh+Gss3aQLgGzYEEeBw+KO+zIyFtkZx/+PHNODoyNNQC/4tVXv8lJJyWzFTNVVZ3TZX0HDw6zYEG8QWmJkZ9vZHg4eptOZ38Mq88bgCtQbFnjIZqLxf9nS8Aceyxs2rSQ0Kj2+PgIWVnVh39nkkDUUxtRZoTs3z/IwmRHMOliJfX170wJmL3s3l05A/MStAne5G3AF9B7rM0FhIDJAEqALpqbsxOyuy4pKaGnx4lwGdLf8yVKVgCsvPLKnZx2WgOinCy9CDEd/lh/f/Zhzo6dimjcH2X257+EEm19H6Sb7u5qXU566eK88+Af/4h+fOdOErT0nz20Bln29oLZPFcHK36I1avvZdu24P3F5cqntFSrPyoxQgWMKCdOy2anKS7OxusNChafb4DiYllClgpSwMwwmZmi3OaJJ0QTrDq5wB3A99LwjqVAN7W1YDS6SJ+AKeHAARE92Lu3icWLZ2fxk5OTS3//a/zrX5W85z3JbMHCggUHOTg11mH//kkWLdK6OSZHUZGRvr7JqMedzlEcDq2sUQ3wRd3vITIw4v+zJWDWr4dNm1YjankVhjm8lqCpkZeXyeCgULNNTePU1c2kKf8JmM2vTPViTLB7tzEttduJkJHBlJOP1mDduYmYBQNwHvB5mpvXJWSdWlJSRk+PD2hlfLw6oWGcGRkwMQFPPLGYD3xgZoxLSkuZzgor9PbmHubS0GqEVfUVwGXMpeVBRoafCdXLtAuXq+KwOJAp5OQIQR05zFcYmBy+/UiFnJwJJiai/769vcY5LGCEyOgIMW09cKCEBQvSYxoz8wLGgNcbvL/4fKNYLIe3j/hIY+5coY5gPv1pMbMhtiXfOkSjWqrYCHqNJ2uxG83ChQYOHFgAeNi82cyxx87OymfjRnj8cejrI8nBZcU4HE1TgxiVWTnpvWCbTBn090ffbZ3OCRyO9FywQi+2syVghL3tIpQJ6YODUFDgYz4JmJUrh9m+vYdAAEZHR2c4e7QQOEBx8TDd3Yvo7+ewDzGrr4emJvH/ffvmT8lLQwNTQYeLgfNpa1uZUL9BSUk1PT2DwCZ8vmMTarhfskRE13fsYMZK/ioqguezgs+Xi8NxuL15fwl8GjFcce5QVubD6YwOCkE3LlfpYRUwAN/6FvzP/wRLmNJZynR4UN/R3t4MzOa564xlMIhhmYol+sGDJSxc2JOWbc+8gAGvN2ga4/X6sVjSn809mpAC5jBw4YWi0fPwIDIwAhfpanA85hjYtu144FNs2nQWxx4b9yUzwoc+JIZlffrTyW4hE4NhcvqGMzo6QW5uGjoAQygqyqKvL3rhIabUp0f4RZaQzUbkTwz/MhEIiL6Epiaoq2vl8E2UTp01a4xs3TpKWxvU1HQxs+LLAFg4/fTHuOWWqzjuuBl8Kw2WLFF6OlIbvni4ycxkKgJvBD7HyEguWQm0wJWUmOjpyQA209OzKiEB8/GPw0UXwcUXz9wCtaIiPLIM4PPlHJbp8uHYgfeS/CyymaGycoy2NjWXg25crpLD/jnZ7XDccfDkk+L7XbvmTzAgFnNdwACceiq8MtWq1dJioqIiesBtMhweAVOEMo7C5yuguHhunWfzDSlgjjhCBcwIiU1j1sZigd7eegKBj9HUtCLtJ7deHA5obBSLieQxkJMjojgGwwiQ3tV/UVEW/f1qAiYHhyM9AqakRNh5gnC4S3TYXbpYsCCXQ4dEOquxEerru5hri59YrF5dxLvv5vDmm7Bhw1ZmPnv0I84//1na2hbyyVkIci9ePD8FDEBtrciYut0k7DokXMyWAJP09GQlJGDWr4f/+z/47GcTe89EUBcws5GBmZtUVmoNs3ThdJoPewYG4BvfEC50w8Pwl7/AR5NzpJ9T9PZmYTbP7bKmjRvh6afF/aakZDihctBYhAoYjydexUziCAFTCnQCAXy+grS/x9GGFDBHHKECJr0sXmzgyScvo6Iic1ZT5em4YC1eDL/6FRx//D7SZTWtIErIopvznE4zDkd6PjijEfx+cfOcLfECcOyxhWzaJOxsGxv7qK9PfXDq4aSmppKDB/P4wx/gjDNeI3UTjXjUUVFxP3/8o2FWxMOSJcok8fknYM46C55/Ht5+m4SzV3l5MDy8CHiQnp7EZ7Zs2JCe644WJpNooA7F682dNz0VM40YZqk2X2oElytrVgRMURFcf70Ipv3737FMeuYiGRgM0eJ4YgKysua2Df4xx4hr1w03GDn99Ja0bbe4WAgXv19kWtO9xikpgZ4eZXZaN15vmRQwKTJ3u7UkSVINtCDSlOmN3l16KXzkI/Doo2nd7CyQy2c/O8LKlbls3foscHZat15UBH190RNL3W4T1jSuj6urxQTw5cvTt81EOfZYI/fdt5CLL55k/34f55yTwjS/WcBgMHPNNX9h27blNDS4mE/Zo2SorAzOsPD55k8TP8B73gOXXSas6S+5JPHXGwwGoDQpATPTqC2YRAZmdvZnrlFVZeb558dVf+ZyJdsPmTobN4rrcFUVh32sQGrkYTD4VYY1TgJzW8AYDHDnnbBzZ4Dh4fQFaw0GEaTYswcW6JsnnRB5eTAyUgS8BeTh85XNixlcc5l5dcpJ9OAAuoBW0l0Os369KD9Zvz6tm50FSlm0yMWjj8KSJY2k2xa1qAj6+yO36cfvN6Y1irtuHfzmN7ByZfq2mShLlsCePccA77J//yQLF85SbWEKnHtuG9df/zTpGCI71zEYRG33M8/Mv5p9q1VkQrq6Es/AQLDRei4KGDXGxjIoiI6DHJVUVdloa1NbrhjweGb377l8ucigzS/yyc4eZ3w88jMNMB+WhXV1sHFj+ssrjzkG/vhHMd9pZsgHGoEWfD6bzMCkyNw/UiUJooTxdgPpHzJxeAerzRR2oJvzzpuZrZtM0NcXGcXyAemdunvGGaJ2fuPGtG42ITIyICenhEOHNmEytWMwzJK7Q0qcAtzE3Jl7MbN85CPCFfHDH47/3LnGt78Nv/99cq/NyoKxsbkrYMQk8OD3gcCRnQ1MBJOpUqWvcBzIwO+fb9mPuUA+BQUjqsMsj2aOO04ImNNOm6l3KEAImAN4vcVSwKSIPO2PSOyIQWSHcUrevKKU8Nkl6UVkYPJR3EYAAgEnkN5mlSVL4Ne/Tn+zYaJceqmFiy8+gzPOaGU+DUcMcgnwP8AsKsHDyDnnCEvg00+f7T1JHIMh+cVqVRW0tYmSo0RNAA4H9fWiMRlgchKMRtnAHySH0OupwEm6DViOHvLJzx9RHWZ5NPPBD8KWLcK2fWbIAtqAVxkaspE3t/0S5jzy6D0iWQrcDXx+tndkjmJHCJhB0uXSForIwJiBAUDUFvh8PVhmW2nMEOedl01xcRWnnDJfC/bzOVrEi8Lhnj8zF6itFfao7e2iF2iusXAh7N8vZs14PPlYrWpN60c3gUBor1A7gUDlPJq9MncIBPIpKBgOEzAjI5CTo95ndLSQmTmz5YCZmQbGxk4lO/sdhJHCzL3X0YDMwByRXAH8HdHQL4mmATgI7AXSb8MkMjBmIGgr5HT2HbENuRkZcOqphRgMsiNRMnepqRECZq6WHC1aBAcOiP87nQU4HIOzu0NzDKt1EI9nJOSRdvr7a49KMZ46eRQUDDEyEiwh6+0Fs1mK5plEWDV/GbhxtnfliGAOXsYlqVMCzGJn95xnKbBn6iv9ZXYiA1OE6HsROJ3DOBwy4SmRzBa1tcJCOn+OmiwtXBgqYAopKxuI/YKjjKqqCdranCGPtONy1c2KhfL8J5+ioiGGhoL3JCFgpGieSaqqoL29hKGh9XP2OjSfkAJGchRSgCgfmxmjg5wcGB3NJVzAjONwpNftTCKR6KehAf72N5HpmIuUlQUH6ckMTDRVVVm0tblDHunA6aw8YjPbM0s+hYUDjIyECpiAzMDMMJWVog+vs1NkYySpIQWM5CglE9jJTJSQCbIILyEL4HDMo6EbEskRhs0G114Lp54623uijsEgytsmJqC11UR5uczAhFJVZaKtrS/kkXY6O0upqJi1XZrH5FFUNMDQUGgJ2Thm89gs7tORj8jAQEeHFDDpQAoYyVHKexAONjM1aCGL8AxMBg7HvBsWIJEcUVx2mXBhm6scdxy88ooBozFAVlak69bRTWVlGW1toyGP9NDRUSgFTFLkYzb3RQiYUczmiVncpyOf0AyMPG5TRwoYyVHK14BfzeD2IwWMibIy6bkvkUi02bgRrr3WyJIlntnelTlHVVUZbW3h19CODoNcCCZFPhZLLwMDwcFuIgMjRfNMUlkpMjCtrXPTCXG+IQWMRDIDZGZmMTERXIS43aY5OXtCIpHMHU44AX7960nOPffAbO/KnMNqzcDjUSzHeoEiOjpkJDsZAoE8LBYfg4ORJWRy9tBMUlgoBum++SYcOx9nPs8xpICRSGaA4uJsfL5gD8zkpJFMaUImkUhiYDDA+vWQnS0j4ZGImRmZQA/CAn8pXi+Y5+Ps3Fknn+LiHgYHQzMwk/KzPAwsXw7d3TM7b+ZoYd4ImJ6eHj75yU9iMpmwWCxcfvnlDAzEbnIcGRnhqquuwmq1UlhYyEc+8hGcTmfYcwwGQ9TXn//855n8VSRHAcXFOfT0KPXag0DGbO6ORCKRzHsCgWICgTcRDpLLAOQwwKTIp7Cwj9HR4H2ppyeA1TqLu3SU8KlPwYc/PNt7cWQwbwTMJz/5SXbu3Mmzzz7LE088wb///W+uvPLKmK/5+te/zj/+8Q8efvhhXn75ZTo6OviwypFz33330dnZOf11wQUXzNBvITlaKCnJoKdHGL0HAi4CgZxZ3iOJRCKZ31RUFNDZuQN4HVg727szj8knMzPcccztNmCzzZsl4bzl2GPhy1+e7b04MpgXRS27d+/m6aef5u233+bYqcLBX/ziF7z//e/n1ltvpUKlCLa3t5ff/va3/PGPf+TMM88EhFBZtmwZb7zxBieccML0cy0WC2VlZYfnl5EcFZSUgNcrHM76+tyYTHmzvEcSiUQyv1m40MqBA21UVLTg9S6SJU9Jk09m5kjYIwMDUFCQrfF8iWTuMS/k9uuvv47FYpkWLwBnn302RqORN998U/U1mzdvZnx8nLPPPnv6saVLl1JTU8Prr78e9tyrrroKm83G8ccfz+9+9zsCgdiNbKOjo/T19YV9SSShFBdDT08hEMDp7MLhmBexAolEIpmzLFyYwf793wSuZ/9+WLx4tvdovpJPZuZoxGOTGAxyPLxk/jAvBExXVxd2uz3ssczMTEpKSujq6tJ8TXZ2NhaLJexxh8MR9pof/OAH/OUvf+HZZ5/lIx/5CF/60pf4xS9+EXN/br75Zsxm8/RXdXV1cr+Y5IilpAQ8HjvQR1eXh7IyeWOQSCSSVFi0CA4cqAZOZ98+KWCSJwuDQRhFBOO1k8zcXDSJJP3MqoD51re+pdpEH/q1Z8+eGd2H733ve5x88smsXbuW66+/nuuuu45bbrkl5mtuuOEGent7p79aW1tndB8l84/KSujoqAK6aG4eoba2ZLZ3SSKRSOY1CxbA/v3i//v3C0EjSZYAubkTBL2QpICRzC9mta7lG9/4Bp/5zGdiPqehoYGysjJcLlfY4xMTE/T09Gj2rpSVlTE2NobP5wvLwjidzpj9Lhs2bOCHP/who6Oj5OSoN17n5ORo/kwiAaiuhpaWGmAbTU2FvO99hbO9SxKJRDKvyc+HwUGRNdi3TwqYVCksHMPng9xcyM0dBWSlgGT+MKsCprS0lNLS0rjPO/HEE/H5fGzevJn169cD8MILL+D3+9mwYYPqa9avX09WVhbPP/88H/nIRwDYu3cvLS0tnHjiiZrvtWXLFoqLi6VAkaSEyQT9/XbgNZqb30td3WzvkUQikcx/6uvh0CHweOQsjVQxmcZwOiErC2y2XkC6IkjmD/Ois3jZsmWcc845XHHFFdx9992Mj49z9dVX8/GPf3zagay9vZ2zzjqLBx98kOOPPx6z2czll1/ONddcQ0lJCSaTiS9/+cuceOKJ0w5k//jHP3A6nZxwwgnk5uby7LPP8qMf/YhvfvObs/nrSo4YioCHcTo/T0QLl0QikUiS4JRT4Ec/gnXrZntP5j9W6xDt7QZyc8Fm8wJVs71LEolu5oWAAfjDH/7A1VdfzVlnnYXRaOQjH/kIP//5z6d/Pj4+zt69exkaGpp+7I477ph+7ujoKBs3buRXv/rV9M+zsrK48847+frXv04gEGDhwoXcfvvtXHHFFYf1d5McmRiNBiYnvwY0yGFrEolEkgY+/GF4+WX45Cdne0/mPzbbMK2tIpNls3kAWeosmT/MGwFTUlLCH//4R82f19XVRdkf5+bmcuedd3LnnXeqvuacc87hnHPOSet+SiQKZWXQ0vJ1MufNWSaRSCRzm9xc+PWvZ3svjgQMWK0DtLUZyM6GhQubEFUDEsn8YF7YKEsk85EVK+CWW+Dkk2d7TyQSiUQiCTIxkYPd3ktbG1OW1K3Mo5i2RCKPVolkpvjEJ6CqCt55Z7b3RCKRSCSSIJOTORQVDTA4CI2NUFfnnO1dkkgSQgoYiWSGsFrhr3+FpUtne08kEolEIgkyMZFLZuYoY2Pi+8xM/+zukESSILKETCKZQc45B9nAL5FIJJI5hRAwI3ztawHq62d7bySSxJEZGIlEIpFIJJKjCEXAvOc9Ac49d7b3RiJJHJmBkUgkEolEIjmKmJjIJSNjZOq7QMznSiRzESlgJBKJRCKRSI4iJidzyMxUBMwokDObuyORJIwUMBKJRCKRSCRHEUoJmWAAOQNGMt+QAkYikUgkEonkKGJiIi9EwPQjBYxkviEFjEQikUgkEslRxNhYAdnZA1PfDQCFs7k7EknCSAEjkUgkEolEchQhBMzg1HcyAyOZf0gBI5FIJBKJRHIUMT5eSFaWkoHpQwoYyXxDChiJRCKRSCSSo4jwDIwHsM3m7kgkCSMFjEQikUgkEslRRCCQicEwOfWdGylgJPMNKWAkEolEIpFIjlo8gHW2d0IiSQgpYCQSiUQikUiOWmQGRjL/kAJGIpFIJBKJ5KhFZmAk8w8pYCQSiUQikUiOMgIBIzABDAH5s7w3EkliSAEjkUgkEolEcpQxPl4A+ADDLO+JRJI4UsBIJBKJRCKRHGWMjRUiysckkvmHFDASiUQikUgkRxkDA2UYDAdnezckkqSQAkYikUgkEonkKKOvrwqD4SVkA79kPiIFjEQikUgkEslRhhAw9wOnz/auSCQJIwWMRCKRSCQSyVHG2JgZyATeM9u7IpEkjBQwEolEIpFIJEchfv8dQO1s74ZEkjBSwEgkEolEIpEchQQCF8/2LkgkSSEFjEQikUgkEolEIpk3SAEjkUgkEolEIpFI5g1SwEgkEolEIpFIJJJ5gxQwEolEIpFIJBKJZN4gBYxEIpFIJBKJRCKZN2TO9g4cCQQCAQD6+vpmeU8kEolEMp+ZmJhgaGgIEPeUzEx5m5aklyPhGDsSfgc9KOtKZZ0pCXJk/sUPMx6PB4Dq6upZ3hOJRCKRSCQSyZGEx+PBbDbP9m7MKaSASQMlJSUAtLS0yAPsMNLX10d1dTWtra2YTKbZ3p2jCvnZzx7ys5895Gc/e8jPfvaQn/3s0dvbS01NzfQ6UxJECpg0YDSKViKz2SxP7lnAZDLJz32WkJ/97CE/+9lDfvazh/zsZw/52c8eyjpTEkR+IhKJRCKRSCQSiWTeIAWMRCKRSCQSiUQimTdIAZMGcnJyuOmmm8jJyZntXTmqkJ/77CE/+9lDfvazh/zsZw/52c8e8rOfPeRnr40hIL3ZJBKJRCKRSCQSyTxBZmAkEolEIpFIJBLJvEEKGIlEIpFIJBKJRDJvkAJGIpFIJBKJRCKRzBukgJFIJBKJRCKRSCTzBilgdHDnnXdSV1dHbm4uGzZs4K233or5/IcffpilS5eSm5vLypUrefLJJw/Tnh55JPLZ33///RgMhrCv3Nzcw7i3Rw7//ve/Of/886moqMBgMPD444/Hfc1LL73EunXryMnJYeHChdx///0zvp9HIol+9i+99FLUcW8wGOjq6jo8O3yEcPPNN3PcccdRVFSE3W7nggsuYO/evXFfJ6/3qZPMZy+v9+nhrrvuYtWqVdNDKk888USeeuqpmK+Rx3x6SPSzl8d8OFLAxOGhhx7immuu4aabbuKdd95h9erVbNy4EZfLpfr81157jUsuuYTLL7+cd999lwsuuIALLriAHTt2HOY9n/8k+tmDmBTc2dk5/dXc3HwY9/jIYXBwkNWrV3PnnXfqen5jYyPnnXce73nPe9iyZQtf+9rX+NznPsczzzwzw3t65JHoZ6+wd+/esGPfbrfP0B4embz88stcddVVvPHGGzz77LOMj4/zvve9j8HBQc3XyOt9ekjmswd5vU8HVVVV/PjHP2bz5s1s2rSJM888kw996EPs3LlT9fnymE8fiX72II/5MAKSmBx//PGBq666avr7ycnJQEVFReDmm29Wff7HPvaxwHnnnRf22IYNGwKf//znZ3Q/j0QS/ezvu+++gNlsPkx7d/QABP7617/GfM51110XOOaYY8Ieu/jiiwMbN26cwT078tHz2b/44osB/n979x9TVf3Hcfx1+XGBvAbapQtOowy6mplcLTZoU0ZIq8XWH9Xsjzu0acuhRn+00dZw1eZWq1zZD1zNynJi07DNFlpilMzmDyBAnRkZrS1wZlIKyXbv5/vH9+udFy/I5XsvxyPPx3Y27ud8zs77vPfeh/vmnHuRzF9//TUuMU0Up0+fNpJMU1PTsHNY7+NjNLlnvY+fKVOmmA8++CDiPmo+vkbKPTUfjjswIxgcHNSRI0dUWloaGktISFBpaakOHDgQ8ZgDBw6EzZekBx54YNj5iGwsuZek8+fPKycnRzNmzLjqXzIQO9S99fLz85Wdna3FixerubnZ6nBsr6+vT5I0derUYedQ9/ExmtxLrPexFggEVFdXpwsXLqiwsDDiHGo+PkaTe4mavxwNzAjOnDmjQCAgj8cTNu7xeIZ9vrynpyeq+YhsLLn3er3atGmTvvjiC3366acKBoMqKirS77//Ph4hT2jD1f3ff/+tgYEBi6KaGLKzs1VbW6sdO3Zox44dmjFjhoqLi9XS0mJ1aLYVDAZVVVWl++67T3fdddew81jvY2+0uWe9j52Ojg65XC6lpKTo6aefVn19ve68886Ic6n52Iom99R8uCSrAwBipbCwMOwvF0VFRZo9e7Y2btyol19+2cLIgPjxer3yer2h10VFRerq6tL69ev1ySefWBiZfVVWVqqzs1P79++3OpQJZ7S5Z72PHa/Xq7a2NvX19Wn79u2qqKhQU1PTsG+kETvR5J6aD0cDMwK3263ExET19vaGjff29iorKyviMVlZWVHNR2Rjyf1QycnJ8vl8+vnnn+MRIi4zXN3feOONSktLsyiqiaugoIA332O0atUq7dq1S999952mT58+4lzW+9iKJvdDsd6PndPpVG5uriRpwYIFOnTokN58801t3LjxirnUfGxFk/uhJnrN8wjZCJxOpxYsWKC9e/eGxoLBoPbu3TvsM4qFhYVh8yXp66+/HvGZRlxpLLkfKhAIqKOjQ9nZ2fEKE/9D3V9b2traqPsoGWO0atUq1dfXq7GxUbfddttVj6HuY2MsuR+K9T52gsGgLl68GHEfNR9fI+V+qAlf81Z/i8C1rq6uzqSkpJiPPvrIHDt2zDz11FMmIyPD9PT0GGOM8fv9prq6OjS/ubnZJCUlmddee80cP37crF271iQnJ5uOjg6rLsG2os39iy++aHbv3m26urrMkSNHzJIlS0xqaqo5evSoVZdgW//8849pbW01ra2tRpJ54403TGtrq+nu7jbGGFNdXW38fn9o/i+//GJuuOEG89xzz5njx4+bd955xyQmJpqGhgarLsG2os39+vXrzc6dO83JkydNR0eHeeaZZ0xCQoL55ptvrLoEW1q5cqVJT0833377rfnjjz9CW39/f2gO6318jCX3rPexUV1dbZqamsypU6dMe3u7qa6uNg6Hw+zZs8cYQ83HU7S5p+bD0cCMwoYNG8wtt9xinE6nKSgoMD/88ENo36JFi0xFRUXY/M8++8zccccdxul0mjlz5pgvv/xynCO+fkST+6qqqtBcj8djHnroIdPS0mJB1PZ36at5h26X8l1RUWEWLVp0xTH5+fnG6XSamTNnmg8//HDc474eRJv7V155xdx+++0mNTXVTJ061RQXF5vGxkZrgrexSDmXFFbHrPfxMZbcs97HxpNPPmlycnKM0+k0mZmZ5v777w+9gTaGmo+naHNPzYdzGGPM+N3vAQAAAICx4zMwAAAAAGyDBgYAAACAbdDAAAAAALANGhgAAAAAtkEDAwAAAMA2aGAAAAAA2AYNDAAAAADboIEBAAAAYBs0MACAES1dulSPPPKIZef3+/1at27dqOYuWbJEr7/+epwjAgBYyWGMMVYHAQCwhsPhGHH/2rVr9eyzz8oYo4yMjPEJ6jI//vijSkpK1N3dLZfLddX5nZ2dWrhwoU6dOqX09PRxiBAAMN5oYABgAuvp6Qn9vG3bNtXU1OjEiROhMZfLNarGIV6WL1+upKQk1dbWjvqYe++9V0uXLlVlZWUcIwMAWIVHyABgAsvKygpt6enpcjgcYWMul+uKR8iKi4u1evVqVVVVacqUKfJ4PHr//fd14cIFLVu2TJMnT1Zubq6++uqrsHN1dnbqwQcflMvlksfjkd/v15kzZ4aNLRAIaPv27SovLw8bf/fdd5WXl6fU1FR5PB49+uijYfvLy8tVV1f3/ycHAHBNooEBAETt448/ltvt1sGDB7V69WqtXLlSjz32mIqKitTS0qKysjL5/X719/dLks6dO6eSkhL5fD4dPnxYDQ0N6u3t1eOPPz7sOdrb29XX16d77rknNHb48GGtWbNGL730kk6cOKGGhgYtXLgw7LiCggIdPHhQFy9ejM/FAwAsRQMDAIjavHnz9MILLygvL0/PP/+8UlNT5Xa7tWLFCuXl5ammpkZ//vmn2tvbJUlvv/22fD6f1q1bp1mzZsnn82nTpk3at2+ffvrpp4jn6O7uVmJiom6++ebQ2G+//aZJkybp4YcfVk5Ojnw+n9asWRN23LRp0zQ4OBj2eBwA4PpBAwMAiNrdd98d+jkxMVE33XST5s6dGxrzeDySpNOnT0v674fx9+3bF/pMjcvl0qxZsyRJXV1dEc8xMDCglJSUsC8aWLx4sXJycjRz5kz5/X5t2bIldJfnkrS0NEm6YhwAcH2ggQEARC05OTnstcPhCBu71HQEg0FJ0vnz51VeXq62traw7eTJk1c8AnaJ2+1Wf3+/BgcHQ2OTJ09WS0uLtm7dquzsbNXU1GjevHk6d+5caM7Zs2clSZmZmTG5VgDAtYUGBgAQd/Pnz9fRo0d16623Kjc3N2ybNGlSxGPy8/MlSceOHQsbT0pKUmlpqV599VW1t7fr119/VWNjY2h/Z2enpk+fLrfbHbfrAQBYhwYGABB3lZWVOnv2rJ544gkdOnRIXV1d2r17t5YtW6ZAIBDxmMzMTM2fP1/79+8Pje3atUtvvfWW2tra1N3drc2bNysYDMrr9YbmfP/99yorK4v7NQEArEEDAwCIu2nTpqm5uVmBQEBlZWWaO3euqqqqlJGRoYSE4X8VLV++XFu2bAm9zsjI0Oeff66SkhLNnj1btbW12rp1q+bMmSNJ+vfff7Vz506tWLEi7tcEALAG/8gSAHDNGhgYkNfr1bZt21RYWHjV+e+9957q6+u1Z8+ecYgOAGAF7sAAAK5ZaWlp2rx584j/8PJyycnJ2rBhQ5yjAgBYiTswAAAAAGyDOzAAAAAAbIMGBgAAAIBt0MAAAAAAsA0aGAAAAAC2QQMDAAAAwDZoYAAAAADYBg0MAAAAANuggQEAAABgGzQwAAAAAGzjP1k9YOj67AdjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 800x420 with 7 Axes>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f6_6.plot_joint()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f3069e7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Effective window size : 1.024 (s)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<MNELineFigure size 1000x350 with 2 Axes>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spectrum = mne.time_frequency.Spectrum(f6_6,'welch',0,20,0,3.5,None,False,False,n_jobs=1)\n",
    "spectrum.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7056a489-c173-48f1-9dfc-ae089d4aac27",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NOTE: plot_psd() is a legacy function. New code should use .compute_psd().plot().\n",
      "Effective window size : 1.024 (s)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<MNELineFigure size 1000x350 with 1 Axes>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prueba.plot_psd(tmax=np.inf, fmax=60, average=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d743d1b5-8d1c-4a34-8fdb-d7cad67ef2f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "comienzo = 0\n",
    "termino = 4.000\n",
    "\n",
    "tmin = comienzo\n",
    "tmax = termino\n",
    "fmin = 1.\n",
    "fmax = 15.\n",
    "sfreq = epocas.info['sfreq']\n",
    "\n",
    "spectrum = epocas.compute_psd(\n",
    "    'welch',\n",
    "    n_fft=int(sfreq * (tmax - tmin)),\n",
    "    n_overlap=0, n_per_seg=None,\n",
    "    tmin=tmin, tmax=tmax,\n",
    "    fmin=fmin, fmax=fmax,\n",
    "    window='boxcar',\n",
    "    verbose=False)\n",
    "psds, freqs = spectrum.get_data(return_freqs=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "45586085-7cb0-4a0a-86a1-ef4ae51228a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"table table-hover table-striped table-sm table-responsive small\">\n",
       "    <tr>\n",
       "        <th>Data type</th>\n",
       "        <td>Power Spectrum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th rowspan=1>Units</th>\n",
       "        <td class=\"justify\">eeg: V²/Hz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>Data source</th>\n",
       "        <td>Epochs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>Number of epochs</th>\n",
       "        <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>Dims</th>\n",
       "        <td>epoch, channel, freq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>Estimation method</th>\n",
       "        <td>welch</td>\n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        <th>Number of channels</th>\n",
       "        <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>Number of frequency bins</th>\n",
       "        <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>Frequency range</th>\n",
       "        <td>1.00 – 15.00 Hz</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Power Spectrum (from Epochs, welch method) | 101 epochs × 2 channels × 57 freqs, 1.0-15.0 Hz>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spectrum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b7289c2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Averaging across epochs...\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<MNELineFigure size 1000x350 with 2 Axes>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spectrum['6.6 Hz'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e213b58b-89e6-4896-8bcf-26158213c441",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(101, 2, 57)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "psds.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2c5834ee-0d41-4cf1-8eeb-d5c7f4335fed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.  ,  1.25,  1.5 ,  1.75,  2.  ,  2.25,  2.5 ,  2.75,  3.  ,\n",
       "        3.25,  3.5 ,  3.75,  4.  ,  4.25,  4.5 ,  4.75,  5.  ,  5.25,\n",
       "        5.5 ,  5.75,  6.  ,  6.25,  6.5 ,  6.75,  7.  ,  7.25,  7.5 ,\n",
       "        7.75,  8.  ,  8.25,  8.5 ,  8.75,  9.  ,  9.25,  9.5 ,  9.75,\n",
       "       10.  , 10.25, 10.5 , 10.75, 11.  , 11.25, 11.5 , 11.75, 12.  ,\n",
       "       12.25, 12.5 , 12.75, 13.  , 13.25, 13.5 , 13.75, 14.  , 14.25,\n",
       "       14.5 , 14.75, 15.  ])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freqs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "cc617cb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def snr_spectrum(psd, noise_n_neighbor_freqs=1, noise_skip_neighbor_freqs=1):\n",
    "    \"\"\"Compute SNR spectrum from PSD spectrum using convolution.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    psd : ndarray, shape ([n_trials, n_channels,] n_frequency_bins)\n",
    "        Data object containing PSD values. Works with arrays as produced by\n",
    "        MNE's PSD functions or channel/trial subsets.\n",
    "    noise_n_neighbor_freqs : int\n",
    "        Number of neighboring frequencies used to compute noise level.\n",
    "        increment by one to add one frequency bin ON BOTH SIDES\n",
    "    noise_skip_neighbor_freqs : int\n",
    "        set this >=1 if you want to exclude the immediately neighboring\n",
    "        frequency bins in noise level calculation\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    snr : ndarray, shape ([n_trials, n_channels,] n_frequency_bins)\n",
    "        Array containing SNR for all epochs, channels, frequency bins.\n",
    "        NaN for frequencies on the edges, that do not have enough neighbors on\n",
    "        one side to calculate SNR.\n",
    "    \"\"\"\n",
    "    # Construct a kernel that calculates the mean of the neighboring\n",
    "    # frequencies\n",
    "    averaging_kernel = np.concatenate((\n",
    "        np.ones(noise_n_neighbor_freqs),\n",
    "        np.zeros(2 * noise_skip_neighbor_freqs + 1),\n",
    "        np.ones(noise_n_neighbor_freqs)))\n",
    "    averaging_kernel /= averaging_kernel.sum()\n",
    "\n",
    "    # Calculate the mean of the neighboring frequencies by convolving with the\n",
    "    # averaging kernel.\n",
    "    mean_noise = np.apply_along_axis(\n",
    "        lambda psd_: np.convolve(psd_, averaging_kernel, mode='valid'),\n",
    "        axis=-1, arr=psd\n",
    "    )\n",
    "\n",
    "    # The mean is not defined on the edges so we will pad it with nas. The\n",
    "    # padding needs to be done for the last dimension only so we set it to\n",
    "    # (0, 0) for the other ones.\n",
    "    edge_width = noise_n_neighbor_freqs + noise_skip_neighbor_freqs\n",
    "    pad_width = [(0, 0)] * (mean_noise.ndim - 1) + [(edge_width, edge_width)]\n",
    "    mean_noise = np.pad(\n",
    "        mean_noise, pad_width=pad_width, constant_values=np.nan\n",
    "    )\n",
    "\n",
    "    return psd / mean_noise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1644db4e-628e-4cd7-85f3-d98b4cae8d0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "snrs = snr_spectrum(psds, noise_n_neighbor_freqs=3,\n",
    "                    noise_skip_neighbor_freqs=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "00d41c5c-28d1-4faa-8d38-1ab26a3410db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snrs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "961a2929-6d8a-4835-a3d4-99db6d389083",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import ttest_rel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4796d11c-9715-42fe-83aa-4c57f48c16aa",
   "metadata": {},
   "outputs": [],
   "source": [
    " #psds_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a1f4f073-1850-495e-b874-2a89927edcd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axes = plt.subplots(2, 1, sharex='all', sharey='none', figsize=(8, 5))\n",
    "freq_range = range(np.where(np.floor(freqs) == 4)[0][0],\n",
    "                   np.where(np.ceil(freqs) == fmax - 1)[0][0])\n",
    "\n",
    "psds_plot = 10 * np.log10(psds)\n",
    "psds_mean = psds_plot.mean(axis=(0,1))[freq_range]\n",
    "psds_std = psds_plot.std(axis=(0,1))[freq_range]\n",
    "axes[0].plot(freqs[freq_range], psds_mean, color='b')\n",
    "axes[0].fill_between(\n",
    "    freqs[freq_range], psds_mean - psds_std, psds_mean + psds_std,\n",
    "    color='b', alpha=.2)\n",
    "axes[0].set(title=\"PSD spectrum\", ylabel='Power Spectral Density [dB]')\n",
    "\n",
    "# SNR spectrum\n",
    "snr_mean = snrs.mean(axis=(0,1))[freq_range]\n",
    "snr_std = snrs.std(axis=(0,1))[freq_range]\n",
    "\n",
    "axes[1].plot(freqs[freq_range], snr_mean, color='r')\n",
    "axes[1].fill_between(\n",
    "    freqs[freq_range], snr_mean - snr_std, snr_mean + snr_std,\n",
    "    color='r', alpha=.2)\n",
    "axes[1].set(\n",
    "    title=\"SNR spectrum\", xlabel='Frequency [Hz]',\n",
    "    ylabel='SNR', ylim=[-2, 30], xlim=[fmin, fmax])\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1f71b14",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ffe3b63c",
   "metadata": {},
   "source": [
    "# PRUEBA EXTRACCION DE FEATURES\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "901537af",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import cross_val_score, KFold\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import make_pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "70e4ee9b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using data from preloaded Raw for 101 events and 1001 original time points ...\n"
     ]
    }
   ],
   "source": [
    "import mne_features\n",
    "mne_features.univariate.get_univariate_funcs(128)\n",
    "\n",
    "data = epocas.get_data()\n",
    "labels1 = epocas.events[:, -1]\n",
    "y1 = labels1\n",
    "\n",
    "selected_funcs = {'max_cross_corr',\n",
    " 'nonlin_interdep',\n",
    " 'phase_lock_val',\n",
    " 'spect_corr',\n",
    " 'time_corr',\n",
    " 'app_entropy',\n",
    " 'decorr_time',\n",
    " 'hurst_exp',\n",
    " 'katz_fd',\n",
    " 'kurtosis',\n",
    " 'line_length',\n",
    " 'mean',\n",
    " 'samp_entropy',\n",
    " 'skewness',\n",
    " 'spect_edge_freq',\n",
    " 'spect_entropy',\n",
    " 'spect_slope',\n",
    " 'std',\n",
    " 'svd_entropy',\n",
    " 'svd_fisher_info',\n",
    " 'teager_kaiser_energy',\n",
    " 'variance',\n",
    " 'wavelet_coef_energy',\n",
    " 'zero_crossings'}\n",
    "X_new = mne_features.feature_extraction.extract_features(data, 250, selected_funcs)\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train1, X_test1, Y_train1, Y_test1 = train_test_split(X_new, y1, test_size=0.25, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "8a6b35e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
       "                (&#x27;binaryrelevance&#x27;,\n",
       "                 BinaryRelevance(classifier=GaussianNB(),\n",
       "                                 require_dense=[True, True]))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
       "                (&#x27;binaryrelevance&#x27;,\n",
       "                 BinaryRelevance(classifier=GaussianNB(),\n",
       "                                 require_dense=[True, True]))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">binaryrelevance: BinaryRelevance</label><div class=\"sk-toggleable__content\"><pre>BinaryRelevance(classifier=GaussianNB(), require_dense=[True, True])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">classifier: GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                ('binaryrelevance',\n",
       "                 BinaryRelevance(classifier=GaussianNB(),\n",
       "                                 require_dense=[True, True]))])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.multiclass import OneVsOneClassifier\n",
    "\n",
    "from sklearn import tree\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "pipeL = make_pipeline(StandardScaler(),\n",
    "                    #OneVsOneClassifier(LinearSVC(random_state=42,class_weight='balanced'))) #76.92\n",
    "                    #tree.DecisionTreeClassifier()) #46.15\n",
    "                    #RandomForestClassifier(random_state=42)) #76.92\n",
    "                    #BinaryRelevance(GaussianNB()))#69.23\n",
    "                    #LogisticRegression())#80.7\n",
    "                    #LogisticRegression(penalty='l1', solver='liblinear',multi_class='auto'))#73.08\n",
    "                    #LinearSVC(random_state=42))#80.77\n",
    "                    #OneVsOneClassifier(RandomForestClassifier(random_state=42)))#76.92\n",
    "                    \n",
    "\n",
    "pipeL.fit(X_train1,Y_train1)\n",
    "#print(\"Accuracy de entrenamiento:\", pipeL.score(X_test1, Y_test1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "28626b59",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'pipeL' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[1;32mIn [1]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m predictions \u001b[38;5;241m=\u001b[39m \u001b[43mpipeL\u001b[49m\u001b[38;5;241m.\u001b[39mpredict(X_test1)\u001b[38;5;241m.\u001b[39mtoarray()\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmetrics\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m accuracy_score\n\u001b[0;32m      4\u001b[0m accuracy_score(Y_test1,predictions)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'pipeL' is not defined"
     ]
    }
   ],
   "source": [
    "predictions = pipeL.predict(X_test1).toarray()\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(Y_test1,predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5a572ddc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jarv_\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_split.py:700: UserWarning: The least populated class in y has only 2 members, which is less than n_splits=5.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for RandomForest: 62.67%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "# Random Forest Classifier\n",
    "rf=RandomForestClassifier(random_state=42)\n",
    "score_rf = np.mean(cross_val_score(rf,  X_test1, Y_test1, scoring='accuracy'))\n",
    "rf.fit(X_train1, Y_train1)\n",
    "print(\"Accuracy for RandomForest: %s\" % '{:.2%}'.format(score_rf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c3de030",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(Y_test1,predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3f03dd5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "16d5d80c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy (Polynomial Kernel):  19.23\n",
      "F1 (Polynomial Kernel):  6.26\n"
     ]
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "import sklearn.model_selection as model_selection\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.model_selection import cross_val_score\n",
    "rbf = svm.SVC(kernel='rbf', gamma=1, C=0.001).fit(X_train1, Y_train1)\n",
    "poly = svm.SVC(decision_function_shape='ovo').fit(X_train1, Y_train1)\n",
    "poly_pred = poly.predict(X_test1)\n",
    "rbf_pred = rbf.predict(X_test1)\n",
    "poly_accuracy = accuracy_score(Y_test1, poly_pred)\n",
    "poly_f1 = f1_score(Y_test1, poly_pred, average='weighted')\n",
    "print('Accuracy (Polynomial Kernel): ', \"%.2f\" % (poly_accuracy*100))\n",
    "print('F1 (Polynomial Kernel): ', \"%.2f\" % (poly_f1*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d09a7fdb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RepeatedStratifiedKFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7ae76223",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jarv_\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "logit = LogisticRegression()\n",
    "logit.fit(X_train1, Y_train1)\n",
    "Y_pred = logit.predict(X_test1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b161a1d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept: [ 0.09050682  0.0864206  -0.04060156 -0.14439918  0.00807332]\n",
      "Coeficiente: [(0, 0.027348013356244285), (1, -0.04832563481834438), (2, -1.9829936215432602e-08), (3, -1.9444508754484706e-08), (4, 0.24043343716586543), (5, 0.2883167298897134), (6, -5.5123914019629725e-15), (7, -4.752954820160768e-15), (8, -0.26190747978648876), (9, -0.028759730684009423), (10, 0.026771600472055724), (11, -1.8468904875657185e-16), (12, 3.1609380929677397e-15), (13, 7.182104925459767e-14), (14, -4.273524807729914e-12), (15, -2.206789264517546e-12), (16, 4.6458871751086414e-14), (17, -1.4352980660628067e-16), (18, 1.7533999504180255e-15), (19, 2.862244454545165e-14), (20, -4.039901468690176e-12), (21, -1.6645737558983535e-12), (22, -5.38955133368381e-14), (23, 0.028757840996153253), (24, -4.124677008316354e-09), (25, -3.990688837968369e-09), (26, 0.0740068329091579), (27, 0.08926687510507018), (28, 1.8511179313274053e-10), (29, 1.5769783530290207e-10), (30, 0.0760877669268415), (31, -0.03879785045375286), (32, 0.02414560432990767), (33, -0.18220549552675247), (34, -1.1192898312513793), (35, 0.12262388033597668), (36, -0.06752762776943455), (37, -0.707556467799963), (38, -0.588103621962298), (39, 0.22429441514609252), (40, 0.05827463340588494), (41, -0.5942896762241143), (42, -0.7065101866064439), (43, 0.29463156346708713), (44, 0.06024115308794349), (45, 0.5710257989540373), (46, 0.7300196595747829), (47, -0.06474812828220655), (48, 2.4252193191023757e-16), (49, 0.12949625656441302), (50, 0.0035430150996058553), (51, 0.005022915098620787), (52, -0.02308661489299427), (53, -0.0359319763174087), (54, -2.846645640436294e-14), (55, -1.176561667124184e-13), (56, -5.4813795342085225e-15), (57, 1.2377715549373857e-14), (58, 1.14408774320426e-13), (59, -4.234784110169525e-14), (60, 1.401149972961754e-14), (61, 3.4874678637630045e-14), (62, -6.957392407911099e-16), (63, 5.3852716717914216e-15), (64, 1.4558221208010844e-17), (65, 1.5179430234249866e-16), (66, -3.268521879676626e-19), (67, -5.0771228433297175e-18), (68, -5.935129196859337e-14), (69, -2.84251551101548e-13), (70, -6.635587446393735e-15), (71, 6.391062746052192e-15), (72, 1.4759166687326341e-13), (73, 3.767832141038303e-14), (74, 1.2932487563864521e-14), (75, 2.6968806620233652e-14), (76, -9.795251950529409e-16), (77, 2.978969609593176e-15), (78, 9.404809997822937e-18), (79, 8.121702873790986e-17), (80, -2.5299249320925265e-19), (81, -3.9170923407810584e-18), (82, -0.0647481282822065), (83, -9.886959133143617e-17), (84, 0.1294962565644131), (85, 0.07053040564468446), (86, 0.07740293750375919), (87, -0.09682347716378402), (88, -0.016463618001255183)]\n",
      "Accuracy de entrenamiento: 0.5769230769230769\n"
     ]
    }
   ],
   "source": [
    "print(\"Intercept:\", logit.intercept_)\n",
    "print(\"Coeficiente:\", list(zip(pd.DataFrame(X_new).columns, logit.coef_.flatten(), )))\n",
    "print(\"Accuracy de entrenamiento:\", logit.score(X_test1, Y_test1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36b6d442",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "3e155838",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>BinaryRelevance(classifier=GaussianNB(), require_dense=[True, True])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">BinaryRelevance</label><div class=\"sk-toggleable__content\"><pre>BinaryRelevance(classifier=GaussianNB(), require_dense=[True, True])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">classifier: GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "BinaryRelevance(classifier=GaussianNB(), require_dense=[True, True])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from skmultilearn.problem_transform import BinaryRelevance\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "# initialize binary relevance multi-label classifier\n",
    "# with a gaussian naive bayes base classifier\n",
    "classifier = BinaryRelevance(GaussianNB())\n",
    "\n",
    "\n",
    "# train\n",
    "classifier.fit(X_train1, Y_train1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "16b0f304",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = classifier.predict(X_test1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "4eefacea",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions=predictions.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "6db98c26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6923076923076923"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(Y_test1,predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9bf3d2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "OneVsOneClassifier(tree.DecisionTreeClassifier())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5ebaa7d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "51acc62f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "971f6e3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "epocas._data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "2df415ac",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'NoneType' object is not subscriptable",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[1;32mIn [60]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m X \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mmoveaxis(\u001b[43mepocas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_data\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43mpick_types\u001b[49m\u001b[43m(\u001b[49m\u001b[43mepocas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minfo\u001b[49m\u001b[43m,\u001b[49m\u001b[43meeg\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43meog\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43mstim\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m,\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m2\u001b[39m);\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28mprint\u001b[39m(x)\n",
      "\u001b[1;31mTypeError\u001b[0m: 'NoneType' object is not subscriptable"
     ]
    }
   ],
   "source": [
    "X = np.moveaxis(epocas._data[:,pick_types(epocas.info,eeg=True,eog=False,stim=False),:],1,2);\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "153b76f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy.random import seed\n",
    "seed(1017)\n",
    "import tensorflow as tf\n",
    "tf.random.set_seed(1017)\n",
    "\n",
    "import os\n",
    "from glob import glob\n",
    "from collections import OrderedDict\n",
    "\n",
    "import mne\n",
    "from mne.io import RawArray\n",
    "from mne import read_evokeds, read_source_spaces, compute_covariance\n",
    "from mne import channels, find_events, concatenate_raws\n",
    "from mne import pick_types, viz, io, Epochs, create_info\n",
    "from mne import pick_channels, concatenate_epochs\n",
    "from mne.datasets import sample\n",
    "\n",
    "from mne.time_frequency import tfr_morlet\n",
    "\n",
    "import numpy as np\n",
    "from numpy import genfromtxt\n",
    "\n",
    "import pandas as pd\n",
    "pd.options.display.precision = 4\n",
    "pd.options.display.max_columns = None\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams[\"figure.figsize\"] = (14,14)\n",
    "\n",
    "import keras\n",
    "from keras import regularizers\n",
    "from keras.callbacks import TensorBoard\n",
    "from keras.models import Sequential, Model\n",
    "from keras.layers import Dense, Dropout, Activation, Input\n",
    "from keras.layers import Flatten, Conv2D, MaxPooling2D, LSTM\n",
    "from keras.layers import BatchNormalization, Conv3D, MaxPooling3D\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "\n",
    "from sklearn.utils import class_weight\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from tensorflow.keras.optimizers import SGD #NUEVA PRUEBA\n",
    "\n",
    "\n",
    "class Feats:\n",
    "  def __init__(self, num_classes=2, class_weights=[0.2,0.2,0.2,0.2,0.2], input_shape=[16,], \n",
    "               new_times=1, model_type='1', \n",
    "               x_train=1, y_train=1, x_test=1, y_test=1, x_val=1, y_val=1):\n",
    "    self.num_classes = num_classes\n",
    "    self.class_weights = class_weights\n",
    "    self.input_shape = input_shape\n",
    "    self.new_times = new_times\n",
    "    self.model_type = model_type\n",
    "    self.x_train = x_train\n",
    "    self.y_train = y_train\n",
    "    self.x_test = x_test\n",
    "    self.y_test = y_test\n",
    "    self.x_val = x_val\n",
    "    self.y_val = y_val\n",
    "\n",
    "\n",
    "\n",
    "def FeatureEngineer(epochs, model_type='NN',\n",
    "                    frequency_domain=False,\n",
    "                    normalization=False, electrode_median=False,\n",
    "                    wavelet_decim=1, flims=(3,30), include_phase=False,\n",
    "                    f_bins=20, wave_cycles=3, \n",
    "                    wavelet_electrodes = [11,12,13,14,15],\n",
    "                    spect_baseline=[-1,-.5],\n",
    "                    test_split = 0.2, val_split = 0.2,\n",
    "                    random_seed=1017, watermark = False):\n",
    "    \"\"\"\n",
    "    Takes epochs object as \n",
    "\n",
    "  input and settings, \n",
    "  outputs  feats(training, test and val data option to use frequency or time domain)\n",
    "  \n",
    "  TODO: take tfr? or autoencoder encoded object?\n",
    "\n",
    "  FeatureEngineer(epochs, model_type='NN',\n",
    "                    frequency_domain=False,\n",
    "                    normalization=False, electrode_median=False,\n",
    "                    wavelet_decim=1, flims=(3,30), include_phase=False,\n",
    "                    f_bins=20, wave_cycles=3, \n",
    "                    wavelet_electrodes = [11,12,13,14,15],\n",
    "                    spect_baseline=[-1,-.5],\n",
    "                    test_split = 0.2, val_split = 0.2,\n",
    "                    random_seed=1017, watermark = False):\n",
    "    \"\"\"\n",
    "    np.random.seed(random_seed)\n",
    "\n",
    "  #pull event names in order of trigger number\n",
    "    epochs.event_id = {'cond0':1, 'cond1':2,'cond2':3,'cond3':4,'cond4':5}\n",
    "    event_names = ['cond0','cond1','cond2','cond3','cond4']\n",
    "    \"\"\"i = 0\n",
    "  for key, value in sorted(epochs.event_id.items(), key=lambda item: (item[1],item[0])):\n",
    "    event_names[i] = key\n",
    "    i += 1\"\"\"\n",
    "\n",
    "    #Create feats object for output\n",
    "    feats = Feats()\n",
    "    feats.num_classes = len(epochs.event_id)\n",
    "    feats.model_type = model_type\n",
    "\n",
    "    if frequency_domain:\n",
    "        print('Constructing Frequency Domain Features')\n",
    "        #list of frequencies to output\n",
    "        f_low = flims[0]\n",
    "        f_high = flims[1]\n",
    "        frequencies =  np.linspace(f_low, f_high, f_bins, endpoint=True)\n",
    "\n",
    "        #option to select all electrodes for fft\n",
    "        if wavelet_electrodes == 'all':\n",
    "            wavelet_electrodes = pick_types(epochs.info,eeg=True,eog=False)\n",
    "\n",
    "        #type of output from wavelet analysis\n",
    "        if include_phase:\n",
    "            tfr_output_type = 'complex'\n",
    "        else:\n",
    "            tfr_output_type = 'power'\n",
    "\n",
    "        tfr_dict = {}\n",
    "        \n",
    "        for event in event_names:\n",
    "            print('Computing Morlet Wavelets on ' + event)\n",
    "            tfr_temp = tfr_morlet(epochs[event], freqs=frequencies,\n",
    "                            n_cycles=wave_cycles, return_itc=False,\n",
    "                            picks=wavelet_electrodes, average=False,\n",
    "                            decim=wavelet_decim, output=tfr_output_type)\n",
    "            # Apply spectral baseline and find stim onset time\n",
    "            #tfr_temp = tfr_temp.apply_baseline(spect_baseline,mode='mean')\n",
    "            stim_onset = np.argmax(tfr_temp.times>0)\n",
    "\n",
    "            # Reshape power output and save to tfr dict\n",
    "            power_out_temp = np.moveaxis(tfr_temp.data[:,:,:,stim_onset:],1,3)\n",
    "            power_out_temp = np.moveaxis(power_out_temp,1,2)\n",
    "            print(event + ' trials: ' + str(len(power_out_temp)))\n",
    "            tfr_dict[event] = power_out_temp\n",
    "\n",
    "        #reshape times (sloppy but just use the last temp tfr)\n",
    "        feats.new_times = tfr_temp.times[stim_onset:]\n",
    "\n",
    "        for event in event_names:\n",
    "            print(event + ' Time Points: ' + str(len(feats.new_times)))\n",
    "            print(event + ' Frequencies: ' + str(len(tfr_temp.freqs)))\n",
    "\n",
    "        #Construct X and Y\n",
    "        for ievent,event in enumerate(event_names):\n",
    "            if ievent == 0:\n",
    "                X = tfr_dict[event]\n",
    "                Y_class = np.zeros(len(tfr_dict[event]))\n",
    "            else:\n",
    "                X = np.append(X,tfr_dict[event],0)\n",
    "                Y_class = np.append(Y_class,np.ones(len(tfr_dict[event]))*ievent,0)\n",
    "\n",
    "        #concatenate real and imaginary data\n",
    "        if include_phase:\n",
    "            print('Concatenating the real and imaginary components')\n",
    "            X = np.append(np.real(X),np.imag(X),2)\n",
    "\n",
    "        #compute median over electrodes to decrease features\n",
    "        if electrode_median:\n",
    "            print('Computing Median over electrodes')\n",
    "            X = np.expand_dims(np.median(X,axis=len(X.shape)-1),2)\n",
    "\n",
    "        #reshape for various models\n",
    "        if model_type == 'NN' or model_type == 'LSTM':\n",
    "            X = np.reshape(X, (X.shape[0], X.shape[1], np.prod(X.shape[2:])))\n",
    "\n",
    "        if model_type == 'CNN3D':\n",
    "            X = np.expand_dims(X,4)\n",
    "        \n",
    "        if model_type == 'AUTO' or model_type == 'AUTODeep':\n",
    "            print('Auto model reshape')\n",
    "            X = np.reshape(X, (X.shape[0],np.prod(X.shape[1:])))\n",
    "\n",
    "\n",
    "    if not frequency_domain:#este es el que nos sirve\n",
    "        print('Constructing Time Domain Features')\n",
    "        \n",
    "        #if using muse aux port as eeg must label it as such\n",
    "        eeg_chans = pick_types(epochs.info,eeg=True,eog=False,stim=False)\n",
    "\n",
    "        #put channels last, remove eye and stim\n",
    "        X = np.moveaxis(epochs._data[:,eeg_chans,:],1,2);\n",
    "\n",
    "        #take post baseline only\n",
    "        stim_onset = np.argmax(epochs.times>0)\n",
    "        feats.new_times = epochs.times[stim_onset:]\n",
    "        X = X[:,stim_onset:,:]\n",
    "\n",
    "        #convert markers to class\n",
    "        #requires markers to be 1 and 2 in data file?\n",
    "        #This probably is not robust to other marker numbers\n",
    "        Y_class = epochs.events[:,2]-1  #subtract 1 \n",
    "\n",
    "        #median over electrodes to reduce features\n",
    "        if electrode_median:\n",
    "            print('Computing Median over electrodes')\n",
    "            X = np.expand_dims(np.median(X,axis=len(X.shape)-1),2)\n",
    "\n",
    "        ## Model Reshapes:\n",
    "        # reshape for CNN\n",
    "        if model_type == 'CNN':\n",
    "            print('Size X before reshape for CNN: ' + str(X.shape))\n",
    "            X = np.expand_dims(X,3 )\n",
    "            print('Size X before reshape for CNN: ' + str(X.shape))\n",
    "\n",
    "        # reshape for CNN3D\n",
    "        if model_type == 'CNN3D':\n",
    "            print('Size X before reshape for CNN3D: ' + str(X.shape))\n",
    "            X = np.expand_dims(np.expand_dims(X,3),4)\n",
    "            print('Size X before reshape for CNN3D: ' + str(X.shape))\n",
    "\n",
    "        #reshape for autoencoder\n",
    "        if model_type == 'AUTO' or model_type == 'AUTODeep':\n",
    "            print('Size X before reshape for Auto: ' + str(X.shape))\n",
    "            X = np.reshape(X, (X.shape[0], np.prod(X.shape[1:])))\n",
    "            print('Size X after reshape for Auto: ' + str(X.shape))\n",
    "\n",
    "\n",
    "    #Normalize X - TODO: need to save mean and std for future test + val\n",
    "    if normalization:\n",
    "        print('Normalizing X')\n",
    "        X = (X - np.mean(X)) / np.std(X)\n",
    "\n",
    "    # convert class vectors to one hot Y and recast X\n",
    "    Y = to_categorical(Y_class,feats.num_classes)\n",
    "    X = X.astype('float32')\n",
    "\n",
    "    # add watermark for testing models\n",
    "    if watermark:\n",
    "        X[Y[:,0]==0,0:2,] = 0\n",
    "        X[Y[:,0]==1,0:2,] = 1\n",
    "\n",
    "    # Compute model input shape\n",
    "    feats.input_shape = X.shape[1:]\n",
    "\n",
    "    # Split training test and validation data\n",
    "    val_prop = val_split / (1-test_split)\n",
    "    (feats.x_train,\n",
    "    feats.x_test,\n",
    "    feats.y_train,\n",
    "    feats.y_test) = train_test_split(X, Y,\n",
    "                                     test_size=test_split,\n",
    "                                     random_state=42) # SE CAMBIO random_seed x 42\n",
    "    (feats.x_train,\n",
    "    feats.x_val,\n",
    "    feats.y_train,\n",
    "    feats.y_val) = train_test_split(feats.x_train, feats.y_train,\n",
    "                                   test_size=val_prop,\n",
    "                                   random_state=42) # SE CAMBIO random_seed x 42\n",
    "\n",
    "    #compute class weights for uneven classes\n",
    "    #y_ints = [y.argmax() for y in feats.y_train]\n",
    "    #feats.class_weights = class_weight.compute_class_weight('balanced',\n",
    "     #                                            np.unique(y_ints),\n",
    "      #                                           y_ints)\n",
    "  \n",
    "  \n",
    "  \n",
    "    #Print some outputs\n",
    "    print('Combined X Shape: ' + str(X.shape))\n",
    "    print('Combined Y Shape: ' + str(Y_class.shape))\n",
    "    print('Y Example (should be 1s & 0s): ' + str(Y_class[0:10]))\n",
    "    print('X Range: ' + str(np.min(X)) + ':' + str(np.max(X)))\n",
    "    print('Input Shape: ' + str(feats.input_shape))\n",
    "    print('x_train shape:', feats.x_train.shape)\n",
    "    print(feats.x_train.shape[0], 'train samples')\n",
    "    print(feats.x_test.shape[0], 'test samples')\n",
    "    print(feats.x_val.shape[0], 'validation samples')\n",
    "    print('Class Weights: ' + str(feats.class_weights))\n",
    " \n",
    "    feats.class_weights = dict(enumerate(feats.class_weights))#Configuracion realizada para la clasifiacion multiclase\n",
    "\n",
    "\n",
    "    return feats\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def CreateModel(feats,units=[32,16,8,8,4,8,16], dropout=.3,  #cambié el dropout de .25 a .3\n",
    "                batch_norm=True, filt_size=3, pool_size=2):\n",
    "    \n",
    "    print('Creating ' +  feats.model_type + ' Model')\n",
    "    print('Input shape: ' + str(feats.input_shape))\n",
    "    \n",
    "    nunits = len(units)\n",
    "\n",
    "    ##---LSTM - Many to two, sequence of time to classes\n",
    "    #Units must be at least two\n",
    "    if feats.model_type == 'LSTM':\n",
    "        if nunits < 2:\n",
    "            print('Warning: Need at least two layers for LSTM')\n",
    "            \n",
    "        model = Sequential()\n",
    "        model.add(LSTM(input_shape=(None, feats.input_shape[1]),\n",
    "                   units=units[0], return_sequences=True))\n",
    "        \n",
    "        \n",
    "        if batch_norm:\n",
    "            model.add(BatchNormalization())\n",
    "        model.add(Activation('relu'))\n",
    "        \n",
    "        if dropout:\n",
    "            model.add(Dropout(dropout))\n",
    "\n",
    "        if len(units) > 2:\n",
    "            for unit in units[1:-1]:\n",
    "                model.add(LSTM(units=unit,return_sequences=True))\n",
    "                \n",
    "                if batch_norm:\n",
    "                    model.add(BatchNormalization())\n",
    "                model.add(Activation('relu'))\n",
    "            \n",
    "                if dropout:\n",
    "                    model.add(Dropout(dropout))\n",
    "\n",
    "        model.add(LSTM(units=units[-1],return_sequences=False))\n",
    "        if batch_norm:\n",
    "            model.add(BatchNormalization())\n",
    "        model.add(Activation('relu'))\n",
    "        if dropout:\n",
    "            model.add(Dropout(dropout))\n",
    "\n",
    "        model.add(Dense(units=feats.num_classes))\n",
    "        model.add(Activation(\"softmax\"))\n",
    "\n",
    "        \n",
    "                \n",
    "    ##---DenseFeedforward Network\n",
    "    #Makes a hidden layer for each item in units\n",
    "    if feats.model_type == 'NN':\n",
    "        \n",
    "        model = Sequential()\n",
    "        model.add(Flatten(input_shape=feats.input_shape))\n",
    "        \n",
    "        for unit in units:\n",
    "            model.add(Dense(unit))\n",
    "            if batch_norm:\n",
    "                model.add(BatchNormalization())\n",
    "            model.add(Activation('relu'))\n",
    "            if dropout:\n",
    "                model.add(Dropout(dropout))\n",
    "\n",
    "        model.add(Dense(feats.num_classes, activation='softmax'))\n",
    "\n",
    "    ##----Convolutional Network\n",
    "    if feats.model_type == 'CNN':\n",
    "        if nunits < 2:\n",
    "            print('Warning: Need at least two layers for CNN')\n",
    "        model = Sequential()\n",
    "        model.add(Conv2D(units[0], filt_size,\n",
    "              input_shape=feats.input_shape, padding='same'))\n",
    "        model.add(Activation('relu'))\n",
    "        model.add(MaxPooling2D(pool_size=pool_size, padding='same'))\n",
    "        \n",
    "        if nunits > 2:\n",
    "            for unit in units[1:-1]:\n",
    "                model.add(Conv2D(unit, filt_size, padding='same'))\n",
    "                model.add(Activation('relu'))\n",
    "                model.add(MaxPooling2D(pool_size=pool_size, padding='same'))\n",
    "\n",
    "        model.add(Flatten())\n",
    "        model.add(Dense(units[-1]))\n",
    "        model.add(Activation('relu'))\n",
    "        model.add(Dense(feats.num_classes))\n",
    "        model.add(Activation('softmax'))\n",
    "\n",
    "    ##----Convolutional Network\n",
    "    if feats.model_type == 'CNN3D':\n",
    "        if nunits < 2:\n",
    "            print('Warning: Need at least two layers for CNN')\n",
    "        model = Sequential()\n",
    "        model.add(Conv3D(units[0], filt_size,\n",
    "                     input_shape=feats.input_shape, padding='same'))\n",
    "        model.add(Activation('relu'))\n",
    "        model.add(MaxPooling3D(pool_size=pool_size, padding='same'))\n",
    "\n",
    "        if nunits > 2:\n",
    "            for unit in units[1:-1]:\n",
    "                model.add(Conv3D(unit, filt_size, padding='same'))\n",
    "                model.add(Activation('relu'))\n",
    "                model.add(MaxPooling3D(pool_size=pool_size, padding='same'))\n",
    "\n",
    "\n",
    "        model.add(Flatten())\n",
    "        model.add(Dense(units[-1]))\n",
    "        model.add(Activation('relu'))\n",
    "        model.add(Dense(feats.num_classes))\n",
    "        model.add(Activation('softmax'))\n",
    "\n",
    "\n",
    "    ## Autoencoder\n",
    "    #takes the first item in units for hidden layer size\n",
    "    if feats.model_type == 'AUTO':\n",
    "        encoding_dim = units[0]\n",
    "        input_data = Input(shape=(feats.input_shape[0],))\n",
    "        #,activity_regularizer=regularizers.l1(10e-5)\n",
    "        encoded = Dense(encoding_dim, activation='relu')(input_data)\n",
    "        decoded = Dense(feats.input_shape[0], activation='sigmoid')(encoded)\n",
    "        model = Model(input_data, decoded)\n",
    "        \n",
    "        encoder = Model(input_data,encoded)\n",
    "        encoded_input = Input(shape=(encoding_dim,))\n",
    "        decoder_layer = model.layers[-1]\n",
    "        decoder = Model(encoded_input, decoder_layer(encoded_input))\n",
    "\n",
    "\n",
    "    #takes an odd number of layers > 1\n",
    "    #e.g. units = [64,32,16,32,64]\n",
    "    if feats.model_type == 'AUTODeep':\n",
    "        if nunits % 2 == 0:\n",
    "            print('Warning: Please enter odd number of layers into units')\n",
    "\n",
    "        half = nunits/2\n",
    "        midi = int(np.floor(half))\n",
    "\n",
    "        input_data = Input(shape=(feats.input_shape[0],))\n",
    "        encoded = Dense(units[0], activation='relu')(input_data)\n",
    "\n",
    "        #encoder decreases\n",
    "        if nunits >= 3:\n",
    "            for unit in units[1:midi]:\n",
    "                encoded = Dense(unit, activation='relu')(encoded)\n",
    "\n",
    "        #latent space\n",
    "        decoded = Dense(units[midi], activation='relu')(encoded)\n",
    "\n",
    "        #decoder increses\n",
    "        if nunits >= 3:\n",
    "            for unit in units[midi+1:-1]:\n",
    "                decoded = Dense(unit, activation='relu')(decoded)\n",
    "\n",
    "        decoded = Dense(units[-1], activation='relu')(decoded)\n",
    "\n",
    "        decoded = Dense(feats.input_shape[0], activation='sigmoid')(decoded)\n",
    "        model = Model(input_data, decoded)\n",
    "\n",
    "        encoder = Model(input_data,encoded)\n",
    "        encoded_input = Input(shape=(units[midi],))\n",
    "\n",
    "    \n",
    "\n",
    "\n",
    "    if feats.model_type == 'AUTO' or feats.model_type == 'AUTODeep':\n",
    "        opt = tf.keras.optimizers.Adam(lear=0.001, beta_1=0.9, beta_2=0.999, \n",
    "                                       epsilon=0.0000001, decay=0.0, amsgrad=False)#lr=0.001\n",
    "        model.compile(optimizer=opt, loss='mean_squared_error')\n",
    "\n",
    "\n",
    "\n",
    "    if ((feats.model_type == 'CNN') or\n",
    "      (feats.model_type == 'CNN3D') or\n",
    "      (feats.model_type == 'LSTM') or\n",
    "      (feats.model_type == 'NN')):\n",
    "        \n",
    "        # initiate adam optimizer\n",
    "        opt =  tf.keras.optimizers.Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999,epsilon=0.0000001, amsgrad=False) #lr=0.001 \n",
    "        ##SGD(lr=0.0001, momentum=0.9)#\n",
    "        # Let's train the model using RMSprop\n",
    "        model.compile(loss='categorical_crossentropy',\n",
    "                  optimizer=opt,# se cambio opt por 'adam'\n",
    "                  metrics=['accuracy'])\n",
    "        encoder = []\n",
    "\n",
    "\n",
    "    model.summary()\n",
    "\n",
    "    return model, encoder\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def TrainTestVal(model, feats, batch_size=2, \n",
    "                train_epochs=20, show_plots=True):\n",
    "    \n",
    "    print('Training Model:')\n",
    "    # Train Model\n",
    "    \n",
    "    if feats.model_type == 'AUTO' or feats.model_type == 'AUTODeep':\n",
    "        print('Training autoencoder:')\n",
    "        \n",
    "        history = model.fit(feats.x_train, feats.x_train,\n",
    "                        batch_size = batch_size,\n",
    "                        epochs=train_epochs,\n",
    "                        validation_data=(feats.x_val,feats.x_val),\n",
    "                        shuffle=True,\n",
    "                        verbose=True,\n",
    "                        class_weight=feats.class_weights\n",
    "                       )\n",
    "        \n",
    "        #Cambiar el class weight = none por = feats.class_weights\n",
    "        \n",
    "        # list all data in history\n",
    "        print(history.history.keys())\n",
    "        \n",
    "        if show_plots:\n",
    "            # summarize history for loss\n",
    "            plt.semilogy(history.history['loss'])\n",
    "            plt.semilogy(history.history['val_loss'])\n",
    "            plt.title('model loss')\n",
    "            plt.ylabel('loss')\n",
    "            plt.xlabel('epoch')\n",
    "            plt.legend(['train', 'val'], loc='upper left')\n",
    "            plt.show()\n",
    "        \n",
    "    else:\n",
    "        history = model.fit(feats.x_train, feats.y_train, batch_size=batch_size, \n",
    "                                epochs=train_epochs, validation_data=(feats.x_val, feats.y_val),shuffle=True,verbose=True,class_weight=feats.class_weights)\n",
    "        #Cambiar el class weight = none por = feats.class_weight\n",
    "        \n",
    "        # list all data in history\n",
    "        print(history.history.keys())\n",
    "        \n",
    "        if show_plots:\n",
    "            # summarize history for accuracy\n",
    "            plt. clf()\n",
    "            plt.plot(history.history['accuracy'])\n",
    "            plt.plot(history.history['val_accuracy'])\n",
    "            plt.title('model accuracy')\n",
    "            plt.ylabel('accuracy')\n",
    "            plt.xlabel('epoch')\n",
    "            plt.legend(['train', 'val'], loc='upper left')\n",
    "            plt.show()\n",
    "            \n",
    "            \"\"\"\n",
    "      # summarize history for loss\n",
    "      plt.semilogy(history.history['loss'])\n",
    "      plt.semilogy(history.history['val_loss'])\n",
    "      plt.title('model loss')\n",
    "      plt.ylabel('loss')\n",
    "      plt.xlabel('epoch')\n",
    "      plt.legend(['train', 'val'], loc='upper left')\n",
    "      plt.show()\"\"\"\n",
    "\n",
    "\n",
    "    # Te1st on left out Test data\n",
    "        score, acc = model.evaluate(feats.x_test, feats.y_test,\n",
    "                                batch_size=batch_size)\n",
    "        print(model.metrics_names)\n",
    "        print('Test loss:', score)\n",
    "        print('Test accuracy:', acc)\n",
    "\n",
    "        # Build a dictionary of data to return\n",
    "        data = {}\n",
    "        data['score'] = score\n",
    "        data['acc'] = acc\n",
    "\n",
    "        return model, data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "ec15c6d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Constructing Frequency Domain Features\n",
      "Computing Morlet Wavelets on cond0\n",
      "Using data from preloaded Raw for 20 events and 1001 original time points ...\n",
      "Not setting metadata\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.1s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cond0 trials: 20\n",
      "Computing Morlet Wavelets on cond1\n",
      "Using data from preloaded Raw for 20 events and 1001 original time points ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Not setting metadata\n",
      "cond1 trials: 20\n",
      "Computing Morlet Wavelets on cond2\n",
      "Using data from preloaded Raw for 20 events and 1001 original time points ...\n",
      "Not setting metadata\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.1s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.1s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cond2 trials: 20\n",
      "Computing Morlet Wavelets on cond3\n",
      "Using data from preloaded Raw for 20 events and 1001 original time points ...\n",
      "Not setting metadata\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.1s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cond3 trials: 20\n",
      "Computing Morlet Wavelets on cond4\n",
      "Using data from preloaded Raw for 21 events and 1001 original time points ...\n",
      "Not setting metadata\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.1s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cond4 trials: 21\n",
      "cond0 Time Points: 1000\n",
      "cond0 Frequencies: 49\n",
      "cond1 Time Points: 1000\n",
      "cond1 Frequencies: 49\n",
      "cond2 Time Points: 1000\n",
      "cond2 Frequencies: 49\n",
      "cond3 Time Points: 1000\n",
      "cond3 Frequencies: 49\n",
      "cond4 Time Points: 1000\n",
      "cond4 Frequencies: 49\n",
      "Computing Median over electrodes\n",
      "Normalizing X\n",
      "Combined X Shape: (101, 1000, 49)\n",
      "Combined Y Shape: (101,)\n",
      "Y Example (should be 1s & 0s): [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "X Range: -0.77186805:17.594185\n",
      "Input Shape: (1000, 49)\n",
      "x_train shape: (60, 1000, 49)\n",
      "60 train samples\n",
      "21 test samples\n",
      "20 validation samples\n",
      "Class Weights: [0.2, 0.2, 0.2, 0.2, 0.2]\n"
     ]
    }
   ],
   "source": [
    "features_DEEP = FeatureEngineer(epocas, model_type='NN',\n",
    "                    frequency_domain=True,\n",
    "                    normalization=True, electrode_median=True,\n",
    "                    wavelet_decim=1, flims=(4,20), include_phase=False,\n",
    "                    f_bins=49, wave_cycles=3, \n",
    "                    wavelet_electrodes = 'all',\n",
    "                    test_split = 0.2, val_split = 0.2,\n",
    "                    random_seed=42, watermark = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33d0bf49",
   "metadata": {},
   "outputs": [],
   "source": [
    "Flatten(input_shape=features_DEEP.input_shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1cc2dc36",
   "metadata": {},
   "outputs": [],
   "source": [
    "modelo, _ = CreateModel(features_DEEP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2c829f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "TrainTestVal(modelo, features_DEEP,train_epochs=150,batch_size=64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "74e4ad3d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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