Explore Temple Hospital Seizure EEG dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Explore Temple Hospital Seizure EEG dataset\n",
    "\n",
    "Previously, I attempted to load the data with pyedflib, but was unsuccessful. I reimplemented a file parser using Python based on the byte by byte explanation of the .edf file found on their website. Seems to be working pretty well. In the present notebook, I will perform some preliminary analysis of the EEG data, using my previous knowledge, in order to gain some preliminary understanding of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "academic mode\n"
     ]
    }
   ],
   "source": [
    "%run '/Users/edward/Documents/Scripts/Python/init_py.py'\n",
    "addpythonpkg('ReadNWrite')\n",
    "from scipy import signal, stats\n",
    "from IPython import display\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "import json\n",
    "from edf import * # custom parser"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "hide_input": true
   },
   "source": [
    "## List all the files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "deletable": false,
    "editable": false,
    "hide_input": true,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train: 1984\n",
      "test: 1013\n"
     ]
    }
   ],
   "source": [
    "basedir = '/Volumes/Storage/TUH_EEG/'\n",
    "P_train, N_train = SearchFiles(os.path.join(basedir, 'train'), '*/*/*/*/*.edf')\n",
    "P_test, N_test = SearchFiles(os.path.join(basedir, 'dev_test'), '*/*/*/*/*.edf')\n",
    "P = P_train + P_test\n",
    "N = N_train + N_test\n",
    "print('train:', len(N_train))\n",
    "print('test:', len(N_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Double check if all the record duration is 1.0 second\n",
    "This way, we can simply use `.num_records` for duration of the sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "deletable": false,
    "editable": false,
    "hide_input": true,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Progress: |██████████████████████████████████████████████████| 100.0% Complete\n",
      "Finished Checking\n"
     ]
    }
   ],
   "source": [
    "l = len(P)\n",
    "printProgressBar(0, l, prefix = 'Progress:', suffix = 'Complete', length = 50)\n",
    "for n, p in enumerate(P):\n",
    "    printProgressBar(n+1, l, prefix = 'Progress:', suffix = 'Complete', length = 50)\n",
    "    eeg_data = EDF(p, header_only=True)\n",
    "    if abs(eeg_data.header.duration - 1.0)>1E-6:\n",
    "        print(p)\n",
    "        \n",
    "print('Finished Checking')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion: All duration is 1.0 second. We can use `.num_records` for duration of the sample"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate the average durations and # channels of the samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "deletable": false,
    "editable": false,
    "hide_input": true,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [],
   "source": [
    "def duration_x_channel(P):\n",
    "    l = len(P)\n",
    "    eeg_durations, eeg_channels = np.zeros(l), np.zeros(l)\n",
    "    printProgressBar(0, l, prefix = 'Progress:', suffix = 'Complete', length = 50)\n",
    "    for n, p in enumerate(P):\n",
    "        printProgressBar(n+1, l, prefix = 'Progress:', suffix = 'Complete', length = 50)\n",
    "        eeg_data = EDF(p, header_only=True)\n",
    "        eeg_durations[n] = eeg_data.header.num_records\n",
    "        eeg_channels[n] = eeg_data.header.num_signals\n",
    "\n",
    "    print('Durations: ', np.mean(eeg_durations), '±', np.std(eeg_durations))\n",
    "    print('min: ', np.min(eeg_durations), 'max: ', np.max(eeg_durations))\n",
    "    print(eeg_durations)\n",
    "\n",
    "    print('# channels: ', np.mean(eeg_channels), '±', np.std(eeg_channels))\n",
    "    print('min: ', np.min(eeg_channels), 'max: ', np.max(eeg_channels))\n",
    "    print(eeg_channels)\n",
    "    return eeg_durations, eeg_channels\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "all\n",
      "Progress: |██████████████████████████████████████████████████| 100.0% Complete\n",
      "Durations:  600.6252919586253 ± 449.9848120385344\n",
      "min:  16.0 max:  3526.0\n",
      "[1205. 1442. 1197. ...  425.  365.  395.]\n",
      "# channels:  32.46813480146813 ± 3.5574416390896606\n",
      "min:  25.0 max:  41.0\n",
      "[36. 36. 36. ... 32. 32. 32.]\n"
     ]
    }
   ],
   "source": [
    "print('all')\n",
    "eeg_durations, eeg_channels = duration_x_channel(P)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "deletable": false,
    "editable": false,
    "hide_input": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train\n",
      "Progress: |██████████████████████████████████████████████████| 100.0% Complete\n",
      "Durations:  598.2066532258065 ± 456.4903538756773\n",
      "min:  16.0 max:  3526.0\n",
      "[1205. 1442. 1197. ...  375.  373.   55.]\n",
      "# channels:  32.65625 ± 3.768607229949684\n",
      "min:  25.0 max:  41.0\n",
      "[36. 36. 36. ... 32. 32. 32.]\n"
     ]
    }
   ],
   "source": [
    "print('train')\n",
    "eeg_durations_train, eeg_channels_train = duration_x_channel(P_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test\n",
      "Progress: |██████████████████████████████████████████████████| 100.0% Complete\n",
      "Durations:  605.3622902270483 ± 436.9241476493356\n",
      "min:  17.0 max:  3405.0\n",
      "[ 20. 272. 234. ... 425. 365. 395.]\n",
      "# channels:  32.09970384995064 ± 3.0692774199648047\n",
      "min:  27.0 max:  41.0\n",
      "[32. 32. 32. ... 32. 32. 32.]\n"
     ]
    }
   ],
   "source": [
    "print('test')\n",
    "eeg_durations_test, eeg_channels_test = duration_x_channel(P_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "source": [
    "## Data structure explore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "deletable": false,
    "editable": false,
    "hide_input": true,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length (s): 1205\n",
      "# Channels:  36\n",
      "Sampling rate (points per sec):  [250, 250, 250, 250, 250, 250, 250, 250, 250, 250, 250, 250, 250, 250, 250, 250, 250, 250, 250, 250, 250, 250, 250, 250, 250, 250, 250, 250, 250, 250, 250, 250, 250, 1, 1, 1]\n"
     ]
    }
   ],
   "source": [
    "eeg_data = EDF(P_train[0])\n",
    "print('Length (s):', eeg_data.header.num_records)\n",
    "print('# Channels: ', eeg_data.header.num_signals)\n",
    "print('Sampling rate (points per sec): ', eeg_data.header.sample_rate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Do some plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "data": {
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Cl893wORfq7wbL0Y2wZ38Uny5LxXzngoBUQkGYSM6ed0pLBnRFs62Vgj2dISVhQzjvzuOBvbWWDOmPYrLKvFH0i2M69KEP2f5zisoLpOLhpY4sgpK4eFsC0DZWQLKxt/e2gJyBcW/5+/wFTMjLkbj/CM6OhGpSZoXMvPxzu/iyTuUgm9w/3w9Eo42lmjq7oCztx5qnA8oRVD0qoNwsrFEp6ZueLlbAN79/RxaeTvhl1c7K/OVmoPrucV4vrPyOVy6k4/zmfl4ur0vbj98hGaNHEUeunv5pfjtxE282y8YhBBRpzNr03ksezZUlIfUrEJ8sitFNAv9p6M38HafIHy6NxXvDWiOj3cpR0nyH1Vg3lMhovM7LNyNjLgYdI3bo2HAZcTF4M7DR7y4H9HeF+Mim+D2g0fYfOY29l7JwoV5A+BkK/YulZTJcTO3BC/9oFwl4GBqDho6WKOpu4Noy9+/z9wWvQNKKeb/qxQyGaqQjojFSgHy5m+ncXJ2P+Xfv57G8et5aOXtjJECscaVi3v5peiyVHnelD5BUFDgvYEtAChXL9DFW7+dxtbzSqF2IiMPvVo0glSUwJhvjkmef3Bmb2w9fxcvRDbB1wfSRca1v5sdAGUnmFNUjuhVYjG68rlQPN3eDxVyBVp9sEPt3V9A7KCWmPZHlbgQhs5wx4nAY/f6ulNY9mwousXtxcX5A+FoY4nOKkE3M7oFlu2o8h5zcAKlSGCA/Ha8Kjb49sNHuP3wEfJLKpCaXSSaGNzyg+346/WuAJThIZtOZ2JEBz++4wSUbeOlOwXwc7Xj67s6O3RMUvv+0HWMiWiMsko57z0Xws0tidt+xaCQm6PpuQjzdwUA7FXzBn+WmIrPEquMg4+eaYsRHfyQphI/w8J8kFNUhsOqeF1h6M3aA2m8cJVCoaDo+8l+/vOne1MR4uMCH9eqZ7IqQeyYURd0HRclwM3BGvmPKjTK46NyOXKKyuDvZo+QD6uM/5t5JWj5wQ68GFnVLg/97BC8XcTv4rDA4TJw1QGUlCsNY/W2d8inVSLsjV9PobWPC1p6OvHH1Fc6KSqthFxBsWTbZTjYWOLFyCZo6GjDfy8c7bvz8BF8XO1QXFbJh/tkCYzNPVeyMFFlaHL52n7hrqgvE1JWKYeMECzdrn+5WG5kM3LpHiRM7wl7KwtYWoh9jOduPURhaSXe+k0cD37ieh76rdiPXyd0howQNHJS/r71J5T1qLRCgW0X7mHbBWU5f1Qhxye7UvBZYipSFg3C0E8P4er9Qgxq44URHfyQV1yGjaduY0rfYOQWlyHhchb+PXcH3x++jh9e7oSXVe2snbUFBrXxwsKtyfjrVCZWG+jYq5Ar+LlFCgXFTVVISq+P9/Fp1A3njaczsfF0JjLiYvi5A1JGPwB8uucaNpy8hdsPH2HNnmtIXzIYN7SE7L3y40kkXq3SQkuebquRJj27CIGNHA36beaAPC6T2cLDw2lSkvRLq204yzUq2F00EUfI5je64ukvjsCvgR2yC8tMYkFdXzqYF7TX7hfCx9UOPZfvQ05RGfa91wsB7g6SHlxCNIfbZg9uhcV6whw49s/oBRkh+GjHFV5gqHNgRm/0WF6zmc0/vtwJJzPy8Hmicjm/9RO74MLth1iyzbA1sq8vHaw1jjwjLgYKBcWAVQf4+M/q8NfrkQgPcOMNEG0MD/NBeIAbH7pzft4AnL35kPfIt/V1wQWJTXSaujvgek4xtr7dHW18XXArr0RkGO14JwqpWUWIDvHCkbRc/nqGcvz9vryQ0sdvEzvj0LUco5dXXPx0G8zerBmyxPHpmPb49tB1nNNinFSXs3P7Q0GVhoE67w1ojrziCvx24gY/BM1x6H+963RWPveO1Xm2ox/e6ReMzAePMHqttEivCelLBhscf1td+rb0wB6BGF0/sYtWgwMAEt/rhUq5Av3Vhmlf7d4U3x2Sjl1fNLyNZEjc4wBXr8d+c0zS8Pd1tZMM6XOytURhqeGhQJGBDXE0PRftG7vizE3T1bPPxrbXEI3Gsu7VzvzIQPcgd6yb0Blnbz3E7uR7WH/iFm+YxY1oi9ERjTHjz3P4U0tYAMeud3tg9Z5riNfSP6UtGYxmOsp+xyYNcOqG9lAtV3srnJ07AJRS7E/JRqWcahWN6kztG4xnO/ppOLjqExlxMdhy9rbRq8KsHh1m9DnhTRogt7hcsg00hJnRLfBGr6BqnVsTCCGnKKXhetOZS0QTQqIBrAZgAeBbSmmcrvTmENGUUnyw5SLWHav+rO3oEC+dnhZdfDWuI2ysZLzlqc6cmFYGDZk+TljIiMErIVxZGK0xdMeREReD5Tuv8AK9JkzpG2xw+E51cXOwxon3++L1dad1hr4wqnicxVVdkDSnH8J1rE5RGwxq42XUWr3/BY7O6sOH4/wXea1HIL7WMiKrTsqiQdVatUWdiVFNazShWJ1GTjYGL7fHMC2v92yG2EEt6/y+9VpEE0IsAKQA6A/lDoYnAYyhlCZrO8ccInrkV0dqNLGIUbt8/1I4XvlRukxcWzwIwbMf29UTGQwGg8H4zzOyox+WjwzVn9DEGCqizTWxMAJAKqU0nVJaDmADgGFmyotWmICu32w8rX15tcdhaRwGg8FgMBjakQqDrE+YS0T7Argl+JypOiaCEDKJEJJECEnKzq7eRDzGk4swHm7FKLGl2m9F/V8ah8FgMBgMhnau1MG69TXBXCJaakV0jbgSSulaSmk4pTS8UaNGdZAtxuNKiI+LubPAYDAYjDrEx8UWP70SYe5sMGoRJ5v6vYicuUR0JgB/wWc/AHfMlJc6IfG9XubOwhNNc09HxLSt2e5YDIaxJC8YaO4sMOoJHZs0MHcW6hVWFtK7B5qSI7P6omfzRpLLoDKeDNaMbW/uLOjEXCL6JIBgQkhTQog1gNEA/jFTXrRybu4Ak12riZu9Qen6tvTAxfmsYzaGP1+PBCEEE3sEmjsrRiHsdN0dbfDtC3rnMGilU0DtdeAWMtN1hp+YYYJIbWJvXf+8JA3s9e8SVxcMC/MBAJz+oL+Zc1I3PN+5sbmzYBDc+uXa+HRMzUVLdIgXLsyr237swIzeRnstmzVyqPF9p/Sp+fJrrz1mfVddEurnau4s6MQsIppSWgngLQA7AVwG8AeltOZ72ZoYR1vDK+TCYSGws7LQ+r1MRrD82XZ6r/P1+I5wNKAhiJ/SHZ/VgoX2br/mJr+mFCM6aITAG8zX4zuKPncKcAMgHSOki+gQL71p2jd2xbgu2jvHl7sFAAD/zoRL8XDfAcp1i9XZOLkrDs7sjeQFA5E0px+8XKQ3oxCizcD647VIBHvUzoL0hpRbbUzr3xzpSwZj0xtd8cnIUDzT0c/gc60ttTdPv7wqPYTb0stJ8rg+Vj5nvLhfp9pgx1h6taid0LSfXonAqTn9+E1qpFhTDYGUMK2nVoHYq4XSC9i+sWZHt3p0e2TExcDNwZo/9vnYDlrvMzO6heiz8LzaZO6Q1gan7dfKU+t3A0O8RIZxwrSecLGzgq+rpmg1pI03Bftn9MJaVXtJiHLpz4Mz+yAjLgbhqrwuGFa1IdOyZ9thSDvpEb1wIzztHz3bDrZWFpLl7alQH8RP6Y5Tc/rB3toCf7/ZTet1Fg4L0fqdOo0b2qNvKw+D0zvZWGLP9F74ZGQoPhzaGtunRmmkiQ7xQvqSwciIi8HF+QMxKlzcfg0M8cS0AS2QtmRwjUalpg8Ql/2+LcW/Y3r/mvfL+2f0qrbR0NhAJ2BtUFftQHUx27bflNJtlNLmlNJmlNLF5sqHLqQccJGBDTUPAhgfGYDLC6N1Xm9kuL/OYae+LT34XZKuqF1r4+SuuLIwGgdn9saOd6IQ4uMCZ7Wd45bpEDvaGsCd7/QQCZKXuwfAxU55XctqeiCFu2Np44XIANHnY7P6Su5WpM4Xz3fAwBAvNGmoWamNXayxtY8z/3dzT0ckzemHl7pW5cvV3grT+jeHrmWrX+/ZDBlxMVg0vA0AIERwzYaCyt8tyF3yfH83e96bya2P7eZgLdlorh4dBkcbS9hbK401oWAkhGBAiPYOHoBol7Kd7/Tg/+4e5I7nwv2lTgEA9GiuKfoM8U5vmNQFU/oGQyYj6NC4gVECGlCuGbthUhfJ75p7OmH/jF7447VIUajUugnVE7bhTdy0ficlggAgTEI4Akqhwr0jdQ7H9sGPL0egt4mEtKvA69yzeSM0dLTR2GlNyFOhPkbfw9ZKhsVa6uaPLyvbDltL8e+V8mY2drNHTDtvURvYJdANLb2csGJUqIbHaf+MXkbnFYDRxiS3vbaQwW2rDOyhgmcm1e5wONhYYuPkrvznIA9HnPtwAA7H9tFIG/eM/rYOAJY9o2zTIwLE5bNrM+l+SJ0mDR3Qv7Unlj3TDpfUDPAg1XOyEpSXUeH+IIRoTNT2dbXDL692xtVF0ejfWrqdaetbNS+F60OeCvVB8oKBuLZ4EH56JQLvDWiONWPaI8THBQ0dbZC8IJrfQVKKsZ2b4K3eQYhRCXtudEMb6ruw6uLQ/5Tv5ZmOfni5W1O08nbWSPPV+I6Qqdo6RxtLUVkQYiEjsLe2FD0DQLmhmD52vtNDw2HwgqAfAsQ7k1aXJg0d8FRo9ZxX2yQMDCH/ixav4zz/qZD/THiT2UT04wBRK7l9W3rgZy0eMF0kTOuh9Tt3R2v8NrEztk+NwmcCL42tlQVSFg3CB0NaY+VzoejYpAFsrSzg72aPll7Kyh4V7I45Ma14kWNnZYFnJYRKSy8nfPeSdGX2d7NDVHBVh+5sa8VXWKFHS9gYLBd4KzycbPgGDlCGVswdqtt78MdrkQjzd8W2KVUV08vFVu8w4/AwHwxWxT3vn9FbZ1pDUAjWSA/1c4W7o43IK3V27gBEBTfC9P7NMaKDL47O0uwMrVUd0LAwH+x7rxeighthWv/m6Nm8kWjLa0P2j+G2fW7sZg9PgeDdM70n/n2rO4aFKRtA/wbKjnzlc2Gi84maL/7qIrEhJvxtLQQCfN2EzvhIhwHmLtiml0MocPwaSL+3LloMzi06PE9c561NuAJKD7+Hkw2aNHRARFM3NHV3QAeVoHV3tMGxWX3x1biOOD/P8HAsfzd7jO8ibfz9oKUj5GI+deVVHS6ttuKgTXxrQ6aqrM09DReO5+YOQPyU7vznUH9XkVGl7R66sLUSdyXqYuPa4kHYO72nxnn9Wnlixzs9MKKDH8oF+63vfreH0UYxf+60qvukLBqkN716SE7ie73Qu0WVJ3D+U1XtWeemVWJ2wbCQasfiDmnng73Te4q8wFL4qdrECoVCJGy/EYR+aQsn4Dy8hBCM6uSv8Tu5/k0hsVfEiA5+GC4QrN+/1Al21hawsbTQWk+CtZRBe2tLWFnI0LN5I7zVJ1gyjRTOtpawkBG8N7AF75Do0Fi3MNMmcgFoOApcqhH2pO48UH+mwtFHQLlbKYfUnJ0XIpvwbfHUvlXPpqea40KuZwPkNr6aBoAUcoHBaEyYnnDk5KtxHTQ88pN7NRN9frFrgMig1Iah+a7PMBFtIHNiWmHl6DBYWcjw8chQnZ47dZqp7fu+dnxHJL7XC2fn9sexWX3RtZk7Wnk7w06tA7W2lOHV7k3xdHtpDx4hBBOiAuHppBQ5MqIMGfniefGQ6Y53evCeAY1r6AiCoAAuL4hG2pLB6CXoVGSE8A3w7JhWog6nU4CbzsqZEReDCFVHJPQEA1XPaVr/5rzH5+0+QegS6IZXuzfFh3rEufrGQcPDfDS8Jq0F3gaFQNkuVHmSZRJ5b+hogxWjwuDtUiWWvhrXERsnR6KBqnEnhCBA1WBO6RuMn16JEHnyuY5K19AUl8ZSRkR5a9bIEW39qjwcVCUv9GkbG0vN8mQILb2c8OfrkVq/XzEqlC9PbXydeY+OkH06JtKG6vA8jejgh73Te/LeO6m9oD4cGqJh4G6YFMmHuni52CK6jRecba0wJ6YV3uodhJRFg/QODXNlQEhGXIzIs7lKYLhwz3fXuz004n71dU/ajKrRnarCJtRHo9QZFe7H5+0vAzosDhd7K4T4uOD60sE4/n5fbHmzm8ioUsdHJfy3T43CiPbSnqzARrpFvJWFTNJDLnyP5ZXKDt7d0RrBnk6gWoTD4dgZCdiqAAAgAElEQVQ+2DhZunx2CVS2LQdm9MbudzU9fBxCD756GJ6DjQWcBKF8wjrbPVg5omRrJdMYTdPF4LZeWDS8DXxd7fhRwcBGjvBy1h3CZaPKf6Wc8isQWVvK4GBjyefrha4B2P2u0giKHdQS15cOxoV5A3R6eIGqeRTBHtLvXuihFpYPbfVXfWTUGKQ868J+xFXV3gjfldQ5YyL8se+9XvBQ9YnCeU2NnKqcAf30hH209HKSDONT7y/nqfVJIzpU9dUudlai36BuIK8eHYb3B7fiP0/pq93A0Ob956iUG2ZyygUN6htqwledqGDp0dPoNt6iZ2koUqGiW9+OMkhs12eYiDaQCVGBfCPxbEc/kadQXxiCeoc/IMQLTd0d4GpvrXPo1VDmDGmNN3o1w8AQTxBCeG+tIUgJMa6Rt7IgsLO20BDFMplYJDyjim/2dDa+YgnxcbXDtcWD8HafIN7yHdDaCxsmReKDIa15wWoMQs+ReiyqsEGxFTTOlxdEaxUwGyZ1Qf/WnhjQ2hMddYQAAICHoIPkQjWCdIgNriGUyYhB3j919J1i6CVtrCwQ0FAcOyf0Hj/d3hdOtlb4alwHfjhfnQB3w2LvpEZO9AkyKawtZZJxphOiAvHewBawtpRhfGQA9s/ohWkGxBf2aenBj/Bw9XdC96YYLiEihYKGo42v7iUX1RcukArvsNUyx4LzjA9v74u1L4TjtwmdqyVgCCHw1CPihLTydsbzWuYHCD1Khhhrz6jEhvAxcGVuqqqzdbS1FIVHcfi62mmte1yVbtzQHsGeSuHnoCZe3B2tMblnlYCQq1lqlXIKG9WzVxcSdlYW6N/aU+QJNoQvnu+IcV2a4HBsH0mDR2q05/OxHWBnpSzTFXIFWng5Yevb3ZGsMhY5p4GMEAR7OuHKwmi81iMQhBA4GVAeRnTww+HYPrxTQx1LLatrCMWgMI5dm6PGEH6b2AVfjRPPdRGO5L3ZJwhzYlqJQsKkJvVyzoyNk7sibkRbkbdZeL3nO0t707mQjh3v9MCi4Zr9enpOkeizlDebmzzdobErLGVVdUFYyiZ0b4phYb6iOq7ez3KhPEBV6N7TgvaHM5wAINDAWOfmnlXGkL75T7/omPPBVZkR7X3xx2vaHS4cz3dujKn9pI2EdgIHkTljr6tL/ZtaXs+IaOqGoRITLSZENcXtB48w76nWBjVYtYmLnRVmRuvfW/6TkaFo5uGI4Z8f1pluzZj2SM8u1vhdAQ3tkZFbAgLCCzJKlQ3X1+M7asSDVQfO++HpbItLdwp0dsg73okS5VHdFvd0scXznZtgUfxlAMAPL3XCj0cyMP/fZLT1ddHqDVQfERDSJbCh1jAFdQYIvAecQHi6gy9OZORJpg8PaIBhYT54p19z+DWww7HruRhowORHDn0amYBg33u9cC2rSGe6SrmC93ZzCL1PnKiMblP9JQUdrC0wtV8wJvVoho9HhiIgNl4ynXo+akqThg6Y0jeY9/qo3/fV7k1x+uYDfK8W/mTssP2CYW0wcNUBWMoIH6YjJO6Zdvh6fzrSsouwPyUb3YLckXhVuaHU4LZe2HbhntZrd2vmLspPVy3x9oCyzv7wcgR6f7zPqPxrQzi6IVwRhjs+qI0XvlQTQ1I4SUzabuHlhBOz+6KRSlBayAjip0Rh1FdHtdYZANj6dndsv3gXnyem6SwtEQFuOJGRBwsZwcSoQCzdfgWAeEQKABrYVxlE6g4QQojRAloX3PVD/Vyw50qW6LuYdt64X1AKAPzETaFxxmWby6E2o0sXukKRtBnyVhYyXFkYDWsLGWQygmU7rgKomYgGqkbiokO80NbPRTTB0cbSAhOixCtYWFtqb/H83ewxOkJs8L3avSkIAYa084ZfA2mxtmFiF9zMK9F6XeHo4ta3u0umaeioLD9yqt1xoc/IBoBRnfwxc+N5AFWTQh8Ul2PzmdtwtbfinQ2EAMufDYWNpQU2n9G+iy+gjFFv6eXMjyycnzcAiVeyMHXDWQDAwZm9EbUskU9/dFYf5BaVa1yHqzHNPBy1GmFCpOZUcOdZWcjw1+uReParoyaJ/a5rmIjWgzYry9nWCp+M0j6jP35Kd8SsOVRb2aoWnBX/8ysReOH7EwCkK7m9taVkJW/r56oU0aSq4eYaPm1ir18rDyRcVnYOhkyy4Fg5Kgw7k+/pHGbmYsPVCfV3xctdAzCorRdsLC3g42KLO/mlIITgpa4BGBXuDxtLGZbvUjb+Mwa2kLxOTRF2wF4utkhdPAiWFjLM2nRBMr2VhQyrR1dNyFoxKkwyHX99ddksuN9swTCh4AQEuDvo9RLLFZT3NFRn2M4QLi3QHapgLj4wYKWGrW93lxTGQvh46QZ26NrMHetP3BR97+lsi7lDW6O8UoGiskpsPJUJQPkKPxvTAYrR2q9vSChjW18XXLidj6/Hh4viMo1FfQhaKKyE3m8uJtvQCW+cF1W9/fFw0vSMuzvpHoFq4+uCorJKfJ6YpnN28cLhSsPGghBR2BYXVvbZ2PYY0k4ZU+usEvm+rsr8rHwuFPklFVqv3bCaKwgIf/7Wt7vjwu18BHk48uEIns622P1uDzSWmkit5RmaCk4wzhuqWSekBLtzDUU0N1pnISN4s7f+ZeOMmSTrYmcFa0sZXu+pO4TBxd4Kbe21C1wLgWdZmxDmRhUCGtqLwtE4cQ1Ix6HrgutLuHIrl1O+7HQPcoeDjSWigt01RHRGXIzIUUAIEfWpzrZWGBbmy4tofzVPsLeLnSiMkYPLfnXL3sX5A/n5RIAyZBIQ14f1E7vgZl5x9W5Qh9RIRBNClgMYCqAcQBqAlymlDwkhAVAuXXdVlfQYpfR11TkdAfwIwA7ANgBTqXow62PMxsldUVJeWa930OvRvBFkROnJ4ITYuC6NceK6dm8PUFXxhTHR+lg7PhyB728DAFFctT5c7K0wyoi4cyEEEA27757WE2WqeEtCCD+sx3mgantTgABVB2iK0B1AGSOdcr+IFzjcCIDwV/RuqRkeYGiISKVARBv6ZBKm9UQDeyt0XJRg4BkGUsstw9FZfZBXrOlp0YUhXiROY1vICJaOaIv1J25KDlVaW8rgZmmNwe28sTIhBWMi/JXhPKonLzRCo4Ld8emY9gaVo42Tu6Jcrqj2UmqD2ihjeBuqhRkIh5zHCpa9C/JwwpkP+otijXXBvVZDytfSEe3QPagR3t8sbXwKryM1csEd4dsvNSuknZ8rzn04QORJ7djEDZ+NbY++LZWjSdrmpQDKiYiuNRSQFMpyJVW2gj2lHQn8M6wlFc0JRn0GI6CcICo1umAM2t6PNhq7GWYcHp3VB/ZWpvEXGtKEt/F1wbpXO6NT0wYiw+vdfs3x9f50ANJzPbg0wjrUzs8F5zPz+c9cX+VsZwWZjCBhWk/4qAw99WLAGbYx7bzRtGHN18MW8kr3AJy79VA0h4NDV7jc9qlRsJQRjXZJwRuEBFHB7hjc1huRzRoi0kCj3JzUtGTtBjCLUlpJCPkIwCwA/1N9l0YplXKlfQlgEoBjUIroaADba5iPeoNwWZfwJg148VbXvNQ1AD8eydCbjmuvpOK/1BHG4PGdlp72tS6HZ7TlxcHGEg4SDlUuPiyoltZXBpSxxOrWfU35eGQoxkQ0hr+bPZIXDBTF3QHKCSpBHlW/LVUVvqHvVcwb2hrz/k2Gj6udwZMXObhn2MLTCVfvFxr+Y1TEDmopuTRZbVvX2jwt1aG1tzOS7xYAENcVADgxu6/OjVl8Xe2QLOGdF3p7LWQErvaGeTytLWUGTySVYsnTbSXnIHAiolkjB/RVWzPZmDkLVZ4s/QXMxc4KYzs3RoVcge8OXZdMw11HV3vEeTqllu6UCkXgvNL6qImnnzPoq+PJrqk3UB9cTLRcj4jmQouOpuXW6H7cev9jIgxznhi6uoSp6rcxcBNQPZyrPPa2VhZY9kw7zNx4Xmufox43vG5CZ9x9WMp/tre2xMLhbdBTtaKWrr5rrCqcRdfa7FK826+53hFIDydbrNey/Kj6BEl3gQdeahlBACKnja5Y7PpIjUQ0pXSX4OMxAM/qSk8I8QbgTCk9qvr8M4DheIJEtBBjZsybmnlPhRgkoo3xYnCr48gI0DnQDZvO3EYzPQK0trwkuu9pWLpnO/qhja+L1optCnStRFFdHGws+UlvQmFGJCybP1+LRI/liSgsrdTwRI9o74tNguE/LsZOIfJEG/f+/nm7GyoMnCkuRNswq42aEFRfSqk+8c9b3fhJatyk0jGqjkwqTMEQzDVEp23UgvtdUh4oY3i7bxDuFZQatenSi10D8KJg/dzVo8P4Ns6QOs95VA31dNYFXQLdEDeirc6l2bTRv7UnNp+5LRoWNyXchK/mWjzh6jTSE3ajDx9XO6PmHtTSz9aJusPCWEaG+6Fni0YGT+h1trWCs5fYwNO2xCDHkHbeeCpUc2UqQ9E2AbA6/PRKhIHLbxo59FmPMGVM9CsAfhd8bkoIOQOgAMAcSulBAL4AMgVpMlXHGHVMsIfxHkPhkMuocH/0aN7ILFa+NrjhREO3WCeE1KqArmukBG8DB2u08XHB0fRcDaGx4rkwrBAs2cZ5NYaGesPbxRYvdQ3gRSCg3CVwz2Xx5Cd1bCwtYMrN2Do2aYAFw0LQtZk78orLDZrEYi4sLWR8g+piZ1XtNYSFCCPd6jToTUtn5mxrmt/l4WRb4wl6w8J8+bXTq8I5NOG+47y9g2swIdbUEEI0JsAZykfPtMP/oltWa0KhOlITjoe080EbHxeDV9rhJuutHq17LoepqM4KRjWFW4WnhYGGhTrGrohj1LVVJd1CRjDAiAnpHB61MP9Ffb3rJxG93R0hJAGA1BuZTSndokozG0AlgF9V390F0JhSmquKgf6bEBIC6aZZa9dACJkEZegHGjeumeeDIea3iZ2RfLfAqAXXwxq7Ylfyffg1sAMhxGAB/XafoDoxMJt7OuHbF8LRNaj+x1HVBi28lCJYPYaSD83Qc76Pqx1SFw+ChUwZ8z7vKfEaqFHBjUQb89QFhBCj1uN90jCVbt7yZjfczX9kcPp65Kw1CE5PSU3Yej+mFWZvvghvF1ucndufD5EZ1MarXnmljcXaUgYvF9MIMm0Tjg0V0IAyXMEUBpahGNN3mQpuHooxIYBT+waLlnGrjxz6X2+zrjLm18Ae/m52eveCqI/oFdGU0n66vieEvAhgCIC+3ARBSmkZgDLV36cIIWkAmkPpeRbO0PADcEfHvdcCWAsA4eHhT8zkw7pi/lMhWncBbOhoY7Qger1HMwxo7cnH2xrK9AHaV7/o39rT4CXjDKFfNYewngSi23hjxztRGquW8LrCgD7HVJMfGfWLUH9XvaFFVxZGI3T+LpRVKswShlUTghop26RJasugAco1gbl1gYUx5YYsxVcd+rT0wN4rukdsGDUnu7AMTUw8YU4fYf6uWDqirVF7MbxrwLr05kbbkn91ha2VBQ7O1Ny063GgpqtzREM5kbAnpbREcLwRgDxKqZwQEgggGEA6pTSPEFJICOkC4DiAFwB8WpM8MLQjjB80BTIZMVhAc8vK6cOUa64ypJf9e7ajH45fz9PYOZNR/3lvQAskJN9Hcbm81jcisLWyQKifK05k5ElOvqvPuNibJszEFKivMc6oHWq6LnV1IISIQtzqE9yohK4NvWqTzW90xZEaTi59HKlp9OJnAGwA7FZ5Lril7HoAWEAIqQQgB/A6pZRbP20yqpa4244ndFLhf51Nb3TDpTv5+hMyap2R4f4YWc3lAhnmxd/NHpcWRGN38n2t2/Cakm9eDMe1+4UmibNlMGoT9V1C/+t0CWyIP16LFK0QZkpaejnhlW5NtX7fvnEDtG9cO/euz9R0dQ7JFdEppRsBbNTyXRKANjW5L6P+4+Via7J4PQbjv051Z9obi4udFcID6u/kTQaDwxwx0fWd2px4veOdHvoT/QdhOxYyGHXMX69Hmm39cAaDwXicsbGUoaxSwUQ0o17ARDSDUccwTx+DwWBUjyAPR1y6UwArNgmaUQ9gIprBYDAYDMZjwU+vROBCZj6L22fUC5gpx2AwGAwG47HA3dEGvVt6mDsbDAYAgNA63Qar+hBCsgHcMMOt3QHkmOG+DO2wd1L/YO+k/sHeSf2DvZP6B3sn9Y/68E6aUEr1bqbx2Ihoc0EISaKUssWM6xHsndQ/2Dupf7B3Uv9g76T+wd5J/eNxeicsnIPBYDAYDAaDwTASJqIZDAaDwWAwGAwjYSJaP2vNnQGGBuyd1D/YO6l/sHdS/2DvpP7B3kn947F5JywmmsFgMBgMBoPBMBLmiWYwGAwGg8FgMIyEiWgGg8FgMBgMBsNImIjWASEkmhBylRCSSgiJNXd+/gsQQvwJIYmEkMuEkEuEkKmq426EkN2EkGuq/xuojhNCyBrVOzpPCOlg3l/w5EIIsSCEnCGEbFV9bkoIOa56J78TQqxVx21Un1NV3weYM99PKoQQV0LIX4SQK6r6EsnqiXkhhLyrarcuEkLWE0JsWT2pewgh3xNCsgghFwXHjK4bhJAXVemvEUJeNMdveVLQ8k6Wq9qv84SQzYQQV8F3s1Tv5CohZKDgeL3SZY9NTLS7uzsNCAgwdzYYDAaDwWAwGE8wp06dyjFksxXLusiMKQgICEBSUpK5s8FgMBgMBkMH9wtKkf+oAs09ncydFQajWhBCDNoh+7ER0QwGg8FgMOo/XZbuAaVARlyMubPCYNQqLCaawWAwGAyGyXhMokQZjBrDRDSDwWAwGAwGg2EkTEQzGAwGg8FgMBhGwkQ0g8FgMBgMBoNhJExEMxgMBoPBYDAYRsJENIPBYDAYDAaDYSRMRDMYDAaDwTA5j8tmbgxGdWEimsFgMBgMhsmRK5iIZjzZMBHNYDAYDAbD5MiZJ5rxhGMyEU0IySCEXCCEnCWEJKmOuRFCdhNCrqn+b6A6TgghawghqYSQ84SQDqbKB4PBYDAYDPOjUJg7BwxG7WJqT3RvSmkYpTRc9TkWwB5KaTCAParPADAIQLDq3yQAX5o4HwwGg8FgMMwI80QznnRqO5xjGICfVH//BGC44PjPVMkxAK6EEO9azguDwWAwGIw6gsVEM550TCmiKYBdhJBThJBJqmOelNK7AKD630N13BfALcG5mapjIgghkwghSYSQpOzsbBNmlcFgMBgMRm2iYCKa8YRjacJrdaOU3iGEeADYTQi5oiMtkTimUdsopWsBrAWA8PBwVhsZDAaDwXhMYOEcjCcdk3miKaV3VP9nAdgMIALAfS5MQ/V/lip5JgB/wel+AO6YKi8MBoPBYDDMC/NEM550TCKiCSEOhBAn7m8AAwBcBPAPgBdVyV4EsEX19z8AXlCt0tEFQD4X9sFgMBgMBuPx50FJhbmzwGDUKqYK5/AEsJkQwl3zN0rpDkLISQB/EEJeBXATwEhV+m0ABgNIBVAC4GUT5YPBYDAYDEY9wEImFbnJYDw5mEREU0rTAYRKHM8F0FfiOAXwpinuzWAwGAwGo/6hYDHRjCcctmMhg8FgMBgMk1NeyXZbYTzZMBHNYDAYDAbD5JTLmYhmPNkwEc1gMBgMhpmglII+oWEPzBPNeNJhIroeknyn4IltVBkMBoNRRdiC3eixPNHc2agVKurIE00pxa28kjq51+PA1XuFyCkqM3c2/hMwEW1myirlSL5TwH/eeekeBq85iF+P3zRjrhgMBoNRF+Q/qsCtvEfmzkatUFee6PUnbiFqWSJO33xQJ/er7wxcdQDhixKQz5YYrHWYiK4j5AqKrefvoPXcHQiIjUelykKf908yBq85iLv5ykb0tV9OAQDm/H3RbHllMBiMx53/wmheYWlFnQulhOT7yC40zMtZV57opBt5AIC0rKI6ud/jQuiCXZj0c5K5s/FEw0R0DcjIKTZoCEmuoFh7IB1v/XYGJeVyAMCmM7cBAOczHwIAIpfuxf/+Ol97mTUzyXcK0GlxAnLZEBPjMYJSyhu4dUFWQSn2p2TX2f2qg1xBUVohr9E1yirleFRe/WtsPJWJjJxinWmaztqGgNh4nWkOp+Ygq7AUmQ8ez1CAtvN2IXTBLoPSpmYVaby3uO1X8Mavpwy+n0JBMeHnJIz86ohB6VPuV0/UZj4owZo91ww2hAh0r0d99V4hLt8t0DguV9Bqe8sTr2TVmZFQE3Yl38fth0/mSEd9gInoGtDr432IWqY/lq3HskR8tOOK6NjK3SkAABvLqlfwe9It02bQTBSWVqC4rFJ07Kv9acguLMOh1ByN9JRS3jNvCH+cvIVZmy7w5357MP2Jiv8qrZAb9TwM4V5+KVKzCk12vcSrWQiIjceVe5odkyGUVcpBKcW9/NJa9RjuT8lGQGy8TjFFKcXVe9LP5qv96Yhcuhdp2XXj4Rrx5RG8+P0J0bGA2HgEz94GQDn0bwrPY3mlAlvO3galFNsv3MWD4nKkZxchIDYehyXqqJAJP51Eyw926Ewz5+8L2HL2ttbv+63Yj1Zzpa+Rnl2EC5n52H7hrqhs5BaVIXLpHiRezcL0P8+h18f7oFBQPCgu17iG0PCRal8u3s5HSXklnv/2OCIW70H3jxKx69I9nb9Jir/P3EZAbDwKS8XvZNmOK1ihauOFFJVV4vPEVEnhJnUsIDYeY9YeE/0WuZ6ttLecvY1NpzNxMkPpnT2SloO84nL0W7Ef0/44y18nNasIX+1Pw7YLyt+dcr8Qvxy7IXnND/6+iDFrj0Gueh8ZuSWid3Pieh4vUoVCXeoZGMIrP57Eit0pBoe53MgtVv0vbQwNXHUAg1YfFB3bdzULwbO3ofmc7Ubl7YO/L2LIpwfx8o8n8dneVKPOrQ4KBUV6Dduf2w/qt4iulCtqbJibCyaiq8l1HV6Q3KIy3svyoLhc0gq8m1+KSrkCyWrWsa2Vca8kv6RCqzc8r7hc1NCdz3yIgNh45JdU4NSNPFHno6uSnr31kP8913OKNQTFsfRclFVWVYC283Yh5MOdou//OXcHAFBSLhdVFkopVuxOQdDs7aJr6GLmxvNYf0IZM375biEWxV9G+KIEDeHOkVVQCoWC4nBqDg6kZOOr/WnIKigFoGygvtyXhqxCZRqOgNh4DP/8sMa1HpaU814ruYJi+OeHMe33s1rz+uZvpzHjz3Max8sq5SjSkt+WH+xA70/2ab0md742L8jZWw+xOuGaqFPusnQP+q04oJH28t0C0e8GlOU3IDZe1AE+KC7nn69cQfHyDycBAF/tS+PTZBWUYvK6U/yz1UZphRwt5uzAa7+cQpelezBo9UFk5BRjxe4U3MgtRsTiBJxSDc9yE4aGfX6Yz+f9glJeICgUlBfJJ67nadxLXZByHEnLQVp2EX4+moGms7Zh4KoD2HXpHv9ML98tQEBsPG/8nryeh4Tk+1W/tbAUN3KLkZpVhBl/nhOJtFUJKei/Yj//Oa+4nB9x0kX3j/YiU9XZccYJJ5gr5MrfHjpf6Xmc8/cFfLTjCo6k5WDjqUxsPpOp9/ocZZVyNJ+zHVM3nEXk0r2Y/OtptF+4G1M2nAEAbD1/F/Hn7+Li7Xx8d+g6X45u5ZUgIDYeiVfFnvLkOwUawn7dsZuYuuEsKuQKbDqdiXO3xL+fE0cBsfEanv4+n+zH0M8OYfKvp7HzUtUz35V8H3fzS/myByiNjvYLd+Nu/iNczynGu7+fRXmlApFL9/Jpms7ahqDZ2xEQG4/3/jyHsd8cw5BPD6H13J2i+57PzIdCQZH/qAL3C0pRUq4s7yn3CxEQG4+/TmVid/J9JKnKXkl5Jd5R1f3YjRcQEBuP+PN3AQBf7EvDmj3XNJ59mw93YvnOq5LC7YfD17F85xXM/EtZnrh29mh6LgpUIv2t9WfQ7P1tOg3DqRvOYtof5zDyq6PYcvY2xn5zHB9sUYYH7lO9uz+SbqGfoIxWyBUYsPIAPvj7Ig6ojYQExMbjl2M3cDQ9F3suV72PH49k8H+P+vooL1L1GVjqZBWWarQZD1XlSUEpfjqSgU6LE3ReI+mGMhb6s8RU0bVO3cgTec13XLzHP8uXfjgJrunLLlS2eYlXskTXpZQi8WoWKKUoKa/Eyt0p+OXYDVy8rayfXx9I09oOl1bI0eeTfdh6/g6mrD/DlydDWfBvMgJi47H2YDr6fLIfxwTlQEj+owp0WLgbRWWV2HgqE3Hbr2ik0eUxT75TgFRVGMzvJ29i+c4r/DkL/k1Gn4/3SZY1uaJqVZlH5XKcufkAX+xLRXZhGXZcvIuySjkUCoptF+5CoUpbKVfg1I0HqJArRP3Oyz9WGebbLtxFQGw8/ki69Vh4+om54sYIIdEAVgOwAPAtpTROV/rw8HCalGSe2J6r9wpRVFYBKwsZEq9kw9vFFjM3VoVeZMTF8H/fyivhvdPv9AvGhcx87FGrmBxdAt1wLF2z45e6LqUUBY8qkV1Uhiv3CjAwxAtWFjKEzt+F/EcVGNu5MbIKyvDti+HYcOImYlWeWo70JYMR+L7Sm9XSywlXBJ43J1tLFJZWwsfFFgnTe2Lr+bv4M+kW1k/sgq5xe5Glin9bMSoU0/5QCsKkOf0QvqiqYRsT0RhD23nDzdEa0avEFr8U5+cNQEZOMZ76rEqonvmgPxo4WKO8UoE3fj2Nll5OGNzWG3bWFthz+T5GdPDDhpM3sWzHVf75qP/WmLbeGNu5MboFuQMAwhbs4htkdY7O6oNVu6+JRgBSFw9C0OyqDo57B/mPKnAztwRDPzskea34Kd3h52qP4vJKPCgpR4iPCwDwjc/5eQPgbGuFjJxi9Pp4n8b1OcorFXwHeyS2D3xc7UTftV+wC/5u9rhyrxA+Lra4k1/K33/iT0n4c3JXdIurEhBpSwajx7JEkSF3+oP+uF9QisOpOVgUfxlD2nljat9geDjZYtx3xzGigy/m/5sMAJg9uBUWb7vMn7vquTCcufkAPx2t8lh980I4JpsJrdcAACAASURBVGqJu5v/VAjGd2kCmYzgy31pOJKWgxaeTvj20HXJ9EIOx/bB2v1ponttmNQFo1WeuYy4GBxPz8VzAk+dr6sdDsf24T8LG//fJnTG898dx4dDWmOe6vdJ8fHIULwnYfhwCJ87x/T+zVFcLsfXB9IgbFJf6xmIr/ena1xjxztROJqWixZeTgj1cxUZnRwLhoVg7pZL/GcXOyvkP9LuhZ7WvznCmzTA2G+PY0qfIKw/eYuPXT0c20dULnTh6WyD+wVVIzuD23rhi+c7anSkCdN6IsjDkT8+vX9zvN03GAC0CrzfJnZGZGBDNJ21TXQ8Ktgdfg3seeOYY+6Q1nhYUo41e1PxVKgPb4yrY2slQ2lF3Xa4o8L98EeSpvHy6Zj2eHu90iC5tngQDqfm4LO9qbzQ4+jc1A3PdvTDDIkwvq7NGqK5p5NIqE7tG4zVEsIcUNbLPq084GxrpTeEZfXoMJy5+VB0bXXSlwzGiYw8WFkQPPPlUZ3XE5IRF6Nxf66N+/X4DUSHeKGhow0u3cnHF/vS8NEz7dBGVfbTlgzG8fRcNHKyQf+Vmgb/xfkDcfvBI7g5WMPO2gK9lu/DkHbe+HBoa43ylBEXgwq5AsGzpb3MUvnk2PFOFFp6OQMA1h27gTl/X8TyZ9th6fYryJMY9QCAvdN7oklDB9wrKMXVewVo6eWMzxJT8ZvaAgH9W3siMrAhvF1s8eepTHz/Uid8uucaUrOLMDDEC2/8ehoA8O0L4ZigpU3947VIjPr6KFY9F4aW3k4G9bczo1vgjV5ByHxQgtIKOY6k5SLzwSOsPVDVNnE6gCMiwA0nMqr0ya8TOiOnqAxTN4idRu38XFAppxoOweaejnrDef55qxt+O34TG04q++Bjs/qiy9I9ojQbJ0eiYxM3vb/R1BBCTlFKw/WmM4eIJoRYAEgB0B9AJoCTAMZQSrX2bOYQ0QevZePHwxlaRXBt8+mY9mjoaI2x3xw3qEDq4pVuTfH9Yf3CxdysGdMeU1QdkD7+fD0SI7+SbuCdbS1RUGqc5a+Nv9/sJumVrg1e7haAHw5n1Mm9TEVrb2eNBlRIj+aNQACTx/q+1iMQXx/QFKgAcHlBNB6UlKOrgcKRUT2igt1x8Jru8I+a4O9m98SuXFEbHJjRu94tl3d96WDM3XJJa6hIbaBLJAPAuC6Nse7Yf2sFrN8ndRE5HB4XJkY1xeyY1nV+3/ouoiMBzKOUDlR9ngUAlNKl2s4xh4jWZ9EzGAwGg8HQzk+vRGgNq6otTszui4jFe/QnZDwWqI/Y1gWGimhzxUT7AhDOostUHWMwGAwGg/GEoGvOSG3BBDSjrjCXiJZaj0bDJU4ImUQISSKEJGVn1+9lnxgMBoPBYIjJ1RJHbApWjw4TfY4O8aq1ezHMg721hbmzoBNziehMAP6Cz34ANGaMUErXUkrDKaXhjRo1qrPMcUQFu9f5PRkMBuNJYuGwEHNngfGEMizMF442lgCAOTGtsKMayxQaSkxb71q7NkM7nz/fwdxZ0Im5RPRJAMGEkKaEEGsAowH8Y6a8aOWXVzsbnNbaQoaYdtKV7KtxHbB9apTomIMO6yojLkbDwlZn4+Su+P4lzXCdfq08DchtFWMi/EWf66rDixvRtkbn/y+6Jf+3h5MNTn/QHxO6N+WPtfRyMup6Us8yIy4GF+YNwNwh2ic1fD2+Iz4d0x673+2Bp9v74tL8gQhoaA9AOZueY3Qnf41zD/2vN//3sDAf/PByJ735XD+xi+TxjLgYTOoRKDr2Zu9mWq+z5c1ueu/F8c0LesPCtPL52A5YM6Y9Xu4WAABIWTRII427o3W1r9+xSQOkLq665vWlg9E9SGz8Tu/fvNrXByAqV+okzemHAa2Nq3O66BJo3Cx0IjGmV1vxg71aNMKYiMYax+2sLDA01Efj+Os9myEjLgbjIwP4Y+/203wXT4X64ODM3hrHdfH+4Jb6E9UCJ97vq/U7YTkElBOfOYFXXRo6VNUNYZs2PEzzeQuZGd0CABDs4Yizc/sjIy4GCdN6orW3Mw7H9sHcIa2xcHgbAMC8oVXt28bJkfhSi2iJaeeNt/sEwcPJRm++ryyM1vpd6uJBfNsKAMufbSeZLirYHStGhQIAYgdpf98X5w9ERlwMJkQFoqm7g968SfHzKxGS9aapuwPSlwxGRlwMVkn0yVxbf3lBNK4vHYxL8weKvm/sZq/zvg7WFkhbMljj+I9qfcHYzpr1ri6ZE9NK9LlvS486u7d/Azv9icyIWUQ0pbQSwFsAdgK4DOAPSukl3WeZHwdrCxyd1Ufyu5TFg/D5WOnGJ7qNN1p5O+Pk7H7YP6MXMuJicHpufwBAK29n/PxKBLoFNQQAvjMZFuaLw7F9EOjugNd6BCIjLkb0r2OTBogKboQXIpvgk5HKhmb/jF74dEx7/lqG8OHQEFHjMT4ygO8okxdUNQgjOlSFrHdo7MqLwqUj2uK1nmLx9t2LukXXvKGt8VwnfyRM68Efu7Z4kGQHteyZdpg1qCWGh/ng2Ky+ODd3AA7H9sHkXlUCcc2Y9nBzsMYzHf0AKDubHe/0QEZcDC/eOLjlzxo6WIs6gz4tPbF+YhcN48XJ1gqvdG+KjLgYvNU7iD/epKE93B2t0buFB4aG+iDY0wkrnwuDg40ltk/tgXNzB8DV3opPLyUy/BrY8+9z9ej2sJIpq2O3oIZ8Bwcol09aPToMf74eichmDRHk4QhAU/i72FmJPs8YKO54Nk7uioRpPfDtC+EI9XcVfRcZqL3M9G/tiVmCTuy1noFImNYDwR6O6Nlc+whRRlwMYtp546lQH76cWVvKkBEXg42TI3Fu7gBkxMUgaU5//pznVZ3F9qlRyIiLwVfjOmpc95sXwrHr3R7o29IDv7waAUsLGRYOb4OYtt4ghOCLcR3wbEc/XJinvP7bfYOx7Nl2eKlrgNa8Csu6kHWvdsZsQQciLLPXlw6Gu6MN1r4QLqpDm97oqvOZZMTFaH3ewR5OSJjWU+v5QJWw+v6lcL4eToxqijMf9NcpXgDlSilC2vm54NriQejXSnun+O9b3ZG+ZDB+fDlCZKj1a+WBjLgYXF4YjYEhVYbE1+M74uL8gZg5sAV/7PdJXRA/pTum9gsWPau3+wThk1Gh8HezF9W9S/MHan0n/7zVDZN6VNV/qRHDd/s1x4V5A7B/Ri/JawiNAXVBfmBGb3w9vqrcrRM4U2x1OD8sLWSi39YpwI0XeM62luijEh4NHayRLiGcpIifEoU+LT2wcXJXvD+4qhyuGt2e//v3SZqG9bIdV5ERF4Pd03rC1V5ZXoI8HLFtahR8Xe3wSvemGNe5Mba82Q0vdasyEjs2cUNvCYF0dFYffD62A6YPaIEtb0kb4ELHhq2VBfZMF5fjsZ0bY+VzobC0ULZzTrZWyIiLwchwf0mn0g8vdcKIDn7IiIvBaz0C8dMrEfj5lQj+e6n6HBGgaYSue7Uzdr/bAx+PDOXbhZXPhfLCcGRHP416cXBmb7w/uCX2Tu8JmUxpqVpZyDTau30zeiMjLgZ21hYghMDBxlLURvTRIzbHdm4MCxnh2wWOXi3E580bquncEvbze6f3hLNtzQw2dToFNOD/flXgSEhZNAhvCPpCQ1F/xoC0E0CdIA/jHGJ1jWmfuhFQSrcB2KY3YT3h45GhGNLOG1aqBiDU3xVv9Q7CxJ+TDPIgAkAjJxsASuFmY2khqjRhjV1x8XY+/AWWq6+rHfa+10vr9awsZFgwTCm2OAEJAL9O6CJaWSSwkQPWjg9H/5X7ob4Yi0xVip8K9cGRNOVSVQuHhWD6gOawt7bElje7oUlDe7jaW2PTaeUOZC91a4ohbb3xTEc/dFI1WoHuDrieU4Kv9qchKrgRbCxlKFPbgSt+Snd+/WRAXDmsLGTwcLbVWJqohZeThuBzsReLxS4qQcLt5EUENdPDyZb/+5dXI+Draoetb3eHp7Mt1h27gdV7rsFNJUoimymvc+rGA/RqoVnh3xvYApvP3Mbth4/w9xvd0MBB2oNqZ20BO2sLhAvWtvRVrffcr5UnEgQbFggJ9XeBh5MN3unXHNYWVfZtYCNHBDZy5D9zv87XVVlWOK+AcKWdxU9XiXAOSxlBkIeTRqP06Zj26NvKA/mPKhC5dC9aeTtrbJH7Ws9mWKpaxH/WIGXns3taT5RVyrH59G2Ndck/ekb3SIP6up+73u0BZ1sreLnYYvHTVedKdQz9VZ7f716qqnfjuzTB+C5NVOdY4WOVYckxKlwpNl/t3hS7k+9jwdaq1TRbezvD3toS5z4cgND54i2Uu6sE2uhO/ngqzAf/Z++8w6Mo3jj+nUvvIT0QkkBCh9ACJPROICD4AxUUBCyIigVFjBRFuqAo2FEUGyhSFAm990BCCSWVJIQQElIglbS7/f1xu3u7d3sthbvAfJ6Hh9zW2Z3ZmXfe9533DfZy0qvp7ebfBKsmhGDu1nj4ONuiokaO++XVIo27tsGjZws3BHs5InnpSNzIK9XIsgYAZ9nJppWFDP1aeWLW4FZ8+5JC+Fy/vtATgVHRcLa1xKF3B7J9EvDj1B5aoxF18lN9s8KJp7+bSuvX3lcZV3fF/zphhIRfai+1ScOknv7YfC4Tbg7WfJ/KfbejQ3zhwGpwN0wNxc2CclF9hfiJ+4Mfp4YiLa8MI9eeQBtvJ+ybrRJinGzFfQXHiv914mNSc0Imh4udFboHKAWIX17oib6tPBDRwQd5pZWws9IU9tJXjBL1OVLEL1JOCCqq5SAEkMkIYhcMxco9idgap4o1vW5SV7T3dULczXvYFncb3s42+Ilt51eyigCoLDeu9la4X15da+0rIYTvW5/t5c/HNLa1ssCiMe3x5/lbfD4BXxdV+3LQol1v6mor+t1SUK71U7pjuA5/5fMLhmLnpWxRP2Ip6AMJIRoC7MtqljflOeJ6mBzmz3/DrbydMIEdJ5/s6oevDitjbj8QJP+a1jsQG09noLmbvWiixvH20FZ6Q3UGezkh+s2+cLW3Rn5JJR+Pe2g7LxxMUIXLXT+lu4awLGTbq+F8jG4rC4K2Pk4Y380Py3Yn4L0RbfD6oGAERkWjUzMXtPR0xOKxHfH2X5cQGeKL9r7OWL0vSfK6m17qhbzSSozt0gx7r97BzN8v8Pu+m9wdM39XpoH/e2ZvtP9wL8qrxEnQrC1l6B7QBBkrI/He35dxIfMeDr07UNR/eDrZ8DHqAaW1wNHGkj/GzcEaT3X3wwej2uH9rfF8rgYuBr8p4r7XFpMJ0Y2NCQIh9VTUYHg4WmsIwoBSa/N33C1Ex98xakGFs60Vegc1jA/24XcHAgCuLhqBGjmDYyl5fCxmdpKNdZNUmg1LCxk8HJWDpboACyiFMZmM8AI0ADzTQ6nZ4cxuNRKpaYUCtC4Sl0SgtLIG0fF3EOKn/ZyPxrRH8yaqSYeCFSIFfS96B6kG736tlJ1wx2bKa3JCt7pGg5uYSLFlZjhOJOdpFaCFCIWPQA8HnI4aDB9nWz7pjTpOtlY4N38o/5tL0KLOZ093xucHktHS00EtIY/qmCFtNV0M5GozqCBPB9zIK+O15PbWlshYGQmGYZBXUomeyw/x7UAbNpYWmNjTnx/82ng7ISm3hG8PhtLa++FoG5q72eOFvi0wvU8gPtufjEm9/HkB1MXOCt8+1w1p+WUa5Vk5XtrkrI0nOjfF98du4OMnOqJvKw9kFpTDSTAhWPm/EHx2IAnuDjb46VQ6/tetGd4b0YYXVqwtZWjn6wxfF1vcUUvsYkGISDsmJUBHdPDB3ms56B7QRMNCcWTOQLjYWfGTR208Hx6AVmrvwcFG2UaOJN5Fb4EmrKWnI64sGm6w+4KznfI4W4FQOrSdFwa28RRpNIe088aDKjk2nEyXzPwKAJYyGf8tc+9FiIwACkYpPDz7Ywyau4nfVwCruFg8tgPGhDTlJ+nCb+s7gWb6yJyBcLO3RufF+7H55TC9ArQQ4fN6ONpgbkQbbI3Lwq8v9ER4kDs/oQj2ctL4hjr5uWDj9B680oDL+CYchzghhXODMJTlT3bCcsHkdVqfFrhyu1iUlIvD2dYKM/q3xPrjafj8mc6Y/ddlyAiQXyoe7wghSF46kh8vdGFvbckLuxO6+2m4D0gh1e6f7eWPP2Iy8fuLvdDax1GkRFEnPMgDQDIiOqqE+4/GtMdCHe57Xf2bYMnYDlj4r27DOTfWCSdd1pbKuh3bpSnKq+SSk4q1E7vw/YRQ0UAIwd63lZND4eThn9f7oIW7A3uMcpuMELw+KBhyBcNnnZURIG2F5uQ/oqOvSHElfBcAcHzuIBQ9qNbaxlc/Jd3O9rzVD9n3HyA+qwg9W7iJ+gUnW0tcWKiyPn4yIQRzRrRBj2UHMbyDD4a190aghwPO3ChAoo78A+YCFaL1sHVmOPyaiP2adGl9Ovm5oJOfC57rFYARXxzHDh2m3Ybk4yc64KOd4g+d0yA80bkpfj6VjouZ93lNtDG08nLUeww3qG2c3gPTBGl6pVD3sbO1soCtlQWm6jC/A8D0PmJfVe6eFoJn6tzcFTeWj4KFRCderVDOdNW1F7po5mqHiRJ+oYbQVEe7kUJKgAaUmrifp/fU2M4NVG8ODoaPi+bgoZ7e+99ZfVEskQGPEMJ3muraJV1EdvLFmmc68ymq6wN7NaFMmzuVsRBCMEfgbsAxsp4WDynN2QP53/7u4j7E390ea1mT/IdjtA/aA9t4YvM5pZamf2tPkTlbF18+2xUbT2XgBQl/bkM0l8lLR/KDvhRSJn9tWl8p3hrSCraWFiLlhL21JTZKtGs7awucihqsVVMuIyqLmqudZhksZAQKOQM3VoNrKRM/V6+W7jj4Tn8EeToaJBBz768+fM+9nGyNuo5Qc8nNiYngcb6f0h37rymzu9aV/q09sO1CFn6RaHPzRrXj3UsC3B2UFpdqOZbsAmwE7UZXG1KHc2+rCx2auhh8DU6bKoQQAn3DwZTwQLT0dEQXCQWTOsKJKjeBGtzWC2O7SEf0Vd/+0Zj2+OboDa3X11WGN4e0wptDWiH7/gPR5E2KDVND0Uzge8xZOzwcbfQqUtQ5/O4A/jx1q5E2dzNPJxucmDsIPi62/ERS2DeYM1SI1kOohI+VIbTx0W/2bUim9g7UEKKFbJzeE+n5ZXo1BEIuLhyGrHsPNLRTuhjQ2hOnowZrNV+fmDsIzhIDX23grtOONS1zSAnQADCwtRe+P5amsRCtvqnFPKVWTOsdiOz7DzBjgMoMKcxUqV4ORxtLrZpDTycbfPZUZ/RrrXo3e97qhzi11MUcwrZex7VUIro0d8UXz3TBO1suQcGIzcrmxvH3BulMy10bFAKLpjGJsawsZCKN1amowcgXmFdNjb21JWYbueDznWGt8cXBZP73Hy/1wj8Xb4MQgna+Tpg3qi2e7Ko58FrICKrlDKprWG01+x2sndiFF5rN3e9Siqm9A/HVkVTYWqoEpBEdfCTdaWrD2C7NMLCNl4YlQ51u/krXl9OpSndAdTe+R5E+RowZ7wxrjR6Bbmjr4wQnG0uM7Gj4RH16nxYayiJtcBYKzq2NwxDlzRBBQIJrH4/QOmYagtD1UB1dwnxzPYswzRUqRD/iaItS4WJnZdBMWkgTB2uD3BgAYMsr4dh95Q4IITo/4vr8cII8HbHllXCdLiBCwoPcG3yis2pCCLr5i9/zgsh2KKmnlORCHGwsRf7EgFLL+fqgIGyKyeQHO0MZr6YJaOfrrDFBeRiM69oMPVq4IVnCtGxOqGub6wOFQHAO0jE46aOZq51OCxqgXKh0KrVA477mAqdZ4+gT7MELM4QQSR9WAJgf2R4L/7mKlp4O8HWx5c312rSBdeVU1GAUljZcbGSOOSPaSFpT6hN9ArSQtmzfYGyEmUcdYZv9WIerYF3xdjbOqqENbT7vFGlMkva7Npgi7Xdj50GVHBYyYpRJjUKhmA/vbrmMbRey8GTXZvhkfEiDf8vxWfex4+JtfDi6vVG+vhTKw4Jz6zGlpfdxRN97T8wpRtzNe3iuV4Dk/saGoWm/6ZTjEcbOzDP9UCgU3XBybL9WHg9lMhzi56rhx0ihmBN/zwzH4cS7+g+k1CvBXo54uZ9215K2Ps5o6/PwLZWmhgrRFAqFYqbMG9UOdlYWGB2iO7kGhfK40CPQTRQZivJw0Be//nGFCtEUCoViprg5WIsS71AoFArFfGg0PtGEkDwAN01waw8A+Sa4L0U7tE7MD1on5getE/OD1on5QevE/DCHOglgGEZ7Sl6WRiNEmwpCSKwhzuWUhwetE/OD1on5QevE/KB1Yn7QOjE/GlOd0LANFAqFQqFQKBSKkVAhmkKhUCgUCoVCMRIqROtnvakLQNGA1on5QevE/KB1Yn7QOjE/aJ2YH42mTqhPNIVCoVAoFAqFYiRUE02hUCgUCoVCoRgJFaIpFAqFQqFQKBQjoUK0DgghEYSQJEJIKiEkytTleRwghDQnhBwhhCQQQq4RQt5it7sRQg4QQlLY/5uw2wkhZB1bR/GEkG6mfYJHF0KIBSHkIiFkF/u7BSEkhq2Tvwgh1ux2G/Z3Krs/0JTlflQhhLgSQrYSQhLZ7yWcfiemhRAym+23rhJCNhNCbOl38vAhhPxECLlLCLkq2Gb0t0EImcoen0IImWqKZ3lU0FInq9n+K54QsoMQ4irY9wFbJ0mEkBGC7WYllzUan2gPDw8mMDDQ1MWgUCgUCoVCoTzCxMXF5RuSbKXRpP0ODAxEbGysqYtBoVAoFArFxNTIFZAzDGwsLUxdFMojCCHEoAzZjUaIplAoFAqFQgGA4Pl7AAAZKyNNXBLK4wz1iaZQKBQKhUKhUIyECtEUCoVCoVAaDRXVclMXgUIBQIVoCoVCoVAojYjb9x/wf98vrzJhSSiPO1SIplAoFAqF0mjILCzn/y4so0I0xXTUWYjWEdd3ESHkNiHkEvtvlOAcyfh/FAqFQqFQKLrYdfkO//fBhFwTloTyuFMf0TlqALzLMMwFQogTgDhCyAF23+cMw3wqPJgQ0h7ARAAdADQFcJAQ0pphGOrkRKFQKBQKRSfXsov4vzs2czFhSSiPO3XWRDMMc4dhmAvs3yUAEgA003HKWAB/MgxTyTBMOoBUAD3rWg4KhUKhUCiPPv5u9vzf1J2DYkrq1SeaTVvaFUAMu2kWm87xJy7FJpQC9i3BaVnQLXRTKBQKhUKhABALzjFphSYsCeVxp96EaEKII4BtAN5mGKYYwLcAggB0AXAHwGfcoRKnS+YeJ4TMIITEEkJi8/Ly6quoFAqFQqFQGimxN+/xf9NwdxRTUi9CNCHECkoB+g+GYbYDAMMwuQzDyBmGUQD4ASqXjSwAzQWn+wHIlrouwzDrGYYJZRgm1NNTbwpzCoVCoVAojxEONjTxMsV01Ed0DgJgA4AEhmHWCLb7Cg57EsBV9u+dACYSQmwIIS0AtAJwrq7loFAoFAqF8nix8XSGqYtAeYypD010HwBTAAxWC2e3ihByhRASD2AQgNkAwDDMNQBbAFwHsBfA6zQyR+PgZEo+AqOiUVBaaeqiNGqqahSorHl4TX77hSxabxQKhUKh1DN1toMwDHMS0n7Ou3WcswzAsrrem/Jw+eFEGgAg/nYRBrXxMnFpGi89lx9E0YNqpK+IfCj3++3sTQBARkE53B1tHso9KRQKhUJ51KEZC82I0soaUxdBJ5KrPylGc7+8Ggx9mRSK0fx+9iYu3bpfb9f7+kiqKOYwhUKpHUUPqpF6t9TUxXjoUCHaTDhzowAdP9qHEynmH4VEyuxgzuy4mIV1h1JMXQyD2X4hC7fvPzB1MRqMqhoFAqOi8cPxtIdyvxq5AiUV1Q/lXo0ZhYLBxlPpZh3tYME/VzHu61P1dr3V+5IQue5kvV2PQjEHPt2XhMh1Jx7qPSd8expD1xyrt+sFRkVjxZ6EerteQ0GFaDNh//UcAMCyaPNtNEwd1KecP3XWvfJ6LJFhzP7rMtYcSH7o960NlTVyvLPlMp7+7oypi9JgPKhSCmnrDj+cic1bf15Cp0X7jT7vdGo+jiTdbYASmZ6YtAIERkUjs0D1PUZfuYNF/13Hqr1JRn3rRxLv1uukr6yyBkXlDT/pqUt/Zi488/0ZDP/ceMHl8q37CIyKxo08Tc3h3qt30HbhHrOeTAnpGehm6iKYHV8dScW17OKHes+UBtBCf3/s4Sha6gIVohuA4opqnEzJN+qcxDslyv9zSmp1z3tlVYj44jjSJDrF+kYZkMUwjiXnoehBNeb/cwUA8MRX9adFqi0V1XIs/u86yszQfUahUP5fUGb8IsD4LOXAeDzZ/K0ZD5PoK3cAACPXnkBgVLTB5z37Ywym/3y+Vveslivw+qYLSDLye47Puo9P9iYCABLuFONucYVof9a9ctwqFE9ET6YYL+xvic0CAJxNL+C3ce5kP51Kx/dGWAmmbzyPUWvrT+vVe+VhdF5s/KRHndLKGlTLFVr3C2XoVXsTUV5VP/3B3ZKKh9IPA0BMeiGSc42/146LtwEAx5I0+4pP9iaholqhdWK046KmpUyuYKBQqF7oO1suGayVrKyR4+D1XNG20soag/uxcxmGJ1spqah+rDIc1uho/+ZE1r1y/BFz09TFqBVUiG4AIj4/jskbYnDPiI/VCLlUkn3XcpCYU1LvM7ecoopaR5LIL63E1J/O4eVfY3GT1XiZQwe2eNd1/HQqHdN+Poecogr9J0hQLVcgv47RLtot3KuxTc6O7LJaNIi9V5XWjOd/apiIkYVlVbgu0G4UlVcjJq1AxxmGkVlQDrmCwau/x+Fi5j39J0igUDB6z024oyp76t1SkRYur6QSaw4k43xGISpr5Fi9L5Hft+X8LaO1lgl3ihEdfwfv/n2J38Y9JwBcyy6SvOYTX53Ct0dvAFAK/X1XHRHt7/vJEfRT2zZ5g3HCflWNAtsuKIVoba1s5Z5EHE7M88t+GgAAIABJREFURbVcgR0Xs/Q+f9ED4zXH1XKFSPBSv9bgz47yEWU4zbkhZOSXYd2hFHT8aB8Gf3YUP55IQ/C83fy9Csuq8MH2eDwQaFq/OXoD3xy5YfQzSNFz2SEM/ky7APmgSo7c4gqUVdbgZkFZre8jtCLUJ1zXIyzb7fsPkJJbghq5ArP/uoynvj0tOido3m6MZCdSZZU12H7htsH+sXO3xuOlX2NxIfMe7pdXoaJajrc2X8TzPxnWPztYWxj4ZECnRfvRbckBg4/Xx+nUfPx4wrAx98cTafjumHQbe+2POPx76bbWc6f9fA4RXxw3unwDPz1q9Dkc/1y8jcCoaPRafhD3yqpqPVYawnM/xmD+jquN0u2OCtE6uH3/gYbJE1CaAdVNXRXVcly9XYTiimpks43t77hbMJTTN+omjHBapL9ibyHhTjECo6IRd9MwgaS4olqrm0XYikN4Y9NFACrNjXDg1TV4cmb7c+m6NQXP/3QOreZrBnPptGgfXt90QWe5J64/IxLMyypr9LqMbIrJBACcz7iHsBWH+O0KBWOwsPT+tniELj2oU9MlxZ0ilQbngYS5VMHe38IAIZphlD6sBaWVYBhG9J6F7ZMr43ZWcFLnenYxMvKVA2ZljRyT1p/FHlZ7K2TMlycxSuBn13nxfjyz/iyKK6r5b2L98RtGvZPuSw6g/+ojGLbmGPZczcGT35xGl8X78e6Wy1rP+eZoqoZ7ztPfn8GT35zGnit39Gp/1x5MwdA1xzBEIOi8+nsc1h1KwVPfncHvZzPxtUCgmrstXqMN77iYhWM6NGU1rMB29bZScE/JLUH/1Ufw1eFUnLlRgMh1J7F6X5LW87l2WFWjwNXbRchm+6LawjAMfj97E8UV1chU02TvvnIH98ur+O+C44WNsZjxayxm/3UZG09niOo1p6gCvZYfrPVCIuU3vwct5+3G4USlFvKD7VcwSDDop+WVYT+rodx0Tly2iC+OY7egjcbdvIfAqGi88lssBn56lG8ftwofYGl0AmoUDP9tfbInEZvP3eK1sRxVtdTaFZRWYuL6M6iqEZ8/4nNNoefNzRfR7sO96LX8EJ77MQYDVh/FsDXH8OQ3SgvdvB1XRFrZgauPYN6OK4i7eY+fgB1NuouKajn6rxZPpvJKKnE06a5OYUyd5NwS/M5G7zmadBfJuSUoKFX2py9sjAWg9E3ts/IwhgmeJ6e4ArcKy7H5XCavZEnKVX53HT7ap3Efbf2qQsHg30vKXGsV1XJ0WXwAE9ef5S2y6n3JnL8v89GGACC8pTvaN3U2+HmFlFbWIDAqGkeS7ho9Sa6skeO/y9l49scYLI1OQNxNcf+Qda8c0fF3MPuvSzhwPRe/nM7A0ugErNyTiB+Op2m0ld1XcvDWn5egjaNJeUjMKUH3JQcwYPURlFRU4+rtIly9rVoUWyNX4LtjN0R9f9a9B3hz80WsP657glhRLcfy3Qn4+kgqv+3tv5TlyS2uRNclB0RjpZB913L0CtjnMwrR/sO9fH+x/UKWqO/gxnBF41Cci6CpfnTQZ+VhAED/1UeQsVIVjmzTuUzM33EVe9/uh2d/iEFhWRUiQ3wRHX8H7XxVH/Ty3YkIa+mOED9X3Cosx4TvTmPrzN5o7maPYWuOITLEF28Pba1x30MJuRjSztugMjIMg3WHUvH5QZVQcThRado9mJCL7gFN+O05RRXYdC4Trbwc4WJnhaaudgj2csTwNceRU1yBG8tHwUKmEt7ulig/jP3Xc5GRX8YPQi/9Gotz84YgKacEz6w/iw1TQ1GjYPDJ3kTsf7s//rmUjTl/X8ZHY9pLljkwKhqpy0bC0kKG7PsPRGa7arkCmYXlCPJ0RElFDaLj7+DrZ6WfPYT1c524/gz2zx6AwrIqXsvA1Vd/gdZu1qYL6BPsIXmtkopqdFq0H/NGtcWM/kH44mAySitqsGC0+Bm2xmWhR2AT7GQ7fmHfW1JRjeTcEmQWluPJrn6i83KKKnA56z6+OCj2A575WxwSc4qx9+3+sLWy4LVlJQa4mpxJK8Ci/64r/VgnhIhS4bZduBf/vN4HXZq78oLcHzGZWPZkJ/4YhmGw4+JtvMMKrBkrI3EqNR9n0gpwJq0AyUtHYv/1HER08IGlhUxkwhVOVEIW7cf4bn68djM+SxkCcXx3P8gVDA4n3kWInwu8nW2V92XjvFRWK1BSoXzOtHyV1ut+eTW2XcjCZ093BqCcqIUtP4QN00LRO8gDq/Yqhc93hqm+He7ZX/1DOek69O4AWMlk8He313hvwm+Fo7BcNRGTMoF+tPMa9rzVD6PWnYSrnRXOsBr4gW08sXF6T/684Pl7AADT+wTy517JKsKuK9n8vZ/qrmwb3xy9gXeGtYalhQwHrueiuZsdf46wXY3+UnPh25Jd19EjsAlm/q6aZF7LLkKHpi4ax94sKENMeiEW/HMVJ1Py8e5w1Xu7U1SBNQeSEdbSDVdua0apOMKa+z/+7zris4rw+TNdAIAfUIUm+7NpBQhr6a5xjWq5Agev5yKiow/vCib85qO2XcG5+d7YrCYoA0DinWJsOX+LF7T47TkleO2PC7ixfBQKyirx06l0AMC+a7ka1+AInr8HFxYO46096pZC4bz1nS2XIFcwcLCxxKaYTLwxOBhfHk7F0TkDEejhgJyiCly5XYSOzZwRvkI5Tjzx1UmROx4nVArZeVn1HFyUEc6X9HBiLjbFZGJTTCbff2UUlCOjQLmtq78r5o5oi2kSVofs+w/Qmx2vAKCbfxP0W3UE214NR/cApc9wfmklGAb48nAKfj2jFET/PH8LOH8L3s62ePnXWI3rqrsl5pYoLQMKBrxFJCNftzb99v0H6LPyMNY83Rn/6ybuF+ftuML/bWdlwb8XTydlKM6tcVnoE+yB3VfuYNETHbA1Lgtb41TKgDMGWMKu3i7C6C9P4rvJ3UXbO7LC/vSfz8PD0QY7Z/VBU1fVN5iSW4I/YjKxcHR7WMgIKmvkuFlQjrLKGjz5jVgTP/7bM+gZ6IaoUW3Rzb8J+n6iGnfUJ2vLdicg5W4Jsu9X4Psp3VEmcCNKyimBj7MtXOytsPvKHRxMyMWap7vw+wvKqlBQVoXwFYd5xdmq8SGY0N2P73tKK8Rjx87L2dh5ORsMA7wyIIjfnnWvHKl3SzXa0+uDgrW+y8CoaKz4XydM6ukPABj86VFR3w0oxxVCCEauPYGEO8U4+8EQPMWu8Rm65hi+fa4bP+ZcWTQc1XKm0QUrEEKFaCOIu3kP4wVmrIgvVFq56HilVkRoMgY0fYD/On8L47v7IeVuKb44mILRIU0R7OUoOubFX2Lx3og2eH1QMJJzS7A0OgFrn+kCFzsryARCbrVcgd/O3NQQCrhOjRPIYjMKcTwlH/9dzka6WoP/ZHwn5LB+l6PWnsC+2f2RV1KJssoa0UIBoVmoqkaBLotVJrEZv8XxWpLC8ireFP7xf9ehDe6DFzJp/VnklVYi9W4pzn4whN9eUlGN++XVOJNWgHtlVZjaOxC2VioTHucTmJijevfzd1zBH2qatV3xd7ArXlPDOmVDDBzZ1LHLdyeioKyKd4sRCtHTfj6Ho2o+hIQoO41/L2XzM3cAGNnRF3+ey8QitXfg7mAt+r33mtIF41ZhOS5nFWHO3yoN7C+nMzC1dyAAoNNH+1BSWYM5w1vj0/3JODF3EJ79IYY/du7WeI3nGvf1KVxcOEy0bcWeBAR5OGJrXJaGL2HWvXJe+wQArReo6qhzc1fRseqWmG0CLTf3noO8HEWRFFp7O+LPGeF8h6lP8/floRRsv3ibb7PLdydgzvA2/P4HVXLYaTHlclrm1GUjdd6jqkbplpOWp/ouhBNJjsScEoQuPYgCNcHraFIebhWWw8nWUuTq8vOpDP7vMV+JheC/BUJAYXkVdl7KxlK1BcXcQmNtbDiZjg0n00XbItedxI/Ph+Kjndfw3og2yCmuwPD23iLXgr3XckR9AKexPZum3690x8Xb+OypzqI+SMjE9WeRsTISBaWV6L70IADg3Pwh2HgqA98cVWnCPNRilVtquR4A/HJGt59k0Dyt6Qgk6bbkANqzio7P1KwZ3x9Lg1zOYFIvf2y/IBZ8vjys1NCdSM3XaiKXWs9SWlnD9y0zJIRUIcJv7zi7lkTIxcz7mPTDWclzOU02B+c28M6Wyzj23iAAQChbJ1JICdCA0lVISB+BoM4h9J9/Qq2t5xRV4JXfYvmyDGvvDSdbK37/n+dV1toUgW83p4FeeygFa9nISoue6KC1/LqYzwrqM3+P07g+R35pJXqvPIzUZSM1xqa+wR4Y2t4bH/17DX+evwVbK2kD/rmMQvzvm9PYOauP3jJxaxLUtfYjWJeN5KUj8RqrELCz0uzjhOFw526Lx9xtqv7/66OpGscDgBs79lRUy3E0KQ9vbL6AarmmBr5arkArifGZ44PtV/DB9ita968/noaeLdx4WajPJ+I2wyk6AGgs+K6skeP2/Rp8vPOa1uubG6SxrFAODQ1lYmN1d0L1jbr5dNagYHx1RLqB1oUuzV0lY59mrIwUleHFvi1Q9KAaW+Oy4GRryWvxGornwwN4jYWh2FjKUFlTd5vMllfC8fT32iNUJC6JQFs1n2K/JnbIule/oeE2vxwGBgwu3brPa0CFbHqpF579MUbiTIo2JvVsjs3nDHd10sV/s/ri4q17+PBf6U7306c6iyYm6rwyoGW9rSMY3t6bdz8wlDbeTpIaS3PltYFBIoHYEFp6OGhoqx4nMlZGYuDqI8hoIB9mffw9M5zXBJoDx94biAB3BwCaY6wuJvX017BWNHWxRXZRBa+5zy2uQH5ppcgiI3WPn6f1wPSNmhr9Ha/11tAyA8C2V8Mx/lvzeYe1YUhbL2yY1gPzdlzRcN0yZ+IXDYezYOL1sCCExDEME6r3OFMJ0YSQCABrAVgA+JFhmJW6jjcHIfphc/y9QSK/t4chODcWZg4I0rpIg0KhUMyF+EXDedczSsOQvmIUCCH8mH1kzkC08DBeUH/UeWtIK16z31j47KnOGN/dT/+B9YyhQrRJFhYSQiwAfA1gJID2ACYRQqQdaB9j1BeOUAFaBRWgKRRKY4AK0A3PjTyxpYNboCoV/eVxprEJ0AB0uo6YA6byie4JIJVhmDQAIIT8CWAsAO1OtBQKhUKhUChqpOeXaawtohroR4PaRs15WJgqxF0zAEKnyCx2mwhCyAxCSCwhJDYv7+EnkPhvVt+Hfk8KhUKhUCiG8+uZDFMXgfKYYiohWmo5tobdhWGY9QzDhDIME+rp6fkQiiWmk59muKiGZvtrvR/6PSkUCoVCaWy0YrXPNpaGJ1yhNC5+f7GXqYugE1MJ0VkAmgt++wHI1nKsSUnREyLLUJxs9XvO2FtboJt/E73HUR4Nnuvlb+oiUCgUSqPlPhsG8GCCcVFxDKWlp0ODXJdiOC52Dz8yhzGYSog+D6AVIaQFIcQawEQAO01UFp1YWcgQ/abSrWN8Nz+snhCiEXsXAL6b3A3NBIHa1Wnqon0fx8dsHMxLH2peX4qN03uIfo/vpn0Fq42l+SenXDi6PSb2aK7/QJbJYf6YxsZRVmdBZLt6KlXDsWRsR3RsVrtsWw+TQW0evhXIEJz1TEzN8d3OFCQ7qG+ECaHMgbgFQ7H55TAAwKhOPiYuTf1zQWIcoCjZ9YZyzAxwt8dvL/ZskHt4OFqLFBF/x+oOm9kz0E3rPq6dqnP43YE6rznQTPvGRwlLC/NOxWISyYphmBoAswDsA5AAYAvDMGYbXbtDUxckLI7AZ093xlOhzdHEwVoj4HpER1+cihqs9RpyhsF3k7uJtl37eITo91OhSgHS1d4anQ1wJVH3f+nq7yp53NtDWyFpqWEadfWsThyTw/wxpK2XQdfQx4apobj04TBcFTx/5+aueLFvC6wcH6L3/EPvDkDikggsHdcJoYHSmvuX+rXUOakxFE8nG/yvm4a7viQx84bg0ofDEDWyLT4a0x6rJ+h+FpmMYNcb/XDt4xFIWhqBfW/3r3U5R4f4GnW8MQLXc70C1H4brkHv7OeCI3MGat3/1bNdsWGqdBQhqckqR8y8IYhfNELrfgDY9UY/g8oIKCcKxgqhMfOGSG4/quN5tSVqqC+OvTcQHo7W+g80goPv9Eeylv7jxvJRWDKuoygzKoe7ow3Cg9xx9eMRWDexK/oGe8DXxbZeyybFsPaGZXvleG1g7SY2bg6a71nY1q8sGq713P2z+6O1tyPWTepq0L38mmjvy4SZLtVp6emAab0D8XK/FjgVNRgz+rdE2vJRWo9fOq4j4hYMrdOE7MicgejQ1BnvDGuNv2eGo18rT8QuGMrvv/rxCJybNwRLx3UEoMxHUBsGt/USWW7fk0g4JaS72lix+eUwDGnrBRtLGcKD3HHwHen+d96othjS1gttfZw0NNPuDqrEQYvH1i4ZDKDZH4/p3FT0W5gB1Rh0TfR6tdA+qagr9ak95pIVmSsmU08yDLObYZjWDMMEMQyzzFTlMBT1zGg9tMxqZwvSeK+f0h1vDFam0JQrGFjKVK87aWkEHGwssUhLaux/1RY1zmFT9VrICNKWj0LGykgNTZyVBZEUpKVSi3N0aCrW1kV09EHGykhcXzxC9JFZW1jAQU9jNlSQG9LOG6721qKPY/PLKr8nbe+EI8jTkc9YOKKDUsMlHMS51OunogbrHAzUy6uurT/4Tn+cjhqs09/uq2dVgyAhygnQzAFBmN6nBZ/i+H/dmmHbq+Far+FgYwkbSwu08XHChqmhfEY1Ds4n7Nh7A7Ve4wm1TtcY/pvVFyffH6R1f41CvDray0m/INTWxwkZKyPx76y+aOHhwNe1uqVhdEhTre+3iYO11vdmqG7CUHPsD8/rDQcKAKIIAN7OtjgxV/O9BXo4wF5LJsUpYQHoHeSOblomvMbi4yyuiwB3B8QuqF8Nqau9Nay1WLIsZARTwgLwYt8Wou1/zlBp9hxtLGFpIcPvL/XCmQ+GaBXIE5dEiH7X1pWuUzPj1rJ4q73Drv6uGC4QxOdGqLJkrtIxMf72uW58bGIAcLK1wtkPhki2rdbeTtg/ewC8nWw09klhYymTFNqV+1RtraWHuL0ffncgFj3RAfMj26OZqx3mjWoHmYzg0LsDsO3V3shYGSmy5j3TozncHTXLtOO13pgSphJ2dVmnWng4gBCCN4e04vsKD0cbHJ0zENc+HgFHG0t4OdviuV7+WDquI6JGtpW8jpOtJd4e2krrfSaHBaBnLQXBnoFuCA9yx4ZpPXgFU7CXk+SxM/oHYcO0Htj7dn94qr0bhmGw+81+uLBwGJ4PDxTVvzptfcTX/5DNhislzHZX6x/Us3wairY2AwBzRrTRuk+d8/OH6j9IwO63+uGnaYb1qfpo7mZfL9dpKMzfxm+m/DJd2kT1luCjH97Bh3exkCsY+LoqO5RZg4L5jm8im4NequM/MLs/Nr8choyVkZg1uBXSV4zCjeWj+LS73QPc8PWz3bB2YhcAQJfmTWBBjDN9/PZiL0mNmr21Jf56JRwL2Q+dAaO58pOlXysP+DVRdtAhfi78OcZgb60SqKf1acELwkI8nWywSk1TbWUhQ8bKSPz9Sjg/e+/f2kPrfYQpnWWCdzU6xBeJSyLQO8gdgDIderCXE6wsZHipXwt4aRnsujR3xfH3BuH9iLY6hUtDw5UOaeeN3W/1Ewn/fVt5IGNlJALcHXD14xGSGvYwttxCpLSDUnTyc4FfE3FHFdlJNcFoqnY/YZZme2sLyfajnsPp2HsDMaiNJz6UmCC1EAi6L/QRC2PackFx38/1xWJttHrKbkO/B0sL7V2h0Axsb22BH54P5b/r5m72mDkgCKFq71pbud0dbbDp5TC4OegfFPUJg062lsgprtB7HSH9Wqm+jYyVkcq+ZVCwznMMGcC5b8nfzR6xC4YirKVme+QQCuRvDlbd29bKAq72Kg2WBSGw1lEv2nhd8DzT+wRKpk0Wor7/+fAATGUFy/CW7nxdRob4IqyF8rk4zfC/r6tSPI/spKlE8HGxNVozLoT7DgkhiH6zLy94CRGmTTc0M2SQpyPfPwjTacu0fC9d/ZvwAquviy1+nt4TS1hNshBdLlaBHg4iRQwhBJPDAkR9vxBrCxlaeionrZEhvvh5eg+kr1Bp0T2dbHhliiEIn0w4yTMGda2ogmHQvqkzL6x+ojY+Ca1pXdXWO73QtwViFwzFrxLuLpxc0JAYk2evib1Ys6w+WVOnmasdBrdVtfuN03vg3Hxpy11jhwrRtUQm6LjiFohnaR2bOeMZ1jXDnTWtTujuhw5NXbDrjb6i2bWtlQUyVkbivzc0w+m18nZCuEA4IhIdXGSIL8Z2aYaUZSPRxscJK8eHiAQgfdhZWWhoYoRwH0sbbyfUaInX6O9mj5PvD0ZTVzvsnNVXQyslxFAzoZosBL8mdvj1hZ54WovPtExG8OWkrjj5/iDMHSGt2QBUq7kBla/V0HZeWDUhBIQQ/DStB7bODMczPVSdWJCno4arzpuDg0GIUsDwd7fHq7pMwoxxHZYuHG0seUFROBA421ph/iixH/i2V8WRXgxZ3Aoon+kTgcYtxM9VpEURtn0CTU0eALzYT9wG3B1t8PP0nrC3tkSfYLGAJZwUqJv7pV7b3zPD4cJ26vbWljgdNRifPdWZL0/fYJWgyL0roTYxbsFQnPlAt5VCiIJhMKN/SwDKSduw9t747OnO/P6okW2xVe1da59ysvvVGsR7Eloh9QmBOuunqDQ9ujR2Qn6TWOk+e1hrnNbhimYIXFFbezsapTVT79NC/FQaOJmMIHnZSA1t4+Qwf41BnSPI00H03j4ao9/Erl5XljIZFGz9WMgI5OwMuIW7puDQubnhFgV1Ez0g/pbU2T+7P6axZvzUu6XwdbHDCxJ9qzAl8pcGuodoQ6o4y5/sBEBZ/n9e78O3lSlhASKNfdyCoTi/wDiNJYeTFisnXx4GGNTGS9Re1LXCHO9HqPr/4++pLEWcImTF/zrpfO+6UI9ZLFf7zNXbahMdmmBA2ddyCgGhZdSQycHQdqp3X5tF6sZkqxa+96sfj8AoI2QMABjYxktSyaTLutpYoEJ0Hfjh+VBsfjlMw/y1641+vBDiZGuFlGUjebeOjs1cdGq9aosVe81gL0d8/Vw3PUer0KeoG9TWC7ve6ItnejTHnqs5ADQHd23ai/pg9YQQfDi6PU6+P1hSO62OXxN7rcKHuusKV+7hHXx4bYitlQVCJVx11DWaL/VvifQVkTo7O+EZ3KAcYkTYxC2vhEv6a3JFURde5Xo6RXUtvjqcICflY7lX4K8tfBVSEzsAeDpU+wLRP17S1AL9+kJP7Hu7P17o2wKvDQzS6cun7krV1NUOY7soBRQXOyv8PL0HEhZHiMrXv5Unry1iAPgasNCXQ84w/IBlaEvXZ3lQr6sm9pqDraWWdvzP633wXC9/hLV0403xr/Sv/YJFCxnRsDYYy4A2nhjbpalIq2kI6s9oiD/30nGdcPFDlb/xawOD+PUEUn1RM1ZrvOkl5QTCydZS5Dqi/tl0aubCC86EKIU3ABhaB41yxspIowXc1t5OWt2ChCx9siNmDQrGjeWjMKZzUxx+dwAA4OV+mgK3PoTfM7cWRGg56NLcVXTMF6wVFFBOlGsbau7k+4Ox7EmxZpsQ7d8AoN16JLS2uDqoJhhTwgOxdmIXXsFVG9T7O4UBguirA4Pw7XPd9FpE1K8kFJKl+OF51fqlZU924i3SHL0lrJNCZY9UybVZEoRP7WhjyU88Xx+k2e8Ys3aquZoFVJc7jLli3h7bZo6hZjqrBhCa65sfnw9FuhZTYEc1s/KTXZWd69a4LADAuK6198fVBtdXtfVxrrd43dFv9kPEF8f531z/bMiMXNh3fje5u0j7YwjcLfR1pEJ6tnCT9PnjBAX1cuvr0B1sLNE32AMnU/Ml93MuCVYymc53MqG7H1btTQJguFCpj/6tVT6WcyO0WxK0YWkhw9JxHdE32ANWFjJwr5n79BQMI6hv/ddzd7BGQVkVe65SwwkAUwxcBPX20FZYtTcJzraWKK6oAQBRlAJOyH5lQEtU1zB4OtQPZZU1eL53ANos2AtAU0sZ3tIdv73YE5YWMnRhNaALR7fH7GGtNdZsuDlYo5Atf205MXcQ8korte4XLsSysbTA2onGa0HVy714bEdsv3BbfJCe+iJEpU3mvg1PJxvklSjLvumlXoi9eY+/V0sPB9Hkl7v8wDaeGNnRB4EeDnBkhYkX+rZAJz8X3mpRxIZU49ZiNBSc1YgTSoXmcx9nW+QUV8DD0Qb5pZVwtbcS+be29HTEuflDRIveagNhv25d/Yo2VwxjcbG30vDvBwiGtPPG1PAAzBpsmKUFULqNcAiVHxYygrFdDFskrg03NQuIIWMHpxnv39oTP51KBwANgVd5MfHPH6eGas266KBncrXppV7oHSx2a1T3ybayINj+Wm8cSbyL0EA3NHO1RbCXk+Q91eemwvHsya7N4GJnhY2nMwAAG6aJo4bpQiYj2PNWP9wpeoAXNsYafJ45QYXox4jYBUORde8Bxn19it/GDTqGaFlORQ3G8DXHsHRcRyz45yoA5UKb7gHaF3dc+nAYuiw+UOsy6zOLG4JQkBHCPbsh/spCDURER+MGUAZA+6bOsLIgeGNwK8RsiDHqfI2ysP+rl1tKQHeysURJpfLZZay7SmWNXGs5dSEjyns2sbeGtYUMVXIFWvtIL8apL4xxg5kcpingcoOoXMHwdahr4Dv07gDkl1Sie0ATBM/fAwBQKBi4O9rodf8I9nJE6t1SAEot0qq9SfB0ssGgtl54tqc/egn8hCf1aI7jyXl4oU8L3qLwMusyol52/reMaGjfLGREciX8sz398dWRVMlytvZ2lBQCbywfha6L96O4ogYXFw5DEwdrnYt6tC3EMoRpvQPDfVINAAAgAElEQVSx8XQGLGQEFxYO431Nda3E/+a5bpJrEwgIuLWv3Cs7MXcQL/x5OdtiVCdfXL51H4D2du7jbMu7cXloqW8XOyvELRgKVwnLQW2QmoQenTMQznydMhoH7n+nP0oravDEV6c0zuUwZPGvPoSuFLqwt7ao08JmDnVlSZCnA6wsZPh4rKbvtZAZ/Vti/fE0/rewH7SQEUwND9BICa6Nv2aE4d/L2dgUkym5305t0qCQ8HB0sbPiJ1tCHGwsMbZLU/x7KVuyXzNEq83BQLNv5H5byIhIgP5rRhieWX8W1XIF34xc7KzQzb8JCCEauSmGtvPWiLutroFnBNs/f0Y5IeCEaGNp5+vMWzvMO5idNFSIfozwcLTRGKSM8cRo5mqHa6yZ3NDvvbaDzSfjQ/DZ/mS09al7rN9j7w1CiZoQ/fWz3XiNrKGd16xBwRjSznBTVRtWwOwT7AEXOyukLNMeWsoY2vg4IS2/TEOLNzksAJ/sTURFtapnj36zH/qvPgJAOShaW8q0RlrgXgMh2t00OM4vGIqjSXcxsHX9hD3UhjF+e1Jw2lwFw0BKJtj+Wm+RK0WQpyOC2MVM/83qizFfnTS4fex+sx/vBsBZKkID3ET+5RwjO/nqFcrnRrTBk9+c5n8b862+PbQVzqUX4lxGIQDVBAgA9s8eIHmO0A1K6l59gt1xKrXA8ELowJMVht0crHVGEBDi7mAt6Wo1tXcg7pYoF1hy7VbKzYp7Jq4+ucgqJ1KkLTNayyHhi2uIq5mhCDWpLT0cNeLhO9tase2rnhZaaEH9fWnj+uIInfsNRV3wF/r866KVmoBsISNwsLZAWZVSWaBPCBfSq6U7erV01ypEC92NnGwt8cqAlhrHTAkL0DqBVSlANN8pt+kDiWglUv0gt6UHG7qvlbfyPSxXc4vhJunVcoav05f6ttDax+vSgKuXVXiJ+aPa4XvBZEYfwrUT9bVmyBRQIfoR5c8ZYZi4/qzGdlsrC6yd2AVv/XkJQN1nfsacry2OtRQdmrrgJyPMQrpo4mDNL/CYNTgYszZdRP/WHjh9gxOiDbuOMSGBAKUbDKfRq08+faozJocFaETpsLKQ4dUBwfj8YDIfccHf3R69WrghJr1Qb2VxHTUhSo1goLs9npVYsEKg1GQITaM/TQtFwp0SpOSW4FhyXt0eUFgm9v/OzV15TaIxqDTRqg5f2GHryhAa5KUMzffucO0hIoUIJyc+LrbY+3a/Wvn4/fh8KE7dyEdX/yaIDPFFdPwdAPoXGgqxtJChq78rL0TvnNVXY02AFEINkzo/T+uJK7fvY/y3ZwwuhzZe6d8Sfk3sDNJg6rNGeTrZIJeNUqLrDancoJS/OS07d/3aLu2IXTAUDrV0a+CikUzq2RwTujfHJbU2LpMRLB3XSec1SAPp7/j31SBXl6apiy2yiyrww/Oh/OJhfUzo7oeKajkW/qtKNfH3zN7YFZ9d70nGuIg5YS3d8OcM6fCb3jpioau3QSFcOwxwV1l//p4ZjoMJuZLhZS1kBLve6Msf36GpC2IXDIW72njDuSYFeznybUVfnf6vazNEhvhi3eFUyX6Xs34J3Rpf7t9Sw5oGQFQeb2cb5BZX4uA7/aUjFBGlq0t7X2cM+/w4WnsbZkEwJVSIfkTRFWJqbJdmmP3XJYOFRyn0DWw9W7jhXLpyAN/0ci8k55RgQh0WdNQXo0OaYnSIcuDW5ltcn9S3AA0ozYJ9gqXD+EkJAi08HBCTXqg3AD4vQLEd7VHBynbltYlWlcHgtt6ikEb1Bacl7tTMuVZCtFATPaiNF/48f0tDg68Ne2tLUUIgY6mtFWVoe2/evUro0qEe/k8fk8MCsO1CFt4a0kpjXYM2pDRMHNaWMn4BYl3CtgFKId9Y/1R1wd7aUoaqGqXVhSu3zACZSb3fG92pKf6OzcJrA3WH+tOGMdFIhrf3Fi1+DPZywp8zwtCluStsrSwMDksJKMPf/XLmpkGLD2uDoZro+uTwnIGoUTBGJdgghGBKeCAmdG/OC83tmzqjvQGTRmOxYS0cutY5cb7dg6UW2Ol4py/1a4l913JF1pYegW7oEeiGarkC1XIFxnVthp7LDvHtXf27lmqLHo422PxyGDo2c0ZmYTm+OpKK4R10f79rWBeN/q09eeuauKwt4GhriUl6QvFtnN6Dt8gCwIm5g8GAkViAqroH1y8ce2+gwVYqU1InIZoQshrAGABVAG4AmM4wzH1CSCCUmQiT2EPPMgwzkz2nO4CNAOwA7AbwFtOQUgxFkul9WmDDyfQ6R9bQZhLaOL0H7hYrF/b0DvJA7yDtsZtNRbcAV/x29qbB/nLmSN9gD1Gw/tbeyg6rlWAGv+iJDhjR0Qcdmoo73KXjOuK4QGvMaTRGmlGK5jY+SiGjq78rRnXyRbGEr6EuVo0PwecHk9HNvwm6BzTBrMHB9ZpNq6H5aEx7NLG3woLR7Y1eoNzczd7oxCsejtYorazRGl/b18UOv73YUyPmbX0S4ueC+KwivcftfrMvztxQupdwQomu/kxliRAPNy72VvhHEO+5IVkvkXhFl8JDFx+O6YC3h7bWmwTLEJaM64jtF7JE24gOrWlDYUzcZ3UMnRwbQp9gd7STmARzYQ61KTEA8WJmdTjNrY3Ec/YIdNPq4mVlIcOM/kFgGAYjO/oYvMCZgwuV26Gpi1HZKIWLtNW3C5PvaGNgG/FEQnvSJuV2J4FmO0AipKQ5Utev7wCADxiGqSGEfALgAwDvs/tuMAwjsQQV3wKYAeAslEJ0BIA9dSwHxUgWRLbjs1fVBq4z8dSShMTe2hKBHoY1r1oWoc482dUPoQFuZp8RSRe/vySO+xvR0Qd73uon8tG0tbLgw3QJmRwWIFqM59fEHtcXjzA6FFNtMTTdLydk1GYiFujhIIoaoZ5Uxtxxd7QxyqezrvzxchhOpebrFMz6tdKera4+2PZqb5H2q42PE85n3BMlYgGUWlxucSMnsOjqSjh3GC4+fEOxc1Yf3Cp80KD3AJTPU1+WrilhARpCEb+GwMx0XHVJSW4oUmE4AaV7XOyCoXDTsdZHxruQab63uRFt4ONia1QuByGEEHw7ubv+AxsZge72WBDZjrcSNybqJEQzDLNf8PMsgAm6jieE+AJwZhjmDPv7VwDjQIXohw4hBHUZS14bFIzOzV1Foclqw643+tY6pWl90JgFaG3UZZGTrpBVL/ZtgfXH04zOiinFwxgIKcbTzNVOZ4zvh4G69mvh6PZ4onMz3soihS5fbo423k6YOSCoVokpjCHEz1WUNKax0rOFG/6IyUSbeljc/Sihb7ziJmtScw97a0vMHFD7mO6mJMTPBVPDAxvk2oQQvNRP05+6MUDqa5ZJCPkPwF8Mw/zOunNcA5AMoBjAAoZhThBCQgGsZBhmKHtOPwDvMwwzWss1Z0CptYa/v3/3mzdv1ktZHxeOJt2Fp5ONhhmfYloOJeSiolqByJDaaSNMhWrhYWMMRER5lCmpqEbnj/djw9QeGGREsgeKbnKLK3RmtKVoUlJRjZFrT2DtxK5G+bhTzAtCSBzDMHpDxOgVogkhBwFIOUnOZxjmX/aY+QBCAfyPYRiGEGIDwJFhmALWB/ofAB0AtAGwQk2InsswzBh9BQ0NDWViYxtnMG4KhUKhUCgUSuPAUCFarzsHJ/DquNFUAKMBDOEWCDIMUwmgkv07jhByA0BrAFkA/ASn+wHI1lcGAIiLi8snhJhCFe0BwLhAopSGhtaJ+UHrxPygdWJ+0DoxP2idmB/mUCcGLdqpa3SOCCgXEg5gGKZcsN0TQCHDMHJCSEsArQCkMQxTSAgpIYSEAYgB8DyALw25F8MwDbuaRQuEkFhDZiOUhwetE/OD1on5QevE/KB1Yn7QOjE/GlOd1DU6x1cAbAAcYP0kuVB2/QEsJoTUAJADmMkwTCF7zqtQhbjbA7qokEKhUCgUCoXSyKhrdA7J6PQMw2wDsE3LvlgADy9mE4VCoVAoFAqFUs/Ub07MR5P1pi4ARQNaJ+YHrRPzg9aJ+UHrxPygdWJ+NJo6qbcQdxQKhUKhUCgUyuMC1URTKBQKhUKhUChGQoVoCoVCoVAoFArFSKgQrQNCSAQhJIkQkkoIiTJ1eR4HCCHNCSFHCCEJhJBrhJC32O1uhJADhJAU9v8m7HZCCFnH1lE8IaSbaZ/g0YUQYkEIuUgI2cX+bkEIiWHr5C9CiDW73Yb9ncruDzRluR9VCCGuhJCthJBE9nsJp9+JaSGEzGb7rauEkM2EEFv6nTx8CCE/EULuEkKuCrYZ/W0QQqayx6ewOTEotURLnaxm+694QsgOQoirYN8HbJ0kEUJGCLablVxWZyH6URV6CCEWAL4GMBJAewCTCCHtTVuqx4IaAO8yDNMOQBiA19n3HgXgEMMwrQAcYn8Dyvppxf6bAeDbh1/kx4a3ACQIfn8C4HO2Tu4BeJHd/iKAe2z0ns/Z4yj1z1oAexmGaQugM5R1Q78TE0EIaQbgTQChDMN0BGABYCLod2IKNgKIUNtm1LdBCHED8BGAXgB6AviIk2MotWIjNOvkAICODMOEAEgG8AEAsGP+RCgzXUcA+IZV4pidXFbnhYWEEF8AvgzDXCCEOAGIAzAOwDQoE66sZGcLTRiGeZ8QMgrAGwBGQdk41zIM00vffTw8PJjAwMA6lZVCoVAoFAqFQtFFXFxcviFJ/uqabAUMw9wBcIf9u4QQkgCgGYCxAAayh/0C4CiU2Q3HAviVTRF+ljVJ+rLX0UpgYCBiY2PrWlwKhUKhUCiNnCe/OYXITr54qV9LUxeF8ghCCLlpyHH16hPN+nR1hTKltzcnGLP/e7GHNQNwS3BaFrtN6nozCCGxhJDYvLy8+iwqhUKhUCiURohCweBi5n0sjU7QfzCF0oDUmxBNCHGEMkvh2wzDFOs6VGKbpE8JwzDrGYYJZRgm1NNTr1adQqFQKBTKI87+67mmLgKFAqCehGhCiBWUAvQfDMNsZzfnsv7SnN/0XXZ7FoDmgtP9AGTXRzkoFAqFQqE82ty+/8DURaBQANRPdA4CYAOABIZh1gh27QTAhYSZCuBfwfbn2SgdYQCK9PlDUygUCoVCoQDAkl3XTV0ECgVAPSwsBNAHwBQAVwghl9ht8wCsBLCFEPIigEwAT7H7dkMZmSMVQDmA6fVQBgqFQqFQKI8Bvi62uFNUYepiUCj1Ep3jJKT9nAFgiMTxDIDX63pfCoVCoVAojx+lFTWmLgKFAoBmLKRQKBQKhdKIeGtoK/7vuua6oFDqAhWiKRQKhUKhNBrsrVVG9GvZuoKBUSgNCxWiKRQKhUKhNBpqFAr+74Q7VIimmA4qRFMoFAqFYiIW/3cda/YnmboYjYpqucqFg2qiKaaECtEUCoVCoZiIn06lY93hVFMXo1HxoEq1sPDirfsmLAnlcYcK0RQKhUKhUBoN2y/c5v+mCwsppoQK0RSKAIWCwZQNMTiZkm/qolAoFApFgtDAJvzfz/RoruNIMQl3ihHxxXHUyBX6DzYxReXVSL1bWuvz39lyCe9vja/HElGkoEI05ZGmuKIai3ZeQ0W13ODjT6Tk47U/4hq4ZBSKYey8nI0TKXmmLgaKyqtx8HquSctQXFFd79e8klWEwKhoxGc1PreAV36LxdSfzpm6GKiWKyBXqDTCDMPUSVDNKarAb2cytO7fEpvF/70pJlPrceVVNYjaFo8cNjHLyLUnkJhTghN1UJL8EXMT98qqan2+oTzx9UkMXXOsVueu2puI7Rdu46/YW/VcKoo6VIiuAzN/i6MzPTPny0Mp2Hg6A3+e097RClFmsQeohfDRoKi8GhtPpTdqk++bmy9iygbTC0ozf4/DS7/GIq+kss7X+v3sTazelyi5T6FgRAIZR3JuCUIW7ccWAwQDhmEMrvMD13MAAEcSTT9RMZZ913JxLLn25T6cmIttcUqBtLiiGpU1hikb1Gk1fw8ivjjO/37uxxgEz99T63K9+Mt5LPz3Gu4UPdB7rK6Fhe0/3Ic/z99C2IpDiM0o5LdnFpbXqlwJd4oxf8dVzN5ySe+xJRXV2Hctp1b3AYCbBcoy5pVUYvF/1w2alKTeLcXd4gp8c/RGre9LMQ4qRNeBvddyzGamd6+sCq0X7MG59EL9B5sRJQ2gWRJSww7GNRKDspDCsirIFQwsZEohWqFlAM4pqsBvZ2/WbyHrmb9jb+FyAy+2Ka2sQbVEp56SW4KU3BKDr3O/vGE1OnO3Xcai/67jQubD0zLuv5aDRTuvGX2eXMHwFpMeyw5ixZ4Ercfuis9G3M17AIANJ9Px08n02hVWC3E3CxEdf0e07UxaAQBxeLHasuCfq/j6yA0ERkXzWkKOZ388i6B5uzXOSWbb1dyt8VhzIFnn9YPn78HoL0+Ktl29XYRCNQ0iwzDYc1Up6FhaaEu8axjn0guRllfK3+tSA3yDm2IyERgVjQdVxgm7FdVyBEZF4+sj4gWML2yMxbt/XwYAhCzaj6e+O6Nx7uQfY7ArPhuAUuMcGBWNFbsTNCYpKQLXg9M3lG2ltpPX++XKcUFqMqVO/9aektszC8SC8gTBs3H9+7a4LDz9veYza4P7Po8m6Z+4zP7rMl75LQ4Z+WUGX1+Kj3ZexU+n0nHEgHsOXXMMPZcfqtP9hFTVKPDloZRaT64M4X55FWrkikbhYiMFFaLricOJuQiMiubNPNvistBp0T6cuVGA0krVSuKi8mqsOZAMhaBz2HwuE/1XHcGRxLu1vn/czXuoqlHgu2OqGahcweB+eRVu39eczd8tVg1ciTnFvDDLMAyyJY7nSMop4QczKTgN0OZzmcjIL0Pq3VIERkVrnJN9/wFW70tEp0X7sWqvtEbKGH44nob9ErN+Tmu2NDqB7wAPXM/FdYH24l5ZFbotOYBxX58CK0NDW9/94i/nsfCfq7h6u8igcqXlleJmgaoTrZYrsPNytkGDy4MquVY3lMGfHsXwz6VNfe9tjcfYr08ZVD4pauQKnYM0wzDo+NE+TP/5PL9NrmDw5aEUDPv8OIZ9fhxZ9/Rreq7eLkKXxQfw/TGx1kTYHjmy7pVj9b5EnEsvRPb9B6Lvp6pGgaoa6Q64oFT5PWqbFAHA9exinEoVm3e5+tlxMQuBUdE4npyHeTuuSE4c1JnxWxw2ns7Qexyg7A+4ewXN2422C/fi1d/jkFdSie+PpWk9b9amixj/7WkAwJJd17F413WD7gcA6fllaL1gD7ou3o9J688iMCoagVHRAJSTxLvFFRj/7Rm8vukCAqOicUtNa7fr8h2py9aabReyRL/PpikVAVwdFz2oRmBUNGZtusgfs+5QCl/uwZ8ehULBILOgHB//dw0lFdWQKxhcyy7GlawiPKiSY9+1HIz+8iRGrzuB/ddysGpvIhiGwVeHU3nhT0YIMgvKcTTpLq/Rm/7zOd6FZOflbBxLztMQxAHl5PHp789g8GfKb3L0lycxTuIb/PJQCrou3o/yKv1pq4vKlc89hxVyAeCrwykAgEI9k8+SimqR6wvXj6w/rr1NAUB8VhGuZxeL+qeTqfn8u+fO//54Glp8sFtDuNp5OVv02xBhk4NhGOy8nI0HVXLJMUsKSxlBp2bOkvvyy7RbTLbEZmHdoRS8+/dlXvFUUS0XjdWSZRT8fSo1X7Ifzy2uQFpeKQ4mKF2f0gvKRP1ZYFS05AT5fnkVfjyRhi8OJqOgVFV2Lpyftj6sWq7A0STtsoOhroxS/HI6A58dSMaPJ1STdGOE3coaOfp+cljDovD72ZuY8O1pVMsV6LL4AILn70Hw/D24lm3YuGpOWOo/5PFGrmBwKjUfKXdL8WLfFlqP4zqXVfuSYGMp4wfRST+cBQD8PK0HBrX1QufF+wEoB4GMlZEAgA+2XwEATN94HhkrI3H1dhG8nW3h6WSjt3xVNQpUyxWIy1RqpWoUDCatP4s5I9rgl9MZfKd2Y/kolFfV4IfjaTh1owBxN+8hrKUbvpzUDRFfnEA3f1dsf60PtsZl4T3WRSV9xSh0WXwARQ+q8elTnfFE56YYwZrsuLLLFQze3xaP3OIK3s+stbcjknNL4WpvhefDAwEAnx9IxreTu+OPmJtwsLbE4l3X+cHom6M38EyP5ghwdwCgHDRHfH4cS8Z1xOC2XshghdAgT0eN5z+SdBdbzt/itUnJS0fC2lI5NzyadFcUiP/FX87jj5fC8PKvsQCAr5/thvn/XOG1HlduF0HGunPIJTqshf9c5U2Hz3x/BtcWR+itH25QTVwSgSe+OgkXOyucz7gHGQFGhzSVPCfrXjmautih3Yd7ASjrbsPJNIzp3BS+LnYAgDRWu1FSUY1Ve5PQ2scJz/X0R1v2HEA5KOUUV+Cv87fwxcEUfjtXd2duFGDTuUyM6uiDpq526NzcFVU1CszadAH7r+ciftFwhCzaj2m9A7FwdHsEzduNOcNb8+/3ZGo+7pVVIbOwXENof37DORyeM5D/XfSgmhesOzR1QY1cgRh28FqxJxGvDAgCoBSsOQ2iq70VLn04HADQ95MjAICvj6gE7sQlEWi7UPW8nZu7oqSiGoffVd2Xs0D8dzkbPQLdcCHzHtr6OOF0agGsLGWwtpDx32jGykhU1SjQe+Vh5JdWImFxBLacVwp4z7N+pwTAsic78devqJbjSOJdeDnboHuAm2R9Ci0cQjLyyzDw06P4+IkOmBwWwG/n2jIg1uRtOJmOMSG+8HK25bcN+vSo6JrXs4sx7edz2PNWP7g72iDuZiF8XOzQzNVO45yqGgWvXQaAmLQCPLP+rEY5jybdxRT2OwaAZbsTsGx3Ar6c1BVjOku3YV2czxBby86lF8LeOh3T+7QQDfhPfX8GX07qiht5uhdXpeWX4Y9zmdgUk4mEO8UiDfqYr06iVws3vq1lF1Vgxm/K9Q7HU/Jw9baqf9gadwufCCb0vi7/Z++sw6M43jj+nbvLxd3dhRCBKIQkEJIQo6U4FFq00OJSKF6clP7qAqXUnbbUcHcN7hAgFCe4BiL7++Nu93b3ds+i0P08Dw+51dmd2ZnvvO87Mxa4fKcMf+y9iJeb+2PYTyohyS6Xvb/aZZBQPF16H5tPluIdtQW939dF+GlAM84xn6wvxvbTN7Cl+Dr+GZKKt9VzR/+25wJyGnsgI9wVl9RW++2nbyAl2Jk59/r9x1h37BpsLBRwslaiGysf33wuEl0SVIPv7jwqx7JDl5Ef7Sma1vwPNyM1xAVbiq9j67jWzHa6o8UmfNIK9Ge1i8N+2ofnWWXi1z3nkRHhpvf9AMD3O//F5D8Pc7Y9rqjC3BXH8emG0+ifGoiFW85i5gtRzP6KKgpfbinBmJwIretduCUuxI9dvstpG8rKK5m6pHWEG77snag3vT0W7sQH3ZqgcPlxxPs74uMX4wAAyTxLMNvY8GkP1TGfbTyDa3cfI87PAd2S/GAml+GN3w9i5RGV8GbX16vV4xAePK7Ag8cVsDZX4NGTSlgq5QBUoTS6+HhdMV7PCedsu3KnDN/vOIdR2WH4ZnsJNp4sxVe9E5lwRkBV99B19uPySnyyvhhvr1SVybmdYpgyBajEclUVsPzwZSzYdAY9kv3QI9kf4ZNU77T5nHXYNSGTqbsm8fKZZu+5W1DIZIzOODo9h7M6ZUOEPC2xggkJCVRRUVGd3vOm2kJJs2tiJn7aeR790wIx4pf9TOFuEeKMrcU3xC7DUFJYwKmITs7MQ0VVFSKnrGS2nZ2Tj8DxKlfm8Rm5KL33GMWl93GvrALDftqH3ROzUFZeCU97CyjkMnSZvx27SvSHcKwckY5hP+3DCR1W5JMz8zBu8UHO9EFsXG3NGctuSWEBLt1+hIHf7cEhA62y+jgxMxfmCjk2nSxlRAubuZ1iUBDtidVHr+KFpt4AhCv2ns38MDE/khGhbM7MzkeQgKuY5o9BKWj/6Tbm94oRaYjwsANFUUy+0LyY7IfnYrxga6FAlLc9KqsoyIgmrrrz/G3YXaLq3Pz4SjJe/Hyn1v2OTc+FpVKOqioKZ64/wAufbNWyhrQMc8XGk6WI8LCFo5USp67dw/X72pYodv7QBLpY4yzPndgsyAmxPg74jGeV2jauNVIK1zG/J+RHYPYy070EJYUFuHa3DKlz13Msxadn52u567/qnYifdv2LVbyBa/smZ+Pi7UdarnkA6JHshx8EBhXRnQSAWz7e6xqLkb8c0Dqehu786aMgxhOfqBtMofInRklhAaqqKNx8+AQuNuYY9ct+LN53ES425rh+3/A44+ntGmPKX9rhIlPaRmLlkSvYefYmxudF4KN1xUxZ2jUxEwQE9pZmCJsk3OiGutlwXPJsjk7P4dRTNEem5aDxm6rtS4amwtpcgYz/bcDXfRLRKtwN760+iQ/WnkJqiAve7hyDlYev4MTVe/hpl3YYXNGkLEz84xAjJIzFxUYp+F1Ul45xPlrWcrqMCeX/l70T0PdrVVu1Y3wmNp0qxViBsTOd433wdudYg8vQi8l+OgfRGcuigc2ZUIY3ciM4HYjq8GrLYI5HtKSwABtPlsLRygwRHnZQKmToPF9Vx/74SjNUVlGwMJNj+M/78Nf+S2KX1Qn7m6eJmLwcZeWGWU07NPXG4n2adu/l5v7onxoEH0dLrDl2FfaWZpjwxyEMzgjBqEXCdcjfQ1rg+Y+N9wDmRXlwOs6G0j81EIuKzuNumX6vBv1+jl+5iy2nrmPtsWucDjQADM4IRmYjd3RQt38pwc5MWI6fk5VgHPn3/ZIhI8CLC7XbNrojxmbTmAykv72e+f37aymMR00IFxsltrzRGhZmcr3PWNMQQvZQFJWg97j6EtGEkFwAHwCQA1hIUVShruPrQ0SLVW5N/Rywz4QYyz2TshA/cw3z+72usWji68ixJolVlM2CnBg357NKpKcdRrcJQ79v9Ofz3E4xaBHighYs0WcIs9tHY8Ifh4w6x9laiRsGjsYuiEmj9l8AACAASURBVPZEtI89Cpcb1iBFedthydA0vP7rAfy254L+E54iaEteXXN8Ri7MFTJcvP2IsWDXNNHe9uidEsDEkxpLViN3xt37rDKlbaRRYSZPG+HutjqNEobA96Y8i7zTOZb5Tron+WJOhxittnVSQSPMXCo+BkAfQiK6yfRVjJfRVApiPLXGBkjULVOfi0TvFuJRALVFgxbRhBA5gJMAsgFcALAbQHeKokRr3IYkoiUkapIB6UF6YxUlJCSePcwVMjwWieeXMJySwgL8tOtfvNDEmwlzkNrvZ4MRWaEYkRVW5/c1VETX18DCJADFFEWdoSjqCYCfAbSrp7RISNQrkoCWkPhvIgnommHpwcsYv/iQYAifxNMNOz68IVJfItobADso7oJ6m4SEhISEhISEwRgy24mERG1QXyJaaEJOrbgSQsgAQkgRIaSotPTpmwhfQkJCQkJConYZIy16JlFP1JeIvgCAveC9DwCtYbkURS2gKCqBoqgEV1fhCdUlJCQkJCQkJADVGgR3HtXuIl4SdUdrA6dIrC/qS0TvBhBKCAkkhCgBdAPwdz2lRZTiWXkYlV33Ae0Sugl1054vWkJCQkKi4bBmVMt6uW/fr3cjdtqqerm3RM0zhjfHdUOjXkQ0RVEVAIYAWAngGIBFFEUZv05uLaOQyzAsMxQ7xmcadPw7nWNr7N5CU/Y8a/g6Weo/iMfBqW2welRLnJ2TXwsp4uJlb6H/IAmJ/xCJAY61ct3jM/QvXNSQMZMTRHjYcrZ5NpD6w1pZ93PsHp2egxA3G/St5anJlHJtCXP8Cnfawa/6JKJNpDsGZwTXalokagc3Axadq0/qbdlviqKWURQVRlFUMEVRs+orHYbgYWBl2DHex+hrd0v01drWxNdBa1ugi7XR127ovNDE+LGkdhZmAFQLmiwfniZ4jI159Vc4CnC2wjYDO09s3nwuEnM7xqB3SoDR57rYNMzKopGnHbO4SHVpqM/YUFAIrGzYUPigWxNMyG/E2dYhTvc3vHJEOtaO1m2RDHKxrpfFFEzB3U67/B54sw1OzcpH9yQ/zvZNYzMwvV1j0WvN7xmv934twzRhjG/kaq/KZwjrXm+FH19JRvck7bbGWEoKC1CgY7VDQNV+0avMjc8XTrPQe9RFmLuw99HBykzvuRnhbljwcgI6xKna5zWjWqKksIB5n9He9qL3+7pPIvZPycb28a2xY3wmhmWG4vTsfHjaW+D9rk0E75cW6sL8/W4X3Ya1ghjd79JUYgU0hLEcM2BF3rrAuYG3GfUmop829kzK4ixvyufbvkmC219u7o8veolPNVjYMQZ2FlzR9+fgFgCAvZOzmW3rRrfEnA6a5YbFKqEAZyvB7cTAtvm9rrHM/dm0CHEWONp0GnvZ4aXm/voPZOHPe7ZGnnbM3018HZAd6Q4ACHazQVKgE9oKVFBt1MfwGc0L21k0sDkA1YpMI7JC8eurzQXPe72N5ryB6UHo0yIQXRJ98XpOOOwtdVfwJYUFODUrD/1TA7F3cjZWjUxn9r2Spilr3/XTlK25HWOYv11slDqvv3OC4Z2AsbniLrPmQc6cyp7dQbA0k2M9a3lvIeZ0iEbxrDwkBTrhw+7CDY8QYt8UX5Rls/I0L8qD+dvbwRIlhQWiXh1TvD1rR7fENyLpMgRdLu5lw9KYJdV11Rk0Yh3rpEDh5ccB1QIoxvJGbgRmvBCFdk28IeNVJKkhLvh5QDOmjHZo6o1dE1Xlzs5CgXAPWwS7agsgCzPVc7YIccbP6uWvg1yrbyjwc9LUESEmhn3F+jpg45hWeKmZP7aNa43iWXnMvrWsJeVp6O/85eb+HEFmJpehZ7K/aAc0N8oDb3eKEdwHqPKK3UmJ8LQVPG5W+yjB7cdn5OLvIS3gbmeBlGAXzOkQg5LCAo4wZ9dfAFe086HFYecEjbGI7U2M8LDF7olZnPbDTMBSvGtCJra+0Rrf90tGhIctNo/N0DomyNUas9pHYXBGMEoKCwSXDm8e5IxeRhgrgl1tUFJYwJQLujMwt1MMrHjW+jkdohHn54Dmwc5wsFLC094SHvYWGJUdBrmMYPv4TGbVXDYHp7bBgpc0365cRpDVSLjNCXWzEW2Pqsv/OsUY1Enjs45Vt1rWgwfjaUQS0QbibGMOO54ootuT/GgPpPMqHzO5ameUlz0yG7njwJQ2mPmCqrLLjnTH5rEZTONxcGoO3uuq6rFmNdJUFk7WGpFECEG3RF+sG90Sp2fnY/u4TCx4SfWRpARrBG5CgBOGZ4Zy0pIU4IR/hqSic7wP4v257lh+T14ukwlawrsl+mltA1TC0dtBU5EKVYhCLB2WBjdb/Rb+JUNT8WH3pgBUy8ny+fGVZABA/7RADGql2b9oYHN8/GKcVtiHm0jnY2hmKBYPSmEdp0pbaqgLRmSFIcHfEe2aeGmd1yslgBFq41lWOhtzBYomZel9PjO5DJPaRsLJWgknayVyG3ugd0oAQt01DWZaqCvTuCnkhAkzEerssHG3477fn15phs5qb8mQjBBme/GsPLwm8G5p3sjjCuypz2usazYWCp1ekm6Jvuie5AeFXIZFA5sjJVhjpRFrXGjYFh02wa42ODM7H53ifbB8eBrYC0bNYzUc+dEaQc0WK0en5+DItBzONQ2xaNH3bhnmimPTczH1uUicmJmLSQVc62y2QMP4WqtgnJ2TjxA3GxyfkYukAG2hG+llB/pRmgXp77QKXQNQdbTEOnB9eYaANpHuSAt1wUfqb0yIrom+eKmZqsNLi+gobzsUTcpChzgfNAtyRlqoK7aOa413usTCxdoc2ZHuWNgrUetaJ2bm4p3OseikLodtIj2Yb21Y61Ct48XYOKYV8/craYGI8VGJV2dWx5JdH+gikyfQfhnQDP7O1pjxQhS8HCyhkMvwTd8kfPJiHOTq51fKZVg2LA37p2gMHYQQ/DM0lXMtmYzotDbKdXge+qYGarU5gGoFVTapIarvhF0Pf9S9KSzM5Ijx0a7LP3spXusaNOz2hw4DmdspBpPbRuILdX62CndjPCZmMo2EiPK2h6uA651tsfWyt4CbnQUUchlSQ12wYkQ6fJ20DT9WSjl6JPtjTI7KWtw53geEcPO9X2qgwXkshJ+zFUoKC9DI0w4H32zDqRPi/Z2weFALmCt0C8lt41rjva6x2D8lG38PaQE7CzNYKuWMUcbT3pJpp/mMbhOOWIH8EeL4jFycma1py+wsFJwOz/ye8TjF6uyFutsiN8pD0DhCh9i80MSL+W5oggQ6vHwGZwRjIs8j9V9GEtFGUMVqrEsKC3B2TgGKZ+Xh4+7algbaskj32u2tzNCzmT92T8zCgpfi4etkxWkoLdQfK79SZVtWCCEIcrWBXEYgkxFkR7pjTodofNk7kRGaNuYKjMwOw4vJGtG76NXmiPK2x9udY/H7aymc6++ckInD03JgqXanuqgr18WDUvDmcyqrlZe9BZ6L1QjIWB97OFkrsXtiFsbnN0J7dY98dHaYYIXIJiPclWNNpdk3ORvLh6cxnQkac4UMz8d6YcsbGYKhLynBLjgyLQdtY7y050hUvzM2PZtprN+0NYwWQXF+4vGehBB80E0jNKK87QSvz4ZthenTIgCAfrE2/6V4TH2+sdYckDZqb4VCLuPcs6SwAOPzInRakNiMahMGf2crdEvyZZ6ff01A1UDR6GpI+On8oX8y57dMh0hY2CtB0FvAXJsQ/I83zmCy2pIqk6n2NfK0g5cDN7Z+iVrI5LKs0p0TfPFVn0T0Tw2ElVIBa17Ij7VSOARIrJG2VMrRu0UgzBVy9EsN5FhwPu2hqQ+UChnjOqbfsYWZHDKRmpdSl2JDPEcyGYEjqzzRnbkAF2sYuhJtcpAzvuuXzPm+AZWni+5wczvzqv+rqrRDc7wdLEGIqm76/OUEjkV889gM7JqYCXOFHB3jfUAEZjk11Fs2uW0k/J01HbcJ+Y2Yjl0V67E7xPlgD68j+3Jzf3jwOpdZBlgDW4a5oiDGE5Xq96qQE0R62cHBSrc3iObtTjFMuQTAeLb4ln0+tNiNZHndonjhB3Q986RSs3ALPz/ZWJjJ0UPdPtBhcfR9eiT7M95MT/V35WBphn6pgYyXBABWqr1m7FI2o52wRdzbUfN95kYZFr7w2UtcT0yImy3Ozing5HuLEBed9a8xKOQyrTrBELwcLNG+qQ8crJScDsugjBD8/lpzJAU6idaBuVEeCHCxFvSI8cOkzBUyyGSE8VoTQjihIrlRHoJW/0GtQrS2vZ4ThpLCArzfrSm+65csKvLZsEMnx+REoF9qID7q3pTpTPE9XO0FrPRi0GXP39mKY/h4WpBEtBHQbiD2aFGFXMb5SFqFuyLIxRq2FmYIc7fV+shdbc0FP3w6JrCsnLuC1T9DU7FBxF1OCEH3JD9YmMlREO2JsbnhTNpmt48WPIePuUIOG3MF9r+ZjXk94pCitmrE+Tky4qSxutKmhdor6UHYOzmbsTp0iveBnYVC0L0FAMFqNy0hwFd9ktBFQAw7WivRyNMO7Zty48rLK1XVtI+jlWiFya/8xKrV97rGIsLDTn09S8a6xuabvklYMypdazufr/skYcFL8Xrjr397tTmKJmWhT4pKlPLDRsTgSyDa2hTubst0rOhGbWDLYLzdWdwtTCMjKsvIxjEZ8HG0wsYxGRxrNv3cX/VJRH91OIlY2NBf6vPoLPlf51i83NwfLUJcGMtbj2Q/jBUYWd0m0h1d1J3Lj7o3ZSwsNuYKJpavWZBKgHk5cAVPP4GQKn6cbpS3PUoKCxDvz7XUZoS7YZJIOINCLlxqBqYH6w37oDu3NGZyGeMepvONj5jGpbcLiUyao9Nz0C3RF2NzwvH3kFQMywzFYZ5lPVFtpR6dHYYDU9oIXsdcIeN0NNgoZDL8/lqK1rMzItpAkU7j62TF8TzRYQDs8sUXh2LwxxsQwnpbFIUZL0QxBgB2PGXzIGdMbxeFHRMyOR4z/qPosg5XqlW6rmOE6Jzgy3k+On+EBFZGuCvTWaTr4IIYT2aAVaSXHceT5KgW8iOzwiAjwLg8/bHTA1sGo1dzf/RpEYBGnnbYOSETx2fkQiYjzDszV9cvQnlNp5rdWRNz/8f42COrkRt+fbU5JhYIWzDH5oYznbK8KA+OVV0M/v3oPK8Ov7+WUiMDEOUyolX/6KNDnDfG50Vg+fA0vNuFG/ZGt32LB2nqXTqfhmXq9uAMbBkEAGjq54CTM/OYeHVAFYrUprGqDuCHS7IJc+eGEslkBM/FemH3xCxsG9day8P1XtcmeutNT3sLpIW6YP2YVhjUKhjf9EnCpz2MD0Gpb6o/Aus/REG0J3wHW2m5QNh83ce0eElakPJHdNtbmumNrQVUH61Qr1MXbLForpAjjzdghBZrCWqLFC0M+NaTABdrHJyqacT3TMpC/Mw1zO9XWwZjzG8HBWXBoFbBggM7aIxtrHRBC/Rlw9LgaW8BmYzg/uNK9EjWiGlDLbouNuZM5aOLBNrlbgMcmZYDK6Uck//SPxENP56zW6Ivchp7wMlaiXk947C75BZHlLjZWmBCfgRmLzsOAJjfU9s7wu+EuNtZcEI+QtxsmYrv8p1HqnNExBxdXun9neJ9GBf9B12b4K2OMaIdjAUva6xMhBAQAmwd1xrWSjkoClh/4prGBW6AVrMwk2Pf5GzcLTN9btgob3ucu/FQa7u92tLr52RllOvY19EKJ67e0wr1EGPFCJWl58veifhmWwkszGSY2zEGX249qzXbgLlCjkK1N8fRWik4DSdtlQp2s2GegU2Utx2WDBUemAsAn78sHJNNN8D8UCFj6ZcahFA3W7QK13xvwa42OD07H8ETlgEA9k/Jxq2H5bj18Al2nLmBuStOAFB1BvnQdRIFCHaOAa6HgO0xy2rkhsLlCnzbLxm2FgpBix5NlYkiWgy5gGHgK1Yb4mJjztQbhBD8ObgForzsoJDLMH/jaVRUUZDLCPPdsj2QurA2V2Aay3KskMtAO5zm94zHmmNXsflUKY5cuotKgZXJCet9z+kQLTrwD1CVV6HQHjaDWoXA38kag3/ca1D62aSGuODE1XvonRKAKgqYseSo0degifd31Ap5rAsMHZ/BD8Mx5LyxORFIDXFBWqh427ZnUhanU8Iv3mKl3dFaCf7bosNUdbF9fGt42ms6SmNNHDTbEJBEtBEQQmpk1KsQUd72WPhyAlqIWK5qki1vZICioDf0opGnHTaOacWIadoioa/94I+mpSvcBIGeudDH80P/ZFiYyXDq6n2EewgPphHCUONYpJfGNcoerGkMVVWUzlAFIYxxF8b5OWLZsDRGABFCGLe6g5VSMO6WdmUPTA8SdJua0u6LiQXac9KY9S5pFHIZbHQIESHYlid6FD3Adc/rwtFaCUeROE9drB6Zjkt3ypAU4ISlBy+LHrfJwFh/moW9ErB470XReHH6sULdbHDq2n3GQ9IixIWpA7ok+mLPuVtaIlpl/TNdxB2a2objmqdpG+OJJep34CdilQp0scYH3ZoY3NkUQy4jgoPF2OXNwUoJByslAmGNOD9HRkQLeaQMsZCLfX9udhYcI4AuHKzM0DslgDO4rjrQn0l2pDtWH70qeAw73ezxKvQzU4b0NI3A1dYc3ZP8sKX4OgAwISxsaDe+nYWZ1qwk1cXYCI3vWSFk7Zt6Y8aSo/Cws8CVu2V6rbR1xfbxrTHyl/3YceYmxuSEI9xdf7u2eWwG0uau52yjha5YGNzClxNw9voDzja5jOgU0AC3zd41MRPmctV9ApytUHLjoVF5wh/PMTA9CANbBkMuI1h5+AqszOUcAS3Gh92b4tTVe3qPq28kEd2AMCQ2rybwcdQtntmwY9C6J/lh5ZGrRnck5DJVnKoudxEbWkQY6w6jxX4nE6YaNBQzOUF5JYWKKgrKWp6OLFJAoOqCdjWLhb0YUxF62FlgYHqQqFhwslZi0cDmRqfRWGpaIPAJdbflDOKsKXydrDA8S7wB79siALvO3sTPA5oZPIXTH4NScLesAgoDOiipoS5YceQKMzNGq3BXbDhRCgCwtRD2bH38YhzWHluBR+WVOiV6OxOmpjSG0dlhOMMTAvqgPSJCGlohI6ioorTKkr6p9wTvQwhnYK0Qvk6WOH/zkeC+JUNTORY/PydV/Rrv74gpbSMFB+bpSgtAGWw8MBbaSl4l0JP1dbLC1OciDY5xNoRW4a5IDXHB2BzTrZJO1kr8PaQFQtxsOGEL9Y2nvSVeSQvCjjM3kRvlIThjDR9fJyuMz4vAO6tPMtsszOTYPyVb1MtXExqC7eFcPjwdZeWVJsee8y3lQqGcfD7o1gSNvewQ4lbz9XJt0HBKmUStYCYS62kKrcLdTJoWLN7PSdSyVZO42prX+iI1Tf0csevszWqJu2/6JuFJhYCPtJrQ8YlsjeVsrcSNB08AcDtE+iCEcGYbEULXVGo1RXKgM9rGeOL1NuEIqOW50lePTMe0f44ixM0G7Zp46XTrC/FdvyRcvl1m0LG5UZ5GldU5HaLRVMfAVz49kv2QF+XBCPSveica1BB+1L0pPt1QzAw0rg+GmmA9pGfd4c+SBKgscRVV2mLTECFjCiuGp+NReaXgPn7cd6SXHTa8rvL2GevZ0sQlm5JK/RTEeOLvA5dEY9V71/BCKtbmCo5V2VSEZiRpCGQ2cje6fRrYMhgDeTMnGTqYtSawVMrrfKq72u6k1zSSiH6G2TQmA9bm9dMYTipohA/WnsIhA92ktclzsV4oKrlZI9f6olcCiq/d1zv1kS6q6woXg46/ZrvTlgxLxcmr92vknhEetlqhBbWNUiHDxzW00Is+Qt1tq9WI63OZmgLdWTPW6UEI4Vi4DbUkZUW615lHzFhahrli48lSwX3udhbYMT5T0JJLh4hUGBobVE2szbVnf9GFqZ3DMHdbHLp4x+jwB0PJaeyBs3Pya2wGDAmJZxFJRD/D1IX1V4z+aUHonxZUb/dno2sOXGOxtTAzyiJYlzQLcsaRaTmcBtzT3tKg+DNDWPRqc1y7a5ilVaJmoHWfrtk6/it89lI87pVViO4XW1n20x5xmLfhNKyeklURDeWbvkk4fPFOra72KAloCUNZPjwNRedu1Xcy6hxJREtIPEOYMtepodhZmDHLrkvUDYyrXtIysDCTcwRjkKs1zpTqj51uFe6GVuHagxifdpyslYLhKxIStYG1Uo6ROqZobeRpx1lF+L+CJKIlJCQkGijM4iv1nI6GyG+vpqDkhnEDECWefsLcbXDy6v36TsZ/jiPTc+s7CQ0SSURLSEhINFTocA7Jra6Fk7WSs5qixH+Dvwanokxk4KaERF0jiWgJCQmJBooUzSEhwaU+ZoyQkBCjWst+E0LeJoQcJ4QcJIT8QQhxYO0bTwgpJoScIITksLbnqrcVE0LGVef+EhISEs8yaaGqOdMjPJ+OOVMlJCQk/ksQqhqTTBJC2gBYR1FUBSHkLQCgKOoNQkgkgJ8AJAHwArAGAB2RfhJANoALAHYD6E5RlN51OhMSEqiioiKT0yohISHxNHL74ZM6nRtWQkJC4r8OIWQPRVEJ+o6rliWaoqhVFEXRcw7tAEAvb9YOwM8URT2mKOosgGKoBHUSgGKKos5QFPUEwM/qYyUkJCQkBJAEtISEhETDpCZjovsC+EX9tzdUoprmgnobAJznbRdd3YAQMgDAAPXP+4SQEzWTVKNwAXC9Hu4rIY6UJw0PKU8aHlKeNDykPGl4SHnS8GgIeeJvyEF6RTQhZA0AD4FdEymK+kt9zEQAFQB+oE8TOJ6CsOVbNJ6EoqgFABboS2NtQggpMsSkL1F3SHnS8JDypOEh5UnDQ8qThoeUJw2PpylP9IpoiqKydO0nhPQC0BZAJqUJsL4AwJd1mA+AS+q/xbZLSEhISEhISEhIPBVUd3aOXABvAHieoqiHrF1/A+hGCDEnhAQCCAWwC6qBhKGEkEBCiBJAN/WxEhISEhISEhISEk8N1Y2J/hiAOYDV6sUAdlAU9SpFUUcIIYsAHIUqzGMwRVGVAEAIGQJgJQA5gC8pijpSzTTUNvUaTiIhiJQnDQ8pTxoeUp40PKQ8aXhIedLweGrypFpT3ElISEhISEhISEj8F6lWOIeEhISEhISEhITEfxFJROtAWl2x7iGE+BJC1hNCjhFCjhBChqu3OxFCVhNCTqn/d1RvJ4SQD9V5dJAQEle/T/DsQgiRE0L2EUKWqH8HEkJ2qvPkF/U4B6jHQvyizpOdhJCA+kz3swohxIEQ8pt61dhjhJDm0ndSvxBCRqrrrcOEkJ8IIRbSd1L3EEK+JIRcI4QcZm0z+tsghPRSH39KPYmChImI5MlTv+q1JKJFIITIAXwCIA9AJIDuRLUSo0TtUgFgNEVRjQA0AzBY/d7HAVhLUVQogLXq34Aqf0LV/wYAmFf3Sf7PMBzAMdbvtwC8p86TWwD6qbf3A3CLoqgQAO+pj5OoeT4AsIKiqAgAsVDljfSd1BOEEG8AwwAkUBQVBdW4n26QvpP64GsAubxtRn0bhBAnAG9CtZZFEoA3aeEtYRJfQztPVgOIoigqBqrVrMcDgLrN7wagsfqcT9VGnAany56amGgXFxcqICCgvpMhISEhISEhISHxDLNnz57rFEW56juuJlcsrFUCAgJQVFRU38mQkJCQkJAQZfvpG/j35gN0TfSr76RISEiYCCHknCHHPTUiWkJCQkJCoqHT/fMdACCJaAmJ/wBSTLSEhESNUXrvMc6U3td5TGUVhQ6fbsX649fqKFWmse74Vaw4fLm+kyHBYs3Rq3rL17PO4Yt3sPLIlWpdo6qKwtbi63hawjnZ3Lj/GMXX/ttlQKLhIIloCYlnnDuPyvFr0fk6uVfy7DVo/c5GncfcL6vA3n9vY/jP++okTabS9+sivPr93vpORp1RWUVh3obTePikor6TIkr/b4v0li9jWLz3Av698VD/gQ2Ith9twcDv9lTrGt9uL0GPhTsFxThFUaiorKrW9WuTVv/bgKx3a64MSEhUB0lESxjMk4oqbC2+Xt/JaBCUV1bh+v3HJp1bfO0eTly5V8Mp0nD5ziPceVTO/H7jt4MY89tBHL54p9buSVP19Bm2GhR3HpZj77+36uXeSw5ewlsrjmPSn4dRVcsZuf7ENXy3w6CQQ5M5ceUebuj5RkctOoCW/1uPl77YiWv3ymo1PQ2J06UPAABX72q/n0/WFyNk4nLcLSvX2lfb7D9/W2/Zu1fWcDt5DZGy8kr8ue/iU+l1eBqQRPQzSFUVhZ93/YvyGrYmzFl+DD0W7sTBC7dr9LqGcM/ACv2PfRe0Kv9lhy5j4eYzNZqeCYsPIWHmGjyuqDT63Kx3NyHn/U01mh42zeesQybLWleqFhKPyo1P67PAkUt38NnG09hx5gbmbThd38nRyUtf7kSHT7cZ1eA9eFyB+4+5wmLLqesIGLcUNx88ETyHoigcv3KXs61MXT4W772Id1efNDLlxtHnq92Y/Odh3HlYXmtWz5z3N6HNe+LfGf2OKQrYfOo6vthytlbSweZM6X1cvvOoxq63/NBlLN57wejzKtRCVSEnWvt+UXutbomUHVMoK69E0Pil+OfAJdFjdpfcxAufbMW8jQ37G30aqKqimO+5cPlxjPhlP7YW3zD6Gu+vOYnbD5/gXlm5aF3yX0cS0c8gv+45j3GLD+FznnB8UlFVLWskHYf2y+7zSH1rXY2I9CcVVajUY3k4fPEOoqeuwt86KmBAJZZG/nIA434/yNk+6Ie9mLn0mMhZprHskCpWtrzS9N59WXklFu+9gIBxS7U6CU8qqjBzyVGTrUFsK7lM3U7WpiFi7orj6DhvW+3doBoUfLgFc5YfR7cFO/DWiuMAVA12UclN5pird8swf+Np5r2Vmdjh+PfGQ4M7fDSPKyqZ+x28oP19PnhcgdsPn2DlkSto/+lWPP/xFs7+mGmrWhXH0QAAIABJREFUEPXmSs62Bepv/8CF27h+/zEO8a773Y5zyH1/M7af1jSs7PKxopoxt4YSO30V3vj9EGdb+0+3ImzicgDA3wcu4dei83hcUYlFJoQk3XjwBH/tvyi4j1/tKGTagpKm+Np9pM1dZ7L3iab1OxvRfM46AKp813e9k1fvIWDcUiw9KByb/9oPezFq0QGj00F3XMxk2hKALgcyIv4+jOXCrUeoooB3Vp3gbL92t4ypSy/dVnUuFhWdx+2HT7DttMrrWVZeifM3n66QGzHE6nNT6g1dTPn7MCImr0BVFcW8V35HWx8bT5Xi/TWnMOnPw2g2ey3iZqzGd9tL8MDI65jK4Yt3cPthwxfukojWw7HLd+s9RrC8sgoB45bih52GuT9vPVR9jHcecj/Kaf8cQduPtphcIRF1pfrDzn9x4dYjo3qme/+9hYBxS3HtLtdlGjZpOXou3Knz3KOXVRazjSdKdR738IlKiAi5KNlQFIU9527ViHuLvsaAb4uw6sgVbDt9Hd8b6Kb+YstZxjJ66Tb3vfyx7wIWbjmL/608IXSqUdDCjN9ZOXrpLgLGLRUN0fm16Dxa/2+DQff4dMNp7DlXvTCE8zcfaokFISvltXtluHq3eq73zvO3o9P87QCAbaevI3n2WhQuP46EmWsAABGTV+g8f8+5mzh5VTskJ/3t9YieugrPfbRF4CwuK49cwf3HFch+d5PW/dhFs/GbK9Fk+moM/G4P9v17W0toC3VCGflDAQkz1+C5j7dgyUFNJ5TuTP9784Fg2uR6BFRlFWVUJ/rk1Xui9c7vLEvqk4oq7Pv3Np6orz3sp30Y89tBvL/mFMb+dlDw/IrKKq26jo3oeVXc9CsEBCXNws1ncP7mI6w+elX0GD5i4p2m/zdFTHkTg7akf7lV20peHcugLks0XfZG/rJf8Nx/DlxihJkQf+67iGOXNV6Oi7cficYwd12wA4N+2MvptJ678RBNpq/Gi5/vxMXbjzD0p31Im7ter7GlofPPgUuImbpKq0MLqOqNTvO219i9vt/xL/M3/drEPumtxdfxzbYSre0VagNRWXklHqjb1sl/HcGbfx8RvA5FUTUaMtL2oy3o0EANM2wkEa2DsvJK5H2wGYN/EB9ctLvkJufj3lp8HcsPGT+i/86jcnyyvliwEN5Xx4C9baCgYi7B+2h+2Kn6sEb/egBHLlU/PvZJRRUePdG22I36ZT+G/qQaNHbnUTl6fbkLhctVFsBf91zARV4FvP3MjTqN11p26Ao6ztuGX/dw3aC3Hz7BgG+LjHJjvvnXEYxffAirjl7FgO/24MXPd2LSn4f1nwhV56hK/dxsI9j9xxV4UlGlPsbw93Ly6j0tYXPowh08Vl+L/453nlVZIcXK1ZjfDuLMdY3IoigKv+z+l7GmXL7zCIHjlyJHh8vcUB4+qUDa3PUY/ONe3CtTufiXHryMkInLcZo3G0PSrLVInr2Ws42iKOad6ePoJW4Yw4ufcztxfIs6ezaIdcevYvOpUnSct11nqMChi3dQUVklKhyLr93DwO/2IPf9Tfi3FqxsdINJQZPnRSWqTo7Kqqsq+4cv3sWcZce0ysaJq/fw1/6LCJmwDAHjlmqFIHRbsB2hamtxwLilWhZGPm3e24S0uet1HtNp3jaETVouuO+ajo7x+MWHEDt9lajIkssItpy6joz/bcDDJxX4a/9FBIxbisMXueVAlyWaz/yNp/V6Xob/LCxCaTaf0nRev956Fot2n0dVlbAQEdoWN2M183d5ZRWm/XMEF26Jl6VHTyqZd0SLaLnAM9P1c5G6U1x67zF+U9eVVVUUhv60D53nawQfRVGcDuWIX/Yj74PNzO9TrH2Ep+TOquuXiMkrBL+Vh48rmI6LsW1E8bX7BofbbThxzaiwIoqi8MPOc4LtH6DSDnxv1uZTKiPQ0cvCbe8JgU55dSEEWHNM9f6OXLqLopKb6rKvSUOPhTtFhbEQV+5oGzAoikJK4ToEjl9mkLa486icU18HjFuKgHFLtfL4TKlwJ78hIc0TrQNakOxWNz5X75YhefZazO0Ugy4Jvth55ga6LtiBUdlhGJYZCkBVIAHATE5weFoOzBVy5no37j+GlVIBS6Ucv++5AGtzBXKjPAAAsdNWAQBWHL6Cf4amCqaHolSFlV8RASpBGzZpOSa3jWQazpLrD3DtXhncbC04x+46exMFH27ByZl5WFR0Ht2T/DiVKUVRuH7/CUb/egAEwDd9kwBoaXJ0mLcNpfceo6SwgLN98T6VBWZb8XVEeNpyYrHeXnkCb688gTOz8znnBI5fhpLCAqw/cQ3h7rbwcrAUfAeGwG5MVxzWdkvTHzm7cqcoCk2mqxqlCM8SjMwK1XrPa49dRaCLNfb+q4kJp5+Vz+GLd9BWbY38/OUEpIe54Oqdx7C3MmOOkRHCSBz6XpVVFM81r91wPHxSgTOlDxDlbQ+KonDk0l2cvf6A6biwYQ+W2nf+NpoFOUMmI1hy8BL+UKd9//nb2HSyFOlhrqiqovDJ+mK8nBLAnHfx9iN4O1ji0MU7eOP3Q9h4shQfd49j3NKGVP4B45YCADrH+2BCfiNm+7HLd5H3wWY4sN5L9NRVKIjxZCrUjSdKEexqg6t3y0SF8pt/H8G328/h7Jx8EEIwY8lROFiaYaj6u2ST/6Gmgf95179a+/kW9dbvbMSSoamI8rZH36+1F3x69KQSFCgtK2W3BTtQdO4Wto5rDW8HS7T7ZCucrMzQKyUAO86oQkku3BK26FEApv9zVNACCaisnD6OVoj3116FeM+5W0xDt/yQdvk/x5qNgh7c18jTTus4tghsPmcd853feVjO1Ik0H60rxsaTpWge5IzxrPwFYLBLtoj33ouvsb5Pge8AAM7deMB0hnss3IEdZ26iQ1NvzncpIwQ9v1DVyz/vOo/pS44CALp+xrX8yeUElVUUPlx7Cq625ujZzF/wntfvP2aMAt/tOIfJ6k7zqpHpCHO3FTzn/M2HcLRWMr+H8b7Vqf+o0jT294PIjnTH5y8ncPaz6xwh/jlwCV9tLcFXW0u06mOaRlNWoHWEG77snagJ55Cr7Ghl5ZXIfGejloEDANq8txG3HpZj7G8H0DzYGQBw6c4jBIxbilA3G4S622DZoSv48ZVkpAS7aJ1v6CDq99ec0tp2i+Vh4NfHu87eRFKgk+C1bj98gqx3N6JDU2+827WJ4DFnSu9rzfQi9u4A4Psd5zDpz8PYNzkbyw5fxsQ/DuPopbuYkN8I1uZcKUV7lk7OzIOZnHDSrq8vcK+sHI/KKznt9tdbz+LGgycYmRWG9SeuoXWEG37ZfR42Fgq0jfECoGlzvuuXxLlegr8jis7dwo87z+GkOi/afrQFg1oFc8rr3wcuwdZCgYxwN875a45xpyLlh51sOHENi/dexGV1nfPxumLM6xkPQNXGdpq/HdmR7hiSEYIAF2sAGr3Df993HpXDwUqJpwlJRBvA/ccVGPHzPqbBWrT7PB4+roCdparhP3XtPj7beBpz1BUroLIgLjlwGe2bekMmI7h+/zESZq5BhIctWoS4MINYlg9PQwnL2neIF7N87sYDDFJbwu88Kkfg+GXYNzmbUyEDYKxZH687hReaegMAVh65ipVHrqJtjCcmFnAbNgD4bONpvLP6JCb9eRhZjdywsFcibtx/jDd+P8j5cG49eIKmLKsHTek9lXXoSUUVRi7aD0szOfayGsIbD56IDmZIm7teq8KmhRYArB3dEhdvPcKGE6WMkKiiKCzcfAY9m/nDwkyOyioKM5YcRf+0QPg4WjHn7j9/G0cv3UWouw1e/V57KqhP1SEUn28+i4kFkXj0pBLDWNOtfbj2FGzM5RiQHgwATN4ZAzuk45VvhVfalMsIU6ESomrI+ALup13nMadDDMrKK3H1bhkePqlkrDxHpuVgzbGrGP7zfkFX3Z5ztzhxjW+vPIGFm8/g7U6xGPIjtxH/36oTqKIozFp6DKeu3cf2M5p86zJ/Oz7pEYcFm1TvrfjafY4QFSJg3FK83iYMQ1qH4sB5jQD4dc8FJq/ullUwz3Kb545fevAy/J1Vx01fchQ+jpYYwJvW63TpfXg7WMLCTI5vt6ve99HLd3HpdhnzfR3WYxUZt/iQzv00F249QpS3veC+JtNXMdZ+NrQo/GR9MYJcrJn3sF5PWBIAlNx4ICqgAY3A/WcIt8O94vAVTplne1u+3laCAelBgiEJI37Zj96sjpMQp0vv40zpA9HyfPDCHRy8cAfRPvZMww6AEa2Aqg6bLTA+QeiaWe9qLP184RE/YzVyozwY7xoApmPC79iyY0H3scpiBc9y/eWWszh++R4z9kJMRLPrgsksr9OuszdFvRNpc9cjnCdYxFh99Cq+2HIW/VIDOdurqihsPClcdib+oUnHgfO3EevrwNlPG4TWHb+GPeduYrnauLC1+Dryoz3x/Y5zggI6efYaRshWUWDqczo/Tl27j1PqsTIvfr5TUISy28Wz18Uti/z8ADTWW0AV9sO2VHb5bDs2j82Ar5OV1nn0e/rrwCWOiJ7wxyGUlVfi3S5NsMGA75AN7WE8cuku875/2Pkvftj5L07NymM6JGxoz0pJYQGI2gxVUUXh661n4WFvgdwoT47Rh90GbhqTAT91HUh3srwcLDF+8SEMbR2Cj9YVAwDzre06qyr/a1lt96ZT1xltcP3+E85Yh095g6zpjh2dh2J+mQO8cJTeX+3m/F5++AqKSm6i0/ztkMtUHdPf9lzA73svYFjrUHSK9+E87ytpmnJ+5W4ZHKyUmM8aXFpVRUFmhJeoriFPy7QnCQkJVF0v+32vrBzRU1dV6xqutuZY/FqKXlcmm5LCAlRUViFkorBr87VWwSiI9sSNB0/Q68tdaBvjiSUiA0904WVvgUss18zMF6IMDkVg066JF/7ar3vQX00gI5r4rs1jM1By4wFe+mIXAODY9Fw0mqI7jrVoUhYszeRozBuE5WSt1IovDHK1xrrRrQCoRsC/piOkR4iMcFeDxBLN+tdb4YM1J/GnwHssKSxA3693Yx1vcRKlQqY3hKFTvA/jiq0PEgMctayWNU3PZn6cGMDaoHdKANzszDF3BTdsoaSwgNPwVYcxOeFMaI2NucLogUBrRrWs9flzX27uz3RYAOD4jFyteO7WEW7oluiLX/dcwLtdYpH61npmysWBLYPw2UbjZ8qpqzqGzYoRaQhzs8X6E9fQ7xtV28POIz4RHrY4zrO60oJEVxkRK0P87bruzad4Vh6G/7wfdx6V46s+iUzojRgD0oOwYFP1ZzDaNSETSepQK7FnPzEzFzJCMGfZcZ0dRUC7jRJCSLh3nLeNMUiw99NpKSksQNuPNmuF9NDHdvlsO6K97XGvrBzdkvwQ5+fInPv3kBZ4/uOtWvdcPTIdoe62RumGdaNbis57bmEmw/EZeXrrlwNvtmEsu4CqrvpaIMbZUAwps4en5aDZ7LVG11H6+GtwC8T6OnDuXRDtiU96xNXofQyBELKHoqgEfcdJlmgd1ET3ovTeY6MENKAKG3lNwIJKM2/Dac5UXaYIaABalZMpAhpAnTVubEPF9zvOcZ672wL9gzLErMlCA3TOlKpCYayVCk5csKEYI6AB1SA+IQENiFdmhsQA16eABlDrAhpArQtoAKKNUk0JaIAbm25K4yRmpaxJ2AIaEI6nP3rpLuM14IuJy7dNGxBa1wIaAHLf1/a26Fopjy+gDUWsDPEHS+48e1PwOCH6flOETerysOqI/sGQNSGgATACmiaaZ7AAgPBJK1DYIVqvgAa02yghKIrCrrOqsUkpIapwEv4MEocv3oG5QmMp/mv/RS0BzWbX2ZuMZXdR0QVsGpPB7LsoEoKVbcLYEF0LB5WVV2G0ATOvxPO8xNUR0IbyzbaSGhfQANDuk61IC+WGBC09dBmf1Pidag7JEq2Daf8cwVdbS+r0ngBgpZQzM01ICNMyzLVORIOEhISEqYS726JnMz9M/svwgVs0gzOC8cn6p3fO5Jr00hgKHZceN2O1SbOXHJmWg9d+2Mt0QGjaN/VmxpC0iXTHKiNmaXna6JcaiCUHL+md5aou0RWrXls0eEs0ISQXwAcA5AAWUhRVWF9pEaM+BDQASUAbgCSgJSQkGjonrt4zSUADeKoFNADsPGPc4h41wcYTpbCzMDN5+j9+qB/NH6w4+2dZQAOok0WHniXqZYo7QogcwCcA8gBEAuhOCImsj7RISEhISEhI1CxdF+yo83vOWnYMzeas1X+gxFODdzVm6qoL6mue6CQAxRRFnaEo6gmAnwG0q6e0SEhISEhISEhINDAcrc30H1SP1JeI9gbAXsP1gnqbhISEhISEhISEhM4BoA2B+hLRQpP+aY1wJIQMIIQUEUKKSkulGFgJCQkJCQkJif8K3ZN86zsJOqkvEX0BAPvN+ADQmsOIoqgFFEUlUBSV4OrqWmeJowlw1p7EXUJCQkJCQkJCovZpGeam/6B6pL5E9G4AoYSQQEKIEkA3AH/XU1pE2TAmAz++klzfyZCQkJCQkJBg0S81EL+92hyvpAXiz8Et6js5ErWEmbzhrlYI1JOIpiiqAsAQACsBHAOwiKIo0+YBqmVSgjUTfw/JCKnHlEhISEhISEgAqtVYEwKcMLEgEk14y5wbyxu5ETWUKomaRiGwnHpDot5SR1HUMoqiwiiKCqYoalZ9pcMQ/NVhHe72FrV6n+RAp1q79vEZubV27YbIZy/F13cSJJ4isho1bJehRMPj+Viv+k6C0YzICtW5v02ke7Xv0b4pd46As3PyUVJYAIVM2KIYa6IAFrsem3B3W73HdE3wxWutgg26Z8uwug8r/a9jZkA+1ycNW+I3EFaOSMfhaTnolqgK4x6WqbsiEuODbk04vxcPSuH8XvCy+OI4kZ52Jt0TAGQEsDCTm3x+bTAgPQhzO8aYdO7rbcIwqaARlgxNxftdm2jtd7FRoq4W4ozzc8BH3ZuiIMaz1u8V6GJdrfN7pwQwfz9XDQGQEuxcrXSwebm5v+i+RQObV+vafxnh4u2S4AsvIzvJv7+WIrj96z6Joud0jvcx6h4NgR9fScbZOfmi+3UJsyhvVb3Vp0WA3vtEeOgXPPVNOktE5UV5VPt6vk61PwfuihFpzN/Px3rh+36qEEUPO+3ybm4mx5ic8Grd7z1WnRzhYQtCVCKoeLZwGTK1MyLjiat+qYGc30q5DN/2S9J5DaVchrc66W+HOsR54+cBzfBKWhBne2Mv09tlNtVp3591JEv0M4CFmRw25gqYyWUoKSzAqOwwrWP+GZKqd2lKRysl5zd/EnF7SzN81L0pACA70h3vdY1l9i0dlop5PeIEr0vHDBXEeGL1yHQURHMFnVhjr4/a/LAn5DdCl0TTRt0OzghB/7QgRHnbw9pce9FNG4FtxhLjY4+3OkbrPe79rk3xXKwXGtWwAAhy1RbM8f6O1bomu9FvyrL+jM+LwN9DWuBDddnTR7S3vUHHudiY6z1merso0X0J/o5oFS5s+flrcAsM0mM9ivV1QJKB3h0zhQzbxmcadCyg6tCI5Qf/O2fjLiBcaoo5HaJB9BhtOsQZP5MoAWGEkBAjssIwWqBO/KJXApYMTUNJYQHefK6x3vsMrqdwuRkvcMvg4AzxctWaVR6z9Fhtz4iIRkC1jHFJYQEGtzL8mekOiTGcmpWHCA87jM9ThSu42JojNdQFJ2fm4eMXtb/3Xs39dZZfQ5nfU9VW+TlxB+fTddAP/ZNRNCkLG15vhb4GdLCEOH75Hud3iJsN5/eOCZlwttY8SyNee7ZxTCscnNpG5z02j83A+tdb4d0uTdAsyBl8o6hnNbzT7PKxZGgqp72d3FZae47GSZon+tlkWGtu5Rftoy0sVo9Mx9JhqeidEoChrUMQ4KwRRjKialD57rPnYr1wcmYePusZj/ZNNVYrQgjyoj1RUliA4zNycXxGLlMRd05QidFmQc4IdbfFJzyx3dTPcPFVUliAsbkqS0SmiS7ugelBsFYaZ/leM6olxuSEY93oloIuuOMzchlBxG7Q2daUV9ICmf1iDU6sQD7RsCvhcHdbdE30Q0lhAU7PzsfbeqwVHeJ84GVvgc1jM/D7ayno2yIQPZL9mP3dk/y0zjk8LYfzO0PdQLcKd8XrbYStQbpmjCmalKUzjVUs6zzbgjKwZTBifBzg72TYbDTBrjZYPTIdAOBlb4HEAOHy9deQFpiY3whrRqWjTaQ75veME7Ro/tBfM3hXqdBUSTIZwbg84VhFJ2slhmWGwtVWW6i/1zVW41I20CNBGeG6GJgehC96iXuN5Drcj+znYzOpoJHB9xeje5KflqWMz7tduJ6bwRnBemch0iXM6XEicQIdisxG3LrtlbRATCpoJCjeAKBtjCfSQjVjUHRZv3URzOqAFs/K03u8qw1XNIa523Ks4sPVnsdezf3xYrI/eiT7Yf+UbJjxLGQuNkqOVV4mI1g6LLVGQsucrZXoHK/f6BDKE5J0GgekB+HM7HzYWagEiVIhQ0IAt4P5epswJAQ46e2IGdKJzo3yRGGHaLzdKZazfV7PeJQUFqBFiAtcbMwR4GKts4OmC75oDlO3G2Nzw7F1XGs4WSs5Vswf+ycjzF11zkvN/OHvbK3XQ+vrZMX1APKSyk779/2SsWlMhsHpZ1vSZTLCeR7+G6npwXWjssPw26uGe/oMDXepDdi6qSEiiWgTsbUQ7h3Rlp45HaIR6m6Lxl72mPp8Y4xuEw4/Zyumgtqo/tjeUoc0OFhprqdUyLRcVWwszOScj7+T2kWcGuIidoooQhbPV9ODMbt9NIawOgq63P/8UIZ0PXFj3QQs0CFuNhicEYIgVxusVAs0Nkq5DL8MaKZl3Yn2sceyYWn4qk8iuiZqhKqPoxWivO1gZ8G1SrOlEjsdXvYWWDYsjUk7+zi5jKBtjPDz26qv7+VgiW3jM+HrZIV4f0dMeS4SiaxGSsgCaGOugIedBSbkR+DUrDx82TsRb3eKwftdm3DCUfqr3ZQEqhlj+I0HAOydnK3XgkSxnio5SDskQ8ZqENgiYkhGCKY+p7GMdE7wgY36uaso4JcB2pWxu505vB0s8Up6EELcbLHg5QTkRnmCEII9PLHPjm3spSO8g2Z0dhh8HC1hYSbH7onca4W42aB9Ux/GpSwkaJv6qazw7PAqXRqa75EZn98IQa7aeUCjK+yG7pjyvSXGhB8tGaryeh2brj3OYXxeBE7rsIDyeT7WG/YmWh5fbRmMkWoLdBXvAX5/TbtMTCyIRP+0IMFvqWOcj5aYIoTg1Kw8WBnZIWd/H4a4gvnvnhCC0axOrLejymPY2NseSoUMs9pHw0H9zqa301jYiyZlY0QW1yLf2MseOY3Fwz6M0Y9ioU8WZppndLJWMnUS9z5EsE0pKSzAO51jMSA9CENaqzoA+pLE9qAK3YumW5If7K2qZ0XU1bm0t+ReO97fEdvGtcZrLYMFl4p2ZFmlezTTNmro8yQD3DoSUHmMaWNWaqgL/EyYFjczQlUnsL8hB957m5BvfCdbLFTonyGpGJYZqtWJGq4jTLVDU+M8WN/3S8aLLCNSSWEBvuotHuamC1M7WXWFJKJNhBIxcb3bpQlKCgsELY+AxtVLNwwOVmbo1dyfiVPjIxS3xifOzxElhQUmxcx+J3BfmYzgxWQ/mCvkGJ8XgaRAJ3zYrYmoBYJtOT40tQ1ahLjoNAA62xjfaFdRlGhDEOllh4xwN7ior0sL/iVD0/ASr+FhV1TsaykVMigVMgzPVHUc+INdhL7jTWMyOBWzLsSqgR0TMjEgPRhmchkIIeic4AsHKyVastzGAep8pdMgdC0na6Xexk+fVZa+fqSnHX5jhQCNzA5D7xaBrOMIiPpuVRQlmCc7J4hbxZ11hHnwBY2QuByaGSpasbItmaq0ah8TqLZsOLDEY5WBIpYfA7lmlHaHz9pcIWqNbuxlj5LCAq1vKUHAmi8WLuKhdiFbKuUcqyugyhtdlnA2JYUFCPew1avgxa6WF+XB3It9iWZBToj3N3yQ9ImZuaKeHjO5jBMScOBN3e53wPQxKzT85+0c74Pv+iUJxrO/3DygWveyt9Rdf2weq7FsipV5T3tL9f8WeKdLLOZ0UIWhGTpIsGO8D0ek6RtAH6q25irlMq13xQ8lrA750R6IUn8nSQHa5UkmoF68HCx1iq5mauOBg573Lgb7yken56BLgn7vgKWZHL++2pxTN9HjgfZNzsa8nipPBfsrfKGJ8WFXfNG8fnQrNA9yZkJErZVyVd0j4I19u1OMTg9DqAEDNNmkhrpgdntuOGRGhGme7QY+rlAS0aYS7mFavHCMugCbqd26hBBMaxfFVBZ8/hmaij8GmRbTbAgWChmnx8hnYMtgLBrYHIQQxPlxhaW3gyUm5EdwXD20hV5Xu5wcaPjAtI+6N8VXfRINsig5WClxZFoORrAaUTmvQmWni/1xxqlDXuL9nbD+9VboyXsnfAvE+12bGGV1MLYzzbZU8s8Vu5a+e+jTifQzVlEUp7EQEmX0ppoYv2nJsjTS16NXqTLEQstunPjhH0LnD8oIwZe9Ezgj7fmWVM41oBlXwC8HIW62WDkiXSu8g1/u+PB3N/VzxImZuRyxYIgFtiam5tI3O4LYt8d+Z3QDPL9nPL7ta9zc+uYKuU7PGy2K3u/aBPaWZjoHgEZ62qGxl3Bdyh7jwK5ThXKejsP0cVSJsrRQ12pbxPgdPEC/QKDfMZ3GIHWHmu0d8XJQvY8Z7aLg42iFtjFeRo1x4NMiWDudiwelYM+kLPw9pAU6xqk6Ex68fBiYHoRp7fTHvgsh9Grf6czyyBHVAFZ2vajvG6MZlR3GDECf3DYSa0a11Eq7obiow8c6xvnASql/7M3GMa1wbEYuEgOcmDZ+TE44Mx7I0VqpCfFit00ywvEw8J800MUatjxvVlWV5u9YXwco5DIhBIh1AAAVq0lEQVT8NKAZM+uXlY6xQhkRbgZ3vusayRL9jNIyzBUbx7Qy+rxPXozDH4NSmNg0fbjamhsV06yLkzPzBF29s9tH44NuTfQO1KIZ2joE60a3xNZxrRkravckP2YwCSBuqT84tY3ecA82jlZKZIQb3oO1NldwGmRdH2B+lMpq8mH3ppjdQdPABgrE6fHrFyHLIR/6HbRr4gUP++qPwqetv7SQm85rsPRVNrqEIqCx7LAPE4vhpe9FxxL3542MN4Zob3s8F+uFnwc0Y7YFuYiHS/Bhe1PMFVzhKeZubh3BtdLpGmVPoOlICLUz4R62WvG/QlYyzjUFrmOukOOrPomMUBLLLvapbRp7YMsbGZwZGIxlUkEklgxNFd1Pd575Fl4nlhfG0VqJksIC5EZ5iJYZNq3VVqkFvHhhoWemn5cO01g5Mh07JwgPAhV6ZbQl203t1QtwtuLUqdrhHCojiaOVGQo7mDaDEJ/iWXn4po/2TBH6vln+u3xB7Vbnj3sBuN93jI+DyTMyKXgCDlAZGZxtzBHjo+lwEcLtYI/Pb2TQYGIhdk7IxKz2mgGeztZKTueaQDWAlT2OxFDRNywzlHlvZnKZYDicoQS72mDJ0FQU6hh0zh6Y6s+K56WzRyzL+W3m8Rni8fzrX2+FPZOzOdtcRDy8hhg6XGzMkR7myowrqisMCd9r6Egiuhr4O1tjXo84LB0m3gDxsTZX1JgoNhalQqZV8dCVeLsm3hhroFXL2VqpFRM6p0M0cqP0u/IM7TzQVFZzrjp+LDf7cikhLigpLMDzsV56GxyheE19MJUmVFb7XRMyMSBd98AvITIj3GFrrkBv3ij2BANc5jNZMw/oe5W0OGe/c7GnpIsRHQYxqW2kQTGFQhBC8FH3poyrVbVN9b9YZ4yPu51w4/1WxxhMyI/gNL7sQYQvJvtBRlQx9LqgBYqhVhGFgIrePr611rZBrYI5niBrcwXWvd6K8y71ueV9HK0QwfOMZTUyfL5fpUIm6gkDNM88KjsMW8dpnsG/GgN+Pu0Rh81jM9BGR7wwDb9zZ2thxpnlZM2odGZlWaEBokwZUv/H9yYIlTEbcwX2TWmDVAHrMZ8pbSPxC6sDKIRCLjzORUgHPh/rhSltI3F0eg6Uai8A/VxDW4fg+IxcOLDigVuox8JUJz84aZIRHJ+Rhy4JwtMxsozDTAz4yhHaYU3G4GZrwek4M6Fr6v8FvWH1ZDmN8rbXGlTKJl8d0sIfIE97lqxE2hpd9TO73mkuMJ7l7U4x+FzHYGdAf6y7XEYwscC4WUF6NvPjjJcBgF0iHVwh2GGCAOBYzRj6+qD6c4H9x8mrwRiwmqSRpx2OXb6r97jaqoaqO09ziJsNiq/d12s9NeQ6bOjrNQsybmEbfn1tyHujK365WgW42VlgQn4jLNh0xqh7u9uZ4xBLBDJWYD0Cc8f4THjYW2DlkSvYfOo6KADvdI7FiiNX9N5Tn1Zkh37UJPRgIbFpCvdPyRbcvnJEOm49LNfa7mitxIB0roeF3SDPbh+tFbvHhxCNiDG03VYqZMBjzW9zhYyJXQU0XoVmQc6inplp7Rpj9tJj6JcaiFVHrxp2Y1aaawP6+Q0Zq6ELCzM5fA2cDYYdf8/G0kyOR+WV8HKwRHmljnAc9S76/JoWX32r4YXhC3oAnDCMsvIqzj5CCCzM5Cgrr2S2vdTMH53ifBhLe00hlDY+vVIC0Is1/3xNkxjghN4pAYLGB0PDOeoauuPDDxkZkB4EuYygRzNh6ytdL+map7tLgo9WfaWQEWaGLiFqY82EVuGu2HCiFLYWZujdIhCPK6owZ/lxAFwPlT78nazQPckXP+06DwDYN6UNAsYtrfkE1yKSJfoZZflw0927NQF/lLqxvNslFj6OltWeG5nN0ek5zOCGD7sZFy/ItgQEOFsJTq3GJy/KE71TAkyevoyZlYNvBVf/r69ypCtxwhK8HeN98LnIoj5syzlzLz3x11WGjsgzkFdbBmN6u8ZMo8C3+DiIzCThYKXUO7CWTrMxlTxNlYgVUww3dfmg53bn39OQywS72uCL3okGhUfwqS15ISZoaxOx+HumDLJ2CCWL3kYxeaj6n54BgS/GxGKqDWXVyHTROf1/7J/MWM0BzTPwx5vog+05o4AaF9CAJm3a4wyM88qYeHcAKmE59fnG8BKYbaOhxvCGuttibscYrcXVLMzkGJwRImrFnvZ8Y/RtEYiBrA4DPxzNXCFnxijQr1/Ii/omyzpMl3P+YjSAKvRkaGvj5mcvKSxgPF10+ga21BgqjCkXMhnBnA4xaN/UmzPV6dOEZImWqBVeaxWM/GgP9P16N06XPjD6/BgfB2x5Q9v9XR2slAq83iYcvZoHVKvR2WDgXKBKhQxTnzdtoA2gCpGYJDDpPhPqICAYcht7YMWRKxxXIlOl6dE9tGWbXQcSETlGW/MseYPfLMxkWtYzY1AqZJwZD0LdbDClbSSei/UyqOOiCwdLM9x6WG7wTBxs4vwc8VIzf4PDceiGxNfJCnM6RGtNP9muiTc2n7qOYAPiM/nJNST5utqxbom+OmPAJ+RHYNmhK9h//rbodWtLQgvmMS/+nkbG2s4P//m6TyLuPOJ6JgJcrOBkrWRE4c4JmaAolSVvQHoQvt5agieVVUZPqccnzN2WmbOYT0oIf/YYVcKtzRWwUsrx8Eml0Gla75sjok3/3PSgShtfyNFeoiYmLtcthqGhWzSGdmiNobr1F40pC4k525hjCi804q/BLbCl+DpjTGKv3WAml+GN3Ahksba52Jjj+v3HzEB5QFVWxELtxuSIh3Cmh7li08lSwX3dk/xAURS6CcxCxs6VT3vECX4Lk9tGcmYDeU9g5eGnBUlES9Qa/s7WWDu6lV73jCnzW5uKXEZMHpkNwODBl7oYmRWGK3cfmXy+rnjhGF97rDhyBa0iNCECtCWCb9G0tVDgXlkF85uO47W3NNM7CMbOwgwT8iOQHcmNad04JgNX7pQZ9Ty6IIRUy13Ohon5NlJF0wOo+Cvb8fmmbxLOlN5XncPaLjTdZad4H3SM8641a54ugVHYUXywXJCLNQakB2NAerDgd6urA1cTzHghCn/su8i9p/p/XZZo+nnp/1uxBiM72yhx8fYj2JibYS9rMBZ7EOqE/EaQEYL5G0/XyIqnhiJjvc81o1ri3I2HnP26Soe9pRnuPCqvNa+AWF672VlgydDUag3Q08cLTfQvBV4bluglQ1Ox8eR1pIe6CK6GW9cEudow44/OzM7XCkXiL4KydlRL3C3TDmszhr+HtMD9sgrEBzii47xtOHxROyxULiN4SWR6R3bVky8S7ipkFX9aqf9SIlFr9Gzmpzd20Zg2nHZDyWtwLXtTB6PVBzWV1uFZ1ZvH1tZcJYplhOCzl+IFl5JmW5BnvBCFaG97pARzB6SsHtkSF25pGu1gV2tMaRuJtrGeBtmE+LHGgGoVTkOXtuanp7ahGyBdoiMvygPLD3NjxgMNnCmkZZgrM23eqOwwvPJdETPHtxCGCmh2cl1tzbUWmRDCFCtd0aQsvVZYF2tzPBfrhd61FAcrJGA1Yo6bb5qwJgqhbjYYmB4kOF3nwpcTsO74Nb2d57E54RiWGWLQ1GU1BTs8xsvBUitsgRZyQwSWRK9trwCN0PV1DUQ1lWC1WHzz/+3de4xU5RnH8e+z3BbkslC7oAsoBFwFKqBEUWuLQnVVFGxtxJpIlJaYNoqmjUgwJSW9eUnRXrQx2qKtFVLwHm/U2tSkXZStRK2ILmAFofWCiJcIrj7947yzDLszu3t2Lufs7u+TnOyc97zLnJ2Hd+aZc97LuROY34H5t0vRm2Nc9SDGVcebE7lcOtKXf8iAPgUvcJM9A8uay0/mo31NbdRuLe1T0hWbkuhu7Mdzcw+Y2vjDrzFl+brY/96iWeMxI++o7fbEHcxXLOdPreHRF3cl8tylcMtFU1jb8CYTDx/c6sMsV344uLIP386xHPSIIZUHJRbZV30/DG+cpXo7fOaa0zo9HVZn/XHBiax+bntzf+VcMgsfbNy+h88+/5y9nzQ1z7Max6wJw9n2s+J+QZw6uor7v3tKxyp3InAt43H1rKNYv+3dg8oqKqx58YZyOdBto0V51kIvZsaSPKu6VQ+uzHnbudXzVFhZE2jInuUmdyrct3dF3i/v2V8iSuFAN7Dy9H8fPrgy1oWKnpasJaHl6sjl9POvf4k/PftGIs8dh5LoHqhqQF8GV/Zm7ydNefu85jKosk/sKXCyrcwxT2o5rLhwSpfuc9VS9aDKVrfxWir086VUH8wZHZ2ZoZhqRwxq1ecwn2L39yxM/FhkEs9MH8nOiO6YFHbXpBiaV0VsUZ75L17OQY7FdmDQb/zfbf5yUcwTylKuK93SPRSrPzlEy4zPO2F0h778Jk1JtJRNUt9oe5LmkfMF/juZD/f+Zb4yJ/nFiWmm7uK6Wo4aPojVG7aztmFHKU6r6K475xiezxrUuOLCKdzxzNaDBktB9lSPXVfzHfpO/BHZs+6UQuYCSxf+jiJl9MgVp1K/9d32K7ajK3XxBCXRPdYNF0zmhideYWCeFd2KadXC6TS1MY+rFM8p4w7lpidfPWhJ684Y2K83i+uO5syJrRftiGZD0K3Ucovzmmeq9u5lTB5VxeRRVe3Oh50WLbse1VT1Z9m5rWe5+en5k1j+8Msd6iOeVoUkwqUe5JmvL7pILuOqB5Z0sGlaFZRBmdmNwLnAfmALcKm77zGzI4FNwOZQtd7dLw+/czywEugPPAoscrXSsqubNIK6Se2vFlYM03OssCSlMXX00KJ9k8/XZSTqg1qUp5AOmDJqKJecdATfydGvPZ/Taqt5cOPOVisZdid1kw7r0CqpaVY7IhrE1l73rFy+efxIbv1b6WYTKeAiuUiPUWjrWwcscfcmM7seWAIsDse2uHuujqi3AQuBeqIkug54rMDzEJESq7Co76Y+VMurV4WxfE7b0+u1NHdqDbMmDC/rdG0S35D+fTr9pfcHZ9Ry5czxJesmp7tNUmy//tZUPvwk3mwfaVfQXGXu/qS7Z16ReqDNaRvM7DBgsLv/M1x9vhuYW8g5iEh5NPdB1Y2jLkEJdPdWUWElHWcyozbqElbMVWOlayt0RqXZxx7eJQYLxlHMd9nLgNVZ+2PM7HlgL3Cduz8D1ADZo1t2hLKczGwh0VVrRo/uXi+8SFdz5/xprPzH66ldbldEimdGbTWNPzmreZnptBh2SF92f7Q/6dPocVYtnM7YNua976naTaLN7C9Ars6zS939wVBnKdAE3BOO7QJGu/u7oQ/0A2Y2kdwDzPNe1nL324HbAaZNm6bLXyIJmlFbfdBKcCLSvaUtgQZ4/KpT2bmneKuiSsdobFNu7SbR7j6rreNmNh+YDczMDBB0933AvvC4wcy2AEcRXXnO7vIxEtjZuVMXERGRnqR6UCXVgzq2KqpIqRU6O0cd0UDCr7r7x1nlXwR2u/tnZjaWaMb+re6+28w+MLPpwHrgEuBXHXmuhoaGd8zsP4WcbycdCryTwPNKfopJ+igm6aOYpI9ikj6KSfqkISZHdKSSFTJIyMwagX5AZobtene/3My+ASwn6uLxGbDM3R8OvzONA1PcPQZckeYp7sxsg7tPS/o85ADFJH0Uk/RRTNJHMUkfxSR9ulJMCroS7e7j8pSvBdbmObYBiDdfk4iIiIhIiqRv1ICIiIiISMopiW7f7UmfgLSimKSPYpI+ikn6KCbpo5ikT5eJSUF9okVEREREeiJdiRYRERERiUlJtIiIiIhITEqi22BmdWa22cwazezapM+nJzCzUWb2tJltMrN/m9miUD7MzNaZ2Wvh59BQbmb2yxCjF8zsuGT/gu7LzHqZ2fNm9kjYH2Nm60NMVptZ31DeL+w3huNHJnne3ZWZVZnZGjN7JbSXk9ROkmVmV4f3rZfM7F4zq1Q7KT8z+52ZvWVmL2WVxW4bZjY/1H8tLCwnnZQnJjeG968XzOx+M6vKOrYkxGSzmZ2ZVZ6qvExJdB5m1gv4DXAWMAG4yMwmJHtWPUIT8H13PwaYDnwvvO7XAk+5+3jgqbAPUXzGh20hcFv5T7nHWARsytq/HlgRYvIesCCULwDeC1Ngrgj1pPhuAR5396OByUSxUTtJiJnVAFcC09x9EtALmIfaSRJWAnUtymK1DTMbBiwDTgROAJZlEm/plJW0jsk6YJK7Hwu8CiwBCJ/584CJ4XduDRdxUpeXKYnO7wSg0d23uvt+YBUwJ+Fz6vbcfZe7/ys8/oAoMagheu3vCtXuAuaGx3OAuz1SD1SZ2WFlPu1uz8xGAucAd4R9A04H1oQqLWOSidUaYGaoL0ViZoOBrwB3Arj7fnffg9pJ0noD/c2sNzAA2IXaSdm5+9+B3S2K47aNM4F17r7b3d8jSvhaJoHSQbli4u5PuntT2K0HRobHc4BV7r7P3bcBjUQ5WeryMiXR+dUA27P2d4QyKZNwe3Mq0RLxw919F0SJNlAdqilO5XEzcA3wedj/ArAn6w0w+3Vvjkk4/n6oL8UzFngb+H3oYnOHmR2C2kli3P1N4CbgDaLk+X2gAbWTtIjbNtRmyusyolWsoQvFREl0frmuCGg+wDIxs4FEq15e5e5726qao0xxKiIzmw285e4N2cU5qnoHjklx9AaOA25z96nARxy4PZ2LYlJi4Vb/HGAMcDhwCNFt55bUTtIlXxwUnzIxs6VEXTnvyRTlqJbKmCiJzm8HMCprfySwM6Fz6VHMrA9RAn2Pu98Xiv+Xuf0cfr4VyhWn0jsFOM/MXie6fXY60ZXpqnDbGg5+3ZtjEo4PofWtVSnMDmCHu68P+2uIkmq1k+TMAra5+9vu/ilwH3AyaidpEbdtqM2UQRiwORu42A8sXNJlYqIkOr/ngPFhZHVfok7uDyV8Tt1e6BN4J7DJ3X+RdeghIDM6ej7wYFb5JWGE9XTg/cwtOykOd1/i7iPd/UiidvBXd78YeBq4IFRrGZNMrC4I9XUFp4jc/b/AdjOrDUUzgZdRO0nSG8B0MxsQ3scyMVE7SYe4beMJ4AwzGxruMpwRyqRIzKwOWAyc5+4fZx16CJgXZrAZQzTo81nSmJe5u7Y8G3A20YjRLcDSpM+nJ2zAl4luz7wAbAzb2UR9BZ8CXgs/h4X6RjRadwvwItHI+MT/ju66ATOAR8LjsURvbI3An4F+obwy7DeG42OTPu/uuAFTgA2hrTwADFU7STwmPwJeAV4C/gD0UztJJA73EvVL/5To6uWCzrQNon66jWG7NOm/qytveWLSSNTHOfNZ/9us+ktDTDYDZ2WVpyov07LfIiIiIiIxqTuHiIiIiEhMSqJFRERERGJSEi0iIiIiEpOSaBERERGRmJREi4iIiIjEpCRaRERERCQmJdEiIiIiIjH9HzH2lEky+TsKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x360 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "eeg_data = EDF(P_train[0])\n",
    "fig, axs = plt.subplots(5, 1)\n",
    "fig.set_size_inches(12, 5)\n",
    "for n, ax in enumerate(axs):\n",
    "    time_base = np.arange(0, eeg_data.header.num_records, 1/eeg_data.header.sample_rate[n])\n",
    "    ax.plot(time_base, eeg_data.data[n])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "eeg_data = EDF(P_train[1])\n",
    "fig, axs = plt.subplots(5, 1)\n",
    "fig.set_size_inches(12, 5)\n",
    "for n, ax in enumerate(axs):\n",
    "    time_base = np.arange(0, eeg_data.header.num_records, 1/eeg_data.header.sample_rate[n])\n",
    "    ax.plot(time_base, eeg_data.data[n])\n",
    "    ax.set_ylim(-300, 300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "eeg_data = EDF(P_test[0])\n",
    "fig, axs = plt.subplots(5, 1)\n",
    "fig.set_size_inches(12, 5)\n",
    "for n, ax in enumerate(axs):\n",
    "    time_base = np.arange(0, eeg_data.header.num_records, 1/eeg_data.header.sample_rate[n])\n",
    "    ax.plot(time_base, eeg_data.data[n])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "eeg_data = EDF(P_train[50])\n",
    "fig, axs = plt.subplots(5, 1)\n",
    "fig.set_size_inches(12, 5)\n",
    "for n, ax in enumerate(axs):\n",
    "    time_base = np.arange(0, eeg_data.header.num_records, 1/eeg_data.header.sample_rate[n])\n",
    "    ax.plot(time_base, eeg_data.data[n])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hide_input": true
   },
   "source": [
    "## Finding common channels across the dataset\n",
    "At least for the first pass of analysis, do not deal with missing data / channel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "deletable": false,
    "editable": false,
    "hide_input": true,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [],
   "source": [
    "def find_common_channels(P):\n",
    "    eeg_data = EDF(P[0], header_only=True)\n",
    "    available_channels = [ll.replace(\"EEG \",\"\").replace(\"-REF\",\"\").replace(\"-LE\",\"\") \\\n",
    "                          for ll in eeg_data.header.labels]\n",
    "\n",
    "    l = len(P)\n",
    "    printProgressBar(0, l, prefix = 'Progress:', suffix = 'Complete', length = 50)\n",
    "    for n, p in enumerate(P):\n",
    "        printProgressBar(n+1, l, prefix = 'Progress:', suffix = 'Complete', length = 50)\n",
    "        eeg_data = EDF(p, header_only=True)\n",
    "        labels = [ll.replace(\"EEG \",\"\").replace(\"-REF\",\"\").replace(\"-LE\",\"\") \\\n",
    "                          for ll in eeg_data.header.labels]\n",
    "        available_channels = list(set(available_channels) & set(labels))\n",
    "        \n",
    "    print(len(available_channels), 'channels')\n",
    "    print(available_channels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "deletable": false,
    "editable": false,
    "hide_input": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Progress: |██████████████████████████████████████████████████| 100.0% Complete\n",
      "19 channels\n",
      "['T4', 'O1', 'F8', 'FZ', 'P4', 'F4', 'F3', 'FP2', 'O2', 'T6', 'CZ', 'PZ', 'F7', 'C3', 'P3', 'T5', 'C4', 'T3', 'FP1']\n"
     ]
    }
   ],
   "source": [
    "find_common_channels(P)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Progress: |██████████████████████████████████████████████████| 100.0% Complete\n",
      "19 channels\n",
      "['T4', 'O1', 'F8', 'FZ', 'P4', 'F4', 'F3', 'FP2', 'O2', 'T6', 'CZ', 'PZ', 'F7', 'C3', 'P3', 'T5', 'C4', 'T3', 'FP1']\n"
     ]
    }
   ],
   "source": [
    "find_common_channels(P_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Progress: |██████████████████████████████████████████████████| 100.0% Complete\n",
      "19 channels\n",
      "['T4', 'O1', 'F8', 'FZ', 'P4', 'F4', 'F3', 'FP2', 'O2', 'T6', 'CZ', 'PZ', 'F7', 'C3', 'P3', 'T5', 'C4', 'T3', 'FP1']\n"
     ]
    }
   ],
   "source": [
    "find_common_channels(P_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion: should only use the following 19 channels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [],
   "source": [
    "channels = ['T4', 'O1', 'F8', 'FZ', 'P4', 'F4', 'F3', 'FP2', 'O2', 'T6', 'CZ', 'PZ', 'F7', 'C3', 'P3', \n",
    "            'T5', 'C4', 'T3', 'FP1']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find out the sample rates for the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "deletable": false,
    "editable": false,
    "hide_input": true,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [],
   "source": [
    "def find_sample_rate(P, channels):\n",
    "    sample_rates = [[]] * len(P)\n",
    "    unique_rates = [[]] * len(P)\n",
    "\n",
    "    l = len(P)\n",
    "    printProgressBar(0, l, prefix = 'Progress:', suffix = 'Complete', length = 50)\n",
    "    for n, p in enumerate(P):\n",
    "        printProgressBar(n+1, l, prefix = 'Progress:', suffix = 'Complete', length = 50)\n",
    "        eeg_data = EDF(p, header_only=True)\n",
    "        labels = [ll.replace(\"EEG \",\"\").replace(\"-REF\",\"\").replace(\"-LE\",\"\") \\\n",
    "                          for ll in eeg_data.header.labels]\n",
    "        current_sample_rates = [eeg_data.header.sample_rate[labels.index(c)] for c in channels]\n",
    "        unique_rates[n] = len(set(current_sample_rates)) > 1\n",
    "        sample_rates[n] = current_sample_rates[0]\n",
    "    \n",
    "    sample_rates_unqiue, counts = np.unique(sample_rates, return_counts=True)\n",
    "    print('rates: ', np.mean(sample_rates), '±', np.std(sample_rates))\n",
    "    for sru, cc in zip(sample_rates_unqiue, counts):\n",
    "        print(sru, ':', cc)\n",
    "    print('All has unique rates:', all(sample_rates))\n",
    "                                \n",
    "    return sample_rates, unique_rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Progress: |██████████████████████████████████████████████████| 100.0% Complete\n",
      "rates:  286.63004032258067 ± 66.20304349912291\n",
      "250 : 605\n",
      "256 : 966\n",
      "400 : 369\n",
      "512 : 44\n",
      "All has unique rates: True\n"
     ]
    }
   ],
   "source": [
    "sample_rates, _ = find_sample_rate(P_train, channels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Progress: |██████████████████████████████████████████████████| 100.0% Complete\n",
      "rates:  276.5310957551826 ± 60.40768266421519\n",
      "250 : 139\n",
      "256 : 751\n",
      "400 : 88\n",
      "512 : 35\n",
      "All has unique rates: True\n"
     ]
    }
   ],
   "source": [
    "sample_rates, _ = find_sample_rate(P_test, channels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion: resample everything to 256 samples per second, since most of the data has that sampling rate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resampling example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(77600,)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eeg_data = EDF(P_train[4])\n",
    "wdata = eeg_data.data[0]\n",
    "wdata.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [],
   "source": [
    "warnings.filterwarnings(\"ignore\")\n",
    "time_base = np.arange(0, eeg_data.header.num_records, 1/eeg_data.header.sample_rate[0])\n",
    "wdata2, time_base2 = signal.resample(wdata, eeg_data.header.num_records * 256, time_base)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "77600\n",
      "49664\n"
     ]
    }
   ],
   "source": [
    "print(len(wdata))\n",
    "print(len(wdata2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "deletable": false,
    "editable": false,
    "hide_input": true,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12, 5))\n",
    "plt.subplot(4,1,1)\n",
    "plt.plot(time_base, wdata)\n",
    "plt.title('original vs. time')\n",
    "plt.subplot(4,1,2)\n",
    "plt.plot(time_base2, wdata2, color=tableau10[1])\n",
    "plt.title('resampled vs. time')\n",
    "plt.subplot(4, 1,3)\n",
    "plt.plot(wdata)\n",
    "plt.title('original vs. index')\n",
    "plt.subplot(4, 1,4)\n",
    "plt.plot(wdata2, color=tableau10[1])\n",
    "plt.title('resampled vs. index')\n",
    "plt.subplots_adjust(hspace=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Converting each of the avaiable channels into a spectrogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "deletable": false,
    "editable": false,
    "hide_input": true,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eeg_data = EDF(P_train[13])    \n",
    "c = channels[0]\n",
    "labels = [ll.replace(\"EEG \", \"\").replace(\"-LE\", \"\").replace(\"-REF\",\"\") for ll in eeg_data.header.labels]\n",
    "index = labels.index(c)\n",
    "wdata = eeg_data.data[index]\n",
    "wdata = signal.resample(wdata, eeg_data.header.num_records * 256)\n",
    "fs = 256\n",
    "frequencies, times, spectrogram = signal.spectrogram(wdata, fs)\n",
    "plt.pcolormesh(times, frequencies, spectrogram, cmap='jet')\n",
    "plt.xlabel('Time [Sec]')\n",
    "_ = plt.ylabel('Frequency [Hz]')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion: strong 60Hz artifacts. Need to turn on the notch filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "deletable": false,
    "editable": false,
    "hide_input": true,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def EEG2Spectrogram(eeg_data, channels=None, sample_rate=256, window='hann', T=1, ts=0.5):\n",
    "    \"\"\"\n",
    "    eeg_data\n",
    "    channels: list of subset of channels \n",
    "    sample_rate: samples per second.\n",
    "    window: window type. Default 'hann'\n",
    "    T: window size (sec). Default 1 second\n",
    "    ts: time resolution (sec). Deafult 0.5 second\n",
    "    \"\"\"\n",
    "    warnings.filterwarnings(\"ignore\")\n",
    "    if channels is None:\n",
    "        channels = eeg_data.header.labels\n",
    "    \n",
    "    \n",
    "    labels = [ll.replace(\"EEG \", \"\").replace(\"-LE\", \"\").replace(\"-REF\",\"\") for ll in eeg_data.header.labels]\n",
    "    for n, c in enumerate(channels):\n",
    "        index = labels.index(c)\n",
    "        wdata = eeg_data.data[index]\n",
    "        # resample if necessary\n",
    "        if sample_rate is not None:\n",
    "            wdata = signal.resample(wdata, eeg_data.header.num_records * sample_rate)\n",
    "            fs = sample_rate\n",
    "        else:\n",
    "            fs = eeg_data.header.sample_rate[index]\n",
    "            \n",
    "        # Apply the notch filter to filter out 60 Hz artifacts\n",
    "        b, a = signal.iirnotch(60 / (fs/2), 30)\n",
    "        wdata = signal.filtfilt(b, a, wdata)\n",
    "\n",
    "        # make spectrogram\n",
    "        frequencies, times, Sxx = signal.spectrogram(wdata, fs, window=window,\n",
    "                                                     nperseg=int(fs*T), noverlap=int(fs*(T-ts)))\n",
    "        \n",
    "        # Upon first iteration of running, initialize the spectrogram matrix\n",
    "        if n == 0: \n",
    "            spectrogram = np.empty((len(channels), len(frequencies), len(times)))\n",
    "        \n",
    "        spectrogram[n, :, :] = Sxx\n",
    "            \n",
    "    return frequencies, times, spectrogram\n",
    "\n",
    "eeg_data = EDF(P_train[13])    \n",
    "frequencies, times, spectrogram = EEG2Spectrogram(eeg_data, channels=channels)\n",
    "plt.pcolormesh(times, frequencies, spectrogram[0], cmap='jet')\n",
    "plt.xlabel('Time [Sec]')\n",
    "_ = plt.ylabel('Frequency [Hz]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [],
   "source": [
    "# Loop and write the spectrograms into files later\n",
    "for p in P:\n",
    "    frequencies, times, spectrogram = EEG2Spectrogram(eeg_data, channels=channels)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Explore Excel label file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Index</th>\n",
       "      <th>File No.</th>\n",
       "      <th>Patient</th>\n",
       "      <th>Session</th>\n",
       "      <th>File</th>\n",
       "      <th>EEG Type</th>\n",
       "      <th>EEG SubType</th>\n",
       "      <th>LTM -or- Routine</th>\n",
       "      <th>Normal/Abnormal</th>\n",
       "      <th>No. Seizures/File</th>\n",
       "      <th>No. Seizures/Session</th>\n",
       "      <th>Filename</th>\n",
       "      <th>Seizure Start Time</th>\n",
       "      <th>Seizure End Time</th>\n",
       "      <th>Seizure Type</th>\n",
       "      <th>Seizure Duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>s003</td>\n",
       "      <td>t000</td>\n",
       "      <td>Outpatient</td>\n",
       "      <td>Outpatient</td>\n",
       "      <td>Routine</td>\n",
       "      <td>Abnormal</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>./edf/train/01_tcp_ar/000/00000077/s003_2010_0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>s005</td>\n",
       "      <td>t000</td>\n",
       "      <td>Outpatient</td>\n",
       "      <td>Outpatient</td>\n",
       "      <td>Routine</td>\n",
       "      <td>Abnormal</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>./edf/train/01_tcp_ar/002/00000254/s005_2010_1...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>s006</td>\n",
       "      <td>t001</td>\n",
       "      <td>Outpatient</td>\n",
       "      <td>Outpatient</td>\n",
       "      <td>Routine</td>\n",
       "      <td>Abnormal</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>./edf/train/01_tcp_ar/002/00000254/s006_2011_0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>s007</td>\n",
       "      <td>t000</td>\n",
       "      <td>Inpatient</td>\n",
       "      <td>General</td>\n",
       "      <td>Routine</td>\n",
       "      <td>Abnormal</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>./edf/train/01_tcp_ar/002/00000254/s007_2013_0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>272.0</td>\n",
       "      <td>s007</td>\n",
       "      <td>t000</td>\n",
       "      <td>ICU</td>\n",
       "      <td>BURN</td>\n",
       "      <td>LTM</td>\n",
       "      <td>Abnormal</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>./edf/train/01_tcp_ar/002/00000272/s007_2003_0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Index  File No.  Patient Session  File    EEG Type EEG SubType  \\\n",
       "1     1       1.0     77.0    s003  t000  Outpatient  Outpatient   \n",
       "2     2       2.0    254.0    s005  t000  Outpatient  Outpatient   \n",
       "3     3       3.0    254.0    s006  t001  Outpatient  Outpatient   \n",
       "4     4       4.0    254.0    s007  t000   Inpatient     General   \n",
       "5     5       5.0    272.0    s007  t000         ICU        BURN   \n",
       "\n",
       "  LTM -or- Routine Normal/Abnormal  No. Seizures/File  No. Seizures/Session  \\\n",
       "1          Routine        Abnormal                0.0                   0.0   \n",
       "2          Routine        Abnormal                0.0                   0.0   \n",
       "3          Routine        Abnormal                0.0                   0.0   \n",
       "4          Routine        Abnormal                0.0                   0.0   \n",
       "5              LTM        Abnormal                0.0                   0.0   \n",
       "\n",
       "                                            Filename  Seizure Start Time  \\\n",
       "1  ./edf/train/01_tcp_ar/000/00000077/s003_2010_0...                 NaN   \n",
       "2  ./edf/train/01_tcp_ar/002/00000254/s005_2010_1...                 NaN   \n",
       "3  ./edf/train/01_tcp_ar/002/00000254/s006_2011_0...                 NaN   \n",
       "4  ./edf/train/01_tcp_ar/002/00000254/s007_2013_0...                 NaN   \n",
       "5  ./edf/train/01_tcp_ar/002/00000272/s007_2003_0...                 NaN   \n",
       "\n",
       "   Seizure End Time Seizure Type  Seizure Duration  \n",
       "1               NaN          NaN               NaN  \n",
       "2               NaN          NaN               NaN  \n",
       "3               NaN          NaN               NaN  \n",
       "4               NaN          NaN               NaN  \n",
       "5               NaN          NaN               NaN  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "excelsheet = '/Volumes/Storage/TUH_EEG/_DOCS/seizures_v31r.xlsx'\n",
    "df_train = pd.read_excel(excelsheet, 'train').iloc[1:2895, :15]\n",
    "df_train = df_train.rename(columns={'Seizure Time': 'Seizure Start Time', 'Unnamed: 13': 'Seizure End Time'})\n",
    "df_train.columns = [c.replace(\"\\n\", \"\") for c in df_train.columns]\n",
    "df_train['Seizure Start Time'] = df_train['Seizure Start Time'].astype(np.float64)\n",
    "df_train['Seizure End Time'] = df_train['Seizure End Time'].astype(np.float64)\n",
    "df_train['Seizure Duration'] = df_train['Seizure End Time'] - df_train['Seizure Start Time']\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "deletable": false,
    "editable": false,
    "hide_input": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Index</th>\n",
       "      <th>File No.</th>\n",
       "      <th>Patient</th>\n",
       "      <th>Session</th>\n",
       "      <th>File</th>\n",
       "      <th>EEG Type</th>\n",
       "      <th>EEG SubType</th>\n",
       "      <th>LTM -or- Routine</th>\n",
       "      <th>Normal/Abnormal</th>\n",
       "      <th>No. Seizures/File</th>\n",
       "      <th>No. Seizures/Session</th>\n",
       "      <th>Filename</th>\n",
       "      <th>Seizure Start Time</th>\n",
       "      <th>Seizure End Time</th>\n",
       "      <th>Seizure Type</th>\n",
       "      <th>Seizure Duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>258.0</td>\n",
       "      <td>s002</td>\n",
       "      <td>t000</td>\n",
       "      <td>Outpatient</td>\n",
       "      <td>Outpatient</td>\n",
       "      <td>LTM</td>\n",
       "      <td>Abnormal</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>./edf/dev_test/01_tcp_ar/002/00000258/s002_200...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>t002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>./edf/dev_test/01_tcp_ar/002/00000258/s002_200...</td>\n",
       "      <td>15.9375</td>\n",
       "      <td>136.4375</td>\n",
       "      <td>TCSZ</td>\n",
       "      <td>120.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>258.0</td>\n",
       "      <td>s003</td>\n",
       "      <td>t000</td>\n",
       "      <td>Outpatient</td>\n",
       "      <td>Outpatient</td>\n",
       "      <td>LTM</td>\n",
       "      <td>Abnormal</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>./edf/dev_test/01_tcp_ar/002/00000258/s003_200...</td>\n",
       "      <td>13.0225</td>\n",
       "      <td>143.3225</td>\n",
       "      <td>TCSZ</td>\n",
       "      <td>130.3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>t001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>./edf/dev_test/01_tcp_ar/002/00000258/s003_200...</td>\n",
       "      <td>45.9300</td>\n",
       "      <td>162.2325</td>\n",
       "      <td>TCSZ</td>\n",
       "      <td>116.3025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>t002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>./edf/dev_test/01_tcp_ar/002/00000258/s003_200...</td>\n",
       "      <td>8.8275</td>\n",
       "      <td>202.0000</td>\n",
       "      <td>TCSZ</td>\n",
       "      <td>193.1725</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Index  File No.  Patient Session  File    EEG Type EEG SubType  \\\n",
       "1     1       1.0    258.0    s002  t000  Outpatient  Outpatient   \n",
       "2     2       2.0      NaN     NaN  t002         NaN         NaN   \n",
       "3     3       3.0    258.0    s003  t000  Outpatient  Outpatient   \n",
       "4     4       4.0      NaN     NaN  t001         NaN         NaN   \n",
       "5     5       5.0      NaN     NaN  t002         NaN         NaN   \n",
       "\n",
       "  LTM -or- Routine Normal/Abnormal  No. Seizures/File  No. Seizures/Session  \\\n",
       "1              LTM        Abnormal                0.0                   1.0   \n",
       "2              NaN             NaN                1.0                   NaN   \n",
       "3              LTM        Abnormal                1.0                   6.0   \n",
       "4              NaN             NaN                1.0                   NaN   \n",
       "5              NaN             NaN                1.0                   NaN   \n",
       "\n",
       "                                            Filename  Seizure Start Time  \\\n",
       "1  ./edf/dev_test/01_tcp_ar/002/00000258/s002_200...                 NaN   \n",
       "2  ./edf/dev_test/01_tcp_ar/002/00000258/s002_200...             15.9375   \n",
       "3  ./edf/dev_test/01_tcp_ar/002/00000258/s003_200...             13.0225   \n",
       "4  ./edf/dev_test/01_tcp_ar/002/00000258/s003_200...             45.9300   \n",
       "5  ./edf/dev_test/01_tcp_ar/002/00000258/s003_200...              8.8275   \n",
       "\n",
       "   Seizure End Time Seizure Type  Seizure Duration  \n",
       "1               NaN          NaN               NaN  \n",
       "2          136.4375         TCSZ          120.5000  \n",
       "3          143.3225         TCSZ          130.3000  \n",
       "4          162.2325         TCSZ          116.3025  \n",
       "5          202.0000         TCSZ          193.1725  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = pd.read_excel(excelsheet, 'dev_test').iloc[1:1411, :15]\n",
    "df_test = df_test.rename(columns={'Seizure Time':'Seizure Start Time', 'Unnamed: 13': 'Seizure End Time'})\n",
    "df_test.columns = [c.replace(\"\\n\", \"\") for c in df_test.columns]\n",
    "df_test['Seizure Start Time'] = df_test['Seizure Start Time'].astype(np.float64)\n",
    "df_test['Seizure End Time'] = df_test['Seizure End Time'].astype(np.float64)\n",
    "df_test['Seizure Duration'] = df_test['Seizure End Time'] - df_test['Seizure Start Time']\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "deletable": false,
    "editable": false,
    "hide_input": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Progress: |██████████████████████████████████████████████████| 100.0% Complete\n",
      "True : 2894.0\n",
      "True : 2894.0\n",
      "True : 2894.0\n"
     ]
    }
   ],
   "source": [
    "# Checking if files in the excelsheet really exists in the dataset\n",
    "import time\n",
    "df = df_train\n",
    "basedir = '/Volumes/Storage/TUH_EEG'\n",
    "tse_file_exists = np.zeros(df.shape[0])\n",
    "edf_file_exists = np.zeros(df.shape[0])\n",
    "lbl_file_exists = np.zeros(df.shape[0])\n",
    "l = int(df.shape[0])\n",
    "printProgressBar(0, l, prefix = 'Progress:', suffix = 'Complete', length = 50)\n",
    "for i in df.index:\n",
    "    printProgressBar(i, l, prefix = 'Progress:', suffix = 'Complete', length = 50)\n",
    "    filepath = df.loc[i, 'Filename'].replace('./edf', basedir)\n",
    "    tse_file_exists[i-1] = os.path.isfile(filepath)\n",
    "    edf_file_exists[i-1] = os.path.isfile(filepath.replace('.tse', '.edf'))\n",
    "    lbl_file_exists[i-1] = os.path.isfile(filepath.replace('.tse', '.lbl'))\n",
    "    time.sleep(0.001)\n",
    "\n",
    "print(all(tse_file_exists), ':', np.sum(tse_file_exists))\n",
    "print(all(lbl_file_exists), ':', np.sum(lbl_file_exists))\n",
    "print(all(edf_file_exists), ':', np.sum(edf_file_exists))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Count how many entries correspond to each file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "deletable": false,
    "editable": false,
    "hide_input": true,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train (unique vs. entries): 1984 / 2894\n",
      "test (unique vs. entries): 1011 / 1410\n"
     ]
    }
   ],
   "source": [
    "print('train (unique vs. entries):', len(df_train['Filename'].unique()), \"/\", df_train.shape[0])\n",
    "print('test (unique vs. entries):', len(df_test['Filename'].unique()), \"/\", df_test.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "deletable": false,
    "editable": false,
    "hide_input": true,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Filename</th>\n",
       "      <th>Set</th>\n",
       "      <th>Counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>./edf/train/01_tcp_ar/000/00000077/s003_2010_0...</td>\n",
       "      <td>train</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>./edf/train/01_tcp_ar/002/00000254/s005_2010_1...</td>\n",
       "      <td>train</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>./edf/train/01_tcp_ar/002/00000254/s006_2011_0...</td>\n",
       "      <td>train</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>./edf/train/01_tcp_ar/002/00000254/s007_2013_0...</td>\n",
       "      <td>train</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>./edf/train/01_tcp_ar/002/00000272/s007_2003_0...</td>\n",
       "      <td>train</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            Filename    Set  Counts\n",
       "0  ./edf/train/01_tcp_ar/000/00000077/s003_2010_0...  train       1\n",
       "1  ./edf/train/01_tcp_ar/002/00000254/s005_2010_1...  train       1\n",
       "2  ./edf/train/01_tcp_ar/002/00000254/s006_2011_0...  train       1\n",
       "3  ./edf/train/01_tcp_ar/002/00000254/s007_2013_0...  train       1\n",
       "4  ./edf/train/01_tcp_ar/002/00000272/s007_2003_0...  train       1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Filename</th>\n",
       "      <th>Set</th>\n",
       "      <th>Counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2990</th>\n",
       "      <td>./edf/dev_test/03_tcp_ar_a/065/00006546/s013_2...</td>\n",
       "      <td>dev_test</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2991</th>\n",
       "      <td>./edf/dev_test/03_tcp_ar_a/065/00006546/s013_2...</td>\n",
       "      <td>dev_test</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2992</th>\n",
       "      <td>./edf/dev_test/03_tcp_ar_a/065/00006546/s013_2...</td>\n",
       "      <td>dev_test</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2993</th>\n",
       "      <td>./edf/dev_test/03_tcp_ar_a/065/00006546/s013_2...</td>\n",
       "      <td>dev_test</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2994</th>\n",
       "      <td>./edf/dev_test/03_tcp_ar_a/065/00006546/s015_2...</td>\n",
       "      <td>dev_test</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Filename       Set  Counts\n",
       "2990  ./edf/dev_test/03_tcp_ar_a/065/00006546/s013_2...  dev_test       1\n",
       "2991  ./edf/dev_test/03_tcp_ar_a/065/00006546/s013_2...  dev_test       1\n",
       "2992  ./edf/dev_test/03_tcp_ar_a/065/00006546/s013_2...  dev_test       1\n",
       "2993  ./edf/dev_test/03_tcp_ar_a/065/00006546/s013_2...  dev_test       1\n",
       "2994  ./edf/dev_test/03_tcp_ar_a/065/00006546/s015_2...  dev_test       1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_files, train_counts = np.unique(df_train['Filename'], return_counts=True)\n",
    "test_files, test_counts = np.unique(df_test['Filename'], return_counts=True)\n",
    "df_repeats = pd.DataFrame({'Filename': train_files, 'Set': 'train', 'Counts': train_counts})\n",
    "df_repeats = df_repeats.append(pd.DataFrame({'Filename': test_files, 'Set': 'dev_test', 'Counts': test_counts}))\n",
    "df_repeats = df_repeats.reset_index(drop=True)\n",
    "display.display(df_repeats.head())\n",
    "display.display(df_repeats.tail())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Files with more than 1 entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "deletable": false,
    "editable": false,
    "hide_input": true,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Filename</th>\n",
       "      <th>Counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>./edf/train/01_tcp_ar/004/00000492/s003_2003_0...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>./edf/train/01_tcp_ar/004/00000492/s003_2003_0...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>./edf/train/01_tcp_ar/008/00000883/s002_2010_0...</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>./edf/train/01_tcp_ar/028/00002806/s001_2004_1...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>./edf/train/01_tcp_ar/028/00002806/s001_2004_1...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             Filename  Counts\n",
       "20  ./edf/train/01_tcp_ar/004/00000492/s003_2003_0...       2\n",
       "23  ./edf/train/01_tcp_ar/004/00000492/s003_2003_0...       2\n",
       "36  ./edf/train/01_tcp_ar/008/00000883/s002_2010_0...      10\n",
       "94  ./edf/train/01_tcp_ar/028/00002806/s001_2004_1...       2\n",
       "95  ./edf/train/01_tcp_ar/028/00002806/s001_2004_1...       3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_repeats.loc[df_repeats['Counts']>1, ['Filename', 'Counts']].head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion: All the files mentioned (.tse, .edf, .lbl) in the Excel sheet are available. No missing data files. Some edf files has multiple seizures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Count total duration of seizures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {
    "deletable": false,
    "editable": false,
    "hide_input": true,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>File No.</th>\n",
       "      <th>Patient</th>\n",
       "      <th>No. Seizures/File</th>\n",
       "      <th>No. Seizures/Session</th>\n",
       "      <th>Seizure Start Time</th>\n",
       "      <th>Seizure End Time</th>\n",
       "      <th>Seizure Duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2894.000000</td>\n",
       "      <td>579.000000</td>\n",
       "      <td>1984.000000</td>\n",
       "      <td>579.000000</td>\n",
       "      <td>1327.000000</td>\n",
       "      <td>1327.000000</td>\n",
       "      <td>1327.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1046.844851</td>\n",
       "      <td>5904.765112</td>\n",
       "      <td>0.668851</td>\n",
       "      <td>2.291883</td>\n",
       "      <td>462.227348</td>\n",
       "      <td>530.399231</td>\n",
       "      <td>68.171883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>564.675533</td>\n",
       "      <td>3285.861459</td>\n",
       "      <td>2.632225</td>\n",
       "      <td>6.206603</td>\n",
       "      <td>507.920353</td>\n",
       "      <td>516.553120</td>\n",
       "      <td>154.411548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.104000</td>\n",
       "      <td>2.104000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>564.250000</td>\n",
       "      <td>2868.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>97.683750</td>\n",
       "      <td>161.346400</td>\n",
       "      <td>17.537150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1096.500000</td>\n",
       "      <td>6514.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>300.777300</td>\n",
       "      <td>370.110000</td>\n",
       "      <td>34.156200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1523.750000</td>\n",
       "      <td>8552.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>646.016000</td>\n",
       "      <td>736.976000</td>\n",
       "      <td>74.325500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1984.000000</td>\n",
       "      <td>10798.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>3402.988300</td>\n",
       "      <td>3474.425800</td>\n",
       "      <td>2448.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          File No.       Patient  No. Seizures/File  No. Seizures/Session  \\\n",
       "count  2894.000000    579.000000        1984.000000            579.000000   \n",
       "mean   1046.844851   5904.765112           0.668851              2.291883   \n",
       "std     564.675533   3285.861459           2.632225              6.206603   \n",
       "min       1.000000      2.000000           0.000000              0.000000   \n",
       "25%     564.250000   2868.000000           0.000000              0.000000   \n",
       "50%    1096.500000   6514.000000           0.000000              0.000000   \n",
       "75%    1523.750000   8552.000000           0.000000              1.000000   \n",
       "max    1984.000000  10798.000000          43.000000             71.000000   \n",
       "\n",
       "       Seizure Start Time  Seizure End Time  Seizure Duration  \n",
       "count         1327.000000       1327.000000       1327.000000  \n",
       "mean           462.227348        530.399231         68.171883  \n",
       "std            507.920353        516.553120        154.411548  \n",
       "min              1.000000          3.104000          2.104000  \n",
       "25%             97.683750        161.346400         17.537150  \n",
       "50%            300.777300        370.110000         34.156200  \n",
       "75%            646.016000        736.976000         74.325500  \n",
       "max           3402.988300       3474.425800       2448.000000  "
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.892648721301533\n",
      "177.37530127869857\n",
      "207\n"
     ]
    }
   ],
   "source": [
    "outliers, min_index, max_index, min_C, max_C = detect_outliers(df_train['Seizure Duration'].values, \n",
    "                                                               percentile=[95, 25],\n",
    "                                                               return_index=True, return_threshold=True)\n",
    "print(min_C)\n",
    "print(max_C)\n",
    "print(len(outliers))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60986.689300000005"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test['Seizure Duration'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "deletable": false,
    "editable": false,
    "hide_input": true,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [],
   "source": [
    "# Remove nans\n",
    "durations = df['Seizure Duration'].values[~np.isnan(df['Seizure Duration'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "deletable": false,
    "editable": false,
    "hide_input": true,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Gc75vCHUO3CizywiGfjJnjhbgFREpJamLpItUqyrq0Qvm6MXVoydSblqB2SmvZwFbspUJFxqeQLAuFWY2C/gp8Al3fy2l/KwsdW4Ph3YS/nwjU6Pcfbm7L3b3xc3NzcO8NBEREZHRUT2BXjhHL6EePZFy8xSw0MzmmVkdwaLCK9LKrCBItgJwPvCgu7uZTQR+CVzl7r9PFg6HZO41s5PDbJufAH6Woa5LUraLiIiIlI2cAr0RpDafYmYPmdk+M7shpXyTmf3SzF4xs9Vm9tV8XVA2GropUp7COXdXAPcDLwN3u/tqM7vWzM4Ni90ETDGzFuDzvJkp8wpgAXC1mT0bPpJz7j4N/BBoAV4D7gu3fxU4y8zWAmeFr0VERETKyqBz9FJSm59FMNzpKTNb4e4vpRTrT21uZssI5sJ8FOgGrgaOCR+pvu7uD4V36B8ws7Pd/T5GSTIZi4ZuipQfd78XuDdt2zUpz7uBCzIcdx1wXZY6V3Hw5xLuvhM4c4RNFhERESmqXHr0RpLavNPdHyMI+Pq5+353fyh83gv8kQPny+Rdco5e+ENERERERKRi5RLo5Su1eUbhHJpzCBYmzrT/MjNbZWar2tracqkyo7h69EREREREpErkEuiNOLV51oqD7Hh3AN9293WZyuQrs52SsYiIiIiISLXIJdAbUWrzQSwH1rr7t3IoO2zxhPdHnUrGIiIiIiIilS6XQG/Yqc0HqtTMriMICD83tCYPXWpwp6GbIiIiIiJS6QbNuunuMTNLpjaPAjcnU5sDq9x9BUFq89vC1Oa7CIJBAMxsAzAeqDOz84D3AR3Al4BXgD8Gy1hxg7v/MJ8XlxRLycCioZsiIiIiIlLpBg30YPipzcN9c7NUm2le36iIpQR3CfXoiYiIiIhIhctpwfRyF4+nDN3U8goiIiIiIlLhqiLQi2mOnoiIiIiIVJEqCfQ0R09ERERERKpHVQR6yropIiIiIiLVpCoCvVjKHD316ImIiIiISKWrjkBPPXoiIiIiIlJFqiLQi6fM0YurR09ERERERCpcVQR6B6yjp0BPREREREQqXHUEeqnr6CnOExERERGRClcdgV7qHD316ImUHTNbYmZrzKzFzK7MsL/ezO4K9680s7nh9ilm9pCZ7TOzG1LKjzOzZ1MeO8zsW+G+T5pZW8q+TxXqOkWkcozgc+ssM3vazF4If56RcszDYZ3Jz6dphbsiESk3NcVuQCEcsI6ekrGIlBUziwI3AmcBrcBTZrbC3V9KKXYpsNvdF5jZMuBrwEeBbuBq4JjwAYC77wWOTznH08B/p9R3l7tfMUqXJCIVboSfWzuAc9x9i5kdA9wPzEw57iJ3X1WQCxGRslYVPXpx9eiJlLMTgRZ3X+fuvcCdwNK0MkuBW8Ln9wBnmpm5e6e7P0YQ8GVkZguBacCj+W+6iFSpkXxuPePuW8Ltq4EGM6svSKtFpKJURaCndfREytpMYFPK61YOvLt9QBl3jwHtwJQc67+QoAcv9cPhw2b2vJndY2azMx1kZpeZ2SozW9XW1pbjqUSkSuTrc+vDwDPu3pOy7UfhsM2rzcwynVyfTyIC1RLohcFdbdS0jp5I+cn0RSb9DzmXMtksA+5Ief1zYK67vxX4LW/ecT+wcvfl7r7Y3Rc3NzfneCoRqRIj/twys6MJhnP+Zcr+i9z9WOCU8HFxppPr80lEoEoCvXgigQE1kYjm6ImUn1YgtVdtFrAlWxkzqwEmALsGq9jMjgNq3P3p5DZ335ly9/wHwAnDb7qIVKkRfW6Z2Szgp8An3P215AHuvjn8uRe4nWCIqIhIRlUR6MUSTjRiRCNGPDF4eREpKU8BC81snpnVEfTArUgrswK4JHx+PvBg2lDMbC7kwN48zGx6ystzgZeH1WoRqWbD/twys4nAL4Gr3P33ycJmVmNmU8PntcCHgBdH+TpEpIxVSdZNpyYaBHqaoydSXtw9ZmZXEGSeiwI3u/tqM7sWWOXuK4CbgNvMrIXgjviy5PFmtgEYD9SZ2XnA+1Iy330E+EDaKT9rZucCsbCuT47axYlIRRrh59YVwALgajO7Otz2PqATuD8M8qIEQ8t/ULCLqlC3r9zY//xjJ80pYktE8q8qAr143IlGIkQMzdETKUPufi9wb9q2a1KedwMXZDl27gD1zs+w7SrgquG2VUQEhv+55e7XAddlqVZDyUUkZ1UzdLOmf+imAj0REREREalsVRLoJaiJGBEzJWMREREREZGKVxWBXvyAZCwK9EREREREpLJVxRy9WDxIxqIePRERERERqQZVEejFE05NJNL/XEREREREpJLlNHTTzJaY2RozazGzKzPsrzezu8L9K81sbrh9ipk9ZGb7zOyGtGNOMLMXwmO+bWaWjwvKJJZIaB09ERERERGpGoMGemYWBW4EzgYWARea2aK0YpcCu919AfBN4Gvh9m7gauCLGar+LnAZsDB8LBnOBeQimXUzYmjopoiIiIiIVLxcevROBFrcfZ279wJ3AkvTyiwFbgmf3wOcaWbm7p3u/hhBwNfPzKYD4939cXd34FbgvJFcyEDiWl5BRERERESqSC6B3kxgU8rr1nBbxjLuHgPagSmD1Nk6SJ0AmNllZrbKzFa1tbXl0NyDxeJONBpRMhYREREREakKuQR6mebOpUdLuZQZVnl3X+7ui919cXNz8wBVZpdcR089eiIiIiIiUg1yCfRagdkpr2cBW7KVMbMaYAKwa5A6Zw1SZ97E++foqUdPREREREQqXy6B3lPAQjObZ2Z1wDJgRVqZFcAl4fPzgQfDuXcZuftWYK+ZnRxm2/wE8LMhtz5HMS2YLiIiIiIiVWTQdfTcPWZmVwD3A1HgZndfbWbXAqvcfQVwE3CbmbUQ9OQtSx5vZhuA8UCdmZ0HvM/dXwI+DfwYaATuCx+jIpl1M2aG4jwREREREal0OS2Y7u73Avembbsm5Xk3cEGWY+dm2b4KOCbXho5EPO7URCNE4q4ePRGpOF29cRrrosVuhoiIiJSQnBZML2eJhBP35NBNFOiJSMVwd+5fvY3j/vHXvLp9b7GbIyIiIiWk4gO93ngCIMi6qWQsIlJB7l+9jUdebaM3nuC3L28vdnNERESkhFRVoBdRMhaRsmRmS8xsjZm1mNmVGfbXm9ld4f6VZjY33D7FzB4ys31mdkPaMQ+HdT4bPqYNVFep2dvdx+/W7uDtcyZx1KHjePTVHcVukoiIiJSQyg/0YkGgF41G1KMnUobMLArcCJwNLAIuNLNFacUuBXa7+wLgm8DXwu3dwNXAF7NUf5G7Hx8+3hikrpLStrcHgONmT+DUI5p5+vXd7O+NFblVIiIiUiqqJtBL9uglPJjXIiJl40Sgxd3XuXsvcCewNK3MUuCW8Pk9wJlmZu7e6e6PEQR8ucpY1/CbPzp27OsFoHlsPacsnEpvPMHK9QMtXyoiIiLVpKoCvWgk+K7WF1egJ1JGZgKbUl63htsylnH3GNAOTMmh7h+FwzavTgnmcqrLzC4zs1VmtqqtrW0o15MXO/b1UBs1xjfW8o65k6mrifDYWg3fFBERkUDlB3rhHL1omIwFIJZIFLNJIjI0mXrT0u/W5FIm3UXufixwSvi4eCh1uftyd1/s7oubm5sHOVX+7djXw5Qx9UTMaKiNctK8yfzu1cIHnCIiIlKaKj/Q6+/RixB26KlHT6S8tAKzU17PArZkK2NmNcAEYMBxjO6+Ofy5F7idYIjosOoqhh37epg6tq7/9TvmTmbtG/vo6o0XsVUiIiJSKio+0OtJBnrRYI4eQCyuHj2RMvIUsNDM5plZHbAMWJFWZgVwSfj8fOBBH2AyrpnVmNnU8Hkt8CHgxeHUVQzxhLOrs5epY+v7t81vHgPA+h2dxWqWiIiIlJCaYjdgtPVn3UyZoxfTEgsiZcPdY2Z2BXA/EAVudvfVZnYtsMrdVwA3AbeZWQtB79uy5PFmtgEYD9SZ2XnA+4DXgfvDIC8K/Bb4QXhI1rpKxe7OXhIOU8elBHpTxwJBoLdoxvhiNU1EJO9uX7nxgNcfO2lOkVoiUl4qPtDriQXDmGpS5uj1qUdPpKy4+73AvWnbrkl53g1ckOXYuVmqPSFL+ax1lYod+4KlFVJ79OZObQJgXdu+orRJRERESkvFB3oHzNELe/S0aLqIlLO2/kDvzTl6TXU1zJjQwDoN3RQR6Zdw55WtHTzb2s6+7hjr2vbxp2+fydEzJhS7aSKjruLn6CUTr0SjqT16CvREpHzt2NdLU12UprrgXt3tKzdy+8qNNNZFeWpDyeWNEalKZrbEzNaYWYuZXZlhf72Z3RXuX2lmc8PtZ5nZ02b2QvjzjJRjTgi3t5jZt0txjc9SEkskuOPJjfxk5UbWt+0jnkhw2xOvc96Nv+d7j7xGQjf+pcJVfI9ecimFqKUkY9HyCiJSxoKMm/UHbZ86tp7nWvfg7uj7n0jxmFkUuBE4iyCT71NmtsLdX0opdimw290XmNky4GvAR4EdwDnuvsXMjiGYn5xcO/S7wGXAEwTD2ZcA9xXimspNPOHcsXIjL2/by9nHHMq7Dp9KNGJ84NhDueq/X+Cr973C5t1dHHXoOH1eSsWq+B695DDNiPHmOnrq0RORMtbe1cekptqDtjePq6e7L8HOzt4itEpEUpwItLj7OnfvBe4ElqaVWQrcEj6/BzjTzMzdn3H35BIyq4GGsPdvOjDe3R8PMwHfCpw3+pdSnv7w2g5e3raXc946nVMWNvcn5JvYVMd3Lno7l506n9ueeF3rj0pFq6JAz4iGV6tkLCJSzvb1xBhbf/CAjGQv37o2zdMTKbKZwKaU16282St3UBl3jwHtwJS0Mh8GnnH3nrB86yB1AmBml5nZKjNb1dZWfYHM5j1dPPDyGxx16DhOnp/+loKZceWSozj3uBnc/9J21m7fW4RWioy+qgn0zEgZuqkePREpT919cXpjCcYMEOit36HMmyJFlmksYPqXjwHLmNnRBMM5/3IIdQYb3Ze7+2J3X9zc3JxDcyvLtT9fjeOc89YZWYdlRiLGv5z/VprH1fNff2ylqzde4FaKjL7KD/Q8pUdPyyuISJnbFQ7LzBToTWyqpSZi6tETKb5WYHbK61nAlmxlzKwGmECwdidmNgv4KfAJd38tpfysQeqsemu27eX+1ds5dWEzk8bUDVi2oTbKBSfMYl9PjF88r7dSKk/FB3rJjEqR1AXTNUdPRMrUzn1hoFd3cKAXMWPymDpeU6AnUmxPAQvNbJ6Z1QHLgBVpZVYAl4TPzwcedHc3s4nAL4Gr3P33ycLuvhXYa2Ynh9k2PwH8bLQvpNz86Pfrqa+J8M4MQzYzmTWpidOOmMYzm/bwxLqdo9w6kcKq+EAvOUwzQvAlKNimHj0RKU87O4M19MbURzPunzKmjtbd+wvZJBFJE865u4IgY+bLwN3uvtrMrjWzc8NiNwFTzKwF+DyQXILhCmABcLWZPRs+poX7Pg38EGgBXkMZNw/wg9+t456nW3nrrIk0ZRj1kM3pRzYzsamWL69YTWyQUV/J5WySD5FSVvHLK8Qz9OhpHT0RKVfJoZuZkrEATBxTx/ObtMSCSLG5+70ESyCkbrsm5Xk3cEGG464DrstS5yrgmPy2tHI8tWEXsYTzrsNz681Lqo1G+MAx07n9yY3c8eRGLn7n3NFpoEiBVXyPXsZkLAr0RKRM9Q/dzBLoTW6qo7M3zp79fYVslohIUbk7T7++m/lTx3DI+IYhH3/0jPGcPH8y1z+wlv29sVFooUjhVX6PXoZkLBq6KSLlamdnL1Ez6msy36dLrq+3aff+QRMRiIhUihc3d7Czs5fTjhg4y2i24ZZmxv95/1F8+Lt/4Md/2MBfn75gNJopUlA59eiZ2RIzW2NmLWZ2ZYb99WZ2V7h/pZnNTdl3Vbh9jZm9P2X735jZajN70czuMLOh337JQSJlHb2wQ09DN0WkbO3q7GFMfTTrsMxkcNe6u6uQzRIRKapfPL+FiMGiGeOHXccJh03ijKOm8f1H1tHepVERUv4GDfTMLArcCJwNLAIuNLNFacUuBXa7+wLgmwTrvhCWWwYcDSwBvmNmUTObCXwWWOzuxwDRsFzexVKGbr6ZdVM9eiJSnnZ19mYdtgl0fW3IAAAgAElEQVQwqSkI9DbtUkIWEakO7s4vnt/KwmnjaMqQkXgovvC+I2jv6uOmR9flqXUixZNLj96JQIu7r3P3XuBOYGlamaXALeHze4Azw9S/S4E73b3H3dcTZIk6MSxXAzSGa8c0MUprwRzQo5dMxqIF00WkTO3YN3Cg11AbZUJjLZuUeVNEqsQfN+5h854ujp01YcR1HT1jAh88djo3Pbaenft68tA6keLJJdCbCWxKed0abstYJkwp3A5MyXasu28Gvg5sBLYC7e7+60wnN7PLzGyVma1qa2vLobkHiiW8f8hmco5eXD16IlKmdnX2MqYu89IKSbMnN2ropohUjV+v3kZt1Fg0ffjDNlP9zVkL6eqL871HXhu8sEgJyyXQyzQRJL1LLFuZjNvNbBJBb988YAYwxsw+nunk7r7c3Re7++Lm5oEn2GYSd+9fP69/6KZ69ETKynDnCZvZFDN7yMz2mdkNKeWbzOyXZvZKOFf4qyn7PmlmbSnrV32qENc4kNQ1m7Z3dGddWiFp1sQmDd0UkarxyKttvGPuZBpqB74JlqsF08Zx3ttmcuvjr7O9ozsvdYoUQy6BXiswO+X1LA4eZtlfJhyKOQHYNcCx7wXWu3ubu/cB/w28azgXMJhE4s1AL/lTyVhEysdI5gkD3cDVwBczVP11dz8KeBvwbjM7O2XfXe5+fPj4YR4vZ0T64gl6YokBh27Cmz167vqsE5HKtrW9i1e27R002+ZQfe7MI4glnOW/01w9KV+5BHpPAQvNbJ6Z1REkTVmRVmYFcEn4/HzgQQ++YawAloV32+cBC4EnCYZsnhzeVTfgTODlkV/OwWIJJ5mcTslYRMrSsOcJu3unuz9GEPD1c/f97v5Q+LwX+CPBjaiS1tkTrO00ZpBkA7MnN9ETS9Cm+SUiUuF+92owref0I6fltd45U5pYevwM/mPl65qrJ2Vr0EAvnHN3BXA/QTB2t7uvNrNrzezcsNhNwBQzawE+D1wZHrsauBt4CfgVcLm7x919JcGXsT8CL4TtWJ7XKwsd2KMXbFMyFpGyMpJ5woMys4nAOcADKZs/bGbPm9k9ZjY7y3Ejmj88HJ29cQDG1A88PGnWpEYANu3SPD0RqWyPvNrGoeMbOOKQsXmv+69PX0BPLMFNj63Pe90ihZBTDlp3vxe4N23bNSnPu4ELshz7FeArGbb/A/APQ2nscKQmY7FwLT316ImUlZHMEx644mCo+R3At909OT7n58Ad7t5jZn9F0FN4xkGVuy8nvEG1ePHigtw96u/RG2zo5qQmAFp37+eEwyaNertERIohFk/w6NodfOCY6VnXFh2JBdPG8sFjp3Pr469z2anzmRguX5MqdQH2j500J+9tEBmJnBZML2eJlGQsEAzfVDIWkbIyknnCg1kOrHX3byU3uPtOd0+O0/kBcMIw2513uQZ6M8MePWXeFJFK9lxrO3u7Y5ya5/l5qa44YwH7emL8+A8bRu0cIqOl4gO9eML718+DICFLn3r0RMrJSOYJZ2Vm1xEEhJ9L2z495eW5jNL84eHIdY7e/zyzhTH1NTy85o0D7jaLiFSSJ9btBOCdh+c0Un9Yjjp0PO9bdAg3P7aevd19o3YekdGQ09DNcpaajAXCHj1l3RQpG+4eM7PkPOEocHNynjCwyt1XEMwTvi2cJ7yLIBgEwMw2AOOBOjM7D3gf0AF8CXgF+GM45OeGMMPmZ8P5x7Gwrk8W5EJzsK8nTtSMhtrB79FNbqpld6e+lIhI5Xpy/S6OOGQsk8ccPKQyn644YwG/fmk7tz3xOhMbR/dcIvlU8YFeajIWCBZNjyXUoydSTkY4T3hulmozTuhw96uAq4bV0FG2vzdGU100p7koE5vq2LJHQzdFpPLcvnIj8YTz+LqdvG32xFEfufDWWRM57Yhmfvjoej57xkLqaip+QJxUiIr/TU1NxgIQiZjW0RORstTVF6ehLrcFgSePqWPP/j4SWktPRCrQ1vYuemMJ5k0dU5DzffbMBezq7OXJDblM/xYpDRUf6CXcD7j7HQzdVI+eiJSf7r44jbW5BXoTm2qJu9PRpeGbIlJ51u/oBGBugQK9Ew6bzDvnT+HRtW3K9SBlo+IDvXjCiVpqMhatoyci5am7L5FzoDc5TAO+e78CPRGpPBt2dDJlTB3jG2rzVuftKzf2PzL5zJkL2Nsd4+nXd+ftnCKjqSoCvdTpLBFTj56IlKeuvjiNOQ7dnNQf6PWOZpNERAou4c6GnfsL1puX9M75U5gzuYlHXm1TvgcpC1UR6KUmY6mNRuiJ6Y9TRMpPV288p4ybEAzdNBToiUjl2bGvh66+OHOnNBX0vGbGGUdNo72rj2c37inouUWGo+KzbqYnY6mribC/N168BomIDIO7090XpyHHoZs10QjjGmq0xIKIVJxNu4KMwrMnFTbQA1g4bSwzJzby8KttvG3OJKIpXzJTh3x+7KQ5BW+bSLqK79FLT8ZSF42wvzdWxBaJiAxdTyyBQ85z9CAYvqkePRGpNJt27aehNsLUcfUFP7eZ8Z4jp7Grs5fnW9WrJ6Wt4gO99KGb6tETkXLU3Rd8bg0p0BujQE9EKs+m3fuZPanpgO93hXTU9HEcOr6Bh9e0aQkbKWlVEui9+bouGqFLgZ6IlJmuMNDLdegmwKSmWtr39ykVuIiUnWwZMHticba1dzN7cuGHbSZFzDj9yGba9vWwektH0dohMpgqCfQO7NHr7NHQTREpL8lAL9esmxAM3XRgW3v3KLVKRKSwNu/uwoE5RQz0AI6ZOYGpY+t56JU31KsnJavyAz2HSMpV1tVE+r8wiYiUi+7eoFduSD16Y4IlFjbt2j8qbRKR7MxsiZmtMbMWM7syw/56M7sr3L/SzOaG26eY2UNmts/Mbkg75uGwzmfDx7TCXE3pSH6ezZrUWNR2JHv1tnV0s2bb3qK2RSSbyg/0EgmMA3v0+uJOr5ZYEJEy0jWcOXrhWnqbdivQEykkM4sCNwJnA4uAC81sUVqxS4Hd7r4A+CbwtXB7N3A18MUs1V/k7seHjzfy3/rStnF3F1PH1tFUV/zE8cfNmsikploefOUNXL16UoKqINDjoDl6gObpiUhZGU4ylgmNtUQMWnd3jVazRCSzE4EWd1/n7r3AncDStDJLgVvC5/cAZ5qZuXunuz9GEPBJms1hIpZSEI0Ypx8xjc17ulj7xr5iN0fkIFUQ6CWIRA5cXgGgU0ssiEgZSfbo1ee4YDoEX0LGN9Zq6KZI4c0ENqW8bg23ZSzj7jGgHZiSQ90/CodtXm1WpLSTRdLR1UdHd4yZRR62mepth01kQmMtD6lXT0pQ8fu9R1k84dTVHDh0E9ASCyJSVrr64tTXRIacTnxSU5169EQKL9MfanoUkEuZdBe5+2YzGwf8F3AxcOtBJze7DLgMYM6cylm4e/Oe4LNs5sTCBnrpmT9T1UQinLpwKj9/fivrdnRyePPYArZMZGAV36OX8LShmzUauilSbkYpqcEJZvZCeMy3k3fGzWyymf3GzNaGPycV4hoH090bH1LGzaRJTXWaoydSeK3A7JTXs4At2cqYWQ0wAdg1UKXuvjn8uRe4nWCIaKZyy919sbsvbm5uHtYFlKLNe7owYPqE0unRA1g8dzLj6mt46JWqmzIpJa7iA71YInHQ8gqgoZsi5WIUkxp8l+CO98LwsSTcfiXwgLsvBB4IXxddd198SPPzkiaNqWV7R0//HD8RKYingIVmNs/M6oBlwIq0MiuAS8Ln5wMP+gBj/8ysxsymhs9rgQ8BL+a95SVs8+4upo2v7/8uVypqoxFOWTiVdTs6eX1nZ7GbI9KvtP5SRkFCyVhEyl3ekxqY2XRgvLs/Hn6xuhU4L0Ndt6RsL6quvviQllZImhxm3tyyR8M3RQolnHN3BXA/8DJwt7uvNrNrzezcsNhNwBQzawE+T8pNJTPbAHwD+KSZtYY3t+qB+83seeBZYDPwg0JdU7G5O5v3dBV82GauTpw3haa6KA+qV09KSMXP0VOPnkjZy5TU4KRsZdw9ZmbJpAY7BqizNa3OZKKEQ9x9a1jX1lJZp6q7L8HkcF28oZjYv8RCF/M1d0SkYNz9XuDetG3XpDzvBi7IcuzcLNWekK/2lZuO7hj7emIlG+jV1UQ4ZcFU7n9puxJgScnIqUdvuPNjwn1XhdvXmNn7U7ZPNLN7zOwVM3vZzN6ZjwtKF09AalKqZI+ekrGIlI3RSGownCQIB1ZgdpmZrTKzVW1tbUM5dFiG3aMXBoetmqcnImVsc/gZNrNEllbI5OT5U2isjfLQGvXqSWkYNNAbyfyYsNwy4GiC+S/fCesDuB74lbsfBRxHMLQh7xLuBwzdrFUyFpFyMxpJDVrDejLVuT0c2pkc4pnxf+xCJzvo6ovTOISlFZLGNdRQGzU27dLQTREpX5v3dBExmD6hodhNyaq+Nsq7FkzhlW17Wb2lvdjNEcmpR2/Y82PC7Xe6e4+7rwdagBPNbDxwKsH4dNy91933jPxyDhaLHzh0s15DN0XKTd6TGoRDM/ea2cnhZ9UngJ9lqOuSlO1FE084vbEEDcPIuhkxY+bERvXoiUhZ29rezdSx9dRGSzu9xLvmT6W+JsIND7YUuykiOQV6I1n0M9ux84E2gkU/nzGzH5rZmEwnH+nwqPTlFWoihpl69ETKxSglNQD4NPBDghtQrwH3hdu/CpxlZmuBs8LXRZXMmDmcrJsAsyc3sUlr6YlIGdva3s2MEp2fl6qxLso7D5/CfS9u49Xte4vdHKlyuSRjGcn8mGzba4C3A59x95Vmdj3BF7OrDyrsvhxYDrB48eIhzaGBg5OxmBlj6mro7FGgJ1IuRiOpgbuvAo7JsH0ncOYImpt3yUBvOHP0AGZNauTXq7fns0kiIgXT2ROjvauvpIdtpnr34VN5cv0ubnyoheuXva3YzZEqlkuP3kjmx2Q7thVodfeV4fZ7CAK/vEukJWOB4G5LV5+GbopIeejKQ4/ezs5e9nb35bNZIiIFsbU9WCGn1BZKz2ZMfQ0Xn3wYP39uC+t3aF09KZ5cAr2RzI9ZASwLs3LOI1iU+El33wZsMrMjw2POBF4a4bVkFHcnknaVTXVR9eiJSNnoGmGP3vypwbIK+sIhIuVoa3sw9LxcevQALj1lHrXRCDc+pLl6UjyDBnojmR/j7quBuwmCuF8Bl7t7MsL6DPAf4cKfxwP/lL/L6m878YQfMHQToKmuRssriEjZ6O5LAMPv0Tu8OZgCva5NgZ6IlJ+t7d1MaKxlTH35LP88bVwDF544h58+s1nr6knR5PQXM8L5MV8BvpJh+7PA4qE0dqgS4Yy+tDiPJg3dFJEy0h3emGocRtZNgDlTmogYvNa2L5/NEhEpiC17usqqNy/pr047nNtXbuQ7D7/GP//ZscVujlSh0s5RO0KxRHAXPHpQj56GbopI+Xhz6ObwPrLra6LMmdykHj0RKTt98QQ79vWUZaB36IQGLlg8i3ue3sSWPcp8LIVX0YFeGOcdlIylqS6q5RVEpGx09cWJGNSNYP2o+c1j1aMnImVne0c3CS+fRCzpPn364bjD9x95rdhNkSpUPoOdhyEerpccSRu6OaauRgumi0jZ6OqL01AbPeim1VDMnzqG37fsIJFwIukfiiIiJWrrniDjZqHX0Lt95ca8Hf9nb5/JHU9t4vL3LGDa+PLrmZTyVdE9evF4MtDLsLyCevREpEx098WHnYgl6fBpY+mJJdis4UMiUka2tHdRXxNhYlNtsZsybH99+gJi8QTfe2RdsZsiVaayA72wRy9TMhZl3RSRctHdFx92Ipak+VPDzJtaYkFEysjW9m6mT2g46KZ9OZk7dQwffvssfrLydd1sk4Kq7EAvkblHr6muhq6+OIlkWk4RkRLW1Rsf9hp6SfObg7X0XntD8/REpDwk3NnW3l228/NSfe6sI8Dh+t++WuymSBWp0kAv+MKUzGQnIlLKuvoSIw70po6tY3xDDet2KNATkfKwa18vvfFEWWbcTDdzYiMXnTyHe55upUU33KRAKjvQy5KMpSlccFMJWUSkHIx0jt7tKzdyx5ObmNBYyx9aduaxZSIio2dLezDMsdCJWEbL5e9ZQGNtlG/8Zk2xmyJVorIDvSzJWJrCL0xKyCIipc7d6eqL0zjMNfRSHTK+gW0d3bhr2LqIlL6t7d1EDKaNqy92U/Ji6th6Lj1lPve+sI0XWtuL3RypApUd6GVJxjKmPgj0tGi6iJS6nliCeMJHPHQTgsV79/fG2d7Rk4eWiYiMrq3tXUwb10DNCNYQLTX/65R5TGqq5V/uf6XYTZEqUDl/ORn0z9GLpC+vEAzd7OrT0E0RKW0dXX0AI866CW8uOPzy1o4R1yUiMtqSGTcrybiGWi5/zwIeXbuDR15tK3ZzpMJVR6CXJRmLllgQkVLXHgZ6+ejRS35hekmBnoiUuL3dfeztjjG9Qubnpbr4nYcxd0oT//jz1fTGEsVujlSwqgj00ldeSQZ6GropUh7MbImZrTGzFjO7MsP+ejO7K9y/0szmpuy7Kty+xszeH2470syeTXl0mNnnwn1fNrPNKfs+UKjrzKSjO+zRy0Og11AbZVJTrQI9ESl5W9u7ASquRw+gvibKNecsYl1bJ7c+vqHYzZEKVhWBXjRy8Dp6oKGbIuXAzKLAjcDZwCLgQjNblFbsUmC3uy8Avgl8LTx2EbAMOBpYAnzHzKLuvsbdj3f344ETgP3AT1Pq+2Zyv7vfO5rXN5hkj14+Aj2AQyc0auimiJS8Sg70AM446hBOP7KZ63+7lra9mjcto6OyA71syVjUoydSTk4EWtx9nbv3AncCS9PKLAVuCZ/fA5xpZhZuv9Pde9x9PdAS1pfqTOA1d3991K5gBDq6ghtS+Ri6CcGXpg07OpV1WERK2tb2LiY21fbfnC9nt6/c2P9IdfWHFtEdi/P1+9cMWE5kuCo70MsyR29MuI7evh716ImUgZnAppTXreG2jGXcPQa0A1NyPHYZcEfativM7Hkzu9nMJmVqlJldZmarzGxVW9voTahPDt1syMPyCgCHjm8g4bBm+9681CciMhq27unuTyBVqQ5vHsufv3sedz+9iedb9xS7OVKBqjbQG1dfw7ZwWICIlLT0abYA6QvBZSsz4LFmVgecC/xnyv7vAocDxwNbgX/L1Ch3X+7ui919cXNzc/bWj1D7/vwO3exPyLJFwzdFpDTt742xY19PxQ7bTPWZMxYwdWw9f/fTF/q/t4rkS1UEeulDNwFmTGykdXdXgVskIsPQCsxOeT0L2JKtjJnVABOAXTkcezbwR3ffntzg7tvdPe7uCeAHHDzUs6A6uvuojVre1pGaNKaOcfU1vLBZi/WKjKbhJpEysylm9pCZ7TOzG9KOOcHMXgiP+XY4RL3ivLJtLw7MqIJAb1xDLf9v6dG8uLmDR9dquQXJr6oI9NJ79ABmTmpkyx4FeiJl4ClgoZnNC3vglgEr0sqsAC4Jn58PPOjuHm5fFn6hmgcsBJ5MOe5C0oZtmtn0lJd/CryYtysZho6uWN7m50HweXj8nIk8s3F33uoUkQONJIkU0A1cDXwxQ9XfBS4j+CxbSJBkquIkRxxU+tDNpCXHTOeDb53OA6+8wfaOYLSZ5utJPlR2oOfJQO/A7bev3EhnT4z1Ozr1ByRS4sI5d1cA9wMvA3e7+2ozu9bMzg2L3QRMMbMW4PPAleGxq4G7gZeAXwGXu3scwMyagLOA/0475b+Ed8yfB94D/M2oXuAg2rv68jZsM2nxYZNZs31v//w/Ecm7YSeRcvdOd3+MIODrF96EGu/uj4c3sm4FzhvVqyiSl7Z20FAbYWJTbbGbUjDXnns09TUR/uuPrRrCKXlT/qmMBpAYoEdvYlMdXX1xevqUeU6k1IVLHNybtu2alOfdwAVZjv0K8JUM2/cTJGxJ337xSNubTx3dfXnt0QM44bBJuMMzG/dw2hGjN79QpIplSgR1UrYy7h4zs2QSqR0D1NmaVmd6cikgSBZF0PPHnDlzhtr2olu9pYPpExqp0JGpGU0ZW8+5x83gzqc28VjLDn02S15UdKAXGzDQC+4S7enSHW0RKV0d3fnv0Ttu9gQiBk+/vltfJkRGx0iSSI2kzmCj+3JgOcDixYvLqnsoFk/wytYOFh+WMeFxRUkfVXbszAm8sLmdB17ezhGHjK2aoasyeip76OYAyVgmNoaB3n4FeiJSutq7+misy2+gN66hliMPHc8fX9c8PZFRMpIkUgPVOWuQOsveuh2d9MQSzJhYfUGOmbH0+Jk01kW5feVGujXqTEYop0BvuJmjwn1XhdvXmNn7046LmtkzZvaLkV5IJv3JWNIn6REM3QTY09U7GqcWEcmLIBlL/u/JnXBYkJBFc0FERsVIkkhl5O5bgb1mdnKYbfMTwM/y3/TiejHMCFyNgR7A2PoaPvqO2ezq7OWnz2xmgF8JkUEN+u1hJJmjwnLLgKMJMkN9J6wv6X8TJFcYFf3JWDLsG9dQQ8TUoycipSuRcPaOwtBNCBKydPbGeWWb1tMTybeRJJECMLMNwDeAT5pZa8r3rk8DPwRagNeA+wpxPYX04uYO6msiTB1bX+ymFM38qWM5a9EhvLC5nZXrB+rkFRlYLnP0+jNHAZhZMnPUSylllgJfDp/fA9wQ3m1aCtzp7j3A+vDD7ETgcTObBXyQIEnC5/NwLQcZKBlLxIwJjbXs2a8ePREpTft6YyScvCdjAXjHvMkAPP7aTo6eMSHv9YtUuxEmkZqbZfsq4Jj8tbL0rN7SzlumjyeaYTRWNTn1iGY27Ozkly9s5X+dMp9jZ+lzWoYul/FAmTJHpWd5OiBzFJDMHDXQsd8C/hZIDHRyM7vMzFaZ2aq2tqEtJBkbYOgmBMM3lYxFREpVezjiYDR69GZObGTBtLE8vEYL9IpIaUgknJe2dHDMzPHFbkrRRcz4yAmzGVtfw2W3repfX09kKHIJ9EaSOSrjdjP7EPCGuz892Mndfbm7L3b3xc3NQ8sOlxggGQsECVk0dFNESlV7eCOqKc/JWJJOP6KZJ9fvYn9vbFTqFxEZik2797O3J1bRowyGshB6U30NF598GO1dfVx6y1P6rJYhyyXQG0nmqGzHvhs4NxyDfidwhpn9ZBjtH9BAyytAsMRCR1cfsfiAnYoiIkWxOxxa3lg3OivhnHZkM73xBE+s2zkq9YuIDMWLm4M5w8dUcKA3VDMmNvLvF76Nl7Z08Nk7nlUCLRmSXAK9kWSOWgEsC7NyzgMWAk+6+1XuPiscg74sLP/xPFzPAfqTsWTr0Wuqw4Ft6g4XkRKUHHEwWj1675g7mcbaKI9o+KaIlIDVW9qpiRhHHDq22E0pKWe+5RD+4Zyj+e3L2/nKL0cth6FUoEFvE7t7zMySmaOiwM3JzFHAKndfQZA56rYw2couguCNsNzdBIlbYsDl7l6wRUHeHLqZpUcvXEtva3s3syY1FapZIiI5SSaLyneglzpkaM7kJh5+VYGeiBTfi1s6WHjIOOprRufmVrm6feVGaqMR3nX4FG7+/XqmjK3j8vcsKHazpAzkNB5ohJmjvkKQWTNb3Q8DD+fSjqFKDt2MZgn0xjUEgd4bHT2jcXoRkRHZnUzGMko9egBHHjqOFc9t4dXteznikHGjdh4RkYG4O6s3t3PGUdOK3ZSS9YFjpzNtXD3/ev8a6msifOqU+cVukpS4/K/CW0IGS8YyviGIc5XJSERK0Z79fYypi1ITGb2P6qNnjCdisOLZ9KnXIiKFs72jh52dvRwzU/PzsomY8fULjuMDxx7Kdb98mVsf31DsJkmJq+hA7805epkjvca6KNGI8cZe9eiJSOnZs7+XiU11o3qOcQ21vOvwqax4bgvumuQvIsXx4uZ2ILj5JNnVRCNcv+xtvPcth3DNz1bnlL1TqldlB3qJgZOxmBnj6mt4Y6969ESk9Ozp6mNiU+2on+fc42awcdd+nmttH/VziYhksnpLB2bwlukK9AZTG41w40Vv4z1HNvN3P32BGx9q0Y06yagqAr1syVgAxjXUaI6eiJSk3ft7mTTKPXoA7z/mUGqjxs+f0/BNESmOF7e0M2/qGMbUj85yMpWmvibK9y9ezHnHz+Bf71/DP/78pf4pSyJJFf3XFBukRw+CYUvq0RORUrRnfx8zJzaO+nkmNNbyniOn8bNnN/O3S45UxjsRKbiXtnRwwmGTit2MslJXE+EbHzmeKWPruemx9ezs7OXrF7w1L5/hqUNCP3bSnBHXJ8VR0T16iYQTscF79LarR0+kpJnZEjNbY2YtZnZlhv31ZnZXuH+lmc1N2XdVuH2Nmb0/ZfsGM3vBzJ41s1Up2yeb2W/MbG34s2jfPII5eqM/dBOC/8h37OvlVy9uK8j5RESSdnX2snlPF8fM1LDNoYpEjL//4Fu48uyj+PlzW/jo959ga3tXsZslJaKiA724+6DZ6sY31tLe1Ud3X8GW9xORITCzKHAjcDawCLjQzBalFbsU2O3uC4BvAl8Lj11EsK7n0cAS4DthfUnvcffj3X1xyrYrgQfcfSHwQPi64BIJp72rryBDNwFOXdjM3ClN3Pr46wU5n4hI0uotyUQsyrg5HGbGX512ON/7+NtZu30v5/z7Yzy5flexmyUloLIDvYQzWFbyceFY8DZl3hQpVScCLe6+zt17gTuBpWlllgK3hM/vAc60oCt/KXCnu/e4+3qgJaxvIKl13QKcl4drGLK93TESzqhn3YRgiM6dT21i0YwJPP367v7sdyIihfB8mAhKSyuMzJJjpvM/l7+b8Q21fOwHT3DTY+s1b6/KVfQcvXjCsy6WntS/aPrebmZPbipEs0RkaGYCm1JetwInZSvj7jEzawemhNufSDt2ZvjcgV+bmQPfd/fl4fZD3H1rWNdWM8u4eq+ZXQZcBjBnTv7nL+ze3wvAxMZaemKJvDLRyFwAACAASURBVNefyQlzJvGbl7Zx82Pr+cZHjy/IOUVEnt20h/nNY5jQWJih6pUi29IKHz/5MP7z6Vb+3y9e4o6VG7ntUycyfcLB873Tj6+kuXiaYxio+B696ECZWIDxjUGsq8ybIiUr0x9x+i3KbGUGOvbd7v52giGhl5vZqUNplLsvd/fF7r64ubl5KIfmJBnoTRpTuC8+jXVRTpo3hf95djPrd3QW7LwiUr3cnWc37eH4WROL3ZSK0VAb5eMnzeG842eycdd+3v/N3/GzZzdrCYYqVPWBXrJHb3uHMm+KlKhWYHbK61lA+joA/WXMrAaYAOwa6Fh3T/58A/gpbw7p3G5m08O6pv//9u47PK7qTPz4952i3iXLluUmVzBgDBiMsSGEEghJ8MKSDSEJEAgkG7IpZDcL2X34JWwam03bTaWkQGJIAiQ4QEKJAdNsXHDBXe5yU7F6n5n398e9MoNQGdnT9X6eZ6SZO7e898zcM/fcc+45QG0U9yViTZ29QHyaboY7f0YZGT4P/7dsR1y3a4wZnQ41d1HX2s3pE62gF00iwjlVJfzLRdOZMTafLzyyjpt+vYq9DXYRbzRJ74KeDl/Qy8nw4vMItXaPnjHJahUwQ0SqRCQDp3OVpf3mWQrc4D6/BlimzqXLpcC1bq+cVcAM4A0RyRWRfAARyQXeB7w1wLpuAJ6I0X4NqSms6WY85Wf5+fj8yfz5zQPsrGuL67aNMaPP+v1NAFbQi9CSlfuOPSJRmpfJVWdUcsVpFby2s4FLf7Cc7z+3Pa6dEI40ZhM96V3QCw5f0POIMCY/04ZYMCZJqWoA+BzwDLAF+IOqbhKRu0XkSne2B4BSEakGbsftKVNVNwF/ADYDfwNuU9UgMBZ4RUTWA28AT6nq39x1fQe4VER2AJe6r+OuqcOp0YtXr5vhPv2eaeRk+Pj6XzZbUx9jTEytq2kiw+vh5Ir8RIeStjwiLJpexpcumcn7Tx3H//59Bxd/7yXW7m0kZHl8Wkvvzlh0+M5YAMrzM23QdGOSmKo+DTzdb9pdYc+7gA8Psuw3gW/2m7YLOH2Q+RuAi08w5BPW2NGLiDMETLyNyc/k9ktncveTm3l642E+MKci7jEYY0aH9fubOHl8QVQG+TZDK8j286Nrz+Dasyfx7b9u4dG1NSzfUcdlp4zjpHFW0E5H6V2jF1K83uELemPys2x4BWNMUmnq6KEgyz9sq4RYuX7BZGZXFHD3k5tobO9JSAzGmPQWDCkba5qZO8GGVYinBdNKeeK2hXz0nEkEQ8pDK/by0xd38re3DiX9cAzWDHRk0r+gF0GN3tiCTOuMxRiTVJo6einOSUxX40tW7uMPq2t476xy6tt6+NzDawkE4zPEgzFm9Nh2uJX2niBzJ9n9efEmIpxWWcgXL5nJ1WdU0tUb5DO/XctlP1zOn96sIZjkBT4TmfQu6KniieBqeHl+Fo0dvfTEaawqY4wZTmNHT9x73Oyvsjibf5g7nlerG/j6XzYn/ZVeY0xqWbP3KADzJpckOJLRy+sR5k0p4YuXzORH187FI8KXfr+eHzy/nRW7GuzcOMWl9z16QcUXQUGvLN85mWpo7x5wQEljjIm35s5eSnITW9ADOGtyCaV5mdy7fBdHWrr4wUfmkpuZ1j8dxpg4Wb23kfL8TCYU27lXonk9wuK5lXxoznie33KEu5/czNL1B3lu8xHq2rq4fsEUxhZkDdpk8ngGJY/XoOajefD0tP61DqriiaDp5pi8TADqWq2gZ4xJDo0dPUwbk5foMAC48/0nMbYgi28+tZn3fPdFPnV+FR+bP+nYOKTGGHM8Vu9p5OwpJUgE52omPjwe4X2njKOutZu9DR28Ul3PT1/cyb3Ld/GhOeMZX5TN+CI7V04V6V3QCym+iDpjcQp69W3WIYsxJvFUldqW7mN5U6I9/MZ+sv1ebjl/Ksu21vKdv27lh89v57xpZVwwYww3LpyS6BCNMSnmUHMnB5o6uXlRVaJDMQMQEaaU5TKlLJeF00v51at7+OPq/bT3BKkqy2XhtFJmjStIWIdhJjJpX9CLpDOWsrAaPWOMSbSWrgDdgRDlSVLQ6zO5NJdPLqyiprGDl7bXsWxrLev3N3G4pYtJJTnH5httTWOMGYiIXA78CPAC96vqd/q9nwk8CJwFNAAfUdU97nt3AjcDQeDzqvqMO30P0OpOD6jqvLjsTAys3tMIwLwpxQmOxAxncmkuX7vyFL506UzueGwDr+1s4Lcr91GY7efsKcXMm2L3WCartC7ohSLsjOXtGj3rQtwYk3i1bi/A5QVZCY5kYBOKc/jY/MnsrGvjsbU13P/yLm5eVMXk0txEh2ZMUhARL/AT4FKgBlglIktVdXPYbDcDjao6XUSuBe4BPiIis4FrgVOA8cDzIjJTVYPucu9V1fq47UyMrNnbSLbfy8kVBYkOxUSoMNvP+TPGcN60MrYdbmHl7qM8v6WWZVtr2VjTzMfOncSCqaXWFDeJpHWvm4EIO2PJ8nvJz/JZjZ4xJinUunlRstXo9TdtTB63XTidohw/D76+l9pWG6bGGNc5QLWq7lLVHuARYHG/eRYDv3GfPwpcLM4Z8mLgEVXtVtXdQLW7vrSyas9R5k4swu9N61PRtOT1CLPHF/LJhVXcfulMzptWxivV9Vx330ou/cFy7n95l/0eJImIji4RuVxEtolItYjcMcD7mSLye/f9lSIyJey9O93p20TkMnfaRBF5QUS2iMgmEflCtHYoXKSdsYDTIYsV9IwxyaDvBzLZC3oAuZk+bjyvCo9HePiNfQRC1hW3MUAlsD/sdY07bcB5VDUANAOlwyyrwLMiskZEbh1s4yJyq4isFpHVdXV1J7QjsdDU0cPmQy2cO7U00aGMOkMNOH48g5GX5WVyxWkVrPzqxfzPh08nL9PHN57awoJvL+PGX73B0vUH6U2icVhH24DrwzbdjEXzAyAAfFlV14pIPrBGRJ7rt84TFgopmf7IrhSV5WdSZ52xGGOSwJEWt0YvSZtu9leSm8HVZ1Ty0Iq9vLKjnusXTEl0SMYk2kBXmfsPRDnYPEMtu1BVD4pIOfCciGxV1eXvmln1XuBegHnz5iXdAJgrdjWgCgunW0EvXWT5vVxz1gSuOWsC1bWtPL72AH9+8wCff/hNMn0eThlfyOyKAjp7gmRneBMd7qgRSSko6s0PVPWQqq4FUNVWYAvvvtJ1wgKhEdTo5WdSbzV6xpgkUNvSTW6Gl7wUGq/u5IoCTq0sZNnWWnbXtyc6HGMSrQaYGPZ6AnBwsHlExAcUAkeHWlZV+/7XAn8iRZt0vrazgZwML3MmFCU6FBMD08vz+crlJ/HKv1/Eklvmc+r4QjYfaua3K/dyxn89yy0Pruah1/dwuKWLkCbddYi0EslZxEBNCOYPNo+qBkQkvPnBin7LvqNA5zbzPANYOdDG3aYJtwJMmjSyntxCqhF3+zomL5PlVtAzxiSBI61djE2R2rxwH5pTwY4jrXzzqS3cf0PKdgZoTDSsAmaISBVwAKd103X95lkK3AC8DlwDLFNVFZGlwBIR+T5Oa6gZwBsikgt4VLXVff4+4O747E50vVpdzzlVJWT43lnfMFqa06WaSD6X/vNcN38SHo9w3rQy9pzVweLQePbUdxAIhXh+8xGe23wEgGy/l0klOexpaKeqLJeqslzK8zPJy/KRn+kny++JqHMX++4MLJKCXqyaHyAiecBjwBdVtWWgjZ9I84NIO2MBp0avtTtAV2+QLL9VKRtjEqcuicbQG4n8LD8XzhzDM5uPsGJXg91/Y0Yt96L354BncIZX+KWqbhKRu4HVqroUeAB4SESqcWryrnWX3SQifwA249zqcpuqBkVkLPAn96TXByxR1b/FfedO0JGWLnbWtfORsycOP7NJGz6Ph+nleVw3fxJfv/IU9h/t5Ed/38GehnZqGjv49Wt76Am8+14+EfCK4PEIHoGQgkdAEP7n2W14BPIyfWT5vUwsyWFmeT7ji7Ks509XJAW9kTQ/qIm0+YGI+HEKeb9T1cePK/phhEbYGQs4Y+lNDBsPyhhj4u1Ia1fKNmk6b3oZGw80862nt/Dnzy6MaIgbY9KRqj4NPN1v2l1hz7uADw+y7DeBb/abtgs4PfqRxtdrO52RIc6bVpbgSEyiiAiTSnM4a3IxZ012xlG89uyJHGjqZHd9O0fbe2jtDtDWFaCjJ0AwpITUOa/ffLCFkCqqML08j5AqTR29rNpzlK2HW3lu8xHG5GeyYGop86YU4/OM7l5dIynoxaL5geBcydqiqt+Pzq68WzCk+LyR1+gB1LVZQc8YkziqSm1LN2NTsEYPwO/18OX3zeLLf1zPXzYcZPHcqN9+bYxJYS/vqKcox89sGz/PhPF4hIklOcOeg4c30bxu/qR3TG/rDrD1kDO+39L1B3m1up4Pzqlg1rjR+10btpjrdvnb1/xgC/CHvuYHInKlO9sDQKnb/OB24A532U1AX/ODv+E2PwAWAp8ALhKRde7jiijvG8ERdMZS5tboWYcsxiSfeA7xIiJfE5EDscybhtLaHaCzN0h5QWoW9ACuOqOS2RUFfPeZbXQHgsMvYIwZFYIh5aVtdbxn5hir7TdRl5fpY96UEj574TRuPG8KHhF+8/repBviIZ4i6tItBs0PXmHg+/eiKhhhZyxLVu6jubMXgKc2HqK+recdVwmMMYmToCFefqCq/xOfPXynWndohVTsjKWPxyN89YqT+fgDK3nwtb3ccsHURIdkjEkC62uaaGjv4aKTyhMdikljIsLMsflMvTiXZzcd4ZXqemoaO7h+wZSU6s06GtK64WogGHmvm30ffFtXIJYhGWNGLmWHeDkefYOlp2JnLH2WrNzHvqMdzCjP43vPbaPexig1xgDLttTi9QgXzrSCnok9n8fDFadVcN05kzjc3MUvXtpJY3tPosOKq7Qu6IVU8UbYdNPrEXIyvLR1W0HPmCQz0BAv/Qtl7xjiBQgf4mXIZQcZ4uVzIrJBRH4pIsUDBSUit4rIahFZXVdXN9J9GlQ61Oj1+cCcCnoDyree3pLoUIwxSeDvW2uZN7mYwhx/okMxCbJk5b5jj2jMF4lTKwu5eVEVHT1B7n9lF00dPVHfRrJK64JeMBR5jR44tXqtVqNnTLKJ9xAvPwOmAXOBQ8D3BgpKVe9V1XmqOm/MmDFD78EI9NXoladwjV6f8vwszp9ZxuNrDxzrac8YMzodbOpky6EWLj7ZavNM/E0uzeWmhU5h74FXdtPa1ZvokOLCCnphinMyODrKqnSNSQEjGeKFEx3iRVWPqGpQVUPAfThNR+PmSEs32X5v2txH8N5Z5UwqyeGrj2+k3VpMGDNqPbvpMAAXnTQ2wZGY0aqyOJtPnjeFlq5efrti76jooCW9C3oRdsbSp6Ioi9rWrlHxwRuTQo4N8SIiGTidqyztN0/fEC8QNsSLO/1at1fOKiIY4kVEKsJeXgW8FfU9GsKh5k7GFabPYK9+r4f/vmYOe4928I2nNg+/gDEmLS1df5CTxuUzvTwv0aGYJBTejDKWTSknleby4bMmsr+xk8fW1uCcKqSv9C7ojaAzFoDxhdmEFA43d8UwKmPMSCRgiJf/FpGNIrIBeC/wpfjsqWNXXTtTy3LjucmYO3dqKZ++YBoPv7Gfv711KNHhGGPibF9DB2v3Ndm4miYpnFpZyGWzx7KhppllW2sTHU5MpUfboEEER9AZC0BlUTYAB5s7YxWSMeY4xHOIF1X9xInGe7yCIWVXfTvvmRm9e/6Sxe2XzuT1nfX86x83ML3cruobM5r8ZYPT2v5Dp1cMM6cx8XHBzDHUtXXz9621lOVlpu2wauld0BvhPXpFOX6y/V4ONllBzxgTf/uPdtATCDFtTHoVgvqa4Vx2yjh+8kI1tz60miduW0h+lvW8Z8xosHTdQc6eUsyE4pxEh2LiJB49WZ7INkSEf5hbydH2Hh5bW8MnFkzm1MrCKEaXHNK76eYIC3oiwviiLA42WdNNY0z87axrA2BamtZ2FeVk8NH5k9jX0MGXfr+eUCi9740wxsDGmma2HWnlSmu2aZKMz+vhuvmTyc308emH1tCQhmO+pndBb4SdsQCML8rmcEsXPQHrkMUYE1/VtU5Bb3qa1eiFm1qWx+WnjuP5LUe45cHVaT1+kTEGfvP6HnIyvCyeOz7RoRjzLnmZPj4+fzL1bd3ctmRt2nXImLYFvVBIUeW4CnrBkLKjtjVGkRljzMB21rVRlpeZ9oMJL5hayhkTi/j71lrW7W9MdDjGmBg52t7D0vUHufrMSgqsqbY5TrHujbOyOJtvX30aK3Yd5VtPb4nJNhIlbQt6Qbe71JF0xgJvd8jy1oHmqMdkjDFDqa5tY3p5evW4ORAR4aozKqkqy+WxNQd4eUddokMyxsTA71ftpycQ4voFUxIdijFDuvrMCdy0sIpfvbqHx9bUJDqcqEnfgp5774dnhDV6JbkZFGb7efiN/Wk/toYxJnmoKjvr2tOuI5bB+LwePnHuZMbkZ3LLg6t5absV9oxJJ92BIA++vodzp5Ywc2x+osMxZlhfveIkzptWyp1/2siGmqZEhxMVaV/Q842woOcR4eKTylm3v4lnNx+JRWjGGPMu9W09NHf2jqphB7L8Xm5aVMXUsjxu+c1qnlh3INEhGWOi5OGV+zjU3MVt752e6FCMiYjP6+HH153JmLxMPv3QGo60pH7njOlb0OtrujnCgh7AGZOKmToml+8+s41Amt2UaYxJTn0dsYyWGr0+eZk+Hr7lXE6fWMgXHlnHnY9vpK07kOiwjDEnoKMnwI9fqObcqSUsml6W6HCMiVhJbgb3XT+Pls5ebvr1qpT/PUrfgl7w+At6Xo/w5UtnUV3bxss76qMdmjHGvMuWQy0AzBg7ugp6AIU5fpbcci6fec80Hn5jH/O/9Xdu+91aHnh5t/XKaUwK+uUru6lv6+HfLpuFjLCvBGMSbfb4An7ysTPZeriVf/7tGroDwUSHdNzSt6B3AjV6ABefXE6Gz8Or1VbQM8bE3ovb65halktFYXaiQ0kIv9fDHe8/iT/ftpDy/Eye2niIb/91Cw++vof7X97Fqj1HaenqTXSYxphh7Kxr4/+WVXPZKWM5a3JJosMx5rhcOKucb199Gi/vqOfzD7+ZssMu+BIdQKz0jYN3vAW9LL+XeZOLeW1nQzTDMsaYd2nvDrBiZwPXL5ic6FASon+t3acWVXGwqYu1+xvZfriVbzz1dnfXlUXZFGb7mVKWy8zyPMoLsrhu/qR4h2yMGUAwpHzl0Q1k+b381+JTEx2OMSfkn+ZNpKM7wNf+spnPP/wmP7x2Lpk+b6LDGpG0LehtqHGGR5g5Np8dR9pGvPySlfvIzfTx2s4G7l++i09dMDXaIRpjDACvVNfTEwxx0cnliQ4lKYgIlcXZVBZnwxxo6erlUFMnh5u7ONTSxYHGTjYfauFpnIKfz+sM1+D3pm0jFWNSwv8t28GavY18/59Op7wgK9HhGHPCblxYRSCkfOOpLbT9ZjU/+/hZ5GWmTvEpdSIdoRW7Gsjyezh9QtFxFfQAppXl8hywq749usEZY0yYZVtqyc/0cfYUa+Y0kIIsPwXj/MwaV3BsWlNHD5sOtrBmbyNfeXQD3356C5ecPJZ7/nHOiIfVMcacuD+/eYAfPr+Dq8+s5KozKhMdjjFR86nzp1KY7eeOxzdy1U9e5d7r51FVlhpj3qZ1QW/e5BIyfMd/hbeyOIcMn4eddW08u+kwvUHlitPG2Y3FxpioCYWUF7bVcsHMMVYjNQJFORksnF7GedNK2X6klee31PLHNTVsO9LKf35gNudUHV+huX8zUmsWaszwnt54iK88uoFzp5bwnavn2HmSSTsfnjeRyqJsbluyliv/7xW+duUpXH1mZdJ/19PyrOJoew9bD7eyYFrpCa3H6xGqSnNZtecotz60htuWrOVTv1lNXWv3gPN3B4KEQjbIujEmcs9uPkJtazcXW7PN4yIizBpXwD9fOI0PnzWB2pZu/ukXr/Pph1az6WBzosMzJq2FQsrPX9rJbUvWctqEQn7x8XkndIHdmGR23vQyln5uESdV5PPlP67nk79eRXVta6LDGlJENXoicjnwI8AL3K+q3+n3fibwIHAW0AB8RFX3uO/dCdwMBIHPq+ozkazzRLyx2+lA5dypJ94M6rQJhexv7OCik8oJKTy3+TCX/3A5Ny+q4rPuIKDBkLJk5V6++8w2ThpXwE8/fiZleZknvG1jjCOeeZCIVAGPACXAWuATqtoTi/1q7uzlrife4uSKAj50+vhYbGLU8IhwxqRiThlfyCvVdby4rY5nNh3hvGmlXHn6eM6pKmFyaS5ej9AbDHGwqZO9DR3sPdrBvoZ29jZ0sO+o8wiGFJ9HyMvysWzrEaaNyWNaeR7TxuQxfUwehTn+RO+uSQGpdu40UpsONnPXE5tYs7eRK04bx/f/aS5Z/tTqqMKYkZpYksMjty7g16/t4YfPbeeyH77M4rnjuWlhFadWFiY6vHcZtqAnIl7gJ8ClQA2wSkSWqurmsNluBhpVdbqIXAvcA3xERGYD1wKnAOOB50VkprvMcOs8bq/vbCDb72XOhKITXteZk4o5c1LxsdeTSnL41au7ue/lXRxq7qKtO8Cr1fXUtnYzqSSHtfsaueR7L3FyRQGLZpQxtSyXrAwvO2vbyPR5mDWugFnj8inMPv4ThebOXnbXtzN34onvnzHJLgF50D3AD1T1ERH5ubvun0V7v3qDIb6+dBP1bd3cf8M8a7YZJRk+DxedNJYFU8tYsbuBHUdauePxjYDTSsMjzsW58MYXGT4Pk0tymFyaQ2G2H59H6A0qrd0B9h3tYPl2p7OcPmV5GccKf00dvZTk+CnKyaAo28/N51eNqCmPqtLRE6StO0Bbd4Cj7T0cbu7iSEsXh5q7ONzSxRH3f0tnL129IXxeISfDS7bfy/TyPCqKshlfmEVFYTYVRVlUFmUzrjAr5XqHSyepeO4UibbuAC9uq+UPq2tYvr2Oohw/3/vw6SnRhM2YaPF6hJsXVXHVGZX8eFk1j6zax+NrDzC7ooAPzKng/BllnDK+8Lh7/o+mSGr0zgGqVXUXgIg8AiwGwjOWxcDX3OePAj8W54hfDDyiqt3AbhGpdtdHBOs8bit2HWXelOKYnDhNKsnh5kVVPLqmhic3HMTr8TB/agkfmlNBQ1sPB5o6eWLdQd7c38jruwYfmmFCcTYfmFPBBTPG0BsMUZSTwayx+WRneAkEQ+xxrzC3dQdo7QrQ3h2gOCeD7mCIHz2/nfq2Hq45awJ3vv8kCtwTk2BI2Xigmb0NHUwdk8ukkhyy/F4yfZ53ZcAtXb1srGkmEFLK8zMZW5BFcY7/uDJqVaWxo5fa1i5qW7rJ8ns5ZXwBuRH2StTeHeD1nQ00dvQwb0oJE4uz8YgM2qGCqtLSFaC2pYva1m56gyFOrSwcsBa1vTvA5kMttHUHGJufxdiCTEpyM6Lyg9QTcGoFGtp76OwJMq4wkymlufgG+d6pKu09QQ43d7Kzrp2j7T2EVBmbn8WscfmUF2QOeWLWHQjy1oEW6lq7GVvgfGZj8jOj/j0PhhSPOE3igiGlOxAk2+8lpNDY0YPf66Egy3fsJHRKaS61rV38dsVecjN8fGLBZCYU50QzpLjlQSKyBbgIuM6d5zfueqNS0AuFlL1HO1i3v5GfvbiT7Ufa+JeLpkflopR5p+wML++dVc69nziLrYdbeetAM3sa2tlQ04zXI5TkZFCSl0Fpbib5WT48g+QJ182fRCAYoqaxk511bc6jtp2ddW08vfEQTR3vHNvv+89vZ3xRNnmZPkSgoc2pDA6EQgSCSpbfS3cgSFdviO5AkO5ACB2kxb/fK05HNNl+SnMzmFCcg88j9ARDdPYE6ewNsvVwKyt3H6Wj590D+uZkeCnOySDL7yHL78XjHtPBkBJU539zZy+hkBJSp/Cbl+nD5xUyvB58XsHv9eDzevC7+XHAnTcQdP73X5/PI/i8HnyevmXlWNqG3/bg9QgnV+RTkptBSU4GpXmZlOZlUJaXSWG2nwyfhwyvB7/Xg+KsOxRy0tHZbl+B3Xn4vZ5jy7zjv8+JJQGFkJQ6d1J1Lmx0ud+rjp4gzZ29HGru5GBTFweaOtl0oJnNh1roDSpj8jP5t8tm8fH5k62G24xaJbkZ3PWh2Xzhkhk8tqaGv2w4yHef2cZ3n9lGtt/LtPJcZpTnM70879h5dnFuBgVZfid/8wk+jwe/m9dm+b1RLxxGciZeCewPe10DzB9sHlUNiEgzUOpOX9Fv2b6umIZb53FRVT6xYDLl+bFrOjmhOIcvXjLzHdOOtvciIkwozuE2t0lnd2+QurZueoNOYao3GOJISxeHW7rZ29DOfct38YuXdh1bh4gzaHAopASGuNdvYnE2i6aX8diaGh5dUwOAR5ymSwMtJwJZPi/h352O3uC7Ti761tH3eygIHHvu0GN/QN0n/a+O920zO8ImHN2BEMFB9tcjzgmBiOAVQXFOMAbaz0yf51h8fbH1Bt89n3MicuIH0kAnaB4Bn6dfwcvdlKoOGE+4bL+Xwc5HBksn5yTGKZj1fYbqpoHzn2Np0vdaVY/Nf+w/zklgX81Fps9DtzseZXjBr+91/1Cy/B56g8r9r+zmrg/O5obzpgy5ryMQzzyoFGhS1cAA85+wn7xQzfee2+4EXJTNfdfP49LZY6O1ejOAh994+2OuLMqhsmhkFyH6d86Sl+nn9IlFnO62qGjvDtDU0UtTZw9NHb2MK8ziQGMn7T3OV6i1M4CiZPv9bxec3MKQ3yPMnVREXqaPvCwfeZk+1u1roiDbT0GWnyz/uy/SDaYnEKKls5emzl6aO3tp7uyhtSvAhOIcunqDdPUGCalyuLkLr0fwezx4BAqz/WF5v6D6zoJbMKT0BAIEVRGc+fp+z+GrQAAADcpJREFUJzwi+Hxu/uwRBAiqEgopQXVi6uzVY78ZGWEXpXpDIbYdbqWxo5fGjp5BC7vRIOLkk6owpSyX529/T+w29raUOnfq6Aky52vPDvp+UY6fk8blc/OiqVx0UjlnTS5OitoKY5JBYbafmxZVcdOiKmpbunh9VwPr9jdRXdvGyl0N/OnNAxGt57c3z2fRjLKoxhZJQW+gI7l/ljzYPINNH6gKYsBsXkRuBW51X7aJyLZB4hxKGVB/HMslypDx7gVeiV8skUqrNE5SKRPzjd+GGyOPd7hRwuOZB0WyLWeDJ5g37QXed2dEs6bM5+5KpXgTHutvRzZ7wuMdoaSLdycgXx7wrYFiHS5vGkqqnDtF9BntBdYDvx9uxuOT6O9JorefDDEkevsJj+FjSZAG598zohgiyp8iKejVABPDXk8ADg4yT42I+IBC4Ogwyw63TgBU9V7g3gjiHJSIrFbVeSeyjnhKtXgh9WJOtXgh9WKOYrzxzIPqgSIR8bm1ejHNmyIxij/3mEulWMHijaUYxJoS507J8BklOoZEbz8ZYkj09pMhhkRvP1YxRHJzzypghohUiUgGzg3CS/vNsxS4wX1+DbBMVdWdfq2IZLo92c0A3ohwncYYA3HMg9xlXnDXgbvOJ2K4b8aY9GTnTsaYhBu2Rs9tN/454Bmc7nx/qaqbRORuYLWqLgUeAB5ybxg+ipP54M73B5wbhQPAbaoaBBhondHfPWNMqktAHvTvwCMi8g3gTXfdxhgTMTt3MsYkA9FY3gGdJETkVrcZQ0pItXgh9WJOtXgh9WJOtXiTVaqlYyrFm0qxgsUbS6kUazQlw34nOoZEbz8ZYkj09pMhhkRvP1YxjIqCnjHGGGOMMcaMJjZCrzHGGGOMMcakmbQu6InI5SKyTUSqReSORMcTTkT2iMhGEVknIqvdaSUi8pyI7HD/F7vTRUT+192PDSJyZhzi+6WI1IrIW2HTRhyfiNzgzr9DRG4YaFsxjvlrInLATed1InJF2Ht3ujFvE5HLwqbH5XsjIhNF5AUR2SIim0TkC+70pE3nIWJO2nROZcmYRtHKG+IUa9SOsTjEmiUib4jIejfWr7vTq0RkpRvr78XphANxOur4vRvrShGZEq9Y+8XtFZE3ReTJZI5Xkvw3N95E5LsistXdvz+JSFHYewPm2TGIIe7520jzhBjGEdFxE8PtF4nIo+53YIuILIhnGojIl9z0f0tEHnbzv5imgSTBb9cgMcT2WFTVtHzg3Ki8E5gKZOAMATM70XGFxbcHKOs37b+BO9zndwD3uM+vAP6KM7bOucDKOMR3AXAm8NbxxgeUALvc/8Xu8+I4x/w14F8HmHe2+53IBKrc74o3nt8boAI4032eD2x340radB4i5qRN51R9JGsaRSNviGOsUTnG4hSrAHnucz+w0o3hD8C17vSfA//sPv8s8HP3+bXA7xP0fbgdWAI86b5OynhJ8t/cBHxu7wN87vN7wvZ9wDw7BttPSP420jwhhnFEdNzEcPu/AT7lPs8AiuKVBkAlsBvIDtv3G2OdBiTBb9cgMcT0WEznGr1zgGpV3aWqPcAjwOIExzScxTgHH+7/fwib/qA6VuCM81URy0BUdTlOL2AnEt9lwHOqelRVG4HngMvjHPNgFgOPqGq3qu4GqnG+M3H73qjqIVVd6z5vBbbgZIBJm85DxDyYhKdzCkvKNIpS3hAXUTzG4hGrqmqb+9LvPhS4CHh0kFj79uFR4GIRGWig7ZgRkQnAB4D73ddCEsc7gKT7HsSLqj6rzlihACtwxuSDwfPsaEtI/nYceULUjfC4icX2C3AKHA8AqGqPqjYRxzTA6fU/W5zxI3OAQ8Q4DZLht2ugGGJ9LKZzQa8S2B/2uoahT0jjTYFnRWSNiNzqThurqofAyYyAcnd6suzLSONLlrg/51aJ/zKsKUJSxew2YzoD5yp+SqRzv5ghBdI5xaRSGiV73nWix1i8YvSKyDqgFueCzU6gKewkIDyeY7G67zcDpfGK1fVD4CtAyH1dSvLGm4q/ufFyE07tBcRv3xOexhHmCbEwkuMmFqYCdcCv3Oaj94tILnFKA1U9APwPsA+ngNcMrCG+adAn2fKAqB+L6VzQG+hKYTJ1MbpQVc8E3g/cJiIXDDFvsu/LYPElQ9w/A6YBc3EylO+505MmZhHJAx4DvqiqLUPNOsC0ZIk56dM5BaVDGiXFPkThGIsLVQ2q6lycK7rnACcPEU9CYxWRDwK1qromfPIAsyZFvKTXb25EROR59x6o/o/FYfP8B85Yfb/rmzTAqmKx74n+/kaaJ0R7uyM9bmLBh9N88GeqegbQjtNsMS7cC8GLcZojjgdycY7L/hJ5zCXivComx+KwA6ansBpgYtjrCcDBBMXyLqp60P1fKyJ/wvlRPyIiFap6yK0irnVnT5Z9GWl8NcCF/aa/GIc4j1HVI33PReQ+4En35VBpGre0FhE/zo/N71T1cXdyUqfzQDEnezqnqGQ57iORtHlXlI6xuFLVJhF5EefekCIR8blXusPj6Yu1xm3+VEjkTdejYSFwpTgdL2UBBTg1FUkZb4r+5p4QVb1kqPfF6bjrg8DFqtp3AhmvfU9YGo8wT4i2kR43sVAD1KhqX2ucR3EKevFKg0uA3apaByAijwPnEd806JMUeUAsj8V0rtFbBcxwe/HJwLn5e2mCYwJARHJFJL/vOc6NmG/hxNfXY+INwBPu86XA9W4vQOcCzX1VzXE20vieAd4nIsXuFZz3udPipl+b6qtw0rkv5mvF6QmuCpgBvEEcvzduu/wHgC2q+v2wt5I2nQeLOZnTOYWlUholZd4VxWMsHrGOEbe3NRHJxjkZ2gK8AFwzSKx9+3ANsCzsBCHmVPVOVZ2gqlNwvpvLVPVjyRhvCv/mxoyIXA78O3ClqnaEvTVYnh1tCcnfjiNPiKrjOG5iEcNhYL+IzHInXQxsJk5pgNNk81wRyXE/j77txy0NwiQ8D4j5sagx7NUn0Q+cXnO249zn8B+Jjicsrqk4PemsBzb1xYbTTvvvwA73f4k7XYCfuPuxEZgXhxgfxmmC14tzVeHm44kPp71xtfv4ZAJifsiNaYN70FSEzf8fbszbgPfH+3sDLMKpht8ArHMfVyRzOg8Rc9Kmcyo/kjGNopU3xCnWqB1jcYh1DvCmG+tbwF3u9Kk4P+7VwB+BTHd6lvu62n1/agK/Exfydu+BSRcvKfCbm4DPrBrn/p++4+LnYe8NmGfHIIa4528jzRNiHMuwx00Mtz0XWO2mw59xeuyOWxoAXwe2unndQzg9S8Y0DUiC365BYojpsSjuiowxxhhjjDHGpIl0brppjDHGGGOMMaOSFfSMMcYYY4wxJs1YQc8YY4wxxhhj0owV9IwxxhhjjDEmzVhBzxhjjDHGGGPSjBX0jDHGGGOMMSbNWEHPDEhEgiKyTkQ2ich6EbldRKL2fRGRG0VkfNjr+0VkdrTWP8D2KkTkySHezxCR5SLii1UMxpjosPzJGJOMLG8yycYKemYwnao6V1VPAS7FGVD0/41kBSLiHeLtG4FjmZWqfkpVNx9PoBG6HbhvsDdVtQdnsMyPxDAGY0x0WP5kjElGljeZpGIFPTMsVa0FbgU+J44bReTHfe+LyJMicqH7vE1E7haRlcACEblLRFaJyFsicq+7/DXAPOB37pWvbBF5UUTmuev4qIhsdJe5J2w7bSLyTfcq2QoRGetO/7A773oRWT7Ibvwj8Dd3/lNE5A132xtEZIY7z5+Bj0Uz7YwxsWX5kzEmGVneZJKBFfRMRFR1F873pXyYWXOBt1R1vqq+AvxYVc9W1VOBbOCDqvoosBr4mHvlq7NvYXGaJNwDXATMBc4WkX8IW/cKVT0dWA7c4k6/C7jMnX5l/4BEpApoVNVud9JngB+p6lycTLPGnf4WcHaESWKMSRKWPxljkpHlTSbRrKBnRkIimCcIPBb2+r0islJENuJkQKcMs/zZwIuqWqeqAeB3wAXuez1AX1vxNcAU9/mrwK9F5BZgoCYPFUBd2OvXga+KyL8Dk/syS1UNAj0ikj/8bhpjkozlT8aYZGR5k0kYK+iZiIjIVJyMqBYI8M7vTlbY8y73oEdEsoCfAteo6mk47bzD5x1wU0O816uq6j4PAj4AVf0M8J/ARGCdiJT2W64zfLuqugTn6lUn8IyIXBQ2bybQNUyMxpgkYvmTMSYZWd5kEs0KemZYIjIG+DlOUwIF9gBzRcQjIhOBcwZZtC+DqBeRPOCasPdagYGu/qwE3iMiZeLckPxR4KVh4pumqitV9S6gHifTCredt69g9WW8u1T1f4GlwBx3eilQp6q9Q23PGJM8LH8yxiQjy5tMMrDuUM1gskVkHeDHuQr1EPB9971Xgd3ARpy22WsHWoGqNonIfe58e4BVYW//Gvi5iHQCC8KWOSQidwIv4FyhelpVnxgm1u+6NwULTu9P6/vF0S4iO0VkuqpW4/QO9XER6QUOA3e7s74XeHqYbRljEs/yJ2NMMrK8ySQVebs215j0JSJXAWep6n8OMc/jwJ2qui1+kRljRjvLn4wxycjyptRnNXpmVFDVPw3Q/vwYEckA/mwZlTEm3ix/MsYkI8ubUp/V6BljjDHGGGNMmrHOWIwxxhhjjDEmzVhBzxhjjDHGGGPSjBX0jDHGGGOMMSbNWEHPGGOMMcYYY9KMFfSMMcYYY4wxJs38f44AbX9SCmXLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "warnings.filterwarnings('ignore')\n",
    "fig = plt.figure(figsize=(15,4))\n",
    "plt.subplot(1,3,1)\n",
    "sns.distplot(durations)\n",
    "plt.subplot(1,3,2)\n",
    "sns.distplot(durations[durations<500], bins=50)\n",
    "plt.subplot(1,3,3)\n",
    "sns.distplot(durations[durations<100], bins=50)\n",
    "_ = [ax.set_xlabel('Durations (s)') for ax in fig.axes]\n",
    "_ = plt.suptitle(\"Distribution of durations within the data set\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification of seizure vs. non-seizure EEG using Dynamic Time Warping (DWT)\n",
    "\n",
    "Using a simple method to normalize the time series before doing classification, with K-nearest neighbor.\n",
    "\n",
    "## Data Preparation\n",
    "Separating time series into periods of seizure vs. non-seizure. Classify into 10 types of seizures (FNSZ, GNSZ, SPSZ, CPSZ, ABSZ, TNSZ, CNSZ, TCSZ, ATSZ, MYSZ) plus the non-seizure. To balance the data, only take 500 samples of non-seizure events (given 1327 total seizure events).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare the csv file to run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [],
   "source": [
    "basedir = '/Volumes/Storage/TUH_EEG'\n",
    "def make_dtw_csvs(df):\n",
    "    gp = df.groupby(by='Filename', sort=False, as_index=False)\n",
    "    Summary = gp.mean()\n",
    "    nn = Summary.shape[0]\n",
    "    out_df = pd.DataFrame({'Path':[[]]*nn, 'Seizure Type':[[]]*nn, 'Class': [[]]*nn})\n",
    "    for i in Summary.index:\n",
    "        out_df.loc[i, 'Path'] = Summary.loc[i, 'Filename'].replace('./edf', basedir).replace('.tse', '.edf')\n",
    "        szt = df.loc[df['Filename']==Summary.loc[i, 'Filename'], 'Seizure Type'].iloc[0]\n",
    "        out_df.loc[i, 'Seizure Type'] = szt\n",
    "        out_df.loc[i, 'Class'] = 1 if isinstance(szt, str) else 0\n",
    "\n",
    "    return out_df\n",
    "\n",
    "df_train_out = make_dtw_csvs(df_train)\n",
    "df_train_out.to_csv(os.path.join(basedir, '_DOCS', 'train_files.csv'), index=False)\n",
    "\n",
    "df_test_out = make_dtw_csvs(df_test)\n",
    "df_test_out.to_csv(os.path.join(basedir, '_DOCS', 'test_files.csv'), index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define some functions to compute KNN for each test sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "from scipy.spatial.distance import euclidean\n",
    "from fastdtw import fastdtw\n",
    "from scipy.stats import zscore\n",
    "from collections import Counter\n",
    "from joblib import Parallel, delayed\n",
    "\n",
    "def load_EEG_traces(path, fs=None,\n",
    "                    channels = ['T4', 'O1', 'F8', 'FZ', 'P4', 'F4', 'F3', 'FP2', 'O2', \n",
    "                                'T6', 'CZ', 'PZ', 'F7', 'C3', 'P3', 'T5', 'C4', 'T3', 'FP1']):\n",
    "    \"\"\"Load and preprocess EEG traces\"\"\"\n",
    "    warnings.filterwarnings(\"ignore\")\n",
    "    eeg_data = EDF(path)\n",
    "    # Parse labels\n",
    "    labels = [ll.replace(\"EEG \",\"\").replace(\"-REF\",\"\").replace(\"-LE\",\"\") \\\n",
    "                for ll in eeg_data.header.labels]\n",
    "    if fs is None:\n",
    "        fs = eeg_data.header.sample_rate[0]\n",
    "    \n",
    "    # channel by time_index \n",
    "    wdata = np.empty((len(channels), eeg_data.header.num_records * fs))\n",
    "    # Make a matrix of the data\n",
    "    for n, c in enumerate(channels):\n",
    "        index = labels.index(c)\n",
    "        if eeg_data.header.sample_rate[n] != fs:\n",
    "            wdata[n, :] = signal.resample(eeg_data.data[index], eeg_data.header.num_records * fs)\n",
    "        else:\n",
    "            wdata[n, :] = eeg_data.data[index]\n",
    "        \n",
    "    # Apply the notch filter to filter out 60 Hz artifacts\n",
    "    b, a = signal.iirnotch(60 / (fs/2), 30)\n",
    "    wdata = signal.filtfilt(b, a, wdata, axis=1)\n",
    "    \n",
    "    # Normalize\n",
    "    wdata = zscore(wdata, axis=1)\n",
    "            \n",
    "    return wdata.T\n",
    "\n",
    "# Define the KNN\n",
    "def EEG_dtw_KNN(df, predict, k=3, dist=euclidean, fs=None):\n",
    "    \"\"\"\n",
    "    Classify a new data point using KNN with fast dynamic time warping distance\n",
    "    \"\"\"\n",
    "    # Training: Find the distances between the test data and training data\n",
    "    l = df.shape[0]\n",
    "    distances = [[]] * l\n",
    "    \n",
    "    # Generator of distance\n",
    "    def dist_gen():\n",
    "        for i in df.index: # iterate over all data points within the group\n",
    "            X = load_EEG_traces(df.loc[i, 'Path'], fs=fs)\n",
    "            y = df.loc[i, 'Class']\n",
    "            dtw_distance, _ = fastdtw(X, predict, dist=dist)\n",
    "            yield i, dtw_distance, y\n",
    "            \n",
    "    def write_dist(i, d, y, distances):\n",
    "        distances[i] = [d, y]\n",
    "        \n",
    "    # Parallelize jobs\n",
    "    #Parallel(n_jobs=8, backend=\"threading\")\\\n",
    "    #        (delayed(write_dist)(i, d, y, distances) for i, d, y in dist_gen());\n",
    "    for i, d, y in dist_gen():\n",
    "        distance[i] = [d, y]\n",
    "    \n",
    "    #print('here are distances')\n",
    "    #print(distances)\n",
    "\n",
    "    # Test: find K nearest neighbors. Sort\n",
    "    # First sort the distances from smallest to the largest,\n",
    "    # and then take the top k distances, then look at which groups those top\n",
    "    # k distances belong to\n",
    "    votes = [i[1] for i in sorted(distances)[:k]]\n",
    "    # Count the most common groups\n",
    "    vote_result = Counter(votes).most_common(1)[0][0] # return most common 1\n",
    "    # Confidence: number of counts / k neighbors\n",
    "    confidence = Counter(votes).most_common(1)[0][1] / k\n",
    "\n",
    "    return vote_result, confidence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "basedir = '/Volumes/Storage/TUH_EEG/'\n",
    "df_train_out = pd.read_csv(os.path.join(basedir, '_DOCS', 'train_files.csv'))\n",
    "df_test_out = pd.read_csv(os.path.join(basedir, '_DOCS', 'test_files.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "code_folding": []
   },
   "outputs": [
    {
     "ename": "OSError",
     "evalue": "[Errno 5] Input/output error: '/Volumes/Storage/TUH_EEG/_DOCS/KNN_results.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m-------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mOSError\u001b[0m                           Traceback (most recent call last)",
      "\u001b[0;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mOSError\u001b[0m: [Errno 5] Input/output error: '/Volumes/Storage/TUH_EEG/_DOCS/KNN_results.csv'"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "fid = open(os.path.join(basedir, '_DOCS', 'KNN_results.csv'), 'w')\n",
    "fid.write(\"Path,Class,Result,Confidence\\n\")\n",
    "#df_train_out = df_train_out.iloc[:2, :]\n",
    "#df_test_out = df_test_out.iloc[:1, :]\n",
    "\n",
    "l = df_test_out.shape[0]\n",
    "printProgressBar(0, l, prefix = 'Progress:', suffix = 'Complete', length = 50, mode='counts')\n",
    "for i in df_test_out.index:\n",
    "    path = df_test_out.loc[i, 'Path']\n",
    "    label = df_test_out.loc[i, 'Class']\n",
    "    predict = load_EEG_traces(path, fs=128)\n",
    "    vote_result, confidence = EEG_dtw_KNN(df_train_out, predict, k=5, fs=128)\n",
    "    fid.write(\"{},{},{},{:.4f}\\n\".format(path, label, vote_result, confidence))\n",
    "    printProgressBar(i+1, l, prefix = 'Progress:', suffix = 'Complete', length = 50, mode='counts')\n",
    "fid.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [],
   "source": [
    "  Parallel(n_jobs=4, backend=\"threading\")(delayed(core_func)(repeat_index, G, numpy_array) for repeat_index in range(nRepeat));\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification of seizure vs. non-seizure EEG using a 2D convolutional neural network (2D)\n",
    "\n",
    "## Data Preparation\n",
    "\n",
    "The minimum duration of the training set is 16 seconds, the maximum duration is about 3000 seconds.\n",
    "\n",
    "The duration of the seizure is minimum 2 seconds and maximum 2448 seconds.\n",
    "\n",
    "I will \"divide\" the EEG spectrograms (0.5 second window, 128 frequencies, 19 channels) into 10 seconds chunks. This will yield a 20 x 128 x 19 matrix to feed into a 2D convolutional neural network for classification (inspired by [CNN Architectures for Large-Scale Audio Classification](https://arxiv.org/abs/1609.09430)). Essentially, the resulting trained model will be able to distinguish between seizure vs. non-seizure events given a 10-second EEG recording. Future application could be trained on shorter window for faster detection.\n",
    "\n",
    "The \"divide\" part can be done in two ways: one deterministic and one by sampling. \n",
    "\n",
    "The deterministic way is the following: given a recording of duration D (e.g. 300 seconds of EEG recording), sample a window W (e.g. 10 seconds) from the beginning, either with overlapping of d (say 1 seconds, i.e. each successive window overlaps each other by 1 second), until reaching the end of the recording. Discard the last window less than W, or sample a window W backwards from the end. This will yield a total `floor(D / (W-d))` windows (plus 1 if choose to sample backwards).\n",
    "\n",
    "The sampling way is the following: take total N samples from the entire dataset of total duration L. For each recording of duration D, take `round(D / L * N)` samples from the recording, uniformly across time.\n",
    "\n",
    "We would need a lot of samples to train a proper neural network. The original MNIST dataset has 60,000 training images of 28 x 28, and 10,000 test images. The extended MNIST data (EMNIST) has 240,000 training images and 40,000 test images ([MNIST database](https://en.wikipedia.org/wiki/MNIST_database), a 4-fold augmentation. The training set has a total duration of 1186842 seconds, meaning a 10-second window with non-overlapping deterministic sampling would yield about 100,000 training samples. Since the seizure duration is 90464 seconds, there should be about 9,000 seizure samples. The test set has a total duration of 613232 seconds, and should yield about 60,000 test samples. Since the seizure duration is 60986, this should yield about 6,000 seizure test samples. The data can be futher augmented, of course, when adding overlapping windows.\n",
    "\n",
    "The size of each sample should be kept small. The 20 x 128 x 19 matrix can be quite large (48640 points) in comparison to the MNIST dataset images (784 points). WE can discard the frequencies above 80 Hz, given that 1) most seizures occur at theta band frequencies (4-8 Hz, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2936439/) 2) the highest frequency in the brain is the gamma band (30-80 Hz, https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1001045), and 3) the previous exploratory spectrogram plots revealed little to no activity at high frequencies above 60 Hz. The resulting matrix is now 20 x 80 x 19 (30400 points), about 37.5% reduction of data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Multivariate LSTM-FCNs for Time Series Classificatio](https://arxiv.org/pdf/1801.04503.pdf)\n",
    "\n",
    "[LSTM Fully Convolutional Networks for Time Series Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8141873)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "code_folding": [
     36
    ],
    "deletable": false,
    "editable": false,
    "hide_input": true,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [],
   "source": [
    "# Make the data\n",
    "def makeSpectrogram(eeg_data, channels, fs=256, window='hann', T=1, ts=0.5):\n",
    "    \"\"\"\n",
    "    eeg_data\n",
    "    channels: list of subset of channels \n",
    "    fs: samples per second.\n",
    "    window: spectral window type. Default 'hann'\n",
    "    T: window size (sec). Default 1 second\n",
    "    ts: time resolution (sec). Deafult 0.5 second\n",
    "    \"\"\"\n",
    "    \n",
    "    warnings.filterwarnings(\"ignore\")\n",
    "    # Parse labels\n",
    "    labels = [ll.replace(\"EEG \",\"\").replace(\"-REF\",\"\").replace(\"-LE\",\"\") \\\n",
    "                for ll in eeg_data.header.labels]\n",
    "    \n",
    "    # channel by time_index \n",
    "    wdata = np.empty((len(channels), eeg_data.header.num_records * fs))\n",
    "    # Make a matrix of the data\n",
    "    for n, c in enumerate(channels):\n",
    "        index = labels.index(c)\n",
    "        if fs is not None and eeg_data.header.sample_rate[n] != fs:\n",
    "            wdata[n, :] = signal.resample(eeg_data.data[index], eeg_data.header.num_records * fs)\n",
    "        else:\n",
    "            wdata[n, :] = eeg_data.data[index]\n",
    "        \n",
    "    # Apply the notch filter to filter out 60 Hz artifacts\n",
    "    b, a = signal.iirnotch(60 / (fs/2), 30)\n",
    "    wdata = signal.filtfilt(b, a, wdata, axis=1)\n",
    "    \n",
    "    # Make spectrogram\n",
    "    frequencies, times, spectrogram = signal.spectrogram(wdata, fs, window=window,\n",
    "                                                        nperseg=int(fs*T), noverlap=int(fs*(T-ts)))\n",
    "    \n",
    "    return frequencies, times, spectrogram # channels x frequencies x times\n",
    "\n",
    "def tileSampleSpectrogram(frequencies, times, spectrogram, subset_time=None, subset_freq=None, \n",
    "                          duration=50, lag=0, sample_end=True, calculation_only=False):\n",
    "    \"\"\"\n",
    "    Sample the spectrogram, given the returned frequencies, times, and \n",
    "    spectrogram (channels x frequencies x times), as well as\n",
    "    * subset_time: use a subset of time window [start_sec, end_sec] to sample within\n",
    "    * subset_freq: use a subset of frequencies [start_freq, end_freq]\n",
    "    * duration: duration of each sample in seconds\n",
    "    * lag: overlapping between each successive windows, in seconds\n",
    "    * sample_end: whether or not to do backward 1 sample when the last sample leaks\n",
    "    * calculation_only: return only the shape of the resulting matrix\n",
    "    \"\"\"    \n",
    "    if subset_time is not None:\n",
    "        a = np.where(times <= subset_time[0])[0][-1] # last point less than\n",
    "        if not a: a = 0\n",
    "        b = np.where(times >= subset_time[1])[0][0] # first point greater than\n",
    "        if not b: b = None\n",
    "        spectrogram = spectrogram[:, :, a:b]\n",
    "        times = times[a:b]\n",
    "    if subset_freq is not None:\n",
    "        a = np.where(frequencies <= subset_freq[0])[0][-1] # last point less than\n",
    "        if not a: a = 0\n",
    "        b = np.where(frequencies >= subset_freq[1])[0][0]+1 # first point greater than\n",
    "        if not b: b = None\n",
    "        spectrogram = spectrogram[:, a:b, :]\n",
    "        frequencies = frequencies[a:b]\n",
    "        \n",
    "    ts = times[1] - times[0] # sampling rate of the spectrogram\n",
    "    \n",
    "    # Calculate the shape of the resulting matrix after sampling\n",
    "    shapes = list(spectrogram.shape)\n",
    "    shapes[2] = int(duration / ts) # time points\n",
    "    shapes.append(int(np.floor((times[-1]-times[0]+ts) / (duration - lag)) + sample_end))\n",
    "    \n",
    "    if calculation_only: # only calculate the shape of the resultign matrix\n",
    "        return shapes\n",
    "    \n",
    "    wdata = np.empty(shapes)\n",
    "    wtime = np.empty(shapes[2:])\n",
    "    start_time = times[0]\n",
    "    #print(wdata.shape)\n",
    "    \n",
    "    for i in range(shapes[-1]):\n",
    "        end_time = start_time - ts + duration\n",
    "        if end_time > times[-1]: break\n",
    "        start_index = np.where(np.abs(times - start_time) < 1E-6)[0][0]\n",
    "        end_index = np.where(np.abs(times - end_time) < 1E-6)[0][0]+1\n",
    "        #print('start:', start_time, 'end:', end_time)\n",
    "        #print('starti:', start_index, 'endi:', end_index)\n",
    "        wdata[:, :, :, i] = spectrogram[:, :, start_index:end_index]\n",
    "        wtime[:, i] = times[start_index:end_index]\n",
    "        start_time = end_time - lag + ts\n",
    "        \n",
    "    if end_time > times[-1] and sample_end: # back sample 1\n",
    "        end_time = times[-1]\n",
    "        start_time = end_time - duration + ts\n",
    "        start_index = np.where(np.abs(times - start_time) < 1E-6)[0][0]\n",
    "        end_index = np.where(np.abs(times - end_time) < 1E-6)[0][0]+1\n",
    "        #print('start:', start_time, 'end:', end_time)\n",
    "        #print('starti:', start_index, 'endi:', end_index)\n",
    "        wdata[:, :, :, -1] = spectrogram[:, :, start_index:end_index]\n",
    "        wtime[:, -1] = times[start_index:end_index]\n",
    "\n",
    "    return frequencies, wtime, wdata # channel x frequency x time x subsets    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {
    "deletable": false,
    "editable": false,
    "hide_input": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[19, 81, 20, 23]"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eeg_data = EDF(P_train[13])    \n",
    "frequencies, times, spectrogram = makeSpectrogram(eeg_data, channels=channels)\n",
    "frequencies_sub, times_sub, spectrograms_sub = tileSampleSpectrogram(frequencies, times, spectrogram, \n",
    "                          subset_time=None, subset_freq=[0, 80], duration=10, lag=2, sample_end=True)\n",
    "tileSampleSpectrogram(frequencies, times, spectrogram, \n",
    "                      subset_time=None, subset_freq=[0, 80], duration=10, lag=2, sample_end=True, \n",
    "                      calculation_only=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The tiling sample method is going to separate the spectrograms into 3 categories: 1) tiles from non-seizure recordings (\"non-seizure\"); 2) tiles from seizure recordings, during seizure period (seizure type, e.g. \"SPSZ\"); and 3) tiles from seizure recordings, during non-seizure period (\"background\")."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "deletable": false,
    "editable": false,
    "hide_input": true,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [],
   "source": [
    " # base directory of the project\n",
    "# Where to save the project\n",
    "def run_data_creation(P, df, duration=10, lag=2, sample_end=True, subset_freq=[0, 80], \n",
    "                      fs=256, window='hann', T=1, ts=0.5, \n",
    "                      basedir = '/Volumes/Storage/TUH_EEG',\n",
    "                      savedir = '/Volumes/Storage/TUH_EEG',\n",
    "                      channels = ['T4', 'O1', 'F8', 'FZ', 'P4', 'F4', 'F3', 'FP2', 'O2', \n",
    "                                  'T6', 'CZ', 'PZ', 'F7', 'C3', 'P3', 'T5', 'C4', 'T3', 'FP1']):\n",
    "    fid = open(os.path.join(savedir, \"progress.csv\"), \"w\")\n",
    "    fid.write(\",\".join([\"Path\", \"Seizure Type\", \"\\n\"]))\n",
    "    l = len(P)\n",
    "    printProgressBar(0, l, prefix = 'Progress:', suffix = 'Complete', length = 50)\n",
    "    for i, p in enumerate(P):\n",
    "        printProgressBar(i+1, l, prefix = 'Progress:', suffix = 'Complete', length = 50)\n",
    "        # Subsetting the spectrogram\n",
    "        eeg_data = EDF(p)\n",
    "        frequencies, times, spectrogram = makeSpectrogram(eeg_data, channels=channels, fs=fs, window=window,\n",
    "                                                         T=T, ts=ts)\n",
    "        frequencies_sub, times_sub, spectrograms_sub = tileSampleSpectrogram(frequencies, times, spectrogram, \n",
    "                          subset_time=None, subset_freq=subset_freq, duration=duration, lag=lag, \n",
    "                          sample_end=sample_end)\n",
    "        # Parse the tile\n",
    "        q = p.replace(basedir, \"./edf\").replace(\".edf\", \".tse\")\n",
    "        df_subset = df.loc[df['Filename']==q, :].reset_index(drop=True)\n",
    "        \n",
    "        # Looping over all the tiles\n",
    "        for t in range(spectrograms_sub.shape[3]): # for each tile\n",
    "            savepath = q.split(\"/\")\n",
    "            savepath[2] = savepath[2]+ \"_spectrogram\" #(\"_nolabel_spectrogram\" if df_subset.shape[0] == 0 else \"_spectrogram\")\n",
    "            savepath = \"/\".join(savepath)\n",
    "            savepath = savepath.replace(\"./edf\", savedir)\n",
    "\n",
    "            if df_subset.shape[0] == 0:\n",
    "                seizure_type = \"no label\"\n",
    "            else:\n",
    "                for j in range(df_subset.shape[0]):\n",
    "                    if np.isnan(df_subset.loc[j, 'Seizure Duration']):\n",
    "                        seizure_type = \"non-seizure\"\n",
    "                    else: # has seizure\n",
    "                        if times_sub[:, t][0]<=df_subset.loc[j, 'Seizure End Time'] and \\\n",
    "                           times_sub[:, t][-1]>=df_subset.loc[j, 'Seizure Start Time']: \n",
    "                            # within seizure period: (StartA <= EndB) and (EndA >= StartB)\n",
    "                            seizure_type = df_subset.loc[j, 'Seizure Type']\n",
    "                            break\n",
    "                        else: # outside of seizure period\n",
    "                            seizure_type = 'background'\n",
    "\n",
    "            current_savepath = savepath.replace(\".tse\", \"_{:04d}.npz\".format(t))\n",
    "            current_folder = os.path.dirname(current_savepath)\n",
    "\n",
    "            if not os.path.isdir(current_folder):\n",
    "                os.makedirs(current_folder, exist_ok=True)\n",
    "\n",
    "            np.savez_compressed(current_savepath, file_ref=q, \n",
    "                     frequencies=frequencies_sub, times=times_sub[:, t], \n",
    "                     spectrogram=spectrograms_sub[:, :, :, t].squeeze(), \n",
    "                     seizure_type=seizure_type)\n",
    "            \n",
    "            fid.write(\",\".join([current_savepath, seizure_type, \"\\n\"]))\n",
    "            \n",
    "    fid.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Progress: |██████████████████████████████████████████████████| 100.0% Complete\n",
      "Finished\n",
      "CPU times: user 1h 33min 5s, sys: 12min 54s, total: 1h 46min\n",
      "Wall time: 9h 8min 42s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "run_data_creation(P_train, df_train)\n",
    "print('Finished')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Progress: |██████████████████████████████████████████████████| 100.0% Complete\n",
      "CPU times: user 41min 57s, sys: 7min 2s, total: 48min 59s\n",
      "Wall time: 2h 46min 36s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "run_data_creation(P_test, df_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Organize the csv file a little"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [],
   "source": [
    "import time\n",
    "def organize_spectrogram_csv(df):\n",
    "    df['Path_base'] = [p[:-9] for p in df['Path']]\n",
    "    C, IC, counts = np.unique(df['Path_base'], return_inverse=True, return_counts=True)\n",
    "    df['Num'] = IC\n",
    "    df['Num Tiles'] = np.nan\n",
    "    \n",
    "    l = df.shape[0]\n",
    "    printProgressBar(0, l, prefix = 'Progress:', suffix = 'Complete', length = 50, mode=\"counts\")\n",
    "    for i in df.index:\n",
    "        printProgressBar(i+1, l, prefix = 'Progress:', suffix = 'Complete', length = 50, mode=\"counts\")\n",
    "        df.loc[i, 'Num Tiles'] = counts[df_spec_train.loc[i, 'Num']]\n",
    "        time.sleep(0.001)\n",
    "\n",
    "    df = df[['Num', 'Path', 'Seizure Type', 'Num Tiles']]\n",
    "    return df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [],
   "source": [
    "csv_file = os.path.join(basedir, '_DOCS', 'train_spectrogram_labels.csv')\n",
    "df_spec_train = pd.read_csv(csv_file)\n",
    "df_spec_train = organize_spectrogram_csv(df_spec_train)\n",
    "df_spec_train.to_csv(csv_file, index=False)\n",
    "df_spec_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "endedess:  |██████████████████████████████████████████████████| 77239 / 77239 Complete\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Num</th>\n",
       "      <th>Path</th>\n",
       "      <th>Seizure Type</th>\n",
       "      <th>Num Tiles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>/Volumes/Storage/TUH_EEG/dev_test_spectrogram/...</td>\n",
       "      <td>non-seizure</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>/Volumes/Storage/TUH_EEG/dev_test_spectrogram/...</td>\n",
       "      <td>non-seizure</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>/Volumes/Storage/TUH_EEG/dev_test_spectrogram/...</td>\n",
       "      <td>non-seizure</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>/Volumes/Storage/TUH_EEG/dev_test_spectrogram/...</td>\n",
       "      <td>background</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>/Volumes/Storage/TUH_EEG/dev_test_spectrogram/...</td>\n",
       "      <td>TCSZ</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Num                                               Path Seizure Type  \\\n",
       "0    0  /Volumes/Storage/TUH_EEG/dev_test_spectrogram/...  non-seizure   \n",
       "1    0  /Volumes/Storage/TUH_EEG/dev_test_spectrogram/...  non-seizure   \n",
       "2    0  /Volumes/Storage/TUH_EEG/dev_test_spectrogram/...  non-seizure   \n",
       "3    1  /Volumes/Storage/TUH_EEG/dev_test_spectrogram/...   background   \n",
       "4    1  /Volumes/Storage/TUH_EEG/dev_test_spectrogram/...         TCSZ   \n",
       "\n",
       "   Num Tiles  \n",
       "0        3.0  \n",
       "1        3.0  \n",
       "2        3.0  \n",
       "3       34.0  \n",
       "4       34.0  "
      ]
     },
     "execution_count": 420,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csv_file = os.path.join(basedir, '_DOCS', 'dev_test_spectrogram_labels.csv')\n",
    "df_spec_test = pd.read_csv(csv_file)\n",
    "df_spec_test = organize_spectrogram_csv(df_spec_test)\n",
    "df_spec_test.to_csv(csv_file, index=False)\n",
    "df_spec_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore the distribution of number of tiles in the non-seizure recordings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "deletable": false,
    "editable": false,
    "hide_input": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Num</th>\n",
       "      <th>Seizure Type</th>\n",
       "      <th>Path</th>\n",
       "      <th>Num Tiles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2378</th>\n",
       "      <td>1734</td>\n",
       "      <td>non-seizure</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2379</th>\n",
       "      <td>1732</td>\n",
       "      <td>non-seizure</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2380</th>\n",
       "      <td>1733</td>\n",
       "      <td>non-seizure</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2381</th>\n",
       "      <td>1737</td>\n",
       "      <td>non-seizure</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2382</th>\n",
       "      <td>1736</td>\n",
       "      <td>non-seizure</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Num Seizure Type  Path  Num Tiles\n",
       "2378  1734  non-seizure     7          7\n",
       "2379  1732  non-seizure    30         30\n",
       "2380  1733  non-seizure    47         47\n",
       "2381  1737  non-seizure    47         47\n",
       "2382  1736  non-seizure     7          7"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "basedir = '/Volumes/Storage/TUH_EEG/'\n",
    "csv_file = os.path.join(basedir, '_DOCS', 'train_spectrogram_labels.csv')\n",
    "df_spec_train = pd.read_csv(csv_file)\n",
    "\n",
    "df_spec_train.loc[np.logical_and(df_spec_train['Seizure Type'] != 'background',\n",
    "                 df_spec_train['Seizure Type'] != \"non-seizure\"), \"Seizure Type\"] = \"seizure\"\n",
    "\n",
    "gp = df_spec_train.groupby(by=['Num', 'Seizure Type'], as_index=False, sort=False)\n",
    "Summary = gp.count()\n",
    "Summary.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 448,
   "metadata": {
    "deletable": false,
    "editable": false,
    "hide_input": true,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15, 3))\n",
    "plt.subplot(1, 3, 1)\n",
    "F = Summary.loc[Summary['Seizure Type']=='non-seizure', 'Num Tiles']\n",
    "sns.distplot(F)\n",
    "_ = plt.title('Non Seizure (Total {:d})'.format(F.sum()))\n",
    "plt.subplot(1, 3, 2)\n",
    "F = Summary.loc[Summary['Seizure Type']=='background', 'Num Tiles']\n",
    "sns.distplot(F)\n",
    "_ = plt.title('Background (Total {:d})'.format(F.sum()))\n",
    "plt.subplot(1, 3, 3)\n",
    "F = Summary.loc[Summary['Seizure Type']=='seizure', 'Num Tiles']\n",
    "sns.distplot(F)\n",
    "_ = plt.title('Seizure (Total {:d})'.format(F.sum()))\n",
    "_ = plt.suptitle(\"Data distribution\", y=1.1, fontweight='bold')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [],
   "source": [
    "Summary_non_seizure = Summary.loc[np.logical_and.reduce([Summary['Seizure Type'] == 'non-seizure', \n",
    "                           Summary['Num Tiles']>25, Summary['Num Tiles']<40]), :]\n",
    "print(\"non-seizure:\", Summary_non_seizure['Num Tiles'].sum())\n",
    "Summary_background = Summary.loc[np.logical_and.reduce([Summary['Seizure Type'] == 'background', \n",
    "                           Summary['Num Tiles']>25, Summary['Num Tiles']<125]), :]\n",
    "print(\"background:\", Summary_background['Num Tiles'].sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion: Apply a filter such that num tiles between 25 and 40 is used for non-seizure (14769 samples). For the dataset with seizures, randomly take 50% of the background periods. This shoudl help mitigate the unbalanced data problem."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make the balanced subset of training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14769, 4)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csv_file = os.path.join(basedir, '_DOCS', 'train_spectrogram_labels.csv')\n",
    "df_spec_train = pd.read_csv(csv_file)\n",
    "df_non_seizure = df_spec_train.loc[np.logical_and(df_spec_train['Seizure Type']=='non-seizure',\n",
    "                                    [df_spec_train.loc[i, 'Num'] in Summary_non_seizure['Num'].values \\\n",
    "                                     for i in df_spec_train.index]), :]\n",
    "\n",
    "df_non_seizure.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13446, 4)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_background = df_spec_train.loc[df_spec_train['Seizure Type']=='background']\n",
    "np.random.seed(42)\n",
    "for n in df_background['Num'].unique():\n",
    "    indices = df_background.loc[df_background['Num']==n, :].index\n",
    "    if len(indices) < 2:\n",
    "        continue # keep evreything\n",
    "    selected_indices = np.random.choice(indices, size=(len(indices)//2), replace=False)\n",
    "    df_background = df_background.drop(index=selected_indices)\n",
    "\n",
    "df_background.shape\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12716, 4)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_seizure = df_spec_train.loc[np.logical_and(df_spec_train['Seizure Type']!='background', \\\n",
    "                                          df_spec_train['Seizure Type']!= 'non-seizure'), :]\n",
    "df_seizure.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Num</th>\n",
       "      <th>Path</th>\n",
       "      <th>Seizure Type</th>\n",
       "      <th>Num Tiles</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>133786</th>\n",
       "      <td>1982</td>\n",
       "      <td>/Volumes/Storage/TUH_EEG/train_spectrogram/03_...</td>\n",
       "      <td>non-seizure</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133787</th>\n",
       "      <td>1982</td>\n",
       "      <td>/Volumes/Storage/TUH_EEG/train_spectrogram/03_...</td>\n",
       "      <td>non-seizure</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133788</th>\n",
       "      <td>1982</td>\n",
       "      <td>/Volumes/Storage/TUH_EEG/train_spectrogram/03_...</td>\n",
       "      <td>non-seizure</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133789</th>\n",
       "      <td>1982</td>\n",
       "      <td>/Volumes/Storage/TUH_EEG/train_spectrogram/03_...</td>\n",
       "      <td>non-seizure</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133790</th>\n",
       "      <td>1982</td>\n",
       "      <td>/Volumes/Storage/TUH_EEG/train_spectrogram/03_...</td>\n",
       "      <td>non-seizure</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Num                                               Path Seizure Type  \\\n",
       "133786  1982  /Volumes/Storage/TUH_EEG/train_spectrogram/03_...  non-seizure   \n",
       "133787  1982  /Volumes/Storage/TUH_EEG/train_spectrogram/03_...  non-seizure   \n",
       "133788  1982  /Volumes/Storage/TUH_EEG/train_spectrogram/03_...  non-seizure   \n",
       "133789  1982  /Volumes/Storage/TUH_EEG/train_spectrogram/03_...  non-seizure   \n",
       "133790  1982  /Volumes/Storage/TUH_EEG/train_spectrogram/03_...  non-seizure   \n",
       "\n",
       "        Num Tiles  Class  \n",
       "133786       38.0      0  \n",
       "133787       38.0      0  \n",
       "133788       38.0      0  \n",
       "133789       38.0      0  \n",
       "133790       38.0      0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final = df_non_seizure.append(df_background).append(df_seizure)\n",
    "df_final = df_final.sort_values(by='Path')\n",
    "df_final['Class'] = 0\n",
    "df_final.loc[df_final['Seizure Type']=='non-seizure', 'Class'] = 0\n",
    "df_final.loc[df_final['Seizure Type']=='background', 'Class'] = 1\n",
    "df_final.loc[np.logical_and(df_final['Seizure Type']!='non-seizure', \n",
    "                        df_final['Seizure Type']!='background'), 'Class'] = 2\n",
    "#df_final.to_csv(os.path.join(basedir, '_DOCS', 'train_spectrogram_balanced.csv'), index=False)\n",
    "df_final.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make the balanced subset of test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8586, 4)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "basedir = '/Volumes/Storage/TUH_EEG/'\n",
    "df_spec_test = pd.read_csv(os.path.join(basedir, '_DOCS', 'dev_test_spectrogram_labels.csv'))\n",
    "gp = df_spec_test.groupby(by=['Num', 'Seizure Type'], as_index=False, sort=False)\n",
    "Summary_non_seizure = gp.mean()\n",
    "Summary_non_seizure = Summary_non_seizure.loc[np.logical_and.reduce([\n",
    "    Summary_non_seizure['Seizure Type']=='non-seizure', \n",
    "    Summary_non_seizure['Num Tiles'] > 25, \n",
    "    Summary_non_seizure['Num Tiles'] < 40],\n",
    "), :]\n",
    "\n",
    "df_non_seizure = df_spec_test.loc[np.logical_and(df_spec_test['Seizure Type']=='non-seizure',\n",
    "                                    [df_spec_test.loc[i, 'Num'] in Summary_non_seizure['Num'].values \\\n",
    "                                     for i in df_spec_test.index]), :]\n",
    "# Randomly take a subset of samples from the number filtered data\n",
    "num_rows = df_non_seizure.shape[0]\n",
    "np.random.seed(42)\n",
    "for n in df_non_seizure['Num'].unique():\n",
    "    indices = df_non_seizure.loc[df_non_seizure['Num']==n, :].index\n",
    "    if len(indices) < 2:\n",
    "        continue # keep evreything\n",
    "    selected_indices = np.random.choice(indices, size=(round(len(indices)*(1-8574/num_rows))), replace=False)\n",
    "    df_non_seizure = df_non_seizure.drop(index=selected_indices)\n",
    "\n",
    "df_non_seizure.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8577, 4)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_background = df_spec_test.loc[df_spec_test['Seizure Type']=='background', :]\n",
    "np.random.seed(42)\n",
    "for n in df_background['Num'].unique():\n",
    "    indices = df_background.loc[df_background['Num']==n, :].index\n",
    "    if len(indices) < 2:\n",
    "        continue # keep evreything\n",
    "    selected_indices = np.random.choice(indices, size=(round(len(indices)*(1-8574/21045))), replace=False)\n",
    "    df_background = df_background.drop(index=selected_indices)\n",
    "\n",
    "df_background.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8408, 4)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_seizure = df_spec_test.loc[np.logical_and.reduce([df_spec_test['Seizure Type']!='background',\n",
    "                                                    df_spec_test['Seizure Type']!='non-seizure',\n",
    "                                                    df_spec_test['Seizure Type']!='no label']), :]\n",
    "df_seizure.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Num</th>\n",
       "      <th>Path</th>\n",
       "      <th>Seizure Type</th>\n",
       "      <th>Num Tiles</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>74778</th>\n",
       "      <td>1009</td>\n",
       "      <td>/Volumes/Storage/TUH_EEG/dev_test_spectrogram/...</td>\n",
       "      <td>non-seizure</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74779</th>\n",
       "      <td>1009</td>\n",
       "      <td>/Volumes/Storage/TUH_EEG/dev_test_spectrogram/...</td>\n",
       "      <td>non-seizure</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74781</th>\n",
       "      <td>1009</td>\n",
       "      <td>/Volumes/Storage/TUH_EEG/dev_test_spectrogram/...</td>\n",
       "      <td>non-seizure</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74782</th>\n",
       "      <td>1009</td>\n",
       "      <td>/Volumes/Storage/TUH_EEG/dev_test_spectrogram/...</td>\n",
       "      <td>non-seizure</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74783</th>\n",
       "      <td>1009</td>\n",
       "      <td>/Volumes/Storage/TUH_EEG/dev_test_spectrogram/...</td>\n",
       "      <td>non-seizure</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Num                                               Path Seizure Type  \\\n",
       "74778  1009  /Volumes/Storage/TUH_EEG/dev_test_spectrogram/...  non-seizure   \n",
       "74779  1009  /Volumes/Storage/TUH_EEG/dev_test_spectrogram/...  non-seizure   \n",
       "74781  1009  /Volumes/Storage/TUH_EEG/dev_test_spectrogram/...  non-seizure   \n",
       "74782  1009  /Volumes/Storage/TUH_EEG/dev_test_spectrogram/...  non-seizure   \n",
       "74783  1009  /Volumes/Storage/TUH_EEG/dev_test_spectrogram/...  non-seizure   \n",
       "\n",
       "       Num Tiles  Class  \n",
       "74778       35.0      0  \n",
       "74779       35.0      0  \n",
       "74781       35.0      0  \n",
       "74782       35.0      0  \n",
       "74783       35.0      0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final = df_non_seizure.append(df_background).append(df_seizure)\n",
    "df_final = df_final.sort_values(by='Path')\n",
    "df_final['Class'] = 0\n",
    "df_final.loc[df_final['Seizure Type']=='non-seizure', 'Class'] = 0\n",
    "df_final.loc[df_final['Seizure Type']=='background', 'Class'] = 1\n",
    "df_final.loc[np.logical_and(df_final['Seizure Type']!='non-seizure', \n",
    "                        df_final['Seizure Type']!='background'), 'Class'] = 2\n",
    "df_final.to_csv(os.path.join(basedir, '_DOCS', 'dev_test_spectrogram_balanced.csv'), index=False)\n",
    "df_final.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2D Convolutional Neural Network with Keras\n",
    "\n",
    "Recall the data is a channel x freqs x times (19 x 81 x 20) matrix. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/edward/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "import tensorflow as tf\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D, MaxPooling2D, AveragePooling2D\n",
    "from keras.layers import Dense, Activation, Dropout, Flatten, BatchNormalization\n",
    "from keras.regularizers import l1, l2, l1_l2\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [],
   "source": [
    "basedir = '/Volumes/Storage/TUH_EEG/'\n",
    "df_train = pd.read_csv(os.path.join(basedir, '_DOCS', 'train_spectrogram_balanced.csv'))\n",
    "df_test = pd.read_csv(os.path.join(basedir, '_DOCS', 'dev_test_spectrogram_balanced.csv'))\n",
    "df = df_train.append(df_test).reset_index(drop=True)\n",
    "df = df.sample(frac=1, random_state=1) # shuffle\n",
    "\n",
    "# Split df_test further into df_test and df_val\n",
    "df_train, df_test = train_test_split(df, test_size=0.3, random_state=42, stratify=df['Class'])\n",
    "df_test, df_val   = train_test_split(df_test, test_size=0.5, random_state=43, stratify=df_test['Class'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>All</th>\n",
       "      <th>Train</th>\n",
       "      <th>Test</th>\n",
       "      <th>Val</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22023</td>\n",
       "      <td>15416</td>\n",
       "      <td>3303</td>\n",
       "      <td>3304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21124</td>\n",
       "      <td>14787</td>\n",
       "      <td>3503</td>\n",
       "      <td>3504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23355</td>\n",
       "      <td>16348</td>\n",
       "      <td>3169</td>\n",
       "      <td>3168</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     All  Train  Test   Val\n",
       "0  22023  15416  3303  3304\n",
       "1  21124  14787  3503  3504\n",
       "2  23355  16348  3169  3168"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def print_split(df, df_train, df_test, df_val):\n",
    "    gp = df.groupby(by=['Class'], sort=False, as_index=False)\n",
    "    Summary_df = gp.count()['Path']\n",
    "    gp = df_train.groupby(by=['Class'], sort=False, as_index=False)\n",
    "    Summary_df_train = gp.count()['Path']\n",
    "    gp = df_test.groupby(by=['Class'], sort=False, as_index=False)\n",
    "    Summary_df_test = gp.count()['Path']\n",
    "    gp = df_val.groupby(by=['Class'], sort=False, as_index=False)\n",
    "    Summary_df_val = gp.count()['Path']\n",
    "    Summary = pd.DataFrame({'All': Summary_df, 'Train': Summary_df_train,\n",
    "                           'Test': Summary_df_test, 'Val': Summary_df_val})\n",
    "    \n",
    "    return Summary\n",
    "print_split(df, df_train, df_test, df_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "code_folding": [],
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Is shuffled\n",
      "\n",
      "\n",
      "call on_epoch_end:  0.016196999999976924 from  train \n",
      "\n",
      "\n",
      "call on_epoch_end:  4.9999999873762135e-06 from  test \n",
      "\n",
      "\n",
      "call on_epoch_end:  4.9999999873762135e-06 from  val \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Generating data\n",
    "batch_size = 50\n",
    "num_classes = 3\n",
    "epochs = 100\n",
    "\n",
    "class DataGenerator(keras.utils.Sequence):\n",
    "    \"\"\"Generate data for Keras\"\"\"\n",
    "    def __init__(self, df, batch_size=50, num_classes=3, shuffle=False, dataset=None):\n",
    "        self.batch_size = batch_size\n",
    "        self.num_classes = num_classes\n",
    "        self.shuffle = shuffle\n",
    "        self.df = df\n",
    "        self.dataset = dataset\n",
    "        self.on_epoch_end()\n",
    "    \n",
    "    def __len__(self):\n",
    "        \"\"\"Denotes the number of batches per epoch\"\"\"\n",
    "        num_batches = self.df.shape[0] // self.batch_size + (self.df.shape[0] % self.batch_size > 0)\n",
    "        return num_batches\n",
    "    \n",
    "    def __getitem__(self, index):\n",
    "        \"\"\"Generate one batch of data\"\"\"\n",
    "        start_index = index * self.batch_size\n",
    "        end_index = (index+1) * self.batch_size\n",
    "        if end_index > self.df.shape[0]:\n",
    "            end_index = None\n",
    "        rows = self.df.index[start_index:end_index]\n",
    "        \n",
    "        X, y = self.__data_generation(rows)\n",
    "\n",
    "        return X, y\n",
    "    \n",
    "    def on_epoch_end(self):\n",
    "        \"\"\"Update indexes after each epoch\"\"\"\n",
    "        start_time = time.clock()\n",
    "        if self.shuffle:\n",
    "            self.df = self.df.sample(frac=1, replace=False).reset_index(drop=True)\n",
    "            print('\\nIs shuffled\\n')\n",
    "        print('\\ncall on_epoch_end: ', time.clock()-start_time, \"from \", self.dataset, \"\\n\")\n",
    "\n",
    "            \n",
    "    def __data_generation(self, rows):\n",
    "        nrows = len(rows)\n",
    "        # Initialize\n",
    "        X = [[]] * nrows\n",
    "        y = [[]] * nrows\n",
    "        #start_time = time.clock()\n",
    "        for n, r in enumerate(rows):\n",
    "            #print(self.df.loc[r, 'Num'])\n",
    "            X[n] = np.load(os.path.join(self.df.loc[r, 'Path']))['spectrogram']\n",
    "            y[n] = int(self.df.loc[r, 'Class'])\n",
    "        X = np.array(X)\n",
    "        y = keras.utils.to_categorical(y, self.num_classes)\n",
    "        #print('\\ndata loading time: ', time.clock()-start_time, \"from \", self.dataset, \"\\n\")\n",
    "        return X, y\n",
    "        \n",
    "            \n",
    "train_generator = DataGenerator(df_train.reset_index(drop=True), batch_size=50, num_classes=3, shuffle=True, dataset='train')\n",
    "test_generator = DataGenerator(df_test.reset_index(drop=True), batch_size=50, num_classes=3, dataset='test')\n",
    "val_generator = DataGenerator(df_val.reset_index(drop=True), batch_size=50, num_classes=3, dataset='val')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "def data_generator(df, batch_size=50, num_classes=3, shuffle=True, dataset=None):\n",
    "    while True:\n",
    "        if shuffle:\n",
    "            df = df.sample(frac=1)\n",
    "        df = df.reset_index(drop=True)\n",
    "        num_batches = df.shape[0] // batch_size + (df.shape[0] % batch_size > 0)\n",
    "        for index in range(num_batches):\n",
    "            start_index = index * batch_size\n",
    "            end_index = (index+1) * batch_size\n",
    "            if end_index > df.shape[0]:\n",
    "                end_index = None\n",
    "            rows = df.index[start_index:end_index]\n",
    "            nrows = len(rows)\n",
    "            # Initialize\n",
    "            X = [[]] * nrows\n",
    "            y = [[]] * nrows\n",
    "\n",
    "            for n, r in enumerate(rows):\n",
    "                X[n] = np.load(os.path.join(df.loc[r, 'Path']))['spectrogram']\n",
    "                y[n] = int(df.loc[r, 'Class'])\n",
    "            X = np.array(X)\n",
    "            y = keras.utils.to_categorical(y, num_classes)\n",
    "            yield X, y\n",
    "            \n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "_df = df_train.reset_index(drop=True).loc[1:200, :]\n",
    "train_generator = data_generator(_df, batch_size=50, num_classes=3, shuffle=True, dataset='train')\n",
    "train_steps = _df.shape[0] // 50 + (_df.shape[0] % 50 > 0)\n",
    "val_generator = data_generator(_df.loc[1:50, :], batch_size=50, num_classes=3, dataset='val')\n",
    "val_steps = _df.loc[1:50].shape[0]//50 + (_df.loc[1:50].shape[0] % 50 >0)\n",
    "history = model.fit_generator(train_generator, steps_per_epoch=train_steps,\n",
    "                              validation_data=val_generator,\n",
    "                              validation_steps = val_steps,\n",
    "                              epochs=3,\n",
    "                              verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 16, 81, 20)        2752      \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 16, 81, 32)        5792      \n",
      "_________________________________________________________________\n",
      "average_pooling2d_1 (Average (None, 8, 40, 32)         0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 8, 40, 32)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 8, 40, 64)         18496     \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 8, 40, 64)         36928     \n",
      "_________________________________________________________________\n",
      "average_pooling2d_2 (Average (None, 4, 20, 64)         0         \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 4, 20, 64)         0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 5120)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 512)               2621952   \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 512)               2048      \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 64)                32832     \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 64)                256       \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 3)                 195       \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 3)                 0         \n",
      "=================================================================\n",
      "Total params: 2,721,251\n",
      "Trainable params: 2,720,099\n",
      "Non-trainable params: 1,152\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "model = Sequential()\n",
    "model.add(Conv2D(16, (3, 3), padding='same', input_shape=[19, 81, 20],\n",
    "          data_format=\"channels_first\", activation='relu'))\n",
    "model.add(Conv2D(32, (3, 3), padding='same', activation='relu', kernel_regularizer=l1_l2(0.001, 0.01)))\n",
    "model.add(AveragePooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))\n",
    "model.add(Conv2D(64, (3, 3), padding='same', activation='relu', kernel_regularizer=l1_l2(0.001, 0.01)))\n",
    "model.add(AveragePooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dense(512))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dense(64))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(0.25))\n",
    "model.add(Dense(num_classes))\n",
    "model.add(Activation('softmax'))\n",
    "\n",
    "# initiate optimizer\n",
    "opt = keras.optimizers.Adam(lr=0.0001, decay=1e-6)\n",
    "\n",
    "# Let's train the model\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer=opt,\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "\n",
      "data loading time:  0.1800969999999964 \n",
      "\n",
      "\n",
      "data loading time:  0.18613599999999764 \n",
      "\n",
      "\n",
      "data loading time:  0.18253999999999593 \n",
      "\n",
      "\n",
      "data loading time:  1.221063000000001 \n",
      "\n",
      "  1/932 [..............................] - ETA: 11:46:42 - loss: 3.9253 - acc: 0.3800\n",
      "data loading time:  2.4558139999999966 \n",
      "\n",
      "\n",
      "data loading time:  2.494997000000005 \n",
      "\n",
      "  2/932 [..............................] - ETA: 8:47:58 - loss: 3.9070 - acc: 0.3200 \n",
      "data loading time:  2.5261270000000025 \n",
      "\n",
      "\n",
      "data loading time:  2.510569000000004 \n",
      "\n",
      "  3/932 [..............................] - ETA: 7:51:59 - loss: 3.8321 - acc: 0.3267\n",
      "data loading time:  2.4734400000000036 \n",
      "\n",
      "\n",
      "data loading time:  2.406404000000002 \n",
      "\n",
      "  4/932 [..............................] - ETA: 7:20:08 - loss: 3.8415 - acc: 0.3150\n",
      "data loading time:  2.4163840000000008 \n",
      "\n",
      "\n",
      "data loading time:  2.3177030000000087 \n",
      "\n",
      "  5/932 [..............................] - ETA: 7:11:23 - loss: 3.8701 - acc: 0.3400\n",
      "data loading time:  2.3461929999999995 \n",
      "\n",
      "\n",
      "data loading time:  2.450057000000001 \n",
      "\n",
      "  6/932 [..............................] - ETA: 7:03:55 - loss: 3.8369 - acc: 0.3567\n",
      "data loading time:  2.4611810000000105 \n",
      "\n",
      "\n",
      "data loading time:  2.451362000000003 \n",
      "\n",
      "  7/932 [..............................] - ETA: 7:07:08 - loss: 3.8353 - acc: 0.3571\n",
      "data loading time:  2.4514099999999956 \n",
      "\n",
      "\n",
      "data loading time:  2.4317100000000096 \n",
      "\n",
      "  8/932 [..............................] - ETA: 7:00:06 - loss: 3.8254 - acc: 0.3525\n",
      "data loading time:  2.4308459999999883 \n",
      "\n",
      "\n",
      "data loading time:  2.1317169999999948 \n",
      "\n",
      "  9/932 [..............................] - ETA: 6:40:35 - loss: 3.8054 - acc: 0.3489\n",
      "data loading time:  2.0518320000000045 \n",
      "\n",
      " 10/932 [..............................] - ETA: 6:20:49 - loss: 3.7968 - acc: 0.3540\n",
      "data loading time:  2.077405000000013 \n",
      "\n",
      " 11/932 [..............................] - ETA: 6:03:54 - loss: 3.8003 - acc: 0.3473\n",
      "data loading time:  2.1076160000000073 \n",
      "\n",
      " 12/932 [..............................] - ETA: 5:50:22 - loss: 3.8122 - acc: 0.3483\n",
      "data loading time:  2.1091559999999987 \n",
      "\n",
      " 13/932 [..............................] - ETA: 5:40:06 - loss: 3.7995 - acc: 0.3415\n",
      "data loading time:  2.113742000000002 \n",
      "\n",
      " 14/932 [..............................] - ETA: 5:30:13 - loss: 3.7893 - acc: 0.3486\n",
      "data loading time:  2.0080290000000076 \n",
      "\n",
      " 15/932 [..............................] - ETA: 5:22:12 - loss: 3.7801 - acc: 0.3493\n",
      "data loading time:  2.1045110000000022 \n",
      "\n",
      " 16/932 [..............................] - ETA: 5:15:36 - loss: 3.7809 - acc: 0.3475\n",
      "data loading time:  2.131484999999998 \n",
      "\n",
      " 17/932 [..............................] - ETA: 5:09:21 - loss: 3.7768 - acc: 0.3529\n",
      "data loading time:  2.0769759999999877 \n",
      "\n",
      " 18/932 [..............................] - ETA: 5:05:57 - loss: 3.7666 - acc: 0.3567\n",
      "data loading time:  2.103934999999993 \n",
      "\n",
      " 19/932 [..............................] - ETA: 5:00:14 - loss: 3.7594 - acc: 0.3600\n",
      "data loading time:  2.0909130000000005 \n",
      "\n",
      " 20/932 [..............................] - ETA: 4:55:48 - loss: 3.7527 - acc: 0.3580\n",
      "data loading time:  2.0879719999999935 \n",
      "\n",
      " 21/932 [..............................] - ETA: 4:51:47 - loss: 3.7589 - acc: 0.3610"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-23-87954810255e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m                               \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m                               \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_generator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m                               verbose=1)\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/keras/legacy/interfaces.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     89\u001b[0m                 warnings.warn('Update your `' + object_name + '` call to the ' +\n\u001b[1;32m     90\u001b[0m                               'Keras 2 API: ' + signature, stacklevel=2)\n\u001b[0;32m---> 91\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     92\u001b[0m         \u001b[0mwrapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_function\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m   1416\u001b[0m             \u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1417\u001b[0m             \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1418\u001b[0;31m             initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m   1419\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1420\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0minterfaces\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegacy_generator_methods_support\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/keras/engine/training_generator.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m    179\u001b[0m             \u001b[0mbatch_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0;32mwhile\u001b[0m \u001b[0msteps_done\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 181\u001b[0;31m                 \u001b[0mgenerator_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput_generator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    182\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    183\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgenerator_output\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'__len__'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/keras/utils/data_utils.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    593\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    594\u001b[0m             \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_running\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 595\u001b[0;31m                 \u001b[0minputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mqueue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mblock\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    596\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mqueue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtask_done\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    597\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0minputs\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/multiprocessing/pool.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    636\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    637\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 638\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    639\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mready\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    640\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/multiprocessing/pool.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    633\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    634\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 635\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_event\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    636\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    637\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    549\u001b[0m             \u001b[0msignaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_flag\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    550\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 551\u001b[0;31m                 \u001b[0msignaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cond\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    552\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    553\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    293\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m    \u001b[0;31m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    294\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 295\u001b[0;31m                 \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    296\u001b[0m                 \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    297\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "data loading time:  2.1284629999999964 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "history = model.fit_generator(train_generator, \n",
    "                              epochs=epochs,\n",
    "                              validation_data=val_generator,\n",
    "                              verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = model.evaluate_generator(test_generator, verbose=1)\n",
    "print('Test loss:', scores[0])\n",
    "print('Test accuracy:', scores[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Serialize the model to json\n",
    "model_json = model.to_json()\n",
    "with open(os.path.join(basedir, \"results/EEG_TUH_spectrogram_CNN_model.json\"), \"w\") as json_file:\n",
    "    json_file.write(model_json)\n",
    "#Serialize weights to HDF5\n",
    "model.save_weights(os.path.join(basedir, \"results/EEG_TUH_spectrogram_CNN_weights.h5\"))\n",
    "# save history\n",
    "with open(os.path.join(basedir, 'results/EEG_TUH_spectrogram_history.json'), 'w') as f:\n",
    "    json.dump(history.history, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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    "editable": false
   },
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   "source": [
    "# Calulate the prediction\n",
    "y_test = df_test['Class']\n",
    "Y_pred = model.predict_generator(test_generator, verbose=1)\n",
    "y_pred = np.argmax(Y_pred, axis=1) # convert one-hot into indices\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "# Visualizing the confusion matrix\n",
    "df_cm = pd.DataFrame(cm, range(3), range(3))\n",
    "plt.figure(figsize=(3,3))\n",
    "sns.set(font_scale=1.4) # for label size\n",
    "sns.heatmap(df_cm, annot=True, annot_kws={'size':12}) # font size\n",
    "plt.show()"
   ]
  }
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