extract_features_segment_powers.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import mne\n",
    "from scipy import stats\n",
    "import scipy.io\n",
    "import h5py\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "mne.set_log_level('error')\n",
    "\n",
    "from utils.load import Load\n",
    "from config.default import cfg\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "loader = Load(cfg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change this one-by-one to process all subjects in the dataset\n",
    "# Yeah, Im lazy, shut up!\n",
    "subject_id = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_runs = loader.load_subject(subject_id = subject_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "preprocessed_runs = raw_runs.copy()\n",
    "\n",
    "drop_bad = False\n",
    "for run in preprocessed_runs:\n",
    "    run = run.resample(200)\n",
    "    run = run.notch_filter(freqs=[60])\n",
    "    if drop_bad:\n",
    "        run = run.drop_channels(cfg['bad_channels'][cfg['subjects'][subject_id]])\n",
    "        run = run.set_eeg_reference('average', projection=False)\n",
    "        not_ROI = [x for x in cfg['not_ROI_channels'] if x not in cfg['bad_channels'][cfg['subjects'][subject_id]]]\n",
    "\n",
    "        run = run.drop_channels(not_ROI)\n",
    "    else:\n",
    "        run = run.set_eeg_reference('average', projection=False)\n",
    "        run = run.drop_channels(cfg['not_ROI_channels'])  \n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ORIG\n",
    "preprocessed_runs[0].plot_psd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Default\n",
    "mu_plot = preprocessed_runs[0].copy().filter(l_freq=8, h_freq=12, method  = 'fir')\n",
    "mu_plot.plot_psd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Default\n",
    "beta_plot = preprocessed_runs[0].copy().filter(l_freq=13, h_freq=25, method  = 'fir')\n",
    "beta_plot.plot_psd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "The features extracted are band power features for the\n",
    "mu and beta bands\n",
    "'''\n",
    "\n",
    "method = 'fir'\n",
    "l_trans_bandwidth = 'auto'\n",
    "h_trans_bandwidth = 'auto'\n",
    "both = []\n",
    "mus = []\n",
    "betas = []\n",
    "for i in range(len(preprocessed_runs)):\n",
    "    both.append(preprocessed_runs[i].copy().filter(l_freq=8, h_freq=25))\n",
    "    # mus.append(preprocessed_runs[i].copy().filter(l_freq=8, h_freq=12, l_trans_bandwidth= l_trans_bandwidth, h_trans_bandwidth=h_trans_bandwidth,method = method))\n",
    "    # betas.append(preprocessed_runs[i].copy().filter(l_freq=13, h_freq=25, l_trans_bandwidth= l_trans_bandwidth, h_trans_bandwidth=h_trans_bandwidth,method = method))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "The EEG data were band-pass filtered for the respective frequency\n",
    "band, and the power was calculated by squaring each time\n",
    "sample. \n",
    "Then the power was estimated in non-overlapping\n",
    "0.25 s segments by averaging the power samples and applying\n",
    "a centered moving average with a 0.75 s window length.\n",
    "band power features were log-transformed\n",
    "power shift compensation was applied, subtracting the mean band power over the last 25 s.\n",
    "the band power features were epoched using 0.5 s pre-and 7 s post-cue.\n",
    "'''\n",
    "\n",
    "\n",
    "        \n",
    "def get_feature(data, freq = 200):\n",
    "    # Calculate power by squaring each time sample\n",
    "    power_eeg_data = np.square(data)\n",
    "\n",
    "    # Define segment parameters\n",
    "    segment_length = int(0.025 * freq)\n",
    "   \n",
    "   \n",
    "    power_eeg_data = power_eeg_data.reshape(power_eeg_data.shape[0], -1, segment_length)\n",
    "    power_eeg_data = np.max(power_eeg_data, axis=-1)\n",
    "\n",
    "\n",
    "\n",
    "    def centered_moving_average(data, window_size):\n",
    "        half_window = window_size // 2\n",
    "        cumsum = np.cumsum(data, axis=-1)\n",
    "        cumsum[..., window_size:] = cumsum[..., window_size:] - cumsum[..., :-window_size]\n",
    "        return (cumsum[..., window_size - 1:-window_size + 1] / window_size)\n",
    "    power_eeg_data = centered_moving_average(power_eeg_data, 3)\n",
    "\n",
    "\n",
    "    power_eeg_data = np.log(power_eeg_data)\n",
    "    return power_eeg_data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "thumb\n",
      "(50, 158, 296)\n",
      "index\n",
      "(50, 158, 296)\n",
      "middle\n",
      "(50, 158, 296)\n",
      "ring\n",
      "(50, 158, 296)\n",
      "little\n",
      "(50, 158, 296)\n"
     ]
    }
   ],
   "source": [
    "sfreq = raw_runs[0].info['sfreq']\n",
    "\n",
    "\n",
    "tmin = -0.5\n",
    "tmax = 7\n",
    "\n",
    "\n",
    "\n",
    "def get_baseline(data, length = 25): # 25s\n",
    "    end = data[..., -int(length*sfreq):]\n",
    "    end = get_feature(end, sfreq)\n",
    "    return np.mean(end, axis=-1)\n",
    "\n",
    "\n",
    "\n",
    "features = {'thumb': [], 'index': [], 'middle': [], 'ring': [], 'little': []}\n",
    "for i in range(len(preprocessed_runs)):\n",
    "    events, _ = mne.events_from_annotations(preprocessed_runs[i])\n",
    "\n",
    "    # mu_baseline = get_baseline(mus[i].get_data())\n",
    "    # beta_baseline = get_baseline(betas[i].get_data())\n",
    "    both_baseline =get_baseline(both[i].get_data())\n",
    "\n",
    "    for trigger in events:\n",
    "        if trigger[-1] in [2, 3, 4, 5, 6]: # Drop 'No instruction' and 'Rest' events\n",
    "            # Epoching\n",
    "            # mu_time_baseline =  mus[i].get_data()[...,trigger[0]+int(baseline_min*sfreq):trigger[0]+int(baseline_max*sfreq)] \n",
    "            # beta_time_baseline =  betas[i].get_data()[...,trigger[0]+int(baseline_min*sfreq):trigger[0]+int(baseline_max*sfreq)]\n",
    "\n",
    "            # mu_time_baseline = mu_time_baseline.mean(axis=-1)\n",
    "            # beta_time_baseline = beta_time_baseline.mean(axis=-1)\n",
    "          \n",
    "            # mu_data = mus[i].get_data()[...,trigger[0]+int(tmin*sfreq):trigger[0]+int(tmax*sfreq)] \n",
    "            # beta_data = betas[i].get_data()[...,trigger[0]+int(tmin*sfreq):trigger[0]+int(tmax*sfreq)] \n",
    "            both_data = both[i].get_data()[...,trigger[0]+int(tmin*sfreq):trigger[0]+int(tmax*sfreq)]\n",
    "            \n",
    "            \n",
    "            # mu_data -= mu_time_baseline[:,np.newaxis]\n",
    "            # beta_data -= beta_time_baseline[:,np.newaxis]\n",
    "\n",
    "          \n",
    "\n",
    "            # feature extraction\n",
    "            # mu_data = get_feature(mu_data, sfreq)\n",
    "            # beta_data = get_feature(beta_data, sfreq)\n",
    "            both_data = get_feature(both_data, sfreq)\n",
    "\n",
    "            # power shift compensation\n",
    "            # mu_data-=mu_baseline[:,np.newaxis]\n",
    "            # beta_data-=beta_baseline[:,np.newaxis]\n",
    "            both_data-=both_baseline[:,np.newaxis]\n",
    "\n",
    "\n",
    "            # Format data\n",
    "            data =  np.stack((mu_data, beta_data), axis=-1)\n",
    "            \n",
    "            # Append data to the right list\n",
    "            #features[cfg['mapping'][trigger[-1]]].append(data)\n",
    "            features[cfg['mapping'][trigger[-1]]].append(both_data)\n",
    " \n",
    "for feature in features:\n",
    "    features[feature] = np.array(features[feature])\n",
    "    print(feature)\n",
    "    print(features[feature].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x18116cef190>]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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Gq2tGtH7ZuKoSvTHpmpGj/BNSoNEpiWyH9M6Z8ZAEZbvGZsR8iV8cPBXKe7Yjqqri1rtewCfvfL5pPkAvn5zDX3zvORybjucoASdIowQAn//ZyxEeCdMMrBqROG0mKeBIJhTk0gnWiCxV5kPKiFAgE6eLGPCW8YlLaUbW15yY1dwNM6kE0knjkg07I/KVnxujB2ii8nLk2HQe//zgy/i3h/bhFwenmvIzrr3tYdzxyH78xfeea8r7NwNVVVGW7LS/9nhrp4MzzWdWGioHxMuCYVbShyiKIrLXnBFZYogebZeMgRe6YluaqT9LxyjNRHvscpR/YkYLNLoy5uBgWG/hPTnXeCAyVyjj2zuOiL8XSvFZgFrN80dnxJ+/8URzHrZ0Lb54PF6GUW7M5MuiRAiYB6QxtbSTqzaguTfTM4A0IsVKFYVyPNYCq+GmMDSL2XMmCByISNDDz8ljwyuxFauK0kz9rpnFUgXFcjWyxUQ+d1SasXYzUUZkPISMyBP7Jk0/s1BevvX/F44ZgcidTx8NPaDOS0HepqGuUN+7mZycNc8dWVzGwaoXbvyvZ/DK/323mBEVd+QsrGmWS0yyItZmA6ER4dLM0kFVVcwVwynN0KyauA2981KakR1Xf+/fn8Rln74/ksyOHBRQmciq3QkzI3LK4kWSX6IPGS/mby+MGYHIfLGCu545Fuox7JK6kqgU2A5QQNyvm13lS9EF6nFnfK6Abz51CONzReGKHHco45BJJtCZSSGX1h6PQda/L//8AB4M+fees2S0ja6Z9s/McSCiY1UkN0JcMyJGD7rz75dMKCJj8uCLJ7F/YsH04GgVdlG+NVMVpkbE+lnlS0svI/KZn+zG1k/8BC8dd/88Xzimff28tb0AgKdDblV97qjxfnEL1t2g62z9YKf4t+WcOXPjh8+OgeQ07dKBJmswAATuStk/Po+Pf+dZfOSrO1CthheoGqV1bf3uielzJggciOjQh5lQICLhoMRVI1Jv8i7Ra9mlTs633rnU7uaylmbI5n16sdRwHdfai78UMyIP7RnHfLGCh/aMO75mvlDG/gmtc2jbxhUAEHqN/NkjRsZloY3OMwUiowNGILLYRoFUK/nB00fFn59pm0DEnHEIuo5T5mx6sYRDpxbCOz5raYZ9RJYeshBIUZSG3iuuPiLGwCT3QMT69ckIJtzaBiKW0kxfRxrppPZZNToTwtqunA/x4btYrOAzd79oygREAZ3TAxPOi+OusVmoqpZtWqOPPw87O/S8dB4WY+Y+7AYFIqv6csjo3VusE6nlxGwej+2bFH9//thMW8yDmrVkjINOUZ+Syryy8LtR5iwjSDr142unrKITHIjoCFdVl7KFV4wLOF4XiJeuGaC2bh+WT4cf7EoznZbSTCKhYEVINu90bigbFubD9yfPj+Eff/oS/u7ul0J7zyDQOT0w4TxRlISq56zulc5FeNdxqVLFC1Kpr50WUdrpDvdkkW3CuVkq3LfrBFQVuGBdH7oySeRL1dhNsbXDuj7SA9+vWFW2PnguzECkYC4dkafNUsjKcSCiY8yZadw/Iu4Zke46GpiwSjOqquKGr+/EX/3ged/fazenx66kJDpnGhSs0iJE7xdm7Z/aj6ciHs43m6+fEdl7UmunPWtVD3L6YMR8iOfi8KlFUwtsOy2iFOyO9GTRofvMcEaklif3a2aArz1jCOeu6QOAtrDEn7EGIgFnuciByPPHwgtEZi1dj3QNFivVtsg4ucGBiI61R7sRKJhZLFVQCVGs1Chef8feXDiByKHJRXxrxxF8/mf7fN8odnXPThvH27AEq5SWJd1JmDvdqUXt/EW5+69Wja6wQ6cWHK9L8o8Z6MyIXX+YnipHp8xOqu2UETkpZUQ69N0oZ0RqeUp3Jb54wwDO1QXP7aATqSnNZINltqcWpEAkxIyIVSPSIfkqtXtAzIGIjuGqGkJpRnrQx2kCr1UV7sSC5ZiDZhtk+26/03ztNSK12aoVXZrN+0SDgloKfJoRiJzSFybreW0lCyWjK6xUUWsCAoJ2f925VFMyIvRz1+r6k3ZaQMnhd7gna5ybJdhd1QiT80W8rI9JuHB0AOev1TIi7TAyYb7GpyNp+nevyBmRsZk8JkKwFwBkjYj2jMqmEkjocsZ2yizawYGIjlG2aLw0k00lkNKvkLiUZ4plY5ptvUDE+pAKmhEZk4yM/FrG2503a9cMEJ4lPQVpojQT4gNmWg9E5iNYLI5OLeLeXcdrvAacyjNUD+/OJoXNfbgZEe2a2DSsGZm1ywJaLFdFQDnSk0NuCdXnw2SHHnBsHu7CQFcGrzljCAlFawE/6FISjAN0z1MmMOiojinLWhRWeWbe8oxSFGXJlAg5ENGxutY1gqIosWvhlY/D7oEu82vnrzb9PXAgMh08ELErzdjpd0QgstBYIEK7DTJJC7NrhkozUTy0/uQ/n8b77ngSP3vR3LJ7/+4T+L/376nJ/Mg6omaIVSlLtnm4G0D71Lcn9M6xVEJBf0caHWnumrHjqQNaIHLR+gEAWtC2fbPWBv59qaU3jtBGLatnu4L6QdFaR82XYY0xoGtNnoPVsUQ6ZzgQ0fEq5PRKd8D6YrOg3y+XNg+Os+O9r96I2995Ee78yGsAaGWPIA6Sx6RAZMZvRsSmjGEXQJHLZeMZEbNYNdTSzDxlRMotd+IkTxBr6/C/PrQPn/rRbnxHmq8DmEXbtCCHKdw9ql8Tm0e6xb+1g5cICY6HurNIJBTxMGCNiBkKRC7eMCD+7dqtawEA39sZ70AkLx702vrYkwu2mZzWRenU0bcQ0mY0bxOIUOcMByJLBMPsK5ypq10B64vNwk/GJ5NK4JrzVxu71nI1kHvf8QZKM1Y7Y6DWRwQwOnwo6xAEWcjZjK4Z+t1VtfWaglN6NuvgpH1a/GmLiFC0sTcpI0Jlv40rutqqvj2pP1wGdU1SBwciNVSqqnDhvUgKRK46bxUyyQR2H5/FrrHwxJthE1ZGhEozq/rCza7S2tEhZ0SWyHXIgYiOlzksfoibzTsJJeuVZWQ6MklxoQcpzxxroDRD521lrzF8yj4jktHfP/h5niuWhZCzOWJV49y1UrCaL1WELsUpEJm0eMTIgx+NXX84wZOqqjimByKr+3NtZchEGT0qBS6V2nyY7Dkxh8VSBZ2ZpNjEANo5o/LMI3smojq8utA9n03pGpEGSzOr9LUrrPvHLiPSwRmRpYWRkg4nEImblwiJEO1aYN1Y0R28KyVoRqRQrqBU0SKDlb1Z8e+dNl0z9GDwW/qRkYdd9enTh8NaPArlimmRaOWCIbcRHjqlBQDbNg7iFaP9Yl6KHKCYBj9mU2JBLpQroZSUZhbLIjBa09chLaLxuEfcsAYi2ZCDtKXA04enAADnre1DMmF2p36lniHZcWiqxUflHcqI0IPe6Jrxfs9Wq6pY60ZEIBJWRsRcOgLk0kz87yE3OBDRofSwXfo/CEEV182CjsOuBdYN0R7r0121UlWFEyVQa6HuhuyqurLHyIjYlZXC6JqR25qzqXBTndbjamUgImdiyERsZW8O37n+1fjCe14FADg0uSCCDHnwY3cuJR62VRUiMGyEo7pQdaAzjY5Msq2cIan9vLfDbCYVdkZkNl/Cu/7tMXz55wdCfd9WQGWZrev6ar52oS5e3RHjNt6ajEiANXy2YGRXae0KLSNiCZQALs0sOSiilD/kRiA78riIVWkn2ukz40M1cb/zZsbnCibTLD8ZC8pOdWaSJo2I1eIdAPr1QGSuUEYpYPfFrDTDQbSslsMZ8T5l6eZppa/MKZssFpUe1w1oPh6zhbI4RlpwE4q2wMk7rzDq3NQxQzNs2qm8Qdcvmf11ZPSumZCDqJ+/PImfvTSOf7jnxZYIm1VVDW2MPOmNzl/XX/O1raN9UBTNWZf8WOKGNSMSpDRD3XsdaWPtCuPeKVWqYj0lDxuASzNLDvog7dL/QaDde1xSZgsi5e4zI6JrJvyWZmR9COAvYyELa+XAyS4jItvRBy3PyBkR+eEbhmDVGogsSIFpparik3c+jx8/N9bwz7Fj0sZSnhbHXDopyl5UnpHLk4qiIJNMiBbEMHZcR3QPkdV9WiDSTop/yujR9ZYTHUXhHju5t47PFV2t+MPilu89h4s+eTdePD5b/8UuFMtVvKC7iNplRHpyaZw50gMA2HFwqqGf1SwMsWrwrhla5/o706H68Mj3X9a2NBP/e8gNDkR0aFcWViASN7FqYI0IZUR8lmbGps2maL4CEcmKnoIPRTGrxYlkQhGzF4KWZ4wZDmlTRiwMU7NTlmBADkzv3XUC//bQPvyPLz/VlPKEXUakRwrmrDoRa2eVoiiGTiSEc0FC1TX9Wsq6o51KM7oYujdnttcO+9jlUQVPHmh+GePJ/adQqqh4tkEL9hePz6JYqaKvIy2uKysXbegHEONAxCIGFV5QxQqqHkd1UPdeX0c61AGa9B6KYgRKAJdmlhy0oHSEnBFpd43IYEALdTIzoxslUEYklxLH25XRdul29HVSC2+DgUguhVRCEW2lYaRUrUZr8s5FDhR+8Myxhn+Wlcn52vMhZ5VG6wQiAKRSVePngq4Jyoh0pJvbNXNociE0e226fulayzWprCSPU3jqwGSo720H/V6NiL1VVcVXHtM0LRes63O8Ty8c1XQi3/rFYfznU4db7qlTD2tGRL4PvJZUKQOqBSLh3TuyfkU+v2xotsQQgUhIGhF6gPodId0s6EbyqxGhdlY/dV1VVbFLH/V+5kqtjW/GR3stCWP7OtJiV+KWqWpUsGoEImkoSrhmVVZ/E3lBOyk9dL7+xMGGf5YVazYGMM9Sop3rIQpE8kZphghzpsq4HngN6Z1YzVT8v3xyDq/91H14x+d/Hsr7idJMzhyIhN01IwciT+xvfkZEBCI+Z0HJ/NtD+/DVxw9BUYD3vXqj4+suP3sEq/tyODFbwB9/85f4j8fDv+Ybwdoem00lRPeP13V8elEORMLMiNg/n8IozZQq1cglBByIQHtwLpSClS6ciFtpZkHMEPH3+1l3zfUolqt42+cexdeeOARAa+UD/AUJu/V69ekj3eI8uh13oy281mGAYT5kTlkyInIqX25vfmL/Kew5EY4VNGHn/SKLf62lGQqS5NeICbwh7OooA7TCEog0ozTzT/ftAaDZa3tNq7shxKpN9hGRA5E9J+YwZRNMhkWpYhgVBg3iq1UVf3/PSwCAP/+1s3HZlhHH167ozuInf/Q6vPfVpwEA/u7ul2KTMQZqMyKKoogNpdd13KQRCbEDj9YiazNFI6WZhWIZ//bQPrz+U/fh9vv3NnyMjcCBCLR5F6RIXrKlGcqI+Pz9NqzQHlZHp/KeulIOTMzjqQOnkEoouP6yzfjg6zcD0HaUXh8Iu/VsypZVPaLDYnV/zvH1ZPNuFYZ6hTIiVP/PpcJzFK3pminYByJA+BNK7TIidhoREkWKjEimORkRCowGdBM6ofgP+WGeL1Vwl1TqCuP9RfuupWsm7No8aUQo+/7zl5tnACYHH0GD+BOzBcwVykgmFLz70tPqvr4nl8ZN15yN9YOdGJ8r4AsP7Qv0c5uBKH9IYlC6Rr1+zkYgkjG8ZsIozZRrzczk4wuS0XjfHU/gk3c+j6PTefzw2bFQAvagcCACIF80FtmlKlZdCOiTMtKTRS6dQKWq4sgp+9Hxdj9nZW8OH7tqi7BMV1WtVdQLlBE5c2UPtq7rwxff8yr87du2Or6+8dIMZUSsafcwAhHt4ZvRgxt5wSCfFdqB2Q36awS7jIjsHLxaD/JOzOY1MzPKmuVkjUh4D1w6HprB0ayMyPd+edQUOM01eF5VVZUyItasWdgZEe0cXb5lJQDgG08eDvX9ZeQg2Y/PjwzNMlrb31F3hhWRSSXw0SvPBAB8NSblmXKlirJNe6zfeUt0vzerNCMLVYHGSjO04fvTq7fgzo+8BomEvbanFXAgAmChpC1UqYTi+Waqh6G4jkcgIrw5fLbvKopi7Jw9lGcWLKLfXDopbh4vu67J+aLYFZ65sgeKouCyLSMiM2JHXwfZvDeWEaEHcEY4iobXvrtWP355waBBaqePkI4mHD8HgkohcqmlR9KIkFajVNHcIOdtxKrZkDIii8WKKGMMdGnHYFi8h3uPPPjiSdPf5wrGec2XKrjtvj34lwe9p6IXSxXxkLJqRMIszeRLFXGf/v7lpwMA7tt9AodPNaeNd1rSLwW9dw7q2TTKnHrlVzZplu9jM3mT31BUFKVsr5wRkd2FvUD3e68kVo1raYbWt187f1Vo/llB4UAEtQ/PMDBKM/EQqwaZNUOsH+wCABzUdz9u2Imq7DIWE3MFHJmqzbBQlL5+sNPzsdL7By7NFMzaiHDFqtoxUcsqBaaqqgoBMAUioWdE9N2ZPPfDGmRQOWp8rmCavCteE5JGhI4lnVTEMRg6i3AFn9ZyKGV6js/kce0/PYy//fFu/PVduzxnAei6TSUUsQMVx14M79gpAM+kEjh/bR9effoKqCrwdV1vFTamjEjAWU2UETltRZev71vRlYGiaK69YXU2NYIcaGdTdte/t8+Z7mFT10yIGRHZ5wgIbmimqqrt7Jqo4EAERmo4rLIMYCzmsSnNFIJb2NNux4vBkl1Q12sjJv2N//sIrvq7B2seGrv16ZxnruzxfHyNlmbmpK4ZQCpHhJIR0R7A1LJK19qphZKwTd84pC3iQdPjdiwWK2JxlQORHstQxyG9dHZytii17xpZk0aFu/lSBc8emRY+NINdGdF+aHhxhHuPWBdl+nw//+DLouyn/Vxvi7fwEOlIG8fehNIMdVENd2ehKAreccl6ADDpXcIkjNLMgYAZkVQyIXXkNR6ILBYr+MOv7QhsDkiBdjqpmObkiNKMx+t/RjZH1LMpsgYxKE5BgxEQ+7sOy1UVdEhyKSoqOBCBbGYWTscMYOw8i+VqYOvxMAkqVgWkQMRDaWbRQ0akXKni4OQC5gplMRaeoAfFllXeAxESqwYtbdAiRGnYMDMidExr+igjYuzOAW1nSO61YVltA0YGIpNKYK1u555KKDU15mH9Z5+cK0hGcsZn1+i5+MT3n8N/++xD+PaOIwAMoSrQPFdIa7mESjMvWrqSih4DTaN1t9ZfJcxAZFx/IFPJbNOQXrILOVNGyL47TkH839/zoquO48CklhHZ4DMjAmj6M8CfNYAT3//lUXxn59HA5oCi9GF5KPstzcjCd5M5YoMZRRGIWI4v6ARrJ6fWqIj+CGIAfYhhpqjkskIcOmeoRBSkNCNaeD1kRBZtAh5rICJ3MVjbW6k0c5aPQESUZhaDtTpS/T+d1HZCwr67wYdMuVIVgccqPSOyoF8LFIgM92TFAy7M0gzpQwY7MxjUA7WeXK0pHGVExmcLIliVxarZBvUyT+peGPftPgHAaN0FjGA17ECE3s8qAt43bglEPG4QrK27gHHs5aoa2kaDhKok8A5TKGzHtNRVNVco13RNHJxYwN/f8xL+4nvP2ZqPqaqKA+PamnCaz4wIIAUiM41nREpV4zP41g7/Al+xGbE8lP1e/7OS34z8PGlUY0Xfb5UPiKyiz2vEXIqKPgyI/ghigN3Ds1HSyYQQPUZdnlFVVTxkunyKVQFgg+Q3Uc8N0S0jQjtLecdi7ew4ri9Kow420XY0Wpop6yWSVIIyIuGo3eXAYlWftujSQ5LS0St7c6JcEmZpRrTKdmUwoLvjdudqg1DKiIzPFcTxmtp3G3gYlipVoSHYN67935wR0X5O2F0z9H4j+iyduUIZhXIFhy1dX/4zIlLJKmMsnWEJVk+KjIh23H5bR/0iZ0TsutoOT2lBRqFctQ0WTy2UxPf4uV+JEX067fEQAhH5GvrCQ/t8t6LSvZ6tyYh475pRVVVkr3pyaSQTitjcNPoZ1tOI+L2H5CywkxNuK+FABOEPvCPiIljNl6piNHUQjci6gU4kFG3BPVmnnmunEaHSydi09r1yhsjqdWE1FfJCo4EI7YxT+qKRDWmgGR1PZyYpHmIkGj6hZ0RGerLia2FmREgIPNKTFZN2aSy5DJUBTs4WjK4ZuQRBXTMBzsWBiQWhgyFodhEg+4iEG6hTYEBB1ly+jAMTC1BV7Z6kMpnXTMa06IQwzkvYAwEBw8yMAhE696WK2pTOEqu421raHJMGV9rdWxRkru7LBcom09DFMEoz8mZv78l53548lP2syYiQWNXDZ7xYqojPSQjfQzI1y4vAwVKa0c97sVJF2Udmzgi84hECxOMoIsZuFx8GcRGsyi3EQX7HTCohxJaH6niJ2J3LV4z2AzDMmRZcMiJF/YbL+LhBOsXOsRrIlIduYGrdDisjIu+kafc/b5sRCT8Q2S1Z7F+0fgD/57e24ta3nl/zOioDjM8V7Nt3xULs/1zYOcUOdNVqRMLOiFCwRzvuuUIZL5/UHpqbhrvEteU9I2J0QhCKohiC1ZA6Z4xARDtH5tR++JsZa3Bh/bs8QduuI+3ABOlD/GdDAGC4V/t8whCrWr1ixmb8BTf5slNGxHtphkTNSam7Ktug2Fscn1P7rrTh85OZi1PHDMCBCIDwB94RlH2IWiNCP78zkwxsWkNBVb0F0a4DaftmzTPg+WMzmJwvmm4Yq4U1ZScyPvxczKIw/zc8lWaMQCScXQwtTH0d6ZqHLmlEVvZmjdLMYim0QWAvSqZwiqLgNy9ehzNsOpGGRGmmKNLs3TazZqzZoby0+3Ni78naQGTQJhAJUyNSrapi0abSzGyhLEpDG4e6xOfsWyMilWaA8G3eKRAZ1gMoebcatpU8UDsk0loaPCZN0LbLiJDB4ehAsEDEEKuGEIhY1li/a27BofThpzQjj4qgcofRgdec0oxWWtH+7Cegp9+HA5EY0ezSTNQDhRoRqhJ0Q9bbRdLNkJPO5UhPTgy/e3TvhGmRkCfEqqoq3t+PkltesIOUU0joltKDtLCsmUVGpCMljOTmi2WoqoqxGeOhQyLIsvQQ9cr0Qgl//u1nsEtveyYoEKkn+qVAxFSasZm+Kx/XUwdO4bxbfoyzb/4RfudfH3MM2OwyIoM2pZnFUiW0AEz+zOTSDAlVNw4FyYjUilWB8DtnKANBJYuE1OXUlIyIvgmgvYnf0gxlSeTP1A8UiJz0mb2ww6pvmfNZDncqCfvpmpmRpngTYV0jThkRRVFEecZPQC9KUVyaiQ9C15AOr30XMCbd+r0pwkaYmTUQaGU83pBieKDlhrl08xAA4OG946bIXdaIyL3t2aT3Y00lEyKICJICrc2IhFSakXbSlB1TVe19D+ut0OsGOtCVSYqHgd8W3q88fgBfeewgbr1rl/i38bkCxueKUBTDLM0JKs2MzeTFua9n8f79Xx5FuaoFjQ/tGcdzR6dt39s2EOms7ZpR1XBcbAHzYkwdQebSTLf/QIR8RCxiXzo3YWQrypWqCETWSRmGZlnJA7LZnlZ2tZqayaUZu9b4KZtuIj+slEozjc45odIMZVL9ZkScShV+SpO2ouYGSpu2x2cTOHSQ6NtPacZhdk1UcCAC40MOPyOi74KjLs2IjE8jGRFvtdK8w8969elaIPLInnHTw0IOROQHgx+NCBB8wVZVVbTvWsWqDZdmpJ20rJkZnytgQtfGrF/RCUUx3Eb9ds7sPaE9YJ86cEqUSl6U3GnrfeZyOy2gZYXkY7VLTVsHsdmJsatVVZRmtq7rE/8+KP08U0ktJHdVkZFLJ8QDQcuI6IGIVJqxCmmdmHZ44AZtnbTj+GwBlaqKdFIRmQJANk4L14uoWlXF70Uaj9rSjKQRsWmNlyfNBoGyceWqajug0Q9Umlmpd6f5Ls04ZkT8lGZsMiJhiVVdNB00gNFfRoQyLPEIAeJxFBFDGYNmaUSiFquSd0WQ1l3C6/yVBZvSDABcsnEQALB/YsFUEz41H1YgEqwWW5Z2YmlL+26ju3R5J52QHvC79EChvzMtHpbCfdanYJVKDnOFMl44NoM9J2bx9BEtQ+HFnVa2eQc0PY/czpe1ZESmForCdI68I+xKj8dm8lgoVpBKKHjtGcPi3+WMSDqZEC6WYUwoBczmhJTZOTq9KAK/04a6xMOmWPH2M4/rXR3WEkRYfjMARIZsTX+HScfVLC+R2XxZdNKRxkMuv+RLFZOQ3K40Q//WFzAjkkklxDltVCdCGZFVvYZA2Q9OD/qcjxEHs7YZEe+BjOvxlZ11jJ1p/23wTl04URFuLaJNMUozYXfNxEOsaswQaTwjUlcj4lCa6etII5NMoFipYkwSwcmLHYkHkwmzzbK34wu2cyxLu+KUxdAszIwIoAWCi6WKsLFfL3kvaJ0zi747Z2inDwC3fO85PHXAaFs8y6NN/lB3VgRAb71orelr1kzT4/smoarA5uEurOnvwP6JBdudGDnmrunvwOYRw3VzwPIwz6YSWChWQsuIyPcyZZnIP2RFVwbd2ZRI33spzeRLFezXz7H1fIaZEaF267WW4Y7NGK4HGBmOzkxSZCbk8stxi27DLhCZaTAQATSdyOR8ESdmCzh7deC3MTIieiASRUaENh49NqWZsDQidoGDVWt1+NQi1g10uPqD5DkjEj+aMWsGkH1EIs6I6L9fEA8RIuPxhnTrQCIfBtnAaCZfFu2z9GDw0zFDBL3h5c4JEYiENKzKulDTOXlBz4jI3QZy54xXTs0XTc60chACAGd6dKeVjb6uOneV6WuGWFI7F4/tmwQAbNu0wrXrRS53kv13Ty5VM906F5IwmJCzm9a5OuSnYnTN1C/NvHR8DlUVGOhMCz0NIYIEj+27boJc+gzoGK0/I+zSDAlN+zvS4r6Us3FHp6yBSO0aRu/RUCDSS6ZmjQlWKRthZER8ilVL9hkCUZL2cP7lrhnx/aGJVe27ZgDZobiMf3nwZbz2U/fh3x894Pp+hm9KPDIiHIhA8r4IuzQTE7FqI3NmCL8ZEdtARN8pWHv8SfRGQY7fsgwQXCMimwBZSzONPhynLW2fq3u1h8xDL40DMLtRBjE122czDXlVbw7nr+1DdzaFbXo5rB6UHl/RlanRlBipZSMjAgDbNg6KwNauNEMLdzadxAVr+3DtK9bgI288veZ1YXeFyJsKawaQZu74EatSN9KWVb01O0z5AVCP937xcVz5dw86mu5RK+zafnMrbJiCWBlZaErXnnxsYzNmvyC30kx/R7CuGQBYP6h9Jnat3l5RVVVkRFb1NZYRqWmP9VGaseuuMgwBQxKr2gQOsjXArT/UROu3fO859/cLYBzZTOJxFBHTrNJMXMSqCyG073rumnE5l7RTkNsCAUMnUmzg5gi6cySNSDKhiNp8eGJVmtqq/d7bNmmBAS3g602BiH+x6j69E+SVGwbEOfvI5afjex9+NZ78X1eINHU9/uG3X4HXnTmM//rQpTVfs55XKlOcu6bPdQR5XrKQTiUT+IffvhAfeN1mx/cPq2tGNtTrzlozItr59heIaNmrLatrs0teDdnmCmXct/skXjoxh7+7+0Xb15CdujUj0owpv4CReevvTBsjGKRgg4SqwrW4xgG5Is51IxmRc1ZrQubnj87UeaUzi6WK6PgSpRmflglGacaaEfEvVu2t03UWBKf2XQCm+/AMqUvOTSdTcHm/KOBABPIuKlzJjNCIROwjMldoXUbErQOJdgo07px4x+cfw5V/94B4CAfLiPibkkmQzXcqUSvQ9Nre6YTVCOtXNq0wfX100Hjo9IjBdz4CET0oOHNVD/7qN87H/3jdJvx/rxyFoii+Fphtm1bg3993CU4bqp2gavVRoACjK5t0Lc14XejCzojInkC1gYi5NOPF4p0yImev6q35mmFR737sh6Sp1V99/GBNIA5IGRGH0kwYglgZudWU7ss9J+ew89AUAOCYXpohHxonF1ZFQU0JzA/nrNHO6/NHZwJ7ydD6llCMTpzgYtXgQ+/sumaCate8Hh9glgDI5cOH94w7vx+178ZErLosA5ETM3l875dH8ePnxgAYsy6aV5qJWiPSuFjVS9eMqqpGjd7mAUQPZKsj5/hcAS8en8Nz+q4oUCASMIth9RABIISy5Qa9Dayp2ovWD4ghWIAlI9IRoDQjtaS+7eJ1uOnXzq7RYDSKnBGpVFXR8ppNJaUR5DalGY/ZrbAssAlZoyRbbQNGIOI1qAaAXccaz4jIgUihXMUXHt5n+nq1qgpNhpNGJOzSjOjo6kjjwvX9WD/YiamFEt52+yPYPTYr5r+QEaE1EKEguyebCuzWDABbVvUgmVAwMV8MPPyOOma6sykRBAQXqzpkRDycfzsH3vAyIs6BA/3Oc4Wy6fe+b9eJuu/nxziymcTjKFrMLw9P4/e/ugP/dO8eAIbYLGyxqp8Fr5kYU1UbyYjUd1YtVqoiRWoX1NXbOVGbcTCxatDSjHngHWBkRxodNGa07xpi1a3r+gFou7c1/bUZET9i1Zcl2/JmIWea5GxTLp3wJFatF4jkPJb8vGKUZrTzKWdFSH8hSjN1MiInZzW/F0UBzhixC0S8OSdb5zMdsGh7Ts4VUKxUkUwoQmxJNEusap2D9P0PvwYXrOtDuarikb3joptt87ARiMimY4aHSHB9CKD9fpuHtev3+WP2xnj1oI1eTy4tdSr6u54cMyI+Wvlnpcm7hFVjFZR82TnD2J3VNzGFsmnTe9/uE45ZJqGJ4YxIdKzWBU00S2HRZRffCDRWvhmTM/1Aivw1ltZAP3ixOpZ3hrYZEUst2RqY0OIYRCMSNMVPO3z6rIBwMiLFctW2hk7lmTX9HabsRZDBdwfF0LEmBiLSBFh5cc+mksI5eMFm0fc6yyLsjIh1XIPsErtWlGa0z7feBoEGB562oss2sDbEqu7X3OFTukeIvu5YHUzp66t6c0jVdBU1R6wqdu+6fqmvMy2GU07MFYXvCgW5VRWYkwKuRj1EZM5ZrZVnnjsSTCdCGZGubFJ4JdEoBa84Z0Soa8a7j0iPrUYk+PVdqlTFM8RuXaVrfC5vDkSOzxRqrO8Jt1JPFMTjKFoMPZDH54rIlyqGLXnIGZGwUvyNoKqqEBjaaQC84iW7Q4tlJpmoWVCBWots63vR4hakNJMNKHo0SjNGRoQChEYCSFnrIT8Mrz5vFVIJpUYv4rdrplSpCsfcoe7GdqVuyKlb+nzSSc3nRcy4sFmkC+WIMiIWc8IePVga6EyL7EhGHx9QLyNCGxW5hCbjvTSjvQ/pIayCZCpJrOmvFRc3TayaN2frAGBFl6YvmJgviIzImv4OcT9OS63iYbTuEueu0QWrx4IFIvKwRvqMVdWf06hTqcKXj0jeKHcRYVj0y99rV0qha3w2X6rJBI07GMWJrjbumomOgc60+AAOTi4Ih8GwNSL0cIsyIzI5r01VVRTnBdULXjQiC0X3KLvHMr2UFmaCdoqNiFWD+ojIpRkRQHqczmqHGICVTZnM2c5b24dHbnwj/vdbzjO9nnZRj++fxLX/9JBJV2CHPPa8Ee1PPeTULQUi9G+iNGOz63IzYDK9f5MyIvQAp3Mjz2/x2jUzPqc9jK02+ITICHnMiJyjP3CtweaCi1i+aaWZxdrdO/2eJ2YKpoF2/dQ5I5UNRUYkoL27DK0DzwXsnBEakZw2SoFuNz86kboZkTrXSqWqSiWicC3e6bNXFPvAQZR1pYwIBWQT8/bW+QWeNRM9iqKIrMjLUv962KUZIyMSnUZk/4S2CK7uzTV00XnKiNTpPqI0MPEnV23B/3j9pppZFy3ViOjBhlwmCUMj4jSfBNBMnKyfhWwf/svD0/jRs2Ou708LTkc6GbpAVSaRUMTnMa27cdKuzO1BbCx0dcSqYfuIWLKb3SIQMcqStEGo1zUzoXd30RRfK24ZIUJVVRFUUgnCmhFxS5M3y+LdzvOCMmt79DVRUYCBzoxte2+YpRm6/+26ibwgAoBsCoqiBBqtITJ4AX1E5J9lNjRrvDQj663s3FIp40oCY8DYdE7M2WdExEaBSzPRQjqRvboXg1M5oRFId1D2OFyrGYRRlgG8CfzqGcP1ZM2L1sreLG665mwxE4UWtyDzDwzjIJ9dM3qwkbbRiJQaCETsdpxunLumF9dfZvhs1BsCRg+S7gZaJ71CnyftkrOWjIidfsFph2kldB8RB42IbJ3uVUQ+ri/ijhkRUZpxfuCdWiiJEhoFItqcF+PaMh40zjqUZvmImEozesB1UA+c+jvSSCYUEWxMNSkQoWCxWKkGykJaswBBBKvCp8Oha6ZUUV03JjOLhr5N/hzDcA52MzMDjN/7+LR2vSYTitBDUVbP8T1ZrBotq/vMjn5hl2WAeGhEDoQkaDTa2OqXZpwyS9bsAC0YdIM1ohEJunMs2ZRmwhAZ2+043VAUBR+7agtu+NUzAdQPROak0k+zoc+T7ORpF0X/bpcC99oeaIxJD9dHhK6py84awWBXBpefvVK8Ju1x1gyltUk7YcXN0I2gssxwT1Z4PFSqqul7nFw9AUP7FLpY1WK2B2jOugBEqZqydH12pZkQNSLy2lvPk8WOWVGaSZn+P1vw3oHmmBGR1iK368WuYwYIp7TmFCQRtNmhTWKXND9o3CEj4lVM3ipaEojcdtttOO2005DL5bBt2zY8/vjjrfixrpAwbO8JLRAJW6gKxEMjQqUZmpQaFErPF9wyIi5zZoDa0gy9jgSLjQUi9QMlOyhbJWfDUtLnFtRkydq66xUaCjfpUNsl7OrRzcLIiGjHRAsiBZJ2Yk2jPbBeaaZZGRHt2N68dQ2e+l9XYPtmQxzstX2XdpNDPQ6lmUz9qackVB0d6EAunRBrglyeKbjseKPIiIi/6wGYbSAi7N0bD0QyyYQoh9p1YNVjTg84mpMRMa5ft/IMDRHss6xxOR9dN47HVqfM2W3JNHdnU6LMNlEnI7JsxKpf//rXccMNN+CWW27BL37xC2zduhVXXXUVTpxwNltpBZQReUYfmS4PIAuLMESPjbJ/IpzSTNbDznWx5O7gat0tUI2dHnT0cG1lRoT0O+lErY8IEDyIzNcpUzkxqPsynJp3381Zd4HNJCcyInogQhqRjHOrpDxrxv29m6sRAVBTV/cuVtVLM13upRk35+RDekZkdLATiqKIB7/cwuvmEdEMsWq+VBGBn5yx682lTN1jIiPS6SJWDSEQURRF3CdeXaiL5ar4/OYsjqZBRmsUHDJ4qWRCrONuwbIIWrvtByOG0TXjlL2wbka6cylxzU7MO2hERAZomWREPvOZz+C6667De9/7Xpxzzjn43Oc+h87OTnzhC19o9o92hTQi9Jy5xOOAMD8IjUhEGRFVVYX75mkNlmbE6HTXjIh7uk9u382kDE0OvZ6eZQ2JVX1bvFNGpLZrBgj+2VX1XyZZqy1zZaBLW9gn65RmZi118WbSoS/OThqRqlq7SHtt3w1rrg+xYGnftcOLxXu1qoqslPXhQtDPyJeqJrMvGZoqS8PYjA4H46HutjtthliVglhFMZf2FEUxiaYHu82lmSm5fTfEQAQwJoPXa4Um3nfHE9j21/fg1HyxViMSQKzqFgx6mcBLbbLW7FkYGqh8naC+M5OEHGt3ZVPiOKwakUK5gtl8SRrBEI+MSFNXsWKxiKeeego33XST+LdEIoErrrgCjz76aM3rC4UCCgVpRPxMsHYuL6y29Ow3IxBJJqPViEwtlMSi00jrLiD5dLhqRNwzIl2ZFBKK9uCSX2O9+YMZmtnvHI9OLaJYrjpmhEq2XTPGn4NmROj7/Npf04PgVJ3SjGGeFM6DwA0qQdCDyMiIGMvHYrFi+hy9zpoxnFtDtnh3+bleSjNTiyXxGQ7WyYgAWibGro36hP6AGunR1hvDyr82EGlVaUYInW3s2Vd0ZYWvCWXnXLtmQmjfBYBOH1mMF4/P4iF9jsrOQ1NGICIyIv5s3lVVdR24mU0lsFCsuJZmKPMwZLlWwggkRZbP4ZpWFAXd2ZSRJc2mRFnNqhF52+2P4tCpBfHaII0BzaCp4dD4+DgqlQpWrlxp+veVK1dibKy2PfHWW29FX1+f+G90dLRpx0alGUDbAV+8YSD0nxG0DfTZI9P4nX99DE8fnmro55Oj6srebMNiXC8ZEVGOcLhhEglFLBLyTWWNyoMZmtXe8Kqq4jf+78N4w6fvF6JBK26zZuSv+4WCz5TfQIRKMwtFx102YJ6v0WxqSzPa35MJRXxW1pS654xIyA9aL+aEWQ9iVWp77OtIO16Psp7ASbB6Ug9ESKhqW5pxMZdqxqwZO30IIXcIuYpVQ86ICE8aD7/nnb88Kv689+ScS9eMt0BEDoLtA5H6WY3xWfvsWRgaqHwd7R1gzmw5aUTmCmU8c2QaUwtGkB2XjEg8jkLnpptuwvT0tPjv0KFDTftZvbmUmL1y3tq+pphCyYGIH9HjDd/YiYf2jOPa2x5u6OePU5TukFr2Q8aD6GrBww1DO8JO6Xy7CcS8YmccVChXxe7u60/YX0slmjXjoBEJ6gFDQUTSZyBCszuqaq3fhEw0YtVaC/4u0cJqvi68GpoZPiLhD71zIp2i0ozzPWnU/J1daxMJRQTdTiUFStmTF4ldacbNXCoMi3Ardg6ghLxWUFDSb9GIlCuGPqMrpInlYm5PHYGpqqq48+lj4u97TsyJe5zuHWPYqLfgzRyIOH8GbhkRo9XbvNZScrUSUPQOyPOTnK9pWSvWlU2J45heLInP6uBE7WZsWXTNDA0NIZlM4vjx46Z/P378OFatWlXz+mw2i97eXtN/zUJRFKzWvQW2NaEsA5hT/H7KM5TO9XrtlitV/PMDe/HsEfPQqEl9MXVKLfsh6yGd7eWGoV2YvGO1PjQaE6saxyfviH7w9DHbYNAuI5JIKMKdMXBpRv9ZCRsDIjcyqYTY2bl1ztiNHG8WpBGxZkQA4wEyb3kQezU0C2soGKB9VvRQcTLVA6Tsnqv40P7BYsXYydvvvikjMtJrzojI7qp5lzJWGGJHK0ZGpPYcycJcp4yIvJalQ+q68CL8BTQbeBr2CAAP7x3HydkCkgkFZ+l+RH7FqnTtKYp51APhxbpgfN4+cE3q97+qInAH3mIdsSpgLtF2Z1PCAwYw1pGDNm7Ny6JrJpPJ4OKLL8ZPf/pT8W/VahU//elPsX379mb+aE+86rQBJBTgqnNX1n9xAJLJYN0XcgdPPfdHALh/90nc+sNd+Ph3nzX9Oz04wgxECuWq4w1lNZOygx6cHW6lmQbEqvLuRm7fe3l8HjsOTdV8n52PCNC40DhoRgQwBKunFpwzIkY9uPkaEetwN3nxMibwWkszrc+IyOULLxoRt3Q5lWbqzfEhbYNdaWaxWBGiYlGa6aidsuwuVjWua7dS3aHJBXzsm78UvkhuzLpkRAZdSjPUvi1vRuwe3EHwKlZ97OVJAMYAQWqPPmtlj9jQiIyIjw4cwNm51MsEXiexajKEDjwjy+e8LnZbSjOJhCE8pqD64OR8zfcti4wIANxwww34/Oc/jy996Ut44YUX8KEPfQjz8/N473vf2+wfXZdb3nwuHvyTy3DxhmZlRIJ1X/RLAjAvC8vL49prdo/NmhYrMmQKJxAxOlucfhcvkTstfl0upZlMEGdVIaY1FjLr7urOXx6DFUPLYb4VkgH1Pdb3DRKIGC28zhkR8k5oRUYkZwkszRkRp9KMV0Oz8DIidAyK4p6JceqaGZvO4wV98JpTO6aVzrTzA5QeANlUQtTwaedq6ppx6diQAyq3B+Eb/8/9+OZTh/HJO593PV75Z9tpRIYk8zYSPAqBbaGMalU16abSidZmRKgk+SpLFvsV6/vFn4NqRJw2QPUmj6uqKj7roS7nQCTopoY6Af2UZgBILbz2GZFkQmnqeAg/NH0Ve/vb346TJ0/i5ptvxtjYGF7xilfgRz/6UY2ANQpy6aRpGFbYmPwofIge5bTt80dnsGWVe4mKLrCFYgVHpxfF7yRKM52NByJyuaRQrtpewHTju+ltaPGTyzHWB11DpRlpsbDu0mmaqowxa8aaEWms46ki2neDZER0UzOXFt5W+ojQw5aQ2wgN/wdracbdDZII0ydD7pix29kSThbv773jCbxwbAb/cd020QXh5KpKuLmr0uyP4Z6sOB4qh8hiVcPQzDkjAmiBvp325cRsXuhd6nVbaT+bXH9tSjNSRoQyc5QRUVXtuqN7JqH47wpzwuskYzrPK7qyWNvfgSNT2j39inX94jV+u2bqed7UE5zOFw1flqEeS2lGOj/VgKWZvIdOMLNYVXudFkTPiuzeAYtGJC5lGaBFYtUPf/jDOHDgAAqFAh577DFs27atFT82cuSLsORD9Ci39j3vYSIlpScB4KUTRgZFZERCGBMvBwdOtXX6eQMugQ/t4LvkQKQmIxJcrCrPhLCK1WTVP2HnIwJIrdc2pbHdY7P40/98WiyCdjRSmvGWEWmhxbslJWwWq1JGwKE043HoXRgZEdJp1Bteade+u1isiGzIn33rGaHtcJozQziVpoDajhnAyC6YxarOGRFt16pdQ1adyNOHp/B7X3oCH/+OUZId9dCm75YRIU1MTzYlHsDZVFKc0+nFkjSxOrzHR6cIHuoFIoZFwKZhoyV/62i/+LN/sap7h1c9HxEqy3RmkjXaJFkj5pRddSu5AVKm2aXkbSrN6GssXbuUrbFO9I5LWQaIWdfMUkNRlEApftmI54UxL4GIcYHtlQIR0og4OUP6IZlQRJbA6aFBoii3xZumoK7qNXxcasSqDWhEAGPBto6nn7LRXNj5iADuGpEvPLQPX3/yEL711GHH46FnXJAdo1tG5MfPjeGuZ45JbpKt04gQObuMiLTolypVcb3X23UFtea3w054bAd9vVI1gtYDUv18/8QCHnxJ86moW5px2ckLoaoUiBilGVms6j6AzEmw+oWH9uGeF07gx88ZzQBeRK1i/ICNRuSc1b14/ZnDeO9rNpr+3Rh8VxTnOch96gS18y86iH4Jyoh0ZpPYPNyt/TmTxOkj3eI1OZ/BLW2snDZA9SbwiuyZzbpnyojYXOKP7BnH5j+/C1/++QHH41usM2sGMK8DtDmgbN7EXBHlSlXYORBxyog0fzu1zEkmFFSqqq8Uv7U0o6qqY6q5WlVNF9hLx41AZNJDhsIP2VQC5WLFMSNCpSC3wOed2zZgVV8Orz9zWPybNSUdzNDM+J68bi5F5YLeXAoz+bJtRkRM360JRJwDyGO6W6Zbe23Fpi3YK06mZvlSBR/5jx2oqMYDtJUW74RtRsTSNu30vbXvXVtSC0rFYxZKfuCUKlUkE0nsO2kW8tE1Lu+67eigtlOXQMSUEaGBbHZiVYfsUS6dxGy+XOMl8rSlS87pOKwYGZHaayeTSuBL77uk5t/7OtIYm8ljerEkgi9rFrERfGdE0klsWaV1yWxd12/6zL2YL8rUE1Y7mSUSJx08RABzadauhfcHzxyDqgJfemQ/3vUrG2zf30tLurwOWIf/zRfLODadR7mqIpVQxJpXb8RBK+FApMmkEwqK8K4RsU7mPLVQwqmFkqPg9Phs3pRifunErPjzRJ0x5n7JpBKmeqhMoWx0CLjV1TsySfy3C9aY/i0MH5GEbq5VLFeF+I9qxGv6OzAzNmvqVCBE14zl4eU2OfmEHoi4pX6Dtu8CRuA4aZk3c3K2UNM+3RKLdxexaodNaUIWDNfbNVvHrAcpZRHinNe5fGQ9UKFcRS6dxD59JtN/u2A1tm1agUqlii2re3Gm3hLqhLGTtwlE5shDxMj+GaUZm/Zdx4xIbWfRbL4kxjc8fOMb8cS+Sfzh13d6Mj4zNCLes2nyvBl64FoF3o3Q5VLikjEyIilc+4o1ODqdx6+db7aCyHroipIpSF0zdtQrHwqhqk0gkkgoUBQS+dcez/N6OXDPiTnsH5+3dYCuZxQJ1Bqaya9fKFZE1nz9YKdof6434buVcCDSZIwHmrebYk5aoCh6ncuXHQMRMqmh1/7i4BRu/u6z2LZxhVjsBusI7ryiPTRKtpE0ZV9SCcVWBOdGGD4i2vHpgYh+45ICf21/B3aNzWK2oAnt5Nq23fRd7e+UEan9XWl+iJsYjuKFQBoR0b5rXijsRnq3IhCxtmPLGaxOm9JMXupCqFeakt+rUK64+n/UQ+hy6gR/cnBEgShlRM4Y6XHcmdphF4jd9cwx/Nm3nxGlQDeNiKqqdaer2tm8P3d0BqqqXdtr+zvwsr7Z8DKrhT4rP9eO7CVC5ywTYkbESfT774/uR75UwQdet1n7esGwCMimkrjhV8+seS+/uqO6pZk6gc1EHfO7pKKgrKo1pZlKVcWuY8bG8Z4XjuP3Xrup5vu9dCPadc3QvZkvVXCAApEVRiAS4VD4GuJTJFqi0APOa2lmtmC4V5JToNvwJuqYuWi9YVH/748ewA3f2AlAU7aHZcOccbnB6WYc6Mq4dizYEYZYFaitpdOiJc8VkneigP30XUCenGyZKluuCH8Pt0CkIR8RB7GqdYBVVybZUAbBK7WlGePvtOjJD0CnSaZ2yO/VaOeM1/k+iqLUmJrRlOqNdUoxVjotD9BypYq/vusFkx7JrBFJiZ+bL1VQrFSFcaFT14adRoTMC89bq3XUdbhkZqxQVs3PfSYPvis5BO+N0GVjjDe9WMLN330Of33XLpH9IUGym6OrKM14zojUEavWeT+3jAhgXI/W0sz+iXnT53XPC2bjT8JTaUYKKik7Igd3tHmSR5vECQ5EmozTA82JWUmEKBwCXceMa/qQzSPdJk8Jumn6OzOhPazcdgZCqBpAGGt9YAUPRMwpbArgenNpcaNadSJOi6pT++6JGSMr4fa5NOQjYjEiIqx/b4U+BLATqyZqviafC69mZoC5K6TRzhk/LdP0MykQoQfdRp9Tqq1i1R88c6xGFChnRLozKTEpdTZftuhpnDUigDlQe/qwFohcoLetdnhsfwWM39mPh4Q8+K7sYALYCMZ5NK4j2UPp0b0TALyNkZDbs724mdbXiLiPtxAuvA5rH12P1u4Y6oikSfBP7D9lq2PzVJpxyYgsFCvivPXkUqGKjMMifke0xPA7+E627jba0FwCET0jMjrYgY9ddZapjQ0Ix8yMcHOknGzAPE1zNJR+TsAbJSesmPWMSNHwNbG6QxLOPiL2mSzyhgDchXXVBnxE1uqdRTP5ssnmndoEiVZ0zADuGpGubO0D0M0p1I5cHTGgV6o+ymF0LZcqVczkSyLbdNqQP1+hDmknr6oqbr9/LwCzyFUOROTBjzP5kjhXiuJ83dsNvntGZET6tOPwkREpOVzzbvSbSjO6wDtEjYidWFXuAHz0ZT0Q0b/umhGRrQY8OFMX67Sa1yvNHNXb+Ed6c7Zfd+qcJH3IG7eMYKQni0pVxYGJWvdTL6MzKBBRFCMAkecgUfa2M5M0GWbGBQ5EmgztGjxrRCTHTC8OgQclEdLvbj8N373+1XjT+avF18MMRJyMoIDGXFwVRTGVZxouzZRJI0KLVtJ2gigAlJy6Zhw0IqaMiKtGxFuZwI7OTApr9TlIe6TF2JoRaX5RRsO6AMoLvV3XiOGL4e1ztJucHAS6x7wIhOWger+eDRnqzvoO7rqknfwvDp7CrrFZdGaS+I/f+xWcubIbZ4x0m0ozgLlluSBN3nUqaVJ3C5Xq5gplkcE5nwIRHxkRQ+PhIyMiiVXJEymdakJGRLoG9kqdTI/unYCqqmJz4ZYRsZov1kOUZhydVZ1LM4VyBS/oOo9z19gbTzoJ3ykjcs6aXqzUgxh5fSFEIOJi8b62vxND3RlcsK5fXEcd0jkVIt9MEp962wUAgD++slZfExUsVm0yfmeWzErj3WnBcnvgkaBRrk+es6YXP3hGszMPw1WVcM+INDbpN5dOiBuu0dIMLe5iFyBlRGoCkbJ9mtmppEa1VsA9UyVaSQOu1Wes7MaRqUW8dGIWl+h21laNyInZ2kWrGbhlRITWQtp5+inNyK9rZFQ6IGWhPAR/aem4Kau40Wc2BDDX4b/1iyMAgKvPW4VVfTnc+ZHXIpVQaoLRtCRgz3sQIm7UOymos2dMdwjuzaVE4E/ut+WqilLF3vmYKHn0W5GRNSJC4B1mRiRTu9bJpZnxuQL2nJgTD1TKxNkhB1iFUhWwT1SYXwOXjIiLj8iuY7MoVqoY6ExjvYOZHF2PVmdVyoics7pXZM1O2gjSKbh0u0Y6Mkk8+CeXmT7TTml+j2EEl8IbzhrBM39xZcsyql7gjEiTCa4RSXlyCCzZCM/kyDwMV1WCHhi2GZEGJ/3Ku26vDzAr1ozIgtQd4BSICB8Ry6LqVFI7Lj383TwbRCASsMx0hm7QJPvCWBcpu3pyM3DLiGT0XbE8t8XrnBnxfiFlRPyYyInSTLkq/D6CCPlosZ9eLInx9G+9cJ34GXbHQnqkUkWt27oLSIGIniGge02eDJyTdsv1vEQoaPQzObdXun+cypmNQKWWQtkww6NAhLJOD7x4Utyv1rEDMoqi+OqcofNRTyNiVzr85eEpAJqzq1NGizJ08lpSlK6701Z0iayZNSNSlSZK13MM7sykLIGI0dFlDeDiFIQAHIg0naAake5s2tM4azuXw3PX9Ik/h+GqSrh2zTQ4YC9nCkSCtu+atQbzxdq66PSCVaxaJyNiDUSkjMh8sewohmtk1gygtZECtbvCKLDuxMwZkdrg1OucGfF++utmFkue54PYQfeYFxM5OZPjdaG3gxb7547OYHqxhJW9WWzfvML1e+haK1Wq4l5yK2OJQEQvx9jpsTLJhLhm3QI6VVUDlWZkjYgIZEIUPcpZt4ViGaVKVVgTkO/Qk/tP2b7eDj9eImLoncO64+RsCwA79YneW6VZN1boNMnPADmb2pNLGYGIpEEDzEZ/9X5nK7KPiKERiWcRhAORJuNXI0JzZnpyKaOlzWVxtnuQDvdkxYUdlqsq4K4RaaRrBrBP9/t/D/POms6ba0bEoWsmLdqunTUiquq8+zTad/3/HoDWBQVYMiL6DuqTbzkPK3uz+NzvXBzszX2STiZMu185wBDdJ3JpxmdGhD63D3z5KVzxmQcCZ3r8CIRlsapRHvH/YVkfDte+Ym19Z1e6tqSMiFsWkAKRsZk85gtl26BfURSTONGJSlUV7cK+NCKmrpnw23ezKSOQWihWcGBiHuWqiq5MEuet0zZWNNspnVTqlm/9uKsW6oir3YTAFIi8wtIkICO6ZqRNC/lFdaSTSCUTRmnGUm6VP0uvgb047oxR8qQNrpvIN0o4EGkyyYSx6HhBDDPz2DXj1IpHugK/vghuNKtrBjAeAnY1de/vYc2IkEArJcR2UzWlGX8+InJGBHAOEimTEsRZFYCYnTE2kxfdFbSYvPmC1Xjsz67A1eetcnuLUDFlrKQHdsYmOM0LjYjH0oy0wB6bzuMbTxwKdIyGQLj+a2UfES86DSdks7eNQ1344Os31/0ekRGpeguC+jsz4r7aPzHvGPTnpB2wEyXpevYjNqVAZLZQFrt06z3TCIqiCJfahWIFe05o2Z/NI91C50aBiJddfbDSjPf2aUDb1Lysl8suWNdX832EGKApZUTIL4pa8Id7dLGqJRCR7yW/66J8bdI10+mirYkSDkSajN9x8rJGxMs4a3pf6+7mf7/lPPzHddvw+jOG7b4tEO4akcbs5OlmDypU1d7DnBGhoXddWZeumTo+ItaSmnWhmHfKiKj2U3290teRxspebZe058Sc2AWnk0poBnV+kBc1ecFOJ2sDkYLPB7v1IfylR/f7GhJJ+BGrykF1vs4YeDfW9negO5vCUHcG//6+SzwF4ilpc0IP9Xo/Wy7POAX9dp0nVuTPyU9pRc780HoUpo8IYDwk5wtlUZLcPNyNAd1pWDxMPZQo3DZNVihr4rT20O9uLc3QtOZ1Ax0mvY4VOx8ReZ0HUDcj4rcsA5gzKDRAkzMiyxS/03dlQzM/YlXrotDfmcGlm4cCZxfscCrNFMtV4VjqNmfGjY4QAhHZCrtaVY32XVexqneNSL5UEd9vp/KXqTSYEQEMncie43PCQ2RFV9a3c20YyJ+P/POzUomDqDe7w4o1YDl8ahF3Pz/m+xj9nPO0STAavDTTk0vjwT+5DA/+yWUYdeiaqP3ZhkZEiFW9BiIn5x31WHZW8FbkEpqfgYzyRoe8PsLUiADmAYo0z2lNf66mvOwlEHHbNFmp1+Vl+NyYzyut1fU6BRM2zwAxPVtf40ekQETWndWbzFzv59I1QW/p5dxFAQciTSYdUCPSnU0JhbPTw04TnvlvxQuKU7qTWoiTieC7dZERaeD3kMVZ8q6wK5NCf4e2mFnFqk4jzWnXKi8etFvJpBJYpff9O5XNvE6CdeOMlVp5ZtfYrGEj3ROe5scP9PnkLMGFXWnGqLl7bd+t7fj6y+8/bzJz84Kfcy4fd96nuNbKYFfGlwjQCIKk0kydoM2cEbHPPuYc5rXIyEJVPwFtKpkAnVZqBQ17zemQgvs50fGWtglE/JRm/AQiThkR7d+tmaZ61vAEZURki3daN7otGZFipWraLBkeIsGuTev3dbVgNlUQOBBpMo1oRERpxsFKXN6ttyIQsXvoAEbKdKAzHTgDQ7oDrwJHOzqkHRWds4Si7XSdMiK0Q3Savivv9Ol7BzrTdc3mDB+R4IEIdT89e3S67jyLZkMLmnXnLmcWCL+GZvLD4vZ3XoxNQ104Op3HH3xthyeLbiKIWLVYrjSkEQlCShKriodgnZ+9SQ9EXh6fN+Y6WR/QHtxVg7iqEnTOKEjwk1HxQpdkjkfBTne21gnUW0bEu0aEXuM89M5eBFzwWNITPiLSsimaErLa75ZLJ4VxnVye8eIh4oa1E4wzIsuUZlq8yw/JMHv6nXDaZdDC10hrWEcIGRF57se8ZAWtKIrRvuuxa8ZwVjU+N/revo60ka1y2H1WfOgVnKCBZs8fnRGLU2SBiP75OM0F0oa3ab+zX0MzqrUD2nTQz73rYqQSCn720njN3BY3fPmI2JRmgraN+8XW0KxeRkQXnb98ck4Sq5qvBcNdtX6XnR8PEYLOmciIhHy+ZHO4OandNJdOmh6gngIRP10zda5XuftE1nl4LUHaTWCftWREAMMiXtahGfbuwc61fK6SCaVl17hf4nlUSwgnPwonjPbddF2xaqkcj4yI4bQY/KFriFWDR+xym53hqqr9GxkyLZYqpl2S86yZ2s9NDkTqfTaNTN8lTh/uRjaVwFyhjJ/uOgEAoiTUamhBs5Yv5OuOsiJ+H+wjescAnaszV/YIEZ8fkzM/3i0mHxGPOo2wSImWZ9WzsHfjUBeSCQUz+bJ4UFnNCr207xbL9qVIL9C9KTQiYWdEsoYB17wQmmvXgZz96fRQXgizNCN/NvL7eS7N2DirzkkO2oSdl0i+wdKMNYCLQl/mBQ5EmoyREal/Q6iqatQOs/KsGfuFpSS9Z9hpUjucrLgp0m/koUtp/IbEqnZOgnqWpidrTD2VsyIlYYJl/rlJG40IfV9vzkNpRg0+a4ZIJRPYskoTrO44OAUAeP1Z4XVB+UFoRFxcVqnMZZRmvC2ef/3W8/Cm81fjB7//GvFvsgW7V/wEf9S6WihXRedKELFqEIRHTUXSp9T52dlUEhtWmMWw1vZdY7aI8zkrNWBGRp81ZUTC9BEBjIzqfMGYjUIPauqcAYwSlJdj9dS+W8/QTPp3OTD2mvkznFWNf6PMd6+UEbHrnBFdMwGDZNOAyph2zAAciDSdlE0N3YlCuSpep5VmKP1v7+Ap13tbEek6tcQJR8sQhKZOg6f8vIc8bZIChkRCEQr1mUUjeAiaEaFF06lsRlmiRjQiAHDuWsOfYGVvFhevH2jo/YIiPh/LYm3KiJQpEPFnaHb6SA9ue+dF2LLKGE2QlnQUXvEzaJAcYUsRZETk3y3vQ9h7pt5FBWi7W+vxGtd//dJMkICfvqdZ7bvdohRdMkoz+hpoyoiE3b5bJ7ORSiZEBknW39SbUUPYdU5axaoAbG3eFxvUL5kyIjH1EAE4EGk6fjQickq1M2OIVZ0cPKk004qyDGCkc627jHI1zNJMCBoRSawq34gUlMjn2UkjkrTJZImMSIdhv+/orKo2fk4A4DzJrv+a81aH2o7tByexajKhiHNF2Yt8yVvt3A25xdUrhli1/mvNXTOtzYjQNVGqykFQ/Z99pt5FBdgbB/rxEQmiKaPPk3RRjei57KDswGy+bHJFBoKUZny073oQndrNQ/LbNSOXZuRRHsSwKM3YaUSCBiLGueKMyDLGj0aEFt2Eon1fRzopWubsSgBiHHeLAhFjCqW9RqSx0oz9jtvXe0hiNxp4J7eryaUbwjiHlgmp1HZdsc+I1BMS+9mduyEPMHzTBasbeq9GcMqIAJLNuyUj0kiGwU8mkfCXETGO2U9WIgzE71ZWpSDIQ0ZklZERsRulkEt7CEQaKM1Q8EZGgWFO3wUMHdfMYsnkAQRonWpE6KUZD7N37GzevZZm7J4BcwVjlAdBWim5NJNvwNDM+n1x7ZgBgPiGSEsEPxoR6yKhKAq6MinMFsqYK5QxYnl9I614QaDdCUXzBP1ujez+X3/mMLaO9uOtF60L/B50o+VLhupeDkTk9kCi7ODDItquHUoztLtxFKuq9D6NfTZnr+7FOat70ZlJRlaWAZw1IoC2gOdLVXH9kqapkUBE9trwih+xKu1+86WKZ1OxsMhI3kLGpGIPgchKIxCxy4jIXSdONOI7RA/quSaVZuihPDFfFEEtTd7t95sRoU2Tn1kzLlkpO5t332JVG2dV29KMJFZtNCMif19cPUQADkSaTspmzoATdg/FrqwWiNgJVltdmqH06NSC2WhKlGYaWJhGBzvx3etfHfzgYDY0o6xHl7QLsC7Uqqo6lpXsSmozUiBCuyHnWTOU3Wpssc6kErjrD17b0HuEAT34+jprDeu0boqyeHgYfivBzdcyPo0AAX9iVdmjpxFn1SDI2R4RBHnIBJ62oguphIJyVcWgjYOxHIg7EWTyLiEyIk0rzWjX1ti08SCmh6ccePlxVg2jawawd631qhGxdVYlv6hsHbFqmBoRzogsX1I+DM3sMhwkWLUrAbS6NEMPllOWQKTi0HnSajqkGjmZLsk10k5LaUZO+ztpRMo2GpG+jrTQoDh1NNG3taKbqRW85cK1mCuU8etb19R8LWPRc9D1YTWi8gN9HsWyn9KM9n8vpZkuKbvn1/ekUcTmpOIvI5JJJbBxqAsvnZiznenkbegd+YgENzQLY+NhB5VmKBDJpBJibZOvpTANzVRVlYbeOb8vBamyvsxzaUY/TRWb9t2enPF7UWlmJq8Fx7l0EotF7WeEUZqJs0Ykvke2RPCjEbGr37r5VVCXQtgLghP9egtdvlQVNwpgPNAbLUM0CgUdqmrsKno77AIRbTGRgwynrhlbQ7POtGgFdnK9DcPiPU70daRx/WWn235NFn5Wq6o4T40EIn5HIwBGidBLaYbExnJQ3bL23YRRdjLs5b397LNX9+KlE3MijS/jzUekAY1IjelfuOeLSjNk9iVnM81dM+H5iJQqqpjD4iaUF6WZshyI+CvNVOqUZno7UsikEiiWqzg5W0AioYh1P7hYtT26ZjgQaTJ+NCJOpRnA/oFXcpiT0ix6simRGj61UMTqvg4A4WhEwkC+WanOKu84Oi0aEVNGxOojUkesSouKo1iVfERiaiAUJrLnx0y+JBZ2mu/TyHsG0oj4yIiQXTrQ+vbdUtW7oRnxB1ecgXUDHfhNGy2Vt9JMI4ZmltbtkO/33pw5cJU1DXJppstPRqSORkTOmLiWZjK1QZ5fZ1VaMwrlith0yoZmiqJguDuLI1OL+Mnzx/HJO583fn4IGhEuzSxjgnTNmEszzt0ZrS7NkFX6+FwRp+ZLIhBpVqrWL8mEInYUx/XpnbJhkGEBr53LcsU5I5K2iFWrVdWsEdEXuAWH0sxSy4i4IWdEpvShgl2ZZEOt2HYzbOohSjMegj8RiMwbAxtbdR/JpRm/5m+bh7vxJ1dvsf1ah6/STAiBSNgaEcvATPkhLWfXvJQpDI2I/blQVRV/fdcLkJdlt4BCTOA1DXf0OWtGD5TnJLF/t0VAOtKrBSL/8dgB888PXJqR17/4Pu7je2RLBHnAVT3sFO1xKs0Amnp9fK5oEqzGRSMCaItxsVwVdWY5IyKme4rSjCFUtRrCWQPIuWJZLFp9HWnxedQdercMAhGRESlXJX1IY1OChddGEB8RD5ch3VdFn6WRMJBbwws+7fDdyEkaKSeEi2gDzqpEs7pmCHkHb8qI+OiacXLmPXxqEZ//2T7x93rTiGntyBf9l2YMZ1Xt+qSyTFcmWbM+DOuzpPaNz5v+Pej1afJRinFGJPonxxLHzqHTCVp0U6bSDIlVbbpmGmjFC8qgEKxKNukx0YgAxo03QxbKkkbE2r5rnO/a4zaG3mmvoWxIJpVALp00dV3Yud6GMWumXaCdcqmiioxII/oQwNixN8tZ1fowa1VZBjAC9mLFaHluJHtEeCnNFBto+bcGL2GvO+lkwrHdtCOdFIFKf0f9a6teaebg5ILt653I2RqaBSvN2LmqEiO9WiBifVyE4iPC7bvLFzuHTieM1rra0ozdzpuEfK3SiADGA+aUKSMSD40IUFtL7c3Vqu0XLV0zaZtMjsiI6K+R9SGA8blUVW0Hak17UuDZqMV7O2AMkKuguKgt1I207gKG/sCXRsTHOe/ORBeIyEFWIQQXWsJXaSYMjUgT1p2eXEpkdKz6iX/87Qtxcq4gptS6Ua9995A1EKkjVLYzi/PeNWOfEbGWZQCjc4b46K+eicmFIrZvWuH6M5yQzd+4a2YZ4y8jYlOayTgHIsUISjN2XiJx0YgAtTuHXpvSjOiaccuIWHYx1kBEDnjmC7WBiDH0LuAv0kaIjEjZGNpo5zfihyAakaovsar5OvE6FycM0mJNqKIQYkZEdv9UVdW21BBmINKM+723Iy0szq331GVbrJaOztRr3z10yhyI1NvM2Qci3mYqic2oSoGIMWHdyrDUDaUowHWv29RQkNzRJl0zy2CZjBZ/GpHaB6OoddpE9lGUZqiFVy7NVERpJvrLyZoR6bERq1q7ZuzaEFMWseqMJRBJJBRRc7ULEqsx0s00GzGDqFLFlDAzaywQSQXpmvFRDkslE6YsRKs8ROhnA0Cxoor7OoyfTw8dVXXOBIiumSBi1aT5GO0yiY0ii8u7G3hw1ht6d2hy0fT3eoJTw9DMRqzq01lVmJnZlWakQGRtf0fDmbrONvERWfqrZMT4GXpnt1uRF3krUZRm7EzNSiEMvQsLa0bEHIiQRkTvmqGuI5vjTll8LKwZEcC9o2k5ZUTSIiNSFZmyRlp3gVqTNC9UfVi8A+bUeKs8RABDnyFPyQ0zIwIAX370gO3mpZGhd63IiMhZgkYsyUVpxkEjYs2IBNGIeDFCA2RnVe3vcwXn0oycEdk03F3zdb902Bg6xpFlsExGi51DpxNlmx7/jMsUyWhKM9pCMSVnRMhIKgaBiNXSWM52+MmI1NOIAM4dTdWqYZK0vDQi1dDEqs0eegeYH3S5FmZEaKMhC9DD0Iikkgmcv1ab1vxXd72Av7vnxZrXGDo0/7+v9RibkYnttQn0g0CZ5In5At74f+7H//rOM6avWzMi9QJBV4t3j9N3jdKMW0bE0IhsHu5yfV8vdDqIf+MGByJNxk9GpGhTmpE9GqxEUpqxyYhQ+aJVw/fckHeFVoOkGmdVlw4CJ42IyZckay8OlK2c4xCcNZuMbhdeCrF9N5Chmf5Sr+e8K6KMCF1b8hTosLKa3/zgdrzjklEAtYJMIByLd6IZ97v8cG6k3TQrdXK9fHIe3/rFEdHdtlisYHyuYPt6J6waEVVVAziraufebiAnsaI7I1ybw8mIcPsuA387O9vSjEsgUm5AeBYUQ6waU42IlIqs9SUwt+8aAZRdRsSsEbEtzWTsSzNy0LksAhEpIzIdkkYkLZl+ecV/acZYmFvaNaOfL8qkZZIJz1mceuTSSbxitB+AvdV7mM6qzdA/9YZcmiEWihUx9uHwqdoArV55JWfJiJSrqmixrds1YynN0OdiVypJJxNYoXumbB5qPCOSTSXwmtOHsHVdH1Z0144FiAvxzdUsEfxkRGxLM9Iib8XOibXZDNi07zpNsI0CU0akwz4jYrTveu+amVkkXxIPpZlllhFphqFZWhJ0eqWh0kwrfUT0640C2DD0ITJuw+8amTVj3flnAmRV6iH7/thpKLxil6XYNz6Pn+46gWeOTIvXkJjVa2lmUS/HyCJYr10ztC5QMONk2/4Hl5+BJ/afwqs2Drq+rxcURcGX33+J+HNc4UCkyfjRiNiVZrIuGZFiBKWZAT1an14soVJVkUwo4neLw0NX3mVYMyKifVdvbxQaEZudnSFWNeZDAOYHlpNYVW7VXg6zZkTWLlSNiP+MiJg14/GUR1eaIeFjeB4iMiLzZ2NsZjdY0ys1Q++akBExzYYKQSMic/fzx/GvDxluqltH+/H4vkkAQL2li+57csItSOe2XnbJ6qy6WGe+0Lu2n4Z3bT/N/YB8EOcAhIg+l77EabhrJmalGXI1VFWjXFGJk0Yk46YRMabz5ktVV42IIVbVXmOX0u5y0IhUl1tpRr9GF4sVIcRr1NAsE0Aj4tfNVjY1a2X7rjWTEHZGxJr5k2kki9oSH5Gw2ndt1sTv7Dxq+vuWVT3iz5TxdKIjo1/jFIhIVvn1MnB0KCIQ0deLoG6pSxEORJoMLYpeNCJ203fl3aaVKEozqWRCZBooDV+Kk0Yk7ZIRkb62UCxLbcfOPiK0eBRtUrhdDmZzJo1IG+xGGoWuV6rBA+YHShDErBkPATzRSGmmlYZm1ust7EDEatwnI7pmYjj0DgivayaVTNQEpLJAVVGAN5w1LP4+tViEGxSoUhDh1d4dMNZFa0Yk6ETdpQiXZpoM3az+MiJS10zSOSMSRWkG0Ha7s/my8IyoxEgjIpdmrBqRZEJBLp1AvlTFQrEiugqGempFXNahd3a1dafSDJUIFMX7Q7GdocWYHDF7cynblmg/yN4kXmlIrNpSQzPz8YVfmqkdWU+UysHXjGbPmgHMAWyjBlyZZAKL1dpz8LnfuQivPn3IVAaSxfd2dFjm+Hh1VQVq23fzdUozy5Hot7BLHOsDzQ27+q2bQ2AUpRnA6ByhdGbZZ0q8mXS4aEQAc+fMjoOnAEB0GchYS2p2w8mcxKp+Zp4sBej6OzGrTTxuVKgKGK6dXu4bol3Eqtb7NfTSTFr7veym8DakEWlJaSacjAjgPHl362h/jb06OQI7kbM4qxoeIvWvGzrVVDqk9+CMiAEHIk0mZekhd8O1NGMzMyGK0ozpmPSfT79bLDQiLj4igOwlUsaOg1MAgAvX99e8zuqsaudIKQYSWn1EfD4Q2x26HsbnqGOmMaEqYPhc+PIR8WkiF5VY1RoEhK1PoWCcZs7IhFqaaUb7rpTFbNQJtGISjWv/H+nJYpU0NO+0FZ0AgEtOc+9QoXWlWKmiUlV9lWYSlk1NPbHqcoRLM03GT0bEtTRjqxGJpjQjBvnpPz9WGpG6GRHt6y8en8XEfBGZZALnrumteZ1VI2K3gJNYtdZZld5jeQQi1vJjn4cx7fWg8+/P0Mxf91Z3VO27luMLe0RDpzRzJl+qmu6JRjYvtc6q4V/fIz1Z/Oo5K9GTS4X6mawb6MTByQVsHe03dZF85bpfwX88dgC/W6dLRd7gPHNkGpPzWtDtJaCzlmZYrFoLByJNxs/QO7u0aVYqzVgnajYySbMRxLRVkRFpD40IYBiePbRnAgBw9ppe2x2pVWRszJXwIFb1qVVod6yLcRiBSJDpu42VZiLMiIT8s3MWUbb8wCtK3R5+sdrCN6MUqygKPv+7rwz1PTvSSZw21KUFIuv6TF9b29+Bj121pe57yPf9W257WFzj9YblAbKhmTcfkeUIByJNJoihmV1pRlW1rIq8C3Ez5GomoqNB//lx0ojkTKUZm4yI/vVH9owDAC600YcAtZ9byY9YVd+ZL5vSjOX6CycQCeCsShbvnksz8RCrhp0RSSYUYda1UKxghfS1RrKocsCZTipt4U8BAINdGXzkjadjsDONd27bEOg9EgkFmVRCBHJkXeCta8a+NMOBiAEHIk3Gj6GZbWlGutCL5appAWnErrkRrLtVevC2OiCyo1NS2ttpROjhM6GnVu30IUDt5+YmVq2ZNeNz5km708yMiC9nVcpEebwdoirN1GZEwv/ZnZkkCuVqjWA1LLFqM8zMmsWK7gxeddogXlVHB1IPu85FP4FIVVWhqqqhEcm0zzlsNnwmmgw9nAMbmiXNgUi917cC+nn0kHZzKG01Zh8R59IMcdH6Adv3sX5udu27NPTOadbMsglELCn7UDQiQZxVqTQTQKzaSh8Rq7aiGRsJ61wlwtA6NWZoFgdhej3o9nvjlpGm/QwvQmPZWVUrsWv/zhkRA86INJmURWvghl3aNJVMIKEAVbVWsBpZaUb/efRwjtODt1svxyiKeW4FIY/FXj/YidHBTtv3SVnaR0VGJFmbEZkvlE36Hb9+Fu2O9aEURtdMIGdV1d91KGdEWumsag3YmxEEdTh4idiVGL2SNQUi0W866vHjP3wdHnjxZF0hqleuPGclHt4zbuqS8+QjIg29y0sZKu6aMeBApMlYuy/ccAosMinNhKs2IxJtaYYe0nEaetedTeGma7YgmVBMZRqiU9IFXLp5Rc3XCfpdVFX77MS5NnXNaO9ftXQnxEkz0wqaUZrxM7WaaB+xaisyItTCa87WNaIRSSUUKIp2T8ShDFuPM1b24IyVPfVf6JHP/c7FmC+WceXfPYhj05pnjj+NiFEqSyeVtgjmWgUHIk0miEbEujBlklogYjU1i640o2d5REYkPhoRAPgfr9/s+DW5q+bS04ccX5eUfpdiuSoecvJnI2dX5qXuhDhliFqB9fqz61by/54BfER8GsnJn18rd6eKoiCVUETA2pSMiM0EXlVVbbVOXlEUBZmkJoJdjg/RREJBTy6N4Z6sFIh46JoR7btGhoqzIWaW39XUYvxpROx3Kxn9YnfSiLQ6ABBiVcqIxEgjUg9yRASA7ZvqZ0QAs0NlWlrAEwlFBDZyC6/fEkG7Y90Vhtu+27zSTCKhYNNwFzrSSYzY2Pw3E9OE7SZmRORARPYyChpIUACzHAMRYrjbuFZ8iVWrKnfMOMAZkSYj+1FYfUCsOAUWdLHXakSiMjQjb5T4te/Wg7plAGDY5eEj/y4L0hRTa7aqK5vCQrFiEqxSYNYGpyMUrNdfmIGIF/8dwq9YFQD+84OXYqFYthU2N5N0IoE8gmcn6mGnEZGDuqDloGwqgVnEowwbFSO9UiDiIZslO6uSvTtnRMws37C2Rcg2yPWSIo6lGWHzbg5Eyg6vbzZW+21haBaT0owb1712E1b2ZvHJt5zn+jr5c5MXc2t93xCsGq9ZbhmRpmhE9HPnNC/EDrq//FyHg10ZrBuwFyw3E1NGpAlC2Y50bdeMvH4E7XqhtabRoYbtjDkj4qc0o7KZmQOcEWkystagXK0imXC+AIWhmWVhzzoEIuSx0PLSjLDfJrGqnslpgwfvOWt68difXVH3dQlJmEfp1EwyUZPREjbvUtbE0Igsj8VazogkE4qpGyUoFNwEGnrXBt1KdqaFYWJM4DWuSwrqFCV4kEzHajWxW07ImVS/hmZCI8L27iaWx0oZIfLDuZ5OhBaKmlkUojRj7wkQlY+IcFZtI42IH+hzoF2l3S6y08bm3a+xVrsjP0j7OtKhOG7KzrZVj8FIO4mE7cY4hEmnNPiOkEu5QT8j+qyXdUakxxia5zsQERmR5Xv+7OCz0WTkRbFeK6JTYCEG38WkNGOYTZnbd9vhAeAH+n1oF2O3c5W9RIgKDQFsg515GGQtgUgYyFnBkoeOM6C9/FtSDu7JYdFhI1YtNTBnhhCByBK71/1gyoh4mjWj/b+qsljViaY9wf7qr/4Kl156KTo7O9Hf39+sHxN7ZK1BvYxI2carQv57bftuRKUZS2tlO2lE/EAZngWXQMSYN2Ms+JQRWS6zZuTAOYzWXcB833j1EjF8REI5hKYiP8iboRHpdBGrNuKKSkFMM4KndkHusPLS8SQ7qwqNCJdmTDTtaioWi/it3/otfOhDH2rWj2gLSGsA1PcSqVuakQIR2RMgstKMaN9tH42IH0RGRJgQ2WVE9J2n3L4bI4O3VpBMKOJchZYRkbVVHgWr7SQSbrZGpMPG4j2M9YIzIuaMSMHDtWmrEeGMiImmiVU/8YlPAADuuOOOZv2ItiGVUFCqqHUzInVLM9JFL79X60szeiBSNrfvLjWNCD0MSfBn98AgjchcsVYj0g6iybBIJxVUqir6QwpE5GDCa+eMX0OzKGm2RkQYmtloRBoJfMjTaDlrROQgYkafwuuGvUaEAxGZ5Xs1tRDhrlonxVyvNCNnROR0datLIqSYpwyP0IgssdKMNSNiF/DRZ1MqG59HO4kmw4LOTVgZEXLxBLx7ifi1eI+SZmtE7LpmnOwB/EDf2w5D71qBl8yQ3L7LgYg9sWrfLRQKKBQK4u8zMzMRHk14aJmCqmsrYrWqOs5ssQtE5F1iVIZmFAxVlmgpwotGJG1j4b8sA5FUuIEIoD2sixXv7qp0e7VFRiTR5IyIjVjVboK0X+hYl7OzKgD81W+chx8+M4b/vm193dfKzqr5ImtE7PB1Nd14441QFMX1v127dgU+mFtvvRV9fX3iv9HR0cDvFSdo9/Orn3kAf/tj+/MjdwZ48REpmwKRFotVKQtQqUJV1SUbiFi7ZuwWX0pRy0HmsgxEQs6IAHKbuLeMCAWD7XDeyRQQaFJGJF3bvis0Iqng58fQiCzvQOSd2zbg//3eNk+OvMJZVcqIsEbEjK+MyEc/+lG85z3vcX3Npk2bAh/MTTfdhBtuuEH8fWZmZkkEIymxa1Zx23178bGrttS8Rk4/2w29A8xZENExk1BC8W3wQ1oqNckP4KW2OFl9ROxS2kbZzfhs2qmNNCzSTciI+J03Q7F8W5RmTBmRZnTNaEv7ok37bjgZkfif47hA60C1CizqFu9cmjHjKxAZHh7G8PBws44F2WwW2Wxrh0+1Ai87NHmx9VKaicrMDDCyAMVK1SSaXeoaEWumCjAWZDmQLLeRViEsKEgLq30XsD+3blTaKABMmyzeW+QjEsJsKh565x95AnueMyK2NE0jcvDgQUxOTuLgwYOoVCrYuXMnAOD0009Hd3d3s35sLPGSKXCzX7bzEQnDEyAoaUmsas6IxP8B4AcKuBZdMyK1pZlqG3VvhMX5a/twYGIB567pDe090zaZQDfay0ekVRbvRiBCwxsbeQgas2aWz7XdKEbXDCQfkTa4SFtI0wKRm2++GV/60pfE3y+88EIAwH333Yc3vOENzfqxscTLTVt2sV/OJLWFw640E4WxkEiZl1XhIgoswUBElGaofbf290tbOogASSOyjBbrT//WVvzFteeiN8QptimLcZ4bsg18OwSAqSZnRCgQKVaqKFeqSCUTmNZbTQc6g39GF4z2I6FogSfjDSFWVQ0fES7NmGlaIHLHHXewh4iOdVlUVbUm2BAZDpuHuVtpJgpdhmFoVjWJbNtBJOiHpC+NiCRWbaPujbBIJJRQgxAAvtp3qSwDtMd1KF9LzdhMyFmPhVIFvVIg0oiO59e3rsHlW0aEozBTH9lZlcWq9nB+qAUcnc6b/m7XBSACEZtFyVUj0oACPijyrBm5Q6TVotlmk7JoROzbd+26ZtqneyPO+MmIyFqldtDmmHxEmqC3yKYSoNNAu/CpBS0QadR0joMQf7ChWX04EGkB1mF1hXKl5jVuQjI3Q7MoRGMZqZthqQ68A7y27xodUQQ9N5eTs2oz8NM1U1XbrTSj/W7JhNIUl1JFUUTAcHRqEQAwRRmRzkzoP49xRp4kzT4i9nAgEgHW4XWAe2kma9u+S6+PomuGdqqGRsTuuNsdehC6GZq5te8uNc1Mq0lbjPPcMHVvtcF5p/ulGfoQ4nVnah2On3tgLwBgaqEIoPGMCOMPOx8RzoiY4UAkAlwDEZfSjJxJibQ0kzB2qqUlXIbwYvGettEx0J/boUQQZ+jarjcsEjA8RID2yETRddNMsfkfXn4GFAX48XPH8eyRaTEXpb8BsSrjH8NHhDUiTnAg0gLOGDG3KxdKS6M0U5Z8RJbiECzqPCCRn2tGRO6aEVNgm32ESxsKeK2lTTvaTaxK90szMyJnrOzBtVvXAAC++PB+ozTDGZGWQknrclVFngzNuDRjgpfKFvDVD/wKvvS+SzDUrZm10cUoY3TB2HTNJO0CkXiUZmj33w6Lv1+oC4SCLbugL22jEVmOPiLNQGSb6kytBixi1TY47XTdNLv9/spzVwEA9k/MG2JVzoi0FLt1gEszZjgQaQFD3Vm8/sxh5NK1JRZCTMZ0Lc3Eo2tGbt+lTMBS1Ij0dpi7A+wzIrWlGSMjwrdXI6T9+Ijo5zyhoC26tyjb0wx7d5mVvTkAwLGpRczkKSPCYtVWYmexwKUZM9yH1UKyNgEF4ak0Y2NoFkVphh4QqmpkaZaieZfVF8M2I+I6fbeJB7cM8DP0rt0GDdIGohmtuzKr+rRARLYQ4NJMa7HGIZlUom2u01bBS2ULod2Pm1jVrjRjN303DoZmgCHkXGoD74DauSl2GRG36bssVm2MID4i7bLAU0k1m27ufTPSk4WcIOrMJCNxY17OWK9JLsvUwldkC6FFx16sWr80IwciZfH66AzNAEPv0i4PAD/UlGZssj62zqqsEQkFWRRdj3abeEz3ULMzIulkAiu6jEGi3LrbeqxdXLkmB5/tCJ+RFpJzzYg4l1qyNqWZYpSlmYRdRqQ9HgB+sJZmbJ1Vhcss+4iEDT2siz5KM+2ShUq1oH2XWK2XZwA2M4sCzojUhwORFiIyIj5LM2LoXUxKM4mEIm4ucgpcitM4ezxoRIz2XclHpM0einElHSQj0ibnfKRHy1KQmLSZyD+DMyKtx5qlY6FqLSxWbSGU2cjblWbK/mbNRFmaAbSASZ6dsBQ7RGpLMy6GZty+Gzp+LN7pJe1yzi/fMoJ/edfFuHjDQNN/1qo+ozTDQtXWk0goUBRN3A+wh4gdHIi0EDexKj3I7B52FIiUqyqqVRWJhBJpaQbQjrNQroqgaimWIbyUZuws3tutTBBX0pJfTT2oa6ldznkqmRAeH81mlZwRYQ+RSEgqCsp6JMKlmVqW3jY2xmRtrNqJoltpRnoA0uuiLM0ARilmSWtErF0ztu27Nl0zrBEJBXmUQD2qbZYRaSVyaaaPA5FIkANkDkRq4UCkhRhdMzYakbKe4bArzUgPQMqmlCM0NAOMTIwIRJagRqQrkzS5dNpmRGyn77aXXiGu0Pn2VJppM41IK1nVJ2tEWKwaBXKAnOPSTA0ciLQQ99KMrvlwsREHDJ0Ipaub3f7nBAUiJFZdihoRRVFMWRFXQzO70gzvzhsiZdMa7YRRDmvqIbUlXJqJnhRnRFzh27aFuFm8u5VmFEWpcVctxqQ0Qz4iS7UMIetE3AzNqqohUm23Do64QoFfcQn6iLSSlXL7LotVI4FLM+5wINJCXJ1VXUozADCg72QOTy4AiE9pJl9euhoRwNw549a+CxjlGS7NhEPaplvMCRYIO9OTTYlJ0ty+Gw3yWsBdM7VwINJC3Np3xfA4h1LLKzcMAgAe3zcJIPrSDAUei0vYRwQwZ0TsRrbLZTP6DDkQCQfaOeY9BCLcMu2MoijYuq4fmWQCm0e6oz6cZYlcpmUfkVq4fbeFuA29o12fnY04AGzbNIgfPHMMj+/XAhG3Uk4roDLFUvYRAcyBiO+MCD8UG4JKmXaBuxUWq7rzxfe+CjP5EkZ6mm+gxtQiLx1cmqmFA5EWktUvQLuuGVpsnaLlSzZqGZGnDpxCqVKVSjPRZkSWso8IYC7N2Fq8SwEYiSpJW8llgsYQGREvgQgLhF3JpZO8E48QWcvHs2Zq4TPSQtx8REj0aZf+B4AzR3rQ35nGQrGCZ45Mu86maQVCI7LExao9poxI7e9IromAUZqhMsFSPSetggIRKv+5wQJhJs4kOCPiCgciLcRNrErBSdbhIk0kFLzqNEMnQt4Kdg/HVrAcfESA+l0zgGRqpgeH7ebyGVeyQiPiJSOi/Z/PORNH5DIti1Vr4UCkheRcht5RZsEtfUpzKZ49Mi0FIlFlRMxi1aW6EzWVZhzONQVhVB5gl89wMDIi3rtmlmg8zLQ5coDMJbJaOBBpISIjYlPzpl2fU2kGMFrv8qVK5KWZVNIsJIzKz6TZUEYknVSgOAQWFIRRcGgIJ1twgEsYEbh70IhwaYaJM6aMCAciNfBS2ULI4t3OF6HgISOSlTIq0ZdmlotYVQtE3NqkrRN4y6J9l2+vRqAU9iKLVZk2h31E3OGVsoW4+YhQRiTnkhHJJA2NSdQZEatGJLlEc+K9Oa0049adlLRYkQtPC767GoJ2juWqWnfeDGdEmDiTZGdVV3ipbCGuYlXqmnHLiKTsMiJRte8a1uba35fmA2BNfwcAYKg76/gaMW/GYmjGu/PGkLOD9Vp42USOiTNJ1oi4wj4iLYQCiZl8CW+7/RFcuL4ff/6mcwAYXTNuPeYZyfI66tJMxmItv1TLEKODnfjie1+FtXpAYod1Ai/vzsNB1kstliqmVmorHPwxcSbBXTOuLM2nR0whjUepouLJA6fw+Z/tE18TXTMpLxmRiigDRJ0RIdJL+KF72VkjOHNlj+PXa9t3ORAJA0VRRBrbzgRQhoK/pZqZY9obLs24w4FIC8m6BBmUes56zIgUI2/fNf/cpaoR8UIqaS7N8NyT8KAMYT3BKvuIMHGGAxF3OBBpIXZlF1VVUa5UxS7aPSMii1Xj0TVDLOedaNKSEeG5J+Hh1eZdnHMO/pgYIl+XbhYNyxXWiLQQuxbQYqUqHmCAu5CJMiKFUkWIRKPzEVkeGhEvpJMOYlUORBom59HmvaIH5hz8MXGErstsKsHrgg0ciLSQVDKBVEIR2Q9A04aUpdZEt2iZvlasVCMPRKw/N6rMTBywtu9WeNZMaIhApG5GRPs/L/JMHKHrkoWq9nAg0mKyqQTK0u4uX6qIB1cm6R4tZ20s4qOa8VKjEVnGDwAhVq2aAxHu4GgcWrjz9cSqbPHOxBi6LlkfYs/yzadHhNUnJF+qeBKqAkBWNzRTVe0/wN3xs5mwRsRAZES4fTd0SFflVSPCGREmjlDpmgMRezgj0mJKFjOzfKkqdtD1jG7sApW4tO8uZ42I6JqpmDUiHIg0jmexKncqMTGGlmk2M7OHA5EWM1som/6eL1XEDrqemtou+xFZacZyrMs5I+I8a2b5npOwyHrUiFT5nDMxJskaEVc4EImYvNQBUy9aTiQUpJOKmDMDGPqEVmM1MFvj4jy61HGcNcO784bp8CxW5dIME19IL8alGXuWbz49JuTLVWPgXR2NCGDOiqQSSmQLb0o6jkwygQvW9UVyHHGA9DKVqtYBRQ9N3v00jlGa8SpW5UCEiR+UMebSjD0ciERMvlQxBt65mJkRstg1qrIMYBarnr+ub1nfYKSPKVVUjM8VUVW1LInboDzGG37FqlyaYeIIt++6w6WZiJEXWL8ZkaiEqtaf/arTBiM7jjggT98dm8kDAIa7s/xQDAHvYlXt/9wyzcSRpCjN8N7fDj4rLeZ9r94IwBCmyu27bvbuhNw5E1XrLmANRAYiO444ILfvjk1rgcjKvlyUh7RkyGW8OasaLdNNPySG8Y0Qqy7jzLEbfNu2mI//t7Pxi4//Ki4/ewSAVvsmg7J6PiKAubMmytLMzGJJ/PniDcs7ECG9TLmi4sSsFois6uWyTBhQcF5/6B2LVZn4QtdljksztnBppsUoioLBroxYYPOlCiib7CUjkknFozSzbsDokunvzER2HHHAmDVjZERW9XJGJAy8OquyjwgTZ4Z1vdhqXhds4UAkInLSAktrp9V11Q5Z0BplaeaSjYO4/Z0XYcvq3siOIS4Y7buGRoRLM+HgVazKbrZMnPnA6zbh/LV9eM0ZQ1EfSizhQCQiREakXAGtnV7GQ5vadyMszSiKgmvOXx3Zz48TlJmqVFUcn+GMSJj4dVZlsSoTR7qyKVxxzsqoDyO2cCASEfJOjxZPLy2wso4kytIMY0C78FKFSzNh43X6LmdEGKZ94UAkInLSTs8IRNqnfZcxkNt3j88UAHBpJiy8BiI834dh2hcORCLCyIhUxeLp19DMOgGXiQYyNJteLGFOnyXEGZFwoNJMoa5YVfs/l2YYpv3gQCQi5IxIMsEZkXaGtDpHTi0CAHqyKXRl+dYKA+qa8V6aafohMQwTMrxaRoTcvks7ataItCc0R+KwHohwWSY85PvEjTKLVRmmbeFAJCLk9t1UUltE/XbNcGkmHpCh2Rh3zIROLqOd28VSBaqqQnEINGjoXYo1IgzTdvCWOiJyZPFeNobecUakPbEGhCPsqhoadE+oKoQDsR0sVmWY9oWfZBGRk8ab58v6rBlPFu+yWJU/vjhgffit6FreTrNhIs/mcBOs0vRdtnhnmPaDn2QRIYtVaYH11DUTk1kzjEE6Yb6N+jrSER3J0iOdTIhyi5tgtcoW7wzTtnAgEhGyoZm/jEg8pu8yBtaMCAci4ZLz4K7KGRGGaV/4SRYR8uJKC6yXjEhcht4xBtbMVC8HIqFC98pC0SUQYbEqw7Qt/CSLCKMtsSpEeH4zIlyaiQcpS2lmuU8jDpuBTi2wm5gvOL7Gj+CbYZh40bRAZP/+/Xj/+9+PjRs3oqOjA5s3b8Ytt9yCYrHYrB/ZVlBbYr4cPCPCpZl4YA0IuTQTLqODnQCAQ5OLjq8plOke4nuCYdqNpvmI7Nq1C9VqFf/8z/+M008/Hc8++yyuu+46zM/P49Of/nSzfmzbILcl5v2073LXTOywtu9yIBIu6wY6AACHTy04voayil6CeYZh4kXTApGrr74aV199tfj7pk2bsHv3btx+++0ciMAozchkfVq8c2kmHiS5a6apGIGIc0ZEZBU93EMMw8SLlt6109PTGBwcbOWPjC3ppAKrrs4uOLHChmbxI235IHtzbFgcJqMDemnGU0aE7wmGaTdatmLu2bMHn/3sZ12zIYVCAYWCIUibmZlpxaFFgqIoyKWTohMgoXizbJdTz6wRiQdy+25PNiUs35lwWKcHIm4ZES7NMEz74nvFvPHGG6Eoiut/u3btMn3PkSNHcPXVV+O3fuu3cN111zm+96233oq+vj7x3+joqP/fqI2QNSF9HWnHORoyGe6aiR1y4MGtu+EzOqiVZk7OFhy9RAol7148DMPEC98ZkY9+9KN4z3ve4/qaTZs2iT8fPXoUl112GS699FL8y7/8i+v33XTTTbjhhhvE32dmZpZ0MJKTgopz1/R5+p4s+4jEDtm7gvUh4dPXkUZ3NoW5QhmHTy3i9JHumtdwRoRh2hffgcjw8DCGh4c9vfbIkSO47LLLcPHFF+OLX/wiEgn3B2c2m0U2u3wGhtEEXgA4d22vp+/h9t34IWem+js5EAkbRVGwbqADu8ZmcfjUQk0gUq5UUa56n2DNMEy8aNpde+TIEbzhDW/A+vXr8elPfxonT57E2NgYxsbGmvUj2w5ZnHpegIwIl2bigZyZ4oxIc1gnBKu1OpFixRiGx10zDNN+NE2sevfdd2PPnj3Ys2cP1q1bZ/qaqs+FWO7I4tTz13oLRNjiPX4kuTTTdNy8RPLSVF7OEjJM+9G0u/Y973kPVFW1/Y/RkHd363X3yHqwoVn8kKfvciDSHMhd9cB4bSBCrqqphMIdSwzThvBdGyGT84bdvdepoWaxKpdm4kBS+hy4a6Y5bF2nZQzv3XUCx6bN5RmeM8Mw7Q0HIjGgO+u9QiannjkjEg9kQzMWqzaHizcM4JKNgyhWqvine/eYvsZmZgzT3vCdGyF/89bz0ZtL4Uvvu8Tz9yQSighGOBCJB7JGpDfHgUgzUBQFH/3VMwEA33jykCkrwgPvGKa9YS/qCPntS9bj7a8a9WRkJpNJJVCsVLk0ExNkXYKf7Bbjj22bVuCc1b14/tgMfnloCqv7NAGryIhwaYZh2hLeQkSM3yAEMHZ+nBGJB3JA2Jnhh2Ez2TjcBcBs9y4G3nFGhGHaEr5z25CNQ11IJRSs6e+I+lAYmEszXZwRaSp2k3hJrMqBCMO0J7xqtiF3vO8STC+WMNyzfFxo44zcvksPSqY5rNOD7yNTskaESzMM085wINKGdGdTrEWIEYmEgjs/8hoUK1X0d2aiPpwlzVq7jAiLVRmmreGnGcOEwHkenXGZxiCr9yOSwyoPvGOY9oa3EAzDtA1r9dLMTL6MmXwJAFAgsSrPmWGYtoTvXIZh2oaubAoDumncEb08k2dDM4Zpa/jOZRimrSCdCAUiRtcMl2YYph3hQIRhmLZiXb+mE6FJvCRWzXFphmHaEr5zGYZpK0RGRG/hZbEqw7Q3HIgwDNNWWE3NuH2XYdobvnMZhmkraMbMsek8AEkjwqUZhmlL+M5lGKatoHk+NGOGSzMM095wIMIwTFtBJZhiRQtAeOgdw7Q3fOcyDNNW0EwZKslQRiTHs2YYpi3hQIRhmLaCMh8UgLBYlWHaG75zGYZpKzJUmilbNSK8nDFMO8J3LsMwbUVNRkR0zXBphmHaEQ5EGIZpK6g7plCuQlVVLs0wTJvDdy7DMG1FRgo4ShUV+RKLVRmmneFAhGGYtkLOfBTKFdaIMEybw3cuwzBthTkQqXJphmHaHL5zGYZpKxRFQSZJnTNVIyPCpRmGaUs4EGEYpu2g7Ee+VEGRSzMM09bwncswTNtBA+7mCmXj3zgQYZi2hO9chmHaDirNzCwagQh3zTBMe8KBCMMwbQfpQWbyJQBAQgFSCSXKQ2IYJiAciDAM03ZQGWZmsaT/PQlF4UCEYdoRDkQYhmk7yNSMMiKkGWEYpv3gu5dhmLbDyIiUTX9nGKb94LuXYZi2g+bNzOZLpr8zDNN+cCDCMEzbQaWZKV0jkuPSDMO0LXz3MgzTdlApZnK+CADoyqaiPByGYRqAAxGGYdoOCkROLWiBSDcHIgzTtnAgwjBM20Glmck5LRDpzLBGhGHaFQ5EGIZpO0icOrnApRmGaXc4EGEYpu0wht5pA++6MhyIMEy7woEIwzBtR8biG8IZEYZpXzgQYRim7bD6hnSxRoRh2hYORBiGaTuslu6dnBFhmLaFAxGGYdqOTNK8dHVnOSPCMO0KByIMw7QdNRkRFqsyTNvCgQjDMG2HVSPChmYM075wIMIwTNth7ZphQzOGaV84EGEYpu3IcvsuwywZOBBhGKbt4ECEYZYOHIgwDNN21BqacWmGYdoVDkQYhmk7ag3NOCPCMO0KByIMw7QdcmlGUYCONGdEGKZd4UCEYZi2Qw5EOtNJJBJKhEfDMEwjcCDCMEzbIZdmWKjKMO0NByIMw7QdsrMqByIM095wIMIwTNshl2a4Y4Zh2hsORBiGaTvk9l2eM8Mw7Q0HIgzDtB3y9F2eM8Mw7Q0HIgzDtB2pZAIpvVOG58wwTHvT1EDk13/917F+/XrkcjmsXr0a73rXu3D06NFm/kiGYZYJVJ7hjAjDtDdNDUQuu+wyfOMb38Du3bvxX//1X9i7dy/e9ra3NfNHMgyzTCDBKmtEGKa9aeod/Ed/9Efizxs2bMCNN96It7zlLSiVSkin08380QzDLHE0L5ESurlrhmHampZpRCYnJ/GVr3wFl156KQchDMM0DJVmOrk0wzBtTdMDkT/90z9FV1cXVqxYgYMHD+K73/2u42sLhQJmZmZM/zEMw9hBpRk2NGOY9sZ3IHLjjTdCURTX/3bt2iVe/7GPfQw7duzAT37yEySTSfzu7/4uVFW1fe9bb70VfX194r/R0dHgvxnDMEsaclft4q4ZhmlrFNUpKnDg5MmTmJiYcH3Npk2bkMlkav798OHDGB0dxSOPPILt27fXfL1QKKBQKIi/z8zMYHR0FNPT0+jt7fVzmAzDLHHe9W+P4WcvjePL778Erz1jOOrDYRhGYmZmBn19fZ6e375zmsPDwxgeDnbTV6tVADAFGzLZbBbZbDbQezMMs7y45c3n4vF9k7h081DUh8IwTAM0rbj62GOP4YknnsBrXvMaDAwMYO/evfj4xz+OzZs322ZDGIZh/HD6SDdOH+mO+jAYhmmQpolVOzs78a1vfQuXX345zjrrLLz//e/HBRdcgAceeICzHgzDMAzDAGhiRuT888/Hvffe26y3ZxiGYRhmCcCzZhiGYRiGiQwORBiGYRiGiQwORBiGYRiGiQwORBiGYRiGiQwORBiGYRiGiQwORBiGYRiGiQwORBiGYRiGiQwORBiGYRiGiQwORBiGYRiGiQwORBiGYRiGiQwORBiGYRiGiYymzZoJA1VVAQAzMzMRHwnDMAzDMF6h5zY9x92IdSAyOzsLABgdHY34SBiGYRiG8cvs7Cz6+vpcX6OoXsKViKhWqzh69Ch6enqgKEqo7z0zM4PR0VEcOnQIvb29ob73UoTPl3/4nPmDz5c/+Hz5h8+ZPxo5X6qqYnZ2FmvWrEEi4a4CiXVGJJFIYN26dU39Gb29vXxB+oDPl3/4nPmDz5c/+Hz5h8+ZP4Ker3qZEILFqgzDMAzDRAYHIgzDMAzDRMayDUSy2SxuueUWZLPZqA+lLeDz5R8+Z/7g8+UPPl/+4XPmj1adr1iLVRmGYRiGWdos24wIwzAMwzDRw4EIwzAMwzCRwYEIwzAMwzCRwYEIwzAMwzCRsSwDkdtuuw2nnXYacrkctm3bhscffzzqQ4oNf/EXfwFFUUz/bdmyRXw9n8/j+uuvx4oVK9Dd3Y3f/M3fxPHjxyM84tby4IMP4s1vfjPWrFkDRVHwne98x/R1VVVx8803Y/Xq1ejo6MAVV1yBl156yfSayclJvPOd70Rvby/6+/vx/ve/H3Nzcy38LVpHvfP1nve8p+Z6u/rqq02vWU7n69Zbb8WrXvUq9PT0YGRkBG95y1uwe/du02u83IMHDx7Em970JnR2dmJkZAQf+9jHUC6XW/mrtAwv5+wNb3hDzXX2wQ9+0PSa5XLObr/9dlxwwQXCpGz79u344Q9/KL4exfW17AKRr3/967jhhhtwyy234Be/+AW2bt2Kq666CidOnIj60GLDueeei2PHjon/HnroIfG1P/qjP8L3v/99fPOb38QDDzyAo0eP4q1vfWuER9ta5ufnsXXrVtx22222X//Upz6Ff/zHf8TnPvc5PPbYY+jq6sJVV12FfD4vXvPOd74Tzz33HO6++27ceeedePDBB/GBD3ygVb9CS6l3vgDg6quvNl1vX/3qV01fX07n64EHHsD111+Pn//857j77rtRKpVw5ZVXYn5+Xrym3j1YqVTwpje9CcViEY888gi+9KUv4Y477sDNN98cxa/UdLycMwC47rrrTNfZpz71KfG15XTO1q1bh7/5m7/BU089hSeffBJvfOMbce211+K5554DENH1pS4zLrnkEvX6668Xf69UKuqaNWvUW2+9NcKjig+33HKLunXrVtuvTU1Nqel0Wv3mN78p/u2FF15QAaiPPvpoi44wPgBQv/3tb4u/V6tVddWqVerf/u3fin+bmppSs9ms+tWvflVVVVV9/vnnVQDqE088IV7zwx/+UFUURT1y5EjLjj0KrOdLVVX13e9+t3rttdc6fs9yPl+qqqonTpxQAagPPPCAqqre7sG77rpLTSQS6tjYmHjN7bffrvb29qqFQqG1v0AEWM+Zqqrq61//evUP/uAPHL9nuZ+zgYEB9V//9V8ju76WVUakWCziqaeewhVXXCH+LZFI4IorrsCjjz4a4ZHFi5deeglr1qzBpk2b8M53vhMHDx4EADz11FMolUqm87dlyxasX7+ezx+Affv2YWxszHR++vr6sG3bNnF+Hn30UfT39+OVr3yleM0VV1yBRCKBxx57rOXHHAfuv/9+jIyM4KyzzsKHPvQhTExMiK8t9/M1PT0NABgcHATg7R589NFHcf7552PlypXiNVdddRVmZmbErncpYz1nxFe+8hUMDQ3hvPPOw0033YSFhQXxteV6ziqVCr72ta9hfn4e27dvj+z6ivXQu7AZHx9HpVIxnUAAWLlyJXbt2hXRUcWLbdu24Y477sBZZ52FY8eO4ROf+ARe+9rX4tlnn8XY2BgymQz6+/tN37Ny5UqMjY1Fc8Axgs6B3fVFXxsbG8PIyIjp66lUCoODg8vyHF599dV461vfio0bN2Lv3r34sz/7M1xzzTV49NFHkUwml/X5qlar+MM//EO8+tWvxnnnnQcAnu7BsbEx22uQvraUsTtnAPDf//t/x4YNG7BmzRo8/fTT+NM//VPs3r0b3/rWtwAsv3P2zDPPYPv27cjn8+ju7sa3v/1tnHPOOdi5c2ck19eyCkSY+lxzzTXizxdccAG2bduGDRs24Bvf+AY6OjoiPDJmKfLbv/3b4s/nn38+LrjgAmzevBn3338/Lr/88giPLHquv/56PPvssyaNFuOO0zmTNUXnn38+Vq9ejcsvvxx79+7F5s2bW32YkXPWWWdh586dmJ6exn/+53/i3e9+Nx544IHIjmdZlWaGhoaQTCZrFMDHjx/HqlWrIjqqeNPf348zzzwTe/bswapVq1AsFjE1NWV6DZ8/DToHbtfXqlWraoTR5XIZk5OTfA4BbNq0CUNDQ9izZw+A5Xu+PvzhD+POO+/Efffdh3Xr1ol/93IPrlq1yvYapK8tVZzOmR3btm0DANN1tpzOWSaTwemnn46LL74Yt956K7Zu3Yp/+Id/iOz6WlaBSCaTwcUXX4yf/vSn4t+q1Sp++tOfYvv27REeWXyZm5vD3r17sXr1alx88cVIp9Om87d7924cPHiQzx+AjRs3YtWqVabzMzMzg8cee0ycn+3bt2NqagpPPfWUeM29996LarUqFsflzOHDhzExMYHVq1cDWH7nS1VVfPjDH8a3v/1t3Hvvvdi4caPp617uwe3bt+OZZ54xBXB33303ent7cc4557TmF2kh9c6ZHTt37gQA03W2nM6ZlWq1ikKhEN311YjSth352te+pmazWfWOO+5Qn3/+efUDH/iA2t/fb1IAL2c++tGPqvfff7+6b98+9eGHH1avuOIKdWhoSD1x4oSqqqr6wQ9+UF2/fr167733qk8++aS6fft2dfv27REfdeuYnZ1Vd+zYoe7YsUMFoH7mM59Rd+zYoR44cEBVVVX9m7/5G7W/v1/97ne/qz799NPqtddeq27cuFFdXFwU73H11VerF154ofrYY4+pDz30kHrGGWeo73jHO6L6lZqK2/manZ1V//iP/1h99NFH1X379qn33HOPetFFF6lnnHGGms/nxXssp/P1oQ99SO3r61Pvv/9+9dixY+K/hYUF8Zp692C5XFbPO+889corr1R37typ/uhHP1KHh4fVm266KYpfqenUO2d79uxR//Iv/1J98skn1X379qnf/e531U2bNqmve93rxHssp3N24403qg888IC6b98+9emnn1ZvvPFGVVEU9Sc/+YmqqtFcX8suEFFVVf3sZz+rrl+/Xs1kMuoll1yi/vznP4/6kGLD29/+dnX16tVqJpNR165dq7797W9X9+zZI76+uLio/s//+T/VgYEBtbOzU/2N3/gN9dixYxEecWu57777VAA1/7373e9WVVVr4f34xz+urly5Us1ms+rll1+u7t692/QeExMT6jve8Q61u7tb7e3tVd/73veqs7OzEfw2zcftfC0sLKhXXnmlOjw8rKbTaXXDhg3qddddV7MpWE7ny+5cAVC/+MUvitd4uQf379+vXnPNNWpHR4c6NDSkfvSjH1VLpVKLf5vWUO+cHTx4UH3d616nDg4OqtlsVj399NPVj33sY+r09LTpfZbLOXvf+96nbtiwQc1kMurw8LB6+eWXiyBEVaO5vhRVVdVguRSGYRiGYZjGWFYaEYZhGIZh4gUHIgzDMAzDRAYHIgzDMAzDRAYHIgzDMAzDRAYHIgzDMAzDRAYHIgzDMAzDRAYHIgzDMAzDRAYHIgzDMAzDRAYHIgzDMAzDRAYHIgzDMAzDRAYHIgzDMAzDRAYHIgzDMAzDRMb/D9ibjKo/tDPQAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(features['thumb'][0][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_dir = 'features'\n",
    "tag = 'more'\n",
    "# Save the dictionary to an HDF5 file\n",
    "file_path = os.path.join(target_dir, tag+'_'+cfg['subjects'][subject_id] + '.h5')\n",
    "with h5py.File(file_path, 'w') as h5file:\n",
    "    for key, value in features.items():\n",
    "        h5file.create_dataset(key, data=value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "9cefe38f745df9e33a66570f2e5a410ba71c4ae3bf929b6ad1b474ac5f904d76"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}