{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Demonstration of subsampling feature dimensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import openensembles as oe\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "## Lu (2005) mRNA \n",
    "# Lu, J., Getz, G., Miska, E. a, Alvarez-Saavedra, E., Lamb, J., Peck, D., … Golub, T. R. (2005). MicroRNA expression profiles classify human cancers. Nature, 435(7043), 834–838. https://doi.org/10.1038/nature03702\n",
    "# Originally, data was clustering 89 cell lines in 14,546 dimensions, which is fraught with \n",
    "# problems with dimensionality. Instead, here, we will randomly subsample a smaller number of dimensions many times\n",
    "fileName = 'Common_Affy.txt' #can be found in OpenEnsembles/data\n",
    "raw_mRNA89 = pd.read_csv(fileName, sep='\\t',skiprows=2)\n",
    "raw_mRNA89.set_index('Name', inplace=True)\n",
    "raw_mRNA89.drop('Description', axis=1, inplace=True)\n",
    "raw_mRNA89_filtered=raw_mRNA89[~(raw_mRNA89<7.25).all(axis=1)]\n",
    "\n",
    "D = raw_mRNA89_filtered.transpose() #final data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#setup oe.data object\n",
    "d = oe.data(D, list(D.columns))\n",
    "\n",
    "#transform, take the zscore so that mean values don't dominate clustering\n",
    "d.transform('parent', 'zscore', 'zscore')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# select a random susbampling of n_features to cluster num_repeats times\n",
    "n_features = 100\n",
    "num_repeats = 2000\n",
    "names = []\n",
    "for i in range(0, num_repeats):\n",
    "    name = 'zscore_'+str(i)\n",
    "    names.append(name)\n",
    "    d.transform('zscore', 'random_subsample', name, num_to_sample=n_features)\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cluster all subsamples using agglomerative clustering\n",
    "c = oe.cluster(d)\n",
    "K=15\n",
    "for name in names:\n",
    "    c.cluster(name, 'agglomerative', name, K)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "comat = c.co_occurrence_matrix('zscore')\n",
    "fig = comat.plot(linkage='ward', threshold=1.5)\n",
    "fig.savefig('comat.eps') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How sensitive is feature subsampling to transforming before or after sampling?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#setup a fresh oe.data object from D\n",
    "dRz = oe.data(D, list(D.columns))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# select a random susbampling of n_features to cluster num_repeats times\n",
    "# apply the zscore after random sample is taken\n",
    "n_features = 100\n",
    "num_repeats = 2000\n",
    "names = []\n",
    "for i in range(0, num_repeats):\n",
    "    name_base = 'random_'+str(i)\n",
    "    \n",
    "    dRz.transform('parent', 'random_subsample', name_base, num_to_sample=n_features)\n",
    "    name = 'zscore_' + name_base\n",
    "    dRz.transform(name_base, 'zscore', name)\n",
    "    names.append(name)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cluster all subsamples using agglomerative clustering\n",
    "cRz = oe.cluster(dRz)\n",
    "K=15\n",
    "for name in names:\n",
    "    cRz.cluster(name, 'agglomerative', name, K)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9l3xs94WXz7r7Gc0vzOwaSV+R9OlmzN2Xm9l17n6GmR0sySXtK2lvSR/v5k4BAABQPxLvyHgt4FuXl4yXlJezr4oGOjdI2qWxDOVuSdua2cdULDU518yukvRdSZ+S1Nph5d2SdjCzS9z9kaoOCgAAAL0j8Y6MbgHfvPLdVNG+3kEDnYNavryg5d/njhrXvN0xo+In9nRA6M7kpORs8jrtsawQcOqGcfzOu9pCUSdKKSmilCR9oT208UfjoVnF3/Qt2kLLk0qlqIgyjc+YEQ9+emkcX3+94Ni2jMfOelkcvzPrb9hu883jCpyttmqPbZC0PMwej7Wmxq+bSWlp3uBlBYnZD5Lo1LMCyDKdK0dKjJXiorDsmLOCs2h8NjYr4ovKoqso+ENnqnhMs+c8Lm0vN/aJEveZFXmWOccyhZhS/FrNjrmK13VWcB09fiPJ2MeSzsZRo+GsI27VSLy71Vxecvpbn78spamz4sqyDXTWbNxuO0nHSloq6T2Naee5e1ZgDAAAgAEj8e7VeMtSAj000FluZtdJ2kfScndfLumM3k4AAAAAdSDx7tXoZSnd66iBjrtf2Wiis5niv5YAAABgCJF4d6rMLifdGbeBTiPh/qCkF6lYggIAAIBVBIl3J7pYTtKJ1uJKd3dJn8m+3/j6QUkn9OVg0L01k7dRUBipxUkvrilB0aAUFmiusfvu8dikG2VYSLlZ0h5x8ifi+LrtBYxZceXdyd9horrSbWbfFo7N5o7mePLJ+Lw3ui2e+7Gb2p+X7CmcNy+OrxVUGb4gqRNdkhT3TJ4cF1EOc1FdVjCVxaOisKyIMnmYwscjK4LKGlNEHTSnlbi/7D7LFmiOBLGsG2gZZbp+ojfZc569fqPPlpHkCXsqmSN6XWeFzmWUbeQS3ee2ydg/JvHocSrznpPi7pcbJ2O33z6Ozw0+29u3EOgPEu9OjLXLSW+dKzsprvyVVnZOnadiy0CX9O/ufnPX5wQAAIBakXj3qoutBUsWV369eTszMxUJ+yRJ91d1CgAAAOg/Eu8qdN+5slUnxZVTJF2uosHOqyWd3+udAgAAoB5ll/igf8YtrlSRnO8n6QMqlp0AAABgFcEV7241dznpYYeTLoorn5H0t13dGfpnt93i+NSgleHISDz2xS+O40GV4aIzfx0O3fKw18VzRN0osyLKDU6O47qiLXJXUk00PSkcejqoiNlw+6SP4TPJJJu3j18nq8TcbrswvEEUjJ4rSdv/Ji4RelEw9ZZBN0tJeiqptMuahCaNLodCcoppgWBU1zQrGZv9MIoKr+J+ovH9SVJUfpsVYz2QxKNzzLoHZsWmUZFn2cKySHYc/TTMRcD9lL0+svftjcFHWVB2LykuGszi9yZjyzwvWRFwJjqO7H07ksSj90Z2zFnBaiR7XpIae0UFcg+XuL9ekHh3I9vlpLXQsn/Fle+RdLW7n9n7iQAAAKAuJN7daN3lpHWHkw4LLbstrmyMe6hlHAAAAFYRrPGuwvOWnXRdaNlR50oAAACsmjq+4v2ja+7U+XPu6eex9M99b9dZ2/RpA5Dqmut0UlwpFft472Bml7j7I1XdOQAAAPqr48T7/Dn36KZFSzRry7gIacLKlp10oGxxZSN2YjeHiT56NikNieLLV8RjRx6N40+2l79snFWFTZ+exIN+XEEnysIVSXy/tshmSXHgplEFmeKuk5q8bonByfglyWOXtaMMuoFq7XJ/bJo2LQgGhZ+StG7WunLt4DgkPaP28xlEIVv059CRZGxWBBX1ac3GZsVRkayYMOkLGxaFZd0ey3TnzJ6XLB7NUabrJwYvKyTOimGnlRibdaOM3otVdDytYq3xExXMkcmWZKxRYmz282hK0GG5ruy21OM+a8upOuuIvft1LP1z+pfqu6/mspP+FFfeKWkPSdtJ+mhjlxMAAACsAiiurFKHy056Ka6UdLmZHSNpHeUXbQAAADBkSLyr1LrspLyOiivN7NWSFrr7Y93eEQAAAOpH4j08xi2uNLMtJH1J0nlmtqG7J4tbUaejX3VZGL8ziGUNErJ1ntFvYq9L1niv+R8/jucOJs96zmRNcaL13J9Z6vHg6/4yjgfNg27+zv+EQ8M11JLuDbpGnHFhPHaaFoTx6NSnJAsEZyeLdaPmNyMXl1mlLD3zTPxqyF4LdSvTuCb701vU5CNbnx29XzLTkng2d/RIl2nOkY3PGgplr4RoDXpSmTGQpjiRbO1stE45O+YqahSy4ygzd5bwlHnPnZD0y9t99zj+pf9oj/1lsph7p53i+Devb4/tGg9VcHeS4nNM2rbp1iQerYG+KhlbphFYNjZ7b0Tx7FyymqjtgzXeSTu3ypF4D1AXnSvvl/Taeo4OAAAAVSLx7kZrh8pM+eLKeZJ+KOllko5T0dH0FZLul7S1pPVU/HL/a0mXSTpKRQHmH9z9kpa5P9q43Y/dPb4UCwAAgNqReHejww6Vmai4UpLM7DoV+7a17pn2rKSfq0jIH5X0CxV7eX/V3R81s2MlXdIy9zfN7IsqdkMBAADAkCDx7lb3HSrHcqOkD6q48r2+pEfc/dTG9y43sx0lfV3S77MJGlfTn3X3ZMNoAAAADMLETrw7WTISue9GaXpSYdG7z6q4yr2XpI3M7EhJd0hySS9VsfTkTEmfNrPHJF036vZvVLF0BTU58DVxfP789ljQD0eS9HhSlTQ56JKQjd1rrzg+JahcuTsoLJGk6UmVXNiEICui/IufxXG1F3++bP31kgOJG/xseefCttjixZeHY7OimqeCSrtNsqZEycfD696zdXvwgfuTSWL+TFzSdejfl5qmVmWL5KKiqaSfRdr8JpIVKWeNSUaCWNasJDuOaO6sQDM7vujxKFeSW7/sOY8+yvrZ6KmKuasoXI4+1yVphx3iePR6mpp8zt50UxyPXiPJYaSvvTLF0lmRbFTgmr3nsgZQ0XOQ3V9WtL1dEFuQjM36l90WxMp8BvViYifePS4ZeZ7RSXwHV8OD4snWrxeoSLBbXdDy788mc1Z+GR4AAAC9m9iJt9TdkpGoNXyJJL5kceUiSbdI2ljSTBW/qD2vuFLSRio6yC5QscvQ5KjdPAAAAAaHxLtKHSbxJYsrJWmpiq1rFyoornT3E1rGXtHobAkAAIAhQuLdrfvmPv/KdzXrvnsurgQAAMBwIvHuxs4H93P2noorzezdkvY1s23dvb0SDZWLiheluPvimsk7blLSli2ae1lSIZQdRxRfP6mIeTqpzAnHB50oC3EHzeIPNaNM+mE8dO2oZ6ekKe3FmNm5TI3arCl+rKNOlJJkWbu85cvaY8/2s7RsOGQPR6ZMMVvZuSPZM9CvzolVzLv6v2pWL08nFX8PJ5V5QX18GJPyj5DoY7mKTp5VFJtmRcpVyM4lejzS937yjUG+70i8u7H74cV/rU5/6/OvgnfWQGffRmL9jUbnysMlfVUr13g3LVOxxnsdSUHz2OfZX9IvJT3Y6ekAAACg/0i8q1LiKnjLGu8pKna6ep+ZzVNx9Xo/lVzjbWatxZUPq0jSvfuTAQAAQNVIvKsSXQUf37PufkbzCzPrao23u7duO3iFme2jYseU1u0HAQAAMEAk3sOn1zXen1ax7eCX6ztkAAAAjIfEe4D61EDnq5UcHDq25wd2jONR8WHSdvKp+x4N41Eh4DlJN8U990tKdmbMaAttMzvq2yVtuP3m8RyTR69+km7+zv+EQ9NulFEh5SsvjMfqv+Lwlu3Ht/enR+KxW8XdL3VbcO7bbBsO3f+B8+I5XvyS9ljwOEuSngkKMSXZ5HXC+NqaHc8zBMp2qZsexGYlY8sUO8Wv3vj+pLhzZVKLXKqTZNZZLzuX6B2aFbj1s2itClUU5q2Ksk+sxZfF8QVB7M5kjmeSF1T0es+6pmbvxTKy1290n1EXSam4Wtjr/WUdLSPZeWePadS1s66EmMS7KiU6V/a5gc42Kroyn+fuf67q9AAAANAbEu+qlOhc2c8GOmZ2lqRrGrcBAADAkCDxrlI37eefr4oGOiOSvivpCElf6+VgAAAAUB0S7+HTU3GlpD9JOkbsaAIAADBUSLw7MXr9dqSLlvF9Kq7kKnfdkiI5LWkvRfElj4VDnyxREbMiq2pK2561rzpans2RVaIE7SGjzpySpOlJUWPYjTIpotRfJ/E57aGsg2bWJnT5iiAWPyDp8zJ9i/ZYUkSpp5+K48nxZQV7wyA7tiwePXxZwdRIiePIirHKFjuWEc1RtntgmYJEOloOp6wod9MkHr1Gss6VayTx6JOiiuStijn6+Tqtoptt2sH4vvZY9vhXjcS7E52u335sUdnOlf0ornxJY45T3P2usqcKAACA/iDx7tR467dPf2tx1fuJh8ZN0PtcXPmwpNdr4u72BAAAMJSquJKPpqVLei2wbBZX/qbx9SPufqq7X+7u/6zi6vbXx5rA3edKOkVSvDExAAAABmLiXPG+b+7KZSDPxcqvy07tfHAxX++6Lq40szUlHa0i6T5BAAAAGBoTI/He+eD+38fuh49fgDlK1cWV7r5ctIqvX1ZMGBQ7WlBwKUmb3HtvPMea7QWJkybFBZpRd0lJUtBJMqiVLGzeeefK7JC3vHNh/I0pQUfLoBNlYU4S37U9lHWo3DwogJTiItSoWFLSlKySKirozIo5s6LLVVDZP5FG47MfOmU6NWbHkc1dRXFadJ9lC8ui8dkcFFcOp8VJ/O4kHhX8ZkXATyfxaN1oNraMKgq5+9lhNVsvG8XT7pdJNXf0+NXV/GRiJN67H178N9roK+BVaF5ZH1xx5Q2S/svdX1f5uQEAAKBrEyPxrkuHV9b7XFz5kcYYAAAADBES7yplV9Y711PnSjNrXhWfZWZ7ufvVvRwMAAAAqkPiPZZm45wqizDH13VxpbsvlnS0mR1D0g0AADBcSLzH0myc0yf96FwZzYs+mzMnjv/VX7XHslaIc/4Qx+fPbws9Gtdn5hWTQfHnk0+2zytJ62QtLZc82hY648J46OLFl3d8eHt/eiSeJOtGGRVS7n1JPFbficMviood4+6Se++TTB1V7GSdQyclpYBJx9OoA94gNuWPjnqrZGz2knxxENsl6SQ3NZsk8LMkvkHnU5RWRefKbUrcX5kfzjRtqE9W1Bg0QpQUFx9mr5sSb4FKnvOsg2Z2jtHrup9JZHaO0UdI9phemWwCEJ3jQ+MfUiVIvMczXrdKKW4p311x5QEq1nMvbPx/JxWvsa9K+oi7n2Rma0v6gooOy/eoKL48TtL3JO3j7h9q3McUSR+S9FpJn3L32zo6XwAAAPQFiXcVOm0p3xAVV5rZTpL+6O4XNL4+1t1PMLPNJb2z5eZvlHS2u99oZseqSLz/KOkqSdNb7uPJxn1sStINAAAweCTevZp9+so14N13rGzyLsedKelQSc/7G7qZbaZiTTgAAAAGjMS7V3PPLVrF98jd55nZQWb2EhVLTW4ws6O1cqnJSY1Cy+slHWJmb5L058bNl6vYv3/GqGkPlvSTng8OY8sauGy8cXvs2RXx2JkvjOPBmvCXbn9rPHbXXeP4rJe1hTa6LfkjyHbbxfGgOcw0LQiHRqctSVOjhXnZY5c1owmb4iRrufWhOPzwx9pjyUHfnXTFmDFjUXswWbOdrv1+Om7XEH2aDEszleyTLotHD99dyeA7ShxHtvYzXqlfrvFP9lhX0UDn4QqOA8Mp+WQP11FnjWvK1gz0qop14smnXl/PJaqUyu5vkyQeVURlfeWqRuJdhXWSaqFxBMWVJ44a0noJ/SMt//5NMF3bLibu/q9dHRgAAAAqR+JdlWbHyqbuiisPlPSEVjbM+bqkKZIOU/FL3tPu/iMz+6GkeyVdKOmVks6S9FZJ/ybpPZLWkrS7pDkk3wAAAMOhzF/gkFlvs44LK6WiuLLRGOdKdz/M3c9QsbvNje5+ioq/Tv1c0pslvUPST1U8V+s1ppgr6SQVu3vdI+kzkv7N3Zc35rpfxe5G2V+/AAAAUDOueHdq9BXt5+KVFVZKK4smTdJ8SW9v/HsTFVsLrmtmF0naWdI/SzrV3a82s23dvXW51loqdjl5qZlt5u4PVnFwAAAA6B6Jdyd2Prjvd9EorvwrM3uxpKfcfamZPaFiKcqbJB2loo7hAyqueJ8i6WQFa7slXS7p8yqS9sV9P/iJ7qab43hUTbgkqSxbHD9ND17dXnJ2b9IQQFf/Lo7feWdb6LGb7gqHpg1IgsLBtMAt6b4Q9pHJijyXJ3+siQoVw4Y4iosoJWmT04Lgl8Khm0XdbCRpv/3aY9OmxWOzosv1skf7hiRer+jpyn5gZH86jcZvlAxes0TVVXYc1vkU6RxlzjEbmxXPTetw3rHiGKzpSfxFSTwqhV83GZsV90WvhSpeH1Fzn7FE95l8+lZSRFnmPVo2mY3G1/WeI/HuxO6HF/9FTtutuOo9+mp4B1fAg+LKL436+uRRN3lKxZXupo8m8zyiIlEHAADAkCDx7tUTD0rPPFHqJmMUVx6nolX8yZIOkfQHd7/UzG5RsdTkE61Jtpl9QtJGkv5bxRaEBzS+dZm7JxuhAQAAYBBIvLO129LK9dvjWXu9Umu8o86VkmRmV0m6TNIuKjpSbtv41m8kvTuYagNJJ0g60d3/TtIZHR8EAAAAajWxE+8a1m6XtKeKZUZzJb1Q0kwz+09Jt0uaqfZdSq5XsdyE4kkAAIAhN7ET77HWbkv5lfBWnVwR79w17n6SmR3j7seb2XYq9ueWpLNV7Os92hqSzqvyIFDSLslrYP2geG7tpJwlKbrcbJv2vmeTJpWpXoxljSHj9pKS1l6rLTQlubtNks6V4alvs20QlLQ8Kd2cHnWuTPoVZi00w0LKz4UjFy/+fBjf8vHH24MjI8n9JdaPS6mGuague9lkRVpTgpglJ7h2iWqs7JlNXk3hlYnNk7FZ7XL0zsgq17PC46wQLTLMr4OJ7KGS8eCTIi08zF5P0fgqihezIuBMdJ/9TCKzc4y6gWbvufafXIWo+2U0bz9M7MS7KtFylc4a6OzbaAP/DXf3xtf/JOlkM7tA0p2STlOxV/fBkq4zs5mSDlLxM+0GFe/Vk8zsl5JOd/dHG/fxXkmbSjrP3f8sAAAADBSJd6+6WK7SssZ7iqQRSe8zs3kq9t5urvG+StIlKpLn17r7l8xsTUnfkbShin2+/8fdLzazjRoNeVq9VdI1KrpgAgAAYMBIvHs13nKVsT3b6DQpSTKzz6r4C+Npkj4oaW9JHx91m/mS/lFFq/ivqLGlYGBE0nclHSHpa90eIAAAAKpB4j1crmnuctLY4eS7kj4l6adm9jkVDXT+XdJhkraWNNaWgX+SdIykC/p6xAAAAOgIifcABY1vTgr+fUzj/60dKheMNU8jxlXumvzgo3HHyJft0B5L6ul07bVx/O7gV6trk4qYtb8Zl/dsvnl7fN68eI7tf/PH+BuB2Vnly7lxOCqq2/+BuC74yajyRdKUoFpv733isdFjJ8XdKNMiym97GF94eHuPxKyWc4NpSZnc5Lh/3aeDWPZQJ/09KxEVJU1PKhKfSV6Tu+3WHps5Mx6bFfxGNcNf/FY8dq9k7rsXtMeyDoRZs9KtglhWUJcVrb1+m/bYT9sby0qStkvmiNxYYix6kxW9PprEo4+hrBg5KxCMlC2MjCSl9GFBaCbpPVxK9phmn3vRcWeP3RVJPNrOoMzj3wsS7/E8tqiz3U1GG2dfbzP7vIqiyReq+Bl3roqOsW9X0VH2S5Le03IF/AJJv1Kxv/ciSX/VGH+Eu69ojPl/kg6X9CV3/6/yBw0AAIB+Ycei8TzxYLFrSfWWqVgucomki919diP+jIriydG/kF3l7l9XkahL0pkqiief++XJ3f9Jxfrw3/bjgAEAANA9rnh3YvrOpTpTlnCmpI9J+klL7DwVO5HsPc5tD5O0jaTT+3FgAAAAqBZXvAfI3f+kYrlIq0MkvUfSLZK2NbMjzWwvFXt8HyWpuSf3jySdIunQ5g3NbBNJb5T0v/t97AAAACiHK94D0ly77e6Ht8Su0PNrAT7S8u+DOpjzYRVXwlGjt74ljkfNDZ9Omk5uFVVuKS4mXDE/Hrvrrp3PvVZS3fOipKJr2rT2WNaE83Xv2Tr+xvKgb9+LXxKPDTtUKu4OGT3QkmbMWBTPsd9+baGwE6XiIkpJ2vb0qOjyxPj+tF4Sj3uqffXT/6ctVkWXuirMfCCOx31XpVdd3h7bPWkPd2Xy3ogsSOKLk2/cGsSyY74viUeHlxWhZc/XjUEhZVYktyCJY7BekcTfMSuOf/Om9ljycZ8W90Wv1ax76x1JPJIVeWaihDHqTluVLEGNOsBm5/L6JB4VRmeF1VUj8R7P0uzjuWH26dLcYBuHzjpX7i9ptqSFjW/9bxX7dJ+s4sr3H9z9UjO7RdJOkj7RuoOJmX1dRTH1SOP/l7n7WFsMAgAAYEBIvMey88HSfeNs1DT33KL4cvrOHU/b0rly8qhEerpWdq5cJGnbxrd+I+ndwVQPS5os6cfunlyLAgAAwDAg8R7L7ofHV7NHq674ck8V6+7nqti9ZKaZ/aek2yXN1Khte939y2a2lYqr4/9UxQEAAACgPyiuHC7XuPsJknZ09+Ml/YOk5ibiZ0vat3WwmX1YRb+NK2s9SgAAAJTGFe9O3Dc3b6Jz343S9F26mjbrXNny/zv0/FqJt4wan/RvQ502mhEXz230zNK22LIn4/KZpUmF1TpBIVrWkXF60opvg6DN1wtmxGO3zKp+Nm9vWThycbK66YH74/izQcnZjORAnonKZxS3N4zmlaTJ68TxqFI0KtpU3o0yLqT8TDJ2ThJ/IoxGL4VhKa5MGoqmRYZRU8YZSRFl1gUykhWhZccX/aDLfvhlV6PKFqJF2j8R8ue2ri56KGeDJB51WJWkNYJY9n7JXpNl3v/Z2OjwynSozOboZ3Fl9l58JIhl75e4PD5+P9d1JZrEezw7H9z52NZCy86KKw9QUbB8qaR5ko5TUcyeFlc2xm2q4ufUppJ2cPdjRt8HAAAAhguJ93h2P7z4L9N6JbzDQssxiiuv0jjFle5+YetcZkbSDQAAsApgjXfVeiu03FNFAxxXUVy5j5lN1sriyuivVgAAAFgFcMV7uFzj7ieZ2THufryZbafnF1d+vXWwmb1VRUfLnd19bt0Hi8LSxfFa3UjWQOepZHHqI4vbY48lC/OeTOaImvAsSbanz45j3ewGvcrWcj/9VOfjs8WVZdd+BzaYll2biNb1z0nG7prEo7YusWG5QpIdRxaPeuUk/XPSePQsZj+44pZEsX4+ptk62+wcI8PynOP5suclKj+RRm1FNs4cZVRRA1A2AYxe1yX6XpWWvY/K1FtEj78UP3511dKQeFehWXxZstCyguLKn0uqZB9DAAAA9Be/VPdq54Ofv6b7sUX5DigNZraWmR1pZhea2Y/M7P1mtruZXWBm/2RmM83saDN7Y2P8LY3bHGNmb2uMf1sj9rNG4x0AAAAMMa5496q1+LJ51Xvx7WPepKriSjM7XNLFlZ0LAAAA+oYr3lVbukTa9lXd3rpsceXOkl6lUY11AAAAMHy44l2lnQ8urnh3r1RxpbsfZWbvl3RVL3eK3mRFNWtMaS8BWXvtuFPOukkXgslBNdYaya/LaycVJ2tNbZ9k8uS4JCabQ2u3FyQ+80w8hz/TedmPZYWO2YMayeZ4OmpXImm9oAXG+usnc6+b3GlUxpcV2WZFlNuH0ejMh6WBThVNZ9ZLJlm7gmqxbNun6PjKFoqW+WGZzVGm+BPD6dEknhW9R5KS8lT0/i/7dunXZ0gVV2+rOLZsjqQ3Xfj41dW0isS7SrsfvrKBTgd6La5sjDmjm0MFAABAvUi8+2HhlWN+u8LOlRurWG5yi7v/Wz9OBQAAANUg8a5aBy3mK+5ceZSk/6rk2AEAANA3FFdWbffD6+5cubG7B21WAAAAMEzM3cf6/nPfPOTbv5MknXXE3n0+pNWGjTugKKI8afTXLf/fTtIrJL1E0k8lfd3d39IyfpakXdz9zOoPH50ys/BNFP1Wm9UuZkUdVRR7RMeRFaIkNZ5hgUoVx5Y9HllBTGTTJF6m12Z2BeLTSfyrQazMMUv5nxuXhp/JfeocKqncM/nTJB4Xit71t69uv7fk7l546B4dH8WOb7k2jG+TjI8W/2WvvawTX/R8JY1e00c02u/q6mTsjCQeWVBiLNC0QxKfX+tRlLddELsjiFXF3cfN58pgqckAVdC58iZJN/X5MAEAAFABEu8+OPziw3X6gaePOcbMPi/pHhVLStaSdK6k9SW9XcXFmHMb/15f0hGSDpO0uaRXqijGPEjFhZyrG7H9Jf3U3f+9Mf80SadK+pa7ZxdVAAAAUBMS78FZJmlrSZdIWtPdZ5vZfpLOUvEXyQ0knali15I1Jb3A3U8wsz0kTZV0n6TrJP3W3a9qFGr+e3Nydx8xszNqPB8AAACMgeLKwTpTxbaBrd4haamKRZ2HqWigsyK47ZGSNpJ0VB+PDwAAABXhivcAufufzGx0y7zz3P3qxtXvH0laR9Khku4ys09ImibpeEmflLSepNnR3I29wg+WtMzMfu/uUfKOmmVFjcPSmTAzzMeXFbL1sxtaFM/GZlc38uOLCimnlp6lc2WKK7P2fHE/vyeD6sNns0N+8qk4Pqn9EcwKIMsWO5YZW+Y5z0THXbbjHlfLMNGVLWQfNryHB6CRFD9tZhdIukHSrxvf+oSk/21mMxv//hsVS0puU7Gn9zRJ/y7pIyp2nLlK0vTGPE+b2YYtd/N+Fa/Pr5N0AwAADB5XvPtg/uKxN+NpbaCjoojyfWYmSXNVNMPZRUVSfYmK3dJe6+5fMrM1VSwtWVvFbib/4+4Xm9lG7n6qmU0zs/c37uY+Sa9XNbu+AQAAoEck3gPm7ndLOkOSzOw8FQ10TpP0QUl7S/p4cLMvq9jJ5CuSPtoy10hzrsZ896u4Ur6oH8cOAACAzpF4D5drmnt4N9rHf1fSpyT91Mw+p2K997+rKLrcWtLd0SSNK+NHq0i6T6jhuAEAADAOEu8enHPrObrojova4k8uz0p8ni9roDPq38c0/t+6F/eCceZZruKqOIAeDE9xRNnSzTLKzJEdx5gdkJ9nRdmKxLQaEwBWPSTePbjojot0y+Jb9NKNX1rqdo3iyv8r6QAVWxhcKmmepONUJNUnS/onSXeqWHayUWNs84r3QSq6e9+g4qr22yT9UsXWAt6YY1dJk0cn5QAAABgMEu8evXTjl7Z1qdz5ezuPeZvW4srWxLixvOQydVlcOepurjCzYwQAAIChwHaCw2VPFcWVl0vaV9IXFRdGflnF+u6v1HZkAAAA6AlXvIdLJcWVjdu/W9K+Zratuy/s+5EDAABgTCTeA9Sv4spG7MeSflzJgQLAmGzQBzCmMuWZ/SxjrQKlpsCqjcS7DybZ2B/RNRRXLlSxB/i2kj7n7g9WeHoAAADoAol3HzzrY1+TqKm48leNLpbTJJF4AwAADNiw/PUMhcqKK81sB0kbuPufqj9MAAAAlMUV7z4Yb6nJGKrqXLmWpH+TdK6ZvcDd7+r2gAAAAFANEu8+mLLmlI7G9bFz5TJJr+rsaAEAAFAHEu8BqKlz5Q2S/svdX1fPWQEAAGAsJN59sMPGO4z5/TqKK83sI5J+Ud1ZAQAAoBcUVw6XSoorzWxjSTMl7W1me/XjQAEAAFDOhL/ifc6t5+iiOy7q6rbzF88f9+p2SZUUV7r7YklHm9kx7n51NAbAsFreYUyq5tpJ9mOgzHEsC6OTgsNLd1t9dtVrDbPqHTGwEldeB2PCJ94X3XGRbll8i1668Utrv+9+dq4cKw4AAID6TfjEW5JeuvFLdfqBp5e+3eEXH971fZrZ5yXdI+mFktaSdK6k9SW9XcUa7nMb/15f0hEqrnJvrqIhzvGSvtq4/dWSXilp/5avF0i6S9I7JU139yO7PlAAAABUgsS7D+Yvnt/JsGUqlotcImlNd59tZvtJOkvFVoAbSDpT0s4qnqcXuPsJZraHpP0krSvpOkm/dferRhdqNpxkZif0fkYAAADoFUt8ButMSYeMir1D0lIV2wweJumtklYEtz1SxTaDR2WTm9k7VBRqAgAAYMC44t0HnRZcuvufzGz9UeHz3P3qxtXvH6koqDxU0l1m9gmtXGrySUnrSZodzW1mO6tYonKxmf3S3b38maBqq+pvutFxD0thWdnHtMz4tUrOvWrKCibL/HiIH9WouDJt7BsNTuLpFCXiVbxusjmG5b0BjGVVfZ2uqj9Hm0i8B6S5LMTdD2+JXRH9O5EWZja+nivpwF6PEwAAANUg8R6QRnHlXZIeVrFn93xJT0jaR90XV14i6fHGXdwmaXdJb5b0v9y9GQcAAMAAkHj3QYc7pCyT9AIVCfLF7n5FBcWV3xx1H1ea2SYk3QAAAIO3qi+VWdX1u7hyN0l/qOJAAQAA0BuueA9QP4srG94i6R8rPmyMMj2JR4Urk5Oxz5SIZ2OzudcOYlk53VZJfCSIPZSMzQp2ot/yR7/4m7JzjObIjnlJEo8+9LIPwumbx/GZD7THnkzmyK5u5B++Pw1i2R+tqiiPyl4NkU8m8evD6It2Cl6VM2fGU8zYuuOj2Ec3hPFk5vC1MDUZe18SnxbE7kzGLk7ie5WYY1YSj9xbYiz6I/v8jd5dZd+10fgqCnujnw1lTUni2edhFWYGsew9kJ1j9DOmroSYK94VOufWc3T4xYd32lhnLTN7v6Rvj4ofYmb/LMlUJNx/I+nHwe03V5EPPWRmR0ra18z+dtSYSySNXn4CAACAAeCKd4VKtp9vrvF+VNKeZjZTRXFl12u8Jf2kkcxLxRKTHSXdXs3ZAQAAoBeVJ94/uuZOnT/nnqqn7ZtbFr9Wr3jFryubr2T7+TMlfUzST1qKK98h6W6tXOO9jaRowiNVLCU5StLJkuTuI5LOkKTGXFuouBI+w93vLn82AAAAqErliff5c+7RTYuWaNaW2Qq61csti295bmnJ/MXzO26eI/V3jXdjH/ArzMxIugEAAAavL0tNZm05VWcdsXc/pq7c4Rd/p+vbvmW7t3R923430BkvjuqU2asxK3zJigmjwpysFK5M/8FsbFYgFBWnZcdcRlaAU2burIgyi0eFLWkBTnIg0dzZ66Bsl0Vp+yD2aDI2a0hr6eztliXx6AjjIkppt87v7r6kfHGHl3U8RfbcPpbEo9dZ9l4s837OXr/Z3NFxP52MzeIYTtlnVr+6Q9ZdVt3POcqK3ndlj2MQx93EGu8evHP7d+qd27/zua8Pv/jw566Aj7fcpIYGOvNUXClfV9IX3P3hnk8YAAAAXSPxrlDJK+B9baDTWMKyuYpLYiM9nRgAAAB6RuJdodFXwDvwXHFlS6zr4spRNpf0nyoS9J2kZONbAAAA1ILEe4D63EDnCUnvVHFl/bOVHzwAAABKIfEekH4XV7r7/ZI+1OtxYnwvTuJR8UbW5WskiUcFO1lXvE2TeHSfWVFYVNaX3WfWKSw7x+jxyLp+lukCmT3+2VY+0Ydedsy7JTWDr7q8PZZ1ICzTUVSS7vrbV7fFnuxjG7hJSZVnFA87UY7l6KfaYz98STx2auc7YR25Xxx/7RuTR/XY9nfSZsnQOUmVXPRa/W08VHck8U8d3B679dx47CHJG9qDqrorsw8FVC57305L4tFTk82RFfyV6bJYpmiwigLD7DiqKL7PvGdae+zGkXhsdnxlOktXjcS7C+fceo4uuuOiMcd0WVy5kVZ2qXyzpA1VFE1+393PadzuRZL+VtLGkj7i7t4y5zRJf9X48mp3n1/itAAAANBHJN5dKNmhMhMVV76/5fvu7qc2iibPaYkvV9EY55nWpLtxgxE1GugAAABguJB4d6lkh8rM6OLKZZLWavl3ZFtJX5N0gJlNayTbAAAAGHIk3gMUFFf+RtL/UdH94p+Tmz3SGCPl/SIAAAAwZEi89fy2750o2xo+khRX3iXpM9G4lq/nSvp/Pd05KlXmlTB6C5umh5L4SBDLStBmJvHNg1jSO1CzkniZTo1ZkWeZ+8vmjj6wdkkekLuS9oYbBUWDlhQYzpwZx3cPqnBmJK0Gs4Kd9ZJP3+VBxdOzSZu6FRW0r4uK9aTkMckekKwbZVRI+b//lBzJl5J4uzlz4vjTT8clXYuCmJfoSirFz2PWXTIrWrslqLrJ3ouXJx8KayTjUY+sELtMUXmmTGfjKoorq5Cdd5l68LyLb+yOkc7nmJHEo/dd5+XdvZnwiXcvbd+lvNCyx86V66r4KfRZFT8zFqt4TbxW0jWSfiHpEBXLUc5299815txP0ofd/dCeTgoAAACVm/CJdxdNb553dbyHQsusc+UzKnauWiJpoaSvqFhacpmKPb2/5+53mNkakp5296ubEzbm2KvsgQAAAKD/JnziXYUeCi2jzpXnSVoqaW8VhZTfkXSJu19rZndJOs7M/r7HQwYAAEDNyi6tQYXc/U9qX/Z7iKT3SLpFxRXvD0na38xerqKF/CQlRZWNMfua2Zv7dtAAAADoCle8u9QsyOy20HKMzpVXtAxrFlZ+pPH/G1q+1zquefsbJB1U+mDQk9uSeFSMlRVXZo3nokKvrBgr6xQWjc8KyLIPhAeCWNapMTuX6Lf8rJhoJIlHHd+mJieTdQ9cM7jTtZMDWTN5QK4MquqyAtm0c2VSBfW5Q/doDz4ZdICsSla5GbWunLF1PHaHl8XxsBtlVkT5uSTe7r6Rz4fx7ZI3QdRlNXvtZR1Po2KxrHtrVhx8W/Bhkb1f2LJqOGWfnVlRY1aAGylTK11FZ8gyx5bJXutllK0Rj56D7Fyyn1PR49fHBsHPQ+LdhV4LMqXSxZW3qUi6/z8VPxeu1criyhslNRvpLJG0i6Rb3f1HPR8kAAAAKkPi3YXWgsxoG8LDLz68kzXfZYsr12vEzsuKKyXJzH6hlft8AwAAYEiQeFdg9D7g8xcHm7XGKi2ubCTjn5b0j2XPAQAAAP1F4t2jXpadBJ0rpWIJybOS/kkrr3j/i5n9SdIBGqO4UtI/qHhO95Z0SdcHhlIq6GFSu+yYV8Vz6adomXNZlTym2YFk67P7eZ+rCV7rAAaBxLtH0T7gnXTB7FNx5bHjHzEAAAAGgcR7QPpUXHmHpL0k3e/u36vrXAAAADA+Eu8+6LCZTuXFlY3Y2xTv/gYAAIABWr0X8Q2/M1VcuW51nqSv6vnFlfe7+7WSviXp02a2VTSZu69w909LijbPBQAAwABxxXuAqi6uNLODJe2kvK8D+iBrkBI1rtm4xFgpbhiTNeHJGgiMlJgjawITNUmYVvI4IlnzoazwLbpS8LNkbNbQIvrQy56XL34rji/o8f7GsuNbrm2LVdHooqzosd7neWUmK2VNRY7crz02Z048NmuKEznRPfnO18PoQYoaEG0QT3H77Dg+fYu20B8O+1o49IzkRfmud7XH1rsoHvvmA+N4VE+7PV0bOhJ9nkrlmtFkn/dTknj0uVDm802Kjy+7vzINbaooMN4lic9J4tE5Zp+d6X0GlxZ3SD6Esuc2OvdNSx5Ht0i8B6RPxZXnSjq3wsMEAABARUi8K/DIWWdryYUXPi+27X9+f8zbjFFceZe7X21mx7j7SWZ2jKSrJb3X3T/Q+PqXkvZX8cvjA1r5C+MFkk6W9K3RjXUAAAAwWCTeFVhy4YV6ev58Td5hhzI3y4orM0+a2Usa/z7A3U+MBpnZGWUOAgAAAPUg8e5QdFW7qZl0j3eVOxB1rhxr7KEqEnZJkpm9V8XOJmeXvWMAAADUi8S7Q11e1R5TVlxpZntJetzMPq6VtRLLVezhPUPSxWb2GRV1c8/VUpjZWpIOlrTMzH7v7isqO1iktk3i0dYyWfFGVvQTFaLcmYzdPIl3Oq8kbZ/EFwexrIgnK9yMilmmJ2OzgpjoAyspkQvL6STJglj2HO41M44vXtAeezKZY60kvkYSjx7XbO5+il4jM5OxWSvd176x/ZX99NPxs7tdmQq3pIhSOioO/yQoAdt8s3Do0osuD+PrbNVegnvppfHd3RSHNTX4ULglqWjeeySO97NZ6equbBFfGdlneKSK7eSqOJcq5sgKq8vMXfYlvWbwgyDbQzn7eZQddx1IvEvIrmov/Jv36un587Xwb977XKyDq99rmdn7JX1bzy+UPitY4y1JH2pZ4y0VTXM2kjSv5bYbSLpd0gtIugEAAIYLiXcFpr7tbd3crLnG+1FJe5rZTDWKK5Px4RpvM5vWSOClogjzNkm7dnNAAAAA6B8S7wpsdMi7tNEhwQat43tujXcHxZVjrfE+o2XcfDPb0szWcfel3RwUAAAAqkfnygFy9z+pfQnSIWZ2pPI13mtJuqyxxns9tSylMrPtG/G/UEuCDgAAgMHjiveAdNhAZ7TWvbl/H8x5q6Rwm0H0T1bEFxV7ZH+CiIoXpbgYM+ucVqbrZFbkOT+JR50Ty3TKlOICmjJjpXIfWGWuKjyYxO9eEMdvDWJlP0yz44u6mJYtgqqi/i46vqwgKS3+PLa9YnJRMrRMu924E6XiIkpJ+l83BsGkiPLV70vudau2yIdue0M4csoP4hl++9v22KykKvrxpHqZ4sruZZ+dVRQvZ3NEnwul6ogTWTFnmS63VRRX7prEs2LH6OWbHXP2GXlr8AMz2/Yie7tEhf1ZsX/VJlziPda2gGPpZEeT1rnHKq5s7D7yf1U0wZmtojW8JG3v7se2FFYeqiLZvkRFK/hPSPqOiuUpj6vIF86X9FfN20ta0kzqAQAAMDwmXOLdj20By87t7ssknWpmk1uT5JYdS0b7jaR3N/79bklfd/fWi09ndDAHAAAABmjCJd5Svi3gWFq3Cqx67jGspWKt9u0qttB9botAM3u9pF3dPdvQFgAAAEOE4soKPHLW2c/t5d2jfRuFlfeY2dGSZmnl0sezJe0r6ccqlpy8TKOWDJvZuxtzZP1AAAAAMCAT8op31ZpLTMoavRbb3Q9KhjbHvaXx/+OS+X6sIjFHjTZK4lFt1LRkbFagGRUDZW/arNgmGp8dR3tvvkJUDJQV2pXp4DalxFipmg+saI6s62dWbBOde3Zs2dWNLJ4V2tYtOr6oG6uUFzBtFrwYPKksy+aI40m/0qQbZVxI+fp46IovxPE12h+Re5KK0AeSJ3GXoPZzSfJGeiZ7nCiu7Fq/io6l/HMvehqzz4oyx1fFVdMq5sg6GGcFpNE5ln1eop+L2f2VKUItU5jaCxLvijTXdTc7WHZYXHmAip/hl6roQHlAs1tlieLKpSpehwsl7a2i+/Xn3D3bqAEAAAADQOJdoU47WI5RXHlAcpPxiiubftXoYjlN+Q5pAAAAGAAS7wr10MEyU6q40sx2kLRBozEPAAAAhgiJdwnNZSRRvKLtCUcXV06T1GzFcLakr0t6j4olJw+opbiysXzl3ySda2YvcPe7qjggAAAAVIPEu0OdLiMpo8riysbylVdVd3To1NwkHtVdZcVpWdfJ9UvMEXU8lOKOalkxS9ZtrEyHszLFgdn9VaFM98usa2LW4TN6rMsWKmXjowKfQXSujGSvsazAak5Q8ZQV5d5d5kBunx2Gl15UohtlWkT598md/ldbZKd37RiOvP22P4bxO+9sj+26a3xvy5MnfcWQFFdGr99+HlqWrJR5b2TF3GWK6soUUUrlPheyxy+ao4rCyKyTZyZ6DrLHdFoSr6JbZvR5s1MydmES3yKI0blyyIy1jGTh37xXyx588HlXwwdQXLlA0ktUbDN4Cle8AQAAhguJd0VWPPywVjz8cEdLTvpVXGlmD6vYI6uKXyoBAABQIRLvijz72GOa8spXVtm1UipZXOnuc83sFBVbCi6q8kAAAADQGzpXVmDq296mSRskDR3K6bpzpZmtaWaflfT/KV+KCQAAgAEZ6BXvH11zp86fc0+t97l009fpGw/9qtI5NzrkXVpy4YWlb1dxceVySV8ufRDoWdYrLyrYyQojs7VB04JYVoATjZXiN3lUtCnlxTbR8WVFctnc/SoyKjM2i2fPy1ZJPHpuy3TslPIP3zLFsJl+dbWbVnKOqFgpe41F551PHJVGSetslfVeDZ7JoBNlob2IsvDXbZFlc/5XOHLBgniG7bdvj92bVPbOmBHHh+XP1HXXeFaxfjIrgKxC9rouUyydPbfRcVfxHi/7eETPeVaYmhVcl+lcmX2mRj9jFiRj10jijwWxacnYqg30PXz+nHt006IlmrVl9mNv1dK63eCAiiu3UbERw3nu/uc+nCIAAAC6NPBfnmdtOVVnHbF3bfe38G/+tS/zltlusI/FlWdJukYtS1AAAAAwHEol3jctWqJDvv27ccesLlewy+hD10qpZHGlpBFJ35V0hKSvVX0wAAAA6F7Hiffbd926n8eBQtedKxv+JOkYSRfUcbAo/CGJR2vfsnWA2Vq7kSA2Kxm7IIlHa2ezN36ZdXnZ+uds7Xc0R9mmE9EcZcZK8blnTX+yxkbZ41SF6HXTz/W0ZdbIB/1fJOXrs38bxLI1oVkTo8gfDouvK1x6aTz+Q7e9oS12T3KHWVOcaD33Wl/2cOzf7b5rGL/whBvaYod/Ml6XPveKMq2o+qeK2owqTEvi0edN2TXDZWpHsoYxZdZLZ49pdnz92h94WhLPGptF575bMnZZiXj2mZDtEhG1zwrKJyQVSVFkwyAWrfvuh44T78P23EaH7bnNuOPGuyKOlaosrmzcnqvcAAAAQ2rga7xXRY+cdfa4u5iMt5+3mX1e0j2SXqhiScm5ku6Q9Hcqfom/QsX2gZMkrevuJ7YUXb5Y0t+o+EV4uaRXSrpV0k8lTXb3Kxr3MVPSqZI+7O5sMQgAADBA7OPdhSUXXqin58/vdZplkrZWsWPJxe4+W0Xh5MnufrKKv5A86+6nSrq9kWw3HSzpeHf/B0km6VpJT2jUL1LuvkBFMg4AAIAB44p3QydXsZuenj9fk3fYoYoulWeq2BrwJy2xeNFgd3MBAABgSJB4NzSvYk/eYYfa7tPd/2RmrXvBnynpU2b2qIqlJpMaxZbruvvZZtYsvrxS0hfNbJmkiyS9IZhLZraJpDdK2kTSyX0/oQkqK6qJ4lnBTpmGNtkcWYFKVJiTFcNlRTzRfWYFONlxdDrvWPFex0rx85Kdd/bcVnF82Z8b+1VIVVZ03Fm5X3aOdwSx7PzKFKye8bM4flMyfsoP2mMPJCdz+21/DONRU5ysiFLvmBOGz/9ra4steyY+kN7/qNpfdTfQyfZKi143ZQqrs7lHkrFZvExBZ/YZWeYzoVTDqURWPF7Gcd+JN9644Ftxc8SpwYN91BXx3NnPxduC2JbJ2LWSePSYlvnZ1QsS7xadXsVuNsnpRbOw0t0Pb4k9LOkLLcOuG3Wb1uLLK0ePa52rZb7Dej5YAAAA9IzEu0utXSojHRZX3iXpYRVFlPNV7JLzTpUrrrxIxW4ndzXmeIGk9Rp3c3Njjg3c/fiuThQAAACVIPHuQpkulWNYpiJJvk1FceUVZvZ/VRRXLjGzqZL2dvfTzOxdSXHls2Z27Ki5HnT3c5oDzexlKoo4AQAAMEAk3l2osEtl34sr3f0MM/tYl3MCAACgIhMy8Y6WidRdWCnVUly5h6Q3KG7ShIq8PIlPC2LrBTEpLkKT4gKarCDmtUl8jSA2kozdNIlHhShZoV3W2S2SdfIsU+SZtfV6OIlPC2JZl7XXJ5PfGLRwHN1Gtik7x6zop0y3x7rtlcSzbqWfOrg9dktSNHhbVDGVeFdy3SMq3JKk3wYP6i67xGPvTNpzbh+0xos6UUpxEaUkfdfbr6t8Y6N4bHaOzwZvgmN/FI+tQpni4H4WXGZdU8vIPrPKFDRnHXuzePSyzsZmBaTRHFnyVqYgfHoyNnucos+hF30oLqK8O5mjjOx5+UIQ+5dkbFagGRXlZo9/1SZc4t3pMpEy2wtGOijSXMvM3i/p21r5s9k19hXvq9z91Mayk9Zx0VxS8R7cWlLSSBkAAAB1mXCJd7ZMZPQV8Bq2F2yuy35U0p6NLpNbq/c13ps1knBJWqjil9Tslz4AAADUZMIl3mVU1CRnLM+ty24prux5jXdrcaWkX5nZiT0eJwAAAHpE4j1ANazx3lnS25QvYQUAAEBNSLwHpKYGOnMlza3okJH4XRKPilyyN1yZoqTNk/ivknhUoJIV/GXHEc2RFeZkHS2juct2aayieCvrDhf5aVLRVaYraRXq7hKYyQrcsuLPW89tj92XjM0KuiLrXRTHb0kqj2cFb5olSUXorrvG8XvvbY8d/smNw7FZN8qokPLvHkn+yPnU0XF8UlAu/aP+/VEze7+UeR9V8frN1ktGr73scyXqIinFx5cd80gSz4reowL57LVepmAyO5cyn6nZ+7ZMF86smD4rII2KGndOxv4hiV8RxLKfi2Ue67q6BpN4t2jd7aST9d1jFWB22UDnCRXFwAdKOl3SoSo2pXhcxYYTp7j74sbtd5P0JknruPsXW+adIemAxpeXuXsVxcUAAADoEYl3QzdNcXoswIwa6Own6dOSPuXuC8xMkla4+zfMbFMVRZXfkSR3v17S9Wb25dZJG4n2Gd0cEAAAAPqHxLth9G4nY7WDb9VjAWbU9OZ+SS9WvHXn8/4maWZHSDq72zsHAABAfUi8BygqiFRxtXpvM3uk8fUaZvZ3KpaafL05yMwOVNEcZ7mkuIsDAAAAhgaJ9xiiDpejv9/tPt9JceUVjX9e3fj/NWPc/mJJF3d156hUFZ0asyKXqIApKtaRqinczAqYovuMimTKKlPslMXLnnf0mGaFYtsl8QVBLCvMKVOEJsXdQ7NudHWblcSz1+8hwclcnlShPVbiON58YBzfeySOPx68WJ9JHtTlyRM5Y0Z7bO4VcenW/KQ7Z9iNMiuiXPcrcTx8pfWvuLLM50c/i4Czz70y91mmq2NmJImXKXYs89k01vheZR1nM9H7PHtMsyLqSJboZJ8rLwti5yVjy7xu6mp4QuKd6GbNdxldFleeJWn3xhTzJR0j6cPuXuY1DgAAgAEg8U5kHS5bZVfDF/7NeztZ991NceVfu/t3mhOY2U87OhkAAAAMHIl3j6LlKE9nf2ds11NxJQAAAFYdJN496HU5So/FlZtIeqOkTSSd3NOBAAAAoO9IvHuQLUfpZCvCCoorH5Z0WOdHi2FWtgAPE0vZ18ewdLr04ECCvoulPZucYJl4NnZFEq/ih2V4n1EnSkl5uS4/todRmfccn/cTG+/gAamguPLmxu02cPfjaz14AAAAlEbi3QcdNtSporjyZZK2rvjwAQAA0Af8xWOwzpR0yKhYs7gy8rziSnc/Q9LC6g8LAAAAVeOK9wD1WFy5h4rOlRvWcawAAADojbmPuUNd6e3rDvn27yRJZx2xd6VjV0E26ANAPcyMbR4BDEz6c/z3yc5bz65oj916azx2Sdzf8NHb2luQbrjrzHDsVz68IJ67hF8nbQzXCWK/TeZImqaGf/qfVnKO1UnZDstlRB0+/zYZOy2J/2OJsWWe88wK90rzOa54d+GP/3OPbr32/jHHvOMTu6XfM7O1JP1fSftLmq2Vy0VmSVokaamK19HXVXQkP0xF59MpKgotj5A0R8VSlVeqKLp8q7t/qzH/TEmniq6WAAAAQ4PEuwu3Xnu/Hrr7cW06Y/Qqkc64+zJJp5rZ5Oa2gma2k6TF7v4zMztK0s8lvVnSxpJ+qmKnk6XufoaZTZf0XRX7eN8j6TMqGvE0519AV0sAAIDhQuLdpU1nrD/mVe0uNf9eaCq2F3x749+bSNpJ0rpmdpGknSX9s6RT3f1qM9vW3bNNXwEAADAESLxbdLKERJIeuvsxbTpjg0rv293nmdlfmdmLJT3l7kvN7AlJSyS9SdJRKpazfUDSXEmnqOhYefXouehqCQATSLaW+5UXJje4vC3y8A/2D0def308w223tcd2uH5BOHZZcllo333aY088GY/99ew4Hq073jgeqgeSeNT8JjmMCeGZPs4dvRQeTMbeXmKOsuvPB9lkjMS7Ra9LSMpqLjNp+fpLo74enTQ/peJKd9NHk3noagkAADBkSLxH6WQJyXknx5cAWq+Yd1hceYCKK9qXSpon6ThJ10u6TtKslvXf/6qiU+V67n6imV0g6U5Jp7n7LY0xMySdqOI5/bK7z+vsjAEAAFAHEu8KdXrFPCqulCQzWy5pM7X/5WWhu59mZgeb2faSrpJ0iYq9vW9pjHlMRXv52ZIWVHE+AAAAqA6Jd5ceuvvxtivfzbXfPRRdXiPpKyraxmdc0r6S9pb08Zb4mioS7ktV7IZyTrcHAQAAgOqReHdh+z226NfU+0r6rKQbJO1iZkdKulvStmb2MRVLTc41s6tUbCf4KUnHSMW6bjNbIel9kv6lXwcIABgyUUMcSVERZeH1bZFp0+KRW24Vxx8KOpNsnFQ1Lk6KK6M57k8qINeLw2GhXdnCuaiZCslRf0SPdVbMGTXbWR3w2urCjq/eWju+euu2+HknX//clfBOrnoHRZEHtXx5Qcu/z01ud8yo+OfHvVMAAAAMBIl3hTq9Ej5GceWBkp5Q3rnyaXf/kZn9UNK9kv5b0ockXStpU3c/tuU+DpS0n7s/LzkHAADAYKx2ifePrrlT58+5p6vbPjTyuD46bZOu7zu7Ej7aGJ0rbxync2Xzr21ztbJz5fmSXq2WzpVmtkVj7EjXJwMAAIBKRcttVmnnz7lHNy1aMujD6NbozpU7S5qhlZ0rdzezrbWyc+Wf3f3Hku4c1bnyNZK2kbSvmU2t6+ABAACQW+2ueEvSrC2n6qwj9i59u2x/7jpU2bnS3c+RJDNbx91X2d9CAAAduPXWMJx1o4wKKdf4B28PStrpK+vG8d2D4HYvCsdue8Ufw/i997bH7h4Jh2qtOKwZQSxpcqkpSTy6ArldMnZOEl+dZJ0/g1rYSubO1hm8IjmQSxe3x7LnNutAOsjCzdUy8e6XTlvKSx030Nm3sXPJN9zdR405Vs9f4/1+FWu8/7mxxvtLks4xs09K+qGZfdjdv9W47a4q1n5fWub8AAAA0D+r3VKTfmo2yOmVuy9z91Ml/U7FOuz3mdkhKtZ4nyJpslau8X6HijXek/T8Nd4nSdpK0j2SPiPpv83s/Wb2fkkbSXpa0tidfAAAAFAbrniX1ElL+RKedfczpOeKK1/YiDfXeL+98e/mGu91zewirVzjfaq7X21m27r7QklntMz9KzM7saoDBQAAQG9IvIdElWu8zWxnSW+TtKyu4wcAAMDYSLwHKGig86VRX5886iZPqbjS3fTRZJ65KpJzrIay9WFlu7X1aliOAxiUsms1o/dGJe+jJXEN/fXJfgFRN8qsiFJHPxXHz9mpPZa0v7xyJJ4iKtZLhuo1SfyOIJYtCM22+IoK8LJuimWfryrW8/brMzU7tiwxrOJcoiLIrAPKDjvE8Y1/2x7bPpkj2zIjK8asA4n3KM3Ok/H3HtO6G6zd0e4nHRZXtjbQeVhFS/gzzezzkh6TtL+kX6ql+NLMzpb0G3f/5qg5ZzTmk6TL3P3ucQ8SAAAAtSHxbtFJ58mnHntGTz22TJvO6L5uMWmgM1PSXo0hy9y9+f1TR918saQNzcxad0JpJNpndH1QAAAA6Ku+JN43LVqiQ779u47GzdpyePq7jNd58ryTr9dDdz9WdYFl0zLlW5U+x90/bGaHqSi2ZDkJAADAKqLyxPvtu47fMh2heyXNMrOjJd0YDTCz9SX9P0nbqmgVDwAAgFVE5Yn3YXtuo8P23KajsZ1cFR82zzy1orK5WosiG8tGPpN9v/H145LYInCCy960yyuYu0wRT7bYKusUFqnimIEqVVHsWKbQroqCtUdvi3sK3nZbPP6hYHjYiVKKiygl6Z3zguB/h0M30Fvi4whi2WP3myQefR6W7VYYjR9Jxmafe9lnWZmC2slJPCoWzeZ4OolHsjmyc3ygxNzZ8xidS9CIUpI0KTnAPYJY1lUze0yjtRZ1dbNkjXcJ2++xhR66+7Hwe6O7WnZRXDmv8fVdKl5Dr1CxVeDbVXTFvVnSz9x9dmOOUyVd7e5n9nhaAAAAqAGdK0vY8dVba9MZG4TfK9PVsqVz5ZXufpi7n9FIqP9S0qPufknL8LPc/Z0q2sXPbomf2s05AAAAYDC44t2FaMvBouhyg16LLu+WNEvShb1MAgAAgOFD4l1SJ1sO9uB6SdeZ2QdbYu8ys9ep2M+71bsl7WBml7j7I/08KAAAAPSOxLukbMvBTprqjBYUTza/bk2yr0huS5HlBJV1VKtbtrCKzpVYlVVRRFn33BvuOjOM73D9gjC+8cZBcLsXxZMn3SjjQso3hyOnJzMsC2JZUhL35oyL57I5qig87GxB6Uplnsfs+Pr1mZoVhGY/Y6o4jmju7LyjImAp7laaFdSOJPFofF1rr4c68f7RNXfq/Dn3lLrNIPcGb12CUmFx5V9KelDSr7WyK+rNKprtbCfpWHd/ovqzAQAAQJWGOvE+f849Q9dkJ1NmCUrUuVKSzOw0SSe4+yVm9opG+Gx3v9rMjnX3E1rGriVpH7EjGwAAwCphqBNvSZq15VSddcTeHY8f1N7g43W97FDHxZXufqWZbSZps8btAAAAMMSGPvGeYDoqrmwk3B+U9CK1F10CAABgCJF4D1C3xZXu/qCkE0bHgTpRRAn0ror30Vc+vCCML0sWIi4O4tte8cdw7JUj8RxRN8qsiPIE9/gbp0xrC91/66Ph0H2+FU8RdTHcLjmO+Uk8SoSyBa79/PPysHymZkWGUbzsMUeFlLODmCRdnTxhtwaxsh1nB7lGl8S7QzdedrFuvuqKjscfctxJ6ffGKK48To2r3pJ2lrRURffWEySdJmlDSX+WtI6KQsvvu/s5jTm3lfS/JG3g7seXOjkAAAD0HYl3h26+6go9uODP2mzmC3uea4ziyuUq1mw/KGmFu3/NzD6q4pe5SZJOcfdrG2OPaSbdjTkXmtliST0vNAcAAED1SLxL2GzmC8e8kl2BayR9RdKnJa1hZl+QNNXdnzazoyS9z8y2cfdzoxu7+xlm9rF+HiAAAAC6M2ES77JLRUZ7YMEd2nxmtnKsMvtK+qykGyTt6O5fNrPPmtnLJb1JxRK630c3NLM9JL1BxXIUAMAEt+8+cTxqTHLvvcnYZO4oHjXEkRSu5ZYkfXykLbTFv8Zb8z6jB8J4tIb3Bclh3JTEo/W+WcOeiSB+pKtZg17msS7TLG5Y1sd3YsIk3lUuFalKUFx5UMuXFzRiX258fcM4t71W0rV9OEwAAABUYMIk3lJvS0XOOv4YSZ1fOe+huHKBpJMlHSLpD+5+qZndImknSZ8YtSb8cBVXwv8o6XR3Zz9vAACAITWhEu8qVHHlfIziyqskXSZpF0mLJG3b+NZvJL07mGofSb+T9AuSbgAAgOFG4l3Cgwv+LJdr85nb9avIck8Vu5fMlfRCSTPN7D8l3S5ppqQVo8a7u3/DzI5XcbUcAAAAQ4rEu0Mv23c/SUWRZR9d4+4nNbYKPN7MtpP01sb3zpb09VHjrzWzz0ua08+DAgCsep54Mo7fH1TP3T0Sj03CYTFbllBkTXHCQsqP3B+OfeX/sTD+4de0x476TXIgiajgL2r0MlH0s1AxanSTbVuRFcN2Oq80nEWXJN4d2uWAA7XLAQfqrOOP0YML/vzcmu9MJ1fEs86VLf+/Q1Jrpv+WUeP/rbOjBwAAwKCReJfUvPLdiy47V35D0m2S1pC0q6QXSfpaS+fKdVXs//2Au/9rzwcJAACASpF4l9S88t2LLjtXriHpf7LOlSr28J6k9nXgAAAAGALZshgMxjUqroS/Ris7V85096clHSXplWZ2cHLbtSRdJWkdM9uslqMFAABAx1bLK943LVqiQ779u+fFHly6s45YZ+6AjqhjXXeulHS5pM9LMkmL6zhYDEb2po0KhPppVSpmATpV5v01LRk7NYnfGcTWT8YmdZGhXyeVgL+eHcfXC2JrJXMHtYuSij1uR8s6EO7zrTgedaPMiij/yz2M3/W37eOfToort4nD2jiIbZqMvSyJr06fe2XOZUoSz94DuwaxHUrOfXEQ2zwZm3VeHeRV59Uu8X77rlv3Zd6yLefHK65s7EZyl5ntJWmyu18h6X2S/k7S9pJ+YWYfk/SEpLe4+4mN5SXXmtmLJa3bmOMiFUWXd6nY+WSyu69ouZ8DJe3n7mNXgwIAAKCvVrvE+7A9t9Fhe7b/XnvW8ef3NG8fWs4vU9Eg5yUqlpU8rmIf75PdfYmZTZW0t7ufZmbvaiTbTQdLOl7SVpJOleQqfsG8rfUOzGwLFRc3Rqo6aAAAAHRntUu8+6mXlvOJH0n6mKSfuPtsM9tTRRLdEXe/28yuV/GXoZ8052oZ8hpJMyTta2ZT3T37SyAAAAD6bEIn3mWWjzyw4A5tPjPb5r077v4nM2td4nempE+Z2aOSrpA0ycyOlLSuu59tZvs2vr5S0hfNbJmKpSZvCOZSy1aD65B0AwAADNaETrz7sHykYy1Ncg5viT0s6Qstw64bdZuDWr68cvS41rmi+8Lqoe4iyszqVEwENJV5f2VXMx4vMUfWIbHM+2udknNH5zgjGZv1ao6Sh8nJ2D2SeHSOUSdKKS6ilKQX/Hv7H4k3/o94bHYu9wWxDZOxE+Fzb+0kHr1usiLg7LU3P4hl76ORJB7Jiiiz9zPFlQPU6fKRTjtWNo01Z0sDnf0lzZa0sPGt7d392EYR5UlmdqikqyVdImknSZ+Q9B0VS0oel/Sgu3+vZd6NJP28MUaSLnP3uzs6YAAAAPTVhE+8O1VFx8qmMRroZFn9byS9u/Hvd0v6erJ05FBJF7r7GZUdLAAAACpB4t2hKjpWdmEtFbuf3C5pplq6UprZ6yXt6u5fb3y9cWPMLDPby92vrvtgAQAAkKNz5XBpFk/eY2ZHS5ol6d7G985W0WDnxyqWnLxM0tLmDd19sbsfLekqkm4AAIDhM6GueI9eo92PnUrKGF30OKp4slVz3Fsa/z+u0zkBAP2RFdqVKcCrolj6t0k86sgoxceXNLlMC0Wjorosoch+yr4giB2VdJ3MulFGhZRXJV0u17G46DIqJnwkvrsJ4ZkkXuZ1nY2Nis6ijq5llX0fDbJIdsIk3mOt0S7blbITHRZXHqCioPdSSfNUJNTXq9ilZFYziTazf5V0s6T1Gh0sL1DxWj1N0itUFJPfpaIAcztJx7r7E5WeEAAAAHoyYRLvaI128+p33dsKjlFcuVzSZpIeHHWThY0Olgeb2faSrlKx08mm7n5my+2XStpHw7PjHAAAABomTOI9nj50pezGNZK+IunTY4xxFWu995b08ed9w/1KM9tMRfLONoIAAABDhOLK4bKvpM9KuknSGmZ2pJkdLGlbM/uYpJe4+59UXPH+gKQPNW9oZpuZ2bGS3qZ8P3oAAAAMyIS/4v3ggj/L5QMpshynuPKCln+fm9zumJbYg5JOqPoYMXzKdBWT+ldEkn14sM4J/Vb2PRB1VMzeF1OSeFRwlh1H9t5YXGKOrMAtknXte6DEHNl5T03i0XGX6VYoFVeYOrVNEo+6UWZFlEuTosutg/HZc5h158yuYkaPSTY2i0fHkr1usqtu0es9O5eZSTx6nWWv082TeHSf2Z/ns9de5C+T+JlJPHo9ZV04qzahE+9mweUDC+7QE48s7rgrZSdqKq78Z63sxjtPxRKV37j7Nys7EQAAAFRiQifezYLLs44/Rg8suENPPvpoLQWWFRZXbtTapdLMFkva0MzMPfnVHgAAAAMxoRPv0YagwLLX4soPm9lhKrYVnNuvgwQAAEB5JN7DpVlceYOkXRpdLO/WyuLK9dz9XDO7StJ3JX1KjXXeZra+pP8naVtJ5w/g2FGTutdyZ1jLjUEps/5ZKrd2M1unHMnW5GZrU6P3TBVNeLIf5Nkc0XFn55I9dmUe0zL1INnnStYM6L4glq1/jtZyS9I9wR+I35CMLfu5Fx1L2ec8em6y90D22ovmzpojZe+BqA4gO45pJeILkrEjSTxax/6dZGz2fEVz1PUzjcS74Zkn61pWv1LFxZWPSzqx6mMEAABANUi8VRRZVl1g2WVx5QEqOlA+pKIj5dUqinUflHS5u1/TMsfZkq7Uyl/c5rl71vUXAAAAA8Y+3iqKLDefuZ2eeHREDy74c9/vz92Xufupkq5098Pc/YxG0vyXkh5190tahp/t7idK2n/UNIslbSDpey23BwAAwJDiineLZ558UjNm7TTIAsu7Jc2SdOF4AymkBAAAWLWQeDc0l5sM2PWSrjOzD7bE3mVmr5P0y2aAQsqJre4iSgCx7L04UsEcZUxL4lnlUvSDP2shlxXPjQSxssWmUYFbVti3aRLfMIg9kozNEp6okPIXyY68ayRFl9nzGBXsZY9TNkeZgt8yY7P7y4pT7w1iWYHmjUl8qyCWPR5ZU6fodVO2YLVMoXPVSLwbdjngQN181RW13mdQXNn8+pct4SuC21FICQAAsIoh8R7lwQV/rqTAsoLOlTtLWippfRWt4E9T8cv9nyWtI2l7Sd9393Mac+4m6U2S1nH3L/Z8AgAAAKgUiXeLZgv5fuuwc+UKd/+amX1UxV9iJkk6xd2vbYw9ppl0N+a8XtL1ZvblWk4CAAAApZB4t2i2kB+g1s6Va5jZFyRNdfenzewoSe8zs23c/dzoxmZ2hKSz6ztcAAAAdIrEe7i0dq7c0d2/bGafNbOXq1hGMl3S76MbmtmBkt6gombghpqOF4qLRaS4UKOKrpN7JPGsyCUqsMre+A8k8clB7IRd4rHz58fxp4MqrWz7nqyoZnE0bzI2Mz2IPZSMzYp+XhHENig5x6NJ/MdJfFUUFWmVKZjKRK/HsrLnJSssi2TnMpLEo8+KbGwWj2Sv3zLmJPHscVo/iGWfQXeXPpp2lyXxMp+d2esm+lzOiihXJEWXj/xdPP4HP2iPbbNNfByveU0cv/LK9tijyRvme7fF8egcs6LX7Pl6SxC7KBmb/YwZCWJvTMZmBZrRZ3j0s0Eq1uRGyrx+q0bi3aHHr1mkJ+c82PH4zY9IspIWnXSudPfm0pEbRo0dfduLJV3c8QECAACgVgNPvG9atESHfPt36fdmbZn9TlavJ+c8qGWLHtdaW0a/J5UzTnHlAkknSzpE0h/c/VIzu0XFft2fGLUm/POSXiPpVyoKLau4sAAAAIA+GGji/fZdtx7k3Ze21pbrd3QlezxjFFdepeIvartIWqRin25J+o2kdwfz/IOZvV6SkXQDAAAMt4Em3oftuY0O2zNZ7CSlV8L7LVpWUtXV7nHsqWJJ3VxJL5Q008z+U9LtkmZKWtE62My2kLSvu/9Dvw8MAAAAvRn4UpNhVOWykpKucfeTGlsFHm9m20l6a+N7Z0v6+qjx35N0mZnt6O5/rPVI8ZyseGMkiGXFlWULBCMvSOIrglhWyJYVi0XvhN13j8fusEMcfzioflmcVExlXeqiP+vcl4yNzluSXhTEsuK0rADyHbPaY5OSKrQ1k0/Zx5JKnh8nxVHDICtOy7obTgti2Wsse29EsjmywsiowDgbm51LdO7Z2KzgLyquzF572fFFc2fHUYUynf/6qYquglmhaJn7y4ooN/pGXHT56L+0j382mXxqsrL2oeBFsmnyITkSh8PXSPYay+a4M4mXEZ36FcnY7PiitRLZsWWfFdHPjTKvj16QeCdGLyt54Ns3atmix/XAt7M62+cba0lKyxrvfc3sSEnfcHdvfP1Pkk42s6NVrPH+SWON98kqlpy0ulTSGpJ2k0TiDQAAMMRIvDs0ZdfNwqsm3WhZ4z1FxS+X7zOzeZLGXeNtZm9TcUHwIRW/zL1Axf7fAAAAGGIk3h1af88ttf6eW1Y97bPufkbzCzP7rMZZ4+3uF7aMnyHpyyr2/h59NRwAAABDpK4lLejMNe5+gormOcdL+gc9f433vqPGz5D0BUk313eIAAAA6AZXvAcoaIJz0qj/3yHpjpYhbxk1/nP9PkaML1uCFBUkZgVJ2yXxkSB2WDJ4wYJkkkBW8HdjUqU1LYh96T/isVkHsag4bUEyNrsiUKZAKCsEvDWIle24982b2mNrJGOzIs9VUdnOq9FroYoC47KFfdHLPStIzF57ZY4ve01GdbPZ50fdxYuZ7PGInvMqCiD7KXsOo+cre/yjTpRSXEQpSZ9b1l506cfFY+fNi+deHhyMJU/MzDistYLYY8nYbFuJqGNk9tmZxaPC/qxbS/bauyeIzUjGbpLEo3Mp0z23FyTeHSjbtVLquLiytYHOw5LWc/czG41xHpO0v6RfamXxpczsfEn3Svq5pJdI2khF85wh3gsBAAAALDXpQHN7waq4+zJ3P1XSle5+WGOd99yWIc3vX+XupzaT7oa/k/TrxlrvtSV9SVJrq3kAAAAMoQl3xbuTq9fRHt5Vda0c624V/zVoLN7yHwAAAIbYhLviXfXV6wrdK2lWY//usTYLf7ekN5vZRiqS9c9JunCM8QAAABgCE+6KtzT+1esHvn2jVjy29LlmOf3qYtlaXNlYTvKZ7PstsRNbvjyl8oNCaVkxYfTmKlOEJsUFYLPvCIJjzBEV940klWV3JXNEr/6/TCrIpiZzR8WOWbexMh0SyxRiStK6QSx7XrIiuagDYfbrfHZ8y5L4MCtbPJc9fr3KjqPMa6HsD7+o2K5sIWb0usnet2XPsW7DchxllCkUzcZus00yR/KERYWUdnz8R+qdv/fCMH7vvQvaYlsn1YSTZsfx6DM1K67MPn+jU8wK0LPi1Oi9mGVXWdfJMoWRDyfxaUFsaTK2ahMy8e7Es48v07OPL2tLuDsttOyiuHJe4+ulkhY2/r+TpA1V5EOvl/QVFVfDv6Biw4t7VDTaea+7f6DRav6kxn1sLOlDKjbM+Ki797OrMAAAAMaxKv7SWpvmlfHW5LuKpSpJceXTkv7o7qe4+39Jerm7f0XSqSoujl3n7rMlvVHS2e7+NRVNdiTpSTN7yaj7WNxIwu+QtE5PBwwAAICeccW7Q8sWPa4Hvn3jc8tO+lRo2WmR5OhxZ0o6VKP+em1mr5a00N2zvygBAACgJiTeHZiy62Zpk4OquPs8MzuoceV6oaQbGoWWUyV9VdJJZnakpOslHWJmb5L058bNl6tYavXcqi8z20LFVoPnmdmG7v5on08BAAAAYyDx7sD6e26p9ffcUpKeK7isQtC58sRRQ37e8u+PtPz7N8F0V4+a635Jr+3pANGRrJAqKkQp240umuOBZGzcC016Iog9lYwt09ltp53isTcFXR2luPjomaTyIOsCGR1fVlSTraOLCnmyArfs+YrifJi2ix6nrGiwTOFmFWskyx5H9PxmxaPZ8UUd+rLXzbCvA40K8Mp8Fg5CFV04X/OaOD41ab8YdaPMiij1vj+H4ek/bf90Xy+pPMyKtqvovBp9LpctHivzPiqj7ALg6Fzq2peZnxUJfzpv9NxcdjKWCosrm1e8P+LuJ5nZ2sqLK49VsS2hGvMtlvQ1d//rsc8WAAAA/UbiHZiy62ZpAWUVy07cfZmkU81scssuJDupKK68oPH1se5+gpltLumdLTdvFlfe2Ei0F2llceWzjUJNmdkkFbuaXNvj4QIAAKACJN6B9ffcMt0ysHXZSR9UWVz5UhXbXf6Fmc1y92QBAAAAAOpA4j0kqi6udPebJX2xsbc3SXcf3ZvE+7Wu8fIknq1Hjv5CU7bxRzT+m9fHY7M16NHctyVjsw+mMmvkyzQgKftcRevKy84xLOteyyh7zNH6zyoaCvSzKUGZJjxlagCk+PWencuwvD6y44j+Jjwsx5wps54++yy88so4/tBDcXx58GKIGuJI8VpuSXr5ee3X5Oa9Mx77ju3i41g7WEj9g/nx2FlxWDODOc5PXsBZU5zoNZI8dOnrKfoZk63xvi+JR8dXZh18L0i8B6ifxZXZfQAAAGAwSLw70Gm3ylZDUFx5s6S9VHSuPNbdo80tAAAAUBMS7w40u1WObh/frTqKKxtzrCVpH5XfxQ4AAAAVW2UT7x9dc6fOn3NP6dstW/SEvrXl5h2OraVbZatKO1e6+5VmtpmkzVSsAQcAAMCArLKJ9/lz7tFNi5Zo1pbJrvU9qqNbZas+dK7cTNIHJb1I0i9rO5EJaOMkHtWclGnOkdktiWdNYKKik6xhQVYoGjXL2DUZm9TrhAVMWfOb7PGIil/K/jmnTHFlVviWPeeRsgV42WMyDMo0IJHi5zF7bssUTCa9Q9LHNHq9Z+dSNh7Jfnb0q3lIXUVhrYa9wU8ke6zLvPYeTd6gm24axy14oLae0R6T8qY4USHlTufE1+n+e7246HJx8KIcie8uLYycFJxLNnZaEo8evuwzIZujTGHk9CQeXX4dScZWbZVNvCVp1pZTddYRe5e6TaedJ/vVrbJVnztXPijphJ4OEAAAAJVZpRPvOnXSrbJVl8WVx6m4on2dpJ1VFFiuryKB/oaK3aiulfQXkvaX9H13P6cx58zGmMslPSppnrvPLnGKAAAA6CMS7w5UvewkKq6UJDNbrmI99oOSVrj718zsoyr+qreGpP9x92sl/U/jtue0zLnAzM6TdLG7Z1tXAgAAYEBIvDvQ526Vra6R9BVJn5a0hpl9QdJUd3/azI6S9D4z28bdz63jYAAAAFAdEu/hsq+kz0q6QdKO7v5lM/usmb1c0ptU1An8PrqhmW2iYqvBTSSdXNPxQnnXrX7JOldmBW5lusmVGfsfSTwrconmrrOAuUp3BLFh79pXhbLnGBU7VrG3adalLjOI4sNIme6Xw25YHtMyssLlaIuG7Py+l7TbHUnGzwxik5JFoMvicNiNMiui/NQT2eZop7ZFtrSPJ2NjM4IHJXtMy/wcyD5Xsg0DOp1Xkm5N4lHyW9d7kcS7AlGDnU62HgyKKw9q+fKCRuzLja9vGOe2D0s6rOODBgAAQK1IvCtQtsFOhcWVFzbGSNJlKjpd7ufux1RyYgAAAKgMiXdFyjTYqbC48tstt91C0nqqbytKAAAAlDChE+9oiUg3Kmwn30tx5WtUNNDZ18ymuvsw9+EAAACYcCZ04l12iUgNui6ubNnPex2S7omp7uK+rBBlIhQZRsp28puoj9NExnM+WFUUoGefe1n3y7WCWNQJWMoLEtcOWm5GnSgLpybxI4PjKFdcOVJibPaYRo9f2Y64q7oJnXhL5ZaIZB749o1tDXY6bKCzb6MN/DfcvVmKvJYkU3HF+5ONr2+W9GEVa7zXadxmXzN7Z0vCvaukD6lYLw4AAIAhM+ET7yqUbbDTssZ7iopfIt9nZvNU/DLY8RpvSb8zs/c3pl2o4hfmobl8DwAAgJVIvCvQQ4OdZ939jOYXZlZqjbe73y3pjJb5fmVmJ3Z5GgAAAOijsssSVwvNZSHLFpVtw9B3zTXeN6m44v33kh5prPH+P5JeJOnO6IZmtrOZfUb5HvwAAAAYoAl3xbvsspB+qriBzlxJc/twmBhHUPciqX+FIdOTePa6jo4j+407myP6oHhxMjbr5FmmK1j2wRQVMGWFStn9Rc9XVhgVdbTL5sh+jS/7ITut5PhelSlsyl7r2WNd5jkv0wmxTOFWFs/OO3s9Ra+RacnY7D0QvXez8y5TIf9AibFlZY9TFB/2LpzZcxu9d7PXWJnPBEl6rMPYWH4wvz02kozNulFGhZR/9rjL5bYWd8WMOklmj0eZQtbNk7FZ58qNg1j2GZ4dR/S5XFdCPOES79ZlIc1iyKq2FWw1XsGmmX1e0j2SXqiigPJcFeuz367iPXxu49/rSzpCRVfKzVV81h8v6asqPi8mSdpOxdrwM9393xvzm6RjVFwBP8XdV1R5fgAAAChnwiXekQFtK7hM0taSLpG0prvPNrP9JJ0l6VWSNpB0pooOlmtKeoG7n2Bme0jaT9K6kn4m6dfu7mZ2TDPpbthZ0laSbq/ndAAAADAWEu+GKrYV7MKZkj4m6SctsXdIulvFXxoPk7SNpNOD2x4p6S2SjpJ0cvD9tVQsPVkkaTcl+38DAACgHhOyuHJYuPuf1L7933nu/k+Nf/9I0imSDpV0l5l9QtJBkq6Q9HEVCfVtyfQ3SNpB0gGSbq32yAEAAFDWhL/i3dzZpO7ulc3iSHc/vCV2RfTvxOjiytFfL1dxNRx9lhV19Mu9SbyKrmxlZL/NldkrqIpjK1OUV9Yg9j26bwD3ie6ULWqMisWqeA+UKQjNRAVrUp4kRD8xs/vLrvBFj1/Zz7EyxdIzk3j0GZIVS96dxEeSePQ4Zc9Xdp+zOpy3rKyIcmFSdHl4MP6mZO4pSTx6LWRF/VnhZvS6yR67bO7o9V7X5/3QJ943LVqiQ779uzA+a8vsaelMc4eT0dsKVlFs2WHnyv0lzVbR/EaStlfRPGehpKWSdlLx2vuqpI+4+0lmtrakL6h4n9+jYinJe939A2b2RUkLGnNdraLb5dXufqaZfVzSRpK+7+7ZVXIAAAD0yVAn3m/fdeu+zt/c4aS11bvU/2LLls6Vk5tXqs1sJ0kPu/sFja+PbRRTbi7pnS03f6Oks939RjM7VkXi/aSZvUTS06Ma8pwqaa/Gl2tL+pKKhP+UvpwYAAAAUkOdeB+25zY6bM9twu9FV8F70Wyq0/z3gIot47/vjD/uTBXrwMdqnuMt/wEAAKBmQ51412UYmuq4+zwzO6hx5XqhpBvM7GitXGpykpkdKel6SYeY2Zsk/blx8+Uqlp7NGDXtuyXtYGaXqEjKPyfpP/t+MgAAAGhjniyibxjaq6PNK95nHbF35XM3r3z3eMU7rlrAamcNs/B9UqZoqkx3uGnJ2KwwJOomlxWilClU3KrkcUTKdOfLlC1Oix7TsnNEVyzKdIAcy7B3/1vdZVejoucxKyDLRO+v7P76WTRcRpmtz7LXelXvjU7nzubdNIlHz2PWNfEtSfzOJB51K82Ob40k/srgA3tS8qA+lLxwRoLYb5P72yOJnx7kixskBZqvSuaIilOzxzp73USdYbP3YvazLnotZD+PFrpXms9xxTvRuvSkGx0WVx6g4rm+VNI8ST+U9DJJx0m6WdIrVKzhvkXSy1t3LjGzn0j6gLs/2hI7UtJbJf1K0qXuPrvrEwAAAEClSLwD/V56EhVXSpKZXaeVXSlbLW39otG98hRJB6rodNmc91Qzu1HFXzJIugEAAIYIiXegudvJANwo6YMqrnyvL+kRdz9Vei7Zbtpf0lMqWs4/l3ib2RaS9nX3f6jrgAEAANAZEu/h81kVS8D2krRRY/nIHZLWaPz7XkkrGle332tmW7j7/Y3bfk/SZWa2o7v/cQDHPiF9MomPBLFs25lXx5v3aNq09tiNyQqo7E8cTwSxbZOx2QdCNMdVydhsE85ord12ydgyzTKyY14niUfPQTZHtuF9tJ4wW2OYrdXN1i9mDSmGQXaO2br06HHN5ihTG5At5MvWaO5a4v7KPI+7JWOP+068Fe6LPnRPx/dXpvnNsLcmrmItd/Y4lfnrdPaYRvHs9XFRifvL5sma8GTHd37wjexztkzdTNYJJfsMOjdYz/1YUif4q9fGy6KjtemHXhHfX1ZHFL2eXpOM3T6JR+vK66qvIfEeoKDbZOvXC1RsE9j0s+D23x/19YFVHh8AAACqQ+I9htmzZ2vu3Lld3fbwww9PvzdGceVxKhLukyX9k4pC6dNUdJw8QMVFvH+XdJCKCwA3SHqbpH0l/V7Sp5rFlmb2b405v+Pug94tEQAAYMIj8R7D3Llzdd9992n69GhDoO6NUVx5laTLVPwl9SpJl6jY9ea17v4lM1tT0lEq/uJ+k6T/cfeLzeyY0VfPJT0saWMN8ZaQAAAAEwmJ9zimT58+5tXriu2pYunnaSqKLPeW9PFg3JclvVLSVyR9NJrI3Y82s31UXCm/oC9HCwAAgI6t9ol3L8tF+nG1exzXNK9cN65+f1fSpyT91Mw+p5VLTQ5TsaNJVqMhM/u0pJkqknT00WlJPCoAyYqMLki6L0wO4psnc8xP4lHBSFZ5O5LEI1UUO91RYmxZ/WzaMVGVXbMWFYtVse5tThLPiqMeCGJZIdu0JB4VyWXF0hd8q72IUoo/sLMCsvuS+LCo+32UFQJGRa/ZsWWfndOCWNbFo2zSFL1uyhbxRYWU05KxWTF3mZ9H2Wd71BQnK6J83a+TP7Z/Y6P247hiJByafVZEhZFZgXH2czF6PVXR0K0Tq33i3a/lIlUYq7iy5d/HNP5/dcvQBWPN04h9tZqjBAAAQBVW+8Rb6n65yOmnn94W6/QKehfFlQ9LWs/dzzSzz0t6TMV+3b+U9A33Ys8eM9tO0tfc/a+DeQ+VNFnSAne/YtyDBAAAQG0mROJdpSquoEfFlWY2U8Xe3ZK0rLFP9+RmA53GmEmS3ijp2mTeM6M4AAAABo/Eexz33Xff8658N5PuPhRcLpO01jhjXippuqS/MLNZ7j7MvTYAAADQgsR7DDvvvHOdd3evpFlmdrSSug53v1nSFxvbB5J0D4msUKZMAU3WJS0qAMvetGWKarIikio6d9XV/Ws8FFGuvrLC2UyZwrLs9RuNz4orp2aVgIEyHTszUUdXKT+X6PHbOBlbptNoVrCafTZFx5c9t7sm8ah4Ltt1YHISnxbEsqLXkSSevZ6iz9rscco+28t8hpd5XWdjs+cgelyjTpSSwiJKSdLfPdIWmn5kXKCZdYaNjiPr5Jm9B6LXQtnPlW6ReI9h99131+677/682Omnn952FTzS4RrvfRtt4L+hoov3Dc013ma2fev3W9Z4f1TSMjPb2N2j4l4AAAAMIRLvkqq4Ct6yxnuKil+g36eiuLLpuTXekr4p6X1mJkl/lrSPpOuU/9IMAACAIUTiXVJ0FbwHz7r7GZJkZltLev3oAe6+XFJzzCYqthK8VNKbJZ1T1YEAAACgv+pa0oLxdbLG+2FJK1RcIb+uxmMDAABAj6yxdDgz5jcH6ZBv/043LVqiWVuOXcVy33336f3bjNTZ9r0prhbAasfMan2fbJPE703iUQFN9qeuMuuXsuKjqEtgZlgKMTP9vDIxUYs/q+gomr1+yxRSZfeXFRlGxYS7JmOzjnvzgti+ydhrknjkfUn8wSQevc83ScZukcSjAqOsiHJ2Eo8KBLdLxmaFdtHeulcHMSnvflmmyPONSfyKEveZvU6zItmHglj2HihT+JX9LMlev9F9Zu+jLB5txHxDkoee9/I4jfpocGnyvcn9XZnENwtiayRjz3GvNJ9bZZeavH3XrWu5n25bzo+X6Dea5Nwj6YUqthE8V0Vh7ttVvP8+2oh9UNKnVaztPkrSAa2dKs3sVElXs4c3AADAcFtlE+/D9txGh+2Z/b620ni7j4ynjy3nl0naWtIlktZ099lmtp+ksyS9StKrJZ0i6Q2SLlaRlD8azHOqVjbeAQAAwJBaZRPvOvWpYY4knSnpY5J+0hJ7h4ptKveV9JSK5PznjbGHKt86FgAAAEOMxFtjLyfp09VuSZK7/8nMRu/7fp6KZWufbGwp+F4Vy+wWqkjIZ4wa/25JO5jZJe7evjM9AAAAhgKJt/q6nCTVXKft7oe3xK5oGfLVRuz7LbG2mhF3P7FPh4gOlSnAywpOyrwRt0/iWVFNVEiVdfnKuqFF55i9W7ICzejcs6592WMaFSWVLdCsokAomqOKosFuxmN8VRTJRoVv9yVjy3TR+0MyNitUjExL4rcn8ehcXpFUle6wQxyPOhY+FFUBSro6ai+p+LMia8mcddAcSeKRrLgymiO7v3DLMeWFkdFrL5s7E30mTEvGZj8HojmysSUar6ZF9lmBZlQkmxVRvuOGuOjyO9Y+/vX7xPe3e/JDbdq09tjGWWV1xUi8G7LlJJ12qhytw86V+6so+F7Y+NbnJb1E0nGSbpb0CkmLJN2ioth+pqQ7Gv8+UMWe3he6+yUtcx+vYjea37j75aUOGgAAAH1D4j2OKjpVjtbSuXLyqB1K3ihpP0nrjrrJUhW/4C5UUYz5bknvcfdHzezYRqxpqqQNVSxLAQAAwJAg8R5HxZ0qx3Ojiu0Df6jir5WPuPupje9dbmY7Svq6pN+PMcctkn6sYmvXW/t3qAAAACiDxHv4fFbFcqy9JG1kZkeqWF7ikl4q6X4VO5x82sweU3sHy50kfUbSz+o6YAAAAIxvle1c2anmGu2xCieb3694y0A6V04QeyedK6Pikuw33awQJbJlEl8via8IYmUKt6S4o1f2jsrmLnufkag4LSsEKtN9LepsONbc0Z6e2ZZCWdFVJuryV6ajaFWiorCZydjssX7PtPbYHSPx2KywN7JL8sSsmbzBbg2qyLLnPCv4jQoms46MtyXx/y+IXZGMfVkSj3w3iZcpPM5ep1m92R5B7I5kbNSxcxCyzh93lphj1yS+URK/J4hlBYmZqKAzO5es+DMqrpyZjM26D0dzZK+PrHAzep1lxci7JPH/DvLWUzaMU647kg+WbYIK1yxN/Jvb6VxZStk12t12qhytw+LKA1T8vLlUxWfTAZLuUtEh9hUqdjH5SxWdfy9392ta5thO0tfc/a/N7FAVP0cWjNoZBQAAAENitU+8O1mj3bpjSR1bC45RXHmapBPc/RIze0UjfLa7X90oorymMW6SpDeq2O9btIsHAAAYfqt94t2NPnaqHM/dkmZJunCccS9V8Zf+vzCzWe6ebX0KAACAIVFFXwFU53pJ15nZB1ti7zKzz0j6ZTPg7je7+xclXUXSDQAAsGrgivcAtS4zGfX1L1vCV3R6ewzGnCReprCpTCezVyXxS5P4SBDLCmKyoproN/QXJ2MXJPGoMKdMUWk2R3b1IItHH3rZc5XFowKhsh00y3bLHAb3JvHs3G8caY9lz0uZ4tsdkoKp7PUbNV/MClZ3SuILgljWRTYrgP6XILZ5Mva8JB6ZlsTLPKbZZ1B2jlGTyuz9XKarazY2e5yi48hej3+ZxL8TxLL3YVY0mBVozghiWSFxVtgbxbPntsznR/YeKNOF8zXJ2Gwv46iQMmk6mXajjAopP/5ovBfIha+M6yLf9n+2bQ8uzp7dapF4NzS7U0bru7spuOyyuPI4Na56S5rVTKzN7F9V7M+9r6SfS/qApLmSTnP3Wxpjpkj6kKTXSvqUu2eF9QAAABgAEm+Nv/NJ1QWXYxRXLpe0mYpdTFotdPdTzexuFbsFTVfRrXJTFQm53P3JxpybknQDAAAMHxJvPX/nk9YdTlrVVHB5jaSvSPr0GGNcxZXvvSV9vPUbZraZigY7AAAAGDIk3sNlXxWdK2+QtEuja+XdkrY1s49JWs/dzzWzq1T0TPiUpGNabn+wpJ/Ue8j4UrL7/+3Bwrynkjk2TeLRG/SYI+Ox//mDOP5YsKBw+2Th5m3J30o2DQ5w42Sh+JJkAeOzweLDZ5JFhlOTBimPB4/plcnC403icGitJH5FEn99EMs6LEQNjKR8feWpSXwYlG0GFL1+ozWvUrkmJtljlzXiiNa9ZueyMIlHTaT+lIzNXk/R8WWrSsvUP5RpPpTJ7u/6JB41IBpJxpZZd5yNjdZyS+VqK7J9d6M5suPI1rxna+Sjz6GHk7GZ+4JY9vf3bG11JDvH7LUQfeRnj8f8JB491lcmY3dPXthRU5x0Lffvkz6QX1m3PXbAG5IjqRaJd6C53rv1637s6x0UVx7U8uUFLf8+N7ndMaPi/1rpAQIAAKAyJN6jjLfeu9NCyy6LK3+oolPwcZJuVtG9cpGKddwbq+jwepukyyQdJemJRnxe47/rVSTj20r6nLuPXisOAACAASHxHiXqdFl1Z8sxiiuvk7SfpNF/A1mqYseohZJ+Iendkr7q7o+a2bHufkbL2BPM7P2N8STeAAAAQ4LEuwt9LLS8UdIHVVz5Xl/SI+5+auN7l5vZjpK+Lun32QRmtoOkDdw9W34IAACAASDx7tBY+3xX7LMqah72krRRo8DyDhW7mbxUxa4lZ0r6tJk9pmLPb0nPLWH5N0nnmtkL3P2ufh4oCv+RdD2IClSyYpZpJe5vxwvj+FVJ9dHdQWzuvHjszcl9Tgkm2T6aWMVaqEh07lnB1KSomkhx04iskURWaBd96GXFRFnRWnTcWbFe2eY8wywraszOJXrOk6c2nbvTeaX8+Yo+sbPXzRZJ/LEgtmEyNns8oo+K7IdwmYLEMg1qMtnrNysajOqfs/dRFa/1Mo2ysvPeJolHr5vsmLPPlex1Hb32piVjowLe7D6zhkJlkrpsbNZgLdoEICsOTurjw6Lc9ZKx06bF8W2CF2XYEEeKiygl6ehgq4N7Ptge6wMS7w6Mt+67W2N0rpSKRmmji7BbCy4/G8y3THljQwAAAAwQiXcHsn2+xyq07EPnyptVbCd4opldoGL3rdPc/RYzO1TFL5JLJb3T3f+6l/MFAABA9Ui8e9BtoWWXnStPM7ODzWx7SVeppXOlu59pZpNUtIy/todTAgAAQJ+QeHdh9HrvCgste+lc+VIVy8n+wsxmuftNVR0UAAAAekfiXVK/1ns3dN250t1vlvRFMzuGpLteWZFLVICXFexkBUyRrOAkExXmZAVkWUe1qFAmO++s2CaSfQBlRUZLg1hWoJkVQUXFWFHBj5Q/X1GRUdkCt2zuBUl8GJT9gRE9rlnRVZlOjVmn10yZ4srs75fTglhUcDnW3NG5ly3ii5R57KoSFWNm74FhUab4M3vfJrX06blHhZvR55hUXFWLRK+nkWRsmULW7P2cnWOZItSs0Dl6nLLPhKw7crjIYHHykyfrRhkVUm793eRIqkXiXdLo9d6ju1w2dbjGe99Gcv0Nd3cVV7yfkPQCFe/Bb6nIyaar+Mxo5hg7q0i4LzSzT0o6S9Jb3f1b7n6SmW0r6X+p2Fbw+J5PGgAAAD0j8e5Bt1e/W9Z4T1Hxi+v7zOwpSTe6+8/M7ChJP5f0ZhU7+/xU0oFauevOXBVXvA+U9CJJZ0g6tmX+hWa2WNLWXR0gAAAAKkfi3YOoy2VJzza7TprZTpJe2IibpPmS3t749yaSdpK0rpldpOKK9z9LOtXdf9hYXnJN68TufkZjeQoAAACGAIn3kHD3eWb2V2b2YklPuftSM3tCxVKpN0k6StI6kj6g4or3KZJOlnT16LnMbA9Jb1De3wEAAAA1s2JpMQAAAIB+4or3AJnZmpLe0/hynrvP7mGuGSoa8kjSZe6eNPQGAADAIHDFGwAAAKjBsG+7CQAAAKwWSLwBAACAGpB4AwAAADUg8QYAAABqQOINAAAA1IDEGwAAAKgBiTcAAABQAxJvAAAAoAYk3gAAAEANSLwBAACAGpB4AwAAADUg8QYAAABqQOINAAAA1IDEGwAAAKgBiTcAAABQAxJvAAAAoAYk3gAAAEANSLwBAACAGpB4AwAAADUg8QYAAABqQOINAAAA1IDEGwAAAKgBiTcAAABQAxJvAAAAoAYk3gAAAEANSLwBAACAGpB4AwAAADUg8QYAAABqQOINAAAA1IDEGwAAAKgBiTcAAABQAxJvAAAAoAYk3gAAAEANSLwBAACAGpB4AwAAADUg8QYAAABqQOINAAAA1IDEGwAAAKgBiTcAAABQAxJvAAAAoAYk3gAAAEANSLwBAACAGpB4AwAAADUg8QYAAABqQOINAAAA1IDEGwAAAKgBiTcAAABQAxJvAAAAoAYk3gAAAEANSLwBAACAGpB4AwAAADUg8QYAAABqQOINAAAA1IDEGwAAAKjBmoM+AGBVZ2beFhvn61VhXN33uVqce3JDy74eJz7ufMm4rm5X8TF0fWxVP0Z9OPfeD6qXg43m72XcIO4zut9BPJZdP/FJfFX6lByWcxrEuE5vI1133XWXuPuByQ26QuINVGDSavD/YTiGQf2/69tOGvX/NRr/tyQ+Kfl+2dtn82T3M8bte7ltr/c96NuXva2NvlMbPWnj/2sk8dH/H337Tm9Xxf0P8r5XtXMfik+pqv4/DMcwqP+XvY1kZpuqYpPGHwIAAACgVyTeAAAAQA1IvAEAAIAakHgDAAAANSDxBgAAAGpA4g0AAADUgMQbAAAAqAGJNwAAAFADEm8AAACgBiTeAAAAQA1IvAEAAIAakHgDAAAANSDxBgAAAGqw5qAPAFgN/PFZ6WlJenbQR4I6bapn9ZCklU/88sEdDOrgkrSptKJ43rVikAeDem0qNd7vmEgmVz0hiTfQu6fdffdBHwTqZWazed4nHp73iYnnfWIys9lVz8lSEwAAAKAGJN4AAABADUi8gd59Z9AHgIHgeZ+YeN4nJp73iany593cveo5AQAAAIzCFW8AAACgBiTeQMLMDjSzW8zsNjM7Jvi+mdlpje/faGa7dXpbDK8Onvf/3Xi+bzSz35rZy1u+t8DM5prZnH5Uw6N/Onje9zOzRxvP7Rwz+0Knt8Xw6uB5/1TLcz7PzFaY2caN7/F+X0WZ2X+Y2QNmNi/5fv9+vrs7//Ef/436T9Iakm6XtJ2ktSXdIGnWqDFvkfTfkkzSXpKu6fS2/Dec/3X4vO8jaaPGv9/cfN4bXy+QtOmgz4P/+vK87yfpwm5uy3/D+V/Z507SQZIub/ma9/sq+p+k10jaTdK85Pt9+/nOFW8gtoek29z9Dnd/RtKZkt4+aszbJX3fC1dLmmZmW3Z4WwyncZ87d/+tuz/S+PJqSTNqPkZUr5f3LO/3VVfZ5+7dkn5cy5Ghr9z9N5IWjzGkbz/fSbyB2NaS7mr5+u5GrJMxndwWw6nsc/e3Kq6KNLmkS83sOjP7UB+OD/3R6fO+t5ndYGb/bWY7lrwthk/Hz52ZTZF0oKSftIR5v6+++vbznc6VQMyC2OgtgLIxndwWw6nj587MXqci8X5VS3hfd7/XzDaX9Aszm9+4soLh1snzfr2kbd39cTN7i6SfSnpJh7fFcCrz3B0k6Sp3b71Kyvt99dW3n+9c8QZid0t6QcvXMyTd2+GYTm6L4dTRc2dmu0j6N0lvd/eHm3F3v7fx/wcknafiz5IYfuM+7+6+xN0fb/z7IklrmdmmndwWQ6vMc3eoRi0z4f2+Wuvbz3cSbyD2e0kvMbMXmtnaKj50fzZqzM8kvbdR/byXpEfdfVGHt8VwGve5M7NtJP2XpL9x91tb4uuZ2QbNf0t6o6SwYh5Dp5PnfbqZWePfe6j4+flwJ7fF0OrouTOzDSW9VtL5LTHe76u3vv18Z6kJEHD35Wb2UUmXqKhi/g93/6OZfbjx/W9JukhF5fNtkp6UdPhYtx3AaaCkDp/3L0jaRNK/NPKw5e6+u6QtJJ3XiK0p6UfufvEATgMldfi8HyzpI2a2XNJTkg71YpsD3u+rqA6fd0l6h6RL3f2Jlpvzfl+FmdmPVexUtKmZ3S3pOElrSf3/+U7nSgAAAKAGLDUBAAAAakDiDQAAANSAxBsAAACoAYk3AAAAUAMSbwAAAKAGJN4AAABADUi8AQAAgBqQeAMAAAA1IPEGAAAAakDiDQAAANSAxBsAAACoAYk3AAAAUAMSbwAAAKAGJN4AAABADUi8AQAAgBqQeAMAAAA1IPEGAAAAakDiDQAAANSAxBsAAACoAYk3AAAAUAMSbwAAAKAGJN4AAABADUi8AQAAgBqQeAMAAAA1IPEGAAAAakDiDQAAANSAxBsAAACoAYk3AAAAUAMSbwAAAKAGJN4AAABADUi8AQAAgBqQeAMAAAA1IPEGAAAAakDiDQAAANSAxBsAAACoAYk3AAAAUAMSbwAAAKAGJN4AAABADUi8AQAAgBqQeAMAAAA1IPEGAAAAakDiDQAAANSAxBsAAACoAYk3AAAAUAMSbwAAAKAGJN4AAABADUi8AQAAgBqQeAMAAAA1IPEGAAAAakDiDQAAANSAxBsAAACoAYk3AAAAUAMSbwAAAKAGJN4AAABADUi8AQAAgBqQeAMAAAA1IPEGAAAAakDiDQAAANSAxBsAAACoAYk3AAAAUAMSbwAAAKAGJN4AAABADUi8AQAAgBqQeAMAAAA1IPEGAAAAakDiDQAAANSAxBsAAACoAYk3AAAAUAMSbwAAAKAGJN4AAABADUi8AQAAgBqQeAMAAAA1IPEGAAAAakDiDQAAANSAxBsAAACoAYk3AAAAUAMSbwAAAKAGJN4AAABADUi8AQAAgBqQeAMAAAA1IPEGAAAAakDiDQAAANSAxBsAAACoAYk3AAAAUAMSbwAAAKAGJN4AAABADUi8AQAAgBqQeAMAAAA1IPEGAAAAakDiDQAAANSAxBsAAACoAYk3AAAAUAMSbwAAAKAGJN4AAABADUi8AQAAgBqQeAMAAAA1IPEGAAAAakDiDQAAANSAxBsAAACoAYk3AAAAUAMSbwAAAKAGJN4AAABADUi8AQAAgBqQeAMAAAA1IPEGAAAAakDiDQAAANSAxBsAAACoAYk3AAAAUAMSbwAAAKAGJN4AAABADUi8AQAAgBqQeAMAAAA1IPEGAAAAakDiDQAAANSAxBsAAACoAYk3AAAAUAMSbwAAAKAGJN4AAABADUi8AQAAgBqQeAMAAAA1IPEGAAAAakDiDQAAANSAxBsAAACoAYk3AAAAUAMSbwAAAKAGJN4AAABADUi8AQAAgBqQeAMAAAA1IPEGAAAAakDiDQAAANSAxBsAAACoAYk3AAAAUAMSbwAAAKAGJN4AAABADUi8AQAAgBr8/+e+AL64Sb1PAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "comat = cRz.co_occurrence_matrix('parent')\n",
    "fig = comat.plot(linkage='ward', threshold=1.5)\n",
    "fig.savefig('comat_subsampleFirst.eps')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finish the ensembles using the co-occurrence matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#cut the co-occurrence matrices and compare across the two methods of clustering\n",
    "c_ComatCut = c.finish_co_occ_linkage(threshold=1.5, linkage='ward')\n",
    "cMV = c.finish_majority_vote(threshold=0.5)\n",
    "\n",
    "cRz_ComatCut = cRz.finish_co_occ_linkage(threshold=1.5, linkage='ward')\n",
    "cRz_cMV = cRz.finish_majority_vote(threshold=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'majority_vote': 'majority_vote'},\n",
       " {'co_occ_linkage': 'co_occ_linkage_2'},\n",
       " {'majority_vote': 'majority_vote_3'}]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "#Merge the finished ensembles so that we can simply calculate their similarity by mutual information \n",
    "transDictList = c_ComatCut.merge([cMV, cRz_ComatCut, cRz_cMV])\n",
    "transDictList"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>co_occ_linkage</th>\n",
       "      <th>majority_vote</th>\n",
       "      <th>co_occ_linkage_2</th>\n",
       "      <th>majority_vote_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>co_occ_linkage</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.775337</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.775337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>majority_vote</th>\n",
       "      <td>0.775337</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.775337</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>co_occ_linkage_2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.775337</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.775337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>majority_vote_3</th>\n",
       "      <td>0.775337</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.775337</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 co_occ_linkage majority_vote co_occ_linkage_2 majority_vote_3\n",
       "co_occ_linkage              1.0      0.775337              1.0        0.775337\n",
       "majority_vote          0.775337           1.0         0.775337             1.0\n",
       "co_occ_linkage_2            1.0      0.775337              1.0        0.775337\n",
       "majority_vote_3        0.775337           1.0         0.775337             1.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MI = c_ComatCut.MI(MI_type='normalized')\n",
    "MI.matrix\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the MI matrix above, the non-numbered keys are the parent, which was created by first applying the z-score and then subsampling. It's clear that it makes no difference (or very little difference, depending on the specicific random starting positions) if you z-score before or after subsampling feature dimensions here. Also, both majority vote solutions are similar, although they differ from the solutions built by ward-linking and cutting the co-occurrence matrix. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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