{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re \n",
    "\n",
    "def sorted_nicely( l ): \n",
    "    \"\"\" Sort the given iterable in the way that humans expect.\"\"\" \n",
    "    convert = lambda text: int(text) if text.isdigit() else text \n",
    "    alphanum_key = lambda key: [ convert(c) for c in re.split('([0-9]+)', key) ] \n",
    "    return sorted(l, key = alphanum_key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "import matplotlib.ticker as plticker\n",
    "from matplotlib.colors import LogNorm\n",
    "\n",
    "\n",
    "def plots_with_1_level(group_x):\n",
    "\n",
    "    change_group_level_1 = df_2_or_uniq.groupby(['chrom_x'])\n",
    "    nrow = int(len(change_group_level_1.groups.keys())/3) + 1\n",
    "    ncol = 3\n",
    "    fig, axes = plt.subplots(nrows=nrow, ncols=ncol, figsize=(4*ncol, 4*nrow),sharex=True,sharey=True) \n",
    "\n",
    "    for key_level_1,ax in zip(sorted_nicely(change_group_level_1.groups.keys()),axes.flatten()):\n",
    "        given_group_level_1 = change_group_level_1.get_group(key_level_1)\n",
    "        #given_group2 = given_group_level_1.groupby(['order_diff', 'category']).mean().groupby(level=2)['exp'].plot(ax=ax)\n",
    "        given_group2 = given_group_level_1.groupby([group_x]).mean()['exp'].plot(ax=ax)\n",
    "        given_group2 = given_group_level_1.groupby([group_x]).mean()['exp (GK)'].plot(ax=ax)\n",
    "        #given_group2 = given_group_level_1.groupby([group_x]).mean()['SRP063477'].plot(ax=ax)\n",
    "        #given_group2 = given_group_level_1.groupby([group_x]).mean()['SRP026208'].plot(ax=ax)\n",
    "        #given_group2 = given_group_level_1.groupby([group_x]).mean()['SRP115956'].plot(ax=ax)\n",
    "\n",
    "        ax.axhline(y=0.5, color='r', linestyle='-')\n",
    "        #ax.set_xlim([0, 2000])\n",
    "        ax.legend()\n",
    "        rects = ax.patches  \n",
    "        ax.set_title('%s' %(key_level_1))\n",
    "        #ax.set_ylabel('%Population')\n",
    "        ax.set_xlabel('Distance between gene pairs (MB)')\n",
    "        ax.set_ylabel('Co-expression')\n",
    "        \n",
    "\n",
    "def plots_with_2_groups(group_2='None', group_3_x='category'):   \n",
    "    change_group_level_1 = df_2_or_uniq.groupby(['chrom_x'])\n",
    "    change_group_level_2 = df_2_or_uniq.groupby([group_2])\n",
    "    nrow = len(change_group_level_1.groups.keys())\n",
    "    ncol = len(change_group_level_2.groups.keys())\n",
    "    fig, axes = plt.subplots(nrows=nrow, ncols=ncol, figsize=(8*ncol, 8*nrow),sharey=True) \n",
    "\n",
    "    for key_level_1,ax_row in zip(sorted_nicely(change_group_level_1.groups.keys()) ,axes):\n",
    "        given_group_level_1 = change_group_level_1.get_group(key_level_1)\n",
    "        change_group = given_group_level_1.groupby([group_2])\n",
    "        for key,ax in zip(change_group.groups.keys(),ax_row):\n",
    "            given_group = change_group.get_group(key)\n",
    "            #given_group2 = given_group.groupby([group_3_x, 'category']).mean().groupby(level=1)['exp'].plot(ax=ax)\n",
    "            given_group2 = given_group.groupby([group_3_x]).mean()['exp'].plot(ax=ax)\n",
    "            given_group2 = given_group.groupby([group_3_x]).mean()['exp_georg'].plot(ax=ax)\n",
    "            ax.legend()\n",
    "            rects = ax.patches  \n",
    "            ax.set_title('%s, %s' %(key_level_1,key))\n",
    "            ax.axhline(y=0.5, color='r', linestyle='-')\n",
    "            ax.set_ylabel('%Population')\n",
    "            ax.set_ylim([0.3, 0.9])\n",
    "            \n",
    "            \n",
    "def plots_with_2_groups_same_plot(group_2='None', group_3_x='category'):   \n",
    "    change_group_level_1 = df_2_or_uniq.groupby(['chrom_x'])\n",
    "    change_group_level_2 = df_2_or_uniq.groupby([group_2])\n",
    "    nrow = int(len(change_group_level_1.groups.keys())/3) + 1\n",
    "    ncol = 3\n",
    "    fig, axes = plt.subplots(nrows=nrow, ncols=ncol, figsize=(4*ncol, 4*nrow),sharey=True, sharex=True) \n",
    "\n",
    "    for key_level_1,ax in zip(sorted_nicely(change_group_level_1.groups.keys()) ,axes.flatten()):\n",
    "        given_group_level_1 = change_group_level_1.get_group(key_level_1)\n",
    "        change_group = given_group_level_1.groupby([group_2])\n",
    "        for key in change_group.groups.keys():\n",
    "            given_group = change_group.get_group(key)\n",
    "            #given_group2 = given_group.groupby([group_3_x, 'category']).mean().groupby(level=1)['exp'].plot(ax=ax)\n",
    "            given_group2 = given_group.groupby([group_3_x]).mean()['exp'].plot(ax=ax)\n",
    "            #given_group2 = given_group.groupby([group_3_x]).mean()['exp_georg'].plot(ax=ax)\n",
    "            #ax.legend()\n",
    "            rects = ax.patches  \n",
    "            ax.set_title('%s, %s' %(key_level_1,key))\n",
    "            ax.axhline(y=0.5, color='r', linestyle='-')\n",
    "    ax.set_ylabel('Co-expression')\n",
    "    ax.set_xlabel('Distance between gene pairs (MB)')\n",
    "    #ax.set_ylim([0.3, 0.9])\n",
    "            \n",
    "\n",
    "def plots_with_1_level_3d(df, group_x):\n",
    "    change_group_level_1 = df.groupby(['chrom_x'])\n",
    "    nrow = int(len(change_group_level_1.groups.keys())/3) + 1\n",
    "    ncol = 3\n",
    "    fig, axes = plt.subplots(nrows=nrow, ncols=ncol, figsize=(4*ncol, 4*nrow)) \n",
    "\n",
    "    for key_level_1,ax in zip(sorted_nicely(change_group_level_1.groups.keys()),axes.flatten()):\n",
    "            given_group_level_1 = change_group_level_1.get_group(key_level_1)\n",
    "            H = given_group_level_1.pivot_table(index='gene_order_tss_x', columns='gene_order_tss_y', values=group_x)\n",
    "            #print (H)\n",
    "            print ((H - H.T).max().max())\n",
    "            #H.mask(H < 0, inplace=True)\n",
    "            #elix.reset_index(drop=True, inplace=True)\n",
    "            print (H.max())\n",
    "            vmax= 10000\n",
    "            cmap = LinearSegmentedColormap.from_list('mycmap', [(0/ vmax, 'red'), (1./2, 'white'), (vmax / vmax, 'blue')])\n",
    "            current_cmap = cmap\n",
    "            current_cmap.set_bad(color='grey')\n",
    "            #      vmax= 1000000\n",
    "            #      current_cmap = LinearSegmentedColormap.from_list('mycmap', [ (0/ vmax, 'white'), (vmax / vmax, 'blue')])\n",
    "            #      imgp = ax.imshow(H.T,origin='low', aspect='auto' , cmap=current_cmap, norm=LogNorm(vmin=1, vmax=vmax))\n",
    "            \n",
    "            imgp = ax.imshow(H,origin='low', aspect='auto' , vmin=100, vmax=vmax, cmap=current_cmap)\n",
    "            #imgp = ax.imshow(H,origin='low', aspect='auto' , norm=LogNorm(), cmap=current_cmap)\n",
    "            #sns.heatmap(H, annot = False) \n",
    "            ax.set_title('%s' %(key_level_1))\n",
    "            ax.set_yticklabels([])\n",
    "            ax.set_xticklabels([])\n",
    "            ax.set_xlabel(\"Genes in chromosome order\")\n",
    "            ax.set_ylabel(\"Genes in chromosome order\")\n",
    "            \n",
    "    cbar = ax.figure.colorbar(imgp, ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2_or = pd.read_hdf('/data/lohia/gene_distance_expresseion/dist_files/11_dist_with_georg_hic_sub_median_hic_500.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 499,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2_or = df_2_or[df_2_or['exp_georg'] >= 0] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n",
      "gene_order_tss_y\n",
      "6       18171.0\n",
      "9       11136.0\n",
      "11      14490.0\n",
      "12      14490.0\n",
      "14      14490.0\n",
      "         ...   \n",
      "3345     6781.0\n",
      "3347     6746.0\n",
      "3348     6746.0\n",
      "3349     5868.0\n",
      "3355     5847.0\n",
      "Length: 1004, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plots_with_1_level_3d(df_2_or, 'hi-c-rao')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n",
      "gene_order_tss_y\n",
      "6       30508.0\n",
      "9       30508.0\n",
      "11      30508.0\n",
      "12      30508.0\n",
      "14      30508.0\n",
      "         ...   \n",
      "3345    23677.0\n",
      "3347    23677.0\n",
      "3348    23677.0\n",
      "3349    23677.0\n",
      "3355    78596.0\n",
      "Length: 1004, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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EFgeBZLEYGJBocleX2CcqFenv2DFZCDgy0nzvu3fD1VfLMEoTQMN+sW2bGI4LqFZhZKzMgulnaQq+SM9xHMdxnDObB9mKIPvTvYpMK0ZnWkSXY69hr6OWiSKsgCwSr3klPYvuL7IstMpKkY9rrpjuy0FuS7HXtufpHMUo4+/oSHYJfX8qlVT4RUtNd3RIijd9P48fT8+aG3nxYtnX2ZkW4C1fLj7jZctSxgqlt5d796wG4N9d0EiMsfn5hG0RtkmTTZskY8Wb3wwvfdG4WDkefESe87LVJzl9juM4juPMD85MrKzdxXlaErmVBznvq52IcqnUnP9XfbVFhUM0b7CORXMAF6WRm84DnedDtiWni/zMGsJsFXHOaedLQ9GiRzsnKnh1n86J7uvoaM4sUa+nwiA2j3O5LGK4WoWDB2Vb95fLIqjtl6HBwfSebNwo6renp/neL7+c3V+RzZGR4ik47zx45Ssb9UR27xYhPj4uEemitIItcA+y4ziO4zinXyC3+g07F8aQSiLnKcHyc2aqRGejwUWV89RmoAJYha8WE1GhrOPSfoeHp96LWjhgah5kpaj4iRXuRRkxiu5dKcpYkWOLoVSrzdYRFbcqhu2+ECQifPy4PLRktF5DBa9Gio8cEWF6+LDMz+CgXKenR4739Ehpu95eabNmDTzxBJTL3F39d3R3r2bvGGzfnoZ+6x/Dv/xL43IbzpN8x9t+wP33ixYGeO11I3DoEHzhCylirMVGZumZcIHsOI7jOPObs0cg50U5bETXRm2tR9imJSuyUbTyJA8Pp77VZ2zToKk4ViFpx6Tn2muB9KNV95SFC+VZfbw5uRKzRUfye8qxc9iq8Eret63019WVHiHIs95jCCIyOztl7BMTcvzYsTSusbH0BcLee62WPMaHDjUXFxkbkzLT69en8Y2PnxDrf/d3cNddEiXesSMNfWjI3MeuXQTE8/zCSyfYvLnhR96xSyr9HTkij7VrRZxrkZM28Qiy4ziO4zinXyCXy6lss60BnKuSWq04QtsqmmwFtkaEFRWQ1ntsPbh5qjW7v1oVX6yOSaPa2s5aErRf3bd2rTyr8D14UJ412mqFrR2PRpj37m2+R3vftpqd3da+8nlTsV+piMd3fFweWnZ79WoRxXoNFcWTkykK3NkpAlfHou+Rit96PVkm6vW0r1SSe9H5HByU53PPhXXrTnxJ+ZO3Ukhc/5wT9xN2ijj+7GeBAwc44S5WS0y1Kgp7ZETuUxMizwIXyI7jOI4zv/H1+o5j8CwWjuM4juOc2QiyjYhq1HRyslmhaARyuoV66qe13mONCOfR5qLob57azV6nVpOIZ29vquam52k0Nb8XJY/squVifHxqW0jeWe1/usintQ20a7HQCPjAgNxPR4e8XrAgXVtTuHV1pfvStG35Ij2d3+HhZHfJ31ObRk7TwCm7d8vcjo83rv/aE4fimrWp3eTUkO6GDYiFQ9m/X4zL27fL/VWrcnzVKjk2CzyC7DiO4zjzmzMjkHXBli2HpsJV/ax20ZxaA7SdFWF6LN8PzR5mtWZYIZ1noMgX/dmFer29IrqsDcFmubB96PFcuFohbsen56mAVnKBbbECuUic5/tUAD/1lNxTX5+MT8Vwbq3o7ExfFrq6xBZy6JBcV9+X3COeW1xqtTT3+uVCxfTkpNguhoamCucijD3mxhvhio3H4BNfFz8ziAjes0d8yJWKjPXAgeQlbxP3IDuO4ziO4z8mO06GC2THcRzHmd+cWYFsF81p5gZd2NXdnRZ76TNMrbCni/NsARDbNq9s1yozRFGeYD2/VmtO36ZjtRX5iqrxKZrFIi9QopYHjZhrZF2ZLn2bjU7nlo1yufj+NE1bqSSVNbS4h0bGjx1L96x5kDXiOzaWtvVebLYQjRrbCLw91tUlKeGOHk399vcne4WxtsR154KdwslJwp4nAJmi/+9Do3D7t6QPzQc3NCSWjUOH5N9OCBJBLlroOQMukB3HcRxnfuMRZOfsZe9eyZN8GvFFeo7jOI7juBRwzl5OszgG9yA7juM4jnMmBHK93pxLV1H7gl2Qp0U8bI5jRV/bUtT6879aImwJZfva9qH7rUXDlmCuVlO+5pER+SlfK1cMDU1dSKdll3Uc+VghVaPT9moz0PzIiuZNzu/Z3isUL+bLFwjq/K5cKXaHWi1ZMWz95o6O5lzT3d1pAV1fX7K72AIq+qzWCZ1nW8ilVpPHokXik9Dc0qtWpffr5kbOY5rfa7VXAFx4IYzSRZdaQGxmkXXrxGJRqcDixbJ/8eJZLdLTrhzHcRzHmb94BNlxMlwgO47jOM785vQLZE2dBlNz+ebV5Ioix9C8oC6PAkNK+6ap4mxbu6gv789eS9v29MijWk0LzvKKfHrNon4gRWrzVGYdHXJMz4lSJa4pQt0KGyG2uZt1TEW5kSsVWLpUnnVM4+Nyfm9vc5lpRRcOLlggEeShIWlXrTbnP7ZzrXOh6dw0yqxp5apVWLFC+tu0SeZz3Trin10Ek/Le2aixZcGCxpq/5culDx3fyIhca8MGyYO8YkWqguhp3hzHcRzHmQWnXyBbBWJFqrUjWNFrBRekzAj6E749X4WYFar2p34VktZyoW20FLPNkqF5jjs7Ux99fek6WjJZxwXNYkwtE7ovzzYxPi6iWEWpzoEK41YZN/JjRQI5z5ihNoyjR8XmoGMaGxPRmueTVrHc1SWPahX27Uv2Cm3X05PmqlxOY+npEQGtlpNly+Qex8fh6qth82bYtYvHKhexczfsuQduaPQbdj/e8ravvrqxsXSpiOBFi+T16Khk4ahW5V6XLZP57O6ePp90AS6QHcdxHGd+c/oF8uioFHOAZj+wjT7ayngaubX+YBXQg4Npv3pSQc6p1VIE0xasKJUkWqrpvzSlmVaJ03FoYZBKJVXT6+9v9jxDc1U/K+wBvvvd5jYqgJU8LZoe13vK21tyL3VRv5adO+V+liwR4f7IIynFnPan4lLnXqPnY2PiFb70UnjwQXm9ZImMb8kSEcYqsru6JMy7aJFEc7UYjBZCuewymJzk4EAHX7zzIt785jTEX+RhLr0Uvvc92PzYP6UDfX0cvuRaAJYOPgZheZqnI0dke2hIRPFrXgN33y3e4w0bUmGTNvEsFo7jOI7jnBmLhRWjdr9GItXGMDkpEdvh4WYhrBFSXdwFzQvLNGqskUMVfCoa+/pE+Nk8ySqiQdotWdIsYGs1EWE2WtvX1xwxzhf52UqBkASvtRzYY9q3PudRYIsdRysbikWr33V3y3Z/f5pz/SKiYljnX98nXYx47bVy3vHjEsHt6ZG+9EtMqQTnny/ZJ0ZGRKBqhPehh2BigmPX/SdqNXjLW+CWW+RQ3HIlbN3Kpz4ZueGnjsEnPgF33ZXGvmABS1/bWNi5e7f0q5YQHaOK+xe/WCwWtZpEqTWf9izwCLLjOI7jzG/OjMXCWiFOjMTYKHJLhe6D5oitij7bhx5T4af2CLsfUiRYhZ0tb63XqVaTzWJsTAShtXLY6KQdV263sEVE8vvWMWi0u6h9ETNFkHNUyOo99vY2i2y1IlSrzdF79V+vWiXCEyRarJFmjbRr9H94WB59fdLu2DGJKl9yCZTLHDoEv/mbRhxf8xIYA7ZsYd06pFx07qHu6mp+n22pby3CUi7LNX/wAxH0+l5NTqYsJG3gHmTHcRzHcfzHZMfJcIHsOI7jOPObs0cg53mKNYqp23n0VSO1trSztThYS0VusRgbS9kYbFlkq4y0r97e5kV+dlzQvEgvt1hMFwGG5oh3vtBupvPtGHKPbX4v2pdGqtXGYi0Wmr/Ytq3XU4npo0fFP374sNgnSiWJDtdqKeoM0qavT7aXL5f9a9ZIf6tW8cBBuPlmORwveyEMTRIeFK923PwUPLRfSkTv35/GvnRpslLs2yfXO3BA5k8XQmrJ7L4+iXTbRZiexcJxHMdxnFlw9ghkaM4gYf2/MNWWkO8rWlmVC+vctlGU6s0KdBWJ6lO2Pme9ph2Lbqtgnc5DrNcvusdWCq1oDlq1a7Vwr1ZrXqCYZ/pQ24rNFDI+LiJzaCilgNOFeWqvCEFeDw/Lvo6OtB9g40ZYupSbf1dexsuvgPsfIBDTGAcH5RrWFgPSl015pxYO+6VifDzZM44flzHs3i1WCy1M0yYukB3HcRxnfnP6BXKplBbPFUVIx8ZS9ohSKYk5JRe81oecR241zRc0Z8PQBXE2h68u5lLRODSURJhGIdeta85i0d8/1TdtRbZeO6+oV1Sxz95jkUc7xwrIdhfp6Xx2dqZFijpOXUyndHRI1omFC2Xh3aJF8NRTU6+jY9XUceoJXr48RXfrdbjmGv7Xn3Zwyy0Qr3oRTJbg8svhfmny+tcji+927pRnS3d3msPly5vvKa9a+PWvp3zVDz0k9zmLNG+excJxHMdxHJcCjpPhEWTHcRzHmd+c2UIhuQVBLQfqa7X2A9sGptoNNNJr+1UPrW2f51TOvc92v/XgaoYHm2Ehz7aR95OPudXrVu3bVWpFfuOiPvWe+vvlftQ2AmJRyO0i4+PpvkZHxRe8YkWK1mqGC43Ud3ZKxPmFL5Qo8J13nigS8pOv7+DrX2+M5Z574KqrCFvvBeANb4APfQjYtk0iyHn+58HB9D5qDutqFQ4eTH7n8XF4/HF48klJ0VepwN69KRtJm7gH2XEcx3GcaQVyCKEMvD/G+I45u+LERHEJZSvO9CdyWxbaij49Ds3V8fJKedZ+YcW4/uRu08ENDTVXievuTovySqWUWzdfoGf9xrbwCIhA0/HaseZCVO9PF6IpRQv38vnKt4te6z6dz0pFRKcV4j09zT7rycmU+m3XrlS6eWhI5kGLiIyOiqo8ejR5hScnZf+VV54oGPKy3fD5z0Pc9FzCIxHukUv/0R9JBrlDh2D5Jz+ZfMh2kV5PjxQf0TlftUr8xkNDsmhQ5x9g7Vp5rwYGRKxDc+nsNnCB7DiO4zjzm2kFcoxxIoRwRQghxBjjdG3bJoTpF7HZHLfqBVb/rB7XBWSQ+rIRZI0Kq89Yo4jWg2yLilQqSRBrPxo11iiylku21+zvn5pj2Ua9Nf9uLnyLcvrabAu5R7mImRYo5qjwHRtLuZzV660FP2zbjo6UGWL9ejnnySclMjw+nr4MLFki97dgQVrUd9llcOGFEiletYpPfX4xv/rJF/CrmycJ2x6mtxd++7fhHa/aJpkwJibgaw+kRX71uvi9lVpNMmeAVMg7eDDNnZ2zoSHYvl1E8vi4jHeWzGUEOYTwq8B/ASLwPeBNwGrgZmAJ4sD+hRjjWAihC/g0cAVwGPiZGOOuRj/vAn4JmADeGmP8covrXQ98ECgDH4sxvj87vh74FNDXaPPOGONtc3O3juPMFv/MOs7ZSzsWi+8AXwgh/D1wXHfGGD93ykalzCT8VODZiHIRGjWdLqtEkdC2oleFpO7X8eWZJ+x1dPx5kRMrim1p6pPNYnEyVgxrB9Ey3TYLhwpoEBvD4GBagKdt6vUkpK3tBDg6VGZxZyfHe1Zy991wg/nicMEFja5jnPq+6RcFa4sIobmSol5XM2mY6zZ9wQIZs7Zpk7lYpBdCWAu8FbgkxjgSQrgFeAPwY8CfxBhvDiH8BSJ8P9p4PhJjvDCE8AbgD4GfCSFc0jjvecAa4F9CCBtjjBPZ9crAR4AfBfYA94YQbo0xPmSa/TZwS4zxo41+bwM2PP27dRxntvhn1nHObtpRVkuQiNa/B3688Xj1qRzUFEsFNAtVK+bsOdP1Z8mjpUUC0/pzp+tbxVwuMs9mZko/14reXongLluWSnkvWybPvb1iwzj/fB6vXizukvPP5/774aMfBR544MTcPOc58Cv/bVwi8BpdL5cl6tsOY2PS1lonQkiiOPccz8JioRHkmR5tUgEWhBAqQDewD/kcfbZx/FPATzS2X9t4TeP4K0IIobH/5hjjaIzxMWAHcFXBta4CdsQYd8YYx5Ao9WuzNhHQsoKLgb1t34njOHONf2Yd5yxmxlhZjPFNT/ciIYQbgRsB1oNEI1th8/Wq9SFfWAciktRHm6sWGwEdHp7q7+3tlYcK8VotLQTTRXi6GEy9yUND4nuFJJzzcs2Tk+J91X0HDshzLkiLIrcdsGkAACAASURBVMVFUfCZFvPl/c3Uxn7J0HmpVMTva49Z+0m1Kovn+vqkXPTRoxL5rVRg5cr0HqjfevNmPvMZuZX3vOccAOKG8wi7Ip98Oxx6NSy9/R/gE4fSl5A1a6TvD30opeCzc6HFTUAsFBs2yPEFC5L1AuT1858vpal37hQxPzo66whymwJ4WQhhq3l9U4zxJn0RY3wyhPDHwG5gBPgKcB8wEGPUfxB7gLWN7bXAE41z6yGEo8DSxv67zHXsOZYT55t2L8ravAf4SgjhV4CFwCtb3VzTZ3b9+lbNHMc5eebsM+ufV8eZe2YUyCGEjchPwCtjjJtDCJcCr4kx/l67F2kIh5sAtoTQ2sucC8ai7bydHm91br4vr17XKutEvrivyDecV9XT6nSthO1M9zFX5PNh9+UZMqbLmJFXGqzVZOGb3vfEhOQ6jlG2u7qgs5PPfQ4efFC6iOufQ9j1OHfcAddufAoGhqSAh+ZILpelUl5nZ1oUmWMLvtj7GB9vzngRQno9Niav8zYzMAsP8qEY45bW/YRzkGjQecAA8PfAqwqa6uchtDjWav+US7bR7meBT8YY/3cI4cXAX4UQNscYp/yDbfrMbtkyN+sPHMexzNln1j+vjjP3tCMF/hJ4FzAOEGN8EPFEOs6zkjmyWLwSeCzGeDDGOA58DrgG6GtYLgDWkX4y3QOcC9A4vhjot/sLzrG00+6XgFsAYozfBqrAsrbuxnGcucY/s45zFtPOcqTuGOM9ITR92T1JE2uDmbIz2MVi+c/+djGcjdbmlehyy4C1YtjXajOw2TE0GwOkTBaaDk7Tx+kxzVRh8yZrxHJgoHnMeQRZx6n928j0TPM03Xy2igZbD7fei52nPI+zWixKJbFYLF0Kq1enrB6rVokHWaPxPT1w/fVs/SmIGy+GsTHCrscBuHbzEXhkZ5o79QWXShJFPnZMzh8eTpF4Ow5NzadVALXSny4c7OpKNoyurvS+zCJ6PN30nQS7gatDCN2IxeIVwFbgG8DrEL/hDcAXGu1vbbz+duP412OMMYRwK/CZEMIHkEV6F3EiSV4T9wIXhRDOA55EvsT+XMGYXgF8MoTwXOSP7cE5uVvHcWaLf2Yd5yymHYF8KIRwAY2ffkIIr0MWGznOs465KjUdY7w7hPBZJJVbHckGcxPwz8DNIYTfa+z7eOOUjyM/n+5AIsdvaPTz/UYGjIca/fxynsGi0a4eQngL8GUkHdQnGue+F9gaY7wV+B/AXzbSz0XgF+csfaPjOLPCP7OOc3bTjhT4ZeQP+6YQwpPAY8DPn/QVZ8qDrFFMXZhlC4ZAc8TYRoBtXxoVtSnbNLqokWKNguqiPE0fVq+n6nBapU0r+ym6redqv+WyRDp1EaJGMXXBWSsPcq7IbJ8nS1FU2Uap9d41rZrN7KH3pdHmFSvkXkZGUl7pvj5ZFLdsWVpo193NSC0QL3shjFVOXOY3fxMe3n8Oz73qKnj4YXjpS+HuuyWfcbUqVUKOHpXraKGR/P3UOTpHFv6xdq3Mq0aWdRHmwIAszluyBJ56atZzOJd5kGOM7wbene3eSUEWihhjDfjpFv28D3hfG9e7DUkDZff9T7P9EPCSGQfuOM5pwT+zjnP20k4Wi53AK0MIC4FSjPHY07pijMU/e1tVoou1VKTaxXBW/OYFP6w1QyvBaZYFW8Wur6+5yt7wcMqeoBkeVqyQZ5sFQ8etYq2vr9lGodkXVKz198tznv/YLhRU7D3m7YuYrYrTeVMhrPelFKXP031794oQvu46yXih4+zuFmG7eDE89BCUSix4yUtgcJCw84cAbNokiSSe+7U/g888BRdfDF/7mijR5zxH5rS/XwTtjh1pDq1Atjmo9++XIiTbtkkfGkwJIWXaGB2VstNLl55USrtnQqY+x3Ecx3FOHS0Fcgjh7S32AxBj/MBJX7WVJ1f3WaGb+1HttgrhPNWbRiDtqirbr/pv7Xk2Kpx7kjXarOQFQGyf1r88XYGQfB70XtrlZAuF2Puy0WQbUdZ50Sh+tQrLl8uXAa1SVy7DypU8/12vZtu2dIl4/gWwfTux1BCud9whbb95XDJX3Hdfyi6xY4eUotYvO1bE5/dnC4Dol52OjqlZRu67T+5tfFzS8rlAdhzHcRxnlkwXQV7UeL4YuBJZRARSKOSOk77idBYLKxxVtOWp2PJS0zYvshWcGtHVn+AtGp1WUdrTk65Zr8s5w8Oy31ab09zJ1gKhx60VRO9LS1NrRDmPEOfkpaanYzqBXNR/Ln51rCqA88ix3vvChZLveNkyicZ3dcnCuOXLIcYmcQycmJtjQ4FF+38Au3bJ/TzveRLhfeQRiSL390s0GGS+9+yRiPLhw8Vp+/T1kiXyet06EezLl8t+Xdyndpi+Pik1PT4+qy8ec2mxcBzHcRznmUlLgRxj/B2AEMJXgMvVWhFCeA+S0/XksAqkaDWU9SBrlDOPDhfl8s0r36lQzTNg5KWdre1B+8mjyjoWjVxacW6zJah4VgGt9gtFz8+zVLQScO1GP2eTxSKP0NoKgDZbREeHiOHFi2UeOjrEdqJfKNSn3SBe9kKYnCQ8+DBxx6MsUsuECuRSSURxtSp9VKsyd93d8liyRPb19CS7i6JV+3Rb+9BIsd7fxERz3ma9h1CUbrT96XQcx3EcZ37RziK99YAt8TbGqagFb0VrnnC2SLHk4jKPqLYSn/YYNItq21aFpP70X3RtjVSCRDDHx5MndjrsFwNre5iJVlHjVhHjVvtanWvHFWPzuMrlJE57euTR+HIQt1wJWx+Ayy6TtrVaqkKolexqtWRD0UivClrdPzGR/Me5rUZfq93FRu/tmLVqXh4pb5O5ymLhOI7jOM4zl3akwF8B94QQ/hFJM/OTwKdO+ooqvIrIxStMzehgMxpYy0IeDbbt7HEbHbaRYOtbhnQ8X0hnMyzYKHMI4rGdnEw5fvPS0Tk2E0dR23atAbMJedpr6nU1oqv77D2qmFW7ikafe3uhr0/EMcCWLYSt98r24KAI5M5OyXRRqci+kRH5EqGR9Y4Oeej7olF4zSdt50HnWfdrKfL83rS9jebr+9EmHkF2HMdxnPlNO1ks3hdC+L/AtY1db4oxfueUjKZoEVvuR7XHrQUjJz9WtFhPX9tFd3aRnp6jYtH6ke0zpAjrxESKYrZzv0X3OF3kvB2mO69en7q4sRUhJKGpFgcVx0uWpOg5JHEMzVHhEJKdQss+qz1jcjJlNdHXrX4RUB+3HUcIYqHQa+qjXG7+wjSLtKHuQXYcx3EcZ1qBHEIoAQ/GGDcjBQ/mhqI0ZjZ6bAVp3jYXULYKnY0s5wvmphPLNhOC5lTWRX42mmmrz4HYDIaG0j2obUDb557j3Hus186jtu2keZst9to2Em7n0dog7PuhAnfxYhGoXV1y7ytXQqlEuOfuE5f5wAdIthSNputcjo7K/AwNSbRdx6OqVCPPdi4gVfSDNEZdFKmV9NSLXCol0T0xkY7PAhfIjuM4jjO/mVYgxxgnQwjfDSGsjzHunpMrWotFLgDzVGy6WK/IJ2vtAbo/F9b6s72lVEoLw6zYVhuBCu2eHulveLg5X7KODVL2C6WjQ/rQCLK2z++3lQc5X5Q3nUBu5R1udZ7Oj85nnnfZLmq0XzBALBG9vXDppTzx8l/gda+De+6BeOkL4J57iI98VxbgDQ1J/1ooBSQLxqJFcP/9Uk66q0va6vui2UQWL5b9uY1F50jbazR7aCjlrFb0WKkkx2dbshuPIDuO4ziO054HeTXw/RDCPcBx3RljfM3Tv/o0WSx02+7P22kaOJhqldA2KoRttNSKab2eCkL12Gof1WpzlNemeVPfLqRoZbmcBHKeti0XbNYPbI8X2QyK5mm6ucn32bRu3d3NmSI0Qmszh4B8AVi6VETuJZfApk38/u83xPGWKwlbv0t88F4Rqfv3izDWqHtHh8zDwYOSuq27WwTy0JC00bnp6pLXx44l64V9r+z4LEWZS+zYrcCfpeL1RXqO4ziOM79pRwr8zpxeUUUrNC/EsqJOI8eaj7hI4FiRqhHG3EJRq0kf9XpKx6Y/zw8NpWijZqPo7k7ibXBQfLZ6fqmU8hovXCjP4+OpwlutJhaC7u60CG3Fiub7zEtOt1JiuWAuIo9C5xSJZvvFoacnRY67u2UxnXp3dU4XLBCBfOmlsHkz1GrceSfEy68ADVLX61IqWgXp0JB8Uejqkgp8+/fLe7Vqlczb4KDM4/HjsmhPOXCgudT0dJlDII1f39fRUfl1YmAgHdOFfLNQvB5BdhzHcRynnUV6/xpCWIkUCwG4J8Z44GldtcgCkOcqzj2o+U/u+SKzPFWaCin9CT4v6GEX5FnPsRYc0bzHuoBM+7MRZHvNIhGWe5Dz+25lqWjHe5z7t6c7nl/T5oe2c209u+WyqMWhIbE+HD4M4+NSGOSyScID32H1akQEazlvjSBryjZNB6dzXatJpHjhwvQlQscwMZGixzq+6e63s7M5v/H4+FT/so5lpmwiGS6QHcdxHGd+M6NADiG8HvhfwO1AAD4cQnhHjPGzp3hszYvFbMYIe0zJbQW28Ed+XLfziLXm2NWosb1mnhoNpJ3aKTT6Wi4nUd5KaeU//beyVDydRXq5wFTRr9FjSEKys7O5oEZHR4qq9/XB855H/I+v5sABtVZ8h5/8SfjgB4HbDqU5qdflvKNHxVM8NtacYq2jQ/zINvVad3fz4kq1s1jveJHtJvemayTcXlMj4e5BdhzHcRxnFrTz2/NvAVdq1DiEsBz4F+DUC2THOQO4QHYcx3Gc+U07ArmUWSoOAycvIawH2UY51fZgHyA/xRfZF2yJZI06ar/qM56chPXrm6O/k5PJNqB9VKviN9bIamcn7N4tntilS5O9Yv9+8c1qZHl4OOXh1Up65bL4bSH5YzUV3NGj8qwpyXS8Oi+HDsm2RqhtNoh8vuyitTy6biOuyjnnSFR3wQK4+GI5fuCAnLtkiSzCW7UKNmwg9i5m+3b41KfgD/4A4p0vIXzgA6zs6iLecy/x8/8o5/z1t1LWjhhle/9+ec+qVXnu75frb9yYIsTd3XDlleJDVj93fz+85CWwb5/MV56dQv3fagPRxZSqZkNIHuTJSXnvjh+XcVSrcOedtIsLZMdxHMeZ37QjkL8UQvgy8LeN1z8D3HbSVwxhqqCDqaqkszMJKiv4ct+vZj6wP8lrG7vPliAeHGxOc6bXt1kxhodlW0VvRwece64IMO1rfLx5XCCCWbdV1OViVYVybgmxghmmZm6w/di2eTuYOscqjmOUcVerIog7OmD5crj6ag6OLWbwEBx4CL72NfjIR8z53/gGXH+9bHd0iLgeGEjZKbRIip1zFbqVinxZGB6Wc3Rejh5Ni+v274cNG5I3Wb+s6L3Y+W21cHN0VL5kaH7q4WEZk10MOANeatpxHMdxnHYW6b0jhPBTwEsRD/JNMcZ/POUjc84ufuRHzvQITgvuQXYcx3Ecp61YWYzxc8Dn5uSK1mJhsQvVOjtlMdfEBOzZ0/xTukZ+OzubMyTY7AWaKkyzInR1ScT0+HHZpxFkXVimxSomJlL/w8PSLgSJmF5wgURhq1WJmIKcp7aI4WGJLOsiNBB7BjRHQyEthiuX05hDSKWbNUJdlMtYUWsHJJuCbZdHlVetknENDMhYL75Y2lQqcMklfPmuxezeDQ8/DHfdBd/+tpwWt1wJkxUYHeWS/V/noX/+Inz/+zLHu3bJmG1e44EBmedFiyTKrOn3envlPTl8OM39rl0ydk0V97znSXW+0dHmct36HoH0obYNrdQH8t4dOCD96Pu5f39zPuQ2cYHsOI7jOPObM/NjclFqNM1JrAJ5YkIEai5+bSo0W9I4T/mmHmS1aIyPTy34oSndVDRrez1Xc+ouXSrnHzokokvLJB88mITq6KiIta6u5D3WYyriVCiPjjbv13vK8yWrEFdsujJroci/cJTLUwWyFvxYt078wBs2NPm0b/8L+PKX4TvfSafEy6+AkqTBC1vvZfdu4E++LnNQrcr9q91B537/fpnDkZE0l6USbNsm1xoZkS89Olc2k4jOUQjNtohKRTzUdk70y40tNa0p+GwOZPWZzwIXyI7jOI4zvzn9AtmaPK0SscUxrOdYX1thnJdJ1mfbRy6ENe2bCqu8P42C2uOaB/nAATmmC8RibD4XRPSqB1eFsF5DF+Xp/droqE1VpqJ3wQJ5zoWvbasiHKZGkLu6pkatFyyQ/vr75XHZZSfm+b77A+9/f3PzeMnz4P6HCMQTlzv3gX9KUfhjx5LQjzEtmrOlonPBr9X7ajWZq4GB5PseHEye4XK5efz1eppznU+NvtsqhRr51wIzutBvFnmQ3WLhOI7jOE5bAjmEsABYH2N89GlfsdUiPct0+X9zQVy0eM8K5OHhJN7UcpG3t+dZgWx/2teMCB0dKbqZF6XQPjTCrEpLBaxGP4uq4Nmy1TbPcitshDiPFnd1NecfBlG4PT1Nlod9Awv44aPwve81N42bnguUJLPFQ7LvHe9ALBEDA9LHyEgqzKGE0Lyw0s5frSbCVudXhbJGsWu1ZhFsVWpnZ3PkeHQ0LeLMfzWw72G9LlaQmf69ZbhAdhzHcZz5zYxSIITw48ADwJcary8LIdx6qgfmOGcC/YFjpofjOI7jOM9e2vlT/x7gKqSSHjHGB0IIG57WVfNKcvm2RkSLqqDltgxrl2jVv0Yo84ix+pb1uPpX8/46OyViGqNETTWvcb2eFtzpgrdSSRaoQbJBaARTI8tqudDqe/ra3hdM9SDnY1KKIsiLFzfv6+2VR18fXHYZf//FBQwPi2X4z/88NYubnw/bHoFNmwiPPAzAi18Mr3gFcFc92SPyedJIus5z/r7V6+k+a7XkZdCHLfmdp+CrVJpT61krRz5v1pqjczMLResWC8dxHMdx2lEO9Rjj0aBC8OkSYxIv1kKg4lQzMOiiKw3Z5WLaip6istN2nxXatnx1kdjWbT3PXk8tGla82VLTmstYLRVqldDXumhPBW13t+xTL68K6CNH5FkXoNn7KsJaE1q1XbNGFrp1dPCDodV85jPw+ONpUV689AWNewc2byZsS76Lq6+WuhtUKvK+WEFfNCYrnvW42h70vdH5sqWj9QvH6OjUa+gXghibPek2u0kuuIv+7bSBC2THcRzHmd+0I5C3hRB+DiiHEC4C3gp866SvaMWOxXp4S6UUmVXhas8pEmC6v2jbUrS/1T67yA9kodvoaHFhDhWzNlKat9N7UFGtPuGOjub70EIaWjRDsV9S7JeLJUumjmf16ubXfX0S2d67l/WXSyIJm7HixD1m0dYXv1jqo3R2klLaKfn7kM+j9SNbIWuvab+4VCppHnMPsr2mPud92eu1+nWhDVwgO47jOM78ph2B/CvAbwGjSDW9LwO/e9JXDCEJRyteplMlRdHhmc6B1gKpyGLRqj891tEhtoU8gqqUy80iGZLYVCGsQk8jyTZCXa2m1xoR1mwW9hqKzWKhKdCUri6pjmfp6xNRXa2ycyds3Tr1FnQs4cHvnnj5vOfJ2r5qlebyz1aQwtTsIpYi24r2oXOiAj1GedjMHDaSPtOXILvg0grmNnGLheM4juM47VTSG0YE8m/NyRVLpSQQba5bFTLqYdW0bLnA0X16fLqf+HPBlqd3K/Ik2z4004IK+np9anYIRUsa12rJMpFHZa0I1P7qdelf7wmSaNTCIUVYj3Ge5q2nZ+o+7XNggAtf2nworjsXHtkDmzZNucyaNbB+PSzvPCoRZB27TdkGUy0sup3PrWatKJebs4oMDTW/X+rjboz5xJcSm4LPvrdFwnhsrHVZ6hZ4qWnHcRzHcWaUAiGELcBvAhts+xjjpSd1Ra1MB82RQS0KoqnB1EKgwjGPVNqor/qDrU3DCibtz4rmVpFIe1zbDw1JH319Iu51bJVKiqpq+jStIjcdVizqgjXrldXnvB8r9Kx4zkW7ViK09PbKtY4fp2PwMLC0+XijcIguzAN41avgmmtEILN3rzx0nm0qPJ0Le3+2jZ3LPBVbbmPRfNT2Xru7xTAN8uVjcLB5HiG9z/bfh1ssHMdxHMc5CdqJlf0N8A7ge8gSrqdHjEm0aPQRUjEPK5Sm+7neFqLI/a/5dlEkOm9bFFm2kciBARHH9XoSaGNjzeNVkWhz/xY9q7c4BImMjo6K0NaosM1yYbGL8Wz0PRfD3d1T504jt11dTZ7euP458q7W64Q9TwAiiN/5TrjqKrhiw2H5gnDXgynvcV6gA6YKZLttqx3ac/I5P3pUvjSVSikKr/OW/9pQVGzG9mW/hPgiPcdxHMdxZkE7AvlgjHHu8h53dkq5Y2gWyLYS3eSkbA8NFasV+xN+HsnU/UV2CRWJdr+KN1t1zfbV2SnR2sWLU6YFW5xExaZmTOjqSvaGyy+XZ71fTQ934EDzfXR3y7mXXCKvDx+W53/7t+b7tuPStgCXNgfzn+rv4C/+ovnUL34RduyAzs4f5cCBhq0CYJITwhjghS+Et70NrrsOVlcOwo6dMt69eyVSfeRImkudz1IpzZ++ZzYSX69L9T7dNzjYLHC1j66utBDR/tuo11NlvaNHmxfi6ReUoi9F4GneHMdxHMeZNe0oh3eHED4GfA1ZqAdAjPFzp2xUjnMGcYHsOI7jOPObdgTym4BNQAfJYhEBF8jOsw6PIDuO4ziO045AfkGM8flzelX9+dsuNNOUZsePi22hsaCMgQF5nRf9sAv3ch9stZo8wdaOoefbDBj6yBecVSpy3d5eqZLR2Sk2goGB5EGuVGDlStmOUWwifX3pvlqledO8xZWKtNWx5YVB9u9vfm2tAjZHcoGiu//+5tdT0ro15k/tFatWye4XvUhud3XfCBzIFiCuXSvV/TQvdT7Hlnx+1YKh9ovOTrFNlEpTfcK1WnMVwWo1ZRKJUeYsz54BYlXR/Vq0pbdXrrN9+9QxtsCzWDiO4zjO/KYdKXBXCOGSGONDc3LFkRF4qNHVNdek/eeeK6Kqpwd27YInnxQ/qy44swu7VJTpAjrNp6tCU8Vzd3cSpdbTatOqqZDL08upL3jpUhGGhw+LiXd4uPXiwY6OJCYBfvhDedasFzt3yrN6kjdt4khtAedUR0Qc79sn+x98UJ6/ldVjsWJw7960rT7cBis3buTtb7+gad+txkUeCYRdsuDvNa+BL/z1Mcari9ixQ275op59cOsdaT6/9z25h82b4ZFH0tzlFQrzFG86lyDzoucMD8t70NubfN1jY+JT7u4W8b9nTxrwkiWSZUP7KSpqotdds0Y806WSCOlNm+TL15130g4eQXYcx3Ecpx2B/FLghhDCY4gHOQDxaaV5U2FjC09o1PDwYYmcHjkyNXUXJBGmUWWbJsyKMRVReo08s4Ht00aObRqySkUE/cAAHDxYnNNXy0LrvXV1peinZmLQaK+OScVtXx/nrFgBew4kQQkpwpznQbbK7fzz0/bmzU3NDlbP5b/8FwqJ684l7BFxvGIF/PZvA3fdRUelwnO1mMgDO+U9GB6WuTx8OIn3wcEU8dW5KormK9pW51ZF8sBAeq3vzbFj8r6OjjYvuhsfF/EMaUFlq/Rtk5PpS8rkpHzZskVV2uCZKpBDCNcDHwTKwMdijO8vaPN64D2ITeq7McafO62DdBznBP6ZdZyzl3YE8vVzesVyOQkWm793/34RTJpzWLGp3KA5bVtnZ3F6MZtOzP7Un4tp294Kbj0XZDwHD4rwzYVWnjnDlsiGlHlBha61eYCIzQUL5KFZMiClNNu4sfl62h+kDBnAd/vPbWr2yU9KsDsnrlnblKhv6dLGLaxfLzuq1STurYVC083ZUtM2YpwXY1ERbDNMFPkWhoeTuNYiLPps51oj/jm5SNZfE2yxEPtlqQ3mMoIcQugDPgZsRv64vRl4FPg7JK/4LuD1McYjIYSA/KH8MWAY+MUY4/2Nfm4AfrvR7e/FGD9VcK0y8BHgR4E9wL0hhFvtLz+NUvHvAl7SuOaKublTx3Fmi39mHefspp1Keo+HEF4AXNvY9W8xxu9Od860dHTIz+A5GmU9flwihipYbVo1aBax6hXWQiFKLnJzgVyUBi73NWub4eHkRV6yRMSaepC1yIftw+YuXrtWnlWAamRYBVtXl+QwrlY5Xu9ixyOyu7tbIsIXXZd5ko1o/Mau805sv/GNzc2sO0GJq1ZLSrf9+07s27evkVFu47LUUAVyX19zwZJaTd4jGw3OBaq1WOTYyohWQOs812rpC4L1HEOywOTX0mOQvgzZ6LEWeTF5n9thDiPIHwS+FGN8XQihE+hGiu58Lcb4/hDCO4F3Ar8BvAq4qPF4EfBR4EUhhCXAu4EtiMi+r/FH9Eh2rauAHTHGnQAhhJuB1wLWGvVfgY/ouTHGA3N2p47jzBb/zDrOWUw7lfTehnxINWvFX4cQbooxfvjkrliBZQ1Blkf2hoclehpC8pIWiTAbvcx9sFCcE9kKKZh6Tr5PI821moyrt1fEry4A1Pa2BHKpJF5iLeihBTzySHIjH/JE9yKGh6F/v2jur3+9eYiXXfaCplvfbRbatbJQKHFJVimvXpoSxb3wwoaet5UIjxyRwQwNyUMtFSMjaTvPcWzRuSsqFW6jzbmYtfmnq9Xk2waZY80hrVFh7cPmpFY7jl5XS03PMoI8F4v0Qgi9wMuAXwSIMY4BYyGE1wIvbzT7FHA7IpBfC3w6xhgR339fCGF1o+1XY4z9jX6/ivyq87fZJdcCT5jXexChbdnY6OObyE+674kxfqnF+G8EbgRYr1/wHMeZS+bsM+ufV8eZe9qRAr8EvCjGeBwghPCHwLeBtgVy04d3pjLMjnOGaTOCvCyEYHOD3BRjvMm8Ph84CPyfxi8w9wFvA1bGGPcBxBj3mZ9Mi/5Yrp1mf04o2Bez1xUkQv1yYB3wbyGEzTHGgSknyr3cjWAIXAAAIABJREFUBLBly5a8H8dxnj5z9pn1z6vjzD3tCOQA2N/6Jyj+YLek6cO7fn08kcXBelp/8AOJvPb3y0/iixY1R//0Z3IbtczTg+UljG05an2tkc/cg2xLKNvjGuXU6m+2T1vJTc8rl9PCQM1a0bCUjPYuB6DSnb4kLJo8yljPYh54AG6/XfbdcUfz7SgaRJ2J2LsYJqeqvLB/HxdeCB/7WLrWq64dgkd3SIT4+HHJoKFln8fHJYo/NibvlS6Osz7uoghynjYvt7bY90/Ra2jk2FosxsdT1NiOQedcz7cRa30vh4ZOVSW9QzHGLdMcrwCXA78SY7w7hPBBxE7R8tIF++I0+3P2ANaMvg7YW9DmrhjjOPBYCOFR5I/vvdOMy3GcU4N/Zh3nLKYdKfB/gLtDCO8JIfwOcBfw8VM7LGfOGJgSHHRmwDp3Wj3aYA+wJ8Z4d+P1ZxHB/FTDOkHj+YBpX/THsp0/oiB/MC8KIZzX8Du/AchLxH8e+JHGtZchP9/ubOtuHMeZa/wz6zhnMTP+qY8xfgCpptffeLwpxvinp3pgzhyRp4pzZmQuBHKMcT/wRAjh4sauVyCLb24FbmjsuwH4QmP7VuCNQbgaONqwYnwZuC6EcE4I4Rzgusa+/Hp14C2NYw8Dt8QYvx9CeG8I4TWNZl8GDocQHgK+Abwjxnh4drPjOM5c4J9Zxzm7aWeR3gXA92OM94cQXg5cG0J4rMi36DjPdOa4UMivAH/TiA7tRL5oloBbQgi/BOwGfrrR9jYkxdsOJM3bmwBijP0hhN8l/aT6Xl2wlxNjvK3Rj933P812BN7eeDiOc4bxz6zjnL20Y878B2BLCOFCJKfrPwGfQf6Yzx5NnQbieVVibPb97to1NZXXlNHPMPxW59lUce20b4c8bZylYR7u0nLaY2IhjT3iRbYZ1WD2rojYd042ltZt3/teeOlLJcXy9u1I1orhYfEcj4w05w0eGkpZOvS+itLj2YwiT5dWBUBmap9f276exbjmKosFQIzxASQ9W84rCtpG4Jdb9PMJ4BNzMyrHcRzHcWaiHSkwGWOshxB+CvhgjPHDIYTvzMnVrRJUYWzTghXlLc73tRK7rXIe521mwi4Im5goPm7bxTi1qpwKziPNqWvDwABMTlIulejrWzR3kcvBwSlFTcKAXPuVr4Tyrh9S6b2ATZuAwZrMu1YM1C8vuihuYiIV4LCL6orS78HU9HlFuZGLjuXp2Sx5erj830O+3+a8huIiI9PwTK2k5ziO4zjO3NCOQB4PIfws8Ebgxxv7OqZpPz02gmyxeW6taJpOreRCqOj4dJFF+7rVdWxREqVViDEXbzbzhkXPt4UxgLvuKu62iNi72Fw3O9jXd0IQW9atg+W1J2D7dpZfs0wyRtz7qFQLmZyUEs9a/GR0VB7j49MLzJmEctG82i81eURaX+dfRmYTWc7bzlLxukB2HMdxnPlNO1LgTcCLgffFGB8LIZwH/PWpHZbjnBnUgzwHWSwcx3Ecx3mG0k6p6YdCCL8GbAwhbAYejTG+/9QPzXHODC6AHcdxHGd+004Wi5cjJXF3IUULzg0h3BBjvOPUDs1xTj9znMXCcRzHcZxnIO14kP83cF2M8VGAEMJG4G+BK07lwBznTDFXWSwcx3Ecx3lm0o4U6FBxDBBj3B5COPlFeo5zFuMRZMdxHMdx2hHIW0MIHwf+qvH6PwP3nfwVK7BsmWwfOJD2a4o3zXBRqUi6r3pdtouyJdiMF/V6cyqwvI1u1+vFmRPqdckqUZRSrtZIhdbd3dx/Ph7drynS+hv1HPSe1q0DYKJb8h+XR4ZOZIvYv7+D/funnzrNmyzXazwPDbVM6Zbz6U8DX/oS7GxUKl2xAh57TMY7Oiq5kA8dkmNjY7J/cFBUo86Bhld1zvS1TcVWNCfaXp+h+b21x4eHYdUqc+MxzaH+m5juPdCx6Os8bdwMuEB2HMdxnPlNOwL5vyMFDN6KeJDvAP78VA7KmQU9PWd6BM8qPILsOI7jOE47WSxGgQ80Ho7zrMcFsuM4juPMb9rJYvFq4HeB5zTaB6Qybu+0JzrOM5C5LDXtOI7jOM4zk3akwJ8CPwV8L8YYn/YVS6Xk5dVKcpC8pWNjxeWKbdlg3VZvaV5RLy83bH3IrSrvqVfV+mjtubVaGlte9thSr0v1OUhKS0tqN+579255uWxZD4t6Ojg21sVTT00dUuxemI3RbJswZxg8OvXkjI99DH5kw2Nw81YZz6pV4jfu75d7O3oUjh2TfSGI73doSI51dooHOK9yaJ/tfBSFYIvew9wfrPOr/me7Xz3Iudfcjsf2a9/7WeIRZMdxHMeZ37QjkJ8Ats2JOHacsxz3IDuO4ziO045A/nXgthDCvwKjujPG6J5k51mJC2THcRzHmd+0I5DfBwwBVaBzhraO84zGI8iO4ziO47QjkJfEGK+bsyuGkLzH1Wo2moqok/wBzc/W99pKzRT5jO05RXmV7TH7WsdWLsvDXlPvRcfa1SUPkDzDAFddBcA/fL4MwBe/mE5ftqyLP/uzZsvtiXzH09xCGDrW+iBTs7+tX4/kNLY+YZ3vEKTRxIQcK5eb771V/uii7Va08n3P5FdW8nbtXLPVuXPb3HEcx3GcZxntSIF/CSHMnUB2nLOcMDkx48NxHMdxnGcv7USQfxn49RDCGNBIz+Bp3pxnKTHOLjrtOI7jOM6zjnYKhSyaqY3jPKtwgew4juM485q2SiKEEF4DvKzx8vYY4xena+84z1g8guw4juM48552Kum9H7gS+JvGrreFEF4aY3znKR2Z45wpXCA7juM4zrymnQjyjwGXxRgnAUIInwK+A5ycQC6VUvaKq69O+/fvT8/1ulR7q9cl00K1mqrSabYI3d/ZKYLGVuWzVd66u+WYTU0wNpYyKOijWk3XqdVS/3190Nsrlef0WlqdrVqF1atTv+Pj0n7ZMgCOXfMfAHj1q+XwHXdMPzVx2XLZqDfuNasCN1PmCkteLvmBB2DDT7yAi37u56R63oteJAe0ct7ixXIvO3fKyYODsGCBzN/oqNx3b2/KMFGpND8rWm2vKPuIPmv2DH0PNEtGd7ek31iUuXoqFVizRrY13YdWPcxvVMek+/U91Up87eAC2XEcx3HmNW1ZLIA+oL+xvfgUjcVRBgZEaDunnxhPqjy14ziO4zjPHtoRyH8AfCeE8A0gIF7kd53SUc13XByfOdyD7DiO4zjznmkFcgghAHcCVyM+5AD8Roxx/0lfcWIChoZku9dkilu1So4BHD8uP4uPjYmtwf5krnR2NlsnrHVjbCzt05/zK5WpRT3UHqD9VCrJIqDte3vhnHOk7wUL0hhB2qiYjTG9XrUKgDe+UQ61ba2w/QJh4Mj0JzZoXO4EV189VeOdcBmsXw/r1sk4y2W5t+HhZEVYtUosFaWS7C+XZR4mJtL8gOyr1ZrtFGbsJ7a1vc61tUXovOujr29q8RjFFpep1dK/C3tta7XR5+7u2Vf+cIHsOI7jOPOaaQVyjDGGED4fY7wCuPU0jclxzhweQXYcx3GceU87obW7QghXnvKROM7Zgi5EnO7hOI7jOM6zlnY8yD8C/D8hhMeB44jNIsYYLz2pK46MwLZtsr1kSdo/MSGPjg55QMpqkP+UPjaWbBf2J3r9Ob+7O4mY3IqhmS00U4W1U+hjaEiee3qk7aJFyQ5Sq6Xr9PbC85/ffH/r18NllwFw/4emn4p44UWNPqupbyAcOljY/sMfTtsrV6bt3GIBkqjCcv758mBPHdauhUcflQMjI3D0aLr3hQuTxQJkbiYm5F6XLBHbxeSkvAelUsrsAUk8FmWxUIuFZh7Js1poZpFaTZ5HRtLgq9Xm9r29xVkpqtXm912vazOczIQv0nMcx3GceU87AvlVc3rFiYmk3qxAVqrVJJRVULXymdpIngotSN7XXFhrO21j29v+csFcrUoKt5EREY+jo6m/devSdk8PnHsuR5ZcAMDu3cVTENc/RzbqzWMJe55oavdrv9Z83lv+q7mu+riLGBqCS5sX+h0rLWZh/SisWsWR8R7OqddFZB46BMeOib+6WoWuLpn7Iw3/s3qN6/W0re+B7oOUqk2xHm9onlvFvo+lUvrio+JbaZXKLd+ngt36yHt63IPsOI7jOM6smFE5xBgfB/YA40A0D2cu2LXrTI/AsagH2S0WjuM4jjNvaaeS3q8A7waeAlQZRODkLBZOMxs2nOkRODkugB3HcRxnXtOOxeJtwMUxxsNzcsXOzoYZNkM9p0NDkuZNvaDDw1M9xPYn9Hw/yLO+LrJa1OvNqeCsNUDPGRtL1drGxlJVvVoteaSPH28WU4ODUC63tLzGNWsbYzVjbowr7PjBlPaHDjW/fmxv14nt9evTdvnAvuaGpRITPc31XBYNH5MOt23jnDVrkpfYWk1GR2X/+Hg6cWxMKu2Njsr96f3WalOtENaDrD5lfa/sce1Dz9f3KgSx15TLUtnPop5j+7427vUE9n3V7YGB1qnjWuEC2XEcx3HmNe0I5CeAo6d6II5zVuBp3hzHcRxn3tNSIIcQ3t7Y3AncHkL4Z+DEKrEY4wdO8dievezZ07y4zzl78CwWjuM4jjPvmW6R3qLGYzfwVaDT7Ft06of2LMbF8dnNM3SRXgjh+hDCoyGEHSGEd07T7nUhhBhC2HI6x+c4TjP+mXWcs5eWEeQY4++c8qtboRGCPFcqkos3RvGO2ny7eo6mF+vuTqnENP2YttGHpmlTn636k+31tUx1T0/y5uqxjg45tnp1KnOsKdD6+0/kLqZeP5G3+Vvfar7NE2ndrPdYbztL7WZ59aubX5+33pS5tnmACyKe+a5yT4/4cWs1iWBfeKEcWLasOT9xV5f4jTs6pK16jWOEffumzp3FpssrZfeq6de0fDikudP3dOFCuW5XV7NvuFKBww0LvP6bsKn8tA/1PRd5zmfDHArgEEIZ2Ao8GWN8dQjhPOBmYAlwP/ALMcaxEEIX8GngCuAw8DMxxl2NPt4F/BIwAbw1xvjlFtf5CPCjSNaZe0MIt8YYH8raLQLeCtw9ZzfpOM6s8c+s45zdzJjmLYTw1RBCn3l9Tghhyh9ox3lWMPdp3t4GPGxe/yHwJzHGi4AjiPCl8Xwkxngh8CeNdoQQLgHeADwPuB7488Yf1pyrgB0xxp0xxjFEhL+2oN3vAn8E1GZzE47jzDn+mXWcs5h2FuktjzEO6IsY45EQworZXCSEcCNwI8D6RYtSkQutTgcSoQWJjNbrkiFCK7wVVUPTyLBGFG0BES1soVXXbPW2UkkiqXpcH93d0lYjkrpv5UqxRJx/vlTUGxiAvXulzd69cMEFaUx9fTw6fC7XXdeYq40Xy0bdRK+ZPmoM8LKXyfN/uu5Y84Hb72m61gm6u5vblUp0DWbV+Pr6JEK+alXaBrnnwUEp2tLZKfc4Pi73efy4ZJTQaLVWIARpa6PBip0/W1HPZiLR4i8aadb3YvnyVCimbDRguSzHQCLJNvJclMXCVkfUfyOzYY4iyCGEdcB/BN4HvD2EEIB/D/xco8mngPcAH0X+ML6nsf+zwJ812r8WuDnGOAo8FkLYgfxh/XZ2ubXIglplD/CibDwvBM6NMX4xhJCVoZky9vSZXb++zTt2HGcWzNln1j+vjjP3tFNibCKEcOITF0J4DrMsFBJjvCnGuCXGuGV5LuYc52yi/QjyshDCVvO4saC3PwV+nZQ/fCkwEGNUz8ce5I8kmD+WjeNHG+2L/oiuZSqh6G5OHAyhhESm/8eMc0D2mdUvJ47jzCVz9pn1z6vjzD3tRJB/C7gzhPCvjdcvo/FN1XGelbTnWT4UY2y5YCaE8GrgQIzxvhDCy3V3QdM4w7Fp/4ga9gDnmtfrgL3m9SJgM5KRBmAVcGsI4TUxxq2t7sNxnFOGf2Yd5yxmRoEcY/xSCOFy4Grkj/WvxhgPzXBaa+p1OHBAtrWoA6TiFLVa+pm9Wp1azEIXdFUqYhPQn9LVZgHp53ztq6tLHrpAT3/61z7VXqGWD13o1dOT7ACLFknxio4OWUwG8OSTcMUVJ27h8QMLeP1rsvvdvh02bgQg7Pxh4ZRUq7BiBVx+ubz+9KcbB/LVfjpvALt3p+0lS5rb6fgt558Px47Bjh0yF89pLBxUu4haW4aG0oK6jg65966uZOno7EyL+myhELVa6P7OTpl/O8da4EN/RRgaSossQdqrtSK3bqjNQ4/rda3Fwlo68gIm7TJ3eZBfArwmhPBjQBXoRSLKfSGESiNKbP8g6h/LPSGECrAY6GfmP6LKvcBFjUWATyK+ZbVyEGM8CizT1yGE24Ff8z+0jnPG8M+s45zFtGOxIMZ4KMb4xRjjPz0tcTwfaYhj5xnEHCzSizG+K8a4Lsa4AfnD9/UY438GvgG8rtHsBuALje1bG69pHP96jDE29r8hhNDV+EN6EWDM6CeuVwfeAnwZWRR4S4zx+yGE94YQ8q9tjuOcYfwz6zhnN+1YLOaW0VHYtUu2bQTZUq1KNFcjkDYaqhFjjRDbRWG2v2pVoo5DQ7J/YiL9dK4RzN5eETu9vRIt1uPr10u0dvFi6WP3bolcrl6dyicD9PXxD7ctOHHJ97wHtm2DeNkLZUcj6Bm2P9p0e7fcIs8rVqRLA1w02Wj3pQfl+cEHm+el1mIRc6nge06+MO2RR+T83bslpd7KlTIPOmf6rOfVahLV1zR3GslfsULm8vDhlD7PLpYbG5Ob0r41ymwjyPah/YIsDNy3b+r463VZSJjfrxWqtsy0njs5Ke9fq3kr4tRX0vsN4OYQwu8B3wE+3tj/ceCvGovw+hFRTeMP5i3AQ0Ad+OUY48TUbiHGeBtwW7bvf7Zo+/KnfyuO4zwd/DPrOGcvp18gO87ZzhwL5Bjj7cDtje2dSBaKvE0N+OkW578PyYThOI7jOM5poC2B3Mi7utK2jzHubn3GNIyPJ99rXgxCo4mdnRIxHBsT72vuR9XzNJqrEUyNRPb1NXuVq9UU9S2V5HilIt5dLRIyOirRQ5AI6apVklqsVJIQ7/i4+HeHhyWqCtDRwX33yeY3vynR4xP3wtTI8Ve+Is/XXJOGD3J7C/f/EG79ouzYs0eeH3mk+b5H///2zj0+ruq6978ljaTxaCTLkmzLL1kYG4wxDgmOgVwSyA0k0KRAb5PmRdu03JA3TZPcJDRp87oNaUrzoE2bkDQP0tC8bkodCqUBQhwaDDHEgF8YY4xxbFmWZVmW5NFopH3/WGd5r9k6MxrJsjQw6/v5zGfOnLPP2fuc0bF/Z81vrzXkl2UM4XWUcwyvWSrFkWMi9lN3d/t0eENDfE3q6vg8BwbYr1xf7yPx2SyntBsY4H1aWrxvfGTEL9fW+gh/aytH8CXaL95luUaNjd6T3NwMHIpS042O8vj0+cRFjUOfsZyz/Oqg0/uVipWaNgzDMIyKZ1yBTETvA/AJAAfh01U5AGtOuvfwJ/Iwr622U0wEEUihBUPn3pX8ygAv19Tk5/VNJlkgjoywgKuq8pPWZkW2ilQKN/5lftdu3flAVf5lveYafl+1it/rE0PRO+Bq60C9R/ihQRSzrs6nEREKeDEv5xYS7ivnnM36iXTZrJ+cl0z6Y4otobo6//oJRCymR0b4mCMjvvKeniQneYj1GMLorP5+ZZuudijobfId6THJ5EGdf1kemib691OmpaQNwzAMw5geSokg/xmAM51zh6ekR+3xjPMgize1qcn7irXIkklSUmBCxLDsJ21EeEn54TCiqkUZkc+OINkbEgleJ0Uz6utZFM6axf5dIL+YBQB3wYXAKEAbuYbDmugR4uMf5/dFo1E6291RoZSREc7hdfAg99HTw+v7/fY8tHDTy7rstJwbBdnBpPiGRIyd8xHkkRGOGA8P8zopt93fz9saGnwUuKUFOH6cjyEPFgAfT655Os3Lvb35DyQ604gu6iGfdaQ5zE4RlrgOS03LrwlyHO1zngin3oNsGIZhGEaZU4pAfg5ctGBqEGEGjBVBgI/49fXl/1wf/rQuFgptnZBlPaksFEgSgZTotAisgYH8KnENDfki7PhxnpgGeFG4eLE/rQsuBDZuBC644MS69ev5femxyHvxwBNjzzWV8hMBe6OChfI+MJA/9kKTGsMIqbYkCCMjXjiLhUVEajbL5zdrFj8EZDI+VVx9fX66PC3a5SFiZISPqwWvRJflJVFh+d5CsT866vuNi36HfytyDLkmcjz94BOX8q0UTCAbhmEYRkVTikDeDU5U/h8ATphgnXNfmFSPOkI3nnCRSLDYJfQ+YcotnelCRJlkTpD2IrTk5/i6Ot5WU8PRU2mbyXifciLBYnlgwB9D+pw1C+6il0fHrwIuuOBE9BgAluJZXpCsHSKwxaKRTnOUVoSeCGI51zD7ghbIcQ8XmjAPslxLnQEkjLomEvz9iO2itpYFsHP8HlZBJOJrNzLio8NxFhnJiawzZsi11VYI6TdMpRZ6kOX70zmUpZ2ct2zTD0GlYgLZMAzDMCqaUpTD3uhVG71Ojupqn9eskLATYSXCFOBlgCOdgBdeQLxnVfuLAV8EZGiIBV1fH68bHvb2iaNRoLylhcfW1saTxzIZLrQhk+TEO9HeDnz96yfOhTZwscEbbojGMG8ev0uRDTmHcFLd4GC+X1faidUiPKeov6KE28UO4RwL26YmH0UeHfVjBPh6OOe/i6YmnnCXyfjIcDLJ10uKsMjDhjxkSF8AHz+dzhfe2ioTWmlCgSzt9TYp4KILyGirSTLJ453MJD0TyIZhGIZR0ZRSSe9T0zEQwygLLIuFYRiGYVQ8BQUyEX3JOfd+IvopOGtFHs45q/QDABs2AK94xUyPwphKLIJsGIZhGBVNsQjyd6P3m6a0x2yWJ6QB+Z5W7SsVn28qBbzoRTxpTewP6TSv7+vj1Gji021u9sKmo4OP97KX8Uy5bJYzRKRSvP61r+Ucx3v28LpduziJcV8f2wlGRoCvfpX7uv9+tmJs3gx0dgJtbXCXXgaAh+k+/3nuM5eDe9kNnMN4c+Q17ngk/9wlMqntHzrThlgqdKYHzckIN+kzleJXd3e8LUXGI9vEopJKcdnszk7v425sBJ56iscpqeHE/pDJeFtLba3P7Vxby20TiXyrTTrN30Ohc9Q2CT0JU7eXSY+5HFdCTKXyU/2ViglkwzAMw6hoCioH59wj0fsvprTHWbOAlSt5OS4rg4ic1lYWWPv2AXPmeI+spFaTdGv19SyIRAADXBK6pYUF8Lp13M+CBfzz+dGj7Gv+1a+AM89kwbZiBXDuuSyQBwdZCN5zD/5h6O04/fQ3YNsW4Ic/eQ0efji6Nj9+Kb+vBXDlv/DKvXu5v0WLWDQCLMIBFpWA98gWykYh5ZT1ZMFCTDQzg878kUyyf1qXeg6PpyfQzZ3L+xw/zpk7JFtFJsMPJlJeWlLEieCVPMvZLLBwoZ8Q2No6tu9Mho8t10jEs1yHcJJmKpVfphrwkw6rqvy4xKtcKuZBNgzDMIyKZ/pLTSeTvmLGYZVaWSLEUuHu3HP5s6jSOXP4vbeXheS8eXwciRw/+qg/1vbtPMFu2za2PyxezK+uLt7/vvt4WSadrVwJXHopi+X9+1lk/eQnuOobb8fOncCmTX4YbvU5QC7BEeW1a300fPdu3re52Rf0uOQSfhfBfOAAv0u+40yG20oBEpnUJ5HkUCCH0VIhFIBxHlrJJNHVxZ+bm72olcl40reO7tbWsujPZIBf/pL3kzRx/f0+VZxEc0X8J5N8rUVMd3TwMTo7WSBns/zwIyK8rw9YvZrfpcqekMn4ynq6QqK+DrJ+cNBHmIs9YBTDBLJhGIZhVDTTL5BfKIiAN15Y2CQ9wzAMw6h4JiSQiagKQNo51zf5HqMIccjx4776W2MjWykGBrw3V7yw8pP9wEB+ijCNRBwlGqqjo0NDHNnMZDiCXVUFHDrE2xYu5HY9PUBfH5YkDmBT3wJ8//u82q08i/MdRwKKNv8G7vG/441btnDkcXjYR5DF8iHHDfMbDw97y0gy6QuQ6Gul0cJNbyvFYyup86QISVsbfx4YYJuK5IKW65VK+RR4km5PLBSZDF8vub4SQQbyvwvJjSwvsUDoinr6vNJpnwaupcVvHxjwUXWxaYQ5qWVbWIxER5tLwSwWhmEYhlHxjGvOJKLbiKiRiOoBbAPwJBH9n1M/NMOYIbSoL/QyDMMwDOMFSymhtVXOuT4ieiuAOwF8BMAjAP52Uj065yOsOmI6Z4734tbX82tgwBelkAhyXR1HBevqeOJeczNHNVtbvXA5fNhHTJubgSVLeJJeNsvRT8mCIV7V+nqOqLa383579gCJBI6lF+CP/iga9tqXApt2ACtXgrZtHXteEv12zkeO5V0mGEohDF0oRMo419b666LLNWsKCTPpWyORaUGurUxak4weEmGtr/eltZNJX+2vqYnbNjX5rBv6JePVy7pSYjhmyYAB+Cp3evJgU9NYT3V1tfcky4S9sBCMTNjThWayWb99IpgANgzDMIyKphSBXENENQCuBvAPzrlhIhqTF7lkcjngyBFeFhEG8CSuujoWSS0tLBaHhli4ikADeLmhgQWTTOzr7mYhJFaGZNJPhFu1itdns9xHUxML4MFBPkZNDYvrvj5u097OfaxciRtv5F/23aWXAZkksHYtaNOvTwx53Tr4CnJNTWwTGRryokzGd+wYv4cWC6L8ayBVAsWmoCcxyrUTtMgOK/PF2QpEYLa0cFaKoSEvNo8e9SJbynoTsWgeHubtw8PA8uV+EmMiwf2KGNWIwBXxm0jwdyT7ZTJsp2hr8xksJCsFwN+Nroonwl6uXSoVXylQn3MqxfvI+/79KAmzWBiGYRhGxVNK/quvAdgDoB7ABiJaCmDyHmTDKHfMYmEYhmEYFU0ppaZvBnCzWvUsEb1y0j1mMpx+DfBpzQCOCudyHPEbGeGJc4cBaC7LAAAgAElEQVQO+cjxwAC/j4xwtDCV4sjsoUM88aypyUdmGxs5AtnRwX309wMHD3K0NpnkiPLevd6eMW+eL4DR0cHrL7oIN74NcBdcCOSSoAd+CQC4+WZOkwxEOmnjIX8OMoFN+CXvc8I6IWnepE0i4SfHSc5gwOdDltRmQqE0b3E2gjDCKgVWZILkM8/4iXPZLF8fibaKCEwmeT/Jd9zWxqnZwoivjmzLOl3Io6rKn1tVlZ9wl0xyBL6qitt2dvrroCPIyaSPgEtqOTlnOU9JJ5dO+1zMki4ujLAXw7JYGIZhGEbFM65AJqL5AD4LYKFz7goiWgXgQgD/PKketQDRYrKnxwuZdNp7efv62IMqfuXqahY/si6Xy98m7zU1/JKqbjU1fFztv62v57bz5vE2EeOJhBfvVVWg+39+YliLFwcJG2TMvb1elMn5iVVCznNoiN9le5htQdZPVKCF7eMKY4iAzOX4wUJnlRB0HmP5DPC1E/+xnG9tbb6HWISwFP7QeZHFdxxW5xsd5e9Zi1x9TfTYx7smuhBKeA0KFWYphEWIDcMwDKOiKcWD/G0A3wLwsejzTgA/wGQFsi7gID5hwIuopib2yFZXexELeK+v+GNnz/ZCq78/qvscWaNF6Oq0ZY2N3L6mhiPIbW1cQa+nh83EIuba23E4MR8tL58Pt+58IOGjj+9+N9twhYcfhhdiyaRP2yYiLaygJ15kXXJaPLm6nbwXE4WFUr7JNY4TeaOjvgTz4GC+KJdJbdJO3pNJP7FPRLBE8GVinbYe6Mlzsl2EtfQnk/rC85NJlDI2fa6Snk7GoSv96XMO99Mp6ErBPMiGYRiGUfGUohxanXM/JKIbAMA5lyOikfF2KohzXhCGYiaRYAHX1cU/6c+bx8JY5zIGWCDLBLjjx1kgS1lhgMVZXR2/duzwuZNTKRawc+ZwZoslS1jxJpMc3Y3sA80XzcfXv5HG20dHQRu40vYNN/Crof8AnsstONH1iXFVV/sMGyJwpWqdWAq0bQDwIk+EqbSTz8UEso6QliLoRHRKDuO46LFEieV4kuNYykaHtgldolrEqc5moSfpSRt5QJKxDA76fiQ/dRhB1mI6FLvykKTzI+vznYzYNYFsGIZhGBVNKQJ5gIhaADgAIKILAByddI/OFf/JW8SQWC6amryYEhoauF1npxe/s2blpxfLZDhSfPgwR24HB1l0NzWxz/jIEeA1rwGqqvCW97WgqQl45JHVvqT0mhfhy3/6GEYfBmjLExwu/sBGoL8fS9raAADvXbMG2LqLdxgc5H5GRrwQlghyaLnQ5wr4cYcPDqUK5FLQYlVEuRa0EkGXdtJHLseCOpPhbB868iwPAzpirUVwWCBE2ojtQqLnYr+QY8UJZEHOQa6ljnqLwJdxyXlO5FpZBNkwDMMwKp5SBPIHAKwHcDoR/TeAuQBeP+kexQMM+Hcg3+OaSnHUMpnkyWM1Nfl5fbu6WBDPmcPWib4+jt5K3uD+fhar/f08o66mhkV1QwMfc/NmYPduYNMmYGQEHR2vw403+sO7lWeBHt+O3rcB1HuE+1i1ikPGTz3FAhvwog7gMUhkNcx/HKZ309FQEXRh5DaOQqKxFAEoglRXytPiUa6/jhDLpDexqGiPcNy4gfxjZjL+s/6udURZ3iVSLTmOw3PVExFFDOsHCO1B1tYL/RBQKiaQDcMwDKOiKSWLxaNEdDGAMwEQgCedc8NT0rsWLrp4RE2NF1GZDAtfLZCrq3mdTPAS0Sdt+vp4vyNHgGXLeJ14mIeHOYNFby/nxk2n8893+QrIZZnd6IB9/X7jrFncn2SZyGT8ci7HY5J+AH8O4Xvc9tCKMN71KmV92Jf27YbCVhcBkTFoUToy4u0POnqrBbWgLSLF0qJpESvHEK+yjraHVg4Zu+5Xe5C10JdtpWJZLAzDMAyj4il19tI6AB1R+5cQEZxzt06qx+pqn2pMC0aZzCX+Yck+cewYR341YtNIJnkSHxELODne0aPcprs7f2KZCOvBQbZeRNtl/pdbdTaQqwLt2I777wfwwAM+1VlrK0eOtXDbs8dnppA0dPoneokgh95jOYY+/7iqc8U+h/uOhxbfemKhiMm4Yh86qj8y4lOshRkwdNQ2FMehXSR8ENCRXxHHUoSk0LkXs+iEUWk5h4lgEWTDMAzDqGhKSfP2XQCnA9gMQCbnOQCTF8iS0zb8iVxXYGtt5e27dvHP8xKZHRpisZZMciYKPcFLxOqxYxyF7unxOXtFcPf1Ac89x20yGaCnB3v2RGPYto2tFAAu7ngWeLSbxfH+/SyOFy7M90PLJDw5FxF+ch5BdDpPJEo7LcZEFOrMEoUYr4RyKPJ02rVczqe8E4GsxyOCOZHghw/J0CET6iTjhbZYhJaPRMJ7iuVaSJYK8RxL9hGAtzU25o9Bn4uIYu0r1n8zcgz5HgqlfBsP8yAbhmEYRsVTSgR5LYBVzrnJl5cOiRMgEu0Tq0KYyksEk4hg2abz62okX7KgBZeUUG5uBohw113sOwZWgrZt5TY9PSyyh4ZYVMvP/jpyKvmY9XiIfLo5aSeFQkKBLMs6klqoXUj4cKEplOJN3nVfOutE+C42C4m8632KvULCYxej0ATFQuMtFGU/GZFrAtkwDMMwKppSBPIWAG0ADkxJj9ksR2SB/Cio/qlfCkgsXswe4sZGL0TFntHYyOK1v5+jx/pn+SgKjNZWjh4PD7PIlWpyl14K9Pfjmde9D48/HvmOq2q9OAbwYObFuPCsRuDRRzlSfPQoj3tgwFsnpBAJwAJSJgmKwNq9m9/DPMdC6DkOJ/GNl+1jItvk+kqEVfoSUas9yPrhJJHg61Zby9defMip1FhPs5yTrJdcyXIu+jy1LUO+d0nzVso5ad+yfJbzkHOZjL0CMIFsGIZhGBVOSXmQAWwjoocBDMlK59yVk+pRfloH8qO6oagSkTZ3ro/ACuJVPXzYF5Coq/OCO5nk5XnzWBjLRDopO9zRAeRy+PGPgfvuA64CQNu24qabvM34nnuAC1/dzT7j/ftZjEupaEnftmpVvvdYSkaLyJOxyecwuqkFqbYRlBJBnqjw0wJW9yFjyGTis1jU1vI2sWTIuKRN3ES8MBuHbqf7FrEu16BY7mI9fhmvXl8oEj+RIiGATdIzDMMwDKMkgfzJKe1RcuQCYyPIIqQAnkCXSgGrV/vJYYCv7Nbby15i8bm2teULs3QaWLOGFXBfH0eAW1p4/StegWcaX4QP/y9uTngKt94K/GHrXRwdbm7G3+0/k7NddHWx3aKri4VyLuc91G1t+d5ogMcj5yfCvNDP/locFprQNhmK2RyEMFIbl91Crxsc5AcOsZjoVG+hR7jQ/vLQo9Pciae7ttZH2EPBHaZt05FhLfTD6zkZzINsGIZhGBVPKWnefkFESwGscM7dQ0QpANXj7VcQyYoAFM7iIAK3rY3FqRZ38vN8MsmV8I4fZ3FaX58fsayq4pzFYqtYtIjbRJkb7rgDcO1LgUQCH3n907jySgC3d7GoTSTwwT89AjyQZHHe18cp3pqbue/WVu6nrQ3YuZOX9dh1RT+gtKwL4TWaCKV4kKWdiMcwUqxFqF6v8yb39+dv00I4Li+xCGnZX4S0Frc6F7QI5jgvs/Ye6/71Q5ae3BimfZsIJpANwzAMo6IpJYvF2wFcB6AZnM1iEYCvAnjVpHsNf2oH8nPdyrbeXqC93YtiaZdK8bqBAV+9rqbGe4BralgkLVnCuZAHB9n+INHetjZcfz3wvnaAdj+N9RcBs594gO0UkrHhrLM46qwzY0h0uKaG13V15YupoaH8SXyFLBNxk9biLAqlCrVSRXbo2Q3318IztH8A/LChLRBx0W9ZH1o4tP86zGEMjC0OUuzci12ruAjyRAWvCWTDMAzDqGhKsVi8B5wH+SEAcM49RUTzTq7XAoUzAC+2cjmO2KZSPq0Y4FOBSXU3iYYuWOA9v7NmARdd5CO+AEd9ly3DwdolaItG/+UPPIvua4CWX/0U+OUWYMcO3jeTAe69ly0cvb38kujn6ChHrQFuF0680+JsPKGlfbyltC+VQuI4ro8wihy215+lyp2MOYzqas+vRJTls1Tu0+OQ71pSyul+i41F91tM6Eshk4lgFgvDMAzDqHhK+e15yDl3wiNARAlwHuTJQRSfGix8yUQ7/TN/IpGf5k38yPIze00Nv5qafO7j9nbOX7xyJbYcXYING/xQVq8GWrIHvMiWKn7JJGfNkPzL0peMQfdb7BziqtfFidE4O8F4toBi/Y63j+6zkF+52EuLar1PmAda2zDCvvR3Kq9C5x53rUJxHrYLI9QTQcR7sVcZQkSXE9GTRLSLiD4as/0DRLSNiB4nonsj65RhGDOE3bOGUb6UEl77BRH9BYBZRHQZgHcD+Omke6yu9mnSdLW01lYWNE1NHBluaeFo7vCwF6uAL1YRTaZDby+vS6V8erdVq4D2djyxowZr1s0/0YVbvgKrczm45QngppuA49XAfx7i/tJpYMUKYPZsPtbTT3OauTe9ie0VBw4A27dz9FgEuc61LDmStTBra+N3ydohUfAw0hkWBtET3goxUfGn07aJ11ceDPRYws8S3ZUHgu5uv10yX8hnHWUX5Fzku9bH1F7owUH+PvV11Ocq16aYwNfiVY4rDzEyYXI8nqdZLIioGsBXAFwGYB+AXxPReufcNtXsNwDWOucGiehdAD4P4I3TP1rDMOyeNYzyphSB/FEA1wJ4AsA7ANwJ4BuT7rGmBpgfidb6er++oYGjtek0Z0uYN49Fzs6d3mYB+LRjzc18nI4OnsiXTAKnncZtqqowjJoTOhwA3BlnAqMAEgnQrqfgHvoLYM4cFnyLF3OUOZXyRTEee4xFeFsbC6y5c1lI79vnK+T19nrhJ5PPJAUd4EW9FvdA4Vy/YQnqUvMgl2Ij0BaG2lo/Ju39DidN6vLfgBeZIoZ1eWgdXZZjZTL+usjEOdkHyM/aoXMrJ5OFs1jIMYrZc4DCBWRKYQoixES0BFxtsg38l3eLc+7LRNQM4Afg0u17APyBc+4IERGALwP4HQCDAN7mnHs0OtYfA/h4dOj/65z7TkyX6wDscs7tjvb5PjiD4Yn/bJ1zP1ftNwK45qRP1DCMyWL3rGGUMeOGIZ1zo865rzvn3uCce320PCGLBRFdR0SbiGjToWKi71Sza9fM9W08PxAP8slbLHIAPuicOwvABQDeQ0SrwA+c9zrnVgC4N/oMAFcAWBG9rgPwTwAQCepPADgf/B/qJ4hoTkx/iwA8pz7vi9YV4loAdxXamHfPHjo03rkahjFxpuyetfvVMKaegqFHIroKwGLn3Feizw8BmBtt/ohz7kelduKcuwXALQCwds4ch9mzo95V93V13p9cW+stF8eOcWQxztfa0MBRXUlBJtklqqowOOidHG7lWRzDO+MM0M4ncdNNALZ2coTTOY4EL1zIkUvJmCHZL3p7eTyNjcDy5flpxY4e9ctxXluJNIcT0DSTTfMWRntLba+j1KFXWo9JR49l4qJYRaTNeBMS9Tpt65Axh1aMdDo+w4VmvLRtsu/J+IWnIILsnDuAqPqkc+4YEW0H/+d3FYBLombfAXA/gI9E62+NHj43ElETES2I2v7MOdcDAET0MwCXA/jXoEuKG0bc2IjoGnAJ+YuLjN/fs2vXTl2ZecMwhCm7Z+1+NYypp9hv8x8G8Cb1uQ7ASwHUA/gWgJIFsmE8byg9i0UrEW1Sn2+J/pMaAxF1AHgxOBPM/Eg8wzl3QGWEKRRNKjXKtA/AEvV5MYD9MWO5FMDHAFzsnBsKtxuGMW3YPWsYZUwxgVzrnNP/MT/gnDsM4DAR1RfaaVxGRnwksrnZryfiqGVzMxf1kFRqTU0cEZTosC7CIRXturt5OQoZH03Ox7ZtwPr1UfQYAKqqQDu2A4gCu3V1fEyJqPb25k/Okklpzc3sQ+7v57RvnZ2+3YED+dXy5FgSZZaMG2E+5EKTwMJ0b6UWGCmUiSJunXiLdXRV/L/62FLdTrzEzvE17unhz/I9xEV8JVKsvcPynct1kqi19grHTe6TcYdReD3BUJDUcjoyrlPIlUppArnbObd2vEZElAbw/wC83znXx1bj+KYx61yR9SG/BrCCiE4D8Fvww+1bgrG8GMDXAFzunOsab+yGYZxS7J41jDKmmHLI8zk6596rPs7FZHHOT1LTOYT1hL3hYRY4qRRPpJOf+QEWTqmUt0TIMdJpjLTy5L9HNwB33smJKm7EDmDlyhPiGIg0bTrNr54e78UYHPTWgnnzuJ+mJhZM+/cDzzzD7cOsExrJyAB4ERmer85Soa0GoYAulk0htC+E24oJZDl2aEcIi7WILUImxYWlwfV7ePzx0smFlplEwhdZ0cVWZBxh4Y840Vuo/4lk/JjCLBZEVAMWx99zzv0kWn2QiBZE0eMFAOQ/vULRpH3wlgxZf//YYbscEb0XwN3gSpffdM5tJaJPA9jknFsP4G8BpAH8KBLqe51zV07JyRqGMSHsnjWM8qaYQH6IiN7unPu6XklE7wDw8KR7HB7mCnRAvqe1v58FWGcn8OSTvO6884ArrgAOHcoXSI2NLJB27PAT71pbUd17GACwYUMLbroJcMtOB6qWg3Zsx4tfDFx2GSe92LkTwNWv4756e7nv7ds5ItzUxP7nl72MRfG2bSxwu7q4r2yWvc8AcPbZYwWiCEqA9wfGCt5ColGEdCkCeaJp3rTXWFKpxVUy1IJdR27TaX/dJXOFzjYRRt/1cQWJ7kp2CxmLtJGHj9A7rMcB+KizTpGnr4n0IdHviTI1WSwIwD8D2O6c+4LatB7AHwP4XPT+72r9e6OZ7OcDOBqJ6LsBfFZNzHs1gBvi+nTO3QnOMqPX/ZVavvSkT8wwjCnD7lnDKF+KCeQ/B3A7Eb0FwKPRuvPAXuSrJ92jjtCFE8OyWS+uqqtZCNXXs6gW0SICtrubQ8Ei1lpbcTTRAgD45CcB174UQALnJJ/Cxo3AmjV8uOrcEJ7aWwfsi2wEfX0skI8c4c+ZDHD4sI/ydnbycm+vF5Zi/9CT2xIJjhBrq0UodOOEl568FloNprogha5epyfM6W2hQNZCNJMZa82ImxCnjyH76xzREgGWyLSkgtPXITz38cYq6+XvQc5T8iCXytRV0vsfAP4QwBNEtDla9xdgYfxDIroWwF4Ab4i23QlO8bYLnObtT3g4roeIPgP+ORYAPi0T9gzDMAzDODUUFMiR3+llRPQ/AZwdrf4P59x9J9djIr5QiAgZqVK3dCnnNe7u9vYKwIu7+fOBdet4ezoNrFyJzRtUP3v3guDwoQ/l216r+/uxoqMKuHtrvhiaN4990HV1XCxk3z7eoaaGfdONjT7q2trK+6TTXiCL2NNRTfH1hoI5jCBLRFQLzDgmIvTiLA26X7EthFYEnW9YR50L5RXWQlfnIQ5FsvYQh1aP8BhhP3HjCqPdsk3bVyb7oDE1WSweQLx/GABeFdPegcu6xx3rmwC+edKDMgzDMAyjJMadvRQJ4pMTxfkH9AJEJt4BXmw2NXHUeHSU2zY350eam5pwvHY2+vuBxmVnoa71EDBrFr5zK+Ftb4u6aF8K2svzmC6/nAvrzc4cBI4NAwMDfOyREf/z+/Cwf0lpaRGHs2fzuv5+nnQ3NMTRZIDXafEbV50OGDvBLBSlpQqy8SbmjbdNT1yTMev1hSwWsr62ls9ZR29DG0i4Lq6IiB5jnG84PNdwmxbLxdqE1oxSmerIvWEYhmEYzysmoR5Okupqnx9YvLwAZ4qYNYsr1jU0sEhOp9kCUVXlM1ZUVaGzE7jvPuD++4GFC+dizx7ghz/0h6K9z+LCC4EPfpD1djYLH/0dHeWdN27k4/f3+5LQDQ38Ghlhv/Hcub6SXm8vj6W314uugwfzq+OFk/d0podo7LGfZTkUrsVsBuMJv7gIsi4brSPdWkjqMeoMELKPCGBtidBoQSwPDeFYQmuKHCOV8vuEwltX3wsn7YXj1pX2womH4/E8LTVtGIZhGMbUMf0CuarKC+OWFr9eskqkUr6sc2srF+NYtOiEreG3fQ0YGADuuAO4/fb8Q7sOLjVNe57BjTcCF7/kGEZSDajujoTscBRB3r2bj0vEYmhw0GfRkAleXV08lmPHWGXX1vqMFsLx42MniYXiDyjsLdb2DI1EtotFMsNsE+OhI8HA2PRnIs51pFdbK+S6lDKeMIuG/qwFc+i71tdNp3kLM2joCHbcJEl9TD0JsBSmzoNsGIZhGMbzlAmmQihz9uyZ6REYLwT0RMFCL8MwDMMwXrBMfwTZOY7kjsfICAuR2bOB5mYcHeWoc08PZ1574IHgsB2nAeg48XnhQgDd3ahuhc+Ccfw4H0CyZdTU+LFkMr5PIZNhz/GRI7zv6ChbMqTYQ3V1/iDCnLulCimdCWIyxO1Xiq0gjN4WGrvYFoqNbyL+6DATRtx4CvWlJ+mFExvD6PFkJulZBNkwDMMwKp7pF8iJhK+g51RBMO3hFetFYyOQyWAoORt7dvCqzZuBr32Nk1cI2loBANdfD6xY7oAfbwLa2/1kukyG07aJ3UIsA8eOcZ/V1T6VWSbDfuPRUZ+NIpHw/mnAV9ED8ifkhdaJ0JscZrOQZW1/GI+JWiwE7RHW/YV9hlkjslk/QU/Wh4wnoMMHCF2sRMYhyzrDSThxME6Ay7HEUiHXWPIhTwQTyIZhGIZR0Uy/QK6tZdEK5Htaq6q8JxgAXv5yHMVszP6vH6Fu+XI89xxnvBgdBc49F3jwwagQCAAEeubDHwYr6V/9youj1lZfFGTHDk7jNm8eb2tqYtE+Oso5kKuqWEinUuxZHhhgH/Ty5fmT9B5/3Ct1EWJ6Apk8CEhBFBF2oZc3zPIQepTjKLYtTkBKhFUeFtJp70PWglLa6rRutbX8UNHd7R8WJOezHDfMSiFFOmRbOu2r5MmxdUq2XM6n0pOJhMLoqJ8MKWMNM1/oKLcW9eG5lYIJZMMwDMOoaKZfIBP59G46zVtdHYuv5magowOP7JqN3buBNyxbhgc31eBTn+JmmzbFHLOqCrT76RMf580DsDsSpe3tXkQ1N7NAFGtEdTXbKpJJFnCZDC9LqeumJv5MxAI7lfIRZiA/Whzm9dXrJDqrJ54JpVoTwrbB+Y/5XMh2IWPWOZt1Bo1wv3Cino7kxqWtk2OE1olCeYzj0r+FFoswE0ZcNFrOTRehmegEPcCyWBiGYRiGMQMC2TDKGfMgG4ZhGEbFM/0CeXgYOHCAlxcs8Oubmzla29QEzJ+PxY2cje0Hu87DRz86NkGFlJIGkBc9BoBHHwXOb2sDLrmEj9fXx2nbhoc5r/E553DDuXM5ytjUxJHlvj7+Kb+2Fli8mNuKWDp6FNi/n9tIVHJgwHcal8ZMPMoSORarhbYW6P3FeiERTLEVxKEjo2GUNE7g6TRuEmmV/sT2oO0hYhmRaHAux1F2KRmto8ph3zo6LGPTHnM5fx2B13mqc7mx/u4wh3Rc3xLtLuatLgUTyIZhGIZR0Uy/QB4ZYbEJ+GwQANsXsln2ofb3I9XG+vBP/iR/Lh8AuMVLgL37gPZ20N5nT6y/+mp+TyTgxff+/fzS9obmZha/DQ3+Z/iuLl8hr7aWt8s4R0a8B3doyOdMrqnJL6ShC2gAXuSFwlAILQayPXyPI/QMhxQSriKOQxuECMu4AiLhpL7QbyzH19kwxLZRqOhJiPYsx2Wx0A8BoVjWy3HjnCgmkA3DMAyjopl+gTw66iOvTU1+/cAAK+H+fqCrCw3J7ejpOWusOG5fCqAKWLz4hDh+17t4Tt5nPsNtGhrAQra1lSfqDQ6y+JUo7cKFwJIlvJzNct89PdxGqrnNm8cT9mSsmYxPESfr2tvj/bMizMIIsI6chvvoSWulikp9TUMKiWY9gU33o8WtTpcmkWKJMIfbhbiS03qynwjycFKfFu4SYQ8FcjiBUU8gDMcRN+FxIkLZLBaGYRiGUfHMTJo3SeOmBXIq5avp1dcDixfjttv8ZhbGHtr33Inll74UOP98YM4c/rwofRTo7GYRKCJVRFMmw5Hdvj4v6mSiXnW1F1SZTL5YSiZ5XImEz+QQhxZjujxyXJuw4l4YMQ6Fmv48mVLTWoyH1gOJIGtBqcepI8th5FbvE6awCyO7xcRn2H84djneeMcRAT6ZPMiTaW8YhmEYxgsKm6RnGBrLYmEYhmEYFc/0C+Tqah851mneJKVaUxOwZAm272s44WRwi5f4XMdBdHHtWrYUS4Y4AEB/lifk9fdz9HNoKP+nfIDXVVd7MTQ87M3Oo6McVQ6jrKHfY2RkbFEQzXhe2DBaOhErwETSw0kbPZ64SGwxW8J4UdW4tG/6WKUU+gj9zHHH1m3HO+8wUl8qFkE2DMMwjIpm+gVyVZWf5KYFaCrFuZCbmjDUthTf/RSwfXv8IfTEPIB17rx5wKzcMV6hJ8mJSEomvbUgm+XS0TKxrrqaxyTlpBMJrq6XTudnRJAsDjJ+wIt8yfQAFC70UcgvK9tCQVfMYjGesI6zKWgKFdwIx60n6mkLSJzNIRTh+nroBwl9flpA6/cwS0ehnMuhlSMU2hPFPMiGYRiGUfHMTBYLyRaxcKFfX1XF0eNFi9DZyVo1Di2O3/te4C1vAS5cO4yh0Rpgf1TVTgROfb1P36YFVDIJzJrFwlgKSvT3s19Zyk23tnLb4WEec18ft5GJfQCbnoeHeVlEnBZXYdo2XcRCzlnQWSUKZb3QTDTyLA8jWuyG0eMwi4UcW0/gk+WwWIc+d709jCCHDw36oUBP/gsLjWhRXcirrMcUJ7RLxQSyYRiGYVQ0k8yDNc3s2zfTIzAqBYkgj/cyDMMwDOMFy/RHkHM5Tp8GcK5hYfVqIJ3GE0c4e8WXvsSrXcdpoD0OCDQyEfD3NzuuCnL7btStXu2Pe845wM6dwNlnAw8+yOHo3tWUn2QAABNDSURBVF7O/1ZVFdWihs9TnMkABw+yrSKZ5IhxKsXbxYvc28u5krPZ/CIWEuUNU7UBPm2ZRDplm7aWSOQ0lxub/zgudVoccVaOQrYLSdfW35/vOQ7LT4fR4NpazlHd2xvvX9Z2ikKFQPT5ylgkyptIcJ5pnRJOn1+hCHLomxYLjRxXznci2CQ9wzAMw6hopl8g19UBK1bw8vnn+/Vr1uDZzjrc9lXgc5/jVW7Z6SgU5L74YmDLVsLpq87DLICF7ezZvPG551gw7dgBdHRw0ZDDh1k4DQ2xwH3mGbZTVFWxj7itjS0ZySS/BgdZhTc2cptFi1hs9fd7gdvaCnR28rKI3GTSby9krYh7F3EHeHErFo3xKJYWTRCRKX2l0/nH10JSV/qTdVrMxk3mk6Ig2g4h515V5T3g4XHlOHLt4qK04bURkR0eQ+danmyaN/MgG4ZhGEbFM/0CuaUFuOYaAMDTqXNOrP7KDaxn77qLP7szzgRGq0C7nsrbfe1a9h03N/Mkvu3bgVe/+jzM7nnGN3rmGRa3v/gFsG4dr2tv98VAdu8GHnuMBa4I2rY2Hlt3N6/bsgVYs4bF8/Awb1sa5WKW/Ma7dvlJeiIqpdAIAJx2Gr+LENVFR4Cxk+HkuJK7WT4LhSbphdk24ry3qRT3KwJWKhfq9mGWC6GxkR8qenp8W72v9vvqPMjary2e7myW+w6j7YOD3EZErj4n3VaEuBxDR5alPxHzcQVNSsEEsmEYhmFUNNMukI9mZ+Gne1gYRzoZQL5rwZ1xJlskzjgDAPD+9/OcOgD47A3HgH37cGzxWRgcZHvybBwFtm7liDDASntwEHj6abZuSAGQXI7Xb97Mkd9cjkVVWxtHVJNJFlx9fd5SsXIlCzcRjwMDbLuQQR9TmTNGR/MjyEuW8LuM68ABfteRWyI/0U/S33VHkw3FoiEUKhQSCunaWmDZsvx1qZQvuT1nDo8hk/HjTya92JfzE2Qyo1wvwAvgcHzZrBepg4NePGcy+RMAtSiXNqlU4ewYwuBg/nF1dgzZV8qHy3ml0/xQVAoWQTYMwzCMimfaBfKhQ8Att/CyFsWCW76Ccx6fcQZo55P46EeBd75T2ZV//RgwMICGTAYNuRzmDw0BP93D23Re5WefZTvH6CgLvF27vH9YBFAm48tQ79nDQkpENMAR02SSPctHjwLbtrFAloF3d+f7hUdH+ViSkFmEsaSFS6f5nYjf6+q8Z3Z01AtlEZGS7UMoVbiNjrIY1jQ0cF8LF/LFfOwxbzEZHPT2i9paHocIWnlgyGb5WmhftfZNS78iVMP1cjxZlocROY62gMj3IqRSvnqhXPsw24X2Ucu2ZJLtNeEDRCnXzzAMwzCMimX6I8hHgTvuGLv+RCnpURZNtPNJAGynWFq9D9j8W97+29+ySOvtZSGVy7G4TSR8BLari4VsNgvs3cvrenpY+Mj6bNbvPziYH93s7+ftvb28LBMLjxzhyXwSYR0czPcZi0gWESkRVvFGS1RTBHMqxSI1mWRRKgJZorrF8iAXWx/aFwCOjNbUsGBcupQnN2qRGwpYbaEQa4b2Bsu56sitful10l6OJ5FoibSHE+50TmnA2zLC66zPXUeipU1tLT8YmEA2DMMwDGMCWKlpw9BYqWnDMAzDqHjKRyCr6KEUA7niCuCcxHZg89PebnDoEEdoUymOEGYyHBXWZaA7O7l9NssRX4DbOec9rBI5lQiyzsIgEeZcjo8j0eowGqojnWKT0JFoiVxK9FOM1DJWsRjU1HBbsWTo89Bo4aYnsYl1Q5BJeJp02q/r72cbyOCgt5XIWHWkV5+jRJZ15FZHiItFj8OCJPLKZLwlQvrQ6dnizjW8Dnq8chw95uHhsfsXwzzIhmEYhlHxlI9ANoxywQSyYRiGYVQ0MyqQ3cJF/kOkSWjfcydWveEN4GwW+/d7329Xl/fIiqd3ZIQjhTpaPDzMbQYGODIrn48dy48yCnqdRI+zWfYed3Vx/uTh4bFR3jgk+iptZVKeRGnFmyyRY/EeS4RXPMhhFFhnjdDbwgipZG7QyEQ3KZYi7WSinh57XCaJ0PurKSYoC/mo5T0sea3HEbesI9PhevneShlXMUwgG4ZhGEZFM+0C+bzqzdg0u4U/5KLuu7qAhQtB+397ol1jI/CKVwD49qZ8wTY0xDP9jh71qS1GR7langjRw4dZdPb08Ety5mYy3E5+iheRncmwoNQ/9cskskOHWFRWV/MkwPp6X7FPp+EoNCEN8JYKmaw3Z07+RRHrxcqV/L5mDb/LpENBC2Q9CU8EtmbRovzPfX3cbvduvh5EPD7JpCHnoB8cZFmuv84soc97IoTXSdsxJPuFpHwTdGYKPbawUAgwthrhgQNjr2MxzGJhGIZhGBVPeVgspPSzYZQDJpANwzAMo6IpD4FsGOWCZbEwDMMwjIrHBLJhhFgE2TAMwzAqmukXyOJ9BfIiddR18MTy614HXHcdcPrgE1xCuqXF+0g7O9kDXF/P3uD6ep5s1tXlJ7cdOcJ96IIi6bSvgtfTk+8/rq3lVyLhyxiL/1baLlvG3uGGBn8uBw96X7Cu6CbrJDWdeHxl4pwIsN5ePr5U3hNPs7RbvTr/2mnfsfYDS2lqQVeeEzIZPv6OHTzWFSv4uo2OsuF7ZIT93f393turvcFyjnIu4hUWZFkm+YXV9PQxa2vHFhnRkwCTSX9NhH37/LIekxazcgxJnyffQ1gSuxjmQTYMwzCMiqdq/CaGUWGEVQHjXmUIEV1ORE8S0S4i+mjM9joi+kG0/SEi6pj+URqGIdg9axjliwlkw9BIBPl5JpCJqBrAVwBcAWAVgDcT0aqg2bUAjjjnlgP4IoC/md5RGoYh2D1rGOWNCWTDCNE5sQu9yo91AHY553Y757IAvg/gqqDNVQC+Ey3/GMCriCQ3omEY04zds4ZRxkyLB5mIrgNwXfRxiLq7t8S0OrF0xx38mhK0dzVE+3h9WedWAGzq7e/nIiUA8PDDE+97166J71McP7aTZdOmye2nfcX5FB+bzhldyBP8+OOTG1NILuf72LxZxra0lF0fAe4mbj8eU/M9TB2LADynPu8DcH6hNs65HBEdBdCCmHMZc88SxdyzZcHU3RNTTzmPDSjv8ZXz2M6couNM2T1r9+uUUc7js7FNnknds9MikJ1ztwC4BQCIaJNzbu109DsZynl8NrbJEY2to5S2zrnLT/FwThVxUSU3iTa88nlyz9rYJk85j6/cxzZVh4pZN6l71u7XqaGcx2djmzyTvWfNYmEYLwz2AViiPi8GsL9QGyJKAJgNoGdaRmcYRojds4ZRxphANowXBr8GsIKITiOiWgBvArA+aLMewB9Hy68HcJ9zLjaCbBjGKcfuWcMoY2aiUMgtM9DnRCjn8dnYJkc5j21KiPyJ7wVwN4BqAN90zm0lok8D2OScWw/gnwF8l4h2gaNQbyrx8OV8/Wxsk6ecx/eCH9spvGdf8NfuFFLO47OxTZ5JjY/sYdQwDMMwDMMwPGaxMAzDMAzDMAyFCWTDMAzDMAzDUJhANgyj7EveljC+DxDRNiJ6nIjuJaKS8l5Px9hUu9cTkSOiaUuHVMrYiOgPomu3lYhum66xlTI+Imonop8T0W+i7/Z3pnFs3ySirkI5hYm5ORr740T0kukaW9R/2d6zdr+e2vHN1D1bcferc85e9rJXBb/AE4SeBrAMQC2AxwCsCtq8G8BXo+U3AfhBmY3vlQBS0fK7pmt8pYwtatcAYAOAjQDWlsvYAKwA8BsAc6LP88rse70FwLui5VUA9kzj+F4B4CUAthTY/jsA7gLnKr4AwENldu1m5J61+/WUX7sZuWcr8X61CLJhGOVe8nbc8Tnnfu6ck9KYG8E5ZctibBGfAfB5AAXKSM7Y2N4O4CvOuSMA4JzrKrPxOQCN0fJsjM0TfMpwzm1A8ZzDVwG41TEbATQR0YLpGV1Z37N2v06ecr5nK+5+NYFsGEZcydtFhdo453IApOTtdFDK+DTXgiMF08G4YyOiFwNY4py7Y5rGJJRy3c4AcAYR/TcRbSSi6awkWcr4PgngGiLaB+BOAO+bnqGVxET/Lqe775m6Z+1+nTzlfM9W3P1algKZiOYT0W1EtJuIHiGiB4no92ZgHGuJ6OYpOM4niehDUzGmSsCu17QzpWWqTwEl901E1wBYC+BvT+mIVJcx606MjYiqAHwRwAenaTyaUq5bAvyT7SUA3gzgG0TUdIrHJZQyvjcD+LZzbjH4J9LvRte0HCj3e2Kmxmf36+Qp53u24u7Xchn4CaKfgG4HsME5t8w5dx7YPzVdP8GcwDm3yTl3/XT0FRnIy+77ONUQUfUUHGMmCt68kCj3kreljA9EdCmAjwG40jk3VCZjawCwGsD9RLQH7H1bP00Tf0r9Xv/dOTfsnHsGwJPg/3yng1LGdy2AHwKAc+5BAEkArdMyuvEp6e9yBvueqXvW7tdTNz5pMxP3bOXdr9NloJ6A0fpVAH5RZHs1+Gnz1wAeB/COaP0lAO4He612APgefCGU8wD8AsAj4KpFC6L11wPYFh3n+zF9XQLgjmj5kwC+GfWxG8D1BcZ3OYBHwQb2e4vtC6ADwHYA/wg23S8FP4E9AWALgL9Rx+0H8DfROdwD9gPJ8a6M2iQBfCva/zcAXhmtPxvAwwA2R+e6Ilr/gaifLQDer8a0A8A3ovXfA3ApgP8G8BSAdVG7+uicfh31dVXMtaDou9oSjemN6rr+HMBtALZF6z4GvtHvAfCvAD4UrT8dwH9G5/1LACuj9d8G8IXoOH8303+3z+cXOCKxG8Bp8JMvzg7avAf5E35+WGbjezF4AsmKcrt2Qfv7MX2T9Eq5bpcD+E603Ar+CbKljMZ3F4C3Rctngf9Do2n8fjtQeNLPa5E/6efhcvq7m6l71u7XU37tZuSercT7ddr+MCdwgtcD+GKR7dcB+Hi0XAdgU/SFXQL2WC0GR8YfBHARgBoAvwIwN9rnjeCSnoi+vLpouSmmr0uQL5B/FfXZCuAwgJqg/dzoj/W06HNzsX2jL3MUwAVRu4UA9kbHSQC4D8DV0TYH4Ipo+d8A/Fd0jBcB2Byt/yCAb0XLK6NjJQH8PYC3RutrAcwCPzQ8ARa6aQBbwf9odQDIATgnuo6PgIUwgU3ut0fH+SyAa+TaAdgJoD64Hr8P4Gfgh5r50XgWRNd1QF0nGUsKbPDfBS+Q74UX9OcDuC9a/jaAOwBUz/Tf7AvhBf45bCf4P62PRes+jfyHrx9F383DAJaV2fjuAXAQ/BC4GcD6chlb0PZ+TO9/uONdNwI/aG6L7sE3ldn3ugr8cP5Y9L2+ehrH9q8ADgAYBkefrgXwTgDvVNfuK9HYn5jO77XEazdj96zdr6f02s3YPVtp92vZlZomouvBwunPo89fAQvdrHPupUT0YwBrAMgM2NkA3gEgC/7CLov2+yfwF7UZLE53R+2rARxwzr2aiP4THJm9HSz8+oOxXAIWaq8jok8CGHbO/XW0bTuAy5xz+1T73wX/sb41OE7svmAR/HPn3GnR+qsA/L5z7o+iz9eCn9A+QERDAJLOOUdEnwYw5Jz768iW0eOcayKifwPw9865+6L9fwmOIqwGR2hvBfAT59xTRPRn4KfOv4rafgbAIQDrAfzMObciWn8rgLudc98jomXR/ucS0SbwP8C56DSbAbzGObddnfcXATzhnPtm9Pm74H+w+wB8wjn3ymj9+8EPEzKWL4AfXr4ajelJdTnrnHNnEdG3o2v3HRiGYRiGYUwh5ejd3AqOPAIAnHPvIaJWcKQY4KeA9znn7tY7RWJW+5hGwOdHALY65y6M6eu14Nx5VwL4SyI62/Fs30LEHT9vGChs+i6070CwfyGGnX+aGZXjOedGlQc3dn/n3G1E9BD4fO8mov89Tl96rKPq86gaN4HFvBavIcX6GAg+x123KgC9zrlzSzyGYRiGYRjGSVOOk8LuA5AkonepdSm1fDeAdxFRDQAQ0RlEVF/keE8CmEtEF0bta4jo7CjyusQ593MAHwbbBNInOfYHAVxMRBIRbp7g/g9F+7dGk9feDPZOl8oGAG+N+j4DQDuAJ6PI727n3M3gCPGaqO3VRJSKrt/vgT2+pXI3gPdJXs0oNU7ceN5IRNVENBf8MPJwgXa/R0SziKgBwO8CgHOuD8AzRPSGqA8iohdNYIyGYRiGYRgTpuwiyJGF4GoAXySiD4N/Yh8A8JGoyTfAPtlHI3F2CMDVRY6XJaLXA7iZiGaDz/lLYB/Nv0TrCOx77j3JsR8iousA/CQS4F1gK0Wp+x8gohvAE88IwJ3OuX+fwBD+EcBXiegJsPXhbc65ISJ6Izg34TCATgCfds71RDYFEazfcM79hkovR/oZ8HV8PPoe9gB4XdDm3wBcCPYjOQAfds51EtHK4LwfJaIfgO0wzyJfqL8VwD8R0cfBnuvvR8czDMMwDMM4JZSdB9kwDMMwDMMwZpJytFgYhmEYhmEYxoxhAtkwDMMwDMMwFCaQDcMwDMMwDENhAtkwDMMwDMMwFCaQDcMwDMMwDENhAtkwDMMwDMMwFCaQDcMwDMMwDEPx/wFY57mSnXOguwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plots_with_1_level_3d(df_2_or_2, 'hi-c-rao')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n",
      "gene_order_tss_y\n",
      "6       0.999501\n",
      "9       0.999501\n",
      "11      0.999501\n",
      "12      0.999501\n",
      "14      0.999501\n",
      "          ...   \n",
      "3345    0.999501\n",
      "3347    0.999501\n",
      "3348    0.999501\n",
      "3349    0.999501\n",
      "3355    0.999501\n",
      "Length: 1004, dtype: float64\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from lohia_utilities.create_corr_network import rank\n",
    "df_2_or = df_2_or[df_2_or['hi-c-rao'] >= 0] \n",
    "ranked_matirx = rank(df_2_or['exp'])\n",
    "df_2_or['exp'] = ranked_matirx\n",
    "plots_with_1_level_3d(df_2_or,'exp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n",
      "gene_order_tss_y\n",
      "6       0.999501\n",
      "9       0.999501\n",
      "11      0.999501\n",
      "12      0.999501\n",
      "14      0.999501\n",
      "          ...   \n",
      "3345    0.999501\n",
      "3347    0.999501\n",
      "3348    0.999501\n",
      "3349    0.999501\n",
      "3355    0.999501\n",
      "Length: 1004, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from lohia_utilities.create_corr_network import rank\n",
    "df_2_or_2 = df_2_or_2[df_2_or_2['hi-c-rao'] >= 0] \n",
    "ranked_matirx = rank(df_2_or_2['exp'])\n",
    "df_2_or_2['exp'] = ranked_matirx\n",
    "plots_with_1_level_3d(df_2_or_2,'exp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 592,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n",
      "gene_order_tss_y\n",
      "6       0.883556\n",
      "9       0.883556\n",
      "11      0.883556\n",
      "12      0.883556\n",
      "14      0.883556\n",
      "          ...   \n",
      "3345    0.943445\n",
      "3347    0.943445\n",
      "3348    0.943445\n",
      "3349    0.943445\n",
      "3355    0.943445\n",
      "Length: 1293, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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fyuLFEdNG791j/p7KJfoz161Zrc2XjAEDYMKEYGNEokIFc88TEvxx5faXJAiJxzEo+FprnZZT9yVKpOmaNXP+C123TgXq72wgLS1NL18e01MrCGcdShXsZ1+eV0HwE+szWzBikC+5BL74Ivs2rVu79RUrct85Bli5ksO1U7Fkh/nTnzznWrSADz809cGDXXu5ctEdUoDrrjOld2XWu+vO4u0NF/iOW159vrNJzw6fLl5MRxVCPl6khFMvai+379rlNli1KrJzfMkl8P337L7R5MuuFNRBnjIlorncqgUR7c4KfJ8+5j0sXGiON2wwy8o2q63dgo09mtZ2EDa4X0iysvz6yD6h7FNDVpAFQRAEoXBTMBxkQSggiIqFIAiCIAjiCghCCLKCLAiCIAiFG3GQBcGDqFgIgiAIglAwHOSc4o/Br4UL0KABrFuX61Mp8fdbI5+w448BHn44eIfvvRduu/LKMNON/33eb5iJKw1hv8/ff3c3CoZQ1KvzvGqVKaMoXviw7n2l7d/m0DCEDh0ib7bzxgV76dsX0tN5b2NTruvSJXrijgcfDLdFiNmmQwe/ysXixTnPOSDiIAuCIAhC4aZgOMiCUIAQB1kQBEEQCjfiIAuCB9mkJwiCIAiCuAKC4EFikAVBEARBOHMc5NB4Wq92bm5x5Ah8841zaCd5y3VWrAi3hcbQZmZC6dKmbgsiHzoUlhHQwZt8JEjscSihMd45MW1aZPuaNdledl2tb+HlWdEbBE28snJlsPnEgDjIgiAIglC4yX8HOTnZpBX28uijJoPdwIFuzuMQB+jRrt879ccuXcC3ZdvSdOZjxrB3L4wZE3m8Sy4xZd26ri093a2vXm2SUjRvzs/vfs2IEfDGSpPUom9f+C5LO9M5/6OnAJhS4wHn8t27oWdPt7uKSf4Mcu/PNNfb++0AJk4Mn+bBhqN9x6VKuY6anbivSBEoWTLy29yyxa0/95wpz0k+4RqzskxSjkaN2LHdzZ64eTNUqwZXXWWOZ292k6DU6NLCVLxJQaxvDV3urMKM9lYiknnz+HWn6dO+3WtD5je5zSv0+PxzGnVrSvv2L9Ft/kvOubbtlMl+9+67XBxnEo182aat+8affNLtqHFjJwHK7CWVHPP//uc2eSrrfPNFYt48N4HIScRNiIMsCIIgCIWbM2cFWRDyAQmxEARBEARBaa1zbpWLpCmlz5Qs8Q8/pHn8CZVzw9wg1CuLJUziVEhONuXOncHa9+kTWarNDgEJWerudpNm6jTF3Xdp4uJgzNhTuK8tW5r75Q1LmTzZrffoEXaJgkC52MuWTdMXXpjzX+inn8aW2/1MJC0tTS9ffqY8tYKQtyhVsJ99eV4FwU+sz6ysIGdDvjnHkP8OcShBHWObaDrGUWJApk4z9/KFj+r746VjYenScFsEpzgWRMVCEARBEIQzxxVo1cqtL15sjk8lOUTr1rBoEQwY4Ca8mDULypaF9u0B+HKe5uLFz5hzw4bFPlYo3gQXNvPm+Y9//91d1bU39W3fboKGI/Hss2791ijJTmys9+cb105AkpQU3r56dbe+dSsAf/yuKV/B8wWiVy//2O3a+fvIyICkJIb89QdGt+zhbgrcvt2UycnGSb/+enP87rvR59+7N7z+ut9mJ0cBaNIk+rUBkBALQRAEQSjcSIjF6cDrcNpYjmfMeL26WFajx40z5V13BWtfr97JrQRbDvBvGTpsI2OeYt0XlZUV6CeWxMQ03bZtzn+hs2cX7J9ZcxP5yVYQXCTEQhDOLM7+EIt69dz6jz9Co0awNlQrIZfp2RPefDP3+23RItx2qg5y8+ZuPZZ/HG21h6CsXRs5FqFfP1O+/LLPvHfdDsolKr7/HtqA69BnZRlVipMdP5TWrd36okVu/ZFHTDliROCuZAVZEARBEAo3BcNBrlAhR/3bH2b94NTrv9CfZb1fpMV7w42hY0fo0AGuvdYcz5zpXmiHDHgd7EWLoHt3mDKFZ57W3D9MQdmyfDx9L59+Cs89b1Y4y818g72LLIfv+uvdcIDp0/2T8666hsbyjh1ryk2bHFOPjHBJujJ3+o//9jc4fNjUS5UyZbNmUD4x8or/x3PdVdnLa1r3au5ct8Hzz8MTTxinf8AA1z5/PqxaxY0zbgTg7bGe+Q8aZEqPxvA7cabdi5fCF/b5sWP57FMzL7vr7/E7yF27wseTJ3P11VC/lXa6XrQIXqjszn3BfNNP23aeVWavI56ZCfv28e3GMjTN+Mwx95j0F6c+eZGC1FQzmXXrOBlExUIQBEEQhILhIKek5Ogg+0JjExONH9qtm79RiRLhF9qxtXZp07o1TJni7inbt4/LW/7B+vXl/e0szV3S0kyMMvjjgEuXdvV6AQ4c8Jfx8WFTql8/fJqh+T+qVXN97Vq1TFm+7AnYFfI+LEqWrBI+B+89rVfPTThivyd74Dp13Gl6V19tPE5my16mnDED87lZ1KljSmX7tc2b+xKi1K4NlCjBiRNGE9p+v61bAxPiTAiG/QUkFLtzey579rBlSxma/qmWY7ZzqjjExZn3X7Zs5D6zQRxkQRAEQSjcFAwHWRAKCKJiIQiCIAiCuAKCEIKsIAuCIAhC4aZgOMghaaUjYcesAtSrN4ofp8OshMYA/PQTPPy5Zv58c/5RpnJJe80Xb+2AffsAmPCJG9cwYKAyQbH9+/PiizAQaJGm2dPSRE/8NEgzpt17HOoKj628xvR58xEnxGJy5Xt8c7t51R1OPX7Pb6byj3/ACy+g/z6AUH4bEv7+unb1HyckuGElR45YxmPHgoUM2JJrDRq4tunTuWVQed6gD3ra245Z/fgDLFrE+hctw0LPpkQr7mJgxqOO6bMr3FMDk0xa6vEMceb/88+m/PKjr3n3XRg/wcRc/OtfsGXL3wCcz8l+SzdmZZkY8ilTmDrV2Nv26wdXX20OZsxwL1i3jpvvr8HKlVDqn+c5Zls1DjB5twcP5uiQByj+0fvR71MEJAZZEARBEIRsHWSlVDzwtNb6vlwbMTXVjeW1OJFyHvFFQqS/bCm0rVvhrbf4+nH31ObNxgeyZYIBLkn5iUEzagPwKEYc4YFxVdm7typgnGibUhM1/arBUWDDBmPr0MEIL5QqBSNHAmW7kIlmyhRzfsDiG6n4o1GKmNDd7SsryxN3C2zbVhGA+++5B556ikmTjN3y0wF4663w27Jnj/9YKTh40NQTE02ZkBAhxtpiwQK3PnjoagDatHFtL46yQo9bt/bJIL/2Wn3qHpjrfEe5e2hx59wLO3YA8Ok3bnt7r+HRo2bToI0dL2078yNGwPjxwASgQQOOrYNt28x9KFvW/U4UHw8LF2hmzIDR9Za6ctBT+/HexqYAXDegmjtQQgLLPjJhEF45aZ889LZtfHjrO1yRPp4Htg8Mu1c5IQ6yIAiCIBRuctRBVkp9DvxF55Jgcq7pILdr51+KjJGPP9Jc3knB0KEwapQvALVuHc36IzXMwanKsHmxk2F48W5EA3j66dwbLwB795iPt1xiBI1ib3a8hQsBuKj/+XyVUdfYNmzg153m+j59jOmD2f5+Wl+kue026N8f/vQnWHHnq+bEV1+FJ/3IgW9WaDIzoUVLd4z3Z7p/ntdcG/4egqaaTkpK03/9a85/oW++mbOuolKqE/ACEA+8prV+OuT8ucBEoBLwO9Bdax3zH1qA8VKAN4BEq80wrfWcnPoVXVVBcMlNHeS8eGbleRUEP3mpg/wN8L5S6h3goG3UWs+Ifkk+EMQ5HjvWH5vhZeVKaNaMRYvgcoD16xk4KJ7x+/dDmTIADBkClB5l2vfsmRuzNmSXJe40Yd/OzpFOPvCAW7eWiK+8EuhnKV5UrkyVXWbVum5dE/bCxIm+jH67d0N6unGOzzkHV/mjQwe/g2x/UbCX9iMwd67px8s1Kd9EbhwDubFJz/r15Z9AR2ArsEwpNUtr7RXvfh54U2v9hlKqA/AUEFPO7IDjPQRM11q/pJRqBMwBasYyniAIp4Y8s4JQsAniClQAfgM6eGwayFcHudtN7grhzTebMAU7u/LevZB6URmWfLofgAtbKT6Ypel813lOTMAb8851rr/l7Stp3rspKzBO22PAUy1mcOBHuPAvpVlitevfH6a1M/7Kl5MV9LB8F4982vHS5SlazLNiud/MwXaynRTVnrTYL3X9Iuz9FSvmP76tymyoYa1ez7EWDHbu9MdqeHi9netk9r41cqa63bs0lSorli1176W9CjvK+h5QfoF7rk1bq58HH3Rstj7x1q3QpEklAK4BujxqHGNbgnr05o5GGs9aydiwwfi+GzbAd99B/xomHvmnn2D6nu6Uy/wNkpKY/Mh6AHosdOO6SU93qmtWa97oasJO/nLInetll7nNFwB8+in89788UWG0MT4cLHtfLsYgtwQ2aK03mn7VNMyt8v7n1wgYbNW/AGYSO0HG04AdxF4OiKKrJwhCPiDPrCAUYCTVdHZUqxZdm1cIzGMjNI+OsBzUBg1OOnlHbhA0xKJSpTTdpUvOf6Hp6eoXwCtKna61djx5pdT1QCetdR/ruAdwodZ6gKfNW8ASrfULSqkuwH+AJK31b0Hf10mOVxWYC5QHSgGXaq2/jtJfX6AvQEpKygW//PLLyU5JEM5KcivEIjefWXleBSE6sT6zOa6VKaXqKaU+U0qtsY6bKKUeimWSZxziHOcKjnMMp8U5PhnsFeScXkCG1jrN80oP7SpC96HfRu8FLlZKfQNcDGwDMmOdeoDxugGTtNbVgSuByUqpiP8GaK3T7fdWqVKlGKckCEI25NozK8+rIOQ+QX5MfhV4ADgOoLVeBdyUl5MShNNJQAc5J7YCNTzH1Qn5eVRrvV1r3UVrfT7woGXbG+O0cxwPuA2Ybo3zFZAAJCEIwulAnllBKMAE+a++pNZ6aYgt1lUuQSjw5JKDvAyoq5Q6TylVDPOl0qdvqJRK8qwGPYBRtIiVHMcDNgN/scZuiPnPdvcpjCkIQuzIMysIBZgg/9VnKKVqY/30Y8VN7cjTWeUnzZv7j73ej5OhoxCQkmJeQYkk5gwm88exY66OtU2aCf9Zs1ozcEAuxL1PPBVfMjp2qumcXjmhtc4EBgAfA99jdqJ/p5QaqZSyMqDQHvhBKfUjUAV4MtZ5BxzvHuB2pdS3wFSgV27JNwqCcHLIMysIBZsgOsi1gHSgNfAH8DNGr3VTLANWrZqme/f2b4LavRtefc0Kx7IlxW680ZTNmrF3j6Z8ebd9YqIRi7AThRw8aJTCbCW2vfsURYtoGjRwfdxjx9zr//xn+OAD2H9AodBoFGVKaw4cMOdTU2H1U7NRna9ynKFbb4VXnjBf3CvUd2O8jh/3+4KVK5vy6qvhnp67uehq09buG4yKQyh16/qPjxxx52wnIilXzp+UxMsff7j1qiY3Chdc4Nq2bnWVJLxhwC1bwoABRlYaoGZN99zPy81esaLJFR1bpue3A7vtz5sUxYuZvyN7zu3aGbGP1WsUXy3StG5tlDq8n4PNiBFG5OPDm95A9boFgEmTQrLjWQwZ4vjaNGrk2letcuvTpxsn9rq497noaZMJcfHiYEH6yclp+v/+L+dNeqNH554WakFHdFUFwSU3dZDzAnleBcFPnukgWxI0lyqlSgFxWuv9MUzO3WFbpgyjjvsT820b8Ry8Zh0UtzK5edK0lUtUNG7iOvKdOhmH6tprzfHOnXDN2qcYMcJyrofA8bSL4K67oLGRH3v/p1Tn+mtGXcgdNy+BdGjSBFhlsue9/LJRhVud9SfovJYiRTQ3WdHWAwcC48YBcMcdblq/TZuMw21z3XWmPGfjAmh2E59v2AbgZogD+vYNv0feXBxgnE9bTc520hs0CM+4Z7PXE7na2RIz9mbMy8yEJUtMmu0pk917edFFUHvuS5QufSdgbpnD9OkAdO9+p2NaYmngrV/vkVZLh9atTdV+n1/+WNWktysGF7VWKKX5+99hzBiTRvs8K0u01vDozjth+M3QrhcNGxoH+Zbmq7mlp/WZWclJAPbWbEvjxlC0qF8L2aO8xw0dfjN6c3v22B8ZLVtGvm+RkEx6giAIglC4ieogK6WGRLEDoLUeHXQQa4d/OkBacrL8PCQUaMRBFgRBEITCTcaE8ZkAACAASURBVHYryFamC+oDLXA3D3QGYs/x/Ouv8PzzPtM53uMRIyJeNneuW6/S6jyzQmjHoQ4aBLWGM/imVf6LevUyWsbANd7f4K9dyvOfAumYFeJVULasWXHdvx+YanTa69SByROPm2uaN4c1awB4aqZnpb5Pqn9yzR4xZYaRyC3xjycAuOKgk4SQIUOeCnt/oQn/9uyBbWbxmYpWhEPVYr85/YbyW1J9p15x5N2mcs9qt8EXX9Cia1cAuu9/ybXfNBGWL+fKrmaVeHCt991z1/YH4PkMdwV5wgRTfvwxvNLoBcfeqVPIhObt9GU/ufhiuP9++Ne/TGiEnUn7yBGg08tOLPj48dYFTZpEfJ/lgJEfaOLj4YpHWjj2zs2WuY2S3E3eLdLLcjLkYqIQQRAEQRDOUKI6yFrrxwCUUnOB5nZohVJqBPBOvszOQ5W1nuxzmzYZ59dyWLn9dlN6stWRkWECXq1Meiz1C3GU6dIRcPxnwPz0vz80gMQOzt21y7Xt3OnWK1aEWrX843rZbW049mTAOxASTgEmRthLYqJbty+tmpgZ1XureMAjDG/P74cf/I3s+IxZs8LaOvsRI2Tqq1jEjd+oWbMcYEIjHG85AOXLQ5Vdq0lMbExWlhu/7GSUXmTSVqemRrzcR6lSViyz90tPcpTGUb5wZYc4yIIgCIJQuAmSajoF8G6tOobkghfOUmwVC0EQBEEQCi9BXIHJwFKl1HsYqbfrgDfydFaReO45//HKlW79lVdMaa8Wg2dp0sJebbb59FMAbul+AnrBbXGv81KD3jzyCEZMB2uf4LCXPQcW/foFn7e9S8zDw2ObhbdLGRRmSrVlOrwr1tHwhDNElIoANxTko4/CTtkbHh0pEC+jRjnV1n2eAWDtWuBZ9x7bt2fjRsswz99FxYrA9u1UqNCYWrXg3JkmPOPcBg1Mg0mTAKgy5/XIc/dwSdp+8/l73ueMGZ4GnlsRSzZEWUEWBEEQhMJNEBWLJ5VSHwJtLVNvrfU3sY9YxBcjCsCcOSYGtXp195zXAQZ4yhO3++ij0KqVK7nw7LN8du1459f0BQs9WmjdupkyNMjXokWreJYBDZ/tTZEipsvjd90F48YxezYwzfL8pk3DlrRonOruMwxNHPHNSjP2bxmaivUqwlVXAfBVP/c7Ra9e4fM4VvNu33HPnq6/v2KFKTt0iO4rp3sSHdtKeT//7No+nPQrE96pwoCBITpxR47w/cbi9LfkoHt7gon1nA8B/61rYkWfv/UWPOPp5hEr9Hr9elO+AkYWpF8/aNOG+fOB1zqxpojm6FFovMa836JFYYWnn2WpvQFo0XuBa+zY0dNgGZQty333ap7zOPNeFZAp0zQ3/vi4O6mTQGKQBUEQBEHIVgfZyvK1SmsdIDI0GGlK6ZgUGr3aaDNmGF22YcPMsR03azvXUTay+ejeHaZMMZ7ezTcbm+1QjRwJQI/umsmbLza2+bHvSwyErZNmY8XkBsbRXMO/adBLUlL0e2OvxnpXoiMx2oiX9F8/mJ9+MqaP5yqzoxG4o4PxkF+ZUgoOHXIue3+m5prWu+kxpBKJiTC+1hhzYvNmGDvW7d/WpvPq4oXSvbv5RvCf/7i2LVvc+vDhpnzrLce7V7t2BdJBPOecNN2/f85/oQ89VLC1UHMT0VUVBBfRQRaEM4tYn9ls18q01lnAt0qpk0ixJghnLvYKci6kmhYEQRAE4QwlSAxyVeA7pdRSwNEq01pfHf2SbChdOjy9c7VqJoQhG67Y/IpTv3faK/z733DHXCM/lpUFF42/Gf1vk/5YxSmWLNZcOP85uP56AKb87zzn+u49FAMTJzOeKTQc2Y3vuRn276d5u9KUKgULMCvIU6bAheO/BGDApJ/Bkkn7cKQrKbZrF9zyF48ExdGjprRWVHnzTVN6ZClunNI57P01CwlLTu7jZq0rXdrtMjmKWsNrr7n1x5pbMRbeVeguXdjR9W6qVlOsWe3+apD66t2Qnk7DJkUBeHmee+7i5808X7ryA8f2gVX95BNXPATgoiSzcrzcUt5rM/EgPYpMdVbnBw2CVbdW4t//Ngk+umUMBkzIyBeZz/PHvnjKV1B0KW/USmZ0vdEJafGl1Ovenc5PXMiey2DkyO6O+S5PvMfqm1YZ9ZFatXhh+K/GOChKCsIIyCY9QRAEQSjcBHEFHsvzWQhCAUFikAVBEARBCLJJ70ulVBVMshCApVrrXdldky0HDsQUzzvak7ev4aq3Sbr7RpoO7mAMf/87TJ2KOvdcp82FrawVw6FDAehuqSTYDBsGTLAWKUcADz7Irbe+YDJTX1oEMjNp1gwG/Nnaj7h4HVhxXVcs9XxnSEyEGpE3AALuKq4nXXKDLuEryA90CdEsrlOHbTvjATinmKWl/PnnMG52xGEee/FF92CopXfsvc/z51PVWqVPXT3Vta9bBykpXHSRObx46IXuOUs7uo0rYuFswgN4se4Yp24LezxjreT26Olfsa1eHR6+9CtGjbqIEyfcRCFLlwJFilDeajfjJWvFN3m6k+rax4QJTNmjKZdwFC691DF37uzZ1PfUNOcXibsHDAAgm08oDHGQBUEQBKFwk6MroJTqCiwFbgC6AkuUUtfn9cQE4XQgMciCIAiCIAQJsXgQaGGvGiulKgGfAu/m5cRCafjIDe7Bu+/StNMk+MLKrrfMigm2lyUjEaKtdk51s8L56N4hxjBuHAOergaJnZzg37JlcVd+bUk5cFNcg5tpLxovvxxmemzo5Ajze9F/3KwZ55S31lW/+861e7PgefGqYEQYEzDB2uCqf4BRkQAmzrJWsBssJZTGT9zo1Efb1zZpAkWGOPZbWpnrL5hupbxu7O9jwYzdkNyGVu1O0LMnnHuVaXBuyZC0gl41jiiUG9DDBIi3bOnYRjV9O3Ljk8j2ZyMOsCAIgiAUboI4yHEhIRW/EWDlObc5Mc3Nbh1f61wj+2YnvGje3IQTJCSYYydvsocrrnDrH37oVA8/OZoSY8ZAmzYcH3I/RQ+5aZV37cI4gqFYMnAA1KtnnLVo3HuvKQ8ccEz/KdkjrFmV5/y2cuVczeOW91u2+APw/fcRh1lwpIVTb3uFtZOueHG3wcyZ0L+/iWn4619d+0svAXDfa8axfa53b/fc6yZpx/cjXOfT3ji4MB3u9E7A2j34DyuWISzdR1ISPPYYO/9tdJ17W5snadDAxLlUr27ybdthFXYCkUjceqtJ753mqra88bsbtnILNxm5usREf4rwgIiDLAiCIAiFmyAO8kdKqY9x8stxIzAn76YkCKcPSTUtCIIgCEKQTXr3KaW6AG0ABaRrrd/L85kJwmlAVCwEQRAEQcg2k15eEHMmPa928ooV0dudKnPmwJVXAqCzNCrOUmOYNClyjuigeGNtZ84MO70ssaPvuMWhL60gaNzsdxs2RA4fASeWGHAz6a1d629TrJibMS+UyVZcdA9PqIcdXx1JdeSqqzC5uEOw1TT69488Tvv2ULUqTJ0a+bwdNhEkE5SVxhuANm3cujfG2kJBoEw6NWum6YcfznnsPn0Kdjat3EQycwmCi2TSE4Qzi1ifWfkxWRBCkBVkQRAEQSjciIMsCB4kxEIQBEEQhEAOslKqBJCitf4hx8Z5xfvvu/UaNaBnTzeN86lw7bVuyENGBu98XhFbUK5nT3AE2TZtcq/xpnAuWdJJQQ3Ajz/6+x9sUipTtKhjWp3sD6cASEzwH/9S+WInGuKI9SnVbNUx6gYyW/EC4Lxx8ZEb2R3WquXaLrwQlizh9eMmbXPv7h+752x1Dq+yhSU59+W9H3DxbE8yEOuzGJNhQjQG4w+xWLhA06at4u4mX/DCs0fhxAlzonVrk4faYssMI9lXIyWb1NCzZplQkznuXtFvO93v1Jt6Qyyef96UtppIAMRBFgRBEITCTZBEIZ2BlcBH1nEzpVQUMV5BOLOxVSxyegmCIAiCcPYSZK1sBNAS2AOgtV4J1My7KQnC6UMy6QmCIAiCEGQtLFNrvVepbH7yFoSzCHGABUEQBKFwE8RBXqOUuhmIV0rVBe4CFuVwTXQaN4b//tdn+mBlDTpfbTngPXuaMiS++PLbqjv1TqM1u3fDA/veAExCvRuG12Xv8vUAlEtUcN11sHq1Gy/syWRHvXqwfTtUrswle97jCxRkZtLlhnjWrYMbihSBzEz+/W/oMsPI4F16KZSx0iDfMekip6tDh2CyV05t1SpTtmkDhw4x+fzRgBnO5oXLw29LQkgMcnIyHD/utyUmwsGD4dcCHD7s1jsNNfG91aq5toE1P2DMhs4MHqL4cMJPjv3AATjQEYYPtwyj3DTYvS83E73v2+6O7bc/mfKrfvC9HdNcrBjXvGtijz//3JgGA6SkOPJz990Hw2dpXuwCixYVp25dk53vxCJ4e1qyucFJSY5y27ejR0OHDgDsrtbUGb/S2xN4Zm1nVq6EZs3c9OMvX+u+15/79IFbbgFg/De2/JvEIAuCIAiCEIwgrsBA4E/AUUw2vX3AoGyvEIQzFAmxEARBEAQhSCa9Q8CD1ksQzmok1bQgCIIgCDm6AkqpNGA4ZmOe015r3SSmEY8fh61bfaayZWu4B145NQ+lSnnbw65dUCbjZwAaNjwPDh2iXLwnjOK990ycwrRp4Z1lZpp4BaB0acs2ezZVqlzD+vXWeYsGDUxZpuQJE08BVKzodlWyJP74CTvEwmprh04kJ7tNPIpvUW3R2njvgxdvOIadgK9RI0+D/3xI6nWdAX/oBcC6da5T6NwPgNmfAFCvjRti8bO55ZQoge+zqlnTlNZthQP4s/sBSUlmnDJl3Ht48CBGGq9yZd/cWbbMmWilJv4/tYQE+NOfTGJAmxIlPA3mzYNOnaByZXc+J4GsEAuCIAhC4SbHVNNKqR+A+4DVQJZt11r/EsuAaWXL6uUXXug3TphgHMru3cPTI9t4UyyHBuzaVKhgyt9/j2VqMH48A38YwPgJJh764Yc0jz9hxUZPnAgDBgDw+gQ3EPjIEbizyKtuH337+rr8ZVP4/fXI9zqsXOk/LlvWDZsOsqLZurVbv/56UxY9tNc1duoEdeoYbeOrr3bM3z/9PuPGQcuW5rj3E7XdazZuDB+oXj1Tbt4cOe11Tqmmx40zXvioUeZ4wwbf6S2bzf3y6SB7v114BZ/vvNOtW+nBAejcOWzYoKmm69ZN0+PG5Zym9corC3a62dxEUtcKgoukmhaEM4u8TDW9W2ude7rHBw7AwoV+28KFZknYt3wZgneVFqBfP3j5Zb8tVsfYw7XXAhNMfdcuzwl7ZRh3tRSsvBmbIziSFtbCKBkZrs12Rr149xDax/ZKZryV96N4cbe/ULyrwkWXf2UqXq97+3bIsr7fPPGEY16+HO66C9assQzeLy+RHGT7zXfqZJxdm+pmE+VPlxmntTaRHeT1nQZSN8uTb6ZJE9+9tT/mGt6LGjZ067aD3L493Haba09KijjeySKZ9ARBEARBCOIgP6qUeg34DLNRDwCt9YyYRtQ6fOWxT5+cr/N6q6mpMGKE69Vt3hz2c35gBg2CsWNNvWJFUlPdUw0a4DpvPXs6YROXZH3mtGlzx1+g5PNRuy8xy6g11PCElZS+9Z6wdhdkLvEbmjVj/7HiAJQ5sMPY9u3zOZM+mndy60MtBZAZno9o1y43JsHjTfdgNQx7iE39rEyFS0LmAf6V2rZtAVjTuBupXgfZunFfWb65Zx0agGVLNS1aKvr0gXnz6qOaNzcnKlc276l9e5g3j5SUCO/tiy/8x0OHmpXwHTtcW5Uq4delpLjhMqFfsLJBHGRBEARBKNwEcZB7Aw2AorghFhqIzUEWhAKMrCALgiAIghDEQW6qtW6c5zMRhAKCqFgIgiAIQuEmyFrZYqVUo5ybRUcp1VcptVwptXz3qXQkCHmM6CALgiAIghBkrawNcItS6mdMDLIC9MnIvGmt04F0gDSlwmUdmjeHFSuy7WMJ7uaxCzt14p35Vbjh/vuNYds2s2nPS/XqRk7O3tVWp457btEiI1FWsyYv1RvDnZgY5N2XdmP2LLC3fn3yCXToYBbPm3o3AHriiYvOfi97j2nfPlN6YqgXLw5vdkUNv37b8bjibrf2juRjx/wqDh5enVbGqd9ua6VVd7MPcuCAE/t9uKSrU1ei12WwYgUf1bTm4b3G2qT38TUvOqb1663rlkCqHbs9aBDMMvs41z4acXpMmABvjB3L5rFmr+CxSSY2u1gxiJ8wAR56CObNY+ZM0/7OyN0YHnoIli6Fy92UhGt+cHXxUgF69YKjR+Hxx43R+/nnQG45wEqpTsALQDzwmtb66ZDzKcAbQKLVZpjWOoLGSe6MZ7XpCozAhEl9q7W+OdbxBEE4NeSZFYSCSxCZt3Mj2WOWeVNKiwDNaWLBArj/fjf99umgdevTMn5QmbeGDdP0xIk5/4W2bp29bIxSKh74EegIbAWWAd201ms9bdKBb7TWL1m/0szRWtfMcfDYx6sLTAc6aK3/UEpV1lrvitihB5GNEgSX3JJ5y6tnVp5XQfCTZzJvWutflFJNgbaWaYHW+tuTHeiMoXt3o5AARo9t6dLcHyOSzlvoOJ06uVk39uwxZWJi1BVkX6aMDz+M3KZjx8jaxbGQkhJZOeSRR0w5cmTEy/Z/9D/KNKsdWUIO4IorTBntPUSjVi23Hq3vgOTSCnJLYIPWeiOAUmoacA3gFfrWgJ0apRwQXGojtvFuB/6ptf4DIIhzLAhCniHPrCAUYIJk0rsb85DaqhVTlFLpWuvxeTqzULySZV26GFkxR7z3FKhQwdVPfughkwzEcpD/1XcJty21ElbMnGmJJGO0e20qVgyXIfNiS6odO+ba7DgCL6HvpUkTd7eY7SAfOBDd+fM6iLbWmn2dPY8mTeCjj/zXpaSYc8OHm2NPEpFsGTLEhFaEsOyqxwBoEcVBHjkSnlu+HLp2NYYff/Q72u9bcnPeNHmRSEmB2293j71ayXamlBg4iVTTSUop7zJNuhVKZHMOsMVzvBUIyZDDCGCuUmogUAq49KQnfHLj1QNQSv0P85PuCK11yB+EQSnVF+gLkBJRe08QhFMk155ZeV4FIfcJ4grcBlyotT4IoJR6BvgKyF8HWRDyiYAryBk5/GSjIthC45m6AZO01v9QSl0ETFZKpWqtsyJcmxNBxisC1AXaA9WBBdZ4e8Iu9O4bSEvLPg5LEIRYyLVnVp5XQch9gjjICjjhOT5B5Ac7GFWqQI8ePtP3tz5Hw0Y5dJkW4otkl3XvZGjSBObNM/Vdu3h1VhXsdckpU9wNe7z8srNy/OtcN8Lk0CE4r50n75tnA5/TCXCi3SWOqVMnwti3r6rvODnZ3d+XkGA21SUlwZ49TSO+jXbt3PrGm/4AzGqozYsvKTdZhjcd37BhcPPNjH+zHAADI3Vur5yDu/o9I0QG21opz2n1tX9/TDruNm2M4eqrTSo/m0jeqXfF3k6UEhLe8R/+5tT/Ruzkog7yVvwJAasTHkJxG9AJQGv9lVIqAUgCYvkZNch4W4HFWuvjwM9WGvm6mNhHQRDyF3lmBaEAE8QVeB1YopQaoZR6DFgM/CtvpyUIp49cknlbBtRVSp2nlCoG3ASEpmzfDPwFQCnVEEgAYlVCDDLeTOASa7wkzM+3pxawLQhCrMgzKwgFmCCb9EYrpeZh5N4U0Ftr/U1eT0wQThe5sYKstc5USg0APsbEDk7UWn+nlBoJLNdazwLuAV5VSg3G/LTaS+ckK3Nq430MXKaUWov5Jeg+rfVvp/peBUE4eeSZFYSCTZBNerWB77TWK5RS7YG2SqmfI8UtCsKZTm6mmrY0jeeE2B7x1NcCf86d0QKNp4Eh1ksQhNOMPLOCUHAJEoP8HyBNKVUHeA34AHgLuDKWAY9Xqc6OIc/5bK8+B6MzM02yj23bjDFE5mtvWTdUqxyYOFpbuSEuDjZsiGU6/ljm3btZ6xHY8cbwctddjjzbXM8/Z9u3w/033OAaxozxdb++uok9PrDKtdmxxV68Ihdg8orYimxZ1pat33+H48cjvw1vSLAdB5ya6mmQmGgUOzZuNCogNs2acbxkOSc82ecdNmhgyqNHXZudkCUxEebPd+1WTLEdzn1+5GmycCGct26diX2GsFjybTvjAbO925mLN2bay9//7lRrRvrRMS4ORoww9UceidAgnJNQsRAEQRAE4SwliCuQZf0U1AV4QWs9XiklIRbCWYukkhYEQRCEwk0QB/m4Uqob0BPobNmKZtM+WzIz4beQCKr9+zE6wAcPQv36xvjJJ+4F+BcAXxg8mDeajuaWWy3N3GrVzOpucrI59ibTePNNU27a5No8nX157wdcPNssFX959wy+HOw2270bJ1Xx5IwraHfA2L05PTIzgec8mr8hK8gTJpgyPt617YqgUaBCRDyOHHHeusOxY0Y1IxL797v1ppbQhVcamWnToEMHKFaMo2NfcswrVsCmd2H2bHN880pXsKTxmFsB+KTbRMdm3+KXXoIXW80zB4sXs+V5kzr6TUtG2XMbAbjvXs1zzytGj4bSI1dzzFrw37cPbo+Lg/R06NPHyS/ySp067mB24haAGTP4uNadNGgAC/7rml95xa0vAHjxRWjUiDErLraswVaQQRxkQRAEQSjsBHGQewP9gCe11j8rpc4DpuRwTVROnPDnrwDLIdm+3TjIe/caY4h36Msbse+g8Z3spHC2Q2zLgXkdZDvlZpT0xt5f79PSjAqdTUICJj0zsPag66+d8IjeKQXb9rphAudEHAW8W6+yIqjcJiSE2+yf+r3tvY62l1Kl3HrEZHnJyY7MW/Fjrjd97FgZ31i+iIdy5cLm1rimubZSpTJQtqxjr5FlMo+XLx8xMzmNGplSKSNXZ5OYCDRv7niljqO/fbszmeMV3A+laGYmR46YORX1fE2rVClkQOuPLNK9zo7cjEEWBEEQBOHMJIiKxVql1L1APaVUKvCD1vrpvJ+aIJwexEEWBEEQhMJNEBWL9sAbwCaMzFsNpdQtWuv52V0nCGcisoIsCIIgCEKQEIt/AJdprX8AUErVA6YCF8Qy4OHD8N13ftvGjfBN8hWc/+6lrhrF66/72tjhDQA8n87lFSrAkiXmuHZtU86dGz6gHYMcGtdh0XDS/U69VMYv7NnjhggcOeL2mXQZHLBikHd7UjkcOeJP6hYaYmGHRHhjhCPFEe8OSQ9RsaIbZWKHTBQrFj0G2RtiYItOeEUm7quz0chgAHz0kWNfm3EDa9dCiRLm2HefU1IAaJvgSdrUfxwAfUZNhpGe+23JcFhRGWH0nt/b9NUW2qYddqU8KleGTsth6FAAathiJZmZTtx40czDbkeTJnFNzyz01XdTs6ZrDgsrWb8emjd3RDfuvTfyvCIhKhaCIAiCULgJ4goUtZ1jAK31j0qpmDfpCUJBRlaQBUEQBEEI4iAvV0r9C5hsHf8f8HXeTUkQTi/iIAuCIAhC4SaIg3wn8HfgLkwM8nzgxbyclCCcLmQFWRAEQRCEICoWR4HR1ksQznrEQRYEQRCEwk0QFYurgMeBc632CpMivmy2FwrCGYikmhYEQRAEIYgrMBboAqzW2pvuQhDOTmQFWRAEQRAKN0Ec5C3AGnGOhcKAxCALgiAIghDEQR4KzFFKfQkctY1aa4lJFs5KxEEWBEEQhMJNEAf5SeAAkAAUO9UBT5xwc0TYxMVZOSyOHoVduyJed/hwiCE52c0q0aSJKVu1MuXixW67ChVMGSVRCAkJbr10aZRyD4t61J6LFfMfeymWzV2xrzlxwjdM1HY23jhYe4rZxcYWLx7eV2Kip0GtWlC9uqnb9wvYNc2UpUqZ0k5O4hu4YUPXdtVVAOzcCTU8TSlbNnzMCOcdkpIA+GOPojyYpCQZGW4ilAoV3Bvl/YwyMyEuDpV1goSEeMcclqCkbVsoWZKsrCjziYKsIAuCIAiCEMRBrqC1viy3BtQ6POtZWBa0CBw9GmJo3tx4aQCpqaasV8+UXge5cmVTbtwYuWOPs/gbFX0Z77xO+bFjro/tdfCPHYPt293j0PSCx4+b0uufR8qGF/qloXx591rbaU1IcBLWheF1BO37mZHhaVC6tPvlw+Os2vfVnp+VvA6AxnXqALD3hOvRl+vQAYANn0ILT/c/7KkCwLp1kednp+g7cgR+2FzC8Xk3boRLAKpVgxUrXAe9ZEnny83RY+63luIdOkBCAvsPxfven52A0SExEXbuzPbLSzTEQRYEQRCEwk0QV+BTpVSuOciCUNBRWSdyfAmCIAiCcPYSZAX578BQpdQxwFrTFJk34SxFa046LkMQBEEQhLOKIIlCyuTmgNn6H7/9Fh5rYBEahrG/WVvK2LENdsjA7beb8s033YZ2iIVdgj/OuUoVpxoa+uD9ef7AAfOrP7ihD2DeS7VqEafsa3sih0XH0FCA+Hj3Gjv2uEgRfziuF29YgF333bPERCvQG/aXruqYT5zwxx37+rfihJcudU2NGlUCzP3wEhoOEsaVV8Lw4ezZ4w9LOXjQOt+8OcyebU/R3JAGDQB/2Ef9mjUhIYGSJf0hMBHv76+/Rvtzyh5xkAVBEAShUBMoJYJS6mqgnXU4T2s9O++mJAinEVlBFgRBEIRCT5BMek9j9mP92zLdrZRqo7UelqczE4TThTjIgiAIglCoCbKCfCXQTGudBaCUegP4BojJQS5SJLIUWEYGHG93LnHVzwUgPuR8qIxXQgKu9IIt7fDdd+EdW6oLzJoVeUJNmzrVX37x/OSPP5QiIcFEgID/p32loirTAW64g9fncsIIPISGCMTHu+Pbb69IkehqdV4VNXssn583ezZUrAhAmRVfOubk5ItZt84NmfCGJBxudD4AqZ4xqy7/AICUlM6+8VNrmg7i4iJo2IETu9GoETRO1bzzrlGmcMJa5s0DXGESDhxw3uzatW439Y8dg7Jlid+1gxIl7ykpRwAAIABJREFU3FARXzhJ6dJG2aR06ajiJdkiDrIgCIIgFGoChVgAiYDt1oUqzp4y8fGQlgZFt//iBvqGeJahMm+ZmVDUbpuaCjVrwp//HN65Lf0WhTdmlOEWq75jh/mF3eaPP9z6hg3QsqWpe/WI4+KiO63gfhkIvSaUUAe5eHHXZrdXCp9OczQixiBXq2buEfgCjTMzjQLb6tXm2Otor1ljSu+9r2rdzxUz4PLWrY1x0SK2/GEc42gydP9aWJ/bMPHEXy1WfPONse/Zg7n/rVvD/PmOHjNFijh/C74+L70UihblYNmqvvvu+wzi4syrSBFHBjswWmcTSC0IgiAIQmEgiIP8FPCNUuoLQGFikR84mUGUUn2BvgDly6ec7BwFIf+QGGRBEARBKPRk6yArpRSwEGiFiUNWwP1a653ZXReK1jodSAdISUnTOTQXhNOLOMiCIAiCUKjJ1kHWWmul1Eyt9QVAlCBeQTiLkBVkQRAEQSj0BMmkt1gp1SLnZoJwlpCVlfNLEARBEISzliAxyJcAdyilfgEOYsIstNa6SZ7OTBBOB7JJTxAEQRAKPUEc5Ctyc8CEBCP15WXxYqi98RNYuBA6djTGkFU6bzY1hg5l5ky4uksPANatgws2bYLOfukxwFVueMCzr/Cpp5zqhg1WpXLlMDWKI0eAa68FoE0baNXK2BcudNscO+aqW0TCVmDwSttFWoAsWtR/7M2aZ5fFi+OqPITgVaywx/IqUpCa6qTr+6PehY65QxGjivbBB+H9tNjzCQD/2dfRsT36SX3A3HMWLXLskyaZMiMj8vzs+zBnjpmfpTjHli3AdddB+/bw9NPu+7vuOpNdD7ixztduR6XLQp06TJ8Eq1a5Zt992bEPli+HjRvZ3vqSyBPKDlkhFgRBEIRCTZBU078opeKBKkHaC8IZjcQgC4IgCEKhJ0gmvYHAo8CvgO05aEBCLISzE3GQBUEQBKFQE2RF+G6gvtb6t7yejCAUCMRBFgRBEIRCTRAHeQuwN68nIggFAgmxEARBEIRCT1QHWSk1xKpuBOYppf4LOEmHtdaj83hugpD/iIqFIAiCIBR6sltBLmOVm61XMet1SpQ9sJ2O8x/22TpOegguewLmz4eRIyNed+utnoMOz9LtriPQdyIAF7Rubew+qQuLUMmMEB5fbol07NrF7XH/Yt+A2+BeYzr/fGDmTABuGbfFka+4/vpuzvVbt0Lt72dH7X+I9TXj7beznUaYgkZSEuzaZerx8aZMTHTroZQu7dZ79jRlE2+UeHIT6NULgPIT/+GYK3e9hyuvhH37zHHjKfe710yYAMDf7A6Bv9kSHqNGwXS36cMbewOQlv66MVzpn9+d042axPDhcNddoIYMtt4oMOU9eO89wPM5XznGvbibe7+ZOhW6daN3pUq+v5Xatcu5bQYCN90EaWlc7FHaCEwurSArpToBLwDxwGta66dDzo/ByCgClAQqa60TiZGcxvO0ux54B2ihtV4e63iCIJwa8swKQsElqoOstX4sPyciCAWGXHCQLeWXfwIdga3AMqXULK31WruN1nqwp/1A4Py8HM9qVwa4C1gS61iCIJw68swKQsEmx0x6SqlPlFKJnuPySqmP83ZagnCasGOQTz2TXktgg9Z6o9b6GDANuCab9t2Aqacw86DjPQ48CxyJcE4QhPxDnllBKMAESTVdSWvtBABorf8AKufdlAThNBPMQU5SSi33vPqG9HIOZoOrzVbLFoZS6lzgPODzU5h1juMppc4Hamito8cEuW372u9t9+7dpzAtQRCikGvPrDyvgpD7BFGxOKGUStFabwbnP3Odt9MShNNEcBWLDK11WjbnVaTeo7S9CXhXa30iyMCxjKeUigPGAL2CdKa1TgfSAdLS0uR5F4TcJ9eeWXleBSH3CeIgPwgsVEp9aR23A0JXy4ITF+ffUQawe7fZoJcNyckhhjp1oLK1kL15c8zTYeVKt56URPtm7qEv/fPcuXDoEACprVxz+fLAq+9E7X6JFTV2442ubdu28HYpKf7jffvcFNP2PIoWhePHI49zwuNa2bc3fv4XrrFYMTh40NSrV3fM9mbAatUsw6+eAaz36/u82rQxZeiuQiuvdfv2kedHq1Ywb56b/rp27fCJ46ak9nEk5JfFiy+GL7+E/fsdU8OG5Qijf3+oVcvUZ+e4aOqSOyoWW4EanuPqwPYobW8C/p7H45UBUjGKNADJwCyl1NWy6UcQTgvyzApCASZIqumPlFLNgVaYb7yDtdYZeT4zQTgd5J4O8jKgrlLqPGAbxgm+ObSRUqo+UB74Ki/H01rvxWiG2OPOA+6V/2gF4bQhz6wgFGCU1vn7a0yaUoXn6S5dGg4cON2zyF/spejt0RZLgZIl3dXpfELB1zmERACQ1qCBXv7qqzn3165djv0ppa4ExmIknCZqrZ9USo0ElmutZ1ltRgAJWuthAd5G9nMKMJ6n7TwC/meblpamly8vNE+tIGSLUirQvyUB+8r1Z1aeV0HwE+szGyTEQhAKD7mYSU9rPQeYE2J7JOR4RK4MFnA8j719bo0rCEJsyDMrCAWXICoWhYd69SDN/ZKhs05xdf1MWj0uWdK8TpVp08wrGhkZcPfdpz5OXpI7Mm+CIAiCIJyhBHKQlVLxSqlqSqkU+5XXE8s3vLv/EhPB89NU6D60QDRrZl5nGgsXOpkCA7FhQ0Tztlpt2VarbfgJa0PdM69VhHvuMeEnoZs1gxK2YxPz5cZ+nQp2qumcXoIgCIIgnLXkGGJhZfh6FPgVsJfONNAk6kWCcCYjK8SCIAiCUKgJEoN8N1Bfa/1bXk/mtFCtGuzcaeojRxr5uDp1ALOPrPxpnFp+crSRyXJcPKeGvXqZMi7kx4dLLwWMZByEZ8T49qfSNAXWrIElGypyYYUKbj/79p3cZAcNgo8+gnnzXFsTz/e1H388uf685GIMsiAIgiAIZyZBHOQtwN48nUW3bjA1hyy7zz7r1ocOzb2x16xx6zNmMKbBKwy2Du+915P7t3Jl1/t7+WX3mk6d4NNP3eM+ffz99+xpyhtucEw/NbwqbBpLl/qPGzVyf8m3BSGqVYuiE4xfR/mcfwwxlXc8+sxbt7rqEUU8H/u990KtWhQvYukRe0MUIjmakyaZ8vHH/fZ77wXgf/8zh+eHXNa0mdHEv/xyK8zbDq84dsznIP+WYeK+KyZF0tC3GDYMRo2CDh0c0+5+Dzv1Su9mc20QxEEWBEEQhEJNEAd5I0ao/L/AUduotR6dZ7MShNOJOMiCIAiCUKgJ4iBvtl7FrJcgnL1IiIUgCIIgFHqCZNJ7LD8mIggFAlvFQhAEQRCEQktUB1kpNVZrPUgp9QFGtcKH1vrqmEY87zx48kmf6aeW3ah9xx2wdi3MnWuMM2f62sxueJ9Tv4qh8NprsHixMfx/e+ceblO1Pv7PS7FT5J6E5BK5lLST+pbLSS4dJDkqoY7cD4rT0eWo5BSny1cdKpEkhC6PhJ9LF4mShLZrUUnSTUhyfGu7jN8fY8w151p7rb3W3vZlsd7P86xnjjHmmGO8c8w9eOeY73jfBg3sxq3c8MQTMGQIAEefncjOu/xT69cHhfwKHn0UgH982S9U/OMImP5wq5jNf9DnJQA2bvTLlr+ctV5k4LlgsDlvP1x2XtGqVvXT555rrV+6LvetYM67/U923K+4gsOHDofKJ06Eokdg5W02P/3VV/2GPHd1K1f6Zc5Y+qV3q3BrUIDTTwd8G+RBEfIdzjScWkz4z3+sDtrDjTlly0LXrqF606bZ49DgxePG+ekhQ2D5cihVCnPhRaHip0f6VR6qXNkOYJUq/gbMzz8nYXQFWVEURVFSmuxWkKe74xN52uORI7A33CFG6dLAtkNWiYmhyNSvH1HQrBm87DTNU0/NvTwBrbPojq+oVq1m9HqffRbaCVfqx4hzixfHbP4c587hl1/8smJRDFWi6WRFi9qjd3tpaTG7CcOL9hwW92PvXlhoAzaduvzdUHH9+lezY0egbrQQ0MHNes45dMVId8ObNwNQvfqVUWU69aAdgJDzCy+IiufNwlGhQpSLfw3sES1b1sq4bRsS8Fxx6qmBjXne20arVlC+vE2rgqwoiqIoSoLEVJCNMWvd8f2CE0dRChm1QVYURVGUlCeRTXqKklqogqwoiqIoKY0qyIoSRDfpKYqiKErKkyMFWUSKAGcYY3IU+kxE+gJ9AaqddRZccknY+TlzoE/vttYQtlcvW+gis7FnDwBjA16Xx//1r2z8vTYNh7mAGBUqwIQJdlMW2KAYHtWr26NnmAvhm87atg0l395ekzlz/A1ihw8TanN9sUu5qHVFK1JAln374Idh/qa9s/v3D7s3b9Pad9/5ZV98QRaCNsoAv//u62negub+/bY8GsFbLl3aHoMb/+7u399uhnv4YX6od3WoPGM2bN8O337rZCt/eehcbW88mzYNlf1xk92at+IhaBcUoFs3ABY3s9kxEfJ9c6AM5wK//QZr1kCPfw8EYO+h0ygHdiPekCEhOahRw7+RP//Zb2jrVr6o0YYarWDR//OLnQm0pV49u2nzggvgrLNs2euvkxBqYqEoiqIoKY8Yk8VBRXgFkZlAf+AosBY4ExhrjHk8Nx2mi5g1kYXTpvkR52IR6ckgvxg7FpzivWmjoUFDt/mrShVWzLTa21WfP+/X37kTHn44dnue3N5mMWBr45uzVFu+PDwfXMQsWdIeS5d2SnsUgh4urmnsNkEGIs2xYUP0C73deV40wCuuiF4vkvR0q+lGMmKEPcYak23bwqP1RTJ6tD3ed198GerV89NduvjpUaPssXXr0M5GmTdvrTEmPV6T6dWrmzX33x+vGtK7d0LtnQykp6ebNdGetaKkICKS1HNf56uihJPbOVskfhXquRXjTsBCoBrQI6cdKcoJw7Fj8X+KoiiKopy0JGJicaqInIpVkJ82xhwWkeyXnXNKvNVjyN9V4yD33BNKNvh2kV++axdXZYzPuSyef+aAj7Y631+bpVq17meG5U/L/NW/xrOV2LHD/qJxYTM/3cf5jI61ahzEc+s2dWr8ukGcu7csxFo5LlsW9u1j7YHaXBK9hiWRlWOPLVv89JQpWc+/9RZcGd3tXEzUxEJRFEVRUp5EFOSJwA5gPbBcRM4FcmSDHJfq1WMrfo6Xpvo6+a2rBvBulwlcXWyFLcjIsEqrZ7fsmQsEmTTJT/fty/vLDM1bCFs/N9Spa80ovt1pSEuDChVtvvE/2zFqvu23/VW/+jaxwc9XpUqF2TGzfXt4v57JQcAG+qGnwpVhgEERkTV+PHBmyN64SpXzAChZpYovQwQvfeD7b77Vc6AcpFo1a5KweDHf7fLH8pzuLWHdOpp/PhGA9yc38a/p3Ruw4+LhxWZZuhQmXHuHzYwbF1KYG15p723jpoBfYuCqentZ8exGOl0LDdoaJk+25du2Qcs/iVVwe/Xizbm2r+s6hV8f5I05husv/Mr3pQw8PN8PGjLifoHu3a1t+h1ORs8WPRFUQVYURVGUlCaRUNPjgIABMN+ISMs8lSKOcgxwa8XAau5zz3F1t27QubPN16hhj9EUY4+ZM8OyzX9fAkCdiv7uuKqHtkLrTqH8iy/6m9fCFNP0HJiyePawAR7sFCWwSPnwyIHlEu8BgFtbt/YzXjTCIDt32h9wzr5AWL9ly4BAIMLOvbNcWnW77wq7aq1SAPz008XwWoZfyb0kNGr0kc1vCm+jSBEgI4O0tIY0bQrnrHnTylLRbnxk924AOiYQn7F2baBWrbCyEcEXIIAZM+zxhx/iNxhEvVgoiqIoSsoT1wZZRM4SkRdEZJHL14PwKMOKclKhNsiKoiiKktIkYmIxFXgR+KfLbwNeAV7IJ5misrai71Tsko4drbeFxo0B+GnaEs6qJH7Y4n37rNnFyJEw163MBldVixWDJtaUYPqCMnbHYUYGTy6sQ6m7PuP23vbz/pAh/pf59o0bw7p1NhMMj9yvH4yJdGoWwPPYEAzX7LmnCzI3fAWZtDRo0cKmswllHaJmjBDZQZo0gdWr+ehgw1DR5T17wrRpvs7XqJFfP8OuEP9Ut3mo6Kmn7PH00wmLZf1QW7tyvNvzoOdsjj2+/x7+6NqDnb1h/XrgWmdy4p7h0bvupug99/DII7Z4RDa3sWMHNHjggdCqM8D6Jn1C6YusR0FLLFvpWKgNsqIoiqKkPIkoyOWNMa+KyL0AxpgjInI0tx2axpdweFW4C5q5c+EvXZ3NaYMG9rgp/Bv9bbf56Q4d3mRFC2jTxppJbL4TphwyIQ9ln+4Tpm+4iM8ueINL3f6/1wJr3jNr1ODWO8vwEnZPWQ+g3T0XsXKl3bN2+7PPwsCBLF/ue2l7bdtaLrjAprt2DQoPF900OpR99ll7LF0apIjQpvpWAA6f41/yaRQzgrRK4fseq1WD393evMy69njaabHdvKV94qdLtrRCBD34de8OxYtD9x4SsiMGmJr2EhVHvMQjf7H5W275NHRumnsfaBHwpubtG8zIgK3drdnLdITZs235l1+6iptW2reLtDSoXJkvv7RmxpmZMH8+nPnupaE2f+3Zkw8+gOb4ljAtVpiouu3mzTDjXnjkjIdCfyoAUwNWL5nHDDt3wrk7V/D851fZwsWxbZqzoAqyoiiKoqQ0ifhBXgbcALxtjGksIk2BR40xzbO9MAZR/SAnI3Pm+DbOSt5SCGMrkJgf5CpVzBpvY1927Q0fntS+UPMS9auqKD7qB1lRTizy0w/yMGAeUFNEPgSmAYNz2lG+UiSR20iAdu38Y+fO/nJwKnDKKfZ3vPXq1QsP4BHBbwcMQ5dfnwsBCxC1QVYURVGUlCYRLxbrRKQ5UAcQYKsxJsaH/kIirxSWN97guz3FOaeK/Ry/oOoA2jMwb9pOdjzPHEH7i2h4Hh5KlPB9KAf58cdsL8/MtJ7x8py8irSoXiwURVEUJeVJxAYZoAlQ3dVvLCIYY6blm1RR+HSdbwpy8eqJ/Ny5HxUG3WgLnnsO/vY3uPdem7/wQv9Cb9OZt8EO7Irz5MnQuzd/vc3w4lTh+UmGZ5tCpUqwqF07WLSIW26BXz//3F7TqxesdDvQQoa2cLR6TYoO7Oe37Ta2sX+/3Zjnbb7bsydU5fljt2e5v0j3xl26wK5dNu3thSvH3jDfv0H+W/7cUPr0Hs58oUIFv0LFitC+PTRtGu7ybq8NS915qXXEPGfPXv+cC499ONMf+4nWXTJTpsC6Dg/azKhR3DHE1vH2E27dF27z+9fbDGecAdNqWFEechv4lqwuQ5u2AvPmQceOfL3dtnNeDQkLrhKiUSNmDfmIo0eh++VfhYqv6e9vUnybIXaD45Yt8MQTtjCRYDQeukKsKIqiKClNXAVZRKYDNYEMwNucZ7CmFopycqFeLBRFURQl5Ulkk95nQD0Tr2KC5HqTXtC12r590Lq177qte3c/MEQO+WWfoUxZ4aGRhsqV7aLv40/Y1c92bQ2LcuL9IFG84BhBAi7LCgXPrCCafXEwXLNzy8Yjj0DJkqHiFybbP48DLsbi0GHh4zZmtOHeTzpzd+05LF0Kn6xx50uV8i8Kth9c8Y9k9Wr7FSAYsMULOwhRV54T3qRXubJZ06dPvGrIqFFJvVEnL9FNP4rio5v0FOXEIrdzNhETi01AJSCHIcnymIBPXSDcr3EulWOAMjPGA/DgyKyK8KIWj0ICLohzTGErw9HIZmMdH3yQtezDD8Oynu/oWPTvDxS5jMppAeUYwpVjyF4x9pg8OTx0OEQ3x8gtuoKsKIqiKClNQn6QgS0ishr4wys0xiQQFPgEYMgQXphsoit499xT8PIUFsFAJtnhKcsutHSilClrx/cO8mBMI5XjvEQ36SmKoihKypOIgjwyv4VIhLVrfAuPS4pthLp1CUWn6NTJfqpfscLmr3LBIdq2hVq1bLpvILrahReyYrnhqmZC7VqGL3oLv+wzDBtmg80NGGiVuTrnG7au+sVeEzTxmDLFTx88CHfd5ee9ep43h0qV7DEQoe7+9EVZ7s8T0+PW3ydC/fo289NP9rhhQ7gpQYDPbns0lL5geAdfNo8NG+A//4EePeDOO/1yFxqvZQs7vu+VDrhgcxsM/++QP/aPPWaPs2fDZw/Msplu3Xhpqq0zdqwtWr9BrLmEWxE+u5Khb18YPdre60DnHOTQIbj7HoFBg+Dpp2HjRnuioR/tL5LfDhgWL4a/7PzfUNmNq/8eSr/yqnvZ6doVeve26datY7YXhtogK4qiKErKE9cGGUBEzgVqG2PeEZESQFFjzG+56TDPAoU4TxO5xoVYfmik8c0r2ra1Gtvy5QAMHmQY/3Q+2CB3jLL4/s474floLtSyI2hiEEOJzhbP5/PAOG7tnI3wjKFr6d4jMDZdugDwfOvXAOjTN3zcXpxi+Gsv4fKmhuefhwZ9Lrcn9uwJ8wqSa4YO9dNPPpnldMI2yJUqmTU9esTtTp54IqntEPMStWlUFB+1QVaUE4vcztlENun1AfoCZY0xNUWkNvCcMebq3Ah6wkTSU04qcqQg33JL/PbGjk3q/yTzEv0PV1F8VEFWlBOL/Iyk9zfgf4ADAMaYL4Aobhjymdat/V+VKjlfYY2g8cX2xWDF8sALwuTJYXVu7WmsY9/F+bFTL8nYvTuhzYPf7TJ8t8sweJCx3kO6d7cnWrWCVq3o19fQr2/Wl67GFxvIyKB4MWNXjZ991v6866+4ImFRf93v2i9WzP/t3+//gvTu7ZtZJIJnYqGR9BRFURQlZUnEBvkPY0ymiP1kLiKnYP0gFyxBrxXgR8/IJes+tfdzVbOAKUCEIvXSBzVh2vbj6ueEoXLlhKp5UQbHR55wJiITm1wW9bp1la6FRot4booJBSAJwwvCkgBnlnbPLDPTL4yMtOIR8dKTEHmkAItIW+A/QFFgsjHm31HqdMXa+RtgvTGmW371JyLDgN7AEeBnoJcx5pvc9qcoyvGhc1ZRkpdEFOT3ReQ+4DQRuQYYCMzPX7Gi0L69n16wwG6G81y/padDvE9KNWr46e0BpXfDBj/y3rFjNiqfs8PtdtlXzKzW0p5btuz45A/iRfwLMmZMeL5UKd+uOBG3cEGfwLn5vOZ5sQiOk0e7dn76Rhu9cEbRW8NtkJ2C/OZBa3lzXadwG+SXblzIrYuE2bPhv+MNgwa781dcEa4ce5sps/NUUbGifUEKhrX2IhZCjj1shJFHXixEpCjwDHANsAv4RETmGWO2BOrUBu4F/scY84uI5PrLTCL9AZ8C6caYQyIyAHgMuDG3fSqKknt0zipKcpOIicU92DfXjUA/YCEwIj+FUpRCJW9MLJoAXxpjthtjMoHZwHURdfoAzxhjfgEwxhyPg+y4/Rlj3jPGeLZJq4Aqx9GfoijHh85ZRUli4q4gG2OOAc+7n6Kc3CTu5q28iASX6icZY4LL3ucA3wbyu4BI+5PzAUTkQ+wn1pHGmNwavCfSX5DbgZhuYESkL3ZzLtWqVculSIqiZEOezVmdr4qS98RUkEXkOqCKMeYZl/8YqOBO322Mea0A5FOUgicxBXlPnF2x0fwDRtrunwLUBlpgV4ZWiEgDY8z+yAsTIJH+bEWR7kA60DxWY07ZnwR2V3wu5FEUJXvybM7qfFWUvCe7FeThwE2BfHHgUuB04EWgYBXkBQvC88HQ04nY3G6P2GxXooT1hPHii35ZkXCLkwEDgGbLciRmQuzYkbXMCyji8eOPWcMwZ4fnDQJyZ4OcXYjnoL9pl26z+9bwOq1aAXBdDJm9/XTVq8OgAUdhsDsRuTkvkSh5L7wAN9wQvkkvIyN63Tlz7LFz5/jtQl4GCtkFVA3kqwDfR6mzyhhzGPhaRLZiFeZP8qk/RKQV8E+guTHmj8jziqIUGDpnFSWJyU5BLmaMCX7++cAYsxfYKyKn57Nc+Y/nJu7wYb+sUSPrYcFtONuzJ5/6vv/+rGWRwSnGjvU9dXib9HbvDn8xCBL5ApBTmjXLUfUKB7+OfuKb6Bus+7S25UWLEh4YpHz58IFu6TZFvvde7M47dMha1rRp9LqJKsZB8kZB/gSoLSLnAd9hXzYjPVTMBW4GpopIeazJRW4fZNz+RORiYCLQ9jjtnRVFOX50zipKEpOdglwmmDHGDApkK6AoJyN55MXCGHNERAYBS7D2xVOMMZtFZBSwxhgzz51rLSJbgKPAP9xLaH719zhwBvCac9u40xgTJayjoij5jc5ZRUluYkbSE5GXgWXGmOcjyvsBLYwxN+emw/Rq1cyav/89vLBbN+u6KxseGunL+eBb/8MvCz6kzJT/BWBBnb/TvoOEAkiE/ORC9EAUAwda/7i9e9OhvWH+AoGNGxn4bAPWroWPV9vra9cyfLHwC3vNE0+EAoZMf9hfJT1yJBRlGYCSpSLMyj7/3B5LlQoVNf7z2Vnur3798HyNGr6FxSnuNaZ8eevdLhq7dvnpnj3tMbhQ22b7BP/etwS8CG3aBDNmcNGONwFY37Sff84zd/jNjyo+YfoZgPV698qorbawbl0mPGvHfvRoW/Rt5lk2LLUbs4YNDBvr3Ujxua9QvToMH+53c3tvwRwzSBHh6fG2nUGDxb/ZunX9yitXwlNPwZAh9pk4Wi78Ryj93jKxg3bkCIwbB4AMGZJYJL1y5cyaBNzEycyZSR1NKy/RyFyK4qOR9BTlxCLPQ007n6xzgT8Az0D1EqwtcidjzE+5EbQgQ03Pmmm4uVu0fRAB2raNGSnvvnsNo8fEuf5koVYtewxq1dnx2GPhWq7HzJn22C3CmqB6dbjzTu798Q7GDPkh4cAkUalVy5rBVK/ulwVfgKIEHUk41HTZsmZNmzZxRZDZs5P6P8m8RP/DVRQfVZAV5cQit3M2pomFs3e6QkT+BHjrm//PGLM0lzIWLOPHx1eOIdsw0hdckIfyJDsNG9pjggryx83+wWUEFGRPwXYxsSNeAAAWPUlEQVQrtpHMH/c1HToKK5vdQctVZ5ONhXF8vvwyXDmG7DcZ5hQNJa0oiqIoKU0ifpCXAieGUhxk8OD4deLQo2eKrB4DvPFGjqpf1jRibDzFOoaC3aGjrf/+8nwa099/z7u2VEFWFEVRlJQmkVDTyYHndQLgyivtz/uMdOwYrFrlrwa3bQvXXw8ffwzfZ/GaE8YP3xvOrmyVtgcfMKxbh7VJBq7raHhznlPojhzxjYFXrfIbqFgRHn/cz0+YEN7BTc5TnnODBjBgze1xb7dvXzh40Ka9iMoVK8Ze4PXqAtxxrbObbtTILzx0CKZN8w2Ug/Lv3s2A/tbUZkL5gIeNhx+2x9Wr/bKOdn9I8/N/4P0pX9myWrVCXjR6/es8AKa8GK4It2trWDTzFxo2K8Oll8I119jyvXvxw04Db861coSFqg7eR6NGfPfwizzzDIy+7M1Q8c2z/QBUs2a7a9etY8bmi226R4KKeR5t0lMURVEU5cQlpg1ynnYSjPIDl0R3BBaHUaP89AMPWOU4PWBS0r27/3k/1k62aKxYAVddZdMbNvBb9YahjXYXNzJ8muEUq8aN8+4zfqR5AMD8+eH5/YFYEc+7fZLbtoUr50Fat/bTb72VM3nKluXX7dZ5QtgGR4+0ND99550AmNFjkIrOmcmePaF7enKIdf82dFh4Ox+vMlzWVLjoQkPnzvDgVmej/H//B3Pn5kzeKVPsS9FTT4WKPpmyMZS+tEnWe0jYBrl0abOmecz4GX578+YltR1iXqI2jYriozbIinJikec2yHlJWJQfEY3yoyQ3amKhKIqiKCnNiWNi8cAD4fn0iJeB3but66+c4q0eA4wZQ8mjR0PZlSsBF6sjTzeBRYukFxn8Ilqd7MjpqnGQffs4M7127PNB+95//xsA+eab8AAfTt6hv4+J2sRlrUoCsH7OV7y3syaMnJV7eXv1ylJ0adfzct9ekLyLpKcoiqIoygnKiaMgO8UMgHvusT+v7KmnQp/+YxJUqIOfnw4csD6KnYnGC3PKcPvrr8OxY/TqBbOaNLH1gna4Awb46Tp1su/b8+FbrVqoaO3oJVmqXXL+b+EFCxf6niE8h8ibNsGGDdH76RjwHT9smD1GGiyXKmXbCsjCzp0AvDba2i3/pWsUE4tglD33EvJR5Ru4fFZAyd1oTRzunt4AgEe5L6yJTR/9RoOGwhU9apKWBi29+9izB/70J7/i0KH2+OST0e8T7LPq0gUmTgwVLbljYSjdpu1xbgRUBVlRFEVRUpoTR0FWlIJCFWRFURRFSWlUQVaUIOrFQlEURVFSnhNHQfZMHQAqVbLu07xQw/HMKyDcrCKI50FhxgzIzKTr5Fegt11BrF8feGd71mu88NEAhw9n329mpj0GorsFIyeHmBPhySEtzTeRyMiwx++/h2+/jd5PcNUz1gqoZ6oRxcQi2+jKy5f76aJFASgy5gY/fPaBA3CxdadW6bHo43HmmX66Zs3Aibffjl0xFo0b2+cW8DbRptZX0es2sCYfbNoUv11QG2RFURRFUU4gBVlRCgpVkBVFURQlpUkOBblFC1i2LNsqY1a1DKWvXfwDPXvCem9VePZsu2HPy0d6uIBw38j79tnAGSVK2JXonj2hdWt+fvoVxo4Gzw/DkiUwYs4cm3nnHd8Xc2BzGMuXw+TJsQX3VrcDG+bmzctarVOXHmH5zEwoUsSmS1aubBPr1oGXjmBJmh8oo02DKTZRsaJf4eBBuxlu3Dh4/XW/fORIeO45xribHh0MJDJtmj16YwCsr3E9ABP/A5d5myQHDgyt1HoL8kNdABKP4cNhFjboSc2aMH+HDW1d6fqGXPrmm3Y1etUq1nd6EICLZszwA7MEV+ybNGFJvaG0afw+Wyv5K8irPvCr3Ool5s3zo6z07UvCqIKsKIqiKClNcijIcZRjgHsHB0LFffEF06Zd7NsFeEpQNMXYY9++8Pz559ujp3y99RYV5kykWbN+4PS+q64i3IND5LWJEMX13M3BwBse3R4Ly57meZxIkDbB+4umgYNvZhCI6ueVVXAxPxgzLet1AROWix6zJiO33HIj3BRwvefMQJoOd/nlvnIM0K0bMBuqVrXD1+Ej5+Vi/37YssVv/8AKm4gVMnD1atq0NvDICup8/0qouE6bNlnrBj17JIqaWCiKoihKylPwCnLp0tCyZXjZrFnh0dqi0K3vGaH0zEm1WTgeLpo50xasXm3dvnkrvJE+kwHat/fTCxZAvXqwaxdrMxtyCUDZsnzapB/P/wvauWrz58Pohc59WJMm4Pr7uv3gUFObN0P7zwKhpocPJ8hnW2xcFE+HBxsILpLqIx4Nyx854u8V84ameHE47bSs10a2P2K/XeE+80DAXnnNGmvvO2FC+Ip3xYpQuTKLnS45NGjjfJ1dlT66em2oaNs2e3xiGFzdubPNTJoES5cCsNjp0uF3Y4euA/Dzz7aNvU+NBqyXtzrvvmtX+Fet4sF3rF/qh+rW9ZfQg27gVq5k+gyh4+ARYQvhu30dm3vBuoFr356fr3XryRVz4PpNFWRFURRFSWmSYwVZUZIF9WKhKIqiKCmPKsiKEomuICuKoihKSqMKsqIEURtkRVEURUl5xBhToB2mi5gYHoljcvdww6OPHWf4YCU5mDnT7dgrWATWGmOy2cVpSS9RwqypXTt+exs2JNTeyUB6erpZE8uPuKKkGCKS1HNf56uihJPbOasryIoSRFeQFUVRFCXlOSEUZF09PokohNXjHKOb9BRFURQlpSlS2AIojiJFwn8nK1WqFLYE2eOtIMf7KYqiKIpy0nJCrCArSoGiCrCiKIqipDSqICtKELVBVhRFUZSURxXkZCFVlLJduwpbgvikyrNQFEVRFCUqqiBHUqIEHDpk0w0awKZNWevUrRuenjs3Z32sWJG1rE6d8Py8eTYMNECpUn65VxbJ44Fw117c6UWLEpepZ097nDYt8WuiMXJk+DGnNGpkjxkZxyfH8aAKsqIoiqKkNKogK0oQDTWtKIqiKClPcijIrVvDgQOwbh1kZhauLN7qMURfPQYrr0duVpDffjtrWdWq4fnGjaFsWZsuV84vL148ept79/rpc85JXJYmTeDgweNfOfbI7cqxR2GuHIPaICuKoiiKkiQKsqIkE6ogK4qiKEpKU/AKctGiULJkeNl990GxYjBsGKxaVeAiJUTTpr5sbdv65SVKJHZ9s2Z++sors5z+6si5YfnvD55L5j6X+dIv9xaVI7m4f38/461wT54cX65Onez9NG4cv25BMH68PQ4eXHgyqIKsKIqiKClNgSvIv9VsxPuTwuPEL1gAj7d/P3wzWiS//+6n09KgRQtYtizP5ftul+GcKjZy32uvGv7S1UXxGzkSdu8GYG+TdqH6+/dDzZtv9huYNSu8wW+/tccKFUJFN9+W1Uzip5/C88WLh1t7gL3tWLpbjRq+TKc7C44+W0yo7IJ6MaIRVqwIJUowf56t26FjglELb7oJZs/OUvzdLtuON4aRHD1iKHqK+Jvxdu6Effv8Cn362GNQQW7Z0k+/956ffuyxUPKX3v8IpcuUDfR9xRX2uHJlNjcTQE0sFEVRFCXlURMLRYlEFWRFURRFSWkKXEEuWeIozS/8JaK0jP3Uv39/7AuDq7SQL6vHAOcsfD6U7tQpcGLq1NCKabnxv4aKy5Url3XVOMjLL9vjwYOhol69/pWl2pYt4fnff/cXzb3I02lpsRfZ69f30zVr2uPZY/8RvXKQQYPgyivpMGVK/LpBvv8+avE5T2XfZ9GdX9tErM14Dz2UtSy4ahxk1KhQskz58tHrJLpy7KFeLBRFURQl5dEVZEWJRFeQFUVRFCWlUQVZUYKoDbKiKIqipDxFClsARUk6jh2L/0sAEWkrIltF5EsRuSfK+dtE5GcRyXC/3scjdgL9FReRV9z5j0Wk+vH0pyjK8aFzVlGSF1WQFSWIt4J8nAqyiBQFngHaAfWAm0WkXpSqrxhjGrlfAn75jqu/24FfjDG1gCeBR3Pbn6Iox4fOWUVJblRBVpRIjhyJ/4tPE+BLY8x2Y0wmMBu4Lh+lTqS/64CXXPp14GoRSdCvn6IoeYzOWUVJYsQYE7/W8XYi0hfo67INgBgxnE9aygN7CluIAibZ7vlcY0yFeJVEZDFW9nikAQHn3EwyxkwKtNMFaGuM6e3yPYDLjDGDAnVuA8YAPwPbgKHGmG8T6Dua3In0t8nV2eXyX7k6WZ7TCTRnk+3vLEgyywbJLV8yy1bHGFMyfrXsycs5q/M1z0hm+VS23JOrOVsgm/Sc4jAJQETWGGPSC6LfZEHv+cTBGNM2fq2EiLbKE/k2Oh+YZYz5Q0T6Y1eK/pSP/SVSxxaeIHNWZcs9ySxfssuWV01FKcvVnNX5mjcks3wqW+7J7ZxVEwtFyR92AVUD+SpAmPNoY8xeY8wfLvs8cEl+9hesIyKnAGcC+1AUpTDQOasoSYwqyIqSP3wC1BaR80SkGHATMC9YQUTODmQ7Ap/lZ38uf6tLdwGWmoKwsVIUJRo6ZxUliSkMP8iT4lc56dB7TjGMMUdEZBCwBCgKTDHGbBaRUcAaY8w8YIiIdASOYFeFbsvn/l4ApovIl66/mxJsPpmfpcqWe5JZvpNetnycsyf92OUjySyfypZ7ciVfgWzSUxRFURRFUZQTBTWxUBRFURRFUZQAqiAriqIoiqIoSgBVkBVFSfqQtwnIN0xEtojIBhF5V0TOTRbZAvW6iIgRkQJzh5SIbCLS1Y3dZhGZWVCyJSKfiFQTkfdE5FP3bK8tQNmmiMhu54s42nkRkXFO9g0i0rigZHP9J+2c1fmav/IV1pxNuflqjNGf/vSXwj/sBqGvgBpAMWA9UC+izkDgOZe+CRsiO5nkawmUcOkBBSVfIrK5eiWB5cAqID1ZZANqA58CZVy+YpI910nAAJeuB+woQPmaAY2BTTHOXwsswvoqbgp8nGRjVyhzVudrvo9doczZVJyvuoKsKEqyh7yNK58x5j1jzCGXXYX1KZsUsjn+BTxGePTFZJCtD/CMMeYXAGPM7iSTzwClXPpMsvoJzjeMMcvJ3ufwdcA0Y1kFlI5w3ZifJPOc1fmae5J5zqbcfFUFWVGUc4BgiOtdrixqHWPMEeBXoFyBSJeYfEFux64UFARxZRORi4GqxpgFBSSTRyLjdj5wvoh8KCKrRCSvIkkmQiLyjQS6i8guYCEwuGBES4ic/l0WdN+FNWd1vuaeZJ6zKTdfk1JBFpGzRGSmiGwXkbUi8pGIXF8IcqSLyLg8aGekiNyVFzKlAjpeBU6ehqnOBxLuW0S6A+nA4/kqUaDLKGUh2USkCPAk8PcCkidIIuN2CvaTbQvgZmCyiJTOZ7k8EpHvZmCqMaYK9hPpdDemyUCyz4nCkk/na+5J5jmbcvM1WQQP4T4BzQWWG2NqGGMuwdpPFdQnmBDGmDXGmCEF0ZczIE+655HfiEjRPGijMALenEwke8jbRORDRFoB/wQ6Gj+Ed2HLVhJoACwTkR1Y27d5BbTxJ9Hn+qYx5rAx5mtgK/Y/34IgEfluB14FMMZ8BKQB5QtEuvgk9HdZiH0X1pzV+Zp/8nl1CmPOpt58LSgD6hwYWl8NvJ/N+aLYt81PgA1AP1feAliGtbX6HHgZPxDKJcD7wFps1KKzXfkQYItrZ3aUvloAC1x6JDDF9bEdGBJDvrbAOqwB+7vZXQtUx4YXfhZrdH8u9g1sI7AJeDTQ7kHgUXcP72Dtgbz2Oro6acCL7vpPgZauvD6wGshw91rblQ9z/WwC7gzI9Dkw2ZW/DLQCPgS+AJq4eqe7e/rE9XVdlLEQ96w2OZluDIzre8BMYIsr+yd2or8DzALucuU1gcXuvlcAdV35VGCsa+d/C/vv9kT+YVcktgPn4W++qB9R52+Eb/h5Ncnkuxi7gaR2so1dRP1lFNwmvUTGrS3wkkuXx36CLJdE8i0CbnPpC7D/oUkBPt/qxN7082fCN/2sTqa/u8Kaszpf833sCmXOpuJ8LbA/zBzc4BDgyWzO9wVGuHRxYI17YC2wNlZVsCvjHwFXAqcCK4EK7pobsSE9cQ+vuEuXjtJXC8IV5JWuz/LAXuDUiPoV3B/reS5fNrtr3cM8BjR19SoDO107pwBLgU7unAHaufQbwFuujYuADFf+d+BFl67r2koDxgO3uPJiwGnYl4aNWEX3DGAz9h+t6tjQxw3dOK7FKsKCNXKf69oZDXT3xg7YBpweMR43AG9jX2rOcvKc7cb1v4Fx8mQpgTXw/xJfQX4XX6G/DFjq0lOBBUDRwv6bPRl+2M9h27D/af3TlY0i/OXrNfdsVgM1kky+d4CfsC+BGcC8ZJEtou4yCvY/3HjjJtgXzS1uDt6UZM+1HvblfL17rq0LULZZwA/AYezq0+1Af6B/YOyecbJvLMjnmuDYFdqc1fmar2NXaHM21eZr0oWaFpEhWMVpqMs/g1V0M40xl4rI68CFgLcD9kygH5CJfWDXuOsmYB9UBlY53e7qFwV+MMa0FpHF2JXZuVjF72CELC2wilp7ERkJHDbGPOLOfQZcY4zZFajfAfvHektEO1GvxSrB7xljznPl1wE3GGN6uvzt2De0YSLyB5BmjDEiMgr4wxjziDPL2GeMKS0ibwDjjTFL3fUrsKsIDbArtNOAOcaYL0TkDuxb5wOu7r+An4F5wNvGmNqufBqwxBjzsojUcNc3EpE12H+Aj7jbLAu0McZ8FrjvJ4GNxpgpLj8d+w/2AeBBY0xLV34n9mXCk2Us9uXlOSfT1sBwFjfGXCAiU93YvYSiKIqiKEoekoy2m5uxK48AGGP+JiLlsSvFYN8CBhtjlgQvcsps0I7pKPb+BNhsjLk8Sl9/xvrO6wjcLyL1jd3tG4to7YeJQWyj71jX/jfi+lgcNv7bzDGvPWPMsYANbtTrjTEzReRj7P0uEZHecfoKynoskD8WkFuwynxQeY0kuz7+G5GPNm5FgP3GmEYJtqEoiqIoinLcJOOmsKVAmogMCJSVCKSXAANE5FQAETlfRE7Ppr2tQAURudzVP1VE6ruV16rGmPeA4VgzgTOOU/aPgOYi4q0Il83h9R+768u7zWs3Y22nE2U5cIvr+3ygGrDVrfxuN8aMw64QX+jqdhKREm78rsfa+CbKEmCw51fTucaJJs+NIlJURCpgX0ZWx6h3vYicJiIlgQ4AxpgDwNci8hfXh4jIRTmQUVEURVEUJcck3QqyMyHoBDwpIsOxn9j/C9ztqkzG2smuc8rZz0CnbNrLFJEuwDgRORN7z09h7WhmuDLB2j3vP07ZfxaRvsAcp4DvxppSJHr9DyJyL3bjmQALjTFv5kCEZ4HnRGQj1vThNmPMHyJyI9Y34WHgR2CUMWafM1PwFNbJxphPJfFwpP/CjuMG9xx2AO0j6rwBXI61RzLAcGPMjyJSN+K+14nIK1hzmG8IV9RvASaIyAiszfVs156iKIqiKEq+kHQ2yIqiKIqiKIpSmCSjiYWiKIqiKIqiFBqqICuKoiiKoihKAFWQFUVRFEVRFCWAKsiKoiiKoiiKEkAVZEVRFEVRFEUJoAqyoiiKoiiKogRQBVlRFEVRFEVRAvx/tfRFRxrg3icAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ranked_matirx = rank(df_2_or['exp_median'])\n",
    "df_2_or['exp_median'] = ranked_matirx\n",
    "plots_with_1_level_3d(df_2_or,'exp_median')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "resoultion=500"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2_or = pd.read_hdf('/data/lohia/gene_distance_expresseion/dist_files/combined_dist_with_georg_hic_sub_median_hic_%s.h5' %resoultion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2_or = df_2_or[['tss_tss', 'exp', 'hi-c-rao', 'Gene stable ID_x', 'Gene stable ID_y', 'chrom_x','gene_order_tss_x', 'gene_order_tss_y']]\n",
    "df_2_or['hi-c-rao'].isnull().astype(int).sum() / df_2_or.shape[0]\n",
    "df_2_or = df_2_or[df_2_or['hi-c-rao'] >= 0] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chr1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lohia/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  import sys\n",
      "/home/lohia/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py:1596: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.obj[key] = _infer_fill_value(value)\n",
      "/home/lohia/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py:1765: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  isetter(loc, value)\n"
     ]
    }
   ],
   "source": [
    "import itertools\n",
    "change_group_level_1 = df_2_or.groupby(['chrom_x'])\n",
    "matrix_all_dist = []\n",
    "for chrm in change_group_level_1.groups.keys():\n",
    "    print (chrm)\n",
    "    df_chr = change_group_level_1.get_group(chrm)\n",
    "    df_chr['pairs'] = [x+\"_\"+y for x,y in zip(df_chr['Gene stable ID_y'], df_chr['Gene stable ID_x'])]\n",
    "    df_chr.set_index('pairs', inplace=True)\n",
    "    unique_genes = list(set(df_chr['Gene stable ID_x'].unique()))\n",
    "    gene_resolution_pairs= {}\n",
    "    for each_gene in  unique_genes:\n",
    "        gene_resolution_pairs[each_gene] = df_chr[(df_chr['Gene stable ID_x'] == each_gene) & (df_chr['tss_tss'] <=resoultion * 1000)]['Gene stable ID_y'].to_list()\n",
    "    counter = 0\n",
    "    for pairs in itertools.combinations(unique_genes, 2):\n",
    "        index_list = [r[0]+ '_' + r[1] for r in itertools.product(gene_resolution_pairs[pairs[0]], gene_resolution_pairs[pairs[1]])] \n",
    "        df_chr.at[index_list, 'exp_mean'] = df_chr.loc[index_list,:]['exp'].mean()\n",
    "        df_chr.at[index_list, 'exp_median'] = df_chr.loc[index_list,:]['exp'].median()\n",
    "        index_list = [r[0]+ '_' + r[1] for r in itertools.product(gene_resolution_pairs[pairs[1]], gene_resolution_pairs[pairs[0]])] \n",
    "        df_chr.at[index_list, 'exp_mean'] = df_chr.loc[index_list,:]['exp'].mean()\n",
    "        df_chr.at[index_list, 'exp_median'] = df_chr.loc[index_list,:]['exp'].median()\n",
    "    matrix_all_dist.append(df_chr)\n",
    "prot_list = pd.concat(matrix_all_dist)    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000\n"
     ]
    }
   ],
   "source": [
    "counter = 0\n",
    "for pairs in itertools.combinations(unique_genes, 2):\n",
    "    counter = counter +1 \n",
    "    index_list = [r[0]+ '_' + r[1] for r in itertools.product(gene_resolution_pairs[pairs[0]], gene_resolution_pairs[pairs[1]])] \n",
    "    if counter == 1000:\n",
    "        print (counter)\n",
    "    df_2_or.at[index_list, 'exp_mean'] = df_2_or.loc[index_list,:]['exp'].mean()\n",
    "    index_list = [r[0]+ '_' + r[1] for r in itertools.product(gene_resolution_pairs[pairs[1]], gene_resolution_pairs[pairs[0]])] \n",
    "    df_2_or.at[index_list, 'exp_mean'] = df_2_or.loc[index_list,:]['exp'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "        matrix_all_dist.append(df_2_or)\n",
    "    prot_list = pd.concat(matrix_all_dist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2_or_u = df_2_or[df_2_or['Gene stable ID_x'] != df_2_or['Gene stable ID_y']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 500)"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "ax = sns.distplot(df_2_or_u['hi-c-rao'], bins=10000, hist=True, kde=False, hist_kws={\"range\":(1, 10000)})\n",
    "#ax.set_ylim(0, 500)\n",
    "ax.set_xlim(0, 500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 600,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lohia/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py:2446: PerformanceWarning: \n",
      "your performance may suffer as PyTables will pickle object types that it cannot\n",
      "map directly to c-types [inferred_type->mixed,key->block2_values] [items->Index(['chrm', 'plot', 'pr_curve', 'Gene stable ID'], dtype='object')]\n",
      "\n",
      "  encoding=encoding,\n"
     ]
    }
   ],
   "source": [
    "# import numpy as np\n",
    "import pandas as pd\n",
    "import warnings\n",
    "from lohia_utilities.calculate_auc import *\n",
    "from pandas.core.common import SettingWithCopyWarning\n",
    "warnings.simplefilter(action=\"ignore\", category=SettingWithCopyWarning)\n",
    "from lohia_utilities.create_corr_network import rank\n",
    "\n",
    "#df_2_or = pd.read_hdf('/data/lohia/gene_distance_expresseion/dist_files/11_dist_with_georg_hic_sub_median_hic_500.h5')\n",
    "\n",
    "#df_2_or = df_2_or[df_2_or['exp_georg'] >= 0] # liming the matrix to only chosen values for rank standerization\n",
    "#df_2_or = df_2_or[df_2_or['hi-c-rao'] >= 0] # liming the matrix to only chosen values for rank standerization\n",
    "df_2_or_u = df_2_or[df_2_or['Gene stable ID_x'] != df_2_or['Gene stable ID_y']]\n",
    "#ranked_matirx = rank(df_2_or['exp_georg'])\n",
    "#df_2_or['exp_georg'] = ranked_matirx\n",
    "#df_2_or.rename(columns={\"exp_georg\": \"exp (GK)\"}, inplace=True)\n",
    "\n",
    "ranked_matirx = rank(df_2_or['exp'])\n",
    "df_2_or['exp'] = ranked_matirx\n",
    "\n",
    "#ranked_matirx = rank(df_2_or['hi-c-rao'])\n",
    "#df_2_or['hi-c-rao'] = ranked_matirx\n",
    "m_l = []\n",
    "change_group_level_1 = df_2_or.groupby(['chrom_x'])\n",
    "for chrm in change_group_level_1.groups.keys():\n",
    "    df = change_group_level_1.get_group(chrm)\n",
    "    num_pairs = df['Gene stable ID_x'].nunique()\n",
    "\n",
    "    prot_list_sp = np.array_split(df, num_pairs, axis=0)\n",
    "    for i in range(0,num_pairs):\n",
    "\n",
    "        long_form_top = prot_list_sp[int(i)]\n",
    "        long_form_top['dist'] = long_form_top['hi-c-rao']\n",
    "        long_form_top = long_form_top[long_form_top['tss_tss'] >= 10000000] # liming the matrix to only chosen values for rank standerization\n",
    "\n",
    "        long_form_top = long_form_top[long_form_top['Gene stable ID_x'] != long_form_top['Gene stable ID_y']] # remove all the self pairs from each set\n",
    "        long_form_top['hi-c-rao'].fillna(-1, inplace=True)\n",
    "        mp =  long_form_top['Gene stable ID_y'].values[0]\n",
    "        #print (long_form_top.shape)\n",
    "        \n",
    "        exp_median = long_form_top['exp'].median()\n",
    "        exp_mean = long_form_top['exp'].mean()\n",
    "        exp_var = long_form_top['exp'].var()\n",
    "\n",
    "        long_form_top = long_form_top.reset_index()\n",
    "        if long_form_top['exp'].values[0] >=0:\n",
    "    \n",
    "       \n",
    "\n",
    "            for dist_thresh in [1,5,10,100,int(num_pairs/2),700]:\n",
    "            #for dist_thresh in [100000,1000000,10000000,100000000]:\n",
    "            #for dist_thresh in [4000]:\n",
    "            #for dist_thresh in [df_2_or_u[\"hi-c-rao\"].min(), df_2_or[\"hi-c-rao\"].max()-1, df_2_or[\"hi-c-rao\"].mean(), df_2_or[\"hi-c-rao\"].median()]:\n",
    "                #long_form_top[\"True_sim\"] = [1 if score > dist_thresh else 0 for score in long_form_top[\"dist\"]]  \n",
    "                \n",
    "                long_form_top = long_form_top.sort_values(by=['dist'], ascending=False) \n",
    "                long_form_top[\"True_sim\"] = [0 if score > dist_thresh else 0 for score in long_form_top[\"dist\"]]  \n",
    "                for ind_val in long_form_top.index.values[0:dist_thresh]:\n",
    "                    long_form_top.at[ind_val, 'True_sim'] = 1\n",
    "                    \n",
    "                #long_form_top = long_form_top.sort_values(by=['dist'], ascending=True) \n",
    "                #long_form_top[\"True_sim\"] = [1 if score > dist_thresh else 1 for score in long_form_top[\"dist\"]]  \n",
    "                #for ind_val in long_form_top.index.values[0:dist_thresh]:\n",
    "                #    long_form_top.at[ind_val, 'True_sim'] = 0\n",
    "                #\n",
    "                #long_form_top[\"True_sim\"] = [1 if score <= dist_thresh else 0 for score in long_form_top[\"dist\"]] \n",
    "                #long_form_top[\"True_sim\"] = [1 if score >= dist_thresh else 1 if score2 <= 1000 else 0 for score, score2 in zip(long_form_top[\"dist\"],long_form_top[\"tss_tss\"])]    \n",
    "                long_form_top[\"true_pos\"] = [score for score in long_form_top[\"True_sim\"]]\n",
    "                long_form_top[\"true_neg\"] = [1 if score==0 else 0 for score in long_form_top[\"True_sim\"]]\n",
    "                long_form_top[\"predicted_sim_from_exp\"] = [score for score in long_form_top[\"exp_median\"]]\n",
    "                ca = calc_auroc (long_form_top,predicted_score='predicted_sim_from_exp')\n",
    "                m_curve = calc_auc_curve (long_form_top,predicted_score='predicted_sim_from_exp')\n",
    "                pr_curve = prec_recall (long_form_top,predicted_score='predicted_sim_from_exp')\n",
    "\n",
    "                tpd = pd.DataFrame(m_curve)\n",
    "                if m_curve:\n",
    "                    tpd[0] =  tpd[0].astype(float).round(2)\n",
    "                    tpd = tpd.groupby([0]).mean()\n",
    "                    m_curve  = dict(zip(tpd.index, tpd[1]))\n",
    "                else:\n",
    "                    m_curve  = {}\n",
    "                tpd = pd.DataFrame(pr_curve)\n",
    "                if pr_curve:\n",
    "                    tpd[0] =  tpd[0].astype(float).round(2)\n",
    "                    tpd = tpd.groupby([0]).mean()\n",
    "                    pr_curve  = dict(zip(tpd.index, tpd[1]))\n",
    "                else:\n",
    "                    pr_curve  = {}\n",
    "                m_l.append((chrm, num_pairs,dist_thresh, ca, m_curve, pr_curve, long_form_top[\"true_pos\"].sum(), long_form_top[\"true_neg\"].sum(), exp_median, exp_mean, exp_var, mp))\n",
    "        else:\n",
    "            pass\n",
    "\n",
    "df_scores = pd.DataFrame(m_l, columns =['chrm', 'num_pairs','dist_thresh',  'auc', 'plot', 'pr_curve', 'true_pos', 'true_neg', 'exp_median', 'exp_mean', 'exp_var', 'Gene stable ID'])\n",
    "df_scores.to_hdf('/data/lohia/gene_distance_expresseion/dist_files/chr11_100kb.h5', key='df', mode='w')    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 601,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_scores = pd.read_hdf('/data/lohia/gene_distance_expresseion/dist_files/chr11_100kb.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 493,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chrm</th>\n",
       "      <th>num_pairs</th>\n",
       "      <th>dist_thresh</th>\n",
       "      <th>auc</th>\n",
       "      <th>plot</th>\n",
       "      <th>pr_curve</th>\n",
       "      <th>true_pos</th>\n",
       "      <th>true_neg</th>\n",
       "      <th>exp_median</th>\n",
       "      <th>exp_mean</th>\n",
       "      <th>exp_var</th>\n",
       "      <th>Gene stable ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chr11</td>\n",
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       "      <th>1</th>\n",
       "      <td>chr11</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>{0.0: nan, 0.01: nan, 0.02: nan, 0.03: nan, 0....</td>\n",
       "      <td>{}</td>\n",
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       "      <td>932</td>\n",
       "      <td>0.732026</td>\n",
       "      <td>0.656574</td>\n",
       "      <td>0.070272</td>\n",
       "      <td>ENSG00000183340</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chr11</td>\n",
       "      <td>1004</td>\n",
       "      <td>4000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{0.0: nan, 0.01: nan, 0.02: nan, 0.03: nan, 0....</td>\n",
       "      <td>{}</td>\n",
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       "      <td>647</td>\n",
       "      <td>0.392391</td>\n",
       "      <td>0.379631</td>\n",
       "      <td>0.036577</td>\n",
       "      <td>ENSG00000182791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>chr11</td>\n",
       "      <td>1004</td>\n",
       "      <td>4000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{0.0: nan, 0.01: nan, 0.02: nan, 0.03: nan, 0....</td>\n",
       "      <td>{}</td>\n",
       "      <td>0</td>\n",
       "      <td>828</td>\n",
       "      <td>0.822697</td>\n",
       "      <td>0.728352</td>\n",
       "      <td>0.067169</td>\n",
       "      <td>ENSG00000132275</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chr11</td>\n",
       "      <td>1004</td>\n",
       "      <td>4000</td>\n",
       "      <td>0.561063</td>\n",
       "      <td>{0.0: 0.0, 0.01: 0.0, 0.02: 0.0104166666666666...</td>\n",
       "      <td>{0.0: 0.0, 0.08: 0.021899579772188874, 0.17: 0...</td>\n",
       "      <td>12</td>\n",
       "      <td>696</td>\n",
       "      <td>0.650224</td>\n",
       "      <td>0.608971</td>\n",
       "      <td>0.084299</td>\n",
       "      <td>ENSG00000168496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>chr11</td>\n",
       "      <td>1004</td>\n",
       "      <td>4000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{0.0: nan, 0.01: nan, 0.02: nan, 0.03: nan, 0....</td>\n",
       "      <td>{}</td>\n",
       "      <td>0</td>\n",
       "      <td>896</td>\n",
       "      <td>0.710174</td>\n",
       "      <td>0.617268</td>\n",
       "      <td>0.067416</td>\n",
       "      <td>ENSG00000151702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>chr11</td>\n",
       "      <td>1004</td>\n",
       "      <td>4000</td>\n",
       "      <td>0.820235</td>\n",
       "      <td>{0.0: 0.0, 0.01: 0.0, 0.02: 0.0, 0.03: 0.0, 0....</td>\n",
       "      <td>{0.0: 0.0, 0.33: 0.011573465587966434, 0.67: 0...</td>\n",
       "      <td>3</td>\n",
       "      <td>738</td>\n",
       "      <td>0.580503</td>\n",
       "      <td>0.522850</td>\n",
       "      <td>0.076284</td>\n",
       "      <td>ENSG00000156587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1001</th>\n",
       "      <td>chr11</td>\n",
       "      <td>1004</td>\n",
       "      <td>4000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{0.0: nan, 0.01: nan, 0.02: nan, 0.03: nan, 0....</td>\n",
       "      <td>{}</td>\n",
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       "      <td>646</td>\n",
       "      <td>0.653405</td>\n",
       "      <td>0.605870</td>\n",
       "      <td>0.066594</td>\n",
       "      <td>ENSG00000173715</td>\n",
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       "    <tr>\n",
       "      <th>1002</th>\n",
       "      <td>chr11</td>\n",
       "      <td>1004</td>\n",
       "      <td>4000</td>\n",
       "      <td>0.427163</td>\n",
       "      <td>{0.0: 0.0, 0.01: 0.0, 0.02: 0.0, 0.03: 0.01739...</td>\n",
       "      <td>{0.0: 0.0, 0.04: 0.03137291143578475, 0.09: 0....</td>\n",
       "      <td>23</td>\n",
       "      <td>913</td>\n",
       "      <td>0.657461</td>\n",
       "      <td>0.592015</td>\n",
       "      <td>0.051981</td>\n",
       "      <td>ENSG00000166086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003</th>\n",
       "      <td>chr11</td>\n",
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       "      <td>4000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{0.0: nan, 0.01: nan, 0.02: nan, 0.03: nan, 0....</td>\n",
       "      <td>{}</td>\n",
       "      <td>0</td>\n",
       "      <td>647</td>\n",
       "      <td>0.728689</td>\n",
       "      <td>0.649545</td>\n",
       "      <td>0.071352</td>\n",
       "      <td>ENSG00000239306</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1004 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       chrm  num_pairs  dist_thresh       auc  \\\n",
       "0     chr11       1004         4000       NaN   \n",
       "1     chr11       1004         4000       NaN   \n",
       "2     chr11       1004         4000       NaN   \n",
       "3     chr11       1004         4000       NaN   \n",
       "4     chr11       1004         4000  0.561063   \n",
       "...     ...        ...          ...       ...   \n",
       "999   chr11       1004         4000       NaN   \n",
       "1000  chr11       1004         4000  0.820235   \n",
       "1001  chr11       1004         4000       NaN   \n",
       "1002  chr11       1004         4000  0.427163   \n",
       "1003  chr11       1004         4000       NaN   \n",
       "\n",
       "                                                   plot  \\\n",
       "0     {0.0: nan, 0.01: nan, 0.02: nan, 0.03: nan, 0....   \n",
       "1     {0.0: nan, 0.01: nan, 0.02: nan, 0.03: nan, 0....   \n",
       "2     {0.0: nan, 0.01: nan, 0.02: nan, 0.03: nan, 0....   \n",
       "3     {0.0: nan, 0.01: nan, 0.02: nan, 0.03: nan, 0....   \n",
       "4     {0.0: 0.0, 0.01: 0.0, 0.02: 0.0104166666666666...   \n",
       "...                                                 ...   \n",
       "999   {0.0: nan, 0.01: nan, 0.02: nan, 0.03: nan, 0....   \n",
       "1000  {0.0: 0.0, 0.01: 0.0, 0.02: 0.0, 0.03: 0.0, 0....   \n",
       "1001  {0.0: nan, 0.01: nan, 0.02: nan, 0.03: nan, 0....   \n",
       "1002  {0.0: 0.0, 0.01: 0.0, 0.02: 0.0, 0.03: 0.01739...   \n",
       "1003  {0.0: nan, 0.01: nan, 0.02: nan, 0.03: nan, 0....   \n",
       "\n",
       "                                               pr_curve  true_pos  true_neg  \\\n",
       "0                                                    {}         0       650   \n",
       "1                                                    {}         0       932   \n",
       "2                                                    {}         0       647   \n",
       "3                                                    {}         0       828   \n",
       "4     {0.0: 0.0, 0.08: 0.021899579772188874, 0.17: 0...        12       696   \n",
       "...                                                 ...       ...       ...   \n",
       "999                                                  {}         0       896   \n",
       "1000  {0.0: 0.0, 0.33: 0.011573465587966434, 0.67: 0...         3       738   \n",
       "1001                                                 {}         0       646   \n",
       "1002  {0.0: 0.0, 0.04: 0.03137291143578475, 0.09: 0....        23       913   \n",
       "1003                                                 {}         0       647   \n",
       "\n",
       "      exp_median  exp_mean   exp_var   Gene stable ID  \n",
       "0       0.592583  0.547375  0.063044  ENSG00000174669  \n",
       "1       0.732026  0.656574  0.070272  ENSG00000183340  \n",
       "2       0.392391  0.379631  0.036577  ENSG00000182791  \n",
       "3       0.822697  0.728352  0.067169  ENSG00000132275  \n",
       "4       0.650224  0.608971  0.084299  ENSG00000168496  \n",
       "...          ...       ...       ...              ...  \n",
       "999     0.710174  0.617268  0.067416  ENSG00000151702  \n",
       "1000    0.580503  0.522850  0.076284  ENSG00000156587  \n",
       "1001    0.653405  0.605870  0.066594  ENSG00000173715  \n",
       "1002    0.657461  0.592015  0.051981  ENSG00000166086  \n",
       "1003    0.728689  0.649545  0.071352  ENSG00000239306  \n",
       "\n",
       "[1004 rows x 12 columns]"
      ]
     },
     "execution_count": 493,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 595,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "803"
      ]
     },
     "execution_count": 595,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scores[df_scores['dist_thresh'] == 4000]['auc'].isnull().astype(int).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>14</th>\n",
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       "      <td>773</td>\n",
       "      <td>2000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{0.0: nan, 0.01: nan, 0.02: nan, 0.03: nan, 0....</td>\n",
       "      <td>{}</td>\n",
       "      <td>0</td>\n",
       "      <td>437</td>\n",
       "      <td>0.486055</td>\n",
       "      <td>0.477094</td>\n",
       "      <td>0.069154</td>\n",
       "      <td>ENSG00000198931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>chr16</td>\n",
       "      <td>773</td>\n",
       "      <td>2000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{0.0: nan, 0.01: nan, 0.02: nan, 0.03: nan, 0....</td>\n",
       "      <td>{}</td>\n",
       "      <td>0</td>\n",
       "      <td>420</td>\n",
       "      <td>0.554352</td>\n",
       "      <td>0.556762</td>\n",
       "      <td>0.077769</td>\n",
       "      <td>ENSG00000243716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>chr16</td>\n",
       "      <td>773</td>\n",
       "      <td>2000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{0.0: nan, 0.01: nan, 0.02: nan, 0.03: nan, 0....</td>\n",
       "      <td>{}</td>\n",
       "      <td>0</td>\n",
       "      <td>428</td>\n",
       "      <td>0.828832</td>\n",
       "      <td>0.726001</td>\n",
       "      <td>0.064853</td>\n",
       "      <td>ENSG00000168434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3844</th>\n",
       "      <td>chr16</td>\n",
       "      <td>773</td>\n",
       "      <td>2000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{0.0: nan, 0.01: nan, 0.02: nan, 0.03: nan, 0....</td>\n",
       "      <td>{}</td>\n",
       "      <td>0</td>\n",
       "      <td>428</td>\n",
       "      <td>0.425241</td>\n",
       "      <td>0.405120</td>\n",
       "      <td>0.049482</td>\n",
       "      <td>ENSG00000260807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3849</th>\n",
       "      <td>chr16</td>\n",
       "      <td>773</td>\n",
       "      <td>2000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{0.0: nan, 0.01: nan, 0.02: nan, 0.03: nan, 0....</td>\n",
       "      <td>{}</td>\n",
       "      <td>0</td>\n",
       "      <td>432</td>\n",
       "      <td>0.490119</td>\n",
       "      <td>0.454270</td>\n",
       "      <td>0.064232</td>\n",
       "      <td>ENSG00000169627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3854</th>\n",
       "      <td>chr16</td>\n",
       "      <td>773</td>\n",
       "      <td>2000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{0.0: nan, 0.01: nan, 0.02: nan, 0.03: nan, 0....</td>\n",
       "      <td>{}</td>\n",
       "      <td>0</td>\n",
       "      <td>442</td>\n",
       "      <td>0.474922</td>\n",
       "      <td>0.465165</td>\n",
       "      <td>0.052825</td>\n",
       "      <td>ENSG00000248124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3859</th>\n",
       "      <td>chr16</td>\n",
       "      <td>773</td>\n",
       "      <td>2000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{0.0: nan, 0.01: nan, 0.02: nan, 0.03: nan, 0....</td>\n",
       "      <td>{}</td>\n",
       "      <td>0</td>\n",
       "      <td>494</td>\n",
       "      <td>0.360949</td>\n",
       "      <td>0.368468</td>\n",
       "      <td>0.032043</td>\n",
       "      <td>ENSG00000102935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3864</th>\n",
       "      <td>chr16</td>\n",
       "      <td>773</td>\n",
       "      <td>2000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{0.0: nan, 0.01: nan, 0.02: nan, 0.03: nan, 0....</td>\n",
       "      <td>{}</td>\n",
       "      <td>0</td>\n",
       "      <td>495</td>\n",
       "      <td>0.572479</td>\n",
       "      <td>0.551079</td>\n",
       "      <td>0.061528</td>\n",
       "      <td>ENSG00000177200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>773 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       chrm  num_pairs  dist_thresh  auc  \\\n",
       "4     chr16        773         2000  NaN   \n",
       "9     chr16        773         2000  NaN   \n",
       "14    chr16        773         2000  NaN   \n",
       "19    chr16        773         2000  NaN   \n",
       "24    chr16        773         2000  NaN   \n",
       "...     ...        ...          ...  ...   \n",
       "3844  chr16        773         2000  NaN   \n",
       "3849  chr16        773         2000  NaN   \n",
       "3854  chr16        773         2000  NaN   \n",
       "3859  chr16        773         2000  NaN   \n",
       "3864  chr16        773         2000  NaN   \n",
       "\n",
       "                                                   plot pr_curve  true_pos  \\\n",
       "4     {0.0: nan, 0.01: nan, 0.02: nan, 0.03: nan, 0....       {}         0   \n",
       "9     {0.0: nan, 0.01: nan, 0.02: nan, 0.03: nan, 0....       {}         0   \n",
       "14    {0.0: nan, 0.01: nan, 0.02: nan, 0.03: nan, 0....       {}         0   \n",
       "19    {0.0: nan, 0.01: nan, 0.02: nan, 0.03: nan, 0....       {}         0   \n",
       "24    {0.0: nan, 0.01: nan, 0.02: nan, 0.03: nan, 0....       {}         0   \n",
       "...                                                 ...      ...       ...   \n",
       "3844  {0.0: nan, 0.01: nan, 0.02: nan, 0.03: nan, 0....       {}         0   \n",
       "3849  {0.0: nan, 0.01: nan, 0.02: nan, 0.03: nan, 0....       {}         0   \n",
       "3854  {0.0: nan, 0.01: nan, 0.02: nan, 0.03: nan, 0....       {}         0   \n",
       "3859  {0.0: nan, 0.01: nan, 0.02: nan, 0.03: nan, 0....       {}         0   \n",
       "3864  {0.0: nan, 0.01: nan, 0.02: nan, 0.03: nan, 0....       {}         0   \n",
       "\n",
       "      true_neg  exp_median  exp_mean   exp_var   Gene stable ID  \n",
       "4          495    0.345003  0.322651  0.030117  ENSG00000245694  \n",
       "9          496    0.619299  0.585025  0.087750  ENSG00000245694  \n",
       "14         437    0.486055  0.477094  0.069154  ENSG00000198931  \n",
       "19         420    0.554352  0.556762  0.077769  ENSG00000243716  \n",
       "24         428    0.828832  0.726001  0.064853  ENSG00000168434  \n",
       "...        ...         ...       ...       ...              ...  \n",
       "3844       428    0.425241  0.405120  0.049482  ENSG00000260807  \n",
       "3849       432    0.490119  0.454270  0.064232  ENSG00000169627  \n",
       "3854       442    0.474922  0.465165  0.052825  ENSG00000248124  \n",
       "3859       494    0.360949  0.368468  0.032043  ENSG00000102935  \n",
       "3864       495    0.572479  0.551079  0.061528  ENSG00000177200  \n",
       "\n",
       "[773 rows x 12 columns]"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scores[df_scores['dist_thresh'] == 2000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "fig, axes = plt.subplots(figsize=(20,10))\n",
    "#grouped = df_scores.groupby(['threshold'])\n",
    "\n",
    "#bp = grouped.boxplot(subplots=False, sym='k+', figsize=(8,10))\n",
    "#bp = df_scores.boxplot(column=['auc'], by=['chrm', 'dist_thresh'], ax=axes,rot=40, fontsize=8,layout=(2, 1))\n",
    "sns.boxplot(y='auc', x='chrm', \n",
    "                 data=df_scores, \n",
    "                 palette=\"colorblind\"\n",
    "                 ,hue='dist_thresh'\n",
    "           )\n",
    "#bp = axes.boxplot([[x if x>=0 else -1 for x in top_500_score_auroc_0_9], [x if x>=0 else -1 for x in top_500_score_auroc_0_7], [x if x>=0 else -1 for x in top_500_score_auroc_0_5], [x if x>=0 else -1 for x in top_500_score_auroc_0_4]] , sym='k+')\n",
    "#axes.set_title('Predicting structure similarity from expression')\n",
    "axes.yaxis.grid(True)\n",
    "#axes.set_xlabel('Co-expression')\n",
    "axes.set_ylabel('AUC')\n",
    "axes.set_ylim([0.0,1.101])\n",
    "#plt.setp(bp['fliers'], markersize=3.0)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "fig, axes = plt.subplots(figsize=(20,10))\n",
    "#grouped = df_scores.groupby(['threshold'])\n",
    "\n",
    "#bp = grouped.boxplot(subplots=False, sym='k+', figsize=(8,10))\n",
    "#bp = df_scores.boxplot(column=['auc'], by=['chrm', 'dist_thresh'], ax=axes,rot=40, fontsize=8,layout=(2, 1))\n",
    "sns.boxplot(y='auc', x='chrm', \n",
    "                 data=df_scores, \n",
    "                 palette=\"colorblind\"\n",
    "                 ,hue='dist_thresh'\n",
    "           )\n",
    "#bp = axes.boxplot([[x if x>=0 else -1 for x in top_500_score_auroc_0_9], [x if x>=0 else -1 for x in top_500_score_auroc_0_7], [x if x>=0 else -1 for x in top_500_score_auroc_0_5], [x if x>=0 else -1 for x in top_500_score_auroc_0_4]] , sym='k+')\n",
    "#axes.set_title('Predicting structure similarity from expression')\n",
    "axes.yaxis.grid(True)\n",
    "#axes.set_xlabel('Co-expression')\n",
    "axes.set_ylabel('AUC')\n",
    "axes.set_ylim([0.0,1.101])\n",
    "#plt.setp(bp['fliers'], markersize=3.0)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 602,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "fig, axes = plt.subplots(figsize=(20,10))\n",
    "#grouped = df_scores.groupby(['threshold'])\n",
    "\n",
    "#bp = grouped.boxplot(subplots=False, sym='k+', figsize=(8,10))\n",
    "#bp = df_scores.boxplot(column=['auc'], by=['chrm', 'dist_thresh'], ax=axes,rot=40, fontsize=8,layout=(2, 1))\n",
    "sns.boxplot(y='auc', x='chrm', \n",
    "                 data=df_scores, \n",
    "                 palette=\"colorblind\"\n",
    "                 ,hue='dist_thresh'\n",
    "           )\n",
    "#bp = axes.boxplot([[x if x>=0 else -1 for x in top_500_score_auroc_0_9], [x if x>=0 else -1 for x in top_500_score_auroc_0_7], [x if x>=0 else -1 for x in top_500_score_auroc_0_5], [x if x>=0 else -1 for x in top_500_score_auroc_0_4]] , sym='k+')\n",
    "#axes.set_title('Predicting structure similarity from expression')\n",
    "axes.yaxis.grid(True)\n",
    "#axes.set_xlabel('Co-expression')\n",
    "axes.set_ylabel('AUC')\n",
    "axes.set_ylim([0.0,1.101])\n",
    "#plt.setp(bp['fliers'], markersize=3.0)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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URYsW5YorrsgrXvGK/Md//EeOPvro3Hzzzbn00ktz6623Dlnm3ZRHAAAAAPvYsmlL/vz17xuy833hri8/7+9dsWJF1qxZkyS5+OKLM2vWrFxxxRU57bTT9sw59dRTs379+hcac1DKIwBoYIsXL05PT0/VMQbo7e1NkrS0tFSc5A9aW1vT2dlZdQwAgBesKIqcddZZKYoif/7nf55LL700GzZsSHNzc5Kkubk5GzdufNb3LVu2LOecc05dMimPAICD8sQTT1QdAQDgsPWDH/wgU6ZMycaNGzNnzpy85jWvec7vWb16dZYtW5b//M//rEsm5REANLBGXE3T3t6eJFm6dGnFSQAADj9TpkxJkkyaNCnnnXdefvKTn2Ty5Mnp6+tLc3Nz+vr6MmnSpD3z77777rS3t+fmm2/OscceW5dMnrYGAAAA0AAef/zxPProo3u+/va3v52TTjopc+fOzfLly5Mky5cvz7x585Ikv/vd73L++efnX//1X/PqV7+6brmsPAIAAABoABs2bMh5552XJNm+fXve+9735uyzz86b3vSmvPvd786yZcty/PHH5+tf/3qS5PLLL8/mzZvzF3/xF0mSMWPG5Pbbbx/yXMojAAAAgH0c03TMC3pC2mDney6vfOUrc9dddz3r+LHHHptVq1Y96/jSpUsPyVYCyiMAAACAfSz5/GeqjtAw7HkEAAAAQE3KIwAAAABqUh4BAAAAUJPyCAAAAICalEcAAAAA1ORpaxxSvb29GfvY1hxz9/VVR2EYG/vYpvT2bq86BgAAwJDq6enJBRdcsGd877335vLLL89FF12UCy64IPfdd1+mTZuWr33tazn66KNTlmUuu+yydHd3Z/z48bn66qszffr0Ic+lPAIAAADYx1/9xZ/n4U39Q3a+o5sm5jOf/cJ+57S2tmbt2rVJkh07duS4447Leeedl0WLFmX27NlZuHBhFi1alEWLFuWKK67IzTffnHXr1mXdunW59dZb8+EPfzi33nrrkGXeTXnEIdXS0pLe/x6TLSefX3UUhrFj7r4+LS3NVccAAAAOYw9v6s8HT28ZsvP9y/d7D2r+qlWr8qpXvSovf/nLs2LFiqxZsyZJcvHFF2fWrFm54oorsmLFilx00UUpiiKnnnpqtm7dmr6+vjQ3D+3PS/Y8AgAAAGgwX/3qV3PhhRcmSTZs2LCnEGpubs7GjRuTJA888EBaWv5QcE2dOjUPPPDAkGdRHgEAAAA0kKeffjo33nhj3vWud+13XlmWzzpWFMWQ51EeAQAAADSQm2++OdOnT8/kyZOTJJMnT05fX1+SpK+vL5MmTUqya6VRb+8fbodbv359pkyZMuR5lEcAAAAADeTaa6/dc8taksydOzfLly9Pkixfvjzz5s3bc/yaa65JWZb58Y9/nAkTJgz5fkeJDbMBAAAAGsYTTzyRW265JV/4wh+ezLZw4cK8+93vzrJly3L88cfn61//epKkra0t3d3dOeGEEzJ+/Ph88YtfrEsm5REAAADAPo5umnjQT0h7rvMdiPHjx2fz5s0Djh177LFZtWrVs+YWRZErr7xySPLtj/IIAAAAYB+f+ewXnnvSCGHPIwAAAABqUh4BAAAAUJPyCAAAABiRyrKsOsIh93x+zcojAAAAYMQZN25cNm/ePKIKpLIss3nz5owbN+6gvs+G2QAAAMCIM3Xq1Kxfvz79/f1VRzmkxo0bl6lTpx7U9yiPAAAAgBFn7NixecUrXlF1jGHBbWsAAAAA1KQ8AgAAAKAm5REAAAAANSmPAAAAAKhJeQQAAABATcojAAAAAGpSHgEAAABQk/IIAAAAgJqURwAAAADUpDwCAAAAoCblEQAAAAA1KY8AAAAAqEl5BAAAAEBNyiMAAAAAalIeAQAAAFDTmKoDALwQixcvTk9PT9UxGtruz6e9vb3iJI2ttbU1nZ2dVccAAICGozwChrWenp784u7bc9xLd1QdpWGN3rFrkekjv7u14iSN64Gto6uOAAAADUt5BAx7x710Ry6b/VjVMRjGPr3qyKojAABAw7LnEQAAAAA1KY8AAAAAqEl5BAAAAEBNyiMAAAAAalIeAQAAAFCT8ggAAACAmpRHAAAAANSkPAIAAACgJuURAAAAADUpjwAAAACoSXkEAAAAQE11LY+Koji7KIqeoijuKYpi4SCvH18UxeqiKO4siuLuoija6pkHAAAADkZ/f3/mz5+fTZs2VR0FKlO38qgoitFJrkxyTpLXJrmwKIrX7jPtb5N8rSzLNyR5T5LP1isPAAAAHKwlS5bkjjvuyJIlS6qOApWp58qjNye5pyzLe8uyfDrJV5PM22dOmeQlz3w9IcmDdcwDAAAAB6y/vz/d3d1JkpUrV1p9xIhVz/LouCS9e43XP3Nsb59I8v6iKNYn6U7yV3XMAwAAAAdsyZIl2blzZ5Jk586dVh8xYo2p47mLQY6V+4wvTHJ1WZb/WBTFW5P8a1EUJ5VluXPAiYri0iSXJsnkyZOzZs2aeuTlEDj99NPzhjdvy44jJlQdhWFs9Enn5cg/Gps1a9bk9NNPz46np2frUTuqjsUwds67Rmf0i17sz5cDtHXr1iTxeQFw2Nu96mi3lStX5swzz6woDVSnnuXR+iQte42n5tm3pc1PcnaSlGX5o6IoxiVpSrJx70llWV6V5KokmTFjRjlr1qw6Rabe2tvb88N7+rLl5POrjsIwdszd38xpJzTn4osvTnt7ex753a25bPZjVcdiGFu+6shMOP4tufjii6uOMix86UtfSpL48xiAw92oUaP2rDzaPfbnHyNRPW9buy3JiUVRvKIoihdl14bYN+4z53dJZidJURR/nGRckv46ZgIAAIADcvbZZw8Yn3POORUlgWrVrTwqy3J7ko8k+fckv8yup6r9vCiKy4uimPvMtP87SUdRFHcluTbJJWVZ7ntrGwAAABxyCxYsyKhRu35sHj16dBYsWFBxIqhGPW9bS1mW3dm1Efbex/5ur69/keR/1DMDAAAAPB8TJ05MW1tbbrrpprS1taWpqanqSFCJupZHAAAAMJwtWLAgDz74oFVHjGjKIwAAAKhh4sSJWbZsWdUxoFL13DAbAAAAhrX+/v7Mnz8/mzZtqjoKVEZ5BAAAADV0dXXlzjvvTFdXV9VRoDLKIwAAABhEf39/VqxYkbIsc8MNN1h9xIilPAIAAIBBdHV1Zdu2bUmSbdu2WX3EiGXDbADYy+LFi9PT01N1jIa2+/Npb2+vOElja21tTWdnZ9UxAHgBbrrpppRlmSQpyzLf+ta38vGPf7ziVHDoKY8AYC89PT257e612Xb0i6uO0rBG73g6SfLD3nUVJ2lcYx9+vOoIAAyB5ubm3HvvvXvGU6ZMqTANVEd5BAD72Hb0i7NpzklVx2AYa7rlZ1VHAGAIPPTQQwPGfX19FSWBatnzCAAAAAZx7rnnZtSoXT82jxo1Ku94xzsqTgTVsPIIAGAEabR9vXp7e5MkLS0tFScZyJ5VQJJ0dHRkxYoVefrppzNmzJh0dHRUHQkqYeURAACVeeKJJ/LEE09UHQNgUBMnTsy8efNSFEX+5E/+JE1NTVVHgkpYeQQAMII02mqa3U/tW7p0acVJAAbX0dGR3/zmN1YdMaIpjwAAAKCGiRMnZtmyZVXHgEopjwAAgOfNPloHxj5awHBmzyMAAOCwYR8thtqvfvWrnH766fn1r39ddRSojJVHAADA89Zoq2nso8VQ++u//us89thj+djHPpYbbrih6jhQCSuPAAAAYBC/+tWv8rvf/S5Jcv/991t9xIilPAIAAIBB/PVf//WA8cc+9rGKkkC1lEcAAAAwiN2rjna7//77K0oC1VIeAQAAwCCKotjvGEYK5REAAAAMYvbs2QPGc+bMqSgJVEt5BAAAAIOYP3/+fscwUoypOgDAC9Hb25vfbx2dT686suooDGPrt47O74veqmMAAA3m+uuvHzD+xje+kY9//OMVpYHqWHkEAAAAg+ju7h4wXrlyZUVJoFpWHgHDWktLSx4pH8xlsx+rOgrD2KdXHZkJLS1VxwAAGswZZ5yRm266ac/4zDPPrDANVMfKIwAAAABqUh4BAADAIFavXj1g/N3vfreiJFAt5REAAAAM4owzzhgwdtsaI5XyCAAAAICalEcAAAAwiFWrVg0Yf+c736koCVRLeQQAAACDaG5uHjCeMmVKRUmgWmOqDsDIM/axTTnm7uurjtGwRj/5SJJkxxETKk7SuMY+tilJ83POAwCAF6Kvr2/A+MEHH6woCVRLecQh1draWnWEhtfT8/skSesJypHaml1LAADU3YQJE/Lkk0/uGb/0pS+tMA1UR3nEIdXZ2Vl1hIbX3t6eJFm6dGnFSQAAYGR76KGHBoz3XYkEI4U9jwAAAACoSXkEAAAAgxg3btyA8RFHHFFREqiW8ggAAAAGURRF1RGgISiPAAAAYBB7b5Y92BhGCuURAAAADOKoo44aMH7JS15SURKolqetAQAA0DAWL16cnp6eqmMkSSZPnpxHH310wHj305Gr1tra6mnWHDJWHgEAAMAgJkyYsOfrUaNGWXnEiGXlEQAAAA2j0VbTvOtd78o999yTz33uc3nzm99cdRyohPIIAPbS29ubsQ8/nqZbflZ1FIaxsQ8/nt70Vh0DgCEwYcKEvPGNb1QcMaK5bQ0AAACAmqw8AoC9tLS0pDdPZdOck6qOwjDWdMvP0tLSUnUMAIAhYeURAAAAADUpjwAAAACoSXkEAAAAQE3KIwAAAABqUh4BAAAAUJPyCAAAAICalEcAAAAA1KQ8AgAAAKAm5REAAAAANY2pOgAAwOFs8eLF6enpqTpGw9r92bS3t1ecpLG1trams7Oz6hgAjFDKIwCAOurp6cnP196WKUdsrzpKQxr136OTJA/3/KjiJI3rwSf9lR2AavmTCACgzqYcsT0fOvGRqmMwTH1+3YSqIwAwwtnzCAAAAICalEcAAAAA1KQ8AgAAAKAm5REAAAAANSmPAAAAAKhJeQQAAABATcojAAAAAGpSHgEAAABQk/IIAAAAgJqURwAAAADUpDwCAAAAoCblEQAAAAA1KY8AAAAAqEl5BAAAAEBNyiMAAAAAalIeAQAAAFCT8ggAAACAmpRHAAAAANSkPAIAAACgJuURAAAAADUpjwAAAACoSXkEAAAAQE1jqg4AAHA46+3tze+fGJPPr5tQdRSGqQefGJPHenurjgHACGblEQAAAAA1WXkEAFBHLS0tefiJ9fnQiY9UHYVh6vPrJuTolpaqYwAwgll5BAAAAEBNyiMAAAAAalIeAQAAAFCT8ggAAACAmpRHAAAAANTkaWsAADBMLF68OD09PVXHaGi7P5/29vaKkzS21tbWdHZ2Vh0DGCaURwAAMEz09PTkF3f/PMdNaK46SsMavX3XzRWP3L+l4iSN64FH+qqOAAwzdS2PiqI4O8mnk4xOsrQsy0WDzHl3kk8kKZPcVZble+uZCQAAhrPjJjRnwUyranj+lnxvadURgGGmbuVRURSjk1yZZE6S9UluK4rixrIsf7HXnBOTfDzJ/yjL8uGiKCbVKw8AAAAAB6+eG2a/Ock9ZVneW5bl00m+mmTePnM6klxZluXDSVKW5cY65gEAAADgINWzPDouSe9e4/XPHNvbq5O8uiiKHxRF8eNnbnMDAAAAoEHUc8+jYpBj5SDvf2KSWUmmJvl+URQnlWW5dcCJiuLSJJcmyeTJk7NmzZohDwuNYuvWXZe/6/zAnH766dnx9PRsPWpH1VEYxs551+iMftGLs2bNmpx++ul5w9NvyvaXHFF1LIaxMX96co580bg919T2N70hT43z3ymen7a3jM6YcUfuuZ52vGV7Hjlye9WxGMbOedm5Gf2iMf6+eYD8/RzqWx6tT9Ky13hqkgcHmfPjsiy3JfltURQ92VUm3bb3pLIsr0pyVZLMmDGjnDVrVr0yQ+W+9KUvJUlc5wemvb09j/zu1lw2+7GqozCMLV91ZCYc/5ZcfPHFaW9vzw9712XTnJOqjsUw1nTLz3Jay4l7rqmHe36UD534SNWxGKa6103I0a1v3XM9PXL/Fhtm84Is/97KTHj5Mbn44ourjjIs+Ps51Pe2tduSnFgUxSuKook38PkAACAASURBVHhRkvckuXGfOTckOSNJiqJoyq7b2O6tYyYAAAAADkLdyqOyLLcn+UiSf0/yyyRfK8vy50VRXF4Uxdxnpv17ks1FUfwiyeoknWVZbq5XJgAAAAAOTj1vW0tZlt1Juvc59nd7fV0m+d/P/AMAAABAg6nnbWsAAAAADHPKIwAAAABqquttawAwHI19+PE03fKzqmM0rNGPPpUk2XHUuIqTNK6xDz8+8JmzAADDmPIIAPbS2tpadYSG19PTkyRpbTmx4iQNrMW1BAAcPpRHwLD3wNbR+fSqI6uO0bD6H9t1h/LEI3dWnKRxPbB1dCYcv+vrzs7OasMMA+3t7UmSpUuXVpwEAIBDQXkEDGv+z/5ze+iZVSITjvdZ1TLheNcSAADUojwChjWrRJ6bVSIAAMAL4WlrAAAAANSkPAIAAACgJretAQAAjFCLFy/e8xRNBrf789m9FQCDa21ttaXEYUx5BAAAMEL19PTk5/+1Nscd48m1tYze+XSSZOsD91ScpHE9sOWxqiNQZzXLo6Io3p7kqLIsr9vn+PuSbCzL8pZ6hwMAAKC+jjvmyPxF2+urjsEw9tnuu6qOQJ3tb8+jv0/yH4McX5Xk8vrEAQAAAKCR7K88Gl+WZf++B8uyfCjJi+sXCQAAAIBGsb/yaFxRFM+6ra0oirFJjqhfJAAAAAAaxf7Ko+uTdBVFsWeV0TNff/6Z1wAAAAA4zO2vPPrbJBuS3F8UxU+LorgjyX1J+p95DQAAAIDDXM2nrZVluT3JwqIo/j7JCc8cvqcsyycPSTIAAAAAKlezPCqK4vx9DpVJXloUxdqyLB+tbywAAAAAGkHN8ijJ/xzk2DFJTi6KYn5Zlt+tUyYAgMPKg0+OyefXTag6RkPa9N+jkyRNf7Sj4iSN68Enx+ToqkMAMKLt77a1Dwx2vCiKlyf5WpK31CsUAMDhorW1teoIDW1jT0+S5GifU01H5w/XUW9vb36/9fdZ8r2l1YZiWFu/tS+/H/V41TGAYWR/K48GVZbl/UVRjK1HGACAw01nZ2fVERpae3t7kmTpUmUIADSqgy6PiqJ4TZL/rkMWAABgP1paWvLIzi1ZMLO96igMY0u+tzQTWo6pOgYwjOxvw+xvZdcm2Xs7JklzkvfXMxQAAAAAjWF/K48+tc+4TLIluwqk9yf5Ub1CAQAAANAY9rdh9n/s/rooilOSvDfJu5P8Nsk36h8NAAAAgKrt77a1Vyd5T5ILk2xO8v8lKcqyPOMQZQMAAACgYvu7be1XSb6f5H+WZXlPkhRF8b8OSSoAAAAAGsKo/bz2ziQPJVldFEVXURSzkxSHJhYAAAAAjaBmeVSW5TfLsrwgyWuSrEnyv5JMLoric0VRnHWI8gEAAABQof2tPEqSlGX5eFmWXy7L8h1JpiZZm2Rh3ZMBAAAAULnnLI/2VpbllrIsv1CW5Zn1CgQAAABA4zio8ggAAACAkUV5BAAAAEBNY6oOAAAAQDV6e3vz+4cfy2e776o6CsPYA5sfy6M7e6uOQR1ZeQQAAABATVYeAQAAjFAtLS3ZOuq/8xdtr686CsPYZ7vvykuPa6k6BnVk5REAAAAANSmPAAAAAKhJeQQAAABATcojAAAAAGpSHgEAAABQk6etAQDAMPLAI31Z8r2lVcdoWP2PbU6STDzy2IqTNK4HHunLhBxTdQxgGFEeAQDAMNHa2lp1hIb3UE9/kmTCy5UjtUzIMa4l4KAojwAAYJjo7OysOkLDa29vT5IsXWp1FsBQsecRAAAAADUpjwAAAACoSXkEAAAAQE3KIwAAAABqUh4BAAAAUJPyCAAAAICalEcAAAAA1KQ8AgAAAKAm5REAAAAANSmPAAAAAKhJeQQAAABATWOqDgAAAEB1HtjyWD7bfVfVMRrWpt8/mSRpeskRFSdpXA9seSwvPa7qFNST8ggAAGCEam1trTpCw9vwWE+S5KXHnVBxksb10uNcS4c75REAAMAI1dnZWXWEhtfe3p4kWbp0acVJoDr2PAIAAACgJuURAAAAADUpjwAAAACoSXkEAAAAQE3KIwAAAABqUh4BAAAAUJPyCAAAAICalEcAAAAA1KQ8AgAAAKAm5REAAAAANSmPAAAAAKhJeQQAAABATcojAAAAAGpSHgEAAABQk/IIAAAAgJqURwAAAADUpDwCAAAAoCblEQAAAAA1KY8AAAAAqEl5BAAAAEBNyiMAAAAAalIeAQAAAFCT8ggAAACAmpRHAAAAANSkPAIAAACgJuURAAAAADUpjwAAAACoSXkEAAAAQE3KIwAAAABqUh4BAAAAUFNdy6OiKM4uiqKnKIp7iqJYuJ95f1oURVkUxYx65gEAAADg4NStPCqKYnSSK5Ock+S1SS4siuK1g8w7KsmCJLfWKwsAAAAAz089Vx69Ock9ZVneW5bl00m+mmTeIPP+3ySfTPJUHbMAAAAA8DzUszw6LknvXuP1zxzboyiKNyRpKcvypjrmAAAAAOB5GlPHcxeDHCv3vFgUo5L8U5JLnvNERXFpkkuTZPLkyVmzZs3QJIQGtHXr1iRxnTNkXFMMNdcUQ8n1xFBzTTHUXFNQ3/JofZKWvcZTkzy41/ioJCclWVMURZK8LMmNRVHMLcvy9r1PVJblVUmuSpIZM2aUs2bNqmNsqNaXvvSlJInrnKHimmKouaYYSq6n4W/x4sXp6empOsYeGzZsSPKHa6tRtLa2prOzs+oYPA/+OwX1LY9uS3JiURSvSPJAkvckee/uF8uyfCRJ0+5xURRrknx03+IIAADgQI0fP77qCACHnbqVR2VZbi+K4iNJ/j3J6CT/Upblz4uiuDzJ7WVZ3liv9wYAAA4Nq2kADn/1XHmUsiy7k3Tvc+zvasydVc8sAAAAABy8ej5tDQAAAIBhTnkEAAAAQE3KIwAAAABqUh4BAAAAUJPyCAAAAICalEcAAAAA1KQ8AgAAAKAm5REAAAAANSmPAAAAAKhpTNUBoGqLFy9OT09P1TH22J2lvb294iQDtba2prOzs+oYAAAAHGLKI2gw48ePrzoCAAAA7KE8YsSzmgYAAABqs+cRAAAAADUpjwAAAACoyW1rAAAjiAdFHBgPigCAP1AeAQBQGQ+KAIDGpzyCBtPf35+FCxfmiiuuSFNTU9VxADjMWE0DABwsex5Bg+nq6sqdd96Zrq6uqqMAAACA8ggaSX9/f1asWJGyLHPDDTdk06ZNVUcCAABghFMeQQPp6urK9u3bkyTbt2+3+ggAAIDKKY+ggaxcuTI7d+5MkuzcuTM33XRTxYkAAAAY6ZRH0EBe9rKXDRg3NzdXlAQAAAB2UR5BA+nr6xswfvDBBytKAgAAALsoj6CBzJ49e8D4bW97W0VJAAAAYJcxVQcA/uCpp57a7xgAAA53ixcvTk9PT9Ux9tidpb29veIkA7W2tqazs7PqGIwQyiNoIKtXrx4w/u53v1tREgAAIEnGjx9fdQSonPIIGsjuJ63VGgMAwOHOahpoPPY8ggYyatSo/Y4BAADgUPOTKTSQM844Y8D4zDPPrCgJAAAA7KI8ggYybty4/Y4BAADgUFMeQQOxYTYAAACNRnkEDaStrW3A+Nxzz60oCQAAAOyiPIIGsu+eR7Nnz64oCQAAAOyiPIIG8qlPfWrA+JOf/GRFSQAAAGAX5RE0kHvvvXfA+De/+U1FSQAAAGAX5RE0kJe//OX7HQMAAMChpjyCBnL88ccPGCuPAACgWv39/Zk/f342bdpUdRSojPIIGsiPfvSjAeMf/vCHFSUBAACSpKurK3feeWe6urqqjgKVUR4BAADAIPr7+3PjjTemLMusWLHC6iNGLOURNJCzzz57wPicc86pKAkAANDV1ZWdO3cmSXbu3Gn1ESOW8ggayIIFCzJq1K7flqNHj86CBQsqTgQAACNXd3d3tm3bliTZtm1bVq5cWXEiqMaYqgMAfzBx4sS0tbXlpptuSltbW5qamqqOBFRs8eLF6enpqTrGALvztLe3V5zkD1pbW9PZ2Vl1DAAOM21tbbnhhhuybdu2jB07Nueee27VkaASVh5Bg1mwYEGmT59u1RHQsMaPH5/x48dXHQMA6q6jo2PPnQGjRo1KR0dHxYmgGlYeQYOZOHFili1bVnUMoEFYTQMA1Zk4cWLmzp2b6667LvPmzXNnACOW8ggAAABq6OjoyG9+8xurjhjR3LYGDaa/vz/z58/3GFAAAGgAu+8MsOqIkUx5BA1myZIlueOOO7JkyZKqowAAAIDyCBpJf39/uru7k+x6LKjVRwAAAFRNeQQNZMmSJdm5c2eSZMeOHVYfAQAAUDnlETSQf/u3fxswvvnmmytKAlCbvdkAAEYW5REAcFC6urpy5513pqurq+ooAAAcAsojaCBnn332gPE555xTURKAwfX39+fGG29MWZZZsWKF1UcAACOA8ggayPve974B4/e///0VJQEYXFdX15692Xbu3Gn1EQDACKA8ggZy/fXXDxh/4xvfqCgJwOC6u7uzbdu2JMm2bduycuXKihMBAFBvyiNoIN3d3QPGfigDGk1bW1vGjBmTJBkzZkzOPffcihMBAFBvyiNoIGecccaA8ZlnnllREoDBdXR0DLhtraOjo+JEAADUm/IIGshTTz213zFAIymKouoIAAAcAsojaCBr1qwZMF69enU1QQBq6OrqGlAa2TAbAODwpzyCBlKW5X7HAFXr7u7Ojh07kiQ7duywNxsAwAigPIIG8rKXvWzAuLm5uaIkAIN761vfOmB82mmnVZQEAIBDRXkEDeShhx4aMO7r66soCcDg1q1bN2D861//uqIkAAAcKsojaCC7n2BUawxQtfvvv3+/YwAADj/KI2ggo0aN2u8YoGqvfOUrB4xf9apXVZQEAIBDxU+m0EDOOeecAeO2traKkgAM7qMf/eiA8cc+9rGKkgAAcKgoj6CBLFiwYM8jsIuiyIIFCypOBDDQ6tWrB4xXrVpVURIAAA4V5RE0kIkTJ+bcc89NkrzjHe9IU1NTxYkABuru7h4wXrlyZUVJAAA4VJRH0GAWLFiQ6dOnW3UENKS2traMHTs2STJ27Ng9hTcAAIcv5RE0mIkTJ2bZsmVWHQENqaOjY89m/qNGjUpHR0fFiQAAqDflETSY/v7+zJ8/P5s2bao6CsCzTJw4MXPnzk1RFJk3b56iGwBgBFAeQYPp6urKnXfema6urqqjAAyqo6Mjb3jDG6w6AgAYIZRH0ED6+/tz4403pizLrFixwuojoCG5vRYAYGRRHkED6erqys6dO5MkO3futPoIAACAyimPoIF0d3dn27ZtSZJt27Z5BDYAAACVUx5BA2lraxsw9ghsAAAAqqY8ggZy/vnnDxi/853vrCgJAAAA7KI8ggby2c9+dsD4yiuvrCgJAAAA7KI8ggby/e9/f8D4e9/7XkVJAAAAYBflEQAAAAA1KY+ggRx55JH7HQMAAMChpjyCBvLJT35ywPgf//EfK0oCAAAAuyiPoIGccMIJA8avfOUrK0oCAAAAuyiPoIF0dXVl9OjRSZLRo0enq6ur4kQAAACMdMojaCDd3d3ZsWNHkmTHjh1ZuXJlxYkAAAAY6ZRH0EDa2toyZsyYJMmYMWNy7rnnVpwIAACAkU55BA2ko6MjO3fuTJKUZZmOjo6KEwEAADDSKY+gQZVlWXUEAAAAUB5BI+nq6sqoUbt+W44aNcqG2QAAAFROeQQNpLu7O9u3b0+SbN++3YbZAAAAVE55BA2kra0tY8eOTZKMHTvWhtkAAABUTnkEDaSjoyNFUSRJiqKwYTYAAACVG1PPkxdFcXaSTycZnWRpWZaL9nn9fydpT7I9SX+SD5ZleX89M0EjmzhxYqZOnZp77703LS0taWpqqjoSB2nx4sXp6empOsYAu/O0t7dXnOQPWltb09nZWXUMAADgANRt5VFRFKOTXJnknCSvTXJhURSv3WfanUlmlGV5cpLrknyyXnlgOOjv78/69euTJL29vdm0aVPFiTgcjB8/PuPHj686BgAAMEzVc+XRm5PcU5blvUlSFMVXk8xL8ovdE8qyXL3X/B8neX8d80DD6+rqys6dO5MkO3fuTFdXVz7+8Y9XnIqDYTUNAABwuKnnnkfHJenda7z+mWO1zE9ycx3zQMPztDXqob+/P/Pnz7eSDQAAeF7qufKoGORYOejEonh/khlJ/q8ar1+a5NIkmTx5ctasWTNEEf//9u4vtM77vAP495GtumWDQSJR7HgkjbPM/XPRQCiUjq5x0i6x5Thzsq676Rjh9KrkKoStgxEKzlhXOvDYxXqW0tGm+5cGx5blOtCEFpawNsNNk7QrixPapXapRMdgvUhs67eLo2Q5rqU4ceX3lfX5gEHPe34+fAXWhb963vdAv1xzzTV56qmnxmb/3rlQDz74YI4dO5Z77703d9xxR9dxAACANaZaO2efc+FvXPX+JPe21n5naf6TJGmt/flZ525K8tdJfru19tPXe9/rr7++Pfnkk6uQGLq3Z8+e/OhHP3p1vvLKK3PgwIEOE7HWzc/PZ/fu3XnppZeyadOmzM7OehA7AADwC6rq31tr15/rtdW8be3bSX6jqt5RVW9J8rEkB88Kdl2Sv01y6/kUR3Cpe21xlCQ//KEPH+TCDIfDnDlzJkly5syZDIfDjhMBAABrzaqVR62100k+meRoku8n+efW2rNV9emqunXp2F8m+dUk/1JV36mqg8u8HawLV1999di8bdu2jpJwqfAcLQAA4EKt5uZRWmtzrbVrW2vbWmv7lq79WWvt4NLXN7XW3t5ae+/Sn1tXfke4tO3bt29svu+++zpKwqXihhtuGJt37NjRURIAAGCtWtXyCHhjtm/f/ur20bZt23Lttdd2nAgAAID1TnkEPXP33XdnYmIi99xzT9dRuAQ89thjY/Ojjz7aURIAAGCtUh5Bz8zNzWVxcTGzs7NdR+ESsHPnzmzcuDFJsnHjxuzatavjRAAAwFqjPIIemZ+ff/WBxocPH87CwkLHiVjrBoNBNmzYkCTZsGFDBoNBx4kAAIC1RnkEPbJ///601pIki4uL2b9/f8eJWOump6dz6623pqqyZ8+eTE1NdR0JAABYY5RH0CNHjhwZm+fm5jpKwqVkMBjkuuuus3UEAAC8KRu7DgD8vzNnzqw4w5sxPT2d+++/v+sYAADAGmXzCHqkqlacAQAA4GJTHkGPTExMrDgDAADAxeZ/ptAjW7duXXEGAACAi015BD0yPz+/4gwAAAAXm/IIemTXrl1j88zMTEdJAAAAYER5BD1yww03jM033nhjR0kAAABgRHkEPfLZz352bP7MZz7TURIAAAAYUR5Bjzz//PNj8/HjxztKAgAAACPKI+iRq6++emzetm1bR0kAAABgRHkEPbJv376x+b777usoCQAAAIwoj6BHtm/f/ur20bZt23Lttdd2nAgAAID1TnkEPXP33XdnYmIi99xzT9dRAAAAQHkEfTM3N5fFxcXMzs52HQUAAACUR9An8/PzOXz4cJJkdnY2CwsLHScCAABgvVMeQY/s378/rbUkSWst+/fv7zgRAAAA653yCHrkyJEjY/Pc3FxHSQAAAGBEeQQ9sri4uOIMAAAAF5vyCHpkw4YNK84AAABwsSmPoEduvvnmsfmWW27pKAkAAACMKI+gR+66665MTIx+LCcmJnLXXXd1nAgAAID1TnkEPTI9PZ0dO3YkSW666aZMTU11nAgAAID1TnkEPfPWt741SbJp06aOkwAAAIDyCHplfn4+jzzySJLk6NGjWVhY6DgRAAAA653yCHpkOBzm5ZdfTpK8/PLLGQ6HHScCAABgvVMeQY/Mzs6OzYcOHeooCQAAAIwoj6BHLrvssrH58ssv7ygJAAAAjCiPoEdOnDgxNv/4xz/uKAkAAACMKI+gR1prK84AAABwsSmPoEeuuOKKsXnr1q0dJQEAAIAR5RH0yDvf+c4VZwAAALjYlEfQI0888cTY/Pjjj3eUBAAAAEaUR9AjO3fuzMTE6MdyYmIiu3bt6jgRAAAA653yCHpkMBisOAMAAMDFpjyCnqqqriMAAACA8gj6ZDgcvnrbWlVlOBx2nAgAAID1TnkEPTI3N5fTp08nSU6fPp3Dhw93nAgAAID1TnkEPeKB2QAAAPSN8gh6ZO/evVlcXEySLC4u5vbbb+84EQAAAOud8gh65KGHHhqbv/rVr3aUBAAAAEaUR9Ajs7OzY/OhQ4c6SgIAAAAjyiPokc2bN4/NW7Zs6SgJAAAAjCiPoEd+8pOfjM0nT57sKAkAAACMKI+gR87+dLWZmZmOkgAAAMCI8gh6ZO/evWOzT1sDAACga8oj6JGHHnooVZUkqSqftgYAAEDnlEfQI3Nzc2mtJUlaazl8+HDHiQAAAFjvlEfQIzt37szk5GSSZHJy8heegQQAAAAXm/IIemQwGGRiYvRjOTExkcFg0HEiAAAA1jvlEfTI9PR0PvzhDydJPvKRj2RqaqrjRAAAAKx3yiMAAAAAlqU8gh6Zn5/PI488kiQ5evRoFhYWOk4EAADAeqc8gh4ZDoc5depUkuTUqVMZDocdJwIAAGC9Ux5Bj8zOzqa1liRpreXQoUMdJwIAAGC9Ux5Bj2zevHls3rJlS0dJAAAAYER5BD1y8uTJsfnEiRMdJQEAAIAR5RH0iM0jAAAA+kZ5BD1i8wgAAIC+UR5Bj8zMzIzNu3fv7igJAAAAjCiPoEf27t07Nt9+++0dJQEAAIAR5RH0yAMPPDA2f/nLX+4oCQAAAIwoj6BHvva1r43NR44c6SgJAAAAjCiPoEdaayvOAAAAcLEpj6BHrrjiirF569atHSUBAACAEeUR9MjCwsLYPD8/31ESAAAAGFEeQY/s2rUrVZUkqarMzMx0nAgAAID1TnkEPTIYDDI5OZkkmZyczGAw6DgRAAAA653yCHpkeno6e/bsSVXltttuy9TUVNeRAAAAWOc2dh0AGDcYDHL8+HFbRwAAAPSCzSMAAAAAlqU8gp4ZDoc5duxYhsNh11EAAABAeQR9Mj8/n4cffjittRw4cCALCwtdRwIAAGCdUx5BjwyHw5w6dSpJcurUKdtHAAAAdE55BD0yOzub1lqSpLWWQ4cOdZwIAACA9U55BD2yefPmsXnLli0dJQEAAIAR5RH0yMmTJ8fmEydOdJQEAAAARpRH0CM2jwAAAOgb5RH0iM0jAAAA+kZ5BD0yMzOTqkqSVFV2797dcSIAAADWO+UR9MhgMMjk5GSSZHJyMoPBoONEAAAArHfKI+iR6enp7NmzJ1WV2267LVNTU11HAgAAYJ3b2HUAYNxgMMjx48dtHQEAANALyiPomenp6dx///1dxwAAAIAkblsDAAAAYAWrWh5V1c1V9YOqeq6q/vgcr2+qqn9aev3fquqq1cwDa8H8/HzuvPPOLCwsdB0FAAAAVq88qqoNSf4myS1J3pXkD6rqXWcduzPJf7fWrknyV0n+YrXywFoxHA5z7NixDIfDrqMAAADAqm4evS/Jc62151trLyf5xyR7zjqzJ8nfL339YJIbq6pWMRP02vz8fA4ePJjWWh5++GHbRwAAAHRuNcujK5L812vmF5eunfNMa+10kv9JcvkqZoJeGw6HWVxcTJIsLi7aPgIAAKBzq/lpa+faIGpv4kyq6hNJPrE0/m9V/eACs0Evve1tb3tvkg2vzM8888yZT33qU9/pMBIAF2YqiTVSAGAtuHK5F1azPHoxya+/Zt6a5MQyZ16sqo1Jfi3Jz85+o9ba55N8fpVyAgCsiqp6srV2fdc5AAAuxGretvbtJL9RVe+oqrck+ViSg2edOZjkD5e+viPJo621X9g8AgAAAKAbq7Z51Fo7XVWfTHI0o9twvtBae7aqPp3kydbawST3J/lSVT2X0cbRx1YrDwAAAABvXFn0AQBYHVX1iaXb7wEA1izlEQAAAADLWs1nHgEAAACwximPAADehKr6YlXdcZ5nv1BVP62qZ866/ntV9WxVLVaVT2UDAHpJeQQAsEqqasPSl19McvM5jjyTZG+Sb16sTAAAb5TyCADgPFTVx6vqu1X1VFV9aenyB6vq8ap6/pUtpKr6UFU9VlVfSfJ0krTWvpnRJ8uOaa19v7X2g4v2TQAAvAkbuw4AANB3VfXuJH+a5AOttYWquizJ55JsTvJbSbYnOZjkwaW/8r4k72mtvdBFXgCAXyabRwAAr29HkgdbawtJ0lp7ZYvoQGttsbX2vSRvf835bymOAIBLhfIIAOD1VZJ2jusvnXXmFT9f3TgAABeP8ggA4PV9PclHq+ryJFm6bQ0AYF1QHgEAvI7W2rNJ9iX5RlU9ldHzjs5bVf1DkieS/GZVvVhVdy5d/92qejHJ+5Mcrqqjv+ToAAAXrFo71wY2AAAAANg8AgAAAGAFyiMAAAAAlqU8AgAAAGBZyiMAAAAAlqU8AgAAAGBZyiMAgAtQVV+sqju6zgEAsFqURwAAq6yqNnSdAQDgzVIeAQC8AVX18ar6blU9VVVfWrr8wap6vKqef2ULqao+VFWPVdVXkjxdVVdV1X9U1d9V1TNV9UBV3VRV/1pV/1lV7+vuuwIAWF611rrOAACwJlTVu5M8lOQDrbWFqrosyeeS/EqS30+yPcnB1to1VfWhJIeTvKe19kJVXZXkuSTXJXk2ybeTPJXkziS3Jvmj1tptF/c7AgB4fTaPAADO344kD7bWFpKktfazpesHWmuLrbXvJXn7a85/q7X2wmvmF1prT7fWFjMqkL7eRr/JezrJVasfHwDgjVMeAQCcv0pyrrXtl84684qfr3Bu8TXzYpKNF5wOAGAVKI8AAM7f15N8tKouT5Kl29YAAC5pfsMFAHCeWmvPVtW+JN+oqjNJjnWdCQBgtXlgNgAAAADLctsaAAAAAMtSHgEAAACwLOURAAAAAMtSHgEAAACwLOURAAAAAMtSHgEAAACwLOURAAAAG3M8OgAAABBJREFUAMtSHgEAAACwrP8DF5YCRi/YfY0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "fig, axes = plt.subplots(figsize=(20,10))\n",
    "#grouped = df_scores.groupby(['threshold'])\n",
    "\n",
    "#bp = grouped.boxplot(subplots=False, sym='k+', figsize=(8,10))\n",
    "#bp = df_scores.boxplot(column=['auc'], by=['chrm', 'dist_thresh'], ax=axes,rot=40, fontsize=8,layout=(2, 1))\n",
    "sns.boxplot(y='auc', x='chrm', \n",
    "                 data=df_scores, \n",
    "                 palette=\"colorblind\"\n",
    "                 ,hue='dist_thresh'\n",
    "           )\n",
    "#bp = axes.boxplot([[x if x>=0 else -1 for x in top_500_score_auroc_0_9], [x if x>=0 else -1 for x in top_500_score_auroc_0_7], [x if x>=0 else -1 for x in top_500_score_auroc_0_5], [x if x>=0 else -1 for x in top_500_score_auroc_0_4]] , sym='k+')\n",
    "#axes.set_title('Predicting structure similarity from expression')\n",
    "axes.yaxis.grid(True)\n",
    "#axes.set_xlabel('Co-expression')\n",
    "axes.set_ylabel('AUC')\n",
    "axes.set_ylim([0.0,1.101])\n",
    "#plt.setp(bp['fliers'], markersize=3.0)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "fig, axes = plt.subplots(figsize=(20,10))\n",
    "#grouped = df_scores.groupby(['threshold'])\n",
    "\n",
    "#bp = grouped.boxplot(subplots=False, sym='k+', figsize=(8,10))\n",
    "#bp = df_scores.boxplot(column=['auc'], by=['chrm', 'dist_thresh'], ax=axes,rot=40, fontsize=8,layout=(2, 1))\n",
    "sns.boxplot(y='auc', x='chrm', \n",
    "                 data=df_scores, \n",
    "                 palette=\"colorblind\"\n",
    "                 ,hue='dist_thresh'\n",
    "           )\n",
    "#bp = axes.boxplot([[x if x>=0 else -1 for x in top_500_score_auroc_0_9], [x if x>=0 else -1 for x in top_500_score_auroc_0_7], [x if x>=0 else -1 for x in top_500_score_auroc_0_5], [x if x>=0 else -1 for x in top_500_score_auroc_0_4]] , sym='k+')\n",
    "#axes.set_title('Predicting structure similarity from expression')\n",
    "axes.yaxis.grid(True)\n",
    "#axes.set_xlabel('Co-expression')\n",
    "axes.set_ylabel('AUC')\n",
    "axes.set_ylim([0.0,1.101])\n",
    "#plt.setp(bp['fliers'], markersize=3.0)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_scores_plot = pd.concat([df_scores.drop(['plot'], axis=1), df_scores['plot'].apply(pd.Series)], axis=1)\n",
    "df_plot = df_scores_plot.groupby(['chrm']).median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       {0.0: 0.15891472868217105, 0.1: 0.358914728682...\n",
       "1       {0.0: 0.19172932330827042, 0.1: 0.479591836734...\n",
       "2       {0.0: 0.027435610302351626, 0.1: 0.14389697648...\n",
       "3       {0.0: 0.03850267379679142, 0.1: 0.107486631016...\n",
       "4       {0.0: 0.3575498575498566, 0.1: 0.7236467236467...\n",
       "                              ...                        \n",
       "999     {0.0: 0.0858757062146893, 0.1: 0.3598870056497...\n",
       "1000    {0.0: 0.09021739130434761, 0.1: 0.192391304347...\n",
       "1001    {0.0: 0.03621346886912323, 0.1: 0.156289707750...\n",
       "1002    {0.0: 0.09125188536953226, 0.1: 0.208898944193...\n",
       "1003    {0.0: 0.06352459016393448, 0.1: 0.174863387978...\n",
       "Name: plot, Length: 1004, dtype: object"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scores['plot']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for thresh in df_plot.index.tolist():\n",
    "    axes = df_plot.T.reset_index()[11::].plot.scatter(x='index', y=thresh)\n",
    "    #axes = zt.reset_index().plot.scatter(x='x_p', y=0, s=1)\n",
    "    #axes.plot([0, 1], [0, 1], 'red', linewidth=1)\n",
    "    #axes = df_plot.T.reset_index().plot(x='index', y=0.3)\n",
    "    axes.plot([0, 1], [0, 1], 'red', linewidth=1)\n",
    "    #bp = axes.boxplot([[x if x>=0 else -1 for x in top_500_score_auroc_0_9], [x if x>=0 else -1 for x in top_500_score_auroc_0_7], [x if x>=0 else -1 for x in top_500_score_auroc_0_5], [x if x>=0 else -1 for x in top_500_score_auroc_0_4]] , sym='k+')\n",
    "    #axes.set_title('Predicting structure similarity from expression')\n",
    "    #axes.yaxis.grid(True)\n",
    "    #axes.set_xlabel('Co-expression')\n",
    "    #axes.set_ylabel('AUC')\n",
    "    #axes.set_ylim([0.0,1.101])\n",
    "    #plt.setp(bp['fliers'], markersize=3.0)\n",
    "    #0.5\n",
    "    #fig, axes = plt.subplots()\n",
    "    #axes.scatter(x, y)\n",
    "    #axes.plot(x, y)\n",
    "    axes.set_ylim([0,1])\n",
    "    axes.set_xlim([0,1])\n",
    "\n",
    "    #plt.show()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for thresh in df_plot.index.tolist():\n",
    "    axes = df_plot.T.reset_index()[11::].plot.scatter(x='index', y=thresh)\n",
    "    #axes = zt.reset_index().plot.scatter(x='x_p', y=0, s=1)\n",
    "    #axes.plot([0, 1], [0, 1], 'red', linewidth=1)\n",
    "    #axes = df_plot.T.reset_index().plot(x='index', y=0.3)\n",
    "    axes.plot([0, 1], [0, 1], 'red', linewidth=1)\n",
    "    #bp = axes.boxplot([[x if x>=0 else -1 for x in top_500_score_auroc_0_9], [x if x>=0 else -1 for x in top_500_score_auroc_0_7], [x if x>=0 else -1 for x in top_500_score_auroc_0_5], [x if x>=0 else -1 for x in top_500_score_auroc_0_4]] , sym='k+')\n",
    "    #axes.set_title('Predicting structure similarity from expression')\n",
    "    #axes.yaxis.grid(True)\n",
    "    #axes.set_xlabel('Co-expression')\n",
    "    #axes.set_ylabel('AUC')\n",
    "    #axes.set_ylim([0.0,1.101])\n",
    "    #plt.setp(bp['fliers'], markersize=3.0)\n",
    "    #0.5\n",
    "    #fig, axes = plt.subplots()\n",
    "    #axes.scatter(x, y)\n",
    "    #axes.plot(x, y)\n",
    "    axes.set_ylim([0,1])\n",
    "    axes.set_xlim([0,1])\n",
    "\n",
    "    #plt.show()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "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>chrm</th>\n",
       "      <th>num_pairs</th>\n",
       "      <th>dist_thresh</th>\n",
       "      <th>auc</th>\n",
       "      <th>pr_curve</th>\n",
       "      <th>true_pos</th>\n",
       "      <th>true_neg</th>\n",
       "      <th>exp_median</th>\n",
       "      <th>exp_mean</th>\n",
       "      <th>exp_var</th>\n",
       "      <th>...</th>\n",
       "      <th>0.82</th>\n",
       "      <th>0.83</th>\n",
       "      <th>0.85</th>\n",
       "      <th>0.86</th>\n",
       "      <th>0.88</th>\n",
       "      <th>0.89</th>\n",
       "      <th>0.9</th>\n",
       "      <th>0.91</th>\n",
       "      <th>0.92</th>\n",
       "      <th>1.0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chr11</td>\n",
       "      <td>1004</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.603357</td>\n",
       "      <td>{0.0: 1.0, 0.01: 1.0, 0.02: 1.0, 0.03: 1.0, 0....</td>\n",
       "      <td>566</td>\n",
       "      <td>2</td>\n",
       "      <td>0.818331</td>\n",
       "      <td>0.709948</td>\n",
       "      <td>0.077252</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.863074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chr11</td>\n",
       "      <td>1004</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>{0.0: 1.0, 0.01: 1.0, 0.02: 1.0, 0.03: 1.0, 0....</td>\n",
       "      <td>480</td>\n",
       "      <td>1</td>\n",
       "      <td>0.711796</td>\n",
       "      <td>0.638367</td>\n",
       "      <td>0.072380</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.975000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chr11</td>\n",
       "      <td>1004</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.990826</td>\n",
       "      <td>{0.0: 1.0, 0.01: 1.0, 0.02: 1.0, 0.03: 1.0, 0....</td>\n",
       "      <td>436</td>\n",
       "      <td>1</td>\n",
       "      <td>0.680995</td>\n",
       "      <td>0.613632</td>\n",
       "      <td>0.075320</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.995413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>chr11</td>\n",
       "      <td>1004</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.964930</td>\n",
       "      <td>{0.0: 1.0, 0.01: 1.0, 0.02: 1.0, 0.03: 1.0, 0....</td>\n",
       "      <td>499</td>\n",
       "      <td>2</td>\n",
       "      <td>0.690213</td>\n",
       "      <td>0.630627</td>\n",
       "      <td>0.070869</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.994990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chr11</td>\n",
       "      <td>1004</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.937400</td>\n",
       "      <td>{0.0: 1.0, 0.01: 1.0, 0.02: 1.0, 0.03: 1.0, 0....</td>\n",
       "      <td>623</td>\n",
       "      <td>2</td>\n",
       "      <td>0.712888</td>\n",
       "      <td>0.625489</td>\n",
       "      <td>0.079132</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.981541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>chr11</td>\n",
       "      <td>1004</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.074297</td>\n",
       "      <td>{0.0: 1.0, 0.01: 1.0, 0.02: 1.0, 0.03: 1.0, 0....</td>\n",
       "      <td>498</td>\n",
       "      <td>1</td>\n",
       "      <td>0.061497</td>\n",
       "      <td>0.109356</td>\n",
       "      <td>0.028743</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.537149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>chr11</td>\n",
       "      <td>1004</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.426445</td>\n",
       "      <td>{0.0: 1.0, 0.01: 1.0, 0.02: 1.0, 0.03: 1.0, 0....</td>\n",
       "      <td>571</td>\n",
       "      <td>2</td>\n",
       "      <td>0.264574</td>\n",
       "      <td>0.274264</td>\n",
       "      <td>0.040111</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.834501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1001</th>\n",
       "      <td>chr11</td>\n",
       "      <td>1004</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.650485</td>\n",
       "      <td>{0.0: 1.0, 0.01: 1.0, 0.02: 1.0, 0.03: 1.0, 0....</td>\n",
       "      <td>618</td>\n",
       "      <td>1</td>\n",
       "      <td>0.480949</td>\n",
       "      <td>0.495839</td>\n",
       "      <td>0.064536</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.825243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1002</th>\n",
       "      <td>chr11</td>\n",
       "      <td>1004</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.793478</td>\n",
       "      <td>{0.0: 1.0, 0.01: 1.0, 0.02: 1.0, 0.03: 1.0, 0....</td>\n",
       "      <td>552</td>\n",
       "      <td>3</td>\n",
       "      <td>0.684659</td>\n",
       "      <td>0.632675</td>\n",
       "      <td>0.075314</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.987319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003</th>\n",
       "      <td>chr11</td>\n",
       "      <td>1004</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.855932</td>\n",
       "      <td>{0.0: 1.0, 0.01: 1.0, 0.02: 1.0, 0.03: 1.0, 0....</td>\n",
       "      <td>472</td>\n",
       "      <td>1</td>\n",
       "      <td>0.745523</td>\n",
       "      <td>0.660855</td>\n",
       "      <td>0.078882</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.927966</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1004 rows × 66 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       chrm  num_pairs  dist_thresh       auc  \\\n",
       "0     chr11       1004          2.0  0.603357   \n",
       "1     chr11       1004          1.0  0.950000   \n",
       "2     chr11       1004          1.0  0.990826   \n",
       "3     chr11       1004          2.0  0.964930   \n",
       "4     chr11       1004          5.0  0.937400   \n",
       "...     ...        ...          ...       ...   \n",
       "999   chr11       1004          2.0  0.074297   \n",
       "1000  chr11       1004          6.0  0.426445   \n",
       "1001  chr11       1004          6.0  0.650485   \n",
       "1002  chr11       1004          6.0  0.793478   \n",
       "1003  chr11       1004          6.0  0.855932   \n",
       "\n",
       "                                               pr_curve  true_pos  true_neg  \\\n",
       "0     {0.0: 1.0, 0.01: 1.0, 0.02: 1.0, 0.03: 1.0, 0....       566         2   \n",
       "1     {0.0: 1.0, 0.01: 1.0, 0.02: 1.0, 0.03: 1.0, 0....       480         1   \n",
       "2     {0.0: 1.0, 0.01: 1.0, 0.02: 1.0, 0.03: 1.0, 0....       436         1   \n",
       "3     {0.0: 1.0, 0.01: 1.0, 0.02: 1.0, 0.03: 1.0, 0....       499         2   \n",
       "4     {0.0: 1.0, 0.01: 1.0, 0.02: 1.0, 0.03: 1.0, 0....       623         2   \n",
       "...                                                 ...       ...       ...   \n",
       "999   {0.0: 1.0, 0.01: 1.0, 0.02: 1.0, 0.03: 1.0, 0....       498         1   \n",
       "1000  {0.0: 1.0, 0.01: 1.0, 0.02: 1.0, 0.03: 1.0, 0....       571         2   \n",
       "1001  {0.0: 1.0, 0.01: 1.0, 0.02: 1.0, 0.03: 1.0, 0....       618         1   \n",
       "1002  {0.0: 1.0, 0.01: 1.0, 0.02: 1.0, 0.03: 1.0, 0....       552         3   \n",
       "1003  {0.0: 1.0, 0.01: 1.0, 0.02: 1.0, 0.03: 1.0, 0....       472         1   \n",
       "\n",
       "      exp_median  exp_mean   exp_var  ... 0.82  0.83  0.85  0.86  0.88  0.89  \\\n",
       "0       0.818331  0.709948  0.077252  ...  NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "1       0.711796  0.638367  0.072380  ...  NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "2       0.680995  0.613632  0.075320  ...  NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "3       0.690213  0.630627  0.070869  ...  NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "4       0.712888  0.625489  0.079132  ...  NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "...          ...       ...       ...  ...  ...   ...   ...   ...   ...   ...   \n",
       "999     0.061497  0.109356  0.028743  ...  NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "1000    0.264574  0.274264  0.040111  ...  NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "1001    0.480949  0.495839  0.064536  ...  NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "1002    0.684659  0.632675  0.075314  ...  NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "1003    0.745523  0.660855  0.078882  ...  NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "\n",
       "      0.9  0.91  0.92       1.0  \n",
       "0     NaN   NaN   NaN  0.863074  \n",
       "1     NaN   NaN   NaN  0.975000  \n",
       "2     NaN   NaN   NaN  0.995413  \n",
       "3     NaN   NaN   NaN  0.994990  \n",
       "4     NaN   NaN   NaN  0.981541  \n",
       "...   ...   ...   ...       ...  \n",
       "999   NaN   NaN   NaN  0.537149  \n",
       "1000  NaN   NaN   NaN  0.834501  \n",
       "1001  NaN   NaN   NaN  0.825243  \n",
       "1002  NaN   NaN   NaN  0.987319  \n",
       "1003  NaN   NaN   NaN  0.927966  \n",
       "\n",
       "[1004 rows x 66 columns]"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scores_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "fig, axes = plt.subplots(figsize=(20,10))\n",
    "#grouped = df_scores.groupby(['threshold'])\n",
    "\n",
    "#bp = grouped.boxplot(subplots=False, sym='k+', figsize=(8,10))\n",
    "#bp = df_scores.boxplot(column=['auc'], by=['chrm', 'dist_thresh'], ax=axes,rot=40, fontsize=8,layout=(2, 1))\n",
    "sns.boxplot(y='auc', x='chrm', \n",
    "                 data=df_scores, \n",
    "                 palette=\"colorblind\",\n",
    "                 hue='dist_thresh'\n",
    "           )\n",
    "#bp = axes.boxplot([[x if x>=0 else -1 for x in top_500_score_auroc_0_9], [x if x>=0 else -1 for x in top_500_score_auroc_0_7], [x if x>=0 else -1 for x in top_500_score_auroc_0_5], [x if x>=0 else -1 for x in top_500_score_auroc_0_4]] , sym='k+')\n",
    "#axes.set_title('Predicting structure similarity from expression')\n",
    "axes.yaxis.grid(True)\n",
    "#axes.set_xlabel('Co-expression')\n",
    "axes.set_ylabel('AUC')\n",
    "axes.set_ylim([0.0,1.101])\n",
    "#plt.setp(bp['fliers'], markersize=3.0)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}