mammoviews/plot_auroc_difference_pvalue.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fn = \"../tables/auroc_delong_comparison-e5ce2d69b035975cb5336cec0da9a32a.csv\"\n",
    "df = pd.read_csv(fn, index_col=0)\n",
    "# df.drop('score_wire', axis=0, inplace=True)\n",
    "# df.drop('score_wire', axis=1, inplace=True)\n",
    "df.drop('wire', axis=0, inplace=True)\n",
    "df.drop('wire', axis=1, inplace=True)\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "modelnamemap = {'rpart':'RPART', 'gbm':'GBM', 'glmnet':'GLMNet', 'xgb':'XGB', 'gbmt':'GBMT',\n",
    "                'image':'avg(image)',\n",
    "                'image_max':'max(image)',\n",
    "                'wire_max': 'max(wire)',\n",
    "                'max_image_wire_max': 'max(image, wire)',\n",
    "                'image+gbmt': 'image+GBMT',\n",
    "                'max_wire_max_image+gbmt': 'max(image+GBMT, wire)',\n",
    "                'max_image_wire':'max(avg(image), avg(wire))',\n",
    "                'max_wire_image+gbmt': 'max(avg(image)+GBMT, avg(wire))',\n",
    "#                         'avg(image)+gbmt':'avg(image)+GBMT',\n",
    "                'max(max(wire), avg(image)+gbmt)':'max(wire, image+GBMT)',\n",
    "                'ViewModifier':'ViewModifier',\n",
    "                    } "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def namemap(x,n=8):\n",
    "    if x!=x:\n",
    "        return \"\"\n",
    "    elif x==1.0:\n",
    "        return \"1.0\"\n",
    "    elif x>0.01:\n",
    "        return \"%.2f\" % x\n",
    "    else:\n",
    "        return \"1e{:.0f}\".format(pd.np.log10(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# df.applymap(namemap)\n",
    "df[df==1] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ViewModifier               12\n",
       "rpart                      11\n",
       "gbm                        10\n",
       "glmnet                      9\n",
       "xgb                         8\n",
       "gbmt                        7\n",
       "image                       6\n",
       "image_max                   5\n",
       "wire_max                    0\n",
       "max_image_wire_max          3\n",
       "image+gbmt                  3\n",
       "max_wire_max_image+gbmt     1\n",
       "max_image_wire              1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(~df.isnull()).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nbonferroni = (~df.isnull()).sum().sum()\n",
    "# df_bonferroni = np.minimum(1.0, df*nbonferroni)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.18"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thr_bonferroni = -np.log10(0.05/nbonferroni)\n",
    "round(thr_bonferroni,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.rename(columns=modelnamemap, index=modelnamemap)\n",
    "df = df.loc[df.index.map(lambda x : 'avg(wire)' not in x).tolist(),\n",
    "            df.columns.map(lambda x : 'avg(wire)' not in x).tolist()]\n",
    "\n",
    "keep_rows = ~df.isnull().all(1)\n",
    "keep_cols = ~df.isnull().all(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RPART                     True\n",
       "GBM                       True\n",
       "GLMNet                    True\n",
       "XGB                       True\n",
       "GBMT                      True\n",
       "avg(image)                True\n",
       "max(image)                True\n",
       "max(wire)                False\n",
       "max(image, wire)          True\n",
       "image+GBMT                True\n",
       "max(image+GBMT, wire)     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "~df.isnull().all(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/rfUbZhfQ4ZQ0zIzm3RIp2x2kaJvX38w+yQQS6v+/wYIl0L4NtGsNsTEwehhM+7Luxy9eCpu3weAB9e6CiEjElPwbzzCg1Dn39J4C59yaPSP2/XgF7yIB4DzgzX3q84GpwP/b90AzyzKzD83sWzObbmZHmdmJwFnAQ2Y2z8yy6vNmfGa8cfNlTL9rLDNWrGVhpVE/QEaTZHK3FwAQDDkKiktIS4ynY4umOGDSlefy2k2XctXJ/esTfr+KtpaR2HzvJFdC8xiKtpY3yLnzNkPLjL37memwaXP1dlM+h7OvhF/9D2zM88pCIXhgIvz2+gbpiohInSn5N55ewHf1OG4qcLKZ+fEuAl6toc39wG/CbSqbBNwcnmUYB0x0zn0NvAv81jnXzzm3ct+Tmdm1ZjbHzOZsmzdj32oAQs5x/hP/ZNj9z9KnbUu6ZDbf9xzVjnGA3+fj2A6tue3VD/j5M/9meK8sBma1O8CPIEI1TCRU7009T12Hcw89Eaa+Cu+8AIOOgzvHe+Uvvw0nD4RWGdVOISJySCn5HybMbIKZzTez2QdoGgS+BC4CEpxzq/dt4JzLBmYBl1Y6fzJwIvCamc0DnqH6kkGNnHOTnHP9nXP9m/YbtN+2BcUlzMpez5BuHauUb9pRQMu0FAD8PiMlPo4dhcVs2lHAnOz1bC8sprisnOlLV9OzdcNmw4TmMRRu2TvSL9pSRnyzhrndJTMdcvP27m/Kh4wWVds0TYXY8CLMhWfC4mXe9rzF8K+3YPhF8OBT8M5H8MgzDdItEZH9UvJvPIuBY/fsOOduBIYD6XU49hXgCeDf+2kzHridvf+NfcD28Oh+z6tHvXq+j6ZJCaTExwEQF/AzKKs92flVb6D7dMkqzj7Wu61gZO+uzFy5DoCvlq2hW8sWxMcE8PuM/p3asjKvfjff1aZ1/xTWfL4d5xxblhUSk+gnoWlMg5y7z1GwZj2s3wilZTB5mnfDX2V5W/ZuT/sKOocfKnjobpj2mjcrcNv1cPZp8JuxDdItEZH90t3+jWcaMN7MrnfOPRUuS6zjsdOB+4CXa2vgnPvBzL4HzgRmOed2mlm2mV3onHstfMPh0c65+UABkFLfN5KeksT4C0/DZ4bPjI8WLuPzH7K5acQgFuds4tMlq3hjziLuH3M6H4y7kh2FxYx7eTIAO4tL+PuX3/HqjZfinGP60tV8sTQ7ovgzH19H/uJCSgrKeX/sUnqOySAU9Objs0Y2o+WxyeTOLeDDm5fjj/XR/8Y2Fcd+enc2BTkllBeHeH/sUo67vg0t+yXXOXYgAL+/Ba4e563hnzcaunby7uDvfRQMGwz/94aX9AN+SE2B++6I6O2JiDQ4cxHeWS0Nx8xa4T3qNxDvRr3dwNPAJuADoNKYkQvxEv4459ycfc6zq9Kjfu8553qHy/sCc4GrnHMvmlkn4Cm86f4Y4BXn3J/MbDDwN6AEuKCmdf89et35WKP9wpxzyfTGCs29GYsaLTaAr+WyRo0vEiUa6nYgAIaden+D/b2c9vEdDdo3jfwbkXNuI3vv3N9XQg1lQ2s5T3L439VA70rl86m0tBO+F+D0Go7/ioN81E9ERI4cWvMXERGJMkr+IiIiUUbJX0REJMoo+YuIiEQZJX8REZEoo+QvIiISZZT8RUREfgTMzG9mc83svQO1VfIXERH5cfgVsKQuDZX8RUREjnBm1hY4A3i2Lu2V/EVERA5zlb9aPfy6dp8mjwO3AaG6nE8f7ytyBAjldmu02PpeAZH62dQ/rsHO5T52k4BJNdWZ2ZlAnnPuWzMbWpfzaeQvIiJyZBsMnGVmq/G+8n2Ymf3f/g5Q8hcRETmCOefudM61dc51xPuyuGnOuZ/t7xglfxERkSijNX8REZEfCefcZ8BnB2qnkb+IiEiUUfIXERGJMkr+IiIiUUbJX0REJMoo+YuIiEQZJX8REZEoo+QvIiISZfScvxy02ICff1w7htiAH7/Px5RFy5nwyYwqbcYcfzSXDOpLKBSisLSMe976hJV5WwHo1rIFfzh3OMlxcYSc46IJ/6K0PFjn+HMm5rDx2wLiUgOMfLRLtfq107ez9O3NAPjjfRx7TWvSOsYDsPz9LWRP3QYOOo1oStczmkf03qfPhPFPQCgEF5wB11xWtT4nF37/AGzdDqlN4MG7oGWGV7dhE9z9IOTmgRk88wC0aRVR+APGf/FVeP198PuhWRr8+XZo09Kre/hp+Pwbb/v6y2H0sMhii8iRS8n/CGdm7YAvgOOcc1vNrCnwHTAUiAUeA3oA24GdwB+cc1+Y2RXAQ0AOEIP3HdCXO+cKI+1DaXmQq559ncLSMgI+Hy9dN4bpS7NZsC63os3783/g37MWAPCTHp257YxTGPvCW/h9xv1jTufOf3/I0tzNpCbGUx6s05dSVegwNI2s05sx+8mcGusTM2I55Y+diE32s3FuAd8+s4Hh93Vmx9pisqduY9h9nfEFjC//soaWxyaT0qpuX8YRDMK9j8Nzj0BmOowZCz8ZDF067m3z0EQ4+zQ453T45jt4dBI8+Huv7o7xMPZnMHgA7C4EX4TzcHWJ36MrvDYJEuLh5be9hP/YPfDZDPh+Gbz1LJSWweW/gpMHQnJSZH0QkSOTpv2PcM65dcBTwP3hovvxvvlpE/A+MMk5l+WcOw64Gehc6fBXnXP9nHO9gFLgovr2o7C0DICA30fA58PtU7+7pLRiOyE2Bue8Fid27cCy3M0szfVG5jsKiwm5fY/ev/SeScQm+2utb9E9saK+eddEirZ4fS3IKaFZ1wQCcT58fqNFz0Q2zCqoc9wFS6B9G2jXGmJjvJHztC+rtlmxBk441tseeAxM+ypcvtpL3oMHePtJiV6CjkRd4g88du95+/aETfne9srVMKAfBAKQmADds7xZBBGJDkr+Pw6PASeY2S3AEOAR4DJghnPu3T2NnHOLnHMv7nuwmQWAJGBbfTvgM+ONmy9j+l1jmbFiLQsrjfr3uOSEvnww7kp+ffpJjP/PZwB0bNEUB0y68lxeu+lSrjq5f327UCfZ07bR8phkAJq0i2fzkkJKCsopLwmR+90uCjeX1flceZv3TuGDN/retLlqm6OyYMoX3vbH02F3obFtB6xeBynJcPPv4bxfwENPeRcDkahL/MremAwnDQz3q4uX7IuKYdt2mDUXcvMjiy8iRy4l/x8B51wZ8Fu8i4BbnHOlQC+86f/9ucjM5uFN/TcD/lNTIzO71szmmNmcbfNm1NSEkHOc/8Q/GXb/s/Rp25IumdXXzl/+Zj6jHn6Bxz6cznXDvCzk9/k4tkNrbnv1A37+zL8Z3iuLgVnt6vbGI5S3aDerp22jz88yAWjSNo7uZ7dg+r1r+PIva0jrGI/VPoFQTU0TFLbP/m03wOx5XoKfMw8y0x0Bv5fov13g1f/7GVi3Ad76MLL3U5f4e7w7BRYthV9c7O0PHgAnnwCX3gi/+RP06+XdFyAi0UHJ/8djFLAR6F1TpZm9ZWaLzOzNSsWvOuf6AS2BhXgXENU45yY55/o75/o37Tdov50oKC5hVvZ6hnTrWGubyQuWMqxnFgCbdhQwJ3s92wuLKS4rZ/rS1fRsnVHrsfW1fU0x3z6dw4m3tScuZe+tLp2GN2XEg1kM/VMnYpL9dV7vB2+knZu3d39TPmS0qNomowU88Wd48zn41dVeWUqyd2yPrt6UfSAAw4d4a/CRqEt8gK/nwDMvwcTxEBu7t/y6n8Nbz8Hzj3oXEh3aRhZfRI5cSv4/AmbWDzgVOAG41cxaAYuBY/e0cc6dC1yBN8KvwnkL8P8BTq5P/KZJCaTEe0kzLuBnUFZ7svO3VmnTvnlaxfYp3TuzZvN2AL5atoZuLVsQHxPA7zP6d2pb8RRAQynML2XGQ+sYcHNbUlpXTe7FO8or2myYuZN2g1PrfN4+R8Ga9bB+o3fT3ORp3g13lW3b7t2JD/C3f8J5o/Yeu7PAewoAYOZ3kNUxsvdVl/jfL4N7HoEJ90HzpnvLg0HYtsPbXroSlq6CwYd2xUVEDiO62/8IZ2aGd8PfLc65tWb2EPAwcDVwp5mdVWndP3E/pxoCrKxPH9JTkhh/4Wn4zPCZ8dHCZXz+QzY3jRjE4pxNfLpkFZcO6segLu0pDwbZWVTC7177CICdxSX8/cvvePXGS3HOMX3par5Ymh1R/JmPryN/sbd2//7YpfQck0Eo6M2JZ41sxvev51O6q5y5f9sIgM8Pwx/wZh5mPLyO0oIgvgD0u7rVfm8c3FcgAL+/Ba4e5yX480ZD107w1+eg91EwbDDMmufd4W8G/fvC/9ziHev3w2+vhytv9UbdvbrDhWdG9LbrFP+hp6GwCG79g3dMqwyYeB+Ul8PPb/bKkpK8RxAD+msgEjXMRXhntRxezOxaYLhz7qLwvh+YBfwa747/R4GjwtsFwIPOuU/2edTPB6wHrnDO5VULUkmvOx9rtF+Ycy6Z3lihuTdjUaPFbmy+lhGuR4gcuWq7baZeGvLv5eL7bm3Qvula/wjnnJuE92jfnv0gcFylJqNrOe5F4MVD2TcRETk8ac1fREQkyij5i4iIRBklfxERkSij5C8iIhJllPxFRESijJK/iIhIlFHyFxERiTJK/iIiIlFGyV9ERCTK6BP+ROrg7rwavyzxv6KxP1o4lNutUePr44VFGp5G/iIiIlFGyV9ERCTKKPmLiIhEGSV/ERGRKKPkLyIiEmWU/EVERKKMkr+IiEiUUfIXERGJMkr+IiIiUUbJX0REJMoo+YuIiEQZfba/HLTYgJ9/XDuG2IAfv8/HlEXLmfDJjGrtTuvTjRuHn4ADlm7M57ZXP+D4zm25/YxTKtp0Sm/GuFcmM+37lXWOP2diDhu/LSAuNcDIR7tUq89bvJuvH1hLUkYsAG0GptDzwgyCpSE++5/VhMpDuCC0OaEJvS7KiOi9Hyj2zpwS5kzIYXt2Mb0uyaD7WS2q1LugY+odq4hvFmDInR0iig0wfSaMfwJCIbjgDLjmsqr1b30ADz0Fmene/qXnwoVnetsPPw2ff+NtX385jB7WsLFffBVefx/8fmiWBn++Hdq09OoeesqL7UJwYn/43S/BLLL4IlJ/Sv5HADPLBB4DTgC2AaXAg+Htd4BsvFmcPOBS51yemV0BvACMcM5NDZ/nXOBN4ELgMqATkAykh88BcINz7utI+ldaHuSqZ1+nsLSMgM/HS9eNYfrSbBasy61o0755GtcMHcDPnn6VncUlNEtKAGDWqvWc/8Q/AUhNiOODcVfx9fI1Ef18OgxNI+v0Zsx+MqfWNi16JFZLrr4Y45Q/dCCQ4CdU7vj07mxaHpNM826JDRY7NtlPv6tasWHWzhrrl0/eQkqbOMqKgnWOuUcwCPc+Ds894iX3MWPhJ4OhS8eq7UYNg7tvqVr22Qz4fhm89SyUlsHlv4KTB0JyUsPF7tEVXpsECfHw8tvexcZj98DcRd7rnee9dpfdBLPnwfHHRPwjEJF60rT/Yc7MDHgb+MI519k5dxxwMdA23GS6c66fc+5oYDZwY6XDFwKXVNq/GJgP4Jw71znXD7i60jn6RZr49ygsLQMg4PcR8Plw+9RfOKAPL8+Yz87iEgC27i6qdo6RvbsxfVk2xWXlEcVO75lEbLI/4j6bGYEE77hQ0OGCDiIcfR4odnxqgGZdErBA9RMXbilj43e76DQ8LbKgYQuWQPs20K41xMZ4I/dpX9bt2JWrYUA/CAQgMQG6Z3kj+YaMPfBYL/ED9O0Jm/L31pWUQlm5d+FRHoTmTeseW0QOnkb+h79hQKlz7uk9Bc65NcATZjZ0T1n4IiEFWFHp2OnASWYWA8QBXYB5h6KTPjNeu+lS2jdP4+Vv5rOw0qgfoEMLL8H939iL8PmMiVNn8OWyqiP8UX278fcvvzsU3WPrsiI+HreC+KYxHH15JqntvKzkgo5Pbl/FrtxSsk5vSvOudR/1H6z5L+Ry9M8yKSsO1ev4vM3QstIqRWa6l5T3NeVzmDMfOraDO26CVhlwVBeY8CJcMQaKi2HW3OozBg0Re483JsNJA73tY3rDwGPg5PPAObjsXMiKILaIHDyN/A9/vYD9ZcSTzGwesBYYATxfqc4BnwCnAWcD79anA2Z2rZnNMbM52+asNpOgAAAgAElEQVRVX8sHCDnH+U/8k2H3P0ufti3pktm8Sr3f76N9izSu+Ntr/PaVyfzxvFNJiY+rqG+RkkTXzBZ8tSyyKf+6aNopntETu3Lqw13oMqoZMx5ct/e9+Y1TH87ijGe6sW1FETvWFjd4/Jps+LaAuFQ/TbMS6n0Ot+/0CtUnLoaeCFNfhXdegEHHwZ3jvfLBA+DkE+DSG+E3f4J+vby1+YaMvce7U2DRUvjFxd7+mvWwcg18+hp89jp88x3Mnl/32CJy8JT8jzBmNsHM5pvZ7HDRnin7dnhr/A/uc8greNP9FwMv1yemc26Sc66/c65/036D9tu2oLiEWdnrGdKtY5XyTTt28en3KykPhcjZtpPV+dsqZgMATu/Tjanh+oYWk+ivmN5vdWwKoaCjZGfVpYXYJD/pvZLInberwePXZMsPhWycU8DkG5Yx87H15C/azay/ro/oHJnpkJu3d39TPmRUvZ+QpqkQ693nyIVnwuJle+uu+zm89Rw8/6iXzDu0pc7qEhvg6znwzEswcfzefnwy3VsGSEr0XicNhPmL6x5bRA6ekv/hbzFw7J4d59yNwHC8m/T29S5wcuUC59wsoDfQwjm3rIZjDlrTpISKUXxcwM+grPZk52+t0mba9ys4PqsdAGmJ8XRo0ZR1W3dU1I/u253J8384FN2jeFsZLjxU3bq8EBeC2BQ/JTvKKd3t3WgXLAmxacFuUtrE7e9UDabPZZmc8Ux3Rk/sxsBb25LeO4njfxlB9gX6HOWNotdv9NbOJ0/zbrqrLG/L3u1pX0Hn8D2PwSBsC//4l66EpatgcP+Gjf39MrjnEZhwX9U1/VaZ3ki/vNxb958zH7Iif9BBRA6C1vwPf9OA8WZ2vXPuqXBZbQvTQ4CanpG7Ezhk89npKUmMv/A0fGb4zPho4TI+/yGbm0YMYnHOJj5dsoovl63hxK4dePeWywk6xyMffMGOQq9LrdOa0DI1hdnZkY1895j5+DryFxdSUlDO+2OX0nNMBqGgl+yzRjZj/Tc7WTVlG+YHf6yPgbe2xcwo2l7OnCdzcCGHc9B2UBNaH5fSoLGLt5Ux9Y5VlBWFMIMV729h5GNdiEmM/AbFfQUC8Ptb4Opx3uN2542Grp3gr89B76Ng2GD4vze8pB/wQ2oK3HeHd2x5Ofz8Zm87KQkevMs7X0PGfuhpKCyCW//gHdMqAybeB6edAjO/g7Ov9B7vG3J89QsHETm0zNW0eCeHFTNrhfeo30AgH9gNPA1sYu+jfgbsAK52zi0LP+rX3zl30z7nehF4zzn3enh/KDDOOXdmXfrS687HGu0X5pxLpjdW6EZ1b8aixu5Co/K1PCQTViI1adBPm2jIv5eL77u1Qfumkf8RwDm3EW/NviaptRzzIvBiDeVX7LP/GfDZQXRPRESOMFrzFxERiTJK/iIiIlFGyV9ERCTKKPmLiIhEGSV/ERGRKKPkLyIiEmWU/EVERI5wZhZvZrPCH/++2Mz+uL/2es5fRETkyFcCDHPO7Qp/k+uXZvaBc+6bmhor+YuIiBzhnPdxvXu+mSwm/Kr1EwaV/CUirR/4utFiv81JjRa7uF9ho8V+ZclxjRb74h7fNlrsCnnnNVrovxz9ZqPFFomUmfmBb4EuwATn3Mza2ir5i4iIHAINOWgws2uBaysVTXLOTarcxjkXBPqZWRrwlpn1ds7V+OUgSv4iIiKHuXCin3TAhl7b7Wb2GXA6UGPy193+IiIiRzgzSw+P+DGzBGAE8ENt7TXyFxEROfK1Av4eXvf3Af92zr1XW2MlfxERkSOcc24BcExd22vaX0REJMoo+YuIiEQZJX8REZEoo+QvIiISZZT8RUREooySv4iISJTRo37SIF5aNYGigmJCwRDB8iA3Hn9HlfrktCR+89wNtM7KpLS4jEd+MZHVi9cBcO4vRzPq6uGYGZOf/YS3/ndynePGBvz849oxxAb8+H0+pixazoRPZlRpc/sZp3B857YAxMfG0CwpgUF/egqAVqkp/PH8U2mZmgwOrnvxbTZs31nn+K0SUnh44Fm0SEgm5ByvrpzLi8tn19i2T7NWvDH8Cn454y0+XO999sZ5HftwY88hAEz4/kveXL2wQWOPaN2NW/ucTMhB0IW4d+4Uvt28nhMyOnBXv1Mr2mU1ac6vZrzFxznL6hR7zsQcNn5bQFxqgJGPdqlWX7oryJyJOezeVIovxkf/G1qT2j4egGXvbWb11O1gkNo+jv43tMEfG9k45EDxy3YHmfXEego3l+GC0O2s5nT8SVMACvNLmfP0Boq2lAHGkN+1JykjNqL4Ikc6Jf9GZGaPA286574ws2eBR51z3zdCPx4GJjvnph3MecYNu4edWwpqrLvkd+excn42fzz/Idp1b83NT17Nbaf+iY692jHq6uHcPPBOykrLue+Du5j1/nfkrMitU8zS8iBXPfs6haVlBHw+XrpuDNOXZrNg3d7jH3j/84rtSwf1o0fr9Ir98WNOY9Kns5ixYi2JsTGEXK1fglWjcucYP38qi7flkhSI5Z2RV/HlpmxW7NxcpZ3PjNuPHsb03FUVZamx8dzc6yTO+fh5nIN3Rl7FJznL2VlW3GCxv87L5pOPvITePTWDJ048l5EfPMM3eWv46ZRnK/oxbfQNVfp2IB2GppF1ejNmP5lTY/0Pb+aT1imeE29rz86cEuY+u5FT/tCRoi1lrJi8ldMe64I/zsc3j65j3Vc7KhJzQ8Vf8dFWUtrGMfiODpTsKOfDX62g/ZBUfDE+Zj2ZQ4/z0snsm0x5URB8FlFskR8DTfs3EjNrBpzgnPsCwDl3dWMk/rAngDsO2OogdOjRlrlTvY+YXrd0A5kd00nLSKV9jzb8MHM5JUWlhIIhFnzxPYPPPT6icxeWlgEQ8PsI+Hy1f4clMLpvdybPXwpAVkYzAj4fM1asrThPcVl5RLHzi3exeJt3obG7vJQVO7eQmZBSrd3lXfvz4fof2FKyu6Ls5Jad+WpTNjtKi9lZVsxXm7I5pVXnBo1dWF5WsZ0YiKnxZzOqbQ8+z11JcbDu7z29ZxKxyf5a63euLyGjdxIATdrEUZhfSvF27/wu5AiWhggFHeUlIeKbxdQ5bl3jm0F5UQjnHOXFIWKT/Zjf2LmuGBeEzL7JAAQS/ATi9GdQoo9+6+vBzN42s2/NbLGZXWtm15vZg5XqrzCzJ8Lbd5vZD2b2sZm9bGbjws0uAD6sdMxnZtY/vL3LzB4Ix/jEzI4P168ys7PCbTqa2XQz+y78OjFc7jOzieG+vWdmk83sgnDdcWb2efi8H5lZKwDn3BqguZm1rO/PxDm4/6PfM2H2A4y+ZkS1+lULVjPkvIEAdB/QhcwO6aS3bc7qRevoc1IPUpolE5cQy/GjjiW9XYuIYvvMeOPmy5h+11hmrFjLwnU1zxq0SkuhbdNUZq70lhs6tGjKzuISHr/sTF6/+TJ+M+okfFb/UWCbxFR6pWUyf0vV0WhmQgoj23TnXyu/q1a+sXDvEkNuYUGNFw4HExtgZJvuTBk1lmdPuog7ZlX/tM8z2/fkP2sW1ytubVI7xpMz05sF2rq8kML8Moq2lJHQPIZuP23B+9cv571rlhKT6KdlOBE3pKzTm1GQU8L71y5jym9W0u/KlpjPKNhYSkySj68fWssnv13Jgn/k4oKRzfaI/Bgo+dfPVc6544D+wC+BN4HKXzp+EfBqOJmfj/eRi+eF2+8xGO97l2uSBHwWjlEA/Bk4FTgX+FO4TR5wqnPu2HC8v4bLzwM6An2Aq4FBAGYWgzfCvyB83ueBv1SK+V24T9WEL3DmmNmc9a7mqeFbh/yeG/rfzl2j/8JZN5xGn5N6VKl/5f63SUlL4unvHuKcm0axYm42wfIga3/I4dUH3+GBKXcz/oO7WLVgNcHyYC0/lpqFnOP8J/7JsPufpU/blnTJbF5ju9FHd2fKomUVU/sBn4/jOrbh4cnTuWjCv2jXLJVzjusZUew9EgMxTBx8PvfO/Zhd5aVV6n5/zKk8uGBatSUFo/qFRn3S0P5iA0zJWcrID57huq9e49bep1SpS49PpltqekRT/nVx1DktKN0d5ONxK1nxwVbSOsVjfqN0V5ANswsYPaErZ07qTrAkxJovtjdobIBN83aR2jGeMyZ149SHOjP3uY2UFQZxQcfmJYUcfXlLht3fmd15paz+rOHjixzutOZfP780s3PD2+2ATsAqMzsBWA50B74CfgW845wrAjCz/1Q6Rysgv5bzl7J3VmAhUOKcKzOzhXiJHSAGeNLM+gFBoFu4fAjwmnMuBOSa2afh8u5Ab+Bj80a3fmBjpZh5QOuaOlP5qyRP9V1YY37asnEbANvzd/LV27PofnwXFk5fUlFfWFDEw7+YWLH/0qoJ5GbnAfDh89P48HnvdoOr/nIJ+eu31PJj2b+C4hJmZa9nSLeOrNhU/Ryj+nbnz+/sva0hd0cBSzbksX7bDgCmfr+Svu1a8iaRjYID5mPCiefzzppFTMlZWq2+T9NW/O8g79elaWwiQ1t1IehC5BbtZGBGh4p2LRNTmJm3pkFjVzY7fx3tk5vSNDaBbaVFAJzRrgcf5yyj3IUiinsgMYl+BtzYBgDnHB/cuJykjBg2zd9FUkYMcanen542A5uwZWkhHU5Oa9D4qz/dTvdzW2BmJLeKIykjloKcEhKax5DWKZ7kTO8Gv9YDmrB1eSEQ2T0HIkc6jfwjZGZD8b4qcZBzri8wF4gHXgXG4I3033LOOahhaLdXUfi4mpSFjwcIASUA4YS+54LtVmAT0BdvRmHP7cq1xTRgsXOuX/jVxzk3slJ9fLhPEYtPjCMhOb5i+7hT+7J60boqbZJSEwnEeF0fdfVwFn6xhMICL1xaehMA0tu1YPC5A/n05a/qHLtpUgIp8XEAxAX8DMpqT3b+1mrtOrZoSpOEOOat3Xu9s2j9JlIT4mmalADAwM7tWJlX/dgDuf/4M1hZsIXnl82qsX7o+xM45T3v9eH6JfzPtx/ycc4yvshdxZDMzjSJiadJTDxDMjvzRYQj8APF7pC8N6n1atqSGJ+/IvEDnNmhF/9Z27BT/gClu4OEyrwLiuyp22jRI5GYRD8JLWLYuryI8hJvPT5v4S6atI1r8PiJLWLIW+jdX1G8vZyCDSUkZcbSLCuBst1BSnZ49x/kLdpNyiGIL3K408g/cqnANudcoZkdBZwQLn8TuAtYA9weLvsSeMbM7sP7WZ8B/C1ctwToAnx2EP1Y75wLmdn/wxvJ74n5/8zs70A6MBT4F7AUSDezQc65GeFlgG7OuT1/+bsBr9WnI2mZqdzz5m8B8Af8fPryl8z5aB5njvUeJXvvmY9p36Mtt//9JoLBEGu/X88jVz9Vcfz/vD6OJs1TKC8r58mbnmXX9t01xqlJekoS4y88DZ8ZPjM+WriMz3/I5qYRg1ics4lPl3jJdHTf7nwwv+pjbCHneGjyFzz3i/MxM77P2cTrs+v+qB3AcS3acm7Ho/lh+yb+M/JqAB5Z+CmtElMBeHmfdf7KdpQW8+T3X/L2qVcC8MT309lRWrc7/esa+7S2R3Fuxz6Uh0IUB8v45Yw3K45vk5hKq4QmEc82AMx8fB35iwspKSjn/bFL6Tkmg1B47TxrZDMK1pcw+8kczAcpbePof703C9C8ayJtTmjC1NtWYn4jrWM8nUZEPuo+UPweF6Qze0IOU369AoA+P8skron35+7on7fkiz+txjlo2jmBzsM16pfoYy7CR5uinZnFAW8DbQgnVOAe59xnZvYe0NM517lS+3uAS/AuCvLx1vL/ZmYnAWOdcz8Lt/sMGOecm2Nmu5xzyZWO3+Wcezi8v8s5l2xmXYE3gELgU+DmcLkPmAicDCwD4vAeIfw4vETwV7wLhwDweLgvMcACoI9zbr+3fNc27f/fsOH2ExsrNMX9ChstdmO6uEdtt6VEh78c/eaBG8mPSYM+95n16l8a7O/lyovuatC+aeQfIedcCTCqlrozayh+2Dl3j5klAl8Aj4TbTjez+8wszTm33Tk3tNJ5kitt37NPjOTwv8uBoytV3RkuD5nZOOfcLjNrDszCu28A59w8vIuCfZ0JvH6gxC8iIj8OSv6H3iQz64m3pv5351zleeDfAO2Bhr7d+D0zS8O7D+Be59yBPjEnQPiiREREfvyU/A8x59yl+6mbeYhiDo2wfb3W+kVE5Miku/1FRESijJK/iIhIlFHyFxERiTJK/iIiIlFGyV9ERCTKKPmLiIhEGSV/ERGRKKPn/EVEanBvxiJCud0O3PAQ8bVcduBGIvWkkb+IiEiUUfIXERGJMkr+IiIiUUbJX0REJMoo+YuIiEQZJX8REZEoo+QvIiISZZT8RUREooySv4iISJRR8hcREYkySv4iIiJRRp/tLw3ipVUTKCooJhQMESwPcuPxd1SpT05L4jfP3UDrrExKi8t45BcTWb14XZ2O3Z/YgJ9/XDuG2IAfv8/HlEXLmfDJjCptbj/jFI7v3BaA+NgYmiUlMOhPT3FUq3TuPmcYyXFxBEMhJn06iw8XRvZ56q0SUnh44Fm0SEgm5ByvrpzLi8tnV2kzonU3bu1zMiEHQRfi3rlT+Hbz+r0/m0AsH426jik5S/njdx/912K/cPLF9Gvehjmb13HN9H9H9L7nTMxh47cFxKUGGPlol2r1pbuCzJmYw+5NpfhifPS/oTWp7eMByJ1bwLwXcnEh6DQ8jaPOTY8odl3iL31nM2un7wDAhRw715dw1nPdKdkZ5JvH9v7sd+eV0uuiDLqe0bzOsafPhPFPQCgEF5wB11xWtf6+J2HWXG+7qBi2bodZ73v7Dz0Fn38DLgQn9off/RLMInvvIg1Byb8Rmdk5wNHOuT+Z2XVAoXPuH43QjzOBAc65PxzMecYNu4edWwpqrLvkd+excn42fzz/Idp1b83NT17Nbaf+qU7H7k9peZCrnn2dwtIyAj4fL103hulLs1mwLreizQPvf16xfemgfvRo7SWborIy7vz3R6zdsp30lCReu+kyvlq+hoLikjrHL3eO8fOnsnhbLkmBWN4ZeRVfbspmxc7NFW2+zsvmk4+8i4ruqRk8ceK5jPzgmYr6W/ucwqz8NRG/94ON/bcfviE+EMMlWcdEHLvD0DSyTm/G7Cdzaqz/4c180jrFc+Jt7dmZU8LcZzdyyh864oKOuc9t5KS7O5LYLMDUO1fRun8KTdrFN2j87me3oPvZLQDYMKeA5e9tITYlQGxKgFMfzgLABR3vjV1G6+NT6hw3GIR7H4fnHoHMdBgzFn4yGLp03Nvmzpv2bv/fG7Bkubc9d5H3eud5b/+ym2D2PDg+8h+/yEHTtH/jug2YCOCce7oxEn/Y+8BZZpZ4qAJ06NGWuVMXAbBu6QYyO6aTlpHaIOcuLC0DIOD3EfD5cPtpO7pvdybPXwrAms3bWbtlOwD5BbvZuruQpkkJEcXOL97F4m3ehcbu8lJW7NxCZkLVZFJYXlaxnRiIqdK/3k1b0iI+iS9zsyOK2xCxv85bze6yul/oVJbeM4nYZH+t9TvXl5DROwmAJm3iKMwvpXh7OVtXFJHcMpbkzFh8MT7aDU5lw5zIL/oOFL+ydV/uoN2QJtXKNy3aTXLLGJLSY+scd8ESaN8G2rWG2BgYPQymfVl7+/enwujhe/dLSqGsHErLoDwIzZvWObRIg1LyrwMz62hmP5jZs2a2yMz+aWYjzOwrM1tuZseHX1+b2dzwv93Dx/7azJ4Pb/cJH59oZt2AEufc5nDdPWY2Lrz9mZk9ZmZfmNkSMxtgZm+GY/25Ur/eNrNvzWyxmV1bqfwXZrYsfJ6/mdmT4fJ0M3vDzGaHX4MBnHMO+Aw4s74/I+fg/o9+z4TZDzD6mhHV6lctWM2Q8wYC0H1AFzI7pJPetnmdjj0Qnxlv3HwZ0+8ay4wVa1lYadRfWau0FNo2TWXmynXV6vq0zSTg97Fu6/aI4+/RJjGVXmmZzN9SfTQ6sk13poway7MnXcQds94DwIA7+43g/nlT6x2zvrEPtdSO8eTM9JL61uWFFOaXUbSljKKtZSQ0j6lol9AshqIt5YesH+UlIXLn7aLtwOrJf/1XO2g3OLIL0LzN0DJj735mOmzaXHPbnFxYvxFOONbbP6Y3DDwGTj7Pew0ZAFkdIwov0mA07V93XYALgWuB2cClwBDgLOB3wOXAyc65cjMbAYwHzgceBz4zs3OBu4CxzrnCcOL9bj/xSp1zJ5vZr4B3gOOArcBKM3vMObcFuMo5t9XMEoDZZvYGEAfcDRwLFADTgPnhc/4v8Jhz7kszaw98BPQI180BTgKqLf6GLyyuBTiKY2lrnat19tYhv2fLxm2kpTfh/il3s+6HHBZOX1JR/8r9b3PD41fy9HcPkb1wLSvmZhMsD9bp2AMJOcf5T/yTlPg4/vqzn9IlszkrNm2p1m700d2ZsmgZIVd1bqBFShL3jTmd3732EW5/0wb7kRiIYeLg87l37sfsKi+tVj8lZylTcpYyIL0dt/Y+hcs//xc/69KfzzeuYGNR5CPfg419qB11TgvmvZDLx+NWkto+jrRO8Zi/lsXtQ7jmvXFOAS2OSiA2peqfulBZiA1zCuh9aWZE56vp96O27k+eBqedAv7wBMWa9bByDXz6mrf/i9/A7PkwoG9EXRBpEEr+dZftnFsIYGaLganOOWdmC4GOQCrwdzPrCjggBsA5FzKzK4AFwDPOua/C52sF5O8n3rvhfxcCi51zG8OxVwHtgC3AL8MXFYTLugItgc+dc1vD7V8DuoXbjAB62t47jJqYWYpzrgDIA1rX1BHn3CRgEsCpvgtrTI9bNm4DYHv+Tr56exbdj+9SJYEXFhTx8C8mVuy/tGoCudl5dTq2rgqKS5iVvZ4h3TrWmPxH9e3On9+ZVqUsKS6Wp/7f2fx1ytdV7hOIRMB8TDjxfN5Zs4gpOUv323Z2/jraJzelaWwCx7Row4AW7bisy3EkBmKJ8fkpLC/loQWfHvLY20qL6hyjPmIS/Qy4sQ0Azjk+uHE5SRkxBEtCFG3ZuxRRtLWMhGaH7s/QulpG97nzdpHWKZ74tMhiZ6ZDbt7e/U35kNGi5rYfTIW7b927/8l06NsTksKLaycNhPmLlfylcWjav+4qL46GKu2H8C6i7gU+dc71Bn4KVL6DqSuwi6rJtWifNrXFqxyrIp6ZDcVL5oOcc32BueHz7W8c5Qu37xd+tQknfsLH1isjxCfGkZAcX7F93Kl9Wb2o6tR6UmoigRjvD+2oq4ez8IslFBYU1enY/WmalEBKfBwAcQE/g7Lak52/tVq7ji2a0iQhjnlrN1aUxfh9/PVnP+XduUuYsmh5ZG+6kvuPP4OVBVt4ftms/8/eecdXVaT///3ce9MTQkkBEiDSkd5FEBUEFCui2P2h4mLDdVfWhu66+pW14FrBjtjWhgXBAkoH6R2k1wQSCCEhIT25z++Pc5LcQBJyITcRMu/XK6+cM/Oc+cycm9xn5pmZc8rMbxZaMrHbvl5D/BxOUvOy+fvSaVww400unDGR59fO5rs9G7xy/Kej7WvyMgtx57sB2D07lYh2wfgFO6nXMohjiXlkHszDne8mfvFRGvWo/II7b8jPLCT5jywa9zwx5L9v0VGa9vN+zUnHttYIPiHRmrf/aY614O94du+Do8egS/uStEbR1ki/oMCa91+5Dlo087oKBkOVYEb+VUc4UDThOrIoUUTCscLt/YE3ReQ6VZ0KbAZuPU29VHsKoS1wnp2+HHhFROphhf2HY0UPAGYBDwAv2XXroqpr7bzWwMZTqUjd6HCe/vYfADhdTuZ+voiVM9dyxehBAMx451eatovl0Y8eoLDQzb4/Enh51FsVXltZIsNCGH/9EBwiOESYuWEb87fs5oFL+rBp/0Hmbt4FWAv9fl5XehvfkI6t6X5ODHWDA7mm27kAjJs6iy2JFQVkStM9IpZhcZ3YknaQ6YNHAfDyhrk0CrYcy+c7VzMkti3D4jpS4HaTU5jPg0u+rXT5vtT+YsBtNA9rQIjLn0VXjuHxFT+yMGlXpbSXvRpP8qYscjMK+HH0Vs4dEYW70AoKtRhcn4yEXFa8uR9xQFhsAD3utaIADqfQ5a5GLHxuL+pW4i6uR7iXK/0row+wf3k60Z1DcAWWHuMU5Lo5tD6T7n8pM9BVIS4XPPkQjBprbfW7dii0Ogde/wA6tIUBdkfgx9nWYkDPbXxDLoRlq+HqO6z0fr3K7jgYDNWB6KlOctYiRCQOmGGP6hGRKfb51KI84G7gI6xQ/hzgNlWNsxf7rVXV10WkCTAXOB8rErAC6GBPHzwNHFPVCSIyDxirqivtEf5YVb3C1p4HjMVy6N8DMcBWIBJ4WlXn2XP0Y4EDWJ2MI6o6TkQigIlY8/wuYIGq3mOXOwN4vGhqozzKC/tXBwcePb+mpMnpklVj2jXJje1W1XQVaoxno06pL1xlOBp698wJQ5VQpStQWnz5XJV9X+68YVyV1s2M/CuBqu4BOnicjywnr7XHZU/Z+Xd62MZjLRwEQER+AwYCv6nq0x52F3kcz8NaiX9CHnBZOVX+n6q+KyIu4DusET/2zoIbjjcWkWgg6GSO32AwGAxnB2bOv2YZD/hib/3TIrIWK4y/GytCUBFNgYd9UA+DwWAw/AkxI/8aRFUPUrKqvyrLHeul/YqTWxkMBoPhbMGM/A0Gg8FgqGUY528wGAwGQy3DOH+DwWAwGGoZxvkbDAaDwVDLMM7fYDAYDIZahnH+BoPBYDDUMozzNxgMBoPhDEdEmojIXPs18JvsN8KWi9nnbzAYDAbDmU8B8LCqrhaRMGCViPyqqn+UZWycv8FgKJcvNnev6SrUGDX9bH93UuuTG/kI816BMw/7te+J9nGGiGzGeveLcf4Gg8FgMFQXVfliLPuFbX/xSHpXVd8txzYO6AosK6884/wNBoPBYPiTYzv6Mp29JwSBBbgAACAASURBVCISCnwDPKSq6eXZmQV/BoPBYDCcBYiIH5bj/0xVv63I1jh/g8FgMBjOcEREgA+Azar635PZG+dvMBgMBsOZT1/gNmCAiKy1f4aWZ2zm/A0Gg8FgOMNR1UWAVNbejPwNBoPBYKhlGOdvMBgMBkMtwzh/g8FgMBhqGcb5GwwGg8FQyzDO32AwGAyGWoZZ7W+oEj7ZNZHsjBzchW4KCwq5v9djpfJD64bw8Af30bhFNHk5+bx81yT2bIoHYNiDQ7ls1EBEhJ/e/43vXvup0rr+Licf/2UE/i4nToeDWRu3M/G3JaVsGoWHMf76IYQFBeAQ4ZWZi1i4dQ8dY6N5etglAIgIE39bwuw/dnrV7kZBYUzofRURQaG4Vfly5xqmbF9xgl3vyKY82XUwLoeD1Nwsbp77KQDP97yCAY1bkpKbyWW/vFfl2r0jm/JOv+uJzzwKwMyELbz5x6Jq0b6qWXtGt+0DQFZBPk+t+pktaYcAGNmqJze06AIIX+5aw5RtJ96z09FuHtaAF3pdQft6Dfnvhnm8v7XkKaenow0w7nmYtwTq14PpU07MV4Xxr8OCZRAYAOMfh/b2Y/q//wXe+tg6vvd2uOZSr6QBWLgMxr8BbjdcdzncfUvp/P1J8OQLcCQNwuvAi+OgYRQsWw3PTyyx27UPXv4nXHKB93UwnPkY538cInIN0ElVnxGRe4AsVf24puvlDSLyu6qe7+U1vwHXq2rqqeqOHfA06SkZZebd9MS17Fy3m38Pf4kmbRoz5s1RPDLoGeLaN+GyUQMZ0/tx8vMK+M/P41j+42r270iqlGZeQSF3vj+VrLx8XA4Hn9wzgoVbd7M+vuT60QN688uGbXy5bD0tourz1shrGPziZLYfTGHExP9R6FYiwkL49sFbmbdlF4VurXSbC1QZv242m1KTCHH5M23wnSw6uJsd6YeLbcL8Avh390u5Y8EXJGal0yAguDjvmz3r+GTHSib0vrLSmt5oA6w4HM/dC7864XpfayccS+OmOZ+Snp/DhQ1b8FyPoQz/bQqtwyO5oUUXhv36IfnuQj7sfxPzDuxgz7HK/elVRvtoXjbPrJnF4Jg2pa49XW2Aay6Dm6+Fx8aXnb9gGexNgF8+g3V/wDP/hS/fhrR0mDgFvn4XROC6u+HivhAeVmlpCgvh2Vfhg5chOhJGjLbKaBlXYvPSJLh6iNWxWLoa/vsuvPgk9O4G331g2aSlw6U3Q9+eldc2nF2YsP+JPAJMAlDVt880xw9QluMXEedJLvsEuM83NYJm7WJZM9t6S1r81gNEx0VSNyqcpu1i2LJsO7nZebgL3axf8Ad9h/XyquysvHwAXE4HLoeD4123qhIa4A9AaGAAh9IzAcjJLyh29AEuJ6qVd/pFJOccY1Oq1dHILMhjR3oK0UGlv82vataBWQlbScyyHrOdkptVnLciOZ603GyvdSurXRG+1l6dsp/0/BwA1qTsp2FQHQBahDVgTcoBcgoLKFRlefI+BseWdtKnq52Sm8WGI4nkuwtLpZ+uNkDPzlC3gts8Z5HlfEWgS3tIPwaHUmDxcji/B9StYzn883vAonJfu1I26zdD0xho0hj8/WDoAEvPkx174bxu1nHvrjBn8YnlzJoHF/SGoEDv9A1nD2ek8xeROBHZIiLvi8hGEflMRC4RkcUisl1Eetl2vUTkdxFZY/9uY6f/XUQm28cd7TKCRaQ1kKuqh+28p0VkrH08T0ReEZEFIrJZRHqKyLe23v951O17EVklIpvstzAVpd8lItvsct4TkTft9EgR+UZEVtg/fU/S9kkicpV9/J1HO+4qqoeIHLN/XyQic0Xkf8AGO+1WEVluP/3pHY9OwQ/ATaf6majC8zOfZOKKFxh69yUn5O9av4d+1/YGoE3PlkQ3iyQytgF7NsbT8YJ2hNUPJSDIn16XdSOySYRX2g4RvhlzCwvHjWbJjn1siC8dNZg4eylXdG3H7MdG8dbIaxj/w9zivI5NGjLtodv5/q+38cz3s70a9R9PTHA47etGsy5lf6n0c8LqU8c/kM8uvpVpg+5kWFzHU9bwVhuga4MYZgwZxeT+N9Kqjnf39nS1ixjRvDPzk6wplW1Hk+kV2YS6/kEEOl1c2KgFjYLr+Ezbk6rULo+Dh60wexENI+FQ8onp0ZFWmjccqkQZbVvArAXW8a8LITNLSD1a2uanOTB0oHfahrOLMzns3xK4HusVhyuAm4F+wFXAE8A1wBagv6oWiMglwHhgOPAqME9EhgHjgNGqmmU73tUVaOapan8R+SswDegOHAF2isgrqpoC3KmqR0QkCFghIt8AAcBTQDcgA5gDrLPLfA14RVUXiUhTYCbQroI6LAAuwHLWMUAjO70f8EUZ9r2ADqq6W0TaATcAfVU1X0QmAbcAH6tqqogEiEgDux3FeL5Ksi3diJXmJ4j8rd+TpCSmUjeyDs/Peor4LfvZsHBzcf4Xz3/Pfa/ewdurX2L3hn3sWLObwoJC9m3Zz5cvTuOFWU+RfSyHXev3UFhQeEL5FeFWZfgbnxEWGMDrt15Jy+gG7DhY0oTLO7fh+1Wb+GjRajo3bcTzIy7l6tc+RhU2xCdx9asf0zyyPuOvH8LCbXvI81IfINjlx6S+w3l2za8cK8grlecUBx3qN+K2uZ8R6HQx9ZKRrDm8nz3Hjnit4632ptQk+s94k6yCfC5q1IK3+13PwJ/eqhLdk2kXcV5UM65v3oUbZltBtJ0ZKbyzeQkfXXQzWfl5bEk7RIHb7RPt46kq7YooK4AkUn76aZd93Pkj91lTA9//DD06Q3Sk4vKI+x1KgW27oJ93ATbDWcaZ7Px3q2rRaHYTMFtVVUQ2AHG2TTjwkYi0AhTwA1BVt4iMBNYD76hqUWCsEZBcgeYP9u8NwCZVTbT1dwFNgBTgQbtTgZ3WCmgIzFfVI7b914C9BIhLgHOl5FugjoiEqWrZk+ewEHhIRM4F/gDqiUgjoA/wYBn2y1V1t308EKvDssLWCwIOedgeAhrb7SjG81WSgxzXlzk0Tkm05kzTktNZ/P1y2vRqWcr5Z2VkM+GuScXnn+yaSNJuS/qXyXP4ZfIcAO587iaSE0rJV5qMnFyW706gX+u4Us7/2h4dGP2h9YKrdfsS8fdzUS84iCOZJSHvXclHyM7Lp1V0BJv2H/RK1yUOJp4/nGl7NzJr/9YT8pOy0knNzSK7MJ/swnyWJ++jXd2oKnH+J9P2dIrzEnfy7+4O6vkHkZp3auF+b7QB2oRHMb7n5dw5/wvSPDS/3r2Or3db/d+HO15EUnZ5f+6nrl0ep6t9MhpGQpLHf1VSMkRGWOnL15akH0yGXl28Kzv6uLIPJkPUccGcqAh4w45FZmZZUYCw0JL8X+Zai/z8zuRvf8Npc0aG/W1yPY7dHuduSjo1zwJzVbUDcCXgOcPVCjiG5eyKyD7OpjxNT71iTRG5CMuZ91HVzsAau7yK+vcO276L/RNTgeNHVfcD9YBLsaIAC4ERwLFyrsv0OBbgIw+tNqr6tEd+INY98IrA4ACCQgOLj7sP6syejfGlbELCg3HZ3zaXjRrIhgWbycqwpOpGWmHXyCYR9B3Wm7mflzFJWQ71QoIICwwArHn7Pi2asju5tFNNTEvnvBZNAWgeWZ8Al5MjmdnE1KuD02F9NI3qhhEXWY/9x8dHK8HzvS5nZ0YKk7ctLzP/t/3b6BnZBKcIgU4XXRo0ZmfGqXVwvNWOCAwpPu5UvzEOpEocf2W0GwXX4a2+wxm7dNoJHZ2iRY+NguswJLYN0/duqlLtijhd7ZNxcV+YNtMapa/dBGEhENUA+vaCxSvgaIb1s3iFleYNHdtaiwkTEiEv3wrfX3zcRGFqmrUTAOC9z+Day0rn/zgbLjch/1rP2d73CweKJgNHFiWKSDhWuL0/8KaIXKeqU4HNwK2nqZdqTyG0Bc6z05cDr4hIPayw/3DsOXhgFvAA8JJdty6qutZet/CAqt5ehs4S4CFgANAAmGr/nIzZwDR7iuKQiNQHwlR1r/06yIbAHm8bXTc6nKe//QcATpeTuZ8vYuXMtVwxehAAM975labtYnn0owcoLHSz748EXh5VEnr+59Sx1GkQRkF+AW8+8D7H0jLL1CmLyLAQxl8/BIcIDhFmbtjG/C27eeCSPmzaf5C5m3fx0k8L+PewQdzerxuqyripMwHoFhfDqAt7UlBYiFuVZ6fNIS0rx6u2d4+IZVhcJ7akHWT64FEAvLxhLo2CwwH4fOdqdmaksCBxFz8OuRtF+XLXWrYdtQJMr553Db2jmlEvIIhFV47htY0LikelVaF9WWw7bm7ZjUJ1k1NYwF+XfFd8va+1x7S/gLoBQfy7u+V9CtXNNb9OBmBi3+HU9Q+iQN08vWpm8cLAqtKOCAzh+0F3EuoXgKoysnUvLv35HY4V5J2WNsDD/7ZG8GlH4aLr4IE7oKDAyrvxarjwPFiwFIbcbG/1s3e91q1jbe8bMdo6v+//WWne4HLBkw/BqLGWg792KLQ6B17/ADq0hQF9rbr9195R0KMz/POhkuv3J1qRg55eRhwMZx9yKiucaxoRiQNm2CN6RGSKfT7VM09E+gAfYYXy5wC3qWqcvUhuraq+LiJNgLnA+ViRgBVYc+QqIk9jjagniMg8YKyqrrRH+GNV9Qpbfx4wFsuhf481F78ViASeVtV59rz5WOAAVifjiKqOE5EIYCLWPL8LWKCq94jIdcAgVR1dRvvvAp5V1cYi4gek2W371s4/pqqhx9fTzrsBeBwr4pAP3K+qS0WkB/C4qg6v6N6XF/avDg486tXuxSolp0vWyY0MZxXbL/yopqtQYzgabqvpKtQUXq7CqJhx66+tsu/L5zp9W6V1OyOdvy8RkdeA6ar6WxWXG6qqx0TEBXwHTFbV7yqwfwn4RFXXV2U9KtB7DfhBVWdXZGecv6G2YJx/raTWOP8zec7fV4wHgk9q5T1Pi8haYCOwGytCUC6q+o/qcvw2G0/m+A0Gg8FwdnC2z/l7jaoepGRVf1WWO7aqy6xKVNW757saDAaD4YzFjPwNBoPBYKhlGOdvMBgMBkMtwzh/g8FgMBhqGcb5GwwGg8FQyzDO32AwGAyGWoZx/gaDwWAw1DKM8zcYDAaDoZZh9vkbDIZyubHdqpquQo3x1KEONab9bNTGGtN+6lAHOHRtjekDPNfp2xrVrw2Ykb/BYDAYDLUM4/wNBoPBYKhlGOdvMBgMBkMtwzh/g8FgMBhqGcb5GwwGg8FQyzDO32AwGAyGWoZx/gaDwWAw1DKM8zcYDAaDoZZhnL/BYDAYDLUM4/wNBoPBYKhlGOdvMBgMBkMtwzzb31AlhIQH8/f37iWuQxNQZcJdb7F56bZSNve9dge9LutGblYuL90xkR1rdgMw6vlb6DW0GwCf/d83zP/q90rr+rucfPyXEfi7nDgdDmZt3M7E35aUsnn08gvp1TwWgEB/P+qHBNHnmbdoVDeM1269EqcILqeTz35fy1fL13vV7kZBYUzofRURQaG4Vfly5xqmbF9RyuaqZu0Z3bYPAFkF+Ty16me2pB3C3+HkiwG34+904hQHv8Rv4bVNC6pFuwiHCN8PupOD2RncvfCrSmuvnLSfxFUZBIS7GPzflifkb512mH0LjwKgbiU9IZerPmiDf5iLbTMOs2d2GgiENw2gx30xOP29G4ecTB/g0KZM1n2YhBYq/mFOLnrmHArz3Mz75x7cBW60EGLOq0P7G6KqVFtVWfdhEomrj+EKEHrcH0O95kEArP80iaTVxwBoNzySJn3DvdIGWLgMxr8BbjdcdzncfcuJNj/PgYlTAIG2LWDCP630CW/D/KXW8b23w9ABldetzD0HOLIjmzlP7OK8v8US2yecQxszWTclqTg/40AuvR+KJaZXncqLG6oc4/wBEfldVc+v6XqUh4jcCjwCOIECYAUwVlXTRGQe0AjIBgKAV1T1Xfu6PUC8ql7gUdZarM/9YeAFO7klsN8uY72q3u5tHe979Q5WzlzDsyNexuXnIiDYv1R+r8u6EtOyESNbj6Fd71Y8OOluHuzzBL2GdqNl1+bc0/Uf+Af48fK8f7Pi5zVkZWRXSjevoJA7359KVl4+LoeDT+4ZwcKtu1kfX/Jl88KP84uPb+7ThXaNIwE4nJHJLW99SX5hIcH+fnz/0G3M3byT5IzMSre7QJXx62azKTWJEJc/0wbfyaKDu9mRfrjYJuFYGjfN+ZT0/BwubNiC53oMZfhvU8hzF3LrvE/JKsjHJQ6+HHg785N2sDblgM+1ixjZqic70w8T6hdQ6TYDNLuoLi0urc+KN/eXmd/m6gjaXB0BwIGVGWyfkYJ/mIvslHx2/HSEIa+0xBngYOl/44lffJS4i+tVqX5eZiFr3kvkgnFNCY70J+doAQAOP+HCfzXDFeTEXaDMfWo3DbuG0qB1cJVpJ605RkZiHpe+0ZIj27NZ/V4iA//TnMRVGaTtyuGSl1rgzlfm/8vS9gt2Vlq7sBCefRU+eBmiI2HEaLi4L7SMK7HZkwDvfQafTYTwMEhJtdLnLYE/tsF370NePtz+V+jfG0JDqqbdAFqobPj0IA27hBanRXUIYdCEFgDkZRTw85gdRHcOLa8IQzVhwv7An8Hxi0ic7ciPT78U+Btwmaq2B7oBvwPRHma3qGoXoC/wgoh4et4wEWlil9WuKFFVZ6pqF/u6lUVlnIrjDw4LomP/c/n5gzkAFOQXkHk0q5RNn6t78tsnlhPevGw7oXVDqN+wLs3OjWX9gk24C93kZOWyc/1eelzaxSv9rLx8AFxOBy6HA63AdmjnNvy0bisA+YVu8gsLAfBzOXGIeKULkJxzjE2pVkcjsyCPHekpRAeFlbJZnbKf9PwcANak7KdhUMmIJ6vArrvDgcvhRCuqfBVrNwwK4+LGLflq19rKi9pEnhuCf2jlnFb8oqM06Veiq26lMM+Nu1ApyHUTWN+vyvXjFx0lpncYwZHWv0JguDXOERFcQdZ17kJFCxW8/NhPpn1gRQbNLqyLiNCgdTD5mYVkp+aTnpBLZPsQHE7BFeggvFkgSWuPeaW9fjM0jYEmjcHfzxq5z1lU2ubr6XDTMMvxAzSw+1U790DPLuByQXAQtGlhRRGqqt0AO345Qsx5YQTUKXtcmbA0nYZdQ3EFGNdT05hPABCRY/bvi0Rkvoh8JSLbROR5EblFRJaLyAYRaWHbXSkiy0RkjYj8JiLRdnqkiPwqIqtF5B0R2SsiEXberXY5a+28ynb3x2GN8vcDqGqhqk5W1a1l2IYCmUChR9pXwA328U3A517enpPSqHk0R5PT+cfk+3lr1Yv8/b17CAwuPZKMaFyfQ/EpxeeHE1KIiKnPrnV76HVpVwKC/KnTIIwuF7UnqkkDr/QdInwz5hYWjhvNkh372OAx6i9Vz7phxNYLZ9nO+OK0huGhfPvgrcx+dBQfzF/p1aj/eGKCw2lfN5p1KeWPjEY078z8pJ2l6j598CiWX/03FiftYt2Ryo36q0L7ya6DeGHdHNze9Di8pCDXTdLaY8T2tpx/UAM/Wl8ZwY/3bmfG3VvxC3bS0AejwIwDueRnFjLvX7v57ZGd7J2fVpynhcqvY3cy/a6tRHUKoUGryo/6K0P2kXyCG5Q4v6AGfmQfKSA8LpCkNRkU5LrJTS8geVMm2Sn5XpV96DA09JiliI6Eg4dL2+xNgD3xcPP9cMO9JQ6+bUvrODsHUtNg+RpISj7VVp5Idko++5el02JQ/XJt4hen06Sf91MdhqrHOP8T6Qz8FegI3Aa0VtVewPvAGNtmEXCeqnYFvsAKyQP8C5ijqt2A74CmUDzivgHoa4+0C4EyZurKpD2w+iQ2n4nIemAr8Kyqejr/qUDRy7mvBKZXUrcYEfmLiKwUkZUJuuuEfKfLQatu5zD97Znc2/0RcjJzueGxa44v44TrVGHVr+tZ/vMaXlv8HE/87yH+WLKNwgK3V/VzqzL8jc8Y8Pz7dIxtSMvosjsPQzu1YdbGbaWcXdLRY1z7+qdcNuFDru52Lg1CT80RBLv8mNR3OM+u+ZVjBXll2pwX1Yzrm3fhxXVzStX9ylnv03f663Su35jW4ZHVon1xo5ak5GaxMbXsjlJVkbgyg4i2QfiHWc4w71ghB1ZkMHRiK654tw2FuW72Lkg7SSneo4WQuiuHfo8344Inm7F5ajIZB3IBEKcwaEILLn+nNak7sjm6L6eKxU9MEqBh51Aadg1j7rjdLHs1gfqtgxGHd2GHsvppx5dQUGh1AD56DV7+Jzz1EqRnQN+e0P88q1Pw8DPQpT04Kz/jcFLWTkmi463RiLPsNmWn5nN0X45POnsG7zHO/0RWqGqiquYCO4FZdvoGIM4+jgVmisgG4B9YDhqgH1ZnAFX9BbBn2xgIdAdW2HPuA4HmACLynZ32E9DDjgysFZE7jq+YiHS083aKyA0eWbeoaieszsZYEWnmkXcESBWRG4HNQOl4fCVQ1XdVtYeq9oiV5ifkJyccITkhhS3LdwCwYOoSWnUtbZe8P6XUiD4itgEpB44A8L/x33JPt3/w2JBnERH2b0/0tooAZOTksnx3Av1ax5WZf5lHyP+ENmRksuNgCt3jYrzWdYmDiecPZ9rejczaX3b5bcKjGN/zckYv/Jq0vBPXM2Tk57I0eR/9G554f32h3T0iloGNWzH/ivt5rc8w+kTF8XLvq7zSrgzxi4+WWtR2aMMxQqL8CAh34XAJMb3rkLLV6z/JkxLUwEV0l1BcgQ4C6riIaBfM0b2lnbx/iJPI9iFeh95Pru1HVkpB8Xl2Sj6B9a3OT7vhkQya0IL+/4wDhdBG/uWUUjbRkZBUsl6Tg8kQFVHapmEkDOwHfi6IbQTnNLE6AwD33AbffQCT/2t1JJrFnkoLyyZ1ZzbLXk3gp/u2kbA0nTXvJ7J/eXpxfsLv6cT0qoPD5f30mqHqMc7/RHI9jt0e525KFki+Abypqh2B0UCgnV7eX7UAHxXNsatqG1V9GkBVh9nRgKHASg+bD+1rN2HN86OqG2zbn4Gg40VUNRkrStD7uKwvgYn4IOQPkHowjeT4FGJbNwag68CO7N2cUMpmyQ8rueS2CwFo17sVmUezOJKUhsPhIKy+NRI4p2NTzunUlJWz1lVau15IEGGB1hRDgMtJnxZN2Z185AS7uIh61AkKYO2+ko5FdJ1QAlzW0KdOYABd4xqXee3JeL7X5ezMSGHytuVl5jcKrsNbfYczduk09hwrKb9+QDBh9kK7AKeLvtFx7ExPKbOMqtaesGEe/aa/wYUzJvLXJd+x5NAeHl72g1faJyM/s5DkP7Jo3LNkvj8owo8j27MpyHWjqhzacIw6sd4tNqwMjXvW4fDmrOJ1BUd2ZBMWE0Du0QLyMq3AWGGum4PrMwmLqVr9xj3C2Ds/DVUlZVsWfsFOgur5oYVKbobVKUjbm8PRfTleL3zr2NZy5AmJ1qK9n+ZYC/48GdgPlq2xjlPTrCmA2MbWYsFUawMGW3fC1l3Qt8fptraEoZNaF//EnleHrqMalVrRH7/4qAn5/4kwq/1PjXCs1fEA/88jfREwAmvR3WCgaAnzbGCaiLyiqodEpD4Qpqp7K6H1H2CCiFytqkUe9QTHDyAiwUBX4MXjsr7D2hEwE2hcCU2vmfjgZB7/9EFc/i4Sdx1kwp2TuGL0IABmvPMry39aTe+hXflo+xvkZuUx4c6JADj9nLyy4FkAstKzeOG2N3AXVj7sHxkWwvjrh+AQwSHCzA3bmL9lNw9c0odN+w8yd7M1TTG0cxt+Xld662HzqPr8Y2j/4vMpC1ax/aB3zrd7RCzD4jqxJe0g0wePAuDlDXNpFGx9yX2+czVj2l9A3YAg/t39MgAK1c01v04mMjCUl3pbWw0dIvy4bzNzE3dUi/bpsuzVeJI3ZZGbUcCPo7dy7ogo3IVWTLrFYGvOd//ydKI7h+AKLBljNGgVTMx5dZj9yE7EKdSNC+ScS7xb6V8Z/TqxATTsEsqvD+9EHHDOwHqENw0kbW8OK9/cj7oVVYjtU4fG3cNOouaddsNuoSStyeCXMdtx+jvocb8VTXIXKvOe2gOAX7CDXmNicJQTIi8PlwuefAhGjbW2+l07FFqdA69/AB3awoC+0K8XLF4BV9wODgeMvRfqhUNuLtxmT1yGhMCL46zyqqrdFZF5KI+sw/lEnlu16ysMp46oDxf7nCmIyDFVDRWRi7AW111hp8+zz1d65onI1cArWB2ApUBPVb1IRKKwRtf1gPlY8/znqGquHaZ/HCvakg/cr6pLPeoQB0xR1YvKqN//A8ZibfVLAzYC/1LVxDK2+n2iquPt6/YAPVT1sEdZccAMVe3gkVbczpPdq0GO62vsD+bAozW3KSOnS9WHps8Ebmy3qqarUCt5NmpjjWk/dajDyY18zHOdvq0p6Sqdkxi3/toq+758rtO3VVo3M/IHVDXU/j0PmOeRfpHHcXGeqk4DppVR1FFgiKoWiEgf4GJ77QCq+iVW+L28OuwBLion7yPgo3LyyrzGzosrR6fDcWnllmEwGAyGsw/j/KuWpsBXIuIA8oC7a7g+BoPBYDCcgHH+VYiqbseaczcYDAaD4U+LWe1vMBgMBkMtwzh/g8FgMBhqGcb5GwwGg8FQyzDO32AwGAyGWoZx/gaDwWAw1DKM8zcYDAaDoZZhnL/BYDAYDGc4IjJZRA6JSKUeD2n2+RsMhnL5YnP3mq5CjVGTjzauyUfs1uSjhcFq+7j1157c0AfU4GOFq4IpwJvAx5UxNiN/g8FgMBjOcFR1AdYr3CuFcf4Gg8FgMPzJEZG/iMhKj5+/nE55JuxvMBgMBoMPqMoplOdU3wXeraryzMjfYDAYDIZahnH+BoPBYDDUMozzNxgMBoPhDEdEPgeWAG1EJEFE7qrI3sz5GwwGg8FwhqOqN3ljb0b+BoPBYDDUMozzNxgMBoOheuncNgAAIABJREFUlmGcv8FgMBgMtQzj/A0Gg8FgqGWYBX+GKiEkPJi/v3cvcR2agCoT7nqLzUu3lbK577U76HVZN3KzcnnpjonsWLObqKYR/Oubf+B0OnD6OZn25s/MeOfXSuv6u5x8/JcR+LucOB0OZm3czsTflpSyaRQexvjrhxAWFIBDhFdmLmLh1j0AtG4Ywb+GDSQ0IAC3KjdM/B95BYWV1m8UFMaE3lcRERSKW5Uvd65hyvYVJ9j1jmzKk10H43I4SM3N4ua5n+LvcPLFgNvxdzpxioNf4rfw2qYFVardO7Ip7/S7nvjMowDMTNjCm38sqhbtSxq35m8d++NWKFQ3z66ZxarDCQB82P9GujSIYeXheO5e+FWldSurfVWz9oxu2weArIJ8nlr1M1vSDgHwfM8rGNC4JSm5mVz2y3teaQOsnLSfxFUZBIS7GPzflifkqyrrPkwicfUxXAFCj/tjqNc8iEMbM1k3JanYLuNALr0fiiWmVx2fa6ftzmb1e4kUZLsRB7S9NpImfcO9bvvCZTD+DXC74brL4e5byrabOQ8e+pfw9TtKh7Yw/VeY/EVJ/tad8M170K5V5bVP1vZDmzL5/YV9hET5AxDTO4xzr48qztdCZfZjuwis76Lf480qL3yWUmPOX0SuATqp6jMicg+QpaqVeiFBdSMi0cArwHlAKpAHvKiq34nIRcA0YDdWJOUQcLOqHhKRkcCHwCWqOtsuaxjwLXA9cAtwDhAKRNplANynqr+fYl2vAs5V1ee9uKYj8LCqjjwVTYD7Xr2DlTPX8OyIl3H5uQgI9i+V3+uyrsS0bMTI1mNo17sVD066mwf7PMGRxDQe6juO/LwCAkMCeW/Dyyz5YSUpiamV0s0rKOTO96eSlZePy+Hgk3tGsHDrbtbHl3zJjh7Qm182bOPLZetpEVWft0Zew+AXJ+N0CM+PuJTHv/qFrUmHCQ8OpKDQ7VW7C1QZv242m1KTCHH5M23wnSw6uJsd6YeLbcL8Avh390u5Y8EXJGal0yAg2Kq7u5Bb531KVkE+LnHw5cDbmZ+0g7UpB6pMG2BFGQ62OrR/P7Sb32ZaHcA24VG8cf4wBv/8DgDvbVlKoMuPm1p0rZSet9oJx9K4ac6npOfncGHDFjzXYyjDf5sCwDd71vHJjpVM6H2l19oAzS6qS4tL67Pizf1l5ietOUZGYh6XvtGSI9stpzvwP82J6hDCoAktAMjLKODnMTuI7hxaLdrOAAc9x8QQ1iiA7CP5zH50F9FdQvEPcVZau7AQnn0VPngZoiNhxGi4uC+0jCttl5kFn3wDnc7V4rQrB1k/ANt2wv3jvHP8lWk7QES74HId+/afUgiLCSA/u/Kd+7OZmgz7PwJMAlDVt/8Mjl9EptjO3DNNgO+BBaraXFW7AzcCsR5mC1W1i6p2AlYA93vkbQA8t2DcCKwDUNVhqtoFGOVRRpdTdfx2mT+U5fhFpNyOnqpuAGJFpOmpaAaHBdGx/7n8/MEcAAryC8g8mlXKps/VPfntk/kAbF62ndC6IdRvWJeC/ALy8woA8Atw4XB4/yeZlZcPgMvpwOVwoMflqyqhAVZnJDQwgEPpmQCc36oZ25IOszXJchpHs3Jw6/FXV0xyzjE2pVodjcyCPHakpxAdFFbK5qpmHZiVsJXErHQAUnJL7k1WgV13hwOXw4k38pXRrghfaxeVDxDs8iv1ufx+aA+Z+bmVF/RSe3XKftLzcwBYk7KfhkElo+sVyfGk5WafkjZA5Lkh+IeW7zQPrMig2YV1EREatA4mP7OQ7NT8UjYJS9Np2DUUV4B3f++nqh3WOICwRgEABNX3IyDcSW56gVfa6zdD0xho0hj8/WDoAJiz6ES71z6Au26CAP8T8wB+nA2XD/RKGjh52ysiKyWfxNXHOGdg3VO6/mykwr88EYkTkS0i8r6IbBSRz0TkEhFZLCLbRaSXbddLRH4XkTX27zZ2+t9FZLJ93NEuI1hEWgO5qnrYzntaRMbax/NE5BURWSAim0Wkp4h8a+v9n0fdvheRVSKyyfMFByJyl4hss8t5T0TetNMjReQbEVlh//St5D0aAOSp6ttFCaq6V1XfKON+CRCGFR0oYiHQS0T8RCQUaAmsraT28eU7RWSXWNQVEbeI9LfzFopISxEZ6dHmKSLyXxGZC7wgIiH2O59X2J/V1R7FT8fqmHhNo+bRHE1O5x+T7+etVS/y9/fuITA4oJRNROP6HIpPKT4/nJBCREx9ACJjG/DO2gn8b9/bfPni95Ue9RfhEOGbMbewcNxoluzYxwaPUT/AxNlLuaJrO2Y/Noq3Rl7D+B/mAhAXUQ8F3r1jGF8/cDN39u9xCq0vISY4nPZ1o1mXUnpkck5Yfer4B/LZxbcybdCdDIvrWKru0wePYvnVf2Nx0i7WHancyLuy2gBdG8QwY8goJve/kVZ1IqpVe3BMG2ZdNpr3L7iBx5bPOKXyT1W7iBHNOzM/aWeVa5dH9pF8ghuU9LWDGviRfaS0o41fnE6Tft6H3atC+8j2LNwFSmh0Od65HA4dhoYlUXSiI+Fg6SATf2yDpENw8fnll/PzXBh6Cs6/MhzZls2vY3ew8Lm9HI3PKU5f92ESnW6NBof4RvgMpDLdzpbAa0AnoC1wM9APGAs8YdtsAfqralfgn8B4O/1VoKUd6v4QGK2qWUBfYHUFmnmq2h94Gyukfj/QARgpIg1smzvtUXgP4EERaSAijYGnsMLzg+z6FvEa8Iqq9gSGA+9Xou0A7U9SV4ALRGQtsA+4BJjskafAb8AQ4Grgh0rqnoCqFgLbgHOxPoNVtnYAEKuqO8q4rDXWtMPDwDhgjn0PLgZeEpEQ224lcEFZup5vk0rQXSfkO10OWnU7h+lvz+Te7o+Qk5nLDY9dc3wZZbTH+p2ckMLoLmMZ2WoMg26/iLpR3n0pulUZ/sZnDHj+fTrGNqRldINS+Zd3bsP3qzYx8Pn3uXfK9zw/4lJEwOlw0K1ZYx758mdue+crBrZvQe8WTbzSLiLY5cekvsN5ds2vHCvIK5XnFAcd6jdi1IIvGTn/cx44tx9xofWL637lrPfpO/11OtdvTOvwyCrV3pSaRP8Zb3LFzPf5ePsK3u53fXGer7UBZu3fyuCf3+GexV/ztw4Xel3+6WgDnBfVjOubd+HFdXOqVLtCyoigeP71Z6fmc3RfDg29DPlXlfbyN/bT474YxEtHWFZkyLMEtxuenwiP3ld+Gev+gMAAaN3cK+lKUe+cQIZOasWgCS1peVl9lrwYD8CBVRkEhDup1yKo6kXPYCrj/Her6gZVdQObgNmqqljh7DjbJhz4WkQ2Ys2NtwewrxkJfALMV9XFtn0jILkCzSIHuQHYpKqJqpoL7AKKvp0fFJF1wFI7rRXQy9Y5oqr5wNceZV4CvGk76R+AOiISJiJDRGStnX4V8L59vqysionIRBFZJyKeK4yKQvZNsDo5Lx532RdYo+obgc8raHdlWAj0t3/+g9UJ6Ik13VAWX9udBoDBwGN2W+cBgUBRqP8Q0LisAlT1XVXtoao9YuXE/9rkhCMkJ6SwZbnV91gwdQmtupa2S96fQlSTEqccEduAlAOlXz2dkpjK3k3xdLygXTlNqZiMnFyW706gX+u4UunX9ujAzA3W3PO6fYn4+7moFxzEwaMZrNydQFpWDjn5BSzcuodzG0eVUXLFuMTBxPOHM23vRmbt33pCflJWOgsSd5JdmE9qXjbLk/fRrm5pnYz8XJYm76N/Q+++FU+mfawgrzj8Pi9xJy6Hg3r+pb8EfaXtyYrkeJqG1jtB+1SpjHab8CjG97yc0Qu/Ji3v1MP83hLUwI+slJLRdnZKPoH1S0bjCb+nE9OrDg5X1Y9CK9LOzypk8X/20eGmKBq0Dva67OhIa1RfxMFkiCoJJJGZBdt3w+0PwcAbLEd/3xOwcUuJzU9zTi3kXxn8gp24gqxpgUbdwnAXKrnpBaRsySJxZQY/3beNZa8kkLwxk+WvJ/imEmcQlXH+nhNzbo9zNyULBp8F5qpqB+BKLKdSRCvgGKUdS/ZxNuVpeuoVa9rz8pcAfVS1M7DGLq+i/yaHbV80rx6jqhmqOrMoDatTMMo+721ftwnoVlSIqt4PDMRaoFcWP2A55mJUdTlW5CJCVbeVeVXlWYg1Qu8F/ATUBS4CyluqnelxLMBwj3vQVFU323mBWJ+L16QeTCM5PoXY1tZH3HVgR/ZuLv3PteSHlVxymzXya9e7FZlHsziSlEZETH38A+35+LohtO/bhvitlQ8/1wsJIizQmmIIcDnp06Ipu5NLdyoS09I5r4XVx2keWZ8Al5Mjmdks3raX1g0jCPRz4XQIPc6JZeehIydonIzne13OzowUJm9bXmb+b/u30TOyCU4RAp0uujRozM6MFOoHBBPmZ9fd6aJvdBw701PKLONUtSMCQ4qPO9VvjAMhNS+7WrSbhdYrPm5fryF+DiepVeSET6bdKLgOb/Udztil09hzzPvP9HRo3COMvfPTUFVStmXhF+wkqJ5fcX784qM+CflXpO3Od/P7S/E0u7AusX1OTbtjW9ibAAmJkJdvOfKLPSZPw0JhyQ8w+0vrp/O5MGk8dLDjr263tQvAVyH/nNR81A5PHNmehbrBP8xJx1uiufydNgyd1Jref4slskMIvR6MPUlpZz9Vtdo/HCiadBtZlCgi4Vjh9v5Yo+7rVHUqsBm49TT1UlU1S0TaYoX5AZYDr4hIPSADK7y/wc6bBTwAvGTXrYuqVmbufQ4wXkTuVdW37LSKus39gLImGB8HcspILxMR+Q+wXFW/Oy5rGfAxsEtVc+xR/GjgikoUOxMYIyJjVFVFpKuqrrHzWgOn/PLpiQ9O5vFPH8Tl7yJx10Em3DmJK0Zby3tnvPMry39aTe+hXflo+xvkZuUx4c6JADRtF8voCbejqogIX788nT0b91VaNzIshPHXD8EhgkOEmRu2MX/Lbh64pA+b9h9k7uZdvPTTAv49bBC39+uGqjJu6kwA0nNy+WjRar68/2ZUlYVb97Bg6+6TKJame0Qsw+I6sSXtINMHjwLg5Q1zaRRsfcF+vnM1OzNSWJC4ix+H3I2ifLlrLduOJtMmPIqXel+J0677j/s2MzexrJmbU9e+LLYdN7fsRqG6ySks4K9LrD+nyMBQn2sPiW3LsLiOFLjd5BTm8+CSb4uv/2LAbTQPa0CIy59FV47h8RU/sjDpxCmlU9Ue0/4C6gYE8e/ulwHWVsNrfrVm41497xp6RzWjXkAQi64cw2sbF/D17nWVbvuyV+NJ3pRFbkYBP47eyrkjonAXWk6nxeD6NOwWStKaDH4Zsx2nv4Me98cUX5t5KI+sw/lEnuv9yPt0tOOXpHN4cyZ5GYXsmZsGQM/7G1P3nMpHYlwuePIhGDXWcuTXDoVW58DrH1gOfsBJVlGtXGdFD5qUGV88/bYnLE1n16xUxAlOfwe9/xZb5nSjwUK0giW+IhIHzLBH9IjIFPt8qmeeiPQBPsIK5c8BblPVOHux31pVfV1EmgBzgfOxIgErgA62E3oaOKaqE0RkHjBWVVfaI/yxqnqFrT8Pa63BBqwV+DHAVqxR+NOqOs9e/DcWOIDVyTiiquNEJAKYCLTD6vQsUNV7jmvvFGCKqs47Lr0R1nRGb7uNmcDbqvqllN7qJ8BRrOjBNrG2+vVQ1QfK0Jlhd4Q4vp122gzgOVUtvWndyluINdXwhIjcjLVror6quj01y9AJwlqHcb5d1z0e9/ZNYKaqTj9ez5NBjuu9Ww5fhRx4tIJVRD4mp0vWyY0MZxU3tltV01WoEZ6NOuUxQJXw1KEONab9XKdvq7S34E5qXWXfl46G26q0bhU6f18iIq8B01X1tyouN1RVj4m1te07YHIZo+c/PSIyU1WHVJNWADAf6KeqFe7/Mc7fUFswzr9mMM6/bKra+dfkPv/xVBw+P1WetkPhG7FG49/7QMPnVJfjt2kKPHYyx28wGAyGs4Mae8Kfqh7kNLa9VVDu2Kou82xHVbcD22u6HgaDwWCoHsyLfQwGg8FgqGUY528wGAwGQy3DOH+DwWAwGGoZxvkbDAaDwVDLMM7fYDAYDIZahnH+BoPBYDDUMozzNxgMBoOhlmGcv8FgMBgMtYwae7yvofYhIn9R1Xdro77Rrhlqa9trq/afQd8T83hfg8HiL7VY32jXPn2jXTv1zwiM8zcYDAaDoZZhnL/BYDAYDLUM4/wN1UlNz8PVpL7Rrn36Rrt26p8RmAV/BoPBYDD4ALPgz2AwGAwGw58G4/wNBoPBYKhlGOdvMBgMBkMtwzh/g88QEaeIvFRD2uNrQvfPgoicU5m0swUROa+m61CbEZF6ItJeRJqLSLX5FRGZUl1aZxuumq6A4exFVQtFpLuIiFb/ytJLgSeqWRMRubaifFX9tpqq8g3Q7bi0qUB3XwmKSCBwA5AKTAceAS4AdgLPquphX2kDkzixvdWOiEQBfYHGQDawEVipqm4f6wYCV2Ddb0/tH1V1k480w4H7gZsAfyAZCASiRWQpMElV5/pC24NOPi7/rMU4f4OvWQNME5GvgcyixGpwgk4RqQeUuUJWVY/4SHcqsNb+4Th9BXzabhFpC7QHwo/riNTB+mL2JR8D+UAI8DCW83kT6AdMwXJOZyUicjHwGFAf62/+ENb9vgZoISJTgZdVNd0H2k8DVwLzgGUe2q2B5+2OwcOqur6KpadifeYXqGracXXqDtwmIs1V9YMq1vUkWES6Uv7/+Wofap/RmK1+Bp8iIh+WkayqeqePdXOB/ZT9paCq2txHusOwRr8tgWnA56q6wxda5ehfjeVwrgJ+8MjKAL5Q1d99qL1RVTuIiAtIUNWGHnnrVLWzD7XTgAXl5avqVb7StvVfAt5Q1X1l5LmwOj5OVf3GB9qXq+qPFeRHAU1VdWVVa9c0IpIBrKD8//MB1VylUvyZt/oZ5284KxGRNaratQb1Q4CrsToCDYBxqjq/GvX7qOqS6tKzNVerarfjj8s694H2dmBUefnVee9rGhEJUdXMk1tWmZ4AtwDNVfUZEWkKNFTV5dWgXaP/5yfjz+z8Tdjf4FNEpDXwFhBtjwo7AVep6v/VcNV8TQ5wFEgHmuL7kPvxpIjIbKr3vseKyOtYo7CiY+zzGB/qAhz7Mzh4EYkGxgMxqnqpiJwL9PFx6LtI+3zgfSAUaCoinYHRqnqfj6UnAW5gAPAMVpTpG6Cnj3UNp4FZ7W/wNe8Bj2PNBWPPO95YDbqvlZchIs18JSoiF4vIu8Aq4GLgNVXtqqozfaVZDjVx3/+B1e6VHsdF54/4WHu3j8uvLFOAmUAj+3wb8FA1ab8CDAFSAFR1HdC/GnR7q+r9WB1eVDUVawFgdfBoNemcdZiRv8HXBKvqcisyWEyBr0VVdYqI9MEacS5Q1UP26PcxrBXRTXwkPRtYDywCAoDbReR2j3o96CPd46n2+66qH/my/JPwaUU7Lapxl0WEqn4lIo/bugUiUlhN2qhq/HGfeXVo54uIE2tBKyISiRUJqA4mHNfeIgRrzt/sBigH4/wNvuawiLSg5IvhOiDR16Ii8iLWCui1wKMiMgO4Dysk68vFhnf4sGxvqPb7LiL9sOZ9P7bPp2Ktfgf4P1Wd40P5Gt1l4UGmiDSg5L6fhzX9Ux3E26F/FRF/4EFgczXovg58B0SJyHPAdcCT1aALVidDgf9hbS/NribdMx6z4M/gU0SkOdZbts7H2v+9G7hVVff4WPcPoJuq5thb/g4AnVR1uy91/yzUxH231xiMUdU/7PMNwEisrX9PqOqlPtSu0V0WHvXoBrwBdMDa6hgJXOeDbXZlaUdgTXddgtX5mQX8VVVTqkG7LTDQ1p2tqtXR6fDUvgmrs/8HVkdglqr6PMJ4Mv7MC/6M8zdUC/bqd4eqZlST3ipV7e5xvlZVu1SDbgTWg09SgcnAS5Q86Obh6nZI1XnfRWSFqvb0OP9WVa+1jxerat9qqEON7rKw6+AC2mA5wq2qml+d+tWJ/TS/9araoabrAiAiNwATgRdUtUaeLurJn9n5m7C/wSeIyK2q+qmI/P24dABU9b8+rkILEfHc5x7nee7Dfd//w1rg1gpYDnyINRq7AGsl9kU+0gXAc33BcekAFIXkfURdz5Mix28T7UNdT2p0l0UZ6w5ai8hRYIOqHvKx9utlJB/FesLgNF9oqqpbRNaJSNOynnFQHYhIDNZi1mFYne6/YU1DGCrAOH+Drwi2f4fVkP7Vx52/XE260ar6hL33ea/H6GOLiNxfDfplba8SrJBoDNYT2XzFlrIeOCMiVwBbfahb9IS9m4BewG9Yuyxq4qE2dwF9gKLH2l4ELMXqBDyjqp/4UDsQaAt8bZ8PBzYBd4nIxarqq10HjYBNIrKc0k/x9OmDlQBEZD7Wd8xXWFNMRU/u9BeR/9/evYfZWVV3HP/+CLeYclfkHkoACcpVEOoFBIqiaKWiCIhFWxCeiqaASh+k5ZICgq1UQUO5FpUS1ApCRQighFAIIBCaIFRiAAmikooahHAJv/6x9smcjHORJPt957I+zzPPzHtmJmtnkjP7vHvvtda6FSt5Dns5+adaJpT3P7b9rQG/soL+lnolbUrcJdRaCl5c4ltS71r21U9A2/5k5+Ou4isnEBPQ6ZXDHwf8Vzlc2Cmr+kbi3EHt0r5DJcviZWCi7V/Ckrz/KcBuRAXCmpP/lsDenb1uSVOIff99gdkV455a8c8ezHjiwN9RwMfLY53lcQNVKnmOBDn5p1reLekkIte88cm/W9mH/yBxZ7gxdZcEtyjbC+r6mHLdSFe9suf8UaK+/p3EgbOqd97FIqLRyoeJ/gIQE97RxIrETyrGHipZFpt3Jv7iV8DWtn8tqfbe/8bE4cpOdsE4YKPSYOv5WkHbLK5ke/O2Yg93OfmnWq4HFgDjJHU3M+nk365ZM7ikNYg9wEOJBidXEWlom9SMy9LbDf9c3rvXdTVla2EScSe8n+3HasfsMh04H/hi193na4mzDq+jYsW3lmsMdJtR0kq7l95vLQcRf9P/t60QZwOzJN1CPM/2AM4osW9a0cEk3Wb7raW+fvfBtkae413jWBlYXFbbNiVWWebanjXIt45qedo/VSXpu7Z77783Efc54sDdScBt5RfDvFoNfbrivg/YxPZXyvVdRLqXgRNqb4FIepm423yKvn8hVyt6UlIqP08s808CtiO2As4GprhiW9uhkmVRtloOJNr6itiG+E839ItW0obEuQcBd9n+eRNx2yLpSOAs4BlgMlFZ8l5gJ+AS22e1OLwhfdo/J/80Ikk6ltjbH0ecwL8SuLGByf+/gYNtP16uZxH5z+OAS23vUzn+gKWLm1gJkDSJKDX7c2B32/MbiDmNyLJYg/h5X0oUfXkb8GHbb689hqGgvADbiq4sB9v9djtcQTFPI7Z37miyoVCJ/QDRMnoNoqDReNsLJL0KuNv26wf8AyobypN/LvunKvpYElT3+9pLgrbPAc4pxW4OAa4GNpJ0AnCV7Vr7z6t2Jv7itlJk5f/K8mtVDS/zL0XS2sRd2G7AfsC7ge9LmlS5uh+0n2UBLKnody4wkahvPwb4fRNL4JKOIFZcNiEqHe4O3EE03KnpUWJ77dzyfJ9BlNSukl7Yywull8DTkubaXgBg+1lJLzQQf9jKyT9VYfut5X1bqX6dccwjTrmfLmk74oXA9+nJRljR1ukV/5iuy9dUirlEf/uvNPOi616iw9snyp7/NEk7Al+V9JjtQyrGbjXLost5xIrTt4BdgL8iTuE3YRJxrmKm7b1K5bvqJ/FtXwJcImkD4CDg08TJ+yae+2Ml7UQ0qVu1fKzy1nQnzWElJ/9UhaR1B/p8E/m3kg4gfvHOtn2D7dlEytOJFcPeKelI2xf2GstRxBmE2m4GNiBq2U9tuPDKHr2X+MuhqzeXvdmaWs+y6LA9V9IY24uBSyXd3lDoRY5y1khazfZDkl5XO6iki4BtgV8Sd/3dqZ61PQl0Cob9ouvjznXqR07+qZZ76Lnj3Iw4iCWiCtzPqPwLWdJXiXSz24HJkt5ke3LNmMWxwNWSDmXpXPfVgANqB7d9gKS1gPcDF0panTjvMLX2C66B9vZ7vxiqoNUsiy7PKprqzFI0l3qSOO/RhPll6+Vq4EZJTxPnLmpbj9je+A1RZGeBG6qrb3uvJuKMRHngL1Ul6XzgGtvXlet3AX9u+/jKcecAO5Qc51cBM9xV6782SXvTk+v+QAN73n2NYSWixv25wBkNlFRuTdtZFl3jGE9kW6xCvBBcC/hqU9kGXePYs8S+3nYje9+SJgLvJP7eYxpIq0XSmsR5j4fL9QeBseXTN/SqudC4Jg/8SdqPKCU+BrjI9ucH/Pqc/FNN6tVgpzz2I9u7VI57r+2d+7seyRRtXQ8hTrrfBlxpe0a7o6qr7SyLoaKc9t+UrlVd21WX4Ev55rcRdQXWIQ4ZzihnAaqSdAFwu+1/L9dziTM9Y4GXbB9dewwDaWrylzSGKKK1LzAfuBs4xKXDZl9y2T/VtqBU+vsGcRd2GFC9xSiwjaROG1URjX7+hwby3dsk6VFi+XUqceiqU2xnZ6g/EbSo1SyLjjIRTibKzq5MgwVvJE0mKjvOo+eQo6l/2v9dRKrfl1qoK7ArUdq3Y6FLiWtJtzU8lja9iShsNA9A0lRiKywn/9SaQ4CT6Smpe2t5rLaJDcQYih4lfuG/E3gHPXXOoZmJoC2tZll0+VfivMXspgr7dDkImNDUMn+H7cZSKfuwcq+f80e6Pl679xc3bUXm5kv6OD39CwAusH1B+XhjoPvF73wi5bZfOfmnqsohs0llb+5l2880FLfPfPeyPHYw0Fo+fE0DFbMpOegjVdtZFh2PA3NamPgB5hATXtXWwUPMy5I2sP0LANtzYEmb3yZTPKsrE/0F/Xy6rxcZA/4fzMk/VVVy678GrFswz0qmAAAMeElEQVSuFwCHd56kFeOuSZR73Ri4BrgROIbIQZ4FXF4z/hD1TSLzYiRqNcuiy2eB6xStZpc002nosOWZwH3lsGt37OqtdVv0BeBaSccD95XHdiYyPL7Q73eNPPOJsx4dmzBIpkce+EtVlRznz9n+Ybl+O3Hy/M2V436XSC+8gzj4tQ5RcW2SR2nDD0mP29508K8cvtrOsihlhp8h6kksufO0Xb3YTil1+299xG6t614Tyin3E+n5d58DfN7299sbVbMUzY1+Qvyue4I48Heo7Qf6/Z6c/FNNku63vcNgj1WIO9v2duXjMUSHwc1sL6wZdyiT9DPbI/XOf0hoIpNlgNjTbe/ZRuxe47gMeBb4Su0VvtRD0ruJMydjiKZGpw/09bnsn2qbJ+kfgK+X68OARxqIu6R3esn1f2Q0TPySrqXvvT4RxVhSXTdJeoftaS3EvkfSmcQ2V/eyf9MZHucR20sfAU5oMrCkwz102js3qtRSue6P/fq8809VlbzjU4nOWyJO+59SmnHUjLsY6HQYE5H3+ywN9xpvWinu0lvnSa6RvgTcttJbYRwx+b5Is6l+P+zjYdtuJMND0jg33NWvjzGMmnoeyyvv/FNVZZL/VAtxxzQdc4hYmwEq3bU5sNHALTayaqvUbSkqdRHwJ8BmknYAjrL9t22MJ/1xcvJPVXQ1VunTCD+B3KbPEqmMHasS3eXGET3uGylzO9pI2qY00unzrrPm0rukw2x/Q9Jx/cSunWlwDlFX4poS735Je1SOuURZ8ej0EdlS0g/oWXEZqXUtlltO/qmWPyNynq8A7qTvPNS04g2JSnej0HFEAZZ/6eNztYsrdf5d21x1eFxa6im+uMHwHy3vBXwP+FiDsYet3PNPVZQT9vsS1fy2J56UVwyUepKWn6S5tvvsHy/pp7YnND2mNLJJ+jbRSvc8YHdim28X2wcP+I11xpJ7/n+kldoeQBqZbC+2fb3tw4lfCHOBWyR9suWhjXR3Sjqy94MtVLobVSS9dZDPrynpDZVinyRp3QE+v3fpOVDL0fQU1JoP7Fiu2/BoS3GHnbzzT9VIWg3Yn7j735zYE7zE9hNtjmskk7Q+0c/9efqodNd2i9ORStI5RC3164F7gKeA1YEtgb2IRj/H2767Quz3EWc9FhH/5p3YWxET8U1EYa2nVnTsNHzl5J+qKIU+3kC015yaxT6a1Xalu9GopLV+AHgLsCHwHPAg8D3b1TvMSdqqj9i32n6uctwv9/Hwb4Ef2f5uzdhdYxDwYWAL26dJ2gzYwHaudvUjJ/9UhaSX6cmzh65cc0Zwnn1KbZG0uu1FvR57te0FleNeAGxDTybJgcADRK35ebb/rmb8MoYpREnjvW1PLC/EptnetXbs4SpP+6cqbOd5kjTq9JNu91vgngZ6StxdOhvOLGM5kGj2s3XluFsSk+5LJe4UYBpx4Hd25dgdu9neWdJ9EPVFJK3aUOxhKSf/VJWk04AZwO1tV/9KqQG7lLdry/X+RJOVoyV9y/bZFWMfClwi6RZgI6KccxN57hsT6Ya/LdfjgI1KWe3n+/+2FerFkmFkAEmvYYS19F3RcvJPtT1KHPj7cil9OoPYh2xkLzClhq0H7Gz7GQBJJwPfBvYgDgJWm/xtz5Z0OtFHYyGwh+35teJ1ORuYVV50iPi7nlHqStzUQHyALwNXAeuXn8EHgJMaij0s5Z5/aoSkDYCDgE8D67RZBjWlWiQ9COxg+4VyvRowq+xD32d7p4qxLwYmEEVutiY6vJ3XKfVck6QNgTcRk/9dtgfsJV9pDNsQLW0F3Gz7wabHMJzknX+qStJFwLbAL4m7/g/Qk4KW0kjzH8BMSZ2VrfcCV5S74B9Xjj0HOMJxR/eIpN2J4jtNWAQ8SUlvlLSl7Vsbik2pc/AroqJo57FVbL/Y/3eNbnnnn6qSdBWx//hjYDqx5D+v3VGlVI+kN9LTxfI22z9qeUhVSToCmARsAswiinrd0WRdfUmPEtkFTxM/97WJFyO/Ao60fU9TYxkucvJPjZA0kWj+cSwwxvYmLQ8ppRVO0peAK23f3kLsrYjT/dsSd+AA2N6ictzZwK7ATNs7luX3U21/qGbcXmM4H7jK9g3l+h3AfsA3gS/Z3q2psQwXmY6VqpL0HklnAZcQZUB/APxju6NKqZp7gZMkzZX0BUm7NBj7UmAK8BJRVfBrxOG/2hZ16gtIWs32Q8DrGojbbZfOxA9gexpx4HEmUd0y9ZJ7/qm2dwG3Eq++Gz8ElFKTbF8GXFb2oA8EzpK0me2tGgg/1vbNkmT7MeAUSTOAkyvHnS9pbaKs9I2Sngaafq7/WtIJwNRy/SHg6ZL+lyl/fcjJP1Vl+xOSxhNLkT+XNBZY2fbCloeWUk1bElXvNqf+Qb+ORZJWAh6WdAzwBLB+7aC2/7J8eIqkHwJrET0OmnQo8SLnaspZi/LYGCLLKPWSe/6pqtJh7uPAurYnlH3J823v0/LQUlrhyhbX+4GfAlcS+9C/aSj2rkQ9/7WBycCawNm272wg9jrEgbslN5S2M6tnCMs7/1TbJ4j83zsBbD9cOs+lNBI9ArwZ2ILYa95eEg2lvZnY4x8PrFIeuxDYvmZQSZOBjwLz6FliN81UF+yM4TVEZ8PXs/Rhx8bGMNzk5J9qe972C9F0CyStTE+Tn5RGmsXEodal0t5oZiK8HPgMUU+/yX3ug4AJncJGLbmcWGl5D3Gw+HCitXHqR572T7VNl3QiMFbSvkTnr2sH+Z6UhqtPEWlvj9neC9iJ5iahp2xfY/sR24913hqIO4fYamjTerYvBl60Pd32XxMvvFI/8s4/1fb3wN8QdyNHAdcBF7U6opTqWWR7kaQlaW+Smkp7O7lU1LwZWNJQx/Z3Ksc9E7hP0pxecf+ictxunUp+T0ran8g2yFoiA8jJP1Vl+2Vi3/HCtseSUgPaTHv7GJFhsApL773XnvwvA86i+e2Gbv8kaS3geOBc4rDjsS2NZVjI0/6pCknftH1Qqf71B//JbFc9hJRS2yTtSUl7a2I/XNJs29vVjtNH3Om292w6blo+eeefalko6S1EY5N8hZlGHdvTGw45U9K2tpuqK9Bxj6QzgWtYetm/sVQ/SX8KfJKoq9Cdbtjk1sOwknf+qQpJk4CDgQ2JU7hX2J7V7qhSGrlKO+EJRLrh80SxG9deZSuFfXpzw4197gcuptfWQwsvwIaNnPxTVaW638HlbXWi5eZU2z9pdWApjTDlufYHGjrx3ypJd2bznlcmJ//UGEk7EQ1+trc9pu3xpJSWnaTDbH9D0nF9fd72Fxscy6HAVsA0Wtp6GG5yzz9VJWkVorXmwcA+wHTg1FYHlVJaEcaV92u0OoqwHfARophSK1UGh5u8809VlII+hwD7A3cR3bautv37VgeWUhpxJD1ErCi2WWVwWMkKf6mWE4myphNtv9f25TnxpzRySDqptC7u7/N7S3pPQ8O5n/arDA4rueyfqiilTVNKI9ds4FpJi4B7iTLGqxN77zsCNwFnNDSW1wIPSbqb9qoMDiu57J9SSmmZlTbdbyHSep8j2grfavu5BsfQZ5GhTPXrX07+KaWUlpmk1W0v6vXYq20vaGtMaXC5559SSml53C1pSQc9SQcCtzcRWNJt5f1CSb/relso6XdNjGG4yjv/lFJKy0zSdkT9jluAjYD1gCNsz29zXGlgOfmnlFJaLpIOAL4OLAT2sD235SGlQeRp/5RSSstM0sVET4Htga2JDIDzbH+l3ZGlgeSef0oppeUxB9jL9iO2bwB2B3ZueUxpELnsn1JKKY0yueyfUkppmZU8/zOBbYkiPwDY3qK1QaVB5bJ/Siml5XEpMAV4CdgL+Bpx+C8NYTn5p5RSWh5jbd9MbCM/ZvsUspvekJfL/imllJbHIkkrAQ9LOgZ4Ali/5TGlQeSBv5RSSstM0q5EPf+1gcnAWsDZtme2OrA0oJz8U0oppVEml/1TSiktM0m7AJ8DxtM1p9jevrVBpUHlnX9KKaVlJul/gc8As4GXO4/bfqy1QaVB5Z1/Siml5fGU7WvaHkR6ZfLOP6WU0jKTtA9wCHAz8HzncdvfaW1QaVB5559SSml5fAzYBliFnmV/Azn5D2E5+aeUUloeO9jeru1BpFcmK/yllFJaHjMlbdv2INIrk3v+KaWUlpmkB4EJwCPEnr8AZ6rf0JaTf0oppWUmaXxfj2eq39CWk39KKaU0yuSef0oppTTK5OSfUkopjTI5+aeUUkqjTE7+KaWU0ijz/5LQe8EK3fetAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ef90c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap = mpl.cm.get_cmap('viridis_r', 6)    # 11 discrete colors\n",
    "\n",
    "fig, ax = plt.subplots(1, figsize=(7,7))\n",
    "hmobj = sns.heatmap(df.loc[keep_rows, keep_cols].applymap(lambda x: -pd.np.log10(x)),\n",
    "           cmap=cmap, vmin=0, vmax=6,\n",
    "           cbar_kws={},\n",
    "           square=True,\n",
    "           annot=df.loc[keep_rows, keep_cols].applymap(lambda x: -pd.np.log10(x)), \n",
    "           fmt = '.2f',\n",
    "                    ax=ax)\n",
    "# ax.set_title(\"$-\\\\rm{log_{10}}$ p-value\\nBonferroni-adjusted $\\\\alpha=0.05$ threshold = %.2f\" % thr_bonferroni)\n",
    "# ax.set_title(\"$-\\\\rm{log_{10}}$ p-value\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfstr = df.applymap(lambda x: ('{n:5.{s}{c}} '.format(n=x, c='e' if x < 1e-3 else 'f',\n",
    "                                                        s = 3- 2*(x < 1e-3)\n",
    "                                                     ).replace('-0','-').replace('nan',''))\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ViewModifier</th>\n",
       "      <th>RPART</th>\n",
       "      <th>GBM</th>\n",
       "      <th>GLMNet</th>\n",
       "      <th>XGB</th>\n",
       "      <th>GBMT</th>\n",
       "      <th>avg(image)</th>\n",
       "      <th>max(image)</th>\n",
       "      <th>max(wire)</th>\n",
       "      <th>max(image, wire)</th>\n",
       "      <th>image+GBMT</th>\n",
       "      <th>max(image+GBMT, wire)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>RPART</th>\n",
       "      <td>0.048</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>GBM</th>\n",
       "      <td>0.028</td>\n",
       "      <td>0.617</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>GLMNet</th>\n",
       "      <td>8.8e-4</td>\n",
       "      <td>0.099</td>\n",
       "      <td>0.287</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>XGB</th>\n",
       "      <td>4.4e-4</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.109</td>\n",
       "      <td>0.513</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>GBMT</th>\n",
       "      <td>2.1e-4</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.073</td>\n",
       "      <td>0.260</td>\n",
       "      <td>0.519</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>avg(image)</th>\n",
       "      <td>1.0e-6</td>\n",
       "      <td>1.7e-4</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.014</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>max(image)</th>\n",
       "      <td>1.1e-6</td>\n",
       "      <td>1.9e-4</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.168</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max(wire)</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>max(image, wire)</th>\n",
       "      <td>1.0e-6</td>\n",
       "      <td>1.5e-4</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.108</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>image+GBMT</th>\n",
       "      <td>8.1e-7</td>\n",
       "      <td>1.9e-4</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.081</td>\n",
       "      <td>0.202</td>\n",
       "      <td></td>\n",
       "      <td>0.034</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max(image+GBMT, wire)</th>\n",
       "      <td>9.4e-7</td>\n",
       "      <td>1.4e-4</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.086</td>\n",
       "      <td>0.095</td>\n",
       "      <td></td>\n",
       "      <td>0.338</td>\n",
       "      <td>0.029</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      ViewModifier    RPART     GBM  GLMNet     XGB    GBMT  \\\n",
       "RPART                       0.048                                             \n",
       "GBM                         0.028    0.617                                    \n",
       "GLMNet                     8.8e-4    0.099   0.287                            \n",
       "XGB                        4.4e-4    0.052   0.109   0.513                    \n",
       "GBMT                       2.1e-4    0.030   0.073   0.260   0.519            \n",
       "avg(image)                 1.0e-6   1.7e-4   0.004   0.004   0.010   0.014    \n",
       "max(image)                 1.1e-6   1.9e-4   0.005   0.005   0.013   0.017    \n",
       "max(wire)                                                                     \n",
       "max(image, wire)           1.0e-6   1.5e-4   0.002   0.003   0.005   0.006    \n",
       "image+GBMT                 8.1e-7   1.9e-4   0.006   0.006   0.016   0.023    \n",
       "max(image+GBMT, wire)      9.4e-7   1.4e-4   0.002   0.003   0.005   0.006    \n",
       "\n",
       "                      avg(image) max(image) max(wire) max(image, wire)  \\\n",
       "RPART                                                                    \n",
       "GBM                                                                      \n",
       "GLMNet                                                                   \n",
       "XGB                                                                      \n",
       "GBMT                                                                     \n",
       "avg(image)                                                               \n",
       "max(image)                0.168                                          \n",
       "max(wire)                                                                \n",
       "max(image, wire)          0.100      0.108                               \n",
       "image+GBMT                0.081      0.202                      0.034    \n",
       "max(image+GBMT, wire)     0.086      0.095                      0.338    \n",
       "\n",
       "                      image+GBMT max(image+GBMT, wire)  \n",
       "RPART                                                   \n",
       "GBM                                                     \n",
       "GLMNet                                                  \n",
       "XGB                                                     \n",
       "GBMT                                                    \n",
       "avg(image)                                              \n",
       "max(image)                                              \n",
       "max(wire)                                               \n",
       "max(image, wire)                                        \n",
       "image+GBMT                                              \n",
       "max(image+GBMT, wire)     0.029                         "
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfstr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4goKviIiIiPiCgq+IiIiI+IKCr4iIiIj4goKviIiIiPiCgq+IiIiI+IKCr4iIiIj4goKviIiIiPiCgq+IiIiI+IKCr4iIiIj4goKviIiIiPiCgq+IiIiI+IKCr4iIiIj4goKviIiIiPiCgq+IiIiI+IKCr4iIiIj4gjnn6rsHaUSys7P1DSMiItIAZWVlWX33AHBSzDlRywrvh16J6pxiozmY+MMlr35c3y3ss2fGHAfAlLg76rmTfTe67FYA+ra9oJ472Xdzc14gKyurvtvYL9nZ2ZpDPcvOzgbQHOqZ5tAw7JiD1E1bHURERETEFxR8RURERMQXFHxFRERExBcUfEVERETEFxR8RURERMQXFHxFRERExBcUfEVERETEFxR8RURERMQXFHxFRERExBcUfEVERETEFxR8RURERMQXYuu7ARERERGRfWVmy4FCoAIod871r61WwVdEREREGrsTnHMbdlekrQ4iIiIi4gsKviIiIiLSmDlgmpllm9nYugq11UFEREREoqZieFbUxgoH2aphdrJzbvIuZYOcc2vMLB1438wWO+c+rmk8BV8RERERaZDCIXfXoLtrzZrwP/PM7DXgaKDG4KutDiIiIiLSKJlZMzNL2vEaGAF8U1u9Vnwlqi4Z1JezjzoC5xxLcjdw66vTKC2vqFaT0TyJO885maT4JsSYMeG9T5j53fK9fq8Rvbox4aJRnPvIv1iYkxulGXjWzS1k3lPrcCHoNLwFPc5qFVFTURZizsQcNi3bRjApwMDx7WiWHiRUFiJ78lo2LS3BYuDIyzJIP7wZAKtmFbB4ynpcCNr0S6T3z9pEte+qZn4Od06EUAjGnAZXXhRZ8/RL8Op/IBCA1BZw+43QNtzSfY/CR7PBheDY/nDLdVBcAhdfW3n/uvXwPyfBLddGji0iIvITaA28Zmbg5dp/Oefera1YK771yMxam9m/zGxZeEP2Z2Z2lpkdb2Zv1VD/oZmttPDvbvjc62a2Nfy6o5k5M7u2yvVHzOzS3fRxppn13N/5pCc346Jj+3LuI89z5kPPEmMxnNq7e0TdL4cN4N0F3zNm4vPc8OLb3HbGsL1+r4RgHBcd24f5K9fub9sRXIVj7hNrGXxrB06e0IVVswrYsmpbRN3yGZsJJgYY+Ug3Dh2VxoLnvPC9bPomAEb8rStDbuvI1/9chws5theW8/WzuRz3x46MmNCV7QXl5C7YGvX+ASoq4K8PwuR74c1/wn+mww/LI+sO6wavTIY3noIRQ+H+x7zzc7/xvt54EqY+DQsWw5x50CwBXnui8iuzNZx03AGZgoiIyG4555Y5544Mfx3unLujrnoF33oSDq+vAx875zo757KA84F2u7l1MzAoPEYLIGOX63nAr80suBftnAnsd/AFCMTE0DQulkCM0TQYS15hZLBzzpHYxGsvsWkT8rYUARBjxvUjh/DSNRcw5bqLOefoI2p9n+tGHMuTH3/J9vLyaLRdTf4PJSS2CZLYOkhMXAztBzVnzZeFEXVr5myhw9AWALQdmEzeN0U45yhcvZ30I7wV3qbNY4lrFmDT0hKKcstIygzSpLn3Fy3pRySSM3tL1PsH+HoRHNIW2mdCMA5OHQYzPomsG9AP4pt6r4/sCbnrK69tL4Wycigtg/IKSEupfu/y1ZC/Cfr3PiBTEBERiToF3/ozDCh1zj2244RzboVzbuJu7nsRLyADjAam7HJ9PTAd+PmuN5pZFzN7N7y6PNPMepjZscDpwH1mNs/MuuzrhPK2FPH0zGw+uPEKPrx5LFu3befTJSsj6iZNn82ovocx/aYrePTSM7lz6n8BOLt/L7Zu2855k17gvEkvMOaoI2ibkhxxf4+MVrRpnsRHi3/c11brVJJfRnxa3M7j+NQ4SjZGBuyS/HLiW3p1MQEjLiGG0sIKmndoypo5hYQqHEW5pWxeVkLxxnIS2wQpzNlOUV4poQrHmjlbKNlYdkDmkLcB2qRXHrduBbm7eaz3v9+GIQO81317wYC+cNxo72vwUdClY/X6/3wAI4dB5d8/iIiINGza41t/Dge+2of7pgP/MLMAXgAeC9y2S83dwDtm9uQu5ycDv3LOLTGzAcDfnXPDzGwq8JZz7tV96Gen5KZNGNazMyPue5LCku387cLTGNWnB2/NW1yt7rQju/N69kL++clXHHlIBnefewpnPPQMx3brwKEZLRnRqxvgrQZ3aNmCnE2Vq6JmcOOoodz6yrT9aXXv1RTuXM11HYelsCVnO9NvXEZCqzjSuicQE4BgYoC+V2Yye8JqzCCtewJFuaUHpF1XQ2915dOp0+Cb7+DZh7zjFath6Qr47yve8eXXw5z5cNSRlfe8MwPuuTVqLYuIiBxwCr4NhJlNAgYDpcANdZRWAJ8A5wHxzrnltsuSm3PuRzP7AriwyviJwLHAK1Xqm+xhbzufoXfLLbcALWusG9j1EFbnb2FTUQkAHyz8gb4dMiOC7+j+vfjlU95C9fyVawnGxZKSEI8Z3Dn1v8xasqJa/XUjjmVo904A/HzyK3Rr3ZKnx44BoGViMx655HTGPTM1ah9w81Z4K1diS/LLiE+N/KMSnxZLyYYyEtLiCFU4yopDBBMDmBl9Lq3cgTLj1mUktvG2dmT2TyKzfxIAy97Pxw7Q37m0bgXr8iqPc9dDes2/bXz6JTz+LDzzMATDG2Q+mOltfWiW4B0PGQDzF1YG38U/eNsfDo/cwi0iItJgaatD/VkI9Ntx4Jy7BhgORD4+INKLwETg5Tpq7gRupPL3OAbY7JzrU+XrsD1p1Dk32TnX3znXf/To0bXWrS0o5MhDMmga54XEgV0PYWlefmTd5i0M7HIIAJ1bpdIkNkB+UQmzvl/BeQN6ExvjtdyhZQvi42J5eNqnnD3xec6e+Dxbt5cy+PbHGHHvk4y490nmr1ob1dALkNI1nq1rSynKLSVUFmLVrAIywmG1qoz+Saz4aDMAObO3kN6rGWZG+fYQ5dtCAOTO30pMwEhu722k3VbgbZko3VrB0vc20Wl4SsS40XBED2/VdvVab4/u2zPghEGRdd9+D39+ACbdVX0Pb0Zrb4W3vNzb5/vlfOjSofL6f6bDacMPSOsiIiIHjFZ8688M4E4zu8o592j4XMIe3jsTuAt4obYC59xiM/sWGAV84ZzbYmY/mtk5zrlXwh+u6+2cmw8UApHJbi8tWLWOad8s4ZVxF1ERCrFo7Xpe+WIBAONOPIaFObn8d9Ey7nv7Y/5y1klcMrgfzjluffU9AF79cgGZKcm8cu1FGLCpqIRrn526v23ttZiA0efyDGbesQIXcnQ8IYXm4eC68MU8Uro0JfOoZDoNS+GLiTm8M24JwcQAA8Z7n0vcXlDOzNtXYDHe6vFR17bdOfb8p9ayefl2AHqe04qkzD1adN9rsbHwh9/AFb/zHmc2+lTo5i2a8/AT0KsHDBsE9z3mPaJs/J+8axnp8Pe74OSh8PlXcMZl3vaSwUdXD87v/hcev+eAtC4iInLAKPjWE+ecM7MzgQlm9nu8D6UV4a3SAgw3s9VVbjmn6r3A/XvwNncAc6scXwQ8amZ/AOLwVo7nh//5DzO7DhjjnFu6j9Ni0gefMemDzyLOP1Ll3NK8fC5+/KWIGufgoWmzeGjarD1+v8v+sV/bkmuV0S+JjH6RPwscfn7lJ8YCwRiOub59RE2z9CCnPNytxnEH/Cay/kAZOtD72tV1l1e+fupvNd8bCMBfflf72O+/uH+9iYiI1AcF33rknFtL5RMadhVfw7njaxknMfzP5UCvKufnU2U7i3PuR+CUGu6fRZQeZyYiIiLSUGmPr4iIiIj4goKviIiIiPiCgq+IiIiI+IKCr4iIiIj4goKviIiIiPiCgq+IiIiI+IKCr4iIiIj4goKviIiIiPiCgq+IiIiI+IKCr4iIiIj4goKviIiIiPiCgq+IiIiI+IKCr4iIiIj4goKviIiIiPiCgq+IiIiI+IKCr4iIiIj4goKviIiIiPiCgq+IiIiI+II55+q7B2lEsrOz9Q0jIiLSAGVlZVl99wAw7KS7o5YVZrx/U1TnpBVfEREREfGF2PpuQBqfS179uL5b2GfPjDkOgClxd9RzJ/tudNmtAPRte0E9d7Lv5ua80Kj7B28OWVlZ9d3GfsnOzm7Uc8jOzgbQHOqZ5tAw7JiD1E0rviIiIiLiCwq+IiIiIuILCr4iIiIi4gsKviIiIiLiCwq+IiIiIuILCr4iIiIi4gsKviIiIiLiCwq+IiIiIuILCr4iIiIi4gsKviIiIiLiCwq+IiIiIuILCr4iIiIi4gsKviIiIiLiCwq+IiIiIuILCr4iIiIi4gsKviIiIiLiCwq+IiIiIuILCr4iIiIi4gux9d2AHHxizHh53IXkbtnKNf98o9a6Eb26MeGiUZz7yL9YmJO71++zv/fXZd3cQuY9tQ4Xgk7DW9DjrFYRNRVlIeZMzGHTsm0EkwIMHN+OZulBivJKee83P5CU2QSAtEPj6Tc2k/LtIWY/sIqi3FIsxsjISuKIi1tHte+qZn4Od06EUAjGnAZXXhRZM2c+3DURvl8GD/wRTj6+8trr78Kjz3ivr7oEzjzFe/32DHj8WagIwdCBcMNVB2wKezSH196B+x6F1uHfogvPgnNGea+vvAHmfwv9joDH7q6859Z7YOF34Bx0bA933gTNEg7cPEREpGFQ8G3kzKw98DGQ5ZzLN7MU4CvgeCAITAAOAzYDW4A/Oec+NrNLgfuAHCAOWARc4pwr3t+efjaoL8vy8mnWNFhrTUIwjouO7cP8lWv36T329/66uArH3CfWMuS2jiSkxjL95mVk9k8iuX3TanXLZ2wmmBhg5CPdWDWrgAXP5TLwt+0BSGwT5KT7u0SMfejpLUnv1YxQWYiP/ncFa+cWktE3KepzqKiAvz4ITzzgBcJzfwknDIKuHavXZabDXTfDky9WP795C0x6Gl6ZDGYw5krv/lAI7n8UXv0HpLaAm+6Ez7LhmKyoT2GP5wAwchjc9pvI8784H7Ztg5ferH7+5nGQ2Mx7ffcj8K/Xag7VIiJycNFWh0bOObcKeBTYsZ51NzAZyAX+A0x2znVxzmUB1wKdq9z+knOuj3PucKAUOG9/+2mdnMhx3Tvx7znf1Fl33YhjefLjL9leXr7zXIwZ148cwkvXXMCU6y7mnKOP2Kv7oyX/hxIS2wRJbB0kJi6G9oOas+bLwoi6NXO20GFoCwDaDkwm75sinHO1jhvbJIb0Xl7aiomLIaVTU0o2lkW9f4CvF8EhbaF9JgTj4NRhMOOTyLq2GdC9C8Ts8m+CWV/Asf2hRTI0T/Jef/I5rF4DHdp7oRe8wDvtowMyhT2eQ12Oyap5JXdH6HUOtm0HbL/bFRGRRkDB9+AwARhoZr8BBgMPABcBnznnpu4ocs5945x7etebzSwWaAZs2t9Gbhp1PA+8M5NQHQGwR0Yr2jRP4qPFP1Y7f3b/Xmzdtp3zJr3AeZNeYMxRR9A2JXmP74+Wkvwy4tPidh7Hp8ZRsjEyYJfklxPf0quLCRhxCTGUFlYAUJRXygc3LOXDP/7I+kVFEfeWFlWwNruQ9CMSD8gc8jZAm/TK49atIHfDnt+fW8v9h7SDH1dCzlooL4fpn8C6vOj1XdXezGHaR3DGZfDrP8LaPeznlrtgyFnefC4evf/9iohIw6etDgcB51yZmd0AvAuMcM6VmtnheFse6nKemQ0GMoDvgTd3U1+noT06kV9UzLdr8jiqU7saa8zgxlFDufWVaRHXju3WgUMzWjKiVzcAEps2oUPLFuRs2rJH9x9QNa0I1pTtDZqmxHLqo4fSJCmWTUtL+PS+lYz4W1fiEgIAhCocnz+4mq6nppHYuvbtIPujpp879mZRs8b7zVv9/dN4+O1fvOO+vWDVmn1uc+97qKHu+GPhtOEQDMKLb8DNd8LTD+5+/Dtv9rZT3P4QvDMDRp+63y2LiEgDp+B78BgJrAV6Ae/vetHMXgO6Ad8753asb73knBtnZgZMAm6gcstE1XvHAmMBbrnlFqBljQ307ZDJ8Yd1Zkj3jjSJjaVZkyB3n3sKN7387s6aZsEg3Vq35OmxYwBomdiMRy45nXHPTMUM7pz6X2YtWVFt3OtGHMvQ7p1mMeNvAAAgAElEQVQA+PnkV2q9P1ofcPNWeCu3IJTklxGfGvlHJT4tlpINZSSkxRGqcJQVhwgmBjAzAnHeX6akdImnWesghWtLSe0SD8BXj68hKSNIt9PSotJvTVq3qr4Sm7se0mv+batRm1bwxbzq9x/dx3t9wiDvC+DlqZHbJKJlT+eQ0rzy9Tmj4IHH9/w9AgFvf/CTLyr4ioj4gYLvQcDM+gAnAQOBT8zsRWAhcNyOGufcWWbWH7h/1/udc87M3sTbAxwRfJ1zk/H2DZOdne1ef/XjGvt48L1ZPPjeLACO6tSOS4/LqhZ6AbZuL2Xw7Y/tPH7qyjHc//ZMFubkMuv7FZw3oDefL11FeShEh5YtyCvYysPTPuXhaZ/uvKe2+6MlpWs8W9eWUpRbSnxqLKtmFXD0ryNXsDP6J7Hio82kdU8gZ/YW0ns1w8zYXlDuBeCAsTW3lK1rS0lM97ZEfPNCLmXFFWT9KjNq/dbkiB6wYjWsXuuFxbdnwH237fn9g46GCf+AgvDW5llzYPxY7/XGTZCW4l174Q3425+j3j6w53PI2wjp4Z8hZsyCzh3qHtc5WJkDHdp5rz/8FDofEv3+RUSk4VHwbeTCq7WPAr9xzq00s/vwwu0VwM1mdnqVfb51PbBpMLD0QPU57sRjWJiTy38XLau15tUvF5CZkswr116EAZuKSrj22am11h8oMQGjz+UZzLxjBS7k6HhCCs3DT3RY+GIeKV2aknlUMp2GpfDFxBzeGbeEYGKAAeO9cLx+UTHfvpSHBcBijH5jMwkmxVK8sYzFUzaQ1DbIB7/3fh26jkyl0/CUqM8hNhb+8Bu44nfekxhGnwrdvEVzHn4CevWAYYNgwSK49jbYUgj//RQmPgVv/dP7UNtVl3hPUgC4+ufeOYA7H4bvwt8pV/0cOrWPevt7NYfn/u0F3tiAtxXjrpsqx7h4HCxbCcUlcPwYuP333gf1br4LthZ5u1V6dIE//fbAzEFERBoWq+tT6NLwhbchDHfOnRc+DgBfAL/Fe7LD34Ae4deFwL3OuQ92eZxZDLAauNQ5V+dHg7Kzs90ltaz4NgbPjPEWwafE3VHPney70WW3AtC37QX13Mm+m5vzQqPuH7w5ZGUdgOe4/YSys7Mb9Ryys7MBNId6pjk0DOE/zw3iGTXDTro7auFyxvs3RXVOWvFt5KpuQwgfVwBV/+TWuHMx/HSHpw9kbyIiIiI/hfDC35dAjnNuVG11epyZiIiIiDR2v8b7n3HVScFXRERERBotM2sHnAb83+5qFXxFREREpDF7EPg9ENpdofb4ioiIiEjU5PZvErWxqv6/BMImhz/ftOP6KCDPOZdtZsfvbjwFXxERERFpkHb9EH8NBgGnm9mpQFMg2cyec85dXFOxtjqIiIiISKPknLvZOdfOOdcROB+YUVvoBQVfEREREfEJbXUQERERkUbPOfch8GFdNVrxFRERERFfUPAVEREREV9Q8BURERERX1DwFRERERFfUPAVEREREV9Q8BURERERX1DwFRERERFfUPAVEREREV9Q8BURERERX1DwFRERERFfMOdcffcgjUh2dra+YURERBqgrKwsq+8eAA6/eULUssLCu8ZHdU5a8RURERERX4it7wak8bnk1Y/ru4V99syY4wCYEndHPXey70aX3Qo0/jn0bXtBfbexX+bmvHBQzCErK6u+29hn2dnZAJpDPdMcGoYdc5C6acVXRERERHxBwVdEREREfEHBV0RERER8QcFXRERERHxBwVdEREREfEHBV0RERER8QcFXRERERHxBwVdEREREfEHBV0RERER8QcFXRERERHxBwVdEREREfEHBV0RERER8QcFXRERERHxBwVdEREREfEHBV0RERER8QcFXRERERHxBwVdEREREfEHBV0RERER8Iba+G5CDR5vmidx1zimkJSXgHLzyxQKe+3RuRF2nVincPmYEPTPTeWjapzw9M3uf3i+jeRJTx1/CpOmz93mMuqybW8i8p9bhQtBpeAt6nNUqoqaiLMSciTlsWraNYFKAgePb0Sw9SP6SYrIfXxuucvQ8J522A5L3eNz67n/lzM1898bGnXUFK7dx4j2dadEpnpm3r2Db5jJcBbQ8LIG+l2dgATsgc5j5Odw5EUIhGHMaXHlRZM3TL8Gr/4FAAFJbwO03Qts23rU1uXDbvbAuD8zg8XugbQasXgvX/wU2b4Geh8I9t0Iw7oBMYY/m8No7cN+j0Dr8W3ThWXDOKO/1lTfA/G+h3xHw2N2V98z+Cu79O5SVw+GHwu2/h1j9G11EpE7612QjYGatgQnAQGATUArcG379BvAj3up9HnChcy7PzC4FngJOdM5ND49zFjAFOAe4COgEJAKtwmMAXO2c+3Rf+iwPOe59+2MWrckjIRjHK9dexGc/rGBpXn61uoLibdz15ocM69llX95mpxtHDWXm98v3a4zauArH3CfWMuS2jiSkxjL95mVk9k8iuX3TanXLZ2wmmBhg5CPdWDWrgAXP5TLwt+1JPqQpw+/pTEzAKNlUxge/W0pG/yQM9mjc+u7/kCEtOGRICwAKVmzj03tX0qJTPAADf9uOuIQAzjlmP7CK1bO30H5Q86j2D1BRAX99EJ54wAuE5/4SThgEXTtWrzusG7wyGeKbwguvw/2PwYQ/e9duuhN+eTEMOgqKiiEm/HdcDzwGl5wDpw2HPz8A//4PXHBm1Kewx3MAGDkMbvtN5PlfnA/btsFLb1aeC4Xg5jvhyQnQqT08/AS8/p4XrEVEpHba6tDAmZkBrwMfO+c6O+eygPOBduGSmc65Ps653sAc4Joqty8ALqhyfD4wH8A5d5Zzrg9wRZUx+uxr6AXYUFjEojV5ABSXlrEsL5/05MSIuvyiEr5ZnUt5RSji2qg+PXjx6gv497UX8aczhxNjNa8kDuvZhVX5BfyQu7HG6/sr/4cSEtsESWwdJCYuhvaDmrPmy8KIujVzttBhqBcQ2w5MJu+bIpxzxDaJISa8ChoqdWB7N25991/VylkF1YJtXEIAAFcBofLqtdH09SI4pC20z/RWY08dBjM+iawb0M8LvQBH9oTc9d7rH5Z7wXPQUd5xswSvzjmYPRdOHuqdP+NkmF7DuD/lHOpyTJbXe1Wbt0Aw6IVegGP7w7SPotOziMjBTMG34RsGlDrnHttxwjm3wjk3sWpROCAn4a0C7zATONrM4swsEegKzPsJeiazRTKHZbbi61Xr9viezq1SGdm7Oxc/9hJnT3yekHOM6tMjoi4+LpbLh/bn0emzo9lyNSX5ZcSnVf7dd3xqHCUby2uoKye+pVcXEzDiEmIoLawAYOOSYqaN/4Fp1y+l35WZ3urvHo7bEPrfYfWnBbQfXH1Fd+bty3nzisXENg3QbmBy1PsHyNsAbdIrj1u3gtwNdd/z77dhyADv9fJVkJQI1/4BRl/ubSWoqIDNBZCcWLktoE367sf9KeYw7SM44zL49R9hbV7d46Y097Y4fLO48t51u7lHRES01aExOBz4qo7rQ8xsHpAGFAG3VLnmgA+Ak4HmwFS87Q0HVEIwjgcvHsXdb31E0fbSPb5vYNf29GybzkvXeIvUTeJi2bi1OKLumhOP4ZlP5lJcWha1nvdITYvPNS14huvSuiUwYkJXtqzezpxHcmjTN3L1u9ZxD4S97B+88B4IxtD8kOpbJIb8oSMVpSG+eHg1ed8U0frIWua2H1wNvdX1SzV1GnzzHTz7kHdcUQHZX8OU/4OMdPjtX+C1d2HYoL0bd3/s6RyOP9bbdhEMwotveNsYnn6w9nHN4IE/wt2PQGkZHHsUxAai1raIyEFLwbeRMbNJwGC8fb434G1TGBW+diPe3t9fVbnlReA6vOB7PdWD8Z6+51hgLMAtt9wCtKy1NjYmhgcvGsV/5i3mg4U/7O078cZX3/Lge7OqnR3eswtXDx8IwB+nfEDv9hmMOKIb148cTFLTJjgHpeXl/Ouz+Xv5frXzVkgrg3VJfhnxqZF/XOLTYinZUEZCWhyhCkdZcYhgYvUEktyuCbFNjYJV2/d43IbS/6pZkau9OwSCMWT0T2bNnMIDEnxbt6q+ipm7HtJr+db79Et4/Fl45mEvPO64/7Bu3jYDgOGDvQ+JnX0qbNkK5eXequ+6vNrH/anmkFLll/icUfDA47sfu28veO4R7/WsObBi1f71KiLiB9rq0PAtBPrtOHDOXQMMx/tA2q6mAsdVPeGc+wLoBbR0zn2/Lw045yY75/o75/qPHj26ztr/Pfsklq3P55+f1LVIXbPPl65kRK9upDbzPkTVPL4JGS2SmP7tUs6e+DxnT3yehTm5XDL5ZUbc+yQj7n2SZ2fNZfKHX0Q19AKkdI1n69pSinJLCZWFWDWrgIz+SRF1Gf2TWPHRZgByZm8hvVczzMy7r8Jb7itaX0rhmlKatYrb43Hru38AF3LkfFb9g2vlJRWUbPICdajCse6rQpLaBqPeP8ARPWDFau8JDKVl8PYM74Nhu/r2e+8DapPugrSU6vdvKYR8b3p8/hV06eitlg7oA++F98S+8V7Nq8A/5RzyqmxVnzELOnfY/dgbw5uaSkvh//4F550RnZ5FRA5mWvFt+GYAd5rZVc65R8PnEmqpHQwsreH8zcC2A9FcVf06ZHJGv558t3Y9/77We2bTg9NmMfO75Zx7dG8AXv7ia1omJvDSuAtJbBIk5Bw/G9SX0yc8w9K8fB6e9in/+MVozIzyUIjb35jB2s3R//DX7sQEjD6XZzDzjhW4kKPjCSk0Dz8RYeGLeaR0aUrmUcl0GpbCFxNzeGfcEoKJAQaM9z5zuGFxMd+9vgELGBYDfa/IoEmy98ettnEbUv8A6xcVE58WR2LrymBbvt3x6T0rCZU5XAha9WpG5xGpUe8fvNXYP/wGrvid9xSD0adCt/BGnYefgF49vMB632NQXALj/+Rdy0iHv9/lPd7shqvgsvHeloPDu1c+Iuz6X3mPM3v4CTis64F7GsKezuG5f3uBNzYAzZPgrpsqx7h4HCxb6c3x+DHeY8sGHw1PvggffgohB+efAQP71dyDiIhUsl0/wS0Nj5ll4D3ObACwHm8v72NALpWPMzOgALjCOfd9+HFm/Z1z43YZ62ngLefcq+Hj44Hf7dgusTvZ2dnuklc/jsKs6sczY7wF8Slxd9RzJ/tudNmtQOOfQ9+2F+y+sAGbm/PCQTGHrKys+m5jn2Vne8/v1hzql+bQMGRnZ5OVlfVTfWqkToffPCFq4XLhXeOjOiet+DYCzrm1eI8iq0mNGzCdc08DT9dw/tJdjj8EPtyP9kREREQaBe3xFRERERFfUPAVEREREV9Q8BURERERX1DwFRERERFfUPAVEREREV9Q8BURERERX1DwFRERERFfUPAVEREREV9Q8BURERERX1DwFRERERFfUPAVEREREV9Q8BURERERX1DwFRERERFfUPAVEREREV9Q8BURERERX1DwFREREZFGycyamtkXZjbfzBaa2V/qqo/9qRoTEREREYmy7cAw59xWM4sDPjGzd5xzs2sqVvAVERERkUbJOeeAreHDuPCXq63evHqRPZOdna1vGBERkQYoKyvL6rsHgMNvnhC1rLDwrvG7nZOZBYBsoCswyTl3Y2212uMrIiIiIg2SmY01sy+rfI3dtcY5V+Gc6wO0A442s161jaetDrLXbjrq7vpuYZ/dPecmAC559eN67mTfPTPmOADO/eHdeu5k373c9ZRG3T94c5gSd0d9t7FfRpfd2qjnMLrsVgCysrLquZN9l52dDWgO9e1gmkNDsK1PcdTGcs5NBibvYe1mM/sQOAX4pqYarfiKiIiISKNkZq3MrEX4dTxwIrC4tnqt+IqIiIhIY5UB/DO8zzcGeNk591ZtxQq+IiIiItIoOee+Bvruab22OoiIiIiILyj4ioiIiIgvKPiKiIiIiC8o+IqIiIiILyj4ioiIiIgvKPiKiIiIiC8o+IqIiIiILyj4ioiIiIgvKPiKiIiIiC8o+IqIiIiILyj4ioiIiIgvKPiKiIiIiC8o+IqIiIiILyj4ioiIiIgvKPiKiIiIiC/E1ncDcnC5/omrGHBaFpvzChjb+/pa665+6DKOHtmP7cXbue+ySfww98e9ep/eQ3ty9YTLCMQF2LKhkOtP+NP+tr7TX88+iaE9OpO/tZgzH3q2xprLhmQxqk8PAAIxMXROT2XI7Y9RULJ9r94ro3kSU8dfwqTps3l6ZvZ+917VcW06c1vfEQTMeGnZPB5f/FlETTAmwP0DTqdXShs2lZZw3aevkVNcAMCvDjuWczsdSYVz/O/cacxct2znfTFmvH7SL8gtKeTKmS9Hte+DbQ7r5hYy76l1uBB0Gt6CHme1iqipKAsxZ2IOm5ZtI5gUYOD4djRLD7K9sJzZD6wi/4dtdDy+BX2vyNh5z6alJcyZlENFqSOjXyJHXtYGM2tUc9hh1t0rKcorZcTfuh6Q/kVEdtCKbz0yswfN7Ljw6/8zs5711Mf9ZjYsGmNNe/pDbhl5R501R4/sS9uuGVx66LU8+MvHue7vV+7VezRrnsB1k67ktjPu4cojfstfz31gf1qO8Hr2t/zyqdfqrHlqZjZnT3yesyc+z4PvzeLLH1fvdegFuHHUUGZ+v3wfO61djBl/zjqFX3z8Iie/+zj/0+Fwuia3jKg7p3MfCkq3MeztR3nquy+48Ujv26BrcktGHdKTU96dzGUfv8Bfsk4hpkqourTbUSzdsiHqfR9sc3AVjrlPrGXwrR04eUIXVs0qYMuqbRF1y2dsJpgYYOQj3Th0VBoLnssFIBAXw+HnpdP7ktYR93z1j7Vk/TKTUyZ2pXBtKevmbW10cwDI+XwLsU31nyIR+Wno3zb1xMxSgYHOuY8BnHNXOOe+rad2JgI3RWOgBTMXUZhf93+AjznjKD549iMAFn2+hMQWzUht0wKAc353Oo98fhePz7ufS/58bo33D7twMJ+89jnrV3mhZfP6LdFofafs5TkUFEf+h702px7Znbfnf7fzeFSfHrx49QX8+9qL+NOZw6uFraqG9ezCqvwCfsjduN897+rI1ExWFOazqmgzZaEQb638lhPbHhpRd2JmN6Ys/xqAd1Yv4pjWHb3zbQ/lrZXfUhqqYHVRASsK8zkyNROANvFJnJDZlZeXzYt63wfbHPJ/KCGxTZDE1kFi4mJoP6g5a74sjKhbM2cLHYZ6fwbaDkwm75sinHPENo2h5WHNCMRV/x4q2VRGeUkFad0TMDM6DG3Bmi8ix23IcwAoL6ng+zc3ctjZkT/QiIgcCAq++8DMXjezbDNbaGZjzewqM7u3yvVLzWxi+PVtZrbYzN43sxfM7HfhsjHAu1Xu+dDM+odfbzWze8Lv8YGZHR2+vszMTg/XdDSzmWb2Vfjr2PD5GDP7e7i3t8zsbTMbE76WZWYfhcd9z8wyAJxzK4A0M2vzE/zy0TIzlbxVlWFvw+qNtGybStZJvWnbLYNxA27mV31voFu/zhwx5LCI+9sdmklSSjPun/FnJs25hxN/dtxP0XaNmsbFMvjQjrz/zRIAOrdKZWTv7lz82EucPfF5Qs7t3BJRVXxcLJcP7c+j02cfkL5axyextqQynKwr3kLr+KSIujYJSawt9n5wqHCOwrLtpATjvfuLK3+gWFdSuPP+P/Q9iXvmzyDk3AHp/WCaQ0l+GfFpcTuP41PjKNlYXkNdOfEtvbqYgBGXEENpYUUd45ZXHzctlpL8sih2XvW9DswcAL55KY9D/yeNQBP9p0hEfhra47tvfuGcyzezeGAOMByYBfw+fP084I5wkD0b6Iv3a/0VsGMj5yDg1VrGbwZ86Jy70cxeA24HTgJ6Av8EpgJ5wEnOuW1m1g14AegPjAY6AkcA6cAi4Ekzi8Nb2T3DObfezM4D7gB+EX7Pr8I9/Xt/fmH2RE37EJ2DrBFHknVSbx776j4AmiY2pW23DBbMXFStNhAboFu/zvz+xP8lGB/k4U/vYNHsJeQsWXugW49wfI/OzF2xZuc2h4Fd29OzbTovXXMBAE3iYtm4tTjivmtOPIZnPplLcemBCSs1rzHXFPJq+L2o5X6H44SMrmzcXsw3m9YxoNUh+9Xj7hwMc6hRzY3tWV0d9Qdoe2/NojCHzT+WULSulD6XZlCUVxqtzkRE6qTgu2+uM7Ozwq/bA52AZWY2EFgCdMcLwr8G3nDOlQCY2ZtVxsgA1tcyfimVq8ELgO3OuTIzW4AXagHigEfMrA9QAez4O+DBwCvOuRCwzsz+Gz7fHegFvB8OngGgalLMAzJrasbMxgJjAW655ZZaWt5z63M2kt4+jYXh45bt0ti4Jh8z48W7X+M/kz+oVn/61Sdz6hUnAnDraXeyfvVGCjZsYVvxdrYVb+frmYvocmSHegm+I4/sztvzF1c5Y7zx1bc8+N6sanXDe3bh6uEDAfjjlA/o3T6DEUd04/qRg0lq2gTnoLS8nH99Nj8qfa0rKSSjyupom4Rkcksit6CsK95CRkIy60oKCZiRFNeEzaUl3v0JyZX3xyeRV7KV4ZndGJ7ZjeMzutAkJpbEuCY8MOB0rv98alT6Ptjm4K2OVv5wU5JfRnxq5L9249NiKdlQRkJaHKEKR1lxiGBioPZx02Krj7uxnKYpcbXW748DNYeN35ewadk23r76e1yFY1tBBR/+6UeO/0unAzIPERFQ8N1rZnY8cCJwjHOu2Mw+BJoCLwHnAouB15xzzur+iHVJ+L6alDm38+9gQ8B2AOdcyMx2/J6NB3KBI/G2rOzYlFrbexqw0Dl3TC3Xm4Z7iuCcmwxMBsjOznZf3HV3LUPsmc+mfskZ15zCf1+cxWEDulFUUEz+us18+d48fv6/5zP9+U/YVrSNtMxUKsrKmfr395j69/cq739jDuMmXk5MIIa4YCw9ju7KlAlv7VdP+yKxSZCjOrXjppfe2Xnu86Urmfiz03nmk6/ILyqheXwTEpoEmf7tUqZ/u3Rn3SWTK58icPXwgRSXlkUt9AJ8nb+GjkmptGvWnNySQkYd0pPxn70eUTd9zRJGd+zN3I05jGx3GJ/lLvfO53zPhGPO5MnvPic9PpGOSanMz1/D3I053L/gQwAGtDqEK3oMPCCB8WCZQ0rXeLauLaUot5T41FhWzSrg6F+3i6jL6J/Eio82k9Y9gZzZW0jv1azOJzTEp8QRGx/Dxv9n777joyqzP45/zqQRSAKEGgJIF5BqpCiKCIigWBYRO7tr72XFXRRdcW2sZS0sKth1sfBDXMsioCCCgBJCFUGatEBoARJIIGWe3x93gGAShCRjCPN9v1555c695957TuqZZ557Z0UW8c2jWfftLpr1i69QNTQ9L56m53k5792aw6wR69X0ikjQqfE9dlWBnYGmtyXQNbB+AjAMWAf8LbDuO2C0mT2F97W+AHgtsG0Z0AyYXoo8Ngaa4T/ijeAeOOcfzewdoBbQA3gf+BmoZWanO+fmBKY+tHDOHRh4bQH8XwlzOejBsXfTrscpVK0Zy/vrX+Xd4eOY9OY0+t98LgBfjP6KuRPn0+X8jryzciT7s3J49rpRAKR8tZiGrerz0mzvrhDZe/Yx4tqXCl28tn55KsmTFzJm0XP4/X6+fGMqa5duKG3qBz1zRT86NW5AtSqVmDr0BkZ9PYcJ85YyqHM7AMbN9S6k6n1KM2atXEd27qH5jqu3pvPSlNm8dt0AzIw8v5/HP53G5l3BufCoOPnO8ej8ybx99pX4zMf4NYtYGbiDwT1turMkfTNTN61k3JqFPNf1Yqadfyu7cvZx9xzvbhYrM7Yzcf0yJvW7mXy/n+Epk4M+H/ZErMEXZnS4PoGZT6zD+R2NzqlO1Qbe892lH26letNK1OsUR+Oe1Zk7MpUv71hJZEwYXe491FhOvG0FuVl+/HmOTckZnPXQScQ1qETHG+sxb1Qq+Tl+6naIpW7HmApXg4jI783c7/yPoKIzsyjgv0AigWYSGO6cm25mXwCtnXNNCsQPB67Ea4i34c3dfc3MzgJuds5dE4ibDgxxzs0zsz3OuZgC++9xzj0beLzHORcTmNf7MZAFfAPcGVjvA14GugMrgCjgX865rwLTIl7Ca5rDgRcCuUQAi4G2zrnCV60UkJKS4oZ2Kt2Ib3kakezdvGLw+BnlnEnJvTvQu5hv0KpJvxF5/BrXrG+Fzh+8GiZEHPnWfce7AbnDKnQNA3KHAZCUlFTOmZRcSop32YdqKF8nSg1JSUm/52z/YjX96Ikyay5XXz6sTGvSiO8xcs7tB/oVs61/Eaufdc4NN7PKwAzguUDsTDN7ysyqOed2Oed6FDhOTIHl4b86R0zg80qgXYFNDwTW+81siHNuj5nVAObizRPGObcQryH+tf7A+N9qekVEREQqMjW+wTcm8MYUlYB3nHPzC2y7D2gI7Crjc35hZtWASOAx51zab8SHE2jIRURERE5UanyDzDl31RG2/RCkc/Y4xvhSz+0VEREROd7pruEiIiIiEhLU+IqIiIhISFDjKyIiIiIhQY2viIiIiIQENb4iIiIiEhLU+IqIiIhISFDjKyIiIiIhQY2viIiIiIQENb4iIiIiEhLU+IqIiIhISFDjKyIiIiIhQY2viIiIiIQENb4iIiIiEhLU+IqIiIhISFDjKyIiIiIhQY2viIiIiIQEc86Vdw5SgaSkpOgHRkRE5DiUlJRk5Z0DQNOPniizXmH15cPKtCaN+IqIiIhISAgv7wSk4hnaaUR5p1BiI5KHAjB4/IxyzqTk3h3YHYBBqyaVcyYlN65Z3wqdP3g1TIh4orzTKJUBucMqdA0DcocB0DHxynLOpOQWpH4AQFJSUjlnUnIpKSmAaihvB2qQI9OIr4iIiIiEBDW+IiIiIhIS1PiKiIiISEhQ4ysiIiIiIUGNr4iIiIiEBDW+IiIiIhIS1PiKiIiISEhQ4ysiIiIiIUGNr4iIiIIfyZsAACAASURBVIiEBDW+IiIiIhIS1PiKiIiISEhQ4ysiIiIiFZKZNTCzb8xsmZktNbO7jxQf/nslJiIiIiJSxvKA+5xz880sFkgxs6+ccz8VFawRXxERERGpkJxzm51z8wPLmcAyILG4eDW+IiIiInJcMrObzGxegY+bjhDbCOgI/FBcjKY6iIiIiEiZuaJVSpkd6wnnxgBjfivOzGKAj4F7nHMZxcVpxFdEREREKiwzi8Bresc65yYcKVaNr4iIiIhUSGZmwBvAMufcv34rXlMdpEzd98atdLkgiV1bd3NTu/uKjGlwcj2GvHk7zU5tzFsPfcD45z4/5vM0btuQe169mcpx0Ti/4/bOQ8ndn1va9AF47NJzObtlE9L3ZHHJi+8VGRNXKYrHBvahQXxVcvLyeejjKazasuOYz5VQNZbP7h3MqKnf8/bMsntpCKB73SY83LEPYWZ8tGYho5fPKRQT6Qvj2S4X0aZ6XXbmZHPX7E9IzdoNwC2tzmBQ4/bkO8c/FkxhZtoaAL7tfzt7c3PId4585+eSr94s07xPtBrSFmSy8K00nB8a96pGyz/UKhSTn+sneWQqO9fsIzI2jK731qdK7Uj2Z+bx/XMbSF+1j0Y9qtHxhoSD+8x8fB37duXi8qFmq8p0vD4BC7MKU0Nudj7TH157cP/s9FwanlWVDn9OKHTssjDzB3hyJPj9MPACuPHqwjHJi+CpkbBiDTz3dzivx6Ftm7bAw09D2lYwg9H/hMQEmJMCz7wCzkHlaHhyKJxUPygliEjRugHXAkvMbGFg3YPOuYlFBavxLUdmdgnQzjn3DzO7Bchyzr1bDnn0Bzo55x4p7bGmvD2dT/89ib++c0exMZnpexh195t0u6Rzic7hC/Mx9L27+OfgkaxZvI7Y+Bjyc/NLmnIh/035iffnLOKpy84rNubGczqzfPM27v7P5zSuVZ2HLurJ9W98fMzn+lv/s5m5Ym0psi2az4zhSX354/T3ScvO4JNzr2PqppWsyth+WNxlTTqwO2cfPSe+Qv8Grflb+57cNecTmsXVpH/D1vSdNIba0TG82+Nqek98Bb9zAFz9zX/YmZNd5nmfaDW4fMeCNzZz1sONqBwfztQH1lDvtFjiGlQ6LG7ttF1ExoTR79/N2TBrN0v+s4Wuf2lAWISPUy6vze4N+8lYv/+wfbr+pT4RlcNwzvH9cxvY+H0GDbpVrTA1RESHce6zTQ8+/vqvq0nsElfm+QPk58NjL8Abz0GdWjDoZjinGzRrdHhcvdrw1APw5oeFjzH0Sbj5GujWCfZmgS/weumj/4JRT0DTRvD+J/Dqe94xROT34Zz7DjjqZ/2a6lC+/gq8DOCce7U8mt6A/wEXmVnl0h5oycxlZKbvOWLMrm0ZrJi3mrzcvELbel19FiO/f4pX5z/D3a/ehM9X+Ef0tD7tWbN4HWsWrwO8Rtrv95c29YNS1qayO2vfEWOa1o7nh1XrAfhl207qVY+jRoz35evfoSUf3nYlH995NY9c0gufFf372LN1Uzak7y7RSPFvaR9fj3WZ6WzYu4tcv58v1v9E78QWheJ612vOhLWLAfhy4zJOr9PIW5/Ygi/W/0SOP5+Ne3ezLjOd9vH1yjzPIzkRakhflU1M3Uhi6kTii/DRoFtVNs3LLBS3KTmDk86uBkBi1zi2/rgX5xzhlXzUbFWFsIjCP0MRlcMAcPngz3MVsoYDMjfvZ39GHjVblfpPUJEWL4OGidCgHkRGwPk9Ydp3heMSE+Dkpoea2gNWrfWa526dvMdVKkN0oO83gz1Z3vKevVC7RlBKEJEyosb3KJhZIzNbbmavm9mPZjbWzHqb2SwzW2lmnQMfs81sQeDzyYF9/2JmbwaW2wb2r2xmLYD9zrntgW3DzWxIYHm6mT1vZjMC70TSycwmBM71eIG8/mtmKYF3KrmpwPrrzWxF4Divmdm/A+trmdnHZpYc+OgG4JxzwHSg/+/zFS1aw5aJnD3oDO458yFuOfV+/Pl+el59ZqG4xBYJ4OCpL4fx8rx/Muj+i373XH/evJ3ebZoB0LZ+HepVi6NOXAxNasXTr93JXPPqR1w6cix+5+jfoWWh/aMjwrn+7NN4Zer3QcmvTnQsm7MPNSdpWRnUiY4tFFe3ciybs7yLX/OdIzN3P9Ujo739sw5dFJuWnXlwf+fg7R5X8em513FFk45Byf9EqSE7PZfoGhEHH0fHR5C9o/ATvuz0PKJrenG+MCOiso+czN9+FWPm42v5/IblhFcKo37X4IyWBrsGgA3f7ab+GVWxYp4kltbW7VC39qHHdWrBlu3Fx//a2g0QGwN3PgQDrvemNuQHSnvsfrj5b9BjIHw2pegpFCJy/NBUh6PXDLgMuAlIBq4CzgQuAh4EBgPdnXN5ZtYbeBK4FHgBmG5mfwCGATc757ICTef8I5wvxznXPfDWe58CSUA6sNrMnnfO7QCuc86lm1k0kGxmHwNRwMPAqUAmMA1YFDjmi8DzzrnvzKwhMBloFdg2DzgLGFe6L1PJdezVlhZJTRg1dwQAkdGR7Nq6u1BcWHgYp5zZkjs6D2V/1n6e/voRVqasYcG0H3+3XF//NpkH+vfg4zuvZsWWHSzfvJV8v5+uzRrQOrE2H91+JQBREeHsODAcVMDtvU/n3e8WkJVTNvOSf63o9qGoUcHCka6Y/V1g/0FT32Hrvj3UiKrMOz2uYnXmdpK3bSh5ssU4EWooUtGJHV3cr5z1UCPyc/zMfWkjW3/cS532MaXN7uiUYQ0AG2Zl0PnOYu83X2quiNyOpcXOz4eUxTDhdUioDX95FD6Z5M0Vfuf/vPm+7VvDGx/AiFHw+F/LLHURKWNqfI/eL865JQBmthSY6pxzZrYEaARUBd4xs+Z4/wIiAJxzfjP7E7AYGO2cmxU4XgKw7Qjn+yzweQmw1Dm3OXDuNUADYAdwV6ChJrCuOVAX+NY5lx6I/z/gwOvDvYHWBUZV4swsNvBOJ1uBIl8HDowm3wTw4IMPHulrVDoGU979ljcffP+w1d0u6cy1f78MgH/d+ArbN+5gybc/kbHDGw2c++V8mp3a5HdtfPfuz+Ghj6ccfDzlr9excWcGSY3r8+n8n3hh8qzD4nu1bsptvboC8PcJX9OuQQJ92jbnvn5nElspCucgJy+P9+csoiykZWeSUGB0tG7lOLZkF56CkpaVQULlONKyMwkzIzYiil052d7+lQ+NINaNjmVrYP+t+7zPO/ZnMWXjz7SPrxeUpvFEqMEbHT305CY7PZfo+MJ/dqNrhJO9PZfKNSLw5ztys/xExoQd1TnCIn0knBbHpuTMoDS+wa5h19p9OL+jetPoMs27oDq1vIvSDtiyDWrXPLb9WzX3pkoA9DoTFv0E6d3g59Ve0wvQryfcdH/Z5S0iZU9THY5ewStL/AUe+/GeQDwGfOOcawNcCBS88qM5sIfDG8vsX8UUd76C5zp4PjPrgdfInu6caw8sCBzvSAMZvkB8h8BHYqDpJbBvkVf6OOfGOOdOc86dNmDAgCMcvnQWTP2R7pd2pVotr1mJrR5D7YY1mfXfudxy6v3ccur9rEhZw7zJi2jcriFR0ZH4wny0696adT9tDFpeRYmtFEVEmPfrM7BTG+b9ksre/Tn8sHo9fdo0J76K90+8anQUCdVimfrTai4dOZZLR45laeoWBo8ZR5+n36TP02/y3qwFjJk+t8yaXoDF6ZtoFBtP/SpVifD56N+wNVNTVxSKm7ppJQMatQOgX/1WzNmy1lufuoL+DVsT6QujfpWqNIqNZ1H6JqLDIqgSHglAdFgEZ9VtwordR3r+Fto1VG8WzZ7NOezdkoM/18+GWbtJOK3wdI2E02JZ9+0uAFK/z6B2mypHfNk/Lzuf7J1eM+rPd6TNzyQ2MbJC1XDAhu92B+WivILatoR1G2HjZsjJhYnTvIvbjmX/jExI98rjh/nexWxxMZC5F34JPGeaPQ+anFTm6YtIGdKIb9mpCqQGlv90YKWZVcWbYtAd+LeZDXTOjcd7L+lrSnm+nYFpEy2BroH1c4Hnzaw63lSHS/FGjQGmAHcAzwRy6+CcO3DrjxZAqYdMHxx7N+16nELVmrG8v/5V3h0+jklvTqP/zecC8MXor6hepxqjkkccvBXZgLsv4IZT7mX9so289fCHjJj8MOYz8nLz+fcdr7N1/eGT8fbs2svHz3/Bv+eOwDnH3C8XMHfikWaNHJtnruhHp8YNqFalElOH3sCor+cwYd5SBnX2mqtxcxfTpHY8T112Hvl+x+qtO/j7x18BsHprOi9Nmc1r1w3AzMjz+3n802ls3lX4YqBgyneOR+dP5u2zr8RnPsavWcTKwN0Q7mnTnSXpm5m6aSXj1izkua4XM+38W9mVs4+753wCwMqM7Uxcv4xJ/W4m3+9neMpk/M5Rs1IVXjlzIABh5uPzdUuZEbhFmGoozBdmdLg+gZlPrMP5HY3OqU7VwN0Qln64lepNK1GvUxyNe1Zn7shUvrxjJZExYXS599D9sCbetoLcLD/+PMem5AzOeugkImPDmf3P9fhzHc4PtdpUoUmf+ApVw4G7Qmycs5tuDwa3WwwPh4fugRuGeLczG3A+NG/sbXvpDWjTEnp2gyXL4M6HvSb3m9kw8i344h0IC4P7b4U/3+tNmzjlZLisv3fcfwyBux/2LoiLi4Un/hbUUkSklMwVNflJDhN47+cvAqO5mNnbgcfjD2wDbgTewZu+MA241jnXKHBh20Ln3Etm1gD4BjgDbwQ4GWgTmDIxHNjjnHvWzKYDQ5xz8wIju0Occ/0D554ODMFrZv8LJAI/A7WA4c656YGpCUOATXgNdrpzbpiZ1QRG4c3rDQdmOOduCRz3C+CBA9M5ipOSkuKGdhpR0i9luRuRPBSAweNnlHMmJffuwO4ADFo1qZwzKblxzfpW6PzBq2FCxBPlnUapDMgdVqFrGJA7DICOiVeWcyYltyD1AwCSkpLKOZOSS0nx7kGuGspXSkoKSUlJwblC9BgNWzygzJrLJ9pNKNOaNOJ7FJxza4E2BR7/qZhtBe+19HBg+3UFYjfgXSQHgJl9DfQCvnbODS8Q16PA8nS8Oy4U2gb0Kybl951zY8wsHPgEb6SXwB0kLv91sJnVAaJ/q+kVERERqcg0x7d8PQkE48aVwwPvXvIj8AveyPCRNASKfps1ERERkROERnzLkXNuC4fu3lCWxx1yjPHJZZ2DiIiIyPFGI74iIiIiEhLU+IqIiIhISFDjKyIiIiIhQY2viIiIiIQENb4iIiIiEhLU+IqIiIhISFDjKyIiIiIhQY2viIiIiIQENb4iIiIiEhLU+IqIiIhISFDjKyIiIiIhQY2viIiIiIQENb4iIiIiEhLU+IqIiIhISFDjKyIiIiIhQY2viIiIiIQEc86Vdw5SgaSkpOgHRkRE5DiUlJRk5Z0DwLDFA8qsV3ii3YQyrUkjviIiIiISEsLLOwGpeIZ2GlHeKZTYiOShAAweP6OcMym5dwd2B2DQqknlnEnJjWvWt0LnD6rheDCuWV8AOiZeWc6ZlNyC1A+AE6OGpKSkcs6k5FJSUoATowY5Mo34ioiIiEhIUOMrIiIiIiFBja+IiIiIhAQ1viIiIiISEtT4ioiIiEhIUOMrIiIiIiFBja+IiIiIhAQ1viIiIiISEtT4ioiIiEhIUOMrIiIiIiFBja+IiIiIhAQ1viIiIiISEtT4ioiIiEhIUOMrIiIiIiFBja+IiIiIhAQ1viIiIiISEtT4ioiIiEhIUOMrIiIiIiEhvLwTkBPLfW/cSpcLkti1dTc3tbuv2LjbXvwznfudyv6s/Tzz51GsWvDLMZ2n3dmtue35PxMWEUbG9kzuO+eR0qZ+0GOXnsvZLZuQvieLS158r8iYTo3rM3LwRaSm7wbg66WreGXaD8d8roSqsXx272BGTf2et2emlCrvX+tetwkPd+xDmBkfrVnI6OVzCsVE+sJ4tstFtKlel5052dw1+xNSs7yabml1BoMatyffOf6xYAoz09aQEB3Ls10uomZ0DH7n+Gj1At5emVymeQe7hkhfGB/2HExkWBhh5mPShuW8uHRGhaoB4Nv+t7M3N4d858h3fi756s0KV0NsRBRPdbqAFlVr4YChc79gwY7UoNQw8wd4ciT4/TDwArjx6sIxyYvgqZGwYg0893c4r8ehbf+dBK+86y3fOhgu6estL/0ZHngK9udA9y7w4F1gFpQSglbD/76G0f/x8q5dE54eBtWrBacGkfKmEd9fMbNLzOzvgeVbzGxweed0rMxsdgn2+drMqpf23FPens6D/Z44Ykznfh1JbJbAn1rcyQs3j+aul288pnNUqVqZu0bdyMMX/5Mb2/6FxwY9V5qUC/lvyk/c/NYnvxmXsjaVS0eO5dKRY0vU9AL8rf/ZzFyxtkT7HonPjOFJfbluxoecN2k0F550Cs3iahaKu6xJB3bn7KPnxFd46+e5/K19TwCaxdWkf8PW9J00hj/P+IBHk/riMyPPOZ5cNJXzvhzNwK/f5prmSUUe93iuIcefzzXT/0P/ya9z4eTX6Z7QhA416lWoGg64+pv/cOGU14Pa9Aazhr937MOMtDX0+XI0/Se/xqqM7UGpIT8fHnsBxjwNn78D/5sKq9YWjqtXG556AC7odfj6XRkw6m346FUYN9pb3p3pbXv0X/DoEJg0FtZt9JrTilRDXp7XTL/zAnz6FrRoAmN/+8+fSIWlxrewvwIvAzjnXnXOvVvO+Rwz59wZv15nZmG/sdt7wG2lPfeSmcvITN9zxJjTL+7E1+99C8CyH1YSU60K8XW94YXLhlzEv394itELn2Xw8EFF7t/zqjP57pMf2LbB+ye5a1tGadM+TMraVHZn7Svx/v07tOTD267k4zuv5pFLeh3WqBTUs3VTNqTvZtWWHSU+V3Hax9djXWY6G/buItfv54v1P9E7sUWhuN71mjNh7WIAvty4jNPrNPLWJ7bgi/U/kePPZ+Pe3azLTKd9fD227dvD0p1pAOzNy2FVxg7qRMeWef7BrAEgKy8XgHCfj3BfGM4FpYSg1vB7CVYNMeGRdKrVkHFrFgKQ6/eTmbs/KDUsXgYNE6FBPYiMgPN7wrTvCsclJsDJTcH3q/+Ms+bCGadBtTioGustf/cDbN0Be7KgYxtvtPTi82BqEcc9nmtwgHOQtc/7vDcLatcITg0ix4MK2fiaWSMzW25mr5vZj2Y21sx6m9ksM1tpZp0DcZ3NbLaZLQh8Pjmw/i9m9mZguW3gGJXNrAWw3zm3PbBtuJkNCSxPN7PnzWyGmS0zs05mNiFwvscL5PZfM0sxs6VmdlOB9deb2YrAcV4zs38H1tcys4/NLDnw0e03an/ZzC4KLH9SoI7rD+RhZnsCn3uY2Tdm9j6wJLDuGjOba2YLzWx0gYb4M+DK0n1njk7NevFs3XCo2du+cQc1E+NJOrcdic0TuKPLA9zS8X6an9qEtme1KrR//Rb1iK1ehWenDWdU8j/pfW333yPtQjo0TGDCXdfw6p8uoWngP0WTWvH0a3cy17z6EZeOHIvfOfp3aFlo3+iIcK4/+zRemfp9UHKrEx3L5uzMg4/TsjKKbFDrVo5lc5b3xCHfOTJz91M9MtrbP+vQE4q07MxC+ydWrsop1eqwKEgvTQezBp8Zn/e5gbkX38ustDUsSt9U4WpwDt7ucRWfnnsdVzTpGJT8g1lDg5jqpO/P4unO/fmsz/U82ekCosMiglLD1u1Qt3aBmmrBlmMYXN5SzP5bt3nLJT3usQhWDRHh8Mhf4OI/Q/cB3ijypReUWdoix52KPMe3GXAZcBOQDFwFnAlcBDwIXAIsB7o75/LMrDfwJHAp8AIw3cz+AAwDbnbOZQWazvlHOGeOc667md0NfAokAenAajN73jm3A7jOOZduZtFAspl9DEQBDwOnApnANGBR4JgvAs87574zs4bAZKBwt3fIDOAsvEY1EUgIrD8T+LCI+M5AG+fcL2bWCrgc6OacyzWzl4GrgXedczvNLMrMagTqOCjQwN8E8OCDDx4htaNjRYyAOgdJfdqTdG47Xp3/DACVYiqR2DyBJTOXHRYbFh5G81Ob8Nfe/yAyOpKXZj/Bsu9Xkrpyc6lzO1o/bdrKuf98g6ycXM46uREjr72Q8597m67NGtA6sTYf3e49h4iKCGfHnqxC+9/e+3Te/W4BWTm5Qcmv6DHmooY1i/heFLO/K7B/5fAIXu52KY8t+Io9eTklS/I3BLMGv3NcOOV1YiOieLXbQFpUrcWK3dtKnmwxglnDoKnvsHXfHmpEVeadHlexOnM7yds2lDzZYgSrhnDzcUr1ujw6fzKL0jfxcMdzuaXVGTz/47elyrcoRY3oH8s03CL3t9If91gEq4bcPPjwU5jwujea/PiLMGasNwdY5ERUkRvfX5xzB0YxlwJTnXPOzJYAjQIxVYF3zKw53t/gCADnnN/M/gQsBkY752YF4hOAI/33+yzweQmw1Dm3OXD+NUADYAdwV6ChJrCuOVAX+NY5lx6I/z/gwGuFvYHWBZrBODOLdc4dGmI53EzgHjNrDfwEVDezBOB04K4i4uc65w5cOdYLr1lPDpwvGthaIHYrUC9Qx0HOuTHAGICUlBQ396kRxaR2dLal7qB2gxosDTyuWb8GOzalY2Z8OOIT/jfm68PiL7rtPM6/oTcAwy54km0bd7B7ewb7svazL2s/i2cuo2n7k37Xxnfv/kPN3syf1/LwxT6qVa4EGJ/O/4kXJs86LL5X66bc1qsrAH+f8DXtGiTQp21z7ut3JrGVonAOcvLyeH/OIspCWnYmCQVG5epWjmNLduEpKGlZGSRUjiMtO5MwM2IjotiVk+3tXznu0P7RsWwN7B9uPkadcSmfrvuRKak/l0m+v3cNB2Tm7uf7bevpXrdJUBrfYNawdZ/3ecf+LKZs/Jn28fWC0vgGq4bN2RmkZWccHG3/csNybmlVaJZWmahTC9IK/KXbss27iOto1a0Fcxcevn/nDlCntrdc0uMei2DVsHyl97hhove57znw2tjS5ytyvKqQUx0CCk4G8xd47OdQQ/8Y8I1zrg1wIVCpwD7NgT14jd4B2b+KKe6cBc938Jxm1gOvkT3dOdceWBA43pGemPsC8R0CH4lHaHpxzqUC1YG+eKO/M4FBwJ5i9ttbYNmAdwqc62Tn3PAC2yvhfQ2Cas5n8+h97dkAtOrSnL27s0hP28W8yQs57889qVTF+xbUqBdPtVpxfPbyZG459X5uOfV+dmzeyZxPk2l7Zit8YT6ioiNp2bkZ65cF5+X24tSMqXxwuW39OvjM2JW1jx9Wr6dPm+bEV4kGoGp0FAnVYpn60+qDF8ItTd3C4DHj6PP0m/R5+k3em7WAMdPnllnTC7A4fRONYuOpX6UqET4f/Ru2ZmrqikJxUzetZECjdgD0q9+KOVvWeutTV9C/YWsifWHUr1KVRrHxBxuUEZ0vYHXmDt5cMbfM8v09a4iPqkxsRBQAUWHhdKvTiNUZZT/POpg1RIdFUCU8EoDosAjOClLjHswatu/by+asDBrHxgNwRp1GrMoITg1tW3oXnm3cDDm5MHEanHPESWWH69YZZiV7F4PtzvSWu3X25sJWiYaFS70R1U8nQ88zg1JC0GqoU8ub3pC+y4ubPQ+anhSUEkSOCxV5xPdoVAUOdER/OrDSzKriTTHoDvzbzAY658YDy4BrSnm+nYFpEy2BroH1c4HnA3dNyMSbbrEksG0KcAfwTCC3Ds65hYF5ync454p6wWkOcA/QE6gBjA98/JapwKeBaRlbzSweiHXOrTNvCLgusPaYqy7gwbF3067HKVStGcv761/l3eHjmPTmNPrffC4AX4z+irkT59Pl/I68s3Ik+7NyePa6UQCkfLWYhq3q89Js764Q2Xv2MeLalwpdvLZ+eSrJkxcyZtFz+P1+vnxjKmuXlt1I1zNX9KNT4wZUq1KJqUNvYNTXc5gwbymDOnv/1MfNXUyfts25vEt78v1+9uXmMeSDiQCs3prOS1Nm89p1AzAz8vx+Hv90Gpt3FftcJijynePR+ZN5++wr8ZmP8WsWsTJwxfw9bbqzJH0zUzetZNyahTzX9WKmnX8ru3L2cfcc73LulRnbmbh+GZP63Uy+38/wlMn4nSOpZn3+0Kgdy3dt4fM+NwDw3JJvmL55dYWpoValGJ7pciFhZvjM+N/6ZXyzeVWZ5x/MGmpWqsIrZw4EIMx8fL5uKTMCtwirKDUAPDp/Cs93vYQIn48Ne3bx17lfBKWG8HB46B64YYh3K7AB50Pzxt62l96ANi2hZzdYsgzufBgyMuGb2TDyLfjiHe+CsFsHw6CbvX1u+6O3Drz5sQ+MgP374awu3i3NKloNt/8Jrr3TO0e9OvDkA8GpQeR4YC5YlzMHkZk1Ar4IjORiZm8HHo8vuM3MTgfewZu+MA241jnXKHBB2ELn3Etm1gD4BjgDbwQ4GW9OrDOz4Xgjqc+a2XRgiHNuXmBkd4hzrn/g/NOBIXjN7H/x5t7+DNQChjvnpgfmyQ4BNuE12OnOuWFmVhMYhTevNxyY4Zy7xcwGAuc6524uov7rgcecc/XMLALYFahtQmD7HudczK/zDGy7HHgAb6Q5F7jdOfe9mZ0GPOCcu/RIX/uUlBQ3tFPppjqUpxHJQwEYPD54920NtncHehfzDVo1qZwzKblxzfpW6PxBNRwPxjXzbkTbMfF3uS43KBakfgCcGDUkJSWVcyYll5Li3Ue9oteQlJQUrGnmx2TY4gFl1lw+0W5CmdZUIUd8nXNrgTYFHv+pqG3OuTkcmksL3gVmOOeuKxC/Ae9COcC7ny3eXNivC04DcM71KLA8HZhe1DagXzFpv++cG2Nm4cAneCO9BO4gcXkR8V3wGuJCnHNvAG8ElnOBKr/aHlNUnoF1HwEfFXHYawncxk1ERESkIggMZvYHth4YXoqxmQAAIABJREFUED2SijzHN1ieBCr/ZtSxG25mC4EfgV/wRoaL5Zy73zm3OAh5FOdH59zU3/F8IiIiIqX1Nt51T0elQo74BpNzbguH7t5QlscdUtbHLEvOudfKOwcRERGRY+GcmxGY5npUNOIrIiIiIiFBI74iIiIiUmYeq/1jmR2r4JtoBYwJvL9AiajxFREREZHjUsE30SoLmuogIiIiIiFBja+IiIiIVEhm9gHeG3udbGYbA+91UCxNdRARERGRCsk5d0zv/qIRXxEREREJCWp8RURERCQkqPEVERERkZCgxldEREREQoIaXxEREREJCWp8RURERCQkqPEVERERkZCgxldEREREQoIaXxEREREJCWp8RURERCQkmHOuvHOQCiQlJUU/MCIiIsehpKQkK+8cAPxpLcqsV/DVXVGmNWnEV0RERERCQnh5JyAVz9BOI8o7hRIbkTwUgMHjZ5RzJiX37sDuAAxaNamcMym5cc36Vuj8wathQsQT5Z1GqQzIHVahaxiQOwzghKihY+KV5ZxJyS1I/QA4Mb4PSUlJ5ZxJyaWkpJR3ChWCRnxFREREJCSo8RURERGRkKDGV0RERERCghpfEREREQkJanxFREREJCSo8RURERGRkKDGV0RERERCghpfEREREQkJanxFREREJCSo8RURERGRkKDGV0RERERCghpfEREREQkJanxFREREJCSo8RURERGRkKDGV0RERERCghpfEREREQkJanxFREREJCSo8RURERGRkBBe3gnIiWXAPRfQ7/peOOdYu2Q9z1z3Mrn7cw+LaXtWK259/k80aXcST1z5AjM//v6YznHZkIvoddVZAPjCfTRsVZ/Lal9P5s49ZVLDY5eey9ktm5C+J4tLXnyvyJi4SlE8NrAPDeKrkpOXz0MfT2HVlh3HfK6EqrF8du9gRk39nrdnppQ29cN0r9uEhzv2IcyMj9YsZPTyOYViIn1hPNvlItpUr8vOnGzumv0JqVm7Abil1RkMatyefOf4x4IpzExbA0BsRBRPdbqAFlVr4YChc79gwY7UMs39RKohbUEmC99Kw/mhca9qtPxDrUIx+bl+kkemsnPNPiJjw+h6b32q1I5kf2Ye3z+3gfRV+2jUoxodb0g4uI8/18+CN9LY9tNezOCUK+tQv2vccVfDlkV7WDJ2C/48hy/caHdtHWq3jQFg5uPr2LcrF5cPNVtVpuP1CViYHXc1+PMcKa96653fcdLZh/af93Iqm1MyiaoaTp9/NQtK7gfM/AGeHAl+Pwy8AG68unDM2x/B+P9BWBjEV4PH/waJdb1t/50Er7zrLd86GC7pC9n74J5HYMMm8PngnDPgvpuDV0Npvg/pK7NIGb05EOVofVltErsc+pl3+Y6pQ9dQKT6cMx84KXhFSIWmEV/AzGaXdw5HYmbXmNliM1tqZovM7HUzqxbYNt3MfjazhWa2zMxuKrDfWjOb+atjLTSzH83svMDyQjPbU+AY75Y0zxr14rnkzvO5vdNQbmp3H74wH+dc0a1Q3Nb123nmz6OY9v53JTrP/z37Gbecej+3nHo/bz74Pou//anMml6A/6b8xM1vfXLEmBvP6czyzdsY8NJ/eOD/JvFA/x4lOtff+p/NzBVrS7TvkfjMGJ7Ul+tmfMh5k0Zz4Umn0CyuZqG4y5p0YHfOPnpOfIW3fp7L39r3BKBZXE36N2xN30lj+POMD3g0qS8+8xqSv3fsw4y0NfT5cjT9J7/GqoztZZ7/iVKDy3cseGMzZw47ifOeb8qGWbvJ2LCvUNzaabuIjAmj37+b06J/DZb8ZwsAYRE+Trm8Nu0G1ym0z7IJ24mqGkbfl5rT5/lm1Gpd+bisITIujG5DG9LnX83odEcic0ceeoLR9S/1OffZZpz7r6bsz8hj4/cZx2UNG+fsJj/X0edfzej1z6as+Wone7fmAHBSj2qcOSz4TVZ+Pjz2Aox5Gj5/B/43FVatLRzXqjn83xj49C3oczY8+6q3flcGjHobPnoVxo32lndnetuuuxwmvgcTXocFS2DGsY1FHLXSfh/iGlai1z+bcO6zTTlz2EnMH7MJf747uN/KiTuITYwKTvJywlDjCzjnzijvHMyskZlNL2J9X+BeoJ9z7hTgVGA2UPA/4dXOuQ5AN+CfZhZZYFusmTUIHKvVgZXOucnOuQ6B/eYdOIZzbnBp6ggL9xEVHYkvzEdU5Sh2bEovFLNl3TZ+WbIe53eFtl025CL+/cNTjF74LIOHD/rN851zxZl882HJGujipKxNZXdW4T/GBTWtHc8Pq9YD8Mu2ndSrHkeNGK/x6N+hJR/ediUf33k1j1zS62Cz9Ws9WzdlQ/ruEo0U/5b28fVYl5nOhr27yPX7+WL9T/RObFEorne95kxYuxiALzcu4/Q6jbz1iS34Yv1P5Pjz2bh3N+sy02kfX4+Y8Eg61WrIuDULAcj1+8nM3V/m+Z8oNaSvyiambiQxdSLxRfho0K0qm+ZlForblJzBSWdXAyCxaxxbf9yLc47wSj5qtqpCWEThn6G13+w8OFpmPiMqLjgv4JW2huqNo4mOjwAgrkEU/lxHfq4fgIjKYQC4fPDnFf57cLzUgBn5+/348x35OX584UZEtPfvs1brKkTGhAUt9wMWL4OGidCgHkRGwPk9YVoRf/q6nArRlbzl9q1hyzZvedZcOOM0qBYHVWO95e9+8GK7nOrFREZA6xaQti04NZT69yHKhy/wioA/x0GBX4usHblsnr+Hxr2qBSd5OWGo8QXMbE/gcw8z+9bMxpnZCjMbYWZXm9lcM1tiZk0DcRea2Q9mtsDMvjazOoH1tczsKzObb2ajzWydmdUMbLsmcJyFgW1H+5dyGDDEOZcK4JzLd8696Zz7uYjYGGAvkF9g3Tjg8sDylcAHx/jlOWo7NqUz/rnPGbvuFT7a9Bp7d2eR8tXio94/6dx2JDZP4I4uD3BLx/tpfmoT2p7Vqtj4qOhITuvbge8+/qEs0j8mP2/eTu823suabevXoV61OOrExdCkVjz92p3MNa9+xKUjx+J3jv4dWhbaPzoinOvPPo1XpgZnaKVOdCybsw/9Q0nLyqBOdGyhuLqVY9mc5Y2y5TtHZu5+qkdGe/tnHRp9S8vOpE50LA1iqpO+P4unO/fnsz7X82SnC4gOi1ANxchOzyW6xqFjR8dHkL0jr4i4PKJrenG+MCOiso+czPxCcQfk7PW2Lf1wK1//dTVzntvAvl2Fj1sWyrKG1O8zqNa4EmERh/71zHx8LZ/fsJzwSmFBm6pR2hrqd40jLMrHFzf+zMRbV9DiwhpExv6+MwW3boe6tQ89rlMLtvzGCxUfT4SzunjLW45i/4xM+GY2nJ5UNjn/Wln8LO1YmcWUe1cx5b7VnHpjvYON8KK30mh3TR3wBWeqjJw41PgW1h64G2gLXAu0cM51Bl4H7gzEfAd0dc51BD4E/hpY/wgwzTl3KvAJ0BAOjrReDnQLjLDmA0XMzirSKcD834gZa2aLgZ+Bx5xzBf/bjAcGBJYvBD4/yvMeZGY3mdk8M5s3YcKEYuNiqlXh9Is6cW2T27ki8SYqVYmi19VnHfV5kvq0J+ncdrw6/xleSXmaBi0TSWyeUGx81wtPY+ms5WU6zeFovf5tMnGVKvHxnVdz1RkdWb55K/l+P12bNaB1Ym0+ut0b8e3StAH146sW2v/23qfz7ncLyMrJLeLopVf0n/6iRtQKR7pi9nc4ws3HKdXrMnbVfC6a8gbZeTnc0io4L5icCDUUqejEji7uQHi+I3tHHjVaVqb3002p0SKaxe+mlVWGv60ENezesI8lY7dw6k31Dgs566FG9B9zMv48P1t/3FumaR7RMdSQviob8xn9x5xMv1EtWPH5DvZsyQl2hoenVkRuR2rxPpsCP/4M119xhP0LHCAvD4b8A6651BtV/t0c489SjeaV6fN8M3qNaMLyT7aTn+NnU0omUVXDqN40OpiZyglCF7cVluyc2wxgZquBKYH1S4BzAsv1gY/MLAGIBH4JrD8T+AOAc26Sme0MrO8FJAHJ5v2liQa2Bs7xCdA4cJyGZrYwsM+Lzrm3CiZmZm2B94BY4EHn3EeBTVc75+aZWS1gtplNcs6tC2xLB3aa2RXAMiDrWL8gzrkxwBiAlJQUN/epEUXGndq7LWlrt7J7uzfK9t0nP9D6jJOZOnZmkfG/ZmZ8OOIT/jfm68PWX3TbeZx/Q28Ahl3wJDs2e1/WHpd345sPZx1rOWVi7/4cHvp4ysHHU/56HRt3ZpDUuD6fzv+JFyYfnlev1k25rVdXAP4+4WvaNUigT9vm3NfvTGIrReEc5OTl8f6cRWWSX1p2JgkFRkfrVo5jS3bhJwhpWRkkVI4jLTuTMDNiI6LYlZPt7V/50Ohb3ehYtmbvYXN2BmnZGSxK3wTAlxuWB61pPBFq8Ea0Dj25yU7PJTq+8J/d6BrhZG/PpXKNCPz5jtws/xFfPo+MDSMsykjs7H196p9elbXTdpV9AZRNDVk7cpnzzAY63VGfmLqRhfYNi/SRcFocm5IzqdM+5rirYcN3u6jbIQZfuFGpajg1W1Zm5+psYuoUriVY6tSCtK2HHm/ZBrULT3kHYPY8GP0evPsSRAZSrFsL5i48fP/OHQ49fuRZOKk+/PGyss/9gLL8fYirH0V4JWP3hv3sWJ7F5nmZTFywgvwcR152PnNf2kjnu+oHrxipsDTiW1jByX7+Ao/9HHqiMBL4t3OuLXAzEJhRVewTcAPeOTCn1jl3snNuOIBz7g+BUeDzgXkFYg40vUvx5vXinFsSiP0Sr3k+jHNuG97ocJdfbfoIGEUQpzmAd9Faqy7NiYr2/tJ27NmW9cs2HvX+8yYv5Lw/96RSFe/LWaNePNVqxfHZy5MPXsx2oOmtHFeZdme3Zs6nyWVfyFGIrRRFRJj36zOwUxvm/ZLK3v05/LB6PX3aNCe+ivftqRodRUK1WKb+tJpLR47l0pFjWZq6hcFjxtHn6Tfp8/SbvDdrAWOmzy2zphdgcfomGsXGU79KVSJ8Pvo3bM3U1BWF4qZuWsmARu0A6Fe/FXO2rPXWp66gf8PWRPrCqF+lKo1i41mUvont+/ayOSuDxrHxAJxRpxGrMoIzIfBEqKF6s2j2bM5h75Yc/Ll+NszaTcJphadrJJwWy7pvvcY19fsMarepghUzNxy8J4kJSbFsW+qNkG5dsofY+sG5qKe0NeTszWfWU+toc1UdarY8dAFeXnY+2Tu9Jsif70ibn0lsYnAaydLWEF0z4uA807x9fnasyP7dL6Jq2xLWbYSNmyEnFyZOg3MKXzvMTytg+HMw6imoUf3Q+m6dYVayd0Hb7kxvuVtnb9sLr0PmXnjgzsLHK0ul/T7s3ZJz8GK2vdtyyNyUQ5VaEbS9ug4XjD6Z819uQZd761OrTRU1vVIsjfiWTFXgwKXJfyyw/jtgEN4FZn2AA392pgKfmtnzzrmtZhYPxBYYlT2Sp4Bnzexi59yBLrLI13PMrDLQEXj6V5s+ARKAyUDQXsRaPncVMz/+npdTniY/L5/VC9YyMTB6+8dHL2fFvNXM+XweLU5ryvAJ9xNTvQpdL0xi8PBB3Nj2L6R8tZiGrerz0uwnAMjes48R177Erm2Fr/Q+8w+dSZmyiH1ZZX9R0jNX9KNT4wZUq1KJqUNvYNTXc5gwbymDOnvN1bi5i2lSO56nLjuPfL9j9dYd/P3jrwBYvTWdl6bM5rXrBmBm5Pn9PP7pNDbvKnwBRzDlO8ej8yfz9tlX4jMf49csYmXgzgX3tOnOkvTNTN20knFrFvJc14uZdv6t7MrZx91zvLtZrMzYzsT1y5jU72by/X6Gp0zGH3it9NH5U3i+6yVE+Hxs2LOLv879QjUUwxdmdLg+gZlPrMP5HY3OqU7VBt4Tu6UfbqV600rU6xRH457VmTsylS/vWElkTBhd7j30T3vibSvIzfLjz3NsSs7grIdOIq5BJdpeU4fkkaksejuNyLhwOt0WnF/t0tawelI6e9JyWDZ+G8vGe08wznr4JHAw+5/r8ec6nB9qtalCkz7xx2UNzc6LJ/nlTXz1l9U4B43OqUa1k7z9f3hhA9uWZrE/M4//3fwzrQfVpnGv6sXmUlLh4fDQPXDDEO92ZgPOh+aNvW0vvQFtWkLPbvDMq5CVDfc+4m1LqA0vP+Vd1HbrYBgUuFXZbX/01qVthdHvGU0aOi690dt21R/gsv5lXkKpvw/bl2fx83+3Y2GG+aDjDQlBu6hTTlzmipr4E2LMbI9zLsbMeuBdSNY/sH564PG8gtvM7GLgebzm93ugk3Ouh5nVxhtVrQ58izevt7Fzbr+ZXQ48gDfKngvc7pz7vkAOjYC3nXM9isjvj8AQIAzYBfwIPOKc2xzIMQHIBqKA95xzTwb2Wwuc5pzbXuBYjYAvnHNtCqw7WOdvfa1SUlLc0E5FT3WoCEYkDwVg8PgZ5ZxJyb07sDsAg1ZNKudMSm5cs74VOn/wapgQ8UR5p1EqA3KHVegaBuQOAzghauiYeGU5Z1JyC1K9FxNPhO9DUlKQruz7HaSkpJCUlHRcXN3nT2tRZs2lr+6KMq1JT5UA51xM4PN0YHqB9T0KLB/c5pz7FPi0iEPtBs5zzuWZ2enAOc65/YF9PsKbclBcDmuBHsVsewd4p5htRe4T2NaomPO0+dW6Yo8hIiIicqJQ41u2GgLjzMwH5AA3lnM+IiIiIhKgxrcMOedW4s2xFREREZHjjO7qICIiIiIhQY2viIiIiIQENb4iIiIiEhLU+IqIiIhISFDjKyIiIiIhQY2viIiIiIQENb4iIiIiEhLU+IqIiIhISFDjKyIiIiIhQY2viIiIiIQENb4iIiIiEhLU+IqIiIhISFDjKyIiIiIhQY2viIiIiFRYZtbXzH42s1VmNvRIsWp8RURERKRCMrMwYBTQD2gNXGlmrYuLV+MrIiIiIhVVZ2CVc26Ncy4H+BC4uLhgc879bplJxZeSkqIfGBERkeNQUlKSlXcOAP60FmXWK/jqrjhiTWY2EOjrnLsh8PhaoItz7o6i4sPLKjERERERkd9qVo+Fmd0E3FRg1Rjn3JiCIUXsVmzjrcZXjtnQTiPKO4USG5HszXkfPH5GOWdScu8O7A7AoFWTyjmTkhvXrG+Fzh9Uw/FgXLO+AEyIeKKcMym5AbnDgBOjho6JV5ZzJiW3IPUD4MT4PpxoAk3umCOEbAQaFHhcH9hUXLDm+IqIiIhIRZUMNDezxmYWCVwBfFZcsEZ8RURERKRCcs7lmdkdwGQgDHjTObe0uHg1viIiIiJSYTnnJgITjyZWUx1EREREJCSo8RURERGRkKDGV0RERERCghpfEREREQkJanxFREREJCSo8RURERGRkKDGV0RERERCghpfEREREQkJanxFREREJCSo8RURERGRkKDGV0RERERCghpfEREREQkJanxFREREJCSo8RURERGRkBBe3gnIieUPd51Pvxt6YWZMfP1rPnlxYrGxLU5ryktznuSJK55n5sffH/U5LhtyEb2uOgsAX7iPhq3qc1nt68ncuafU+QM8dum5nN2yCel7srjkxfeOGNumfh3ev/UKhnwwkSk/rjzmc5V2/yPpXrcJD3fsQ5gZH61ZyOjlcwrFRPrCeLbLRbSpXpedOdncNfsTUrN2A3BLqzMY1Lg9+c7xjwVTmJm2hoToWJ7tchE1o2PwO8dHqxfw9srkMs072DVE+sL4sOdgIsPCCDMfkzYs58WlMypUDQDf9r+dvbk55DtHvvNzyVdvVrgaYiOieKrTBbSoWgsHDJ37BQt2pAalhrQFmSx8Kw3nh8a9qtHyD7UKxeTn+kkemcrONfuIjA2j6731qVI7En+eI+VVb73zO046+9D+OXvzSXllExkb9oHBabcmUuPkysdfDbl+UsZsZufqbMwH7f+cQO1TqgAw/ZFf2Lczj7BIbyzsrIdPolLV4LQHM3+AJ0eC3w8DL4Abry4c8+Gn8P4nEBYGlaPh0SHQrBEsXgaPPOvFOAe3/wnO7e49fnscjP8fmEGLxvDkUIiKCkoJpfo+bFm0hyVjt+DPc/jCjXbX1qF22xgANszazfIJ23B+qHtqDO2urRucAkJcuY34mtklZvb3wPIt9v/t3XecVPX1//HXe2HpvUuXZq+AWIAo9l5j1Jj81Gg00cTEmGg0+aoxdqNfe4n9a4uxoomCggJWEAXBShEEREDpsLTd8/vjcweGZRd2F5bP3J3zfDz2wcy9U85nd5g587nnno/081ixbIqktpKelDRV0lhJ70k6Ptm3v6RFksZJ+kTSG5LaJPvOkGSSDsx6rOOTbSdJeiG53+Ssxxgnad/NiPUYSZdW8j67SHqkqs+Z0XWnThx+9oH8pt+fOXf3i9n7yN506FH2f9yCggLOvv50xg4ZV+nn+ffNgzlvzz9y3p5/5KHLnuSTEZ9tsaQX4MWxn3Huwy9s8nYFEhcd1p93Jk2v0vNs7v039dhX9j6Ms0Y+zaGv3cfRXXaiR5NWG9zux912Z9GqFQz67z08/OVoLtltEAA9mrTiqM47cthr93PmyKe4qvdhFEisMePa8cM49NX7OOmNRzi9Z+8yHzeXx7CqpJjT33qco4Y8wNFDHmDgNt3YvWX7VI0h46dvPs7RQx+o1qS3OsfwP3scwsjvpnLIq/dx1JB/Mnnx99UyBis2Pn5wNv0v78Kht3ZnxjuLQqJayrThC6nTqBaH39mTXke1ZMLjcwCY+d4iilcbh9zSgwNv6M7U1xewbO4qAMY/PJt2ezTi0Nt6cvBN3WncsXqyrc0dw9RhCwA45JYeDPhrVz559DusxNbeb68LO3Lwzd05+Obu1Zb0FhfD1f8L998ILz8K/xkGk6dteLujDoLBj8ALD8IvToUb7grbe24L/74vbL//JrjyH7BmDcyZB48/B8/eDy8/EpLq/w6vliFs9t+hTpNa7HdpZw65pQd9L+jA6DvCF72VS9bwyf/NYeD/dOWQW3uwctEa5kzYcp9rbp2YpQ5/Au4GMLN7zeyxiLEAIOkRSfuX2ibgRWCkmXUzs97AKUDHrJuNMrPdzWxXYAxwfta+CcCpWddPAcYDmNnxZrY7cHbWY+xuZu9WdQxmNtjMri9jbOW+k5nZBKCjpM5VfV6Azjt04IsPJrGyaBUlxSV8MvIz9jt+rzJve+xvDuPt599n4dzF623/8cXHcOcH13HfuJv5+ZUnb/I5DzilP28+/fbmhL2BsdNmsWj5hm9kpf103915feJk5i9dvt72Mwf05l/nn8rzvz2d8w/ap9L33xJ2a9Ge6UvmM2PZQlaXlPDKN59xUIdeG9zuoPY9eX7aJwC8OvNz9mnbNWzv0ItXvvmMVSXFzFy2iOlL5rNbi/bMW7GUTxd8B8CyNauYvPgH2tZvvMXjr84xACxfsxqA2gUF1C6ohdkGD5vzY9haqmsMjWrXoW/rzjwzNXz5XV1SwpLVK6tlDPMnF9GoXR0ata1DQWEBnfZryrcfLtngdt+OWUyXHzUDoMPeTZg7cRlmBhLFK0soKTaKV5VQUFsU1i9g9fJi5n22nK6Dwn0KCguo07BWTo5hycyVtNklzPDWa1qbwoa1WDClqFpiLc8nn0PnDtCpPdQphCMGwfAy3r4bNVx3uagIMl/16teD2skn2apVYXY3o7gYVqwMiXDRSmhTPd/HN/vv0Hzb+tRvUQhAk051KVltFK8uYdmc1TRuX4e6yZeONrs0Ytb7izd4XLf5Npr4Suoq6QtJD0iaKOkJSQdJekfSJEl7JbfbS9K7kj5O/t0u2X6RpIeSy7skj9FAUi9gpZl9n+y7UtLFyeW3JN0qaaSkzyX1lfR88nx/z4rtxWT29VNJv8za/gtJXyWP809JdybbW0t6TtKY5Ge/Cv6OBgGrzOzezAYzm25md5Tx+xLQGFiQtXkUsJekQkmNgB5A5ac5w+PXSmadJamZpBJJA5N9oyT1SGaZM2N+RNItkt4EbpDUUNJDyfg/lnRs1sO/TEjKq2zaxBnsMmAHGrdoRN36ddjr8D1p3WnDd5+W7VvQ/7h+vHLv6+tt733wrnTouQ0X9Psz5+3xR3ru2Y1dBuxQ7vPVrV+HPoftztvPfbA5YVdJmyYNOXDHHvzrg0/W275vz850adWcn9z1FCfe8Tg7dmhD764dKnz/LaVt/cbMLlr3Zvzd8sVlJqjtGjRm9vLw5lpsxpLVK2lep364//J1b7rfFS3Z4P4dGjRlp2ZtGV9Nh6arcwwFEi8fcjajj/0973w3lfHzv03dGMzgkf1P46WDz+KUbntUS/zVOYZOjZozf+VybtzrKAYf8guu7Xsk9WsVVssYiuavpn7LdY9dv0UhRT+sKeN2a6jfKtyuoJYobFDAqiXFdNy7CbXqFvDKOV/y3199Ra+jW1KncW2WzVlF3Sa1+fCub3njj1P48J5ZrFlRkpNjaNqlHt+OWUJJsbFszioWTi1iedb9P7xrFq9fPIXPnp0bkv1qMPd7aNdm3fW2rWFOOZP8T7wAh5wKN98Ll124bvv4z+Co/wfHnglXXBQS4bat4cxT4MCTYeAJ0Lgh7Ne3Woaw2X+HbLPeX0yzbetRq7CARu3qsGTWSpbNXUVJsfHtmMUU/bC6egaR5ypyPKMH8GPgl4TZzNOA/sAxwGXAccAXwEAzWyPpIOBa4ETgf4G3krKAy4FzzWx5knR+tJHnXGVmAyVdCLwE9AbmA1Mk3WpmPwBnmdl8SfWBMZKeA+oCfwX2BJYAw0lmV4HbgFvN7O1kZnMIUH5Wtc5Om4gVYICkcUBLYFnye8kw4A3gUKApMBjYtgLPuwEzK5b0FbBj8hhjk+f+AOhoZpMl9S91t17AQcl9rwWGm9lZkpoBoyW9YWbLgA+BS4EbqxIbwDdfzOJfN77EDUP/StHSFUz9ZBrFa4o3uN2vbz2DBy59nJKS9T+KSeceAAAgAElEQVQgeh+yG70P3pV7P7oJgHqN6tGh5zZMGPV5mc+399F9+PSdL7ZomUNFXXrU/tzy2ihKSn1A7NujC/v27MxzvwmFaw3q1qFLq2aMnTarQvffUlTm1rKea8NbWjn3t6z7N6hdyN37ncjVH7/O0jWrqhbkJlTnGErMOHroAzQurMu9+51Er6at+WrRvKoHW47qHMPJwx5l7oqltKzbgEf3P40pS75nzLwZVQ+2HNU1htoqYKfm7bjqoyGMn/8tf93jYM7bYV9unThis+KtsLIDK/N28ycXoQJx1P3bsWpZMW/99Wva7NqIkhJY+HURu/+iHS17NmDcQ7P54sV57HxK2+qOfm1sGyhnDF0HNWfxrJUMu2QqDVoX0nK7BhQkk9P9ftuR+i0LWV1UzHs3z+CbkYvWzlZuSWW93ZX9+oKfHh9+Xnkd7n0Mrk8+VXfbEV55FKZMgz9fBwP7wYpVYeb49aehcSP4/RUweCgcc8gWH0LZKvF3yFg0YwUTnpjDgL90BaBOo1rscU573r91JhK03K4By+ZUz3trvqtI4vt1cigcSZ8Cw8zMJE0Auia3aQo8Kqkn4c9dCGBmJZLOAD4B7jOzd5LbbwNs7FNmcPLvBOBTM5udPP9UoBPwA/DbTJ1tsq0n0A4YYWbzk9v/m5D4ARwE7Kh1x0aaSGoM7AvckGzrDPSXtJQwI92vdGCS7iIk/qvMLPOdcpSZHZXsv4SQPJ6Xdbengd8mv6c/sH5iXFmjgIGExPc64BxgBOFLSVn+bWaZ7PMQ4JjM7DpQjzDmz4G5QJnHUJMZ9V8CXHbZxkN/7aHhvPZQKK4665pTmTfzhw1u07NPdy576ncANG3VhL5H7EHxmmIk8fT1L/Cf+99Y7/bH/PpQjjj7IAAuP/JafpgdJtT3/8l+vPn0O8SwU4e23HzqEQA0b1CfAdtty5qSEiTxz7fG8O/RE9a7/al778ZJfXcG4LxHXiz3/sM/m7JF4vuuaAnbZM3KtWvQhDlFG35B+G75YrZp0ITvipZQS6JxYV0WrioK92/QZN396zdmbnL/2irgrn1P5KXpExk668stEu/WHkPGktUreX/eNwxs161aEt/qHMPcFeHfH1YuZ+jML9mtRftqSXyrawyzixbzXdHitbPtr874gvN2qPLpDRsVZuXWzZ4VzV9N/RYbfvzVb1mbou9X06BlISXFxurlJdRpVIsZby+k3e6NKKgt6jWtTavtG7BgShGtd2hA/ZaFtOwZTmbrsE8TvnyheuqUN3cMktj9jG3W3m745VNp1K5Ocp8wM1lYvxad+zdl/qSiakl827aG7+auuz5n3qZLEo44EK66dcPt3buG0odJX8PM2dBhG2iRhHzQAPh4YvUkvpv7dwBY/sNq3rtpBn0v6Lj2bwDQvk9j2vcJ/9emvj4fed+talGRX2t20VVJ1vUS1iXOVwNvmtnOwNGEhCqjJ7CU9ZOqolK3Ke85s59v7XMmdbgHAfuY2W7Ax8njlfflEcJY98mqo+1gZkvMbEhmGyHhPju5nkl6PyXMIANgZucDBwIbnsYZDCYkpmuZ2WhgZ6CVmX21kRgrYhQwANgL+C/QDNgfKO+09GVZlwWcmPU76GxmmenUeoS/ywbM7H4z62NmfU444YSNBtesdfiAa92pFfsd3483n9owMf159/P5WbfwM+rZ97nj/Ad496UxfDhkHIeeOYh6DcNLo2X7FjRr3YTBdw9ZezJbJult0KQBu/5oR957qfo6CmzMoTc9xCE3hp+hEyfx95eGM/yzKbwzaRon9NmJBnXCB0mbJg1p0bA+T70/nhPveIIT73iCeUuWlXv/LeWT+d/StXELOjZsSmFBAUd13pFhszZ86Q37dhIndN0VgMM77sB7c6aF7bO+4qjOO1KnoBYdGzala+MWaxOU6/c6kilLfuChr0ZvsXi35hha1G1A48JwAlLdWrXZr21Xpize8AtaLo+hfq1CGtZOkpZahQyopsS9Osfw/YplzF6+mG0btwBg37Zdmby4esbQvEd9ls5exbI5qyhZXcKMdxaxTZ8NyzW26dOY6SMWAuEwdJudGyKJ+q0K19ZorllRwg9fFdG4Q13qNS+kfstClswKH1NzJyyjSTWd3La5Y1izsmRtGcac8UspqCWadKpHSbGxcnE4VF+yxpg9dilNOlfPGHbZHqbPDInqqtXhBLQDyig6nDZz3eUR70GX5IyambNDDS/ArO/g6xnQoR1s0zaUQBStCLPK738E3btUyxA2+++walkx71w3nZ1Pa0ur7dfv/rFiURjcqqXFTBmygG0PbF49g8hzW+rUzaZA5ljuGZmNkpoSSgwGAndKOsnMniXMMJ6+mc+3ICmb2B7YO9k+GrhVUnNCqcOJhFljgKHABcBNSWy7m1lFam2HA9dK+pWZ3ZNs21ivmv5AWRnMn4FNnzGVkHQdMNrMSrcX+AB4DJhqZiuSEotzgaMq8LBDgN9I+k0ya7+HmX2c7OsFTKxofOX5n2cvpknLxqxZvYY7L3iApQtD3n3UuQcD8Mp9r5d737Gvf0LnHTpy+7vXAFC0dAXX/+x2Fs7bsMC///F7MXboeFYs3/Inw9x0yuH03bYTzRrWY9ilZ3PXG+/x/IefcvJe4UP9mdHl1+W+O+kburVuyRO/CuXSy1et4tJ/vcb8ZVv3JJJiM676aAiP/OhUClTAs1PHMyk5Y/53Ow9kwvzZDPt2Es9MHcc/9j6W4Uf8ioWrVnDhe+HlNmnx9/z3m8957fBzKS4p4cqxQygxo3erjhzfdVe+WDiHlw85G4B/THiTt2ZvuaS9usfQul4jbup3NLUkCiT+883nvDl78haPvzrH0KpeQ+7pfxIAtVTAy9M/ZWTSIiwtYwC46qOh3Lr3cRQWFDBj6UL+NPqVahlDQS2x+y+2YdQ107ESo+sBzWnaKXzB/vTpuTTvXo/2fZuw7aDmjL5jFq9eMIk6jWrR7/ch4+pxaAvG3P0tr180BTPoekAzmnUJ99/jrHaMvn0mJWuMhm3r0OfXG9b058IYVi5aw6i/T0cFYday729CnCWrjVF/n44VG1YCbXZpSLdqSrhq14a//A7Ovjh0XjjhiNCpAeD2B2Hn7WHQfvDk8/DuWCisDU0awXV/DrcZ+wn888mwXYL/+T00bxZ+Dv0RnHhOaIG2Qw84+ehqGcJm/x2mvDafpd+t4vNn5/H5s+GLXqZ93PiHZ7NwWvhM2/HHrWncvpr6seU5bayIXVJX4JVkJpek5dUrZvZs9j5J+wCPEsoXhgM/M7OuyYlt48zsdkmdgDcJpQVLCYfmd04SsCuBpWZ2s6S3gIvN7MNkZvfirDKCt4CLCcnsi0AH4EvC7OuVZvZWclj+YuBbQoI938wul9QKuItQ11ub0KUhuxwhM75HzOytUtu3AW4F+iVjXAbca2b/SmJ8CfiaMKO6iDBr/FVS5tHHzC4o43leSb4EUHqcybZXgGvMbIOGmZJGEcorLpN0GqE7Rous0pI+ZnZBGc9Tn1B3vW8S67Ss3+2dwBAze7n082UbO3asXdp3g6YRqXH9mNDp7efPVl/f1ur22EnhgMLJk1+LHEnVPdPjsFTHDz6GXPBMj8MAeL7wmsiRVN0Jqy8HasYY9uhw6iZumbs+nvUUkP6/Q+/evTd25NuxiRlfM5tGOESfuX5GWfuS5Cy7v81fk+1nZd1+BuFEOQAkvUEoGXjDzK7Mut3+WZffAt4qax9weDlhP2lm9yu073qBMNNL0kHiJ+UOttT4Sm2fTTkdD5IYm5az7xHgkU09T+lxJgrLSnqT2w/Iuvwk8GRZz1nG8xQRZofXI6ku0Af4XVnP55xzzjlXE8Qsnb6WjZcMVNWVyeH/iYRZ2Ber4TmqnZkduhWfrjNwqZlt2JPFOeecc66GiLZksZnNYV33hi35uBdv+lYum5lNArbsernOOeeccznGm2U455xzzrm84Imvc84555zLC574Ouecc865vOCJr3POOeecywue+DrnnHPOubzgia9zzjnnnMsLnvg655xzzrm84Imvc84555zLC574Ouecc865vOCJr3POOeecywue+DrnnHPOubzgia9zzjnnnMsLnvg655xzzrm84Imvc84555zLC574Ouecc865vOCJr3POOeecywue+DrnnHPOubwgM4sdg0uRsWPH+gvGOeecy0G9e/dW7BhynSe+LqdI+qWZ3R87js3hY8gNPobc4GPIDT6G3FATxpB2Xurgcs0vYwewBfgYcoOPITf4GHKDjyE31IQxpJonvs4555xzLi944uucc8455/KCJ74u19SE2icfQ27wMeQGH0Nu8DHkhpowhlTzk9ucc84551xe8Blf55xzzjmXFzzxdc4555xzecETX+dcjSFp24psc1uepL1jx+BqHknNJe0kqZskz1ncZvMaXxeNpFrA9Wb2x9ixVJWka83ssthxbA5JJ2xsv5k9v7Vi2VySPjKzPUttG2tmvWPFVBGS6gE/ARYALwN/AgYAU4Crzez7iOFVSFm/+7SS1AbYD2gPFAETgQ/NrCRqYJWQvKaOIryOssfxHzP7NGZsmyKpKXA+cCpQB5gH1APaAu8Dd5vZm/EirBhJj5jZGbHjcOurHTsAl7/MrFhSb0my9H4DOwxIdeILPAuMS34Aspe8NCDnE19J2wM7AU1LJfJNCB+Yue4xYDXQEPgDIUG5E+gPPEJIYFw1k3QAcCnQAvgYmEt4/RwHdJf0LPAPM1scL8pNk3QlcDTwFvAB68bRC7g+SYr/YGafxIpxE54l/J8YYGYLs3dI6g38TFI3M3swSnQVt2vsANyGfMbXRSXpH0BP4N/Assz2tMwyShoP7M/6yeJaZjZ/qwZUBZKOJ8w29gBeAp4ys8lxo6ocSccSkpNjgMFZu5YAT5vZu1ECqyBJE81sZ0m1gZlm1i5r33gz2y1ieBUiaSEwsrz9ZnbMVgynSiTdBNxhZt+Usa824QtILTN7bqsHVwmSjjSz/2xkfxugs5l9uBXDyjuSviDMWpf3+fDR1o3IgSe+LjJJD5ex2czsrK0eTBVIWgnMouw3NjOzbls5pCqT1BA4lpAEtwQuN7MRcaOqHEn7mNl7seOorOwygdIlA2kpIZA0CTi7vP1pey3VJJIamtmyTd8yt0gS8FOgm5n9TVJnoJ2ZjY4cWoVIWgKMofzPh0FbOSSHlzq4yMzszNgxbKbPzGyP2EFsISuARcBioDPpKBEo7QdJw4C2yQzqrsAxZvb32IFtQkdJtxM+IDOXSa53iBdWpSytKcmtpLbAtUAHMztM0o7APik4tL4eSfsCDwCNgM6SdgPONbNfx42swu4GSoBBwN8IR3CeA/rGDKoSJntym3v8DEkXlaRekoZJmphc31XSX2LHlU8kHSDpfmAscABwm5ntYWZDIodWFf8E/kyolyWpYTwlakQV80fC7//DrMuZ63+KGFdlfB07gC3oEWAIsE1y/Svgd9GiqbpbgUOBHwDMbDwwMGpEldPPzM4nfCnHzBYQTnZzrsp8xtfF9k/CB/19EBIVSU8CuT5Dl3FbeTskdTGz6VszmCoaBnwCvA3UBX4u6eeZnWb221iBVUEDMxsdjpCutSZWMBVlZo/GjmELeHxjHULSUrefaGVmz0j6M4CZrZFUHDuoqjCzGaX+P6RpHKuT7j8GIKk1YQY4LS6JHYDbkCe+LrZUJioZZvaIpH0Ih6NHmtnc5PD6pYQ2Qp2iBlgxaS83yfa9pO6s+6A8CZgdN6RNk9SfUMf4WHL9WUJnAYC/m9nwaMFVXOq7g2RZJqkl615HexPKgNJmRlLuYJLqAL8FPo8cU2XcDrwAtJF0DXASkKYjgjeX+mzLEKHG17s+ROCJr4stlYlKhqQbCW2DxgGXSHoF+DWhPjAVJ+jVkNnGjPOB+4HtJc0iHH4/PW5IFXIV8Jus69sBZxDam10GpCHxPZFwYuSupLQ7SJaLCN1Bukt6B2hNSLrS5jzCUakOwExgKOH/SCqY2ROSxgIHEpLF48wsTYl7CeGz7UlCf+6iuOE48K4OLjJJ3QiJyr6E5v1fA6eb2bSYcVWUpM+APc1shaTmwLfArmY2KXJoFSapFeHDcAHwEHAT6xZP+EMak5ekQ0WBmS2JHUtFSBpjZn2zrj9vZickl98xs/3iRVc5NaE7CKxtX7YdIeH60sxWRw4prySrtH1iZjvHjmVzJD3GTyVMkHxGSIKHmllqjmzWND7j66Iys6nAQWlLVLIUmdnaEy8kfZmmpDfxJOEkqp7AaOBhwizRAMIZ4ftHi6yCsmuSS20HIFNCkMOaZV/JJL2Jtls5ls2V+u4gZdQq95K0CJhgZnNjxFQVWd1Bsi0irEL30taOpzLMrETSeEmdy+qrnBZm9gVwBXCFpJ8QFua4gTDB4CLwGV8XhaTTzexxSReVtd/MbtnaMVVFGU37B2ZfT0nT/vFmtlvSM3O6mXXO2jfOzHaPGF6FSLqjrM2EWZYOZpbTX/IlvQzcW3rRAUlHAb8ysyPjRFZxyapnpwJ7AW8QFg5J5QIJkv4D7ANklsXdn7BUbi/gb2b2f5FCq5SkW8v2hAWCIJSjfEo492CqmeV0pwpJwwmty0az/gJHOf++miGpA6GzzPGEo2rPAC+Y2dKogeWxnP4wcDVag+TfxlGj2HzHlrr+jyhRbJ5iCGdaSPq+1L5UnEFtZmvrY7Oa3l9CSFauiRVXJVwEvJLUuGdWc+pNKAFKy3LFNak7SAmwg5nNgbV9fe8B+hG+2KYi8SWsxjgoc1hd0j2EOt+DgQkxA6ugq2IHsDkkjSB8xj1DqNnPrORZR1KLNKzsWRN54uti6Z78+5mZ/Xujt8xh5dUuSupE+JafhtrGbpIGE2ZIM5dJrm8bL6zKSWoyzwD+AHwAnGRmX0YNquJWEE4K+ymwU7JtJOHkpL6EPrK5riZ1B+maSXoTc4FeZjZfUppqfTsQTpDMdKRoCLQ3s+Jk1cmclsba8FK6EE5uOxf4ZbIt0+bBgNSs7FmTeOLrYjkiWajiz6w7DJdqyUliPyYc7u1AaMOTBtmz1jcn/1qp6zlN0vnAhYRZx8NS0j852wjgXuCWrNm5toQa6+1IwUpVNaw7yKikQ0t2icDI5FyEhfHCqrQbgXGS3iIkXAOBa5NxvBEzsI2R9LaZ9U+W/M2ux8y0AWsSKbRKMbOusWNwG/IaXxeFpJsI34AbAsuzd5GiNzZJjQm1W6cR6v9eAH5iZh2jBlYJko4FOprZXcn10YT2TQZckoYZeUklhFm5eZT9QZnT/TKTjiDXE0obLgR2IZQ/3AjcY2Y5X3JSk7qDJOUyJwL7EV5DbwPPWQo/MCVtQ6i7FjDazL6NHFJeSY5EFSelZJ0I5TKTzWzcJu7qqoknvi4qSS+ZWek62dSQVEQ48eIvwNvJm9tUM0vNIaykT+kpZjYjuT6O0DezIfCwmR0YM76KkNRlY/vTMgMs6ULCMrPfAnub2czIIVWYpKGE7iCNCa+fhwm9SwcAPzWz/eNFl7+SL1U9yequYWYjy79H7pD0N0LJz3tmtmxTt881ks4hdHBYClxNWKX0I2AP4CEzuyFieHnLE1/nNoOk3xNqeRsS2oL9C3g9ZYlv6R6yd5rZBcnl981s73jR5QdJzQgfkP2APwFHEJLHC1OyaluN6A6SkazUdgewA1AHqAUsS8uRqAxJZxOOIHQkLLKzNyGJHBQ1sAqSdBbQn9BhYwkwirBCZk63YsuQ9Ckh/saEFfO6mNn3khoAY8xsp40+gKsWBbEDcPlJ0tvJv0skLS79b+z4KsrMbjWzfsAxhEOJLwLtJV0iqVfc6CqsefaVTNKbaL2VY6mSrNfP4pS+nj4CJgF9zGxo0mbqZ8DfJT0VN7QKW9sdBEhld5AsdxJq9ScB9YGzCYlw2lxIqA+fbmYHEGYa58UNqeLM7CEzOws4AHiccA7F43GjqpRVZrYg6UM82cy+BzCz5cCquKHlLz+5zUVhZv2Tf9PezgxYuxDHNcA1knYhfGi+yrruFbnsA0nnmNk/szdKOpdQxpEGw4B2wPOE/rFpa3g/sHRZQ1IDuG9yuDQNakR3kAwzmyyplpkVAw9Lejd2TFWwIllVEkl1zewLSdvFDqqiJD0A7AjMIcz2Zrf7S4P6kvYgTDLWSS4r+Undwi41hZc6uCgktdjY/jT1N5R0HKFf5gQzGxI7nsqS1IYwU72S9XvI1gWOK9XWKWdJagqcQCg9qUcoO3k6Ta+lNJP0ozI2Zz5glKbWVJJGAgcRump8B8wGzjCz3aIGVkmSXiC0mfsdMIhw4mGhmR0RNbAKSuJvT1jqdwShzGFq3KgqTtKbG9ufzMK7rcwTXxeFpK8JH4oiLGu6ILncDPjGzFIxQyTpbkLf1XcJNZkvm9nVcaOqGkmDWNdD9tO01JaWJqkA+Anh0PS1aVkFMO1qQneQjORkyblAIfB7oClwd5o6U5SWfDFpCrxmZqk6zC5pB+BQwt+iVpq65rjc44mvi0rSvcBgM/tvcv1w4CAz+0PcyCpG0kRgt6QhfANglJn1jh1XPpK0L6HEZACh/dS/zGxU3KjyR03oDlITJV0dOpFV2mhmqSgXSJbsHkDoP9wceI/wHvtQ1MAqSFIToK2ZTUqu/5hQMw4wJC1H02oar/F1sfU1s/MyV8zsVUlpmjFdldQAYmbLkzPa3VYmaRphYYGnCf2hM4tA7Anp+aBPuTqZpDfxtpn9APyQLJiQGknCdTVh5a3apKy/eEbyXnoGMJV1JxgaoewhDQ4ntDO7LaX9h28mHA2clFy/jnDuR31Cz+7zyrmfq0Y+4+uikjSEcNLC44Q35NMJJ/ocGjWwCpK0HMgc/hThZLbJpGThhJoiWZkq82aWKaHJsLS0b0ozSZPNrEc5+6aYWRpO9ATCWAj14hPSuGhFhqQvgV3SVtpQU0j6GNgz8xqS9LGZ7ZFcfjtzkrfbunzG18V2KnAF65b3HZlsS4sdYgfgYGOLIyQ9WV31qwndQTJmABPTnPQmJhLOm5gbO5A8VbvUa+hnWZebbe1gXOAzvi4nJLVQJWa2NHYsW4KkWoR6xydix5LvJH2TvZiCqx41pTsIgKS+hFKHEYTxAJC2EyUl9QFeIiTA2eM4JlpQeUTSeOBQM/uu1PYOwKt+RDAOn/F1USU9bx8DWiTXvwf+n5lNjBpYBSUJ+/lAB2Aw8DpwAXAxYaUkT3zj87rrrcDM5hL6Dmd3B/lPSruDXENYZrYeYeW2tHqUsCLgBNK3iEhNcBPwsqQ/AB8n2/Yk1P7eFC2qPOczvi6qpCn85Wb2ZnJ9f0ILqn2jBlZBkl4itGJ7j3AGe3PCB+WFyQIELjKf8XWVJelDM+sTO47NJWmEmZXVXzmVJD0KLAfuStHkyGHAZaz7MjgRuN7MXo0XVX7zxNdFJWl86abwZW3LVZImmNkuyeVahKVaO5vZkriR5RdJL7Pu5Lb1dgGDzCxVXQVcXJKuB4ab2dDYsWwOSbcQShwGs36pQyq7nCQlKJ2BvczsktjxuHTyxNdFlazM8xHwf8mm04E+ZnZcvKgqTtJHZrZnedfd1lGTVg1z8UlaQug/vBJYTXrbmZW1cljqupxIamhmy2LHsbkk/T8zezR2HPnOE18XVdJc/SqgP+HDZSRwpZktiBpYBUkqBjJvyCL0Z1xOSj8o06omrRrmnAuSRWkeABqZWWdJuwHnmtmvI4dWJT4xkhs88XXOpZ6vGua2BEnbm9kXmYVPSktLiYCk083scUkXlbU/Ld0pJH0AnERY3TPT/3aime0cN7Kq8cQ3N3hXBxeFpMEb2+/tdlwl1ZhVw1xUFxFW/vtHGfvStOJZ5jXfOGoUW4CZzSi1IGZxrFiqIik3ySyq00PScNYdEUzL66lG8RlfF4WkeYQm8U8BH1Cq5ZTXZLrKqEmrhjnnAknPArcAdwJ7A78lnANyStTAKkFSl8xF4D/AEZl9ZjY9SlB5riB2AC5vtSO0eNkZuA04GPjezEZ40uuq4ANJ55TemNJVw1wkkja6hKykJpJy/jC7pL9IarGR/YMkHbU1Y6qi81jXJ30msHtyPTXMbHryMw1YmXXdk95IfMbXRSepLmGZ4puAv5nZHZFDcilTk1YNc/FIuhXoB7wGjAXmERax6AEcAHQB/mBmY6IFWQHJyZ5/AlYQ/j9kxtGTkDy+QeiXPi9akHlI0vNmdkLsOPKdJ74umiThPZKQ9HYl9Jp8yMxmxYzLpVepVcM+TemqYS6ipNPMScB+wDZAEfA5YRW6t2PGVlmSerLhOEaaWVHUwCpI0u1lbF4EfGhmL23teFzN4ImviyJZgWdn4FXg6bSswuOcc2khqZ6ZrSi1rZWZfR8rpsqQdD+wPZBpR3gi8CnQCZhqZr+LFVtlKJyd91Ogm5n9TVJnoJ2ZeRlWBJ74uigklbCu/y1kLTaA9791zkVUThuwRcDYNC1FLmkCcI6ZvZ9cPxG4zsx6xY2sYpIOCIeY2Zrkem1gKOGckAlmtmPM+CpK0j1ACWEVyR2SowpDzaxv5NDykrczc1GYmZ9Y6ZzLVX2Sn5eT60cCY4DzJP3bzG6MFlnlnAY8JOktoD3QkvS0ZINwUltDwpcOksvtzaxY0sry75Zz+pnZnpI+BjCzBZLqxA4qX3ni66KS9DdgFPBuTViS0jlXI7QE9jSzpQCSrgCeBQYSTnpLReJrZhMkXUNYEn4JMNDMZkYOqzJuBMYlibsIv/9rk97cb8QMrJJWS6pFcmRTUmvCDLCLwBNfF9s0wsltt0taQkiCR/qJC865iDoDq7Kurwa6mFlRmmYaJT0IdAd2BXoBL0u6M7O0d64zswcl/RfYi5D4XmZm3ya7/xgvskq7HXgBaJN8ETkJ+EvckPKX1/i6nCCpHXAycDHQ3MxSv+KQcy6dJP0VOB7IfAE/mtB15h/A/Wb201ixVYak3wP/a8kHvZ19LycAAAebSURBVKSmwC1m9ou4kVVcUg/bk9CODQAzGxkvoqqRtD1hGXUBw8zs88gh5S1PfF1Ukh4AdgTmEGZ73wY+ypzM4JxzMUjqDfQnJCpvm9mHkUPKO5LOBi4EOgLjCKu3vZe2pX7LWUxkiZmt3urBOF+5zUXXEqgFLATmE1Zv86TXOReNpNuAumZ2m5n9b1qTXkk9JT0r6TNJUzM/seOqhAuBvsB0MzsA2IOwGEfaZBYR+QqYlFz+WtJHyRcstxV54uuiMrPjzawf4SSGZsCbktJ08oVzrub5CPiLpMmSbpLUJ3ZAVfQwcA+whrDy3GOEE93SYkWmD7Gkumb2BbBd5Jiq4jXgCDNrZWYtgcOBZ4BfA3dHjSwPeamDiypZL34A4Wzd5sB7wCgzeyhqYM65vJccoj4ROAXobGY9I4dUKZLGmllvSRPMbJdk2ygzGxA7toqQ9AJwJvA7Qhu2BUChmR0RNbBKkvShmfUpa5ukcWa2e6zY8pF3dXCxHQ6MBG7LOlvXOedyQQ/CymFdgc/ihlIlKyQVAJMkXQDMAtpEjqnCzOz45OKVkt4EmhJmT9NmvqRLgKeT6z8BFiQtzryt2VbmM74uOkldgJ5m9oak+kBtM1sSOy7nXH6SdANwAjAF+BfwgpktjBtV5UnqC3xOKCO7GmgC3GhmH0QNrBKSrg6dyJqoM7OP4kVUeZJaAVeQdbIkcBVhYY7OZjY5Ynh5xxNfF5Wkc4BfAi3MrLuknsC9ZnZg5NCcc3lK0nnAc0A3oG5me9raaCW1yZcDXYDCZLOZ2a7xoqo4SVcDZwBTWTczamnr6uByi5c6uNjOJzQn/wDAzCZJSs2hOOdcjVQMDKdUGy3StdwvwBOEhR4mkM5D6icD3c1s1SZvmcOSldr+BOzE+v2I0/Z6qhG8q4OLbWX2m5qk2iTLOjrnXCS/pWa00ZpnZoPN7Gszm575iR1UJUwklGmk3RPAF8C2hBKHacCYmAHlM5/xdbGNkHQZUF/SwYT2Li9Hjsk5l99WmNkKSWvbaElKYxutK5JFgoYBa5daNrPn44VUKdcBH0uayPrxHxMvpCppmSy/fKGZjSB87o2IHVS+8sTXxXYp8AvCobhzgf8CD0SNyDmX72ZKaga8CLwuaQGQxq4zZxK6UhSSVSMLpCXxfRS4gfSWamRkVmibLelIwmupY8R48pqf3Oacc86VQ9KPSNpopa3WNLt/bxpJGmFmP4odx+ZK+tWPInSnuIPQXeMqMxscNbA85Ymvi0LSM2Z2sqQJlFHTm5azjp1zLldJ+idwq5mlsQcxkm4hlDgMZv1Sh1S1M3O5xRNfF4WkB4GHCA3Vy0p803QChnPO5RxJnwPdga8JiaNIVzuzN8vYnLp2ZpK2BX5DWAglux9x2mqVawRPfF0Uki4kLAO6DaFB/FNmNi5uVM45V3MkiwNtwCcWti5J44EHKVWrnJzo5rYyT3xdVMkb8ynJTz3gKeBpM/sqamDOOeeikHS6mT0u6aKy9pvZLVs7ps0h6QMz6xc7Dhd44utyhqQ9COUPu5pZrdjxOOec2/oknWtm90m6oqz9ZnbV1o5pc0g6DegJDMVrlaPzxNdFJakQOIww43sgMIJQ9vBi1MCcc865LUDSdcDPgCn40svReeLrokgWqzgVOBIYDTwNvGhmy6IG5pxzLipJfwHuNrP55ewfBDQws1e2bmRVI+kLwpHMVLXDq6l8AQsXy2XAk8DF5b25Oeecy0sTgJclrQA+IiwXXY9QLrA78AZwbbzwKm08YenlubEDcT7j65xzzrkcJKknsB+h+08R8Dkw0syKogZWSZLeAnYFxpDupZdrBE98nXPOOZdzJNUzsxWltrUys+9jxVQVyep/G/B2ZnF44uucc865nJOs7HmOmb2fXD8RuM7MesWNzKWZ1/g655xzLhedBjyUlAq0B1oCqemEIOltM+svaQnrr1CaWUGvSaTQ8prP+DrnnHMuJ0k6Dvg/YAkw0MwmRw7JpZzP+DrnnHMu50h6EOhOODGsF6HTw51mdlfcyFyaFcQOwDnnnHOuDBOBA8zsazMbAuwN7Bk5JpdyXurgnHPOOefygpc6OOeccy7nJH18rwN2JCxgAYCZdYsWlEs9L3VwzjnnXC56GLgHWAMcADxGONHNuSrzxNc555xzuai+mQ0jlGVON7MrSVE7M5ebvNTBOeecc7lohaQCYJKkC4BZQJvIMbmU85PbnHPOOZdzJPUFPgeaAVcDTYEbMyu5OVcVnvg655xzzrm84KUOzjnnnMs5kvoAlwNdyMpXzGzXaEG51PMZX+ecc87lHElfAn8EJgAlme1mNj1aUC71fMbXOeecc7lonpkNjh2Eq1l8xtc555xzOUfSgcCpwDBgZWa7mT0fLSiXej7j65xzzrlcdCawPVDIulIHAzzxdVXmia9zzjnnctFuZrZL7CBczeIrtznnnHMuF70vacfYQbiaxWt8nXPOOZdzJH0OdAe+JtT4CjBvZ+Y2hye+zjnnnMs5krqUtd3bmbnN4Ymvc84555zLC17j65xzzjnn8oInvs4555xzLi944uucc8455/KCJ77OOeeccy4veOLrnHPOOefywv8H1m5tNJUN4zsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a20925080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap = mpl.cm.get_cmap('viridis_r', 6)    # 11 discrete colors\n",
    "\n",
    "fig, ax = plt.subplots(1, figsize=(12,9))\n",
    "hmobj = sns.heatmap(df.loc[keep_rows, keep_cols].applymap(lambda x: -pd.np.log10(x)),\n",
    "            cmap=cmap, vmin=0, vmax=6,\n",
    "            cbar_kws={\"shrink\": 0.8},\n",
    "            linewidths=0.8,\n",
    "            linecolor=[0.8]*3,\n",
    "            square=True,\n",
    "            annot=dfstr.loc[keep_rows, keep_cols], \n",
    "            annot_kws = {'horizontalalignment':'center',},\n",
    "            fmt = 's',\n",
    "            ax=ax)\n",
    "plt.yticks(rotation=0)\n",
    "# plt.xticks(rotation=90)\n",
    "pass\n",
    "# ax.set_title(\"$-\\\\rm{log_{10}}$ p-value\\nBonferroni-adjusted $\\\\alpha=0.05$ threshold = %.2f\" % thr_bonferroni)\n",
    "# ax.set_title(\"$-\\\\rm{log_{10}}$ p-value\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [],
   "source": [
    "fn = \"../tables/auroc_mcnemar_comparison-e5ce2d69b035975cb5336cec0da9a32a.csv\"\n",
    "df = pd.read_csv(fn, index_col=0)\n",
    "# df.drop('score_wire', axis=0, inplace=True)\n",
    "# df.drop('score_wire', axis=1, inplace=True)\n",
    "# df.drop('wire', axis=0, inplace=True)\n",
    "# df.drop('wire', axis=1, inplace=True)\n",
    "\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 11)"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.rename(columns=modelnamemap, index=modelnamemap)\n",
    "\n",
    "df = df.loc[df.index.map(lambda x : 'avg(wire)' not in x).tolist(),\n",
    "            df.columns.map(lambda x : 'avg(wire)' not in x).tolist()]\n",
    "\n",
    "keep_rows = ~df.isnull().all(1)\n",
    "keep_cols = ~df.isnull().all(0)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 11)"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfstr = df.applymap(lambda x: ('{n:5.{s}{c}} '.format(n=x, c='e' if x < 1e-3 else 'f',\n",
    "                                                        s = 3- 2*(x < 1e-3)\n",
    "                                                     ).replace('-0','-').replace('nan',''))\n",
    ")\n",
    "dfstr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1f141710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# df\n",
    "cmap = mpl.cm.get_cmap('viridis_r', 8)    # 11 discrete colors\n",
    "\n",
    "fig, ax = plt.subplots(1, figsize=(12,9))\n",
    "hmobj = sns.heatmap(df.loc[keep_rows, keep_cols].applymap(lambda x: -pd.np.log10(x)),\n",
    "            cmap=cmap, vmin=0, vmax=4,\n",
    "            cbar_kws={\"shrink\": 0.8, \"ticks\":np.arange(0,4.01)},\n",
    "            linewidths=0.8,\n",
    "            linecolor=[0.8]*3,\n",
    "            square=True,\n",
    "            annot=dfstr.loc[keep_rows, keep_cols], \n",
    "            annot_kws = {'horizontalalignment':'center',},\n",
    "            fmt = 's',\n",
    "            ax=ax)\n",
    "plt.yticks(rotation=0)\n",
    "# plt.xticks(rotation=90)\n",
    "pass\n",
    "# ax.set_title(\"$-\\\\rm{log_{10}}$ p-value\\nBonferroni-adjusted $\\\\alpha=0.05$ threshold = %.2f\" % thr_bonferroni)\n",
    "# ax.set_title(\"$-\\\\rm{log_{10}}$ p-value\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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