examples/Scikit-survival_Example.ipynb

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   "source": [
    "# Use SurvivalEVAL for scikit-survival model\n",
    "\n",
    "In this notebook we introduce the use of `ScikitSurvivalEvaluator` in the package to evaluate the performance for a scikit-survival model.\n",
    "The first half of this notebook follows the procedure described in the Scikit-Survival [example](https://github.com/havakv/pycox/blob/master/examples/01_introduction.ipynb).\n",
    "\n",
    "\n",
    "In the following, we will:\n",
    "1. Load the German Breast Cancer Study Group 2 dataset;\n",
    "2. Train the `CoxPH` model using 70% of the data;\n",
    "3. Perform evaluation on the 30% testing set."
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "!pip install scikit-survival"
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Import Dependencies"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sksurv.datasets import load_gbsg2\n",
    "from sksurv.linear_model import CoxPHSurvivalAnalysis\n",
    "from sksurv.preprocessing import OneHotEncoder, encode_categorical\n",
    "from sksurv.metrics import check_y_survival\n",
    "\n",
    "from Evaluator import ScikitSurvivalEvaluator"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
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     "start_time": "2023-08-12T01:11:40.590350881Z"
    }
   },
   "execution_count": 1,
   "outputs": []
  },
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   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ],
   "metadata": {
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    "pycharm": {
     "name": "#%%\n"
    },
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  },
  {
   "cell_type": "markdown",
   "source": [
    "## Load and prepare data."
   ],
   "metadata": {
    "collapsed": false
   }
  },
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   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/shiang/anaconda3/envs/SurvivalEVAL/lib/python3.10/site-packages/sksurv/column.py:137: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  columns_to_encode = {nam for nam, s in table.iteritems() if _is_categorical_or_object(s)}\n",
      "/home/shiang/anaconda3/envs/SurvivalEVAL/lib/python3.10/site-packages/sksurv/column.py:142: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  for name, series in table.iteritems():\n",
      "/tmp/ipykernel_246416/2164041626.py:3: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
      "  X.loc[:, \"tgrade\"] = X.loc[:, \"tgrade\"].map(len).astype(int)\n",
      "/home/shiang/anaconda3/envs/SurvivalEVAL/lib/python3.10/site-packages/sksurv/column.py:142: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  for name, series in table.iteritems():\n"
     ]
    }
   ],
   "source": [
    "X, y = load_gbsg2()\n",
    "Xt = encode_categorical(X)\n",
    "X.loc[:, \"tgrade\"] = X.loc[:, \"tgrade\"].map(len).astype(int)\n",
    "Xt = OneHotEncoder().fit_transform(X)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2023-08-12T01:11:43.864605747Z",
     "start_time": "2023-08-12T01:11:43.842880665Z"
    }
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  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "n = len(Xt)\n",
    "\n",
    "tr_size = int(n*0.70)\n",
    "\n",
    "Xt_train, Xt_test = Xt.iloc[:tr_size, :], Xt.iloc[tr_size:, :]\n",
    "y_train, y_test = y[:tr_size], y[tr_size:]\n",
    "train_event_indicators, train_event_times = check_y_survival(y_train)\n",
    "test_event_indicators, test_event_times = check_y_survival(y_test)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
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  },
  {
   "cell_type": "markdown",
   "source": [
    "## Build the model and train it."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "source": [
    "est = CoxPHSurvivalAnalysis(ties=\"efron\").fit(Xt_train, y_train)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2023-08-12T01:11:46.094299090Z",
     "start_time": "2023-08-12T01:11:46.033149921Z"
    }
   },
   "execution_count": 5,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Predict individual survival functions\n",
    "Get probability of remaining event free from 1 year to 5 years (=1825 days)."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [],
   "source": [
    "survs = est.predict_survival_function(Xt_test)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2023-08-12T01:11:47.235399826Z",
     "start_time": "2023-08-12T01:11:47.219269656Z"
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  },
  {
   "cell_type": "markdown",
   "source": [
    "## Build the evaluator.\n",
    "Some arguments includes:\n",
    "1. Training set information (`durations_train` and `events_train`) is optional if you don't need Brier score, integrated Brier score, and L1-margin loss.\n",
    "2. `predicted_time_method` argument indicates whether using median or mean survival time as the predicted time."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
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  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "eval = ScikitSurvivalEvaluator(survs, test_event_times, test_event_indicators, train_event_times, train_event_indicators, predict_time_method=\"Median\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2023-08-12T01:11:48.309783763Z",
     "start_time": "2023-08-12T01:11:48.305590692Z"
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   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Make some example plots"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "(<Figure size 640x480 with 1 Axes>,\n <Axes: xlabel='Time', ylabel='Survival probability'>)"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
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4k3TGcm/mHgy9voCH3jA8XvkKzGgFZ9aY9DKOdjaMeqgGy19sS4i3E5dvZvHxajN2jBZCCFFA9+7d+fjjj3nkkUfueE5RFKZOncp7771H3759ady4MT///DNXr169o4VHDVLcVEDN/JvxW4/fqOJWhasZVxny1xDWRa+z7Jt2eAfCXzJcprpxDuYPMHQ4zs0w6WX83ByYNtDQ8rTyyFUW7i/+0HMhhCgTFMXws6+0b2ZcbSkqKor4+HgiIiKM2zw8PAgLC2P37t1me5+SslU7gFBHDc8azOs5jze3vcmuq7t4a9tbNPJtRJBrkGXeUKuFrv+F9m/C/EFwaaehw3FOKvT5yqSXal7Fixc61ODrLReYvOYMjzSrjL2t1OlCCCuRlwmfBJf++75zFezNs5ZffHw8AAEBAQW2BwQEGJ9Tk3wjVGAeDh7M7DyTet71yFfyzbPu1L04esCwVdBrquHxoZ9h77cmv8yrD9fGz82BGxm5zNt7ybwZhRBCWDVpuangbLW2PFjpQU4lneK7o9/RpVoXnGydLPumWi00HwZRW+HEMvjrTcOfNTpDq5GGeXPuwc5Gy4i21Zi85gxrTsQzvG2oZTMLIYS52DkbWlHUeF8zCQwMBCAhIYGgoNst/gkJCTRt2tRs71NS0nIjGN5wOP7O/sSkxTD3+NzSeVOtFvr/CNU7Gh7H7IbNH8Mvj4C+eJP19Who+A+1LyqJ+BT1e+cLIUSxaDSGy0OlfdNozPYRQkNDCQwMZOPGjcZtqamp7N27l/DwcLO9T0lJcSNwt3fn9ZavAzD/9HxydKW05IFGA08thed3Q4/Pwd4N4iJh83+hGBMMVvN1oXU1b/QKzNp6gfScfMtnFkKICiI9PZ3IyEgiIyMBQyfiyMhIYmJi0Gg0jBkzho8//pg//viDY8eOMXToUIKDg+nXr5+quUGKG/G3h6s+jLejNzdzbnLqRikOsdZqIaA+tH4WWg43bNv+OUyuDseW3PPwJ1qFADB3VzQNJ6zlP78ckPlvhBDCDA4cOECzZs1o1swwQnXs2LE0a9aM8ePHA/Dmm28yevRoRo0aRatWrUhPT2fNmjU4OjqqGRuQ4kb8zVZrSzX3agBcTVfhWjBA21ehxQhAA9nJsO69ex7yaLNKvBpRGxutobl17YkE3l12DMWMQx6FEKIi6tChA4qi3HGbO3cuYFj4+MMPPyQ+Pp7s7Gw2bNhA7dq11Q39NyluhFFNz5oAzIicwfRD08nTl/IaTi4+0HuqYfkGgLQ4iN1f5CFarYZXImqx/90I+reoDMCGU4n8tlfmvxFCiIpKihthFB5s6AQWmxbL7GOzeXPrm6Vf4IBhtFToQ4b7CwZBcuw9D/F2sefzx5vwTo+6ALy3/Dizt12UjsZCCFEBSXEjjDpX6cy3Ed/yVqu3sNPasSFmA98d/U6dME/8Av71IeMaTG0Ic3sZ5sO5x0iqoeHVqOZjGO743z9P8cCkjczYVDYWchNCCFE6ZJ4bYaTRaGhTqQ1tKrXB1d6V93e+z7xT8zh+/TjOts4MrjeY5gHNSyeMkyc8+p1hNuOUWIjebrjF7oMuH4N74TMpO9rZsOLFB/nj6FW+336RSzcy+WK9YfXz1qE+NK7sgaOdTel8BiGEEKrQKBWs52VqaioeHh6kpKTg7u6udpwyKys/i4jFEaTmphbY/kTtJxjTYgxu9m6lE0RR4PpZiPwNdk4zbHPxgxF/gW+tIg/N1+kZ+N0eDly6adxmq9VQL8gdT2c7wkK9eaZddSl2hBAWk52dTVRUFKGhoWViFFFZV9T5MuX7W4obcVcXky9y7PoxAA4kHGD5+eWAYdj4Fx2+KP1AMXvhj9Fw/QzU6QmD5t3zkOw8HQv2xbDnYhIHY25yLa3gHD5aDVTzcaFTXX9aVvPC3dGO1qHe2NrIFVshxP2T4sY0UtyUkBQ3JfdX1F+8ue1N/J382fjExnsfYAlXDsLszoACTy6G2l2KfaiiKFy+mcXxKynM2Hyeq8lZ3My8s8N0kIcjr3Wpw2PNK6Ex44yeQoiKR4ob05iruJE+N6LYmvg1ASA5JxlFUdT54q/UAsKeg73fwOFfTCpuNBoNId7OhHg7071REIqicC0thxmbz3MqznD57cK1DOJSsnl98RF+2XOJXo2C8HCyo1M9f3xdHSz1qYQQQpiRFDei2DwdPAHI1eeSlZ+FsxkXYTNJw0cNxc3ZtXDtDPjVKdHLaDQa/N0d+bBvQ+O2nHwdc3ZEM23jWY7EJnMkNhkAT2c7HmtemUGtq1DT39Ucn0IIIYSFSMcCUWxOtk7Yae0AuJlz8x57W1DlVoYFN3U5sOljs760g60Nz3eowdY3OvJOj7p0axCIl7MdyZl5/LAjiq5TtzFz83my8+699pUQQgh1SHEjik2j0RDsGgzAivMr1AwCER8Y7p/fCPm5Zn+LAHdHRj1Ug1lDWrB7XGdmPdWCh2r7odMrTFl7hpYfb+DVhZHM2HSOX3ZHk5atwmSHQghhQdu2baN3794EBwej0WhYvnx5gecVRWH8+PEEBQXh5OREREQE584VnFcsKSmJwYMH4+7ujqenJyNHjiQ9Pd3i2aW4ESZ5semLAHx39DuiU6LVCxLYGJx9IC8DordZ9K0c7Wzo1jCQucNb8XqX2gR7OJKek8+yw1f4fN1Z3l9xggc+2cgX686QnGn+QksIIdSQkZFBkyZNmDlzZqHPT548menTpzNr1iz27t2Li4sLXbt2JTv79szwgwcP5sSJE6xfv55Vq1axbds2Ro0aZfHsMlpKmOyFDS+w/cp2+tfuz4TwCeoFWfUqHJgDTQbBI7NK7W31eoXDsTdZdzKB5Iw8tpxNJCHVMMTcwVbL4ufCaVzZs9TyCCHKrvIyWkqj0bBs2TL69esHGFptgoODee2113j99dcBSElJISAggLlz5zJw4EBOnTpF/fr12b9/Py1btgRgzZo19OjRg8uXLxMcHHzH+5hrtJS03AiTjWg4AoC10WvRK0Uvh2BR1doZ/rxeussraLUaWlT1Zlz3enzWvzFLX2hLo0oeAOTk6+kzYydNP1zHrvPXSzWXEMI6KIpCZl5mqd/M2ZYRFRVFfHw8ERERxm0eHh6EhYWxe/duAHbv3o2np6exsAGIiIhAq9Wyd+9es2UpjIyWEiZr6t8UJ1sn0nLTOHfzHHW8SzZa6b4FNzX8GRcJOengoM4opkqeTvzxUlu+2XqByWvOAJCcmceT3++lSYgnHev48WizylTxUWl0mRCiTMnKzyJsXlipv+/eJ/eabZRrfHw8AAEBAQW2BwQEGJ+Lj4/H39+/wPO2trZ4e3sb97EUabkRJrPT2tE6sDUAG2I2qBfEKxQ8qoA+H2J2q5cDQ5PtCx1qcv6/3dkwtj2tq3kDcCQ2makbzhHx5VZ+2BGlakYhhKgopOVGlEj30O5svbyVpeeWMqrRKOxs7Eo/hEYD1R+Cw7/ChU1Q6+HSz/AvtjZaavq7svA/D3DhWgaHLt1kycHL7ItO4qNVJzl4KYnw6j7U8HPF1dEWTyd7adERooJxsnVi75OWvSxzt/c1l8DAQAASEhIICrq9kHFCQgJNmzY17pOYmFjguPz8fJKSkozHW4oUN6JEHq76MP878D8SMxPZHLuZLtWKP1OwWVXvaChu9nwNVyPh8R/BzbL/aYpDo9FQ09+Vmv6uPNaiMg0mrCE7T8+fx+L581jB5thmVTwZE1GbuoFuONra4OGsQqEohCg1Go1GvUlQzSQ0NJTAwEA2btxoLGZSU1PZu3cvzz//PADh4eEkJydz8OBBWrRoAcCmTZvQ6/WEhVn2spwUN6JE7G3saVe5HUvPLeVCygX1gtTvZ2i1ifwNYnbB/+rAYz9Ao/7qZfoXG62GveMiWHcynvOJ6ZxPTOfi9Qxy8nQkpOVwOCaZYXP2GfefPqgZfZrcOYpACCFKU3p6OufPnzc+joqKIjIyEm9vb6pUqcKYMWP4+OOPqVWrFqGhobz//vsEBwcbR1TVq1ePbt268eyzzzJr1izy8vJ46aWXGDhwYKEjpcxJihtRYoHOfzdLZiSoF8LGFvp9DVXbwArDHDzs+QYaPma4bFVGeDjb8XjLkDu2xyZl8uzPBzifmE6+3jCS4eX5h6kX6EatALfSjimEEEYHDhygY8eOxsdjx44FYNiwYcydO5c333yTjIwMRo0aRXJyMg8++CBr1qwpMIT7t99+46WXXqJz585otVoee+wxpk+fbvHsMs+NKLFl55Yxftd42ga3ZdbDpTfPzF2lxcO0ppCfBU8ugtpd1U5kkriULMInbQKgc11/vnmqBfa20udfCGtWXua5KS0yz41QXaCLoeXmasZVlZP8zS0QWj9ruL/nG3WzlECQhxNLngsHYOPpRNpN3sT+6CSVUwkhhPWR4kaUWKhHKBo0RKVEEZkYqXYcg6ZPGv6M3g7rJ0CmdRUHLat58/Xg5gAkpObw+KzdPPPTAc4lpKmcTAghrIcUN6LEAl0C6VuzLwCT909Wd7biW/zqQt1ehrlvdk41dDA+u07tVCbp0SiI+c8+wBMtK2Oj1bDhVAJdp25j5ZEy0kImhBBlnBQ34r680vwVnG2dOXb9GH9F/aV2HEMn4gG/wqAF4BYEulyY9zjorGvV7vAaPkzu34S1Yx6iQx0/9AqMnn+YQzE3ORKbXOjtXEKaWadXF0IIayWjpcR98XXyZXC9wcw+Nputl7fSs3pPtSMZCpw63SGoKXxR17BtyQhoNgSqdwBbBzXTmaSmvyuT+zem9X83AvDo17uK3L9Pk2Am92+Mo51NacQTQhST/OJRPOY6T1LciPt2a22pczfPoSgKmrIyBNs9CHpPg5WvwKmVhpuDO9TqAh6VDJevQlqrnfKe/N0ceb5DDVYdvYr+Llf+8vV6ElJz+OPIVY5fSWHK401oUdWrdIMKIe5gZ2eYlDMzMxMnJ/PNEFxe5ebmAmBjc3+/oMlQcHHfbmbfpMuSLmTrsvmhyw+0DipjBcOl3XBiqaG4SYsr+NyDYyG0HYS2B611t3ZsPpPIW0uOkpiWg4OtluUvtqVekPwbF0JtcXFxJCcn4+/vj7Ozc9n5BbCM0ev1XL16FTs7O6pUqXLHeTLl+1uKG2EWH+z6gN/P/c5T9Z7irdZvqR2ncHo9XN4PZ1bDzmkFn3P2hVbPgIsvNBmk2grj9yslK49RPx9gb1QSVbyd6dk4iFr+rvRpEoytjXSxE0INiqIQHx9PcnKy2lHKPK1WS2hoKPb29nc8J8VNEaS4sYw/L/7JW9vfooFPAxb0WqB2nHtLT4Rz6+D8BjixrOBzbkHw2PdQ7UF1st2nE1dTePTrXeTk376G5elsh5OdDXY2Wvo0CaZdLV9stBoqezkT4O4gv0kKUQp0Oh15edY1uKG02dvbo9UW/ouYVRU3M2fOZMqUKcTHx9OkSRO++uorWre++2WNqVOn8s033xATE4Ovry/9+/dn0qRJxZ75UYoby4hLj6PL712w0djwY7cfaebfTO1IxXdiGUTvAEWBCxvhZjTYOBiWdShjyzgU1/ErKaw8cpXsPB0rj8aRlJF7133dHG2pHeBGnUA3nmxdhYaVPEoxqRBCFI/VFDcLFy5k6NChzJo1i7CwMKZOncrixYs5c+YM/v7+d+w/b948nn76aebMmUObNm04e/Ysw4cPZ+DAgXzxxRfFek8pbixn8OrBHL1+FA0ahtQfwivNX8He5s6mxTItLwt+fwZOrzI8DmgEbgGGS1Z1uqubrYQyc/O5eC0DgOgbGfy4M5qbmbnk6xSuJGeh0xf8EdCiqhdhod40DfE0jrrydLajUSUPaeERQqjGaoqbsLAwWrVqxYwZMwBDZ6KQkBBGjx7N22+/fcf+L730EqdOnWLjxo3Gba+99hp79+5lx44dxXpPKW4sJzU3lcn7JrPiwgoAGvs15ov2XxDgEqByMhPp8mHzf2HP15CfbdimtYXwFyF8NLj6qZvPjHLydURdz+BsQjobTyWwIvLuEwXW8nflgeo+PPVAVeoEyqKeQojSZRXFTW5uLs7OzixZssS4PDoYVhtNTk5mxYoVdxwzb948XnjhBdatW0fr1q25ePEiPXv2ZMiQIbzzzjuFvk9OTg45OTnGx6mpqYSEhEhxY0FbYrfw7o53Sc1NxcfRh196/EKI250rYpd5afEQtR12fAmJJwzb3ILgqaUQUF/dbBYSm5TJnos32BuVxOn4VPR6SMvJIzYpq8B+H/ZtwJAHqkpLjhCi1FhFcXP16lUqVarErl27CA8PN25/88032bp1K3v37i30uOnTp/P666+jKAr5+fk899xzfPPN3RdJ/OCDD5g4ceId26W4sazYtFhe3vQy55PPU8urFr92/xVnO2e1Y5WMXg9n18D68XDjHLhXgj5fQY1OVtkfpyT2XrzBnotJLNgfQ1yKoTWra4MAqvm4AKDVamgY7MGDNX3xcLZTM6oQopwqt8XNli1bGDhwIB9//DFhYWGcP3+eV155hWeffZb333+/0PeRlhv1JGQkMGDVAG5k36Brta5MeWiKdf+mn3IFZj0IWX8vxlmppWFElYsftBwB9i7q5isFer3CV5vOM23jWfSF/OTQaqBNDV8m9m1ADT/rHE4vhCibrKK4KcllqXbt2vHAAw8wZcoU47Zff/2VUaNGkZ6eftfhY/8kfW5K1+HEwzy99mny9fm8/8D7PFHnCbUj3Z/Uq7BzOhz88XZ/HACfWjB0hWHm4wpgX1QSG08loP/7x0dmro69UUmcT0wHwNFOy8Q+DXiiZYh1F7RCiDLDlO9v1Wb1sre3p0WLFgU6B+v1ejZu3FigJeefMjMz7yhgbk3RXMGm67Eazfyb8XKzlwFYdGaRymnMwD0Yun8KY45B5/HQciTYuxouV31ZHw7/pnbCUtE61JtxPerxbs/6vNuzPv99pBEbxrZn6xsdCAv1JjtPz1u/H+Ot34+S+4/5doQQojSoOmXp2LFjmT17Nj/99BOnTp3i+eefJyMjgxEjRgAwdOhQxo0bZ9y/d+/efPPNNyxYsICoqCjWr1/P+++/T+/eve97HQphOY/UfARbjS1nbp7hYvJFteOYh6s/tHsNen0Bj8y6vX3FC3B8qXq5VFbVx4Wfnm7NUw9UAWDRgcv0nL6d+JTsexwphBDmo2pxM2DAAD7//HPGjx9P06ZNiYyMZM2aNQQEGIYOx8TEEBd3ey2g9957j9dee4333nuP+vXrM3LkSLp27cq3336r1kcQxeDp6El4sKE17q/ov1ROYwH1esMLe24/XjIC/lcPrhxUL5OKHO1s+LhfI34c3gobrYZziek8MGkjcSlZ9z5YCCHMQPUZikub9LlRx8oLK3lnxztUda/K8r7LsdWWwwXpz62H3/rffuxRBfpMg9AOUIz+YOXR99sv8vHqU8bHzap40r62H8+1r2GcIFAIIYrDKjoUq0WKG3Vk5GXQcVFHsvKzaFepHZ+3/9x6h4YXJT8HLu2CeU+A7u8lD7yrQ6f3DEs5VEDrTsQz6peCrVhOdjZ8/VRzKns6UStAJgQUQtybFDdFkOJGPZtiNvHWtrfI1mXTtVpXPm//udqRLCfuiGHl8XPrISfVsK3H59D6WXVzqeRcQhqXbmRyLT2HcUuPFXhuYKsQ3ulZD3dHmR9HCHF3UtwUQYobde2+uptR60fhZOvEjoE7rG/tKVPlpMPGibDvO8Pj53ZAYCN1M6nsu20X+OPIVY5fSTVu83S249FmlXmvZz20Whk6LoS4kxQ3RZDiRl16RU+nRZ24kX2DbtW68Wm7T7HRlvO+F7o8+Mj39mOPKhDSCpx9/p4AcCS4+KiXTyWJadl8vz2K5YevkJh2e6JNRzsttlotQ8Kr8niLygWOcXW0xd/NsbSjCiHKACluiiDFjfp2XtnJS5teKj8T+xXHufWGFpyEE6D8a94XRw/oP8fQ8VijrXCdj9Oy8/ho1UkWHbhcrP3nDG9Jp7pWthirEOK+SXFTBCluyoZZR2YxM3ImnUI6Ma3TNLXjlJ6cNMMQ8SsHITcTTv0B18/eft7WCZ74GWp3US+jSq6n55Cdp0NRYOLKk+yLulHg+dTsfAA61fVnzvBWakQUQqhIipsiSHFTNhyIP8CItSOo5FqJNY+tUTuOerKS4dt2kBxze5udC4yLhfJ+uc5Eu85f58nvDWvOTR/UjC71A2Q4uRAViCnf3+VwshFhDSq7GfpSxGfEczntsvFxhePkCaMPQ24aRO+EhYMhLwNOroCGj6qdrkyp7ueKVgN6BV6efxhXB1t8XAt2SK/s5cRHfRtSXRbtFKJCq1gX90WZEeAcQIuAFugUHeN3jVc7jrpsbMHJC+r0MFyWAji6CCpWo+o9BXo4svA/4bzQoQaVPJ1Iz8nn0o3MAred52/Qd+ZOft1ziZNXUzl5NZXsPJ3a0YUQpUwuSwnVxKbF0mNpDwB2DNyBh4OHyonKgMRT8HU4oMCDYw2Lc8qq2nfQ6xVOxaeSnXe7c3a+Ts/ktWc4eOnmHft/M7g5VXycaRAs/8aEsFbS56YIUtyULe0XticpO4nGfo15u9XbNPKr2HPAALB1Mmz+r+F+k0HQ839g76JuJiuRr9Pz8+5L/LQ7mtSsPG5m5hV4fnibaox6qDrBnk4qJRRClJQUN0WQ4qZs+f3s73y2/zOy8g2LKr4T9g6D6g5SOZXKcjNgwZNwcYvhsV89wwgqv9qqxrJG32+/yNoT8eTpFCJjk43bm1Xx5KmwqjzWooL29RLCCklxUwQpbsqexMxEPt//OX9F/0UVtyqsfnS12pHKhqjt8PtISE8wjKB6aglUbaN2Kqu16XQC32y5wIFLN43dmcKr+xDs6USral48VNtPWnSEKMOkuCmCFDdlU0pOCu0WtENBYfMTm/F18r33QRVBWgIsGQGXdkL1jjB0udqJrF5CajYPTd5MTr7+judq+Lng4+pgfGyr1dA61JvuDYOoHeCKRvo/CaEaKW6KIMVN2fXIikc4n3yeLzt8SUTVCLXjlB0xe2BOV8P9vl+Dw11W0baxh+rtwU5aH+7l0o0Mtpy5hqIoJGXmsePcNSJjk9EX8dOwdoArXw9uTk1/WcVcCDXIPDfCKtX3qc/55PNcTLmodpSyJbj57fsrXih634b94bHvZYTVPVT1cWFYm9udtMc+XJuUzDwOxiQVGIGVkpXHxlMJbDiVyNmEdCK+2EZ1XxeGt63G4LCq2Mgin0KUSdJyI8qM7499z7RD03C3d+fTdp/SrnI7tSOVHSeWw77ZoNxlzpbsVEg8Ybgf/hI8/FGFW6PKki5cS6fvjJ2k5+Qbt7k62BYoboI8HOnbtBKPNKtEoIcs7imEucllqSJIcVN2peSk8MKGFzh6/SgAb7Z6kyH1h6icyors+QbWvG2433wo9PlK3TzlzI30HOJSsjkQncTn684WKHT+SauBga2r8EHvBtjbSoEphLlIcVMEKW7Ktuz8bAauGsiFlAs09mvMbz1+UzuSdYmcb7h0pejhqd+hpvRdsoSMnHziU7ONjxVF4UD0TX4/dJn90YZJBF0dbLGzKfqyVcNKHkwf2AwvF/si9xNCSHFTJCluyr5LqZfotawXDjYOHHjqgNpxrM/KMXDwR6gSDt0+Be9QcJSZeUvLljOJjJ5/mLTswlt2/q1DHT/mjmht4VRCWD/pUCysmpejFwA5uhxydbnY28hvtSZpPMBQ3MTshu/ag4O7YRkH92CwdYCqD4Kd9AmxlA51/NkzrjNXk7OK3C8hNYfhP+5jy5lrzN8Xw8BWITLUXAgzkZYbUebo9Dqa/tIUgC1PbMHHyUfdQNZo32zY9RXkpEFWUsHn/OtDy6dBa2OY/Ti4mRQ7Knnht4P8eSwegOp+LvgUcnnKyd6WCb3rU0NWOhcVnFyWKoIUN9ah8+LOJGYm8mm7T+lZvafacayXLg92fAnn1gMK3DgPWf9aWNLG3lDgVAmHsOfAPUiVqBVRek4+3229wA87osjIvfvq5dV8nGlU2dP42NPJjuf+Xh1diIpCipsiSHFjHb47+h1fHf6Ket71WNR7kdpxyo+0eNj2uWFJh/wcuHoYMhJvP+/sC53ehXp9wUVazEpLUkYuB6KT0P/rx3GuTmHc70cLLXw8ne2YPrAZD9X2K62YQqhKipsiSHFjHW5m36TTok7kK/ms6LuC6p7V1Y5UPikKJF2E2L2w52uIP2bYrrGBKg8Y+uuAYdbjzu+Dt/w9lLbjV1LYF3X70qICrIi8wtHLKTjb27D59Q4EuMtlRVH+SXFTBClurMdz659j59WdvN7ydYY1GKZ2nPIvLxv2zoLjv0P80cL3CXkA2oyGer1KN5soICdfx4Bv9xAZm0znuv6M7VK8FeO1Gg21/F2xtZH5d4T1keKmCFLcWI85x+fw5cEvsdHY8Fitx3ih6QvSubi03LhgWNNK0UHKFdj6acHnZQ4d1Z28mkq/mTvJ1d25AGhRejQK5OvBLSyUSgjLkeKmCFLcWI/MvEze3fEuG2I2AODt6M3SPkulwFHD9fOQeBKWPQd5GWDrBMNXQeWWaier0BYfiGXqhnPk6+9d4OgVuJaWA0CbGj7MeLI53jJ5oLAiUtwUQYob63Mg/gDv7XyPK+lX5BKV2jKTYP5AQx8drR3YOxu2a2wg9CFo9hTU6GQYZi7KFEVR6PD5Fi7dyASgT5NgHqh++xcFe1stnev6y2zJosyS4qYIUtxYp0VnFvHRno9ws3Pjt56/EeoRqnakiisnHX591FDgFMYrFJ5cBH7F6wciSs/19Bw+WnWSFZFXC33ew8mOVyNq8dQDVaVfjihzpLgpghQ31ilXl8sz657hcOJhanvVZnHvxWg18sNXNbdGWSl/Xw7JSobjS+DoQsM8Oi5+8Oxm8AxRNaa4U06+jkl/nubKv2ZQjrqewfnEdABah3oz/9kHCqx6LoTapLgpghQ31ut61nV6L+tNel46UztOpXOVzmpHEv+WcQN+7gMJx8HWEUZtBf+6aqcSxZCv07NgfywfrjpJbr6etWMeok6gm9qxhDAy5fvb5F99J0yYwKVLl0ocToiS8nXy5ZFajwCw/fJ2ldOIQrn4wOM/Gfrj5GfDb49DWoLaqUQx2NpoeeqBqtTyNyzzcDo+VeVEQpScycXNihUrqFGjBp07d2bevHnk5ORYIpcQharlWQuAqJQolZOIu/KtCa8eN/S9SYmBeY+DrngrZAv1dazjD8CSg5dVTiJEyZXostThw4f58ccfmT9/Pvn5+QwcOJCnn36aVq1aWSKjWcllKesWmxZLj6U90Gq0bOi/AT9nmXq+zLp+Hma2NsyV4+wDr56UBTqtQGxSJu0mbwbgja51sC9hx+J2tX2pGyg/Y4X5lFqfm7y8PFauXMmPP/7I2rVrqVu3LiNHjmT48OF4eHiU9GUtSoob6zdw1UBO3DjBfx/8L31q9FE7jijK6tdh/2zD/Q7joPUocPZWN5O4pyE/7GX7uev39Rr2tlq+G9KCDn+3BAlxv0z5/ra9nzdSFIW8vDxyc3NRFAUvLy9mzJjB+++/z+zZsxkwYMD9vLwQhWoZ0JITN05w9NpRKW7Kup5/L9J56g/YMslwq9bO0C9HFuYssyb0bsD32y+Sm2/a7Me3RN/I4FBMMqN+OcikRxpRxce50P2q+7rg4+pwP1GFKFSJipuDBw8aL0s5ODgwdOhQZs6cSc2aNQH46quvePnll6W4ERZRzaMaAFfTC5+rQ5QxfWfA9bNw7bThcfR2+LEbPLMR7JzB5r5+xxIWUNPflU8fa1zi43Pz9bw07xDrTibw2uIjd93Pyc6G6YOa8XD9gBK/lxCFMfmyVKNGjTh9+jRdunTh2WefpXfv3tjYFJyN9Pr16/j7+6MvxpTgpU0uS1m/XVd28Z8N/6GmZ02W9V2mdhxhisRT8PUDtx87+8LzO8EtUL1MwiJy8/V8vPokO+5yeSszV0d8ajZaDUzs04Ah4dVKN6CwOha9LPXEE0/w9NNPU6lSpbvu4+vrWyYLG1E+hLgZJoa7lHqJ1NxU3O2lSLUa/vWg4WOGlccBMq/D/+rAgF+hXm91swmzsrfV8mHfhnd9Pk+n5/3lx1mwP5b3V5zg4vUMJvRuUIoJRXlmcjf4W31r/i0rK4sPP/zQLKGEKEplt8rU9KxJnj6PjZc2qh1HmKr/HHg3Afp+fXvbwqdgdifIy1YvlyhVdjZaJj3aiGceNCyl8tueGCrYnLLCgkwubiZOnEh6evod2zMzM5k4caJZQglRFI1GQ8eQjgDsj9+vchpRInaO0GwwjLsMjR43bLtyEP4bAJ+Fwue1IXK+uhmFxWk0GsZ2MaxBlqvTczMzT+VEorww+bKUoihoNHeuN3LkyBG8vWWIpygdTf2bAnD65ml1g4j74+AGj30PAQ1hwwTDtqwkw5/Ln4OdU4F//Lxx9IBHvgHv6qWdVFiIk50NzvY2ZObqmPDHCb4a1EztSKIcKHZx4+XlhUajQaPRULt27QIFjk6nIz09neeee84iIYX4t0quhj5f8enxKicRZvHgGENfnLxM0OXB3B6QnXJ7hNU/7fseun1S6hGFZWg0Gt7oWoeJK0+y8shVHmteSebGEfet2MXN1KlTURSFp59+mokTJxaYpM/e3p5q1aoRHh5ukZBC/FuQSxAAaXlppOWm4WYvC/xZvX+uIP5ypGHxzX86thgO/QxXDpRqLGF5I9qGculGJnN3RTNu6TG2vNEBB1ubex8oxF0Uu7gZNmwYAKGhobRp0wY7OzuLhRLiXpztnPFy8OJmzk2upl+ljncdtSMJc3L2htCHCm7T2BiKm9i9kBwDnlXUySYs4s1udfj90GXiUrK5kJhB/WAZBSlKrljFTWpqqnFMebNmzcjKyiIrK6vQfWXuGFFaKrlW4mbOTaJSoqS4qQjcg2/fn94cmj5p6KvzTz41oEYnKKRfoCjbnO1t8XdzIC07n9XHrnIqzrAqubeLPQ/V9sNGK3+noviKVdx4eXkRFxeHv78/np6ehXYovtXRWKfTmT2kEIVp7NeY4zeOE3ktkm6h3dSOIyzNOxT6zoTIeXBpJxz6qfD9Or0PD71eutmEWfi4OHDhWgYzN18osP3bIS3o2kAmehTFV6ziZtOmTcaRUJs3b7ZoICGKq6p7VQCuZV5TOYkoNc2eMtwu7YLDv0Fexu3ncjPg3DrY9JGhM/KtIea+tcDOSZ28wiQvd67FjzujyNcb5rs5HZ9KQmoOMTcyVU4mrE2xipv27dsXel8INd3qRJyWm6ZyElHqqrYx3P5t62TY/F/YNd1wA/AKNSzxYOsEWpOn9hKl6MFavjxYy9f4ePyK4/y8+xJp2TL/jTBNsYqbo0ePFvsFGzcu+WJrQpji1rILyTnJ6gYRZUf7Nw39brZ8CumJkJ0MN6Pgk2DwqAIDf4Mg+RllLTyd7QFITMtROYmwNsUqbpo2bYpGo7nn1NjS50aUpgAXw0rCCZkJKicRZUrDxww3gHkD4exfhvspMfBTb0N/nGZDwMlTtYiieOoEGFpnT8VL66wwTbGKm6ioKEvnEMJkwa6G0TNJ2Ulk5mXibOesciJR5gyab5gYMDcDFjwJl/fDuvdg8yQIfwE6vad2QlGEekGG4uZ0XCpZuTqc7GXuG1E8xSpuqlataukcQpjM3d4dNzs30vLSiM+Ip7qnTMkv/kWjAXsXw234aji6EPbMgsQTsG0KpMWBVzWo3R0C776CtVBHNR8XgjwciUvJ5sNVJ5n0aCO1IwkrUazi5o8//qB79+7Y2dnxxx9/FLlvnz59zBJMiOIIdg3mzM0zXEm/IsWNKJqtAzQfargk9XktyLgGh381PLfpYwhsBI0HFJwcUGsH1R4ER5m/Sw1arYYp/ZswZM5e5u+LoW1NH3o1Dr73gaLCK1Zx069fP+Lj4/H396dfv3533U/63IjSFuQaxJmbZ7iaflXtKMJaaDQw4Dc4sRQUBVIuG4aQxx8z3P7N3hWaDILWo8CvdunnreAerOXLCx1qMHPzBcYtPUaHOv64Opi85rOoYIr1L0Sv1xd6Xwi13VpA80rGFZWTCKtSJcxwuyUzyVDsnP4T8v4x+3panGG01f7ZcOAHeHIR1Hq49PNWcK9G1Gb54atcSc7iQHSSLKwp7knKX2HVgl0MTdRx6XEqJxFWzdkbWj1juP2TokDUVtj2OURvh8XDwbMqdBwH9XqrErUisrXREl7DhyUHL7P88BXydLdH7tYOcKWqj4uK6URZVKLiZuPGjXz55ZecOnUKgHr16jFmzBgiIiLMGk6Ie7nVcrM/fj8XUy5S3UP63Qgz0migegfwqwtftYTcNENn5IVPQf1+4OB6e1+vUGj5tKFQEmbXupq3obiJvMryyNuXoW20GkY+GMqYiFo428vv68JAo9xr8pp/+frrr3nllVfo378/4eHhAOzZs4clS5bw5Zdf8uKLL1okqLmkpqbi4eFBSkqKLPJZDmTmZfL4yseJSYvBw8GDpX2W4u8sTdbCAtITITnWMOJq37eF72PnAq1GQtsx4OJTqvHKu/ScfF5fdISEtGzjtuw8vXGBzcpeTnzUryEd5ZJVuWXK97fJxU3lypV5++23eemllwpsnzlzJp988glXrpTtvg9S3JQ/SdlJDPtrGNGp0fg5+bGkzxK8HeW3Z2FB5zZA/D9mbld0cHLF7Q7J3jXg6bXg6qdOvgpk0+kE3l9+givJhr5SvRoHMb53ffzdHFVOJszNosWNq6srkZGR1KxZs8D2c+fO0axZM9LT001PXIqkuCmfpuyfws8nfwZgVONRjG42WuVEosJRFMOoq9WvQUosoAHNPday8qsDI/4EJ69SiVheZeTk8+X6s8zZGYVeAXdHW8Jr+KBBU+pZHqrtx5NhVe69ozCZKd/fJl+g7NOnD8uWLeONN94osH3FihX06tXL1Jdj5syZTJkyhfj4eJo0acJXX31F69at77p/cnIy7777LkuXLiUpKYmqVasydepUevToYfJ7i/Lj6YZPs/LCSm7m3CQqRWbUFirQaKB2V/CuDj/3g9TLhhadoiSehA0fQO9ppZGw3HJxsOW9XvXp16wS45Ye49iVFNaeUGdZlrUn4+lY148gD1mJXk3FKm6mT59uvF+/fn3++9//smXLlgJ9bnbu3Mlrr71m0psvXLiQsWPHMmvWLMLCwpg6dSpdu3blzJkz+Pvfed00NzeXhx9+GH9/f5YsWUKlSpW4dOkSnp6eJr2vKH98nHz4vP3njFw3ksjESBRFQaMp/d/ahMC3FrwSCRnXi94v/ijMewIO/QIPfyQTBZpBw0oeLHuhDRtOJXA9PbfU33/e3hhOxqWy9NAVXuxY894HCIsp1mWp0NDQ4r2YRsPFixeL/eZhYWG0atWKGTNmAIY5dEJCQhg9ejRvv/32HfvPmjWLKVOmcPr0aezs7Ir1Hjk5OeTk3F5RNjU1lZCQELksVQ7l6HJoM68Nufpc/uj3B6Eexft3K4RqpjWBm9HQ4BHo+YWMtLJyiw/E8saSo4T6urDptfbyC5aZmf2ylCUWzszNzeXgwYOMGzfOuE2r1RIREcHu3bsLPeaPP/4gPDycF198kRUrVuDn58eTTz7JW2+9hY1N4QuqTZo0iYkTJ5o9vyh7HGwcaOLfhP3x+9kfv1+KG1H2PfQG/DEaTiwzdFIurO+NvQs88g0ENyv9fMIkPRoFMeGPE0Rdz+DgpZu0rCbFqlru0dvNcq5fv45OpyMgIKDA9oCAAOLj4ws95uLFiyxZsgSdTseff/7J+++/z//+9z8+/vjju77PuHHjSElJMd5iY2PN+jlE2dIqoBUABxIOqJxEiGJo9hSMXA8+tQxz6KTE3Hm7dgpWvQq7v759O7oY8kv/sosomouDLT0aBQGw5OBlldNUbCWa8ejy5cv88ccfxMTEkJtb8D/YF198YZZghdHr9fj7+/Pdd99hY2NDixYtuHLlClOmTGHChAmFHuPg4ICDg4PFMomypZ5PPQCiU6LVDSJEcVVuCS/shoQTd3ZAzk6B3x6Hq4cNt386vgSe+NmwIKgoM/q3qMySg5dZdTSO8b3ry8SCKjH5rG/cuJE+ffpQvXp1Tp8+TcOGDYmOjkZRFJo3b17s1/H19cXGxoaEhII92hMSEggMDCz0mKCgIOzs7ApcgqpXrx7x8fHk5uZib29v6scR5Yxxran0sj3fkhAF2NhBcNPCn+v3jWGI+S2KAqdXwdk1sPRZQ4EjyozW1byp4u1MTFImrT7egI3WtH43NloNrz5cm6Hh1SwTsIIw+bLUuHHjeP311zl27BiOjo78/vvvxMbG0r59ex5//PFiv469vT0tWrRg48aNxm16vZ6NGzcaR2H9W9u2bTl//nyBxTvPnj1LUFCQFDYCMBQ3tlpbUnNTmXt8rtpxhLh/jZ+Ax76/fev/AwxeDFpbw8SB/27REarSajUMb1MNgIxcHanZ+SbdbmbmsfSQ/HJ2v0yexM/NzY3IyEhq1KiBl5cXO3bsoEGDBhw5coS+ffsSHR1d7NdauHAhw4YN49tvv6V169ZMnTqVRYsWcfr0aQICAhg6dCiVKlVi0qRJAMTGxtKgQQOGDRvG6NGjOXfuHE8//TQvv/wy7777brHeUybxK/++P/Y90w4Z5g15J+wdBtUdpHIiISzg92fh2CKo3Q2eXKh2GvEvV5KzyM67xzxH/3Lo0k3eWHKUuoFurBnzkIWSWS+LTuLn4uJi7GcTFBTEhQsXaNCgAWDoJGyKAQMGcO3aNcaPH098fDxNmzZlzZo1xk7GMTExaLW3G5dCQkJYu3Ytr776Ko0bN6ZSpUq88sorvPXWW6Z+DFGOjWw4koy8DL4/9j2f7P2EQOdAOlbpqHYsIcyr/ZtwYqnh8tSHvsU7RqOFB181rGouLKqSp+mT+CVnGr5bs0wsisSdTG656devHz179uTZZ5/l9ddfZ8WKFQwfPpylS5fi5eXFhg0bLJXVLKTlpmJQFIXxu8az/PxyKrlWYs1ja9SOJIT5bZgIO0owiGPMMfCUJQLKmhNXU+g5fQf+bg7sezdC7ThljkVbbr744gvj+lETJ04kPT2dhQsXUqtWLYuOlBLCFBqNhg4hHVh+fjlX0q+Qp8vDzqZ4Ez8KYTUiJsADL4A+79775mfD9OaAAlMbwTMbDSO1RJnhZGcYLCMtN/fP5OKmevXqxvsuLi7MmjXLrIGEMJf2ldsb7x+/cZxm/jIJmiiHTFl5PPxF2G2YEZ6tkw1rYd1i6wh1e8ginipysjcUN6b21RF3KvEA/AMHDnDq1CnAsN5UixYtzBZKCHOw1drStVpX1kavZf6p+dT1rouTrSxmJyqwrv81tNYsHg7n1hpu/7S5MvSfA1XCVIlX0d1qucnTKeTp9NjZqDbPrtUzubi5fPkygwYNYufOncYFK5OTk2nTpg0LFiygcuXK5s4oRIn1CO3B2ui1/BX9F8dvHOeD8A9oHXT3VeeFKPfq9oKw5yHlX7O1xx+F5Bj4sTt0+RjCX1AnXwXmaHd7DreDl27ibF/4skKFqezljLeLTIlyi8kdirt160ZycjI//fQTderUAeDMmTOMGDECd3d31qwp2x03pUNxxbMldgsf7fmIxMxEAB6r9RjtKrejmX8zvB1l7RchAMhJMyzzcGyxYVTV2FPgVviEqsIyFEWhxjt/ojfpW9nA2d6GXW93wtO5/BY4pnx/m1zcODk5sWvXLpo1K9h/4eDBg7Rr147MzEzTE5ciKW4qpvTcdKYemsrCM7fnA2ns15jfevymYiohyhhFgS8bQupl6XCsko9WnWTN8cLXV7ybhNRs8vUKS19oQ/Mq5bfPlEVHS4WEhJCXd2fPfJ1OR3BwsKkvJ0SpcLV35b0H3qNHaA8+2vMR55PPc/LGSZadW0avGr2w08pIKiHQaMCjkqG4uXZaihsVvN+rPu/3qm/SMX1m7ODo5RSS0mUx1VtM7q00ZcoURo8ezYEDt1ddPnDgAK+88gqff/65WcMJYW7NA5qzpPcSqrpXJV+fz/hd4+m1tBeHEg6pHU2IsqFGJ8Ofp/9UN4cotlt9bZIypLi5pVgtN15eXmg0txf/ysjIICwsDFtbw+H5+fnY2try9NNP069fP4sEFcJcbLQ2LOq1iEVnFjH3xFyuZlxlwq4JvNj0RYJdg2nk26jAv3chKpS6PWHLJLiwEXIzwN5F7UTiHm4VNzekuDEqVnEzdepUC8cQonQ52zkzvOFwetXoRcdFHYlOjeaNbW8AMKb5GEY2GqlyQiFUEtAQPKtC8iX4qoVhxfKSaPAoRHxguNQlLMrH2HKTo3KSsqNYxc2wYcMsnUMIVfg6+TKm+Rh2Xt1Jri6XI9eOMPXQVKq5V6Nz1c5qxxOi9Gk00GQQbP0U0uJK/jo7pxomBHxwjLmSibvwkpabO5RoEj+dTsfy5cuNk/g1aNCAPn36YGNT/DH5QpQVIxuNNLbUfLbvM3499Ssf7fmIloEt8XDwUDmdECpo/xbU6wX5JWwJiNoGGycabr61794x2dkHtPK9cb9utdzclOLGyOTi5vz58/To0YMrV64Y57mZNGkSISEhrF69mho1apg9pBCl5dUWr7Lz6k6iUqKYe2IurzR/Re1IQpQ+rRYCG5X8+Eot4MYFiPwVFgy6+35eoTB0OXhVK/l7CbxdHADpUPxPJo+Wevnll6lRowaxsbEcOnSIQ4cOERMTQ2hoKC+//LIlMgpRauxt7BnTfAwAP5/4me2Xt6NX9OqGEsLaaDTQ839Qp0fR+92Mgl8egfRrpZOrnJIOxXcyeRI/FxcX9uzZQ6NGBav6I0eO0LZtW+OK4WWVTOIn7kVRFMZuGcuGmA0AaDVaXmz6IqMaj1I5mRDlSGoczOliWPIhoCEMXw1OnmqnskrR1zPo8PkWnO1tOPlhN7XjWIwp398mt9w4ODiQlpZ2x/b09HTs7cvvtM+i4tBoNEx+aDI9q/cEQK/o+fH4j6TkpKicTIhyxD0IhiwHZ19IOA6/Pqp2Iqvl7Wr47s3M1cmK4n8zubjp1asXo0aNYu/evSiKgqIo7Nmzh+eee44+ffpYIqMQpc7Oxo5P233KrkG7CHELIT0vneFrhhOXfh+jR4QQBfnUgH5fG+5fjTQs/yBM5uZgi52NYci9XJoyMLm4mT59OjVq1CA8PBxHR0ccHR1p27YtNWvWZNq0aZbIKIRq3Ozd+KLDF/g5+XE++TwvbXqJPP2dy48IIUqoalvDn4oO8sr22oRllUajwevvBTNlCQYDk0ZLKYpCamoqCxYs4MqVK8ah4PXq1aNmzZoWCSiE2up61+W3Hr8xYNUAzt48y/xT8xnaYKjasYQoH+xdDKuQK3o4/BuESd+2kvB2sScxLYfP153B381B7ThU9XHmpU61VHt/k4ubmjVrcuLECWrVqiUFjagwglyDeL7p83yy9xP+ivqLIfWHyBINQpiDRmNovYnebpj4r/kQsHNSO5XVqeLtzOn4NLaeLRsjz5pX8bSe4kar1VKrVi1u3LhBrVrqhRZCDR1DOvLZvs84fuM443aM44PwD3C0dVQ7lhDWb/ASw1IPqZdh77cyq3EJTOjTgJbVvMjXl41+S4Hu6v5sNHko+MqVK5k8eTLffPMNDRs2tFQui5Gh4OJ+LDu3jAm7JqCg0Ny/OR+1/QgPBw+ZyViI+xU5D5Y/D44e8OpJcHBVO5EoY0z5/ja5uPHy8iIzM5P8/Hzs7e1xcirYfJiUlGR64lIkxY24X5tiNvHWtrfI1mUDYKOx4euIr2kT3EblZEJYMb0OptSArJvw3I77myFZlEumfH+bvPyCrBAuKrpOVTrxU/efeHv728SmxZKvz+eDXR+wot8KnGylr4AQJaK1ASdvQ3GTU7YngxVln8ktN9ZOWm6EOWXmZdJ7WW8SsxJpHdiaLzp8IZeohCipb9tDXCS4+IPd3302nH3g8Z/Aq6qq0YT6LDpDMRhWBV+yZAkfffQRH330Eb///jv5+fklCiuENXO2c6ZDSAcA9sXvY+6JuarmEcKqBf7djzMj0bAsQ3IMXD0M59apm0tYHZMvS504cYI+ffoQHx9vXBX8s88+w8/Pj5UrV1plJ2Mh7seoxqP448IfZOuyuZB8Qe04QlivXtOg1bOG/jcA2ybD2TWQK5ephGlMbrl55plnaNCgAZcvXzauCh4bG0vjxo0ZNUomXxIVT4BLAP/r8D8ANsdu5tsj36qcSAgrZWMLwU2hcgvDzbOKYbv0wREmMrnlJjIykgMHDuDl5WXc5uXlxX//+19atWpl1nBCWIt63vWM92dEzkCr0TK43mCc7ZxVTCWElXP8u/9aVtkehSvKHpNbbmrXrk1CQsId2xMTE2XGYlFh+Tn78dejfxkfTz88nQVnFqiYSIhywKua4c8bcrlXmMbk4mbSpEm8/PLLLFmyhMuXL3P58mWWLFnCmDFj+Oyzz0hNTTXehKhIKrtVZmbnmTxY6UEAtl/ernIiIaycb23DnzfOq5tDWB2Th4JrtbfroVtr69x6iX8+1mg06HQ6c+U0GxkKLiztSvoVei7tiU7R0a1aNya2mSiXp4QoicwkmBxquD/mOHiGqJtHqMqik/ht3ry5xMGEqAgquVaiX81+/H7ud9ZEr+F00mk+b/85dbzrqB1NCOvi7G1YVPPSTljzNgz8Te1EwkrIJH5CWECuLpd1l9Yx9eBUEjIT8HfyZ0W/Fbjay3o5Qpgk4QTMageKDp5cBLW7qp1IqMTik/gJIYpmb2NPr+q9WNJ7CVXcqpCYlcj3x75XO5YQ1iegAYS/YLj/5xugy1M3j7AKUtwIYUGejp6Mbj4agB+O/4Be0aucSAgr1P5tw7Dw5EsQf0ztNMIKSHEjhIWFB4Ub75+4fkLFJEJYKQdXCG5uuB8XqWoUYR1M7lAshDCNh4MH3o7eJGUn8cLGF+hTow+P1X6M6h7V1Y4mhPUIbgoXN8PhXyH16u3tQU2hbk/4e7SuECAditWOIyqIbZe38eHuD0nIvD0BplajJcgliM8e+owmfk1UTCeEFTi1ChYOLvy5Rk9Ary/Awa10M4lSZcr3d7GKm2bNmhnnsLmXQ4cOFS+lSqS4EWrR6XXsvLqTxWcXs+3yNmP/G1c7V37o+gP1feqrnFCIMkyvg53TIC3+9rbcdDiywDCSyqcmPD4XAhupFlFYltmLm4kTJxb7zSdMmFDsfdUgxY0oCzLyMkjLTWPc9nEcSDhAy4CWjGw0Ehc7Fxr7NsZGa6N2RCGsQ8weWPI0pF4BGwfo/im0GCGXqcohsxc35YkUN6IsOXbtGE/++WSBbe+FvceAugNUSiSEFcpMgmXPwbm1hscNHoXwl0CN+sanFjjKd4slWHSGYiGE+TTwbcBjtR7j5I2T3Mi+QWJmIsdvHGcAUtwIUWzO3jBoAeyeARsnwomlhpsavEJh9EGQ1ldVmdxyo9Pp+PLLL1m0aBExMTHk5uYWeD4pqWwvTS8tN6KsWnlhJe/seAeAA08dwMHGQeVEQlih2H2wZhykJ5b+e6fFgT4Phv8J1dqW/vuXcxZtuZk4cSLff/89r732Gu+99x7vvvsu0dHRLF++nPHjx5c4tBAV3T+Hhq+/tJ5e1XupmEYIKxXSGp7dqM57L3sejsyDU39IcaMykyfx++2335g9ezavvfYatra2DBo0iO+//57x48ezZ88eS2QUokKo71MfVzvD2lPRKdHqhhFCmK5eb8Ofp1ZCxerOWuaYXNzEx8fTqJFhqJ2rqyspKSkA9OrVi9WrV5s3nRAViEaj4eXmLwMQeS1S3TBCCNPV6AR2LoaRW1fK9rQo5Z3JxU3lypWJi4sDoEaNGqxbtw6A/fv34+AgfQSEuB+3lmrYH7+fiykXVU4jhDCJnSPU7mK4f+oPdbNUcCYXN4888ggbNxquZ44ePZr333+fWrVqMXToUJ5++mmzBxSiIqnmUY2OIR3RK3rmHJujdhwhhKnq9TH8eeoPuTSlovue52bPnj3s2rWLWrVq0bt3b3PlshgZLSXKurXRa3l96+sATGk/hW7VuqmcSAhRbDnpMLk66HKg5xfg6m/a8RobqPagzJVTCItO4pednY2jo+N9BVSTFDeirEvPTafvir4kZhqGsv7U7SeaBzRXOZUQotjmD4Izf5b8+AaPGJaSEAVYdCi4v78/jzzyCE899RSdO3dGqzX5ypYQogiu9q6se2wdz6x7hgMJB3h1y6ss7r0Yf2cTfwMUQqij/ZuQmwH52aYdl5UM18/AzWhLpKpQTG65WbZsGfPmzWP16tV4eHgwYMAAnnrqKVq2bGmpjGYlLTfCWmTmZfLEqie4lHqJFgEt+L7L99hqZVJxIcqti1vg577gXx9e2K12mjLHlO/vEnUoXrx4MQkJCXzyySecPHmSBx54gNq1a/Phhx+WOLQQoiBnO2dmdp6Js60zBxMO8tup39SOJISwJFsnw595WermKAdKfE3Jzc2NESNGsG7dOo4ePYqLi4tJq4cLIe6tqntVnmvyHAAHEg6onEYIYVF2f/dnNfVylrhDiYub7OxsFi1aRL9+/WjevDlJSUm88cYb5swmhABqetYEYOeVnSonEUJYlK0UN+Zi8gX8tWvXMm/ePJYvX46trS39+/dn3bp1PPTQQ5bIJ0SFF+IWAkCePo/LaZfxdvQudD9brS32NvalGU0IYU63ips8KW7ul8nFzSOPPEKvXr34+eef6dGjB3Z2dpbIJYT4W1X3qsb73Zd2v+t+Wo2Wd8Pe5Yk6T5RGLCGEudn93ecmP8swAaBGo24eK2ZycZOQkICbm5slsgghCqHRaBjRcAQ/Hv+xyP30ip4fj/9I/9r90WpkigYhrI7tP5Yw0uUWfCxMUqziJjU11TjsSlEUUlNT77qvDK8WwvzGthjLC01eQK/oC30+R5dDj6U9uJx+mXmn5vFU/adKOaEQ4r7dGi0FhpXF7ZyLf6xPTfCrbf5MVqpY89zY2NgQFxeHv78/Wq0WTSFNZYqioNFo0Ol0FglqLjLPjSivFp5eyMd7P8bBxoFFvRdR3aO62pGEEKZQFPjID/R5ph+rtYOxp8DVz/y5ygizz1C8adMmvL29jfcLK27ux8yZM5kyZQrx8fE0adKEr776itatW9/zuAULFjBo0CD69u3L8uXLzZpJCGvzRJ0n2BS7iV1Xd/H+zvf5tfuvZv+/KoSwII0GOr0Lp1aZdlz8McNaVjejynVxY4r7Xjjzfi1cuJChQ4cya9YswsLCmDp1KosXL+bMmTP4+999uvno6GgefPBBqlevjre3d7GLG2m5EeVZfEY8vZb1IkeXw/ye82no21DtSEIIS/u2PcRFwpOLoHZXtdNYjEVnKK5VqxYffPAB586dK3HAf/riiy949tlnGTFiBPXr12fWrFk4OzszZ86cux6j0+kYPHgwEydOpHp1aXoX4pZAl0A6hnQEYOvlrSqnEUKUCicvw5+ZSermKENMLm5eeOEFVq9eTd26dWnVqhXTpk0jPj6+RG+em5vLwYMHiYiIuB1IqyUiIoLdu+++rsaHH36Iv78/I0eOvOd75OTkkJqaWuAmRHlWx7sOAOdumucXECFEGef899xXWVLc3GJycfPqq6+yf/9+Tp06RY8ePZg5cyYhISF06dKFn3/+2aTXun79OjqdjoCAgALbAwIC7low7dixgx9++IHZs2cX6z0mTZqEh4eH8RYSEmJSRiGsTdvgtgBsitlExOII3t7+Ntky46kQ5ZfT38WNtNwYlXgyjNq1azNx4kTOnj3L9u3buXbtGiNGjDBntjukpaUxZMgQZs+eja+vb7GOGTduHCkpKcZbbGysRTMKobZ6PvV4uOrDKCgkZCaw+uJqXtn8Cl9Hfs2Px3/kRtYNtSMKIczJ2HJzU90cZYjJk/j90759+5g3bx4LFy4kNTWVxx9/3KTjfX19sbGxISEhocD2hIQEAgMD79j/woULREdH07t3b+M2vd4w74etrS1nzpyhRo0aBY5xcHDAwUEmQhIVy2ftPmNU41FEp0Qzbvs4dl3dxa6ruwBYcX4F0zpNw8nWCT8nPxlRJYS1u9XnRi5LGZlc3Jw9e5bffvuN+fPnExUVRadOnfjss8949NFHcXV1Nem17O3tadGiBRs3bqRfv36AoVjZuHEjL7300h37161bl2PHjhXY9t5775GWlsa0adPkkpMQf7OzsaOud13qetfF18mXtdFrUVDYFLOJCykX6LWsF2BYt6pn9Z70DO1JNY9q6oYWQpSMXJa6g8nFza2OxC+++CIDBw68o7+MqcaOHcuwYcNo2bIlrVu3ZurUqWRkZBgvcQ0dOpRKlSoxadIkHB0dadiw4NBWT09PgDu2CyEMWga2pGVgSwAeq/UYb257kyvpV9ApOmLTYpl1ZBazjsyioU9D3g9/n/o+9VVOLIQwiXQovoNJxY1Op+Pbb7+lf//+eHl5mSXAgAEDuHbtGuPHjyc+Pp6mTZuyZs0aY9EUExODVivr5AhhDvV86rHykZUAZOZlsil2E6svrmb31d0cv3Gc5zc8z6/dfyXEXVpBhbAaxpYb6XNzi8mT+Dk6OnLq1ClCQ0MtlcmiZBI/Ie50Pes6z294ntNJp3GwceCJOk/wdMOn8XUqXsd9IYSKki7C9GZg5wLvXlU7jcVYdBK/hg0bcvHixRKHE0KUPb5OvszoNINm/s3I0eXwy8lf6Ly4My1/bUnLX1vSbkE79sTtUTumEKIwtzoU52VAfo66WcoIk4ubjz/+mNdff51Vq1YRFxcnE+QJUU4EuATwU7efmBUxi0a+jdArenJ0OeTockjOSeaTvZ/w2b7POHvzrNpRhRD/5OABmr+/zqVTMVCCy1L/7P/yzyGksiq4EOWHoihcz7pOvj6fxKxEhvw5BAXDj4pgl2DWPLZGhpALUZZMrg6ZN+D5XRDQQO00FmH2VcH/afPmzSUOJoSwDhqNBj9nw+rCQa5BTO80nWPXj/HDsR+4mnGVmZEzeanZndM1CCFU4uRtKG5unL/dwVhNNnbgol6fPdVXBS9t0nIjRMlN2DWBpeeWYq+1Z3nf5TKqSoiy4vuH4fI+tVPcVrk1PLPerC9p0Zabbdu2Ffn8Qw89ZOpLCiGsxAfhH3A1/Sp74vYwcfdEHqv9GAC2WltaB7bGw8FD5YRCVFANH4W4I6DPVzuJgdZG1be/rz43xhf5x7V36XMjRPl2MeUij/3xGPn/+iHqbu/Oc02eY2CdgdjZ2KmUTghRXll0KPjNmzcL3BITE1mzZg2tWrVi3bp1JQ4thLAO1T2qMyF8AmFBYYQFGm5V3KqQmpvK5P2TeXzl4yRly4gNIYR6zNbnZuvWrYwdO5aDBw+a4+UsRlpuhDC/fH0+y88v56vDX5GUnUSHyh2Y3mm6jKgSQpiNRVtu7iYgIIAzZ86Y6+WEEFbEVmtL/9r9+e7h77DT2rHl8hbWXzJvZ0IhhCgukzsUHz16tMBjRVGIi4vj008/pWnTpubKJYSwQnW86/BUvaf48cSPvLb1Nf7w+oNQD+tcqkUIYb1K1KFYo9Hw78MeeOAB5syZQ926dc0a0NzkspQQlnUj6wYdFnUA4IGgB/ju4e/k8pQQ4r5ZdCh4VFRUgcdarRY/Pz8cHR1NfSkhRDnk4+TDn4/8Sb8V/dgTt4d1l9bRtVpXtWMJISoQk/vcVK1atcAtJCREChshRAEh7iGMbDQSgGmHpt3R0iuEEJZU7OJm9+7drFq1qsC2n3/+mdDQUPz9/Rk1ahQ5ObIaqRDCYHiD4dhqbIlNi2XYmmHoFb3akYQQFUSxi5sPP/yQEydOGB8fO3aMkSNHEhERwdtvv83KlSuZNGmSRUIKIayPs50zbSu1BeBw4mHOJMloSiFE6Sh2cRMZGUnnzp2NjxcsWEBYWBizZ89m7NixTJ8+nUWLFlkkpBDCOn3Z8UtstYaufYvPLuZa5jW5RCWEsLhiFzc3b94kICDA+Hjr1q10797d+LhVq1bExsaaN50QwqrZae0Y2dDQ92bx2cV0WtyJFza+QJ4uT+VkQojyrNjFTUBAgHGkVG5uLocOHeKBBx4wPp+WloadnawnI4QoaGSjkdT3qY+d1g4NGnZc2cEn+z5RO5YQohwrdnHTo0cP3n77bbZv3864ceNwdnamXbt2xuePHj1KjRo1LBJSCGG9nGydWNhrIYeGHGJm55kA/H72d9ZfWi/9cIQQFlHseW4++ugjHn30Udq3b4+rqys//fQT9vb2xufnzJlDly5dLBJSCFE+PFjpQVzsXMjIy2DslrFoNVpW9F1BNY9qakcTQpQjJs9QnJKSgqurKzY2NgW2JyUl4erqWqDgKYtkhmIh1DX/9HxWX1xNTGoMN3NuArCo1yLq+dRTOZkQoiyz6MKZHh4edxQ2AN7e3mW+sBFCqG9Q3UH82uNXXmr2knHbsDXD2Hhpo4qphBDlidlWBRdCCFM8UecJFvRagJeDF1n5Wby65VVO3jjJzeybpOWmqR1PCGHFTL4sZe3kspQQZcuZpDP0X9n/ju1jmo8xLuEghBCmfH9LcSOEUN2uq7sYs3kMWflZxm2eDp60r9y+wH7uDu481+Q53O3l/64QFY1FVwUXQghzaxPchr1P7gUgV59LxOIIknOSWXFhxR37+jn5MaLhiNKOKISwIlLcCCHKBI1GA4CDjQOzHp7F3ri9BZ4/fv046y+t51DiIUYgxY0Q4u6kuBFClDkNfBrQwKdBgW1bY7ey/tJ6rmVeUymVEMJayGgpIYRVcLV3BeDEjRPo9DqV0wghyjIpboQQVsHD3sN4f+hfQzl/87yKaYQQZZlclhJCWIXqntV5rNZjrLq4iqPXj/L4ysfxdfYtsE+QSxBfdvgSHycflVIKIcoCGQouhLAqCRkJfLz3Y7bEbin0+faV2/NVp6+MHZSFEOWDzHNTBCluhLB+iqJwKfUSGfkZxm03sm4wZvMY8vR5PFXvKePyDs62zlLoCFEOSHFTBCluhCi/VpxfwXs73yuwrW2ltsyKmKVSIiGEuVh04UwhhCir+tbsy/jw8TjZOhm37byyk/d3vs+vJ3+VUVZCVBDSciOEKHfy9Hnk6/Pp9ns3krKTjNtHNhzJmBZj1AsmhCgxWX5BCFGh2WntsNPa8XXnr9kdt5uk7CR+OfkLPxz/gZi0GEY3G02oR6jaMYUQFiLFjRCi3Grg24AGvoaZjj3sPZgZOZP1l9azKWYTg+sN5o1Wb6icUAhhCdLnRghRIfynyX9Y0mcJHSp3QKfo+Pnkz6yJXsOB+APk6/PVjieEMCPpcyOEqHAeXvIw8RnxxsdBLkEMrjeYR2s9ipu9m4rJhBB3I0PBiyDFjRDi97O/s/DMQgCuZlwlJScFACdbJwKcA4z72Wpt6R7ancH1BuNi56JKViGEgRQ3RZDiRgjxT9n52ay+uJqfT/7MxZSLhe7j6eDJiIYjGN5gOFqNXM0XQg1S3BRBihshRGEUReFU0imy8rOM2y6nXeb7Y98TnRoNwFut3uKp+k+plFCIik2KmyJIcSOEMEW+Pp+vI79m9rHZ2Gpt8Xfy57WWr9GlWhe1owlRoUhxUwQpboQQpkrJSaHXsl4k5yQDoNVoGdZgGJ4OnsZ9qrpXpWNIR7lsJYSFSHFTBCluhBAlkZGXwbXMa/x44keWnlta6D4NfRryRqs3aB7QvJTTCVH+SXFTBCluhBD3Q6/oWXhmIcevHzdu0yk6NsdsJjM/E4Ah9YfwZqs31YooRLkkyy8IIYSFaDVaBtUddMf261nXmRk5k9/P/s4vJ39BURQquVYq8rWqe1SnTaU2looqRIUlLTdCCGFGs4/OZvrh6cXe/5fuv9DUv6nlAglRTkjLjRBCqGRko5E42Tpx9PrRIveLTonmVNIpvjj4BT93/7mU0glRMUhxI4QQZqTVaIs1F05iZiIRiyM4nHiY61nX8XXyLYV0QlQMMmZRCCFU4O/sTw3PGgCM3zme5OxkdQMJUY5InxshhFDJpphNvLH1DXL1ubjbuxfaeuNs68zEthOp7VVbhYRClB0yFLwIUtwIIcqSM0lneH3r68YlHgoTHhTOiIYjjI99nHyk2BEVjhQ3RZDiRghR1uTocjh54yT5+vwC229m3+S1ra8VesyzjZ7l5eYvl0Y8IcoEGS0lhBBWxMHGgWb+zQp97j83/8Pm2M3Gx3pFz/nk88w+NhsPBw+GNRhWWjGFsBrSciOEEFZmzvE5fHnwS+y19qx/fD3ejt5qRxLC4kz5/pbRUkIIYWVGNBhBQ5+G5OpzWXhmodpxhChz5LKUEEJYGY1Gw9AGQ3lz25v8dOIn8nR5xTpOq9HStVpXannVsnBCIdQll6WEEMIK6RU9A1cN5FTSKZOOa+jTkPm95lsolRCWI6OliiDFjRCivIhOiWbx2cV3jLIqTI4uh9/P/Q7AY7Ue463Wb+Fk62TpiEKYjdWNlpo5cyZTpkwhPj6eJk2a8NVXX9G6detC9509ezY///wzx48fB6BFixZ88sknd91fCCHKq2oe1Xij1RvF2ldRFLZd3sa1rGv8fu53Gvg24PHaj1s4oRDqUL1D8cKFCxk7diwTJkzg0KFDNGnShK5du5KYmFjo/lu2bGHQoEFs3ryZ3bt3ExISQpcuXbhy5UopJxdCCOuh0WiY3WU2lVwrATD14FT6LO9jvD2+8nGWnVtGBWvMF+WU6pelwsLCaNWqFTNmzABAr9cTEhLC6NGjefvtt+95vE6nw8vLixkzZjB06NA7ns/JySEnJ8f4ODU1lZCQELksJYSokM7dPEf/lf3RK/pCn+8R2oPx4eNxsXMp5WRCFM1qLkvl5uZy8OBBxo0bZ9ym1WqJiIhg9+7dxXqNzMxM8vLy8PYufJ6HSZMmMXHiRLPkFUIIa1fLqxYr+60kMbNg6/jBhIN8c+Qb/oz6k4spF5nfcz622jLRc0EIk6n6L/f69evodDoCAgIKbA8ICOD06dPFeo233nqL4OBgIiIiCn1+3LhxjB071vj4VsuNEEJUVFXcq1DFvUqBbS0DW9I6qDXPrnuW00mniU6JpqZXTZUSCnF/rLos//TTT1mwYAFbtmzB0dGx0H0cHBxwcHAo5WRCCGF9mvk3o5JrJS6mXORa1jUpboTVUrVDsa+vLzY2NiQkJBTYnpCQQGBgYJHHfv7553z66aesW7eOxo0bWzKmEEJUGLcm+Nsbt1flJEKUnKrFjb29PS1atGDjxo3GbXq9no0bNxIeHn7X4yZPnsxHH33EmjVraNmyZWlEFUKICiGiiuES//pL62XklLBaql+WGjt2LMOGDaNly5a0bt2aqVOnkpGRwYgRIwAYOnQolSpVYtKkSQB89tlnjB8/nnnz5lGtWjXi4+MBcHV1xdXVVbXPIYQQ5UG7yu1wsHEgJi2GaYem4Wpfsp+rD1V+iNpetc2cTojiUb24GTBgANeuXWP8+PHEx8fTtGlT1qxZY+xkHBMTg1Z7u4Hpm2++ITc3l/79+xd4nQkTJvDBBx+UZnQhhCh3XOxcaBvclk2xm/jh+A8lfp1ZR2Yxp+scGvtJtwFR+lSf56a0yfILQghRtEupl/jl5C/k6HLuvXMhzt08x4kbJ/Bx9GFez3kEuwabOaGoiGRtqSJIcSOEEJaVkZfB0L+GcvbmWdzs3HB3KPxnbQOfBnzQ5gPc7N1KOaGwRlYziZ8QQojyx8XOhZmdZ/LUn0+RkJlAWl5aoftdSb9CfEY8sx6eJQWOMCtpuRFCCGER2fnZnLt5rtDnbubc5J0d75CSk0J9n/p8E/EN3o6FzzQvBMhlqSJJcSOEEGXDmaQzjFw3kpScFOp512NR70VqRxJlmCnf36qvCi6EEKJiquNdh8ntJgNwOun0XRfzFMJUUtwIIYRQTYvAFgAoKMRnxKucRpQXUtwIIYRQjYONA+72hksMn+77VOU0oryQ4kYIIYSq3mj1BgCbYzez48oOldOI8kCKGyGEEKrqV7MfQ+oPAeDjPR+Tp89TOZGwdlLcCCGEUN1LTV/Cxc6FK+lXiEqJUjuOsHIyiZ8QQgjVOds54+fkR0ZeBvvi9nEz+yYAng6e1PaqjUajUTmhsCZS3AghhCgTbi3T8Nn+zwpsnxUxi7aV2qoRSVgpuSwlhBCiTBhSbwh1vetS07MmNT1rGpdkuJB8QeVkwtpIy40QQogyoVtoN7qFdjM+/njPxyw8s/Cua1MJcTfSciOEEKJM8nTwBOBa5jV1gwirI8WNEEKIMqmmV03AsAaVEKaQ4kYIIUSZ1MC7AQBnb54lOTtZ3TDCqkhxI4QQokyq7FaZUI9QcvW5vLb1NZncTxSbFDdCCCHKJI1Gwxftv8DZ1pl98fuYsn+K2pGElZDiRgghRJlV06smk9pNAmD+6fnEpsaqnEhYAyluhBBClGmdqnSiZUBLALZc3kJGXobxplf0KqcTZZHMcyOEEKLMa1upLQcSDjB5/2Qm759s3B7oEsioxqPoV7Mfdlo7FROKskRaboQQQpR53ap1w8PB447t8RnxfLj7Q/ou78vKCyvR6XUqpBNljUZRFEXtEKUpNTUVDw8PUlJScHd3VzuOEEKIYtLpdQVGTOXr81l+fjmzj80mKTsJgBoeNXix2YuEBYWhofQX23S1c5VFPi3ElO9vKW6EEEJYtcy8TOadnsec43NIy1V3qYbGvo35ufvP2GhtVM1RHpny/S2XpYQQQlg1Zztnnmn0DGseW8OoxqNwtXNVLcvR60f5K/ov1d5fGEjLjRBCiHJFp9ehU0q/782c43OYGTmTau7VWN53ubTemJkp398yWkoIIUS5YqO1wYbSLyyeqvcUv5z8hejUaNZGr6VH9R6lnkEYyGUpIYQQwgxc7V0ZUn8IAN8e/VZGbqlILksJIYQQZpKWm0bX37uSlpuGm52bySOnbDQ2PNfkOZ6s96SFElov6VAshBBCqMDN3o1RjUYBkJaXRmpuqkm3mzk3+ePCHyp/CusnfW6EEEIIMxrWYBhdqnUhR5dj0nEnb5zk7e1vk5GXYaFkFYcUN0IIIYQZaTQagl2DTT4uV5cLoPpcPeWBXJYSQgghygBXe8P8POl56SonsX7SciOEEEKUAbcmH8zR5XA96zq2muJ/RbvYu8jCof8gxY0QQghRBrjYuRjvd1zU0aRjA5wDWN53ubH1p6KTy1JCCCFEGWCrtaVTSKcSHZuQmcCppFNmTmS9pOVGCCGEKCOmdZqGXtGbdMyLG19kx5UdRKdG0yqwlYWSWRdpuRFCCCHKEK1Ga9Ktmns1AC6lXFI3eBkixY0QQghhxaq6VwXgUqoUN7dIcSOEEEJYMWNxkybFzS1S3AghhBBW7NZlqdi0WPL1+eqGKSOkQ7EQQghhxQJcAnCwcSBHl0NMagyV3SqrHQkNGuxs1Jt3R4obIYQQwoppNVpC3EI4n3yeviv6qh0HgCZ+Tfi1x6+qvb9clhJCCCGsXJeqXdSOUKZoFEVR1A5RmlJTU/Hw8CAlJQV3d3e14wghhBBmkZmXafIcOZai1WhxtnM262ua8v0tl6WEEEKIcsDcxYQ1k8tSQgghhChXpLgRQgghRLkixY0QQgghyhUpboQQQghRrkhxI4QQQohyRYobIYQQQpQrUtwIIYQQolyR4kYIIYQQ5YoUN0IIIYQoV6S4EUIIIUS5IsWNEEIIIcoVKW6EEEIIUa5IcSOEEEKIckWKGyGEEEKUK2WiuJk5cybVqlXD0dGRsLAw9u3bV+T+ixcvpm7dujg6OtKoUSP+/PPPUkoqhBBCiLJO9eJm4cKFjB07lgkTJnDo0CGaNGlC165dSUxMLHT/Xbt2MWjQIEaOHMnhw4fp168f/fr14/jx46WcXAghhBBlkUZRFEXNAGFhYbRq1YoZM2YAoNfrCQkJYfTo0bz99tt37D9gwAAyMjJYtWqVcdsDDzxA06ZNmTVr1j3fLzU1FQ8PD1JSUnB3dzffBxFCCCGExZjy/W1bSpkKlZuby8GDBxk3bpxxm1arJSIigt27dxd6zO7duxk7dmyBbV27dmX58uWF7p+Tk0NOTo7xcUpKCmA4SUIIIYSwDre+t4vTJqNqcXP9+nV0Oh0BAQEFtgcEBHD69OlCj4mPjy90//j4+EL3nzRpEhMnTrxje0hISAlTCyGEEEItaWlpeHh4FLmPqsVNaRg3blyBlh69Xk9SUhI+Pj5oNBqzvldqaiohISHExsbKJS8zkXNqXnI+zU/OqfnJOTW/8nBOFUUhLS2N4ODge+6ranHj6+uLjY0NCQkJBbYnJCQQGBhY6DGBgYEm7e/g4ICDg0OBbZ6eniUPXQzu7u5W+4+nrJJzal5yPs1Pzqn5yTk1P2s/p/dqsblF1dFS9vb2tGjRgo0bNxq36fV6Nm7cSHh4eKHHhIeHF9gfYP369XfdXwghhBAVi+qXpcaOHcuwYcNo2bIlrVu3ZurUqWRkZDBixAgAhg4dSqVKlZg0aRIAr7zyCu3bt+d///sfPXv2ZMGCBRw4cIDvvvtOzY8hhBBCiDJC9eJmwIABXLt2jfHjxxMfH0/Tpk1Zs2aNsdNwTEwMWu3tBqY2bdowb9483nvvPd555x1q1arF8uXLadiwoVofwcjBwYEJEybccRlMlJycU/OS82l+ck7NT86p+VW0c6r6PDdCCCGEEOak+gzFQgghhBDmJMWNEEIIIcoVKW6EEEIIUa5IcSOEEEKIckWKGzOZOXMm1apVw9HRkbCwMPbt26d2pDLrgw8+QKPRFLjVrVvX+Hx2djYvvvgiPj4+uLq68thjj90xcWNMTAw9e/bE2dkZf39/3njjDfLz80v7o6hi27Zt9O7dm+DgYDQazR3rqimKwvjx4wkKCsLJyYmIiAjOnTtXYJ+kpCQGDx6Mu7s7np6ejBw5kvT09AL7HD16lHbt2uHo6EhISAiTJ0+29EdTzb3O6fDhw+/4N9utW7cC+8g5vW3SpEm0atUKNzc3/P396devH2fOnCmwj7n+n2/ZsoXmzZvj4OBAzZo1mTt3rqU/niqKc047dOhwx7/T5557rsA+FeacKuK+LViwQLG3t1fmzJmjnDhxQnn22WcVT09PJSEhQe1oZdKECROUBg0aKHFxccbbtWvXjM8/99xzSkhIiLJx40blwIEDygMPPKC0adPG+Hx+fr7SsGFDJSIiQjl8+LDy559/Kr6+vsq4cePU+Dil7s8//1TeffddZenSpQqgLFu2rMDzn376qeLh4aEsX75cOXLkiNKnTx8lNDRUycrKMu7TrVs3pUmTJsqePXuU7du3KzVr1lQGDRpkfD4lJUUJCAhQBg8erBw/flyZP3++4uTkpHz77bel9TFL1b3O6bBhw5Ru3boV+DeblJRUYB85p7d17dpV+fHHH5Xjx48rkZGRSo8ePZQqVaoo6enpxn3M8f/84sWLirOzszJ27Fjl5MmTyldffaXY2Ngoa9asKdXPWxqKc07bt2+vPPvsswX+naakpBifr0jnVIobM2jdurXy4osvGh/rdDolODhYmTRpkoqpyq4JEyYoTZo0KfS55ORkxc7OTlm8eLFx26lTpxRA2b17t6Iohi8irVarxMfHG/f55ptvFHd3dyUnJ8ei2cuaf38R6/V6JTAwUJkyZYpxW3JysuLg4KDMnz9fURRFOXnypAIo+/fvN+7z119/KRqNRrly5YqiKIry9ddfK15eXgXO51tvvaXUqVPHwp9IfXcrbvr27XvXY+ScFi0xMVEBlK1btyqKYr7/52+++abSoEGDAu81YMAApWvXrpb+SKr79zlVFENx88orr9z1mIp0TuWy1H3Kzc3l4MGDREREGLdptVoiIiLYvXu3isnKtnPnzhEcHEz16tUZPHgwMTExABw8eJC8vLwC57Nu3bpUqVLFeD53795No0aNCqwO37VrV1JTUzlx4kTpfpAyJioqivj4+ALnz8PDg7CwsALnz9PTk5YtWxr3iYiIQKvVsnfvXuM+Dz30EPb29sZ9unbtypkzZ7h582YpfZqyZcuWLfj7+1OnTh2ef/55bty4YXxOzmnRUlJSAPD29gbM9/989+7dBV7j1j4V4Wfvv8/pLb/99hu+vr40bNiQcePGkZmZaXyuIp1T1WcotnbXr19Hp9MV+McCEBAQwOnTp1VKVbaFhYUxd+5c6tSpQ1xcHBMnTqRdu3YcP36c+Ph47O3t71jcNCAggPj4eADi4+MLPd+3nqvIbn3+ws7PP8+fv79/gedtbW3x9vYusE9oaOgdr3HrOS8vL4vkL6u6devGo48+SmhoKBcuXOCdd96he/fu7N69GxsbGzmnRdDr9YwZM4a2bdsaZ5I31//zu+2TmppKVlYWTk5OlvhIqivsnAI8+eSTVK1aleDgYI4ePcpbb73FmTNnWLp0KVCxzqkUN6LUde/e3Xi/cePGhIWFUbVqVRYtWmQ1/3FExTJw4EDj/UaNGtG4cWNq1KjBli1b6Ny5s4rJyr4XX3yR48ePs2PHDrWjlBt3O6ejRo0y3m/UqBFBQUF07tyZCxcuUKNGjdKOqSq5LHWffH19sbGxuaOXf0JCAoGBgSqlsi6enp7Url2b8+fPExgYSG5uLsnJyQX2+ef5DAwMLPR833quIrv1+Yv69xgYGEhiYmKB5/Pz80lKSpJzXEzVq1fH19eX8+fPA3JO7+all15i1apVbN68mcqVKxu3m+v/+d32cXd3L7e/KN3tnBYmLCwMoMC/04pyTqW4uU/29va0aNGCjRs3Grfp9Xo2btxIeHi4ismsR3p6OhcuXCAoKIgWLVpgZ2dX4HyeOXOGmJgY4/kMDw/n2LFjBb5M1q9fj7u7O/Xr1y/1/GVJaGgogYGBBc5famoqe/fuLXD+kpOTOXjwoHGfTZs2odfrjT8Mw8PD2bZtG3l5ecZ91q9fT506dcrt5RNTXL58mRs3bhAUFATIOf03RVF46aWXWLZsGZs2bbrjcpy5/p+Hh4cXeI1b+5THn733OqeFiYyMBCjw77TCnFO1ezSXBwsWLFAcHByUuXPnKidPnlRGjRqleHp6FuiRLm577bXXlC1btihRUVHKzp07lYiICMXX11dJTExUFMUwRLRKlSrKpk2blAMHDijh4eFKeHi48fhbwxm7dOmiREZGKmvWrFH8/PwqzFDwtLQ05fDhw8rhw4cVQPniiy+Uw4cPK5cuXVIUxTAU3NPTU1mxYoVy9OhRpW/fvoUOBW/WrJmyd+9eZceOHUqtWrUKDFtOTk5WAgIClCFDhijHjx9XFixYoDg7O5fLYcuKUvQ5TUtLU15//XVl9+7dSlRUlLJhwwalefPmSq1atZTs7Gzja8g5ve35559XPDw8lC1bthQYlpyZmWncxxz/z28NW37jjTeUU6dOKTNnzrTKYcvFca9zev78eeXDDz9UDhw4oERFRSkrVqxQqlevrjz00EPG16hI51SKGzP56quvlCpVqij29vZK69atlT179qgdqcwaMGCAEhQUpNjb2yuVKlVSBgwYoJw/f974fFZWlvLCCy8oXl5eirOzs/LII48ocXFxBV4jOjpa6d69u+Lk5KT4+voqr732mpKXl1faH0UVmzdvVoA7bsOGDVMUxTAc/P3331cCAgIUBwcHpXPnzsqZM2cKvMaNGzeUQYMGKa6uroq7u7syYsQIJS0trcA+R44cUR588EHFwcFBqVSpkvLpp5+W1kcsdUWd08zMTKVLly6Kn5+fYmdnp1StWlV59tln7/jlRc7pbYWdS0D58ccfjfuY6//55s2blaZNmyr29vZK9erVC7xHeXKvcxoTE6M89NBDire3t+Lg4KDUrFlTeeONNwrMc6MoFeecahRFUUqvnUgIIYQQwrKkz40QQgghyhUpboQQQghRrkhxI4QQQohyRYobIYQQQpQrUtwIIYQQolyR4kYIIYQQ5YoUN0IIIYQoV6S4EUIIIUS5IsWNEMKqDB8+nH79+qkdQwhRhtmqHUAIIW7RaDRFPj9hwgSmTZuGTKwuhCiKFDdCiDIjLi7OeH/hwoWMHz+eM2fOGLe5urri6uqqRjQhhBWRy1JCiDIjMDDQePPw8ECj0RTY5urqesdlqQ4dOjB69GjGjBmDl5cXAQEBzJ49m4yMDEaMGIGbmxs1a9bkr7/+KvBex48fp3v37ri6uhIQEMCQIUO4fv16KX9iIYQlSHEjhLB6P/30E76+vuzbt4/Ro0fz/PPP8/jjj9OmTRsOHTpEly5dGDJkCJmZmQAkJyfTqVMnmjVrxoEDB1izZg0JCQk88cQTKn8SIYQ5SHEjhLB6TZo04b333qNWrVqMGzcOR0dHfH19efbZZ6lVqxbjx4/nxo0bHD16FIAZM2bQrFkzPvnkE+rWrUuzZs2YM2cOmzdv5uzZsyp/GiHE/ZI+N0IIq9e4cWPjfRsbG3x8fGjUqJFxW0BAAACJiYkAHDlyhM2bNxfaf+fChQvUrl3bwomFEJYkxY0QwurZ2dkVeKzRaApsuzUKS6/XA5Cenk7v3r357LPP7nitoKAgCyYVQpQGKW6EEBVO8+bN+f3336lWrRq2tvJjUIjyRvrcCCEqnBdffJGkpCQGDRrE/v37uXDhAmvXrmXEiBHodDq14wkh7pMUN0KICic4OJidO3ei0+no0qULjRo1YsyYMXh6eqLVyo9FIaydRpGpPoUQQghRjsivKEIIIYQoV6S4EUIIIUS5IsWNEEIIIcoVKW6EEEIIUa5IcSOEEEKIckWKGyGEEEKUK1LcCCGEEKJckeJGCCGEEOWKFDdCCCGEKFekuBFCCCFEuSLFjRBCCCHKlf8DR25s1yKU61AAAAAASUVORK5CYII="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eval.plot_survival_curves([1, 10, 100])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2023-08-12T01:11:49.715033012Z",
     "start_time": "2023-08-12T01:11:49.607413185Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Calculate the concordance index.\n",
    "`ties` argument indicates the way ties should be handled.\n",
    "`ties=\"None\"` will throw out all ties in true survival time and all ties in predict survival times (risk scores).\n",
    "`ties=\"Time\"` includes ties in true survival time but removes ties in predict survival times (risk scores).\n",
    "`ties=\"Risk\"` includes ties in predict survival times (risk scores) but not in true survival time.\n",
    "`ties=\"All\"` includes all ties."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Concordance index is 0.6612887062831947, meaning that the model can correctly order 6599.0 pairs among 9979.0 comparable pairs in the testing set.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "cindex, concordant_pairs, total_pairs = eval.concordance(ties=\"None\")\n",
    "print(\"Concordance index is {}, meaning that the model can correctly order {} pairs among {} comparable pairs \"\n",
    "      \"in the testing set.\".format(cindex, concordant_pairs, total_pairs))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2023-08-12T01:11:51.642368767Z",
     "start_time": "2023-08-12T01:11:51.447053572Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Calculate the Brier score\n",
    "Target time is at time 175. You could use any time as preferred."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Brier score at time 175 is 0.009235037097327974.\n"
     ]
    }
   ],
   "source": [
    "bs = eval.brier_score(target_time=175)\n",
    "print(\"Brier score at time 175 is {}.\".format(bs))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2023-08-12T01:11:55.526978059Z",
     "start_time": "2023-08-12T01:11:55.423838816Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Calculate the integrated Brier score (IBS)\n",
    "- You can specify the number of points `num_points` to integral. If it is not specify, the default setting will use the censored time points obtained from testing set.\n",
    "- You can also set the `IPCW_weighted` as True or False to calculate the IPCW-weighted IBS or non-weighted IBS. The default setting is True."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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eeuihSuumpaUBAIKY6IWoDK1Wbha8apX8kixvi5WSMjNl74Uu0Cp5jG++kb1WV64A3bsDixdzJaEjqeqcuRs3gOhoTo4n16LocGFcXBxGjBiBzp07o2vXrkhMTER+fj5GjhwJABg+fDgaNGiAhIQEAHIi+9GjR/W3MzMzkZaWBi8vLzRt2lR/3OLiYixbtgwjRoxAtWqGb/HUqVNYuXIlHnzwQdStWxe///47Jk2ahPvuuw9t27a10TsncgzGhgXNMXo0kJYme7iuXDF8LCBADiG5u99uK8mRnD3LTaXJhQiFLVy4UDRs2FC4u7uLrl27il27dukf6969uxgxYoT+fnp6ugBQ5tK9e3eDY/70008CgDhx4kSZ1ztz5oy47777hJ+fn/Dw8BBNmzYVU6ZMERqNxqx2azQaAcDs5xE5iq+/FkKlEkL2W1n+olLJ1yDHsnKlZf7tQ0OFuHlT6XdDrsiW398qISrr+Cdj8vLy4OPjA41Gw/lZ5HS0WjmsU9UeLFOFhgLp6eavSiTlbN0q591ZwpYtpk22J7IkW35/K76tDhHZH3NXkFVVRoZ8LXIckZEyh5Yl5tF9++3tH4PInjHIIqIyqrqCzN5fi26fWi3zmwG3H2glJnISPDk3BllEBMBwBWCpzCpWxUW9jqe87PG1apl3HJVKZo9nWgdyVgyyiAhJSXIOVlSUzGdUWd4qlUoOGcXH397r1qsnh5/I8ZTcZmflSnmt0QAvvWT6MYTgkDE5N6fO+E5EldMlFzV1CYxuiGjBAvlF27Zt2TQPbm4y0WhlPvyQk94dmbEs8fPmAeHhwAsvABcumHYcDhmTs2JPFpEL02orTy5aOgiqaONgXY/GmjWVv/aUKTK4I+czaJAMnN5/37T6HDImZ8UUDlXEFA7kSMrbzNnU5fjvvy+Th5qzEXRSEjBmDHDpkmG5tzfwySfAo49W6a2QA9GlAsnMNB7I64admcaDbMmW398cLiRycsaytoeEyOG+U6dMO0ZAADB0qHmvGxsrN4veulVeADm01KMHv1BdhW4l4qBBMqAyFmglJvL/Azkv9mRVEXuyyBGYO9+qPEwaSbejvO2ZXnkFaNXKvB5Sottly+9vBllVxCCL7J2pWds9PYHr140/xuEcshTdkHVmJjB+PPDvv4aP63pXuZ8hWRszvhPRbTM1a/uMGTKYKp1YUnefwzlkCbqViDVqlA2wABl8DRrE5KTkXBhkETkpU5fFN2tmPLFk6VWERLdLt5rVGN2YCpOTkjPhxHciJ2XqsvigINnD0L+/8RWIRJZSWe9qyeSkjjIHsLyVu0QAgywip6XbyLey5fO6jOvGEksSWZKpvaubNjlG0FLRyl32ABPA4UIip1VyI9/SON+KlGBq7+rbb8vtnaKi5OINe5unpdUCs2cDAweW7Znj3DIqiUEWkROLjQWef75sOedbkRIiI8vO/auMvQUtSUlAo0bl79vJuWVUEoMsIien+9B/7LFb296kpzPAIttTq+UuAOYQQl7sIWjR5Z3LzKy4Hje+Jh3OySJycvv3y+vYWGDwYGXbQtSsWdWep/SEeFP2+SyNG18Te7KInFhhIXDokLzdqZOybSECbm8z6Pfes1w7zGVq3rmSuPE1McgiclJaLfD558CNG0CtWnICMZHSdKteq2LjRjmXUAnm9EqpVEBo6K2Vu+S6GGQROaGkJBlU6ea/5OcDjRvbz+Rhcl0VrXo1xahRyszNMrdXiit3CWCQReR0dJNzubSc7FVsLPD110DduuY/Ny8P2LrV4k2qVGQkUL9+5fXc3blyl25hkEXkRCqanMul5WRPYmOBnBxg1izAz8+85y5dap02VSQ/v+z+nqWpVHIeZMuWtmkT2T8GWUROxJxtS4iUplYDM2cC588DmzcDnp6mPe/nn23/Q2HsWBkU1qsHBAcbPhYaKnvm+veX9z/6yLZtI/vFIIvIiZg6OZdLy8meqNVAz57AtGmm1c/Ls+0PhS+/lBc3N+Cbb4AzZ2S+udJ55559Vtb//HPg2jXbtY/sF/NkETkRczaFJrI3r70GzJkjV8RWxlY/FP7+G3jhBXk7Ph645x5521i+rgcekNng//kHeOYZOQx6xx3y+e7utmkv2Rf2ZBE5kchIwMen/Me5tJzsmVoNvPyyaXVt8UOhqAgYOhS4ckX+zbzySsX11Wr59wXIXq5Fi4BJk4CaNYGpU63fXrI/DLKInMhPP8mhFGO4KTQ5gldeMW3V4cWL1m9LfDywZw/g6yuHCyv7u5k6Ffjtt7LlWi0wbx4DLVekEsKcTQJIJy8vDz4+PtBoNPD29la6OeSCtFo5LyUrS/6qr1sXuPdeGWRFRwPHjxtOgg8NlQEWl5aTvVu7Vu61WZHQUDkXylo/GH75Rf4dCSFTMgwcWHH9wkLZY1XRhHy1Gigo4NCh0mz5/c05WUQOKClJpmooGUSp1fID/r77gO+/l/dLBmGRkezBIsdQr17lday5l+HFi8CTT8oAa/ToygMsAPjww8pXPGq1st7EiRZpJjkABllEDkaXbLR0H7TuA/6pp279UlZqM12i26HkKlkhZFb5c+dkvqv33zfteadOWbYeOQfOySJyIBUlGwXkvKv4eCYbJcdm6qR2f3/D+1qtzAa/apW8rsrfwZIlwHffyR8qq1fLfT9Ncccdlq1HzoFBFpEDYbJRcgW6TaQry7A+Zgzwv//JYEq3X2dUFPD44/I6LMy8baQOHwYmT5a3584F2rUz/bkvvFD5cLxafSsdBLkGxYOsxYsXIywsDJ6enggPD8eePXvKrXvkyBEMHDgQYWFhUKlUSExMLFPn9ddfh0qlMri0LLXHwfXr1zF27FjUrVsXXl5eGDhwIHJyciz91ogsjslGyRWU3ES6dKClu+/jA5w+DQwfLjc/Hzjw9vbrvHYNGDIEuH4dePBB4MUXzWuzuzsQF1dxnbg4Tnp3NYoGWWvWrEFcXBzi4+Nx4MABtGvXDjExMTh//rzR+gUFBWjSpAnmzJmDwMDAco/bqlUrZGVl6S+/lVpTO2nSJGzYsAFr167Ftm3bcO7cOcRyyRU5ACYbJVcRGytX9TVoYFgeEiK3sMnMBBISZLCVkWH8GObs1/nSS8CRI0BAALBsWeW9aMbMnQtMmVK2R0utluVz55p/THJwQkFdu3YVY8eO1d/XarUiODhYJCQkVPrcRo0aiffff79MeXx8vGjXrl25z8vNzRXVq1cXa9eu1ZcdO3ZMABCpqakmt12j0QgAQqPRmPwcott186YQISFCqFRCyK8Qw4tKJURoqKxH5Axu3hRiyxYhVq6U16X/b3/3nfG/hdKXLVvKf43162/V++mn22/zjRtCzJlz65j//HP7xyTLseX3t2I9WYWFhdi/fz+io6P1ZW5uboiOjkZqauptHfuvv/5CcHAwmjRpgmHDhuHMmTP6x/bv34+ioiKD123ZsiUaNmxY4eveuHEDeXl5BhciJYwcaXziO5ONkjNSq+Uq2aFD5XXp/9tXr5p2nPKG0DMzgaeflrcnTwZ69apqS29xd5f7MOpmqixaVPWJ+OTYFAuyLl68CK1Wi4CAAIPygIAAZGdnV/m44eHhWL58OZKTk7FkyRKkp6cjMjISV65cAQBkZ2fD3d0dvr6+Zr1uQkICfHx89JdQ3d4JRDaSlCQzT7/xhvHHQ0Lk8ApHvsmV3M4QulYr82Fdvgx07Ai8/bbl2pWUJDeSBmS296pMxCfHp/jEd0vr06cPHn30UbRt2xYxMTH44YcfkJubi6+++uq2jjtjxgxoNBr9JaO8SQBEVpCUJCf2VvSr/bHHGGCR66lsJWJF+3XOnQts2SLTNKxaZblJ6bpcdgUFhuXmTMQn56BYkOXv7w+1Wl1mVV9OTk6Fk9rN5evri+bNm+PkyZMAgMDAQBQWFiI3N9es1/Xw8IC3t7fBhcgWtFpg/PjK6yUmyq09iFxJRSsRATm0bmwIffdu4LXX5O2FC4HmzS3Tnopy2ZkzEZ+cg2JBlru7Ozp16oSUlBR9WXFxMVJSUhAREWGx17l69SpOnTqFoP/6ijt16oTq1asbvO6JEydw5swZi74ukaVs3y6zT1dGt2UHkaspbyUiIJN/PvKIYVlensylpdUCgwfLXRIshbnsqCRFt9WJi4vDiBEj0LlzZ3Tt2hWJiYnIz8/HyJEjAQDDhw9HgwYNkJCQAEBOlj969Kj+dmZmJtLS0uDl5YWmTZsCAF566SX07dsXjRo1wrlz5xAfHw+1Wo2hQ4cCAHx8fDBq1CjExcXBz88P3t7eGD9+PCIiInD33XcrcBaIKmZOzitu2UGuKjYW6N//1n6dnp5yvtWpUzIAe/TRW3XHjgX+/hto1AhYurRq6RrKw1x2VJKiQdbgwYNx4cIFzJw5E9nZ2Wjfvj2Sk5P1k+HPnDkDN7dbnW3nzp1Dhw4d9PffffddvPvuu+jevTu2bt0KADh79iyGDh2KS5cuoV69erj33nuxa9cu1Cux4+j7778PNzc3DBw4EDdu3EBMTAw+ZBcA2Slzcl5xyw5yZbqViDovvQTMmgVMny4noZ8+Dfz7L7Bihay7cqVcTGJJzGVHJamEKG8XNKpIXl4efHx8oNFoOD+LrEqrBRo2rHzIUK2WE22ZUZpIunJFJhe9dq3sY/fcA5TKU20RWq1cRZiZWX6qlZAQID2dqVaUYsvvb6dbXUjkbNRqOTG3Mtyyg8jQG28YD7AAYMcOYOpUy7+mKVsCMZed62CQReQAYmPlEnNj3Ny4ZQdRaYWFwPz5FdeZP986K3Ir2hKIuexcC4MsIgfRpo289vQEnngCGDAAePdd+UudARaRoQ8/rDxNgjVX5MbGyjlgDz4o7z/9tBwiZIDlWhSd+E5EpvvrL3ndujXwv/8p2xYie2fqSltrrshVq4EWLYAffgD8/TlE6IrYk0XkIP78U143a6ZsO4gcgakrba29ItfHR15zu1vXxCCLyM5ptXJz2eRkeZ9pGogq98ILlfccqdWynjXpFq9pNNZ9HbJPDLKI7FhSkkyYGBUl91gD5MqktWsVbRaR3XN3lytuK2KLFbleXvL62DH5Y4nb6bgWBllEdkq3KXRmpmH51atyM2hrLD8nciZz58qVt6V7tNRq26zITUoCZsyQt9PS5I+lsDBuEO1KmIy0ipiMlKxJq5VJFC9dqrje2rXAoEG2aRORoyoslKsIT52Sw+0vvGD9HqykJPm3WV5CUqZyUI4tv78ZZFURgyyqClM/7FNSgOjoyo9Xr57cA42rlojshy7re0UbRYeGMuu7UpjxncgJTZ0K1KwJTJoELFokr2vUkPurlSQE8Mknph3zwgW5IS4R2Y/t2ysOsAAgI4N/u66AQRaRDUydCsybV3bSa3Ex8N57MrHo9esyuGrdGli92vRjZ2VZtKlEdJtKz6O83XrkuJiMlMjKTNne49tvgfr15Ya2gMzqfv26accPCrq99hGRZV24YNl65LjYk0VkZaZs7wHIACs0VPZsnTsH+PlV/pyQECAy8vbbSESWU6+eZeuR42KQRWRl5mzbsWyZzN1Tpw7w8ceV11+wgBNniexN6Y2hb7ceOS4GWURWdvWq6XXPn791OzYW+PproG7dsvXq1pWPcQk4kf2JjJS9zBUJDWUvtCvgnCwiK0pKApYvN71+6flVsbFA//4yU/TWrbKsRw95YQ8WkX1Sq2Uvsy6HXelESSqV3LmBf8POj3myqoh5sqgypuTKKYl5c4icS1ISMGFC2c+AxERZTspgniwiJ2BKrhwd/rIlcj6xscDp03Lf0ZUrgYgIWX75sqLNIhtikEVkJabmr6pbl1tsEDkrtVoO7w8dCowdK8tWrza+3Q45HwZZRFZiav6qNWsYYBG5gn79ZA68P/8EDh1SujVkCwyyiKxEt8JIpTL+uEol52H16GHTZhGRQmrXBh56SN6eOxdYtUouaDEljx45JgZZRFaiW2FkjC7w4jwsItcSFiavV60CHn8ciIqSZUlJSraKrIVBFpEVxcYaX0UUEsJ5WESuJinJ+BZbmZky3QMDLefDPFlEVlbtv7+y/v2BwYPlXK3ISPZgEbkSrVb+4DI24V0I2bs9caL8nOBng/NgkEVkZQcPyuu+feUKIyJyPZWldBECyMiQ9ThP03lwuJDIioQADhyQtzt0ULYtRKQcU1O6mFqPHAODLCIrOnMG+PdfOWTYqpXSrSEipZia0sXUeuQYGGQRWUlhITB7trxdv375qRyIyPmZmtKFm0Y7FwZZRFYwdSpQsybw2Wfy/rlz8v7Uqcq2i4iUoUvpUl6mdyGY0sUZceI7kYVNnQrMm1e2XKu9VT53rm3bREREtqcSgjsoVYUtd/Emx1FYKHusKsrgrFYDBQWAu7vt2kVEytJqZdLR8lYYqlRyODE9nb1Z1mbL72/FhwsXL16MsLAweHp6Ijw8HHv27Cm37pEjRzBw4ECEhYVBpVIhMTGxTJ2EhAR06dIFtWvXRv369TFgwACcOHHCoE6PHj2gUqkMLs8995yl3xq5oGefrXyLDK0W+PBD27SHiOyDOSkcyHkoGmStWbMGcXFxiI+Px4EDB9CuXTvExMTg/PnzRusXFBSgSZMmmDNnDgIDA43W2bZtG8aOHYtdu3Zh06ZNKCoqQq9evZCfn29Qb/To0cjKytJf5nL8hm5TUhKwfLlpdU+dsmpTiMjOMIWDa1J0Ttb8+fMxevRojBw5EgCwdOlSfP/99/jss88wffr0MvW7dOmCLl26AIDRxwEgOTnZ4P7y5ctRv3597N+/H/fdd5++vGbNmuUGakTlKSyUvVCnTgF33AG88IIc9tNlczbVHXdYr41EZH+YwsE1KdaTVVhYiP379yM6OvpWY9zcEB0djdTUVIu9jkajAQD4+fkZlK9YsQL+/v5o3bo1ZsyYgYKCggqPc+PGDeTl5RlcyLXoVgxOmgQsWiSvdSsGKxsKKEmtlsEZEbmObt0qn2ulVst65DwU68m6ePEitFotAgICDMoDAgJw/Phxi7xGcXExJk6ciHvuuQetW7fWlz/++ONo1KgRgoOD8fvvv2PatGk4ceIEkirYnTMhIQGzZs2ySLvI8VS2YvCrr0w/VlwcJ70TuZqdO02br7lzJ7fVcSZOncJh7NixOHz4MH777TeD8jFjxuhvt2nTBkFBQejZsydOnTqFO8oZx5kxYwbi4uL09/Py8hAaGmqdhpNdKSwE5s+vuM4//5h2rMGDmb6ByBVxTpZrUizI8vf3h1qtRk5OjkF5Tk6OReZKjRs3Dhs3bsSvv/6KkJCQCuuGh4cDAE6ePFlukOXh4QEPD4/bbhc5ng8/rPwXKADUqiVTM5SXFKVBA2DFCsu2jYgcg6lzrf76y7rtINtSbE6Wu7s7OnXqhJSUFH1ZcXExUlJSEBERUeXjCiEwbtw4fPPNN/jll1/QuHHjSp+TlpYGAAjijEMywtSVgLp1FaW3zVCp5OWDD5j/hshVVbatjs7rr8uVyuQcFE3hEBcXh48//hiff/45jh07hueffx75+fn61YbDhw/HjBkz9PULCwuRlpaGtLQ0FBYWIjMzE2lpaTh58qS+ztixY/Hll19i5cqVqF27NrKzs5GdnY1r164BAE6dOoU33ngD+/fvx+nTp/Hdd99h+PDhuO+++9C2bVvbngByCKauBOzVC1i3TvZYlRQSIstjYy3fNiJyDJVtq1PSxImm9Z6T/VM84/uiRYswb948ZGdno3379vjggw/0w3c9evRAWFgYlv+XfOj06dNGe6a6d++OrVu3AgBU5fxMWLZsGZ566ilkZGTgiSeewOHDh5Gfn4/Q0FA88sgjePXVV83K/MqM767D3CzuWq1cbZiVJYcIIiPZg0VE0uzZQHx85fW2bOEEeGux5fe34kGWo2KQ5VrKW12oM2UKJ7QTUeVWrQIef7zyeitXAkOHWr89rsiW399OvbqQyFLmzpXd/O++a1iuVsuUDAywiMgUTErqWtiTVUXsyXJOWi2wdau8ALK7vkcPGUydPAk0ayZvjxkDNG9+K+M7EZEpdBtFZ2Yan5/FjaKtjz1ZRApISpLB06VLt8refBOoWxf46CPg5k1Z1rEjN3gmoqrRTYAfNEgGVCUDLd2U4sREBljOQtHVhUT2IikJGDjQMMDSuXRJPvbBB/J+YCBX/hBR1cXGciWyq2CQRS5PqwVefLHyejt2yOsNG2R3P3PZEFFVxcYCp08DDzwg7z/zjBwi1AVYuqkLq1bJa/6wc0wMssjlbd8u50eYIzNTdvcz0CKiqlKrgS5d5O30dPlZpNXKz5WwMCAqSq5EjIriDztHdVtB1vXr1y3VDiLFVGWvMN08CiYNJKKqSkoCliyRt1NSZDAVECCnJ5w9a1iXP+wck9lBVnFxMd544w00aNAAXl5e+PvvvwEAr732Gj799FOLN5DI2qq6VFoIICND/vokIjJHUpIMmv7917Dc2LxQQH7eCAFMmMAfdo7E7CDrzTffxPLlyzF37ly4l1i73rp1a3zyyScWbRyRLURGlp2Aao6q9IQRkevSamWwVJUESmfPAm+9Zfk2kXWYHWR98cUX+OijjzBs2DCoS6wxbdeuHY4fP27RxhHZglp9a+VgVTBpIBGZY/v2ssOB5oiP57ChozA7yMrMzETTpk3LlBcXF6OoqMgijSKytdhYYNEi858XGip7woiITGWJ3m/OB3UMZgdZd911F7YbmYSybt06dOjQwSKNIlKCbh2HOUkAmTSQiMxlid5vzgd1DGZnfJ85cyZGjBiBzMxMFBcXIykpCSdOnMAXX3yBjRs3WqONRDaxfr28Hj4cWLas4rpqNbB6NZMGEpH5IiNl4tHyttYxFeeD2j+ze7L69++PDRs2YPPmzahVqxZmzpyJY8eOYcOGDXhAl1WNyMHk5NxKNjprFjBlSsX1V62SK4OIiMyl21oHuLWVjk7p+xXhfFD7Z1aQdfPmTcyePRuNGzfGpk2bcP78eRQUFOC3335Dr169rNVGIqvbsEH+ouzcWc6zmjsXWLsWqFfPsF5oKPD118CjjyrTTiJyDuVtraP7zKks2OJ8UMdgVpBVrVo1zJ07Fzd1O+USOQndUOGAAbfKBg2S3fFbtgArV8rrktteEBHdDt3WOh07yvsvvwz07i1vR0XJQKu8YGvqVM4HdQRmDxf27NkT27Zts0ZbiBRx5QqwebO8XTLIAuSHWI8ewNCh8pofakRkSWq17JUCAF9f2bsFAG+8Ybynq3p1eZ2YCFy4YKtWUlWZPfG9T58+mD59Ov744w906tQJtWrVMni8X79+FmsckS389BNw4wbQtClw111Kt4aIXI3ua/Szz4CCAuDOO4GICNmL1b+/XEWYlSXnYDVrBtx7L3DqFNC3L/DLL0DNmsq2n8pndpD1wgsvAADmz59f5jGVSgUtE3eQgyk5VGjOpFMiIkvQBUm6fN5PP33rs0jXm17Sjz8C3boBu3cDw4bJHi/2stunKu1dWN6FARY5mqIiQJd5pPRQIRGRtSUlyXQwJb3/fsUZ3Vu2BL79FvDwkD8SJ026vVQQZD1mB1lEzmTbNkCjAerXB+6+W+nWEJEr0W0SffWqYXlWliyvKNCKjAS++ELeXrhQBmZkf6oUZG3btg19+/ZF06ZN0bRpU/Tr189oFngie/fNN/K6Xz92txOR7VS0SbSurLKtcx57DJg3T96ePFmmndFqga1bZS6/rVu59Y7SzA6yvvzyS0RHR6NmzZp48cUX8eKLL6JGjRro2bMnVq5caY02EllFcbHscgc4VEhEtlXZJtFCmLZ1zuTJwLhx8vbjj8vJ8VFR8nZUFBAWxs2klaQSwryR3DvvvBNjxozBpEmTDMrnz5+Pjz/+GMeOHbNoA+1VXl4efHx8oNFo4O3trXRzqAr27gW6dgW8vORSaE9PpVtERK5i1SoZCFVm5UqZQqYiWq2c7rBvX9nHdBPo161jjj8dW35/m92T9ffff6Nv375lyvv164f09HSLNIrIFnSrCvv0YYBFRLZl6pY4ptYrbx9DU4ceyTrMDrJCQ0ORkpJSpnzz5s0I1WVUI3IAxrK8ExHZgm6T6PLSxqhUpm+ds3273Gy6PKYOPZLlmZ0na/LkyXjxxReRlpaGbt26AQB27NiB5cuXY4Fux0siO/fnn8DRo0C1asCDDyrdGiJyNbpNogcNkgFVyYk7usArMdG0BTnl9WKVlpJyK6lpZCQX+9iC2UHW888/j8DAQLz33nv46quvAMh5WmvWrEH//v0t3kAia9BNeI+KkltZEBHZmm6T6AkTDCfBh4TIAMvUOVSmDim++abhayxYwHla1mb2xHeSOPHdsd1zD7BzJ7B4MfDfJgZERIrQag23zjG3l0mrlasIMzNNT0rqyhPibfn9bXaQtXfvXhQXFyM8PNygfPfu3VCr1ejcubNFG2ivGGQ5ruxsIDj41jyFkBClW0REdHt0iU0B8wKtkBAgPd21hg7tenXh2LFjkZGRUaY8MzMTY8eOtUijiKzpu+/kh1CXLgywiMg56IYeGzQw/TmcEG99ZgdZR48eRceOHcuUd+jQAUePHrVIo4isSbeq8JFHFG0GEZFFxcYCp08DW7bI/Fqvvmra80ydOE/mMzvI8vDwQE5OTpnyrKwsVKtm9jx6IpvKy5MrbACmbiAi56NWAz16yASmPXua9hxTJ86T+cwOsnr16oUZM2ZAo9Hoy3Jzc/Hyyy/jgQceMLsBixcvRlhYGDw9PREeHo49e/aUW/fIkSMYOHAgwsLCoFKpkJiYWKVjXr9+HWPHjkXdunXh5eWFgQMHGg0cyfkkJwOFhUDz5nIneyIiZ2XJXFxUNWYHWe+++y4yMjLQqFEjREVFISoqCo0bN0Z2djbee+89s461Zs0axMXFIT4+HgcOHEC7du0QExOD8+fPG61fUFCAJk2aYM6cOQgMDKzyMSdNmoQNGzZg7dq12LZtG86dO4dYV1te4aJKJiAt74OHiMgZ6HJxAWU/78zNxUVVU6UUDvn5+VixYgUOHTqEGjVqoG3bthg6dCiqV69u1nHCw8PRpUsXLFq0CABQXFyM0NBQjB8/HtOnT6/wuWFhYZg4cSImTpxo1jE1Gg3q1auHlStXYtB/SzGOHz+OO++8E6mpqbj77rtNajtXFzqewkKgXj05ZLhzJxARoXSLiIisLympbC4uPz9Z9sorrhdk2fXqQgCoVasWxowZg8WLF+Pdd9/F8OHDzQ6wCgsLsX//fkRHR99qjJsboqOjkZqaWpVmmXTM/fv3o6ioyKBOy5Yt0bBhwwpf98aNG8jLyzO4kGPZulUGWAEBQKkMJERETks3If7112+VXb4MxMfL/FpJSQo1zAWYHGT9+eefZeY2paSkICoqCl27dsXbb79t1gtfvHgRWq0WAQEBBuUBAQHIzs4261jmHDM7Oxvu7u7wLZXmu7LXTUhIgI+Pj/7CfRodj26osH9/wK1KPy+IiBzTt98Cs2aVLc/MlPm1GGhZh8lfNdOmTcPGjRv199PT09G3b1+4u7sjIiICCQkJ5U5Edwa6yf66i7FcYWS/iou5ITQRuSatVg4NGpscpCubOFHWI8syOefCvn37MHXqVP39FStWoHnz5vjpp58AAG3btsXChQvLzJEqj7+/P9RqdZlVfTk5OeVOarfEMQMDA1FYWIjc3FyD3qzKXtfDwwMeHh5Vahcpp7AQ+PBDYMcOmQumVi3g/vuVbhURke1s3244H6u0kklJe/SwWbNcgsk9WRcvXkRIifTYW7ZsQd++ffX3e/TogdOnT5v8wu7u7ujUqRNSdEmLICepp6SkIKKKM5JNOWanTp1QvXp1gzonTpzAmTNnqvy6ZJ+mTgVq1gQmTZKZkAEgPx947TVl20VEZEumJhtlUlLLM7kny8/PD1lZWQgNDUVxcTH27duHuLg4/eOFhYUwd6FiXFwcRowYgc6dO6Nr165ITExEfn4+Ro4cCQAYPnw4GjRogISEBP1r6LLKFxYWIjMzE2lpafDy8kLTpk1NOqaPjw9GjRqFuLg4+Pn5wdvbG+PHj0dERITJKwvJ/k2dCsybZ/wxXfncubZrDxGRUkxNNsqkpFYgTPT444+Lhx9+WJw5c0a89957wsvLS1y9elX/+Lp160Tbtm1NPZzewoULRcOGDYW7u7vo2rWr2LVrl/6x7t27ixEjRujvp6enCwBlLt27dzf5mEIIce3aNfHCCy+IOnXqiJo1a4pHHnlEZGVlmdVujUYjAAiNRmP2eybrunFDCLVaCNkJbvyiVst6RETO7uZNIUJChFCpjH8eqlRChIbKeq7Alt/fJufJOn36NB544AGcOnUKarUaH3zwAZ5//nn94wMGDEDjxo3x/vvvWz4StEPMk2U/tFqZnuGXX4AzZ4DsbGDz5sqf9/77crInEZGzS0qSqwgBwwnwuqSk69bJVA+uwJbf3yYPF4aFheHYsWM4cuQI6tWrh+DgYIPHZ82aZTBni8gWkpKAMWOAS5fMf+6pU5ZvDxGRPYqNlYFU6aSkDRrIrPCuEmDZmlnZgqpVq4Z27dqVCbAAoF27dqhbt67FGkZUmaQkYODAqgVYAHDHHZZtDxGRPdMlJd28WS4KAoCvvmKAZU1MyUgOSasFXnyx6s9Xq4EXXrBce4iIHIFaDfTsCeg2PfntN2Xb4+wYZJFD2r5dZiquqrg4wN3dcu0hInIk3bvL661bFW2G02OQRQ6pqvlc1GpgyhSmbyAi16ZLOvrbb8DNm4o2xamZFWTdvHkTs2fPxtmKUscS2YC5+VwGDJCrCQsKGGAREbVrB/j4AHl5QFqa0q1xXmZPfJ83bx5uMuwlhUVGylUxpggJkatqJk7kECERESB79SMj5e1t25RtizMze7jw/vvvxzb+i5DC1Grggw9Mq7tggaxPRES3cF6W9ZmcJ0unT58+mD59Ov744w906tQJtWrVMni8X79+FmscUUViY4H33gMmTzb+eN26wEcfcXkyEZExunlZW7YAK1bI0YHISP4otSSzg6wX/lv3Pn/+/DKPqVQqaLXa228VkYk8PeV1p05ATIzM+N6wIXD//fIDhB8WRETGpafLjO/5+cATT8iykBAmJ7Uks4Os4uJia7SDqEp0OV769QNmzlS2LUREjiIpCRg82HCLHUCmxhk0yLW22bGm20rhcP36dUu1g6hKduyQ1/feq2w7iIgchVYrt9cxtnOxrmziRFlPV3/rVmDVKnnNASvTmR1kabVavPHGG2jQoAG8vLzw999/AwBee+01fPrppxZvIFF5MjLk8KBaDYSHK90aIiLHsH274f6FpQkhP1+3b5c9XmFhQFQU8Pjj8josTJZT5cwOst566y0sX74cc+fOhXuJ9fCtW7fGJ598YtHGEVVE14vVoQNQav0FERGVw9Rkzp9+KocOSwdkuiFFBlqVMzvI+uKLL/DRRx9h2LBhUJeYVdyuXTscP37coo0jqohuPtY99yjbDiIiR2JqMucvvzR9SJGMMzvIyszMRNOmTcuUFxcXo6ioyCKNIjKFLsjifCwiItNFRspVhCpV+XUqS9xcckiRymd2kHXXXXdhu5Gzum7dOnTo0MEijSKqjEYD/PGHvM2eLCIi06nVMk0DUDbQUqnk5b9sTZWq6j6yrsLsFA4zZ87EiBEjkJmZieLiYiQlJeHEiRP44osvsHHjRmu0kaiMXbuA4mKgSRPz9zEkInJ1sbEyTcOECYZzrkJCgMREwM9PXleGn78VMzvI6t+/PzZs2IDZs2ejVq1amDlzJjp27IgNGzbggQcesEYbicpg6gYiotsTGwv07y+H/LKyZMCky/iu1cqAKzPT+LwsAAgNvbX/IRlndpAFAJGRkdi0aZOl20JkMk56JyK6fWr1re11SpcvWCBXEapUxgOtJ5/krhqVua1kpERKKCoCdu+Wt9mTRURkHbohxQYNDMtr1pTX8+dzc+nKmBRk+fn54eLFiwCAOnXqwM/Pr9wLkbWlpQEFBUCdOkDLlkq3hojIecXGAqdPy02kV66U15cuyWHG69eBvn2BPXuUbqX9Mmm48P3330ft2rUBAImmzIQjsqKSQ4Vu7IslIrIqY0OKq1cDDz8MpKQAvXsD27YBbdoo0jy7ZlKQNWLECADAzZs3oVKpEBMTg4CAAKs2jKg8uknvnI9FRKQMT09g/XqgVy8gNRV44AH5A9hIGk2XZlY/QLVq1fDcc89xY2hSjBBMQkpEZA+8vIDvvwfatQNycoDoaJmglG4xe7Cla9euOHjwoDXaQlSpv/+Wf8zu7kDnzkq3hojItdWpA/z0E9C8OfDPP7JH6/x5pVtlP8xO4fDCCy9g8uTJOHv2LDp16oRapXbmbdu2rcUaR1Sarherc2fZXU1ERMoKCAA2bZKjCydOADExcoK8r6/SLVOe2UHWkCFDAAAvvviivkylUkEIAZVKBS13iyQrYhJSIiL707AhsHmzTE6algY89BDw889AqX4Yl2N2kJWenm6NdhCZhElIiYjsU/Pmskere3dg507gkUeADRsADw+lW6YclRDlJcyniuTl5cHHxwcajQbe3t5KN8clXLoE+PvL2xcu3LpNRET2Y9cuOQk+P18GWl99BVSr0v4y1mHL72+zJ75funRJfzsjIwMzZ87ElClTsH37dos2jKi0nTvldcuWDLCIiOzV3XcD330ne7C++QZ4+mmguFjpVinD5CDrjz/+QFhYGOrXr4+WLVsiLS0NXbp0wfvvv4+PPvoIUVFRWL9+vRWbSq6OqRuIiBzD/ffLHiy1Gvjf/4AXXyx/o2lnZnKQNXXqVLRp0wa//vorevTogYcffhgPPfQQNBoN/v33Xzz77LOYM2dOlRqxePFihIWFwdPTE+Hh4dhTSY7+tWvXomXLlvD09ESbNm3www8/GDyuUqmMXubNm6evExYWVubxqrafbINJSImIHEe/fsAXX8gNphcvBl59VekWKUCYqG7duuLQoUNCCCGuXLkiVCqV2Ldvn/7xY8eOCR8fH1MPp7d69Wrh7u4uPvvsM3HkyBExevRo4evrK3JycozW37Fjh1Cr1WLu3Lni6NGj4tVXXxXVq1cXf/zxh75OVlaWweWzzz4TKpVKnDp1Sl+nUaNGYvbs2Qb1rl69anK7NRqNACA0Go3Z75nMd+2aEO7uQgBC/PWX0q0hIiJTLV0qP7sBId55R+nW2Pb72+SJ725ubsjOzkb9+vUBALVr18ahQ4fQpEkTAEBOTg6Cg4PNTuEQHh6OLl26YNGiRQCA4uJihIaGYvz48Zg+fXqZ+oMHD0Z+fj42btyoL7v77rvRvn17LF261OhrDBgwAFeuXEFKSoq+LCwsDBMnTsTEiRPNaq8OJ77b1o4dcpiwfn0gO1v+MiIiIscwdy4wbZq8vWQJ8NxzyrXFbie+q0p9s5W+b67CwkLs378f0dHRtxrk5obo6GikpqYafU5qaqpBfQCIiYkpt35OTg6+//57jBo1qsxjc+bMQd26ddGhQwfMmzcPN2/eLLetN27cQF5ensGFbKfkfCwGWEREjmXqVOCVV+TtF14AVqxQtj22Ytaiyqeeegoe/yW8uH79Op577jl9xvcbN26Y/eIXL16EVqsts9l0QEAAjh8/bvQ52dnZRutnZ2cbrf/555+jdu3aiI2NNSh/8cUX0bFjR/j5+WHnzp2YMWMGsrKyMH/+fKPHSUhIwKxZs0x9a2RhzI9FROTY3ngDyMsDFi4ERoyQex/27690q6zL5CBrxIgRBvefeOKJMnWGDx9++y2ysM8++wzDhg2DZ6k9WOLi4vS327ZtC3d3dzz77LNISEjQB5IlzZgxw+A5eXl5CA0NtV7DSa+4+Fb6Bq4sJCJyTCoVkJgoA63PPwceewz44QegZ0+lW2Y9JgdZy5Yts/iL+/v7Q61WIycnx6A8JycHgYGBRp8TGBhocv3t27fjxIkTWLNmTaVtCQ8Px82bN3H69Gm0aNGizOMeHh5Ggy+yvuPHgcuXgRo1gA4dlG4NERFVlZsb8MknwJUrQFKS7MnatAmIiFC6ZdZhdjJSS3J3d0enTp0MJqQXFxcjJSUFEeWc8YiICIP6ALBp0yaj9T/99FN06tQJ7dq1q7QtaWlpcHNz00/sJ/uhS90QHg5Ur65sW4iI6PZUqwasXAn06iWzwj/4IHDokNKtsg7FE93HxcVhxIgR6Ny5M7p27YrExETk5+dj5MiRAOQQZIMGDZCQkAAAmDBhArp374733nsPDz30EFavXo19+/bho48+MjhuXl4e1q5di/fee6/Ma6ampmL37t2IiopC7dq1kZqaikmTJuGJJ55AnTp1rP+mySxMQkpE5Fw8PGRPVkyM/CHdqxewfbvc/9CZKB5kDR48GBcuXMDMmTORnZ2N9u3bIzk5WT+5/cyZM3Bzu9Xh1q1bN6xcuRKvvvoqXn75ZTRr1gzr169H69atDY67evVqCCEwdOjQMq/p4eGB1atX4/XXX8eNGzfQuHFjTJo0yWDOFdkPJiElInI+tWoBGzcCUVFAWprc7/C334AGDWTAlZUFBAUBkZEyc7wj4gbRVcQ8WbaRlQUEB8sJk//+C/j4KN0iIiKypAsXgPvuk/Nvg4JkWVbWrcdDQoAFC4BSSQKqzG7zZBHZmq4Xq00bBlhERM6oXj05+b1ePRlclQywACAzExg0SA4vOhoGWWSXtFpg61bg00/l/W7dFG0OERFZUVBQ+UOCuvG2iRPld4MjYZBFdicpCQgLk+P0ycmy7KuvHPNXDBERVW77drllWnmEADIyZD1HwiCL7EpSkuwWPnvWsPzffx23u5iIiCpWeojwduvZCwZZZDe0WmDChFtdwyU5cncxERFVTDfh3VL17AWDLLIb27eX7cEqyVG7i4mIqGKRkXIVoUpl/HGVCggNlfUcCYMsshvO2l1MREQVU6tlmgagbKClu5+Y6Hj5shhkkd1w1u5iIiKqXGwssG6dTEZaUkiILLdUnixbYjLSKmIyUsvTauWqwsxM4/OyVCr5x5ae7ni/ZoiIyDRarXUzvtvy+1vxbXWIdHTdxQMHln3MkbuLiYjIdGo10KOH0q2wDA4Xkl155BGgSZOy5Y7cXUxERK6JPVlkV376Cfj7b6BGDWDVKqCgwPE3CCUiItfEIIvshhDAG2/I288/D/Tvr2x7iIiIbgeHC8lubNsG7NwJeHgAkycr3RoiIqLbwyCL7Mabb8rrUaOA4GBl20JERHS7GGSRXUhNBVJSgGrVgKlTlW4NERHR7WOQRXbhrbfk9fDhQKNGyraFiIjIEhhkkeIOHgS+/x5wcwOmT1e6NURERJbBIIsUp+vFGjIEaNZM2bYQERFZCoMsUtTRo0BSkrz98svKtoWIiMiSGGSRohISZH6s2FigVSulW0NERGQ5TEZKNqfb/DMtDVixQpa98oqiTSIiIrI4BllkU0lJwIQJwNmzt8o8PYHTp4GOHRVrFhERkcVxuJBsJikJGDTIMMACgOvXZblubhYREZEzYJBFNqHVyh4sIcqvM3GirEdEROQMGGSRTWzfXrYHqyQhgIwMWY+IiMgZMMgim8jKsmw9IiIie8cgi2wiKMiy9YiIiOwdgyyyichIICQEUKmMP65SAaGhsh4REZEzYJBFNqFWAwsWGH9MF3glJsp6REREzoBBFtlMbCywdq3cCLqkkBBg3Tr5OBERkbNgMlKyqfBwoLhYBlqffQY0aiSHCNmDRUREzoZBFtlUWpq8vusuYMQIRZtCRERkVXYxXLh48WKEhYXB09MT4eHh2LNnT4X1165di5YtW8LT0xNt2rTBDz/8YPD4U089BZVKZXDp3bu3QZ3Lly9j2LBh8Pb2hq+vL0aNGoWrV69a/L2RoYMH5XWHDsq2g4iIyNoUD7LWrFmDuLg4xMfH48CBA2jXrh1iYmJw/vx5o/V37tyJoUOHYtSoUTh48CAGDBiAAQMG4PDhwwb1evfujaysLP1l1apVBo8PGzYMR44cwaZNm7Bx40b8+uuvGDNmjNXeJ0m6IKt9e0WbQUREZHUqISra6MT6wsPD0aVLFyxatAgAUFxcjNDQUIwfPx7Tp08vU3/w4MHIz8/Hxo0b9WV333032rdvj6VLlwKQPVm5ublYv3690dc8duwY7rrrLuzduxedO3cGACQnJ+PBBx/E2bNnERwcXGm78/Ly4OPjA41GA29vb3Pftstq0gRITwd++QWIilK6NURE5Gps+f2taE9WYWEh9u/fj+joaH2Zm5sboqOjkZqaavQ5qampBvUBICYmpkz9rVu3on79+mjRogWef/55XLp0yeAYvr6++gALAKKjo+Hm5obdu3cbfd0bN24gLy/P4ELmyc2VARbAniwiInJ+igZZFy9ehFarRUBAgEF5QEAAsrOzjT4nOzu70vq9e/fGF198gZSUFLzzzjvYtm0b+vTpA+1/uw9nZ2ejfv36BseoVq0a/Pz8yn3dhIQE+Pj46C+hoaFmv19Xp5v03qgRUKeOok0hIiKyOsXnZFnDkCFD0K9fP7Rp0wYDBgzAxo0bsXfvXmzdurXKx5wxYwY0Go3+kpGRYbkGuwhdkGWvk96XL18OlUqFffv2Gdwvealfvz6ioqLw448/Gj3GH3/8gUGDBqFRo0bw9PREgwYN8MADD2DhwoVWb/+NGzcwbdo0BAcHo0aNGggPD8emTZtMeu7Vq1cRHx+P3r17w8/PDyqVCsuXLy9Tb+/evRg3bhxatWqFWrVqoWHDhnjsscfw559/Gj3uX3/9hSFDhiAkJAQ1a9ZEy5YtMXv2bBQUFNzOWyUicgiKpnDw9/eHWq1GTk6OQXlOTg4CAwONPicwMNCs+gDQpEkT+Pv74+TJk+jZsycCAwPLTKy/efMmLl++XO5xPDw84OHhYcrbonI46qT32bNno3HjxhBCICcnB8uXL8eDDz6IDRs24OGHH9bX27lzJ6KiotCwYUOMHj0agYGByMjIwK5du7BgwQKMHz/equ186qmnsG7dOkycOBHNmjXTt3PLli249957K3zuxYsXMXv2bDRs2BDt2rUr9wfJO++8gx07duDRRx9F27ZtkZ2djUWLFqFjx47YtWsXWrdura+bkZGBrl27wsfHB+PGjYOfnx9SU1MRHx+P/fv349tvv7Xk2ycisj9CYV27dhXjxo3T39dqtaJBgwYiISHBaP3HHntMPPzwwwZlERER4tlnny33NTIyMoRKpRLffvutEEKIo0ePCgBi3759+jo//fSTUKlUIjMz06R2azQaAUBoNBqT6pMQbdoIAQjx3z+D3Vm2bJkAIPbu3Wv0vs7ly5dF9erVxeOPP25Q/uCDD4p69eqJf//9t8yxc3JyrNZuIYTYvXu3ACDmzZunL7t27Zq44447RERERKXPv379usjKyhJCCLF3714BQCxbtqxMvR07dogbN24YlP3555/Cw8NDDBs2zKD8rbfeEgDE4cOHDcqHDx8uAIjLly+b+vaIiCzGlt/fig8XxsXF4eOPP8bnn3+OY8eO4fnnn0d+fj5GjhwJABg+fDhmzJihrz9hwgQkJyfjvffew/Hjx/H6669j3759GDduHAA57DFlyhTs2rULp0+fRkpKCvr374+mTZsiJiYGAHDnnXeid+/eGD16NPbs2YMdO3Zg3LhxGDJkiEkrC8l8168Dx47J2/Y6XGgqX19f1KhRA9WqGXYEnzp1Cq1atYKvr2+Z55SeA2hp69atg1qtNkhD4unpiVGjRiE1NbXS4W0PD48Ke4N1unXrBnd3d4OyZs2aoVWrVjim+wf+j25xSOk5lEFBQXBzcytzHCIiZ6N4xvfBgwfjwoULmDlzJrKzs9G+fXskJyfrP5jPnDkDtxKb3XXr1g0rV67Eq6++ipdffhnNmjXD+vXr9cMUarUav//+Oz7//HPk5uYiODgYvXr1whtvvGEw3LdixQqMGzcOPXv2hJubGwYOHIgPPvjAtm/ehRw5Aty8Cfj5yb0KHYlGo8HFixchhMD58+excOFCXL16FU888YRBvUaNGiE1NRWHDx82GDarTFFRETQajUl1/fz8DP4edA4ePIjmzZuXWY7ctWtXAEBaWprVFmuI/4ZRW7VqZVDeo0cPvPPOOxg1ahRmzZqFunXrYufOnViyZAlefPFF1KpVyyrtISKyG1bvK3NSHC40zyefyKHCnj2Vbkn5yhsuLH3x8PAQy5cvL/P8n3/+WajVaqFWq0VERISYOnWq+Omnn0RhYWGFr7tlyxajr2Pskp6ebvQYrVq1Evfff3+Z8iNHjggAYunSpSafh4qGC4353//+JwCITz/9tMxjb7zxhqhRo4bBe3jllVdMbgsRkaXZ8vtb8Z4scg2OvJ3O4sWL0bx5cwBykcWXX36JZ555BrVr10ZsbKy+3gMPPIDU1FQkJCTgp59+QmpqKubOnYt69erhk08+Qb9+/Ywev127diavAixvSO/atWtGF2Z4enrqH7eG48ePY+zYsYiIiMAII5tRhoWF4b777sPAgQNRt25dfP/993j77bcRGBioH+InInJWDLLIJhx1ZSEgh9xKJq4dOnQoOnTogHHjxuHhhx82mFvUpUsXJCUlobCwEIcOHcI333yD999/H4MGDUJaWhruuuuuMsevU6dOmQS75qpRowZu3LhRpvz69ev6xy0tOzsbDz30EHx8fPRzwkpavXo1xowZgz///BMh/40Rx8bGori4GNOmTcPQoUNRt25di7eLiMheKD7xnZxfcTFw6JC87Yg9WaW5ubkhKioKWVlZ+Ouvv4zWcXd3R5cuXfD2229jyZIlKCoqwtq1a43WLSwsRHZ2tkkXXULd0oKCgpCVlVWmXFdm6QUdGo0Gffr0QW5uLpKTk40e/8MPP0SHDh30AZZOv379UFBQgIO6yJuIyEmxJ4us7uRJID8f8PQE/ht1c3g3b94EIFezVkbXC2YsCAJu5dcyRXp6OsLCwsqUt2/fHlu2bEFeXp7B5HfdNlHtLdiFeP36dfTt2xd//vknNm/ebLR3DpBDq3WMpPYvKioCcOscEhE5KwZZZHW6Dou2bYFqTvA/rqioCD///DPc3d1x55136su3bNmCHj16QKVSGdT/4YcfAAAtWrQwejxLzMkaNGgQ3n33XXz00Ud46aWXAMgM8MuWLUN4eLh+ZWFBQQHOnDkDf39/+Pv7m/SaJWm1WgwePBipqan49ttvERERUW7d5s2b4+eff8aff/6pn9MGAKtWrYKbmxvatm1r9usTETkSJ/jKI3tn79vpVObHH3/E8ePHAQDnz5/HypUr8ddff2H69OkGvUbjx49HQUEBHnnkEbRs2RKFhYXYuXMn1qxZg7CwMH3ut9IsMScrPDwcjz76KGbMmIHz58+jadOm+Pzzz3H69Gl8+umn+np79uxBVFQU4uPj8frrrxscY9GiRcjNzcW5c+cAABs2bMDZs2f1783HxweTJ0/Gd999h759++Ly5cv48ssvDY5RMq3FlClT8OOPPyIyMhLjxo1D3bp1sXHjRvz444945plnmJOOiJyf1dcvOimmcDBdTIxM37BkidItqZgpKRw8PT1F+/btxZIlS0RxcbHB83/88Ufx9NNPi5YtWwovLy/h7u4umjZtKsaPH2/1jO9CyAzvL730kggMDBQeHh6iS5cuIjk52aCOLl1EfHx8mec3atSo0tQR3bt3rzDFRGm7d+8Wffr0EYGBgaJ69eqiefPm4q233hJFRUXWOAVERJWy5fe3SgghbBrVOYm8vDz4+PhAo9GUSQBJtwgBBAYC588Du3YB4eFKt4iIiFyZLb+/ubqQrCo7WwZYbm5AmzZKt4aIiMh2GGSRVekmvbdoAdSsqWxbiIiIbIlBFlmVI2d6JyIiuh0MssiqdCsLHTHTOxER0e1gkEVWxZ4sIiJyVQyyyGry8oBTp+Rt9mQREZGrYZBFVqPbrzAkBKhCcnEiIiKHxiCLrIZDhURE5MoYZJHVcNI7ERG5MgZZZDXsySIiIlfGIIusorAQOHJE3maQRURErohBFlnF0aNAURHg6ws0aqR0a4iIiGyPQRZZnFYLrFolbzdqBBQXK9seIiIiJTDIIotKSgLCwoC5c+X9Q4fk/aQkJVtFRERkewyyyGKSkoBBg4CzZw3LMzNlOQMtIiJyJQyyyCK0WmDCBECIso/pyiZOlPWIiIhcAYMssojt28v2YJUkBJCRIesRERG5AgZZZBGHD5tWLyvLuu0gIiKyFwyy6LZcvw68+SYwebJp9YOCrNseIiIie1FN6QaQ/dNq5TBfVpYMkiIjATc34LvvgEmTgPR0Wc/dXebGMjYvS6WSG0VHRtq27UREREphkEUVSkqSE9pLzrcKDAQCAmR6BgBo0ACYN08GWY8+KgOqkoGWSiWvExMBtdpmTSciIlIUhwupXOWlZMjOlgFWtWrAjBnA8ePA0KHAwIHAunUy6CopJESWx8baru1ERERKUwlhbHCHKpOXlwcfHx9oNBp4e3sr3RyL02plEtGKVgwGBckVg6V7p4wNL7IHi4iI7IEtv785XEhGVZaSAZBB1PbtQI8ehuVqddkyIiIiV2MXw4WLFy9GWFgYPD09ER4ejj179lRYf+3atWjZsiU8PT3Rpk0b/PDDD/rHioqKMG3aNLRp0wa1atVCcHAwhg8fjnPnzhkcIywsDCqVyuAyZ84cq7w/R2RqqgWmZCAiIjJO8SBrzZo1iIuLQ3x8PA4cOIB27dohJiYG58+fN1p/586dGDp0KEaNGoWDBw9iwIABGDBgAA7/l6ipoKAABw4cwGuvvYYDBw4gKSkJJ06cQL9+/coca/bs2cjKytJfxo8fb9X36khMTbXAlAxERETGKT4nKzw8HF26dMGiRYsAAMXFxQgNDcX48eMxffr0MvUHDx6M/Px8bNy4UV929913o3379li6dKnR19i7dy+6du2Kf/75Bw0bNgQge7ImTpyIiRMnVqndzjonSzefKjNTpme4cMF4PV1KhvR0zrciIiLHYcvvb0V7sgoLC7F//35ER0fry9zc3BAdHY3U1FSjz0lNTTWoDwAxMTHl1gcAjUYDlUoFX19fg/I5c+agbt266NChA+bNm4ebN2+We4wbN24gLy/P4OJskpLkZPeoKOCJJyoOsACmZCAiIqqIohPfL168CK1Wi4CAAIPygIAAHD9+3OhzsrOzjdbPzs42Wv/69euYNm0ahg4dahCxvvjii+jYsSP8/Pywc+dOzJgxA1lZWZg/f77R4yQkJGDWrFnmvD2HokvXYEq/ZkiIDLCYkoGIiKh8Tr26sKioCI899hiEEFiyZInBY3Fxcfrbbdu2hbu7O5599lkkJCTAw8OjzLFmzJhh8Jy8vDyEhoZar/E2pNXKhKOmBFizZgGvvMIeLCIiosooOlzo7+8PtVqNnJwcg/KcnBwEBgYafU5gYKBJ9XUB1j///INNmzZVOu4aHh6Omzdv4vTp00Yf9/DwgLe3t8HFWZiSrkHnk0+s2xYiIiJnoWiQ5e7ujk6dOiElJUVfVlxcjJSUFERERBh9TkREhEF9ANi0aZNBfV2A9ddff2Hz5s2oW7dupW1JS0uDm5sb6tevX8V347jMScOQkSGDMiIiIqqY4sOFcXFxGDFiBDp37oyuXbsiMTER+fn5GDlyJABg+PDhaNCgARISEgAAEyZMQPfu3fHee+/hoYcewurVq7Fv3z589NFHAGSANWjQIBw4cAAbN26EVqvVz9fy8/ODu7s7UlNTsXv3bkRFRaF27dpITU3FpEmT8MQTT6BOnTrKnAgFmZuGgbmxiIiIKqd4kDV48GBcuHABM2fORHZ2Ntq3b4/k5GT95PYzZ87Aze1Wh1u3bt2wcuVKvPrqq3j55ZfRrFkzrF+/Hq1btwYAZGZm4rvvvgMAtG/f3uC1tmzZgh49esDDwwOrV6/G66+/jhs3bqBx48aYNGmSwZwrVxIZKSezmzpkyNxYRERElVM8T5ajcrY8WUlJcoPnijA3FhEROTqXyZNF9iM2Fvj6a8Dd3fjjzI1FRERkHgZZpBcbC3TqJG97eRk+FhICrFvH3FhERESmUnxOFtkX3T7ayclAUZGc5B4UJOdtsQeLiIjIdAyySK+4WO5ZCAANGwJOkmuViIhIERwuJL0LF4CbN+X8q3JywRIREZGJGGSRnq4XKzAQqF5d2bYQERE5OgZZpKfLk9WggbLtICIicgYMsgiA3CR6yxZ528ND3iciIqKqY5BFSEoCwsJkDiwA2LFD3k9KUrBRREREDo5BlotLSgIGDSq7pU5mpixnoEVERFQ1DLJcmFYLTJgAGNtYSVc2cSKHDomIiKqCQZYL27694k2hhQAyMmQ9IiIiMg+DLBeWlWXZekRERHQLgywXFhRk2XpERER0C4MsFxYZKTd+Lo9KJbfWiYy0XZuIiIicBYMsF6ZWAw88YPwxlUpeJyZyY2giIqKqYJDlwvbuBb78Ut729TV8LCQEWLcOiI21ebOIiIicQjWlG0DKyM0FBg8GioqAgQOB1auB336Tk9yDguQQIXuwiIiIqo5BlgsSAhg9GkhPBxo3Bj75BKhWDejRQ+mWEREROQ8OF7qgJUvkUGD16sCaNWWHComIiOj2MchyMWlpwKRJ8vY77wBduijaHCIiIqfFIMuFXLkCPPYYUFgI9Osnt8whIiIi62CQ5SKEAJ59FvjrL5n7atmyW2kaiIiIyPIYZLmITz8FVq2SKwZXrwb8/JRuERERkXPj6kInpdXKjZ2zsoDr14Fx42T5W28B3bop2zYiIiJXwCDLCSUlARMmAGfPGpa3bw9MmaJIk4iIiFwOhwudTFISMGhQ2QALAA4dAtavt3mTiIiIXBKDLCei1coeLCHKrzNxoqxHRERE1sUgy4ls3268B0tHCCAjQ9YjIiIi62KQ5USysixbj4iIiKqOQZYTCQqybD0iIiKqOgZZTqaiBKMqlUxEGhlpu/YQERG5KqZwsCNaLfDzz8D8+cC//wLBwcAjjwAaDVCvHhAYKOts2QLs3SvzX4WFASNGAJ6eQN++tya9q1SGE+B1wVdiokxISkRERFYm7MCiRYtEo0aNhIeHh+jatavYvXt3hfW/+uor0aJFC+Hh4SFat24tvv/+e4PHi4uLxWuvvSYCAwOFp6en6Nmzp/jzzz8N6ly6dEk8/vjjonbt2sLHx0c8/fTT4sqVKya3WaPRCABCo9GY/kYr8PXXQnh6CiFDo6pfevYUYuVKIUJCDMtDQ+VrEBERuTJLf39XRPHhwjVr1iAuLg7x8fE4cOAA2rVrh5iYGJw/f95o/Z07d2Lo0KEYNWoUDh48iAEDBmDAgAE4fPiwvs7cuXPxwQcfYOnSpdi9ezdq1aqFmJgYXL9+XV9n2LBhOHLkCDZt2oSNGzfi119/xZgxY6z+fo1JSgIGDpQ9U7dr1Chg6FDg9GnZ47VypbxOTwdiY2//+ERERGQalRAVZVWyvvDwcHTp0gWLFi0CABQXFyM0NBTjx4/H9OnTy9QfPHgw8vPzsXHjRn3Z3Xffjfbt22Pp0qUQQiA4OBiTJ0/GSy+9BADQaDQICAjA8uXLMWTIEBw7dgx33XUX9u7di86dOwMAkpOT8eCDD+Ls2bMIDg6utN15eXnw8fGBRqOBt7d3ld+/VivnSVlqxV9IiAywOCRIRERUlqW+v02haE9WYWEh9u/fj+joaH2Zm5sboqOjkZqaavQ5qampBvUBICYmRl8/PT0d2dnZBnV8fHwQHh6ur5OamgpfX199gAUA0dHRcHNzw+7du42+7o0bN5CXl2dwsQTd/oKWcvYs82ARERHZA0WDrIsXL0Kr1SIgIMCgPCAgANnZ2Uafk52dXWF93XVlderXr2/weLVq1eDn51fu6yYkJMDHx0d/CQ0NNfFdVswaOauYB4uIiEh5is/JchQzZsyARqPRXzIyMixyXGvkrGIeLCIiIuUpGmT5+/tDrVYjJyfHoDwnJweBgYFGnxMYGFhhfd11ZXVKT6y/efMmLl++XO7renh4wNvb2+BiCZGRlg2KQkKYB4uIiMgeKBpkubu7o1OnTkhJSdGXFRcXIyUlBREREUafExERYVAfADZt2qSv37hxYwQGBhrUycvLw+7du/V1IiIikJubi/379+vr/PLLLyguLkZ4eLjF3p8p1Grgvzn/FrFgASe9ExER2QWrJ4moxOrVq4WHh4dYvny5OHr0qBgzZozw9fUV2dnZQgghnnzySTF9+nR9/R07dohq1aqJd999Vxw7dkzEx8eL6tWriz/++ENfZ86cOcLX11d8++234vfffxf9+/cXjRs3FteuXdPX6d27t+jQoYPYvXu3+O2330SzZs3E0KFDTW63veXJql2bebCIiIgqY8s8WYpnfB88eDAuXLiAmTNnIjs7G+3bt0dycrJ+4vqZM2fg5narw61bt25YuXIlXn31Vbz88sto1qwZ1q9fj9atW+vrTJ06Ffn5+RgzZgxyc3Nx7733Ijk5GZ6envo6K1aswLhx49CzZ0+4ublh4MCB+OCDD2z3xkuJjQWuXq16xvf772cPFhERkT1RPE+Wo7Jlng0iIiKyDJfJk0VERETkrBhkEREREVkBgywiIiIiK2CQRURERGQFDLKIiIiIrIBBFhEREZEVMMgiIiIisgIGWURERERWwCCLiIiIyAoU31bHUekS5efl5SncEiIiIjKV7nvbFhveMMiqoitXrgAAQkNDFW4JERERmevKlSvw8fGx6mtw78IqKi4uxrlz51C7dm2oVKrbOlZeXh5CQ0ORkZHBfRBtjOdeOTz3yuG5VxbPv3J05/7o0aNo0aIF3NysO2uKPVlV5ObmhpCQEIse09vbm39wCuG5Vw7PvXJ47pXF86+cBg0aWD3AAjjxnYiIiMgqGGQRERERWQGDLDvg4eGB+Ph4eHh4KN0Ul8Nzrxyee+Xw3CuL5185tj73nPhOREREZAXsySIiIiKyAgZZRERERFbAIIuIiIjIChhkEREREVkBgyw7sHjxYoSFhcHT0xPh4eHYs2eP0k1yaK+//jpUKpXBpWXLlvrHr1+/jrFjx6Ju3brw8vLCwIEDkZOTY3CMM2fO4KGHHkLNmjVRv359TJkyBTdv3rT1W7F7v/76K/r27Yvg4GCoVCqsX7/e4HEhBGbOnImgoCDUqFED0dHR+OuvvwzqXL58GcOGDYO3tzd8fX0xatQoXL161aDO77//jsjISHh6eiI0NBRz58619luze5Wd+6eeeqrM30Hv3r0N6vDcV01CQgK6dOmC2rVro379+hgwYABOnDhhUMdSnzNbt25Fx44d4eHhgaZNm2L58uXWfnt2zZRz36NHjzL/95977jmDOjY794IUtXr1auHu7i4+++wzceTIETF69Gjh6+srcnJylG6aw4qPjxetWrUSWVlZ+suFCxf0jz/33HMiNDRUpKSkiH379om7775bdOvWTf/4zZs3RevWrUV0dLQ4ePCg+OGHH4S/v7+YMWOGEm/Hrv3www/ilVdeEUlJSQKA+OabbwwenzNnjvDx8RHr168Xhw4dEv369RONGzcW165d09fp3bu3aNeundi1a5fYvn27aNq0qRg6dKj+cY1GIwICAsSwYcPE4cOHxapVq0SNGjXE//3f/9nqbdqlys79iBEjRO/evQ3+Di5fvmxQh+e+amJiYsSyZcvE4cOHRVpamnjwwQdFw4YNxdWrV/V1LPE58/fff4uaNWuKuLg4cfToUbFw4UKhVqtFcnKyTd+vPTHl3Hfv3l2MHj3a4P++RqPRP27Lc88gS2Fdu3YVY8eO1d/XarUiODhYJCQkKNgqxxYfHy/atWtn9LHc3FxRvXp1sXbtWn3ZsWPHBACRmpoqhJBfXm5ubiI7O1tfZ8mSJcLb21vcuHHDqm13ZKW/6IuLi0VgYKCYN2+eviw3N1d4eHiIVatWCSGEOHr0qAAg9u7dq6/z448/CpVKJTIzM4UQQnz44YeiTp06Bud+2rRpokWLFlZ+R46jvCCrf//+5T6H595yzp8/LwCIbdu2CSEs9zkzdepU0apVK4PXGjx4sIiJibH2W3IYpc+9EDLImjBhQrnPseW553ChggoLC7F//35ER0fry9zc3BAdHY3U1FQFW+b4/vrrLwQHB6NJkyYYNmwYzpw5AwDYv38/ioqKDM55y5Yt0bBhQ/05T01NRZs2bRAQEKCvExMTg7y8PBw5csS2b8SBpaenIzs72+Bc+/j4IDw83OBc+/r6onPnzvo60dHRcHNzw+7du/V17rvvPri7u+vrxMTE4MSJE/j3339t9G4c09atW1G/fn20aNECzz//PC5duqR/jOfecjQaDQDAz88PgOU+Z1JTUw2OoavD74dbSp97nRUrVsDf3x+tW7fGjBkzUFBQoH/MlueeG0Qr6OLFi9BqtQb/0AAQEBCA48ePK9QqxxceHo7ly5ejRYsWyMrKwqxZsxAZGYnDhw8jOzsb7u7u8PX1NXhOQEAAsrOzAQDZ2dlG/010j5FpdOfK2Lksea7r169v8Hi1atXg5+dnUKdx48ZljqF7rE6dOlZpv6Pr3bs3YmNj0bhxY5w6dQovv/wy+vTpg9TUVKjVap57CykuLsbEiRNxzz33oHXr1gBgsc+Z8urk5eXh2rVrqFGjhjXeksMwdu4B4PHHH0ejRo0QHByM33//HdOmTcOJEyeQlJQEwLbnnkEWOZ0+ffrob7dt2xbh4eFo1KgRvvrqK5f/UCLXMWTIEP3tNm3aoG3btrjjjjuwdetW9OzZU8GWOZexY8fi8OHD+O2335Ruissp79yPGTNGf7tNmzYICgpCz549cerUKdxxxx02bSOHCxXk7+8PtVpdZsVJTk4OAgMDFWqV8/H19UXz5s1x8uRJBAYGorCwELm5uQZ1Sp7zwMBAo/8musfINLpzVdH/78DAQJw/f97g8Zs3b+Ly5cv897CwJk2awN/fHydPngTAc28J48aNw8aNG7FlyxaEhIToyy31OVNeHW9vb5f/wVjeuTcmPDwcAAz+79vq3DPIUpC7uzs6deqElJQUfVlxcTFSUlIQERGhYMucy9WrV3Hq1CkEBQWhU6dOqF69usE5P3HiBM6cOaM/5xEREfjjjz8MvoA2bdoEb29v3HXXXTZvv6Nq3LgxAgMDDc51Xl4edu/ebXCuc3NzsX//fn2dX375BcXFxfoPxoiICPz6668oKirS19m0aRNatGjB4SoznD17FpcuXUJQUBAAnvvbIYTAuHHj8M033+CXX34pM6Rqqc+ZiIgIg2Po6rjy90Nl596YtLQ0ADD4v2+zc2/WNHmyuNWrVwsPDw+xfPlycfToUTFmzBjh6+trsOqBzDN58mSxdetWkZ6eLnbs2CGio6OFv7+/OH/+vBBCLq1u2LCh+OWXX8S+fftERESEiIiI0D9ft7y3V69eIi0tTSQnJ4t69eoxhYMRV65cEQcPHhQHDx4UAMT8+fPFwYMHxT///COEkCkcfH19xbfffit+//130b9/f6MpHDp06CB2794tfvvtN9GsWTODNAK5ubkiICBAPPnkk+Lw4cNi9erVombNmi6fRqCic3/lyhXx0ksvidTUVJGeni42b94sOnbsKJo1ayauX7+uPwbPfdU8//zzwsfHR2zdutUgTUBBQYG+jiU+Z3RpBKZMmSKOHTsmFi9e7PIpHCo79ydPnhSzZ88W+/btE+np6eLbb78VTZo0Effdd5/+GLY89wyy7MDChQtFw4YNhbu7u+jatavYtWuX0k1yaIMHDxZBQUHC3d1dNGjQQAwePFicPHlS//i1a9fECy+8IOrUqSNq1qwpHnnkEZGVlWVwjNOnT4s+ffqIGjVqCH9/fzF58mRRVFRk67di97Zs2SIAlLmMGDFCCCHTOLz22msiICBAeHh4iJ49e4oTJ04YHOPSpUti6NChwsvLS3h7e4uRI0eKK1euGNQ5dOiQuPfee4WHh4do0KCBmDNnjq3eot2q6NwXFBSIXr16iXr16onq1auLRo0aidGjR5f58cZzXzXGzjsAsWzZMn0dS33ObNmyRbRv3164u7uLJk2aGLyGK6rs3J85c0bcd999ws/PT3h4eIimTZuKKVOmGOTJEsJ25171X6OJiIiIyII4J4uIiIjIChhkEREREVkBgywiIiIiK2CQRURERGQFDLKIiIiIrIBBFhEREZEVMMgiIiIisgIGWURERERWwCCLiFzSU089hQEDBijdDCJyYtWUbgARkaWpVKoKH4+Pj8eCBQvADS+IyJoYZBGR08nKytLfXrNmDWbOnIkTJ07oy7y8vODl5aVE04jIhXC4kIicTmBgoP7i4+MDlUplUObl5VVmuLBHjx4YP348Jk6ciDp16iAgIAAff/wx8vPzMXLkSNSuXRtNmzbFjz/+aPBahw8fRp8+feDl5YWAgAA8+eSTuHjxoo3fMRHZIwZZRET/+fzzz+Hv7489e/Zg/PjxeP755/Hoo4+iW7duOHDgAHr16oUnn3wSBQUFAIDc3Fzcf//96NChA/bt24fk5GTk5OTgscceU/idEJE9YJBFRPSfdu3a4dVXX0WzZs0wY8YMeHp6wt/fH6NHj0azZs0wc+ZMXLp0Cb///jsAYNGiRejQoQPefvtttGzZEh06dMBnn32GLVu24M8//1T43RCR0jgni4joP23bttXfVqvVqFu3Ltq0aaMvCwgIAACcP38eAHDo0CFs2bLF6PyuU6dOoXnz5lZuMRHZMwZZRET/qV69usF9lUplUKZbtVhcXAwAuHr1Kvr27Yt33nmnzLGCgoKs2FIicgQMsoiIqqhjx474+uuvERYWhmrV+HFKRIY4J4uIqIrGjh2Ly5cvY+jQodi7dy9OnTqFn376CSNHjoRWq1W6eUSkMAZZRERVFBwcjB07dkCr1aJXr15o06YNJk6cCF9fX7i58eOVyNWpBFMeExEREVkcf2oRERERWQGDLCIiIiIrYJBFREREZAUMsoiIiIisgEEWERERkRUwyCIiIiKyAgZZRERERFbAIIuIiIjIChhkEREREVkBgywiIiIiK2CQRURERGQF/w95zVjgKM1V/QAAAABJRU5ErkJggg=="
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Integrated Brier score is 0.12827381547056485.\n"
     ]
    }
   ],
   "source": [
    "ibs = eval.integrated_brier_score(num_points=None, IPCW_weighted=True, draw_figure=True)\n",
    "print(\"Integrated Brier score is {}.\".format(ibs))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2023-08-12T01:11:57.865253914Z",
     "start_time": "2023-08-12T01:11:57.587504111Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Calculate the MAE-PO loss\n",
    "You can set `method` as `Hinge`, `Uncensored`, `Margin`, `IPCW-v1`, `IPCW-v2`, or `Pseudo_obs` for different MAE calculation.\n",
    "\n",
    "You can also determine if you want to use weighting for calculating the l1-loss. If True, the weight is 1 for uncensored patients and $1 - \\text{Kaplan-Meier}(C_i)$ for censored patient i."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE-PO loss is 1717.0362381265797.\n"
     ]
    }
   ],
   "source": [
    "mae_score = eval.mae(method='Pseudo_obs', weighted=False)\n",
    "print(\"MAE-PO loss is {}.\".format(mae_score))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2023-08-12T01:11:59.029830457Z",
     "start_time": "2023-08-12T01:11:58.945052861Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Calculate the 1-Calibration\n",
    "Run one calibration test at 50% time of the testing set."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model is one-calibrated at time 858.\n"
     ]
    }
   ],
   "source": [
    "t_time = round(np.percentile(test_event_times, 50))\n",
    "p_value, ob, exp = eval.one_calibration(target_time=t_time, method=\"DN\")\n",
    "if p_value >= 0.05:\n",
    "    print(\"The model is one-calibrated at time {}.\".format(t_time))\n",
    "else:\n",
    "    print(\"The model is not one-calibrated at time {}.\".format(t_time))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2023-08-12T01:12:00.495250605Z",
     "start_time": "2023-08-12T01:12:00.388655156Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "###  Plot the histogram for 1-Calibration.\n",
    "As presented in [Effective Ways to Build and Evaluate Individual Survival Distributions](https://jmlr.csail.mit.edu/papers/v21/18-772.html)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [],
   "source": [
    "# Reverse the bins to let it start from small to large observations\n",
    "ob.reverse()\n",
    "exp.reverse()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2023-08-12T01:12:01.652947915Z",
     "start_time": "2023-08-12T01:12:01.650871540Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.arange(10)\n",
    "plt.bar(bins - 0.15, ob, width=0.3, label=\"Observed\")\n",
    "plt.bar(bins + 0.15, exp, width=0.3, label=\"Expected\")\n",
    "\n",
    "plt.xlabel('Bin')\n",
    "plt.ylabel('Survival Probability')\n",
    "plt.text(0.5, 0.5, r'$p-value = {:.3f}$'.format(p_value), verticalalignment='top', horizontalalignment='left', fontsize=12, color='Black')\n",
    "plt.legend()\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2023-08-12T01:12:02.490279843Z",
     "start_time": "2023-08-12T01:12:02.331710624Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Calculate the distribution calibration (D-Calibration)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model is d-calibrated!\n"
     ]
    }
   ],
   "source": [
    "p_value_2, bin_statistics = eval.d_calibration()\n",
    "if p_value_2 >= 0.05:\n",
    "    print(\"The model is d-calibrated!\")\n",
    "else:\n",
    "    print(\"The model is not d-calibrated!\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2023-08-12T01:12:03.954038810Z",
     "start_time": "2023-08-12T01:12:03.851952185Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "data": {
      "text/plain": "array([0.11772446, 0.10876726, 0.09986195, 0.09651766, 0.10408213,\n       0.08713236, 0.09791648, 0.09659758, 0.09812724, 0.09327287])"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# An ideal model should have bin statistics equally distributed. That means 0.1 for each value since we are using 10 bins.\n",
    "bin_statistics /= bin_statistics.sum()\n",
    "bin_statistics"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2023-08-12T01:12:04.525873619Z",
     "start_time": "2023-08-12T01:12:04.476915671Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Plot the sideways histogram of D-Calibration\n",
    "As presented in [Effective Ways to Build and Evaluate Individual Survival Distributions](https://jmlr.csail.mit.edu/papers/v21/18-772.html)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bars = ('[0.0, 0.1)', '[0.1, 0.2)', '[0.2, 0.3)', '[0.3, 0.4)', '[0.4, 0.5)',\n",
    "        '[0.5, 0.6)', '[0.6, 0.7)', '[0.7, 0.8)', '[0.8, 0.9)', '[0.9, 1.0]')\n",
    "y_pos = np.arange(len(bars))\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.barh(y_pos, bin_statistics)\n",
    "ax.plot(np.ones(len(bars)+2) * 0.1, np.arange(-1, len(bars)+1),  '--', color=\"black\")\n",
    "ax.invert_yaxis()  # labels read top-to-bottom\n",
    "ax.set_xlabel('Proportion in Bin')\n",
    "ax.set_ylabel('Bins')\n",
    "ax.text(0.14, 0.5, r'$p-value = {:.2f}$'.format(p_value_2), verticalalignment='top', horizontalalignment='right', fontsize=10, color='Black')\n",
    "plt.ylim([-0.8, 9.8])\n",
    "plt.yticks(y_pos, bars)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2023-08-12T01:12:06.530781184Z",
     "start_time": "2023-08-12T01:12:06.426962975Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Plot the Q-Q plot of D-Calibration\n",
    "As presented in [Countdown Regression: Sharp and Calibrated Survival Predictions](https://proceedings.mlr.press/v115/avati20a.html) and [Inverse-Weighted Survival Games](https://arxiv.org/abs/2111.08175)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "default = np.linspace(0, 1, 11)\n",
    "plt.plot(default, default, linestyle='dashed', label=\"x=y\")\n",
    "\n",
    "fraction = np.concatenate((np.array([0]), bin_statistics))\n",
    "plt.plot(default, fraction.cumsum(), 'o-', label=\"CoxPH\")\n",
    "\n",
    "plt.xlabel(\"Cumulative Risk\")\n",
    "plt.ylabel(\"Observed Cumulative Fraction\")\n",
    "plt.legend()\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2023-08-12T01:12:08.176061171Z",
     "start_time": "2023-08-12T01:12:08.107184655Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}