survival-analysis/intro-survival-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "\n",
    "# Plotting syle\n",
    "plt.style.use('dark_background')\n",
    "\n",
    "# Plotting functions\n",
    "def clean_ax(ax, clean_all : bool = False, inverted : bool = False):\n",
    "    \"\"\"\n",
    "    Cleans the borders of a matplotlib.axis object.\n",
    "    Parameters\n",
    "    ----------\n",
    "    ax: matplotlib.axis\n",
    "        axis object to be modified\n",
    "    clean_all: boolean (default = False)\n",
    "        whether to clean the entire boundary\n",
    "    inverted: boolean (default = False)\n",
    "        whether to clean bottom and right borders\n",
    "    \"\"\"\n",
    "    if clean_all:\n",
    "        ax.spines['top'].set_visible(False)\n",
    "        ax.spines['right'].set_visible(False)\n",
    "        ax.spines['left'].set_visible(False)    \n",
    "        ax.spines['bottom'].set_visible(False)\n",
    "    elif inverted:\n",
    "        ax.spines['bottom'].set_visible(False)\n",
    "        ax.spines['right'].set_visible(False)\n",
    "    else:\n",
    "        ax.spines['top'].set_visible(False)\n",
    "        ax.spines['right'].set_visible(False)\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to Survival Analysis\n",
    "\n",
    "In this notebook we will take a quick review of the collection of statistical techniques known as **survival analysis**. In survival analysis, we are generally interested in the time until an event happens. Thus, the response variable is usually called **survival time**, **failure time** or **event time**. \n",
    "\n",
    "Some examples of such situations are:\n",
    "* Survival time of a patient after a surgery or treatment.\n",
    "* Time until a former prisoner goes back to jail.\n",
    "* The time taken for a farm to experience its first case of an exotic disease.\n",
    "* The time until a person gets inquired at Serasa.\n",
    "* ... or the time until a person (Data Scientist) dies.\n",
    "\n",
    "## Censoring\n",
    "When analyzing (accompanying) subjects on a survival experiment (such as the ones listed above) typically some subjects will have **censored** survival times. That is, the survival times of such subjects are actually not observed. This is the case when dealing with death as the event, for instance. In other words, censoring occurs when we are unable to collect a complete set of data for a subject (because he dropped out of the study, for instance). \n",
    "* *Time-to-censoring* $C$, is the duration between the start event and the censoring.\n",
    "* *right-censoring*: censoring that occurs before the event, this is, no information could be collected about the event of interest for that subject.\n",
    "* *left-censoring*: occurs when a person's true *survival* time is less that or equal to the time information was observed, e.g., time until COVID exposure.\n",
    "\n",
    "Often, a dataset with right censoring would look like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>birth</th>\n",
       "      <th>death</th>\n",
       "      <th>status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.51</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.32</td>\n",
       "      <td>7.73</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.56</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.56</td>\n",
       "      <td>5.81</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.58</td>\n",
       "      <td>4.22</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.66</td>\n",
       "      <td>12.33</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   birth  death  status\n",
       "0   3.75    NaN       0\n",
       "1   9.51    NaN       0\n",
       "2   7.32   7.73       1\n",
       "3   5.99    NaN       0\n",
       "4   1.56    NaN       0\n",
       "5   1.56   5.81       1\n",
       "6   0.58   4.22       1\n",
       "7   8.66  12.33       0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_seed = 42\n",
    "study_duration = 15\n",
    "\n",
    "np.random.seed(seed = random_seed)\n",
    "births = np.reshape(np.round(np.random.rand(8,1)* 10, 2), -1)\n",
    "survival_df = pd.DataFrame({'birth': births,\n",
    "                            'death': np.reshape(np.round([x +np.random.random()*20 for x in births],2), -1) })\n",
    "                          \n",
    "survival_df.loc[survival_df.death > study_duration, 'death'] = np.nan\n",
    "survival_df['status'] = 1 - survival_df.death.isna().astype(int)\n",
    "# Censoring an arbitrary observation\n",
    "idx = survival_df[~survival_df['death'].isna()].index[-1]\n",
    "survival_df.at[idx, 'status'] = 0\n",
    "\n",
    "survival_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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CVFQUsrKy4OTkhGPHjsHJyQlJSUkq/1gPGjQIlStXxt69e4u1j15W2Hv5VTVr1gQAREdHq11eUD9Q8PV3OTk5Bd6gVFL5/8l6+UheQUxMTAAAM2fOLHScuiN8SUlJKn35hdrLr6W474/8/VrQFDj5/epukCrJz+9l6n4e6l5DaX7mJG8s+Eir5OXlYffu3WjTpg2++eYb9OrVS1Hw5R9tePlOzHzqPlhfVthRlF9++UVRrK1btw52dnZo27Yt9u/fj9jY2GLlDg8Px+nTp9GnTx80b94c9+/fVxSN27dvVxrr7u4Oa2trzJs3T2X+tS5dumDGjBnF2mZB3n77bQwdOhQnT57EgAEDlI5A6OjoFPkPZnGYmpri448/RnBwMLp27apyA8jIkSNLtD5vb284Oztj0qRJ+Oqrr+Du7o709HRs3bpVZezFixcxaNAg6Ovr4+2330b//v3x0UcfKe4mVXf69GVHjhzBkSNHYGhoiM6dO2PgwIGYOnUqDh06hHbt2uHu3btF5j19+jSmTJmCjh07wsnJCY8fP1acpr98+TJ69+6NevXqoWXLljh48KDKTT9DhgxBUlKSxi+gz78Dvm7dumqXF9RfXvKLGHNz8yJPNeYXbTVq1EBKSorGMhXn/ZGfpV69emrXkX/HsLpCs6RHdEtK23/mJB2e0iWtlP+B/vIUJfnX4DRq1EhlvLrrcIrr6dOnOH36NLp06YK33nqrwOvuipJ/+tfNzQ22trawtbXF0aNHVYrGpk2bAnhxt+WrymKqhPz1HzhwQOV0U6dOnWBoaFjqbVhbW0NPTw8nTpxQKfbMzc1LdJoVAA4dOoTHjx9j/Pjx6NOnD1q0aIHffvut0Ls8s7KycOHCBXh6euLjjz8G8OKateLKn57n888/x+LFi1GlShU4OzsX67n5RWW/fv3Qs2dPpSODp06dQtu2bRWn918tQA0MDNC3b18cPnwY2dnZxc77Oq5fvw4AKlPFAICRkRHs7OzKZDsFHQUuysWLFwGgWPs9f2z+6XJNK+z98fJ+Vfea86fgyb9EozwV9jPX0dFRTIlFFQ8LPpLEiBEj0Lt3b7VzztWtWxeTJk0CAJw5c0bRn3/ty/jx45U+ZBs2bFisOdQKk1+sTZw4ESNHjkRsbKzKFCtF2bt3L5KSkjBmzBiMGzdOab0vy795xMHBQanfzs4OX331VQmTqypo/aampli7dm2p1//yNrp37w5d3f8+RoyMjLBhwwa185sVRgiB9evXo27duti8eTMAKE6vv6x79+6oUaOGSn/+UYuivqWgV69eqFq16ms/P5+/vz/y8vLg4eEBY2NjpaLu9OnT0NXVxaxZsxSPX+bs7AxDQ8MymWy5KH5+foqph9q2bau0bM6cOWV2LV7+DQ2NGzcu0fO8vb2RnZ2Nb775Bi1btlRZ/vL1cz/99BOysrKwatUqNGvWTGVs5cqV1RY5JVHc90d4eDhOnDgBKysrlSPynTp1wqhRoxAfH18uP+NX/fnnn3j06BF69eqF/v37Ky2bPHkymjdvXu6ZSDvwlC5JonPnzpgxYwYiIyNx7tw5hIaGAgCsrKzg4uICQ0ND7N+/H7///rviOZcvX0ZgYCDs7e1x+fJlnD59GnXr1sWgQYNw/PjxEv9j87L8Ym3GjBnQ19fHDz/8UOh8bupkZGRg9+7dcHd3h4eHB2JjY9XOw7V161Z88cUXWL16NRwdHfHw4UM0a9YMAwcOxN69e5Vu/HgdV65cwblz5+Dq6oo///wT586dQ926deHs7Iz79+8X63qpokRHR2PHjh0YOXIkbty4gRMnTqBmzZro06cPMjIycP36dbUXrBdm48aNmDt3Lho2bIibN28qjui87PPPP0ffvn0REBCAkJAQpKamonXr1nB2dkZ8fLzijuKCrFy5EpaWlggICEBYWBiysrLw9ttvw8nJCWFhYUVeA5gvLi4ON2/eVBwhe7mou3DhAtLS0lC3bl08e/ZM5YadIUOGID09vVg3KpRWSkoKPDw88Ouvv+L8+fNK8/DZ2toiICAADg4Oam/OKIn81zxjxgzUrl1bcZ3Yjz/+WOi3f9y9exceHh5Yt24drl+/Dj8/Pzx8+BAmJibo0KEDUlJSFDe+3L9/HxMmTMDmzZtx+/ZtHDt2DA8ePEDlypXRuHFj9OjRAzExMWoLx+IqyftjypQp+PPPP7FixQr07dsXV69eVczDl5eXh/Hjx6ud61LThBBwd3fHsWPHcODAAcU8fG3btkWfPn1w5MgRDBgwoNQ/c3ozST43DFvFaw0bNhQeHh5i79694t69eyIpKUlkZmaKiIgIcfjwYTF69GilOdXyW82aNcX69etFdHS0yMjIEMHBwWLSpElFzsNXnPnrNmzYoJijqn379mrHFDXfWLdu3RTr+OGHHwrcVsuWLYWfn5+Ijo4Wqamp4urVq2LixIll8joAiFq1aom1a9eK0NBQkZ6eLh49eiQWLVokDAwMRGhoqAgNDS3R61I3P5iBgYH49ttvxcOHD0V6erp48uSJ+Omnn0Tt2rUV85+9PF7dPHyvtr179wohhPDw8FC7vE+fPmLz5s3i9u3bIjExUaSmpop79+6JNWvWqMzjqG4evmHDhont27eLBw8eiJSUFJGUlCSCg4PFt99+K+rUqVOi93D+nIC3bt1SWZY/R9vOnTuV+itVqiTi4+OFn59fiX9n1O3T4v4M+/fvL/7880+RlpYm4uPjxf79+0Xz5s3FwYMHhRBC1KxZUzG2oPdgUTn69esnzp8/L1JSUhS/A8V9v3bp0kX8/vvvIjo6WmRmZorw8HBx9OhR4erqqjLWxsZG+Pj4iLCwMJGRkSHi4uJEcHCwWLdunXB0dCz2PlO3v0r6/mjQoIHw8vISYWFhIjMzU8TExIh9+/aJDh06lOjnU9jv2evMPwpAdOrUSZw4cUIkJyeL5ORkcfLkSdGlSxfx448/CiGEsLW1LfF7kO2Nb5IHYGNjYxM6Ojri77//FqmpqaJ69eqS59FE69OnjxBCiHHjxkmeRVdXV4SFhYnIyEjJs7CVXzt37pzIzs4WhoaGkmdhK9/Ga/iISCu89957sLa2xtatWzV6F6aUhgwZgpycHBw8eLDctlmzZk0YGBio9M+ZMwcWFhavNTUMaTcDAwPF9Cwvc3NzQ7du3XDixIliX69K8qGDF5UfEZEkvvzyS9SuXRuTJ09GpUqV0Lp1azx58kTqWLLRr18/7Nq1CydOnEBYWBiqVauGLl26oF27dnjy5Ak6dOiAmJgYqWNSGWrevDmuX7+OkydP4tGjR6hUqRLatWuHHj16ICEhAV27dsW9e/ekjkkSkPwwIxsbW8VtQgiRmZkprl+/Lnr37i15Hrk1S0tL8euvv4rQ0FCRmpoqMjIyxKNHj8Tq1auFmZmZ5PnYyr4ZGxuLDRs2iAcPHojk5GSRmZkpHj9+LDZt2qT2e7zZKkbjET4iIiIimeM1fEREREQyx4KPiIiISOZY8BERERHJHAs+IiIiIpmT/Ver9Z06EQkRkbjid0TqKERERKRl3NycYGlphrCwZ/D1PVX0E95Qsj/C18/DHR0Gu0gdg4iIiLSQ2zgneM4bBbdxTlJH0SjZF3xEREREFR0LPiIiIiKZY8FHREREJHOyv2nj0ZVriLj3UOoYREREpIWCboQo/SlX/Go1IiIiIpnjKV0iIiIimWPBR0RERCRzsr+Gr0mHdkhPSUXEfV7HR0RERMpsba1gbGyExMQ0BAWFSh1HY2R/Dd/K4At4dOUavCdMkzoKERERaZnT/ovh4NAGAQHB6OU4W+o4GsNTukREREQyx4KPiIiISOZY8BERERHJHAs+IiIiIpljwUdEREQkcyz4iIiItNwHdq2RNf//0L5BXY1vq38zK3zj0FXj26HyxYKPiIiIFPo3s8Y3jiz45EarCj4fHx8IIQpsnTt3ljoiERER0RtHqyZetra2hqmpqUr/wYMHkZmZCQsLC+Tl5UmQjIiISDof2LXGpiHO6PLzL7gWEa12TNfG5pjr2BUdzetDT0cHQVHPsPTMJRx9GKIYY1C5Ejwdu2FIq7dQv5oRnmdnIzQhCavPX8WuW/ew8d3+GNvORmXdzVatx+PEZI29PtI8rfpqtZCQEISEhCj19ezZE6ampli4cCGLPSIiIjV6WDTE0bHDEBwdgw/9jiMzJwcfdrLDvlFD8MHvh7D79n0AwPJ+Dhht2xqep8/hRuQzGFWujNZ166C2oQEAYHHgBRjpV4Zr6+bovmGbYv2RKWmSvC4qO1pV8KkzceJE5OXlYfPmzVJHISIi0kqL+vREQkYGem/ZhbSsbADA4QchuDp1LJb2s1cUfF0bm+OPv8Pww4W/FM99+QhgSEISolOfAwAuP40sx1dAmqbVBV+NGjXw3nvv4dSpUwgLCyt0rKmpqdrTwTExMYiJidFQQiKisuXu7g5ra2upY5DEQkJCsHHjxmKNNaxcGZ3M6+PnqzcUxR4A5AmBbUF3sKSvPZrXqY37sfG4Eh6FkW1aYlHvHjj+KAyXn0YiIydHUy+DtIhWF3wjR46EoaEhNm3aVORYDw8PzJs3T6X/+1sX8ejKNXhPmKaBhEREZcva2ho2NqrXUBEVpJZBFejq6iBKzWnXyJRUAEBtg6oAgE+PnEZ4cgqGtW6OL3p0Rnp2Nk4+CsOsE4F4FJ9YnrG1xmn/xXBwaIOAgGD0cpwtdRyN0eqCb+LEiYiNjcW+ffuKHOvl5YXdu3errmNX0cUiEZG2ePU6ZqqYSvI+SEjPRG5eHupVN1JZVr96NQBA3PN0AMDz7Gws8D+PBf7nYWZkiH7NrLCod0/sGzUEbX7yKZvwpJW0tuBr06YNOnbsiNWrVyMrK6vI8Tx1S0RyUNzTeET5nmdn4/LTSLzbshm+PB6oOEWrowOMatsK/yQl40FcgsrznqU9xy83bqNtPVN88k4HGFSuhPTsHGTm5gIAqlaqxNO9MqK1Bd/EiRMB8MOPiIgon6NVY1ga11Tpn/PHWRwdOwwnx72PVeevIis3Fx92tENrszr44PdDinHnJo3GkQd/IzgqBgkZmWhhWhuj27bChSfhSM9+Udzdin5x8OSL7p1w7GEIcoVAcHQMsnM5U8abTCsLPn19fYwZMwaXLl3C7du3pY5DRESkFZb0tVfb32zVevT1/Q1zHbti47v9oaujg5vRMRi6Yx+OPPjv9HBA6BMMbN4UH3d5G4aVKyM8JRW/Bt3B0jMXFWN2Bt9F18bm+LCjHb62fwe6ujqch08GtLLge/fdd2FiYoJZs2ZJHYWIiEhyv9y4jV9uFH4A5HFiMvr7ql7L/rI5f5wFcLbQMdm5eZh64ASm4kRJY5IW06qvVss3ceJEpKamYufOnVJHISIiInrjaeURvn79+kkdgYiIiEg2tPIIHxERERGVHR0AQuoQmtSkQzukp6Qi4v5DqaMQERGRlrG1tYKxsRESE9MQFBQqdRyNkX3BR0RERFTR8ZQuERERkcyx4CMiIiKSOdkXfFM3r8XgmTOkjkFERERaaNUqd5z2X4xVq9yljqJRWjktS1lq2rG91BGIiIhIS9naWcPBoY3UMTRO9kf4iIiIiCo6FnxEREREMseCj4iIiEjmWPARERERyRwLPiIiIiKZY8FHREREJHMs+IiIiIhkTvbz8B332oiEiEipYxAREZEW8t1yCoEBwQgLeyZ1FI3SASCkDkFEREREmsNTukREREQyx4KPiIiISOZkfw1f36kTkRARiSt+R6SOQkRERFrGzc0JlpZmCAt7Bl/fU1LH0RjZH+Hr5+GODoNdpI5BREREWshtnBM8542C2zgnqaNolOwLPiIiIqKKjgUfERERkcyx4CMiIiKSORZ8RERERDLHgo+IiIhI5ljwEREREckcCz4iIiIimZP9xMuPrlxDxL2HUscgIiIiLRR0I0TpT7nSASCkDkFEREREmsNTukREREQyx4KPiIiISOZkX/A16dAODZo3kzoGvYGKetfwXUVE9OaztbWCvb0NbG2tpI6iUVpZ8HXr1g2HDx9GfHw8nj9/jgcPHmDOnDmvtS4PHy8M/nJG2QYk2fMEEAzApYDlLv8u9yy3REREpAmrVk+Cf8ASrFo9SeooGqV1Bd/IkSMRGBiIpKQkjB07FgMGDMCyZcugo6MjdTSqIJoB+ApAFQB7oFr0ufzbX+XfcTzSR0RE2k6rpmVp0KAB1q9fj59//hnTpk1T9AcEBEgXiiqchwBc8V9Rt+ffx4ehXOxl/tvPSX+IiEjbadURPnd3d1SrVg3Lli2TOgpVcIfxopjLxH9FnxdUi73DUgUkIiIqAa06wtezZ0/ExcWhRYsW8PPzg42NDeLj47F3717MnDkTKSkpBT7X1NQUpqamGs3n7u4Oa2trjW6DtMsBAEPwosibCgAhIcjcuJHFHhERvVG0quAzNzeHoaEhdu/ejSVLlmDGjBno2LEj5s+fDxsbG/To0aPA53p4eGDevHkq/d/fulhm+aytrWFjY1Nm66M3w1MAli893gwWe0RE9GbRqoJPV1cXBgYGmD9/vuK0bmBgILKysrBmzRo4OTnh1KlTap/r5eWF3bt3q/RP3LWpzPKFhMj7a1dIVVMADV/uCAnBBLwo+Fj0ERHRm0KrCr64uDgAwPHjx5X6jx49ijVr1qB9+/YFFnwxMTGIiYnRaL6NGzdqdP2kXVwAfIYXvySZeHFkbwJUb+QgIiLSdlp108bNmzfV9udPyZKXl1eecagCU3c3rgdUb+QoaJ4+IiIibSO0pfXp00cIIcRXX32l1D9jxgwhhBDdunWTPCOb/FszQGQAQvz7p8sry11eWd5MCzKzsbGxsbEV0SQPoNT8/PxEenq6+Prrr4WTk5P48ssvxfPnz8WBAwckz8ZWcZon1Bd7+S2/6PPUgqxsbGxsbGxFNZ1//6I1qlatCk9PT4waNQr169dHREQEtm3bhvnz5yMrK0vqeFSBNEPhkyoXtZyIiEhbaF3BR0RERERlS6tu2tCElcEXMHXzWqljEBERkRY67b8YeeIgTvsvljqKRsm+4CMiIiKq6FjwEREREckcCz4iIiIimWPBR0RERCRzLPiIiIiIZI4FHxEREZHMseAjIiIikjkWfEREREQyJ/tv2mjSoR3SU1IRcZ9fgkVERETKbG2tYGxshMTENAQFhUodR2NkX/ARERERVXQ8pUtEREQkcyz4iIiIiGRO9gXf1M1rMXjmDKljEBERkRZatcodp/0XY9Uqd6mjaFQlqQNoWtOO7aWOQERERFrK1s4aDg5tpI6hcbI/wkdERERU0bHgIyIiIpI5FnxEREREMseCj4iIiEjmWPARERERyRwLPiIiIiKZY8FHREREJHOyn4fvuNdGJERESh2DiIiItJDvllMIDAhGWNgzqaNolA4AIXUIIiIiItIcntIlIiIikjkWfEREREQyJ/tr+PpOnYiEiEhc8TsidRQiIiLSMm5uTrC0NENY2DP4+p6SOo7GyP4IXz8Pd3QY7CJ1DCIiItJCbuOc4DlvFNzGOUkdRaNkX/ARERERVXQs+IiIiIhkjgUfERERkcyx4CMiIiKSOa0q+Ozt7SGEUNs6d+4sdTwiWWlWyuVERPTm0MppWb766iv4+/sr9d26dUuiNETy4wngKwCuAA6rWe4CYA+AJQDml2MuIiLSDK0s+B4+fIhLly5JHYNIlprhRbFXBS+KuleLvvxir8q/47YDeFjOGYmIqGxpZcFXlh5duYaIe/zniijfQ7wo8vKLupeLvpeLvcx/+/nbQ0RyFnQjROlPORPa0uzt7YUQQkRFRYns7GyRlJQkjh07Jrp16yZ5NjY2uTUXQGQAQvz7p9crj120ICMbGxsbW9k0nX//ohXs7Ozg5uaGgIAAxMXFoWnTpvjiiy/w1ltvwcXFBSdOnCjwuaampjA1NVXpj4mJQUxMjCZjlzt3d3dYW1tLHYNkoCmAIXjpUH9ICDI3bizw2j4iInozaVXBp07NmjURHByM+Ph42NnZFTjO09MT8+bNU+mfN28e5s+X12Xnixcvho2NjdQxSCbaALDMf3DrFrxnz4aHdHGIiEgDtP4avqSkJBw6dAhTp05F1apVkZGRoXacl5cXdu/erdJv0KAuGjRvhoj78rkSKSRE/tcZUPloCqDhyx0hIZiAF0f3eISPiCoCW1srGBsbITExDUFBoVLH0SjJzysX1by9vYUQQlSpUqXEz10ZfEFM3bxW8tfAxqZtjdfwsbGxsUGc9l8s8sRBcdp/seRZNNm0auJldYyNjTFw4EBcv34dmZmZUschkgV1d+N6/PtnJv67e9dFqoBERFSmtOqU7rZt2/DkyRNcvXoVsbGxaNasGT7//HPUrVsX48aNkzoekSw0g2qxl3/69jBUp2xpA07NQkT0ptOqgu/mzZsYPnw4pkyZgmrVqiE+Ph7nzp3DBx98gKtXr0odj0gWHuLFN2gU9E0bLxd9S8Bij4hIDrSq4Fu2bBmWLVsmdQwi2ZuPwr9B4zB4ZI+ISE60/ho+ItKMooo5FntERPLBgo+IiIhI5ljwEREREckcCz4iIiIimdP6r1YjIiIiotLhET4iIiIimWPBR0RERCRzLPiIiIiIZE72Bd/K4AuYunmt1DGIiIhIC532X4w8cRCn/RdLHUWjZF/wEREREVV0LPiIiIiIZI4FHxEREZHMseAjIiIikjkWfEREREQyx4KPiIiISOZY8BERERHJHAs+IiIiIpnTASCkDqFJTTq0Q3pKKiLuP5Q6ChEREWkZW1srGBsbITExDUFBoVLH0RjZF3xEREREFR1P6RIRERHJHAs+IiIiIpmTfcE3dfNaDJ45Q+oYREREpIVWrXLHaf/FWLXKXeooGlVJ6gCa1rRje6kjEBERkZaytbOGg0MbqWNonOyP8BERERFVdCz4iIiIiGSOBR8RERGRzLHgIyIiIpI5FnxEREREMseCj4iIiEjmWPARERERyZzs5+E77rURCRGRUscgIiIiLeS75RQCA4IRFvZM6igapQNASB2CiIiIiDSnVKd0GzVqhEqV1B8k1NPTQ6NGjUqzeiIiIiIqA6Uq+EJDQ9GuXTu1y2xtbREaGlqa1QMAJk6cCCEEUlJSSr0uIiIiooqoVNfw6ejoFLhMT08PQpTubHGDBg2wYsUKhIeHo2bNmq+1jr5TJyIhIhJX/I6UKgsRERHJj5ubEywtzRAW9gy+vqekjqMxpb5LV11Rp6+vD2dnZ8TGxpZq3evWrcOZM2dw8uTJ115HPw93dBjsUqocREREJE9u45zgOW8U3MY5SR1Fo0pc8M2dOxc5OTnIycmBEAIXL15UPM5vz58/x9y5c+Hn5/fawUaPHg17e3t4eHi89jqIiIiI6DVO6V6+fBleXl7Q0dGBh4cHfv/9d0RHRyuNyczMRHBwMLZv3/5aoUxNTbF69WrMmjUL4eHhxX6Oqanpa23vTePu7g5ra2upYxAREb3xrK3sAZjB2qo2Fi9eLHUczJ49WyPrLXHBd+zYMRw7dgwAYGRkhAULFiAsLKxMQ3l5eeH+/fvw9vYu9nM8PDwwb948lf7vb10sw2TawdraGjY2NlLHICIieuMZGZkCqA4jIyHrf1tLddPGhAkTyiqHwtChQzFo0KAC7/4tiJeXF3bv3q3SP3HXprKKpjVCQkKkjkBERCQLbduawKSODtLSYnDr1i2p42hMqQq+mTNnomHDhvj4449Vlv3www94/PgxVq5cWez1GRkZYe3atfjxxx8RERGhuDNXX18fAFCzZk1kZ2fj+fPnKs+NiYlBTEzMa76SN8vGjRuljkBERCQLXd5ZjMYWbRASGqyx06naoFR36bq5uRVYDQcFBcHNza1E66tTpw7q1auH//u//0NiYqKijRo1CtWqVUNiYiK2bdtWmshEREREFU6pjvBZWFjgwYMHapc9evQIlpaWJVpfVFQUHBwcVPpnzZoFe3v7MpnqhYiIiKiiKVXBl52dDTMzM7XL6tatW+KJlzMzMxEYGKjSP27cOOTm5qpdVpRHV64h4t7DEj+PiIiI5C/oRojSn3KlA+C1vw4jf0LkPn36qF2mq6sLJ6fST2To4+OD9957D9WrVy/1uoiIiIgqmlIVfP369cPhw4dx9uxZeHl5ITw8HA0bNsSUKVPQs2dPDBgwACdOnCjDuERERET0OkRp2oQJE0RCQoLIzc0VOTk5Ijc3V8THx4vx48eXar1sbGxsbGxsbGxl00p1hC+foaEhunXrhjp16iAmJgbnz59XO3WKFJp0aIf0lFRE3Od1fERERKTM1tYKxsZGSExMQ1BQqNRxNKZMCj5ttjL4Ah5duQbvCdOkjkJERERa5rT/Yjg4tEFAQDB6OXIevgLVqVMHixcvxvnz53H//n20atUKADB58mTY2dmVdvWSqvTvhM9EREREb7JSFXyWlpYICgrCxx9/DCEEmjRpgipVqgAA2rZtq/YbOLSRXT8ndB0+VPHYpFFDfLF/O5ZcPo1pW7xhUIN3BxMREdGbq1QF33fffYfExEQ0a9YMPXv2hI6OjmLZuXPn0K1bt1IHLA/240ZB38BA8XjQ59NhWKM6zm77DWZWFnByd5MwHREREVHplKrgc3Jywvz58xEZGakyyXJkZCQaNGhQqnDlxaShOaIe/Q3gxWnc5l0749AqLxxY/gOO/rQeNr16SpyQiIiI6PWVquCrWrUq4uPj1S4zMjJCXl5eaVZfbvSrVkVWegYAoHHb1qikXxn3zl0AAET/HYqaZqZSxiMiIiIqlVIVfPfv30fv3r3VLuvZsydu3bpVmtWXm+TYWDRo3gwA0KJbFzwLe4K0hEQAgGGN6sjOyJAwHREREVHplOq7dDds2IDvv/8eERER2LZtGwBAX18frq6u8PDwwPTp08skpKYF/xEI548/RJMO7dCi+zvw3/yLYln9t5oi9p9wCdMRERERlU6pCj5vb2/Y2dlh1apVWLlyJYAXN2vo6Ohgw4YN2Lp1a5mE1LSjP62HvqEBLO3a4PqREzjts02xrFXPbnh48YqE6YiIiIhKp0wmXu7cuTNcXFxQt25dxMbG4tChQ7hw4UIZxCMiIiKi0pL9N20QERERVXSlOqUrJ1bt2qLdgL6o1aAeKv87ebSCANZN+kiaYERERESlVOKC7++//8aQIUNw8+ZNhISEqMy/9zIhBOLi4nD+/HksWLAACQkJpQqrKR3fdcH782cjPSkZMY//QU5WlvIAHfXPIyIiInoTlLjgCwwMRHJysuLvhRV8AFCzZk24ubnB3Nwc77///uulLIWVwRfw6Mo1eE+YVuAYx/FjEHT8FHZ8vRC52dnlmI6IiIikdNp/MRwc2iAgIBi9HGdLHUdjSlzwTZgwQfH38ePHF+s5w4cPh5eXV0k3VW5q1a+HfUu+Z7FHREREslSqiZeL688//8SaNWvKY1OvJTo0DNVNakkdg4iIiEgjSl3wVa9eHbNmzcLx48dx9epVHD9+HLNmzULNmjUVY54+fYoFCxaUdlMac3TNOvSaOBY1+BVqREREJEOlukvX0tIS/v7+aNy4MR4/foyoqCg0a9YMvXv3xpQpU+Do6IjQ0NCyyqox3Ua4omo1I8w6uAsR9x/ieWKS0nIBAZ+Pv5QoHREREUmtfv3aqF7dAA8evPj2LV1dXXz++bto174JTp64Dh+fPyROWLhSFXxr1qxB1apV0a1bN1y8eFHR/84772Dv3r1YvXo1Bg8eXOqQmlb/rabIy81DanwCapjWQQ3TOq+M4FSFREREFdm6n6fhnycxmD59HQDgm2+G45u5I5CYmIZhw7ohKysH27YFSBuyCOJ1W0pKipgwYYLaZRMnThQpKSmvve6yaiuDL4ipm9dKnoONjY2NjY1N+9pp/8UiTxwUp/0XFzrun6dbhKtrV6XHK1a8qIG8vKaKP88vl/y1FNZKdQ1fZmYm/vnnH7XLnjx5gszMzNKsnoiIiEgrmJhUR1RUIgCgRYuGqF+/FrZsOQUA2LPnPJo3N5cwXdFKVfD5+flh2LBhapcNGzYMhw4dKs3qiYiIiLRCUlIazMxqAgB69rRBfHwqbt16DAAQQkBfX7u/vKzE6dq1a6f4+/bt27Fp0yb89ttv2L59O6KiolCvXj2MHj0aHTp0wMSJE8s07OvwGu+B9JRUlf7lN87hhzGT8c+tO1ge9CdQxDeGzGzXQ5MxiYiISAKfztgAY2MjJCamFTru8uWHmPmlK7Kzc/DxJ4Nw4sR1xTJr63qIiIjXdNRS0cGLc7vFlpubq/TtGjo6L753rKC+SpW0s+LtO2UCLu45gOSYWPSdOrHQgg8ATqzbXE7JiIiISNvY2lrhxMmFMDGpjoSEVNj3/Ap37jwBABw8NBcJCakY+8H3EqcsWIkLvrFjx5ZoA1u3bi3ReCIiIiJtZGBQBS1aNMSjRxFISUlX9A8Y0AEPH0bg4cMICdMVrsQFHxERERG9WbTzfGsZmrp5LSLuPYTfd6sLHPP2IOcClwmRh/TkVITfvY/kmFgNJCQiIiKprFrlDls7awTdCMGnn25UWtajR+sSrevs2dtlGa1Mlarg27RpU6HLhRBwd3cvzSZKrWnH9kWOGfHtnP+u4fv3+kMASn0iLw9/HTqG3fOWIi83VwNJiYiIqLzZ2lnDwaGN2mX+AYsV9yjo6Ogo3a+gTuVK75Z1vDJTqoKvV69eKi/exMQE1apVQ2JiIhITE0uz+nKzZuREfLBiIR6cv4zrR08iJS4e1euYoJ1zHzTv2gl7Fi5Hw1Yt0GfKeCSER/IGDiIiogqgl+PXir9Xr26AH3/6EPfvh2PH9kBERSWgXr1aGDXaAc2bm2P6tHUSJi1aqQo+Kysrtf2Ojo7w8vIqcI4+bdNjzPu4deoMDq78UdEXE/YEIVevY9D/fYROQwbily++gUGN6mjv0o8FHxERUQVw5swtxd/Xrp2KM2duY5zbKqUxW7eehu/WzzBoUCccPnylvCMWW6kmXi6Iv78/fvrpJ6xZs6ZEz7O1tcWhQ4fw+PFjPH/+HHFxcTh//jxGjx6tiZgKrey74d6fF9Uuu3fuIpp37QwAeHT5Kozr19VoFiIiItI+7w3rhu0FfFfu9m0BGDL0nfINVEIaKfgA4M6dO+jUqVOJnmNsbIx//vkHs2fPxoABAzB27FiEhYXh119/xddff130Cl6Trq4eTBqp/0qUOo0aKq7ry83OQU5WtsZyEBERkXYyNKwCMzNjtcvq1jWGoWGV8g1UQhq7S9fe3h6xsSW7qzUwMBCBgYFKfYcPH4aVlRUmT56MRYsWlWVEhfvnL8F5+mTEPw3Hgwv/HY5t3rUz+k+fhAcXLgMAzKwskBARqZEMREREpL3Onr2Nbxd9gOvX/8bt208U/TY2Flj47RitvkMXKGXB980336j0ValSBW3btoWzszOWL19emtUrxMbGwszMrEzWpc7+ZavhsXktJnmvQmba8xc3bZjURhUjQ8T9E479y1Yrxp75dZfGchAREZF2mvHJBgSeWYLrN37A7dtPFDdttG7dGHFxyZjxyQapIxaqVBMv56qZniQzMxNhYWH45ZdfsHz5cuTk5JQ8lI4OdHV1UatWLQwbNgxr1qzB9OnTsX79+gKfY2pqClNTU5X+ibs24dGVa/CeMK3QbVauWgUdB7vA+m07GBnXRFpiEv6+eh1XDxxBdkZmiV8DkRy4u7vD2tpa6hhERBozapQ9GluY4cnjZ9i+PbDQsYaGeujYqQ4aN64GAwM9pKfn4snjVFy5Eofnz0te76gze/bsMlnPq0p1hE9PT6+scijx8vLClClTALwoID/++ONCiz0A8PDwwLx581T6l/z2K676HS5ym9kZmTi/ay/O79r7WpmJ5Mja2ho2NjZSxyAi0piEBB1kZCQjPV2nWJ93cbEv2n8MYG2tesBJ25TZV6vVqlULM2fOhI2NDcLDw7FmzRrcvXv3tdbVqFEjmJmZwczMDIMGDcLkyZPx5ZdfYuXKlQU+p6AjfDExMYiJiSnWds2sLGDdoR2MjGvi8t6DSImLRw3TOnienIKcTB7lo4qHR/iIiMqXpo7wlbjgW758Od5//31YWFgo+gwNDREcHAxLS0tFX0pKCjp16oQHDx6UOqSXlxfc3d3RoEGDEt8IUhw6uroY5jkLHQcPeHFHrhBYPXICwu8+wIQflyP83gMcX6vd5+aJiIhIs0aPdsDIUfawsDCDgYG+0jIhBJo1nSxRsqKVeFqWrl27YufOnUp906dPh6WlJVatWgVjY2N07doVqampmDVrVpmEvHz5MipXrqyxIw29J49D+wF9cfD7n7BiyGilr1e7d+4CWnTropHtEhER0Zth5kxX+G79FI0bmyIoKBSBgbeU2pkz2n2XLvDiCF+xW2RkpBg2bJhS39mzZ0VUVJTQ1dVV9H322WciJCSkROsuqPn6+oqcnBxRp06dEj+379SJouPgAYWOmX30d+Hk7iYACB1dXbE86E9h3vItAUC06N5FzA88Uiavg42NjY2NjU27mpubk/D0HCnc3JwKHffw0Xrxww+TJc/7uq3EN20YGxsjMvK/uej09PTQsWNH7N+/H3l5eYr+69evo379+iVa988//4zk5GRcvnwZ0dHRqFOnDoYNG4YRI0bgu+++e63Tuf083PHoyjVc8TtS4JiaZqYICwpWuyw7MwtVjAxLvF0iIiLSfm7jnODg0AYBAcHw9T1V4Lh69Wph3z7138r1JihxwRcdHa1UyLVv3x6VK1fG1atXlcbl5eUhs4Q3Oly4cAHjx4+Hm5sbjI2NkZqaiqCgIIwZMwbbtm0radRiS41PgElDc/x95ZrKMjNLCyRFF++mDyIiIpKnv/56hCZN6sHf/6bUUV5Lia/h++uvvzBp0iTF49GjR0MIgVOnlKviFi1aKB0JLI4tW7bA3t4eZmZm0NfXR+3ateHo6KjRYg8A7p69gN6T3VDD7KW7fAVQtZoRuo8ehtuB5zS6fSIiItJun3+2CZ99PgTt2zeROsprKfERvmXLluHPP//E/fv3ERsbiy5duuDs2bO4fv260rhBgwbhypUrBaxFuxxbux4tunfBzP3b8feVvwAh4PzJFNRvao3cnBycXOcjdUQiIiKS0GafT2BiUh2XLq9EVFQi4uKSlZYLAbSz+1iidEUrccF3+fJlDB48GF988QVMTEywceNGlbtx69ati4YNG8LH580olFLjErB65AT085iEVj27Ii8vDw3eaoo7Z/7E8Z82ID05ueiVEBERkWzFxaUgNvbNrQde65s2jhw5giNHCr4JIjo6GnZ2dq+bSRKpcQnYs/A77JE6CBEREWmdXo6amRC5vJTqq9XkqIZpHRga18TzxCQkx5T9JM9ERERE5Y0F37/aONljwCdTUadxQ0Vf3D/hOPLDOtw86S9hMiIiItIGderUwGefvQt7hzaoU6cGhry7CHfuPMHkyf1x+fID3LgRInXEApX4Lt03zaMr1xBx72GhY+z6OWHs94uRl5eHk+s2Y8+iFTj5sw/y8vLwwfKFsOvnVE5piYiIqDwF3QhBQEAwgooo1iwt6+JG0A/46ONBEELA2rouqlSpDABo29YSH308qDzilorksz9L3b7Yt01MXLtC6OjoKPXr6OgId+/vxRf7tkmekY2NjY2NjU26tuu3L8Wt22tF/fq1hZ6ersjJ9RPt2jURAMSIET3FvfvrJM9YWJP9Eb7iMGlkjvM790IIodQvhMD5nXth0shcomRERESkDZycbLFg/g5ERsbjlXIBkZHxaNCgtjTBiokFH4CEiChUNqiqdpl+1SpIjHpWzomIiIhIm1Stqo/4+FS1y4yMqiIvT6hdpi1kX/A16dAODZo3K3RM4NYd6DtlAoyMayr1V6tdC70/HI8A3+2ajEhEREQSsbW1gr29DWxtrQodd//+U/Tubat2Wc+erXHr1mNNxCszsr9L18PHC4+uXIP3hGlK/e/O+lTpcRUjQ3x9fC8eXrqKlNh4VK9TG806d0BaQhLqWluWY2IiIiIqL6tWT4KDQxsEBAQXOtfexg0nsPL7iYiIiMe2bYEAAH39SnB17YqpHgPw0fSfyyvya5F9wVeQbiPfU9vfyr670mPj+lXRfeR78Fu2uhxSERERkTby9j4COzsrfL/KHStWTgQAnD23DDo6L4rBrVtPS5ywcBW24PvCtpvUEYiIiOgN8uGHa7F58x9wcemAunWNERubjEOHruDChXtSRytShS34iIiIiErq0qX7uHTpvtQxSkz2N20QERERVXQs+IiIiIhkjgUfERERkcyx4CMiIiKSORZ8RERERDKngxdfqluhDfnqM5zb8Ttiwp5IHYWIiIi00A8/TMZPPx3GgwfhUkd5LSz4AHx7/iSqGBrg0eW/cG7H77jtf1bqSERERKRFEhJ3olq1qjh9+ibW/nQYBw5ckjpSibDgA6BvUBUd/jcAXYcPRd0mVkiMisaF3/bh0p4DSEtMkjoeERERSczQsArc3Jww1WMAWrVqhH/+icU676PYuPEE4uKSpY5XJBZ8r2ja6W10G/keWtt3R15uLm4c/wPntv+Op3e0fxZtIiIi0jxHx7aYNt0FgwZ1Qk5OLnbtOoe1Px3CX389kjpagWRf8K0MvoBHV67Be8K0Ej2vZl1TjPx2Lpp0bAcA+Of2PfyxfgvuBJ7TREwiIiKSwGn/xXBwaIOAgGD0cpxdoueam5tgi++ncHCwAQBcvfoIi77dhUOHrmgiaqnwLt1XVKpSBZ2HDsLEH1egSaf2iA4Jw4l1m6Grp4vxa5ai94fjpY5IREREEqpaVR8TJ/bFgYPfwNGxDe7efYoF83dCT08X+/Z/jTlzhksdUQW/S/dfJg3N0W2kKzoOdkEVI0PcO3cRB7//CQ8vvqjST67bDOePp6D7yPfwx88+EqclIiKi8mZtXQ/TprnAbZwTatQwxNGjf2HmFz44dSoIALBw4U4sWvQBpn80EN9+u0vitMpY8AFw9/4eb3XpiKz0DFzZfxjntu9G3FPV267vBJ5Dr4kfSJCQiIiIpHT4yDz06WOHtLQMbPH5Az/+eAghIVEq4w4evIwvZ70nQcLCseADUKehOQ4sX4PL+w4jKz29wHFRD0PgPXF6OSYjIiIibdCkST189ulG+Pj8gbS0jALH3br1BL0cvy7HZMXDgg/A0kHFO9ee+fw5Qq5e13AaIiIi0jYtmk8p1rjU1HScOXNLw2lKjjdtEBEREclchT3CN/voHpRkRprFztp3Pp6IiIg05++QjRCi+LVC0yaTNJimdCpswRfy13WlH2LTTm+jRh0ThN0IRkpsHKrXMYGlXRskx8Th0eW/JExKRJrWDMDDUiwnInkKDLylVCv06tUW9erVwvnzdxEVlYB69Wqha9eWiIyMh//pmxImLR6hLc3R0VFs2rRJ3L17V6SmpoqnT5+K/fv3i/bt27/2Opt0aCcaNG9W6Ji3B/YXsw7uEsb16ir116pfT8w6uEt0+N8AyfcNGxubZponIDIA4VLAcpd/l3tqQVY2Nrayb7a2VsLe3kbY2loVOm7MGEdx7/460aiRqVJ/48am4t79dWLs2F6Sv5YimuQBFO23334Tp06dElOmTBE9e/YUrq6u4vz58yIrK0s4OjpqbLtf7Nsm2jn3UbusvUtfMdNvh+T7ho2NrexbM7wo5gTUF30uryxvpgWZ2djYpGnBt9aKESN6ql02apS9uH3HS/KMhTWtOqU7bdo0xMTEKPUdO3YMjx49wuzZs+Hv76+R7Zo0Mkd6aqraZc+TU1C7QX2NbJeIpPUQgCuAPQCq/PunK4DDAFxe6s/8t5+ndYkqriZN6iEpKU3tsoSEVFhampVzopLRqrt0Xy32ACAtLQ137txBo0aNNLbdhIgodB4ySO2yLq6DkRCpOrEiEcnDYbwo5jLxX9HnBdVi77BUAYlIK4SFPcOEiX3VLnOf1A+PH6vWMNpEq47wqVOjRg20b98ep0+fLnScqakpTE1NVfr7fDUDYcF34Pfd6gKfe3rTVry/4Gt8sn0Trh89qbhpo51zHzRs1Ry/zVtS2pdBROXI3d0d1tbWJXrOAQBD8KLImwoAISHI3LiRxR6RzK1a5Q5bO2sE3QjBp59uLHDcsqW/Y9Pmj3Hx0krs3HFGcdPGiJE98fbbTTDJ/cdyTF1yWl/wrV27FkZGRli0aFGh4zw8PDBv3jyV/u9vXURWdnahz73idwQA0P+jDzHo/z5S9KfExGH3vKW4sp8f90RvEmtra9jY2JT4eU8BWL70eDNY7BHJna2dNRwc2hQ5ztf3FABg4bdjsGLlBEV/ZGQCJk/6CVu2nNJYxrKg1QXfggULMGbMGEyfPh3Xrl0rdKyXlxd2796t0j9x16ZibeuK3xFc8TsCMysLGNasiedJSXgW+vi1chORtEJCQkr8nKYAGiqvBBPwouBj0UdEwIuiz9f3FJo3bwgTk+qIi0vB/ftPpY5VLFpb8M2dOxfffPMNZs+ejbVr1xY5PiYmRu01gCXFIo/ozbdxY8GnZdRxAfAZXnwgZuLFkb0JUL2Rg4gIwBtT5L1MKwu+uXPnYv78+fD09MSSJZq/fu7tQc5Fjvnr4FGN5yCi8qfubtz8o3rq7t4loorpgw8cixzzyy+amU2kLGhdwTdnzhzMnz8fCxcuxIIFC8plmyO+naN+wUuza7PgI5KfZij4btz8u3dfLvragFOzEFVUPltmqO1/+Zs4WPAV02effYaFCxfi6NGjOHz4MDp37qy0/NKlSxrZ7uL+rip9RrVqorVjT9j1c8KvM+dqZLtEJK2HAJYA+Arqj+C9XPQtAYs9oorM2spdpa9OnRoYPLgL3h/eHSNHLJcgVfFpVcE3aNCLufCcnZ3h7Kx6mlVHR0cj21U3z15CZBSe3rkPvUqV0GPM+9g551uNbJuIpDUfwHYUXMwdBo/sERHw5InqfQJPnsTg2rW/UbmyHj7+5H+YMH51+QcrJq2aeNnR0RE6OjoFNik8vHQVrR16SLJtIiofRRVzLPaIqDCnTgXhf//rJHWMQmnVET5NOO61EQkRka/9/Fr16yEvN7cMExEREZG28N1yCoEBwQgLe/ba67CwMENubl4Zpip7si/4TngXPQ+f9dt2Kn2V9CujfrOmcHIfi4eXrmogGREREUktf0LlovTo0Vqlr0qVymjb1hKzvhqGU6eCyjpamdIBIIocJXPLg/5UuiMXAPDvKeSHF69g+1fzkRqfIEEyIiIi0gY5uX5Kd+QC/91b8McfQfhgzErExCRJEa1YWPABaNKhncpOyMnMRHxEJFLjWOgRERFVdPb2NirHhjIyshAW9gzPniVKkqkkZF/w9Z06EQkRkYrvyyUiIiLK5+bmBEtLM4SFPSv26d03kVbdpasJ/Tzc0WGwS6Fjlt84h0Y2rdQua9iqOZbfOKeJaERERCQxt3FO8Jw3Cm7jnAodl52zHx07NlO7rH37JsjO2a+BdGVH9gVfsRQy5YuOjq7KOXsiIiKqWAqbHk5XV1fldK+2YcGXr4CfVMNWLZCRmlbOYYiIiEjbFFTUvf12EyQlaXetIPtpWQrSY/T76DHm/RcPhMD4NcuQk52lNKZylSqoVrsWbp7U3u/GIyIiIs34+ONB+PiT/wF48Z25+/Z/jczMbKUxBgb6MDOrid9/Py9FxGKrsAVfanwCoh6FAgBqNaiPuKfhSE9JVRqTk5WFqId/48y236SISERERBJ69iwJt28/AQBYWpohJCQKiYnKR/IyM7NxKzgMa9YclCJisVXYgu/60ZO4fvQkAGDqpp+w59vleBb6WOJUREREpC127jyDnTvPAABOnV4Ej6neuH//qcSpXk+FLfhe5j1xutQRiIiISIs59fpa6gilwps2ADTt9Dba9u2leFzNpBbcvVbC0/8QRi6ai0r6+hKmIyIiIqk5OrbFe+91Uzw2MzPGocOeiIjcii2+n6JKlcoSpiua7Au+R1euIeLew0LH9J82CfWsLRWPB346HVbtbRF2Ixht+zjCYfxoDackIiIiKQTdCEFAQDCCboQUOm7+gtFo1aqR4vGy78ahR49WOH/+Lt57rxu++GKopqOWmqjobV7AYdHKvrsAIHT19MSiC3+IrsOHCgDCwW2U+GL/dskzsrGxsbGxsUnXoqJ/EQMHdhQAhJ6erkhM2iWmTh0gAIjPPx8ibt1eK3nGwprsj/AVR9VqRkhPSQHw4ps19A2q4nbAWQDAk1t3UKt+XSnjERERkcRq1DBU3KH79ttNYWRUBQcOXAIAXL78AI0bm0oZr0gs+PBiihbTxi8O0zbr0hEJkVFIio4BAFQxNERuTo6U8YiIiEhiz54loVmzBgCA3r3t8PhxDMLD4wAA1asbIDs7V8p4RZL9XbpNOrRDekoqIu4XfB3fvT8vwvmTKajb1AodB7vg6oEjimVmVhZICI8qj6hERERUzmxtrWBsbITExDQEBYUWOO74sb+waPFYtG7dGG7jnLDV97RiWYsWDREWFl0ecV+b7I/wefh4YfCXMwodc3TNOkTce4guroMRfu8BTv68RbGsnUtfhAUFazYkERERSWLV6knwD1iCVasnFTpu9uxfcONGCNwn9cP16yH49ttdimUjR9njwvl7mo5aKrI/wlccaYlJ2DD1U7XLvCdMQ05mltplREREVDHExSVjgPM8tct6Oc5GRka22mXaggVfETLTnksdgYiIiLRYSkq61BGKJPtTukREREQVHQs+IiIiIpljwUdEREQkcyz4iIiIiGSOBR8RERGRzLHgIyIiIpI5Hbz4Ul0iIiIikike4SMiIiKSORZ8RERERDLHgo+IiIhI5mRf8K0MvoCpm9dKHYOIiIi00Gn/xcgTB3Haf7HUUTRK6wq+atWqYdmyZTh+/DiePXsGIQQ8PT2ljkVERET0xtK6gs/ExASTJ09GlSpVsH//fqnjEBEREb3xKkkd4FWPHz9GrVq1ALwo/iZNmiRxIiIiIqI3m9Yd4SMiIiKissWCj4iIiEjmtO6U7usyNTWFqamp1DGIiN5I7u7usLa2ljoGUbmztrIHYAZrq9pYvFj6O3Vnz56tkfXKpuDz8PDAvHnzVPq/v3Wx/MMQEb1hrK2tYWNjI3UMonJnZGQKoDqMjISsfwdkU/B5eXlh9+7dKv0Td22SIA0R0ZslJCRE6ghEkmjb1gQmdXSQlhaDW7duSR1HY2RT8MXExCAmJkal32u8B9JTUiVIRET05ti4caPUEYgksWuXFYyNjZCYmIagoFCp42iMbAq+gvx99brUEYiIiEhLybnIe5lWFnz9+/eHkZERqlevDgBo1aoVXF1dAQBHjhxBenq6lPGIiIiI3ig6AITUIV4VGhoKS0tLtcssLS3x+PHj8g1ERERE9AbTyoKvLE3dvBYR9x7C77vVUkchIiIiLbNqlTts7awRdCMEn34q32tZtfKUbllq2rG91BGIiIhIS9naWcPBoY3UMTSO37RBREREJHMs+IiIiIhkjgUfERERkcyx4CMiIiKSORZ8RERERDLHgo+IiIhI5ljwEREREcmc7OfhO+61EQkRkVLHICIiIi3ku+UUAgOCERb2TOooGiX7b9ogIiIiquh4SpeIiIhI5ljwEREREcmc7K/h6zt1IhIiInHF74jUUYiIiEjLuLk5wdLSDGFhz+Dre0rqOBoj+yN8/Tzc0WGwi9QxiIiISAu5jXOC57xRcBvnJHUUjZJ9wUdERERU0bHgIyIiIpI5FnxEREREMseCj4iIiEjmWPARERERyRwLPiIiIiKZY8FHREREJHOyn3j50ZVriLj3UOoYREREpIWCboQo/SlXOgCE1CGIiIiISHN4SpeIiIhI5ljwEREREcmc7K/ha9KhHdJTUhFxn9fxERERkTJbWysYGxshMTENQUGhUsfRGNlfw7cy+AIeXbkG7wnTpI5CREREWua0/2I4OLRBQEAwejnOljqOxvCULhEREZHMseAjIiIikjkWfEREREQyx4KPiIiISOZY8BERERHJHAs+IiIiIpnTuoLPyMgIq1atQnh4ONLT03H9+nUMHz5c6lhEREREbyytm3h579696NixI2bNmoUHDx5g1KhR2LlzJ3R1dbFjxw6p4xERERG9cbRq4mVnZ2ccOXIEI0eOxM6dOxX9x48fR+vWrdG4cWPk5eVJmJCIiIjozaNVp3SHDBmClJQU7N69W6nfx8cH5ubm6Ny5s0TJiIiIiN5cWnVK18bGBnfv3kVubq5S/82bNxXLL1y4oPa5pqamMDU1VemPiYlBTExM2YelN4q7uzusra2ljkFERFSo2bM18/VuWlXwmZiYICQkRKU/Pj5esbwgHh4emDdvnkr/vHnzMH/+/DLLSG8ma2tr2NjYSB2DiIhIElpV8AGAEAVfUljYMi8vL5VTwQAwcdcmTLUwg/eEaWWSj95M6v4jQURENGqUPRpbmOHJ42fYvj1Q6jgao1UFX1xcnNqjeLVr1wbw35E+dXjqlgqzceNGqSMQEZEW6vLOYjS2aIOQ0GCNnU7VBlp100ZwcDBatmwJPT09pf42bdoAAG7duiVFLCIiIqI3mlYVfPv27UP16tXh6uqq1O/m5obw8HBcunRJomREREREby6tOqV77NgxnDhxAt7e3qhRowYePXqEkSNHwtnZGaNHj+YcfERERESvQasKPgAYOnQoFi1ahAULFqB27dq4d+8eRowYgV27dkkdjYiIiOiNpHUFX1paGmbMmIEZM2ZIHYWIiIhIFrTqGj4iIiIiKnss+IiIiIhkTgdAwbMZy0CTDu2QnpKKiPsPpY5CREREWsbW1grGxkZITExDUFCo1HE0RvYFHxEREVFFx1O6RERERDLHgo+IiIhI5mRf8E3dvBaDZ86QOgYRERFpoVWr3HHafzFWrXKXOopGad08fGWtacf2UkcgIiIiLWVrZw0HhzZSx9A42R/hIyIiIqroWPARERERyRwLPiIiIiKZY8FHREREJHMs+IiIiIhkjgUfERERkcyx4CMiIiKqAIRcm6mpqfD09BSmpqaSZ9Gmxv3CfcL9wv3C/cJ9wv2ifU3D+0X6F6ip1qpVKyGEEK1atZI8izY17hfuE+4X7hfuF+4T7hfta5rcLzylS0RERCRzLPiIiIiIZI4FHxEREZHMseAjIiIikjlZF3wxMTGYN28eYmJipI6iVbhfVHGfqMf9oh73i3rcL6q4T9TjflFPk/tFBy/u3iAiIiIimZL1ET4iIiIiYsFHREREJHss+IiIiIhkjgUfERERkczJsuAzMjLCqlWrEB4ejvT0dFy/fh3Dhw+XOpakHB0dsWnTJty9exepqal4+vQp9u/fj/bt20sdTatMnDgRQgikpKRIHUVy3bp1w+HDhxEfH4/nz5/jwYMHmDNnjtSxJGVnZ4d9+/YhPDwcaWlpuHv3Lr755hsYGBhIHa1cVKtWDcuWLcPx48fx7NkzCCHg6empdmy7du1w8uRJpKSkICEhAXv27IGVlVU5Jy4fxdkvurq6+PTTT3H06FH8888/SEtLw507d7BkyRLUrFlTouSaU5L3yssCAwMhhMCPP/5YDinLX0n2S6VKlfDpp5/i5s2beP78ORISEvDnn3/inXfeea1ty7Lg27t3L9zc3DB//nw4OzvjypUr2LlzJ0aOHCl1NMlMnToVlpaWWLNmDQYMGIBPPvkEZmZmuHjxIhwdHaWOpxUaNGiAFStWIDw8XOookhs5ciQCAwORlJSEsWPHYsCAAVi2bBl0dHSkjiaZli1b4vz587C0tMSMGTMwcOBA7Ny5E3PnzsWOHTukjlcuTExMMHnyZFSpUgX79+8vcFzz5s0REBAAfX19vP/++5gwYQLeeustnD17FnXq1Cm/wOWkOPvFwMAA8+bNw+PHjzFjxgwMGDAAGzZswOTJk/Hnn3+iatWq5Rtaw4r7XnnZtGnT0LRpU80Gk1hx94uuri727dun+HxxdnbG6NGjcezYMRgZGb329iX/suCybM7OzkIIIUaMGKHUf/z4cfH06VOhq6sreUYpmqmpqUqfkZGRiIyMFCdPnpQ8nza0AwcOCD8/P+Hj4yNSUlIkzyNVa9CggUhJSRFr166VPIs2tYULFwohhLC2tlbqX7dunRBCCGNjY8kzlmczMTERQgjh6empsmzXrl3i2bNnonr16oq+xo0bi8zMTLF06VLJs0uxX3R1dUXt2rVVxru6ugohhBg9erTk2aV4r+Q3CwsLkZycLN59910hhBA//vij5Lml3C+ffPKJyMnJEZ07dy6z7cnuCN+QIUOQkpKC3bt3K/X7+PjA3NwcnTt3liiZtNRN4ph/SqFRo0YSJNIuo0ePhr29PTw8PKSOIjl3d3fFaQf6T3Z2NgAgKSlJqT8xMRG5ubnIysqSIpbW0dPTw8CBA7Fnzx6lSyOePHkCf39/DBkyRMJ00snLy0N8fLxK/+XLlwGgwn8Or1+/HidPniz20UC5++STT3DmzBlcunSpzNYpu4LPxsYGd+/eRW5urlL/zZs3FcvphRo1aqB9+/a4ffu21FEkZWpqitWrV2PWrFk8nQugZ8+eiIuLQ4sWLXD9+nVkZ2cjOjoa3t7eqF69utTxJOPr64uEhAR4e3vDysoK1apVg4uLCz788EOsXbsWz58/lzqiVmjSpAkMDQ0Vn7kvu3nzJpo2bYoqVapIkEw79erVCwAq9OfwxIkT0alTJ0yfPl3qKFqhYcOGsLKyQnBwMBYtWoSoqChkZ2fj1q1bGDt27GuvV3YFn4mJidr/ReX3mZiYlHckrbV27VoYGRlh0aJFUkeRlJeXF+7fvw9vb2+po2gFc3NzGBoaYvfu3di1axd69+6N5cuXY+zYsThy5IjU8STz+PFjvPPOO7CxsUFISAhSUlJw6NAh+Pr64pNPPpE6ntbI/4wt6HNYV1cXtWrVKu9YWqlBgwZYunQprly5gkOHDkkdRxL5107PnDkTkZGRUsfRCubm5gAANzc3DB48GNOnT8eAAQNw584d+Pr6wt3d/bXWW6ksQ2oLIcRrLatIFixYgDFjxmD69Om4du2a1HEkM3ToUAwaNAjt2rWTOorW0NXVhYGBAebPn684rRsYGIisrCysWbMGTk5OOHXqlMQpy5+FhQUOHjyI6OhouLq6IiYmBp07d8acOXNQrVq11/4Qlit+DheuVq1aOHLkCHR0dDB8+PAKu0/WrVuHoKAgbNiwQeooWkNX98WxuKpVq2LAgAF48uQJAODkyZO4evUq5s6di40bN5Z4vbIr+OLi4tQexatduzYA9f/rrGjmzp2Lb775BrNnz8batWuljiMZIyMjrF27Fj/++CMiIiIUUyPo6+sDAGrWrIns7OwKd6ouLi4OAHD8+HGl/qNHj2LNmjVo3759hSz4li5diho1asDOzk7xnjh79ixiY2Ph4+ODrVu34syZMxKnlF7++6egz+G8vDwkJiaWcyrtYmxsjJMnT8Lc3By9evVCaGio1JEk4erqiv79+6N79+4qU9Po6+ujZs2aSEtLQ05OjkQJpZH/O3Tv3j1FsZfv+PHjmD17NkxNTdVem18Y2Z3SDQ4ORsuWLaGnp6fU36ZNGwDArVu3pIilNebOnYv58+fD09MTS5YskTqOpOrUqYN69erh//7v/5CYmKhoo0aNQrVq1ZCYmIht27ZJHbPcqbv2CoBiSpa8vLzyjKM17OzscOfOHZX/AFy5cgUArw/O9/fff+P58+eKz9yXtWnTBo8ePUJmZqYEybSDsbEx/vjjD1hZWaFPnz4IDg6WOpJkbGxsULlyZVy6dEnpMxgAJk+ejMTERLi4uEgbUgJ///030tLS1C4rzeew7Aq+ffv2oXr16nB1dVXqd3NzQ3h4eJne8fKmmTNnDubPn4+FCxdiwYIFUseRXFRUFBwcHFTasWPHkJ6eDgcHhwo50fCePXsAAM7Ozkr9AwYMAABcvHix3DNpg4iICLRu3VplDqz8SVCfPn0qRSytk5ubi4MHD2Lo0KGoVq2aor9Ro0ZwdHTE3r17JUwnrfxiz9raGn379sWNGzekjiSpLVu2qP0MBl78W+7g4IBz585JG1ICubm58PPzQ8uWLWFhYaG0rH///nj06JHiKGBJST4XTVm348ePi7i4OOHu7i4cHBzEzz//LIQQYtSoUZJnk6p99tlnQgghjhw5Ijp37qzSpM6nTa2iz8MHQPj5+Yn09HTx9ddfCycnJ/Hll1+K58+fiwMHDkieTao2aNAgkZubK86fPy+GDRsmHB0dxVdffSWSk5PFrVu3ROXKlSXPWB6tf//+wtXVVYwbN04IIcSuXbuEq6urcHV1FQYGBgKAaN68uUhOThYBAQGif//+4t133xU3b94UT58+FXXq1JH8NUixX6pWrSouXbokcnNzxUcffaTyGfzq/I5yaMV5r6hrcp+Hrzj7xdraWsTHx4u7d++K4cOHC2dnZ7Fnzx6Rm5srXF1dX3fb0r/4sm5GRkZi9erVIiIiQmRkZIgbN26I4cOHS55Lyubv7y8KI3U+bWos+CCqVq0qlixZIh4/fiyysrJEWFiYWLRokdDX15c8m5TNwcFBHDt2TERERIi0tDRx7949sXz5crUT6sq1hYaGFvg5YmFhoRjXvn17cfLkSZGamioSExPF3r17ZVnUFHe/WFhYFPoZ7OPjI/lrkOq98moTQt4FX3H3S+vWrcXBgwdFUlKSeP78uTh//rxwcXF57e3q/PsXIiIiIpIp2V3DR0RERETKWPARERERyRwLPiIiIiKZY8FHREREJHMs+IiIiIhkjgUfERERkcyx4CMiIiKSORZ8RERERDLHgo+IiIhI5ljwEZHGderUCXv37sXjx4+RkZGBqKgonD9/HitWrJAsk6enJ158q6Dm+Pj4IDQ0tMhxI0eOxCeffKJ2mRACnp6eZR2NiCoYfrUaEWnUgAEDcODAAQQEBGDDhg2IjIxE/fr10aFDB4wYMQKNGjWSJJe5uTkaNmyIS5cuaWwbPj4+cHBwgJWVVaHjDh48CBsbG7XjOnfujKdPnyI8PFxTMYmoAmDBR0QaFRAQAHNzc7Ro0QK5ublKy3R0dMrsKFvVqlWRkZFRJusqK2VR8BERlQWe0iUijTIxMUFsbKxKsQdApdgr6PRlaGgofHx8FI/d3NwghECfPn2wadMmPHv2DOnp6Rg+fDiEEOjVq5fKOqZMmQIhBNq0aQNA9ZTuvn37EBYWBh0dHZXnXrx4EX/99ZfisYeHBwIDAxEdHY3U1FTcvHkTX3zxBSpVqlSMPaLM398fAwcOhKWlJYQQilbQPsl/7Y6Ojli/fj1iY2ORlJQEX19fGBoaom7duti1axcSEhIQERGB5cuXq+SqXLkyvv76a9y9excZGRl49uwZNm/ejDp16pQ4PxG9GVjwEZFGXbhwAV26dMGaNWvQqVOn1yqKCrJ582ZkZ2fjgw8+gKurK/bt24fo6GiMHz9eZey4cePw119/ITg4uMB1WVhYqBSLzZs3R+fOnZUKziZNmmD79u344IMPMHDgQGzatAlffPEFfv755xK/Bg8PD5w7dw6RkZHo0qWLohVl48aNSEpKwogRI/Dtt99i1KhR2LBhAw4fPoygoCC899578PX1xf/93//ho48+UjxPR0cHfn5+mDVrFrZv3w4XFxfMmjULffr0QUBAAKpWrVri10BEbwbBxsbGpqlWu3ZtcebMGZEvMzNTnDt3Tnz55ZfCyMhIaawQQnh6eqqsIzQ0VPj4+Cgeu7m5CSGE2LJli8rYFStWiLS0NFGjRg1FX4sWLYQQQkybNk3R5+npKcSLQ2kCgNDT0xORkZHi119/VVrf0qVLRUZGhqhdu7ba16ejoyP09PTEmDFjRHZ2tjA2NlYs8/HxEaGhoUXuo4MHDxY47tV9kv/a16xZozRu7969QgghZsyYodR/7do1cfXqVcXj4cOHCyGEGDJkiNK4t99+WwghxJQpUyR/z7CxsZV94xE+ItKo+Ph49OzZEx06dMCXX34JPz8/vPXWW1i6dCmCg4NhYmLy2uves2ePSt/mzZthaGiI4cOHK/rGjx+PjIwMbN++vcB15ebm4tdff8XQoUNRo0YNAICuri4++OAD+Pn5IT4+XjHWzs4Ofn5+iI2NRV5eHnJycvDLL7+gUqVKeOutt1779ZTEoUOHlB7fvXsXAHD48GGVfgsLC8XjgQMHIiEhAQcPHoSenp6i3bhxA5GRkXBwcNB4diIqfyz4iKhc/PXXX/juu+/w/vvvo0GDBvj+++9hZWWFmTNnvvY6IyMjVfru3LmDy5cvK07r6urqYsyYMfDz80NCQkKh69u8eTMMDAwwYsQIAEC/fv3QoEEDpdO5jRo1wtmzZ2Fubo5PPvkE3bt3R4cOHeDh4QEAMDAweO3XUxIvF6AAkJWVVWD/y6dp69ati1q1aiE7Oxs5OTlKrX79+ryOj0imyu5iGiKiYsrJycH8+fPx2WefwcbGRtGfkZGBKlWqqIwv6ChgQXf4+vj4wNvbGy1atIC1tbVK0VaQu3fv4tKlSxg/fjzWr1+P8ePHIzw8HCdOnFCMeffdd1GtWjUMHToUT548UfTb2dkVuX5tEBsbi9jYWPTv31/t8pSUlHJORETlgQUfEWlUvXr1EBUVpdLfsmVLAEBERISiLywsDG3btlUa5+joiOrVq5domzt27MD333+PcePGwdraGk+fPlUq2grj4+ODdevWoVu3bhg0aBC+//575OXlKZbnF5mZmZlKz5s0aVKJMr4sMzOz3I4MHjp0CCNHjoSenh4uX75cLtskIumx4CMijTp+/DiePn2KgwcP4t69e9DV1YWdnR0+//xzpKSkYM2aNYqxv/zyCxYuXIj58+cjMDAQrVq1wvTp05GYmFiibSYlJWHfvn0YN24cjI2NsWLFimLP95dfLO7YsQNVq1bFli1blJafPHkSmZmZ2LFjB7777jtUrVoVU6dORa1atUqU8WXBwcFwdXXFlClT8NdffyEvL09pGpiytHPnTowePRpHjhzBmjVrcPnyZWRnZ6Nhw4ZwdHSEn58f9u/fr5FtE5G0JL9zhI2NTb5t2LBh4tdffxX3798XycnJIjMzU4SFhQlfX1/RokULpbGVK1cWS5cuFY8fPxZpaWnC399ftG3btsC7dN9+++0Ct9u7d2/FncFNmzZVWf7qXbovt19//VUIIcTZs2fVLndxcRHXr18Xz58/F//8849YtmyZ6NevnxBCCHt7e8W44t6la2xsLH777TcRHx8vcnNzlXIVdJfuq689//WYmJgo9fv4+IiUlBSlPj09PfHZZ58pXkNycrK4c+eO8Pb2Fk2aNJH8PcPGxlb2jd+0QURERCRzvEuXiIiISOZY8BERERHJHAs+IiIiIpljwUdEREQkcyz4iIiIiGSOBR8RERGRzLHgIyIiIpI5FnxEREREMseCj4iIiEjmWPARERERyRwLPiIiIiKZY8FHREREJHMs+IiIiIhk7v8BeWrSr6KPHTEAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(dpi =120)\n",
    "# Fix plot limits\n",
    "ax.set_xlim([0, 16])\n",
    "ax.set_ylim([-0.5, 7.5])\n",
    "xlim = ax.get_xlim()\n",
    "xlim_rng = xlim[1]-xlim[0]\n",
    "# Study reference initial and final points\n",
    "ax.axvline(x=0, linestyle = '--', c= 'C0')\n",
    "ax.annotate('study begins', xy = (0.2, 1.5), c = 'C0', rotation = 90)\n",
    "ax.axvline(x=15, linestyle = '--', c= 'C1')\n",
    "ax.annotate('study ends', xy = (15.2, 1.5), c = 'C1', rotation = 90)\n",
    "\n",
    "# Survival lines\n",
    "for i, j in survival_df.iterrows():\n",
    "    if j['status'] != 0:\n",
    "        ax.axhline(y = i, \n",
    "                    xmin = j['birth']/xlim[1], \n",
    "                    xmax = j['death']/xlim[1],\n",
    "                    alpha = 0.7)\n",
    "        ax.scatter(x = j['death'], y = i, marker = 'x', c = 'r')\n",
    "    else:\n",
    "        if not np.isnan(j['death']):\n",
    "            ax.axhline(y = i, \n",
    "                        xmin = j['birth']/xlim[1], \n",
    "                        xmax = j['death']/xlim[1],\n",
    "                        alpha = 0.7)\n",
    "            ax.annotate( 'Lost', xy = (j['death']+0.1, i-0.1), c = 'C3')\n",
    "        else:\n",
    "            ax.axhline(y = i, \n",
    "                        xmin = j['birth']/xlim[1],\n",
    "                        alpha = 0.7)\n",
    "clean_ax(ax, clean_all = True)\n",
    "ax.set_ylabel('Subject')\n",
    "ax.set_xlabel('Survival time')\n",
    "ax.set_title('Survival analysis w/ right censoring')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above picture, subjects 2, 5 and 6 had actually suffered an event while subject 7's track was lost. This is, we do not know whether subject 7 has suffered an event or just his/her records disappeared for some reason (e.g. moving away from the the location where the study is being performed).\n",
    "Note that in the case of studying a death event, the censoring considers unregisterd deaths, such as COVID-19 sub notification issues around the world.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Describing time to event\n",
    "Let us start with some terminology and definitions. \n",
    "\n",
    "> **Definition: [Survival Time]** Let $T \\in \\mathbb{R}^{+}$ be a random variable (survival time) and $t$ an particular arbitrary value for $T$. \n",
    "\n",
    "> **Definition: [Censor variable]** Let $d \\in \\{0,1\\}$ be the response variable such that $d=1$ if the event has happened while $d=0$ in the case of censoring; This is, the event has not been observed whether because it has not happened or because we have lost track of the subject.\n",
    "\n",
    "\n",
    "### Death density\n",
    "When considering the time until an event happens (e.g. death) we can construct a histogram of the count of events as a function of time. In simple terms, we could fit a curve to this histogram (e.g. using Kernel Density Estimation) and produce a *death* probability density function $f(t)$. After normalization, at any given time $t$ the proportion of observed events in the population is given by:\n",
    "$$F(t) = \\int_{0}^{t}f(u)du,$$\n",
    "the cumulative death distribution function. Essentially, we are assuming $T$ (time-to-event) to be a random variable with cumulative distribution function:\n",
    "$$F(t) := \\mathbb{P}(T \\leq t)$$\n",
    "\n",
    "To illustrate this, consider the following dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "colon.csv     lung.csv      stanford2.csv\n",
      "kidney.xlsx   ovarian.csv   veteran.csv\n"
     ]
    }
   ],
   "source": [
    "!ls data/healthcare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order</th>\n",
       "      <th>ctryname</th>\n",
       "      <th>year</th>\n",
       "      <th>aclpcode</th>\n",
       "      <th>cowcode</th>\n",
       "      <th>cowcode2</th>\n",
       "      <th>qogctycode</th>\n",
       "      <th>qogctylett</th>\n",
       "      <th>qogctyyear</th>\n",
       "      <th>ccdcodelet</th>\n",
       "      <th>...</th>\n",
       "      <th>regime</th>\n",
       "      <th>tt</th>\n",
       "      <th>ttd</th>\n",
       "      <th>tta</th>\n",
       "      <th>flagc</th>\n",
       "      <th>flagdem</th>\n",
       "      <th>flagreg</th>\n",
       "      <th>agedem</th>\n",
       "      <th>agereg</th>\n",
       "      <th>stra</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1946</td>\n",
       "      <td>142</td>\n",
       "      <td>700.0</td>\n",
       "      <td>700</td>\n",
       "      <td>4</td>\n",
       "      <td>AFG</td>\n",
       "      <td>41946</td>\n",
       "      <td>AFG</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1947</td>\n",
       "      <td>142</td>\n",
       "      <td>700.0</td>\n",
       "      <td>700</td>\n",
       "      <td>4</td>\n",
       "      <td>AFG</td>\n",
       "      <td>41947</td>\n",
       "      <td>AFG</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1948</td>\n",
       "      <td>142</td>\n",
       "      <td>700.0</td>\n",
       "      <td>700</td>\n",
       "      <td>4</td>\n",
       "      <td>AFG</td>\n",
       "      <td>41948</td>\n",
       "      <td>AFG</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1949</td>\n",
       "      <td>142</td>\n",
       "      <td>700.0</td>\n",
       "      <td>700</td>\n",
       "      <td>4</td>\n",
       "      <td>AFG</td>\n",
       "      <td>41949</td>\n",
       "      <td>AFG</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1950</td>\n",
       "      <td>142</td>\n",
       "      <td>700.0</td>\n",
       "      <td>700</td>\n",
       "      <td>4</td>\n",
       "      <td>AFG</td>\n",
       "      <td>41950</td>\n",
       "      <td>AFG</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   order     ctryname  year  aclpcode  cowcode  cowcode2  qogctycode  \\\n",
       "0      1  Afghanistan  1946       142    700.0       700           4   \n",
       "1      2  Afghanistan  1947       142    700.0       700           4   \n",
       "2      3  Afghanistan  1948       142    700.0       700           4   \n",
       "3      4  Afghanistan  1949       142    700.0       700           4   \n",
       "4      5  Afghanistan  1950       142    700.0       700           4   \n",
       "\n",
       "  qogctylett  qogctyyear ccdcodelet  ...  regime   tt  ttd  tta  flagc  \\\n",
       "0        AFG       41946        AFG  ...     5.0  0.0  0.0  0.0    1.0   \n",
       "1        AFG       41947        AFG  ...     5.0  0.0  0.0  0.0    0.0   \n",
       "2        AFG       41948        AFG  ...     5.0  0.0  0.0  0.0    0.0   \n",
       "3        AFG       41949        AFG  ...     5.0  0.0  0.0  0.0    0.0   \n",
       "4        AFG       41950        AFG  ...     5.0  0.0  0.0  0.0    0.0   \n",
       "\n",
       "   flagdem  flagreg  agedem  agereg  stra  \n",
       "0      1.0      1.0    18.0    18.0   0.0  \n",
       "1      0.0      0.0    19.0    19.0   0.0  \n",
       "2      0.0      0.0    20.0    20.0   0.0  \n",
       "3      0.0      0.0    21.0    21.0   0.0  \n",
       "4      0.0      0.0    22.0    22.0   0.0  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel('data/ddrevisited_data_v1.xls')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset contains information about countries from 1946 to 2008. Thus, this is a right censored dataset. Among the information from countries there are two special columns named `bornyear` and `endyear` which stand for the years that a specific country (with that name) was born and was ended. We can use the countries' duration time as our survival time random variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ctryname</th>\n",
       "      <th>bornyear</th>\n",
       "      <th>endyear</th>\n",
       "      <th>status</th>\n",
       "      <th>duration</th>\n",
       "      <th>q_duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1919.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>(71.7, 104.2]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1912.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>(71.7, 104.2]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>1962.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>(43.0, 48.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>1993.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>(1.999, 17.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>1975.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>(24.2, 34.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ctryname  bornyear  endyear  status  duration     q_duration\n",
       "0  Afghanistan    1919.0   2008.0       0      89.0  (71.7, 104.2]\n",
       "1      Albania    1912.0   2008.0       0      96.0  (71.7, 104.2]\n",
       "2      Algeria    1962.0   2008.0       0      46.0   (43.0, 48.0]\n",
       "3      Andorra    1993.0   2008.0       0      15.0  (1.999, 17.0]\n",
       "4       Angola    1975.0   2008.0       0      33.0   (24.2, 34.0]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survival_df = df.groupby('ctryname')[['bornyear', 'endyear']].first().reset_index()\n",
    "survival_df['status'] = survival_df.endyear.apply(lambda x: 0 if x == 2008 else 1)\n",
    "survival_df['duration'] = survival_df['endyear'] - survival_df['bornyear']\n",
    "survival_df['q_duration'] = pd.qcut(survival_df.duration, q= 10)\n",
    "survival_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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s0Lln1cbGBnPmzNH2yffTTz8BAGbNmgVLS0tt3MTEBF988UWJPk/+Y62e/U/lyy+//NxFVkSVDS/rE8nY0qVLYWxsjHnz5uHs2bP4+++/cfbsWe3rS93c3NCqVSucOXNGO+bUqVP48ssvMW3aNFy+fBm//fYb0tPT4eHhAWdnZ5w8eRIrVqzQOc7KlSvRv39/7Nq1C4GBgXj06BFeeeUVNGvWDMHBwdrZo9I4deoU0tPTMWXKFFhbW2vv11u9erX2MmlRff/991i1ahWA0s2ajh49WlsM1KhRAw4ODnBzc4ONjQ1iY2MxduxY/P333zpjdu3ahWvXrmHEiBFo1KgRTp8+jSZNmsDT0xO7du3CsGHDip3HqlWrsG7dOly4cAHbt29Hbm4uunfvjrZt22L37t0YPHiwZMyRI0fg7e2N3bt349y5c8jLy8OJEycKfZvVBx98gL///hsrV65Ev379cPbsWe1zTjUaDcaMGVOsVe6G1KdPH/zvf/9DSEgIoqKiEB8fj0aNGsHT0xNqtVrnZ/zq1au4d+8ehg8fjpycHNy5cwdCCGzZsgV37tzBypUr4eHhgTfffBOXLl3Cvn37YG5ujrfffhv16tXDl19+qfNzcOLECaxbtw4TJkxARESE9nv2xhtv4PHjx1CpVHoXChbmp59+wtSpU/H111+jV69euH79Olq2bInXX38dO3bswPDhw8vs3BFVBAZ/nhUbG1vhrU2bNuLbb78V4eHh4vHjxyI7O1vExsaKffv2ibFjx+p969OwYcPEyZMnRUpKisjMzBSXL18WM2fOLPDNMG+88YY4c+aMyMzMFA8fPhS//PKLaNKkyXPfEKVvX/nP9Xw23r9/fxESEiJSU1O1z7PU94ao552PWrVqiby8PJGZmSmsra2LfT6ffWNVbm6uSEpKEpGRkSIgIED4+PgIc3PzAsc3atRIBAQEiMTERJGRkSH++ecf4eXl9dznnBaWk4+Pj7hw4YJIS0sTCQkJYseOHaJdu3YFnhdbW1vx888/iwcPHoi8vDyd4xb2/bGzsxN+fn4iJiZGZGdni4SEBLFz507RuXNnvTkJUfCzaYUo+Fmrz7ayfkNUmzZtxFdffSXOnDkj4uPjRVZWloiOjhZBQUE6zyTNb507dxZ//vmnSE5OFmq1WnJOzczMxIwZM0R4eLjIyMgQKSkp4uTJk2L48OF681QoFGLKlCkiMjJSZGVlCZVKJb777jthaWkpUlJSxIULF4p1LgEIR0dHsWvXLhEXFyfS0tLE2bNnxbhx4wr8fhb2zN3C/jwVJRc2NgM3gyfAxsbGVqyWXwT+9NNPBs+Fje3p1qJFCyGEENu2bTN4LmxsFbXxnlMiqnA+//xzAMB3331n4EyoqqpXr57kuazVq1fH119/DQAV5t5dIjniPadEVCG0a9cOr7/+Ojp16oSBAwdiz549hT7QnOhFmjJlCry9vXHs2DHcv38f9evXh7u7Oxo3box9+/YhKCjI0CkSVWgGn75lY2Nje17Lv08uOTlZBAYG6n0XPRtbebXevXuLffv2idjYWJGVlSXS0tLE+fPnxWeffSaMjY0Nnh8bW0Vuiv//DRERERGRwfGeUyIiIiKSDRanRERERCQbLE6JiIiISDZYnBIRERGRbLA4JSIiIiLZYHFKRERERLLB4pSIiIiIZIPFKRERERHJBotTIiIiIpINFqdEREREJBssTomIiIhINlicEhEREZFssDglIiIiItlgcUpEREREssHilIiIiIhkg8UpEREREckGi1MiIiIikg0Wp0REREQkGyxOiYiIiEg2WJwSERERkWywOCUiIiJZmjdvHoQQsLGxKfbY4OBgBAcHv4Cs6EVjcUpEREREsmFs6ASoals0cnGZ73POz7PLfJ9ERERUPlicEhERVTJfhZ8ydAp6fercrdT7aN26Nfbt24eEhAS88cYbSEhIwNSpUzFp0iTUq1cPV65cwaxZs/SOrVmzJubOnYuhQ4eiYcOGSEhIQFBQEGbNmoWMjIxS50Zlg8UpERERVQhubm7YuXMnTpw4gREjRiAzMxPz5s3D/PnzsWHDBvz2229o3LgxfvjhByiVSly9elU7tnr16jh+/DgaNWqEL774AmFhYXBycsLChQvh7OyMPn36GPCT0dNYnBIREZHsjRw5Ehs3bsT333+PTz75BEIIWFlZYdq0adixYwfee+89bd+IiAiEhIToFKcff/wxXnrpJbi4uODcuXMAgKNHj0KlUmH79u0YMGAADhw4UO6fi6S4IIqIiIhkbdasWdi8eTOmT5+OKVOmQAgBAOjWrRuqV6+On3/+Waf/qVOnEBMToxN7/fXXcfnyZVy8eBFKpVLbDh48CI1Gg549e5bTp6Hn4cwpERERydqoUaOgUqkQEBCgE89/xNSDBw8kY56N1atXDy1btkReXp7eY9SpU6eMsqXSYnFKRERUyZTFwiM5GTBgAAIDA3Hy5Em4u7vjzp07AIDExEQAQP369SVj6tevrzN7+vDhQ2RmZmLs2LF6j/Hw4cOyT5xKhJf1iYiISNZu376N1157DdnZ2Th58iRatGgBAAgNDUVmZiZGjhyp079bt25o2rSpTmzv3r1wcHBAYmIizp07J2m3b98ur49Dz8HilIiIiGTvwYMH6NGjBxISEnDixAk4OTkhOTkZK1euxJAhQ/DDDz+gX79+GDduHH799Vfcv39fZ/zXX3+Nq1ev4sSJE/jkk0/g7u6Ovn37Yty4cQgMDETXrl0N9MnoWbysT0RERBVCYmIievfujT/++APHjx9H//79MXfuXKSnp8PX1xfvvvsuoqKi8MEHH+Czzz7TGZuRkYHXXnsN06dPx/vvv49mzZohMzMTd+7cwZ9//ilZQEWGowAgDJ0EVV18QxQRERE9jZf1iYiIiEg2WJwSERERkWywOCUiIiIi2WBxSkRERESyweKUiIiIiGSDxSkRERERyQaLUyIiIiKSDRanRERERCQbLE6JiIiISDZYnBIRERGRbBgbOoFnWVhYYPHixXjnnXdgbW2NqKgoLFu2DIGBgc8da2tri+XLl+P111+Hubk5Ll26hNmzZ+Po0aM6/QYNGoR33nkHHTp0QJs2bWBiYgKFQvHc/bu7u+PPP/8EANSpUweJiYkl+5BERETl4EW8Irq4SvNK6Xnz5mH+/PnP/Tc3Ojoax44dw5gxY4q8727duqFfv374+uuv8fjx4xLnSGVPdjOnO3bsgI+PDxYsWAAPDw+cOXMGAQEB8Pb2LnScqakpjhw5And3d0yePBmenp6Ii4vDgQMH4ObmptPXy8sLrq6uuHLlCi5dulSkvCwsLPDDDz9ApVKV+LMRERFR2fPy8sKiRYuKNeaVV17B/PnzUatWrReTFJWYrGZOPTw80K9fP3h7eyMgIAAAcOzYMdjb22PFihUIDAyERqPRO3bcuHFwdnZGt27dEBoaCgAIDg7GpUuXsHz5cri6umr7vvfeexBCAABWr16Nzp07Pze3ZcuWISkpCX/88QfmzJlT2o9KREREZeTixYuGTqHYjI2NIYSAWq02dCqyI6uZUy8vL6SmpiIoKEgn7u/vj4YNG8LFxaXQsVFRUdrCFADUajW2bt0KFxcX2NnZaeP5hWlRvfrqq3j//fcxfvx4/hARERGVs3r16mHbtm1ITk7GgwcPsHHjRlhaWmq3R0dHw9/fX/u1QqHArFmzEBUVhYyMDCQlJeHSpUv4+OOPATy5XWDlypUAgJiYGAghIIRAjx49tOOnTp2KyMhIZGVlIS4uDj/++CMaNmwoyW3GjBmIiYlBZmYmzpw5gz59+iA4OBjBwcHaPj169IAQAqNGjcLKlStx7949ZGdno0WLFqhTpw7WrFmDiIgIpKamIi4uDkeOHMGrr76qcxx7e3sIIfDZZ5/h888/R3R0NDIyMhAcHIyWLVvC2NgYS5cuhUqlQnJyMnbs2AFbW9uy+yaUI1nNnLZr1w6RkZGSAjAsLEy7/dSpUwWOPXnypCSeP9bJyQmxsbHFzqlatWrYuHEjvv76a1y4cAGDBw8u8lhbW1u9PxgJCQlISEgodi5ERERV0fbt2xEYGIiNGzfC2dkZS5cuBfDkqqk+n3/+OebPn4/FixfjxIkTMDExQZs2bbSX8Dds2ABra2t8/PHH8PLywv379wEAV65cAQCsXbsWEyZMwOrVq7F37140bdoUixYtQs+ePdGxY0ft/a9LlizBzJkzsW7dOuzYsQONGzfGhg0bYGJigmvXrknyWrp0KU6dOoUPPvgAGo0G8fHx2jphwYIFePDgAWrUqAEvLy8cO3YM7u7uOH78uM4+Jk2ahLCwMEyaNAm1atXCV199hT179uD06dPIzc3F2LFjYW9vj5UrV2LDhg3w9PQs/TegnMmqOLWxscGtW7ck8UePHmm3FzY2v19xxxZm0aJFUCqVmDdvXrHH+vr6Yv78+ZL4/PnzsWDBghLlQ0REVNVs3LhRO9N55MgRtGjRAmPHji2wOO3evTvCw8N1/q09dOiQ9vcqlQp37twBAFy4cAG3b9/WbmvdujUmTJiANWvWaGda8/v9888/+OSTTzB79mzUqlUL//3vfxEQEIAPPvhA2+/y5csIDQ3VW5zevHkT77zzjk4sKSkJkyZN0n5tZGSEgwcPomnTpvj4448lxWlycjLefPNN7VXgOnXq4JtvvkFUVBTefPNNbb82bdrgk08+Qc2aNZGamqr3PMmVrC7rA4Vfcn/e5fjSjNWnS5cumDJlCiZMmICsrKxij/fz84OTk5Ok+fn5FXtfREREVdXu3bt1vg4LC0P16tVRt25dvf3/+ecftG/fHmvWrEG/fv1Qs2bNIh+rV69eAIDNmzfrxM+cOYMrV67A3d0dAODq6opq1arh119/1el3+vRpREdH69339u3b9cYnTJiAc+fOITMzE2q1Gnl5eejTpw8cHR0lffft26dT00RGRgIA/vjjD51++fEmTZoU9FFlS1Yzp4mJiXpnOK2trQFA78xoWYwtyKZNm7Bjxw6cPXsWVlZWAJ5c5gcAS0tLZGdnIy0trcDxvHxPRERUes8+Rio7OxsAUL16db39ly5divT0dIwaNQoffPAB1Go1Tpw4gWnTpuHcuXOFHiu/lsi/1P+02NhY2Nvb6/SLi4uT9NMXK2ifn3zyCf73v/9h7dq1mDNnDh4+fAi1Wo1FixbpLU6frWdycnIKjefXLRWJrGZOw8PD4ejoCKVSqRN3dnYG8GSqvLCx+f2KO7Yg7dq1wzvvvIPk5GRtmz59OgDg1q1beu9xJSIiIsNSq9VYtWoVOnXqBGtra3h7e6Nx48Y4ePBggQVtvvxCuEGDBpJtdnZ2ePjwoU6/evXqSfrVr19f7771XcUdNWoUgoOD4evri3379uGff/7BuXPnijXbW9nIqjjduXMnatasiaFDh+rEfXx8oFKpcPr06ULHOjo6omvXrtqYUqnEqFGjEBoaqvd/K8/Ts2dPScuf5vf09MT48eOLvU8iIiIqP48fP8b27duxZs0a2NjYoGnTpgAKnn3Nf3HPqFGjdOKdO3dG27ZtceTIEQBPLt9nZWVh2LBhOv1cXFy0xygKIYQ2l3z5j8asqmR1Wf/AgQM4dOgQ1q5dC0tLS9y4cQPe3t7w8PDAyJEjtc843bBhA3x8fODg4KC9oXnTpk2YNGkSgoKCMH36dMTHx8PX1xetW7dGnz59dI7TpEkTdOnSBQDg4OAAANqCOCYmRjvl/+xNyMCTghUA/v77b74hioiISIZ2796Ny5cv4+zZs0hISIC9vT2mTJmCmJgYXL9+HcCTK64AMHnyZPz444/Izc3F1atXce3aNaxbtw4fffQRNBoN9u/fr12tf+fOHaxatQrAk4VM//vf/zBz5kwkJSVh586daNSoEebNm4fY2NgCn8v+rL1792LOnDmYP38+jh8/jtatW2Pu3LmIjo6GsbGsyrRyI7tPPWTIECxZsgQLFy7Uvr50+PDhOq8vVSqVMDY21nnlaE5ODtzd3bF8+XKsXr0a5ubmuHjxIjw8PHDixAmdY/Tq1Utyo/Nvv/0G4MkN0MV5/RkRERHJS3BwMIYOHYrx48fD0tISDx48wOHDh7Fo0SLk5eUBeDIB9cUXX8DHxwfvvfcelEolevbsiePHj2PixIm4efMmxo0bh0mTJuHx48c4cOAAZsyYoXNv56xZs5Ceno4PPvgAY8aMQVRUFCZOnIglS5YgOTm5SLkuWbIE5ubmGDduHD7//HNcuXIFH3zwAby8vLQTYlWNAkDxl7ETlZEX8d7n0rzHmYiIqDSaNm2KqKgoLFiwQPs8Vioe2c2cEhEREVUEL730Ery9vRESEoKUlBS0bt0an3/+OVJSUrBx40ZDp1dhsTglIiIiKoH09HR07twZ48aNQ61atfD48WMcO3YMs2bNQnx8vKHTq7BYnBIRERGVwM2bN9G3b19Dp1HpyOpRUkRERERUtbE4JSIiIiLZYHFKRERERLLB4pSIiIiIZIPFKRERERHJBotTIiIiIpINFqdEREREJBssTomIiIhINlicEhEREZFssDglIiIiItlgcUpEREREssHilIiIiIhkg8UpEREREckGi1MiIiIikg0Wp0REREQkGyxOiYiIiEg2WJwSERERkWywOCUiIiIi2WBxSkRERESyweKUiIiIiGSDxSkRERERyQaLUyIiIiKSDRanRERERCQbLE6JiIiISDZYnBIRERGRbLA4JSIiIiLZYHFKRERERLLB4pSIiIiIZIPFKRERERHJhuyKUwsLC6xatQoqlQqZmZm4cOEChg0bVqSxtra28Pf3R0JCAtLT0xESEoLevXtL+g0aNAg//vgjwsLCkJOTAyGE3v117NgR3333HcLCwpCSkoIHDx7g8OHD6NWrV6k+IxERERHpJ7vidMeOHfDx8cGCBQvg4eGBM2fOICAgAN7e3oWOMzU1xZEjR+Du7o7JkyfD09MTcXFxOHDgANzc3HT6enl5wdXVFVeuXMGlS5cK3Ke3tze6du2KTZs2wdPTE+PHj0d2djaOHj2Kd999t0w+LxERERH9SwFA/7ShAXh4eGDfvn3w9vZGQECANn7w4EE4OTmhSZMm0Gg0esdOnDgRfn5+6NatG0JDQwEASqUSly5dQlpaGlxdXbV9FQqFdrZ09erV+PDDD6FQKCT7tLW1RUJCgk7MyMgI58+fh4WFBVq2bFnqz1zVLRq5uMz3Oefn2WW+TyIiIiofspo59fLyQmpqKoKCgnTi/v7+aNiwIVxcXAodGxUVpS1MAUCtVmPr1q1wcXGBnZ2dNl7QZfxnPVuYAoBGo8G5c+fQuHHjIu2DiIiIiIpOVsVpu3btEBkZCbVarRMPCwvTbi9sbH4/fWOdnJzKJEelUonXXnsNERERZbI/IiIiIvqXsaETeJqNjQ1u3boliT969Ei7vbCx+f2KO7Y45s+fj5YtW8LT0/O5fW1tbWFrayuJJyQk6J2VJSIiIqrqZDVzChR+yf15l+NLM7Yoxo0bh9mzZ2PlypXYvXv3c/v7+voiIiJC0nx9fUudCxEREVFlJKuZ08TERL0znNbW1gCgd2a0LMYWxejRo7Fu3TqsW7cOU6dOLdIYPz8/yf2zgP57WYmIiIhIZsVpeHg4vL29oVQqde47dXZ2BgBcvny50LH5/Z5WlLHPM3r0aGzYsAE//vgjPvjggyKP4+V7IiIiouKR1WX9nTt3ombNmhg6dKhO3MfHByqVCqdPny50rKOjI7p27aqNKZVKjBo1CqGhobh//36JcvLx8cGGDRuwdetWjB8/vkT7ICIiIqKikdXM6YEDB3Do0CGsXbsWlpaWuHHjBry9veHh4YGRI0dqn3G6YcMG+Pj4wMHBAXfu3AEAbNq0CZMmTUJQUBCmT5+O+Ph4+Pr6onXr1ujTp4/OcZo0aYIuXboAABwcHABAWxDHxMTg3LlzAIC33noLGzduxMWLF7Fu3TqdwhcALly4gJycnBd3QoiIiIiqGFkVpwAwZMgQLFmyBAsXLoS1tTWioqIwfPhwBAYGavsolUoYGxvrPDg/JycH7u7uWL58OVavXg1zc3NcvHgRHh4eOHHihM4xevXqhc2bN+vEfvvtNwDA5s2bMWbMGABPXnOqVCrRqVMnhISESHJt2rQpbt++XVYfnYiIiKjKk9Uboqjq4RuiiIiI6GmyuueUiIiIiKo2FqdEREREJBssTomIiIhINlicEhEREZFssDglIiIiItlgcUpEREREssHilIiIiIhkg8UpEREREckGi1MiIiIikg0Wp0REREQkGyxOiYiIiEg2WJwSERERkWywOCUiIiIi2WBxSkRERESyweKUiIiIiGSDxSkRERERyQaLUyIiIiKSDRanRERERCQbLE6JiIiISDZYnBIRERGRbLA4JSIiIiLZYHFKRERERLJhbOgEiApkbASjBpZQVDeB5nEmRFyaoTMiIiKiF4zFKcmPkQLGrvYw6WoPRbV/f0Q1SRnIDb4B9bUEAyZHRERELxIv65O8KI1gNvQlmLo56BSmAGBU2xxmQ16CiZuDgZIjIiKiF43FKcmHAjAb4gylQ51Cu5m80hTGnRqVU1JERERUnlickmwYd2ny3MI0n4l7Sxg1qvViEyIiIqJyx+KUZEFhYw4Tt+aSuNAIqO+nSPsbGcF0QBvASFEe6REREVE5YXFKsmDSswUUxkqdmBACOXsikP3jGeSevycZY1THgpf3iYiIKhkWp2RwRvVrwrilrSSed+Yu1JFxAIDcP69BrXos6WPSvRlgqpTEiYiIqGJicUoGZ/Kq9HK+5nEmck/cfCogkHMoCkIInX6KaiYwfrnhi06RiIiIygmLUzIoRe3qULaQLoLK/TsGyNPoxERcGtRh9yV9jTs35r2nRERElQSLUzIo4w7SWU9NcibUl6VFKADkhsZIZk+NLKtB2abuC8mPiIiIypfsilMLCwusWrUKKpUKmZmZuHDhAoYNG1aksba2tvD390dCQgLS09MREhKC3r17S/oNGjQIP/74I8LCwpCTkyMpdp5mbGyMuXPnIjo6GllZWYiMjMSHH35Y4s9H/zI2M4Oxs50knnf+HqDR/z0RSZlQX5W+Icr4Jel+iIiIqOKRXXG6Y8cO+Pj4YMGCBfDw8MCZM2cQEBAAb2/vQseZmpriyJEjcHd3x+TJk+Hp6Ym4uDgcOHAAbm5uOn29vLzg6uqKK1eu4NKlS4Xu18/PDzNmzMCaNWvQv39/7Ny5E9988w1mzJhR6s9a1bXr+SoU1U10YiJPjbyw2ELH5Z29K4kpm1pDYVWtTPMjIiKi8qcAUPC0YTnz8PDAvn374O3tjYCAAG384MGDcHJyQpMmTaDRaPSOnThxIvz8/NCtWzeEhoYCAJRKJS5duoS0tDS4urpq+yoUCu1s6erVq/Hhhx9CoZDes9i2bVuEh4dj1qxZWLZsmTa+bt06jBo1Co0aNUJSUlKZfPaqaMw3y9Cudw+dWF74feT8ceW5Y6u95wojGwudWO7f0cg9eQtzfp5dpnkSERFR+ZHVzKmXlxdSU1MRFBSkE/f390fDhg3h4uJS6NioqChtYQoAarUaW7duhYuLC+zs/r3sW9hl/Ke9+eabMDIygr+/vyQfc3NzDBgwoEj7IalqNWugzavdJPG8Au41lfTTszBK6Viv1HkRERGRYRkbOoGntWvXDpGRkVCr1TrxsLAw7fZTp04VOPbkyZOSeP5YJycnxMYWfrlY3z7j4+MRFxdXYD6FsbW1ha2t9PmdCQkJSEiQ3jdZlTi794CxqalOTKRlQ3OnaDPR6oj7ED0ddGa8jazNoahbo0zzJCIiovIlq5lTGxsbPHr0SBLPj9nY2LyQscXdZ0ZGBrKzs5+7T19fX0REREiar69vsXOpbJyfuZwPAHlR8UW+yUSk5UBzN1kSN+aqfSIiogpNVjOnQOGX3J93Ob40Y1/EPv38/CS3KACo8rOmxqamaOHSWRJXR8Xp6V0wdVQ8lE1q68SUbXhpn4iIqCKTVXGamJiodzbS2toaAPTOYpbF2ML2+fLLL0vi5ubmMDMze+4+efleP4fOHWBmXl0nJjJzodHzetLC5F2Nh0nfVpJL+3Wb2SM++naZ5EpERETlS1aX9cPDw+Ho6AilUvdd6c7OzgCAy5cvFzo2v19xxxa2z7p166JePd3ZuNLskwBHt1ckMfWtxOI/NyI9B5p70oJW30IrIiIiqhhkVZzu3LkTNWvWxNChQ3XiPj4+UKlUOH36dKFjHR0d0bVrV21MqVRi1KhRCA0Nxf37RVsF/rRdu3ZBo9HAx8dHJz569GhkZGTgwIEDxd4nFVCc3nxYon2pb0nHOb7G4pSIiKiiktVl/QMHDuDQoUNYu3YtLC0tcePGDXh7e8PDwwMjR47UPuN0w4YN8PHxgYODA+7cuQMA2LRpEyZNmoSgoCBMnz4d8fHx8PX1RevWrdGnTx+d4zRp0gRdunQBADg4OACAtiCOiYnBuXPnAABXrlzBxo0bsWDBAqjVapw5cwb9+vXD+++/j9mzZ/MZpyVQt5k96jRupBMTGvFk5rQE1DcTgR4tdGLNO70M0+rVkZOZWeI8iYiIyDBkVZwCwJAhQ7BkyRIsXLgQ1tbWiIqKwvDhwxEYGKjto1QqYWxsrHOvYU5ODtzd3bF8+XKsXr0a5ubmuHjxIjw8PHDixAmdY/Tq1QubN2/Wif32228AgM2bN2PMmDHauK+vL1QqFT766CPUr18fMTExmDx5Mr777rsX8OkrvzZ6ZjU1qsdAVl6J9ifi06BJzYZRTTNtzNjUFC1dOiHi2F8lzpOIiIgMQ1ZviKLKb9yalWjr1l0nlnPsBvJCS76AydTDEcbt7XRiIYE7sH3xihLvk4iIiAxDVvecUuVmpFSieceXJXF1dPGfpKAzXs99p/pmaImIiEj+WJxSubFr3RLValjoxERWHkR8aqn2q455BKHW6MSs7RrA5pl7W4mIiEj+WJxSuWnRpaMkprmXVPobS7LV0MSmSI/XVXo8IiIikjcWp1RuHPQUp+o7yWWyb80d6ZMT9BXDREREJG8sTqlcKIyM0Kxje0lcX1FZEmo9+3HozOKUiIioomFxSuWiYZuWqF6zhk4sMzUNmri0Mtm/RvUYIk/3vlOreraoY9+4TPZPRERE5YPFKZWL5p07SGLRFy4BooyeZJangea+9FWmvLRPRERUsbA4pXLRtL2zJHbr7IUyPYZGz/2r+u5zJSIiIvlicUrlQl9xGn0+rEyPob7NRVFEREQVHYtTeuFq1asLq3q2OrG83Fzci7pWpsfRxErvO7W0rQPrRnYFjCAiIiK5KXVxamJiUhZ5UCXWpH07SSw26jrysrPL9kB5GmjuS5932vRl6awtERERyVOpi1OVSoUvvvgCjRtzVTTpZ/+SkyR2O+zyCzmWJla6KErfLQVEREQkT6UuTvfs2YOPP/4YN2/exI4dO9C7d++yyIsqEfuXpDOnty+9oOJUxeKUiIioIit1cTpu3Dg0atQIs2bNQvv27XHo0CFcuXIFkyZNQo0aNZ6/A6rUlMbGaNS2tST+omZO1XqK0watHGBmbv5CjkdERERlq0wWRCUnJ2PFihVwcHCAl5cX7t69i2+++QYqlQqrV69G69bS4oSqBrs2rWBiZqYTS018hEeq+y/mgOk5eHj3nk7ISKlEE+e2L+Z4REREVKbKfLX+nj17MG3aNBw/fhw1atSAr68vIiIi8Ntvv8HW1vb5O6BKpTzvN9XuX88tA/ZcFEVERFQhlFlxqlQqMXz4cJw8eRJnz55F8+bNMW3aNDRt2hRTpkzBa6+9hp9++qmsDkcVRGMnR0nsTtiVF3rMmIvhklhTPU8MICIiIvkxLu0O7OzsMGHCBLz33nuoV68eTp48iXfeeQc7d+6E+P9XU3733XdQqVTYunVrqROmiqWhYytJ7G5E+Ren9u3bQaFQaH8miYiISJ5KXZzGxMQgLy8PAQEB+Oabb3Dp0iW9/W7duoW4uLjSHo4qEJNqZqjXvKkkroos24fvP+vBjVvISk9HNQsLbczc0hJ1m9kj7lbMCz02ERERlU6pL+svWLAATZo0wdixYwssTAHg0qVLaN68eWkPRxWIXeuWMFIqdWKPYu8jPVm6or4sadRq3AmXzs7yYfxERETyV+ri9M6dO9BoNHq31a5dG++++25pD0EVVCNH6VMa7l25Wi7H1rsois87JSIikr1SF6f+/v5wcHDQu61Zs2bw9/cv7SGogmqopzhVRZZPcRpzSXrfaeN20sVZREREJC+lLk4VCkWB26pVqwa1Wl3aQ1AFpXfmtJyK07uXIyWx+g7NYFq9Wrkcn4iIiEqmRAuiGjdujKZNm2q/7tChA6pV0/1Hv3r16nj//fdx586dUiVIFZOxqSnqt5DeY3zvSlS5HD89KRmPVPdh3bCBNmakVKJhm1aIvhBWLjkQERFR8ZWoOB0zZgzmzZsHIQSEEPDz85P0yZ9RnTx5cukypAqpfovmUJro/ng9jktAWmJSueVwNyJSpzgFgEZOjixOiYiIZKxExemvv/6Ky5cvQ6FQ4Ndff8XMmTNx/fp1nT7Z2dm4fPkybt++XSaJkmEtGrm4WP2V7e0ksRqpxd9Pady9fAXt+/XWiTXhfadERESyVqLiNCoqClFRTy7PjhkzBnv37sWjR4/KNDGq2Izq15TENA9SyzWHuxHSWwj0vbGKiIiI5KPUD+HnK0lJH6N6hi9O9d3fatu0CarVrIGs1LRyzYWIiIiKpkTF6Zw5c7Bhwwbcv38fc+bMKbSvEAKLF5ffpVySASMFjOrWkIQ1ceVbnGalpSM++jbqNrPXiTd2csT10DPlmgsREREVTYmK0/nz5+PAgQO4f/8+5s+fX2hfFqdVj8LGHApj3TdDifQciNTscs/lbkSknuK0DYtTIiIimSpRcap86pWUymdeT0lkZKtn1jS+fGdN8929HIlOrw/QifG+UyIiIvkq9UP4iZ6lvzg1zD2e+h7GzzdFERERyVepi1MzMzPUrKm7+OXtt9/G0qVL4e7uXuz9WVhYYNWqVVCpVMjMzMSFCxcwbNiwIo21tbWFv78/EhISkJ6ejpCQEPTu3VtvX3d3d4SEhCA9PR0JCQnw9/eHra2tpJ+DgwN++ukn3L59GxkZGbhx4wa++uorWFtbF/uzVRV67zdNMExxqrp6Deq8PJ1Y7Qb1UcOmtkHyISIiosKVujjdsmULvv32W+3XH330EQICAvD555/j4MGD8PDwKNb+duzYAR8fHyxYsAAeHh44c+YMAgIC4O3tXeg4U1NTHDlyBO7u7pg8eTI8PT0RFxeHAwcOwM3NTaevm5sb9u/fj7i4OHh6emLy5Mno06cPjhw5AlNTU22/OnXqIDQ0FN27d8ecOXMwcOBArFmzBu+99x7+/PPPQl/dWpUp9M2cGqg4zc3KRtzNaEm8cVvOnhIREclRqR8l1bVrV0ybNk379ccff4ytW7fiww8/xMaNG/HZZ59h//79RdqXh4cH+vXrB29vbwQEBAAAjh07Bnt7e6xYsQKBgYHQaDR6x44bNw7Ozs7o1q0bQkNDAQDBwcG4dOkSli9fDldXV23fFStW4Nq1a3jrrbegVqsBANHR0QgJCcHYsWPx/fffAwA8PT1Rp04dDBs2DEePHtXmY2ZmhqVLl6J9+/a4ePFi8U5YZWdmDCNL3VfZCo0G4mGGgRJ6cmnfrnVLnVjjdo6IPBlioIyIiIioIKWeObW1tYVKpQIANG3aFM2bN8fq1auRmpqKjRs3ol27dkXel5eXF1JTUxEUFKQT9/f3R8OGDeHi4lLo2KioKG1hCgBqtRpbt26Fi4sL7OyevLHIzs4OXbt2xZYtW7SFKQCcOnUKV69ehZeXlzaWm5sLAHj8+LHOsZKTkwEAWVlZRf5sVYW+S/riUSag1v+fivJwJ4L3nRIREVUUpS5OMzIyYGVlBQB47bXXkJaWhrNnzwJ4UrzVqCEtVgrSrl07REZG6hSNABAWFqbdXtjY/H76xjo5Oenso6C+Tx/j999/x+3bt/HVV1+hbdu2sLCwwGuvvYbp06dj9+7d2rdk0b/ktBgq3z19xSlX7BMREclSqS/rh4eHY9KkSbh9+zZ8fX0RHBys3dakSRM8ePCgyPuysbHBrVu3JPH8V6Pa2NgUOlbfK1SfHZv/a0F9nz5GSkoKXF1dsX37dkRERGjjv/76K959993nfh5bW1u9i6wSEhKQkJDw3PEVkZzuN813/9pN5OXkwPip+4lr2lijVr26SI6LN2BmRERE9KxSz5wuWrQIPXr0wMWLF9G+fXssX75cu23QoEE4f/58sfYnhCjRtuKOLajv0/FatWph165dsLS0xIgRI/Daa69h4sSJePXVV7F79+7nPuPV19cXERERkubr61vouIpM72V9Axen6rw8xF67IYk3cmpjgGyIiIioMKWeOQ0ODoajoyM6deqEixcvIjr635XRR48eLdaCocTERL2zo/mPbdI321ncsYmJiQD0z8JaW1vrHGPatGl4+eWXYW9vr50B/uuvvxAVFYXg4GCMHDkSP/30U4E5+fn5Se6fBVBpZ00BwKiOhSRm6JlTALgXEYUm7drqxBo5tcHloycMlBERERHpU+riFADu3LmDO3fuSOLr168v1n7Cw8Ph7e0NpVKpc9+ps7MzAODy5cuFjs3v97Rnx+b/6uzsLHmKgLOzs84xXn75ZahUKsmtCWfOPHn15fMWe1Xmy/f6KKyqQWGm+yMlsvMgHht+4di9K9L7gxu15cwpERGR3JTZG6JsbW3RuXNnvPbaa5JWVDt37kTNmjUxdOhQnbiPjw9UKhVOnz5d6FhHR0d07dpVG1MqlRg1ahRCQ0Nx//59AEBsbCxOnz6NUaNGwcjo34/v4uKCNm3aYMeOHdpYbGwsGjVqpF3pn69bt24AgHv37hX5s1UFcnr4/rPuRugpTh1bGyATIiIiKkypZ07r16+PLVu2oFevXpJtCoUCQggYGxftMAcOHMChQ4ewdu1aWFpa4saNG/D29oaHhwdGjhypfcbphg0b4OPjAwcHB+2M7aZNmzBp0iQEBQVh+vTpiI+Ph6+vL1q3bo0+ffroHGfatGk4fPgwgoKC4Ofnh7p162LZsmUIDw+Hv7+/tt+aNWswcuRIHD58GMuWLcPdu3fRrl07zJ49Gw8ePMDPP/9c0tNWKclxMVS+BzdvITc7GyZmZtpYTRtr1KpfD8kP4gyYGRERET2t1MXpd999hw4dOmDatGkICwtDdnZ2qfY3ZMgQLFmyBAsXLoS1tTWioqIwfPhwBAYGavsolUoYGxvrvKEpJycH7u7uWL58OVavXg1zc3NcvHgRHh4eOHFC977C48ePY+DAgVi4cCH27NmDjIwM7N27F1OnTkVOTo623/nz5+Hq6oo5c+ZgyZIl2me67t69GwsXLtTev0pP6HuMlDDwY6TyafLUuH/tJpo4P3Pfads2LE6JiIhkRAGg8CXwz5GQkICpU6di8+bNZZMRydKikYuf26faeFfJgqisreeguZf8grLSb87Ps/XGh8z6DN2H694y8uf6zdi/el15pEVERERFUOp7ToUQuHv3blnkQhWZsREU1uaSsFwu6wPAvStXJTEuiiIiIpKXUhenQUFBeP3118siF6rAFDYWUBgpdGKax1lAdp6BMpK6q+dNUY3aclEUERGRnJT6ntNff/0VP/zwA4yMjLBnzx6992FeuHChtIchmZPjw/efFXcrWrIoqoZ1bdRuUB9J94v+JjMiIiJ6cUpdnB49ehQA8OGHH2LSpEk624q7Wp8qLrk+fP9pmjw1Yq/egP1LTjrxRm1bszglIiKSiVJXjWPGjCmLPKiCM7LRU5w+TDdAJoW7dyVKWpw6OSL8yHEDZURERERPK3VxWtjrO6nqUOibOZVjcarnYfyNed8pERGRbJTZG6IAoFWrVnjllVdgbi5dtU2VmIkSRrWq64SEEBCJ8itO717RtyiKK/aJiIjkokyK03fffRd3797FlStXcOLECbRu/WQmKjAwEOPHjy+LQ5CMGdlI/zMikjOBPI0Bsilc3M0Y5GbpvijConYt1Larb6CMiIiI6GmlLk7feustbN68GefPn8eHH36o89am8+fP45133intIUjm9F3Sl+OsKQBo1Gqorl6TxDl7SkREJA+lLk5nzJgBf39/eHp6Yv369TrbIiMj0bZt2wJGUmWhd6W+DO83zceH8RMREclXqYtTR0dHBAQE6N326NEj2NjYlPYQJHMVrzjVsyjKicUpERGRHJS6OM3IyICVlZXebQ0bNkRSUlJpD0Eyp9DzGCkh4+L0rp4V+5w5JSIikodSF6d///03PvzwQ73bRo8ejWPHjpX2ECRnxkZQPLNSHwA0iRkGSKZo4m/FICczSydmUcsK1g0bGCgjIiIiylfq4nThwoVwdXXFP//8g48//hhCCAwZMgS7d++Gm5sblixZUhZ5kkwpbCx0FsEBgCY5E8hVGyij59Oo1Yi9el0S5+wpERGR4ZW6OD137hw8PDxQo0YNfPXVV1AoFJg5cyZatWqFgQMHIiIioizyJJnSd7+pnC/p59N33ymLUyIiIsMrk5feHzt2DG3btkXz5s1Rr149PHz4ENevS2emqPLR+9pSmT5G6mlcFEVERCRPpSpO69SpgwkTJsDNzQ12dnYAgNjYWAQHB2P9+vV49OhRmSRJ8qWwrVgr9fNxURQREZE8lbg47d27N7Zv3w5LS0uo1Wo8fPgQCoUCrVu3Rp8+ffDZZ5/By8sLJ0+eLMt8SWb0zZxWhMv68dG3kZOZBdPq1bQxcytLWDeyw6N7sQbMjIiIqGor0T2nderUQWBgIB4/fox33nkHVlZWsLOzQ4MGDWBlZYXhw4cjPT0dv/32G6ytrcs6Z5KLAlfqy7841ajVUEXxTVFERERyU6LidNy4cVAqlejevTu2b9+OzMxM7bbMzEwEBQXh1VdfhYmJCcaNG1dmyZK8KKzNoTB6ZqX+4ywgR74r9Z+m977Ttq0NkAkRERHlK1Fx2q9fP2zatAkqlarAPnfv3oW/vz8GDBhQ4uRI3vSu1K8As6b59L7G1MnRAJkQERFRvhIVp46Ojvjrr7+e2+/kyZNwdOQ/9pWV3pX6D9MMkEnJ3I2IlMQaceaUiIjIoEpUnNaqVQvx8fHP7RcfH49atWqV5BBUASj0zJxWhJX6+eKjbyM7I1MnZm5pCZtGDQ2UEREREZWoODUzM0Nubu5z++Xl5cHU1LQkh6AKoKI+gD+f0GgQq29RFJ93SkREZDAlfpRU69atkZeXV2ifNm34j3ylpVRAUdtcEq4IK/WfdvdKFJp1bK8Ta9S2NS4dPGKgjIiIiKq2Ehenmzdvfm4fhUIBIURJD0EypnelfkoWkF0xVurn07coqnFb3idNRERkKCUqTseMGVPWeVAFY1SnhiRWkVbq57vHRVFERESyUqLi9KeffirrPKiC0Xe/aUVaDJUvPuYOsjMyYGb+7y0K1S1rwqZxIyTevWfAzIiIiKqmEi2IIlLofYxUxStOhUYDVaR0URQfxk9ERGQYLE6pRCr6Sv2n6X0YP19jSkREZBAsTqn4jBRQWFeXhCvaSv18+l5jysdJERERGQaLUyq2Jyv1dX90RFo2kFX4o8XkSu+bohx5WZ+IiMgQWJxSsel/bWnFnDUFgITbd5GdkaETy18URUREROVLdsWphYUFVq1aBZVKhczMTFy4cAHDhg0r0lhbW1v4+/sjISEB6enpCAkJQe/evfX2dXd3R0hICNLT05GQkAB/f3/Y2trq7evk5IRff/0V8fHxyMrKQnR0NNasWVPiz1jR6X1taQW9pA88WRR1L1LP8055aZ+IiKjcya443bFjB3x8fLBgwQJ4eHjgzJkzCAgIgLe3d6HjTE1NceTIEbi7u2Py5Mnw9PREXFwcDhw4ADc3N52+bm5u2L9/P+Li4uDp6YnJkyejT58+OHLkiOR1qz179sQ///wDS0tLfPDBB+jXrx/mzJmDrKysMv/sFYW+mdOKuhgqHxdFERERyUOJ3xD1Inh4eKBfv37w9vZGQEAAAODYsWOwt7fHihUrEBgYCI1Go3fsuHHj4OzsjG7duiE0NBQAEBwcjEuXLmH58uVwdXXV9l2xYgWuXbuGt956C2r1kzcaRUdHIyQkBGPHjsX3338PAKhevTp+/vlnHD16FG+88YbO8bZu3Vrmn7+iMKpT8V9b+ix9i6I4c0pERFT+ZDVz6uXlhdTUVAQFBenE/f390bBhQ7i4uBQ6NioqSluYAoBarcbWrVvh4uICOzs7AICdnR26du2KLVu2aAtTADh16hSuXr0KLy8vbeztt9+GnZ0dVqxYUVYfseJTKKCw1lOcVvSZ0whpcdrQsTUUCoWe3kRERPSiyKo4bdeuHSIjI3WKRgAICwvTbi9sbH4/fWOdnJx09lFQ36ePkX87gFKpxMmTJ5GdnY1Hjx5h27ZtaNCgwXM/j62tLdq2bStpBd3bWhEoalWDwlipExMZOUBGroEyKhsJMXeQla5bYFevWQM2jRsaKCMiIqKqSVbFqY2NDR49eiSJ58dsbGxKPTb/14L6Pn2Mhg2fFCbbt2/H33//jf79+2P69Ono27cvjh8/jurVpc/6fJqvry8iIiIkzdfXt9Bxcqb3taUV/JI+AAgh9L8pysnRANkQERFVXbK65xR4UiSUZFtxxxbU9+m40f8/yzMwMBDTp08H8OQe2AcPHmDXrl0YMWIENm7cWOAx/fz8JLcoAEBCQkLBH0Lm9L+2NENPz4rn3pUoOHTuoBNr1LYNLuw/bKCMiIiIqh5ZFaeJiYl6Z0etra0B6J/tLO7YxMREAPpnYa2trXWOkd/34MGDOv0OHjwIjUaDjh07FlqcJiQkVOhCVB+9ry2tBDOnAHBXz32nfFMUERFR+ZLVZf3w8HA4OjpCqdS9p9HZ2RkAcPny5ULH5vcrbGz+rwX1ffoY+u5LfVpBTw6ozPTPnFaO4lTva0y5KIqIiKhcyao43blzJ2rWrImhQ4fqxH18fKBSqXD69OlCxzo6OqJr167amFKpxKhRoxAaGor79+8DAGJjY3H69GmMGjVKe9keAFxcXNCmTRvs2LFDZ58ajQYeHh46x/Lw8ICRkZHOkwGqisr4jNN8D2/fRVaa7mepVsMCNk34pigiIqLyIqvL+gcOHMChQ4ewdu1aWFpa4saNG/D29oaHhwdGjhypnancsGEDfHx84ODggDt37gAANm3ahEmTJiEoKAjTp09HfHw8fH190bp1a/Tp00fnONOmTcPhw4cRFBQEPz8/1K1bF8uWLUN4eDj8/f21/a5evYo1a9bA19cXqamp2L9/P1q1aoXFixfj/Pnz+PXXX8vv5MiAwqoaFKbPrNTPzoNIyzZQRmVLCIF7kVfRoktHnXiTdo54ePuugbIiIiKqWmQ1cwoAQ4YMwZYtW7Bw4UIcOHAALi4uGD58OLZt26bto1QqYWxsrHO5NScnB+7u7ggODsbq1auxZ88eNGjQAB4eHjhx4oTOMY4fP46BAweiQYMG2LNnD1avXo3g4GC4u7sjJydHp++UKVMwc+ZMDB48GPv27cPs2bMREBCA3r17Ize3Yj8+qbgq8yX9fPqed9rE2ckAmRAREVVNCgCFL4EnArBo5GIYd20C094tdeJ5YbHI2RdpoKz0m/Pz7BKPfalfb/h8tUQndufyFXzjPa60aREREVERyG7mlORL3/2mlW3m9PalcEmsYetWMDYzM0A2REREVQ+LUyoyRSV9AP/THsclIPlBnE5MaWKMxnykFBERUblgcUpFVplX6j8t5qJ09rTpy9JHjxEREVHZY3FKRaKoYQpFNd2HO4hcNcTjLANl9OLEXJI+T7dp+3YGyISIiKjqYXFKRaJvpX5leTPUs/Tdd2rfnjOnRERE5YHFKRWJvteWVrbFUPlUkdeQm6377NaaNtawbmRnoIyIiIiqDhanVCRVYTFUPnVent7nnfLSPhER0YvH4pSKpKoshsqn977Tl18yQCZERERVC4tTKpKqdFkfKOC+05c4c0pERPSisTil57KoXQsKc1OdmMjTQCRXvpX6+fTNnDZo5QDT6tUNkA0REVHVweKUnqueQzNJTDzKAETlffNt6sNEJN6L1YkpjY3R9GXOnhIREb1ILE7pueo1byqJVdbFUE+7de6iJNa8U4fyT4SIiKgKYXFKz6W3OK3E95vm01+cvlzueRAREVUlLE7pufRe1q8SM6cXJLEmzm1hbGqqpzcRERGVBRan9FxVdeb04Z17SEl4qBMzMTND43aOBsqIiIio8mNxSoWqVrMGrOra6sSERgORlGGgjMoXL+0TERGVLxanVCh9s6YiKRNQV96V+k/TV5w6sDglIiJ6YVicUqHqt2guiVWFlfr5buopTpt2eAlGSmX5J0NERFQFsDilQukrTkVC1SlO427cQsbjFJ2Ymbk5Gjq2NlBGRERElRuLUypUgxYOkpgmIc0AmRiGEAK3zl+UxHlpn4iI6MVgcUqFqt9Sz2X9KrBS/2m3zl6UxBy6diz/RIiIiKoAFqdUoBrWtVHTxlonJtSaJ68urUJunpU+79ShcwcYGfO+UyIiorLG4pQKpP/h+xmApmqs1M+nirqm975Te2cnA2VERERUebE4pQLpXan/sOrcb5pPaDS4fvqsJN6qW1cDZENERFS5sTilAuktTqvQSv2nXQ+VFqctXbsYIBMiIqLKjcUpFaiBvsdIVcGZUwC4FnpGEmvi3BZmFuYGyIaIiKjyYnFKBeLM6b8S797DI9V9nZjS2BgtunDVPhERUVkyNnQCJE9W9WxR3bKmTkzkqiGSMw2UUdEtGrn4hew3PPQMXIYO1om1dO2CiGN/vZDjERERVUWcOSW96ut7+H4Ve77ps66d+kcS432nREREZYvFKeml937TKvRmKH2u/3NOEqvv0AyWdW0NkA0REVHlxOKU9OKboaTSk5Khirwmibfp7mqAbIiIiConFqekl/7FUFV75hQArp46LYk5ur1igEyIiIgqJ9kVpxYWFli1ahVUKhUyMzNx4cIFDBs2rEhjbW1t4e/vj4SEBKSnpyMkJAS9e/fW29fd3R0hISFIT09HQkIC/P39YWtb+OVZd3d3CCEghICNjU2xP1tFoTAyQr3met4OVUVX6j8t8kSIJNb6la5QmpgYIBsiIqLKR3bF6Y4dO+Dj44MFCxbAw8MDZ86cQUBAALy9vQsdZ2pqiiNHjsDd3R2TJ0+Gp6cn4uLicODAAbi5uen0dXNzw/79+xEXFwdPT09MnjwZffr0wZEjR2Bqaqp3/xYWFvjhhx+gUqnK7LPKlXVDO5hWr6YTE5m5EGnZBspIPmIuhiMjRfoqU4fOHQyUERERUeUiq0dJeXh4oF+/fvD29kZAQAAA4NixY7C3t8eKFSsQGBgIjUajd+y4cePg7OyMbt26ITQ0FAAQHByMS5cuYfny5XB1/fe+wBUrVuDatWt46623oFarAQDR0dEICQnB2LFj8f3330v2v2zZMiQlJeGPP/7AnDlzyvqjy0oDvfeb8pI+AGjUakT9FYqOA/vpxB3dXtG7mp+IiIiKR1Yzp15eXkhNTUVQUJBO3N/fHw0bNoSLi0uhY6OiorSFKQCo1Wps3boVLi4usLOzAwDY2dmha9eu2LJli7YwBYBTp07h6tWr8PLykuz71Vdfxfvvv4/x48frjKms+PD9wkWe+FsSa9ujuwEyISIiqnxkVZy2a9cOkZGRkgIwLCxMu72wsfn99I11cnLS2UdBfZ89RrVq1bBx40Z8/fXXuHDhQjE+TcVl17qlJCaq+Er9p0X9FQrNMz+jdRo3Qt1m9gbKiIiIqPKQ1WV9Gxsb3Lp1SxJ/9OiRdnthY/P7FTY2/9eC+j57jEWLFkGpVGLevHlF/BT/srW11bvIKiEhAQkJCcXeX3mxa9VCEtPEpxogE3nKeJyCmIvhaN7pZZ14W7fuiI++bZikiIiIKglZzZwCgBCiRNuKO7agvk/Hu3TpgilTpmDChAnIysoq9Nj6+Pr6IiIiQtJ8fX2Lva/yYlq9GmyaNJLENfG85/RpkSelq/bb9nzVAJkQERFVLrKaOU1MTNQ7O2ptbQ1A/2xncccmJiYC0D8La21trXOMTZs2YceOHTh79iysrKwAPLnMDwCWlpbIzs5GWlrBRZufn5/k/lkAsp41rd/SAUZGuv9nSbynQvWcyn+vbXFcOf43Bk3R/U9Gsw4voYZ1baQ9SjJQVkRERBWfrIrT8PBweHt7Q6lU6tx36uzsDAC4fPlyoWPz+z3t2bH5vzo7O2P//v2Svk8fo127dmjXrh3eeecdyX5v3bqFixcvokOHgh8hJPfL9/o0bN1KEou9egMOBshFzh7cuIXEeyrYNGqojRkplWjX2w2hv+0yYGZEREQVm6wu6+/cuRM1a9bE0KFDdeI+Pj5QqVQ4fVr6dp6nxzo6OqJr167amFKpxKhRoxAaGor79+8DAGJjY3H69GmMGjVKZ4bQxcUFbdq0wY4dO7Sxnj17StrmzZsBAJ6enhg/fnxZfGxZsWstvd809up1A2Qif2GHgiWxl/r2MkAmRERElYesZk4PHDiAQ4cOYe3atbC0tMSNGzfg7e0NDw8PjBw5UvuM0w0bNsDHxwcODg64c+cOgCeX4CdNmoSgoCBMnz4d8fHx8PX1RevWrdGnTx+d40ybNg2HDx9GUFAQ/Pz8ULduXSxbtgzh4eHw9/fX9jt+/Lgkx549ewIA/v77b+0tApWJvpX6sVevAw2kb4yq6i4dOopeY0fpxFp07QSLWlZIT35soKyIiIgqNlnNnALAkCFDsGXLFixcuBAHDhyAi4sLhg8fjm3btmn7KJVKGBsbQ6FQaGM5OTlwd3dHcHAwVq9ejT179qBBgwbw8PDAiRMndI5x/PhxDBw4EA0aNMCePXuwevVqBAcHw93dHTk5OeX2WeVGoVCgQSvpBXzOnOp3NyISj1T3dWJKY2M49XIrYAQRERE9jwJA4UvgqcqwadwIM/fpLuDKTE3D7Ff6YtHIxQbKSj7m/DxbEnvj04/Qc/QInVjkX6ewYeJ/yystIiKiSkV2M6dkOA3bSC/p3792wwCZVByXDh+VxFq5dEF1S0sDZENERFTxsTglLb33m7I4LdSdsAgk3X+gE1OaGKNd79cMlBEREVHFxuKUtPS9GSo26poBMqlYwv48Jol1GjSg/BMhIiKqBFicklYDvY+R4szp81w88Kck5tC1I2rVr2eAbIiIiCo2FqcEAKhuaQlruwY6MY1ajfs3bhkoo4rjTlgEEmLu6MSMjIzQ6XXOnhIRERUXi1MCoH8xVMLtu8jLzjZANhXP2T37JbHOgz0MkAkREVHFxuKUAACN2raRxFS837TIzu05IInVbWaPJs5tDZANERFRxcXilAAAjdq2lsTuRkQaIJOKKen+A9z455wk3nnwQANkQ0REVHGxOCUAQGMnR0ns3pWrBsik4jq7e58k1sGjL4xNTQ2QDRERUcXE4pRQrWYN1GnSSCem0WigimRxWhxhh48hOyNTJ2ZuZYmX+vUyUEZEREQVD4tTQiNH6SX9h7fvIjs9wwDZVFzZGRkI1/PM0+7DhpZ/MkRERBUUi1NCYyfpYijeb1oyIb/ukMSavuyMhm1aGSAbIiKiiofFKeldqc/7TUvm9qXLUEVKn3LQbZiXAbIhIiKqeIwNnQAZXiM9M6f3rkQZIBN5WzRycZH6KWPUwDPry1wHv4H2d2sD2XmS/nN+nl0W6REREVUKnDmt4qpb1kSdxvoWQ/EZpyWlvvIAIku3CFWYKmHs3KCAEURERJSPxWkVp28xVELMHWRncDFUieVqkHf5viRs3KkRoFAYICEiIqKKg8VpFcdL+i9G3vl7kphRbXMoW9saIBsiIqKKg8VpFadvMdTdCBanpSUeZUB986EkbuJqb4BsiIiIKg4Wp1Vck3bSd79z5rRs5J6+LYkZ1beEkX1tA2RDRERUMbA4rcJq1rGBdUPdRToatZqLocqI5k4y1LGPJXHOnhIRERWMxWkVZv+SkyR2/9pN5GRm6ulNJZEXKp09VTazgVH9mgbIhoiISP5YnFZh+orT22GXDZBJ5aW+ngBNYrokbvJacwNkQ0REJH8sTqsw+/bOkljMJRanZUrov/dU6VAHRo1qlX8+REREMsfitIoyUirR2MlREufMadlTX34ATZL0ubEmPTh7SkRE9CwWp1VUg5YOMK1eTSeWnvwYD2/fNVBGlZhGIPevaElY2bg2jJpZGyAhIiIi+WJxWkU14f2m5Up95QE0CWmSuGkPByiM+MeQiIgon7GhEyDDaKrnftPbYREGyKSKEEDuyVswG/KSTtioviW6vjkIp3fsMVBi8rFo5OIXst85P89+IfslIqIXg1M2VVTTDtLi9A5nTl8o9bUEqO+nSOIDJ09EtZo1DJARERGR/LA4rYIs69qiTuNGOjGNWs2Z03KQe+S6JFbDujb6TxxvgGyIiIjkh8VpFdS8Y3tJTBV1Ddnp0hXlVLY095KRF/FAEu/uPRT1W3D1PhEREYvTKqh5p5clsVvnLpZ7HlVV7rEbEDlqnZjS2Bhvz5/OxVFERFTl8V/CKkh/cXqp/BOpokRqNnJPSR8t1bS9M9xGDTNARkRERPLB1fpVjEUtKzRo6SCJR19gcVqe8v65C2NnOxhZm+vEPT6agIjjf1Wp583WbWaPpi+/BJOeDlDUqg6FiRIwUkBk50E8zoImMR2amCSIlCxDp0pEROVAdjOnFhYWWLVqFVQqFTIzM3HhwgUMG1a02SRbW1v4+/sjISEB6enpCAkJQe/evfX2dXd3R0hICNLT05GQkAB/f3/Y2trq9OnYsSO+++47hIWFISUlBQ8ePMDhw4fRq1evUn9OQ2mm537TBzduIT0pufyTqcrUGuTsi4QQQidsUs0MwxfNhpFSaaDEykcT57bwmvFfzP1zN6btDsCwhTNh4toUxm3qQelQB8pmNjBuUw8mLvYwG9gW1X27o9p4Vxh3aQyY8f/URESVmeyK0x07dsDHxwcLFiyAh4cHzpw5g4CAAHh7exc6ztTUFEeOHIG7uzsmT54MT09PxMXF4cCBA3Bzc9Pp6+bmhv379yMuLg6enp6YPHky+vTpgyNHjsDU1FTbz9vbG127dsWmTZvg6emJ8ePHIzs7G0ePHsW77777Qj7/i6avOOX9poahuZeMvHP3JPFmHV5CP99xBsjoxTI2NUW3t70wbXcAJm/biFdHvA2rerbPH/j/jOpYwNS9FapPehUmrzYDTGT31xcREZUBBQDx3F7lxMPDA/v27YO3tzcCAgK08YMHD8LJyQlNmjSBRqPRO3bixInw8/NDt27dEBoaCgBQKpW4dOkS0tLS4Orqqu17+vRpWFhYoH379lCrnyxM6datG0JCQjBx4kR8//33AJ7MxCYkJOgcx8jICOfPn4eFhQVatmxZpp+/PHwSuBmN2rbWiW39fC4u7D9c6LgX9YD0Ks/ECBnvtJQ82gsAfpj4CaL+CjVAUmXLzMIc3YcPxWujhsGyjk2Z7VeTkoWcA1HQ3EostB8fwk9EVLHIaurBy8sLqampCAoK0on7+/ujYcOGcHFxKXRsVFSUtjAFALVaja1bt8LFxQV2dnYAADs7O3Tt2hVbtmzRFqYAcOrUKVy9ehVeXl7a2LOFKQBoNBqcO3cOjRs3LvHnNBSL2rUkhSkA3OTMqeHkahA4Zwk0arVk04gv5qFWvboGSKpsGBkr0X34UMzc9xsGTfEt08IUAIwsq6HaOy/DpE8rQCmrv8qIiKgUZHXzVrt27RAZGalTNAJAWFiYdvupU6cKHHvy5ElJPH+sk5MTYmNj0a5dO534s327d+9eaI5KpRKvvfYaIiKe/8B6W1tbyX2swJOiV1/h+6K17NpJEntwMxop8eWfC/3r1rmLOOi3AR4fTdCJW9SuhdHffgm/0b7Iycw0UHYl49TrNbz+ySTUbWb/3L55OTm4HRaBJjm1oUnKgMjIAQSgMDeBkY0FjJpaQ9nAssDxJp0bw6h+TWRvDwMyc8vyYxARkQHIqji1sbHBrVu3JPFHjx5ptxc2Nr9fYWPzfy2ob2HHAID58+ejZcuW8PT0LLQfAPj6+mL+/Pl697FgwYLnji9rrbp1lcSuh54p9zxI6sgPP6JZh/Zo86qrTrxx2zYY9eUC+E+ZDlHALS1y0tjJEW989hEcOnd4bt8bZ87j3O79CPszGFlp6XpvHVEDwPGbUNSuDuPOjWH8ckMo9MySKhvVQrX/dEb2rxchkipWIU9ERLpkVZwCkKxeLuq24o4tqG9h+xg3bhxmz56NlStXYvfu3YXmAgB+fn6SWxQA/bcLlIeW3bpIYtdOsTiVAyEEts1cgP8G/Si5lO/U6zUMnTMV2xcuf+6fAUOpbVcfAydPRMeB/Qrtp1GrcfHgEQRv2orYq9JXuRZEJGUi9/A15J29C9O+raFsLv1PpFFtc1Qb2QlZ285DPOLbzoiIKipZFaeJiYl6Zy6tra0B6J/tLO7YxMQniycK6lvQMUaPHo1169Zh3bp1mDp16nM+yROGunyvT50mjWBt10Anps7Lw82z5w2UET0rPSkZm6fMwCR/P5hUM9PZ1u2tN6HOzcPOL74yUHb6VbesCffxPnht5NswfupJF/qc3bMfB/024NG92BIfTyRlIvvXizDu3BgmvVpIZlEVNcxQbURHZP1yASIxvcTHISIiw5HVKoLw8HA4OjpC+cwzHp2dnQEAly9fLnRsfr/Cxub/WlBffccYPXo0NmzYgB9//BEffPBBET+NvLR0lc6a3gmLQHY6Z5jk5O7lK/h5xny9T6V41fstvDN/hiyegao0MYHbf4Zj5r7f0GvMyEIL0xv/nMOqYaPxy8yFpSpMn5Z39i6yAy5A6LnHVFHDDGbDXoaippmekUREJHeyKk537tyJmjVrYujQoTpxHx8fqFQqnD59utCxjo6O6Nr13/sqlUolRo0ahdDQUNy/fx8AEBsbi9OnT2PUqFEweuo95i4uLmjTpg127NghOfaGDRuwdetWjB8/viw+pkG0fkX6pINrvN9UlsL/PIbdK77Vu81l6GCM+eZLmJmb693+ohkZK9HlzUGYtjsAnlMnw9yq4IVKcbdisGHSZ1g77kPcu3K1zHPR3E1G1paz0DyWvjnKyLIazN55mQ/sJyKqgGT1N/eBAwdw6NAhrF27FpaWlrhx4wa8vb3h4eGBkSNHameTNmzYAB8fHzg4OODOnTsAgE2bNmHSpEkICgrC9OnTER8fD19fX7Ru3Rp9+vTROc60adNw+PBhBAUFwc/PD3Xr1sWyZcsQHh4Of39/bb+33noLGzduxMWLF7Fu3TqdwhcALly4gJycnBd8VkrP2NSUi6EqmJNbA2FarRoGTpbO1Lft0R1TAjZh8yczEHczulzyUZqYoMubg9B77LuwaWRXaN/UxEc4uGYDTu/YrfcRWWVJPMpA9rZzMBvREUZW1XW2GdnWgNnQl2C81RR5FeDPKRERPSGr4hQAhgwZgiVLlmDhwoWwtrZGVFQUhg8fjsDAQG0fpVIJY2NjKBQKbSwnJwfu7u5Yvnw5Vq9eDXNzc1y8eBEeHh44ceKEzjGOHz+OgQMHYuHChdizZw8yMjKwd+9eTJ06VafYHDRoEJRKJTp16oSQkBBJrk2bNsXt27dfwFkoWy26doSZue4/3BmPU3A77PmPwyLDObLhRyiNleg/6T3JtrrN7DHll004uOYHnNgaqLcILJMXJ5ibwPglOxh3bAQjy2qFdhW5auSduQNl6G0MNO2EgcOljy57EcTjLGRvO49q73aGoobupXxlk9oYOnsqAucuKZdciIio9GT1hih6MYbM+gzdh+veKnFu7wFsm1H0x1nxDVEvzvPeYNTd+y28Of0TndtQnqaKuoZ9336PqJO6zwAu8fdMoYBRk1owdm4AZZt6UBgXfveP0Aiow2ORezIaIi27ZMcsA4p6NVBtRCco9FzK3754BUICd+gZRUREciO7mVMqe217SF8scOXYXwbIhEri719+Q0rCQ3gvmaP3XtOGbVrhPb//4c7lKzj16++4dPAIsjOKudDNxAhGjWpB2dIWxq3rQmFR+Mr7fOobD5Fz/AZEguFXxou4NGTvDIfZ2+0lq/jfnPYJ7l+/iejzlwyUHRERFRVnTis5u9Yt8elvP+nE1Ll5mOs2AFlpRS8oOHP64hT13e91m9nDZ9VS1HdoVmi/vNxc3Dp7Ac3UdaCJS4UmJRvIyAGEgBCAoroJFBamUNSqDqO6NWBUryaMGljqfbh9QdQ3EpD7dww091OKPKa8KNvbwczDURJPTXyEVcNG43GcPB7vRkRE+nHmtJJr2/NVSezWuYvFKkxJHuKjb+Mb77Hw+OgDvDry7QIv8xubmOhdAFdaQgioryUgNyQaIi6tzPdfVtSXYpFbvyZMOjTSide0scZ/Vi7BmjETocl7sQu1iIio5GT1KCkqey/16SmJRRznJf2KKiczC7uWf43Vo97DnfAr5XJMkZGD3FMxyPo+BDk7w2VdmObLPXwN6nvJknjTl53h8dGE8k+IiIiKjMVpJVa3mT0atmkliUccO2mAbKgs3Qm/gm9GjMOmjz/H3YjIMt+/yFMj72o8sn8PR+aav5F7/CaEnueJypZGIHtnODSp0px7j30XbV51NUBSRERUFLysX4m93N9dErsTfqXM3tJDhhcRfBIRwSfRqG0bdPV6HU69XkOtenVLtC9NShY0t5Ogjk6E+sZDIKeCX/pOz0HO75dh4v0ylMa6f9V5L5mLr972QUo87z8lIpIbFqeV2MsefSWxiwf+NEAm9KLduxKFe1eisGPJSjRo1QL2LznhzcHvwqh29SfP/jRTAlBAoQBEjhoiPRsiLQfiUQY08anQxKVBJGca+mOUOY3qMQ58tx6DpvjqxGtY18ao5Qvw/biPXviLAoiIqHhYnFZSDVo5oF7zppL4pYNHyj8ZKlf3r93A/Ws3MMisi6FTkYXgTVvh0Lmj5FK+Q6cO6PvBWBxc84OBMiMiIn14z2kl9fIA6axp9PlLSI6LN0A2RIYjhMAvsxbisZ5L+H3eH42WriziiYjkhMVpJWSkVKLL4IGS+MWDvKRPVVPaoyRsnTZPcgnfyMgII5fNR806NgbKjIiInsXitBJq82o3WNWz1Ympc/NwkZf0qQq7dfYCDn2/SRKvaWONUcsXwkipNEBWRET0LBanlZDL0DcksYjjfyEtMckA2RDJx5/rN+N66FlJvEWXjujnO84AGRER0bNYnFYylrZ14PjaK5L46R27DZANkbwIjQY/T5+HlIeJkm193x+D1t35/FMiIkNjcVrJdPV6XfJMx+QHcbj692kDZUQkL6mJj7D187l6HyE1cum8Ej8nloiIygaL00rE2NQU3b3fksT/2bkXQqMxQEZE8nTzzHkc9NsgiVvUroVRKxbByJj3nxIRGQqL00qk0xsDYPnMqmN1Xh7+2bnXQBkRydeRH37E1b9DJfFmHV7CwI8nGiAjIiIC+BD+SkOhUKCnzwhJPOzQUSTdf2CAjKioFo1cbOgUqiQhBH6esQD/DfpRcim/15iRuBcRySdcEBEZAGdOKwmnXq+hbjN7STx4888GyIaoYkhPSsbWqXOgzsuTbBu+eA4aOzkaICsioqqNxWkloDAywoAP35fEr4eehSrymgEyIqo4oi+EYd8330viJtXMMObbL2FZ11bPKCIielFYnFYCnV4fgAYtHSRxzpoSFc3xH7fh0qGjkrhVXVuM+WYZTKqZGSArIqKqSQFAGDoJKjljU1NM2xMAa7sGOvGYi+FY/a50NrWkeF8kVXomRqg2shOM6ltKNuVdT0DOznBA8+Svyzk/zy7v7IiIqgzOnFZwPXy8JYUpAOxdtcYA2RBVYLkaZG8Pg0jLlmwybmkL04G8/5SIqDywOK3AbJs2Qd8JYyTxiGN/Ifr8JQNkRFSxidTsJwVqnvQB/cbtGsCkTysDZEVEVLWwOK2gFAoF3p43HSZmuvfCadRq7PtWuriDiIpGcz8FOXuuQAjpHU8mnRvDpG8rKBQKA2RGRFQ1sDitoHr4jIBD5w6S+PGfAvDg+k0DZERUeaivxiP34FW920w6NcbQuZ+zQCUiekFYnFZAzTu9jIGTP5DEH969h4N+PxggI6LKJ++iCjnH9f9Hr9tbb+LdlYthWr1aOWdFRFT5sTitYGo3qI93VyyC0lj6cq/ti5YjN0u6mIOISibvVAxy/47Wu619v97w3byWz0ElIipjLE4rkJo21pjww7ewtK0j2Xbsx224duqMAbIiqtxyT95Czgn9M6iN27bBJwGb0Kpb13LOioio8mJxWkFY1bPFhB++ha19Y8m2W+cu4o+v/QyQFVHVkBcSg5yj1/Vus7Stgwnrv8HgqR/D2NS0nDMjIqp8WJxWAA3btMLknzfqfQvU4/gEbJk6Bxo9j74horKT988dZP8eDpGr/89aj/94Y+rOn+H42ivlnBkRUeXCN0TJmEKhgNu7w+Hx8QTJI6MAIONxCtaMnogHN2698Fz4hiiiJ4waWCJnYDO9t9fkizwZgkNrN+JO+JVyzIyIqHLgzKlMNe/cAR//vAGDp36stzDNSk/H+g8+KZfClIj+pbmfglXDxuBaaMH3eDu+9gomb9uICT98i7Y9XoWRUlmOGRIRVWyyK04tLCywatUqqFQqZGZm4sKFCxg2bFiRxtra2sLf3x8JCQlIT09HSEgIevfurbevu7s7QkJCkJ6ejoSEBPj7+8PWVrrq1tjYGHPnzkV0dDSysrIQGRmJDz/8sFSfsSDGpqZ4qW8v+Pr7YZK/H5o4t9XbLyXhIdaOnYS7lzkrQ2QIKQkPsf79ydi94lvk5eYW2K+VaxeM+24F5hz+HW98+hGad3qZhSoR0XNIn0dkYDt27ECXLl0wffp0XLt2DSNGjEBAQACMjIzwyy+/FDjO1NQUR44cQa1atTB58mTEx8dj0qRJOHDgAPr06YMTJ05o+7q5uWH//v34448/4Onpibp16+LLL7/EkSNH0LlzZ+Tk5Gj7+vn54d1338WcOXNw5swZ9O/fH9988w1q1qyJpUuXluqzKhQK2DZtAvv27dCmuyvavNYN1SwsCh0Te+0GNk76DMkP4kp1bCIqHSEEjv/0C6L+DoXXjP+ipUvnAvta2tZBz9Ej0HP0CGQ8TkH0+Uu4HRaB22GXoYq6jsyUlHLMvOrSuT3JzBgKM2Og2pNfFWbGT2LVjLXbnt6ujVUzAcyUgNG/L2HQaDTa3+fl5CI3Kws5mVnIycp68vusLGSlpiPjcQoyU1KRkZKCzJQUZKSk/ht7ahvXEFBVJ6t7Tj08PLBv3z54e3sjICBAGz948CCcnJzQpEkTnb8EnjZx4kT4+fmhW7duCA0NBQAolUpcunQJaWlpcHV11fY9ffo0LCws0L59e6jVT/4S6NatG0JCQjBx4kR8//2T13+2bdsW4eHhmDVrFpYtW6Ydv27dOowaNQqNGjVCUlJSsT/niKXzUKdJY9Rt2gTVLWsWedzxLQHY9833yMsu/2eZ8p5Ton/N+Xm2JPZSv954/ZNJsGlkV+z9pSclIz7mDh6pYpGSkIiUhw+R+vARMpIfIzs9A1kZGchOS0dWegZys7KgzsuDKODvwspEoVBAaWICpYkxjE1NoTQxgbGpCYxNTKA0MYFZ9eowszD/t5mbo1oNC+3vzSzMUb1mTVSraYHqNWuifr2GUJg9KS7l/Iav7IwMZKamPVXIpv7/71P///cpyExNR252NnJ1iuBs5GZlIzcrC7nZ2VDn5kKdp4ZGra4SPy9Uechq5tTLywupqakICgrSifv7++OXX36Bi4sLTp06VeDYqKgobWEKAGq1Glu3bsXSpUthZ2eH2NhY2NnZoWvXrpg+fbq2MAWAU6dO4erVq/Dy8tIWp2+++SaMjIzg7+8vyef999/HgAEDCp3NLUin1wcUq78q6hp2ffk1bp69UOxjEVH5CDt0FJePHkenQf3Re9x/ULeZfZHHWtSuhWa1a6FZh5eKPEaj0UCTlwd1fsvNgyZPDY3myd9rQgjt1IMQAvlfSOLiqTj+jT9dvCkUCkCh+P9f//9rKJ7a9uRXbb//36btm78tv7++fT7pDAAwUhrB2MQUShNZ/RNVbszMnxTXterVLdP9qnOf/Kxo1E8K1vzf5/8cCFHAz8iT3/37+yL8/Dwbo/K37r2PkZLw0NBplIis/uS3a9cOkZGROkUjAISFhWm3F1SctmvXDidPnpTE88c6OTkhNjYW7dq104k/27d79+46+4yPj0dcXJykX/72F+nBjVs4unELzv9xkH/IiSoATZ4aZ3btw9k9B9Cmuyu6vDkITr1eg7GJSZkfy8jICEampny2KhWZ0sS4yhb8VZG+N0lWFLLK3MbGBrduSVefP3r0SLu9sLH5/Qobm/9rQX2fPkZB+8zIyEB2dnah+QBPFmjpW2RVmIyUFFw5/jfO7tqH66fPFmssEcmD0GgQeTIEkSdDYFHLCm17voq2bt3R6pWuz72vnIioqpNVcQoUfhngebOHxRlbUN+i9itKPr6+vpg/f74kPn/+fCxYsKDQsXJga2sLX19f+Pn56b3Hjkrm6fOakJBg6HQqDbme1/Tkxzjz+x848/sfhk6lROR6XisyntMXg+f1xTDEeZXVo6QSExP1zkZaW1sD0D/bWdyxiYmJAPTPwlpbW+sco6B9mpubw8zMrNB8gCcr/Z2cnCTNz69ivGrU1tYW8+fPL/bsLxWO5/XF4Hl9MXheyx7P6YvB8/piGOK8yqo4DQ8Ph6OjI5TPPAfQ2dkZAHD58uVCx+b3K2xs/q8F9X36GOHh4ahbty7q1atX7HwAICEhAVeuXJE0/o+OiIiISD9ZFac7d+5EzZo1MXToUJ24j48PVCoVTp8+XehYR0dHdO3aVRtTKpUYNWoUQkNDcf/+fQBAbGwsTp8+jVGjRsHI6N+P7+LigjZt2mDHjh3a2K5du6DRaODj46NzrNGjRyMjIwMHDhwo1eclIiIiIikhp3bw4EGRmJgoxo8fL3r27CnWrVsnhBBixIgR2j4bNmwQubm5okmTJtqYqampCA8PF7dv3xbe3t7C3d1dbN++XeTk5Ag3NzedY/To0UPk5OSI7du3C3d3d+Ht7S1u374twsLChKmpqU7f9evXi8zMTPHpp58KNzc3sXjxYqFWq8WMGTMMfq5edGvbtq0QQoi2bdsaPJfK1HheeV4rUuN55TmtKI3ntVKdV8N/8KebhYWF+Prrr0VsbKzIysoSFy9eFMOGDdPp4+/vL4QQwt7eXidet25dsXnzZvHw4UORkZEhQkJChLu7u97j9OnTR4SEhIiMjAzx8OFDsXnzZmFrayvpZ2xsLObNmydiYmJEVlaWiIqKEh9++KHBz1N5NP5B53mtSI3nlee1ojSeU57XitRYnLLJqtna2op58+bpLdrZeF7l1nheeV4rSuM55XmtSM0Q51VWry8lIiIioqpNVguiiIiIiKhqY3FKRERERLLB4pSIiIiIZIPFKRERERHJBotTkrCwsMCqVaugUqmQmZmJCxcuYNiwYYZOS5Zq1KiBL7/8EgcPHkR8fDyEEJg3b57evh06dMDhw4eRmpqKpKQkbN++Hc2aNdPb98MPP0RkZCSysrJw69YtzJ07F8bGxi/yo8hGr169sHHjRkRGRiItLQ337t3D77//jo4dO0r68pwWXfv27bF3717cvn0bGRkZSExMREhICEaOHCnpy/NacuPGjYMQAqmpqZJtPK9F16NHDwgh9DYXFxedvjyvxdO9e3f88ccfePToETIyMnDt2jXMnj1bp48czqnBH1PAJq928OBB8ejRI/H++++Lnj17ivXr1wshhPD29jZ4bnJr9vb2IikpSRw7dkx7nubNmyfp17p1a/H48WNx/Phx4eHhIby8vER4eLi4d++eqFOnjk7fmTNnCrVaLZYsWSJ69OghPvvsM5GVlSXWrVtn8M9bHu3XX38VR44cER988IFwc3MTQ4cOFSEhISInJ0f06tWL57SErUePHmLt2rVi5MiRomfPnmLQoEFi27ZtQgghZs2axfNaBs3Ozk4kJSWJe/fuidTUVJ1tPK/F/3kVQojp06cLFxcXnWZhYcHzWsLm7e0t8vLyxLZt28Trr78uevbsKcaNGyfmzJkjt3Nq+JPFJp/m4eEhhBBi+PDhOvGDBw+Ke/fuCSMjI4PnKNdmY2NTYHEaGBgo4uPjRc2aNbWxJk2aiOzsbLFs2TJtzNraWmRkZIjvv/9eZ/yMGTOEWq0Wjo6OBv+cL7rpe5aehYWFuH//vjh8+DDPaRm3U6dOidu3b/O8lkHbvXu32LVrl/D395cUpzyvxWv5xenQoUML7cfzWvRmZ2cnUlNTxZo1ayrCOTX8CWOTT1u/fr1ISUkRSqVSJz58+HAhhBDdunUzeI5ybQUVp0qlUqSnp4u1a9dKxhw4cEBcvXpV+/WIESOEEEK4uLjo9Ktfv74QQlSJ1+YW1I4cOSKioqJ4Tsu47dmzR9y8eZPntZRt5MiR4vHjx6Jhw4aS4pTntfitKMUpz2vx2ty5c4UQQufV73I9p7znlHS0a9cOkZGRUKvVOvGwsDDtdioeBwcHmJuba8/h08LCwtCiRQuYmZkB+Pf8hoeH6/R78OABEhISquz5t7S0RMeOHREREQGA57Q0FAoFlEol6tSpg4kTJ6J///748ssvAfC8lpStrS2+/vprTJ8+HSqVSrKd57Xk1qxZg9zcXDx+/BgHDhxA9+7dtdt4XovHzc0NiYmJaNOmDS5cuIDc3FzExcVh7dq1qFmzJgD5nFMWp6TDxsYGjx49ksTzYzY2NuWdUoWXf84KOq9GRkaoXbu2tm9WVhYyMjL09q2q53/NmjWwsLDAkiVLAPCcloafnx/y8vKQkJCAVatW4eOPP8b69esB8LyWlJ+fH65evYq1a9fq3c7zWnyPHz/G119/jQkTJqBXr16YPHkyGjdujGPHjqFfv34AeF6Lq2HDhjA3N0dQUBACAwPRp08frFixAv/5z3+wb98+API5p1VriRoViRCiRNuocEU9rzz/uhYuXIhRo0bhww8/xPnz53W28ZwW3xdffIENGzagbt26eOONN/Ddd9/BwsICX331lbYPz2vRDRkyBG+88QY6dOjw3L48r0V38eJFXLx4Ufv1X3/9hZ07dyI8PBzLly/HoUOHtNt4XovGyMgI1atXx4IFC7RXS44fP46cnBx88803cHd31xaahj6nnDklHYmJiXr/t2NtbQ1A//+mqHCJiYkA9M86W1tbQ6PRIDk5Wdu3evXqqF69ut6+Ve38z507F3PmzMHMmTOxZs0abZzntOTu3r2Lc+fOYf/+/fD19cX69euxdOlS1KlTh+e1mCwsLLBmzRqsXr0asbGxsLKygpWVFUxNTQEAVlZWMDc353ktI48fP8bevXvRvn17VKtWjee1mPLP18GDB3Xi+/fvBwB07NhRNueUxSnpCA8Ph6OjI5RKpU7c2dkZAHD58mVDpFWh3bx5ExkZGdpz+DRnZ2fcuHED2dnZAP69d+fZvvXq1YOtrW2VOv9z587FggULMG/ePCxdulRnG89p2fnnn39gYmKC5s2b87wWU506dVC/fn189tlnSE5O1rYRI0agRo0aSE5Oxs8//8zzWoYUCgWAJ7NyPK/Fo+8+UuDfc6rRaGR1Tg2+goxNPm3AgAFCCCHeeecdnfi+ffv4KKnntMIeJRUQECAePHggatSooY01btxYZGVliaVLl2pjtWvXFhkZGcLPz09n/LRp06rM404AiNmzZwshhFi4cGGBfXhOy6b9+OOPIi8vT/v8Qp7XojczMzPRo0cPSdu/f7/IyMgQPXr0EE5OTjyvZdRq1aol7t69K86fP6+N8bwWvfXt21fvKvopU6YIIYTo3r27nM6p4U8Ym7zawYMHRWJiohg/frzo2bOnWLdunRBCiBEjRhg8Nzm2AQMGiKFDh4rRo0cLIYQIDAwUQ4cOFUOHDhXVq1cXwJOHGqekpIhjx46JAQMGiDfffFOEhYUV+lDjxYsXCzc3N/Hpp5+KzMzMKvOg6P/+979CCCH27dsnefj2048s4TktXlu3bp1YsWKFePvtt4Wbm5sYMmSI+OWXX4QQQnz55Zc8r2XY9D3nlOe1eO3nn38WS5cuFUOHDhU9evQQ48ePF5GRkSInJ0e4u7vzvJaw7dq1S2RmZopZs2YJd3d3MW3aNJGRkSF2794tt3Nq+JPFJq9mYWEhvv76axEbGyuysrLExYsXxbBhwwyel1xbdHS0KIi9vb22X8eOHcXhw4dFWlqaSE5OFjt27BDNmzfXu8+PPvpIREVFiaysLBETEyPmzZsnjI2NDf5Zy6MFBwcXeD7FkzvseU5L0EaPHi2OHz8u4uPjRU5Ojnj06JEIDg4WI0eOlPTleS1d01ec8rwWr02bNk2cP39eJCUlidzcXBEXFye2b98uOnfuzPNailatWjWxdOlScfv2bZGTkyNiYmLEkiVLhKmpqazOqeL/f0NEREREZHBcEEVEREREssHilIiIiIhkg8UpEREREckGi1MiIiIikg0Wp0REREQkGyxOiYiIiEg2WJwSERERkWywOCUiIiIi2WBxSkRERESyweKUiCo0Hx8fCCG0LTMzE/fv38fRo0cxffp02NraGjpFzJgxA56enpJ4jx49IIRAjx49yj2n6Oho7TlbvXp1uR+/rFlZWen8HHz66aeGTomISojFKRFVCqNHj4arqyv69u2LSZMm4eLFi5g2bRoiIyPh7u5u0NxmzpyJN998UxI/f/48XF1dcf78+fJPCsAff/wBV1dXrFy50iDHL0upqalwdXWFl5eXoVMholIyNnQCRERl4fLlyzh37pz26x07dmDVqlX466+/sGPHDrRs2RLx8fGlPo6RkRGMjY2Rk5NT6n2lpqbi9OnTpd5PSSUkJBj0+EVRrVo1ZGVlPbefRqPB6dOnYW9vXw5ZEdGLxJlTIqq07t69i08//RSWlpaYMGGCNh4cHIzg4GBJf39/f0RHR2u/tre3hxACU6dOxaxZs3Dr1i1kZ2ejV69eMDMzw8qVK3HhwgUkJycjMTERISEhGDx4sM4+hRCoUaMGRo8erb3knH/sgi7rv/HGGwgJCUF6ejpSUlJw6NAhuLq66vSZN28ehBBo27Yttm3bhuTkZDx48AAbN26EpaVlic6XhYUFkpKS8P3330u22dvbIy8vD5999pk2Vq9ePXz//fe4e/cusrOzcevWLcydOxdKpVJn7Ny5cxEaGorExEQ8fvwY586dw9ixYyXHiI6Oxp49e+Dl5YXz588jMzMT8+bNAwC89dZbCA0NRXJyMtLT03Hz5k1s3LixRJ+TiOSNM6dEVKnt27cPeXl5cHNzK/E+Pv74Y1y7dg2fffYZUlJScP36dZiZmcHa2horV66ESqWCqakp+vTpgx07dmDMmDHYsmULAMDV1RVHjx5FcHAwFi1aBABISUkp8Fje3t7Ytm0bDh48CG9vb5iZmeHzzz/HsWPH4O7ujr///lun//bt2xEYGIiNGzfC2dkZS5cuBQCMGzeu2J8zPT0dmzZtwvvvv4/PP/9cJ09fX1/k5ORg06ZNAJ4Upv/88w80Gg0WLlyImzdvolu3bpg9ezaaNm2qU3w2bdoU69atw507d7TnZPXq1WjYsKH2nOTr2LEjHB0dsXjxYkRHRyM9PR2urq4IDAxEYGAg5s+fj6ysLNjb26N3797F/oxEVDEINjY2torafHx8hBBCdOrUqcA+9+/fFxEREdqvg4ODRXBwsKSfv7+/iI6O1n5tb28vhBDi+vXrwtjYuNA8jIyMhFKpFD/88IM4d+6czrbU1FTh7+8vGdOjRw8hhBA9evQQAIRCoRD37t0Tly5dEgqFQtvPwsJCPHjwQPz111/a2Lx584QQQnz22Wc6+/zuu+9ERkbGc89bdHS03pyaNWsm8vLyxOTJk7UxMzMzkZCQIDZu3KiNrV27VqSkpIjGjRvrjP/vf/8rhBDC0dFR73EVCoVQKpVi9uzZIiEhQZJTbm6uaNmypd59WlpaPvdz5X/PPv30U4P/bLKxsZWs8bI+EVV6CoWiVON3796NvLw8Sfytt97CX3/9hdTUVKjVauTl5WH8+PFwdHQs0XFat26Nhg0bYsuWLRBCaOPp6enYvn07XF1dUb16dUluTwsLC0P16tVRt27dEuUQHR2NvXv3wtfXVxsbMWIE6tSpg++++04be/311xEcHIzY2FgolUpt279/PwDo3KrQq1cvHD58GMnJydBoNMjLy8OiRYtQp04dSZ5hYWG4fv26TuzMmTMAgF9//RVvv/027OzsSvTZiKhiYHFKRJWaubk5bGxsEBsbW+J93L9/XxLz8vJCUFAQVCoVRo0aBVdXV3Tu3BkbN26UFJBFZWNjU+Dx8ovA2rVr68QTExN1vs7OzgaAEucAAN988w1atWqFvn37AgAmTZqEkJAQXLhwQdunXr16GDx4MPLy8nTalStXAAB16tQBAHTp0gWHDh0CALz33nt45ZVX0LlzZyxevFhvnvo++8mTJ+Hp6QljY2P89NNPUKlUCA8Px/Dhw0v8GYlIvnjPKRFVaoMGDYKxsTGOHTumjWVlZcHKykrSN7+getbTs5j5Ro0ahVu3bmHYsGE6cTMzsxLnml9oNmjQQLLNzs4OarUaSUlJJd5/UQUHByM8PBwffvgh0tLS0KlTJ4wcOVKnz8OHDxEWFoZZs2bp3Uf+fwaGDx+O3NxcvP7669rCGYDeR2sB+s818GSGePfu3TA1NYWrqytmzJiBX375BTExMQgNDS3BpyQiuWJxSkSVVuPGjbFy5UokJydj3bp12nhMTAzefvttmJqaah8JZW1tjVdeeaXQxUpPE0JIHidVr149vQ/bz87OLtJM5tWrV3Hv3j2MGDFC59mj5ubmGDp0KE6dOoXMzMwi5Vda3377Lb7//ntYWVnhwYMHCAoK0tm+d+9eDBw4EDdv3kRycnKB+xFCIC8vD2q1WhurVq0a3n333RLllZOTgxMnTiA5ORkDBgxAhw4dWJwSVTK8rE9ElUK7du3g4uKC7t27w8vLC//73/8QFhYGCwsLeHl54eHDh9q+W7ZsgY2NDbZu3Yq+ffti+PDh+PPPP4tcmAJPirM2bdpgzZo16NWrF/7zn//gr7/+0ntZOjw8HD179sTrr7+OTp06oVWrVnr3KYTA559/jg4dOmDv3r1444038NZbbyE4OBi1atXC9OnTi39iSmjr1q1ISkpCjx498MMPPyA3N1dn+9y5c5Gbm4uQkBB88MEH6NWrFzw8PDBx4kTs2bMHDRs2BPDkQf81a9bEtm3b0KdPHwwbNgwnT57UmUV9ngULFmDjxo0YMWIE3NzcMHjwYKxatQo5OTk4fvx4mX5uIpIHg6/KYmNjYytpy1+tny8rK0s8ePBABAcHi+nTp4s6deroHffuu++KiIgIkZGRIS5fvizefvvtAlfrF7Ty+/PPPxe3bt0SmZmZIiIiQowbN067iv7pfi+99JI4efKkSEtLE0II7ZMCnl2tn98GDx4sTp06JTIyMkRqaqo4fPiw6Natm06f/OPY2NjoPR/29vaFnreCVus/3TZt2iRycnKEnZ2d3u02Njbi66+/Fjdv3hTZ2dni4cOH4syZM2LRokXC3Nxc22/06NEiMjJSZGZmihs3bohp06aJMWPGSPKMjo4We/bskRxn4MCB4o8//hB3797Vfn/37t0runfvLunL1fpsbJWiGTwBNjY2NrZybtHR0WLz5s1CqVTqPLYqv5mYmAiVSiUCAwMNnmtRm1KpFM2bN2dxysZWwRsv6xMRVVE+Pj7Iy8vDt99+q43VqVMH3bt3x9q1a1GvXj0sW7bMgBkWnZWVFfLy8nDz5k1Dp0JEpcQFUUREVdAbb7yhfbJAfHy8Nj5o0CBs3rwZsbGx8PX11Xl8lJylpqaic+fO2q/v3r1rwGyIqDQUeDKFSkRERERkcLysT0RERESyweKUiIiIiGSDxSkRERERyQaLUyIiIiKSDRanRERERCQbLE6JiIiISDZYnBIRERGRbLA4JSIiIiLZYHFKRERERLLB4pSIiIiIZOP/AFmLyjcLQ1LrAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(dpi = 120)\n",
    "survival_df.duration.plot.kde(ax = ax, color = 'C0', linewidth = 3, label = 'kde')\n",
    "survival_df.duration.plot.hist(density = True, ax = ax, color = 'C7', alpha = 0.7, label = 'histogram', bins = 15)\n",
    "ax.legend(loc = 'best', frameon = False)\n",
    "clean_ax(ax, clean_all = True)\n",
    "ax.set_xlim([-50, 600])\n",
    "ax.set_ylabel('Density')\n",
    "ax.set_xlabel('Duration [years]')\n",
    "ax.set_title('Country Duration Histogram')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Survival function\n",
    "\n",
    ">**Definition:** The survival function $S(t)$ gives the probability that a person survives longer than some specified time $t$, in other words:\n",
    ">$$S(t):=\\mathbb{P}(T > t)$$\n",
    "Given a death density function $f(t)$. The area under the curve to the right of time $t$ is the proportion of subjects in the population who have survived past time $t$. We call this quantity $S(t)$ and it is given by:\n",
    "$$S(t) = 1 - \\int_{0}^{t}f(u)du = 1 - F(t).$$\n",
    "The survival function is related an instantaneous risk rate which we introduce next.\n",
    "\n",
    "### Instantaneous Hazard function\n",
    "\n",
    "> **Definition:** The instantaneous rate at which a randomly-selected individual known to be alive at time $t$ will die at time $t+ \\Delta t$ is called the conditional failure rate or instantaneous hazard, $\\lambda(t)$. Given by:\n",
    ">$$\\lambda(t) = \\lim_{\\Delta t \\rightarrow 0}\\dfrac{\\mathbb{P}(t\\leq T < t + \\Delta t | T \\geq t  )}{\\Delta t},$$\n",
    ">where $\\Delta t$ is an infinitesimal time interval.\n",
    "\n",
    "Note that, in contrast to the *survival function* the *instantaneous hazard rate* focus on the event rather than surviving to the event. The above expression can be written as:\n",
    "\\begin{align*}\n",
    "\\lambda(t) &= \\lim_{\\Delta t \\rightarrow 0} \\dfrac{\\mathbb{P}(t\\leq T < t + \\Delta t \\cap T \\geq t)}{\\mathbb{P}(T \\geq t)\\Delta t}, \\\\\n",
    "            &= \\lim_{\\Delta t \\rightarrow 0} \\dfrac{\\mathbb{P}(t\\leq T < t + \\Delta t)}{S(t) \\Delta t}, \\\\\n",
    "            &= \\lim_{\\Delta t \\rightarrow 0} \\dfrac{f(t)\\Delta t}{S(t)\\Delta t}\\\\\n",
    "            &= \\dfrac{f(t)}{S(t)}.\n",
    "\\end{align*}\n",
    "Where in the second line we have used the fact that $\\mathbb{P}(t\\leq T < t + \\Delta t \\cap T \\geq t) = \\mathbb{P}(t\\leq T < t + \\Delta t)$ and then we used the fact that for very small $\\Delta t$ we can write $\\mathbb{P}(t\\leq T < t + \\Delta t) \\approx f(t) \\Delta t$. In the last line: $f(t)$ is the *death density* and $S(t)$ is the survival function. \n",
    "\n",
    "Furthermore, by noting that:\n",
    "$$\\dfrac{d}{dt}S(t) = -f(t),$$\n",
    "we can write for the hazard function:\n",
    "$$\\lambda(t) = -\\dfrac{d}{dt}\\log S(t)\\;\\;\\; \\text{or:  } \\;\\;S(t) = e^{-\\int_{0}^{t}\\lambda(u)du}$$\n",
    "\n",
    "> **Example:** This relation allows us to analyze a very simple scenario, the one where the hazard risk does not change with time $t$. This is: $\\lambda = c$, where $c$ is a constant value. Which leaves for the survival function:\n",
    ">$$S(t) = e^{-c t}$$\n",
    ">The above is straightforward to illustrate with a few code lines:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c = 0.65\n",
    "x = np.linspace(0, 10, 100)\n",
    "S = np.exp(- c * x)\n",
    "f, ax = plt.subplots(1,2, dpi = 120, figsize = (12,4))\n",
    "ax[0].axhline(y = c, xmin = x[0], xmax = x[-1], label = 'hazard rate')\n",
    "ax[0].set_xlim([x[0], x[-1]])\n",
    "clean_ax(ax[0])\n",
    "ax[0].legend(loc = 'best', frameon = False)\n",
    "ax[0].set_ylabel(r'$\\lambda(t)$')\n",
    "ax[0].set_xlabel('Time')\n",
    "# Survival function\n",
    "ax[1].plot(x, S, label = 'survival function')\n",
    "ax[1].set_xlabel('Time')\n",
    "ax[1].set_ylabel(r'$S(t)$')\n",
    "ax[1].legend(loc = 'best', frameon = False)\n",
    "clean_ax(ax[1])\n",
    "f.suptitle('Constant hazard rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Definition:** It is also natural to define the *cumulative hazard function* as:\n",
    "> \\begin{align*}\n",
    " \\Lambda(t) &:= \\int_{0}^{t}\\lambda(u) du \\\\\n",
    "            & = - \\int_{0}^{t} \\dfrac{d}{du}\\log S(u) du \\\\\n",
    "            & = - \\log S(t)\n",
    "\\end{align*}\n",
    "\n",
    "So we can rewrite the survival and death density functions in terms of this cumulative hazard function\n",
    "\\begin{align*}\n",
    "S(t) &= e^{-\\Lambda(t)}\\\\\n",
    "f(t) &= \\lambda(t)e^{-\\Lambda(t)}.\n",
    "\\end{align*}\n",
    "Note that the hazard function might be of more intrinsic interest than the p.d.f. to a patient who had survived a certain time periotd and wanted to knwo something about their prognosis. There are also other reasons for introducing the hazard functions $\\lambda(t)$ and $\\Lambda(t)$:\n",
    "* **Interpretability:** Suppose T denotes time from surgery for breast cancer\n",
    "until recurrence. Then when a patient who had received surgery visits\n",
    "her physician, she would be more interested in conditional probabilities\n",
    "such as “Given that I haven’t had a recurrence yet, what are my chances\n",
    "of having one in the next year” than in unconditional probabilities (as\n",
    "described by the p.d.f.).\n",
    "*  **Analytic Simplifications:** When the data are subject to right censoring,\n",
    "hazard function representations often lead to easier analyses. For example, imagine assembling a cohort of $N$ patients who just have turned $50$\n",
    "years of age and then following them for 1 year. Then if $d$ of the men\n",
    "die during the year of follow-up, the ratio $d/N$ estimates the (discrete)\n",
    "hazard function of $T$ =age at death. We will see that $\\Lambda(·)$ has nice\n",
    "analytical properties.\n",
    "* **Modeling Simplifications:** For many biomedical phenomena, T is such\n",
    "that $\\lambda(t)$ varies rather slowly in t. Thus, $\\lambda(·)$ is well-suited for modeling.\n",
    "\n",
    "The **Expected Survival Time** or life expectancy is given by:\n",
    "$$\\mu := \\mathbb{E}[T] = \\int_{0}^{\\infty} t f(t)  dt$$\n",
    "after integrating by parts, and making use of the fact that $-f(t) = d/dt S(t)$, we get:\n",
    "$$\\mu = \\int_{0}^{\\infty}S(t)dt.$$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## The Likelihood Function for Censored Data\n",
    "Suppose we have a *cohort* of $N$ observations governed by a survival function $S(t)$ with *death* density $f(t)$ and hazard $\\lambda(t)$. If the $i$-th subject is observed at time $t_i$ and the subject died, its contribution to the likelihood function is:\n",
    "$$L_i = f(t_i) = S(t_i)\\lambda(t_i).$$\n",
    "If the subject is alive at time $t = t_i$, all we know is that the survival time $T$ of subject $i$ exceeds $t_i$. The likelihood of this event is:\n",
    "$$L_i = S(t_i),$$\n",
    "the contribution to the likelihood of a censored observation.\n",
    "\n",
    "Let $d_i\\in \\{0,1\\}$ be the death indicator variable for the $i$-th observation, that takes the value $1$ if the subject died and $0$ otherwise. Then, we can write the likelihood function as:\n",
    "$$\\mathcal{L}= \\prod_{i = 1}^{N}L_i = \\prod_{i=1}^{N}\\lambda(t_i)^{d_i}S(t_i).$$\n",
    "This leaves for the log-likelihood:\n",
    "$$\\log\\mathcal{L} = \\sum_{i = 1}^{N}\\left\\{d_i\\log\\lambda(t_i)- \\Lambda(t_i)\\right\\}.$$\n",
    "\n",
    "> **Example:** Let us go back to our good old exponential distribution example. The hazard rate function is constant $\\lambda(t)=\\lambda$. Then, for the cumulative hazard we have $\\Lambda(t) = \\lambda t$. The log-likelihood then reads:\n",
    ">$$\\log\\mathcal{L} = \\sum_{i}^{N}\\left\\{d_i \\log\\lambda - \\lambda t_i \\right\\},$$\n",
    ">if $D = \\sum_{i}^{N} d_i$ is the total number of deaths and $T= \\sum_{i}^{N}t_i$ is the total observation time. We can write the log-likelihood as:\n",
    ">$$\\log \\mathcal{L} = D \\log\\lambda - \\lambda T.$$\n",
    ">We obtain a score function by diferentiating this expression with respect to $\\lambda$:\n",
    ">$$u(\\lambda) = \\dfrac{d}{\\lambda} - T,$$\n",
    ">setting this score to zero gives the maximum likelihood estimator of the hazard:\n",
    ">$$\\hat{\\lambda}_{ML} =\\dfrac{D}{T} $$\n",
    ">the total number of deaths divided by the total duration time, a.k.a. death rate. The expected information is obtained from:\n",
    ">\\begin{align*}\n",
    "I(\\lambda) &= -\\dfrac{d^2}{d\\lambda^2}\\log\\mathcal{L}\\\\\n",
    "            &=\\dfrac{D}{\\lambda^2}.\n",
    "\\end{align*} \n",
    "> using the maximum likelihood estimate $\\lambda_{ML}$ we get an estimator for the variance:\n",
    ">$$\\widehat{\\text{var}}(\\hat{\\lambda})=\\dfrac{D}{T^2},$$\n",
    "> which is used as a confidence interval for $\\lambda$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Approaches to Survival Analysis\n",
    "\n",
    "### Estimating the Survival function\n",
    "We could be tempted to estimate this quantity as:\n",
    "$$\\hat{S}_{\\text{Naive}}(t) = \\dfrac{n_s(t)}{n},$$\n",
    "where $n_s(t)$ is the number of subjects surviving beyond $T=t$ and $n$ is the total number of subjects. But note that in the presence of censoring this estimator cannot be used since the numerator is not always defined. To see this consider the data in the example below: the subject indexed as `13` has a survival time $T=25$ days but the response variable `status` has a value of $0$ which means the observation was censored at the 25th day. For this subject we do not have information about the occurrence of the event. The only thing we know is that the event has not occurred for the 25 first days of observation, point at which we have lost track of the subject. This right censoring does not allow us to calculate the real number of surviving subjects, the numerator of the $\\hat{S}_{\\text{Naive}}(t)$ estimator.\n",
    "\n",
    ">To illustrate the survival function estimation, we will use a dataset concerning **Veteran's Administration Lung Cancer Trial** dataset. It consists of a randomized trial of two treatment regimes for lung cancer. The dataset contains the following variables:\n",
    "* Treatment: denotes the type of lung cancer treatment; standard and test drug.\n",
    "* Celltype: denotes the type of cell involved; squamous, small cell, adeno, large.\n",
    "* Karnofsky_score: is the Karnofsky score.\n",
    "* Diag: is the time since diagnosis in months.\n",
    "* Age: is the age in years.\n",
    "* Prior_Therapy: denotes any prior therapy; none or yes.\n",
    "* Status: denotes the status of the patient as dead or alive; dead or alive.\n",
    "* Survival_in_days: is the survival time in days since the treatment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(137, 8)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/healthcare/veteran.csv')\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>status</th>\n",
       "      <th>karno</th>\n",
       "      <th>diagtime</th>\n",
       "      <th>age</th>\n",
       "      <th>prior</th>\n",
       "      <th>trt</th>\n",
       "      <th>celltype</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>58</td>\n",
       "      <td>63</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>9</td>\n",
       "      <td>52</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    time  status  karno  diagtime  age  prior  trt  celltype\n",
       "11     8       1     40        58   63     10    1         1\n",
       "5     10       1     20         5   49      0    1         1\n",
       "32    20       1     30         5   65      0    1         2\n",
       "13    25       0     80         9   52     10    1         1\n",
       "23    59       1     30         2   65      0    1         2"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[[11,5,32,13,23]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Consider our naive estimator $\\hat{S}_{\\text{Naive}}(t)$. From the above table, we can compute $\\hat{S}_{\\text{Naive}}(t=11)= \\frac{3}{5}$. But we cannot compute $\\hat{S}_{\\text{Naive}}(t = 30)$ because we do not know whether subject number 4 (indexed `13`) is still alive at $t=30$, all we know is that the subject was still alive at $t = 25$.\n",
    "\n",
    "### Kaplan-Meier Estimator\n",
    "An estimator that can be used for right censored data (as it is our case) is the so called **Kaplan-Meier** estimator.\n",
    "> **Definition: [Kaplan-Meier]** Let $d_i$ be the number of events at time $T=t_i$. Let also $n_i$ be the number of subjects *known to be survived*, this is, the number of subjects with no event or that are censored up to time $t_i$. The Kaplan-Meier estimator for the survival function is defined as:\n",
    ">$$\\hat{S}_{KM}(t) := \\prod_{i:t_i\\leq t}\\left(1 - \\dfrac{d_i}{n_i}\\right)$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sksurv.nonparametric import kaplan_meier_estimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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+WXFxcbr++utd/kjXqFGj2GSjJJ599lk9++yzpXrNu+++q/79++uzzz6TJL333ntF7u/4nH/7298UHx9fto6e9D6NGjVSenp6md+nNMcqTZ+Tk5N11113SfrzLt3VV1+tUaNGaezYsfLz89Mzzzzjtv6W1lNPPaV69eopIiJC3333ncu2MWPG6KabbirV+1XUNT5ZaX9+YmJi9Oyzz2r48OH64IMPNHz4cElyuetXHMdKVZK0cuXKQve75557nO/vSGwL629B7Y6+9+nTR3Pnzi1x/wCgOiD/Iv8q7ljkXyVD/kX+heL5eboDQGXLy8vTbbfdpg8++EBhYWH67rvv8j0m3KpVKyUmJuZLsGrUqKEuXboU+t7nnXdegUswO5KytWvXFtm3Vq1aSZLmzJmT7y7UZZddpnr16hX5eneJi4vTzp071bx5c3333XfasmVLkfs7/lhdddVV5TpuRb1PZRwrMTFR//nPf5yPSZ+ctBR0R7eytWrVSqmpqfkSLEnOpbRPlZeXV2if3XFtunTpoho1auRrL+zn5+DBg/r888/VuXNnXXHFFYqMjNSOHTu0ePHiEh/TcRdx6dKlmjp1aoGRmZmpyMhI1a9f36Ufl19+eYH9Leh3RGV+LwNAVUT+VXrkX8Uj/yo/8i9URx6fGIwg3B0Op7Y7VvTYtm2by4SbGzdutCNHjlizZs1c9h83bpzzvbp161bgMaZPn17g6j65ubkuE1gWNBlmp06dzMzs888/d3nvwMBA++WXX8zM8k1EWdikmSf3a+nSpSU+V6dOfOqISy65xPr27WsXXnihS3tBE582adLEDh06ZPv377eOHTvmO0aNGjXynb+CJj4NCQmxnJwc27x5s7Vu3Trf+9SqVctlaWPpr0kyS/s9Uto+t23bNt9y25Ls0ksvNTOzlStXOtvatGljZmYffPBBgccuauLTwiYeLepzFnQuFyxYYGZm7dq1c9nXsWpUQcf/+eefLSsrK9+qP2W9xsV9z5mVfHUfR1xxxRVmZpaUlGRmZmPGjCnxNXesVHXs2LF8P+snx0cffWRmZnfddZez7dtvvzWz/Kv7XHfddQWez5o1a9rvv/9uGRkZhU5u2rlzZwsICCj19y5BEERVDfIv8q/igvyL/Kuw/ci/iLIGwxXh00aMGKGsrCw99NBD+uGHH3T11Vdr69atmjhxoqKjo7V27Vp98cUXOnbsmK688kqFhoZqzpw56tOnT4Hv99tvv6lTp0769ddftXjxYjVu3FgDBgzQ6aefrkcffbTACSxPtnr1ai1fvlz9+/fXjz/+qOXLl+uss85S7969tXnzZiUnJ7vjNJTI2rVri70T6nDo0CH94x//0OzZs7Vy5UotWbJEGzZs0IkTJ3Teeefp8ssvV9OmTfNNvHmqzZs3a+jQoYqJidGGDRu0cOFCbdmyRbVq1dJ5552nq666SikpKWrTpk25P19p+3zNNdfojTfe0IoVK7Rp0yYdOHBA5557rvr27au8vDxNmDDB5XPs3r1bAwcOVG5urnbt2iUz03//+1/t2rWr3H0viTfffFO9evXS8uXL9dlnn+nIkSPq0KGDunTpopkzZ+qWW27J95olS5bosssu08KFC/X9998rJydHv/32m+bNm1dh1/hkCxYs0Ouvv67evXvrt99+U6tWrXTzzTcrKytLw4YNK3CS0RUrVmjdunW6+OKLlZubq5iYmBIfz/GzOWfOnCLnkJg6daruuOMO3XPPPc4JVUeNGqUff/xRU6ZM0fXXX6/169erZcuW6t+/v7788kvddNNNLhPgHj9+XDfffLMWLVrkMkluZmammjdvro4dOyo4OFhnn322cxJXAKiuyL9KjvyL/Iv8i/wLpefxShtBuDsKu5PoiBdeeMHMzPbs2WOhoaEm/Xl3Z+3atfbHH39YSkqKzZo1y8LCwpzLURd0J3Hp0qXWrFkz++9//2v79++3rKws+/XXXy0yMjLfMQtb1vj000+3qKgo27Fjh2VlZdnWrVtt/PjxFhAQUOCdpcq6k1hYFHQn0REtWrSwSZMm2ZYtWywrK8uOHDliGzdutI8++sj69u3rsm9Bd78cERYWZrGxsbZz507Lzs621NRUi4+Pt3feeSffEs5lvZNY2j5fdNFF9vrrr9vq1avtwIEDlp2dbTt27LCZM2fa5Zdfnu99O3ToYN98840dPnzY8vLyXL6HKuNOoiT7+9//bj/99JMdPXrU0tLSbNGiRXbVVVcVevx69erZ5MmTLSkpyY4dO2ZmZrGxsWW+xsV9z40dO9Y6d+5scXFxduTIETt69KgtWrTIOnToUOTrHcs/f/bZZ6W61suXLzczsxtvvLHYfTdt2mRmZhdffLGzLSQkxL744gtLS0uzP/74w1asWGHXX3+9Pfzww2ZmBX7+wMBAe+mllyw+Pt4yMjIsPT3dtmzZYjNnzrTbb7/d/P39y/y9SxAEUdWC/Iv8q6RB/vXX/uRffwX5F1HG8HgHCKJaRGmTGYIgKiYcSeXVV1/t8b5Iso8//tjMLN/QEoIgCKLig/yLIDwT5F9EVQ0mngcAeK1zzz1XAwcOVGJior799ttKO26NGjUKXMXn6quv1oABA7Rhw4ZiJwgGAADwRuRfqMqYkwsA4HUiIyN14YUXauDAgapbt66efvrpSj1+7dq1lZSUpKVLl2rTpk06fvy42rZtq549eyo3N1ejRo2q1P4AAAC4G/kXvIXHHycjiOoQPC5PEJUXS5cutby8PNu5c6c98MADlX58Pz8/e/vtty0hIcEOHz5subm5tmfPHps+fbrLvBEEQRCEe4P8iyAqL8i/CC8Jj3eg1NGgQQN75ZVXbNGiRXbgwIECJ48sKgIDAy02NtZSUlIsIyPDVqxYUWXGEhMEQRAEQVTVIAcjCIIgCKIqh1fOydW0aVPdc889qlOnjr788stSvbZ27dpasmSJevTooQceeEB9+/bV/v37tXDhQnXt2tU9HQYAAKgGyMEAAEBV5/FKW3miadOmpbqLOGLECDMz69y5s7PN39/fEhISbOXKlR7/PARBEARBEN4Q5GAEQRAEQVS18MonucqjX79+2rRpk1auXOlsy8vL08cff6xOnTrpnHPO8WDvAAAAqidyMAAA4G4+t7piWFiYfvjhh3zt69evlyS1bdtWe/bsKfC1gYGBCgwMdGmrXbu2GjRooNWrVysnJ6fiOwwAAFANkIMBAAB387kiV9OmTXXo0KF87Y62pk2bFvrakSNHaty4cQVua9u2rRITEyukjwAAANUNORgAAHA3nytySZKZlWnb5MmTNXPmTJe24OBgzZkzp8L6BgAAUF2RgwEAAHfyuSJXampqgXcKmzRpIkkF3mF0SElJUUpKitv6dqq+jz2ocy5qXaJ9czIyFRcdq6QE7mQCAICqx5tyMAAA4J18rsgVHx+vdu3a5Wt3tCUkJFR2lwp1zkWt1apjeKleE3Pfo27qDQAAQNl5Uw4GAAC8k88VuWbPnq0pU6bosssu06pVqyRJ/v7+GjRokFauXKm9e/d6uId/2bPp9xLtFxTSWgGNGqpO/Xpu7hEAAEDZeFMOBgAAvJPXFrl69eql+vXrq2HDhpKk0NBQ9e/fX5L09ddfKysrS1OnTtXgwYMVHBysXbt2SZJiYmI0atQozZw5U2PGjNGBAwc0cuRIhYSE6JprrvHY5ynIV6++WaL9RsRElfqJLwAAgLLwhRzMMWUE00EAAOBdvLbINWXKFJ1//vnOr2+99VbdeuutkqTzzz9f//vf/+Tv76+aNWuqRo0azv1yc3PVo0cPvfrqq5o0aZLq1aundevWqXfv3vr+++8r+2MAAAB4FV/IwU6dMoLpIAAA8A41JBW+lA2KFRoaqg0bNnh0+WrHk1xZR9OVvLlkQxyLw51LAABQlbkzB+v72IPqescASdLW1Ws0ZeioCn1/AADgHl77JBf+kpORKUkKaNSwwoctcucSAAD4mq9efbNMCwABAADPoshVDcRFx0pShU08z0T2AAAAAADA21DkqgaSEhIr9IkrJrIHAAAAAADexs/THQAAAAAAAADKiyIXAAAAUIigkNYaOmmCmoeFerorAACgGAxXRKGCQlprRExUkfuwCiMAAKiOTl7Yp21EF0ksyAMAQFVHkQv5lGW1RpI+AABQnTgW9mkZ3p4FeQAA8BIUuZBPSVdrZBVGAABQXTkW9mFBHgAAvAdFLuRT0tUaSfoAAAAAAEBVwcTzAAAAAAAA8HoUuQAAAAAAAOD1KHIBAAAAAADA61HkAgAAAAAAgNdj4nmUW1BIa42IiSp0e05GpuKiY5WUkFiJvQIAAAAAAL6EIhfKLCcjU5IU0KhhiVZZLMmKjQAAAAAAAGVBkQtlFhcdK0mqU79eofsEhbRWQKOGRe4DAAAAAABQXhS5UGZJCYnFPp01IiaqRE95AQAAAAAAlAcTzwMAAAAAAMDr8SQXKkVxk9NLTFAPAAAAAADKjiIX3Kq0k9NLTFAPAAAAAABKjyIX3Kokk9NLTFAPAAAAAADKhyIX3Kokk9NLf01Qz7BGAAAAAABQFhS5UCUwrBEAAAAAAJQHRS5UCaUd1tgyvD1PfAEAgErTqmO4moeFklcAAFCFUeRClVDSYY1DJ01Q24guPPEFAAAqheNpc0nqOXwIeQUAAFUYRS54FSayBwAAlSkuOlZtI7pIKj7/AAAAnkWRC16ltBPZAwAAlEdSQqK2rl5DXgEAgBfw83QHAAAAAAAAgPKiyAUAAACUgGPyeQAAUDVR5AIAAACKcOrk8wAAoGqiyAUAAAAUwbHwjcTk8wAAVGVMPI9qLSiktUbERBW5T05GpuKiY5WUkFhJvQIAAN6EyecBAPAOFLlQLTmGFQQ0aljihLQkqzYCAAAAAICqiSIXqiXHsILihhQEhbRWQKOGDD0AAAAAAMDLUeRCtZSUkFiiJ7NGxEQx9AAAAAAAgGqAiecBAAAAAADg9ShyAQAAACXUqmO4moeFerobAACgABS5AAAAgGI4FrWRpJ7Dh3iwJwAAoDAUuQAAAIBiOBa1kYpf2AYAAHgGRS4AAACgGEkJidq6eo2nuwEAAIpAkQsAAAAAAABejyIXAAAAAAAAvF5NT3cAqAqCQlprRExUodtzMjIVFx2rpITESuwVAACoihwrLJIXAABQtVDkgk9zrJQU0KihWnUML3b/mPsedXeXAABAFXXqCovkBQAAVC0UueDTHCslFbVKUlBIawU0ashKSgAA+Li46Fi1jegiiRUWAQCoiihywaclJSQWexd2REyUWnUML3ZIo8SwRgAAqjPHCoslefobAABUPopcQDFKO6RRYlgjAAAAAACVjSIXUIySDGmUGNYIAAAAAIAnUeQCilGSIY3SX8MaAQAAAABA5fPzdAcAAAAAb9OqY7iah4V6uhsAAOAkPMkFVDAmqAcAoPpyzNUpST2HD2EeTgAAqhCKXEAFYYJ6AACqv7joWLWN6CKp+Pk6AQBA5aLIBVQQJqgHAKD6S0pI1NbVa5iHEwCAKogiF1BBSjtBPcMaAQAAAACoOBS5gErGsEYAAAAAACoeRS6gkpV2WGPL8PY88QUAAAAAQDEocgGVrKTDGodOmqC2EV144gsAAAAAgBKgyAVUUUxkDwAAAABAyVHkAqqo0k5kDwAAAACAL/PzdAfKon79+po4caKSk5OVlZWltWvXasCAASV6bUREhBYvXqz9+/crPT1dv/32m+677z75+XnlqQAAAKg05GAAAKAq88onuWbNmqWOHTtqzJgx2rJli2677TZNnz5dfn5++vTTTwt9XY8ePbRo0SJ9//33uvvuu5WRkaE+ffro7bffVnBwsB588MHK+xAAAABehhwMAABUZV5X5Ordu7euvfZaRUZGavr06ZKkZcuWqUWLFpowYYJmzJihEydOFPjaO++8U8eOHdMNN9ygzMxMSdKSJUsUEhKiO++8kwQLXi0opDWrMAIA3IYcDAAAVHVeV+Tq16+f0tPTNXPmTJf22NhYffrpp+rUqZN++umnAl977Ngx5ebmKisry6X98OHDys7OdlufAXfKyfjzPwuswggAcCdyMAAAUNV53SQIYWFh2rhxo/Ly8lza169f79xemHfeeUe1a9fW22+/rWbNmqlx48YaNGiQ+vXrp1dffdWt/QbcJS46VhuWLdfW1WuKjKyj6ZKKX60RAICCkIPl16pjuJqHhXq6GwAA4P953ZNcTZs21fbt2/O1Hzp0yLm9MKtWrdLVV1+tmTNn6t5775UkHT9+XI8//rjeeOONYo8dGBiowMBAl7bg4ODSdB+ocKzCCACoDORgf3E8RS1JPYcP4QlpAACqCK8rckmSmZVpW3h4uGbPnq2ff/5Zw4cPV0ZGhq6++mq98MILqlu3rl544YUijzty5EiNGzeurN0GAADwauRgf4qLjlXbiC6SeEIaAICqxOuKXKmpqQXeKWzSpImkv+4mFiQqKkr79+9Xv379nBOjLlu2TCdOnNC4ceM0bdo07dixo9DXT548Od88FMHBwZozZ05ZPgoAAIDXIAf7S1JCorauXsMT0gAAVDFeV+SKj49XZGSk/P39XeaEaNeunSQpISGh0NdefPHF+vTTT/Ot/LN69Wr5+/urTZs2RSZYKSkpSklJKecnAAAA8D7kYAAAoKrzuonnZ8+erYYNG6p///4u7YMHD1ZycrJ+/vnnQl+7Z88edejQQX5+rh/78ssvlyTt3r274jsMAABQDZCDAQCAqs7rnuRauHChFi9erClTpqhRo0baunWrIiMj1bt3b91+++3OO4RTp07V4MGDFRwcrF27dkmSJk6cqEmTJmnu3LmKjo5WZmamevTooYcfflhxcXHO1YGA6iwopLVGxEQVuj0nI1Nx0bFKSkisxF4BAKo6cjAAAFDVeV2RS5JuvvlmjR8/Xs8995yaNGmiTZs2aeDAgZoxY4ZzH39/f9WsWVM1atRwtv3nP/9RcnKyHnroIU2dOlUBAQHauXOnnn32WU2cONETHwWoNI6VoAIaNSzRHCKsFAUAOBU5GAAAqMpqSCp8KRwUKzQ0VBs2bFDbtm2VmMiTL6i6moeFqufwIUWuAhUU0loBjRpq6+o1mjJ0VCX2DgCA0vF0DjYiJkqtOobzNxMAgCrEK5/kAlB6SQmJxT6d5UjYAQAAAADwNl438TwAAABQVbTqGK7mYaGe7gYAABBFLgAAAKDUHHNdSlLP4UM82BMAAOBAkQsAAAAopbjoWOe/i5rvEgAAVB6KXAAAAEApJSUkauvqNZIYsggAQFXBxPMA8gkKaa0RMVFF7pOTkam46FglJbCqKADAN506ZLG4BV4AAIB7UeQC4ORI1gMaNSzxKosk9AAAXxUXHau2EV0kMWQRAICqgCIXACfH/CLFJepBIa0V0KihWoa354kvAIDPcgxZLOmNIQAA4F4UuQA4JSUklujJrKGTJqhtRBee+AIAAAAAVBkUuQCUWmmf+GIIBwAAAADA3ShyASi1kj7xNSImiiEcAAAAAIBK4efpDgAAAAAAAADlRZELAAAAAAAAXo/higDcLiikNaswAgAAAADciiIXALfJyciUJFZhBAAAAAC4HUUuAG7DKowAAAAAgMpCkQuA27AKIwAAAACgsjDxPAAAAFBOrTqGq3lYqKe7AQCAT6PIBQAAAJSRY/5JSeo5fIgHewIAAChyAQAAAGXkmH9SKn4OSgAA4F4UuQAAAIAySkpI1NbVayQxZBEAAE9j4nkAVUZQSGuNiIkqcp+cjEzFRccqKSGxknoFAEDRTh2yWJJFVwAAQMWjyAXA4xz/OQho1LDEqyzyHwgAQFURFx2rthFdJEltI7qoeVgoN2MAAPAAilwAPM4xn0lxc5kEhbRWQKOGzHkCAKhSkhIStWHZcmehi6e5AADwDIpcADwuKSGxRP8ZGBETVeInvQAAqEwnP83FzRgAADyDiecBAACAcjp5AnoAAOAZPMkFwOsUN0E9k9MDAAAAgO+hyAXAa5R2gnrmQwEAAAAA30GRC4DXKMkE9UxODwAAAAC+iSIXAK9RkgnqmZweAAAAAHwTE88DAAAAAADA61HkAgAAAAAAgNdjuCKAaqm4FRglVmEEAAAAgOqEIheAaqW0KzBKrMIIAKhYrTqGq3lYKDdRAACoZBS5AFQrJVmBUWIVRgBAxXPcaJGknsOHcBMFAIBKRpELQLVSkhUYpb9WYSzJsMaSYvgjAPi2uOhYtY3oIqn4my0AAKDiUeQC4JPKMqyxpLhzDwC+KSkhUVtXr1GrjuEMWQQAwAMocgHwSSUd1lhSDH8EAEgMWQQAwJMocgHwSSUd1lhSjuGPAADfxpBFAAA8x8/THQAAAACqC8eQRQAAUPkocgEAAAAAAMDrMVwRAAAAcAPHCr6svgsAQOWgyAUAAABUoMJW8GUSegAA3IsiFwBUIMdd+6JwRx8AqreTV/Bl9V0AACoPRS4AqACF3bUvCnf0AaB6OnkFX8fqu606hqt5WCg3OAAAcCOKXABQAU6+a18U7ugDgG9x3ASRpJ7Dh3CDAwAAN6LIBQAV4OS79kVx3NEvblgjQxoBoHqIi45V24gukoq/EQIAAMqHIhcAVKLSDmvkjj8AeLekhERtXb2mxEPZAQBA2VHkAoBKVJJhjQxpBAAAAIDSo8gFAJWoJMMaHUMaAQAAAAAl5+fpDgAAAAAAAADlRZELAAAAAAAAXo8iFwAAAFAJgkJaa+ikCWoeFurprgAAUC1R5AIAAADc6OSVddtGdFHP4UM83CMAAKonJp4HAAAA3Mixsm7L8PasngsAgBvxJBcAAADgRo6VdZM3/+7prgAAUK1R5AIAAAAAAIDXo8gFAAAAVKJWHcOZfB4AADegyAUAAABUAscE9JKYfB4AADegyAUAAABUAscE9JKYfB4AADfwyiJX/fr1NXHiRCUnJysrK0tr167VgAEDSvz6Pn36aNmyZTpy5Ij++OMPJSQk6O6773ZjjwEAALwfOVj5JCUkauvqNZ7uBgAA1VZNT3egLGbNmqWOHTtqzJgx2rJli2677TZNnz5dfn5++vTTT4t87ejRozV+/Hi98847eumll3Ts2DFddNFFql27diX1HgAAwDuRgwEAgKrOvCl69+5tZmYDBw50aV+0aJHt3r3b/Pz8Cn1teHi4HT9+3B599NEK609oaKiZmYWGhnr83BAEUT1iREyUvR7/k42IifJ4XwiCIBxBDlYxwe94giAIgnBfeN1wxX79+ik9PV0zZ850aY+NjVVQUJA6depU6Gvvvfde5eTkaNKkSe7uJgAAQLVCDgYAAKo6rytyhYWFaePGjcrLy3NpX79+vXN7Ybp27aqNGzeqf//+2rRpk44fP66kpCS99NJLqlWrVrHHDgwMVGhoqEsEBweX7wMBAAB4AXIwAABQ1XndnFxNmzbV9u3b87UfOnTIub0wQUFBCgwM1Ntvv62nn35aiYmJ6tGjh8aMGaPmzZtr0KBBRR575MiRGjduXLn6DwAA4I3IwSpWq47hah4WqqSERE93BQCAasPrilySZGZl2ubn56dGjRpp4MCBmjFjhiRp2bJlql+/vh566CGNHTtW27ZtK/T1kydPzveIfnBwsObMmVPKTwAAxQsKaa0RMVFF7pOTkam46Fj+kwSgUpCDlV9ORqbz3z2HD1HMfY96sDcAAFQvXlfkSk1NLfBOYZMmTST9dTexsNc2a9ZMixYtcmlfsGCBHnroIYWHhxeZYKWkpCglJaWMPQeAknH8ByigUUO16hheotfwnyQA7kYOVjHiomPVNqKLJKlO/Xoe7g0AANWL1xW54uPjFRkZKX9/f5c5Idq1aydJSkhIKPS169evV7NmzfK116hRQ5J04sSJCu4tAJReXHSspOL/8xMU0loBjRrynyQAlYIcrGIkJSRq6+o1Jb6JAQAASs7rilyzZ8/WPffco/79++uzzz5ztg8ePFjJycn6+eefC33tF198oeuuu069e/fWp59+6my//vrrlZeXp9WrV7u17wBQEkkJiSV6MmtETBT/SQJQacjBAABAVed1Ra6FCxdq8eLFmjJliho1aqStW7cqMjJSvXv31u233+68Ezh16lQNHjxYwcHB2rVrl6Q/l7gePny4Jk+erDPOOEOJiYm65pprNGrUKE2ePNm5HwAAAFyRgwEAgKrO64pcknTzzTdr/Pjxeu6559SkSRNt2rTJZSJTSfL391fNmjWdj8FL0vHjx9WzZ0+9+OKLeuKJJ9SkSRPt2LFDY8aM0RtvvOGJjwIAAOA1yMEAAEBVVkNS4UvhoFihoaHasGGD2rZtq8REVjcDUHkcwxW3rl6jKUNHebo7AFCpvDkH4/c3AADu4efpDgAAAAAAAADlRZELAAAAAAAAXo8iFwAAAAAAALweRS4AAADAA1p1DFfzsFBPdwMAgGrDK1dXBAD8JSiktUbERBW5T05GpuKiY5WU4F2TMwNAdZSTken8d8/hQxRz36Me7A0AANUHRS4A8FKO/yQFNGqoVh3DS/Qa/iMFAJ4XFx2rthFdJEl16tfzcG8AAKg+KHIBgJeKi46VVPx/kIJCWiugUUP+IwUAVURSQqK2rl5T4hsUAACgZNxe5KpRo4bq1q2rrKwsdx8KAHxKUkJiiZ7MGhETxX+kAB9EDgYAAHxNhU88X6dOHQ0ePFifffaZkpOTlZubq/T0dGVkZGj16tV65ZVX9Le//a2iDwsAAODTyMG8E5PPAwBQcSrsSa66devqscce0wMPPKDGjRtr06ZNWrJkiQ4cOKDs7Gw1adJELVu21N13362HH35YK1as0GOPPaaVK1dWVBcAAAB8DjmYd2LyeQAAKl6FFbl+//13ZWRk6IUXXtC0adN04MCBQvft3r27hgwZoqVLl+ree+/V+++/X1HdAAAA8CnkYN6JyecBAKh4FTZc8ZlnnlFoaKgmTpxYZHIlSUuXLtU///lPhYaGauvWrRXVBQAAAJ9DDuadHJPPSwxZBACgolRYkSs2NlYnTpyQJB0+fFhhYWHFvmbHjh367rvvKqoLAAAAPocczHudOmQRAACUT4VPPC9JDRs2VEBAQIHbmjdvrgceeMAdhwUAAPBp5GDeJS461vlvhiwCAFB+FVbk6tKli2655Ra1bNlSkmRmBe7XrFkzvf766xV1WAAAAJ9GDua9GLIIAEDFqrAiV0REhKZPn64tW7bIzDR58mS98cYbuv3223XRRRc59zvrrLOUkZFRUYcFAADwaeRg3o0hiwAAVJwKK3K98MILuuiii3THHXeoRo0aMjNFRkbqo48+UkJCgo4ePapVq1bpgw8+0C+//FJRhwUAAPBp5GDe7eQhi20juvA0FwAA5VChc3L9/vvv+vTTT7VmzRoNHz5czZo103nnnaebb75ZEydOVHJysubMmaNhw4ZV5GEBAAB8GjmY90pKSNSGZcudX/M0FwAAZVfTHW/asWNH57+Tk5OdiRUAAADchxzMO8VFx6ptRBdJTEAPAEB5uKXIBQAAAKBkHBPQt+oYrqCQ1hoREyXpz/m64qJjlZSQ6OEeAgDgHShyAQAAAB7mmIA+oFFDteoY7rIt5r5HPdElAAC8ToXNyRUfH6+bbrqpxPufffbZeuuttzR69OiK6gIAAIDPIQerHuKiY7Vh2XJtXb1GW1evUdbRdEkMXwQAoDQq7Emuzz77TB999JHS0tI0bdo0LVu2TGvWrNHBgwclSXXr1lVwcLA6d+6svn376rrrrtPq1av1zjvvVFQXAAAAfA45WPWQlJDo8sTWiJiofE90AQCAolVYkev555/Xe++9pwcffFB33XWXRo8eLTOTmenYsWOqXbu2JKlGjRr64YcfNHDgQM2ePbuiDg8AAOCTyMEAAAD+VKFzcu3bt09jxozRU089pU6dOumKK65Qs2bNFBAQoIMHD2rTpk1atmyZkpOTK/KwAAAAPo0crPpiInoAAErOLRPPHz9+XD/++KN+/PFHd7w9AAAACkAOVn0wET0AAKXn1tUVP/74Y/3222/O2L9/vzsPBwAAAJGDVQdx0bGS/pp4PiiktQIaNWQiegAAiuDWIldkZKQ6duyonJwchYSEKC0tzSXh+u2335SQkODOLgAAAPgccjDvx0T0AACUnp873/yGG25QZmamNm7cqPDwcPXq1UuffPKJatasqSFDhmjJkiXuPDwAAIBPIgcDAAC+yK1Pci1YsEALFizQHXfcoS+//FKLFi3Ss88+qw8//NCdhwUAFODkyYsLw6TGQPVADgYAAHyRW5/kcvjvf/+rtm3baseOHVq9erXGjh2rgICAyjg0APi8UycvLiraRnRRz+FDPNxjABWFHKz6adUxXM3DQj3dDQAAqiS3PsnlcPbZZ6tNmzbKycnRihUr9Mwzz2jFihWKi4urjMMDgE87dfLiwjCpMVD9kINVH44bFpLUc/gQVlgEAKAAbi1y/fjjjwoJCdGhQ4eUkJCghIQEffnll3r++ee1efNmdx4aAPD/Tp28uDBMagxUH+Rg1U9cdKzaRnSRVPxNCwAAfJVbi1ydO3fW1q1btWLFCq1bt865mk9aWpo7DwsAAODTyMGqn6SERG1dvYabEQAAFMGtRa4LLrhA7du3V/v27dWlSxeNGjVKF1xwgfbu3etMtp566il3dgEAAMDnkIMBAABf5NYi19GjRzV37lzNnTvX2VavXj397W9/cyZeAAAAqFjkYNVbUEhrDZ00gdVwAQA4hVuLXLNnz9Y111yjvLw8Z1tmZqZWrlyplStXuvPQAAAAPoscrHo6ebVcx/xcTEAPAMBf/Nz55vv379d7771X4LaAgAB9+umn7jw8AACATyIHq57iomO1YdlyZR1Nl8QE9AAAnMqtRa5//vOfuuiii/T444+7tLdq1UqrVq1S/fr13Xl4AAAAn0QOVj05VstN3vy7JKlVx3A1Dwv1cK8AAKg63Frkys3N1U033aS77rpL/fv3lyT17dtXP//8s6ZPn64+ffq48/AAAAA+iRysenMMW5SknsOHeLAnAABULW6dk6tOnTo6cOCAbrzxRn3zzTfq1auX+vTpowEDBuibb75x56EBAAB8FjlY9RYXHeuck6ttRBc1DwtlAnoAAOTmItcff/yhLVu26LffflNCQoJuuukm9enTRz/99JM7DwsAAODTyMGqt6SERG1YttxZ6Oo5fAgT0AMAIDcPVwwODtaYMWOUmJioI0eOKC0tTd9//71SUlL0zTff6LXXXnPn4QEAAHwSOVj1Fxcd6/w3E9ADAPAntz7JtWvXLu3atUtz5851ttWrV09/+9vf1L59e7Vv396dhwcAAPBJ5GDVX1JCorauXqNWHcMVFNJaQydNUFx0LMMWAQA+rcKKXK+99pquuuoqderUKd+2Cy+8UHXr1lV8fLwyMzO1cuVKrVy5sqIODQAA4LPIwXyXYwL6gEYNnUMXGbYIAPBlZR6u2KJFC5evIyIi9Ouvv7q01axZUwsXLlRiYqLWrFmj7du364orrijrIQEAAHweORgc4qJjtWHZcmUdTZfEsEUAAMpU5Bo1apR++OEHl7YWLVro+++/d2kbMmSIevbsqQ0bNmjq1KkyM82fP1/nnntu2XsMAADgo8jBcLKkhETF3Peokjf/7umuAABQJZR6uOLSpUtVv359devWzaW9YcOGSk5OdmkbMGCAsrOz1b17dx06dEinnXaafv75Zz388MN66KGHytdzAAAAH0IOBgAAULRSP8nVoUMHLViwQDt27HBpT01N1dlnn+38unbt2rriiiu0dOlSHTp0SJJ0+PBhTZ48Wb169SpntwEAAHwLORgAAEDRSl3kat++vbp27apFixa5tC9fvly333678+sePXqoTp06Wrp0qct+CQkJOu+888rYXQAAAN9EDobitOoYruZhoZ7uBgAAHlPqItf27dvVvXt3zZs3z6V94sSJuuGGG/TRRx/pjjvu0Pjx42Vm+fbLy8tTXl5e+XoNAADgY8jBUBjHKouS1HP4EA/2BAAAzyrz6oqTJk1y+XrlypUaOXKkbrnlFsXGxqp9+/aaNWuWNm/e7LJfWFiY9u/fX9bDAgAA+DRyMJwqLjrW+W9WWAQA+LJSTzxflHfffVdz585VRESE0tPTNX/+/Hz7DBw4UOvWravIwwIAAPg0cjDflpSQqK2r16hVx3BPdwUAAI+q0CKXJO3du1effvppgdvOOussZWVl6auvvqrowwIAAPg0cjAAAODrKrzIVZT9+/erZ8+elXlIAAAAn0cO5jsck88nJSR6uisAAFS6Ms/JBQAAAKBqYPJ5AAAocgEAAABej8nnAQCgyAUAAAB4Pcfk89JfQxYBAPA1FLkAAACAaoAhiwAAX+eVRa769etr4sSJSk5OVlZWltauXasBAwaU+n2ef/55mZni4+Pd0EsAAIDqhRysamPIIgDA11Xq6ooVZdasWerYsaPGjBmjLVu26LbbbtP06dPl5+dX6NLZp2rfvr0eeeQR7du3z829BQAAqB7Iwao2x5DFVh3DWWURAOCTvK7I1bt3b1177bWKjIzU9OnTJUnLli1TixYtNGHCBM2YMUMnTpwo8j38/f0VGxur6OhotW/fXmeccUZldB0AAMBrkYN5h1OHLMbc96gHewMAQOXyuuGK/fr1U3p6umbOnOnSHhsbq6CgIHXq1KnY9xgzZoyaNGmiJ5980l3dBAAAqFbIwbwDQxYBAL7M657kCgsL08aNG5WXl+fSvn79euf2n376qdDXt2nTRk899ZRuvvlmZWRkuLWvAOCNgkJaa0RMVJH75GRkKi46lmEwgA8hB/MOJw9ZDAppraGTJvD7GgDgM7yuyNW0aVNt3749X/uhQ4ec2wtTo0YNxcTEaNasWVqwYEGpjx0YGKjAwECXtuDg4FK/DwBURY4hLgGNGqpVx/ASvYZhMIDvIAfzHif/Pm8b0UUSv68BAL7B64pckmRmZdr273//W61bt1afPn3KdNyRI0dq3LhxZXotAFR1jiEuxQ1vCQpprYBGDRkGA/ggcjDv4Ph93jK8Pb+vAQA+xeuKXKmpqQXeKWzSpImkv+4mnqp58+Z67rnnNGbMGOXm5qpx48aSpJo1a8rPz0+NGzdWTk6OsrOzCz325MmT881DERwcrDlz5pT14wBAlZGUkFiiO/0jYqJK/KQXgOqDHMx7OH6fO35fs9IiAMBXeF2RKz4+XpGRkfL393eZE6Jdu3aSpISEhAJf17JlS9WrV09vv/223n777XzbDx8+rDfffFMPPfRQocdOSUlRSkpKOT8BAACA9yEH8z6stAgA8DVeV+SaPXu27rnnHvXv31+fffaZs33w4MFKTk7Wzz//XODr1q1bp4iIiHztb775pho3bqwhQ4Zo9+7d7uo2AACAVyMH8z5x0bHOObkYsggA8AVeV+RauHChFi9erClTpqhRo0baunWrIiMj1bt3b91+++06ceKEJGnq1KkaPHiwgoODtWvXLh05ckTfffddvvc7fPiwatasWeA2AAAA/IkczPucvNIiAAC+wOuKXJJ08803a/z48XruuefUpEkTbdq0SQMHDtSMGTOc+/j7+6tmzZqqUaOGB3sKAABQfZCDAQCAqqyGpMKXwkGxQkNDtWHDBrVt21aJiUzmCaD6c0xkvHX1Gk0ZOsrT3QHgo8jBSobf2QAAX+Ln6Q4AAAAAAAAA5UWRCwAAAAAAAF6PIhcAAAAAAAC8nldOPA8AAACg5IJCWmtETJRyMjIVFx2rpATmMQMAVD8UuQAAAIBqKicjU5IU0KihWnUMd7bH3Peop7oEAIDbUOQCAAAAqqm46FhJUp369RQU0loBjRqqTv16Hu4VAADuwZxcAAAAQDWVlJComPse1ZSho5S8+XdPdwcAALeiyAUAAAAAAACvR5ELAAAAAAAAXo8iFwAAAAAAALweRS4AAAAAAAB4PVZXBACUSVBIa42IiSpyn5yMTMVFxyopIbGSegUAKE5QSGsNnTSB388AgGqHIhcAoFRyMjIlSQGNGqpVx/ASvSbmvkfd2SUAQAmc/Pu7bUQXSfx+BgBULxS5AAClEhcdK0mqU79ekfsFhbRWQKOGxe4HAKgcjt/fLcPb8/sZAFAtUeQCAJRKUkJiie78j4iJKvGTXgAA93P8/nb8fmbYIgCgumHieQAAAMCHnDpssefwIR7uEQAAFYMnuQAAAAAfwrBFAEB1xZNcAAAAgA9xDFtM3vy7pL9Wyx06aYKah4V6uHcAAJQdT3IBAAAAPqiw1XJZcREA4K0ocgEAAAA+6OTVclkRFwBQHTBcEQAAAPBBjmGLU4aOcg5dBADAm1HkAgAAAAAAgNejyAUAAAAAAACvx5xcAAAAACT9tdJiTkam4qJjlZSQ6OkuAQBQYhS5AAAAAB/HSosAgOqAIhcAAADg41hpEQBQHTAnFwAAAODjWGkRAFAdUOQCAAAAAACA16PIBQAAAAAAAK9HkQsAAABAPkEhrTV00gQ1Dwv1dFcAACgRilwAAAAAnE5eabFtRBf1HD7Ewz0CAKBkWF0RAOBWQSGtNSImqtDtORmZiouOVVJCYiX2CgBQGMdKiy3D27PKIgDAq1DkAgC4xclPArTqGF7s/jH3PeruLgEASsCx0uKImKgS/f4GAKCqoMgFAHALx5MART0BEBTSmqcEAAAAAFQIilwAALdwPAlQFJ4SAAAAAFBRKHIBAAAAKJRjbkXmUAQAVHUUuQAAAADkU9jcisyhCACoqihyAQAAAMjn5LkVmUMRAOAN/DzdAQAAAABVj2NuxSlDRyl58++SpFYdw9U8LNTDPQMAoGAUuQAAAAAUyTF0UZJ6Dh/iwZ4AAFA4ilwAAAAAiuQYuiiJIYsAgCqLIhcAAACAIiUlJGrr6jWS/lxtceikCQxbBABUORS5AAAAABTr5NUW20Z0YdgiAKDKYXVFAAAAAMVyDFlsGd6elRYBAFUST3IBAAAAKJZjtUXHSosMWwQAVDUUuQAAAACUGMMWAQBVFcMVAQAAAJQYwxYBAFUVT3IBAAAAKLFThy0CAFBVUOQCAAAAAACA16PIBQAAAAAAAK9HkQsAAABAmbXqGM4KiwCAKoGJ5wEAHhcU0lojYqKK3CcnI1Nx0bFKSkispF4BAIriWGVRknoOH6KY+x71YG8AAKDIBQDwoJOXoW/VMbxEr+E/UQBQNcRFx6ptRBdJYoVFAECVQJELAOAxjmXoi/vPUVBIa5apB4AqJikhUVtXr1GrjuEFPpHLE7gAgMpGkQsA4DGOZeiLMyImqsRPegEAKk9JnsjlCVwAQGWhyAUAAACgTAp7IpcncAEAnkCRCwAAAECZFPZEruMJ3FOHMTKEEQDgThS5AAAAAFSo4oYxMoQRAOAOFLkAAAAAVKiChjEyhBEA4G4UuQAAAABUqIKGMRY2hFFiGCMAoGL4eboDZVG/fn1NnDhRycnJysrK0tq1azVgwIBiX9evXz998skn+v3335WZmakdO3bo448/VqtWrSqh1wAAAN6NHAzlceoQxpOjbUQX9Rw+xMM9BAB4O698kmvWrFnq2LGjxowZoy1btui2227T9OnT5efnp08//bTQ140ePVr79u3T+PHjtX37djVv3lxPPPGE1qxZo86dOysxkTtHAAAAhSEHQ3mwEiMAoDKYN0Xv3r3NzGzgwIEu7YsWLbLdu3ebn59foa8NDAzM19asWTPLycmx9957r0z9CQ0NNTOz0NBQj58bgiCI6hojYqLs9fifbERMlMf7QhC+GuRghLvC8Tv+9fifrHkY15MgCIIoe3jdcMV+/fopPT1dM2fOdGmPjY1VUFCQOnXqVOhrU1JS8rXt3btXu3fvVvPmzSu8rwAAANUFORjcxTGMURJDFgEA5eJ1wxXDwsK0ceNG5eXlubSvX7/euf2nn34q8ftdcMEFatGihb788sti9w0MDFRgYKBLW3BwcImPBQAA4K3IweAucdGxahvRRVL+oYwAAJSG1xW5mjZtqu3bt+drP3TokHN7Sfn7++v999/XH3/8oYkTJxa7/8iRIzVu3LgSvz8AoGIVtCLXqVihC3APcjC4S1JCorauXsPKiwCAcvO6IpckmVmZtp3q/fff11VXXaX+/ftr9+7dxe4/efLkfI/oBwcHa86cOSU+JgCg9E5dkaskTl26HkD5kYPBXUrye57f6wCA4nhdkSs1NbXAO4VNmjSR9NfdxOJMnTpVgwYN0uDBg0ucIKWkpBQ4pwQAwL0KW5HrVKzQBbgPORjciZUXAQAVweuKXPHx8YqMjJS/v7/LnBDt2rWTJCUkJBT7HlOnTtWQIUM0bNgwTZs2zW19BQBUjKSExBLdwR8RE1XiJ70AlA45GNypsN/zjt/rDGMEAJSE162uOHv2bDVs2FD9+/d3aR88eLCSk5P1888/F/n69957T0OGDNHw4cP1wQcfuLGnAAAA1Qc5GDzh1GGMJ0fbiC6sxggAcOF1T3ItXLhQixcv1pQpU9SoUSNt3bpVkZGR6t27t26//XadOHFC0p93CgcPHqzg4GDt2rVLkvT222/rrrvu0vvvv6/4+HiXpa5zcnK0bt06T3wkAACAKo8cDJ7AMEYAQGmZt0X9+vXtzTfftD179lh2dratW7fOBgwY4LJPbGysmZm1aNHC2bZjxw4rzI4dO8rUl9DQUDMzCw0N9fh5IQiC8PUYERNlr8f/ZCNiojzeF4KojkEORlSV4Pc9QRAEUVDU+P9/oIxCQ0O1YcMGtW3bVomJzAcAAJ7kmLtl6+o1mjJ0lKe7A8CNyMF828lzML4ZOYx5uQAAkrxwTi4AAAAAvs0xV5ck5uUCADhR5AIAAADgVRxzdUn55+sCAPgur5t4HgAAAIBvS0pI1NbVa9SqY7iCQlprREyUc1tORqbiomMZwggAPogiFwAAAACv4xiyGNCooXN+rpPF3PdoZXcJAOBhFLkAAAAAeB3HkMWThysGhbRWQKOGDGEEAB9FkQsAAACA10lKSMz3tJZj1cWThzAyfBEAfAdFLgAAAADVQlFDGBm+CADVH0UuAAAAANXCqUMYGb4IAL6FIhcAAACAauHUIYwFDV+UGMIIANUVRS4AAAAA1RIrMAKAb6HIBQCodk69Y18Q7uIDQPXHCowA4FsocgEAqo3i7tgXhLv4AFB9lXQFxlNxIwQAvBNFLgBAtVHQHfuCcBcfAHxXaW6IcCMEALwLRS4AQLVR0B37gjju4gMAfE9xN0S4EQIA3osiFwAAAACfUdwNEW6EAID38vN0BwAAAACgqgkKaa2hkyaoeViop7sCACghilwAAAAA8P9OnrOrbUQX9Rw+xMM9AgCUFMMVAQAAAOD/Oebsahnenrm5AMDL8CQXAAAAAPw/x5xdyZt/l8SwRQDwJhS5AAAAAOAUDFsEAO/DcEUAAAAAOAXDFgHA+1DkAgAAAIBTOIYtjoiJUquO4QoKaa0RMVElfn1ORqbiomOVlJDoxl4CAE5GkQsAAAAACnHysMVWHcNL/fqY+x6t6C4BAApBkQsAAAAACuEYtlia4YpBIa0Z4ggAHkCRCwAAAAAK4Ri2WBqOIY4AgMpFkQsAAAAA3KA083gxhxcAlB9FLgAAAACoQOWZx4s5vACg7ChyAQAAAEAFKu08XszhBQAVgyIXAMBnlWQYCcNHAAClVdp5vBxzeBX1d4m/RwBQPIpcAACfU5ZhJAwfAQC4S2n+LvH3CAAKR5ELAOBzSjqMhOEjAIDKUNzfJf4eAUDJUOQCAPickg4jYQl4AEBlKO7vUkmGM0oMaQQAilwAAAAAUIWVdpg9QxoB+CqKXAAAAABQhZVkmD1DGgGAIhcAAAAAVGklGWZ/6pBGhi4C8EUUuQAAAADAyxU2pJGhiwB8CUUuAAAAAPByJw9pZOgiAF/l5+kOAAAAAADKxzGkccrQUUre/LunuwMAHkGRCwAAAACqoaCQ1ho6aYKah4V6uisAUCkocgEAAABANXLy/FxtI7qo5/AhHu4RAFQO5uQCAKAYjpWqCsMKVgCAqsQxP1fL8PbMzQXAp1DkAgCgEIWtVFUYVrACAFQFjvm5RsREqVXHcOewRW7IAKjuKHIBAFCIk1eqKgwrWAEAqqpThy1K3JABUL1R5AIAoBCOO+FFcdwlBwCgqmHYIgBfQ5ELAAAAAKqhgoYtMsckgOqMIhcAAAAAVGPMMQnAV1DkAgAAAIBqjDkmAfgKilwAAAAAUI2VZo7J4oY0lhdDIgG4E0UuAAAAAPBxpR3SWF4MiQTgDhS5AAAAAMDHlWRIY3kxJBKAu1HkAgCgApRkeAdDNAAAVVVJhjSWl2NIJAC4C0UuAADKoSzDOxiiAQDwZe6e96s8uCEFeDeKXAAAlENJh3cwRAMA4Osqe96v8uCGFOCdKHIBAFAOJR3ewRANAICvq4x5v8qDG1KA96PIBQAAAABwu8qY96s8HDekyjOckuGOgGdR5AIAAAAA+LyKHE5ZlYt5QHVGkQsAAAAA4PPKO5yS4Y6A51HkAgCgElXlFaWqA4aJAADKqrzDKStiuCPgLapqzkWRCwCASuBNK0pVBwwTAQBUNv7WwxdVtZzLK4tc9evX1wsvvKBbb71VTZo00aZNm/Tyyy9rxowZxb42MDBQr776qm644QbVq1dPv/32m5566il9++23ldBzAICvquorSlUHDBNxP3IwACgcf+vhK6p6zmXeFosWLbJDhw7ZPffcYxEREfbuu++amVlkZGSRr6tdu7atX7/edu3aZbfddptdc801Nnv2bMvNzbWuXbuWqS+hoaFmZhYaGurx80IQBEEQvhwjYqLs9fifbERMlMf7Ul2DHIwgCIIgCEfO9cKPi21ETJQNnTTBmodVmb/HHu9AqaJ3795mZjZw4ECX9kWLFtnu3bvNz8+v8AsxYoSZmXXu3NnZ5u/vbwkJCbZy5coy9YcEiyAIgiCqRlDkcm+QgxEEQRAEIcmGTppgr8f/5BJDJ03weL8kmZ+8TL9+/ZSenq6ZM2e6tMfGxiooKEidOnUq8rWbNm3SypUrnW15eXn6+OOP1alTJ51zzjlu6zcAAIA3IwcDAADSn0NzNyxbrq2r1yjraLqkqjNM1+uKXGFhYdq4caPy8vJc2tevX+/cXtRrHfsV9Nq2bdtWYE8BAACqD3IwAAAg/bUS6ZSho5S8+XdPd8eF100837RpU23fvj1f+6FDh5zbi3qtY7/Svlb6c8LUwMBAl7aLLrpIkhQcHFx0xwEAgFs1O+10Na0ToIzTTldoaKhbjrFt2zbl5OS45b2rOnIwAABwqqqWf3ldkUuSzKxM28r72pEjR2rcuHEFbpszZ06RrwUAAJWkdXs93S/SLW/dtm1bJSYmuuW9vQE5GAAAKFAVyb+8rsiVmppa4N2+Jk2aSFKBdwkr4rWSNHny5HzzUDRo0EAXXnihEhISlJubW2z/UbDg4GDNmTNHffr00bZt2zzdHZ/FdagauA5VB9eiaqhK18HTx/ckX8rBqtL3nC/gfFcuznfl45xXLs535XP3OS/Ne3pdkSs+Pl6RkZHy9/d3mROiXbt2kqSEhIQiX+vY72Qlea0kpaSkKCUlJV/7qlWrStR3FG/btm0+fYe8quA6VA1ch6qDa1E1cB08yxdzML7nKhfnu3Jxvisf57xycb4rX1U451438fzs2bPVsGFD9e/f36V98ODBSk5O1s8//1zka9u0aaPLLrvM2ebv769BgwZp5cqV2rt3r9v6DQAA4M3IwQAAQFXndU9yLVy4UIsXL9aUKVPUqFEjbd26VZGRkerdu7duv/12nThxQpI0depUDR48WMHBwdq1a5ckKSYmRqNGjdLMmTM1ZswYHThwQCNHjlRISIiuueYaT34sAACAKo0cDAAAVHVeV+SSpJtvvlnjx4/Xc889pyZNmmjTpk0aOHCgZsyY4dzH399fNWvWVI0aNZxtubm56tGjh1599VVNmjRJ9erV07p169S7d299//33nvgoAAAAXoMcDAAAVHVGEJ6OwMBAGzt2rAUGBnq8L74cXIeqEVyHqhNci6oRXAeisoPvOc53dQ7ON+e8ugfn27fPeY3//wcAAAAAAADgtbxu4nkAAAAAAADgVBS5AAAAAAAA4PUocgEAAAAAAMDrUeQCAAAAAACA16PIBY8YNmyYzEzp6en5tl1yySWKi4tTenq60tLS9MUXX+iCCy7wQC+rn27dusnMCoxOnTq57Mt1cL8rr7xS8+fP16FDh5SZmaktW7boqaeectmH6+A+sbGxhf48nPozwXVwr4svvlizZ89WcnKyMjIytHHjRj399NMKCAhw2Y/rAHeqX7++Jk6cqOTkZGVlZWnt2rUaMGCAp7vlVbp37673339fGzdu1B9//KHdu3fryy+/VHh4eL59S/PzfO+992rjxo3Kzs7W9u3b9cwzz6hmzZru/jheqaJybM550So6h+N8F84dOQLn+08NGjTQK6+8okWLFunAgQMyM40dO7bAfd1xfgMDAxUbG6uUlBRlZGRoxYoVuvrqqyvks3l8iUfCt+Kcc86xtLQ02717t6Wnp7tsCwkJsSNHjth3331nvXv3tn79+ll8fLzt3r3bzjjjDI/33dujW7duZmY2ZswY69Spk0vUr1+f61CJERkZacePH7dPPvnEbrjhBouIiLBhw4bZ008/zXWopGjZsmW+n4NOnTrZgQMHLCkpyfz8/LgOlRBt2rSxzMxMW7t2rd1yyy3WvXt3Gzt2rB07dsy+/PJL535cB8LdsWjRIjt06JDdc889FhERYe+++66ZmUVGRnq8b94Sn332mS1ZssT+9a9/WdeuXa1///62YsUKy83Nte7duzv3K83P8xNPPGF5eXk2fvx469atmz3yyCOWnZ1t0dHRHv+8VS0qKsfmnBcdFZ3Dcb4LD3fkCJzvv6JFixaWlpZmy5Ytc/7NGzt2bL793HF+a9eubevXr7ddu3bZbbfdZtdcc43Nnj3bcnNzrWvXruX9bJ4/uYRvxZw5c+yrr76y2NjYfH+AZ8yYYQcOHLCGDRs628477zzLycmxl19+2eN99/ZwFLn69+9f5H5cB/fGOeecY+np6RYVFcV1qGLRtWtXMzN77rnnuA6VFM8//7yZmbVs2dKl/Z133jEzs9NOO43rQLg9evfubWZmAwcOdGlftGiR7d6921n0JoqOwMDAfG3169e3vXv3WlxcnLOtpD/PTZo0sczMTHvnnXdc3vPxxx+3vLw8a9Omjcc/c1WKisixOedFR0XncJzvoqOicwTOd+HRtGnTQotc7ji/I0aMMDOzzp07O9v8/f0tISHBVq5cWd7P4/kTSvhO3H777XbkyBELCgrK9wfY39/fMjIybMqUKflet3DhQtu8ebPH++/tUZIiF9fB/fHMM8+Ymdl5553Hdahi8eGHH1peXp6df/75XIdKCsfPQ9OmTV3aX375ZTt+/LjVq1eP60C4Pd599107evSo+fv7u7QPHDjQzMwuv/xyj/fRm2PJkiW2adMmk0r3e/W2224zM7NOnTq57Hf22Webmdnjjz/u8c9WVaKicmzOedFR0Tkc57tk57uicgTOd+FRWJHLXed38eLFtnHjxnzvOWbMGDMzO+ecc8r8WZiTC5UmMDBQb775psaMGaPk5OR824ODg1WvXj2tX78+37b169erVatWqlOnTmV0tdqLiorSsWPHdOTIES1cuFBXXnmlcxvXwf26du2q1NRUXXTRRVq7dq2OHTum/fv3a8qUKWrYsKEkroMnNGrUSP/4xz+0ZMkS7dy5UxLXoTJ8+OGHSktL05QpU3TBBReoQYMG+vvf/67hw4crKipKmZmZXAe4XVhYmDZu3Ki8vDyXdsf3XFhYmCe6VS00atRI4eHh2rBhg6TS/V51nPf4+HiX/fbt26eUlBSuy/+ryBybc160is7hON9Fq+gcgfNdeu46v2FhYYW+pyS1bdu2zH2myIVKM3nyZG3evFlTpkwpcHvTpk0lSYcOHcq37dChQ/Lz89Ppp5/u1j5Wd0eOHNGbb76p4cOHq3v37nrggQfUvHlzLVu2TNdee60krkNlCAoKUr169TRz5kzNmDFD11xzjSZMmKB//vOf+vrrryVxHTwhMjJS9erV0/vvv+9s4zq43//+9z9dfvnlCgsL0/bt25Wenq558+bpww8/1AMPPCCJ6wD3a9q0aaHfX47tKJuoqCjVr19f48ePl1S6n+emTZsqOztbmZmZBe7LdflTRebYnPOiVXQOx/kuWkXnCJzv0nPX+XXn313fW0IAHnHzzTfrxhtv1CWXXFLsvmZWpm0o3rp167Ru3Trn18uXL9fs2bMVHx+vV199VYsXL3Zu4zq4j5+fnwICAvTss8/qlVdekSR99913ys3N1VtvvaUePXo4/zhwHSrPsGHDdPDgQc2ePTvfNq6D+7Ro0UJz587V/v371b9/f6WkpKhTp0566qmn1KBBA911113OfbkOcCe+vyrec889p0GDBunee+/VmjVrXLaV9HxzXYrmjhybc144d+RwnO/CuSNH4HyXjTvOr7uuBU9ywe3q16+vqKgoTZo0SXv27FHjxo3VuHFj1a5dW5LUuHFj1atXT6mpqZIKrto2adJEJ06c0OHDhyuz6z7hyJEjmjdvntq3b6+6detyHSqB4xwvWrTIpX3BggWSpPDwcK5DJWvXrp06duyojz/+WLm5uc52roP7vfzyy2rUqJGuu+46zZo1Sz/88INee+01Pfjggxo2bJhzaIjEdYD7pKamFvr9JRV8BxtFe+aZZ/T000/riSeeUFRUlLO9ND/PqampCggIUEBAQIH7+vp1cUeOzTkvWkXncJzvolV0jsD5Lj13nV93/t2lyAW3O+OMM3T22WfrkUce0eHDh51x2223qUGDBjp8+LCmTZumbdu2KTMzU+3atcv3Hu3atdPWrVuVk5PjgU9Q/dWoUUPSnxVzroP7FTT+XPrrOpw4cYLrUMmGDRsmSZo6dapLO9fB/S6++GIlJibme7R99erVkv6cs4HrAHeLj49XmzZt5O/v79Lu+J5LSEjwRLe81jPPPKNnn31WY8eO1UsvveSyrTQ/z455XU7d96yzzlJgYKDPXxd35Nic86JVdA7H+S5aRecInO/Sc9f5jY+PL/Q9pfL/3fX4TP5E9Y46depYt27d8sWCBQssMzPTunXrZm3btjVJNn36dNu3b581aNDA+frmzZtbdna2vfTSSx7/LNUxTjvtNEtKSrI1a9Y427gO7o2ePXsWuILLgw8+aGZmV155JdehEqN27dp28ODBQpcr5jq4N5YsWWL79++3+vXru7TfddddZmbWp08frgPh9ujVq5eZmd16660u7V9//bXt3r3b/Pz8PN5Hb4mnnnrKzMyee+65Qvcp6c/z6aefbpmZmTZ58mSX148ePTrfcvS+GO7IsTnnRUdF53Cc76KjonMEznfhUdjqiu46v//617/MzOyyyy5ztvn7+1t8fLz99NNP5f08nj+hhG/GqcsbS7KQkBA7evSoLVu2zHr16mU33XSTrV+/3nbv3m1nnHGGx/vs7TFt2jR76aWXrH///tatWze76667bOPGjZabm2s9evTgOlRifPXVV5aVlWVPPvmk9ejRw0aPHm2ZmZk2Z84crkMlx6233mpmZnfddVeB27kO7o0bb7zR8vLybMWKFXbLLbdY9+7d7fHHH7ejR49aQkKC1apVi+tAVEosWrTIUlNT7a677rKIiAiLjo42M7PbbrvN433zlvj3v/9tZmZff/21derUKV849ivNz/MTTzxheXl59sILL1jXrl3t4YcftqysLIuOjvb4562qUd4cm3NedFR0Dsf5LjzckSNwvl2jV69e1r9/f7vzzjvNzGzGjBnWv39/69+/vwUEBLjt/NauXdvi4+Ptf//7n0VGRlqPHj3siy++sNzcXOvatWt5P5fnTyzhm1HQH2BJFh4ebnFxcfbHH3/Y4cOHbdasWdayZUuP97c6xOjRo23NmjWWlpZmx44ds/3799sXX3xhHTp04DpUctStW9deeukl+9///me5ubm2c+dOGz9+vNWuXZvrUMmxaNEiS09Pd7k7dWpwHdwbERERtnDhQtuzZ49lZGTYpk2bbMKECdakSROuA1FpUb9+fXvzzTdtz549lp2dbevWrbMBAwZ4vF/eFEuXLrWinLxvaX6e77vvPtu0aZNlZ2fbzp07bezYsVazZk2Pf96qGhWRY3POCw935HCc78LDHTkC5/uv2LFjR6G/s1u0aOHW83vmmWfaBx98YAcPHrTMzExbsWKFy4MXZY0a//8PAAAAAAAAwGsx8TwAAAAAAAC8HkUuAAAAAAAAeD2KXAAAAAAAAPB6FLkAAAAAAADg9ShyAQAAAAAAwOtR5AIAAAAAAIDXo8gFAAAAAAAAr0eRCwAAAAAAAF6PIhcAAAAAAAC8HkUuAFXCfffdJzNTfHy8R/vRrVs3mZkzLr30Uo/2pyI88MADLp+padOmnu4SAACV6uS/g0VFt27dFBsbqx07dni6y6UyePBgmZlatGjh6a4UqHfv3ho7dqynu6HHH39cffv29XQ3yqwseWpV+H5u3LixS78ffvhhj/YH1RtFLgBVwtChQyVJYWFhuuyyyzzcG2nkyJHq3LmzNm7c6OmulNv06dPVuXNnTZ061dNdAQDAIzp37uwS8+fPV2ZmZr72NWvW6Pnnn1e/fv083eVSmT9/vjp37qy9e/d6uisFuv766zVu3DhPd0NPPPGEbrrpJk93o9y8LU9NT09X586dve7nCt6ppqc7AACXXnqpLr74Ys2bN0833HCDhg0bplWrVnm0T4mJifr555892oei1KxZU2amvLy8Yvfdv3+/9u/fr169elVCzwAAqHpO/ZuekpKiEydOFPi3Pj09vbK6VWEOHjyogwcPerobFaZu3brKzs72dDeqrKqep57K8bNWVZ80RPXCk1wAPG7YsGGSpDFjxujHH3/UwIEDFRAQkG+/oKAgzZw5U0ePHlVaWpo+/vhjdejQQWamwYMHu+x76aWX6quvvlJqaqqysrK0Zs0a3XLLLWXu46BBg2Rm6ty5c75tTz/9tHJzc9WsWTNnW48ePfTNN9/oyJEjysjI0PLly3X11Ve7vC44OFgxMTHasmWLMjIytHv3bs2ZM0dhYWEu+zkeTR80aJBee+017d69Wzk5OWrVqpUCAgI0YcIEbd++XVlZWUpNTdXq1as1cODAMn9WAAB8WUHDu8xMkyZN0p133qlNmzYpMzNTq1evVqdOnSRJjzzyiLZv36709HQtWbJEwcHB+d63JLlBQWrUqKEnn3zSedy0tDT99ttvuv/++537FDRccenSpYqPj1eHDh30/fffKyMjQ9u2bdPo0aNVo0YNl2M0btxYr732mrZt26bs7Gzt379f8+fPV0hIiHOfWrVq6cknn9TGjRuVnZ2tAwcOKCYmRmeccUax5/Pee+91nkdHOPrqOLfDhw9XYmKicnJynHldq1atNG3aNO3fv1/Z2dlKTEzUyJEjXd6/Tp06eu2117R27VodPnxYqampWrFihfr06eOyn5mpQYMGuvPOO519WLp0qcv56969u959910dPHhQR44c0Ycffqh69erprLPO0owZM5SWlqY9e/ZowoQJqlnT9XmRkp6fHTt2aO7cubruuuv066+/KjMzUxs3btSQIUOKPI8lMXjwYG3atMl5ru64444C93vmmWe0cuVKpaam6siRI/r111+doyocpk6dqtTU1AJz8iVLlighIcH59T/+8Q+tXLlShw8fdn6fvf/+++X+PEBZGUEQhKeibt26lpaWZj///LNJsqFDh5qZ2T//+U+X/erVq2dbtmyxgwcP2ogRI6xnz572+uuv27Zt28zMbPDgwc59IyIiLDs727777ju75ZZb7Nprr7WYmJh8+xUU3bp1MzOzbt26ubTXqlXL9uzZY//9739d2v39/W337t02Y8YMZ9vtt99ueXl5NmvWLLvpppvs73//u82ZM8eOHTtmV199tXO/q666yiZMmGA333yzXXXVVda3b1+bNWuWZWRk2IUXXpivT0lJSfbZZ5/ZDTfcYNdff72dfvrpNmXKFPvjjz/swQcftG7dutn1119vjz32mI0aNSrfZxs7dqyZmTVt2tTj150gCIIgPBmxsbGWnp5e6LYdO3a4tJmZ7dixw5YvX2433XST9e3b1zZt2mQHDx60119/3WbPnm3XX3+9RUZG2t69e23dunUury9pblBQjB492o4dO2Zjx4617t2727XXXmv333+/PfPMM859Bg8ebGZmLVq0cLYtXbrUUlJSbPPmzXbPPfdYjx497D//+Y+Zmd1xxx3O/Ro0aGDx8fGWnp5uTz31lPXs2dP69etnEydOtIiICJNkNWrUsK+//trS09Pt6aefth49etjQoUMtKSnJEhISrG7duoX2v2XLlvbZZ5+ZmVmnTp2cUbt2bee5TUpKsnXr1tnAgQMtIiLCQkNDrU2bNpaWlma//fabDRo0yK655hqbMGGCHT9+3OWzN2rUyGJiYuz222+3iIgIu/baa+3VV1+148ePu3zOTp06WUZGhs2bN8/ZhzZt2ricv23bttmECRPsmmuusUcffdSOHTtm06ZNs19++cWeeOIJ69Gjh7300ktmZvbQQw8537s052fHjh22a9cuS0hIsEGDBlnPnj1txowZZmZ21VVXFfm9UFieevJnmD17tv3973+32267zbZs2WL/+9//8n0/x8TE2JAhQ6xHjx7Wo0cPe/LJJy0jI8Oefvpp5z7t2rUzM7Nhw4a5vLZNmzZmZjZixAiTZJ07d7a8vDz75JNPrFevXhYREWGDBw+2Dz/8MF8fW7RoYWZmDz/8sMd/BxDVOjzeAYIgfDgGDRpkZmb33HOPSbL69evb0aNH7bvvvnPZb8SIEWZmdt1117m0T5kyJV/xKjEx0X799Vfz9/d32XfOnDmWnJxsNWrUKLQ/RSUPY8eOtezsbAsMDHS23XLLLS5JSUBAgB08eNC++uorl9fWqFHD1q5daytXriz02H5+flazZk3bvHmzvf766/n6tGzZsnyvWb9+vc2aNatE55oiF0EQBEH8GWUpcu3Zs8fq1avnbOvTp4+Zma1Zs8Zl3/vvv9/MzMLCwkwqX24g/Zm/nHqMU6OwIpeZWceOHV32TUhIsAULFji/fuqpp8zMrEePHoW+/4ABA8zMrF+/fi7tl156qZmZ/etf/yqyf5MmTTIzK3CbmVlaWpqddtppLu0LFiywXbt2WcOGDV3a3377bcvMzMy3vyP8/PzM39/f3nvvPfv1119dtqWnp1tsbGyh5++tt95yaZ81a5aZmT344IMu7WvWrLFffvmlTOdnx44dlpmZac2bN3e21alTxw4ePGhTpkwp8jwWlqfWqFHDdu/e7dInSXbeeedZTk5Ovu/nU1/r7+9vTz31lKWkpLhsW7p0ab7vvaioKDt8+LDVr1/fJNm///1vMzNr1KhRsT93FLmIygiGKwLwqGHDhikzM1PTp0+XJGVkZGjmzJnq2rWrWrVq5dyvW7duOnr0qBYtWuTy+k8//dTl6+DgYLVp00bTpk2TJPn7+zvj66+/1jnnnOPy6H1pTJkyRZJ09913O9vuvfderV+/Xj/88IMk6YorrlDTpk314Ycfuhzbz89PCxcuVMeOHVWvXj1n3x5//HFt2LBBOTk5ysvL07Fjx3ThhReqTZs2+Y7/xRdf5GtbtWqVevfurZdeekndunVT3bp1y/TZAABA0ZYuXarMzEzn145JvxcsWOCyn6PdMRyvNLlBQVatWqX27dsrKipK1157rRo2bFjiPu/du1erV692aVu/fr3LsMbevXtr8+bNWrJkSaHvc8MNNygtLU1z5851+Qzr1q3T3r17FRERUeI+FeTbb7/V4cOHnV/XqVNHPXr00OzZs5WZmZkvnwsICHCZQuIf//iHli9frvT0dOXl5en48eO66667CsynijJv3jyXrx3Xcv78+fnaTz6HpT0/69atU1JSkvPrnJwcbdmypcxzVoWEhCgoKEiffPKJS/uuXbu0YsWKfPt3795dcXFxOnz4sE6cOKHjx4/r+eef1xlnnKEzzzzTud9bb72lSy65RFdccYUkqWHDhrrjjjv04YcfKiMjQ5Kc31+fffaZbrnlFp1zzjll+gxARaHIBcBjgoOD1bVrV82fP181atRQ48aN1bhxY33++eeS5DI3QNOmTbV///5873Fq21lnnSVJev3113X8+HGXcBSpips7ojAHDhzQjBkzNHz4cPn5+aldu3bq2rWr/vOf/+Q7/hdffJHv+GPGjJGfn5+aNGkiSXrjjTf0/PPP68svv9SNN96oyy67TB06dNC6desKnP+goBWT7r//fr3yyiu66aabtGzZMh06dEizZ892KRACAIDyO3TokMvXubm5RbY7bjyVJjcoyEsvvaRHHnlEnTt31oIFC5SamqpvvvlGl156abF9Tk1NzdeWk5PjkmcEBgZq9+7dRb7PWWedpdNPP13Hjh3L9xmaNWtW5tzK4dQcp2nTpqpVq5buv//+fMdzFBUdx+zXr59mzpyp5ORkDRo0SJ07d1aHDh30/vvvF5hPFaU01/jkG4ulPT8luS6l0bRpU0nSvn378m07ta1jx45avHixpD9v3F5xxRXq0KGDXnjhBUly6cNXX32lHTt2aNSoUZKkO++8U/Xr11dUVJRznx9++EF9+/ZVzZo19dFHHyk5OVnx8fHMDwuPYXVFAB4zdOhQ+fn56ZZbbilwUvjBgwfrqaee0okTJ5SamqrLLrss3z5nn322y9eOlYVefPFFzZo1q8Djbt68ucx9fuutt/TPf/5Tffv2Va9evZSWluZ8auzk4997771auXJlge/hKMwNGjRIH330kZ588kmX7WeccYbL3UyHP5/yd5WZmalx48Zp3LhxOvPMM9W7d2+9/PLLmjt3bqnvXgIAgIpXmtygIHl5eZo4caImTpyoxo0b65prrtGLL76oRYsWqXnz5srKyipX/1JSUnTuuecWuY9j9cbCVmou74qUp+Y4aWlpOn78uP773/+6FFRO5lgcYNCgQdq+fbsGDBjgsr1OnTrl6lNpuPv8FMdRNDs1Ly6obeDAgTp27JhuuOEG5eTkONtvuummfK81M0VFRenFF1/Uww8/rJEjR2rJkiXasmWLy35z5szRnDlzVLt2bXXu3FmPP/64Pv30U+3cubPQ73nAXShyAfAIPz8/DR48WFu3btVdd92Vb/sNN9ygRx55RL1799b8+fP13XffacCAAerVq5cWLlzo3O/Uu0RbtmzRli1b1L59+3zFo4qwZs0a/fjjjxo9erTCwsL07rvvugxd+PHHH5WWlqbQ0NBCkzIHM3NJLiTp+uuv17nnnqutW7eWum8HDhzQhx9+qPbt2+uhhx5SQEBAuRNfAABQPqXJDYpz5MgRffHFFwoKCtJbb72l888/3zmkrqwWLFig559/Xt27d3euNniqefPmKTIyUv7+/lq1alWpj+HId+rWravs7Oxi98/KytLSpUt1ySWXaP369Tp27Fih+5qZ84krh7POOkt9+/YtsB9lfVqqKOU9P+W1efNm7dmzR5GRkXrjjTec7eedd56uuOIK7dmzx9lmZjp+/Ljy8vKcbXXr1i10JcapU6dq3LhxmjZtmi666CKNHj260H7k5ubq+++/1+HDh9WrVy9dcsklFLlQ6ShyAfCI3r17KygoSI899pi+++67fNsTEhJ07733atiwYZo/f74+/PBDPfTQQ/r444/11FNPaevWrerdu7euu+46SdKJEyecrx0+fLgWLFighQsX6oMPPlBycrKaNGmiNm3aKDw8XLfeemu5+v7WW2/ps88+04kTJzR58mSXbRkZGbrvvvv04YcfqkmTJvr888914MABBQYGqn379goMDHQufT1v3jznUuTr16/XpZdeqkcffdRljobirFy5UvPmzdP69euVlpamNm3a6I477tCKFSsocAEAUAWUJjcoyJw5c5SQkKBffvlFKSkpatGihR588EHt3LlTv//+e7n79+abb2rAgAH66quv9PLLL2vVqlUKCAhQt27dNG/ePC1btkzTp0/X7bffrq+//lpvvfWWVq1apWPHjuncc89V9+7d9dVXX+nLL78s9Bjx8fGSpNGjR2vBggXKy8srtnj1wAMPaPny5frhhx80ZcoU7dy5Uw0bNlSrVq104403qkePHpL+zKf69++vqKgoff7552revLmefvpp7d27N9/8ZfHx8YqIiNANN9ygvXv3Kj09Pd9TSWVR3vNTXmamp59+Wu+//75mz56t9957T6eddprGjRuXb7ji/Pnz9fDDD+uTTz7Ru+++q6ZNm+qRRx7Jd+PV4ciRI/roo480cuRI7dy5U3PnznXZ/uyzz+rcc8/VkiVLtHv3bp122ml64IEHlJubW2COD1QGj89+TxCE78WsWbMsOzvbzjjjjEL3+eSTTyw3N9fOPPNMk2Tnnnuuff7553b06FE7cuSIzZw503r16mVmZjfeeKPLa9u1a2fTp0+3ffv2WU5Oju3Zs8e++eYb5yqOhUVRqys6olatWpaVlWVff/11oftcddVVNnfuXDt48KDl5ORYUlKSzZ071/r37+/cp3Hjxvbee+/Zvn377I8//rDvv//errzySlu6dKktXbo0X59Ofq0jXnzxRVu1apWlpqZaVlaWbd261V5//XVr0qRJvn1ZXZEgCIIg/oyyrK44adIkl7bCVoor7O92SXKDguKhhx6y5cuX24EDByw7O9t27txp7733np133nnOfQpbXTE+Pr5En69x48Y2ceJE27lzp+Xk5Ni+ffts7ty5duGFFzr38ff3t3//+9+2du1ay8zMtKNHj1piYqJNmTLFgoODi/wMtWrVsnfffdf2799veXl5Ln0t6NyefI6nTp1qSUlJlpOTY/v377fly5fbE0884bLfY489Ztu3b7esrCzbsGGDDRs2zJn3nLzf3/72N/vhhx/sjz/+MDNz5luO83fppZe67F9Y7lTQ909Jz8+OHTts7ty5+T7rqflfQVFcnjp06FDbvHmzZWdn26ZNm+zOO+8s8HrfeeedtnHjRmfuOHr0aBsyZEi+7yFHdO3a1czMHnvssXzbrr/+eps/f74lJSVZdna27du3z+bNm2dXXnllgdeT1RWJSgiPd4AgCKLM8fjjj1teXp4FBQVVyPs5koerr77a/P39C9znhhtuMDOz3r17e/zzlzT8/f3t2WefpchFEARBEAThpVGSPNUd8dprr1lGRkaBN1FLGv7+/tayZUuKXITbg+GKALyGY2WXTZs2qVatWrr66qt1//336+OPP1ZycnKFHsuxjHaHDh3066+/SpLatGmjFi1a6PXXX9fatWvzLRleVT3wwAN68803Pd0NAAAAVICC8lR36NSpky688EKNHDlS0dHR+VaZLKnGjRsXuKgS4A419Ge1CwCqvCFDhuihhx7S+eefrzp16mjXrl365JNP9MILLxQ5p0NpNGjQQCEhIc6vExMTnXNbLV26VFdeeaXWrFmjwYMHl2uVxsoUGBio8847z/n1unXrXCYbBQAAQNVXVJ7qDmamjIwMff311xoyZIgyMjLK9D5+fn665JJLnF8nJSXpwIEDFdVNwAVFLgAAAAAAAHg9P093AAAAAAAAACgvilwAAAAAAADwehS5AAAAAAAA4PUocgEAAAAAAMDrUeQCAAAAAACA16PIBQAAAAAAAK9HkQsAAAAAAABejyIXAAAAAAAAvB5FLgAAAAAAAHg9ilwAAAAAAADwev8H2F780ZvHJLkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1440x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Event variable\n",
    "E = df['status']\n",
    "x_age, y_age = kaplan_meier_estimator(E.astype(bool), df['age'])\n",
    "x_time, y_time = kaplan_meier_estimator(E.astype(bool), df['time'])\n",
    "f, ax = plt.subplots(1, 2, dpi = 120, figsize = (12, 4))\n",
    "# By age\n",
    "ax[0].step(x_age, y_age, where=\"post\")\n",
    "ax[0].set_ylim(0, 1)\n",
    "clean_ax(ax[0])\n",
    "ax[0].set_ylabel(r'$\\hat{S}_{KM}(t)$')\n",
    "ax[0].set_xlabel('Age [years]')\n",
    "ax[0].set_title('Kaplan-Meier estimate by Age')\n",
    "# By survival time in days since the treatment\n",
    "ax[1].step(x_time, y_time, where=\"post\")\n",
    "ax[1].set_ylim(0, 1)\n",
    "clean_ax(ax[1])\n",
    "ax[1].set_ylabel(r'$\\hat{S}_{KM}(t)$')\n",
    "ax[1].set_xlabel('Time since treatmen [days]')\n",
    "ax[1].set_title('Kaplan-Meier estimate by Age')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Patients in this study were given two treatments, this information is stored in the `trt` column of the dataset. We can use this flag to investigate the impact of the two different treatments on the above Survival curves:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    69\n",
       "2    68\n",
       "Name: trt, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['trt'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "x_standard, y_standard = kaplan_meier_estimator(df.query('trt == 1')['status'].astype(bool), df.query('trt == 1')['age'])\n",
    "x_test, y_test = kaplan_meier_estimator(df.query('trt == 2')['status'].astype(bool), df.query('trt == 2')['age'])\n",
    "\n",
    "f, ax = plt.subplots(1, 2, dpi = 120, figsize = (12, 4))\n",
    "# Standard treatment by age\n",
    "ax[0].step(x_standard, y_standard, where=\"post\", label = 'standard')\n",
    "ax[0].step(x_test, y_test, where=\"post\", label = 'test')\n",
    "ax[0].set_ylim(0, 1)\n",
    "clean_ax(ax[0])\n",
    "ax[0].set_ylabel(r'$\\hat{S}_{KM}(t)$')\n",
    "ax[0].set_xlabel('Age [years]')\n",
    "ax[0].set_title('Kaplan-Meier estimate by Age and Treatment')\n",
    "ax[0].legend(loc = 'best', frameon = False)\n",
    "\n",
    "\n",
    "x_standard, y_standard = kaplan_meier_estimator(df.query('trt == 1')['status'].astype(bool), df.query('trt == 1')['time'])\n",
    "x_test, y_test = kaplan_meier_estimator(df.query('trt == 2')['status'].astype(bool), df.query('trt == 2')['time'])\n",
    "\n",
    "# Standard treatment by age\n",
    "ax[1].step(x_standard, y_standard, where=\"post\", label = 'standard')\n",
    "ax[1].step(x_test, y_test, where=\"post\", label = 'test')\n",
    "ax[1].set_ylim(0, 1)\n",
    "clean_ax(ax[1])\n",
    "ax[1].set_ylabel(r'$\\hat{S}_{KM}(t)$')\n",
    "ax[1].set_xlabel('Time since treatment [days]')\n",
    "ax[1].set_title('Kaplan-Meier estimate by time and Treatment')\n",
    "ax[1].legend(loc = 'best', frameon = False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above Kaplan-Meier estimates for the survival curves allow to study the effect of covariates on the survival function. This approach of splitting the dataset into smaller groups according to a variable is useful but not feasible when we want to consider more than a couple of variables, because subgroups will become very small very quickly. For this kind of situations we need models! BAM!\n",
    "\n",
    "## Cox Proportional Hazard Models\n",
    "\n",
    "This family of models introduced by Cox (1972) focuses on the hazard function insted of focusing on the survival function. The simplest member of this family of models is the **proportional hazards** model. For this model, the hazard at time $t$ for an individual with covariates $\\vec{x}_i$ is assumed to be:\n",
    "$$\\lambda(t| \\vec{x}_i) = \\lambda_0(t)\\exp \\left\\{ \\vec{x}_i \\cdot \\vec{\\beta}\\right\\},$$\n",
    "where $\\lambda_0 (t)$ is a baseline hazard function that describes the risk of individuals with $\\vec{x}_i= 0$, who serve as a reference. The multiplicative term $\\exp \\{\\vec{x}_i\\cdot \\vec{\\beta}\\}$ is the relative risk, a proportionate increase or reduction in risk that depends on the set of covariates $\\vec{x}_i$. Note that this increase or reduction of risk does not change with time. In fact, the **proportional hazard assumption** is satisfied by two samples $\\vec{x}_1$ and $\\vec{x}_2$ when:\n",
    "$$\\dfrac{\\lambda(t|\\vec{x}_1)}{\\lambda(t|\\vec{x}_2)} = \\dfrac{\\lambda_0(t)\\exp\\{\\vec{x}_1\\cdot \\vec{\\beta}\\}}{\\lambda_0(t)\\exp\\{\\vec{x}_2\\cdot \\vec{\\beta}\\}} = e^{(\\vec{x}_1-\\vec{x}_2)\\cdot \\vec{\\beta}},$$\n",
    "is constant with time. Taking the logarithm we obtain:\n",
    "$$\\log\\lambda_i(t| \\vec{x}_i) = \\alpha_0(t)+ \\vec{x}_i\\cdot \\vec{\\beta},$$\n",
    "which yields a linear model for the logarithm of the hazard. Returning to the expression for the hazard, we can integrate it to obtain the cumulative hazard functions:\n",
    "$$\\Lambda_i(t| \\vec{x}_i) = \\Lambda_0(t) \\exp\\{\\vec{x}_i\\cdot \\vec{\\beta}\\},$$\n",
    "exponentiating we obtain the survival functions:\n",
    "$$S_i(t| \\vec{x}_i)= S_0(t)^{\\exp \\{\\vec{x}_i \\cdot \\vec{\\beta}\\}}.$$\n",
    "\n",
    "Thus, the effect of the relative risk factor on the survival function is to raise it to a power. The likelihood of an event to be observed occuring for subket $i$ at time $t_i$ can be written as:\n",
    "$$\\mathcal{L}_i(\\beta) = \\dfrac{\\lambda(t_i| \\vec{x}_i)}{\\sum_{j:t_j \\geq t_i}\\lambda (t_i| \\vec{x}_j)},$$\n",
    "that represents the probability of subject $i$ having an event at time $t_i$ among those at risk at time $t_i$. Note that the baseline hazard gets canceled out in the above expression, so we can write for the likelihood\n",
    "$$\\mathcal{L}(\\beta) = \\prod_{i}\\dfrac{e^{\\vec{x}_i\\cdot \\vec{\\beta}}}{\\sum_{j: t_j \\geq t_i}e^{\\vec{x}_j\\cdot \\vec{\\beta}}}$$\n",
    "The above expression has to be corrected for *tied* events, this is, when the number of deaths $d_i$ at time $t_i$ is greater than 1. This yields:\n",
    "$$\\mathcal{L}(\\beta) = \\prod_{i}\\dfrac{e^{\\vec{x}_i\\cdot \\vec{\\beta}}}{\\left(\\sum_{j: t_j \\geq t_i}e^{\\vec{x}_j\\cdot \\vec{\\beta}}\\right)^{d_i}}.$$\n",
    "> **Example:** Let us go back to the lung cancer dataset and try to estimate the risk scores and survival curve. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sksurv.linear_model import CoxPHSurvivalAnalysis\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sksurv.datasets import load_veterans_lung_cancer, get_x_y\n",
    "\n",
    "# data_x, data_y = load_veterans_lung_cancer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['status_bool'] = df['status'].astype(np.bool_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CoxPHSurvivalAnalysis()"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = ['karno', 'diagtime', 'age', 'prior', 'trt', 'celltype']\n",
    "targets = ['status_bool', 'time']\n",
    "\n",
    "data_x, data_y = get_x_y(df[features+ targets], attr_labels= targets, pos_label=True)\n",
    "\n",
    "from sksurv.preprocessing import OneHotEncoder\n",
    "\n",
    "data_x_numeric = OneHotEncoder().fit_transform(data_x)\n",
    "data_x_numeric.head()\n",
    "\n",
    "\n",
    "estimator = CoxPHSurvivalAnalysis()\n",
    "estimator.fit(data_x_numeric, data_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test = data_x_numeric.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(dpi = 120)\n",
    "pred_surv = estimator.predict_survival_function(x_test)\n",
    "time_points = np.arange(1, 1000)\n",
    "for i, surv_func in enumerate(pred_surv):\n",
    "    ax.step(time_points, surv_func(time_points), where=\"post\",\n",
    "             label=\"Subject %d\" % (i + 1))\n",
    "ax.set_ylabel(\"est. probability of survival $\\hat{S}(t)$\")\n",
    "ax.set_xlabel(\"time $t$\")\n",
    "ax.legend(loc=\"best\", frameon = False)\n",
    "clean_ax(ax)\n",
    "ax.set_title('Cox Proportional Hazard model')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exponential and Weibull models\n",
    "Different kinds of proportional hazard models are obtained depending on the different choices for the baseline survival function. For example, we could (for some reason) set the baseline hazard function $\\lambda_0(t) = \\lambda_0$ to be a constant value. Then the hazard of subject $i$ at time $t$ reads:\n",
    "$$\\lambda_i(t| \\vec{x}_i) = \\lambda_0\\, e^{\\vec{x}_i\\cdot \\vec{\\beta}}.$$\n",
    "\n",
    "Another common model is obtained by assuming the death density function follows a *Weibull* distribution from which the survival function is:\n",
    "$$S(t) = e^{-(\\lambda t)^p},$$\n",
    "and hazard function\n",
    "$$\\lambda(t) = p \\lambda (\\lambda t)^{p-1},$$\n",
    "where $\\lambda >0$ and $p > 0 $ are parameters. If $p=1$, this model reduces to the exponential model and has constant risk over time. If $p>1$, then the risk increases over time. If $p<1$, then the risk decreases with time. this can be seen by taking the log of the above expression:\n",
    "$$\\log \\lambda(t) = \\log(p) + \\log(\\lambda) + (p-1)\\log(\\lambda t).$$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Time-Varying Covariates\n",
    "The nature of the model allows for considering time dependent covariates.\n",
    "Let $\\vec{x}_i(t)$ denote the value of a covariate vector for subject $i$ at time (or duration) $t$. The proportional hazard model now reads:\n",
    "$$\\lambda(t|\\vec{x}_i) = \\lambda_0(t)\\;e^{\\vec{\\beta} \\cdot \\vec{x}_i(t)}.$$\n",
    "\n",
    "### Time-dependent \"Effects\"\n",
    "The model can also be generalized so the coefficients $\\beta(t)$ are now a function of time. Example: it is possible that cetain social characteristics might have a large impact on the hazard for children shortly after birth, but may have a relatively small impact later in life. So, we would like something like:\n",
    "$$\\lambda_i(t|\\vec{x}_i) = \\lambda_0(t)\\; e^{\\vec{\\beta}(t) \\cdot \\vec{x}_i}.$$\n",
    "\n",
    "### The General Hazard Rate Model\n",
    "Naturally, we can let everything depend on time to obtain the most general version of a hazard rate model:\n",
    "$$\\lambda_i(t|\\vec{x}_i) = \\lambda_0(t)\\;e^{\\vec{\\beta} (t) \\cdot \\vec{x}_i(t)}.$$\n",
    "Or... more generally:\n",
    "$$\\lambda_i(t|\\vec{x}_i) = \\lambda_0(t)\\; f_{ML}(\\vec{x}_i,t).$$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Discrete Time Models\n",
    "There are cases where the time variable is actually a \"discrete\" value, which basically means that is not continuous. One example is using the age as a predictor for death risk. Having a discrete description is useful. In practice, it is the discrete description that is used to approximate the functions using our computers. \n",
    "\n",
    "Let the survival time $T$ be a discrete random variable that takes the values $t_1 < t_2 < \\dots < t_n$ with density function:\n",
    "$f(t_j) = f_j = \\mathbb{P}(T = t_i).$\n",
    "The survivor function at time $t_j$ is then:\n",
    "$$S(t_j) = S_j = \\mathbb{P}(T \\geq t_j) = \\sum_{k = j}^{\\infty}f_k.$$\n",
    "Next, we need to define the hazard at time $t_j$ as the conditional probability of dying at that time given that one has survived to that point, so that:\n",
    "$$\\lambda(t_j) = \\lambda_j = \\mathbb{P}(T = t_j | T \\geq t_j)=  \\dfrac{f_j}{S_j}.$$\n",
    "Another result of interest in discrete time is that the survival function at time $t_j$ can be written in terms of the hazard at all prior times $t_1, \\dots, t_{j-1}$ as:\n",
    "$$S_j = (1-\\lambda_1)(1- \\lambda_2) \\dots (1-\\lambda_{j-1}),$$\n",
    "which makes sense, since one needs to survive $t_1$, then one must survive $t_2$ given that has survived $t_1$, and so on, finally surviving $t_{j-1}$ up to that point."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Measuring the Performance of Survival Models\n",
    "Usual metrics such as RMSE or correlation might not be suitable for censored variables. The most frequently used evalution metric of survival models is the concordance index (c-index, c-statistic). It is a rank correlation between predicted risk scores $\\hat{f}$ and observed time points $t$ that is closely related to Kendall's $\\tau$. It is defined as the ratio of correctly ordered (concordant) pairs to comparable pairs. This is, two samples $i$ and $j$ are **comparable** if the sample with lower survival time $t$ experienced an event, i.e., if $t_j > t_i$ and $d_i=1$. A comparable pair is **concordant** if the estimated risk $\\hat{f}$ by a survival model is higher for subjects with lower survival time, i.e., \n",
    "$$\\hat{f}_i > \\hat{f}_j \\; \\land t_j > t_i, $$\n",
    "otherwise the pair is said to be **discordant**. Essentially, we are talking about the following quantity:\n",
    "$$\\boxed{\\mathbb{P}(\\hat{f}_i > \\hat{f}_j| t_j > t_i) .}$$\n",
    "[Harrell](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3079915/) proposed an estimator of this quantity and that is implemented in `scikit-survival`. Known as Harrell's concordance index, c-index or c-statistic. The interpretation is identicall to our usual ROC curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sksurv.metrics import concordance_index_censored, concordance_index_ipcw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7051340299863699"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction = estimator.predict(data_x_numeric)\n",
    "result = concordance_index_censored(data_y[\"status_bool\"], data_y[\"time\"], prediction)\n",
    "result[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While Harrell’s concordance index is easy to interpret and compute, it has some shortcomings: \n",
    "1. it has been shown that it is too optimistic with increasing amount of censoring \n",
    "2. it is not a useful measure of performance if a specific time range is of primary interest (e.g. predicting death within 2 years).\n",
    "\n",
    "The first point is addressed by a weighing procedure known as IPCW (Inverse Probability of Censoring Weights). \n",
    "\n",
    "The second issue is adressed by extending the well known ROC curve to possibly censored survival times. Given a time point $t$, we can estimate how well a predictive model can distinguishing subjects who will experience an event by time $t$ (sensitivity) from those who will not (specificity). The resulting AUC for this time-dependent cumulative/dynamic ROC curve is:\n",
    " $$\\text{AUC}^{C,D}(t) = \\mathbb{P}(\\hat{f}_i > \\hat{f}_j | T_i \\leq t, T_j > t).$$\n",
    " An estimator for this quantity is implemented in `scikit-survival`, defined as:\n",
    " $$\\widehat{AUC}(t) = \\dfrac{\\sum_{i=1}^{N}\\sum_{j=1}^{N}\\mathbb{I}_{t_j>t}\\,\\mathbb{I}_{t_i\\leq t}\\,\\omega_i\\, \\mathbb{I}_{\\hat{f}_i > \\hat{f}_j}}{\\left(\\sum_{i = 1}^{N}\\mathbb{I}_{t_i> t}\\right)\\left(\\sum_{i = 1}^{N}\\mathbb{I}_{t_j\\leq t}\\omega_i\\right)},$$\n",
    "where $\\omega_i$ are inverse probability of censoring weights (IPCW).\n",
    "To estimate IPCW, access to survival times from the training data is required to estimate the censoring distribution. Note that this requires that survival times survival_test lie within the range of survival times survival_train. This can be achieved by specifying times accordingly, e.g. by setting times[-1] slightly below the maximum expected follow-up time. IPCW are computed using the Kaplan-Meier estimator, which is restricted to situations where the random censoring assumption holds and censoring is independent of the features.\n",
    "Finally, the function also provides a single summary measure that refers to the mean of the $\\text{AUC}(t)$ over the time range $(\\tau_1,\\tau_2)$:\n",
    "$$\\widehat{\\text{AUC}}(\\tau_1, \\tau_2) = \\dfrac{1}{\\hat{S}(\\tau_1)- \\hat{S}(\\tau_2)}\\int_{\\tau_1}^{\\tau_2}\\widehat{\\text{AUC}}(t)d\\hat{S}(t),$$\n",
    "where $\\hat{S}(t)$ is the Kaplan-Meier estimator of the survival function.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sksurv.metrics import (\n",
    "    concordance_index_censored,\n",
    "    concordance_index_ipcw,\n",
    "    cumulative_dynamic_auc,\n",
    "    integrated_brier_score,\n",
    ")\n",
    "from sksurv.datasets import load_flchain\n",
    "\n",
    "from sklearn.impute import SimpleImputer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us load another built-in dataset from `scikit-survival`. The dataset has 7874 samples and 9 features:\n",
    "\n",
    ">* age: age in years\n",
    ">* sex: F=female, M=male\n",
    ">* sample.yr: the calendar year in which a blood sample was obtained\n",
    ">* kappa: serum free light chain, kappa portion\n",
    ">* lambda: serum free light chain, lambda portion\n",
    ">* flc.grp: the serum free light chain group for the subject, as used in the original analysis\n",
    ">* creatinine: serum creatinine\n",
    ">* mgus: whether the subject had been diagnosed with monoclonal gammapothy (MGUS)\n",
    ">* chapter: for those who died, a grouping of their primary cause of death by chapter headings of the International Code of Diseases ICD-9\n",
    ">* The endpoint is death, which occurred for 2169 subjects (27.5%)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "x, y = load_flchain()\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us treat some missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_columns = ['age', 'creatinine', 'kappa', 'lambda']\n",
    "\n",
    "imputer = SimpleImputer().fit(x_train.loc[:, num_columns])\n",
    "x_test_imputed = imputer.transform(x_test.loc[:, num_columns])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to be a little bit careful when selecting the test data and time points we want to evaluate the ROC at, due to the estimator’s dependence on inverse probability of censoring weighting. First, we are going to check whether the observed time of the test data lies within the observed time range of the training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_events = y_train[y_train['death']]\n",
    "train_min, train_max = y_events[\"futime\"].min(), y_events[\"futime\"].max()\n",
    "\n",
    "y_events = y_test[y_test['death']]\n",
    "test_min, test_max = y_events[\"futime\"].min(), y_events[\"futime\"].max()\n",
    "\n",
    "assert train_min <= test_min < test_max < train_max, \\\n",
    "    \"time range or test data is not within time range of training data.\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When choosing the time points to evaluate the ROC at, it is important to remember to choose the last time point such that the probability of being censored after the last time point is non-zero. Here we use a more conservative approach by setting the upper bound to the 80% percentile of observed time points, because the censoring rate is quite large at 72.5%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 470.3        1259.         1998.         2464.82428571 2979.\n",
      " 3401.         3787.99857143 4051.         4249.         4410.17285714\n",
      " 4543.         4631.         4695.         4781.         4844.        ]\n"
     ]
    }
   ],
   "source": [
    "times = np.percentile(y[\"futime\"], np.linspace(5, 81, 15))\n",
    "print(times)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Time dependent AUC')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_cumulative_dynamic_auc(risk_score, label, color=None, ax = None):\n",
    "    auc, mean_auc = cumulative_dynamic_auc(y_train, y_test, risk_score, times)\n",
    "    if ax == None:\n",
    "        plt.plot(times, auc, marker=\"o\", color=color, label=label, alpha = 0.7)\n",
    "        plt.xlabel(\"days from enrollment\")\n",
    "        plt.ylabel(\"time-dependent AUC\")\n",
    "        plt.axhline(mean_auc, color=color, linestyle=\"--\", alpha = 0.7)\n",
    "        plt.legend(loc = 'best', frameon = False)\n",
    "    else:\n",
    "        ax.plot(times, auc, marker=\"o\", color=color, label=label, alpha = 0.7)\n",
    "        ax.set_xlabel(\"days from enrollment\")\n",
    "        ax.set_ylabel(\"time-dependent AUC\")\n",
    "        ax.axhline(mean_auc, color=color, linestyle=\"--\", alpha = 0.7)\n",
    "        ax.legend(loc = 'best', frameon = False)\n",
    "    \n",
    "\n",
    "\n",
    "f, ax = plt.subplots(dpi = 120)\n",
    "\n",
    "for i, col in enumerate(num_columns):\n",
    "    plot_cumulative_dynamic_auc(x_test_imputed[:, i], col, color=\"C{}\".format(i), ax = ax)\n",
    "    ret = concordance_index_ipcw(y_train, y_test, x_test_imputed[:, i], tau=times[-1])\n",
    "    clean_ax(ax)\n",
    "ax.set_title('Time dependent AUC')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot shows the estimated area under the time-dependent ROC at each time point and the average across all time points as dashed line.\n",
    "\n",
    "We can see that age is overall the most discriminative feature, followed by $\\kappa$ and $\\lambda$ FLC. That fact that age is the strongest predictor of overall survival in the general population is hardly surprising (we have to die at some point after all). More differences become evident when considering time: the discriminative power of FLC decreases at later time points, while that of age increases. The observation for age again follows common sense. In contrast, FLC seems to be a good predictor of death in the near future, but not so much if it occurs decades later."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluating model's predictions\n",
    "### Cox Proportional Hazards model\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import make_pipeline\n",
    "\n",
    "va_x, va_y = load_veterans_lung_cancer()\n",
    "\n",
    "va_x_train, va_x_test, va_y_train, va_y_test = train_test_split(\n",
    "                                                    va_x, va_y, test_size=0.2, stratify=va_y[\"Status\"], random_state=0\n",
    "                                                )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fit the Cox prorportional hazard model to the train data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('onehotencoder', OneHotEncoder()),\n",
       "                ('coxphsurvivalanalysis', CoxPHSurvivalAnalysis())])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cph = make_pipeline(OneHotEncoder(), CoxPHSurvivalAnalysis())\n",
    "cph.fit(va_x_train, va_y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the test data, we want to assess how well the model can distinguish survivors from deceased in weekly intervals, up to 6 months after enrollment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "va_times = np.arange(8, 184, 7)\n",
    "cph_risk_scores = cph.predict(va_x_test)\n",
    "cph_auc, cph_mean_auc = cumulative_dynamic_auc(va_y_train, va_y_test, cph_risk_scores, va_times)\n",
    "\n",
    "f, ax = plt.subplots(dpi = 120)\n",
    "ax.plot(va_times, cph_auc, marker=\"o\")\n",
    "ax.axhline(cph_mean_auc, linestyle=\"--\")\n",
    "ax.set_xlabel(\"days from enrollment\")\n",
    "ax.set_ylabel(\"time-dependent AUC\")\n",
    "ax.grid(False)\n",
    "ax.set_title('Cox Model: Time dependence of performance')\n",
    "clean_ax(ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot shows that the model is doing moderately well on average with an AUC of $\\approx 0.72$ (dashed line). However, there is a clear difference in performance between the first and second half of the time range. The performance on the test data increases up to 56 days from enrollment, remains high until 98 days and quickly drops thereafter. Thus, we can conclude that the model is most effective in predicting death in the **medium-term**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Survival Forests\n",
    "The downside of Cox proportional hazards model is that it can only predict a risk score that is independent of time (due to the built-in proportional hazards assumption). Therefore, a single predicted risk score needs to work well for every time point. In contrast, a Random Survival Forest does not have this restriction. So let’s fit such a model to the training data.  Details of the inner workings of Random Survival Forests can be found [here](https://arxiv.org/pdf/0811.1645.pdf)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('onehotencoder', OneHotEncoder()),\n",
       "                ('randomsurvivalforest',\n",
       "                 RandomSurvivalForest(min_samples_leaf=7, random_state=0))])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sksurv.ensemble import RandomSurvivalForest\n",
    "\n",
    "rsf = make_pipeline(OneHotEncoder(),\n",
    "                    RandomSurvivalForest(n_estimators=100, min_samples_leaf=7, random_state=0)\n",
    "                    )\n",
    "rsf.fit(va_x_train, va_y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For prediction, we do not call predict, which returns a time-independent risk score, but call predict_cumulative_hazard_function, which returns a risk function over time for each patient. We obtain the time-dependent risk scores by evaluating each cumulative hazard function at the time points we are interested in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict\n",
    "rsf_chf_funcs = rsf.predict_cumulative_hazard_function(va_x_test, return_array=False)\n",
    "rsf_risk_scores = np.row_stack([chf(va_times) for chf in rsf_chf_funcs])\n",
    "\n",
    "# Dynamic AUC\n",
    "rsf_auc, rsf_mean_auc = cumulative_dynamic_auc(va_y_train, va_y_test, rsf_risk_scores, va_times)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(dpi = 120)\n",
    "\n",
    "ax.plot(va_times, cph_auc, \"o-\", label=\"CoxPH (mean AUC = {:.3f})\".format(cph_mean_auc))\n",
    "ax.plot(va_times, rsf_auc, \"o-\", label=\"RSF (mean AUC = {:.3f})\".format(rsf_mean_auc))\n",
    "ax.set_xlabel(\"days from enrollment\")\n",
    "ax.set_ylabel(\"time-dependent AUC\")\n",
    "ax.legend(loc=\"lower center\", frameon = False)\n",
    "clean_ax(ax)\n",
    "ax.set_title('Cox model vs Random Survival Forests')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Indeed, the Random Survival Forest performs slightly better on average, mostly due to the better performance in the intervals 25–50 days, and 112–147 days. Above 147 days, it actually is doing worse. This shows that the mean AUC is convenient to assess overall performance, but it can hide interesting characteristics that only become visible when looking at the AUC at individual time points."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions and further references\n",
    "\n",
    "IMHO Survival Analysis is about asking the right questions: \n",
    "* Does it make sense to tackle this or that problem as a usual classification problem?\n",
    "* What are we trying to model?\n",
    "* Is time an important variable for prediction?\n",
    "\n",
    "There is a considerable amount of (open source) resources out there that are written in python such as [pySurvival](https://square.github.io/pysurvival/index.html) and [scikit-survival](https://scikit-survival.readthedocs.io/en/stable/index.html). The people at Loft made a clever use of XGBoost and some other techniques to tackle the survival analysis problem know as [XGBoost-survival-embeddings](https://github.com/loft-br/xgboost-survival-embeddings).\n"
   ]
  }
 ],
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