{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>week</th>\n",
       "      <th>indicator</th>\n",
       "      <th>inc</th>\n",
       "      <th>inc_low</th>\n",
       "      <th>inc_up</th>\n",
       "      <th>inc100</th>\n",
       "      <th>inc100_low</th>\n",
       "      <th>inc100_up</th>\n",
       "      <th>geo_insee</th>\n",
       "      <th>geo_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>202019</td>\n",
       "      <td>7</td>\n",
       "      <td>312</td>\n",
       "      <td>0</td>\n",
       "      <td>757</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>202018</td>\n",
       "      <td>7</td>\n",
       "      <td>849</td>\n",
       "      <td>98</td>\n",
       "      <td>1600</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>202017</td>\n",
       "      <td>7</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>658</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>202016</td>\n",
       "      <td>7</td>\n",
       "      <td>758</td>\n",
       "      <td>78</td>\n",
       "      <td>1438</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>202015</td>\n",
       "      <td>7</td>\n",
       "      <td>1918</td>\n",
       "      <td>675</td>\n",
       "      <td>3161</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1531</th>\n",
       "      <td>199101</td>\n",
       "      <td>7</td>\n",
       "      <td>15565</td>\n",
       "      <td>10271</td>\n",
       "      <td>20859</td>\n",
       "      <td>27</td>\n",
       "      <td>18</td>\n",
       "      <td>36</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1532</th>\n",
       "      <td>199052</td>\n",
       "      <td>7</td>\n",
       "      <td>19375</td>\n",
       "      <td>13295</td>\n",
       "      <td>25455</td>\n",
       "      <td>34</td>\n",
       "      <td>23</td>\n",
       "      <td>45</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1533</th>\n",
       "      <td>199051</td>\n",
       "      <td>7</td>\n",
       "      <td>19080</td>\n",
       "      <td>13807</td>\n",
       "      <td>24353</td>\n",
       "      <td>34</td>\n",
       "      <td>25</td>\n",
       "      <td>43</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1534</th>\n",
       "      <td>199050</td>\n",
       "      <td>7</td>\n",
       "      <td>11079</td>\n",
       "      <td>6660</td>\n",
       "      <td>15498</td>\n",
       "      <td>20</td>\n",
       "      <td>12</td>\n",
       "      <td>28</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1535</th>\n",
       "      <td>199049</td>\n",
       "      <td>7</td>\n",
       "      <td>1143</td>\n",
       "      <td>0</td>\n",
       "      <td>2610</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1536 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        week  indicator    inc  inc_low  inc_up  inc100  inc100_low  \\\n",
       "0     202019          7    312        0     757       0           0   \n",
       "1     202018          7    849       98    1600       1           0   \n",
       "2     202017          7    272        0     658       0           0   \n",
       "3     202016          7    758       78    1438       1           0   \n",
       "4     202015          7   1918      675    3161       3           1   \n",
       "...      ...        ...    ...      ...     ...     ...         ...   \n",
       "1531  199101          7  15565    10271   20859      27          18   \n",
       "1532  199052          7  19375    13295   25455      34          23   \n",
       "1533  199051          7  19080    13807   24353      34          25   \n",
       "1534  199050          7  11079     6660   15498      20          12   \n",
       "1535  199049          7   1143        0    2610       2           0   \n",
       "\n",
       "      inc100_up geo_insee geo_name  \n",
       "0             1        FR   France  \n",
       "1             2        FR   France  \n",
       "2             1        FR   France  \n",
       "3             2        FR   France  \n",
       "4             5        FR   France  \n",
       "...         ...       ...      ...  \n",
       "1531         36        FR   France  \n",
       "1532         45        FR   France  \n",
       "1533         43        FR   France  \n",
       "1534         28        FR   France  \n",
       "1535          5        FR   France  \n",
       "\n",
       "[1536 rows x 10 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import isoweek\n",
    "\n",
    "data_url = \"https://www.sentiweb.fr/datasets/incidence-PAY-7.csv\"\n",
    "raw_data = pd.read_csv(data_url, skiprows=1)\n",
    "raw_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "missing cells?"
   ]
  },
  {
   "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>week</th>\n",
       "      <th>indicator</th>\n",
       "      <th>inc</th>\n",
       "      <th>inc_low</th>\n",
       "      <th>inc_up</th>\n",
       "      <th>inc100</th>\n",
       "      <th>inc100_low</th>\n",
       "      <th>inc100_up</th>\n",
       "      <th>geo_insee</th>\n",
       "      <th>geo_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [week, indicator, inc, inc_low, inc_up, inc100, inc100_low, inc100_up, geo_insee, geo_name]\n",
       "Index: []"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data[raw_data.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "looks good! - convert it now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = raw_data\n",
    "\n",
    "def convert_week(year_and_week_int):\n",
    "    year_and_week_str = str(year_and_week_int)\n",
    "    year = int(year_and_week_str[:4])\n",
    "    week = int(year_and_week_str[4:])\n",
    "    w = isoweek.Week(year, week)\n",
    "    return pd.Period(w.day(0), 'W')\n",
    "\n",
    "data['period'] = [convert_week(yw) for yw in data['week']]\n",
    "\n",
    "sorted_data = data.set_index('period').sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "some validation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "periods = sorted_data.index\n",
    "for p1, p2 in zip(periods[:-1], periods[1:]):\n",
    "    delta = p2.to_timestamp() - p1.end_time\n",
    "    if delta > pd.Timedelta('1s'):\n",
    "        print(p1, p2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbf9c135fd0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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eX7ZpN07+xQvYYqm3r8CHIeUwBDDG1jHGZvt/7wKwCMAIwy3nAXiIMdbCGFsBYCmASf7PUsbYcsZYK4CHAJxH3tHnFAB/8e+/F8D5pb5QUlT7W1f7+e0Ns0iK12knT29HkVRzWwF/fG1lkCOiubVQVWWw6smue7ROhxGzkvJXvuo9qyGScsFvXliGFZv3YPrCjuJQ2b456TsiEukwiGg0gKMAcKPwrxLRPCK6h4j6+WUjAKwWblvjl+nKBwDYzhjLS+Wq519JRLOIaNamTZtUVRKj2jqMji6SaisUcfvMpYGMvJJor5Mqh+uef+uMJfje4wuwwVcwy0rvfQ0X72sdtDqMWLlX8Lt/rsBbK7dany8iW6UwKNzqrb7KAUM5+EGnK1maOY88EfUE8CiAbzDGdgK4A8BBACYAWAfgxnbpoQDG2F2MsYmMsYmDBg2qUKOVaaaTPt6KP72+Cj+fthi/+2fpsZh0EBP6tAdcDwPbmqJWLve9tiq6CSToXyW4JVW/XffojIZiyPfzamu378VFv3nN+vxS+lJpcKu3tdv24sl55YeOKRdcFJWKpCQQUR08YnE/Y+wxAGCMbWCMFRhjRQC/hSdyAoC1AEYJt4/0y3TlWwD0JaKcVL5PUO1PXW3HQY4Vm/fgM3e+FgtBsd3fTFtLNFF1QXtxea5DK2+AL76/CTsSmkpWMiudmsMoDzqz2lJQLZEUj5d14/T38dUHyg8dkxTPLdyAU298IfifHypSpbcAX8dwN4BFjLGbhPJhQrVPAeC5Rp8AcDERNRDRAQDGAHgTwFsAxvgWUfXwFONPMG/HnAngQv/+ywA8XsrL5AtFPPPuukSbcLU37I4y1Z5buAFvrtiKv82J0mq+SBtKFAOYiEG54c3bc+yS7gHtrvR2fICrp7dx07eJpKpEMEp1xKwUpj72LpZt2hP8X0w5DCVOAPB5AKdIJrQ/I6J3iWgegJMBfBMAGGMLADwCYCGAfwC42udE8gC+CmAaPMX5I35dAPg2gP8ioqXwdBp3l/Iy9762Cl+5fzYen+vOrlb9cFDt5/vg+4ns7NZaJsEI249vMqX6YbhuV+UcBgqdVJOpoytJ9njbqLW37kkHVbQAG7btaTXHMUsA+bU5/epKOoycrQJj7GWo1+jThnuuB3C9ovxp1X2MseUIRVolg3tcLt+8x1JTeHa5Dy0THU3pLXMEPESGTDDeXbMDuSzhsGG9S35WqToM1+rljGypXuKVQDlTwtVx7w+vriz5+VUjGCVwGB/76fPY01rAyhvOKfv58tgWu6BIykowOhP6dq8DgEQeo+0pkmKMWUUJHWWqBad9qUOhSCobKf/kbS8DgHUhGoc3CD7YPqNQDjEu5TQLVEaBrxoPOZ+FDq5mteWgWiKpUk7ye1orZ90nj23e70++CxGMjmGfViHwTU2WdR78nafxmTtfU93Srhu2y0QaV8YJvZLQbQF84yzXlFHVfqnBB91FUsnaFSHOIReCVskttByzWh2SBFC0va8r8ao0qpzUMMZhcN1F1yEXNUYw+IlSXhz5IsObKzxb89hibMev3eJwSu3fw4uCcsSIjkE4ZPDhKWWxLvhwB6YtWK+9XvYGYPl2rp9W1Y2O5rjnCh1XlWSsO6ofRrUR21f8Q0W1DWf2JWpKJMU/nGk6y6enSopD3l61FYN7NQb/t7QV0LPBPMR8X6rmnPvbnLW47smFfj8q15Fzbn3ZeD3UYVTXrFYFkTvc199GNR6ue7SOzlVD6c0Yw6Oz1+LsI4eie33ltxoXkW8lEVd6+xxG16EXtUUw+GLRTejNu1uwpyUq06zkx77gjqjYy0VJFyjOqjjpbp2xJPjb1o0PtjRhVP9uFXluuWa1dilN6YPa0RSZrtuijsNIosOwBh90pD5vrtiK//nzO3hrxVb89MKPOD/fFYztWzGV/CxuGNHRDFfaEzVFMGxJZCYqMoa157duaXMgGFwOWs1QFOLfhm7MW7Md5972Cn5w7uGJn6Fa2IEOI3FrPmwiqTKG9DHBHyVJO5XgWFUtuJ6kh/RuVJZXcmN1FUntafWi/Wzc1T4Z6YqMIWMgpR/6KXddMPXReRjVvzuuPvlgbR2Z6HLnzq5DLmqMYARJZCqo4HPB1j2t+Kwi57KLDqMjHGRVGd5U11Zu8ezZ5dhDLljw4c5YWdkchgErNu/BrFXbKtKWywmykhtyKeNRlyW0FZjh4JGEwzBfd+UwKoEde9uQzZBStGtbO8ff8Lzzcx56ywtzZyIY8lv/5kUvpHwXYjBqVentfk8lPvZT8z6MhdQAEEnzqUPIYZTfj1LhwmEQUcl+ExyyA1XQXoXPaIwxnPyLF7B0426n+uJ8ufjYUbHr+/zTqKykDHN69damQDyyL/qaTbhrlNOn8T94FhN+8CyA+IYtE/K7X16B0VOfUgbKrIQRQ7X8TzoSaopg8PmTSF5bgefqbL3dOIzOIwfl41rqBh/LgKfx/bDB9nlXJHDclKE6Pe9zpbcq+KCh/s3T3xdvLv/5ljbqDRSjUGQVF0HpzNPlft71knfi394UjwPmGlZk7Peewfodmv6n9KK2CAbfdJM4FlVCd9DUkleWuyhOAyupsntROsQhMPrZVViEFHIYlUU5jmVq+bx7DyviuFdGGzpiXsnDcc5AMH427T1Mun5GJBmS7dGuofNlUXOSQ5arc11zWxEzF29UP9/5abWLGiMY3u8kH7YSC1wXydVljhYD07xqKr2TmZBWKlgglSvj0qAsglEih9He5p3G9oVL7eVpLsLEYTznJzfatsc92sIvpi1265f0cokIRoKwIlqOxrmF2kVNEQw+0ZMs3hnvqU8TSZDTbFAuRCDQYZTdi0pB3xNTHuhSYLOSKpWI5spwRVYSjJJbKw1KKynHG/JFlig0jrI5ywvzzISVwoc73K2ZRMjdNPU7yVQqVDAq7pwPtuGBNz6oWHvVRm0RjECHsW+fqzvRunAYHcH5JyKSMvajsgNrE3HZxkR3Ei7n+6u+5b7WLyV13BM33O/+bT4mXDc9JrNPxHVbrpsIMj+s3TJjSRDN1daea2Bg+SDINPepxirJN9TGnSxhGnzq16/iO399N/mNDmjJF/DBlspE4nVFTRCMx+euxcQfPRcskqTSgXLFQVoOw2GGhZ7eHYPHcMrBXXLb0TttVlKlGoiK97k4GYpxmkoVSameXSqStDF39Xa8vjxu5ixbBdnWRMS02vLCLuvryXnr8KwiLMze1gL+Nmdt5BmlEmTTffI7JHmCTvfYMVZoiG/9eR4+/vOZ2FvBAIs21ATB+O5f52Pz7hbsbvaUz0nN38q1uNPZpbu0yzqASCqq9Nb3RBXKoxyPaBuHodsQbE8Ub0s6F1RKb5c3rKQ6RtWG7j1WbSndIkxEErNT46FC+FtlJfjDpxbiGw/PjRC5UqeQiWDIbSbjMHRWWaV/3Iff+gAvvr+p5PtVeMFXzruY71cKNUEw+CzlHzqpRKJckYNeJGVvN4h4WSWK8fKSzVjr4BHLGAvNaoW+/lIIK2JtQ/rflkCpEmOSdC6oOQwXrXfCBxnwlGO+6kKR4esPzVVeSzqnS0xhbmkzvh65yeoewbKw1I3YRGjk90+kw2gHDuPbj76Ly+55s4wW4tClJGhP1ATB4BOyVOecsgmG5vTnpvR270NrvohbZyzB6KlP4Vt/fsdaf+32vdbc1J+7O+qh/uqyLdq6/DX/uXRzUPa6ob4NtvHRRl61tSss7aTWS+WKpCqB7/99YaxM9R6PzFqtbSPpUhDH2v6+Bi5U6OYz89dra4v1xBN9kjwkJrFTOQSjo8UR60ioDYLhz74wGFiy+8vNxqkT47i0yxJwGA+8sQo3+U5af357jbX+CTc8jym/fMnesIA126LcxuL1u4L+8UVe6eRCld6QxfZcyIW4eamtpPbNBrK9qTVR2JVrHtMrU+UN05ZPI1rd/L6u38v1ACe2979/0R+E4p7ebm16dQ3iK6mhVVvViuQOomYMUA3HcyvBIKJRRDSTiBYS0QIi+rpf3p+IphPREv93P7+ciOhWIlpKRPOI6Gihrcv8+kuI6DKh/Bg/P/hS/95EQ8Fr8zzMSRd4uRyGbuK6tJqEKyolReU6ndeqARffFUbdXeKH19COaaIvpT716TiNUr9L5K6Ei0plwGC11mIMf5291n926XPphmfew0W/USf6Sro36CyItPWTOCcm7EvkXsVgit95uu/H0ZovWs2DTRxoEoIh6yz+8vYaNLXGnXH31cEhKfalFZ8Lh5EH8N+MsXEAJgO4mojGAZgKYAZjbAyAGf7/AHAWgDH+z5UA7gA8AgPgWgDHwcvffS0nMn6dLwn3TUnyEnwx5Us0US13wJMqbVV1XMRX+yqWjcrqBlC/ZzlOkkz67fI8t+cIIqmE9zbWZWNltn7M/mA7Nu5qMVdywBJD7Kukn74sHUYZy8HVo1p8nwhH6F+46v63MeG66cY2TEYR6yTfDtM7qQ5tuxXRGzoch+H/3pcSNCvBYIytY4zN9v/eBWARgBEAzgNwr1/tXgDn+3+fB+A+5uF1AH2JaBiAMwFMZ4xtZYxtAzAdwBT/Wm/G2OvMW+n3CW05gU+yfIkB2ModcO3ETaTDsD+nWrmUAW+xqBZWko1Mvr1cDsNFxJVUh9FYF18SNi6wwSF9rduBQH9Nl7xHh+QEQ9BhWOqa3sXuO2N+Nn/P5xbZHWpN3/9rD81x7pfKmkuVmqCjEQyOfWmSn0iHQUSjARwF4A0AQxhj6/xL6wEM8f8eAUDUxq3xy0zlaxTlqudfSUSziGjWpk2hiVrIYXgfOeliaa8BTxQaxIHM6fw99gUYU79PklzTMUUkzBxhJb6Ky7cV30DFYdhEgS6ez6rw7vF+6NuRr734vnlDlb+V1Q9DeMVSNn0OVxGr+D7ivEiW6Eld/sis1Zi/NjrepvXF9XQiXAKHdhTozIDbA84Eg4h6AngUwDcYY5Gv4XMG7d5rxthdjLGJjLGJgwYNEvrm/W7N8yNrsnbLDX2ss6pIJpKyPydbRsiLcsGg3nyTcRhqHYb2mSWu2YhfScJPq+IWbEr+co0mAiTgMHa3mG3vkx6Ckhyykop3rM8Wxq8S8+kmMXpvUFffjir4YamGHfsSnHvuUCIpACCiOnjE4n7G2GN+8QZfnAT/Nz/yrAUgJhUY6ZeZykcqyhPAF0kFSu9ksA14c1sBo6c+hTv9hCkyypHB8zAEbiIpe532AmOs7JNMUk5CH3m1/cLXN5TAYbgEb3Q5ORtFUnKfbESsDD2ebty/edohGD+qr7EdG8FQjY8rhyFfSkLkzE5+8WsqZziRCO8/oLvzs9sb+9IM2MVKigDcDWARY+wm4dITAC7z/74MwONC+aW+tdRkADt80dU0AGcQUT9f2X0GgGn+tZ1ENNl/1qVCW1osWrcz5hLPdRhJB9B2GtvZ7Pky/PafK5TXdZPRZUKHz7bXrRSHwZgpM5vmHkAZgqAcEQL/Vz9+zk1L7YY3JhVPNubiBGNYH3Xa0+B5Do9w+XRG8Z40zjbv3vZUepsuuyq9xVeNEgz9LW+tjGZQTDI/TO+kuqYiyGK1f//YAUp9VzWwYWdzTMnfXnB54xMAfB7AKUQ01/85G8ANAE4noiUATvP/B4CnASwHsBTAbwFcBQCMsa0AfgjgLf/nOr8Mfp3f+fcsA/CMrVP5IsOGnZ7JqOwfUGkOg08o3d5Yjn9BoKiv4CFh/todxjAE33h4Lg645ulkjTLgW3+ZFysuR4TAX1o/fuZB+faj8f7I7bmlVw1font9nGCo9Bq65+mQIcI1j83D6KlP6esk2H/sHEa0U1ZnR5HD0HKCzDdZTXZaj7ahukf8L8kBJIkYzaCoV/RKpcMQm1BxovsafKQu/M1r+OhP3NPRlgNrTm/G2MvQf8VTFfUZgKs1bd0D4B5F+SwAR9j6IoMrG/niedN3enKZR3+64rjAy9k0yXc2twVKMd3ppxw/giRiNNdT47/86mXj9cfnuoWeEFEJG/S40ltdHtY3t7d5t9pOX7yNMU+UcvNzcbm2CqV4ert8lxcXb8KDb+o9swGb0juKSutVoqFB1O/DmP2A4Jp3QmwmYgataV8lNUjCYRjDiCi6nFcUiuPSkMt0WKup9nN+PdQAACAASURBVETH4KlKBP+msqjEZQGLUUxluau4GM+/7RVc6seA0YlfdE9zmdCchU8mvtr3mGrwKnaFzqxWb09vf9/RU5/CD5+MhtKQT8tfP20MDh3Sy6mPqk9sG3eXr3L904uCv3VyfmOOJOmazYonqUjKJZ4YYD//l6T0Fm7RHcqaFSK4+Dsm4yL07QA2upfLVJ5gPLtgPU676UV3oiuN1X2vraxshxTo1ASDK2FLcbzLEOHGi8YDiJ4gX1i8EYd89xnMW7MdALBcyA+tWyzl+GEkEUlVMK9LYuj616CQ+evb0J9ck5TLuPvlqG5JvG2Xr3+KOorpG1ad8lXf97mFGzDp+ufQki8knn97NSlJjQpf6f+kIikbLrjj1eBv3a3crNvUtE2HoRr7bYJHt24MVPqzREpvw3CpulxQcRhCvWyGKu5h/e1H52Hpxt3W+G8homO1LxI1dWqCUc5Gnc1QIDNWhSaYt2ZH7B6ddU55OgxfJOVQWTWJq41uCpm/DjEOw9/a73/jAyzZsCsY++B6qUpv4b6dzep86zqok+/Ey657ciE27mrB+h3Nifupy1+QRB/UZMmBEPfDKN+HJ5fNgIgSn9ZVEPsjxi/T9VJFZJOMu5lbdeMwxFq5DFXcj4CLQ125PRk9G6wahrLRuQmGRpzj6jWdCeyYwxv4xOyWQKmle57L4mkLHPfs+N7jC5z71B5QWQs1Ong5cyxeLzlTCS99+s0v4Uv3zYqw45U8wYkblKlZ12xt0Q0o/PsXz9rzU+9tLeDm6e/jJ4KYCjCLe+Q5ZrOSKkd8qbu1Lmt301RxGJX4jCpfiUQEw6TDUFxT6jBEDiMb5TBU450kjzgQztFzb3vFsX70/x4pwTAjFElFy11k3xkiwfElrM8nZpKTszZaraL4b3PW4sybwwiyrila1zsGESzXCVGEvAjGj4zb4CcJVyITPFVP1dtwFPYnxu907aVqSzQSGFDiOEx72wq4ZcYS3PnS8mhbBi5A/ha2z9wepvkuIqkhvdxMkGev2oZTfvEC/rkkas2na3pva3zzbU8/DHX9sKxO0mEoxVoJKWW5gRz2RSCIzk0wdIlOHL5ThsI8FmIzXFyg4jD0IjD1M1T1v/HwXCzesCvgjtocRVKTfzLDeJ2jkh6q8viqTrW6MXHJAqa6NbIIS/SgV112lci4cBh3vrgMq7eGYoPEIimtDkN/T3woogXXnXe4VL8MDkOzbXsiKfP7nnTIIP1FAbfMWILlm/fg83e7JRX65G1xy79KeacrOQxtYm8P8kFJtRcl/QS6vDqusHS5IujUBENvL26HJ5Ly/hYnHrc+qVeIWnSfU7fZ/3WO3mGdP7PUgIk6VDJdo7wG3t8QjabauzGn/QY7muyKO9XG5MIdqhZGxDLKcv8ryzZrrxHMgf6WbtyFnzzzXuR6YqW3Vv+g3zBUYTCG9G4I/p9yxFDnPr37/TOM/dMdOjyRlF2HMVwSXZblq2OsG/3fTBT0F20h11Xty/HDbPVdkFTPJNfeF1aUnZpg6DkMB5FURhBJCesjSWyn8B51+durtqkvIGRXXUVSrthUgTDbHPIikJVx9bmM9t1LfZ0om6/hMCynORuHwXNX6HD3ZRMj/4vz7LSb4gmp3ltnDywoYm+bWhGfhMMoMhYRn8nWamY9DeE7Z4/VXv+PP81WlucyGatsr8BYWQr2JEH/kmyQSZItAWpdjMgZOnEYCVdBudGoKymO1qFzE4yEIiIRWVJzGCbfgKRWUiZwItVWYoRdjteWbcHjc8MNcP3O5AmTdLD1ybNFL2OSquS+xfi3cOlXVPeh0mG4xSgiitdtMygviaIpVccOtft7qOTxgNmsdufetkg/GIv2Ww6aaPp2BODKjx+Ew4b1Vl7fvDt+6DjriKE45yPDgmer8My767B8057Yxhch5pZNtMkSVFGEy/44oEd98GR9OwoOQ2q8UGQRyzQ5crRqL0q6NMQmXdaVnINlXyRS6tQEw8WuX1dHZyXFUSmFmumeQpEFfS31U1/y29fx9YfmAgDueXkFHpllT93qCtuCzGX1poWqIbn8hNHROor73hMsqXTjqjpJ2XJSux964yKXtgTC4YE9QzGRbu7pdBimPq7dvhdf+dPbYduIEpj6rEww9G3x25KcZ2/8zHg01mW90CCaOl+5fzbmrt5uDPduWypJskq+ZAh/I8Po6e3AYeyUfCNkoqj2RE+2qsXvafNnOfLaafHn7QOr+05NMHRzS/xQunEnAur8U1nk5Ob/fvit1XjkrdWxeyqFrXtao6aCFTgcXPfkQvz9neRhP3Swsbg5g/OS6sQlK/VK9aBW3WYlGMYnCfWkioN7NShNLDk27opydLZ+AKU57gHRpELyuGfkDUy6rmo6Sewq3jcyUQwfMvFqL9w2c6m1zqBeHgE3BSRVzUN57svfLCcNntKcWPEsUz/Eb6RTuu9pySNfKGKXIiPgvsiL0ckJhnqA/vx2eMrWchhEgSVURETgV39m/nr8rxTcThvnJsGH4m2c+LOZ+OoDnqzYtPFWCy35AuavjTsvisgQaQlyQTHhkygpvevuxCiRTb7hGkltHXfgAKPFzAV3qHNwm56zV5EvOni4j+F9GvGF40dr2wYzH2CMG5P/oCSRhkOuxH5PXTaDX11yVOxeEz4/eX8AwAVHj7TU1EP1xt8+a6z2WnCfRTSquj+m9Ha0kjJt6uL32KARLR9+7TR85X61jikVSVmwYvMejJ76lDETnW4Is5mQYIgnfbPst3wdhtjCzMUeS12fy7Rr9in5FOyC7z+xEJ/93RvmSqQfL9Wp/PXlWyL/u0YJlmEzYVTKyY1hN0ioRpG26jKUSEziIg7V6TDEHtbnMvj+uYcr6wFxkZSMSoukxGfZ9BC5LOGT44eH9R0md7f6LHo35tCrsXLOZ+ccOSwIV5/UD0Pe2OVvKXPLSkLgQIgibQr72Cd+8QKWbIhnAgQQi4jAsS/SYnRqgvHCYo9FN8n7dPOEiIJ49iK7aTx9JuQwQoWbGbkMtatJ3KTr1T4cpsn7zurt1nYzRNoBU43JwnU7I3FybCeiJB704iZ28/QlsevujnvRV8plyWqTr+uHDi4iKVsrRcbMHIbDfEpizZQVRFK2put8kZQqX4Tu3nyB+cS6cusgXyyGiuSEOgyZQ+bd+vGnjsTvLz8Wowf2wNDeofmwTa/Godvsgfj3OP3muEWeESmHYcazhsEPYjQZZgrPcxAhGBbrEhW0snZtS1HsbM6jyID3NSeK9oLN+seGjJHDUJeLrLZqrH//ysrwumYE1Ysz/Pu5RfF5UarjXi6bMeowZEQ5HTV0Iilxg7WL67z52EsTDiKeDyM+AEmsOAOuxOEersNIku+d96eSW16+wALdjtGsVvFUef7e4WfbbMhlcPKhgwEAf7v6BJw/weOkVFNE9cg7XlBn7QQqkFGzkkpWDTo1wTBht68UMi08TjBanDkMnUhKfZc6B7b+o76yVO1QpisvFyZRi4t82wuLoSEYmlO5uNmrFvGT89aF17VGDfEy28k0yVIS26rPZhJ5z4u90HWpVTM2SezwuUjqpf89GS9+6xPx6y4iqUQ6DHfuh8v3k+xfDB6HUUk5fL7Igu9uFklF//e4qLBwxqINQSRY8Z2G9mnESYd6nu1qvVq8zHRIS6JTqhZqlmDwE4Jp/tX5E1s8TRgXmqZct7ElnfqqzG7rdzQrdQkHD+6ZsPU42gwbocvcNYknXExi7dnZ3AnxM++uN7ZlQsQPQ4gNNXZoL+Qy5J52FIh89CT998rt93IUfRajX4967D+gBwBPXBK57mPJhl2YuzruRFqKnxhBLTYSy+oCDiMO03vJBgflIl8sBoTOrPSOXpWNOUSHVXld8LTJynDoimfVGdiIjmb4okLNEQzO1of+DXYldjTbmAGaBWbKUCbDpDdQxa/aoxFfVCKUsc7aAnA7fZqspHSb7NTHQsuzUpXeqrZvmRHXW4hwPU0TeSFPAODY0f2RzSYkGAKSEtOowtzWeHw6Hio4DYpdPv3mlzD7g1Anxee9akw2Whw/dWor8XlcJJU037tBJVYSPL2I3z/DNxSvnXPksJioVVyz8jtx3Y6KcVB95zpDdOf5a80RA6qZQI2j5gjGpAMGAAg3cSPH4L89c1ypWg5Dc0vSD2zLHR1pW/rflCtahzdXbNVfdFKaGkxfNYMiLgqrH4bmsirMug1JDtMfPWgAbv7X8fi/cw5DJqEi1nRA6dOtDiP6dsOidWpdVZJQEgwstnmJHIPR2o/i9Tkm/dgtyKUM8Xk8Dps6e6GpFaooh1EossCCUjUfl27cjbZCMRj1V6eeglsunuCLxqLt6MAZBmUdRdHIvt3ihQCecPCf6gD0wk4wiOgeItpIRPOFsu8T0Voimuv/nC1cu4aIlhLRYiI6Uyif4pctJaKpQvkBRPSGX/4wEbmZFmkQmNj6g3u4wiOSgy848UO8o0icxLFs0x7l5qHbeF2/75mHDwGQzKqkvU4bpx02GGOH9nLybs6Q3tPbJa6N3UpKfV0VfkM1Hj/+1JGY873TASRTehMRPnXUSDTWZZE1cFHqPuuv9e1eh+a2At7V+LfYzi2ijqNYjL9TxMrK4eCTVClt6pv4vXs0eAefxMH0CKgkj5EvskAEJOsOVm9twmk3vYgbnnkv+GY9GnLIZTOe8t2RwzBFi1C9yVPvrsPyTbtj5V97cI7yHZKIcPcFXDiMPwCYoii/mTE2wf95GgCIaByAiwEc7t/zayLKElEWwO0AzgIwDsAlfl0A+Knf1sEAtgG4opwXymqsIiaMiudykBVi7xqIBYfMNu5qbsMHW5vUlaU+6Bbx2KG9I/0wNuIj6eRxJTBZw4lMhslKyo1gmK8neUNV3X7d69DPN202xpIS/1ac2pOF0Q7rqqLSmpSe4lO4QcKdnz8mKBNt/xlY7J3ErrsYdiXx9A6foT4kiEPEE/kkUnozZtVhfPa4/dwbhKfD4ARDNvDg8bJmrdwafLNMwHlFle+iSDKuw9CvF927fFtyCDZh0ToxVI7zbe0G65RhjL0EwCC7iOA8AA8xxloYYysALAUwyf9ZyhhbzhhrBfAQgPPIW52nAPiLf/+9AM5P+A4RBElepGn9zdMPwevXnBopCzgM//+dzclDcnNroG+deSj+7+zDpLpR6D54Qx1XnCmep5ODF5Nl9HKdbNzyycWUVGbdRbjI/UvlMJQ+Um4SAb+uu9iRO/KVwtF98d63Ym3nLEpPvmlxYnPm4UNxsm+JI27wXOYvQjz9mkVSFKvvCuIPV/Sdo2d9Ttu+aRRtPh7/cdJBkVhdNuQLLNCnyFZ7wX+Co6Y4LjqRVIzD4ATDMiYi5Mx4prnVGTkMHb5KRPN8kVU/v2wEADEA0xq/TFc+AMB2xlheKleCiK4kollENEtXh39AeWwH92pA3+51Unveb/4hXJZPLKyxf2+vxhwOGNgjck2eCLrTJZ/Urmwtr7swQVhtV18CvmhdOASTDsOkZORw8TVQlivLLI2V6IehEluaIFab80HU+ZGIzJs0CzcTlXNfNkIQ4txQhMMw9Lcc400X59XunMNI0C4DrLk2PHGhoQ3pIxWKDHU57wZ57QVEAmHf+dImiauMcBjSM206EhX6dIvuQzv3hoYtZx8ZzWviasG5r1AqwbgDwEEAJgBYB+DGivXIAMbYXYyxiYyxibo6WYlr4Ohen41t9nzy8Q/RpE1sE0Je8OFkoxiLL/dBd+rmoald49Hw8iS5A1x9z7ilirMOox05jETKZgvXUeomKYfAH9VfrbQ09cMVDKH1m8pfJWphw2Lv5KrD4FCJaV37KSNqJWXwwzARMguHQZRM61Iw6DB4R+au3o55vk4pCLCIaD/EdSl3L7SSinf80nvU2QRla8jNe8Iw5QcPiprL1wSHwRjbwBgrMMaKAH4LT+QEAGsBjBKqjvTLdOVbAPQlopxUXjLCvMMMW4S4/t3rc8HHPXCQxwmEp0fvQ+hMWEXEgpL5/4o5wuVrHDoREk9+k4StLTIWjXZrgWskS25nX74Ow06hrBxGifeF9+tlzzrIW5IscrC9llHkYnk2Y+EGJ/rZ8DEWY6YxFtdBiATD9L15tW+cdkjEd8MFKj3D6q1NuPfVlcH/2aBjCc1qYR6/jIXDkNEm6DDkfPLi9H5KcBYFvG8ejXit71XgSZ5AwSBHFt7dHO472VgU3Hgk7WqiJGN+IhrGGOOj/CkA3ILqCQAPENFNAIYDGAPgTXhzYQwRHQCPIFwM4N8YY4yIZgK4EJ5e4zIAj5f6MkD4MfIFhmN++lxQ3qMhi0yG8IfLj8Xhw/t4dQMLB6+Oi0evfFLhG2uG4tyHa14FboaojGmjmYhFxtDS5s5hOGfj8jkMFwJj8vR2Ua/YTAmTLEKlmCrCYbjtNOWKpMphMZj//MeuOh779+8elIdjHNVRxIibq0jKf6dshnD4cHUSJR08y7ho4xff9XrEuc0UDNQEOfCj6tlJeIyCoMMAvPlk2uD5t5Y5ZxORyhp0GBw/+fSROGXsYBznmyzLgQvFNSRHwe1oHIaVYBDRgwA+AWAgEa0BcC2ATxDRBHhzfCWALwMAY2wBET0CYCGAPICrGWMFv52vApgGIAvgHsYYJ/nfBvAQEf0IwBwAd5f1QpxgSEdBHrXyE34cGCBuJeXyPWRRSyCSEnKEc8jt6TbteoNIyqT4TSaSctNJcMvGQ4f0sqZ7NYkQksRf0kHLYSiuKL2PE7YLxM/EskjKJuoxDrNlr+On7KP36xcp50Mp7jMM8Y0sct1xcxEPOS552IkoxmXJobj5phyIfIVrWidX7jdVhg5DxkGDewbRHABPgtCrsS7WJ7F9IJllXGCFZViL9dkMhgiBCmP7hPC3KfUrsyypcnRTrrASDMbYJYpi7abOGLsewPWK8qcBPK0oX45QpFU2shqlt8wGAnEdhsupWqc8yygUmnJrNqW3iqCYOAzTpB7UqyGy4btxDKFvxdihvfCyIoaVSCQyRNr+/W1OWZJFABWwkhIKTRtN5JqGw+CvaRtF20ajSn/K4RGBeEdVRhmelVS0bms++WlUbOLqB/Se/2J9uW35EMUPbaoh1zmnBlZfVg7DDfd+cRImjOob2YDnrt6OE8cMCp4XbVvot8H6T76vr6/A3m4gtnHCLouuBQ7DQDA6AodRc57e8gI3gYgilj4uH0Q2zxNFUrHNU/r3q5oFyc1qk6SKLTLz5vXrzx6t7KcJfDyKjGkJzH+eMib4O5PR5BIosiDXhw625EyAl69chk5vY3u7UnUYsiWdaYr06VZXUiwyDu6LIEM3L+S6e9tCWbgrgyceclZv0/gTRerb2zQFUezXXe+XawsNomp1l2AKLxKujx08EH261UU25+Wb9gR/y5xM9/qcYFbrzqHx99nW1KqtEwsnEsuOqL+WrwDB2LantWKZOGuOYMgL3Fof4QdzITLyxsufk80QmgSluTf5o3V1XuQms1pd1FcGlkjG70QwECo1xa5ccPTIIL6SKH/V6TB+9bw5rhMAfO0htWdrtJ1oCs7H567F2O/9A8sUnrLqgHjh3646HK0Ow9+ATSKTMYN7Ovk/6KASMwFhOlB545Hr9mwIzTVd57+oY3WhqbJTmwo5SSTl0u6JYwZpAxuKzzaNoSgWUtEsMfCf/Jhu9SHn4/KOHDzh065mvcFMfE5F/xcfJXMY4vpXTeHffO6YeKGEr9z/Nv7zwTlYt2Ovta4NNUcwOFw3CFGJ57IBy2IlfgsRYXdLePr9j5MOctZ/5rIGpbfWz8FsuiqvF5fx6NWY8z15o+KuC44egRH9PCWs6HemOxG6JF9KQuw4eP6TRQr/E1Vr4ua+19FAwK7D0N87sGdDeWa1jCk3xBs/Mx4AcNR+oRms5+QXrXvo0F645eIJ1n6KSOq853Gg5jomDkO1EX/2uP0w5Yih1nwYtr6KOj3VOIqWe2IiLyBq6io77olcp3xgCPy+jD2LwpR/ffnmPZFr4n6jJqYMkw7ob+zDmm0eoWjLlzE5fdQcwZAV2QCMliDi5FjwoV1MIls6iU4/IoeRJBmMyflHGzqdMSfTVQ5TSAqO/fp3DzgM0SeFIRxXcbLrotW6bEIiITxXSOdpRML5Lq4vk1LSdGqNmdWaZOwZi9JWfyuAUOktY3jfbhgzuKek1FbX5ZuHM4chNKIah99ffixm+/G4eB2buIYTDJVPlOrOno08lIg5bhdl4qf1JOcO8YB1lRSpubvAYaj0NCYQXzTa69FOx3Sdwq0jpOCEYkgT3bve84VjMbxPo/WUkPBsoETtEQyFDuPyEw4w3BBOjkdmrbG2v7slejIJRFJEmLh/f6FZ9yin3JTOJbdw+Fw35ziOZocTdkNd1jOrZcBf3g7HIl8Mw0RHRFIaWa/KwECGSOuSWmG6K73Dv10Ipgrx4HImgpAsUKGMQPGrapuiY8aYmjAn0eH5LSv+CtG7MYf+Qqphk7NmUMf/oKp5oJwvEYc5m0jK3p4OxtQCkkgqCacoirVVkIdBNqvl73DTZ8bH9irxoKOMBME8Z8+xw3o7K+rLQc0RDA5xcphSH2Yslhky/jYnqjwKTR4J44b3RjZDGD2gu1GBx52yuKdtDz/2jkpMo+MiiszsXCcvrOa83ckvl1HbuReKxZBgSByGajKaciocf5Affj5iwaTeJT8/eX9rnzlsYVVcCUaDZMUj+2EYldqWk6nthKcKKCj2Q+ReeAIl3TNK4TBU7cmOZOTQdhjML96gajoHfShB6Z1kMzQdsPga5P0R39EWdobIHNJEhs6sdlT/7jFxXlvBTDDENvVWhczvp3MXtSg/C08HhbghmUQkSRRcAPDa8qjljhyH5r0fTkGGCLc89752Ml/7yXEY0bcbhvXphqUbdweWFir9tm6fKzK9QlyFZoewJ9kMKbmGfCHcyMSx1E1SnXL/qP36BvWjMmI15HDvxkWp5DDCQtexkhNTxXQYhnuTnkxlzPlgu3FTEy+JYkK5D4D+5L3/gO6R/23iQ1kJK4taVVZrnJtVcY6q+SJyGHazWr3834YkHEZkrC2PsEikYs+VD0imWHYiwXj07bip+jBfhGU0BdY8txTUHIcRnrDEMv1A2dhJjtPHDVGWi1ZSgGeJkbXEMMhlMjhwUE90q8/iyJF9AksVNYcRLeORS13Dd3CogtnJyPrOh3KrBVEkJR1Jk4hgGnKZ4IQpitp03ydJ26qqEdl5iTt5zA+DMZxz5DBlXU5sW/IF/GLa4vh1ixZj464WbN2jNs8UvaDX72jW5mCxiaSeuPpjyvpA1Ow0fG68fXGTvuGZ92L3tPjcrJLDUPQrYDAsJ3XVNCkyYPmm3dZMgYD50NDDoMOw+TDZzIFloiZzEfyyah2IIqn731gVuXbaYUMCKYVJnMeEuVsuaoZgcI/OMO2qyGHo71OdCr97zmEYP7JPpGxkv2743OR4PH6+AGImj/5vl48U5vCI15XL/uv0Q3HpR/eHF4Jc3faJYwbGyjjBWL8jurAuP2F02A//BFdkDL2Ek3a+GNr8R5XeySZhVojRI96XNKeGSoGt7IdQJC76RPJpfgAphhzGgJ5qXwJ+Mn3krdW4bebS2PVyDngZ4fh998vLAQBvrowTDZkjktFHE7FZh5gISdKlrFH4bgQchnLhxftFAoeRdE9jjOGUG1/EpB/PiMxZFUxGIt0iIqmo/tHOYZg5S5s0NOAwFMPVKhA5OR/4OMGYxyQpcck+6oqaIRg5SdYqntaNyXMUYpUvHD8av7z4qGg9EAb08GLxq7JxyR+b/+/ykTixUZ1kZKJAFG5MugXw9VPHQGZw+SK+QsrRII5TJhBJhfblgP+Ofh9FhV1SEYw4qcXXkpPbcOiI0a6W0BqNZ98Tg/UF90P8Tvp+mfZMWemts04CwrnkwvqrsiuaIJqzrtzSFPQlXs+fSyX6nchok+aYiwXcsaP7+XW9/6OHA30f+NzTQfQSPy6wBguv20LlmHUYkkhKaCoSnkPVBJnFpfI6ldvg/6vGVjwcyXGmImL3jH7seHkSiYQONUMwzjrCiyM/op8n0xM3XxOHoTIT9MJ8ROu15AvKQHTPLlwPIM5mciLl8omCTUnxQafNXx/5f7e/WcocxoEDe0RSl47qFzXP4xyGHMJAHKesb4XCpH6fcPDAYJPkh5xchpSe3ib/CiIK9DTifTqTV5f5fdFELwjykRJHCES/U6nsuJzBUecrAYQnTZ6iVIZobRTMD2fldDhPTcH9dKFxSoUsxokphBXPOXhwL79unHPWWfoAsObDEDF6QI9Ye62FIj591Aj84fJjlfeIc130aQHMZrW2jdZmOMPXaZDGQBoD/s6qryrWlTkMsRkychjxtkpFzRCMq04+CLO/dzpG+hulqw5DJbPnp3gRTa0FpQXKnS8u99uRCIbidKWDLq0s4OUAFlEs+g5bLDqRl2/eE8nkNVgIdgaESm85JPZhw0K2NpslwN/0xHcc1KtBCMwW6mpUnt6qU9zgXg2Y/s2PR0RYYt91J0NxgX+4fS8WrdulrDewZ4Ny7MQinRx6R1Mb7nxpufIaELc6Yj6zJdvLA6GIrludWjTyq0uOipW5rmFR12ZyjOOXnMPZG9bGAQN7xHyYTBGKAS+Gk9y2KrcH4OWPj/bFfTzqNZvvmCG9IgFGRRQE4ic/p3tDVCQVVXqbO2UbE36IevabH/fai10Pn6u7F4gTjKjYXc/t8/IKMBi1QzDqshn071EfOu6JIimLDkP+2Dymkoim1nzIYivaiYmkoK8rL/hgkTt80Y8eNCA45dn8MMTncGsWUTz3y3+dgH+btB++fNKBXv3gvVlscoUcBicYITfCsWrLHqzYHFecHn/QAIwZ0gtZosjGC3ic4V5NHhJxoR5/w/PKthnz0praPMd1Y/vSEnPMK9nqiMEbw/8+45BIPVFUKFrciBggpBdNav4KCnNpyxuHnIutEgAAIABJREFUCC6adU3faxIwzfyfT8TSicqiD7n3UZGl9zvqPxLeIYuReXumvvPmOcGQh68hpx+baPa66I0ih+G9o8BhuCi9DVX43Nt/QA+lk98fX18VtBO7V6grSw3EVswRdvmaSzmMAHwjCx33otRXB1E2LEK+p3+PemXbwfO1HEa87Yn795PqRs1Zd+xtw7JNuyMJoHibROQneLETmFn/dxre+M6pyGUoEEmJtMozoyUc4osQuJVUkeknFx/n+lwmcqrZuLMZJ/38BZz5y5e0/eGhsd/fsCsQrfXtXo8NO9URXF0d2Xc15/HQW6tjYbajIinhb6GOLXeDykpKNWdCcR6LzQURR4zwTuzBwcZxDYvcmWk+c+MPl4yJpUA03/xw+148/97GyHXxkKLKFSF+B55BkBdxSzAdJylCx2E0GHRDEV2EdC0eGkTNFSkPi5ry4LmitENqb8vuFrz4vndoUVqBCZUnjpbC3kt7nM1xL+UwBPBTl2ohmrYEXdIWcVHecvEEfPeccUYiIFuEcOIiymS712fxpRMPUFqPZIiChXXRb17FqTe+GPP5CGS9nMOwbAr9etRjSO9GdKvLCgRDv6B5iHYvdHq0reB9/PJcJhOR9bqExuanoAfe+CBS/uF2dVA019M3fzcuHlTdryOuNq90lR8GIc7RZHziq1uUpx3mmWXz8Nocssz+k5owKeLB1ETkiAjZDEXykYi6rXLh9cPryBX3zopdFxWzapGUIGJR+XgwfaRkEXWa+Gs8e6UKpsivosOm/B1t3Kst8ZOo9JZNh4/5UZjkTek0azIYEIlvlrS6QF4t1WEI4Jsf53LFSargfMNrpD5Ni4lXzpswAj0acoKiUt2OCmLdfJHFPGeD/hMFljzvb9gde4fos7wJGre+0HAFWQonvdBPWdmezXALERZri4dv5qx7XY4iHIYczE3Zj4xa1ju4V4OitpscPqr4k64Jf+sWi43DCLhKHq2WqS3rsr6hBGNMqbiVxVRxD3LvjwMH9tD2g7ebzZr7XJel4DCxcvOeIPicul1jUzHwfCkAsEMR0jsaCcD7rVN6y6I1Pj9U8/4XF42X+uH9luepyfosYq0l7a312Wi/bYp6ER6Hoa8jStgUEqnwuYqui/fGDEyE/3s31mnXoEpvWCpqhmDUZTiHERcbmR33vEn6/oYoG8wtWkTP2FCHodr0okpmpTyyyCKESIQsNwXiFipc0eqqwwj7HZ6YxNNSfS7KYWQzoZhJbpqbMQ7r4/WhLhvlMFwOL7wf0bFh+PNXjsfDV07GDZ+O5pd2aVO0diEAd7ywLPh/0ugwtpd+kSblMJhv7cVi9XRjx/sm/obUrs6fR2zfhcMAvLXATZU/8YsXsLsljwE96vHcf52k6FcyiiFupqr3zEUIhlnpXZeLsuzcik61sfXvEfUf0TkoqjiMn1/4EfEx/n3RG0V9ingQaisU7ZktLTqMmHJa24z3TtecNTYoixK56J3iM/t0q8PetkLgNBmpp6hfKmqGYASssOJUY1oSfCH+VcoQR0SY8d8n4dGvHC+U8baF52YIX/74gRjVPxpyQeZGuGe2zsJFlb1uvhA991eXHIXHrjo+qOuJjaL1dYRRXOSrhdPmSYd41iTnjh+OU8cOxjdOG4MMeQREbvsH5x6B1645BYcN64UzDx+CW/71qMjCMp3C+BXyldPyJjWibzccd+CA2Bi6sNATBaJABPz0H6Hn8aGCKKagmQ+2zVd2qmQsLocGgPGj+gYEVMXp8U8T44Kk8dPps0VLHH4y/9aZhyrr5gQOg2PLnlalr0pSDoOIBCfG+HuqOIx8xDop/FtWemf9+SGK00SiDwBH+Z7NPISLPNYqpfdFE0dheJ9GozlwXU4kGOH3OOGG5/H4XHPyIZtvSmRdG5TTfLzOOiKMJKAjtvL/3CFTlZfDZY26wkowiOgeItpIRPOFsv5ENJ2Ilvi/+/nlRES3EtFSIppHREcL91zm119CRJcJ5ccQ0bv+PbdSiQFPAqW3/784mGalt3fqFTN3cRw0qCcGCpYtssVMW6GIfJHF4g957Xq/+aLi3IBug8oqTq2/f2Vl8PcpYwcHeYF5n13jI4kyWT4BLzxmZDBmvRrrcPcXjsWQ3o2BWECeW/W5DIb16YZcNoM7Pz8RR47sEwlE59IT3rZuj5bHMSkHbZo6oqGB2KxJQQ0olN6Ibpocd37+mICDUnU75DDkecoiv7U+HsIpluA5ml198sHKurlsxjmnuo1gmvqh2n/k4JSAnNMhrCtz29mMd2gSu37IUI/I8VzcN1zwETx+9QkY2sdbC3EOQ72lxfQSUufrJN0Lv75RymuvOwyYNmNREkCAdrHYrKRMIin+3ipuKElGURtcOIw/AJgilU0FMIMxNgbADP9/ADgLwBj/50oAdwAegQFwLYDj4OXvvpYTGb/Ol4T75Gc5IVB6S3J5r0x/H7dO2m3ImCWDN73Nj/ujMqPkj+TfiG/uOc0RMpMxK87EhcjzQm930BsAaj2NbqPglkwukyti+12iSEp8zAjBbDCXiW/K5eB3l01Ulpt8GgCVH4ZnJXXWkUMjvhi9GuuMHMZpfiyyz03eH4cP741LjtvPbxd+u95v3eFG3KgLLJ48ScSmXS148M3VWKkwQ5bBTX1N5qgiRJGK0vdFcVATvcXFecX1eUyoX2BRDuO754zDrz97NI71OY3GuizGj+qrDYGiyxkui3zlrov6FG4p6AqVXkKcu7KYTiuSUnxTsR3TeuD9Vym++SHRJbGZDdZZwhh7CYActOY8APf6f98L4Hyh/D7m4XUAfYloGIAzAUxnjG1ljG0DMB3AFP9ab8bY68z7mvcJbSV7kahEytmsln9Aq5xSbMdvetKPZwDQEIxo1WAR6DbqDJmVUuI7rNribQSzV22z9pnfK7etk91nfe9tJ4KRScZhkOIdvyDEshK5uVzWHkX4rf87TXvt00ePiPzfq7EOBygUyjaGNuaH4YukBvdqxCtTT4m25cfnlru9+EdT8C8f8ayfhvZpxFNfOxFDfJ2XrJDU0a/WfDHIxsaYXfcCAHMdN4hPHz0iMvYmiOIaFWEUuYnBvb02uejp7+98iNkfbEd9LoPfXToxUFDzN8lmKBDdcjTWZXG2ItijzsRd56MiO9eZ7pNjSdkgWz41txWw1TcIIAK+dOKBQl39xk9CHQ5RiCDf9s3TQl8gbmasCuPP97bv/32h9V1sKFWHMYQxxl2Q1wPgoVxHAFgt1Fvjl5nK1yjKlSCiK4loFhHF7PnCBO5xRZtpT+AbtcsGKZ82OborOYzoRsM5DN2JNpuJi6REiITmyx8/KOiPS0wilY22nnCpRVJqkHBCVt9w2LDe+M9TxgDwxD9MOh2PHarOhliXzQTjrDO7HSRZV4lvdNAghbxe0Ybtu4siqeff2xBJJhWvC+XYqRSxoZVPtB+6+TH7g+0BZ2kS64mwcU8cSRTforhGNXai/8ePzj8CAIJAntc96W1Y+/fvjtPGDRHMxMO1Wyi6RWFWhekB4vGWwvrhoaY1X4zfJ3EBui7w6LAiZA7j3NtexkTfXPa754yLHCgJ+sOVOrqvmsgdOKhHJJBkvYLDaG4r4Ft/fidRojUbylZ6+5xB5XpkftZdjLGJjDG1fAHqTd20IPhGzcdUZ+IJhB/05aWb8eOnFwXlsoWU2I+Qw7CIpCynGvFEyU8Trfli5GSkJQIKCyzdZsJlvW4iKSDwItXUeebrJwbK1mAhOuxPHsHw/nYJzQ64tSvD9pqiF/4X/zDLf4xunAl7WguO3Fn0hFyUNk8Zn/DD2hf9w42Ja77omJGRtm3gYln3ut7fqjsOGRISas7VcRrCCUGQg5pxrsonGBm9lZQMnUhKb2Xmzb2ZizfikO8+E4saIJ7MVTqJsUN7YeUN52D/ASouNToW3CweCNdqWFcvelZ1PSKSMgxLvUKH8cz8dfjz2/YsoklQKsHY4IuT4P/m7p5rAYwS6o30y0zlIxXlzpDFDGqlt/7+XCaDfKEYCf2gA/+g//ngHNwlxB8a1idOMDiYtFhMJ3tXO2m+KFoLxYjs+eZ/nYB//9gBOHq/frH68gL47HHxUO1AqAR2PeXJMnhj/Ywnf3c50dYJviM2KxUOsV3ThhoNXW3uuEr0oWv6DT9HBQ/1YO4r/Hb93xaR1DH+Ny0whkLRLEq7yleGu0pVTKdeGRHLOGmOfP3UMYFymiMr6KL4piw73fFXCa2kknD70XL9Qcj7hjMlz3QOsRkVh2Hm1vREoDFGMPQ+G3zOikRG5/Qo94ZzGJEMfaVlJTaiVILxBIDL/L8vA/C4UH6pby01GcAOX3Q1DcAZRNTPV3afAWCaf20nEU32raMuFdpywn1fnIS5/y9MUq8yqzXJe3NZ3zLDYePTLVI5rWekLvNkyb950fMPMImkeB/koGzxut5vmcMY3rcbvvsv42LvG1jvCC/HI4rKyGgWogriScwlyiiPDOwiKcllQpHUrTOWxK5f/6kjlP3h0MWnkmF7T1X0V13312z1wo67KBflSAA2kRR31iv4TpXWtMNIymE4VY1Yxsl54i+ZFD+EZCn0Og/1NNF3F1950bqdTul0dToM3dziHLzqPQf2bAjMdXkbqgjWOnBOe/PuFtwu5UGRCagsvopc8x8xpHcjbvUDVT67cENwPWrIE+2PKHUI6lfAKkqGNUUrET0I4BMABhLRGnjWTjcAeISIrgCwCsBn/OpPAzgbwFIATQAuBwDG2FYi+iEAnozhOsYYV6RfBc8SqxuAZ/wfZ3Srz6Jv93jYaNfQILkMSSca/SDr2lGZZvISBobzb38lKNc57onKsN7SJPvrVcdH/hfl6qZAdJG2maNsOIGZpXjadJmbWYWVlA71uYxxMz91bDwDotjsToPVm5hW1K7DUNTTvACXn7uckGUZvE0kxTlTrm+zGXLwNns35oxjAXjWSq7RbUUrHzGPyaQD+gemrtG2wwgG8rjI7zxzsRdT6b8eecepH0CcEzaKpIrqg82/HjsyMu4qjty0Lry1C3zz4bn455LNkWtyqHtVOgXxGse544fjaw/OiVx3EUmJBKMd6IWdYDDGLtFcOlVRlwG4WtPOPQDuUZTPAhA/LjpCFvEErKqBGovgtt9yrCQVtJNRsWcHDEaMtdWEBhGU3uJkHdizAUcpREwcsoxU2T9/Y0+ykbkgwmEomr5bMmXlSuHAF8EoKjRbSamG0WRn7z3Q+/Xcoo3meuItwuYbPFvo9+8vPzYwrw4ixSYSqfDxM4ukskLbhaLNtyhs2+WQkMsQNu1SB4CUIYfNsMFbX94mNnZoL8xbsyMIohf6njg3F+kHELcKsomkVF2Xy7hpuep5yrb9PB47FWbuMhcmKt/j7ZhhGvfArLag5zBKGWcZVoLR0REPFR4/eZg3pgzyBQZuyOKiw4j1wchhRCEHXBPbCOTZwk0qnYf4zi4cRpJQIkn8uHQx+Gd/73QQvOCHIkjS05ijrmbMBENxrxhby3VPc1V6i30R82mfLOReyGXjc8/WbqDjkhTAMkQOgzFmiY/GDz8MbQ59eXXZZmudAJpvruW+M2HSrKP364d5a3aEZrIs2t8k0HEYJmMO3UjI5SqiaHLw5CI91fqSEzWJVmZ7W6PGHNr+MYbZH2zHH15dqe2DyqxW7s4nP6IObJkEnZ5gyOEFVFZSpgnJI3tmuBORcZPStyGDhEVrq8v7LeaNNtWPcBiWQHS8vhfd1i4bdjXFBKLybPE9u9dnlQ5U2Uw0OZPpUXW5jFFpp1rAEXbcUY3ralYrvp/Ov0Hc1G2QZfD8Ft3GxL9Lvlh0FkkVim55MbY1uTmAem17v12tqkQOo1BkkayDLvNAB/768YyAOpGU12dVr+VX4QehaApUA8GAt2bl737hMSNj/i2iDmPllqills4/o1BkuOCOVyNlhw6J6iC50rvFoMNw9f43odMTjHhqVA9RgqG/P5clNOcZcjwaqeFZJnPKWF2+sBTPU4GLxva05PH3dz6MlKvqcriIpLiprEuOhCSRWUTvdBuRA8KTGz9Nm55VZxVJxe9tEk5sleMwws3Xdo8Ltye3K1sc6YZEJEYF5qKEBXY2t7kZL7h1GUBI0FzzbWQFrjJfLEY45kCHIfWgZ0MuyJeiA39/+VRvitXmGX7Er8lDyR1SxUyMRpEU8ejR0cY/okgbLJrgyvV180rWLz3wpeNi/iANSh1GeF+3umxFcqR0+uCDWh2GqKM0LIkcj19jkMXLbcswiqQcvxHnAh56a3WkXM1hhH+7iqQYYxFlrw4ODEsAnQ5Dd0rOEKGptSCktdW3ncu6h0rhiBAMVX8VZVYOQxEu37VPw/s04omvnqCsKx9swtO2mcNYubkJzy/aYN3AAGD9jmZ9pUh9p2oAwtAbclTUEw4eqKzPD0KAR2TE9cqHXn5+W6GI4X0asfC6M7X9CAiGrMMwcBgq34rRA7rjqk8cFCnnYqPnBV2XbR0zxPVFOsmDLraT/P9UP2qtzBgcf9BAdK+PnvXrFGa1Yn/696h3zsJoQqcnGPGTZpTVB8wLIuvrMHhtE6utOxErTzWS6SDHdg37H3pY2xVV4ju7EQzvdOXiAJfESooE+3PxFKRrQ2Xuq0MSz3eOJsGUVnXrHZ87JlZmowM6WbmyT9K3+OO/H4ePjIx7BgMhIZKtpHS6Cc6ZXvLb17GnteDEYYj5Ea78+IGa2kASHqPR91qW59JXNYEQRYKRLxQjY6Qjki35Ino05GKbogj+jjKHodXtBDrCsP6kA/rjhW+dHDN9DXRzQreMxjAZr1l5vqrmqCiSiluNqfUmLqIktVlteH1gz/qIQrxUdHqRlAy1lZS+fs7XYaj0BzJMNt6xfvA/ZMWTJqNaVuJ0TBBPUW5WUt5k3GNh84GEIikKCaKLlY08fvtJ4cwBL5TIonU7laaNIkSHRa503NMiiqTi945WeOnaZPGZBItWHjk301fv+YUg1pjOik6tq1PX9S7yDeK+L07Cxw8ZpK2fRIfAHdGaWqIEQx+bTBRJsYhIlo+98lBkmYf8sqvSm3PZD765WigzcyPiVdPhhcepcjHx5XMVCPeoXo05FIsMw/tGc3YH0QCEqTfl8KHKPtj8MHp3q1OGPk+KTksw+narw+vXxCx7lTJn40k26/lhBINbikgqgQ5DxxHwPN3yQbalLb5RRZXe7joM2SpDhU073cQYvB9JwtTI3+H2zx4dq/PQlyZj9bYm/HzaYmPbImHLEiHPGHYKIeptsmov6qy9/8FJ1kH+G+MOrXeE37s17/2hmx/yadXE8fCxacubA16G9R066oOLpFw3H5FTzBdYkOgMELgqzcZqAgWE3FXpHRdx6p7B57UpWKHcjkrpzdMRxPoBfkjwft/5uWNwvEKkF+ithGf371kfq8frEoUiqUKRRb5Rj/qcs4jShE4rksoQKR2FZNkwYJ58dRkv2UzRTi+MJ5J4P6JKTRv4qUaemKo8HeJhk58sTNFG+elqjwPBeHNlaDI6fmSfwOPU1GcRqoiwHDL30q97fPL36V6HI0b0UXrb6vvhtSsuEJU9jPj00ITZ/AxVXmod5BqmeRfOJS7f52Ez3MSeJhNp2UchieWbDd0CguFmWRV13CtG+iJ7eovJkmwchlaHYTG4ENeJTrfJdXOikth4eEHcrHbcsN74mIIIkNBW4PluEeGK3K0uECcR+ZkWvTa/9/h83CJESOjRkHOSMNjQaQmG7vgms/qAebE35LJoyYcB45KENQ66YjghuZp3ZgMdRrRcZS0iLopchnDjReNj3uAiuHhHlyRehCjWOXfCCJyrEaEBCGzyxZP9UMWpKui3NE6mfSxJbC3e7C4LhyHiT368J1dP72kL1lv7oTLP1LfL56n3P98UdByGPHYmjke2INJZ5nEkiVbb4EdHtlkxcXhWUsWgPyJB/Nzk/dGQy+DUwzyv/ds+Gx5OTH4mgF6HoVN6c0V2XyHCq0nE7BGAcL2onPLEthmiYvBvnDZGSQhy2Uxg+so5B1NSNcATSfHX2ronnkc96LcQZPSx2dGggz0bss7fzITOSzA0kOW3gJlgNNZl0NxWDBZgKRyGqR9JAgp62cZkRZi5H7ks4YJjRsbSm6ra5gvg0a98VFv3S4Jy1CbK2O7H/J/66LygzKRTkZsz6Uu4iM4Fx/jZ9MTNQ3Wr+Lxrn1iApta8s1ntDCFoneyMxZHEA5orjf/iRxO1iaTiHIae+AfBKfOcwzAvc9VnGKgRffBNTLT3P1ozHoAn8g2V3iyi9B47tDcW/+isQHYviqtK5TB0p3XxZG+rGzi6CkRZjm4rt81Y1DFWp7Dff0B3rNjsRbPNWzgMUSTVy89GucNAuMRDlkw467KZioQ5rzmCUZ+LLhbAbDPeWJ9Fc5vIYejbTiLr5R/7oTdXW2p6yGTgZ7pzqCvK7x3EDR6LHY7DoJ56LuCUsaHnsq3tZZu8if/h9lA2asrcJi8Ms8jGW4Tvb9hl7APgRekFoputUoch/T/u/02zRpZVjcGvNGI6Vxk5AGzc5Y0Zj3psE0nJY2fWYSDSplWHoSi7/98nG/shmmiP0QSyBKJ+GG2ForEvWSlNqgn8susmmBEdIKQ2VHWLjLlbFSl0GN3q1eugb/f6gIu/Y6YfkFQn6uYEo8AC8fsVHztA2w0xWoRMcEXCXQ5qjmBwKxOdPbKMxlwWLfliwLb/4fJjtXVL4TBuk6JXmuq7ZrqLiqTsn5BnMgtiHhlEFKIS3UYw+GmndzchkYuBYIjiAMAusikyhuelcNRTDh8aM3bgCWpEGa1Sh6F43NuWrIWqzyG+b6Ru7Hnu80UO/S1D/hSmQxAfV17HbnEUv65LzMU3/GbhQGazQiwwhgUf7sAbK7YazcCjHIaxy1rHPW39TJwD1I0LEWHn3jyaHXR+vB05U+ChmsRg3Cpze1NroC/UR7D2fhcYQ102g1PHDsalHx2t7QfXvQDxAwb3NysXndZKSjef6hTmZSb2nVt9NLUUcPq4ITjuwAGJn6mCy0YuIkPewnLRoYhzwYXDyBBh/tqdeGultzmaCIZ4wjXFzwEQ5EEX42OZCEbMbNBBhyGz4P161MeMHZSbh9JKKrnyV3ZQ+8Lxo7WbXjwktqFdyfqNb+7uOgyTSIq36XMYFh3GqP7dsFZSpmqVx355i8BhGDnFDKFQYPjUr1+11hX76SqSSiLy3by7JSIS0z0hQ57IcO+O8B1N85orvcW+9GxQb608aq4IPcHwnsnTL9h8pPihE1AZA3giqVJ0tCJqjsPgG95eYUGaFLH8JLWrJe9gfui+4YhNudzmTSTmJpKSlN42EEUdreoMxCxirmppu5svpxUX03fOPkxfX4ovZeQwMjyej1SuuEVVVsqy6KPgHOT0qmOGxFO/Bs+URR6GI4bsdc83dy5SlSHPPVNQwZDDcLOS+tkF42NlWn8QhQ7DdJTiHAbXzTUZTu3RNKmmHseJog3/XLIZK7c0YenGMBuebu2opuX3ztHPa+5b4eKrwzkM8XCjJRgCUSwW7Xlk+CFrV3NbzCIyUKCXyWTUHsHwJ/pyX77+pyuOw4GK/M4cOeHEpEufylHCAdW7z/8tmg3K4AmUXLyxswk2dSC+MdtOnK5t//ZSz3OaK/h+8ukjjea9SYZPZbIL6Lxn42WlJI956Vsnx8r2G+AZE+zv/zZm8pPIlGm+9JHEc6G+wVHpbdgoQx2G2QqHo3e3+GnYFPMMiBM8HTK+KKRXo/eMeWv0yaWIKGjfVYdRjphFZwwgfuNudVl8+eMH4jPHjlLWBRCEN3c97HlBIR0IhhCWpsiYdT1yQ5EL73gtdi3M1VKet3ftEQz/tLvBd0A7en+9BQcQntZbC0Vt6PGgrmIS/+TTRyrrinMnmEhGEYw3MTY6eUwn4zDkKq5B8mwTdGS/7qjPZoLNw+ZEmCQ0SCCikzZhHppChKqZUjhveRPnmLh/v+DTmcR0ss+Kac/70omeNRr3/Od5E1RRfoH4N7Q5NRKJVlLJOWetqSc/YCXQYWze3YphfTxxpC0AHm/fNq11jntJoJuu4rwc0LMe15x9WIzTjPbFfb7xPC8iZ6SPUMxT2XoBO21ENEOeae9ihaEIH9dyA9bWHMHgE517Ndt0CSKLbaXgisuqtJSAZgIZJhVX8oqRagF1vnDxlVxiP8U4DEdHLlcLLM4VNWgUpWE/zP/L7aomt2pcVYSnfPVeiFyWgs3XtGbHDu2Nb5w2JvjfJJKqy2Zw0KAegUyZE12dsjnjuJEG9YmcRVKqy6ZUwgAiOe1NrfPYaQvX7TT2gaMueM/K6jBUcOEwdARchmsvshlCvlCM9NvGYXi6B7tOkftyybjso/sHa75cDqPzKr01g8dPz82OYRH4QmTMLF8V23aBq8Meh6iw4pj9vdOVZpbZhByGPFaunr+u4i4e9M90ClP1w5gJkdSpLFsUohA1h1E5kpHLZAITS9tGJn4bmwiTh8UGQqKr5zC8xup8x69fXBTXO8j9aHUmGCoOw000BpjfU47B9LnJ6gNW8NxsBoA5uCKQXIehgm451+VEguEWesc9ooMnNhI3blNIdsAPae+gwyCKB+wc0rsBPzjvCPz+lRVBW+WgLA6DiFYS0btENJeIZvll/YloOhEt8X/388uJiG4loqVENI+Ijhbaucyvv4SILiunT9kMBeIdIgfLAmFiviWExVBBlOuOHdoLt/2bPmxG0v2KK6xEP4b+PepjkTR5XQ6bU5ZXP/q/q/LedqLxnk+BgYHJBwNQBefT19XFqTrlsHgub9XmogpsWCpyWQqsmmz6H3G+2UZP1NO0tBVApB/DMHIpw9ihvXDhMSONbbcWikGoFNt3VBIMnQ5DUdfESYn1R/brhh+drxbhBs8NdBjGaok5jOHKfOPqsW4QKIlsqKHui/sBJZf1lN6iaK6HxskvJ4ikiow5WUnJ3fjZheODa0Cxa3nuAAAZM0lEQVR5IjygMiKpkxljExhjPIHzVAAzGGNjAMzw/weAswCM8X+uBHAH4BEYANcCOA7AJADXciJTKvjgOMn3hRHoppCPixBl9GcePhT/kjTloWmDzPAkR/YTU1IrqVJSYALuIilur24jGHI/zJ7eiFjXAMDyH5+NkxRRV8Vufuzggfj9F47FN08/xNp3V+QyGezyfTySnNZdxCrcHPPW55eCMf2YiErepN+zzkbIVSIprVw9GYeRSzhXOaFyjVb78lK39LJ3fyHuX6Xrj2j15yKSInIXSXGzWpHQdW/QcJVcJFXgVlJ2rksmoHy9hDqM6hMMGecBuNf/+14A5wvl9zEPrwPoS0TDAJwJYDpjbCtjbBuA6QCmlNMBvuiSWhDZThN1CZzakn6WDTubsWLznsRmb07vWOJXdh0/7rWsc2gT67qCO0OJJzFt2AcijPczkGUzhJPHDk4kPkwC26YnPtZFJFVkLObroW6XlH+7wGqMoOioa04TwMJJCRdtVohAeKp2MSEFgOWbwpAdpvWrTERWKYIBcyh+EdysVjwY2nxvPA7DgVPM6HPI5DoIh8EAPEtEbxPRlX7ZEMbYOv/v9QC4DGEEADFOxhq/TFceAxFdSUSziGjW3r1N2k7xgXX1guawcRgim24lGIoPd5FBjPDmiqg47DMTzSIHVZ90cAk6GGkz405wsxkKckIfPFhvvgwkM0vmIim+sKzEPMNl/KVxUybkIpu1++ZrC+pHgC/PdnHWFDkXa/VI3hUb8UxCf5RmzY4ftpIchnz9vAnD8dTXPuZc39QfcbxURicyiNxSBwChJOGx2WutdUNTWM9Kyh6QUa33A6JOgOWgXKX3xxhja4loMIDpRPSeeJExxoioYtpHxthdAO4CgJFjjtC2y+eB04Yn6gMsk1Q8qdkm9ETJ52LBD85ED433p4ypZ43Fl40Z0kK4vKMpwqUKGU+47shheL/rsxnrxpSMYEQDwD37zY9b6rsRufEj++CdNTti/bruvCO094giHTuHIRAM2wL3o4u2ORD0KOdiH8gewuGnFKV3krrjhqvDYABRoukyn0Idhl38IuKcI4cZ/a1Uz9atd/Ebq3JayCC4n9x52zyG2cmHmhJbhWIkxuwiKTFuV+yaYHFVDsriMBhja/3fGwH8FZ4OYoMvaoL/mwcDWgtA9H4Z6ZfpyksGH1iXdZDEezsqkjLXHdG3W5AWc2jvRiuxOOfIYcHfvRpzFT21iW393hArS27TTYfhc3MOJ/skGxP5k7+tUMTIft2M0XiBUOxm4ypVuo3jDxqAz0/eX3tPkvhaEYJhrOmfCGH3TQCSeeADdm452rb6bxXkZ9/7xUlGzllcJ04chqNISh7cFgvRVREHW0gOwC1BWSZDzly8/MyrNKltgXAsQispu16nyIDJB8YdhEMOo0qOe0TUg4h68b8BnAFgPoAnAHBLp8sAPO7//QSAS31rqckAdviiq2kAziCifr6y+wy/zNIB/SWuoNTlzxaRRB4snjSTbH7rHbLYnXlEmHpxkMFbWoaLlZTYU5e2s46ndbFupZXv3OKjrcic85bz+2ztyrCZA4vhOqxm2hGzWtsC92Tfn7/7DWM9ILrhuUzZ7okIRtjgtf8yDitvOEffD+nhhw3rZTZeSEjoShVJ2TzPVctE159IiBKHPjfkMpF88sZ+SP02zaeMwBW0FZg1HXM244UXUuVKCUKll+m4V45IagiAv/qTJQfgAcbYP4joLQCPENEVAFYB+Ixf/2kAZwNYCqAJwOUAwBjbSkQ/BPCWX+86xpjZvvX/t3emQXJV1wH+zoxGI2m0S6OFVQuSEAjtgABZiE0IVUBgSEyiAGGJg4MJwWAbbBPKdkhBYruCKVwUBaIg2DjlhJRxyglLANsJhiBsgRAgJBQIu1iELGILhLj58d6bvv3ofu/cN93T3TPnq5qante379zTbzn3nHvuOTUkZG3U3wmuufhffrf6Oksa/8KZoDCDK32uGv7loxl3snCmDSkEfW3xhEP2HpXbds/HkbtGsy7RplRclcNCsymbZeY0DtmFn6xhbPbyG2n61RQ8Ck1+2dN3zoM6vS8hbwbudxfiktL463125VkYFf53tX1XZQEGCuXcOag9dw9XQvqayDpPPZFNcar1/LWoqNz0ugoZmEthtQ3auOec2wp8YveQc+4d4BPFtl20GnNRlb7WAmuLjqU3+BdenmMgJEoq3T4Pv78JI0IsDIUCUOwqrdReozB6XFIaS8f71yfPm1y9IcniYFT0SdN3MhMsYmHkGT55dTbK+6/8uUqExO/7X8F/5+wXiv53wIJRakxZpK2x/PxrpQ7zLDkoldkNXcNYMrV6njaoPFFI6rmkCQlugcjC0JY/TU+ssly5fmTT7j0fM1ixB6iapVOyMJovrLZPKHY7fJKQJHWhLqkQheHPPLIS+KXRXNC+jJpxJ801fvCePS+KB5T/ELhgafaifqmIjcvdRxC1RzWOIvWt/ZtcW6HPH1NWW//cVMtLlu5XgyaEtRKah/qcvUuL3CEWnWbX9Na4sl1I5ui/Oe0QZkysXsQJKruW3t9V7eHqne/MXiM6O/QWRlphZCn25Jz/xV2/xjl4Oyd4pV3KSyyX9dUkYbUtj2bBMaE8vFKjMMJ89gka905C3mY5KM+MqXleJg8xTQx6MmPZoVgvKnuYKh40ez52fKRICpm0B0VEUMX3sz9TZmHkPELKFr2VG/cS0nU+qvWrQZsvLE1ePjCAy1bM6nkdErKrzcsEul3yCZqF3EoWxoXLp1du63WeVTs9oXNQqfzprIkj+KcLq5dATk9QNS6phC1vZrsu/TQ9aQa8hVEr/Avi0uOzdweXuaQUM76QSWFRn7NmRuhfpCFrGJoZYVJ4Z6fCJA95hvk73zWWmtY1Vum8VatfnRBiYQQ92KX83GStB4Ru1k8eEFnRX5XQTEBCFrKlzMLQK4yQMagScFZoM2Vc5cg7X9mqLAzvO7v4uAM+EVLvk35gZ31/n8yMkD2OUUM7eH1H5QCbdlMYtcFfBFo6Y3xmW//kai7SkHxSRVwl2s/5ZqjGtXH7uYex8uBJqpDCEDSLtQmJWB965XOzSIZaZA3jypOqF8cB+PTC0j7SPBfmc2/k1yBPiLe79JDUjKjcNuz6SB7UoYpGMwEJUvzeAF7d/ruMlhGXHBdl+9VYZwmnzMtP0VN57ary//Bri2vWmPyQ+TzLLm2xZE2G0rVx8q7tyaOHVA0vHlSjjXsDXmGEuKR8ap15QlvUqMjn/PwxGkW3bGY3N521qFBJ0yxCuvPTzqti4bVRUqn3h3cOqloHI2HyqKEsi3Py5F0tmk14CemdudXKegKMGZZtBaVJ+g09gxoLQ2NdJ3xqZmkStkuRAmXvMUNz20DpWhKhYoLONCFjLrMwFI8Hf49QXph7eiNg1v2bTqCZpzAqVYxMf3bArmFoZqtXnnRgbpui6ZGLJvSrRlGfc6iFkVWprd5olFW67QdKC6PoPgzNRkaAi489gK7B7SzYN7sgVzW/eCXSaxhZLpv2NmFsl15pJKc8VOlr1jBC+jxm1oRS3wplFFqr5YyFuhQ6mv+d4Ke40ZQpGOsp87zxrzpkUtnfWe2HdLRzplfpb/bk6jvqAYZkWIcll9RArbinuK7yvmCAmRk1mrMouuZQ6/6qpUb28c3Q7oCQ3VpTxJXxwe49qjWMZLEvVGFoH1CHThnLxm+sZHTOTD/LSkgjqTWMrNrzoNt1nJCc89CJjcYlpa2Kl0YTpq2lo72NJ752fGZkmU/IZGXyqKFcsHQqoKuB3REQhisiHHdgSYnmRbP54/7iibMyWmYr+2bYuNcvWLT/WH555bE9JSS1hLikqlXlK++vmIUxa1J2OCGUHh73Xbqsp/52IwhZw0i+39d27OJwxZf90Ka3AHhsa/YehbRrotaWYkgotcQJFrtHdHL87ImqegcAX1mVbzknQQjDM9ZFKqHZIZ4MM/S70yx6J3nPstZzEsYFhJ+HkqxjaMLu/aAITXvfYs677ZMH/aihHbnXlv/9ThvfxWs7SmtGtbIw+rXCqJbqN02osoCwm2XuPtm7miF85r/x6yeqlUzyPdQr5beWooulIeHJu3NuiPRpq7XCCFH8ycY9zaYsn+MrFJFKs3NXFOY8MlBhaCyknnKxgZMcPxy3GifP24sHn9vGRRk5lnrLnecfzhHTx2W2Sa4LzSPEVxhZ6wgJm7zAiDyrKxlHXiZoKHdJPfCFo8veq1V6836tMHpbLCSLWj9oukd0ctQB4zhwUr4bDVBnvoWShVF0naRWhPi+/ZlayCa0PBnTSeJqfBqDECKXR5T+RL9TXqOU/mDxvtz56EucdEj2jvo0wzQKI1kvCvzyNA+9iSOH8IM/XRLUbyidHW35rqP4tyZKyncVLtgvv/bbi+9EKYMOnTIm955Irud9FMEAvksqrczbeiwMUxhV6e2XU4kl08by6NZ3VeZ10kb7oP7+BfW5UZLvIWRDYChHHZA9Y4MwC8PfhRviu897EKQVbSMVRpStNkosp9vNrp/1zp48ks3XrAoe07CA/GFF3aiNJiSZZYiFEbpG8/VTqqfUT0i+Y02UXNb6U4+FUTAqNKFlF701l2pI2g8tO34XPcg0pudlK2byueXTOXVBxXpQfUZfWBg3/OHC3DYhFoafaiFk3HnBA3uNHlpWnKrWlmIIUQp3VInlovbR7/rZzcpNcFJs0btZ0ExAku9at+jdVvaZPJLiVll1REoDiX5pnjdZG20TK723yQdbVmFoqEcE6VdXzWbKuGHMUERXjRjSwZdXHtjwtYNEYWhmsUUJyWyrmZmWlbAMGLem75le3qF66Qvt95HIqVnDKM1666ky8ulRGC2qMfx09dVIdmsv2C87jBrC1wav/8x8Nv21rgp1YhFo8rplWRhJWHFe3ZA8+rVLqh4umKUzxvPwF4+peb/1pEdh1DgU2EcT5y4BM9Pd3tROk0sqIfRR2lsTvRL3X7osN/wWImX12ziaSfPQufrkg7jqx0+z1+jwII08HrniWLVF3tUZPZhCS/82C5rv+uiZ3fzqqhNUe1+6R3QyqE1U+74gUrSdbTr3Vc+9qxhzloWRuMfz6obk0boKQ/EM8eOdBzR94HPWuTLi4Sim9f4Gw5BFb02gw8F7laLWPlDsPg4lL3NqQpsIr8W5f9a9tJ0/y2m/fNYEfvGlY3s5usqEKKHEPaLN0NpsaHaGA+qNkqOGdrD5mpNqnhkBSi4kTaRg1rrqkHhCt2v3AHVJaU5Nq5rMtebuzx3Jny+fXrhGQq2QgOgaP2WLptZAsiP288fmh2MeMX0c07q7gN7fQL1h/cvv9bx+77dhddcbifaBm7Dy4En5jfqQMTmpYIpQD2UBJQtDM9nL8qgMam9jUJsMXAtjZOBFO5CZs/co5uRUuCvKQ5cv53+VlQUT33voGoYmNci1p8/l2tPnqsYB0UPsew+/wOg6PDy0+JlFP61McdEMtLcJFx49neWzulXtbzprUZ1HpOOa0+bwwDNv1u3hXg8Sl6lmkpXX5qOPHbf+5//0ajwtqzCytGnX4HY6a5iGwKjO1PFdTB3fpWqb3KiTM+o+JPgK49yjphYbXAaXrZjFyjmTytxTfY1IKWzTzxnUClyh9Nc3E2sO3581h4ele280iZspnbm2ElPGd3HNaXNYNqO6IrdF7wqsv3pFo4dgVGB45yD+/jPzc3fZQqQk7t34JhBWgVBLe5swd5/8CJh60tHWxod7PuaACcNbatZr9B0z49Q/2tDyeivEplEYIrISuB5oB25xzl1btK9Gh7Ea1dHuSVkyLV+ptDqJjrjpj5vDZWM0H2sO249hHe2snp9f86MvaAqFISLtwI3ACcArwOMico9z7pnGjsxoJL/40jG9XqRrZvYfN4zn33w/KPW2MbBoaxNOX1Sb9a0xwzrYriilnEVTKAzgMGCLc24rgIj8EFgNmMIYwOw7tnIJzf7Creccyo+eeEWVJ8gwesuv/ypy1ct1xftolqnN3sDL3t+vxMfKEJHPisg6EVn31ltv9dngDKMe7Dt2GF84YaatXxgtQ7MoDBXOuZudc4udc4u7u3UhfYZhGEZtaBaF8SrgxxXuEx8zDMMwmoRmURiPAzNEZKqIDAbOBO5p8JgMwzAMj6ZY9HbOfSQinwfuJQqrXeuc29jgYRmGYRgeTaEwAJxzPwV+2uhxGIZhGJVpFpeUYRiG0eSYwjAMwzBUmMIwDMMwVEijyz0WRUR2Am8AO5QfGVWntqHtxwNvN8E4TMbejcNk7LtxmIy96zst4yznnK7KVxrnXEv+AOuAmwPa16Vtgb7XNck4TMbejcNk7LtxmIw1lDFE5vRPq7ukftIEbYu0r1e/JmPftA3FZCzett59hzAQZMyklV1S65xzixs9jlBaddwhmIz9A5Oxf5CWsTcyt7KFcXOjB1CQVh13CCZj/8Bk7B+kZSwsc8taGIZhGEbf0soWhmEYhtGHmMIwDMMwVJjC6CUislZEtonI096xeSLySxHZICI/EZGR8fHBInJbfPxJEVnufeZhEdkkIuvjnwkNEKciIrKviDwkIs+IyEYRuSQ+PlZE7heRzfHvMfFxEZHvisgWEXlKRBZ6fZ0Tt98sIuc0SqY0NZZxj3cemybrcgEZD4yv4w9E5PJUXyvj63WLiFzRCHkqUWMZX4zv1fUisq4R8lSigIxr4mt0g4g8IiLzvL7CzmPReFz76YlpXgYsBJ72jj0OHB2/Pg/4Zvz6IuC2+PUE4AmgLf77YWBxo+WpIuNkYGH8egTwPHAQ8LfAFfHxK4Dr4tergH8DBFgCPBYfHwtsjX+PiV+PabR8tZQxfu/9RstTIxknAIcC1wCXe/20Ay8A04DBwJPAQY2Wr5Yyxu+9CIxvtEw1kPHI5D4DTvLux+DzaBZGL3HO/Rx4N3V4JvDz+PX9wOnx64OAB+PPbQPeA5o+pM8597pz7lfx653As0QldFcDt8fNbgdOjV+vBu5wEY8Co0VkMnAicL9z7l3n3Hai72ZlH4pSlRrK2LSEyuic2+acexzYnerqMGCLc26rc+5D4IdxHw2nhjI2LQVkfCS+3wAeJSpQBwXOoymM+rCR0hf/+5SqCT4JnCIig0RkKrCI8kqDt8Xm71UizVnoWUSmAAuAx4CJzrnX47feACbGr6vVaFfVbm80vZQRYIhEtecfFZFTaUKUMlajP53HLBxwn4g8ISKfrcsge0kBGc8nsoyhwHlsmnoY/YzzgO+KyFVElQM/jI+vBWYTpTV5CXgE2BO/t8Y596qIjAD+GTgLuKNPR52DiAwnGttfOud+4+s055wTkZaP0a6RjPvH53Ia8KCIbHDOvVCnIQdj51Et49L4PE4A7heR52KPQlMQKqOIHEOkMJYW/Z9mYdQB59xzzrkVzrlFwF1EfkKccx855y51zs13zq0GRhP5H3HOvRr/3gn8gMhcbBpEpIPo4vy+c+7u+PCbiRsm/r0tPl6tRntT126vkYz+udxKtDa1oO6DVxIoYzX603msincetwH/QhPdk6Eyishc4BZgtXPunfhw8Hk0hVEH4hkJItIGfA24Kf57mIh0xa9PAD5yzj0Tu6jGx8c7gN8Dnq7YeQOI3WO3As86577jvXUPkEQ6nQP82Dt+tkQsAXbEpvK9wAoRGRNHcKyIjzWcWskYy9YZ9zkeOAp4pk+EyKGAjNV4HJghIlNFZDBwZtxHw6mVjCLSFVv7xPfsCprkngyVUUT2A+4GznLOPe+1Dz+P9VzNHwg/RBbE60SLZq8QmXyXEFkOzwPXUtpRPwXYRLRI9QCR6wKgiyhi6imi9Y/rgfZGy+bJuJTIn/sUsD7+WQWMA/4D2BzLMzZuL8CNRJbVBrzoLyJ33Zb459xGy1ZrGYkiUjYQrVdtAM5vtGy9kHFSfE3/hihA4xVgZPzeqvj6fgH4aqNlq7WMRJFDT8Y/G1tcxluA7V7bdV5fQefRUoMYhmEYKswlZRiGYagwhWEYhmGoMIVhGIZhqDCFYRiGYagwhWEYhmGoMIVhGHVARC4UkbMD2k8RL+OxYTQjlhrEMGqMiAxyzt3U6HEYRq0xhWEYFYiTuv070YbKhUSbt84mygX2HWA48DbwJy7a4f0w0aaopcBd8S7h951z3xKR+US7/YcRbZA6zzm3XUQWEeUXA7ivj0QzjMKYS8owqjML+J5zbjbRTuCLgBuAM1yUJ2wtUR2FhMHOucXOuW+n+rkD+LJzbi7R7u+r4+O3ARc75+ZhGC2AWRiGUZ2XnXP/Fb++E/gKMIcocylEBWhe99r/Y7oDERkFjHbO/Sw+dDvwIxEZHR9Psp/+A1FxG8NoWkxhGEZ10nlzdgIbnXNHVGn/f3Uej2E0FHNJGUZ19hORRDn8EVG1su7kmIh0iMjBWR0453YA20XkU/Ghs4CfOefeA94TkaQ2wZraD98waospDMOozibgIhF5lqgG+Q3AGcB1IvIk0SL3kYp+zgH+TkSeAuYD34iPnwvcKCLribLfGkZTY9lqDaMCcZTUvzrn5jR4KIbRNJiFYRiGYagwC8MwDMNQYRaGYRiGocIUhmEYhqHCFIZhGIahwhSGYRiGocIUhmEYhqHi/wHN6oBcJ4FBiwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sorted_data['inc'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1990-01-01/1990-01-07 1990-12-31/1991-01-06 4\n"
     ]
    },
    {
     "ename": "AssertionError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-88348e001840>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweek1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweek2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mone_year\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m     \u001b[0;32massert\u001b[0m \u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mone_year\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m52\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m     \u001b[0myearly_incidence\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mone_year\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m     \u001b[0myear\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweek2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myear\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAssertionError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "first_january_week = [pd.Period(pd.Timestamp(y, 1, 1), 'W')\n",
    "                     for y in range(1990,\n",
    "                                    sorted_data.index[-1].year)]\n",
    "year = []\n",
    "yearly_incidence = []\n",
    "for week1, week2 in zip(first_january_week[:-1],\n",
    "                        first_january_week[1:]):\n",
    "    one_year = sorted_data['inc'][week1:week2-1]\n",
    "    if not (abs(len(one_year)-52) < 2):\n",
    "        print(week1, week2, len(one_year))\n",
    "        \n",
    "    assert abs(len(one_year)-52) < 2\n",
    "    yearly_incidence.append(one_year.sum())\n",
    "    year.append(week2.year)\n",
    "yearly_incidence = pd.Series(data=yearly_incidence, index=year)\n",
    "\n",
    "yearly_incidence.plot(style='*')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ... in 1990 there are some weeks missing... so leave this year out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbf958c7828>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "first_january_week = [pd.Period(pd.Timestamp(y, 1, 1), 'W')\n",
    "                     for y in range(1991,\n",
    "                                    sorted_data.index[-1].year)]\n",
    "year = []\n",
    "yearly_incidence = []\n",
    "for week1, week2 in zip(first_january_week[:-1],\n",
    "                        first_january_week[1:]):\n",
    "    one_year = sorted_data['inc'][week1:week2-1]\n",
    "    if not (abs(len(one_year)-52) < 2):\n",
    "        print(week1, week2, len(one_year))\n",
    "        \n",
    "    assert abs(len(one_year)-52) < 2\n",
    "    yearly_incidence.append(one_year.sum())\n",
    "    year.append(week2.year)\n",
    "yearly_incidence = pd.Series(data=yearly_incidence, index=year)\n",
    "\n",
    "yearly_incidence.plot(style='*')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "zoom:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbf9589d9e8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "yearly_incidence[2005-1991:2015-1991].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(561400, 27)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yearly_incidence.min(), yearly_incidence.argmin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1992    656000\n",
       "1993    825671\n",
       "1994    601390\n",
       "1995    657596\n",
       "1996    667294\n",
       "1997    632212\n",
       "1998    624302\n",
       "1999    760258\n",
       "2000    660461\n",
       "2001    656975\n",
       "2002    563415\n",
       "2003    589547\n",
       "2004    678928\n",
       "2005    832896\n",
       "2006    655727\n",
       "2007    574493\n",
       "2008    778119\n",
       "2009    738993\n",
       "2010    847724\n",
       "2011    781579\n",
       "2012    633840\n",
       "2013    698277\n",
       "2014    658318\n",
       "2015    648607\n",
       "2016    635356\n",
       "2017    736724\n",
       "2018    564245\n",
       "2019    561400\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yearly_incidence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}