diff --git a/module3/exo1/Untitled.ipynb b/module3/exo1/Untitled.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..7fec51502cbc3200b3d0ffc6bbba1fe85e197f3d
--- /dev/null
+++ b/module3/exo1/Untitled.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/module3/exo1/analyse-syndrome-grippal.ipynb b/module3/exo1/analyse-syndrome-grippal.ipynb
index 59d72b5b58a3ae26346460dd39e62a39c55243d7..1d23ff9a909a0559f3a191f335816adcadee573e 100644
--- a/module3/exo1/analyse-syndrome-grippal.ipynb
+++ b/module3/exo1/analyse-syndrome-grippal.ipynb
@@ -9,9 +9,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'pwd' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-17-971fa534f554>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0misoweek\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mpwd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'pwd' is not defined"
+     ]
+    }
+   ],
    "source": [
     "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
@@ -28,10 +40,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 2,
+   "metadata": {},
    "outputs": [],
    "source": [
     "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\""
@@ -61,11 +71,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "File b'/home/e158401a/T\\xc3\\xa9l\\xc3\\xa9chargements/incidence-PAY-3.csv' does not exist",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-18-b307e371285d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mraw_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"/home/e158401a/Téléchargements/incidence-PAY-3.csv\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskiprows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mraw_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, skip_footer, doublequote, delim_whitespace, as_recarray, compact_ints, use_unsigned, low_memory, buffer_lines, memory_map, float_precision)\u001b[0m\n\u001b[1;32m    707\u001b[0m                     skip_blank_lines=skip_blank_lines)\n\u001b[1;32m    708\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 709\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    710\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    711\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    447\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    448\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 449\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    450\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    451\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    816\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'has_index_names'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'has_index_names'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    817\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 818\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    819\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    820\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m   1047\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'c'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1048\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'c'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1049\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1050\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1051\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'python'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m   1693\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'allow_leading_cols'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex_col\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1694\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1695\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1696\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1697\u001b[0m         \u001b[0;31m# XXX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;31mFileNotFoundError\u001b[0m: File b'/home/e158401a/T\\xc3\\xa9l\\xc3\\xa9chargements/incidence-PAY-3.csv' does not exist"
+     ]
+    }
+   ],
    "source": [
-    "raw_data = pd.read_csv(data_url, skiprows=1)\n",
+    "raw_data = pd.read_csv(\"/home/e158401a/Téléchargements/incidence-PAY-3.csv\", skiprows=1)\n",
     "raw_data"
    ]
   },
@@ -78,9 +107,73 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "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>1671</th>\n",
+       "      <td>198919</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        week  indicator  inc  inc_low  inc_up  inc100  inc100_low  inc100_up  \\\n",
+       "1671  198919          3    0      NaN     NaN       0         NaN        NaN   \n",
+       "\n",
+       "     geo_insee geo_name  \n",
+       "1671        FR   France  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "raw_data[raw_data.isnull().any(axis=1)]"
    ]
@@ -94,9 +187,976 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "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>202120</td>\n",
+       "      <td>3</td>\n",
+       "      <td>13803</td>\n",
+       "      <td>9830.0</td>\n",
+       "      <td>17776.0</td>\n",
+       "      <td>21</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>202119</td>\n",
+       "      <td>3</td>\n",
+       "      <td>9774</td>\n",
+       "      <td>7030.0</td>\n",
+       "      <td>12518.0</td>\n",
+       "      <td>15</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>202118</td>\n",
+       "      <td>3</td>\n",
+       "      <td>12135</td>\n",
+       "      <td>9165.0</td>\n",
+       "      <td>15105.0</td>\n",
+       "      <td>18</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>202117</td>\n",
+       "      <td>3</td>\n",
+       "      <td>12058</td>\n",
+       "      <td>8891.0</td>\n",
+       "      <td>15225.0</td>\n",
+       "      <td>18</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>202116</td>\n",
+       "      <td>3</td>\n",
+       "      <td>16505</td>\n",
+       "      <td>12735.0</td>\n",
+       "      <td>20275.0</td>\n",
+       "      <td>25</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>202115</td>\n",
+       "      <td>3</td>\n",
+       "      <td>19306</td>\n",
+       "      <td>15398.0</td>\n",
+       "      <td>23214.0</td>\n",
+       "      <td>29</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>202114</td>\n",
+       "      <td>3</td>\n",
+       "      <td>21073</td>\n",
+       "      <td>17099.0</td>\n",
+       "      <td>25047.0</td>\n",
+       "      <td>32</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>202113</td>\n",
+       "      <td>3</td>\n",
+       "      <td>26413</td>\n",
+       "      <td>22094.0</td>\n",
+       "      <td>30732.0</td>\n",
+       "      <td>40</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>202112</td>\n",
+       "      <td>3</td>\n",
+       "      <td>30658</td>\n",
+       "      <td>25919.0</td>\n",
+       "      <td>35397.0</td>\n",
+       "      <td>46</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>53.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>202111</td>\n",
+       "      <td>3</td>\n",
+       "      <td>24988</td>\n",
+       "      <td>20718.0</td>\n",
+       "      <td>29258.0</td>\n",
+       "      <td>38</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>202110</td>\n",
+       "      <td>3</td>\n",
+       "      <td>19539</td>\n",
+       "      <td>15951.0</td>\n",
+       "      <td>23127.0</td>\n",
+       "      <td>30</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>202109</td>\n",
+       "      <td>3</td>\n",
+       "      <td>17572</td>\n",
+       "      <td>13926.0</td>\n",
+       "      <td>21218.0</td>\n",
+       "      <td>27</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>202108</td>\n",
+       "      <td>3</td>\n",
+       "      <td>20882</td>\n",
+       "      <td>16907.0</td>\n",
+       "      <td>24857.0</td>\n",
+       "      <td>32</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>202107</td>\n",
+       "      <td>3</td>\n",
+       "      <td>22393</td>\n",
+       "      <td>18303.0</td>\n",
+       "      <td>26483.0</td>\n",
+       "      <td>34</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>202106</td>\n",
+       "      <td>3</td>\n",
+       "      <td>23183</td>\n",
+       "      <td>19134.0</td>\n",
+       "      <td>27232.0</td>\n",
+       "      <td>35</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>202105</td>\n",
+       "      <td>3</td>\n",
+       "      <td>22426</td>\n",
+       "      <td>18445.0</td>\n",
+       "      <td>26407.0</td>\n",
+       "      <td>34</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>202104</td>\n",
+       "      <td>3</td>\n",
+       "      <td>25804</td>\n",
+       "      <td>21491.0</td>\n",
+       "      <td>30117.0</td>\n",
+       "      <td>39</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>202103</td>\n",
+       "      <td>3</td>\n",
+       "      <td>21810</td>\n",
+       "      <td>17894.0</td>\n",
+       "      <td>25726.0</td>\n",
+       "      <td>33</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>202102</td>\n",
+       "      <td>3</td>\n",
+       "      <td>17320</td>\n",
+       "      <td>13906.0</td>\n",
+       "      <td>20734.0</td>\n",
+       "      <td>26</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>202101</td>\n",
+       "      <td>3</td>\n",
+       "      <td>21799</td>\n",
+       "      <td>17778.0</td>\n",
+       "      <td>25820.0</td>\n",
+       "      <td>33</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>202053</td>\n",
+       "      <td>3</td>\n",
+       "      <td>21220</td>\n",
+       "      <td>16498.0</td>\n",
+       "      <td>25942.0</td>\n",
+       "      <td>32</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>202052</td>\n",
+       "      <td>3</td>\n",
+       "      <td>16428</td>\n",
+       "      <td>12285.0</td>\n",
+       "      <td>20571.0</td>\n",
+       "      <td>25</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>202051</td>\n",
+       "      <td>3</td>\n",
+       "      <td>21619</td>\n",
+       "      <td>17370.0</td>\n",
+       "      <td>25868.0</td>\n",
+       "      <td>33</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>202050</td>\n",
+       "      <td>3</td>\n",
+       "      <td>16845</td>\n",
+       "      <td>13220.0</td>\n",
+       "      <td>20470.0</td>\n",
+       "      <td>26</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>202049</td>\n",
+       "      <td>3</td>\n",
+       "      <td>12939</td>\n",
+       "      <td>9923.0</td>\n",
+       "      <td>15955.0</td>\n",
+       "      <td>20</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>202048</td>\n",
+       "      <td>3</td>\n",
+       "      <td>13804</td>\n",
+       "      <td>10641.0</td>\n",
+       "      <td>16967.0</td>\n",
+       "      <td>21</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>202047</td>\n",
+       "      <td>3</td>\n",
+       "      <td>19085</td>\n",
+       "      <td>15285.0</td>\n",
+       "      <td>22885.0</td>\n",
+       "      <td>29</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>202046</td>\n",
+       "      <td>3</td>\n",
+       "      <td>24801</td>\n",
+       "      <td>20503.0</td>\n",
+       "      <td>29099.0</td>\n",
+       "      <td>38</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>202045</td>\n",
+       "      <td>3</td>\n",
+       "      <td>42516</td>\n",
+       "      <td>36857.0</td>\n",
+       "      <td>48175.0</td>\n",
+       "      <td>65</td>\n",
+       "      <td>56.0</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>202044</td>\n",
+       "      <td>3</td>\n",
+       "      <td>44567</td>\n",
+       "      <td>38521.0</td>\n",
+       "      <td>50613.0</td>\n",
+       "      <td>68</td>\n",
+       "      <td>59.0</td>\n",
+       "      <td>77.0</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>1878</th>\n",
+       "      <td>198521</td>\n",
+       "      <td>3</td>\n",
+       "      <td>26096</td>\n",
+       "      <td>19621.0</td>\n",
+       "      <td>32571.0</td>\n",
+       "      <td>47</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>59.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1879</th>\n",
+       "      <td>198520</td>\n",
+       "      <td>3</td>\n",
+       "      <td>27896</td>\n",
+       "      <td>20885.0</td>\n",
+       "      <td>34907.0</td>\n",
+       "      <td>51</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>64.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1880</th>\n",
+       "      <td>198519</td>\n",
+       "      <td>3</td>\n",
+       "      <td>43154</td>\n",
+       "      <td>32821.0</td>\n",
+       "      <td>53487.0</td>\n",
+       "      <td>78</td>\n",
+       "      <td>59.0</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1881</th>\n",
+       "      <td>198518</td>\n",
+       "      <td>3</td>\n",
+       "      <td>40555</td>\n",
+       "      <td>29935.0</td>\n",
+       "      <td>51175.0</td>\n",
+       "      <td>74</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>93.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1882</th>\n",
+       "      <td>198517</td>\n",
+       "      <td>3</td>\n",
+       "      <td>34053</td>\n",
+       "      <td>24366.0</td>\n",
+       "      <td>43740.0</td>\n",
+       "      <td>62</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1883</th>\n",
+       "      <td>198516</td>\n",
+       "      <td>3</td>\n",
+       "      <td>50362</td>\n",
+       "      <td>36451.0</td>\n",
+       "      <td>64273.0</td>\n",
+       "      <td>91</td>\n",
+       "      <td>66.0</td>\n",
+       "      <td>116.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1884</th>\n",
+       "      <td>198515</td>\n",
+       "      <td>3</td>\n",
+       "      <td>63881</td>\n",
+       "      <td>45538.0</td>\n",
+       "      <td>82224.0</td>\n",
+       "      <td>116</td>\n",
+       "      <td>83.0</td>\n",
+       "      <td>149.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1885</th>\n",
+       "      <td>198514</td>\n",
+       "      <td>3</td>\n",
+       "      <td>134545</td>\n",
+       "      <td>114400.0</td>\n",
+       "      <td>154690.0</td>\n",
+       "      <td>244</td>\n",
+       "      <td>207.0</td>\n",
+       "      <td>281.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1886</th>\n",
+       "      <td>198513</td>\n",
+       "      <td>3</td>\n",
+       "      <td>197206</td>\n",
+       "      <td>176080.0</td>\n",
+       "      <td>218332.0</td>\n",
+       "      <td>357</td>\n",
+       "      <td>319.0</td>\n",
+       "      <td>395.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1887</th>\n",
+       "      <td>198512</td>\n",
+       "      <td>3</td>\n",
+       "      <td>245240</td>\n",
+       "      <td>223304.0</td>\n",
+       "      <td>267176.0</td>\n",
+       "      <td>445</td>\n",
+       "      <td>405.0</td>\n",
+       "      <td>485.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1888</th>\n",
+       "      <td>198511</td>\n",
+       "      <td>3</td>\n",
+       "      <td>276205</td>\n",
+       "      <td>252399.0</td>\n",
+       "      <td>300011.0</td>\n",
+       "      <td>501</td>\n",
+       "      <td>458.0</td>\n",
+       "      <td>544.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1889</th>\n",
+       "      <td>198510</td>\n",
+       "      <td>3</td>\n",
+       "      <td>353231</td>\n",
+       "      <td>326279.0</td>\n",
+       "      <td>380183.0</td>\n",
+       "      <td>640</td>\n",
+       "      <td>591.0</td>\n",
+       "      <td>689.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1890</th>\n",
+       "      <td>198509</td>\n",
+       "      <td>3</td>\n",
+       "      <td>369895</td>\n",
+       "      <td>341109.0</td>\n",
+       "      <td>398681.0</td>\n",
+       "      <td>670</td>\n",
+       "      <td>618.0</td>\n",
+       "      <td>722.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1891</th>\n",
+       "      <td>198508</td>\n",
+       "      <td>3</td>\n",
+       "      <td>389886</td>\n",
+       "      <td>359529.0</td>\n",
+       "      <td>420243.0</td>\n",
+       "      <td>707</td>\n",
+       "      <td>652.0</td>\n",
+       "      <td>762.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1892</th>\n",
+       "      <td>198507</td>\n",
+       "      <td>3</td>\n",
+       "      <td>471852</td>\n",
+       "      <td>432599.0</td>\n",
+       "      <td>511105.0</td>\n",
+       "      <td>855</td>\n",
+       "      <td>784.0</td>\n",
+       "      <td>926.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1893</th>\n",
+       "      <td>198506</td>\n",
+       "      <td>3</td>\n",
+       "      <td>565825</td>\n",
+       "      <td>518011.0</td>\n",
+       "      <td>613639.0</td>\n",
+       "      <td>1026</td>\n",
+       "      <td>939.0</td>\n",
+       "      <td>1113.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1894</th>\n",
+       "      <td>198505</td>\n",
+       "      <td>3</td>\n",
+       "      <td>637302</td>\n",
+       "      <td>592795.0</td>\n",
+       "      <td>681809.0</td>\n",
+       "      <td>1155</td>\n",
+       "      <td>1074.0</td>\n",
+       "      <td>1236.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1895</th>\n",
+       "      <td>198504</td>\n",
+       "      <td>3</td>\n",
+       "      <td>424937</td>\n",
+       "      <td>390794.0</td>\n",
+       "      <td>459080.0</td>\n",
+       "      <td>770</td>\n",
+       "      <td>708.0</td>\n",
+       "      <td>832.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1896</th>\n",
+       "      <td>198503</td>\n",
+       "      <td>3</td>\n",
+       "      <td>213901</td>\n",
+       "      <td>174689.0</td>\n",
+       "      <td>253113.0</td>\n",
+       "      <td>388</td>\n",
+       "      <td>317.0</td>\n",
+       "      <td>459.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1897</th>\n",
+       "      <td>198502</td>\n",
+       "      <td>3</td>\n",
+       "      <td>97586</td>\n",
+       "      <td>80949.0</td>\n",
+       "      <td>114223.0</td>\n",
+       "      <td>177</td>\n",
+       "      <td>147.0</td>\n",
+       "      <td>207.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1898</th>\n",
+       "      <td>198501</td>\n",
+       "      <td>3</td>\n",
+       "      <td>85489</td>\n",
+       "      <td>65918.0</td>\n",
+       "      <td>105060.0</td>\n",
+       "      <td>155</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>190.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1899</th>\n",
+       "      <td>198452</td>\n",
+       "      <td>3</td>\n",
+       "      <td>84830</td>\n",
+       "      <td>60602.0</td>\n",
+       "      <td>109058.0</td>\n",
+       "      <td>154</td>\n",
+       "      <td>110.0</td>\n",
+       "      <td>198.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1900</th>\n",
+       "      <td>198451</td>\n",
+       "      <td>3</td>\n",
+       "      <td>101726</td>\n",
+       "      <td>80242.0</td>\n",
+       "      <td>123210.0</td>\n",
+       "      <td>185</td>\n",
+       "      <td>146.0</td>\n",
+       "      <td>224.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1901</th>\n",
+       "      <td>198450</td>\n",
+       "      <td>3</td>\n",
+       "      <td>123680</td>\n",
+       "      <td>101401.0</td>\n",
+       "      <td>145959.0</td>\n",
+       "      <td>225</td>\n",
+       "      <td>184.0</td>\n",
+       "      <td>266.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1902</th>\n",
+       "      <td>198449</td>\n",
+       "      <td>3</td>\n",
+       "      <td>101073</td>\n",
+       "      <td>81684.0</td>\n",
+       "      <td>120462.0</td>\n",
+       "      <td>184</td>\n",
+       "      <td>149.0</td>\n",
+       "      <td>219.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1903</th>\n",
+       "      <td>198448</td>\n",
+       "      <td>3</td>\n",
+       "      <td>78620</td>\n",
+       "      <td>60634.0</td>\n",
+       "      <td>96606.0</td>\n",
+       "      <td>143</td>\n",
+       "      <td>110.0</td>\n",
+       "      <td>176.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1904</th>\n",
+       "      <td>198447</td>\n",
+       "      <td>3</td>\n",
+       "      <td>72029</td>\n",
+       "      <td>54274.0</td>\n",
+       "      <td>89784.0</td>\n",
+       "      <td>131</td>\n",
+       "      <td>99.0</td>\n",
+       "      <td>163.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1905</th>\n",
+       "      <td>198446</td>\n",
+       "      <td>3</td>\n",
+       "      <td>87330</td>\n",
+       "      <td>67686.0</td>\n",
+       "      <td>106974.0</td>\n",
+       "      <td>159</td>\n",
+       "      <td>123.0</td>\n",
+       "      <td>195.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1906</th>\n",
+       "      <td>198445</td>\n",
+       "      <td>3</td>\n",
+       "      <td>135223</td>\n",
+       "      <td>101414.0</td>\n",
+       "      <td>169032.0</td>\n",
+       "      <td>246</td>\n",
+       "      <td>184.0</td>\n",
+       "      <td>308.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1907</th>\n",
+       "      <td>198444</td>\n",
+       "      <td>3</td>\n",
+       "      <td>68422</td>\n",
+       "      <td>20056.0</td>\n",
+       "      <td>116788.0</td>\n",
+       "      <td>125</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>213.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1907 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        week  indicator     inc   inc_low    inc_up  inc100  inc100_low  \\\n",
+       "0     202120          3   13803    9830.0   17776.0      21        15.0   \n",
+       "1     202119          3    9774    7030.0   12518.0      15        11.0   \n",
+       "2     202118          3   12135    9165.0   15105.0      18        14.0   \n",
+       "3     202117          3   12058    8891.0   15225.0      18        13.0   \n",
+       "4     202116          3   16505   12735.0   20275.0      25        19.0   \n",
+       "5     202115          3   19306   15398.0   23214.0      29        23.0   \n",
+       "6     202114          3   21073   17099.0   25047.0      32        26.0   \n",
+       "7     202113          3   26413   22094.0   30732.0      40        33.0   \n",
+       "8     202112          3   30658   25919.0   35397.0      46        39.0   \n",
+       "9     202111          3   24988   20718.0   29258.0      38        32.0   \n",
+       "10    202110          3   19539   15951.0   23127.0      30        25.0   \n",
+       "11    202109          3   17572   13926.0   21218.0      27        21.0   \n",
+       "12    202108          3   20882   16907.0   24857.0      32        26.0   \n",
+       "13    202107          3   22393   18303.0   26483.0      34        28.0   \n",
+       "14    202106          3   23183   19134.0   27232.0      35        29.0   \n",
+       "15    202105          3   22426   18445.0   26407.0      34        28.0   \n",
+       "16    202104          3   25804   21491.0   30117.0      39        32.0   \n",
+       "17    202103          3   21810   17894.0   25726.0      33        27.0   \n",
+       "18    202102          3   17320   13906.0   20734.0      26        21.0   \n",
+       "19    202101          3   21799   17778.0   25820.0      33        27.0   \n",
+       "20    202053          3   21220   16498.0   25942.0      32        25.0   \n",
+       "21    202052          3   16428   12285.0   20571.0      25        19.0   \n",
+       "22    202051          3   21619   17370.0   25868.0      33        27.0   \n",
+       "23    202050          3   16845   13220.0   20470.0      26        20.0   \n",
+       "24    202049          3   12939    9923.0   15955.0      20        15.0   \n",
+       "25    202048          3   13804   10641.0   16967.0      21        16.0   \n",
+       "26    202047          3   19085   15285.0   22885.0      29        23.0   \n",
+       "27    202046          3   24801   20503.0   29099.0      38        31.0   \n",
+       "28    202045          3   42516   36857.0   48175.0      65        56.0   \n",
+       "29    202044          3   44567   38521.0   50613.0      68        59.0   \n",
+       "...      ...        ...     ...       ...       ...     ...         ...   \n",
+       "1878  198521          3   26096   19621.0   32571.0      47        35.0   \n",
+       "1879  198520          3   27896   20885.0   34907.0      51        38.0   \n",
+       "1880  198519          3   43154   32821.0   53487.0      78        59.0   \n",
+       "1881  198518          3   40555   29935.0   51175.0      74        55.0   \n",
+       "1882  198517          3   34053   24366.0   43740.0      62        44.0   \n",
+       "1883  198516          3   50362   36451.0   64273.0      91        66.0   \n",
+       "1884  198515          3   63881   45538.0   82224.0     116        83.0   \n",
+       "1885  198514          3  134545  114400.0  154690.0     244       207.0   \n",
+       "1886  198513          3  197206  176080.0  218332.0     357       319.0   \n",
+       "1887  198512          3  245240  223304.0  267176.0     445       405.0   \n",
+       "1888  198511          3  276205  252399.0  300011.0     501       458.0   \n",
+       "1889  198510          3  353231  326279.0  380183.0     640       591.0   \n",
+       "1890  198509          3  369895  341109.0  398681.0     670       618.0   \n",
+       "1891  198508          3  389886  359529.0  420243.0     707       652.0   \n",
+       "1892  198507          3  471852  432599.0  511105.0     855       784.0   \n",
+       "1893  198506          3  565825  518011.0  613639.0    1026       939.0   \n",
+       "1894  198505          3  637302  592795.0  681809.0    1155      1074.0   \n",
+       "1895  198504          3  424937  390794.0  459080.0     770       708.0   \n",
+       "1896  198503          3  213901  174689.0  253113.0     388       317.0   \n",
+       "1897  198502          3   97586   80949.0  114223.0     177       147.0   \n",
+       "1898  198501          3   85489   65918.0  105060.0     155       120.0   \n",
+       "1899  198452          3   84830   60602.0  109058.0     154       110.0   \n",
+       "1900  198451          3  101726   80242.0  123210.0     185       146.0   \n",
+       "1901  198450          3  123680  101401.0  145959.0     225       184.0   \n",
+       "1902  198449          3  101073   81684.0  120462.0     184       149.0   \n",
+       "1903  198448          3   78620   60634.0   96606.0     143       110.0   \n",
+       "1904  198447          3   72029   54274.0   89784.0     131        99.0   \n",
+       "1905  198446          3   87330   67686.0  106974.0     159       123.0   \n",
+       "1906  198445          3  135223  101414.0  169032.0     246       184.0   \n",
+       "1907  198444          3   68422   20056.0  116788.0     125        37.0   \n",
+       "\n",
+       "      inc100_up geo_insee geo_name  \n",
+       "0          27.0        FR   France  \n",
+       "1          19.0        FR   France  \n",
+       "2          22.0        FR   France  \n",
+       "3          23.0        FR   France  \n",
+       "4          31.0        FR   France  \n",
+       "5          35.0        FR   France  \n",
+       "6          38.0        FR   France  \n",
+       "7          47.0        FR   France  \n",
+       "8          53.0        FR   France  \n",
+       "9          44.0        FR   France  \n",
+       "10         35.0        FR   France  \n",
+       "11         33.0        FR   France  \n",
+       "12         38.0        FR   France  \n",
+       "13         40.0        FR   France  \n",
+       "14         41.0        FR   France  \n",
+       "15         40.0        FR   France  \n",
+       "16         46.0        FR   France  \n",
+       "17         39.0        FR   France  \n",
+       "18         31.0        FR   France  \n",
+       "19         39.0        FR   France  \n",
+       "20         39.0        FR   France  \n",
+       "21         31.0        FR   France  \n",
+       "22         39.0        FR   France  \n",
+       "23         32.0        FR   France  \n",
+       "24         25.0        FR   France  \n",
+       "25         26.0        FR   France  \n",
+       "26         35.0        FR   France  \n",
+       "27         45.0        FR   France  \n",
+       "28         74.0        FR   France  \n",
+       "29         77.0        FR   France  \n",
+       "...         ...       ...      ...  \n",
+       "1878       59.0        FR   France  \n",
+       "1879       64.0        FR   France  \n",
+       "1880       97.0        FR   France  \n",
+       "1881       93.0        FR   France  \n",
+       "1882       80.0        FR   France  \n",
+       "1883      116.0        FR   France  \n",
+       "1884      149.0        FR   France  \n",
+       "1885      281.0        FR   France  \n",
+       "1886      395.0        FR   France  \n",
+       "1887      485.0        FR   France  \n",
+       "1888      544.0        FR   France  \n",
+       "1889      689.0        FR   France  \n",
+       "1890      722.0        FR   France  \n",
+       "1891      762.0        FR   France  \n",
+       "1892      926.0        FR   France  \n",
+       "1893     1113.0        FR   France  \n",
+       "1894     1236.0        FR   France  \n",
+       "1895      832.0        FR   France  \n",
+       "1896      459.0        FR   France  \n",
+       "1897      207.0        FR   France  \n",
+       "1898      190.0        FR   France  \n",
+       "1899      198.0        FR   France  \n",
+       "1900      224.0        FR   France  \n",
+       "1901      266.0        FR   France  \n",
+       "1902      219.0        FR   France  \n",
+       "1903      176.0        FR   France  \n",
+       "1904      163.0        FR   France  \n",
+       "1905      195.0        FR   France  \n",
+       "1906      308.0        FR   France  \n",
+       "1907      213.0        FR   France  \n",
+       "\n",
+       "[1907 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "data = raw_data.dropna().copy()\n",
     "data"
@@ -122,7 +1182,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -152,10 +1212,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 7,
+   "metadata": {},
    "outputs": [],
    "source": [
     "sorted_data = data.set_index('period').sort_index()"
@@ -179,9 +1237,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1989-05-01/1989-05-07 1989-05-15/1989-05-21\n"
+     ]
+    }
+   ],
    "source": [
     "periods = sorted_data.index\n",
     "for p1, p2 in zip(periods[:-1], periods[1:]):\n",
@@ -199,9 +1265,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f917f08f160>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZQAAAEKCAYAAAA1qaOTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJztnXm4HUWZ/7/vWe65e/aNBEgCkRBAthiCIqKRxWUERxjjRsZBcZRxG2cUcBxGFBF/OiguKI4ILuygIAgYgoCyBJKwhGwkJCH7epObm7uepX5/dFWf7j5V3X3O6bPdvJ/nuc/p2921dHV3vfUuVU1CCDAMwzBMucRqXQGGYRhmeMAChWEYhokEFigMwzBMJLBAYRiGYSKBBQrDMAwTCSxQGIZhmEhggcIwDMNEAgsUhmEYJhJYoDAMwzCRkKh1BarJ2LFjxdSpU2tdDYZhmIZi6dKle4QQ44LOO6QEytSpU7FkyZJaV4NhGKahIKI3wpzHJi+GYRgmEligMAzDMJHAAoVhGIaJBBYoDMMwTCSwQGEYhmEiIVCgENHNRLSLiF517BtNRAuJaK38HeU4dgURrSOiNUR0rmP/qUS0XB67gYhI7k8R0Z1y/2IimupIs0CWsZaIFjj2T5PnrpVpm8pvCoZhGKYcwmgotwA4z7PvcgCLhBAzACyS/4OIZgGYD+A4meZnRBSXaW4EcCmAGfJP5XkJgH1CiKMBXA/gOpnXaABXATgNwBwAVzkE13UArpfl75N5MAzDMDUkUKAIIZ4C0OXZfT6AW+X2rQAucOy/QwgxKITYAGAdgDlENAlApxDiWWF9c/g3njQqr3sAzJPay7kAFgohuoQQ+wAsBHCePPYuea63/GFNz0Aa97+0tdbVYBiG0VLqxMYJQojtACCE2E5E4+X+yQCec5y3Re5Ly23vfpVms8wrQ0TdAMY493vSjAGwXwiR0eQ1rPnPu1/BIyt2YObEThwzsaPW1WEYhnERtVOeNPuEz/5S0vjlVVghokuJaAkRLdm9e7fptIZgU1cfACCdzdW4JgzDMIWUKlB2SjMW5O8uuX8LgMMd500BsE3un6LZ70pDRAkAI2CZ2Ex57QEwUp7rzasAIcRNQojZQojZ48YFLkVT12RzltxMxHUylWEYpraUKlAeAKCirhYAuN+xf76M3JoGy/n+vDSP9RDRXOkDudiTRuV1IYDHpZ/lUQDnENEo6Yw/B8Cj8thf5bne8oc16ZylmSRiLFAYhqk/An0oRHQ7gLMAjCWiLbAir74L4C4iugTAJgAXAYAQYgUR3QVgJYAMgMuEEFmZ1WdhRYy1AHhY/gHArwD8lojWwdJM5su8uojoWwBekOddLYRQwQFfA3AHEX0bwIsyj2GP0lDiMZ4+xDBM/REoUIQQHzEcmmc4/xoA12j2LwFwvGb/AKRA0hy7GcDNmv3rYYUSH1JkspZAYf2EYZh6hIe6DYTSUHLCGIPAMAxTM1igNBBZKUhYnDAMU4+wQGkglKlLsIbCMEwdwgKlgSApUVieMAxTj7BAaSBI6ig5FigMw9QhLFAaCFtDYS8KwzB1CAuUBkL5UHK88grDMHUIC5QGQn5ChsOGGYapS1igMAzDMJHAAqWBUD4U1lAYhqlHWKA0EBw2zDBMPcMCpYHIhw2zRGEYpv5ggdJA5MOGGYZh6g8WKA0EL73CMEw9wwKlgciHDde4IgHM/MbD+No9r9S6GgzDVBkWKA1EXkOpaTUCGUjncOeSzbWuBsMwVYYFSiPBYcMMw9QxLFAaiEbRUBiGOTRhgdJAKB8KO+UZhqlHWKA0EPbikCxPGIapQ1igMAzDMJHAAqUB4e+hMAxTj7BAaSB4LS+GYeoZFigNBAsShmHqGRYoDQjLFYZh6hEWKAzDMEwksEBpQHgeCsMw9QgLlAaCl69nGKaeYYHSQLBiwjBMPcMCpRHxESznXP8kfvHk69WrC8MwjKQsgUJEXyaiFUT0KhHdTkTNRDSaiBYS0Vr5O8px/hVEtI6I1hDRuY79pxLRcnnsBpKLVhFRiojulPsXE9FUR5oFsoy1RLSgnOsYTry28yCufXh1ravBMMwhSMkChYgmA/gCgNlCiOMBxAHMB3A5gEVCiBkAFsn/QUSz5PHjAJwH4GdEFJfZ3QjgUgAz5N95cv8lAPYJIY4GcD2A62ReowFcBeA0AHMAXOUUXMMdninPMEw9Uq7JKwGghYgSAFoBbANwPoBb5fFbAVwgt88HcIcQYlAIsQHAOgBziGgSgE4hxLPCCl/6jSeNyuseAPOk9nIugIVCiC4hxD4AC5EXQsMWninPMEw9U7JAEUJsBfB9AJsAbAfQLYT4C4AJQojt8pztAMbLJJMBOD/jt0Xumyy3vftdaYQQGQDdAMb45MUwDMPUiHJMXqNgaRDTABwGoI2IPu6XRLNP+OwvNY23npcS0RIiWrJ7926f6jUOrKEwDFOPlGPyejeADUKI3UKINID7ALwVwE5pxoL83SXP3wLgcEf6KbBMZFvktne/K400q40A0OWTVwFCiJuEELOFELPHjRtX4qXWFyxPGIapR8oRKJsAzCWiVunXmAdgFYAHAKioqwUA7pfbDwCYLyO3psFyvj8vzWI9RDRX5nOxJ43K60IAj0s/y6MAziGiUVJTOkfuYxiGYWpEotSEQojFRHQPgGUAMgBeBHATgHYAdxHRJbCEzkXy/BVEdBeAlfL8y4QQWZndZwHcAqAFwMPyDwB+BeC3RLQOlmYyX+bVRUTfAvCCPO9qIURXqdfSaPDSKwzD1CMlCxQAEEJcBSt818kgLG1Fd/41AK7R7F8C4HjN/gFIgaQ5djOAm4usMsMwDFMheKZ8A8L6CcMw9QgLlAaELV4Mw9QjLFAYhmGYSGCB0pCwisIwTP3BAqUBORRMXl+640X8dfWu4BMZhqkbWKAwdckfX9qGT97yQvCJDMPUDSxQGpBDQEFhGKYBYYHCMAzDRAILlAbkUPChMAzTeLBAaSBILrLMH9hiGKYeYYHSQAQJkuGyxtdwuQ6GOdRggdKAmPrb4dIPD5frYJhDDRYoDUTe5KVnuPTDOZYoDNOQsEAZRgwXU9HwuAqGOfRggdKAmATHcOmIh4lcZJhDDhYoDUSwU75KFakwHMXGMI0JC5RhxHDpiIeLYGSYQw0WKA2E7ZTnDpdhmDqEBcowoh4ETRSBAfVwHQzDFA8LlAaknk1bUQgDDhtmmMaEBcowYrj0w8PkMhjmkIMFSgNinClfB11xFDUYLvNpGOZQgwVKAzLsl16pdQUYhikJFijDiHroiNkpzzCHLixQGhDjWl510BNHUoPaXwbDMCXAAqUBqQfBUUnCRHn1DmbQ3ZeuQm0YhgkLC5QieHVrNxau3FnrahipBzEThawLk8XcaxfhxKv/Un5hDMNERqLWFWgk3v/jvwMANn73fTWth9nkVdVq6OsQgVgLo4H1DGTKLodhmGhhDWU4UQcCJQqGyWUwzCEHC5RGpJ7noURh8qr9ZTAMUwIsUBoQk+AYLh1xPQhGhmGKpyyBQkQjiegeIlpNRKuI6HQiGk1EC4lorfwd5Tj/CiJaR0RriOhcx/5TiWi5PHYDEZHcnyKiO+X+xUQ01ZFmgSxjLREtKOc6hgvDphseNhfCMIcW5WooPwLwiBBiJoATAawCcDmARUKIGQAWyf9BRLMAzAdwHIDzAPyMiOIynxsBXApghvw7T+6/BMA+IcTRAK4HcJ3MazSAqwCcBmAOgKucgmu4Y54pX/ueOJrFIcvPg2GY6lOyQCGiTgBnAvgVAAghhoQQ+wGcD+BWedqtAC6Q2+cDuEMIMSiE2ABgHYA5RDQJQKcQ4llh9Yi/8aRRed0DYJ7UXs4FsFAI0SWE2AdgIfJCaNhi6W0+UV5Vq0llYZMXwzQm5Wgo0wHsBvBrInqRiP6PiNoATBBCbAcA+Ttenj8ZwGZH+i1y32S57d3vSiOEyADoBjDGJy+mxkQTNhxBRRiGqTrlCJQEgFMA3CiEOBlAL6R5ywBp9gmf/aWmcRdKdCkRLSGiJbt37/apXuNQz4tDVmtiI8Mw9Uc5AmULgC1CiMXy/3tgCZid0owF+bvLcf7hjvRTAGyT+6do9rvSEFECwAgAXT55FSCEuEkIMVsIMXvcuHElXGb9ENRZDxdTUT34ghiGKZ6SBYoQYgeAzUR0jNw1D8BKAA8AUFFXCwDcL7cfADBfRm5Ng+V8f16axXqIaK70j1zsSaPyuhDA49LP8iiAc4holHTGnyP3HRIYBUcd9MPRfA8lgkwYhqk65S698nkAvyeiJgDrAXwSlpC6i4guAbAJwEUAIIRYQUR3wRI6GQCXCSGyMp/PArgFQAuAh+UfYDn8f0tE62BpJvNlXl1E9C0AL8jzrhZCdJV5LaERQoBIZ3WrLLZTvn7lCWsXDHMIU5ZAEUK8BGC25tA8w/nXALhGs38JgOM1+wcgBZLm2M0Abi6mvlGRzQkk4tUXKEEMl75crTZcA5nNMEwZ8Ez5EsjWuOc2hw3XXqKwyas2zLnmMVz2+2W1rgZziMMCpQSyNZp5F+iUHyYd8TC5jKqyq2cQDy3fXutqMIc4LFBKoFYCxaaOJUc0i0PW7/VVm3W7erB2Z0+tq8EwoeDvoZRALlebchtipjzPQ4mUd//vUwBq/w0ehgkDayglUGsfionhMrJXl8E++cZiycYu7Dk4WOtqMDWEBUoJ1NrkVdcz5aNxy0eQR/nsOTiIL97xInoH+euQYbjw58/in37+bK2rwdQQFiglkKt1lFc9SA4DUa42XIu5Pk6uX/ga7n9pG+5btiX45EOcTNayA6/f01vjmjC1hAVKCdRaQzFRx3KmKOrlOlIJ6+sKA+kaOc0aiMHM8Gmj+Tc9i9nfXljrajQk7JQvgVoLlGE/D6WK13FwMIPBdBZj2lMFx5qT1nhrMJMtOMa4UQKlKd74Y9Tn1ldt0Y1hR+Pf/RpQa5OXiXqoVhTmuGo65d/1/Sdw6rcf0x5jDSU8A2lL6DYluEs5lOG7XwKZWmsodSA4Kkk1r29XjzkqKSU1lKEsC5QgMlnrpsU4NO+QhgVKCeRqLVCK3F9NGs3k5UdC9o6qs2TM1Ms9Y2oLC5QS4HkolcU2edV4tBuXAiVbxkxWIQQWrdpZ80FIpRkmjx5TJixQSqDmTnnD21sP73Q0S69Yv1TjqY0J6WBOl3G/H3xlOy65dQlueWZjRLWqDNmcwD1Lt5T8bAvPL3NowgKlBGq19EoQ9TBKjOSb8nXSLSmTV7YMk5fy0Wze1xdJnSrFbYvfwH/c/TJ+99wbJaUfLtoxUx4sUErAz+T1y6fWY92ug1WsjZPh8VLXS9+kTF7lBGGoz+bUu8lrb++Q9Vvi0imiYKM2PPLqDpx+7SKkIwikYCFZPCxQSsBkFhjK5HDNn1fhQzc+U9Hy63npleG0OGQiAh9KTOVRFzfHTEw6rEqtpbq8Wl/lN/+0Atu7B7DbJ3ovLHV+y+oSFiglYJqHovYfrNHaT/Xw/Efzga16uJI85WgoqqMud8Bc6TZR3qrSixEyfW3vXXNSzR0qfzJqvc43q2dYoJSASUNR+yv9Upl8DMPl+bcvo8ZRXrkIRt32yL/Mm1Ote1uu/8qUeueBgaqsRJySEyujmIxa51bKuoSXXikBkz1cmTUq/SDWs+CI8gNbtZ4jJyKw46iVSMqNDMwKgVgFW6TcEO2g+37adxYhRsD6ayv7XZeU1FAODKTLzos1lOJhDaUI1EtnMoHU2vFaL9FR5VIv73EU1VAaSrmPRqVD1cnWpEpLb4cN+6Svxutx5OhWAMAbe8tf9bhensNGggVKEcTJ38FarSVZjDPl6+AFiCZsuD5QGko516Qixcod7VbP5FViujq5aWpBzyhgDaV4WKAUgT3arLGGUs9RXlFObKw1UdQjitBjoHpRYqVrKOUL3yiJ5rs89XEtjQQLlCJQJi+jU77WH96qk5e5XGwfSo2dKPXUmpU3eVm/pT5D9db3RlEddsoXDwuUIsjbww0mryotIljPUV6RhA1HkEeocgIaTN3numjXiocN2xKlJOz4hRq3lbqOKINDmPCwQCmCmK2h6I+zihzt91AqTdAItF46SaDyo2UqT57UnXYcxbvIGkrxsEApgliAU75ai0bWsw8lCvJhw5W1eQV1OlE2Z/nzUMzpoxDi5bZ0vcyUV4IxGoFS66tpPFigFAEFrMtU61WI64FITA1FlVd6gYECJYIoL6qCIyjKfq+Sgq8aBL2jxcACpXhYoBSBvS4TO+UrSjHfQymnyYPS1pPJy68KUVTPNnlV4FqrK2SimfcD1Md9bzRYoBRBfl2mWpu86tcpHwXFCMZyRpFBaRtlhBpFPW1ndonp/YRvLZqRTV61oWyBQkRxInqRiB6U/48mooVEtFb+jnKcewURrSOiNUR0rmP/qUS0XB67gaSdgIhSRHSn3L+YiKY60iyQZawlogXlXkcYgnwotf5OSj08/tWeh1JOcaGd8mWUERV+bRJFm5erofgNAqqqn0SoabEFu3ii0FC+CGCV4//LASwSQswAsEj+DyKaBWA+gOMAnAfgZ0QUl2luBHApgBny7zy5/xIA+4QQRwO4HsB1Mq/RAK4CcBqAOQCucgquShELmIeSkRKl0mZzs1N+eLwBxflQSi+nqk75CPPyUg8jaT/hW4v6RaKhsEQpmrIEChFNAfA+AP/n2H0+gFvl9q0ALnDsv0MIMSiE2ABgHYA5RDQJQKcQ4llh9Yi/8aRRed0DYJ7UXs4FsFAI0SWE2AdgIfJCqGIEzUNR+yvthjU95vXw+Eey9EoR7VhOeSJAo7Sd8nXQsNXyj5U8sdHnWDUFinpm2IdSG8rVUH4I4KsAnK/mBCHEdgCQv+Pl/skANjvO2yL3TZbb3v2uNEKIDIBuAGN88qooQRqKmp8Sq9EUb+cL8NAr22teh3LzCBMhVVENxT5chtAqo4KutD7ZROJDKXdxSFv4FmbAPpRDh5IFChG9H8AuIcTSsEk0+4TP/lLTuAslupSIlhDRkt27d4eqqAkKcMrX2uTlbILLbltW2UpUkGqNxoNNXhF2SiVkFbY/i8SHUmZ63yi0KvbLeR9KYwuU7r40fvTY2oabilCOhvI2AB8goo0A7gDwLiL6HYCd0owF+btLnr8FwOGO9FMAbJP7p2j2u9IQUQLACABdPnkVIIS4SQgxWwgxe9y4caVdqSQmW8to8qqSU76ew4OjqFlRTvmyNJRwxysZmuyb1rDtJRoNpbz09edDqY88SuWbD67A9Y+9hsdX7wo+uY4oWaAIIa4QQkwRQkyF5Wx/XAjxcQAPAFBRVwsA3C+3HwAwX0ZuTYPlfH9emsV6iGiu9I9c7Emj8rpQliEAPArgHCIaJZ3x58h9FSXoc64q+qvSM7xN1IOGXu2lV8rprIIEcxRRXuV0SmHbMtLggbInNhbuq64PJZrPBQC1DXLplZ8Rz5T77egqU4l5KN8FcDYRrQVwtvwfQogVAO4CsBLAIwAuE0KoDz9/FpZjfx2A1wE8LPf/CsAYIloH4N8hI8aEEF0AvgXgBfl3tdxXUQKd8rL3qFmUVwR5D6SzuG/Zlpq+TH72TtO5JZUTqKGUH2RRlg/FlY/PeRH0OfY35X3O2d0z6POt9voIG1aDhEbXUBS1XnG7WCL5BLAQ4gkAT8jtvQDmGc67BsA1mv1LAByv2T8A4CJDXjcDuLnUOpeC/cVGw6rCmSoJFBNRyIDvPbIGNz+9AWPaU3jHm4o3EUZj8gqfS1RLrwghCoIAar3QZWgfSgStHsYp/5ZrHsNbjxqD2z49t7AOFRZ4YcmbKRvbh9Ko8Ez5Igi7OGSlTV7GsOEIXoC9vYMAgH29Q2XnVSrFXEVU5ii9qaaMzO08ZPRTCTV1pvFLH0U9w34P5ZnX92r310vYsCqq0aO8GlWWsUApAtVhD2b0ar9tIqn4RBT90xbFM5iQkQdDJdpuowkblplUeC0v58Q1XecRiR2+nLSho7wi0FDKTO+roZSZdzGotuB5KLWBBUoRqOerb1AvUGyTV6XKD3jAo3gBmhJW7dMlOwMrbyYKOz+jmHL00Uml553PowwfSoAGlS+j5CLyBJi8gmaN+wk11lAOHVigFIN8vnqHMtrDead8jUxeEXTmTXHrkUhnahddEuSUd77nUS0OqcsmP9qNRigUndZl8gp3XrmYckoHxMTXi8kryq9s1tIpny+6sbzyLFCKQN1kU6RLttIaCgJelghegGS8HkxeAccN28XiEiianHIRmE/Kmykf7XlhMjHlFfR5a1+TVxU7ZlXUcPkeSqNFebFAKQLVOZhermyNo7yiIB7zn2sTRCR9W+D8EKHdLpawTvnytCBz/kG4BKdPBpEI8YDjmSCTl1/YcBX75SgGAYrhsthqNWGBUgT26McU5WU75SsjUfIT7SrnlLfDRyNc5LH4dP7HXYKgpBJUOf4mryjMJ5Gt5eVDtCvr6vMqZ4JdVUf6kfpQys7ikIMFShGoZ9Q0Wqu1hhLFe1v2dzFCOpJ987Drom9Il2+hjGt2C6bCjKJw8JZlLtPUJfi8EoW4/DW5SoI0lEovXhmW/CAgSiFbfRpVOWKBUgKmBdsqvXy9PYY0PGyRTHCzyyi1YwrnSPbNIyD8WgQIgrA4OzrdLVUdSnnLu5RO2GLd4c+VKSvY5FV63lGStyKUn1c9aCiNZj1ngVIEqvMyzpTPVtrkJUdfxuPllxHlF+8qZfJyn1xSEQAKZ8oXHnf/lkJZI+USkpYq/IImYAaZvMI65Svtl1D3yjT5uBjYh1I8LFCKQD1fldZQBtJZdPeli04XxeMfs30opRHFFJEgrSNIs/ByxX2v4FnNDO/geSjlm0/K8cOE1b7c7VHeU2BKng6K8vKdyV++BhUWv++yFJ1X2Tk0bumlwgKlCPI+FP1oLe9DKU+kXPDTp3Hi1X8pLN9Tj8L6RWfyima11sqkK8bklc0J3P78Znzkl88VHHNpKJpbGsWs68h8PKE1gFLL8td+1TMfC2GGNOUNmAdjUWH7vSKYRlUPYcONBguUEjB/YMvf9h+W1Tt6SkoXxeNP9orKpaWPwr+h8jBObDSUp0PNGUpoesIgp3ykYcOlrOUVME8mX4bTdFd0Ma50pjZX5lzT10h9fSiObb+2/P6ja3D+T5/2ySmYfNhw+W9DPfhQ6qEOxcACpQjUC26yz9oz5StWAfVTuacsVqYTJYoIrKBk7o7Wn34pUFKJwkc9qCOOZGJjGfcqbMqwHbYfecGnRw2WYgYVxX+eTDiT3E/+ug4vb97vX9EA8pF5ZWUj8zBn8uiKHXjLNY8Z1/UrF3uKQINpSSxQikDdWuPExhA3f+3OHuzqGSirfJ+1V8pGyZNajoyCP83r2A44t39ICpRkvDCfgI4uiqXQ8yP/4ocZYU1ZYTts37ICHh7llDeZvPxwPkthnqtyzGJRhg375XH1n1Zid88gdh0YLDrfu17YjC/c/mKoc6MILqgmLFCKIMgpH+ZFOPv6p3DadxZFWS2bKDQX1WGUa64qC9UJm8wrDvt4UHmDck2yYJOXppwIzCdKay13+Xrf84rssIPy0GFrKKWYvBwHw7wjpS9M6gwbjsDkVaHl7L567yt44GXtF8sL69BY8oQFSjGoFzxIoATOAQv5kHgnVlUnbDj4Q0sAsGZHD9bu9Pf1lG7yCtJQwpvVVOekFSg5/3xsk1cZHUtZtyRA4OmOlR6qHaSh+AuUsBMbw9TPT6Cksznc/vwm46TDcgMpitX2Kq1A1HJyZSlE8sXGQ4V8lJe/ySsqu+dQNofmmL+pRle/KAjK6twfPgUA2Pjd9/nkUZ6WE2a14WBTjdn2746iMpu8oljRuLSw4eLKsLaLLwcIrt9QVgY3xPV3xa+Nio3yGvJZ6fqXf1uP7z2yBjECPvyWIwqOizLvWVhtT8nVoFWYSyVKTauasIZSBOrWGuehyP1R2T295QTlGkWpeR9K+S9kxZzyhvJ0qBc+rhEoQc79MMJg7c4efOz/nrN9NYVlmPMPwt2W5hzcnWBpjR7Uzw/5mA4Bf0FRrEnulS3dxmNdB60viXb36+dplbv+WlhtSr0nfsKvHKL8UFg1YYFSBEE+FKW5RKWmmgRTJeeh5KO8SksfzdIr/sddI96Ak5fLzimuMdUEzfPICwNzGd/800o8vW4vXtjYpT0uyujgqjmxMags5YvSCWagGIESXL8n1uwKPMdYVhHl+KW38jCfp4IsyvH3hKHRTF4sUIrCurkmk5ftgI3oGSj0ofifH4mGosqucJSMb7ogH4qzgwp44a56YAUAk8nLKfx0Jq/gUWKQ/M2nLb4twq6qHIVWGLTMfl5D0XcZfoI9rMnruMM6AQATRjQbzwmKai/XTFmscNYJlD0HB/HbZzcGPv9h3o9GM3mxD6UI8hqKYaZ8BFFBrvwKTF5SYEWSu55YSKe8CRGyE/RDXbbZ/5vPOXAVXIleQ/H3PRTTORn9WijddBG0vL7uWMnPnh3woU+vPrhm8qH4CYqwnbT9LR6fZV6CVqEo3ymf3/arq18Azud+twzPb+zCO940HkeMaTXmkROAoTnz5TSYQGENpQjUrQ1avj4qLbV4k1f5ZUY5D6Xk+tgJgyOKws5Z0PtQnNtmDcXvOihg7bNyRsxhkziFgKk5tu7v9zUlqWSmMtPlmLwc22F82GEGCaYzbDNlhX2A9ruuqetru6zox55B//X4TEs4OWkwixcLlGKwZ8oHCJSoRhXe5y042+ievpIjtIz/lJhHwPFyBEoU35S3cw0Q8uV3DH4agGPbUND5P3ka//zrF3zy8L/WgLUhfYVA2FDcMBFaQfMqy9EIvWX7aig+mlDPQMb1a8wjhBDmmfLDmGANxfqtuFPe0LlE8ezlR3ilpnd00iEkyid+tRg//es6bR1MFOOUV+gFin+ZSqCX9T0UW8upoIYSwkex56A1o1utbWYqq9SAD3vZIU2P76yS3/1SdQ+loRhOKfeeubSpMBqKphx7YBlwHWGus9KLaUYNC5QiCIryUr6VoLW+wmJyyhtf+qJyN5QZ4YgoTFZ/W7sH/+/RNZ50/gmLccorAjUUP6e8j2Ui75Q33POIorz80jubwGRGUdd/wBh1Om8DAAAgAElEQVRuq379r8OE30z6INOitwzfTjSgvaumofgIFEWQwPDzFUWn2VYXFihF4DR56V4M9XwIoe/oijWFFTs6iURDkb9RjPBKrU4xJq+wphadUz74m/KF53nJf+HSVAeVlzmPrfv78au/bzCmDSZ/4lBGnygpvb+DhnkTQQEfQY+iuj6di8Ud5RWch99zH7QmWrnrr7mX9fERfiH8pUGDnTAaSqOFDXOUVxF41WFvhIbz5meFQMzz8BcrILwCKHjRxPIfvjAdYJj01nZ5eZg/AewYRYac/6CLdnUvWliYT5iIoaClasI45T/z2yV4desBnHf8REwe2ZIv31kXcxVc9TPNi1AC1XQ8yJlt199QEb+vlYYd9avrCPOeBJnmopiP41eNMHPOAjWUMAKFfSjDmADTgvMB0T0sxT4cRc9DiURDKd1EY6VzmpFKI+jLl8J1H4JNKEAYk5c5fRgnsemMMCaYPjnLvm/Q7cQNK5Cdp5kEhpqHY2qvoAmYYf1aOg3FmdY3vNj2oZjVmKBvDdkDohLnGwYNMhRhpgiYphcowkyKbDAFhQVKMTjvre7FcL6sfs66sHg1lHxnb+gUispdT7G22wKh5zxWpoQzdtLODirEiBfQ2/YDnfIh2iI/0c7UUfsfB4AWubR+v8dhHlS//HkOk5dJoITUUIw+lJDmm2AfijkPdS+jWL4+momNPueFMHkFyQvWUA5xgqJpgmYEFztq8uZh24eLy0ZTD4HbFm8K+DhQuFK8i+OF7Tz8CNTEnGaJkBqKbg0qtw/FfD+j+Ka8X9+RiFuvYWFn79SgzBm4NRT9eeryTd/yCZopH/TsZX0EilPoh4ny8vehqHrqz7FNURU22YYpJ2ieyXD0oZQsUIjocCL6KxGtIqIVRPRFuX80ES0korXyd5QjzRVEtI6I1hDRuY79pxLRcnnsBpKGWCJKEdGdcv9iIprqSLNAlrGWiBaUeh3FIJB3buoeeuc+3bMUZiKTk2LnoYTt+P786nZc+Yfl+PGidQXHbBt0yKp6O7AwIaxBBGpiJZi8VKdtOq7LpRh/kumcMBqK6iQL2zKw2IK8TYsVKpOfUUMJMM0FCVd1r7VhwwGauzePsKsf6FBtWGoWYf1zYc4Jev7DzEM5lGbKZwB8RQhxLIC5AC4jolkALgewSAgxA8Ai+T/ksfkAjgNwHoCfEZFam/1GAJcCmCH/zpP7LwGwTwhxNIDrAVwn8xoN4CoApwGYA+Aqp+CqFEIASdkx6R56lw9FGwXmPyL2O1+msutRDmql1q6+oYJjed9ruBcl4+mgwtqg/ch3wobjzvJ8BUphnq7jOee5Zg0lTOdkMm8Usx6YV3tw/ufXlM5DRh+KbfIqTfAFBSj4aSi5kJ10JoyGErCWl3oe/Z69d37/CXzu90u1x9zPrz69W0AaiwkxD8U8agsTEHLrMxuxuavPt4xqU7JAEUJsF0Isk9s9AFYBmAzgfAC3ytNuBXCB3D4fwB1CiEEhxAYA6wDMIaJJADqFEM8KqxV/40mj8roHwDypvZwLYKEQoksIsQ/AQuSFUMXICWGbToI0lCCTV5hOyvtS5M0OwSN3P+woKm2ZwXk5XwSvzb6Yj1+ZI4pUXnrCTmwMWorc3dGZ04cRjOZJqObyvfiZD/1w1i9IoJg6MVsDMZZRWJYT9bxrw+kD3gu7jBACJYi8Kcp8zoY9vfjz8h3aY+4oL30mG/f2Bp4DlKmhBAj4AwNpXPXACnz8V4t9y6g2kfhQpCnqZACLAUwQQmwHLKEDYLw8bTKAzY5kW+S+yXLbu9+VRgiRAdANYIxPXrq6XUpES4hoye7du0u7QIlTQ9EKlABzj/N4qHV8CqK8/F+W0J+Mlb8680SYqCS3huI5sQgNJci8EibKK0wHBeg7/KB8wgjXckf2QP46CzWUcMLZecxk8gryoQRN1Mt/PE5fB7/jYR3dYTSUoHyUQC3V7xUmECIo+EZ3ng6TtujM19QWar7Mnp7iv2lfScoWKETUDuBeAF8SQhzwO1WzT/jsLzWNe6cQNwkhZgshZo8bN86nesEICF+BEtSBuVTlED4Kk1Pe/PnT4DydJ+omiYUxeTlfFO+I2Jmq1BGanz3eW4r/Krf67fw+fwFfzJwG80fXCsvyouZueM2HpWko+kQUEOUV7JT37+D8BFJYM2g+bDh45G4Kyc1kw98zff7BwsK5Rpf/vBr/OvgFxQSZStXzWo6/qRKUJVCIKAlLmPxeCHGf3L1TmrEgf9USp1sAHO5IPgXANrl/ima/Kw0RJQCMANDlk1dFyQkgmTDH87tGLgHHw2goBWHDQaOWkM+Wr4YSMBIF3EtGeDuosKNR77lh9uvyDeuU9/vEL+CvoZQjUIpZCiRdoJEW5qMvw5GHaWJjzN+HkvMRCM66BJq8fI75pQfCaSh2aLEhH/VelToPxZmtqRoHHfOFvOU433uTNqgwfeXTma+pvYrR5qpJOVFeBOBXAFYJIf7XcegBAAvk9gIA9zv2z5eRW9NgOd+fl2axHiKaK/O82JNG5XUhgMeln+VRAOcQ0SjpjD9H7qsoOSGQjCkNpfCJDQobDooCK8jPG+Wl8gmw1wfnqzQUzTGZiV9H7bT1+0UmBZkdgj5UZjTticJz/fKx8tIJDKeANx8PI6jNwhEyDx8NRdXBxx/lRzFRXkE+FBNBAsdfQwmnUYYJGw7SYtT1RzMPxaSh5NdD876LYc1hQOG8I109TM+3GjjUm4ZSztIrbwPwCQDLieglue9KAN8FcBcRXQJgE4CLAEAIsYKI7gKwElaE2GVCCNWinwVwC4AWAA/LP8ASWL8lonWwNJP5Mq8uIvoWALUe99VCCP03WCNCCAEh8h8Y0g0Eg6K8gkwsBfkVaCgynzIforyGYo7ICRsO6WfyCqqm0XwSYGoK75R3bGua29kR60aTxXyB0xTllfexBOdR4EMR+m1TGYC5PdStNmkowW3u/i1I79NWzvvsq/kGaODWMflruI6BjPKhmMvxI8yA6KDD5OU9x+VfNFxHczKGgXQOB/rNy9v7LY8PBGs/taJkgSKE+DvMftN5hjTXALhGs38JgOM1+wcgBZLm2M0Abg5b33JRz00+bNh/6RXt4pABAseLySlvepZKWaqjoExbQzELvIzL5KWvIxCsjpuEVtBnAMKOeINGm27TmeZ+hvChqCNmU1FwHnZnX3KUV37b1B4xg5/GW1apPpSMj0DZ3j0QWD8hhGMeivnZy/r4DrI5EbGGoj/HaZr0nuO8h6ZrndDZjDf29mH97oM+9Sisj5Ni57RVC54pHxJ1W32jvAIERtjwSdM5dudltNeHQ52nXybDOvroip2h6lWgoTi1goCX2rjMf8DozDUiD+1D8T+uXUqnCAdvkDYVJg+/KC8/wkzGsxeHLMGp7iwjaGkWXZ2dnycI0oCsvLSnWPX0EWzOb72ULlCc24a2ypqFhlNzCnrHe319KP7t7RchVktYoIRE3Vg1U147QhICTUqD0dzwogWK52EKGiWGlSiqc9B/DCmfycpt+qA9V3BBiaGuQGkRQ958/e3tju1ADUV33F+wOTE75eWvTx4q2s5POIfRKoEQJi+DjyXoWoOiwMLM/3CWU5g+p9324jebvt8lUPTpg7X4YA3FWbY3P3fgjf/z3T9kNnkFaoQsUBobdYMT0ilvMmk1JWKu813HizAH6cpQyYv9kmPBeSE7J9NCgxmXU74MDSXghTOG4pZg8tJrjP7miaxPx+F3rrsOMr1vagtvBxS+ywihoYR1yhsKDfJvhF33LMgH41eG85guKMb5rZeg4IEw9QtaZsZ7vveYKbRZZeunoeQHVfrjXvNovcDfQwmJ7UNJ+C+90pSIAYMh5qmUoKGo/4LmoRw7qROapasKCPoYUtL7wRdJxjdsOL8d5Cfys6dbv/p0zt1hZ8rrinJGROnCbb0dh645gkxBoeay2JMOvcLZIdB8xEuYNg8OG1Z5mY77X0dYDSVoEOHMS3+e+Rx1P2MU7OsxESbKK+PzHqdd5jB9GXkNxSxQ8j6t0gRjrWANJSTqvjb5LA6Zywm7E9ZqKOX6UGynvElDsQj4ZITdOemivMJFNDkFir6O3u2gfFz7A67TpXn4zjbW10vhFChBKxuYOhc1ygzy94Sx6Re0ZWAKC/dzpT8nP3nSv81NZQatrusc5IT5zG9B+aE1lJzxHHU/m5PxULPctcezzntuqoP5uQijoai27vMxeQVphGG+pVILWKCExGvyMvlQ8k77wjzCjsK8ZSpsk1eAhhKLhTNr6eehBL/YYWfKB45WjZ1LYV1cZQSYsvLl+wuEwazTbq8bIOjzUqzb1YPnN3TJOvsLPz8LhenDUu5O2id9CMGnMjCZvII0qSATjDNf7zkzJ3YE1s9v1O+qhyg8X6Fmnrck4z6+h3w9g9YdC1NX7ylhfCiq3GWb9huFb+BMefahNC6bu/rw2Cor6knNQwnyoZSy1ldhfu7/VYqg0W7Qd7ft5AEmL+OcBT+B4kgSNF/GGBYs9wuhf+lDO+WdHa2mHw3SUJydpK7JVziCFoLMkH6jdhV55e0kwk5ay4SILFK7g+6pqZpBfi3nY+Bst2sfXoXVO3owtj1VcJ6rfsVqKJrrcGooYaKjgla7MLeF47nx0VDMYfH5/QcG9FpKsImxPjUU9qGE4L0/+ht65HILQcvXqyivKExe3jwCF4yzTVmwF4/zy1cneJxZm+YsOPf7mbxK1lA8eXh9Fy6fgZ9ACdAwnAJFfz/Dpy81BNoqx8rH25ZOQeHXlGFMc6qdgtfyCu4EhRAF5lJnJzuUySGVsL5M8Ysn1wOArykYCDeyd9bDz4fS0hTHYJ/eP+GyEmQFknHzcZNA8JsNnw7QelUZ8RghmxPYc3AQI1qSBeeopBw2PAzpcazdE7R8vZ+GEnaGt31+gQ9F7defr44TkW8HpDoqv7W8APML4bcmmTNFkCZl/HpggNkh/Ex561g8RtrR5lAmh+akvF+ajjbQqe9IEzQ3yHexzazqJN11cGtIfoIzuBNU12IaJNhRWoYy/CKbgLwpCtAv/6I62jCj9jBRXroRujJhNidj5ugo52BIk4ef6c6uQzY/cPTeljAmMyGAKaNaALhn3evyYZPXMCcRoIE0+U58dJ8bhOmDXOU65dVLo8vG+Y6ZRrPOuns7D5fJK0CghFpaRdNOLpNXiCXAEzHSttlQNofWJktJ15o/nAJDkz6dCe58wixfr8r201D8CPOJXdWO2xyz1nXHS/VxuDQUR7upAdagPYNdm72dZ1M8Fk6g+Ji8fH0oHg3FlL9VV3MeSYPp2zWfxicAoj1lPXd9hkiv/JdTTXWoT5MXC5QiMWkomWwOvUNZpJKm74N7bK8BDjvA7JQ1T2y09sfIf0SbX6nUf1RueiH8zBMixMQwhbMDdS/978hPk0d4p7z1a+qkhjI5tDZZNg/9xEbHFwI176/L9GYUjvLXpzGU4PJqDy57vjF1Pm8iHx+FrN/OA3qBYq8KEJAeMGvnCucgQ7WvEijmVYKlQEnE/E1eorAMb7l+PhRnG+u0NZeZ0Uc4q+kD3utJh/JnCbRJgTJgWCAyaNkf1lCGCQmDLfgXT1m24qfX7QWgt3GG0VBM/gHhOsd/FKoi0UzkHaz+5ZtGQU5B5J157UwfpKG4YvYNglS/yKazLsEaitVJ6Tsg1eGZnPJJH400Z6iz7hy/TlId856T8fglTKi6J+Mxn3pYvyYTizL/mGqZMQgMbx28x1ulk2LIXrTRv51SiZjvc6OePd2kW5dAMbSDc/Kjbhkat39EXwdLQ4lpz8kGPLuAJbSVhmJacVhlY9a08tfxzOt79BWtASxQisS0ltfW/f2u//0WG9Sl16VzyqQw4bxqd4A8yduztRFUZu0jnz6/v/AbHsGdrJ2/YTQXZHYIPVPeOerVmUiyObQok5d2tWEgGTM7k8OEuuZNNMECwavVOuv0Pw+sMKZX9UjFY4EmL1NUkSrLPCrP120wW9gJOq/f2WmrkbjuPF351r0qvq2c5bYk48Z2cNZNV07W5UMxt0UiRlpLQCinvENDMZm8gj7h4HwHv/6HV/Un1QAWKEViMnkNpt0Pp34UF2zyyhjMQGFMXkFOaG/6oMCBoM/FAv4TrIJNXvqXz+U70mQfRltzlm8yowxmcmhOxKSpSD8ASBhGot6ygzpyv6gcdcwvbHj1jh5jenuduYRZQ1H1MH0l0G+1YG/dAjUUx33taHYLFNMz4dQm/QYJqh66527IMQ/FlIV7dQTdICOchhKPEWJEGtO345kwBZ0IgfaUpbmZNRR/Ae8UhsdO6tCeUwtYoBSJySmvXlSlyvqFNQJ+GoqhYw3x2Vu1X7eKsK4Mk98gYS/TYQgbdgqUMpzyLpOX0Z/iL/S6eoeM+ecX9NSPeocyOTQlYkjEqNAXJKzl1JXzVTsJzmVv19dB5TuUzQWO/vs8nYvfSN2d3vpNxvXBB0C+rU2ms4yP1grkvzNiyiOTE2jxmLcAYHRbEwDgs2cdZeUf8Nw3xf19KEoj1vpQsvmwYbPJK9/GOivCgENjMM50zwlLQ4lRgdBxWxj0FgAhgDapGQ8YNBR70BfgcxrZmrRDtOsBFighcK551SlHXN53fUBqKGpEph9BhYtR1227NJQAB3A8RgFhquYQzpzIhz6b62i+DveS8UECxaENGV5EP7PciJYkNnX1Fxz3pjV1UtZ8iRjiGoGi/lX+KN2VqA5sREsyUMh7t52oUe0+j3AMO7HRKThNZai6Dmb0gs3WUAxlOFfG1fkvsjlh+6Ocz7kQwKxJnfj8u4521bUwvZUmlfT3oagBjFa7yATn4XoHNXkojaGzOWG/04V1VRqKbh6K9X/KoGmpfa2BUV7Wb5DJq60p4etfqzYsUELgHPGrMFNnp3f5va/YM+kPG2nFl+se+MEQGopTELm+T+30KwSYvBIBJi8/DSWbE2iWI03TCNl5Hd7OxW2aM9cBMJu8XI59TR1VGeM7UjjQny44nk9r/SZ9fCiWhlJ43HZ0J8w+lEE5j8VPM/Bb90yhrt2rbYXVUGz/Q9zckaZ97plVN38NpW8oaw+mTCavZo2Gks5ZEVHqHQpabieViAcEMAQ75VMJy+SlXb/NNSm3MA/VwY9uazJHYOUEErEYkvFYQR7qfqcMZlbbDBsnNMVjRpOXHeVlHIRY5bY2xV3vY61hgRIC54PxlqmjALhv9B0vbLa3b/jIyQAMGoohqsmJs1NxnuPsEII6ryCTl59jM50VaE7E7G0dvYPWSzCqNan5DnqeokxeBoGp911Alt/kK1BU5FJLUj9afH33QcRjMSTiVGDesAVKzOxDGcrk0BS3OsswE/ZMS46rTnJv76BnfzgNJZ3NIUb+GkraMRlPJxC27JOanqHI/qEsRknzlVagCIeG4rqvyoGtTIf6/FUbNMVjEMKvIzWbvAblACFpL9WvGQSk9YMYhergR7QkXWY+d10tDaVJI1DU/6ZIM/VOxGKElqa4UWjlV1jwH4S0phJGv1gtYIFSBO9/8ySM61BrEulv9Hh5XDe6/N4j+S/Xmeyzb+zts7dzhs4oaK6AZfIy4/dSZnM5e6RpusbewQxiBLQ3JwKWXglv8jKZhvx8KCNak+gZzASakpqT8YLOfH/fEIQA/vTyNq0PRZXrt3bbYCaLVDKuTa+7FtPHrTI5q7MfSOdcK9AWI1CaEjHEYqTVCoUQGMrm0C41DO+IdsnGLnvbdM/601mMlEuE6EbE2ZxAi8bklc4KOyJKnadD3Z4mw/wOhRJWJpNyKhGz66HrrIM0lP6hDFqScTQnzZ19NieQiJOloWTc9VT3rDkZ1/po7HeUCC3JuHHF4aDPCaSzlqBOJWJs8mpUZh3WiZhPGCmQd2gPeTpab1ixyZrxr79bmj/HqaGEmTAVUkOxHbS6iV05gZQUKKYRde9QBm2phK/KDwQvhR8mysvP5KU6OJOWkrU1lHjBqPegYzkdta6S7jqShiU2AGu0qzow07ctXGtH6UasOctJqwYq2/YHf3/dy1DWmi8Tjxlm9MtnRwWMeAWCaxCjKVIIgf50FiNafTQUg1Nedb5xQ3SkwtZQfJYuAvyjvAalQFEDIp05KYwPpbXJEiiDBoGiOvNkgjSh3srs5u9DiRGhtSmOfo2fRghhv3umweNAOoeWZBypRIxNXo3GO940DgDwmTOPsr/NbXrgiQjJOBVoKHsPus0ZYb5n4HzgXQ+qyeQlhP3i+jnElSqvjdbJCnt9K1PYcO9gBm1NCSRjsYKONOwy5IDX5KX3HekuQ3U+ygTjFA7u/POjRcAtIJXD9b/fPwuJWKxA07InifpMbLzvxa3Ysq8f7amEsQ5BkxNVnWZMaAcArN6RX8HY2T5nHD1Wm7/KV5nedG2u8lECxVuPUW35xQl1wRwD6RyEQGgNxftZgEQsZi8mGTQpNyggJG1rKKLgGVftoATbwJBO6PhHefUNZdGcjKM5GTM65QdldGAyHisYlDmfOW1Yvjw9FiM0J/UDEeva5Pk+GmNzU5w1lEbkO/94Ap78z7MQj5FtAvETCLqRu/I7/Gj+SQCAK+5bXpDu9d0HAQAXnTrFsuE6RkjqQR3RkkSvofPKCUuVBvkv1aFCFU0jzZR6qQ3X2DuURWsqjvbmBDbs7XUdC1r/yolpKfGgtakyjraw6uPfmetMeKptJ49qkRqKyYfi3xEC1uQ9v3ui8Psq5JGjW61rceSTyQrECDh9+hjfTiMtNZQYkbaeKm3e5OXuxPplx3vC5BHaEbEyy4xqVQJFP7FRF+WVkaN5wBIWQcvnq2fPNIfDGRXlFWwqDFwJNp2G4hQSuroMODSUAYNvYihjtXdTvLAzV8+cSUNRx+MEqaEUPjfO9jUNHgfSWbQk42hKxNiH0mhMHtmCI8e0AbAiSJriMe33oH8sHfKJGBU8rKqj8E70cjLvB08CACZ0NheosqozGteRQt9QVmvfzeUEiKxO0E87UC/KoNbkZb0syThpl6ZQ19LWlMBR49oKlvII8z0J+1zXukpCu63tIGW6kbKDM3Xm+dFiYZCBegmbk3Ek4oX3y+7gkv4mGAC+Goqy6wP6Ubcqt1MKR2eHqUb3qaR/p5HOCiQTlllJb/Ky2qtDmbw8I2/V8SqB4EXVaXxnMwCgRzPb3hTlpUxeAOQz7W8aTPn4UHI5y/Rmug5boPiYvHoG8uZRnQbeNyQFSsLsQ0lnrXtqCUivQMk/N6YFSQHruTOZSp1Cz/TY9Q9lpckrrjVd1woWKCXQ3pywO1Kn2v3+N08CAO2Dtq/Pit5SYcV+nHXMOMuG63gx1cN97KROAMCKbd0F6VR8vOUs1D9kQgi7g9Cdo/KIxwrNdordPYMY296EtlSi4KVNBwgD97l6H4qzQ7rl6Y2YevlDBSN3ABjZYpm8lPbnRZ3XotFQ1Ki8ORFDeypRoOV4J6p6BY7TLLe/L43VO3pwYKDQl3NwMGNP7tNpGd19Vho10HAKlN7BDFqkWcNkfrHSZNCaTCBuMHmpDkdpKN4OSM0xaUsltPdsV4/l15koBUq3xmfljPJya9Y5ey5PKmEOcR3wtLeurdSzNrq9yZXGeZ1NTh+KprN2Cn6dyas/hMlLaSiWJcLjlFeDmERcK7CUEFT11M1DcQpdk+lambx0WlItYYFSAqlEDE+vsxZkc4b5KjtxeypRsGaSOm/KqFZ8+u3T0JSIuR6WE/7nUUf+qhOxHiwhBD74s2cAAG87egwA4PXdblPTzgMDePK13YgTIRGPFQQFKP71d0uxbpdlWtPPR7CicpIav0K+rEFM6GxGiyYSxvXxrQAfSq9hVnI6m7Oj5X773BsA8gIZyHcEQRqK1+TlrJuqd0tTHG1NhSYr1Zm0pfQTVZ0C8HkZJfVZR0AFYHW83f1pW6DoNJTP3Wal6WhOYnRbEzZ35R3ku3oGML4jJTtivdDs6h3Coyt2Ys3OHsRieieu6nCU0DJpKB2aqD0A+NCNzwLIt/f/e3RNwTl9Q1m0pRJoTyVcGowKsQWkhmLopA/0W2kmSKGl0y6Wb7UGUaNkcIBXYBwctCK0/KK8Dgxk7NUPdNdqO+V9QnqV4ErGqeAdUs+YaQkZJVBTiTg6NAMZwD1AMrVXfzqLlqTSXvPnCCHwiydfd/niqgkLlBLY3j2A9Xt6sb27H5/7/TIAwCfmHmkfP3x0a4EGcecSa65KW1McY9tTGMrk7A510aqdrpcw5XlQnJ+anSpNb97IpgU3P4+1uw5aDsM4GVcKfnTFTntbN7LpG8qgpSkh52ZoRqsHBrDnYF6gpLPCqGn0aEbsTpwPfcZljsphfGfKda7zeNrjQzGZm1SH0KZZDmfAYfJqb04UmHFUB95hECj/dtsye/uRL70dAHDE6DbXOZ/89fMA8suP7O5xB2YAwKtbrTZobYpjdFuTS8vZ1TOI8Z0ptDcXDlAUf1u7295OxmPayLy7l24BkG8Hr3ByTubrN/ijgLxg9pLO5jCUyaGtKYHO5oRrPo0yIQHWc23qpNWzoqLddNrFV+95BUBeQ3IKHSEEVm8/gKPHd9jrZOk0xp6BjC2QCuZQCYHXdx3E5FEtaJbalM7xv3VfPyZ2Nmt9pemctVxP3PANHtX2qUTMZelwsnK71XdM7Gw2vkO2D8Wjoby+uxfXPrwaX77zZW26SsMCpQz29Axh8QZrdDrv2PH2/lOPHIX1u3vtl+fVrd1YLzUKIrKjk9RSGy9vcQuf5oTbhuvsAGYdZpm8vB2MWjxQhZCaTF5OdN/G2NeXxqjWpNHZt1CuCHD2rAnakWAml7MmfSViWtOIYu3OHnupf8C9jlU6m8OEjmbX+U7zhuoIRmj8Ds78v/Pn1QDyy+U4hZLqsJoTcYPJS43qrTK8I9G/rLTa4b/edyxmTuyUHaG7A1m2aT8A4AjpcF+93TxqjJFlqhxyzGvYdWAQ4zuaMa49hX19QxP2aTwAABp1SURBVL5OfcBahkPXQd34xOsAgHHyu+7egUR/OosmafrrS2eNZpYZ49u1+1X7tzbFcfSEDqxyXGfPQNrWjI4c3Yo1O/WLXB4YyKA5GbPvqU5DOX6y9ex/+ew3AXA/d939aRwYyOCocW2YNMIyKztDsJ31UQLFq0Ef6M+gdyiLqWPabOHpNdEdHMwgkxOYNMLyc3rvyUA6i1Qirg1Ft45LDSUZs7U5b3s/+qr1bM2Y0K71V/UMpPHKlm4k4jH5nubrsEN+QG2Vz7NWSViglMC0sdZIVNmWgbwaDuS1iC37LPPFh258xpV+tDxXmcFuWLTWddyroaiH6o5L5yIlO0DTdy0Aa6kRrTrv6XgHMzmXULlt8SZ096cxsrUJY9pS2HvQvRTI8i3d+PofXkWMLF+OLt5fmcxGtCRt/4CX3T2DuO4Rt9lkv8Oklc4WaijOuqsO0S9s+D/uzo/QVCiqUyioTrClKY62VNxlZtjRPWAvpdPeXOhDcXZkKoKprSle4MtRHen4jmZM7GwuiIhz4nXydvUOYev+fgxlcxjXkYIQ+oUw75Hax6xJnejwaFpCCFddTz3SWuXB20n2Sy2ipcmar+P1HZz5pnE4YfIIjO9sxqfOmFbgvFf3bmRrEw4b0Yz98r7fs3QLBtI5WyifMHkE1u06qBWMB/rT6GhO2ksbeZ/VldsO4M/LdwDITx52PnfKZDay1fLtNSVi2N9fuJTNmh099ud3vRrKTvk+T+hstgM5vPVQHXWrnIdVIJxlW1o+yMJ3UA2yRrQk0d6cQCYnCu7HxBHWYOrkI0bh4FCmYC7WLU9vBAAsXLkTqYQVnqyE1w75Pqvghjtf2ISvSc2uGrBAKQEV+vvy5v32PmXWACyTFwBskvZw9cB8/b3HWudKp+ITa3Zj457CTiaViEmV23qYVYepyrA6DvPoX8XHe80Lmxz2eYXypxwYSOPKP1ihzKNbkxjXkcJuz9yZf/jJ3wHI8GTHbPzXd+Wv4eBgBh3NCYxsSRo1lO89strusK//8IkA4BJe/ems7ZxVOI9396fRkoyjrcmapa7zoagJiYDVyQD5jm/x+r24bfEmEFmhsO2ppEsoffSXz+EXT64H4Fg92tH57HcISqUp7jwwiAde3mZ3MP9+50t25/6vZ03HtLFt2OC516oTGNeRwjuOGYemeH6inNJmjhrbhsNGWh3Ms6/vdaX//O0v4hm57/efOg0dzUnXc3Hvsq2Y+Y1HAADHTOiwR+5eAawihsZKDcY5UAIsH1Vni9UOrakE+oayrk5u5wHrOZnQmcL4zmbsPjiIrt4hW6grZ/4qqUX/+PF18NIzkEFnc8IYoeWcGKwGMs7nWwkPpeF0asyYz7y+F31DWcyeOhqA14yas/2iEzqbbX/Rfs8z/LH/WwzAWiJGJ1CUic804dApUJQ51VvPvb1DmDqmFZ3NCQgB9Hjul5pc/cm3Tc0PlmRZqs2T8Rjed8Pf8LV7l+POJZuNfsaoYYFSAidMHoGmeAw3OF6MSSPyJhpl4nhjb5/98n5kzhH49JnTAeQ1lOsfew1nff8JlzAC8iGFuw4MIpsT9gOnOrf2VAJ7HaNVp8r83hMm2nVRnQlgjfDO/eFTAIAHP38GHvrCGQDyD7jTvj9CCRSPzX/mxA7X9anJmg8t3wbAiny6bfEm7Dk4hDHtTfZoyYtTsF1w0mTEY2Q73Z98bTcG0rmCDzNtdIzuu/qGMLqtCUSWULv/pW2ucx9fvRNL3thn13msFOC/e+4NZHMCH77pOazZ2YNRrU1IxGNoT8UxlMnZL6VTaxut0YKcAQJ/WbnDdd7K7Qfwiydfx30vbgVgrVSdSsQx67BOrNh2wDXi/c2zGwEA/3nOMUgl4i6bvBLmHzhpMk6cMhIA8KU7X7LT9gyk8aeX89c9qq0JHc0J9A5lbUGlAhoA4JoPHo/xHSk0J2O2+RUAvnrPy7h76Ra0NMVx+CjrvtrregF4Zt0eLH1jn73cepvUTpwmStVeEzqbMfvIURDCMjkqTj/KCiRREYrr5XwrxcKVO/HQ8u3oaE6ipUlqBo78B9JZfPo3SwAA5x03MS90ZKTeG3t78YGfPA0gL7w6mpMFfsaLb37erk9LMm7P+wKAu5Zsxjf/tFJeRwpj2izh6pyQ/PDy7fZ2TGnhnjK6eofQ0ZxEW1NCu6yKU6CoUHGndg4Ay97Yh9FtTfZxr6b/2s4ejG5rwn+/f5Yt6L0m0YODGZfv9ZUthVGhlaChBQoRnUdEa4hoHRFdXsVy8ekzp9n/X33+cfaMagB2B/bNP63EJrmshXOm8+RR7tDhI8e02tFbgDX6mXfseOzqGcSLm/bZo05lQlm76yAeX70LJ1z1KHb3DGKffOC+OG8Gfvjhk22V3sktz2ywt2dN6rQ7wJ/+1RKK6uWLxwjvPGY8xnWksL17wDUSHcrkMHVMqy2MPjLnCADAMROtjuLmp/NlvHnKSLy8eb/rhVyxrRszv/EwFm/owuGjW7DumveAZKjrT/9q2fkXyJd+2/5+fOFdR2P6uDbEyC1QFq3aZXeaM8a3Y+v+ftwrTT/pbA7/cssS+1oe+dKZtu/gjy9tw48ee83OR/lWlPBSo7gz5coIQF543rdsK3777EYAboFy3YfeDAC4/dNzAQBX3rcc1z682j7+pgmWEJ595CgMZXI44X8exZodPbjklhfsDuyEKSMAKM3Suq4nX7Oc7RM6U64Bh+rUvEv5APnnQ5lDnaPnU48chViMcNS4dqzf4+xIrXab2NlsPzfKVCuEwEfliFwNauwvDToE7KsyAGVCR7Nt7lWdWWdzwtbYvzRvBoC8FqFQwuKlzfvtL2g687/gp0/b21e8d6YjyMESZHfLawDy71ZHcwIPvrIds7/9GABrkKGYMb4dZ8wYa0fnAcAzDn/ehM5mu4w9UjMeSGfx2d/nAzHefex4jG1PYV9f2m7nR17dgb+v24OTDh+JNo0md+/SLfjWgytl/ZL2FIJt3fkBTO9gBtu6+zF1TJv97G30mEpf2rwfc6aOBhHZz9fSN/Zpgz4UH/nlc9jebf7UQ1Q0rEAhojiAnwJ4D4BZAD5CRLOqVf5/njvT3lY2XUfd7O0PSDPR4aPznbzTHAMAL27aj+lj2zFb2rhjMcLbjrIE0IU/f9Z2LisNRZ3XM5jBW655DPcts16oYyZ2oCkRwxlHj7XV6WfW7cH63Qdt+/s/njIZsRjZTu8V2w7gLyt22GaLP37ubbYtHAB+sNDydezvG8L6Pb244OTJtk1cdWB3vbAZz63fixfkC/qDi07E2bMmICes0edQJofBTBbLNu237fP/9b5ZLiEMWCMvxduOHot/P+cYPP6Vs3DC5BH2COuy3y9Dd3/a1n6+et4xAICvSFVfmfCc7eXskJ1a5bxjJ8jrsK6nq28ISzZ24eFXd9jnqM7vydd24xv3r8C9S7fYJq8r3zsTxx1mCYMjxrRiYmczVnqcoXOnWwMF5RPK5ASu/MNyLFq9yz5HObtbknG8vHk/nnptN7bt70cyTmhPJVzP02d/vwz//OvnscbxBcfvX2SZDSfLDupBqTF29w2hORnDF+bNsPNIxGN4Ys1uDGay2OYQSgveOhWTRjQjHiM8+/pebNzT6/KlfP19lrlWjZo3dfWhZyCNp9ftsc2DnS0J2zx3tew4ncEjsRjh9Olj8JeVO5HNCTy2cmeB+W1su6VpLZUaJpAPOGlPJXDkmDaMbW9CKhHDtu4BZHMCf3xpq32ums2vns09BwfRM5C2Bxlzpo1GWyqBaWPbsLmrD7vkc/SQFNQfPe0INCfj9jut2uj6hfmByB8+91a0NiXsiDQV1abW4XvLtNG2leBBh1bzFYdfLx4jjGlTvlQrfS4ncPl9yyEE8J4TJmG69NU6TaXff3QN3tjbh5nyK42nHDEK7akEHnh5m23uUh8zW3D6kXj+ynmoJuZp2/XPHADrhBDrAYCI7gBwPoCV1arAV85+E36w8DVMHdtWcOyGj5yML9z+oh2qqkYSijsunYv5Nz1n/3/ClBG4/D0z7VHG+M7mApVadQq3fXouzvvhU1gvH7RvP7QKgDWaVefd8Zm5eN8Nf7dHmDMnduD06WPwA9n5xGKEK987E9/582pc+tv8/Ikjxlijog+dOgU3/W09fvrX123tAXCP3pXpYfnWbvtaZk7swIdOnWJrVZfftxyXe5aZuf7DJ+Js2ZkDwE8+ejL+7bYX8dFfWnX90fyTcP5Jk+3j75o5Adc/9hrm/eAJe/7Nzf88GwDw1qPymt/Uyx9ylXP1+ccBQIHgAqxVDf7hxMMAANPHWfdPrVSgaErEbL+CwtkpXOCoI4ACE98lZ0yzBZ6aXwHA1Vku+8bZdv1OOXIkHlmxwzbNnDNrgn3PH/rCGXjfDdbg5Ik1u/HEGkuDefm/z8EI2YkeP9kSbl//w6v2d8Y/c+Z0/LuMigLyfr9j/usR/Ns7j7b3v+NN45CQ31P540vb8MeXttmDkms+eLydt/p8w4U/f9Z1re9/8yQQWUsTOYMDFn75TNd575o5Hs+u34ujrvwzvPz0o6cglYjj3cdOwN1Lt9jhzorbPn0aAOv5HszkcNNT63HTU+vt4xuufa/dXsdM6LSjCE/4n7/Y5/zww5b/c2JnM9JZgTnfWeQq4zsfPAFAPnz56gdXIicEfiHLef7KefaKAZOk8Dz92scLrlFpiV+4/UWs2NptR0cCwD/NngIg79v78p0v4+9r9yKTy9lmzLfPGGsHalz1wAqMaEkiGY/hJ9KicNYxVlRpPEY4+YiRWLgyn/+/vfNofHHeDNvXdOPHTsHzG7tsc2AlaVgNBcBkAJsd/2+R+6rG5+fNwLJvnI2Z0uTj5AMnHoYTpSnjvOMmFsTwz50+Bjd94lQAwD+ePBkfPHky2lIJl3B66qvvtLd//cm32NtNiRge/4+zcOelc+19bU1x20YNoKBOq3f04C3TRrtGu59++3R84/15pW58R8oekbc2JXDle44tuK6TpD0fsF7sBacf6To+/y2HA7BG/RecdFhB+qZ4DB88eYrtWASAdx87AZNHtmDPwUHE5SjWyXtOmAggP5nz0jOn410zLYHUnIzj5x8/taCc1d86zyWUvvH+WbaJDgDOOS4v0JRvyMnvLjkNr337PWhpimPl1edqzxnn0UzVyFDxtfNm2p9nnTKqFefMmuA6/s0PHOfSni489XDX8SOlcAeA4w4bges+dIIrwmryyBZbmFhlFJo63yId0IrvXfhme1t1Ti9fdY79fL7zmHz4u3IGTx+bDxee2Nls+1EUiRjhJx89xf5/6X+djfOOm4j//acTMcMzkPrU26fZGraTFd88F++TK018ztOOAHDbp07Dmx3P3nnHTXQd/+GHT3I926dNd183YAk3ZWY6Y4Z7sU0i4PGvvMPxP9n3XA3Y5r/lcFuYAMBp00bjpMNHuvL5+9feifZUAhNHNGOObPtfPLXe9ltd+48n2EJrpMP0d++yLbj/pW2YObEDf/jcW9GcjIOI8M5jrAHcl+58CZfJuU8Pfv4MV7kfdTzX13zweLSlEq7+5j0nTMJV/3Ccq30qBQV9prVeIaKLAJwrhPiU/P8TAOYIIT7vOe9SAJcCwBFHHHHqG2+8UZBXpegfyuKepZtx7vETMb6jcHSglgVXoZKlsG1/P7bs68ecaYUv0Ctb9uPFTfuxeMNeNMVjuOaDJxQ4u1Uef3p5Gy44ebJrJA3Ixf2kszhO5BIEimxOYMW2bhx/2IiC4129Q7ju4dXoS2fx8dOOwClHjiow+alybnlmI+ZOH2OPhp3s6hnAQ69sx6auPnz57Dehs9lth39tZw+27uvHsk378Km3Ty+w0yv+smIHTjp8pKtjAKy5Pk3xGJZt2o/pY9vskGQnQghccusSpLM5/OCiEwvyACwb+Ffuehkfm3sE3j5jnPb4U6/tRnd/GheeOqVAe9rXO4RNXX1Y8sY+XDR7SsF1KpZv6ca0cW0F0XCDmSwODmRw37KtOG5yp0uDc17Hf9z9Cu5dtgX/d/FsvNsj6HZ0D+DXT2/AfS9uxSVnTMNnzpzu6oyyOYG7l2zGiJYkRrQkMdPhkwuDEAIvbd6PjuYkbnlmAz531tEFSxIdGEhj7c4eLNm4Dx+fe2TBc9s3lMGGPb2IEWFka9KOYFPkcgKPrNiBkS1JbOrqw4mHj3QNuNR1bNjTi0kjmrHzwACmjyucZ9MzkMZrO3vw6Iqd+PhpR9oavLeuf1mxE6ccMdKVx+odB3D74k0Y2dqEc46bgJGtTbZZUrFuVw9+8+wbmHfsBBzoT+OsY8bZJljAel7++NJWrNp+AIePasUpR44qGCQA+XXMKgURLRVCzA48r4EFyukA/kcIca78/woAEEJca0oze/ZssWTJkirVkGEYZngQVqA0ssnrBQAziGgaETUBmA/ggRrXiWEY5pClYZ3yQogMEf0bgEcBxAHcLIRYUeNqMQzDHLI0rEABACHEnwEUhoswDMMwVaeRTV4MwzBMHcEChWEYhokEFigMwzBMJLBAYRiGYSKBBQrDMAwTCQ07sbEUiKgfgF9o8QgAfus8HwFgU0AxQXmUezzMOUH1jKKMKK6j3HpW4zr4nkd3HOB7HvZ4mHOqec+PEUIUrj/kRQhxyPwB2B1w/KZy0ofMo6zjUdQzojKiuI5y70c1roPvOd/zQ/6eA1gSlJcQ4pAzee0POP6nMtOHyaPc42HOKfc6w5wTxXWUW89qXAff8+iOA3zPwx4Pc0493HMXh5rJa4kIsR5NpdJXC65ndDRCHQGuZ5Q0Qh2B6tYzbFmHmoZyU43TVwuuZ3Q0Qh0BrmeUNEIdgerWM1RZh5SGwjAMw1SOQ01DYRiGYSrEIS9QiOhmItpFRK869p1IRM8S0XIi+hMRdcr9SSK6Ve5fpb7BIo89QURriOgl+TdeV16V6tlERL+W+18morMcaU6V+9cR0Q0U4WfcIqxjxdqSiA4nor/K+7eCiL4o948mooVEtFb+jnKkuUK21xoiOtexv5JtGWU966Y9iWiMPP8gEf3Ek1dF2jPiOtZTW55NREtlmy0lonc58qrYs+lLmFCw4fwH4EwApwB41bHvBQDvkNv/AuBbcvujAO6Q260ANgKYKv9/AsDsOqnnZQB+LbfHA1gKICb/fx7A6QAIwMMA3lOHdaxYWwKYBOAUud0B4DUAswB8D8Dlcv/lAK6T27MAvAwgBWAagNcBxKvQllHWs57asw3AGQD+FcBPPHlVpD0jrmM9teXJAA6T28cD2Frptgz6O+Q1FCHEUwC6PLuPAfCU3F4I4EPqdABtRJQA0AJgCMCBOqznLACLZLpdsMILZxPRJACdQohnhfXU/QbABfVUx6jq4lPH7UKIZXK7B8AqAJMBnA/gVnnarci3y/mwBhGDQogNANYBmFOFtoyknlHVJ6p6CiF6hRB/BzDgzKeS7RlVHStNCfV8UQixTe5fAaCZiFKVfjb9OOQFioFXAXxAbl8E4HC5fQ+AXgDbYc1Q/b4QwtmB/lqqwd+okoppqufLAM4nogQRTQNwqjw2GcAWR/otcl891VFR8bYkoqmwRnmLAUwQQmwHrBcbltYEWO2z2ZFMtVnV2rLMeirqpT1NVKU9y6yjoh7b8kMAXhRCDKI27zkAFigm/gXAZUS0FJbqOST3zwGQBXAYLLPCV4houjz2MSHECQDeLv8+UcN63gzrIVoC4IcAngGQgaX+eql0mF+xdQSq0JZE1A7gXgBfEkL4aZmmNqtKW0ZQT6C+2tOYhWZfpO0ZQR2BOmxLIjoOwHUAPqN2aU6rSjgvCxQNQojVQohzhBCnArgdlj0asHwojwgh0tJM8zSkmUYIsVX+9gC4DdUxN2jrKYTICCG+LIQ4SQhxPoCRANbC6sCnOLKYAmCbN98a17HibUlESVgv7O+FEPfJ3TulqUCZX3bJ/Vvg1pxUm1W8LSOqZ721p4mKtmdEday7tiSiKQD+AOBiIYTqp6r+nitYoGhQkRtEFAPwXwB+Lg9tAvAusmgDMBfAamm2GSvTJAG8H5appyb1JKJWWT8Q0dkAMkKIlVJd7iGiuVJVvxjA/fVUx0q3pbzuXwFYJYT4X8ehBwAskNsLkG+XBwDMl7bpaQBmAHi+0m0ZVT3rsD21VLI9o6pjvbUlEY0E8BCAK4QQT6uTa/Ge20Tt5W+0P1ij5u0A0rAk+yUAvggrwuI1AN9FfgJoO4C7YTnAVgL4T5GPClkK4BV57EeQETY1qudUAGtgOfUeA3CkI5/ZsF6C1wH8RKWplzpWui1hRe8Imf9L8u+9AMbAChJYK39HO9J8XbbXGjiiZSrclpHUs07bcyOs4I2D8jmZVcn2jKqO9daWsAZovY5zXwIwvtLPpt8fz5RnGIZhIoFNXgzDMEwksEBhGIZhIoEFCsMwDBMJLFAYhmGYSGCBwjAMw0QCCxSGqROI6F+J6OIizp9KjpWdGabWJGpdAYZhrElzQoifB5/JMPULCxSGiQi5oN8jsBb0OxnWZM6LARwL4H9hTYzdA+CfhRDbiegJWGuYvQ3AA0TUAeCgEOL7RHQSrFUFWmFNTvsXIcQ+IjoV1jpofQD+Xr2rY5hg2OTFMNFyDICbhBBvhvVpg8sA/BjAhcJaz+xmANc4zh8phHiHEOIHnnx+A+BrMp/lAK6S+38N4AtCiNMreREMUwqsoTBMtGwW+XWVfgfgSlgfP1ooVzqPw1qeRnGnNwMiGgFL0Dwpd90K4G7N/t8CeE/0l8AwpcEChWGixbuWUQ+AFT4aRW8ReZMmf4apG9jkxTDRcgQRKeHxEQDPARin9hFRUn6/wogQohvAPiJ6u9z1CQBPCiH2A+gmojPk/o9FX32GKR3WUBgmWlYBWEBEv4C1OuyPATwK4AZpskrA+qDYioB8FgD4ORG1AlgP4JNy/ycB3ExEfTJfhqkbeLVhhokIGeX1oBDi+BpXhWFqApu8GIZhmEhgDYVhGIaJBNZQGIZhmEhggcIwDMNEAgsUhmEYJhJYoDAMwzCRwAKFYRiGiQQWKAzDMEwk/H8fvmK9zAiC3wAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "sorted_data['inc'].plot()"
    ]
@@ -215,9 +1304,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f917cee1c18>"
+      ]
+     },
+     "execution_count": 10,
+     "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": [
     "sorted_data['inc'][-200:].plot()"
    ]
@@ -252,10 +1364,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 11,
+   "metadata": {},
    "outputs": [],
    "source": [
     "first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n",
@@ -274,7 +1384,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -298,9 +1408,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f917ce825c0>"
+      ]
+     },
+     "execution_count": 13,
+     "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.plot(style='*')"
    ]
@@ -314,9 +1447,55 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2014    1600941\n",
+       "1991    1659249\n",
+       "1995    1840410\n",
+       "2020    2053781\n",
+       "2012    2175217\n",
+       "2003    2234584\n",
+       "2019    2254386\n",
+       "2006    2307352\n",
+       "2017    2321583\n",
+       "2001    2529279\n",
+       "1992    2574578\n",
+       "1993    2703886\n",
+       "2018    2705325\n",
+       "1988    2765617\n",
+       "2007    2780164\n",
+       "1987    2855570\n",
+       "2016    2856393\n",
+       "2011    2857040\n",
+       "2008    2973918\n",
+       "1998    3034904\n",
+       "2002    3125418\n",
+       "2009    3444020\n",
+       "1994    3514763\n",
+       "1996    3539413\n",
+       "2004    3567744\n",
+       "1997    3620066\n",
+       "2015    3654892\n",
+       "2000    3826372\n",
+       "2005    3835025\n",
+       "1999    3908112\n",
+       "2010    4111392\n",
+       "2013    4182691\n",
+       "1986    5115251\n",
+       "1990    5235827\n",
+       "1989    5466192\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "yearly_incidence.sort_values()"
    ]
@@ -331,9 +1510,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f917cddbc88>"
+      ]
+     },
+     "execution_count": 15,
+     "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.hist(xrot=20)"
    ]
@@ -341,9 +1543,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": []
   }
@@ -364,7 +1564,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,