From 5f0ea7bee845466e51f4e217659761d62a67b1c8 Mon Sep 17 00:00:00 2001
From: 6b9fcb02f874d4fa73786fcf6e721560
 <6b9fcb02f874d4fa73786fcf6e721560@app-learninglab.inria.fr>
Date: Sun, 3 May 2020 13:01:35 +0000
Subject: [PATCH] =?UTF-8?q?Travail=20termin=C3=A9?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 module3/exo3/exercice.ipynb | 430 +++++++++++++++++++++++++-----------
 1 file changed, 307 insertions(+), 123 deletions(-)

diff --git a/module3/exo3/exercice.ipynb b/module3/exo3/exercice.ipynb
index ceab3e8..9b981bc 100644
--- a/module3/exo3/exercice.ipynb
+++ b/module3/exo3/exercice.ipynb
@@ -23,35 +23,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Requirement already satisfied: pandas==1.0.0 in /opt/conda/lib/python3.6/site-packages (1.0.0)\n",
-      "Requirement already satisfied: pytz>=2017.2 in /opt/conda/lib/python3.6/site-packages (from pandas==1.0.0) (2019.3)\n",
-      "Requirement already satisfied: python-dateutil>=2.6.1 in /opt/conda/lib/python3.6/site-packages (from pandas==1.0.0) (2.8.1)\n",
-      "Requirement already satisfied: numpy>=1.13.3 in /opt/conda/lib/python3.6/site-packages (from pandas==1.0.0) (1.15.2)\n",
-      "Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.6/site-packages (from python-dateutil>=2.6.1->pandas==1.0.0) (1.14.0)\n",
-      "1.0.0\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     " %matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
-    "!pip install pandas==1.0.0\n",
     "import pandas as pd\n",
-    "print(pd.__version__)\n",
     "import isoweek\n",
     "import math"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -86,7 +71,6 @@
        "      <th>1/26/20</th>\n",
        "      <th>1/27/20</th>\n",
        "      <th>...</th>\n",
-       "      <th>4/22/20</th>\n",
        "      <th>4/23/20</th>\n",
        "      <th>4/24/20</th>\n",
        "      <th>4/25/20</th>\n",
@@ -96,6 +80,7 @@
        "      <th>4/29/20</th>\n",
        "      <th>4/30/20</th>\n",
        "      <th>5/1/20</th>\n",
+       "      <th>5/2/20</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -112,7 +97,6 @@
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>...</td>\n",
-       "      <td>1176</td>\n",
        "      <td>1279</td>\n",
        "      <td>1351</td>\n",
        "      <td>1463</td>\n",
@@ -122,6 +106,7 @@
        "      <td>1939</td>\n",
        "      <td>2171</td>\n",
        "      <td>2335</td>\n",
+       "      <td>2469</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -136,7 +121,6 @@
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>...</td>\n",
-       "      <td>634</td>\n",
        "      <td>663</td>\n",
        "      <td>678</td>\n",
        "      <td>712</td>\n",
@@ -146,6 +130,7 @@
        "      <td>766</td>\n",
        "      <td>773</td>\n",
        "      <td>782</td>\n",
+       "      <td>789</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -160,7 +145,6 @@
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>...</td>\n",
-       "      <td>2910</td>\n",
        "      <td>3007</td>\n",
        "      <td>3127</td>\n",
        "      <td>3256</td>\n",
@@ -170,6 +154,7 @@
        "      <td>3848</td>\n",
        "      <td>4006</td>\n",
        "      <td>4154</td>\n",
+       "      <td>4295</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -185,7 +170,6 @@
        "      <td>0</td>\n",
        "      <td>...</td>\n",
        "      <td>723</td>\n",
-       "      <td>723</td>\n",
        "      <td>731</td>\n",
        "      <td>738</td>\n",
        "      <td>738</td>\n",
@@ -194,6 +178,7 @@
        "      <td>743</td>\n",
        "      <td>745</td>\n",
        "      <td>745</td>\n",
+       "      <td>747</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -211,13 +196,13 @@
        "      <td>25</td>\n",
        "      <td>25</td>\n",
        "      <td>25</td>\n",
-       "      <td>25</td>\n",
        "      <td>26</td>\n",
        "      <td>27</td>\n",
        "      <td>27</td>\n",
        "      <td>27</td>\n",
        "      <td>27</td>\n",
        "      <td>30</td>\n",
+       "      <td>35</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -285,11 +270,11 @@
        "      <td>4</td>\n",
        "      <td>4</td>\n",
        "      <td>4</td>\n",
-       "      <td>4</td>\n",
        "      <td>8</td>\n",
        "      <td>8</td>\n",
        "      <td>14</td>\n",
        "      <td>16</td>\n",
+       "      <td>16</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>263</th>\n",
@@ -310,10 +295,10 @@
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
-       "      <td>1</td>\n",
        "      <td>6</td>\n",
        "      <td>6</td>\n",
        "      <td>7</td>\n",
+       "      <td>10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>264</th>\n",
@@ -335,9 +320,9 @@
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>0</td>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
+       "      <td>3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>265</th>\n",
@@ -359,13 +344,13 @@
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>0</td>\n",
        "      <td>15</td>\n",
        "      <td>15</td>\n",
+       "      <td>76</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>266 rows × 105 columns</p>\n",
+       "<p>266 rows × 106 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
@@ -382,11 +367,11 @@
        "264            NaN                Comoros -11.645500  43.333300        0   \n",
        "265            NaN             Tajikistan  38.861034  71.276093        0   \n",
        "\n",
-       "     1/23/20  1/24/20  1/25/20  1/26/20  1/27/20  ...  4/22/20  4/23/20  \\\n",
-       "0          0        0        0        0        0  ...     1176     1279   \n",
-       "1          0        0        0        0        0  ...      634      663   \n",
-       "2          0        0        0        0        0  ...     2910     3007   \n",
-       "3          0        0        0        0        0  ...      723      723   \n",
+       "     1/23/20  1/24/20  1/25/20  1/26/20  1/27/20  ...  4/23/20  4/24/20  \\\n",
+       "0          0        0        0        0        0  ...     1279     1351   \n",
+       "1          0        0        0        0        0  ...      663      678   \n",
+       "2          0        0        0        0        0  ...     3007     3127   \n",
+       "3          0        0        0        0        0  ...      723      731   \n",
        "4          0        0        0        0        0  ...       25       25   \n",
        "..       ...      ...      ...      ...      ...  ...      ...      ...   \n",
        "261        0        0        0        0        0  ...        6        6   \n",
@@ -395,23 +380,23 @@
        "264        0        0        0        0        0  ...        0        0   \n",
        "265        0        0        0        0        0  ...        0        0   \n",
        "\n",
-       "     4/24/20  4/25/20  4/26/20  4/27/20  4/28/20  4/29/20  4/30/20  5/1/20  \n",
-       "0       1351     1463     1531     1703     1828     1939     2171    2335  \n",
-       "1        678      712      726      736      750      766      773     782  \n",
-       "2       3127     3256     3382     3517     3649     3848     4006    4154  \n",
-       "3        731      738      738      743      743      743      745     745  \n",
-       "4         25       25       26       27       27       27       27      30  \n",
-       "..       ...      ...      ...      ...      ...      ...      ...     ...  \n",
-       "261        6        6        6        6        6        6        6       6  \n",
-       "262        4        4        4        4        8        8       14      16  \n",
-       "263        1        1        1        1        1        6        6       7  \n",
-       "264        0        0        0        0        0        0        1       1  \n",
-       "265        0        0        0        0        0        0       15      15  \n",
+       "     4/25/20  4/26/20  4/27/20  4/28/20  4/29/20  4/30/20  5/1/20  5/2/20  \n",
+       "0       1463     1531     1703     1828     1939     2171    2335    2469  \n",
+       "1        712      726      736      750      766      773     782     789  \n",
+       "2       3256     3382     3517     3649     3848     4006    4154    4295  \n",
+       "3        738      738      743      743      743      745     745     747  \n",
+       "4         25       26       27       27       27       27      30      35  \n",
+       "..       ...      ...      ...      ...      ...      ...     ...     ...  \n",
+       "261        6        6        6        6        6        6       6       6  \n",
+       "262        4        4        4        8        8       14      16      16  \n",
+       "263        1        1        1        1        6        6       7      10  \n",
+       "264        0        0        0        0        0        1       1       3  \n",
+       "265        0        0        0        0        0       15      15      76  \n",
        "\n",
-       "[266 rows x 105 columns]"
+       "[266 rows x 106 columns]"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -431,7 +416,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -466,7 +451,6 @@
        "      <th>1/27/20</th>\n",
        "      <th>1/28/20</th>\n",
        "      <th>...</th>\n",
-       "      <th>4/22/20</th>\n",
        "      <th>4/23/20</th>\n",
        "      <th>4/24/20</th>\n",
        "      <th>4/25/20</th>\n",
@@ -476,12 +460,13 @@
        "      <th>4/29/20</th>\n",
        "      <th>4/30/20</th>\n",
        "      <th>5/1/20</th>\n",
+       "      <th>5/2/20</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>0 rows × 104 columns</p>\n",
+       "<p>0 rows × 105 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
@@ -489,10 +474,10 @@
        "Columns: [Country/Region, Lat, Long, 1/22/20, 1/23/20, 1/24/20, 1/25/20, 1/26/20, 1/27/20, 1/28/20, 1/29/20, 1/30/20, 1/31/20, 2/1/20, 2/2/20, 2/3/20, 2/4/20, 2/5/20, 2/6/20, 2/7/20, 2/8/20, 2/9/20, 2/10/20, 2/11/20, 2/12/20, 2/13/20, 2/14/20, 2/15/20, 2/16/20, 2/17/20, 2/18/20, 2/19/20, 2/20/20, 2/21/20, 2/22/20, 2/23/20, 2/24/20, 2/25/20, 2/26/20, 2/27/20, 2/28/20, 2/29/20, 3/1/20, 3/2/20, 3/3/20, 3/4/20, 3/5/20, 3/6/20, 3/7/20, 3/8/20, 3/9/20, 3/10/20, 3/11/20, 3/12/20, 3/13/20, 3/14/20, 3/15/20, 3/16/20, 3/17/20, 3/18/20, 3/19/20, 3/20/20, 3/21/20, 3/22/20, 3/23/20, 3/24/20, 3/25/20, 3/26/20, 3/27/20, 3/28/20, 3/29/20, 3/30/20, 3/31/20, 4/1/20, 4/2/20, 4/3/20, 4/4/20, 4/5/20, 4/6/20, 4/7/20, 4/8/20, 4/9/20, 4/10/20, 4/11/20, 4/12/20, 4/13/20, 4/14/20, 4/15/20, 4/16/20, 4/17/20, 4/18/20, 4/19/20, 4/20/20, 4/21/20, 4/22/20, 4/23/20, 4/24/20, 4/25/20, 4/26/20, 4/27/20, ...]\n",
        "Index: []\n",
        "\n",
-       "[0 rows x 104 columns]"
+       "[0 rows x 105 columns]"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -508,17 +493,181 @@
    "source": [
     "Il n'y a pas de données manquantes. \n",
     "\n",
-    "## Seconde partie : génération montrant l'évolution de nombre de cas cumulé\n",
+    "## Seconde partie : formattage du dataset\n",
+    "\n",
+    "Pour formater le dataset, nous allons créer une fonction faisant office de filtre : si le pays est défini dans le tableau (qui représente tous les pays demandés dans l'énoncé) alors on le garde dans le dataset; sinon on le retire. Certains pays devant être affichés sans leurs colonies, le filtre le prendra en compte et retirera les lignes ou le champ *Province/State* est défini.\n",
+    "\n",
+    "La Chine est un cas particulier : on va récupérer toutes les rows de Chine dans un second dataset et les fusionner (sauf Hong Kong) pour obtenir le nombre de cas en Chine, puis on ira la fusionner avec le premier dataset plus tard.\n",
+    "\n",
+    "Voici le résultat :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "scrolled": true
+   },
+   "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>Lat</th>\n",
+       "      <th>Long</th>\n",
+       "      <th>1/22/20</th>\n",
+       "      <th>1/23/20</th>\n",
+       "      <th>1/24/20</th>\n",
+       "      <th>1/25/20</th>\n",
+       "      <th>1/26/20</th>\n",
+       "      <th>1/27/20</th>\n",
+       "      <th>1/28/20</th>\n",
+       "      <th>1/29/20</th>\n",
+       "      <th>...</th>\n",
+       "      <th>4/23/20</th>\n",
+       "      <th>4/24/20</th>\n",
+       "      <th>4/25/20</th>\n",
+       "      <th>4/26/20</th>\n",
+       "      <th>4/27/20</th>\n",
+       "      <th>4/28/20</th>\n",
+       "      <th>4/29/20</th>\n",
+       "      <th>4/30/20</th>\n",
+       "      <th>5/1/20</th>\n",
+       "      <th>5/2/20</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Country/Region</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>China</th>\n",
+       "      <td>1061.0367</td>\n",
+       "      <td>3570.2197</td>\n",
+       "      <td>548</td>\n",
+       "      <td>641</td>\n",
+       "      <td>918</td>\n",
+       "      <td>1401</td>\n",
+       "      <td>2067</td>\n",
+       "      <td>2869</td>\n",
+       "      <td>5501</td>\n",
+       "      <td>6077</td>\n",
+       "      <td>...</td>\n",
+       "      <td>82849</td>\n",
+       "      <td>82864</td>\n",
+       "      <td>82872</td>\n",
+       "      <td>82875</td>\n",
+       "      <td>82881</td>\n",
+       "      <td>82903</td>\n",
+       "      <td>82907</td>\n",
+       "      <td>82919</td>\n",
+       "      <td>82920</td>\n",
+       "      <td>82920</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1 rows × 104 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      Lat       Long  1/22/20  1/23/20  1/24/20  1/25/20  \\\n",
+       "Country/Region                                                             \n",
+       "China           1061.0367  3570.2197      548      641      918     1401   \n",
+       "\n",
+       "                1/26/20  1/27/20  1/28/20  1/29/20  ...  4/23/20  4/24/20  \\\n",
+       "Country/Region                                      ...                     \n",
+       "China              2067     2869     5501     6077  ...    82849    82864   \n",
+       "\n",
+       "                4/25/20  4/26/20  4/27/20  4/28/20  4/29/20  4/30/20  5/1/20  \\\n",
+       "Country/Region                                                                 \n",
+       "China             82872    82875    82881    82903    82907    82919   82920   \n",
+       "\n",
+       "                5/2/20  \n",
+       "Country/Region          \n",
+       "China            82920  \n",
+       "\n",
+       "[1 rows x 104 columns]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def filter_countries(row):\n",
+    "    countries = ['Belgium', 'France', 'China', 'Germany', 'Iran', 'Italy', 'Japan', 'Korea', 'Netherlands', 'Portugal', 'Spain', 'United Kingdom', 'US']\n",
+    "    if row['Country/Region'] in countries:\n",
+    "        if str(row['Country/Region']) in ['France','Netherlands','United Kingdom']:\n",
+    "            if str(row['Province/State']) == \"nan\":\n",
+    "                return True\n",
+    "            else:\n",
+    "                return False\n",
+    "        if str(row['Country/Region']) == \"China\":\n",
+    "            if str(row['Province/State']) == \"Hong Kong\":\n",
+    "                return True\n",
+    "            else:\n",
+    "                return False\n",
+    "        return True\n",
+    "\n",
+    "    return False\n",
     "\n",
-    "On crée le tableau, en supprimant les pays et régions qui ne nous intéressent pas. Pour cela, on crée un dictionnaire contenant les pays qui nous intéresse et retirer les autres, dont la Chine que l'on a séparé du reste. \n",
+    "only_china = raw_data.loc[raw_data['Country/Region'] == 'China']\n",
+    "only_china = only_china.loc[only_china['Province/State'] != 'Hong Kong']\n",
+    "only_china = only_china.groupby(['Country/Region']).sum()\n",
+    "only_china"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Le calcul suivant va appliquer le filtre sur le dataset, renommer la ligne pour la Chine (qui représente seulement Hong Kong) en Hong Kong (on le considèrera comme une région), fusionner la ligne pour la Chine du second dataset (qui est la somme de toutes les régions) avec le premier dataset puis enfin nettoyer le dataset des colonnes devenues obsolètes et transposer le dataset pour que les rows soient maintenant des dates.\n",
     "\n",
-    "On change Hong Kong en tant que pays pour pouvoir le voir plus tard sur le graphe.\n",
-    "On en profite pour retirer les colonnes longitude/latitude qui ne servent à rien ici :"
+    "Seulement, ces dates sont en format texte : on utilise une fonction lambda pour les convertir en format datetime, puis on les réindexent pour qu'elles soient triées par temps. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -543,6 +692,7 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th>Country/Region</th>\n",
        "      <th>Belgium</th>\n",
+       "      <th>Hong Kong</th>\n",
        "      <th>France</th>\n",
        "      <th>Germany</th>\n",
        "      <th>Iran</th>\n",
@@ -564,6 +714,7 @@
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
+       "      <td>0</td>\n",
        "      <td>2</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
@@ -575,6 +726,7 @@
        "    <tr>\n",
        "      <th>2020-01-23</th>\n",
        "      <td>0</td>\n",
+       "      <td>2</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
@@ -591,6 +743,7 @@
        "      <th>2020-01-24</th>\n",
        "      <td>0</td>\n",
        "      <td>2</td>\n",
+       "      <td>2</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
@@ -605,6 +758,7 @@
        "    <tr>\n",
        "      <th>2020-01-25</th>\n",
        "      <td>0</td>\n",
+       "      <td>5</td>\n",
        "      <td>3</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
@@ -620,6 +774,7 @@
        "    <tr>\n",
        "      <th>2020-01-26</th>\n",
        "      <td>0</td>\n",
+       "      <td>8</td>\n",
        "      <td>3</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
@@ -646,25 +801,12 @@
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2020-04-27</th>\n",
-       "      <td>46687</td>\n",
-       "      <td>164589</td>\n",
-       "      <td>158758</td>\n",
-       "      <td>91472</td>\n",
-       "      <td>199414</td>\n",
-       "      <td>14153</td>\n",
-       "      <td>38245</td>\n",
-       "      <td>24027</td>\n",
-       "      <td>209465</td>\n",
-       "      <td>157149</td>\n",
-       "      <td>988197</td>\n",
-       "      <td>82881</td>\n",
+       "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2020-04-28</th>\n",
        "      <td>47334</td>\n",
+       "      <td>1037</td>\n",
        "      <td>167605</td>\n",
        "      <td>159912</td>\n",
        "      <td>92584</td>\n",
@@ -680,6 +822,7 @@
        "    <tr>\n",
        "      <th>2020-04-29</th>\n",
        "      <td>47859</td>\n",
+       "      <td>1037</td>\n",
        "      <td>165093</td>\n",
        "      <td>161539</td>\n",
        "      <td>93657</td>\n",
@@ -695,6 +838,7 @@
        "    <tr>\n",
        "      <th>2020-04-30</th>\n",
        "      <td>48519</td>\n",
+       "      <td>1037</td>\n",
        "      <td>165764</td>\n",
        "      <td>163009</td>\n",
        "      <td>94640</td>\n",
@@ -710,6 +854,7 @@
        "    <tr>\n",
        "      <th>2020-05-01</th>\n",
        "      <td>49032</td>\n",
+       "      <td>1039</td>\n",
        "      <td>165764</td>\n",
        "      <td>164077</td>\n",
        "      <td>95646</td>\n",
@@ -722,96 +867,103 @@
        "      <td>1103461</td>\n",
        "      <td>82920</td>\n",
        "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-05-02</th>\n",
+       "      <td>49517</td>\n",
+       "      <td>1039</td>\n",
+       "      <td>166976</td>\n",
+       "      <td>164967</td>\n",
+       "      <td>96448</td>\n",
+       "      <td>209328</td>\n",
+       "      <td>14571</td>\n",
+       "      <td>40236</td>\n",
+       "      <td>25190</td>\n",
+       "      <td>216582</td>\n",
+       "      <td>182260</td>\n",
+       "      <td>1132539</td>\n",
+       "      <td>82920</td>\n",
+       "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>101 rows × 12 columns</p>\n",
+       "<p>102 rows × 13 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "Country/Region  Belgium  France  Germany   Iran   Italy  Japan  Netherlands  \\\n",
-       "2020-01-22            0       0        0      0       0      2            0   \n",
-       "2020-01-23            0       0        0      0       0      2            0   \n",
-       "2020-01-24            0       2        0      0       0      2            0   \n",
-       "2020-01-25            0       3        0      0       0      2            0   \n",
-       "2020-01-26            0       3        0      0       0      4            0   \n",
-       "...                 ...     ...      ...    ...     ...    ...          ...   \n",
-       "2020-04-27        46687  164589   158758  91472  199414  14153        38245   \n",
-       "2020-04-28        47334  167605   159912  92584  201505  13736        38416   \n",
-       "2020-04-29        47859  165093   161539  93657  203591  13895        38802   \n",
-       "2020-04-30        48519  165764   163009  94640  205463  14088        39316   \n",
-       "2020-05-01        49032  165764   164077  95646  207428  14305        39791   \n",
+       "Country/Region  Belgium  Hong Kong  France  Germany   Iran   Italy  Japan  \\\n",
+       "2020-01-22            0          0       0        0      0       0      2   \n",
+       "2020-01-23            0          2       0        0      0       0      2   \n",
+       "2020-01-24            0          2       2        0      0       0      2   \n",
+       "2020-01-25            0          5       3        0      0       0      2   \n",
+       "2020-01-26            0          8       3        0      0       0      4   \n",
+       "...                 ...        ...     ...      ...    ...     ...    ...   \n",
+       "2020-04-28        47334       1037  167605   159912  92584  201505  13736   \n",
+       "2020-04-29        47859       1037  165093   161539  93657  203591  13895   \n",
+       "2020-04-30        48519       1037  165764   163009  94640  205463  14088   \n",
+       "2020-05-01        49032       1039  165764   164077  95646  207428  14305   \n",
+       "2020-05-02        49517       1039  166976   164967  96448  209328  14571   \n",
        "\n",
-       "Country/Region  Portugal   Spain  United Kingdom       US  China  \n",
-       "2020-01-22             0       0               0        1    548  \n",
-       "2020-01-23             0       0               0        1    641  \n",
-       "2020-01-24             0       0               0        2    918  \n",
-       "2020-01-25             0       0               0        2   1401  \n",
-       "2020-01-26             0       0               0        5   2067  \n",
-       "...                  ...     ...             ...      ...    ...  \n",
-       "2020-04-27         24027  209465          157149   988197  82881  \n",
-       "2020-04-28         24322  210773          161145  1012582  82903  \n",
-       "2020-04-29         24505  212917          165221  1039909  82907  \n",
-       "2020-04-30         25045  213435          171253  1069424  82919  \n",
-       "2020-05-01         25351  213435          177454  1103461  82920  \n",
+       "Country/Region  Netherlands  Portugal   Spain  United Kingdom       US  China  \n",
+       "2020-01-22                0         0       0               0        1    548  \n",
+       "2020-01-23                0         0       0               0        1    641  \n",
+       "2020-01-24                0         0       0               0        2    918  \n",
+       "2020-01-25                0         0       0               0        2   1401  \n",
+       "2020-01-26                0         0       0               0        5   2067  \n",
+       "...                     ...       ...     ...             ...      ...    ...  \n",
+       "2020-04-28            38416     24322  210773          161145  1012582  82903  \n",
+       "2020-04-29            38802     24505  212917          165221  1039909  82907  \n",
+       "2020-04-30            39316     25045  213435          171253  1069424  82919  \n",
+       "2020-05-01            39791     25351  213435          177454  1103461  82920  \n",
+       "2020-05-02            40236     25190  216582          182260  1132539  82920  \n",
        "\n",
-       "[101 rows x 12 columns]"
+       "[102 rows x 13 columns]"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "def filter_countries(row):\n",
-    "    countries = ['Belgium', 'China', 'France', 'Germany', 'Iran', 'Italy', 'Japan', 'Korea', 'Netherlands', 'Portugal', 'Spain', 'United Kingdom', 'US']\n",
-    "    if row['Country/Region'] in countries:\n",
-    "        if str(row['Country/Region']) in ['France','Netherlands','United Kingdom']:\n",
-    "            if str(row['Province/State']) == \"nan\":\n",
-    "                return True\n",
-    "            else:\n",
-    "                return False\n",
-    "        if str(row['Country/Region']) == 'China':\n",
-    "            return False\n",
-    "        return True\n",
-    "\n",
-    "    return False\n",
-    "\n",
-    "only_china = raw_data.loc[raw_data['Country/Region'] == 'China']\n",
-    "only_china = only_china.loc[only_china['Province/State'] != 'Hong Kong']\n",
-    "only_china = only_china.groupby(['Country/Region']).sum()\n",
-    "\n",
     "if len(raw_data.index) > 0:\n",
     "    new_data = raw_data[raw_data.apply(filter_countries, axis=1)]\n",
     "\n",
     "new_data = new_data.set_index('Country/Region')\n",
+    "new_data = new_data.rename(index={\"China\": \"Hong Kong\"})\n",
     "new_data = new_data.append(only_china, sort='False')\n",
     "new_data = new_data.drop(columns=['Lat', 'Long','Province/State'])\n",
     "new_data = new_data.T\n",
-    "\n",
     "new_data.index = [pd.to_datetime(datetext) for datetext in new_data.index]\n",
     "new_data = new_data.sort_index()\n",
     "new_data"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Troisième partie : affichage\n",
+    "\n",
+    "Nos données sont prêtes : on les affiche en utilisant la fonction plot() fournie par matplotlib :"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbed8413240>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f13330c1b00>"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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RdLy3Jk8/5Gft7gASXK61cSpcPFK+bdZpBX0/uO1qixYtYsOGDWRnZ5ORkcHatWsZMGAAV69eJS8vj3fffZcBAwYQFxdH37596dy5M7///jv16tVjzZo1ODs7c/LkSSZMmEBiYiK2trb88MMPNG7cuNg0/kKIqsNo1Pz9h0MAfDy4dYU9n+V2yZMoK4GC9C9BQUEMGjTIUr57924WL17Mtm3bcHJyYvXq1Rw4cIDt27fzyiuvWJJWxsTE8MILL3D06FFq1KjBqlWrAHjyySd54YUXOHToEL///jt169a9Jo1/REQE+/fvZ8eOHVY5byFE2S3dd4a9sUm8FRJAg5p39kbJW5GRS2GlGGGUh5tNi/31r3+lZs2agGmx7o033mDHjh3Y2Nhw/vx5EhISAPD397fkJmvbti1xcXGkpaVx/vx5S7BycjJlQS1JGn8hRNWQkpnHPzedoOO9NRnSrn7xFe4gCS6VmKurq+X9kiVLSExMZP/+/djb2+Pn52dJnV84Tb6trS1ZWVk3TcVfkMZ//PjxFdt5IUSFm70lmpSsPN4KaWG1+1luRqbFqoiUlBR8fHywt7dn+/btXJ+A83oeHh7Ur1+fn376CYCcnBwyMzNLlMZfCFH5xSSk8e2e0wzv0JCAezys3Z0byMilinjyySfp168f7dq1IygoiGbNmhVb59tvv2X8+PG89dZb2Nvb88MPP5Qojb8QonLTWvP2+ihcHWx5pdf91u5OkUqUcr+6aNeunQ4PD7+mTFK0Vy/yfYq7wZaoBMZ8E870fgE83cm/wo9XmpT7Mi0mhBBViCHfyPsbj9G4tisjOjaydnduSoKLEEJUIasPnudUYgZTejfD3rby/gmvvD0TQghxjRxDPp9tiSGwvie9W/hauzu3JMFFCCGqiGV7z3A+OYspve+vdJceX0+CixBCVAGZuQbmbj9Jx3tr0rlJLWt3p1gSXIQQogr4+rc4LqfnMqV3s0o/agEJLpVCQkICTzzxBPfeey9t27blwQcfZPXq1dbulhCikkhIzeaLX0/Rs5kPbRt5Wbs7JSLBxcq01gwcOJCuXbvyxx9/sH//fr7//nvOnTtXovr5+fnF7ySEqLK01vxj9RFyDUbeDAmwdndKTO7QL+TDfR9yPOl4ubbZrGYzXuvw2k23b9u2DQcHByZMmGApa9SoES+++CL5+flMnTqV0NBQcnJyeOGFFxg/fjyhoaHMnDmTunXrEhERwf/+9z/69OlD586d2bNnD61bt+bpp59m+vTpXLp0iSVLltChQwf27dvHyy+/TFZWFs7Oznz99dfcf//9LFq0iLVr15KZmcmpU6cYNGgQH330EQsWLCAyMpLZs2cD8OWXX3Ls2DE+/fTTcv0dCSFubk3EBbYcu8SbjzTHv5Zr8RUqCRm5WNnRo0dp06ZNkdsWLFiAp6cnYWFhhIWF8eWXXxIbGwvAvn37mDVrFlFRUQCcPHmSSZMmcfjwYY4fP87SpUvZtWsXn3zyCe+99x4AzZo1Y8eOHRw8eJC3336bN954w3KsiIgIli9fzpEjR1i+fDlnz55l2LBhrF27lry8PAC+/vprnn766Yr8dQghCrmUls2MdUdp07DGHbkTvzzJyKWQW40w7pQXXniBXbt24eDgQKNGjTh8+DArV64ETMkrY2JicHBwoEOHDvj7//kfm7+/v+VRyS1atKBnz54opWjVqhVxcXGW+qNGjSImJgallCVoAPTs2RNPT08AAgICOH36NA0aNKBHjx6sX7+e5s2bk5eXd83jmIUQFWv6mqNk5ubz0eDW2FaSh4CVlIxcrKxFixYcOHDA8vnf//43W7duJTExEa01n3/+OREREURERBAbG0uvXr2Aa9Pxw7Vp921sbCyfbWxsMBgMAEybNo3u3bsTGRnJunXrLCn7r69va2trqTNmzBgWLVokoxYh7rD1hy+wMfIik//SlCY+btbuzm2T4GJlPXr0IDs7m3nz5lnKMjMzAejduzfz5s2zjDCio6PJyMgo9bFSUlKoV68eYHqMckkEBwdz9uxZli5dyvDhw0t9bCFEyV1Oz+GtNUdpXd+TsV2q1nRYAQkuVqaU4qeffuLXX3/F39+fDh06MGrUKD788EPGjBlDQEAAbdq0oWXLlowfP94yoiiNV199lddff51OnTrd1lVmjz/+OJ06dcLLq2pcAilEVffWmkjSsw18PKQ1dpU4f9itSMp9SdFerJCQECZPnkzPnj2t3ZViyfcpqrr1hy8wcelBpvS+nxe6N7F2dwBJuS/KWXJyMk2bNsXZ2blKBBYhqrrC02Hju95r7e6USZmCi1JqslLqqFIqUim1TCnlpJSqqZTarJSKMb96Fdr/daXUSaXUCaVU70LlbZVSR8zb5ihzbgOllKNSarm5fK9Syq9QnVHmY8QopUaV5TxE0WrUqEF0dDQ//PCDtbsiRLWntebN1VV/OqxAqXuvlKoHvAS001q3BGyBYcBUYKvW+j5gq/kzSqkA8/YWQB/gP0opW3Nz84BxwH3mnz7m8meBq1rrJsBs4ENzWzWB6UAw0AGYXjiICSFEVbP64Hl+PnqR/+vVlKa+7tbuTpmVNTTaAc5KKTvABbgADAAWm7cvBgaa3w8Avtda52itY4GTQAelVF3AQ2u9W5sWgL65rk5BWyuBnuZRTW9gs9Y6SWt9FdjMnwFJCCGqlPPJWUxfc5T2fl6M7VK1p8MKlDq4aK3PA58AZ4B4IEVrvQnw1VrHm/eJB3zMVeoBZws1cc5cVs/8/vrya+porQ1ACuB9i7ZuoJQap5QKV0qFJyYmlu5khRCighiNmik/HMKoNf8cElTlbpa8mbJMi3lhGln4A/cArkqpEbeqUkSZvkV5aetcW6j1fK11O611u9q1a9+ie0IIcect3h3H76euMC0kgIbeLtbuTrkpy7TYX4BYrXWi1joP+BF4CEgwT3Vhfr1k3v8c0KBQ/fqYptHOmd9fX35NHfPUmyeQdIu2qiQ3t6p3960QouyiLqTy/sbj9Gjmw9D2DYqvUIWUJbicAToqpVzM6yA9gWPAWqDg6q1RwBrz+7XAMPMVYP6YFu73mafO0pRSHc3tjLyuTkFbg4Ft5nWZX4BeSikv8wiql7ms2pBU+kJUb+k5BiYuPUANZ3s+GhxYJR4AdjtKnbhSa71XKbUSOAAYgIPAfMANWKGUehZTABpi3v+oUmoFEGXe/wWtdcFf0OeARYAzsNH8A7AA+FYpdRLTiGWYua0kpdQ7QJh5v7e11kmlPZcCF997j5xj5Zty37F5M+oUyj58K9en0o+KimLgwIGcPXuW7OxsJk2axLhx4wDTaGfSpEmsX78eZ2dn1qxZg6+vb7n2XQhRMUyXHR8h7koGS8d2pJabY/GVqpgyZUXWWk/HdElwYTmYRjFF7T8LmFVEeTjQsojybMzBqYhtC4GFt9nlSm/fvn1ERkZaMh4vXLiQmjVrkpWVRfv27Xnsscfw9vYmIyODjh07MmvWLF599VW+/PJL3nzzTSv3XghREj+En+OniAv831+b0vFeb2t3p0JIyv1CSjrCqEjXp9KfM2eO5ZHHZ8+eJSYmBm9vbxwcHAgJCQGgbdu2bN682Sr9FULcnpiENN5aG8lDjb0rTXqXiiDBpZIpnEo/NDSULVu2sHv3blxcXOjWrZslTb69vb1ljrZwinwhROWVY8jnpe8jcHWw47Nh1eey46JIcKnEUlJS8PLywsXFhePHj7Nnzx5rd0kIUQYf/XyCY/GpLBjVDh93J2t3p0JV7eQ11VyfPn0wGAwEBgYybdo0OnbsaO0uCSFKaUd0Igt2xTLywUb0bF79L76RlPuSor1ake9TVEZX0nPo86+d1HC2Z92LnXGyty2+UiVSmpT7Mi0mhBAV7J31UaRk5rH46Q5VLrCUlkyLCSFEBQqPS+KniAuMf/heAu7xsHZ37hgJLkIIUUHyjZrpa49S19OJ57o1tnZ37igJLkIIUUGWh53l6IVU3vhbc1wc7q5VCAkuQghRAVIy8/j4l+N08K9JSGBda3fnjpPgIoQQFWD2lmhSsvKY3i+g2iWlLAkJLpVAQcr9uLg4li5dWuz+cXFxtGx5Qyo2IUQlcfxiKt/uOc3wDg1pcY+ntbtjFRJcKpGSBhchROWltWb6mqO4O9nx9173W7s7VnN3rTAVY+eKaC6fTS/XNms1cKPL401LtO/UqVM5duwYQUFBjBo1ikGDBvHUU0+RkZEBwNy5c3nooYeuqdOlSxc+//xzgoKCAOjUqRPz5s0jMDCwXM9DCFEy6w/Hszc2iXcHtsTL1cHa3bEaGblUIh988AFdunQhIiKCyZMn4+Pjw+bNmzlw4ADLly/npZdeuqHOmDFjWLRoEQDR0dHk5ORIYBHCSjJzDbz3v2O0uMeD4R0aWrs7ViUjl0JKOsK4U/Ly8pg4cSIRERHY2toSHR19wz5DhgzhnXfe4eOPP2bhwoWMHj36zndUCAHAv7efJD4lm7lPPFCtMx6XhASXSmz27Nn4+vpy6NAhjEYjTk43ZlF1cXHhr3/9K2vWrGHFihVcnztNCHFnxF7O4MsdsTz6QD3aNqpp7e5YnQSXSsTd3Z20tDTL55SUFOrXr4+NjQ2LFy8mPz+/yHpjxoyhX79+dOnShZo15T9qIe40rU134jva2TC1bzNrd6dSkDWXSiQwMBA7Oztat27N7Nmzef7551m8eDEdO3YkOjr6mgeJFda2bVs8PDx4+umn73CPhRAAGyMvsiM6kVd6NcXHo3o/p6WkZORSCaSnm65Qs7e3Z+vWrddsO3z4sOX9+++/D4Cfnx+RkZGW8gsXLmA0GunVq9cd6K0QorD0HANvr4sioK4HIzo2snZ3Kg0ZuVRx33zzDcHBwcyaNQsbG/k6hbjT5myN4WJqNu8Oaomdrfw/WEBGLlXcyJEjGTlypLW7IcRd6cTFNBbsimV4hwa0aehl7e5UKhJmhRCiFAz5Rl5ddRgPJzte7S2L+NeTkYsQQpTCgl2xHDqbzOfDH7ir78S/GRm5CCHEbTp5KZ1/bo6mdwvfuzKdfklIcBFCiNuQb9S8uvIQLg62vDOw5V2ZTr8kyhRclFI1lFIrlVLHlVLHlFIPKqVqKqU2K6VizK9ehfZ/XSl1Uil1QinVu1B5W6XUEfO2Ocr8bSmlHJVSy83le5VSfoXqjDIfI0YpNaos52FtBSn3hRCV39e/xXLgTDIz+rXAx13uabmZso5c/gX8rLVuBrQGjgFTga1a6/uArebPKKUCgGFAC6AP8B+llK25nXnAOOA+808fc/mzwFWtdRNgNvChua2awHQgGOgATC8cxIQQoiJEJ6Tx0S8n+EtzXwYE3WPt7lRqpV7QV0p5AF2B0QBa61wgVyk1AOhm3m0xEAq8BgwAvtda5wCxSqmTQAelVBzgobXebW73G2AgsNFcZ4a5rZXAXPOopjewWWudZK6zGVNAWlba8wHYvmg+l07/UZYmbuDT6F66jx5X7H7p6ekMGDCAq1evkpeXx7vvvsuAAQOIi4ujT58+BAcHc/DgQZo2bco333yDi4sLb7/9NuvWrSMrK4uHHnqI//73vyil6NatG8HBwWzfvp3k5GQWLFhAly5dyvW8hLjb5BjymfR9BO6Odrz/aCuZDitGWUYu9wKJwNdKqYNKqa+UUq6Ar9Y6HsD86mPevx5wtlD9c+ayeub315dfU0drbQBSAO9btHUDpdQ4pVS4Uio8MTGxtOda4ZycnFi9ejUHDhxg+/btvPLKK2itAThx4gTjxo3j8OHDeHh48J///AeAiRMnEhYWRmRkJFlZWaxfv97SnsFgYN++fXz22WfMnDnTKuckRHXy6eZojsWn8uFjgdR2d7R2dyq9slyKbAe0AV7UWu9VSv0L8xTYTRQV5vUtyktb59pCrecD8wHatWtX5D4FSjLCqChaa9544w127NiBjY0N58+fJyEhAYAGDRrQqVMnAEaMGMGcOXP4+9//zvbt2/noo4/IzMwkKSmJFi1a0K9fPwAeffRRwJR3LC4uzirnJER1sfvUFebv+IMnghvylwBfa3enSijLyOUccE5rvdf8eSWmYJOglKoLYH69VGj/BoXq1wcumMvrF1F+TR2llB3gCSTdoq0RYumUAAAgAElEQVQqa8mSJSQmJrJ//34iIiLw9fUlOzsb4Ibht1KK7Oxsnn/+eVauXMmRI0cYO3asZX8AR0fTv6xsbW0xGAx37kSEqGaSM3N5ZUUEft6uvPlIc2t3p8oodXDRWl8EziqlCh4S3ROIAtYCBVdvjQLWmN+vBYaZrwDzx7Rwv888dZamlOpoXk8ZeV2dgrYGA9u0aa7oF6CXUsrLvJDfy1xWZaWkpODj44O9vT3bt2/n9OnTlm1nzpxh9+7dACxbtozOnTtbAkmtWrVIT09n5cqVVum3ENWZ1pq//3CYxPQcZg8NwsVB7jsvqbL+pl4EliilHIA/gKcxBawVSqlngTPAEACt9VGl1ApMAcgAvKC1LnhAyXPAIsAZ00L+RnP5AuBb8+J/EqarzdBaJyml3gHCzPu9XbC4X9UYDAYcHR158skn6devH+3atSMoKIhmzf5MJ9G8eXMWL17M+PHjue+++3juuedwcXFh7NixtGrVCj8/P9q3b2/FsxCielqwK5YtxxKYFhJAUIMa1u5OlaIKFo3vBu3atdPXP6nx2LFjNG9uvaHuoUOHGDt2LPv27Stye1xcHCEhIdek2Bc3Z+3vU1QfB89cZcgXu+nRzIf/PtX2rr46TCm1X2vd7nbqyB36VvTFF18wfPhw3n33XWt3RQhRSEpmHhOXHqSOpxMfD259VweW0pIJRCuaMGECEyZMuOU+1z8YTAhR8aavjSQhNZuVzz2Ep4u9tbtTJcnIRQghCtkSlcBPEReY2KOJrLOUgQQXIYQwS8nK443VR2hWx53nuzWxdneqNJkWE0IIs1kboriSkcuCUe1xsJN/e5eF/PaEEALYEZ3IivBzjOt6L63qe1q7O1WeBJdKQCnFK6+8Yvn8ySefMGPGjFvWCQ0N5ffff7d8Hj16dJlvpPTz8+Py5ctlaqOAPEZAVCUZOQZe//EI99Z2ZVLP+6zdnWpBgksl4OjoyI8//nhbf9ivDy5lobXGaDSWS1tCVEWfbo7mfHIWHz4WiJO9bfEVRLFkzaWQ5HWnyL2QUa5tOtzjSo1+jW+5j52dHePGjWP27NnMmjXrmm2JiYlMmDCBM2fOAPDZZ59Rr149vvjiC2xtbfnuu+/4/PPPAdixYweffvopFy9e5KOPPmLw4MEAfPzxx6xYsYKcnBwGDRrEzJkziYuLo2/fvnTv3p3du3fz008/XXPcgQMHcvbsWbKzs5k0aRLjxpmSerq5uTFp0iTWr1+Ps7Mza9aswdfXl9jYWJ544gkMBgN9+vSxtBMfH8/QoUNJTU3FYDAwb948Sf8vKpVDZ5P5+rdYnghuSHu/mtbuTrUhI5dK4oUXXmDJkiWkpKRcUz5p0iQmT55MWFgYq1atYsyYMfj5+TFhwgQmT55MRESE5Y91fHw8u3btYv369UydakpQvWnTJmJiYti3bx8RERHs37+fHTt2AKZU/iNHjuTgwYM0atTomuMuXLiQ/fv3Ex4ezpw5c7hy5QoAGRkZdOzYkUOHDtG1a1e+/PJLSz+fe+45wsLCqFOnjqWdpUuX0rt3byIiIjh06BBBQUEV8wsUohTy8o1M/fEItdwcmdq3WfEVRInJyKWQ4kYYFcnDw4ORI0cyZ84cnJ2dLeVbtmwhKirK8jk1NZW0tLQi2xg4cCA2NjYEBARY0vVv2rSJTZs28cADDwCmh5LFxMTQsGFDGjVqRMeOHYtsa86cOaxevRqAs2fPEhMTg7e3Nw4ODoSEhACmdP6bN28G4LfffmPVqlUAPPXUU7z22msAtG/fnmeeeYa8vDwGDhwowUVUKgt2xXIsPpUvRrTBw0lulixPElwqkZdffpk2bdrw9NNPW8qMRiO7d+++JuDcTEGafcDyoDGtNa+//jrjx4+/Zt+4uDhcXV2LbCc0NJQtW7awe/duXFxc6NatmyULs729vSUVxvXp/ItKkdG1a1d27NjBhg0beOqpp5gyZQojR44s9lyEqGjnk7P4bEs0vQJ86dOyrrW7U+3ItFglUrNmTR5//HEWLFhgKevVqxdz5861fI6IiADA3d39piOYwnr37s3ChQtJT08H4Pz581y6dOmWdVJSUvDy8sLFxYXjx4+zZ8+eYo/TqVMnvv/+e8D0bJoCp0+fxsfHh7Fjx/Lss89y4MCBYtsS4k74+OfjaA3T+7ewdleqJQkulcwrr7xyzVVjc+bMITw8nMDAQAICAvjiiy8A6NevH6tXryYoKIidO3fetL1evXrxxBNP8OCDD9KqVSsGDx5cbFDq06cPBoOBwMBApk2bdtOps8L+9a9/8e9//5v27dtfs24UGhpKUFAQDzzwAKtWrWLSpEnFtiVERYs4m8xPERcY08WfejWKnxUQt09S7kuK9mpFvk9RHK01Q77YTdyVDEKndMfNUVYHiiMp94UQohgbIy8Sfvoqr/S6XwJLBZLgIoS4a+QY8nl/4zGa1XHn8XYNrN2dak2CixDirrH49zjOJmXxxt+aY2sjDwCrSBJchBB3haSMXD7fdpJu99ema9Pa1u5OtSfBRQhxV5izNYaMHANv/E0u+LgTJLgIIaq9PxLT+W7PaYa2b0hTX3drd+euIMGlErC1tSUoKIiWLVsyZMgQMjMzb6v+e++9VyH9iouLo2XLlhXSthB30oc/H8fRzobJf5V0+neKBJdKwNnZmYiICCIjI3FwcLDcKFmcglT5FRVchKgO9v5xhV+OJjDh4cb4uDtZuzt3DbnIu5CNGzdy8eLFcm2zTp069O3bt8T7d+nShcOHDwPw6aefsnDhQgDGjBnDyy+/fEOq/KCgILKysggKCqJFixbMmjWLkJAQIiMjAdODx9LT05kxYwZhYWE8++yzuLq60rlzZzZu3EhkZCRxcXE89dRTZGSYHjcwd+5cHnrooXL9PQhhDflGzTsboqjj4cSYLvdauzt3FQkulYjBYGDjxo306dOH/fv38/XXX7N371601gQHB/Pwww/j5eXFiRMn+Prrr/nPf/4DwA8//GDJORYXF3fT9p9++mnmz5/PQw89ZEnJD+Dj48PmzZtxcnIiJiaG4cOHc30mAyGqoqV7TxN5PpXPhz+As4M8BOxOKnNwUUrZAuHAea11iFKqJrAc8APigMe11lfN+74OPAvkAy9prX8xl7cFFgHOwP+ASVprrZRyBL4B2gJXgKFa6zhznVHAm+ZuvKu1XlzWc7mdEUZ5Khh5gGnk8uyzzzJv3jwGDRpkyVz86KOPsnPnTvr373/LVPk3k5ycTFpammVE8sQTT7B+/XoA8vLymDhxIhEREdja2hIdHV2OZyeEdVxOz+HjX07wUGNvQgIl6/GdVh4jl0nAMcDD/HkqsFVr/YFSaqr582tKqQBgGNACuAfYopRqqrXOB+YB44A9mIJLH2AjpkB0VWvdRCk1DPgQGGoOYNOBdoAG9iul1hYEsaqmYM2lsFvlfLtZqnwwPdWy8COLC1Ll36q92bNn4+vry6FDhzAajTg5yby0qPo+3HiczNx83h7QosjHQYiKVaYFfaVUfeAR4KtCxQOAglHEYmBgofLvtdY5WutY4CTQQSlVF/DQWu/Wpr+A31xXp6CtlUBPZfqvpDewWWudZA4omzEFpGqja9eu/PTTT2RmZpKRkcHq1atv+nhge3t78vLyAPD19eXSpUtcuXKFnJwcy+jEy8sLd3d3S/r8gvT4YEqxX7duXWxsbPj222/Jz8+v4LMTomLtP53ED/vP8WwXf5r4yKXH1lDWq8U+A14FjIXKfLXW8QDmVx9zeT3gbKH9zpnL6pnfX19+TR2ttQFIAbxv0Va10aZNG0aPHk2HDh0IDg5mzJgxlqdJXm/cuHEEBgby5JNPYm9vz1tvvUVwcDAhISE0a/bno1sXLFjAuHHjePDBB9Fa4+npCcDzzz/P4sWL6dixI9HR0bccGQlR2RnyjUz76Sh1PZ14qYdcemw1WutS/QAhwH/M77sB683vk6/b76r59d/AiELlC4DHgPbAlkLlXYB15vdHgfqFtp3CFFymAG8WKp8GvHKTfo7DtCYU3rBhQ329qKioG8qqq7S0NMv7999/X7/00ktW7E3FuJu+T1G0r3b+oRu9tl5vOHzB2l2pNoBwfZsxoiwjl05Af6VUHPA90EMp9R2QYJ7qwvxa8NjDc0DhNKT1gQvm8vpFlF9TRyllB3gCSbdo6wZa6/la63Za63a1a9/d+YQ2bNhguVlz586dvPnmm8VXEqIKuZiSzaebTtDt/tr0bVnH2t25q5U6uGitX9da19da+2FaqN+mtR4BrAVGmXcbBawxv18LDFNKOSql/IH7gH3aNHWWppTqaF5PGXldnYK2BpuPoYFfgF5KKS+llBfQy1wmbmHo0KGWmzU3bNjA3R5sRfXz9vqjGIyat/u3lEV8K6uI+1w+AFYopZ4FzgBDALTWR5VSK4AowAC8oE1XigE8x5+XIm80/4Bp6uxbpdRJTCOWYea2kpRS7wBh5v3e1lonVcC5CCGqiO3HL/G/Ixf5e6+mNPR2sXZ37nrlEly01qFAqPn9FaDnTfabBcwqojwcuCGJldY6G3NwKmLbQmBhafsshKg+snLzeWttJI1ruzK2q9yJXxnIHfpCiCrvs63RnE3KYtnYjjjayZ34lYEkrhRCVGmR51P4amcsQ9s14MHG3tbujjCT4FIJzJo1ixYtWhAYGEhQUBB79+697TbWrl3LBx98UAG9E6Lyyss38urKw9R0dZCHgFUyMi1mZbt372b9+vUcOHAAR0dHLl++TG5u7m23079/f/r3718BPRSi8vpqZyxR8anMe7INni721u6OKESCSyHR0e+Qln6sXNt0d2tO06bTbro9Pj6eWrVq4ejoCECtWrUA8PPzY+jQoWzfvh2ApUuX0qRJE9atW8e7775Lbm4u3t7eLFmyBF9fXxYtWkR4eDhz585l9OjReHh4EB4ezsWLF/noo48YPHhwuZ6XENYWezmDz7ZE07uFL31bSWLKykamxaysV69enD17lqZNm/L888/z66+/WrZ5eHiwb98+Jk6cyMsvvwxA586d2bNnDwcPHmTYsGF89NFHRbYbHx/Prl27WL9+/TXp9YWoDoxGzWsrD+NgZ8PbA+RpqZWRjFwKudUIo6K4ubmxf/9+du7cyfbt2xk6dKhl7WT48OGW18mTJwNw7tw5hg4dSnx8PLm5ufj7+xfZ7sCBA7GxsSEgIICEhIQ7czJC3CELf4tlX1wSHw8OxNdDsnhXRjJyqQRsbW3p1q0bM2fOZO7cuaxatQrgmjuMC96/+OKLTJw4kSNHjvDf//7XklL/egXTbHDrdPtCVDWnEtP5+JcT9Gzmw+C29YuvIKxCgouVnThxgpiYGMvniIgIGjVqBMDy5cstrw8++CBgSo9fr54pAfTixWV+PpoQVUq+UfP3Hw7hZG/L+4+2khQvlZhMi1lZeno6L774IsnJydjZ2dGkSRPmz5/P+vXrycnJITg4GKPRyLJlywCYMWMGQ4YMoV69enTs2JHY2Fgrn4EQd86XO//g4Jlk/jUsCB+ZDqvU1N00ZdKuXTt9/bPhjx07RvPmle/6eD8/P8LDwy1Xj4mSqazfpyi7s0mZ9Pz0V3rc78O8EW1k1HIHKaX2a63b3U4dmRYTQlQJH/x8HBsF0/sHSGCpAmRarJKKi4uzdheEqDTC4pLYcDiel/9yH3U9na3dHVECMnIRQlRqRqPm7XVR1PV0YnzXxtbujighCS5CiErtx4PnOXI+hdf6NMPZQTIeVxUSXIQQlVZGjoGPfj5OUIMa9G99j7W7I26DBBchRKX1+baTXErLYVpIADY2sohflUhwsbK4uDhatrw2N9KMGTP45JNPblkvPDycl156CYDQ0FB+//332z62n58fly9fvmX5/v378ff35+DBg+Wa1j80NJSQkJByaUtUT6cS01mw6w8Gt61P20Ze1u6OuE1ytVgV1a5dO9q1M112HhoaipubGw899FC5HuPw4cMMHjyY5cuX88ADD/DAAw9IWn9xR2itmbH2KE72tkzt28za3RGlIMGlkGkx54hMzyrXNlu6OfPOfaXPf9StWzeCg4PZvn07ycnJLFiwgC5duhAaGsonn3zC3Llz+eKLL7C1teW7777j888/p1mzZkyYMIEzZ84A8Nlnn9GpUyeuXLnC8OHDSUxMpEOHDrfMOXbs2DFGjRrFt99+S4cOHQBKlNbfaDQyceJEfv31V/z9/TEajTzzzDMMHjyYn3/+mZdffplatWrRpk0by7GSkpJ45pln+OOPP3BxcWH+/PkEBgYyY8YMYmNjiY+PJzo6mk8//ZQ9e/awceNG6tWrx7p167C3l2d4VEc/R15kZ8xlZvQLoJabY/EVRKUj02JVgMFgYN++fXz22WfMnDnzmm1+fn5MmDCByZMnExERQZcuXZg0aRKTJ08mLCyMVatWMWbMGABmzpxJ586dOXjwIP3797cEn6IMGDCAuXPn0rlz55vuU1Ra/x9//JG4uDiOHDnCV199xe7duwHIzs5m7NixrFu3jp07d3Lx4kVLO9OnT+eBBx7g8OHDvPfee4wcOdKy7dSpU2zYsIE1a9YwYsQIunfvzpEjR3B2dmbDhg23/8sUlV5mroF31kfRrI47Izo2snZ3RCnJyKWQsowwSutmdxoXLn/00UcBaNu2bYlurtyyZQtRUVGWz6mpqaSlpbFjxw5+/PFHAB555BG8vG4+j/2Xv/yFr776it69e2NrW/Tln0Wl9d+1axdDhgzBxsaGOnXq0L17dwCOHz+Ov78/9913HwAjRoxg/vz5ljoFmaB79OjBlStXSElJAaBv377Y29vTqlUr8vPz6dOnDwCtWrWSG02rqc+3neRCSjb/Gv4Adrby79+qSr45K/P29ubq1avXlCUlJV2TU6wgfb6trS0Gg6HYNo1GI7t37yYiIoKIiAjOnz+Pu7s7cPNgdr25c+cC8Pzzz990n6LS+t9qqu1mxy6qTsG+BcewsbHB3t7eUm5jY1Oi34WoWk5cTOPLHX/wWJv6tPerae3uiDKQ4GJlbm5u1K1bl61btwKmwPLzzz/fcjrqeu7u7qSlpVk+9+rVyxIcwJTGH6Br164sWbIEgI0bN94Q1AqzsbFh2bJlnDhxgrfeeqvEfencuTOrVq3CaDSSkJBAaGgoAM2aNSM2NpZTp04BWLI8X9+v0NBQatWqhYeHR4mPKaoHo1HzxuojuDvZ8Y9HJPloVSfBpRL45ptvePfddwkKCqJHjx5Mnz6dxo1LnuaiX79+rF69mqCgIHbu3MmcOXMIDw8nMDCQgIAAvvjiC8C0trFjxw7atGnDpk2baNiw4S3bdXR0ZM2aNaxdu5Z///vfJerLY489Rv369WnZsiXjx48nODgYT09PnJycmD9/Po888gidO3e2PLMGTJdeF/R36tSp8pyau9T3YWfZf/oq/3gkgJquDtbujigjSbkvKdrLXXp6Om5ubly5coUOHTrw22+/UadOnTtybPk+q6ZLadn85Z+/EnCPB8vGdpSsx5VEXl4qGZkxeNVod9sp90u9oK+UagB8A9QBjMB8rfW/lFI1geWAHxAHPK61vmqu8zrwLJAPvKS1/sVc3hZYBDgD/wMmaa21UsrRfIy2wBVgqNY6zlxnFPCmuTvvaq3ln7uVREhICMnJyeTm5jJt2rQ7FlhE1aS1KTFldp6RWYPk6ZLXy829THJyOCmpB/FwD8THpy9KlXzSKT8/B4Mhhby8ZPLyrpKdE09O9kVyci+Sb8jEqHMxGnPROr9QnUwyMk6Sm3up1P0uy9ViBuAVrfUBpZQ7sF8ptRkYDWzVWn+glJoKTAVeU0oFAMOAFsA9wBalVFNtOqN5wDhgD6bg0gfYiCkQXdVaN1FKDQM+BIaaA9h0oB2gzcdeWxDEhHUVrLMIURI/hJ9j/eF4XvlrUxrXdrN2d6xKayMZGSdJST1IakoEySkHyMw8ad5qAxhxP92Cexv/H941HyY37wqZGX+QlXWa3NxEcnITyc29TG7uZXJyLpGbm0h+fkaRx7Kz88DWxhWwRxvt0EYbjPna9GOwIy+jOdlXu5N2yRd4+bbPpdTBRWsdD8Sb36cppY4B9YABQDfzbouBUOA1c/n3WuscIFYpdRLooJSKAzy01rsBlFLfAAMxBZcBwAxzWyuBucr0z5rewGatdZK5zmZMAenPVWIhRKV3LD6VaWsi6dykFs93b2Lt7twxOTmJ5OQmkJd7ldy8K2SknyA19TCpaZHk56cDYGdXA0/PIOrWfRSvGu1xc2tBYuLPnPpjNocOPYutrQv5+ZnXtGtr64aDQy0cHGrh7NgMV4eHMOZ5kJftSm6GE9mpzmRcdScj0Y20K2DINd7QNztHW1w9HXCr4YirpyM+zRy4o8GlMKWUH/AAsBfwNQcetNbxSikf8271MI1MCpwzl+WZ319fXlDnrLktg1IqBfAuXF5Enev7Ng7TqKjYBWwhxJ2Tlp3H80sO4Olsz2fDgrCt5okpc3OTSLi0gYsXV5OaeuiabUrZ4+bWjDp1BuDhEYinRxtcXPxvmCKsU2cAPj59OXtmBclJUei8ehgy6pCVXJuMKy4kXdakJWWTlZZ3w/GVjTIFDS9HatZ1omFzR1xrOOLi6YBrDUdcza8OTuVz+2OZW1FKuQGrgJe11qm3mC8taoO+RXlp61xbqPV8YD6YFvRv1jkhxJ2jteb1H49w+koGy8Z2rFYpXgyGNC5fCeXy5a1kZ50jPz8TQ34mOTkX0ToPN7dmNG78Kq4u/tjb18TevibOzvWwsbn2d2A0alISM0lJzCLlUhYpiZkkX8wk6WIG6Un3YFpdMLG1N+Je04CHtxO1Grrj6umIi4eD5cfNywkXT4c7mlm6TMFFKWWPKbAs0Vr/aC5OUErVNY9a6gIFK0LngAaFqtcHLpjL6xdRXrjOOaWUHeAJJJnLu11XJ7Qs5yKEuHOW7TvL+sPxTOl9P8H3elu7O2WWn59NYuIm4i/+yNWre9A6D3t7b9zc7sfBoRa2tq44OtXB1ycEd/drr2Y05OZz9WIWKZdSSb6UydWLmSSdTyfpQgaGvD+nrewcbKjh60LdxjXw6uRCDV8X3L2d8PB2xtndvkIuhMgxGjmYmln8jkUoy9ViClgAHNNaf1po01pgFPCB+XVNofKlSqlPMYXc+4B9Wut8pVSaUqojpmm1kcDn17W1GxgMbDNfRfYL8J5SqiB/SS/g9dKei7XFxcUREhJCZGSkpWzGjBm4ubnRuXNnJk2aRE5ODjk5OQwdOpQZM2ZYr7NClNHJS2m8vf4oXe6rxXMPV93HFufn55CSEs6lxE0kJKzFYEjFyak+DRqMpnbtv+LpEYRSptRJhtx8kuIzuBCVydX4UyQnZJKWlF3kFJazuz3e9dxo0bUe3vVc8fRxwbO2My4eDhV+JZ3WmqiMbDZdTuH35HTCUzLIMpZuwqcsI5dOwFPAEaVUhLnsDUxBZYVS6lngDDDE3OmjSqkVQBSmK81e0H9e+/Ycf16KvNH8A6bg9a158T8J09VmaK2TlFLvAGHm/d4uWNyvbkaNGsWKFSto3bo1+fn5nDhxwtpdEqLUsvPyeXFZBC4OdvxzSOsq8QAwrTU5uQlkZZ4hO/ssWVnnSEk9SHLyPozGHGxsHPGp3Ye6dQdTo0YwmSkGLp9J49T5s1w+m87lc+mkXMqk4JZCZaPwrO1smcJy93LEo7YznrVdqOHjjKPLnc30rbUmIi2LDYnJrE9MJi4rF4AAVydG3ONNpxru9C1Fu2W5WmwXRa99APS8SZ1ZwKwiysOBlkWUZ2MOTkVsWwgsLGl/S2LmuqNEXUgtzyYJuMeD6f1alLr+pUuXqFu3LmDKLRYQEFBeXRPijvvo5xMci09l4eh2+Hg4Wbs7NzAac0hPP0FqWiTpaVGkZ0STkRGNwZBWaC+Fq2sT6tYZhqNNewxpzbgcY+TU1jQSz/xOdvqfIxF3bydq1XejSVsfvOu5UbOuK54+ztja3aHkKJlJcCkKEk/A5RhITwCPe0ivcS8HnO/llxwnNmbac8Fohy2aLnYZjHdOppNKwCUrgcykq2TmJJfq0JIVuZKbPHky999/P926daNPnz6MGjUKJ6fK9z+lEMXZEpXAwt9iGf2QHz2a+Vq7OwAYDOkkp4STfHUvV5P3kpYWhdam4GBn54mba1O8vR5B5zQgJ9WXjMueJMe7cS4hj/SrOebLiGKxsVF43eOKf2AtajVwo1Z9d7zru+HoXAF/Yo1GyE6GjETTT/olyLwChhzIz4G8LAyXjpGRcJjM1PMk29hx1O1eDnu2JMqzNSfz/UnIqI/OtMXGmIN35kH8MsOwzTpINFkcVYpPi+9FsSS4FFKWEUZZ3Crt/ltvvcWTTz7Jpk2bWLp0KcuWLZObFEWVc/DMVV5cdpCW9Tys+mRJozGXlNRDXE36jaSrv5Gaegit81HKHg+P1vjWeoq8tHtJPV+fy3HOxMRnkpP5Z/ZtBydbatSx4Z4mNSxrITV8XfCu54qdfdGPpriprGRIPmP6SfoDkk6ZXnMzwKMeeNYHF29Iv0Re8hkupp7hUvZlEvMyuGyruGJrw1VbW67a2JBsa0Oqjekn2c6TNOem5DV6HIPjfeQ5+IP5SjSVn4Z97h+4pR7EyxhPfX0RbwcXvGrWwN2+G272rrjbueBi74qrszcuTjVxdnChC11u+3ctwaUSuFnafX9/fwAaN27Mc889x9ixY6lduzZXrlzB27vqX2Ej7g4nL6XzzKIwfDwc+Xp0B5xu949wGWVknOLKlVCSrv5GcnKY+cZDG9zdW1HbazS5V1uQeLI+h2OyyEo1rTfYOeRTu6GiSTtfatZ1pWZdF7zqut7eonpuBuRl//k5/SLE7vj/9s48yq6jPPC/usvb3+t9Vbc2W5IlS7JkLZaNQxSzGBsDMgkx9hBMCGZOhnBsYIYJy+Q4CSHAwCR6XaUAACAASURBVGQ7OcmAgUAweAjEwTaLY/CGha3NsmXJsizJ2nrf3/7e3Wr+uLe7X7e6tbakllS/c+rUvVV169a791V9t5bvK98dfR6Kw0ggrWmMaBqZWDXZVAuDoTCd6d10D75ApwadpkmPruNGgWgY8AWFgUa1mSAanYcTXUHOXMCQ1soIvkVxHY/2kMXiiMPVcVidjLE40U5ddBUJM3HOFwco4TILqDS7/5a3vGXM7P69997LT3/6U2699VaEEOzfvx9d16murr7QRVYoTonudJEPfnMLuqbx3Q+vpyF57vVZpJTk86/T1/cL+vp/Tj6/H4BIeAEx/RZK6asZfGMBBw9J7LK/pihRU6J9aQ0tV1TTvDBFbUsc7XQ3KuveBU//NQwehGwPOStDp2HQZ+j06zp9hs6ArjMQSdI/p5UB0UK/W8SusOkF/iZ5hKGhup3WeDPXJNu5NTGHtmQbzbFmwqE69pXjbM95bB7OsbfkC8RqQ2ddVZz1gVuZjBG9gJutKeEyS/jud7/Lxz72MT71qU8BjJnd/9znPscnPvEJYrEYhmHw4IMPTrszpEIxmxjIlfngN7eSKTk89NENzKuLn7N7WdYQw8PPMzj0a4aGfk253AMIdG8l7sA99L22jHSPb7dMNzTq22NcdUOKpnlJWq6sJlkXOeMvedvKc+ypP+fwrgd5I5rgtaoG9la3cNStOi5tlZmkId5EXbSONdFG6mP1NEQbqInUkAqlSIVS1ERqaI43E9Z9QVx0fV2TF9I5vt2TZUcmj0eRlKFxQ3WCe9obeFN1giXxCNosMvqphMssYdmyZTz11FPHhT/00EMXoDQKxdkxnLf4wANbODZc4NsfWs/yOcc3tGeD55UZGdlOf/+zDPQ/R8naB0ikE6fQv5RMx9vJda/ELVWRaojSNC/JijdX0bygivr2xGmt1rI9m/5CP8PlYTLlDJlyhmODr/J630vsTx/icHkYRwCN/s6Zc2IJltUtY1PtUuam5tIUa6Ix1kh9tJ6QfvJ9akZsh2eH82xND7IlneelTAFLSgSwMhnl3nlN3FSXYnUyhjGLl3Ir4aJQKGaUdMHmA9/cwhsDeb519zquv2Jm5gctK83RQ4/S2/NLSvY20EpIT6c4cAX53ndjjawgHl1GTXOK9tVx6t+doGFukkj85HojJafEsewxjmSOcCh9iEPpQxzJHqEn10N/sR85hXWpVtthsWWxUY+xcPldzF98G/Or5pMMJU/5N0kpOVS02JrOsT1dYFsmz768P09jCFiZjPGRtgY2VPtDXdXmxdNkXzwlVSgUs55MyeaD39rC/t4cX//gGm5cVH/GeUkpSffnOLTvcQZHHoXIFoRuY+frKPRdT1jbQF399Sy8qpH6tyVI1p7a0FZvvpfdA7vZPbibPQN7OJQ5RE++Z0KapmgD8124wSrSkivRXMxR43n+0FXrGprbrifZsgoalkKi4bR+V2fJ4peDGX4zkuOFkRy9lr8arcrQWZOKsamxmuuqEqxKxYhdwDmTs0UJF4VCMSMULIcPf3sbe7oy/PMH1rBxSePJL6pASslwd4GuAyN0HX6ZvPMzYk2bMaIZPDMB2bdTX/Mu5q7eQG1r4pS1+4dKQ2zp3jLmOnK+EXZDGCyqWcS6pnXMTc1lbnIu86rmMT/RTvzBO6BjKzQth/aboeUamHsDNC6F05zXkFKyO1fkPwcyPD6QZleuCEBzyOSG6gTXVye4rjrBolh4Vs2ZnC1KuCgUirOmZLvc893tvHh0mH+481reuuzkSpKjwqTz9WE6Xx+m62A/ZtUL1Fz5FNG2N0hKnYi4npbW32X+lTejG6e20szxHPYN7+O5jud4tvNZXul/BYkkaSZZ27yWu5bexcqGlVxVe9XYpPkEfv01OPob2PRPsOqu030UAOQcl2eGs/xyMMOTgxl6LQcBrE3F+fzCFm6ur+LKWPiS3nVTCReFQnFWWI7Hf3vwRX5zcJCvve8a3rmyZcp05YJN3+EsvYczvjuUppi1MaJDNF79HHPf+ixCTxMOzad97mdpad5EKHTi+RrHcziaPcqB4QO8NvQau/p3sWtgF0XH7x0sr1vOH6/6Y35rzm+xtHYpunaSlZadL8JTX4Rlm+CaO0/5GbhSsjdXZPNIjl8NZnh+JI8tJSlDY2NtirfUpripLklD6PzaDbuQKOGiUCjOmJLt8rEHX+TJ1/r44u0reO+147tnZAaKHNs7RPfBNL2HMoz0jptur2mOMnd1H9HWX1DyngEk9fU30db2B9TW3DDlHvFSSg5nDrOrfxevDLzC7oHdvD78Orbnm2vRhc7imsVsunIT1zRcw3Ut11EfPY05HysP/34PJJrgtr854fCX7UleyRb8eZN0nq3pHBnHN4+/KBbmnrYG3lqXYl1VHHMWr+iqREqJLJfx8nnfFYt4hQKyWDyj/JRwmQX09PRw3333sW3bNsLhMPPnz2fTpk088sgjPPbYY8el/8hHPsInP/lJZcRScUHJlmw+8p3tbD08xBc2LeeOtW0c2zvEoV0DHN0zSLrPb5SiSZOmBVUsua6JhnlhiD9Lb9/3yeb24GhJ5s75EG1tf0A02n7cPUZKIzzT8czYfElf0d8eKm7GWV63nA8s/QCLahZxZfWVLKhaQMQ4Dbt7dhG6Xx437Hj0eV8B8u5HIFY7IamUkn2FEk8PZnlmOMsLI3mKni9MroyFeU9jDRuq4lxXnaAtcvLlxjPJmFDIZnGzWbxMBjeXHxcSuRxuLouXyeLlc7jZHF4uh5fN4hXyuPk8Xr6Al8+D45z8hqeIEi4XGCklt99+O3ffffeYTstLL73Eo48+Ou01DzzwwPkqnkIxJcN5iw99eyu7OzN8Zv1Cmvbm+PYPn6NccDBMjTlLaljx223MvbqW6qYYxeIROju/z+HuH+P0jhCPL2LJ4r+guXkThjFRuTJdTvPk0Sd5/PDjbOnegiMdaiO1rG9ez/qW9axpXMP8qvloU/RuTkim25+kP7YVjr7gC5ag14MZh4Yl8M6vwYI343iSV3JFtqfzbE3n2ZbO02P5aRfFwtzVUhtMxMfPeqhLSukLgHQaN532hUMmi5fNjAuCXA43n8PL5vzwTBY3mwnOs0j7+G2NJyMiEbRkAj2RREsm0eIxQo0NaPEEWjzuu0QCLRFHi8XQojG0WBQtGoV16077dwkpL5+df9euXSu3b98+IWzv3r0sXRrsDPfzP4WeV2b2ps0r4JYvTRv95JNPcv/99/Pss89OCH/66ae5//77qa+vZ/fu3axZs4bvfe97CCHYuHEjX/3qV1m7di2JRIJ7772Xxx57jGg0yk9+8hOampp49NFH+cIXvoBlWdTV1fHggw/S1DQ7LNGeFVL6Di+wSOsF5xIk7N23j6W1ElzbbzhcCxwLnFJgNdYC6YL0wAt88H0Z5DV27E0fzmg5YGyH7dFyjB0zMX663zNduunq5nHhcpq4yWWRUxzL8WcwIY+prvX9XMHiuf39FB2Hdkcj6Ql0Q5Co9vdnj6VMNE0gpUu51EuxdBTL6gc0wqEmYtG5mGYNoqLcnucxUBqgJ9/DQHEAKT2iRpSmaCMNsXqSoSQCMfVvn/wewH//5QyU0oGf8f8DAAgIpyCS9P1QHPQQZU8y4jikLZuc4+BKDyElYU2Q0AUpXSOpa4TEVM8yOPJcsMtIJ/jfeQ7S88CT4Lnjx9ILjr2Jz3s6hEBoAoTmD9dpwh861IR/LjR/cUBFmrHz0esnD/OdRtsvPn1gh5Ry7SlfgOq5XHBGBcdU7Ny5kz179tDa2sqb3vQmNm/ezI033jghTT6fZ8OGDfzVX/0Vn/70p/nGN77B5z//eW688UZeeOEFhBA88MADfOUrX+FrX/vazP8A6YHrBA25A9KpEACjDZc8PkxK8JzAuUzfsI5e6x2fZiqyffCj35/JXzgNQaVmvPJODIcJ2x2dcFXQidJNFzcp3XRxYtLB5DILbVKZK9JX/A7PkziOxLJcMq5kORDXdKIJAyOso5saAoEsCLycRdnN4bp5pPQIC524WYVhJNHKBpT7QPTjISk4RXJWjrydx5UecU2nOZQkFUoRNiIINCikoZie/rcj/SEuu+D7VsE3PT+KHvIFSLwBwkn/WOhIION6DJddRpwChWDHRUPTqI7EqDYMUppGyHORto20HWTRwRo9tm0820ZagbMtcL3g7yqO+8uKUAhhRgLf9P1o4Jsh36+I00wTTNOPm/weT5dp/3+nmt/fnfYtlXCp5AQ9jAvB+vXraWvzJ0hXrVrF4cOHjxMuoVCI2267DYA1a9bwxBNPANDR0cEdd9xBd3c3lmWNWVg+ZaT0v/Bde9x5VnBsjQsU7zTGaCsbMqGBpoNmgB6e9B+f1BCKioZ8QmM4OU7AgAfv/4HfoOhm4MJghMGIgG6A0P17C23cVZZrND9N99MKEfhaxf0ubaySQ+e+YY6+OsSxV4dI9xcZ0jx+lLJxdPiH96zkd9a1jn0N2/Yw3d0P09n1/ygUDqBpCRoa3ktz83uorbkRTTNwPZc9Q6/xQvcLbO3Zyou9L1JyIRVqZWP7Rm6efzM3tN6AoU3RLEkJw4egYweMHIZsj+8GD/jzJaOteKrN10dpuQbmXAstq45TciyVy/zbG518vS/Dfg/C0mNNboTruo+x/uA+mg8dwB0cYGiwj8Fy+biiAIhoFKOmAb2mBr22FqO2Fr22Fr22BqO2zg+vrkavqfb9VApxUdsEVMLlouPqq6/mRz/60ZRx4fD4Gnxd13GmmGwzTXOsglem+fjHP84nP/lJ3v3ud48NsZ0UKf2hg+Kwv9eEO0XF0oygwQ6+BnVzPEwzfCcEoAUy4jw3yGYMrrr1/NzrEsJ1PPoOZ+h8fZhje4fpOZjG8yRGWGfOoipyVyd5+PUuDGHw/T9az9WtVb4GffpFOjq+R2/fz5HSIpVazdKrvkRj4y2gRdg3vI+f7n2QbT3b2NG7g5ydA+DK6it576L3srF9I2ub12Jqk+YtpPT3Njn4JLzxNBzb4m+MNUqkGpItUDMfrr4dWq9FNq7AKUjsnl6cnm7s5zpxel/E7uvloOWyM17NrrpGXli8jKGqGq44dpjP/vKnvHnnFsKeh1FXh15fh1FXT2ThQvS6Ooy6WvTaOozamgq/1p+HUJwQJVwuMDfddBOf/exn+cY3vsE999wDwLZt23jmmWfOKt90Os2cOXMA+M53vnPyC+yS/0VoB8sOQ0mI14FmTuwFnO4kqmLWMtJb4MjuQY7sGaT7wAiO5c+91LcnWPW2ubQvq4W6EH/+s7088VInS1tS/MOdq1lQZ9DV9UM6Or5HNrcHXU8wZ84dzGm9kz7H5CdHHmfH7vvY1T+ubzIvNY93LHgH65rWsb5l/cQlwsVhOPIbvxeS7oRMpz/3OXLEj6+eB1e+Fdm6Fju0ADtnYHX3Ynd0Yu/oxH50F3bXL3D6+saGU3ORKDuWrmDryjVs+Z1NDCb8PU6qbIs1xSx3jXTw5vZqQn/6CYzGRvSamou8ZzH7UMLlAiOE4OGHH+a+++7jS1/6EpFIZGwp8tlw//338773vY85c+awYcMGDh06NH3iYtqvyEL4u99Fqn1BorjkGOzMsX9bLwd39o/pndQ0x1j6plbaFtfQuqiaSMKkYDl8e/Nh/un7B3E8j8/cchX/ZV2M3p6vs3nfD7DtIeLxxSxZ8peYVTfyn0ef4bGn/4xXB19FIFhSu4T3XPEerm26ltWNq2mON08sSO8eePkhOPSMvw9KMKwlzRSWbMZy27G0G7CKcexdI1g/fQO76zlwK/Y+MQzMlhYK8xdw4LZN7Gubx76ael4LxzmChgRSusbGuhS/XZNkfVX8kteKnymklFiuh+1KbMc7+QVToFaLVa4Wu9SRwSqq0Ul38FfR5HrAjELNQjDO7xr9meayep+ngJSSwc48b7zUz8EX+xjqyiM0wZzF1Sy4pp55y+upahgf4ik7Lt/fcpR/fOoAAzmLty5t5H++vRE38y26un6IlDb19W+hqfUudmbyPPLGozzf9TyudFlWt4zbFt7GLQtumVp5sTgMr/0MdvwL7hvbKGcjWMZiLLeJcsbA6h7C6uyaoGuhpVKE5s4lNLcds30uofY2nDntPF/bwC9djd+kCxwLNssCmBsJsSIZZXkiyvXVCdam4rPaLP3pIqWkaLsM5S1GCja5skO+7JArOxQsl4LlUrJ9ZzkeZcej7LiU7HHfcnxXdj3K9mh6P95yfIFiuRMFypEv36ZWi132uE6w7LY0vgzXLY8vzZ2KaC1UtYOmhrwuBRzbpWv/CEf3DHHo5X4yAyUQ0HJFFW9+/2KuuLaRWGriR4SUkkd3dfOVX7xGx3CRDQtq+Jv3VtNo/JyOfT9ASo+m5vcyGF7LQ53befKVT1NwCjTHm/nD5X/Iuxa+i4XVC/3MPBeGj8DwIdxjeym//Dzlva9Q7hygnDaxshGcwqiJmH6EOUJo/jzCS64i+Y5bCC2YT2jePELz56NXV9NdttmVLfJytsDL2QLPj+QodmWoMnR+qybB3a11rErFWJ6IznqT9LbrkS7ajBRshgsWQ3mL4bxFtuQLCF9IOOTKLoWyQ95yyJfdwHcYKdiUT9KTCAUuqmvEDZ2ooRExNCK6TsTQSGmCiK4R1nViEYNoTCOqCcKaICw0QkL4DjABE8FNZ/BbZ/ebUEyNlL7OhlMM/MC55Umrt8T4aqlINJg/MSsm3YMVUWbsslgBdalilRz6jmTpeSNN94ERul4fwbE9NEPQtqSWa2+ex4JrGo4TKOALla2Hhvjiz1/j5WMjXFnncP/GF5gX+RlWT44ONDLhFfymVMXml54haz1GMpTkHQvewa0LbmVd8zpEMYPz8q/Ibf8a5Vdfony0CystsLIGbnl8HkOEqwnPayd+/TWEr7yC0MIrCF+xELOtbWy+w/Eke/NFtqXzbO1Ks3VvF11l/6NIAxbFI9zZUsct9VVsqE5cMNMqUkpGCjbHhgt0p0ukizaZwI0EwiNdtMmUbHKB4MgUbcqWSxxBHIggCI/5ggiQ0nVShkaboZPQNOKaIK5pxAgT0yNEU0F6CaYHuifRXYlwJcLxwK3U9QnccetyZEXkuUMJF/w/yqwdh3Vtf5J9tDcyelyp+KaZ/lLbSJW/3NaI+Od66LISGpf6EK+UkvyIxVB3joGOHAPHcgx25hjuzo+NctY0x1h6Yytzl9UyZ3ENZnjqSepsyebHOw7w/S1HeL3PpTaa58NXP8z1rdsoiiQvl8K8mLXZX9LIeftZHJvP7ZEN3BCex5LeHN6OvVhHPsXhriGsERfpjvd69USK0JxGkmvnE1q0lNCy1YQXLcZsbUFM6h2P2A7bR/JsD7Tgd2YLFIIhmeaQyXXVcdZVxVmdjLE0EZ3R/U08T5Iu+j2IkaJNumAzmLcYypcZzPm9ilzZoVCyccsu2B6a7aE7HsWCg7RdwgiigbDwhYag3dBZqWlUCY0EgpjUiXg6YS908gZ3skDQJCIEWkhDhHVESEeYGiKko5maf2zqYAiEGcQZGsIQCF0DfVT5UgQr+cV4mC78tLrmX69XXDd6rgvQNfjy6T/fy164RCIRBgcHqauru/ACxrV9RTCrMK4QVjmUpRm+4IjVgRH150mMsN/7uMyRUjI4OEgkchq2pWYxju0y1JVn4FiOgWNZBjpzDHXlKRfGe6aJmjD17UkWrm6geUEVTQtSJ9x1cThziP94cTNPvJphR2czZTdMe7KDP1i6maVV2+nvcXnulyaJIY35VoxrC3GqRyzCQ3m87AHgAAD9AEJiJgSh5hTx1e2ElqwgvGYjoaUrMWpqjr+37XA4V+JQscz+fIn9hRL78iX2F/xWVBdwdSLK+5trWVsVZ00qxtxI6JTrpON6ZEoOIwWL4YJNumgFvQmHTMEim7UpZEuUcxZWzsEt2IiyS0JCCkESQQpBAsF8BCsQpIQgKiE0QQlLC9w0Tacu0CIGWtRAixiIqIEW0f3jSHAc1hFhA0ICTwdXeHi6xBUST/NwBbjCxcXD8Vw8z8N1XVy38tjGcZwx57qu79subslPJ6Ucc57njbnRcynl+HUV+Y86EWj1n2m7eNkLl7a2Njo6Oujv7z954pnEtYO5kFHt9kkKiaO6JKPLgDUzECIOkA2copJIJDKmdHoxIaVkqDtPx95heg9n/N5ITwEZaIybEZ36OQmuXNtEbUuMmnqT6qRHyC0Gxgo7cY/spbg7Sy6TpVzuIa8dIWseo5siO+12dhWv4pXhZZTcGhKGyQ36Dt6W3cJV+44R+olAzwS6SQDk0SNZjIiHEXExGz2MpSnM5mbMK5YRWnYdxoqNiOrWsd9geR4dJZsjxTJHOgc4UixztGRxrGRxpGiRdsaHYDRgfjTMoniY32uqZW1VjFWpGHFdx/Mk2ZLDcKbMy8cyZNJlchmLfLZEPmdTzFu4RQfKLlrZQ3dcDFsSkhALhpYiCKJADYJYcKxN0kSXGDholLCwNRc7BF5IIEMaIiogJMgZkqwukAZ4GnjaaOMvcfFwpYsTCABHOjieg+04Exp213Wx0zbOoDOlMJgphBAYhoGu6+i6jqZpE4RDZVhlnK7rGIZBOBw+7tpK4XQmXNTCRQjxDnzVUR14QEp52ir2pmmevvb6qSClb8I73+cbzMt0QaYDul6CI5vHFcL0MNQvgvrF0LoK5qzxtYvDp74Pt2L24doeVskJnEshY5EdLJEdKlEcKSEd33yIVSjT21GmEKgXxUybKrPA4vAQKaufRL6DcF8XcncWL5vFKecYSTgMVkm8KombAq9a4taAWyNx6iUdRhM7+1ays+/dHM7MRaJRpaVZJ17muvwrXDtygFpZoIYikSoPrd5DD0n0VBJ93nL0uVcj6hf6+iU186BmAbZmMmw7HLFsuks2nTmLI/2dHCiUOFgoc6xkUTnNbAJNQqfBhetsqC9r1BU9GvMujQUPs1xCOP4wU8mR7HAkYU8SkRAPrk/gEsGlRjj+VzwervBw8Bt1W7iUNQ8nJHE1cHWPrCZJ6xJPeEjdFwae8HBxcaSL7TlYrk2xXMJxJzXuVuByp/aOdV3HNE0Mw8A0zTE3upuk9DyEgGjIRI/4u0xqQiDwR6l0TUPXNAxN9xt7pB8npW8STEqE9HBtG9e2cC0b6dpI1w1ccOy4SNfBs3x7ZdLzcGwLx7KwrTJ4HpphoOk6mqYHJuI8pOf3YMpy/DrPO/7Yc89sbuaiFS5CCB34R+BtQAewTQjxiJTy1XN2Uykh1+trDg8fgXLW7304JV+jPdPpC5JsF+T6/Qn3yaTmwBU3wfwbYe71ULtQDWvNQqSUOJaLUyhTzhSwhrNYI1nKI3nKmQKlTJly3qJcdCmVJeWyoORolByTshfCZurhKeG5mHZ2zGijkB6p7BvMlTtJhPejR7PIhIZXZeAkBYNJgR2TFMMGhbBOUa+i6ETJWgnS5STpchVpK0G6HCc9GGeos5Z02Td3Msc4xB3h/2CTeIn5SUk21UY21kImeg0HI40UQzUUjSryRoKsWUtGi5JzPTKOy3DRYyTvku4okeZVclOMjIRdSWvBpb3gsCFv0VKwaSiWqSuWSVgWHi42Hl4gFDwkDi5p4VDGxRIOtnBxhIdjur4QkC4eLvJU7MhNeGGAA5qmYWBgGLrvazqGbqDrGqZuENXDGJqGaegYmsDAb8x9W3cu0vWQroN0bDzHtx3mOTau44DjIAPjk9J18IoOnuPg2jaOY+PaNiXbxioVT8so5KlgmCGMSAQjFEI3DHTdQDMM9FGhoRt+j8MwEJpGLFSNEQpjhEIIoeG5Dp7r4rmuL8iCuS9N0xCajtA0hBb0agI3GqdpGvzgJ6dd5otWz0UIcT1wv5Ty5uD8MwBSyr+e7por2mrkl//ktxFINCnR8NDx0PAwpYOJgyldTGzC0iYkbcLYRKRFRJaJyRIRpl7OWyLEoFbFgFbFoFbFsJYkLRKMaMkgvJoBrYqiqJgTuDgf/TlFnqlhvmnzCxjNVk6d//irEBPOp7M/LEfFgwyylBXxwj/xpJ8uGN0aO5dS4EmB9Hzfk+BJDc/TcD3f953AcwXSO/EktjAEwhBgCgjpiNoQoi6EiOiUDB37FDXPhScJuw5hxyFq20Qdi4htE7HLhO0yIbtMzCoSLxdIlItEbOvU35YMvsoBDRk40ALLwyL4SsfzEJ4bWAv2EJ5vvVo6foMvbRvpuX56L2gsRy0Ly7G3clYYoTBmODzWOBumiW6aaIaJpvuNrmaYExt50/TTGSahWJxwLEY4FscMh9GD63Vj1DfQjYkfH36ehi8kdC3wdXTDxAyH0S6w9QAhxGWl5zIHOFZx3gFcNzmREOKjwEcBrm3ReWdxs1/hEbhoOIF4KWNiSQMbgzImJUKkZZQSIQqEKcowOaIclY0clU0clY2kZZwyJmVMHHTO2GKp4qLFFyaTAseMCQt/giEwfS5HW9fRcRGBbzp9dI7YJBgL9899m5rCX7EzaudTl2iab39TNyWGLtGlxPBcdM/B8MoImUfkQeTAdF0ijkXEtgg7NmHHIeQ4mI6D6TkYjovh+QJF91y0wHK1HNvWwAt6EV4gEATBQqMKQcF4uOYP/eiaQBcCnWDcv0I+isDWnB9ujH0ta5oefIUHX9LC/5oGETTeht/AV6w4E5rmN9pjX/HG2PWjeY022Lphjn3thyJRjHAYMzzaGxjPY/KKNsWZcTELl6la8uM+W6SUXwe+DrBo8SL51A1/e9o3GlVKqsKXaNdPV6Azki1nK5DOrUATU5pvP/lVU6FNfkATFuGMm3fXJ8VrQUsmoMJq8aiZs4o9LQJjmX7bpY+Vf3zLXBHkN24FebyhEuiaETR4/odC2AyhCR00gS40hPCHUsYaTl34JuFHjThrjI2rj/4kIYKv0tEyT/En8dMdHz85rNI/m1U8CsX54GIWLh1A5b6obUDXiS6oE20DSwAABmZJREFUSlVx683vPKeFUigUCsX42sOLkW3AIiHEAiFECHg/8MgFLpNCoVAouIh7LlJKRwjxJ8Dj+EuRvyWl3HOBi6VQKBQKLmLhAiCl/BnwswtdDoVCoVBM5GIeFlMoFArFLEUJF4VCoVDMOEq4KBQKhWLGUcJFoVAoFDPORWv+5UwQQmSBfWeRRRWQPsti1AMD5zD/6VB5+1Q+/3NZ7nOd/6WY94nqxtnmPRNcis98Oia/iyVSytOzpltpVvlSd8D2s7z+6+eyDDORv8r71J//uSz3xfZcZkPes6F+zsbnciHynvwuzuTdqGGx0+PRizh/lff5zftc56/yPr95n+v8L9a8p+VyGxbbLk/TsuelWIbLGfX8Zy/q3cweJr+LM3k3l1vP5esXugDMjjJczqjnP3tR72b2MPldnPa7uax6LgqFQqE4P1xuPReFQqFQnAeUcFEoFArFjKOEywwjhHCFEC9VuPknSLtRCPHY+SvdpY8QQgoh/rXi3BBC9KvnPDsQQtwevKOrLnRZLlfOVx1RwmXmKUopV1W4wxe6QJcZeWC5ECIanL8N6DydDIQQF7W18FnOncBz+PsvnTJidGtRxUxw1nXkVFDC5TwghNCFEP9bCLFNCLFLCPFfK6JTQoiHhRCvCiH+WQih3snZ83NgdMvRO4EfjEYIIdYLIX4jhNgZ+EuC8A8JIf5NCPEo8J/nv8iXPkKIBPAm4I8IhEvQe392qjoghMgJIf5CCLGF6XcXV5wZZ1JHfi2EWFWRbrMQYuV0N1AN2cwTrRgSezgI+yMgLaVcB6wD7hFCLAji1gOfAlYAVwDvPe8lvvR4CHi/ECICrAS2VMS9BrxZSrka+DPgixVx1wN3SylvOm8lvbzYBPxCSvk6MCSEuDYIn64OxIHdUsrrpJTPnffSXtqcSR15APgQgBBiMRCWUu6a7gaq+z/zFKWUqyaFvR1YKYT4veC8ClgEWMBWKeUbAEKIHwA3Aj86X4W9FJFS7grmuu7k+M3kqoDvCCEWARIwK+KekFIOnZdCXp7cCfxtcPxQcP5Tpq8DLvDjC1DOS54zrCP/BvwvIcT/AD4M/MuJ7qGEy/lBAB+XUj4+IVCIjfgvrxKleDQzPAJ8FdgI1FWE/yXwlJTy9qByPV0Rlz9PZbvsEELUATfhj/VL/K3JJX7DNl0dKEkp3fNXysuO06ojUsqCEOIJ4D3A7wMn1NhXw2Lnh8eBPxZCmOB3KYUQ8SBuvRBiQTDOfAf+ZKfi7PkW8BdSylcmhVcxPnn5ofNaosub3wO+K6WcJ6WcL6VsBw7h91JUHbgwnEkdeQD4e2DbyXr5SricHx4AXgVeFELsBv4v473G54EvAbvxK9vDU+agOC2klB1Syr+bIuorwF8LITbjfz0rzg93cvx/+8fAXag6cEE4kzoipdwBZIBvnyx/Zf5FoVBcMIKh4f8upbztQpdFcXKEEK34w2RXSSm9E6VVPReFQqFQnBQhxAfxV5V97mSCBVTPRaFQKBTnANVzUSgUCsWMo4TLWSKEaBdCPCWE2CuE2COEuDcIrxVCPCGE2B/4NUH424QQO4QQrwT+TRV5rQnCDwgh/l4IIS7U71IoFIqzQQmXs8cBPiWlXApsAD4mhFgG/CnwKynlIuBXwTnAAPAuKeUK4G7gXyvy+ifgo/gKlouAd5yfn6BQKBQzixIuZ4mUsltK+WJwnAX2AnPwFY2+EyT7Dr7pC6SUO6WUXUH4HiAihAgLIVqAlJTyeelPhH139BqFQqG42FDCZQYJtFlX46+oaJJSdoMvgIDGKS75XWCnlLKML5A6KuI6gjCFQqG46FDmX2aIwOLrj4H7pJSZk02XCCGuBr6Mb3cMfBMxk1FL+RQKxUWJ6rnMAIFZlx8DD0op/z0I7g2Gugj8vor0bfhayB+UUh4MgjuAtops24AuFAqF4iJECZezJFjR9U1gr5Ty/1REPYI/YU/g/yRIX41vCfYzUsrNo4mDobOsEGJDkOcHR69RKBSKiw2lRHmWCCFuBH4NvAKMaq1+Fn/e5YfAXOAo8D4p5ZAQ4vPAZ4D9Fdm8XUrZJ4RYi2/GOoq/mc/HpXpBCoXiIkQJF4VCoVDMOGpYTKFQKBQzjhIuCoVCoZhxlHBRKBQKxYyjhItCoVAoZhwlXBQKhUIx4yjholAoFIoZRwkXhUKhUMw4/x8dJNsYa6l9wwAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -826,6 +978,38 @@
     "new_data.plot()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f13330c1198>"
+      ]
+     },
+     "execution_count": 20,
+     "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": [
+    "new_data.plot(logy=True)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-- 
2.18.1