diff --git a/module2/exo1/toy_notebook_fr.ipynb b/module2/exo1/toy_notebook_fr.ipynb
index 0bbbe371b01e359e381e43239412d77bf53fb1fb..972d8674f25466eef26a52b3c4d530d860bf822d 100644
--- a/module2/exo1/toy_notebook_fr.ipynb
+++ b/module2/exo1/toy_notebook_fr.ipynb
@@ -1,5 +1,33 @@
 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 1. À propos du calcul de π\n",
+    "## 1.1 En demandant à la lib maths\n",
+    "Mon ordinateur m’indique que π vaut approximativement"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from math import *\n",
+    "print(pi)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.2 En utilisant la méthode des aiguilles de Buffon\n",
+    "Mais calculé avec la **méthode** des [aiguilles de Buffon](https://fr.wikipedia.org/wiki/Aiguille_de_Buffon), on obtiendrait comme **approximation** :"
+   ]
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
@@ -16,10 +44,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-
diff --git a/module2/exo2/exercice.ipynb b/module2/exo2/exercice.ipynb
index 0bbbe371b01e359e381e43239412d77bf53fb1fb..0d39d963fae9b6c843731b93a1d6b24cbc5963ef 100644
--- a/module2/exo2/exercice.ipynb
+++ b/module2/exo2/exercice.ipynb
@@ -1,5 +1,77 @@
 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(14.113000000000001, 2.8, 23.4, 14.5, 4.334094455301447)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "a = np.array([14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2, 14.9, 18.1, 7.3, 9.8, 10.9,12.2, 9.9, 2.9, 2.8, 15.4, 15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2, 16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9, 16.8, 11.3, 14.4, 15.7, 14.0, 13.6, 18.0, 13.6, 19.9, 13.7, 17.0, 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6, 19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15.0, 14.3, 16.8, 14.0, 6.8, 8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2, 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0])\n",
+    "\n",
+    "np.mean(a), np.min(a), np.max(a), np.median(a), np.std(a,ddof=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "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": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.plot(a, c='blue')\n",
+    "plt.xlim(0,100)\n",
+    "plt.ylim(0,25)\n",
+    "plt.grid(linestyle='--')\n",
+    "plt.show()\n",
+    "\n",
+    "plt.hist(a, color='blue',edgecolor='black', linewidth=1.2)\n",
+    "plt.xlim(0,25)\n",
+    "plt.ylim(0,25)\n",
+    "plt.grid(linestyle='--')\n",
+    "#plt.axis(\"equal\")\n",
+    "plt.show()"
+   ]
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
@@ -16,10 +88,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-
diff --git a/module3/exo3/exercice_fr.ipynb b/module3/exo3/exercice_fr.ipynb
index 0bbbe371b01e359e381e43239412d77bf53fb1fb..4f01c34ae23fdbe6894b817f1c30a14270918aa8 100644
--- a/module3/exo3/exercice_fr.ipynb
+++ b/module3/exo3/exercice_fr.ipynb
@@ -1,6 +1,658 @@
 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import datetime"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "source": [
+    "# Analyse de la concentration de CO2 dans l'atmosphère depuis 1958\n",
+    "# 1. Chargement des données"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "source": [
+    "Récupérons directement les données sur le site [officiel](https://scrippsco2.ucsd.edu/)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "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>Yr</th>\n",
+       "      <th>Mn</th>\n",
+       "      <th>Date Excel</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>CO2 [ppm]</th>\n",
+       "      <th>seasonally adjusted [ppm]</th>\n",
+       "      <th>fit [ppm]</th>\n",
+       "      <th>seasonally adjusted fit [ppm]</th>\n",
+       "      <th>CO2 filled [ppm]</th>\n",
+       "      <th>seasonally adjusted filled [ppm]</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1958</td>\n",
+       "      <td>1</td>\n",
+       "      <td>21200</td>\n",
+       "      <td>1958-01-01</td>\n",
+       "      <td>-99.99</td>\n",
+       "      <td>-99.99</td>\n",
+       "      <td>-99.99</td>\n",
+       "      <td>-99.99</td>\n",
+       "      <td>-99.99</td>\n",
+       "      <td>-99.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1958</td>\n",
+       "      <td>2</td>\n",
+       "      <td>21231</td>\n",
+       "      <td>1958-02-01</td>\n",
+       "      <td>-99.99</td>\n",
+       "      <td>-99.99</td>\n",
+       "      <td>-99.99</td>\n",
+       "      <td>-99.99</td>\n",
+       "      <td>-99.99</td>\n",
+       "      <td>-99.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1958</td>\n",
+       "      <td>3</td>\n",
+       "      <td>21259</td>\n",
+       "      <td>1958-03-01</td>\n",
+       "      <td>315.71</td>\n",
+       "      <td>314.43</td>\n",
+       "      <td>316.20</td>\n",
+       "      <td>314.90</td>\n",
+       "      <td>315.71</td>\n",
+       "      <td>314.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1958</td>\n",
+       "      <td>4</td>\n",
+       "      <td>21290</td>\n",
+       "      <td>1958-04-01</td>\n",
+       "      <td>317.45</td>\n",
+       "      <td>315.15</td>\n",
+       "      <td>317.30</td>\n",
+       "      <td>314.98</td>\n",
+       "      <td>317.45</td>\n",
+       "      <td>315.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1958</td>\n",
+       "      <td>5</td>\n",
+       "      <td>21320</td>\n",
+       "      <td>1958-05-01</td>\n",
+       "      <td>317.51</td>\n",
+       "      <td>314.69</td>\n",
+       "      <td>317.88</td>\n",
+       "      <td>315.06</td>\n",
+       "      <td>317.51</td>\n",
+       "      <td>314.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1958</td>\n",
+       "      <td>6</td>\n",
+       "      <td>21351</td>\n",
+       "      <td>1958-06-01</td>\n",
+       "      <td>-99.99</td>\n",
+       "      <td>-99.99</td>\n",
+       "      <td>317.27</td>\n",
+       "      <td>315.14</td>\n",
+       "      <td>317.27</td>\n",
+       "      <td>315.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1958</td>\n",
+       "      <td>7</td>\n",
+       "      <td>21381</td>\n",
+       "      <td>1958-07-01</td>\n",
+       "      <td>315.87</td>\n",
+       "      <td>315.20</td>\n",
+       "      <td>315.85</td>\n",
+       "      <td>315.21</td>\n",
+       "      <td>315.87</td>\n",
+       "      <td>315.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1958</td>\n",
+       "      <td>8</td>\n",
+       "      <td>21412</td>\n",
+       "      <td>1958-08-01</td>\n",
+       "      <td>314.93</td>\n",
+       "      <td>316.23</td>\n",
+       "      <td>313.95</td>\n",
+       "      <td>315.28</td>\n",
+       "      <td>314.93</td>\n",
+       "      <td>316.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1958</td>\n",
+       "      <td>9</td>\n",
+       "      <td>21443</td>\n",
+       "      <td>1958-09-01</td>\n",
+       "      <td>313.21</td>\n",
+       "      <td>316.12</td>\n",
+       "      <td>312.42</td>\n",
+       "      <td>315.35</td>\n",
+       "      <td>313.21</td>\n",
+       "      <td>316.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1958</td>\n",
+       "      <td>10</td>\n",
+       "      <td>21473</td>\n",
+       "      <td>1958-10-01</td>\n",
+       "      <td>-99.99</td>\n",
+       "      <td>-99.99</td>\n",
+       "      <td>312.41</td>\n",
+       "      <td>315.40</td>\n",
+       "      <td>312.41</td>\n",
+       "      <td>315.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>1958</td>\n",
+       "      <td>11</td>\n",
+       "      <td>21504</td>\n",
+       "      <td>1958-11-01</td>\n",
+       "      <td>313.33</td>\n",
+       "      <td>315.21</td>\n",
+       "      <td>313.60</td>\n",
+       "      <td>315.46</td>\n",
+       "      <td>313.33</td>\n",
+       "      <td>315.21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>1958</td>\n",
+       "      <td>12</td>\n",
+       "      <td>21534</td>\n",
+       "      <td>1958-12-01</td>\n",
+       "      <td>314.67</td>\n",
+       "      <td>315.43</td>\n",
+       "      <td>314.76</td>\n",
+       "      <td>315.51</td>\n",
+       "      <td>314.67</td>\n",
+       "      <td>315.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>1959</td>\n",
+       "      <td>1</td>\n",
+       "      <td>21565</td>\n",
+       "      <td>1959-01-01</td>\n",
+       "      <td>315.58</td>\n",
+       "      <td>315.52</td>\n",
+       "      <td>315.63</td>\n",
+       "      <td>315.57</td>\n",
+       "      <td>315.58</td>\n",
+       "      <td>315.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>1959</td>\n",
+       "      <td>2</td>\n",
+       "      <td>21596</td>\n",
+       "      <td>1959-02-01</td>\n",
+       "      <td>316.49</td>\n",
+       "      <td>315.84</td>\n",
+       "      <td>316.29</td>\n",
+       "      <td>315.63</td>\n",
+       "      <td>316.49</td>\n",
+       "      <td>315.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>1959</td>\n",
+       "      <td>3</td>\n",
+       "      <td>21624</td>\n",
+       "      <td>1959-03-01</td>\n",
+       "      <td>316.65</td>\n",
+       "      <td>315.37</td>\n",
+       "      <td>316.99</td>\n",
+       "      <td>315.69</td>\n",
+       "      <td>316.65</td>\n",
+       "      <td>315.37</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Yr  Mn  Date Excel        Date  CO2 [ppm]  seasonally adjusted [ppm]  \\\n",
+       "0   1958   1       21200  1958-01-01     -99.99                     -99.99   \n",
+       "1   1958   2       21231  1958-02-01     -99.99                     -99.99   \n",
+       "2   1958   3       21259  1958-03-01     315.71                     314.43   \n",
+       "3   1958   4       21290  1958-04-01     317.45                     315.15   \n",
+       "4   1958   5       21320  1958-05-01     317.51                     314.69   \n",
+       "5   1958   6       21351  1958-06-01     -99.99                     -99.99   \n",
+       "6   1958   7       21381  1958-07-01     315.87                     315.20   \n",
+       "7   1958   8       21412  1958-08-01     314.93                     316.23   \n",
+       "8   1958   9       21443  1958-09-01     313.21                     316.12   \n",
+       "9   1958  10       21473  1958-10-01     -99.99                     -99.99   \n",
+       "10  1958  11       21504  1958-11-01     313.33                     315.21   \n",
+       "11  1958  12       21534  1958-12-01     314.67                     315.43   \n",
+       "12  1959   1       21565  1959-01-01     315.58                     315.52   \n",
+       "13  1959   2       21596  1959-02-01     316.49                     315.84   \n",
+       "14  1959   3       21624  1959-03-01     316.65                     315.37   \n",
+       "\n",
+       "    fit [ppm]  seasonally adjusted fit [ppm]  CO2 filled [ppm]  \\\n",
+       "0      -99.99                         -99.99            -99.99   \n",
+       "1      -99.99                         -99.99            -99.99   \n",
+       "2      316.20                         314.90            315.71   \n",
+       "3      317.30                         314.98            317.45   \n",
+       "4      317.88                         315.06            317.51   \n",
+       "5      317.27                         315.14            317.27   \n",
+       "6      315.85                         315.21            315.87   \n",
+       "7      313.95                         315.28            314.93   \n",
+       "8      312.42                         315.35            313.21   \n",
+       "9      312.41                         315.40            312.41   \n",
+       "10     313.60                         315.46            313.33   \n",
+       "11     314.76                         315.51            314.67   \n",
+       "12     315.63                         315.57            315.58   \n",
+       "13     316.29                         315.63            316.49   \n",
+       "14     316.99                         315.69            316.65   \n",
+       "\n",
+       "    seasonally adjusted filled [ppm]  \n",
+       "0                             -99.99  \n",
+       "1                             -99.99  \n",
+       "2                             314.43  \n",
+       "3                             315.15  \n",
+       "4                             314.69  \n",
+       "5                             315.14  \n",
+       "6                             315.20  \n",
+       "7                             316.23  \n",
+       "8                             316.12  \n",
+       "9                             315.40  \n",
+       "10                            315.21  \n",
+       "11                            315.43  \n",
+       "12                            315.52  \n",
+       "13                            315.84  \n",
+       "14                            315.37  "
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Step 0: Load the raw data without the readme info.\n",
+    "data_url = \"https://scrippsco2.ucsd.edu/assets/data/atmospheric/stations/in_situ_co2/monthly/monthly_in_situ_co2_mlo.csv\"\n",
+    "df = pd.read_csv(data_url, skiprows=60).reset_index()\n",
+    "\n",
+    "# Step 1: Concatenate the first three rows to form the header\n",
+    "header = df.iloc[:3].astype(str).agg(' '.join).str.strip().tolist()\n",
+    "\n",
+    "# Step 2: Create a new DataFrame with the correct header\n",
+    "df.columns = header\n",
+    "df.columns = [\" \".join(col.split()) for col in df.columns]\n",
+    "\n",
+    "# Step 3: Drop the first three rows (now redundant)\n",
+    "df = df.iloc[3:, :-1].reset_index(drop=True).apply(pd.to_numeric)\n",
+    "df['Date'] = df.apply(lambda row: datetime.date(int(row.Yr),int(row.Mn),1), axis=1)\n",
+    "\n",
+    "# Display the updated DataFrame\n",
+    "df.head(15)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "source": [
+    "## 2. Analyse des données manquantes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "source": [
+    "Affichage du nombre de données manquantes par colonne. Les données manquantes sont remplacées par la valeur -99.99."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Yr                                   0\n",
+       "Mn                                   0\n",
+       "Date Excel                           0\n",
+       "Date                                 0\n",
+       "CO2 [ppm]                           17\n",
+       "seasonally adjusted [ppm]           17\n",
+       "fit [ppm]                           13\n",
+       "seasonally adjusted fit [ppm]       13\n",
+       "CO2 filled [ppm]                    12\n",
+       "seasonally adjusted filled [ppm]    12\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(df==-99.99).sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "source": [
+    "Keep only the non missing data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "outputs": [],
+   "source": [
+    "df = df.loc[(df!=-99.99).all(1)].reset_index(drop=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Nous pouvons voir si tous les mois sont représentés de la même façon:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Mn\n",
+       "1     67\n",
+       "2     65\n",
+       "3     66\n",
+       "4     66\n",
+       "5     67\n",
+       "6     66\n",
+       "7     67\n",
+       "8     67\n",
+       "9     67\n",
+       "10    66\n",
+       "11    67\n",
+       "12    67\n",
+       "Name: Yr, dtype: int64"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby(\"Mn\").count()[\"Yr\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "source": [
+    "## 3. Caractérisation des phénomènes périodiques sous-jacents"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Affichons les tendances annuelles, qui sont relativements courtes, comparativement à la tendance globale."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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PcdXMG+j8z28BeHRqL24cFdvAnZqOUmq71npIQ6+THoQQQvwOlTV23lx7hDLnHodFW1MYFOJgsfUZ/CpSuaHmfmydJmCuN8cQHdhy8iv9HhIghBDid1ixL4N5Kw7w9DfxJOeVcywrlzfNzxNNFrc6HmSDo48rTfd057LXHk1cKvRMaZMBojUPm4nTI3/HoqlU2exU2eo2osalGXUcUgrKOZRRwCuWVwgt2st74Q/zU1V3AFehn39N78feJ86nY7B30zf8DGhzAcJqtZKXlyc/QNowrTV5eXlYrdbmboo4C1zwn1+44D+/uJ7vSC4AIKe4kvYbH2GSeQdlE+aS1e5812tqU3YrpfC1Wpq2wWdQm9sHER0dTWpqKjk5Oc3dFNGIrFYr0dHRzd0M0cbV2B2uVUrl1TbMJsW+dKMHcWnRx3QvXsqbehq3jLqVmA1JALiZFFaLubmafEa1uQBhsViIjW05qwWEEK1XbU4l43E5VTY71TY7Twd9z9Xln7PKYyIrfP/KrUoR48yt5OfZensMv9bmAoQQQvxRDocmpaDcNWeQVljhOpdRVMHPe4/wrvu/mVi+jWX2c7i36Bqu7OEPwLDYICb0CGNs97ZTClkChBBCOD377X7eXZfIoptHMLxTMBlFdbuky1P3cPOBOwg3ZZM89FHu/KUHoFxpur3c3XjvuqHN1PLG0eYmqYUQ4o/a46z8tmKfUTP6QEYxJgWXmdYxZeMs3OyVLOz1Gm4jbweMfQ7hfm13sYQECCGEcCqvNja/pRVWUF5t44udqbwa+R0vub/OIXM3plY9g3vsSCLqBYW2HCBkiEkIIZzyS6sBo6b0liPZ/NP2Bhfmr+FH6yRuLrwaG27Ehvgcl4m1fVDr3CV9OiRACCEEzv01ZUaAKC0povMPNzHWbR3lI+5lad5UbIXGsFNtJbiHLuhBbkkV0YEtrxLcmSIBQgghgOySKqpsDoJVMS+U/4t2FYk8pW/i0cmPEfntfsAoDxriY6TqvnVM5+ZsbpOQOQghxFlr2uvreWJZHAC/HM7Fgo0lAa/SjWSe8JzNvsgrUEoR6az+FuBlQamWX+jnTJEAIYQ4KyXllrEzuZAPnTugE7JLecTyKbEV+/hHzW18VNCbgR0DAAj1NQr9eLm3jR3Sp0sChBDirJSYV7dLurLGTmjSMq41rySl+3Usdxb6qU26N7hjID0ifPnX9NZR6OdMkQAhhDgrVNbYWbY7HYfDSOSZXm+XdMqB7fw5az6HrX0pPu9R1/FwX2MJa1SAJyvvHk3/9gFN2+hm1ugBQillVkrtVEp943wepJRapZQ67Pw9sN5rZyulEpRSB5VSkxu7bUKIs8e/Vx3izoU7WRlnrEbKKDR2SftQTvDyGynRnuw55yWig/1d17TlPQ6noyl6EHcB++s9fwhYrbXuCqx2Pkcp1QuYAfQGpgCvK6XOrgE/IUSj2Z1aCMDGI3kAJOaWEe1n4QXLm/hXpnJ79Z106NgJ/3rJ9mqHmM5WjRoglFLRwEXAu/UOXwoscD5eAFxW7/hnWusqrXUikAAMa8z2CSHaLrtDu4aTANKdPYba/ErpmRm8bZrLZPM25tr/wlbdw1XH4d5J3bhzQlf8vdpOZtY/orH3QbwEPADUr7cXrrXOANBaZyilwpzH2wGb6r0u1XlMCCF+t8FzVtEnyp+P/zocu0O75hyyiis5HL+T+UX/IMacw3Med/Bu0TmYTYow52qlOyd0bc6mtxiN1oNQSk0FsrXW20/3kpMcO6EsnFLqZqXUNqXUNikKJIQ4Ga01heU1rEvIBYxU3TZnb6JD4WY6LL2YAFVK8ZX/x5aACwGI8LPiZpZ1O/U15p/GSOASpVQS8BkwXin1MZCllIoEcP6e7Xx9KtC+3vXRQPqvb6q1fltrPURrPSQ0tO3kXRdCnDk5pVWux+XVNlLyKwDNI6G/8B/bHPJMwdzs8TyBPce6dka3C2y7OZX+qEYLEFrr2VrraK11DMbk849a678Ay4BrnS+7FvjK+XgZMEMp5aGUigW6Alsaq31CiLYrMaduj0NaQQVbD6fxguVN/lryBmsc/ZlpfxrviC4AtHfmUoryP7tXLJ1Mc+RimgcsVkrdCCQDfwLQWscppRYD8YANuF1rbW+G9gkhWrm9zroOADmpCUzefAPdzUc52vvv3LR9BLrGxGjnCiV3N+N7crCPR7O0tSVrkgE3rfUarfVU5+M8rfUErXVX5+/59V73jNa6s9a6u9Z6RVO0TQjR+h3LK+PhL/ZSZTO+U/6wPwsPNxPnmvYxaOVlRDoy+LLni1SOfBDt/LHXMdgIEJcNbIef1Y1Zwzs0W/tbKpmREUK0erf8dzufbE5mS2I+FdV2Nifm81zvZD6yzCMPfy6tnoPqPsW1jBVw1Z3uFu7Lnicm0ynUp7ma32JJum8hRKtX5qwEtyu5EH9PC6PVbi459CL7zV2ZWXo/xdqLzqE++HnW/cir7UGI3yY9CCFEq1deZQwtHc4uJW/fat6yvEhNcHdejpxLsTYCQadQb5RSrp3SEiAaJj0IIUSrU2WzY1YKN7OJKpvdVQnOPX0rIw8/RhLhdL7uKwJWpgMpRPlb8XI3ftx98bdzcXcz4eEmmXwaIj0IIUSrM+TpH7jtkx2AsYwVYLh7Io8XP0a+KYinguZi9gkhwrl0tUO93kKnUJ82XSb0TJIAIYRo0Rz10mQApBVWUFJlY1V8FjV2B3tSixhn2slH5qcp1D78lcfwD40GINi5Ca5+Aj5x+iRACCFatJdWH+bceT+6gsS2JNfKeI7klJK9+lXedX+Bcr9OXFH9BPtKfV2b387tHMLQmEBmX9CzWdre2kmAEEK0aEu2pQCwM9lI173pqBEgFA5Mqx7j5tLXORpwDkenLiYbo7xM+yBjOWuXMB8+v/VcYkK8m6HlrZ8ECCFEi1absTs+o4gau4Pv4jKZ3MWL1ywv0+3IByywTWL3qDfoEBHmuqa9zDGcEbKKSQjRYlVU28kqMeo3pBVUsC4hl24Vu3gp/wPcTem85n4D/6qcwBdhfq6kewDtz/JCP2eKBAghRIt1JKcU7exB5BQUErD2LT5zX4jD2pnb7HP5rshIj9E5zAelFFeP6MiGI7nH7ZgWf5wECCFEi3Lf57sZ2SWYaQOjOZxdAsAl4bncm/0cMTqNJeYLmX7ru+S8uwuKCgnz9cDPaqxSevqyPs3Z9DZH5iCEEC3GwcwSlmxP5Z5Fu53PSxnidoTnS2fj4ahgtvdTLGt3D7h7u3IndZYcSo1GehBCiBZj45Fc1+OSyhocxzazwO1ZajxCuLzsATKqgrm+h7EiqWekHwDRUuin0UgPQgjRbH45nMMF//mFoooaAPak1tVxyI3/hbsyH6TMEkTc+Z+SQTBQ12P405BoZg3vwDXnxDR5u88WEiCEEM3mvxuPsT+jmGW70gBjUjrAy8IQdYDo5bPIcvjz3dD3CG3XyXVNlzAjQPhZLTwzrS99o/2bpe1nAwkQQohm42ExEualOPMpJeWVc1f4Hha4P0euCmJG9aN06tT1uFVJMufQdCRACCGazLG8Mipr6ioJpxaUA5BRVMn2hHQerHmD6zOe5rDqyKVls8kmkM6hPlgtdZlX6+93EI1LJqmFEE1id0ohl762npnD2jP38n4UlFW70meY8w7ScemNDHY7QvU5d/H4wXFkp5fj4WYizNeoFf3V7SMJ8fVAKdWcH+OsIj0IIUST2JlcAMDCLSlorZm7Yj+gudr6C3Nz78SjKo/7PR7DffJTRAUZK5Q6BHlhMhkBoX/7ANkA18QkQAghGsW+tCLmf3cQ7dwKnV5U6TqXW1rN3oRj/NfvDZ7mDXY7OnOj9UUK240BjMBQ/3fRPGSISQjRKC5/fQPVdgdXDI4mNsSbtHo1HVJ2fs97FXcRbipmR9c7+fPeYTjyTNzez5iA7hRq7HWwmOU7bHOSP30hRKOotjsA2Oqs37A/vZjOQRbud/uMgT9eTZW2sHX8QvIH/h2H80dRt3BfAC7p346L+kZy/ciYZmm7MDTYg1BKWYGpwHlAFFAB7AOWa63jGrd5QojWqLza5nqcnFdOTkkV5ryD/DfwXSLcDrHKYxJ3Fc3k6x7nEuNe92Ooa5gRIDzdzbw2a1CTt1sc75QBQin1BHAxsAbYDGQDVqAbMM8ZPP6htd7TuM0UQrR0WmvXCqPaOtEAafml5H7/At+4v4jJ7sutNfewsnIobiZFhyCv44aRaoeWRMvQUA9iq9b6id8496JSKgzocGabJIRobVLyy7nqrY1M6RPJYxf3YmuSsWKph7WQG4/Oo2fNHn5kMKNu+5Rtr+6F0mo6BNcFh1X3jCYuvfi4/Q6i+Z1yDkJrvbyB89la620nO6eUsiqltiildiul4pRSTzqPD1BKbVJK7VJKbVNKDat3zWylVIJS6qBSavIf+UBCiKa3en8W6UWVvL8+EYdDs2x3GpcEJrPEPJvYmkO87HMXb0fNwT0gglBfK3D8juiu4b5cNrBdczVf/IaGhpiWneq81vqSU5yuAsZrrUuVUhZgnVJqBfAU8KTWeoVS6kLgeWCsUqoXMAPojTHX8YNSqpvW2v5bbyCEaB52h6ba5sDT3fjGX38Ja0pBORHHvma++9uUeEQyrfwu0mvaMau7kTOpd5Qf+zOK6SR1olu8hoaYzgFSgIUYcxCnvYVRG4ufS51PLc5f2vnLz3ncH0h3Pr4U+ExrXQUkKqUSgGHAxtN9TyFE05j5ziYSc8vY+NB43Mwm0l1LWDWFK57mJcsb5AQNZf3gl0hadgzsDmKCjYBw6YAoth8rYGz3sN9+A9EiNBQgIoBJwEzgz8ByYOHprl5SSpmB7UAX4DWt9Wal1N3Ad0qp+RhDXOc6X94O2FTv8lTnMSFEC6K1ZkuisXT1cHYpPSJ82Z9RTIy/G3eXv0T/hA18bhtN16nvEu5wA44BEOPsMZzXNZSf7hvbTK0Xv0dDcxB2rfVKrfW1wAggAVijlLrjdG7uvH4AEA0MU0r1AW4D7tFatwfuAd5zvvxkvRP96wNKqZudcxfbcnJyTqcZQogzqLC8xvU4Pr2Y5PxycnOy+NQ6l8vMG3jVNIv7bbfQMSzwuGI+faL8TnY70YKdzj4ID+AijF5EDPAysPT3vInWulAptQaYAlwL3OU89TnwrvNxKtC+3mXR1A0/1b/X28DbAEOGDDkhgAghzrySyhp8PNxQSpFVUjffkFpQQUhNBkvdHyeiJI/79N0sKR+Gv6eFQG93fK11P2KCfTyao+nif3DKHoRSagGwARiEMbE8VGv9tNY6raEbK6VClVIBzseewETgAMYP/THOl40HDjsfLwNmKKU8lFKxQFdgyx/4TEKIM6igrJphz6zm7wt3ApCaX7fHwZS+nSGrryRYFVP156Xs9BsH1A0nuZlNvHvNEH55YFzTN1z8zxrqQVwNlGFsjLtLKVX7jV1hzEOfqs8YCSxwzkOYgMVa62+UUoXAf5RSbkAlcDPGzeKUUouBeMAG3C4rmIRofj8fzqGixs7yPRm89mfYl16EUnB94B5uTvw3RaZAHgl4lre7nEeE/yaO5JQRE1yXZG9ir/BmbL34X5wyQGit/3CuJufu6oEnOb4OGPwb1zwDPPNH31MIceYdyCxxPS6qqGH7gaO86/sOE8p/It7UjdvtD3BuTA8AAryMYj61K5ZE69bQPggrcCvGKqQ9wPtaa9uprhFCtC1JuWWux+mb/o8Xch4k2FTCz1E3cuPRMdTgxp9rA4JzjKF/e6kT3RY0NMS0AKgBfgEuxNjEdtcprxBCtGqlVTayiyvpFOqDw6HZk1pErK+DOyrfpOfadcTrjiRPXsBRe0dqjsYD0N5Zt+He87vRp50/42SPQ5vQUIDopbXuC6CUeg+ZNBaizXtmeTwLt6Sw9G/nYlKKosI8fgh7mZDqfXzsMZMniy7g557DyU8tcl3T0Tnn0DnUh9vG+vzWrUUr01CAcC141lrbpBasEG3foq0pAGxPKsBSU8x/3ecRXprE7bY7+bZoGL5WNyL8rOQFVLuuaS+V39qkhiah+yulipVSJUqpEqBfvefFTdFAIUTj0Vrz+poEV6qMvNIqHM55hPTMDMZsvpk+piTUlR+x02c0AN0V+MugAAAgAElEQVTDfVFKHbcJzsdDilO2RQ3tpDZrrf201r7OX271nsu2SCFaucPZpTy/8iA3fWQkZT6UZaRPC6CEaw/fSbvqI3zUYQ70uMg1jNQtwijq4+9p4eoRHXnpqgHN03jR6GQVkxBnsf0ZxkBAXLrx+3dxmXQxZ/OB9UXCazK4qfofDIudBECYM013d2dZUKUUT1/WpxlaLZqKrGIS4iy26aiRdM/DzUSN3UHWjm9Y5vEyGhPXVj/ARkdvLncOJf31vFg8LWam9IloziaLJiSrmIQ4iyzYkMTulEKevbwv7mYTq/dnAVBls5P97Vxe0/+i2KcbizvPY+NGY16idq6hX3QA/aYHNFvbRdOTVUxCtFGpBeX8eCCbGUM74O5morzaxuPLjEz9k3qF0zHYm+ySKi7p4c35R+bSbvsmvnaMoMOl7+OeWY2R9QaiA2WF0tmqoQDRv95qJQV4Op+fTi4mIUQzeufnoyzYeAy7Q3P9yFjXfANAcn45ZVU2ppl+YW7GYiymfP7rcwOP5k5gd2QYbp7lrteGShbWs1ZDuZikgrgQrVSac+nqyn2ZXD8y1jURDVCdvpehaS8y3X03NYEDuezYPezN7USwtzv+Xhb8POu++5lMMnJwtmoo3XeDWyJP5zVCiMaXXVLJkZxS1/NEZw6lfWlFOByaXSmF9PEq4FX/T/jbwesJKDvK+0H34Hbzag67dQEg1pmmWynFj/8Yw1e3j2z6DyJajIY2yn2llHpBKTVaKeVKz6iU6qSUulEp9R1GESAhRDP78zubmfDCWooqasgpqeJIThntgzwpq7ZzdOePnL/vfpY57mBK1Xd8ZZrEmIr5lPeZhTKZXfMMnULrsrB2CvWhf3uZlD6bNTTENEEpdSFwCzBSKRWIUavhIEZ96mu11pmN30whxKlU2ewkZBu9h01H81zHb++cR5fS5+ny9SFClRfpvW9mqeVCXtxkvLZLmDEAUFv5LTZEBgREnQb3x2utvwW+bYK2CCH+oCPZdSm5D2eVoKvLedxtAVft/Z4MFchb3rfwn7zhbJx6CYG70gBjNVPnUCMgxIZ4szO5kL7tJE23qCMJVIRoAw5n1xX1MSet5U/p/yLELZOKATcwadNIyqo8CffzwN/TQnS9xHodnOkzHprSg0v6RzGyS3CTt120XH+4YpwQovkk55Uz+vmf2JNaCMDBzBIsJs1rgQu5LfkflNsVL7V/GfdLXqTSZASB2gno9vX2NXi4GQsVw/ysjO0ehux1EvVJgBCiFfpwQxLJ+eV8sTMNgEOZxcz1WcxFFV/zsb6ASRVzsXYZhdmkCPY2yoB2cg4nxQR7MW1gO16ZeUJFYCGOI0NMQrQCO5ILKCirZkLPcMBY0gqQVVyJ3aHplbSA6XzF9ogreSTpUkDRK9LYy2AxG98DOzl7EG5mE/+WDKziNDS0D6KvUmqTUipFKfW2cxVT7TnJyyREE7n89Q3cuGAb+WVGkZ7UAmMTXHx6MTnrPuRePiYlagoHBvwTI9EB9IoyAkRtMKm/hFWI09HQENMbwBNAX+AQsE4p1dl5ztKI7RJCOKUW1KW92JlcgNaaY3nGqqWOBRsJ++k+1tt7U3D+y0QF1gWBEGeKjPO6hgJwbueQJmy1aAsaGmLy0VqvdD6er5TaDqxUSl0N6MZtmhBnp6LyGl5cdZC/j+9KqK8HPx3McZ1LyC4lwt9KeXkZj0Tu5s/5r5PuEcstFfewPiwIT2dvoX1QXbW3l2cOpKzKhtUimXPE79NQgFBKKX+tdRGA1vonpdQVwP8BQY3eOiHOQq+tSWDBxmNYzCYemdqLPSmFronmvMxjlB95jQ0eiwkuKGG37sTj6p9YvHycOZTcePziXkztF+W6n4+Hm5QEFX9IQ/9qngN6AptqD2it9yilJgCPNmbDhDhb1eZQ2pNaBBjV3oZGmrgs500m7v8RE3Y2uw8n9qL7uPSzKqhWDOxQl0Pp+pGxzdZ20bY0lGrj09rHzqR8WmtdprVOBm5q7MYJcTaqnYA+klNKtc1BeM46/lX6Hp41+XxtmcIrZROZMuJcBvTqChgjwLHBMgEtzrwG90EopW5TSiUDx4AUpdQxpdTfTuM6q1Jqi1Jqt1IqTin1ZL1zdyilDjqPP1/v+GylVILz3OQ/+qGEaE0e/mIvs5fuAUBrTZpzUrqirJj8xbfzgds8sPrxZte3uKdkFkcd4XSP8MXT3Yy7m/FfOCZEAoQ4807Zg1BKPQKcC4zVWh91HusE/EcpFaS1nnOKy6uA8VrrUqWUBWMF1ArAE7gU6Ke1rlJKhTnv2wuYgVH3Ogr4QSnVTWtt/x8/oxAtVnZxJZ9sTgbg4Yt6kVVcSXGljZlROdya+wzhh3J423YR5896BUdcPuw9BEAHZ7qMapsDgI7BUvVNnHkN9SCuBi6vDQ4AzsdXAtec6kJtqE1Ob3H+0sBtwDytdZXzddnO11wKfKa1rtJaJwIJwLDf+XmEaNFsdgdLd6RSYzd+sMfVq/J2IKOYLUfz+It5FXMK/oFZObjf51leNl9Lh7Agwv3qKrvF/qrH0DNSijuKM6/BISatdeVJjlUAjoauVUqZlVK7gGxgldZ6M9ANOE8ptVkptVYpNdT58nZASr3LU53Hfn3Pm5VS25RS23Jycn59WogWJSG79LgiPku2p3Lv4t18sD4RgAMZdUn2kjJz6LL+PuZYPqCi/WguqnqWJbkd6RXph8mkCPezul4b4GWsarplTCeiAz3pGiZpusWZ11CASHWuWDqOUmo8kNHQzbXWdq31ACAaGKaU6oMxrBUIjADuBxYrI0PYybKEnbDXQmv9ttZ6iNZ6SGhoaENNEKLJpOSXc/V7m127nQEmvriWCS+sdQ0F7Us3VibVrlA6mFlMuJ8Hnc1ZjF47k8HFq/g66Hoq//QpRRg/9HtG+gJ1w0qDO7oSGjD7gp6se3C8JNkTjaKhZa53YlSVWwdsx/iBPRQYiTEkdFq01oVKqTUY1edSgaVaaw1sUUo5gBDn8fb1LosG0k/3PYRobvNWHuCXw7l8F5fJzGEdKK6scZ1LzC2je4Svqy50dnEVWhtlQG/w286s6pfQlWauq36AcYNnEuxT11toH1Rb7c2HL28fSfdw36b9YOKsdcoehNY6DugD/AzEAJ2cj/s4z/0mpVSoUirA+dgTmAgcAL4ExjuPdwPcgVxgGTBDKeWhlIoFugKS70m0GoXlRs8hybmPYcexAte5zOJKHA7NwUxjSCm9qIL9SencUfwCt+Q+S5olhikVc/jZ0Z8uYT4opXAzGb2C6MC6XdED2gfg6S47okXTaGgVUxcgXGv9/q+On6eUStdaHznF5ZHAAqWUGSMQLdZaf6OUcgfeV0rtA6oxypZqIE4ptRiIxyhrerusYBKtSe3+hRTnMtXt9QJEVlElx/LLKa+2E+ztTnjJPjp8fifdTankD7mX98qnkrbDqN4b49zTcOmAdvzfjlRZwiqaTUNDTC8B/zzJ8QrnuYt/60Kt9R7ghITzWutq4C+/cc0zwDMNtEmIFuGKNzbQPtCTl2YMpNrmICXfCAwZRca6jm1JBXQL9+FQVimZRRUcitvJX8yruNL7KD1t6yitCeGGmkf5eMo9RPyUABgBIirA6DE8P70fV5/TkR4RskJJNI+GAkSM8wf9cbTW25RSMY3SIiFagbIqG9uPFbD9WAH/vmoAyfnlODSYlNFbqLE7OJySzgNd0rAWr2b05jgCarKZbIGKmkgW2sezyHItpYE+uLuZiHYGBXc3E2bn0JLZpBjQPqA5P6Y4yzUUIKynOOd5inNCtCnl1TY2HsljfA+jLOeO5OPnF2rzJ50fYyYq5Ssq3nuRDaaNuCfaKVE+HHAfxCr7Zbh1HsMFo0fx2GvrIR/GdTeGj2p7DaE+Hie+uRDNpKEAsVUpdZPW+p36B5VSN2KsahLirPDAkj18syeDt64ezOTeEWxJzHedO5pTRkJqFneYl3JH9grc3crIzY9hoX0K02fexP2brRzIKie9vJJHOvQkst6kc2yIsZR1SEwg14+M4aqh7U94byGaS0MB4m7gC6XULOoCwhCMlUfTGrNhQrQkR3OMHsKP+7OZ3DuCzYn5hPt5kFdchnnH+8w8+AYBlnyyIiZx9dGJ1Pj0oMrbzi29xxF2YA8/HjICSvcIX4Kcm9wAYp1V3qwWM49f3LvpP5gQp9BQNtcs4Fyl1DiM5a4Ay7XWPzZ6y4RoRs9+u58ofyvXOVNnpxcZK5QOZJVQWWNnb0oec7sepH/V28TGZ7LV0Z19veYx9LwpHHplHeSWMa67sZGz/g7obuG+mEx1m9o6yQol0YKdVhURrfVPwE+N3BYhWoTc0ire/tlIPzZjWAeqahwUlhub3pKyi0hf8z7fmv5FbGIWCaYY7lUPsrSyH58OHXFcMOgcagwf1T8W5mvMMfha3SiptNEjQja9iZZLykwJ8Su7kgtdjxNzy9iZXIjCwcPt45iY9T4x67PYRwyFl3zAI1vC2ZRkvD42xNtV+Q2gszM/Um1Q6Bft70qJ8clfh1NRbSdYJqVFCyYBQgigssbuqtmcUC+5XmJuGVt+Xs5K7/fonnOYfcQw1/9xPi/pw/aBk4g8vBuSCvFwMxHuaz1u+Ki2BzG8UxCzhnfgzgldXef6RcvyVdHyNZjNVYi2btnudHo8utK1VHVrYj7+nhaiVTZd1/6dl8oeIspUyJGR87m4eg5vZXWnR6QfSiki/Y3how5BXq7gcF7XEABXziRfq4VnpvU9bqhJiNZAAoQ46/3f9lQAlu9Jp9rmYO2hHB7uksgqjwdon/cL/665glUTlmMZNAvt/C/T3Tl3UBsgvDzqOuNvXT2Y3Y+dj7+XpYk/iRBnlgwxibNOdnEl3h5ueDt/qOeVVQFwJKeMpLwyLmUt0xPeIcHciWvK7iSTYL5sF05YvYI9PZ3pL2qHkSb3Dned83J3MxaCC9HKSYAQZxW7QzPs2dUM6hDA0r+NRGvNsVwjh9KxvDLs617mBfc3KY0cySvuD5F5oASloFu4j2uOAup6EOd0DubHf4w5ocKbEG2BDDGJs8quFGPF0Y7kQuwOTV5ZNSVVNkBzSc7b9Nz7PN/ah2H+yxJCgoMBY37By934LtXLWdqzNkAopegU6iMFe0SbJD0I0aZ9syed2f+3l7UPjCPI2/248p8ZRRUcySkjkGLeDPyU4RU/85PPVJ6zX8eFXl60c+ZHiqg3ufzB9UOxmE3H9SaEaKukByHatMe+iqOkysYP8VkApDpTcgMcyysna9Nn/ODxAEMqNzCvZgbX586kc7ixBLVXlNFbmNw7wnVNuJ+VIG+ZYBBnB+lBiDZl4ZZkekf50S86gGqbg7IqGwAHnJXcjuaWoRQE6mKif7iNkZnfk2ztSt5Fr/Hmp7kAdHFucDu3cwjbH5kom9nEWUt6EKLN2JdWxOyle7nhw60AHMoqocrmACC1oByHQ7MnIYX/tPuJ1R730y7rJ56vuZKfRn1KeJdBrvvUBghAgoM4q0kPQrQZcelFAOSWVlNtc5CUZ2x8i/K3UpSXSe6yx/jG/i5+ueVscRvE09Uz2Wtvx+LokOP2LHStFyCEOJtJD0K0WhlFFXyy+RhGSfO6mtAAyfnlJOWW4Ucpc3yX8EHRDYTtepn1jt4U/GUVb7Z/nr017YC6gFA7Kd1ZAoQQgPQgRCv2xLI4vovLIjrQizHdQl01oQGOZeYRte9N1lsX4pNbzpf2c1kV9BeSTO25oMswOu6PAyDI251A56Tzt3edR3x6MX5W2QEtBEiAEK1YknOD25bEPEZ3DWFrUgGD2vvRJf0rRiy/G++qbOJ8zqFgxEPc800ZZMGVQ4yVSTHBxsY2P2vdfwF/TwvndA5u+g8iRAslQ0yiVdBaM+ebeDYeyXMdqy3ik5hbRlJeOR5FR3ij5hGet7xDhg7myqpHOTj+XQJiB7quqU2NMbhjIAA1dt2En0KI1kV6EKJV2JtWxLvrEvl0SzLxT02huLKGkkpjCWtSdjEF3z3HCvfXcavw5inzHbxfPAJQ/KtjIP6edUNGtQGid5Qf150bw0X9Ipvj4wjRKkgPQrRICdklvPvLUdcE9E8HcgAor7ajtSbNOSE90ieD5wvvZdDh/7DJPAjT3zezPXAKoHA3m4gO9DouQNQuYVVK8cQlvRkaE9S0H0yIVkR6EKJFuuy1DZRW2TivayjdI3zZklQ3tJRZXMmexEzud/uMW23LycebO213o3tdyhjfCML8UoEiOgZ7YXbWaBjVJYR1CblEB3o20ycSovWRACFaBJvdgZvZ6NBW2eyUOndAbzySS9cwH/akFNE51JsjOWXk7FnF+J8fItQtjYzYy7lg/2QK8eXWQC8Awp1puetnWP3ohmEUVtS43kMI0bBG+9+ilLIqpbYopXYrpeKUUk/+6vx9SimtlAqpd2y2UipBKXVQKTW5sdomWpbKGjsj5v7InG/iATjoTIsBkF5USWJeGSVVNq7qCv9ye5N+q6+mssbOf6JfIG/iSxRiZFZtF2Ak1fPxMIaUop0BA8BkUpJDSYjfqTG/TlUB47XW/YEBwBSl1AgApVR7YBKQXPtipVQvYAbQG5gCvK6UkpSZZ4H1Cbnkllbx7rpEwJiQBnAzKdIKK0iK28y/La9x087Lucy8ntUhf2Fi5TyCek8kpF4qjOggIyAM7xSEu5uJ6YOjm/7DCNGGNNoQkzZmF2tzK1ucv2rXFP4beAD4qt4llwKfaa2rgESlVAIwDNjYWG0ULcP3cVmux6VVNvalFeFvdWN66DEuSprPoEPbKTN5oIfdwmXb+pGUHUgVdobEBB3XK+jtrNUwrnsYB5+eIjUahPgfNeqArFLKrJTaBWQDq7TWm5VSlwBpWuvdv3p5OyCl3vNU57Ff3/NmpdQ2pdS2nJycRmu7aDwJ2SW8vy4Rm91IpLfH2WMASMsvx5L4I4vdn+TRnPuJqU7gE59ruT38v5gumAv+0ZRV23EzKTqH+uDuZiI60JNALwth9eo2SHAQ4n/XqJPUWms7MEApFQB8oZTqBzwMnH+Sl5/sf/QJu5i01m8DbwMMGTJEdjm1Qg8s2cOO5ELC/axc1C+StIJy+kb5EZm5mojFz/BUSRxF7uH81Ol+btvfG22zclVX47tCpL+VuPRiYkK8cXczvt8sv/M8PNxk8lmIM61JVjFprQuVUmswhpFigd3Ob3jRwA6l1DCMHkP7epdFA+lN0T7RuBwOjcm53LSyxs6OZKPs5+HsEorKQ3CrzGe+4xW6u/9Cblk7Hqi5iTGX/Z0ym5nK+D1gc7j2L0T4G72E+hlX6+9zEEKcOY25iinU2XNAKeUJTAR2aq3DtNYxWusYjKAwSGudCSwDZiilPJRSsUBXYEtjtU80jcTcMgbPWcVjX+0DYE9q3XBScl45m77/jO88HqRr8Wbm2q9mYvV8FtvH0ad9qCu7KtTtgK4t/9kp1BshRONqzB5EJLDAuRLJBCzWWn/zWy/WWscppRYD8YANuN05RCVasa92pVFQXsMnm5N58pLeHMgsBqB7sBsTk+YzueJrjpg6EHrzt6z6JJfCnDL8rG50CPLCUW8AsTZA/Hl4RzKLK5k+uP3J3k4IcQY15iqmPcDABl4T86vnzwDPNFabROP7alcaq/dn858ZA1BKcSDD2NNgd2hySqs4kF7ITOsG7qteSnBNOl9ZL+WrkJt4P6IP7QI2czSnjD7t/FFKEelfN+lcu/ktyNudOZf1bZbPJsTZRnZSizPqrs92ATBzWAfO6RzMwawSvN3NlFXbKNi6hBv3zqUzqeR4dOfPZf9kY1Ufru1l5EMK8zUCQt92/gBYLWbuntiVbuG+sipJiGYgSz/EGVNZUzciGJdeREW1naS8Mm7smMXX7g/T/efb0Q4HGwe/yIYJ/8cGRx+0rqvgNiTGSME9qVe46z53T+zGhX0l46oQzUF6EOIPs9kdfLghiYv7RxHuZ3XVgAZIyC7lx537mWt+mxkpa0hTwbzofQ+v5g1m2aDR4EzVDdDFOb9w1ZD2TBvYDqtFNtAL0RJIgBB/2OJtqcxZvp/txwp44y+DOZJtBAgPN0VM6leMiX8Hq1sJ+tw7mb5xKBl5ZswmRZcwH9IL6+pHdw4zViSZTAqrSYKDEC2FDDGJ0/bSD4e4+JV1VNmMoaRNR40U3LVLV4/klBKtsvnC+zluLZhPiorkiYg3UOc/TVCgMXwUG+KN1WImvN6u59B6+ZSEEC2H9CDESTkcmq92pzGxZzi+Vgtaa1764TAAGxLyGNcjjN2pxoa3tMIKyiprCNz/Md97vImpxsQTjr+ysHI817SPBSAqwJO49GK6RxiZV7093HhwSg98PMwyAS1ECyU9CAFAakE5++rlRPp2Xwb3LNrNKz8mAJBdUuU6dyyvjIKyao7lldM/2p9I8qhZcBlX575Epm9fFg1dzIfV46myQbdwIyDU7nbuFubrus9tYztz9TkxTfDphBB/hAQIAcA1721h6ivryCs1AsH6hFwA9mcYG9sO1KvRkFJQwe7UQjqrNJ7y+ozvPB7AK2s7D9fcwNEpHxMW3cX12toew9UjOtIhyIvzurnKfwghWjgZYhJorTmaa0wwbztWwOTeEcSlG4EhLr0YrTUbj+ThZlJ08LbTMelzuh9cxWqPvehUN1Y4BvG5/838VO7FzRG+qHp5F2tzKPVvH8DPD4xr+g8nhPjDJEAIUvLrVhQdySmlvNrG/oxifDzcyC+rJjO/CFv813zk9zODqzbjkVtFmqUjb3jcwK13zOaeedupynbg4WYiOtALU70pBS93+ScmRGsl/3vPUlnFlYT5eqCUYmdKget4QnYpu5MLCXdkc3enQmyHfyLkrVt5pLqYcrcAdgVfxMs5gzike3Bep1CUTxjtAjw5mltG51AfzM7osO7BcZRVSSotIVozCRBnoZ8OZHP9h1t5Y9YgLugbyc7kQrzcTfw9cAuDEl6mf8Jh1nnkQSKUmq3EeY/l36X9uXr6NSQVVLN++X6oqqZftJESI8oZILqG16Xgrl8PWgjROkmAOAuUVdmw2TX+XsZKou/jjRKf64/kckHfSPYk5/KyzwImFq0gRYezP2gI31ZF848bZjHgjVT8i73Ic1Qzv2MoNup6G/3aBwAQ4LxvTLCk4BaiLZEAcRa46aNtbDiSx4Gnp2C1mMlxLlndn1FCZUkB92Y/zCjTXnbF3Mi0A+PwyrMwolMwnh0H4+OVR15ZNdGBnoT6etA+qK5GQy9nDeiJPcP5Zk8GvaL8muXzCSEahyxzbWO+3ZvB1e9tJq1eKosNR4wdz9uPGd/+M4qMc/lpCTjeO5/hKp69Q54lc8gDaEyUVduZ2t9IkBflbwSEAc7eQm1dhq5hPq6cSZcNbMdP943l/HpJ9oQQrZ8EiFZuR3IBWxLzXc8Xbknml8O5vOrc4JZaUO46dyCzhMNZJRxKz2eGdROLzY+gitO5puYhwkffSHRgXe/ggj5GgLA4az0P7GCkyrBazOx4dBKf3DT8uHbEhnjLjmgh2hgZYmrlLn99AwCJcy8EqLd/wdgVvXhbKkqB1pCZkUr24bf5xWMhERRwSLfjZn0f6T4dCPOz4ufc7Rzu5+HqHVw5JJq0ggrGdAt1vWeQt3uTfT4hRPORANGK1Q4VARzOLsXL3Ux+WTUmZdSCttkd/LJlO7Mjj9C5dBvnxa/HXVez030Qnhe/zuRP7WhMjOtgzB1YLWY2PDSe+h2BWcM7Mmt4x6b+aEKIFkACRCuitWZrUgEDOwRgMZuISyt2nVt7MId2gZ6YcPBQ5xSCjy2n5oW7+aImHfKhzOTL9+axvFoxkQnnjOG+vt2xmFdSbXfQM7JucjkqwPNkby2EOAtJgGjBEnPLcDMp2gcZewq+2ZPBHQt3ct25MTxxSW/iM4pRCjzcTORkJtPt0Ep+9vic6NRc8ky+7DcN5MuaSdx143X8e5eZj7ekAnBvdABKKartDoDjAoQQQtSSANFCaa0ZN38NAEnzLgJgZVwmADuTjdVI+zOKGRJQxm22Txgd/wtu2Nlt6Y9l4rOM/NKKrcqNKH8rT3UaRETSYde9e0QYAaFnpB/7M4oZHhvUhJ9MCNFaSIBoIVLyy/n3D4d4cEoPwv2sHM4udZ2rqLbj6W4mzpmO+3B2KcVlZXQ/8j4vsQSFg+XWqbxbOY6+vYfw5NDe6GUrwaHp71yeGuFfN3RUu1rpnWsGk1daTVi94j1CCFFLAkQL8cL3B/lyVzre7m48fVkfDmfVBYijuaUEermTlFdOpL+VjiU70G/M5m59lPzoibzldRNv7THyHs1q54/FbMLLYqakyuYKEJH+dUHA5MyXFB3oJSkxhBC/SfZBtBC19RYOZxu/H8ysm4BOyS9nzbbdXGv+jqXWp/nMfQ7VleX8teY+rNcswj+qrv5C33r5kQD6RxsBolu4L5H+Vp6/ol+TfB4hROsnPYhG8H1cJmF+Vtfu48Lyaj7ZnMyNo2KxWsxorfl2byaxId70ivKjxu7gaI5Rj+FIThn5ZdV8uiWZUaGVdMpfS79V84ko2onJoqk092BezQw+qr6A8KAAvNzd6B5eV6WttoLbyzMHsiUp3zW/EOrrwcbZE5r4T0II0Zo1WoBQSlmBnwEP5/ss0Vo/rpT6//buPTyq8k7g+PeXKySEQAIhN8JlSQoBEUKKVkVuBQVW0bW2gBaseOmugvVZu5W127VL3V20T2tbq5Z1sfVWvNWqrRYQFwrKRbknmMQEIgRIAgQIF3OZ5N0/zjuTA51Ag5lLOr/P88wzZ95zzszvTCbzm/e873nfx4DrgCagAviWMea43WcRMB9oARYaY1YEKr5A8bS0ctfzW4C2xuVl6/fy8/fLaWk1LJycy64DJ7jnpa0AFP/wGkprTjrdTdOT8NR8QtWba1jW9AdGevZCLBw4M4ifNt9E+hWzmDZhPE8vXgVAju3dVESXy+EAAA4BSURBVGCvcgaIjXYqhV9KT/LN5qaUUhcjkDWIRmCSMeaUiMQC60XkXWAVsMgY4xGRJcAi4Hsikg/MAoYDmcB7IpJnjAnrSQVON3pYW3aYaSPSEZGzpub0Ni6vLTsMQGmNs857tTPA2rLDHDj2OV+JKubx1rfpF78TymAbuXgm/TsLt2byTrXzRb98WAG9E2KJj4mi0dPqSxC9E+P41+lDyeql7QlKqc4TsARhjDGAt6U11t6MMWala7ONwNfs8kxguTGmEdgrIuXAWGBDoGK8GM9tqGRI3x5cMcSZW/lHf9zNbzfv58lbCph+SQblrt5HB45/Tve4aHZUOb2P9h11xkX6qLKObrFRNDS3crJsHePLf8mdcdtp8qTzcPNc1sRcSXRyOquvnkD0gW1QfRBwTh+JCC2tBoChGW01hLuu/rtgHL5SKoIEtJFaRKJFZDtQC6wyxmw6Z5PbgXftchaw37WuypaFTMXhU/zb74to9DiVmEZPCz94s5g5z7QdxgY7Uup7do6FvXZuZ3AGyltR5Fy7MC63D/vqzuBpaWVD0acsztnOy93+i2/supM+DZW8nHovNbdt4Nct11LZmMRgO2pqjh1eu1dCrG8MJO88z942DqWUCoSANlLb00OjRKQX8IaIjDDGFAGIyEOAB3jRbu5vKFBzboGI3AXcBZCTkxOQuL2eXlPBq1uqGJbRkzmX5VDkGtqi0dNCQ3MrlbZWsKPqOAAl1fW+U0BVdWdYV1TBxL4nuSGtiQF71nLqf37KOtlIzIFWaiSNF3rM50eHr2LBiJGkp7Z94Q/u40y+MyDFue8R3/aneu72sRQdPMHwzOSAHr9SKrIFpReTMea4iKwBrgWKRGQe8PfAZHsqCpwaQ3/XbtnAQT/PtRRYClBYWPgXCaQz1Tc0A20jo3qvYAaoqD3NsTNNAAxNT+LY0RpOFa9geMXvuLd3FSn1JaSvPM6txuPssAVmxkJ1XQ7Pt1zPrLn/xAN/Nqwrd2oglw1K8TUwgzN8NsCILCcJzB7blgzTenZjkl7cppQKsED2YuoLNNvk0B34KrBERK4FvgeMN8acce3yFvCSiPwEp5E6F9gcqPj8eWpNBTkpCcwY6cyFUGobnL1dULftP+7btqy6ntP7dnBf9OvMadpFv+gyeBUWAg0yiPUxw9gQl05JfRxTCvPJyOzPbb+vZU9DJkPTk1iQdxn9i3dB+VF6JcQyZkDvs2LxJoj8zJ7s/o9rSIjTHslKqeAK5LdOBvAbEYnGaet4xRjzB9v4HA+sshPMbDTGfNsYUywirwC7cU493RPIHky1JxuoOdHou7Bsz+FTLPlTCQDTRkynwdPCZ3VO/vrsqJMgiitrWDhwP8lVa5i48rskNxygNVY41n00j574Omf6juL9+izW3j+TZc9s8s3kdl3hlSSnJrLnDad93jv5zqXZyby0CVpbjW+ynQem5vHjlWXkuq5t0OSglAqFQPZi2gmM9lM+xM/m3nWPAI8EKia363/xAdX1DWx+aDJpSd34oPyIb13tyUbKak5iDIxL+5whdSs5/ewTvNu4nu7VTTTGxFIaVcCLzdMZOv5mhg0ZwpNLN0I1jM/ri4j4xjuKEueahPiYttNHo3Octoar7SQ8yQmxvnX3Tspl3hUDSerWVqaUUqEQkT9NjTFU1zcAsHFPHddfmslK2wsJ4GD1QY78+SVei3+bwvoSiIG66mzebJnIjBu/yYINCWyqcvZ/LS+X3LS2X/vDM52RUr1jHGX26u6bnc1rXK7TRTYjuTtP3lJAru2V5KXJQSkVDiIyQdSdbvItb6g4yqisntSUb+ORrCrSaj9k1PIdFBgP1XE5lH/pfu7Yks2JqBxSesfxrTET6FO6FaoO0T02mpHZvYhz1Q68PYv62+6pfZPifeuenz8WT4shwzWy6vRLMgJ9uEopdVEiMkHsP+ZM1Xlj1DquLd5Ov5ISVsYfg6NwIKoP7yXN5GeHC5h/3Uzy0ntS+fF6ONPMlPx+QNtw2WMG9PYlh7iYKJo8reTbGsTU/HSmjajhHwqyfa87LrcvSinVVURkghiUmsjSb44h6/1fk3zkUzZ3K2RVcy4PL7ibm56uoLq2gSiBiUP70WraetJ6Z17rneBcsOaeaOe528fy8kf7GWCHv0iMj+GpW8cE8aiUUqpzRWSCSE6IZerwdDbFPM6MZ7dDI0wemkZUykAG962mur6B/Mye9E6Mw/hJEF8v7E9MlDDvioG+dZcPTuXywanBPhSllAqYiJ4PIiut7dqDPDvyqfd6hD49nLYDb/dTgGF2qs6UxDjuGDf4rAvblFLqb01Ef8O5G4u9PYmm5qcD+MY9Alh5/9UsvmHEWd1RlVLqb11EnmLyio4Svj9jGCcbPFwz3EkMl2Qn88zcQgpcVzbn9UvyTcSjlFKRIqITBMAd4wb/RdlXbW8lpZSKZBF9ikkppVT7NEEopZTySxOEUkopvzRBKKWU8ksThFJKKb80QSillPJLE4RSSim/NEEopZTyS9yD0XU1InIY+CyAL9EHOHLBrcKLxhwcXTFm6Jpxa8ydb4Ax5oLzD3TpBBFoIvKxMaYw1HF0hMYcHF0xZuiacWvMoaOnmJRSSvmlCUIppZRfmiDOb2moA7gIGnNwdMWYoWvGrTGHiLZBKKWU8ktrEEoppfyKqAQhIstEpFZEilxll4rIBhHZJSJvi0hP17qRdl2xXd/Nlo+xj8tF5Ofinpc0hDGLyC0ist11axWRUcGO+SLijhWR39jyT0RkkWufcH2v40TkWVu+Q0QmhCjm/iLyf/Z9KxaR+2x5ioisEpFP7X1v1z6LbGylInJNsOPuaMwikmq3PyUiT5zzXOEa8xQR2WJj2yIik4Idc6cwxkTMDbgaKACKXGUfAePt8u3AYrscA+wELrWPU4Fou7wZ+AogwLvAtHCI+Zz9LgH2uB4HLeaLeK/nAMvtcgJQCQwM5/cauAd41i6nAVuAqBDEnAEU2OUkoAzIBx4FHrTlDwJL7HI+sAOIBwYBFcH+XF9EzInAVcC3gSfOea5wjXk0kGmXRwAHgh1zpxx3qAMI+gHDwHO+AOppa4vpD+y2y9OBF9r5oJS4Hs8GfhUOMZ+zz38Cj4Qq5g6+17OBt3GScqr950sJ5/ca+CVwq2u71cDYUL3Xrtd7E5gClAIZrr9/qV1eBCxybb/CflmFLO4Lxeza7jZcCaIrxGzLBTiKk5RD+vno6C2iTjG1owi43i7fjPMlAJAHGBFZISJbReRfbHkWUOXav8qWBVN7Mbt9A/itXQ6HmKH9uF8DTgOHgH3Aj40xdYRH3O3FvAOYKSIxIjIIGGPXhSxmERmI88t1E9DPGHMIwN6n2c2ygP1+4gtJ3H9lzO3pKjHfBGwzxjQSHp/pv5omCOe0wT0isgWn6thky2NwqrW32PsbRWQyzq+BcwW7K1h7MQMgIpcBZ4wx3nPp4RAztB/3WKAFyMQ57fHPIjKY8Ii7vZiX4fxzfww8DnwIeAhRzCLSA3gd+I4xpv58m/opM+cpD5gOxNzuU/gpC6uYRWQ4sAS421vkZ7Ow7UoaE+oAQs0YUwJMBRCRPGCGXVUFrDXGHLHr3sE5P/0CkO16imzgYNAC5rwxe82irfYAzrGENGY4b9xzgD8ZY5qBWhH5ACgE1hGm77UxxgPc791ORD4EPgWOEeSYRSQW50vrRWPM72xxjYhkGGMOiUgGUGvLqzi7xumNL6ifkQ7G3J6wjllEsoE3gLnGmIpQxPxFRXwNQkTS7H0U8H3gabtqBTBSRBJEJAYYj3P++RBwUkQut70P5uKcjwyHmL1lNwPLvWXhELONrb249wGTxJEIXI5znjbkcbcXs/1cJNrlKYDHGBP0z4d9jf8FPjHG/MS16i1gnl2e54rhLWCWiMTbU2O5wOZgxn0RMfsVzjGLSC/gjzjtPR+EIuZOEepGkGDecH5VHwKacTL5fOA+nEbRMuC/sQ2SdvtbgWKc89CPusoLbVkF8IR7nzCIeQKw0c/zBC3mjsYN9ABete/1buC74f5e4zRmlwKfAO/hjI4ZipivwjlFsRPYbm/TcRr7V+PUalYDKa59HrKxleLqQROsuC8y5kqgDjhl/zb54Rwzzo+J065ttwNpofhf/CI3vZJaKaWUXxF/ikkppZR/miCUUkr5pQlCKaWUX5oglFJK+aUJQqkgE5G7xTV4nlLhShOEUhcgIjeKiBGRoZ3wXD8A6owxxzohNKUCSru5KnUBIvIKziBrq40xD4c4HKWCRmsQSp2HHXvnSpyL5mbZsgkiskZEXhOREhF50Tumv4hUisgP7QCPu7y1DhFJFGe+iY9EZJuIzLTl0SLymC3fKSJ3txOKUkGnCUKp87sBZ5yoMqBORAps+WjgOzhzAgzGSSJeR4wxBcBTwAO27CHgfWPMl4GJwGN2qI75wAlb/mXgTjsEhlIhpwlCqfObTdu4VsvtY3DGL6oyxrTiDKMw0LWPdyC3La7yqcCDIrIdWAN0A3Js+Vxbvgln6IbcQByIUh0V8aO5KtUeEUkFJgEjRMQA0Tjj8bwDNLo2beHs/6VGP+UC3GSMKT3nNQRYYIxZ0flHoNQXozUIpdr3NeA5Y8wAY8xAY0x/YC/OwG0dtQJY4GqrGO0q/0c7lDQikucdJVapUNMEoVT7ZuOM5+/2Os78FR21GIgFdopIkX0M8AzOCLZbbfmv0Jq9ChPazVUppZRfWoNQSinllyYIpZRSfmmCUEop5ZcmCKWUUn5pglBKKeWXJgillFJ+aYJQSinllyYIpZRSfv0/KmVejWuoRq4AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "CO2 = df.iloc[:,4].values\n",
+    "dates = df.loc[:,'Date'].values\n",
+    "df_yearly_mean = df.groupby(\"Yr\").mean()\n",
+    "CO2_yearly = df_yearly_mean.loc[:,\"CO2 [ppm]\"].values\n",
+    "dates_yearly_f = df_yearly_mean.index.values\n",
+    "dates_yearly = [datetime.date(int(d), 7, 1) for d in dates_yearly_f]\n",
+    "\n",
+    "plt.plot(dates, CO2, label=\"Évolution globale\")\n",
+    "plt.plot(dates_yearly, CO2_yearly, label=\"Annuel\")\n",
+    "plt.ylabel(\"CO2 (PPM)\")\n",
+    "plt.xlabel(\"Année\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Affichons maintenant la tendance sur une année. Moyennons pour chaque mois sur toutes les années."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [
+    {
+     "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": [
+    "df_mn = df.groupby('Mn').mean()\n",
+    "CO2 = df_mn.iloc[:,4].values\n",
+    "months = df_mn.index.values\n",
+    "\n",
+    "plt.plot(months, CO2)\n",
+    "plt.ylabel(\"CO2 (PPM)\")\n",
+    "plt.xlabel(\"Mois\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "On observe une tendance annuelle dû aux saisons. On a également observé une tendance globale à la hausse."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Prédiction pour les années futures"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Nous proposons une régression linéaire simple pour estimer les valeurs pour les prochaines années."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pente = ((CO2_yearly-CO2_yearly.mean())*(dates_yearly_f-dates_yearly_f.mean())).sum()/((dates_yearly_f-dates_yearly_f.mean())**2).sum()\n",
+    "origine = CO2_yearly.mean() - pente * dates_yearly_f.mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-2930.5194959134415"
+      ]
+     },
+     "execution_count": 129,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "origine"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7fe92d60e400>"
+      ]
+     },
+     "execution_count": 137,
+     "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": [
+    "plt.plot(dates_yearly, CO2_yearly, label=\"Annuel\")\n",
+    "plt.plot(dates_yearly, pente*dates_yearly_f+origine, label=\"Modèle\")\n",
+    "\n",
+    "dates_yearly_pred_f = np.arange(2025,2031)\n",
+    "dates_yearly_pred = [datetime.date(int(d), 7, 1) for d in dates_yearly_pred_f]\n",
+    "plt.plot(dates_yearly_pred, pente*dates_yearly_pred_f+origine, label=\"Prédictions\")\n",
+    "\n",
+    "plt.legend()"
+   ]
+  }
+ ],
  "metadata": {
+  "hide_code_all_hidden": false,
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
@@ -16,10 +668,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-