journal/Journal_de_bord.md

@@ -2,7 +2,7 @@
 
 > Autrice : Flavie B. 
 >
-> Modifié le : 2025/08/25.
+> Modifié le : 2025/09/20.
 
 ## Préambule - Posons le décor
 
@@ -31,7 +31,7 @@ __Contenu :__
 - [x] Fiches supplémentaires
 - [x] Exercices
 
-Liens vers les exercices réalisés : [Exercice](module1/exo2) 
+__Liens vers les exercices réalisés :__ [Exercice](module1/exo2) 
 
 __Références utiles (pour moi) :__
 - [Tuto Markdown](https://www.jdbonjour.ch/cours/markdown-pandoc/) [depuis le cours]
@@ -59,19 +59,105 @@ Je choisis donc de suivre le parcours Jupyter dans un premier temps, mais je com
 __Contenu :__
 - [x] Vidéos de présentation/cours
 - [x] Tutos
-- [ ] Exercices
+- [x] Exercices
+
+__Tips jupyter :__
+
+`Ctrl + Enter` pour exécuter la cellule; `Alt + Enter` pour exécuter la cellule et créer une nouvelle cellule en dessous;
+
+`Esc + A` pour créer une nouvelle cellule au-dessus, `Esc + B` en dessous; 
 
-Tips : 
+`Esc + M` pour transformer une cellule en Markdown, `Esc + Y` pour la transformer en code.
 
 Pour personnaliser l'export jupyter notebook : 
 ```
 jupyter nbconvert --to mypackage.MyExporter notebook.ipynb
 ```
 
-Liens vers les exercices réalisés : 
+**Note pour l'exercice 4**
+
+Apparemment suivre des consignes n'est pas mon fort, je n'ai pas noté de données quotidiennes dans mon journal de bord... 
+
+Pour pouvoir faire l'exercice 4 je récupère donc les données de température pour une même ville via le site data.gouv.fr :
+[Données climatologiques quotidiennes](https://meteo.data.gouv.fr/datasets/donnees-climatologiques-de-base-quotidiennes/).
+
+Fichier "Q_06_latest-2024-2025_RR-T-Vent.csv" téléchargé le 2025/08/26. 
+
+Etapes de l'analyse : 
+
+1. Préparer/filtrer les données avec un script R. 
+
+Pour perdre moins de temps je filtre les données en utilisant un script R ([prep_data.R](module2/exo4/prep_data.R)), 
+afin de ne garder que les données pour Nice du mois d'août, entre 2025/08/01 et 2025/08/25, pour les colonnes suivantes
+
+```
+NUM_POSTE = Numéro de la station météo
+NOM_USUEL = Nom de la station météo
+AAAAMMJJ = Date de la mesure au format année mois jour
+RR = Quantité de précipitations sur 24h (en mm)
+TN = Température minimale ce jour (en °C)
+TX = Température maximale ce jour (en °C)
+TM = Température moyenne ce jour (en °C)
+```
+
+J'exporte les données filtrées en format csv, pour servir de données d'entrée à charger depuis mon document computationnel.
+
+2. Faire les analyses dans un document computationnel.
+
+J'importe les données depuis le document computationnel ([exercice.ipynb](module2/exo4/exercice.ipynb)), je corrige quelques formats, 
+puis je suis enfin prête à réaliser des statistiques de base et quelques visualisations.
+
+__Liens vers les exercices réalisés :__
+- [Exo 1](module2/exo1/toy_notebook_fr.ipynb)
+- [Exo 2](module2/exo2/exercice.ipynb)
+- [Exo 3](module2/exo3/exercice.ipynb)
+- [Exo 4](module2/exo4/exercice.ipynb)
+- [Exo 5 sans correction](module2/exo5/exo5_fr.ipynb)
 
 __Références utiles (pour moi) :__
-- [Critiques article Reihart-Rogoff sur dette](https://en.wikipedia.org/wiki/Growth_in_a_Time_of_Debt)
+- [Critiques article Reinhart-Rogoff sur dette](https://en.wikipedia.org/wiki/Growth_in_a_Time_of_Debt)
 - [Défaut dans méthode stat IRMf](https://www.pnas.org/doi/10.1073/pnas.1602413113) & [Blog sur cet article et ses suites](https://www.cogneurosociety.org/debunking-the-myth-that-fmri-studies-are-invalid/)
 - [Problèmes de reproductibilité en science avec recours aux ordinateurs](https://theconversation.com/how-computers-broke-science-and-what-we-can-do-to-fix-it-49938)
 
+## Module 3 - La main à la pâte : une analyse réplicable
+
+> Suivi la semaine du 2025/08/25.
+
+S'entraîner à réaliser une analyse réplicable à l'aide d'un document computationnel. 
+
+__Contenu :__
+- [x] Vidéo de cours
+- [x] Etude de cas
+- [ ] Exercices
+
+__Liens vers les exercices réalisés :__
+- [Exo 1](module3/exo1/analyse-syndrome-grippal.ipynb)
+- [Exo 2](module3/exo2/exercice.ipynb)
+
+**Exercice 3 - évalué par les pairs**
+
+Choix du sujet : Sujet 3 - L'épidémie de choléra à Londres en 1854.
+
+Représentation de données géographiques.
+
+Ayant eu du mal avec la librarie "folium" (carte ne montrant pas les marqueurs créés sans erreur évidente), 
+je mets en pause la réalisation de l'exo 3 pour la reprendre après le MOOC Reproducible Research II.
+
+## Module 4 - Vers une étude reproductible : la réalité du terrain 
+
+> Suivi la semaine du 2025/09/20.
+
+Module qui illustre bien les différents niveaux de difficultés qu'on peut rencontrer 
+quand on analyse un grand volume de données, hétérogènes, avec des calculs plus ou moins complexes...
+
+Avec R je trouve la fonction `set.seed()` absolument indispensable et ai commencé 
+à l'ajouter par défaut au début de la plupart de mes scripts.
+Mais elle est loin de garantir de pouvoir la reproduction des résultats, 
+surtout quand on compare des calculs sous linux/Ubuntu et MacOS... 
+
+__Contenu :__
+- [x] Vidéo de cours
+- [ ] Exercices
+
+J'ai pu rééxecuter avec succès le script en Rmd des analyses du Challenger sur ma machine (Exo1).
+Je garde l'exo 3 ("Répliquer un papier de ReScience ou RunMyCode") pour après le MOOC Reproducible Research II. 

module2/exo2/exercice.ipynb

 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Savoir faire un calcul simple soi-même"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = [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]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[14.0, 7.6, 11.2, 12.8, 12.5]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[:5]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "On va calculer : \n",
+    "- Moyenne des données\n",
+    "- Ecart-type\n",
+    "- Min\n",
+    "- Médiane\n",
+    "- Max"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Moyenne = 14.11\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Moyenne =\", round(np.mean(data), 2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ecart-type = 4.33\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Ecart-type =\", round(np.std(data, ddof=1), 2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Min = 2.8\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Min =\", np.min(data))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mediane = 14.5\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Mediane =\", np.median(data))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Max = 23.4\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Max =\", np.max(data))"
+   ]
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
@@ -16,10 +159,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-

module2/exo3/exercice.ipynb

 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Réaliser un affichage graphique"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = [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]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[14.0, 7.6, 11.2, 12.8, 12.5]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(data[:5])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Sequence plot**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f713fd202b0>]"
+      ]
+     },
+     "execution_count": 11,
+     "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": [
+    "%matplotlib inline\n",
+    "plt.plot(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Histogramme**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([ 4.,  3.,  5.,  9., 16., 20., 22.,  9.,  8.,  4.]),\n",
+       " array([ 2.8 ,  4.86,  6.92,  8.98, 11.04, 13.1 , 15.16, 17.22, 19.28,\n",
+       "        21.34, 23.4 ]),\n",
+       " <a list of 10 Patch objects>)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "plt.hist(data)"
+   ]
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
@@ -16,10 +143,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-

module2/exo4/Q_06_latest-2024-2025_RR-T-Vent_filtered_Nice_20250801-26.csv

+"NUM_POSTE";"NOM_USUEL";"AAAAMMJJ";"RR";"TN";"TX";"TM"
+6088001;"NICE";20250801;0;20.8;26.5;24.1
+6088001;"NICE";20250802;0;20.2;27.8;24.2
+6088001;"NICE";20250803;0;21;27.4;24.5
+6088001;"NICE";20250804;0;22;27.2;24.6
+6088001;"NICE";20250805;0;21.7;27.1;24.6
+6088001;"NICE";20250806;0;21.5;27.5;25.1
+6088001;"NICE";20250807;0;22.7;28.4;25.9
+6088001;"NICE";20250808;0;24.3;29.1;26.4
+6088001;"NICE";20250809;0;24.4;29.7;27.3
+6088001;"NICE";20250810;0;24.8;30.7;28.3
+6088001;"NICE";20250811;0;26.5;34.7;30
+6088001;"NICE";20250812;0;28.7;33.6;30.1
+6088001;"NICE";20250813;0;27.6;35.1;29.7
+6088001;"NICE";20250814;0;26.7;34;29.5
+6088001;"NICE";20250815;0;25.5;35.7;29.1
+6088001;"NICE";20250816;0;25.8;34.9;29.2
+6088001;"NICE";20250817;0;26.5;31.4;28.8
+6088001;"NICE";20250818;0;26;30.1;27.8
+6088001;"NICE";20250819;0;24.8;28.7;26.8
+6088001;"NICE";20250820;13;19.8;27.4;24.5
+6088001;"NICE";20250821;2.2;20.4;28.7;22.9
+6088001;"NICE";20250822;0;19.3;27.2;23.8
+6088001;"NICE";20250823;0;20.9;27.2;24.4
+6088001;"NICE";20250824;0;20.9;27.1;24.6

module2/exo4/exercice.ipynb

 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Analyse de données liées au journal de bord"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np \n",
+    "from datetime import datetime\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.dates as mdates\n",
+    "import re"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "La première étape consiste à importer puis corriger les données."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([(6088001, '\"NICE\"', '20250801', 0., 20.8, 26.5, 24.1),\n",
+       "       (6088001, '\"NICE\"', '20250802', 0., 20.2, 27.8, 24.2),\n",
+       "       (6088001, '\"NICE\"', '20250803', 0., 21. , 27.4, 24.5),\n",
+       "       (6088001, '\"NICE\"', '20250804', 0., 22. , 27.2, 24.6),\n",
+       "       (6088001, '\"NICE\"', '20250805', 0., 21.7, 27.1, 24.6)],\n",
+       "      dtype=[('NUM_POSTE', '<i8'), ('NOM_USUEL', '<U10'), ('AAAAMMJJ', '<U10'), ('RR', '<f8'), ('TN', '<f8'), ('TX', '<f8'), ('TM', '<f8')])"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Importer donnees\n",
+    "data = np.genfromtxt('Q_06_latest-2024-2025_RR-T-Vent_filtered_Nice_20250801-26.csv', delimiter=';', names = True,\n",
+    "                     dtype=[('NUM_POSTE', '<i8'), ('NOM_USUEL', '<U10'), ('AAAAMMJJ', '<U10'), ('RR', '<f8'), ('TN', '<f8'), ('TX', '<f8'), ('TM', '<f8')])\n",
+    "data[:5,]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([(6088001, 'NICE', '2025-08-01', 0., 20.8, 26.5, 24.1),\n",
+       "       (6088001, 'NICE', '2025-08-02', 0., 20.2, 27.8, 24.2),\n",
+       "       (6088001, 'NICE', '2025-08-03', 0., 21. , 27.4, 24.5),\n",
+       "       (6088001, 'NICE', '2025-08-04', 0., 22. , 27.2, 24.6),\n",
+       "       (6088001, 'NICE', '2025-08-05', 0., 21.7, 27.1, 24.6)],\n",
+       "      dtype=[('NUM_POSTE', '<i8'), ('NOM_USUEL', '<U10'), ('AAAAMMJJ', '<U10'), ('RR', '<f8'), ('TN', '<f8'), ('TX', '<f8'), ('TM', '<f8')])"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Corriger colonne NOM_USUEL\n",
+    "data['NOM_USUEL'] = [re.sub(\"\\\"\", \"\", x) for x in data['NOM_USUEL']]\n",
+    "# Convertir colonne en date\n",
+    "data['AAAAMMJJ'] = [datetime.strptime(x, '%Y%m%d') for x in data['AAAAMMJJ']]\n",
+    "data[:5,]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Il est temps de faire quelques statistiques de bases à partir de ces données."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Moyenne des températures minimales sur le mois d'août à NICE : 23.4 °C.\n",
+      "Ecart-type = 2.81\n",
+      "Moyenne des températures maximales sur le mois d'août à NICE : 29.9 °C.\n",
+      "Ecart-type = 3.09\n",
+      "Moyenne des précipitations sur le mois d'août à NICE : 0.6 mm.\n",
+      "Ecart-type = 2.67\n",
+      "Plus fort taux de précipitations pour le mois d'août à NICE : 13.0 mm.\n",
+      "Plus fort taux de précipitations le 2025-08-20 .\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Moyenne des températures minimales sur le mois d'août à\", data['NOM_USUEL'][0], \":\", round(np.mean(data['TN']), 1), \"°C.\")\n",
+    "print(\"Ecart-type =\", round(np.std(data['TN'], ddof=1), 2))\n",
+    "print(\"Moyenne des températures maximales sur le mois d'août à\", data['NOM_USUEL'][0], \":\", round(np.mean(data['TX']), 1), \"°C.\")\n",
+    "print(\"Ecart-type =\", round(np.std(data['TX'], ddof=1), 2))\n",
+    "print(\"Moyenne des précipitations sur le mois d'août à\", data['NOM_USUEL'][0], \":\", round(np.mean(data['RR']), 1), \"mm.\")\n",
+    "print(\"Ecart-type =\", round(np.std(data['RR'], ddof=1), 2))\n",
+    "print(\"Plus fort taux de précipitations pour le mois d'août à\", data['NOM_USUEL'][0], \":\", data['RR'].max(), \"mm.\")\n",
+    "indices = np.where(data['RR'] == data['RR'].max())\n",
+    "print(\"Plus fort taux de précipitations le\", data[indices]['AAAAMMJJ'][0], \".\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Maintenant nous allons pouvoir visualiser ces données météo."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Avec Q1 = 27.35 , Q2 = 28.7 , Q3 = 31.95\n"
+     ]
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "plt.style.use('seaborn-whitegrid')\n",
+    "plt.boxplot(data['TX'])\n",
+    "plt.title(\"Températures maximales du mois d'août à \" + data['NOM_USUEL'][0])\n",
+    "plt.xticks([])\n",
+    "plt.show()\n",
+    "print(\"Avec Q1 =\", round(np.quantile(data['TX'], 0.25), 2), \", Q2 =\", round(np.quantile(data['TX'], 0.5), 2), \", Q3 =\", round(np.quantile(data['TX'], 0.75), 2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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/P7du3Xrk9WeffRaLxULBggUpX748AAUKFODmzZsULFiQKVOm0KlTJ1atWsW1a9fut7t06RInT56kb9++dO7cmR07dnD58mUqVqzI3r17GT58OCdPnozX+KYG7brUaDTOYfVqWLYM8uTh2LJlPLl2LUyaBIGBsH07fPst5M7tbi3dzqpV8Z8/ePAg5cqVS3G/Bw86dl316tUZPXo0fn5+vPLKK/Fe4+HhcX/fiOchxf71uNeOGzeON998k1q1ajF79mxu3759/3UvLy8KFy5McHCwnd4HKVy4MMuXL2fHjh38+OOP7N27lz59+jj2hhxAj+g0Gk3quXsX+vVT+6NGEV2sGEycCD//DHnywI8/QrVqcOiQe/XU4OXlxQsvvMDixYt5+eWXnd7/tWvXKFmyJJGRkWzatImoqKj7r+XLlw+AI0eOABAcHMyJEyfYunUrW7dupWbNmgwbNox//vnHqTrpEZ1Go0k9n30GR49ChQrQty+YNzLatFHnWrWCf/6BF16AOXOgZUv36pvFadSoEVeuXCFv3rxO77tTp068++67lChRgs6dOzNmzBgaN258//Vx48bx4Ycf3h/d9ejRgwIFCjBw4ECCgoKwWCz0sz00OYuE6vek103Xo8uYemdV2RlV72S1P3rUMHLkMAwwjI0b429744ZhtGmjrgHDGDLEMKKiUi/byW3dKTuj6u1u2TZ0PTqNRuM6AgOV67JjR0goiCBvXli4UM3ZeXjAp59Co0Zw8WLa6qrJkrjMdSmEyAV8DxQBcgBjgFeA/wG2MJ6JUspVcdpNBaoBBhAopfzLVTpqNJpUsmIFrFwJPj7KiCWGxQLvvw+VK8Mbb0BIiEpBWLwYXnwxbfTVZElcOaJrBuySUtYG2gJTgDxAgJSyjrnFNXK1gSellP8DAoAZLtRPo8m8/PUXhSdMgP/+c52MO3ceBKCMHu14cnidOhAaCv/7H5w+DS+9BLNm6RQEjctwmaGTUv4kpfzMPCwBnAaSmvmsBywz24cBvkIIH1fpqNFkSi5ehCZNKDhnDjRsCFevukbO+PEqQfzZZ+Hdd5PX9rHHYONGFbgSGQm9e0P37sp4ajROxuVzdEKIrcAPwHuoEd0IIcRGIcQ8IUSBOJf7A/ZO+//McxqNxhEMA3r1ejD3tW8fNG0KERHOlXPkCEyYoPa//BI8UzAL4u2tksnnzYOcOVU0ZvXqcOyYc3XVZHksRhq4C4QQFYG5wAjggJTykBBiKOAvpexrd923wEop5XLz+A+gu5TysO2a3bt3G/Fl8ieXu3fvJriAqavbu6utlp35vy+fX37hsSFDiMmdm6PTp1N66FC8zp/nVs2ahM+YoYxLamUbBiV69SLPli1ca9mSc598kmq9s0tJ8cBAvE+dIsbHh6Off05MtWoOt0+NbGe2z0i/lcwi28bt27epXLmyJd4XEwrHTO1mtVorW63WEnbHYVartbDdcXmr1bopTpuRVqv1bbvjY1arNa/9NTq9IGPqnVVlp6ne4eGGkS+fCt+fPVu1/fdfw/DzU+def90woqNTL3vJEtVfvnyGcf586vW2cfWqYTRvbhhg3KpSJfntUyPbSe3T828lPDzcsFqtxp49ex46/9prrxlvv/22w3I2bdpkzJ8/P1my7Vm8eLHx6aefpqhtYrgrvaAW8D6AEKIIym0ZJIQoab5eB4ib/r4OaGO2eR44K6W86UIdNZrMgWFAz55w/bpyVXbvrs4LAb/+qqIif/5ZzYWlxosTEQHvvaf2x46FIkVSr7uN/PmV+9LDg1x79rhubjELk1iZHkepVasWHTp0cLZqLsWVK6PMAmYLIbYAOYF3gXvAYiFEBBABdAcQQixAuSi3CiF2m/N6sWYbjSZjcvw4RcaPh5EjoUwZ18qaNQvWrYMCBdSakhY7D87zz6sUgIYN1Wu+vg/m15LLJ5/AqVOqz969naO7PfnzQ82aWDZtUu/njTecLyMLk1iZnhUrVhAcHEy2bNl48sknGTNmDDNmzCBXrlz06NGDL7/8Ei8vLwoVKsThw4fp2LEjgwYNomTJkuzYsYOuXbsipeTvv/+mY8eOdOzYMd4+7Zk/fz4LFy4kd+7c1K9fnx49ehAWFsaoUaPw9vbG29ubqVOn4uOTuphElxk6KeUdID6zvy6ea9vZ7Q9xlU4aTZryyScUCA6GzZtVuZrHHnONnCNH4IMP1P5XX8Uf5v/SSypfrUULtVxXgQIweHDy5Eip1q8EFYBit5ivU2nSBDZtUotEZ0ZD16SJem9xSPlyzkDjxknnMfJwmZ7q1asTEhJCnz59+Omnn7h9+zZBQUH4+PjQsWNHpJS89dZbdOzYkZo1a7Jp0ybmz5//UNmdgwcP8uWXXxIaGkpgYCAhISHcu3ePvn370rFjx3j7tBEeHs7atWsZP3485cqVo3379jRq1IglS5bQvn17WrZsybZt27h48WL6NXQaTZbnt9/U35MnoUEDZfAKFXKujJgY6NYNbt+Gdu2gbduEr23cGObOVSuYDBmiRk9vv+2YHMNQqQBRUdCjh8qBcxVNmsCgQbBmDcTGQja9gJMzsZXp8fPze6hMT758+XjnnXcAOHr0KNeuXcPb25v+/fvToUMHZs2ahZeX10N9lSxZEl9fX3x9fSlQoABFihQhIiLivjs0vj5t7N+/n5MnT/Lxxx+TO3duIiIiOHPmDPXq1WPkyJGcOHGCxo0bU7Zs2VS/Z23oNBpXcPIkHDtGTN68eJQsCQcOKEMTEqKWw3IWU6ao0WLRomqUlRTt26t5vN691ZY/v2OjpsWLYf165fb89NPU650Y5coR+dhjeJ85A3/9BVWrulZeWpNAnZ7UlulxtE5PfGV6oqOjGT16NMuXL8fPz4+37R6ALl26hI+PD+fPn3+kL/sSPZ5xUkwiIyMT7BPU6LJOnTq0b9/+kfe9aNEifv/9d4YMGcKgQYOolsIIXBv6UUmjcQW//w7A7RdeUHNNpUurm3aLFmpdSGfwzz/w8cdqPyhIuSMdoVcvNddmGNCpkxo5JcatW6qgKqh2fn4p19kRLBZu2dbMTKh4mybFxFem586dO3h4eODn58e5c+f4559/iIqK4ubNm8yZM4eFCxcSFBSUrMCViIiIePu0UaFCBXbs2MG9e/cwDIOxY8dy9+5d5s2bx7Vr12jevDldu3bloKOF9hJBGzqNxhWYhi6ialUoVkyNhvz91fl27SA6OnX9R0ZCly7q75tvqtFichgyRM3rRUdD69bwxx8JXztmjFqqq3JlJSsNuFWrltrRhs4lNGrUiPLly98v05M3b15q1KhB69atmTFjBgEBAYwfP54pU6bQvXt3ChUqRKdOnZgyZYrDMnx9fePtM9r87RcrVowuXbrw0Ucf0bZtW/z8/MiRIwclS5YkMDCQrl27snLlSpo1a5b6N5xQ3kF63XQeXcbUO0vJjo01jOLFDQOMo8uWPTi/f79h+PqqHLQuXQwjJiblsocNU/2ULq1K4KRE79hYw+jZ80FOXJz8qrCwMMMICzMMT0/DsFgMY8eOJPV1WHYSHAwNNYycOZVuZ88mq22G+q04qW1Wlm1Dl+nRaNKSI0fUCKhQIe498cSD808/raLtcudWQSH9+6csp+2vv5QL0WKB779P+ZyfxQJff62Ko16/Dq+88nAFcMOAPn3UqO/NN9O0woCRIwfUq6cO4olQ1GiSgzZ0Go2zMd2W1K37aMRgtWqwbNmDdR5Hj05e33fuKJdlTIwylDYXX0rx8FBrTTZsCBcuqOjQ06cB8FmzRkWOFiigDGta06SJ+qvdl5pUog2dRuNsbGkFdevG/3r9+vDjj8oIjhwJ06Y53vdHH8G//0K5cjBuXKpVBSB7dliyRKUMnDqljN2xY6rMD6goy4IFnSMrOdjmHdevh3v30l6+JtOgDZ1G40wM48GIzoxoi5dWrdQqJaCW1Jo7N+m+N26Ezz9Xo7C5c8EJC+HeJ3duNXJ65hllSJ95Bq+LF5W7smdP58lJDiVLKn1u3YItW9yjgyZToA2dRuNMwsKUC7BYMbBaE7+2Rw+YPPnB/vLlCV978+aD9SuHDoUqVZyjrz2+vioVomxZuH0bw2KBmTPdm7Ct3ZcaJ6ANnUbjTOzdlpb4K4Y8xIABKhcuJkYlbttGg/Fdd+IEVKr0IHfOFfj7K1fhyy9zYeBAlVLgTrSh0zgBbeg0GmfiiNsyLqNHqwrd9+5B8+YqqtKeVatUQnj27MplGWcZJqdTujSEhHClWzfXynGEatXUSPPwYbVpUszp06cRQrB3796Hzrdq1YppyZknzoBoQ6fROIvYWDWPBgkHosSHxaIiMDt0UPNRjRopFyjgce0aBASo68aOhQoVnKtzesfTU30eoEd1TsAZZXoyInqtS43GWfz9t6qhVqqUGhUlh2zZVE7cjRuqpE6DBvDnnxQZMwbOn1fVB2zLcGU1GjdWUaqrVj2ohadJEYmV6dmxYwdTp07F09OTIkWKMH78eDp27MjkyZMpWbIk58+f55133uHnn39m2LBhhIeHEx0dTb9+/cifPz+dO3emevXqbN++natXrzJr1izCw8OZP38+FouFY8eO8corr9CnTx+OHDnC6NGjsVgsxMbG8uWXX5IzZ04GDhzIxYsXiYyMpG/fvtRKbfqMiR7RaTTOwjY/lxy3pT1eXrBwocqNO3sWXniBfGvWqIjI7793XVmc9E6jRmrUu2mTCsrJ4DRp0gSLxfLIVr58+XjPO7I1sc1lJoF9mR6AkJAQapvrio4YMYKpU6cyb9488uXLx4oVK2jRogWrzYT9kJAQmjRpwooVK/Dz8yM4OJgvv/yST+xyLPPkycOcOXOoVasW69apimz79u3j008/ZcGCBQQHBwMwZswYRo8ezZw5c6hYsSLz58/n0KFDXL16lfnz5zN79myuX7/utM9cGzqNxlnYJ4qnlJw5YcUKFXRy6ZI6N3my6wu3pmcKFVJzdVFRsGGDu7XJ8NjK9Bw+fPh+mZ5bt25hsVgoWrQoAFWqVOHgwYM0adLkvsHauHEjTZo0Yc+ePYSEhNC5c2cCAwO5d+/e/cWaq5jRwP7+/ty6dQuA8uXLkzNnTnLnzn1fh3379jFs2DA6d+7Mpk2buHz5MmXKlCEiIoKBAweyfft2h423I2jXpUbjDKKi1IgDUmfoAHx8YO1a6NyZq76++L71Vur1y+g0aQLbtqnlwF57zd3apIpVLirT4+gq//GV6bFYLBh2y9HFxsZisVjw9fXF39+fffv2ERsbi7+/P15eXvTq1YumTZs+Itu+bI+tv7jlewBy5szJ3LlzsVgsD73vhQsXEhoaytKlS/n9998ZP358Mj+F+NEjOo3GGezerQJJrFbnVBL384O1azk/fLhjaQqZHdvT/erVKVsfVHOf+Mr05M6dG4vFwtmzZwHYuXMnTz/9NAAtWrRg9OjRNDKDgp577jk2mCPry5cvJ6uigY2nnnqKzZs3A7Blyxa2bdvGgQMHWLFiBVWqVGHkyJEcPXo01e/Vhh7RaTTOICVpBRrHee459QBx5gzs3QvPP+9ujTI0jRo14sqVK/fL9ICaN3v//ffx9PSkePHi912HdevWZdiwYTRs2BCAV199le3bt9OuXTtiYmLo06dPsuUPHTqUYcOG8e233xIdHU3btm2xWCxMmTKFn376CQ8PD3o6cUUebeg0GmeQ1PqWmtRhsajoy2+/VdGX2tAlm+LFi/OpWR2+Tp061KlTB4CqVavi4+NDuXLl+PHHHx9pFxoaSt26dcmXLx+gXJHj4qyzevDgwfuBJgCdOnW6v1/VrkK8LQimbNmy/PDDD/fb5s+fH4DZs2en9m3Gi3ZdajSp5d49+PNPtW/ePDQuQK+SkuZMnz6dKVOmMGDAAHerkiq0odNoUsuOHap8zjPPQOHC7tYm81KvnipvtGMHXLzobm2yBP369WPhwoUUKVLE3aqkCm3oNJrUot2WaUOePGrEbBgqKlWjcRBt6DSa1KIDUdIO7b7UpABt6DSa1HD7tsrvslhSX+1bkzQ2Q/frrxAd7V5dNBkGbeg0mtSwdatKFq9USa2yr3EtZcuCEHDtmvrsNRoH0IZOo0kNqV3fUpN8tPtSk0y0odNoUoMOREkwD0XMAAAgAElEQVR7tKHTJBNt6DSalHLjBuzapWqm1azpbm2yDjVrQt68cOAAnDzpbm00GQCXrYwihMgFfA8UAXIAY4C/ge8ALyAK6CSlPG/XpjKwHDhintovpezrKh01mlSxZQvExMD//qduvJq0wdsbGjaExYvVqO6dd9ytkSad48oRXTNgl5SyNtAWmAKMBb4xzy0F4qbb5wEWSSnrmJs2cpr0i04rcB/afalJBi4b0Ukpf7I7LAGcBt4B7prnLgKV4jTTj8WajIOen3Mfr76q/v72m0rxyJXLvfpo0jUWw8UlL4QQW4HiQFMp5T7znAfwGzBaShlid2074AOUEcwNjJBS/m7f3+7du41cTvhR3717lxw5crilvbvaatnOk53t2jWsNWpgeHpyaMcOjAT6Tm96ZybZpV5/nZwHDhD+1VfcMqtkp5Xs9NY2K8u2cfv2bSpXrhx/TSvDMFy+Wa3WilardZ/VarVYrVYPq9X6g9VqHRHPdeWsVmtzc99qtVpPWa1Wb/trdu3aZTiDsLAwt7V3V1st24ltlywxDDCMOnVcJje17TO97OHD1XfQu3fay05nbbOybBumbYjXBrlsjk4IUVkIUQJASrkX5Sb1QwWjHJZSjorbRkp5UEr5i7l/CDgPOKGKpUbjZLTb0v3oYqwaB3FlMEot4H0AIUQRVKBJAyBSSjkivgZCiB5CiH7mvj8qYvOMC3XUaFKGDkRxP1WqqGoRJ09CWJi7tdGkY1xp6GYBhYUQW4BVwLvmVkkIsdHcZgIIIRYIIXKiIjEbCSE2o9IMekspI12oo0aTfP77T+Vw5coFL77obm2yLtmyPQhK0dGXmkRwZdTlHaBDnNMrEri2nbl7B2jsKp00GqewcaP6W7OmyunSuI8mTWDOHGXoBg1ytzaadIpeGUWjSS56fcv0Q8OGamWaP/+Eq1fdrY0mnaINnUaTXHQgSvohXz41so6JgXXr3K2NJp2iDZ1GkxzCw+HIEfDxUaV5NO5Hr5KiSQJt6DSa5GCLtqxVS7nMNO7HZujWrFEjO40mDtrQaTTJQacVpD+eegpKl4ZLl+Cvv9ytjSYdog2dRuMohqHn59IjFot2X2oSRRs6jcZRjh2DU6egQAF49ll3a6OxRxs6TSJoQ6fROIrNbVm3rkpW1qQf6tRRCfx79uB54YK7tdGkM/R/q0bjKNptmX7JkQPq1QMgz+bNblZGk97Qhk6jcQTD0IEo6R3TfZln0yY3K6JJb2hDp9E4wr//wvnz4O+vovw06Y/GavXA3Fu36lVSNA+hDZ1G4wj283OW+Gs7atxMiRLw0ktku3MHOnaE2Fh3a6RJJ2hDp9E4gp6fyxjMnUt0vnwqeXzUIyUvNVkUbeg0mqSIjdXzcxmFUqU4O3myioodPRpWrnS3Rpp0gDZ0Gk0SZD90CK5cgZIloUwZd6ujSYKI6tVh3Dh10KkTHD7sXoU0bkcbOo0mCXLv2KF29PxcxmHwYHjtNbh+HVq1glu33K2Rxo1oQ6fRJEGunTvVjnZbZhwsFvj+exUh+88/EBCgUkQ0WRJt6DSaxIiOJpdtoWAdiJKx8PGBJUsgTx746SeYOtXdGmnchDZ0Gk1ihIbicesWPPGECl/XZCzKlYM5c9T+oEGwcaNb1dG4B23oNJrE0NGWGZ9WrWDIEFWrrm1bOH3a3Rpp0hht6DSaxND5c5mDsWOhfn24eBFat4Z799ytkSYN0YZOo0mIpUsfGLo6ddyqiiaVeHjAjz+qFJGdO6FfP3drpElDtKHTaOJj4UJ4/XWIjuZSQIBa41KTsSlUSAWnZM8O33wDs2e7WyNNGqENnUYTl/nzoX17Nafz4Ydc7N/f3RppnEXlyjBrltp/912wRdRqMjXa0Gk09nz/PXTurJb9GjFCrbChk8QzF926Qe/eap6udWs1b6fJ1GhDp9HY+OYb6N5dJRaPGwcjR2ojl1n5/HOoVg3Cw6FdO4iOdrdGGheiDZ1GA/Dll/D222p/4kT46CP36qNxLd7esGgRFC6sAo70952p0YZOo5k6Ffr0UfvTpsEHH7hXH03a8Nhj8PPPKiJz4kS1r8mUeLqqYyFELuB7oAiQAxgD/A0EAx7AOaCzlPJenHZTgWqAAQRKKfVsscZ1TJigkokBvvoKevVyrz6atKVWLZg8Gd57T7mtK1TQ7upMiCtHdM2AXVLK2kBbYAowGvhSSvkScALoYd9ACFEbeFJK+T8gAJjhQv00WZ0xY5SRs1hUqHk6MHL37t3j5MmTbN++nWXLlvHvv/+6W6XMT79+0KEDRETAa6+R7eZNd2ukcTIuG9FJKX+yOywBnAbqALa7yXJgAPCV3XX1gGVm+zAhhK8QwkdKecNVemqyIIYBw4er1TKyZXsQaelCbt26xblz55Lcrly58kjbSpUqERAQQPv27cmfP79L9cySWCwqEGn/fti/n6LDhsHatXpkl4lwmaGzIYTYChQHmgIb7FyV54GicS73B3bbHf9nntOGTuMcDEON4j77TM3NBAernDkXsHXrVnr06MGZM2e45WA9NA8PD/z9/fH396dw4cL8+eefhIaG8s477/D+++/z+uuvExAQQM2aNbHoG7HzyJ1bJZNXqoTPunXw7bfw1lvu1krjJCyGAzWahBCPo0ZlAKellCeSI0QIURGYCxSRUhYxzz0BzJFS1rC77ltgpZRyuXn8B9BdSnm/RPDu3buNXLlyJUd8vNy9e5ccOXK4pb272mZ52dmzU3jCBArOnYvh6cmZSZO42bChS2TfuXOHZs2acfbsWQCyZ8+On5/f/a1QoUIPHds2X19fsmV7MKNw/fp1/vjjDxYtWsQOWwFYoFSpUrRu3ZoWLVpQqFAhp+ntrPYZVbbPypU8NmgQsdmzc3zhQiKffDLNZGfUz8zdsm3cvn2bypUrx//0ZxhGvJvVas1mtVoDrVartFqt261W62Jz226ee89qtXok0r6y1WotYXccZrVaj1mt1pzmcW2r1booTpuRVqv1bbvjY1arNa/9Nbt27TKcQVhYmNvau6ttlpb9zz+G8c47hgGG4eVlGMuXu1T2xx9/bACGEMK4cuWKERsbm+w+4so+cuSI8dFHHxlFixY1UMFahqenp/Haa68Zq1atMqKjo1Ott7PaZ2TZV1u2VL+TChUMIyIizWRn5M/MnbJtmLYhXnuUWDDKapRrs7KUspqUsrW5VQMqoQJZVibSvhbwPoAQogiQB9gAtDZfbw2sjdNmHdDGbPM8cFZKqWeGNakjNhb/kSNh5ky1zuGyZdC8ucvEHT58mM8++wyA4cOH4+vr6xQ3Y9myZRk3bhynTp1ixYoVtGjRAsMwWLp0KU2aNKFUqVIMHz6cEydOpFpWVub80KEgBBw4AAMGuFsdjRNIzNC9JaWcLKV8ZHJBShkhpZwCJObEngUUFkJsAVYB7wIjgK7muQLAHAAhxAIhRE4p5VZgtzmv94XZRqNJOdHR0KMHvosWQc6csGIFNG7sMnGGYdC3b18iIyPp3r07zz//vNNleHp60rRpU5YtW0Z4eDjjx4+nbNmynD59mjFjxlCmTBkaNmzIypUrOXXqFIYD0xOaBxi5c8OCBSqp/OuvVWK5JkOTWDBKtBDid6CFLepRCFEJ+AxoLaW8LqUMT6ixlPIO0CGelxrEc207u/0hjiqv0SRKSAgEBsKBA8TmzEm21atdXm5n6dKl/Prrr+TPn59PP/2Uy5cvu1Re0aJFGTJkCIMGDWLz5s0EBQWxaNEi1q9fz/r16xk0aBAFCxakUqVKD21lypR5aC5QE4eKFWHSJJV6EBAAVapAqVLu1kqTQhL7pX8FfG0f2i+lDAW+RY22NJr0yYkT0KaNKrR54ACUKcOpoCCXG7mIiAjee+89AMaNG0fhwoVdKs+ebNmyUadOHebNm8e5c+f44osvqF69OgULFuTy5cusX7+eCRMm8MYbb/Dkk0/i6+tLnTp1GDBgAPPmzSMsLIyYmJg00zdD0KePcnFfv67y7KKi3K2RJoUkNqIrKKVcEPeklPInIURvF+qk0aSM27dV2sCECXD3LuTKBR9/DP37c+f4cZeLHzt2LOHh4VSqVIm3betmugFfX1/69OlDvXr1eOqppwgPDyc0NPSh7dy5c2zatIlNmzbdb5crVy6ee+65+6O+UqVKUa5cObe9D7djscD//Z8a3W3bpqpZfPKJu7XSpIDEDF1i8Z6+zlZEo0kxhgGLF8P778OpU+pchw7K4BUvniYq/Pvvv0yePBmAmTNn4uHhkSZyk8JisVCyZElKlixJy5Yt758/d+4ce/bsecj4nTx5km3btrFt27b7bRs1akRAQABNmzbF29vbXW/DfRQsqOoT1q0Ln34K9eqpTZOhSMzQ7RFCfCClnGQ7IYTwBEYBG12tmEbjEPv3q3m4339XxxUrwhdfQM2aaaaCLQAlKiqKgIAAqlatmmayU0rRokUpWrQoje0Ccy5fvszevXsJDQ1l27ZtrFy5kjVr1rBmzRr8/Pzo2rUrPXv25KmnnnKj5m6gVi21ks7IkdCpE/z9t6p6oMkwJDZH9x5QTghxVAixUgixBjgGFAT08u4a93LlCvTtC88/r4xcwYKqcvSuXWlq5AAWLVrEhg0bKFCgAOPHj09T2c6kYMGC1KtXj4EDB7JkyRI2bdrE559/ToUKFbh48SKTJk2iXLlyvPTSS8yZM4eIiAh3q5x2fPyxMnjnz0PXrqowrybDkKChM1MIeqJy5kYAHwPPSCl7SSn1rKzGPcTEqJBvqxVmmGt+9+0Lhw6penJp7DK8efMm/fv3B2D8+PEJrlKSEcmfPz+BgYHs37+f7du3ExAQQO7cufnjjz/o1q0bxYoVo3fv3uzevTvzpzB4eCgXZoECah3MqVPdrZEmGSS61qUQ4kWgMxAspdyZNippNAnwxx/KqO3dq47r1lX14555xm0qjRkzhjNnzvDCCy/Qs2dPt+nhSiwWC1WrVqVq1apMmTKFhQsXEhQUxPbt25k1axazZs2iYsWKBAQE0KFDB3x9E5/Cj46O5uLFiwkubH358mWsViuDBg3CarWm0bt0gOLF4bvvoEUL+PBDNcJ74QV3a6VxgKQWdR4HtERVFHgk/02jSRPOnKHYwIGwapU6LllS1RBr3dqtK8yHhYUxdepULBZLugpAcSV58+alZ8+e9OzZkwMHDjB79mzmzp3L3r176dOnDx988AGtW7emYsWK/P777/EasgsXLhCbhOtvy5YtzJ49m1q1ahEQEEDr1q1xxhq3qaZ5c/Ww9cUX0K4d7NkDPj7u1kqTBEkZuv2o1Un2p4EuGs3DGIaqLtCvH/muX4ccOWDwYBg0SKUOuFU1g3fffZfo6Gh69epFlSpV3KqPO6hQoQJTpkxh/PjxLF++nKCgINavX8/8+fOZP39+om39/PzuB8TE3XLlysXs2bNZu3YtmzdvZvPmzfTt25eOHTsSEBDgktVmksVnn8GWLcqz0Ls3zJunS/qkcxI0dEIIDynlACFEYSnlhUSu0VmmGudz4YKac1u2DICbdeuS97vv4PHH3ayYYsGCBWzcuJFChQoxbtw4d6vjVrJnz07btm1p27Ytx48f57vvvmPr1q2ULVs2XkNWpEgRvLy8Eu2zdOnSfPfdd/z0008EBQWxc+dOZs6cycyZM91fny9HDrVEWOXK8MMP0KABdOuW9npoHCaxEd1qIcRHUsrd8b0ohKgMjAVedYlmmqzL0qXKyF28qNxC06dz+oUXKJdOjNyNGzd4//33AZgwYQIFChRws0bph9KlSzN69GgOHjyY6mRzHx8f3nzzTd588032799PUFAQwcHB6aM+nxAqGKp7d3j3XahWDbJa2kUGIjFD1xP4QghRDNiCqhBuoIqo1gLOkfiizhpN8rh2Ta0tGBysjuvVUytTlCwJBw+6Vzc7Ro4cyblz56hWrRrd9JN8mvDMM88wbdo0JkyYwNKlS5k9ezYhISHMnTuXuXPnYrVa6dmzJ127dk07pbp2hQ0bVDRmu3awfXvaydYki8TSC05LKV8D3gAOALlRpXbCgHZSypaJLeqs0SSL9etV9GRwsKoy8MUXsG6dMnLpiP379zN9+nSyZcvGl19+qRdGTmNy5MhB+/bt2bBhA0ePHmXo0KEUK1aMQ4cOMXjwYIoXL87gwYPTJsfPYoGvvoKyZVUS+aBBrpepSRFJBaNgVhP/zvWqaLIkEREwcKC6YYByAc2Zo/Lk0hm2AJSYmBjeffddKlWq5FJ54eFw7Vrmj+RMKWXKlGHs2LGMHDmStWvXEhQUxMqVK1mxYgWNGzdm5cqV5M2b17VK5M2r5uuqV4cvviCP1QruWB/03Dksd++mvdwMgn4c1biPP/+E555TRs7LSy2Yu2VLujRyAPPmzWPLli34+fkxduxYl8o6fVrdL5s1K8ORIy4VleGxr8+3f/9+ChcuzObNm2nUqBE3btxIuoPUUqWKWgcTKDZ0KBw75nqZ9mzaBKVKUbpNG/XgqHkEbeg0ac/duypN4KWX4OhRePZZ+OsvlYTrmaSTwS1cu3aNDz5QK99NnDjR5dF+U6eqe9bly57Urw9nzrhUXKahXLlyzJkzhxIlSrB161YaNGjA1atXXS/4vfegaVM8btxQ+Z23b7teJqgnorZtITKS7MeOafdpAiRp6IQQHkKI14UQ/c3j8ubizhpN8tmzRz0Bf/aZmuP48EPYuVON7NIxI0aM4MKFC9SoUYPOnTu7VNaVK/DNN2q/TJl7nDypItgvXXKp2EzD448/zubNmylVqhQ7d+6kfv36Li+AS7ZsEBxMZIkSKr+uVy+VB+pK7t1TdRcvXIAXXsDw9ISZM9V8t+YhHBnRfQNUA9qbxw2AOS7TSJM5iY6m4KxZ8OKLqhjqk0+qJb0++QSyZ3e3doly8OBBZsyYgYeHBzNnznR5AMrMmXDrFjRsCPPmnaRCBRV02rgx3LzpUtGZhlKlSrF582bKli1LaGgoL7/8MhcvXnSt0Pz5Of3FF2oxg+Bg9UW6ksBA2LFDBWytXs3Fd99V57t3VxHMmvs48h/7uJTyfSACQEo5DZVioNE4Rng41KhB4enTITpaVW7eswf+9z93a5YksbGxjB07ltjYWPr06cOzzz7rUnl37sD06Wp/8GDInz+GdeugdGnl3W3RQnl+NUlTokQJNm/ejBCCffv2UadOHc6fP+9SmfesVpg9Wx28956ah3YF//d/anHz7NlhyRIoVIjLPXtC1arKzx0Y6Bq5GRRHDJ23EMIHlUOHEMJK4kVZNZoH/P23iqTcuZMof3+Vd/TFF5A7t7s1c4i5c+eyZ88e/P39GTVqlMvlffedypOvUkWtWQ1QrJjyRvn7q4pE7dqp5wVN0hQrVoxNmzZRoUIFwsLCqF27NmdcPeHZrh0MGKC+pDZt4Nw55/a/axe8847a/+ortUILqPntOXNUes7cufdXFdI4Zug+BjYBVYQQ+4EV6Hp0GkdYv14FnJw9Cy+9xLElSzJUdeZdu3bdL8EzadIk8uXL51J50dEwySxzPGTIw8snli2r0gp9fWH5cujZU5dEc5QiRYrw+++/8+yzz3Lo0CFq167NKVslelcxYQLUrq3q173+OkRGOqffixehVSs1P9erl3JT2iPE/QhQ3npLzd9pHDJ0OaSUzwNPAXWllEJKucXFemkyOt9//2BSqW1bWLeOWHesS5hCtm/fTr169bh27RoNGjSgQ4cOLpf5889w/LiavmzZ8tHXn3kGVq9WU0Bz50L//q6Pd8gs+Pn58dtvv1GpUiWOHj1K7dq1OX78uOsEenrCTz/BY48p96W5ZFyqiI6G9u3VVEC1avD55/Ff16ePcgdcvJg2QTEZAEcMXX8hhI+U8qyUUsd9aRLHMGD0aPWkGR2tksF//FEthJtB+OOPP2jYsCE3btygTZs2TJo0yeXrKBqGGgSA+sgSqvhTrZrySHl7q7m80aNdqlamomDBgoSEhFC1alVOnDhB7dq1OeLKJMUiRWDxYvVlzZjxYGm7lDJ0KISEQOHCsGhRwkFc2bIpH3jevGrd2HnzUic3E+CIocsFhAshdgkhtgohtgkhtrpaMU0GJCoKAgJgxAjld5sxQ6URZKBlsjZu3EijRo24efMm7du358cff0xypX1nsG6dms7094ekshcaNFDPDtmywciRD4JXNEmTP39+1q1bR40aNQgPD6d27dr8+++/rhNYtaqakwblStyzJ2X9/Pyz+l/y8FD7jz2W+PWPP66KEoOqnxeetVdrdOQO1AOohFrzsjPQyfyr0Tzg5k1o1kxFg+XMqZ4kbeHOGYT169fTuHFjIiIi6NKlC8HBwXimUQK7bTT33nuODX5btYJvv1X7gYHKlalxDB8fH9auXUvt2rU5e/YsderU4cCBA64T+OabalL17l31xV25krz2YWEP5uImTVKVzR2hWzf1P3n9upKfhV2Yjhi6IcDgeDaNRnH2rPrn+/VXKFRIhQa2aOFurZLFmjVraNasGXfu3CEgIIDvvvsuzSqG79ypPjIfHzWl4ig9eqhC67b95ctdo19mJE+ePKxevZr69evz33//UadOHf7++2/XCLN5N6pUgRMnoEMHiHGwjOf16/Daa2qZnPbtk5c2YLGolQcKFlSBYbb1ZLMgjhi6lcAqc1sH3AZcvMyAJsNw4IDKh9u7F554ArZtU+6aDMQvv/xCy5YtuXfvHr179+brr79O06oEttFc796Q3MDOAQPU1E1MDLzxhjKYGsfIlSsXv/zyC40aNeLSpUu8/PLLhIaGukZYjhxqvq5QIfVAOGJE0m1iY1UpoEOHVCTSt98mv5K5vz/MmqX2Bw4kqy6cmuR/s5Ryud22SEr5HuDarNn0zKFD0K0b+RYvVstXZGU2boQaNeDUKRUlsXWrMnYZiMWLF9O6dWsiIyMJDAxM89I7Uiovr7d3ynN8x4xRaVX37kHz5iqxXOMYOXPmZNmyZTRr1owrV67w8ssvu25kV7KkqnSQLRuMG5f0EPzTT9U1+fOrH0lKc0/btFGjwdu3lTvT0dFkJsKRtS4bxtm6AmXTQLf0x/Xr0LQpzJlDsWHDoGhR5X/fsSPr+b9/+AFeeeWBayUkBPz83K1VsliwYAFvvPEG0dHRDBw4kKlTp6ZtlWrUlIthqAf3okVT1ofFouIdOnRQz16NGqlpHY1jZM+enUWLFtGqVSuuX79OQEAAf7pqRZN69R7kuXXurJ504uPXX+Hjj9WXO3++SqRMDTNmqB/Yn38+8HdnIRyZabcPPDGAG6iAlCQRQnwGvGTKGY9aL9N2NywAbJdSvmV3fWVgOWAbX++XUvZ1RJbLiY2FLl3g8GEoX57bOXKQKzQUgoLU9vTTKuKwUyflE8+s2OLgP/xQHffrB1OmJBwPn04JDg6mW7duxMbGMnToUMaMGZPmRu7sWRVEYrHAB6lcgiFbNpW6eP06rFqlIjPnzPFyS2m0jIi3tzcLFiygS5cuLFiwgFdeeYVVq1ZRu3Zt5wv74AM1MbtokQpO2b5dpQLYOH5cjcAMA0aNUvmoqaVAAbU0WePGMGwYvPqqcodmFQzDSHSzWq3d4zkX6EC7ulardbW5X9BqtZ6K8/r/Wa3WF+Ocq221Wj9PrN9du3YZziAsLCx5DcaMMQwwDF9fwzh6VLU/eNAwPvjAMPz81GtgGN7ehvHGG4axfr1hxMQ4R7aT2qZa9r59htG794P3OnmyYcTGpo1sJ77v2bNnGxaLxQCMUaNGuVR2Ym0HDlQfY5s2zpN9+7Zh1Kql+i1R4p4RHp6s5qmS7ay27pQdFRVlNG/e3ACMnDlzGhs2bHCN7Bs3DKN8+Qc/gNhY1TYiwjAqVlTnmzZN8B6SYtlvvaX6rljRMO7dS77eqZHtgrb2mLYhXruRoOtSCFFPCDEeGCGE+MRumwI4UvRoM/C6uX8VyC2E8DD7FkB+KeXOOG1cXA44haxZA8OHq0fvH36AMmXU+aeegokTVU2oRYuUzygqSq2I0KCBcjeMHatez+hERFC8Xz8VuZU9OyxcqCIh0ngUlFpmzZpFz549MQyD8ePHM3z4cLfoce3agxiBwU6MYc6ZE1asgEqVIDzcm2rVYN8+5/Wf2fH09GTcuHH06NGDO3fu0LRpU9auXet8QXnzqsWY8+ZV947Jk9XjY69eKrCrbFmVYO7s+eJJk9QK4Xv3qsndLEJin+IuYANwFzhqt+0HGiXVsZQyRkppK3cbAKyWUtpmQQOBL+JplgeoKYRYI4TYLISo69jbcCFHj6rJD9uKH43ieeve3qrY4po1Knx41Cg18XzihHITPP64mttbtkwZwozEP/+otaZKliTvxo3KBbJhg1q/L4Mxffp0evfuDcDkyZMZMmSI23SZNUulHr78soo6dyY+PiqavFKl25w5o5YbDQlxrozMjIeHB99++y29evXi7t27tGjRghUrVjhfkBAPEiAHD6bosGHKuOXKpYJPXLFkXt68auFniwXGj1cu1CyAxXAgiEIIkRPwNQ+zA19KKR1yHAshWgAfAQ2llNeFEN7ALinlI5GbQohywJNSyl/MKgkbgCeklPdXRN29e7eRK1cuR0Qnyt27d8mRRGau5c4dSnXoQA4puVm3rqo1ZT5hJdk+Jobc27eTf9Ei8oaEYDGXm48uWJCrdesS9dxz3C1XjntPPKEMpRP1Tm17S0QEPmvXkn/RInLZRaDdLleOcxMnEmkb0bpAtivaAnzzzTd8bq4NOHToUDp27JgmsuNre++ehfr1n+DyZU++/fYUNWpEJNA6dbKvX7/HqFFlWLvWB09PgzFjztGixfVU6Z4WbdOLbNuof968eXh6ejJ58mQaNGjgdNl+06ZR6Ouv7x+fmTSJGymYl0uO7MKffUbB77/nXunSHF+8mDuQYb8vG7dv36Zy5crxu5gS8mnaNqvV+qHVaj1htVpvWK3Wg1ar9brVamvmQFEAACAASURBVJ2SVDuz7StWq3Wn1WotYHeugdVqDXKw/U6r1Vra/lyazdHFxhpGx47Kn/3kk4Zx7Vry2ttz4YKazypX7sH8lm3z8jKMSpUMIyDAMGbONIzt29VES0r1ToIE28fGGsa2bUqPPHke6OfjYxi9ehnGrl1G2IEDrpHt4rbjxo0zUIFUxtdff52msuNrO2uW+miffz7pKc7Uyo6JUdPItq9zzBjHp1Wz4hxd3PaxsbHG+++/bwCGh4eHsWDBAufLjo42jFdeUV/QgAHJb58S2XfuPJgjDAzM0N+XjcTm6ByJumwBlAZ+k1LWFUK8CDRPqpEQIh8wEagvpbRf8+YFIN5EFSFEDyCPlHK6EMIfKAK4uHhUAsyYocJ6c+dWboTUlGjx81PzWf37w86d/LdoEUXOnIHQUJWXFxqqNhseHlCunJposW0VKz4cmeUsLl1Si74GBankbxs1a6oo0jZtHuTvHDzofPkuJDY2liFDhjBx4kQsFguzZ8+me9yyJmlMTMyDUjyDB7t+ijNbNjWN/PjjKkB22DA4eVIVv06DJTwzPBaLhYkTJ+Lt7c348ePp0KEDUVFRdOrkUOC5Y3h4wIoVHF+6lNJpNSWQI4dym1atCtOmkatiRTJziK4jhi5GSmkIIbyEEDmklDuFEJMcaPcGUAhYqGJPAOgCFEXN9d1HCLEA6A4sBeYLIdqgXKS97d2WacaWLcowgVoFvEIF5/RrsUDVqlzx8aGI7Ud186Zazddm7EJDVRLUP/+ozX4RQ6uVx0qXhvLlVU5M3C1/fsfunLGx8NtvyrgtXfqgVpafn0ro6tlTBdpkYO7evUu3bt346aef7gcYuNvIgYo/OHJExTO1bp12cvv0geLFVdR6UJCKj/r5Z8iTJ+10yKhYLBbGjRtH9uzZGTlyJF26dCEqKsq5vycvL+4+80zaBndVrqxy9UaNotigQZCKoBt/i+XR+5K/v6q0kEbrxSaGIxosE0L0B34E9gghzgP3kmokpfwG+Caelx7Ji5NStjN37wBOSBpJBWfPqkCL6GiV7+LqJ6y8edXoqWbNB+fu3IH9+x8Yvt271fGhQ/gcOqSSSeMje/ZHf2j2x4UKUXD+fPjlFxUoA+ofq1EjNXpr1ixZ84XplStXrvDaa6+xefNm8ubNy+LFiylevLi71cIwHuQKf/BB2v//t2yplghr1kzd02rXVjl3/v5pq0dGxGKxMGLECLy8vBg6dCg9evQgKiqKt956K+nG6ZmhQ2HlSrx271bR4inEN6EXsmVTD9DxPZibW1qsROTIv9qvUsp9AEKINahR2m6XauUuIiOVq+6//1ThwvHj3aNHzpzw4otqs9ctLIwzv/7KY9mywblzj243byoDZjNi8VDYtlOypBq5deum9jMJJ06coHHjxhw8eJBixYqxevVqnnvuOQ6mA7drSIh6bilcWH3s7qBaNbUc6auvKl2qVVPBwpnYa+VUPvroI7y9vRk4cCBvv/02kZGR9OnTx91qpRwvL/j1V07PmUPxYsVS1kdsLOfDwvA3jIfvR2fPquKv//2ntr17423+RJ48cOaMChd2EY4YumlCiPpmusAx4JjLtHE3/furu0CJEurpJh0Mue/j7Q0VK3Ije3YeS+iuFBERvwG0bf/9x42iRfEZMEAtRZTBVjNJitDQUJo0acL58+d5+umnWb16NSVKlHC3WvexLd4cGKieZdzFE0+oZUmbN1eLclSvrpZUdLT6S1bngw8+wNvbm8DAQPr27UtkZCQD/r+9M4+zsfof+HsMxpbd15rdPZahECWJZClLqyJZfhWFshSlUklZWuzZI0lpl2RokchS2QnjhKGsQ3bGDDNzf398nsvQzJ07d5m7nffr9bzmLs/n+XzuM+d5Ps8553M+H8dURzBSrBhn777bo6edk7GxlEpP/tIlcXJO7ksJBQtynY8vCFfu5GcArZTaDFyeL9Nad/aZVf5gzhyZoc+dW7KMB1neRkCCRqpWdZpY+WBsLAVD8PF9yZIlPPTQQ5w/f57mzZvz9ddfU9gX65DcZMMGWX5YoIBUKfA3JUpID/PRR2V5p6QMg06dMpc1QL9+/cidOze9e/dm4MCBXLx40a/rMgOWXLlkctjJ1MGB2Fhq+DgyyhVHF/r1izduvFIIbPJkaNDAv/YYssT7779P7969SUlJoUuXLsyaNYvcATbX6OjNPfUUFMlwQiN7yZdPknI8+6wkhX7kESlEPWhQ0CW88Qu9evUid+7c9OjRg5deeomLFy/yUBAmUggHXJkFXI4kYK6jtf4ZOAys8KVR2crx45JYNTFRKhH06OFviwwuYrfbefXVV3nyySdJSUlhyJAhfPTRRwHn5HbvlkGCXLnEqQQSkZEwYcKVJQ8vvAB9+4ZlJRe3ePzxx5kzZw45cuRg6NChTJ482d8mGdLBWa7LFtbLGcAtSOUBgJbAHB/blT2kpMhj7N9/S+DHe+llJTMEIhcvXqR79+4MHz6cyMhIpk+fzvDhw7O9AoErjB4tKzq6dIGyZf1tzX+JiICBA2VaOnduGdR48EG4cCHwzmUg0rVrV+bNm0dkZCRTpkxhd5gWNw1knPXonlJK1QQqaK0HAucBtNYTAP/HanuDV1+VpIAlSsgYTlSUvy0yuMDp06dp06YNc+fOJX/+/CxcuDBgw7yPHYvkww/FmTz/vL+tcc7DD8s8YpEiEpzSu/f1l5dYGpzTsWNHunXrht1uZ8KECf42x3ANzhzdw8AlILdSqiCSQgkrB6Xnicn8zHVLl8rygchIeZQNoOg8Q8bs37+f2267jZ9//pmSJUuyYsUK2nijXpePmDu3KElJcO+9wRHC36SJRGSWKQNr1+bnySfDr6awuzxrjUt/8MEHnDx50s/WGNKSoaPTWtu11ruAV5E5uZuUUtuA74AAfzbNhJ07Ke0oHPr227JmzhDwbN26lUaNGrFt2zaqV6/Ob7/9Rv369f1tVoacOQOffy6RJ94sxeNrqleXUj9586YyZw6MHOlvi1wnORkSE/0z5Fq7dm1uvfVWEhISmDEjvVwZBn+RaTCK1nqF1rouUB1oqrVWWutffW+aD3nqKSLPn5exmmBe/xJGrFmzhttuu42DBw/SpEkTVq9eTaVKlfxtllPeew/Ono3k9ttlYXYwUa8evPPOQSIiJEuUB0kzso2VK6XyTePGNp5/XpZvZTfdu3cHpCTURTPuGzBk6uiUUl2VUv8gJXNWKaXilFIP+940H3LLLZy94w4pLR+AwQuGKyQlJTFhwgR69erF2bNnefjhh/nxxx8pWrSov01zyj//XOkJ+am2q8fceec5xoyR1927Sy6FQCQxUZZENG0KcXFw4UIORo+GihVlcf7BbEwLf9ttt1GzZk0OHTrEl19+mX2KDU5xZXnBQKC+1rqm1roG0AgY4luzfMzbb3Ng8mST0TaAuXDhAu+99x5Vq1ZlwIABJCcnM2jQID799FOv1K7yNc89BwkJ0Lr1Ge6809/WuM+AAbLA3THPGBdgeZHWr5fe55gxklbxlVdg3rx93HuvOMCJEyWBdu/eElztayIiIi7P1Y0dOxa7meAMCFxxdIe01sccb7TW8VxTfcBg8Bbnzp1j9OjRVKpUiX79+nHgwAGio6MZP3487777brYkgPWUH36QdXP588PgwX4YP/MiERHiLFq3lrSFbdvCqVP+tkoyS73+ugwJx8bKkOWaNfDmm3DjjRdYsEBSKz70kOw7bZokDHriCVnX6EseffRRSpQowcaNG/n11+Ce5QkVXLlrnFRKbVRKjVVKjVdKrQVSlVIjlVJBNE1tCGTOnDnDyJEjqVixIs8//zzx8fHUrVuX+fPns2XLFlq1auVvE10iKUlK4gAMHQqlSiX71yAvkDMnfPEFREfDzp2S9/zSJf/Zs2MHNGoEw4bJUtj+/SW5Udoc6AA33CB2b9smqc5SU+GDD8Qpdu0qv8UX5M2blz59+gDSqzP4H1cc3TJgMrAd+BOYDixBenWmZ2fwiJMnT/L6669ToUIFhgwZwvHjx7n55ptZtGgRGzZs4P777w+KXpyD0aOlx1CjhtyAQ4WCBWHRIihZUnJk9umT/csOUlJkiLJePckdWqGClFUcP17SmWVEzZpSW3jnTnjsMRni/Phj+bxjR6mA5W169+5NVFQU3333HX/99Zf3FRiyhCt3kM+QtF8JSL24C8AFrfUsrfUsXxpnCF2OHTvGyy+/TIUKFRg2bBinTp2iSZMm/Pjjj/z222+0bds2ILOcOGPfPhgxQl5PnhwSpf2uokIFKWWYJ48Ub3333ezTHRcnq4AGDZJe8+OPw9atWVsZVK2a9Oh27ZKco46eap06cP/94jy9RcmSJenSpYtZQB4guOLoVgCPAg2BBtZ2ky+NMoQuR44cYdCgQVSsWJFRo0Zx9uxZWrRowYoVK/j1119p2bJl0Dk4BwMGSM3cRx4J3aWZDRvC3LnyevBgqZjuS+x2mD5dnNHKldKjXLhQAqbdLV9WsaLM2cXFSV7PPHmkgsNNN8kc5MGD3smk7whKmT17NidOnPDKMQ3u4dIcndb6Ua31s2k2s/jMkCUOHDjAiBEjqFSpEmPGjCEhIYE2bdqwZs0afvrpJ24P8mJoMTGSNqtAgSsJkkOVDh2uVErv0gXWrfONnoMHoU0bKSziWPa6fbtUSPcG5cpJoM3evdJTzJcPFi+Gnj2vxxt+qVatWtx1111cuHCB6dOne35Ag9u44ug+VEqNU0p1U0p1dmw+t8wQMsycOZOqVavyySefkJiYyH333cf69euJiYmhUaNG/jbPYy5cgH795PWwYZI+K9R54QWJYLxwQRzPP/9479h2OyxaVJDoaPj+e8m9+emnsmi9WDHv6XFQqpQMw+7bJwEs+/ZF8eCDeCXPp6Mg63vvvWcWkPsRVxzdIKA0UBczdGnIAhcuXOCJJ56gZ8+eJCUl0apVK7Zs2cI333wT0Km7sso778gwWHS0DIWFAxERMHUqNG8uGUjatpWUZ56QlCQ51jt0gBdeKMupU3D33RI1mR0FYUuUkICbEiUusXw5Xsnz2aJFC6Kjozl8+DCfB0N6mRDFlcKrJ7TWpu6wIUvs27ePBx98kI0bN5InTx6mT59OgwYNqBEMmY2zQFyc5AYHCUDxcaHkgCJXLin6ceut4ow6dpQcmTlduatYHDokw4UxMVI54dw5+TxfvhTGj4+kR4/sTV5UrhxMmXKA7t0rMWcO2Gzw8svuHy8iIoLnnnuOxx9/nLFjx9KlS5egnYMOZlzp0a1VSr2ulGqjlGrl2HxumSFo+f7776lfvz4bN26kcuXK/Pbbb3Tr1s3fZvmE/v2lJ9KlCwT5NKNbFCkiTqp4cRlm7NfPeS8oJQV+/10qZNWrJ/X5evaUYJBz56B2bXjxRfj227307OmfDH21aiUyb57oHjJEIjM9oXPnzpQsWZLNmzezfPlyr9hoyBquOLpyQBWk8GpXa+viS6MMwUlqaipvvvkmbdq04cSJE7Rt25b169dz4403+ts0n7BwoQx1FSyYvaH2gUblyhKIExUlw5nXRtOfOiXza926yXxYo0YwfDhs2gR580K7diL399+yZGDUKChb1o8r0pF0Z46gom7dxDm7S1RUFE8//TRgFpD7i0wHGbTWXZVSuYBSWuv92WCTIQg5efIkXbt2JSYmhoiICN58801efvnloFrsnRUSEq4EoLz5ptzAw5lbb4XZs6FzZ8nzOXRoYfLmld7e6tXSk3NQsaLM6bVtC82aibMLRJ59Fv76S5Y33HMP/PEHuFswo1evXowcOZJFixahtUYp5V1jDU5xpXpBB2AT8L31fryJujSkZfPmzdx0003ExMRQtGhRlixZwiuvvBKyTg4kvP7vvyVKz8r2FPY88gi88YYMXb7+emkGD4Zff5UhwGbNpNe7Y4fMa06aJIEmgerkQOx+7z1o1crzPJ8lSpS4PHw/fvx4L1ppcAVX7kT9kSjLo9b7l4F+PrPIEFR89NFHNGrUiLi4OOrVq8eGDRto3bq1v83yKbt2Sb1ekACUrARfhDqvvAJPPw2lSl2ie3eZ3/r3X/jlF1mrVqNGcFXGypVLfkOtWpI82pEk2h0GDBgAwJw5c/j333+9aKUhM1xxdCla60TAMcV8wYf2GIKEpKQk+vTpQ/fu3UlMTOSJJ55g9erVVKxY0d+m+RS7XZYQXLwI//d/0Lixvy0KLCIipLe2bNluPvxQHEOhQv62yjMKFZK52P/9TyJDn37avWUHNWrUoE2bNmYBuR9wxdH9rpSaDZRVSg0EfkESPRvClAMHDtC0aVOmTp1KVFQU77//PjNnzgyKOnGesmCBlOEpXPhKr84Q+lSseCXP5/vvc7kgbVZxLCCfNGkSSUlJ3jPQ4JRMHZ3W+kVgjrVFAK9qrT1YWWIIZpYtW0a9evX4448/KF++PKtWraJHjx7+NitbOH/+SkWCESPkCd8QPtx885U8ny+8AN98k/VjNG/enDp16nDkyBE+++wz7xpoyJAMZxeUUl9prTsAaK2XA8uzenCl1DtAE0vPKOAOpEK5tSyUd7XWMdfIjANuQYZK+2utfZRJz5AV7HY7s2bNYty4caSmptKqVSs++eQTihcv7m/Tso0RI2D/fqhbV7LfG8KPDh1k+cNLL0mNuxUroEED1+UdC8j/7//+j7Fjx9KtWzezgDwbcNajK+rJgZVSdwDRWutGwF3AeKAA0ENr3czarnVyTYFqlkwPYJInNhg8x263s3TpUpo2bcqYMWNITU3llVdeYfHixWHl5LS+sq5qyhSIjPSvPQb/MXjwlTyf99yT9TyfnTp1olSpUmzdupVly8wsUHbgLF6sqtUjSxet9QuZHPtXYK31+iSQHyicicydwALr+DuUUkWUUgW11h5m0TNkFbvdzpIlS3jzzTf53VotW6hQIebOnUt7b6WPDxLsdqkafukS9OgBt9zib4sM/iQiQh529u6Vwq/t2sGqVa6XDYqKiuKZZ57hlVdeYezYsdx5552+NdhAhD2D8CGlVCzwVkaCWus5ripRSj2JDGGWQKI2iwAHgH5a6xNp9psBxGitv7XerwSe0FpfLtG7YcMGez5n5YRdJDEx0aPgCU/k/SXrinxqairLli1j+vTpbN++HYAiRYrQvXt3HnjgAY96cYH8u53x3Xd5GDy4EgULprBkyR6KFEnJXMgLej2VN7p9q/v06Rw8+mhF4uKiuO22c0yZsp/kZNdkT506RfPmzUlMTOS7776jSpUqYXHOfKHbQUJCAvXr109/HNhut6e72Wy2XzL6LiubzWa712az/WGz2QrZbLb7bTabzfp8iM1me++afd+32Wz3pnm/ymazVUu7z/r16+3eYMeOHX6T95esM/nk5GT7559/bq9du7YdmR+1lyxZ0j569Gj7uXPnfKrb17KeyJ89a7eXLHnRDnb7tGnZp9cb8ka37+X37LHbixe328Fu79PHbt++3XXZ3r172wF7z549s6w3PYLlnHlbtwPLN6Trh5zN0XlcWF4p1RoYAtyttT6ttf4mTe/sG6DONSIHgbTJlMoARzy1w5AxycnJzJ07l+joaDp27Miff/5J2bJlmThxInv37mXgwIHkz5/f32b6hYQEWeQcH5+LBg1k2NJgSEvaPJ9TpsDHHxdxWdaxgPyjjz7i2LFjvjLRgJNgFK31IE8OrJQqBLwLtHMMTyqlFiqlylu7NAO2XSP2I9DB2rcucEhrfdYTOwzpc/HiRWbNmkX16tXp1q0bO3fupGLFikybNo09e/bQt29f8gZyfiYfsnGjpPUqXVryHEZE2Jk82QSgGNLHkecTYNy4/+Hq8jibzUb79u1JSkpi6tSpvjPQ4NKCcXfpCBQHvlBKLVdKLQe+Ar62XrcFhgEopT5TSuXVWq8BNiil1gDvAU/70L6w5OLFi0ydOpVq1arRo0cP9uzZQ9WqVfnggw/466+/eOqpp4iKivK3mdnOqVPyRF6vHtSvL9n0z5yBhg1hwoSDWQohN4QfjzwC1atDYmIOtl37+O4ExwLyyZMnmwXkPsRnWfq01jOAGel89VE6+3ZK8/pFX9kUbFy6dIn4+HgOHz58eYuNjaVEiRJuHS8hIYGZM2dy9KikLa1RowZDhgyhY8eO5AzDhI12O6xcCTNnwpdfQmKifF60KHTtKiHktWtDbKwZVDBkzk03wc6dsG6dPCy5QtOmTalbty6bNm1i0aJFIVvS6louXbpEbGwsGzdu5OzZsz4vyBx+d7cAIDExkbi4uKscWHrbv//+i92dpHqZUKdOHV599VUeeOCBkK4wkBHx8TBnjji4XbuufH7nnTIPd999kurJYMgKDRrAxx/D+vWuyzgWkHft2pU5c+YwZMiQkFtAnpiYyLZt29i4cePlbevWrVf1YDt37kyxYsV8ZoNxdNnA+fPnWbZsGTExMSxZsoR/XFxhGhERQcmSJSlduvTlLUeOHPzPg9xTZcuWpVevXmHn4FJSYPFicW7ffQfJyfJ5mTLw2GPw+OMSWGAwuMtNN8nfdVnM5fTwww8zePBgdu/eTUxMDO3atfO+cdnE+fPn2bJly1VObfv27SQ7Lrg0VK5cmXr16nHjjTdStKhH+UkyxTg6H7F3715iYmKIiYnhl19+uerpJWfOnJQpU+YqB5beVqJEif8MKcbGxnrUzY+NjQ1pJ2e3w/HjcPjwlW3nTvjww6ocseJ3IyOlgnSPHnDXXabMjsE73HgjREba2b49goQEcHW5b+7cuRk4cCADBw6kX79+NG/eHG+sFc4OLly4wOzZs1myZAl79uxBa01qaupV+0RERFCjRg3q1atHvXr1qFu3LjfeeCNFikiEamxsrM97seYS9xKXLl1i1apVl53bzp07L38XERFBw4YNadu2LW3btiVPnjzUqlXLj9YGH8nJcOjQ1Q4sve3IkYzqheWiShVxbt27S0SlweBN8uWDqlWT0DoPmzdLNKar9O3blxkzZqC1ZujQobz77ru+M9QL2O125s+fz8CBA/n7778vf54zZ05q16592anVq1ePOnXqUKBAAT9aaxydR8THx7NkyRJiYmL48ccfOXPmSqayggUL0rp1a9q2bcvdd9991XBjbGysP8wNSux26NcPpkypzjUPihlSuLA4stKloVQpKFsWatX6m27dKgRV0U9D8BEdnYjWeVi/PmuOLleuXLz55pt06tSJsWPH0qlTJ+q7GtGSzWzbto3+/ftfztN5ww03cN9999GuXTuio6MDslyXcXSZYLfbOXHiBEeOHLkcJLJ7926++eYb/vzzz6v2rVGjxuVeW+PGjcmVK5efrA4dvvpKCnlGREhZHIcDy2grVQrSW/4XG5tgnJzB59SqdYGvvy6c5Xk6gOjoaJ599lnGjBlDjx49WLt2bUDdQ06ePMnrr7/O5MmTSUlJoWjRoowYMYKePXvy119/+Txy0hPC1tGlpKRkGvV4+PBhjhw5wsWLF9M9RlRUFHfcccdl51apUqVs/hWhzYkTkkwZYOjQIwwdasYbDYFNdLSsUclK5GVahg0bxvz589m8eTNjx45l8ODBXrTOPVJSUpg1axZDhgzh33//JUeOHDz99NO88cYbPg8i8RZh6ej69OnD9OnT/zNpmhEFCxa8KkikbNmyVK5cme7duwfNpHEw8vzzcPQo3H47dOhwCjCOzhDY2GxJ5M4tZZ3OnHG9ooGD/PnzM336dFq1asXrr7/OAw88QLVq1XxjrAusXr2avn37smnTJkDW/U2cOJE6da7N3hjYhKWji4+PJzU1leLFi2ca+Vi6dOl0nVlsbKxxcj7k55/hgw8kh+CMGbg8P2cw+JPcue3UqSM9uo0boVmzrB+jZcuWdO/enTlz5vDkk0+ybNmybF9bd/DgQQYPHswnn3wCQLly5RgzZgwPPfRQUK7zC0tH9/XXX/Pnn39Su3Ztf5tiSIeEhCsVvF97DZQCE79jCBYaNBBHt369e44OYMyYMSxevJjly5fzwQcf8MQTT3jVxoxISkpi3LhxDB8+nPPnzxMVFcULL7zA4MGDgzq5e+guqMqEcEx5FSwMGwZ79kj6reef97c1BkPWcHfheFqKFSvGxIkTARg0aBCHDx/2gmUZY7fbWbRoEbVq1eKll17i/Pnz3H///cTGxvLGG28EtZODMHZ0hsBk40YYMwZy5JAsJgEUdGYwuIQjAbi7ASkOOnbsSJs2bTh16hT9+vXz3LAMiIuLo1evXrRv3549e/ZQo0YNfvzxR+bPnx8yAXbG0RkChuRkWdCdkgL9+0vlAIMh2KhRQ5a4xMVJlh53iYiIYOrUqeTPn5+vvvqKBQsWeM9IizVr1tCwYUNWrlxJwYIFGTduHFu2bKFly5Ze1+VPjKMzBAzjxsGmTVChArzxhr+tMRjcI2dOKfcEsMHD8tXly5dn1KhRADz99NOcPn3aQ+uuMH/+fO68806OHz9OkyZN2LVrFwMGDAiotXvewjg6Q0Cwe7cEngBMmwZ+zhhkMHiEN+bpHPTp04dbbrmFQ4cO8eKL3qliNmHCBDp06EBiYiI9e/Zk8uTJHiWLD3SMozP4HbtdoiwTE6FLF0m0bDAEM96apwOIjIxk5syZ5MqVi2nTprFy5Uq3j5Wamspzzz3HgAEDsNvtjBgxgunTp4d8cJ5xdAa/8+GHsGwZFC8uw5cGQ7DjzR4dcDkaEqBnz54kOqoEZ4HExEQ6duzIuHHjyJUrF3PnzuXll18OynVxWcU4OoNfOXIEBg6U1+PHi7MzGIKdatUkK8rBg1JVwxu8/PLLVK9eHa01I0aMyJLs8ePHadGiBV999RUFCxbk+++/p0uXLt4xLAgwjs7gV/r3h5MnZbiyc2d/W2MweIccOcBRfMAbw5cguXVnzpwJwFtvvfWfpPIZERcXR+PGjVm9ejXlypVj1apVNG/e3DtGBQnG0Rn8xsKFdjQ8JQAAIABJREFU8MUXkD+/BKCEwQiKIYxwDF96y9EBNG7cmD59+pCcnEyPHj1ISUlxuv+6deto1KgRWmvq1KnD77//HpYZoYyjM/iFM2egTx95PWKELCkwGEIJbwakpGXUqFGULVuWtWvXMmnSpAz3W7RoEc2aNePo0aO0bNmSlStXUrZsWe8aEyQYR2fwCy+9JPMXDRteKcVjMIQSaQNS7HbvHbdgwYJMmTIFgCFDhrBv377/7DNt2jTuvfdeEhIS6N69OzExMRTMaimFEMI4OkO2s3o1TJkiC2tnzoTISH9bZDB4n4oVoVgxOHYM9u/37rHvueceHn74Yc6fP0+vXr2wW540NTWVl156id69e5Oamsprr73G7NmzQ3IReFYwjs6QrSQlSZovgBdflMTNBkMoEhHh/WUGaZk4cSJFihThhx9+YN68eVy8eJGuXbvy1ltvXV57N2zYsLBYPpAZxtEZspWRI2HnTim9M2SIv60xGHyLr+bpAEqWLMno0aMB6N+/Pz179mTevHkUKFCAmJiYbCvtEwwYR2fINnbtisJK28f770OePP61x2DwNb7s0QE89thjNG/enOPHj7Nu3TpKly7Nr7/+SuvWrX2jMEgxjs6QLaSkwKuvlubSJejVC5o08bdFBoPvSduj82ZAioOIiAimT59OiRIlUErx22+/UbduXe8rCnJCO8GZIWCYMgW2bs1LmTLw1lv+tsZgyB7KlIHSpSU7yu7dkjHF21StWpW///6bvXv3UsGs00kXnzo6pdQ7QBNLzyhgHTAbyAVcArporY+k2b8+8C2w2/roT611X1/aaPA9u3fLcgKAyZOhUCH/2mMwZCcNGkhyhPXrfePoAPLmzWuCTpzgs6FLpdQdQLTWuhFwFzAeGA7M0Fo3Bb4BnrtGrADwlda6mbUZJxfknDwJbdvC+fPQqtUZ7rvP3xYZDNmLr+fpDJnjyx7dr8Ba6/VJID/QB3Ck3T4G1LtG5jof2mPIZi5ehAcegL/+kmUEI0YcBsJ30aohPPFl5KXBNXzm6LTWKcB5620PYLHW+jyAUioSeBq4to50AeA2pdQSxDEO1Vr/4isbDb7Dbpegk+XLoVQpWLQIzp9P9bdZBkO240juvHGjBGWZBAnZT4TdF6FAaVBK3Qu8DLTSWp+2nNxcQGuth12zbw2gmtZ6oVLKBiwFqmqtLzr22bBhgz1fvnwe25WYmEgeD+LbPZH3l2x26p4xoxjjx/+PPHlS+eijv4mOTgyL3+1NWaM7dHS3aFGFQ4dy8+23e6hW7WI6koFpdzDodpCQkED9+vXTn6i02+0+22w2W2ubzbbWZrMVTfPZRzabbZiL8mttNlultJ+tX7/e7g127NjhN3l/yWaX7i++sNvBbo+IsNvnz89e3b6QD0e7jW7vynboINfEhx/6Rq+n8sGs24HlG9L1Jb4MRikEvAu001qfsD57FLiotR6agczjSql+1utSQEngoK9sNHif33+Hbt3k9TvvwP33+9cegyEQMAEp/sWXwSgdgeLAF0opx2flgVNKqeXW+x1a6z5Kqc+Ax5BIzE+UUh2AKKB32mFLQ2Czbx/cey8kJsKTT16pHG4whDsmIMW/+DIYZQYww8V9O1kvLwBtfGWTwXecPi3LCI4ehZYtYdIkU0jVYHBQz4ov37xZopFz5/avPeGGSQFm8JhLl+Chh2DHDqhZU6qGh3lVEIPhKgoXBptNqnds3+5va8IP4+iCiAsX4I8/8nHmjL8tuYLdDn37wk8/QYkSsoygcGF/W2UwBB5mns5/GEcXJJw6BXfcAY89VoHixeHOO2HsWNDaN8liXWXsWJg+HaKiJM1RpUr+s8VgCGTMPJ3/MI4uCDhxAlq0gD/+gAIFUkhJgWXLJNijenXJn9e/P/z4owyNZBcLFsDzz8vrjz6CW27JPt0GQ7BhenT+wzi6AOfYMWjeHDZsgCpVYMGCOI4ehU8+gc6doWhR2LMHJk6E1q2hWDG47z6p93bQhwszNmwQ/XY7jBgBDz/sO10GQyhQty7kyAHbtsk0hCH7MI4ugImPl+HKLVtkInvFCihTJplixcTJfPKJRDmuWiXVAerUkeTJ334r4f3lysnFNWQIrFkj6Ye8wf790L69XKzdu1+pTGAwGDImf34J1kpOlmvakH0YRxegHDoEzZpJhFbNmuLkypb9736RkdC4MYwcKRfP/v0yZ3bPPZAvn4Qzjxwp+5QsCf36lWX4cFi8GI4c+e/xMuPsWWjXTuprNWsGM2aYZQQGg6uYeTr/YAqvBiD798tw5e7d0ktbulQiGl2hXDnpzT35pCzcXrECYmJki4uDpUsLsnTplf1Ll5Y1Pmm3669P33klJ0OnTrB1q/Qwv/7arAcyGLLCTTfB7NnG0WU3xtEFGPv2iZPbu1eGHX/6Sebd3CFPHpm3a90aJkyAXbtgwYKDxMeXZeNGyaZ++PAVR+igWLH/Or/KleHtt0uyeLF8HxMj84MGg8F1TECKfzCOLoDYs0ec3D//QMOG8P33UKSId44dESG9sPbtz1CjhoyBpqaKQ3U4vY0bJcjk+HFxsD/9dEW+QAE4d64ouXPDN99A1aresctgCCduuEGSKcTGwrlzcl0ZfI9xdAGC1uLkDh2CW2+VObRChXyrM0cOieSsUkUym4BEUR448F/nd/iwfD9rFjRp4lu7DIZQJSpKihA7rq3bb/e3ReGBcXQBwI4d4uTi46XhL1oE1/mp1npEhMzRXX+9JGh2cOQIbNmym9atTVfOYPCEBg3Eya1fbxxddmGiLv3M1q0SvRgfL85u8WL/OTlnlCoF5ctf8rcZBkPQY+bpsh/j6PzIxo2yTu7YMQkYWbRI1toYDIbQxSwxyH6Mo/MTa9dKvsoTJ2Rd2oIFkDevv60yGAy+pmZNiYjevRtOnvS3NeGBcXR+YNOmvLRoIYma779f1qPlyeNvqwwGQ3aQK5csHQIJ9DL4HuPosplff4WePa/n7FnJD/n552bRtcEQbph5uuzFOLpsZPZsmYtLSIikSxfJVWkKlBoM4YeZp8tezPKCbCApCfr1k7yQAB07nuTDD4sQGelfuwwGg38IlR7dqVPQpw/s3Vve7UC6kiVLMneurOv1FWHp6E6fhn//zR4v888/0KGDNOioKJg8GW699QiRkV5KeWIwGIIOpSQryv79srSoZEl/W+Qezz8Pn34K4H64eM6cRZg0yXtZoNLV4btDBy533QXr1lWjb18YOhQKF/aNnqVLJQny8eNQoYIEndSvL+l/DAZD+JIjh9wLVqyQgJQ2bfxtUdb55ReYOVNiDN5++wDR0eXcOk5q6h6KFPFtIoqwdHTt20u17vHjZZ5s5Eh47DG8NpSYmgpvvQWvviqvW7cWPe4mZzYYDKFHgwbi6NatCz5Hd+GCVEgBuc+1bn2WGjXcO1ZsrO8TUYRlMMrLL8OXX+7ltttksXbPnnDzzfDbb54f27FkYMgQcXKvvSaZ/o2TMxgMaXHM0wVjQMobb8g6wOhoeOEFf1uTOWHp6ABq1kzi119h3jwpaLphgyRT7tbtSgLjrPLnn/KUtnChDId+9x0MG+a9nqLBYAgd0gak2O3+tSUrbN4M774reXEdQ5eBTtg6OpB/1COPwM6d0gPLnRvmzpVyNu+8I9GSrjJvHtxyizzl3HCDPKW1a+c72w0GQ3BTubIEYMTHw8GD/rbGNZKToUcPSEmRSPKbb/a3Ra4R1o7OQYECMHy4VBG4916pEzV4sJTTWLzYuezFi/IPf/RRSEiQHuGaNVL6xmAwGDIiIsJ7ywzi4yExMcJzozJhwgQZ/SpfXu6ZwYJxdGmoUkVyTn7/vYT/7toFbdtKz2zXrv/uf+iQJGV+7z1Z+D11Knz4IeTLl+2mGwyGIMTTheMnTkDfvlCmDDzwQCXi471n27XExUngCcC0acFVNNY4unRo3VrK54wZIyVzYmKgVi148UU4e1b2WbcuH/XqSe+tXDlYuRJ69ZKnNIPBYHAFd3t0KSkwfbpMs0yaJIFv+/ZF0bq1BMR5G7sdnnpKoi07d4a77/a+Dl9iHF0G5M4Nzz0nPbnHH4dLl+Dtt6Wn98wz8Pjj5S/XkNuwIXjGqg0GQ+CQtkfnakDKqlXiIHv1kjW6zZrBzz9DxYpJbNkiI1AJCd6186OPZF1wsWKyLCvY8Ok6OqXUO0ATS88oYB0wF4gEDgNdtdZJ18iMA24B7EB/rbVfk+SULAmzZsnTTL9+sv5u8mSACF58Ed58E3KG5WpEg8HgKWXLyj0mPh72789FzZoZ73vwoITyz5sn76+/XkadOnRwRED+w2OPVWP1anjwQfj2W+9ERMbHw7PPyutx46BECc+Pmd34rEenlLoDiNZaNwLuAsYDbwCTtdZNgH3A49fINAWqWTI9gEm+si+rNGwow5Qffii9uIkT9zNqlHFyBoPBfSIirvTqtm9Pv1ZXYiKMGiWjSfPmSSrB116TaPGHHroyXVKmTDI//QTFi0ucQdeuMsTpKQMGSN28Vq2gSxfPj+cPfDl0+SvwkPX6JJIMrRmw0PrsW6DFNTJ3AgsAtNY7gCJKqYI+tDFL5MgB3bvLMEGLFuf8bY7BYAgBHPN027ZdXXnZbpe1uNHRkuTi/Hl44AFJIThsWPpBb0rBDz9AwYLwxReScNmTNXqLFsFnn4muadOCNwbBZ45Oa52itT5vve0BLAbypxmqPAKUvkasFHAszft46zODwWAISRw9um3brvTodu6UgI977oE9e6Qq+dKlki+3UiXnx6tXTxxknjxSMeWll9yz6+xZ6N1bXg8fnrneQCbC7uMl+Uqpe4GXgVbAX1rrktbnVYE5WuvGafZ9H1iktf7Wer8KeExrfTm4f8OGDfZ8XojfT0xMJI8HZb09kfeXrNFt/l9Gd+DpPn48kiZNbOTLl8KyZbuZNq04H39clOTkCK67LoVnnjlGp04nM61dea3uFSsK0LdvOZKTI3juuaP06HE8S3YPH16SefOKUrv2BebN2+c0w5M//18OEhISqF+/fvp9Trvd7rPNZrO1ttlsa202W1HrfZzNZstrvW5qs9m+umb/120221Np3sfZbLbr0u6zfv16uzfYsWOH3+T9JWt0B5es0R0+usuXt9vBbi9cWP5GRNjtPXva7UePeqZ73jw5Ftjt06e7Lrt6tcjlzGm3b97snm5X8fT/5cDyDen6Il8GoxQC3gXaaa1PWB8vBR60Xj8IfH+N2I9AB0u+LnBIa33WVzYaDAZDIOCYpzt1Cho1knV1M2Z4HuH4yCOOKHFZjvD555nLJCVJmi+7XaI8b7jBMxsCAV/GDHYEigNfKKUcn3UHZiqlngL+BuYAKKU+Q4Yo1yilNiil1gCpwNM+tM9gMBgCgkGDID7+HL16FeDRR70b9NG7t0RNDhkikZiFCklNzox46y0JeLHZrmRCCXZ85ui01jOAGel81TKdfTulef2ir2wyGAyGQKRRI3j//f3UcLeoWya89JI4u9GjJXLzp5+gceP/7rdjB4wYIa9nzJCAllDAZEYxGAyGECciQiqyPPGEpPFq21bK7aQlNVWGLC9dkqKqTZv6x1ZfYBydwWAwhAEREZIfs0MHOH1acvqmTVY/daoUny5dWtIdhhLG0RkMBkOYEBkJH38sWU6OHoUWLeDAATh8OCcvWpNGkyZJ4ehQwiSwMhgMhjAiKgrmz4eWLaUH17IlFC1amnPn4P77ZQ4v1DCOzmAwGMKM/Pml/FjTpvDnnwAFKFhQenOhiBm6NBgMhjCkSBHJi1mlirx/910p4BqKmB6dwWAwhCmlS0stvMWL99G5c0V/m+MzTI/OYDAYwpjChaFu3Qv+NsOnGEdnMBgMhpDGODqDwWAwhDTG0RkMBoMhpDGOzmAwGAwhjXF0BoPBYAhpjKMzGAwGQ0hjHJ3BYDAYQhrj6AwGg8EQ0hhHZzAYDIaQxjg6g8FgMIQ0EXa73d82ZIkNGzYEl8EGg8FgyBbq168fkd7nQefoDAaDwWDICmbo0mAwGAwhjXF0BoPBYAhpjKMzGAwGQ0gT9o5OKZXu5GV2HkcpFeGpvLuynsp76/y5o9dfut3Bm7a6eyxvnDN/tRXTzlzH3/c0f7ez9DDBKBaOE6u1dumEKKWuBxQQp7WOc0NfacAG/K61TrrWFmd2KKVuBI4CR7TWqdZnubXWF5VSORyfOZF/EPhea33+Wp0u6C4DlNFar0/nu8xkhwK/aa1/VErlAOyunm9L/n9AdeCPdM6Z09+tlMoF1ELO20Xgotb6jCt2W/vkB2oD/wKRwGngmNY6xZVznuY4WWpnlozbbc2Tdmbt43Zb81c7s/Zxu6150s6sfdxua95qZw5dkD33NH+2M1cIW0enlMoJNAb2aa3/Tuf7go7Gmc53+YBZQDmgKLBVa/1Imu//hzTOdE+uUiovMAOIBgoArYEHgYPAT1rrY07szgssAzSwFzgEbAXuBCakvalkIJ8f+FFr3dh6fwPwMLDb0n0gE93TgTNAX+SCfBjY4YLd+YF/gEXARK31Bmd2ZqB7CnLOCgMdgBbIefjp2ovrGtk8wDDk5hUJVAS+xLoRuqj7XeBG4ALyfzoCbNJaf56JrNvtzPre7bbmSTtLI+9WW/NXO0uj26225kk7s+TdbmuetDNL3i/3NH+2M1cJ56HLO4CxQD+l1FtKqV5KqaZKqcLWiX/TiWxrAK11E+Bm4JBSqhdIYwLeyOQJpgWQQ2tdF3gVuagrA22A5Uqpm5zIpiKNogjwF1AIeALoB/RRSnXM5HffCmyybL0ReAO4DmgILFFK1XMi2xYoqbV+BqgDjEQuiruBZUqphpnonQs8B4xUSo1WSpV1fKmUiszE7juBXFrrBsDzwCTk6bMPsFkpVcuJ7F1AJa31vcA9wEzL/oFKqR+sJ3Bn3AWU1lrfhvyPxgNxQBel1Aar95ERnrQz8KytedLOwLO25q925tDtblvzpJ2BZ23Nk3YG/run+bOduUQ4O7pdyJPDbmsrBTwAvAh8DlzvRDYVSFZK5dVanwM+Rf6pII2tQCa6KwKnrNclgC1a695a6y7ABOC+jAStJ8qhyJNPPa31aKRxrEWGO3JmovsRwHHBNASWaa37aa17AxOBe53I7gfilVLFkae3RVrrPlrrrsA4oL0T2buB3Vrr45YNe4FHHTcOrXVKJnZXBU5arxsAf2qtn9RaO24I9zuRLQicUUoVtoZAjgDrtNatgeVA90x05wROWfKXtNabtdYztNbtgTnW78kIRzvbRdbbGUhbu6SUyuNGW6vIlXOWpXYGV7W1OLLe1h4BSluvs7OdgZyfXW62tWrACet1VtsZSFs7rZQq5EZby2nJFnGjnYG0scNk/z2tIm7ez+ByO3sd9+9pmRK2jk5rvQ+IQS7AmcAHSGNYgvyzvnMivhJIQsakseYRdiqlngRuAL7PRH0M0qBLIsMko6x5BJAL7XBGgkqpXFrrZK31AKRhPgU0Bd7XWs/OxG6AhcDfSqnZwGAgf5rvbMiFmRGbkMb4OeK4bldKNbeGa6KRMfaMOAB8DaC1PgF8C+QBPlNKHVFKNcnE7hjk98YgT4v5lVLR1ndVgONOZJci/69RSqnlwKPITQfk4s+bie5lQALwrlKqp1KqlVLqBqtnUBW4lJGg1c6WACutdvY+chNxpZ1h2ZmCzPk42tp2F9vaQuC8UqocMJmrn+idtjO4PLeSrLV+FnG2PYAmuNbW5gMHlFJzkZ5RVJrvMmtn65Ghx/lIT6OxUqqZUiqKzNsZSBv9Fi63tflAblxrawsAlFI/IDfwKKVUTeu7zNoZyP8jBRijlFoGdETaD2Te1lYgTnZkmnZWx5V2Bpfb2gLgZzfa2kqkjae9p7nazmKA49b9zJ12ltNy7AO4up3NcPGelilhO0fnIL1xa6XUb8CjziZklVJFgJS0stZN+G6gstXonOmNRsbSz1nvr0fG5/MAvbTWGd4IrCfdJGvrhDwl3u9sruca+WLI014JZNx9u1LqFeTJrZvW+mAm8jcDlYC6yEV9O3IzmaW1Pp2BTAQyJJGitT6bzvF2ZiSbZr+GlvwGpVRvoDhQE3nafFJr7ewBoSLyhL4P0FrrM0qpecjDXn+tdXwmunMi8w61kJtVBDLMtRIZ1vk3A7nIjHoQSqnfgc7O2pm131WT8ZYtC5Ghripa671OZK/XWu9P8746MofkaGfOzllOIEJrfUkpVRT4P2SurHVm/6s08mWQobuzWusVSqlXudLOMpyns+RrIHNOtwDngZuAb4CZWutTTuSKA8czmE+6BYh1Zr9SSgF2pCc+AGlfFZGh155a60OZ2F0EGf5LAv7WWscppT5F5uz6OmtrSqlqwG3I9ZXfsiPTdpZGviCQrLVOuObzTNuaUqoyEgySYL3PSjtrgvRcE633LrezNL/7IPJA9AQyN+pSO3OFsHR0SqnayBBEQeTmmwxsAX5Ghmlu0lr/4UT2PqTxF0WesrYiTx1Hge7W05Qz3fciN8v/pdEdZx1rU0YXcRrZ64BiyMV/GjintX7HejJKduF357NsTwU2IxdRUWRoMd0Gec05K4A0yD3AL1rr1UqpKJ3BRL3l1NsjY/BFrN+8FelN64zsTed3RwElgUTkXF8C1gA7rGGqjGTvQy64YpbMBus3xwM5nd000zleUaRHaUeGx1x6uLBkI5GbXTLiKGtqrf/MgnwOy96L1lzPQ1rreVnUXd7aVmqtnfYQ0jlGReTa+CqLcjmR35sTedjYnIWHsvzINWrP7GaZgXwOS68dcQBZuuFZ82K3Iu1tndb6ghs2RCBOKymjc66Uug1ppzmR62oe0tajkOvybHpy6cjnQ4YtJ2iJ1IxAHuZqaK23OZF1XF/7HLLWd7mADlrrT53I3gPkQoacHXorAxWQdubsnuSQz4M8FIxRSpUAmmit5zv7zVnB47HPIKUXMnexyvpbAemy90C6y3+ojENieyHj0b8jwwwO2WeBqc6c3DXy69PIV0WefD/UWp+69gk+Hbt/S2N3OWQYr4rWeo8Tu9Pq/sPSXR6ZaO+M9MYOu6B7tfW3LDKU014pdSQT3b0tvWu4+nw/rpSaobXek/HpusruDcjQUQXL9tzAQa318UzsTitb0dJdA/lfOz1nSqmxiHP+FokgO4E1h6OUutnqsa3JRHaBJZuA9IAdvVin118G8hetr+vhZPjPki2K9IDS6t5t9ejrIe3AZd3WKMU+y/bcWuuVmehegEReOnoXl5RSSci5z+hB8lq955EHOsc5y6W1XpXF333RIa8kZN1lu63e21eW7joZ2X2N7d+m/d1allPUQhzObxmId0aG938HngHKa62ft457u/UQuywD2bTyMZZ8BaXUIKs33hhxROk6Okv2IHJ9XiULNEIepp3pvVb2OasXWxbpka9wwe7fgGeUUuW11v2B+Uqp24EorfVPTuRdIlwd3S1AS+um5QhvLYsMy4xSSj3rZPguM9kBmQxtZCTfCXjLDd3lkW6+J7o7uqE7H947Z85kM5K/3pLPqt1r0/xmV3QXRxzrYGCGUuoI8AsSWfY4MmSbmeyLwPtKqUOW7AwXZF2R/9oN2ZmW7DdZ1H0YmS/Myu92nLPDyDyVK7rT0+vNc5ZVu5enkXX1nDnkHbrfd0G+HvCaNTS5TCn1nVKqvdb6O6Abzv/X18r/opT6FmiFOL7M5DOTddaDT0/2bmRpR3cy/39dK79QKXWP1nqhC3a7TLgGo0wF3lBKNQLQWl/QWu/WWo9EhgucDRNkJnvOTd3D3dSttdYjPNTtkM+K7gSt9S43ZbNyvjOS/8uDc+bqbwYJbf9Qa90Imdd8DnlA/ALoiZMn/HRkByG90C9dkHVFfq0bsp9bsr9nUfdA3P/dA7OgOz3ZXC7qTU8+K+c8o9/s7jlz6HbY7kz+I2QJgiMjyGjgEWuovCIZ9wQzkh+LRJq6Ip+ZbFbsHgt0TiOb7miHE/kxQKcs/G6XCNce3bfIcOF4pVQqEhW0CZm/OZvJ/IEnsuGqO1jtBon+KwxgDY0uBZYqyQQxQzuf3/NENlx1B6vdnsp/C9xqDXNGINMqLYC3kcCazHR7Iu8vWW/Iu0RYBqOkRUmghEJC9LcA87XWJ51LeS4brrqD1e50jtUOKKu1np6dsuGqO1jt9kReyXKKRUiWkoFu6HVb3l+y3pBPF7vdHnabzWaLtNlsOW02Ww7rfffskA1X3cFqt7W/Qy7C+vuszWYr5Di2r2TDVXew2u0F3ZHXyA6y2Wy5rde5XWnn7sr7S9Yb8q5uXjlIMG82m62EzWbbnd2y4ao7WO1OI6+zWzZcdQer3d7U7XAA2SXvL1lvyDvbwi4YRSlVUSlVJ81H1yFpjhxrbnwiG666g9VuJ/JveKDbJdlw1R2sdvtSN7L+0G3dmcn7S9Yb8lkhrObolFITkQXPqUjqrUtIBoO/tdY/+Eo2XHUHq91Gt2krRnfg2u0OYePolFKFkXUsXZBFjP2wSmAgyWdnaq03e1s2XHUHq91Gt2krRnfg2u0u4TR0eSOSx+0gkiKnoNa6A1KDaTuywNQXsuGqO1jtNrpNWzG6A9dutwgnR7cVyKOU2o6kqtkOoCV58k4kc7cvZMNVd7DabXSbtmJ0B67dbhE2Q5cASpLL1gX+BEYgWbkPILkLv9ZOktV6IhuuuoPVbqPbtBWjO3DtdoewcHRK8jLehuReu4SklfkSySh+M1K/apNOJymwJ7LhqjtY7Ta6TVsxugPXbk8IlxRgjwDtkDxsNZGnhyFImZaBWusNPpINV93BarfRbdqK0R24druPNxflBepms9mm2my2Dul8fpfNZptks9kq+0I2XHUHq91Gt2krRnfg2u3JFi49um1AW6XUP8AOpHCnXWv9vZJK1dWRwqfelg1X3cFqt9Ft2orRHbh2u01YzNEBKKV6IWs2jgIbgWNImOvdQHutdYYlbjyRDVfdwWrIdKH4AAAEV0lEQVS30W3aitEduHa7S8g7OpWmerSSdDNNgZuQqtO7gG1a6+Xelg1X3cFqt9Ft2orRHbh2e4wvxkMDfbPZbPVsNluNNO9dTiDqiWy46g5Wu41u01aM7sC1OytbOC0Yd6zdAJgIJFmfXX7K8JVsuOoOVruN7uzXHax2h6tuf9rtFr7wnoGy2Wy20jabrUo6n3/sS9lw1R2sdhvdpq0Y3YFrtze2UI+67AocBvYAKKWaA4WQJKK+lA1X3cFqt9Ft2orRHbh2e0yoD13eCqwDUEqNBDoDzwPblVLdfSgbrrqD1W6jO/t1B6vd4arbn3Z7TMg6OqVUcaAWcNIaD24IPKu1vhW4AbhHKVXM27LhqjtY7Ta6TVsxugPXbm8RykOXxYE/gA5ARSQj9qU035fQWh/3gWy46g5Wu41u01aM7sC12yuEbI9Oa70TeBk4CJwEfkdKQ5RFcqvt9IVsuOoOVruNbtNWjO7AtdtbhGyPTinVDuku7wMmAKla6wtKqTuBlcCvvpANV93BarfRbdqK0R24dnuLkOzRKaUqA/2BfMD9wDIgBUBr/TWSKftfb8uGq+5gtdvoNm3F6A5cu71JSDo6pPTDZq31UK11J+Ab4C0ApVQN4Bmdcb0jT2TDVXew2m10m7ZidAeu3V4jVB3dJqCeUsqmlIoAZgBVlFIVkDDXfT6SDVfdwWq30W3aitEduHZ7jVB1dBuQqrVHtNZ2rfUJYAnwKNDKeu0L2XDVHax2G92mrRjdgWu31whJR6e1voiUaFfKyqmmtZ4GlAHuRUJdvS4brrqD1W6j27QVoztw7fYmIVemRyn1CNAGOA2UtrY9wCdaivtV01rv8rZsuOoOVruNbtNWjO7AtdvbhOLygg7AbORJIQkoBTQCHlBK2bXWP6iMs2R7IhuuuoPVbqPbtBWjO3Dt9ioh5eiUUrmA/wEHtNbHrI/PKKUOAHuB15RSsVrrf7wpG666g9Vuo9u0FaM7cO32BaE4dNkUaA+sB37XWu+zPi8JfK21vs0XsuGqO1jtNrpNWzG6A9dubxNqPbq6wGNAN+ujI0opx2RoPLJY0euy4ao7WO02uk1bMboD125fEFKODpgEvAMsBO4A5gDHgDrIb13oI9lw1R2sdhvdpq0Y3YFrt/fJjuqu2bHZbLbiNptteZr3d9hstg1p3v9us9kqe1s2XHUHq91Gt2krRnfg2u2rLZTW0UUCx5VSAwG01r8AHyulJiil8gL/aq3jfCAbrrqD1W6j27QVoztw7fYJIePotNbxwCvA8jQff4x0kz/FSaoZT2TDVXew2m10m7ZidAeu3T4jO7uP/thsNlt9m8221maztclO2XDVHax2G92mrRjdgWu3p1vILS9IDyUF/o5pSUeTbbLhqjtY7Ta6TVsxun0n6w15dwkLR2cwGAyG8CVk5ugMBoPBYEgP4+gMBoPBENIYR2cwGAyGkMY4OoPBYDCENMbRGQwGgyGk+X+9gxPui33QDgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 504x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "fig, ax = plt.subplots(figsize = (7,5))\n",
+    "l1 = ax.plot(data['AAAAMMJJ'], data['TN'], color = 'blue', linewidth = 2, label = 'Minimales') \n",
+    "l2 = ax.plot(data['AAAAMMJJ'], data['TX'], color = 'red', linewidth = 2, label = 'Maximales')\n",
+    "l3 = ax.plot(data['AAAAMMJJ'], data['TM'], color = 'black', linewidth = 2, label = 'Moyennes')\n",
+    "ax.tick_params(axis = 'x', labelrotation = 75)\n",
+    "ax.set_ylabel('Température (°C)')\n",
+    "ax.legend()\n",
+    "plt.title(\"Suivi des températures pour le mois d'août à \" + data['NOM_USUEL'][0])\n",
+    "plt.show()"
+   ]
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
@@ -16,10 +211,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-

module2/exo4/prep_data.R

+#!/usr/bin/env Rscript
+
+data = read.table("data/Q_06_latest-2024-2025_RR-T-Vent.csv", sep = ";", header = TRUE)
+str(data)
+
+# Keep only columns of interest
+data_interest = data[, c("NUM_POSTE", "NOM_USUEL", "AAAAMMJJ", "RR", "TN", "TX", "TM")]
+
+# Keep only data for Nice and for august 2025 
+filtered_name = data_interest[which(data$NOM_USUEL == "NICE"),]
+nrow(filtered_name)
+
+filtered_date = filtered_name[which(filtered_name$AAAAMMJJ > 20250731),]
+nrow(filtered_date)
+
+# Export filtered data
+write.table(filtered_date, file = "data/Q_06_latest-2024-2025_RR-T-Vent_filtered_Nice_20250801-26.csv", sep = ";", row.names = FALSE, col.names = TRUE)

module3/exo1/inc-3-PAY.csv

+# {"source":"réseau Sentinelles, INSERM, Sorbonne Université, https:\/\/www.sentiweb.fr","meta":{"period":[198444,202534],"geo":["PAY",0],"geo_ref":"insee","indicator":"3","type":"all","conf_int":true,"compact":false,"age_group":false,"span":"all"},"date":"2025-08-27T10:12:14+02:00","bundle":"1756282334"}
+week,indicator,inc,inc_low,inc_up,inc100,inc100_low,inc100_up,geo_insee,geo_name
+202534,3,23555,17312,29798,35,26,44,FR,France
+202533,3,16937,12133,21741,25,18,32,FR,France
+202532,3,19900,14303,25497,30,22,38,FR,France
+202531,3,18470,12625,24315,28,19,37,FR,France
+202530,3,19166,14283,24049,29,22,36,FR,France
+202529,3,18673,13815,23531,28,21,35,FR,France
+202528,3,23285,18131,28439,35,27,43,FR,France
+202527,3,21453,17129,25777,32,26,38,FR,France
+202526,3,21945,17422,26468,33,26,40,FR,France
+202525,3,23323,18546,28100,35,28,42,FR,France
+202524,3,23154,18577,27731,35,28,42,FR,France
+202523,3,24391,19307,29475,36,28,44,FR,France
+202522,3,18755,14333,23177,28,21,35,FR,France
+202521,3,23760,18671,28849,35,27,43,FR,France
+202520,3,20265,15814,24716,30,23,37,FR,France
+202519,3,16264,12394,20134,24,18,30,FR,France
+202518,3,18115,13975,22255,27,21,33,FR,France
+202517,3,22150,17291,27009,33,26,40,FR,France
+202516,3,28564,22550,34578,43,34,52,FR,France
+202515,3,35721,29592,41850,53,44,62,FR,France
+202514,3,37579,31232,43926,56,47,65,FR,France
+202513,3,39673,33686,45660,59,50,68,FR,France
+202512,3,52543,45627,59459,78,68,88,FR,France
+202511,3,59469,52154,66784,89,78,100,FR,France
+202510,3,60334,53048,67620,90,79,101,FR,France
+202509,3,84531,74994,94068,126,112,140,FR,France
+202508,3,136020,124824,147216,203,186,220,FR,France
+202507,3,208952,195988,221916,312,293,331,FR,France
+202506,3,273519,258159,288879,408,385,431,FR,France
+202505,3,334395,318416,350374,499,475,523,FR,France
+202504,3,350043,332885,367201,522,496,548,FR,France
+202503,3,252772,238917,266627,377,356,398,FR,France
+202502,3,257247,242991,271503,384,363,405,FR,France
+202501,3,231549,214627,248471,345,320,370,FR,France
+202452,3,201726,185870,217582,302,278,326,FR,France
+202451,3,201697,187843,215551,302,281,323,FR,France
+202450,3,136694,126369,147019,205,190,220,FR,France
+202449,3,108487,99037,117937,163,149,177,FR,France
+202448,3,87381,78687,96075,131,118,144,FR,France
+202447,3,76286,67626,84946,114,101,127,FR,France
+202446,3,56399,49006,63792,85,74,96,FR,France
+202445,3,47347,40843,53851,71,61,81,FR,France
+202444,3,36039,30122,41956,54,45,63,FR,France
+202443,3,46572,39928,53216,70,60,80,FR,France
+202442,3,67785,60009,75561,102,90,114,FR,France
+202441,3,79435,71386,87484,119,107,131,FR,France
+202440,3,84965,76555,93375,127,114,140,FR,France
+202439,3,91660,82937,100383,137,124,150,FR,France
+202438,3,91786,82903,100669,138,125,151,FR,France
+202437,3,56460,49319,63601,85,74,96,FR,France
+202436,3,33657,27906,39408,50,41,59,FR,France
+202435,3,27454,22069,32839,41,33,49,FR,France
+202434,3,26717,21003,32431,40,31,49,FR,France
+202433,3,20623,15349,25897,31,23,39,FR,France
+202432,3,23187,17532,28842,35,27,43,FR,France
+202431,3,26035,20267,31803,39,30,48,FR,France
+202430,3,36393,28593,44193,55,43,67,FR,France
+202429,3,39560,32592,46528,59,49,69,FR,France
+202428,3,54342,45781,62903,81,68,94,FR,France
+202427,3,47364,40234,54494,71,60,82,FR,France
+202426,3,44219,36956,51482,66,55,77,FR,France
+202425,3,47204,40300,54108,71,61,81,FR,France
+202424,3,41110,34671,47549,62,52,72,FR,France
+202423,3,35875,30610,41140,54,46,62,FR,France
+202422,3,33772,28274,39270,51,43,59,FR,France
+202421,3,21963,17556,26370,33,26,40,FR,France
+202420,3,20057,15780,24334,30,24,36,FR,France
+202419,3,15375,11274,19476,23,17,29,FR,France
+202418,3,22409,17653,27165,34,27,41,FR,France
+202417,3,27042,21410,32674,41,33,49,FR,France
+202416,3,28882,23305,34459,43,35,51,FR,France
+202415,3,30229,24648,35810,45,37,53,FR,France
+202414,3,31813,26529,37097,48,40,56,FR,France
+202413,3,35090,29607,40573,53,45,61,FR,France
+202412,3,40639,34582,46696,61,52,70,FR,France
+202411,3,50268,43331,57205,75,65,85,FR,France
+202410,3,60107,52623,67591,90,79,101,FR,France
+202409,3,71121,62920,79322,107,95,119,FR,France
+202408,3,104566,94520,114612,157,142,172,FR,France
+202407,3,138078,127050,149106,207,190,224,FR,France
+202406,3,190062,177955,202169,285,267,303,FR,France
+202405,3,216237,203595,228879,324,305,343,FR,France
+202404,3,213196,200547,225845,320,301,339,FR,France
+202403,3,163457,152276,174638,245,228,262,FR,France
+202402,3,129436,119453,139419,194,179,209,FR,France
+202401,3,120769,109452,132086,181,164,198,FR,France
+202352,3,115446,103738,127154,174,156,192,FR,France
+202351,3,148755,136546,160964,224,206,242,FR,France
+202350,3,147971,136787,159155,223,206,240,FR,France
+202349,3,147552,136422,158682,222,205,239,FR,France
+202348,3,124204,113479,134929,187,171,203,FR,France
+202347,3,110948,100694,121202,167,152,182,FR,France
+202346,3,83894,75134,92654,126,113,139,FR,France
+202345,3,72003,63178,80828,108,95,121,FR,France
+202344,3,49952,42813,57091,75,64,86,FR,France
+202343,3,44982,38170,51794,68,58,78,FR,France
+202342,3,56842,49277,64407,86,75,97,FR,France
+202341,3,58357,51032,65682,88,77,99,FR,France
+202340,3,68894,60069,77719,104,91,117,FR,France
+202339,3,72003,63452,80554,108,95,121,FR,France
+202338,3,63218,55227,71209,95,83,107,FR,France
+202337,3,49085,42079,56091,74,63,85,FR,France
+202336,3,38247,32237,44257,58,49,67,FR,France
+202335,3,31695,26013,37377,48,39,57,FR,France
+202334,3,26663,21057,32269,40,32,48,FR,France
+202333,3,19144,13161,25127,29,20,38,FR,France
+202332,3,14641,10285,18997,22,15,29,FR,France
+202331,3,15286,10705,19867,23,16,30,FR,France
+202330,3,13205,8647,17763,20,13,27,FR,France
+202329,3,11122,7113,15131,17,11,23,FR,France
+202328,3,9179,5703,12655,14,9,19,FR,France
+202327,3,8999,5763,12235,14,9,19,FR,France
+202326,3,9023,5934,12112,14,9,19,FR,France
+202325,3,10090,6739,13441,15,10,20,FR,France
+202324,3,11308,7639,14977,17,11,23,FR,France
+202323,3,14300,10661,17939,22,17,27,FR,France
+202322,3,18303,13822,22784,28,21,35,FR,France
+202321,3,16460,12188,20732,25,19,31,FR,France
+202320,3,16162,11963,20361,24,18,30,FR,France
+202319,3,16901,12577,21225,25,18,32,FR,France
+202318,3,19929,15402,24456,30,23,37,FR,France
+202317,3,27007,21779,32235,41,33,49,FR,France
+202316,3,27875,22767,32983,42,34,50,FR,France
+202315,3,37455,30993,43917,56,46,66,FR,France
+202314,3,48060,40671,55449,72,61,83,FR,France
+202313,3,64859,56800,72918,98,86,110,FR,France
+202312,3,72750,64499,81001,109,97,121,FR,France
+202311,3,74638,66420,82856,112,100,124,FR,France
+202310,3,76368,68243,84493,115,103,127,FR,France
+202309,3,62062,54778,69346,93,82,104,FR,France
+202308,3,76391,68065,84717,115,102,128,FR,France
+202307,3,89851,80397,99305,135,121,149,FR,France
+202306,3,97368,87636,107100,146,131,161,FR,France
+202305,3,95469,86268,104670,144,130,158,FR,France
+202304,3,74901,66916,82886,113,101,125,FR,France
+202303,3,69570,61893,77247,105,93,117,FR,France
+202302,3,78260,70090,86430,118,106,130,FR,France
+202301,3,121773,111024,132522,183,167,199,FR,France
+202252,3,155371,142004,168738,234,214,254,FR,France
+202251,3,248319,232128,264510,374,350,398,FR,France
+202250,3,234143,219402,248884,353,331,375,FR,France
+202249,3,163384,151691,175077,246,228,264,FR,France
+202248,3,121691,111744,131638,184,169,199,FR,France
+202247,3,96416,87230,105602,145,131,159,FR,France
+202246,3,67735,60075,75395,102,90,114,FR,France
+202245,3,45306,38909,51703,68,58,78,FR,France
+202244,3,34713,28880,40546,52,43,61,FR,France
+202243,3,44769,36884,52654,68,56,80,FR,France
+202242,3,47462,40773,54151,72,62,82,FR,France
+202241,3,48583,42388,54778,73,64,82,FR,France
+202240,3,41927,36115,47739,63,54,72,FR,France
+202239,3,39902,34168,45636,60,51,69,FR,France
+202238,3,28781,23733,33829,43,35,51,FR,France
+202237,3,21395,17076,25714,32,25,39,FR,France
+202236,3,14120,10487,17753,21,16,26,FR,France
+202235,3,9283,6485,12081,14,10,18,FR,France
+202234,3,7498,4731,10265,11,7,15,FR,France
+202233,3,7586,4442,10730,11,6,16,FR,France
+202232,3,12222,7749,16695,18,11,25,FR,France
+202231,3,13257,8905,17609,20,13,27,FR,France
+202230,3,15006,10738,19274,23,17,29,FR,France
+202229,3,20801,15829,25773,31,24,38,FR,France
+202228,3,23387,17970,28804,35,27,43,FR,France
+202227,3,36015,29709,42321,54,44,64,FR,France
+202226,3,29421,24314,34528,44,36,52,FR,France
+202225,3,22840,18538,27142,34,28,40,FR,France
+202224,3,19294,15406,23182,29,23,35,FR,France
+202223,3,17159,13450,20868,26,20,32,FR,France
+202222,3,14230,10924,17536,21,16,26,FR,France
+202221,3,11804,8686,14922,18,13,23,FR,France
+202220,3,17355,13600,21110,26,20,32,FR,France
+202219,3,17178,13462,20894,26,20,32,FR,France
+202218,3,27569,22584,32554,42,34,50,FR,France
+202217,3,35950,30255,41645,54,45,63,FR,France
+202216,3,54405,46976,61834,82,71,93,FR,France
+202215,3,106385,96079,116691,160,144,176,FR,France
+202214,3,159637,147889,171385,241,223,259,FR,France
+202213,3,187355,175153,199557,283,265,301,FR,France
+202212,3,159809,148839,170779,241,224,258,FR,France
+202211,3,113273,104095,122451,171,157,185,FR,France
+202210,3,80177,72368,87986,121,109,133,FR,France
+202209,3,43750,37938,49562,66,57,75,FR,France
+202208,3,28034,23245,32823,42,35,49,FR,France
+202207,3,31636,26432,36840,48,40,56,FR,France
+202206,3,42557,36599,48515,64,55,73,FR,France
+202205,3,60757,53958,67556,92,82,102,FR,France
+202204,3,66266,59163,73369,100,89,111,FR,France
+202203,3,67412,60291,74533,102,91,113,FR,France
+202202,3,52140,45949,58331,79,70,88,FR,France
+202201,3,54128,47329,60927,82,72,92,FR,France
+202152,3,47704,40795,54613,72,62,82,FR,France
+202151,3,35349,29486,41212,54,45,63,FR,France
+202150,3,33994,28706,39282,51,43,59,FR,France
+202149,3,34014,28982,39046,51,43,59,FR,France
+202148,3,34805,29778,39832,53,45,61,FR,France
+202147,3,30075,25306,34844,46,39,53,FR,France
+202146,3,23814,19612,28016,36,30,42,FR,France
+202145,3,15536,12190,18882,24,19,29,FR,France
+202144,3,15187,11671,18703,23,18,28,FR,France
+202143,3,19326,15025,23627,29,22,36,FR,France
+202142,3,22672,18238,27106,34,27,41,FR,France
+202141,3,18960,15099,22821,29,23,35,FR,France
+202140,3,17994,14318,21670,27,21,33,FR,France
+202139,3,15052,11668,18436,23,18,28,FR,France
+202138,3,11419,8602,14236,17,13,21,FR,France
+202137,3,9530,6793,12267,14,10,18,FR,France
+202136,3,7817,5342,10292,12,8,16,FR,France
+202135,3,8055,5388,10722,12,8,16,FR,France
+202134,3,8603,5639,11567,13,9,17,FR,France
+202133,3,6737,3925,9549,10,6,14,FR,France
+202132,3,8699,5303,12095,13,8,18,FR,France
+202131,3,10665,6766,14564,16,10,22,FR,France
+202130,3,8567,5241,11893,13,8,18,FR,France
+202129,3,8493,5378,11608,13,8,18,FR,France
+202128,3,5925,3110,8740,9,5,13,FR,France
+202127,3,5534,3022,8046,8,4,12,FR,France
+202126,3,4073,2066,6080,6,3,9,FR,France
+202125,3,3659,2076,5242,6,4,8,FR,France
+202124,3,3166,1659,4673,5,3,7,FR,France
+202123,3,3842,2184,5500,6,3,9,FR,France
+202122,3,4723,2869,6577,7,4,10,FR,France
+202121,3,5572,3533,7611,8,5,11,FR,France
+202120,3,7469,5128,9810,11,7,15,FR,France
+202119,3,6658,4440,8876,10,7,13,FR,France
+202118,3,6411,4249,8573,10,7,13,FR,France
+202117,3,8211,5657,10765,12,8,16,FR,France
+202116,3,12448,9252,15644,19,14,24,FR,France
+202115,3,15512,12062,18962,23,18,28,FR,France
+202114,3,17316,13724,20908,26,21,31,FR,France
+202113,3,22165,18137,26193,34,28,40,FR,France
+202112,3,25903,21582,30224,39,32,46,FR,France
+202111,3,20134,16351,23917,30,24,36,FR,France
+202110,3,16895,13527,20263,26,21,31,FR,France
+202109,3,13211,10035,16387,20,15,25,FR,France
+202108,3,16398,12827,19969,25,20,30,FR,France
+202107,3,18894,15116,22672,29,23,35,FR,France
+202106,3,19658,15802,23514,30,24,36,FR,France
+202105,3,19086,15419,22753,29,23,35,FR,France
+202104,3,22822,18658,26986,35,29,41,FR,France
+202103,3,19811,16030,23592,30,24,36,FR,France
+202102,3,16315,12933,19697,25,20,30,FR,France
+202101,3,19301,15463,23139,29,23,35,FR,France
+202053,3,14614,10694,18534,22,16,28,FR,France
+202052,3,11028,7506,14550,17,12,22,FR,France
+202051,3,12111,9016,15206,18,13,23,FR,France
+202050,3,12421,9354,15488,19,14,24,FR,France
+202049,3,11427,8505,14349,17,13,21,FR,France
+202048,3,13672,10387,16957,21,16,26,FR,France
+202047,3,15021,11641,18401,23,18,28,FR,France
+202046,3,22523,18413,26633,34,28,40,FR,France
+202045,3,36865,31522,42208,56,48,64,FR,France
+202044,3,38970,33265,44675,59,50,68,FR,France
+202043,3,36794,31144,42444,56,47,65,FR,France
+202042,3,30581,25622,35540,46,38,54,FR,France
+202041,3,24197,19849,28545,37,30,44,FR,France
+202040,3,18194,14366,22022,28,22,34,FR,France
+202039,3,18607,14641,22573,28,22,34,FR,France
+202038,3,23252,18902,27602,35,28,42,FR,France
+202037,3,14028,10699,17357,21,16,26,FR,France
+202036,3,5364,3404,7324,8,5,11,FR,France
+202035,3,3960,2027,5893,6,3,9,FR,France
+202034,3,3098,1118,5078,5,2,8,FR,France
+202033,3,1900,348,3452,3,1,5,FR,France
+202032,3,2073,369,3777,3,0,6,FR,France
+202031,3,3144,1228,5060,5,2,8,FR,France
+202030,3,4833,2546,7120,7,4,10,FR,France
+202029,3,5730,3374,8086,9,5,13,FR,France
+202028,3,4330,2406,6254,7,4,10,FR,France
+202027,3,2029,797,3261,3,1,5,FR,France
+202026,3,1322,344,2300,2,1,3,FR,France
+202025,3,1395,373,2417,2,0,4,FR,France
+202024,3,1166,333,1999,2,1,3,FR,France
+202023,3,1408,434,2382,2,1,3,FR,France
+202022,3,1743,620,2866,3,1,5,FR,France
+202021,3,3318,1749,4887,5,3,7,FR,France
+202020,3,7745,5375,10115,12,8,16,FR,France
+202019,3,8590,6087,11093,13,9,17,FR,France
+202018,3,12223,9182,15264,19,14,24,FR,France
+202017,3,15685,12264,19106,24,19,29,FR,France
+202016,3,21122,17112,25132,32,26,38,FR,France
+202015,3,34593,29369,39817,53,45,61,FR,France
+202014,3,74213,66885,81541,113,102,124,FR,France
+202013,3,110261,101606,118916,167,154,180,FR,France
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+200316,3,50759,39835,61683,85,67,103,FR,France
+200315,3,69269,55778,82760,116,93,139,FR,France
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+200223,3,15468,9806,21130,26,16,36,FR,France
+200222,3,4854,1696,8012,8,3,13,FR,France
+200221,3,5901,2212,9590,10,4,16,FR,France
+200220,3,3803,1215,6391,6,2,10,FR,France
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+199620,3,10619,7365,13873,18,12,24,FR,France
+199619,3,11898,8065,15731,21,14,28,FR,France
+199618,3,18338,13352,23324,32,23,41,FR,France
+199617,3,24954,19100,30808,43,33,53,FR,France
+199616,3,29313,23868,34758,51,42,60,FR,France
+199615,3,51703,43644,59762,89,75,103,FR,France
+199614,3,44414,36774,52054,77,64,90,FR,France
+199613,3,30622,25062,36182,53,43,63,FR,France
+199612,3,31818,25852,37784,55,45,65,FR,France
+199611,3,24427,19499,29355,42,33,51,FR,France
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+199606,3,21810,17153,26467,38,30,46,FR,France
+199605,3,25275,20410,30140,44,36,52,FR,France
+199604,3,43359,36948,49770,75,64,86,FR,France
+199603,3,54691,48016,61366,94,82,106,FR,France
+199602,3,105606,96106,115106,182,166,198,FR,France
+199601,3,223830,209274,238386,386,361,411,FR,France
+199552,3,397122,378252,415992,688,655,721,FR,France
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+199545,3,29791,24523,35059,52,43,61,FR,France
+199544,3,13168,9820,16516,23,17,29,FR,France
+199543,3,16188,12389,19987,28,21,35,FR,France
+199542,3,15178,11387,18969,26,19,33,FR,France
+199541,3,14980,10851,19109,26,19,33,FR,France
+199540,3,16245,11610,20880,28,20,36,FR,France
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+199538,3,13123,9410,16836,23,17,29,FR,France
+199537,3,4895,2666,7124,8,4,12,FR,France
+199536,3,3659,1574,5744,6,2,10,FR,France
+199535,3,2496,644,4348,4,1,7,FR,France
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+199530,3,1898,46,3750,3,0,6,FR,France
+199529,3,2148,687,3609,4,1,7,FR,France
+199528,3,3312,1430,5194,6,3,9,FR,France
+199527,3,4853,2683,7023,8,4,12,FR,France
+199526,3,4986,2911,7061,9,5,13,FR,France
+199525,3,4571,2520,6622,8,4,12,FR,France
+199524,3,8058,5054,11062,14,9,19,FR,France
+199523,3,13820,9801,17839,24,17,31,FR,France
+199522,3,14124,9999,18249,24,17,31,FR,France
+199521,3,11931,8655,15207,21,15,27,FR,France
+199520,3,18846,14311,23381,33,25,41,FR,France
+199519,3,26261,21488,31034,45,37,53,FR,France
+199518,3,39231,33258,45204,68,58,78,FR,France
+199517,3,88640,79343,97937,153,137,169,FR,France
+199516,3,140488,129439,151537,243,224,262,FR,France
+199515,3,181007,169110,192904,313,292,334,FR,France
+199514,3,248704,235144,262264,431,408,454,FR,France
+199513,3,209839,197191,222487,363,341,385,FR,France
+199512,3,122434,112497,132371,212,195,229,FR,France
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+199509,3,39696,33259,46133,69,58,80,FR,France
+199508,3,38008,32225,43791,66,56,76,FR,France
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+199506,3,37351,32007,42695,65,56,74,FR,France
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+199502,3,23480,19249,27711,41,34,48,FR,France
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+199452,3,22036,17495,26577,38,30,46,FR,France
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+199444,3,11307,7688,14926,20,14,26,FR,France
+199443,3,11254,8002,14506,20,14,26,FR,France
+199442,3,14406,10653,18159,25,18,32,FR,France
+199441,3,14073,10341,17805,24,18,30,FR,France
+199440,3,15962,11850,20074,28,21,35,FR,France
+199439,3,15878,11643,20113,28,21,35,FR,France
+199438,3,15366,11100,19632,27,20,34,FR,France
+199437,3,5949,3561,8337,10,6,14,FR,France
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+199434,3,3459,1444,5474,6,2,10,FR,France
+199433,3,4272,977,7567,7,1,13,FR,France
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+199427,3,713,0,1459,1,0,2,FR,France
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+199425,3,5431,3222,7640,9,5,13,FR,France
+199424,3,6288,3723,8853,11,7,15,FR,France
+199423,3,6440,1052,11828,11,2,20,FR,France
+199422,3,845,0,2449,1,0,4,FR,France
+199421,3,2197,395,3999,4,1,7,FR,France
+199420,3,2217,852,3582,4,2,6,FR,France
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+199416,3,5156,3176,7136,9,6,12,FR,France
+199415,3,6952,4678,9226,12,8,16,FR,France
+199414,3,4671,2784,6558,8,5,11,FR,France
+199413,3,3343,1641,5045,6,3,9,FR,France
+199412,3,4305,2412,6198,7,4,10,FR,France
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+199409,3,5635,3251,8019,10,6,14,FR,France
+199408,3,9539,6652,12426,17,12,22,FR,France
+199407,3,14639,11188,18090,25,19,31,FR,France
+199406,3,17308,13395,21221,30,23,37,FR,France
+199405,3,18489,14290,22688,32,25,39,FR,France
+199404,3,23712,19048,28376,41,33,49,FR,France
+199403,3,29882,24817,34947,52,43,61,FR,France
+199402,3,47402,41415,53389,82,72,92,FR,France
+199401,3,118119,108182,128056,205,188,222,FR,France
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+199349,3,898045,873419,922671,1565,1522,1608,FR,France
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+199329,3,3714,1668,5760,6,2,10,FR,France
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+199327,3,2574,918,4230,4,1,7,FR,France
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+199323,3,7424,4302,10546,13,8,18,FR,France
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+199321,3,5942,3670,8214,10,6,14,FR,France
+199320,3,5137,3157,7117,9,6,12,FR,France
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+199318,3,5027,3075,6979,9,6,12,FR,France
+199317,3,8111,5138,11084,14,9,19,FR,France
+199316,3,24932,19468,30396,43,33,53,FR,France
+199315,3,22181,17147,27215,39,30,48,FR,France
+199314,3,38266,30865,45667,67,54,80,FR,France
+199313,3,62987,54506,71468,110,95,125,FR,France
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+199301,3,75288,67244,83332,131,117,145,FR,France
+199253,3,56622,49107,64137,99,86,112,FR,France
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+199251,3,41156,35024,47288,72,61,83,FR,France
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+199245,3,22023,17804,26242,39,32,46,FR,France
+199244,3,27619,22945,32293,48,40,56,FR,France
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+199241,3,24091,19953,28229,42,35,49,FR,France
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+199239,3,13535,10301,16769,24,18,30,FR,France
+199238,3,9837,7106,12568,17,12,22,FR,France
+199237,3,7480,5130,9830,13,9,17,FR,France
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+199235,3,6383,3687,9079,11,6,16,FR,France
+199234,3,2953,922,4984,5,1,9,FR,France
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+199232,3,4963,2215,7711,9,4,14,FR,France
+199231,3,4233,1733,6733,7,3,11,FR,France
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+199225,3,5304,2805,7803,9,5,13,FR,France
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+199223,3,3914,1855,5973,7,3,11,FR,France
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+199215,3,8642,5029,12255,15,9,21,FR,France
+199214,3,11346,7536,15156,20,13,27,FR,France
+199213,3,11693,8096,15290,20,14,26,FR,France
+199212,3,11752,7834,15670,21,14,28,FR,France
+199211,3,11689,7575,15803,20,13,27,FR,France
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+199142,3,27483,20385,34581,48,36,60,FR,France
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+199140,3,26740,16049,37431,47,28,66,FR,France
+199139,3,14373,8734,20012,25,15,35,FR,France
+199138,3,7460,3700,11220,13,6,20,FR,France
+199137,3,2429,434,4424,4,0,8,FR,France
+199136,3,2380,0,4882,4,0,8,FR,France
+199135,3,2372,0,5025,4,0,9,FR,France
+199134,3,2099,0,5104,4,0,9,FR,France
+199133,3,1654,0,4102,3,0,7,FR,France
+199132,3,625,0,2190,1,0,4,FR,France
+199131,3,2464,0,5830,4,0,10,FR,France
+199130,3,2382,0,6857,4,0,12,FR,France
+199129,3,3837,40,7634,7,0,14,FR,France
+199128,3,3297,152,6442,6,0,12,FR,France
+199127,3,3164,934,5394,6,2,10,FR,France
+199126,3,6376,3324,9428,11,6,16,FR,France
+199125,3,8218,4776,11660,14,8,20,FR,France
+199124,3,8223,4660,11786,14,8,20,FR,France
+199123,3,6801,3596,10006,12,6,18,FR,France
+199122,3,6050,2682,9418,11,5,17,FR,France
+199121,3,6391,3385,9397,11,6,16,FR,France
+199120,3,8633,4683,12583,15,8,22,FR,France
+199119,3,7785,3986,11584,14,7,21,FR,France
+199118,3,10856,6457,15255,19,11,27,FR,France
+199117,3,15603,10950,20256,27,19,35,FR,France
+199116,3,15665,11184,20146,28,20,36,FR,France
+199115,3,15062,10715,19409,26,18,34,FR,France
+199114,3,27862,21583,34141,49,38,60,FR,France
+199113,3,32179,25829,38529,57,46,68,FR,France
+199112,3,27873,22312,33434,49,39,59,FR,France
+199111,3,46186,38876,53496,81,68,94,FR,France
+199110,3,88750,77784,99716,156,137,175,FR,France
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+199108,3,216369,198630,234108,381,350,412,FR,France
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+199102,3,29759,22044,37474,52,38,66,FR,France
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+199052,3,41262,31607,50917,73,56,90,FR,France
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+199040,3,20433,14614,26252,36,26,46,FR,France
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+199033,3,0,0,0,0,0,0,FR,France
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+199031,3,881,0,2206,2,0,4,FR,France
+199030,3,425,0,1262,1,0,2,FR,France
+199029,3,1077,0,2643,2,0,5,FR,France
+199028,3,959,0,2252,2,0,4,FR,France
+199027,3,2948,793,5103,5,1,9,FR,France
+199026,3,7236,2651,11821,13,5,21,FR,France
+199025,3,7879,3136,12622,14,6,22,FR,France
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module3/exo2/inc-7-PAY.csv

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