From 0b9d7533a395364f1979d37edc8f14a2fa8662cb Mon Sep 17 00:00:00 2001
From: 6d8af797d01bfc5ebbe3e45be8c278ea
 <6d8af797d01bfc5ebbe3e45be8c278ea@app-learninglab.inria.fr>
Date: Wed, 28 Feb 2024 21:32:30 +0000
Subject: [PATCH] derniers ajustements

---
 ...03\251valuation par les pairs-Copy1.ipynb" |    0
 module3/exo3/exercice.ipynb                   | 1024 +++++++++++++++++
 2 files changed, 1024 insertions(+)
 rename "module3/exo3/Travail pratique avec \303\251valuation par les pairs.ipynb" => "module3/exo3/Travail pratique avec \303\251valuation par les pairs-Copy1.ipynb" (100%)
 create mode 100644 module3/exo3/exercice.ipynb

diff --git "a/module3/exo3/Travail pratique avec \303\251valuation par les pairs.ipynb" "b/module3/exo3/Travail pratique avec \303\251valuation par les pairs-Copy1.ipynb"
similarity index 100%
rename from "module3/exo3/Travail pratique avec \303\251valuation par les pairs.ipynb"
rename to "module3/exo3/Travail pratique avec \303\251valuation par les pairs-Copy1.ipynb"
diff --git a/module3/exo3/exercice.ipynb b/module3/exo3/exercice.ipynb
new file mode 100644
index 0000000..3de25ad
--- /dev/null
+++ b/module3/exo3/exercice.ipynb
@@ -0,0 +1,1024 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__Sujet__ :  \"Analyse des dialogues dans l'Avare de Molière\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Préliminaires"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Avant tout, j'importe les __packages__ qui me seront nécessaires (et je règle la taille des figues) :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import os\n",
+    "import numpy as np\n",
+    "import urllib.request\n",
+    "import pandas as pd\n",
+    "import urllib.request\n",
+    "import subprocess\n",
+    "\n",
+    "plt.rcParams['figure.figsize'] = [14, 7]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Afin d'étudier le texte, je décide d'exploiter la __version .txt (markdown)__ que je télécharge"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_url = \"http://dramacode.github.io/markdown/moliere_avare.txt\"\n",
+    "data = urllib.request.urlopen(data_url)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Je souhaite extraire chaque ligne de dialogue, __mais__ :\n",
+    "1. le texte est encodé en __utf-8__ ;\n",
+    "2. des __sauts de lignes__ sont fréquemment effectués ;\n",
+    "On corrige ces défauts dans le code suivant :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lignes=[]\n",
+    "for ligne in data:\n",
+    "    l1=ligne.decode('utf-8')\n",
+    "    l2=l1.replace(u'\\xa0', u' ')\n",
+    "    if l2.strip()!=\"\":\n",
+    "        lignes+=[l2.strip()]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Certaines lignes ne correspondent pas à des répliques. On veut créer un tableau avec la structure suivante : chaque ligne correspond à une réplique, et les colonnes correspondent successivement au __numéro de la réplique__ (ordre chronologique), au __personnage__ qui la prononce, la __réplique__ elle-même, l'__acte__ et la __scène__ correspondants.\n",
+    "\n",
+    "On enlève les __premières lignes__ qui n'auront pas d'intérêt dans notre analyse, et on supprime également un saut de ligne non conventionnel à l'acte III."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lignes=lignes[26:]\n",
+    "lignes.remove(\"Il crie au Voleur dès le jardin, et vient sans chapeau.\")\n",
+    "target_index = lignes.index(\"Il se prend lui-même le bras.\")\n",
+    "lignes[target_index] = \"HARPAGON\"\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "La fonction suivante permettra de supprimer les didascalies."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def nodidasc(L):\n",
+    "    M=L\n",
+    "    if \"*\" in L:\n",
+    "        for k in range(len(L)-1,-1,-1):\n",
+    "            if L[k]==\"*\":\n",
+    "                M=L[:k]\n",
+    "    M=M.replace(\",\",\"\")\n",
+    "    M=M.replace(\".\",\"\")\n",
+    "    while len(M)>0 and M[-1]==\" \":\n",
+    "        M=M[:-1]\n",
+    "    return M"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pour déterminer l'acte et la scène, on exploite le formatage du texte : les nouveaux actes sont annoncés par __##__ quand une nouvelle scène est annoncée par __###__.\n",
+    "\n",
+    "Pour déterminer qu'une nouvelle réplique commence, on exploite le fait que le nom de personnage qui l'énonce est __toujours en majuscules__."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "repliques=[]\n",
+    "acte=0\n",
+    "scene=0\n",
+    "deb=False\n",
+    "nb=1\n",
+    "for ligne in lignes:\n",
+    "    if ligne.startswith(\"## \"):\n",
+    "        acte+=1\n",
+    "        scene=0\n",
+    "    if ligne.startswith(\"### \"):\n",
+    "        scene+=1\n",
+    "    if not(ligne.startswith(\"#\")):\n",
+    "        ligne2=ligne\n",
+    "        if deb:\n",
+    "            repliques+=[[nb,personnage,ligne2,acte,scene]]\n",
+    "            deb=False\n",
+    "            nb+=1\n",
+    "        if ligne[:5].isupper():\n",
+    "            ligne2=nodidasc(ligne)\n",
+    "            deb=True\n",
+    "            personnage=ligne2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Une mesure du temps de paroles sera donnée par le nombre de mots prononcés. On écrit donc une __fonction__ qui compte le __nombre de mots dans une réplique__ ( _en faisant attention à supprimer la ponctuation_ ) :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compte_mots(replique):\n",
+    "    for ponctuation in [\"...\",\".\",\"?\",\"!\",\",\",\";\",\"'\",'\"']:\n",
+    "        replique=replique.replace(ponctuation,\" \")\n",
+    "    for k in range(10):\n",
+    "        replique=replique.replace(\"  \",\" \")\n",
+    "    return len(replique.split())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "On teste cette fonction sur un fameux adage :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "compte_mots(\"Quand le chat n'est pas là, les souris dansent.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Enfin, pour pouvoir manipuler simplement ces données, on créé une fonction qui change légèrement leur forme :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def traitement(L):\n",
+    "    M=[]\n",
+    "    for k in range(0,len(L[0])):\n",
+    "        M+=[[L[0][k],L[1][k]]]\n",
+    "    return M"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "_Petit bonus : pour des raisons purement esthétiques, on invoquera la fonction suivante, qui transforme un nombre entre 1 et 9 en écriture romaine._"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def roman_num(k):\n",
+    "    return [\"I\",\"II\",\"III\",\"IV\",\"V\",\"VI\",\"VII\",\"VIII\",\"IX\"][k-1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__Il est à présent temps d'exploiter toutes ces informations !__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Temps de parole des personnages"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__Consignes__ : classez les personnages selon la quantité de parole grâce à une analyse syntaxique du texte (scènes / répliques / mots). En particulier, quel est celui qui parle le plus ? Quel est celui qui ne parle pas du tout ? Attention, les noms des personnages ne sont pas forcément homogènes (casse et accents par exemple)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Par nombre de scènes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__Dans combien de scènes__ apparaissent les personnages ? On propose ici un code qui détermine la liste des scènes (au format __[n° acte,n° scène]__) pour chaque personnage."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "liste_scenes=[[],[]]\n",
+    "for replique in repliques:\n",
+    "    if replique[1] in liste_scenes[0]:\n",
+    "        k=0\n",
+    "        while liste_scenes[0][k]!=replique[1]:\n",
+    "            k+=1\n",
+    "        if not([replique[3],replique[4]] in liste_scenes[1][k]):\n",
+    "            liste_scenes[1][k]+=[[replique[3],replique[4]]]\n",
+    "    else:\n",
+    "        #première apparition\n",
+    "        liste_scenes[0]=liste_scenes[0]+[replique[1]]\n",
+    "        liste_scenes[1]=liste_scenes[1]+[[[replique[3],replique[4]]]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "La fonction de traitement construite en introduction permet d'obtenir une liste plus facile à exploiter :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "liste=traitement(liste_scenes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "On peut à présent lister les scène dans lesquelles apparaît un personnage donné : ( _attention à bien mentionner le nom du personnage en majuscules_ )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MARIANE apparaît dans les scènes suivantes :\n",
+      "Acte III, scène 4\n",
+      "Acte III, scène 6\n",
+      "Acte III, scène 7\n",
+      "Acte IV, scène 1\n",
+      "Acte V, scène 5\n",
+      "Acte V, scène 6\n"
+     ]
+    }
+   ],
+   "source": [
+    "nom='MARIANE'\n",
+    "def LS(nom):\n",
+    "    for L in liste:\n",
+    "        if L[0]==nom:\n",
+    "            print(nom+\" apparaît dans les scènes suivantes :\")\n",
+    "            for actscen in L[1]:\n",
+    "                print(\"Acte \"+roman_num(actscen[0])+\", scène \"+str(actscen[1]))\n",
+    "    return None\n",
+    "LS(nom)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "On peut également lister les personnages avec le nombre de scènes dans lesquelles ils apparaissent chacun :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "VALÈRE : 9\n",
+      "ÉLISE : 9\n",
+      "CLÉANTE : 14\n",
+      "HARPAGON : 23\n",
+      "LA FLÈCHE : 5\n",
+      "MAÎTRE SIMON : 1\n",
+      "FROSINE : 10\n",
+      "MAÎTRE JACQUES : 9\n",
+      "LA MERLUCHE : 2\n",
+      "BRINDAVOINE : 2\n",
+      "MARIANE : 6\n",
+      "LE COMMISSAIRE : 3\n",
+      "ANSELME : 2\n"
+     ]
+    }
+   ],
+   "source": [
+    "for k in range(0,len(liste)):\n",
+    "    print(liste[k][0]+\" : \"+str(len(liste[k][1])))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "C'est sans suprise __Harpagon qui apparaît dans le plus grand nombre de scènes (23)__. __Maître Simon__ n'apparaît que dans une scène, mais de quelle scène s'agit-il ?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MAÎTRE SIMON apparaît dans les scènes suivantes :\n",
+      "Acte II, scène 2\n"
+     ]
+    }
+   ],
+   "source": [
+    "LS(\"MAÎTRE SIMON\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Par nombre de répliques"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Compter les scènes peut donner une idée globale, mais la longueur des scènes diffèrent, et on ne pondère pas nos données par l'importance du personnage dans cette scène. Cette section vient corriger ce problème en comptant le nombre de répliques de chaque personnages tout au long de la pièce. Le code suivant produit une liste dont chaque élément correpond à un personnage et est de la forme __[nom,nombre de répliques]__."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nombre_repliques=[[],[]]\n",
+    "for replique in repliques:\n",
+    "    if replique[1] in nombre_repliques[0]:\n",
+    "        k=0\n",
+    "        while nombre_repliques[0][k]!=replique[1]:\n",
+    "            k+=1\n",
+    "        nombre_repliques[1][k]+=1\n",
+    "    else:\n",
+    "        #première apparition\n",
+    "        nombre_repliques[0]=nombre_repliques[0]+[replique[1]]\n",
+    "        nombre_repliques[1]=nombre_repliques[1]+[1]\n",
+    "new_repliques=traitement(nombre_repliques)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Et on peut alors facilement donner la __liste des nombres de répliques par personnages__ :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "VALÈRE : 101\n",
+      "ÉLISE : 51\n",
+      "CLÉANTE : 161\n",
+      "HARPAGON : 355\n",
+      "LA FLÈCHE : 66\n",
+      "MAÎTRE SIMON : 5\n",
+      "FROSINE : 60\n",
+      "MAÎTRE JACQUES : 85\n",
+      "LA MERLUCHE : 5\n",
+      "BRINDAVOINE : 3\n",
+      "MARIANE : 31\n",
+      "LE COMMISSAIRE : 17\n",
+      "ANSELME : 20\n"
+     ]
+    }
+   ],
+   "source": [
+    "for L in new_repliques:\n",
+    "    print(L[0]+\" : \"+str(L[1]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A nouveau, __Harpagon__ est en tête avec 355 répliques. La surprise porte plutôt sur les moins bavards : Maître Simon qui n'apparaissait que dans une scène prononce cinq répliques au cours de la pièce, quand __Brindavoine__, qui apparaît dans deux scènes, n'en prononce que trois."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Par nombre de mots"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "On propose une dernière critique dans cette section : un personnage qui ne dirait qu'une longue tirade lors de la pièce a sûrement plus d'important qu'un personnage qui prononcerait deux ridicules et courtes répliques. On propose ici d'analyser le nombre de mots prononcés par chaque personnage toute au long de la pièce. Le code suivant produit une liste dont chaque élément correpond à un personnage et est de la forme __[nom,nombre de mots]__."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nombre_mots=[[],[]]\n",
+    "for replique in repliques:\n",
+    "    if replique[1] in nombre_mots[0]:\n",
+    "        k=0\n",
+    "        while nombre_mots[0][k]!=replique[1]:\n",
+    "            k+=1\n",
+    "        nombre_mots[1][k]+=compte_mots(replique[2])\n",
+    "    else:\n",
+    "        #première apparition\n",
+    "        nombre_mots[0]=nombre_mots[0]+[replique[1]]\n",
+    "        nombre_mots[1]=nombre_mots[1]+[compte_mots(replique[2])]\n",
+    "new_mots=traitement(nombre_mots)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "On peut alors lister les personnages et le nombre de mots qu'ils prononcent :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "VALÈRE : 2671\n",
+      "ÉLISE : 1050\n",
+      "CLÉANTE : 3284\n",
+      "HARPAGON : 5751\n",
+      "LA FLÈCHE : 1494\n",
+      "MAÎTRE SIMON : 197\n",
+      "FROSINE : 2110\n",
+      "MAÎTRE JACQUES : 1481\n",
+      "LA MERLUCHE : 51\n",
+      "BRINDAVOINE : 43\n",
+      "MARIANE : 907\n",
+      "LE COMMISSAIRE : 289\n",
+      "ANSELME : 513\n"
+     ]
+    }
+   ],
+   "source": [
+    "for L in new_mots:\n",
+    "    print(L[0]+\" : \"+str(L[1]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hideCode": true,
+    "hideOutput": true
+   },
+   "source": [
+    "Une dernière fois, Harpagon est bien en tête. Maître Simon semble posséder des repliques relativement longues : elles sont composées en moyenne du nombre suivant de mots."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "39.4"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_mots[5][1]/new_repliques[5][1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__C'est la moyenne la plus élevé parmi tous les personnages !__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "VALÈRE : 26.445544554455445\n",
+      "ÉLISE : 20.58823529411765\n",
+      "CLÉANTE : 20.39751552795031\n",
+      "HARPAGON : 16.2\n",
+      "LA FLÈCHE : 22.636363636363637\n",
+      "MAÎTRE SIMON : 39.4\n",
+      "FROSINE : 35.166666666666664\n",
+      "MAÎTRE JACQUES : 17.423529411764704\n",
+      "LA MERLUCHE : 10.2\n",
+      "BRINDAVOINE : 14.333333333333334\n",
+      "MARIANE : 29.258064516129032\n",
+      "LE COMMISSAIRE : 17.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "for k in range(0,12):\n",
+    "    print(new_mots[k][0]+\" : \"+str(new_mots[k][1]/new_repliques[k][1]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "On pourrait évidemment questionner la longueur de chaque mot prononcé par chaque personnage au cours de chaque scène, mais l'intérêt serait vite limité."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Nombre des mots par scène"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__Consigne :__ Réalisez un graphique qui montrera le nombre de mots que chaque acteur prononce dans chaque scène. Pour cela, vous pouvez vous inspirer de [l'étude de l'Avare de Molière réalisée par l'OBVIL](https://obvil.sorbonne-universite.fr/corpus/moliere/moliere_avare) (graphe de gauche). Dans ce graphique, les lignes sont de longueur égale et la hauteur représente le nombre de mots prononcés au total dans la scène. La largeur de chaque rectangle indique le pourcentage de la scène qu’un acteur occupe."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Une simple modification du code de la sous-section nous offre une fonction qui à un __personnage__, un __acte__, et une __scène__, associe le nombre de mot qu'il y prononce."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def nb_mots(perso,acte,scene):\n",
+    "    nombre_mots=0\n",
+    "    for replique in repliques:\n",
+    "        if replique[1]==perso and replique[3]==acte and replique[4]==scene:\n",
+    "            nombre_mots+=compte_mots(replique[2])\n",
+    "    return nombre_mots"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "On peut, par exemple, se demander combien de réplique prononce Harpagon dans la première scène de l'acte III :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "602\n"
+     ]
+    }
+   ],
+   "source": [
+    "perso=\"HARPAGON\"\n",
+    "acte=3\n",
+    "scene=1\n",
+    "print(nb_mots(perso,acte,scene))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Harpagon prononce 602 mots durant la première scène de l'acte III ! Représentons graphiquement toutes ces nouvelles données. Pour cela, on veut d'abord lister toutes les scènes et actes. Pour des questions de lisibilité, on fixe la nomenclature suivante : __concaténation(n° acte en chiffres romaines,n° scène)__. Par exemple, la troisième scène de l'acte deux correspondra au sigle _II3_. La fonction suivante liste des sigles pour un acte donné."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def LSigleA(acte):\n",
+    "    sigles=[]\n",
+    "    for replique in repliques:\n",
+    "        s=roman_num(replique[3])+str(replique[4])\n",
+    "        if not(s in sigles) and replique[3]==acte:\n",
+    "            sigles+=[s]\n",
+    "    return sigles\n",
+    "\n",
+    "def LSceneA(acte):\n",
+    "    scene=[]\n",
+    "    for replique in repliques:\n",
+    "        s=[replique[3],replique[4]]\n",
+    "        if not(s in scene) and replique[3]==acte:\n",
+    "            scene+=[s]\n",
+    "    return scene"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Quant au programme suivant, il donne la __liste des personnages__ de la pièce :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "list_perso=[]\n",
+    "for replique in repliques:\n",
+    "    if not(replique[1] in list_perso):\n",
+    "        list_perso+=[replique[1]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "La fonction suivante renvoie la liste contenant pour chaque personnage une liste avec le nombre de mots prononcés à chaque scène d'un acte donné"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def donnee(acte):\n",
+    "    M=[]\n",
+    "    for k in range(0,len(list_perso)):\n",
+    "        m=[]\n",
+    "        for actsc in LSceneA(acte):\n",
+    "            if actsc[0]==acte:\n",
+    "                m+=[nb_mots(list_perso[k],actsc[0],actsc[1])]\n",
+    "        M+=[m]\n",
+    "    return M"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "On trace la donnée calculée avec des __diagrammes bâtons empilés__ :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_acte(acte):\n",
+    "    df=donnee(acte)\n",
+    "    fig, ax = plt.subplots()\n",
+    "    for k in range(0,len(list_perso)):\n",
+    "        ax.bar(LSigleA(acte), df[k], label=list_perso[k])\n",
+    "    ax.set_xlabel(\"Scènes de l'acte \"+roman_num(acte))\n",
+    "    ax.set_ylabel(\"Nombre de mots\")\n",
+    "    ax.set_title(\"Nombre de mots prononcé par scène et personnages durant l'acte \"+roman_num(acte))\n",
+    "    ax.legend()\n",
+    "    plt.show()\n",
+    "    return None\n",
+    "plt.rcParams['figure.figsize'] = [12, 5]\n",
+    "plot_acte(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "On peut évidemment réaliser le même rendu pour tous les actes :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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Occ4dMLPX8JZGS8G74cFjZvYg3l3FfmNmKXhfmV2D97XZ28AVYTfCEBERERE5rVX2nOvaQKyZfYe3RuYW4CG8W4oCTMG7nehv8Baqz3HOHQI2mlkB3kD7g9Iqb9iwoUtMTKys2EVEREREAFi6dOlXzrn48spV2uDaObfZzCbirRt7APinc+6fZvZD/6YHOOe2mlnRbXgbEbmG5heUfYthEhMTWbJkSSVELyIiIiLyPTP7LJpylTbn2p9L3R1IwpvmUc/MSlv4Hkq+neoxc1bMbIiZLTGzJTt27KiYYEVEREREKkBlXtD4I2Cjc26Hf8vWfwDXA9vM7GIA/+d2v/wXQOOw/RPwppFEcM694JxLc86lxceXe2ZeREREROSUqczB9SbgOjOra2YG3Ix3y97ZQNEdqQbw/Tqfs4E+Zna2mSUBTYGPKjE+EREREZEKVZlzrj80s+nAMuAI8B/gBSAOeM3M7sYbgP/EL7/aX1FkjV9+qFYKERERkdPNd999xxdffMHBgwerOhSpBOeccw4JCQnUqVPnhPav1jeRSUtLc7qgUURERE6ljRs3cu6559KgQQO8L+elpnDOsXPnTvbs2UNSUlJEnpktdc6llVdHpd5ERkRERKSmOXjwYI0aWM+YMYPf/OY3VR3GacHMaNCgwUl9K1HZ61yLiIiI1Dg1ZWAN0LNnT/bt28fhw4c566yzqjqcKneyr63OXIuIiIhUIx07dmTu3LkRaU899RT33nsvAJMmTeKcc87h22+/DeUvXLiQ9PT0Euu68sormThxItdccw29evUCICsri0aNGhEIBEhJSSE7Ozu0z8CBA0lKSiIQCBAIBLj++usro5vVls5ci4iIiJyExAffrND6go/dVmZ+RkYGOTk5dO3aNZSWk5PDhAkTAMjOzqZt27bMmDGDgQMHltve1KlTSUs7dirxiBEjGDVqFPn5+bRp04ZevXqFLvKbMGFCaCAukXTmWkRERKQa6dWrF2+88QaHDh0CIBgMsmXLFm688UbWr1/P3r17GTt2bMTZ5pPRtGlT6taty65duyqkvppOg2sRERGRaqRBgwZcc801zJkzB/DOWvfu3RszIzs7m4yMDNq3b8+6devYvn17ObVBv379QlM8MjMzj8lftmwZTZs25Qc/+EEoLTMzM7RPv379Kq5zNYCmhZygrKysqg6h0tTkvomIiNQERVNDunfvTk5ODn/7298Ab6A9Y8YMatWqxY9//GOmTZvG0KFDy6yrtGkhkyZN4sUXX2TDhg2hgXwRTQspnc5ci4iIiFQzPXr0YP78+SxbtowDBw5w9dVXs3LlSvLz8+ncuTOJiYnk5OSc1NSQESNGsG7dOnJzc/nZz36mm+ZESYNrERERkWomLi6Ojh07MmjQIDIyMgDvQsasrCyCwWBoHvbmzZv57LPPTqqtH//4x6SlpTFlypSKCL3G0+BaREREpBrKyMhgxYoV9OnTB/CmhPTs2TOiTM+ePcnJyQFg/vz5JCQkhB4ffPABEDnn+kc/+lGJbY0ZM4Ynn3ySwsJCIHLOdSAQ4PDhw5XVzWpHtz8/QTV5XnJN7puIiMjJysvLo3nz5lUdhlSikl5j3f5cREREROQU0+BaRERERKSCaHAtIiIiIlJBNLgWEREREakgGlyLiIiIVEOFhYV07dqVTZs2VXUoEkaDaxEREZFqaOPGjTz88MM0adKkqkORMBpci4iIiFQzMTEx3HnnnQwfPpxAIMBjjz0GQMeOHSm+TPHChQtJT08HYNu2baSnp5OamkpKSgrdunUDIBgMEhsbG7F29csvv3xqO1VD1K7qAERERESqtazzKri+b8stEhsby/Lly4+76jFjxtC5c2eGDx8OwMqVK0N5ycnJJ1SnRNKZaxEREZEzxNatW0lISAhtt27dugqjqZk0uBYRERGpZg4cOBAxhSM3Nzeq/YYOHcrdd99Np06dGDduHFu2bAnlrV+/PqLOd999t7LCr9E0LURERESkmjnRaSFdu3Zlw4YNzJkzh7feeourrrqKVatWAZoWUlF05lpERETkDHLhhRfSt29fXnnlFdq2bcuiRYuqOqQaRYNrERERkTPEO++8w/79+wHYs2cP69ev11J+FUzTQkRERESqmaI510VuueWW0HJ8t912G3Xq1AGgXbt2DB06NFRu6dKl3HfffdSuXZvCwkIGDx5M27ZtCQaDoTnXRQYNGsSwYcNOUY9qjkobXJvZlUD47PrLgDHAy356IhAE7nLO7fL3eQi4GzgKDHPOza2s+EREREQqRBRL51W0o0ePlpi+cOHCEtM7duwIQGZmJpmZmcfkJyYmcuDAgYoK74xWadNCnHPrnHMB51wAaAPsB2YADwLznXNNgfn+NmaWAvQBWgC3AM+aWUxlxSciIiIiUtFO1Zzrm4H1zrnPgO7AFD99CtDDf94dyHHOHXLObQQKgGtOUXwiIiIiIiftVA2u+wDZ/vMfOue2Avg/f+CnNwI+D9vnCz9NRERERKRaqPTBtZmdBdwBTCuvaAlproT6hpjZEjNbsmPHjooIUURERESkQpyKM9e3Asucc9v87W1mdjGA/3O7n/4F0DhsvwRgC8U4515wzqU559Li4+MrMWwRERGRmuOPf/wjf/rTn6o6jBrvVAyuM/h+SgjAbGCA/3wAMCssvY+ZnW1mSUBT4KNTEJ+IiIhIjRcXF8d9991X1WHUeJU6uDazukBn4B9hyY8Bnc0s3897DMA5txp4DVgDzAGGOudKXmdGRERE5Az25Zdf0qdPH5KTk0lJSaFbt258+umntGzZ8piyAwcOJCkpiT/+8Y8EAgGuv/76iPzu3bvTrl27iLSsrCzq1q3L9u3bQ2lxcXHs3LmTQCBAIBDgoosuolGjRqHtw4cPExMTE9oOBAKhtbfPJJV6Exnn3H6gQbG0nXirh5RUfhwwrjJjEhEREalIraa0qtD6PhnwSZn5zjl69uzJgAEDyMnJAWD58uVs27at1H0mTJhAr169jkn/5ptvWLZsGXFxcWzcuJGkpKRQXsOGDXniiScYP358KK1BgwYsX74c8AbgcXFxjBo1KpQfGxsbyj9T6fbnIiIiItXIggULqFOnDvfcc08oLRAI0Lhx4zL2Ktnrr7/O7bffTp8+fUID9SKDBg0iNzeXr7/++qRjPpNocC0iIiJSjaxatYo2bdoc1z6ZmZmhqRr9+vULpWdnZ5ORkUFGRgbZ2dkR+8TFxTFo0CCefvrpqNspui170SM3N7f8nWqYSp0WIiIiIiJVr6RpIdu2baOgoIAbb7wRM6N27dqsWrUqYt72sGHDCAQCjBw5Mqp2NC1EZ65FREREqpUWLVqwdOnSk64nNzeXXbt2kZSURGJiIsFg8JipIeeffz59+/bl2WefPen2zhQaXIuIiIhUIzfddBOHDh3ixRdfDKV9/PHHfPbZZ8dVT3Z2NnPmzCEYDBIMBlm6dOkxg2uA+++/n+eff54jR46cdOxnAg2uRURERKoRM2PGjBnMmzeP5ORkWrRoQVZWFpdccgnr1q0jISEh9Jg2zbtBdvic60AgwKeffsqmTZu47rrrQvUmJSVRv359Pvzww4j2GjZsSM+ePTl06FC5sRWfc/3ggw9WbOerAXPumDuMVxtpaWluyZIlVdJ2VlZWlbR7KtTkvomIiJysvLw8mjdvXtVhSCUq6TU2s6XOubTy9tWZaxERERGRCqLBtYiIiIhIBdHgWkRERESkgmhwLSIiIiJSQTS4FhERERGpIBpci4iIiIhUEA2uRURERKqZuLi4iO3Jkydz3333RaSlpqaSkZERkTZw4ECSkpIIBAKkpqYyf/78UF7Hjh258sorSU1N5YYbbmDdunWhvB07dlCnTh2ef/75iPr27t3LL3/5S5KTk7nqqqto06ZNxM1tVq9ezU033cQVV1xB06ZN+cMf/kDRMtCTJ0+mVq1arFy5MlS+ZcuWBIPBEzsop4naVR2AiIiISHWW16xi17xuvjbvpOvIy8ujsLCQRYsWsW/fPurVqxfKmzBhAr169WLBggUMGTKE/Pz8UN7UqVNJS0vjhRdeIDMzk9mzZwMwbdo0rrvuOrKzs/nFL34RKj948GAuu+wy8vPzqVWrFjt27OBvf/sb4N1Q5o477uDPf/4zXbp0Yf/+/dx55508++yzDB06FICEhATGjRtHbm7uSff5dKEz1yIiIiI1zKuvvkr//v3p0qVLaIBcXLt27di8eXOJeR06dKCgoCC0nZ2dzRNPPMEXX3wR2mf9+vV89NFHjB07llq1vCFlfHw8v/nNb0Ix3HDDDXTp0gWAunXr8qc//YnHHnssVG96ejqrV6+OOEte3WlwLSIiIlLNFL/N+JgxYyLyc3Nz6d27NxkZGWRnZ5dYx5w5c+jRo0eJef/7v/9Lq1atAPj888/58ssvueaaa7jrrrtCZ5lXr15NampqaGBd3OrVq2nTpk1EWnJyMnv37mX37t0A1KpViwceeIBHHnkk+s6f5jS4FhEREalmYmNjWb58eejx+9//PpT38ccfEx8fz6WXXsrNN9/MsmXL2LVrVyg/MzOTyy67jJ/+9Kc8/PDDEfX269ePQCDAv//9byZOnAhATk4Od911FwB9+vQpdbA+btw4AoEAl1xyCQDOOcysxLLh6X379mXx4sVs3LjxBI7E6UeDaxEREZEaJDs7m7Vr15KYmEhycjK7d+/m9ddfD+VPmDCBgoICxo4dy4ABAyL2nTp1KsuXL2fmzJk0btw4VN/kyZNJTEzkjjvuYMWKFeTn55OSksKKFSsoLCwE4Le//S3Lly8PnZVu0aIFS5Ysiah/w4YNxMXFce6554bSateuzciRIxk/fnylHI9TTYNrERERkRqisLCQadOmsXLlSoLBIMFgkFmzZh1ztrlWrVoMHz6cwsJC5s6dW2p969atY9++fWzevDlU30MPPUROTg6XX345aWlpjB49mqNHjwJw8ODB0Gog/fr147333uPtt98GvKksw4YN44EHHjimnYEDB/L222+zY8eOijoUVUaDaxEREZEaYtGiRTRq1IhGjRqF0jp06MCaNWvYunVrRFkzY/To0Tz++OOl1pednU3Pnj0j0u68887QYP0vf/kLO3fu5PLLL6dNmzb86Ec/Cp2Bjo2NZdasWYwdO5Yrr7ySVq1a0bZt22OWDAQ466yzGDZsGNu3bz/hvp8urOi/i+ooLS3NFf+64VTJysqqknZPhZrcNxERkZOVl5dH8+YVu/yenF5Keo3NbKlzLq28fXXmWkRERESkgmhwLSIiIiJSQTS4FhERERGpIJU6uDaz881supmtNbM8M2tnZhea2Twzy/d/XhBW/iEzKzCzdWbWtTJjExERERGpaJV95vppYI5zrhmQCuQBDwLznXNNgfn+NmaWAvQBWgC3AM+aWUwlxyciIiIiZTh8+DBdunRhy5YtVR1KtVBpg2szqw90AP4K4Jw77Jz7BugOTPGLTQGK7rvZHchxzh1yzm0ECoBrKis+ERERESnfWWedxQsvvEBeXl5Vh1ItVOaZ68uAHcBLZvYfM/uLmdUDfuic2wrg//yBX74R8HnY/l/4aRHMbIiZLTGzJTVhoXERERGR4xUXF1dqXmpqKhkZGaXmZ2Vl0ahRIwKBAIFAgAcffBCAjh07HnNHxYULF3LeeefRo0cPRo4cSSAQCN0U5ssvv6RPnz4kJyeTkpJCt27d+PTTTwkGg7Rs2fKYNotupz5w4ECSkpJC7V9//fUndAxOV7Urue6rgV855z40s6fxp4CUoqSbzx+zCLdz7gXgBfDWua6IQEVERERO1DP3vFOh9Q197qYT3jcvL4/CwkIWLVrEvn37qFevXonlRowYwahRo6Kqs3379rzxxhsRac45evbsyYABA8jJyQFg+fLlbNu2LXTb9LJMmDCBXr16RdV+dVOZZ66/AL5wzn3ob0/HG2xvM7OLAfyf28PKh78aCYAm94iIiIhE6dVXX6V///506dKF2bNnV1o7CxYsoE6dOtxzzz2htEAgQPv27Sutzeqi0gbXzrkvgc/N7Eo/6WZgDTAbGOCnDQBm+c9nA33M7GwzSwKaAh9VVnwiIiIiNU1ubi69e/cmIyMjdIvykkyaNCk0LWM6nVjhAAAgAElEQVTu3Lll1vnuu++GygYCAdavX8+qVato06ZNqfusX78+Yp/nnnsuIj8zMzOU169fv+Pr5GmuMqeFAPwKmGpmZwEbgJ/jDehfM7O7gU3ATwCcc6vN7DW8AfgRYKhz7mglxyciIiJSI3z88cfEx8dz6aWXkpCQwKBBg9i1axcXXHDBMWVPdlpIeZKTk1m+fHloOysrKyK/Jk8LqdTBtXNuOVDSPdhvLqX8OGBcZcYkIiIiUhNlZ2ezdu1aEhMTAdi9ezevv/46gwcPrvC2WrRowfTp0yu83ppAd2gUERERqeYKCwuZNm0aK1euJBgMEgwGmTVrVplTQ07GTTfdxKFDh3jxxRdDaR9//DH/+te/KqW96kSDaxEREZFqZv/+/SQkJIQeTz31FI0aNaJRo+9XMe7QoQNr1qxh69atUdd72223her8yU9+Ahw753r69OmYGTNmzGDevHkkJyfTokULsrKyuOSSS6JqJ3zOdSAQ4PDhw8d3AE5j5lz1Xc0uLS3NFV+P8VQpPneoJqnJfRMRETlZeXl5NG/evKrDkEpU0mtsZkudcyVNd45Q2Rc0ioiISAVpNaVVVYdQaT4Z8ElVhyBSITQtRERERESkgmhwLSIiIiJSQTS4FhERERGpIBpci4iIiJwhjhw5Qs+ePbnrrrsoLCys6nBqJF3QKCIiInKGKCgoYMyYMRw+fJiNGzeSnJxc1SHVODpzLSIiIlLNmBn9+/cPbR85coT4+HjS09MjynXv3p127dqFtps1a8asWbN49913efLJJwkEAqSkpBAbGxuxjvXAgQNJSkoiEAiQmprK/PnzQ3V07NiRK6+8MlS+pNuYb9u2jfT0dFJTU0lJSaFbt24ABINBWrZsCcDChQsxM/7617+G9vvPf/6DmTFx4kQAnHOMHTuWpk2bcsUVV9CpUydWr14dKp+YmMidd94Z2i6KvSrpzLWIiIjISXiid3r5hY7DyNw3yi1Tr149Vq1axYEDB4iNjWXevHkRN5AB+Oabb1i2bBlxcXFs3LiRpKSkiPxnnnkG8Aa86enpLF++PJT3xhtvMGHCBHr16sWCBQsYMmQI+fn5ofypU6eSllb6ks9jxoyhc+fODB8+HICVK1eWWK5Vq1bk5uZy9913A5CTk0NqampEjO+//z4rVqygbt26/POf/+SOO+5g9erVnHPOOQAsWbKE1atX06JFi3KP26mgM9ciIiIi1dCtt97Km2++CUB2djYZGRkR+a+//jq33347ffr0IScn54TbadeuHZs3bz6ufbZu3UpCQkJou3Xr1iWWa9KkCQcPHmTbtm0455gzZw633nprKH/8+PH8z//8D3Xr1gWgS5cuXH/99UydOjVUZtSoUTzyyCPHFV9l0uBaREREpBoqGjQfPHiQlStXcu2110bkFw24MzIyyM7OPuF25syZQ48ePSLS+vXrF5oWkpmZecw+Q4cO5e6776ZTp06MGzeOLVu2lFp/r169mDZtGu+//z5XX301Z599NgC7d+9m3759x8wLT0tLi5gactddd7Fs2TIKCgpOuI8VSdNCRERERKqh1q1bEwwGyc7ODs1pLrJt2zYKCgq48cYbMTNq167NqlWrQvOdo5GZmckDDzzA9u3bWbx4cUReedNCunbtyoYNG5gzZw5vvfUWV111FatWrSqx7F133UXv3r1Zu3YtGRkZvP/++2XG5ZzDzELbMTExZGZm8uijj0ac9a4qOnMtIiIiUk3dcccdjBo16pgpIbm5uezatYukpCQSExMJBoPHPTVkwoQJFBQUMHbsWAYMGHDcsV144YX07duXV155hbZt27Jo0aISy1100UXUqVOHefPmcfPNN4fS69evT7169diwYUNE+WXLlpGSkhKR1r9/fxYtWsSmTZuOO86KpsG1iIiISDU1aNAgxowZQ6tWrSLSs7OzmTNnDsFgkGAwyNKlS09o3nWtWrUYPnw4hYWFzJ07N+r93nnnHfbv3w/Anj17WL9+PU2aNCm1/O9//3vGjx9PTExMRHpmZibDhg3jwIEDALz99tu899579O3bN6JcnTp1GDFiBE899VTUMVYWTQsRERERqaYSEhJCK3IUCQaDbNq0ieuuuy6UlpSURP369fnwww+Puw0zY/To0Tz++ON07doV8OZcx8bGAtCwYUPefvvtiH2WLl3KfffdR+3atSksLGTw4MG0bduWYDBYYhvXX399iem/+tWv2LVrF61atSImJoaLLrqIWbNmhdoOd/fddzN27Njj7l9FM+dcVcdwwtLS0tySJUuqpO2srKwqafdUqMl9ExGpzlpNaVV+oWrqkwGfVHUIUcvLy6N58+ZVHYZUopJeYzNb6pwrfaK5T9NCREREREQqiAbXIiIiIiIVRINrEREREZEKUu7g2sxuMLN6/vOfmtmTZnZp5YcmIiIiIlK9RHPm+s/AfjNLBR4APgNertSoRERERESqoWgG10ect6RId+Bp59zTwLmVG5aIiIiISPUTzeB6j5k9BPwUeNPMYoA60VRuZkEz+8TMlpvZEj/tQjObZ2b5/s8Lwso/ZGYFZrbOzLqeSIdEREREarqYmBgCgUDoEQwGWbhwIeeddx5XXXUVzZo1Y9SoURH7zJw5k9atW9OsWTNatWrFzJkzQ3mLFy/m2muvJRAI0Lx589CyvJMnT+a+++4DvKV669aty/bt20P7xcXFlRrTY489VolH4PQVzU1kegN9gbudc1+aWRNgwnG00ck591XY9oPAfOfcY2b2oL/9GzNLAfoALYBLgLfN7Arn3NHjaEtERETklPriwXcrtL6Ex9qXWyY2Npbly5dHpAWDQdq3b88bb7zBgQMHuOqqq+jZsyc33HADK1asYNSoUcybN4+kpCQ2btxI586dueyyy2jdujUDBgzgtddeIzU1laNHj7Ju3boS223YsCFPPPEE48ePjyqmM1E0Z65HOOeedM69C+Cc24Q3AD5R3YEp/vMpQI+w9Bzn3CHn3EagALjmJNoREREROSPFxsYSCATYvHkzABMnTuThhx8mKSkJ8O7Y+NBDDzFhgne+dPv27Vx88cWAdwY6JSWlxHoHDRpEbm4uX3/99SnoRfUUzeC6cwlpt0ZZvwP+aWZLzWyIn/ZD59xWAP/nD/z0RsDnYft+4aeJiIiISJgDBw6Epl/07NnzmPxdu3aRn59Phw4dAFi9ejVt2rSJKJOWlsbq1asBGDFiBFdeeSU9e/bk+eef5+DBgyW2GxcXx6BBg3j66afLjCkQCJCbm3uy3ayWSp0WYma/BO4FLjOzlWFZ5wL/jrL+G5xzW8zsB8A8M1tbRlkrIe2Ye7P7g/QhAE2aNIkyDBEREZGao7QpGO+++y6tW7dm3bp1PPjgg1x00UUAOOcwixxqhaeNGTOGfv368c9//pNXX32V7OxsFi5cWGLbw4YNIxAIMHLkyKhiOtOUdeb6VeB2YLb/s+jRxjn302gqd85t8X9uB2bgTfPYZmYXA/g/i2bFfwE0Dts9AdhSQp0vOOfSnHNp8fHx0YQhIiIickZo3749K1eu5JNPPuHPf/5zaLDbokULlixZElF22bJlEdM/kpOT+eUvf8n8+fNZsWIFO3fuLLGN888/n759+/Lss89WXkeqsVIH1865b51zQedcBnA+3w+uG5e2Tzgzq2dm5xY9B7oAq/AG6wP8YgOAWf7z2UAfMzvbzJKApsBHx98lERERkTPbFVdcwUMPPRS68HDUqFE8+uijBINBwLv48ZFHHgmdfX7zzTfxVl6G/Px8YmJiOP/880ut//777+f555/nyJEjlduRaqjc1ULMbBjeNIx/+El/N7MXnHP/U86uPwRm+F831AZedc7NMbOPgdfM7G5gE/ATAOfcajN7DVgDHAGGaqUQERERkRNzzz33MHHiRDZu3EggEGD8+PHcfvvtfPfdd9SpU4fHH3+cQCAAwCuvvMKIESOoW7cutWvXZurUqcTExJRad8OGDenZsyeTJk0KpRXNuS5yyy23nJHL8VnRfymlFvDmW7dzzu3zt+sBHzjnWp+C+MqUlpbmin/FcaoUrf9YE9XkvomIVGetprSq6hAqzScDPqnqEKKWl5dH8+bNqzoMqUQlvcZmttQ5l1bevtGsFmJA+Bnko5R88aGIiIiIyBktmpvIvAR8aGYz/O0ewF8rLyQRERERkeqp3MG1c+5JM1sI3Ih3xvrnzrn/VHZgIiIiIiLVTTRnrgE24l1kWBswM7vaObes8sISEREREal+olkt5A/AQGA939/UxQE3VV5YIiIiIiLVTzRnru8Ckp1zhys7GBERERGR6iya1UJW4d1ERkRERESqsSNHjtCzZ0/uuusuCgsLqzqcGimaM9ePAv8xs1XAoaJE59wdlRaViIiIiFS4goICxowZw+HDh9m4cSPJyclVHVKNE82Z6ynAeOAx4Imwh4iIiIhUATOjf//+oe0jR44QHx9Penp6RLnu3bvTrl270HazZs2YNWsW7777Lk8++SSBQICUlBRiY2MJBAIEAgGmT5/OwIEDSUpKIhAIkJqayvz580N1dOzYkSuvvDJUvlevXsfEN3nyZO67776ItNTUVDIyMo4pO3HiRJo1a0bLli1JTU3l5ZdfBuDw4cP8+te/Jjk5mcsvv5z09HQ2bdoEeLdvb9myZUQ9WVlZTJw4ESAi/kAgwPXXXw/Atm3bSE9PJzU1lZSUFLp161b+wT5O0Zy5/so598cKb1lERESkBqjoOxtHU1+9evVYtWoVBw4cIDY2lnnz5tGoUaOIMt988w3Lli0jLi6OjRs3kpSUFJH/zDPPAN5ANT09neXLl4fy3njjDSZMmECvXr1YsGABQ4YMIT8/P5Q/depU0tLKvVlhSF5eHoWFhSxatIh9+/ZRr149AJ577jnmzZvHRx99RP369fn222+ZOXMmAA8//DB79uzh008/JSYmhpdeeonu3buzdOnSqNosij/cmDFj6Ny5M8OHDwdg5cqVUfchWtGcuV5qZo+aWTszu7roUeGRiIiIiEjUbr31Vt58800AsrOzjzkr/Prrr3P77bfTp08fcnJyTriddu3asXnz5pOK9dVXX6V///506dKF2bNnh9IfeeQRnn32WerXrw/Aeeedx4ABA9i/fz8vvfQSkyZNIiYmBoCf//znxMXF8fbbb59wHFu3biUhISG03bp16xOuqzTRDK6vAq4DHuH7KSETKzwSEREREYla0aD54MGDrFy5kmuvvTYiv2jAnZGRQXZ29gm3M2fOHHr06BGR1q9fv9CUi8zMzHLryM3NpXfv3hGx7Nmzhz179pQ477ugoIAmTZqEBt1F0tLSWLNmTVRxZ2ZmhmLs168fAEOHDuXuu++mU6dOjBs3ji1btkRV1/GI5g6NnSq8VRERERE5Ka1btyYYDJKdnX3M3OFt27ZRUFDAjTfeiJlRu3ZtVq1adcw85bJkZmbywAMPsH37dhYvXhyRdzzTQj7++GPi4+O59NJLSUhIYNCgQezatYuYmBjMrMR9nHMl5jnn3XKltP3C00uaFtK1a1c2bNjAnDlzeOutt7jqqqtYtWoV8fHxUfUlGtGcuRYRERGR09Add9zBqFGjjpkSkpuby65du0hKSiIxMZFgMHjcU0MmTJhAQUEBY8eOZcCAASccY3Z2NmvXriUxMZHk5GR2797N66+/Tv369alXrx4bNmw4Zp/LL7+czz77jD179kSkL1u2jLS0NBo0aMCuXbsi8r7++msaNmxYbjwXXnghffv25ZVXXqFt27YsWrTohPtWEg2uRURERKqpQYMGMWbMGFq1ahWRnp2dzZw5cwgGgwSDQZYuXXpC865r1arF8OHDKSwsZO7cuce9f2FhIdOmTWPlypWhWGbNmhWaGvLQQw8xdOhQdu/eDcDu3bt54YUXqFevHgMGDOD+++/n6NGjALz88succ8453HDDDcTFxXHxxReHVjH5+uuvmTNnDjfeeGOZ8bzzzjvs378f8KalrF+/niZNmhx3v8oSzWohIiIiInIaSkhICK18USQYDLJp0yauu+66UFpSUhL169fnww8/PO42zIzRo0fz+OOP07VrV8Cbcx0bGwtAw4YNj7nI8MiRI5x99tksWrSIRo0aRaxk0qFDB9asWcPWrVv55S9/yd69e2nbti116tShTp06jBw5EoBHH32UzMxMrrzySg4cOEB8fDwffPBBaOrHyy+/zNChQ0Plf/e730XM387MzGTs2LGh7Y8++oilS5dy3333Ubt2bQoLCxk8eDBt27Y97mNS5vEqmrtSagGzusBIoIlz7r/MrClwpXPujQqN5ASkpaW5JUuWVEnbFb3szumkJvdNRKQ6azWlVfmFqqlPBnxS1SFELS8vj+bNm1d1GKe1ESNG0LRpU+69994Kqe/LL7/klltu4d5772XIkCEVUmdZSnqNzWypc67ciebRnLl+CVgKFK1A/gUwDajywbWIiIiInF5uvfVWDh8+XKEn6y666KKIdbhPZ9EMrpOdc73NLAPAOXfASrtEU0RERETOaG+99VZVh1Clormg8bCZxQIOwMySgUOVGpWIiIiISDUUzZnr3wFzgMZmNhW4ARhYmUGJiIiIiFRH0dxEZp6ZLcO7S6MBw51zX1V6ZCIiIiIi1Uypg2szu7pY0lb/ZxMza+KcW1Z5YYmIiIiIVD9lzbl+wn88A3wIvAC86D//Y+WHJiIiIiIliYuLKzUvNTX1mDs2hsvKysLMKCgoCKVNmjQJM6NoiePExERatWpFIBAgEAgwbNgwAAYOHEhSUhKBQIDU1NTQTVwAOnbsSPElkidPnsx9990XkRZebu/evfziF78gOTmZFi1a0KFDh9Ba3MX7GF5XVlYWjRo1CsUXCAT45ptvSu3zqVTqmWvnXCcAM8sBhjjnPvG3WwKjTk14IiIiIqe3+e8kl1/oONx80/oT3jcvL4/CwkIWLVrEvn37qFevXonlWrVqRU5ODqNHjwZg+vTppKSkRJRZsGBBibcTnzBhAr169WLBggUMGTKE/Pz8E4538ODBJCUlkZ+fT61atdiwYQN5eXlR7TtixAhGjTr9hqTRrBbSrGhgDeCcWwUEom3AzGLM7D9m9oa/faGZzTOzfP/nBWFlHzKzAjNbZ2Zdj6cjIiIiIme6V199lf79+9OlSxdmz55darkePXowa9YsADZs2MB5551HfHz8cbXVrl07Nm/efMKxrl+/ng8//JCxY8dSq5Y3JL3sssu47bbbTrjO00E0g+s8M/uLmXU0s/9jZi8C0f1L4RlerPyDwHznXFNgvr+NmaUAfYAWwC3As2YWcxztiIiIiJzRcnNz6d27NxkZGWRnZ5darn79+jRu3JhVq1aRnZ1N7969jynTqVOn0JSLSZMmHZM/Z84cevToccKxrl69mkAgQExMycO9AwcOREz7GDNmTET+pEmTQnmdOnU64TgqWjRL8f0c+CXeIBlgEfDnaCo3swTgNmAccL+f3B3o6D+fAiwEfuOn5zjnDgEbzawAuAb4IJq2RERERM5kH3/8MfHx8Vx66aUkJCQwaNAgdu3axQUXXFBi+T59+pCTk8PcuXOZP38+L730UkR+adNCMjMzeeCBB9i+fTuLFy8uM6bS7jsYzf0IY2NjI+7KOHny5Ig53dV2Wohz7qBzbpJzrqf/mOScOxhl/U8BDwCFYWk/dM5t9eveCvzAT28EfB5W7gs/TURERETKkZ2dzdq1a0lMTCQ5OZndu3fz+uuvl1r+9ttv55VXXqFJkybUr18/6nYmTJhAQUEBY8eOZcCAAWWWbdCgAbt27YpI+/rrr2nYsCEtWrRgxYoVFBYWlrJ39RTNtJATYmbpwHbn3NJodykhzZVQ7xAzW2JmS3bs2HFSMYqIiIjUBIWFhUybNo2VK1cSDAYJBoPMmjWrzKkhsbGxjB8/nt/+9rfH3V6tWrUYPnw4hYWFzJ07t9Rybdu25d///jdffvklAEuWLOHQoUM0btyY5ORk0tLS+N3vfodz3pAvPz8/NBe8uopmWsiJugG4w8y6AecA9c3s78A2M7vYObfVzC4GtvvlvwAah+2fAGwpXqlz7gW8ZQFJS0s7ZvAtIiIiUtPt37+fhISE0Pb9999Po0aNaNTo+y/9O3TowJo1a9i6dSsXX3xxifX06dOn1DY6deoUmg/dunVrXn755Yh8M2P06NE8/vjjdO3qrUNx2223UadOHcC74HHatGk8/fTTdOvWjcLCQuLi4sjOzg5dwPiXv/yFkSNHcvnll1O3bl0aNGjAhAkTojoGkyZN4u9//3toe+bMmSQmJka1b2Wyov8Uyi1oVs85t++EGjHrCIxyzqWb2QRgp3PuMTN7ELjQOfeAmbUAXsWbZ30J3sWOTZ1zR0urNy0tzRVfT/FUycrKqpJ2T4Wa3DcRkeqs1ZRWVR1CpflkwCflFzpN5OXl0bx586oOQypRSa+xmS11zqWVt2+500LM7HozW4O/4oeZpZrZsycaLPAY0NnM8oHO/jbOudXAa8AaYA4wtKyBtYiIiIjI6SaaaSGTgK7AbADn3Aoz63A8jTjnFuKtCoJzbidwcynlxuGtLCIiIiIiUu1EdUGjc+7zYkk6oywiIiIiUkw0Z64/N7PrAWdmZwHDOL6byIiIiIiInBGiOXN9DzAUb83pL/BufT60MoMSEREREamOyjxz7d9+vL9zrt8pikdEREREpNoq88y1v1pH91MUi4iIiIhEISYmhkAgQGpqKldffTXvv/8+AMFgkNjYWAKBACkpKfzsZz/ju+++A2DhwoWkp6cD3q3Ea9WqxcqVK0N1tmzZkmAwCEBiYiKtWrWiVatWpKSkMHr0aA4dOhQRw6RJkzjnnHP49ttvAdi3bx8NGjQIbRfp0aMHr732GuCtRd26dWuaNWtGq1atmDlzZqjcwIEDmT59OgAdO3YkLe37Ve+WLFlCx44dQ/0477zzCAQCocfbb799UsezIkUz5/rfZvYnIBcIrXPtnFtWaVGJiIiIVBMXLVheofV92SlQbpnY2FiWL/fanTt3Lg899BD/+te/AEhOTmb58uUcPXqUzp0789prr9Gv37GTEBISEhg3bhy5ubkltrFgwQIaNmzI3r17GTJkCEOGDGHKlCmh/OzsbNq2bcuMGTMYOHAg9erVo0uXLsycOTN0W/Rvv/2W9957j1dffZUVK1YwatQo5s2bR1JSEhs3bqRz585cdtlltG7d+pj2t2/fzltvvcWtt956TF779u154403yj1OVSGaOdfXAy2A3wNP+I+JlRmUiIiIiERn9+7dXHDBBcekx8TEcM0117B58+YS90tPT2f16tWsW7euzPrj4uJ47rnnmDlzJl9//TUA69evZ+/evYwdOzbiFusZGRnk5OSEtmfMmMEtt9xC3bp1mThxIg8//DBJSUkAJCUl8dBDD5V6R8bMzEzGjh1bdudPQ+UOrp1znUp43HQqghMRERGRYx04cIBAIECzZs0YPHgw//3f/31MmYMHD/Lhhx9yyy23lFhHrVq1eOCBB3jkkUfKba9+/fokJSWRn58PeGetMzIyaN++PevWrWP79u0A3HLLLSxdupSdO3cCkJOTQ0ZGBgCrV6+mTZs2EfWmpaWxevXqEtts164dZ599NgsWLDgm7913342YFrJ+/fpy+3CqRHOHxgZm9kczW2ZmS83saTNrcCqCExEREZFjFU0LWbt2LXPmzOFnP/sZzjnAO6scCARo0KABTZo0KXHKRZG+ffuyePFiNm7cWG6bRfWDN2ju06cPtWrV4sc//jHTpk0D4KyzzuKOO+5g+vTpfPXVVyxfvpwuXbqE9jezY+osnhZu9OjRJZ69bt++PcuXLw89kpOTy43/VIlmWkgOsAO4E+jlPy95co6IiIiInFLt2rXjq6++YseOHcD3c64LCgpYvHgxs2fPLnXf2rVrM3LkSMaPH19mG3v27CEYDHLFFVewcuVK8vPz6dy5M4mJieTk5JQ4NWT69Ol0796dOnXqANCiRQuWLFkSUe+yZctISUkptd2bbrqJgwcPsnjx4nKPw+kimsH1hc65PzjnNvqPscD5lR2YiIiIiJRv7dq1HD16lAYNIicWXHzxxTz22GM8+uijZe4/cOBA3n777dDgvLi9e/dy77330qNHDy644AKys7PJysoiGAwSDAbZsmULmzdv5rPPPgOgU6dO5Ofn88wzz4SmhACMGjWKRx99NLQiSTAY5JFHHmHkyJFlxvfb3/6Wxx9/vLzDcNqIZnC9wMz6mFkt/3EX8GZlByYiIiIiJSuacx0IBOjduzdTpkwhJibmmHI9evRg//79vPvuu6XWddZZZzFs2LDQvOkinTp1omXLllxzzTU0adKE559/HvCmhPTs2TOibM+ePUMXMtaqVYs777yTnTt30qFDh1CZQCDA+PHjuf3222nWrBm33347jz/+OIFA2aujdOvWjfj4+Ii04nOui5bwOx1Y+PyZiAyzPYADDKgHFPpZtYC9zrn6pyTCMqSlpbniXy+cKllZWVXS7qlQk/smIlKdtZrSqqpDqDSfDPikqkOIWl5eHs2bN6/qMKQSlfQam9lS51xaKbuElLrOtXPu3AqITURERETkjBHNTWQws9ZAYnh559w/KikmEREREZFqqdzBtZn9DWgNrOb7qSEO0OBaRERERCRMNGeur3POlb5GioiIiIiIANGtFvKBmWlwLSIiIiJSjmjOXE/BG2B/CRzCWz3EOedKv92PiIiIiMgZKJoz138D+gO3ALcD6f5PEREREakCZkb//v1D20eOHCE+Pp709PSIct27d6ddu3YRaVlZWTRq1IhAIEBKSkrE3RUHDhwYsWb0jh07qFOnTmiN6yKJiYnceeedoe3p06czcOBAACZPnkx8fHzEOtRr1qw56T5XF9Gcud7knCv9vpkiIiIiZ7DEByv23nrBx24rt0y9evVYtWoVB9W2fnMAAB13SURBVA4cIDY2lnnz5tGoUaOIMt988w3Lli0jLi6OjRs3kpSUFMobMWIEo0aNIj8/nzZt2tCrV6/QbcrDTZs2jf/f3r2HV1Wd+x7//ggqUEWlYjcSKTmIQrhqA2qVVo9VQEVFLARvqFQ9Fq2b1ipeNofdPiitR2m31u7anha2lZVwUPGu9YL1fgGMCAKKGiFqBaltvWELvOePNclOSAIhXSszIb/P8+TJWmPOMeY7Bwt4M/LOOQ877DAymQwXXnhhrW0LFy5k2bJl9OvXr06/cePGcfPNNzf2lHcqjVm5XiFpjqTxkk7d8pX3yMzMzMysQSNHjuT++7OJfSaTqfWocYA77riDUaNGUVpaWv30xK317t2bTp068dFHH9W7PZPJcMMNN1BVVcW7775ba9tll13Gtddem4Mz2bk0JrnuSLbW+jiy5SBbSkPMzMzMLCVbkuYNGzawZMkSDj300FrbtyTc48ePr1X6UdPixYvp3bs3++67b51ta9as4U9/+hNDhw5l7NixlJeX19o+duxYFi9ezKpVq+r0LS8vr1UW8vnnn/8TZ9q6bLcsJCLObY5AzMzMzKzxBg4cSGVlJZlMhuOPP77Wtg8++IBVq1Zx5JFHIon27duzdOlS+vfvD8DMmTP59a9/zVtvvcVDDz1U7/hlZWWMHTsWyCbyEydO5Pvf/3719oKCAn74wx9y3XXXMXLkyFp9XRayDZIKJd0laa2kDyTdIamwOYIzMzMzs4addNJJXHbZZXVKQsrLy/noo48oKiqiZ8+eVFZW1ioNmTx5MitXrqS8vJyzzz6bDRs21Bk7k8kwa9YsevbsyUknncQrr7zCG2+8UWufs846iyeffJLVq1fn5wRbocaUhfwOuAfYD+gO3Ju0bZOkDpJelPSKpGWS/j1p7yLpEUlvJN/3rtHnSkmrJK2UNLxpp2RmZmbWNpx33nlMnTqVAQMG1GrPZDI89NBDVFZWUllZyaJFi+qtuz711FMpKSlh9uzZtdpXrlzJp59+yrvvvls9xpVXXllnjF122YXJkyfzs5/9LPcn10o1JrnuGhG/i4iNydcsoGsj+n0B/M+IGAQMBkZIOgyYAjwWEb2Bx5L3JA+qKQX6kb3t3y2SCnb4jMzMzMzaiMLCQi699NJabZWVlaxevZrDDjusuq2oqIjOnTvzwgsv1Blj6tSp3HjjjWzevLm6LZPJMHr06Fr7jRkzpt7a7YkTJ7Jx48ZabVvXXD/77LNNOr/WSBGx7R2kR4FZwJbZHA+cGxHHNPogUifgaeAi4L+AoyLifUndgCci4iBJVwJExHVJn4eBaRHxXEPjlpSUxMKFCxsbRk5NmzYtleM2h5353MzMWrMBswdsf6dW6tUJr6YdQqMtX76cvn37ph2G5VF9f8aSFkVEyfb6Nmbl+jxgLPAn4H3gtKRtuyQVSKoA1gKPRMQLwFci4n2A5PuWy1O7A2tqdK9K2szMzMzMWoXG3C1kNXBSUwaPiE3AYEl7AXdJ6r+N3VXfEHV2ki4ALgDo0aNHU8IyMzMzM8uLBpNrSVO30S8i4seNPUhE/EXSE2RrqT+Q1K1GWcjaZLcqYP8a3QqB9+oZ61bgVsiWhTQ2BjMzMzOzfNtWWcin9XwBTASu2N7AkromK9ZI6gh8C1hB9s4jE5LdJgB3J6/vAUol7SapCOgNvLhDZ2NmZmZmlqIGV64j4oYtryXtAVwKnAuUATc01K+GbsDs5I4f7YC5EXGfpOeAuZImAquBbyfHWyZpLvAasBGYlJSVmJmZmZm1CtusuZbUBfg+cAYwGzgkIup/+PxWImIJcHA97euBeu80EhHTgemNGd/MzMzMrKVpsCxE0vXAS8DHwICImNbYxNrMzMzM8mf33Xev0zZt2jS6d+9e6/7Sf/nLX+rs9/rrr3P88cdzwAEH0LdvX8aOHcsHH3wAwNNPP83QoUPp06cPffr04dZbb601viRWrVpV3TZz5kwkseXWyD179mTYsGG1jjd48ODqx64/8cQTnHjiiUD2Ee0nnngigwYNori4uPoR7ps3b+Z73/se/fv3Z8CAAQwZMoS33367eryXX34ZSTz88MP1zkllZSUdO3Zk8ODBFBcXc/bZZ/OPf/yj+vh77rlnrTl69NFHGzPljbatlesfkH0QzDXA1VL1zTxE9oLGzjmNxMzMzKw1mrZnjsf7a5O7Tp48mcsuu6zB7Rs2bOCEE07gxhtvZNSoUQAsWLCAdevWERGcfvrpzJ8/n0MOOYQPP/yQ4cOH0717d0444QQABgwYQFlZGddccw0A8+bNo7i4uNYxPv74Y9asWcP+++/P8uXLG4xl6tSpHHvssdUPwVmyZAmQfQDNe++9x5IlS2jXrh1VVVV86Utfqu6XyWQ48sgjyWQyDB9e/wO9e/XqRUVFBZs2beLYY49l7ty5nHHGGQAMGzaM++67b5vz+M9ocOU6ItpFRMeI2CMiOtf42sOJtZmZmVnrM2fOHA4//PDqxBrg6KOPpn///vziF7/gnHPO4ZBDDgFgn3324ac//SkzZsyo3veUU07h7ruz96J466232HPPPenatfaDu8eOHUt5eTmQTYTHjx9fbyzvv/8+hYWF1e8HDhxY3d6tWzfatcumqYWFhey9994ARATz5s1j1qxZ/OEPf2DDhg3bPN+CggKGDh3Ku+++u/3JyZHGPETGzMzMzFqBmTNnVpc7HH300XW2L126lK997Wv19l22bFmdbSUlJSxbtqz6fefOndl///1ZunQpmUyGcePG1RnntNNO48477wTg3nvvrZXI1zRp0iQmTpzI0UcfzfTp03nvvewdmMeOHcu9997L4MGD+cEPfsDLL79c3eeZZ56hqKiIXr16cdRRR/HAAw9scz42bNjACy+8wIgRI6rbnnrqqVplIW+++eY2x9hRTq7NzMzMdhKTJ0+moqKCiooKFixYsEN9I4IaZcDVtm4rLS2lrKyM+fPnM3r06Dr7d+nShb333puysjL69u1Lp06d6j3e8OHDeeuttzj//PNZsWIFBx98MOvWraOwsJCVK1dy3XXX0a5dO4455hgee+wxILsSXlpaWh1HJpOpd+w333yTwYMH8+Uvf5kePXpUr4pDtixkyxxVVFTQq1evxk1QIzm5NjMzM2sj+vXrx6JFixrctuXCxC0WLVpUp6Z61KhR3HbbbfTo0YPOneuvFB43bhyTJk1qsCRkiy5dunD66adz2223MWTIEJ588kkAdtttN0aOHMn111/PVVddxfz589m0aRN33HEHP/rRj+jZsyeXXHIJDz74IB9//HGdcbfUXK9atYrnn3+ee+65Z5tx5JKTazMzM7M24vTTT+fZZ5/l/vvvr2576KGHePXVV5k0aRKzZs2ioqICgPXr13PFFVdw+eWX1xqjY8eO/OQnP+Hqq69u8DijR4/m8ssvb/CCQ4DHH3+czz77DMheBPnmm2/So0cPFi9eXF0isnnzZpYsWcJXv/pVHn30UQYNGsSaNWuorKzknXfeYcyYMcyfP7/BY3Tr1o0ZM2Zw3XXXbX9ycsTJtZmZmVkr89lnn1FYWFj9deONNwK1a64HDx5MZWVlrX4dO3bkvvvu46abbqJ3794UFxcza9Ys9t13X7p168bvf/97zj//fPr06cPXv/51zjvvvHprpktLS6svfKzPHnvswRVXXMGuu+7a4D6LFi2ipKSEgQMHcvjhh/Od73yHIUOGsHbtWkaNGkX//v0ZOHAg7du35+KLLyaTydQpQxkzZgxz5szZ5lydcsopfPbZZzz11FNA3ZrrefPmbbP/jlJE5HTA5lRSUhJb//qiuUybNi2V4zaHnfnczMxaswGzB6QdQt68OuHVtENotOXLl9O3b9+0w7A8qu/PWNKiiCjZXl+vXJuZmZmZ5YiTazMzMzOzHHFybWZmZmaWI06uzczMzMxyxMm1mZmZmVmOOLk2MzMzM8sRJ9dmZmZmrdBdd92FJFasWAFAZWUlkrjpppuq97n44ouZNWsWAM8//zyHHnoogwcPpm/fvtW33p01axZdu3atde/n1157jcrKSvr371/nuOeccw6dOnWq9WTESy+9FEl8+OGHABQUFNQab8aMGXmahZanfdoBmJmZmbVmub7/eGPv+Z3JZDjyyCMpKyurTpT33Xdffv7zn3PhhRfWeYDLhAkTmDt3LoMGDWLTpk2sXLmyetu4ceO4+eaba+2/9QNoajrggAO4++67OfPMM9m8eTMLFiyge/fu1ds7duxY/aTHtsbJtZmZWSvx8fK2s/pn2/bJJ5/wzDPPsGDBAk466aTq5Lpr164cccQRzJ49m/PPP79Wn7Vr19KtWzcgu7JcXFzc5OOPHz+e8vJyzjzzTJ544gmOOOIIHnzwwSaPtzNxWYiZmZlZKzN//nxGjBjBgQceSJcuXVi8eHH1tilTpnDDDTewadOmWn0mT57MQQcdxOjRo/nVr37Fhg0bqreVl5fXKuP4/PPPt3n83r17s27dOj766CMymQylpaW1tn/++ee1xisvL8/BWbcOTq7NzMzMWpmaCW1paSmZTKZ6W1FREUOHDmXOnDm1+kydOpWFCxdy3HHHMWfOHEaMGFG9bdy4cVRUVFR/dezYcbsxnHrqqZSVlfHCCy8wbNiwWtu2lIVs+Ro3btw/c7qtistCzMzMzFqR9evX8/jjj7N06VIksWnTJiTx3e9+t3qfq666itNOO41vfOMbtfr26tWLiy66iPPPP5+uXbuyfv36JsdRWlrKIYccwoQJE2jXzuu1W3gmzMzMzFqRefPmcfbZZ/POO+9QWVnJmjVrKCoqoqqqqnqfPn36UFxczH333Vfddv/99xMRALzxxhsUFBSw1157NTmOHj16MH369FpJvXnl2szMzKxVyWQyTJkypVbbmDFjuPbaa2u1XX311Rx88MHV72+77TYmT55Mp06daN++PbfffjsFBQVAtub66aefrt73lltuYb/99mPlypUUFhZWt8+cObPWMS688MJ6Y9xSc73FiBEj2szt+LTlJ5jWqKSkJBYuXJjKsbdclbsz2pnPzcysNes55f60Q8ibyhknpB1Coy1fvpy+ffumHYblUX1/xpIWRUTJ9vq6LMTMzMzMLEfyllxL2l/SAknLJS2TdGnS3kXSI5LeSL7vXaPPlZJWSVopaXi+YjMzMzMzy4d8rlxvBH4QEX2Bw4BJkoqBKcBjEdEbeCx5T7KtFOgHjABukVSQx/jMzMzMzHIqb8l1RLwfEYuT1x8Dy4HuwMnA7GS32cApyeuTgbKI+CIi3gZWAUPzFZ+ZmZlZU7Xma9Zs2/7ZP9tmqbmW1BM4GHgB+EpEvA/ZBBzYN9mtO7CmRreqpG3rsS6QtFDSwnXr1uUzbDMzM7M6OnTowPr1651g74QigvXr19OhQ4cmj5H3W/FJ2h24A/jXiPibpAZ3raetzqc2Im4FboXs3UJyFaeZmZlZYxQWFlJVVYUX+XZOHTp0qHX7wR2V1+Ra0i5kE+vbI+LOpPkDSd0i4n1J3YC1SXsVsH+N7oXAe/mMz8zMzGxH7bLLLhQVFaUdhrVQ+bxbiID/CyyPiBtrbLoHmJC8ngDcXaO9VNJukoqA3sCL+YrPzMzMzCzX8rlyfQRwFvCqpIqk7SpgBjBX0kRgNfBtgIhYJmku8BrZO41MiohNeYzPzFJQNeWptEPIq8IZw9IOwczMUpS35Doinqb+OmqAYxroMx2Ynq+YLL925ic77sznZmZmZrmT9wsazcxqOpK/pR1CXlWmHYCZmaXKjz83MzMzM8sRJ9dmZmZmZjni5NrMzMzMLEecXJuZmZmZ5YiTazMzMzOzHHFybWZmZmaWI06uzczMzMxyxMm1mZmZmVmOOLk2MzMzM8sRJ9dmZmZmZjni5NrMzMzMLEecXJuZmZmZ5YiTazMzMzOzHHFybWZmZmaWI06uzczMzMxyxMm1mZmZmVmOOLk2MzMzM8sRJ9dmZmZmZjni5NrMzMzMLEecXJuZmZmZ5YiTazMzMzOzHHFybWZmZmaWI06uzczMzMxyJG/JtaTfSloraWmNti6SHpH0RvJ97xrbrpS0StJKScPzFZeZmZmZWb7kc+V6FjBiq7YpwGMR0Rt4LHmPpGKgFOiX9LlFUkEeYzMzMzMzy7n2+Ro4Ip6U1HOr5pOBo5LXs4EngCuS9rKI+AJ4W9IqYCjwXL7iMzNrKZb36Zt2CHnVd8XytEMwM2s2zV1z/ZWIeB8g+b5v0t4dWFNjv6qkzczMzMys1WgpFzSqnraod0fpAkkLJS1ct25dnsMyMzMzM2u85k6uP5DUDSD5vjZprwL2r7FfIfBefQNExK0RURIRJV27ds1rsGZmZmZmO6K5k+t7gAnJ6wnA3TXaSyXtJqkI6A282MyxmZmZmZn9U/J2QaOkDNmLF/eRVAX8b2AGMFfSRGA18G2AiFgmaS7wGrARmBQRm/IVm5mZmZlZPuTzbiHjG9h0TAP7Twem5yseMzMzM7N8aykXNJqZmZmZtXpOrs3MzMzMcsTJtZmZmZlZjuSt5trMzMzMmt/O/NTX1vDEV69cm5mZmZnliFeuzcysRfLqm5m1Rl65NjMzMzPLESfXZmZmZmY54uTazMzMzCxHnFybmZmZmeWIk2szMzMzsxxxcm1mZmZmliNOrs3MzMzMcsTJtZmZmZlZjji5NjMzMzPLESfXZmZmZmY54uTazMzMzCxH2qcdgJmZWX1GnvJ/0g4hbyrTDsDM8sYr12ZmZmZmOeKVazMzM7OdyHu3/D3tEPKmb9oBNIKTazOzlO1xyq1ph2BmZjni5LqJ/vObp6QdQt5MSzsAMzOzRlrepzWsZTZN3xXL0w7BmsDJtZmZmbVaR/8yk3YIefOntAOwJnFybZZHVVOeSjuEvCmcMSztEMzMzFoc3y3EzMzMzCxHWtzKtaQRwM+BAuA3ETEj5ZDMmuw3HR5LO4S8mYZXrs3MzLbWolauJRUAvwBGAsXAeEnF6UZlZmZmZtY4LSq5BoYCqyLirYj4O1AGnJxyTGZmZmZmjdLSkuvuwJoa76uSNjMzMzOzFk8RkXYM1SR9GxgeEd9J3p8FDI2IS2rscwFwQfL2IGBlswfa/PYBPkw7iBbI81KX56Quz0ldnpO6PCd1eU7q53mpq63MyVcjouv2dmppFzRWAfvXeF8IvFdzh4i4FWhTjzOTtDAiStKOo6XxvNTlOanLc1KX56Quz0ldnpP6eV7q8pzU1tLKQl4CeksqkrQrUArck3JMZmZmZmaN0qJWriNio6SLgYfJ3orvtxGxLOWwzMzMzMwapUUl1wAR8QDwQNpxtDBtqgxmB3he6vKc1OU5qctzUpfnpC7PSf08L3V5TmpoURc0mpmZmZm1Zi2t5trMzMzMrNVyct2CSPok+d5T0tLk9VGS7kte95H0nKQvJF2WZqzNpRFzcoakJcnXs5IGpRlvc2jEnJyczEeFpIWSjkwz3uawvTmpsd8QSZsknZZGnM2tEZ+VoyT9NfmsVEiamma8zaExn5XkfYWkZZL+mFaszaURn5Mf1viMLE3+DnVJM+Z8a8Sc7CnpXkmvJJ+Tc9OMtzk0Yk72lnRX8v/Pi5L6pxlvmlpczbVt05+B7wGnpB1IC/I28M2I+EjSSLJ1X4emHFPaHgPuiYiQNBCYC/RJOabUSSoAfkL2gmn7b09FxIlpB9FSSNoLuAUYERGrJe2bdkxpi4jrgesBJI0CJkfEn9ONKnWTgNciYpSkrsBKSbcnT5duq64CKiJitKQ+wC+AY1KOKRVeuW5FImJtRLwE/CPtWFqKiHg2Ij5K3j5P9t7obVpEfBL/fTHFlwBfWJF1CXAHsDbtQKxFOx24MyJWQ/bf3ZTjaWnGA5m0g2gBAthDkoDdyS5+bUw3pNQVk13cISJWAD0lfSXdkNLh5Np2JhOBB9MOoiWQNFrSCuB+4Ly040mbpO7AaOA/046lBTo8+dX2g5L6pR1MC3AgsLekJyQtknR22gG1FJI6ASPI/pDa1t0M9CX7oLtXgUsjYnO6IaXuFeBUAElDga/SRhe8nFzbTkHS0WST6yvSjqUliIi7IqIP2RKiH6cdTwvwM+CKiNiUdiAtzGKyj/MdBNwEzE85npagPfA14ARgOPBvkg5MN6QWYxTwjEtCgOxnowLYDxgM3Cypc7ohpW4G2R9MK8j+pvBl2uhqvmuurdVL6op/A4yMiPVpx9OSRMSTknpJ2iciPkw7nhSVAGXZ3+CyD3C8pI0R0aaTyYj4W43XD0i6xZ8VqoAPI+JT4FNJTwKDgNfTDatFKMUlIVucC8xISvBWSXqb7LUtL6YbVnqSf0/OBUjKZd5Ovtocr1xbqyapB3AncFZE+D8/QNIByT9sSDoE2BVo0z90RERRRPSMiJ7APOC7bT2xBpD0LzU+K0PJ/p/Qpj8rwN3AMEntkzKIQ4HlKceUOkl7At8kOz8Gq0ku1kvqig8C3ko1opRJ2kvSrsnb7wBP1vwBvi3xynUrIulfgIVAZ2CzpH8FitvqhzcxFfgycEuSI2yMiJJ0Q0rdGOBsSf8APgfG1bjA0aym04CLJG0k+1kpbeuflYhYLukhYAmwGfhNRCxNOayWYDTwh2RF37LldrMkvQqIbNlZW/6ND2Rr0P9L0ibgNbKlmm2Sn9BoZmZmZpYjLgsxMzMzM8sRJ9dmZmZmZjni5NrMzMzMLEecXJuZmZmZ5YiTazOzZiLpOEmD0o7DzMzyx8m1mVkTSbpa0jJJSyRVSDp0O12eBn4g6YDmiG+L5FHejb5FpaRZkk6r0bdnE445WNLxO9inp6SlyeujJN2XvD5H0s07GoOZWRp8n2szsyaQdDhwInBIRHwhaR+yD+xpUER8BpzdHPG1AIPJPhnzgbQDMTNrTl65NjNrmm5kH5P9BUBEfBgR7wFIGiLpWUmvSHpR0h6SCiRdL+klSa9KujDZ96hkdXiepBWSbq/x1MSvSfqjpEWSHpbULWn/nqTXkhXzsq0Dk9RRUlmyvRzoWGPbcZKek7RY0v+TtPt2zvPPwKak7y8lLUxW6/+9xphbn++ewI+AccmK/jhJX5L02+T8X5Z0ctOn3sys5fLKtZlZ0/wBmCrpdeBRoDwi/pg8/rec7JMxX5LUmezTDycCf4uIIZI6AM9KeiQZ62CgH/Ae8AxwhKQXgJuAkyNinaRxwHTgPGAKUJSsmO9VT2wXAZ9FxEBJA4HFAMnq+jXAtyLiU0lXAN8nmwjXKyJOrfH26oj4s6QC4LFk7BX1nO9nZJ+eWhIRFyfHvhZ4PCLOS2J+UdKjfuKfme1snFybmTVBRHwi6WvAMOBooFzSFGAR8H5EvJTs9zfIrhgDRZKOSYbYFfgfwEbgxYioSvarAHoCfwH6A48kC9kFwPtJ3yXA7ZLmA/PrCe8bwH8kx18iaUnSfhhQDDyTjLkr8NwOnPZYSReQ/b+jWzJWNHC+W/c9DjhJ0mXJ+w5AD2D5DhzfzKzFc3JtZtZEEbEJeAJ4QtKrwASyq8RRz+4iu/L7UK1G6SjgixpNm8j+2yxgWUQcXs9YJ5BNoE8C/k1Sv4jYuHV4DcTwSESM386p1e0oFQGXAUMi4iNJs8gmyGrgWPUde0xErNzRY5uZtSauuTYzawJJB0nqXaNpMPAO2TKJ/SQNSfbbQ1J74GHgf0napUb/L23jECuBrsmFk0jaRVI/Se2A/SNiAXA5sBewdd30k8AZSb/+wMCk/XmyJScHJNs6STqwkafcGfgU+KukrwAjk/aGzvdjYI8a/R8GLqlRT35wI49rZtaqeOXazKxpdgduSuqHNwKrgAsi4u9JffRNkjqSrbf+FvAbsuUei5MEcx1wSkODJ+OcBvxHcoFge+BnwOvA75M2ATMj4i9bdf8l8LukHKQCeDEZc52kc4CMpN2Sfa9JxtymiHhF0svAMuAtsrXhbON8FwBTkjKX64AfJ/EvSc6/kuzdVszMdiqKaMxv88zMzMzMbHtcFmJmZmZmliNOrs3MzMzMcsTJtZmZmZlZjji5NjMzMzPLESfXZmZmZmY54uTazMzMzCxHnFybmZmZmeWIk2szMzMzsxz5/3cYd8n0aWVzAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 864x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.rcParams['figure.figsize'] = [12, 5]\n",
+    "for k in range(1,6):\n",
+    "    plot_acte(k)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "On peut alors tout de suite analyser le texte avec beaucoup plus de précision. Par exemple :\n",
+    "1. Harpagon semble énoncer un monologue à la scène 4 de l'acte I.\n",
+    "2. Il est quasiment absent de l'acte II.\n",
+    "3. Le record de la scène la plus longue semble être tenu par _II5_, et Frosine y monopolise la parole."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Graphe des interlocuteurs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__Consignes :__ ( _facultatif_ ) construisez un graphe d’interlocution permettant de visualiser les échanges entre les personnages."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "On introduit un nouveau package :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import networkx as nx"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "On va parcourir les répliques, et estimer que deux personnages s'adressent une fois la paroles si l'un parle immédiatement après l'autre ( _cette convention est évidemment source de nombreuses critiques_ )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "G=nx.Graph()\n",
+    "G.add_nodes_from(list_perso)\n",
+    "for i in range(0,len(list_perso)-1):\n",
+    "    for j in range(i+1,len(list_perso)):\n",
+    "        w=0\n",
+    "        perso1=list_perso[i]\n",
+    "        perso2=list_perso[j]\n",
+    "        for k in range(0,len(repliques[:-1])):\n",
+    "            if repliques[k][1]==perso1 and repliques[k+1][1]==perso2:\n",
+    "                w+=1\n",
+    "        G.add_edge(perso1,perso2,weight=w)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "On observe qu'__Harapagon__ se retrouve à nouveau au centre de l'attention, proche entre autres de __Cléante__ :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "weights = [G[u][v]['weight']/10 for u,v in G.edges()]\n",
+    "nx.draw(G, width=weights,with_labels=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Cette représentation graphique marque la fin de notre analyse de données."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
-- 
2.18.1