From 68a408824eb7e5a2ea1a7c1d8fde461ea8f4077e Mon Sep 17 00:00:00 2001 From: 7404ea6678ce6fbf3a726e36f2bf2079 <7404ea6678ce6fbf3a726e36f2bf2079@app-learninglab.inria.fr> Date: Tue, 8 Oct 2024 20:29:54 +0000 Subject: [PATCH] Move forward on graphic for question 2 lack of few lines of code to write text in the figure properly --- module3/exo3/exercice_fr.ipynb | 646 ++++++++++++++++++++++++--------- 1 file changed, 480 insertions(+), 166 deletions(-) diff --git a/module3/exo3/exercice_fr.ipynb b/module3/exo3/exercice_fr.ipynb index 18a05ef..4fd110f 100644 --- a/module3/exo3/exercice_fr.ipynb +++ b/module3/exo3/exercice_fr.ipynb @@ -374,7 +374,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -383,7 +383,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -439,7 +439,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -565,16 +565,20 @@ " for perso in scenePersoList:\n", " # On fait une recherche ignorant si les lettres sont en majuscules ou minuscules\n", " # grâce à l'option passée à la fonction de recherche d'expression régulière\n", - " # re.IGNORECASE\n", - " m = re.search(perso, currentLine, re.IGNORECASE)\n", + " # re.IGNORECASE. On traite également du problèe posé par les maitre/maîtres\n", + " # car les valeurs utilisées dans scenePersoList sont celles issues du dictionnaire\n", + " # des personnages créé à partir de la liste en début de pièce.\n", + " persoRegex = perso\n", + " if perso.startswith(\"Maitre\"):\n", + " persoRegex = persoRegex.replace('Maitre','Maître')\n", + " m = re.search(persoRegex, currentLine, re.IGNORECASE)\n", " if m:\n", - " # le résultats de la recherche n'est pas vide\n", - " # L'auteur est trouvé. Vérifions si quelqu'un\n", - " # a déjà parlé dans la scène auquel cas l'auteur\n", + " # le résultats de la recherche n'est pas vide, l'auteur est trouvé.\n", + " # Vérifions si quelqu'un a déjà parlé dans la scène auquel cas l'auteur\n", " # courant est le destinataire de l'auteur précédent.\n", " if currentAuthor is not None:\n", " # Il y avait déjà un auteur d'une réplique, currentAuthor\n", - " # le nouvel auteur est donc aussi le destinataire de la réplique précédente.\n", + " # le nouvel auteur est donc le destinataire de la réplique précédente.\n", " # On peut maintenant remplir le dictionnaire pour la réplique précédente,\n", " # car nous avons toutes les informations nécessaires sur celles-ci.\n", " # Pour rappel, la structure du dictionnaire,\n", @@ -589,8 +593,10 @@ " # Fin du si sur le test de savoir si il y a un auteur d'une réplique\n", " # précédent \n", " # Dans tous les cas l'auteur courant est donné par la valeur \"perso\"\n", - " # qui a été trouvée dans la ligne\n", + " # qui a été trouvée dans la ligne.\n", " currentAuthor = perso\n", + " # Le break permet d'arrêter la boucle for car l'auteur courant a été identifié\n", + " break\n", " \n", " else:\n", " isNotActEndDeclarationLine = not currentLine.startswith(\"Fin du\")\n", @@ -642,14 +648,19 @@ " # il faut adapter name Perso pour être une expression régulière\n", " persoRegex = namePerso\n", " if namePerso.startswith(\"Maitre\"):\n", - " persoRegex = namePerso.replace('Maitre','Maître')\n", + " persoRegex = persoRegex.replace('Maitre','Maître')\n", " \n", " m = re.search(persoRegex, currentLine, re.IGNORECASE)\n", " if m is not None:\n", " # Le résultat de la recherche n'est pas vide,\n", " # le personnage fait partie des protagonistes\n", " # on l'ajoute à la liste des personnages de la scène\n", - " scenePersoList.append(m.group(0))\n", + " # On met le nom tel qu'il est écrit dans le dictionnaire\n", + " # créé à partir de la liste en début de pièce.\n", + " #scenePersoList.append(m.group(0))\n", + " scenePersoList.append(namePerso)\n", + " #print(\"scenePersoList : {}\".format(scenePersoList))\n", + " \n", " else:\n", " # La ligne est vide on sait que c'est le cas particulier\n", " # le seul protagoniste est Harpagon\n", @@ -672,7 +683,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -953,7 +964,7 @@ " ...\n", " \n", " \n", - " 931\n", + " 927\n", " 5\n", " Harpagon\n", " Valère\n", @@ -961,23 +972,23 @@ " 2\n", " \n", " \n", - " 932\n", + " 928\n", " 5\n", " Valère\n", - " Maître Jacques\n", + " Maitre Jacques\n", " 5\n", " 6\n", " \n", " \n", - " 933\n", + " 929\n", " 5\n", - " Maître Jacques\n", + " Maitre Jacques\n", " Harpagon\n", " 5\n", " 7\n", " \n", " \n", - " 934\n", + " 930\n", " 5\n", " Harpagon\n", " Valère\n", @@ -985,7 +996,7 @@ " 10\n", " \n", " \n", - " 935\n", + " 931\n", " 5\n", " Valère\n", " Harpagon\n", @@ -993,7 +1004,7 @@ " 10\n", " \n", " \n", - " 936\n", + " 932\n", " 5\n", " Harpagon\n", " Valère\n", @@ -1001,7 +1012,7 @@ " 9\n", " \n", " \n", - " 937\n", + " 933\n", " 5\n", " Cléante\n", " Harpagon\n", @@ -1009,7 +1020,7 @@ " 41\n", " \n", " \n", - " 938\n", + " 934\n", " 5\n", " Harpagon\n", " Cléante\n", @@ -1017,7 +1028,7 @@ " 3\n", " \n", " \n", - " 939\n", + " 935\n", " 5\n", " Cléante\n", " Harpagon\n", @@ -1025,7 +1036,7 @@ " 47\n", " \n", " \n", - " 940\n", + " 936\n", " 5\n", " Harpagon\n", " Cléante\n", @@ -1033,7 +1044,7 @@ " 7\n", " \n", " \n", - " 941\n", + " 937\n", " 5\n", " Cléante\n", " Mariane\n", @@ -1041,7 +1052,7 @@ " 36\n", " \n", " \n", - " 942\n", + " 938\n", " 5\n", " Mariane\n", " Anselme\n", @@ -1049,7 +1060,7 @@ " 36\n", " \n", " \n", - " 943\n", + " 939\n", " 5\n", " Anselme\n", " Harpagon\n", @@ -1057,7 +1068,7 @@ " 61\n", " \n", " \n", - " 944\n", + " 940\n", " 5\n", " Harpagon\n", " Cléante\n", @@ -1065,7 +1076,7 @@ " 11\n", " \n", " \n", - " 945\n", + " 941\n", " 5\n", " Cléante\n", " Harpagon\n", @@ -1073,7 +1084,7 @@ " 6\n", " \n", " \n", - " 946\n", + " 942\n", " 5\n", " Harpagon\n", " Anselme\n", @@ -1081,7 +1092,7 @@ " 13\n", " \n", " \n", - " 947\n", + " 943\n", " 5\n", " Anselme\n", " Harpagon\n", @@ -1089,7 +1100,7 @@ " 13\n", " \n", " \n", - " 948\n", + " 944\n", " 5\n", " Harpagon\n", " Anselme\n", @@ -1097,7 +1108,7 @@ " 12\n", " \n", " \n", - " 949\n", + " 945\n", " 5\n", " Anselme\n", " Harpagon\n", @@ -1105,7 +1116,7 @@ " 8\n", " \n", " \n", - " 950\n", + " 946\n", " 5\n", " Harpagon\n", " Anselme\n", @@ -1113,55 +1124,55 @@ " 12\n", " \n", " \n", - " 951\n", + " 947\n", " 5\n", " Anselme\n", - " le Commissaire\n", + " Le commissaire\n", " 6\n", " 13\n", " \n", " \n", - " 952\n", + " 948\n", " 5\n", - " le Commissaire\n", + " Le commissaire\n", " Harpagon\n", " 6\n", " 14\n", " \n", " \n", - " 953\n", + " 949\n", " 5\n", " Harpagon\n", - " le Commissaire\n", + " Le commissaire\n", " 6\n", " 8\n", " \n", " \n", - " 954\n", + " 950\n", " 5\n", - " le Commissaire\n", + " Le commissaire\n", " Harpagon\n", " 6\n", " 12\n", " \n", " \n", - " 955\n", + " 951\n", " 5\n", " Harpagon\n", - " Maître Jacques\n", + " Maitre Jacques\n", " 6\n", " 12\n", " \n", " \n", - " 956\n", + " 952\n", " 5\n", - " Maître Jacques\n", + " Maitre Jacques\n", " Anselme\n", " 6\n", " 23\n", " \n", " \n", - " 957\n", + " 953\n", " 5\n", " Anselme\n", " Harpagon\n", @@ -1169,7 +1180,7 @@ " 8\n", " \n", " \n", - " 958\n", + " 954\n", " 5\n", " Harpagon\n", " Anselme\n", @@ -1177,7 +1188,7 @@ " 5\n", " \n", " \n", - " 959\n", + " 955\n", " 5\n", " Anselme\n", " Harpagon\n", @@ -1185,7 +1196,7 @@ " 11\n", " \n", " \n", - " 960\n", + " 956\n", " 5\n", " Harpagon\n", " Anselme\n", @@ -1194,7 +1205,7 @@ " \n", " \n", "\n", - "

961 rows × 5 columns

\n", + "

957 rows × 5 columns

\n", "" ], "text/plain": [ @@ -1230,41 +1241,41 @@ "28 1 Cléante Élise 2 150\n", "29 1 Élise Cléante 2 27\n", ".. ... ... ... ... ...\n", - "931 5 Harpagon Valère 5 2\n", - "932 5 Valère Maître Jacques 5 6\n", - "933 5 Maître Jacques Harpagon 5 7\n", - "934 5 Harpagon Valère 5 10\n", - "935 5 Valère Harpagon 5 10\n", - "936 5 Harpagon Valère 5 9\n", - "937 5 Cléante Harpagon 6 41\n", - "938 5 Harpagon Cléante 6 3\n", - "939 5 Cléante Harpagon 6 47\n", - "940 5 Harpagon Cléante 6 7\n", - "941 5 Cléante Mariane 6 36\n", - "942 5 Mariane Anselme 6 36\n", - "943 5 Anselme Harpagon 6 61\n", - "944 5 Harpagon Cléante 6 11\n", - "945 5 Cléante Harpagon 6 6\n", - "946 5 Harpagon Anselme 6 13\n", - "947 5 Anselme Harpagon 6 13\n", - "948 5 Harpagon Anselme 6 12\n", - "949 5 Anselme Harpagon 6 8\n", - "950 5 Harpagon Anselme 6 12\n", - "951 5 Anselme le Commissaire 6 13\n", - "952 5 le Commissaire Harpagon 6 14\n", - "953 5 Harpagon le Commissaire 6 8\n", - "954 5 le Commissaire Harpagon 6 12\n", - "955 5 Harpagon Maître Jacques 6 12\n", - "956 5 Maître Jacques Anselme 6 23\n", - "957 5 Anselme Harpagon 6 8\n", - "958 5 Harpagon Anselme 6 5\n", - "959 5 Anselme Harpagon 6 11\n", - "960 5 Harpagon Anselme 6 6\n", + "927 5 Harpagon Valère 5 2\n", + "928 5 Valère Maitre Jacques 5 6\n", + "929 5 Maitre Jacques Harpagon 5 7\n", + "930 5 Harpagon Valère 5 10\n", + "931 5 Valère Harpagon 5 10\n", + "932 5 Harpagon Valère 5 9\n", + "933 5 Cléante Harpagon 6 41\n", + "934 5 Harpagon Cléante 6 3\n", + "935 5 Cléante Harpagon 6 47\n", + "936 5 Harpagon Cléante 6 7\n", + "937 5 Cléante Mariane 6 36\n", + "938 5 Mariane Anselme 6 36\n", + "939 5 Anselme Harpagon 6 61\n", + "940 5 Harpagon Cléante 6 11\n", + "941 5 Cléante Harpagon 6 6\n", + "942 5 Harpagon Anselme 6 13\n", + "943 5 Anselme Harpagon 6 13\n", + "944 5 Harpagon Anselme 6 12\n", + "945 5 Anselme Harpagon 6 8\n", + "946 5 Harpagon Anselme 6 12\n", + "947 5 Anselme Le commissaire 6 13\n", + "948 5 Le commissaire Harpagon 6 14\n", + "949 5 Harpagon Le commissaire 6 8\n", + "950 5 Le commissaire Harpagon 6 12\n", + "951 5 Harpagon Maitre Jacques 6 12\n", + "952 5 Maitre Jacques Anselme 6 23\n", + "953 5 Anselme Harpagon 6 8\n", + "954 5 Harpagon Anselme 6 5\n", + "955 5 Anselme Harpagon 6 11\n", + "956 5 Harpagon Anselme 6 6\n", "\n", - "[961 rows x 5 columns]" + "[957 rows x 5 columns]" ] }, - "execution_count": 13, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -1302,7 +1313,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -1344,7 +1355,7 @@ "Index: []" ] }, - "execution_count": 14, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -1355,7 +1366,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -1397,7 +1408,7 @@ "Index: []" ] }, - "execution_count": 15, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -1443,7 +1454,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -1504,7 +1515,7 @@ " Anselme\n", " \n", " \n", - " 910\n", + " 923\n", " Mariane\n", " \n", " \n", @@ -1520,7 +1531,7 @@ " Maitre Jacques\n", " \n", " \n", - " 2370\n", + " 2349\n", " Frosine\n", " \n", " \n", @@ -1528,7 +1539,7 @@ " Valère\n", " \n", " \n", - " 3331\n", + " 3339\n", " Cléante\n", " \n", " \n", @@ -1549,17 +1560,17 @@ "197 Maitre Simon\n", "294 Le commissaire\n", "517 Anselme\n", - "910 Mariane\n", + "923 Mariane\n", "1067 Élise\n", "1520 La Flèche\n", "1668 Maitre Jacques\n", - "2370 Frosine\n", + "2349 Frosine\n", "2740 Valère\n", - "3331 Cléante\n", + "3339 Cléante\n", "6160 Harpagon" ] }, - "execution_count": 16, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -1573,10 +1584,7 @@ "persoNbWords = np.zeros(len(avarePersoDict),dtype=int)\n", "#print(textDataSynthesisTableDf.shape)\n", "for index, perso in enumerate(avarePersoDict):\n", - " persoRegex = perso\n", - " if perso.startswith(\"Maitre\"):\n", - " persoRegex = perso.replace('Maitre','Maître')\n", - " m = textDataSynthesisTableDf['author'].str.match(persoRegex, case=False, na=False)\n", + " m = textDataSynthesisTableDf['author'].str.match(perso, case=False, na=False)\n", " tmpDf = textDataSynthesisTableDf[m].copy()\n", " persoList.append(perso)\n", " persoNbWords[index] = tmpDf['speech_length'].sum()\n", @@ -1611,7 +1619,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -1649,7 +1657,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -1708,7 +1716,7 @@ " 'color': array([0.35882353, 0.21994636, 0.99385914, 1. ])}}" ] }, - "execution_count": 39, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -1732,9 +1740,54 @@ "En nous appuyant sur une page internet trouvée sur exemple d'affichage similaire à ce qui est fait dans l'étude \\(à savoir un affichage par Acte, sous forme de barres horizontales pour chaque scène le composant donnant la répartition de la paroles entre les différents protagonistes de chaque scène\\), on va pouvoir répondre à la seconde question. Voici un lien vers celle-ci: [geeksforgeeks_stacked-percentage-bar-plot](https://www.geeksforgeeks.org/stacked-percentage-bar-plot-in-matplotlib/)." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pour l'écriture du nombre de mots au sein des graphiques, il a fallu passer par des sorties intermédiaires pour voir comment fonctionnait matplotlib, dans quel sens il parcours les éléments de son graphiques par rapport au dataframe d'entrée que l'on veut représenter. Une sortie intermédiaire obtenue via des prints, nous à donner ce qui suit:\n", + "\n", + "```\n", + " Cléante Harpagon La Flèche Valère scene Élise\n", + "0 0.000000 0.000000 0.000000 0.561998 1 0.438002\n", + "1 0.831694 0.000000 0.000000 0.000000 2 0.168306\n", + "2 0.000000 0.644044 0.355956 0.000000 3 0.000000\n", + "3 0.141361 0.752618 0.000000 0.000000 4 0.106021\n", + "4 0.000000 0.268701 0.000000 0.695866 5 0.035433\n", + "\n", + "Rectangle(xy=(0, -0.25), width=0, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 0.75), width=0.831694, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 1.75), width=0, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 2.75), width=0.141361, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 3.75), width=0, height=0.5, angle=0)\n", + "Rectangle(xy=(0, -0.25), width=0, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 0.75), width=0, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 1.75), width=0.644044, height=0.5, angle=0)\n", + "Rectangle(xy=(0.141361, 2.75), width=0.752618, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 3.75), width=0.268701, height=0.5, angle=0)\n", + "Rectangle(xy=(0, -0.25), width=0, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 0.75), width=0, height=0.5, angle=0)\n", + "Rectangle(xy=(0.644044, 1.75), width=0.355956, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 2.75), width=0, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 3.75), width=0, height=0.5, angle=0)\n", + "Rectangle(xy=(0, -0.25), width=0.561998, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 0.75), width=0, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 1.75), width=0, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 2.75), width=0, height=0.5, angle=0)\n", + "Rectangle(xy=(0.268701, 3.75), width=0.695866, height=0.5, angle=0)\n", + "Rectangle(xy=(0.561998, -0.25), width=0.438002, height=0.5, angle=0)\n", + "Rectangle(xy=(0.831694, 0.75), width=0.168306, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 1.75), width=0, height=0.5, angle=0)\n", + "Rectangle(xy=(0.893979, 2.75), width=0.106021, height=0.5, angle=0)\n", + "Rectangle(xy=(0.964567, 3.75), width=0.0354331, height=0.5, angle=0)\n", + "```\n", + "\n", + "On voit qu'il y a 25 éléments, qui correspondent à 5 scène x 5 personnages pour l'acte 1.\n", + "En regardant de près les valeurs, on voit que les 5 premièrs rectangles correspondent au premier personnage, la première colonne du dataframe, qui est affiché juste au dessus, les 5 rectangles suivants correspondent au second personnage et ainsi de suite. Il va falloir calculer les positions des textes en conséquences. " + ] + }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -1747,44 +1800,172 @@ "2 0.000000 0.644044 0.355956 0.000000 3 0.000000\n", "3 0.141361 0.752618 0.000000 0.000000 4 0.106021\n", "4 0.000000 0.268701 0.000000 0.695866 5 0.035433\n", - "[array([1. , 0.21994636, 0.11065268, 1. ]), array([1.0000000e+00, 1.2246468e-16, 6.1232340e-17, 1.0000000e+00]), array([0.78627451, 0.9005867 , 0.53165947, 1. ]), array([1. , 0.42912061, 0.21994636, 1. ]), array([0.64509804, 0.9741386 , 0.62211282, 1. ])]\n", - " Cléante Frosine Harpagon La Flèche Maître Simon scene\n", + "Rectangle(xy=(0, -0.25), width=0, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 0.75), width=0.831694, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 1.75), width=0, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 2.75), width=0.141361, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 3.75), width=0, height=0.5, angle=0)\n", + "Rectangle(xy=(0, -0.25), width=0, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 0.75), width=0, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 1.75), width=0.644044, height=0.5, angle=0)\n", + "Rectangle(xy=(0.141361, 2.75), width=0.752618, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 3.75), width=0.268701, height=0.5, angle=0)\n", + "Rectangle(xy=(0, -0.25), width=0, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 0.75), width=0, height=0.5, angle=0)\n", + "Rectangle(xy=(0.644044, 1.75), width=0.355956, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 2.75), width=0, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 3.75), width=0, height=0.5, angle=0)\n", + "Rectangle(xy=(0, -0.25), width=0.561998, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 0.75), width=0, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 1.75), width=0, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 2.75), width=0, height=0.5, angle=0)\n", + "Rectangle(xy=(0.268701, 3.75), width=0.695866, height=0.5, angle=0)\n", + "Rectangle(xy=(0.561998, -0.25), width=0.438002, height=0.5, angle=0)\n", + "Rectangle(xy=(0.831694, 0.75), width=0.168306, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 1.75), width=0, height=0.5, angle=0)\n", + "Rectangle(xy=(0.893979, 2.75), width=0.106021, height=0.5, angle=0)\n", + "Rectangle(xy=(0.964567, 3.75), width=0.0354331, height=0.5, angle=0)\n", + " Cléante Frosine Harpagon La Flèche Maitre Simon scene\n", "0 0.295632 0.000000 0.000000 0.704368 0.00000 1\n", "1 0.250493 0.000000 0.337278 0.023669 0.38856 2\n", "2 0.000000 0.045455 0.954545 0.000000 0.00000 3\n", "3 0.000000 0.301624 0.000000 0.698376 0.00000 4\n", "4 0.000000 0.729400 0.270600 0.000000 0.00000 5\n", - "[array([1. , 0.21994636, 0.11065268, 1. ]), array([1. , 0.61727822, 0.32653871, 1. ]), array([1.0000000e+00, 1.2246468e-16, 6.1232340e-17, 1.0000000e+00]), array([0.78627451, 0.9005867 , 0.53165947, 1. ]), array([0.07254902, 0.78292761, 0.9005867 , 1. ])]\n", - " Brindavoine Cléante Frosine Harpagon La Merluche Mariane \\\n", - "0 0.011880 0.039256 0.000000 0.391012 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1.75), width=0.0454545, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 2.75), width=0.301624, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 3.75), width=0.7294, height=0.5, angle=0)\n", + "Rectangle(xy=(0, -0.25), width=0, height=0.5, angle=0)\n", + "Rectangle(xy=(0.250493, 0.75), width=0.337278, height=0.5, angle=0)\n", + "Rectangle(xy=(0.0454545, 1.75), width=0.954545, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 2.75), width=0, height=0.5, angle=0)\n", + "Rectangle(xy=(0.7294, 3.75), width=0.2706, height=0.5, angle=0)\n", + "Rectangle(xy=(0.295632, -0.25), width=0.704368, height=0.5, angle=0)\n", + "Rectangle(xy=(0.587771, 0.75), width=0.0236686, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 1.75), width=0, height=0.5, angle=0)\n", + "Rectangle(xy=(0.301624, 2.75), width=0.698376, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 3.75), width=0, height=0.5, angle=0)\n", + "Rectangle(xy=(0, -0.25), width=0, height=0.5, angle=0)\n", + "Rectangle(xy=(0.61144, 0.75), width=0.38856, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 1.75), width=0, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 2.75), width=0, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 3.75), width=0, height=0.5, angle=0)\n", + " Brindavoine Cléante Frosine Harpagon La Merluche Maitre Jacques \\\n", + "0 0.011880 0.039256 0.000000 0.391012 0.013430 0.402376 \n", + "1 0.000000 0.000000 0.000000 0.000000 0.000000 0.669065 \n", + "2 0.000000 0.000000 0.633333 0.000000 0.000000 0.366667 \n", + "3 0.000000 0.000000 0.507979 0.000000 0.000000 0.000000 \n", + "4 0.000000 0.000000 0.198473 0.801527 0.000000 0.000000 \n", + "5 0.000000 0.000000 0.038168 0.534351 0.000000 0.000000 \n", + "6 0.000000 0.559501 0.039347 0.175624 0.000000 0.000000 \n", + "7 0.465116 0.000000 0.000000 0.534884 0.000000 0.000000 \n", + "8 0.000000 0.283688 0.000000 0.567376 0.148936 0.000000 \n", "\n", - " Maître Jacques Valère scene Élise \n", - "0 0.402376 0.140496 1 0.001550 \n", - "1 0.669065 0.330935 2 0.000000 \n", - "2 0.366667 0.000000 3 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"Rectangle(xy=(0, 5.75), width=0, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 6.75), width=0, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 7.75), width=0, height=0.5, angle=0)\n", + "Rectangle(xy=(0, -0.25), width=0, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 0.75), width=0, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 1.75), width=0, height=0.5, angle=0)\n", + "Rectangle(xy=(0.507979, 2.75), width=0.492021, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 3.75), width=0, height=0.5, angle=0)\n", + "Rectangle(xy=(0.572519, 4.75), width=0.29771, height=0.5, angle=0)\n", + "Rectangle(xy=(0.774472, 5.75), width=0.225528, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 6.75), width=0, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 7.75), width=0, height=0.5, angle=0)\n", + "Rectangle(xy=(0.857955, -0.25), width=0.140496, height=0.5, angle=0)\n", + "Rectangle(xy=(0.669065, 0.75), width=0.330935, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 1.75), width=0, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 2.75), width=0, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 3.75), width=0, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 4.75), width=0, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 5.75), width=0, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 6.75), width=0, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 7.75), width=0, height=0.5, angle=0)\n", + "Rectangle(xy=(0.99845, -0.25), width=0.00154959, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 0.75), width=0, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 1.75), width=0, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 2.75), width=0, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 3.75), width=0, height=0.5, angle=0)\n", + "Rectangle(xy=(0.870229, 4.75), width=0.129771, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 5.75), width=0, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 6.75), width=0, height=0.5, angle=0)\n", + "Rectangle(xy=(0, 7.75), width=0, height=0.5, angle=0)\n", + " Cléante Frosine Harpagon La Flèche Maitre Jacques Mariane scene \\\n", + "0 0.251282 0.447179 0.000000 0.000000 0.000000 0.242051 1 \n", + "1 0.197183 0.000000 0.760563 0.000000 0.000000 0.000000 2 \n", + "2 0.515413 0.000000 0.484587 0.000000 0.000000 0.000000 3 \n", + "3 0.278232 0.000000 0.230769 0.000000 0.490998 0.000000 4 \n", + "4 0.558219 0.000000 0.441781 0.000000 0.000000 0.000000 5 \n", + "5 0.265625 0.000000 0.000000 0.734375 0.000000 0.000000 6 \n", + "6 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 7 \n", "\n", " Élise \n", "0 0.059487 \n", @@ -1794,28 +1975,135 @@ "4 0.000000 \n", "5 0.000000 \n", "6 0.000000 \n", - "[array([1. , 0.21994636, 0.11065268, 1. ]), array([1. , 0.61727822, 0.32653871, 1. ]), array([1.0000000e+00, 1.2246468e-16, 6.1232340e-17, 1.0000000e+00]), array([0.78627451, 0.9005867 , 0.53165947, 1. ]), array([0.50392157, 0.99998103, 0.70492555, 1. ]), array([0.92745098, 0.78292761, 0.43467642, 1. ]), array([0.64509804, 0.9741386 , 0.62211282, 1. ])]\n", - " Anselme Cléante Frosine Harpagon Le 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\n", 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\n", 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\n", + "image/png": 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\n", 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\n", + "image/png": 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\n", 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hefPmxe0GDBhA1DEravbu3Zt69eoBcNVVV/H++++TkJDAQw89xOLFiwHYtm0bW7ZsYdeuXfTs2ZO6desCMHjwYDYXLZ6WkpLCCy+8AMCIESO45ZZbivdx5ZVXUqlSJVq1asU333zjxY/Dc5rDF5GwNG7cOMaOHcv69et5/PHHyTnmAic1atT4UVv7yYfEZsayZct4++23SUlJYe3atXTo0IGcnJzjLrdckmP7rVatWvH90vQRThT4IhKWsrKyaNKkCQBPP/30CdsuXbqU7777juzsbF588UUuuOACsrKyOPPMM6levTqbNm3io48+AqBTp04sX76c77//nry8PBYtWlTcT7du3Vi4cCEA8+bNo3v37h6NLjQ0pSMihcLsqHXGjBkMHjyYJk2a0KVLF7788svjtu3evTsjRozgs88+Y+jQoSQkJNCmTRtmz55N27ZtadGiBV26dAGgSZMmTJ06lc6dOxMdHU2rVq2oXXR1rYceeojExETuueee4g9tK5KAlkc2s0bAP4Fo51w/M2sFdHXOzS3LYhISEtzR+TsR8VZJS+2WR0fn/WfNmhXwaw4cOEDNmjXJy8tj4MCBJCYmMnDgQA+rLBvBWh45CXgTiC56vBmYGOBrRUTCyowZM2jfvj2tW7emefPmXHnllaEuKSgCndKp75z7j5lNAXDO5ZlZvod1iYgEZPTo0YwePbpUr7n33nu9KSbMBRr4B82sHuAAzKwLkFXm1WxaB13PLvNupUg5uRJTqH330YRQlxAUBdUGk3dga6jL8L3KNZsFb18Btvsz8DJwrpl9ADQAyuHXzERE/CugwHfOrTGznkALwIBPnXO5nlYmIiJlqjTn4XcC2gFxwDVmNtKbkkRETl9eXh6PzXmGI0eOhLqUsBHQEb6ZPQucC6QDRz+sdcAzHtUlIkGWtn9BmfYXX+uak7apc9b57Nu1sdR9L/9vClcNuZ6Ycwo/86tf70zefGU+t//zfmrWqM6fJ/yBypUrkxDXjgmTpvPIA/+gUqXjH99mfrWNKwcnkr5qaalrKU8CncNPAFq5U/g+sZlFAKnA1865y0r7ehGRknTv2pGXkk/8xaiO8e3oGN8uSBWFv0CndDKAs05xHxOA0r+Fi4gvvfr623S78AoSLuhH38uH8s23u0+pn8+/+IpLB46kU49L6dVnEBs3bQHgm293M+iaMcR1vYS4rpfw4UeFX/bMz8/nD2NvpV3H39DviuFkZ+eU2M+mTz8rm4GGQKCBXx/4xMzeNLOXj95O9iIzawpcCvz7dIoUEf+4oGtHPnj3RVI/WMLvfns5994/u8R276esJr5bP+K79eNf9zz8s+03jLuVB+75O6v++xr/umMK4/78VwD+dPMMenTvzJqUN1j9/mvEnv8rALZ8nskfx4xk7eq3qVP7DF546XUA/jh+cnE/M/8xrbif8ijQKZ0Zp9j/A8AtQK3jNTCzMcAYgGZVI05xNyJSUWzfsZOho29i565vyT2SS0xMyd/NOdGUzoEDB1mVms4N4yYXP7cv6wcA3lv+IU89cR8AERER1K59Bt/vy6J5zNm0bxsLQFz7Nny1dTsHDhwkZWUaQ0beWNzPkcOHy2ScoRDoaZnLzewc4Dzn3NtmVh04YTqb2WXAt865NDPrdYK+nwCeAEioWTW8Vm8SkaCbOGk6E8dex+WX9mb5f1O4/Z8PlLqPgoICzqhVk3eWPBfwa6pVrVp8PyKiEtk5+RQUFFCn9hmkfbik1DWEo4CmdMzseiAZeLzoqSbAiyd52QXAADPLBBYCF5nZ/51inSLiE1k//EB0dOFHhs/MW3SS1iU744xaxJxzNs+/8CpQ+Abw8doMAC7qdQGz/10YRfn5+fzww/6T9pO8+DWgcB38tes/OaWawkGgUzo3UXge/koA59wWM2t4ohc456YAUwCKjvAnOeeGn3qpIuKlQE6jLGuHDmUT06Jz8eMJY6/jtil/4pqRfyS68Vl07tiBzK+2nVLfz8x9kLF/+iv/umcWebm5/G7QADq0a819d0/nj+Om8NQzzxEREcGs+++k8VnHj7Oj/fzz7oeL+2nXptUp1RRqgS6PvNI519nMPnbOdTCzysAa51zbgHbyv8A/4WmZCTWrutQ2jQLpUk6F1tIJiF/W0tlZbTAtfqm1q0KtNGvpBGt55OVmNhWIMrPewPPAK4EW6ZxbpnPwRURCK9DAnwzsBtYDfwBeB8rvuUkiIj4U6Bx+FPCkc24OFH97Ngo45FVhIiJStgIN/HeA3wAHih5HAW8B3cq0mpZtIUWXOJTQqhvqAoLkm40bg7oWu4ReoFM6kc65o2FP0f3q3pQkIiJeCDTwD5pZ3NEHZpYAZHtTkoiIeCHQKZ0JwPNmtoPCZZGjgas9q0pEgu52XivT/m7j0pO2MTOGDx/Os88+CxSuYd+4cWM6d+7Mq6++GvC+duzYwfjx40lOTj7lev0g0MBvDnQAmgEDgS4UXd9WRORU1ahRg4yMDLKzs4mKimLp0qU0adKkVH3k5eURHR2tsA9AoFM6f3PO/QDUAXpTuPbNY55VJSK+0a9fP157rfCviwULFnDNNf/7xu+qVavo1q0bHTp0oFu3bnz66acAJCUlMXjwYC6//HL69OlDZmYmrVu3BiAzM5MePXoQFxdHXFwcH374IQDLli2jV69eDBo0iJYtWzJs2DCOfvE0LS2Nnj17Eh8fT9++fdm5c2cwfwRBE2jgH73K1aXAbOfcS0DVE7QXEQnIkCFDWLhwITk5Oaxbt47Onf+31ELLli1ZsWIFH3/8MbfffjtTp04t3paSksLTTz/Nu++++6P+GjZsyNKlS1mzZg3PPfcc48ePL9728ccf88ADD/DJJ5/wxRdf8MEHH5Cbm8u4ceNITk4mLS2NxMREpk2b5v3AQyDQKZ2vzexxCk/NnGlm1Sjd9XBFRErUtm1bMjMzWbBgAf379//RtqysLEaNGsWWLVswM3Jzc4u39e7dm7p1f34SbW5uLmPHjiU9PZ2IiAg2b95cvK1Tp040bdoUgPbt25OZmUmdOnXIyMigd+/eQOGCao0bN/ZiqCEXaOD/DrgEuNc5t8/MGgM3e1eWiPjJgAEDmDRpEsuWLWPv3r3Fz//tb3/jwgsvZPHixWRmZtKrV6/ibTVq1Cixr/vvv59GjRqxdu1aCgoKiIyMLN5WrVq14vsRERHk5eXhnCM2NpaUlJSyH1iYCego3Tl3yDn3gnNuS9Hjnc65t7wtTUT8IjExkdtuu402bdr86PmsrKziD3GTkpIC6isrK4vGjRtTqVIlnn32WfLz80/YvkWLFuzevbs48HNzc9mwYUPpB1EOBHqELyIVXCCnUXqladOmTJjw81VKb7nlFkaNGsV9993HRRddFFBfN954I7/97W95/vnnufDCC4/7l8BRVatWJTk5mfHjx5OVlUVeXh4TJ04kNjb2lMYSzgJaHjlYEhISXGqqllYQCYaSltqV8Bas5ZFFRKScU+CLiPiEAl9ExCfC60PbTeugqy65JmGiol8ScskSOHgw1FVIQkDT72VCR/giIj6hwBeRcufTzEyeevnlUJdR7oTXlI6IhMx3+f9Xpv3VjRh+0jbWsSPD+/Xj2dtvB4qWR+7Xj86tW/Pq/fcf93VTH32UmtWr0+oXvyD78GGqVq5Mt3btTrnWzB07uOxPfyLjuedOuY/yQIEvIiFTIyqKjM8/Jzsnh6jISJauXEmTBg1O+Jqvv/2W8VdfTafYWN5ISWHtli3UjIoqMfDz8vKoXFkxd5RnUzpmFmlmq8xsrZltMLO/e7UvESm/+nXrxmsffADAgrfe4pq+fYu3rdqwgW6JiXQYNoxuiYl8mplJk4YNccDgKVPo0KIFsxct4v4FC2g/dCj//fhjRs+YwZ/vv58Lb7iBWx9+mIPZ2STefjsdR46kw7BhvLR8+Qnrydyxgx7XX0/c8OHEDR/Oh2vXFm+7+5lnaDNkCO2GDmXyww8DkLZxI+2GDqVrYiI3P/ggra8uvDZU0iuvMPbuu4tfe9mf/sSytDQA3vroI7omJhI3fDiDBw/mwIHCK8hOnjyZVq1a0bZtWyZNmnT6P9yf8PKt7zBwkXPugJlVAd43syXOuY883KeIlDND+vTh9n//m8u6d2fdli0kDhjAf9PTAWh5zjmseOIJKleuzNsrVzL10UdZdEyIxkRHc8Nvf0vNqCgmjRgBwNyXXmLz1q28/cgjREREMPWRR7goIYEnb7uNffv302n0aH7TqRM1oqJKrKdh3bosnTWLyGrV2LJ1K9f89a+kPvMMSz74gBeXLWNlUhLVIyP5LisLgN/ffjsPT5pEz/h4bn7wwZOOd8++fdz55JO8/cgj1IiKYuY773DfffcxduxYFi9ezKZNmzAz9u3bd7o/2p/xLPBd4ZoNRy98XqXoFj7rOIhIWGh73nlk7tzJgjffpP8FF/xoW9aBA4z6+9/ZsnVr4fLIeXkB9Tn44ouJiIgA4K2VK3l5xQru/b/CzyhyDh9m665dnN+8eYmvzc3LY+zdd5O+eTMRlSqxeetWAN5etYrfX3451YtW36xbuzZZBw6wb/9+esbHAzCif3+WFF1w5Xg+Wr+eT774gguuvRaAI5Ur07VrV8444wwiIyO57rrruPTSS7nssssCGmtpeDq5ZWYRQBrwS+AR59zKEtqMAcYANKsa4WU5IhKmBvTowaSHHmLZ7NnsLTpyBvjb7NlcGB/P4nvuIXPHDnrdcENA/R179O6cY9HMmbSIiQnotffPn0+junVZO39+4fLK3bsX9kPhNXiP5Zz72XNHVY6IoKCgoPhxzuHDxa/p3bkzC/7xj8INx5yHv2rVKt555x0WLlzIrFmzfnZxl9Pl6WmZzrl851x7oCnQycxal9DmCedcgnMuoUEVnSUq4keJAwZw27XX0uaXv/zR81kHD9KkYUMAko5zUfNa1auz/9Ch4/bdt0sXHv7Pf4ovZ/hx0WUSjyfrwAEa169fuLzy668XL6/cp3Nnnnz5ZQ7l5ADwXVYWdWrVonbNmrxfNAU17403ivuJiY4mffNmCgoK2LZrF6s++QSALm3a8MHatXy2bRsAhw4dYvPmzRw4cICsrCz69+/PAw88QHpRn2UpKB9fF100ZRmFF1HJCMY+RaR0AjmN0itNGzViwjHXsj3qlhEjGPX3v3PfvHlcdJxvpF7eoweDJk/mpeXLefjmn1+X6W/XXsvE++6j7TXX4JwjJjr6Z6d85uXnU61q4VVbbxw0iN/eeivPv/MOF8bHF/+1cEm3bqRv3kzCyJFUrVyZ/hdcwD9vuomnbruNxDvuoHpkJH27dCnu84J27WgeHU2bIUNofe65xLVoAUCDM88kafp0rpk2jcO5uRAVxZ133kmtWrW44ooryMnJwTnH/Sc4LfVUebY8spk1AHKLwj4KeAuY6Zwr+W0aSKhZ1aW2aeRJPSKlVsGXVti4ZAnn168f6jLCwkvLlzPvjTf4z7/+dVr9nNL5/KVYWuF0l0f28gi/MfB00Tx+JeA/Jwp7EZFQuG32bF5asYKk6dNDXYrnwusCKDrCl3CiI3wJhiAe4etTUhERn1Dgi4j4RHgtMtGyLaTomrYiQbFxI4TBNW0LCgro168fc+bMoVmzZqEup0LTEb6IhNSXX37J1KlTFfZBEF5H+CISMvMOJ5Vpf8OqjT5pm4iICNq0aVP8eMiQIUyePJlevXpx7733kpCQQP/+/Zk/fz516tQp0/r8SIEvIiETFRV10m+Uvv7660GqpuLTlI6IhLWYmBj27NnDwYMHufTSS2nXrh2tW7fmuaIvN6WlpdGzZ0/i4+Pp27cvO3fuDHHF4UuBLyIhk52dTfv27Ytvz53gG6pvvPEG0dHRrF27loyMDC655BJyc3MZN24cycnJpKWlkZiYyLRp04I4gvJFUzoiEjKBTOkc1aZNGyZNmsStt97KZZddRo8ePcjIyCAjI4PevXsDkJ+fT+PGjb0suVxT4ItIufCrX/2KtLQ0Xn/9daZMmUKfPn0YOHAgsbGxpKSkhLq8ckFTOiJSLuzYsYPq1aszfPhwJk2axJo1a2jRogW7d+8uDvzc3Fw2bNgQ4krDl47wRQQI7DTKsnZ0Dv+oSy65hLvuuqvEtutu5La2AAAG2ElEQVTXr+fmm2+mUqVKVKlShccee4yqVauSnJzM+PHjycrKIi8vj4kTJxIbGxusIZQr4bV4WkKCS03VN21FgqGkhbgkvGnxNBERCYgCX0TEJxT4Ij4WTlO6cmJl8btS4Iv4VGRkJHv37lXolwPOOfbu3UtkZORp9RNeZ+lsWgddzw51FcFXwa+sJOGp6Zlnsn3GDHb/8pdQScd+ZeFgs3qlfk0NqxlQu8jISJo2bVrq/o8VXoEvIkFT5fvvaT5hQqjLqFDm5TxV6tcE83RYva2LiPiEAl9ExCcU+CIiPuFZ4JvZ2Wb2npltNLMNZqbJQhGREPLyQ9s84C/OuTVmVgtIM7OlzrlPPNyniIgch2dH+M65nc65NUX39wMbgSZe7U9ERE4sKHP4ZhYDdABWlrBtjJmlmlnq7tyCYJQjIuJLnge+mdUEFgETnXM//HS7c+4J51yCcy6hQRV9hiwi4hVPE9bMqlAY9vOccy94uS8RETkxL8/SMWAusNE5d59X+xERkcB4eYR/ATACuMjM0otu/T3cn4iInIBnp2U6594HzKv+RUSkdPQpqYiITyjwRUR8IryWR27ZFlJ0EXMRKZ+GhbqAk9ARvoiITyjwRUR8QoEvIuITCnwREZ9Q4IuI+IQCX0TEJxT4IiI+ocAXEfEJBb6IiE8o8EVEfEKBLyLiEwp8ERGfUOCLiPiEAl9ExCcU+CIiPhFe6+FvWgddzw51FeXX9Z1DXYGUQ7cn/j7UJZQ75x7eXWZ9Das2usz6Ohkd4YuI+IQCX0TEJxT4IiI+4Vngm9mTZvatmWV4tQ8REQmcl0f4ScAlHvYvIiKl4FngO+dWAN951b+IiJROyOfwzWyMmaWaWeru3IJQlyMiUmGFPPCdc0845xKccwkNqoS8HBGRCksJKyLiEwp8ERGf8PK0zAVACtDCzLab2bVe7UtERE7Os7V0nHPXeNW3iIiUnqZ0RER8QoEvIuIT4bU8csu2kJIa6ipEfOW2UBdQHlULdQGnRkf4IiI+ocAXEfEJBb6IiE8o8EVEfEKBLyLiEwp8ERGfUOCLiPiEOedCXUMxM9sPfBrqOkKkPrAn1EWEkMav8ft1/Kc79nOccw0CaRheX7yCT51zCaEuIhTMLNWvYweNX+P37/iDOXZN6YiI+IQCX0TEJ8It8J8IdQEh5Oexg8av8ftX0MYeVh/aioiId8LtCF9ERDyiwBcR8YmgB76ZXWJmn5rZZ2Y2uYTtZmYPFW1fZ2Zxwa7RSwGMf1jRuNeZ2Ydm1i4UdXrlZOM/pl1HM8s3s0HBrM9rgYzfzHqZWbqZbTCz5cGu0SsB/N+vbWavmNnaorH/PhR1esXMnjSzb80s4zjbvc8+51zQbkAE8DnwC6AqsBZo9ZM2/YElgAFdgJXBrDEMxt8NOLPofj+/jf+Ydu8CrwODQl13kH//dYBPgGZFjxuGuu4gjn0qMLPofgPgO6BqqGsvw5/Br4E4IOM42z3PvmAf4XcCPnPOfeGcOwIsBK74SZsrgGdcoY+AOmbWOMh1euWk43fOfeic+77o4UdA0yDX6KVAfv8A44BFwLfBLC4IAhn/UOAF59xWAOdcRfkZBDJ2B9QyMwNqUhj4ecEt0zvOuRUUjul4PM++YAd+E2DbMY+3Fz1X2jblVWnHdi2F7/gVxUnHb2ZNgIHA7CDWFSyB/P5/BZxpZsvMLM3MRgatOm8FMvZZwPnADmA9MME5VxCc8sKC59kX7KUVrITnfnpeaCBtyquAx2ZmF1IY+N09rSi4Ahn/A8Ctzrn8wgO9CiWQ8VcG4oGLgSggxcw+cs5t9ro4jwUy9r5AOnARcC6w1Mz+65z7weviwoTn2RfswN8OnH3M46YUvpuXtk15FdDYzKwt8G+gn3Nub5BqC4ZAxp8ALCwK+/pAfzPLc869GJwSPRXo//89zrmDwEEzWwG0A8p74Acy9t8Dd7nCCe3PzOxLoCWwKjglhpzn2RfsKZ3VwHlm1tzMqgJDgJd/0uZlYGTRJ9ZdgCzn3M4g1+mVk47fzJoBLwAjKsBR3U+ddPzOuebOuRjnXAyQDNxYQcIeAvv//xLQw8wqm1l1oDOwMch1eiGQsW+l8C8bzKwR0AL4IqhVhpbn2RfUI3znXJ6ZjQXepPBT+yedcxvM7Iai7bMpPDOjP/AZcIjCd/0KIcDx3wbUAx4tOsrNcxVkFcEAx19hBTJ+59xGM3sDWAcUAP92zpV4Gl95EuDv/g4gyczWUzi9catzrsIsmWxmC4BeQH0z2w5MB6pA8LJPSyuIiPiEvmkrIuITCnwREZ9Q4IuI+IQCX0TEJxT4IiI+ocAXEfEJBb6IiE/8f0QruAE6oCDyAAAAAElFTkSuQmCC\n", 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\n", 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\n", + "image/png": 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\n", 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" ] @@ -1929,16 +2217,21 @@ " sortedPersos = np.sort(actPersos)\n", " colors = []\n", " for perso in sortedPersos:\n", - " if not re.search('î',perso) and not re.search('le co',perso, re.IGNORECASE):\n", - " colors.append(avarePersoDict[perso]['color'])\n", - " else:\n", - " if re.search('î',perso):\n", - " colors.append(avarePersoDict[perso.replace('î','i')]['color'])\n", - " else:\n", - " colors.append(avarePersoDict['Le commissaire']['color'])\n", - " print(colors)\n", + " colors.append(avarePersoDict[perso]['color'])\n", + " #print(colors)\n", " #plt.sca.ax(axs[actNum])\n", " #df.plot.barh(color={\"speed\": \"red\", \"lifespan\": \"green\"})\n", + "\n", + " #df_total = df[\"Studied\"] + df[\"Slept\"] + df[\"Other\"] \n", + " #df_rel = df[df.columns[1:]].div(df_total, 0) * 100\n", + " \n", + " #for n in df_rel: \n", + " #for i, (cs, ab, pc) in enumerate(zip(df.iloc[:, 1:].cumsum(1)[n], \n", + " #df[n], df_rel[n])): \n", + " #plt.text(cs - ab / 2, i, str(np.round(pc, 1)) + '%', \n", + " #va = 'center', ha = 'center', rotation = 20, fontsize = 8)\n", + "\n", + "\n", " percentDf.plot(\\\n", " x = 'scene', \\\n", " kind = 'barh', \\\n", @@ -1946,7 +2239,28 @@ " stacked = True, \\\n", " title = \"Acte {}\".format(actNum), \\\n", " mark_right = True)\n", - " pass" + " ax = plt.gca()\n", + " ax.invert_yaxis()\n", + " actPersosNumber = len(sortedPersos)\n", + " for i, bar in enumerate(ax.patches):\n", + " print(bar)\n", + " # On calcul l'index du personnage dans la liste des personnages de l'acte\n", + " persoIdx = np.floor(i/actPersosNumber)\n", + " # On calcul la ligne de la donnée\n", + " # On récupère le nom du personnage\n", + " persoName = sortedPersos[persoIdx]\n", + " if bar.get_width() != 0:\n", + " # Si la largeur de la barre en cours est différente de 0\n", + " # l'auteur associé a prononcé un certain nombres de mots\n", + " # on va l'afficher au milieu du rectangle correspondant\n", + " ax.text(bar.get_x()+bar.get_width()/2, \\\n", + " bar.get_y()+bar.get_height()/2, \\\n", + " )\n", + " # cf. (https://stackoverflow.com/questions/66837088/how-to-write-text-inside-the-bar-in-a-horizontal-bar-graph-matplotlib) \n", + " # ax.text(0.1, bar.get_y()+bar.get_height()/2, disease, color = 'white', ha = 'left', va = 'center')\n", + " # Positionnement de la légende en haut à droite en dehors du plot\n", + " # cf. (https://www.geeksforgeeks.org/how-to-place-legend-outside-of-the-plot-in-matplotlib/)\n", + " plt.legend(bbox_to_anchor=(1.05, 1.0), loc='upper left')" ] }, { -- 2.18.1