diff --git a/module3/exo3/exercice.ipynb b/module3/exo3/exercice.ipynb index 254dbcd5eaa86bf3f3e66ab467d27c3db6c8ab95..937d057fdfa353682e629a1cc9b96895fd3dc815 100644 --- a/module3/exo3/exercice.ipynb +++ b/module3/exo3/exercice.ipynb @@ -23,49 +23,37 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 130, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "import isoweek\n", - "import os" + "import pylab as plt\n", + "import matplotlib.patches as mpatches\n", + "import numpy as np\n", + "\n", + "#Version numpy : 1.15.2\n", + "#Version matplotlib : 2.2.3\n", + "#python 3.6.4" ] }, { - "cell_type": "code", - "execution_count": 2, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'/home/jovyan/work/module3/exo3'" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "os.getcwd()" + "## 1. Etude des données" ] }, { - "cell_type": "code", - "execution_count": 3, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "data_url = \"http://dramacode.github.io/markdown/moliere_avare.txt\"" + "Le texte utilisé a été télécharger au lien suivant: http://dramacode.github.io/markdown/moliere_avare.txt, le 1er avril 2023 à 14h09. Il est disponible sur le git, au nom de \"moliere_avare.txt\"" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 133, "metadata": {}, "outputs": [], "source": [ @@ -74,27 +62,7 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "<_io.TextIOWrapper name='moliere_avare.txt' mode='r' encoding='UTF-8'>" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fichier" - ] - }, - { - "cell_type": "code", - "execution_count": 6, + "execution_count": 135, "metadata": {}, "outputs": [], "source": [ @@ -103,7 +71,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 136, "metadata": {}, "outputs": [], "source": [ @@ -114,8 +82,10 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": {}, + "execution_count": 137, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { @@ -123,7 +93,7 @@ "'# ACTEURS.\\n'" ] }, - "execution_count": 8, + "execution_count": 137, "metadata": {}, "output_type": "execute_result" } @@ -134,8 +104,10 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": {}, + "execution_count": 138, + "metadata": { + "scrolled": false + }, "outputs": [ { "name": "stdout", @@ -179,7 +151,36 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 228, + "metadata": {}, + "outputs": [], + "source": [ + "#Vérification structure du texte\n", + "for k in range(34,len(lignes)) :\n", + " l = lignes[k]\n", + " if l[:3] == '###' :\n", + " scène_courante += 1\n", + " dico_scènes[scène_courante]= {}\n", + " for perso in persos :\n", + " dico_scènes[scène_courante][perso] = 0\n", + " elif l[:2] == '##' :\n", + " if acte_courant >= 1 :\n", + " dico_acte[acte_courant] = dico_scènes\n", + " scène_courante = 0\n", + " acte_courant += 1\n", + " dico_scènes = {}\n", + " if l in persos :\n", + " Nombre_repliques[l] += 1\n", + " dico_scènes[scène_courante][l] += 1\n", + " Nombre_mots[l] += len(lignes[k+1].split()) #problème ponctuation\n", + " if not (acte_courant in Nombre_actes[l]) :\n", + " Nombre_actes[l].append(acte_courant)\n", + "dico_acte[acte_courant] = dico_scènes\n" + ] + }, + { + "cell_type": "code", + "execution_count": 139, "metadata": {}, "outputs": [ { @@ -222,7 +223,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 140, "metadata": {}, "outputs": [], "source": [ @@ -252,62 +253,29 @@ }, { "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{' HARPAGON.\\n': [1, 2, 3, 4, 5],\n", - " ' CLÉANTE.\\n': [1, 2, 3, 4, 5],\n", - " ' ÉLISE.\\n': [1, 3, 4, 5],\n", - " ' VALÈRE.\\n': [1, 3, 5],\n", - " ' MARIANE.\\n': [3, 4, 5],\n", - " ' ANSELME.\\n': [5],\n", - " ' FROSINE.\\n': [2, 3, 4, 5],\n", - " ' MAÎTRE SIMON.\\n': [2],\n", - " ' MAÎTRE JACQUES.\\n': [3, 4, 5],\n", - " ' LA FLÈCHE.\\n': [1, 2, 4],\n", - " ' DAME CLAUDE.\\n': [],\n", - " ' BRINDAVOINE.\\n': [3],\n", - " ' LA MERLUCHE.\\n': [3],\n", - " ' LE COMMISSAIRE.\\n': [5]}" - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Nombre_actes" - ] - }, - { - "cell_type": "code", - "execution_count": 52, + "execution_count": 142, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{' DAME CLAUDE.\\n': 0,\n", - " ' BRINDAVOINE.\\n': 193,\n", - " ' LA MERLUCHE.\\n': 255,\n", - " ' MAÎTRE SIMON.\\n': 940,\n", - " ' LE COMMISSAIRE.\\n': 1439,\n", - " ' ANSELME.\\n': 2460,\n", - " ' MARIANE.\\n': 4297,\n", - " ' ÉLISE.\\n': 4515,\n", - " ' MAÎTRE JACQUES.\\n': 6871,\n", - " ' LA FLÈCHE.\\n': 7223,\n", - " ' FROSINE.\\n': 10224,\n", - " ' VALÈRE.\\n': 12759,\n", - " ' CLÉANTE.\\n': 16020,\n", - " ' HARPAGON.\\n': 26239}" + " ' BRINDAVOINE.\\n': 38,\n", + " ' LA MERLUCHE.\\n': 49,\n", + " ' MAÎTRE SIMON.\\n': 186,\n", + " ' LE COMMISSAIRE.\\n': 281,\n", + " ' ANSELME.\\n': 488,\n", + " ' MARIANE.\\n': 854,\n", + " ' ÉLISE.\\n': 893,\n", + " ' MAÎTRE JACQUES.\\n': 1341,\n", + " ' LA FLÈCHE.\\n': 1419,\n", + " ' FROSINE.\\n': 2033,\n", + " ' VALÈRE.\\n': 2532,\n", + " ' CLÉANTE.\\n': 3172,\n", + " ' HARPAGON.\\n': 5179}" ] }, - "execution_count": 52, + "execution_count": 142, "metadata": {}, "output_type": "execute_result" } @@ -318,29 +286,29 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 143, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{' DAME CLAUDE.\\n': 0,\n", - " ' BRINDAVOINE.\\n': 18,\n", - " ' MAÎTRE SIMON.\\n': 30,\n", - " ' LA MERLUCHE.\\n': 30,\n", - " ' LE COMMISSAIRE.\\n': 102,\n", - " ' ANSELME.\\n': 120,\n", - " ' MARIANE.\\n': 162,\n", - " ' ÉLISE.\\n': 300,\n", - " ' FROSINE.\\n': 354,\n", - " ' LA FLÈCHE.\\n': 384,\n", - " ' MAÎTRE JACQUES.\\n': 498,\n", - " ' VALÈRE.\\n': 594,\n", - " ' CLÉANTE.\\n': 960,\n", - " ' HARPAGON.\\n': 2064}" + " ' BRINDAVOINE.\\n': 3,\n", + " ' MAÎTRE SIMON.\\n': 5,\n", + " ' LA MERLUCHE.\\n': 5,\n", + " ' LE COMMISSAIRE.\\n': 17,\n", + " ' ANSELME.\\n': 20,\n", + " ' MARIANE.\\n': 27,\n", + " ' ÉLISE.\\n': 50,\n", + " ' FROSINE.\\n': 59,\n", + " ' LA FLÈCHE.\\n': 64,\n", + " ' MAÎTRE JACQUES.\\n': 83,\n", + " ' VALÈRE.\\n': 99,\n", + " ' CLÉANTE.\\n': 160,\n", + " ' HARPAGON.\\n': 344}" ] }, - "execution_count": 53, + "execution_count": 143, "metadata": {}, "output_type": "execute_result" } @@ -351,548 +319,14 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 221, "metadata": {}, "outputs": [ { "data": { + "image/png": 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\n", 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0,\n", - " ' BRINDAVOINE.\\n': 0,\n", - " ' LA MERLUCHE.\\n': 0,\n", - " ' LE COMMISSAIRE.\\n': 0},\n", - " 8: {' HARPAGON.\\n': 2,\n", - " ' CLÉANTE.\\n': 0,\n", - " ' ÉLISE.\\n': 0,\n", - " ' VALÈRE.\\n': 0,\n", - " ' MARIANE.\\n': 0,\n", - " ' ANSELME.\\n': 0,\n", - " ' FROSINE.\\n': 0,\n", - " ' MAÎTRE SIMON.\\n': 0,\n", - " ' MAÎTRE JACQUES.\\n': 0,\n", - " ' LA FLÈCHE.\\n': 0,\n", - " ' DAME CLAUDE.\\n': 0,\n", - " ' BRINDAVOINE.\\n': 2,\n", - " ' LA MERLUCHE.\\n': 0,\n", - " ' LE COMMISSAIRE.\\n': 0},\n", - " 9: {' HARPAGON.\\n': 6,\n", - " ' CLÉANTE.\\n': 2,\n", - " ' ÉLISE.\\n': 0,\n", - " ' VALÈRE.\\n': 2,\n", - " ' MARIANE.\\n': 0,\n", - " ' ANSELME.\\n': 0,\n", - " ' FROSINE.\\n': 0,\n", - " ' MAÎTRE SIMON.\\n': 0,\n", - " ' MAÎTRE JACQUES.\\n': 0,\n", - " ' LA FLÈCHE.\\n': 0,\n", - " ' DAME CLAUDE.\\n': 0,\n", - " ' BRINDAVOINE.\\n': 0,\n", - " ' LA MERLUCHE.\\n': 3,\n", - " ' LE COMMISSAIRE.\\n': 0}},\n", - " 4: {1: {' HARPAGON.\\n': 0,\n", - " ' CLÉANTE.\\n': 10,\n", - " ' ÉLISE.\\n': 2,\n", - " ' VALÈRE.\\n': 0,\n", - " ' MARIANE.\\n': 6,\n", - " ' ANSELME.\\n': 0,\n", - " ' FROSINE.\\n': 6,\n", - " ' MAÎTRE SIMON.\\n': 0,\n", - " ' MAÎTRE JACQUES.\\n': 0,\n", - " ' LA FLÈCHE.\\n': 0,\n", - " ' DAME CLAUDE.\\n': 0,\n", - " ' BRINDAVOINE.\\n': 0,\n", - " ' LA MERLUCHE.\\n': 0,\n", - " ' LE COMMISSAIRE.\\n': 0},\n", - " 2: {' HARPAGON.\\n': 3,\n", - " ' CLÉANTE.\\n': 1,\n", - " ' ÉLISE.\\n': 1,\n", - " ' VALÈRE.\\n': 0,\n", - " ' MARIANE.\\n': 0,\n", - " ' ANSELME.\\n': 0,\n", - " ' FROSINE.\\n': 0,\n", - " ' MAÎTRE SIMON.\\n': 0,\n", - " ' MAÎTRE JACQUES.\\n': 0,\n", - " ' LA FLÈCHE.\\n': 0,\n", - " ' DAME CLAUDE.\\n': 0,\n", - " ' BRINDAVOINE.\\n': 0,\n", - " ' LA MERLUCHE.\\n': 0,\n", - " ' LE COMMISSAIRE.\\n': 0},\n", - " 3: {' HARPAGON.\\n': 23,\n", - " ' CLÉANTE.\\n': 22,\n", - " ' ÉLISE.\\n': 0,\n", - " ' VALÈRE.\\n': 0,\n", - " ' MARIANE.\\n': 0,\n", - " ' ANSELME.\\n': 0,\n", - " ' FROSINE.\\n': 0,\n", - " ' MAÎTRE SIMON.\\n': 0,\n", - " ' MAÎTRE JACQUES.\\n': 0,\n", - " ' LA FLÈCHE.\\n': 0,\n", - " ' DAME CLAUDE.\\n': 0,\n", - " ' BRINDAVOINE.\\n': 0,\n", - " ' LA MERLUCHE.\\n': 0,\n", - " ' LE COMMISSAIRE.\\n': 0},\n", - " 4: {' HARPAGON.\\n': 8,\n", - " ' CLÉANTE.\\n': 8,\n", - " ' ÉLISE.\\n': 0,\n", - " ' VALÈRE.\\n': 0,\n", - " ' MARIANE.\\n': 0,\n", - " ' ANSELME.\\n': 0,\n", - " ' FROSINE.\\n': 0,\n", - " ' MAÎTRE SIMON.\\n': 0,\n", - " ' MAÎTRE JACQUES.\\n': 17,\n", - " ' LA FLÈCHE.\\n': 0,\n", - " ' DAME CLAUDE.\\n': 0,\n", - " ' BRINDAVOINE.\\n': 0,\n", - " ' LA MERLUCHE.\\n': 0,\n", - " ' LE COMMISSAIRE.\\n': 0},\n", - " 5: {' HARPAGON.\\n': 18,\n", - " ' CLÉANTE.\\n': 19,\n", - " ' ÉLISE.\\n': 0,\n", - " ' VALÈRE.\\n': 0,\n", - " ' MARIANE.\\n': 0,\n", - " ' ANSELME.\\n': 0,\n", - " ' FROSINE.\\n': 0,\n", - " ' MAÎTRE SIMON.\\n': 0,\n", - " ' MAÎTRE JACQUES.\\n': 0,\n", - " ' LA FLÈCHE.\\n': 0,\n", - " ' DAME CLAUDE.\\n': 0,\n", - " ' BRINDAVOINE.\\n': 0,\n", - " ' LA MERLUCHE.\\n': 0,\n", - " ' LE COMMISSAIRE.\\n': 0},\n", - " 6: {' HARPAGON.\\n': 0,\n", - " ' CLÉANTE.\\n': 5,\n", - " ' ÉLISE.\\n': 0,\n", - " ' VALÈRE.\\n': 0,\n", - " ' MARIANE.\\n': 0,\n", - " ' ANSELME.\\n': 0,\n", - " ' FROSINE.\\n': 0,\n", - " ' MAÎTRE SIMON.\\n': 0,\n", - " ' MAÎTRE JACQUES.\\n': 0,\n", - " ' LA FLÈCHE.\\n': 5,\n", - " ' DAME CLAUDE.\\n': 0,\n", - " ' BRINDAVOINE.\\n': 0,\n", - " ' LA MERLUCHE.\\n': 0,\n", - " ' LE COMMISSAIRE.\\n': 0},\n", - " 7: {' HARPAGON.\\n': 1,\n", - " ' CLÉANTE.\\n': 0,\n", - " ' ÉLISE.\\n': 0,\n", - " ' VALÈRE.\\n': 0,\n", - " ' MARIANE.\\n': 0,\n", - " ' ANSELME.\\n': 0,\n", - " ' FROSINE.\\n': 0,\n", - " ' MAÎTRE SIMON.\\n': 0,\n", - " ' MAÎTRE JACQUES.\\n': 0,\n", - " ' LA FLÈCHE.\\n': 0,\n", - " ' DAME CLAUDE.\\n': 0,\n", - " ' BRINDAVOINE.\\n': 0,\n", - " ' LA MERLUCHE.\\n': 0,\n", - " ' LE COMMISSAIRE.\\n': 0}},\n", - " 5: {1: {' HARPAGON.\\n': 6,\n", - " ' CLÉANTE.\\n': 0,\n", - " ' ÉLISE.\\n': 0,\n", - " ' VALÈRE.\\n': 0,\n", - " ' MARIANE.\\n': 0,\n", - " ' ANSELME.\\n': 0,\n", - " ' FROSINE.\\n': 0,\n", - " ' MAÎTRE SIMON.\\n': 0,\n", - " ' MAÎTRE JACQUES.\\n': 0,\n", - " ' LA FLÈCHE.\\n': 0,\n", - " ' DAME CLAUDE.\\n': 0,\n", - " ' BRINDAVOINE.\\n': 0,\n", - " ' LA MERLUCHE.\\n': 0,\n", - " ' LE COMMISSAIRE.\\n': 7},\n", - " 2: {' HARPAGON.\\n': 19,\n", - " ' CLÉANTE.\\n': 0,\n", - " ' ÉLISE.\\n': 0,\n", - " ' VALÈRE.\\n': 0,\n", - " ' MARIANE.\\n': 0,\n", - " ' ANSELME.\\n': 0,\n", - " ' FROSINE.\\n': 0,\n", - " ' MAÎTRE SIMON.\\n': 0,\n", - " ' MAÎTRE JACQUES.\\n': 22,\n", - " ' LA FLÈCHE.\\n': 0,\n", - " ' DAME CLAUDE.\\n': 0,\n", - " ' BRINDAVOINE.\\n': 0,\n", - " ' LA MERLUCHE.\\n': 0,\n", - " ' LE COMMISSAIRE.\\n': 8},\n", - " 3: {' HARPAGON.\\n': 30,\n", - " ' CLÉANTE.\\n': 0,\n", - " ' ÉLISE.\\n': 0,\n", - " ' VALÈRE.\\n': 30,\n", - " ' MARIANE.\\n': 0,\n", - " ' ANSELME.\\n': 0,\n", - " ' FROSINE.\\n': 0,\n", - " ' MAÎTRE SIMON.\\n': 0,\n", - " ' MAÎTRE JACQUES.\\n': 2,\n", - " ' LA FLÈCHE.\\n': 0,\n", - " ' DAME CLAUDE.\\n': 0,\n", - " ' BRINDAVOINE.\\n': 0,\n", - " ' LA MERLUCHE.\\n': 0,\n", - " ' LE COMMISSAIRE.\\n': 0},\n", - " 4: {' HARPAGON.\\n': 4,\n", - " ' CLÉANTE.\\n': 0,\n", - " ' ÉLISE.\\n': 1,\n", - " ' VALÈRE.\\n': 1,\n", - " ' MARIANE.\\n': 0,\n", - " ' ANSELME.\\n': 0,\n", - " ' FROSINE.\\n': 1,\n", - " ' MAÎTRE SIMON.\\n': 0,\n", - " ' MAÎTRE JACQUES.\\n': 1,\n", - " ' LA FLÈCHE.\\n': 0,\n", - " ' DAME CLAUDE.\\n': 0,\n", - " ' BRINDAVOINE.\\n': 0,\n", - " ' LA MERLUCHE.\\n': 0,\n", - " ' LE COMMISSAIRE.\\n': 0},\n", - " 5: {' HARPAGON.\\n': 11,\n", - " ' CLÉANTE.\\n': 0,\n", - " ' ÉLISE.\\n': 0,\n", - " ' VALÈRE.\\n': 14,\n", - " ' MARIANE.\\n': 3,\n", - " ' ANSELME.\\n': 14,\n", - " ' FROSINE.\\n': 0,\n", - " ' MAÎTRE SIMON.\\n': 0,\n", - " ' MAÎTRE JACQUES.\\n': 1,\n", - " ' LA FLÈCHE.\\n': 0,\n", - " ' DAME CLAUDE.\\n': 0,\n", - " ' BRINDAVOINE.\\n': 0,\n", - " ' LA MERLUCHE.\\n': 0,\n", - " ' LE COMMISSAIRE.\\n': 0},\n", - " 6: {' HARPAGON.\\n': 10,\n", - " ' CLÉANTE.\\n': 4,\n", - " ' ÉLISE.\\n': 0,\n", - " ' VALÈRE.\\n': 0,\n", - " ' MARIANE.\\n': 1,\n", - " ' ANSELME.\\n': 6,\n", - " ' FROSINE.\\n': 0,\n", - " ' MAÎTRE SIMON.\\n': 0,\n", - " ' MAÎTRE JACQUES.\\n': 1,\n", - " ' LA FLÈCHE.\\n': 0,\n", - " ' DAME CLAUDE.\\n': 0,\n", - " ' BRINDAVOINE.\\n': 0,\n", - " ' LA MERLUCHE.\\n': 0,\n", - " ' LE COMMISSAIRE.\\n': 2}}}" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dico_acte" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "ename": "ValueError", - "evalue": "shape mismatch: objects cannot be broadcast to a single shape", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;31m# Création du diagramme en bâtons (bâtons côte à côte)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0mpos\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpos\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'lightsteelblue'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 17\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpos\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'IndianRed'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpos\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Scène \"\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscene\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mbar\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 2773\u001b[0m mplDeprecation)\n\u001b[1;32m 2774\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2775\u001b[0;31m \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2776\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2777\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwashold\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1865\u001b[0m \u001b[0;34m\"the Matplotlib list!)\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlabel_namer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1866\u001b[0m RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1867\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1868\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1869\u001b[0m inner.__doc__ = _add_data_doc(inner.__doc__,\n", - "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mbar\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 2236\u001b[0m x, height, width, y, linewidth = np.broadcast_arrays(\n\u001b[1;32m 2237\u001b[0m \u001b[0;31m# Make args iterable too.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2238\u001b[0;31m np.atleast_1d(x), height, width, y, linewidth)\n\u001b[0m\u001b[1;32m 2239\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2240\u001b[0m \u001b[0;31m# Now that units have been converted, set the tick locations.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/numpy/lib/stride_tricks.py\u001b[0m in \u001b[0;36mbroadcast_arrays\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 250\u001b[0m \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_m\u001b[0m\u001b[0;34m,\u001b[0m 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directly\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 189\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mpos\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m31\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mValueError\u001b[0m: shape mismatch: objects cannot be broadcast to a single shape" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "for acte in range(1,6) :\n", - " plt.figure()\n", - " for scene in range(1,len(dico_acte[acte])+1) :\n", - " x = np.zeros()\n", - " labels = []\n", - " for perso in persos :\n", - " if dico_acte[acte][scene][perso] != 0 :\n", - " x.append(dico_acte[acte][scene][perso])\n", - " labels.append(perso)\n", - " plt.title('Acte ' + str(acte))\n", - " #plt.bar(labels,x)\n", - " width = 0.35 # épaisseur de chaque bâton\n", - "\n", - " # Création du diagramme en bâtons (bâtons côte à côte)\n", - " pos = np.arange(2)\n", - " plt.bar(pos - width/2, x, width, color='lightsteelblue')\n", - " plt.bar(pos + width/2,x, width, color='IndianRed')\n", - " plt.xticks(pos, \"Scène \"+str(scene))" - ] - }, - { - "cell_type": "code", - "execution_count": 129, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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96RmSVFpamtC5c+eDkpSQkKDc3Nx9NfV77bXXfrpgwYLUTz75JP5Ya2wuCH8AAKDZq/4atcsuu6z7kfu3b98e/+GHH7YaNmzYF5K0fv36xEGDBu2p3ua8887bvX79+kRJGjt27Cd9+vTpe9lll3V/5JFH2u/Zs6fG14IlJydXXHvttTseeuihjnXVlJmZmfXcc8+1a5zRNi2WfQEAQLNX2xLrihUrknv16pW1cePGxHHjxpV07ty5XJLc3eLiDp/jcneZVWW86dOnf3zjjTeWLly4sM3vfve70+bMmXPasmXLimo69+TJk7fl5ORk3XvvvSVHU1Nzx8wfAAA4YeXl5e1av379eytWrCiYMWNGhyVLlrSWpF69eu195513kqq3XbZsWVLPnj0PLe9mZ2fvnzRp0vYlS5YUFRYWti4pKalxabd9+/YVo0aNKp0+ffrpTTua44PwBwAATnhnn332/gkTJnwcvaZv0qRJJY8//ninoqKilpJUVFTU8rHHHut05513lkjSb3/721MrKyslSWvXrk2Mj4/39u3bV9TW/5QpUz6ZMWNGh4qKihqXh08kLPsCAICTwh133LG9W7duZxQWFra84IIL9k6dOnXLFVdc0ePgwYPWokULf+CBB7ZccMEFeyXppZdeOm3y5MnpiYmJlQkJCf78889/mJBQeyzq1KlT+fDhw3f+6le/OnTtX/Sav+j3Sy+99POnn376X+4Kbm7M3WNdQ5OwNHMbd8KHczSyvw3aG+sSEEMXDmkV6xKAJmNmK909ryn6zs/P35iTk7Mj+j0Wj3pBw+Tn57fPycnJqGkfM38AAKBBCGonNq75AwAACAjhDwAAICCEPwAAgIAQ/gAAwEnr4MGDuuyyy7p/7Wtf61ZRUeuTXILS6Dd8mNkwSZ+4OxeDAgCAmFq3bl3if/7nf27dv39/XGFhYavs7Oz9sa4p1uqd+TOzKWZWYGZrzGy1mQ2q55C3Jd1hZj0ap0TJzAab2SozKzezaxqrXwAAcGIws9yrrrqqa/T7wYMH1a5du5xLLrnksLwxZMiQ7v3798+Mfh8wYMC+efPmtVu0aFHyQw891DEzMzOre/fu2YmJiedE38n761//ut3o0aMz0tLS+mVmZmb17t0769VXX02J9nHuuef2zsjI6Bttf/nll3c7sr7NmzcnXHLJJT169+6d1b179+yLLrqoh1T1cOmePXtmS9LChQtTzCz38ccfbx897u9//3trM8u97777OkpSZWWl7rzzzk5dunTpm5GR0XfQoEG9VqxYkRhtn5aW1u/f/u3fDr3bOFp7Q36Xdc78mdn5kkZIOsfd95tZe0kt6zrG3fdIGtOQIo7CR5JukPTDRu4XAAA00MYxG3Iqv6hstNXDuJS48owXe9S5Yti6devKoqKi1rt27bLk5GR/5ZVX2nTs2PFg9TY7duyILygoOCUpKamisLCwZWZm5oHq+2fOnPmRVBXIRowY0bP6e3kXLlx46rRp07bceOONO3//+9+njB8/vsuVV165Lrr/xRdf/GDw4MF7aqtv0qRJaZdeemnZvffeu02Sli5d2rqmdj179tw7d+7cdrfffvsOSXrppZdSe/fufeghtA899FCHpUuXnrJu3br3UlJSKl9++eU2o0aN6lFUVFSQlJTkkrR27dqkFStWJObl5e2r6Rz1qW/mr5OkHe6+X5LcfYe7b5UkMxtoZkvMLN/MlplZipnFm9kjZrbczNaa2c2Rtheb2SIzm2tmhWb2G4u8WdnMcs1ssZmtNLPXzKzTkUW4+0Z3XyOp8ssMEgAANJ7GDH4N6W/IkCGfz5kzp60kzZo1K3X06NGl1ffPnDmz3dChQz8bNWpU6YwZM1K/bD1DhgzZtW3bthYNOaakpKRFenr6obA5aFDNbxVIS0s7sH///rjNmzcnVFZW6q233jp1yJAhn0f3P/nkk52efvrpzSkpKZWSdPXVV5fl5ubu/uUvf3latM24ceM+mTp16r/kpaNVX/h7XVK6ma03s6fN7CJJMrOWkmZLmuDuOZKGStor6SZJZe4+UNJASTebWXRqdICk2yRlSeom6UIzayHpKUnXuHuupBckPfhlBwMAAE5e3/3ud0tnz57dbs+ePfb+++8nnX/++bur758zZ07qd77zndLrr7++dN68eV86/M2bN+/UoUOHflZ925gxY7pFl31vvvnms448Zty4cdu+//3vZwwaNKjXpEmTzti4cWOt4fGqq67aOXPmzHZvvPHGKf369dvTqlUrl6TS0tK4vXv3xh15XWJubu7ugoKCQ0u/Y8aMKV23bl3SunXrvtRri+pM2u6+y8xyJX1V0iWSZpvZZEkrJX3s7ssj7cqkQzd7dDWzIZEuWqoq6JVLWubuWyLtVkvKkPSZpL6S/hyZCIyX9PGXGUik37GSxkqSTv2yvQAAgOZo0KBBe7ds2dLqueeeSx06dOjn1fdt3rw5YdOmTa2GDRu2Ky4uTgkJCb58+fLEgQMHHvXS6D333HPWvffee1ZpaWnC4sWL36++r75l39GjR5d95StfWfvKK6+c+qc//enU3NzcrLVr1xbU1HbMmDGlo0eP7l5YWNj6uuuuK3377beT66rL3RXJSZKkhIQE/eAHPyiZOnXqGcOHDy872vFF1XvDh7tXuPsid79f0nhJoyWZpJpeCmySprj7xZFPX3d/I7KveoqtUFXwNEkF7t4/8unn7sMaOohqtT7r7nnunqdTvmwvAACgubr88ss/u//++9PHjBlz2JLvjBkzUsvKyuLT09P7paWl9SsuLm41c+bMBs3+TZs2bcumTZvWTp48ufiGG27oWv8Rh+vYsWPFLbfcUjp//vwPzz777N2vv/56jaGuc+fO5S1atPC//vWvbUaOHHkovKWmpla2bt268r333jvs/op33303KSsr67AQe+utt5YuXbo0ZdOmTXXei1GTOsOfmfU2s57VNvWXtElSoaQzzWxgpF2KmSVIek3SLZHl3OjxdcWwIkkdIjeWyMxamFl2QwcBAADCcOutt+644447tp577rmHXVM3d+7c1FdeeeUfxcXFa4uLi9cuXbr0vfnz5zd46Tc+Pl733HPPtsrKSps3b16boz1uwYIFKV988UWcJO3cuTNu06ZNrbp27XqgtvY//vGPix944IEtCQmHL8KOHz++ZNy4cZ137dplkjR//vyU5cuXp/zHf/zHp9XbtWrVym+99dZPfvnLX57eoAGq/uf8JUt6yszaqmrpdoOkse5+wMy+GdnXWlXX+w2V9LyqlnNXRW7o2C7pqto6j/RzjaQnzezUSD0/k3TYNGkkZL4iqZ2kK8zsx+5OSAQAIDDdu3c/GL2jNqqoqKjl1q1bW1566aWHrgHMzMw8kJycXPHWW281eC0wLi5OkyZN2jp9+vQzRo8eXSZVXfOXmJhYKUmpqanlS5YsWV/9mOXLlyfdfvvtnePj493d7bvf/e6Oiy66aE9RUVGNM3OXXXbZ7pq233333dt27twZn5WVlR0XF6cOHTocfPnllzckJyf/y4rrhAkTdjz22GMNvvHD3GtavT3xWZq5jbP6GyIof6v55isE4sIhX+raaOCEYGYr3T2vKfrOz8/fmJOTsyP6PRaPekHD5Ofnt8/JycmoaV+jv+EDAACc3AhqJzbe7QsAABAQwh8AAEBACH8AAAABIfwBAABUs2/fPrvwwgt71vWWjhMZ4Q8AAKCaxMREf+GFFzbl5+cn1t/6xEP4AwAAzV5SUtKA2vb17t0764orrqj1jRwTJ0488/TTTz87+m7e733ve2mSdO655/b+61//mlS97cKFC1NSUlL6X3nllT0mTZqUnpmZmTV//vwUSfroo48SRowY0S09Pb1v9+7dsy+66KIea9asaVVUVNSyZ8+e2Uee87777usoSaNHj85IS0vrFz3/gAEDMo/ld3GseNQLAABokKV/3Z9TfrDxMkRCC5UPGtzqSz0+ZtWqVYnurqVLl6aUlZXFtWnTprKmdrfccssnU6dO/eRo+szLy9v1l7/8ZUP1bZWVlRo5cmSP66677tOFCxd+IElLlixpvXXr1hZ1vckjatq0aVtuvPHGnUdz/qbGzB8AAGiQxgx+x9rfjBkzUr/xjW98Onjw4LJZs2a1bcy6qlu4cGFKQkKC33nnnduj2y644IK9l19++a6mOmdTIfwBAIAT1quvvpo6ZsyYndddd13p7Nmza32X7y9+8YuO0WXX+t7Zu2LFiuRo28zMzKyCgoJWa9asaZ2Tk7OntmM2b97cqvoxL774Yofq+++5556zovtGjhxZ6xL18cCyLwAAOCEtXrw4KTU1tbxXr14HunXrduDWW2/N2L59e3yHDh0qjmx7rMu+r7zySp3HpKen7y8sLHwv+n3ixIlnVt/Psi8AAMAxmjlzZuoHH3yQmJaW1q9Lly79du/eHT9z5sx2TXGufv367c3Pz0+qv2XzR/gDAAAnnIqKCi1cuDD13XffLSguLl5bXFy8dtasWRvmzJlT69Lvsbjiiiu+OHDggD366KPto9sWL16c9Ic//CG5Kc7XlAh/AACg2du3b19cx44dz45+HnjggY4dO3Y80LVr14PRNsOHD/9iw4YNiZs2bTrqhzOPGjWqZ7TP4cOHd5P+9Zq/X//61+3i4uK0YMGCf7755ptt0tPT+/bo0SP7/vvvP7Nz584H6zuHdPg1f5mZmVn79u2zhv8WGoe5e6zO3aQszdzGxez3imbqb4P2xroExNCFQ1rFugSgyZjZSnfPa4q+8/PzN+bk5OyIfm9Oj3pBzfLz89vn5ORk1LSPGz4AAECDENRObCz7AgAABITwBwAAEBDCHwAAqE9lZWUlF9KfICJ/VzW+5k4i/AEAgPqt2759+6kEwOavsrLStm/ffqqkdbW14YYPAABQp/Ly8n8vKSl5vqSkpK+YOGruKiWtKy8v//faGhD+AABAnXJzc7dJGhnrOtA4SO8AAAABIfwBAAAEhPAHAAAQEMIfAABAQAh/AAAAASH8AQAABOSkfdRLbqdcrbh7RazLAAAAaFaY+QMAAAgI4Q8AACAghD8AAICAEP4AAAACQvgDAAAICOEPAAAgIIQ/AACAgBD+AAAAAkL4AwAACAjhDwAAICCEPwAAgIAQ/gAAAAJC+AMAAAgI4Q8AACAghD8AAICAJMS6gKayzbfryX1Px7qMJnXbY+NjXUKtNiwvjOn5Px7fpcbtFw5pdZwrAQCgeWHmDwAAICCEPwAAgIAQ/gAAAAJC+AMAAAgI4Q8AACAghD8AAICAEP4AAAACQvgDAAAICOEPAAAgIIQ/AACAgBD+AAAAAkL4AwAACAjhDwAAICCEPwAAgIAQ/gAAAAJC+AMAAAgI4Q8AACAghD8AAICAEP4AAAACQvgDAAAICOEPAAAgIIQ/AACAgBD+AAAAAkL4AwAACAjhDwAAICCEPwAAgIAQ/gAAAALS6OHPzIaZWU5j9wsAAIBjV2/4M7MpZlZgZmvMbLWZDarnkLcl3WFmPRqnRMnMJprZe5Ea3jSzLo3VNwAAQEgS6tppZudLGiHpHHffb2btJbWs6xh33yNpTOOVKEl6V1Keu+8xs1sl/R9J32zkcwAAAJz06pv56yRph7vvlyR33+HuWyXJzAaa2RIzyzezZWaWYmbxZvaImS03s7VmdnOk7cVmtsjM5ppZoZn9xswssi/XzBab2Uoze83MOh1ZhLv/JRIqJem/JZ3VWL8AAACAkNQX/l6XlG5m683saTO7SJLMrKWk2ZImuHuOpKGS9kq6SVKZuw+UNFDSzWbWLdLXAEm3ScqS1E3ShWbWQtJTkq5x91xJL0h6sJ6abpL0xwaOEwAAAKpn2dfdd5lZrqSvSrpE0mwzmyxppaSP3X15pF2ZVHWzh6SuZjYk0kVLVQW9cknL3H1LpN1qSRmSPpPUV9KfIxOB8ZI+rq0eM/uOpDxJF9Wyf6yksZLULj21nqEDAACEp87wJ0nuXiFpkaRFZrZW0vWSVknyGpqbpCnu/qfDNppdLGl/tU0VkXObpAJ3P7++OsxsqKQpki6KLkPXUOuzkp6VpM65XWqqDwAAIGh1LvuaWW8z61ltU39JmyQVSjrTzAZG2qWYWYKk1yTdElktPJQEAAAT50lEQVTOjR5/Sh2nKJLUIXJjicyshZll11DHAEm/lDTS3bcd/fAAAABQXX0zf8mSnjKztqpaut0gaay7HzCzb0b2tVbV9X5DJT2vquXcVZEbOrZLuqq2ziP9XCPpSTM7NVLPzyQVHNH0kUgtcyLLwx+5+8gGjRQAAAD1XvO3UtIFtexbLum8GnbdHflUtyjyiR47vtrPqyUNrqeOoXXtBwAAwNHh9W4AAAABIfwBAAAEhPAHAAAQEMIfAABAQAh/AAAAASH8AQAABITwBwAAEBDCHwAAQEAIfwAAAAEh/AEAAASE8AcAABAQwh8AAEBACH8AAAABIfwBAAAEhPAHAAAQEMIfAABAQAh/AAAAASH8AQAABITwBwAAEBDCHwAAQEAIfwAAAAEh/AEAAASE8AcAABAQwh8AAEBACH8AAAABIfwBAAAEhPAHAAAQEMIfAABAQMzdY11Dk8jLy/MVK1bEugwAAI4LM1vp7nmxrgPNHzN/AAAAASH8AQAABITwBwAAEBDCHwAAQEAIfwAAAAEh/AEAAASE8AcAABAQwh8AAEBACH8AAAABIfwBAAAEhPAHAAAQEMIfAABAQAh/AAAAASH8AQAABITwBwAAEJCEWBfQVLb5dj257+l/2X7viBdqPebzN1Y0ZUkAAAAxx8wfAABAQAh/AAAAASH8AQAABITwBwAAEBDCHwAAQEAIfwAAAAEh/AEAAASE8AcAABAQwh8AAEBACH8AAAABIfwBAAAEhPAHAAAQEMIfAABAQAh/AAAAASH8AQAABITwBwAAEBDCHwAAQEAIfwAAAAEh/AEAAASE8AcAABAQwh8AAEBACH8AAAABIfwBAAAEhPAHAAAQEMIfAABAQAh/AAAAASH8AQAABITwBwAAEJBGD39mNszMchq7XwAAABy7esOfmU0xswIzW2Nmq81sUD2HvC3pDjPr0TglSmZ2i5mtjZz/bTPLaqy+AQAAQpJQ104zO1/SCEnnuPt+M2svqWVdx7j7HkljGq9ESdJ/ufsvIjWNlPSYpMsb+RwAAAAnvfpm/jpJ2uHu+yXJ3Xe4+1ZJMrOBZrbEzPLNbJmZpZhZvJk9YmbLIzN1N0faXmxmi8xsrpkVmtlvzMwi+3LNbLGZrTSz18ys05FFuHtZta+nSPLGGDwAAEBo6pz5k/S6pPvMbL2kNyTNdvfFZtZS0mxJ33T35WbWRtJeSTdJKnP3gWaWKGmJmf050tcASdmStkr6u6QLzWyppKckXenu283sm5IelPS/jyzEzMZJmqiqmcdLayrWzMZKGitJ7dJTj/qXAAAAEIo6w5+77zKzXElflXSJpNlmNlnSSkkfu/vySLsyqepmD0ldzWxIpIuWkrpJKpe0zN23RNqtlpQh6TNJfSX9OTIRGC/p41pq+bmkn5vZdZLukXR9DW2elfSsJHXO7cLsIAAAwBHqm/mTu1dIWiRpkZmtVVXoWqWal15N0hR3/9NhG80ulrS/2qaKyLlNUoG7n9+Amn8r6ZkGtAcAAEBEndf8mVlvM+tZbVN/SZskFUo608wGRtqlmFmCpNck3WJmLaodf0odpyiS1CFyY4nMrIWZZddQR/Uavi7pH/UPDQAAAEeqb+YvWdJTZtZWVUu3GySNdfcDkevznjKz1qq63m+opOdVtZy7KnJDx3ZJV9XWeaSfayQ9aWanRur5maSCI5qON7Ohkg5K2qkalnwBAABQv/qu+Vsp6YJa9i2XdF4Nu+6OfKpbFPlEjx1f7efVkgbXU8eEuvYDAADg6PB6NwAAgIAQ/gAAAAJC+AMAAAgI4Q8AACAghD8AAICAEP4AAAACQvgDAAAICOEPAAAgIIQ/AACAgBD+AAAAAkL4AwAACAjhDwAAICCEPwAAgIAQ/gAAAAJC+AMAAAgI4Q8AACAghD8AAICAEP4AAAACQvgDAAAICOEPAAAgIIQ/AACAgBD+AAAAAkL4AwAACAjhDwAAICCEPwAAgIAQ/gAAAAJC+AMAAAiIuXusa2gSeXl5vmLFiliXAQDAcWFmK909L9Z1oPlj5g8AACAghD8AAICAEP4AAAACQvgDAAAICOEPAAAgIIQ/AACAgBD+AAAAAkL4AwAACAjhDwAAICCEPwAAgIAQ/gAAAAJC+AMAAAgI4Q8AACAghD8AAICAEP4AAAACQvgDAAAIiLl7rGtoEvFtTvHkc/vE7Pz/766/x+zcdblwSKtYlwAAaAJmttLd82JdB5o/Zv4AAAACQvgDAAAICOEPAAAgIIQ/AACAgBD+AAAAAkL4AwAACAjhDwAAICCEPwAAgIAQ/gAAAAJC+AMAAAgI4Q8AACAghD8AAICAEP4AAAACQvgDAAAICOEPAAAgIIQ/AACAgBD+AAAAAkL4AwAACAjhDwAAICCEPwAAgIAQ/gAAAAJC+AMAAAgI4Q8AACAghD8AAICAEP4AAAACQvgDAAAICOEPAAAgII0e/sxsmJnlNHa/AAAAOHb1hj8zm2JmBWa2xsxWm9mgeg55W9IdZtajcUo8rJZrzMzNLK+x+wYAAAhBQl07zex8SSMknePu+82svaSWdR3j7nskjWm8Eg/VkiLpB5KWNnbfAAAAoahv5q+TpB3uvl+S3H2Hu2+VJDMbaGZLzCzfzJaZWYqZxZvZI2a23MzWmtnNkbYXm9kiM5trZoVm9hszs8i+XDNbbGYrzew1M+tUSy0PSPo/kvY1ysgBAAACVF/4e11SupmtN7OnzewiSTKzlpJmS5rg7jmShkraK+kmSWXuPlDSQEk3m1m3SF8DJN0mKUtSN0kXmlkLSU9JusbdcyW9IOnBI4swswGS0t194bENFwAAIGx1Lvu6+y4zy5X0VUmXSJptZpMlrZT0sbsvj7Qrk6pu9pDU1cyGRLpoqaqgVy5pmbtvibRbLSlD0meS+kr6c2QiMF7Sx9VrMLM4SY9LuqG+wZjZWEljJckS61ydBgAACFKd4U+S3L1C0iJJi8xsraTrJa2S5DU0N0lT3P1Ph200u1jS/mqbKiLnNkkF7n5+HSWkqCogLooExDMkLTCzke6+4ohan5X0rCTFtzmlpvoAAACCVueyr5n1NrOe1Tb1l7RJUqGkM81sYKRdipklSHpN0i2R5dzo8afUcYoiSR0iN5bIzFqYWXb1Bu7+ubu3d/cMd8+Q9N+S/iX4AQAAoH71zfwlS3rKzNqqaul2g6Sx7n7AzL4Z2ddaVdf7DZX0vKqWc1dFbujYLumq2jqP9HONpCfN7NRIPT+TVHBswwIAAEBNzP3kXB2Nb3OKJ5/bJ2bn/393/T1m567LhUNaxboEAEATMLOV7s5zcFEvXu8GAAAQEMIfAABAQAh/AAAAASH8AQAABITwBwAAEBDCHwAAQEAIfwAAAAEh/AEAAASE8AcAABAQwh8AAEBACH8AAAABIfwBAAAEhPAHAAAQEMIfAABAQAh/AAAAASH8AQAABITwBwAAEBDCHwAAQEAIfwAAAAEh/AEAAASE8AcAABAQwh8AAEBACH8AAAABIfwBAAAEhPAHAAAQEMIfAABAQAh/AAAAASH8AQAABCQh1gU0lQG9+mjFGytiXQYAAECzwswfAABAQAh/AAAAASH8AQAABITwBwAAEBDCHwAAQEAIfwAAAAEh/AEAAASE8AcAABAQwh8AAEBACH8AAAABIfwBAAAEhPAHAAAQEMIfAABAQAh/AAAAASH8AQAABCQh1gU0lW2+XU/uezrWZQDBuXfEC7EuAQBQB2b+AAAAAkL4AwAACAjhDwAAICCEPwAAgIAQ/gAAAAJC+AMAAAgI4Q8AACAghD8AAICAEP4AAAACQvgDAAAICOEPAAAgIIQ/AACAgBD+AAAAAkL4AwAACAjhDwAAICCEPwAAgIAQ/gAAAAJC+AMAAAgI4Q8AACAghD8AAICAEP4AAAACQvgDAAAICOEPAAAgIIQ/AACAgBD+AAAAAkL4AwAACAjhDwAAICCNHv7MbJiZ5TR2vwAAADh29YY/M5tiZgVmtsbMVpvZoHoOeVvSHWbWo3FKlMzsBjPbHjn/ajP798bqGwAAICQJde00s/MljZB0jrvvN7P2klrWdYy775E0pvFKPGS2u49vgn4BAACCUd/MXydJO9x9vyS5+w533ypJZjbQzJaYWb6ZLTOzFDOLN7NHzGy5ma01s5sjbS82s0VmNtfMCs3sN2ZmkX25ZrbYzFaa2Wtm1qkpBwwAABCy+sLf65LSzWy9mT1tZhdJkpm1lDRb0gR3z5E0VNJeSTdJKnP3gZIGSrrZzLpF+hog6TZJWZK6SbrQzFpIekrSNe6eK+kFSQ/WUsvoyNLzXDNL/7IDBgAACFmdy77uvsvMciV9VdIlkmab2WRJKyV97O7LI+3KpKqbPSR1NbMhkS5aqirolUta5u5bIu1WS8qQ9JmkvpL+HJkIjJf0cQ2l/F7SrMjS8y2SZki69MhGZjZW0lhJapeeepS/AgAAgHDUGf4kyd0rJC2StMjM1kq6XtIqSV5Dc5M0xd3/dNhGs4sl7a+2qSJybpNU4O7n11PDp9W+Pifp4VraPSvpWUnqnNulpvoAAACCVueyr5n1NrOe1Tb1l7RJUqGkM81sYKRdipklSHpN0i2R5dzo8afUcYoiSR0iN5bIzFqYWXYNdVS/DnCkpPfrHxoAAACOVN/MX7Kkp8ysraqWbjdIGuvuB8zsm5F9rVV1vd9QSc+rajl3VeSGju2Srqqt80g/10h60sxOjdTzM0kFRzT9gZmNjNRQKumGBo0SAAAAkiRzPzlXRzvndvEf/n1SrMsAgnPviBdiXQIQpLI3V65097xY14Hmj9e7AQAABITwBwAAEBDCHwAAQEAIfwAAAAEh/AEAAASE8AcAABAQwh8AAEBACH8AAAABIfwBAAAEhPAHAAAQEMIfAABAQAh/AAAAASH8AQAABITwBwAAEBDCHwAAQEAIfwAAAAEh/AEAAASE8AcAABAQwh8AAEBACH8AAAABIfwBAAAEhPAHAAAQEMIfAABAQAh/AAAAASH8AQAABITwBwAAEBDCHwAAQEAIfwAAAAExd491DU0iLy/PV6xYEesyAAA4LsxspbvnxboONH/M/AEAAASE8AcAABAQwh8AAEBACH8AAAABIfwBAAAEhPAHAAAQEMIfAABAQAh/AAAAASH8AQAABITwBwAAEBDCHwAAQEAIfwAAAAEh/AEAAASE8AcAABAQwh8AAEBACH8AAAABIfwBAAAEhPAHAAAQEMIfAABAQMzdY11DkzCzLyQVxbqOGGovaUesi4ihkMcf8tglxs/4wx1/b3dPiXURaP4SYl1AEypy97xYFxErZraC8Yc5/pDHLjF+xh/u+M1sRaxrwImBZV8AAICAEP4AAAACcjKHv2djXUCMMf5whTx2ifEz/nCFPHY0wEl7wwcAAAD+1ck88wcAAIAjnJThz8wuN7MiM9tgZpNjXU9TM7MXzGybma2rti3VzP5sZv+I/NkuljU2FTNLN7O/mNn7ZlZgZhMi20MZf6KZLTOz/Mj4fxzZHsT4JcnM4s3sXTNbGPke0tg3mtlaM1sdvdMzsPG3NbO5ZlYY+W/A+aGM38x6R/7eo58yM7stlPHj2Jx04c/M4iX9XNJwSVmSrjWzrNhW1eT+r6TLj9g2WdKb7t5T0puR7yejckl3uHsfSedJGhf5+w5l/PslXeruOZL6S7rczM5TOOOXpAmS3q/2PaSxS9Il7t6/2uNNQhr/E5L+5O6ZknJU9c9BEON396LI33t/SbmS9kh6RYGMH8fmpAt/ks6VtMHdP3D3A5J+K+nKGNfUpNz9r5JKj9h8paQZkZ9nSLrquBZ1nLj7x+6+KvLzF6r6j3+awhm/u/uuyNcWkY8rkPGb2VmSvi7p+Wqbgxh7HYIYv5m1kTRY0q8kyd0PuPtnCmT8Rxgi6Z/uvklhjh8NdDKGvzRJm6t93xLZFpqO7v6xVBWQJJ0e43qanJllSBogaakCGn9k2XO1pG2S/uzuIY3/Z5LulFRZbVsoY5eqgv7rZrbSzMZGtoUy/m6Stkv6dWTZ/3kzO0XhjL+6b0maFfk5xPGjgU7G8Gc1bOOW5pOcmSVLmifpNncvi3U9x5O7V0SWfs6SdK6Z9Y11TceDmY2QtM3dV8a6lhi60N3PUdVlLuPMbHCsCzqOEiSdI+kZdx8gabcCXOI0s5aSRkqaE+tacOI4GcPfFknp1b6fJWlrjGqJpU/MrJMkRf7cFuN6moyZtVBV8PuNu78c2RzM+KMiS16LVHX9Zwjjv1DSSDPbqKrLOy41s5cUxtglSe6+NfLnNlVd73Wuwhn/FklbIjPdkjRXVWEwlPFHDZe0yt0/iXwPbfz4Ek7G8LdcUk8z6xr5P6JvSVoQ45piYYGk6yM/Xy/p1RjW0mTMzFR1zc/77v5YtV2hjL+DmbWN/Nxa0lBJhQpg/O5+l7uf5e4Zqvr3/C13/44CGLskmdkpZpYS/VnSMEnrFMj43b1E0mYz6x3ZNETSewpk/NVcq/9Z8pXCGz++hJPyIc9m9jVVXQsUL+kFd38wxiU1KTObJeliSe0lfSLpfknzJf1OUmdJH0n6X+5+5E0hJzwz+4qkv0laq/+57utuVV33F8L4z1bVRd3xqvqfud+5+1QzO00BjD/KzC6W9EN3HxHK2M2sm6pm+6SqJdD/cvcHQxm/JJlZf1Xd7NNS0geSblTk3wOFMf4kVV3j3s3dP49sC+bvH1/eSRn+AAAAULOTcdkXAAAAtSD8AQAABITwBwAAEBDCHwAAQEAIfwAAAAEh/AEAAASE8AcAABAQwh8AAEBA/n+Ld/b3JqoTTgAAAABJRU5ErkJggg==\n", 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\n", + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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" ] }, "metadata": { @@ -950,9 +384,12 @@ } ], "source": [ - "colors = np.random.rand(14,3)\n", + "import matplotlib.patches as mpatches\n", + "import matplotlib.pyplot as plt\n", + "\n", + "\n", "for acte in range(1,6) :\n", - " plt.figure()\n", + " plt.figure(figsize=(8,8))\n", " x = np.zeros((len(dico_acte[acte]),len(persos)))\n", " labels = persos\n", " for scene in range(1,len(dico_acte[acte])+1) :\n", @@ -961,23 +398,18 @@ " plt.title('Acte ' + str(acte))\n", " #plt.bar(labels,x)\n", " width = 1/2 # épaisseur de chaque bâton\n", - "\n", " # Création du diagramme en bâtons (bâtons côte à côte)\n", - " pos = np.arange(len(dico_acte[acte]))\n", " for scene in range(1,len(dico_acte[acte])+1) :\n", - " nb_perso = 0\n", - " for l,perso in enumerate(persos) :\n", - " if x[scene-1,l] != 0 :\n", - " nb_perso +=1\n", - " nb = 0\n", + " bt = 0\n", " for l,perso in enumerate(persos) :\n", - " if x[scene-1,l] != 0 :\n", - " nb +=1\n", - " if scene == 1 :\n", - " plt.barh(scene+(nb-nb_perso//2)*width/nb_perso, x[scene-1,l],width/nb_perso,label=persos[l],color=colors[l])\n", - " else :\n", - " plt.barh(scene+(nb-nb_perso//2)*width/nb_perso, x[scene-1,l],width/nb_perso,color=colors[l])\n", - " plt.yticks(np.arange(scene)+1, np.array([\"Scène \"+str(k) for k in range(1,scene+1)]))" + " plt.barh(-scene, x[scene-1,l],left = bt,color=colors[l])\n", + " bt += x[scene-1,l]\n", + " plt.yticks(-np.arange(scene)-1, np.array([\"Scène \"+str(k) for k in range(1,scene+1)]))\n", + " patches = []\n", + " for l,perso in enumerate(persos):\n", + " if acte in Nombre_actes[perso] :\n", + " patches.append(mpatches.Patch(color=colors[l], label=perso[4:-2]))\n", + " plt.legend(handles=patches,bbox_to_anchor=(1.05, 1),loc='upper left', borderaxespad=0.)" ] }, {