From e9cfa4536dfffc2e610f974e86fb02de25bdb942 Mon Sep 17 00:00:00 2001 From: 7404ea6678ce6fbf3a726e36f2bf2079 <7404ea6678ce6fbf3a726e36f2bf2079@app-learninglab.inria.fr> Date: Wed, 9 Oct 2024 08:34:40 +0000 Subject: [PATCH] Answer to second question done. Initialisation of optional part. --- module3/exo3/exercice_fr.ipynb | 613 ++++++++++++--------------------- 1 file changed, 216 insertions(+), 397 deletions(-) diff --git a/module3/exo3/exercice_fr.ipynb b/module3/exo3/exercice_fr.ipynb index 4fd110f..d0fe649 100644 --- a/module3/exo3/exercice_fr.ipynb +++ b/module3/exo3/exercice_fr.ipynb @@ -374,7 +374,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -383,7 +383,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -439,7 +439,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -683,7 +683,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -1275,7 +1275,7 @@ "[957 rows x 5 columns]" ] }, - "execution_count": 14, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -1313,7 +1313,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -1355,7 +1355,7 @@ "Index: []" ] }, - "execution_count": 15, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -1366,7 +1366,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -1408,7 +1408,7 @@ "Index: []" ] }, - "execution_count": 16, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -1454,7 +1454,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -1570,7 +1570,7 @@ "6160 Harpagon" ] }, - "execution_count": 18, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -1614,17 +1614,19 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "On va utiliser le classement pour associer à chaque personnage de la pièce une couleur unique. On va donc reprendre le dictionnaire des personnages et régler la propriété de couleur." + "On voulais utiliser le classement pour associer à chaque personnage de la pièce une couleur unique qui reflète son temps de paroles au travers de toute la pièce notamment avec des couleurs plus chaudes, par exemple le rouge pour le personnage avec le nombre de mots le plus élevés, ensuite du orange, puis du jaune etc en allant vers le violet. Néanmoins, le nombre de personnages au total fait qu'une colormap à laquelle nous pensions et qui est illustrée en dessous ne convient pas pour l'affectation des couleurs aux personnages de sorte que l'on puisse les distinguer facilement dans les graphiques.\n", + "\n", + "On va donc reprendre le dictionnaire des personnages et régler la propriété de couleur, mais sans s'occuper d'associer des couleurs en fonction de l'importance des personnages mais surtout de manière à les différencier correctement." ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 40, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -1642,7 +1644,7 @@ "# On affiche une sélection automatique de couleur dans une colormap\n", "# matplotlib\n", "nbPersos = len(persoList)\n", - "color = iter(cm.rainbow(np.linspace(0, 1, nbPersos)))\n", + "color = iter(cm.gist_rainbow(np.linspace(0, 1, nbPersos)))\n", "for i in range(nbPersos):\n", " c = next(color)\n", " plt.plot(i, i, 'o', c=c)" @@ -1652,77 +1654,111 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Cette sélection nous convient, on va affecter ces couleurs aux personnages." + "Comme expliqué au-dessus, cette sélection ne convient pas car certaines couleurs sont trop proches les unes de autres. Essayons d'autres colormaps." ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# On affiche une sélection automatique de couleur dans une colormap\n", + "# matplotlib\n", + "color = iter(cm.tab20(np.linspace(0, 1, nbPersos)))\n", + "for i in range(nbPersos):\n", + " c = next(color)\n", + " plt.plot(i, i, 'o', c=c)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Cette dernière semble convenir." + ] + }, + { + "cell_type": "code", + "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[0.5 0. 1. 1. ]\n", - "[0.35882353 0.21994636 0.99385914 1. ]\n", - "[0.21764706 0.42912061 0.97551197 1. ]\n", - "[0.07647059 0.61727822 0.94518383 1. ]\n", - "[0.07254902 0.78292761 0.9005867 1. ]\n", - "[0.21372549 0.9005867 0.84695821 1. ]\n", - "[0.35490196 0.9741386 0.78292761 1. ]\n", - "[0.50392157 0.99998103 0.70492555 1. ]\n", - "[0.64509804 0.9741386 0.62211282 1. ]\n", - "[0.78627451 0.9005867 0.53165947 1. ]\n", - "[0.92745098 0.78292761 0.43467642 1. ]\n", - "[1. 0.61727822 0.32653871 1. ]\n", - "[1. 0.42912061 0.21994636 1. ]\n", - "[1. 0.21994636 0.11065268 1. ]\n", - "[1.0000000e+00 1.2246468e-16 6.1232340e-17 1.0000000e+00]\n" + "[0.12156863 0.46666667 0.70588235 1. ]\n", + "[0.68235294 0.78039216 0.90980392 1. ]\n", + "[1. 0.49803922 0.05490196 1. ]\n", + "[0.17254902 0.62745098 0.17254902 1. ]\n", + "[0.59607843 0.8745098 0.54117647 1. ]\n", + "[1. 0.59607843 0.58823529 1. ]\n", + "[0.58039216 0.40392157 0.74117647 1. ]\n", + "[0.54901961 0.3372549 0.29411765 1. ]\n", + "[0.76862745 0.61176471 0.58039216 1. ]\n", + "[0.89019608 0.46666667 0.76078431 1. ]\n", + "[0.49803922 0.49803922 0.49803922 1. ]\n", + "[0.78039216 0.78039216 0.78039216 1. ]\n", + "[0.85882353 0.85882353 0.55294118 1. ]\n", + "[0.09019608 0.74509804 0.81176471 1. ]\n", + "[0.61960784 0.85490196 0.89803922 1. ]\n" ] }, { "data": { "text/plain": [ "{'Harpagon': {'links': [\"Père de Cléante et d'Élise\", 'Amoureux de Mariane'],\n", - " 'color': array([1.0000000e+00, 1.2246468e-16, 6.1232340e-17, 1.0000000e+00])},\n", + " 'color': array([0.61960784, 0.85490196, 0.89803922, 1. ])},\n", " 'Cléante': {'links': [\"Fils d'Harpagon\", 'Amant de Mariane'],\n", - " 'color': array([1. , 0.21994636, 0.11065268, 1. ])},\n", + " 'color': array([0.09019608, 0.74509804, 0.81176471, 1. ])},\n", " 'Élise': {'links': [\"Fille d'Harpagon\", 'Amante de Valère'],\n", - " 'color': array([0.64509804, 0.9741386 , 0.62211282, 1. ])},\n", + " 'color': array([0.76862745, 0.61176471, 0.58039216, 1. ])},\n", " 'Valère': {'links': [\"Fils d'Anselme\", \"Amant d'Élise\"],\n", - " 'color': array([1. , 0.42912061, 0.21994636, 1. ])},\n", + " 'color': array([0.85882353, 0.85882353, 0.55294118, 1. ])},\n", " 'Mariane': {'links': ['Amante de Cléante', \"aimée d'Harpagon\"],\n", - " 'color': array([0.50392157, 0.99998103, 0.70492555, 1. ])},\n", + " 'color': array([0.54901961, 0.3372549 , 0.29411765, 1. ])},\n", " 'Anselme': {'links': ['Père de Valère et de Mariane'],\n", - " 'color': array([0.35490196, 0.9741386 , 0.78292761, 1. ])},\n", + " 'color': array([0.58039216, 0.40392157, 0.74117647, 1. ])},\n", " 'Frosine': {'links': [\"Femme d'Intrigue\"],\n", - " 'color': array([1. , 0.61727822, 0.32653871, 1. ])},\n", + " 'color': array([0.78039216, 0.78039216, 0.78039216, 1. ])},\n", " 'Maitre Simon': {'links': ['Courtier'],\n", - " 'color': array([0.07254902, 0.78292761, 0.9005867 , 1. ])},\n", + " 'color': array([0.59607843, 0.8745098 , 0.54117647, 1. ])},\n", " 'Maitre Jacques': {'links': [\"Cuisinier et Cocher d'Harpagon\"],\n", - " 'color': array([0.92745098, 0.78292761, 0.43467642, 1. ])},\n", + " 'color': array([0.49803922, 0.49803922, 0.49803922, 1. ])},\n", " 'La Flèche': {'links': ['Valet de Cléante'],\n", - " 'color': array([0.78627451, 0.9005867 , 0.53165947, 1. ])},\n", + " 'color': array([0.89019608, 0.46666667, 0.76078431, 1. ])},\n", " 'Dame Claude': {'links': [\"Servante d'Harpagon\"],\n", - " 'color': array([0.5, 0. , 1. , 1. ])},\n", + " 'color': array([0.12156863, 0.46666667, 0.70588235, 1. ])},\n", " 'Brindavoine': {'links': [\"laquais d'Harpagon\"],\n", - " 'color': array([0.21764706, 0.42912061, 0.97551197, 1. ])},\n", + " 'color': array([1. , 0.49803922, 0.05490196, 1. ])},\n", " 'La Merluche': {'links': [\"laquais d'Harpagon\"],\n", - " 'color': array([0.07647059, 0.61727822, 0.94518383, 1. ])},\n", + " 'color': array([0.17254902, 0.62745098, 0.17254902, 1. ])},\n", " 'Le commissaire': {'links': [],\n", - " 'color': array([0.21372549, 0.9005867 , 0.84695821, 1. ])},\n", + " 'color': array([1. , 0.59607843, 0.58823529, 1. ])},\n", " 'son clerc': {'links': ['assistant du commissaire'],\n", - " 'color': array([0.35882353, 0.21994636, 0.99385914, 1. ])}}" + " 'color': array([0.68235294, 0.78039216, 0.90980392, 1. ])}}" ] }, - "execution_count": 20, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "color = iter(cm.rainbow(np.linspace(0, 1, nbPersos)))\n", + "color = iter(cm.tab20(np.linspace(0, 1, nbPersos)))\n", "for i in range(nbPersos):\n", " c = next(color)\n", " print(c)\n", @@ -1744,7 +1780,7 @@ "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", + "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 (un print du dataframe `percentDf` et de `bar` dans la boucle `for i, bar in enumerate(ax.patches):` du code de tracé du graphique un peu plus bas), nous à donner ce qui suit:\n", "\n", "```\n", " Cléante Harpagon La Flèche Valère scene Élise\n", @@ -1782,328 +1818,17 @@ "```\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. " + "En regardant de près les valeurs de largeur des rectangles `width`, on voit que les 5 premiers rectangles correspondent au premier personnage (cela correspond aux valeurs de pourcentage de paroles pour les 5 scènes de l'acte) = 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": 28, + "execution_count": 43, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 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", - "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", - "Rectangle(xy=(0, -0.25), width=0.295632, height=0.5, angle=0)\n", - "Rectangle(xy=(0, 0.75), width=0.250493, 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, -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=0.0454545, 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", - " Mariane Valère scene Élise \n", - "0 0.000000 0.140496 1 0.001550 \n", - "1 0.000000 0.330935 2 0.000000 \n", - "2 0.000000 0.000000 3 0.000000 \n", - "3 0.492021 0.000000 4 0.000000 \n", - "4 0.000000 0.000000 5 0.000000 \n", - "5 0.297710 0.000000 6 0.129771 \n", - "6 0.225528 0.000000 7 0.000000 \n", - "7 0.000000 0.000000 8 0.000000 \n", - "8 0.000000 0.000000 9 0.000000 \n", - "Rectangle(xy=(0, -0.25), width=0.0118802, 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, 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=0.465116, height=0.5, angle=0)\n", - "Rectangle(xy=(0, 7.75), width=0, height=0.5, angle=0)\n", - "Rectangle(xy=(0.0118802, -0.25), width=0.0392562, 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, 4.75), width=0, height=0.5, angle=0)\n", - "Rectangle(xy=(0, 5.75), width=0.559501, 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=0.283688, 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=0.633333, height=0.5, angle=0)\n", - "Rectangle(xy=(0, 2.75), width=0.507979, height=0.5, angle=0)\n", - "Rectangle(xy=(0, 3.75), width=0.198473, height=0.5, angle=0)\n", - "Rectangle(xy=(0, 4.75), width=0.0381679, height=0.5, angle=0)\n", - "Rectangle(xy=(0.559501, 5.75), width=0.0393474, 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=(0.0511364, -0.25), width=0.391012, 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=(0.198473, 3.75), width=0.801527, height=0.5, angle=0)\n", - "Rectangle(xy=(0.0381679, 4.75), width=0.534351, height=0.5, angle=0)\n", - "Rectangle(xy=(0.598848, 5.75), width=0.175624, height=0.5, angle=0)\n", - "Rectangle(xy=(0.465116, 6.75), width=0.534884, height=0.5, angle=0)\n", - "Rectangle(xy=(0.283688, 7.75), width=0.567376, height=0.5, angle=0)\n", - "Rectangle(xy=(0.442149, -0.25), width=0.0134298, 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, 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=(0.851064, 7.75), width=0.148936, height=0.5, angle=0)\n", - "Rectangle(xy=(0.455579, -0.25), width=0.402376, height=0.5, angle=0)\n", - "Rectangle(xy=(0, 0.75), width=0.669065, height=0.5, angle=0)\n", - "Rectangle(xy=(0.633333, 1.75), width=0.366667, 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, -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", - "1 0.042254 \n", - "2 0.000000 \n", - "3 0.000000 \n", - "4 0.000000 \n", - "5 0.000000 \n", - "6 0.000000 \n", - "Rectangle(xy=(0, -0.25), width=0.251282, height=0.5, angle=0)\n", - "Rectangle(xy=(0, 0.75), width=0.197183, height=0.5, angle=0)\n", - "Rectangle(xy=(0, 1.75), width=0.515413, height=0.5, angle=0)\n", - "Rectangle(xy=(0, 2.75), width=0.278232, height=0.5, angle=0)\n", - "Rectangle(xy=(0, 3.75), width=0.558219, height=0.5, angle=0)\n", - "Rectangle(xy=(0, 4.75), width=0.265625, height=0.5, angle=0)\n", - "Rectangle(xy=(0, 5.75), width=0, height=0.5, angle=0)\n", - "Rectangle(xy=(0.251282, -0.25), width=0.447179, 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, 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, -0.25), width=0, height=0.5, angle=0)\n", - "Rectangle(xy=(0.197183, 0.75), width=0.760563, height=0.5, angle=0)\n", - "Rectangle(xy=(0.515413, 1.75), width=0.484587, height=0.5, angle=0)\n", - "Rectangle(xy=(0.278232, 2.75), width=0.230769, height=0.5, angle=0)\n", - "Rectangle(xy=(0.558219, 3.75), width=0.441781, 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=1, 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, 2.75), width=0, height=0.5, angle=0)\n", - "Rectangle(xy=(0, 3.75), width=0, height=0.5, angle=0)\n", - 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\n", 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\n", 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zYZpZIP3bv/3bruuuuy41MTGxMisr62hxcXHkqdpmZWWVXXPNNb23bdvWYfLkyQdHjx59rLy8vHzhwoUJaWlp/VNTU7/NzMw8Kkm9e/euuuuuu3ZfeOGFGV27dq1KS0sr79ixY40kPfXUU8XTpk1LeeKJJ7o1XBQXoO76hOVTAaANY/nU02vO3PWSkpKQjh071lZVVekHP/hBn5tuuunA1KlTD/uzTl+xfCoAAD669957z0lPT++flpY2oGfPnhU33HBDqwjzM+GUOwDAs2bPnn1Q0sGmvGfhwoU7/VSOXzFCBwDAAwh0AAA8gEAHAMADCHQAQLtTVVWlhx9+OOHbb7+1M7duG1rXRXEFG6ThPc7cDt/1aZu8hqPdWrvr4dNun1g8NUCVINgODuse7BJ8snb3vBZdPnVY97lNXj7VV9nZ2bHXXXddalJSUqUkxcXFVa9Zs6bo7rvvPicmJqbmgQce2BseHq7hw4cfveWWW3r+6U9/2h4aGnrK/RUWFkb86Ec/6rt58+a8ptYSSK0r0AEAaAFZWVllH3744ZbTtRkzZsyxMWPGbA9UTf7GKXcAQJvx0ksvdTzvvPPSMzIy+o8YMSJtx44dzRqY5uXlRY4aNarvgAEDMoYMGdLv888/7yBJO3bsCBs3blxqv379+vfr16//e++9d5Yk1dTU6Nprr+3Vp0+fASNHjuxbVlZmje1n/fr1HU53XH8i0AEAbcbplk09UU5OTkzDsqtz5879Tpvp06f3+v3vf1+cl5eXP2/evJ0zZ87sKdUtkTpq1KgjhYWFm/Ly8jZdcMEF30pScXFxh9mzZ+/bsmVLXseOHWuWLFnSWZJmzJhxfD/z588/vp9g4JQ7AKDNON2yqSc63Sn3kpKSkNzc3LOmT5+e0vBaaWlpqCStWbMmdunSpV9LUlhYmLp06VJz4MCB0KSkpIoRI0aUS9LgwYOPbdu2LbKkpCRk/fr1MVdddVVqw34qKyuDdpEdgQ4AaDPuvPPOnj//+c/3/PSnPy3Jzs6OfeCBB85p6j5qamoUExNTs27dukJf3xMREXF84ZPQ0FBXXl4eUlNTo9jY2OoT12wPJk65AwDajKYsm3oqcXFxtcnJyZUNS6HW1NRo9erVUZI0cuTII/Pnz0+QpOrqah06dOiUOdmwn0WLFnWWpNraWn3yySdRzampJfhthG5mPSQtkdRNUq2khc65J/x1PACA//gyzaylffvttyGJiYnnNTyfOXPm3qYsm3o6L7/88le33nprr/nz53evrq62K6+88tDIkSPLn3rqqeKbbrqpV1paWnxISIiefPLJ7T169Kg6037mzZt3fD/Dhw8vb05N35fflk81s+6SujvnPjezWEmfSfqxc+6UpyayYiJczqBEv9TjecxDb1OYh44G33ceOsunti9BWT7VObfbOfd5/ddHJOVLSvLX8QAAaM8C8hm6maVIGixpbSCOBwBAe+P3QDezGEmvSfqFc660ke23mVmOmeXsr6r1dzkAAHiSXwPdzMJVF+YvOudeb6yNc26hcy7LOZeVEM5F9wAANIffEtTMTNIfJOU75/7TX8cBAAD+HaGPlHSjpLFm9kX9Y6IfjwcAQLvlt3nozrmPJXlmnVkAaM+6rN3dosunHhzW/Yzz2s1syBVXXHHojTfe+FqqW8O8a9eumeeff/7R062ktnLlyuhFixZ1Wbx48Y7s7OzYyMjI2nHjxh1tbq01NTWaPn16j9WrV59tZi4iIsItXbp0a3p6euWYMWP6vPbaa1/Hx8fXNHf/LYVbvwIAWqWoqKjawsLCqLKyMouJiXF/+ctfzk5MTDzlTV4ajB49+tjo0aOPSdIHH3wQGxMTU9NYoFdVVSk8PPyMdTz33HNxe/bsCS8oKMgLDQ3V1q1bw88+++xaSVqxYsVpl2gNJK5CAwC0WpdeemnJq6++2kmSXn755bjJkycfatj24YcfRg8ePDg9IyOj/+DBg9Nzc3MjJSk7Ozv2kksu6VNYWBixZMmShKeffjoxPT29/zvvvBMzefLklBkzZiQPGzYsbdasWcmlpaUhV111VcrAgQMzMjIy+r/wwgudTq5h9+7d4YmJiVWhoaGSpNTU1KqEhIQaSUpKShq0e/fusMLCwojevXsPuOaaa3r17dt3wKRJk3q/8cYbsRdccEF6r169Bn744YfRkrR3797Qyy67LDUtLa1/ZmZm+tq1a6Mk6e677z7nqquuShk6dGi/5OTkQf/+7//etanfKwIdANBq3XjjjYf+/Oc/dz527Jjl5+dHDx8+/PhIOzMz89t169YV5Ofnb7r//vv/e86cOcknvrdfv36VU6dO3X/HHXfsLSgo2DR+/PgySdq6dWuH1atXFz377LM777vvvu6XXHJJ6caNG/NXrVpV+Otf/zq5tLQ05OQa3n///U7p6en9b7311uSG+76fbMeOHR1++ctf7isoKMjbunVrhxdffLFLTk5OwUMPPbTzoYce6i5Jc+bMOSczM/NYUVHRpgcffPC/p02b1rvh/Vu2bOmwYsWKor///e/5jzzyyDkVFRVN+tiaQAcAtFrDhg0r37lzZ+Szzz4bd9lll5WcuO3QoUOhEydOTO3bt++AOXPm9CgqKurgyz5/8pOffBMWVveJ80cffXT2Y4891j09Pb3/xRdf3K+iosK2bNkScWL71NTUqi1btmx84IEHdoaEhGjixIn9li1bFnvyfpOSkiqGDh1aHhoaqrS0tPKxY8eWhoSE6IILLji2c+fOSElat25d7PTp0w9K0qRJk44cPnw47ODBg6GSdPnllx+Oiopy3bt3r46Li6vauXNnkz4W5zN0AECrNn78+MP3339/j7/97W+F+/btO55bc+fOTRozZsyR9957b2thYWHE2LFj+/myv5iYmON3MXPOaenSpVsyMzMbXVe9QVRUlLv66qtLr7766tLExMSq119/vdMVV1xx5MQ2Jy6xGhISog4dOjhJCg0NVU1NjTUc72Rm5iQpMjLyxCVaVV1d3aQReusK9PTzpE9ygl0F4HfDzrD94PdbrwPwlJkzZx7o2LFjzdChQ8uzs7OPj4xLS0tDk5OTKyXpmWeeiW/svbGxsTWlpaWhp9r3JZdcUvroo48mLl68uDgkJESrV6+OGjly5D+tlvbxxx9HJycnV6WkpFTV1NToyy+/jBo0aFCzVlS76KKLjjz//PNd5s+fvzs7Ozu2c+fO1XFxcS1ym9TWFegAgFbJl2lm/pKamlr1m9/8Zt/Jr8+dO3fPjBkzei9YsKDbqFGjvnNrcUmaPHny4SlTpqS+/fbbnR5//PHik7c//PDDu2677bae6enp/Z1zlpycXHHylLg9e/aE3X777b0qKytDJOn8888/+qtf/eo79fhi3rx5u66//vqUtLS0/lFRUbWLFy/+ujn7aYzflk9tjqysLJeTwwgdAHzF8qntS1CWTwUAAIFDoAMA4AEEOgCgMbW1tbXcvrsVqf95nPICOgIdANCYjfv37+9IqLcOtbW1tn///o6SNp6qDVe5AwC+o7q6esaePXue27Nnz0Ax+GsNaiVtrK6unnGqBgQ6AOA7hgwZsk/SpGDXAd/xVxcAAB5AoAMA4AEEOgAAHkCgAwDgAQQ6AAAeQKADAOABBDoAAB5AoAMA4AEEOgAAHkCgAwDgAQQ6AAAeQKADAOABBDoAAB7g02prZpYo6f9IOsc5N8HM+ksa7pz7Q4tWc2CrtGhK0983/bUWLQMAAsa5YFcAj/B1hL5Y0ruSzql/XiTpF/4oCAAANJ2vgR7vnPsv1S2wLudctaQav1UFAACaxNdAP2pmXSQ5STKziySV+K0qAADQJD59hi7pbknLJaWa2WpJCZKa8WE3AADwB58C3Tn3uZmNkdRPkkkqdM5V+bUyAADgM19H6JI0VFJK/XsuMDM555b4pSoAANAkvk5b+5OkVElf6B8XwzlJBDoAAK2AryP0LEn9nfN9wqSZdZC0UlJk/XGWOufub3qJAADgTHy9yn2jpG5N3HeFpLHOuUxJ50saX391PAAAaGG+jtDjJW0ys3WqC2pJknNu0qneUD+aL6t/Gl7/4JZIAAD4ga+B/r+bs3MzC5X0maQ+kv6vc25tI21uk3SbJPXsEtWcwwAA0O75dMrdObdC0jZJ4fVf/13S5z68r8Y5d76kZElDzWxgI20WOueynHNZCTGRTSoeAADU8SnQzexWSUslPVP/UpKkN3w9iHPusKSPJI1vYn0AAMAHvl4U9z8kjZRUKknOuc2Sup7uDWaWYGad6r+OknSZpILmlwoAAE7F18/QK5xzlWYmSTKzMJ35Arfukv5Y/zl6iKT/cs5lN7tSAABwSr4G+gozu09SlJmNkzRL0pune4NzboOkwd+zPgAA4ANfT7n/StJ+SV9Kul3SW5J+7a+iAABA0/g6Qo+StMg596x0fDpalKRj/ioMAAD4ztcR+v9TXYA3iJL0fsuXAwAAmsPXQO/gnGu465vqv472T0kAAKCpfA30o2Z2QcMTM8uSVO6fkgAAQFP5+hn6zyW9ama7VDdd7RxJ17R4NfGp0i1Lm/6+W1q8EgAA2hRfA7236qag9ZR0paSLxEIrAAC0Gr6ecv+Nc65UUidJ4yQtlPSU36oCAABN4mug19T/+0NJTzvnlkmK8E9JAACgqXwN9P82s2ckXS3pLTOLbMJ7AQCAn/kayldLelfS+PqV0+Ik3eu3qgAAQJP4dFGcc+6YpNdPeL5b0m5/FQUAAJqG0+YAAHgAgQ4AgAcQ6AAAeACBDgCABxDoAAB4AIEOAIAHEOgAAHgAgQ4AgAcQ6AAAeACBDgCABxDoAAB4AIEOAIAHEOgAAHgAgQ4AgAcQ6AAAeIBP66EHzIGt0qIpwa6i3Vo74cJglwC0O8O6zw12CfAIRugAAHgAgQ4AgAcQ6AAAeACBDgCABxDoAAB4AIEOAIAH+D3QzSzUzNabWba/jwUAQHsViBH6zyXlB+A4AAC0W34NdDNLlvRDSc/58zgAALR3/h6hPy5pjqRaPx8HAIB2zW+BbmY/krTPOffZGdrdZmY5Zpazv6zCX+UAAOBp/hyhj5Q0ycy2SXpF0lgze+HkRs65hc65LOdcVkJMpB/LAQDAu/wW6M65/+mcS3bOpUi6VtIHzrkb/HU8AADaM+ahAwDgAQFZPtU595GkjwJxLAAA2iNG6AAAeACBDgCABxDoAAB4AIEOAIAHEOgAAHhAQK5y91l8qnTL0mBX0W4NC3YBAIBmY4QOAIAHEOgAAHgAgQ4AgAcQ6AAAeACBDgCABxDoAAB4AIEOAIAHEOgAAHgAgQ4AgAcQ6AAAeACBDgCABxDoAAB4AIEOAIAHEOgAAHgAgQ4AgAcQ6AAAeACBDgCABxDoAAB4AIEOAIAHEOgAAHgAgQ4AgAeEBbuAf3Jgq7RoSrCrANCY6a8FuwJvci7YFcAjGKEDAOABBDoAAB5AoAMA4AEEOgAAHkCgAwDgAQQ6AAAeQKADAOABfp2HbmbbJB2RVCOp2jmX5c/jAQDQXgXixjKXOOcOBOA4AAC0W5xyBwDAA/wd6E7S38zsMzO7rbEGZnabmeWYWc7+sgo/lwMAgDf5+5T7SOfcLjPrKuk9Mytwzq08sYFzbqGkhZKUldKZmxoDANAMfh2hO+d21f+7T9JfJA315/EAAGiv/BboZnaWmcU2fC3pckkb/XU8AADaM3+eck+U9BczazjOS865d/x4PAAA2i2/Bbpz7itJmf7aPwAA+AemrQEA4AEEOgAAHkCgAwDgAQQ6AAAeQKADAOABgVicxXfxqdItS4NdBYDG3BLsAgCcDiN0AAA8gEAHAMADCHQAADyAQAcAwAMIdAAAPIBABwDAAwh0AAA8gEAHAMADCHQAADyAQAcAwAPMORfsGo4zsyOSCoNdR5DESzoQ7CKCiP7T//ba/+/b917OuYSWKgZtV+u6l7tU6JzLCnYRwWBmOe217xL9p//tt//tue9oWZxyBwDAAwh0AAA8oLUF+sJgFxBE7bnvEv2n/+1Xe+47WlCruigOAAA0T2sboQMAgGYg0AEA8ICAB7qZjTezQjPbYma/amS7mdmC+u0bzOyCQNfoTz70/6f1/d5gZmvMLDMYdfrLmfp/QrscWN2jAAADUUlEQVQLzazGzKYEsj5/86X/ZvYvZvaFmeWZ2YpA1+gvPvzf72hmb5pZbn3fbw5Gnf5iZovMbJ+ZbTzFdk//7kMAOOcC9pAUKmmrpHMlRUjKldT/pDYTJb0tySRdJGltIGtsBf0fIalz/dcT2lv/T2j3gaS3JE0Jdt0B/vl3krRJUs/6512DXXcA+36fpHn1XydIOiQpIti1t+D3YLSkCyRtPMV2z/7u4xGYR6BH6EMlbXHOfeWcq5T0iqQrTmpzhaQlrs6nkjqZWfcA1+kvZ+y/c26Nc+6b+qefSkoOcI3+5MvPX5J+Juk1SfsCWVwA+NL/6yW97pwrliTnnFe+B7703UmKNTOTFKO6QK8ObJn+45xbqbo+nYqXf/chAAId6EmSdpzwfGf9a01t01Y1tW/TVfcXu1ecsf9mliTpSklPB7CuQPHl558mqbOZfWRmn5nZ1IBV51++9P1JSRmSdkn6UtLPnXO1gSmvVfDy7z4EQKBv/WqNvHbyvDlf2rRVPvfNzC5RXaBf7NeKAsuX/j8uaa5zrqZuoOYpvvQ/TNIQSZdKipL0iZl96pwr8ndxfuZL338g6QtJYyWlSnrPzFY550r9XVwr4eXffQiAQAf6Tkk9TnierLq/xpvapq3yqW9mdp6k5yRNcM4dDFBtgeBL/7MkvVIf5vGSJppZtXPujcCU6Fe+/v8/4Jw7Kumoma2UlCmprQe6L32/WdLDzjknaYuZfS0pXdK6wJQYdF7+3YcACPQp979L6mtmvc0sQtK1kpaf1Ga5pKn1V3xeJKnEObc7wHX6yxn7b2Y9Jb0u6UYPjMpOdsb+O+d6O+dSnHMpkpZKmuWRMJd8+/+/TNIoMwszs2hJwyTlB7hOf/Cl78WqOzMhM0uU1E/SVwGtMri8/LsPARDQEbpzrtrM7pT0ruquel3knMszszvqtz+tuiubJ0raIumY6v5q9wQf+/+/JHWR9Pv6UWq188hKTD7237N86b9zLt/M3pG0QVKtpOecc41Oc2pLfPzZPyhpsZl9qbrTz3Odc55ZUtXMXpb0L5LizWynpPslhUve/92HwODWrwAAeAB3igMAwAMIdAAAPIBABwDAAwh0AAA8gEAHAMADCHQAADyAQAcAwAP+P6+dLVloH/qZAAAAAElFTkSuQmCC\n", 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\n", 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\n", + "image/png": 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\n", 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DA80BAQGWvXv3ttm/f39bAEhJSbn4448/Bpw9e9ZgMpmwbt26DrbzDBgw4OIHH3zQAQCWLFkSlJycXKllHx3JKRfoiUgUgAEAfmxg2wwAMwAg0tvgjHCIiKi57Fgq50yvvPLKyQcffLBnaGhoTXJy8sXi4mKfxvZNTk6unDhxYveioqI2qamp54cPH36pqqqqaunSpSExMTEJPXv2vNyvX7+LANC9e3fTs88+W3LrrbfGd+rUyRQTE1MVGBhoBoDFixcXT5kyJerdd9/tbLtAz0ndvWmal7gVEX8AWwD8QSn1eVP7Jvt7K2OfUE3jcUu8qQ6RLrjipjoscdu0G1mbX1ZW5hEYGGgxmUz49a9/3Wvq1KnnJk+efEHLOB3FJSVuRcQLwBoAH10v0RMREbmDF198sWtcXFxCTExMYmRkZPUjjzzSIhJ9U7S8Gl8A/BlAnlLqf7Vqh4iIqDGzZs06D+B8c45ZunSp7qZbtRzZDwXwKIBRIrLP+rhLw/aIiIioAZqN7JVS3wOQ6+5IREREmuId9IiIiHSOyZ6IiFodk8mEN954I+Ty5cutYgbavQrhxPUFdhhdHQUR0Q152NUBaOjHkvkOLXE7qMucZpe4tVdWVlbAgw8+2DMsLKwGAIKCgmq3b99+6Lnnnuvq7+9vnjdv3mkvLy8MHjz44uOPPx754YcfHjMYGr/PS0FBgffdd98dffjw4ZzmxuIu3CvZExEROUBycnLlP/7xj8Km9hkxYsSlESNGHHNWTK7EaXwiImoxPv7448C+ffvGxcfHJwwZMiTm+PHjNzRozcnJ8UlJSYlOTEyMT0pKit2zZ08bADh+/Ljn6NGje8bGxibExsYmbNq0qS0AmM1mTJo0qVuvXr0Shw4dGl1ZWSkNnWfv3r1tmmrXVZjsiYioxWiqtO3VjEajv6007pw5c67ZZ9q0ad0WLVpUnJOTkzd//vwTaWlpkUBdGduUlJSKgoKC3JycnNxbbrnlMgAUFxe3mTVr1pnCwsKcwMBAc2ZmZgcAmD59+pXzvPnmm1fO4244jU9ERC1GU6Vtr9bUNH5ZWZnH/v37206bNi3K9lp5ebkBALZv3x6wevXqnwDA09MTHTt2NJ87d84QFhZWPWTIkCoAGDBgwKWioiKfsrIyj7179/rff//9PW3nqampccsL/pjsiYioxXjqqacin3nmmVMPP/xwWVZWVsC8efO6NvccZrMZ/v7+5l27dhXYe4y3t/eVQjIGg0FVVVV5mM1mBAQE1Obn5+c2NwZn4zQ+ERG1GM0pbduYoKAgS3h4eI2tXK3ZbMYPP/zgCwBDhw6tePPNN0MAoLa2FqWlpY3mSdt5li9f3gEALBYLduzY4XsjMWmNI3siIroue5bKOdrly5c9QkND+9qep6WlnW5OadumrFq16ugTTzzR7c033+xSW1sr9957b+nQoUOrFi9eXDx16tRuMTExwR4eHli4cOGxiIgI0/XOM3/+/CvnGTx4cNWNxKQlzUvcNkdycrIyGrnOnojIXixxS1dzSYlbIiIicj0meyIiIp1jsiciItI5JnsiIiKdY7InIiLSOSZ7IiIinXOvdfb52cDgCFdHQVRn5wlXR0CtgRstf25K6Y+zHVriNmjQguuu2xeRpHvuuad07dq1PwF1Neg7derUr3///hebqmi3detWv+XLl3dcsWLF8aysrAAfHx/L6NGjL95orCxxS0REpBFfX19LQUGBb2Vlpfj7+6svvviiXWhoaKM3uLEZPnz4peHDh18CgO+++y7A39/f3FCyN5lM8PLy0iJ0t8NpfCIiclt33HFH2WeffdYeAFatWhWUmppaatv2j3/8w2/AgAFx8fHxCQMGDIjbv3+/DwBkZWUFjBw5sldBQYF3ZmZmyPvvvx8aFxeX8M033/inpqZGTZ8+PXzQoEEx6enp4eXl5R73339/VO/evePj4+MTVq5c2b6peAoKCryTkpJiExIS4hMSEuJtJXAB4He/+11oTExMQmxsbEJ6enoYAGzbts0vNjY2oX///nEzZ84Mj46OTgSAjIyMjpMnT75SIW/kyJG9srKyAgDg888/b9e/f/+4hISE+LFjx/YoKyvzAID09PSwnj17JsbExCTMmDEjvDnvI0f2RETkth599NHSuXPndpk4ceKFvLw8v2nTpp3fvn27PwD069fv8q5du/K9vLywdu3agJdeeil848aNR2zHxsbG1kyePPmsv7+/ed68eacBYNmyZcFHjhxp88MPPxzy9PTEU089FTZy5Mjyzz77rOjcuXOG5OTk+PHjx5e3a9fO0lA8Xbt2rd22bdshPz8/deDAAZ8HH3ywx8GDB/P+9re/tfvqq6867N69Oz8gIMBy+vRpAwBMmzYt6p133ikeN25c5cyZM6+boEtKSjxff/31Llu3bj3Url07yyuvvNL5tddeC33xxRfPbNiwocPRo0cPenh44Ny5c4bmvI9M9kRE5LYGDRpUdeLECZ9ly5YF3XnnnWVXbystLTVMnDixe1FRURsRUSaTya7ysvfdd98vnp516W/z5s3tNm7c2D4jI6MzAFRXV0thYaG3rY59fTU1NTJt2rRuubm5vh4eHjh27JgPAGzatKndI488ci4gIMACAKGhoebz588bKioqDOPGjasEgMcff/z8d999F9hUbJs3b2575MiRNgMHDowDAJPJJElJSZVBQUFmHx8fy6RJk7qNGzeubOLEiWVNnac+zZK9iLQBsBWAj7Wd1UqpuVq1R0RE+jRmzJgLc+fOjfj73/9ecObMmSt5a86cOWEjRoyo2LRp05GCggLvUaNGxdpzPn9//yujdqUUVq9eXdivX79qe479wx/+ENqpUyfTmjVrfrJYLPD19U2ynUfk3//WaOg1G09PT2Wx/GvyoLq62sN2zLBhw8q//PLLn+ofs2/fvrz169e3++STTzosXry4086dOw/ZEzOg7Xf21QBGKaX6AegPYIyI3KZhe0REpENpaWnnnn/++ZMDBw78t2py5eXlhvDw8BoAWLJkSXBDxwYEBJgrKioanfIeOXJk+dtvvx1qS7y2UreNKSsrM3Tp0sVkMBiwaNGijmazGQAwZsyY8g8//DC4oqLCAwBOnz5tCA4ONvv7+5s3btzoDwArVqwIsp2nZ8+eNTk5OX5msxmFhYVe2dnZbQHg9ttvv2g0Gv0PHjzoAwAVFRUe2dnZPmVlZR7WmYyy999//3heXp7f9d63q2k2sld15fQqrU+9rI+WscaEiIj+jT1L5bTSs2dP0+9///sz9V+fM2fOqenTp3fPyMjonJKSUt7QsampqRcmTJjQ8+uvv26/YMGC4vrb33jjjZMzZsyIjIuLS1BKSXh4eHX9ZX0mk0m8vb0tADB79uwzqampPdeuXdth2LBhFb6+vhYAmDBhQvmePXv8+vfvH+/l5aXuvPPOsoULF/785z//uWj69OlRvr6+llGjRl2JcfTo0ZXvvfdedWxsbGJsbGxVQkLCJaDumoAlS5YUTZo0qUdNTY0AwNy5c38ODAy03H333b2qq6sFAP7nf/7neHPeQ01L3IqIAcBuAL0AvKeUmtPAPjMAzACASG9D0rFbumgWD1GzcJ09OcNNfgazxK32Vq5c2f7jjz8O2rBhw9GbOY8z1us3VuJW0wv0lFJmAP1FpD2AL0Skt1LqYL19lgJYCgDJ/t4c+RMRkduYPXt216+//rr98uXLr/kOvSVxyjp7pdQFAJsBjHFGe0RERI6wYMGCkwUFBblDhw6tuv7eTYuNja1x1V34NEv2IhJiHdFDRHwB3AkgX6v2iIiIqGFaTuN3AfBX6/f2HgD+ppTK0rA9IiIiaoCWV+NnAxig1fmJiIjIPrw3PhERkc7xdrlERHRd8/CVQ0vc/jfGaVbitr6ioiKvJ598MuKbb765qaVzLZl7Jfu4vsAOo6ujICIiN3CjJW6vZjKZEBUVZWrNiR7gND4REbmxGylxm5GR0XHs2LE9Ro0a1SslJSWmoKDA21ZatrEStVlZWQEDBw6MHTNmTI/u3bsnjh8/vrvtFrrbtm3zu/XWW2MTExPjhw0bFn3s2DEvp78RN4nJnoiI3Najjz5a+umnn3a4dOmS5OXl+Q0ePPiibZutxG1eXl7u3Llzf37ppZeulJDds2eP/6pVq36qXyzGVqI2Nzc379NPPz367LPPXqkpn5eX5/vee+8dLywszCkuLvbZtGmTf3V1tcyaNSty3bp1R3JycvKmTJly7oUXXghzTu8dx72m8YmIiK5yoyVuU1JSykNDQ831z9dYiVoA6NOnz8WePXuaACAxMfHSkSNHvIOCgmoPHz7sO2rUqBgAsFgsCAkJadZXCe6AyZ6IiNzajZS49fPzszR0rsZK1AKAj4/PlVu2GwwG1NbWilJKevXqVbVv374WfVM4TuMTEZFbu5kSt/U1VqK2MX379r1cWlrq+e2337YFgOrqajEajW1usCsuw5E9ERFdlz1L5bRyMyVu62usRG1j2rRpoz755JMjs2bNiqyoqDCYzWZJS0s7nZycfPlG++MKmpa4ba7k5GRlNHLpHRGRvVjilq7WWIlbTuMTERHpHJM9ERGRzjHZExER6RyTPRERkc7ZlexFJFRE/iwiX1ufJ4jING1DIyKi1s5sNmPYsGHRhw8f9nZ1LC2ZvSP7FQA2AuhqfX4IwGwtAiIiIrLJz8/3efnll0uio6NrXB1LS2bvOvtgpdTfRORlAFBK1YpI03ciICIi3fioeoVDS9w+7DP1uuv2DQZDUnR0dBUAPP/887jvvvtKX3/99VMDBw6Mfeutt44PHz780ogRI3qtWbPmp+DgYOakJtib7C+KSEcACgBE5DYAZU0fcgPys4HBEQ4/LVntPOHqCFqE0p3PuDoEakWCBi1wdQhuy8fHx5Kfn5/b1D5btmyxu659a2bvNP5zANYD6CkiPwDIBPC0ZlERERHZISwsrE9JSYlneXm5x+23394rNjY2ITo6OnHZsmUdAH2Up3UEu0b2Sqk9IjICQCwAAVCglGpxVX+IiKjlqK6u9oiLi0uwPX/++edLnnjiiV8a2vfzzz9v17lzZ9PmzZsLAeD8+fMGW3nar776qrBr1661y5Yt6/DCCy+EffbZZ0VO6oLbaM698QcCiLIec4uIQCmVqUlURETU6tkzjW9zyy23VL3yyisRaWlpYffcc0/ZmDFjKv/5z3+20UN5WkewK9mLyIcAegLYB8B2EYRC3XQ+ERGRS/Xt27d6z549uWvWrAl85ZVXwr799tvyBx544IIeytM6gr3f2ScDGKqUSldKPW19zLLnQBExiMheEcm68TCJiIgaV1RU5BUQEGBJT08vnT179ul9+/b56aU8rSPYO41/EEBnACU30MYzAPIAtLuBY4mIyA3Ys1TO0ep/Zz9q1KiyRYsW/dzQvrt37/Z9+eWXwz08PODp6akWLVp0TC/laR3B7nX2AHJFZBeAatuLSqnxTR0kIuEAxgH4A+qu6CciIrKL2Wxu8A+MXbt2Fdh+/vnnnw8AQGpqanlqauo13+8PGTKkymg0FtR/vbWxN9n/vxs8/wIALwEIaGwHEZkBYAYARHobbrAZIiIiaoxd39krpbYAKALgZf35nwD2NHWMiNwN4IxSqsmpH6XUUqVUslIqOcSLdXmIiIgczd5COE8AWA1gifWlMABrr3PYUADjRaQIwCcARonIyhuMk4iInMtisVjE1UGQ/ay/L0tD2+wdSv8n6pJ3OQAopQ4D6NTUAUqpl5VS4UqpKACTAHynlHrE3qCJiMilDp49ezaQCb9lsFgscvbs2UDUXVB/DXu/s69WStWI1P3ORcQT1vvkExGR/tTW1k4/derUB6dOneoN+weG5DoWAAdra2unN7TR3mS/RUR+C8BXREYDSAfwpb0RKKU2A9hs7/5ERORaSUlJZwA0ueKKWg57/1r7LwBnARwAMBPABgC/0yooIiIichx7R/a+AJYrpZYBdXfFs752SavAiIiIyDHsTfb/H8CdACqtz30B/B3AEIdGE9cX2GF06CmJmivI1QEQETmYvdP4bZRStkQP689+2oREREREjmRvsr8oIrfYnohIMoAqbUIiIiIiR7J3Gv8ZAJ+JyEnULbnrCmDo3t9fAAAI6ElEQVSiZlERERGRw9ib7LsDGAAgEsC9AG4D19kTERG1CPZO4/9eKVUOoD2A0QCWAlisWVRERETkMPYme7P133EA3ldKrQPgrU1IRERE5Ej2JvufRWQJgAcAbBARn2YcS0RERC5kb8J+AMBGAGOUUhdQtxT5Rc2iIiIiIoex6wI9pdQlAJ9f9bwEQIlWQREREZHjcCqeiIhI55jsiYiIdI7JnoiISOeY7ImIiHSOyZ6IiEjn7L1drnPkZwODI1wdBdHN2XnC1RFQS6J453HSHkf2REREOsdkT0REpHNM9kRERDrHZE9ERKRzml6gJyJFACpQVzWvVimVrGV7REREdC1nXI0/Uil1zgntEBERUQM4jU9ERKRzWid7BeDvIrJbRGY0tIOIzBARo4gYz5osGodDRETU+mg9jT9UKXVSRDoB2CQi+UqprVfvoJRaCmApACT7e/PuEkRERA6m6cheKXXS+u8ZAF8AGKhle0RERHQtzZK9iLQVkQDbzwB+BeCgVu0RERFRw7Scxg8F8IWI2Nr5WCn1jYbtERERUQM0S/ZKqaMA+ml1fiIiIrIPl94RERHpHJM9ERGRzrlXPfu4vsAOo6ujICIi0hWO7ImIiHSOyZ6IiEjnmOyJiIh0jsmeiIhI55jsiYiIdI7JnoiISOeY7ImIiHSOyZ6IiEjnmOyJiIh0jsmeiIhI55jsiYiIdI7JnoiISOeY7ImIiHSOyZ6IiEjn3KvEbX42MDjC1VHoxo+fP+XqEIjoOgZ1mePqEKgV4MieiIhI55jsiYiIdI7JnoiISOeY7ImIiHRO02QvIu1FZLWI5ItInogM1rI9IiIiupbWV+O/C+AbpdQEEfEG4Kdxe0RERFSPZsleRNoBGA5gKgAopWoA1GjVHhERETVMy2n8HgDOAviLiOwVkQ9EpG39nURkhogYRcR41mTRMBwiIqLWSctk7wngFgCLlVIDAFwE8F/1d1JKLVVKJSulkkO8eL0gERGRo2mZXU8AOKGU+tH6fDXqkj8RERE5kWbJXil1CsBxEYm1vnQHgFyt2iMiIqKGaX01/tMAPrJeiX8UwGMat0dERET1aJrslVL7ACRr2QYRERE1jVfEERER6RyTPRERkc65Vz37uL7ADqOro9CNQa4OgIiI3AJH9kRERDrHZE9ERKRzTPZEREQ6x2RPRESkc0z2REREOsdkT0REpHNM9kRERDrHZE9ERKRzTPZEREQ6x2RPRESkc0z2REREOsdkT0REpHNM9kRERDrHZE9ERKRzTPZEREQ651717HfvBkRcHQURkfMo5eoIqBXgyJ6IiEjnmOyJiIh0jsmeiIhI5zRL9iISKyL7rnqUi8hsrdojIiKihml2gZ5SqgBAfwAQEQOAnwF8oVV7RERE1DBnTePfAeCIUuqYk9ojIiIiK2cl+0kAVjW0QURmiIhRRIxnnRQMERFRayJK4zWeIuIN4CSARKXU6ab2TRZRRk2jISJyMzf5GSwiu5VSyQ6KhnTKGSP7sQD2XC/RExERkTackewfRCNT+ERERKQ9TZO9iPgBGA3gcy3bISIiosZpem98pdQlAB21bIOIiIiaxjvoERER6RyTPRERkc65V4nbpCTAyMV3REREjsSRPRERkc4x2RMREekckz0REZHOMdkTERHpHJM9ERGRzjHZExER6RyTPRERkc5pXuK2OUSkAkCBq+NwkWAA51wdhAux/+x/a+3/zfa9m1IqxFHBkD651011gILWWpdZRIytte8A+8/+t97+t+a+k/NwGp+IiEjnmOyJiIh0zt2S/VJXB+BCrbnvAPvP/rderbnv5CRudYEeEREROZ67jeyJiIjIwZjsiYiIdM7pyV5ExohIgYgUish/NbBdRCTDuj1bRG5xdoxasqP/D1v7nS0i20Wknyvi1Mr1+n/VfreKiFlEJjgzPq3Z038RuV1E9olIjohscXaMWrHj/36giHwpIvutfX/MFXFqRUSWi8gZETnYyHZdf/aRiymlnPYAYABwBEAPAN4A9gNIqLfPXQC+BiAAbgPwozNjdIP+DwHQwfrz2NbW/6v2+w7ABgATXB23k3//7QHkAoi0Pu/k6rid2PffAphv/TkEQCkAb1fH7sD3YDiAWwAcbGS7bj/7+HD9w9kj+4EACpVSR5VSNQA+AXBPvX3uAZCp6uwE0F5Eujg5Tq1ct/9Kqe1KqV+sT3cCCHdyjFqy5/cPAE8DWAPgjDODcwJ7+v8QgM+VUsUAoJTSy3tgT98VgAAREQD+qEv2tc4NUztKqa2o61Nj9PzZRy7m7GQfBuD4Vc9PWF9r7j4tVXP7Ng11f+nrxXX7LyJhAO4F8L4T43IWe37/MQA6iMhmEdktIpOdFp227On7QgDxAE4COADgGaWUxTnhuQU9f/aRizn7drnSwGv11/7Zs09LZXffRGQk6pL9ME0jci57+r8AwByllLlugKcr9vTfE0ASgDsA+ALYISI7lVKHtA5OY/b0/dcA9gEYBaAngE0isk0pVa51cG5Cz5995GLOTvYnAERc9TwcdX/FN3eflsquvolIXwAfABirlDrvpNicwZ7+JwP4xJrogwHcJSK1Sqm1zglRU/b+/z+nlLoI4KKIbAXQD0BLT/b29P0xAG8opRSAQhH5CUAcgF3OCdHl9PzZRy7m7Gn8fwKIFpHuIuINYBKA9fX2WQ9gsvXK1NsAlCmlSpwcp1au238RiQTwOYBHdTCaq++6/VdKdVdKRSmlogCsBpCuk0QP2Pf/fx2AFBHxFBE/AIMA5Dk5Ti3Y0/di1M1oQERCAcQCOOrUKF1Lz5995GJOHdkrpWpF5CkAG1F3de5ypVSOiDxp3f4+6q7AvgtAIYBLqPtrXxfs7P9/A+gIYJF1dFurdFIRy87+65Y9/VdK5YnINwCyAVgAfKCUanCpVkti5+/+NQArROQA6qa05yildFP2VkRWAbgdQLCInAAwF4AXoP/PPnI93i6XiIhI53gHPSIiIp1jsiciItI5JnsiIiKdY7InIiLSOSZ7IiIinWOyJyIi0jkmeyIiIp37P5s6GxjT/ce0AAAAAElFTkSuQmCC\n", + "image/png": 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\n", 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\n", + "image/png": 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" ] @@ -2174,8 +1899,6 @@ "actNums = pd.unique(textDataSynthesisTableDf[\"act\"])\n", "nbActs = actNums.size\n", "\n", - "#fig, axs = plt.subplots(nbActs, 1)\n", - "\n", "for actNum in actNums:\n", " actDf = textDataSynthesisTableDf[textDataSynthesisTableDf[\"act\"]==float(actNum)].copy()\n", " #actDf\n", @@ -2206,32 +1929,25 @@ " # sur les graphiques.\n", " #print(actDict)\n", " actSceneSynthesisGraphDf = pd.DataFrame.from_dict(actDict)\n", + " #print(actSceneSynthesisGraphDf)\n", " # On va créer une version en pourcentage du dataframe\n", " # de synthèse par scène d'un acte.\n", " percentDf = actSceneSynthesisGraphDf.copy()\n", - " #df.div(df.sum(axis=1), axis=0)\n", + " # On utilise actPersos en indice pour ne traiter que les colonnes de personnages\n", + " # et ignorer la colonne donnant le numéro de scène\n", " percentDf[actPersos] = percentDf[actPersos].div(percentDf[actPersos].sum(axis=1), axis=0)\n", " percentDf = percentDf.sort_index(axis=1)\n", - " print(percentDf)\n", + " #print(percentDf)\n", " #colors = {perso:colors.rgb2hex(avarePersoDict[perso]['color']) for perso in actPersos}\n", " sortedPersos = np.sort(actPersos)\n", " colors = []\n", " for perso in sortedPersos:\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", + " # Pour l'affichage en barres empilées à l'horizonatal, une recherche nous à permis de tomber\n", + " # sur la page suivante qui nous a aidé afin de faire le tracer souhaité:\n", + " # --> https://www.geeksforgeeks.org/stacked-percentage-bar-plot-in-matplotlib/\n", + " \n", " percentDf.plot(\\\n", " x = 'scene', \\\n", " kind = 'barh', \\\n", @@ -2241,26 +1957,131 @@ " mark_right = True)\n", " ax = plt.gca()\n", " ax.invert_yaxis()\n", - " actPersosNumber = len(sortedPersos)\n", + " sceneCount = len(sceneNums)\n", " for i, bar in enumerate(ax.patches):\n", - " print(bar)\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", + " # en prenant la partie entière de la division de i par le nombre de scènes\n", + " # de l'acte. En effet, comme on a pu le montrer un peu plus haut,\n", + " # les données sont parcourues de la manière suivante, les barres du graphique, les rectangles\n", + " # représentant les pourcentage de paroles, sont données pour toutes les scènes pour chaque\n", + " # personnage du dataframe.\n", + " # Pour le premier acte i va aller de 0 à 24 et le résultat de floor(i/sceneCount) va \n", + " # valloir 0 0 0 0 0 1 1 1 1 1 2 2 2 2 2 ... 4 4 4 4 4 où 4 est l'indice du dernier personnage (il y en a 5)\n", + " # On s'assure que persoIdx est un entier pour l'utiliser comme élément d'indiçage de l'array numpy des noms\n", + " # des personnages de l'acte courant trié sortedPersos.\n", + " persoIdx = int(np.floor(i/sceneCount))\n", + " #print(persoIdx)\n", " # On calcul la ligne de la donnée\n", " # On récupère le nom du personnage\n", " persoName = sortedPersos[persoIdx]\n", + " #print(persoName)\n", + " # Ensuite, on va calculer le \"numéro\" de scène auquel correspond la barre courante\n", + " # en utilisant le reste de la division euclidienne de i par le nombre de scènes\n", + " # Pour le premier acte i va aller de 0 à 24 et le résultat de i % sceneCount va \n", + " # valloir 0 1 2 3 4 ... autant de fois qu'il y a de personnages dans l'acte courant\n", + " # et où 0 1 2 3 4 donne le numéro de ligne dans le dataframe\n", + " dataLineIdx = i % sceneCount\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", + " # Si la largeur de la barre en cours est différente de 0, l'auteur associé a prononcé un certain nombres de mots\n", + " # que l'on va récupérer dans le dataframe qui n'a pas été mis en pourcentage et on va l'afficher au milieu du rectangle\n", + " # correspondant.\n", + " #print(actSceneSynthesisGraphDf[persoName])\n", + " nbWords = actSceneSynthesisGraphDf[persoName].iloc[dataLineIdx]\n", " ax.text(bar.get_x()+bar.get_width()/2, \\\n", " bar.get_y()+bar.get_height()/2, \\\n", - " )\n", + " \"{:d}\".format(nbWords), \\\n", + " rotation=20, \\\n", + " ha = 'center', \\\n", + " va = 'center')\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", + " # ax.text(0.1, bar.get_y()+bar.get_height()/2, disease, color = 'white', ha = 'left', va = 'center') --> on comprend\n", + " # que les premiers paramètres donne la position du texte dans la barre notamment grâce à bar.get_y()+bar.get_height()/2\n", + " # qui met la coordonnée en y à la coordonnée de début de la barre + la moitié de sa hauteur. Ensuite, en ayant observé\n", + " # quelques comportements en modifiant les paramètres ha et va on comprend que cela règle l'ancrage du texte, i.e. \n", + " # cela précise à quoi font référence les coordonnées renseignées ha (alignement horizontal) et va (alignement vertical)\n", + " # center indique que la coordonnées renseignée représente le centre de la boîte de texte pour l'axe considéré,\n", + " # center, center --> indique que le centre de la boîte de texte se trouve aux coordonnées passées en entrée.\n", + " \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')" + " plt.legend(bbox_to_anchor=(1.05, 1.0), loc='upper left')\n", + " # Hide ticks of x axis on figures\n", + " plt.tick_params(axis='x', \\\n", + " which='both', \\\n", + " bottom=False, \\\n", + " top=False, \\\n", + " labelbottom=False) \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Réponse à la question facultative" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Un nouveau regard sur le texte de la pièce nous montre que la manière dont nous avons géré auteurs et destinataires des répliques est \"naïf\". Cela n'a pas d'impact sur les réponses aux deux premières questions mais sur la dernière question oui.\n", + "Voici un commentaire de notre code:\n", + "```\n", + " # Il y avait déjà un auteur d'une réplique, currentAuthor\n", + " # le nouvel auteur est donc le destinataire de la réplique précédente.\n", + "```\n", + "\n", + "renseignant sur notre approche et qui montre la manière dont nous identifions auteurs et destinataires. Cette approche fonctionne parfaitement lorrsqu'il n'y a que deux personnages dans une scène mais n'est plus qu'une approximation lorsqu'il y a plus de deux personnages. En effet, cela peut être plus compliqué comme le montre quelques extraits ci-dessous:\n", + "```\n", + "### Scène IV. (Acte 5)\n", + "Élise, Mariane, Frosine, Harpagon, Valère, Maître Jacques, le Commissaire, son Clerc\n", + "\n", + "\n", + " HARPAGON.\n", + "Ah ! fille scélérate ! Fille indigne d'un Père comme moi ! C'est ainsi que tu pratiques les Leçons que je t'ai données ! Tu te laisses prendre d'amour pour un Voleur infâme, et tu lui engages ta foi sans mon consentement ? Mais vous serez trompés l'un et l'autre. Quatre bonnes murailles me répondront de ta conduite ; et une bonne Potence me fera raison de ton audace.\n", + "\n", + " VALÈRE.\n", + "Ce ne sera point votre passion qui jugera l'affaire ; et l'on m'écoutera, au moins, avant que de me condamner.\n", + "\n", + " HARPAGON.\n", + "Je me suis abusé de dire une Potence ; et tu seras roué tout vif.\n", + "\n", + " ÉLISE, *à genoux devant son père*.\n", + "Ah ! mon père, prenez des sentiments un peu plus humains, je vous prie, et n'allez point pousser les choses dans les dernières violences du pouvoir paternel : Ne vous laissez point entraîner aux premiers mouvements de votre passion, et donnez-vous le temps de considérer ce que vous voulez faire.Prenez la peine de mieux voir celui dont vous vous offensez : il est tout autre que vos yeux ne le jugent ; et vous trouverez moins étrange que je me sois donnée à lui, lorsque vous saurez que sans lui vous ne m'auriez plus il y a longtemps. Oui, mon Père, c'est celui qui me sauva de ce grand péril que vous savez que je courus dans l'eau, et à qui vous devez la vie de cette même fille, dont…\n", + "```\n", + "\n", + "Ci-dessus, la première réplique d'Harpagon s'adresse à sa fille Elise et non à Valère.\n", + "\n", + "\n", + "```\n", + "### Scène V. (Acte 5)\n", + "Anselme, Harpagon, Élise, Mariane, Frosine, Valère, Maître Jacques, le Commissaire, son Clerc\n", + "\n", + "\n", + " ANSELME.\n", + "Qu'est-ce, Seigneur Harpagon, je vous vois tout ému.\n", + "\n", + " HARPAGON.\n", + "Ah ! Seigneur Anselme, vous me voyez le plus infortuné de tous les hommes ; et voici bien du trouble et du désordre au Contrat que vous venez faire ?On m'assassine dans le bien, on m'assassine dans l'honneur ; et voilà un traître, un scélérat, qui a violé tous les droits les plus saints ; qui s'est coulé chez moi sous le titre de Domestique, pour me dérober mon argent, et pour me suborner ma Fille.\n", + "\n", + " VALÈRE.\n", + "Qui songe à votre argent, dont vous me faites un galimatias ?\n", + "\n", + " HARPAGON.\n", + "Oui, ils se sont donné l'un et l'autre une Promesse de mariage. Cet affront vous regarde, Seigneur Anselme ; et c'est vous qui devez vous rendre partie contre lui, et faire toutes les poursuites de la Justice, pour vous venger de son insolence.\n", + "```\n", + "\n", + "Ici la première réplique d'Harpagon n'est pas adressée à Valère mais à Anselme, il lui répond.\n", + "\n", + "Dans la scène 2 de l'acte 5:\n", + "```\n", + " MAÎTRE JACQUES, *à part*.\n", + "Voici justement ce qu'il me faut pour me venger de notre Intendant : depuis qu'il est entré céans, il est le favori, on n'écoute que ses conseils ; et j'ai aussi sur le cœur les coups de bâton de tantôt.\n", + "```\n", + "Maître Jacques se parle ici à lui-même.\n", + "\n", + "Afin d'avoir une graphique exact des échanges, il faudrait donc une analyse du texte bien plus poussée et intelligente que celle mise en place. Néanmoins, cette approche permet d'avoir une bonne approximation des échanges entre les personnages. " ] }, { @@ -2270,9 +2091,7 @@ "Notes pour plus tard: lien stack overflow vers code de customisation de graphes de la bibliothèque python networkx\n", "https://stackoverflow.com/questions/25639169/networkx-change-color-width-according-to-edge-attributes-inconsistent-result\n", "lien github vers morceau de code ajoutant de la couleur et le réglage de l'épaisseur des arêtes sur un graphe\n", - "https://gist.github.com/AruniRC/2c53fe7680eeb578593ec816bbfb1653\n", - "Lien vers une page donnant un exemple d'affichage par ensemble de barres\n", - "https://www.geeksforgeeks.org/stacked-percentage-bar-plot-in-matplotlib/" + "https://gist.github.com/AruniRC/2c53fe7680eeb578593ec816bbfb1653\n" ] } ], -- 2.18.1