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Commit 9fedb436 authored by 7404ea6678ce6fbf3a726e36f2bf2079's avatar 7404ea6678ce6fbf3a726e36f2bf2079
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move forward on question 2 answer and bar plots but some corrections need to... 

move forward on question 2 answer and bar plots but some corrections need to be done with names of personnages.
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module3/exo3/exercice_fr.ipynb
View file @ 9fedb436
...@@ -675,14 +675,6 @@ ...@@ -675,14 +675,6 @@
"execution_count": 13, "execution_count": 13,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Exception scene 7 of act 4\n",
"persosList : ['Harpagon']\n"
]
},
{ {
"data": { "data": {
"text/html": [ "text/html": [
...@@ -1278,15 +1270,11 @@ ...@@ -1278,15 +1270,11 @@
} }
], ],
"source": [ "source": [
"# On ouvre le fichier local en lecture 'r' pour en faire l'analyse\n", "# On ouvre le fichier local en lecture 'r' pour en faire l'analyse en utilisant l'instruction with qui se chargera de fermer\n",
"# en utilisant l'instruction with qui se chargera de fermer\n", "# le fichier une fois sortie de l'instruction (pas d'erreur possible par oubli d'appel à l'instruction close).\n",
"# le fichier une fois sortie de l'instruction.\n", "# Un rapide coup d'oeil au fichier texte nous montre une organisation, des symboles en début de ligne etc, que l'on va utiliser\n",
"# (pas d'erreur possible par oubli d'appel à l'instruction close)\n", "# pour \"parser\" le fichier, à savoir le lire de manière à ranger les données de manière intelligente dans une structure de\n",
"# Un rapide coup d'oeil au fichier texte nous montre une organisation\n", "# données facilitant la manipulation et l'analyse.\n",
"# , des symboles en début de ligne etc, que l'on va utiliser pour \"parser\"\n",
"# le fichier, à savoir le lire de manière à ranger les données\n",
"# de manière intelligente dans une structure de données facilitant\n",
"# la manipulation et l'analyse.\n",
"with open(local_filename,'r') as avareFile:\n", "with open(local_filename,'r') as avareFile:\n",
" # On va commencer par parser le fichier afin de récupérer la liste des personnages\n", " # On va commencer par parser le fichier afin de récupérer la liste des personnages\n",
" lineNum = fill_perso_dict(avareFile, avarePersoDict)\n", " lineNum = fill_perso_dict(avareFile, avarePersoDict)\n",
...@@ -1367,7 +1355,7 @@ ...@@ -1367,7 +1355,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 20, "execution_count": 15,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -1409,7 +1397,7 @@ ...@@ -1409,7 +1397,7 @@
"Index: []" "Index: []"
] ]
}, },
"execution_count": 20, "execution_count": 15,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -1661,9 +1649,30 @@ ...@@ -1661,9 +1649,30 @@
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"cell_type": "code", "cell_type": "code",
"execution_count": 18, "execution_count": 39,
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"outputs": [ "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"
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...@@ -1699,7 +1708,7 @@ ...@@ -1699,7 +1708,7 @@
" 'color': array([0.35882353, 0.21994636, 0.99385914, 1. ])}}" " 'color': array([0.35882353, 0.21994636, 0.99385914, 1. ])}}"
] ]
}, },
"execution_count": 18, "execution_count": 39,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -1708,6 +1717,7 @@ ...@@ -1708,6 +1717,7 @@
"color = iter(cm.rainbow(np.linspace(0, 1, nbPersos)))\n", "color = iter(cm.rainbow(np.linspace(0, 1, nbPersos)))\n",
"for i in range(nbPersos):\n", "for i in range(nbPersos):\n",
" c = next(color)\n", " c = next(color)\n",
" print(c)\n",
" perso = sortedData[\"perso\"].iloc[i]\n", " perso = sortedData[\"perso\"].iloc[i]\n",
" #print(perso)\n", " #print(perso)\n",
" avarePersoDict[perso][\"color\"] = c\n", " avarePersoDict[perso][\"color\"] = c\n",
...@@ -1724,93 +1734,138 @@ ...@@ -1724,93 +1734,138 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 19, "execution_count": 50,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"act 1 persos ['Valère' 'Élise' 'Cléante' 'Harpagon' 'La Flèche']\n", " Cléante Harpagon La Flèche Valère scene Élise\n",
" Cléante Harpagon La Flèche Valère scene Élise\n", "0 0.000000 0.000000 0.000000 0.561998 1 0.438002\n",
"0 0 0 0 630 1 491\n", "1 0.831694 0.000000 0.000000 0.000000 2 0.168306\n",
"1 761 0 0 0 2 154\n", "2 0.000000 0.644044 0.355956 0.000000 3 0.000000\n",
"2 0 465 257 0 3 0\n", "3 0.141361 0.752618 0.000000 0.000000 4 0.106021\n",
"3 216 1150 0 0 4 162\n", "4 0.000000 0.268701 0.000000 0.695866 5 0.035433\n",
"4 0 273 0 707 5 36\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",
"act 2 persos ['Cléante' 'La Flèche' 'Maître Simon' 'Harpagon' 'Frosine']\n", " Cléante Frosine Harpagon La Flèche Maître Simon scene\n",
" Cléante Frosine Harpagon La Flèche Maître Simon scene\n", "0 0.295632 0.000000 0.000000 0.704368 0.00000 1\n",
"0 379 0 0 903 0 1\n", "1 0.250493 0.000000 0.337278 0.023669 0.38856 2\n",
"1 127 0 171 12 197 2\n", "2 0.000000 0.045455 0.954545 0.000000 0.00000 3\n",
"2 0 1 21 0 0 3\n", "3 0.000000 0.301624 0.000000 0.698376 0.00000 4\n",
"3 0 130 0 301 0 4\n", "4 0.000000 0.729400 0.270600 0.000000 0.00000 5\n",
"4 0 1496 555 0 0 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",
"act 3 persos ['Harpagon' 'Maître Jacques' 'La Merluche' 'Brindavoine' 'Élise' 'Cléante'\n", " Brindavoine Cléante Frosine Harpagon La Merluche Mariane \\\n",
" 'Valère' 'Frosine' 'Mariane']\n", "0 0.011880 0.039256 0.000000 0.391012 0.013430 0.000000 \n",
" Brindavoine Cléante Frosine Harpagon La Merluche Mariane \\\n", "1 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"0 23 76 0 757 26 0 \n", "2 0.000000 0.000000 0.633333 0.000000 0.000000 0.000000 \n",
"1 0 0 0 0 0 0 \n", "3 0.000000 0.000000 0.507979 0.000000 0.000000 0.492021 \n",
"2 0 0 19 0 0 0 \n", "4 0.000000 0.000000 0.198473 0.801527 0.000000 0.000000 \n",
"3 0 0 191 0 0 185 \n", "5 0.000000 0.000000 0.198473 0.534351 0.000000 0.137405 \n",
"4 0 0 26 105 0 0 \n", "6 0.000000 0.551823 0.039347 0.175624 0.000000 0.233205 \n",
"5 0 0 26 70 0 18 \n", "7 0.465116 0.000000 0.000000 0.534884 0.000000 0.000000 \n",
"6 0 575 41 183 0 243 \n", "8 0.000000 0.283688 0.000000 0.567376 0.148936 0.000000 \n",
"7 20 0 0 23 0 0 \n",
"8 0 40 0 80 21 0 \n",
"\n", "\n",
" Maître Jacques Valère scene Élise \n", " Maître Jacques Valère scene Élise \n",
"0 779 272 1 3 \n", "0 0.402376 0.140496 1 0.001550 \n",
"1 186 92 2 0 \n", "1 0.669065 0.330935 2 0.000000 \n",
"2 11 0 3 0 \n", "2 0.366667 0.000000 3 0.000000 \n",
"3 0 0 4 0 \n", "3 0.000000 0.000000 4 0.000000 \n",
"4 0 0 5 0 \n", "4 0.000000 0.000000 5 0.000000 \n",
"5 0 0 6 17 \n", "5 0.000000 0.000000 6 0.129771 \n",
"6 0 0 7 0 \n", "6 0.000000 0.000000 7 0.000000 \n",
"7 0 0 8 0 \n", "7 0.000000 0.000000 8 0.000000 \n",
"8 0 0 9 0 \n", "8 0.000000 0.000000 9 0.000000 \n",
"act 4 persos ['Cléante' 'Élise' 'Mariane' 'Frosine' 'Harpagon' 'Maître Jacques'\n", "[array([0.21764706, 0.42912061, 0.97551197, 1. ]), 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([0.07647059, 0.61727822, 0.94518383, 1. ]), array([0.50392157, 0.99998103, 0.70492555, 1. ]), array([0.92745098, 0.78292761, 0.43467642, 1. ]), array([1. , 0.42912061, 0.21994636, 1. ]), array([0.64509804, 0.9741386 , 0.62211282, 1. ])]\n",
" 'La Flèche']\n", " Cléante Frosine Harpagon La Flèche Mariane Maître Jacques scene \\\n",
" Cléante Frosine Harpagon La Flèche Mariane Maître Jacques scene \\\n", "0 0.251282 0.447179 0.000000 0.000000 0.242051 0.000000 1 \n",
"0 245 436 0 0 236 0 1 \n", "1 0.197183 0.000000 0.760563 0.000000 0.000000 0.000000 2 \n",
"1 14 0 54 0 0 0 2 \n", "2 0.515413 0.000000 0.484587 0.000000 0.000000 0.000000 3 \n",
"2 418 0 393 0 0 0 3 \n", "3 0.278232 0.000000 0.230769 0.000000 0.000000 0.490998 4 \n",
"3 170 0 141 0 0 300 4 \n", "4 0.558219 0.000000 0.441781 0.000000 0.000000 0.000000 5 \n",
"4 163 0 129 0 0 0 5 \n", "5 0.265625 0.000000 0.000000 0.734375 0.000000 0.000000 6 \n",
"5 17 0 0 47 0 0 6 \n", "6 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 7 \n",
"6 0 0 407 0 0 0 7 \n",
"\n", "\n",
" Élise \n", " Élise \n",
"0 58 \n", "0 0.059487 \n",
"1 3 \n", "1 0.042254 \n",
"2 0 \n", "2 0.000000 \n",
"3 0 \n", "3 0.000000 \n",
"4 0 \n", "4 0.000000 \n",
"5 0 \n", "5 0.000000 \n",
"6 0 \n", "6 0.000000 \n",
"act 5 persos ['Le Commissaire' 'Harpagon' 'Maître Jacques' 'Valère' 'Élise' 'Frosine'\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' 'Mariane' 'Cléante' 'le Commissaire']\n", " Anselme Cléante Frosine Harpagon Le Commissaire Mariane \\\n",
" Anselme Cléante Frosine Harpagon Le Commissaire Mariane \\\n", "0 0.000000 0.000000 0.000000 0.449495 0.550505 0.000000 \n",
"0 0 0 0 89 109 0 \n", "1 0.000000 0.000000 0.000000 0.265693 0.232117 0.000000 \n",
"1 0 0 0 182 159 0 \n", "2 0.000000 0.000000 0.000000 0.403477 0.000000 0.000000 \n",
"2 0 0 0 441 0 0 \n", "3 0.000000 0.000000 0.013333 0.413333 0.000000 0.000000 \n",
"3 0 0 4 124 0 0 \n", "4 0.326052 0.000000 0.000000 0.208738 0.000000 0.155340 \n",
"4 403 0 0 258 0 192 \n", "5 0.272727 0.311005 0.000000 0.212919 0.000000 0.086124 \n",
"5 114 130 0 89 0 36 \n",
"\n", "\n",
" Maître Jacques Valère le Commissaire scene Élise \n", " Maître Jacques Valère le Commissaire scene Élise \n",
"0 0 0 0 1 0 \n", "0 0.000000 0.000000 0.000000 1 0.000000 \n",
"1 344 0 0 2 0 \n", "1 0.502190 0.000000 0.000000 2 0.000000 \n",
"2 11 641 0 3 0 \n", "2 0.010064 0.586459 0.000000 3 0.000000 \n",
"3 7 22 0 4 143 \n", "3 0.023333 0.073333 0.000000 4 0.476667 \n",
"4 7 376 0 5 0 \n", "4 0.005663 0.304207 0.000000 5 0.000000 \n",
"5 23 0 26 6 0 \n" "5 0.055024 0.000000 0.062201 6 0.000000 \n",
"[array([0.35490196, 0.9741386 , 0.78292761, 1. ]), 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([0.21372549, 0.9005867 , 0.84695821, 1. ]), array([0.50392157, 0.99998103, 0.70492555, 1. ]), array([0.92745098, 0.78292761, 0.43467642, 1. ]), array([1. , 0.42912061, 0.21994636, 1. ]), array([0.21372549, 0.9005867 , 0.84695821, 1. ]), array([0.64509804, 0.9741386 , 0.62211282, 1. ])]\n"
] ]
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [ "text/plain": [
"<Figure size 432x288 with 5 Axes>" "<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
] ]
}, },
"metadata": { "metadata": {
...@@ -1820,6 +1875,7 @@ ...@@ -1820,6 +1875,7 @@
} }
], ],
"source": [ "source": [
"from matplotlib import colors\n",
"# Il va nous falloir compter le nombre d'actes de la pièce\n", "# Il va nous falloir compter le nombre d'actes de la pièce\n",
"# et faire une boucle sur ces derniers pour construire \n", "# et faire une boucle sur ces derniers pour construire \n",
"# un dataframe par acte à partir du dataframe global\n", "# un dataframe par acte à partir du dataframe global\n",
...@@ -1830,14 +1886,14 @@ ...@@ -1830,14 +1886,14 @@
"actNums = pd.unique(textDataSynthesisTableDf[\"act\"])\n", "actNums = pd.unique(textDataSynthesisTableDf[\"act\"])\n",
"nbActs = actNums.size\n", "nbActs = actNums.size\n",
"\n", "\n",
"fig = plt.subplots(nbActs, 1)\n", "#fig, axs = plt.subplots(nbActs, 1)\n",
"\n", "\n",
"for actNum in actNums:\n", "for actNum in actNums:\n",
" actDf = textDataSynthesisTableDf[textDataSynthesisTableDf[\"act\"]==float(actNum)].copy()\n", " actDf = textDataSynthesisTableDf[textDataSynthesisTableDf[\"act\"]==float(actNum)].copy()\n",
" #actDf\n", " #actDf\n",
" sceneNums = pd.unique(actDf[\"scene\"])\n", " sceneNums = pd.unique(actDf[\"scene\"])\n",
" actPersos = pd.unique(actDf[\"author\"])\n", " actPersos = pd.unique(actDf[\"author\"])\n",
" print(\"act {0:} persos {1:}\".format(actNum,actPersos))\n", " #print(\"act {0:} persos {1:}\".format(actNum,actPersos))\n",
" actDict = {\"scene\":[]}\n", " actDict = {\"scene\":[]}\n",
" for perso in actPersos:\n", " for perso in actPersos:\n",
" # Création d'une colonne par personnage de l'acte\n", " # Création d'une colonne par personnage de l'acte\n",
...@@ -1862,9 +1918,34 @@ ...@@ -1862,9 +1918,34 @@
" # sur les graphiques.\n", " # sur les graphiques.\n",
" #print(actDict)\n", " #print(actDict)\n",
" actSceneSynthesisGraphDf = pd.DataFrame.from_dict(actDict)\n", " actSceneSynthesisGraphDf = pd.DataFrame.from_dict(actDict)\n",
" print(actSceneSynthesisGraphDf)\n", " # On va créer une version en pourcentage du dataframe\n",
" subFig = plt.subplot(nbActs, 1, actNum)\n", " # de synthèse par scène d'un acte.\n",
" \n", " percentDf = actSceneSynthesisGraphDf.copy()\n",
" #df.div(df.sum(axis=1), axis=0)\n",
" percentDf[actPersos] = percentDf[actPersos].div(percentDf[actPersos].sum(axis=1), axis=0)\n",
" percentDf = percentDf.sort_index(axis=1)\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",
" 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",
" #plt.sca.ax(axs[actNum])\n",
" #df.plot.barh(color={\"speed\": \"red\", \"lifespan\": \"green\"})\n",
" percentDf.plot(\\\n",
" x = 'scene', \\\n",
" kind = 'barh', \\\n",
" color=colors, \\\n",
" stacked = True, \\\n",
" title = \"Acte {}\".format(actNum), \\\n",
" mark_right = True)\n",
" pass" " pass"
] ]
}, },
......
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