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mooc-rr
Commit b9692dc4 authored by ce9a2e8f95a59bf8ceb3cb087dad994a's avatar ce9a2e8f95a59bf8ceb3cb087dad994a
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maj

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module3/exo3/exercice.ipynb
View file @ b9692dc4
......@@ -23,7 +23,7 @@
},
{
"cell_type": "code",
"execution_count": 16,
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
......@@ -36,7 +36,7 @@
},
{
"cell_type": "code",
"execution_count": 17,
"execution_count": 2,
"metadata": {},
"outputs": [
{
......@@ -45,7 +45,7 @@
"'/home/jovyan/work/module3/exo3'"
]
},
"execution_count": 17,
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
......@@ -56,7 +56,7 @@
},
{
"cell_type": "code",
"execution_count": 37,
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
......@@ -65,7 +65,7 @@
},
{
"cell_type": "code",
"execution_count": 38,
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
......@@ -74,7 +74,7 @@
},
{
"cell_type": "code",
"execution_count": 39,
"execution_count": 5,
"metadata": {},
"outputs": [
{
......@@ -83,7 +83,7 @@
"<_io.TextIOWrapper name='moliere_avare.txt' mode='r' encoding='UTF-8'>"
]
},
"execution_count": 39,
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
......@@ -94,7 +94,7 @@
},
{
"cell_type": "code",
"execution_count": 40,
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
......@@ -103,7 +103,7 @@
},
{
"cell_type": "code",
"execution_count": 46,
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
......@@ -114,7 +114,7 @@
},
{
"cell_type": "code",
"execution_count": 47,
"execution_count": 8,
"metadata": {},
"outputs": [
{
......@@ -123,7 +123,7 @@
"'# ACTEURS.\\n'"
]
},
"execution_count": 47,
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
......@@ -134,7 +134,7 @@
},
{
"cell_type": "code",
"execution_count": 61,
"execution_count": 9,
"metadata": {},
"outputs": [
{
......@@ -179,7 +179,7 @@
},
{
"cell_type": "code",
"execution_count": 134,
"execution_count": 22,
"metadata": {},
"outputs": [
{
......@@ -222,28 +222,37 @@
},
{
"cell_type": "code",
"execution_count": 137,
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"dico_acte = {}\n",
"acte_courant = 0\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",
" Nombre_mots[l] += len(l.split()) #problème ponctuation\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)"
" Nombre_actes[l].append(acte_courant)\n",
"dico_acte[acte_courant] = dico_scènes"
]
},
{
"cell_type": "code",
"execution_count": 138,
"execution_count": 51,
"metadata": {},
"outputs": [
{
......@@ -265,7 +274,7 @@
" ' LE COMMISSAIRE.\\n': [5]}"
]
},
"execution_count": 138,
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
......@@ -274,6 +283,703 @@
"Nombre_actes"
]
},
{
"cell_type": "code",
"execution_count": 52,
"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}"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dict(sorted(Nombre_mots.items(), key=lambda item:item[1]))"
]
},
{
"cell_type": "code",
"execution_count": 53,
"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}"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dict(sorted(Nombre_repliques.items(), key=lambda item:item[1]))"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{1: {1: {' HARPAGON.\\n': 0,\n",
" ' CLÉANTE.\\n': 0,\n",
" ' ÉLISE.\\n': 8,\n",
" ' VALÈRE.\\n': 8,\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",
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" ' LA MERLUCHE.\\n': 0,\n",
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" 2: {' HARPAGON.\\n': 0,\n",
" ' CLÉANTE.\\n': 10,\n",
" ' ÉLISE.\\n': 9,\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': 34,\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': 32,\n",
" ' DAME CLAUDE.\\n': 0,\n",
" ' BRINDAVOINE.\\n': 0,\n",
" ' LA MERLUCHE.\\n': 0,\n",
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" 4: {' HARPAGON.\\n': 53,\n",
" ' CLÉANTE.\\n': 29,\n",
" ' ÉLISE.\\n': 23,\n",
" ' VALÈRE.\\n': 0,\n",
" ' MARIANE.\\n': 0,\n",
" ' ANSELME.\\n': 0,\n",
" ' FROSINE.\\n': 0,\n",
" ' MAÎTRE SIMON.\\n': 0,\n",
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" ' LA FLÈCHE.\\n': 0,\n",
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" 5: {' HARPAGON.\\n': 20,\n",
" ' CLÉANTE.\\n': 0,\n",
" ' ÉLISE.\\n': 4,\n",
" ' VALÈRE.\\n': 22,\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",
" 2: {1: {' HARPAGON.\\n': 0,\n",
" ' CLÉANTE.\\n': 21,\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': 20,\n",
" ' DAME CLAUDE.\\n': 0,\n",
" ' BRINDAVOINE.\\n': 0,\n",
" ' LA MERLUCHE.\\n': 0,\n",
" ' LE COMMISSAIRE.\\n': 0},\n",
" 2: {' HARPAGON.\\n': 9,\n",
" ' CLÉANTE.\\n': 6,\n",
" ' ÉLISE.\\n': 0,\n",
" ' VALÈRE.\\n': 0,\n",
" ' MARIANE.\\n': 0,\n",
" ' ANSELME.\\n': 0,\n",
" ' FROSINE.\\n': 0,\n",
" ' MAÎTRE SIMON.\\n': 5,\n",
" ' MAÎTRE JACQUES.\\n': 0,\n",
" ' LA FLÈCHE.\\n': 1,\n",
" ' DAME CLAUDE.\\n': 0,\n",
" ' BRINDAVOINE.\\n': 0,\n",
" ' LA MERLUCHE.\\n': 0,\n",
" ' LE COMMISSAIRE.\\n': 0},\n",
" 3: {' 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': 1,\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': 0,\n",
" ' CLÉANTE.\\n': 0,\n",
" ' ÉLISE.\\n': 0,\n",
" ' VALÈRE.\\n': 0,\n",
" ' MARIANE.\\n': 0,\n",
" ' ANSELME.\\n': 0,\n",
" ' FROSINE.\\n': 5,\n",
" ' MAÎTRE SIMON.\\n': 0,\n",
" ' MAÎTRE JACQUES.\\n': 0,\n",
" ' LA FLÈCHE.\\n': 6,\n",
" ' DAME CLAUDE.\\n': 0,\n",
" ' BRINDAVOINE.\\n': 0,\n",
" ' LA MERLUCHE.\\n': 0,\n",
" ' LE COMMISSAIRE.\\n': 0},\n",
" 5: {' HARPAGON.\\n': 35,\n",
" ' CLÉANTE.\\n': 0,\n",
" ' ÉLISE.\\n': 0,\n",
" ' VALÈRE.\\n': 0,\n",
" ' MARIANE.\\n': 0,\n",
" ' ANSELME.\\n': 0,\n",
" ' FROSINE.\\n': 35,\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: {1: {' HARPAGON.\\n': 34,\n",
" ' CLÉANTE.\\n': 3,\n",
" ' ÉLISE.\\n': 1,\n",
" ' VALÈRE.\\n': 11,\n",
" ' MARIANE.\\n': 0,\n",
" ' ANSELME.\\n': 0,\n",
" ' FROSINE.\\n': 0,\n",
" ' MAÎTRE SIMON.\\n': 0,\n",
" ' MAÎTRE JACQUES.\\n': 27,\n",
" ' LA FLÈCHE.\\n': 0,\n",
" ' DAME CLAUDE.\\n': 0,\n",
" ' BRINDAVOINE.\\n': 1,\n",
" ' LA MERLUCHE.\\n': 2,\n",
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" 2: {' HARPAGON.\\n': 0,\n",
" ' CLÉANTE.\\n': 0,\n",
" ' ÉLISE.\\n': 0,\n",
" ' VALÈRE.\\n': 11,\n",
" ' MARIANE.\\n': 0,\n",
" ' ANSELME.\\n': 0,\n",
" ' FROSINE.\\n': 0,\n",
" ' MAÎTRE SIMON.\\n': 0,\n",
" ' MAÎTRE JACQUES.\\n': 11,\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': 0,\n",
" ' CLÉANTE.\\n': 0,\n",
" ' ÉLISE.\\n': 0,\n",
" ' VALÈRE.\\n': 0,\n",
" ' MARIANE.\\n': 0,\n",
" ' ANSELME.\\n': 0,\n",
" ' FROSINE.\\n': 2,\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",
" 4: {' HARPAGON.\\n': 0,\n",
" ' CLÉANTE.\\n': 0,\n",
" ' ÉLISE.\\n': 0,\n",
" ' VALÈRE.\\n': 0,\n",
" ' MARIANE.\\n': 6,\n",
" ' ANSELME.\\n': 0,\n",
" ' FROSINE.\\n': 5,\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: {' 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': 1,\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': 6,\n",
" ' CLÉANTE.\\n': 0,\n",
" ' ÉLISE.\\n': 1,\n",
" ' VALÈRE.\\n': 0,\n",
" ' MARIANE.\\n': 1,\n",
" ' ANSELME.\\n': 0,\n",
" ' FROSINE.\\n': 1,\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",
" 7: {' HARPAGON.\\n': 9,\n",
" ' CLÉANTE.\\n': 20,\n",
" ' ÉLISE.\\n': 0,\n",
" ' VALÈRE.\\n': 0,\n",
" ' MARIANE.\\n': 10,\n",
" ' ANSELME.\\n': 0,\n",
" ' FROSINE.\\n': 2,\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",
" 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<ipython-input-64-6d44178c73cd>\u001b[0m in \u001b[0;36m<module>\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 \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubok\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msubok\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_m\u001b[0m \u001b[0;32min\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 251\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 252\u001b[0;31m \u001b[0mshape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_broadcast_shape\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[0m\n\u001b[0m\u001b[1;32m 253\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 254\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mshape\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0marray\u001b[0m \u001b[0;32min\u001b[0m \u001b[0margs\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/numpy/lib/stride_tricks.py\u001b[0m in \u001b[0;36m_broadcast_shape\u001b[0;34m(*args)\u001b[0m\n\u001b[1;32m 185\u001b[0m \u001b[0;31m# use the old-iterator because np.nditer does not handle size 0 arrays\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 186\u001b[0m \u001b[0;31m# consistently\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 187\u001b[0;31m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbroadcast\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[0;36m32\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 188\u001b[0m \u001b[0;31m# unfortunately, it cannot handle 32 or more arguments 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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CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"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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ovdqqz9glSYeo7y+Dqqo9wHpgfZItwHJgE7C/J+cAq6rqgR87mCwBdk84tKe7d4CtVbW4Xx1JlgKrgMveXkaTJA3HpMtWSc5OctaEQ+cDzwPbgFOSLOrazUxyLLAOuLFbjnr7+hMmucUzwMndxjxJpic5dz91XAD8O+Dqqnr54IcnSZoK/WYeJwKrk8yit/T0HLCiqt5Mcm137nh6+x1LgbvpLUdt6jbEXwGuOVDnXT/LgLuSnNTVcyewdZ+md3S1/OdueesHVXV100glSQMzsu8wX7hwYY2NjQ27DEk6qgztHeaSpNFneEiSmhkekqRmhockqZnhIUlqZnhIkpoZHpKkZoaHJKmZ4SFJamZ4SJKaGR6SpGaGhySpmeEhSWpmeEiSmhkekqRmhockqZnhIUlqZnhIkpoZHpKkZoaHJKnZscMuYKrs3rWL33viqWGXccjOmH/esEuQpANy5iFJamZ4SJKaGR6SpGaGhySpmeEhSWpmeEiSmhkekqRmhockqZnhIUlqNrLvMP/hd/fyixe8MewyDtn9e4ddgSQd2MBnHkmuTDJ/0P1Kko4cfcMjyaokW5M8mWRzkg/0ueRR4JYkZw6mREjyoSSbkryVZNmg+pUkHZpJl62SLAauAi6sqt1JZgMzJrumqt4ArhtciQD8ALge+PSA+5UkHYJ+M485wI6q2g1QVTuq6kWAJIuSPJbkiSQbksxMMi3JHUk2JtmSZGXXdkmS9UnuS7Ityb1J0p1bkOSRJONJ1iWZs28RVfX9qnoScCdAko4A/cLjQeC0JM8m+WKSywCSzADWAjdV1XxgKbALuAHYWVWLgEXAyiTzur4uAG4GzgHmARcnmQ6sBpZV1QLgHuD2Qx1MkhVJxpKMvcmrh9qNJKmPSZetquq1JAuAS4HLgbVJbgPGge1VtbFrtxN6m+XAGUmu6LqYQS8o3gI2VNULXbvNwFzgVeA84KFuIjIN2H6og6mqNcAagFl5fx1qP5KkyfV9qW5V7QHWA+uTbAGWA5uA/T05B1hVVQ/82MFkCbB7wqE93b0DbK2qxYdSvCRpOCZdtkpydpKzJhw6H3ge2AackmRR125mkmOBdcCN3XLU29efMMktngFO7jbmSTI9ybmHPhxJ0uHQb+ZxIrA6ySx6S0/PASuq6s0k13bnjqe337EUuJvectSmbkP8FeCaA3Xe9bMMuCvJSV09dwJbJ7brQuobwE8CfzPJL1eVISNJQ5Kq0dwamJX31yX58rDLOGT3771o2CVIehdKMl5VC/u1G9mPJzlzwQncP+YTsCRNBT8YUZLUzPCQJDUzPCRJzQwPSVIzw0OS1MzwkCQ1MzwkSc0MD0lSM8NDktTM8JAkNTM8JEnNDA9JUjPDQ5LUzPCQJDUzPCRJzQwPSVIzw0OS1MzwkCQ1MzwkSc1SVcOuYUok+WPgmWHXcRjNBnYMu4jD6N02Xnj3jdnxDsfpVXVyv0bHHo5KhuSZqlo47CIOlyRjjne0vdvG7HiPbC5bSZKaGR6SpGajHB5rhl3AYeZ4R9+7bcyO9wg2shvmkqSpM8ozD0nSFDE8JEnNRjI8knwkyTNJnkty27DrGbQk9yR5OclTE469J8lDSb7X/fmTw6xxkJKcluQ3kzydZGuSm7rjIznmJMcl2ZDkiW68v9wdH8nxvi3JtCS/m+T+7vHIjjfJ95NsSbI5yVh37Kga78iFR5JpwBeAnwXOAf5+knOGW9XA/QbwkX2O3QY8XFVnAQ93j0fFW8AtVfV+4IPAJ7u/01Ed827gw1U1Hzgf+EiSDzK6433bTcDTEx6P+ngvr6rzJ7y346ga78iFB3AR8FxV/d+qehP4GvCxIdc0UFX1W8CP9jn8MeDL3fdfBq45rEVNoaraXlWbuu//mN4TzPsY0TFXz2vdw+ndVzGi4wVIcirwUeDuCYdHdrwHcFSNdxTD433ADyc8fqE7Nup+uqq2Q+/JFvipIdczJZLMBS4AvsMIj7lbwtkMvAw8VFUjPV7gTuBWYO+EY6M83gIeTDKeZEV37Kga7yh+PEn2c8zXI4+AJCcC/wW4uap2Jvv7qx4NVbUHOD/JLOAbSc4bdk1TJclVwMtVNZ5kybDrOUwurqoXk/wU8FCSbcMuqNUozjxeAE6b8PhU4MUh1XI4vZRkDkD358tDrmegkkynFxz3VtV/7Q6P9JgBqupVYD29Pa5RHe/FwNVJvk9vmfnDSf4ToztequrF7s+XgW/QW24/qsY7iuGxETgryRlJZgB/D/jmkGs6HL4JLO++Xw789yHWMlDpTTH+A/B0VX1+wqmRHHOSk7sZB0mOB5YC2xjR8VbVZ6rq1KqaS+/f6/+uqp9nRMeb5IQkM9/+HrgSeIqjbLwj+Q7zJH+D3hrqNOCeqrp9yCUNVJKvAkvofYTzS8A/A/4b8HXgZ4AfAD9XVftuqh+VklwC/DawhT9fE/8svX2PkRtzkr9Kb8N0Gr3/wft6Vf1Kkr/ECI53om7Z6tNVddWojjfJPHqzDehtHXylqm4/2sY7kuEhSZpao7hsJUmaYoaHJKmZ4SFJamZ4SJKaGR6SpGaGhySpmeEhSWr2/wHMkgceePrAYQAAAABJRU5ErkJggg==\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": {
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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"
}
],
"source": [
"colors = np.random.rand(14,3)\n",
"for acte in range(1,6) :\n",
" plt.figure()\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",
" for l,perso in enumerate(persos) :\n",
" x[scene-1,l] += dico_acte[acte][scene][perso]\n",
" 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",
" 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)]))"
]
},
{
"cell_type": "code",
"execution_count": null,
......
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