{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "# Analyse des dialogues dans l'Avare de Molière" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "## Choix du fichier source\n", "\n", "*L'Avare* de Molière est disponible dans plusieurs formats différents. \n", "Cependant, tous ne se prêtent pas à une analyse sémantique d'une pièce de théâtre. \n", "Les formats reposant sur du texte brut (tels que Markdown ou iramuteq, et par extension, les fichiers ne contenant que les prises de parole) compliquent le triage des [didascalies](https://fr.wikipedia.org/wiki/Didascalie_(théâtre)) et autres blocs de texte insérés dans les scènes, et ne relevant pas directement du dialogue. \n", "Notre choix devra donc se porter sur un format plus structuré.\n", "Nous pourrions tenter l'analyse des fichiers epub ou kindle, mais ce sont des formats destinés à la présentation, ce qui rendrait leur analyse inutilement complexe et coûteuse, alors que de meilleurs formats sont disponibles.\n", "\n", "Les formats de fichier constituant de meilleurs candidats pour une analyse sémantique sont basés sur XML, qui permet la structuration du contenu : TEI (conçu par le *Text Encoding Initiative Consortium*), TXM (co-développé par l'École normale supérieure de Lyon et l'université de Franche-Comté), et HTML (le langage de balisage du web). \n", "\n", "Ces trois formats sont basés sur XML, et peuvent donc théoriquement être exploités avec une même API ([XPath](https://fr.wikipedia.org/wiki/XPath)), sans nécessiter de bibliothèque tierce. \n", "\n", "HTML présente toutefois des avantages considérables : son exploitation par, au minimum, quelques centaines de millions de sites web à travers le monde, et sa gouvernance par un consortium d'entreprises comme Apple, Google ou Mozilla outre-Atlantique, ou encore l'Inria en France. \n", "C'est le format qui a créé internet, et il est réutilisé dans des contextes très différents.\n", "De plus, en tant que développeur web depuis 30 ans, l'auteur de la présente analyse ne cache pas son intérêt particulier pour ce format, avec lequel il est bien plus familier qu'avec les autres.\n", "\n", "Nous poursuivrons donc cette étude avec le fichier `moliere_avare.html` [mis à disposition](http://dramacode.github.io/html/moliere_avare.html) par [dramacode](https://dramacode.github.io)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Ouverture du fichier" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Commençons par regrouper les importations, afin d'en avoir une vue d'ensemble." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [], "source": [ "# Parseur XML, requêtes XPath\n", "import xml.etree.ElementTree as ET\n", "\n", "# Analyse\n", "import pandas as pd\n", "\n", "# Utile pour la gestion des caractères accentués\n", "import locale\n", "\n", "# Nous permet de combiner deux listes de longueur indéterminée\n", "from itertools import zip_longest\n", "\n", "# Utilisation d'expressions régulières (regex)\n", "import re\n", "\n", "# Traçage de graphiques\n", "import matplotlib.pyplot as plt\n", "import matplotlib.colors as mcolors\n", "\n", "# Permet de définir et d'afficher la colormap des personnages\n", "from itertools import cycle\n", "import matplotlib.patches as mpatches\n", "\n", "# Utiles aux calculs effectués pour les graphiques\n", "import math\n", "import numpy as np\n", "\n", "# Graphe réseau\n", "import networkx as nx\n", "import bokeh.plotting as bkp\n", "from bokeh.io import output_notebook, show\n", "from bokeh.resources import INLINE\n", "from bokeh.models import (\n", " GraphRenderer,\n", " StaticLayoutProvider,\n", " Circle,\n", " MultiLine,\n", " HoverTool,\n", " Arrow,\n", " NormalHead,\n", " ColumnDataSource,\n", " LabelSet,\n", ")" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
\n", " \n", " Loading BokehJS ...\n", "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", "(function(root) {\n", " function now() {\n", " return new Date();\n", " }\n", "\n", " var force = true;\n", "\n", " if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n", " root._bokeh_onload_callbacks = [];\n", " root._bokeh_is_loading = undefined;\n", " }\n", "\n", " var JS_MIME_TYPE = 'application/javascript';\n", " var HTML_MIME_TYPE = 'text/html';\n", " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", " var CLASS_NAME = 'output_bokeh rendered_html';\n", "\n", " /**\n", " * Render data to the DOM node\n", " */\n", " function render(props, node) {\n", " var script = document.createElement(\"script\");\n", " node.appendChild(script);\n", " }\n", "\n", " /**\n", " * Handle when an output is cleared or removed\n", " */\n", " function handleClearOutput(event, handle) {\n", " var cell = handle.cell;\n", "\n", " var id = cell.output_area._bokeh_element_id;\n", " var server_id = cell.output_area._bokeh_server_id;\n", " // Clean up Bokeh references\n", " if (id !== undefined) {\n", " Bokeh.index[id].model.document.clear();\n", " delete Bokeh.index[id];\n", " }\n", "\n", " if (server_id !== undefined) {\n", " // Clean up Bokeh references\n", " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", " cell.notebook.kernel.execute(cmd, {\n", " iopub: {\n", " output: function(msg) {\n", " var element_id = msg.content.text.trim();\n", " Bokeh.index[element_id].model.document.clear();\n", " delete Bokeh.index[element_id];\n", " }\n", " }\n", " });\n", " // Destroy server and session\n", " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", " cell.notebook.kernel.execute(cmd);\n", " }\n", " }\n", "\n", " /**\n", " * Handle when a new output is added\n", " */\n", " function handleAddOutput(event, handle) {\n", " var output_area = handle.output_area;\n", " var output = handle.output;\n", "\n", " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", " if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", " return\n", " }\n", "\n", " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", "\n", " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", " // store reference to embed id on output_area\n", " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", " }\n", " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", " var bk_div = document.createElement(\"div\");\n", " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", " var script_attrs = bk_div.children[0].attributes;\n", " for (var i = 0; i < script_attrs.length; i++) {\n", " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", " }\n", " // store reference to server id on output_area\n", " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", " }\n", " }\n", "\n", " function register_renderer(events, OutputArea) {\n", "\n", " function append_mime(data, metadata, element) {\n", " // create a DOM node to render to\n", " var toinsert = this.create_output_subarea(\n", " metadata,\n", " CLASS_NAME,\n", " EXEC_MIME_TYPE\n", " );\n", " this.keyboard_manager.register_events(toinsert);\n", " // Render to node\n", " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", " render(props, toinsert[toinsert.length - 1]);\n", " element.append(toinsert);\n", " return toinsert\n", " }\n", "\n", " /* Handle when an output is cleared or removed */\n", " events.on('clear_output.CodeCell', handleClearOutput);\n", " events.on('delete.Cell', handleClearOutput);\n", "\n", " /* Handle when a new output is added */\n", " events.on('output_added.OutputArea', handleAddOutput);\n", "\n", " /**\n", " * Register the mime type and append_mime function with output_area\n", " */\n", " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", " /* Is output safe? */\n", " safe: true,\n", " /* Index of renderer in `output_area.display_order` */\n", " index: 0\n", " });\n", " }\n", "\n", " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", " if (root.Jupyter !== undefined) {\n", " var events = require('base/js/events');\n", " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", "\n", " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", " register_renderer(events, OutputArea);\n", " }\n", " }\n", "\n", " \n", " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", " root._bokeh_timeout = Date.now() + 5000;\n", " root._bokeh_failed_load = false;\n", " }\n", "\n", " var NB_LOAD_WARNING = {'data': {'text/html':\n", " \"
\\n\"+\n", " \"

\\n\"+\n", " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", " \"

\\n\"+\n", " \"\\n\"+\n", " \"\\n\"+\n", " \"from bokeh.resources import INLINE\\n\"+\n", " \"output_notebook(resources=INLINE)\\n\"+\n", " \"\\n\"+\n", " \"
\"}};\n", "\n", " function display_loaded() {\n", " var el = document.getElementById(\"a8e1aee7-1fff-43ed-b9fe-4ab601b77cd7\");\n", " if (el != null) {\n", " el.textContent = \"BokehJS is loading...\";\n", " }\n", " if (root.Bokeh !== undefined) {\n", " if (el != null) {\n", " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", " }\n", " } else if (Date.now() < root._bokeh_timeout) {\n", " setTimeout(display_loaded, 100)\n", " }\n", " }\n", "\n", "\n", " function run_callbacks() {\n", " try {\n", " root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n", " }\n", " finally {\n", " delete root._bokeh_onload_callbacks\n", " }\n", " console.info(\"Bokeh: all callbacks have finished\");\n", " }\n", "\n", " function load_libs(js_urls, callback) {\n", " root._bokeh_onload_callbacks.push(callback);\n", " if (root._bokeh_is_loading > 0) {\n", " console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", " return null;\n", " }\n", " if (js_urls == null || js_urls.length === 0) {\n", " run_callbacks();\n", " return null;\n", " }\n", " console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", " root._bokeh_is_loading = js_urls.length;\n", " for (var i = 0; i < js_urls.length; i++) {\n", " var url = js_urls[i];\n", " var s = document.createElement('script');\n", " s.src = url;\n", " s.async = false;\n", " s.onreadystatechange = s.onload = function() {\n", " root._bokeh_is_loading--;\n", " if (root._bokeh_is_loading === 0) {\n", " console.log(\"Bokeh: all BokehJS libraries loaded\");\n", " run_callbacks()\n", " }\n", " };\n", " s.onerror = function() {\n", " console.warn(\"failed to load library \" + url);\n", " };\n", " console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", " }\n", " };var element = document.getElementById(\"a8e1aee7-1fff-43ed-b9fe-4ab601b77cd7\");\n", " if (element == null) {\n", " console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'a8e1aee7-1fff-43ed-b9fe-4ab601b77cd7' but no matching script tag was found. \")\n", " return false;\n", " }\n", "\n", " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.16.min.js\"];\n", "\n", " var inline_js = [\n", " function(Bokeh) {\n", " Bokeh.set_log_level(\"info\");\n", " },\n", " \n", " function(Bokeh) {\n", " \n", " },\n", " function(Bokeh) {\n", " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.css\");\n", " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.css\");\n", " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.css\");\n", " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.css\");\n", " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.css\");\n", " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.css\");\n", " }\n", " ];\n", "\n", " function run_inline_js() {\n", " \n", " if ((root.Bokeh !== undefined) || (force === true)) {\n", " for (var i = 0; i < inline_js.length; i++) {\n", " inline_js[i].call(root, root.Bokeh);\n", " }if (force === true) {\n", " display_loaded();\n", " }} else if (Date.now() < root._bokeh_timeout) {\n", " setTimeout(run_inline_js, 100);\n", " } else if (!root._bokeh_failed_load) {\n", " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", " root._bokeh_failed_load = true;\n", " } else if (force !== true) {\n", " var cell = $(document.getElementById(\"a8e1aee7-1fff-43ed-b9fe-4ab601b77cd7\")).parents('.cell').data().cell;\n", " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", " }\n", "\n", " }\n", "\n", " if (root._bokeh_is_loading === 0) {\n", " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", " run_inline_js();\n", " } else {\n", " load_libs(js_urls, function() {\n", " console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n", " run_inline_js();\n", " });\n", " }\n", "}(window));" ], "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n var el = document.getElementById(\"a8e1aee7-1fff-43ed-b9fe-4ab601b77cd7\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n }\n finally {\n delete root._bokeh_onload_callbacks\n }\n console.info(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(js_urls, callback) {\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = js_urls.length;\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var s = document.createElement('script');\n s.src = url;\n s.async = false;\n s.onreadystatechange = s.onload = function() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: all BokehJS libraries loaded\");\n run_callbacks()\n }\n };\n s.onerror = function() {\n console.warn(\"failed to load library \" + url);\n };\n console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.getElementsByTagName(\"head\")[0].appendChild(s);\n }\n };var element = document.getElementById(\"a8e1aee7-1fff-43ed-b9fe-4ab601b77cd7\");\n if (element == null) {\n console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'a8e1aee7-1fff-43ed-b9fe-4ab601b77cd7' but no matching script tag was found. \")\n return false;\n }\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.16.min.js\"];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.css\");\n }\n ];\n\n function run_inline_js() {\n \n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }if (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(\"a8e1aee7-1fff-43ed-b9fe-4ab601b77cd7\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(js_urls, function() {\n console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Directive pour bokeh pour inclure le graphe réseau final dans le notebook\n", "output_notebook()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# La définition de la locale nous permettra de gérer correctement les majuscules\n", "# accentuées\n", "# locale.setlocale(locale.LC_COLLATE, \"fr_FR.UTF-8\") # ou \"fr_FR.UTF-8\", \"fr_FR\" selon le système\n", "\n", "ns = {\"x\": \"http://www.w3.org/1999/xhtml\"}\n", "root = ET.parse(\"moliere_avare.html\").getroot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Outils préliminaires\n", "\n", "Nous travaillons sur une pièce de théâtre, par définition divisée en actes et en scènes.\n", "Nous allons donc nous créer quelques outils pour accéder facilement à ces éléments structurés, que nous complèterons de diverses fonctions utilisées à plusieurs reprises dans notre étude." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Nettoyage des titres\n", "def clean_title(el, tag):\n", " return \"\".join(el.find(tag, ns).itertext()).replace(\"§\", \"\").strip()\n", "\n", "# Actes avec ordre explicite\n", "def list_acts():\n", " acts = []\n", "\n", " for idx, act in enumerate(root.findall(\".//x:section[@class='div1 act level2']\", ns)):\n", " acts.append({\n", " \"id\": act.get(\"id\"),\n", " \"title\": clean_title(act, \"x:h2\"),\n", " \"node\": act,\n", " \"order\": idx,\n", " })\n", "\n", " return acts\n", "\n", "# Scènes d’un acte donné, avec ordre explicite\n", "def list_scenes(act=None, act_id=None):\n", " if act is None:\n", " if act_id is None:\n", " raise ValueError(\"Un élément `act` ou un identifiant doit être spécifié\")\n", "\n", " act = root.find(f\".//x:section[@class='div1 act level2'][@id='{act_id}']\", ns)\n", "\n", " if act is None:\n", " raise ValueError(f\"Acte introuvable: {act_id}\")\n", "\n", " scenes = []\n", "\n", " for idx, scene in enumerate(act.findall(\"x:section[@class='div2 scene level3']\", ns)):\n", " scenes.append({\n", " \"id\": scene.get(\"id\"),\n", " \"title\": clean_title(scene, \"x:h3\"),\n", " \"node\": scene,\n", " \"order\": idx,\n", " })\n", "\n", " return scenes\n", "\n", "# On compacte les espaces pour calquer le comptage sur l'OBVIL\n", "# Supprime les balises de l'élément HTML soumis.\n", "# Cela permet de ne conserver que les noms de personnages extraits des blocs de dialogue,\n", "# sans les didascalies.\n", "def text_without_i(el):\n", " parts = []\n", "\n", " if el.tag != f\"{{{ns['x']}}}i\" and el.text and el.text.strip():\n", " parts.append(el.text.strip())\n", "\n", " for child in el:\n", " if child.tag != f\"{{{ns['x']}}}i\":\n", " parts.extend(text_without_i(child))\n", "\n", " # on garde toujours le texte suivant, même si le noeud enfant est une balise \n", " if child.tail and child.tail.strip():\n", " parts.append(child.tail.strip())\n", "\n", " return parts\n", "\n", "# Formate le nom d'un acteur extrait d'un dialogue\n", "def speaker_name(sp):\n", " name = \" \".join(text_without_i(sp)).strip()\n", "\n", " # nettoyage simple de la ponctuation finale\n", " name = name.rstrip(\",;:\").strip()\n", "\n", " return name\n", "\n", "# Résolution d'un nom d'acteur à partir de notre table de correspondance\n", "alias_index = {}\n", "def resolve_name(name):\n", " return alias_index.get(name, name)\n", "\n", "# Extrait le texte brut d'un dialogue soumis sous la forme d'un élément HTML\n", "def speech_text(sp):\n", " parts = []\n", "\n", " for p in sp.findall(\".//x:p[@class='p autofirst']\", ns):\n", " parts.extend(text_without_i(p))\n", "\n", " raw = \" \".join(parts)\n", " return \" \".join(raw.split()).strip()\n", "\n", "# Compte le nombre de mots d'un texte brut.\n", "# On utilise ici une regex simple dédiée à cet usage.\n", "def word_count(txt):\n", " return len(re.findall(r\"\\b\\w+\\b\", txt, flags=re.UNICODE))\n", "\n", "# Conversion d'un texte en nombre de lignes (60 caractères par ligne)\n", "def line_count(txt, line_length=60):\n", " return len(txt) / line_length if txt else 0\n", "\n", "# Retourne l'acteur associé à une réplique (
)\n", "def speech_actor(sp):\n", " speaker_el = sp.find(\"x:p[@class='speaker']\", ns)\n", "\n", " if speaker_el is None:\n", " return \"\"\n", "\n", " return resolve_name(speaker_name(speaker_el))\n", "\n", "# Liste les répliques d'une scène (par noeud ou identifiant) avec texte et nombre de mots\n", "def scene_speeches(scene=None, scene_id=None):\n", " if scene is None:\n", " if scene_id is None:\n", " raise ValueError(\"Un élément `scene` ou un identifiant doit être spécifié\")\n", "\n", " scene = root.find(f\".//x:section[@class='div2 scene level3'][@id='{scene_id}']\", ns)\n", "\n", " if scene is None:\n", " raise ValueError(f\"Scène introuvable: {scene_id}\")\n", "\n", " speeches = []\n", "\n", " for sp_div in scene.findall(\".//x:div[@class='sp']\", ns):\n", " speaker = speech_actor(sp_div)\n", "\n", " if not speaker:\n", " continue\n", "\n", " txt = speech_text(sp_div)\n", "\n", " speeches.append({\n", " \"speaker\": speaker,\n", " \"text\": txt,\n", " \"word_count\": word_count(txt),\n", " \"node\": sp_div,\n", " })\n", "\n", " return speeches\n", "\n", "# Création d'une colormap associant une couleur à un personnage\n", "def create_actors_colormap(personnages):\n", " # Définition d'une palette de couleurs pour les personnages\n", " palette = cycle(plt.cm.tab20.colors)\n", " color_map = {}\n", "\n", " for p in personnages:\n", " color_map[p] = next(palette)\n", "\n", " return color_map" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Vérifions que nous obtenons bien la liste des actes et des scènes :" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Acte I - Scène I01\n", "Acte I - Scène I02\n", "Acte I - Scène I03\n", "Acte I - Scène I04\n", "Acte I - Scène I05\n", "Acte II - Scène II01\n", "Acte II - Scène II02\n", "Acte II - Scène II03\n", "Acte II - Scène II04\n", "Acte II - Scène II05\n", "Acte III - Scène III01\n", "Acte III - Scène III02\n", "Acte III - Scène III03\n", "Acte III - Scène III04\n", "Acte III - Scène III05\n", "Acte III - Scène III06\n", "Acte III - Scène III07\n", "Acte III - Scène III08\n", "Acte III - Scène III09\n", "Acte IV - Scène IV01\n", "Acte IV - Scène IV02\n", "Acte IV - Scène IV03\n", "Acte IV - Scène IV04\n", "Acte IV - Scène IV05\n", "Acte IV - Scène IV06\n", "Acte IV - Scène IV07\n", "Acte V - Scène V01\n", "Acte V - Scène V02\n", "Acte V - Scène V03\n", "Acte V - Scène V04\n", "Acte V - Scène V05\n", "Acte V - Scène V06\n" ] } ], "source": [ "for act in list_acts():\n", " for scene in list_scenes(act=act[\"node\"]):\n", " print (\"Acte \" + act[\"id\"] + \" - Scène \" + scene[\"id\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Obtention de la liste des acteurs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La requête xpath suivante permet d'extraire la liste des acteurs donnée au début du fichier, autrement appelée [_dramatis personae_](https://fr.wikipedia.org/wiki/Dramatis_personæ_(théâtre))." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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DescriptionPersonnage
0Père de Cléante et d'Élise, et Amoureux de Mar...Harpagon
1Fils d'Harpagon, Amant de Mariane.Cléante
2Fille d'Harpagon, Amante de Valère.Élise
3Fils d'Anselme, et Amant d'Élise.Valère
4Amante de Cléante, et aimée d'Harpagon.Mariane
5Père de Valère et de Mariane.Anselme
6Femme d'Intrigue.Frosine
7Courtier.Maitre Simon
8Cuisinier et Cocher d'Harpagon.Maitre Jacques
9Valet de Cléante.La Flèche
10Servante d'Harpagon.Dame Claude
11laquais d'Harpagon.Brindavoine
12laquais d'Harpagon.La Merluche
13et son clerc.Le commissaire
\n", "
" ], "text/plain": [ " Description Personnage\n", "0 Père de Cléante et d'Élise, et Amoureux de Mar... Harpagon\n", "1 Fils d'Harpagon, Amant de Mariane. Cléante\n", "2 Fille d'Harpagon, Amante de Valère. Élise\n", "3 Fils d'Anselme, et Amant d'Élise. Valère\n", "4 Amante de Cléante, et aimée d'Harpagon. Mariane\n", "5 Père de Valère et de Mariane. Anselme\n", "6 Femme d'Intrigue. Frosine\n", "7 Courtier. Maitre Simon\n", "8 Cuisinier et Cocher d'Harpagon. Maitre Jacques\n", "9 Valet de Cléante. La Flèche\n", "10 Servante d'Harpagon. Dame Claude\n", "11 laquais d'Harpagon. Brindavoine\n", "12 laquais d'Harpagon. La Merluche\n", "13 et son clerc. Le commissaire" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Retourne une liste formatée des personnages définis dans la dramatis personae.\n", "# Prend en compte les majuscules accentuées.\n", "def dramatis_personae():\n", " rows = []\n", "\n", " # Requête xpath permettant d'obtenir la liste des balises
  • listant les acteurs\n", " for li in root.findall(\".//x:div[@id='castList']//x:li\", ns):\n", " # L'acteur se trouve dans une balise \n", " span = li.find(\"x:span\", ns)\n", " name = span.text.strip()\n", "\n", " # description = texte qui suit le dans la même balise
  • \n", " desc = (span.tail or \"\").strip()\n", "\n", " if desc.startswith(\",\"):\n", " desc = desc[1:].strip()\n", "\n", " rows.append({\"Personnage\": name, \"Description\": desc})\n", "\n", " return pd.DataFrame(rows)\n", "\n", "dramatis_personae = dramatis_personae()\n", "\n", "# Affichage de la liste\n", "dramatis_personae" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous pouvons déjà constater que le commissaire et son clerc sont considérés comme un acteur unique.\n", "Nous verrons plus tard si cette information est importante (par exemple, si le clerc s'exprime en son nom propre)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous allons confronter cette liste avec la liste des protagonistes mentionnés en introduction de chaque scène, puis avec ceux qui interviennent \"réellement\", c'est-à-dire ceux qui ont une ligne de dialogue.\n", "Cette étape devra nous permettre d'identifier des différences d'orthographe subtiles qu'il sera utile de gérer." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Noms des personnages par scène" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous allons itérer sur chaque acte, puis chaque scène, afin de consulter la liste des protagonistes. \n", "N'oublions pas que ces listes sont facultatives, et ne désignent pas les acteurs dotés d'une réplique.\n", "Néanmoins, nous pourrions identifier des éléments potentiellement intéressants, tels que des orthographes différentes ou une anomalie quelconque." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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    ActeProtagonistesScène
    0Acte Premier[Valère, Élise]Scène Première
    1Acte Premier[Cléante, Élise]Scène II
    2Acte Premier[Harpagon, La Flèche]Scène III
    3Acte Premier[Élise, Cléante, Harpagon]Scène IV
    4Acte Premier[Valère, Harpagon, Élise]Scène V
    5Acte II[Cléante, La Flèche]Scène Première
    6Acte II[Maître Simon, Harpagon, Cléante, La Flèche]Scène II
    7Acte II[Frosine, Harpagon]Scène III
    8Acte II[La Flèche, Frosine]Scène IV
    9Acte II[Harpagon, Frosine]Scène V
    10Acte III[Harpagon, Cléante, Élise, Valère, Dame Claude...Scène Première
    11Acte III[Maître Jacques, Valère]Scène II
    12Acte III[Frosine, Mariane, Maître Jacques]Scène III
    13Acte III[Mariane, Frosine]Scène IV
    14Acte III[Harpagon, Frosine, Mariane]Scène V
    15Acte III[Élise, Harpagon, Mariane, Frosine]Scène VI
    16Acte III[Cléante, Harpagon, Élise, Mariane, Frosine]Scène VII
    17Acte III[Harpagon, Mariane, Frosine, Cléante, Brindavo...Scène VIII
    18Acte III[Harpagon, Mariane, Cléante, Élise, Frosine, L...Scène IX
    19Acte IV[Cléante, Mariane, Élise, Frosine]Scène Première
    20Acte IV[Harpagon, Cléante, Mariane, Élise, Frosine]Scène II
    21Acte IV[Harpagon, Cléante]Scène III
    22Acte IV[Maître Jacques, Harpagon, Cléante]Scène IV
    23Acte IV[Cléante, Harpagon]Scène V
    24Acte IV[La Flèche, Cléante]Scène VI
    25Acte IV[]Scène VII
    26Acte V[Harpagon, Le Commissaire, son Clerc]Scène Première
    27Acte V[Maître Jacques, Harpagon, Le Commissaire, son...Scène II
    28Acte V[Valère, Harpagon, le Commissaire, son Clerc, ...Scène III
    29Acte V[Élise, Mariane, Frosine, Harpagon, Valère, Ma...Scène IV
    30Acte V[Anselme, Harpagon, Élise, Mariane, Frosine, V...Scène V
    31Acte V[Cléante, Valère, Mariane, Élise, Frosine, Har...Scène VI
    \n", "
    " ], "text/plain": [ " Acte Protagonistes \\\n", "0 Acte Premier [Valère, Élise] \n", "1 Acte Premier [Cléante, Élise] \n", "2 Acte Premier [Harpagon, La Flèche] \n", "3 Acte Premier [Élise, Cléante, Harpagon] \n", "4 Acte Premier [Valère, Harpagon, Élise] \n", "5 Acte II [Cléante, La Flèche] \n", "6 Acte II [Maître Simon, Harpagon, Cléante, La Flèche] \n", "7 Acte II [Frosine, Harpagon] \n", "8 Acte II [La Flèche, Frosine] \n", "9 Acte II [Harpagon, Frosine] \n", "10 Acte III [Harpagon, Cléante, Élise, Valère, Dame Claude... \n", "11 Acte III [Maître Jacques, Valère] \n", "12 Acte III [Frosine, Mariane, Maître Jacques] \n", "13 Acte III [Mariane, Frosine] \n", "14 Acte III [Harpagon, Frosine, Mariane] \n", "15 Acte III [Élise, Harpagon, Mariane, Frosine] \n", "16 Acte III [Cléante, Harpagon, Élise, Mariane, Frosine] \n", "17 Acte III [Harpagon, Mariane, Frosine, Cléante, Brindavo... \n", "18 Acte III [Harpagon, Mariane, Cléante, Élise, Frosine, L... \n", "19 Acte IV [Cléante, Mariane, Élise, Frosine] \n", "20 Acte IV [Harpagon, Cléante, Mariane, Élise, Frosine] \n", "21 Acte IV [Harpagon, Cléante] \n", "22 Acte IV [Maître Jacques, Harpagon, Cléante] \n", "23 Acte IV [Cléante, Harpagon] \n", "24 Acte IV [La Flèche, Cléante] \n", "25 Acte IV [] \n", "26 Acte V [Harpagon, Le Commissaire, son Clerc] \n", "27 Acte V [Maître Jacques, Harpagon, Le Commissaire, son... \n", "28 Acte V [Valère, Harpagon, le Commissaire, son Clerc, ... \n", "29 Acte V [Élise, Mariane, Frosine, Harpagon, Valère, Ma... \n", "30 Acte V [Anselme, Harpagon, Élise, Mariane, Frosine, V... \n", "31 Acte V [Cléante, Valère, Mariane, Élise, Frosine, Har... \n", "\n", " Scène \n", "0 Scène Première \n", "1 Scène II \n", "2 Scène III \n", "3 Scène IV \n", "4 Scène V \n", "5 Scène Première \n", "6 Scène II \n", "7 Scène III \n", "8 Scène IV \n", "9 Scène V \n", "10 Scène Première \n", "11 Scène II \n", "12 Scène III \n", "13 Scène IV \n", "14 Scène V \n", "15 Scène VI \n", "16 Scène VII \n", "17 Scène VIII \n", "18 Scène IX \n", "19 Scène Première \n", "20 Scène II \n", "21 Scène III \n", "22 Scène IV \n", "23 Scène V \n", "24 Scène VI \n", "25 Scène VII \n", "26 Scène Première \n", "27 Scène II \n", "28 Scène III \n", "29 Scène IV \n", "30 Scène V \n", "31 Scène VI " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def list_scene_protagonists():\n", " rows = []\n", "\n", " for act in list_acts():\n", " for scene in list_scenes(act=act[\"node\"]):\n", " stage = scene[\"node\"].find(\"x:div[@class='stage stage']\", ns)\n", "\n", " # Si nous trouvons un noeud xpath pour cette requête, c'est un personnage\n", " if stage is not None:\n", " raw = \"\".join(stage.itertext()).strip()\n", " people = [p.strip() for p in raw.split(\",\") if p.strip()]\n", " else:\n", " people = []\n", "\n", " rows.append({\n", " \"Acte\": act[\"title\"],\n", " \"Scène\": scene[\"title\"],\n", " \"Protagonistes\": people,\n", " })\n", "\n", " return pd.DataFrame(rows)\n", "\n", "df_scenes = list_scene_protagonists()\n", "df_scenes" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "Nous voyons ici que la scène VII de l'acte IV ne contient aucun protagoniste déclaré dans la liste attenante[^1], mais nous allons de toute façon la compléter par l'extraction individuelle des interventions concrètes de chaque acteur.\n", "\n", "[^1]: Cette liste n'est pas obligatoire dans le contexte théâtral. Ici, on peut supposer que son absence est dûe à un monologue par exemple." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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    ActeIntervenantsScène
    0Acte Premier[Valère, Élise]Scène Première
    1Acte Premier[Cléante, Élise]Scène II
    2Acte Premier[Harpagon, La Flèche]Scène III
    3Acte Premier[Harpagon, Cléante, Élise]Scène IV
    4Acte Premier[Harpagon, Valère, Élise]Scène V
    5Acte II[Cléante, La Flèche]Scène Première
    6Acte II[Maître simon, Harpagon, La Flèche, Cléante]Scène II
    7Acte II[Frosine, Harpagon]Scène III
    8Acte II[La Flèche, Frosine]Scène IV
    9Acte II[Harpagon, Frosine]Scène V
    10Acte III[Harpagon, Maître Jacques, La Merluche, Brinda...Scène Première
    11Acte III[Valère, Maître Jacques]Scène II
    12Acte III[Frosine, Maître Jacques]Scène III
    13Acte III[Mariane, Frosine]Scène IV
    14Acte III[Harpagon, Frosine]Scène V
    15Acte III[Mariane, Élise, Harpagon, Frosine]Scène VI
    16Acte III[Cléante, Mariane, Harpagon, Frosine, Valère]Scène VII
    17Acte III[Brindavoine, Harpagon]Scène VIII
    18Acte III[La Merluche, Harpagon, Cléante, Valère]Scène IX
    19Acte IV[Cléante, Élise, Mariane, Frosine]Scène Première
    20Acte IV[Harpagon, Élise, Cléante]Scène II
    21Acte IV[Harpagon, Cléante]Scène III
    22Acte IV[Maître Jacques, Cléante, Harpagon]Scène IV
    23Acte IV[Cléante, Harpagon]Scène V
    24Acte IV[La Flèche, Cléante]Scène VI
    25Acte IV[Harpagon]Scène VII
    26Acte V[Le Commissaire, Harpagon]Scène Première
    27Acte V[Maître Jacques, Harpagon, Le Commissaire]Scène II
    28Acte V[Harpagon, Valère, Maître Jacques]Scène III
    29Acte V[Harpagon, Valère, Élise, Maître Jacques, Fros...Scène IV
    30Acte V[Anselme, Harpagon, Valère, Mariane, Maître Ja...Scène V
    31Acte V[Cléante, Harpagon, Mariane, Anselme, Le Commi...Scène VI
    \n", "
    " ], "text/plain": [ " Acte Intervenants \\\n", "0 Acte Premier [Valère, Élise] \n", "1 Acte Premier [Cléante, Élise] \n", "2 Acte Premier [Harpagon, La Flèche] \n", "3 Acte Premier [Harpagon, Cléante, Élise] \n", "4 Acte Premier [Harpagon, Valère, Élise] \n", "5 Acte II [Cléante, La Flèche] \n", "6 Acte II [Maître simon, Harpagon, La Flèche, Cléante] \n", "7 Acte II [Frosine, Harpagon] \n", "8 Acte II [La Flèche, Frosine] \n", "9 Acte II [Harpagon, Frosine] \n", "10 Acte III [Harpagon, Maître Jacques, La Merluche, Brinda... \n", "11 Acte III [Valère, Maître Jacques] \n", "12 Acte III [Frosine, Maître Jacques] \n", "13 Acte III [Mariane, Frosine] \n", "14 Acte III [Harpagon, Frosine] \n", "15 Acte III [Mariane, Élise, Harpagon, Frosine] \n", "16 Acte III [Cléante, Mariane, Harpagon, Frosine, Valère] \n", "17 Acte III [Brindavoine, Harpagon] \n", "18 Acte III [La Merluche, Harpagon, Cléante, Valère] \n", "19 Acte IV [Cléante, Élise, Mariane, Frosine] \n", "20 Acte IV [Harpagon, Élise, Cléante] \n", "21 Acte IV [Harpagon, Cléante] \n", "22 Acte IV [Maître Jacques, Cléante, Harpagon] \n", "23 Acte IV [Cléante, Harpagon] \n", "24 Acte IV [La Flèche, Cléante] \n", "25 Acte IV [Harpagon] \n", "26 Acte V [Le Commissaire, Harpagon] \n", "27 Acte V [Maître Jacques, Harpagon, Le Commissaire] \n", "28 Acte V [Harpagon, Valère, Maître Jacques] \n", "29 Acte V [Harpagon, Valère, Élise, Maître Jacques, Fros... \n", "30 Acte V [Anselme, Harpagon, Valère, Mariane, Maître Ja... \n", "31 Acte V [Cléante, Harpagon, Mariane, Anselme, Le Commi... \n", "\n", " Scène \n", "0 Scène Première \n", "1 Scène II \n", "2 Scène III \n", "3 Scène IV \n", "4 Scène V \n", "5 Scène Première \n", "6 Scène II \n", "7 Scène III \n", "8 Scène IV \n", "9 Scène V \n", "10 Scène Première \n", "11 Scène II \n", "12 Scène III \n", "13 Scène IV \n", "14 Scène V \n", "15 Scène VI \n", "16 Scène VII \n", "17 Scène VIII \n", "18 Scène IX \n", "19 Scène Première \n", "20 Scène II \n", "21 Scène III \n", "22 Scène IV \n", "23 Scène V \n", "24 Scène VI \n", "25 Scène VII \n", "26 Scène Première \n", "27 Scène II \n", "28 Scène III \n", "29 Scène IV \n", "30 Scène V \n", "31 Scène VI " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def list_scene_speakers():\n", " rows = []\n", "\n", " for act in list_acts():\n", " for scene in list_scenes(act=act[\"node\"]):\n", " speakers, seen = [], set()\n", "\n", " for sp in scene[\"node\"].findall(\".//x:p[@class='speaker']\", ns):\n", " name = speaker_name(sp)\n", "\n", " # On évite d'ajouter à la liste un acteur que l'on a déjà vu passer\n", " if name and name not in seen:\n", " seen.add(name)\n", " speakers.append(name)\n", "\n", " rows.append({\n", " \"Acte\": act[\"title\"],\n", " \"Scène\": scene[\"title\"],\n", " \"Intervenants\": speakers,\n", " })\n", "\n", " return pd.DataFrame(rows)\n", "\n", "df_speakers = list_scene_speakers()\n", "df_speakers" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
    \n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
    Intervenant
    0Anselme
    1Brindavoine
    2Cléante
    3Frosine
    4Harpagon
    5La Flèche
    6La Merluche
    7Le Commissaire
    8Mariane
    9Maître Jacques
    10Maître simon
    11Valère
    12Élise
    \n", "
    " ], "text/plain": [ " Intervenant\n", "0 Anselme\n", "1 Brindavoine\n", "2 Cléante\n", "3 Frosine\n", "4 Harpagon\n", "5 La Flèche\n", "6 La Merluche\n", "7 Le Commissaire\n", "8 Mariane\n", "9 Maître Jacques\n", "10 Maître simon\n", "11 Valère\n", "12 Élise" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Tri localisé pour les intervenants et déduplication\n", "intervenants_uniques = sorted(\n", " {name for names in df_speakers[\"Intervenants\"] for name in names},\n", " key=locale.strxfrm\n", ")\n", "\n", "intervenants_df = pd.DataFrame({\"Intervenant\": intervenants_uniques})\n", "intervenants_df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On peut désormais identifier les différences avec la _dramatis personae_, afin de vérifier l'uniformité des orthographes.\n", "Par corollaire, on pourra, en même temps, identifier les acteurs sans réplique." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
    \n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
    Personnage (en-tête)Intervenant (répliques)
    0Dame ClaudeLe Commissaire
    1Le commissaireMaître Jacques
    2Maitre JacquesMaître simon
    3Maitre SimonNone
    \n", "
    " ], "text/plain": [ " Personnage (en-tête) Intervenant (répliques)\n", "0 Dame Claude Le Commissaire\n", "1 Le commissaire Maître Jacques\n", "2 Maitre Jacques Maître simon\n", "3 Maitre Simon None" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "acteurs_set = set(dramatis_personae[\"Personnage\"])\n", "intervenants_set = set(intervenants_uniques)\n", "\n", "# On écarte les noms exactement identiques\n", "communs = acteurs_set & intervenants_set\n", "acteurs_only = sorted(acteurs_set - communs, key=locale.strxfrm)\n", "intervenants_only = sorted(intervenants_set - communs, key=locale.strxfrm)\n", "\n", "df_diff = pd.DataFrame(\n", " list(zip_longest(acteurs_only, intervenants_only)),\n", " columns=[\"Personnage (en-tête)\", \"Intervenant (répliques)\"]\n", ")\n", "\n", "df_diff" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`None` indique simplement un remplissage par `zip_longest` pour que les deux listes aient la même taille." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On identifie bien deux orthographes différentes pour trois acteurs. La liste des personnages initiale omet les accents circonflexes de \"Maître\", \"commissaire\" est écrit en minuscule, et \"Simon\" a perdu sa majuscule." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Enfin, il est clair que Dame Claude n'a aucune réplique (puisqu'on ne la retrouve pas dans la liste des intervenants)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On peut donc créer une table de correspondance, associant un nom correctement orthographié avec les variantes que l'on peut trouver dans le texte initial. Nous utiliserons comme référence la graphie française correcte de \"maître\", et \"Commissaire\" avec une majuscule." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "alias_map = {\n", " \"Maître Jacques\": {\"Maître Jacques\", \"Maitre Jacques\"},\n", " \"Maître Simon\": {\"Maitre Simon\", \"Maître simon\"},\n", " \"Le Commissaire\": {\"Le Commissaire\", \"Le commissaire\"},\n", "}\n", "\n", "alias_index = {alias: canon for canon, aliases in alias_map.items() for alias in aliases}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Quantité de parole par acteur\n", "\n", "Maintenant que nous disposons d'une liste uniformisée des noms des différents acteurs, nous pouvons analyser l'ensemble de la pièce et quantifier le texte prononcé par chaque acteur." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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    ActeScènePersonnageMots
    0Acte IIScène IICléante127
    1Acte IIScène IIHarpagon171
    2Acte IIScène IILa Flèche12
    3Acte IIScène IIMaître Simon197
    4Acte IIScène IIIFrosine1
    5Acte IIScène IIIHarpagon21
    6Acte IIScène IVFrosine130
    7Acte IIScène IVLa Flèche292
    8Acte IIScène PremièreCléante379
    9Acte IIScène PremièreLa Flèche903
    10Acte IIScène VFrosine1482
    11Acte IIScène VHarpagon555
    12Acte IIIScène IIMaître Jacques186
    13Acte IIIScène IIValère92
    14Acte IIIScène IIIFrosine19
    15Acte IIIScène IIIMaître Jacques11
    16Acte IIIScène IVFrosine191
    17Acte IIIScène IVMariane185
    18Acte IIIScène IXCléante40
    19Acte IIIScène IXHarpagon73
    20Acte IIIScène IXLa Merluche21
    21Acte IIIScène IXValère7
    22Acte IIIScène PremièreBrindavoine23
    23Acte IIIScène PremièreCléante76
    24Acte IIIScène PremièreHarpagon747
    25Acte IIIScène PremièreLa Merluche26
    26Acte IIIScène PremièreMaître Jacques779
    27Acte IIIScène PremièreValère249
    28Acte IIIScène PremièreÉlise3
    29Acte IIIScène VFrosine26
    ...............
    65Acte PremierScène IVÉlise162
    66Acte PremierScène PremièreValère630
    67Acte PremierScène PremièreÉlise491
    68Acte PremierScène VHarpagon271
    69Acte PremierScène VValère701
    70Acte PremierScène VÉlise36
    71Acte VScène IIHarpagon182
    72Acte VScène IILe Commissaire159
    73Acte VScène IIMaître Jacques348
    74Acte VScène IIIHarpagon441
    75Acte VScène IIIMaître Jacques11
    76Acte VScène IIIValère641
    77Acte VScène IVFrosine4
    78Acte VScène IVHarpagon124
    79Acte VScène IVMaître Jacques7
    80Acte VScène IVValère22
    81Acte VScène IVÉlise143
    82Acte VScène PremièreHarpagon89
    83Acte VScène PremièreLe Commissaire109
    84Acte VScène VAnselme403
    85Acte VScène VHarpagon258
    86Acte VScène VMariane192
    87Acte VScène VMaître Jacques7
    88Acte VScène VValère376
    89Acte VScène VIAnselme114
    90Acte VScène VICléante130
    91Acte VScène VIHarpagon89
    92Acte VScène VILe Commissaire26
    93Acte VScène VIMariane36
    94Acte VScène VIMaître Jacques23
    \n", "

    95 rows × 4 columns

    \n", "
    " ], "text/plain": [ " Acte Scène Personnage Mots\n", "0 Acte II Scène II Cléante 127\n", "1 Acte II Scène II Harpagon 171\n", "2 Acte II Scène II La Flèche 12\n", "3 Acte II Scène II Maître Simon 197\n", "4 Acte II Scène III Frosine 1\n", "5 Acte II Scène III Harpagon 21\n", "6 Acte II Scène IV Frosine 130\n", "7 Acte II Scène IV La Flèche 292\n", "8 Acte II Scène Première Cléante 379\n", "9 Acte II Scène Première La Flèche 903\n", "10 Acte II Scène V Frosine 1482\n", "11 Acte II Scène V Harpagon 555\n", "12 Acte III Scène II Maître Jacques 186\n", "13 Acte III Scène II Valère 92\n", "14 Acte III Scène III Frosine 19\n", "15 Acte III Scène III Maître Jacques 11\n", "16 Acte III Scène IV Frosine 191\n", "17 Acte III Scène IV Mariane 185\n", "18 Acte III Scène IX Cléante 40\n", "19 Acte III Scène IX Harpagon 73\n", "20 Acte III Scène IX La Merluche 21\n", "21 Acte III Scène IX Valère 7\n", "22 Acte III Scène Première Brindavoine 23\n", "23 Acte III Scène Première Cléante 76\n", "24 Acte III Scène Première Harpagon 747\n", "25 Acte III Scène Première La Merluche 26\n", "26 Acte III Scène Première Maître Jacques 779\n", "27 Acte III Scène Première Valère 249\n", "28 Acte III Scène Première Élise 3\n", "29 Acte III Scène V Frosine 26\n", ".. ... ... ... ...\n", "65 Acte Premier Scène IV Élise 162\n", "66 Acte Premier Scène Première Valère 630\n", "67 Acte Premier Scène Première Élise 491\n", "68 Acte Premier Scène V Harpagon 271\n", "69 Acte Premier Scène V Valère 701\n", "70 Acte Premier Scène V Élise 36\n", "71 Acte V Scène II Harpagon 182\n", "72 Acte V Scène II Le Commissaire 159\n", "73 Acte V Scène II Maître Jacques 348\n", "74 Acte V Scène III Harpagon 441\n", "75 Acte V Scène III Maître Jacques 11\n", "76 Acte V Scène III Valère 641\n", "77 Acte V Scène IV Frosine 4\n", "78 Acte V Scène IV Harpagon 124\n", "79 Acte V Scène IV Maître Jacques 7\n", "80 Acte V Scène IV Valère 22\n", "81 Acte V Scène IV Élise 143\n", "82 Acte V Scène Première Harpagon 89\n", "83 Acte V Scène Première Le Commissaire 109\n", "84 Acte V Scène V Anselme 403\n", "85 Acte V Scène V Harpagon 258\n", "86 Acte V Scène V Mariane 192\n", "87 Acte V Scène V Maître Jacques 7\n", "88 Acte V Scène V Valère 376\n", "89 Acte V Scène VI Anselme 114\n", "90 Acte V Scène VI Cléante 130\n", "91 Acte V Scène VI Harpagon 89\n", "92 Acte V Scène VI Le Commissaire 26\n", "93 Acte V Scène VI Mariane 36\n", "94 Acte V Scène VI Maître Jacques 23\n", "\n", "[95 rows x 4 columns]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# TODO : Est-ce que cette fonction est redondante avec\n", "# les fonctions utilitaires créées précédemment ?\n", "def count_words_by_actor():\n", " rows = []\n", "\n", " for act in list_acts():\n", " for scene in list_scenes(act=act[\"node\"]):\n", " for order, speech in enumerate(scene_speeches(scene=scene[\"node\"])):\n", " txt = speech[\"text\"]\n", " rows.append({\n", " \"Acte\": act[\"title\"],\n", " \"Scène\": scene[\"title\"],\n", " \"Ordre\": order,\n", " \"Personnage\": speech[\"speaker\"],\n", " \"Texte\": txt,\n", " \"Mots\": speech[\"word_count\"],\n", " \"Lignes\": line_count(txt),\n", " })\n", "\n", " return pd.DataFrame(rows)\n", "\n", "df_speeches = count_words_by_actor()\n", "df_counts = df_speeches.groupby([\"Acte\", \"Scène\", \"Personnage\"], as_index=False)[\"Mots\"].sum()\n", "df_counts" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "Le comptage semble s'effectuer correctement, mais un tel tableau n'est pas digeste. \n", "On peut noter par exemple que \"Acte Premier\" est dilué au centre du tableau, en raison de la clause `groupby`, qui trie implicitement le tableau, et ignore donc notre tri initial.\n", "Nous pouvons toutefois ignorer ce détail ici, et regrouper par personnage : de cette manière, nous aurons un aperçu global du temps de parole de chacun à travers l'oeuvre." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Personnage le plus taciturne : Brindavoine (43 mots)\n", "Personnage le plus locace : Harpagon (6132 mots)\n" ] } ], "source": [ "global_df = df_speeches.groupby([\"Personnage\"])[\"Mots\"].sum()\n", "\n", "moins_bavard_nom = global_df.idxmin()\n", "moins_bavard_mots = global_df.min()\n", "\n", "plus_bavard_nom = global_df.idxmax()\n", "plus_bavard_mots = global_df.max()\n", "\n", "print(f\"Personnage le plus taciturne : {moins_bavard_nom} ({moins_bavard_mots} mots)\")\n", "print(f\"Personnage le plus locace : {plus_bavard_nom} ({plus_bavard_mots} mots)\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notons que nous avons déjà établi que Dame Claude n'avait aucune réplique, et bien que le Commissaire soit accompagné d'un clerc, ce dernier ne parle jamais non plus." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Montrons la proportion de dialogues par personnage à travers un diagramme circulaire :" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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    " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "n_small = 5\n", "y0, dy = 1.3, 0.05 # Positionnement du bloc des acteurs les moins locaces\n", "x_col = -0.5\n", "\n", "totaux = df_speeches.groupby(\"Personnage\")[\"Mots\"].sum().sort_values(ascending=False)\n", "labels = totaux.index\n", "values = totaux.values\n", "color_map = create_actors_colormap(labels)\n", "colors = [color_map[p] for p in labels]\n", "\n", "fig, ax = plt.subplots(figsize=(7, 7))\n", "wedges, _ = ax.pie(values, colors=colors, startangle=90, counterclock=False)\n", "\n", "small_roles = global_df.sort_values().head(n_small) # les moins bavards, ordre croissant\n", "\n", "# Placement de l'étiquette pour les petits rôles\n", "for i, (name, val) in enumerate(small_roles.items()):\n", " idx = labels.get_loc(name)\n", " w = wedges[idx]\n", "\n", " theta = np.deg2rad((w.theta1 + w.theta2) / 2)\n", " xw, yw = np.cos(theta), np.sin(theta)\n", " xpos, ypos = x_col, y0 - i * dy\n", "\n", " ax.annotate(\n", " f\"{name} ({val})\",\n", " xy=(xw, yw), xytext=(xpos, ypos),\n", " ha=\"right\", va=\"center\", fontsize=9,\n", " arrowprops=dict(\n", " arrowstyle=\"-\",\n", " color=colors[idx],\n", " lw=1,\n", " connectionstyle=\"angle,angleA=0,angleB=90\",\n", " shrinkA=0, shrinkB=0,\n", " ),\n", " )\n", "\n", "# Autres rôles : nom autour + valeur au centre\n", "for idx, name in enumerate(labels):\n", " if name in small_roles.index:\n", " continue\n", "\n", " w = wedges[idx]\n", " theta = np.deg2rad((w.theta1 + w.theta2) / 2)\n", " r_label, r_value = 1.1, 0.7\n", "\n", " ax.text(r_label * np.cos(theta), r_label * np.sin(theta), name, ha=\"center\", va=\"center\", fontsize=9)\n", " ax.text(r_value * np.cos(theta), r_value * np.sin(theta), str(totaux[name]), ha=\"center\", va=\"center\", fontsize=9, color=\"black\")\n", "\n", "ax.set_title(\"Répartition des mots prononcés par personnage\", pad=50)\n", "ax.axis(\"equal\")\n", "plt.tight_layout()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Statistiques complémentaires\n", "\n", "Inspirées des tableaux de l'OBVIL, nous examinons la place de chaque personnage et les relations directes entre interlocuteurs (une ligne = 60 caractères).\n", "Commençons par la \"Table des rôles\" :" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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    RôleScènesRépl.Répl. moy.PrésenceTexteTexte % prés.Texte × pers.Interlocution
    0[TOUS]32 sc.959 répl.1,8 l.1 769 l. (100 %)1 769 l. (100 %)100 %5 847 l. (100 %)3,3 pers.
    1Harpagon23 sc.354 répl.1,5 l.1 296 l. (73 %)514 l. (29 %)40 %4 729 l. (81 %)3,7 pers.
    2Cléante14 sc.161 répl.1,8 l.900 l. (51 %)285 l. (16 %)32 %3 486 l. (60 %)3,9 pers.
    3Élise9 sc.51 répl.1,8 l.681 l. (39 %)92 l. (5 %)13 %2 667 l. (46 %)3,9 pers.
    4Valère9 sc.101 répl.2,3 l.695 l. (39 %)232 l. (13 %)33 %3 067 l. (52 %)4,4 pers.
    5Mariane6 sc.31 répl.2,5 l.359 l. (20 %)79 l. (4 %)22 %1 638 l. (28 %)4,6 pers.
    6Anselme2 sc.20 répl.2,3 l.143 l. (8 %)45 l. (3 %)32 %749 l. (13 %)5,3 pers.
    7Frosine10 sc.60 répl.3,3 l.466 l. (26 %)201 l. (11 %)43 %1 465 l. (25 %)3,1 pers.
    8Maître Simon1 sc.5 répl.3,2 l.44 l. (2 %)16 l. (1 %)37 %175 l. (3 %)4,0 pers.
    9Maître Jacques9 sc.85 répl.1,6 l.557 l. (32 %)140 l. (8 %)25 %2 670 l. (46 %)4,8 pers.
    10La Flèche5 sc.66 répl.2,0 l.255 l. (14 %)132 l. (7 %)52 %598 l. (10 %)2,3 pers.
    11Dame Claude0 sc.0 répl.0,0 l.0 l.0 l.0 %0 l. (0 %)0,0 pers.
    12Brindavoine2 sc.3 répl.1,1 l.166 l. (9 %)3 l. (0 %)2 %1 146 l. (20 %)6,9 pers.
    13La Merluche2 sc.5 répl.0,9 l.175 l. (10 %)5 l. (0 %)3 %1 189 l. (20 %)6,8 pers.
    14Le Commissaire3 sc.17 répl.1,5 l.110 l. (6 %)26 l. (1 %)24 %418 l. (7 %)3,8 pers.
    \n", "
    " ], "text/plain": [ " Rôle Scènes Répl. Répl. moy. Présence \\\n", "0 [TOUS] 32 sc. 959 répl. 1,8 l. 1 769 l. (100 %) \n", "1 Harpagon 23 sc. 354 répl. 1,5 l. 1 296 l. (73 %) \n", "2 Cléante 14 sc. 161 répl. 1,8 l. 900 l. (51 %) \n", "3 Élise 9 sc. 51 répl. 1,8 l. 681 l. (39 %) \n", "4 Valère 9 sc. 101 répl. 2,3 l. 695 l. (39 %) \n", "5 Mariane 6 sc. 31 répl. 2,5 l. 359 l. (20 %) \n", "6 Anselme 2 sc. 20 répl. 2,3 l. 143 l. (8 %) \n", "7 Frosine 10 sc. 60 répl. 3,3 l. 466 l. (26 %) \n", "8 Maître Simon 1 sc. 5 répl. 3,2 l. 44 l. (2 %) \n", "9 Maître Jacques 9 sc. 85 répl. 1,6 l. 557 l. (32 %) \n", "10 La Flèche 5 sc. 66 répl. 2,0 l. 255 l. (14 %) \n", "11 Dame Claude 0 sc. 0 répl. 0,0 l. 0 l. \n", "12 Brindavoine 2 sc. 3 répl. 1,1 l. 166 l. (9 %) \n", "13 La Merluche 2 sc. 5 répl. 0,9 l. 175 l. (10 %) \n", "14 Le Commissaire 3 sc. 17 répl. 1,5 l. 110 l. (6 %) \n", "\n", " Texte Texte % prés. Texte × pers. Interlocution \n", "0 1 769 l. (100 %) 100 % 5 847 l. (100 %) 3,3 pers. \n", "1 514 l. (29 %) 40 % 4 729 l. (81 %) 3,7 pers. \n", "2 285 l. (16 %) 32 % 3 486 l. (60 %) 3,9 pers. \n", "3 92 l. (5 %) 13 % 2 667 l. (46 %) 3,9 pers. \n", "4 232 l. (13 %) 33 % 3 067 l. (52 %) 4,4 pers. \n", "5 79 l. (4 %) 22 % 1 638 l. (28 %) 4,6 pers. \n", "6 45 l. (3 %) 32 % 749 l. (13 %) 5,3 pers. \n", "7 201 l. (11 %) 43 % 1 465 l. (25 %) 3,1 pers. \n", "8 16 l. (1 %) 37 % 175 l. (3 %) 4,0 pers. \n", "9 140 l. (8 %) 25 % 2 670 l. (46 %) 4,8 pers. \n", "10 132 l. (7 %) 52 % 598 l. (10 %) 2,3 pers. \n", "11 0 l. 0 % 0 l. (0 %) 0,0 pers. \n", "12 3 l. (0 %) 2 % 1 146 l. (20 %) 6,9 pers. \n", "13 5 l. (0 %) 3 % 1 189 l. (20 %) 6,8 pers. \n", "14 26 l. (1 %) 24 % 418 l. (7 %) 3,8 pers. " ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Agrégats au niveau des scènes\n", "scene_totals = (\n", " df_speeches\n", " .groupby([\"Acte\", \"Scène\"], as_index=False)\n", " .agg({\n", " \"Lignes\": \"sum\",\n", " \"Personnage\": \"nunique\",\n", " })\n", ")\n", "\n", "scene_totals = scene_totals.rename(columns={\n", " \"Lignes\": \"scene_lines\",\n", " \"Personnage\": \"participants\",\n", "})\n", "\n", "scene_totals[\"textexpers\"] = scene_totals[\"scene_lines\"] * scene_totals[\"participants\"]\n", "scene_totals[\"SceneKey\"] = scene_totals[\"Acte\"] + \" | \" + scene_totals[\"Scène\"]\n", "\n", "total_lines_play = scene_totals[\"scene_lines\"].sum()\n", "textexpers_total = scene_totals[\"textexpers\"].sum()\n", "\n", "# Statistiques par personnage\n", "speech_stats = (\n", " df_speeches\n", " .groupby(\"Personnage\")\n", " .agg({\n", " \"Texte\": \"count\",\n", " \"Lignes\": \"sum\",\n", " })\n", " .rename(columns={\n", " \"Texte\": \"repl\",\n", " \"Lignes\": \"text_lines\",\n", " })\n", ")\n", "\n", "presence = (\n", " df_speeches[[\"Acte\", \"Scène\", \"Personnage\"]]\n", " .drop_duplicates()\n", " .merge(\n", " scene_totals[[\"Acte\", \"Scène\", \"scene_lines\", \"textexpers\"]],\n", " on=[\"Acte\", \"Scène\"],\n", " how=\"left\",\n", " )\n", ")\n", "\n", "presence_stats = (\n", " presence\n", " .groupby(\"Personnage\")\n", " .agg({\n", " \"Scène\": \"count\",\n", " \"scene_lines\": \"sum\",\n", " \"textexpers\": \"sum\",\n", " })\n", " .rename(columns={\n", " \"Scène\": \"scenes\",\n", " \"scene_lines\": \"presence_lines\",\n", " })\n", ")\n", "\n", "roles = speech_stats.join(presence_stats, how=\"outer\").fillna(0)\n", "\n", "# Ajout des rôles muets (ex : Dame Claude) pour qu'ils apparaissent dans le tableau\n", "for name in (resolve_name(p) for p in dramatis_personae[\"Personnage\"]):\n", " if name not in roles.index:\n", " roles.loc[name] = 0\n", "\n", "roles[\"repl_moy\"] = roles[\"text_lines\"] / roles[\"repl\"]\n", "roles[\"presence_pct\"] = roles[\"presence_lines\"] / total_lines_play\n", "roles[\"text_pct\"] = roles[\"text_lines\"] / total_lines_play\n", "roles[\"text_presence_pct\"] = roles[\"text_lines\"] / roles[\"presence_lines\"]\n", "roles[\"textexpers_pct\"] = roles[\"textexpers\"] / textexpers_total\n", "roles[\"interlocution\"] = roles[\"textexpers\"] / roles[\"presence_lines\"]\n", "\n", "roles = roles.replace([np.inf, -np.inf], 0).fillna(0)\n", "\n", "# Ordre basé sur la distribution initiale\n", "role_order = [\n", " name for name in (resolve_name(p) for p in dramatis_personae[\"Personnage\"])\n", " if name in roles.index\n", "]\n", "role_order += [r for r in roles.index if r not in role_order]\n", "\n", "roles = roles.loc[role_order]\n", "\n", "# Ligne globale\n", "all_row = pd.Series({\n", " \"scenes\": scene_totals.shape[0],\n", " \"repl\": len(df_speeches),\n", " \"repl_moy\": df_speeches[\"Lignes\"].sum() / len(df_speeches),\n", " \"presence_lines\": total_lines_play,\n", " \"presence_pct\": 1.0,\n", " \"text_lines\": df_speeches[\"Lignes\"].sum(),\n", " \"text_pct\": 1.0,\n", " \"text_presence_pct\": (\n", " df_speeches[\"Lignes\"].sum() / total_lines_play if total_lines_play else 0\n", " ),\n", " \"textexpers\": textexpers_total,\n", " \"textexpers_pct\": 1.0,\n", " \"interlocution\": textexpers_total / total_lines_play if total_lines_play else 0,\n", "})\n", "\n", "roles = pd.concat([\n", " pd.DataFrame({\"Personnage\": [\"[TOUS]\"]})\n", " .set_index(\"Personnage\")\n", " .assign(**all_row),\n", " roles,\n", "])\n", "\n", "roles.index.name = \"Rôle\"\n", "\n", "# Quelques utilitaires spécifiques\n", "\n", "def format_number(value, decimals=0):\n", " fmt = f\"{value:,.{decimals}f}\"\n", " return fmt.replace(\",\", \" \").replace(\".\", \",\")\n", "\n", "def format_lines(value, decimals=0):\n", " return f\"{format_number(round(value, decimals), decimals)} l.\"\n", "\n", "def format_percent(value, decimals=0):\n", " return f\"{format_number(value * 100, decimals)} %\"\n", "\n", "def format_people(value):\n", " return f\"{format_number(value, 1)} pers.\"\n", "\n", "roles_display = roles.reset_index()\n", "roles_display[\"Scènes\"] = roles_display[\"scenes\"].fillna(0).astype(int).astype(str) + \" sc.\"\n", "roles_display[\"Répl.\"] = roles_display[\"repl\"].fillna(0).astype(int).astype(str) + \" répl.\"\n", "roles_display[\"Répl. moy.\"] = roles_display[\"repl_moy\"].apply(lambda v: format_lines(v, 1))\n", "roles_display[\"Présence\"] = roles_display.apply(\n", " lambda r: f\"{format_lines(r['presence_lines'])}\"\n", " + (f\" ({format_percent(r['presence_pct'])})\" if r[\"presence_lines\"] else \"\"),\n", " axis=1,\n", ")\n", "roles_display[\"Texte\"] = roles_display.apply(\n", " lambda r: f\"{format_lines(r['text_lines'])}\"\n", " + (f\" ({format_percent(r['text_pct'])})\" if r[\"text_lines\"] else \"\"),\n", " axis=1,\n", ")\n", "roles_display[\"Texte % prés.\"] = roles_display[\"text_presence_pct\"].apply(lambda v: format_percent(v, 0))\n", "roles_display[\"Texte × pers.\"] = roles_display.apply(\n", " lambda r: f\"{format_lines(r['textexpers'])} ({format_percent(r['textexpers_pct'])})\",\n", " axis=1,\n", ")\n", "roles_display[\"Interlocution\"] = roles_display[\"interlocution\"].apply(format_people)\n", "\n", "roles_table = roles_display[\n", " [\"Rôle\", \"Scènes\", \"Répl.\", \"Répl. moy.\", \"Présence\",\n", " \"Texte\", \"Texte % prés.\", \"Texte × pers.\", \"Interlocution\"]\n", "]\n", "\n", "roles_table\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Il existe des différences entre le tableau que nous avons généré et celui de l'OBVIL.\n", "Ces différences peuvent être attribuées à :\n", "\n", "- une méthode de nettoyage des lignes différente (nous avons opté pour un nettoyage agressif des espaces surnuméraires)\n", "- une gestion des décimales différentes (est-ce que l'OBVIL arrondi à l'entier supérieur ou inférieur, ou tronque les décimales ?)\n", "\n", "Ces différences affectent mathématiquement les statistiques qui découlent de ce comptage, notamment l'interlocution." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Statistiques par relation\n", "\n", "Chaque relation s'appuie sur l'enchaînement de répliques adjacentes entre deux personnages (monologues inclus), ce qui reflète les échanges directs plutôt que la simple coprésence sur scène.\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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    DétailInterlocutionRelationScènesTexte
    033 l. (100 %) 1 répl. 32,7 l.1,0 pers.Harpagon1 sc.33 l. (2 %)
    1135 l. (49 %) 97 répl. 1,4 l. - 140 l. (51 %) ...4,6 pers.Cléante / Harpagon9 sc.275 l. (16 %)
    241 l. (60 %) 28 répl. 1,5 l. - 28 l. (40 %) 28...4,7 pers.Harpagon / Élise6 sc.69 l. (4 %)
    396 l. (44 %) 65 répl. 1,5 l. - 121 l. (56 %) 5...4,9 pers.Harpagon / Valère7 sc.217 l. (12 %)
    49 l. (39 %) 7 répl. 1,2 l. - 13 l. (61 %) 5 ré...4,9 pers.Harpagon / Mariane2 sc.22 l. (1 %)
    525 l. (71 %) 11 répl. 2,2 l. - 10 l. (29 %) 8 ...5,3 pers.Anselme / Harpagon2 sc.35 l. (2 %)
    6128 l. (70 %) 39 répl. 3,3 l. - 56 l. (30 %) 3...3,0 pers.Frosine / Harpagon5 sc.184 l. (10 %)
    75 l. (22 %) 3 répl. 1,5 l. - 16 l. (78 %) 4 ré...4,0 pers.Harpagon / Maître Simon1 sc.20 l. (1 %)
    858 l. (38 %) 49 répl. 1,2 l. - 94 l. (62 %) 51...4,9 pers.Harpagon / Maître Jacques7 sc.153 l. (9 %)
    935 l. (62 %) 33 répl. 1,1 l. - 22 l. (38 %) 33...2,0 pers.Harpagon / La Flèche1 sc.57 l. (3 %)
    101 l. (38 %) 2 répl. 0,7 l. - 2 l. (62 %) 2 rép...6,9 pers.Brindavoine / Harpagon2 sc.4 l. (0 %)
    111 l. (11 %) 1 répl. 0,6 l. - 5 l. (89 %) 5 rép...6,8 pers.Harpagon / La Merluche2 sc.5 l. (0 %)
    1211 l. (53 %) 10 répl. 1,1 l. - 10 l. (47 %) 9 ...3,8 pers.Harpagon / Le Commissaire3 sc.21 l. (1 %)
    1366 l. (84 %) 10 répl. 6,6 l. - 13 l. (16 %) 9 ...3,0 pers.Cléante / Élise2 sc.78 l. (4 %)
    1431 l. (60 %) 12 répl. 2,6 l. - 21 l. (40 %) 10...4,8 pers.Cléante / Mariane3 sc.52 l. (3 %)
    153 l. (8 %) 5 répl. 0,6 l. - 37 l. (92 %) 6 rép...4,5 pers.Cléante / Frosine2 sc.40 l. (2 %)
    1614 l. (51 %) 8 répl. 1,8 l. - 14 l. (49 %) 8 r...3,0 pers.Cléante / Maître Jacques1 sc.28 l. (2 %)
    1731 l. (27 %) 25 répl. 1,2 l. - 85 l. (73 %) 26...2,5 pers.Cléante / La Flèche3 sc.116 l. (7 %)
    1865 l. (59 %) 11 répl. 5,9 l. - 45 l. (41 %) 11...2,5 pers.Valère / Élise2 sc.110 l. (6 %)
    191 l. (23 %) 2 répl. 0,6 l. - 4 l. (77 %) 1 rép...4,0 pers.Mariane / Élise2 sc.6 l. (0 %)
    203 l. (41 %) 1 répl. 2,6 l. - 4 l. (59 %) 3 rép...5,0 pers.Mariane / Valère1 sc.6 l. (0 %)
    2119 l. (47 %) 8 répl. 2,4 l. - 22 l. (53 %) 7 r...5,0 pers.Anselme / Valère1 sc.41 l. (2 %)
    2218 l. (49 %) 14 répl. 1,3 l. - 19 l. (51 %) 18...5,2 pers.Maître Jacques / Valère4 sc.37 l. (2 %)
    2318 l. (48 %) 6 répl. 3,0 l. - 20 l. (52 %) 7 r...4,1 pers.Frosine / Mariane4 sc.38 l. (2 %)
    241 l. (42 %) 1 répl. 1,0 l. - 1 l. (58 %) 2 rép...4,7 pers.Frosine / Maître Jacques2 sc.2 l. (0 %)
    2511 l. (42 %) 5 répl. 2,3 l. - 16 l. (58 %) 5 r...2,0 pers.Frosine / La Flèche1 sc.28 l. (2 %)
    2613 l. (76 %) 7 répl. 1,8 l. - 4 l. (24 %) 5 ré...3,0 pers.Le Commissaire / Maître Jacques1 sc.17 l. (1 %)
    \n", "
    " ], "text/plain": [ " Détail Interlocution \\\n", "0 33 l. (100 %) 1 répl. 32,7 l. 1,0 pers. \n", "1 135 l. (49 %) 97 répl. 1,4 l. - 140 l. (51 %) ... 4,6 pers. \n", "2 41 l. (60 %) 28 répl. 1,5 l. - 28 l. (40 %) 28... 4,7 pers. \n", "3 96 l. (44 %) 65 répl. 1,5 l. - 121 l. (56 %) 5... 4,9 pers. \n", "4 9 l. (39 %) 7 répl. 1,2 l. - 13 l. (61 %) 5 ré... 4,9 pers. \n", "5 25 l. (71 %) 11 répl. 2,2 l. - 10 l. (29 %) 8 ... 5,3 pers. \n", "6 128 l. (70 %) 39 répl. 3,3 l. - 56 l. (30 %) 3... 3,0 pers. \n", "7 5 l. (22 %) 3 répl. 1,5 l. - 16 l. (78 %) 4 ré... 4,0 pers. \n", "8 58 l. (38 %) 49 répl. 1,2 l. - 94 l. (62 %) 51... 4,9 pers. \n", "9 35 l. (62 %) 33 répl. 1,1 l. - 22 l. (38 %) 33... 2,0 pers. \n", "10 1 l. (38 %) 2 répl. 0,7 l. - 2 l. (62 %) 2 rép... 6,9 pers. \n", "11 1 l. (11 %) 1 répl. 0,6 l. - 5 l. (89 %) 5 rép... 6,8 pers. \n", "12 11 l. (53 %) 10 répl. 1,1 l. - 10 l. (47 %) 9 ... 3,8 pers. \n", "13 66 l. (84 %) 10 répl. 6,6 l. - 13 l. (16 %) 9 ... 3,0 pers. \n", "14 31 l. (60 %) 12 répl. 2,6 l. - 21 l. (40 %) 10... 4,8 pers. \n", "15 3 l. (8 %) 5 répl. 0,6 l. - 37 l. (92 %) 6 rép... 4,5 pers. \n", "16 14 l. (51 %) 8 répl. 1,8 l. - 14 l. (49 %) 8 r... 3,0 pers. \n", "17 31 l. (27 %) 25 répl. 1,2 l. - 85 l. (73 %) 26... 2,5 pers. \n", "18 65 l. (59 %) 11 répl. 5,9 l. - 45 l. (41 %) 11... 2,5 pers. \n", "19 1 l. (23 %) 2 répl. 0,6 l. - 4 l. (77 %) 1 rép... 4,0 pers. \n", "20 3 l. (41 %) 1 répl. 2,6 l. - 4 l. (59 %) 3 rép... 5,0 pers. \n", "21 19 l. (47 %) 8 répl. 2,4 l. - 22 l. (53 %) 7 r... 5,0 pers. \n", "22 18 l. (49 %) 14 répl. 1,3 l. - 19 l. (51 %) 18... 5,2 pers. \n", "23 18 l. (48 %) 6 répl. 3,0 l. - 20 l. (52 %) 7 r... 4,1 pers. \n", "24 1 l. (42 %) 1 répl. 1,0 l. - 1 l. (58 %) 2 rép... 4,7 pers. \n", "25 11 l. (42 %) 5 répl. 2,3 l. - 16 l. (58 %) 5 r... 2,0 pers. \n", "26 13 l. (76 %) 7 répl. 1,8 l. - 4 l. (24 %) 5 ré... 3,0 pers. \n", "\n", " Relation Scènes Texte \n", "0 Harpagon 1 sc. 33 l. (2 %) \n", "1 Cléante / Harpagon 9 sc. 275 l. (16 %) \n", "2 Harpagon / Élise 6 sc. 69 l. (4 %) \n", "3 Harpagon / Valère 7 sc. 217 l. (12 %) \n", "4 Harpagon / Mariane 2 sc. 22 l. (1 %) \n", "5 Anselme / Harpagon 2 sc. 35 l. (2 %) \n", "6 Frosine / Harpagon 5 sc. 184 l. (10 %) \n", "7 Harpagon / Maître Simon 1 sc. 20 l. (1 %) \n", "8 Harpagon / Maître Jacques 7 sc. 153 l. (9 %) \n", "9 Harpagon / La Flèche 1 sc. 57 l. (3 %) \n", "10 Brindavoine / Harpagon 2 sc. 4 l. (0 %) \n", "11 Harpagon / La Merluche 2 sc. 5 l. (0 %) \n", "12 Harpagon / Le Commissaire 3 sc. 21 l. (1 %) \n", "13 Cléante / Élise 2 sc. 78 l. (4 %) \n", "14 Cléante / Mariane 3 sc. 52 l. (3 %) \n", "15 Cléante / Frosine 2 sc. 40 l. (2 %) \n", "16 Cléante / Maître Jacques 1 sc. 28 l. (2 %) \n", "17 Cléante / La Flèche 3 sc. 116 l. (7 %) \n", "18 Valère / Élise 2 sc. 110 l. (6 %) \n", "19 Mariane / Élise 2 sc. 6 l. (0 %) \n", "20 Mariane / Valère 1 sc. 6 l. (0 %) \n", "21 Anselme / Valère 1 sc. 41 l. (2 %) \n", "22 Maître Jacques / Valère 4 sc. 37 l. (2 %) \n", "23 Frosine / Mariane 4 sc. 38 l. (2 %) \n", "24 Frosine / Maître Jacques 2 sc. 2 l. (0 %) \n", "25 Frosine / La Flèche 1 sc. 28 l. (2 %) \n", "26 Le Commissaire / Maître Jacques 1 sc. 17 l. (1 %) " ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from collections import defaultdict\n", "\n", "# Accumulateur : pour chaque relation (acteur seul - monologues) ou (acteurA, acteurB),\n", "# on stocke les lignes, le nombre de répliques, les scènes concernées,\n", "# les lignes de présence cumulées et un poids d’interlocution (lignes × nb de participants).\n", "relation_stats = defaultdict(lambda: {\n", " \"lines\": defaultdict(float),\n", " \"counts\": defaultdict(int),\n", " \"scenes\": set(),\n", " \"presence_lines\": 0.0,\n", " \"interlocution_weight\": 0.0,\n", "})\n", "\n", "scene_lookup = scene_totals.set_index([\"Acte\", \"Scène\"])[[\"scene_lines\", \"participants\"]]\n", "\n", "for (act, scene), scene_df in df_speeches.groupby([\"Acte\", \"Scène\"]):\n", " scene_df = scene_df.sort_values(\"Ordre\")\n", " speakers = scene_df[\"Personnage\"].tolist()\n", " lines = scene_df[\"Lignes\"].tolist()\n", " scene_lines = scene_lookup.loc[(act, scene), \"scene_lines\"]\n", " participants = scene_lookup.loc[(act, scene), \"participants\"]\n", " scene_key = f\"{act} | {scene}\"\n", "\n", " # Scène à un seul intervenant : on enregistre un monologue\n", " if len(set(speakers)) == 1:\n", " actor = speakers[0]\n", " stats = relation_stats[(actor,)]\n", " stats[\"lines\"][actor] += scene_lines\n", " stats[\"counts\"][actor] += len(speakers)\n", " stats[\"scenes\"].add(scene_key)\n", " stats[\"presence_lines\"] += scene_lines\n", " stats[\"interlocution_weight\"] += scene_lines * participants\n", " continue\n", "\n", " relations_here = set()\n", " \n", " # Pour chaque changement d’intervenant, on attribue les lignes du locuteur au duo (ordre ignoré)\n", " for speaker, next_speaker, speaker_lines in zip(speakers, speakers[1:], lines):\n", " if speaker == next_speaker:\n", " continue\n", " \n", " key = tuple(sorted((speaker, next_speaker)))\n", " stats = relation_stats[key]\n", " stats[\"lines\"][speaker] += speaker_lines\n", " stats[\"counts\"][speaker] += 1\n", " stats[\"scenes\"].add(scene_key)\n", " relations_here.add(key)\n", "\n", " # On ajoute la présence et l’interlocution une seule fois par scène et par relation\n", " for key in relations_here:\n", " stats = relation_stats[key]\n", " stats[\"presence_lines\"] += scene_lines\n", " stats[\"interlocution_weight\"] += scene_lines * participants\n", "\n", "role_order_index = {name: idx for idx, name in enumerate(role_order)}\n", "\n", "def relation_sort_key(rel):\n", " if len(rel) == 1:\n", " return (role_order_index.get(rel[0], len(role_order_index)), -1)\n", " \n", " a, b = rel\n", " \n", " return (\n", " min(role_order_index.get(a, len(role_order_index)), role_order_index.get(b, len(role_order_index))),\n", " max(role_order_index.get(a, len(role_order_index)), role_order_index.get(b, len(role_order_index))),\n", " )\n", "\n", "relation_rows = []\n", "\n", "for rel in sorted(relation_stats, key=relation_sort_key):\n", " data = relation_stats[rel]\n", " total_lines = sum(data[\"lines\"].values())\n", "\n", " # On ignore les relations sans matière (moins de 2 lignes au total)\n", " if total_lines < 2:\n", " continue\n", " \n", " # On ignore les relations où au moins un protagoniste n’a jamais prononcé de réplique dans ce duo\n", " if len(rel) > 1 and any(data[\"counts\"].get(actor, 0) == 0 for actor in rel):\n", " continue\n", "\n", " scenes_count = len(data[\"scenes\"])\n", " interlocution = data[\"interlocution_weight\"] / data[\"presence_lines\"] if data[\"presence_lines\"] else 0\n", "\n", " parts = []\n", " \n", " for actor in rel:\n", " actor_lines = data[\"lines\"].get(actor, 0)\n", " actor_repl = data[\"counts\"].get(actor, 0)\n", " avg_lines = actor_lines / actor_repl if actor_repl else 0\n", " share = actor_lines / total_lines if total_lines else 0\n", " \n", " parts.append(\n", " f\"{format_lines(actor_lines)} ({format_percent(share)}) {actor_repl} répl. {format_lines(avg_lines, 1)}\"\n", " )\n", "\n", " relation_rows.append({\n", " \"Relation\": \" / \".join(rel),\n", " \"Détail\": \" - \".join(parts),\n", " \"Scènes\": f\"{scenes_count} sc.\",\n", " \"Texte\": f\"{format_lines(total_lines)} ({format_percent(total_lines / total_lines_play)})\",\n", " \"Interlocution\": format_people(interlocution),\n", " })\n", "\n", "relations_table = pd.DataFrame(relation_rows)\n", "relations_table" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Bien que le décompte des lignes diffère toujours de celui de l'OBVIL (comme attendu et pour les mêmes raisons que précédemment), l'interlocution est identique.\n", "En effet, les écarts de comptage de lignes n’affectent pas l’interlocution ; seule une différence de liste d’intervenants par scène la ferait varier." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Voyons maintenant si nous parvenons à reproduire le graphique proposé par l'OBVIL :" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": 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    " ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Ordonnancement des actes et des scènes\n", "# On affichera les labels bruts (\"Acte Premier\") mais on utilisera un ordre \"naturel\"\n", "scene_order = []\n", "\n", "for act in list_acts():\n", " for sc in list_scenes(act=act[\"node\"]):\n", " scene_order.append(f\"{act['title']} | {sc['title']}\")\n", "\n", "# Préparation des données\n", "df = df_counts.copy()\n", "\n", "# Création d'une clé unique pour identifier un couple acte/scène\n", "# Cela permet de forcer l'ordre d'affichage, au lieu de suivre un ordre alphabétique\n", "# qui noierait \"Acte Premier\" au milieu de la liste, par exemple\n", "df[\"SceneKey\"] = pd.Categorical(df[\"Acte\"] + \" | \" + df[\"Scène\"], categories=scene_order, ordered=True)\n", "\n", "# Obtention du pourcentage que représente un dialogue particulier au sein d'une scène\n", "df[\"share\"] = df[\"Mots\"] / df.groupby(\"SceneKey\")[\"Mots\"].transform(\"sum\")\n", "\n", "# Calcul du total de mots pour une scène donnée\n", "totals = df.groupby(\"SceneKey\")[\"Mots\"].sum()\n", "\n", "# Paramétrage du graphique\n", "gap = 0 # Espace vertical ajouté entre deux scènes\n", "label_fs = 8 # Taille de la police des étiquettes\n", "min_target = 10 # Hauteur minimale souhaitée\n", "\n", "# Définition de la hauteur d'une scène.\n", "# Nous utilisons ici une fonction racine carré.\n", "# Le but de ce calcul est d'éviter que les scènes contenant le moins de mots\n", "# se trouvent compressées en une ligne si fine qu'il ne serait pas possible\n", "# de distinguer les différents protagonistes.\n", "# On sacrifie donc le rapport proportionnel strict au profit d'une meilleure\n", "# lisibilité.\n", "def scene_height(total):\n", " return max(min_target, math.sqrt(total) * factor)\n", "\n", "create_actors_colormap(df[\"Personnage\"].unique())\n", "\n", "# Calcul de l'échelle des scènes : évite qu'une scène courte soit\n", "# représentée par une ligne trop fine pour être distinguée\n", "min_total = totals.min()\n", "factor = min_target / math.log1p(min_total)\n", "\n", "figure, axis = plt.subplots(figsize=(12, len(scene_order) * 0.5))\n", "\n", "# Affichage de la colormap des personnages\n", "handles = [mpatches.Patch(color=col, label=name) for name, col in color_map.items()]\n", "axis.legend(handles=handles, title=\"Personnage\", bbox_to_anchor=(1.25, 1), loc=\"upper left\")\n", "\n", "y = 0 # \"Curseur\" vertical permettant de positionner les scènes\n", "\n", "# Traçage des scènes\n", "for scene in scene_order:\n", " scene_rows = df[df[\"SceneKey\"] == scene]\n", " h = scene_height(totals.loc[scene])\n", "\n", " left = 0\n", "\n", " for _, row in scene_rows.iterrows():\n", " # broken_barth est la méthode nous permettant de tracer des barres\n", " # horizontales juxtaposés\n", " axis.broken_barh([(left, row[\"share\"])], (y, h),\n", " facecolors=color_map[row[\"Personnage\"]],\n", " edgecolors=\"white\", linewidth=0.5)\n", "\n", " # Décalage horizontal de la prochaine barre\n", " left += row[\"share\"]\n", "\n", " # Étiquette correspondant à la scène\n", " axis.text(1.01, y + h/2, scene, va=\"center\", fontsize=label_fs)\n", "\n", " # Décalage vertical de la prochaine scène\n", " y += h + gap\n", "\n", "axis.set_xlim(0, 1)\n", "axis.set_ylim(0, y)\n", "axis.invert_yaxis() # Acte I en haut\n", "axis.set_xlabel(\"% de la scène (largeur) - une ligne par scène\")\n", "axis.set_yticks([]) # On masque l'ordonnée à gauche\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "L'ordre des dialogues est respecté sur ce graphique, contrairement au graphique de l'OBVIL.\n", "Par exemple, dans la deuxième scène du premier acte, Cléante est bien la première à prendre la parole, et non Élise." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Graphe réseau des dialogues" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "Ici, nous allons représenter les dialogues par un graphe réseau.\n", "Nous allons essayer de reproduire le [graphe](https://obtic.huma-num.fr/obvil-web/corpus/moliere/moliere_avare) proposé par l'OBVIL." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "\n", "
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\n", 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    " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 0. Fonction pour construire les arêtes d'interlocution\n", "def interlocution_edges(df_speeches):\n", " \"\"\"Arêtes directionnelles source -> cible : succession des locuteurs par scène.\"\"\"\n", " df = df_speeches.copy()\n", " df[\"SceneKey\"] = df[\"Acte\"] + \" | \" + df[\"Scène\"]\n", " df[\"order\"] = range(len(df))\n", " df = df.sort_values([\"SceneKey\", \"order\"])\n", " df[\"next\"] = df.groupby(\"SceneKey\")[\"Personnage\"].shift(-1)\n", "\n", " return (df.dropna(subset=[\"next\"])\n", " .groupby([\"Personnage\", \"next\"], as_index=False)[\"Mots\"]\n", " .sum()\n", " .rename(columns={\"Personnage\": \"source\", \"next\": \"target\", \"Mots\": \"weight\"}))\n", "\n", "# 1. Données de base\n", "totaux = df_speeches.groupby(\"Personnage\")[\"Mots\"].sum()\n", "edges_dir = interlocution_edges(df_speeches)\n", "\n", "# 2. Construction du graphe NetworkX\n", "G = nx.DiGraph()\n", "\n", "# Nœuds : taille ~ mots, couleur issue de color_map\n", "for personnage, mots in totaux.items():\n", " taille = 8 + 0.6 * np.sqrt(mots) # on ne calcule plus cette formule qu'ici\n", " G.add_node(\n", " personnage,\n", " mots=int(mots),\n", " label=personnage,\n", " color=mcolors.to_hex(color_map.get(personnage)) if color_map.get(personnage) else \"#cccccc\",\n", " size=taille,\n", " )\n", "\n", "# Arêtes dirigées : poids log1p, couleur héritée de la source\n", "for _, r in edges_dir.iterrows():\n", " if r[\"source\"] == r[\"target\"]:\n", " continue\n", "\n", " src = r[\"source\"]\n", " tgt = r[\"target\"]\n", " w = int(r[\"weight\"])\n", "\n", " G.add_edge(\n", " src,\n", " tgt,\n", " weight=w,\n", " log_weight=float(np.log1p(w)),\n", " color=mcolors.to_hex(color_map.get(src)) if color_map.get(src) else \"#999999\",\n", " label=f\"{src} → {tgt} : {w} mots\",\n", " )\n", "\n", "# 3. Préparation du GraphRenderer Bokeh\n", "node_names = list(G.nodes())\n", "node_indices = list(range(len(node_names)))\n", "name_to_index = {name: i for i, name in enumerate(node_names)}\n", "\n", "graph = GraphRenderer()\n", "\n", "# ----- NŒUDS -----\n", "mots_list = [G.nodes[n][\"mots\"] for n in node_names]\n", "sizes = [G.nodes[n][\"size\"] for n in node_names] # on réutilise la taille déjà calculée\n", "max_size = max(sizes) if sizes else 1\n", "\n", "# radius en coordonnées \"data\" (échelle relative)\n", "radii = [0.03 + 0.12 * (s / max_size) for s in sizes]\n", "\n", "graph.node_renderer.data_source.data = dict(\n", " index=node_indices,\n", " name=node_names,\n", " label=[G.nodes[n][\"label\"] for n in node_names],\n", " mots=mots_list,\n", " color=[G.nodes[n][\"color\"] for n in node_names],\n", " radius=radii,\n", ")\n", "\n", "graph.node_renderer.glyph = Circle(\n", " radius=\"radius\",\n", " fill_color=\"color\",\n", " line_width=1.2,\n", ")\n", "\n", "# ----- ARÊTES -----\n", "edge_start = []\n", "edge_end = []\n", "edge_weight = []\n", "edge_log_weight = []\n", "edge_color = []\n", "edge_label = []\n", "\n", "for u, v in G.edges():\n", " edge_start.append(name_to_index[u])\n", " edge_end.append(name_to_index[v])\n", " edge_weight.append(G[u][v][\"weight\"])\n", " edge_log_weight.append(G[u][v][\"log_weight\"])\n", " edge_color.append(G[u][v][\"color\"]) # couleur de la source\n", " edge_label.append(G[u][v][\"label\"])\n", "\n", "# largeur des arêtes normalisée (contraste lisible)\n", "if edge_log_weight:\n", " min_log = min(edge_log_weight)\n", " max_log = max(edge_log_weight)\n", " if max_log == min_log:\n", " edge_line_width = [5.0 for _ in edge_log_weight]\n", " else:\n", " edge_line_width = [\n", " 1.0 + 11.0 * (lw - min_log) / (max_log - min_log)\n", " for lw in edge_log_weight\n", " ]\n", "else:\n", " edge_line_width = []\n", "\n", "graph.edge_renderer.data_source.data = dict(\n", " start=edge_start,\n", " end=edge_end,\n", " weight=edge_weight,\n", " log_weight=edge_log_weight,\n", " edge_color=edge_color,\n", " line_width=edge_line_width,\n", " label=edge_label,\n", ")\n", "\n", "graph.edge_renderer.glyph = MultiLine(\n", " line_color=\"edge_color\",\n", " line_width=\"line_width\",\n", " line_alpha=0.25, # « fond » des arêtes\n", ")\n", "\n", "# 4. Layout (positions des nœuds)\n", "\n", "LAYOUT_SEED = 42 # layout stable d’une exécution à l’autre\n", "\n", "pos = nx.spring_layout(\n", " G,\n", " k=2,\n", " iterations=200,\n", " weight=\"log_weight\",\n", " seed=LAYOUT_SEED,\n", ")\n", "\n", "# Rayons par nœud (en coordonnées data)\n", "node_radius = dict(zip(node_names, radii))\n", "\n", "# Gros personnages = au-dessus du 75e centile\n", "mots_arr = np.array(mots_list)\n", "seuil_gros = np.quantile(mots_arr, 0.75) if len(mots_arr) > 0 else 0\n", "big_nodes = [n for n, m in zip(node_names, mots_list) if m >= seuil_gros]\n", "\n", "big_sep_factor = 5.0 # écartement spécifique entre gros nœuds\n", "\n", "for _ in range(10):\n", " moved = False\n", " for i in range(len(big_nodes)):\n", " for j in range(i + 1, len(big_nodes)):\n", " ni = big_nodes[i]\n", " nj = big_nodes[j]\n", "\n", " pi = np.array(pos[ni], dtype=float)\n", " pj = np.array(pos[nj], dtype=float)\n", "\n", " diff = pj - pi\n", " dist = np.linalg.norm(diff)\n", " if dist == 0:\n", " diff = np.random.randn(2)\n", " dist = np.linalg.norm(diff)\n", "\n", " min_dist = big_sep_factor * (node_radius[ni] + node_radius[nj])\n", "\n", " if dist < min_dist:\n", " direction = diff / dist\n", " center = (pi + pj) / 2.0\n", " offset = direction * (min_dist / 2.0)\n", "\n", " pos[ni] = (center - offset).tolist()\n", " pos[nj] = (center + offset).tolist()\n", " moved = True\n", " if not moved:\n", " break\n", "\n", "graph_layout = {name_to_index[name]: pos[name] for name in node_names}\n", "graph.layout_provider = StaticLayoutProvider(graph_layout=graph_layout)\n", "\n", "xs = [p[0] for p in pos.values()]\n", "ys = [p[1] for p in pos.values()]\n", "if xs and ys:\n", " margin = 0.3 * max(max(xs) - min(xs), max(ys) - min(ys))\n", " x_min, x_max = min(xs) - margin, max(xs) + margin\n", " y_min, y_max = min(ys) - margin, max(ys) + margin\n", "else:\n", " x_min, x_max, y_min, y_max = -2, 2, -2, 2\n", "\n", "# 5. Figure Bokeh + Hover\n", "\n", "plot = bkp.figure(\n", " width=900,\n", " height=700,\n", " x_range=(x_min, x_max),\n", " y_range=(y_min, y_max),\n", " title=\"Graphe des interlocutions\",\n", " tools=\"pan,wheel_zoom,box_zoom,reset,save\",\n", " active_scroll=\"wheel_zoom\",\n", " background_fill_color=\"#f8f8f8\",\n", ")\n", "\n", "plot.renderers.append(graph)\n", "\n", "# Hover nœuds\n", "hover_nodes = HoverTool(\n", " tooltips=[\n", " (\"Personnage\", \"@label\"),\n", " (\"Mots totaux\", \"@mots\"),\n", " ],\n", " renderers=[graph.node_renderer],\n", ")\n", "plot.add_tools(hover_nodes)\n", "\n", "# Hover arêtes (corps)\n", "hover_edges = HoverTool(\n", " tooltips=[\n", " (\"Interaction\", \"@label\"),\n", " (\"Mots\", \"@weight\"),\n", " ],\n", " renderers=[graph.edge_renderer],\n", ")\n", "plot.add_tools(hover_edges)\n", "\n", "# 6. Flèches (orientation)\n", "\n", "layout_xy = {name: pos[name] for name in node_names}\n", "\n", "x_start, y_start, x_end, y_end, lw_arrow, colors = [], [], [], [], [], []\n", "for (u, v), w, c in zip(G.edges(), edge_line_width, edge_color):\n", " x0, y0 = layout_xy[u]\n", " x1, y1 = layout_xy[v]\n", " x_start.append(x0)\n", " y_start.append(y0)\n", " x_end.append(x1)\n", " y_end.append(y1)\n", " lw_arrow.append(w)\n", " colors.append(c) # couleur du personnage source\n", "\n", "arrow_source = ColumnDataSource(dict(\n", " x_start=x_start,\n", " y_start=y_start,\n", " x_end=x_end,\n", " y_end=y_end,\n", " line_width=lw_arrow,\n", " color=colors,\n", "))\n", "\n", "arrows = Arrow(\n", " end=NormalHead(\n", " size=7,\n", " fill_color=\"color\",\n", " line_color=\"color\",\n", " fill_alpha=1.0,\n", " line_alpha=1.0,\n", " ),\n", " x_start=\"x_start\",\n", " y_start=\"y_start\",\n", " x_end=\"x_end\",\n", " y_end=\"y_end\",\n", " line_width=\"line_width\",\n", " line_color=\"color\",\n", " line_alpha=0.5,\n", " source=arrow_source,\n", ")\n", "plot.add_layout(arrows)\n", "\n", "# 7. Labels sur les nœuds\n", "\n", "node_x = [graph_layout[i][0] for i in node_indices]\n", "node_y = [graph_layout[i][1] for i in node_indices]\n", "\n", "labels_source = ColumnDataSource(dict(\n", " x=node_x,\n", " y=node_y,\n", " label=[G.nodes[n][\"label\"] for n in node_names],\n", "))\n", "\n", "labels = LabelSet(\n", " x=\"x\",\n", " y=\"y\",\n", " text=\"label\",\n", " source=labels_source,\n", " text_align=\"center\",\n", " text_baseline=\"middle\",\n", " text_font_size=\"9pt\",\n", " text_color=\"#111111\",\n", ")\n", "plot.add_layout(labels)\n", "\n", "show(plot)\n", "\n", "# Version statique\n", "\n", "fig, ax = plt.subplots(figsize=(10, 10))\n", "ax.set_axis_off()\n", "\n", "# Nœuds : réutilise la taille et la couleur déjà stockées dans G\n", "node_sizes = [G.nodes[n][\"size\"] * 20 for n in G.nodes()] # facteur à ajuster visuellement\n", "node_colors = [G.nodes[n][\"color\"] for n in G.nodes()]\n", "\n", "nx.draw_networkx_nodes(\n", " G,\n", " pos,\n", " ax=ax,\n", " node_size=node_sizes,\n", " node_color=node_colors,\n", " linewidths=1.0,\n", " edgecolors=\"#111111\",\n", ")\n", "\n", "# Arêtes : on réutilise largeur et couleur calculées pour Bokeh\n", "edges = list(G.edges())\n", "\n", "nx.draw_networkx_edges(\n", " G,\n", " pos,\n", " ax=ax,\n", " edgelist=edges,\n", " width=edge_line_width,\n", " edge_color=edge_color,\n", " arrows=True,\n", " arrowstyle=\"-|>\",\n", " arrowsize=15,\n", " alpha=0.5,\n", ")\n", "\n", "# Labels : mêmes labels que dans Bokeh\n", "labels = {n: G.nodes[n][\"label\"] for n in G.nodes()}\n", "nx.draw_networkx_labels(\n", " G,\n", " pos,\n", " labels=labels,\n", " ax=ax,\n", " font_size=9,\n", " font_color=\"#111111\",\n", ")\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "La pile logicielle employée par l'OBVIL pour son propre graphique repose sur Sigma et ForceAtlas2, des modules `nodejs` que je ne souhaitais pas exploiter dans ce notebook.\n", "Nous avons choisi d'exploiter la librairie bokeh pour sa popularité et sa maintenance active, ainsi que sa disponibilité dans cette instance de Jupyter Lab." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "## Conclusion\n", "\n", "Cette étude a révélé que malgré tout le soin que l'on peut apporter au traitement d'un sujet spécifique, il est possible d'introduire involontairement des \"artefacts\" dans les données sur lesquelles on travaille. \n", "Ici, il s'agit de différences d'orthographes mineures, invisibles lors d'une lecture par un humain, mais qui peuvent rendre une étude assistée par l'informatique plus complexe, voire sujette aux erreurs.\n", "On le répète assez souvent en informatique (et particulièrement en développement web) : on ne doit jamais avoir confiance dans les entrées...\n", "Cela a nécessité un travail assez important en amont, et il faut préciser que ce n'est pas infaillible.\n", "\n", "Une autre source potentielle d'erreur, que ce soit au niveau de l'analyse ou de l'interprétation, consiste en des définitions ou des méthodologies différentes.\n", "Ici, nous avons calculé des statistiques divergentes de celles de l'OBVIL, probablement en raison d'une définition différente de ce qu'est une \"ligne\".\n", "Pour notre étude, nous considérons qu'une ligne est une suite de 60 caractères, dont les espaces surnuméraires ont été supprimés (incluant les sauts de lignes).\n", "\n", "Or, ce n'est qu'après une inspection plus poussée du dépôt de l'outil [dramagraph](https://github.com/dramacode/dramagraph/tree/gh-pages) de l'OBLIV révèle la méthode : après \"nettoyage\" des fichiers TEI source par l'emploi d'une [feuille de style XSL](https://github.com/dramacode/dramagraph/blob/gh-pages/naked.xsl), [un autre fichier XSL](https://github.com/dramacode/dramagraph/blob/gh-pages/drama2csv.xsl#L517) est en charge, notamment, du formatage des paragraphes (séparés par des retours de ligne), de la [gestion des accents](https://github.com/dramacode/dramagraph/blob/gh-pages/drama2csv.xsl#L14) et de [la casse](https://github.com/dramacode/dramagraph/blob/gh-pages/drama2csv.xsl#L524), et enfin de la production [des compteurs](https://github.com/dramacode/dramagraph/blob/gh-pages/drama2csv.xsl#L490).\n", "\n", "Par conséquent, pour retrouver des statistiques identiques à l'OBVIL, il aurait fallut passer par le même _pipeline_.\n", "On aurait du choisir d'utiliser le TEI comme fichier source et lui appliquer les mêmes fichiers XSL, ce qui nous aurait donné directement accès aux statistiques, sans avoir besoin de les recalculer nous-même, réduisant considérablement la taille de ce notebook.\n", "\n", "On en déduit finalement que :\n", "\n", "- l'enthousiasme est parfois un ennemi ! J'aurai du prendre davantage de temps pour examiner comment l'OBVIL a produit ses statistiques, avant de me lancer dans une étude personnelle\n", "- bien que structuré, HTML est un \"produit transformé\" : les fichiers TEI ont manifestement servi comme base à tous les autres formats proposés par l'OBVIL ; j'aurai du l'identifier comme source idéale" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 5 }