From c36e3c1c2b4f9e17bb8f39a7c7d6bce07782e793 Mon Sep 17 00:00:00 2001 From: Konrad Hinsen Date: Tue, 10 Mar 2020 13:35:10 +0100 Subject: [PATCH] Analyse de L'Avare (exo3 module3) --- module3/exo3/exercice.ipynb | 25 - module3/exo3/exercice_R_en.org | 81 - module3/exo3/exercice_R_fr.org | 84 - module3/exo3/exercice_en.Rmd | 33 - module3/exo3/exercice_en.ipynb | 25 - module3/exo3/exercice_fr.Rmd | 33 - module3/exo3/exercice_fr.html | 14080 ++++++++++++++++++++++++++ module3/exo3/exercice_fr.ipynb | 793 +- module3/exo3/exercice_fr.pdf | Bin 0 -> 253450 bytes module3/exo3/exercice_python_en.org | 94 - module3/exo3/exercice_python_fr.org | 93 - 11 files changed, 14870 insertions(+), 471 deletions(-) delete mode 100644 module3/exo3/exercice.ipynb delete mode 100644 module3/exo3/exercice_R_en.org delete mode 100644 module3/exo3/exercice_R_fr.org delete mode 100644 module3/exo3/exercice_en.Rmd delete mode 100644 module3/exo3/exercice_en.ipynb delete mode 100644 module3/exo3/exercice_fr.Rmd create mode 100644 module3/exo3/exercice_fr.html create mode 100644 module3/exo3/exercice_fr.pdf delete mode 100644 module3/exo3/exercice_python_en.org delete mode 100644 module3/exo3/exercice_python_fr.org diff --git a/module3/exo3/exercice.ipynb b/module3/exo3/exercice.ipynb deleted file mode 100644 index 0bbbe37..0000000 --- a/module3/exo3/exercice.ipynb +++ /dev/null @@ -1,25 +0,0 @@ -{ - "cells": [], - "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": "3.6.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} - diff --git a/module3/exo3/exercice_R_en.org b/module3/exo3/exercice_R_en.org deleted file mode 100644 index 2b73d64..0000000 --- a/module3/exo3/exercice_R_en.org +++ /dev/null @@ -1,81 +0,0 @@ -#+TITLE: Your title -#+AUTHOR: Your name -#+DATE: Today's date -#+LANGUAGE: en -# #+PROPERTY: header-args :eval never-export - -#+HTML_HEAD: -#+HTML_HEAD: -#+HTML_HEAD: -#+HTML_HEAD: -#+HTML_HEAD: -#+HTML_HEAD: - -* Some explanations - -This is an org-mode document with code examples in R. Once opened in -Emacs, this document can easily be exported to HTML, PDF, and Office -formats. For more information on org-mode, see -https://orgmode.org/guide/. - -When you type the shortcut =C-c C-e h o=, this document will be -exported as HTML. All the code in it will be re-executed, and the -results will be retrieved and included into the exported document. If -you do not want to re-execute all code each time, you can delete the # -and the space before ~#+PROPERTY:~ in the header of this document. - -Like we showed in the video, R code is included as follows (and is -exxecuted by typing ~C-c C-c~): - -#+begin_src R :results output :exports both -print("Hello world!") -#+end_src - -#+RESULTS: -: [1] "Hello world!" - -And now the same but in an R session. This is the most frequent -situation, because R is really an interactive language. With a -session, R's state, i.e. the values of all the variables, remains -persistent from one code block to the next. The code is still executed -using ~C-c C-c~. - -#+begin_src R :results output :session *R* :exports both -summary(cars) -#+end_src - -#+RESULTS: -: speed dist -: Min. : 4.0 Min. : 2.00 -: 1st Qu.:12.0 1st Qu.: 26.00 -: Median :15.0 Median : 36.00 -: Mean :15.4 Mean : 42.98 -: 3rd Qu.:19.0 3rd Qu.: 56.00 -: Max. :25.0 Max. :120.00 - -Finally, an example for graphical output: -#+begin_src R :results output graphics :file "./cars.png" :exports results :width 600 :height 400 :session *R* -plot(cars) -#+end_src - -#+RESULTS: -[[file:./cars.png]] - -Note the parameter ~:exports results~, which indicates that the code -will not appear in the exported document. We recommend that in the -context of this MOOC, you always leave this parameter setting as -~:exports both~, because we want your analyses to be perfectly -transparent and reproducible. - -Watch out: the figure generated by the code block is /not/ stored in -the org document. It's a plain file, here named ~cars.png~. You have -to commit it explicitly if you want your analysis to be legible and -understandable on GitLab. - -Finally, don't forget that we provide in the resource section of this -MOOC a configuration with a few keyboard shortcuts that allow you to -quickly create code blocks in R by typing ~ -#+HTML_HEAD: -#+HTML_HEAD: -#+HTML_HEAD: -#+HTML_HEAD: -#+HTML_HEAD: - -* Quelques explications - -Ceci est un document org-mode avec quelques exemples de code -R. Une fois ouvert dans emacs, ce document peut aisément être -exporté au format HTML, PDF, et Office. Pour plus de détails sur -org-mode vous pouvez consulter https://orgmode.org/guide/. - -Lorsque vous utiliserez le raccourci =C-c C-e h o=, ce document sera -compilé en html. Tout le code contenu sera ré-exécuté, les résultats -récupérés et inclus dans un document final. Si vous ne souhaitez pas -ré-exécuter tout le code à chaque fois, il vous suffit de supprimer -le # et l'espace qui sont devant le ~#+PROPERTY:~ au début de ce -document. - -Comme nous vous l'avons montré dans la vidéo, on inclut du code -R de la façon suivante (et on l'exécute en faisant ~C-c C-c~): - -#+begin_src R :results output :exports both -print("Hello world!") -#+end_src - -#+RESULTS: -: [1] "Hello world!" - -Voici la même chose, mais avec une session R (c'est le cas le -plus courant, R étant vraiment un langage interactif), donc une -persistance d'un bloc à l'autre (et on l'exécute toujours en faisant -~C-c C-c~). - -#+begin_src R :results output :session *R* :exports both -summary(cars) -#+end_src - -#+RESULTS: -: speed dist -: Min. : 4.0 Min. : 2.00 -: 1st Qu.:12.0 1st Qu.: 26.00 -: Median :15.0 Median : 36.00 -: Mean :15.4 Mean : 42.98 -: 3rd Qu.:19.0 3rd Qu.: 56.00 -: Max. :25.0 Max. :120.00 - -Et enfin, voici un exemple de sortie graphique: -#+begin_src R :results output graphics :file "./cars.png" :exports results :width 600 :height 400 :session *R* -plot(cars) -#+end_src - -#+RESULTS: -[[file:./cars.png]] - -Vous remarquerez le paramètre ~:exports results~ qui indique que le code -ne doit pas apparaître dans la version finale du document. Nous vous -recommandons dans le cadre de ce MOOC de ne pas changer ce paramètre -(indiquer ~both~) car l'objectif est que vos analyses de données soient -parfaitement transparentes pour être reproductibles. - -Attention, la figure ainsi générée n'est pas stockée dans le document -org. C'est un fichier ordinaire, ici nommé ~cars.png~. N'oubliez pas -de le committer si vous voulez que votre analyse soit lisible et -compréhensible sur GitLab. - -Enfin, pour les prochains exercices, nous ne vous fournirons pas -forcément de fichier de départ, ça sera à vous de le créer, par -exemple en repartant de ce document et de le commiter vers -gitlab. N'oubliez pas que nous vous fournissons dans les ressources de -ce MOOC une configuration avec un certain nombre de raccourcis -claviers permettant de créer rapidement les blocs de code R (en -faisant ~. - -When you click on the button **Knit**, the document will be compiled in order to re-execute the R code and to include the results into the final document. As we have shown in the video, R code is inserted as follows: - -```{r cars} -summary(cars) -``` - -It is also straightforward to include figures. For example: - -```{r pressure, echo=FALSE} -plot(pressure) -``` - -Note the parameter `echo = FALSE` that indicates that the code will not appear in the final version of the document. We recommend not to use this parameter in the context of this MOOC, because we want your data analyses to be perfectly transparent and reproducible. - -Since the results are not stored in Rmd files, you should generate an HTML or PDF version of your exercises and commit them. Otherwise reading and checking your analysis will be difficult for anyone else but you. - -Now it's your turn! You can delete all this information and replace it by your computational document. diff --git a/module3/exo3/exercice_en.ipynb b/module3/exo3/exercice_en.ipynb deleted file mode 100644 index 0bbbe37..0000000 --- a/module3/exo3/exercice_en.ipynb +++ /dev/null @@ -1,25 +0,0 @@ -{ - "cells": [], - "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": "3.6.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} - diff --git a/module3/exo3/exercice_fr.Rmd b/module3/exo3/exercice_fr.Rmd deleted file mode 100644 index 7eece5e..0000000 --- a/module3/exo3/exercice_fr.Rmd +++ /dev/null @@ -1,33 +0,0 @@ ---- -title: "Votre titre" -author: "Votre nom" -date: "La date du jour" -output: html_document ---- - - -```{r setup, include=FALSE} -knitr::opts_chunk$set(echo = TRUE) -``` - -## Quelques explications - -Ceci est un document R markdown que vous pouvez aisément exporter au format HTML, PDF, et MS Word. Pour plus de détails sur R Markdown consultez . - -Lorsque vous cliquerez sur le bouton **Knit** ce document sera compilé afin de ré-exécuter le code R et d'inclure les résultats dans un document final. Comme nous vous l'avons montré dans la vidéo, on inclue du code R de la façon suivante: - -```{r cars} -summary(cars) -``` - -Et on peut aussi aisément inclure des figures. Par exemple: - -```{r pressure, echo=FALSE} -plot(pressure) -``` - -Vous remarquerez le paramètre `echo = FALSE` qui indique que le code ne doit pas apparaître dans la version finale du document. Nous vous recommandons dans le cadre de ce MOOC de ne pas utiliser ce paramètre car l'objectif est que vos analyses de données soient parfaitement transparentes pour être reproductibles. - -Comme les résultats ne sont pas stockés dans les fichiers Rmd, pour faciliter la relecture de vos analyses par d'autres personnes, vous aurez donc intérêt à générer un HTML ou un PDF et à le commiter. - -Maintenant, à vous de jouer! Vous pouvez effacer toutes ces informations et les remplacer par votre document computationnel. diff --git a/module3/exo3/exercice_fr.html b/module3/exo3/exercice_fr.html new file mode 100644 index 0000000..c41ce68 --- /dev/null +++ b/module3/exo3/exercice_fr.html @@ -0,0 +1,14080 @@ + + + + +Analyse de L'Avare + + + + + + + + + + + + + + + + + + + + + + + +
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+

Auteur: Konrad Hinsen

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+

Analyse de "L'Avare"

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In [1]:
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+
%matplotlib inline
+import matplotlib.pyplot as plt
+import matplotlib.patches as mpatch
+import numpy as np
+from collections import defaultdict
+from operator import add
+from functools import reduce
+import unicodedata
+import urllib.request
+import os
+
+ +
+
+
+ +
+
+
+
+

Nous utilisons des fonctionnalités introduites avec Python 3.7, c'est donc la version minimale réquise.

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In [2]:
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+
import sys
+if sys.version_info.major < 3 or sys.version_info.minor < 7:
+    print("Veuillez utiliser Python 3.7 (ou plus) !")
+
+ +
+
+
+ +
+
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+
+

Téléchargement

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+

Nous utilisons le texte suivant :

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In [3]:
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text_url = 'https://dramacode.github.io/markdown/moliere_avare.txt'
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+ +
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+
+

Nous en faisons d'abord une copie locale :

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In [4]:
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+
+
text_file = 'moliere_avare.txt'
+if not os.path.exists(text_file):
+    urllib.request.urlretrieve(text_url, text_file)
+
+ +
+
+
+ +
+
+
+
+

Lecture et analyse syntaxique du texte

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+

Attention: le code dans cette partie dépend du format précis du texte!

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In [5]:
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text = open(text_file.strip(), 'r')
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+ +
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+ +
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+
+
+

Les noms des acteurs apparaissent parfois en majuscule, parfois pas. L'usage des accents n'est pas non plus uniforme. Il est pratique d'introduire une forme normalisée, nous choisissons le tout majuscule et sans accents.

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In [6]:
+
+
+
def normalized_name(name):
+    return unicodedata \
+        .normalize('NFKD', name) \
+        .encode('ASCII', 'ignore') \
+        .decode() \
+        .upper()
+
+ +
+
+
+ +
+
+
+
+

D'abord nous lisons la liste des acteurs:

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In [7]:
+
+
+
for line in text:
+    if line.strip() == "# ACTEURS.":
+        break
+characters ={}
+while True:
+    line = next(text)
+    if not line.startswith(" – "):
+        break
+    name, *description = line[3:].split(',')
+    description = (','.join(description)).strip()
+    characters[normalized_name(name)] = ({'name': name, 'description': description})
+assert len(characters) == 14
+
+ +
+
+
+ +
+
+
+
+

Voici donc nos acteurs :

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In [8]:
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+
for name, record in characters.items():
+    print(name)
+    print(record)
+    print()
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
HARPAGON
+{'name': 'Harpagon', 'description': "Père de Cléante et d'Élise, et Amoureux de Mariane."}
+
+CLEANTE
+{'name': 'Cléante', 'description': "Fils d'Harpagon, Amant de Mariane."}
+
+ELISE
+{'name': 'Élise', 'description': "Fille d'Harpagon, Amante de Valère."}
+
+VALERE
+{'name': 'Valère', 'description': "Fils d'Anselme, et Amant d'Élise."}
+
+MARIANE
+{'name': 'Mariane', 'description': "Amante de Cléante, et aimée d'Harpagon."}
+
+ANSELME
+{'name': 'Anselme', 'description': 'Père de Valère et de Mariane.'}
+
+FROSINE
+{'name': 'Frosine', 'description': "Femme d'Intrigue."}
+
+MAITRE SIMON
+{'name': 'Maitre Simon', 'description': 'Courtier.'}
+
+MAITRE JACQUES
+{'name': 'Maitre Jacques', 'description': "Cuisinier et Cocher d'Harpagon."}
+
+LA FLECHE
+{'name': 'La Flèche', 'description': 'Valet de Cléante.'}
+
+DAME CLAUDE
+{'name': 'Dame Claude', 'description': "Servante d'Harpagon."}
+
+BRINDAVOINE
+{'name': 'Brindavoine', 'description': "laquais d'Harpagon."}
+
+LA MERLUCHE
+{'name': 'La Merluche', 'description': "laquais d'Harpagon."}
+
+LE COMMISSAIRE
+{'name': 'Le commissaire', 'description': 'et son clerc.'}
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+ +
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+ +
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+
+

Notons que le clerc du commaissaire est référencé, mais ne dit jamais rien. On peut l'ignorer.

+

Puis nous avançons au texte principal :

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+
In [9]:
+
+
+
for line in text:
+    if line.rstrip().startswith("# L'Avare"):
+        break
+
+ +
+
+
+ +
+
+
+
+

Le reste du texte est une suite d'actes, dont chacun consiste de scènes. Chaque scène commence avec une liste des acteurs, sauf la scene VII de l'acte IV. Nous n'avons pas besoin de cette liste, parce que nous pouvons la reconstruire du dialogue. Nous ignorons donc cette ligne et n'analysons que le dialogue.

+

Dans le dialogue, chaque acteur est introduit sur une ligne qui commence avec quatre espaces, suivi par le nom de l'acteur, optionnellement suivi par un commentaire en gras (étoiles en Markdown), qui peut ou non être séparé par une virgule. Ceci rend l'extraction du nom un peu compliqué.

+ +
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In [10]:
+
+
+
acts = []
+scenes = None
+scene = None
+speech = None
+
+for line in text:
+    line = line.rstrip()
+    if not line:
+        continue
+    if line.startswith("## "):
+        scenes = []
+        acts.append(scenes)
+    elif line.startswith("### "):
+        assert scenes is not None
+        scene_characters = next(text)
+        scene = []
+        scenes.append(scene)
+    elif line.startswith("    "):
+        assert scene is not None
+        character = normalized_name(line
+                                    .lstrip()
+                                    .split(',')[0]
+                                    .split('*')[0]
+                                    .rstrip(' .'))
+        assert character in characters
+        speech = []
+        scene.append({'character': character, 'speech': speech})
+    else:
+        assert speech is not None
+        speech.append(line)
+
+ +
+
+
+ +
+
+
+
+

Quelques petites vérifications s'imposent. Il est assez facile de compter les actes et scènes à la main, vérifions donc :

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In [11]:
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assert list(map(len, acts)) == [5, 5, 9, 7, 6]
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+

Vérifions aussi que chaque scène contient un dialogue :

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In [12]:
+
+
+
for scenes in acts:
+    for scene in scenes:
+        assert len(scene) > 0
+
+ +
+
+
+ +
+
+
+
+

Qui parle le plus ?

Commençons par une vue globale: un tableau qui montre l'importance de chaque acteur. On résume le nombre de scènes où l'acteur apparaît, ainsi que le nombre de répliques qu'il prononce et le nombre total des mots que ces répliques contiennent.

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In [13]:
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+
+
n_scene = defaultdict(lambda: 0)
+n_speech = defaultdict(lambda: 0)
+n_word = defaultdict(lambda: 0)
+for scenes in acts:
+    for scene in scenes:
+        in_scene = defaultdict(lambda: False)
+        for part in scene:
+            c = part['character']
+            s = part['speech']
+            in_scene[c] = True
+            n_speech[c] += 1
+            for line in s:
+                n_word[c] += len(line.split())
+        for c in characters:
+            if in_scene[c]:
+                n_scene[c] += 1
+
+ +
+
+
+ +
+
+
+
In [14]:
+
+
+
print('Acteur         Scènes Répliques   Mots')
+print('--------------------------------------')
+for key, character in characters.items():
+    print(character['name'].ljust(15), str(n_scene[key]).rjust(5), str(n_speech[key]).rjust(7), str(n_word[key]).rjust(8))
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
Acteur         Scènes Répliques   Mots
+--------------------------------------
+Harpagon           23     354     5923
+Cléante            14     161     3203
+Élise               9      51     1034
+Valère              9     101     2626
+Mariane             6      31      878
+Anselme             2      20      488
+Frosine            10      60     2250
+Maitre Simon        1       5      186
+Maitre Jacques      9      85     1607
+La Flèche           5      66     1436
+Dame Claude         0       0        0
+Brindavoine         2       3       38
+La Merluche         2       5       50
+Le commissaire      3      17      281
+
+
+
+ +
+
+ +
+
+
+
+

Un premier constat: Dame Claude ne dit jamais rien. Elle ne figurera donc nulle part dans les analyses qui suivent, dont quelques-unes seront facilitées par sa suppression.

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+
In [15]:
+
+
+
del characters['DAME CLAUDE']
+
+ +
+
+
+ +
+
+
+
+

Une analyse plus fine procède par scène. Comptons d'abord le nombre de mot que chaque acteur prononce dans chaque scène.

+ +
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+
+
+
+
In [16]:
+
+
+
word_count = []
+for scenes in acts:
+    word_count.append([])
+    for scene in scenes:
+        wc = defaultdict(lambda: 0)
+        for part in scene:
+            count = sum(len(line.split()) for line in part['speech'])
+            wc[part['character']] += count
+        word_count[-1].append(wc)
+
+ +
+
+
+ +
+
+
+
+

Un premier graphique montre le nombre de mot que chaque acteur prononce dans chaque scène. Chaque ligne représente une scene, et sa longueur est proporionnelle au nombre de mots prononcés.

+ +
+
+
+
+
+
In [17]:
+
+
+
fig, axes = plt.subplots(nrows = len(acts),
+                         figsize=(8, 12),
+                         gridspec_kw = {'height_ratios': list(map(len, acts))})
+fig.tight_layout()
+fig.subplots_adjust(left=0.03, top=0.82, hspace=0.25)
+colors = plt.get_cmap('tab20b')(2+np.arange(len(characters)))
+colors[::2] = colors[::2][::-1]
+max_x = 0
+for ax, act, scene_wcs in zip(axes, range(len(acts)), word_count):
+    ax.set_title(f"Acte {act+1}", fontsize=10)
+    data = np.array([[scene_wc[c]
+                      for c in characters]
+                     for scene_wc in scene_wcs])
+    offsets = np.hstack([np.zeros((len(data), 1), np.int),
+                         data[:, :-1].cumsum(axis=1)])
+    max_x = max(max_x, offsets[:, -1].max())
+    for i, character in enumerate(characters.keys()):
+        ax.barh(np.arange(len(data)),
+                width=data[:, i], left=offsets[:, i],
+                color=colors[i], height=1,
+                linewidth=1, edgecolor='black',
+                label=characters[character]['name'])
+for ax in axes:
+    ax.invert_yaxis()
+    ax.axis('off')
+    ax.set_xlim(0, max_x)
+axes[0].legend(ncol=3,
+               bbox_to_anchor=(-0.01, 1.3),
+               loc='lower left',
+               fontsize=10)
+
+ +
+
+
+ +
+
+ + +
+ +
Out[17]:
+ + + + +
+
<matplotlib.legend.Legend at 0x114b7c240>
+
+ +
+ +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
+

Une autre vue des même données utilise des lignes de longueurs égales, mais dont la hauteur représente le nombre de mots prononcés en total. La largeur de chaque rectangle indique alors le pourcentage de la scène qu'un acteur occupe.

+ +
+
+
+
+
+
In [18]:
+
+
+
fig, axes = plt.subplots(nrows = len(acts),
+                         figsize=(8, 12))
+fig.tight_layout()
+fig.subplots_adjust(left=0.03, top=0.82, hspace=0.25)
+colors = plt.get_cmap('tab20b')(2+np.arange(len(characters)))
+colors[::2] = colors[::2][::-1]
+for ax, act, scene_wcs in zip(axes, range(len(acts)), word_count):
+    ax.set_title(f"Acte {act+1}", fontsize=10)
+    data = np.array([[scene_wc[c]
+                      for c in characters]
+                     for scene_wc in scene_wcs])
+    scene_lengths = np.sum(data, axis=1)
+    widths = data / scene_lengths[:, np.newaxis]
+    heights = scene_lengths
+    h_offsets = np.hstack([np.zeros((len(widths), 1), np.float),
+                           widths.cumsum(axis=1)])
+    assert (np.fabs(h_offsets[:, -1] - 1) < 1.e-10).all()
+    v_offsets = np.hstack([[0], heights.cumsum()[:-1]])
+    for i, character in enumerate(characters.keys()):
+        ax.barh(v_offsets+heights/2,
+                width=widths[:, i], left=h_offsets[:, i],
+                height=heights,
+                color=colors[i],
+                linewidth=1, edgecolor='black',
+                label=characters[character]['name'])
+for ax in axes:
+    ax.invert_yaxis()
+    ax.xaxis.set_visible(False)
+    ax.axis('off')
+    ax.set_xlim(0, 1)
+axes[0].legend(ncol=3,
+               bbox_to_anchor=(-0.01, 1.2),
+               loc='lower left',
+               fontsize=10)
+
+ +
+
+
+ +
+
+ + +
+ +
Out[18]:
+ + + + +
+
<matplotlib.legend.Legend at 0x115206e80>
+
+ +
+ +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
+

Qui parle à qui ?

La plupart des répliques s'adressent à une personne spécifique, mais il n'est pas facile d'identifier cette personne de façon sure par une analyse du texte. Parfois cette personne est nommée ("Hé quoi, charmante Élise..."), et on pourrait donc envisager d'exploiter les noms contenus dans les répliques. Mais le plus souvent la personne n'est pas nommée, et parfois les acteurs parlent d'une tièrce personne ("Elle se nomme Mariane...").

+

Nous allons adopter une approche imparfaite mais simple, qui devrait donner une vue globale correcte: on suppose que chaque réplique s'adresse à la personne qui a parlé juste avant.

+ +
+
+
+
+
+
In [19]:
+
+
+
word_count = defaultdict(lambda: 0)
+for scenes in acts:
+    for scene in scenes:
+        previous = None
+        wc = defaultdict(lambda: 0)
+        for part in scene:
+            current = part['character']
+            count = sum(len(line.split()) for line in part['speech'])
+            if previous is not None:
+                word_count[(current, previous)] += count
+            previous = current
+
+ +
+
+
+ +
+
+
+
+

L'affichage sous forme de matrice donne une première impression de la diversité dans l'ampleur des échanges :

+ +
+
+
+
+
+
In [20]:
+
+
+
for c1 in characters:
+    print(''.join([str(word_count[(c1, c2)]).rjust(5) for c2 in characters]))
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
    0 1485  364  857   85  189  607   60  665  434   20  134  118
+ 1354    0  702   22  305    0  151    0  162  362    0    0    0
+  314  195    0  499   26    0    0    0    0    0    0    0    0
+ 1452    0  677    0    4  265    0    0  159    0    0    0    0
+  167  278   36  190    0    0  175    0    0    0    0    0    0
+   95    0    0  170  207    0    0    0    8    0    0    0    0
+ 1501  331   46    0  233    0    0    0   12  116    0    0    0
+  102   57    0    0    0    0    0    0    0    0    0    0    0
+ 1026  148    0  267    0    0   11    0    0    0    0    0  111
+  249  883    0    0    0    0  252   13    0    0    0    0    0
+   27    0    0    0    0    0    0    0    0    0    0    0    0
+    8    0    0   11    0    0    0    0    5    0   17    0    0
+  194    0    0    0    0   12    0    0   38    0    0    0    0
+
+
+
+ +
+
+ +
+
+
+
+

Ces données se prêtent à une présentation sous forme d'un graphe. Chaque acteur en est un noeud, représesenté par un cercle dont la superficie indique l'importance de sa prise de parole. Ces cercles ont les mêmes couleurs déjà utilisées ci-dessus pour chaque acteur.

+ +
+
+
+
+
+
In [21]:
+
+
+
colors = plt.get_cmap('tab20b')(2+np.arange(len(characters)))
+colors[::2] = colors[::2][::-1]
+
+character_names = list(characters.keys())
+
+importance = {}
+for c1 in characters:
+    importance[c1] = 0
+    for c2 in characters:
+        importance[c1] += word_count[(c1, c2)]
+max_importance = max(importance.values())
+for c in characters:
+    importance[c] /= max_importance
+r_sq_min = 0.015**2
+r_sq_max = 0.06**2
+
+def radius(c):
+    n = character_names[c]
+    return np.sqrt(r_sq_min + importance[n]*(r_sq_max-r_sq_min))
+
+def draw_circle(c):
+    n = character_names[c]
+    r = positions.get(n, None)
+    circle = mpatch.Circle(r, radius(c), fc=colors[c])
+    ax.add_patch(circle)
+    return circle
+
+ +
+
+
+ +
+
+
+
+

Le positionnement des cercles est important pour la clarté du graphe, surtout quand nous allons rajouter les arêtes. Il y a des algorithmes pour trouver des positions acceptables, mais ils sont assez compliqués, sans pour autant donner un résultat vraiment satisfaisant. Pour nos 14 acteurs, un placement manuel reste faisable et permet une optimisation fine.

+ +
+
+
+
+
+
In [22]:
+
+
+
positions = {}
+positions['HARPAGON'] = (0.5, 0.5)
+positions['VALERE'] = (0.05, 0.5)
+positions['CLEANTE'] = (0.5, 0.95)
+positions['ELISE'] = (0.15, 0.9)
+positions['LA FLECHE'] = (0.9, 0.75)
+positions['FROSINE'] = (0.7, 0.75)
+positions['MARIANE'] = (0.2, 0.7)
+positions['MAITRE SIMON'] = (0.95, 0.5)
+positions['MAITRE JACQUES'] = (0.35, 0.2)
+positions['ANSELME'] = (0.2, 0.1)
+positions['LE COMMISSAIRE'] = (0.6, 0.05)
+positions['LA MERLUCHE'] = (0.7, 0.05)
+positions['BRINDAVOINE'] = (0.8, 0.1)
+
+ +
+
+
+ +
+
+
+
+

Nous pouvons maintenant générer les cercles et la légende. +Les arêtes sont représentés par des flèches transparentes dont la largeur est proportionnelle au nombre de mots qu'un acteur adresse à un autre.

+ +
+
+
+
+
+
In [23]:
+
+
+
for n in positions:
+    positions[n] = np.array(positions[n])
+
+fig, ax = plt.subplots(nrows = 1,
+                       figsize=(9, 12))
+ax.axis('off')
+fig.tight_layout()
+fig.subplots_adjust(left=0.03, top=0.8, hspace=0.25)
+circles = [draw_circle(c)
+           for c in range(len(characters))]
+plt.legend(circles,
+           [characters[character_names[c]]['name']
+            for c in range(len(circles))],
+           ncol=3,
+           bbox_to_anchor=(0., 1.02),
+           loc='lower left',
+           fontsize=10)
+
+transparency = np.array([1., 1., 1., 0.3])
+
+def draw_arrow(c1, c2):
+    n1 = character_names[c1]
+    n2 = character_names[c2]
+    r1 = positions.get(n1, None)
+    r2 = positions.get(n2, None)
+    d = r2-r1
+    l = np.sqrt(np.sum(d**2))
+    x_tail, y_tail = r1 + 1.1*radius(c1) * d/l
+    x_head, y_head = r2 - 1.1*radius(c2) * d/l
+    w = word_count[(n1, n2)] / 50.
+    astyle = mpatch.ArrowStyle.Simple(head_length=w,
+                                      head_width=1.5*w,
+                                      tail_width=w)
+    arrow = mpatch.FancyArrowPatch((x_tail, y_tail), (x_head, y_head),
+                                   arrowstyle=astyle,
+                                   fc=colors[c1]*transparency)
+    ax.add_patch(arrow)
+
+for c1 in range(len(characters)):
+    for c2 in range(len(characters)):
+        if c1 != c2:
+            draw_arrow(c1, c2)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+ + + + + + diff --git a/module3/exo3/exercice_fr.ipynb b/module3/exo3/exercice_fr.ipynb index 0bbbe37..ea6c3ca 100644 --- a/module3/exo3/exercice_fr.ipynb +++ b/module3/exo3/exercice_fr.ipynb @@ -1,5 +1,793 @@ { - "cells": [], + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Auteur: Konrad Hinsen" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Analyse de \"L'Avare\"" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.patches as mpatch\n", + "import numpy as np\n", + "from collections import defaultdict\n", + "from operator import add\n", + "from functools import reduce\n", + "import unicodedata\n", + "import urllib.request\n", + "import os" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nous utilisons des fonctionnalités introduites avec Python 3.7, c'est donc la version minimale réquise." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "if sys.version_info.major < 3 or sys.version_info.minor < 7:\n", + " print(\"Veuillez utiliser Python 3.7 (ou plus) !\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Téléchargement" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nous utilisons le texte suivant :" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "text_url = 'https://dramacode.github.io/markdown/moliere_avare.txt'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nous en faisons d'abord une copie locale :" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "text_file = 'moliere_avare.txt'\n", + "if not os.path.exists(text_file):\n", + " urllib.request.urlretrieve(text_url, text_file)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Lecture et analyse syntaxique du texte" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Attention: le code dans cette partie dépend du format précis du texte!" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "text = open(text_file.strip(), 'r')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Les noms des acteurs apparaissent parfois en majuscule, parfois pas. L'usage des accents n'est pas non plus uniforme. Il est pratique d'introduire une forme normalisée, nous choisissons le tout majuscule et sans accents." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def normalized_name(name):\n", + " return unicodedata \\\n", + " .normalize('NFKD', name) \\\n", + " .encode('ASCII', 'ignore') \\\n", + " .decode() \\\n", + " .upper()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "D'abord nous lisons la liste des acteurs:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "for line in text:\n", + " if line.strip() == \"# ACTEURS.\":\n", + " break\n", + "characters ={}\n", + "while True:\n", + " line = next(text)\n", + " if not line.startswith(\" – \"):\n", + " break\n", + " name, *description = line[3:].split(',')\n", + " description = (','.join(description)).strip()\n", + " characters[normalized_name(name)] = ({'name': name, 'description': description})\n", + "assert len(characters) == 14" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Voici donc nos acteurs :" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "HARPAGON\n", + "{'name': 'Harpagon', 'description': \"Père de Cléante et d'Élise, et Amoureux de Mariane.\"}\n", + "\n", + "CLEANTE\n", + "{'name': 'Cléante', 'description': \"Fils d'Harpagon, Amant de Mariane.\"}\n", + "\n", + "ELISE\n", + "{'name': 'Élise', 'description': \"Fille d'Harpagon, Amante de Valère.\"}\n", + "\n", + "VALERE\n", + "{'name': 'Valère', 'description': \"Fils d'Anselme, et Amant d'Élise.\"}\n", + "\n", + "MARIANE\n", + "{'name': 'Mariane', 'description': \"Amante de Cléante, et aimée d'Harpagon.\"}\n", + "\n", + "ANSELME\n", + "{'name': 'Anselme', 'description': 'Père de Valère et de Mariane.'}\n", + "\n", + "FROSINE\n", + "{'name': 'Frosine', 'description': \"Femme d'Intrigue.\"}\n", + "\n", + "MAITRE SIMON\n", + "{'name': 'Maitre Simon', 'description': 'Courtier.'}\n", + "\n", + "MAITRE JACQUES\n", + "{'name': 'Maitre Jacques', 'description': \"Cuisinier et Cocher d'Harpagon.\"}\n", + "\n", + "LA FLECHE\n", + "{'name': 'La Flèche', 'description': 'Valet de Cléante.'}\n", + "\n", + "DAME CLAUDE\n", + "{'name': 'Dame Claude', 'description': \"Servante d'Harpagon.\"}\n", + "\n", + "BRINDAVOINE\n", + "{'name': 'Brindavoine', 'description': \"laquais d'Harpagon.\"}\n", + "\n", + "LA MERLUCHE\n", + "{'name': 'La Merluche', 'description': \"laquais d'Harpagon.\"}\n", + "\n", + "LE COMMISSAIRE\n", + "{'name': 'Le commissaire', 'description': 'et son clerc.'}\n", + "\n" + ] + } + ], + "source": [ + "for name, record in characters.items():\n", + " print(name)\n", + " print(record)\n", + " print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notons que le clerc du commaissaire est référencé, mais ne dit jamais rien. On peut l'ignorer.\n", + "\n", + "Puis nous avançons au texte principal :" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "for line in text:\n", + " if line.rstrip().startswith(\"# L'Avare\"):\n", + " break" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Le reste du texte est une suite d'actes, dont chacun consiste de scènes. Chaque scène commence avec une liste des acteurs, sauf la scene VII de l'acte IV. Nous n'avons pas besoin de cette liste, parce que nous pouvons la reconstruire du dialogue. Nous ignorons donc cette ligne et n'analysons que le dialogue.\n", + "\n", + "Dans le dialogue, chaque acteur est introduit sur une ligne qui commence avec quatre espaces, suivi par le nom de l'acteur, optionnellement suivi par un commentaire en gras (étoiles en Markdown), qui peut ou non être séparé par une virgule. Ceci rend l'extraction du nom un peu compliqué." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "acts = []\n", + "scenes = None\n", + "scene = None\n", + "speech = None\n", + "\n", + "for line in text:\n", + " line = line.rstrip()\n", + " if not line:\n", + " continue\n", + " if line.startswith(\"## \"):\n", + " scenes = []\n", + " acts.append(scenes)\n", + " elif line.startswith(\"### \"):\n", + " assert scenes is not None\n", + " scene_characters = next(text)\n", + " scene = []\n", + " scenes.append(scene)\n", + " elif line.startswith(\" \"):\n", + " assert scene is not None\n", + " character = normalized_name(line\n", + " .lstrip()\n", + " .split(',')[0]\n", + " .split('*')[0]\n", + " .rstrip(' .'))\n", + " assert character in characters\n", + " speech = []\n", + " scene.append({'character': character, 'speech': speech})\n", + " else:\n", + " assert speech is not None\n", + " speech.append(line)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Quelques petites vérifications s'imposent. Il est assez facile de compter les actes et scènes à la main, vérifions donc :" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "assert list(map(len, acts)) == [5, 5, 9, 7, 6]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Vérifions aussi que chaque scène contient un dialogue :" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "for scenes in acts:\n", + " for scene in scenes:\n", + " assert len(scene) > 0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Qui parle le plus ?\n", + "\n", + "Commençons par une vue globale: un tableau qui montre l'importance de chaque acteur. On résume le nombre de scènes où l'acteur apparaît, ainsi que le nombre de répliques qu'il prononce et le nombre total des mots que ces répliques contiennent." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "n_scene = defaultdict(lambda: 0)\n", + "n_speech = defaultdict(lambda: 0)\n", + "n_word = defaultdict(lambda: 0)\n", + "for scenes in acts:\n", + " for scene in scenes:\n", + " in_scene = defaultdict(lambda: False)\n", + " for part in scene:\n", + " c = part['character']\n", + " s = part['speech']\n", + " in_scene[c] = True\n", + " n_speech[c] += 1\n", + " for line in s:\n", + " n_word[c] += len(line.split())\n", + " for c in characters:\n", + " if in_scene[c]:\n", + " n_scene[c] += 1" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Acteur Scènes Répliques Mots\n", + "--------------------------------------\n", + "Harpagon 23 354 5923\n", + "Cléante 14 161 3203\n", + "Élise 9 51 1034\n", + "Valère 9 101 2626\n", + "Mariane 6 31 878\n", + "Anselme 2 20 488\n", + "Frosine 10 60 2250\n", + "Maitre Simon 1 5 186\n", + "Maitre Jacques 9 85 1607\n", + "La Flèche 5 66 1436\n", + "Dame Claude 0 0 0\n", + "Brindavoine 2 3 38\n", + "La Merluche 2 5 50\n", + "Le commissaire 3 17 281\n" + ] + } + ], + "source": [ + "print('Acteur Scènes Répliques Mots')\n", + "print('--------------------------------------')\n", + "for key, character in characters.items():\n", + " print(character['name'].ljust(15), str(n_scene[key]).rjust(5), str(n_speech[key]).rjust(7), str(n_word[key]).rjust(8))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Un premier constat: Dame Claude ne dit jamais rien. Elle ne figurera donc nulle part dans les analyses qui suivent, dont quelques-unes seront facilitées par sa suppression." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "del characters['DAME CLAUDE']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Une analyse plus fine procède par scène. Comptons d'abord le nombre de mot que chaque acteur prononce dans chaque scène." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "word_count = []\n", + "for scenes in acts:\n", + " word_count.append([])\n", + " for scene in scenes:\n", + " wc = defaultdict(lambda: 0)\n", + " for part in scene:\n", + " count = sum(len(line.split()) for line in part['speech'])\n", + " wc[part['character']] += count\n", + " word_count[-1].append(wc)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Un premier graphique montre le nombre de mot que chaque acteur prononce dans chaque scène. Chaque ligne représente une scene, et sa longueur est proporionnelle au nombre de mots prononcés." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(nrows = len(acts),\n", + " figsize=(8, 12),\n", + " gridspec_kw = {'height_ratios': list(map(len, acts))})\n", + "fig.tight_layout()\n", + "fig.subplots_adjust(left=0.03, top=0.82, hspace=0.25)\n", + "colors = plt.get_cmap('tab20b')(2+np.arange(len(characters)))\n", + "colors[::2] = colors[::2][::-1]\n", + "max_x = 0\n", + "for ax, act, scene_wcs in zip(axes, range(len(acts)), word_count):\n", + " ax.set_title(f\"Acte {act+1}\", fontsize=10)\n", + " data = np.array([[scene_wc[c]\n", + " for c in characters]\n", + " for scene_wc in scene_wcs])\n", + " offsets = np.hstack([np.zeros((len(data), 1), np.int),\n", + " data[:, :-1].cumsum(axis=1)])\n", + " max_x = max(max_x, offsets[:, -1].max())\n", + " for i, character in enumerate(characters.keys()):\n", + " ax.barh(np.arange(len(data)),\n", + " width=data[:, i], left=offsets[:, i],\n", + " color=colors[i], height=1,\n", + " linewidth=1, edgecolor='black',\n", + " label=characters[character]['name'])\n", + "for ax in axes:\n", + " ax.invert_yaxis()\n", + " ax.axis('off')\n", + " ax.set_xlim(0, max_x)\n", + "axes[0].legend(ncol=3,\n", + " bbox_to_anchor=(-0.01, 1.3),\n", + " loc='lower left',\n", + " fontsize=10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Une autre vue des même données utilise des lignes de longueurs égales, mais dont la hauteur représente le nombre de mots prononcés en total. La largeur de chaque rectangle indique alors le pourcentage de la scène qu'un acteur occupe." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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TE5qamuoiItK1a9fQlJSUC/u3Z8+eIdu3b3cvKioyDhgwINhsNodHR0eH/vDDD24i1cff2LFj24uIJCYmdhw/fny7mJiY0MDAwKhFixZdGHLopZde8nOcG6ZOnXrVY9UBaHgEpcvYu3evW3R0dMXllnnnnXdat2jRwrZv376M1NTUjH/9618+mZmZzu7u7vYNGzbsT09Pz9i6dWv2888/H+g4iR86dMh1ypQpxfv3709r0aKFbcmSJV6PPPLIycjIyIolS5b8mpmZme7k5CRTpkxpv3bt2ty0tLSMcePGHX/22WcDbsiGNzKVlZWG0NDQ8NDQ0PCBAwdeGL19165dHitWrDjw/fffZy9ZsqTlL7/84paRkZH21VdfZf/tb38LPHjwoNPChQu9+/fvX5qZmZmekZGRFhcXd8n+rG1/iIgkJSV1mDt37qG0tLSMWbNmHZk0aVL7G7ndtxLHPg4KCop48sknO8yYMePCsDV79+5tNmvWrKO5ublpNdfLyMhwmzNnzuH9+/enHTp0yGXTpk0eFRUV6oknnui4bt26/T/99FNWcXHxhR8Y0dHR53788cfMjIyM9BkzZhydNm1aoIhIYmJiyfLly71FqsN1cXGxU0JCQsW0adP8o6OjK7Kzs9NfeeWVo+PGjQuqrf5FRUVOKSkpmWvXrs2ZMWNGgIjImjVrmu/fv9917969GRkZGel79uxx//zzz2sdoxBA49Wkut4ao82bNzfPzMx0X7dunZdI9UCe6enprkFBQZannnoq8Pvvv/cwGAxSXFzsfOTIEZOISEBAQGWvXr3OiojExMRU5OXlXTKG1d69e11ycnLc+vXrZxap7gb08fFptF1y15Ne11tCQkKZn5+fTURk+/btng888ECJyWSSdu3aWePi4k7v2LHD/fbbbz8zceLEjhaLxTB8+PCTjs/9YrXtj9LSUsPu3bs9RowYcSGYOcZ2Q/27eB9v3ry52SOPPBKUnZ2dJiLStWvXM6GhobVekxYVFXUmODjYIiISERFRkZub6+zp6WkLDAysjIqKqhQRefDBB0988MEHPiIi5wdMDsrLy3NVSmkWi0WJiIwdO/bkgAEDzG+//Xb+kiVLvO65556TIiI//vij5+rVq/eLiAwdOrT8scceM504ccJYsx5Dhw49ZTQaJTY29tyJEyecREQ2btzYfNu2bc3Dw8PDRUQqKioMmZmZroMHD76pB7IGbjYEpcuIioo6+8knn3hdbhlN09Ts2bMPJSYmll08PTk5udWJEydMv/zyS4aLi4sWEBAQdfbsWYOIiLOz84XHoRuNRs0xvWa5nTt3Prtnz54Gvx6jsXJ3d7c7/q/3hPnBgwef3rZtW9bq1atbjB8/PmjKlClFTzzxxImLl6ltf9hsNvH09LRe7tooXB8DBgw4c/LkSVNBQYFJ5Lf7uSYXF5eL951YrVYlIqJU7Zl2+vTpAX369CnftGlTblZWlnO/fv1CRKoHom7ZsqX1hx9+cFuzZo33ggULDorUflwppS6Z6OrqemGaYx1N0+Spp54qeO655xhdAGjC6Hq7jHvuuae8qqpKzZ49u7Vj2tatW933799/4REAAwcOLJ03b56PY7T6vXv3upSVlRlKS0uNrVu3tri4uGiffvqpZ35+/hUfG+Dh4WErLS01ioh07dr1XElJiWnz5s3NREQqKyvVxddQ4Lf69OlTvmrVKm+r1Sr5+fmmH3/80SMhIeFMdna2c0BAgOWZZ545/tBDDx3ftWuXe13K8/b2tgcGBlYtXLjQS6S6Re+7775zu75bARGR3bt3u9rtdvHz87Ney/rdunU7d+TIEee0tDQXEZGVK1d6O+aVlZUZAwMDq0REFixY0Pri9YYPH17y+uuvtykvLzf27NnzrIjI7bffXr5o0aJWItXXRHl5eVm9vb11g9vFBg8eXLZ06dLWpaWlBhGRAwcOOB09epQfp0AT06S+tG3a+FuvdKfa1ZZ3ufkGg0HWrVuXO3ny5HbvvPNOGxcXlwuPB3AsM3Xq1ON5eXkuUVFRYZqmKW9vb8tnn32Wm5SUVDJ48ODOkZGRYRERERVBQUHnrlSfsWPHHv/LX/7S4bnnnrOnpKRkrFy5MnfKlCnty8vLjTabTU2aNKmoR48eVyznevLzbW2ty51qV1NefZTz8MMPn/r22289wsLCIpRS2t///vcj7du3t7777rutkpOT25hMJs3d3d22fPnyA3Utc8WKFb/+6U9/6jBz5sy2VqtV3X///SXx8fGXdN3dbHz9fKx1uVPtasq70jKOa5REqlti5s2bl2cyXVsV3N3dtXfffffgkCFDOnt7e1vj4uJOZ2RkuImITJ8+vTApKSkoOTm5TUJCwm9agR966KGTL730Uvsnn3wy3zFt5syZ+WPGjOloNpvD3dzc7IsXL67z8TNs2LCytLQ019tuuy30fL3sy5cvPxAQEFAvxzyAG4NBcQHgJsCguMD1QdcbAACADoISAACADoISAACADoISAACADoJSHdhsNundu3eXnJycK97iDwAAbh4EpTrIzMx0+etf/1rQpUuX3zViPQAAaFqaVFBq4+sbrZSKra9/bXx9o6/0nkajMTYxMTH4mWeeaR8aGhr+/PPPtxGpHjRz27Zt7iIiffr06Xz8+PFLhjW4Gfm3rd994N/2yvtAKRV73333XRhjy2KxiJeXV3Tfvn07X269bdu2uY8fP76dSPXDAjdt2tTs92y7zWaT8ePHt3MMvBsZGRmWmZnpLHJzHQNt6/l71rYO3zPHQLdXa/369Z5Kqdi33377wsMjd+7c6aaUiv3b3/7mdzVlXTzQ7dW6+HwA4ObSpB44WXTsmOnV3vH1Vt6LO678UD29ccYutnXr1v31VqlGrqDwmOmrhfW3D/pPuPI+cHNzs2dlZbmdPn1aeXh4aB9//HFzPz+/K457d+edd1bceeedFSIiX3/9taeHh4dt4MCBZ2ouZ7FYxMnJ6dICavjggw+8CwsLnTIzM9OMRqPk5uY6NW/e3C5ycx0DhceOmVYP/mO9lZf4+WfX9TzTpUuXs6tWrfKaOnXqcRGRZcuWeYeEhFzVg0EtlltyGEUAddCkWpQaq4CAgKiCggJTWVmZ4a677uocEhIS3qVLl4j333/fS0Rk+/bt7rfddltIREREWO/evbscPHjwyn+V8Rv9+/cv/eijj1qKiKxYscI7MTGxxDFvy5Yt7jExMaFhYWHhMTExoampqS4i1a0Nffv27ZyVleW8ZMkSn/nz5/uFhoaGb9y40SMxMbFjUlJSYFxcnHny5MmBZWVlhhEjRnSMjIwMCwsLC1+2bFnLmnUoKChw8vPzsxiN1Q1HwcHBFh8fH5vI/x0DWVlZzkFBQREjR47s0KVLl4ihQ4cGffLJJ57du3cP7dChQ+SWLVvcRUSKioqMAwYMCDabzeHR0dGhP/zwg5uIyNNPP+0/YsSIjj179gwJDAyMevXVV32v+4fbSP373/9u0bVr19CwsLDwXr16mQ8fPlxr4AoICKiqrKw0HD582GS32+Xrr79u0b9//1LH/LS0NJeEhIQuERERYbGxsSG7d+92FRGpeQxcXGZiYmLHRYsWXRjn8eIWrxdffNHPbDaHh4SEhE+ePDnAMX3FihVeUVFRYR07dozcuHGjh4iI1WqViRMnBkZGRoaZzebwWbNm/WbYFACNH0HpChxDKzj+OcJPbdasWdO8TZs2lqysrPScnJy0YcOGlVVWVqopU6a0X7t2bW5aWlrGuHHjjj/77LMBemWgdg8//HDJhx9+6FVRUaEyMjLc4+PjL7QMRUdHn/vxxx8zMzIy0mfMmHF02rRpv/mjFxISUjV27Nhjjz/+eFFmZmb6oEGDTouI5Obmuu7cuTP7/fffP/L888+37du3b9m+ffsytm/fnvXiiy8GlpWVGWrWYfPmzS1DQ0PD//SnPwXu3Lmz1rHfDh8+7PrMM88UZ2ZmpuXm5rouX768VUpKSuZrr7125LXXXmsrIjJt2jT/6Ojoiuzs7PRXXnnl6Lhx4y50Le7fv99169at2T/99FPGW2+95e8YR/BWM3DgwNN79uzJzMjISB8+fHjJyy+/3EZv2fvuu+/k0qVLvTZv3twsKiqq4uLBcpOSkjrMnTv3UFpaWsasWbOOTJo06UL32sXHQF3q9J///Kf5hg0bvH7++efMrKys9BkzZhQ65lmtVvXLL79kzJw58/DLL7/sLyLyzjvvtG7RooVt3759GampqRn/+te/fBzdtQCahibV9dYQ6tL15tC9e/ezL7zwQrtJkyYF3HvvvaWDBg06/dNPP7nm5OS49evXzyxSPbiqj48P7fxXKS4u7uyRI0dc3n//fe8BAwaUXjyvpKTEOHLkyKC8vDxXpZRmsVjqFCyGDRt20jGe2DfffNP8iy++aJmcnNxGpHoQ4v379zt37979wth6wcHBlv379+/79NNPPb/66qvmf/zjH0OWLFmSe++995ZfXG5AQEClY1BVs9l8tl+/fmUGg0G6d+9e8eqrr/qLiPz444+eq1ev3i8iMnTo0PLHHnvMdOLECaOIyH/913+dcnNz09zc3Kze3t6WI0eOmIKDg2+5Y+bAgQPO9913X+CxY8ecqqqqDO3atavUW3bs2LEliYmJwZmZmW5jxowp2bFjh4eISGlpqWH37t0eI0aMCHYsW1VVdeH4uPgYqItNmzY1f+ihh457enraRUT8/PxsjnkjRow4KSLSq1evM88995yziMjmzZubZ2Zmuq9bt85LRKS8vNyYnp7uGhoayo0hQBNBUKpHXbt2rdy1a1f66tWrW7zwwgsBmzdvLnvggQdOde7c+eyePXsyG7p+Td2gQYNOzZgxo92XX36ZVVxcfOHYnT59ekCfPn3KN23alJuVleXcr1+/kLqU5+HhcWEUeE3TZNWqVfujo6N1/xiLiLi5uWkPPPBA2QMPPFDm5+dnWbNmTcuaQcnZ2flCa4bBYBBXV1dNRMRoNIrNZlOO96tJKaWJiFzcGmI0GsVqtd6SLUpPPPFE+yeffLLwwQcfLF2/fr2no5WmNu3bt7c6OTlp27Zta75w4cJDjqBks9nE09PTqvdj5+Jj4GImk0mz2aozkN1uF0f41jRNlKp9dzj2s8lkung/q9mzZx9KTEwsq3UlAI0eXW/1KC8vz8nT09M+efLkkqeeeqpoz5497l27dj1XUlJi2rx5czOR6paKlJQU14aua1M0adKk488880y+o7XGoayszBgYGFglIrJgwYJarwHx9PS0lZeX696V1rdv37LZs2f72e3Vfzdr61bbsWOHe15enpNI9R/gX375xa1Dhw7X1DJw++23ly9atKiVSPW1VF5eXlZvb+9a/2jfqsrLy43t27e3iIgsXry41ZWW//vf/370lVdeOXJxC5G3t7c9MDCwauHChV4i1aHnu+++q7XL9GIdOnSo+vnnn91FRJYvX97SEVYHDRpUtnTp0tbl5eUGkeprzS5XzsCBA0vnzZvn4+g+3bt3r0vNLl0AjVuTalHy8/Gx1uVOtasp70rLOK5Rcrzu169f6dy5c4/WtuzPP//s9te//jXQYDCIyWTS5s6de9DV1VVbuXJl7pQpU9qXl5cbbTabmjRpUlGPHj3O1VZGY9e2jY+1LneqXU15dV02ODjY8tJLLxXXnD59+vTCpKSkoOTk5DYJCQm1/nJPTEw8NXz48ODPP/+85TvvvHOo5vw33ngj/7HHHmsfGhoarmmaCgwMrNyyZctv7mQrLCw0TZw4sUNVVZVBRKRbt25n/t//+3+X1KcuZs6cmT9mzJiOZrM53M3Nzb548eID11LO9dDGx8dan3eqtanD9+zcuXMGPz+/ro7XkyZNKnrhhRfyR48eHezn51fVo0ePM4cOHXK5XBm13dEoIrJixYpf//SnP3WYOXNmW6vVqu6///6S+Pj4y94V95e//OXYkCFDOkdFRYXdeeedZW5ubnYRkeHDh5ft2rXLvVu3bmFOTk7agAEDSt97771azwciIlOnTj2el5fnEhUVFaZpmvL29rZ89tlnuZf/NAA0Jqq2LoDGIjU1NS86Ovp4Q9cDABq71NTU1tHR0R0buh7AzYYmYAAAAB0EJQAAAB0EJQAAAB3lK5BCAAAgAElEQVQEJQAAAB0EJQAAAB0Epetoy5Yt7hs2bPBo6HoAAIBr06Seo9SmrW90UeGx+nuOUhsfa2FBcare/J49e4ZMnz694OKn6r788su+2dnZrsuWLbvkWTwi1YNnVlRU7BYRSUhIqJgwYUJ7FxcXbcCAAbU+46Wp8ff3iy4oKK6/5yi19bXm5xfp7gMREaVU7L333lvyySefHBCpHund19c3ulu3bmdqPuvoYtu2bXNfuHBhq8WLFx9ev369p4uLi13vWTt1kZWV5TxkyJAuOTk5addaRlPg5+cfXVxcUG/72Ne3rbWoKP+y+/ji701T8eabb/q4u7vbn3jiiRP1UV5MTEzo7t27eYI/0Mg0qaBUVHjM9NaSYfVW3rNj11x2+0eMGHHi/Ej1F4LS6tWrvWfOnFmnATRNJpMsWbKk1kAlUv2UYE3TxDEafVNQUFBs2rD7z/VW3t0xc654DLq5udmzsrLcTp8+rTw8PLSPP/64uZ+f3xXHPrvzzjsr7rzzzgoRka+//trTw8PDVltQslgs4uTkdG0bcBMqLi4w/XlK/WWWOckxTeo8U1fTpk07Vp/l1RaSrFarXM1YdADqH11vl/Hwww+f/Oqrr1qcPXtWiVS3KBQXFzvFxcVVxMfHm8PDw8PMZnP4smXLWta2/ksvveQXGRkZZjabw6dOnervKKNTp04RDz30UPuIiIjw3Nxc5zVr1jTv1q1baHh4eNjgwYM7lZaWsl9q6N+/f+lHH33UUkTkfHgtcczbsmWLe0xMTGhYWFh4TExMaGpqqotI9dAgffv27ZyVleW8ZMkSn/nz5/uFhoaGb9y40SMxMbFjUlJSYFxcnHny5MmBZWVlhhEjRnSMjIwMCwsL092nDllZWc6xsbEh4eHhYeHh4WGbNm1q5pj34osv+pnN5vCQkJDwyZMnB4iIbN++3T0kJCS8W7duoRMnTgzs0qVLhIhIcnJyq7Fjx14Yzb5v376d169f7ykiondcTJ48OSA4ODjCbDaHP/bYY4H19yk3Lvn5+aY//OEPwZGRkWGRkZFhX375ZbOay1itVnnssccCzWZzuNlsDn/ttdd8RUTWrl3rGRYWFm42m8NHjBjR0fEdDggIiHriiScCunXrFhoZGRm2Y8cO9969e3dp165d5JtvvukjUn3c3HbbbSF//OMfO3Xs2DFy8uTJAfPmzfOOiooKM5vN4WlpaS4iIk8//bT/3/72Nz8RkVdffdXXsU+GDBnSSURkw4YNHqGhoeGhoaHhYWFh4SdPnjSUlpYa9M4d7u7uMY73j4uLM99zzz1BISEhESIic+fO9Y6KigoLDQ0NHzNmTAertc4PtAfwO/EH+TLatGlji46OPrN69eoWIiL/+te/vIcOHXrSw8PDvmHDhv3p6ekZW7duzX7++ecDHWOEOaxZs6Z5Tk6O6969ezPS09PTd+3adeF6pby8PNdHHnnkREZGRrqnp6f99ddfb7tt27bs9PT0jO7du1e88sorfg2wuY3aww8/XPLhhx96VVRUqIyMDPf4+PgLLUPR0dHnfvzxx8yMjIz0GTNmHJ02bdpvwkNISEjV2LFjjz3++ONFmZmZ6YMGDTotIpKbm+u6c+fO7Pfff//I888/37Zv375l+/bty9i+fXvWiy++GHi5Mbn8/f2t27dvz05PT8/48MMPf506dWp7EZH//Oc/zTds2OD1888/Z2ZlZaXPmDGjUETk0Ucf7fiPf/zjUF0HRy4oKDDVdlwUFRUZP/vsM6+cnJy07Ozs9Ndff73gWj7PpmDixIntnn766aJ9+/ZlfPzxx7mPP/54x5rLzJ492+fgwYMuaWlp6dnZ2elJSUknKioq1MSJE4M+/PDD3Ozs7HSr1SqzZs3ycazTrl27qj179mTGxcWdnjBhQsdPP/0094cffsh84403Lgy6m5mZ6TZv3rzDGRkZaatWrWqVnZ3t+ssvv2Q8/PDDx2fPnu1bsx7Jyclt9u3bl56dnZ2+ePHig+fr1iY5OflgZmZm+vfff5/p4eFhd3d3v+K5Q0Rk7969zWbNmnU0Nzc3bdeuXa6rVq3yTklJyczMzEw3GAza/Pnzrzj2HYD6QZvuFTzwwAMlH374oddDDz10as2aNd4ffPBBnt1uV0899VTg999/72EwGKS4uNj5yJEjpvbt21/4mbdx48bmKSkpHrfffnuISPXArbm5uS6dO3euatu2bVX//v3PiIh88803zXJzc1179uwZKiJisVhUbGzs6YbZ2sYrLi7u7JEjR1zef/997wEDBpRePK+kpMQ4cuTIoLy8PFellOYY6f1Khg0bdtLRrfHNN980/+KLL1omJye3EakevHj//v3O3bt3r3VMvqqqKvXoo492SE9PdzMYDHLw4EEXEZFNmzY1f+ihh457enraRUT8/PxsJ06cMJaXlxvvvvvu0yIiEyZMOPH111+3uFzd9I4Lb29vm4uLi33UqFEd7r777tKRI0eWXq6cpmznzp3Nc3JyLgxge/r0aePJkycNXl5eF5LF119/3fzxxx8/5ug69fPzs3333XdugYGBlV27dq0UERk/fvyJOXPm+IpIsYjIAw88cEpEJCoqquLMmTMGLy8vu5eXl93FxcV+/Phx4/l5Zzp06GAREWnfvn3l4MGDS0VEoqOjz27dutWzZl1DQkLO3n///UFDhw499eCDD54SEbn99ttPP/vss+0eeOCBktGjR58MDg62V1ZWXvHcISLStWvXM6GhoVUiIhs3bvTct2+fe3R0dJhI9bh4vr6+NCkBNwhB6QoefPDBUy+++GK7HTt2uJ87d87Qu3fviuTk5FYnTpww/fLLLxkuLi5aQEBA1NmzZ3/T+qBpmjz22GPFNa9jyMrKcnZ3d7dfvFzv3r3LPv3000YzKGpjNWjQoFMzZsxo9+WXX2YVF//fBeXTp08P6NOnT/mmTZtys7KynPv16xdSl/I8PDx+sx9WrVq1Pzo6urIu67722mt+vr6+ltWrVx+w2+3i5uYW6yhHqd/mtNqmOZhMJu3iFoXKykqDYx2942LPnj0Z69ata75y5UqvefPm+X7//ffZdalzU6NpmqSkpGR4eHjoDkh5/rPVak67HFdXV01ExGAwiLOz84WFDQaDOEK2i4vLb6ZfvI7NZrtkZ27ZsiXn888/9/zkk09avvnmm/45OTn7Xn/99cL77ruvdO3atS169eoVtnHjxuzt27c3u9K5Q0SkxjlCjRgx4sScOXN0B98FcP3Q9XYFLVq0sN9+++3lSUlJHYcNG1YiIlJaWmps3bq1xcXFRfv000898/PznWuuN3jw4LJly5a1clxXkpub63T06NFLguldd911JiUlxWPfvn0uIiLl5eWGvXv3XnaU9FvVpEmTjj/zzDP5PXv2/M3I72VlZcbAwMAqEZEFCxa0rm1dT09PW3l5ue5V83379i2bPXu2nyO07Ny5001vWZHqY6Bt27YWo9Eoc+fObWWz2UREZNCgQWVLly5tXV5ebhARKSoqMrZu3drm4eFh++KLLzxERBYvXuztKCc4OLgqLS3N3Wazyf79+5327t3bTET/uCgtLTWcb0ErnT9//uGMjAz3K31uTVXv3r3LZs6ceaGb69tvv71knwwYMKBs/vz5PhZL9bX9RUVFxm7dup07evSos+OzW7JkSauEhITy61VPm80mubm5zvfcc0/53Llzj5SXlxtLS0uNaWlpLj179jz72muvFUZFRZ3Zt2+fa13OHTUNGjSobP369V6O80dRUZExOzv7iusBqB9NqkXJr42P9Up3ql1teXVZbtSoUSXjxo0LXrFixa8iIklJSSWDBw/uHBkZGRYREVERFBR0SffMsGHDytLS0lxvu+22UJHqX4jLly8/YDKZfvNz19/f37pgwYK8UaNGdaqqqlIiIjNmzDjq6DZobNq29bXW5U61qymvrssGBwdbXnrppeKa06dPn16YlJQUlJyc3CYhIaGstnUTExNPDR8+PPjzzz9v+c4771xyJ+Ibb7yR/9hjj7UPDQ0N1zRNBQYGVtZ89IDFYlHOzs52EZGnnnqqODExMfiTTz7x6t27d7mbm5tdRGT48OFlu3btcu/WrVuYk5OTNmDAgNL33nvv6P/+7//mJSUldXRzc7P369fvQh0HDhx4es6cOZUhISERISEhZ8PDwytE9I+LFi1a2IcMGdK5srJSiYi8+uqrh+v6+dWVr29ba33eqebr2/aK+/jcuXMGPz+/ro7XkyZNKvrnP/95OCkpqb3ZbA632WwqLi6uvFevXr/Zd1OnTj2WnZ3tEhoaGmEymbRx48Yde/7554/Nnz8/b8SIEcE2m02io6Mrnn322Xq9Q+1iVqtVjRkzJqi8vNyoaZqaOHFiUevWrW3PPPOM/7ffftvcYDBoZrP57PDhw0tPnTplvNK5o6bY2NhzL7744tH+/fub7Xa7ODk5acnJyYfMZnPV9domAP9HXamZuiGlpqbmRUdHH2/oegAiIsuWLWv573//2/uzzz779feUc6s8jwk3Vmpqauvo6OiODV0P4GbTpFqUgIby1FNP+X/++ectFy5cyLVkAHALoUUJAG4CtCgB1wcXcwMAAOggKAEAAOggKAEAAOggKAEAAOhoUne9Bfj7RucXHKu3Ovu39bEezS9OvdwySqnYe++9t+STTz45IFI90ryvr290t27dztR8zs7l5OXlOT3++OPtNm7c+LtuLQcAADdOkwpK+QXHTAVfDqu38tr+15UfXunm5mbPyspyO336tPLw8NA+/vjj5n5+fpareR+LxSIdO3a0EJIAAGha6Hqrg/79+5d+9NFHLUVEVqxY4Z2YmFjimLdlyxb3mJiY0LCwsPCYmJjQ1NRUFxGR5OTkVoMHD+7Ur1+/zgkJCeasrCznLl26RIhUP3AwNjY2JDw8PCw8PDxs06ZNzURE1q9f79mzZ8+QQYMGdQoKCooYOnRokGNIje3bt7vfdtttIREREWG9e/fucvDgQacb/kEAAHCLISjVwcMPP1zy4YcfelVUVKiMjAz3+Pj4M4550dHR53788cfMjIyM9BkzZhydNm1aoGPerl27PFasWHGg5qCl/v7+1u3bt2enp6dnfPjhh79OnTq1vWNeRkaG25w5cw7v378/7dChQy6bNm3yqKysVFOmTGm/du3a3LS0tIxx48Ydf/bZZwNuzNYDAHDralJdbw0lLi7u7JEjR1zef/997wEDBpRePO/8AKVBeXl5rkopzTH6uIhIQkJCmZ+fn61meVVVVerRRx/tkJ6e7mYwGOTgwYMXBsGNioo6ExwcbBERiYiIqMjNzXX29va25uTkuPXr188sImK328XHx+equv8AAMDVIyjV0aBBg07NmDGj3ZdffplVXFx84XObPn16QJ8+fco3bdqUm5WV5dyvX78Qxzx3d3d7bWW99tprfr6+vpbVq1cfsNvt4ubmFuuY5+LicuFR6UajUaxWq9I0TXXu3Pnsnj17Mq/X9gEAgEvR9VZHkyZNOv7MM8/k9+zZ8+zF08vKyoyBgYFVIiILFixoXZeySktLjW3btrUYjUaZO3duK5vtkkan3+jateu5kpIS0+bNm5uJiFRWVqqUlBTXa9wUAABQR02qRcm/rY+1LneqXU15dV02ODjY8tJLLxXXnD59+vTCpKSkoOTk5DYJCQlldSnrqaeeKk5MTAz+5JNPvHr37l3u5uZWa8uTg6urq7Zy5crcKVOmtC8vLzfabDY1adKkoh49epyra/0BAMDVY1BcALgJMCgucH3Q9QYAAKCDoAQAAKCjsQclu91uV1deDABuXefPk5e91hHAtWnsQWnfsWPHWhCWAKB2drtdHTt2rIWI7GvougA3o0Z915vVak0qLCz8oLCwMFIaf6gDgIZgF5F9Vqs1qaErAtyMGvVdbwAAAA2JVhoAAAAdBCUAAAAdBCUAAAAdBCUAAAAdBCUAAAAdBCUAAAAdBCUAAAAdBCUAAAAdBCUAAAAdBCUAAAAdBCUAAAAdBCUAAAAdBCUAAAAdBCUAjZJS6n6llKaUCq3DsuOVUv5XWf6dSqldSimrUmr4tdcUwM2MoASgsRotIjtEZFQdlh0vIlcVlETk0Pn1/n2V6wG4hZgaugIAUJNSykNE7hCRviKyTkT+v4vmTRORh0XELiKfi0iKiPQQkeVKqbMiEi8i4SLyDxHxEJHjIjJe07SCi99D07S88+XZr+/WAGjKaFEC0BjdJyIbNU3LFpESpVR3ERGl1ODz8+I0TYsWkTc1TVsl1WHpQU3TuomIVUTeFZHhmqbFishCEXmtITYCQNNHixKAxmi0iLxz/v8rz7/eJSIDRGSRpmkVIiKappXUsm6IiESKyCallIiIUUQKalkOAK6IoASgUVFKtRKRfiISqZTSpDroaOe73JSIaFcqQkTSNE2Lv741BXAroOsNQGMzXESWaJrWQdO0jpqmtRORAyLSW0S+FJEJSil3ERGllPf5dcpFxPP8/7NExEcpFX9+GSelVMQN3QIANw2CEoDGZrSIfFxj2moRGaNp2kapvrg7RSm1R0SePT9/sYjMPz/NKNVha6ZSKlVE9ohIr5pvopS6TSl1RERGiMgCpVTa9dgYAE2b0rQrtWIDAADcmmhRAgAA0EFQAgAA0EFQAgAA0EFQAgAA0EFQAgAA0EFQAgAA0EFQAgAA0EFQAgAA0EFQAgAA0NGoB8V1MhiqrJrm1ND1AHB1TE4Gu9Vi54cYgCZB0zSlN69RByWrpjm92psBwIGm5sUd3xneWjKsoasBAL8bv/gAAAB0EJQAAAB0EJQAAAB0EJQAAAB0EJQAAAB0EJQAAAB0EJQAAAB0EJQAAAB0EJQAAAB0EJQAAAB0EJQAAAB0EJQAAAB0EJQAAAB0EJQAAAB0EJQAAAB0EJQAAAB0EJQAAAB0EJQAAAB0EJQAAAB0EJQAAAB0KE3TGroOupyMRs1qtzd0NQBcJZOTQawWvrsAmgZN05TePNONrMjVstrt8sHCXxu6GgAA3FBJEzrJq73jG7oaELreAAAAdBGUAAAAdBCUAAAAdBCUAAAAdBCUAAAAdBCUAAAAdBCUAAAAdBCUAAAAdBCUAAAAdBCUAAAAdBCUAAAAdBCUAAAAdBCUAAAAdBCUAAAAdBCUAAAAdBCUAAAAdChN0xq6DrqcnV3tFkulauh6AMCtwsXZJJVV1oauBnBDaZqmmzVMN7IiV8tiqVTpj/y5oasBALeM8EVzpOCn9xq6GkCjQdcbAACADoISAACADoISAACADoISAACADoISAACADoISAACADoISAACADoISAACADoISAACADoISAACADoISAACADoISAACADqVpWkPXQZeryWSrtNkIcwBwg7g4m6SyytrQ1QBuKE3TlN48042syNWqtNkM6Y/8uaGrAQBo4sIXzZEPFv7a0NVocpImdJJXe8c3dDUaFK01AAAAOghKAAAAOghKAAAAOghKAAAAOghKAAAAOghKAAAAOghKAAAAOghKAAAAOghKAAAAOghKAAAAOghKAAAAOghKAAAAOghKAAAAOghKAAAAOghKAAAAOghKAAAAOghKAAAAOghKAAAAOghKAAAAOghKAAAAOghKAAAAOghKAAAAOghKAAAAOghKAAAAOghKAAAAOghKAAAAOghKAAAAOghKAAAAOpSmaQ1dB12uJpOt0mYjzKFemZQSaz0e9yYng1gt9norDwBwY2mapvTmmW5kRa5Wpc1mSH/kzw1dDdxkwhfNkVd7x9dbeS/u+E7eWjKs3soDADQetNYAAADoICgBAADoICgBAADoICgBAADoICgBAADoICgBAADoICgBAADoICgBAADoICgBAADoICgBAADoICgBAADoICgBAADoICgBAADoICgBAADoICgBAADoICgBAADoICgBAADoUJqmNXQddLmaTIWVNptfQ9cDNxeTUppV01S9ledksFstdn50AEATpV3mb0KjDkoAAAANiV/BAAAAOghKAAAAOghKAAAAOghKAAAAOghKAAAAOghKAAAAOghKAAAAOghKAAAAOghKAAAAOghKAAAAOghKAAAAOghKAAAAOghKAAAAOghKAAAAOghKAAAAOghKAAAAOghKAAAAOghKAAAAOghKAAAAOghKABolpdT9SilNKRVah2XHK6X8r7L8p5VS6UqpvUqpr5RSHa69tgBuVgQlAI3VaBHZISKj6rDseBG5qqAkIrtFpIemaV1FZJWIvHmV6wO4BShN0xq6DgDwG0opDxHJEpG+IrJO07TQi+ZNE5GHRcQuIp+LSIqILBaRoyJyVkTiRSRcRP4hIh4iclxExmuaVnCZ94sRkfc0TbvjemwPgKaLFiUAjdF9IrJR07RsESlRSnUXEVFKDT4/L07TtGgReVPTtFVSHZYe1DStm4hYReRdERmuaVqsiCwUkdeu8H6PSnXoAoDfMDV0BQCgFqNF5J3z/195/vUuERkgIos0TasQEdE0raSWdUNEJFJENimlRESMInK51qSHRKSHiPSpr8oDuHkQlAA0KkqpViLST0QilVKaVAcd7XyXmxKRK10voEQkTdO0+Dq81wAReUFE+miaVvn7ag7gZkTXG4DGZriILNE0rYOmaR01TWsnIgdEpLeIfCkiE5RS7iIiSinv8+uUi4jn+f9niYiPUir+/DJOSqmImm9y/rqkBSIyVNO04uu6RQCaLIISgMZmtIh8XGPaahEZo2naRhFZJyIpSqk9IvLs+fmLRWT++WlGqQ5bM5VSqSKyR0R61fI+s6T6Yu+PlFJ7lFLr6n1LADR53PUGAACggxYlAAAAHQQlAAAAHQQlAAAAHQQlAAAAHQQlAAAAHQQlAAAAHQQlAAAAHQQlAAAAHQQlAAAAHY16UFxnZ9fTFktls4auB24dLs4mqayyNnQ1AAA3kKZpSm9eow5KFktlsw8W/trQ1cAtJGlCJyn46b2GrgYAoJGg6w0AAEAHQQkAAEAHQQkAAEAHQQkAAEAHQQkAAEAHQQkAAEAHQQkAAEAHQQkAAEAHQQkAAEAHQQkAAEAHQQkAAEAHQQkAAEAHQQkAAEAHQQkAAEAHQQkAAEAHQQkAAEAHQQkAAEAHQQkAAEAHQQkAAEAHQQkAAEAHQQkAAEAHQQkAAEAHQQkAAEAHQQkAAEAHQQkAAEAHQQkAAEAHQQkAAEAHQQkAAEAHQQkAAECH0jStoeugy9nZ1W6xVKqGrgduHS7OJqmssjZ0NQAA1+Baz+GapulmDdPvqtF1ZrFUqvRH/tzQ1QB+l/BFc+SDhb82dDXQRCRN6CRfLYxv6GoATVL/Cd9JwU/v1WuZdL0BAADoICgBAADoICgBAADoICgBAADoICgBAADoICgBAADoICgBAADoICgBAADoICgBAADoICgBAADoaNRjvTmZDJrV1njrh5sT470BwK2lyY71ZrVp8tZfYhq6GrjFPPvu7nofKwgA0DTR9QYAAKCDoAQAAKCDoAQAAKCDoAQAAKCDoAQAAKCDoAQAAKCDoAQAAKCDoAQAAKCDJ3PjpmQyKrnWY4cncwPAraVJP5k7/ZE/N3Q10ASFL5rDU90BAL8bXW8AAAA6CEoAAAA6CEoAAAA6CEoAAAA6CEoAAAA6CEoAAAA6CEoAAAA6CEoAAAA6CEoAAAA6CEoAAAA6CEoAAAA6CEoAAAA6CEoAAAA6CEoAAAA6CEoAAAA6CEoAAAA6CEoAAAA6CEoAAAA6CEoAAAA6CEoAAAA6CEoAAAA6CEoAAAA6CEoAAAA6CEoAAAA6CEoAAAA6CEoAAAA6CEoAAAA6CEoAAAA6CEoAAAA6CEoAAAA6CEoAAAA6CEoAAAA6CEoAAAA6CEoAAAA6CEoAAAA6CEoAAAA6CEoAAAA6CEoAAAA6CEoAAAA6CEoAAAA6CEoAAAA6CEoAAAA6CEoAAAA6CEoAAAA6CEoAAAA6CEoAAAA6CEoAAAA6CEoAAAA6lKZpDV0HXa4mU2GlzebX0PVA02MyKrHaGu+xDQBoPDRNU3rzGnVQAgAAaEh0vQEAAOggKAEAAOggKAEAAOggKAEAAOggKAEAAOggKAEAAOggKAEAAOggKAEAAOggKAEAAOggKAEAAOggKAEAAOggKAEAAOggKAEAAOggKAEAAOggKAEAAOggKAEAAOggKAEAAOggKAEAAOggKAEAAOggKAFolJRS9yulNKVUaB2WHa+U8r/K8h9XSv2ilNqjlNqhlAq/9toCuFkRlAA0VqNFZIeIjKrDsuNF5KqCkoj8W9O0KE3TuonImyLyj6tcH8AtgKAEoNFRSnmIyB0i8qjUCEpKqWnnW4JSlVJvKKWGi0gPEVl+vnXITSkVq5TaqpT6WSn1hVKqbc330DSt7KKXzUREu46bBKCJMjV0BQCgFveJyEZN07KVUiVKqe6apu1SSg0+Py9O07QKpZS3pmklSqknRORZTdNSlFJOIvKuiNyradoxpdRIEXlNRCbUfBOl1J9F5GkRcRaRfjds6wA0GbQoAWiMRovIyvP/X3n+tYjIABFZpGlahYiIpmkltawbIiKRIrJJKbVHRF4UkcDa3kTTtDmapgWLyPTzywHAb9CiBKBRUUq1kurWnUillCYiRhHRlFLTRETJlbvIlIikaZoWfxVvu1JE5l1LfQHc3GhRAtDYDBeRJZqmddA0raOmae1E5ICI9BaRL0VkglLKXUREKeV9fp1yEfE8//8sEfFRSsWfX8ZJKRVR802UUl0uenm3iORcl60B0KTRogSgsRktIm/UmLZaRMZomjZJKdVNRFKUUlUi8pmIPC8ii0VkvlLqrIjES3XYSlZKtZDq89w7IpJWo8wnlFIDRMQiIidFZNx12h4ATZjSNG70AAAAqA1dbwAAADoISgAAADoISgAAADoISgAAADoISgAAADoISgAAADoISgAAADoISgAAADoISgAAADoa9RAmrjR+hRYAABDkSURBVCZTYaXN5tfQ9QBuNk5OLnaLpbLefyiZnAya1WJX9V3uzcTJ2SiWKltDVwO4qRmNSrPZtEvORU4Gg91it19y7tO0S5d1aNRBqdJm80t/5M8NXQ3gphO+aI7hg4W/1nu5SRM6qbeWDKv3cm8mz45dIxt2c14Drqe7Y+aoR/8af8n0//3v7wyrB//xqsqi6w0AAEAHQQkAAEAHQQkAAEAHQQkAAEAHQQkAAEAHQQkAAEAHQQkAAEAHQQkAAEAHQQkAAEAHQQkAAEAHQQkA/v/27i/E0rs84PjznHnPzPjvRitppMGwIMoYqkkEWc2VSMAKotu9MAohu+7dklwtucpdI6gstGAHhC47dkGwoCvYBmLsRYWALY1uUnGLCutlAtJAsUjPnPPOrxc7pnEzz2ZnnZ33nZnPBxZ25rznvM8kZ8985/e+8x6AglACACgIJQCAglACACgIJQCAglACACgIJQCAglACACgIJQCAglACACgIJQCAglACACgIJQCAglACACgIJQCAglACACgIJQCAglACACgIJQCAglACACgIJQCAglACACgIJQCAglACACgIJQCAglACACgIJQCAglACACgIJQCAglACACgIJQCAglACACgIJQCAglACACgIJQCAglACAChka23oGUrT6VJbLLaGHgNGZbq8FPPNfugxAEZrspSx1b+xb6aTScy33tgVrbWsHqvb29H21mKxFecvnRh6DBiVc49ejmeunB16DIAjwaE3AICCUAIAKAglAICCUAIAKAglAIDCuC8P0E3aYodf74OjzOUBeDOeI7A7N7s8gBUlOGCO8jfAbql8LQO4I8Z9HaW+xcvPuY4ScN3dD1+O84/fP/QYwBFiRQkAoCCUAAAKQgkAoCCUAAAKQgkAoCCUAAAKQgkAoDDqK3OvLi+12Xxr6DGAkeiWMlytn8PG83p4N7sy96gvODmbb8XVU2eHHgMA7pi1jXUXUh0xh94AAApCCQCgMO5zlLqun/W9mANGrcuMxYhfS4eyMp2E80zfnHOU/t9Q/y0O7jlKfT9xjhIwdmsb6/H0Q8eHHmN0nnr+x97YnF0Z4xtfW60BACg49MZrptOVmM9nQ48BB45Dbzsb+tDb0Ptn927l0NudODzn0Bu3ZG1jPS5cvDb0GAB/lDOnj8X5Syfi3KOXHfo7hPb78JzVGgCAglACACgIJQCAglACACgIJQCAglACACgIJQCAglACACgIJQCAglACACgIJQCAglACACgIJQCAglACACgIJQCAglACACgIJQCAglACACgIJQCAglACACgIJQCAglACACgIJQCAglACAChka23oGUqrXdfP+l7M7ZOuW47FYnPoMWAwk6WMrX68r4nAndFay+o2EcJrRBJHwXS6MvQIHGHTiW+7B0039AA3M+v7ydVTZ4ceAzhE1jbW48LFa0OPwcidOX0szl86seePe+7Ry/HdT/3Fnj8ud460BQAoCCUAgMLYT+Z+Zdb3dw09B3B4TKcrW/P5zA+J3FQ3nbTFfKs8wfd2TSeTmG9t7fXD8ke62cncow4lAIAh+akKAKAglAAACkIJAKAglAAACkIJAKAglAAACkIJAKAglAAACkIJAKAglAAACkIJAKAglAAACkIJAKAglAAACkIJAKAglAAACkIJAKAglAAACkIJGKXM/Fxmtsz8wC1s+1hmvuc293Nyez8fuZ37A4ebUALG6pGIeD4iPn8L2z4WEbsOpcx8R0Q8ERH/ttv7AkeDUAJGJzPfHhEfj4gvxQ2hlJlPZubPMvOlzPxKZp6MiI9ExLcy88XMfEtmPpiZP8rMn2TmDzLz7mJXfxURX4uI/72TXw9wcAklYIw+GxHPttZ+GRGvZuYDERGZ+ant2z7aWvtQRHyttfadiHghIr7YWvtwRCwi4usRcbK19mBEXIyIL9+4g8y8PyLuaa390758RcCB1A09AMAOHomIv9n++7e3P/5pRHwyIjZaa7+LiGitvbrDfd8fEfdFxA8zMyJiKSJefv0GmTmJiL+O64fsAEpCCRiVzHxXRHwiIu7LzBbXQ6dl5pMRkRHR3uwhIuLnrbXjN9nmHXE9pv5lO6b+NCK+n5mfaa298Md+DcDh4dAbMDYnI+JSa+29rbV7W2v3RMSvI+KhiHguIk5n5lsjIjLzndv3+W1cj5+IiF9ExLsz8/j2NtPM/ODrd9Ba++/W2p9sP/69EfGvESGSgDcQSsDYPBIR37vhc9+NiC+01p6NiO9HxAuZ+WJEnNu+/ZsR8Y3tzy3F9dj6ama+FBEvRsTH9mNw4PDJ1t5sFRsA4GiyogQAUBBKAAAFoQQAUBBKAAAFoQQAUBBKAAAFoQQAUBBKAAAFoQQAUBBKAACFbugBbmZ5efV/5vPZ24aeg/HqJpOtxdbWYMG/Mp202Xwrh9o/49ItZVv0zfMBDpjW6n+3ow6l+Xz2tgsXrw09BiN25vSxydMPHR9s/089/+N8+bkTg+2fcbn74ct5/vH7hx4D2EMOvQEAFIQSAEBBKAEAFIQSAEBBKAEAFIQSAEBBKAEAFIQSAEBBKAEAFIQSAEBBKAEAFIQSAEBBKAEAFIQSAEBBKAEAFIQSAEBBKAEAFIQSAEBBKAEAFIQSAEBBKAEAFIQSAEBBKAEAFIQSAEBBKAEAFIQSAEBBKAEAFIQSAEAhW2tDz1BaXl7dms9nOfQcjFc3mcRia2uw/a9MJzGbD7d/xqVbylj0431NBXbWWitbo9vPQXZrPp/l1VNnhx4DgCNmbWM9Lly8NvQYRMSZ08fi6YeOD7b/Ua8orXZdP+t7hweBmE5XYj6fDT0GsM+6zFjc4VY5sCtKs76fWFECIvyEDwzDag0AQEEoAQAUhBIAQEEoAQAUhBIAQEEoAQAUhBIAQEEoAQAUhBIAQEEoAQAUhBIAQEEoAQAUhBIAQEEoAQAUhBIAQEEoAQAUhBIAQEEoAQAUhBIAQEEoAQAUhBIAQEEoAQAUhBIAQEEoAQAUsrU29Ayl1a7rZ30v5vgD0+lKzOezocfggJouL8V8sx96DGBEWmtZ3dbt5yC7Nev7ydVTZ4ceg5FZ21iPCxevDT0GB9SZ08fimSteV4BbY7UGAKAglAAACkIJAKAglAAACkIJAKAglAAACkIJAKAglAAACkIJAKAglAAACkIJAKAglAAACkIJAKAglAAACkIJAKAglAAACkIJAKAglAAACtlaG3qG0mrX9bO+F3P8ga5bjsVic+gxiIP5/2K6vBTzzX7oMYARaa1ldVu3n4Ps1qzvJ1dPnR16DKCwtrEeFy5eG3oMgDvGag0AQEEoAQAUhBIAQEEoAQAUhBIAQEEoAQAUhBIAQEEoAQAURn1l7uXp6tZ8MSuvlgm7sbLcxWxzMfQYAIzMgb0y93wxS1f9Za+cOX0sXv73vx16DAAOkHGvKC2vbs3nVpTYG1aUANjJwV1Rms/Se70BAENxMjcAQEEoAQAUhBIAQEEoAQAUhBIAQEEoAQAUhBIAQEEoAQAUhBIAQEEoAQAUhBIAQGHUb4q72nWvzPr+rqHnAAAOr5u9Ke6oQwkAYEgOvQEAFIQSAEBBKAEAFIQSAEBBKAEAFIQSAEBBKAEAFIQSAEBBKAEAFIQSAEBBKAEAFIQSAEBBKAEAFIQSAEBBKAEAFIQSAEBBKAEAFIQSAEBBKAGjlJmfy8yWmR+4hW0fy8z37PLxH8vM32Tmi9t/ztz+tMBhJZSAsXokIp6PiM/fwraPRcSuQmnbP7TWPrz958Jt3B845IQSMDqZ+faI+HhEfCluCKXMfDIzf5aZL2XmVzLzZER8JCK+tb0y9JbMfDAzf5SZP8nMH2Tm3QN8GcAhIJSAMfpsRDzbWvtlRLyamQ9ERGTmp7Zv+2hr7UMR8bXW2nci4oWI+GJr7cMRsYiIr0fEydbagxFxMSK+XOznLzPzPzLzO5l5zx3+moADSCgBY/RIRHx7++/f3v44IuKTEbHRWvtdRERr7dUd7vv+iLgvIn6YmS9GxFMR8Wc7bPePEXFva+3PI+KfI+Lv92584LDohh4A4PUy810R8YmIuC8zW0QsRUTLzCcjIiOivdlDRMTPW2vHb7ZRa+2/Xvfh30XEV29/auCwsqIEjM3JiLjUWntva+3e1to9EfHriHgoIp6LiNOZ+daIiMx85/Z9fhsR79j++y8i4t2ZeXx7m2lmfvDGndxw3tJnIuI/78hXAxxoQgkYm0ci4ns3fO67EfGF1tqzEfH9iHhh+7Daue3bvxkR39j+3FJcj62vZuZLEfFiRHxsh/08kZk/397mibj+m3MAfyBbe7NVbACAo8mKEgBAQSgBABSEEgBAQSgBABSEEgBAQSgBABSEEgBAQSgBABSEEgBAQSgBABS6oQe4mdWue2XW93cNPQewd5aWlqPvN4ceA+A1rbWsbht1KM36/q6rp84OPQawh9Y21uPsE1eGHgPgljj0BgBQEEoAAAWhBABQyNba0DOUVruun/W9mGPXpstLMd/shx6DHTiZmzHyvDzaDvLJ3BMnc3M71jbW45krnjvArfn0/X7JgJ1ZrQEAKAglAICCUAIAKAglAICCUAIAKAglAICCUAIAKAglAICCUAIAKAglAICCUAIAKAglAICCUAIAKAglAICCUAIAKGRrbegZSqtd18/6Xsyxa9PlpZhv9kOPARwQS0vL0febQ4/BQFprWd3W7ecguzXr+8nVU2eHHgOOlLWN9Th/6cTQYwDcEecevRzPXLn1trBaAwBQEEoAAAWhBABQEEoAAAWhBABQEEoAAAWhBABQEEoAAAWhBABQEEoAAAWhBABQEEoAAAWhBABQEEoAAAWhBABQEEoAAAWhBABQEEoAAAWhBABQEEoAAAWhBABQEEoAAAWhBABQEEoAAAWhBABQEEoAAAWhBABQyNba0DOUVruun/W9mIN91HWTWCy2hh4DGJGxvS50SxmLfud+mS4vxXyz39Xjtday3NfuRttfs76fXD11dugx4Mhb21iPpx86PvQYwECeev7Hcf7SiaHHeM25Ry/H+cfv3/m2r1+JZ67sXTtYrQEAKAglAICCUAIAKAglAICCUAIAKAglAICC6yhxR4ztmhvweyvLk5htem7uteWVLjZni6HH4IjYi+sovX4711Fi361trI/qmhvwe+cevRwvP+e5udfufvhy/PRXF4YeA27ZA+87c0vXW7JaAwBQEEoAAAWhBABQEEoAAAWhBABQEEoAAAWhBABQEEoAAAWhBABQEEoAAAWhBABQEEoAAAWhBABQEEoAAAWhBABQEEoAAAWhBABQEEoAAAWhBABQEEoAAAWhBABQEEoAAAWhBABQEEoAAAWhBABQEEoAAAWhBABQEEoAAAWhBABQEEoAAAWhBABQyNba0DOUVruun/W9mDuAum4Si8XW0GPAG6wsT2K2efPn5sp0ErP5wXz+Lq90sTlbDD0GB8x0eSnmm/2gMywtLUffbw6y79ZaVrd1+znIbs36fnL11Nmhx2APrW2sx4WL14YeA27qzOlj8fJzJ4Ye47bc/fDl+OmvLgw9BgfMA+87E89cGfb77afvX4+zT1wZdIadWK0BACgIJQCAglACACgIJQCAglACACgIJQCAglACACgIJQCAglACACiM/S1MXpn1/V1Dz8HemU5XtubzmUBn1A7yW5isrEy3ZrO5f2Psircwqd/CZNShBAAwJD91AAAUhBIAQEEoAQAUhBIAQEEoAQAUhBIAQEEoAQAUhBIAQEEoAQAUhBIAQOH/ALrUOselAnwmAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(nrows = len(acts),\n", + " figsize=(8, 12))\n", + "fig.tight_layout()\n", + "fig.subplots_adjust(left=0.03, top=0.82, hspace=0.25)\n", + "colors = plt.get_cmap('tab20b')(2+np.arange(len(characters)))\n", + "colors[::2] = colors[::2][::-1]\n", + "for ax, act, scene_wcs in zip(axes, range(len(acts)), word_count):\n", + " ax.set_title(f\"Acte {act+1}\", fontsize=10)\n", + " data = np.array([[scene_wc[c]\n", + " for c in characters]\n", + " for scene_wc in scene_wcs])\n", + " scene_lengths = np.sum(data, axis=1)\n", + " widths = data / scene_lengths[:, np.newaxis]\n", + " heights = scene_lengths\n", + " h_offsets = np.hstack([np.zeros((len(widths), 1), np.float),\n", + " widths.cumsum(axis=1)])\n", + " assert (np.fabs(h_offsets[:, -1] - 1) < 1.e-10).all()\n", + " v_offsets = np.hstack([[0], heights.cumsum()[:-1]])\n", + " for i, character in enumerate(characters.keys()):\n", + " ax.barh(v_offsets+heights/2,\n", + " width=widths[:, i], left=h_offsets[:, i],\n", + " height=heights,\n", + " color=colors[i],\n", + " linewidth=1, edgecolor='black',\n", + " label=characters[character]['name'])\n", + "for ax in axes:\n", + " ax.invert_yaxis()\n", + " ax.xaxis.set_visible(False)\n", + " ax.axis('off')\n", + " ax.set_xlim(0, 1)\n", + "axes[0].legend(ncol=3,\n", + " bbox_to_anchor=(-0.01, 1.2),\n", + " loc='lower left',\n", + " fontsize=10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Qui parle à qui ?\n", + "\n", + "La plupart des répliques s'adressent à une personne spécifique, mais il n'est pas facile d'identifier cette personne de façon sure par une analyse du texte. Parfois cette personne est nommée (\"Hé quoi, charmante Élise...\"), et on pourrait donc envisager d'exploiter les noms contenus dans les répliques. Mais le plus souvent la personne n'est pas nommée, et parfois les acteurs parlent d'une tièrce personne (\"Elle se nomme Mariane...\").\n", + "\n", + "Nous allons adopter une approche imparfaite mais simple, qui devrait donner une vue globale correcte: on suppose que chaque réplique s'adresse à la personne qui a parlé juste avant." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "word_count = defaultdict(lambda: 0)\n", + "for scenes in acts:\n", + " for scene in scenes:\n", + " previous = None\n", + " wc = defaultdict(lambda: 0)\n", + " for part in scene:\n", + " current = part['character']\n", + " count = sum(len(line.split()) for line in part['speech'])\n", + " if previous is not None:\n", + " word_count[(current, previous)] += count\n", + " previous = current" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "L'affichage sous forme de matrice donne une première impression de la diversité dans l'ampleur des échanges :" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0 1485 364 857 85 189 607 60 665 434 20 134 118\n", + " 1354 0 702 22 305 0 151 0 162 362 0 0 0\n", + " 314 195 0 499 26 0 0 0 0 0 0 0 0\n", + " 1452 0 677 0 4 265 0 0 159 0 0 0 0\n", + " 167 278 36 190 0 0 175 0 0 0 0 0 0\n", + " 95 0 0 170 207 0 0 0 8 0 0 0 0\n", + " 1501 331 46 0 233 0 0 0 12 116 0 0 0\n", + " 102 57 0 0 0 0 0 0 0 0 0 0 0\n", + " 1026 148 0 267 0 0 11 0 0 0 0 0 111\n", + " 249 883 0 0 0 0 252 13 0 0 0 0 0\n", + " 27 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 8 0 0 11 0 0 0 0 5 0 17 0 0\n", + " 194 0 0 0 0 12 0 0 38 0 0 0 0\n" + ] + } + ], + "source": [ + "for c1 in characters:\n", + " print(''.join([str(word_count[(c1, c2)]).rjust(5) for c2 in characters]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Ces données se prêtent à une présentation sous forme d'un graphe. Chaque acteur en est un noeud, représesenté par un cercle dont la superficie indique l'importance de sa prise de parole. Ces cercles ont les mêmes couleurs déjà utilisées ci-dessus pour chaque acteur." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "colors = plt.get_cmap('tab20b')(2+np.arange(len(characters)))\n", + "colors[::2] = colors[::2][::-1]\n", + "\n", + "character_names = list(characters.keys())\n", + "\n", + "importance = {}\n", + "for c1 in characters:\n", + " importance[c1] = 0\n", + " for c2 in characters:\n", + " importance[c1] += word_count[(c1, c2)]\n", + "max_importance = max(importance.values())\n", + "for c in characters:\n", + " importance[c] /= max_importance\n", + "r_sq_min = 0.015**2\n", + "r_sq_max = 0.06**2\n", + "\n", + "def radius(c):\n", + " n = character_names[c]\n", + " return np.sqrt(r_sq_min + importance[n]*(r_sq_max-r_sq_min))\n", + "\n", + "def draw_circle(c):\n", + " n = character_names[c]\n", + " r = positions.get(n, None)\n", + " circle = mpatch.Circle(r, radius(c), fc=colors[c])\n", + " ax.add_patch(circle)\n", + " return circle" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Le positionnement des cercles est important pour la clarté du graphe, surtout quand nous allons rajouter les arêtes. Il y a des algorithmes pour trouver des positions acceptables, mais ils sont assez compliqués, sans pour autant donner un résultat vraiment satisfaisant. Pour nos 14 acteurs, un placement manuel reste faisable et permet une optimisation fine." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "positions = {}\n", + "positions['HARPAGON'] = (0.5, 0.5)\n", + "positions['VALERE'] = (0.05, 0.5)\n", + "positions['CLEANTE'] = (0.5, 0.95)\n", + "positions['ELISE'] = (0.15, 0.9)\n", + "positions['LA FLECHE'] = (0.9, 0.75)\n", + "positions['FROSINE'] = (0.7, 0.75)\n", + "positions['MARIANE'] = (0.2, 0.7)\n", + "positions['MAITRE SIMON'] = (0.95, 0.5)\n", + "positions['MAITRE JACQUES'] = (0.35, 0.2)\n", + "positions['ANSELME'] = (0.2, 0.1)\n", + "positions['LE COMMISSAIRE'] = (0.6, 0.05)\n", + "positions['LA MERLUCHE'] = (0.7, 0.05)\n", + "positions['BRINDAVOINE'] = (0.8, 0.1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nous pouvons maintenant générer les cercles et la légende.\n", + "Les arêtes sont représentés par des flèches transparentes dont la largeur est proportionnelle au nombre de mots qu'un acteur adresse à un autre." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "for n in positions:\n", + " positions[n] = np.array(positions[n])\n", + "\n", + "fig, ax = plt.subplots(nrows = 1,\n", + " figsize=(9, 12))\n", + "ax.axis('off')\n", + "fig.tight_layout()\n", + "fig.subplots_adjust(left=0.03, top=0.8, hspace=0.25)\n", + "circles = [draw_circle(c)\n", + " for c in range(len(characters))]\n", + "plt.legend(circles,\n", + " [characters[character_names[c]]['name']\n", + " for c in range(len(circles))],\n", + " ncol=3,\n", + " bbox_to_anchor=(0., 1.02),\n", + " loc='lower left',\n", + " fontsize=10)\n", + "\n", + "transparency = np.array([1., 1., 1., 0.3])\n", + "\n", + "def draw_arrow(c1, c2):\n", + " n1 = character_names[c1]\n", + " n2 = character_names[c2]\n", + " r1 = positions.get(n1, None)\n", + " r2 = positions.get(n2, None)\n", + " d = r2-r1\n", + " l = np.sqrt(np.sum(d**2))\n", + " x_tail, y_tail = r1 + 1.1*radius(c1) * d/l\n", + " x_head, y_head = r2 - 1.1*radius(c2) * d/l\n", + " w = word_count[(n1, n2)] / 50.\n", + " astyle = mpatch.ArrowStyle.Simple(head_length=w,\n", + " head_width=1.5*w,\n", + " tail_width=w)\n", + " arrow = mpatch.FancyArrowPatch((x_tail, y_tail), (x_head, y_head),\n", + " arrowstyle=astyle,\n", + " fc=colors[c1]*transparency)\n", + " ax.add_patch(arrow)\n", + "\n", + "for c1 in range(len(characters)):\n", + " for c2 in range(len(characters)):\n", + " if c1 != c2:\n", + " draw_arrow(c1, c2)" + ] + } + ], "metadata": { "kernelspec": { "display_name": "Python 3", @@ -16,10 +804,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.3" + "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 } - diff --git a/module3/exo3/exercice_fr.pdf b/module3/exo3/exercice_fr.pdf new file mode 100644 index 0000000000000000000000000000000000000000..035dab649e13bc1aa76c957038ac13218cdc4fa1 GIT binary patch literal 253450 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Once opened in -Emacs, this document can easily be exported to HTML, PDF, and Office -formats. For more information on org-mode, see -https://orgmode.org/guide/. - -When you type the shortcut =C-c C-e h o=, this document will be -exported as HTML. All the code in it will be re-executed, and the -results will be retrieved and included into the exported document. If -you do not want to re-execute all code each time, you can delete the # -and the space before ~#+PROPERTY:~ in the header of this document. - -Like we showed in the video, Python code is included as follows (and -is exxecuted by typing ~C-c C-c~): - -#+begin_src python :results output :exports both -print("Hello world!") -#+end_src - -#+RESULTS: -: Hello world! - -And now the same but in an Python session. With a session, Python's -state, i.e. the values of all the variables, remains persistent from -one code block to the next. The code is still executed using ~C-c -C-c~. - -#+begin_src python :results output :session :exports both -import numpy -x=numpy.linspace(-15,15) -print(x) -#+end_src - -#+RESULTS: -#+begin_example -[-15. -14.3877551 -13.7755102 -13.16326531 -12.55102041 - -11.93877551 -11.32653061 -10.71428571 -10.10204082 -9.48979592 - -8.87755102 -8.26530612 -7.65306122 -7.04081633 -6.42857143 - -5.81632653 -5.20408163 -4.59183673 -3.97959184 -3.36734694 - -2.75510204 -2.14285714 -1.53061224 -0.91836735 -0.30612245 - 0.30612245 0.91836735 1.53061224 2.14285714 2.75510204 - 3.36734694 3.97959184 4.59183673 5.20408163 5.81632653 - 6.42857143 7.04081633 7.65306122 8.26530612 8.87755102 - 9.48979592 10.10204082 10.71428571 11.32653061 11.93877551 - 12.55102041 13.16326531 13.7755102 14.3877551 15. ] -#+end_example - -Finally, an example for graphical output: -#+begin_src python :results output file :session :var matplot_lib_filename="./cosxsx.png" :exports results -import matplotlib.pyplot as plt - -plt.figure(figsize=(10,5)) -plt.plot(x,numpy.cos(x)/x) -plt.tight_layout() - -plt.savefig(matplot_lib_filename) -print(matplot_lib_filename) -#+end_src - -#+RESULTS: -[[file:./cosxsx.png]] - -Note the parameter ~:exports results~, which indicates that the code -will not appear in the exported document. We recommend that in the -context of this MOOC, you always leave this parameter setting as -~:exports both~, because we want your analyses to be perfectly -transparent and reproducible. - -Watch out: the figure generated by the code block is /not/ stored in -the org document. It's a plain file, here named ~cosxsx.png~. You have -to commit it explicitly if you want your analysis to be legible and -understandable on GitLab. - -Finally, don't forget that we provide in the resource section of this -MOOC a configuration with a few keyboard shortcuts that allow you to -quickly create code blocks in Python by typing ~ -#+HTML_HEAD: -#+HTML_HEAD: -#+HTML_HEAD: -#+HTML_HEAD: -#+HTML_HEAD: - -* Quelques explications - -Ceci est un document org-mode avec quelques exemples de code -python. Une fois ouvert dans emacs, ce document peut aisément être -exporté au format HTML, PDF, et Office. Pour plus de détails sur -org-mode vous pouvez consulter https://orgmode.org/guide/. - -Lorsque vous utiliserez le raccourci =C-c C-e h o=, ce document sera -compilé en html. Tout le code contenu sera ré-exécuté, les résultats -récupérés et inclus dans un document final. Si vous ne souhaitez pas -ré-exécuter tout le code à chaque fois, il vous suffit de supprimer -le # et l'espace qui sont devant le ~#+PROPERTY:~ au début de ce -document. - -Comme nous vous l'avons montré dans la vidéo, on inclue du code -python de la façon suivante (et on l'exécute en faisant ~C-c C-c~): - -#+begin_src python :results output :exports both -print("Hello world!") -#+end_src - -#+RESULTS: -: Hello world! - -Voici la même chose, mais avec une session python, donc une -persistance d'un bloc à l'autre (et on l'exécute toujours en faisant -~C-c C-c~). -#+begin_src python :results output :session :exports both -import numpy -x=numpy.linspace(-15,15) -print(x) -#+end_src - -#+RESULTS: -#+begin_example -[-15. -14.3877551 -13.7755102 -13.16326531 -12.55102041 - -11.93877551 -11.32653061 -10.71428571 -10.10204082 -9.48979592 - -8.87755102 -8.26530612 -7.65306122 -7.04081633 -6.42857143 - -5.81632653 -5.20408163 -4.59183673 -3.97959184 -3.36734694 - -2.75510204 -2.14285714 -1.53061224 -0.91836735 -0.30612245 - 0.30612245 0.91836735 1.53061224 2.14285714 2.75510204 - 3.36734694 3.97959184 4.59183673 5.20408163 5.81632653 - 6.42857143 7.04081633 7.65306122 8.26530612 8.87755102 - 9.48979592 10.10204082 10.71428571 11.32653061 11.93877551 - 12.55102041 13.16326531 13.7755102 14.3877551 15. ] -#+end_example - -Et enfin, voici un exemple de sortie graphique: -#+begin_src python :results output file :session :var matplot_lib_filename="./cosxsx.png" :exports results -import matplotlib.pyplot as plt - -plt.figure(figsize=(10,5)) -plt.plot(x,numpy.cos(x)/x) -plt.tight_layout() - -plt.savefig(matplot_lib_filename) -print(matplot_lib_filename) -#+end_src - -#+RESULTS: -[[file:./cosxsx.png]] - -Vous remarquerez le paramètre ~:exports results~ qui indique que le code -ne doit pas apparaître dans la version finale du document. Nous vous -recommandons dans le cadre de ce MOOC de ne pas changer ce paramètre -(indiquer ~both~) car l'objectif est que vos analyses de données soient -parfaitement transparentes pour être reproductibles. - -Attention, la figure ainsi générée n'est pas stockée dans le document -org. C'est un fichier ordinaire, ici nommé ~cosxsx.png~. N'oubliez pas -de le committer si vous voulez que votre analyse soit lisible et -compréhensible sur GitLab. - -Enfin, n'oubliez pas que nous vous fournissons dans les ressources de -ce MOOC une configuration avec un certain nombre de raccourcis -claviers permettant de créer rapidement les blocs de code python (en -faisant ~