From d8562566a1f955c56682a2166d4f8c56055ea834 Mon Sep 17 00:00:00 2001 From: ce9a2e8f95a59bf8ceb3cb087dad994a Date: Sun, 2 Apr 2023 19:44:36 +0000 Subject: [PATCH] no commit message --- module3/exo3/exercice.ipynb | 825 +++++++++++++++--------------------- 1 file changed, 345 insertions(+), 480 deletions(-) diff --git a/module3/exo3/exercice.ipynb b/module3/exo3/exercice.ipynb index f4a2047..21bd319 100644 --- a/module3/exo3/exercice.ipynb +++ b/module3/exo3/exercice.ipynb @@ -468,593 +468,458 @@ " persos.append(perso.upper())\n", "print(persos)\n", "persos[7] = 'MAÎTRE SIMON'\n", - "persos[8] = 'MAÎTRE JACQUES'\n", - "Nombre_repliques = {}\n", - "for perso in persos :\n", - " Nombre_repliques[perso] = 0\n", - "\n", - "Nombre_mots = {}\n", - "for perso in persos :\n", - " Nombre_mots[perso] = 0\n", - "\n", - "Nombre_scènes = {}\n", - "for perso in persos :\n", - " Nombre_scènes[perso] = 0\n", - "\n", - "Nombre_actes = {}\n", - "for perso in persos :\n", - " Nombre_actes[perso] = []" + "persos[8] = 'MAÎTRE JACQUES'" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 78, "metadata": {}, "outputs": [], "source": [ - "k = 33\n", - "non_habituel = []\n", - "nontraitée = []\n", + "acte = 0\n", "perso_prec = 0\n", + "scene = 0\n", + "k = 40\n", + "non_habituel = []\n", + "new_scene = False\n", "while k < len(lignes) :\n", " l = lignes[k]\n", " est_perso = False\n", " for perso in persos :\n", " if perso[4:-2] in l :\n", - " k+=2 \n", - " est_perso = True\n", - " if (k-perso_prec) > 3 :\n", - " non_habituel.append(k)\n", + " if not new_scene :\n", + " if (k-perso_prec) != 3 :\n", + " non_habituel.append((acte,scene,k,perso_prec))\n", + " else :\n", + " new_scene = False\n", " perso_prec = k\n", - " if l[:2] == '##' :\n", - " k +=2\n", - " elif l == '\\n' :\n", - " k+=1\n", - " elif not (est_perso) :\n", - " nontraitée.append(l)\n", + " k+=2\n", + " if l[:3] == '###' :\n", + " scene +=1\n", + " new_scene = True\n", + " k+=4\n", + " elif l[:2] == '##' :\n", + " scene = 0\n", + " acte +=1\n", + " k+=3\n", + " else :\n", " k+=1" ] }, { "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "' VALÈRE.\\n'" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lignes[47]" - ] - }, - { - "cell_type": "code", - "execution_count": 15, + "execution_count": 197, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "' VALÈRE.\\n'" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "lignes[47]" + "Ajouter_mots = {}\n", + "Ajouter_répliques = {}\n", + "\n", + "## Harpagon\n", + "Ajouter_mots['HARPAGON'] = {}\n", + "Ajouter_répliques['HARPAGON'] = {}\n", + "#Harpagon, acte 1\n", + "Ajouter_mots['HARPAGON'][1] = {}\n", + "Ajouter_répliques['HARPAGON'][1] = {}\n", + "#Harpagon, acte 1, scène 3\n", + "Ajouter_mots['HARPAGON'][1][3] = 0\n", + "Ajouter_répliques['HARPAGON'][1][3] = 0\n", + "for k in [199,202,205,211,214,252,270] :\n", + " Ajouter_mots['HARPAGON'][1][3] += len(split_string(lignes[k]))\n", + " Ajouter_répliques['HARPAGON'][1][3] += 1\n", + "#Harpagon, acte 1, scène 4\n", + "Ajouter_mots['HARPAGON'][1][4] = 0\n", + "Ajouter_répliques['HARPAGON'][1][4] = 0\n", + "for k in [382,385,629] :\n", + " Ajouter_mots['HARPAGON'][1][4] += len(split_string(lignes[k]))\n", + " Ajouter_répliques['HARPAGON'][1][4] +=1\n", + "#Harpagon, acte 1, scène 5\n", + "Ajouter_mots['HARPAGON'][1][5] = 0\n", + "Ajouter_répliques['HARPAGON'][1][5] = 0\n", + "for k in [783] :\n", + " Ajouter_mots['HARPAGON'][1][5] += len(split_string(lignes[k]))\n", + " Ajouter_répliques['HARPAGON'][1][5] += 1\n", + "\n", + "#Harpagon, acte 3\n", + "Ajouter_mots['HARPAGON'][3] = {}\n", + "Ajouter_répliques['HARPAGON'][3] = {}\n", + "#Harpagon, acte 3, scène 1\n", + "Ajouter_mots['HARPAGON'][3][1] = 0\n", + "Ajouter_répliques['HARPAGON'][3][1] = 0\n", + "for k in [1340,1343,1367,1370] :\n", + " Ajouter_mots['HARPAGON'][3][1] += len(split_string(lignes[k]))\n", + " Ajouter_répliques['HARPAGON'][3][1] += 1\n", + "\n", + "#Harpagon, acte 4\n", + "Ajouter_mots['HARPAGON'][4] = {}\n", + "Ajouter_répliques['HARPAGON'][4] = {}\n", + "#Harpagon, acte 4, scène 4\n", + "Ajouter_mots['HARPAGON'][4][4] = 0\n", + "Ajouter_répliques['HARPAGON'][4][4] = 0\n", + "for k in [2355] :\n", + " Ajouter_mots['HARPAGON'][4][4] += len(split_string(lignes[k]))\n", + " Ajouter_répliques['HARPAGON'][4][4] += 1\n", + "\n", + "## Elise\n", + "Ajouter_mots['ÉLISE'] = {}\n", + "Ajouter_répliques['ÉLISE'] = {}\n", + "#Elise, acte 1\n", + "Ajouter_mots['ÉLISE'][1] = {}\n", + "Ajouter_répliques['ÉLISE'][1] = {}\n", + "#Elise, acte 1, scène 4\n", + "Ajouter_mots['ÉLISE'][1][4] = 0\n", + "Ajouter_répliques['ÉLISE'][1][4] = 0\n", + "for k in [624] :\n", + " Ajouter_mots['ÉLISE'][1][4] += len(split_string(lignes[k]))\n", + " Ajouter_répliques['ÉLISE'][1][4] += 1\n", + "\n", + "##Valère\n", + "Ajouter_mots['VALÈRE'] = {}\n", + "Ajouter_répliques['VALÈRE'] = {}\n", + "#Valère, acte 1\n", + "Ajouter_mots['VALÈRE'][1] = {}\n", + "Ajouter_répliques['VALÈRE'][1] = {}\n", + "#Valère, acte 1, scène 5\n", + "Ajouter_mots['VALÈRE'][1][5] = 0\n", + "Ajouter_répliques['VALÈRE'][1][5] = 0\n", + "for k in [813,816] :\n", + " Ajouter_mots['VALÈRE'][1][5] += len(split_string(lignes[k]))\n", + " Ajouter_répliques['VALÈRE'][1][5] += 1\n", + "\n", + "#Valère, acte 3\n", + "Ajouter_mots['VALÈRE'][3] = {}\n", + "Ajouter_répliques['VALÈRE'][3] = {}\n", + "#Valère, acte 3, scène 2\n", + "Ajouter_mots['VALÈRE'][3][2] = 0\n", + "Ajouter_répliques['VALÈRE'][3][2] = 0\n", + "for k in [1631,1663,1666] :\n", + " Ajouter_mots['VALÈRE'][3][2] += len(split_string(lignes[k]))\n", + " Ajouter_répliques['VALÈRE'][3][2] += 1\n", + "\n", + "##Frosine\n", + "Ajouter_mots['FROSINE'] = {}\n", + "Ajouter_répliques['FROSINE'] = {}\n", + "#Frosine, acte 2\n", + "Ajouter_mots['FROSINE'][2] = {}\n", + "Ajouter_répliques['FROSINE'][2] = {}\n", + "#Frosine, acte 2, scène 5\n", + "Ajouter_mots['FROSINE'][2][5] = 0\n", + "Ajouter_répliques['FROSINE'][2][5] = 0\n", + "for k in [1278,1281,1284,1292,1295,1303] :\n", + " Ajouter_mots['FROSINE'][2][5] += len(split_string(lignes[k]))\n", + " Ajouter_répliques['FROSINE'][2][5] += 1\n", + "\n", + "## Maître Jacques\n", + "Ajouter_mots['MAÎTRE JACQUES'] = {}\n", + "Ajouter_répliques['MAÎTRE JACQUES'] = {}\n", + "## Maître Jacques, acte 3\n", + "Ajouter_mots['MAÎTRE JACQUES'][3] = {}\n", + "Ajouter_répliques['MAÎTRE JACQUES'][3] = {}\n", + "## Maître Jacques, acte 3, scène 1\n", + "Ajouter_mots['MAÎTRE JACQUES'][3][1] = 0\n", + "Ajouter_répliques['MAÎTRE JACQUES'][3][1] = 0\n", + "for k in [1409,1525,1528] :\n", + " Ajouter_mots['MAÎTRE JACQUES'][3][1] += len(split_string(lignes[k]))\n", + " Ajouter_répliques['MAÎTRE JACQUES'][3][1] += 1\n", + "## Maître Jacques, acte 3, scène 2\n", + "Ajouter_mots['MAÎTRE JACQUES'][3][2] = 0\n", + "Ajouter_répliques['MAÎTRE JACQUES'][3][2] = 0\n", + "for k in [1608] :\n", + " Ajouter_mots['MAÎTRE JACQUES'][3][2] += len(split_string(lignes[k]))\n", + " Ajouter_répliques['MAÎTRE JACQUES'][3][2] += 1\n", + "\n", + "## Maître Jacques, acte 4\n", + "Ajouter_mots['MAÎTRE JACQUES'][4] = {}\n", + "Ajouter_répliques['MAÎTRE JACQUES'][4] = {}\n", + "## Maître Jacques, acte 4, scène 4\n", + "Ajouter_mots['MAÎTRE JACQUES'][4][4] = 0\n", + "Ajouter_répliques['MAÎTRE JACQUES'][4][4] = 0\n", + "for k in [2297,2317,2320,2328] :\n", + " Ajouter_mots['MAÎTRE JACQUES'][4][4] += len(split_string(lignes[k]))\n", + " Ajouter_répliques['MAÎTRE JACQUES'][4][4] +=1\n", + "\n", + "## Cléante\n", + "Ajouter_mots['CLÉANTE'] = {}\n", + "Ajouter_répliques['CLÉANTE'] = {}\n", + "#Cléante, acte 3\n", + "Ajouter_mots['CLÉANTE'][3] = {}\n", + "Ajouter_répliques['CLÉANTE'][3] = {}\n", + "#Cléante, acte 3, scène 4\n", + "Ajouter_mots['CLÉANTE'][3][4] = 0\n", + "Ajouter_répliques['CLÉANTE'][3][4] = 0\n", + "for k in [1867] :\n", + " Ajouter_mots['CLÉANTE'][3][4] += len(split_string(lignes[k]))\n", + " Ajouter_répliques['CLÉANTE'][3][4] +=1\n", + "#Cléante, acte 3, scène 7\n", + "Ajouter_mots['CLÉANTE'][3][7] = 0\n", + "Ajouter_répliques['CLÉANTE'][3][7] = 0\n", + "for k in [1875] :\n", + " Ajouter_mots['CLÉANTE'][3][7] += len(split_string(lignes[k]))\n", + " Ajouter_répliques['CLÉANTE'][3][7] += 1\n", + "\n", + "##La merluche\n", + "Ajouter_mots['LA MERLUCHE'] = {}\n", + "Ajouter_répliques['LA MERLUCHE'] = {}\n", + "#La merluche, acte 3\n", + "Ajouter_mots['LA MERLUCHE'][3] = {}\n", + "Ajouter_répliques['LA MERLUCHE'][3] = {}\n", + "#La merluche, acte 3, scène 9\n", + "Ajouter_mots['LA MERLUCHE'][3][9] = 0\n", + "Ajouter_répliques['LA MERLUCHE'][3][9] = 0\n", + "for k in [1971] :\n", + " Ajouter_mots['LA MERLUCHE'][3][9] += len(split_string(lignes[k]))\n", + " Ajouter_répliques['LA MERLUCHE'][3][9] +=1" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 204, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['La Scène est à Paris.\\n',\n", - " \"# L'Avare, *Comédie.*.\\n\",\n", - " 'À part.\\n',\n", - " \"J'enrage.\\n\",\n", - " 'Haut.\\n',\n", - " \"Je demande si malicieusement tu n'irais point faire courir le bruit que j'en ai.\\n\",\n", - " 'Il lève la main pour lui donner un soufflet.\\n',\n", - " \"Sors d'ici, encore une fois.\\n\",\n", - " \"Ces grands hauts-de-chausses sont propres à devenir les recéleurs des choses qu'on dérobe ; et je voudrais qu'on en eût fait pendre quelqu'un.\\n\",\n", - " 'Il fouille dans les poches de la Flèche.\\n',\n", - " \"Ici le frère et la sœur paraissent s'entretenant bas.\\n\",\n", - " \"Ô Ciel ! je me serai trahi moi-même.La chaleur m'aura emporté ; et je crois que j'ai parlé haut en raisonnant tout seul. Qu'est-ce ?\\n\",\n", - " \"Je ne veux point me marier, mon Père, s'il vous plaît.\\n\",\n", - " \"Et moi, ma petite Fille ma mie, je veux que vous vous mariiez, s'il vous plaît.\\n\",\n", - " \"Ouais. Il me semble que j'entends un Chien qui aboie. N'est-ce point qu'on en voudrait à mon argent ? Ne bougez, je reviens tout à l'heure.\\n\",\n", - " 'Il aperçoit Harpagon.\\n',\n", - " \"Oui, il faut qu'une fille obéisse à son Père. Il ne faut point qu'elle regarde comme un Mari est fait ; et lorsque la grande raison de *sans dot* s'y rencontre, elle doit être prête à prendre tout ce qu'on lui donne.\\n\",\n", - " 'Fin du Premier Acte.\\n',\n", - " 'Il prend un air sévère.\\n',\n", - " \"J'ai un Procès que je suis sur le point de perdre, faute d'un peu d'argent ; et vous pourriez facilement me procurer le gain de ce Procès, si vous aviez quelque bonté pour moi.\\n\",\n", - " 'Il reprend un air gai.\\n',\n", - " \"Vous ne sauriez croire le plaisir qu'elle aura de vous voir. Ah ! que vous lui plairez ! et que votre fraise à l'antique fera sur son esprit un effet admirable ! Mais, surtout, elle sera charmée de votre haut-de-chausses, attaché au pourpoint avec des aiguillettes.C'est pour la rendre folle de vous ; et un Amant aiguilletté sera pour elle un ragoût merveilleux.\\n\",\n", - " \"En vérité, Monsieur, ce Procès m'est d'une conséquence tout à fait grande. Je suis ruinée, si je le perds ; et quelque petite assistance me rétablirait mes affaires.\\n\",\n", - " 'Il reprend un air gai.\\n',\n", - " \"Je voudrais que vous eussiez vu le ravissement où elle était, à m'entendre parler de vous. La joie éclatait dans ses yeux, au récit de vos qualités ; et je l'ai mise enfin dans une impatience extrême, de voir ce mariage entièrement conclu.\\n\",\n", - " 'Je vous prie, Monsieur, de me donner le petit secours que je vous demande. Cela me remettra sur pied ; et je vous en serai éternellement obligée.\\n',\n", - " 'Fin du Second Acte.\\n',\n", - " 'Elle tient un Balai.\\n',\n", - " \"Bon, vous voilà les armes à la mainJe vous commets au soin de nettoyer partout ; et surtout, prenez garde de ne point frotter les meubles trop fort, de peur de les user.Outre cela, je vous constitue, pendant le souper, au gouvernement des bouteilles ; et s'il s'en écarte quelqu'une, et qu'il se casse quelque chose, je m'en prendrai à vous, et le rabattrai sur vos gages.\\n\",\n", - " \"Harpagon met son chapeau au-devant de son pourpoint, pour montrer à Brindavoine comment il doit faire pour cacher la tache d'huile.\\n\",\n", - " \"Et vous, tenez toujours votre chapeau ainsi, lorsque vous servirez. Pour vous, ma Fille, vous aurez l'œil sur ce que l'on desservira, et prendrez garde qu'il ne s'en fasse aucun dégât. Cela sied bien aux Filles. Mais cependant préparez-vous à bien recevoir ma Maîtresse, qui vous doit venir visiter, et vous mener avec elle à la Foire. Entendez-vous ce que je vous dis ?\\n\",\n", - " 'Il ôte sa Casaque de Cocher, et paraît vêtu en Cuisinier.\\n',\n", - " 'Il remet sa casaque.\\n',\n", - " 'Vous dites…\\n',\n", - " 'Maître Jacques pousse Valère jusques au bout du théâtre, en le menaçant.\\n',\n", - " \"Valère le fait reculer autant qu'il l'a fait.\\n\",\n", - " 'Il lui donne des coups de bâton.\\n',\n", - " 'Apprenez que vous êtes un mauvais railleur.\\n',\n", - " 'Il faut que vous le voyiez de près.\\n',\n", - " \"Nenni, Madame, il est en de trop belles mains. C'est un présent que mon Père vous a fait.\\n\",\n", - " 'Monsieur…\\n',\n", - " \"Il vient trouver Cléante à l'autre bout du théâtre.\\n\",\n", - " 'Il revient à Harpagon.\\n',\n", - " \"Hé bien, votre Fils n'est pas si étrange que vous le dites, et il se met à la raison. Il dit qu'il sait le respect qu'il vous doit, qu'il ne s'est emporté que dans la première chaleur, et qu'il ne fera point refus de se soumettre à ce qu'il vous plaira, pourvu que vous vouliez le traiter mieux que vous ne faites, et lui donner quelque Personne en mariage, dont il ait lieu d'être content.\\n\",\n", - " \"Laissez-moi faire. Hé bien, votre Père n'est pas si déraisonnable que vous le faites\\xa0; et il m'a témoigné que ce sont vos emportements qui l'ont mis en colère\\xa0; qu'il n'en veut seulement qu'à votre manière d'agir, et qu'il sera fort disposé à vous accorder ce que vous souhaitez, pourvu que vous vouliez vous y prendre par la douceur, et lui rendre les déférences, les respects, et les soumissions qu'un Fils doit à son père.\\n\",\n", - " \"Il tire son mouchoir de sa poche\\xa0; ce qui fait croire à maître Jacques qu'il va lui donner quelque chose.\\n\",\n", - " \"Au voleur, au voleur, à l'assassin, au meurtrier.Justice, juste Ciel.Je suis perdu, je suis assassiné, on m'a coupé la gorge, on m'a dérobé mon argent. Qui peut-ce être ? qu'est-il devenu ? où est-il ? où se cache-t-il ? que ferai-je pour le trouver ? où courir ? où ne pas courir ? n'est-il point là ? n'est-il point ici ? qui est-ce ? Arrête. Rends-moi mon argent, coquin…\\n\",\n", - " 'Il se prend lui-même le bras.\\n',\n", - " \"Ah, c'est moi.Mon esprit est troublé, et j'ignore où je suis, qui je suis, et ce que je fais. Hélas, mon pauvre argent, mon pauvre argent, mon cher ami, on m'a privé de toi\\xa0; et puisque tu m'es enlevé, j'ai perdu mon support, ma consolation, ma joie, tout est fini pour moi, et je n'ai plus que faire au monde.Sans toi, il m'est impossible de vivre. C'en est fait, je n'en puis plus, je me meurs, je suis mort, je suis enterré. N'y a-t-il personne qui veuille me ressusciter, en me rendant mon cher argent, ou en m'apprenant qui l'a pris ? Euh ? que dites-vous ? Ce n'est personne.Il faut, qui que ce soit qui ait fait le coup, qu'avec beaucoup de soin on ait épié l'heure\\xa0; et l'on a choisi justement le temps que je parlais à mon traître de Fils. Sortons. Je veux aller quérir la justice, et faire donner la Question à toute la Maison\\xa0; à Servantes, à Valets, à Fils, à Fille, et à moi aussi. Que de gens assemblés ! Je ne jette mes regards sur personne, qui ne me donne des soupçons, et tout me semble mon voleur. Eh ! de quoi est-ce qu'on parle là ? de celui qui m'a dérobé ?Quel bruit fait-on là-haut ? est-ce mon voleur qui y est ? De grâce, si l'on sait des nouvelles de mon voleur, je supplie que l'on m'en dise. N'est-il point caché là parmi vous ? Ils me regardent tous, et se mettent à rire. Vous verrez qu'ils ont part, sans doute, au vol que l'on m'a fait. Allons vite, des Commissaires, des Archers, des Prévôts, des Juges, des Gênes, des Potences et des Bourreaux. Je veux faire pendre tout le monde\\xa0; et si je ne retrouve mon argent, je me pendrai moi-même après.\\n\",\n", - " 'Fin du Quatrième Acte.\\n']" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "nontraitée" + "import copy\n", + "\n", + "dico_persos = {}\n", + "for perso in persos :\n", + " dico_persos[perso] = 0\n", + "\n", + "acte = 0\n", + "perso_prec = 0\n", + "scene = 0\n", + "k = 40\n", + "new_scene = False\n", + "\n", + "dico_acte_nombre_mots = {}\n", + "dico_acte_nombre_répliques = {}\n", + "while k < len(lignes) :\n", + " l = lignes[k]\n", + " for perso in persos :\n", + " if perso in l :\n", + " if not new_scene :\n", + " if (k-perso_prec) == 3 :\n", + " dico_scene_nombre_mots[perso] += len(split_string(lignes[k+1]))\n", + " dico_scene_nombre_répliques[perso] +=1\n", + " else :\n", + " new_scene = False\n", + " dico_scene_nombre_mots[perso] += len(split_string(lignes[k+1]))\n", + " dico_scene_nombre_répliques[perso] +=1\n", + " perso_prec = k\n", + " k+=2\n", + " if l[:3] == '###' :\n", + " if scene > 0 :\n", + " dico_acte_nombre_mots[acte][scene] = copy.deepcopy(dico_scene_nombre_mots)\n", + " dico_acte_nombre_répliques[acte][scene] = copy.deepcopy(dico_scene_nombre_répliques)\n", + " scene +=1\n", + " new_scene = True\n", + " k+=4\n", + " dico_scene_nombre_mots = copy.deepcopy(dico_persos)\n", + " dico_scene_nombre_répliques = copy.deepcopy(dico_persos)\n", + " elif l[:2] == '##' :\n", + " if acte > 0 :\n", + " dico_acte_nombre_mots[acte][scene] = copy.deepcopy(dico_scene_nombre_mots)\n", + " dico_acte_nombre_répliques[acte][scene] = copy.deepcopy(dico_scene_nombre_répliques)\n", + " scene = 0\n", + " acte +=1\n", + " k+=3\n", + " dico_acte_nombre_mots[acte] = {}\n", + " dico_acte_nombre_répliques[acte] = {}\n", + " else :\n", + " k+=1\n", + "dico_acte_nombre_mots[acte][scene] = copy.deepcopy(dico_scene_nombre_mots)\n", + "dico_acte_nombre_répliques[acte][scene] = copy.deepcopy(dico_scene_nombre_répliques)" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 205, "metadata": {}, "outputs": [], "source": [ - "non_traitée = [\n", - " \"J'enrage.\\n\",\n", - " \"Je demande si malicieusement tu n'irais point faire courir le bruit que j'en ai.\\n\",\n", - " \"Sors d'ici, encore une fois.\\n\",\n", - " \"Ces grands hauts-de-chausses sont propres à devenir les recéleurs des choses qu'on dérobe ; et je voudrais qu'on en eût fait pendre quelqu'un.\\n\",\n", - " \"Ô Ciel ! je me serai trahi moi-même.La chaleur m'aura emporté ; et je crois que j'ai parlé haut en raisonnant tout seul. Qu'est-ce ?\\n\",\n", - " \"Je ne veux point me marier, mon Père, s'il vous plaît.\\n\",\n", - " \"Et moi, ma petite Fille ma mie, je veux que vous vous mariiez, s'il vous plaît.\\n\",\n", - " \"Ouais. Il me semble que j'entends un Chien qui aboie. N'est-ce point qu'on en voudrait à mon argent ? Ne bougez, je reviens tout à l'heure.\\n\",\n", - " \"Oui, il faut qu'une fille obéisse à son Père. Il ne faut point qu'elle regarde comme un Mari est fait ; et lorsque la grande raison de *sans dot* s'y rencontre, elle doit être prête à prendre tout ce qu'on lui donne.\\n\",\n", - " \"J'ai un Procès que je suis sur le point de perdre, faute d'un peu d'argent ; et vous pourriez facilement me procurer le gain de ce Procès, si vous aviez quelque bonté pour moi.\\n\",\n", - " \"Vous ne sauriez croire le plaisir qu'elle aura de vous voir. Ah ! que vous lui plairez ! et que votre fraise à l'antique fera sur son esprit un effet admirable ! Mais, surtout, elle sera charmée de votre haut-de-chausses, attaché au pourpoint avec des aiguillettes.C'est pour la rendre folle de vous ; et un Amant aiguilletté sera pour elle un ragoût merveilleux.\\n\",\n", - " \"En vérité, Monsieur, ce Procès m'est d'une conséquence tout à fait grande. Je suis ruinée, si je le perds ; et quelque petite assistance me rétablirait mes affaires.\\n\",\n", - " \"Je voudrais que vous eussiez vu le ravissement où elle était, à m'entendre parler de vous. La joie éclatait dans ses yeux, au récit de vos qualités ; et je l'ai mise enfin dans une impatience extrême, de voir ce mariage entièrement conclu.\\n\",\n", - " 'Je vous prie, Monsieur, de me donner le petit secours que je vous demande. Cela me remettra sur pied ; et je vous en serai éternellement obligée.\\n',\n", - " \"Bon, vous voilà les armes à la mainJe vous commets au soin de nettoyer partout ; et surtout, prenez garde de ne point frotter les meubles trop fort, de peur de les user.Outre cela, je vous constitue, pendant le souper, au gouvernement des bouteilles ; et s'il s'en écarte quelqu'une, et qu'il se casse quelque chose, je m'en prendrai à vous, et le rabattrai sur vos gages.\\n\",\n", - " \"Harpagon met son chapeau au-devant de son pourpoint, pour montrer à Brindavoine comment il doit faire pour cacher la tache d'huile.\\n\",\n", - " \"Et vous, tenez toujours votre chapeau ainsi, lorsque vous servirez. Pour vous, ma Fille, vous aurez l'œil sur ce que l'on desservira, et prendrez garde qu'il ne s'en fasse aucun dégât. Cela sied bien aux Filles. Mais cependant préparez-vous à bien recevoir ma Maîtresse, qui vous doit venir visiter, et vous mener avec elle à la Foire. Entendez-vous ce que je vous dis ?\\n\",\n", - " 'Vous dites…\\n',\n", - " 'Apprenez que vous êtes un mauvais railleur.\\n',\n", - " 'Il faut que vous le voyiez de près.\\n',\n", - " \"Nenni, Madame, il est en de trop belles mains. C'est un présent que mon Père vous a fait.\\n\",\n", - " 'Monsieur…\\n',\n", - " \"Hé bien, votre Fils n'est pas si étrange que vous le dites, et il se met à la raison. Il dit qu'il sait le respect qu'il vous doit, qu'il ne s'est emporté que dans la première chaleur, et qu'il ne fera point refus de se soumettre à ce qu'il vous plaira, pourvu que vous vouliez le traiter mieux que vous ne faites, et lui donner quelque Personne en mariage, dont il ait lieu d'être content.\\n\",\n", - " \"Laissez-moi faire. Hé bien, votre Père n'est pas si déraisonnable que vous le faites\\xa0; et il m'a témoigné que ce sont vos emportements qui l'ont mis en colère\\xa0; qu'il n'en veut seulement qu'à votre manière d'agir, et qu'il sera fort disposé à vous accorder ce que vous souhaitez, pourvu que vous vouliez vous y prendre par la douceur, et lui rendre les déférences, les respects, et les soumissions qu'un Fils doit à son père.\\n\",\n", - " \"Il tire son mouchoir de sa poche\\xa0; ce qui fait croire à maître Jacques qu'il va lui donner quelque chose.\\n\",\n", - " \"Au voleur, au voleur, à l'assassin, au meurtrier.Justice, juste Ciel.Je suis perdu, je suis assassiné, on m'a coupé la gorge, on m'a dérobé mon argent. Qui peut-ce être ? qu'est-il devenu ? où est-il ? où se cache-t-il ? que ferai-je pour le trouver ? où courir ? où ne pas courir ? n'est-il point là ? n'est-il point ici ? qui est-ce ? Arrête. Rends-moi mon argent, coquin…\\n\",\n", - " \"Ah, c'est moi.Mon esprit est troublé, et j'ignore où je suis, qui je suis, et ce que je fais. Hélas, mon pauvre argent, mon pauvre argent, mon cher ami, on m'a privé de toi\\xa0; et puisque tu m'es enlevé, j'ai perdu mon support, ma consolation, ma joie, tout est fini pour moi, et je n'ai plus que faire au monde.Sans toi, il m'est impossible de vivre. C'en est fait, je n'en puis plus, je me meurs, je suis mort, je suis enterré. N'y a-t-il personne qui veuille me ressusciter, en me rendant mon cher argent, ou en m'apprenant qui l'a pris ? Euh ? que dites-vous ? Ce n'est personne.Il faut, qui que ce soit qui ait fait le coup, qu'avec beaucoup de soin on ait épié l'heure\\xa0; et l'on a choisi justement le temps que je parlais à mon traître de Fils. Sortons. Je veux aller quérir la justice, et faire donner la Question à toute la Maison\\xa0; à Servantes, à Valets, à Fils, à Fille, et à moi aussi. Que de gens assemblés ! Je ne jette mes regards sur personne, qui ne me donne des soupçons, et tout me semble mon voleur. Eh ! de quoi est-ce qu'on parle là ? de celui qui m'a dérobé ?Quel bruit fait-on là-haut ? est-ce mon voleur qui y est ? De grâce, si l'on sait des nouvelles de mon voleur, je supplie que l'on m'en dise. N'est-il point caché là parmi vous ? Ils me regardent tous, et se mettent à rire. Vous verrez qu'ils ont part, sans doute, au vol que l'on m'a fait. Allons vite, des Commissaires, des Archers, des Prévôts, des Juges, des Gênes, des Potences et des Bourreaux. Je veux faire pendre tout le monde\\xa0; et si je ne retrouve mon argent, je me pendrai moi-même après.\\n\"]" + "for perso_a_ajouter in Ajouter_mots.keys() :\n", + " for acte_a_ajouter in Ajouter_mots[perso_a_ajouter].keys() :\n", + " for scene_a_ajouter in Ajouter_mots[perso_a_ajouter][acte_a_ajouter].keys() :\n", + " dico_acte_nombre_mots[acte][scene][perso_a_ajouter] += Ajouter_mots[perso_a_ajouter][acte_a_ajouter][scene_a_ajouter]\n", + " dico_acte_nombre_répliques[acte][scene][perso_a_ajouter] += Ajouter_répliques[perso_a_ajouter][acte_a_ajouter][scene_a_ajouter]" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 207, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "27\n" - ] - } - ], + "outputs": [], "source": [ - "print(len(non_traitée))" + "Nombre_mots_perso = copy.deepcopy(dico_persos)\n", + "Nombre_répliques_perso = copy.deepcopy(dico_persos)\n", + "\n", + "for perso in persos :\n", + " for acte in dico_acte_nombre_mots.keys() :\n", + " for scene in dico_acte_nombre_mots[acte].keys() :\n", + " Nombre_mots_perso[perso] += dico_acte_nombre_mots[acte][scene][perso]\n", + " Nombre_répliques_perso[perso] += dico_acte_nombre_répliques[acte][scene][perso]" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 208, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{' HARPAGON.\\n': 0,\n", - " ' CLÉANTE.\\n': 0,\n", - " ' ÉLISE.\\n': 0,\n", - " ' VALÈRE.\\n': 0,\n", - " ' MARIANE.\\n': 0,\n", - " ' ANSELME.\\n': 0,\n", - " ' FROSINE.\\n': 0,\n", - " ' MAÎTRE SIMON.\\n': 0,\n", - " ' MAÎTRE JACQUES.\\n': 0,\n", - " ' LA FLÈCHE.\\n': 0,\n", - " ' DAME CLAUDE.\\n': 0,\n", - " ' BRINDAVOINE.\\n': 0,\n", - " ' LA MERLUCHE.\\n': 0,\n", - " ' LE COMMISSAIRE.\\n': 0}" + "{'DAME CLAUDE': 0,\n", + " 'BRINDAVOINE': 43,\n", + " 'LA MERLUCHE': 55,\n", + " 'MAÎTRE SIMON': 197,\n", + " 'LE COMMISSAIRE': 294,\n", + " 'ANSELME': 517,\n", + " 'MARIANE': 908,\n", + " 'ÉLISE': 1052,\n", + " 'LA FLÈCHE': 1465,\n", + " 'MAÎTRE JACQUES': 1700,\n", + " 'FROSINE': 2357,\n", + " 'VALÈRE': 2791,\n", + " 'CLÉANTE': 3289,\n", + " 'HARPAGON': 5869}" ] }, - "execution_count": 19, + "execution_count": 208, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "dict(sorted(Nombre_mots.items(), key=lambda item:item[1]))" + "dict(sorted(Nombre_mots_perso.items(), key=lambda item:item[1]))" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 210, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{' HARPAGON.\\n': 0,\n", - " ' CLÉANTE.\\n': 0,\n", - " ' ÉLISE.\\n': 0,\n", - " ' VALÈRE.\\n': 0,\n", - " ' MARIANE.\\n': 0,\n", - " ' ANSELME.\\n': 0,\n", - " ' FROSINE.\\n': 0,\n", - " ' MAÎTRE SIMON.\\n': 0,\n", - " ' MAÎTRE JACQUES.\\n': 0,\n", - " ' LA FLÈCHE.\\n': 0,\n", - " ' DAME CLAUDE.\\n': 0,\n", - " ' BRINDAVOINE.\\n': 0,\n", - " ' LA MERLUCHE.\\n': 0,\n", - " ' LE COMMISSAIRE.\\n': 0}" + "{'DAME CLAUDE': 0,\n", + " 'BRINDAVOINE': 3,\n", + " 'MAÎTRE SIMON': 5,\n", + " 'LA MERLUCHE': 6,\n", + " 'LE COMMISSAIRE': 17,\n", + " 'ANSELME': 20,\n", + " 'MARIANE': 30,\n", + " 'ÉLISE': 49,\n", + " 'LA FLÈCHE': 62,\n", + " 'FROSINE': 66,\n", + " 'MAÎTRE JACQUES': 89,\n", + " 'VALÈRE': 105,\n", + " 'CLÉANTE': 160,\n", + " 'HARPAGON': 359}" ] }, - "execution_count": 20, + "execution_count": 210, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "dict(sorted(Nombre_repliques.items(), key=lambda item:item[1]))" + "dict(sorted(Nombre_répliques_perso.items(), key=lambda item:item[1]))" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 222, "metadata": {}, "outputs": [ - { - "ename": "NameError", - "evalue": "name 'dico_acte' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0macte\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdico_acte\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0macte\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpersos\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 8\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpersos\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mscene\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdico_acte\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0macte\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mNameError\u001b[0m: name 'dico_acte' is not defined" - ] - }, { "data": { + "image/png": "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\n", "text/plain": [ - "
" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.patches as mpatches\n", - "import matplotlib.pyplot as plt\n", - "\n", - "colors = np.random.rand(14,3)\n", - "for acte in range(1,6) :\n", - " plt.figure(figsize=(8,8))\n", - " x = np.zeros((len(dico_acte[acte]),len(persos)))\n", - " labels = persos\n", - " for scene in range(1,len(dico_acte[acte])+1) :\n", - " for l,perso in enumerate(persos) :\n", - " x[scene-1,l] += dico_acte[acte][scene][perso]\n", - " plt.title('Acte ' + str(acte))\n", - " #plt.bar(labels,x)\n", - " width = 1/2 # épaisseur de chaque bâton\n", - " # Création du diagramme en bâtons (bâtons côte à côte)\n", - " for scene in range(1,len(dico_acte[acte])+1) :\n", - " bt = 0\n", - " for l,perso in enumerate(persos) :\n", - " plt.barh(-scene, x[scene-1,l],left = bt,color=colors[l])\n", - " bt += x[scene-1,l]\n", - " plt.yticks(-np.arange(scene)-1, np.array([\"Scène \"+str(k) for k in range(1,scene+1)]))\n", - " patches = []\n", - " for l,perso in enumerate(persos):\n", - " if acte in Nombre_actes[perso] :\n", - " patches.append(mpatches.Patch(color=colors[l], label=perso[4:-2]))\n", - " plt.legend(handles=patches,bbox_to_anchor=(1.05, 1),loc='upper left', borderaxespad=0.)" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "metadata": {}, - "outputs": [], - "source": [ - "acte = 0\n", - "perso_prec = 0\n", - "scene = 0\n", - "k = 40\n", - "non_habituel = []\n", - "new_scene = False\n", - "while k < len(lignes) :\n", - " l = lignes[k]\n", - " est_perso = False\n", - " for perso in persos :\n", - " if perso[4:-2] in l :\n", - " if not new_scene :\n", - " if (k-perso_prec) != 3 :\n", - " non_habituel.append((acte,scene,k,perso_prec))\n", - " else :\n", - " new_scene = False\n", - " perso_prec = k\n", - " k+=2\n", - " if l[:3] == '###' :\n", - " scene +=1\n", - " new_scene = True\n", - " k+=4\n", - " elif l[:2] == '##' :\n", - " scene = 0\n", - " acte +=1\n", - " k+=3\n", - " else :\n", - " k+=1" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "metadata": {}, - "outputs": [ + }, { "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAn8AAAHiCAYAAACZXqr7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzt3Xt4VdW97vH3l4QQYsIlBrmEQLjHBAwQELEVVJCKG1HE01ZrUY97g1ZaFHcFBbVFbLViW/VUrVq7MbopGxCl9Gmx1UKPxXKVANGEooIQCBCDRuSaZJw/MhdnQXMhkrCSjO/nedZj1pxjjvkba9Fnv3uMOdc055wAAADgh6hIFwAAAICzh/AHAADgEcIfAACARwh/AAAAHiH8AQAAeITwBwAA4BHCHwAAgEcIfwBOYmYrzOyAmbU8zfZpZubMLOYrnq+Pmb1hZvvNrMTMlptZ36/SFwCgdoQ/ACeYWZqkSyQ5SePO0mnbSloqqa+kDpLWSHrjLJ0bALxD+AMQbqKkf0j6L0k3h+8ws1Zm9oSZ7TCzz83sHTNrJelvQZPPzOygmQ0L2v9vM/sgmEVcbmbdqjqhc26Nc+43zrkS59xxSb+Q1NfMzm2oQQKAzwh/AMJNlPRq8PqGmXUI2zdXUrakiyUlSbpXUoWk4cH+ts65BOfcu2Z2raT7JV0nqb2k/ytp/mnWMFxSkXPu0zMdDADgXxnP9gUgSWb2dUl/ldTJOVdsZvmSfu2c+4WZRUn6UtJFzrncU45Lk/SxpBbOubJg2x8lLXLO/SZ4HyXpoKTznXM7aqihi6TVkv7TOXe6YREAUAfM/AEIuVnSm8654uD9f+v/L/0mS4qT9OFp9tVN0pNm9pmZfSapRJJJSqnuADNrL+lNSc8Q/ACg4Xylu/MANC/BtXvflBRtZkXB5paS2ppZlqTNko5I6ikp95TDq1o+2CnpEefcq6d5/naqDH5LnXOPfIUhAABOEzN/ACTpWknlkjIkDQhe56vyWr2JzrkKSS9J+rmZdTazaDMbFvwczH5VXvvXI6y/5yTdZ2aZkmRmbczsf1V1YjNrLWm5pL8752Y0zPAAACGEPwBS5fLub51znzjnikIvSf9H0neC3/D7T1XOAK5V5TLuY5KinHOHJD0i6e/BMu9Fzrklwf7fmVmppC2SxlRz7vGShki6NbhbOPTq2pADBgBfccMHAACAR5j5AwAA8AjhDwAAwCOEPwAAAI8Q/gAAADxC+AMAAPBIs/2R5+TkZJeWlhbpMgAAOCvWr19f7Jxr30B9nxcTE/OipH5i4qixq5C0pays7N+zs7P3VdWg2Ya/tLQ0rVu3LtJlAABwVphZtc/NPlMxMTEvduzY8fz27dsfiIqK4jfiGrGKigrbv39/RlFR0YuSxlXVhvQOAABq0699+/alBL/GLyoqyrVv3/5zVc7SVt3mLNYDAACapiiCX9MRfFfVZjzCHwAAaPTi4+MHhr9/6qmnzp04ceJJj4Hs27dvxtVXX909fNuECRPSUlJS+qenp2f07ds344033kgM7bvwwgv7pqWl9evbt2/GoEGD0nNzc1uG9u3evTsmJiZm0OOPP54c3t/nn38e9Z3vfKdrampqv/PPPz8jMzPz/CeeeOJEm3Xr1sVddNFFfdLS0vp169at3w9/+MNOFRUVJ2qOiorKXr16datQ+969e2cWFBTEnuHHUyfN9po/AADQMD779H+ynDtWbxnCLLas7bnfzD2TPjZs2BDnnNPq1asTS0tLo1q3bl0R2jdnzpxdt95664Hf//73iVOmTOl2zTXXbAnte/nllz8aPnz4oblz5ybffffdqW+//fa2YHu7rKysLxcuXHjuD3/4w+JQ++985ztpaWlpR7dv374lOjpau3fvjvnVr36VLEkHDx608ePH93ryySc/ue6660q/+OKLqH/7t3/r+dhjj7W/77779ktShw4djs2ePbvTH/7wh4/OZLxngpk/AABQJ/UZ/Oqrv3nz5iV985vf/HT48OGl8+fPb1tVm5EjRx7ct29fi+r27dix48TM38KFC5Pmzp27s6ioqMXHH3/cQpLy8vJabty48Zwnn3yyMDo6WpLUuXPnskceeaRIkl544YVzBw8efPC6664rlaTExMSKZ5999pMnn3yyU9h5Pt+6dWur8FnGs43wBwAAGr2jR49GpaenZ4ReP/3pTzuH73/jjTeSJk6ceODGG28sWbBgQVJVfSxevLjNqFGjPqtq32uvvdYmPT39sCRt27atRXFxcYvLLrvs0Lhx4w7MmzcvSZI2btwYd/755x8KBb9T5eXlxQ0aNOhQ+LbMzMyjhw4diiopKYmSpKioKE2dOrXoxz/+cacqOzkLCH8AAKDRa9myZUV+fv77odd99923O7Rv5cqV8UlJSWV9+vQ5Nm7cuNK8vLz4/fv3n0hos2bN6tKlS5f+kydP7v7ggw/uCe934sSJPdLT0zPefffdhCeffHKnVDmLOG7cuAOS9N3vfrdk0aJFVYbJ6dOnd0xPT88477zzLpAk55yZWZX1h2+fPHnypxs2bEjIz88/q9f6hRD+AABAk5aTk5P00UcfxaWkpPTv1q1b/y+//DI6JyenXWj/nDlzdu3YsWPzjBkzCm+55ZaTbgh5+eWXP8rPz3//L3/5y4e9evU6LkmLFy9OWrBgwbkpKSn9r7vuul4FBQWtNm/e3DIrK+vIBx98EF9eXi5Jeuyxx4ry8/PfP3jwYLQkZWZmHl6/fn18eP/vv/9+bHx8fEW7du1OXIPYokULTZkypWj27NkdG/BjqRbhDwAANFnl5eVatmxZ0nvvvZdXWFi4ubCwcPP8+fO3LVy48KTZuujoaM2aNWtfRUWFLV68uHV1/eXm5rY8dOhQ9L59+zaF+psyZUrRyy+/nNSvX7+jF1xwwZdTp05NKSsrkyQdOnTInKv8FZxJkyZ9unbt2sTXX389Uaq8AeTOO+/s+v3vf7/o1PNMmTLl03feead1SUnJWb/5lvAHAACarD/+8Y+JHTp0ONa9e/fjoW1jxoz5Ytu2bXE7duw46eaOqKgoTZ8+fffcuXOrnXGbN2/euVddddWB8G3f/va3D7z22mtJkvTKK69sLykpienWrVv/zMzM8y+55JI+DzzwwC5JSkhIcK+99tq2n/zkJ53T0tL6ZWRkZA4aNOjL++67718esxYXF+cmTZq0LxLh70RabW4GDx7seLwbAMAXZrbeOTe4IfrOzc3dnpWVdeLnThrjT73gZLm5uclZWVlpVe3jd/4AAECdENSaNpZ9AQAAPEL4AwAA8AjhDwAAwCOEPwAA0CzNmTPnvJ/85CftI11HY8MNHwAAoFlKSEgov+uuuz6NdB2NDTN/AACg0fvkk09ixo4d2yM1NbVfz549M0eMGNFr06ZNLXv37p15atsJEyakpaSk9H/uuec6pKenZwwcODA9fP/IkSN7Dhgw4KRt06ZN69yqVauBhYWFJybG4uPjBxYVFUWHniecnJycdd55510Qen/kyBGLjo7ODn/m8P333x+Rp3bURbOd+du96aB+3PX/RroMr2RfuDzSJQBeGrtoTqRLgGd+lvVu1uEDZfWWIVq1iym7N3dYtT8fU1FRoXHjxvW68cYbP122bNlHkrRq1apWu3fvblHdMXPmzNl16623Hjh1e3FxcXReXt458fHx5fn5+bHp6enHQvvatm1bNmfOnA7PPvtsYWhbx44dy/Pz89+XKgNiQkJC+ezZs/eG9oeeOVz3UUcOM38AAKBO6jP4nU5/y5YtS4yJiXH33nvv/tC2iy+++HD37t2P1XRcVXJyctqNGjXqs/Hjx5fMmzfvpEfA3XDDDZ8uXbo0ae/evdF17bcpIfwBAIBGbdOmTa2ysrIO1eWYWbNmdQktxY4bN657aPvChQuTbrrpppKbb765ZPHixSeFv4SEhPIbbrih+NFHH+1wuuc5evRoVPiy7wsvvNCuLnVGQrNd9gUAAP6qatl3586dMTt27Gg5evTog1FRUYqJiXFr166NGzJkyJFQmxkzZuzLysrKeOCBB4pO5zws+wIAANSz/v37H87NzY0/037mzZuXVFpaGp2amto/JSWlf2FhYcucnJyTZv+Sk5PLx48fXzJ37tzzzvR8jRXhDwAANGpXX331F8eOHbMnnngiObRt5cqV8du2bYutSz+LFi1KWrJkyT8LCws3FxYWbl69evX7r7/+etKp7WbOnLl33rx57cvLy60+6m9sCH8AAKBRi4qK0tKlSz986623Wqempvbr1atX5kMPPdS5a9euxz/++OOWHTp0uCD0eumll9pJJ1/zl56enrFp06aWu3fvjr388su/DPWbnp5+LCEhofztt98+J/x8nTp1KhszZsyBY8eO1Rr+Tr3m73vf+15K/X8C9cucc5GuoUF0jk13kzu+EOkyvMJPvQCRwU+9QJLMbL1zbnBD9J2bm7s9KyurOPT+bP/UC+ouNzc3OSsrK62qfdzwAQAA6oSg1rSx7AsAAOARwh8AAIBHCH8AAAAeIfwBAAB4hPAHAADgEcIfAABo9KKjo7PDf0+voKAgdtmyZYmJiYkDzj///Izu3btnTpo0qUv4MTk5OW379OmT0b1798w+ffpk5OTktA3te+utt8654IIL0tPT0zN69OiROW3atM6S9NRTT507ceLErpI0bdq0zq1atRpYWFh44tdR4uPjB1ZX0/3339+x4T+JM8dPvQAAgDr5YvmTWTp+uP4yRItWZYnfmFrjz8dU9Qzdf/7zny0HDx588K9//eu2gwcPWv/+/TPefPPNA6NHj/7y3XffbTVz5swub7755tb09PRj+fn5saNHj+7Tp0+fo0OHDj182223dZ8/f/6Hw4YNO1xWVqbc3Ny4qs7btm3bsjlz5nR49tlnC0+npqaAmT8AAFA39Rn86qm/hIQEl5mZefiTTz6JlaTHHnus47Rp0/akp6cfkyqf5nH33XcX/fSnP+0oSSUlJTFdu3Y9LkkxMTHKzs4+UlW/N9xww6dLly5N2rt3b/SZ1thYEP4AAECjF/4YtSuuuKLnqfv3798f/fHHH7ccPXr0F5K0devWuKFDhx4Kb3PRRRd9uXXr1jhJmjRp0t7zzz+/3xVXXNHz8ccfTz506FCVj3JLSEgov+GGG4offfTRDjXVlJ6envHCCy+0q5/RNiyWfQEAQKNX3RLrunXrEvr06ZOxffv2uDvvvLOoa9euZZLknLOoqJPnuJxzMqvMeHPnzt1z6623lixbtqz1//zP/5y7cOHCc9esWVNQ1blnzJixLysrK+OBBx4oOp2aGjtm/gAAQJM1ePDgg1u3bn1/3bp1efPmzWu/atWqVpLUp0+fw++++258eNs1a9bE9+7d+8TybmZm5tHp06fvX7VqVUF+fn6roqKiKpd2k5OTy8ePH18yd+7c8xp2NGcH4Q8AADR5F1xwwdGpU6fuCV3TN3369KJf/OIXnQoKCmIlqaCgIPbnP/95p3vvvbdIkn73u9+1qaiokCRt3rw5Ljo62iUnJ5dX1//MmTP3zps3r315eXmVy8NNCcu+AACgWbjnnnv29+jRo2N+fn7sxRdffHj27Nm7rr766l7Hjx+3Fi1auIcffnjXxRdffFiSXnnllXNnzJiRGhcXVxETE+NefPHFj2Niqo9FnTp1KhszZsyB3/zmNyeu/Qtd8xd6f/nll3/+zDPP/MtdwY2NOeciXUOD6Byb7iZ3fCHSZXgl+8LlkS4B8NLYRXMiXQIaATNb75wb3BB95+bmbs/KyioOvY/ET72gbnJzc5OzsrLSqtrHzB8AAKgTglrTxjV/AAAAHiH8AQAAeITwBwAA4BHCHwAAaLaOHz+uK664oudVV13Vo7y82l9y8Uq93/BhZqMl7XXOcTEoAACIqC1btsT96Ec/2n306NGo/Pz8lpmZmUcjXVOk1TrzZ2YzzSzPzDaZ2UYzG1rLIe9IusfMetVPiZKZDTezDWZWZmbX11e/AACgaTCz7GuvvbZ76P3x48fVrl27rMsuu+ykvDFy5MieAwYMSA+9Hzhw4JHFixe3W7FiRcKjjz7aIT09PaNnz56ZcXFxg0LP5P3tb3/bbsKECWkpKSn909PTM/r27ZvxxhtvJIb6uPDCC/umpaX1C7W/8sore5xa386dO2Muu+yyXn379s3o2bNn5ogRI3pJlT8u3bt370xJWrZsWaKZZf/iF79IDh3397//vZWZZT/44IMdJKmiokL33ntvp27duvVLS0vrN3To0D7r1q2LC7VPSUnp/41vfOPEs41Dtdfls6xx5s/MhkkaK2mQc+6omSVLiq3pGOfcIUkT61LEafhE0i2S/rOe+wUAAHX0wXPjs8qPlNbb6mF0XOuy829fUuOKYatWrSoKCgpaHTx40BISEtySJUtad+jQ4Xh4m+Li4ui8vLxz4uPjy/Pz82PT09OPhe/Pycn5RKoMZGPHju0d/lzeZcuWtZkzZ86uW2+99cDvf//7xClTpnS75pprtoT2v/zyyx8NHz78UHX1TZ8+PeXyyy8vfeCBB/ZJ0urVq1tV1a53796HFy1a1O7uu+8ulqRXXnklqW/fvodD+x999NH2q1evPmfLli3vJyYmVrz22mutx48f36ugoCAvPj7eSdLmzZvj161bFzd48OAjVZ2jNrXN/HWSVOycOypJzrli59xuSTKzIWa2ysxyzWyNmSWaWbSZPW5ma81ss5lNDtpeamYrzGyRmeWb2asWPFnZzLLNbKWZrTez5WbW6dQinHPbnXObJFV8lUECAID6U5/Bry79jRw58vOFCxe2laT58+cnTZgwoSR8f05OTrtRo0Z9Nn78+JJ58+YlfdV6Ro4ceXDfvn0t6nJMUVFRi9TU1BNhc+jQoYerapeSknLs6NGjUTt37oypqKjQ22+/3WbkyJGfh/Y/9dRTnZ555pmdiYmJFZJ03XXXlWZnZ3/561//+txQmzvvvHPv7Nmz/yUvna7awt+bklLNbKuZPWNmIyTJzGIlLZA01TmXJWmUpMOSbpNU6pwbImmIpMlmFpoaHSjpLkkZknpI+pqZtZD0tKTrnXPZkl6S9MhXHQwAAGi+vvvd75YsWLCg3aFDh+yDDz6IHzZs2Jfh+xcuXJh00003ldx8880lixcv/srhb/HixW1GjRr1Wfi2iRMn9ggt+06ePLnLqcfceeed+77//e+nDR06tM/06dM7bt++vdrweO211x7Iyclp95e//OWc/v37H2rZsqWTpJKSkqjDhw9HnXpdYnZ29pd5eXknln4nTpxYsmXLlvgtW7a0/CrjqzFpO+cOmlm2pEskXSZpgZnNkLRe0h7n3NqgXal04maP7mY2MugiVpVBr0zSGufcrqDdRklpkj6T1E/Sn4OJwGhJe77KQIJ+J0maJEltojvU0hoAADQlQ4cOPbxr166WL7zwQtKoUaM+D9+3c+fOmB07drQcPXr0waioKMXExLi1a9fGDRky5LSXRmfNmtXlgQce6FJSUhKzcuXKD8L31bbsO2HChNKvf/3rm5csWdLmT3/6U5vs7OyMzZs351XVduLEiSUTJkzomZ+f3+rGG28seeeddxJqqss5pyAnSZJiYmL0gx/8oGj27Nkdx4wZU3q64wup9YYP51y5c26Fc+4hSVMkTZBkkqp6KLBJmumcuzR49XPO/SXYF55iy1UZPE1SnnNuQPDq75wbXddBhNX6vHNusHNucHxU26/aDQAAaKSuvPLKzx566KHUiRMnnrTkO2/evKTS0tLo1NTU/ikpKf0LCwtb5uTk1Gn2b86cObt27NixecaMGYW33HJL99qPOFmHDh3Kb7/99pLXX3/94wsuuODLN998s8pQ17Vr17IWLVq4v/3tb63HjRt3IrwlJSVVtGrVquL9998/6f6K9957Lz4jI+OkEHvHHXeUrF69OnHHjh013otRlRrDn5n1NbPeYZsGSNohKV9SZzMbErRLNLMYScsl3R4s54aOP6eGUxRIah/cWCIza2FmmXUdBAAA8MMdd9xRfM899+y+8MILT7qmbtGiRUlLliz5Z2Fh4ebCwsLNq1evfv/111+v89JvdHS0Zs2ata+iosIWL17c+nSPW7p0aeIXX3wRJUkHDhyI2rFjR8vu3bsfq679j3/848KHH354V0zMyYuwU6ZMKbrzzju7Hjx40CTp9ddfT1y7dm3if/zHf3wa3q5ly5bujjvu2PvrX//6vDoNULX/zl+CpKfNrK0ql263SZrknDtmZt8K9rVS5fV+oyS9qMrl3A3BDR37JV1bXedBP9dLesrM2gT1/FLSSdOkQchcIqmdpKvN7MfOOUIiAACe6dmz5/HQHbUhBQUFsbt37469/PLLT1wDmJ6efiwhIaH87bffrmkSqkpRUVGaPn367rlz53acMGFCqVR5zV9cXFyFJCUlJZWtWrVqa/gxa9eujb/77ru7RkdHO+ecffe73y0eMWLEoYKCgipn5q644oovq9p+//337ztw4EB0RkZGZlRUlNq3b3/8tdde25aQkPAvK65Tp04t/vnPf17nGz/MuapWb5u+zrHpbnLHFyJdhleyL1we6RIAL41dNCfSJaARMLP1zrnBDdF3bm7u9qysrOLQ+0j81AvqJjc3NzkrKyutqn31/oQPAADQvBHUmjae7QsAAOARwh8AAIBHCH8AAAAeIfwBAACEOXLkiH3ta1/rXdNTOpoywh8AAECYuLg499JLL+3Izc2Nq71100P4AwAAjV58fPzA6vb17ds34+qrr672iRzTpk3rfN55510Qejbv9773vRRJuvDCC/v+7W9/iw9vu2zZssTExMQB11xzTa/p06enpqenZ7z++uuJkvTJJ5/EjB07tkdqamq/nj17Zo4YMaLXpk2bWhYUFMT27t0789RzPvjggx0kacKECWkpKSn9Q+cfOHBg+pl8FmeKn3oBAAB1svzWn2Qd/+JQvWWIFonxZd/47f1f6edjNmzYEOec0+rVqxNLS0ujWrduXVFVu9tvv33v7Nmz955On4MHDz7417/+dVv4toqKCo0bN67XjTfe+OmyZcs+kqRVq1a12r17d4uanuQRMmfOnF233nrrgdM5f0Nj5g8AANRJfQa/M+1v3rx5Sd/85jc/HT58eOn8+fPb1mdd4ZYtW5YYExPj7r333v2hbRdffPHhK6+88mBDnbOhEP4AAECT9cYbbyRNnDjxwI033liyYMGCap/l+9xzz3UILbvW9szedevWJYTapqenZ+Tl5bXctGlTq6ysrEPVHbNz586W4ce8/PLL7cP3z5o1q0to37hx46pdoj4bWPYFAABN0sqVK+OTkpLK+vTpc6xHjx7H7rjjjrT9+/dHt2/fvvzUtme67LtkyZIaj0lNTT2an5//fuj9tGnTOofvZ9kXAADgDOXk5CR99NFHcSkpKf27devW/8svv4zOyclp1xDn6t+//+Hc3Nz42ls2foQ/AADQ5JSXl2vZsmVJ7733Xl5hYeHmwsLCzfPnz9+2cOHCapd+z8TVV1/9xbFjx+yJJ55IDm1buXJl/B/+8IeEhjhfQyL8AQCARu/IkSNRHTp0uCD0evjhhzt06NDhWPfu3Y+H2owZM+aLbdu2xe3YseO0f5x5/PjxvUN9jhkzpof0r9f8/fa3v20XFRWlpUuXfvjWW2+1Tk1N7derV6/Mhx56qHPXrl2P13YO6eRr/tLT0zOOHDlidf8U6oc55yJ17gbVOTbdTe74QqTL8Er2hcsjXQLgpbGL5kS6BDQCZrbeOTe4IfrOzc3dnpWVVRx635h+6gVVy83NTc7Kykqrah83fAAAgDohqDVtLPsCAAB4hPAHAADgEcIfAACoTUVFRUXEblBA3QTfVZWPuZMIfwAAoHZb9u/f34YA2PhVVFTY/v3720jaUl0bbvgAAAA1Kisr+/eioqIXi4qK+omJo8auQtKWsrKyf6+uAeEPAADUKDs7e5+kcZGuA/WD9A4AAOARwh8AAIBHCH8AAAAeIfwBAAB4hPAHAADgEcIfAACAR5rtT710viBBD627JNJleIbPGwCAxo6ZPwAAAI8Q/gAAADxC+AMAAPAI4Q8AAMAjhD8AAACPEP4AAAA8QvgDAADwCOEPAADAI4Q/AAAAjxD+AAAAPEL4AwAA8AjhDwAAwCOEPwAAAI8Q/gAAADxC+AMAAPBITKQLaCjlZSU6UPxKpMuo1VODukX0/BOm/Sii5z9T298ZduLvsYvmRLASAACaBmb+AAAAPEL4AwAA8AjhDwAAwCOEPwAAAI8Q/gAAADxC+AMAAPAI4Q8AAMAjhD8AAACPEP4AAAA8QvgDAADwCOEPAADAI4Q/AAAAjxD+AAAAPEL4AwAA8AjhDwAAwCOEPwAAAI8Q/gAAADxC+AMAAPAI4Q8AAMAjhD8AAACPEP4AAAA8QvgDAADwCOEPAADAI4Q/AAAAjxD+AAAAPEL4AwAA8AjhDwAAwCP1Hv7MbLSZZdV3vwAAADhztYY/M5tpZnlmtsnMNprZ0FoOeUfSPWbWq35KlMxsmpm9H9Twlpl1q6++AQAAfBJT004zGyZprKRBzrmjZpYsKbamY5xzhyRNrL8SJUnvSRrsnDtkZndI+pmkb9XzOQAAAJq92mb+Okkqds4dlSTnXLFzbrckmdkQM1tlZrlmtsbMEs0s2sweN7O1ZrbZzCYHbS81sxVmtsjM8s3sVTOzYF+2ma00s/VmttzMOp1ahHPur0GolKR/SOpSXx8AAACAT2oLf29KSjWzrWb2jJmNkCQzi5W0QNJU51yWpFGSDku6TVKpc26IpCGSJptZj6CvgZLukpQhqYekr5lZC0lPS7reOZct6SVJj9RS022S/ljHcQIAAEC1LPs65w6aWbakSyRdJmmBmc2QtF7SHufc2qBdqVR5s4ek7mY2MugiVpVBr0zSGufcrqDdRklpkj6T1E/Sn4OJwGhJe6qrx8xukjRY0ohq9k+SNEmSunQ5t5ahAwAA+KfG8CdJzrlySSskrTCzzZJulrRBkquiuUma6Zz700kbzS6VdDRsU3lwbpOU55wbVlsdZjZK0kxJI0LL0FXU+ryk5yVp4IAeVdUHAADgtRqXfc2sr5n1Dts0QNIOSfmrWWqkAAAUz0lEQVSSOpvZkKBdopnFSFou6fZgOTd0/Dk1nKJAUvvgxhKZWQszy6yijoGSfi1pnHNu3+kPDwAAAOFqm/lLkPS0mbVV5dLtNkmTnHPHzOxbwb5Wqrzeb5SkF1W5nLshuKFjv6Rrq+s86Od6SU+ZWZugnl9Kyjul6eNBLQuD5eFPnHPj6jRSAAAA1HrN33pJF1ezb62ki6rYdX/wCrcieIWOnRL290ZJw2upY1RN+wEAAHB6eLwbAACARwh/AAAAHiH8AQAAeITwBwAA4BHCHwAAgEcIfwAAAB4h/AEAAHiE8AcAAOARwh8AAIBHCH8AAAAeIfwBAAB4hPAHAADgEcIfAACARwh/AAAAHiH8AQAAeITwBwAA4BHCHwAAgEcIfwAAAB4h/AEAAHiE8AcAAOARwh8AAIBHCH8AAAAeIfwBAAB4hPAHAADgEcIfAACARwh/AAAAHiH8AQAAeITwBwAA4BFzzkW6hgYxePBgt27dukiXAQDAWWFm651zgyNdBxo/Zv4AAAA8QvgDAADwCOEPAADAI4Q/AAAAjxD+AAAAPEL4AwAA8AjhDwAAwCOEPwAAAI8Q/gAAADxC+AMAAPAI4Q8AAMAjhD8AAACPEP4AAAA8QvgDAADwCOEPAADAIzGRLqChlJeV6EDxKydta5d8U4SqAQAAaByY+QMAAPAI4Q8AAMAjhD8AAACPEP4AAAA8QvgDAADwCOEPAADAI4Q/AAAAjxD+AAAAPEL4AwAA8AjhDwAAwCOEPwAAAI8Q/gAAADxC+AMAAPAI4Q8AAMAjhD8AAACPEP4AAAA8QvgDAADwCOEPAADAI4Q/AAAAjxD+AAAAPEL4AwAA8AjhDwAAwCOEPwAAAI8Q/gAAADxC+AMAAPAI4Q8AAMAjhD8AAACPEP4AAAA8Uu/hz8xGm1lWffcLAACAM1dr+DOzmWaWZ2abzGyjmQ2t5ZB3JN1jZr3qp0TJzG43s83B+d8xs4z66hsAAMAnMTXtNLNhksZKGuScO2pmyZJiazrGOXdI0sT6K1GS9N/OueeCmsZJ+rmkK+v5HAAAAM1ebTN/nSQVO+eOSpJzrtg5t1uSzGyIma0ys1wzW2NmiWYWbWaPm9naYKZuctD2UjNbYWaLzCzfzF41Mwv2ZZvZSjNbb2bLzazTqUU450rD3p4jydXH4AEAAHxTW/h7U1KqmW01s2fMbIQkmVmspAWSpjrnsiSNknRY0m2SSp1zQyQNkTTZzHoEfQ2UdJekDEk9JH3NzFpIelrS9c65bEkvSXqkqkLM7E4z+1DSzyT9oJo2k8xsnZmtK/60tKomAAAAXqtx2dc5d9DMsiVdIukySQvMbIak9ZL2OOfWBu1KpcqbPSR1N7ORQRexqgx6ZZLWOOd2Be02SkqT9JmkfpL+HEwERkvaU00tv5L0KzO7UdIsSTdX0eZ5Sc9L0sABPZgdBAAAOEWN4U+SnHPlklZIWmFmm1UZujao6qVXkzTTOfenkzaaXSrpaNim8uDcJinPOTesDjX/TtKzdWgPAACAQI3LvmbW18x6h20aIGmHpHxJnc1sSNAu0cxiJC2XdHuwnBs6/pwaTlEgqX1wY4nMrIWZZVZRR3gN/ybpn7UPDQAAAKeqbeYvQdLTZtZWlUu32yRNcs4dM7NvBftaqfJ6v1GSXlTlcu6G4IaO/ZKura7zoJ/rJT1lZm2Cen4pKe+UplPMbJSk45IOqIolXwAAANTOnGuel8YNHNDDvf2X2Sdta5d8U4SqAQCgYZnZeufc4EjXgcaPx7sBAAB4hPAHAADgEcIfAACARwh/AAAAHiH8AQAAeITwBwAA4BHCHwAAgEcIfwAAAB4h/AEAAHiE8AcAAOARwh8AAIBHCH8AAAAeIfwBAAB4hPAHAADgEcIfAACARwh/AAAAHiH8AQAAeITwBwAA4BHCHwAAgEcIfwAAAB4h/AEAAHiE8AcAAOARwh8AAIBHCH8AAAAeIfwBAAB4hPAHAADgEcIfAACAR2IiXUBDiY5JUrvkmyJdBgAAQKPCzB8AAIBHCH8AAAAeIfwBAAB4hPAHAADgEcIfAACARwh/AAAAHiH8AQAAeITwBwAA4BHCHwAAgEcIfwAAAB4h/AEAAHiE8AcAAOARwh8AAIBHCH8AAAAeIfwBAAB4hPAHAADgkZhIF9BQKj4v0hfLHo3Y+Vf+18GInbupGbtoTqRLAADAG8z8AQAAeITwBwAA4BHCHwAAgEcIfwAAAB4h/AEAAHiE8AcAAOARwh8AAIBHCH8AAAAeIfwBAAB4hPAHAADgEcIfAACARwh/AAAAHiH8AQAAeITwBwAA4BHCHwAAgEcIfwAAAB4h/AEAAHiE8AcAAOARwh8AAIBHCH8AAAAeIfwBAAB4hPAHAADgEcIfAACARwh/AAAAHiH8AQAAeITwBwAA4BHCHwAAgEfqPfyZ2Wgzy6rvfgEAAHDmag1/ZjbTzPLMbJOZbTSzobUc8o6ke8ysV/2UeFIt15uZM7PB9d03AACAD2Jq2mlmwySNlTTIOXfUzJIlxdZ0jHPukKSJ9VfiiVoSJf1A0ur67hsAAMAXtc38dZJU7Jw7KknOuWLn3G5JMrMhZrbKzHLNbI2ZJZpZtJk9bmZrzWyzmU0O2l5qZivMbJGZ5ZvZq2Zmwb5sM1tpZuvNbLmZdaqmlocl/UzSkXoZOQAAgIdqC39vSko1s61m9oyZjZAkM4uVtEDSVOdclqRRkg5Luk1SqXNuiKQhkiabWY+gr4GS7pKUIamHpK+ZWQtJT0u63jmXLeklSY+cWoSZDZSU6pxbdmbDBQAA8FuNy77OuYNmli3pEkmXSVpgZjMkrZe0xzm3NmhXKlXe7CGpu5mNDLqIVWXQK5O0xjm3K2i3UVKapM8k9ZP052AiMFrSnvAazCxK0i8k3VLbYMxskqRJkpTavm1tzQEAALxTY/iTJOdcuaQVklaY2WZJN0vaIMlV0dwkzXTO/emkjWaXSjoatqk8OLdJynPODauhhERVBsQVQUDsKGmpmY1zzq07pdbnJT0vSYN6d6mqPgAAAK/VuOxrZn3NrHfYpgGSdkjKl9TZzIYE7RLNLEbSckm3B8u5oePPqeEUBZLaBzeWyMxamFlmeAPn3OfOuWTnXJpzLk3SPyT9S/ADAABA7Wqb+UuQ9LSZtVXl0u02SZOcc8fM7FvBvlaqvN5vlKQXVbmcuyG4oWO/pGur6zzo53pJT5lZm6CeX0rKO7NhAQAAoCq1XfO3XtLF1exbK+miKnbdH7zCrQheoWOnhP29UdLw06q2sv2lp9sWAAAAJ+PxbgAAAB4h/AEAAHiE8AcAAOARwh8AAIBHCH8AAAAeIfwBAAB4hPAHAADgEcIfAACARwh/AAAAHiH8AQAAeITwBwAA4BHCHwAAgEcIfwAAAB4h/AEAAHiE8AcAAOARwh8AAIBHCH8AAAAeIfwBAAB4hPAHAADgEcIfAACARwh/AAAAHiH8AQAAeITwBwAA4BHCHwAAgEcIfwAAAB4h/AEAAHiE8AcAAOARwh8AAIBHYiJdQEOJatNRiWNnROz8Y8dG7NQAAADVYuYPAADAI4Q/AAAAjxD+AAAAPEL4AwAA8AjhDwAAwCOEPwAAAI8Q/gAAADxC+AMAAPAI4Q8AAMAjhD8AAACPEP4AAAA8QvgDAADwCOEPAADAI4Q/AAAAjxD+AAAAPBIT6QIaSnlZiQ4UvxLpMhAhMf/YFekSAABolJj5AwAA8AjhDwAAwCOEPwAAAI8Q/gAAADxC+AMAAPAI4Q8AAMAjhD8AAACPEP4AAAA8QvgDAADwCOEPAADAI4Q/AAAAjxD+AAAAPEL4AwAA8AjhDwAAwCOEPwAAAI8Q/gAAADxC+AMAAPAI4Q8AAMAjhD8AAACPEP4AAAA8QvgDAADwCOEPAADAI4Q/AAAAjxD+AAAAPEL4AwAA8AjhDwAAwCOEPwAAAI/Ue/gzs9FmllXf/QIAAODM1Rr+zGymmeWZ2SYz22hmQ2s55B1J95hZr/opUTKzW8xsf3D+jWb27/XVNwAAgE9iatppZsMkjZU0yDl31MySJcXWdIxz7pCkifVX4gkLnHNTGqBfAAAAb9Q289dJUrFz7qgkOeeKnXO7JcnMhpjZKjPLNbM1ZpZoZtFm9riZrTWzzWY2OWh7qZmtMLNFZpZvZq+amQX7ss1spZmtN7PlZtapIQcMAADgs9rC35uSUs1sq5k9Y2YjJMnMYiUtkDTVOZclaZSkw5Juk1TqnBsiaYikyWbWI+hroKS7JGVI6iHpa2bWQtLTkq53zmVLeknSI9XUMiFYel5kZqlfdcAAAAA+q3HZ1zl30MyyJV0i6TJJC8xshqT1kvY459YG7Uqlyps9JHU3s5FBF7GqDHplktY453YF7TZKSpP0maR+kv4cTARGS9pTRSm/lzQ/WHq+XdI8SZef2sjMJkmaJEldupx7mh8BAACAP2oMf5LknCuXtELSCjPbLOlmSRskuSqam6SZzrk/nbTR7FJJR8M2lQfnNkl5zrlhtdTwadjbFyQ9Vk275yU9L0kDB/Soqj4AAACv1bjsa2Z9zax32KYBknZIypfU2cyGBO0SzSxG0nJJtwfLuaHjz6nhFAWS2gc3lsjMWphZZhV1hF8HOE7SB7UPDQAAAKeqbeYvQdLTZtZWlUu32yRNcs4dM7NvBftaqfJ6v1GSXlTlcu6G4IaO/ZKura7zoJ/rJT1lZm2Cen4pKe+Upj8ws3FBDSWSbqnTKAEAACBJMuea5+rowAE93Nt/mR3pMhAhMf/YFekSAOCsan31feudc4MjXQcaPx7vBgAA4BHCHwAAgEcIfwAAAB4h/AEAAHiE8AcAAOARwh8AAIBHCH8AAAAeIfwBAAB4hPAHAADgEcIfAACARwh/AAAAHiH8AQAAeITwBwAA4BHCHwAAgEcIfwAAAB4h/AEAAHiE8AcAAOARwh8AAIBHCH8AAAAeIfwBAAB4hPAHAADgEcIfAACARwh/AAAAHiH8AQAAeITwBwAA4BHCHwAAgEcIfwAAAB4h/AEAAHgkJtIFNJTomCS1S74p0mUgUsZGugAAONvui3QBaCKY+QMAAPAI4Q8AAMAjhD8AAACPEP4AAAA8QvgDAADwCOEPAADAI4Q/AAAAjxD+AAAAPEL4AwAA8AjhDwAAwCOEPwAAAI8Q/gAAADxC+AMAAPAI4Q8AAMAjhD8AAACPEP4AAAA8QvgDAADwCOEPAADAI4Q/AAAAj5hzLtI1NAgz+0JSQaTriIBkScWRLuIs83HMEuP2iY9jlvwc95mMuZtzrn19FoPmKSbSBTSgAufc4EgXcbaZ2Trfxu3jmCXGHek6ziYfxyz5OW4fx4yzj2VfAAAAjxD+AAAAPNKcw9/zkS4gQnwct49jlhi3T3wcs+TnuH0cM86yZnvDBwAAAP5Vc575AwAAwCmaZfgzsyvNrMDMtpnZjEjXU1/MLNXM/mpmH5hZnplNDbb/yMwKzWxj8Loq7Jj7gs+hwMy+Ebnqz4yZbTezzcH41gXbkszsz2b2z+C/7cLaN+lxm1nfsO9zo5mVmtldzfG7NrOXzGyfmW0J21bn79bMsoN/I9vM7Ckzs7M9lrqoZtyPm1m+mW0ysyVm1jbYnmZmh8O+9+fCjmky465mzHX+N92UxixVO+4FYWPebmYbg+3N4rtGI+eca1YvSdGSPpTUQ1KspFxJGZGuq57G1knSoODvRElbJWVI+pGk/6yifUYw/paSugefS3Skx/EVx75dUvIp234maUbw9wxJjzW3cQfjiZZUJKlbc/yuJQ2XNEjSljP5biWtkTRMkkn6o6QxkR7bVxj3aEkxwd+PhY07LbzdKf00mXFXM+Y6/5tuSmOubtyn7H9C0oPN6bvm1bhfzXHm70JJ25xzHznnjkn6naRrIlxTvXDO7XHObQj+/kLSB5JSajjkGkm/c84ddc59LGmbKj+f5uIaSfOCv+dJujZse3Ma90hJHzrndtTQpsmO2Tn3N0klp2yu03drZp0ktXbOveucc5JeDjumUapq3M65N51zZcHbf0jqUlMfTW3c1XzX1WnW33VIMHv3TUnza+qjKY4bjVdzDH8pknaGvd+lmgNSk2RmaZIGSlodbJoSLBW9FLZE1pw+CyfpTTNbb2aTgm0dnHN7pMpgLOm8YHtzGrckfVsn/x+G5v5dS3X/blOCv0/d3pT9b1XO7oR0N7P3zGylmV0SbGsu467Lv+nmMuaQSyTtdc79M2xbc/6u0Qg0x/BX1TUQzeqWZjNLkLRY0l3OuVJJz0rqKWmApD2qXEKQmtdn8TXn3CBJYyTdaWbDa2jbbMZtZrGSxklaGGzy4buuSXXjbFbjN7OZksokvRps2iOpq3NuoKRpkv7bzFqreYy7rv+mm8OYw92gk/+fu+b8XaORaI7hb5ek1LD3XSTtjlAt9c7MWqgy+L3qnHtNkpxze51z5c65Ckkv6P8v9zWbz8I5tzv47z5JS1Q5xr3BUkhoSWRf0LzZjFuVYXeDc26v5Md3Hajrd7tLJy+RNtnxm9nNksZK+k6wvKdg6fPT4O/1qrz+rY+awbi/wr/pJj/mEDOLkXSdpAWhbc35u0bj0RzD31pJvc2sezBr8m1JSyNcU70Irg35jaQPnHM/D9veKazZeEmhO8qWSvq2mbU0s+6SeqvyguEmxczOMbPE0N+qvCh+iyrHd3PQ7GZJbwR/N4txB06aFWju33WYOn23wdLwF2Z2UfC/k4lhxzQZZnalpOmSxjnnDoVtb29m0cHfPVQ57o+aw7jr+m+6OYw5zChJ+c65E8u5zfm7RiMS6TtOGuIl6SpV3gn7oaSZka6nHsf1dVVO82+StDF4XSUpR9LmYPtSSZ3CjpkZfA4FaqJ3hqnyzu3c4JUX+k4lnSvpLUn/DP6b1MzGHS/pU0ltwrY1u+9aleF2j6TjqpzduO2rfLeSBqsyOHwo6f8o+BH7xvqqZtzbVHmdW+h/388FbScE//ZzJW2QdHVTHHc1Y67zv+mmNObqxh1s/y9Jt5/Stll817wa94snfAAAAHikOS77AgAAoBqEPwAAAI8Q/gAAADxC+AMAAPAI4Q8AAMAjhD8AAACPEP4AAAA8QvgDAADwyP8D3b3Ol8EyGRIAAAAASUVORK5CYII=\n", "text/plain": [ - "' VALÈRE.\\n'" + "
" ] }, - "execution_count": 79, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lignes[47]" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "metadata": {}, - "outputs": [ + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, { "data": { + "image/png": "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\n", "text/plain": [ - "6" + "
" ] }, - "execution_count": 80, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "scene" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "metadata": {}, - "outputs": [ + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, { "data": { + "image/png": "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\n", "text/plain": [ - "3171" + "
" ] }, - "execution_count": 81, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "k" - ] - }, - { - "cell_type": "code", - "execution_count": 82, - "metadata": {}, - "outputs": [ + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, { "data": { + "image/png": "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\n", "text/plain": [ - "[(1, 3, 207, 198),\n", - " (1, 3, 216, 210),\n", - " (1, 3, 254, 249),\n", - " (1, 3, 274, 269),\n", - " (1, 4, 387, 381),\n", - " (1, 4, 626, 621),\n", - " (1, 4, 631, 626),\n", - " (1, 5, 785, 780),\n", - " (1, 5, 818, 812),\n", - " (2, 5, 1286, 1277),\n", - " (2, 5, 1297, 1289),\n", - " (2, 5, 1305, 1300),\n", - " (3, 1, 1345, 1339),\n", - " (3, 1, 1372, 1366),\n", - " (3, 1, 1413, 1408),\n", - " (3, 1, 1530, 1524),\n", - " (3, 2, 1612, 1607),\n", - " (3, 2, 1635, 1630),\n", - " (3, 2, 1668, 1662),\n", - " (3, 7, 1869, 1864),\n", - " (3, 7, 1877, 1872),\n", - " (3, 9, 1973, 1968),\n", - " (4, 4, 2301, 2296),\n", - " (4, 4, 2322, 2316),\n", - " (4, 4, 2330, 2325),\n", - " (4, 4, 2359, 2354)]" + "
" ] }, - "execution_count": 82, - "metadata": {}, - "output_type": "execute_result" + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "non_habituel" - ] - }, - { - "cell_type": "code", - "execution_count": 181, - "metadata": {}, - "outputs": [], - "source": [ - "Ajouter = {}\n", - "\n", - "## Harpagon\n", - "Ajouter['HARPAGON'] = {}\n", - "#Harpagon, acte 1\n", - "Ajouter['HARPAGON'][1] = {}\n", - "#Harpagon, acte 1, scène 3\n", - "Ajouter['HARPAGON'][1][3] = 0\n", - "for k in [199,202,205,211,214,252,270] :\n", - " Ajouter['HARPAGON'][1][3] += len(split_string(lignes[k]))\n", - "#Harpagon, acte 1, scène 4\n", - "Ajouter['HARPAGON'][1][4] = 0\n", - "for k in [382,385,629] :\n", - " Ajouter['HARPAGON'][1][4] += len(split_string(lignes[k]))\n", - "#Harpagon, acte 1, scène 5\n", - "Ajouter['HARPAGON'][1][5] = 0\n", - "for k in [783] :\n", - " Ajouter['HARPAGON'][1][5] += len(split_string(lignes[k]))\n", - "\n", - "#Harpagon, acte 3\n", - "Ajouter['HARPAGON'] = {}\n", - "Ajouter['HARPAGON'][3] = {}\n", - "#Harpagon, acte 3, scène 1\n", - "Ajouter['HARPAGON'][3][1] = 0\n", - "for k in [1340,1343,1367,1370] :\n", - " Ajouter['HARPAGON'][3][1] += len(split_string(lignes[k]))\n", - "\n", - "#Harpagon, acte 4\n", - "Ajouter['HARPAGON'] = {}\n", - "Ajouter['HARPAGON'][4] = {}\n", - "#Harpagon, acte 4, scène 4\n", - "Ajouter['HARPAGON'][4][4] = 0\n", - "for k in [2355] :\n", - " Ajouter['HARPAGON'][4][4] += len(split_string(lignes[k]))\n", - "\n", - "## Elise\n", - "Ajouter['ÉLISE'] = {}\n", - "#Elise, acte 1\n", - "Ajouter['ÉLISE'][1] = {}\n", - "#Elise, acte 1, scène 4\n", - "Ajouter['ÉLISE'][1][4] = 0\n", - "for k in [624] :\n", - " Ajouter['ÉLISE'][1][4] += len(split_string(lignes[k]))\n", - "\n", - "##Valère\n", - "Ajouter['VALÈRE'] = {}\n", - "#Valère, acte 1\n", - "Ajouter['VALÈRE'][1] = {}\n", - "#Valère, acte 1, scène 5\n", - "Ajouter['VALÈRE'][1][5] = 0\n", - "for k in [813,816] :\n", - " Ajouter['VALÈRE'][1][5] += len(split_string(lignes[k]))\n", - "\n", - "#Valère, acte 3\n", - "Ajouter['VALÈRE'][3] = {}\n", - "#Valère, acte 3, scène 2\n", - "Ajouter['VALÈRE'][3][2] = 0\n", - "for k in [1631,1663,1666] :\n", - " Ajouter['VALÈRE'][3][2] += len(split_string(lignes[k]))\n", - "\n", - "##Frosine\n", - "Ajouter['FROSINE'] = {}\n", - "#Frosine, acte 2\n", - "Ajouter['FROSINE'][2] = {}\n", - "#Frosine, acte 2, scène 5\n", - "Ajouter['FROSINE'][2][5] = 0\n", - "for k in [1278,1281,1284,1292,1295,1303] :\n", - " Ajouter['FROSINE'][2][5] += len(split_string(lignes[k]))\n", - "\n", - "## Maître Jacques\n", - "Ajouter['MAÎTRE JACQUES'] = {}\n", - "## Maître Jacques, acte 3\n", - "Ajouter['MAÎTRE JACQUES'][3] = {}\n", - "## Maître Jacques, acte 3, scène 1\n", - "Ajouter['MAÎTRE JACQUES'][3][1] = 0\n", - "for k in [1409,1525,1528] :\n", - " Ajouter['MAÎTRE JACQUES'][3][1] += len(split_string(lignes[k]))\n", - "## Maître Jacques, acte 3, scène 2\n", - "Ajouter['MAÎTRE JACQUES'][3][2] = 0\n", - "for k in [1608] :\n", - " Ajouter['MAÎTRE JACQUES'][3][2] += len(split_string(lignes[k]))\n", - "\n", - "## Maître Jacques, acte 4\n", - "Ajouter['MAÎTRE JACQUES'][4] = {}\n", - "## Maître Jacques, acte 4, scène 4\n", - "Ajouter['MAÎTRE JACQUES'][4][4] = 0\n", - "for k in [2297,2317,2320,2328] :\n", - " Ajouter['MAÎTRE JACQUES'][4][4] += len(split_string(lignes[k]))\n", - "\n", - "## Cléante\n", - "Ajouter['CLÉANTE'] = {}\n", - "#Cléante, acte 3\n", - "Ajouter['CLÉANTE'][3] = {}\n", - "#Cléante, acte 3, scène 4\n", - "Ajouter['CLÉANTE'][3][4] = 0\n", - "for k in [1867] :\n", - " Ajouter['CLÉANTE'][3][4] += len(split_string(lignes[k]))\n", - "#Cléante, acte 3, scène 7\n", - "Ajouter['CLÉANTE'][3][7] = 0\n", - "for k in [1875] :\n", - " Ajouter['CLÉANTE'][3][7] += len(split_string(lignes[k]))\n", - "\n", - "##La merluche\n", - "Ajouter['LA MERLUCHE'] = {}\n", - "#La merluche, acte 3\n", - "Ajouter['LA MERLUCHE'][3] = {}\n", - "#La merluche, acte 3, scène 9\n", - "Ajouter['LA MERLUCHE'][3][9] = 0\n", - "for k in [1971] :\n", - " Ajouter['LA MERLUCHE'][3][9] += len(split_string(lignes[k]))" + "colors = np.random.rand(14,3)\n", + "for acte in range(1,6) :\n", + " présents = []\n", + " plt.figure(figsize=(8,8))\n", + " x = np.zeros((len(dico_acte_nombre_mots[acte]),len(persos)))\n", + " labels = persos\n", + " for scene in range(1,len(dico_acte_nombre_mots[acte])+1) :\n", + " for l,perso in enumerate(persos) :\n", + " x[scene-1,l] += dico_acte_nombre_mots[acte][scene][perso]\n", + " plt.title('Acte ' + str(acte))\n", + " #plt.bar(labels,x)\n", + " width = 1/2 # épaisseur de chaque bâton\n", + " # Création du diagramme en bâtons (bâtons côte à côte)\n", + " for scene in range(1,len(dico_acte_nombre_mots[acte])+1) :\n", + " bt = 0\n", + " for l,perso in enumerate(persos) :\n", + " plt.barh(-scene, x[scene-1,l],left = bt,color=colors[l])\n", + " bt += x[scene-1,l]\n", + " if x[scene-1,l] > 0 :\n", + " if not (perso in présents) :\n", + " présents.append(perso)\n", + " plt.yticks(-np.arange(scene)-1, np.array([\"Scène \"+str(k) for k in range(1,scene+1)]))\n", + " patches = []\n", + " présents.sort()\n", + " for l,perso in enumerate(persos) :\n", + " if perso in présents :\n", + " patches.append(mpatches.Patch(color=colors[l], label=perso))\n", + " plt.legend(handles=patches,bbox_to_anchor=(1.05, 1),loc='upper left', borderaxespad=0.)" ] }, { -- 2.18.1