Guide_extraction_cataloguesBJP.ipynb

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    "# Guide : extraction de catalogues d'exposition numérisés\n",
    "\n",
    "*Artl@s / IMAGO / Université de Genève / ENS*\n",
    "\n",
    "Auteur : Esteban Sanchez Eoconomo, Ecole nationale des Chartes, Paris. Relecture : Béatrice Joyeux-Prunel, Université de Genève\n",
    "\n",
    "**Citation**: Esteban SANCHEZ OECONOMO, *Du catalogue papier au tableur numérique. Un pipeline semi-atomatique pour former des étudiants en sciences humaines aux humanités numériques*, mémoire de master \"Technologies numériques appliquées à l'histoire\", dir. Béatrice Joyeux-Prunel et Simon Gabay, Paris, Ecole nationale des Chartes, 2022. \n",
    "\n",
    "\n",
    "\n",
    "**Ce guide explique comment utiliser le programme `extractionCatalogs`**\n",
    "\n",
    "En partant d'images numérisées de catalogues d'exposition artistiques (XIXe et XXe siècle), le script (programme) nous permet de produire des tableurs (et autres types de fichiers \"structurés\") organisant toutes les informations contenues dans ces catalogues : liste des exposants, de leurs oeuvres, informations complémentaires (adresses, entités spatiales, descriptions, associations académiques, prix, etc.).\n",
    "\n",
    "**Si vous ne savez pas utiliser le pipeline**, suivez toutes les indications de ce notebook.\n",
    "\n",
    "**Si vous savez déjà utiliser le pipeline**, vous pouvez lancer directement le programme `extractionCatalogs` (dans ce cas, recopiez la ligne de la cellule et collez-la dessous; ôtez le dièze de la nouvelle ligne, indiquez les chemins d'entrée et de sortie de votre catalogue, puis exécutez la cellule) :\n",
    "\n"
   ]
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   "id": "78484e91",
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   "source": [
    "#!python3 run.py chemin_input chemin_output nom_catalogue"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c79a9b15",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Introduction\n"
   ]
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   "cell_type": "markdown",
   "id": "52d52709",
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   "source": [
    "\n",
    "\n",
    "**On souhaite passer d'une image de catalogue comme celle-ci :**\n",
    "<img src=\"extractionCatalogs/static/images/image_1.jpg\" width=\"300\"/>\n",
    "\n",
    "**à un tableur et un encodage \"structurés\" :**\n",
    "<img src=\"extractionCatalogs/static/images/tableau_1.png\"/>\n",
    "\n",
    "\n",
    "    ADNET (JACQUES)\n",
    "\tné à Chatillon-Coligny (Loiret). Français. — 1 bis, rue HenriRochefort (Aux Armées). S.A. et. S.A.D\n",
    "\t\t  1. — Un Meuble-Coffre gainé en maroquin. vert, sur un piètement en chêne sahlé \n",
    "\t\t      sur un piètement en chêne sahlé (ferrures dorées de G. Poillerat) édité par la Compagnie \n",
    "              des Arts \n",
    "              Français.\n",
    "\t\n",
    "    ALBE (MAURICE)\n",
    "\tné à Sarlat (Dordogne). Français. — 1, rue Léon-Dièrx (Aux Armées). S.A.\n",
    "\t\t  2. — Illustration \n",
    "\t\t      (aux armées), p.\n",
    "      \n",
    "    ALBIN-GUILLOT (Mme LAURE)\n",
    "\tnée à Paris. Française. — 43, boulevard Beausé jour. S.A.D\n",
    "\t\t  3. — Photographies.\n",
    "\n",
    " \n",
    "\\\n",
    "**C'est à dire que nous voulons, à partir d'un catalogue d'exposition sur un support en papier, extraire des informations, les organiser pour signaler leurs significations, et produire ainsi des objets numériques extrêmement utiles pour la recherche en histoire de l'art.**\\\n",
    "\\\n",
    "Le processus, \"semi-automatique\", implique des interventions/vérifications/corrections manuelles. Si la prise en main de cette \"pipeline\" (cheminement par étapes) nécessite un apprentissage technique, les résultats acquièrent une performance exponentielle dès lors qu'il s'agit de traiter des corpus de grande taille. En effet, saisir à la main des tableurs pour des centaines de catalogues, chacun comportant des dizaines ou des centaines de pages, serait un exercice extrêmement pénible... \n",
    "De plus, les technologies numériques utilisées ici sont des outils \"open source\" fondamentaux en humanités numérique ; ce guide est porté par l'objectif d'en montrer certains usages à des utilisateurs débutants.\n",
    "\n",
    "Notre \"**pipeline semi-automatique**\", accompagnée par le présent guide d'utilisation, a donc un intêret **scientifique** (produire des instruments pour la recherche), **technique** (produire efficacement ces instruments) et **pédagogique** (présenter des technologies numériques fondamentales dans le domaine des humanités numériques). Elle comporte deux grandes étapes :\n",
    "\n",
    "**1. L'utilisation d'eScriptorium, un logiciel de recconnaissance de caractères (OCR) pour transcrire automatiquement des pages (images) numérisées de catalogue**. **eScriptorium** est une interface qui permet de visualiser toute la chaîne de traitement OCR de nos images. Ce traitement est en réalité effectué par **Kraken**, un logicel qui permet d'entraîner et d'appliquer des modèles de reconnaissance de caractères.\n",
    "- **input** : des **images numériques** de catalogues du XIXe et XXe siècle [.jpg, .png, etc.]\n",
    "- l'utilisateur/utilisatrice peut ainsi appliquer un modèle efficace pour que la machine face une **transcription automatique des pages**\n",
    "- l'utilisateur/utilisatrice doit aussi \"segmenter\" manuellement ces pages, cet à dire nommer leurs régions sémantiques (il/elle doit **signaler où se trouvent les entrées de catalogue dans une image**). Dans notre projet, le choix du nom des régions est conforme a une \"ontologie\" appellée **Segmonto**, qui est tout simplement un standard de nommage des zones dans une page. \n",
    "- l'utilisateur/utilisatrice doit faire des **corrections manuelles de la transcription automatique**, sur l'interface même.\n",
    "\n",
    "- **output** : l'utilisateur/utilisatrice obtiendra en sortie des **fichiers XML \"ALTO4\"**, un type de document adapté à l'encodage de nos catalogues. Chaque image numérique sera transcrite dans un fichier Alto ; nous obtiendrons donc **un fichier Alto pour chaque page de catalogue**.\n",
    "\n",
    "**2. L'utilisation d'extractionCatalogs, un progamme qui traite les données produites lors de l'étape antérieure, pour produire les fichiers souhaités (tableurs et autres documents structurés)**\n",
    "- **input** : des fichiers **XML ALTO4** produits par eScriptorium.\n",
    "- lancement du script avec la commande suivante : **\"!python3 run.py   chemin_input   chemin_output   nom_du_catalogue\"**\n",
    "- le script, écrit dans le langage de programmation **Python 3**, comporte de multiples fonctions qui traitent automatiquement les fichiers en entrée. Pour chaque page de catalogue (c'est à dire pour chaque fichier Alto), il analyse le document ligne par ligne pour reconstituer le catalogue, entrée par entrée. Une entrée est normalement constituée des éléments suivants : nom de l'exposant, informations biographiques/complémentaires, liste des oeuvres présentées. \n",
    "- **output** : **fichier XML TEI** (\"encodage structuré\") : un seul fichier contenant tout le catalogue et signalant sa structure à partir de balises. Ces balises (par exemple \\<name>MONET (Claude)<\\/name>) signalent les entrées, les exposants, les oeuvres, les informations complémentaires, etc.\n",
    "- **output** : **tableur CSV** : les mêmes informations sont présentées sous forme de tableau. Celui-ci est beaucoup plus facile à lire par un être humain, et sera utilisé pour **alimenter la base de données Artl@s**, un ambitieux projet en histoire de l'art qui cartographie les expositions du XIXe et XXe siècles. \n",
    "- **output** : **fichier .txt indiquant les éventuels problèmes** d'extraction, pour que l'utilisateur/utilisatrice puisse faire des **corrections manuelles**.\n",
    "\n",
    "**Ce guide ne contient pas uniquement des instructions ; il permet de lancer le script d'extraction directement. Il peut donc être utilisé pour effectuer la pipeline, étape par étape**.\n",
    "  "
   ]
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   "source": [
    "## Installation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d7eaa22",
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   },
   "source": [
    "### Utilisation du guide : Jupyter notebook"
   ]
  },
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   "id": "a491a142",
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   "source": [
    "Ce guide a été créé sur Jupyter Notebook, un logiciel qui permet d'afficher sur un navigateur des fichiers qui présentent des morceaux de code commentés. Sur un notebook Jupyter, il existe deux types de cellule : les cellules de code permettent d'afficher et éxécuter du code; les cellules de texte permettent d'écrire en MarkDown (un langage à balise extrêmement simple). \n",
    "\n",
    "Nous pouvons ainsi vous montrer le script en \"live\", tout en l'accompagnant d'images et de commentaires.\n",
    "\n",
    "Lorsque vous lancez des cellules de code (tapez SHIFT/ENTER, ou bien COMMAND/ENTER sur mac, ou bien le bouton \"Run\" sur le menu de la page), vous exécutez directement le script et des commandes sur le terminal ! (si vous voyez un \"!\" au début de la céllule, cela veut dire qu'on exécute des commandes sur le terminal).\n",
    "\n",
    "**Pour intégrer une table des matières et rendre les chapitres/sous-chapitres \"collapsables\", lancez la commande suivante** :"
   ]
  },
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   "id": "ffc07e58",
   "metadata": {
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      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\demo-R.gif\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\demo_2to3.gif\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\isort.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\isort.yaml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\kernel_exec_on_cell.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\README.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\README_2to3.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\README_autopep8.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\README_code_prettify.md\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\README_isort.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] - Validating: ok\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Installing c:\\python39\\lib\\site-packages\\jupyter_contrib_nbextensions\\nbextensions\\code_prettify -> code_prettify\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\2to3.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\2to3.yaml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\autopep8.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\autopep8.yaml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\code_prettify.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\code_prettify.yaml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\demo-jv.gif\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\demo-py.gif\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\demo-R.gif\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\demo_2to3.gif\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\isort.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\isort.yaml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\kernel_exec_on_cell.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\README.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\README_2to3.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\README_autopep8.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\README_code_prettify.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\README_isort.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] - Validating: ok\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Installing c:\\python39\\lib\\site-packages\\jupyter_contrib_nbextensions\\nbextensions\\code_prettify -> code_prettify\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\2to3.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\2to3.yaml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\autopep8.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\autopep8.yaml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\code_prettify.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\code_prettify.yaml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\demo-jv.gif\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\demo-py.gif\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\demo-R.gif\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\demo_2to3.gif\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\isort.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\isort.yaml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\kernel_exec_on_cell.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\README.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\README_2to3.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\README_autopep8.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\README_code_prettify.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\README_isort.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] - Validating: ok\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Installing c:\\python39\\lib\\site-packages\\jupyter_contrib_nbextensions\\nbextensions\\code_prettify -> code_prettify\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\2to3.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\2to3.yaml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\autopep8.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\autopep8.yaml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\code_prettify.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\code_prettify.yaml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\demo-jv.gif\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\demo-py.gif\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\demo-R.gif\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\demo_2to3.gif\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\isort.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\isort.yaml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\kernel_exec_on_cell.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\README.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\README_2to3.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\README_autopep8.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\README_code_prettify.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\code_prettify\\README_isort.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] - Validating: ok\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Installing c:\\python39\\lib\\site-packages\\jupyter_contrib_nbextensions\\nbextensions\\collapsible_headings -> collapsible_headings\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\collapsible_headings\\collapsible_headings.yaml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\collapsible_headings\\icon.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\collapsible_headings\\main.css\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\collapsible_headings\\main.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\collapsible_headings\\readme.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\collapsible_headings\\screenshot.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] - Validating: ok\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Installing c:\\python39\\lib\\site-packages\\jupyter_contrib_nbextensions\\nbextensions\\comment-uncomment -> comment-uncomment\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\comment-uncomment\\comment-uncomment.yaml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\comment-uncomment\\icon.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\comment-uncomment\\main.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\comment-uncomment\\readme.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] - Validating: ok\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Installing c:\\python39\\lib\\site-packages\\jupyter_contrib_nbextensions\\nbextensions\\contrib_nbextensions_help_item -> contrib_nbextensions_help_item\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\contrib_nbextensions_help_item\\contrib_nbextensions_help_item.yaml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\contrib_nbextensions_help_item\\main.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\contrib_nbextensions_help_item\\README.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] - Validating: ok\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Installing c:\\python39\\lib\\site-packages\\jupyter_contrib_nbextensions\\nbextensions\\datestamper -> datestamper\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\datestamper\\icon.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\datestamper\\main.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\datestamper\\main.yaml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\datestamper\\readme.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] - Validating: ok\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Installing c:\\python39\\lib\\site-packages\\jupyter_contrib_nbextensions\\nbextensions\\equation-numbering -> equation-numbering\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\equation-numbering\\button.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\equation-numbering\\icon.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\equation-numbering\\info.yaml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\equation-numbering\\main.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\equation-numbering\\readme.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] - Validating: ok\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Installing c:\\python39\\lib\\site-packages\\jupyter_contrib_nbextensions\\nbextensions\\execute_time -> execute_time\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\execute_time\\ExecuteTime.css\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\execute_time\\ExecuteTime.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\execute_time\\ExecuteTime.yaml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\execute_time\\execution-timings-box.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\execute_time\\execution-timings-menu.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\execute_time\\icon.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\execute_time\\readme.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] - Validating: ok\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Installing c:\\python39\\lib\\site-packages\\jupyter_contrib_nbextensions\\nbextensions\\execution_dependencies -> execution_dependencies\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\execution_dependencies\\execution_dependencies.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\execution_dependencies\\execution_dependencies.yml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\execution_dependencies\\README.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] - Validating: ok\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Installing c:\\python39\\lib\\site-packages\\jupyter_contrib_nbextensions\\nbextensions\\exercise -> exercise\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\exercise\\exercise.yaml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\exercise\\history.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\exercise\\icon.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\exercise\\image.gif\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\exercise\\main.css\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\exercise\\main.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\exercise\\readme.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] - Validating: ok\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Installing c:\\python39\\lib\\site-packages\\jupyter_contrib_nbextensions\\nbextensions\\exercise2 -> exercise2\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\exercise2\\exercise2.yaml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\exercise2\\icon.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\exercise2\\image.gif\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\exercise2\\main.css\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\exercise2\\main.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\exercise2\\readme.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] - Validating: ok\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Installing c:\\python39\\lib\\site-packages\\jupyter_contrib_nbextensions\\nbextensions\\export_embedded -> export_embedded\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\export_embedded\\export_embedded.yaml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\export_embedded\\icon.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\export_embedded\\main.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\export_embedded\\readme.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] - Validating: ok\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Installing c:\\python39\\lib\\site-packages\\jupyter_contrib_nbextensions\\nbextensions\\freeze -> freeze\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\freeze\\config.yaml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\freeze\\icon.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\freeze\\main.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\freeze\\readme.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] - Validating: ok\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Installing c:\\python39\\lib\\site-packages\\jupyter_contrib_nbextensions\\nbextensions\\gist_it -> gist_it\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\gist_it\\gist_it.yaml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\gist_it\\icon.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\gist_it\\main.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\gist_it\\readme.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] - Validating: ok\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Installing c:\\python39\\lib\\site-packages\\jupyter_contrib_nbextensions\\nbextensions\\help_panel -> help_panel\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\help_panel\\help_panel.css\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\help_panel\\help_panel.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\help_panel\\help_panel.yaml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\help_panel\\help_panel_ext.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\help_panel\\help_panel_ext_fullscreen.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\help_panel\\icon.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\help_panel\\readme.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\help_panel\\img\\handle-v.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] - Validating: ok\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Installing c:\\python39\\lib\\site-packages\\jupyter_contrib_nbextensions\\nbextensions\\hide_header -> hide_header\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\hide_header\\hide_header.yaml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\hide_header\\main.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\hide_header\\README.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] - Validating: ok\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Installing c:\\python39\\lib\\site-packages\\jupyter_contrib_nbextensions\\nbextensions\\hide_input -> hide_input\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\hide_input\\hide-input.yaml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\hide_input\\icon.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\hide_input\\main.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\hide_input\\readme.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] - Validating: ok\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Installing c:\\python39\\lib\\site-packages\\jupyter_contrib_nbextensions\\nbextensions\\hide_input_all -> hide_input_all\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\hide_input_all\\hide_input_all.yaml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\hide_input_all\\hide_input_all_hide.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\hide_input_all\\hide_input_all_show.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\hide_input_all\\icon.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\hide_input_all\\main.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\hide_input_all\\readme.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] - Validating: ok\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Installing c:\\python39\\lib\\site-packages\\jupyter_contrib_nbextensions\\nbextensions\\highlighter -> highlighter\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\highlighter\\demo_highlighter.html\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\highlighter\\demo_highlighter.ipynb\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\highlighter\\export_highlights.html\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\highlighter\\export_highlights.ipynb\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\highlighter\\export_highlights.pdf\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\highlighter\\export_highlights.tex\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\highlighter\\highlighter.css\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\highlighter\\highlighter.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\highlighter\\highlighter.yaml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\highlighter\\icon.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\highlighter\\image.gif\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\highlighter\\readme.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\highlighter\\tst_highlights.html\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\highlighter\\tst_highlights.ipynb\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\highlighter\\tst_highlights.pdf\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\highlighter\\tst_highlights.tex\n",
      "[I 09:17:39 InstallContribNbextensionsApp] - Validating: ok\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Installing c:\\python39\\lib\\site-packages\\jupyter_contrib_nbextensions\\nbextensions\\hinterland -> hinterland\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\hinterland\\hinterland.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\hinterland\\hinterland.yaml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\hinterland\\README.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] - Validating: ok\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Installing c:\\python39\\lib\\site-packages\\jupyter_contrib_nbextensions\\nbextensions\\init_cell -> init_cell\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\init_cell\\cell_toolbar_menu.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\init_cell\\icon.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\init_cell\\init_cell.yaml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\init_cell\\main.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\init_cell\\README.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] - Validating: ok\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Installing c:\\python39\\lib\\site-packages\\jupyter_contrib_nbextensions\\nbextensions\\keyboard_shortcut_editor -> keyboard_shortcut_editor\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\keyboard_shortcut_editor\\icon.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\keyboard_shortcut_editor\\keyboard_shortcut_editor.yaml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\keyboard_shortcut_editor\\kse_components.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\keyboard_shortcut_editor\\main.css\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\keyboard_shortcut_editor\\main.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\keyboard_shortcut_editor\\quickhelp_shim.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\keyboard_shortcut_editor\\README.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\keyboard_shortcut_editor\\readme_add_new_link.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\keyboard_shortcut_editor\\readme_add_new_select_action.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\keyboard_shortcut_editor\\readme_comma.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\keyboard_shortcut_editor\\readme_conflict.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\keyboard_shortcut_editor\\readme_dropdown.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\keyboard_shortcut_editor\\readme_menu_item.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\keyboard_shortcut_editor\\readme_reset_disabled.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\keyboard_shortcut_editor\\readme_shortcut_editor_blank.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\keyboard_shortcut_editor\\readme_shortcut_editor_success.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\keyboard_shortcut_editor\\readme_undefined_key.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] - Validating: ok\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Installing c:\\python39\\lib\\site-packages\\jupyter_contrib_nbextensions\\nbextensions\\limit_output -> limit_output\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\limit_output\\icon.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\limit_output\\limit-output.yaml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\limit_output\\main.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\limit_output\\readme.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] - Validating: ok\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Installing c:\\python39\\lib\\site-packages\\jupyter_contrib_nbextensions\\nbextensions\\livemdpreview -> livemdpreview\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\livemdpreview\\livemdpreview.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\livemdpreview\\livemdpreview.yml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] - Validating: ok\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Installing c:\\python39\\lib\\site-packages\\jupyter_contrib_nbextensions\\nbextensions\\load_tex_macros -> load_tex_macros\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\load_tex_macros\\icon.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\load_tex_macros\\load_tex_macros.yaml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\load_tex_macros\\main.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\load_tex_macros\\readme.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] - Validating: ok\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Installing c:\\python39\\lib\\site-packages\\jupyter_contrib_nbextensions\\nbextensions\\move_selected_cells -> move_selected_cells\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\move_selected_cells\\main.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\move_selected_cells\\move_selected_cells.yaml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\move_selected_cells\\README.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] - Validating: ok\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Installing c:\\python39\\lib\\site-packages\\jupyter_contrib_nbextensions\\nbextensions\\navigation-hotkeys -> navigation-hotkeys\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\navigation-hotkeys\\hotkeys.yaml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\navigation-hotkeys\\icon.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\navigation-hotkeys\\main.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\navigation-hotkeys\\readme.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] - Validating: ok\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Installing c:\\python39\\lib\\site-packages\\jupyter_contrib_nbextensions\\nbextensions\\nbTranslate -> nbTranslate\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\nbTranslate\\demo1.gif\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\nbTranslate\\demo2.gif\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\nbTranslate\\languages.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\nbTranslate\\main.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\nbTranslate\\mutils.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\nbTranslate\\nbTranslate.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\nbTranslate\\nbTranslate.yaml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\nbTranslate\\README.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] - Validating: ok\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Installing c:\\python39\\lib\\site-packages\\jupyter_contrib_nbextensions\\nbextensions\\notify -> notify\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\notify\\notification.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\notify\\notify.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\notify\\notify.mp3\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\notify\\notify.yaml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\notify\\readme.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] - Validating: ok\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Installing c:\\python39\\lib\\site-packages\\jupyter_contrib_nbextensions\\nbextensions\\printview -> printview\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\printview\\icon.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\printview\\main.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\printview\\printview-button.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\printview\\printview.yaml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\printview\\readme.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] - Validating: ok\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Installing c:\\python39\\lib\\site-packages\\jupyter_contrib_nbextensions\\nbextensions\\python-markdown -> python-markdown\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\python-markdown\\main.css\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\python-markdown\\main.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\python-markdown\\python-markdown-post.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\python-markdown\\python-markdown-pre.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\python-markdown\\python-markdown.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\python-markdown\\python-markdown.yaml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\python-markdown\\readme.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\python-markdown\\trusted.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\python-markdown\\untrusted.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] - Validating: ok\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Installing c:\\python39\\lib\\site-packages\\jupyter_contrib_nbextensions\\nbextensions\\qtconsole -> qtconsole\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\qtconsole\\qtconsole.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\qtconsole\\qtconsole.yaml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\qtconsole\\README.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] - Validating: ok\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Installing c:\\python39\\lib\\site-packages\\jupyter_contrib_nbextensions\\nbextensions\\rubberband -> rubberband\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\rubberband\\icon.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\rubberband\\main.css\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\rubberband\\main.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\rubberband\\readme.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\rubberband\\rubberband.yaml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] - Validating: ok\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Installing c:\\python39\\lib\\site-packages\\jupyter_contrib_nbextensions\\nbextensions\\ruler -> ruler\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\ruler\\edit.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\ruler\\icon.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\ruler\\main.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\ruler\\readme.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\ruler\\ruler.yaml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\ruler\\ruler_editor.yaml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] - Validating: ok\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Installing c:\\python39\\lib\\site-packages\\jupyter_contrib_nbextensions\\nbextensions\\ruler -> ruler\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\ruler\\edit.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\ruler\\icon.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\ruler\\main.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\ruler\\readme.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\ruler\\ruler.yaml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\ruler\\ruler_editor.yaml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] - Validating: ok\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Installing c:\\python39\\lib\\site-packages\\jupyter_contrib_nbextensions\\nbextensions\\runtools -> runtools\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\runtools\\annotations.odg\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\runtools\\cellstate.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\runtools\\demo.gif\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\runtools\\gutter.css\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\runtools\\icon.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\runtools\\main.css\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\runtools\\main.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\runtools\\readme.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\runtools\\runtools.yaml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\runtools\\runtools_execute.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\runtools\\runtools_lock.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\runtools\\runtools_marker.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\runtools\\runtools_nb.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\runtools\\runtools_show_hide.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] - Validating: ok\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Installing c:\\python39\\lib\\site-packages\\jupyter_contrib_nbextensions\\nbextensions\\scratchpad -> scratchpad\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\scratchpad\\demo.gif\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\scratchpad\\LICENSE\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\scratchpad\\main.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\scratchpad\\README.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\scratchpad\\scratchpad.css\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\scratchpad\\scratchpad.yaml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] - Validating: ok\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Installing c:\\python39\\lib\\site-packages\\jupyter_contrib_nbextensions\\nbextensions\\scroll_down -> scroll_down\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\scroll_down\\config.yaml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\scroll_down\\icon.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\scroll_down\\main.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\scroll_down\\readme.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] - Validating: ok\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Installing c:\\python39\\lib\\site-packages\\jupyter_contrib_nbextensions\\nbextensions\\select_keymap -> select_keymap\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\select_keymap\\main.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\select_keymap\\README.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\select_keymap\\select_keymap.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\select_keymap\\select_keymap.yaml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] - Validating: ok\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Installing c:\\python39\\lib\\site-packages\\jupyter_contrib_nbextensions\\nbextensions\\skill -> skill\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\skill\\main.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\skill\\README.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\skill\\skill.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\skill\\skill.yaml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] - Validating: ok\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Installing c:\\python39\\lib\\site-packages\\jupyter_contrib_nbextensions\\nbextensions\\skip-traceback -> skip-traceback\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\skip-traceback\\icon.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\skip-traceback\\main.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\skip-traceback\\readme.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\skip-traceback\\skip-traceback.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\skip-traceback\\skip-traceback.yaml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\skip-traceback\\traceback.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] - Validating: ok\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Installing c:\\python39\\lib\\site-packages\\jupyter_contrib_nbextensions\\nbextensions\\snippets -> snippets\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\snippets\\main.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\snippets\\README.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\snippets\\snippets-demo.gif\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\snippets\\snippets.json\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\snippets\\snippets.yaml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] - Validating: ok\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Installing c:\\python39\\lib\\site-packages\\jupyter_contrib_nbextensions\\nbextensions\\snippets_menu -> snippets_menu\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\snippets_menu\\config.yaml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\snippets_menu\\examples_for_custom.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\snippets_menu\\main.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\snippets_menu\\readme.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\snippets_menu\\screenshot1.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\snippets_menu\\screenshot2.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\snippets_menu\\screenshot3.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\snippets_menu\\snippets_menu.css\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\snippets_menu\\snippets_submenu_markdown.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\snippets_menu\\snippets_submenu_python.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\snippets_menu\\thumbnail.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\snippets_menu\\snippets_submenus_python\\astropy.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\snippets_menu\\snippets_submenus_python\\h5py.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\snippets_menu\\snippets_submenus_python\\matplotlib.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\snippets_menu\\snippets_submenus_python\\numba.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\snippets_menu\\snippets_submenus_python\\numpy.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\snippets_menu\\snippets_submenus_python\\numpy_polynomial.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\snippets_menu\\snippets_submenus_python\\numpy_ufuncs.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\snippets_menu\\snippets_submenus_python\\pandas.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\snippets_menu\\snippets_submenus_python\\python.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\snippets_menu\\snippets_submenus_python\\python_regex.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\snippets_menu\\snippets_submenus_python\\scipy.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\snippets_menu\\snippets_submenus_python\\scipy_constants.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\snippets_menu\\snippets_submenus_python\\scipy_special.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\snippets_menu\\snippets_submenus_python\\sympy.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\snippets_menu\\snippets_submenus_python\\sympy_assumptions.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\snippets_menu\\snippets_submenus_python\\sympy_functions.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] - Validating: ok\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Installing c:\\python39\\lib\\site-packages\\jupyter_contrib_nbextensions\\nbextensions\\spellchecker -> spellchecker\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\spellchecker\\config.yaml\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\spellchecker\\download_new_dict.py\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\spellchecker\\main.css\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\spellchecker\\main.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\spellchecker\\README.md\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\spellchecker\\screenshot.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\spellchecker\\typo\\LICENSE.txt\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\spellchecker\\typo\\typo.js\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\spellchecker\\__pycache__\\download_new_dict.cpython-39.pyc\n",
      "[I 09:17:39 InstallContribNbextensionsApp] - Validating: ok\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Installing c:\\python39\\lib\\site-packages\\jupyter_contrib_nbextensions\\nbextensions\\splitcell -> splitcell\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\splitcell\\icon.png\n",
      "[I 09:17:39 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\splitcell\\readme.md\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\splitcell\\splitcell.js\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\splitcell\\splitcell.yaml\n",
      "[I 09:17:40 InstallContribNbextensionsApp] - Validating: ok\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Installing c:\\python39\\lib\\site-packages\\jupyter_contrib_nbextensions\\nbextensions\\table_beautifier -> table_beautifier\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\table_beautifier\\main.css\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\table_beautifier\\main.js\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\table_beautifier\\README.md\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\table_beautifier\\table_beautifier.yaml\n",
      "[I 09:17:40 InstallContribNbextensionsApp] - Validating: ok\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Installing c:\\python39\\lib\\site-packages\\jupyter_contrib_nbextensions\\nbextensions\\toc2 -> toc2\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\toc2\\demo.gif\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\toc2\\demo2.gif\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\toc2\\demo3.gif\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\toc2\\demo_dark.png\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\toc2\\icon.png\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\toc2\\image.png\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\toc2\\main.css\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\toc2\\main.js\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\toc2\\README.md\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\toc2\\toc2.js\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\toc2\\toc2.yaml\n",
      "[I 09:17:40 InstallContribNbextensionsApp] - Validating: ok\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Installing c:\\python39\\lib\\site-packages\\jupyter_contrib_nbextensions\\nbextensions\\toggle_all_line_numbers -> toggle_all_line_numbers\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\toggle_all_line_numbers\\icon.png\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\toggle_all_line_numbers\\main.js\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\toggle_all_line_numbers\\main.yaml\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\toggle_all_line_numbers\\readme.md\n",
      "[I 09:17:40 InstallContribNbextensionsApp] - Validating: ok\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Installing c:\\python39\\lib\\site-packages\\jupyter_contrib_nbextensions\\nbextensions\\tree-filter -> tree-filter\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\tree-filter\\demo.gif\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\tree-filter\\index.js\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\tree-filter\\readme.md\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\tree-filter\\tree-filter.yaml\n",
      "[I 09:17:40 InstallContribNbextensionsApp] - Validating: ok\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Installing c:\\python39\\lib\\site-packages\\jupyter_contrib_nbextensions\\nbextensions\\varInspector -> varInspector\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\varInspector\\demo.gif\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\varInspector\\icon.png\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\varInspector\\jquery.tablesorter.min.js\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\varInspector\\main.css\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\varInspector\\main.js\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\varInspector\\README.md\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\varInspector\\tablesorter_LICENSE.txt\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\varInspector\\varInspector.yaml\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\varInspector\\var_list.py\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\varInspector\\var_list.r\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\varInspector\\__pycache__\\var_list.cpython-39.pyc\n",
      "[I 09:17:40 InstallContribNbextensionsApp] - Validating: ok\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Installing c:\\python39\\lib\\site-packages\\jupyter_contrib_nbextensions\\nbextensions\\zenmode -> zenmode\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\zenmode\\main.css\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\zenmode\\main.js\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\zenmode\\README.md\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\zenmode\\zenmode.yaml\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\zenmode\\images\\back1.jpg\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\zenmode\\images\\back11.jpg\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\zenmode\\images\\back12.jpg\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\zenmode\\images\\back2.jpg\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\zenmode\\images\\back21.jpg\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\zenmode\\images\\back22.jpg\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\zenmode\\images\\back3.jpg\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\zenmode\\images\\ipynblogo0.png\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\zenmode\\images\\ipynblogo1.png\n",
      "[I 09:17:40 InstallContribNbextensionsApp] - Validating: ok\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Installing c:\\python39\\lib\\site-packages\\jupyter_highlight_selected_word\\static\\highlight_selected_word -> highlight_selected_word\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\highlight_selected_word\\configurator.yaml\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\highlight_selected_word\\main.js\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\highlight_selected_word\\README.md\n",
      "[I 09:17:40 InstallContribNbextensionsApp] - Validating: ok\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Installing c:\\python39\\lib\\site-packages\\latex_envs\\static -> latex_envs\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\latex_envs\\bibInNb4.js\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\latex_envs\\bibtex2.js\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\latex_envs\\configuration_toolbar.png\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\latex_envs\\envsLatex.js\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\latex_envs\\envsLatex.json\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\latex_envs\\example.png\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\latex_envs\\example_user_envs.json\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\latex_envs\\icon.png\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\latex_envs\\initNb.js\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\latex_envs\\latex_envs.css\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\latex_envs\\latex_envs.js\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\latex_envs\\latex_envs.yaml\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\latex_envs\\main_toolbar.png\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\latex_envs\\README.md\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\latex_envs\\thmsInNb4.js\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\latex_envs\\doc\\biblio_ipython.bib\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\latex_envs\\doc\\configuration_toolbar.png\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\latex_envs\\doc\\config_toolbar.png\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\latex_envs\\doc\\documentation.pdf\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\latex_envs\\doc\\documentation.tex\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\latex_envs\\doc\\example.png\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\latex_envs\\doc\\header.tex\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\latex_envs\\doc\\IEEEtran.bst\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\latex_envs\\doc\\LaTeX_envs_menu.png\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\latex_envs\\doc\\latex_env_doc.html\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\latex_envs\\doc\\latex_env_doc.ipynb\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\latex_envs\\doc\\latex_env_doc.tex\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\latex_envs\\doc\\main_toolbar.png\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\latex_envs\\doc\\README.md\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\latex_envs\\doc\\Toggles.png\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\latex_envs\\doc\\.ipynb_checkpoints\\latex_env_doc-checkpoint.ipynb\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\latex_envs\\doc\\latex_env_doc_files\\latex_env_doc_23_2.png\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\latex_envs\\doc\\latex_env_doc_files\\latex_env_doc_24_2.png\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\latex_envs\\doc\\latex_env_doc_files\\latex_env_doc_26_2.png\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\latex_envs\\doc\\latex_env_doc_files\\latex_env_doc_27_2.png\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\latex_envs\\doc\\latex_env_doc_files\\latex_env_doc_41_0.png\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\latex_envs\\doc\\latex_env_doc_files\\latex_env_doc_42_0.png\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\latex_envs\\doc\\latex_env_doc_files\\latex_env_doc_43_0.png\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\latex_envs\\doc\\latex_env_doc_files\\latex_env_doc_44_0.png\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\latex_envs\\doc\\latex_env_doc_files\\latex_env_doc_45_0.png\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\latex_envs\\doc\\latex_env_doc_files\\latex_env_doc_46_0.png\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\latex_envs\\doc\\latex_env_doc_files\\latex_env_doc_47_0.png\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\latex_envs\\doc\\latex_env_doc_files\\latex_env_doc_48_0.png\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\latex_envs\\doc\\latex_env_doc_files\\latex_env_doc_49_0.png\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\latex_envs\\doc\\latex_env_doc_files\\latex_env_doc_50_0.png\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\latex_envs\\doc\\latex_env_doc_files\\latex_env_doc_51_0.png\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Up to date: C:\\Users\\joyeuxpr\\AppData\\Roaming\\jupyter\\nbextensions\\latex_envs\\doc\\latex_env_doc_files\\latex_env_doc_52_0.png\n",
      "[I 09:17:40 InstallContribNbextensionsApp] - Validating: ok\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Installing jupyter_contrib_nbextensions items to config in C:\\Users\\joyeuxpr\\.jupyter\n",
      "Enabling: jupyter_nbextensions_configurator\n",
      "- Writing config: C:\\Users\\joyeuxpr\\.jupyter\n",
      "    - Validating...\n",
      "      jupyter_nbextensions_configurator 0.4.1 ok\n",
      "Enabling notebook nbextension nbextensions_configurator/config_menu/main...\n",
      "Enabling tree nbextension nbextensions_configurator/tree_tab/main...\n",
      "[I 09:17:40 InstallContribNbextensionsApp] Enabling notebook extension contrib_nbextensions_help_item/main...\n",
      "[I 09:17:40 InstallContribNbextensionsApp]       - Validating: ok\n",
      "[I 09:17:40 InstallContribNbextensionsApp] - Editing config: C:\\Users\\joyeuxpr\\.jupyter\\jupyter_nbconvert_config.json\n",
      "[I 09:17:40 InstallContribNbextensionsApp] --  Configuring nbconvert template path\n",
      "[I 09:17:40 InstallContribNbextensionsApp] --  Configuring nbconvert preprocessors\n",
      "[I 09:17:40 InstallContribNbextensionsApp] - Writing config: C:\\Users\\joyeuxpr\\.jupyter\\jupyter_nbconvert_config.json\n",
      "[I 09:17:40 InstallContribNbextensionsApp] --  Writing updated config file C:\\Users\\joyeuxpr\\.jupyter\\jupyter_nbconvert_config.json\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: jupyter_nbextensions_configurator in c:\\python39\\lib\\site-packages (0.4.1)\n",
      "Requirement already satisfied: jupyter-contrib-core>=0.3.3 in c:\\python39\\lib\\site-packages (from jupyter_nbextensions_configurator) (0.3.3)\n",
      "Requirement already satisfied: pyyaml in c:\\python39\\lib\\site-packages (from jupyter_nbextensions_configurator) (5.4.1)\n",
      "Requirement already satisfied: traitlets in c:\\python39\\lib\\site-packages (from jupyter_nbextensions_configurator) (5.3.0)\n",
      "Requirement already satisfied: jupyter-core in c:\\python39\\lib\\site-packages (from jupyter_nbextensions_configurator) (4.7.1)\n",
      "Requirement already satisfied: tornado in c:\\python39\\lib\\site-packages (from jupyter_nbextensions_configurator) (6.2)\n",
      "Requirement already satisfied: notebook>=4.0 in c:\\python39\\lib\\site-packages (from jupyter_nbextensions_configurator) (6.3.0)\n",
      "Requirement already satisfied: setuptools in c:\\python39\\lib\\site-packages (from jupyter-contrib-core>=0.3.3->jupyter_nbextensions_configurator) (56.2.0)\n",
      "Requirement already satisfied: nbformat in c:\\python39\\lib\\site-packages (from notebook>=4.0->jupyter_nbextensions_configurator) (5.1.3)\n",
      "Requirement already satisfied: Send2Trash>=1.5.0 in c:\\python39\\lib\\site-packages (from notebook>=4.0->jupyter_nbextensions_configurator) (1.5.0)"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Ignoring invalid distribution -illow (c:\\python39\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -illow (c:\\python39\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -illow (c:\\python39\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -illow (c:\\python39\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -illow (c:\\python39\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -illow (c:\\python39\\lib\\site-packages)\n",
      "WARNING: You are using pip version 21.1.2; however, version 22.2.2 is available.\n",
      "You should consider upgrading via the 'c:\\python39\\python.exe -m pip install --upgrade pip' command.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Requirement already satisfied: ipykernel in c:\\python39\\lib\\site-packages (from notebook>=4.0->jupyter_nbextensions_configurator) (5.5.3)\n",
      "Requirement already satisfied: prometheus-client in c:\\python39\\lib\\site-packages (from notebook>=4.0->jupyter_nbextensions_configurator) (0.10.1)\n",
      "Requirement already satisfied: nbconvert in c:\\python39\\lib\\site-packages (from notebook>=4.0->jupyter_nbextensions_configurator) (6.0.7)\n",
      "Requirement already satisfied: terminado>=0.8.3 in c:\\python39\\lib\\site-packages (from notebook>=4.0->jupyter_nbextensions_configurator) (0.9.4)\n",
      "Requirement already satisfied: pyzmq>=17 in c:\\python39\\lib\\site-packages (from notebook>=4.0->jupyter_nbextensions_configurator) (23.2.0)\n",
      "Requirement already satisfied: ipython-genutils in c:\\python39\\lib\\site-packages (from notebook>=4.0->jupyter_nbextensions_configurator) (0.2.0)\n",
      "Requirement already satisfied: jinja2 in c:\\python39\\lib\\site-packages (from notebook>=4.0->jupyter_nbextensions_configurator) (2.11.3)\n",
      "Requirement already satisfied: jupyter-client>=5.3.4 in c:\\python39\\lib\\site-packages (from notebook>=4.0->jupyter_nbextensions_configurator) (6.1.12)\n",
      "Requirement already satisfied: argon2-cffi in c:\\python39\\lib\\site-packages (from notebook>=4.0->jupyter_nbextensions_configurator) (20.1.0)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in c:\\python39\\lib\\site-packages (from jupyter-client>=5.3.4->notebook>=4.0->jupyter_nbextensions_configurator) (2.8.1)\n",
      "Requirement already satisfied: pywin32>=1.0 in c:\\python39\\lib\\site-packages (from jupyter-core->jupyter_nbextensions_configurator) (300)\n",
      "Requirement already satisfied: six>=1.5 in c:\\python39\\lib\\site-packages (from python-dateutil>=2.1->jupyter-client>=5.3.4->notebook>=4.0->jupyter_nbextensions_configurator) (1.16.0)\n",
      "Requirement already satisfied: pywinpty>=0.5 in c:\\python39\\lib\\site-packages (from terminado>=0.8.3->notebook>=4.0->jupyter_nbextensions_configurator) (0.5.7)\n",
      "Requirement already satisfied: cffi>=1.0.0 in c:\\python39\\lib\\site-packages (from argon2-cffi->notebook>=4.0->jupyter_nbextensions_configurator) (1.14.5)\n",
      "Requirement already satisfied: pycparser in c:\\python39\\lib\\site-packages (from cffi>=1.0.0->argon2-cffi->notebook>=4.0->jupyter_nbextensions_configurator) (2.20)\n",
      "Requirement already satisfied: ipython>=5.0.0 in c:\\python39\\lib\\site-packages (from ipykernel->notebook>=4.0->jupyter_nbextensions_configurator) (7.22.0)\n",
      "Requirement already satisfied: jedi>=0.16 in c:\\python39\\lib\\site-packages (from ipython>=5.0.0->ipykernel->notebook>=4.0->jupyter_nbextensions_configurator) (0.18.0)\n",
      "Requirement already satisfied: colorama in c:\\python39\\lib\\site-packages (from ipython>=5.0.0->ipykernel->notebook>=4.0->jupyter_nbextensions_configurator) (0.4.4)\n",
      "Requirement already satisfied: pygments in c:\\python39\\lib\\site-packages (from ipython>=5.0.0->ipykernel->notebook>=4.0->jupyter_nbextensions_configurator) (2.8.1)\n",
      "Requirement already satisfied: pickleshare in c:\\python39\\lib\\site-packages (from ipython>=5.0.0->ipykernel->notebook>=4.0->jupyter_nbextensions_configurator) (0.7.5)\n",
      "Requirement already satisfied: decorator in c:\\python39\\lib\\site-packages (from ipython>=5.0.0->ipykernel->notebook>=4.0->jupyter_nbextensions_configurator) (4.4.2)\n",
      "Requirement already satisfied: backcall in c:\\python39\\lib\\site-packages (from ipython>=5.0.0->ipykernel->notebook>=4.0->jupyter_nbextensions_configurator) (0.2.0)\n",
      "Requirement already satisfied: prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0 in c:\\python39\\lib\\site-packages (from ipython>=5.0.0->ipykernel->notebook>=4.0->jupyter_nbextensions_configurator) (3.0.18)\n",
      "Requirement already satisfied: parso<0.9.0,>=0.8.0 in c:\\python39\\lib\\site-packages (from jedi>=0.16->ipython>=5.0.0->ipykernel->notebook>=4.0->jupyter_nbextensions_configurator) (0.8.2)\n",
      "Requirement already satisfied: wcwidth in c:\\python39\\lib\\site-packages (from prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->ipython>=5.0.0->ipykernel->notebook>=4.0->jupyter_nbextensions_configurator) (0.2.5)\n",
      "Requirement already satisfied: MarkupSafe>=0.23 in c:\\python39\\lib\\site-packages (from jinja2->notebook>=4.0->jupyter_nbextensions_configurator) (1.1.1)\n",
      "Requirement already satisfied: testpath in c:\\python39\\lib\\site-packages (from nbconvert->notebook>=4.0->jupyter_nbextensions_configurator) (0.4.4)\n",
      "Requirement already satisfied: entrypoints>=0.2.2 in c:\\python39\\lib\\site-packages (from nbconvert->notebook>=4.0->jupyter_nbextensions_configurator) (0.3)\n",
      "Requirement already satisfied: mistune<2,>=0.8.1 in c:\\python39\\lib\\site-packages (from nbconvert->notebook>=4.0->jupyter_nbextensions_configurator) (0.8.4)\n",
      "Requirement already satisfied: jupyterlab-pygments in c:\\python39\\lib\\site-packages (from nbconvert->notebook>=4.0->jupyter_nbextensions_configurator) (0.1.2)\n",
      "Requirement already satisfied: bleach in c:\\python39\\lib\\site-packages (from nbconvert->notebook>=4.0->jupyter_nbextensions_configurator) (3.3.0)\n",
      "Requirement already satisfied: nbclient<0.6.0,>=0.5.0 in c:\\python39\\lib\\site-packages (from nbconvert->notebook>=4.0->jupyter_nbextensions_configurator) (0.5.3)\n",
      "Requirement already satisfied: defusedxml in c:\\python39\\lib\\site-packages (from nbconvert->notebook>=4.0->jupyter_nbextensions_configurator) (0.7.1)\n",
      "Requirement already satisfied: pandocfilters>=1.4.1 in c:\\python39\\lib\\site-packages (from nbconvert->notebook>=4.0->jupyter_nbextensions_configurator) (1.4.3)\n",
      "Requirement already satisfied: async-generator in c:\\python39\\lib\\site-packages (from nbclient<0.6.0,>=0.5.0->nbconvert->notebook>=4.0->jupyter_nbextensions_configurator) (1.10)\n",
      "Requirement already satisfied: nest-asyncio in c:\\python39\\lib\\site-packages (from nbclient<0.6.0,>=0.5.0->nbconvert->notebook>=4.0->jupyter_nbextensions_configurator) (1.5.1)\n",
      "Requirement already satisfied: jsonschema!=2.5.0,>=2.4 in c:\\python39\\lib\\site-packages (from nbformat->notebook>=4.0->jupyter_nbextensions_configurator) (3.2.0)\n",
      "Requirement already satisfied: pyrsistent>=0.14.0 in c:\\python39\\lib\\site-packages (from jsonschema!=2.5.0,>=2.4->nbformat->notebook>=4.0->jupyter_nbextensions_configurator) (0.18.1)\n",
      "Requirement already satisfied: attrs>=17.4.0 in c:\\python39\\lib\\site-packages (from jsonschema!=2.5.0,>=2.4->nbformat->notebook>=4.0->jupyter_nbextensions_configurator) (21.4.0)\n",
      "Requirement already satisfied: packaging in c:\\python39\\lib\\site-packages (from bleach->nbconvert->notebook>=4.0->jupyter_nbextensions_configurator) (20.9)\n",
      "Requirement already satisfied: webencodings in c:\\python39\\lib\\site-packages (from bleach->nbconvert->notebook>=4.0->jupyter_nbextensions_configurator) (0.5.1)\n",
      "Requirement already satisfied: pyparsing>=2.0.2 in c:\\python39\\lib\\site-packages (from packaging->bleach->nbconvert->notebook>=4.0->jupyter_nbextensions_configurator) (2.4.7)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Enabling: jupyter_nbextensions_configurator\n",
      "- Writing config: C:\\Users\\joyeuxpr\\.jupyter\n",
      "    - Validating...\n",
      "      jupyter_nbextensions_configurator 0.4.1 ok\n",
      "Enabling notebook nbextension nbextensions_configurator/config_menu/main...\n",
      "Enabling tree nbextension nbextensions_configurator/tree_tab/main...\n"
     ]
    }
   ],
   "source": [
    "!jupyter contrib nbextension install --user\n",
    "!pip install jupyter_nbextensions_configurator\n",
    "!jupyter nbextensions_configurator enable --user"
   ]
  },
  {
   "attachments": {
    "menu_guide.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "c797c166",
   "metadata": {
    "hidden": true
   },
   "source": [
    "(rappel : SHIFT/ENTER, ou bien COMMAND/ENTER sur mac, ou bien le bouton \"Run\" dans le menu de la page)\n",
    "\n",
    "Vous venez d'installer et d'activer une extension qui vous permet d'activer des add-on pour Jupyter Notebook ! \n",
    "Maintenant :\n",
    "- **1. cliquez sur le logo \"Jupyter\" en haut à gauche de la page**\n",
    "- **2. cliquez sur l'onglet \"nbextensions\"**\n",
    "- **3. activez les extensions \"collapsable Headings\" et \"Table of contents (2)\"**\n",
    "<img src=\"extractionCatalogs/static/images/jupyter_extensions_1.png\" width=\"1000\"/>\n",
    "\n",
    "\n",
    "**En cliquant sur ces extensions, vous accédez à leurs menus, dans lesquels vous cocherez les cases suivantes pour activer des options :**\n",
    "\n",
    "**dans le menu de l'add-on Collapsable Headings :**\n",
    "- Collapse/uncollapse notebook sections when the ToC2 nbextension is used to collapse/uncollapse sections in the table of contents. For the inverse behaviour, see ToC2's configuration\n",
    "\n",
    "**dans le menu de l'add-on Table of Content (2) :**\n",
    "- Skip h1 headings from numbering, so that they can serve as a notebook title. See the README for details, caveats and alternatives\n",
    "- Collapse/uncollapse ToC sections when the collapsible_headings nbextension is used to collapse/uncollapse sections in the notebook. For the inverse behaviour, see collapsible_headings' configuration\n",
    "\n",
    "Vous pouvez maintenant **rafraichir la page du guide**, et profiter d'une interface beaucoup plus agréable pour travailler étape par étape. Fermez les onglets, vous obtiendrez ceci :\n",
    "![menu_guide.png](attachment:menu_guide.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c5cec71",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### Installation du script d'extraction de catalogues\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b15d3444",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Maintenant qu'on a une interface plus adaptée pour le guide, nous allons installer le script extractionCatalogs à proprement parler."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1fe2d9c2",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#ce passage mérite qqs explications. pourquoi passer par github si on peut le faire directement depuis son notebook? \n",
    "#Quel serait l'avantage de choisir l'option 2 avec Github?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01877368",
   "metadata": {
    "hidden": true
   },
   "source": [
    "#### Depuis JupyterHub : tous les packages utiles pour ce notebook sont préinstallés"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c76359c3",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Cédric : donc pas besoin de -r requirements?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd8fcec6",
   "metadata": {
    "hidden": true
   },
   "source": [
    "#### Depuis Colab ou depuis votre ordinateur local "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53b148be",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Pour l'installer, executez la cellule de code suivante :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff70575f",
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "!pip install -r requirements.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0093ecd",
   "metadata": {
    "hidden": true
   },
   "source": [
    "**Cette commande installe les packages python nécessaires pour que le script fonctionne correctement.**\n",
    "Ces packages sont des sous-ensembles spécialisés du langage de programmation python qui ne sont pas intégrés à la version de base. On en a besoin pour faire tourner ce script, écrit lui aussi en python.\n",
    "\n",
    "Il suffit de cliquer sur la cellule et d'appuyer sur SHITF/ENTER (COMMAND/ENTER sur Mac), ou bien le bouton \"Run\" sur le menu de la page. La céllule affichera alors les messages du terminal, qui donnent des informations sur le processus qui vient d'avoir lieu. Vous pouvez augmenter la taille de la cellule du message pour voir son contenu ou la diminuer pour qu'elle ne vous dérange pas. Il suffit de clicker ou double-clicker sur l'espace en blanc à gauche de celle-ci:\n",
    "<img src=\"extractionCatalogs/static/images/cellule_diminuer.png\" width=\"500\"/>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e770c2a6",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### Github (optionnel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47537f3d",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "### BJP : pas très clair pour moi. \n",
    "#\"ou bien\" est l'alternative de quelle première option?\n",
    "#Mettre en premier l'option notebook; puis proposer l'option git (mais si on l'a fait par le notebook, pourquoi proposer par le git?)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cafcf510",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Ou bien, pour télécharger le programme extractionCatalogs sur Github (https://github.com/IMAGO-Catalogues-Jjanes/extractionCatalogs), éxecutez les commandes suivantes sur un terminal :\n",
    "- cloner le répositoire : ```git clone https://github.com/Juliettejns/extractionCatalogs```\n",
    "- créer un environnement virtuel : ```virtualenv -p python3 env```\n",
    "- activer l'environnement virtuel : ```source env/bin/activate```\n",
    "- installer les packages nécessaires : ```pip install -r requirements.txt```\n",
    "\n",
    "Vous pouvez bien entendu faire tout cela depuis ce notebook en exécutant la cellule suivante (il aura fallu rajouter \"!\" au début de chaque commande, pour qu'elles soient exécutées dans le terminal) : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28805407",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "!git clone https://github.com/Juliettejns/extractionCatalogs\n",
    "!virtualenv -p python3 env\n",
    "!pip install -r requirements.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "938a5435",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#BJP : il me semble qu'on a déjà installé requirements.txt auparavant, non ? "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5f215f4",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Reconnaissance de caractères avec eScriptorium"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "692bcc53",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Une fois les installations terminées, nous pouvons commencer à travailler ! \n",
    "\n",
    "**La première étape est l'utilisation d'eScriptorium, un logiciel de reconnaissance de caractères (OCR) qui nous permettra de transcrire automatiquement les pages numérisées de votre catalogue, moyennant quelques interventions manuelles.**\n",
    "\n",
    "\n",
    "En entrée (*input*), nous mettrons des images numériques de catalogues du XIXe et XXe siècle (fichiers .jpg, .png, etc., ou bien un \"manifeste iiif\").\n",
    "En sortie (*output*), eScriptorium nous fournira un fichier XML-ALTO4 pour chaque image traitée ; ces fichiers *output* seront utilisés lors de la deuxième étape (script extractionCatalogs).\n",
    "\n",
    "Dans ce guide, nous allons traiter le **catalogue d'une exposition d'art à Nancy en 1843** : \n",
    "https://gallica.bnf.fr/ark:/12148/bpt6k62251805/f1.item \\\n",
    "Ce petit catalogue de 24 pages comporte seulement 13 pages avec des entrées de catalogue à proprement parler. Il nous permettra d'exposer le fonctionnement de la pipeline du début à la fin. \n",
    "\n",
    "\n",
    "**Vous pouvez suivre le guide en traitant le catalogue de votre choix.**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bf3c673",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### OCR avec eScriptorium et Kraken"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "541b09a0",
   "metadata": {
    "hidden": true
   },
   "source": [
    "L'**OCR (Optical Character Recognition)** et l'**HTR (Handrwritten Text Recognition)** sont des technologies qui permettent qu'un ordinateur reconnaisse du texte contenu dans des images. Ces outils sont très performants quand il s'agit de fontes contemporaines, mais lorsque l'on veut traiter des charactères imprimés antérieurs au XXe siècle, les choses deviennent vite compliquées...\n",
    "\n",
    "Il faut alors entrainer des \"**modèles**\" de reconnaissance adaptés à des cas différents. Pour nos catalogues du XIXe et XXe siècles, nous possédons heureusement des modèles très efficaces, bien qu'il faille vérifier et corriger manuellement les résultats.\n",
    "\n",
    "Il sera également nécessaire de \"**segmenter**\" les images, c'est à dire d'indiquer au logiciel quelles sont les zones sémantiques de la page : le catalogue est en effet structuré, formaté en entrées, titres, numérotations de pages, etc. qu'il est possible d'indiquer à la machine pour qu'elle sache identifier plus vite à quel sens correspond quelle partie de la page (ex.: une entrée est une entrée !). Les modèles de ségmentation automatique étant à ce jours balbutiants, il nous faudra faire le travail manuellement. Notons que notre travail est dès-lors une contribution importante: nos segmentations seront utilisées dans le cadre d'autres projets pour entrainer et améliorer des modèles de segmentation automatique !\n",
    "\n",
    "\n",
    "**eScriptorium** est une **interface qui permet de visualiser toute la chaîne de traitement OCR de nos images**. Ce traitement est en réalité effectué par **Kraken (https://kraken.re/4.1/index.html), un logicel qui permet d'entraîner et d'appliquer des modèles de reconnaissance de caractères, ainsi que des modèles de segmentation.** Kraken est un outil \"open source\" (gratuit et librement réutilisable), qui ne peut être exécuté que dans un terminal. eScriptorium a été développé pour lui fournir une interface \"user friendly\" beaucoup plus accessible, et permet en plus de produire/corriger manuellement des transcriptions.\n",
    "\n",
    "On aura à faire à trois étapes sur eScriptorium : la mise en place du projet (gestion des paramètres préliminaires, des images et des noms des zones), la transcription automatique des images, et la segmentation manuelles des images.  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e08a257d",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### Mettre en place un projet sur eScriptorium"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9401e60",
   "metadata": {
    "hidden": true
   },
   "source": [
    "**Vous possédez un compte eScriptorium (la version de l'université de Genève s'appelle FoNDUE-HTR).**\n",
    "\n",
    "Accès à l'interface : https://test2.fondue.unige.ch/\n",
    "\n",
    "Une fois connecté.e, vous pourrez créér un projet eScriptorium, dans lequel vous pourrez ajouter un document (un catalogue dans notre cas).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2e402de",
   "metadata": {
    "hidden": true
   },
   "source": [
    "#### Créer un projet"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a4840c3",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Allez sur l'onglet **\"my projects\"**, puis cliquez sur **\"Create New Project\"** :\n",
    "\n",
    "<img src=\"extractionCatalogs/static/images/projet_eScriptorium.png\" width=\"\"/>\n",
    "\n",
    "\n",
    "\\\n",
    "Choisissez un nom pour votre catalogue, puis cliquez sur **\"Create\"** :\n",
    "\n",
    "<img src=\"extractionCatalogs/static/images/create_eScriptorium.png\" width=\"\"/>\n",
    "\n",
    "\n",
    "\\\n",
    "Cliquez sur votre projet pour accéder à la page du projet :\n",
    "\n",
    "<img src=\"extractionCatalogs/static/images/document_eScriptorium.png\" width=\"\"/>\n",
    "\n",
    "\n",
    "\\\n",
    "Cliquez sur **\"Create new Document\"** :\n",
    "\n",
    "<img src=\"extractionCatalogs/static/images/new_document_eScriptorium.png\" width=\"\"/>\n",
    "\n",
    "\n",
    "\\\n",
    "[1] Choisissez un nom pour votre document et [2] cliquez sur **\"Create\"**\n",
    "\n",
    "<img src=\"extractionCatalogs/static/images/create_doc_eScriptorium.png\" width=\"\"/>\n",
    "\n",
    "\\\n",
    "Nous avons à présent créé un projet et un document dans ce projet. Nous devons maintenant établir les noms des zones (cela nous permettra ensuite de faire la segmentation) puis ajouter des images. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99a55457",
   "metadata": {
    "hidden": true
   },
   "source": [
    "#### L'Ontologie Segmonto"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1551421",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Pour signaler les zones qui existent dans chaque page de notre catalogue, nous allons utiliser l'\"ontologie\" Segmonto (https://github.com/SegmOnto).\n",
    "\n",
    "##### Un standard pour nommer les zones de la page\n",
    "\n",
    "**Segmonto est tout simplement un standard de nommage**, un vocabulaire contrôlé pour décrire la page manuscrite et imprimée. Utiliser ce vocabulaire a deux avantages : premièrement, des chercheurs ont préalablement réfléchi et discuté pour trouver une manière cohérente de nommer les zones d'une page, et nous n'avons pas à le faire; deuxièmement, ce standard se généralise, et cela renforce la compatibilité de notre projet avec d'autres. \n",
    "\n",
    "\n",
    "Il existe de nombreuses zones de page décrites par l'ontologie Segmonto. Cependant nous n'aurons besoin pour notre projet que des suivantes :\n",
    " \n",
    "\n",
    "- **MainZone** : refère à la totalité de l'espace sémantique de la page. Dans nos catalogues, cette zone doit contenir la totalité des entrées et du texte, et exclure des éléments qui ne font pas partie du contenu sémantique de la page,  tels que le numéro de page.\n",
    "- **CustomZone:entry** : \"CustomZone\" est un terme qui indique que ce type de zone a été créé par l'utilisateur. Nous avons en effet créé un type de zone correpondant à des entrées de catalogue. Une entrée est composée d'un exposant et des oeuvres qu'il expose, avec toutes les informations complémentaires concernées.\n",
    "- **CustomZone:entryEnd** : Ce type d'entrée, plus rare, réfère aux cas où la page commence avec la fin d'une entrée dont le début se situe à la fin de la page antérieure. Puisque le début de l'entrée commence à la page antérieure, ce type de zone permet que la machine comprenne qu'il faut les unir, et qu'il ne s'agit pas d'une entrée dans laquelle aucun exposant n'aurait été signalé. \n",
    "- **NumberingZone** : Ce type de zone encercle les numérotations des pages. Il est très important de s'assurer qu'elle ne soit pas englobée par la \"MainZone\", afin que le script sache qu'il faut mettre de côté cette information qui ne nous intéresse pas dans le cadre de l'extraction.\n",
    "- **GraphicZone:illustration** : Toute image accompagnant le texte. Dans notre cas, il y aura parfois des réproductions d'oeuvres d'art insérées dans les pages.\n",
    "- **GraphicZone:ornamentation** : Des ornements courants.\n",
    "\n",
    "\n",
    "Voici un exemple, extrait d'une page de notre catalogue :\n",
    "\n",
    "<img src=\"extractionCatalogs/static/images/exemples_zones.png\" width=\"500\"/>\n",
    "\n",
    "Ce qu'il faut retenir :\n",
    "- Les Zones peuvent être parfois serrées, et il est important de veiller à ce qu'elles soient correctement imbriquées ou différenciées : la zone NumberingZone est extrérieure à la MainZone (espace sémantique), qui, elle, englobe toutes les CustomZone:entry et une CustomZone:entryEnd.\n",
    "- Nous avons une CustomZone:entryEnd : c'est à dire une entrée coupée qui commence à la page antérieure (cela est évident dès lors qu'on ne voit pas de personne indiquée). Il est important de signaler ce type de zones pour que le script sache qu'il faut l'unir à la dernière entrée de la page antérieure.\n",
    "\n",
    "##### Saisie manuelle des noms des zones (une par une)\n",
    "\n",
    "**Pour intégrer l'ontologie Segmonto à votre projet eScriptorium, il faut [1] aller sur l'onglet \"Ontology\" et saisir manuellement chaque zone de la manière suivante :**\n",
    "- [2] copiez/collez chacune des zones sur la partie \"Region types\" et cliquez sur le bouton vert \"+\" (ajouter)\n",
    "- voici les régions : MainZone ; CustomZone:entry ; CustomZone:entryEnd ; NumberingZone ; GraphicZone:illustration ; GraphicZone:ornamentation\n",
    "Attention : il faut créer chaque zone une par une. \n",
    "- [3] une fois toutes les régions saisies comme sur l'image, n'oubliez pas de sauvegarder avec le bouton \"Update\" !\n",
    "\n",
    "\n",
    "<img src=\"extractionCatalogs/static/images/ontologie_eScriptorium.png\" width=\"1000\"/>\n",
    "\n",
    "\n",
    "Maintenant, lors de l'étape postérieure de segmentation, nous pourrons nommer les zones de la page conformément à l'ontologie Segmonto. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16339fd3",
   "metadata": {
    "hidden": true
   },
   "source": [
    "#### Quelles images utiliser ? \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db1b625b",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Pour ajouter vos images à votre document, \n",
    "\n",
    "[1] **Cliquez sur l'onglet \"Images\" et choisissez une option qui convienne au format de vos images** :\n",
    "[2] **Choisissez le format de votre document\" : \n",
    "\n",
    "\n",
    "\n",
    "<img src=\"extractionCatalogs/static/images/ajouter_images_eScriptorium.png\" width=\"\"/>\n",
    "\n",
    "E-Scriptorium vous indique, au milieu de la page, par une petite icône jaune, que le fichier est en cours d'importation (\"importing\"). Pour voir apparaître vos pages, il faudra peut-être rafraîchir votre navigateur (mais pas trop tôt : vous risquez d'arrêter le chargement des pages). Lorsque l'importation complète est faite, une bulle en haut à droite indique \"import done\". "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa96acc9",
   "metadata": {
    "hidden": true
   },
   "source": [
    "##### Cas de catalogues au standard iiif"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7275297a",
   "metadata": {
    "hidden": true
   },
   "source": [
    "\n",
    "Cliquez sur \"Import\" puis sur \"IMAGES (IIIF)\", puis collez un lien vers les manifeste iiif du catalogue. Cliquez sur \"Start importing\" : eScriptorium s'occupera d'importer chaque image.\n",
    "\n",
    "Pour voir apparaître vos pages, il faudra peut-être rafraîchir votre navigateur (mais pas trop tôt : vous risquez d'arrêter le chargement des pages). \n",
    "Pour voir apparaître vos pages, il faudra peut-être rafraîchir votre navigateur (mais pas trop tôt : vous risquez d'arrêter le chargement des pages). "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98a3bcf0",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Ayant été numérisé par la Bibliothèque nationale de France, le catalogue pris comme exemple pour ce tutoriel est conforme au **standard iiif** (https://iiif.io/), un **outil de partage et de gestion des images** privilégié dans le domaine des humanités numériques. IIIF permet d'exploiter et de faire circuler efficacement des images numériques de haute qualité entre institutions patrimoniales, universités et chercheurs.\n",
    "\n",
    "Quand on utilise des images iiif, le script extractionCatalogs est capable de les découper et de créér des lien vers des images de chaque entrée du catalogue !\n",
    "\n",
    "**Mais si votre catalogue n'est pas conforme au standard iiif, ceci ne pose aucun problème: des fichiers .jpeg, .png, .pdf, etc. suffisent à eScriptorium et au programme extractionCatalogs pour fonctionner.**\n",
    "\n",
    "Il est tout de même conseillé de chercher dans le site source du catalogue l'icône ci-dessous :\n",
    "\n",
    "<img src=\"extractionCatalogs/static/images/iiif.png\" width=\"100\"/>\n",
    "\n",
    "Si cette icône est présente, il doit être possible de retrouver facilement un **lien vers le \"manifeste iiif\" de votre document**. Celui-ci est un fichier indiquant des liens vers les images du catalogue ainsi que diverses métadonnées. Voici comment le retrouver dans le cas du catalogue de Nancy 1843 : \n",
    "<img src=\"extractionCatalogs/static/images/lien_iiif.png\" width=\"500\"/>\n",
    "\n",
    "En cliquant dessus, nous accédons à un \"viseur iiif\" ; pour récupèrer le manifeste iiif, il faut [1] cliquer sur licône d'information, puis [2] scroller jusqu'à la fin :\n",
    "\n",
    "<img src=\"extractionCatalogs/static/images/lien_manifest.png\" width=\"500\"/>\n",
    "\n",
    "https://gallica.bnf.fr/iiif/ark:/12148/bpt6k62251805/manifest.json \\\n",
    "Si vous cliquez dessus, une page affichera le manifeste, qui est un document écrit en langage json. Les liens vers des manifestes iiif sont faciles à reconnaître car ils se terminent toujours par \"manifest.json\".\n",
    "\n",
    "Il suffit de copier/coller ce lien sur eScriptorium au moment de saisir les images. Et comme indiqué, si ce lien n'existe pas, il faudra télécharger des fichiers image classiques (jpeg, png, pdf).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc986c88",
   "metadata": {
    "hidden": true
   },
   "source": [
    "##### Cas de fichiers images classiques (.jpeg, .png, .pdf). Ajouter des images (iiif, jpeg, png, pdf, etc.) dans eScriptorium"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b5fc173",
   "metadata": {
    "hidden": true
   },
   "source": [
    "**Déposez des fichiers images une par une (jpg, png, etc.)**\n",
    "Si vous avez un document pdf : cliquez sur \"Import\" puis sur \"IMAGES (PDF)\", puis ajoutez le document pdf. \n",
    "\n",
    "Cliquez sur `Start importing` :  eScriptorium s'occupera de le découper en images isolées.\n",
    "\n",
    "Pour voir apparaître vos pages, il faudra peut-être rafraîchir votre navigateur (mais pas trop tôt : vous risquez d'arrêter le chargement des pages). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a05ad13",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "## L'ordre dans lequel on charge les images est impoortant je suppose; Ne faut-il pas le préciser?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "961c4446",
   "metadata": {
    "hidden": true
   },
   "source": [
    "\n",
    "Pour le Catalogue de Nancy 1843, nous obtenons une frise avec 24 images :\n",
    "\n",
    "\n",
    "<img src=\"extractionCatalogs/static/images/frize_eScriptorium.png\" width=\"\"/>\n",
    "\n",
    "\n",
    "\\\n",
    "Lors de l'utilisation du script extractionCatalogs, **seules les pages contenant des entrées vont être traitées**. Vous êtes libre de conserver ou d'éliminer les images sans entrées avec la petite croix en haut à gauche de chaque image. Dans notre cas, nous choisissons de garder deux pages sans entrées, afin de montrer explicitement ce que le script fera après avec l'une d'entre elles. Notre frise comporte desormais 15 images, et les deux premières n'ont aucune entrée. \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff2bde7f",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### Transcription automatique des images"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91f19200",
   "metadata": {
    "hidden": true
   },
   "source": [
    "#### Manipulations possibles\n",
    "\n",
    "Dans la frise d'images, vous trouverez pour chaque image une **icône avec des boutons**.\\\n",
    "Voici les manipulations fondamentales qu'ils permettent de faire :\n",
    "- [1] case pour sélectionner des **ensembles d'images à traiter**. Toutes les images dont cette case est cochée subiront les mêmes traitements simultanément. **Nous ne traitons que les pages qui contiennent des listes d'exposants et d'oeuvres**. Ni les listes de jurys, ni les comités, ni les titres, ni les publicités, ni les articles et divers textes commentant ou introduisant l'exposition et ses sections. **Pour aller plus vite, il est possible de cocher plusieurs images successives en cliquant sur la touche \"MAJ\" en même temps qu'on sélectionne avec la souris.**\n",
    "- [2] **editeur d'image** : renvoie vers la page d'édition, qui permet entre autres de saisir des transcriptions/segmentations manuelles, ou bien de corriger des transcriptions/segmentations automatiques. \n",
    "- [3] **binariser** : transforme l'image en tons de gris, ce qui facilite la reconnaissance de caratères.\n",
    "- [4] **segmenter** : permet de segmenter, c'est à dire de reconnaître des régions, mais aussi de **localiser les lignes dans la page**. Dans notre cas, *nous ferons usage uniquement de la reconnaissance automatique des lignes*.\n",
    "- [5] **transcrire** : permet d'**appliquer un modèle de reconnaissance de caractères** au choix.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "<img src=\"extractionCatalogs/static/images/icone_image_eScriptorium.png\" width=\"300\"/>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dac6786b",
   "metadata": {
    "hidden": true
   },
   "source": [
    "#### Manipulations à faire\n",
    "\n",
    "##### Choisir les images à traiter. \n",
    "\n",
    "**Cochez sur toutes les images que vous voulez transcrire automatiquement**. Pour aller plus vite, vous pouvez maintenir les touches COMMAND/SHIFT appuyées et selectionner la première et la dernière image, toutes les images entre les deux seront cochées automatiquement.\\\n",
    "Dans notre cas, nous cocherons toutes sauf la première (page de titre), ce qui nous laisse 14 images, dont une (numéro 2) sans entrées. Maintenant, si on appuie sur n'importe quel bouton de n'importe quel icône, l'**ENSEMBLE DES IMAGES COCHÉES SERONT TRAITÉES**.\n",
    "\n",
    "<img src=\"extractionCatalogs/static/images/cocher_eScriptorium.png\" width=\"\"/>\n",
    "\n",
    "\n",
    "##### Reconnaître les lignes \n",
    "\n",
    "[1] Cliquez sur n'importe quel bouton \"segmenter\" d'une des images cochées, ou bien sur le bouton général \"Segment\". \n",
    "Puis \n",
    "\n",
    "[2] **sélectionnez \"Lines Baselines and Masks\"** (ET SURTOUT PAS L'OPTION PAR DÉFAUT \"Lines and regions\", puisque la ségmentation des régions sera faite manuellement). \n",
    "\n",
    "[3] Cliquez sur \"Segment\". \n",
    "\n",
    "<img src=\"extractionCatalogs/static/images/lines_eScriptorium.png\" width=\"400\"/>\n",
    "\n",
    "\n",
    "Une fois une image segmentée, son bouton \"segmenter\" (petites lignes horizontales) devient vert. En haut à droite de la page, un bouton \"segmentation done!\" vous indique le nombre de pages dont les lignes ont été reconnues. Attendez patiemment que le nb de pages soit celui du nb de pages que vous avez sélectionnées avant de passer à l'étape suivante. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36756582",
   "metadata": {
    "hidden": true
   },
   "source": [
    "##### Transcrire automatiquement le texte contenu dans les images\n",
    "\n",
    "\n",
    "[1] Cliquez sur n'importe quel bouton \"transcrire\" d'une des images cochées, ou bien sur le bouton général \"Transcribe\". Puis [2] **selectionnez le modèle \"19th Century prints - HTRcatalogs Artlas\"**, ou celui de votre choix. [3] Cliquez sur \"Transcribe\". \n",
    "\n",
    "<img src=\"extractionCatalogs/static/images/transcribe_eScriptorium.png\" width=\"400\"/>\n",
    "\n",
    "\n",
    "Une fois une image transcrite, son bouton \"transcrire\" indique la complétion à \"100%\" : le rectangle arrondi indiquant l'état de la transcription doit devenir bleu (ou pas... bref, il finira par devenir bleu).  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43d21056",
   "metadata": {
    "hidden": true
   },
   "source": [
    "#### Correction de la transcription\n",
    "\n",
    "* **Vous pouvez maintenant cliquer sur le bouton \"éditer\" des icones ; il vous mènera vers la page d'édition de chaque image, où vous pourrez corriger manuellement la transcription automatique et segmenter manuellement les régions.**\n",
    "\n",
    "Prenez le temps d'explorer cette page et les options d'édition qu'elle offre.\n",
    "\n",
    "\n",
    "Premièrement, assurez vous de [1] choisir la transcription qui correspond à notre modèle de reconnaissance de caractères. Sur le viseur de la transcription, vous trouverez à gauche [2] l'image courante, avec la segmentation des régions et des lignes (si ces segmentations existent), et à droite [3] la transcription du texte contenu dans l'image (si elle existe). \n",
    "\n",
    "\n",
    "<img src=\"extractionCatalogs/static/images/editer_eScriptorium.png\" width=\"400\"/>\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "Pour corriger la transcription, [1] cliquez sur la ligne que vous souhaitez corriger dans la section \"transcription\" (à droite), puis [2] écrivez le texte souhaité sur l'éditeur qui apparait. Dans notre cas, le texte \"SOCIÉTÉ LORRAINE\" a été très mal reconnu. Cela est normal, puisqu'il ne s'agit pas de caractères typiques :\n",
    "\n",
    "\n",
    "\n",
    "<img src=\"extractionCatalogs/static/images/corriger_eScriptorium.png\" width=\"400\"/>\n",
    "\n",
    "\n",
    "**Il suffit de substituer votre correction au texte affiché. Cliquez sur ENTER ou sur le bouton de flèche vers le bas : vous irez vers la ligne suivante.**\n",
    "\n",
    "\n",
    "**VOUS DEVEZ VÉRIFIER CHAQUE LIGNE DE CHAQUE PAGE, ET FAIRE LES CORRECTION NÉCESSAIRES. SANS CELA, VOTRE TRANSCRIPTION SERA INUTILISABLE, ET VOTRE PROPRE NOM SERA ASSOCIÉ à UN TRAVAIL INSUFFISANT.** Ce qui serait dommage, car la relecture/correction prend peu de temps !  \n",
    "\n",
    "\n",
    "**IL EST TRÈS IMPORTANT QUE LES PONCTUATIONS SOIENT CORRECTEMENT TRANSCRITES, EN PARTICULIER LES CARACTÈRES QUI SIGNALENT DES OEUVRES** (\"1.  Titre de l'oeuvre\"). Vérifiez les numéros, mais aussi les points et les tirets qui délimitent le numéro et le titre de l'oeuvre, les virgules qui séparent les numéros des adresses, etc. ; ces éléments sont importants pour que le script marche correctement.\n",
    "\n",
    "\n",
    "Vous n'avez pas besoin de sauvegarder vos corrections: cela ce fait automatiquement au fur et à mesure des modifications."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db697334",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### Segmentation manuelle des images"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7073839",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Une fois la correction de la transcription automatique terminée, nous pouvons attaquer la segmentation manuelle des images. \n",
    "\n",
    "Cela se passe dans la section de gauche, qui contient l'image de la page. Cette section sert à signaler sur la page des régions et des lignes détectées automatiquement ou saisies manuellement. \n",
    "\n",
    "\n",
    "**Par défaut, quand vous cliquez sur l'image, vous dessinerez des lignes. Vous pouvez (et parfois devez !) modifier des lignes qui sont mal saisies** : elles peuvent parfois [1] ne pas couvrir tout le texte, [2] déborder des régions, ou [3] couvrir des espaces où il n'y a pas de texte. Vous pouvez les manipuler en cliquant dessus, ou les élíminer en cliquant dessus puis sur le bouton rouge \"poubelle\" à gauche de l'image.\n",
    "\n",
    "<img src=\"extractionCatalogs/static/images/lignes_eScriptorium.png\" width=\"400\"/>\n",
    "\n",
    "\n",
    "Pour **dessiner des régions**, vous devez **[1] cliquer sur le bouton \"Switch to region mode\", puis [2] saisir la région sur l'image avec votre souris. Il faut aussi indiquer le nom de la zone ! [3] Cliquez sur le bouton \"T\", puis [4] sélectionnez la zone concernée et appuyez sur ENTER.**\\\n",
    "Vous l'aurez compris, c'est ici que nous faisons usage de l'ontologie saisie précédemment.\n",
    "\n",
    "\n",
    "<img src=\"extractionCatalogs/static/images/regions_eScriptorium.png\" width=\"400\"/>\n",
    "\n",
    "\n",
    "Une fois les eventuelles `CustomZone:entry`, `CustomZone:entryEnd` et `NumberingZone` saisies (ou tout autre type de zone), n'oubliez pas de saisir la `Mainzone`, qui couvre tout l'espace sémantique de la page (donc pas le numéro de page). Tout texte qui ne constitue pas une entrée fait partie de la `MainZone`. \n",
    "\n",
    "<img src=\"extractionCatalogs/static/images/MainZone_eScriptorium.png\" width=\"400\"/>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11a4ac81",
   "metadata": {
    "hidden": true
   },
   "source": [
    "\n",
    "\n",
    "**VOUS DEVEZ SEGMENTER MANUELLEMENT TOUTES LES PAGES DE VOTRE CATALOGUE** Un conseil : sélectionnez d'abord la zone de numérotation (et étiquetez-la), puis toute les zones d'entrées, qu'il suffit de sélectionner toutes avec le curseur pour toutes les associer à l'étiquette \"Entry\". Ensuite occupez-vous de la Mainzone. \n",
    "\n",
    "**VEILLEZ À CE QU'IL N'Y AIT PAS DE CONFLITS D'IMBRICATION (des lignes qui dépassent leurs régions, des zones `CustomZone:Entry` qui dépassent la `MainZone`, etc.)**\n",
    "\n",
    "Dans le cadre de ce guide, nous allons segmenter une page sans entrées et cinq pages avec des entrées (pages 3-7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c433ed1",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "### qUESTION /  La MAinzone doit-elle comprendre la zone de numérotation, ou pas? "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a05f909d",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### output ALTO"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "237757db",
   "metadata": {
    "hidden": true
   },
   "source": [
    "#### Export des fichiers ALTO "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c9da394",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Nous avons maintenant nos pages transcrites et segmentées ! Il ne nous reste qu'à les télécharger pour les passer sur le programme `extractionCatalogs`.\n",
    "\n",
    "\n",
    "[1] Cliquez sur l'onglet \"Images\" pour **retourner sur la frise des images**, puis \n",
    "\n",
    "[2] **cochez les images à télécharger** et \n",
    "\n",
    "[3] cliquez sur **\"Export\"** :\n",
    "\n",
    "<img src=\"extractionCatalogs/static/images/export_eScriptorium.png\" width=\"\"/>\n",
    "\n",
    "\\\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e977357",
   "metadata": {
    "hidden": true
   },
   "source": [
    "**Assurez-vous que [1] le modèle de reconnaissance de caractères de l'export correspond à celui que vous avez utilisé, et que [2] les documents en output soient des fichiers ALTO. [3] cochez sur \"include images\" et [4] vérifiez que toutes les régions de l'ontologie sont cochées. [5] Cliquez sur Export :**\n",
    "\n",
    "<img src=\"extractionCatalogs/static/images/export2_eScriptorium.png\" width=\"400\"/>\n",
    "\n",
    "\n",
    "Un avertissement vert apparaît en haut à droite de la page si l'export est réussi. Cliquez sur **\"Download\"** :\n",
    "\n",
    "<img src=\"extractionCatalogs/static/images/download_eScriptorium.png\" width=\"400\"/>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4f23fd4",
   "metadata": {
    "hidden": true
   },
   "source": [
    "**Vous avez téléchargé un fichier ZIP qui contient les images de votre catalogue, et autant de fichiers ALTO4. \n",
    "\n",
    "Dézipez le fichier. Les documents qu'il contient sont des fichiers XML, dans lesquels vous retrouverez si vous les ouvrez les transcriptions et les segmentations de votre page, dans un langage balisé que le programme `extractionCatalogs` peut traiter.**\n",
    "\n",
    "\n",
    "OPTIONNEL : Vous pouvez renommer ces fichiers pour qu'ils soient plus compréhensibles à l'oeil humain, mais cela n'est pas obligatoire et implique de manipuler les fichiers ALTO (.xml). Veillez seulement à ce que les fichiers gardent un ordre numérique. Dans notre cas, on va renommer les fichiers comme suit : `Cat_Nancy_1843_typo_0000.jpg` - `Cat_Nancy_1843_typo_0005.jpg` / `Cat_Nancy_1843_typo_0000.xml` - `Cat_Nancy_1843_typo_0005.xml`. Si vous faites ce renommage, il faut ensuite aller sur chaque fichier ALTO (.xml) et actualiser le nom de l'image (.jpg) à laquelle ce fichier fait référence dans la balise \\</filename> :  \n",
    "\n",
    "<img src=\"extractionCatalogs/static/images/filename.png\" width=\"600\"/>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6157152b",
   "metadata": {
    "hidden": true
   },
   "source": [
    "#### Précisions sur ALTO"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0da2dc1",
   "metadata": {
    "hidden": true
   },
   "source": [
    "**ALTO (Analysed Layout and Text Object)** est un standard XML permettant de rendre compte de la mise en page physique et de la structure logique d'un texte transcrit par reconnaissance optique de caractères (OCR).\\\n",
    "Si vous saisissez correctement les régions sur eScriptorium, le script `extractionCatalogs` pourra utiliser les informations contenues dans ce fichier pour extraire et reconstruire les données qui nous intéressent entrée par entrée.\n",
    "\n",
    "Voici la structure d'un fichier ALTO :"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42449980",
   "metadata": {
    "hidden": true
   },
   "source": [
    "- **une balise racine \\</alto> contient des sous-ensembles \\</Description>, \\</Tags> et \\</Layout>**\n",
    "\n",
    "- **Description** : métadonnées sur le fichier source. Le nom de l'image est indiqué, mais aussi le lien vers l'image iiif quand le fichier est issu d'un manifeste iiif.\n",
    "<img src=\"extractionCatalogs/static/images/description_alto.png\" width=\"\"/>\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "- **Tags** : établit des ID pour signaler les noms des régions (des tags). C'est ici que nous pourrons retrouver l'ontologie Segmonto. Ces ID seront utilisées dans la balise \\<Layout> pour signaler à quoi correspond chaque bloc de texte.\n",
    "\n",
    "<img src=\"extractionCatalogs/static/images/tags_alto.png\" width=\"\"/>\n",
    "\n",
    "\n",
    "\n",
    "- **Layout** : Contient tout le texte transcrit. Nous retrouverons dedans des balises **\\</TextBlock> : chacune correspond à une des régions signalées manuellement sur eScriptorium**. Leur **Attribut \"TAGFREFS\"** fait réfèrence à l'ID qui permet de connaître le nom de la zone dans la balise \\</Tags>. La balise **\\</Shape> indique la position du TextBLock dans la page**. Un TextBlock doit contenir des balises **\\<TextLine>, qui elles mêmes contiennent des balises \\</Shape> et des balises \\</String>. C'est dans l'attribut \"CONTENT\" de cette dernière que se trouve le texte transcrit.** Dans l'exemple suivant, l'attribut \"TAGREFS=BT15\" fait référence au type de région \"`CustomZone:entry`\" :\n",
    "\n",
    "<img src=\"extractionCatalogs/static/images/textblock_alto.png\" width=\"\"/>\n",
    "\n",
    "\\\n",
    "Vous pouvez corriger directement ces fichiers ALTO (pour préciser le découpage et les imbrications des régions, le contenu des lignes, les noms des zones), mais cela risque d'être un peu difficile pour un.e néophyte. Normalement, toutes vos corrections peuvent se faire sur l'interface eSCriptorium, ou bien sur le tableau produit par le programme `extractionCatalogs`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bee6889",
   "metadata": {},
   "source": [
    "## Utiliser le script extractionCatalogs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68236f68",
   "metadata": {},
   "source": [
    "Une fois le travail terminé sur eScriptorium, nous obtenons un dossier contenant les images de nos catalogues et autant de fichiers XML ALTO4 contenant leurs transcriptions et segmentations. \n",
    "\n",
    "\n",
    "**Placez ce dossier où vous voulez ; vous devrez indiquer son chemin au script extractionCatalogs. Ce chemin peut être \"absolu\" (partir de la racine de l'ordinateur), ou bien \"relatif\" (au script) si vous le placez dans le dossier extractionCatalogs**. Pour le catalogue Nancy 1843, nous avons créé un dossier \"exemples_guide\" dans extractionCatalogs. \n",
    "\n",
    "**Le script `extractionCatalogs` va chercher et traiter les fichiers ALTO contenus dans ce dossier. Il va utiliser les informations de segmentation pour reconstituer les entrées, et leur appliquer des regex (expressions régulières) pour analyser chaque ligne et déterminer si elle correspond à un exposant, à une oeuvre ou à des informations complémentaires.**\n",
    "\n",
    "Ce guide ne détaille pas en profondeur le fonctionnement du script, qui est l'objet d'améliorations continues. **Vous pouvez ouvrir les fichier `.py` du dossier extractionCatalogs, qui contiennent tout le programme python. Le script est abondamment commenté pour vous aider à le comprendre.**\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24c6c86b",
   "metadata": {},
   "source": [
    "### Commandes du script"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2ad7549",
   "metadata": {},
   "source": [
    "Voici le modèle de commande pour lancer le script :\n",
    "**`!python3 run.py input output titre`**\n",
    "\n",
    "Si vous recevez une erreur `\"'python3' n'est pas reconnu en tant que commande interne ou externe, un programme ex‚cutable [sic] ou un fichier de commandes.\"\"`, enlevez juste le 3 et exécutez à nouveau la cellule. \n",
    "\n",
    "\n",
    "Pour traiter le catalogue Nancy 1843, nous devons construire la commande suivante :\\\n",
    "**`!python3 run.py exemples_guide/Cat_Nancy_1843_page/ exemples_guide/extractions/ Nancy_1843`**\n",
    "\n",
    "\n",
    "Voici ses composantes :\n",
    "- **\"`!`\"** indique à Jupyter Notebook que nous voulons lancer la commande sur le terminal. Si nous lançons la commande directement sur un terminal, il ne faut pas utiliser ce point d'exclamation\n",
    "- **\"`python3`\"** indique que le script doit être éxécuté en langage python 3\n",
    "- **\"`run.py`\"** est le fichier python que nous voulons exécuter. Tous les autres fichiers python se trouvent dans des sous-dossiers et sont appelés par celui-ci.\n",
    "\n",
    "- **\"`exemples_guide/Cat_Nancy_1843_page/`\"** est le chemin vers nos ALTO produits sur eScriptorium. Ce dossier ne contient qu'une page de catalogue pour que nos premiers exemples soient simples.\n",
    "- **\"`exemples_guide/extractions/`\"** est le chemin pour le dossier d'extaction que le script va produire. Le script crée ce dossier qu'il n'y a donc pas besoin de créer au préalable. \n",
    "- **\"`Nancy_1843`\"** est le nom que nous voulons donner au catalogue\n",
    "\n",
    "\n",
    "Vous pouvez à présent lancer la commande (COMMANT/ENTER, SHIF/ENTER ou bouton \"Run\") : \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d57d36ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "==================================================================\n",
      "1 – Traitement de BAA_E_1903_GENEVE_15_mai-15_juin_.pdf_page_7.xml\n",
      "\t1 – Vérification de la conformité à l'ontologie Segmonto\n",
      "\t   Bien! le fichier issu d'eScriptorium est conforme à l'ontologie Segmonto\n",
      "\t2 – Restructuration du fichier\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Traceback (most recent call last):\n",
      "  File \"C:\\Users\\joyeuxpr\\OneDrive - unige.ch\\Travail\\ARTLAS-ANR-Labex\\BASART_PIPELINE\\estebansanchezoeconomo_memoireTNAH\\script_extractionCatalogs\\run.py\", line 530, in <module>\n",
      "    extraction()\n",
      "  File \"C:\\Python39\\lib\\site-packages\\click\\core.py\", line 829, in __call__\n",
      "    return self.main(*args, **kwargs)\n",
      "  File \"C:\\Python39\\lib\\site-packages\\click\\core.py\", line 782, in main\n",
      "    rv = self.invoke(ctx)\n",
      "  File \"C:\\Python39\\lib\\site-packages\\click\\core.py\", line 1066, in invoke\n",
      "    return ctx.invoke(self.callback, **ctx.params)\n",
      "  File \"C:\\Python39\\lib\\site-packages\\click\\core.py\", line 610, in invoke\n",
      "    return callback(*args, **kwargs)\n",
      "  File \"C:\\Users\\joyeuxpr\\OneDrive - unige.ch\\Travail\\ARTLAS-ANR-Labex\\BASART_PIPELINE\\estebansanchezoeconomo_memoireTNAH\\script_extractionCatalogs\\run.py\", line 221, in extraction\n",
      "    chemin_restructuration = restructuration_automatique(directory, fichier, extraction_directory)\n",
      "  File \"C:\\Users\\joyeuxpr\\OneDrive - unige.ch\\Travail\\ARTLAS-ANR-Labex\\BASART_PIPELINE\\estebansanchezoeconomo_memoireTNAH\\script_extractionCatalogs\\extractionCatalogs\\fonctions\\restructuration.py\", line 27, in restructuration_automatique\n",
      "    with open(chemin_restructuration, mode='wb') as f:\n",
      "FileNotFoundError: [Errno 2] No such file or directory: 'Geneve_1903_Municipale_output/extraction_Geneve_1903_Municipale/restructuration ALTO/BAA_E_1903_GENEVE_15_mai-15_juin_.pdf_page_7_restructuration.xml'\n"
     ]
    }
   ],
   "source": [
    "#!python3 run.py exemples_guide/Cat_Nancy_1843_page/ exemples_guide/extractions/ Nancy_1843_page\n",
    "!python run.py Geneve_1903_municipale_alto Geneve_1903_Municipale_output Geneve_1903_Municipale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "372a2c40",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "1b2bb9db",
   "metadata": {},
   "source": [
    "Ci-dessus, nous avons les **informations de terminal : pour chaque page traitée, nous aurons un récapitulatif des étapes et une visualisation de l'extraction. Une fois toutes les pages traitées, nous aurons un résumé de ce qui s'est passé pour l'ensemble des pages**. Lisez ces informations... vous comprendrez probablement une très grande partie de ce qui s'est passé ! \n",
    "\n",
    "\n",
    "Allez voir le dossier d'extraction produit sur votre ordinateur : vous verrez qu'un dossier \"extraction_Nancy_1843\" a été produit, qui contient :\n",
    "- un fichier XML (la totalité des entrées extraites)\n",
    "- un dossier CSV avec deux tableurs (un \"simple\" pour l'oeil humain, l'autre plus complexe pour la base de données Artl@s)\n",
    "- un dossier avec des fichiers ALTO \"restructurés\" (les eventuelles erreurs de saisie sur eScriptorium ont été corrigées par le script) \n",
    "- eventuellement, un fichier .txt indiquant des problèmes d'extraction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8925b3fd",
   "metadata": {},
   "source": [
    "### fonctionnement du script"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09410459",
   "metadata": {},
   "source": [
    "Ce guide ne détaille pas en profondeur le fonctionnement du script, qui est l'objet d'améliorations continues. Vous pouvez ouvrir les fichier `.py` du dossier `extractionCatalogs`, qui contiennent tout le programme python. Le script est abondamment commenté pour vous aider à le comprendre. \n",
    "\n",
    "\n",
    "En tout cas, pour utiliser le script, il suffit de savoir comment l'exécuter (commandes expliquées dans la section antérieure) et de comprendre les messages/fichiers en sortie ; il n'est pas nécessaire de comprendre sa mécanique interne. \n",
    "\n",
    "\n",
    "**Cette section décrit sommairement le fonctionnament du script à travers des étapes fondamentales exécutées par le fichier run.py**\n",
    "\n",
    "Le dossier `extractionCatalogs` contient :\n",
    "- **`run.py`** : fichier python principal qui exécute tous les fichiers `.py` secondaires, contenus dans les sous-dossiers `extractionCatalogs/extractionCatalogs/fonctions` et `extractionCatalogs/extractionCatalogs/variables`\n",
    "- **`README.md`** : fichier expliquant comment installer le script (ce sont les mêmes instructions que dans la partie 0.Introduction de ce guide)\n",
    "- **`requirements.txt`** : fichier `.txt` indiquant des paquets python externes dont le script a besoin pour fonctionner (vous les avez déjà installé avec la commante `!pip install -r requirements.txt`)\n",
    "- **dossiers \"exemples...\"** : dossiers contenant des catalogues transcrits sur eScriptorium (images et fichiers alto) pour servir d'exemples.\n",
    "- **dossier \"env\"** : uniquement dans le cas où vous aurez installé le script en-dehors de Jupyterhub ou Colab (en le téléchargeant sur Github pour le mettre sur votre ordinateur personnel par exemple). Environnement virtuel python contenant les paquets nécessaires pour le script\n",
    "\n",
    "\n",
    "**`run.py`** est le fichier exécuté par la commande `!python3 run.py`, suivie des indications secondaires (input, output, nom du catalogue). Ce fichier est la colonne vertebrale du script, puisqu'il contient tout le processus du début à la fin. Il \"appelle\" les autres fichiers `.py`, qui fonctionnent comme des extensions que nous avons séparées pour que le script soit logique et compréhensible par un être humain. Ces autres fichiers `.py` sont des fonctions spécialisées, souvent longues, que nous avons rangées donc dans des sous-dossiers. \n",
    "\n",
    "Le fichier `run.py` exécute les opérations suivantes : \n",
    "- **1.1 On appelle les paquets externes, modules et fonctions nécessaires** : nous allons faire appel, dossier par dossier et fichier par fichier, aux fonctions créés pour ce script et situées en dehors de `run.py` ; par exemple : \n",
    "```python\n",
    "from extractionCatalogs.fonctions.extractionCatEntrees import extInfo_Cat\n",
    "```\n",
    "Mais nous allons aussi appeler des paquets externes. Certains d'entre eux ont été installés avec le fichier `requirements.txt`, d'autres peuvent être appelés sans installation préalable. Pour notre script, nous avons besoin des paquets python suivants : `**lxml**` (implémentation xml pour python, qui nous permettra de créér et de manipuler de objets xml), `**click**` (permet de créér les commandes pour exécuter le script), `**re**` (implémentation regex pour python, qui nous permettra de construire et utiliser des expressions régulières), `**os**` (permet de manipuler avec python des fichiers et dossiers présents sur l'ordinateur).\n",
    "\n",
    "\n",
    "- **1.2 Création des commandes pour lancer le script sur le Terminal** : le paquet **`click`** permet d'établir une communication avec le terminal, afin de demander en input des variables spécifiques. Dans notre cas, outre les commandes fondamentales `!python3 run.py` (suivie de ses attributs), nous utilisons `click` pour créér et demander d'autres éléments en input : les variables \"directory\", \"output\" et \"titlecat\". Ce sont tout simplement des chaînes de caractère, et c'est le script qui va s'occuper de vérifier si elles sont valables.\n",
    "\n",
    "\n",
    "- **1.3 Création de la fonction principale du script** : nous définissons la fonction `extraction()` qui constitue la totalité du script, puisqu'elle appelle toutes les fonctions secondaires et qu'elle produit les documents en sortie (TEI, CSV, restructurations alto et problèmes.txt) : \n",
    "```python\n",
    "def extraction(directory, output, titlecat, segmentationtranscription):\n",
    "    # contenu de la fonction (+500 lignes)\n",
    "```\n",
    "Tout ce qui suit est contenu dans cette fonction. \n",
    "\n",
    "\n",
    "- **2.1 Création d'un dossier pour les output** : nous utilisons le paquet `os` pour traiter les chemins indiqués (input et output), et créér les chemins qui seront nécessaires au script pour aller chercher puis produire les fichiers. \n",
    "\n",
    "\n",
    "- **2.2 Création directe d'un fichier CSV, si la chaine \"csv\" est contenue dans le titre du catalogue** : lorsque le titre du catalogue indiqué dans la commande se termine par `.csv`, le script va tout simplement produire un tableur avec les fichiers qui existent déjà, sans traiter les fichiers ALTO en input ni créér de fichier TEI. Cela est utile lorsqu'on a déjà exécuté préalablement le script, que nous avons fait des modifications manuelles sur le fichier TEI, et que nous voulons simplement actualiser notre tableur CSV. Dans ce cas, le script s'arrêtera immédiatement à cette étape.\n",
    "\n",
    "\n",
    "- **2.3 création du fichier problèmes et traitement de l'option `-st`** : on vérifie les chemins existants pour créér (ou remplacer) le fichier `problemes.txt`. Nous établissons aussi une commande en option, `-st`, qui permet de demander une transcription automatique au logiciel kraken. Dans ce cas, l'input sera constitué uniquement d'images. Cette option ne fonctionne pas pour plusieurs raisons : les commandes kraken indiquées ne sont plus d'actualité, la ségmentation automatique donne des résultats défaillants, et la transcription automatique ne peut pas être corrigée. L'option \"-st\" reste donc une implémentation expérimentale, en prospective d'avancements techniques hypothétiques qui permettraient de faire des transcriptions/segmentations automatiques suffisament performantes comme pour ne plus avoir à utiliser eScriptorium.\n",
    "\n",
    "\n",
    "- **3. Création d'un arbre TEI** : nous utilisons le paquet lxml pour construire un arbre TEI conforme au projet (qui possède un schéma et une ODD spécifiques). Nous créons, un par un, les sous-éléments des l'arbre (tei, teiHeader, text, body, list, etc.). Pour créér le `teiHeader`, **le script utilise un gabarit appelé `variables/gabarit_teiHeader_IMAGO.py`, qui est adapté au projet Artl@s**. Mais **vous pouvez créér un gabarit adapté à tout autre projet** : il suffit d'adapter à vos besoins le document \"variables/gabarit_teiHeader_vide.py\", en remplissant les noms des variables en conséquence, et en inquant le nom de ce document sur le fichier \"variables/contenu_tei.py\".  \n",
    "<img src=\"extractionCatalogs/static/images/gabarit.png\" width=\"400\"/>\n",
    "\n",
    "\n",
    "\n",
    "- **4.1 Traitement préalable des ALTO en input** : nous allons maintenant aller chercher les fichier ALTO contenus dans le fichier signalé par la commande. Nous allons faire une boucle afin que chaque fichier suive le même traitement, un par un. \n",
    "- **4.2 On analyse la conformité et la structure des fichiers ALTO** : une fonction secondaire permet de vérifier si les zones nommées correspondent à l'ontologie Segmonto. Les erreurs éventuelles seront signalées dans le fichier `problemes.txt`\n",
    "- **4.3 Restructuration des ALTO en input** : une première restructuration a lieu. Parfois, les fichiers ALTO produits par eScriptorium présentent les éléments TextBlock (qui correspondent à des entrées de catalogue) de manière désordonnée. Il s'agit tout simplement de les mettre dans le bon odre. La fonction appelle la feuille de transformation xsl \"Restructuration_alto.xsl\" et l'applique. Un dossier \"restructuration ALTO\" et créé et les fichiers restructurés y sont déposés. \n",
    "- **4.4 restructuration eventuelle de la segmentation des ALTO en input** : il existe en autre problème, beaucoup plus complexe, dans les fichiers ALTO produits par eScriptorium. Selon l'ordre de saisie des lignes et des régions sur l'interface, le fichier ALTO produit peut avoir des structures très variables : des fois, les TextBlock qui contiennent le texte transcrit ne correspondent pas à des \"CustomZone:entry\" (comme attendu), mais à des \"MainZone\", ce qui rend le fichier intraitable par le script. D'autres erreurs dans ce registre peuvent avoir lieu. Nous avons donc créé une fonction qui utilise les informations de positionnement spatial de chaque TextBlock pour corriger toutes les erreurs d'imbrication du fichier. Si des erreurs sont détéctées, des fichiers \"resegmentation\" vont être déposés à coté des fichiers \"restructuration\". \n",
    "\n",
    "\n",
    "- **5. Extraction des entrées** : Maintenant que nous nous sommes assurés que les fichiers ALTO sont correctement structurés, nous pouvons extraire leurs informations et reconstituer les entrées du catalogue. Le script fait appel à une série de fonctions qui traitent chaque ligne, de chaque entrée, de chaque page. Ces fonctions vont utiliser des regex complèxes pour déterminer si la ligne courante est le nom-prénom d'un auteur, une oeuvre, ou bien une information complémentaire (adresse, biographie, etc.). Si le travail de correction des transcriptions automatiques et la ségmentation manuelles sont correctes, ces fonctions produiront des résultats très satisfaisants. Après avoir reconstitué chaque entrée de catalogue, ces fonctions les adaptent à l'arbre TEI et l'intègrent dedans. Elles permettent aussi de détecter plusieurs problemes d'extraction, qui seront indiqués dans le fichier problemes.txt afin que l'utilisateur/utilisatrice puisse les corriger facilement (de manière générale, il s'agit de lignes qui n'ont pas été reconnues et qui ont été exclues de l'arbre TEI).\n",
    "\n",
    "\n",
    "- **6. Outputs : TEI et CSV** : nous arrivons aux dernières étapes du script. L'arbre TEI est prêt, on va l'écrire dans un fichier déposé dans le chemin output indiqué dans la commande. Selon les erreurs de transcription, de segmentation, ou de reconnaissance, ce fichier comportera des erreurs. Il sera utilisé par le script pour produire deux tableurs CSV, déposés dans un dossier \"CSV\". Selon les erreurs contenues dans le fichier TEI, ces fichiers CSV comporteront des erreurs. Le premier est une version simplifiée adaptée à la lecture humaine et utile pour les corrections manuelles. Le deuxième comporte de nombreuses colonnes et constitue un gabarit pouvant alimenter la base de donnée BASART du projet ARTl@S. \n",
    "\n",
    "\n",
    "\n",
    "- **7. Informations à afficher sur le terminal** : Finalement, le script construit et affiche des messages d'information ou d'erreur, lesquels ont été produits tout au long du script. Ces messages permettent que l'utilisateur/utilsiatrice puisse évaluer simplement le résultat de la pipeline. Ils signalent comment corriger les eventuelles erreurs, et affichent des résumés des processus réussis. C'est à travers de cet output que l'utilisateur/utilisatrice est susceptible de prendre en main efficacement le programme : il s'agit de produire des explications, même dans les cas ou le traitement des fichiers ne soit pas satisfaisant. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9819fa4",
   "metadata": {},
   "source": [
    "### Regex "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4c77e35",
   "metadata": {},
   "source": [
    "L'utilisation de regex pour reconnaître les lignes du catalogue est une partie fondamentale du script. Le programme est doté de longues et solides regex adaptées à la très grande majorité des cas possibles, mais il est virtuellement possible que vous soyez mené.e à les adapter à votre catalogue. Cette section explique comment le faire. \n",
    "\n",
    "Les catalogues des expositions d'art du XIXe et XXe siècle implémentent une grande variété de formats de présentation des exposants et de leurs oeuvres. Il est possible de dire que chaque catalogue est une configuration unique de conventions qui elles peuvent souvent se répèter. Voici quelques exemples : \n",
    "\n",
    "\n",
    "<img src=\"extractionCatalogs/static/images/1.png\" width=\"400\"/>\n",
    "<img src=\"extractionCatalogs/static/images/2.png\" width=\"400\"/>\n",
    "<img src=\"extractionCatalogs/static/images/3.png\" width=\"400\"/>\n",
    "<img src=\"extractionCatalogs/static/images/4.png\" width=\"400\"/>\n",
    "<img src=\"extractionCatalogs/static/images/5.png\" width=\"400\"/>\n",
    "\n",
    "\n",
    "Pour l'oeil humain, la structure de ces entrées est évidente. Mais la machine est incapable de faire ces abstractions, et il suffit de l'utilisation d'un caractère différent pour que tout change : points, tirets, virgules, chiffres, etc.\n",
    "Le script extactionCatalogs possède plusieurs fonctions qui permettent de détérminer quelle ligne correspond à quoi. Par exemple, une ligne qui commence par un numéro est probablement une oeuvre... mais si ce numéro est suivi d'une virgule, c'est peut-être une adresse... et il existe des cas ou les oeuvres ne sont pas numérotées... il faut d'ailleurs séparer dans une même ligne un exposant ou une oeuvre de ses informations complémentaires...\n",
    "Le script est capable de faire tout cela de manière assez (et même très !) satisfaisante, mais le travail de vérification reste important pour corriger des erreurs qui peuvent parfois être recurentes, voir nombreuses. \n",
    "\n",
    "Allez regarder le fichier **extractionCatalogs/extractionCatalogs/variables/instanciation_regex.py**\\\n",
    "Il contient toutes les regex utilisées par le script. Voici les plus importantes :\n",
    "- exposant :\n",
    "```\n",
    "^[MLE .]*[☙]*[ ]*[a-zà-ü]{0,2}[ ]*[a-zà-ü]{0,2}[ ]*[A-ZÀ-ÜŒ\\(][,]*[ ]*[A-ZŒÀ-Üa-zœà-ü’`´'.\\(\\)-]+[, ]*[A-ZŒÀ-Üa-zà-ü]{0,2}[ ]*[VAON ]*[A-ZŒÀ-Ǘ̧̀̂\\(\\)-][A-Z ŒÀ-Ǘ̧̀̂a-zœà-ǘ̧̀̂’`´'\\(\\)-]*[ ]*[A-ZŒÀ-Ǘ̧̀̂]*[A-ZŒÀ-Ǘ̧̀̂a-zœà-ǘ̧̀̂’`´'.\\(\\)-]*[.]*[,]*|^[A-ZŒÀ-Ǘ̀̂a-zœà-ǘ̧̀̂]{0,2}[ ]*[A-ZŒÀ-Ǘ̧̀̂a-zœà-ǘ̧̀̂]{0,2}[ ]*[A-ZŒÀ-Ǘ̧̀̂][A-ZŒÀ-Ǘ̧̀̂a-zœà-ǘ̧̀̂]*[.,]*[ ]*[A-ZŒÀ-Ǘ̧̀̂a-zœà-ǘ̧̀̂]{0,2}[ ]*[A-ZŒÀ-Ǘ̧̀̂a-zœà-ǘ̧̀̂]{0,2}[ ]*[A-ZŒÀ-Ǘ̧̀̂]+[a-zœà-ǘ̀̂]*[.]*[,]*|^[A-ZŒÀ-Ǘ̧̀̂]+[a-zœà-ǘ̧̀̂]*[.]*[,]*\n",
    "```\n",
    "- oeuvre :\n",
    "```\n",
    "^[*]*[ ]*\\d{1,4}[\\.][ ](Bis|bis|ter|Ter|BIS|TER)*[ ]*[—]|^[*]*\\d{1,4}[ ]*(Bis|bis|ter|Ter|BIS|TER)*[ ]*[\\.]|^[*]*\\d{1,4}[ ]*(Bis|bis|ter|Ter|BIS|TER)*[ ]*[—]|[*]*\\d{1,4}[ ]*[-]|[*]*\\d{1,4}[ ]*(Bis|bis|ter|Ter|BIS|TER)*[ ]*[–]\n",
    "```\n",
    "\n",
    "Si elles sont aussi longues, c'est car elles traitent des cas de figure très divers. Mais **si le script n'est pas capable de comprendre quelles sont les lignes \"auteur\" ou \"oeuvre\" de votre propre catalogue, vous pouvez substituer ses regex par d'autres, beaucoup plus simples, qui soient adaptées à votre cas spécifique**. Il suffit donc de **changer les variables \"exposant_recuperation_regex\" et/ou \"oeuvre_recuperation_regex\"**.\n",
    "Au besoin, vous pouvez aussi modifier les autres regex présentes sur le fichier, qui permettent de détecter des adresses, des mesures, des prix, et des informations complémentaires.\n",
    "\n",
    "Voici un site extrêmement utile qui permet de construire, de tester et d'expliquer minutieusement des regex (vous pouvez aussi vous amuser à tester l'efficacité des longues regex ci-dessus) :\\\n",
    "https://regex101.com/\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc53ec02",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Informations sur le terminal"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc52df5c",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Lorsque vous éxécutez le script, le terminal (ou la céllule sur Jupyter Notebook) affiche un certain nombre d'informations :\n",
    "\n",
    "pour chaque fichier, nous aurons un récapitulatif indiquant :\n",
    "- 1. résultats de la vérification de la conformité à l'ontologie Segmonto\n",
    "- 2. résultats de restructuration du fichier\n",
    "- 3. vérification de la segmentation saisie manuellement sur eScriptorium, et résultats de la tentative de correction s'il y a des erreurs\n",
    "- 4. extraction : affichage des entrées sur le terminal\n",
    "\n",
    "Aprés la traitement de chaque fichier, le terminal indiquera un résumé global :\n",
    "- nombre de pages traitées\n",
    "- nombre d'entrées extraites\n",
    "- nombre d'exposants signalés (normalement, le chiffre est égal à celui des entrées)\n",
    "- nombre d'oeuvres extraites\n",
    "- résultats et problèmes du traitement des fichiers ALTO en input\n",
    "- résultats et problèmes du fichier TEI produit \n",
    "- réussite ou échec de création d'un tableur CSV\n",
    "- chemin vers le dossier d'extraction\n",
    "\n",
    "\n",
    "Pour qu'une extraction fonctionne, il n'est pas nécessaire que le processus soit libre d'erreurs. Le script fait tout ce qui lui est possible, et signale des problème que l'utilisateur décidera de corriger ou pas selon si l'extraction est satisfaisante ou pas. \n",
    "\n",
    "\n",
    "Maintenant que nous comprenons mieux les messages sur le terminal, nous pouvons lancer l'extraction des 5 pages du catalogue Nancy 1843 :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32328d01",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "!python3 run.py exemples_guide/Cat_Nancy_1843/ exemples_guide/extractions/ Nancy_1843"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28a0dbd7",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Comme vous pouvez le voir, **le terminal signale parfois des erreurs de segmentation manuelle, ou de conformité à l'ontologie Segmonto**. Normalement, si l'utilisateur fait une saisie correcte sur l'interface eScriptorium, cela ne devrait pas avoir lieu... mais nous sommes humains ! Et comme vous pouvez le voir, **ces erreurs n'ont pas empêché d'obtenir une extraction très satisfaisante**. L'important, c'est surtout d'avoir un retour clair. Si l'extraction n'avait pas été correcte, **ces informations pourraient nous permettre de repérer des problèmes très rapidement**. \n",
    "\n",
    "Nous pouvons maintenant aller regarder le dossier d'extraction produit par la pipeline."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6106fc5",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Output"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df39eae4",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Le dossier produit (le chemin est rappelé à la fin des informations sur le terminal) contient les fichiers suivants :\n",
    "- fichier XML TEI (la totalité des entrées extraites)\n",
    "- dossier CSV avec deux tableurs (un \"simple\" pour l'oeil humain, l'autre plus complexe pour la base de données Artl@s)\n",
    "- dossier avec des fichiers ALTO \"restructurés\" (correction des eventuelles erreurs contenues dans l'output d'eScriptorium). Ces fichiers ne servent pas à grand chose, si ce n'est à avoir la possibilité de voir le résultat des restructurations/reségmentations et s'assurer qu'elles sont correctes. \n",
    "- eventuellement, un fichier .txt indiquant des problèmes d'extraction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00ef8cb2",
   "metadata": {
    "hidden": true
   },
   "source": [
    "#### Fichier problèmes.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cb2a960",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Le fichier \"nom_du_catalogue_problemes.txt\" permet de signaler les problèmes d'extraction, et notament les lignes qui n'ont pas été intégrées à l'output TEI.\n",
    "Le fichier contient plusieurs sections pour organiser les types d'erreurs possibles :\n",
    "- problèmes liés à l'input ALTO\n",
    "- problèmes de conformité Segmonto\n",
    "- problèmes d'extraction\n",
    "- indications claires des pages ou des numéro d'entrée posant problème\n",
    "- transcription des lignes non extraites, pour faciliter les corrections manuelles"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17d329a2",
   "metadata": {
    "hidden": true
   },
   "source": [
    "#### Fichier TEI"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1767ffed",
   "metadata": {
    "hidden": true
   },
   "source": [
    "**L'output TEI est un fichier structuré qui contient la totalité des entrées de catalogue extraites.**\n",
    "\n",
    "Le teiHeader découle du gabarit dans le dossier \"variables\" et peut-être facilement adapté à tout autre projet de recherche. Le script utilise le fichier \"variables/gabarit_teiHeader_IMAGO.py\", qui est adapté au projet Artl@s. Pour le changer, il suffit d'adapter à vos nécéssités le document \"variables/gabarit_teiHeader_vide.py\", en remplissant les noms des variables selon vos besoins, et en inquant le nom de ce document sur le fichier \"variables/contenu_tei.py\".\n",
    "\n",
    "**Toutes les entrées du catalogue se trouvent dans le \\</body> du fichier, à l'intérieur des balises \\</list>.\\\n",
    "Une entrée est contenue dans une balise \\</entry>, qui est numérotée, possède un id, et contient un lien vers la découpe de l'image iiif (quand elle existe). Une balise \\</desc> (description) contient le nom de l'exposant (balise \\</name>) et se informations complémentaires (balise \\</trait> et \\</p>). Suivent des balises \\</item>, qui sont numérotées et possèdent un id, et qui contiennent le numéro de l'oeuvre (balise \\</num>), son titre (balise \\</title>) et son éventuelle description (balise \\</desc>).**\n",
    "\n",
    "Voici un exemple d'entrée :\n",
    "\n",
    "```\n",
    "        <entry n=\"1\" xml:id=\"Nancy_1843_e1\" source=\"https://gallica.bnf.fr/iiif/ark:/12148/bpt6k62251805/f7/217.0,1320.0,1333.0,735.0/full/0/default.jpg\">\n",
    "          <desc>\n",
    "            <name>M.ANDRÉ,</name>\n",
    "            <trait>\n",
    "              <p> peintre, rue des Carmes, à Nancy.</p>\n",
    "            </trait>\n",
    "          </desc>\n",
    "          <item n=\"1\" xml:id=\"Nancy_1843_e1_i1\">\n",
    "            <num>1</num>\n",
    "            <title>Portrait de M.D.V...... </title>\n",
    "            <desc>(pastel).</desc>\n",
    "          </item>\n",
    "          <item n=\"2\" xml:id=\"Nancy_1843_e1_i2\">\n",
    "            <num>2</num>\n",
    "            <title>Idem de M. C. J...., idem.</title>\n",
    "            <desc>idem.</desc>\n",
    "          </item>\n",
    "          <item n=\"3\" xml:id=\"Nancy_1843_e1_i3\">\n",
    "            <num>3</num>\n",
    "            <title>L'Aumône, idem.</title>\n",
    "            <desc>idem.</desc>\n",
    "          </item>\n",
    "          <item n=\"4\" xml:id=\"Nancy_1843_e1_i4\">\n",
    "            <num>4</num>\n",
    "            <title>Portrait de M. C...., idem.</title>\n",
    "            <desc>idem.</desc>\n",
    "          </item>\n",
    "          <item n=\"5\" xml:id=\"Nancy_1843_e1_i5\">\n",
    "            <num>5</num>\n",
    "            <title>Idem de M. H....., idem.</title>\n",
    "            <desc>idem.</desc>\n",
    "          </item>\n",
    "          <item n=\"6\" xml:id=\"Nancy_1843_e1_i6\">\n",
    "            <num>6</num>\n",
    "            <title>La Sorcière. idem.</title>\n",
    "          </item>\n",
    "          <item n=\"7\" xml:id=\"Nancy_1843_e1_i7\">\n",
    "            <num>7</num>\n",
    "            <title>Portrait de M....... </title>\n",
    "            <desc>(pastel.)</desc>\n",
    "          </item>\n",
    "        </entry>\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb29d2d4",
   "metadata": {
    "hidden": true
   },
   "source": [
    "#### Fichiers CSV"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cf56ac3",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Finalement, le script produit deux tableurs CSV (Comma Separated Value) contenant la totalité des informations extraites.\n",
    "\n",
    "- Une **version simplifiée** permet de lire facilement ces informations, afin de vérifier lesquelles doivent être corrigées manuellement. \n",
    "\n",
    "- Une **version complète** est adaptée au cahiers des charges de la **base de données BASART du projet Artl@S. Ce fichier permettra d'alimenter cette base avec les données que vous avez produites !**\n",
    "\n",
    "\n",
    "Pour que votre fichier CSV puisse alimenter BASART, vous devez l'ouvrir et compléter deux cellules qui se situent en haut à gauche :\n",
    "\n",
    "<img src=\"extractionCatalogs/static/images/csv.png\" width=\"400\"/>\n",
    "\n",
    "Vous possédez normalement un compte utilisateur BASART (https://artlas.huma-num.fr/fr/bases-en-acces-libre/\n",
    ") :\n",
    "- copiez/collez votre nom d'utilisateur dans la cellule \"COLLER ICI LE NOM UTILISATEUR\"\n",
    "- recherchez le nom de votre exposition/catalogue sur la base, puis copiez/collez le nom de l'exposition/catalogue dans la cellule \"COLLER ICI LE NOM DE L'EXPOSITION\"\n",
    "\n",
    "\n"
   ]
  },
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   "source": [
    "### Corrections manuelles"
   ]
  },
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   "source": [
    "Vous pouvez effectuer des corrections manuelles à différentes étapes et sur tous les documents :\n",
    "- sur l'**interface eScriptorium**, vous pouvez corriger la transcription automatique\n",
    "- sur les fichiers **XML ALTO** produits par eScriptorium, vous pouvez corriger la transcription automatique, le découpage des régions, la conformité des régions à l'ontologie Segmonto.\n",
    "- sur le **fichier XML TEI** en output, vous pouvez corriger le contenu et la structure des entrées\n",
    "- sur les **fichiers CSV** en output, vous pouvez corriger le contenu et la scruture des entrées\n",
    "\n",
    "Éditer des fichiers XML peut être difficile pour un.e utilisateur/utilisatrice néophyte. Il faut savoir lire un document complèxe et veiller à ce que sa conformité sot respectée. Il est beaucoup plus simple (et donc conseillé) de faire les corrections sur l'interface eScriptorium ou directement sur les tableurs csv, dernière étape de la pipeline.\n",
    "\n",
    "Vous pourrez utiliser les informations sur le terminal et sur le fichier problemes.txt pour détécter rapidement les corrections à faire. Il s'agira de corriger des problèmes de transcription, mais aussi d'ajouter des auteurs, des oeuvres ou des informations complémentaires éxclus à un moment de la pipeline. Le document problèmes.txt est censé vous fournir une liste complète et explicite des lignes non ajoutées. \n",
    "\n",
    "**ATTENTION : si vous modifiez des documents ALTO ou TEI et que vous relancez la même commande (mêmes chemins, même titre de catalogue), l'output antérieur sera remplacé. Dans ce cas, le terminal affiche un message vous demandant si vous voulez remplacer les documents anciens. Cependant, sur Jupyter Notebook, vous n'avez pas la possibilité d'indiquer ce choix : il faut donc toujours changer le nom de catalogue ou les chemins indiqués, lorsque vous faites des modifications sur les fichiers ALTO ou TEI.**\n",
    "\n",
    "\n"
   ]
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   "source": [
    "–––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––\n",
    "\n",
    "**Pour terminer, voici plusieurs exemples de catalogues que vous pouvez faire passer par la pipeline. \n",
    "Ils fournissent plusieurs exemples d'erreurs récurentes qui ne nuisent pas à l'extraction satisfaisante des entrées.**\n",
    "\n",
    "Si vous lancez plusieurs fois la même commande, veillez à changer le nom du catalogue. Normalement, le terminal demande à l'utilisateur de confirmer qu'il souhaite que l'ancien dossier soit remplacé par un nouveau, mais les céllules Jupyer Notebook ne permettent pas de fournir une réponse oui/non. \n"
   ]
  },
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   "execution_count": null,
   "id": "a7086259",
   "metadata": {
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   "outputs": [],
   "source": [
    "!python3 run.py exemples_guide/Cat_Automne_1940/ exemples_guide/extractions/ automne_1940"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc9792ca",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "!python3 run.py exemples_guide/Cat_Refuses_1863/ exemples_guide/extractions/ refuses_1863"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55c75646",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "!python3 run.py exemples_guide/Cat_Rouen_1853/ exemples_guide/extractions/ rouen_1853"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9851182e",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "!python3 run.py exemples_guide/Cat_Strasbourg_1884/ exemples_guide/extractions/ Strasbourg_1884"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c3803f7",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "!python3 run.py exemples_guide/PCP_1896/ exemples_guide/extractions/ PCP_1896"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a75614e",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "!python3 run.py exemples_guide/Cat_SaoPaulo_1972/ exemples_guide/extractions/ saopaulo_1972"
   ]
  }
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