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gargantext
Commit e6a2bb98 authored by Administrator's avatar Administrator
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indicateurs pour l'analyste ou le tuteur

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.ipynb_checkpoints/gargantext_web_tutorial-checkpoint.ipynb
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{
"metadata": {
"name": "",
"signature": "sha256:a8678c86f8d94c1ba55469d0e0a796c7d25df5de29707f5114b4cb74858855a8"
"signature": "sha256:2afae28d08bbb0945aaca44a5b704550048c5dc193cc3d81cb11a551fcc03864"
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"nbformat": 3,
"nbformat_minor": 0,
......@@ -66,19 +66,32 @@
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 10
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from collections import defaultdict"
"import nltk\n",
"from nltk.stem.snowball import EnglishStemmer\n",
"stemmer = EnglishStemmer()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from collections import defaultdict"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "markdown",
"metadata": {},
......@@ -158,19 +171,18 @@
"4 Thanks anthrax\n",
"\n",
"1 Bees swarm\n",
"|_ 9 : Bees tweets\n",
"|_ 4 : Health academic publications\n",
"|_ 2 : bees and (pesticides or chemicals or neocotinoids)\n",
"|_ 1 : Quand les abeilles meurent, les articles sont compt\u00e9s\n",
"\n",
"6 CIRDEM\n",
"|_ 8 : Zotero fichier du 9 sept.\n",
"\n",
"7 TEST\n",
"\n"
]
}
],
"prompt_number": 16
"prompt_number": 7
},
{
"cell_type": "code",
......@@ -189,20 +201,7 @@
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import nltk\n",
"from nltk.stem.snowball import EnglishStemmer\n",
"stemmer = EnglishStemmer()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
"prompt_number": 29
},
{
"cell_type": "code",
......@@ -248,13 +247,13 @@
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 12,
"prompt_number": 8,
"text": [
"'France/R\u00e9gent TS: contr\u00f4le judiciaire annul\u00e9 pour BASF, confirm\u00e9 pour Bayer'"
]
}
],
"prompt_number": 12
"prompt_number": 8
},
{
"cell_type": "code",
......@@ -297,12 +296,12 @@
"l = set()\n",
"d = defaultdict(lambda : defaultdict(lambda: defaultdict(lambda: defaultdict(int))))\n",
"\n",
"docs = documents.models.Document.objects.all().filter(project_id=1)[:10] \n",
"docs = documents.models.Document.objects.all().filter(project_id=1)[:1000] \n",
"\n",
"for doc in docs:\n",
" sentances = nltk.sent_tokenize(doc.text)\n",
" for sentance in sentances:\n",
" words = nltk.wordpunct_tokenize(sentance)\n",
" sentences = nltk.sent_tokenize(doc.text)\n",
" for sentence in sentences:\n",
" words = nltk.wordpunct_tokenize(sentence)\n",
" #print(len(words))\n",
" for word in words:\n",
" stems = stemmer.stem(word)\n",
......@@ -316,8 +315,17 @@
"new_gramDoc = [ documents.models.NgramDocumentTemporary(terms=k, document=pk, occurrences=d[k][pk]['count']) \\\n",
" for k in d.keys() \\\n",
" for pk in d[k].keys()\\\n",
" ]\n",
"\n",
" ]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"documents.models.NgramTemporary.objects.bulk_create(new_grams)\n",
"documents.models.NgramDocumentTemporary.objects.bulk_create(new_gramDoc)\n",
"\n",
......@@ -332,12 +340,16 @@
" \n",
" delete from documents_ngramtemporary;\n",
" \n",
" INSERT INTO documents_ngramdocument\n",
" SELECT * FROM documents_ngramdocumenttemporary WHERE NOT EXISTS \n",
" ( SELECT 1 FROM documents_ngram WHERE \n",
" documents_ngram.terms = documents_ngramtemporary.terms);\n",
" INSERT INTO \n",
" documents_ngramdocument (terms_id, document_id, occurrences)\n",
" SELECT \n",
" GT.id, DT.id, NDT.occurrences \n",
" FROM \n",
" documents_ngramdocumenttemporary as NDT \n",
" INNER JOIN documents_document AS DT ON DT.id = NDT.document \n",
" INNER JOIN documents_ngram AS GT ON GT.terms = NDT.terms ;\n",
" \n",
" delete from documents_ngramtemporary;\n",
" delete from documents_ngramdocumenttemporary;\n",
" \"\"\"\n",
"cursor.execute(query_string)\n",
"\n",
......@@ -353,7 +365,7 @@
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 21
"prompt_number": 14
},
{
"cell_type": "markdown",
......@@ -366,7 +378,18 @@
"cell_type": "code",
"collapsed": false,
"input": [
"\n"
"\n",
"SELECT t1.terms_id, t2.terms_id, COUNT(*) AS c, t3.project_id\n",
"FROM documents_ngramdocument AS t1\n",
"\n",
"INNER JOIN documents_ngramdocument AS t2\n",
"ON t1.document_id = t2.document_id\n",
"\n",
"INNER JOIN documents_corpus\n",
"\n",
"GROUP BY t1.terms_id, t2.terms_id;\n",
"\n",
"# add corpus_id in column !"
],
"language": "python",
"metadata": {},
......@@ -377,7 +400,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"#Copy Postgres"
"#Coocurrences"
]
},
{
......@@ -497,99 +520,16 @@
{
"cell_type": "code",
"collapsed": false,
"input": [
"x = extractNgrams()"
],
"input": [],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"local variable 'x' referenced before assignment\n",
"local variable 'x' referenced before assignment"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"local variable 'x' referenced before assignment"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"local variable 'x' referenced before assignment"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"local variable 'x' referenced before assignment"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"local variable 'x' referenced before assignment"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"local variable 'x' referenced before assignment"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"local variable 'x' referenced before assignment"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-9-d62560425026>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mextractNgrams\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32m<ipython-input-8-520b3bdbed28>\u001b[0m in \u001b[0;36mextractNgrams\u001b[1;34m(corpus_pk)\u001b[0m\n\u001b[0;32m 6\u001b[0m limit 90;''' % corpus_pk)\n\u001b[0;32m 7\u001b[0m \u001b[0mwords\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgargantext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mNgrams\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m \u001b[0mwords\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdocs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'text'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0munique_id\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"unique_id\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 9\u001b[0m \u001b[1;32mreturn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwords\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/home/alexandre/projets/gargantext.py/gargantext_core/analysis/ngrams.py\u001b[0m in \u001b[0;36mget\u001b[1;34m(self, corpus, lang, key, unique_id)\u001b[0m\n\u001b[0;32m 96\u001b[0m \u001b[0mdate\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdoc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'date'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'XX'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 97\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 98\u001b[1;33m \u001b[0mngrams\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfouille\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 99\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 100\u001b[0m \u001b[1;32mwhile\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/home/alexandre/projets/gargantext.py/gargantext_core/analysis/ngrams.py\u001b[0m in \u001b[0;36mfouille\u001b[1;34m(text, grammar_rule)\u001b[0m\n\u001b[0;32m 69\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0msentance\u001b[0m \u001b[1;32min\u001b[0m \u001b[0msentances\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 70\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 71\u001b[1;33m \u001b[0mt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpos_tag\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msentance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 72\u001b[0m \u001b[0mg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgrammar\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 73\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubtrees\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/home/alexandre/projets/gargantext.py/gargantext_core/analysis/languages.py\u001b[0m in \u001b[0;36mfrench_pos_tag\u001b[1;34m(sentance)\u001b[0m\n\u001b[0;32m 67\u001b[0m \u001b[0mTAGINENC\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'utf-8'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0;31m\\\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 68\u001b[0m TAGOUTENC='utf-8')\n\u001b[1;32m---> 69\u001b[1;33m \u001b[0mtags\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtagger\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTagText\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msentance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 70\u001b[0m \u001b[0mt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtag\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtags\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 71\u001b[0m \u001b[1;32mreturn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/home/alexandre/projets/gargantext.py/env/lib/python3.4/site-packages/treetaggerwrapper.py\u001b[0m in \u001b[0;36mTagText\u001b[1;34m(self, text, numlines, tagonly, prepronly, tagblanks, notagurl, notagemail, notagip, notagdns, encoding, errors)\u001b[0m\n\u001b[0;32m 1085\u001b[0m \u001b[0mintext\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1086\u001b[0m \u001b[1;32mwhile\u001b[0m \u001b[1;32mTrue\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1087\u001b[1;33m \u001b[0mline\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtagoutput\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreadline\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1088\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mDEBUG\u001b[0m \u001b[1;33m:\u001b[0m \u001b[0mlogger\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Read from TreeTagger: %r\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1089\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mline\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"prompt_number": 9
"outputs": [],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"d = documents.models.Document()"
],
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
......@@ -606,610 +546,20 @@
{
"cell_type": "code",
"collapsed": false,
"input": [
"for i in x.stems.keys():\n",
" print(i)"
],
"input": [],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"('m\u00e9decin', 'classiqu')\n",
"('canc',)\n",
"('sourc', 'proch')\n",
"('carriol', '\u00e0', 'bagard')\n",
"('plant', 'cultiv')\n",
"('apr\u00e8s-mid', 'r\u00e9cr\u00e9at')\n",
"('ouest-fr', 'sarth', 'sud')\n",
"('jeun', '\u00e9lev')\n",
"('naus',)\n",
"('adult',)\n",
"('apres',)\n",
"('maison',)\n",
"('l',)\n",
"('jeud', 'soir')\n",
"('unaf',)\n",
"('don',)\n",
"('assurance-malad',)\n",
"('sol',)\n",
"('film', 'nos', 'enfant')\n",
"('concurrent', 'bai', 'cropscienc', 'franc')\n",
"('annul', 'd\u00e9pos')\n",
"('tel',)\n",
"('afriqu',)\n",
"('\u00eatre', '\u00e0', 'pied')\n",
"('group', 'lyon')\n",
"('quincy',)\n",
"('frelon', 'europ\u00e9en')\n",
"('mati', 'premi')\n",
"('d\u00e9sign', 'coupabl')\n",
"('\u00e9poqu',)\n",
"('affair',)\n",
"('bardeau',)\n",
"('jean-luc', 'souli')\n",
"('contr\u00f4l', 'judiciair')\n",
"('commercialis',)\n",
"('lieu',)\n",
"('semenc', 'trait')\n",
"('pr\u00e9par',)\n",
"('bai',)\n",
"('autr', 'problem')\n",
"('allerg',)\n",
"('suspens',)\n",
"('a-t-il', 'ajout')\n",
"('cadr',)\n",
"('absenc',)\n",
"('unit', 'finist\u00e9rien')\n",
"('infos', 'econom', 'r\u00e9gent', 't')\n",
"('commun',)\n",
"('semenc',)\n",
"('march', 'mondial')\n",
"('ventr',)\n",
"('not',)\n",
"('jean-mar',)\n",
"('jeud',)\n",
"('pet', 'boul')\n",
"('arbre',)\n",
"('examen',)\n",
"('asi',)\n",
"('jour',)\n",
"('agricultur', 'biolog')\n",
"('soir',)\n",
"('m\u00e9decin', 'hom\u00e9opath')\n",
"('client',)\n",
"('meus', 'vi', 'quotidien')\n",
"('croissanc',)\n",
"('toit',)\n",
"('mal-\u00eatr', 'g\u00e9n\u00e9ral')\n",
"('excellent', 'miel')\n",
"('\u00e9tudi', 'domicili')\n",
"('nouveaut',)\n",
"('chiffr',)\n",
"('d\u00e9monstr',)\n",
"('sit', 'brestois')\n",
"('bas',)\n",
"('volet',)\n",
"('v\u00e9g\u00e9tal',)\n",
"('histoir',)\n",
"('haute-garon',)\n",
"('jug', 'rama\u00ebl')\n",
"('mariag',)\n",
"('pr\u00e9dateur',)\n",
"('homm',)\n",
"('rachat',)\n",
"('id\u00e9',)\n",
"('\u00e9vident',)\n",
"('produit',)\n",
"('infarctus',)\n",
"('sid',)\n",
"('fruit',)\n",
"('deuxiem',)\n",
"('dgal', 'paris')\n",
"('r\u00e9sum',)\n",
"('visiteur',)\n",
"('public', 'passion')\n",
"('vend\u00f4mois',)\n",
"('\u00e9l\u00e9ment', 'naturel')\n",
"('surmortal',)\n",
"('appris', 'aupres')\n",
"('dat',)\n",
"('r\u00e9guli',)\n",
"('nid',)\n",
"('venin',)\n",
"('recel',)\n",
"('but',)\n",
"('recherch', 'perp\u00e9tuel')\n",
"('chauffag',)\n",
"('vi',)\n",
"('environ',)\n",
"('f\u00e9mur',)\n",
"('long',)\n",
"('f\u00e9vri',)\n",
"('cons\u00e9quent', 'p\u00e9nal')\n",
"('infos', 'econom', 'franc')\n",
"('hygien', 'tres', 'draconien')\n",
"('nichoir', 'r\u00e9alis')\n",
"('patholog',)\n",
"('expos',)\n",
"('\u00e9gal',)\n",
"('illustr',)\n",
"('pert',)\n",
"('magistrat', 'toulousain')\n",
"('macer',)\n",
"('ceven',)\n",
"('rayon',)\n",
"('proven',)\n",
"('l\u00e9gum',)\n",
"('ateli',)\n",
"('bagard',)\n",
"('d\u00e9but', 'mar')\n",
"('group',)\n",
"('conf\u00e9der', 'paysan')\n",
"('moment',)\n",
"('reper',)\n",
"('surexploit', 'industriel')\n",
"('m\u00eam',)\n",
"('plaint',)\n",
"('utilis', 'massif')\n",
"('fractur',)\n",
"('besseg',)\n",
"('\u00e9quinox',)\n",
"('h\u00f4tel', '\u00e0', 'insect')\n",
"('doublon',)\n",
"('dimanch',)\n",
"('produit', 'hom\u00e9opath')\n",
"('\u00e9colog',)\n",
"('aven',)\n",
"(\"c\u00f4tes-d'armor\",)\n",
"('f\u00e9liqu',)\n",
"('vu', '\u00e9conom')\n",
"('canton',)\n",
"('eau',)\n",
"('paris',)\n",
"('charg',)\n",
"('ouest-fr',)\n",
"('coll\u00e8gu',)\n",
"('demand',)\n",
"('anxiet',)\n",
"('saint-gauden', 'jean', 'guary')\n",
"('tres', 'satisfais')\n",
"('imm\u00e9diat',)\n",
"('condit',)\n",
"('bouff',)\n",
"('part', 'civil')\n",
"('mort',)\n",
"('bai', 'toulous')\n",
"('journ', 'port')\n",
"('magistrat',)\n",
"('il',)\n",
"('lydi', 'gravil')\n",
"('croissanc', 'correct')\n",
"('bon', 'rep')\n",
"('comprim',)\n",
"('beau', 'r\u00e9sultat')\n",
"('\u00e9pileps',)\n",
"('part',)\n",
"('inflig',)\n",
"('saint-gauden',)\n",
"('est', 'r\u00e9publicain')\n",
"('centr',)\n",
"('march',)\n",
"('d\u00e9savantag',)\n",
"('point',)\n",
"('europ',)\n",
"('appel',)\n",
"('d\u00e9bat',)\n",
"('gerb',)\n",
"('tub',)\n",
"('product',)\n",
"('d\u00e9tracteur',)\n",
"('ramassag',)\n",
"('aliment', 'viv')\n",
"('vendred',)\n",
"('an',)\n",
"('local',)\n",
"('manifest', 'ax\u00e9')\n",
"('ann\u00e9',)\n",
"('associ',)\n",
"('vin',)\n",
"('r\u00e9colt',)\n",
"('fan',)\n",
"('\u00eatre', 'pr\u00e9sent')\n",
"('union', 'national')\n",
"('fusion',)\n",
"('promen',)\n",
"('tres', 'simpl')\n",
"('\u00e9co-pieg',)\n",
"('fabriqu',)\n",
"('mid', 'libr')\n",
"('seb',)\n",
"('\u00a9',)\n",
"('grand', 'instanc')\n",
"('femm', 'favor')\n",
"('destruct',)\n",
"('particip',)\n",
"('centr', 'cultur')\n",
"('\u00e9nerg', 'nucl\u00e9air')\n",
"('arriv',)\n",
"('sifflet', 'bref')\n",
"('indemnis',)\n",
"('tri',)\n",
"('minister',)\n",
"('victim',)\n",
"('r\u00e9gent',)\n",
"('\u00e9chos', 'de', 'la', 'cez', 'semain', 'bleu')\n",
"('lot',)\n",
"('aub',)\n",
"('tribunal',)\n",
"('direct', 'g\u00e9n\u00e9ral')\n",
"('mck',)\n",
"('emploi', 'anthonin', 'debran')\n",
"('jug', 'guary')\n",
"('saccharos',)\n",
"('teintur', 'mer')\n",
"('m\u00e9dic',)\n",
"('autoris',)\n",
"('insect', 'diver')\n",
"('sos', 'abeil')\n",
"('enqu\u00eat',)\n",
"('solut', 'fiabl')\n",
"('indique-t-il',)\n",
"('amour',)\n",
"('b\u00e9b\u00eat',)\n",
"('\ufffduvr',)\n",
"('mesur',)\n",
"('robert',)\n",
"('semain',)\n",
"('salari',)\n",
"('aliment',)\n",
"('objet',)\n",
"('boiron',)\n",
"('b\u00eat',)\n",
"('situat',)\n",
"('est', 'r\u00e9publicain', 'ver')\n",
"('sein',)\n",
"('kergaradec',)\n",
"('\u00e0', 'savigny-sur-bray')\n",
"('ex-propri\u00e9tair',)\n",
"('ph\u00e9nomen', 'nouveau')\n",
"('confirm',)\n",
"('reproduct',)\n",
"('ouest-fr', 'finister')\n",
"('cha\u00een', 'compl\u00e9mentair')\n",
"('bernard', 'fau')\n",
"('\u00e9cosystem',)\n",
"('sac',)\n",
"('arr\u00eat',)\n",
"('oiseau',)\n",
"('million',)\n",
"('ajout', 'inconsider')\n",
"('apiculteur',)\n",
"('dgal',)\n",
"('lydi', 'villefeu')\n",
"('couleur',)\n",
"('espec', 'animal')\n",
"('societ', 'basf', 'agro')\n",
"('titr',)\n",
"('quart',)\n",
"('nombreux', 'expliqu')\n",
"('samed',)\n",
"('ancien', 'propri\u00e9tair')\n",
"('organis',)\n",
"('ale', 'le', '\u00e9chos', 'de', 'la', 'cez', 'semain', 'bleu')\n",
"('tgi',)\n",
"('\u00e9tabl',)\n",
"('supprim',)\n",
"('condit', 'tres', 'sp\u00e9cif')\n",
"('honneur',)\n",
"('semain', 'bleu')\n",
"('apr\u00e8s-mid',)\n",
"('principal', 'mission')\n",
"('milieu', 'naturel')\n",
"('conseil', 'g\u00e9n\u00e9ral')\n",
"('human',)\n",
"('insect',)\n",
"('\u00e9ventuel', 'nociv')\n",
"('pain',)\n",
"('r\u00e9pet',)\n",
"('octobr',)\n",
"('g\u00e9n\u00e9ral',)\n",
"('\u00e9quilibr', 'parf')\n",
"('lead', 'mondial')\n",
"('\u00e9coul',)\n",
"('bien',)\n",
"('bibliothequ', 'municipal')\n",
"('m\u00e9dic', 'traditionnel')\n",
"('stock',)\n",
"('sourc', 'judiciair')\n",
"('espec', 'v\u00e9g\u00e9tal')\n",
"('vapeur',)\n",
"('contenu',)\n",
"('pharmacien',)\n",
"('trac',)\n",
"('d\u00e9pen',)\n",
"('affair', 'jurid')\n",
"('sp\u00e9cial',)\n",
"('mas', 'serret')\n",
"('format',)\n",
"('vol',)\n",
"('propri\u00e9tair',)\n",
"('sommeil',)\n",
"('docu', 'suit')\n",
"('insecticid', 'r\u00e9gent')\n",
"('caution',)\n",
"('contr\u00f4l',)\n",
"('origin', 'v\u00e9g\u00e9tal')\n",
"('extinct',)\n",
"('bai', 'cropscienc', 'franc')\n",
"('habitud',)\n",
"('problem', 'environnemental')\n",
"('mercred',)\n",
"('machin',)\n",
"('zon',)\n",
"('travail',)\n",
"('f\u00e9vri', 'derni')\n",
"('agricultur',)\n",
"('basf', 'agro')\n",
"('chemin',)\n",
"('frelon', 'asiat')\n",
"('mond', 'enti')\n",
"('vill',)\n",
"('natur',)\n",
"('mid', 'libr', 'lozer')\n",
"('frelon',)\n",
"('parcour',)\n",
"('monstr',)\n",
"('camion', 'frigorif')\n",
"('\u00e0', 'vend\u00f4m')\n",
"('chambr',)\n",
"('cas',)\n",
"('nouveau', 'sp\u00e9cial')\n",
"('mair',)\n",
"('jardin', 'familial')\n",
"('laboratoir',)\n",
"('charpenti',)\n",
"('droit',)\n",
"('exempl',)\n",
"('toulous',)\n",
"('raison',)\n",
"('gu\u00e9rand',)\n",
"('basf',)\n",
"('difficult', '\u00e0')\n",
"('docu', 'communiqu')\n",
"('produit', 'chimiqu')\n",
"('horticulteur',)\n",
"('degr',)\n",
"('milit', 'agriculteur')\n",
"('infirmi',)\n",
"('bateau',)\n",
"('project',)\n",
"('juin',)\n",
"('centr', 'culturel')\n",
"('vice-pr\u00e9sident',)\n",
"('taigni',)\n",
"('conf\u00e9rent',)\n",
"('florenc', 'henry')\n",
"('chenill', 'processionnair')\n",
"('montm\u00e9dien',)\n",
"('renseign',)\n",
"('centre-ouest',)\n",
"('gu\u00eap',)\n",
"('sit',)\n",
"('disparit',)\n",
"('essaim', 'd')\n",
"('fipronil',)\n",
"('exploit',)\n",
"('chass', 'cafard')\n",
"('bernard', 'portales')\n",
"('\u00e9cout',)\n",
"('ccas',)\n",
"('rigolad',)\n",
"('meilleur', 'v\ufffdu')\n",
"('abeil',)\n",
"('chimioth\u00e9rap',)\n",
"('chaleur',)\n",
"('moyen',)\n",
"('diff\u00e9rent', 'pharmacien')\n",
"('journ',)\n",
"('dout',)\n",
"('exploit', 'horticol', '\u00e9colog')\n",
"('air',)\n",
"('public',)\n",
"('autr', 'arr\u00eat')\n",
"('plac',)\n",
"('siecl',)\n",
"('fau',)\n",
"('directeur|directric',)\n",
"('tres',)\n",
"('nouveau', 'ordon', 'conform')\n",
"('diabet',)\n",
"('jug',)\n",
"('cloqu',)\n",
"('cycl',)\n",
"('euro',)\n",
"('anim',)\n",
"('colleret',)\n",
"('action',)\n",
"('br\u00fblur', 'local')\n",
"('porspod',)\n",
"('produit', 'issu')\n",
"('pesticid',)\n",
"('popul', 'd')\n",
"('imag',)\n",
"('trentain',)\n",
"('aux', 'beau')\n",
"('proc\u00e9dur',)\n",
"('terr',)\n",
"('produit', 'diff\u00e9rent')\n",
"('yves-mar', 'robin')\n",
"('moulin', 'robert')\n",
"('instruct',)\n",
"('alcool',)\n",
"('alpe',)\n",
"('dangeros',)\n",
"('societ',)\n",
"('porte-parol', 'jos', 'bov')\n",
"('coll\u00e8gu', 'jean', 'guary')\n",
"('occup',)\n",
"('potag',)\n",
"('ministr',)\n",
"('ami',)\n",
"('sant',)\n",
"('gest', 'simpl')\n",
"('fond',)\n",
"('mis',)\n",
"('insect', 'tres', 'util')\n",
"('ndy', '\u00a9')\n",
"('attent', 'port')\n",
"('avocat',)\n",
"('forc',)\n",
"('f\u00eat',)\n",
"('femm',)\n",
"('mond',)\n",
"('them',)\n",
"('beau', 'affluenc')\n",
"('lutf', 'kocyig')\n",
"('insecticid', 'r\u00e9gent', 't')\n",
"('r\u00e9seau',)\n",
"('patienc',)\n",
"('lactos',)\n",
"('infos', 'fran\u00e7ais', 'r\u00e9gent', 't')\n",
"('particuli',)\n",
"('coup',)\n",
"('florenc',)\n",
"('extr\u00eam', 'orient')\n",
"('d\u00e9cis',)\n",
"('habitat',)\n",
"('gard',)\n",
"('algu',)\n",
"('anciennet', 'derri')\n",
"('transmiss',)\n",
"('facil', '\u00e0')\n",
"('contact',)\n",
"('chim',)\n",
"('lead', 'incontest')\n",
"('princip', 'actif')\n",
"('hom\u00e9opath',)\n",
"('grain',)\n",
"('norm',)\n",
"('gamm',)\n",
"('engrais', 'chimiqu')\n",
"('c\ufffdur',)\n",
"('chl', '\u00a9')\n",
"('concurrent', 'direct')\n",
"('farin',)\n",
"('mar', '@ord@')\n",
"('caus',)\n",
"('nouveau', 'moyen')\n",
"('publiqu',)\n",
"('cultur', 'intens')\n",
"('moulin',)\n",
"('d\u00e9part',)\n",
"('cop',)\n",
"('fleur',)\n",
"('dolisos',)\n",
"('c\u00f4t',)\n",
"('soin',)\n",
"('servic',)\n",
"('bois',)\n",
"('sujet',)\n",
"('maxim', 'ortin')\n",
"('cultur', 'biolog')\n",
"('\u00eatre',)\n",
"('derni',)\n",
"('lois',)\n",
"('orchestr', 'alain', 'mayo')\n",
"('nich',)\n",
"('franc',)\n",
"('bonheur',)\n",
"('centre-ouest', 'loir', 'et', 'cher', 'vendom', 'et')\n",
"('humor', 'andy', 'klein')\n",
"('morbihan',)\n",
"('jus',)\n",
"('pa',)\n",
"('caracter', 'particuli')\n",
"('sel',)\n",
"('jeun',)\n",
"('plui',)\n",
"('cultur', 'biolog', 'accessibl')\n",
"('autr', 'ordon')\n",
"('docu',)\n",
"('retrait',)\n",
"('entretien',)\n",
"('jug', 'patrick', 'rama\u00ebl')\n",
"('dossi',)\n",
"('d\u00e9fens',)\n",
"('cultur',)\n",
"('rest',)\n",
"('curieux',)\n",
"('anthonin',)\n",
"('terrain',)\n",
"('nouvel', 'r\u00e9publ')\n",
"('kutf',)\n",
"('\u00e0',)\n",
"('m\u00e9decin',)\n",
"('fin',)\n",
"('gill',)\n",
"('tout', 'gentil')\n",
"('pollinis',)\n",
"('vers',)\n",
"('cour',)\n",
"('annul',)\n",
"('bl\u00e9',)\n",
"('interdict',)\n",
"('\u00e9gal', 'pr\u00e9dateur')\n",
"('long', 'travail')\n",
"('person',)\n",
"('brest',)\n",
"('dizain',)\n",
"('miel',)\n",
"('enfant',)\n",
"('animal',)\n",
"('copeau',)\n",
"('tiroir',)\n",
"('r\u00e9gent', 't')\n",
"('foi|fois',)\n",
"('organ',)\n",
"('plant',)\n",
"('bon', 'temp')\n",
"('malad',)\n",
"('vent',)\n",
"('apicultur', 'fran\u00e7ais')\n",
"('antonin',)\n",
"('futur', '\u00e9poux')\n",
"('insect', 'nuisibl')\n",
"('d\u00e9p\u00f4t',)\n",
"('stress',)\n",
"('fort', 'progress')\n",
"('madagascar',)\n",
"('sall',)\n"
]
}
],
"prompt_number": 7
"outputs": [],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"corpus.language.language"
],
"input": [],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 72,
"text": [
"'French'"
]
}
],
"prompt_number": 72
"outputs": [],
"prompt_number": 0
},
{
"cell_type": "code",
......@@ -1223,10 +573,7 @@
{
"cell_type": "code",
"collapsed": false,
"input": [
"for doc in corpus_dict:\n",
" print(doc.get('title', ''))"
],
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
......@@ -1253,9 +600,7 @@
{
"cell_type": "code",
"collapsed": false,
"input": [
"gram = documents.models.Ngram()"
],
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
......@@ -1264,9 +609,7 @@
{
"cell_type": "code",
"collapsed": false,
"input": [
"x = documents.models.NgramDocument()"
],
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
......@@ -1275,12 +618,7 @@
{
"cell_type": "code",
"collapsed": false,
"input": [
"jj_nn = r\"\"\"\n",
" NP: \n",
" {<JJ.*>*<NN.*|>+<JJ.*>*}\n",
" \"\"\""
],
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
......@@ -1289,9 +627,7 @@
{
"cell_type": "code",
"collapsed": false,
"input": [
"import nltk"
],
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
......@@ -1300,24 +636,20 @@
{
"cell_type": "code",
"collapsed": false,
"input": [
"grammar = nltk.sent_tokenize(jj_nn)"
],
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 66
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def "
],
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 66
"prompt_number": 12
},
{
"cell_type": "code",
......@@ -1326,30 +658,16 @@
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"documents.models.Document.objects.raw('select count(*),source from documents_document group by source;'):"
],
"input": [],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "InvalidQuery",
"evalue": "Raw query must include the primary key",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mInvalidQuery\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-45-5495b08ea061>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfor\u001b[0m \u001b[0mp\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdocuments\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodels\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDocument\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobjects\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'select count(*),source from documents_document group by source;'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/home/alexandre/projets/gargantext.py/env/lib/python3.4/site-packages/django/db/models/query.py\u001b[0m in \u001b[0;36m__iter__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 1413\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mskip\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1414\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_meta\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpk\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mattname\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mskip\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1415\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mInvalidQuery\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Raw query must include the primary key'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1416\u001b[0m \u001b[0mmodel_cls\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdeferred_class_factory\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mskip\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1417\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mInvalidQuery\u001b[0m: Raw query must include the primary key"
]
}
],
"prompt_number": 45
"outputs": [],
"prompt_number": 12
},
{
"cell_type": "code",
......@@ -1357,24 +675,8 @@
"input": [],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"{'count': 34, 'source': 'Sud Ouest'}\n",
"{'count': 26, 'source': 'Le Progr\u00e8s - Lyon'}\n",
"{'count': 9, 'source': 'Le Figaro'}\n",
"{'count': 9, 'source': 'AFP Infos Fran\u00e7aises'}\n",
"{'count': 8, 'source': 'Les Echos'}\n",
"{'count': 8, 'source': 'Le Monde'}\n",
"{'count': 6, 'source': 'Le Parisien'}\n",
"{'count': 5, 'source': 'Le T\u00e9l\u00e9gramme (Bretagne)'}\n",
"{'count': 4, 'source': 'La Presse'}\n"
]
}
],
"prompt_number": 82
"outputs": [],
"prompt_number": 12
},
{
"cell_type": "code",
......@@ -1388,96 +690,29 @@
{
"cell_type": "code",
"collapsed": false,
"input": [
"result = query_to_dicts('''select to_char(date, 'YYYY-MM'), count(*) \n",
" from documents_document\n",
" group by to_char(date, 'YYYY-MM')\n",
" order by 1 DESC;''')\n",
"for i in result:\n",
" print(i)"
],
"input": [],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"{'to_char': '2001-12', 'count': 9}\n",
"{'to_char': '2001-11', 'count': 10}\n",
"{'to_char': '2001-10', 'count': 2}\n",
"{'to_char': '2001-09', 'count': 10}\n",
"{'to_char': '2001-08', 'count': 36}\n",
"{'to_char': '2001-07', 'count': 15}\n",
"{'to_char': '2001-06', 'count': 12}\n",
"{'to_char': '2001-05', 'count': 13}\n",
"{'to_char': '2001-04', 'count': 13}\n",
"{'to_char': '2001-03', 'count': 16}\n",
"{'to_char': '2001-02', 'count': 14}\n",
"{'to_char': '2001-01', 'count': 4}\n"
]
}
],
"prompt_number": 61
"outputs": [],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"result = query_to_dicts('''select to_char(date, 'YYYY'), count(*) \n",
" from documents_document \n",
" group by to_char(date, 'YYYY')\n",
" order by 1 DESC;''')\n",
"for i in result:\n",
" print(i)"
],
"input": [],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"{'to_char': '2001', 'count': 154}\n"
]
}
],
"prompt_number": 62
"outputs": [],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"result = query_to_dicts('''select to_char(date, 'YYYY-MM'), count(*) \n",
" from documents_document \n",
" group by to_char(date, 'YYYY-MM')\n",
" order by 1 DESC;''')\n",
"for i in result:\n",
" print(i)"
],
"input": [],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"{'to_char': '2001-12', 'count': 9}\n",
"{'to_char': '2001-11', 'count': 10}\n",
"{'to_char': '2001-10', 'count': 2}\n",
"{'to_char': '2001-09', 'count': 10}\n",
"{'to_char': '2001-08', 'count': 36}\n",
"{'to_char': '2001-07', 'count': 15}\n",
"{'to_char': '2001-06', 'count': 12}\n",
"{'to_char': '2001-05', 'count': 13}\n",
"{'to_char': '2001-04', 'count': 13}\n",
"{'to_char': '2001-03', 'count': 16}\n",
"{'to_char': '2001-02', 'count': 14}\n",
"{'to_char': '2001-01', 'count': 4}\n"
]
}
],
"prompt_number": 83
"outputs": [],
"prompt_number": 12
},
{
"cell_type": "code",
......
gargantext_web_tutorial.ipynb
View file @ e6a2bb98
{
"metadata": {
"name": "",
"signature": "sha256:bf2595e789140e8e70cb8461a7bd41413d759a51f3c940626bc8ef6856a320e3"
"signature": "sha256:2afae28d08bbb0945aaca44a5b704550048c5dc193cc3d81cb11a551fcc03864"
},
"nbformat": 3,
"nbformat_minor": 0,
......@@ -558,17 +558,8 @@
"input": [],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 72,
"text": [
"'French'"
]
}
],
"prompt_number": 72
"outputs": [],
"prompt_number": 0
},
{
"cell_type": "code",
......
templates/corpus.html
View file @ e6a2bb98
......@@ -39,13 +39,13 @@
<div class="row">
<div class="col-md-8">
<div class="col-md-6">
<h3>Histogram publications</h3>
<div id="hero-area" style="height: 300px;"></div>
</div>
<div class="col-md-4">
<div class="col-md-3">
<h3>Donut chart publications</h3>
<div id="hero-donut" style="height: 250px;"></div>
</div>
......@@ -63,13 +63,13 @@
data: [
{% if dates %}
{% for d in dates %}
{period: {{ d.to_char }}, Quantity: {{ d.count }}},
{Period: '{{ d.to_char }}', Quantity: '{{ d.count }}', Infos: '10'},
{% endfor %}
{% endif %}
],
xkey: 'period',
ykeys: ['Quantity',],
labels: ['publications',],
xkey: 'Period',
ykeys: ['Quantity', 'Infos'],
labels: ['publications', 'Infos'],
lineWidth: 2,
hideHover: 'auto',
lineColors: ["#939393",]
......@@ -82,9 +82,9 @@
Morris.Donut({
element: 'hero-donut',
data: [
{% if sources %}
{% for source in sources %}
{label: '{{ source.source }}', value: {{ source.count }} },
{% if sources_donut %}
{% for source in sources_donut %}
{label: '{{ source.source }}, {{source.count}} docs', value: {{ source.part }} },
{% endfor %}
{% endif %}
......
templates/home.html
View file @ e6a2bb98
......@@ -26,6 +26,8 @@
<div class="col-md-4">
<h3><a href="/project/{{ project.id }}">{{ project.title }}</a></h3>
<h4>{{ project.subtitle }}<h4>
<h5>Completed:</h5>
<div class="chart" barColor="#fffff" data-percent="15">15%</div>
</div>
{% endfor %}
{% endif %}
......
templates/project.html
View file @ e6a2bb98
......@@ -34,7 +34,8 @@
<h3><a href="/project/{{ project.id }}/corpus/{{corpus.id}}">{{ corpus.title }}</a></h3>
<h4>{{ corpus.subtitle }}</h4>
<p>{{ corpus.language }}, {{ corpus.database}}</p>
<div class="chart" data-percent="73">73%</div>
<h5>Activity:</h5>
<div class="chart" data-percent="50">73%</div>
</div>
{% endfor %}
......
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