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Adding notebook folder.
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{
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   "source": [
    "from gargantext_notebook import *\n",
    "\n",
    "from gargantext.models import *\n",
    "from gargantext.util.db import *\n",
    "from nltk.tokenize import word_tokenize\n",
    "from statistics import mean\n",
    "from math import log\n",
    "from collections import defaultdict\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import datetime\n",
    "%matplotlib inline  \n",
    "\n",
    "corpus_id = 111107\n",
    "path = '/home/alexandre/travail/recherches/controverses/controverseMortAbeille/ConferencesReunions/2016/06/images'"
   ]
  },
  {
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   "source": [
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    {
     "data": {
      "image/png": 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p4jv+54DvV/A8ZtaQrOBLehA4ExGbK2qPmTUguR9f0l3ALcDHZ3rs5CWNBwcHs/phzWx6\nTeyWux54BPjTiPi/GcrGypUrZ9WYuSJn59mc9e2laTc2nZWc9fxTB/DkDBrKGfyTs9/C+Ph4ctmc\nATw56+q//fbbSeXq2C33n4CFwA8l7ZL09aSWmVkrUnfL3VRDW8ysIR65Z1YgB9+sQA6+WYEamZbb\n15f2/pJaDmA2vRW95JxdzymbI+f15kwHTpXTc5Ij5+x6ztTad955J7lsHXzENyuQg29WIAffrEAO\nvlmBHHyzAjn4ZgVy8M0K5OCbFcjBNyuQg29WIAffrEAOvlmBHHyzAjn4ZgWa1WKbWRVIUXcdZu9n\nOVO9kxfbNLP3HwffrEBJu+VOuu/Lks5JWlZP88ysDqm75SJpBFgHvFJ1o8ysXkm75XY9CtxfeYvM\nrHZJ3/El3QocjIjnKm6PmTXgPa+yK2kIeBD45OSbL1Zm48aNFy6vXbuWtWvXvtdqzaxCs900cwWd\nTTOvk/R7wDbgHTqBHwFeBz4SEW9OU9b9+GYZ6ujHn+0RX90fIuJ54KpJjfo1sCYipjsPYGZzUOpu\nuZMFM3zUN7O5ZTZn9e+MiOURMRARV0fEpin3r4qIIymV79ixI6VYNtf7/qyzxHpTtTpyr7Q/Ukn1\nlvRa26w3lYfsmhXIwTcrUCPTcmutwMx66tWdV3vwzWzu8Ud9swI5+GYFcvDNCuTgmxXIwTcr0P8D\n6VBP+/7qvD8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb4f0a01cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(np.array(matrix), fignum=100, cmap=plt.cm.gray)\n",
    "plt.savefig(path + '/matrix.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "corpus = session.query(Node).filter(Node.id==111107).first()\n",
    "docs = (session.query(Node).filter( Node.parent_id==111107\n",
    "                                  , Node.typename==\"DOCUMENT\"\n",
    "                                  )\n",
    "        .order_by(Node.hyperdata['publication_date'])\n",
    "        .all()\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Le corpus ALL (abeille* and (mort* or disparition)) contient 11282 documents.\n"
     ]
    }
   ],
   "source": [
    "print(\"Le corpus %s contient %d documents.\" % (corpus.name, len(docs)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f31f05be4e0>]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0CXV14T/+55+Hr341mfccMCAsALlxYzLvV8iGDWH14htugI4d0ztPtchKU1hz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\nxCIiIolSYhERkUQpsYiISKL+P6Zsfs5fQsffAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f31f05be5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dico_docs = defaultdict(list)\n",
    "for doc in docs:\n",
    "    dico_docs[int(doc['publication_year'])].append(doc.hyperdata['title'])\n",
    "\n",
    "x = list(dico_docs.keys())\n",
    "y = [ len(ys) for ys in dico_docs.values()]\n",
    "plt.ylabel('Quantité moyenne de mots par article')\n",
    "plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f31e3b74668>]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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c9Xn33BMWInz2Wdhnn5o/Z9UqWLYMmjXLPq+F8tproSY1eXL4fX/9NRxxRKhF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\nKDiIiEglCg4iIlKJgoOIiFTy/2ykppKsFaTrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f31e33d8b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dico_words_length = defaultdict(list)\n",
    "for doc in docs:\n",
    "    number_words = len(word_tokenize(doc.hyperdata['title'] + doc.hyperdata['abstract']))\n",
    "    dico_words_length[int(doc['publication_year'])].append(number_words)\n",
    "\n",
    "x_length = list(dico_words_length.keys())\n",
    "y_length = [mean(dico_words_length[k]) for k in dico_words_length.keys()]\n",
    "plt.plot(x_length,y_length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f662ca85898>]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pSLNmcOCBsGxZ/fu8/DKsXu1qCqY8ZW0DUNVXgcb1vP3Nej7zk2IK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\neqKGIsIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f662cb12e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dico_words_set = defaultdict(list)\n",
    "for doc in docs[:1000]:\n",
    "    number_words = len(set(word_tokenize(doc.hyperdata['title'] + doc.hyperdata['abstract'])))\n",
    "    dico_words_set[int(doc['publication_year'])].append(number_words)\n",
    "\n",
    "x_set = list(dico_words_set.keys())\n",
    "y_set = [mean(dico_words_set[k]) for k in dico_words_set.keys()]\n",
    "plt.plot(x_set,y_set)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DSExJ5O+zf7u4VkJYJyoKevc2Akj37nD4MNS0cQs+aYkUcDX9aqbt5psb6RcZ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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f31e05acf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y,label='Articles / year')\n",
    "#plt.plot(x_set,y_set)\n",
    "plt.plot(x_length, y_length, label='Words average / article')\n",
    "plt.legend(loc='center left', bbox_to_anchor=(0.5,1))\n",
    "plt.savefig(path + '/historyQL.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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zEKkOjUFI0XCHCy6AZ5+NWU7NmqUdUXr++c/Y4/vNN2HGDNh777QjkjRpDEJK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\nEBGRrJQgREQkKyUIERHJSglCRESy+v+M47xirLhUWAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f31e9d7d7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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4sJuS1bMn3H71wU0Vvv3WzUY+cwa6d4fnn7ft603KssBiriunI07TfVF3BqwY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\ncWrK/yf5II+fc442AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f31d9ce89b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, ('2002-10-30 00:00:00', 85768.95087396505))\n",
      "(1, ('2008-08-15 00:00:00', 75846.00918598552))\n",
      "(2, ('2012-02-13 00:00:00', 83418.01639344262))\n"
     ]
    }
   ],
   "source": [
    "def homogeinity(docs, size=3, plot1=False, plot2=False):\n",
    "    segment = round(len(docs) / size)\n",
    "    \n",
    "    dico = defaultdict(int)\n",
    "    result1 = []\n",
    "    result2 = []\n",
    "    count = 0\n",
    "    \n",
    "    xxs,yys  = 0,0\n",
    "    xs,ys = [],[]\n",
    "    bag = set()\n",
    "            \n",
    "    for doc in docs:\n",
    "        if count == 1:\n",
    "            date = doc.hyperdata['publication_date']\n",
    "            bag = set()\n",
    "            \n",
    "            xxs,yys  = 0,0\n",
    "            xs,ys = [],[]\n",
    "            \n",
    "            \n",
    "        elif count >= segment:\n",
    "            wn = len(dico.keys())\n",
    "            ws = sum(dico.values())\n",
    "            \n",
    "            result1.append((date, ws*count/wn))\n",
    "            result2.append((xs, ys, str(date)))\n",
    "            \n",
    "            count = 0\n",
    "            dico = defaultdict(int)\n",
    "            \n",
    "\n",
    "        \n",
    "        words = word_tokenize(doc.hyperdata['title'] + doc.hyperdata['abstract'])\n",
    "        \n",
    "        for word in words:\n",
    "            dico[word] = dico.get(word, 0) + 1\n",
    "            \n",
    "            xxs += 1\n",
    "            \n",
    "            if word not in bag:\n",
    "                yys += 1\n",
    "                bag.add(word)\n",
    "            \n",
    "            xs.append(xxs)\n",
    "            ys.append(yys)\n",
    "        \n",
    "        count += 1\n",
    "        \n",
    "    if plot1 == True:\n",
    "        x, y = [], []\n",
    "        for xa, (label, ya) in enumerate(result1):\n",
    "            x.append(xa)\n",
    "            y.append(ya)\n",
    "        plt.plot(x, y,label='homogeinity')\n",
    "        plt.show()\n",
    "        \n",
    "    if plot2 == True:\n",
    "        for x1, y1, label in result2:\n",
    "            plt.plot(x1,y1,label=label)\n",
    "        plt.legend(loc='center left', bbox_to_anchor=(0.5,1))\n",
    "        plt.savefig(path + '/heterogeinity.png')\n",
    "        plt.show()\n",
    "\n",
    "    return(enumerate(result1))\n",
    "        \n",
    "for r in homogeinity(docs[1000:], plot1=True, plot2=True):\n",
    "    print(r)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Temporal TFIDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def temporal_tfidf(corpus_id, start=None, end=None):\n",
    "    \n",
    "    doc_sum = (session.query(func.count(Node.id))\n",
    "                  .filter(Node.parent_id == corpus_id)\n",
    "                  .filter(Node.typename == 'DOCUMENT')\n",
    "                  .all()\n",
    "           )[0][0]\n",
    "    #print(\"Somme des documents du corpus: %d\" % doc_sum)\n",
    "    \n",
    "    mapList_id = (session.query(Node.id).filter(Node.parent_id==corpus_id)\n",
    "                                    .filter(Node.typename=='MAPLIST')\n",
    "                                    .first()\n",
    "             )[0]\n",
    "    #print(\"MapList_id : %d\" % mapList_id)\n",
    "    # do the groups\n",
    "    \n",
    "    Map = aliased(NodeNgram)\n",
    "\n",
    "    idf = (session.query(Ngram.id, func.count(NodeNgram.node_id))\n",
    "                     .join(NodeNgram, NodeNgram.ngram_id == Ngram.id)\n",
    "                     .join(Node, NodeNgram.node_id == Node.id)\n",
    "                     .join(Map, Map.ngram_id == Ngram.id)\n",
    "              )\n",
    "    \n",
    "    tf = (session.query(Ngram.id, func.sum(NodeNgram.weight))\n",
    "                     .join(NodeNgram, NodeNgram.ngram_id == Ngram.id)\n",
    "                     .join(Node, NodeNgram.node_id == Node.id)\n",
    "                     .join(Map, Map.ngram_id == Ngram.id)\n",
    "              )\n",
    "    \n",
    "    if start is not None:\n",
    "        #date_start = datetime.datetime.strptime (\"2001-2-3 10:11:12\", \"%Y-%m-%d %H:%M:%S\")\n",
    "        # TODO : more complexe date format here.\n",
    "        date_start = datetime.datetime.strptime (str(start), \"%Y-%m-%d\")\n",
    "        date_start_utc = date_start.strftime(\"%Y-%m-%d %H:%M:%S\")\n",
    "\n",
    "        Start=aliased(NodeHyperdata)\n",
    "        idf = (idf.join( Start, Start.node_id == Node.id)\n",
    "                  .filter( Start.key == 'publication_date')\n",
    "                  .filter( Start.value_utc >= date_start_utc)\n",
    "              )\n",
    "        \n",
    "        tf = (tf.join( Start, Start.node_id == Node.id)\n",
    "                 .filter( Start.key == 'publication_date')\n",
    "                 .filter( Start.value_utc >= date_start_utc)\n",
    "             )\n",
    "\n",
    "\n",
    "    if end is not None:\n",
    "        # TODO : more complexe date format here.\n",
    "        date_end = datetime.datetime.strptime (str(end), \"%Y-%m-%d\")\n",
    "        date_end_utc = date_end.strftime(\"%Y-%m-%d %H:%M:%S\")\n",
    "\n",
    "        End=aliased(NodeHyperdata)\n",
    "\n",
    "        idf = (idf.join(End, End.node_id == Node.id)\n",
    "                  .filter( End.key == 'publication_date')\n",
    "                  .filter( End.value_utc <= date_end_utc )\n",
    "              )\n",
    "\n",
    "        tf = (tf.join(End, End.node_id == Node.id)\n",
    "                  .filter( End.key == 'publication_date')\n",
    "                  .filter( End.value_utc <= date_end_utc )\n",
    "              )\n",
    "    \n",
    "    idf = (idf.filter(Node.parent_id == corpus_id)\n",
    "              .filter(Node.typename == 'DOCUMENT')\n",
    "              .filter(Map.node_id == mapList_id)\n",
    "              .group_by(Ngram.id)\n",
    "              #.limit(30)\n",
    "          )\n",
    "    \n",
    "    tf = (tf.filter(Node.parent_id == corpus_id)\n",
    "            .filter(Node.typename == 'DOCUMENT')\n",
    "            .filter(Map.node_id == mapList_id)\n",
    "            .group_by(Ngram.id)\n",
    "            #.limit(30)\n",
    "         )\n",
    "    \n",
    "    tf_dict  = defaultdict(int)\n",
    "    idf_dict = defaultdict(int)\n",
    "    tfidf_dict = defaultdict(float)\n",
    "    \n",
    "    for ngram_id, s in idf:\n",
    "        #print(ngram_id, s)\n",
    "        idf_dict[ngram_id] = log(int(doc_sum)/int(s))\n",
    "    \n",
    "    for ngram_id, s in tf:\n",
    "        #print(ngram_id, s)\n",
    "        tf_dict[ngram_id]  = s\n",
    "        \n",
    "    for ngram_id in tf_dict.keys():\n",
    "        tfidf_dict[ngram_id] = tf_dict.get(ngram_id, 0) * idf_dict.get(ngram_id, 0)\n",
    "        \n",
    "    return(tfidf_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3, 45, 7, 2] [2, 54, 13, 15] 0.97228425171235\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "def cosine_similarity(v1,v2):\n",
    "    \"compute cosine similarity of v1 to v2: (v1 dot v2)/{||v1||*||v2||)\"\n",
    "    sumxx, sumxy, sumyy = 0, 0, 0\n",
    "    for i in range(len(v1)):\n",
    "        x = v1[i]; y = v2[i]\n",
    "        sumxx += x*x\n",
    "        sumyy += y*y\n",
    "        sumxy += x*y\n",
    "    return sumxy/math.sqrt(sumxx*sumyy)\n",
    "\n",
    "v1,v2 = [3, 45, 7, 2], [2, 54, 13, 15]\n",
    "print(v1, v2, cosine_similarity(v1,v2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD7CAYAAABKWyniAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAwkRfCAhgg8kRPCBhP4Pm6XprjUlc54AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f31f0673b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "years = range(1998, 2016)\n",
    "vecteurs_dict = defaultdict(dict)\n",
    "\n",
    "for period in zip(years[:-1], years[1:]):\n",
    "    vecteurs_dict[period[0]] = temporal_tfidf(corpus_id, start=str(period[0])+'-01-01', end=str(period[1])+'-01-01') \n",
    "    \n",
    "\n",
    "set_words = set()\n",
    "\n",
    "for year in vecteurs_dict.keys():\n",
    "    for ngram_id in vecteurs_dict[year].keys():\n",
    "        set_words.add(ngram_id)\n",
    "\n",
    "sorted_ngram_ids= sorted(list(set_words))\n",
    "\n",
    "vecteurs_list = defaultdict(list)\n",
    "\n",
    "for year in vecteurs_dict.keys():\n",
    "    vecteur = []\n",
    "    for ngram_id in sorted_ngram_ids:\n",
    "        vecteur.append(vecteurs_dict[year].get(ngram_id, 0))\n",
    "    vecteurs_list[year] = vecteur\n",
    "\n",
    "\n",
    "matrix = []\n",
    "for year1 in vecteurs_list.keys():\n",
    "    \n",
    "    vecteur = []\n",
    "    for year2 in vecteurs_list.keys():\n",
    "        vecteur.append(1-cosine_similarity(vecteurs_list[year1],vecteurs_list[year2]))\n",
    "    matrix.append(vecteur)\n",
    "\n",
    "plt.matshow(np.array(matrix), fignum=100, cmap=plt.cm.gray)\n",
    "plt.savefig(path + '/matrix.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "1998-2004\n",
    "2005-2007\n",
    "2008-2015"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'distance' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-58-2a6a7c1902cc>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mperiods\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[1;33m*\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m/srv/gargantext/periods.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     79\u001b[0m         \u001b[0mcomNode\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcomNode\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munion\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     80\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 81\u001b[1;33m \u001b[1;32mdef\u001b[0m \u001b[0mget_partition\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcorpus\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstart\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mend\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdistance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdistance\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     82\u001b[0m     \u001b[1;31m# implicit global session\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     83\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'distance' is not defined"
     ]
    }
   ],
   "source": [
    "from periods import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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u3EhiYiLLli2TEBJCvBCy/kjP4u3tnX+Vuc/Tds2Vy7H9Lndn1IUCXQBz4GW0CQi7gIvA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\nqKoBq7DUUHtYc1He+LgXigorWhuTV1E31l2M272xFA/1QDW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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d8c4faa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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qmY0J8O9j1tmeApyHKfe8ibkn7/HDh9lw5ZUsjcVU+hEJILVsyriumDwZRkaYQfbWi/Z7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\nfMwkroiIRECc3O6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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d87cd7dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c.zipf(top=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+eto4mzZt4sSJE9x0003VXt9iseDv71+pzWg0cvz4cQBSU1NZuXIlX375JXPm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\nRERERERERFrf/wE0jiGo04VURgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f85080eb080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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SEKkk7MbAe8BSYAlwV/T41dFje4C2rkQnIiJ7pZKwdwH3AmcC7YE7gZZAOXAl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\nIiIiIiIiIiIiwfX/Af/e9IRw/CEHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f85083ae198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c.homogeinity(periods=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "s = c.slice_sample(size=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.timedelta(0, 44, 286598)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import datetime as date\n",
    "d = date.now()\n",
    "words = gargantext.Ngrams()\n",
    "words.get(s, lang='fr')\n",
    "date.now() - d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1050 occurrences, 698 forms, 1.50429799427 avg\n"
     ]
    }
   ],
   "source": [
    "words.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, 0)\n"
     ]
    }
   ],
   "source": [
    "g = gargantext.Graph()\n",
    "g.cooc(words, words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'Ngrams' object has no attribute 'sort_ngrams'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-9-e0a8a9be9f0b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mwords\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/home/alexandre/projets/gargantext/gargantext/analysis/ngrams.py\u001b[0m in \u001b[0;36mhead\u001b[0;34m(self, n, size)\u001b[0m\n\u001b[1;32m    203\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    204\u001b[0m         \u001b[0;31m# TODO : n for ngrams\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 205\u001b[0;31m         \u001b[0;32mreturn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_ngrams\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    206\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    207\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtimeline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'Ngrams' object has no attribute 'sort_ngrams'"
     ]
    }
   ],
   "source": [
    "words.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(<class 'str'>, {'count': 1, 'context': {'WOS:000261906200079'}, 'grams': [[('applications', 'NNS'), ('Jmol', 'NNP')]]})"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stems = list(words.stems.keys())\n",
    "words.stems[stems[9]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#p = gargantext.bdd.Pubmed()\n",
    "#p.add('gargantext/data/pubmed/pubmed_result.xml')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "corpus = gargantext.bdd.Europresse()\n",
    "corpus.add(\"/home/alexandre/projets/abeilles/documents/Europresse/html/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "corpus_2004 = gargantext.bdd.Europresse()\n",
    "corpus_2004.add(\"gargantext/data/html/html_french/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#print(\"%s documents in the corpus, we need a samp<le for tests:\" % (len(corpus.corpus)))\n",
    "#bee.timeline(time=year/month/day, from=, to=)\n",
    "#bee.sources()\n",
    "#bee.wordEvolution()\n",
    "\n",
    "test = gargantext.bdd.Europresse()\n",
    "test.add(\"/home/alexandre/projets/ademe/Corpus/Europresse/html/\")\n",
    "#len(test.corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "import operator\n",
    "import re\n",
    "\n",
    "stop_words = ['givors', 'mercredis', 'nc', 'u', 'bouche', 'nez', 'vin']\n",
    "stop_docs = ['erika', 'givors',]\n",
    "\n",
    "def tf(data, stop_words=['stopwords',]):\n",
    "    \"\"\"\n",
    "    data is a list of dict: gargantext.Corpus.corpus\n",
    "    \"\"\"\n",
    "    text_all = ' '.join([a.get('text', '') for a in data])\n",
    "\n",
    "    words = re.findall(r'\\w+', text_all.lower())\n",
    "    words = [x for x in words if not re.match(r'\\d', x)]\n",
    "    words = [x for x in words if x not in stop_words]\n",
    "\n",
    "    c = Counter(words)\n",
    "    total = sum([x for x in c.values()])\n",
    "\n",
    "    tf = [(x[0], x[1]/total, x[1], total) for x in c.items()]\n",
    "    tf.sort(key=lambda tup: tup[1], reverse=True)\n",
    "    \n",
    "    #XXX\n",
    "    tfd = defaultdict(list) \n",
    "    for x in c.items():\n",
    "        tfd[x[0]] = (x[1]/total, x[1], total)\n",
    "    \n",
    "    return(tfd)\n",
    "\n",
    "#Counter(words).most_common(10)\n",
    "\n",
    "def tfidf(tf, tf_temoin, part=20):\n",
    "    tf1 = sorted(tf.items(), key=operator.itemgetter(1), reverse=True)\n",
    "    tf2 = sorted(tf_temoin.items(), key=operator.itemgetter(1), reverse=True)\n",
    "    \n",
    "    result = dict()\n",
    "    s = set([ x[0] for x in tf2[:round(len(tf2)*part/100)]])\n",
    "    for c in tf1[:round(len(tf1)*part/100)]:\n",
    "        if c[0] not in s:\n",
    "            result[c[0]] = c[1][0]\n",
    "    #result.sort(key=lambda tup: tup[1], reverse=True)\n",
    "    result = sorted(result.items(), key=operator.itemgetter(1), reverse=True)\n",
    "    return(result)\n",
    "\n",
    "def specificities(data, test, part=20):\n",
    "    c = tf(data)\n",
    "    t = tf(test)\n",
    "    spec = tfidf(c, t, part=part)\n",
    "    return(spec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def specificity_historic(term, plot=False):\n",
    "    decoupes = slice_homogeneous(corpus, part=30)\n",
    "    #del decoupes[0]\n",
    "    r = [ tf(x) for x in decoupes ]\n",
    "    d = [ x[0]['date'] for x in decoupes ]\n",
    "    # = map(tf, decoupes)\n",
    "    moyenne = []\n",
    "    for x in r:\n",
    "        try:\n",
    "            moyenne.append(x[term][0])\n",
    "        except:\n",
    "            moyenne.append(0)\n",
    "    m = average(moyenne)\n",
    "    spec = [ x - m for x in moyenne ]\n",
    "    #print(spec)\n",
    "    if plot==True:\n",
    "        plt.bar(np.array(d), np.array(spec))\n",
    "    #plt.plot(np.array(d), np.arange())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff05137fa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "specificity_historic('précaution', plot=True)\n",
    "# seuil à x\n",
    "# si spec > x then print timeline\n",
    "# (david)\n",
    "#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "specificites_global = specificities(corpus_2004.corpus, test.corpus, part=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('abeilles', 0.0059870668700093901), ('régent', 0.0048882169995404807), ('gaucho', 0.0043776402920498768)]\n"
     ]
    }
   ],
   "source": [
    "print(specificites_global[:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "slices = slice_homogeneous(corpus, part=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# XXX\n",
    "#spec = specificities(corpus_an['2010'], test.corpus, part=20)\n",
    "spec = list()\n",
    "for sl in slices:\n",
    "    sp = specificities(sl, test.corpus, part=20)\n",
    "    spec.append((sl[0]['date'],sp[:3]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def timeline():\n",
    "    previous = set()\n",
    "    current = set()\n",
    "    old = []\n",
    "\n",
    "    for n, y in enumerate(spec):\n",
    "\n",
    "        current = set(x[0] for x in spec[n][1])\n",
    "        new = []\n",
    "\n",
    "        for t in current:\n",
    "            if t not in previous and t not in old:\n",
    "                old.append(t)\n",
    "                new.append(t)\n",
    "\n",
    "        print(spec[n][0], '+', new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# zipf, répartition des principales occurrences\n",
    "Prendre un corpus test (les flux rss de presse)\n",
    "Extraire les principales occurences de chaque corpus\n",
    "Pour en faire emerger les termes spécifiques de chaque corpus\n",
    "(TF-IDF) puis spécificités temporelles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# salem methods (lexico)\n",
    "# calcul des spécificités historiques\n",
    "# effet gutman AFC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# AFC methods: travail avec Ludovic Lebart et son fameux DTM-Vic => AFC en fortran\n",
    "# https://pypi.python.org/pypi/MDP/2.3\n",
    "# reinert methods\n",
    "# (see iramuteq methods if needed)\n",
    "\n",
    "s = i.sample(size=400)\n",
    "words = gargantext.Ngrams()\n",
    "words.get_ngrams(s, lang='fr')\n",
    "# keys = ['text', 'title', 'keywords']\n",
    "\n",
    "#words.remove_blackwords(blacklist)\n",
    "#words.fusion_whiteliste(whitelist)\n",
    "#words.fusion_synonyms(cvalue)\n",
    "#words.tfidf('word') > list of documents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# words.stem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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48vPzmTdvHi+88EKtPztp0iSef/55OnTowKmnnsqpp57KqFGjeOihhxg2bBi33noriUSi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\nJEmSJEmSJEmSJElSLvv/7rqwrqkR3jcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0b343beac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "white_liste = words.filtre(inf=0.2, sup=0.85, n_min=2, plot=True)\n",
    "#white_liste.first_ngrams()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(407, 407)\n"
     ]
    }
   ],
   "source": [
    "graph = gargantext.Graph()\n",
    "#graph.cooc(white_liste, white_liste, type='sum', inf=5, sup=20) #other type: tfidf\n",
    "graph.cooc(white_liste, white_liste, type='sum', inf=2, sup=20) #other type: tfidf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "graph.distance(type='inclusion', threshold=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "danc follow\n",
      "southern brazil\n",
      "waggl danc\n",
      "food sourc\n",
      "danc communic\n",
      "social bee\n",
      "stingless bee\n"
     ]
    },
    {
     "data": {
      "image/png": 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NzJkzh379+l2xvMvlYtWqVdx5552MGjWqzb7a2loOHz7M/v37aWhoIC0tjRtu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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0b0dcdfb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------\n",
      "solitari bee\n",
      "similar result\n",
      "signific differ\n",
      "bee speci\n"
     ]
    },
    {
     "data": {
      "image/png": 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nzp04HA6mT5/ugwitpWv1BKioqChGjBjBkSNHmDBhgtXhiJedPn2aF198kYkT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\niIiIiIiIiIiIiIiIiIiIiNjH/wcVEdLzE5FySAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0b0b159ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------\n",
      "mobil phase\n",
      "dissolut rate\n",
      "mobil phase composit\n",
      "develop model\n",
      "elsevi b.v. all right\n",
      "( c\n",
      "activ pharmaceut ingredi\n"
     ]
    },
    {
     "data": {
      "image/png": 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+C1kDnD7P8kD+3MKF6+/w59aq4O+JJ38lAkdazBd7nrvQOoESnt7U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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0b0d4f35c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------\n",
      "mushroom bodi\n",
      "kenyon cell\n",
      "antenn lobe\n",
      "wiley period\n",
      "olfactori pathway\n",
      "nervous system\n",
      "visual system\n",
      "later part\n",
      "olfactori system\n"
     ]
    },
    {
     "data": {
      "image/png": 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cAJY6nfzb0qXMnz+fOXPmdHhcxzAM/vKXvzB69GiuuuoqIiIi2uzbrqurY+/e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\nRERERERERERERERERERE7OP/A3JNZXSWdF22AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0b0dde8f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------\n",
      "escherichia coli\n",
      "staphylococcus aureus\n",
      "honey sampl\n"
     ]
    },
    {
     "data": {
      "image/png": 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TTz9l2bJl1zRxdaa8vJyPP/6Y733ve36po78Klq9+2+7xg3mzjcLCQu6//36r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\nIiL28f8BjQmti+xS9h4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0b0d5023c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------\n",
      "honey bee api mellifera\n",
      "genom region\n",
      "fruit fli\n",
      "a. mellifera\n",
      "first report\n",
      "fruit product\n"
     ]
    },
    {
     "data": {
      "image/png": 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k8bOzZ8/yyiuvMG7cOObMmePT3mpJSQkvv/wy3/ve97y69o9hGHzxxRds27aN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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0b0de12c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------\n",
      "first experi\n",
      "second experi\n",
      "third experi\n"
     ]
    },
    {
     "data": {
      "image/png": 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4/LurqKjg+eefZ+HChSQnJ/vk9x3oVOrpwaqrq/nVr37F9773PV0nX9rldDp5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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0b0de690b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------\n",
      "morpholog trait\n",
      "elong factor\n",
      "bodi size\n"
     ]
    },
    {
     "data": {
      "image/png": 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XX86DDz7Y6xKFw+Fg0aJFPP/880ybNq3LM4XIyEiuvPJKMjMzOXbsGJs3b+at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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0b0ddc97b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------\n",
      "express level\n",
      "quantit real-tim pcr\n",
      "express profil\n"
     ]
    },
    {
     "data": {
      "image/png": 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qKipYvnw5P/3pT/UH3U9VV1ezdetWsrOzSU1NZc6cOd0e1zEMg1deeYXx48cz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\niIiIffx/1lrZZFuOcZ0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0b0f74a0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------\n",
      "bee popul\n",
      "brood area\n",
      "honey product\n"
     ]
    },
    {
     "data": {
      "image/png": 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w4AC7d++mtraWKVOmMGzYMD788EMeeeSRXtUmfvz4cd58801mz57t9VDNHTt2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\nIiIiIiIiIiIi9vH/Af9W/PQrjieMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0b0ddc4d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------\n",
      "sucros stimul\n",
      "probosci extens reflex\n",
      "academ press\n"
     ]
    },
    {
     "data": {
      "image/png": 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/fmpqapg+fTpDhgxhy5YtPProo72qJn7q1Cnefvtt5s+f7/FQzT179uBwOJgx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\nROzj/wN0nt/MfXY6sgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0b0d809eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------\n",
      "random complet block design\n",
      "high outcross rate\n",
      "natur outcross\n"
     ]
    },
    {
     "data": {
      "image/png": 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xL7/8knfeeYdFixZ5PVRz3759OBwOZs+eHYAIraVr9fRS0dHRjBo1ipMnTzJ5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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0b0de1a7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------\n"
     ]
    }
   ],
   "source": [
    "graph.draw(type='louvain', node_min=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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