Teaming1.ipynb

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   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import matplotlib.pyplot as plt\n",
      "from collections import Counter\n",
      "from sklearn.feature_extraction.text import TfidfVectorizer\n",
      "documents = []\n",
      "files = [ 'AHOTA.md', 'ctang7', 'djshouse.md', 'JDUGGAN1.md', 'JWILL221.MD', 'tbwhite2', 'rjlusk10.md', \n",
      " 'markdcarringer.md', 'deytia.md', 'kagrawa1.md', 'rcm684', 'CDAFFRON.md', 'dmercer2.md',\n",
      "   'AmreenS.md', 'auxiliary.md', 'ccrumpton.md' ]\n",
      "\n",
      "text = \"\"\n",
      "for f in files:\n",
      "  a = open (f)\n",
      "  str = a.read()\n",
      "  documents .append(str)\n",
      "  text = text + \" \" + str\n",
      "      \n",
      "#Do tf.idf magic \n",
      "tfidf = TfidfVectorizer().fit_transform(documents)\n",
      "# no need to normalize, since Vectorizer will return normalized tf-idf\n",
      "# we may want to see these, e.g., analyze in R\n",
      "pd .DataFrame.from_records(tfidf.A).to_csv('tfidf.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Lets see the similarity matrix\n",
      "# first create labels\n",
      "idx = {}\n",
      "for i in range(len(files)):\n",
      "    idx[i] = files [i]\n",
      "\n",
      "# Multiply the tfidf matrix to get all pairwise distances    \n",
      "df = pd. DataFrame.from_records((tfidf * tfidf.T).A,columns=files)\n",
      "\n",
      "#do pretty printing\n",
      "pd.options.display.float_format = '{:20,.3f}'.format\n",
      "df = df.rename(index=idx)\n",
      "#Output to a file in case we want to look at it separately\n",
      "#df.to_csv(\"dist.csv\")\n",
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>AHOTA.md</th>\n",
        "      <th>ctang7</th>\n",
        "      <th>djshouse.md</th>\n",
        "      <th>JDUGGAN1.md</th>\n",
        "      <th>JWILL221.MD</th>\n",
        "      <th>tbwhite2</th>\n",
        "      <th>rjlusk10.md</th>\n",
        "      <th>markdcarringer.md</th>\n",
        "      <th>deytia.md</th>\n",
        "      <th>kagrawa1.md</th>\n",
        "      <th>rcm684</th>\n",
        "      <th>CDAFFRON.md</th>\n",
        "      <th>dmercer2.md</th>\n",
        "      <th>AmreenS.md</th>\n",
        "      <th>auxiliary.md</th>\n",
        "      <th>ccrumpton.md</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>AHOTA.md</th>\n",
        "      <td>               1.000</td>\n",
        "      <td>               0.128</td>\n",
        "      <td>               0.146</td>\n",
        "      <td>               0.101</td>\n",
        "      <td>               0.111</td>\n",
        "      <td>               0.011</td>\n",
        "      <td>               0.038</td>\n",
        "      <td>               0.099</td>\n",
        "      <td>               0.014</td>\n",
        "      <td>               0.047</td>\n",
        "      <td>               0.030</td>\n",
        "      <td>               0.051</td>\n",
        "      <td>               0.053</td>\n",
        "      <td>               0.104</td>\n",
        "      <td>               0.295</td>\n",
        "      <td>               0.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ctang7</th>\n",
        "      <td>               0.128</td>\n",
        "      <td>               1.000</td>\n",
        "      <td>               0.173</td>\n",
        "      <td>               0.299</td>\n",
        "      <td>               0.219</td>\n",
        "      <td>               0.102</td>\n",
        "      <td>               0.093</td>\n",
        "      <td>               0.202</td>\n",
        "      <td>               0.161</td>\n",
        "      <td>               0.206</td>\n",
        "      <td>               0.113</td>\n",
        "      <td>               0.206</td>\n",
        "      <td>               0.189</td>\n",
        "      <td>               0.280</td>\n",
        "      <td>               0.249</td>\n",
        "      <td>               0.104</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>djshouse.md</th>\n",
        "      <td>               0.146</td>\n",
        "      <td>               0.173</td>\n",
        "      <td>               1.000</td>\n",
        "      <td>               0.073</td>\n",
        "      <td>               0.200</td>\n",
        "      <td>               0.131</td>\n",
        "      <td>               0.075</td>\n",
        "      <td>               0.135</td>\n",
        "      <td>               0.132</td>\n",
        "      <td>               0.153</td>\n",
        "      <td>               0.082</td>\n",
        "      <td>               0.236</td>\n",
        "      <td>               0.257</td>\n",
        "      <td>               0.128</td>\n",
        "      <td>               0.107</td>\n",
        "      <td>               0.074</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>JDUGGAN1.md</th>\n",
        "      <td>               0.101</td>\n",
        "      <td>               0.299</td>\n",
        "      <td>               0.073</td>\n",
        "      <td>               1.000</td>\n",
        "      <td>               0.153</td>\n",
        "      <td>               0.101</td>\n",
        "      <td>               0.063</td>\n",
        "      <td>               0.088</td>\n",
        "      <td>               0.133</td>\n",
        "      <td>               0.129</td>\n",
        "      <td>               0.106</td>\n",
        "      <td>               0.150</td>\n",
        "      <td>               0.130</td>\n",
        "      <td>               0.136</td>\n",
        "      <td>               0.152</td>\n",
        "      <td>               0.030</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>JWILL221.MD</th>\n",
        "      <td>               0.111</td>\n",
        "      <td>               0.219</td>\n",
        "      <td>               0.200</td>\n",
        "      <td>               0.153</td>\n",
        "      <td>               1.000</td>\n",
        "      <td>               0.065</td>\n",
        "      <td>               0.157</td>\n",
        "      <td>               0.171</td>\n",
        "      <td>               0.104</td>\n",
        "      <td>               0.217</td>\n",
        "      <td>               0.098</td>\n",
        "      <td>               0.387</td>\n",
        "      <td>               0.263</td>\n",
        "      <td>               0.231</td>\n",
        "      <td>               0.168</td>\n",
        "      <td>               0.082</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>tbwhite2</th>\n",
        "      <td>               0.011</td>\n",
        "      <td>               0.102</td>\n",
        "      <td>               0.131</td>\n",
        "      <td>               0.101</td>\n",
        "      <td>               0.065</td>\n",
        "      <td>               1.000</td>\n",
        "      <td>               0.110</td>\n",
        "      <td>               0.069</td>\n",
        "      <td>               0.164</td>\n",
        "      <td>               0.074</td>\n",
        "      <td>               0.149</td>\n",
        "      <td>               0.202</td>\n",
        "      <td>               0.161</td>\n",
        "      <td>               0.070</td>\n",
        "      <td>               0.075</td>\n",
        "      <td>               0.095</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>rjlusk10.md</th>\n",
        "      <td>               0.038</td>\n",
        "      <td>               0.093</td>\n",
        "      <td>               0.075</td>\n",
        "      <td>               0.063</td>\n",
        "      <td>               0.157</td>\n",
        "      <td>               0.110</td>\n",
        "      <td>               1.000</td>\n",
        "      <td>               0.063</td>\n",
        "      <td>               0.130</td>\n",
        "      <td>               0.100</td>\n",
        "      <td>               0.114</td>\n",
        "      <td>               0.099</td>\n",
        "      <td>               0.165</td>\n",
        "      <td>               0.095</td>\n",
        "      <td>               0.094</td>\n",
        "      <td>               0.144</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>markdcarringer.md</th>\n",
        "      <td>               0.099</td>\n",
        "      <td>               0.202</td>\n",
        "      <td>               0.135</td>\n",
        "      <td>               0.088</td>\n",
        "      <td>               0.171</td>\n",
        "      <td>               0.069</td>\n",
        "      <td>               0.063</td>\n",
        "      <td>               1.000</td>\n",
        "      <td>               0.109</td>\n",
        "      <td>               0.137</td>\n",
        "      <td>               0.064</td>\n",
        "      <td>               0.148</td>\n",
        "      <td>               0.131</td>\n",
        "      <td>               0.146</td>\n",
        "      <td>               0.256</td>\n",
        "      <td>               0.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>deytia.md</th>\n",
        "      <td>               0.014</td>\n",
        "      <td>               0.161</td>\n",
        "      <td>               0.132</td>\n",
        "      <td>               0.133</td>\n",
        "      <td>               0.104</td>\n",
        "      <td>               0.164</td>\n",
        "      <td>               0.130</td>\n",
        "      <td>               0.109</td>\n",
        "      <td>               1.000</td>\n",
        "      <td>               0.163</td>\n",
        "      <td>               0.175</td>\n",
        "      <td>               0.164</td>\n",
        "      <td>               0.217</td>\n",
        "      <td>               0.163</td>\n",
        "      <td>               0.171</td>\n",
        "      <td>               0.220</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>kagrawa1.md</th>\n",
        "      <td>               0.047</td>\n",
        "      <td>               0.206</td>\n",
        "      <td>               0.153</td>\n",
        "      <td>               0.129</td>\n",
        "      <td>               0.217</td>\n",
        "      <td>               0.074</td>\n",
        "      <td>               0.100</td>\n",
        "      <td>               0.137</td>\n",
        "      <td>               0.163</td>\n",
        "      <td>               1.000</td>\n",
        "      <td>               0.086</td>\n",
        "      <td>               0.238</td>\n",
        "      <td>               0.163</td>\n",
        "      <td>               0.247</td>\n",
        "      <td>               0.102</td>\n",
        "      <td>               0.054</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>rcm684</th>\n",
        "      <td>               0.030</td>\n",
        "      <td>               0.113</td>\n",
        "      <td>               0.082</td>\n",
        "      <td>               0.106</td>\n",
        "      <td>               0.098</td>\n",
        "      <td>               0.149</td>\n",
        "      <td>               0.114</td>\n",
        "      <td>               0.064</td>\n",
        "      <td>               0.175</td>\n",
        "      <td>               0.086</td>\n",
        "      <td>               1.000</td>\n",
        "      <td>               0.163</td>\n",
        "      <td>               0.234</td>\n",
        "      <td>               0.104</td>\n",
        "      <td>               0.121</td>\n",
        "      <td>               0.242</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CDAFFRON.md</th>\n",
        "      <td>               0.051</td>\n",
        "      <td>               0.206</td>\n",
        "      <td>               0.236</td>\n",
        "      <td>               0.150</td>\n",
        "      <td>               0.387</td>\n",
        "      <td>               0.202</td>\n",
        "      <td>               0.099</td>\n",
        "      <td>               0.148</td>\n",
        "      <td>               0.164</td>\n",
        "      <td>               0.238</td>\n",
        "      <td>               0.163</td>\n",
        "      <td>               1.000</td>\n",
        "      <td>               0.262</td>\n",
        "      <td>               0.217</td>\n",
        "      <td>               0.158</td>\n",
        "      <td>               0.080</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>dmercer2.md</th>\n",
        "      <td>               0.053</td>\n",
        "      <td>               0.189</td>\n",
        "      <td>               0.257</td>\n",
        "      <td>               0.130</td>\n",
        "      <td>               0.263</td>\n",
        "      <td>               0.161</td>\n",
        "      <td>               0.165</td>\n",
        "      <td>               0.131</td>\n",
        "      <td>               0.217</td>\n",
        "      <td>               0.163</td>\n",
        "      <td>               0.234</td>\n",
        "      <td>               0.262</td>\n",
        "      <td>               1.000</td>\n",
        "      <td>               0.182</td>\n",
        "      <td>               0.148</td>\n",
        "      <td>               0.178</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AmreenS.md</th>\n",
        "      <td>               0.104</td>\n",
        "      <td>               0.280</td>\n",
        "      <td>               0.128</td>\n",
        "      <td>               0.136</td>\n",
        "      <td>               0.231</td>\n",
        "      <td>               0.070</td>\n",
        "      <td>               0.095</td>\n",
        "      <td>               0.146</td>\n",
        "      <td>               0.163</td>\n",
        "      <td>               0.247</td>\n",
        "      <td>               0.104</td>\n",
        "      <td>               0.217</td>\n",
        "      <td>               0.182</td>\n",
        "      <td>               1.000</td>\n",
        "      <td>               0.254</td>\n",
        "      <td>               0.142</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>auxiliary.md</th>\n",
        "      <td>               0.295</td>\n",
        "      <td>               0.249</td>\n",
        "      <td>               0.107</td>\n",
        "      <td>               0.152</td>\n",
        "      <td>               0.168</td>\n",
        "      <td>               0.075</td>\n",
        "      <td>               0.094</td>\n",
        "      <td>               0.256</td>\n",
        "      <td>               0.171</td>\n",
        "      <td>               0.102</td>\n",
        "      <td>               0.121</td>\n",
        "      <td>               0.158</td>\n",
        "      <td>               0.148</td>\n",
        "      <td>               0.254</td>\n",
        "      <td>               1.000</td>\n",
        "      <td>               0.053</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ccrumpton.md</th>\n",
        "      <td>               0.000</td>\n",
        "      <td>               0.104</td>\n",
        "      <td>               0.074</td>\n",
        "      <td>               0.030</td>\n",
        "      <td>               0.082</td>\n",
        "      <td>               0.095</td>\n",
        "      <td>               0.144</td>\n",
        "      <td>               0.000</td>\n",
        "      <td>               0.220</td>\n",
        "      <td>               0.054</td>\n",
        "      <td>               0.242</td>\n",
        "      <td>               0.080</td>\n",
        "      <td>               0.178</td>\n",
        "      <td>               0.142</td>\n",
        "      <td>               0.053</td>\n",
        "      <td>               1.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>16 rows \u00d7 16 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "                              AHOTA.md               ctang7  \\\n",
        "AHOTA.md                         1.000                0.128   \n",
        "ctang7                           0.128                1.000   \n",
        "djshouse.md                      0.146                0.173   \n",
        "JDUGGAN1.md                      0.101                0.299   \n",
        "JWILL221.MD                      0.111                0.219   \n",
        "tbwhite2                         0.011                0.102   \n",
        "rjlusk10.md                      0.038                0.093   \n",
        "markdcarringer.md                0.099                0.202   \n",
        "deytia.md                        0.014                0.161   \n",
        "kagrawa1.md                      0.047                0.206   \n",
        "rcm684                           0.030                0.113   \n",
        "CDAFFRON.md                      0.051                0.206   \n",
        "dmercer2.md                      0.053                0.189   \n",
        "AmreenS.md                       0.104                0.280   \n",
        "auxiliary.md                     0.295                0.249   \n",
        "ccrumpton.md                     0.000                0.104   \n",
        "\n",
        "                           djshouse.md          JDUGGAN1.md  \\\n",
        "AHOTA.md                         0.146                0.101   \n",
        "ctang7                           0.173                0.299   \n",
        "djshouse.md                      1.000                0.073   \n",
        "JDUGGAN1.md                      0.073                1.000   \n",
        "JWILL221.MD                      0.200                0.153   \n",
        "tbwhite2                         0.131                0.101   \n",
        "rjlusk10.md                      0.075                0.063   \n",
        "markdcarringer.md                0.135                0.088   \n",
        "deytia.md                        0.132                0.133   \n",
        "kagrawa1.md                      0.153                0.129   \n",
        "rcm684                           0.082                0.106   \n",
        "CDAFFRON.md                      0.236                0.150   \n",
        "dmercer2.md                      0.257                0.130   \n",
        "AmreenS.md                       0.128                0.136   \n",
        "auxiliary.md                     0.107                0.152   \n",
        "ccrumpton.md                     0.074                0.030   \n",
        "\n",
        "                           JWILL221.MD             tbwhite2  \\\n",
        "AHOTA.md                         0.111                0.011   \n",
        "ctang7                           0.219                0.102   \n",
        "djshouse.md                      0.200                0.131   \n",
        "JDUGGAN1.md                      0.153                0.101   \n",
        "JWILL221.MD                      1.000                0.065   \n",
        "tbwhite2                         0.065                1.000   \n",
        "rjlusk10.md                      0.157                0.110   \n",
        "markdcarringer.md                0.171                0.069   \n",
        "deytia.md                        0.104                0.164   \n",
        "kagrawa1.md                      0.217                0.074   \n",
        "rcm684                           0.098                0.149   \n",
        "CDAFFRON.md                      0.387                0.202   \n",
        "dmercer2.md                      0.263                0.161   \n",
        "AmreenS.md                       0.231                0.070   \n",
        "auxiliary.md                     0.168                0.075   \n",
        "ccrumpton.md                     0.082                0.095   \n",
        "\n",
        "                           rjlusk10.md    markdcarringer.md  \\\n",
        "AHOTA.md                         0.038                0.099   \n",
        "ctang7                           0.093                0.202   \n",
        "djshouse.md                      0.075                0.135   \n",
        "JDUGGAN1.md                      0.063                0.088   \n",
        "JWILL221.MD                      0.157                0.171   \n",
        "tbwhite2                         0.110                0.069   \n",
        "rjlusk10.md                      1.000                0.063   \n",
        "markdcarringer.md                0.063                1.000   \n",
        "deytia.md                        0.130                0.109   \n",
        "kagrawa1.md                      0.100                0.137   \n",
        "rcm684                           0.114                0.064   \n",
        "CDAFFRON.md                      0.099                0.148   \n",
        "dmercer2.md                      0.165                0.131   \n",
        "AmreenS.md                       0.095                0.146   \n",
        "auxiliary.md                     0.094                0.256   \n",
        "ccrumpton.md                     0.144                0.000   \n",
        "\n",
        "                             deytia.md          kagrawa1.md  \\\n",
        "AHOTA.md                         0.014                0.047   \n",
        "ctang7                           0.161                0.206   \n",
        "djshouse.md                      0.132                0.153   \n",
        "JDUGGAN1.md                      0.133                0.129   \n",
        "JWILL221.MD                      0.104                0.217   \n",
        "tbwhite2                         0.164                0.074   \n",
        "rjlusk10.md                      0.130                0.100   \n",
        "markdcarringer.md                0.109                0.137   \n",
        "deytia.md                        1.000                0.163   \n",
        "kagrawa1.md                      0.163                1.000   \n",
        "rcm684                           0.175                0.086   \n",
        "CDAFFRON.md                      0.164                0.238   \n",
        "dmercer2.md                      0.217                0.163   \n",
        "AmreenS.md                       0.163                0.247   \n",
        "auxiliary.md                     0.171                0.102   \n",
        "ccrumpton.md                     0.220                0.054   \n",
        "\n",
        "                                rcm684          CDAFFRON.md  \\\n",
        "AHOTA.md                         0.030                0.051   \n",
        "ctang7                           0.113                0.206   \n",
        "djshouse.md                      0.082                0.236   \n",
        "JDUGGAN1.md                      0.106                0.150   \n",
        "JWILL221.MD                      0.098                0.387   \n",
        "tbwhite2                         0.149                0.202   \n",
        "rjlusk10.md                      0.114                0.099   \n",
        "markdcarringer.md                0.064                0.148   \n",
        "deytia.md                        0.175                0.164   \n",
        "kagrawa1.md                      0.086                0.238   \n",
        "rcm684                           1.000                0.163   \n",
        "CDAFFRON.md                      0.163                1.000   \n",
        "dmercer2.md                      0.234                0.262   \n",
        "AmreenS.md                       0.104                0.217   \n",
        "auxiliary.md                     0.121                0.158   \n",
        "ccrumpton.md                     0.242                0.080   \n",
        "\n",
        "                           dmercer2.md           AmreenS.md  \\\n",
        "AHOTA.md                         0.053                0.104   \n",
        "ctang7                           0.189                0.280   \n",
        "djshouse.md                      0.257                0.128   \n",
        "JDUGGAN1.md                      0.130                0.136   \n",
        "JWILL221.MD                      0.263                0.231   \n",
        "tbwhite2                         0.161                0.070   \n",
        "rjlusk10.md                      0.165                0.095   \n",
        "markdcarringer.md                0.131                0.146   \n",
        "deytia.md                        0.217                0.163   \n",
        "kagrawa1.md                      0.163                0.247   \n",
        "rcm684                           0.234                0.104   \n",
        "CDAFFRON.md                      0.262                0.217   \n",
        "dmercer2.md                      1.000                0.182   \n",
        "AmreenS.md                       0.182                1.000   \n",
        "auxiliary.md                     0.148                0.254   \n",
        "ccrumpton.md                     0.178                0.142   \n",
        "\n",
        "                          auxiliary.md         ccrumpton.md  \n",
        "AHOTA.md                         0.295                0.000  \n",
        "ctang7                           0.249                0.104  \n",
        "djshouse.md                      0.107                0.074  \n",
        "JDUGGAN1.md                      0.152                0.030  \n",
        "JWILL221.MD                      0.168                0.082  \n",
        "tbwhite2                         0.075                0.095  \n",
        "rjlusk10.md                      0.094                0.144  \n",
        "markdcarringer.md                0.256                0.000  \n",
        "deytia.md                        0.171                0.220  \n",
        "kagrawa1.md                      0.102                0.054  \n",
        "rcm684                           0.121                0.242  \n",
        "CDAFFRON.md                      0.158                0.080  \n",
        "dmercer2.md                      0.148                0.178  \n",
        "AmreenS.md                       0.254                0.142  \n",
        "auxiliary.md                     1.000                0.053  \n",
        "ccrumpton.md                     0.053                1.000  \n",
        "\n",
        "[16 rows x 16 columns]"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy.cluster.hierarchy import linkage, ward, dendrogram\n",
      "from scipy.spatial.distance import pdist\n",
      "import matplotlib.pyplot as plt\n",
      "import numpy as np\n",
      "import re\n",
      "\n",
      "%matplotlib inline\n",
      "# Clean up labels (remove .md at the end)\n",
      "flab = files\n",
      "for i in range(len(files)):\n",
      "    flab[i] = re.sub(r\"\\.[mM][dD]$\", \"\", files[i])\n",
      "\n",
      "# Calculate cosine distance (clustering need sdistance, not similarity)   \n",
      "dist1 = pdist(tfidf.A, 'cosine')\n",
      "# Do clustering\n",
      "linkage_matrix = linkage(dist1,method='complete')\n",
      "\n",
      "# Plot results\n",
      "plt.figure(1, figsize=(22, 4))\n",
      "plt.clf()\n",
      "ddata = dendrogram(linkage_matrix,\n",
      "               color_threshold=1,\n",
      "               p=17,labels=flab,\n",
      "               truncate_mode='lastp',\n",
      "               show_leaf_counts=True,\n",
      "               )\n",
      "plt.title(\"Clustering of Descriptions\")\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "<matplotlib.text.Text at 0x7f6218c4a090>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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yxJQtW9YcPHjQvPTSS6ZPnz5m+PDhpnnz5qZjx45my5YtxhhjMjMz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\nQgghhBBCCCGEmCT+B/oYaXwDZ01VAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f6218c4fd50>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import networkx as nx\n",
      "import json\n",
      "nxg = nx.Graph()\n",
      "\n",
      "for i in range(len(files)-1):\n",
      "    for j in range(i+1,len(files)):\n",
      "        if (df.iloc[i,j] > .07):\n",
      "            nxg.add_edge(flab[i], flab[j], weight=df.iloc[i,j])\n",
      "    \n",
      "print nxg.degree()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "{'auxiliary': 14, 'djshouse': 15, 'markdcarringer': 11, 'kagrawa1': 13, 'AmreenS': 15, 'tbwhite2': 12, 'ctang7': 15, 'JWILL221': 14, 'ccrumpton': 10, 'JDUGGAN1': 13, 'CDAFFRON': 14, 'dmercer2': 14, 'rjlusk10': 12, 'deytia': 14, 'rcm684': 13, 'AHOTA': 7}\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from networkx.readwrite import json_graph\n",
      "nld = json_graph.node_link_data(nxg)\n",
      "json.dump(nld, open('r/students.json','w'))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import IFrame\n",
      "from IPython.core.display import display\n",
      "# IPython Notebook can serve files and display them into\n",
      "# inline frames. Prepend the path with the 'files' prefix.\n",
      "viz_file = 'files/r/students.html'\n",
      "display(IFrame(viz_file, '100%', '600px'))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "\n",
        "        <iframe\n",
        "            width=\"100%\"\n",
        "            height=600px\"\n",
        "            src=\"files/r/students.html\"\n",
        "            frameborder=\"0\"\n",
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 ]
}