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   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<h1 class=\"alert alert-info\">UPMC HNSCC Cohort <small> <i class=\"icon-globe\"></i> Validation of Primary Findings in UPMC Cohort</small></h1>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Import Data and Packages  \n",
      "For full list of data and packages imported see the [Imports](../Analysis_Notebooks/Imports.ipynb) notebook."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import NotebookImport\n",
      "from Imports import *"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "importing IPython notebook from Imports.ipynb\n",
        "Populating the interactive namespace from numpy and matplotlib"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "changing to source dirctory"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "populating namespace with data"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Read in UPMC Data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "meta = pd.read_csv('../Extra_Data/UPMC_cohort/meta.csv', index_col=0)\n",
      "meta.index = pd.MultiIndex.from_tuples([('-'.join(i.split('-')[:-1]), i.split('-')[-1]) \n",
      "                                        for i in meta.index])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clin = pd.read_csv('../Extra_Data/UPMC_cohort/pitt_broad_data.csv', index_col=0)\n",
      "clin.Tumor_type = clin.Tumor_type.map(str.strip)\n",
      "surv = pd.concat([clin.os_5yr, clin.os_5yr_mons*30.5], keys=['event','days'], axis=1).stack()\n",
      "clin2 = pd.read_csv('../Extra_Data/UPMC_cohort/pitt_broad_data_update.csv', index_col=0)\n",
      "surv2 = pd.concat([clin2.os_5yr, clin2.os_5yr_mons*30.5], keys=['event','days'], axis=1).stack()\n",
      "surv = surv2.combine_first(surv)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Explore Clinical Features of UPMC Cohort"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "keepers = ti(clin.HPV == 'Negative')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cc = clin.ix[true_index(meta.Final_74_exome_analysis[:,'Tumor']>0)]\n",
      "cc = cc.ix[:,cc.apply(pd.value_counts).count().isin([2,3,4])]\n",
      "cc = cc.dropna(1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cox_screen(cc.T, surv)[7:].head(10)"
     ],
     "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>\n",
        "      <th></th>\n",
        "      <th colspan=\"4\" halign=\"left\">hazard</th>\n",
        "      <th colspan=\"4\" halign=\"left\">LR</th>\n",
        "      <th colspan=\"2\" halign=\"left\">concordance</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th>exp(-coef)</th>\n",
        "      <th>exp(coef)</th>\n",
        "      <th>lower .95</th>\n",
        "      <th>upper .95</th>\n",
        "      <th>df</th>\n",
        "      <th>p</th>\n",
        "      <th>stat</th>\n",
        "      <th>q</th>\n",
        "      <th>se</th>\n",
        "      <th>stat</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Neoadj_tx</th>\n",
        "      <td> 0.01</td>\n",
        "      <td> 115.91</td>\n",
        "      <td> 10.25</td>\n",
        "      <td> 1311.28</td>\n",
        "      <td> 1</td>\n",
        "      <td> 4.32e-04</td>\n",
        "      <td> 12.39</td>\n",
        "      <td> 0.00</td>\n",
        "      <td> 0.00</td>\n",
        "      <td> 0.53</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Chemo </th>\n",
        "      <td> 0.04</td>\n",
        "      <td>  27.59</td>\n",
        "      <td>  5.33</td>\n",
        "      <td>  142.93</td>\n",
        "      <td> 2</td>\n",
        "      <td> 1.54e-03</td>\n",
        "      <td> 12.95</td>\n",
        "      <td> 0.00</td>\n",
        "      <td> 0.04</td>\n",
        "      <td> 0.62</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Adj_chemo</th>\n",
        "      <td> 0.04</td>\n",
        "      <td>  27.59</td>\n",
        "      <td>  5.33</td>\n",
        "      <td>  142.93</td>\n",
        "      <td> 2</td>\n",
        "      <td> 1.54e-03</td>\n",
        "      <td> 12.95</td>\n",
        "      <td> 0.00</td>\n",
        "      <td> 0.04</td>\n",
        "      <td> 0.62</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Recurrence</th>\n",
        "      <td> 0.45</td>\n",
        "      <td>   2.21</td>\n",
        "      <td>  1.18</td>\n",
        "      <td>    4.12</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1.41e-02</td>\n",
        "      <td>  6.02</td>\n",
        "      <td> 0.04</td>\n",
        "      <td> 0.04</td>\n",
        "      <td> 0.57</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>PNI</th>\n",
        "      <td> 0.42</td>\n",
        "      <td>   2.36</td>\n",
        "      <td>  0.86</td>\n",
        "      <td>    6.50</td>\n",
        "      <td> 2</td>\n",
        "      <td> 1.49e-02</td>\n",
        "      <td>  8.41</td>\n",
        "      <td> 0.04</td>\n",
        "      <td> 0.04</td>\n",
        "      <td> 0.62</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>DistMets</th>\n",
        "      <td> 0.35</td>\n",
        "      <td>   2.82</td>\n",
        "      <td>  1.33</td>\n",
        "      <td>    6.01</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1.44e-02</td>\n",
        "      <td>  5.98</td>\n",
        "      <td> 0.04</td>\n",
        "      <td> 0.03</td>\n",
        "      <td> 0.55</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>EPS</th>\n",
        "      <td> 1.45</td>\n",
        "      <td>   0.69</td>\n",
        "      <td>  0.30</td>\n",
        "      <td>    1.58</td>\n",
        "      <td> 2</td>\n",
        "      <td> 2.14e-02</td>\n",
        "      <td>  7.69</td>\n",
        "      <td> 0.05</td>\n",
        "      <td> 0.04</td>\n",
        "      <td> 0.62</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Dif</th>\n",
        "      <td> 0.90</td>\n",
        "      <td>   1.11</td>\n",
        "      <td>  0.58</td>\n",
        "      <td>    2.13</td>\n",
        "      <td> 3</td>\n",
        "      <td> 6.69e-02</td>\n",
        "      <td>  7.16</td>\n",
        "      <td> 0.14</td>\n",
        "      <td> 0.04</td>\n",
        "      <td> 0.54</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Ethnicity</th>\n",
        "      <td> 6.71</td>\n",
        "      <td>   0.15</td>\n",
        "      <td>  0.02</td>\n",
        "      <td>    1.16</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1.54e-01</td>\n",
        "      <td>  2.04</td>\n",
        "      <td> 0.28</td>\n",
        "      <td> 0.01</td>\n",
        "      <td> 0.51</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Local_recurr</th>\n",
        "      <td> 0.55</td>\n",
        "      <td>   1.83</td>\n",
        "      <td>  0.84</td>\n",
        "      <td>    3.98</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1.53e-01</td>\n",
        "      <td>  2.04</td>\n",
        "      <td> 0.28</td>\n",
        "      <td> 0.03</td>\n",
        "      <td> 0.53</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "                  hazard                                   LR                         concordance      \n",
        "              exp(-coef)  exp(coef)  lower .95  upper .95  df         p   stat     q           se  stat\n",
        "Neoadj_tx           0.01     115.91      10.25    1311.28   1  4.32e-04  12.39  0.00         0.00  0.53\n",
        "Chemo               0.04      27.59       5.33     142.93   2  1.54e-03  12.95  0.00         0.04  0.62\n",
        "Adj_chemo           0.04      27.59       5.33     142.93   2  1.54e-03  12.95  0.00         0.04  0.62\n",
        "Recurrence          0.45       2.21       1.18       4.12   1  1.41e-02   6.02  0.04         0.04  0.57\n",
        "PNI                 0.42       2.36       0.86       6.50   2  1.49e-02   8.41  0.04         0.04  0.62\n",
        "DistMets            0.35       2.82       1.33       6.01   1  1.44e-02   5.98  0.04         0.03  0.55\n",
        "EPS                 1.45       0.69       0.30       1.58   2  2.14e-02   7.69  0.05         0.04  0.62\n",
        "Dif                 0.90       1.11       0.58       2.13   3  6.69e-02   7.16  0.14         0.04  0.54\n",
        "Ethnicity           6.71       0.15       0.02       1.16   1  1.54e-01   2.04  0.28         0.01  0.51\n",
        "Local_recurr        0.55       1.83       0.84       3.98   1  1.53e-01   2.04  0.28         0.03  0.53"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f = clin.Primary_site.ix[keepers]\n",
      "f = f.replace('Hypopharynx','Other').replace('Sinonasal','Other')\n",
      "get_surv_fit_lr(surv, f)"
     ],
     "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>\n",
        "      <th></th>\n",
        "      <th colspan=\"2\" halign=\"left\">Stats</th>\n",
        "      <th colspan=\"3\" halign=\"left\">Median Survival</th>\n",
        "      <th colspan=\"3\" halign=\"left\">5y Survival</th>\n",
        "      <th colspan=\"2\" halign=\"left\">Log-Rank</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th># Patients</th>\n",
        "      <th># Events</th>\n",
        "      <th>Median</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "      <th>Surv</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "      <th>chi2</th>\n",
        "      <th>p</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <td>   </td>\n",
        "      <td>   </td>\n",
        "      <td>      </td>\n",
        "      <td>      </td>\n",
        "      <td>    </td>\n",
        "      <td>      </td>\n",
        "      <td>      </td>\n",
        "      <td>      </td>\n",
        "      <td> 4.02</td>\n",
        "      <td> 0.259</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Oral cavity</th>\n",
        "      <td> 36</td>\n",
        "      <td> 22</td>\n",
        "      <td>  2.99</td>\n",
        "      <td>   1.3</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0.411</td>\n",
        "      <td> 0.277</td>\n",
        "      <td>  0.61</td>\n",
        "      <td>     </td>\n",
        "      <td>      </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Larynx</th>\n",
        "      <td> 15</td>\n",
        "      <td>  6</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   1.5</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0.574</td>\n",
        "      <td> 0.366</td>\n",
        "      <td> 0.901</td>\n",
        "      <td>     </td>\n",
        "      <td>      </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Other</th>\n",
        "      <td>  8</td>\n",
        "      <td>  4</td>\n",
        "      <td>   3.7</td>\n",
        "      <td>   1.8</td>\n",
        "      <td> NaN</td>\n",
        "      <td>   0.5</td>\n",
        "      <td>  0.25</td>\n",
        "      <td>     1</td>\n",
        "      <td>     </td>\n",
        "      <td>      </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Oropharynx</th>\n",
        "      <td>  4</td>\n",
        "      <td>  3</td>\n",
        "      <td> 0.844</td>\n",
        "      <td> 0.535</td>\n",
        "      <td> NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>     </td>\n",
        "      <td>      </td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
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       "prompt_number": 7,
       "text": [
        "                 Stats          Median Survival              5y Survival               Log-Rank       \n",
        "            # Patients # Events          Median  Lower Upper        Surv  Lower  Upper     chi2      p\n",
        "                                                                                           4.02  0.259\n",
        "Oral cavity         36       22            2.99    1.3   NaN       0.411  0.277   0.61                \n",
        "Larynx              15        6             NaN    1.5   NaN       0.574  0.366  0.901                \n",
        "Other                8        4             3.7    1.8   NaN         0.5   0.25      1                \n",
        "Oropharynx           4        3           0.844  0.535   NaN         NaN    NaN    NaN                "
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f = clin.Nodal_stage.ix[keepers].fillna('.')\n",
      "f = f.replace('N2A','N2').replace('N2B','N2').replace('N2C','N2')\n",
      "get_surv_fit_lr(surv, f)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
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        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th colspan=\"2\" halign=\"left\">Stats</th>\n",
        "      <th colspan=\"3\" halign=\"left\">Median Survival</th>\n",
        "      <th colspan=\"3\" halign=\"left\">5y Survival</th>\n",
        "      <th colspan=\"2\" halign=\"left\">Log-Rank</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th># Patients</th>\n",
        "      <th># Events</th>\n",
        "      <th>Median</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "      <th>Surv</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "      <th>chi2</th>\n",
        "      <th>p</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <td>   </td>\n",
        "      <td>   </td>\n",
        "      <td>      </td>\n",
        "      <td>      </td>\n",
        "      <td>    </td>\n",
        "      <td>      </td>\n",
        "      <td>      </td>\n",
        "      <td>      </td>\n",
        "      <td> 22.1</td>\n",
        "      <td> 0.000192</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>N2</th>\n",
        "      <td> 28</td>\n",
        "      <td> 20</td>\n",
        "      <td>  1.15</td>\n",
        "      <td> 0.978</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0.298</td>\n",
        "      <td> 0.167</td>\n",
        "      <td> 0.532</td>\n",
        "      <td>     </td>\n",
        "      <td>         </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>N0</th>\n",
        "      <td> 19</td>\n",
        "      <td>  6</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>  3.98</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0.672</td>\n",
        "      <td> 0.487</td>\n",
        "      <td> 0.928</td>\n",
        "      <td>     </td>\n",
        "      <td>         </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>N1</th>\n",
        "      <td>  8</td>\n",
        "      <td>  4</td>\n",
        "      <td>   3.7</td>\n",
        "      <td>  2.11</td>\n",
        "      <td> NaN</td>\n",
        "      <td>   0.5</td>\n",
        "      <td>  0.25</td>\n",
        "      <td>     1</td>\n",
        "      <td>     </td>\n",
        "      <td>         </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>.</th>\n",
        "      <td>  5</td>\n",
        "      <td>  2</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>  1.55</td>\n",
        "      <td> NaN</td>\n",
        "      <td>   0.6</td>\n",
        "      <td> 0.293</td>\n",
        "      <td>     1</td>\n",
        "      <td>     </td>\n",
        "      <td>         </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>N3</th>\n",
        "      <td>  3</td>\n",
        "      <td>  3</td>\n",
        "      <td> 0.894</td>\n",
        "      <td> 0.535</td>\n",
        "      <td> NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>     </td>\n",
        "      <td>         </td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
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       "prompt_number": 8,
       "text": [
        "        Stats          Median Survival              5y Survival               Log-Rank          \n",
        "   # Patients # Events          Median  Lower Upper        Surv  Lower  Upper     chi2         p\n",
        "                                                                                  22.1  0.000192\n",
        "N2         28       20            1.15  0.978   NaN       0.298  0.167  0.532                   \n",
        "N0         19        6             NaN   3.98   NaN       0.672  0.487  0.928                   \n",
        "N1          8        4             3.7   2.11   NaN         0.5   0.25      1                   \n",
        ".           5        2             NaN   1.55   NaN         0.6  0.293      1                   \n",
        "N3          3        3           0.894  0.535   NaN         NaN    NaN    NaN                   "
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "survival_and_stats(f.isin(['N2','N3']), surv)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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9RCLhKHfxOzo6pj99+rQZNS2VSo0MDAxkJiYmxbW3NwAAUBXvltbEu6V1td7z8yjPD+Z9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\nwFD/B6ZkWgYFBz8/AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7b82710>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f = clin.clinical_stage.ix[keepers]\n",
      "f = f.replace('IVa','IV').replace('IVb','IV').replace('.', 'Unstaged').fillna('Unstaged')\n",
      "get_surv_fit_lr(surv, f)"
     ],
     "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>\n",
        "      <th></th>\n",
        "      <th colspan=\"2\" halign=\"left\">Stats</th>\n",
        "      <th colspan=\"3\" halign=\"left\">Median Survival</th>\n",
        "      <th colspan=\"3\" halign=\"left\">5y Survival</th>\n",
        "      <th colspan=\"2\" halign=\"left\">Log-Rank</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th># Patients</th>\n",
        "      <th># Events</th>\n",
        "      <th>Median</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "      <th>Surv</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "      <th>chi2</th>\n",
        "      <th>p</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <td>   </td>\n",
        "      <td>   </td>\n",
        "      <td>    </td>\n",
        "      <td>     </td>\n",
        "      <td>    </td>\n",
        "      <td>      </td>\n",
        "      <td>      </td>\n",
        "      <td>      </td>\n",
        "      <td> 4.3</td>\n",
        "      <td> 0.231</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>IV</th>\n",
        "      <td> 43</td>\n",
        "      <td> 27</td>\n",
        "      <td> 1.6</td>\n",
        "      <td>  1.1</td>\n",
        "      <td> NaN</td>\n",
        "      <td>  0.38</td>\n",
        "      <td> 0.258</td>\n",
        "      <td>  0.56</td>\n",
        "      <td>    </td>\n",
        "      <td>      </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>III</th>\n",
        "      <td> 12</td>\n",
        "      <td>  5</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 2.11</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0.583</td>\n",
        "      <td> 0.362</td>\n",
        "      <td> 0.941</td>\n",
        "      <td>    </td>\n",
        "      <td>      </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Unstaged</th>\n",
        "      <td>  5</td>\n",
        "      <td>  2</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 1.55</td>\n",
        "      <td> NaN</td>\n",
        "      <td>   0.6</td>\n",
        "      <td> 0.293</td>\n",
        "      <td>     1</td>\n",
        "      <td>    </td>\n",
        "      <td>      </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>II</th>\n",
        "      <td>  3</td>\n",
        "      <td>  1</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 3.98</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0.667</td>\n",
        "      <td>   0.3</td>\n",
        "      <td>     1</td>\n",
        "      <td>    </td>\n",
        "      <td>      </td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "              Stats          Median Survival             5y Survival               Log-Rank       \n",
        "         # Patients # Events          Median Lower Upper        Surv  Lower  Upper     chi2      p\n",
        "                                                                                        4.3  0.231\n",
        "IV               43       27             1.6   1.1   NaN        0.38  0.258   0.56                \n",
        "III              12        5             NaN  2.11   NaN       0.583  0.362  0.941                \n",
        "Unstaged          5        2             NaN  1.55   NaN         0.6  0.293      1                \n",
        "II                3        1             NaN  3.98   NaN       0.667    0.3      1                "
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f = clin.Alcohol_amt.fillna(0).ix[keepers].replace('.', 0).astype(float) > 13\n",
      "f2 = clin.Alcohol_amt.fillna(0).ix[keepers].replace('.', 0).astype(float) < 7\n",
      "get_surv_fit_lr(surv, 1.*f - 1.*f2)"
     ],
     "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>\n",
        "      <th></th>\n",
        "      <th colspan=\"2\" halign=\"left\">Stats</th>\n",
        "      <th colspan=\"3\" halign=\"left\">Median Survival</th>\n",
        "      <th colspan=\"3\" halign=\"left\">5y Survival</th>\n",
        "      <th colspan=\"2\" halign=\"left\">Log-Rank</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th># Patients</th>\n",
        "      <th># Events</th>\n",
        "      <th>Median</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "      <th>Surv</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "      <th>chi2</th>\n",
        "      <th>p</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <td>   </td>\n",
        "      <td>   </td>\n",
        "      <td>     </td>\n",
        "      <td>      </td>\n",
        "      <td>    </td>\n",
        "      <td>      </td>\n",
        "      <td>       </td>\n",
        "      <td>      </td>\n",
        "      <td> 3.25</td>\n",
        "      <td> 0.197</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-1</th>\n",
        "      <td> 36</td>\n",
        "      <td> 19</td>\n",
        "      <td> 3.98</td>\n",
        "      <td>   1.8</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0.497</td>\n",
        "      <td>  0.357</td>\n",
        "      <td> 0.692</td>\n",
        "      <td>     </td>\n",
        "      <td>      </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 18</td>\n",
        "      <td>  9</td>\n",
        "      <td> 3.65</td>\n",
        "      <td>  1.29</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0.463</td>\n",
        "      <td>  0.276</td>\n",
        "      <td> 0.778</td>\n",
        "      <td>     </td>\n",
        "      <td>      </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>  9</td>\n",
        "      <td>  7</td>\n",
        "      <td>  1.1</td>\n",
        "      <td> 0.953</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0.222</td>\n",
        "      <td> 0.0655</td>\n",
        "      <td> 0.754</td>\n",
        "      <td>     </td>\n",
        "      <td>      </td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "        Stats          Median Survival              5y Survival                Log-Rank       \n",
        "   # Patients # Events          Median  Lower Upper        Surv   Lower  Upper     chi2      p\n",
        "                                                                                   3.25  0.197\n",
        "-1         36       19            3.98    1.8   NaN       0.497   0.357  0.692                \n",
        "0          18        9            3.65   1.29   NaN       0.463   0.276  0.778                \n",
        "1           9        7             1.1  0.953   NaN       0.222  0.0655  0.754                "
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "get_surv_fit_lr(surv, clin.Tob_history.ix[keepers])"
     ],
     "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>\n",
        "      <th></th>\n",
        "      <th colspan=\"2\" halign=\"left\">Stats</th>\n",
        "      <th colspan=\"3\" halign=\"left\">Median Survival</th>\n",
        "      <th colspan=\"3\" halign=\"left\">5y Survival</th>\n",
        "      <th colspan=\"2\" halign=\"left\">Log-Rank</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th># Patients</th>\n",
        "      <th># Events</th>\n",
        "      <th>Median</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "      <th>Surv</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "      <th>chi2</th>\n",
        "      <th>p</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <td>   </td>\n",
        "      <td>   </td>\n",
        "      <td>     </td>\n",
        "      <td>     </td>\n",
        "      <td>    </td>\n",
        "      <td>     </td>\n",
        "      <td>      </td>\n",
        "      <td>      </td>\n",
        "      <td> 0.0913</td>\n",
        "      <td> 0.763</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>yes</th>\n",
        "      <td> 55</td>\n",
        "      <td> 31</td>\n",
        "      <td> 3.65</td>\n",
        "      <td> 1.55</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0.44</td>\n",
        "      <td> 0.324</td>\n",
        "      <td> 0.596</td>\n",
        "      <td>       </td>\n",
        "      <td>      </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>no</th>\n",
        "      <td>  8</td>\n",
        "      <td>  4</td>\n",
        "      <td> 1.43</td>\n",
        "      <td>  1.3</td>\n",
        "      <td> NaN</td>\n",
        "      <td>  0.5</td>\n",
        "      <td>  0.25</td>\n",
        "      <td>     1</td>\n",
        "      <td>       </td>\n",
        "      <td>      </td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "         Stats          Median Survival             5y Survival               Log-Rank       \n",
        "    # Patients # Events          Median Lower Upper        Surv  Lower  Upper     chi2      p\n",
        "                                                                                0.0913  0.763\n",
        "yes         55       31            3.65  1.55   NaN        0.44  0.324  0.596                \n",
        "no           8        4            1.43   1.3   NaN         0.5   0.25      1                "
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "get_surv_fit_lr(surv, clin.PNI.ix[keepers])"
     ],
     "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>\n",
        "      <th></th>\n",
        "      <th colspan=\"2\" halign=\"left\">Stats</th>\n",
        "      <th colspan=\"3\" halign=\"left\">Median Survival</th>\n",
        "      <th colspan=\"3\" halign=\"left\">5y Survival</th>\n",
        "      <th colspan=\"2\" halign=\"left\">Log-Rank</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th># Patients</th>\n",
        "      <th># Events</th>\n",
        "      <th>Median</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "      <th>Surv</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "      <th>chi2</th>\n",
        "      <th>p</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <td>   </td>\n",
        "      <td>   </td>\n",
        "      <td>     </td>\n",
        "      <td>     </td>\n",
        "      <td>    </td>\n",
        "      <td>      </td>\n",
        "      <td>      </td>\n",
        "      <td>      </td>\n",
        "      <td> 6.78</td>\n",
        "      <td> 0.0338</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>yes</th>\n",
        "      <td> 31</td>\n",
        "      <td> 22</td>\n",
        "      <td> 1.55</td>\n",
        "      <td>    1</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0.323</td>\n",
        "      <td> 0.194</td>\n",
        "      <td> 0.537</td>\n",
        "      <td>     </td>\n",
        "      <td>       </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>no</th>\n",
        "      <td> 26</td>\n",
        "      <td>  9</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> 3.98</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0.636</td>\n",
        "      <td> 0.471</td>\n",
        "      <td> 0.858</td>\n",
        "      <td>     </td>\n",
        "      <td>       </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>unknown</th>\n",
        "      <td>  6</td>\n",
        "      <td>  4</td>\n",
        "      <td> 3.25</td>\n",
        "      <td>  1.1</td>\n",
        "      <td> NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>     </td>\n",
        "      <td>       </td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "             Stats          Median Survival             5y Survival               Log-Rank        \n",
        "        # Patients # Events          Median Lower Upper        Surv  Lower  Upper     chi2       p\n",
        "                                                                                      6.78  0.0338\n",
        "yes             31       22            1.55     1   NaN       0.323  0.194  0.537                 \n",
        "no              26        9             NaN  3.98   NaN       0.636  0.471  0.858                 \n",
        "unknown          6        4            3.25   1.1   NaN         NaN    NaN    NaN                 "
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "survival_and_stats(clin['Chemo '], surv, figsize=(5,4), title='Chemo')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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x9Wd3d3dwd3dv8DojhJRHYmIiJCYmfvd5MEjLwWKx2Gw2myW5js1ms7S1tUsl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\nQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7ca1990>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "get_surv_fit_lr(surv, clin.Date_Dx.map(lambda s: s[-2:]).ix[keepers].astype(int)>10)"
     ],
     "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>\n",
        "      <th></th>\n",
        "      <th colspan=\"2\" halign=\"left\">Stats</th>\n",
        "      <th colspan=\"3\" halign=\"left\">Median Survival</th>\n",
        "      <th colspan=\"3\" halign=\"left\">5y Survival</th>\n",
        "      <th colspan=\"2\" halign=\"left\">Log-Rank</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th># Patients</th>\n",
        "      <th># Events</th>\n",
        "      <th>Median</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "      <th>Surv</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "      <th>chi2</th>\n",
        "      <th>p</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <td>   </td>\n",
        "      <td>   </td>\n",
        "      <td>     </td>\n",
        "      <td>    </td>\n",
        "      <td>    </td>\n",
        "      <td>      </td>\n",
        "      <td>      </td>\n",
        "      <td>      </td>\n",
        "      <td> 0.0558</td>\n",
        "      <td> 0.813</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 62</td>\n",
        "      <td> 34</td>\n",
        "      <td> 3.17</td>\n",
        "      <td> 1.5</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0.457</td>\n",
        "      <td> 0.347</td>\n",
        "      <td> 0.602</td>\n",
        "      <td>       </td>\n",
        "      <td>      </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>  1</td>\n",
        "      <td>  1</td>\n",
        "      <td> 3.98</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>       </td>\n",
        "      <td>      </td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "       Stats          Median Survival             5y Survival               Log-Rank       \n",
        "  # Patients # Events          Median Lower Upper        Surv  Lower  Upper     chi2      p\n",
        "                                                                              0.0558  0.813\n",
        "0         62       34            3.17   1.5   NaN       0.457  0.347  0.602                \n",
        "1          1        1            3.98   NaN   NaN         NaN    NaN    NaN                "
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "survival_and_stats(clin.EPS, surv, figsize=(5,4), title='EPS')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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I1QaTtIQcHR3j+Xw+7eDBgzMLCwu1g4KCQtzc3CKIm4YAAHv27JnXu3fvB5s2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\nGEIaAIDFENIAACz2B+hZq5aj70rtAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xa282f50>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "survival_and_stats(clin.HPV, surv, figsize=(5,4), title='HPV')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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lcpydnRNramrUTp8+vdDY2LgA3yYhIcFlyJAhb2g0WoOvr++5pqYmJeFjPH78\n2HrEiBE5NBqtwdXVNb6mpkaN6OsSp7i6usanp6ePITqOzpYbN244DR8+PJdKpXImTpx4++XLl0OJ\njqkzJTo62t3AwKBIRUWFPWXKlJuFhYWGRMfU2ZKXl2euqKjIq6ys1CI6ls6UCRMmpCorK3PxsmzZ\nsuM9fU7CL1rWSktLi5y+vn7JsWPH/Orr6xmLFy/+a/78+WeJjquz1xATEzNDQUGhOSMjw5boeDpT\nKioqBjAYjPqbN29OaWpqUtqxY8cvFhYWz4iOq6OlsrJSC4+fw+FQg4KC9k6ZMuUm0XF19vszbty4\nB2QyWdDbkrSent5bgUBAluQ5Cb9oWSt3794dP3To0Jf4MpPJ1FVRUWE3NzcrEB1bR4u5uXmevLw8\nn0wmC3pbkr548aL31KlTk/FlPp8vTyaTBXV1df2Ijq0j5erVq56Ojo5J+HJ+fr6Zurp6NdFxdabs\n2rXrx/Xr1/+PRCK19qYk3djYSBH+i1lSBdqkJSw7O9vK0tIyB1/W1dUtpVAo3IKCAhMi4+qMvLy8\n4Xw+X0FfX/8t0bF0loODQ8qhQ4e+w5fz8vKGKykp8dp2rZRWnp6eUTdu3HBGCCE+n69w5syZhWPH\njk0jOq6Oev369Vfnzp2bHxISEkJ0LJ1VWFho1NzcrGhtbf1EXV29Zvbs2Zerqqq0evq8kKQlrCN9\nrUHPGTBgQCXeGychIcHV1dU1YdOmTVvJZHIr0bF1FIlEwhISElwpFAp37969axcuXHiG6Jg6orW1\nlezn53f84MGDAUpKSjyi4+msurq6/iYmJgVnzpxZWFhYaKSiotKwaNGi0z1+YqL/hJC1cvDgQf8v\njVrsLcXAwKCotzV3YBiG6urq+s2ePfvSgAEDKs6fP+9DdDziFoFAQL58+bKXkpJSU0VFxQCi4/lS\nCQsLC/Dz8zuGYRhqbW0lkUik1t4Qd3vl/fv3GiQSqbWhoYHWk+eBmrSEdaSvNeg5zc3NilOnTr2p\nrKzcVFBQYOLj4xNBdEydceTIkZVhYWFrEEKITCa3enl5XVFTU6stKysbSHRsX5Kammp/9uzZBRQK\nhYuPGzAwMCiOiYlxJzq2joiPj3e7d+/eeHy5paVFXk5OTtDjfxUQ/b+RrBU+ny+vr69fcvTo0eV1\ndXX9Fi1adMrX1/cc0XGJU3pjTfrMmTMLvvnmm3tExyFuSUhIcNHT03ubm5s7vKmpSen48ePLdHV1\nmW27efbZSEnoAAAgAElEQVSG0tseHJ48eXKJubl5XmFhoSGLxaIvWrToVNu/inuiEH7hslhE9bUm\nOiZxSm9M0mvWrDlAJpMF8vLyfLwoKCg0v337Vo/o2DpaduzY8cvAgQPf0el0loODw53s7GxLomMS\np/TGLnjr16//n7q6erWKigrby8vrsiR+d0kYhvVoTR0AAID4oE0aAACkGCRpAACQYpCkAQBAikGS\nBgAAKQZJGgAxBAQEHJwwYcJd4XVcLpeir6//9s8//1xFVFyg74EkDYAYtm/fvqGgoMDk6tWrs/B1\ne/bsWaetrV2xcuXKI0TGBvoYovsdQoHSW8vZs2fnDxky5E1zc7NCWVmZjqqq6odHjx7ZuLq6xlMo\nlMYhQ4a8SUtLs8O3X7du3W4tLa1KKpXKmTlz5jU2m62CYRhatGjRqfXr1/9v+PDhuXv27FmbmZk5\n2srK6qmysjLXysrqaU5OzgiirxUKcYXwAKBA6c3F3t4+Zc+ePWuXLl16YtWqVYenTJly89dff93O\n4XCocXFxboMGDSrl8/nyN27ccLKxsXlUU1OjVltb29/W1jbj4MGD/hj2MUnr6+uXvHjxYhiGYcja\n2vpxVFSUR0tLi9zvv//+/eTJk28RfZ1QiCvwtnAAuuDQoUPfjR8//p6SkhIvKytrlLm5eX5SUpIT\nmUxudXNzizcxMSm4e/fuBCsrq+xLly7NUVVVrWcymXo0Go1TW1urhtDHWe18fX3PDxs27CVCCDU0\nNKg8ffp0pJWVVfaaNWvCFi9efIrQiwSEgjZpALrA3Nw8f8qUKbdWrlx5hMlk6tXX16vSaDQOhULh\nUigUblpa2tjS0lLdpqYm5fnz558bOnToK39///C6urr+wscRfpv8hQsX5uXk5FhaWlrmmJub5ycm\nJrpI/sqAtICaNABdRKPROFQqtVFDQ6NaQ0OjurKycgD+WUFBgYmmpuZ7f3//8IkTJ94JDQ0NRgih\n9uYh5nK5lLKysoExMTHuAoFALioqytPX1/e8h4fHNWVl5SZJXROQHlCTBqCbGBkZFQ4aNOhdeHi4\nP4/HU3r48OG4r7/++kF9fb0qj8dT4nK5FD6fr5CcnOwYGxs7vampSRkhhDAMI+HHIJFImI+PT8Tt\n27cnkUgkjEajcfr3718HCVp2QZIGoBtdunRpzpUrV7z69+9f5+vre/6PP/74dvDgwSUbNmzYHh8f\n79a/f/+6kydPLt27d+/a8PBw/9zcXAsSiYSRSCQMIYSUlZWbTp06tfjbb7/9Q0VFpeHnn3/+7cKF\nC/OIvi5AHJgFDwAApBjUpAEAQIpBkgYAACkGSRoAAKQYJGkAAJBikKQBAECKQZIGAAApBkkaAACk\nGCRpAACQYpCkAQBAikGSBgAAKQaz4BEIn68BAKIIT+7U3eD7/V/i3G+oSROMyDc+bN68Gc4vw+fv\n699vabnPXb3fkKQBAH1acXEx0SF0CSRpAACQYpCkZZiDgwOcX4bPLysWL15MdAhdAvNJE4hEImFw\n/wFRSCQSwmTkwSFDtR+q/1BHaAzi3m+J9u4gk8mtcnJyAoQ+PuUcMGBApb+/f/gvv/zyv84eKzU1\n1d7f3z88NzfX4tSpU4sjIyO9ExMTXYTXd0fMFRUV2gEBAQcvX748G193+fLl2bdv3550+PDh1W23\nd3Nzi9+0adPWMWPGZCCE0PTp02MjIiJ86HQ6uzviAaA3GbPpBtEhIFbxM/TizE9EhyE2iTd3vHv3\nbhCfz1doaWmRv3r16qzt27dv+Pvvvyd39jj29vapohJxe+vFFRwcHPrtt9/+gS8zmUy9//3vf7+0\nrSUIBAK569evz7h58+ZU4c98fX3P79y5c313xQMAkC2Etknb2dmlm5qavsjJybGsqalRnzZtWhyV\nSm0cNmzYy7t3707AtwsKCtqnpqZW279//7qffvppF0IIpaSkOJiamr549OiR7bJly04kJyc7Tp8+\nPTY1NdXe1NT0BZPJ1COTya3Cb262s7NLj4iI8OFwOLQFCxacVVFRadDV1S2NiYlxFxVfVVWVVmpq\nqv3EiRPvIITQ7t27fzQyMirMycmxbLutpaVlzqxZs64KBAI54fUeHh7XLly4MK+hoUGlu+4b6P0S\nb95GnksC0IzFa5DnkgCUePM20SH1WQyDEUSH0CUST9J4mwyGYaS0tLSxz58/N7OxscmcO3fuRWNj\n4zf19fWqYWFha2bOnBldUVGhHRcXNy0jI2MMk8nUy8vLGx4TE+P+4MGDr/Hj2draPjp58uRSJyen\npNjY2On48fX09Jg2NjaZN27ccEYIofLycp28vLzh7u7uMd9///1+MpncWl5ernPlyhWvFStWHK2p\nqVFvG+v169dnjBs37iG+/OOPP+7m8/kKmzZt2tq2bSkvL284n89X0NfXfyu8XklJiWdtbf0EjwOA\nxJu3UUj4eVSmPw1VDXZDZfrTUEj4eUjUQCSJjzg0MDAoRuhjktbV1S3dvn37BiMjo8K0tLSxcXFx\n0xQUFPiOjo7JU6dOvRkREeEzcODAstraWrX09HS7r7/++sGTJ0+sFRUVmx8+fDgOPyaGYSRRDfJe\nXl5XEhMTXRYtWnQ6NjZ2uouLSyKVSm2MiIjwYTKZenQ6nW1nZ5fu6uqaEBMT47506dKTwvunpaWN\ntbKyym573M42/o8aNSrr/v3733h5eV3pzH6gbzoWcQ2RrOb8ax3Jag7yCz2O9O7zEUII+TkMQX4T\njf+z7/E7b9DxlH/+s16c7XsTVvEzsWvErOJnXT5/SkoKYb1xJJ6kS0pKBmtpaVUJr8vIyBijra1d\noaSkxMPXDRky5J/S0lLdwMDAA2/evDEOCAg4WFJSMtjd3T3m4MGDAR0516xZs67u3LlzfWtrKzk6\nOnrmsmXLTlRVVWlxuVzKoEGD3uHbYRhGMjQ0LGq7f0VFhTbe1CGss0+tNTQ0qrOyskaJ+iwkJOTT\nzw4ODtAtSwa0tPN/PIksJ3J9d2EVP0Osko8J63pJ/x49V3djlYifpLuDTCVpUXR1dUsrKiq0eTye\nEp6o//nnnyF2dnbp2dnZVvPnzz+3cePGbaWlpbqLFi06/fvvv/8wderUm186rpGRUaGBgUFxcnKy\nY2Zmpk1UVJQnmUxulZeXb6mtrVWjUChchBB6+/atvrKyclPb/UkkEkYmk1u7en2tra3tNisJJ2kg\nG+Tb+T8eaxX06HkZBiM+JboZDkNQ7OmDPXq+7lT3Oh2xiv7zR22nTJgw4csbiWBkZIQMDAy6dO6u\nkIokPWjQoHdjx45N+/XXX3fs3LlzfWpqqv3NmzenhoWFrTl27NjyjIyMMRcuXJhHp9PZZDK5VUdH\np1y4yUFOTk7A4XBooo7t5eV1Zd26dXucnJyS8ETs5uYWHxISErJ58+YtxcXFBq6urgkXL16c27aG\nr62tXSGqrbqzzR1VVVVawjV3INuW+3igkPDz/2ryaH0aiY4H+yGXqZM+u6/fRGORzRribL+8w0ch\nnorOV0jdYqLY+784/SPatm2bWPtSqVQUFxcn9rm7SqJJ+nPNBBcuXJi3cOHCMwwGg6Wnp8eMiory\nHDBgQOX333+/38/P77ienh4TIYQ8PT2jVq5ceeT+/fvf4McbM2ZMxk8//bTLy8vrSkBAwEHh83h5\neV3ZsGHD9v/973+/4OuOHDmy0s/P77i2tnYFlUpt3Lhx4zY7O7v0tjGNHTs2TbjtW/g6OtPkkZub\nawHt0QCHJ+ITF6JRcytCimSElgXM/2KClmUKdDXEGCxez1q8TVrcmjRCiNAkDSMOP6O6ulrDzs4u\nvaCgwETc0VMCgUDO1NT0xePHj0czGAyW8Gcw4hAQSRIjDrtrMEtp6jmkaz9frH3xwSxd+V0LCQnp\nctOkuPcb5u74DA0NjWonJ6ek+Ph4N3GPkZCQ4Orq6prQNkEDADqOMVj8h4bd8cCRyAf6kKS/YMuW\nLZtPnjy5VNz9T5w4sWzTpk1buzMmAGQN0QNSiEzS0NxBIGjuAETqTc0dXdEdzR3dAZo7AACgD4Ik\nDQDo04huKukqaO4gEDR3ACLBfNKSBc0dAACpQ/TLXzEMQ3fu3CE8QXcFJGkAAJBi0NxBIGjuAESS\nRHMHfL//P2juAACAPgiSNACgx5BIJIkXNQbjXzGkpKQQc/HdRCpmwQMA9E2lA3Ulfk7dslKJn7Mn\nQU0aANCn9fYXaUBNGgAxZGZmoqdPn35xOzKZjGbPno1UVVUlEBXoi7q9Jl1cXGxAJpNbN27c+K8Z\ntk+dOrXYxcUlsbvP1/bYqamp9hYWFrnddeyKigrt2bNnX0bo47SjP/74424dHZ1yHR2d8gMHDgTi\n28XExLgbGhoW0el09tSpU28WFRUZIoTQ9OnTY9lsNr274gHSYevPP6Pz69ahu79u+GzZEhiIUlNT\niQ5XpvX2Nukeae4gk8mtYWFhawoKCkx64vifY29vn5qbmyve7OAiBAcHh3777bd/IITQnj171qWl\npY3Nz883T0tLG7t79+4fHz58OK6qqkprwYIFZ48dO7a8srJywIgRI56tWLHiKEII+fr6nt+5c+f6\n7ooHSAestRUtVlBEvykpf7aY0US+MAh0URqP9+WN2tHbknaPJGkFBQX+qlWr/vT39w9vb5udO3eu\n19LSqlJVVa3fvHnzFnx9fn6+uaWlZQ6dTmf7+fkdnzRp0u3U1FR7hBDat29fkK6ubimFQuFOnjz5\n77KysoEZGRlj/Pz8jicnJztOnz49NjU11d7U1PQFk8nUI5PJrZWVlQPwY9vZ2aVHRET4cDgc2oIF\nC86qqKg06OrqlsbExLiLirGqqkorNTXVHn8Z7eHDh1eHh4f7q6mp1RoYGBTfvn17komJScGDBw++\nHjt2bNqUKVNuUanUxmXLlp14+vTpSIQQ8vDwuHbhwoV5DQ0NKt11f6VFSkIC+mn6dPSLkxP6afp0\nlJKQQHRIQEakNXc8Sbdtk4Yk/X82b9685cWLF6ZXrlzxavvZ+fPnfS9evDj3yZMn1vn5+eZxcXHT\nYmNjp2MYRvL29o5cvHjxqfr6elVbW9tHd+/enUAikbBXr14NDQsLW5Oenm5XU1OjTqfT2fv27Qsa\nM2ZMxokTJ5Y5OTkl4cdACCE9PT2mjY1N5o0bN5wRQqi8vFwnLy9vuLu7e8z333+/n0wmt5aXl+tc\nuXLFa8WKFUdFvcvw+vXrM8aNG/cQ37+mpkY9Ojp6pqam5nsDA4Pie/fujdfU1Hzv4eFxDT8Pn89X\nOHPmzMKxY8emIYSQkpISz9ra+gn+eV+RkpCAYn5ejwKzspF/3nMUmJWNYn5eD4ka9Ljc5mYU1chB\nC2reiywIIeTi4iKyHDlyhODoO6/HHhzSaDTOgQMHAgMDAw+4urr+6zf3zJkzCzdt2rQVf29hQEDA\nwcuXL882MzN7XlZWNvCHH374HSGEVqxYcXTr1q2bEEJowIABlUlJSU4DBw4sKy8v16HRaJza2lo1\nhD6+GFbUSB4vL68riYmJLosWLTodGxs73cXFJZFKpTZGRET4MJlMPTqdzrazs0t3dXVNiImJcV+6\ndOlJ4f3T0tLGWllZZSOEUE1NjTqHw6GVlJQMLioqMnz9+vVXLi4uiUOGDPnHwcEhhUQiYQkJCa4z\nZsy4TiKRsIiICB/8OKNGjcq6f//+N33pPYcJhw+jdbX/ng9hXW0d2u09F5kwPj4kowf9gBhrg/6z\nL2vvPsTe9/t/1vem7ZvTMxCCpgxCDJKTQyMVlZAHhSry8zs8HlqzZs2n5WfPnqERIz7OhDd48GB0\n6dIlicTZXXq0d4eHh8e1EydOLNuyZctmMzOz5/j6t2/f6vv4+ETgs2RhGEb6+uuvH1RXV2uoq6vX\nCB/DwMCgGN8mKCho3/Pnz81MTEwKuFwuxcTEpOBz5581a9bVnTt3rm9tbSVHR0fPXLZs2Ymqqiot\nLpdLEX57N4ZhJENDw6K2+1dUVGjjTR0qKioNCH18U4uKikrDqFGjsubOnXsxLi5umoODQwpCCLm6\nuiY0NzcrRkVFec6fP//chAkT7g4YMKBSQ0OjOisra5SoGIXfm+bg4NBrugvJNTeLXk/qsVHGoItS\nUlJ63Z/6oqjJySFDeXk0SVm53W1cXFw+/UyhUHrN75UoPd4FLzw83N/Kyip75cqVn/7O0NTUfP/7\n77//4OzsfAMhhOrq6vrX1taqUSgUbmVl5QA+n6+goKDAR+hjosQwjLR37961ampqtXivic2bN29h\nMpl6nzu3kZFRoYGBQXFycrJjZmamTVRUlCeZTG6Vl5dvwc+H0Mf/NJSVlZva7i/8VnBtbe0KRUXF\nZj6fr4B/zuPxlNTV1WuOHj26oqmpSXnNmjVhZDK51cvL68qaNWvCysrKBg4YMKCytbW13Walrr7c\nkigCRUXR62GuBqnVthKwZcuW9jfuQ3pzgkZIAoNZDAwMin/66add+/fv/x5PeLNmzbq6Z8+edVVV\nVVrV1dUa8+bNu3D58uXZAwcOLLO2tn6yY8eOXxsbG6l//vnnqsLCQiOEPiZEHo+nxOfzFR49emR7\n+vTpRU1NTcoIISQnJyfgcDgi//b08vK6sm7duj1OTk5JysrKTYqKis1ubm7xISEhIY2NjdTnz5+b\nTZgw4S5+HmHa2toVeJOKsrJyk7e3d+SmTZu21tfXqz558sQ6KirKc/r06bF6enrMPXv2rMvLyxvO\n4/GUTpw4sUxOTk6A//VQVVWlJVxz7wtcV69Ge9T6/2vdbrV+aGbkRTToHRMNescU2VSAEEKMtUGf\nthEuvWl7Rbsx7d0aIAFjFZXE3re3Je0eSdJtJ/v+8ccfdxsZGRXiy999992hESNGPLOwsMg1NDQs\n0tfXf7tu3bo9CCEUERHhk5SU5KSurl6TlJTkZGpq+oJEImFr1qwJKyoqMlRVVa0PDg4O3bdvX1BC\nQoJrQkKC65gxYzIKCgpMvLy8rgjXfhH6mKSfP39u5u3tHYmvO3LkyMoXL16YamtrV0yaNOn2unXr\n9tjZ2aW3vY6xY8emZWdnW+HLBw8eDGhpaZHX0dEp9/DwuLZnz551I0eOfOri4pK4evXqw05OTkma\nmprvz507Nz8uLm6akpISDyGEcnNzLfAHkH2Fg6srcv9tJwqzHonCh5uhMOuRaOZvvyEHV1eiQwMy\nYKxSx5N02yae3pakpWqqUgzDSKmpqfZ4Gy9CCOno6JTfuHHD2dLSMkfS8VRXV2vY2dmlFxQUmIj7\nlgmBQCBnamr64vHjx6MZDAZL+DOYyrH3mubggDQyHyMzBYXPbndK0IJ2nj+PZsyYIaHIOk4SU5US\nNXeH8O9VSkqKVCRmce+3VA0LJ5FI2OLFi09t3rx5y6JFi07jzQbdOYKwMzQ0NKqdnJyS4uPj3aZN\nmxYnzjESEhJcXV1dE9omaNC7+fj5oTt6euizT64RQuPl5ZGlpaVEYgKiSUOC7gqpqkkj9LHb29Kl\nS08ymUy9ESNGPAsLC1szevTox0TFU11drbFixYqjUVFRnuLsP3PmzOiTJ08uVVNTq237GdSkAZFk\npSYtLcS931KXpGUJJGlApL76Itr+dDqqZf3/P1x7e3MHTFUqw4juMwvnJ/b8kkDEi2eFEzRCCGVn\nZxN09d0DkrQMIzpJwPmJPb+s+PDhA9EhdAkkaQAAkGKQpAEAfVpxcTHRIXQJPDgkEFEPVgDA9cUH\nh9IMencAAEAfA80dAAAgxSBJAwCAFIMkDQAAUgySNAGePHlibWlpmaOiotLg5uYWj0+HKmlubm7x\nGRkZEp9zMykpycnCwiKXRqNxJk2adPvVq1dDJXn+9t7sLmn5+fnmSkpKvKqqKi1Jntfe3j6VQqFw\n8eLn53dc3GN15LuclJTkZGxs/IZOp7MXLFhwlsfjiT/PaBfiGDx4cInwdYeGhgZ3dxwIff73Sqx7\nQcSIIFkuLS0tcvr6+iXHjh3zq6+vZyxevPiv+fPnn5V0DDExMTMUFBSaMzIybCV57oqKigEMBqP+\n5s2bU5qampR27Njxi4WFxTNJnb+yslILPz+Hw6EGBQXtnTJlyk0ivgfjxo17QCaTBZWVlVqSPLee\nnt5bgUBA7o5r+NJ3uaamRk1NTa0mLi7OraamRs3R0TFp48aNod19L78UB5fLVTY0NCzs6X/Tz/1e\niXsvJPrFhIKhu3fvjh86dOhLfJnJZOqqqKiwm5ubFSQVg7m5eZ68vDyfTCYLJJ2kL1686D116tRk\nfJnP58uTyWRBXV1dP0mc/+rVq56Ojo5J+HJ+fr6Zurp6taS/B7t27fpx/fr1/yORSK2STNKNjY0U\nY2Pjgu44Vke+y2fOnFng7OyciC/fu3fvGxMTk9fdeU0dieP58+emkydPvtWT9/ZLv1fi3gto7pCw\n7OxsK+G5sXV1dUspFAq3oKDARFIx5OXlDefz+Qr6+vpvJXVOnIODQ8qhQ4e+E45FSUmJJ6mpXD09\nPaPae7O7pLx+/fqrc+fOzQ8h4N1phYWFRs3NzYrW1tZP1NXVa2bPnn1Z3OaWjnyX224zcuTIp2/e\nvDFubGwU/RbZHoqjsLDQqKysbKCpqekLLS2tquXLlx9r721O4vrS75W49wKStISx2Wx624TEYDBY\n9fX1qkTFJEkDBgyoxF8gjM+1vWnTpq1kMrlVUjHgb3anUCjcvXv3rl24cOEZSZ27tbWV7Ofnd/zg\nwYMB+Jt7JKmurq6/iYlJwZkzZxYWFhYaqaioNCxatOi0OMfqyHeZzWbT6XQ6G1+m0WgcOTk5QXd+\n3zsSB4fDoQ0dOvRVUlKSU25urkVpaaku/jYoSRH3XkjVpP+ygMFgsFgsFkN4HYvFYqiqqtYTFZOk\nffjwod+KFSuO3r17d8K+ffuCfHx8IiQdQ3tvdu/p8x46dOi7oUOHvpowYcJd7P9Gn2E9OOqvrW++\n+eb+rVu3puDLu3fv/lFLS6uKw+HQaDQapzPH6sh3ue02HA6HJhAI5Lrz+96ROObMmXNpzpw5l/Dl\n0NDQYGdn5xuHDx9e3V1xdDbOjt4LqElLmJWVVXZOTs6nV3UwmUw9LpdLwWuXfV1zc7Pi1KlTbyor\nKzcVFBSYSDpBHzlyZGVYWNgahBDC3+yupqZWW1ZWNlAS509NTbU/e/bsAgqFwqVSqY0IfXxZc0xM\njLskzh8fH+9279698fhyS0uLvJycnECcWn1Hvsttt8nKyho1ZMiQf/Br7w4dieP8+fO+ubm5Fvhy\nS0uLfGf/U+ruODt8LyT1wALKx8Ln8+X19fVLjh49uryurq7fokWLTvn6+p4jIhYDA4MiST84PHPm\nzIJvvvnmHlH3PyEhwUVPT+9tbm7u8KamJqXjx48v09XVZTY1NSkREY+kHxyePHlyibm5eV5hYaEh\ni8WiL1q06NScOXMixTlWR77Lwj0aqqur1adOnZq8YcOGbd15TR2JY9OmTVsmTpx4u6KiYsD79+81\nHB0dk3766affeuIet/d7Je69kPiXEgqGMjMzR1tYWDyjUqkcZ2fnxJqaGjUi4iAiSa9Zs+YAmUwW\nyMvL8/GioKDQ/PbtWz1JxbBjx45fBg4c+I5Op7McHBzuZGdnWxL1XSCiC9769ev/p66uXq2iosL2\n8vK63JXvn6jv8unTpxcK9yBJSEhwGTJkyBsajdbg6+t7rif+Q/xSHM3NzQrLli07rqqq+qFfv351\ny5cvP8rlcpV74v4K/151x72ACZYAAECKQZs0AABIMUjSAAAgxSBJAwCAFIMkDQAAUgySdC9XXFxs\nQCaTW2fOnBnd9rNZs2ZdJZPJrW/fvtXvzDFDQkJCVq9efRghhBQUFPjv37/X7K54//jjj29NTU1f\nUCgUrqGhYdHWrVs3Yd04mMPPz+/4nj171omz7+LFi0/99ttvP4v6DMMwkpubW3xDQ4PKoEGD3p04\ncWKZ8OdxcXHT+vXr96GysnKAOOf+ksTERJf2YutNRM3AJ849zcnJsZw0adJtVVXV+v79+9fNmjXr\naklJyeDuijM1NdXewsIiV5x9U1JSHExNTV90VyyEdDuC0n2lqKjIQEFBoVlTU7OqoaGBhq/ncDhU\nTU3NKkVFRV5JSYl+Z44ZEhKyedWqVYe7O9b4+HhXY2Pjgry8PHOBQEB++vSp1VdfffVq//79gUTf\nRwzD0OLFi//67bfffhL12blz53w3bdq0BcM+ThKlo6NTht9vPp8vP3To0JdhYWEBPRmfra1tBlHd\nNburtDcDX2fuKY/HU9TV1WWePHlyCY/HU6yqqtJctWrVYSsrq6dEXx+GYejOnTsOw4YNe9Fdx4Oa\ndB8gJycnsLe3T42Pj3fD1yUmJrrY29unCs+JkZmZaTNixIhnFAqFa29vn4rXkOvr61VnzpwZTaPR\nOOPHj79XVlY2EH+JKJlMbsUn4Pnxxx93DxgwoJJGo3E8PDyuNTQ0qCD0cdKkbdu2bRw+fHgenU5n\nBwUF7RMVZ0pKisP06dNjzc3N88lkcquVlVX2b7/99jM+VLZtTVZ42cDAoHj//v3fa2trV9y9e3eC\nnJycQLiGZWdnl37+/HlffJ/Q0NBgFxeXRPzzly9fDlNVVa1vampS3rdvXxA+Cc/kyZP/Fh5tiLVT\nq9+3b18QPseFt7d3pJmZ2fOdO3euRwihw4cPr6ZSqY3+/v7hiYmJLsbGxm8oFArX09MzCp88p7i4\n2GDixIl3VFRUGgYOHFgWHh7ujx+bTCa3/vnnn6v69+9fV1VVpbVr166ftLW1K1RUVBoWL158is/n\nKyCEkJeX1xXhyal6Gy6XS1FSUuKJmqflc/e07bYFBQUmHA6HtmTJkr8UFRWbNTU13+/Zs2edlpZW\nVVNTk3LbmqzwckhISMjKlSuPTJgw4W5AQMDByZMn/y38nfvzzz9XTZ069WZqaqq9qanpCyaTqUcm\nk1vbftciIiJ8srKyRtnY2GRSqdRGIyOjwitXrnh19z1DCEFNureXoqIiA2VlZe6FCxfmCo8c8/X1\nPSnmkjMAAAsnSURBVHfhwoW5ysrK3JKSEn0Wi0XX1NSsiomJmcHhcKibNm3a4unpeRXDMLR69eo/\n5syZE8nlcpUfP35sTafTWatXr/4Dw/7/iLgbN2442djYPKqpqVGrra3tb2trm3Hw4EF/DMOQvb19\nyrBhw14wmUzdwsJCw379+tWlpqZOaBvrxYsXvTU1Nav2798fmJmZObrt9Kxta7LCywYGBkVOTk43\n8ClNbW1tM06dOrUIwzBUVlamQ6PRGhoaGmj4Ps+fPzelUCiN+GCBnTt3/rxgwYIzr169+mrw4MHF\nTCZTl8PhUN3d3aPXrl27Bz/fzp07f24bd0FBgbG+vn6J8LqXL18O7devX92zZ88sNDU1q9LS0uyK\niooM1NXVqx88eDCOxWLRly5deiIoKGgvhmFo3rx5ETt27PiFz+fL379//2s5ObkWFotFx+/xkiVL\nTnI4HGpubu5wIyOjf6qrq9VramrUxo8ff/fcuXO+GIahvLw8c3Nz8zyiv3Pilry8PHN9ff2SUaNG\nPVFTU6vx8vK6LDyQR9Q9FXWchoYGmrq6evXKlSv/TExMdG7710Xbmqzw8ubNm0MYDEZ9enr6GAzD\n0B9//LHawcHhDr6ts7Nz4okTJ5ampKTY4/uI+q6x2WyVsWPHPjx79uz8lpYWuYiIiHn4lLdQkwYi\nubm5xf/999+TeTyeUnNzs+LNmzenurm5xeOfx8XFTbOzs0ufMWPGdSqV2rhhw4btycnJjjweTyk2\nNnb6r7/+ukNZWbnJ2tr6yYwZM663Pb6VlVX2pUuX5qiqqtbX19er0mg0Dv72CxKJhH377bd/6Orq\nlhoaGhaNHDnyqah2cG9v78jff//9h+TkZEdHR8fkfv36ffD09IwS3hb7TPt0UFDQvn79+n1A6GOt\nMjEx0QUhhGJjY6e7uLgkCs/FYGpq+sLQ0LDozp07ExH6+DYWb2/vSC0traqkpCSngQMHltXV1fUX\nvo72pKWljbWyssoWXjd06NBXq1at+nP8+PH3pk+fHmtnZ5d+4cKFeT4+PhHjxo17SKfT2cHBwaGX\nL1+ejRBC69ev3/n999/vb2pqUiaTya0YhpHq6ur648dbv379TiqV2shms+mNjY3UBw8efK2goMC/\nceOG86xZs64ihJCZmdnzf/75Z0hvnTHxSzPwibqnoo5Do9E49+7dG9/S0iK/Zs2aME1NzfdWVlbZ\nHa3Jurm5xY8ZMyYDIYQ8PDyupaen2zU0NKiw2Wz6gwcPvvb09IwS/h6K+q6pqKg0HDhwINDHxyei\noaFBRUFBgd9Tb1iCJN1H0Ol0tp2dXfqNGzecb926NWXMmDEZwtMivn37Vj8xMdEFf2Cjqqpa39zc\nrFheXq5TXV2toamp+R7f1sDAoLjt8ZuampTnz59/bujQoa/8/f3DhRMMQh+nIMV/VlJS4rW2tor8\nbvn6+p6Pj493q62tVbt///43LS0t8kuWLPlL1LZtEzaeoBH6+FD05s2bU1tbW8nR0dEz586de7Ht\n/vgvV2Vl5YDXr19/5eTklIRhGCkoKGjfkCFD/lmyZMlfHXmoWl5erqOhoVHddv3GjRu3sVgsRnBw\ncChCH+/xn3/+uQq/x6ampi/ev3+vKRAI5HJyciwtLCxyx48ff+/o0aMr5OTkBKKubezYsWnBwcGh\nISEhIZqamu9nz559uaKiQhuhj/8Zqqur17x7927Ql2KWRvgMfObm5vmqqqr1u3fv/jEpKclJeF7n\ntve0Paampi+OHz/u9/r166/Ky8t1Fi1adHrBggVnRb0Kre33SHjWOW1t7QpbW9tHN2/enIo3EQp/\nzxBq/7uWkpLiYGRkVDh16tSb0dHRM8W9L18CSboP8fT0jLp69eqsqKgoT09PzyjhzzQ1Nd97enpG\ncblcCl7S0tLG6unpMfX19d8KPxkvLy/XEd4XwzDShg0btk+cOPHOmzdvjOPi4qaNGDHiWWfjs7Gx\nyYyKivLEl0eOHPl027ZtG7Ozs60Q+piEBAKBHP755xKokZFRoYGBQXFycrJjZmamjfBfDfgvJZ6k\nY2Njp8+cOTNaXl6+Ze/evWvV1dVrioqKDJOTkx0nTZp0+0txk8nkVlHtqHjNHZ/FTFNT8/0PP/zw\nO35/2Ww2PS0tbSyXy6WsWLHi6I0bN5yfPn068siRIyvbO9eLFy9MHRwcUrKyska9e/duUL9+/T5s\n3LhxG/55e//59QYdmYGv7T0VJTw83F+4N5OWllbVDz/88LuZmdnz/Px887bfIyaTqfe5uPDvyfXr\n12d4e3tHtv1c1HftzZs3xjt27Pg1Kytr1KNHj2x76n2JCEGS7lPc3d1jEhISXBMSElzd3d1jhD9z\ndXVNuH379qT79+9/09zcrHjkyJGVvr6+5+Xk5AQ+Pj4R27Zt21hfX6+akZExRjiR4ng8nhKXy6Xw\n+XyF5ORkx9jY2OlNTU3K+Odtayuimi1cXV0TgoODQ7OyskYJBAK5iooK7b17966dOHHiHYQ+vlHj\n9u3bkxobG6lxcXHT0tPT7T53vV5eXlfWrVu3x8nJKUlZWbmp7ecWFha5JBIJO3DgQCBe++HxeEpN\nTU3KfD5f4dGjR7anT59ehF9He00t2traFR35U9bT0zMqIiLCJzc316KpqUk5JCQkZP369TsxDCM1\nNzcrcjgcGovFYvzyyy//a21tJXO5XErbYzx+/Hj04sWLT1VXV2tQqdRGOTk5gY6OTjlCHxN0bW2t\n2sCBA8u+FIs0qqqq0lq9evXhoqIiQzabTV+/fv1OT0/PKHl5+ZbOHGfKlCm3/v7778nHjx/3a2xs\npPJ4PKXIyEjvkpKSwdbW1k90dXVLS0pKBufn55u/e/duUHh4uD/+IFyUWbNmXU1MTHS5c+fORFFd\nWRH673etqalJWSAQyHG5XMr79+818Zp/T7xkl/CHCVC6VoqKigwoFEojvjx58uRbwi9WpVAojXgX\nvISEBBczM7N8RUVF3ujRozPz8/PNMOxjd6eFCxeeplKpnKFDh74MDAzcjz84xGdpe/r0qdWwYcNe\n0Gi0Bm9v74snT55cQqfTWc+ePbNwcHC4ExkZOQc/p7Ozc+Lp06cXto1VIBCQN27cGKqvr1+ipKTU\npKury/T39z/Y2NhIwbCPL6m1s7NLU1ZW5jo7Oyf6+fkdE35w2HbGvtevX5uQSKTW69evT8fXtX34\n+Ouvv27X0tKqxLt9vX37Vm/06NGZFAql0dHRMenq1aueqqqqH+Li4tza64L35s2bIUZGRv+Iuv9t\nZ7E7efLkEiMjo3+UlJSaJk2a9Pe7d+8GYhiGfvvtt59UVVU/DBo0qPTw4cOrZs+efcnOzi4Nw/49\nXSmfz5f/9ttvD2loaLyn0+ksDw+PqA8fPqhi2Mf3MXbnAykiSkdm4OvIzIA3b96cYmtrm0GlUjmq\nqqofHB0dkx49emQjfB4VFRW2gYFB0f79+wNNTU2fY9jH7qX4d1u4jBs37gH+IB3DPj78w/dp77u2\nZs2aAyoqKuwhQ4a8uXLlyixbW9sMb2/viykpKfbC+3a1wCx4AHTA6NGjH58/f9536NChr4iK4dCh\nQ99VVlYO2Lp16yaiYgCSB80dAHTAzz///NuxY8eWExnDxYsX5+IjQYHsgCQNQAfMnj378qtXr4ay\n2Ww6Eee/ffv2pEmTJt3G26eB7IDmDgAAkGJQkwYAACkGSRoAAKQYJGkAAJBikKQBAECKQZIGAAAp\nBkkaAACkGCRpAACQYpCkAQBAikGSBgAAKQZJGgAApBgkaQAAkGKQpAEAQIpBkgYAACkGSRoAAKQY\nJGkAAJBikKQBAECK/T8tKJaKvd0pKAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7d16c10>"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "survival_and_stats(to_quants(clin.Age_Dx, q=.33, labels=True), surv, figsize=(5,4), title='Age')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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w+fNnDR8fH7/x48eHko2GAACBgYHLBg4c+Gjnzp2rlZSUqmWZX4SQbGCQlhFF\nRcWay5cvTw4ICPDU19fPKygo0Nu/f/+PQUFBs8jS9LNnz/qGhIS40Wg0PpkwWCP0bcEueO0EdsFD\nSL7hfNIIIdQOYZBGCCE5hkEaIYTkGAZphBCSYxikEUJIjmGQRgghOYZBGiGE5BgGaYQQkmMYpBFC\nSI5hkEYIITmGQRohhOQYBmmEEJJjGKQRQkiOYZBGCCE5hkEaIYTkGAZpJBPiWKBTmjC/ktfW8iyt\n/GKQlqHnz5/3sbW1TVRTUyt3cXEJKykp0ap7zK1bt0aZmZmlMZlMzsyZM89UVVUpyyKv4oZfSMlq\na/kFaHt5xiDdzgkEAoVJkyZd8fT0DMjPz++sq6tb6OXltU/4mJKSEq3p06ef27dvn1d2drZRYWGh\n7tatWzfIKs8IIenDIC0jjx49Gkin03keHh7HWCwW28/Pz+fatWsT+Hw+jTwmLCzMxd7e/qmLi0uY\nlpZWiY+Pj19ISIibLPONEJIygiAwySDt37/f09XVNUR4n46OTmFycrIlue3t7b1nzZo128nt8vJy\nBoVCqeVyuap1rwcABCZMmOQ7tSRWKAKSCQ6Hw2SxWGzhfSwWi11WVqYufIyRkVE2uc1gMLgKCgqC\nsrIydVVV1Qrhc1uywCVCSP5hdYeMsFgsNpvNZgnvY7PZLHV19bL6juFyuQyBQKAgfAxCqH3DIC0j\ndnZ2CYmJibbkdk5OjiGPx6Obm5un1ndMfHx8b1NT0/S6pWiEUPuFQVpGBgwY8ITH49GPHj264PPn\nzxo+Pj5+48ePD6XRaHzyGGdn5/C4uLh+YWFhLsXFxdp+fn4+bm5uIbLMN0JIymTdgPYtp7i4uL42\nNjYvVVVVuaNHj44oLi7WOn369CwzM7NU8pjw8PAxpqamaQwGo9zd3f1sZWWlsvA1nj171qdnz56J\nDAaj3NnZOay4uFhL1s/VlOTs7Bz25MmT/rLOR2Pp5s2bo6ytrV+pqqpyhwwZEpmSktJd1nlqLF27\ndm28sbFxppqaGmf48OF3MjIyTGSdp6akpKQkKyUlpaqCggJdWeeloTRo0KD7KioqPDLNnz//mCTv\nJ/MHxtTyVFNTo9ClS5fso0ePepSVlbHmzJlzcsaMGWdkna/G8hwaGvoDjUarjo2NtZd1fhpKHz9+\n1GOxWGV37twZXllZqbxt27ZfbGxsXso6Xw2lgoICXTLPXC5X1dvbe8/w4cPvyDpfTflcDBw48CGV\nShXIe5BBFzQ/AAAgAElEQVQ2NDR8LxAIqNK6n8wfGFPLU3R0tGP37t1TyO2cnBwDNTU1TnV1NU3W\neasvWVlZJSkqKvKpVKpA3oP0hQsX3EaMGHGb3Obz+YpUKlVQWlqqIeu81ZcuX748aeTIkbfI7eTk\nZEttbe0iWeersbRz585Va9eu/Y1CodTKc5CuqKigC/+nK42EddJtWEJCgp2trW0iuW1gYJBLp9N5\nqamp5rLMV0OSkpKs+Xw+rUuXLu9lnZfGODk5RQUGBi4jt5OSkqyVlZWr6nadlCeTJk26cvPmzdEA\nAHw+nxYUFDTLwcHhsazz1ZB37951O3v27AxfX19fWeelMRkZGV2rq6uV+vTp81xbW7t46tSpfxYW\nFupK8p4YpNuwpvS1Ri2np6dXQPa2CQ8Pd3Z2dg7fuHHjFiqVWivrvDWEQqEQ4eHhznQ6nbdnz56f\nZ82aFSTrPNWntraW6uHhcSwgIMBTWVm5Stb5aUxpaammubl5alBQ0KyMjIyuampq5bNnzz4t0ZvK\n+t8HTC1PAQEByxsbtSivydjYOFPeqzsIgoDS0lKNqVOnXtTT0/sYHBw8Xdb5aU4SCATUP//8c4qy\nsnLlx48f9WSdH1Fp//79nh4eHkcJgoDa2loKhUKplde8ikqfPn3qQKFQasvLyxmSugeWpNuwpvS1\nRi1XXV2tNGLEiDsqKiqVqamp5tOnTz8n6zw15vDhw4v279//IwAAlUqtnTJlyiUtLa2S/Pz8zrLO\nmyj3798ffObMmZl0Op1H9v83NjbOCg0NHS/rvIkSFhbmEhMT40hu19TUKCooKAgk+l+ArP8SYWp5\n4vP5il26dMk+cuTIgtLSUo3Zs2efcnd3PyvrfDUltYWSdFBQ0Mzvv/8+Rtb5aE4KDw8fY2ho+P7V\nq1fWlZWVyseOHZtvYGCQU7frprwmeW84PHHixFwrK6ukjIwMEzabzZw9e/apuv/NijvJ/KExtS6J\n6mst6zw1JbWFIP3jjz/uo1KpAkVFRT6ZaDRa9fv37w1lnbeG0rZt237p3LlzHpPJZDs5Od1LSEiw\nlXWempraQhe8tWvX/qatrV2kpqbGmTJlyp+S/s5RCIKQWCkdIYRQ62CdNEIIyTEM0gghJMcwSCOE\nkBzDII0QQnIMgzRCEuTp6RkwaNCgaOF9PB6P3qVLl/d//PHHYlnlC7UdGKQRkqBff/11fWpqqvnl\ny5cnk/t27969smPHjh8XLVp0WJZ5Q22ErPscYsLU3tOZM2dmmJqaplVXV9Py8/M7qaurf3769Gk/\nZ2fnMDqdXmFqapr2+PHjAeTxK1eu3KWrq1ugqqrKnTBhwlUOh6NGEATMnj371Nq1a3+ztrZ+tXv3\n7p/j4uL62tnZvVBRUeHZ2dm9SExM7CnrZ8Uk/iTzDGDC9C2kwYMHR+3evfvnefPmHV+8ePGh4cOH\n31m3bt2vXC5X9caNGy76+vq5fD5f8ebNm6P69ev3tLi4WKukpETT3t4+NiAgYDlBfAnSXbp0yX7z\n5o0FQRDQp0+fZ1euXJlYU1Oj8Pvvv68YNmzYXVk/JybxJ1wtHCEpCAwMXObo6BijrKxcFR8f39vK\nyir51q1bo6hUaq2Li0uYubl5anR09CA7O7uEixcvuqqrq5fl5OQYMhgMbklJiRbAl9nt3N3dgy0s\nLFIAAMrLy9VevHjRy87OLuHHH3/cP2fOnFMyfUgkEVgnjZAUWFlZJQ8fPvzuokWLDufk5BiWlZWp\nMxgMLp1O59HpdN7jx48dcnNzDSorK1VmzJhxtnv37m+XL19+oLS0VFP4OsIrxZ8/f35aYmKira2t\nbaKVlVVyRETEGOk/GZI0LEkjJCUMBoOrqqpa0aFDh6IOHToUFRQU6JGvpaammuvo6Hxavnz5gSFD\nhtzz8/PzAQCob65iHo9Hz8/P7xwaGjpeIBAoXLlyZZK7u3vwxIkTr6qoqFRK65mQ5GFJGiEp69q1\na4a+vn7egQMHlldVVSk/evRo4HffffewrKxMvaqqSpnH49H5fD7t9u3bI69fvz6usrJSBQCAIAgK\neQ0KhUJMnz79XGRk5FAKhUIwGAyupqZmKQbo9geDNEIycPHiRddLly5N0dTULHV3dw8+ePDgUiMj\no+z169f/GhYW5qKpqVl64sSJeXv27Pn5wIEDy1+9emVDoVAICoVCAACoqKhUnjp1as7SpUsPqqmp\nla9Zs2bH+fPnp8n6uZD44Sx4CCEkx7AkjRBCcgyDNEIIyTEM0gghJMcwSCOEkBzDII0QQnIMgzRC\nCMkxDNIIISTHMEgjhJAcwyCNEEJyDIM0QgjJMZwFr50g53RASByEJ3MSt2/5s9qS9xVL0u2ItFeM\n2LRpk0xWqpDFfb+lZ22Pn1VZ/x5b877KdZA2MzNLo9FofBqNxqdSqbXkz0pKStU5OTmGrbk2h8Nh\nurm5hTAYDG6XLl3e+/v7e5OvPX782MHS0vI1g8HgOjg4PI6Pj+8NAJCZmWni4ODwWEND4/OKFSv2\nksdnZ2cb+fj4+DV2z9WrV+98/PixAwDAmTNnZpqbm6eyWCz2hAkTrn348KFTQ/mKiIgYs2PHjjWt\neWaEvnVZWVmyzkLzyeqvSnMThUKpLSgo0BXX9VasWPH75MmTL5WUlGi+evXKWk9P7+PDhw8HVlVV\nKenp6X08e/ase0VFBf33339fYWZmlkoQBLi7u589fvz4PC6Xqzpq1Kib9+7dcyKvVVJSotnQ/TIy\nMkzINegSEhJsNTU1SxISEmzLy8sZS5cuDRw3btxfDeWLIAiwt7ePLS4u1qrnrzQhbZs2bZL6PWV1\n32/pWf/5LEmyREnIc2JpsOTqfW2zddK7d+9euXPnztV8Pp+2bNmywK1bt24AADA2Ns5atmxZYEBA\ngCeXy2X4+fn5LF269GDd8yMjI4f+8ccfizU1NUs1NTVLBw8efP/Fixe96HQ6T11dvczd3T0YAGDh\nwoVHvL29/cvLy9UUFBQESkpK1eQ1FBQUBG/evOmhq6tbqKmpWdpQfvfv3//j9OnTzwEA3Lp1a9SU\nKVMu2draJgIAeHt7+/fu3Tu+oXwNHDjw0ZQpUy4FBgYua0qpXRqcnJxadf6tv2/CiSsnQEARgAKh\nAPMmzYNRw0ZL/L4tIYt7yvK+kjbuqrNM7luUVAwdrLUbPOb6xHAp5aZp5Lq6oz7BwcHuR48eXRAf\nH987NTXV/Pbt2yP37dvnRb4eFhbm8vr1a8tHjx4N3LJly0ayukJYQkKCnYODw2OAL0sXPXjw4Pv+\n/fvH9urV68WbN296AADU1tZST506Ncfa2jpJTU2t3M/Pz+fQoUNLunTp8t7GxuaVo6NjTEBAgKeX\nl9e+xvJ8+fLlyUOHDo0EAHBzcwvZsGHDVvK1V69e2ejp6RUAALx48aKXqHwBADg7O4eHhIS4te7d\nE5/WBJBbf9+EX89sA94ILlQPrwTeCC78emYb3Pr7pkTv21IYpNuHxgK0PGqTJelTp07N2bBhw1YD\nA4NcAIDNmzdvWrNmzQ4vL699FAqF8Pb29ldTUyvv3r3722nTpp2/evXqRLKkSiJbmB0cHB7Hxsb2\nd3BweNyjR483AABUKrU2OTnZytbWNrG2tpa6e/fulQAAXbp0ef/w4cPvyGvExcX1s7S0fP3s2bO+\nS5YsOUSn03lBQUGzLC0tXwvfi1x41NjYOAsAwMjIKJt8LSgoaJaXl9e+wMDAZeS968uXpaXl6/T0\ndNOysjJ14QVJ26ITV06AxgTmv/ZpTGCC52FPsOB0AwCAaRbTYZqF+3/OPZ8SDOdTzv1nPx4v3uPb\nm6KkYgAQb6COioqS+B/SNlmSzsvL0+/atWsGud21a9eM3NxcA3KbDIYAANra2sUlJSVa9V3r8ePH\nDnl5efrKyspVv/zyy2/kfisrq+SamhrFyMjIoRs3btwiqjR+5MiRhQsWLDjq4eFxLDQ0dPyBAweW\ni6pa+fDhQ6cOHToUCe/Lzc01GDp0aKSfn59PSEiIG1kV0lC+KBQKoa2tXZyXl6cv6ll8fX2/pqio\nqPoeWS4IKAKR+ylUifX8QvV4/fQNvL2Q+jW1V8VJJWK/pjS+Z22yJG1gYJCbkZHR9bvvvnsIAJCR\nkdHV0NAwh3z9w4cPnXr27PkS4EuPDLIkSqqurlYaNmzY3xEREWPU1NTKO3Xq9MHNzS0kLCzM5fr1\n6+MePnz43fbt29cCADg5OUX17NnzZXZ2tpFwaTwyMnLo999//4DNZrO0tLRKzMzM0szMzNJE9Tqh\nUCgEWUIGACgpKdFydHSMmTp16p83b94cTdZzN5Qv8tza2tp6/7D6+vq24N2UDQVCQeR+ovab7UIr\nM5b2PaA7y/zr9ruQ9heo2dlsyL71Hj4+LWjS8b169Wr0mLFjx4KCgujPsTi1mTUOqVRq7cePHzvq\n6uoWBgcHu2/dunXD3bt3hysrK1eNHTv2xrRp0857eXntMzY2zuratWvGpUuXpmRkZHQdM2ZMxNOn\nT+1NTEwyha/Xv3//2OHDh99dt27dtqKiog5ubm4h7u7uwUOHDo387rvvHoaFhbn069cv7vbt2yNn\nz559Ojk52apjx44fyfPnzZt34vjx4/MBALp16/YuLCzM5f379102b968KSYmxlH4Xjk5OYZ2dnYJ\nxcXF2gAAfn5+PikpKRbBwcH/+d+zvnx5enoG1NbWUlVVVSs+fvzYUUND47PweRQKhWgrv0uA/6+T\nFq7y+HyVDetnrW9S4yGSHAqFAoSEB7NIu+GwprIGXp98A0ajujR6bPTPDyE+Pr7R47S0tODkyZNN\nLhy19H1tMyVp4VFK7u7uwTk5OYa2traJNTU1ikuWLDlENt5RKBTCwsIixczMLI1Op/MOHjy4tG6A\nBvjST3nx4sV/6OrqFuro6HyaNWtW0PLlyw9QKBTC39/fe9asWUEFBQV6lpaWry9dujRFOECHhoaO\nHzdu3HUyTwcPHlw6fPjwu0wmk3PhwoX/1b2XoaFhDoPB4Obm5hoYGBjkPnv2rG9YWJjLxYsXXYWf\nr7q6Wqm+fAEApKSkWJiYmGTWDdBtERmIT149CTVQA4qgCMtmeWKARhKhqKIIypoqwK+oaVKddFNK\n0tLSZkrSTWViYpIZEhLiZm9v/1TWeRG2cuXK3ebm5qmLFi063NJrBAYGLisoKNDbsmXLxrqvtbWS\nNJJf7bEkDQDw9kIqaFtrNakLXlO/S2QbUFO09H1tkw2HbdGKFSv2njlzZmZrrnHhwoX/LVmy5JC4\n8oTQt6QpAbq5pNFFEoO0lBgYGOQOGTLk3v379we35PzIyMihQ4cOjezUqdMHcecNoW+BJPpISyNI\nt7vqjm8VVncgcZFGdYekri0OLA0WlJWKfxgCVncghNqM1sz90Zp07969Ro+RRIBuDSxJtxNYkkbi\nIo2S9Lf4WW33XfAQaqtyc3MhM/M/vUD/g0KhgL29PSgpKUkhV6jNkNW/HeJImZmZxhQKpXb9+vVb\nhfefPHlyzujRoyNk8K+Uk4WFxZv6Xj9w4MCyc+fOTSMIAm7evDnK2tr6laqqKnfIkCGRKSkp3QmC\ngNTUVDMajVatoqLCI1NUVNTg5ORkyyVLlhys79ogg6lKUdOMdvmB0OxsQnQyt2sw0dVYxPXr12Wd\n3XYxVSlLXUPks927d08yb1oTtPR9bfMlaSqVWrt///4fZ8+efdrc3Fxux7NyOBzmyZMn58bFxfUr\nKCjQc3V1vXj58uXJjo6OMf7+/t5Tp0798+XLlz3T09NN3d3dg0+ePDm37jWys7ONUlJSLCwsLFJk\n8QyoZar5NaDjOAs0uzs0eJzCta0gEIie06S96b+x8dkOWyN2S/sZFNXmGw5pNBp/8eLFf5Cj8uoS\nCAQKq1at2qWhofG5Y8eOHw8cOLCcfM3Y2Dhr7969Kzp27Pjx6dOn9k5OTlGbN2/e1KNHjzfa2trF\nR48eXbBixYq9LBaLPWDAgCefPn3SAQCIj4/v3a9fvzhVVdUKcgh6Y/k8ceLEPGdn53AKhUJERUU5\nkcO/lZWVq1atWrUrOTnZqrS0VDMjI6OrqalpuqhrzJkz59SuXbtWtfS9kgcRdyJh0lxP+GHOjzBp\nridE3ImUdZZQG8LOetmq8+t2mZP3icgA2kGQBgDYtGnT5jdv3vQQFSy3b9++9smTJwMyMzNNnjx5\nMsDf3987NDR0PMCXBoybN2+OTklJsSBHKF64cOF/d+7cGXHs2DGPRYsWHWaxWOyCggI9XV3dQjJA\nLl++/ICXl9c+DofD/PXXX9cvXrz4j8byeOnSpSnkfNJOTk5R5NSkAABJSUnWKioqlSwWi52RkdE1\nIiJijL6+fp6RkVH2nj17fiaPGzly5O3Q0NDxDU2yJM8i7kSC74FgyO8yFgqNXCC/y1jwPRCMgRo1\nGTu7dUG6rrYQpNt8dQcAAIPB4O7bt8/Ly8trn7Oz87+WVTh16tScEydOzCNXOlm1atWu48ePzx8/\nfnwowJdVUci5MCgUCrF06dKDBgYGuYqKijUAX9YlpNPpPCcnp6hnz571BQDYt2+fV58+fZ5zOBwm\njUbjNzQVKsCX0vzTp0/t7ezsEgAA9PT0CshJ/sPDw509PDyO+fj4+CkoKAgAAL7//vsH4eHhzllZ\nWcYTJky4ZmBgkOvm5hairq5epq2tXfzy5cue5LXakqPnrgLFzvVf+yh2ruDhdwwMH/ABAMDDyRQ8\nhpj959xj99LgWNR//8FoK8cj8eCXlwI7O6lJx8bExPxn34sXL77Oy0Gj0cSaN0lpF0EaAGDixIlX\njx8/Pn/z5s2bhCfdb2zu6bqTFZHBk6SmplYO8KXum/in+0xUVJTT1KlT/9TV1S3s1q3bu8byVlxc\nrC0QCBSE7/X582eNhQsXHomOjh7k7+/vTc4nLVydYWtrm7hs2bLAP//8c6qbm1sIAICOjs6n/Pz8\nzqKCtPAcAk5OTnK3qkdNPb2PKFTJT/eI6hcVFdUmSpQAABUF6cD7lNWkY9etW/effWVlZaCurg4A\nABoaGtCnTx9xZk8i2k2QBgA4cODAcjs7uwThSYzIuaf19fXzAP4793Rzpaenm27btm1denq6qZaW\nVklmZqbJuXPnpjd0Tt35pKurq5VGjBhxp0ePHm9SU1PNmUwmh3ztt99++2XZsmWBLBaLDQBQU1Oj\nSP6hAGjb80kr1jPQjKj9NhrL5FXdP+ibN2+WXWYaoW7aFwwGz2j0uNgto0WWpOuS9+8MQDsL0sbG\nxlmrV6/euXnz5k3Dhg37GwBg9uzZpzds2LD12rVrE9hsNmvPnj0/C9fz1kU00tm8srJSRSAQKPB4\nPPqnT590yEVhq6qqlOs7R1tbuxgAoLy8XE1NTa08JCTETUVFpTIoKGhW3WNv3rw5uri4WNvPz88n\nIyOj66FDh5b88ccfi8nXCwsLdTt37pzf+LshfxZMnwi+B4L/VeVR+yIEjvl4wJgRQxs812OIWbOq\nEeTteIRaqk02QAmrOw/AqlWrdglXb6xdu3Z7nz59nhsbG2f169cvztPTM4Csj27senV/plAohJWV\nVfLcuXNPWlhYpDg4ODyeOHHi1X79+sXNnj37NHlM3WtSqdTa/v37x7548aIXAMCzZ8/6Pnr0aCCN\nRuOTSUlJqTonJ8fwzJkzM1+8eNFLW1u7ePTo0TdXrFixd8yYMREAX4J8UVFRB3LVmbZmzIih4Lvc\nHQxywkA3OwwMcsJgs+eMRgM0QiSWUc9WnV+3WkfeqgRFwWHhUnLgwIHleXl5+r/99tsvLb1GWFiY\nS0hIiJuoEvi3OtS2LRg2ygXe8XWB2cW6weOKHgTB0T1bYPz48VLKmWjSGBYujX7Sor4P0lg4tj44\nLFzOzZs374SDg8NjPz8/H7LnSHOdOnVqzrp167aJO29IsoY5DYKSK9cAkhseg2TUgQmmpqZSytW3\nqS2UnOvCkrQUHT9+fL6iomLN7NmzTzf33LS0NLNt27atO3HixDxRr2NJGolLe5iqlKWuAWWfSyV9\nm2Zp6fuKQbqdwCCNxKU9z4LXFqs72nzDIUIItWdYkm4nsCSNxKWtVXdoMplQwmaL85ISgQ2HCAFA\nUVERnD59WmTLfl329vYwaNAgKeQK1ZXb2aDxg5rIID9XbNeSRxKp7sjKyjKmUqm1wv2AbWxsXj14\n8OD7xs49derUHLJfMMCXPsaFhYW6ksjnu3fvutnb2z9VVVWt6Nmz58u7d+8OJ187cuTIws6dO+er\nq6uXTZ48+XJRUVEHAIBbt26NMjExyezUqdOH4OBgd/L4Cxcu/C8sLMylofsRBEFxcXEJq6ioUBUI\nBAqrV6/eqaenV6Crq1v4008//S4QCBQAALhcLmP+/PnHO3ToUGRiYpJ54cKF/wF86fP95MmTAZJ4\nL9qLhw8fQsDGjZD267YG011fX9jtK78j65BktJXh78IkVietrKxcxefzaXw+n1ZRUaG6bNmywGnT\npp1vzjXIfw0k9a+Xm5tbyOTJky8XFxdrb9y4ccvUqVP/5PF49MTERNt169Ztu3Hjxtjc3FwDGo3G\n/+WXX34DAFi2bFng9evXx8XFxfXz8fHxq6qqUq6pqVG8ffv2SBcXl7CG7nfu3Lnpffv2faaqqloR\nGBi4LDIycuibN296JCYm2sbGxvbft2+fFwDAypUrd5eVlalnZ2cbXb16deLixYv/yMnJMfT09AxY\ns2bNDkm8F+2JBYMBPqoNJ1clFSAAq4fk3eOqKrFcpy0GZ5JUGg5pNBp/5syZZ/Ly8vQ5HA4TAOD2\n7dsjzczM0lRVVSsmT558mc1ms2JjY/t7eHgcu3379sixY8feIEfWGRoa5nz69EmnuLhYe+zYsTdU\nVVUrLCwsUqKjowcBfJnwyN7e/unSpUsPslgstqOjY8zdu3eHW1lZJWtoaHwWnhaUVFJSopWammq+\natWqXXQ6nTdlypRLSkpK1enp6aaRkZFDJ0+efLl3797xTCaTM2vWrCBytKCiomKNkpJStfDowhMn\nTsybP3/+8cbeB39/f2+y+93NmzdHe3t7+2tpaZV06tTpg4eHx7H79+8P5vF49ODgYPfDhw8vYjAY\nXDs7u4SYmBhHdXX1Mn19/Tw1NbXymJgYR/H9dpovKjwcVo8bB7+MGgWrx42DqPDwxk9CqAUeV4s3\nSLfFftJSqZOuqqpSPnLkyML+/fvHMplMTlZWlrGrq+vF0NDQ8QMGDHji5eW1b/bs2aevXr068fjx\n4/MvXLjwvxs3bowF+FLdkZeXp6+jo/NpxIgRd6ysrJKvXr068d69e0MmTJhw7fXr15YAAM+fP+/j\n6up68cOHD52GDh0aOXfu3JPR0dGDCgoK9AYNGhQ9a9asIOGJjLS0tEo4HA7znwY3Cnk/MzOzNCsr\nq2TyuIqKCtXz589Pc3BweAwAEBgYuGzMmDER1dXVSrt3715JEATl+fPnfRYuXHikofcgLS3NrKio\nqAM5ZH379u1rjYyMssnXX7582VNPT68gMTHRVl9fP2/Dhg1bL1y48L+OHTt+3LFjxxobG5tXAADO\nzs7hISEhbo6Ojo3PHiMBUeHhELpmLaws+f8+qLvXrAUAACdnZ1lkCbVjtyt58JbPb/S4qVOn1vta\n3759xZklqZNYkK6qqlKm0+k8AAA+n0+rra2lktNwBgcHu0+ePPny4MGD7wMA7Ny5c7WOjs6n4uJi\nbVFVGwRBUPLy8vQfP37scOPGjbE0Go0/cuTI2yNGjLhz7ty56X369HmuqalZunLlyt0AAL179453\ndHSMMTExyTQxMclUUVGp/PjxY0fhIA3wpZWZy+Uy1NXVy2pra6menp4BKioqleTrhw4dWrJ8+fID\ndDqdd+vWrVEAAMOGDfs7PT3967Awf39/7yVLlhwKCAjw9Pf397azs0sICgqaVfdejx8/dhCeXpT8\nL4HH49F9fX19z5w5M/PRo0cDU1NTzd++fdt9ypQpl/Lz8zs/evRo4OTJky8/ffrUvlu3bu969er1\n4ujRowta/xtqmfBDh/4VoAEAVpaUwi63/4E568sUkEzvn4D1s/d/zmXv8QeO/+//2S/O44t/3QYE\njwugVe98V6gN6aqgCM50eoPHhFXywNXVtd7XO3XqBHfv3gUA2faTbimJBWllZeUqHo/39d19/vx5\nH2dn53B7e/uneXl5+iYmJl+XT2axWGxtbe3i/Pz8zvVdLzc316Bjx44flZWVv/7/Y2pqmp6bm2vQ\np0+f5zo6Op+EjxcOkgoKCoL66rUZDAa3pqZGMSEhwc7FxSVs8ODB9ydNmnQFAGDJkiWH5s+ff/zg\nwYNLJ0yYcO3Dhw+dhId0s9lsVkZGRldnZ+fwoKCgWUlJSdaBgYHLdu/evXLz5s2bhO/z4cOHTh06\ndCgS3hcTE+M4Z86cU8bGxllPnjwZYG5unkrew9fX11dBQUEwbNiwvwcPHnz/9u3bI7t16/ZOR0fn\nU15enr6oZ5HGfNIK1dWi91Mk1mMLSZg8zydtRqPBWLpqwweVljRYkgaAr0G6LZLaYJY+ffo8d3R0\njHn58mVPAwOD3MzMTBPyNTabzSouLtYm53wWxcDAIPfjx48dhacETU9PN23p3NBPnz61nzNnzily\n287OLmHw4MH3s7KyjLds2bLx4sWLrgAASkpK1YsXL/6jpKREi6xPJ+3bt8/rp59++v3Vq1c2w4YN\n+5vBYHDHjh17Iz4+vnfd+1Gp1FrhOaUfP37sMH78+NAtW7Zs/Pvvv4eRi+gaGRllEwRBqamp+foH\ntKqqSlldXb0MoPH5pMkkqdKCQElJ9H7so91mOTk5/euz0561tVI0gJSCdG1tLTUuLq5fdHT0oO++\n++7h9OnTz12+fHlyTEyMY1VVlfIvv/zym7Ozc7iWllaJgoKCgMvlMr5mkEqt5XA4TH19/TwHB4fH\n69at28bn82l3794dfufOnRHTp08/15LeH127ds24fPny5L/++uuHqqoq5cePHzvcvXt3+LBhw/7W\n0bnIdWwAABghSURBVNH59Ntvv/2SlZVlzOPx6Dt27FhjZ2eXoKmp+fX//E+fPul8/vxZw9TUNN3a\n2jopMjJyaFlZmXpISIibra1tYt37dezY8aPwMlsbNmzYumPHjjXu7u7BwseZmJhkDhw48JGPj49f\neXm52u3bt0c+ffrUnpwfW9bzSTsvWQK7tTT/tW+XlgZMCLkA+nk5oJ+XI7IqAgCA9bP312OEkziP\n1z55HChMpsjXUdvjoCSeaqu2GJy/IghC7CkzM9OYQqHUKioq8hUVFfnKysqVlpaWyefOnZtGHhMR\nETG6a9eu6XQ6vWL8+PHXPn/+rE4QBLx9+7Zbx44dP0yePPkSQRAwfvz4a6qqqtyioiLtT58+dRgz\nZky4iooKz9zc/N29e/ecCIKAe/fuOfXo0eM1ee3Fixcf2rx580ZyW1NTs+Tt27fd6ubz+vXrYy0t\nLZPpdHqFjY3Ny4sXL04lCAL4fL6it7f3ng4dOnzS1NQsGTt27PXMzExj4XN/+eWXbXl5eZ3J7e3b\nt6/R0tIqHj16dERZWRmr7r3S0tJMu3btmk5us1isMvL9IdPw4cPvEAQBubm5+iNGjLitrKxc2a1b\nt7c3b94cRZ4XGBi4dMmSJQfrXv/Lr1I67oWFEavHjSPWjhxJrB43jrgXFia1ezfm2rVrxCgdHSK3\ns0GD6bimNjF2yBBZZ1cu/fNZkkhsIP75rDb2+2lOas5n/969e61/g1qope8rDguXor59+z4LDg52\n7969+9uWXsPV1fXi4sWL/yBXHifJYli4rBphGrpvaGgorHF3h6kKDTe3vOXzgTegP1yPbNpK5fL4\nrJLSnoeF7927F1asWCHO2zcZDgtvA9asWbPj6NGjC3bv3r2yJecXFRV1yM7ONqoboGVFHgNX3759\nYdyCBVBRWyvydZIhADg2I+/y+KxtmawKh58/f278IDmDQVqKpk6d+mdQUNAsDofDrNtFrykCAgI8\nt2/fvlYSeWsv9PX1Ydfv/+22h1BbhUFayq5fvz6upefW7daHEGqerKwsWWeh2bBOup2QxmoX6NvR\nluqk25KWvK8YpBFCSI7hyiwIISTHMEgjhJAcwyCNEEJyDIN0G/f8+fM+tra2iWpqauUuLi5hwkPP\npcHFxSUsNja2vzTudevWrVE2NjavGAwGd+jQoZFv377tLul7hoaGjjcxMclkMpmcESNG3BGec0Ya\nkpOTrZSVlasktTqRsMGDB9+n0+k8Mnl4eBxr6bWa8rm8devWKDMzszQmk8mZOXPmGeF5eVqjKfc2\nMjLKFn5WPz8/H3HcG6Dh70SLnrklwxQxyUeqqalR6NKlS/bRo0c9ysrKWHPmzDk5Y8aMM9K6d2ho\n6A80Gq06NjbWXtL3+/jxox6LxSq7c+fO8MrKSuVt27b9YmNj81KS9ywoKNAl78nlclW9vb33kEP3\npfUeDxw48CGVShUUFBToSvp+hoaG7wUCAVUc+W7sc1lcXKylpaVVfOPGDZfi4mKtkSNH3tqwYYOf\nNO7N4/FUTExMMiTx+2roO9HSZ5bKhw2TZFJ0dLRj9+7dU8jtnJwcAzU1NU51dTVN0ve2srJKUlRU\n5FOpVIE0gvSFCxfcRowYcZvc5vP5ilQqVVBaWqohqXtevnx50siRI2+R28nJyZba2tpF0vr97ty5\nc9XatWt/o1AotZIO0hUVFXQzM7NUcVyrKZ/LoKCgmaNHj44gt2NiYr43Nzd/J417v379usewYcPu\nivs9bOw70dJnxuqONiwhIcFOeMY9AwODXDqdzktNTTWX9L2TkpKs+Xw+rUuXLu8lfS8AACcnpyjh\nZdCSkpKslZWVq1gsVtMmbWiBSZMmXbl58+ZogC8LVwQFBc0iV+iRtHfv3nU7e/bsDF8pzR2akZHR\ntbq6WqlPnz7PtbW1i6dOnfpnS6tYmvK5rHtMr169XqSlpZlVVFQ0Mnl06++dkZHRNT8/v3OPHj3e\n6OrqFi5YsOCo8MybLdXYd6Klz4xBug3jcDjMukGKxWKxy8rK1GWVJ0nR09MrIOfcDg8Pd3Z2dg7f\nuHHjFuE5uiWBQqEQ4eHhznQ6nbdnz56fZ82aFSTJ+wF8mdrXw8PjWEBAgKfwIheSVFpaqmlubp4a\nFBQ0KyMjo6uamlo5uR5nczXlc1l3agQGg8FVUFAQtPaz25R7c7lcRvfu3d/eunVr1KtXr2xyc3MN\nyFWdJKmlz4zDwtswFovFZrPZLOF9bDabRS4Q0N58/vxZY+HChUeio6MH+fv7e0+fPv2cNO7r7Owc\nXl1drXTlypVJM2bMODto0KBoPT29AkndLzAwcFn37t3fDho0KJr4Z4QaIcERgAAA33///YO7d+8O\nJ7d37dq1SldXt5DL5TIYDAa3Oddqyuey7jFcLpc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2/nFgYGBDV1eX5fDw8LqMjIxIxMGt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\n",
       "text": [
        "<matplotlib.figure.Figure at 0xa1601d0>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Process Mutation Data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "maf = pd.read_csv('../Extra_Data/UPMC_cohort/maf.csv')\n",
      "maf = maf.dropna(how='all', axis=[0,1])\n",
      "maf['Tumor_Sample_Barcode'] = maf['Tumor_Sample_Barcode'].str.replace('-Tumor','').copy()\n",
      "maf = maf.set_index(['Hugo_Symbol','Tumor_Sample_Barcode'])\n",
      "maf = maf[maf.Validation_Status != 'Wildtype']\n",
      "non_silent = maf[maf.Variant_Classification != 'Synonymous']\n",
      "non_silent['counter'] = 1\n",
      "hit_matrix = non_silent.counter.groupby(level=[0,1]).sum().unstack()\n",
      "hit_matrix.columns = map(lambda s: s.replace('-Tumor',''), hit_matrix.columns)\n",
      "hit_matrix = hit_matrix.fillna(0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The TCGA has a much higher mutation rate as compared to this data.  This could be due to differences in sample prep, sequencing depth, or bioinformatic processing.  The two cohorts are fairly well matched clinically, so we expect that this difference can be contributed to technical factors as compared to biological ones."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, axs = subplots(1,3, figsize=(8,3), sharey=True)\n",
      "c = pd.concat([(mut.df > 0).sum(), (hit_matrix > 0).sum(0)], keys=['TCGA','UPMC'], axis=1).stack()\n",
      "violin_plot_series(c.clip_upper(500), ax=axs[0])\n",
      "\n",
      "hh = hit_matrix.ix[:, true_index(clin.HPV == 'Negative')].sum()\n",
      "yy = mut.df.ix[:, true_index(clinical.hpv.dropna() == False)].sum()\n",
      "c = pd.concat([yy,hh], keys=['TCGA','UPMC'], axis=1).stack()\n",
      "violin_plot_series(c.clip_upper(500), ax=axs[1])\n",
      "\n",
      "hh = hit_matrix.ix[:, true_index(clin.HPV == 'Positive')].sum()\n",
      "yy = mut.df.ix[:, true_index(clinical.hpv.dropna())].sum()\n",
      "c = pd.concat([yy,hh], keys=['TCGA','UPMC'], axis=1).stack()\n",
      "violin_plot_series(c.clip_upper(500), ax=axs[2])\n",
      "\n",
      "for ax in axs:\n",
      "    prettify_ax(ax)\n",
      "    ax.set_ylabel('')\n",
      "    ax.set_xlabel('Study')\n",
      "axs[0].set_ylabel('# of Mutations')\n",
      "fig.tight_layout()\n",
      "fig.savefig(FIGDIR +  'mutation_rate_comparison.pdf')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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8Ytq0aTvVajV73333rTxw4MD/e/3113+4cOHC58aNG3e4tLT04Pz5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\nRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7c5c050>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hh = hit_matrix.ix[:, true_index(clin.HPV == 'Negative')].sum()\n",
      "yy = mut.df.ix[:, true_index(clinical.hpv.dropna() == False)].sum()\n",
      "c = pd.concat([yy,hh], keys=['TCGA','UPMC'], axis=1).stack()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "stats.mannwhitneyu(hh.dropna(), yy.dropna())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "(5909.0, 0.0002041546634473598)"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The median mutation rate of TCGA HPV- samples is 104 verses 74 in the UPMC cohort."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "yy = mut.df.ix[:, true_index(clinical.hpv.dropna() == False)].sum()\n",
      "yy.median()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "104.0"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hh = hit_matrix.ix[:, true_index(clin.HPV == 'Negative')].sum()\n",
      "hh.median()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "73.0"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "TP53 Mutation"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p53_mut = hit_matrix.ix['TP53'].ix[true_index(clin.HPV=='Negative')] > 0\n",
      "survival_and_stats(p53_mut, surv)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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TvajlrKwsB5lMxnVxcUmvbZubN292atu27eOaavEAAACGoFEkeV9f38symYy7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\nwFBI8gAAAAyFJA8AAMBQSPIAAAAM9X/lx7FtbN3DOgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7e3ded0>"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Broken down by functional type."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import re as re\n",
      "get_nums = lambda s: re.findall(r'\\d+', s)\n",
      "\n",
      "def is_disruptive(v):\n",
      "    c = v.Variant_Classification\n",
      "    if c != 'Missense':\n",
      "        if 'Ins' in c or 'Del' in c:\n",
      "            return 'InDel'\n",
      "        else:\n",
      "            return v.Variant_Classification.split('_')[0]\n",
      "    else:\n",
      "        s = v.Protein_Change\n",
      "        aa = int(get_nums(s)[0])\n",
      "        if int(aa) in range(163,196):\n",
      "            return 'L2'\n",
      "        if int(aa) in range(236, 252):\n",
      "            return 'L3'\n",
      "    return 'other'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p53 = maf.ix['TP53'].reset_index()\n",
      "dd = p53.apply(is_disruptive, 1)\n",
      "dd = dd.replace('Synonymous',nan).dropna()\n",
      "others = hit_matrix.columns.diff(p53.Tumor_Sample_Barcode.ix[dd.index])\n",
      "dd.index = p53.Tumor_Sample_Barcode.ix[dd.index]\n",
      "dd = pd.concat([pd.Series('WT', others), dd])\n",
      "dd = dd.ix[keepers]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s2 = surv.unstack().ix[dd.index]\n",
      "s2.index = range(len(dd))\n",
      "s2 = s2.stack()\n",
      "dd.index = range(len(dd))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "survival_and_stats(dd, s2, colors=colors[:6] + ['grey'] + colors[6:], figsize=(7,6))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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OQpIHAADgKCR5AAAAjkKSV7D4+HgHW1vb6/r6+nlubm5RmZmZxqXLbN++/Qsb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\ngJyCJA8AAADIqf8DBn+MtGafEQkAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7b82fd0>"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "By Validation Status.  Validated mutations do seem to have worse prognosis, leading us to think there could be a number of false positives in the mix."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "v = maf.ix['TP53'].Validation_Status == 'Valid'\n",
      "v = v.groupby(level=0).sum() > 0\n",
      "v.name = 'valid'\n",
      "v = v.ix[hit_matrix.columns]\n",
      "v = v.fillna('WT')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "v.value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "False    37\n",
        "WT       28\n",
        "True      9\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "draw_survival_curve(v.ix[true_index(clin.HPV=='Negative')], surv)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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qeQ2gbM/28csaaBJpfu9YDAAPKngqaQt7HgghhWDyQAgpBAdMuxllViBDSFpH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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7d5b650>"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "MUC5B mutation status.  Can't really say much here."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "survival_and_stats(hit_matrix.ix['MUC5B'].ix[true_index(clin.HPV=='Negative')]>0, surv)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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zEYlExQkJCX3LysqMf/rpp+88PT1vMB2XPpTc3Fxr+trJZDLT4ODgVX379k1g\nOi59KRUVFQbdunU7x+VyVUjyNS8ODg5PVCoVl+k4NAvjAehLOXPmTHc3N7d79HJmZqa9mZmZtLy8\nnMd0bPpQPDw8bhkaGiq5XK4KSb5mZd++faP69et3nF5WKpWGXC5XVVRUZM50bPW9REZGBvTv3z+e\nXr59+3YrS0vL50zHpS9l+fLl38yfP/9nDoejRpKvWSktLeW7uLikMh1H5dIg7snrQk0G1IHq3bp1\nq7VSqeQ5Ojo+YToWfeHj45O4adOmafTyrVu3WhsbGysqj/kA/xUQEBB17NixgYQQolQqeeHh4eO6\ndu16gem49MGDBw8+iIiIGBMaGhrKdCz65NGjR83Ly8uN2rdvf9XS0rJgxIgRf+Xl5VkzHReSfA3V\nZEAdAF2ysbHJpTuGxsbG+vr6+sYuWrToBy6Xq2Y6Nn3A4XCo2NhYXz6fL1+1atWccePGhTMdU32n\nVqu5kydP3r5hw4YZxsbGCqbj0SdFRUWNXF1dU8PDw8c9evSouZmZWcn48eN3MR1XgxgMRxdqMqAO\ngK69ePHC/Msvv9x65syZHqtXrw4ePXr0XqZj0ie+vr6x5eXlRlFRUQFjxoyJ6NGjxxkbG5tcpuOq\nrzZt2jTNzc3tfo8ePc5Q/xv0i8LgXzXy0Ucf/XvixIm+9PKKFSu+sba2zpPJZAKBQCBjKi7U5Guo\nJgPqAOhSeXm5Ub9+/RJMTEzKUlNTXZHga27Lli1T1q9fP5MQQrhcrnr48OEHLSwsCnNycmyZjq0+\nO336dM/du3eP5fP5cnogMGdn58fR0dH+TMdW38XExPidPXu2O71cUVFhaGBgoGK6RQRJvoZqMqAO\ngC7t379/lImJSVl4ePg4oVAoZToefeLo6Phk5cqVc2/dutVaoVAY//77758bGBioWrVqdYfp2Oqz\ngwcPDi8rKzORy+V8uVzOJ4SQjIwMJ39//2imY6vv8vLyrKdOnbo5PT29mVQqFc6fP/+XgICAKEND\nwwom40KSr6HqBtRhOi5gr6SkpA7nz5/vxuPxlHQxMjIqz8zMdGA6tvpu0KBBcVOnTt08YMCAeCsr\nq/yIiIgxR48e/ZjpWpW+4XA4FNMx6IuJEyf+MXjw4CMdO3a8YmtrmyOTyQSbN2+eynRcHIrC7xAA\nAICNUJMHAABgKSR5AAAAlkKSBwAAYCkkeQAAAJZCkgeAdzJjxowNPXr0OKO5Ti6X8x0dHZ/89ttv\nXzEVFwCBPReeAAAgAElEQVT8F5I8ALyTH3/8cUFqaqprZGTkMHrdypUr5zZp0uTZlClTtjAZGwC8\nDkkeAN6JSCSSrFix4pt58+YtUyqVvKdPnzZdtWrVnE2bNk37+OOPj5qampa6uLikXbx4sQv9mm++\n+WaFjY1NrkAgkA0dOvTvkpISM0IImTBhws7vvvvuZ09Pz5urVq2ak5SU1KFt27bX+Xy+vG3bttdv\n3LjRhrkzBWABpqfBQ0FB0c/Ss2fPxJUrV86ZNGnS71999dXmvn37Jnz//fc/ymQy06NHj/rZ2dll\nKZVKw2PHjg3o2LHj5YKCAovCwsJGnTp1urRhw4bpFEWR8ePH73R0dMy4e/duS4qiSPv27ZOioqKG\nVlRUGKxZs2ZWnz59TjB9nigo+lwwQQ0AaGXTpk3TunfvftbY2Fhx7dq1dh4eHrfj4+MHcLlctZ+f\nX4yrq2vqmTNnenh7eycfOHBgpFgsLs7MzHQQCASywsJCC0JejqgWGBi4p2XLlvcIIaSkpMTs+vXr\nbb29vZNnzpy5fsKECTsZPUkAPYfmegDQioeHx+2+ffuemDJlypbMzEyH4uJisUAgkPH5fDmfz5df\nuHCha1ZWln1ZWZnJmDFjItzc3O5Pnz59Y1FRUSPN42jO5Pjnn39+lpKS4uXl5ZXi4eFxOy4ublDd\nnxkAe6AmDwBaEwgEMlNT09LGjRs/b9y48fPc3FwbeltqaqqrlZVV/vTp0zf26tXrVFhYWAghhFQ3\nx7ZcLufn5OTYRkdH+6tUKoOoqKiAwMDAPUOHDv3bxMSkrK7OCYBNUJMHgPfWvHnzR3Z2dtkbN26c\nrlAojM+fP9/tww8/PFdcXCxWKBTGcrmcr1QqecePH+9/5MiRwWVlZSaEvD5XOYfDoUaPHr335MmT\nvTkcDiUQCGSNGjUqQoIH0B6SPADoxIEDB0YePHhweKNGjYoCAwP3/Prrr187OTllLFiw4MeYmBi/\nRo0aFe3YsWPSqlWr5mzcuHH6zZs3PTkcDkXPdGZiYlK2c+fOCV9//fWvZmZmJfPmzVv2559/fsb0\neQHoM8xCBwAAwFKoyQMAALAUkjwAAABLIckDAACwFJI8AAAASyHJAwAAsBSSPAAAAEshyQMAALAU\nkjwAAABLIckDAACwFCao0QI9DCcAQE1ojtGva/g+aji0+RyhJq8liqJYVRYvXsx4DDivhn1ebDwn\niqqb/Mv0OdZ1GT9+POMx6MvnCEkeAAD0irOzM9Mh6A0keQAAAJZCkgdCCCE+Pj5Mh1ArcF76g43n\nBLXD3Nyc6RD0Bqaa1QKHw6Fw3QCgJjgcDqFqueNdQ/s+SkxMbHD/FGr7OUKS10JD/KMCAO0gyYMu\naPs5QnM9AAAASyHJAwCAXklMTGQ6BL2BwXAAAPQch1NrdwPqJVOBGZGVSJkOQy8gyQMA6LnOi44x\nHUKduvTDQKZD0BtorgcAAGApJHkAAACWQnO9nlCr1aSiouKt+3E4HMLj8eogIgAAqO8Yr8m7uLik\n8Xg8JY/HU3K5XDX9s5GRUXlmZqaDrt+PoiiOn59fTGlpqalKpTL49ttvl9vY2ORaW1vnzZ49e41K\npTK4ffu2x9dff/2rrt/7ffTpP5Dw+abEVGD2xmJiYkJu3rzJdLgAAFAPMF6TT0tLc6F/5nK56uzs\nbDtra+u82nq/vXv3ju7QoUOSqalp6fr162eePHmy9927d90VCoXxsGHDItetWxcUHBy8OiMjw+ne\nvXstW7Zsea+2YnkXBYUvSMvxK4jQodUb98vY+w2RStHrFAAA6kFNvjo7d+6cMHTo0L+HDRsW6e/v\nH3369Ome7u7ud+ntiYmJPprLv/zyy3xra+s8sVhcvHjx4iXVHXf16tXB48eP30UIIceOHRsYHBy8\n2sLCorBp06ZPJ0+evP306dM9CSFkwoQJO1esWPFNbZ4jAABAbaq3SZ4QQo4ePfrxxIkT/4iOjvZ/\n03B+e/bsCdy3b9+nV69ebX/79m2Po0ePfnzkyJHBlfdLS0tzef78eePmzZs/IuTlPwZ+fn4x9PYb\nN260sbGxySWEkP79+x+Pjo72V6vV9foaAQAAVKdeJ7AOHTokffzxx0fftl94ePi4RYsW/eDg4JBp\nb2+fNWPGjA1//fXXiMr7Xbhwoau3t3cyvdymTZsbYrG4WC6X8+fNm7ds9+7dY+fMmbOKEELEYnGx\npaVlwY0bN9ro9qwAAADqBuP35N/E3Nz8RXXbNGv2T548cRw9evReDodD0ds+/PDDc5Vf8/Tp06aN\nGzd+rrnu7Nmz3SdMmLDT2dn58cWLF7u4urqm0tusrKzyc3JybDX/MaCFhoa++tnHx6fBzYgEAFVL\nTEzUm2FXJY9vEJEz6jF1gamZ8+p1ktfE4XAolUplQC9r9ry3srLKX7NmzeyBAwceI4SQoqKiRoWF\nhRaVj8HlctVcLldNL1+4cKGrv79/9IYNG2YEBgbuqbz/m5rqNZM8AACt8j/9S5ZU20WIcZIMJPm6\ngiT/Fvb29lkZGRlOt2/f9jA3N3+xcePG6XTNfdiwYZErV66c265du2tcLlc9ZsyYCB8fn8T58+f/\nonmMJk2aPLtw4UJXennhwoVLly1bNq+qBE8IIXl5eda2trY5tXtmAAB1r6wgmzy9EEnyrsYyHYpW\nbG1tmQ7hnbRt25aR961XSZ5O2vTPmsstWrR4GBwcvLpLly4XGzdu/HzWrFlrt2zZMoUQQqZNm7Yp\nIyPDydPT82ZpaanpZ5999ufcuXNXVj5+165dL2j2vE9KSupw5syZHprPxPv4+CQmJCT0KykpMXv+\n/HnjNm3a3Ki9MwYAYIaRuQ2xbjeINO06jOlQ3tn1NYEkKSmJ6TDeycaNGxl533qV5DWb48ePH7+L\nftSN9vPPP3/3888/f0cvBwUFrSOEEENDw4rVq1cHr169OvhNx2/RosXDRo0aFd2/f9/Nzc3tfnFx\nsbi6fU+fPt1zyJAhhzWb95lGqSqIWqV88z4U9cbtAACEEMI1MCQGxqbESGjJdCha0beavJGRESPv\nW6+SfF2YN2/esm3btn2xcuXKuW/ab+fOnRO+//77n+oqrrexs21CEvYueOt+hoYGxNzcvA4iAgCA\n+q7BJfkRI0b8FR4ePk4qlQqFQmGVQ8OlpaW5CIVCadu2ba/XdXzViTt6mOkQAIBlRE7odFdXmHoC\ni4Pm3XfH4XAoXDcAqAkOh0PeNJiXDo5PNcT55Bvad7C2n6N6PRgOAAAAaA9JHgAAgKWQ5AEAAFgK\nSR4AAIClkOQBAABYCkkeAACApZDkAQAAWArPyWsBz8kDQE3VxXPytXXs+kokNifFL4qYDqNOafs5\nanAj3gEAsA0qHVAdNNcDAIBeSUxMZDoEvYEkDwAAwFK4J68F3JMHgJqqi3vy+D5iP4xdDwAAAK9B\nkgcAAL2Ce/I1hyQPAADAUrgnrwXcAwOAmsI9edAFPCcPANBAcTi19j8EYxoJhaRQImE6DL2HJA8A\noOeybO2ZDkHn7HOyqt2WmJhIfHx86i4YPYZ78gAAACyFJA8AAHoFtfiaQ5IHAABgKSR5AADQK3hO\nvuaQ5AEAAFgKSR4AAPQK7snXHJI8AAAAS9VakudyuWoej6fULM2bN3/0ttfk5eVZ6yqGb7/9dvmF\nCxe6EkJIUlJSh86dO18SCoXS3r17n8zMzHTQ3PfKlSsdBw0aFEcvx8XFDVq2bNk8XcUCoO++HDeO\nDPzww7eWYX5+RKlUMh0usBjuyddcrdbks7Oz7ZRKJY8ujx49al6b76cpPT292bVr19p17dr1QnFx\nsXjw4MFHZs+evaaoqKhR9+7dz06aNGkHvW9+fr7VokWLfuBwOK/Ghhw0aFBcVFRUQGFhoUVdxQxQ\nnx04eJAMvXuPBN578MZyLCGByGQypsOFeuSCQlFn74V/AF5X5831RUVFjYYMGXJYLBYXN27c+PmC\nBQt+rLyPQqEwHjNmTIRYLC62sbHJXbNmzWx6W1xc3CAXF5c0Pp8vDwgIiCotLTWt6n3Wr18/c/To\n0XsJISQqKiqgc+fOlz799NN9hoaGFd99993PdC39wIEDI21tbXMSEhL6VT7G8OHDD27atGma7s4e\nQL91NzYhvU3eXAy5uAsIr7tQrtsk/6Z78kjyr6vVYW2rGkx/xYoV39jb22fl5+dbpaenN+vQoUPS\n6NGj93p4eNym99m2bdsXCoXCOD8/3+rx48fOH3744bkRI0b8VVFRYTh27Njdhw8fHuLp6Xlz1qxZ\na0NCQsJWrVo1p/L7REZGDgsKClpHCCEXL17s0rhx4+edO3e+dP/+fbfu3buf3bp165eEEDJy5MgD\nI0eOPLBr167x+/bt+1TzGL6+vrGjRo3aHxISEqb7q9OwJcbGktjNm4lBeTlRGRkR36lTiY+vL9Nh\nAYCORZXKyB+yEhItL33n17Zs2fKd9h8/fvw7vwfb1WqSd3Z2fqy5/Ndff42YMGHCTisrq3y1Ws1V\nKpU8IyOj8oKCAkt6H4qiOCUlJWZPnz5tmpSU1KFTp06X09LSXMzMzEqWL1/+7ejRo/d269btPCGE\nhISEhPXo0eNM5SSfmZnpUFxcLKbfv6CgwPLEiRN94+LiBnl6et5csGDBj4GBgXtOnjzZW/N9K8ff\nqlWrOw8fPmxRXFwsFovFxbq9Og1XYmwsiZ43n8wtLHq1buW8+YQQgkQPwDK9TPjkWnk5mSAwe6fX\n+eTnkkOHDlW57fLly6RTp07/Wd+oUSOyefNmreJkq1pN8hkZGU7W1tZ5muuOHz/ef8iQIYd5PJ6y\ndevWtwwMDFSa2zkcDjVz5sz1BQUFloGBgXuKiooaffbZZ3+uWbNm9pMnTxx///33z7dt2/aF5mvU\najWXy+Wq6eWnT582bdy48XN6WSgUSgMCAqK6du16gRBCQkNDQy0tLQtKSkrMzMzMSqqLn8PhUJaW\nlgXZ2dl2SPK6E7t582sJnhBC5hYWkRWjPiWuIjEhhBBh8GwimhP8n9dKVq0m0tVr/rMe+9fN/gDv\nqhGXSywMDIgLj/fOr62uJv/s2bN3ruU3VHU+C92kSZN27Nq1a3yfPn3+IeS/tX1CXvZ0//bbb5ev\nWLHimwcPHnwwfPjwgxEREWOsrKzyZ8+evYa+n15RUWF469at1poJnpCXyVlznZOTU8bDhw9b0MsK\nhcKYx+MpTU1N39p+pFarq7zBGBoa+upnHx8fPLf5DgzKy6tez8LpMqHhSUxMxH3hWobv25qr8ySv\nUCiMZTKZQC6X8zdv3jw1KyvLXi6X8+ntFEVxdu/ePdbQ0LBi/fr1M8VicTFFUZymTZs+7dChQ9Lg\nwYOPjBkzJsLV1TV16dKlC69cudIxPj5+gOZ7NGnS5Jlmr/jRo0fv7dq164UzZ870aNeu3bXQ0NBQ\nPz+/mMr/HFSmVqu5hYWFFra2tjmVt2kmeXg3KiOjqtdTVJXrAfRJ5X/6lyxZwlww9URXI+M6ey/8\nA/A6DlVLX6wGBgaqp0+fNq3cXB8RETEmODh4tVqt5k6fPn2jVCoV/vnnn5/l5OTYcrlc9bNnz5qo\nVCqDiRMn/nH+/PluxsbGismTJ2//+eefvyOEkD/++GPi0qVLF2ZnZ9t9+OGH53bv3j22qiTs6Oj4\n5Ny5cx86ODhkEvKyh/2cOXNW5ebm2vTu3ftkeHj4OAsLi0J6/127do3fv3//qNjY2Fc3he/cudNq\n2LBhkXfv3nXXPDaHw6Fq67o1BFXdk19hYU4+WbYM9+TrMXNTU3Je3IiI39J7vlVBPsl49oyYm5vX\nUWT1G4fDqbLPjw6PT7F1Pvnqvmcb4nzy2n6Oai3JM23u3LkrXV1dU6dMmbJF22Ns2rRpWm5urs0P\nP/ywSHM9kvz7S4yNJXG//Ua4CgVRGxuTQV99hQRfzyHJawdJXjtI8q9Dkq8kKyvL/tNPP93377//\nfqTtMbp37372wIEDI5s2bfpUcz2SPDREFkIhaW1sQnjcN3/PnC4oIIVFRUQkEtVRZPUbkrx23pTk\nGyJtP0d1fk++rtjb22f16tXr1OnTp3v27Nnz9Lu+/uTJk7179+59snKCB2iojp86RfLz89+632KB\nAAkeoJ5gbU2+NqEmDwA1hZq8dtBc/zptP0cYfxIAAIClkOQBAECvNLRa/PtAkgcAAGAp3JPXAu7J\nA0BN1cU9+do6NpMaCYWkUCKpchvuydcca3vXAwA0FKh0QHVQk9cCavIAUFN1UZPH9xH7oXc9AAAA\nvAZJHgAA9Apm+as5JHkghLD3jwbnpT/YeE5QO5KTk5kOQW8gyQMhhL1fsDgv/cHGc4La8eLFC6ZD\n0BtI8gAAACyFJA8AAHrl8ePHTIegN/AInRbYOvgEANQODIYDuoD55AEAAOAVNNcDAACwFJI8AAAA\nSyHJv4OrV6+29/LySjEzMyvx8/OLKSwstGA6Jl3y8/OLuXTpUmem49CF+Pj4AZ6enjcFAoGsd+/e\nJ+/fv+/GdEy6EB0d7d+sWbN0oVAo7devX0J6enozpmPSldu3b3sYGxsr8vLyrJmORRd69ux5ms/n\ny+kyefLk7doeqybfPfHx8QNcXFzShEKhdOzYsbsVCoXx+50Bs2pyzk5OThma1zgsLCyEiVh16U3f\nw1r9jimKQqlBqaioMHB0dMzYtm3b5OLiYtGECRP+GDNmzG6m49LVuUVHRw/h8Xjlly5d6sR0PO9b\nnj17ZiMSiYoTEhL6lpWVGf/000/feXp63mA6rvctubm51vR5yWQy0+Dg4FV9+/ZNYDouXZSKigqD\nbt26neNyuarc3FxrpuPRRXFwcHiiUqm4urg2b/vuKSgosLCwsCg4evSoX0FBgUX//v3jFy5cGMb0\nNajNc5bL5SbNmjV7xHSsujznN30Pa/s7ZvzE9KWcOXOmu5ub2z16OTMz097MzExaXl7OYzq29y0e\nHh63DA0NlVwuV8WGJL9v375R/fr1O04vK5VKQy6XqyoqKjJnOrb3KZGRkQH9+/ePp5dv377dytLS\n8jnTcemiLF++/Jv58+f/zOFw1GxI8qWlpXwXF5dUXRyrJt894eHhYwcOHBhHL589e/YjV1fXB0xf\nh9o85zt37rj36dPnBNOx6qq87XtY298xmutrKDk52dvLyyuFXra3t8/i8/ny1NRUVybj0oVbt261\nViqVPEdHxydMx6ILPj4+iZs2bZpGL9+6dau1sbGxQiQSVT05tZ4ICAiIOnbs2EBCCFEqlbzw8PBx\nXbt2vcB0XO/rwYMHH0RERIwJDQ0NZToWXXn06FHz8vJyo/bt21+1tLQsGDFixF/a3oaoyXdP5X3a\ntm17PS0tzaW0tNT0/c6EGTU550ePHjXPycmxdXd3v2ttbZ33xRdfbJPJZAJmIn5/b/se1vZ3jCRf\nQ1KpVFg5SYhEIklxcbGYqZigajY2Nrmurq6phBASGxvr6+vrG7to0aIfuFyumunY3heHw6FiY2N9\n+Xy+fNWqVXPGjRsXznRM70OtVnMnT568fcOGDTOMjY0VTMejK0VFRY1cXV1Tw8PDxz169Ki5mZlZ\nyfjx43dpc6yafPdIpVKhUCiU0ssCgUBmYGCg0tfvp5qcs0wmE7i5ud2Pj48fcPPmTc+srCz7uXPn\nrqz7aOuGtr9jw9oPjR1EIpFEIpGINNdJJBKRWCwuZiomqN6LFy/Mv/zyy61nzpzpsXr16uDRo0fv\nZTomXfH19Y0tLy83ioqKChgzZkxEjx49ztjY2OQyHZc2Nm3aNM3Nze1+jx49zlD/G+iDqsWBY+rK\nRx999O+JEyf60ssrVqz4xtraOk8mkwkEAoHsXY5Vk++eyvvIZDKBSqUy0Nfvp5qc88iRIw+MHDny\nAL0cFhYWMnDgwGObN2+eWpex1hVtf8eoydeQt7d3ckpKihe9nJmZ6SCXy/l0jRHqj/LycqN+/fol\nmJiYlKWmprqyJcFv2bJlyvr162cSQgiXy1UPHz78oIWFRWFOTo4t07Fp6/Tp0z137949ls/ny01N\nTUsJIcTZ2flxdHS0P9OxvY+YmBi/s2fPdqeXKyoqDA0MDFTatFbU5Lun8j7Xrl1r16JFi4f0NdU3\nNTnnPXv2BN68edOTXq6oqDB813+g9InWv2OmOxvoS1EqlYaOjo4ZW7du/aKoqMh8/PjxOwMDAyOY\njkuXxdnZOZ0NHe/Cw8PHfvTRR2eZjkPXJTY2dpCDg8OTmzdvti4rKzPevn375/b29pllZWXGTMem\nq8KWjnc7duyY6OHhcevRo0fNJBKJcPz48TtHjhy5X5tj1eS7R7Pn9fPnzy379et3fMGCBUuZvg7a\nlpqc86JFi5b06tXr5LNnz2zy8/Mb9+/fP/7bb79dxnTs71uq+x7W9nfM+AnpU7ly5UoHT0/PG6am\nprKBAwfGFRQUWDAdky4LW5L8zJkz13G5XJWhoaGSLjwer/zJkycOTMf2vuWnn376ztbWNlsoFEp8\nfHxOJScnezEdky4Lmx6hmz9//s+WlpbPzczMpMOHD//rfb4vqvru2bVr1zjNHvyxsbGDWrRokSYQ\nCEoCAwMj9P2fv7edc3l5Oe/zzz/fLhaLX5ibmxd98cUXW+VyuQnTcb9v0fwe1sXvGGPXAwAAsBTu\nyQMAALAUkjwAAABLIckDAACwFJI8AAAASyHJg049fvzYmcvlqj/55JNDlbcNGzYsksvlqp88eeL4\nLscMDQ0NnTp16mZCCOHxeMr8/HwrXcX766+/fu3u7n6Xz+fLmzVrlv7DDz8sonQ4GMvkyZO3r1y5\ncq42r50wYcLOZcuWzatqG0VRHD8/v5iSkhIzOzu77N9///1zze1Hjx792Nzc/EVubq6NNu/9NnFx\ncYOqiw0alqpm29Pmc5mSkuLVu3fvk2KxuLhRo0ZFw4YNi8zIyHDSVZynT5/u6enpeVOb1yYmJvq4\nu7vf1VUsdYrpxwVQ2FXS09OdeTxeuZWVVV5JSYmAXi+TyUytrKzyjIyMFBkZGY7vcszQ0NDFX331\n1WZdxxoTE+Pr4uKSeuvWLQ+VSsW9fv269wcffHB/7dq1QUxfR4qiyIQJE/5YtmzZt1Vti4iICFy0\naNESino5IU/Tpk1z6OutVCoN3dzc7q1fv35GbcbXqVOnS2x7jBTl3Ut1s+29y+dSoVAY2dvbZ+7Y\nsWOiQqEwysvLs/rqq682e3t7X2f6/CiKIqdOnfJp2bLlXabj0KagJg86Z2BgoOrZs+fpmJgYP3pd\nXFzcoJ49e57WHD/+ypUrHdu0aXODz+fLe/bseZquoRcXF4s/+eSTQwKBQNa9e/ezOTk5thwOhyLk\n5Uhv9EQf33zzzQobG5tcgUAgGzp06N8lJSVmhLycoGbp0qULW7dufUsoFEqDg4NXVxVnYmKiz+DB\ng494eHjc5nK5am9v7+Rly5bNo4eOrFyT1lx2dnZ+vHbt2llNmjR5dubMmR4GBgYqzdpJly5dLu7Z\nsyeQfk1YWFjIoEGD4ujt9+7daykWi4vLyspMVq9eHUxPwNGnT59/NEewo6ppVVi9enUwPRb6qFGj\n9rdq1erOL7/8Mp8QQjZv3jzV1NS0dPr06Rvj4uIGubi4pPH5fHlAQEAUPZnF48ePnXv16nXKzMys\nxNbWNmfjxo3T6WNzuVz1b7/99lWjRo2K8vLyrJcvX/5tkyZNnpmZmZVMmDBhp1Kp5BFCyPDhww9q\nTgQEDY9cLucbGxsrqpoX4k2fy8r7pqamuspkMsHEiRP/MDIyKreysspfuXLlXGtr67yysjKTyjVp\nzeXQ0NDQKVOmbOnRo8eZGTNmbOjTp88/mn+3v/3221f9+vVLOH36dE93d/e7mZmZDlwuV13573Xv\n3r2jr1271q5jx45XTE1NS5s3b/7o4MGDw3V9zeoc0/9loLCrpKenO5uYmMj//PPPTzVH+AoMDIz4\n888/PzUxMZFnZGQ4SiQSoZWVVV50dPQQmUxmumjRoiUBAQGRFEWRqVOn/jpy5Mj9crncJCkpqb1Q\nKJRMnTr1V4r6/xHRjh07NqBjx46XCwoKLAoLCxt16tTp0oYNG6ZTFEV69uyZ2LJly7uZmZn2jx49\namZubl50+vTpHpVj3bdv3ygrK6u8tWvXBl25cqVD5WmDK9ekNZednZ3TBwwYcIyevrZTp06Xdu7c\nOZ6iKJKTk9NUIBCUlJSUCOjX3Llzx53P55fSg1f88ssv88aOHRt+//79D5ycnB5nZmbay2QyU39/\n/0Nz5sxZSb/fL7/8Mq9y3KmpqS6Ojo4Zmuvu3bvnZm5uXnTjxg1PKyurvAsXLnRJT093trS0fH7u\n3LluEolEOGnSpN+Dg4NXURRFPvvss70//fTTd0ql0vDff//90MDAoEIikQjpazxx4sQdMpnM9ObN\nm62bN2/+8Pnz55YFBQUW3bt3PxMRERFIURS5deuWh4eHxy2mP3MozJVbt255ODo6ZrRr1+6qhYVF\nwfDhw//SHMyoqs9lVccpKSkRWFpaPp8yZcpvcXFxAyu3EFWuSWsuL168OFQkEhVfvHixM0VR5Ndf\nf53q4+Nzit534MCBcb///vukxMTEnvRrqvp7lUqlZl27dj2/e/fuMRUVFQZ79+79jJ7KGTV5gEr8\n/Pxi/vnnnz4KhcK4vLzcKCEhoZ+fn18Mvf3o0aMfd+nS5eKQIUMOm5qali5YsODH48eP91coFMZH\njhwZ/P333/9kYmJS1r59+6tDhgw5XPn43t7eyQcOHBgpFouLi4uLxQKBQFZYWGhByMuZ2r7++utf\n7e3ts5o1a5betm3b61X1Axg1atT+NWvWzD5+/Hj//v37Hzc3N38REBAQpbkv9Yb788HBwavNzc1f\nEPKyVhsXFzeIEEKOHDkyeNCgQXGa42i7u7vfbdasWfqpU6d6EUJIdHS0/6hRo/ZbW1vnxcfHD7C1\ntc0pKipqpHke1blw4UJXb2/vZM11bm5u97/66qvfunfvfnbw4MFHunTpcvHPP//8bPTo0Xu7det2\nXnQp2uYAAAhmSURBVCgUSkNCQsL++uuvEYQQMn/+/F9mzZq1tqyszITL5aopiuIUFRU1oo83f/78\nX0xNTUulUqmwtLTU9Ny5cx/yeDzlsWPHBg4bNiySEEJatWp15+HDhy30daYzeH9vm22vqs9lVccR\nCASys2fPdq+oqDCcOXPmeisrq3xvb+/kmtak/fz8Yjp37nyJEEKGDh3698WLF7uUlJSYSaVS4blz\n5z4MCAiI0vxbrurv1czMrGTdunVBo0eP3ltSUmLG4/GUb/tb1AdI8lArhEKhtEuXLhePHTs28MSJ\nE307d+58SXOaxCdPnjjGxcUNojvriMXi4vLycqOnT582ff78eWMrK6t8el9nZ+fHlY9fVlZmMmbM\nmAg3N7f706dP36iZoAh5Od0s/bOxsbFCrVZX+VkPDAzcExMT41dYWGjx77//flRRUWE4ceLEP6ra\nt3LCpxM8IS87FSYkJPRTq9XcQ4cOffLpp5/uq/x6+oslNzfX5sGDBx8MGDAgnqIoTnBw8OoWLVo8\nnDhx4h816ZT49OnTpo0bN35eef3ChQuXSiQSUUhISBghL6/xb7/99hV9jd3d3e/m5+dbqVQqg5SU\nFC9PT8+b3bt3P7t169YvDQwMVFWdW9euXS+EhISEhYaGhlpZWeWPGDHir2fPnjUh5OU/U5aWlgXZ\n2dl2b4sZ2Imebc/Dw+O2WCwuXrFixTfx8fEDNOd1r/y5rI67u/vd7du3T37w4MEHT58+bTp+/Phd\nY8eO3Z2ent6s8r6V/xY1Z2Jr0qTJs06dOl1OSEjoR98m1PxbJaT6v9fExESf5s2bP+rXr1/CoUOH\nPtH2utQnSPJQawICAqIiIyOHRUVFBQQEBERpbrOyssoPCAiIksvlfLpcuHChq4ODQ6ajo+MTzV61\nT58+bar5WoqiOAsWLPixV69ep9LS0lyOHj36cZs2bW68a3wdO3a8EhUVFUAvt23b9vrSpUsXJicn\nexPyMompVCoDevubEnDz5s0fOTs7Pz5+/Hj/K1eudNRstaC/kOgkf+TIkcGffPLJIUNDw4pVq1bN\nsbS0LEhPT292/Pjx/r179z75tri5XK66qnugdMsBPSuVlZVV/uzZs9fQ11cqlQovXLjQVS6X87/8\n8sutx44dG3j9+vW2W7ZsmVLde929e9fdx8cn8dq1a+2ys7PtzM3NXyxcuHApvb26f56gYajJbHuV\nP5dV2bhx43TNJ3Ksra3zZs+evaZVq1Z3bt++7VH5bzEzM9PhTXHRf2uHDx8eMmrUqP2Vt1f195qW\nluby008/fX/t2rV2ly9f7hQWFhZS8ytRf+EPFGqNv79/dGxsrG9sbKyvv79/tOY2X1/f2JMnT/b+\n999/PyovLzfasmXLlMDAwD0GBgaq0aNH7126dOnC4uJi8aVLlzprJmKaQqEwlsvlfKVSyTt+/Hj/\nI0eODC4rKzOht1f+T7+qZndfX9/YkJCQsGvXrrVTqVQGz549a7Jq1ao5vXr1OkUIIfb29lknT57s\nXVpaanr06NGPL1682OVN5zt8+PCDc+fOXTlgwIB4ExOTssrbPT09b3I4HGrdunVBdM1BoVAYl5WV\nmSiVSt7ly5c77dq1azx9HtXdKmjSpMmzmjQjBgQERO3du3f0zZs3PcvKykxCQ0ND58+f/wtFUZzy\n8nIjmUwmkEgkou++++5ntVrNlcvl/MrHSEpK6jBhwoSdz58/b2xqalpqYGCgatq06VNCXib4wsJC\nC1tb25y3xQLslJeXZz116tTN6enpzaRSqXD+/Pm/BAQERBkaGla8y3H69u174p9//umzffv2yaWl\npaYKhcJ4//79ozIyMpzat29/1d7ePisjI8Pp9u3bHtnZ2XYbN26cTnfGrcqwYcMi4+LiBp06dapX\nVY/zEvLfv9eysjITlUplIJfL+fn5+VZ0y4NCoTB+t6tSzzDdKQCFXSU9Pd2Zz+eX0st9+vQ50bdv\n3wR6mc/nl9KP0MXGxg5q1arVbSMjI0WHDh2u3L59uxVFvXzUZty4cbtMTU1lbm5u94KCgtbSHe/o\nWcquX7/u3bJly7sCgaBk1KhR+3bs2DFRKBRKbty44enj43Nq//79I+n3HDhwYNyuXbvGVY5VpVJx\nFy5cGObo6JhhbGxcZm9vnzl9+vQNpaWlfIqiyLNnz2y6dOlywcTERD5w4MC4yZMnb9PseFd5xr4H\nDx64cjgc9eHDhwfT6yp33vv+++9/tLa2zqUfOXry5IlDhw4drvD5/NL+/fvHR0ZGBojF4hdHjx71\nq+4RurS0tBbNmzd/WNX1rzyL244dOyY2b978obGxcVnv3r3/yc7OtqUoiixbtuxbsVj8ws7OLmvz\n5s1fjRgx4kCXLl0uUNTr070qlUrDr7/+elPjxo3zhUKhZOjQoVEvXrwQUxRFbt++3UpfOyOh6K7U\nZLa9mswumJCQ0LdTp06XTE1NZWKx+EX//v3jL1++3FHzfczMzKTOzs7pa9euDXJ3d79DUS8fsaW/\nHzRLt27dztGdeSnqZec5+jUUVfXf68yZM9eZmZlJW7RokXbw4MFhnTp1ujRq1Kh9iYmJPTVfq08F\ns9AB6KEOHTok7dmzJ9DNze0+UzFs2rRpWm5urs0PP/ywiKkYAODN0FwPoIfmzZu3bNu2bV8wGcO+\nffs+pUciBID6CUkeQA+NGDHir/v377tJpVIhE+9/8uTJ3r179z5J358HgPoJzfUAAAAshZo8AAAA\nSyHJAwAAsBSSPAAAAEshyQMAALAUkjwAAABLIckDAACwFJI8AAAASyHJAwAAsBSSPAAAAEshyQMA\nALAUkjwAAABLIckDAACwFJI8AAAASyHJAwAAsBSSPAAAAEshyQMAALAUkjwAAABL/R9KJVgTa/Fh\nEQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7e3d610>"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Copy Number Data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cn_tumor = meta.xs('Tumor', level=1)['SNP array ID'].dropna()\n",
      "lu = pd.Series({v:i for i,v in cn_tumor.iteritems()})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 34
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "I am loading the GISTIC2 processed data for this cohort.  The data was processed together with the TCGA samples."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "path = '../Extra_Data/UPMC_cohort/'\n",
      "gistic = pd.read_table(path + 'all_thresholded.by_genes.txt', index_col=[0,1, 2])\n",
      "gistic.index = gistic.index.droplevel(1)\n",
      "cn_upmc = pd.DataFrame({i: gistic[t] for i,t in cn_tumor.iteritems() if t in gistic})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pct_del = (cn_upmc < 0).sum() / (1.*len(cn_upmc))\n",
      "pct_amp = (cn_upmc > 0).sum() / (1.*len(cn_upmc))\n",
      "pct_altered = pct_amp + pct_del\n",
      "pct_altered.name = 'CIN_pct_altered'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 36
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Chromosomal instability in the UPMC cohort."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "survival_and_stats(to_quants(pct_altered, q=.33, labels=True), surv)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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j6Yn+HZOP3+X7qjrjXU1fYvOx3vWYKAfYzi9y6HsT4fC1+RTR/xCDEbEPh8MhuVzOqfZ+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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7b828d0>"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Taking a look at 3p deletion."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "del_3p = cn_upmc.xs('3p14.2',0,1).median().ix[true_index(clin.HPV=='Negative')].dropna() < 0"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b = combine(p53_mut, del_3p).index"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "keepers = meta['SNP array ID'].notnull() & (meta['Final_74_exome_analysis'] == 1)\n",
      "keepers = keepers.groupby(level=0).sum() \n",
      "keepers = (keepers > 0) & (clin.HPV == 'Negative')\n",
      "keepers = keepers.ix[surv.index.get_level_values(0)].dropna()\n",
      "keepers = keepers.groupby(level=0).last()\n",
      "keepers.value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 40,
       "text": [
        "True     49\n",
        "False    25\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "survival_and_stats(del_3p, surv)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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NTU1FFRUVBnTHpg3Fw8MjTV9fX8Zms+VI8qqVQ4cOBfft2/cMtSyTyfTZbLa8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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7b89510>"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Association of TP53 mutation with 3p deletion"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "len(combine(p53_mut, del_3p))"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fisher_exact_test(p53_mut, del_3p, alternative='greater')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 42,
       "text": [
        "odds_ratio    2.50\n",
        "p             0.17\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Survival association"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "combo = combine(p53_mut, del_3p)\n",
      "survival_and_stats(combo, surv, figsize=(5,4), title='PITT')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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WI6+Jx+NpLFmyZNvw4cOj5F2vhqbKykpa//7971OpVKGyB2kDA4MPQqGQ2hhl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\nX4Zyn80Ed2mcAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7d5bdd0>"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.crosstab(clin.Alcohol, clin.Alcohol_amt.fillna(0))"
     ],
     "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>Alcohol_amt</th>\n",
        "      <th>0</th>\n",
        "      <th>.</th>\n",
        "      <th>1</th>\n",
        "      <th>10</th>\n",
        "      <th>12</th>\n",
        "      <th>15</th>\n",
        "      <th>16</th>\n",
        "      <th>2</th>\n",
        "      <th>20</th>\n",
        "      <th>24</th>\n",
        "      <th>27</th>\n",
        "      <th>3</th>\n",
        "      <th>5</th>\n",
        "      <th>6</th>\n",
        "      <th>7</th>\n",
        "      <th>8</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Alcohol</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>.</th>\n",
        "      <td>  1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>  0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>no</th>\n",
        "      <td> 15</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>  0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>yes</th>\n",
        "      <td>  0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 8</td>\n",
        "      <td> 4</td>\n",
        "      <td> 3</td>\n",
        "      <td> 1</td>\n",
        "      <td> 3</td>\n",
        "      <td> 2</td>\n",
        "      <td> 3</td>\n",
        "      <td> 1</td>\n",
        "      <td> 4</td>\n",
        "      <td> 4</td>\n",
        "      <td> 11</td>\n",
        "      <td> 3</td>\n",
        "      <td> 9</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 44,
       "text": [
        "Alcohol_amt   0  .  1  10  12  15  16  2  20  24  27  3  5   6  7  8\n",
        "Alcohol                                                             \n",
        ".             1  0  0   0   0   0   0  0   0   0   0  0  0   0  0  0\n",
        "no           15  0  0   0   0   0   0  0   0   0   0  0  0   0  0  0\n",
        "yes           0  1  1   8   4   3   1  3   2   3   1  4  4  11  3  9"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "alch = clin.Alcohol_amt.fillna(0).replace('.', nan).astype(float)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "alch.hist()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 46,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x970b550>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7ad7090>"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "get_surv_fit_lr(surv, (alch.ix[combo.index].dropna() > 10).fillna('M'))"
     ],
     "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>\n",
        "      <th></th>\n",
        "      <th colspan=\"2\" halign=\"left\">Stats</th>\n",
        "      <th colspan=\"3\" halign=\"left\">Median Survival</th>\n",
        "      <th colspan=\"3\" halign=\"left\">5y Survival</th>\n",
        "      <th colspan=\"2\" halign=\"left\">Log-Rank</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th># Patients</th>\n",
        "      <th># Events</th>\n",
        "      <th>Median</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "      <th>Surv</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "      <th>chi2</th>\n",
        "      <th>p</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <td>   </td>\n",
        "      <td>   </td>\n",
        "      <td>     </td>\n",
        "      <td>      </td>\n",
        "      <td>    </td>\n",
        "      <td>      </td>\n",
        "      <td>       </td>\n",
        "      <td>      </td>\n",
        "      <td> 6.83</td>\n",
        "      <td> 0.00898</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 39</td>\n",
        "      <td> 19</td>\n",
        "      <td> 5.01</td>\n",
        "      <td>   1.8</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0.526</td>\n",
        "      <td>  0.389</td>\n",
        "      <td> 0.712</td>\n",
        "      <td>     </td>\n",
        "      <td>        </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>  9</td>\n",
        "      <td>  8</td>\n",
        "      <td>  1.1</td>\n",
        "      <td> 0.953</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0.111</td>\n",
        "      <td> 0.0175</td>\n",
        "      <td> 0.705</td>\n",
        "      <td>     </td>\n",
        "      <td>        </td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 47,
       "text": [
        "       Stats          Median Survival              5y Survival                Log-Rank         \n",
        "  # Patients # Events          Median  Lower Upper        Surv   Lower  Upper     chi2        p\n",
        "                                                                                  6.83  0.00898\n",
        "0         39       19            5.01    1.8   NaN       0.526   0.389  0.712                  \n",
        "1          9        8             1.1  0.953   NaN       0.111  0.0175  0.705                  "
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "get_surv_fit_lr(surv, clin.Smoking_history.ix[combo.index])"
     ],
     "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>\n",
        "      <th></th>\n",
        "      <th colspan=\"2\" halign=\"left\">Stats</th>\n",
        "      <th colspan=\"3\" halign=\"left\">Median Survival</th>\n",
        "      <th colspan=\"3\" halign=\"left\">5y Survival</th>\n",
        "      <th colspan=\"2\" halign=\"left\">Log-Rank</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th># Patients</th>\n",
        "      <th># Events</th>\n",
        "      <th>Median</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "      <th>Surv</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "      <th>chi2</th>\n",
        "      <th>p</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <td>   </td>\n",
        "      <td>   </td>\n",
        "      <td>     </td>\n",
        "      <td>     </td>\n",
        "      <td>    </td>\n",
        "      <td>      </td>\n",
        "      <td>      </td>\n",
        "      <td>      </td>\n",
        "      <td> 1.14</td>\n",
        "      <td> 0.285</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>yes</th>\n",
        "      <td> 41</td>\n",
        "      <td> 24</td>\n",
        "      <td> 1.85</td>\n",
        "      <td> 1.15</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0.399</td>\n",
        "      <td> 0.273</td>\n",
        "      <td> 0.585</td>\n",
        "      <td>     </td>\n",
        "      <td>      </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>no</th>\n",
        "      <td>  7</td>\n",
        "      <td>  3</td>\n",
        "      <td> 5.01</td>\n",
        "      <td> 1.43</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0.714</td>\n",
        "      <td> 0.447</td>\n",
        "      <td>     1</td>\n",
        "      <td>     </td>\n",
        "      <td>      </td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 48,
       "text": [
        "         Stats          Median Survival             5y Survival               Log-Rank       \n",
        "    # Patients # Events          Median Lower Upper        Surv  Lower  Upper     chi2      p\n",
        "                                                                                  1.14  0.285\n",
        "yes         41       24            1.85  1.15   NaN       0.399  0.273  0.585                \n",
        "no           7        3            5.01  1.43   NaN       0.714  0.447      1                "
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p2 = p53_mut\n",
      "p2.name = 'TP53'\n",
      "p2 = p2.map({True:'Mut.',False:'WT'})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "draw_survival_curves(del_3p.map({0:'3p +/+', 1:'3p +/-'}), surv, p2, legend=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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AN/fBCwsLwdXVtcX+Fi1aBDNmzACANx3QMjIyZPeydXV14datW2BqairbN5vNBnt7e1Ud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\nH1K2E9/QqVWcAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x13d4f890>"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<div id='p53/3p'>This UPMC cohort confirmed that patients with the TP53 / 3p aggregate event have worse prognosis than patients with TP53 mutation alone.</div>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "get_cox_ph(surv, del_3p.ix[true_index(p53_mut)], print_desc=True, interactions=False);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "        coef exp(coef) se(coef)    z     p\n",
        "feature 1.92      6.84     1.03 1.87 0.061\n",
        "\n",
        "Likelihood ratio test=6.37  on 1 df, p=0.0116  n= 35, number of events= 20 \n",
        "   (11 observations deleted due to missingness)\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "exp(1.92), exp(1.92) -  exp(1.92 - 1.03)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 52,
       "text": [
        "(6.8209584692907494, 4.3858288180008751)"
       ]
      }
     ],
     "prompt_number": 52
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.crosstab(p53_mut, del_3p.ix[p53_mut.index].fillna('M'))"
     ],
     "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>d</th>\n",
        "      <th>False</th>\n",
        "      <th>True</th>\n",
        "      <th>M</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TP53</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>False</th>\n",
        "      <td> 5</td>\n",
        "      <td>  8</td>\n",
        "      <td>  4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>True </th>\n",
        "      <td> 7</td>\n",
        "      <td> 28</td>\n",
        "      <td> 11</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 53,
       "text": [
        "d      False  True   M\n",
        "TP53                  \n",
        "False      5     8   4\n",
        "True       7    28  11"
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "two_hit_both = combine(p53_mut>0, del_3p) == 'both'\n",
      "no_cna = true_index(del_3p.ix[true_index(p53_mut==False)].isnull())\n",
      "no_cna = pd.Series(False, index=no_cna)\n",
      "two_hit = two_hit_both.append(no_cna).ix[keepers].dropna()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 54
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "survival_and_stats(two_hit.ix[keepers].dropna(), surv)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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U7phovyjKUmpqatTMzc0zQkJCfEtKSvizZ8/+dfr06YfojktZSk1NjdrZs2fH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\nSUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0xb583450>"
       ]
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Coin Tests"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This was a suggestion from a review.  The R [coin](http://cran.r-project.org/web/packages/coin/index.html) package allows for exact permutation tests for log-rank statistics."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load_ext rmagic\n",
      "coin = robjects.packages.importr('coin')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 56
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Test 1: p53/3p Worse Survival than wt"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "th2 = two_hit[true_index(clin.Age_Dx < 75)].dropna()\n",
      "time = robjects.r.c(*list(surv.unstack().days.ix[th2.index]))\n",
      "event = robjects.r.c(*list(surv.unstack().event.ix[th2.index]))\n",
      "g = robjects.r.c(*list(th2*1.))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 57
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R -i time,event,g\n",
      "\n",
      "exdata <- data.frame(time = time,\n",
      "                     event = event,\n",
      "                     group = factor(g))\n",
      "surv_test(Surv(time, event) ~ group, data=exdata, \n",
      "          distribution=exact(), alternative='less')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "\n",
        "\tExact Logrank Test\n",
        "\n",
        "data:  Surv(time, event) by group (0, 1)\n",
        "Z = -1.5691, p-value = 0.05894\n",
        "alternative hypothesis: less\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 58
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Test 2: p53/3p Worse Survival than p53"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f = del_3p.ix[true_index(p53_mut)].dropna()*1.\n",
      "f = f.ix[clin.Age_Dx < 77]\n",
      "g = robjects.r.c(*list(f))\n",
      "time = robjects.r.c(*list(surv.unstack().days.ix[f.index]))\n",
      "event = robjects.r.c(*list(surv.unstack().event.ix[f.index]))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 59
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cox(f, surv)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 60,
       "text": [
        "hazard       exp(coef)      6.84\n",
        "             exp(-coef)     0.15\n",
        "             lower .95      0.91\n",
        "             upper .95     51.20\n",
        "LR           stat           6.37\n",
        "             df             1.00\n",
        "             p              0.01\n",
        "concordance  stat           0.61\n",
        "             se             0.05\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 60
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R -i time,event,g\n",
      "\n",
      "exdata <- data.frame(time = time,\n",
      "                     event = event,\n",
      "                     group = factor(g))\n",
      "surv_test(Surv(time, event) ~ group, data=exdata, \n",
      "          distribution=exact(), alternative='less')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "\n",
        "\tExact Logrank Test\n",
        "\n",
        "data:  Surv(time, event) by group (0, 1)\n",
        "Z = -2.4025, p-value = 0.009194\n",
        "alternative hypothesis: less\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 61
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Test p53/3p Worst Survival than 3p"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f = p53_mut.ix[true_index(del_3p)].dropna()*1.\n",
      "g = robjects.r.c(*list(f))\n",
      "time = robjects.r.c(*list(surv.unstack().days.ix[f.index]))\n",
      "event = robjects.r.c(*list(surv.unstack().event.ix[f.index]))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 62
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R -i time,event,g\n",
      "\n",
      "exdata <- data.frame(time = time,\n",
      "                     event = event,\n",
      "                     group = factor(g))\n",
      "surv_test(Surv(time, event) ~ group, data=exdata, \n",
      "          distribution=exact(), alternative='less')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "\n",
        "\tExact Logrank Test\n",
        "\n",
        "data:  Surv(time, event) by group (0, 1)\n",
        "Z = 0.0197, p-value = 0.5014\n",
        "alternative hypothesis: less\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 63
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "MIR548K Copy Number"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We do not have expression data for this cohort so we use copy number.  The trend seems to hold here but we do not report this in the manuscript. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cn_upmc = cn_upmc.ix[:, two_hit.index]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 64
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "survival_and_stats(cn_upmc.ix['MIR548K'].ix[0].dropna()>0, surv)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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x4sSYHj16pBsbG0uGDRt25v79+850x6Qt5ciRIz4ODg7ZJiYm4pEjR55+/Phx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\nQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x83faf90>"
       ]
      }
     ],
     "prompt_number": 65
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "st = (two_hit + 1).add(2*(cn_upmc.ix['MIR548K'].ix[0] > 0)).replace(3,0).replace(4,3)\n",
      "st = st.dropna()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 66
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mir = cn_upmc.ix['MIR548K'].ix[0].ix[two_hit.index]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 67
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "eq = cn_upmc.ix['MIR548K'].ix[0].ix[two_hit.index]\n",
      "#eq = cn_upmc.xs('11q13.3',0,1).median()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 68
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "st = (two_hit + 1).add(2*(eq > 1)).replace(3,1).replace(4,3)\n",
      "st = st.dropna()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 69
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "survival_and_stats(st, surv)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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lZXUQiURsGxubtNq2uX379kddunR5WlMtHgAAoCVTqSTv6up6XSQSsXft2vVF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\nkydPjhg1atRxLS2tyo0bNy42MTHJz8jI6HTmzBk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       "text": [
        "<matplotlib.figure.Figure at 0x9736d90>"
       ]
      }
     ],
     "prompt_number": 70
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "get_cox_ph(surv, st, print_desc=True, interactions=False);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "         coef exp(coef) se(coef)    z     p\n",
        "feature 0.433      1.54    0.243 1.78 0.074\n",
        "\n",
        "Likelihood ratio test=3.14  on 1 df, p=0.0763  n= 48, number of events= 27 \n",
        "\n"
       ]
      }
     ],
     "prompt_number": 71
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "HPV+ Combined Patients"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cn_upmc = pd.DataFrame({i: gistic[t] for i,t in cn_tumor.iteritems() if t in gistic})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 72
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cn_upmc = pd.DataFrame({i: gistic[t] for i,t in cn_tumor.iteritems() if t in gistic})\n",
      "del_3p_upmc = cn_upmc.xs('3p14.2',0,1).median().ix[true_index(clin.HPV=='Positive')].dropna()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 73
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "surv_combined = pd.concat([surv, clinical.survival.survival_5y])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 74
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cn_2 = cn_upmc.groupby(level=1).median().ix[:, true_index(clin.HPV=='Positive')].dropna(1, how='all')\n",
      "cn_3 = cn.features.copy().ix[:, ti(clinical.hpv)]\n",
      "cn_3.index = cn_3.index.get_level_values(1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 75
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cn_4 = cn_2.join(cn_3)\n",
      "cn_4 = cn_4.dropna(1, how='all').dropna(how='any')\n",
      "cn_d = (cn_4 < 0)\n",
      "cn_d = cn_d[cn_d.sum(1) > 0]\n",
      "\n",
      "cn_a = (cn_4 > 0)\n",
      "cn_a = cn_a[cn_a.sum(1) > 0]\n",
      "\n",
      "cn_all = pd.concat([cn_a, cn_d], keys=['Amplification','Deletion'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 76
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cox_screen(cn_all, surv_combined).sort([('LR','p')]).head()"
     ],
     "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>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th colspan=\"4\" halign=\"left\">hazard</th>\n",
        "      <th colspan=\"4\" halign=\"left\">LR</th>\n",
        "      <th colspan=\"2\" halign=\"left\">concordance</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th>exp(-coef)</th>\n",
        "      <th>exp(coef)</th>\n",
        "      <th>lower .95</th>\n",
        "      <th>upper .95</th>\n",
        "      <th>df</th>\n",
        "      <th>p</th>\n",
        "      <th>stat</th>\n",
        "      <th>q</th>\n",
        "      <th>se</th>\n",
        "      <th>stat</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Deletion</th>\n",
        "      <th>3p14.2</th>\n",
        "      <td> 0.18</td>\n",
        "      <td> 5.54</td>\n",
        "      <td> 1.51</td>\n",
        "      <td> 20.33</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0.00</td>\n",
        "      <td> 8.21</td>\n",
        "      <td> 0.23</td>\n",
        "      <td> 0.08</td>\n",
        "      <td> 0.75</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">Amplification</th>\n",
        "      <th>8q24.21</th>\n",
        "      <td> 6.06</td>\n",
        "      <td> 0.17</td>\n",
        "      <td> 0.04</td>\n",
        "      <td>  0.76</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0.01</td>\n",
        "      <td> 7.44</td>\n",
        "      <td> 0.23</td>\n",
        "      <td> 0.08</td>\n",
        "      <td> 0.68</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13q22.1</th>\n",
        "      <td> 0.16</td>\n",
        "      <td> 6.25</td>\n",
        "      <td> 1.82</td>\n",
        "      <td> 21.46</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0.01</td>\n",
        "      <td> 7.38</td>\n",
        "      <td> 0.23</td>\n",
        "      <td> 0.06</td>\n",
        "      <td> 0.69</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20q11.22</th>\n",
        "      <td> 7.82</td>\n",
        "      <td> 0.13</td>\n",
        "      <td> 0.02</td>\n",
        "      <td>  0.99</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0.01</td>\n",
        "      <td> 6.69</td>\n",
        "      <td> 0.23</td>\n",
        "      <td> 0.08</td>\n",
        "      <td> 0.64</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8p11.23</th>\n",
        "      <td> 7.71</td>\n",
        "      <td> 0.13</td>\n",
        "      <td> 0.02</td>\n",
        "      <td>  1.02</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0.01</td>\n",
        "      <td> 6.44</td>\n",
        "      <td> 0.23</td>\n",
        "      <td> 0.08</td>\n",
        "      <td> 0.60</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 77,
       "text": [
        "                            hazard                                   LR                    concordance      \n",
        "                        exp(-coef)  exp(coef)  lower .95  upper .95  df     p  stat     q           se  stat\n",
        "Deletion      3p14.2          0.18       5.54       1.51      20.33   1  0.00  8.21  0.23         0.08  0.75\n",
        "Amplification 8q24.21         6.06       0.17       0.04       0.76   1  0.01  7.44  0.23         0.08  0.68\n",
        "              13q22.1         0.16       6.25       1.82      21.46   1  0.01  7.38  0.23         0.06  0.69\n",
        "              20q11.22        7.82       0.13       0.02       0.99   1  0.01  6.69  0.23         0.08  0.64\n",
        "              8p11.23         7.71       0.13       0.02       1.02   1  0.01  6.44  0.23         0.08  0.60"
       ]
      }
     ],
     "prompt_number": 77
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "del_3p_all = cn_all.ix['Deletion'].ix['3p14.2']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 78
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p53_upmc = hit_matrix.ix['TP53', true_index(clin.HPV=='Positive')].dropna()\n",
      "p53_tcga = mut.features.ix['TP53', true_index(clinical.hpv==True)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 79
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<div id='3p_hpv_OR'>We noted that TP53 mutation is very rare in HPV+ patients, consistent with the idea that mutation confers little selective advantage once TP53 is inactivated by HPV.</div>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p53_all = (mut.features.ix['TP53'].append(hit_matrix.ix['TP53']>0)).map({True:'mut',False:'wt'})\n",
      "hpv_all = clinical.hpv.append(clin.HPV=='Positive')\n",
      "pd.crosstab(hpv_all, p53_all)"
     ],
     "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>TP53</th>\n",
        "      <th>mut</th>\n",
        "      <th>wt</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>HPV</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>False</th>\n",
        "      <td> 257</td>\n",
        "      <td> 69</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>True </th>\n",
        "      <td>   2</td>\n",
        "      <td> 52</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 80,
       "text": [
        "TP53   mut  wt\n",
        "HPV           \n",
        "False  257  69\n",
        "True     2  52"
       ]
      }
     ],
     "prompt_number": 80
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "1 / fisher_exact_test(hpv_all, p53_all)['odds_ratio']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 81,
       "text": [
        "0.010326249625860521"
       ]
      }
     ],
     "prompt_number": 81
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fisher_exact_test(hit_matrix.ix['TP53']>0, clin.HPV=='Positive')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 82,
       "text": [
        "odds_ratio    0.00e+00\n",
        "p             5.14e-06\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 82
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print len(combine(hit_matrix.ix['TP53']>0, clin.HPV=='Positive'))\n",
      "pd.crosstab(hit_matrix.ix['TP53']>0, clin.HPV)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "74\n"
       ]
      },
      {
       "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>HPV</th>\n",
        "      <th>Negative</th>\n",
        "      <th>Positive</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TP53</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>False</th>\n",
        "      <td> 17</td>\n",
        "      <td> 11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>True </th>\n",
        "      <td> 46</td>\n",
        "      <td>  0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 83,
       "text": [
        "HPV    Negative  Positive\n",
        "TP53                     \n",
        "False        17        11\n",
        "True         46         0"
       ]
      }
     ],
     "prompt_number": 83
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fisher_exact_test(hpv_all, p53_all)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 84,
       "text": [
        "odds_ratio    9.68e+01\n",
        "p             1.17e-27\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 84
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<div id='3p_hpv_surv'>Moreover, the 25 HPV+ patients harboring a 3p deletion had significantly worse prognosis than the 34 HPV+ patients without the 3p event.</div>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "survival_and_stats(del_3p_all, surv_combined)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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WTXAh6ybQHcX/wzV5JeCaPEDTp2iPd3iJDagDZa/JI8krAUkeoOnLad9B4X7rGRwOsXj+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\nHgAASf0ESQvs2p0T2GIAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x9628cd0>"
       ]
      }
     ],
     "prompt_number": 85
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "log_rank(del_3p_all, surv_combined).ix['p']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 86,
       "text": [
        "0.0038336554312233214"
       ]
      }
     ],
     "prompt_number": 86
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "get_cox_ph(surv_combined, del_3p_all, print_desc=True);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "        coef exp(coef) se(coef)    z      p\n",
        "feature 1.71      5.54    0.663 2.58 0.0098\n",
        "\n",
        "Likelihood ratio test=8.21  on 1 df, p=0.00417  n= 59, number of events= 13 \n",
        "\n"
       ]
      }
     ],
     "prompt_number": 87
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "exp(1.71), exp(1.71) -  exp(1.71 - .663)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 88,
       "text": [
        "(5.5289614776240041, 2.6798704663347901)"
       ]
      }
     ],
     "prompt_number": 88
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "UPMC Validation Cohort Figures"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Figure 3a"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p53_mut.name = 'TP53'\n",
      "del_3p.name = '3p'\n",
      "combo = combine(p53_mut, del_3p)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 89
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = subplots(figsize=(3,2.5))\n",
      "c = {'TP53': colors_th[1], 'both': colors_th[0]}\n",
      "draw_survival_curve(combo[combo.isin(['both','TP53'])], surv, colors=c, ax=ax)\n",
      "ax.legend(loc='lower left', frameon=False)\n",
      "prettify_ax(ax)\n",
      "fig.tight_layout()\n",
      "fig.savefig(FIGDIR + 'fig3a.pdf')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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pmk8//XQLhUKRWlpaiuVGpVJbnz596kR23evWrfuqT58+f7PZ7IagoKALd+7c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\nGgxGS7BoMBgt+X+zgfoA/oEF+gAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x13d44910>"
       ]
      }
     ],
     "prompt_number": 90
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "c = combo[combo.isin(['both','TP53'])]\n",
      "c = c.replace({'both': 'TP53 and 3p'})\n",
      "df = pd.concat([c, surv[:,'days'], surv[:,'event']],\n",
      "                keys=['Molecular Status', 'Days to Death/Censoring', 'Death Indicator'],\n",
      "                axis=1).dropna()\n",
      "df.to_csv(FIGDIR + 'fig3a.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 91
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = subplots(figsize=(3,2.5))\n",
      "surv_fit = get_surv_fit(surv, combo)\n",
      "tt = surv_fit['5y Survival']\n",
      "b = (tt['Surv']).plot(kind='bar', ax=ax, color=[colors_th[2], colors_th[0], \n",
      "                                                colors_th[3], colors_th[1]],\n",
      "         yerr=[tt.Surv-tt.Lower, tt.Upper-tt.Surv], ecolor='black')\n",
      "ax.set_ylabel('5Y Survival')\n",
      "ax.set_yticks([0, .5, 1.0])\n",
      "bar_labels = ['{}\\n\\n{}'.format(j,int(v)) for j,v in \n",
      "              surv_fit['Stats']['# Patients'].iteritems()]\n",
      "ax.set_xticklabels(bar_labels, rotation=0);\n",
      "prettify_ax(ax)\n",
      "fig.tight_layout()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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I7OfndzEyMjKhurp6kImJSR3eQyeusbHRYMGCBafj4+NXl5eXW9fV1ZnExsZGKSIeaYVF\nfyp6E2/lfo2qI5BNutNAdXP7XU+ae/fujadSqYKlS5f+wGQyuTExMdFpaWlzOjs7NfE66enpvmPG\njHno6+ubbmBg0BgdHR1z9uzZ+YqIB4D+pJCkycvLc2Wz2fn4tIWFRRWVShUUFxc7yKrj5ub26Pnz\n5/ZtbW00RcQEQH9RyH0aHo/HYDKZ/zW2HZPJ5La0tOhK17G2ti7Hp+l0Op9MJotaWlp0aTRaW/d1\n9qX/ZYT61p3smfOH+7Suvm6zrwYd79v5yp68vtXrz/iS7y7vU738iit9qtff7x15epLcOttSHvVp\nXX2MTSKRSHqsqJCkYTKZXC6Xy5Sex+Vymfj4Nj3V4fP5dJFIRJaug5MVPACqoJDDM1dX17z8/Hw2\nPl1ZWWkpEAioDg4OxbLq5ObmjrSzs3vR014GALUikUj6vXR2dmpYWVmVf//998uampr0Fi9e/OPC\nhQtTpOs0NDQYGBgYNFy5csWXw+EY+vj4XP/6669jFREPFCj9WRS24uzs7NEsFusxjUbjT58+/WpD\nQ4PBiRMnFtnb2xfjdX755ZcZdnZ2z+l0euvChQtT2tvbKf0ZQ1pa2sc2NjalOjo6PG9v78ySkpIh\nqniTb9265TVs2LAnRJZZvHjxjzt27NgokUjQ5s2bt4SHhx9WZswaGhqddXV1xqWlpTYUCqUdn49h\nmLi2ttZEUdu1s7N7rqGh0amhodGJYZgYf6yhodE5ZMiQEjKZ3IVP6+npNQUHBye3trbSJRIJio6O\n3kahUNq1tbUF2traAgcHh2f4eo8cOfKZubl5NZPJbPHz80utr683etMYlf4FUlapra01YTKZLZmZ\nmd58Pp+2du3aPd7e3pmqiOVtk2bLli2blZ00eCktLbXR1tYW4NOKThrp0n1bXl5et86ePTsPn66o\nqLAcMWJEflRUVIxEIkGBgYGns7KyPLuvJy8vj21oaMjJyckZyeVyGfPnz/9p6dKlR980rve2Gc3d\nu3cnenh4PPD29r5Bo9HalixZkvTo0SO30NDQ4+vWrdszcuTIXDqdzv/ss8++l75/pCgikYi8bNmy\no/r6+k2urq55f//99zCEENq9e/d6ExOTOn19/aaoqKhYhBDatWvXhpMnTwZ//fXX33733Xf/Quj1\nzeBp06Zl0Ol0vo+PTyaPx2P0ddtZWVleY8eO/WP16tXxTCaT6+zsXISfT+7YseNLExOTOl1d3ZbN\nmzdvxZchkUji2tpaUxcXl0KhUEiRvhp64sSJxWZmZjVmZmY10qOAX716dYa9vf1zKpUq8PPzu4jf\nPggJCflx06ZN21ksVsGePXvWve17ibO0tKz08fHJxF9LSUmJrZ2d3Yvu9W7evPmBv79/6siRI3MZ\nDAZv0aJFyY8ePXJ74w2r4tdLWUUsFmMSiQR1dHRobty4ccesWbMuh4SEHDcxMaktLi62b25u1vX0\n9Mzas2fPWkXGcevWLS8Mw8Tx8fGrRCIRKT4+ftWIESPyU1JSFg4dOvRpZWWlRX19vZG7u/vDffv2\nrZZIJCgkJOT4zp07N0gkrw/PKBRK++3btye3tbVRfXx8rm/duvUbItsnk8ld33333XqhUKi1du3a\nPVOmTLmZkpKykM1m51VUVFhWVlZajBw5Mufnn3+eLf0rX1ZWZt19TzN37txzPB5P58aNGx/SaDS+\nUCjUKi0ttTE0NOT8/vvv47lcLiMsLCxp7dq1eySS13tNKyur8idPngwj8r71tKf56aef5uPTlZWV\nFi4uLgUxMTFREokEGRsb102fPv2qvr5+47hx4+5lZ2ePxr8H+HeBz+fTgoKCTq5cuTLhTT9PlX+x\nFV3S09Nn4sfB586dmxsSEnJ8zZo1/8afT0tL+3jixIl3FBnDrVu3vPT19Rul5xkYGDRMmTLlZnJy\ncjA+75dffpnBYrEe41806XOajz766D94vW+//farsLCwJCLbNzQ05ODTt2/fnmxra/ti2rRp11JT\nU/3w+cePHw8JDg5Olv7C9nR4lpub64ZPa2lpCSsqKizj4uI2RUZG7sfnl5aW2lhaWlbgr2XTpk1x\nRN+37knj6emZpaWlJcTPWUxNTWuWL1+eKBAItAUCgbabm1vulStXfNva2qiHDh2KMDMze9XS0sLE\nlz906FAEiUQS0en01rt3705408/zvT08w82cOfOXjo4OrTNnzgQGBQWlvHr1ylz6pqqBgUFjT41J\n+5v0NhFCSE9Pr/nBgwcetra2Jfg8W1vbEnzoeQzDJBiG/XOX09zc/J/GVSQSSYwPV99XxsbG9fhj\nLS2tDrFYTKqoqLBasGDBaSqVKqBSqYLw8PDEly9fDpa3rkGDBlV3j6WiosIqMTExHF+Xk5PTk/r6\nemORSETGMEzS0/03ojAMk5w8eTJYIBBQBQIBtaamxiwxMTFcW1u7XVtbuz03N3ekr69vOpVKFURE\nRBw2NDRs+O233ybjy0dERBwWCATU2NjYqDlz5qR1dXW90X3K9zZpjhw5snz//v2rEHr9wQYEBFww\nNDRs0NLS6qipqTHD65WVldkMGTKkVNHxSG9TKBRSampqzMaNG3e/pKTkn3HGS0pKbC0tLSsR+t8b\nutIJJP34bRgbG9enpaXNwb+Er169Mv/+++8/k7dcT9s3NjauX7NmzV58XTwej3H//v1xZDJZ1B+x\n4rq/L7iioiLnkydPBkvPE4lEZDqdzt+2bds3586dm4fQ6x+M8PDwxMbGRgMi54XS3tuksbKyqti9\ne/f6wsJCF6FQSElKSlpCIpHENBqt7ccffwx58uSJU21trem+ffu++PTTT39SdDy1tbWm+/fvXyUQ\nCKg7duz4cty4cffDwsKOxcXFffXy5cvBHA7HaOvWrZvDwsKOIYSQhoZGl6wPVdYXhyg/P7+Lu3fv\nXl9XV2fC4XCMAgMDz5w/f36udB0ymSwSiUTk9vZ2bVnrwTBM4ufnd/H06dMLCgoKWO3t7dpbtmzZ\nsnHjxp39GS++rZ7ma2pqdkZGRibcvn3bUygUUhITE8N5PB5j0qRJd4yNjeu3b9++qayszEYgEFB3\n7ty50dXVNU9fX7/pTWJ4b/sImDFjxtWIiIjD06ZNy+DxeIxRo0blpKen++7bt+8LLy+vLF9f33QO\nh2P0+eefHwoKCkpRZCwYhknc3Nwe/fbbb5M3bdq0nc1m51+8eNHPzMysprKy0pLNZud3dXVpRERE\nHMb/QuHt7X1j2bJlR42MjDjdD9W6T/c1hu7TK1euPFBeXm7NYrEK2traaIGBgWfWr1+/W7r+oEGD\nqtlsdr6FhUUVh8MxkrVeV1fXvG3btn0zZ86ctJcvXw6eMGHC7/gv/5vE21PMvRk6dOizxMTE8LCw\nsGPV1dWDXF1d8y5fvjxbQ0Oja9myZUefP39u7+7uni0SicgTJkz4PTU11Z9oPP/EJZGo75+eFCE0\nNPS4k5PTkw0bNuxSdSzg3fTeHp71pj8PF8DAMyCTpr9OpMHANOAOzwB4WwNyTwPA24CkAYAgSBoA\nCIKkAYAgSBoACIKkAYAgSBoACIKkAYAgSBoACIKkAYAgSBoACIKkAYAgSBoACIKkAYAgSBoACIKk\nAYCgAZU0KSkpQdbW1uU6Ojqt48ePv5edne2uyngyMjKmsVisAjqdzv/ggw9uPn361LGtrY0WFhZ2\njMlkck1NTWvDwsKOqXp0OE9Pz9t4f2b4sJCqjAcXGxsbJR0XlUoVaGlpdUh3lasQqu4BU1mlqqpq\nMIPB4Obl5bE7Ojo0Dx06FCHdq7yyS01NjSneQXt7ezslLi5uE4vFerxjx46NU6dOzeByuYz6+noj\nHx+f699+++1XqnzvLC0tK0QiEUnVn6G88ujRI1cXF5cCPp9PU+R2BsyeRvL/nWl0dHRoYRgm6ejo\n0Oo+xKEy4Z2Se3t736BQKMINGzbsKioqciaRSGKRSETGe6bs7OzUVGWcAoGASqFQhCQSSayqGPqi\nq6tLIyQk5McjR44sV/jAYKr+dVBm2bVr178wDBNjGCYmkUiiX3/99QNVxVJTU2P67NkzB3w6NzfX\nDe9M3M3NLRePc9iwYU8EAoG2quIsLCx0trKyKh85cmSOgYFBQ0BAwHllDbVBpBw9enSpv7//BWVs\nS+UvVlmloKDARVdXt/nmzZtT2tvbKdu2bYu2t7cvxnuTV2VJT0+faW5uXr19+/YvN27cuMPT0zOr\npqbGtKqqavDYsWMfEBkhoL/LnTt3Jn744Yc3CgsLnZubm3VDQkKOT58+/aqq3zPp0tHRoTlkyJCS\nnJyckcrYnspfsLLKrl27/hUYGHhaep6Ojg6vqKhouKpiampq0ps7d+45U1PTmlOnTi2QSCTI2dm5\nMCMjYype5/z58wGjR4/OVvX7h5f6+nojDMPE+Ohj6lAuXbo0Z8yYMX8oa3vvbbe03YnFYpJQKKRI\nzyORSGIGg8FTRTwdHR1aPj4+mU5OTk+Ki4sd8DhEIhFZOk5VxogQQunp6b5MJpM7adKkOwi9Pncg\nk8kiCoUiVFVM3Z04cWKxv79/qtI2qOpfCWWVgoICFwaDwb1+/bpPe3s75cCBAytU+QuenJwc3NO4\nOBs3btwxYcKEu1VVVYM5HI6ht7d3ZkJCwkpVxXns2LFQZ2fnwpKSkiFcLpexePHiH+fNm3dW1Z8n\nXrq6ush0Or31/v37HsrapspftDLLhQsX/B0cHJ5RKJT2KVOm3CwrK7NWVSyrVq2KJ5FIIumBWDU1\nNTsqKiosP//884O6urrNenp6TZs2bYpT9eXeL7/8cruhoSFHR0eHFxAQcL6hocFA1Z8lXh49euRK\no9H4nZ2dGsraJvSwCQBBA+Y+DQD9BZIGAIIgaQAgCJIGAIIgaQAgCJIGAIL+D6s5Fn5fT7vTAAAA\nAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x13d564d0>"
       ]
      }
     ],
     "prompt_number": 92
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Figure 3b"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = subplots(figsize=(3,2.5))\n",
      "draw_survival_curve(del_3p_all.astype(int), surv_combined, ax=ax, \n",
      "                    colors=[colors_th[3], colors_th[2]])\n",
      "ax.legend().set_visible(False)\n",
      "prettify_ax(ax)\n",
      "fig.tight_layout()\n",
      "fig.savefig(FIGDIR + 'fig3d.pdf')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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wjaura8aOHTvWJScnj3N3d7/PZrNF7u7u99PS0kbp+1gNvei9AVioK1OnTr22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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xd036690>"
       ]
      }
     ],
     "prompt_number": 103
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.concat([del_3p_all, surv_combined[:,'days'], surv_combined[:,'event']],\n",
      "                keys=['3p Deletion', 'Days to Death/Censoring', 'Death Indicator'],\n",
      "                axis=1).dropna()\n",
      "df.to_csv(FIGDIR + 'fig3ba.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 101
    }
   ],
   "metadata": {}
  }
 ]
}