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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<h1 class=\"alert alert-info\">HNSCC HPV- Cohort <small> <i class=\"icon-repeat\"></i> Integrative Prognositic Screen</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": "code",
     "collapsed": false,
     "input": [
      "from Processing.Screen import *"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Setup Survival Screen Functions"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def surv_test(s, surv, cov_df):\n",
      "    s = s.dropna()\n",
      "    try:\n",
      "        return get_cox_ph_ms(surv, s, cov_df, return_val='LR', interactions='just_feature')\n",
      "    except:\n",
      "        return pd.Series(index=['LR','feature_p', 'fmla', 'hazzard'])\n",
      "\n",
      "\n",
      "def run_screen(screen, filters, covariates):\n",
      "    cov_df = pd.concat(covariates, axis=1)\n",
      "    keepers_o = screen.get_patient_set(filters)\n",
      "    cutoff = max(np.ceil(len(keepers_o) * .05), 10)\n",
      "    df = screen.get_data(keepers_o, cutoff)\n",
      "\n",
      "    univariate = cox_screen(df, surv)\n",
      "    vec = univariate.LR.p.sort_index()\n",
      "    univariate = pd.concat([univariate['hazard'], corrections(vec)], \n",
      "                           keys=['hazard', 'p'], axis=1)\n",
      "    #hits = univariate[univariate['q_bh'] < .2].index\n",
      "    hits = univariate.index\n",
      "    \n",
      "    full = df.ix[hits].apply(surv_test, args=(surv, cov_df,), axis=1)\n",
      "    vec = full.LR.ix[univariate.index].sort_index()\n",
      "    full = pd.concat([full[['fmla']], corrections(vec)],\n",
      "                            keys=['fmla','p'], axis=1)\n",
      "    hits = true_index(full.p.bh_all.order() < .1)\n",
      "    res = full.sort([('p','uncorrected')]).head()\n",
      "\n",
      "    return res, full, univariate, keepers_o, df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Screen for Hierarchical Model of Survival"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p53_mut = mut.features.ix['TP53'].ix[keepers_o]\n",
      "del_3p = cn.features.ix[('Deletion', '3p14.2', 'Lesion')].ix[keepers_o]\n",
      "two_hit = combine(p53_mut > 0, del_3p < 0) == 'both'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "screen = Screen(mut, cn, rna, mirna, clinical.binary_df, surv, keepers_o.intersection(ti(two_hit)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "res, full, univariate, keepers_o, df = run_screen(screen, filters=[hpv==1, two_hit==0, age>=85], \n",
      "                                                  covariates=[old, age])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(full.p.bh_all < .1).groupby(level=0).apply(pd.value_counts).unstack()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>False</th>\n",
        "      <th>True</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>clinical</th>\n",
        "      <td>  11</td>\n",
        "      <td>  2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>cna</th>\n",
        "      <td>  71</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mirna</th>\n",
        "      <td> 237</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mutation</th>\n",
        "      <td> 220</td>\n",
        "      <td>  5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>rna</th>\n",
        "      <td> 455</td>\n",
        "      <td>  7</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 2 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "          False  True \n",
        "clinical     11      2\n",
        "cna          71    NaN\n",
        "mirna       237    NaN\n",
        "mutation    220      5\n",
        "rna         455      7\n",
        "\n",
        "[5 rows x 2 columns]"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(full.p.bh_all < .1).value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "False    994\n",
        "True      14\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "muc5b = df.ix['mutation'].ix['MUC5B']>0"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "survival_and_stats(df.ix['mutation'].ix['MUC5B']>0, surv)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
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rEBsbiy1btmDIkCGmx9atW4c+ffpU+1yDwYCoqCjExcVh/PjxiI2NRWxsLDZt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\nnZIkL4QQQujUP5uX4SuZKhq4AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xbf78a10>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rr = cox_screen(df.ix[full.p.bh_all < .1], surv)\n",
      "hits = full.ix[true_index(rr.LR.p < .05)]\n",
      "hits = hits.sort([('p','uncorrected')])\n",
      "len(hits)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "6"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hits"
     ],
     "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>fmla</th>\n",
        "      <th colspan=\"6\" halign=\"left\">p</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th>fmla</th>\n",
        "      <th>uncorrected</th>\n",
        "      <th>bh_within</th>\n",
        "      <th>bh_all</th>\n",
        "      <th>bonf_all</th>\n",
        "      <th>bonf_within</th>\n",
        "      <th>two_step</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>mutation</th>\n",
        "      <th>MUC5B</th>\n",
        "      <td>               Surv(days, event) ~ feature + old\\n</td>\n",
        "      <td> 7.73e-05</td>\n",
        "      <td> 0.01</td>\n",
        "      <td> 0.02</td>\n",
        "      <td> 0.08</td>\n",
        "      <td> 0.02</td>\n",
        "      <td> 0.03</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>rna</th>\n",
        "      <th>BIOCARTA_PYK2_PATHWAY</th>\n",
        "      <td> Surv(days, event) ~ feature + old + feature:old\\n</td>\n",
        "      <td> 1.68e-04</td>\n",
        "      <td> 0.03</td>\n",
        "      <td> 0.03</td>\n",
        "      <td> 0.17</td>\n",
        "      <td> 0.08</td>\n",
        "      <td> 0.13</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">clinical</th>\n",
        "      <th>recent_smoker</th>\n",
        "      <td>               Surv(days, event) ~ feature + old\\n</td>\n",
        "      <td> 6.81e-04</td>\n",
        "      <td> 0.00</td>\n",
        "      <td> 0.06</td>\n",
        "      <td> 0.69</td>\n",
        "      <td> 0.01</td>\n",
        "      <td> 0.02</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>pre_2000</th>\n",
        "      <td>                     Surv(days, event) ~ feature\\n</td>\n",
        "      <td> 6.98e-04</td>\n",
        "      <td> 0.00</td>\n",
        "      <td> 0.06</td>\n",
        "      <td> 0.70</td>\n",
        "      <td> 0.01</td>\n",
        "      <td> 0.02</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mutation</th>\n",
        "      <th>BIOCARTA_PGC1A_PATHWAY</th>\n",
        "      <td> Surv(days, event) ~ feature + old + feature:old\\n</td>\n",
        "      <td> 8.71e-04</td>\n",
        "      <td> 0.04</td>\n",
        "      <td> 0.07</td>\n",
        "      <td> 0.88</td>\n",
        "      <td> 0.20</td>\n",
        "      <td> 0.20</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>rna</th>\n",
        "      <th>RAD51</th>\n",
        "      <td> Surv(days, event) ~ feature + old + age + feat...</td>\n",
        "      <td> 1.29e-03</td>\n",
        "      <td> 0.09</td>\n",
        "      <td> 0.09</td>\n",
        "      <td> 1.30</td>\n",
        "      <td> 0.60</td>\n",
        "      <td> 0.43</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>6 rows \u00d7 7 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "                                                                              fmla            p                                                    \n",
        "                                                                              fmla  uncorrected  bh_within  bh_all  bonf_all  bonf_within  two_step\n",
        "mutation MUC5B                                 Surv(days, event) ~ feature + old\\n     7.73e-05       0.01    0.02      0.08         0.02      0.03\n",
        "rna      BIOCARTA_PYK2_PATHWAY   Surv(days, event) ~ feature + old + feature:old\\n     1.68e-04       0.03    0.03      0.17         0.08      0.13\n",
        "clinical recent_smoker                         Surv(days, event) ~ feature + old\\n     6.81e-04       0.00    0.06      0.69         0.01      0.02\n",
        "         pre_2000                                    Surv(days, event) ~ feature\\n     6.98e-04       0.00    0.06      0.70         0.01      0.02\n",
        "mutation BIOCARTA_PGC1A_PATHWAY  Surv(days, event) ~ feature + old + feature:old\\n     8.71e-04       0.04    0.07      0.88         0.20      0.20\n",
        "rna      RAD51                   Surv(days, event) ~ feature + old + age + feat...     1.29e-03       0.09    0.09      1.30         0.60      0.43\n",
        "\n",
        "[6 rows x 7 columns]"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Step 2- Add year of diagnosis covariate into the mix"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "res, full, univariate, keepers_o, df = run_screen(screen, filters=[hpv==1, two_hit==0], \n",
      "                                                  covariates=[old, age, clinical.binary_df.ix['pre_2000']])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "(1008, 179)"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.groupby(level=0).size()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "clinical     13\n",
        "cna          71\n",
        "mirna       237\n",
        "mutation    225\n",
        "rna         462\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rr = cox_screen(df.ix[full.p.bh_all < .1], surv)\n",
      "hits = full.ix[true_index(rr.LR.p < .05)]\n",
      "hits = hits.sort([('p','uncorrected')])\n",
      "len(hits)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "6"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hits.p.head(4)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th>uncorrected</th>\n",
        "      <th>bh_within</th>\n",
        "      <th>bh_all</th>\n",
        "      <th>bonf_all</th>\n",
        "      <th>bonf_within</th>\n",
        "      <th>two_step</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>clinical</th>\n",
        "      <th>recent_smoker</th>\n",
        "      <td> 1.79e-05</td>\n",
        "      <td> 2.33e-04</td>\n",
        "      <td> 0.01</td>\n",
        "      <td> 0.02</td>\n",
        "      <td> 2.33e-04</td>\n",
        "      <td> 0.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mirna</th>\n",
        "      <th>hsa-mir-548k</th>\n",
        "      <td> 3.40e-05</td>\n",
        "      <td> 8.05e-03</td>\n",
        "      <td> 0.01</td>\n",
        "      <td> 0.03</td>\n",
        "      <td> 8.05e-03</td>\n",
        "      <td> 0.04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>rna</th>\n",
        "      <th>BIOCARTA_PYK2_PATHWAY</th>\n",
        "      <td> 3.15e-04</td>\n",
        "      <td> 3.30e-02</td>\n",
        "      <td> 0.04</td>\n",
        "      <td> 0.32</td>\n",
        "      <td> 1.46e-01</td>\n",
        "      <td> 0.17</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mutation</th>\n",
        "      <th>MUC5B</th>\n",
        "      <td> 3.71e-04</td>\n",
        "      <td> 2.78e-02</td>\n",
        "      <td> 0.04</td>\n",
        "      <td> 0.37</td>\n",
        "      <td> 8.34e-02</td>\n",
        "      <td> 0.14</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>4 rows \u00d7 6 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "                                uncorrected  bh_within  bh_all  bonf_all  bonf_within  two_step\n",
        "clinical recent_smoker             1.79e-05   2.33e-04    0.01      0.02     2.33e-04      0.00\n",
        "mirna    hsa-mir-548k              3.40e-05   8.05e-03    0.01      0.03     8.05e-03      0.04\n",
        "rna      BIOCARTA_PYK2_PATHWAY     3.15e-04   3.30e-02    0.04      0.32     1.46e-01      0.17\n",
        "mutation MUC5B                     3.71e-04   2.78e-02    0.04      0.37     8.34e-02      0.14\n",
        "\n",
        "[4 rows x 6 columns]"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "survival_and_stats(df.ix['clinical'].ix['recent_smoker'].dropna(), surv)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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lPB8Og0fnzkhp085kCtt7A+Y4Iql61z+95g9xubIR+f53LhUxp1Pw/pgu1bruvM2n8HCH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\nJSEvhBBCmCkJeSGEEMJMScgLIYQQZuo/8rFGdhWmZjEAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x8bddd90>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hits.p.iloc[1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "uncorrected    3.40e-05\n",
        "bh_within      8.05e-03\n",
        "bh_all         1.14e-02\n",
        "bonf_all       3.42e-02\n",
        "bonf_within    8.05e-03\n",
        "two_step       4.03e-02\n",
        "Name: (mirna, hsa-mir-548k), dtype: float64"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mir548k = df.ix['mirna'].ix['hsa-mir-548k']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "survival_and_stats(mir548k, surv)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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hwzBtmmVWK+PAO/tS2ep4XAWPrJHYzfXWiLMNqmcz+8kbkpOTg/bt2+PPP/+0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\nRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x8b76e90>"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "survival_and_stats(df.ix['mutation'].ix['MUC5B'], surv)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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v+bLaNjdGjYZ/QgJ8e8fWaZ/6s2eR99wLaPTd3hrb3V41kMiW7JbkH3zwQSxe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\nAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0xc02b890>"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rr = cox_screen(df, surv)\n",
      "haz = rr['hazard'][['exp(coef)','lower .95','upper .95']]\n",
      "p = rr.LR.p\n",
      "uni = haz.join(p)\n",
      "uni['PASS'] = uni.p < .05"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "multi = full.reset_index().sort(['level_0',('p','uncorrected')]).set_index(['level_0','level_1'])\n",
      "multi.index.names = ['data_type','event']\n",
      "multi['p'] = multi['p'].clip_upper(1.)\n",
      "multi.columns = multi.columns.droplevel(0)\n",
      "multi['PASS'] = multi.bh_all < .1\n",
      "f = pd.concat([uni, multi], keys=['Univariate','Multivariate'], axis=1)\n",
      "f[('BOTH','PASS')] = f.Univariate.PASS & f.Multivariate.PASS\n",
      "f = f.sort([('BOTH','PASS'),('Multivariate','uncorrected')], ascending=[False, True])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f.to_csv(FIGDIR + 'supplemental_table2.csv', float_format='%.2e')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    }
   ],
   "metadata": {}
  }
 ]
}