aseyboldt/mc-xarray.ipynb

## mc-xarray.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pymc3 as pm\n",
    "import seaborn as sb\n",
    "import theano.tensor as tt\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "N = 200\n",
    "\n",
    "coords = {\n",
    "    'subject': np.array([f'm{i:03}' for i in range(N)]),\n",
    "    'treatment': np.array(['Sorafenib', 'Lurbinectedin']),\n",
    "    'oncogene': np.array(['P19', 'MYC', 'AKT']),\n",
    "}\n",
    "\n",
    "data = xr.Dataset(coords=coords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data['treated_idx'] = (\n",
    "    'subject',\n",
    "    np.random.randint(2, size=N))\n",
    "data['treated'] = (\n",
    "    'subject',\n",
    "    data['treatment'].isel_points(data.subject, treatment=data.treated_idx))\n",
    "\n",
    "data['genotype_idx'] = (\n",
    "    'subject',\n",
    "    np.random.randint(3, size=N))\n",
    "data['genotype'] = (\n",
    "    'subject',\n",
    "    data['oncogene'].isel_points(data.subject, oncogene=data.genotype_idx))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:       (oncogene: 3, subject: 200, treatment: 2)\n",
       "Coordinates:\n",
       "  * subject       (subject) <U4 'm000' 'm001' 'm002' 'm003' 'm004' 'm005' ...\n",
       "  * treatment     (treatment) <U13 'Sorafenib' 'Lurbinectedin'\n",
       "  * oncogene      (oncogene) <U3 'P19' 'MYC' 'AKT'\n",
       "    treated_idx   (subject) int64 1 1 0 1 1 0 0 1 0 1 0 1 0 1 1 0 1 0 1 1 1 ...\n",
       "    treated       (subject) <U13 'Lurbinectedin' 'Lurbinectedin' 'Sorafenib' ...\n",
       "    genotype_idx  (subject) int64 0 1 2 1 0 2 0 1 1 0 2 2 2 0 2 0 0 0 1 2 1 ...\n",
       "    genotype      (subject) <U3 'P19' 'MYC' 'AKT' 'MYC' 'P19' 'AKT' 'P19' ...\n",
       "Data variables:\n",
       "    *empty*"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.set_coords(data.variables, inplace=True)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data['true_treatment_effect'] = (\n",
    "    'treatment',\n",
    "    0.08 * np.random.randn(data.dims['treatment']))\n",
    "\n",
    "data['true_interaction'] = (\n",
    "    ('oncogene', 'treatment'),\n",
    "    0.05 * np.random.randn(data.dims['oncogene'], data.dims['treatment']))\n",
    "\n",
    "data['true_intercept'] = np.log(30.)\n",
    "data['true_sigma'] = 0.13\n",
    "data['true_expected_survival'] = (\n",
    "    'subject',\n",
    "    data['true_intercept']\n",
    "    + data.true_treatment_effect.sel_points(\n",
    "        data.subject,\n",
    "        treatment=data.treated)\n",
    "    + data.true_interaction.sel_points(\n",
    "        data.subject,\n",
    "        oncogene=data.genotype,\n",
    "        treatment=data.treated))\n",
    "\n",
    "data['survival'] = (\n",
    "    'subject',\n",
    "    data['true_expected_survival']\n",
    "    + data['true_sigma'].values * np.random.randn(N))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                 (oncogene: 3, subject: 200, treatment: 2)\n",
       "Coordinates:\n",
       "  * subject                 (subject) <U4 'm000' 'm001' 'm002' 'm003' 'm004' ...\n",
       "  * treatment               (treatment) <U13 'Sorafenib' 'Lurbinectedin'\n",
       "  * oncogene                (oncogene) <U3 'P19' 'MYC' 'AKT'\n",
       "    treated_idx             (subject) int64 1 1 0 1 1 0 0 1 0 1 0 1 0 1 1 0 ...\n",
       "    treated                 (subject) <U13 'Lurbinectedin' 'Lurbinectedin' ...\n",
       "    genotype_idx            (subject) int64 0 1 2 1 0 2 0 1 1 0 2 2 2 0 2 0 ...\n",
       "    genotype                (subject) <U3 'P19' 'MYC' 'AKT' 'MYC' 'P19' ...\n",
       "Data variables:\n",
       "    true_treatment_effect   (treatment) float64 0.1311 -0.09077\n",
       "    true_interaction        (oncogene, treatment) float64 -0.06961 0.01454 ...\n",
       "    true_intercept          float64 3.401\n",
       "    true_sigma              float64 0.13\n",
       "    true_expected_survival  (subject) float64 3.325 3.439 3.47 3.439 3.325 ...\n",
       "    survival                (subject) float64 3.348 3.657 3.387 3.63 3.326 ..."
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survival</th>\n",
       "      <th>genotype</th>\n",
       "      <th>genotype_idx</th>\n",
       "      <th>treated</th>\n",
       "      <th>treated_idx</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subject</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>m000</th>\n",
       "      <td>3.348438</td>\n",
       "      <td>P19</td>\n",
       "      <td>0</td>\n",
       "      <td>Lurbinectedin</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>m001</th>\n",
       "      <td>3.657182</td>\n",
       "      <td>MYC</td>\n",
       "      <td>1</td>\n",
       "      <td>Lurbinectedin</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>m002</th>\n",
       "      <td>3.386957</td>\n",
       "      <td>AKT</td>\n",
       "      <td>2</td>\n",
       "      <td>Sorafenib</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>m003</th>\n",
       "      <td>3.629847</td>\n",
       "      <td>MYC</td>\n",
       "      <td>1</td>\n",
       "      <td>Lurbinectedin</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>m004</th>\n",
       "      <td>3.325638</td>\n",
       "      <td>P19</td>\n",
       "      <td>0</td>\n",
       "      <td>Lurbinectedin</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         survival genotype  genotype_idx        treated  treated_idx\n",
       "subject                                                             \n",
       "m000     3.348438      P19             0  Lurbinectedin            1\n",
       "m001     3.657182      MYC             1  Lurbinectedin            1\n",
       "m002     3.386957      AKT             2      Sorafenib            0\n",
       "m003     3.629847      MYC             1  Lurbinectedin            1\n",
       "m004     3.325638      P19             0  Lurbinectedin            1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.survival.to_dataframe().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa67db86550>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XYFn88R//Ph/72KdxuTT27n2WT3ziY+TzFx/6qGckScLvD+D3B87bC7Rtm1wux/T0FNPT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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa67dc14a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.violinplot('survival', 'treated', hue='genotype', data=data.survival.to_dataframe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>treatment</th>\n",
       "      <th>Sorafenib</th>\n",
       "      <th>Lurbinectedin</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oncogene</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>P19</th>\n",
       "      <td>-0.069610</td>\n",
       "      <td>0.014536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MYC</th>\n",
       "      <td>0.004711</td>\n",
       "      <td>0.128798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AKT</th>\n",
       "      <td>-0.062306</td>\n",
       "      <td>-0.156841</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "treatment  Sorafenib  Lurbinectedin\n",
       "oncogene                           \n",
       "P19        -0.069610       0.014536\n",
       "MYC         0.004711       0.128798\n",
       "AKT        -0.062306      -0.156841"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.true_interaction.to_pandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "treatment\n",
       "Sorafenib        0.131061\n",
       "Lurbinectedin   -0.090770\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.true_treatment_effect.to_pandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "with pm.Model(coords=data.coords) as model:\n",
    "    intercept = pm.Flat('intercept')\n",
    "    \n",
    "    treat_sd = pm.HalfStudentT('treatment_sd', nu=3, sd=0.1)\n",
    "    treatment = pm.Normal('treatment_effect', dims='treatment')\n",
    "    \n",
    "    interact_sd = pm.HalfStudentT('interaction_sd', nu=3, sd=0.1)\n",
    "    interaction = pm.Normal('interaction', dims=('oncogene', 'treatment'))\n",
    "    \n",
    "    mu = (intercept\n",
    "          + treat_sd * treatment[data.treated_idx.values]\n",
    "          + interact_sd * interaction[data.genotype_idx.values, data.treated_idx.values])\n",
    "    sigma = pm.HalfStudentT('sigma', nu=3, sd=1)\n",
    "    pm.Normal('y', mu=mu, sd=sigma, observed=data.survival)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with pm.Model() as model:\n",
    "    intercept = pm.Flat('intercept')\n",
    "    \n",
    "    treat_sd = pm.HalfStudentT('treatment_sd', nu=3, sd=0.1)\n",
    "    treatment = pm.Normal('treatment_effect', shape=data.treatment.shape)\n",
    "    \n",
    "    interact_sd = pm.HalfStudentT('interaction_sd', nu=3, sd=0.1)\n",
    "    interaction = pm.Normal('interaction', shape=data.oncogene.shape + data.treatment.shape)\n",
    "    \n",
    "    mu = (intercept\n",
    "          + treat_sd * treatment[data.treated_idx.values]\n",
    "          + interact_sd * interaction[data.genotype_idx.values, data.treated_idx.values])\n",
    "    sigma = pm.HalfStudentT('sigma', nu=3, sd=0.2)\n",
    "    pm.Normal('y', mu=mu, sd=sigma, observed=data.survival.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using ADVI...\n",
      "Average Loss = 107.84:   9%|▉         | 17662/200000 [00:03<00:43, 4216.92it/s]\n",
      "Convergence archived at 18000\n",
      "Interrupted at 18,000 [9%]: Average Loss = 487.26\n",
      " 99%|█████████▊| 986/1000 [03:14<00:02,  4.76it/s]/home/adr/git/pymc3/pymc3/step_methods/hmc/nuts.py:456: UserWarning: Chain 1 contains 1 diverging samples after tuning. If increasing `target_accept` does not help try to reparameterize.\n",
      "  % (self._chain_id, n_diverging))\n",
      "100%|██████████| 1000/1000 [03:16<00:00, 10.06it/s]\n"
     ]
    }
   ],
   "source": [
    "with model:\n",
    "    trace = pm.sample(njobs=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def to_xarray(trace, coords, dims):\n",
    "    \"\"\"Convert a pymc3 trace to an xarray dataset.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    trace : pymc3 trace\n",
    "    coords : dict\n",
    "        A dictionary containing the values that are used as index. The key\n",
    "        is the name of the dimension, the values are the index values.\n",
    "    dims : dict[str, Tuple(str)]\n",
    "        A mapping from pymc3 variables to a tuple corresponding to\n",
    "        the shape of the variable, where the elements of the tuples are\n",
    "        the names of the coordinate dimensions.\n",
    "    \"\"\"\n",
    "    coords = coords.copy()\n",
    "    coords['sample'] = list(range(len(trace)))\n",
    "    coords['chain'] = list(range(trace.nchains))\n",
    "    \n",
    "    coords_ = {}\n",
    "    for key, vals in coords.items():\n",
    "        coords_[key] = xr.IndexVariable((key,), data=vals)\n",
    "    coords = coords_\n",
    "    \n",
    "    data = xr.Dataset(coords=coords)\n",
    "    for key in trace.varnames:\n",
    "        if key.endswith('_'):\n",
    "            continue\n",
    "        dims_str = ('chain', 'sample')\n",
    "        if key in dims:\n",
    "            dims_str = dims_str + dims[key]\n",
    "        vals = trace.get_values(key, combine=False, squeeze=False)\n",
    "        vals = np.array(vals)\n",
    "        data[key] = xr.DataArray(vals, {v: coords[v] for v in dims_str}, dims=dims_str)\n",
    "    \n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dims = {\n",
    "    'treatment_effect': ('treatment',),\n",
    "    'interaction': ('oncogene', 'treatment'),\n",
    "}\n",
    "trace = to_xarray(trace, dict(data.coords), dims)\n",
    "trace = xr.Dataset(trace.data_vars, coords=data.coords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:           (chain: 4, oncogene: 3, sample: 500, subject: 200, treatment: 2)\n",
       "Coordinates:\n",
       "  * sample            (sample) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 ...\n",
       "  * chain             (chain) int64 0 1 2 3\n",
       "  * treatment         (treatment) <U13 'Sorafenib' 'Lurbinectedin'\n",
       "  * oncogene          (oncogene) <U3 'P19' 'MYC' 'AKT'\n",
       "    treated_idx       (subject) int64 1 1 0 1 1 0 0 1 0 1 0 1 0 1 1 0 1 0 1 ...\n",
       "    treated           (subject) <U13 'Lurbinectedin' 'Lurbinectedin' ...\n",
       "    genotype_idx      (subject) int64 0 1 2 1 0 2 0 1 1 0 2 2 2 0 2 0 0 0 1 ...\n",
       "    genotype          (subject) <U3 'P19' 'MYC' 'AKT' 'MYC' 'P19' 'AKT' ...\n",
       "  * subject           (subject) <U4 'm000' 'm001' 'm002' 'm003' 'm004' ...\n",
       "Data variables:\n",
       "    intercept         (chain, sample) float64 3.564 3.441 3.435 3.439 3.412 ...\n",
       "    treatment_effect  (chain, sample, treatment) float64 -0.4229 -1.114 ...\n",
       "    interaction       (chain, sample, oncogene, treatment) float64 -0.3714 ...\n",
       "    treatment_sd      (chain, sample) float64 0.2448 0.0599 0.04889 0.06563 ...\n",
       "    interaction_sd    (chain, sample) float64 0.05679 0.1004 0.1127 0.08553 ...\n",
       "    sigma             (chain, sample) float64 0.1442 0.1304 0.135 0.1287 ..."
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>treatment_effect</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chain</th>\n",
       "      <th>sample</th>\n",
       "      <th>treatment</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">0</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">0</th>\n",
       "      <th>Sorafenib</th>\n",
       "      <td>-0.422860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lurbinectedin</th>\n",
       "      <td>-1.113724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
       "      <th>Sorafenib</th>\n",
       "      <td>0.937122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lurbinectedin</th>\n",
       "      <td>-2.412286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>Sorafenib</th>\n",
       "      <td>0.870567</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            treatment_effect\n",
       "chain sample treatment                      \n",
       "0     0      Sorafenib             -0.422860\n",
       "             Lurbinectedin         -1.113724\n",
       "      1      Sorafenib              0.937122\n",
       "             Lurbinectedin         -2.412286\n",
       "      2      Sorafenib              0.870567"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trace.treatment_effect.to_dataframe().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa645452198>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Mjscc/rcDxcWlFBdn9iL4fh1iSikD8GmtNwOblVL3Ae8AQzptZgDtbelY6nFjgeuBmVrr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\ntgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa648038cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "effect = trace.treatment_sd * trace.treatment_effect\n",
    "sb.violinplot(x='treatment effect', y='treatment',\n",
    "              data=effect.to_dataframe('treatment effect').reset_index())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fa647fd6048>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UlPqoKfdzqnOYpCHNlkR60odSzBhJFWkZSa+MR19oZXRsilDQQ9/wFL947gzlsw7Q7tnR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\n3nn9rbrunFcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa645376710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diff = effect.sel(treatment='Sorafenib') - effect.sel(treatment='Lurbinectedin')\n",
    "sb.distplot(diff.values.ravel())\n",
    "plt.xlabel('Sorafenib - Lurbinectedin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray ()>\n",
       "array(0.885)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(diff > 0).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'true_treatment_effect' (treatment: 2)>\n",
       "array([ 0.131061, -0.09077 ])\n",
       "Coordinates:\n",
       "  * treatment  (treatment) <U13 'Sorafenib' 'Lurbinectedin'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.true_treatment_effect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa6477bedd8>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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q9xFqHSDU4iNyQcCVJInc3DyKi0spKiqmqKiEwsJiUlNTkSRp2l2zHWsVFX/i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\nCCHyyv8HZBmdnzOoLLAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa667b59b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interaction = trace.interaction_sd * trace.interaction\n",
    "sb.violinplot(x='interaction', y='treatment', hue='oncogene',\n",
    "              data=interaction.to_dataframe('interaction').reset_index())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<script src=\"https://code.jquery.com/ui/1.10.4/jquery-ui.min.js\" type=\"text/javascript\"></script>\n",
       "<script type=\"text/javascript\">function HoloViewsWidget(){\n",
       "}\n",
       "\n",
       "HoloViewsWidget.comms = {};\n",
       "HoloViewsWidget.comm_state = {};\n",
       "\n",
       "HoloViewsWidget.prototype.init_slider = function(init_val){\n",
       "\tif(this.load_json) {\n",
       "\t\tthis.from_json()\n",
       "\t} else {\n",
       "\t\tthis.update_cache();\n",
       "\t}\n",
       "}\n",
       "\n",
       "HoloViewsWidget.prototype.populate_cache = function(idx){\n",
       "    this.cache[idx].html(this.frames[idx]);\n",
       "    if (this.embed) {\n",
       "        delete this.frames[idx];\n",
       "    }\n",
       "}\n",
       "\n",
       "HoloViewsWidget.prototype.process_error = function(msg){\n",
       "\n",
       "}\n",
       "\n",
       "HoloViewsWidget.prototype.from_json = function() {\n",
       "\tvar data_url = this.json_path + this.id + '.json';\n",
       "\t$.getJSON(data_url, $.proxy(function(json_data) {\n",
       "\t\tthis.frames = json_data;\n",
       "\t\tthis.update_cache();\n",
       "\t\tthis.update(0);\n",
       "\t}, this));\n",
       "}\n",
       "\n",
       "HoloViewsWidget.prototype.dynamic_update = function(current){\n",
       "\tif (current === undefined) {\n",
       "\t\treturn\n",
       "\t}\n",
       "\tif(this.dynamic) {\n",
       "\t\tcurrent = JSON.stringify(current);\n",
       "\t}\n",
       "\tfunction callback(initialized, msg){\n",
       "\t\t/* This callback receives data from Python as a string\n",
       "\t\t   in order to parse it correctly quotes are sliced off*/\n",
       "\t\tif (msg.content.ename != undefined) {\n",
       "\t\t\tthis.process_error(msg);\n",
       "\t\t}\n",
       "\t\tif (msg.msg_type != \"execute_result\") {\n",
       "\t\t\tconsole.log(\"Warning: HoloViews callback returned unexpected data for key: (\", current, \") with the following content:\", msg.content)\n",
       "\t\t\tthis.time = undefined;\n",
       "\t\t\tthis.wait = false;\n",
       "\t\t\treturn\n",
       "\t\t}\n",
       "\t\tthis.timed = (Date.now() - this.time) * 1.1;\n",
       "\t\tif (msg.msg_type == \"execute_result\") {\n",
       "\t\t\tif (msg.content.data['text/plain'].includes('Complete')) {\n",
       "\t\t\t\tthis.wait = false;\n",
       "\t\t\t\tif (this.queue.length > 0) {\n",
       "\t\t\t\t\tthis.time = Date.now();\n",
       "\t\t\t\t\tthis.dynamic_update(this.queue[this.queue.length-1]);\n",
       "\t\t\t\t\tthis.queue = [];\n",
       "\t\t\t\t}\n",
       "\t\t\t\treturn\n",
       "\t\t\t}\n",
       "\t\t}\n",
       "\t}\n",
       "\tthis.current = current;\n",
       "\tvar kernel = IPython.notebook.kernel;\n",
       "\tcallbacks = {iopub: {output: $.proxy(callback, this, this.initialized)}};\n",
       "\tvar cmd = \"holoviews.plotting.widgets.NdWidget.widgets['\" + this.id + \"'].update(\" + current + \")\";\n",
       "\tkernel.execute(\"import holoviews;\" + cmd, callbacks, {silent : false});\n",
       "}\n",
       "\n",
       "HoloViewsWidget.prototype.update_cache = function(force){\n",
       "    var frame_len = Object.keys(this.frames).length;\n",
       "    for (var i=0; i<frame_len; i++) {\n",
       "        if(!this.load_json || this.dynamic)  {\n",
       "            frame = Object.keys(this.frames)[i];\n",
       "        } else {\n",
       "            frame = i;\n",
       "        }\n",
       "        if(!(frame in this.cache) || force) {\n",
       "\t\t\tif ((frame in this.cache) && force) { this.cache[frame].remove() }\n",
       "\t\t\tthis.cache[frame] = $('<div />').appendTo(\"#\"+\"_anim_img\"+this.id).hide();\n",
       "\t\t\tvar cache_id = \"_anim_img\"+this.id+\"_\"+frame;\n",
       "\t\t\tthis.cache[frame].attr(\"id\", cache_id);\n",
       "\t\t\tthis.populate_cache(frame);\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "HoloViewsWidget.prototype.update = function(current){\n",
       "    if(current in this.cache) {\n",
       "        $.each(this.cache, function(index, value) {\n",
       "            value.hide();\n",
       "        });\n",
       "        this.cache[current].show();\n",
       "\t\tthis.wait = false;\n",
       "    }\n",
       "}\n",
       "\n",
       "HoloViewsWidget.prototype.init_comms = function() {\n",
       "\tif ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel !== undefined)) {\n",
       "\t\tvar widget = this;\n",
       "\t\tcomm_manager = Jupyter.notebook.kernel.comm_manager;\n",
       "        comm_manager.register_target(this.id, function (comm) {\n",
       "\t\t\tcomm.on_msg(function (msg) { widget.process_msg(msg) });\n",
       "\t\t});\n",
       "\t}\n",
       "}\n",
       "\n",
       "HoloViewsWidget.prototype.process_msg = function(msg) {\n",
       "}\n",
       "\n",
       "function SelectionWidget(frames, id, slider_ids, keyMap, dim_vals, notFound, load_json, mode, cached, json_path, dynamic){\n",
       "    this.frames = frames;\n",
       "    this.id = id;\n",
       "    this.slider_ids = slider_ids;\n",
       "    this.keyMap = keyMap\n",
       "    this.current_frame = 0;\n",
       "    this.current_vals = dim_vals;\n",
       "    this.load_json = load_json;\n",
       "    this.mode = mode;\n",
       "    this.notFound = notFound;\n",
       "    this.cached = cached;\n",
       "    this.dynamic = dynamic;\n",
       "    this.cache = {};\n",
       "\tthis.json_path = json_path;\n",
       "    this.init_slider(this.current_vals[0]);\n",
       "\tthis.queue = [];\n",
       "\tthis.wait = false;\n",
       "\tif (!this.cached || this.dynamic) {\n",
       "\t\tthis.init_comms()\n",
       "\t}\n",
       "}\n",
       "\n",
       "SelectionWidget.prototype = new HoloViewsWidget;\n",
       "\n",
       "\n",
       "SelectionWidget.prototype.get_key = function(current_vals) {\n",
       "\tvar key = \"(\";\n",
       "    for (var i=0; i<this.slider_ids.length; i++)\n",
       "    {\n",
       "        val = this.current_vals[i];\n",
       "        if (!(typeof val === 'string')) {\n",
       "            if (val % 1 === 0) { val = val.toFixed(1); }\n",
       "            else { val = val.toFixed(10); val = val.slice(0, val.length-1);}\n",
       "        }\n",
       "        key += \"'\" + val + \"'\";\n",
       "        if(i != this.slider_ids.length-1) { key += ', ';}\n",
       "        else if(this.slider_ids.length == 1) { key += ',';}\n",
       "    }\n",
       "    key += \")\";\n",
       "\treturn this.keyMap[key];\n",
       "}\n",
       "\n",
       "SelectionWidget.prototype.set_frame = function(dim_val, dim_idx){\n",
       "\tthis.current_vals[dim_idx] = dim_val;\n",
       "    var current = this.get_key(this.current_vals);\n",
       "    if(current === undefined && !this.dynamic) {\n",
       "        return\n",
       "    }\n",
       "\tif (this.dynamic || !this.cached) {\n",
       "\t\tif (this.time === undefined) {\n",
       "\t\t\t// Do nothing the first time\n",
       "\t\t} else if ((this.timed === undefined) || ((this.time + this.timed) > Date.now())) {\n",
       "\t\t\tvar key = this.current_vals;\n",
       "\t\t\tif (!this.dynamic) {\n",
       "\t\t\t\tkey = this.get_key(key);\n",
       "\t\t\t}\n",
       "\t\t\tthis.queue.push(key);\n",
       "\t\t\treturn\n",
       "\t\t}\n",
       "\t}\n",
       "\tthis.queue = [];\n",
       "\tthis.time = Date.now();\n",
       "\tthis.current_frame = current;\n",
       "    if(this.dynamic) {\n",
       "        this.dynamic_update(this.current_vals)\n",
       "    } else if(this.cached) {\n",
       "        this.update(current)\n",
       "    } else {\n",
       "        this.dynamic_update(current)\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "/* Define the ScrubberWidget class */\n",
       "function ScrubberWidget(frames, num_frames, id, interval, load_json, mode, cached, json_path, dynamic){\n",
       "    this.slider_id = \"_anim_slider\" + id;\n",
       "    this.loop_select_id = \"_anim_loop_select\" + id;\n",
       "    this.id = id;\n",
       "    this.interval = interval;\n",
       "    this.current_frame = 0;\n",
       "    this.direction = 0;\n",
       "    this.dynamic = dynamic;\n",
       "    this.timer = null;\n",
       "    this.load_json = load_json;\n",
       "    this.mode = mode;\n",
       "    this.cached = cached;\n",
       "    this.frames = frames;\n",
       "    this.cache = {};\n",
       "    this.length = num_frames;\n",
       "\tthis.json_path = json_path;\n",
       "    document.getElementById(this.slider_id).max = this.length - 1;\n",
       "    this.init_slider(0);\n",
       "\tthis.wait = false;\n",
       "\tthis.queue = [];\n",
       "\tif (!this.cached || this.dynamic) {\n",
       "\t\tthis.init_comms()\n",
       "\t}\n",
       "}\n",
       "\n",
       "ScrubberWidget.prototype = new HoloViewsWidget;\n",
       "\n",
       "ScrubberWidget.prototype.set_frame = function(frame){\n",
       "\tthis.current_frame = frame;\n",
       "\twidget = document.getElementById(this.slider_id);\n",
       "    if (widget === null) {\n",
       "        this.pause_animation();\n",
       "        return\n",
       "    }\n",
       "    widget.value = this.current_frame;\n",
       "    if(this.cached) {\n",
       "        this.update(frame)\n",
       "    } else {\n",
       "        this.dynamic_update(frame)\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "ScrubberWidget.prototype.process_error = function(msg){\n",
       "\tif (msg.content.ename === 'StopIteration') {\n",
       "\t\tthis.pause_animation();\n",
       "\t\tthis.stopped = true;\n",
       "\t\tvar keys = Object.keys(this.frames)\n",
       "\t\tthis.length = keys.length;\n",
       "\t\tdocument.getElementById(this.slider_id).max = this.length-1;\n",
       "\t\tdocument.getElementById(this.slider_id).value = this.length-1;\n",
       "\t\tthis.current_frame = this.length-1;\n",
       "\t}\n",
       "}\n",
       "\n",
       "\n",
       "ScrubberWidget.prototype.get_loop_state = function(){\n",
       "    var button_group = document[this.loop_select_id].state;\n",
       "    for (var i = 0; i < button_group.length; i++) {\n",
       "        var button = button_group[i];\n",
       "        if (button.checked) {\n",
       "            return button.value;\n",
       "        }\n",
       "    }\n",
       "    return undefined;\n",
       "}\n",
       "\n",
       "\n",
       "ScrubberWidget.prototype.next_frame = function() {\n",
       "\tif (this.dynamic || !this.cached) {\n",
       "\t\tif (this.wait) {\n",
       "\t\t\treturn\n",
       "\t\t}\n",
       "\t\tthis.wait = true;\n",
       "\t}\n",
       "\tif (this.dynamic && this.current_frame + 1 >= this.length) {\n",
       "\t\tthis.length += 1;\n",
       "        document.getElementById(this.slider_id).max = this.length-1;\n",
       "\t}\n",
       "    this.set_frame(Math.min(this.length - 1, this.current_frame + 1));\n",
       "}\n",
       "\n",
       "ScrubberWidget.prototype.previous_frame = function() {\n",
       "    this.set_frame(Math.max(0, this.current_frame - 1));\n",
       "}\n",
       "\n",
       "ScrubberWidget.prototype.first_frame = function() {\n",
       "    this.set_frame(0);\n",
       "}\n",
       "\n",
       "ScrubberWidget.prototype.last_frame = function() {\n",
       "    this.set_frame(this.length - 1);\n",
       "}\n",
       "\n",
       "ScrubberWidget.prototype.slower = function() {\n",
       "    this.interval /= 0.7;\n",
       "    if(this.direction > 0){this.play_animation();}\n",
       "    else if(this.direction < 0){this.reverse_animation();}\n",
       "}\n",
       "\n",
       "ScrubberWidget.prototype.faster = function() {\n",
       "    this.interval *= 0.7;\n",
       "    if(this.direction > 0){this.play_animation();}\n",
       "    else if(this.direction < 0){this.reverse_animation();}\n",
       "}\n",
       "\n",
       "ScrubberWidget.prototype.anim_step_forward = function() {\n",
       "    if(this.current_frame < this.length || (this.dynamic && !this.stopped)){\n",
       "        this.next_frame();\n",
       "    }else{\n",
       "        var loop_state = this.get_loop_state();\n",
       "        if(loop_state == \"loop\"){\n",
       "            this.first_frame();\n",
       "        }else if(loop_state == \"reflect\"){\n",
       "            this.last_frame();\n",
       "            this.reverse_animation();\n",
       "        }else{\n",
       "            this.pause_animation();\n",
       "            this.last_frame();\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "ScrubberWidget.prototype.anim_step_reverse = function() {\n",
       "    this.current_frame -= 1;\n",
       "    if(this.current_frame >= 0){\n",
       "        this.set_frame(this.current_frame);\n",
       "    } else {\n",
       "        var loop_state = this.get_loop_state();\n",
       "        if(loop_state == \"loop\"){\n",
       "            this.last_frame();\n",
       "        }else if(loop_state == \"reflect\"){\n",
       "            this.first_frame();\n",
       "            this.play_animation();\n",
       "        }else{\n",
       "            this.pause_animation();\n",
       "            this.first_frame();\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "ScrubberWidget.prototype.pause_animation = function() {\n",
       "    this.direction = 0;\n",
       "    if (this.timer){\n",
       "        clearInterval(this.timer);\n",
       "        this.timer = null;\n",
       "    }\n",
       "}\n",
       "\n",
       "ScrubberWidget.prototype.play_animation = function() {\n",
       "    this.pause_animation();\n",
       "    this.direction = 1;\n",
       "    var t = this;\n",
       "    if (!this.timer) this.timer = setInterval(function(){t.anim_step_forward();}, this.interval);\n",
       "}\n",
       "\n",
       "ScrubberWidget.prototype.reverse_animation = function() {\n",
       "    this.pause_animation();\n",
       "    this.direction = -1;\n",
       "    var t = this;\n",
       "    if (!this.timer) this.timer = setInterval(function(){t.anim_step_reverse();}, this.interval);\n",
       "}\n",
       "\n",
       "function extend(destination, source) {\n",
       "    for (var k in source) {\n",
       "        if (source.hasOwnProperty(k)) {\n",
       "            destination[k] = source[k];\n",
       "        }\n",
       "    }\n",
       "    return destination;\n",
       "}\n",
       "\n",
       "function update_widget(widget, values) {\n",
       "\tif (widget.hasClass(\"ui-slider\")) {\n",
       "\t\twidget.slider('option',\n",
       "\t\t\t\t\t  {'min': 0, 'max': values.length-1,\n",
       "\t\t\t\t\t   'dim_vals': values, 'value': 0,\n",
       "\t\t\t\t\t   'dim_labels': values})\n",
       "\t\twidget.slider('option', 'slide').call(widget, event, {'value': 0})\n",
       "\t} else {\n",
       "\t\twidget.empty();\n",
       "\t\tfor (var i=0; i<values.length; i++){\n",
       "\t\t\twidget.append($(\"<option>\", {\n",
       "\t\t\t\tvalue: i,\n",
       "\t\t\t\ttext: values[i]\n",
       "\t\t\t}))};\n",
       "\t\twidget.data('values', values);\n",
       "\t\twidget.data('value', 0);\n",
       "\t\twidget.trigger(\"change\");\n",
       "\t};\n",
       "}\n",
       "\n",
       "// Define MPL specific subclasses\n",
       "function MPLSelectionWidget() {\n",
       "\tSelectionWidget.apply(this, arguments);\n",
       "}\n",
       "\n",
       "function MPLScrubberWidget() {\n",
       "\tScrubberWidget.apply(this, arguments);\n",
       "}\n",
       "\n",
       "// Let them inherit from the baseclasses\n",
       "MPLSelectionWidget.prototype = Object.create(SelectionWidget.prototype);\n",
       "MPLScrubberWidget.prototype = Object.create(ScrubberWidget.prototype);\n",
       "\n",
       "// Define methods to override on widgets\n",
       "var MPLMethods = {\n",
       "\tinit_slider : function(init_val){\n",
       "\t\tif(this.load_json) {\n",
       "\t\t\tthis.from_json()\n",
       "\t\t} else {\n",
       "\t\t\tthis.update_cache();\n",
       "\t\t}\n",
       "\t\tthis.update(0);\n",
       "\t\tif(this.mode == 'nbagg') {\n",
       "\t\t\tthis.set_frame(init_val, 0);\n",
       "\t\t}\n",
       "\t},\n",
       "\tpopulate_cache : function(idx){\n",
       "\t\tvar cache_id = \"_anim_img\"+this.id+\"_\"+idx;\n",
       "\t\tif(this.mode == 'mpld3') {\n",
       "\t\t\tmpld3.draw_figure(cache_id, this.frames[idx]);\n",
       "\t\t} else {\n",
       "\t\t\tthis.cache[idx].html(this.frames[idx]);\n",
       "\t\t}\n",
       "\t\tif (this.embed) {\n",
       "\t\t\tdelete this.frames[idx];\n",
       "\t\t}\n",
       "\t},\n",
       "\tprocess_msg : function(msg) {\n",
       "\t\tif (!(this.mode == 'nbagg')) {\n",
       "\t\t\tvar data = msg.content.data;\n",
       "\t\t\tthis.frames[this.current] = data;\n",
       "\t\t\tthis.update_cache(true);\n",
       "\t\t\tthis.update(this.current);\n",
       "\t\t}\n",
       "\t}\n",
       "}\n",
       "// Extend MPL widgets with backend specific methods\n",
       "extend(MPLSelectionWidget.prototype, MPLMethods);\n",
       "extend(MPLScrubberWidget.prototype, MPLMethods);\n",
       "</script>\n",
       "\n",
       "\n",
       "<link rel=\"stylesheet\" href=\"https://code.jquery.com/ui/1.10.4/themes/smoothness/jquery-ui.css\">\n",
       "<style>div.hololayout {\n",
       "    display: flex;\n",
       "    align-items: center;\n",
       "    margin: 0;\n",
       "}\n",
       "\n",
       "div.holoframe {\n",
       "\twidth: 75%;\n",
       "}\n",
       "\n",
       "div.holowell {\n",
       "    display: flex;\n",
       "    align-items: center;\n",
       "    margin: 0;\n",
       "}\n",
       "\n",
       "form.holoform {\n",
       "    background-color: #fafafa;\n",
       "    border-radius: 5px;\n",
       "    overflow: hidden;\n",
       "\tpadding-left: 0.8em;\n",
       "    padding-right: 0.8em;\n",
       "    padding-top: 0.4em;\n",
       "    padding-bottom: 0.4em;\n",
       "}\n",
       "\n",
       "div.holowidgets {\n",
       "    padding-right: 0;\n",
       "\twidth: 25%;\n",
       "}\n",
       "\n",
       "div.holoslider {\n",
       "    min-height: 0 !important;\n",
       "    height: 0.8em;\n",
       "    width: 60%;\n",
       "}\n",
       "\n",
       "div.holoformgroup {\n",
       "    padding-top: 0.5em;\n",
       "    margin-bottom: 0.5em;\n",
       "}\n",
       "\n",
       "div.hologroup {\n",
       "    padding-left: 0;\n",
       "    padding-right: 0.8em;\n",
       "    width: 50%;\n",
       "}\n",
       "\n",
       ".holoselect {\n",
       "    width: 92%;\n",
       "    margin-left: 0;\n",
       "    margin-right: 0;\n",
       "}\n",
       "\n",
       ".holotext {\n",
       "    width: 100%;\n",
       "    padding-left:  0.5em;\n",
       "    padding-right: 0;\n",
       "}\n",
       "\n",
       ".holowidgets .ui-resizable-se {\n",
       "\tvisibility: hidden\n",
       "}\n",
       "\n",
       ".holoframe > .ui-resizable-se {\n",
       "\tvisibility: hidden\n",
       "}\n",
       "\n",
       ".holowidgets .ui-resizable-s {\n",
       "\tvisibility: hidden\n",
       "}\n",
       "</style>\n",
       "\n",
       "\n",
       "\n",
       "</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import holoviews as hv\n",
    "hv.notebook_extension(logo=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "interaction = hv.Dataset(trace.interaction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"hololayout row row-fluid\">\n",
       "  <div class=\"holoframe\" id=\"display_area4f87c68e52a84076a7560314ea60d29b\">\n",
       "    <div id=\"_anim_img4f87c68e52a84076a7560314ea60d29b\">\n",
       "      \n",
       "      \n",
       "      \n",
       "    </div>\n",
       "  </div>\n",
       "  <div class=\"holowidgets\" id=\"widget_area4f87c68e52a84076a7560314ea60d29b\">\n",
       "    <form class=\"holoform well\" id=\"form4f87c68e52a84076a7560314ea60d29b\">\n",
       "      \n",
       "      \n",
       "        <div class=\"form-group control-group holoformgroup\" style=''>\n",
       "          <label for=\"textInput4f87c68e52a84076a7560314ea60d29b_treatment\"><strong>treatment:</strong></label>\n",
       "          <select class=\"holoselect form-control\" id=\"_anim_widget4f87c68e52a84076a7560314ea60d29b_treatment\" >\n",
       "          </select>\n",
       "        </div>\n",
       "        <script>\n",
       "        var vals = ['Lurbinectedin', 'Sorafenib'];\n",
       "        var labels = ['Lurbinectedin', 'Sorafenib'];\n",
       "        function init_dropdown() {\n",
       "        var widget = $(\"#_anim_widget4f87c68e52a84076a7560314ea60d29b_treatment\");\n",
       "        widget.data('values', vals)\n",
       "        for (var i=0; i<vals.length; i++){\n",
       "\t\t\tif (false) {\n",
       "\t\t       var val = vals[i];\n",
       "\t\t\t} else {\n",
       "\t\t\t   var val = i;\n",
       "\t\t\t}\n",
       "            widget.append($(\"<option>\", {\n",
       "                value: val,\n",
       "                text: labels[i]\n",
       "            }));\n",
       "        };\n",
       "        widget.data(\"next_vals\", {});\n",
       "        widget.on('change', function(event, ui) {\n",
       "\t\t    if (false) {\n",
       "                var dim_val = parseInt(this.value);\n",
       "\t\t\t} else {\n",
       "\t\t\t    var dim_val = $.data(this, 'values')[this.value];\n",
       "\t\t\t}\n",
       "            var next_vals = $.data(this, \"next_vals\");\n",
       "            if (Object.keys(next_vals).length > 0) {\n",
       "                var new_vals = next_vals[dim_val];\n",
       "                var next_widget = $('#_anim_widget4f87c68e52a84076a7560314ea60d29b_oncogene');\n",
       "                update_widget(next_widget, new_vals);\n",
       "            }\n",
       "\t\t\tif (anim4f87c68e52a84076a7560314ea60d29b) {\n",
       "                anim4f87c68e52a84076a7560314ea60d29b.set_frame(dim_val, 0);\n",
       "            }\n",
       "\n",
       "        });\n",
       "        }\n",
       "        $(document).ready(init_dropdown)\n",
       "        </script>\n",
       "        \n",
       "        \n",
       "      \n",
       "        <div class=\"form-group control-group holoformgroup\" style=''>\n",
       "          <label for=\"textInput4f87c68e52a84076a7560314ea60d29b_oncogene\"><strong>oncogene:</strong></label>\n",
       "          <select class=\"holoselect form-control\" id=\"_anim_widget4f87c68e52a84076a7560314ea60d29b_oncogene\" >\n",
       "          </select>\n",
       "        </div>\n",
       "        <script>\n",
       "        var vals = ['AKT', 'MYC', 'P19'];\n",
       "        var labels = ['AKT', 'MYC', 'P19'];\n",
       "        function init_dropdown() {\n",
       "        var widget = $(\"#_anim_widget4f87c68e52a84076a7560314ea60d29b_oncogene\");\n",
       "        widget.data('values', vals)\n",
       "        for (var i=0; i<vals.length; i++){\n",
       "\t\t\tif (false) {\n",
       "\t\t       var val = vals[i];\n",
       "\t\t\t} else {\n",
       "\t\t\t   var val = i;\n",
       "\t\t\t}\n",
       "            widget.append($(\"<option>\", {\n",
       "                value: val,\n",
       "                text: labels[i]\n",
       "            }));\n",
       "        };\n",
       "        widget.data(\"next_vals\", {});\n",
       "        widget.on('change', function(event, ui) {\n",
       "\t\t    if (false) {\n",
       "                var dim_val = parseInt(this.value);\n",
       "\t\t\t} else {\n",
       "\t\t\t    var dim_val = $.data(this, 'values')[this.value];\n",
       "\t\t\t}\n",
       "            var next_vals = $.data(this, \"next_vals\");\n",
       "            if (Object.keys(next_vals).length > 0) {\n",
       "                var new_vals = next_vals[dim_val];\n",
       "                var next_widget = $('#_anim_widget4f87c68e52a84076a7560314ea60d29b_');\n",
       "                update_widget(next_widget, new_vals);\n",
       "            }\n",
       "\t\t\tif (anim4f87c68e52a84076a7560314ea60d29b) {\n",
       "                anim4f87c68e52a84076a7560314ea60d29b.set_frame(dim_val, 1);\n",
       "            }\n",
       "\n",
       "        });\n",
       "        }\n",
       "        $(document).ready(init_dropdown)\n",
       "        </script>\n",
       "        \n",
       "        \n",
       "        </form>\n",
       "    </div>\n",
       "</div>\n",
       "\n",
       "\n",
       "<script language=\"javascript\">\n",
       "/* Instantiate the MPLSelectionWidget class. */\n",
       "/* The IDs given should match those used in the template above. */\n",
       "(function() {\n",
       "\tif (jQuery.ui !== undefined) {\n",
       "\t\t$(\"#display_area4f87c68e52a84076a7560314ea60d29b\").resizable({\n",
       "\t\t\tresize: function(event, ui) {\n",
       "\t\t\t\t$(\"#widget_area4f87c68e52a84076a7560314ea60d29b\").width($(this).parent().width()-ui.size.width);\n",
       "\t\t\t}\n",
       "\t\t});\n",
       "\t\t$(\"#widget_area4f87c68e52a84076a7560314ea60d29b\").resizable();\n",
       "\t}\n",
       "    var widget_ids = new Array(2);\n",
       "    \n",
       "    widget_ids[0] = \"_anim_widget4f87c68e52a84076a7560314ea60d29b_treatment\";\n",
       "    \n",
       "    widget_ids[1] = \"_anim_widget4f87c68e52a84076a7560314ea60d29b_oncogene\";\n",
       "    \n",
       "    var frame_data = {\"0\": \"<img 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' 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' 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' 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' 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' style='max-width:100%; margin: auto; display: block; '/>\"};\n",
       "    var dim_vals = ['Lurbinectedin', 'AKT'];\n",
       "    var keyMap = {\"('Lurbinectedin', 'AKT')\": 0, \"('Lurbinectedin', 'MYC')\": 1, \"('Lurbinectedin', 'P19')\": 2, \"('Sorafenib', 'AKT')\": 3, \"('Sorafenib', 'MYC')\": 4, \"('Sorafenib', 'P19')\": 5};\n",
       "    var notFound = \"<h2 style='vertical-align: middle>No frame at selected dimension value.<h2>\";\n",
       "    function create_widget() {\n",
       "        setTimeout(function() {\n",
       "            anim4f87c68e52a84076a7560314ea60d29b = new MPLSelectionWidget(frame_data, \"4f87c68e52a84076a7560314ea60d29b\", widget_ids,\n",
       "\t\t\t\tkeyMap, dim_vals, notFound, false, \"default\",\n",
       "\t\t\t\ttrue, \"./json_figures/\", false);\n",
       "        }, 0);\n",
       "    }\n",
       "    \n",
       "\n",
       "\n",
       "    create_widget();\n",
       "    \n",
       "\n",
       "\n",
       "})();\n",
       "</script>"
      ],
      "text/plain": [
       "b':HoloMap   [treatment,oncogene]\\n   :Distribution   [sample,chain]   (interaction)'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interaction.to(hv.Distribution, ['sample', 'chain'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}