{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:stancache.seed:Setting seed to 1245502385\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline\n",
    "import random\n",
    "random.seed(1100038344)\n",
    "import survivalstan\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from stancache import stancache\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_code = survivalstan.models.pem_survival_model_randomwalk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/*  Variable naming:\n",
      " // dimensions\n",
      " N          = total number of observations (length of data)\n",
      " S          = number of sample ids\n",
      " T          = max timepoint (number of timepoint ids)\n",
      " M          = number of covariates\n",
      " \n",
      " // main data matrix (per observed timepoint*record)\n",
      " s          = sample id for each obs\n",
      " t          = timepoint id for each obs\n",
      " event      = integer indicating if there was an event at time t for sample s\n",
      " x          = matrix of real-valued covariates at time t for sample n [N, X]\n",
      " \n",
      " // timepoint-specific data (per timepoint, ordered by timepoint id)\n",
      " t_obs      = observed time since origin for each timepoint id (end of period)\n",
      " t_dur      = duration of each timepoint period (first diff of t_obs)\n",
      " \n",
      "*/\n",
      "// Jacqueline Buros Novik <jackinovik@gmail.com>\n",
      "\n",
      "\n",
      "data {\n",
      "  // dimensions\n",
      "  int<lower=1> N;\n",
      "  int<lower=1> S;\n",
      "  int<lower=1> T;\n",
      "  int<lower=0> M;\n",
      "  \n",
      "  // data matrix\n",
      "  int<lower=1, upper=N> s[N];     // sample id\n",
      "  int<lower=1, upper=T> t[N];     // timepoint id\n",
      "  int<lower=0, upper=1> event[N]; // 1: event, 0:censor\n",
      "  matrix[N, M] x;                 // explanatory vars\n",
      "  \n",
      "  // timepoint data\n",
      "  vector<lower=0>[T] t_obs;\n",
      "  vector<lower=0>[T] t_dur;\n",
      "}\n",
      "transformed data {\n",
      "  vector[T] log_t_dur;  // log-duration for each timepoint\n",
      "  int n_trans[S, T];  \n",
      "  \n",
      "  log_t_dur = log(t_obs);\n",
      "  \n",
      "  // n_trans used to map each sample*timepoint to n (used in gen quantities)\n",
      "  // map each patient/timepoint combination to n values\n",
      "  for (n in 1:N) {\n",
      "      n_trans[s[n], t[n]] = n;\n",
      "  }\n",
      "\n",
      "  // fill in missing values with n for max t for that patient\n",
      "  // ie assume \"last observed\" state applies forward (may be problematic for TVC)\n",
      "  // this allows us to predict failure times >= observed survival times\n",
      "  for (samp in 1:S) {\n",
      "      int last_value;\n",
      "      last_value = 0;\n",
      "      for (tp in 1:T) {\n",
      "          // manual says ints are initialized to neg values\n",
      "          // so <=0 is a shorthand for \"unassigned\"\n",
      "          if (n_trans[samp, tp] <= 0 && last_value != 0) {\n",
      "              n_trans[samp, tp] = last_value;\n",
      "          } else {\n",
      "              last_value = n_trans[samp, tp];\n",
      "          }\n",
      "      }\n",
      "  }\n",
      "}\n",
      "parameters {\n",
      "  vector[T] log_baseline_raw; // unstructured baseline hazard for each timepoint t\n",
      "  vector[M] beta;                      // beta for each covariate\n",
      "  real<lower=0> baseline_sigma;\n",
      "  real log_baseline_mu;\n",
      "}\n",
      "transformed parameters {\n",
      "  vector[N] log_hazard;\n",
      "  vector[T] log_baseline;\n",
      "  \n",
      "  log_baseline = log_baseline_raw + log_t_dur;\n",
      "  \n",
      "  for (n in 1:N) {\n",
      "    log_hazard[n] = log_baseline_mu + log_baseline[t[n]] + x[n,]*beta;\n",
      "  }\n",
      "}\n",
      "model {\n",
      "  beta ~ cauchy(0, 2);\n",
      "  event ~ poisson_log(log_hazard);\n",
      "  log_baseline_mu ~ normal(0, 1);\n",
      "  baseline_sigma ~ normal(0, 1);\n",
      "  log_baseline_raw[1] ~ normal(0, 1);\n",
      "  for (i in 2:T) {\n",
      "      log_baseline_raw[i] ~ normal(log_baseline_raw[i-1], baseline_sigma);\n",
      "  }\n",
      "}\n",
      "generated quantities {\n",
      "  real log_lik[N];\n",
      "  vector[T] baseline;\n",
      "  int y_hat_mat[S, T];     // ppcheck for each S*T combination\n",
      "  real y_hat_time[S];      // predicted failure time for each sample\n",
      "  int y_hat_event[S];      // predicted event (0:censor, 1:event)\n",
      "  \n",
      "  // compute raw baseline hazard, for summary/plotting\n",
      "  baseline = exp(log_baseline_raw);\n",
      "  \n",
      "  for (n in 1:N) {\n",
      "      log_lik[n] <- poisson_log_lpmf(event[n] | log_hazard[n]);\n",
      "  }\n",
      "  \n",
      "  // posterior predicted values\n",
      "  for (samp in 1:S) {\n",
      "      int sample_alive;\n",
      "      sample_alive = 1;\n",
      "      for (tp in 1:T) {\n",
      "        if (sample_alive == 1) {\n",
      "              int n;\n",
      "              int pred_y;\n",
      "              real log_haz;\n",
      "              \n",
      "              // determine predicted value of y\n",
      "              // (need to recalc so that carried-forward data use sim tp and not t[n])\n",
      "              n = n_trans[samp, tp];\n",
      "              log_haz = log_baseline_mu + log_baseline[tp] + x[n,]*beta;\n",
      "              if (log_haz < log(pow(2, 30))) \n",
      "                  pred_y = poisson_log_rng(log_haz);\n",
      "              else\n",
      "                  pred_y = 9; \n",
      "              \n",
      "              // mark this patient as ineligible for future tps\n",
      "              // note: deliberately make 9s ineligible \n",
      "              if (pred_y >= 1) {\n",
      "                  sample_alive = 0;\n",
      "                  y_hat_time[samp] = t_obs[tp];\n",
      "                  y_hat_event[samp] = 1;\n",
      "              }\n",
      "              \n",
      "              // save predicted value of y to matrix\n",
      "              y_hat_mat[samp, tp] = pred_y;\n",
      "          }\n",
      "          else if (sample_alive == 0) {\n",
      "              y_hat_mat[samp, tp] = 9;\n",
      "          } \n",
      "      } // end per-timepoint loop\n",
      "      \n",
      "      // if patient still alive at max\n",
      "      // \n",
      "      if (sample_alive == 1) {\n",
      "          y_hat_time[samp] = t_obs[T];\n",
      "          y_hat_event[samp] = 0;\n",
      "      }\n",
      "  } // end per-sample loop\n",
      "}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(model_code)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:stancache.stancache:sim_data_exp_correlated: cache_filename set to sim_data_exp_correlated.cached.N_100.censor_time_20.rate_coefs_54462717316.rate_form_1 + sex.pkl\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:stancache.stancache:sim_data_exp_correlated: Loading result from cache\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>rate</th>\n",
       "      <th>true_t</th>\n",
       "      <th>t</th>\n",
       "      <th>event</th>\n",
       "      <th>index</th>\n",
       "      <th>age_centered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>male</td>\n",
       "      <td>54</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>1.013855</td>\n",
       "      <td>1.013855</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>male</td>\n",
       "      <td>39</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>4.890597</td>\n",
       "      <td>4.890597</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>-16.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "      <td>45</td>\n",
       "      <td>0.049787</td>\n",
       "      <td>4.093404</td>\n",
       "      <td>4.093404</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>-10.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>female</td>\n",
       "      <td>43</td>\n",
       "      <td>0.049787</td>\n",
       "      <td>7.036226</td>\n",
       "      <td>7.036226</td>\n",
       "      <td>True</td>\n",
       "      <td>3</td>\n",
       "      <td>-12.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>female</td>\n",
       "      <td>57</td>\n",
       "      <td>0.049787</td>\n",
       "      <td>5.712299</td>\n",
       "      <td>5.712299</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "      <td>1.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      sex  age      rate    true_t         t  event  index  age_centered\n",
       "0    male   54  0.082085  1.013855  1.013855   True      0         -1.12\n",
       "1    male   39  0.082085  4.890597  4.890597   True      1        -16.12\n",
       "2  female   45  0.049787  4.093404  4.093404   True      2        -10.12\n",
       "3  female   43  0.049787  7.036226  7.036226   True      3        -12.12\n",
       "4  female   57  0.049787  5.712299  5.712299   True      4          1.88"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = stancache.cached(\n",
    "    survivalstan.sim.sim_data_exp_correlated,\n",
    "    N=100,\n",
    "    censor_time=20,\n",
    "    rate_form='1 + sex',\n",
    "    rate_coefs=[-3, 0.5],\n",
    ")\n",
    "d['age_centered'] = d['age'] - d['age'].mean()\n",
    "d.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f91143b96a0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "survivalstan.utils.plot_observed_survival(df=d[d['sex']=='female'], event_col='event', time_col='t', label='female')\n",
    "survivalstan.utils.plot_observed_survival(df=d[d['sex']=='male'], event_col='event', time_col='t', label='male')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:stancache.stancache:prep_data_long_surv: cache_filename set to prep_data_long_surv.cached.df_14209590808.event_col_event.time_col_t.pkl\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:stancache.stancache:prep_data_long_surv: Loading result from cache\n"
     ]
    }
   ],
   "source": [
    "dlong = stancache.cached(\n",
    "    survivalstan.prep_data_long_surv,\n",
    "    df=d, event_col='event', time_col='t'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>rate</th>\n",
       "      <th>true_t</th>\n",
       "      <th>t</th>\n",
       "      <th>event</th>\n",
       "      <th>index</th>\n",
       "      <th>age_centered</th>\n",
       "      <th>end_time</th>\n",
       "      <th>end_failure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>male</td>\n",
       "      <td>54</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>1.013855</td>\n",
       "      <td>1.013855</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.12</td>\n",
       "      <td>1.013855</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>male</td>\n",
       "      <td>54</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>1.013855</td>\n",
       "      <td>1.013855</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.12</td>\n",
       "      <td>0.808987</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>male</td>\n",
       "      <td>54</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>1.013855</td>\n",
       "      <td>1.013855</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.12</td>\n",
       "      <td>0.377535</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>male</td>\n",
       "      <td>54</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>1.013855</td>\n",
       "      <td>1.013855</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.12</td>\n",
       "      <td>0.791192</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>male</td>\n",
       "      <td>54</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>1.013855</td>\n",
       "      <td>1.013855</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.12</td>\n",
       "      <td>0.009787</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sex  age      rate    true_t         t  event  index  age_centered  \\\n",
       "0   male   54  0.082085  1.013855  1.013855   True      0         -1.12   \n",
       "58  male   54  0.082085  1.013855  1.013855   True      0         -1.12   \n",
       "65  male   54  0.082085  1.013855  1.013855   True      0         -1.12   \n",
       "72  male   54  0.082085  1.013855  1.013855   True      0         -1.12   \n",
       "73  male   54  0.082085  1.013855  1.013855   True      0         -1.12   \n",
       "\n",
       "    end_time end_failure  \n",
       "0   1.013855        True  \n",
       "58  0.808987       False  \n",
       "65  0.377535       False  \n",
       "72  0.791192       False  \n",
       "73  0.009787       False  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dlong.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:stancache.stancache:Step 1: Get compiled model code, possibly from cache\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:stancache.stancache:StanModel: cache_filename set to anon_model.cython_0_29_2.model_code_17281568671805165521.pystan_2_18_1_0.stanmodel.pkl\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:stancache.stancache:StanModel: Loading result from cache\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:stancache.stancache:Step 2: Get posterior draws from model, possibly from cache\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:stancache.stancache:sampling: cache_filename set to anon_model.cython_0_29_2.model_code_17281568671805165521.pystan_2_18_1_0.stanfit.chains_4.data_75284643319.iter_5000.seed_9001.pkl\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:stancache.stancache:sampling: Loading result from cache\n"
     ]
    }
   ],
   "source": [
    "testfit = survivalstan.fit_stan_survival_model(\n",
    "    model_cohort = 'test model',\n",
    "    model_code = model_code,\n",
    "    df = dlong,\n",
    "    sample_col = 'index',\n",
    "    timepoint_end_col = 'end_time',\n",
    "    event_col = 'end_failure',\n",
    "    formula = '~ age_centered + sex',\n",
    "    iter = 5000,\n",
    "    chains = 4,\n",
    "    seed = 9001,\n",
    "    FIT_FUN = stancache.cached_stan_fit,\n",
    "    )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            mean   se_mean         sd        2.5%        50%       97.5%      Rhat\n",
      "lp__ -287.781216  1.521948  25.841239 -337.843663 -288.32069 -236.827395  1.006161\n"
     ]
    }
   ],
   "source": [
    "survivalstan.utils.print_stan_summary([testfit], pars='lp__')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                          mean   se_mean        sd      2.5%       50%     97.5%      Rhat\n",
      "log_baseline_raw[1]  -2.266052  0.022797  0.757871 -3.781383 -2.264353 -0.801449  1.002332\n",
      "log_baseline_raw[2]  -2.364431  0.022200  0.747522 -3.852205 -2.352824 -0.913209  1.002224\n",
      "log_baseline_raw[3]  -2.469519  0.022284  0.742275 -3.943979 -2.457068 -1.035849  1.001857\n",
      "log_baseline_raw[4]  -2.566836  0.022849  0.744848 -4.040161 -2.548941 -1.108471  1.001946\n",
      "log_baseline_raw[5]  -2.662735  0.023252  0.749227 -4.152525 -2.639411 -1.206405  1.001873\n",
      "log_baseline_raw[6]  -2.754253  0.024026  0.759507 -4.251543 -2.735682 -1.266185  1.001951\n",
      "log_baseline_raw[7]  -2.843475  0.024597  0.768474 -4.378099 -2.824548 -1.343800  1.001972\n",
      "log_baseline_raw[8]  -2.924604  0.025082  0.776434 -4.479474 -2.907346 -1.419350  1.002177\n",
      "log_baseline_raw[9]  -3.003173  0.025526  0.782436 -4.581052 -2.986473 -1.474753  1.002179\n",
      "log_baseline_raw[10] -3.073193  0.026218  0.791575 -4.648971 -3.066341 -1.511074  1.002226\n",
      "log_baseline_raw[11] -3.139566  0.026516  0.796587 -4.732211 -3.129224 -1.578292  1.002020\n",
      "log_baseline_raw[12] -3.209124  0.026946  0.799748 -4.801769 -3.197802 -1.646758  1.001927\n",
      "log_baseline_raw[13] -3.270523  0.027736  0.804584 -4.867368 -3.257556 -1.714762  1.001913\n",
      "log_baseline_raw[14] -3.324910  0.028008  0.806302 -4.952066 -3.322625 -1.770464  1.002156\n",
      "log_baseline_raw[15] -3.375416  0.028060  0.808821 -4.986150 -3.358505 -1.787048  1.002112\n",
      "log_baseline_raw[16] -3.418015  0.028232  0.809641 -5.020435 -3.403251 -1.825160  1.002190\n",
      "log_baseline_raw[17] -3.460291  0.028885  0.812362 -5.056463 -3.444036 -1.880696  1.002066\n",
      "log_baseline_raw[18] -3.498345  0.028933  0.812367 -5.117012 -3.495259 -1.892893  1.002061\n",
      "log_baseline_raw[19] -3.535051  0.028993  0.814328 -5.132985 -3.522936 -1.952877  1.002407\n",
      "log_baseline_raw[20] -3.568242  0.028936  0.815294 -5.190021 -3.565835 -1.994228  1.002462\n",
      "log_baseline_raw[21] -3.596620  0.028745  0.812500 -5.214018 -3.583318 -2.004260  1.002160\n",
      "log_baseline_raw[22] -3.627828  0.028897  0.816340 -5.249507 -3.621776 -2.027126  1.002112\n",
      "log_baseline_raw[23] -3.664812  0.028823  0.817502 -5.285838 -3.660719 -2.057037  1.002035\n",
      "log_baseline_raw[24] -3.694122  0.028644  0.816023 -5.313456 -3.679695 -2.101070  1.001962\n",
      "log_baseline_raw[25] -3.726449  0.028639  0.817342 -5.351640 -3.718952 -2.122747  1.001737\n",
      "log_baseline_raw[26] -3.754691  0.028893  0.819345 -5.393803 -3.751139 -2.151594  1.001739\n",
      "log_baseline_raw[27] -3.783266  0.028977  0.821166 -5.411232 -3.778064 -2.168727  1.001889\n",
      "log_baseline_raw[28] -3.808153  0.028980  0.821477 -5.440836 -3.796426 -2.203063  1.001925\n",
      "log_baseline_raw[29] -3.835505  0.028949  0.821662 -5.474397 -3.827508 -2.242330  1.002038\n",
      "log_baseline_raw[30] -3.860725  0.028877  0.817381 -5.478707 -3.848681 -2.272787  1.002150\n",
      "log_baseline_raw[31] -3.885441  0.028842  0.816953 -5.513805 -3.874989 -2.308918  1.002068\n",
      "log_baseline_raw[32] -3.908965  0.029221  0.816717 -5.532251 -3.896064 -2.326129  1.001938\n",
      "log_baseline_raw[33] -3.931273  0.029425  0.818813 -5.564209 -3.914277 -2.364230  1.001907\n",
      "log_baseline_raw[34] -3.953029  0.029330  0.820281 -5.578620 -3.943326 -2.385194  1.001686\n",
      "log_baseline_raw[35] -3.973201  0.029288  0.821152 -5.607299 -3.967871 -2.381836  1.001760\n",
      "log_baseline_raw[36] -3.995195  0.029151  0.819997 -5.641221 -3.982282 -2.410138  1.001859\n",
      "log_baseline_raw[37] -4.013542  0.028848  0.821193 -5.670351 -4.006696 -2.440001  1.001911\n",
      "log_baseline_raw[38] -4.032756  0.028732  0.821102 -5.672715 -4.031058 -2.435735  1.001869\n",
      "log_baseline_raw[39] -4.054814  0.028657  0.821730 -5.677297 -4.052578 -2.462860  1.002034\n",
      "log_baseline_raw[40] -4.077615  0.029048  0.820922 -5.714226 -4.078718 -2.484970  1.002175\n",
      "log_baseline_raw[41] -4.100401  0.029174  0.822995 -5.752051 -4.096290 -2.507281  1.002144\n",
      "log_baseline_raw[42] -4.120889  0.029157  0.823284 -5.771448 -4.114912 -2.524585  1.002094\n",
      "log_baseline_raw[43] -4.139251  0.029130  0.822729 -5.767588 -4.132056 -2.544751  1.001926\n",
      "log_baseline_raw[44] -4.163014  0.029071  0.821213 -5.812907 -4.159177 -2.570496  1.002177\n",
      "log_baseline_raw[45] -4.179724  0.028949  0.820050 -5.835372 -4.163769 -2.615221  1.002185\n",
      "log_baseline_raw[46] -4.197610  0.028892  0.822176 -5.823872 -4.189961 -2.597658  1.002236\n",
      "log_baseline_raw[47] -4.212008  0.029008  0.819808 -5.849129 -4.202241 -2.633780  1.002305\n",
      "log_baseline_raw[48] -4.225084  0.028957  0.818729 -5.848362 -4.215952 -2.643008  1.001915\n",
      "log_baseline_raw[49] -4.241355  0.029079  0.818991 -5.863295 -4.227776 -2.670875  1.001969\n",
      "log_baseline_raw[50] -4.256881  0.029008  0.818179 -5.891835 -4.254293 -2.664914  1.002075\n",
      "log_baseline_raw[51] -4.270769  0.029369  0.818646 -5.904414 -4.265443 -2.694613  1.002035\n",
      "log_baseline_raw[52] -4.282680  0.029372  0.818056 -5.931432 -4.278622 -2.698225  1.001975\n",
      "log_baseline_raw[53] -4.294047  0.029321  0.816835 -5.942168 -4.289579 -2.713233  1.002032\n",
      "log_baseline_raw[54] -4.305103  0.029514  0.818933 -5.933277 -4.298894 -2.706766  1.001916\n",
      "log_baseline_raw[55] -4.312760  0.029303  0.818738 -5.933212 -4.312261 -2.728168  1.001963\n",
      "log_baseline_raw[56] -4.324978  0.029258  0.820226 -5.964142 -4.319273 -2.726846  1.001836\n",
      "log_baseline_raw[57] -4.337596  0.029456  0.822980 -5.957788 -4.334267 -2.729425  1.001769\n",
      "log_baseline_raw[58] -4.350086  0.029573  0.823873 -5.990426 -4.349105 -2.740828  1.001933\n",
      "log_baseline_raw[59] -4.363347  0.029452  0.820318 -5.998213 -4.363577 -2.763006  1.001743\n",
      "log_baseline_raw[60] -4.376516  0.029280  0.821438 -6.019750 -4.373609 -2.763041  1.001752\n",
      "log_baseline_raw[61] -4.381173  0.029498  0.821282 -6.026957 -4.378750 -2.777419  1.001712\n",
      "log_baseline_raw[62] -4.390519  0.029471  0.821330 -6.018604 -4.387542 -2.796227  1.001823\n",
      "log_baseline_raw[63] -4.394140  0.029188  0.822449 -6.040613 -4.391103 -2.784246  1.001807\n",
      "log_baseline_raw[64] -4.400284  0.029012  0.820515 -6.045551 -4.401524 -2.807080  1.001888\n",
      "log_baseline_raw[65] -4.410931  0.029522  0.823018 -6.024781 -4.408396 -2.811094  1.001875\n",
      "log_baseline_raw[66] -4.417436  0.029669  0.823858 -6.064181 -4.411878 -2.834595  1.001887\n",
      "log_baseline_raw[67] -4.427447  0.029746  0.825849 -6.073543 -4.427379 -2.846127  1.001931\n",
      "log_baseline_raw[68] -4.438940  0.030008  0.828293 -6.087548 -4.432626 -2.846181  1.001923\n",
      "log_baseline_raw[69] -4.450288  0.030352  0.833324 -6.133451 -4.438808 -2.833132  1.002091\n",
      "log_baseline_raw[70] -4.459671  0.030378  0.836460 -6.138809 -4.454855 -2.830563  1.002030\n",
      "log_baseline_raw[71] -4.470068  0.030428  0.839366 -6.146245 -4.471940 -2.844583  1.001988\n",
      "log_baseline_raw[72] -4.481865  0.030131  0.841145 -6.155597 -4.480798 -2.860652  1.001826\n",
      "log_baseline_raw[73] -4.496755  0.030241  0.853279 -6.209481 -4.500568 -2.843853  1.001606\n",
      "log_baseline_raw[74] -4.515678  0.030588  0.864968 -6.246099 -4.505534 -2.848791  1.001712\n",
      "log_baseline_raw[75] -4.541684  0.031342  0.883239 -6.323431 -4.536108 -2.856660  1.001737\n"
     ]
    }
   ],
   "source": [
    "survivalstan.utils.print_stan_summary([testfit], pars='log_baseline_raw')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "survivalstan.utils.plot_stan_summary([testfit], pars='log_baseline_raw')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "survivalstan.utils.plot_coefs([testfit], element='baseline')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "survivalstan.utils.plot_coefs([testfit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f9024bef470>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "survivalstan.utils.plot_pp_survival([testfit], fill=False)\n",
    "survivalstan.utils.plot_observed_survival(df=d, event_col='event', time_col='t', color='green', label='observed')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "survivalstan.utils.plot_pp_survival([testfit], by='sex')"
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   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": []
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