Bayesian survival model - time-varying effect of mutation count.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T04:56:06.016320",
     "start_time": "2016-08-18T04:56:04.037829"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import sys \n",
    "from os import getcwd, path\n",
    "import os\n",
    "sys.path.append(path.dirname(getcwd()))\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "import ggplot as ggplot\n",
    "import scipy.stats as stats\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "import lifelines as ll\n",
    "import patsy as patsy\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import functools\n",
    "import survivalstan\n",
    "import stanity\n",
    "%matplotlib inline\n",
    "from utils import data\n",
    "from utils import paper\n",
    "from cohorts.functions import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## prep for rpy2\n",
    "## this will be the r-lib-path, location where installed R packages will be stored\n",
    "r_lib_path = os.path.join(os.path.dirname(getcwd()),'Ranalyses','.R_env')\n",
    "if not os.path.exists(r_lib_path):\n",
    "    print(\"R lib path doesn't exist. Creating it.\")\n",
    "    os.mkdir(r_lib_path)\n",
    "assert(os.path.exists(r_lib_path))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T04:56:26.045030",
     "start_time": "2016-08-18T04:56:26.007905"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## set up rpy2\n",
    "import rpy2\n",
    "from rpy2 import robjects as ro\n",
    "from rpy2.robjects import pandas2ri\n",
    "pandas2ri.activate()\n",
    "R = ro.r\n",
    "%load_ext rpy2.ipython\n",
    "ro.globalenv['r_lib_path'] = r_lib_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tavi/miniconda2/lib/python2.7/site-packages/rpy2/rinterface/__init__.py:185: RRuntimeWarning: Error in .External2(C_X11, paste(\"png::\", filename, sep = \"\"), g$width,  : \n",
      "  unable to start device PNG\n",
      "\n",
      "  warnings.warn(x, RRuntimeWarning)\n",
      "/home/tavi/miniconda2/lib/python2.7/site-packages/rpy2/rinterface/__init__.py:185: RRuntimeWarning: In addition: \n",
      "  warnings.warn(x, RRuntimeWarning)\n",
      "/home/tavi/miniconda2/lib/python2.7/site-packages/rpy2/rinterface/__init__.py:185: RRuntimeWarning: Warning message:\n",
      "\n",
      "  warnings.warn(x, RRuntimeWarning)\n",
      "/home/tavi/miniconda2/lib/python2.7/site-packages/rpy2/rinterface/__init__.py:185: RRuntimeWarning: In (function (filename = \"Rplot%03d.png\", width = 480, height = 480,  :\n",
      "  warnings.warn(x, RRuntimeWarning)\n",
      "/home/tavi/miniconda2/lib/python2.7/site-packages/rpy2/rinterface/__init__.py:185: RRuntimeWarning: \n",
      " \n",
      "  warnings.warn(x, RRuntimeWarning)\n",
      "/home/tavi/miniconda2/lib/python2.7/site-packages/rpy2/rinterface/__init__.py:185: RRuntimeWarning:  unable to open connection to X11 display ''\n",
      "\n",
      "  warnings.warn(x, RRuntimeWarning)\n"
     ]
    },
    {
     "ename": "RRuntimeError",
     "evalue": "Error in .External2(C_X11, paste(\"png::\", filename, sep = \"\"), g$width,  : \n  unable to start device PNG\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mRRuntimeError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-4-5271d020526b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun_cell_magic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mu'R'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34mu''\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34mu\"## make sure R packages can be installed to the lib-path\\n.libPaths(new = r_lib_path)\\noptions(repos = c(CRAN = 'https://cran.rstudio.com'))\\nif (!require('survival', lib.loc = r_lib_path))\\n    install.packages('survival', lib = r_lib_path)\\nif (!require('dplyr', lib.loc = r_lib_path))\\n    install.packages('dplyr', lib = r_lib_path)\\n\\nlibrary('survival', lib.loc = r_lib_path)\\nlibrary('dplyr', lib.loc = r_lib_path)\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m/home/tavi/miniconda2/lib/python2.7/site-packages/IPython/core/interactiveshell.pyc\u001b[0m in \u001b[0;36mrun_cell_magic\u001b[1;34m(self, magic_name, line, cell)\u001b[0m\n\u001b[0;32m   2118\u001b[0m             \u001b[0mmagic_arg_s\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvar_expand\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstack_depth\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2119\u001b[0m             \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2120\u001b[1;33m                 \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmagic_arg_s\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcell\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2121\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2122\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<decorator-gen-130>\u001b[0m in \u001b[0;36mR\u001b[1;34m(self, line, cell, local_ns)\u001b[0m\n",
      "\u001b[1;32m/home/tavi/miniconda2/lib/python2.7/site-packages/IPython/core/magic.pyc\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(f, *a, **k)\u001b[0m\n\u001b[0;32m    191\u001b[0m     \u001b[1;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    192\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 193\u001b[1;33m         \u001b[0mcall\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    194\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    195\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/tavi/miniconda2/lib/python2.7/site-packages/rpy2/ipython/rmagic.pyc\u001b[0m in \u001b[0;36mR\u001b[1;34m(self, line, cell, local_ns)\u001b[0m\n\u001b[0;32m    663\u001b[0m                     \u001b[0mro\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0massign\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlocalconverter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpy2ri\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mval\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    664\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 665\u001b[1;33m         \u001b[0mtmpd\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetup_graphics\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    666\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    667\u001b[0m         \u001b[0mtext_output\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m''\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/tavi/miniconda2/lib/python2.7/site-packages/rpy2/ipython/rmagic.pyc\u001b[0m in \u001b[0;36msetup_graphics\u001b[1;34m(self, args)\u001b[0m\n\u001b[0;32m    413\u001b[0m                 \u001b[1;31m# Note: that %% is to pass into R for interpolation there\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    414\u001b[0m                 ro.r.png(\"%s/Rplots%%03d.png\" % tmpd_fix_slashes,\n\u001b[1;32m--> 415\u001b[1;33m                         **argdict)\n\u001b[0m\u001b[0;32m    416\u001b[0m             \u001b[1;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdevice\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'svg'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    417\u001b[0m                 self.cairo.CairoSVG(\"%s/Rplot.svg\" % tmpd_fix_slashes,\n",
      "\u001b[1;32m/home/tavi/miniconda2/lib/python2.7/site-packages/rpy2/robjects/functions.pyc\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    176\u001b[0m                 \u001b[0mv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    177\u001b[0m                 \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mr_k\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mv\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 178\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mSignatureTranslatedFunction\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    179\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    180\u001b[0m \u001b[0mpattern_link\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mre\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mr'\\\\link\\{(.+?)\\}'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/tavi/miniconda2/lib/python2.7/site-packages/rpy2/robjects/functions.pyc\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    104\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    105\u001b[0m             \u001b[0mnew_kwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconversion\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpy2ri\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 106\u001b[1;33m         \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFunction\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mnew_args\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mnew_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    107\u001b[0m         \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconversion\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mri2ro\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mres\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    108\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mRRuntimeError\u001b[0m: Error in .External2(C_X11, paste(\"png::\", filename, sep = \"\"), g$width,  : \n  unable to start device PNG\n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "## make sure R packages can be installed to the lib-path\n",
    ".libPaths(new = r_lib_path)\n",
    "options(repos = c(CRAN = 'https://cran.rstudio.com'))\n",
    "if (!require('survival', lib.loc = r_lib_path))\n",
    "    install.packages('survival', lib = r_lib_path)\n",
    "if (!require('dplyr', lib.loc = r_lib_path))\n",
    "    install.packages('dplyr', lib = r_lib_path)\n",
    "\n",
    "library('survival', lib.loc = r_lib_path)\n",
    "library('dplyr', lib.loc = r_lib_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prep/load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T04:56:11.383824",
     "start_time": "2016-08-18T04:56:06.018333"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inner join with ensembl_coverage: 25 to 25 rows\n",
      "inner join with tcr_peripheral_a: 25 to 25 rows\n",
      "inner join with ensembl_coverage: 25 to 25 rows\n",
      "inner join with tcr_peripheral_a: 25 to 25 rows\n",
      "{'dataframe_hash': -7522244498661454348,\n",
      " 'provenance_file_summary': {u'cohorts': u'0.4.0+3.gda968fb',\n",
      "                             u'isovar': u'0.0.6',\n",
      "                             u'mhctools': u'0.3.0',\n",
      "                             u'numpy': u'1.11.1',\n",
      "                             u'pandas': u'0.18.1',\n",
      "                             u'pyensembl': u'1.0.3',\n",
      "                             u'scipy': u'0.18.1',\n",
      "                             u'topiary': u'0.1.0',\n",
      "                             u'varcode': u'0.5.10'}}\n"
     ]
    }
   ],
   "source": [
    "## adapted from TCR plots.ipynb\n",
    "cohort = data.init_cohort(join_with=[\"tcr_peripheral_a\",\"ensembl_coverage\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T04:56:25.518952",
     "start_time": "2016-08-18T04:56:11.385582"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inner join with ensembl_coverage: 25 to 25 rows\n",
      "inner join with tcr_peripheral_a: 25 to 25 rows\n",
      "inner join with ensembl_coverage: 25 to 25 rows\n"
     ]
    }
   ],
   "source": [
    "cols, d = cohort.as_dataframe(on={\"peripheral_a_clonality\": lambda row: row[\"Clonality\"], \n",
    "                                  \"missense_snv_count\": missense_snv_count,\n",
    "                                  \"expressed_neoantigen_count\": expressed_neoantigen_count,\n",
    "                                 }, return_cols=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T04:56:25.545136",
     "start_time": "2016-08-18T04:56:25.520707"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T04:56:25.657884",
     "start_time": "2016-08-18T04:56:25.546569"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## construct log-transformed / centered missense_snv_count metric\n",
    "d['missense_snv_per_mb'] = d.missense_snv_count / d.MB\n",
    "d['log_mut'] = np.log1p(d.missense_snv_count)\n",
    "d['log_mut_centered'] = d.log_mut - d.log_mut.mean()\n",
    "d['log_neoant'] = np.log1p(d.expressed_neoantigen_count)\n",
    "d['log_neoant_centered'] = d.log_neoant - d.log_neoant.mean()\n",
    "d['neoant_mutation_ratio'] = d.expressed_neoantigen_count / d.missense_snv_count\n",
    "\n",
    "## define output variables of interest\n",
    "time_col = 'pfs'\n",
    "event_col = 'is_progressed_or_deceased'\n",
    "\n",
    "## reshape to long\n",
    "dlong = survivalstan.prep_data_long_surv(df = d, time_col = 'pfs', event_col = 'is_progressed_or_deceased')\n",
    "dlong_os = survivalstan.prep_data_long_surv(df = d, time_col = 'os', event_col = 'is_deceased')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T04:56:25.845617",
     "start_time": "2016-08-18T04:56:25.659554"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5dfdcef350>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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r++ijj5gwYQKGYRAcHOzY6kREpMqwFDhPPfUUpmmyevVq2wy13//+92RmZgIwZMgQx1co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\nIyIiLqHAERERl1DgiIiIS/x/ogJxaykFVYkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5dffee0bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.boxplot(y = 'missense_snv_per_mb', x = 'benefit', data = d, whis=[2.5, 97.5],)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T04:56:25.875141",
     "start_time": "2016-08-18T04:56:25.847419"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def coxph_model(formula, data, time_col, event_col, **kwargs):\n",
    "    sdata = patsy.dmatrix(\n",
    "        formula\n",
    "         , data = data\n",
    "         , return_type = 'dataframe'\n",
    "         ).join(data[[time_col, event_col]])\n",
    "    sdata = sdata.ix[:, sdata.columns != 'Intercept']\n",
    "    \n",
    "    if not(hasattr(kwargs, \"penalizer\")):\n",
    "        kwargs[\"penalizer\"] = 0.1\n",
    "    if not(hasattr(kwargs, \"normalize\")):\n",
    "        kwargs['normalize'] = False\n",
    "    \n",
    "    cf = ll.CoxPHFitter(**kwargs)\n",
    "    cf.fit(sdata\n",
    "          , time_col\n",
    "          , event_col\n",
    "          )\n",
    "    cf.print_summary()\n",
    "    return cf\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T04:56:25.896621",
     "start_time": "2016-08-18T04:56:25.876640"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "coxph_pfs = functools.partial(coxph_model, time_col = 'pfs', event_col = 'is_progressed_or_deceased', data = d)\n",
    "coxph_os = functools.partial(coxph_model, time_col = 'os', event_col = 'is_deceased', data = d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## fit cox model using Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T04:56:26.006355",
     "start_time": "2016-08-18T04:56:25.898150"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n=25, number of events=19\n",
      "\n",
      "                         coef  exp(coef)  se(coef)          z         p  lower 0.95  upper 0.95   \n",
      "missense_snv_count -8.044e-02  9.227e-01 8.402e-02 -9.573e-01 3.384e-01  -2.452e-01   8.428e-02   \n",
      "---\n",
      "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 \n",
      "\n",
      "Concordance = 0.550\n",
      "n=25, number of events=17\n",
      "\n",
      "                         coef  exp(coef)  se(coef)          z         p  lower 0.95  upper 0.95   \n",
      "missense_snv_count -4.926e-02  9.519e-01 8.334e-02 -5.911e-01 5.545e-01  -2.126e-01   1.141e-01   \n",
      "---\n",
      "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 \n",
      "\n",
      "Concordance = 0.544\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tavi/.local/lib/python2.7/site-packages/lifelines/fitters/coxph_fitter.py:285: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
      "  df.sort(duration_col, inplace=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<lifelines.CoxPHFitter: fitted with 25 observations, 8 censored>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coxph_pfs(data = d, formula = 'missense_snv_count')\n",
    "coxph_os(data = d, formula = 'missense_snv_count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## fit cox model using R"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T04:56:26.215262",
     "start_time": "2016-08-18T04:56:26.046718"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## prep data to export to R environment\n",
    "## (in absence of this, get strange behavior for some logical columns)\n",
    "## for now, export the minimal number of columns necessary\n",
    "columns = ['benefit', event_col, time_col, 'missense_snv_count', 'patient_id']\n",
    "d_for_r = d.loc[:,columns]\n",
    "dlong_for_r = survivalstan.prep_data_long_surv(d_for_r, event_col = event_col, time_col = time_col)\n",
    "dlong_for_r['end_failure'] = dlong_for_r['end_failure'].astype(int)\n",
    "dlong_for_r['benefit'] = dlong_for_r['benefit'].astype(int)\n",
    "dlong_for_r['is_progressed_or_deceased'] = dlong_for_r['is_progressed_or_deceased'].astype(int)\n",
    "\n",
    "## export data to R\n",
    "ro.globalenv['d'] = d_for_r\n",
    "ro.globalenv['dlong'] = dlong_for_r\n",
    "ro.globalenv['time_col'] = time_col\n",
    "ro.globalenv['event_col'] = event_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T04:56:26.365639",
     "start_time": "2016-08-18T04:56:26.217009"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## prep data for OS-based analysis\n",
    "columns = ['benefit', 'is_deceased', 'os', 'missense_snv_count', 'patient_id']\n",
    "d2_for_r = d.loc[:,columns]\n",
    "dlong2_for_r = survivalstan.prep_data_long_surv(d2_for_r, event_col = 'is_deceased', time_col = 'os')\n",
    "dlong2_for_r['end_failure'] = dlong2_for_r['end_failure'].astype(int)\n",
    "dlong2_for_r['benefit'] = dlong2_for_r['benefit'].astype(int)\n",
    "dlong2_for_r['is_deceased'] = dlong2_for_r['is_deceased'].astype(int)\n",
    "\n",
    "## export data to R\n",
    "ro.globalenv['d_os'] = d2_for_r\n",
    "ro.globalenv['dlong_os'] = dlong2_for_r\n",
    "ro.globalenv['time_col_os'] = 'os'\n",
    "ro.globalenv['event_col_os'] = 'is_deceased'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T04:56:27.930787",
     "start_time": "2016-08-18T04:56:26.367286"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%R\n",
    "library(survival)\n",
    "library(dplyr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T04:56:27.965088",
     "start_time": "2016-08-18T04:56:27.932694"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'data.frame':\t25 obs. of  6 variables:\n",
       " $ benefit                  : logi  FALSE FALSE FALSE TRUE FALSE TRUE ...\n",
       " $ is_progressed_or_deceased: logi  TRUE TRUE TRUE TRUE TRUE TRUE ...\n",
       " $ pfs                      : int  20 61 58 398 41 265 121 560 655 646 ...\n",
       " $ missense_snv_count       : num  9.898 3.7204 0.4263 0.0377 4.2359 ...\n",
       " $ patient_id               : Factor w/ 25 levels \"0040\",\"0471\",..: 1 2 3 4 5 6 7 8 9 10 ...\n",
       " $ key                      : int  1 1 1 1 1 1 1 1 1 1 ...\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%R str(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T04:56:28.000050",
     "start_time": "2016-08-18T04:56:27.966792"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'data.frame':\t263 obs. of  8 variables:\n",
       " $ benefit                  : int  0 0 0 0 0 0 0 0 0 0 ...\n",
       " $ is_progressed_or_deceased: int  1 1 1 1 1 1 1 1 1 1 ...\n",
       " $ pfs                      : int  20 20 61 61 61 61 61 61 61 58 ...\n",
       " $ missense_snv_count       : num  9.9 9.9 3.72 3.72 3.72 ...\n",
       " $ patient_id               : Factor w/ 25 levels \"0040\",\"0471\",..: 1 1 2 2 2 2 2 2 2 3 ...\n",
       " $ key                      : int  1 1 1 1 1 1 1 1 1 1 ...\n",
       " $ end_time                 : int  20 19 20 61 58 41 37 60 19 20 ...\n",
       " $ end_failure              : int  1 0 0 1 0 0 0 0 0 0 ...\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%R str(dlong)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T04:56:28.035782",
     "start_time": "2016-08-18T04:56:28.001980"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'data.frame':\t320 obs. of  8 variables:\n",
       " $ benefit           : int  0 0 0 0 0 0 0 0 0 0 ...\n",
       " $ is_deceased       : int  1 1 1 1 1 1 1 1 1 1 ...\n",
       " $ os                : int  24 24 329 329 329 329 329 329 329 329 ...\n",
       " $ missense_snv_count: num  9.9 9.9 3.72 3.72 3.72 ...\n",
       " $ patient_id        : Factor w/ 25 levels \"0040\",\"0471\",..: 1 1 2 2 2 2 2 2 2 2 ...\n",
       " $ key               : int  1 1 1 1 1 1 1 1 1 1 ...\n",
       " $ end_time          : int  24 22 24 329 76 54 44 182 266 163 ...\n",
       " $ end_failure       : int  1 0 0 1 0 0 0 0 0 0 ...\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%R str(dlong_os)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T04:56:28.078786",
     "start_time": "2016-08-18T04:56:28.037823"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "glm(formula = \"benefit~missense_snv_count\", family = \"binomial\", \n",
       "    data = d)\n",
       "\n",
       "Deviance Residuals: \n",
       "    Min       1Q   Median       3Q      Max  \n",
       "-1.2762  -0.9370  -0.8243   1.3605   1.5787  \n",
       "\n",
       "Coefficients:\n",
       "                   Estimate Std. Error z value Pr(>|z|)\n",
       "(Intercept)         -0.9114     0.5726  -1.592    0.111\n",
       "missense_snv_count   0.1152     0.1282   0.899    0.369\n",
       "\n",
       "(Dispersion parameter for binomial family taken to be 1)\n",
       "\n",
       "    Null deviance: 32.671  on 24  degrees of freedom\n",
       "Residual deviance: 31.847  on 23  degrees of freedom\n",
       "AIC: 35.847\n",
       "\n",
       "Number of Fisher Scoring iterations: 4\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## example calling GLM \n",
    "%R print(summary(glm('benefit~missense_snv_count', data=d, family = 'binomial')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T04:56:28.106771",
     "start_time": "2016-08-18T04:56:28.080634"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%%R\n",
    "print_for_hyper <- function(label, value) {\n",
    "    print(paste('{{{',label,':',value,'}}}', sep=''))\n",
    "}\n",
    "format_hr <- function(coxres, coef, digits = 2) {\n",
    "    confint <- summary(coxres)$conf.int[coef,]\n",
    "    formatted <- paste('n=25, HR=', \n",
    "                       round(confint['exp(coef)'], digits),\n",
    "                       ' (95% CI ',\n",
    "                       round(confint['lower .95'], digits),\n",
    "                       ' - ',\n",
    "                       round(confint['upper .95'], digits),\n",
    "                       ')',\n",
    "                        sep = '')\n",
    "    formatted\n",
    "}\n",
    "format_hr_inline <- function(coxres, coef, digits = 2) {\n",
    "    confint <- summary(coxres)$conf.int[coef,]\n",
    "    formatted <- paste(round(confint['exp(coef)'], digits),\n",
    "                       ' (95% CI ',\n",
    "                       round(confint['lower .95'], digits),\n",
    "                       ' - ',\n",
    "                       round(confint['upper .95'], digits),\n",
    "                       ')',\n",
    "                        sep = '')\n",
    "    formatted\n",
    "}\n",
    "format_p_zph <- function(test_zph, coef, digits = 2, n = NULL) {\n",
    "    p_value <- test_zph$table[coef, 'p']\n",
    "    p_value <- format.pval(p_value, digits=digits)\n",
    "    if (is.null(n)) {\n",
    "        return(p_value)\n",
    "    } else {\n",
    "        return(paste('n=',n,', p=',p_value,sep=''))\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### primary model for PFS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T04:56:28.133605",
     "start_time": "2016-08-18T04:56:28.108376"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Surv(pfs, is_progressed_or_deceased) ~ .\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R \n",
    "surv_time = as.formula(paste('Surv(',time_col,',',event_col,') ~ .'))\n",
    "print(surv_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T04:56:28.171553",
     "start_time": "2016-08-18T04:56:28.135094"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Call:\n",
       "coxph(formula = update(surv_time, \"~ missense_snv_count\"), data = d, \n",
       "    na.action = na.exclude, method = \"breslow\")\n",
       "\n",
       "  n= 25, number of events= 19 \n",
       "\n",
       "                       coef exp(coef) se(coef)      z Pr(>|z|)\n",
       "missense_snv_count -0.08049   0.92266  0.08406 -0.958    0.338\n",
       "\n",
       "                   exp(coef) exp(-coef) lower .95 upper .95\n",
       "missense_snv_count    0.9227      1.084    0.7825     1.088\n",
       "\n",
       "Concordance= 0.55  (se = 0.077 )\n",
       "Rsquare= 0.041   (max possible= 0.984 )\n",
       "Likelihood ratio test= 1.03  on 1 df,   p=0.3091\n",
       "Wald test            = 0.92  on 1 df,   p=0.3383\n",
       "Score (logrank) test = 0.93  on 1 df,   p=0.3346\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R \n",
    "coxres <- \n",
    "    coxph(\n",
    "        update(surv_time, '~ missense_snv_count'),\n",
    "        data = d, \n",
    "        method=\"breslow\",\n",
    "        na.action=na.exclude\n",
    "    )\n",
    "print(summary(coxres))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### test for non-proportional hazard of missense_snv_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T04:56:28.229072",
     "start_time": "2016-08-18T04:56:28.173453"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                     rho chisq      p\n",
       "missense_snv_count -0.45  4.05 0.0441\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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oGBwKBoeCwaFgcCgYHAoGh4LBoWBwKBgcCgaHgsGhYHD+AQMzdikhn0uUAAAAAElFTkSu\nQmCC\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "test_nph = cox.zph(coxres, transform = 'log')\n",
    "plot(test_nph)\n",
    "print(test_nph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T04:56:28.255758",
     "start_time": "2016-08-18T04:56:28.231300"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] \"{{{p_nph_pfs:n=25, p=0.044}}}\"\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "print_for_hyper(label = 'p_nph_pfs', value = format_p_zph(test_nph, 'missense_snv_count', n=25))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### log-transformed mutation count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T04:56:28.311899",
     "start_time": "2016-08-18T04:56:28.257549"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Call:\n",
       "coxph(formula = update(surv_time, \"~ log_mut\"), data = d2, na.action = na.exclude, \n",
       "    method = \"breslow\")\n",
       "\n",
       "  n= 25, number of events= 19 \n",
       "\n",
       "           coef exp(coef) se(coef)      z Pr(>|z|)\n",
       "log_mut -0.3304    0.7186   0.2966 -1.114    0.265\n",
       "\n",
       "        exp(coef) exp(-coef) lower .95 upper .95\n",
       "log_mut    0.7186      1.392    0.4018     1.285\n",
       "\n",
       "Concordance= 0.55  (se = 0.077 )\n",
       "Rsquare= 0.05   (max possible= 0.984 )\n",
       "Likelihood ratio test= 1.29  on 1 df,   p=0.2552\n",
       "Wald test            = 1.24  on 1 df,   p=0.2653\n",
       "Score (logrank) test = 1.26  on 1 df,   p=0.2615\n",
       "\n",
       "           rho chisq     p\n",
       "log_mut -0.343  2.44 0.118\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeAAAAHgCAMAAABKCk6nAAAABlBMVEUAAAD///+l2Z/dAAANoklE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\nLA6CxUGwOAgW5x+993bScKdtowAAAABJRU5ErkJggg==\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R \n",
    "d2 <- \n",
    "  d %>%\n",
    "  dplyr::mutate(log_mut = log1p(missense_snv_count))\n",
    "\n",
    "coxres2 <- \n",
    "    coxph(\n",
    "        update(surv_time, '~ log_mut'),\n",
    "        data = d2, \n",
    "        method=\"breslow\",\n",
    "        na.action=na.exclude\n",
    "    )\n",
    "print(summary(coxres2))\n",
    "test_nph2 = cox.zph(coxres2, transform = 'log')\n",
    "plot(test_nph2)\n",
    "print(test_nph2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### alternate long-data spec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T04:56:28.346258",
     "start_time": "2016-08-18T04:56:28.313463"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'data.frame':\t263 obs. of  8 variables:\n",
       " $ benefit                  : int  0 0 0 0 0 0 0 0 0 0 ...\n",
       " $ is_progressed_or_deceased: int  1 1 1 1 1 1 1 1 1 1 ...\n",
       " $ pfs                      : int  20 20 61 61 61 61 61 61 61 58 ...\n",
       " $ missense_snv_count       : num  9.9 9.9 3.72 3.72 3.72 ...\n",
       " $ patient_id               : Factor w/ 25 levels \"0040\",\"0471\",..: 1 1 2 2 2 2 2 2 2 3 ...\n",
       " $ key                      : int  1 1 1 1 1 1 1 1 1 1 ...\n",
       " $ end_time                 : int  20 19 20 61 58 41 37 60 19 20 ...\n",
       " $ end_failure              : int  1 0 0 1 0 0 0 0 0 0 ...\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "str(dlong)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T04:56:28.384221",
     "start_time": "2016-08-18T04:56:28.347784"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%R\n",
    "## add start time to timepoint\n",
    "coxres_alt <- \n",
    "  coxph(as.formula('Surv(end_time, end_failure) ~ missense_snv_count + cluster(patient_id)'),\n",
    "        data = dlong,\n",
    "        method=\"breslow\",\n",
    "        na.action=na.exclude\n",
    "       )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T04:56:28.414171",
     "start_time": "2016-08-18T04:56:28.385944"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Call:\n",
       "coxph(formula = as.formula(\"Surv(end_time, end_failure) ~ missense_snv_count + cluster(patient_id)\"), \n",
       "    data = dlong, na.action = na.exclude, method = \"breslow\")\n",
       "\n",
       "  n= 263, number of events= 19 \n",
       "\n",
       "                       coef exp(coef) se(coef) robust se      z Pr(>|z|)\n",
       "missense_snv_count -0.10340   0.90176  0.08558   0.11657 -0.887    0.375\n",
       "\n",
       "                   exp(coef) exp(-coef) lower .95 upper .95\n",
       "missense_snv_count    0.9018      1.109    0.7176     1.133\n",
       "\n",
       "Concordance= 0.562  (se = 0.076 )\n",
       "Rsquare= 0.007   (max possible= 0.488 )\n",
       "Likelihood ratio test= 1.72  on 1 df,   p=0.1892\n",
       "Wald test            = 0.79  on 1 df,   p=0.3751\n",
       "Score (logrank) test = 1.5  on 1 df,   p=0.2208,   Robust = 0.73  p=0.3915\n",
       "\n",
       "  (Note: the likelihood ratio and score tests assume independence of\n",
       "     observations within a cluster, the Wald and robust score tests do not).\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%R print(summary(coxres_alt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T04:56:28.443819",
     "start_time": "2016-08-18T04:56:28.415875"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Call:\n",
       "coxph(formula = update(surv_time, \"~ missense_snv_count\"), data = d, \n",
       "    na.action = na.exclude, method = \"breslow\")\n",
       "\n",
       "  n= 25, number of events= 19 \n",
       "\n",
       "                       coef exp(coef) se(coef)      z Pr(>|z|)\n",
       "missense_snv_count -0.08049   0.92266  0.08406 -0.958    0.338\n",
       "\n",
       "                   exp(coef) exp(-coef) lower .95 upper .95\n",
       "missense_snv_count    0.9227      1.084    0.7825     1.088\n",
       "\n",
       "Concordance= 0.55  (se = 0.077 )\n",
       "Rsquare= 0.041   (max possible= 0.984 )\n",
       "Likelihood ratio test= 1.03  on 1 df,   p=0.3091\n",
       "Wald test            = 0.92  on 1 df,   p=0.3383\n",
       "Score (logrank) test = 0.93  on 1 df,   p=0.3346\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## compare to original spec of model\n",
    "%R print(summary(coxres))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] \"{{{hr_missense_snv_pfs_cox:n=25, HR=0.92 (95% CI 0.78 - 1.09)}}}\"\n",
       "[1] \"{{{hr_missense_snv_pfs_cox_inline:0.92 (95% CI 0.78 - 1.09)}}}\"\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "print_for_hyper(label = 'hr_missense_snv_pfs_cox', \n",
    "                value = format_hr(coxres, 'missense_snv_count')\n",
    "               )\n",
    "print_for_hyper(label = 'hr_missense_snv_pfs_cox_inline', \n",
    "                value = format_hr_inline(coxres, 'missense_snv_count')\n",
    "               )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] \"{{{hr_log_missense_snv_pfs_cox:n=25, HR=0.72 (95% CI 0.4 - 1.29)}}}\"\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "print_for_hyper(label = 'hr_log_missense_snv_pfs_cox', \n",
    "                value = format_hr(coxres2, 'log_mut')\n",
    "               )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### test for difference at 90d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T04:56:28.506239",
     "start_time": "2016-08-18T04:56:28.467323"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Call:\n",
       "coxph(formula = as.formula(\"Surv(end_time, end_failure) ~ missense_snv_count:lt_91_days + missense_snv_count:gt_91_days + cluster(patient_id)\"), \n",
       "    data = dlong2, na.action = na.exclude, method = \"breslow\")\n",
       "\n",
       "  n= 263, number of events= 19 \n",
       "\n",
       "                                  coef exp(coef) se(coef) robust se      z\n",
       "missense_snv_count:lt_91_days  0.03803   1.03876  0.08203   0.10457  0.364\n",
       "missense_snv_count:gt_91_days -0.53482   0.58577  0.27137   0.29608 -1.806\n",
       "                              Pr(>|z|)  \n",
       "missense_snv_count:lt_91_days   0.7161  \n",
       "missense_snv_count:gt_91_days   0.0709 .\n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "                              exp(coef) exp(-coef) lower .95 upper .95\n",
       "missense_snv_count:lt_91_days    1.0388     0.9627    0.8463     1.275\n",
       "missense_snv_count:gt_91_days    0.5858     1.7071    0.3279     1.047\n",
       "\n",
       "Concordance= 0.693  (se = 0.076 )\n",
       "Rsquare= 0.038   (max possible= 0.488 )\n",
       "Likelihood ratio test= 10.17  on 2 df,   p=0.006181\n",
       "Wald test            = 4.24  on 2 df,   p=0.12\n",
       "Score (logrank) test = 5.89  on 2 df,   p=0.05266,   Robust = 7.72  p=0.02106\n",
       "\n",
       "  (Note: the likelihood ratio and score tests assume independence of\n",
       "     observations within a cluster, the Wald and robust score tests do not).\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "## add start time to timepoint\n",
    "dlong2 <- \n",
    "    dlong %>%\n",
    "    dplyr::mutate(gt_91_days = ifelse(end_time > 91, 1, 0),\n",
    "                  lt_91_days = ifelse(end_time <= 91, 1, 0))\n",
    "\n",
    "\n",
    "coxres_alt_time <- \n",
    "  coxph(as.formula('Surv(end_time, end_failure) ~ missense_snv_count:lt_91_days + missense_snv_count:gt_91_days + cluster(patient_id)'),\n",
    "        data = dlong2,\n",
    "        method=\"breslow\",\n",
    "        na.action=na.exclude\n",
    "       )\n",
    "print(summary(coxres_alt_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T04:56:28.536992",
     "start_time": "2016-08-18T04:56:28.508084"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] \"{{{num_patients_gt_90d_pfs:11}}}\"\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "## print out number of patients with >90d pfs \n",
    "## in hyper-friendly way\n",
    "num_patients <-\n",
    "    dlong2 %>% \n",
    "    dplyr::filter(gt_91_days==TRUE) %>%\n",
    "    distinct(patient_id) %>%\n",
    "    tally()\n",
    "print_for_hyper('num_patients_gt_90d_pfs',num_patients)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T04:56:28.562643",
     "start_time": "2016-08-18T04:56:28.538589"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] \"{{{hr_lt_90d_pfs_cox:n=25, HR=1.04 (95% CI 0.85 - 1.28)}}}\"\n",
       "[1] \"{{{hr_gt_90d_pfs_cox:n=25, HR=0.59 (95% CI 0.33 - 1.05)}}}\"\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "print_for_hyper(label = 'hr_lt_90d_pfs_cox', \n",
    "                value = format_hr(coxres_alt_time, 'missense_snv_count:lt_91_days')\n",
    "               )\n",
    "print_for_hyper(label = 'hr_gt_90d_pfs_cox', \n",
    "                value = format_hr(coxres_alt_time, 'missense_snv_count:gt_91_days')\n",
    "               )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-07T22:24:26.358804",
     "start_time": "2016-07-07T22:24:26.334646"
    }
   },
   "source": [
    "#### same analysis using log-mut-count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T04:56:28.601741",
     "start_time": "2016-08-18T04:56:28.564566"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Call:\n",
       "coxph(formula = as.formula(\"Surv(end_time, end_failure) ~ log_mut:lt_91_days + log_mut:gt_91_days + cluster(patient_id)\"), \n",
       "    data = dlong2, na.action = na.exclude, method = \"breslow\")\n",
       "\n",
       "  n= 263, number of events= 19 \n",
       "\n",
       "                       coef exp(coef) se(coef) robust se      z Pr(>|z|)  \n",
       "log_mut:lt_91_days  0.09686   1.10171  0.32440   0.40902  0.237   0.8128  \n",
       "log_mut:gt_91_days -1.53533   0.21538  0.65495   0.71831 -2.137   0.0326 *\n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "                   exp(coef) exp(-coef) lower .95 upper .95\n",
       "log_mut:lt_91_days    1.1017     0.9077    0.4942    2.4560\n",
       "log_mut:gt_91_days    0.2154     4.6429    0.0527    0.8803\n",
       "\n",
       "Concordance= 0.693  (se = 0.076 )\n",
       "Rsquare= 0.042   (max possible= 0.488 )\n",
       "Likelihood ratio test= 11.2  on 2 df,   p=0.003697\n",
       "Wald test            = 5.94  on 2 df,   p=0.05125\n",
       "Score (logrank) test = 8  on 2 df,   p=0.01836,   Robust = 9.31  p=0.009499\n",
       "\n",
       "  (Note: the likelihood ratio and score tests assume independence of\n",
       "     observations within a cluster, the Wald and robust score tests do not).\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "## add start time to timepoint\n",
    "dlong2 <- \n",
    "    dlong %>%\n",
    "    dplyr::mutate(gt_91_days = ifelse(end_time > 91, 1, 0),\n",
    "                  lt_91_days = ifelse(end_time <= 91, 1, 0),\n",
    "                  log_mut = log1p(missense_snv_count))\n",
    "coxres_alt_time <- \n",
    "  coxph(as.formula('Surv(end_time, end_failure) ~ log_mut:lt_91_days + log_mut:gt_91_days + cluster(patient_id)'),\n",
    "        data = dlong2,\n",
    "        method=\"breslow\",\n",
    "        na.action=na.exclude\n",
    "       )\n",
    "print(summary(coxres_alt_time))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### primary model for OS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T04:56:28.628007",
     "start_time": "2016-08-18T04:56:28.603632"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Surv(os, is_deceased) ~ .\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R \n",
    "surv_time_os = as.formula(paste('Surv(',time_col_os,',',event_col_os,') ~ .'))\n",
    "print(surv_time_os)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T04:56:28.658471",
     "start_time": "2016-08-18T04:56:28.629716"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Call:\n",
       "coxph(formula = update(surv_time_os, \"~ missense_snv_count\"), \n",
       "    data = d_os, na.action = na.exclude, method = \"breslow\")\n",
       "\n",
       "  n= 25, number of events= 17 \n",
       "\n",
       "                       coef exp(coef) se(coef)      z Pr(>|z|)\n",
       "missense_snv_count -0.04929   0.95190  0.08338 -0.591    0.554\n",
       "\n",
       "                   exp(coef) exp(-coef) lower .95 upper .95\n",
       "missense_snv_count    0.9519      1.051    0.8084     1.121\n",
       "\n",
       "Concordance= 0.544  (se = 0.077 )\n",
       "Rsquare= 0.015   (max possible= 0.977 )\n",
       "Likelihood ratio test= 0.37  on 1 df,   p=0.5404\n",
       "Wald test            = 0.35  on 1 df,   p=0.5544\n",
       "Score (logrank) test = 0.35  on 1 df,   p=0.5533\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R \n",
    "coxres_os <- \n",
    "    coxph(\n",
    "        update(surv_time_os, '~ missense_snv_count'),\n",
    "        data = d_os, \n",
    "        method=\"breslow\",\n",
    "        na.action=na.exclude\n",
    "    )\n",
    "print(summary(coxres_os))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### test for time-varying effect using OS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T04:56:28.701132",
     "start_time": "2016-08-18T04:56:28.660250"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                      rho chisq      p\n",
       "missense_snv_count -0.435  3.02 0.0821\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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WAuo7BfsAc/IU7MTlU9RBd2sK9mRQBFGlQ8GunL/EE+gTEhSs5+WjPeHDKBPB5/cbCj7S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\nYII=\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "test_nph_os = cox.zph(coxres_os, transform = 'log')\n",
    "plot(test_nph_os)\n",
    "print(test_nph_os)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T04:56:28.725730",
     "start_time": "2016-08-18T04:56:28.703054"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] \"{{{p_nph_os:n=25, p=0.082}}}\"\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "print_for_hyper(label = 'p_nph_os', value = format_p_zph(test_nph_os, 'missense_snv_count', n=25))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### test for difference at 90 days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T04:56:28.766451",
     "start_time": "2016-08-18T04:56:28.727488"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Call:\n",
       "coxph(formula = as.formula(\"Surv(end_time, end_failure) ~ missense_snv_count:lt_90_days + missense_snv_count:gt_90_days + cluster(patient_id)\"), \n",
       "    data = dlong2_os, na.action = na.exclude, method = \"breslow\")\n",
       "\n",
       "  n= 320, number of events= 17 \n",
       "\n",
       "                                  coef exp(coef) se(coef) robust se      z\n",
       "missense_snv_count:lt_90_days  0.30708   1.35945  0.09771   0.11206  2.740\n",
       "missense_snv_count:gt_90_days -0.21155   0.80933  0.13162   0.13567 -1.559\n",
       "                              Pr(>|z|)   \n",
       "missense_snv_count:lt_90_days  0.00614 **\n",
       "missense_snv_count:gt_90_days  0.11894   \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "                              exp(coef) exp(-coef) lower .95 upper .95\n",
       "missense_snv_count:lt_90_days    1.3594     0.7356    1.0914     1.693\n",
       "missense_snv_count:gt_90_days    0.8093     1.2356    0.6204     1.056\n",
       "\n",
       "Concordance= 0.814  (se = 0.08 )\n",
       "Rsquare= 0.036   (max possible= 0.406 )\n",
       "Likelihood ratio test= 11.89  on 2 df,   p=0.002619\n",
       "Wald test            = 15.62  on 2 df,   p=0.0004055\n",
       "Score (logrank) test = 24.07  on 2 df,   p=5.92e-06,   Robust = 4.56  p=0.1022\n",
       "\n",
       "  (Note: the likelihood ratio and score tests assume independence of\n",
       "     observations within a cluster, the Wald and robust score tests do not).\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "## add start time to timepoint\n",
    "dlong2_os <- \n",
    "    dlong_os %>%\n",
    "    dplyr::mutate(gt_90_days = ifelse(end_time > 90, 1, 0),\n",
    "                  lt_90_days = ifelse(end_time <= 90, 1, 0))\n",
    "coxres_alt_time_os <- \n",
    "  coxph(as.formula('Surv(end_time, end_failure) ~ missense_snv_count:lt_90_days + missense_snv_count:gt_90_days + cluster(patient_id)'),\n",
    "        data = dlong2_os,\n",
    "        method=\"breslow\",\n",
    "        na.action=na.exclude\n",
    "       )\n",
    "print(summary(coxres_alt_time_os))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T04:56:28.793009",
     "start_time": "2016-08-18T04:56:28.768308"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] \"{{{hr_lt_90d_os_cox:n=25, HR=1.36 (95% CI 1.09 - 1.69)}}}\"\n",
       "[1] \"{{{hr_gt_90d_os_cox:n=25, HR=0.81 (95% CI 0.62 - 1.06)}}}\"\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "print_for_hyper(label = 'hr_lt_90d_os_cox', \n",
    "                value = format_hr(coxres_alt_time_os, 'missense_snv_count:lt_90_days')\n",
    "               )\n",
    "print_for_hyper(label = 'hr_gt_90d_os_cox', \n",
    "                value = format_hr(coxres_alt_time_os, 'missense_snv_count:gt_90_days')\n",
    "               )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## fit survival model in stan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### fit both rw & gamma PEM models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T04:56:28.815994",
     "start_time": "2016-08-18T04:56:28.794649"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../utils/stan/logistic_model.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_custom_coefs_expressed_missense_and_neoant_mutations.stan\n",
      "../utils/stan/logistic_model_by_group.stan\n",
      "../utils/stan/pem_survival_model_unstructured_varcoef.stan\n",
      "../utils/stan/pem_survival_model_unstructured_varcoef_hsprior.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_tvc.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_alt.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_bspline_est_xi.stan\n",
      "../utils/stan/pem_survival_model_varying_coefs3.stan\n",
      "../utils/stan/pem_survival_model_randomwalk.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_custom_coefs_rate_only.stan\n",
      "../utils/stan/pem_survival_model_gamma.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_custom_coefs_missense_and_neoant_rates.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_tvc2.stan\n",
      "../utils/stan/pem_survival_model_varying_coefs2.stan\n",
      "../utils/stan/pem_survival_model_varying_coefs4.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_bspline_est_xi2.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_bspline.stan\n",
      "../utils/stan/pem_survival_model_randomwalk2.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_bspline2.stan\n"
     ]
    }
   ],
   "source": [
    "## load stan models\n",
    "models = survivalstan.utils.read_files('../utils/stan', pattern = \"*.stan\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T04:57:27.984268",
     "start_time": "2016-08-18T04:56:28.817846"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NOT reusing model.\n",
      "Ran in 56.141 sec.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tavi/miniconda2/lib/python2.7/site-packages/stanity/psis.py:228: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  elif sort == 'in-place':\n",
      "/home/tavi/miniconda2/lib/python2.7/site-packages/stanity/psis.py:246: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  bs /= 3 * x[sort[np.floor(n/4 + 0.5) - 1]]\n"
     ]
    }
   ],
   "source": [
    "gamma_model = survivalstan.fit_stan_survival_model(\n",
    "    formula = '~ missense_snv_count',\n",
    "    df = dlong,\n",
    "    timepoint_end_col = 'end_time',\n",
    "    sample_col = 'patient_id',\n",
    "    event_col = 'end_failure',\n",
    "    chains = 4,\n",
    "    iter = 1000,\n",
    "    model_code = models['pem_survival_model_gamma.stan'],\n",
    "    model_cohort = 'gamma prior'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T04:58:23.964766",
     "start_time": "2016-08-18T04:57:27.986155"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NOT reusing model.\n",
      "Ran in 49.720 sec.\n"
     ]
    }
   ],
   "source": [
    "rw_model = survivalstan.fit_stan_survival_model(\n",
    "    formula = '~ missense_snv_count',\n",
    "    df = dlong,\n",
    "    timepoint_end_col = 'end_time',\n",
    "    sample_col = 'patient_id',\n",
    "    event_col = 'end_failure',\n",
    "    chains = 4,\n",
    "    iter = 1000,\n",
    "    model_code = survivalstan.models.pem_survival_model_randomwalk,\n",
    "    model_cohort = 'random-walk prior'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T04:59:36.046303",
     "start_time": "2016-08-18T04:58:23.966929"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NOT reusing model.\n",
      "Ran in 61.971 sec.\n"
     ]
    }
   ],
   "source": [
    "rw2_model = survivalstan.fit_stan_survival_model(\n",
    "    formula = '~ missense_snv_count',\n",
    "    df = dlong,\n",
    "    timepoint_end_col = 'end_time',\n",
    "    sample_col = 'patient_id',\n",
    "    event_col = 'end_failure',\n",
    "    chains = 4,\n",
    "    iter = 5000,\n",
    "    model_code = models['pem_survival_model_randomwalk_alt.stan'],\n",
    "    model_cohort = '(alt) random-walk prior'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{{{bayes_hr_missense_snv_pfs:HR=0.78, 95% CI (0.63, 0.91)}}}\n"
     ]
    }
   ],
   "source": [
    "## summarize HR estimated in bayesian survival model\n",
    "rw2_model['coefs']['exp(beta)'] = np.exp(rw2_model['coefs']['value'])\n",
    "paper.hyper_label_printer(formatter=paper.hr_posterior_formatter, label='bayes_hr_missense_snv_pfs', series=rw2_model['coefs']['exp(beta)'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T05:00:31.618407",
     "start_time": "2016-08-18T04:59:36.048482"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NOT reusing model.\n",
      "Ran in 48.880 sec.\n"
     ]
    }
   ],
   "source": [
    "uns_model = survivalstan.fit_stan_survival_model(\n",
    "    formula = '~ missense_snv_count',\n",
    "    df = dlong,\n",
    "    timepoint_end_col = 'end_time',\n",
    "    sample_col = 'patient_id',\n",
    "    event_col = 'end_failure',\n",
    "    chains = 4,\n",
    "    iter = 1000,\n",
    "    model_code = survivalstan.models.pem_survival_model_unstructured,\n",
    "    model_cohort = 'unstructured prior'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T05:00:35.086153",
     "start_time": "2016-08-18T05:00:31.620529"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#print(rw_model['fit'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### inspect coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T05:00:35.371919",
     "start_time": "2016-08-18T05:00:35.087864"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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MzNSvv/6qAwcOyGq16oEHHtDChQuVkZGhffv2OXRRfEREhDIyMvTHH3/o66+/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\niIiIJMSGnIiIiIhIQmzIiYiIiIgk9C9f55S400l9awAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5dfb815f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "survivalstan.utils.plot_coefs([gamma_model, rw_model, rw2_model, uns_model])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### inspect baseline hazard functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T05:00:36.919980",
     "start_time": "2016-08-18T05:00:35.373907"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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57N27FzU1NUJbnn32WbRs2RKDBw+Gp6cnxo4dC19fX3Ts2BGRkZH48ccfRe+jX79++Pnn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\nCZGMvFloOSsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5dfbe86190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "survivalstan.utils.plot_coefs([rw_model, rw2_model], element='baseline_raw')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### compare rw & gamma fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T05:00:36.951324",
     "start_time": "2016-08-18T05:00:36.921788"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'diff': 21.861866886560342, 'se_diff': 5.8804260953187848}"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stanity.loo_compare(gamma_model['loo'], rw_model['loo'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T05:00:36.978093",
     "start_time": "2016-08-18T05:00:36.953067"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'diff': -8.5080703429676703, 'se_diff': 3.0586310236426866}"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stanity.loo_compare(rw_model['loo'], rw2_model['loo'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### bayesian p-value for p(mutation count >= 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T05:00:37.003113",
     "start_time": "2016-08-18T05:00:36.979745"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.049000000000000002"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coefdata = rw_model['coefs']\n",
    "np.mean(coefdata.loc[coefdata['variable']=='missense_snv_count','value'] >= 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-07-08T19:06:33.386418",
     "start_time": "2016-07-08T19:06:33.343282"
    },
    "collapsed": true
   },
   "source": [
    "## fit tvc-survival model in stan (naive)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we will fit the \"naive\" time-varying covariate model in Stan, using the same method as above.\n",
    "\n",
    "We will estimate a separate HR for `log_mut_centered` in (a) the first 90d, and (b) in the post-90d period.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T05:00:40.545708",
     "start_time": "2016-08-18T05:00:37.005874"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n",
      "Ran in 3.272 sec.\n"
     ]
    }
   ],
   "source": [
    "dlong['lt_91_days'] = (dlong['end_time']<=91).astype(int)\n",
    "dlong['gt_91_days'] = (dlong['end_time']>91).astype(int)\n",
    "\n",
    "rw2_model_90d = survivalstan.fit_stan_survival_model(\n",
    "    formula = '~ missense_snv_count:lt_91_days + missense_snv_count:gt_91_days',\n",
    "    df = dlong,\n",
    "    timepoint_end_col = 'end_time',\n",
    "    sample_col = 'patient_id',\n",
    "    event_col = 'end_failure',\n",
    "    chains = 4,\n",
    "    iter = 1000,\n",
    "    model_code = survivalstan.models.pem_survival_model_randomwalk,\n",
    "    model_cohort = 'random-walk baseline hazard, time-varying HR at 90d'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n",
      "Ran in 2.357 sec.\n"
     ]
    }
   ],
   "source": [
    "uns_model_90d = survivalstan.fit_stan_survival_model(\n",
    "    formula = '~ missense_snv_count:lt_91_days + missense_snv_count:gt_91_days',\n",
    "    df = dlong,\n",
    "    timepoint_end_col = 'end_time',\n",
    "    sample_col = 'patient_id',\n",
    "    event_col = 'end_failure',\n",
    "    chains = 4,\n",
    "    iter = 1000,\n",
    "    model_code = survivalstan.models.pem_survival_model_unstructured,\n",
    "    model_cohort = 'uns baseline hazard, time-varying HR at 90d'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### compare coefficients for each model type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T05:43:21.505906",
     "start_time": "2016-08-18T05:43:21.198667"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GDhzIuHHjjC7qAIsXLyYyMpK1a9dSvHhxTp48Sdu2ba0SAHd3Zb/ftfUoFStWLMvzktHb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\n/BnFs3TpUry8vB5JPI+LUaNGsX79+kz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bAAAAAElFTkSu\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5dfc456450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "survivalstan.utils.plot_coefs([rw2_model_90d, uns_model_90d])\n",
    "_ = plt.vlines(0, -10, 10, linestyles='--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{{{hr-survival-by-90d}}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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jx49H3bp1TVoLERGZnt6gPH78uPS9o6MjOnbsiOzsbGzYsCHf9adMmWL04nIL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\nyWBQEhERyWBQEhERyfg/pTl3nTs+46gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5dfbb8b190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('{{{hr-survival-by-90d}}}')\n",
    "value, variable, coefdata = survivalstan.utils._prep_data_for_coefs([rw2_model_90d], 'coefs')\n",
    "coefdata['exp(beta)'] = np.exp(coefdata[value])\n",
    "coefdata['Missense SNV Count / MB'] = coefdata['variable']\n",
    "sb.boxplot(data=coefdata, x='exp(beta)', y='Missense SNV Count / MB', fliersize=0, whis=[2.5, 97.5])\n",
    "_ = plt.yticks([0, 1], ['t <= 3m', 't > 3m'])\n",
    "_ = plt.vlines(1, -10, 10, linestyles='--')\n",
    "_ = plt.ylabel('# Missense SNVs / MB')\n",
    "_ = plt.xlabel('HR for {}'.format(cohort.hazard_plot_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{{{hr_gt90d_rw2:HR=0.69, 95% CI (0.38, 0.99)}}}\n",
      "{{{hr_lt90d_rw2:HR=0.91, 95% CI (0.75, 1.07)}}}\n"
     ]
    }
   ],
   "source": [
    "## summarize coefficient estimates for caption\n",
    "coefdata['group_label'] = coefdata.variable.apply(lambda x: 'hr_lt90d_rw2' if x=='missense_snv_count:lt_91_days' else 'hr_gt90d_rw2')\n",
    "for name, group in coefdata.groupby('group_label'):\n",
    "    paper.hyper_label_printer(formatter=paper.hr_posterior_formatter, label=name, series=group['exp(beta)'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{{{hr_gt_90d_pfs:n=11, HR=0.69, 95% CI (0.38, 0.99)}}}\n",
      "{{{hr_lt_90d_pfs:n=25, HR=0.91, 95% CI (0.75, 1.07)}}}\n"
     ]
    }
   ],
   "source": [
    "coefdata['group_label'] = coefdata.variable.apply(lambda x: 'hr_lt_90d_pfs' if x=='missense_snv_count:lt_91_days' else 'hr_gt_90d_pfs')\n",
    "for name, group in coefdata.groupby('group_label'):\n",
    "    if name=='hr_gt_90d_pfs':\n",
    "        n=11\n",
    "    else:\n",
    "        n=25\n",
    "    paper.hyper_label_printer(formatter=paper.hr_posterior_formatter, label=name, series=group['exp(beta)'], n=n)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### compare model fit using LOO-PSIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T05:44:16.284563",
     "start_time": "2016-08-18T05:44:16.253135"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'diff': 8.2401167563348423, 'se_diff': 3.5026600683610822}"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stanity.loo_compare(rw2_model['loo'], rw2_model_90d['loo'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bayesian p-value for p(post-90d coefficient >= 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T05:44:16.311967",
     "start_time": "2016-08-18T05:44:16.286472"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.024"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coefdata = rw2_model_90d['coefs']\n",
    "np.mean(coefdata.loc[coefdata['variable']=='missense_snv_count:gt_91_days','value'] >= 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bayesian p-value for p(pre-90d coefficient > post-90d coefficient)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T05:44:16.350523",
     "start_time": "2016-08-18T05:44:16.313633"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.097999999999999976"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coefdata = rw2_model_90d['coefs']\n",
    "1-(pd.pivot_table(coefdata,\n",
    "                  index = ['iter', 'model_cohort'],\n",
    "                  values = 'value', columns = 'variable').rename(\n",
    "        columns = {'missense_snv_count:lt_91_days': 'lt_91_days',\n",
    "                   'missense_snv_count:gt_91_days': 'gt_91_days'}).eval('lt_91_days > gt_91_days').mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## bayesian tvc model for os endpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n",
      "Ran in 4.042 sec.\n"
     ]
    }
   ],
   "source": [
    "dlong_os['lt_91_days'] = (dlong_os['end_time']<=91).astype(int)\n",
    "dlong_os['gt_91_days'] = (dlong_os['end_time']>91).astype(int)\n",
    "\n",
    "rw2_model_90d_os = survivalstan.fit_stan_survival_model(\n",
    "    formula = '~ missense_snv_count:lt_91_days + missense_snv_count:gt_91_days',\n",
    "    df = dlong_os,\n",
    "    timepoint_end_col = 'end_time',\n",
    "    sample_col = 'patient_id',\n",
    "    event_col = 'end_failure',\n",
    "    chains = 4,\n",
    "    iter = 1000,\n",
    "    model_code = survivalstan.models.pem_survival_model_randomwalk,\n",
    "    model_cohort = 'random-walk baseline hazard, time-varying HR at 90d'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{{{hr-survival-by-90d-os}}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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Z0YL86quvtHPnTl27dk2SMgVks2bNnFchAABulG1QxsTEaP369dqyZYuuXLkiKXM41qpV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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5dfc456510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('{{{hr-survival-by-90d-os}}}')\n",
    "value, variable, coefdata = survivalstan.utils._prep_data_for_coefs([rw2_model_90d_os], 'coefs')\n",
    "coefdata['exp(beta)'] = np.exp(coefdata[value])\n",
    "coefdata['Missense SNV Count / MB'] = coefdata['variable']\n",
    "sb.boxplot(data=coefdata, x='exp(beta)', y='Missense SNV Count / MB', fliersize=0, whis=[2.5, 97.5])\n",
    "_ = plt.yticks([0, 1], ['t <= 3m', 't > 3m'])\n",
    "_ = plt.vlines(1, -10, 10, linestyles='--')\n",
    "_ = plt.ylabel('# Missense SNVs / MB')\n",
    "_ = plt.xlabel('HR for {}'.format(cohort.hazard_os_plot_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{{{hr_gt90d_rw2_os:HR=0.80, 95% CI (0.60, 1.00)}}}\n",
      "{{{hr_lt90d_rw2_os:HR=1.02, 95% CI (0.79, 1.22)}}}\n"
     ]
    }
   ],
   "source": [
    "coefdata['group_label'] = coefdata.variable.apply(lambda x: 'hr_lt90d_rw2_os' if x=='missense_snv_count:lt_91_days' else 'hr_gt90d_rw2_os')\n",
    "for name, group in coefdata.groupby('group_label'):\n",
    "    paper.hyper_label_printer(formatter=paper.hr_posterior_formatter, label=name, series=group['exp(beta)'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{{{hr_gt_90d_os:n=11, HR=0.80, 95% CI (0.60, 1.00)}}}\n",
      "{{{hr_lt_90d_os:n=25, HR=1.02, 95% CI (0.79, 1.22)}}}\n"
     ]
    }
   ],
   "source": [
    "coefdata['group_label'] = coefdata.variable.apply(lambda x: 'hr_lt_90d_os' if x=='missense_snv_count:lt_91_days' else 'hr_gt_90d_os')\n",
    "for name, group in coefdata.groupby('group_label'):\n",
    "    if name=='hr_gt_90d_os':\n",
    "        n=11\n",
    "    else:\n",
    "        n=25\n",
    "    paper.hyper_label_printer(formatter=paper.hr_posterior_formatter, label=name, series=group['exp(beta)'], n=n)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## fit tvc-survival model in stan (b-splines)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T05:44:16.377684",
     "start_time": "2016-08-18T05:44:16.352316"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../utils/stan/logistic_model.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_custom_coefs_expressed_missense_and_neoant_mutations.stan\n",
      "../utils/stan/logistic_model_by_group.stan\n",
      "../utils/stan/pem_survival_model_unstructured_varcoef.stan\n",
      "../utils/stan/pem_survival_model_unstructured_varcoef_hsprior.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_tvc.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_alt.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_bspline_est_xi.stan\n",
      "../utils/stan/pem_survival_model_varying_coefs3.stan\n",
      "../utils/stan/pem_survival_model_randomwalk.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_custom_coefs_rate_only.stan\n",
      "../utils/stan/pem_survival_model_gamma.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_custom_coefs_missense_and_neoant_rates.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_tvc2.stan\n",
      "../utils/stan/pem_survival_model_varying_coefs2.stan\n",
      "../utils/stan/pem_survival_model_varying_coefs4.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_bspline_est_xi2.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_bspline.stan\n",
      "../utils/stan/pem_survival_model_randomwalk2.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_bspline2.stan\n"
     ]
    }
   ],
   "source": [
    "## load stan models again (for convenience)\n",
    "models = survivalstan.utils.read_files('../utils/stan')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### tvc with linear spline; fixed knot position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T05:44:23.451505",
     "start_time": "2016-08-18T05:44:16.379493"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NOT reusing model.\n",
      "Ran in 75.378 sec.\n"
     ]
    }
   ],
   "source": [
    "spline_model = survivalstan.fit_stan_survival_model(\n",
    "    formula = '~ missense_snv_count',\n",
    "    df = dlong,\n",
    "    timepoint_end_col = 'end_time',\n",
    "    sample_col = 'patient_id',\n",
    "    event_col = 'end_failure',\n",
    "    chains = 4,\n",
    "    iter = 10000,\n",
    "    model_code = models['pem_survival_model_randomwalk_bspline2.stan'],\n",
    "    model_cohort = 'random-walk prior - linear spline',\n",
    "    stan_data = {'H': 1, 'xi': [90], 'power': 1},\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T05:44:23.452947",
     "start_time": "2016-08-18T05:44:16.277Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#print(spline_model['fit'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T05:44:23.453305",
     "start_time": "2016-08-18T05:44:16.278Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'diff': -2.028895510333026, 'se_diff': 3.349026124632057}"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stanity.loo_compare(rw_model['loo'],spline_model['loo'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T05:44:23.453691",
     "start_time": "2016-08-18T05:44:16.280Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'diff': -1.7609419237001978, 'se_diff': 2.4879856829408116}"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stanity.loo_compare(rw2_model_90d['loo'], spline_model['loo'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T05:44:23.454112",
     "start_time": "2016-08-18T05:44:16.282Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def extract_time_betas(stanmodel, bins=20, element='beta_time', value_name='beta'):\n",
    "    time_betas = stanmodel['fit'].extract()[element]\n",
    "    time_betas = pd.DataFrame(time_betas[:,0,:])\n",
    "    time_betas = pd.melt(time_betas, var_name = 'timepoint_id', value_name=value_name)\n",
    "    timepoint_data = stanmodel['df'].loc[:,['timepoint_id','end_time']].drop_duplicates()\n",
    "    time_betas = pd.merge(time_betas, timepoint_data, on = 'timepoint_id')\n",
    "    time_betas['exp({})'.format(value_name)] = np.exp(time_betas[value_name])\n",
    "    time_betas['model_cohort'] = stanmodel['model_cohort']\n",
    "    time_betas['alt_timepoints'] = pd.cut(time_betas['end_time'],\n",
    "                                          bins=bins,\n",
    "                                          precision=0,\n",
    "                                         )\n",
    "    time_betas['alt_log_timepoints'] = pd.cut(np.log(time_betas['end_time']),\n",
    "                                             bins=bins,\n",
    "                                             precision=1,\n",
    "                                             )\n",
    "    rename_log_timepoints = np.exp(time_betas['alt_log_timepoints'].cat.categories.str.extract(', ([\\d\\.]+)\\]', expand=False).astype(float)).astype(int)\n",
    "    rename_timepoints = time_betas['alt_timepoints'].cat.categories.str.extract(', ([\\d\\.]+)\\]', expand=False).astype(float).astype(int)\n",
    "    time_betas['alt_timepoint_end'] = time_betas['alt_timepoints'].cat.rename_categories(rename_timepoints)\n",
    "    time_betas['alt_log_timepoint_end'] = time_betas['alt_log_timepoints'].cat.rename_categories(rename_log_timepoints)\n",
    "    time_betas['90d_post_therapy'] = time_betas.end_time.apply(lambda x: '>90d' if x > 90 else '<=90d')\n",
    "    return(time_betas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T05:44:23.454498",
     "start_time": "2016-08-18T05:44:16.285Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "spline_model['time_betas'] = extract_time_betas(spline_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T05:44:23.454862",
     "start_time": "2016-08-18T05:44:16.287Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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g5PLVV199BR8fHzzzzDMIDw+vtEoSEVHNZHSIqFohY8eOVS8LDAzE8uXL8ffff+PEiRPm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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5dffa32ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.boxplot(x = 'end_time', y = 'exp(beta)',\n",
    "           data = spline_model['time_betas'],\n",
    "           fliersize=0,\n",
    "           whis=[2.5, 97.5],\n",
    "          )\n",
    "_ = plt.ylim([0, 4])\n",
    "_ = plt.hlines(1, -1, 20)\n",
    "_ = plt.xticks(rotation=\"vertical\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T05:44:23.455224",
     "start_time": "2016-08-18T05:44:16.288Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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sLEx0vZycHBw8eBBxcXEYPXo0AMDPzw+hoaFISkrC6tWrmx49ERE1G4PGIBwc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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5dfb77a950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "spline_model['time_betas'].boxplot(\n",
    "    column='exp(beta)',\n",
    "    by='end_time',\n",
    "    whis=[2.5, 97.5],\n",
    "    positions=np.log(spline_model['time_betas'].end_time.drop_duplicates())\n",
    ")\n",
    "_ = plt.ylim([0, 4])\n",
    "_ = plt.hlines(1, -1, 20, linestyles='--')\n",
    "_ = plt.xticks(rotation=\"vertical\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T05:44:23.455648",
     "start_time": "2016-08-18T05:44:16.290Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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d5atPP/0U3t7eeOKJJ7Bt2zadBUlERIZJ7SRS3wp57bXXpNv8/f3x8ccf48aNGzh58qT2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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5dfbaf7590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.boxplot(data = spline_model['time_betas'],\n",
    "          x = 'alt_log_timepoints',\n",
    "          y = 'exp(beta)',\n",
    "           whis=[2.5, 97.5],\n",
    "          fliersize=0,\n",
    "          )\n",
    "_ = plt.ylim([0, 4])\n",
    "_ = plt.hlines(1, -1, 20, linestyles='--')\n",
    "_ = plt.xticks(rotation=\"vertical\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T05:44:23.456073",
     "start_time": "2016-08-18T05:44:16.292Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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zmDVrFlxcXLBr1y5MnDgRe/bsgbu7ux6jfvEZw1/wurgsx4RKZDj1kkR69eqF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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5dfc3d4410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.boxplot(data = spline_model['time_betas'],\n",
    "          x = 'alt_log_timepoint_end',\n",
    "          y = 'exp(beta)',\n",
    "           whis=[2.5, 97.5],\n",
    "          fliersize=0,\n",
    "          )\n",
    "_ = plt.ylim([0, 4])\n",
    "_ = plt.hlines(1, -1, 20, linestyles='--')\n",
    "_ = plt.xticks(rotation=\"vertical\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T05:44:23.456446",
     "start_time": "2016-08-18T05:44:16.294Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PR3p6ugkj1h/HmhBRU9IgVyK9e/dGSkoKAODbb7/FkSNH9DouPT0de/bswcKF\nCzFy5EgAQEBAAIKDg7FixQqsWrXKZDFrU3OcCcCxJkTUtDSqNpHk5GRYWVlh6NChyjJLS0sEBwcj\nJSUFCoWiAaMjImp6GtWI9czMTLi5uaF58+Yq5Z6enlAoFMjKykKnTp1qrWfGjBlqdQBAfn4+gP9d\nTdzPyckJy5YtkxA5EdGDqVElkaKiIjg4OKiVOzo6AgAKCwv1qud6QQHa2KnX09zCEgBw71aJ2rYb\nZXcMCdUguiaBBNg4T0Tmq1ElEWNxbN4C/3rmefT66C2N23/9LFqtbHri9/j+++9x6NAhtW0XL17U\nWM+jjz6qsVzb/tnZ2WjWrOojKSoq0rhPXern/tyf+3N/Q/evbr/WplElEXt7e+Tm5qqVV1+BVF+R\nNDbVjfOPPvooOnbsCAC4cOGCynYiInPUqJKIp6cnkpKSUF5ertKmkZGRASsrK7i7uxtUn6YrDl2e\nf/55g3pdacv4uvavbo/Zv3+/Sern/tyf+3N/Q/bPzs7WeVyjSiJBQUGIiYlBQkKCsovvvXv3kJCQ\ngL59+8LKyqpe4nj77bdRUFCgcZuuxnk2zBPRg6bBkkhiYiIA4OzZsxBC4ODBg2jdujVat26NgIAA\n5ObmYvDgwYiMjERERAQAoEuXLhg2bBiioqKgUCjg5uaGuLg45OTkGPTjXFheiumJ3xsU742yO7CR\nCQBAQUEB8q/lobWNeg+v5hZVAyfv3Sq87/hyg56PiKgxaLAk8uabbypHqstkMnz66acAqgYPbt68\nGUII5V9NCxcuRHR0NJYvX47i4mLI5XLExsZCLpfXa/ytbZpj6TO99d5/RuIJE0ZDRNQwGiyJ1DZN\nSfv27XH+/Hm1cmtra8ycORMzZ86U/NzVvbMMMT3xe1jatZL8nERED6JGNWKdiIjMC5MIERFJ1qh6\nZxmLtob124oKAICtlbXathtld+BsX3U7q7i4GGVl5Qa1c9woK4eNrFhixERE5qlJJpGHnZxgqWHu\nrPL/ds+1t1dv+3C2bwUnJyeTx0ZE1Jg0ySSydOlSuLm5qZXrO427nZ0dWop7BvfOsqwxHxYR0YOg\nSSYRc6BtwCJnEiaixoRJpIFUD1h0tFEtt/5vVwfFrTy1Ywp1LJjImYCJqCEwiTQgRxtgztM2te/4\nX5/8pN+yu9XL89rx9hkRmRiTiEQ3tPTOuq24CwCwtWqmtr+zveniqblMb1NfolfXVRmvyIiMi0lE\nAl29tP7Oqs59AAAgAElEQVTXw0t1Wnpne93HkWnwqozItJhEJNDVsN3UrwLMAa/KiOoPR6wTEZFk\nvBIhIq1qti8BbGOqK3N5P43Zbtjkk0jNN/P+MRqm/ECrpk7Rv8cVUNXFl1OnUENiG5NxmcP7WdcY\nmnwSqcnGRv/utkRNQc32JYBtTHVlLu+nMdsNm3wSuf9DrS92dnawEXcMHidixX8BEpEZYcM6ERFJ\nxiRCRESSMYkQEZFkTCJERCRZk29Yr6v7+30b0k24UEMX3zuKqv+2tNK8vynn3yIiMhSTiJHp201Y\n2zxaFf9NQg72zmrbOP8WEZkbJpE6ktpFWNv8W+yHT0SNCdtEiIhIMiYRIiKSjLezGilta7QDXKed\niOoPk0gjVVBQgGvX8mDfQn1bs/9eX5YVq6/TfqvUxIERUZPCJNKI2bcAIoYZ9hGu+vddE0VDRE0R\n20SIiEiyBrkSuXr1KhYsWICjR49CCIE+ffrggw8+QLt27Wo9Vi6Xq5XJZDLs2rVL4zYiIjKdek8i\nZWVlGD9+PJo3b47FixcDAKKjoxEaGordu3frNVhv9OjRCAkJUSnz8PAwSbxk/tjJgKjh1HsS2bFj\nB3JycrB371506NABAPDYY4/hmWeewfbt2xEWFlZrHS4uLvD19TVxpNRYFBQUIO9aHtBKpr7RUgAA\n8u5cU99WIkwcGdGDr96TyM8//4zu3bsrEwgAuLm5oWfPnkhOTtYriRCpaSWDxSu2Bh1S+fVtEwVD\n1HTUexLJyMjAoEGD1Mo9PT2RmJioVx1xcXH46quvYGlpie7du2PatGnw9/c3dqj1Rtc674Bp13on\nIqqLek8ihYWFcHBwUCt3cHDArVu3aj1+xIgRGDBgAFxcXJCbm4vY2FiEhYVhw4YNCAgIMEXI9Yrr\nvBNRY9LoxoksWrRI+f+9evVCUFAQnnvuOSxfvhxbtmxpwMikkzKJY3FxMUpLDR/3casUUKDYoGOI\niLSp93EiDg4OKCoqUisvKiqCvb3hi2XY2tqif//+OHPmjDHCIyIiA9T7lYinpycyMjLUyjMyMtCp\nU6f6DqfRsrOzgxXuSBqxbmNnZ6KoiKipqfcrkaCgIKSmpiI7O1tZlp2djf/85z8aG9xrU1JSggMH\nDrDLLxFRA6j3K5EXX3wR27ZtQ0REBN58800AwIoVK+Dq6qoygDA3NxeDBw9GZGQkIiIiAADr16/H\npUuXEBgYCCcnJ+Tk5GD9+vUoKCjA0qVL6/ulEBE1efWeRFq0aIFNmzZhwYIFmDlzpnLak1mzZqFF\ni/9NSSuEUP5V8/DwQFJSEvbt24fi4mK0atUKvXr1QlRUFLy9vev7pRARNXkN0jurbdu2WLFihc59\n2rdvj/Pnz6uUDRw4EAMHDjRlaEREZADO4ktERJI1unEiRPcrLi4GSoXh05iUCBTf45gZorrglQgR\nEUnGK5FG7JaWEeulFVX/bWGt+RibB2yYiJ2dHe5YlkqagNGu5QP2ZhDVMyaRRsrJyUnrtuL/TuJo\nY+ests3GTvexRESGYBJppHQtpFQ9A/DmzZvrKxx6gHCRLzIEkwgRqaha5OsaZLbqc9kJy6qfjGu3\ny9S33a59Fm568DCJEJEama09WvzzDYOOKd2me+xXUyX1yq6xXNUxiRARmVBBQQHyr+XjoeYPqW2z\nllX1frlbpNpB5mb5zXqJzRiYRIiITOyh5g9h6ZNL9N5/xpF3TRiNcTGJEBE94OrSWaJly5Y662YS\nISJ6wFXdUruG1hoGiTW3qEoD926Vqm27UVYMew3LmdfEJEJE1AS0trFD9GDDluF+K2kdaluAm0mE\nHgwlWubOKvvvUgI2Mo3HQPeVOhHVgkmEGj1dI/Dzb1fd73VuqT56Hy05ep+orphEqNHj6H2ihsNZ\nfImISDImESIikoxJhIiIJGObyANi3bp1OHz4MADNg4f69euH114zrHsfNU3FxcUQpaUGz4Ulbt9C\ncaXCRFGRuWISeQDZ2Ng0dAhE1EQwiTwgXnvtNV5pkFHY2dmh1MJK0iy+drb8B8z9iouLUVZWZtB8\nWDfLbsLGonG8l2wTISIiyXglQkRkQnZ2dmhR2cLgWXyb2Rnv57nqaqgUbyWtM+i4G2XFsFBY6tyH\nVyJERCQZr0SIiB5wdnZ2aCmaSZuAsbnuaw1eiRARkWRMIkREJBlvZxGRGnH7lsbBhqK8auEiWfMW\nGo8Bu/g2OQ2SRK5evYoFCxbg6NGjEEKgT58++OCDD9CuXbtaj62oqEB0dDTi4+NRXFyMLl264J13\n3oG/v389RE6Nga7R+xy5XzudU+vfKQYAOGtKFrY2nFpfi5vlNzWOE7mtqFoDx9bKVm1/Z2hYvsAM\n1XsSKSsrw/jx49G8eXMsXrwYABAdHY3Q0FDs3r271tHWs2bNwuHDh/Hee+/Bzc0NW7duRXh4OHbs\n2AG5XF4fL4EaEY7eNxyn1jcuXYm1Ir8CAOBw3xK0znA2ekK+UVassYvvbUUZAMDWSv27cqOsGPbN\nzWx53B07diAnJwd79+5Fhw4dAACPPfYYnnnmGWzfvh1hYWFaj01PT8eePXuwcOFCjBw5EgAQEBCA\n4OBgrFixAqtWraqPl0BmjqP3yZyYQ1LWlZDK80sAAPb26rcone1boGVL3ct/1nsS+fnnn9G9e3dl\nAgEANzc39OzZE8nJyTqTSHJyMqysrDB06FBlmaWlJYKDg7Fu3TooFApYWVmZMnwiokanLoksOzsb\nP//8s9bj6713VkZGBjp37qxW7unpiczMTJ3HZmZmws3NDc2bN1c7VqFQICsry6ixEhGRbvV+JVJY\nWKh2/w+ouid469YtnccWFRVpPNbR0VFZNxEZT81OCgA7KtSVubyfxux80qS6+N67dw9AVe8wIqpd\nUVERysvLlY8tLKpuXlSXFRUVITs7u0Fia4zM5f2sGUdtMVT/Xlb/ft6v3pOIg4MDioqK1MqLiopg\nb2+v81h7e3vk5uaqlVdfgVRfkWhTnXFffvllfcMlIh1+//13rFtn2KR+pJ05vJ/aYsjPz8cjjzyi\nVl7vScTT0xMZGRlq5RkZGejUqVOtxyYlJaG8vFylXSQjIwNWVlZwd3fXeby3tze2bt0KZ2dnWFrq\nnpmSiIiqrkDy8/Ph7e2tcXu9J5GgoCAsWbIE2dnZcHNzA1DV+v+f//wH77zzTq3HxsTEICEhQdnF\n9969e0hISEDfvn1r7ZllY2PDQYlERAbSdAVSzXLu3Llz6y8UwMvLC//+97+RmJgIFxcX/P3335gz\nZw5atGiBzz77TJkIcnNzERgYCJlMhoCAAACAs7MzLly4gG3btsHR0RG3bt3C559/jjNnzuDzzz/n\naFkionpW71ciLVq0wKZNm7BgwQLMnDlTOe3JrFmz0KLF/wa7CCGUfzUtXLgQ0dHRWL58OYqLiyGX\nyxEbG8vR6kREDUAm7v+VJiIi0hOngiciIsmYRIiISDImESIikqxJjVgHgO+++07ScYMHD1YOZqxr\nHcuXL5d0/D//+U84O1etMfDee+9JquPNN99E+/btzaYOc4jBnOowB+bwXphDDOZShzFiMMbvnjZN\nrmFdLpdDJpOp9frSRSaT4bvvvkO3bt2MUoeUnmSaYnBycoK1tbXedVy5csXs6jCHGMylDqkzKcyf\nPx+PPvooAOmzMdSswxzeC3OIwVzqMFYMdf3d06bJXYkAQExMDLp06aLXvvfu3cOQIUOMXsc333wD\nX19fvY6/e/euxtGiq1ateiDqMIcYzKGOX3/9FV27doWtra2Oo1SdOnUKt2/fNmodQMO/F+YSg7nU\nYYwYjPG7p0mTSyKWlpZwcXHR+/ZBZWUlLC0tIZPJjFaHu7u7Qf+qsLCwgLu7u8pUL0888QRatWql\ndx2WlpZqx5hDHeYQgznVMXfu3Dr/WNS1DnN4L8whBnOpwxgxGON3T5smdzuLyFx99NFHeP3115XT\nAdVGCIGPP/4YU6dORbt27YxWB5EhmESIiEiyJnc7S5eCggIAutcjNkR5eTmuX78OV1dXlfLTp0/D\nx8dHr0vFulIoFMjPz1eLoVpeXh4OHjyIyspKDB06FA4ODsjPz0dsbCwyMzPRtm1bvPTSS7U2rhUW\nFqJZs2Yql9CnT5/GpUuX4O7uju7du2s91lgxaHL79m2MGzcOn332mV7H37p1C4cOHcJff/2FwsJC\nWFhYwNnZGf7+/ujdu7fBzw8Aly9fxpkzZwAAPj4+KktDN2a6zq1///vf6NevH+zs7BosBuDBPrek\nnlc5OTnIyMhQLsnh4OAAT09PyT0Em9yVSHJyMnr37q1ycsfHxyM6OhpXrlwBALRr1w7vvvuuylru\nUiQmJmL69Ok4f/68SrlcLoezszNGjBiBkSNHwtPTU1L927Ztw1dffYVr167Bw8MDr7/+OoKDg1X2\nSU1NxUsvvaQWAwD88ccfCA0NVa7H4urqik2bNmHixIm4ceMG3N3dcenSJSgUCsTFxWm8/15WVoYZ\nM2Zg//79AIAXXngBn376Kd59913s2bMHQgjIZDL0798fMTExajMtGyOGy5cva32PSkpKMGrUKCxd\nulTZTqDty7Z69WqsXr0a5eXlyl4sNXu0dOrUCYsWLdI6JXZUVBTCwsKUt4UqKysxZ84c7Ny5E5WV\nlQCq2rdeeuklzJ49W2vM9/vll19w+vRpAED37t0RGBiodd+CggKUl5er/CCkpaUhNjZWeQ74+vpi\n0qRJOpdeqOu5JZfL0bx5cwQFBWHkyJHo16+fcvEjfZnD+W0O55axzqtDhw5h6dKl+PPPP9V6aclk\nMnTu3BkzZsxA//79tdahkWhi5HK5SE1NVT7+6aefhJeXlxg9erRYt26dWLdunRg1apSQy+XiyJEj\ndXquvXv3Crlcrlbu5eUlgoODhVwuF3K5XDz//PNiy5Yt4ubNm3rXXR13aGioWLJkiXjxxReFXC4X\n77//vrh7965yv99//11jDEIIMXXqVDF8+HCRmZkprl27JqZOnSqeffZZMXr0aFFYWCiEEOLmzZti\n1KhRYvLkyRrrWLlypfDx8RFLly4VX331lejbt694//33Rc+ePcX3338v/vzzT7Flyxbh6+srtm3b\nZpIYvLy8lO+lpr/7t2uyceNG0bVrV/Hpp5+Kn3/+WRw5ckTExMSI3r17ix07dojU1FTx1ltviZ49\ne4qMjAyNddx/bq1du1bI5XKxYMECkZqaKlJTU8W8efNEly5dxNatWzW+FxcvXlQ+LisrExMnTlS+\nhurXMWnSJFFRUaExhgkTJoiFCxcqHx87dkx069ZNPPHEE2LKlCliypQp4vHHHxd+fn4iLS1NYx3G\nOLe8vLxEWFiY6Nmzp5DL5eLJJ58UCxcuFH/88YfG/U0Rw4NybtX1vBJCiMTERCGXy0VoaKjYuXOn\nSE1NFZcuXRKXLl0SqampYufOnSI0NFR06dJF7Nu3T/OHokWTSyJeXl4qH8iLL74oXnnlFXHv3j1l\n2d27d8XYsWPFhAkTNNYRExOj19/06dO1JpHU1FSRk5MjvvjiCzFkyBDh5eUlvL29xbRp00RycrLK\nF0WTkJAQ8f7776uUbd++Xfj4+IjJkyeL8vJyIYTuL9mTTz4pEhISlI+zsrKEl5eXSExMVNlvz549\nom/fvhrrePbZZ8WqVauUj48cOSK8vLzEmjVrVPZbsmSJeOGFF0wSg6+vr+jbt6/YtGmT+P7771X+\ntmzZIry8vMTnn3+uLNNkyJAhYtmyZWrl+/btE/7+/sr3Mzw8XEybNk1jHfefW0OGDBEff/yx2n7v\nv/++GDVqVK3HL1myRHTr1k1s2rRJXL9+XVy/fl1s2LBBdOvWTXz55ZcaY+jdu7fYv3+/8vGYMWPE\n+PHjxe3bt5VlxcXF4p///KeYOHGixjqMcW5Vv5bS0lLxww8/iLCwMNGlSxchl8vFqFGjxObNm8X1\n69c1HmusGB6Uc6uu55UQQgwfPlzMnj1b47aaZs+eLYYPH17rfjU1+STi7e2tdlIJIcSPP/4oAgIC\ntNZR81+Huv50JZGafvvtN/Hxxx+L3r17C7lcLp544gkxf/58rf9a9Pf3FykpKWrlv/zyi+jZs6cY\nN26cKCkp0fkl8/PzE7/88ovycXFxsfDy8hInTpxQ2e/o0aPCz89PYx2+vr7i2LFjyse3b98WXl5e\n4uTJkyr7HThwQAQGBpokhqysLDFx4kTx5JNPit27d6tsu3Xrlsb67uft7S2OHj2qVl59/F9//SWE\nqDovevfurbGO+z/Xrl27ioMHD6rtt3//fo2v5f7jBw4cKBYvXqy234IFC0RwcLDGGHx8fFTe+27d\nummMISkpSev7aYxzS9M5fvXqVbFmzRoxbNgw4eXlJbp16yYiIiLETz/9ZJIYHpRzq67nlRBV58Xx\n48d1ximEEMePHxc+Pj617ldTk587y9LSEg8//LBaubOzM+7cuaPxGCcnJ7z00ktIS0vT+bds2TK9\n4+jRowc+/fRTHD58GMuWLYOPjw+2bduG0aNHa9zfwsIC9+7dUysPDAzEhg0b8OeffyIsLEx5P1gT\nNzc3HD9+XPn4+PHjsLCwUCkDgJMnT2ptdLO2tkZFRYXycfVYlpprw1TvV1ZWZpIYOnTogNjYWMyc\nOROLFi1CaGgoLly4oHFfbVxcXJTtDjWlpqZCJpPBwcEBQNV5oel1aGJnZ6cytqeajY2N8l62Llev\nXkXfvn3Vyp988kmt9+ofeeQRnD17Vvm4VatWUCgUavvdvXtXaxuFMc4tTdq0aYNJkyZhz549+Oab\nb/Diiy/i119/xbRp00wSw4N6bkk5r5ycnHDu3Lla6z537pzBHYuaZO+smJgYPPTQQwAAKysrZGdn\no1evXir75OXlaZ0zxtvbG+fOnat1nXYp67hbW1tj6NChGDp0KK5fv474+HiN+z3yyCM4ceIEnnrq\nKbVtvr6+2Lx5M8LDw3XOuzN69GgsXrwYWVlZsLOzw+7duzFt2jSsXr0aQgh069YNZ8+exVdffYUp\nU6ZorKNdu3b4+++/lXFYWloiPj5ebTnN7OxsjSenMWKo9txzz6F///5YvHgxRo4ciQkTJmDcuHE6\nj6n2/PPP44svvsDdu3fRr18/WFtb4/fff0dMTAwCAgKUc5ZlZ2dr7QkEAFOmTFF2Hrh9+zYyMjLU\nGsIvX76s8R8uQFWjaWVlJYQQaN26tcZpKmQymdYEMHr0aKxevRqBgYHo0qULRo8ejZUrV6J79+7K\n9//atWv44osvtPYIMsa5VRtfX1/4+vpi1qxZOHjwoElieJDOrbqeVyEhIVi2bBlKSkowfPhwuLu7\nqx27e/durF27ttb34n5NLom4uroiMzNT+djOzg5nzpzBiBEjVPY7ePCg1l5TgYGB+Pbbb2t9rvbt\n2yvXgpfi4YcfRlhYmMZt/fr1w/bt2zFt2jSN/yrx8vLCli1bMHHiRNy6dUtjHePHj8eNGzewa9cu\nKBQKhIaGYsqUKXBwcMDChQuVVxgDBgzAxIkTNdbRv39/XLp0SaWsc+fOavvt3bsXfn5+JomhJnt7\ne3z22WcYOXIk5syZg++//16vrtRTpkxBXl4evvjiC6xcuRIAlKtuRkVFKfcrLy/X+uMxatQotTJN\n/5qOj49H165dNdYxduxYlcfnzp1Dnz59VMouXryo/OG53/jx43HmzBmMGTMGAQEB8PT0RFZWFgYP\nHqzsjZWRkQE7OzvExMRorMMY55a+rKysMHjwYJPE8KCcW8Y4ryZNmoQ7d+5gzZo1+OKLL2Btba3s\noVpcXIyKigpYWlpiwoQJmDx5cq2vqaYm18VXX3v27IGbm5vO8Q1SnThxAt26dTNofqP7lZSU4MqV\nK+jQoQNsbGy07nfjxg1kZGQY3A+9qKgIFy9ehJOTk1FmmP3jjz/g5OSk9V9KpohBoVBg48aNuHDh\nAiZNmgQPD49aj7ly5QpOnz4NS0tLeHh46OwGK1Vubi5sbW2VtzGqVf/A1OTu7o7hw4erlD3//PPw\n8PDA0qVLtT7Hnj17EBcXh9TUVOXtLJlMhg4dOmDw4MEIDw/X+lkY49zKycmBs7OzQdP7GDsGXR7E\nc0vbeVXTjRs3cOjQIWRmZirHidjb28PT0xNPPfUUWrdubfDzMokQPcDu3r2LwsJCVFZWwsHBQeO/\n6onqgkmEiIgka3JtIvr66KOPUFlZiQULFjRYHRMmTEBlZSU2bdokOYYhQ4agsrISSUlJjboOc4ih\nvuq4fv06Dh06pHFqiqeeesqgW4KmZA7vpznEYC516Do+PT0dHh4eKleiJ0+exPLly5VTp/j6+uKt\nt95Cz549DXpeJhEtvv/+ewgh6pRE6lpHdna2Xl1BdWnXrt0DUYc5xGDqOiorKxEdHY2NGzdCoVCg\nRYsWsLe3B1A171JpaSmsrKwQGhqKGTNm1GnutW7dukEIoVe3T0NfR33WYQ4xmEsduo4fNWoUduzY\noZye5dSpU5gwYQJcXFzw/PPPAwAOHDiA0NBQrVPAaMPbWURm4ssvv8SXX36JyZMnY8SIEWrTuefk\n5OD//u//sHr1akyZMsXgrpg1ffDBBxBCqPQOogeXXC5XWQgvNDQURUVF2Lp1q7KDT0lJCcaOHQt3\nd3d88cUXetfNKxEiM/Htt9/i7bff1tqtu3379oiIiEDLli2xefPmOiWRulxhU+OXmpqKefPmqfQQ\nbdWqFSZOnIhFixYZVFeTTyKlpaXKfub29vZqI60bo7pOaX/48GHlrKg+Pj544oknaj3m4sWLqKys\nRMeOHZVl+/fvR2ZmJtq0aYOnn3661vf22rVr+Ouvv1BUVASZTAZnZ2d4e3vr7OJpCG1T89eH8vJy\nLFu2DOPGjdM602tBQYHWfv41de3aVfkZm5oxPhNjTz1uDPfu3UNBQQEefvhhNGsm7Wewvr9nxviO\nVbt3757G70H79u1RUlJiwKtookkkLy8PX331FZKTk5XTv1dr164dBg0ahFdffRVt2rTRWkdpaSkS\nEhKQl5cHT09PDBo0SG0U8eXLl7Fq1SqNtwySkpKwc+dONGvWDOPGjUNgYCAOHjyIRYsWISsrCx06\ndMAbb7yhdTp6Y0xpHx0dDUtLS7zxxhsAqtYEefXVV5GWlqYyXXVgYCC+/PJLjSfojRs3EBERgdTU\nVABVgw9XrFiBadOmqYxEdnV1xfbt2+Hi4qJWx8mTJ7FkyRJlA19NNjY2GDFiBN5++21l+4BUBw4c\n0Dg1f7Vz585h8+bNyMvLQ6dOnRAaGqr2g3/+/HlERkYiOTnZoOeuqKjA5s2b8fTTT2tNIp06dcKe\nPXtqHfPw448/qvyQ1GSstWqM8ZnUdepxY3wemzZtUn7PwsLCMHz4cHzzzTdYtGgR7ty5g5YtWyIi\nIgLh4eEajzeH75kxvmMAsGPHDvz8888AAFtbW1y7dk1tn4KCAoPXgGlybSJ//vknxo8fDyEEBg4c\nCE9PT+XgnKKiImRmZirXxvj666/x2GOPqdVx48YNhISEqMxf1LlzZyxbtkxltLa2tQ4OHjyIyZMn\no23btrCzs8PFixcRHR2Nt99+G927d4e3tzd+/fVXnD17Ftu2bdM40rtLly4qDWVJSUmIjIyEt7c3\nnn32WQBVCwOdP38esbGxaqOeAWDQoEGYNm2aclT9zJkzceDAAcyZM0c5Z9OhQ4fw6aefYsSIEfjw\nww/V6pg3bx727NmD6dOnw87ODqtWrUKHDh1w9uxZLF68GL6+vvj999/x/vvvIygoCJ9++qnK8Skp\nKXj99dfRsWNHPPnkk7C2tsZ//vMf5ZxKFhYW+O6772BtbY1t27bVKZFoW98FqOq9EhISgubNm+PR\nRx/FX3/9BQCYPXu2yohhXetXDBgwQOtzCyGQl5eH1q1bw9raGjKZTPmFrpaUlIQ33ngD/v7+GD58\nODp37qzSsP7XX38hPj4eJ0+exIoVKzSO9DbGWjXG+Ez27duHN998E4GBgRg+fDg8PT2V0wgVFhYi\nIyMDu3fvxokTJ7B8+XI8/fTTKscb4/PYvXs33nvvPfj5+cHR0REpKSn48MMPMW/ePIwYMQLe3t44\nfvw4fvrpJ6xZs0bjFCvm8D2r63cMqDov7jdixAi1W1dz5szBX3/9hW3btqntr5VB0zU+AMLCwsS4\nceNEcXGx1n2Ki4vFuHHjtE4FP2fOHNGvXz9x8uRJUVZWJg4ePCieeeYZ0bNnT5VZQ7XNMDpu3Dgx\nefJk5XTvMTExomfPnmL69OnKfSorK8WECRPElClTNMZgjCntvb29VWZ8DQgIEN98843aflu2bNE6\nVXZQUJDKOiGpqanCy8tL7NixQ2W/rVu3ikGDBqkdP2bMGPHGG2+olX/55ZdiyJAhQgghSkpKxDPP\nPCM+++wzjTHUdWp+IYR4/fXXRUhIiPK8KCwsFG+//baQy+Uq09rXNnNt9Xoq9/+9/fbbwsvLS4SH\nhyvLNDl06JAYOXKkxnUsvLy8xIgRI8SBAwc0HlsdQ13XqjHGZ1LXqceN8Xm88MILKu/z9u3bhbe3\nt/jkk09U9ps+fbrW74g5fM/q+h0zRGxsrMpSAvpocknEz89PHD58uNb9Dh06pHVa5cGDB4tvv/1W\npaykpERMmjRJ+Pr6iuTkZCGE9hM8MDBQuY8QQuTn5wsvLy+1H4c9e/aIgQMHaozBGFPa37/egre3\nt0oSrJaSkqJ1emhfX1+VqbC1TQX/yy+/CF9fX43HHzp0SK38xo0bwsvLS1y4cEEIIcQ333wj+vfv\nrzGGuk7NL0TVe6FpSvLVq1cLLy8v5ZTsun60fvzxR/Hkk0+K8PBwkZWVpbKtqKhIr2nDq125ckUc\nOnRIxMfHi/j4eHHw4EGRm5tb63HGWKvGGJ9JXaceN8bn4e/vrzJl+s2bN4WXl5fa93/fvn3iqaee\n0liHOXzP6vodM7UmNxV88+bN9Zo0rri4WOu8P9euXcOjjz6qUmZra4tVq1Zh8ODBeOONN7TOvgtU\ntbNa3/YAACAASURBVKfUXIu8ekbh+xvonJ2d9W5AlTKlfb9+/fD1118r+5YHBARg3759avvt27dP\nbVbemvXXnBq7+v///vtvlf0uXLigcdLAli1b4vr162rl+fn5kMlkypmQH3nkEY37AcaZmv/27dsa\n7wVPnjwZc+bMwfr16zFnzhyd/fiDg4ORkJCAdu3aYfjw4Vi5cqVykj9D2yjatm2Lfv364R//+Af+\n8Y9/4KmnnlIuj6oPV1dXREREIDExEXFxcRg1ahSOHz+OqVOnol+/fliwYIHWMSLG+kzqMvW4MT6P\nu3fvqnyHq+ur/r5Vc3R0xM2bN2uNFWiY71ldv2O6lJSUIDU1FampqSgtLTXo2GpNrmF90KBBWLx4\nMZydnREQEKBxn1OnTmHJkiUa7zkDVV+Qixcvwt/fX6Xc0tISn3/+OVq2bImZM2cqB/Hc7+GHH8bV\nq1eVjy0sLDBhwgS1L1N+fr7ORq66Tmk/ffp0jBkzBi+99BJeeeUVhISE4MMPP8TVq1eV93YPHz6s\nbPDXZMCAAVi+fDksLCzQqlUrrFq1Ck8//TRiYmLg7u6unG77yy+/1HjPecCAAVi2bBnc3NyU7+eF\nCxfw4Ycfws3NTTll9c2bN7WO1DbG1Pyurq5IS0vTuH752LFj0aJFC3z44Ycqa3VoYmdnh3nz5iln\neo2Pj8fs2bOV99QbQo8ePdCjRw989NFHSE5Oxg8//IBt27bh66+/1tq2U9fPpK5Tjxvj83B2dkZO\nTo7ysaWlJT7++GO1XklXr17VOWlhQ3/P6vodA6rabSoqKpTtMpWVlVi8eDG2bt2Ku3fvAqj6B/Zr\nr72GqVOnan0vNGlySWTmzJmYPHkyxo8fDxcXF3Tu3FmlYT0jIwN5eXno3r07Zs6cqbEOPz8/JCQk\n4IUXXlDbJpPJlP2vN27cqPFfoHK5HCdOnFDOziqTyTQ+12+//aZxWnXAOFPat2nTBnFxcZg9ezbe\nffddyGQyCCGQnJys7O3i5OSEqKgotZlkq0VGRuLs2bP4+OOPAVT9K2vx4sX46KOPEBoaqnz9zs7O\nGhcfeu+99zB+/Hi88sorsLGxQbNmzVBSUgJHR0eVWW3T0tI0NloCxpma//HHH8euXbu0Tgk+cuRI\n2NraYsaMGbU+DwD06tULP/zwA9auXYuIiAg88cQTde4xVU3q9Bj6rlVjjM+krlOPG+Pz6Nq1K44d\nO6aysNvLL7+stt/Ro0e1dq02h+9ZXb9jALB69Wq8+OKLyserVq3C119/jTFjxqg07q9atQoODg56\nr5UCNMHeWdWSkpLw888/IyMjQ7k6WnX/9aCgIAwaNEjrl/7YsWPYvn075s6dq3ZpXNPatWtx+PBh\nfP311yrlubm5uHPnTq09Z1auXImuXbsiKCjIwFf3P/pOaX/58mX8+uuvuHbtGoQQcHR0hKenJ/z8\n/PRaXOvixYtQKBQqSe/AgQP4888/4ezsjKefflrlFl5NFRUVSEhIwOnTp2FhYQEPDw8899xzBnc1\nrIuLFy/iyJEjCA4O1vovSqCq6+vx48cRGRmpd92XLl3CZ599hszMTPzrX/+q81VJaGgoKisr1c4r\nQH1kslTG+kykTj1ujM/j9u3bUCgUOo8Hqrq+PvbYY+jRo4cBr0xVfXzP6vId69GjB1atWqUcizJg\nwACMGjUKb775psp+n3/+OZKSkrB37159XjaAJpxEiB5Exlirhh48AQEBWLp0qfJ2V7du3bBx40a1\nW/rHjh3DpEmTNI4P0qbJ3c6qD9evX4eDg4PkkbB1VVFRge3bt+OZZ57ROWDS2BQKBbKyspRXdo6O\njnB3d1cu69lYNObXYejiTPXtwoULSE9PB1A1SlvbwMuaKisrkZWVpRw17+LigrZt25o6VKMrLy+H\nEEJltP8ff/yhHHF+fzuLMXXv3h179+5VJpGOHTsiLS1NLYmkpaVxjXUpbty4gS1btuD06dMAqto8\nxo0bp/MyePv27fjhhx8ghEBYWBiGDh2KH3/8EfPnz0dhYSGaN2+OsWPH4r333pN8L/zQoUP45JNP\nDB4dXV5ejqioKHTt2lVnEunevTsGDRqEkSNHom/fvlrX7a5Neno6VqxYgZSUFOUqetWsrKzQt29f\nvPHGGxoHPAF1H71vLq/DGK9FEynT0AB1m7IkLy8P33zzjXJGhtGjR6vdysrMzMQnn3yCzZs3qx3/\n9ddf4969e8p5wMrLy/HOO+8gKSlJZZT2qFGjMG/ePI23ci5cuICYmBgcOHAAZWVlKtvatWuHsWPH\nYsKECTr/sVbX12GMKdRLS0vx4YcfYt++faisrMTYsWPx8ccfY+7cudi+fbvyvfD29sb69esl38bV\nNZg2MjIS48aNg729PSb8f3tnHlfT9v//16lkSIPIdA3Xh3TSaSAKIboyRJR5qAwlJPOUqajulamL\nEKlMySwyk8iQBjOJiFDGaJDSuH5/9D3712nvczp1dteh/Xw8ejza+6z1PmvtaZ291vv9ek+ahAUL\nFlDrM8J1rRs3bmDr1q1itdvEUeOms0xMTLBr1y7o6ekBKE1ZOWbMGKSnp1Nuu69evULTpk1x+PBh\nxlH52LFjWLZsGYyMjFC/fn3ExMRg1apV8PDwwIABA2BgYIAHDx7g7Nmz8PDwwJgxY6rUVkkXBdMC\noZDi4mLcv38f7du3h6qqKng8HkJCQmjl+Hw+lJSUUFxcjIYNG2LIkCGwsbFhjNIXx+3bt+Ho6Ihm\nzZph0KBBjJHJ586dQ1paGoKCgmgebWxE78tDP9joCxsyNIDskiWpqakYPnw4srOzoampiS9fvqBh\nw4ZYv369yAAmKVp8wIABcHR0xMiRIwEA3t7eOHr0KGbMmEFbyJ0yZQptTePx48dwcHBA3bp1YWxs\nDGVlZTx8+BBpaWmYOHEicnJycP78ebRv3x6BgYGMGRvZ6Ef5iPXbt29j4sSJaNy4MSXXcvXqVaSn\np4uVUN+4cSN27doFBwcHqKqqYu/evbCwsMCZM2fg5uYGfX19PHjwAGvXrsWYMWOwcOFCxvNSEZKe\nF8J2Ll26FBkZGVBXV0deXh7lgg6UqioIB/VKzaL855EpP5nywUPz5s0j3bp1IwkJCdS+hw8fElNT\nU7ERt7a2tiKfHTp0iAgEAlr07qpVq4iNjQ2tflxcnFR/fn5+EqOjzczMiJ2dHe1v7NixVHSzcJ84\nG7du3SJhYWFkwoQJRFdXl/D5fGJra0v27dtHvn79Kv5A/h+jR48mrq6uEgPYioqKyMyZM8moUaNo\nn7EVvf+z+8FGXywsLEhYWBi1vWjRImJiYkLOnDlDsrKySFZWFjl16hTp0qWL2Ejx69evEz09PWJt\nbU18fHyIr68vsbe3Jx06dCD+/v5kx44dxNLSkgwaNIhkZWUx2pg/fz4ZOHAgSUtLI4QQ8uLFCzJ+\n/Hiip6dHwsPDqXKSAv0MDAxEgg27detGgoODaeV27NjBGFBrb29P7OzsSG5uLrWvpKSErFq1igwb\nNowQQsiHDx9Ijx49yKZNm6qtH+WfFw4ODmTo0KEkJyeH2vft2zcyePBg4uLiwmijX79+JDAwkNqO\njo4mfD6fdjx27txJ+vfvT6sfFhYm1Z+np6fYfgjJyckhR44cIUuWLCGOjo5k8uTJZN68eWTbtm0k\nKSlJYl1x1PhBxMTEhOzZs4dWLigoiPTu3ZvRRseOHUl0dDS1nZ2dTT3IynLjxg3SqVMnxjaUl7Rg\n+pMUYb1jxw5iZGRE3N3daQ8DaaOjyx+L9+/fk+3bt5OBAwdSUc4zZswgly5dIoWFhYw2DAwMaP1m\nIjo6mjGatjqi939GP9joCxsyNGxIlvTu3ZucPn1aZF9RURFZsWIF0dXVJfv37yeESH74mpqakoiI\nCGpbT0+P8XqMjo4mAoGAtt/IyIhcuXKFtv/jx4+Ez+dTigB79+4llpaW1daP8teWoaGhyAAk5Pjx\n48TU1JTRRvkBVVLEuaGhIWMbZFVkqE5q/JrIt2/fGH3EO3TogM+fPzPWqVOnjkh0p/D//Px8kXI/\nfvxgfM1WUVGBmZkZxo4dK7Ft8fHx8Pf3Z/zM2dkZAwcOxMqVKzFgwAAsWrSIioGo6hpM06ZNMXXq\nVEydOhUPHz5EWFgYzp49i4iICDRo0AC3bt2i1VFVVUVqamqFtlNTUxnneqsjev9n9IONvqirq4vs\nz8vLowXpAcCff/5JucuW59mzZ4yxAqNHj8bGjRvx6tUrtGnTBo6Ojti6dSujqGZGRgZNCVZRURGe\nnp5QU1ODl5cXcnJyGAMBhZiamuLYsWP466+/AJR6A8XGxtIWcmNiYhglyZWUlGjrIMD/X5wWrllp\na2uLBO6y3Y/yVEVCXVNTU0QtXPh/+Xa/f/+eMWRAXV0dFhYWFeaPuXbtGv7+++8K+8A2NXIQefTo\nEb5//w6g9AQznfycnByxc866urrYs2cPunfvjjp16iAgIABNmjRBSEgIzMzMoKSkhKKiIoSGhjLG\ngnTo0AE5OTkVLpBWJM/SsmVLBAUF4dSpU/Dx8cHRo0exatUqsVLQlcHAwAAGBgZYunQprly5ghMn\nTjCWs7a2xtq1a6GkpISBAwfSBs38/HycO3cO69evZ4zgZyt6/2f3g42+COUx+vXrBwUFBUoeo/xD\nTpIMDRuSJU2aNEFSUhKjosOCBQugoqICX19fsdHRADBr1iyMGjUKs2bNwsSJEzF79mzMnTsX2dnZ\nIgu5Bw8exIIFC2j1u3Xrhs2bN0MgEFAZHrOysuDt7Y1GjRqhTZs2AErvU3FrO2z0A5BdQt3ExAR+\nfn7Q0tJC/fr1sW7dOhgbG2PLli0wNDREy5Yt8fbtW2zfvp1xzU8gEODt27eMPyjKUlnJE7aokYOI\nt7c3AFCLlXFxcTQZ78TERLHJi1xcXDB58mR06dIFtWrVAiEEe/fuxaxZs2BlZQUdHR08e/YMb9++\nRUBAAK2+QCDA8ePHK2xn3bp1pdJLsra2hrm5OdasWQMbGxuMGDGCtejoWrVqoV+/fujXrx/j53Pn\nzsWnT5/g5uaGFStWoEWLFiIKAKmpqSgsLISVlRXmzp1Lq89G9L489IONvrAlQyOrZEnnzp1x6tQp\nsc4b06dPh4qKisTUum3btsXu3bvh5uZG2SGEYN++fVSQZK1atTBt2jRGb6BFixZh7NixGDBgAFq3\nbo1atWrh9evXKC4uxoYNG6jrOz4+Xmw+cDb6AZQ60pTl6tWrNO+62NhYamArz5w5czBx4kQqZ0nr\n1q0RGhqK2bNno1+/flBTU0N2djZUVFQYgyb19PQYHWPKo6mpyejwUd3UOO+suLg42j5VVVXo6uqK\n7Fu4cCG0tbXh7OzMaOfZs2c4c+YMCgsLMWzYMGhra+P169fYsGEDnj9/jkaNGsHOzg79+/en1f3+\n/TsyMzOrJbPb7du34eHhgeTkZOzbt0+sPhhQGhE/cuRIVmJJnj59isuXLzNGJltYWNCOrxA2ovfl\noR8AO31JS0uDu7s7bt68ScljlKVRo0ZYsGCBWPmWjIwMODg44MWLF4ySJcJYBF9fX6SnpzOmyX30\n6BHOnj2LKVOmiI0oB0qjtG/cuCHxIUwIQUxMDO7evUuL0u7Vq5dEN/rMzEyEhoaKRM2PGTNGJLak\nqKhI5A2ruvpREcHBwWjTpg369OnD+HleXh7u3r2LwsJCdO/eHcrKyigoKMCRI0eoiHNbW9ufmu2x\nqtS4QYSD41dAFnkMeZCR4ag5cIMIBweH1BQVFSEqKgrGxsYValJVlw022vA7kJOTQ4lDtm/fXur8\n6mxT4/KJ/ErEx8fDwcFBJhvXrl2jPGSqy0ZeXh6OHz8Of39/Kiq3PG/fvsWSJUsqrH/p0qVK12fL\nxqNHj+Dt7Q0fHx/q5kxISMCUKVPQv39/TJkyBXfv3hVbny0b5Xn58iXOnj2Ls2fPiqRk/hnk5eXB\n1dUVL168+Gk2pK2fk5NDmw589eoVFi9eTKkZL1myBCkpKXJt4+zZsyIOISUlJfDx8UG3bt0wZswY\njBkzBt26dcPWrVvFtkFaTpw4gRs3blSqDvcmIsdUFIEqDzZkzTcva322bNy/fx92dnbg8XhUDvSA\ngABMnToVmpqa0NHRwePHj5Geno5jx44xLozLaoMNqRBAdumVRYsWMe4HSt8Czp49CzMzMzRs2BA8\nHo9xkV9WG2y0oXy0eVJSEuVWL1wbunPnDpSUlHDo0CFaojl5sTFkyBCMGjWKkmffsmUL/P39aTLu\nx44dw5IlSyol414ePp8PHo+Hdu3aYfr06bCysqqwTo30zvrZvHv3TqpyX79+FftZfHy8VDaeP39e\nrTY2b96M/Px8hISEQF9fH7Gxsfjnn38wZswYbNu2rUIffFnrs2Vj27ZtEAgECAoKQt26deHp6YlZ\ns2ZBIBAgICAAtWrVQl5eHhwcHLBjxw6sX7+edRv79++nPHgAYN26dbh+/TrmzZtHkwpp3rw5oydP\nVFQUXF1dKekVJycnEekVc3Nz3LlzB/Pnz0ezZs0YXUrDw8OhqqrKuIZCCAGPx8OzZ8+ogZIJWW2w\n0Ybyv4///fdfaGhoYP/+/ZSA47t372BnZ4etW7di3bp1cmnj7du3aNu2LbV99OhRODs7i8i49+3b\nF2pqaggJCZFpENm7dy/y8vJw584dhISESDWI1LiIdXmAjYh1ebEha755WeuzZcPMzIycP3+e2k5L\nSyM6Ojq0PN9hYWFiI6RltSGrVAgh7MjIrFixghgZGZEdO3bQZGCkVUOQ1QYbbSgfbW5sbMyoABAa\nGkrMzMzk1kb5XPEdOnSoVPR/dcO9ifwE6tSpg86dOzO6/5bl8ePHOHz4MONnbES9s2FDUr75RYsW\nYdasWVi9erXYQClZ67NlIzs7WyRuQhiwWV5y/I8//sDHjx+rxUbdunXx7ds3EXtMMRD6+vrw8/Nj\nbMPz58/xzz//UFNdY8aMwZYtW0Rcgnk8HkaMGMH4NgUAnp6eGDp0KFauXImTJ09i5cqVlKu4tPFH\nstpgow3lycvLw//+9z/a/v/973+U7L882qhOGXc24AYRMcTHx6Nhw4aMJ1tWG3w+H4qKipTCqTjU\n1NTEDiJsRL2zYUPWfPOy1mfLhoaGhkgksqKiIvr160fz/snIyBDrBSOrDVmlQgD2ZGSMjY0RFhaG\nwMBAyilg8eLFlcqpIqsNNtoQGRmJpKQkAKXyIRkZGbQyWVlZEpN4/Wwb1SnjHhMTQ6XAMDQ0rJQE\njBBuEBGDvb09eDweevXqBRcXlwrTXlbGhp6eHi5cuCCVDSLG74GNqHc2bMiab17W+mzZaN++Pe7e\nvUvNAfN4PGzevJlW7vHjx2Ijk2W1IatUCMCujIySkhKmTZsmotHm5ORUqTcBWW3IWn/79u0i2zdu\n3EDfvn1F9t2/f1/iW+rPtmFkZIQtW7Zg6dKl2LNnD9TV1ZGfnw8fHx+qDPk/GfcZM2Ywfr+rqysW\nLlxIyeXk5+fDxcUF0dHRIk4bvXr1wpYtWyqXgO0/n0D7RTh+/DgJCQkh8+bNI+bm5qza+PDhg8jc\nd1XIyckhqampP91GdHQ0mTVrVoVy6zt27GCUpJe1Pls2Hj9+LKLMLI4VK1aQo0ePVpuNhw8fEisr\nK9p6lPBPX1+f+Pn5ibU9ffp0smzZsgrb4OXlRSZMmFBhubKcOHGCdOvWTar1iOqyUZn6qamptL9P\nnz7Ryvn4+Ig9H/JigxDZZNzLr8usW7eO6OnpkT179pAvX76QL1++kF27dhE9PT3i7+8v0VZ5OBdf\nDg45g8ggFcKG9IokcnNzkZGRAS0tLSgrK1eqLls22GhDTYPP5+Pw4cOUm7GFhQUGDhxIS4C1evVq\n3Lx5E6dPn5baNjedxcEhZ/B4PHTr1k3qNLhlEbdWUh4m92BpqFevHurVq1elumzZYKMNNZ0PHz5Q\nbuNlMTMzo1L2SgsXsV6G79+/w9bWFgkJCVWq//btW7mJLObg4OAoS0lJCUpKSlBcXAxNTU3G9VYe\njwcFhcoNCzXuTUTSwz0nJweJiYlISUmhchSUVQwVsnr1akycOJFabC4pKYGHhweOHTtGyW0oKChg\nzJgxcHd3r4ZecPyuPHr0CCdPnoSSkhJGjhyJtm3bIiEhARs3bsSbN2/QqlUrTJ8+HZ06dfrZTeX4\nxSjvyv/kyRPKYUNISkpKpfOS1Lg1EWFYvzjI/0XDCmGSyCgvY7Bz5074+vrCwcEBgwYNAlAacRsa\nGorly5dj3LhxLPeC43eEDekVDg4mtmzZQtvXqlUrKveNkGHDhqFNmzbYsGGD1LZr3CBiaGgINTU1\nTJkyhebimJubCy8vL0yZMoWK7bC1taXZKL9I1b9/f5iamsLT01Ok3JIlS/Ds2TOp3Gg5OJydnZGd\nnS0imxIREQFtbW2abErr1q3FBgtycPyX1Lg1kdOnT6N9+/YICAiAkpISbG1tqT/hqNyrVy9qnzSk\npqbSfL4BoF+/fnj16hWr7ef4fXny5AkmTZoEFRUVKCgowNnZGenp6Rg/fjzlt1+3bl2MHz+eChDj\n4PjZ1LhBRJiXfPHixVizZg0mTJiAly9fymRTVVWVlpMbKJU3YZIkl5b4+HiZ21YVaWd5tCEPbahu\nG2xIr/yXyMPxlIc2yIsNNtpQJSoVVfKbkZWVRZYtW0b09fWJr68v+fTpk9TCbt27dyfm5ubE3Nyc\nCAQCEhISQit36NAhsUJ50iAMNHN2dib379+XycbgwYPJmTNnflkb8tCG6rbRs2dPke2SkhIyc+ZM\n8vbtW5H6Fy5cIKamplX6biF+fn7k0KFD5MePH1W2IQ/HUx7aIC822GhDhw4diK6ubqXq1Lg1ESaE\necmzs7ORnp6OvXv3SsxNzpTYSFdXl5ZAyt7eHurq6oyLWtIQFhaG3Nxc3L17F3fu3MHVq1crbSMu\nLo6Sdr59+zZCQ0N/SRvy0IbqtuHk5IQ///wTy5cvl1jf19cX8fHxOHDgQKW/WwifzwcAaGpqYuLE\niXB2dq60DXk4nvLQBnmxwUYbli5dCkJIpfLNc4PI/1FYWIjdu3fj5cuXcHZ2FquPVBnevXsHFRUV\nqKurs9BCjt+dhIQEZGdnVxhk6O7uDkNDQwwfPrzK35WWlkY9cOLj47lFeo4qww0iHBwcHBxVpsYF\nG/5KvHv3DnFxcSK5IITs3LkTf/31l0xS9UIKCgooLSKmaFVhEKa4Kb6nT5/i3LlzKCkpwYgRI9C6\ndWu8ePECmzZtQnJyMpo0aQIHBwf06dNHbBuePHkCRUVF6OjoACgN4Dx//jwVYGdpaSlRWZSNNogj\nJycHFhYW2L59u1RBfsnJyYiIiEBSUhKysrKgoKAALS0tKoeMvEt2pKenIz8/H3/88Qe1LyEhAUFB\nQVTclIGBAZydnUUy7jFR1Wvrd7u+xVGZa0vW6yovLw93795FSUkJunXrBiUlJfz48QNHjhxBcnIy\nmjZtChsbG5ojR0VwbyJi6NevH0pKShAREfHTbEjKbS4MmhQIBLC1tcWgQYMqPW1GCMH69esREhKC\ngoICqKmpwdHREU5OTiI3m6Tc5Ldv38bkyZNRUlKCWrVqoVatWggODsaUKVOgoqICPp+PxMREvHv3\nDsHBwbSpmszMTEyePJmy3b17d/j5+WHq1Kki6Xs7dOiAkJAQxhtF1jYAwK1bt8Qep7y8PLi4uMDN\nzY0a5JhsFBUVYdWqVSLKBUCpnLmqqioyMjKgqamJf/75B7179xb7fWxTUFCAU6dOUW7BRkZGGDx4\nsNhBefLkydDR0cHixYsBlOaccHJygpqaGpVO9969e/jx4wf279+PDh060GzIem39Ltc3IPu1xcZ1\nlZaWhkmTJuHt27cghKBDhw4ICgqCk5MTEhISoKamhuzsbKirq4vNFS+WKi3h1wAcHBzEyob/VzbO\nnz8vMbXtwoULiaWlJdHR0SH6+vpk5syZ5MqVK7R0ouI4cuQI4fP5ZMmSJSQ0NJTMmTOH8Pl8MmnS\nJJKTk0OVk5RWdtKkScTOzo7k5OSQoqIi4u7uTnr06EEmT55MCgoKCCGE5Ofnk4kTJxJ7e3tafR8f\nH9KlSxdy8OBBcu7cOTJo0CDi5OREzMzMSGxsLPn+/Tu5cuUKMTExITt27KiWNgiPp1B6vfyftKmC\nN27cSAwNDUlQUBBJSkoiKSkpJCwsjPTq1YsEBQWR9PR0SoL73r174k+MDNjY2IjIgmdmZhJra2ui\no6NDjIyMiJGREdHR0SG2trbk27dvjDZMTExIZGQktT1y5Eji4OBAvn//Tu379u0bGTduHJk8eTKj\nDVmvrd/l+hb2RZZri43ratGiRaRPnz7k2rVr5N69e2TcuHFk9OjRpF+/fiQlJYUQQsjLly9Jv379\nyLx586Q6vkK4QeQnYG9vL9Xf4MGDJQ4iwvwA8fHxZNmyZaRz586Ez+eT7t27k9WrV5PExESJ7bC1\ntSU+Pj4i+65du0ZMTEzI8OHDSUZGBiFE8k3WtWtXKoc5IaW5UnR0dMjVq1dFykVERJBu3brR6lta\nWpLdu3dT2/fu3SM6Ojo0l+mtW7eSoUOHVksbCCnNj96jRw9y7NgxEhsbK/IXGRlJdHR0yO7du6l9\nTPTu3ZsEBgbS9t+6dYsYGhqS3NxcQggh8+bNE/vwlZXyeSPc3d1Jx44dSUREBLXv4sWLxMjIiKxd\nu5bRhr6+PomPj6e29fT0RHJ8C4mIiCBGRkaMNmS9tn6X65sQ2a8tNq4rc3NzEhYWRm0nJSURHR0d\nEh4eLlLu+PHjpHfv3ow2xFHjgg3lgfj4eKSmpqKoqEjiX3FxsVT2OnfuDG9vb9y8eRPr169Hhw4d\nsG/fPtja2mLo0KHYvXs3Y72UlBSYm5uL7OvZsyf279+Pjx8/ws7OTiTdKxMFBQUiqV6F003lgy/r\n1KmD/Px8Wv0PHz6ITIcIXU91dXVFyhkaGiItLa1a2gAA586dQ58+feDl5YW7d++iY8eOMDExsq5i\nlwAAIABJREFUgYmJCZV2t0OHDtQ+JtLT0xmndvT09PDjxw+kpqYCKJXJefDgAaMNtrl8+TKcnZ2p\nlLsAYGlpicmTJ+PSpUuMdVq3bo3Hjx9T2/Xr10dhYSGtXFFRkVjFVzauLSG/8vUNyH5tsXFdZWRk\niKQJEK53lc9Y2rx5c8bUvZKosYNISkoKLRo8MjISO3fuRHh4OPLy8qSyk5aWhqioKISHhyM8PBxR\nUVFiH3ZCWrVqBRMTE4SGhkr8mzVrVqX6pKysjEGDBmHnzp2IiorCggULUFJSgjVr1jCWr1OnDr5/\n/07b365dO4SGhiI3Nxfjx4+nLlImWrduLfIwunDhApSVlXHlyhWRcleuXGFM/Vm3bl2RNgjn6cvf\npIQQFBUVVUsbgFLVAU9PTwQGBuLMmTOwtrZGTEwMY1lxtGjRgjFi+Pr161BQUKBS1KqpqUn9A0FW\nvn79yrhga2xsjPfv3zPWGT58OLZv306tEQwfPhxbtmwRycn+6dMnbN26VeyAysa1VZ5f8foGZL+2\n2LiumjZtKpLi4tGjRwAg8mNBuN2kSROp2wbUQO+sr1+/wsXFhRqxzc3NsXnzZsycORNRUVFUuebN\nm+PgwYOU9ER5rl27hg0bNiApKYmmy8/j8aCtrY358+fTfgkBpbnNy588Jng8ntgc6xXRqFEjODo6\nwtHRkXHBEADatm2L6OhokV+pQlq2bInQ0FBMnDgRS5cuFfs99vb2WLJkCZ48eQI1NTVER0dj1apV\n8PT0RFZWFvT09PD48WOEh4czBtG1bNkSSUlJ1IKgoqIi7t69K/LrDwBev34t9uKWtQ1lMTY2RlhY\nGHbu3ImpU6eib9++cHFxkVinbDu8vLyQkZGBnj17olatWnjw4AFCQkLQt29fNGjQAADw6tUrtGjR\nQiqbTJw4cQKNGjViTCoEAB8/fqRSHmhqauLHjx+0MgUFBWKzAjo4OODRo0cYOXIkunTpgnbt2uHN\nmzfo27cv5Y314sULqKqqws/Pj9EGG9eWJH6V67ssVb222LiuBg0ahM2bNyMzMxOqqqrYu3cvxo0b\nh61bt0JNTY3qx/bt2zFixIgK2yRCpSa/fgM8PT2JqakpOXDgADl9+jSxsrIiU6dOJWZmZuTmzZvk\n27dv5Pr168TMzIysWLGC0caFCxcIn88nEyZMIMeOHSMPHjwgr1+/Jq9fvyYPHjwgx44dIxMmTCC6\nurrk4sWLtPqRkZFk6dKlFbY1LS2NHD9+nPGz8nPfVSEgIIAYGxuTrKwssWW+fPlCbGxsxM4ZE0LI\nwYMHyejRo8mwYcOoHNGXLl0i3bt3pxZFvby8SHFxMa3url27yKZNmyps6+jRoyUeM1naII6UlBQy\nadIkYmRkRPh8vlQ5wbdv3046duxILZzq6uqS5cuXiyxinzlzhpw9e1bqdpRHkrwF02ItU072nTt3\nEisrK4nfc/r0aTJ+/HgiEAhEFoItLS3JmjVrSHp6uti6sl5bv8v1LY7KXluyXld5eXlk3rx5pEOH\nDkRHR4csXryYFBcXk/Xr14ss/A8dOpRkZmZK3Q9CaqDsyV9//QUnJycqQcvDhw8xatQoeHp6YtSo\nUVS50NBQBAcHM7rnDh06FEZGRli1apXE7/Lw8MD9+/dx8uRJdjvBEiUlJfjx4wfq1KkjMZtZfn4+\n0tPTReIGpCU9PR1qamoy58LOycmBsrJylezI2oazZ8/i5cuXGDZsmFTpZwsKCpCUlARFRUW0bt2a\n9bgQSfIWYWFhtPJaWlq0txZHR0e0a9eOUcKnPEVFRcjMzERJSQnU1dUZxUbL819cW/LQhv/y2mLj\nuvrx4weKi4uhoqJC7Xvx4gWSkpLQqFEjdO7cudKZDWvcIGJoaIjAwEAqsCg3NxedOnVCSEgItcgF\nALGxsXB2dmZcqDIwMEBgYKDY+WAhcXFxcHJy4mS7OTg4fltq3MK6lpaWyIK68P/yeT9evnwpNk1k\no0aN8OTJkwq/68mTJ9SiFwcHB4e8k5+fj3fv3lWqTo1bWO/duzc2bdoEBQUF1K9fH9u2bYOlpSX8\n/PzQqlUraoHJ398fvXr1YrQxevRo+Pr6IicnB0OGDKF5Zbx9+xbh4eEICAjA9OnTq9xWNqLm9fT0\nQAiRatCTZxvy0IafZSMmJoZ6mzU0NISpqWmFdRITE3H58mW8ePECWVlZAAB1dXW0a9cOFhYWjC6j\nlUUejqc8tEFebLDRhqtXr4pVyRBHjRtEXF1d8fjxY6xYsQIA0KVLF6xduxbLly/HhAkTqPzqWlpa\nmDlzJqMNZ2dn5ObmYseOHdi6dSuUlZWpVLvfvn1DQUEBFBUVMWnSJEydOrXKbW3WrJlMSa2A0vUb\nWWcs5cGGPLShum24urpi4cKFaN26NYDSX4UuLi6Ijo6myvN4PPTq1QtbtmxhlC3Jz8/HkiVLcO7c\nOSgpKaFVq1aUXMjz588RGRmJrVu3YsCAAfDx8ZFqfaOy/fgvbchDG+TFBhttqAo1bk1ESEpKCgoL\nC6GtrU3tu3r1KpKSkqClpQVLS0vUr19foo2vX7/i2rVrSE5Opn7tqampoV27dujVqxc0NTWrtQ8c\nvxd8Ph+HDx+GgYEBAGD9+vXYvXs3Fi1ahMGDBwMAwsPDsX79eri6umLatGk0Gz4+Pjh+/DiWLl0K\nKysr2oJvQUEBzp07h3/++Qe2trZwc3Or/o5x/HSkzWmUnJyM8+fPV+pNpMYOIhwc8kb5QcTCwgID\nBw7EwoULRcqtXr0aN2/exOnTp2k2evTogTlz5lTo63/kyBFs2rTp56RT5fjPEQpaSvO45/F43HRW\nTefTp09ITU1Fy5YtxToHSCIvLw9JSUkAgPbt29MC/yoiNzcX4eHhIrLh1tbWYt0g2ZKoJoTg/v37\neP78OTIzMymp7E6dOqFly5aV6oM4vn79iuTkZImZL9niw4cPjAGFZmZmOHjwIGOdb9++SRXI2KJF\nC3z79o3xs4cPH0JfX5+a2pUFWc9JcXEx7ty5w7i206lTJygp/bxH2M+8zyp7jzVq1AiWlpYVBkRe\nuHAB8+fPl7odAGpesKG0SFLQTUxMpOWmjouLI+PHjycGBgbEwMCA2NnZkTt37oi1n5CQQBYvXkwm\nTpxIvLy8yJs3b2hlnjx5QiwsLBjrFxcXk7///pt06tSJmJiYEH9/f0IIIb6+vqRDhw6Ez+cTXV1d\nsmrVKrFt2LNnDy1gzN/fnwqA4vP5xMjIiFH8TYibmxvx8vKitt++fUv69OlDdHR0iLGxMTE2NiY6\nOjpk0KBB5MuXL7T6qampxNLSkgp2srW1JV+/fiXDhg0jOjo6pEuXLkRHR4eYmJiQV69eiW1HeHg4\n6dmzp0igXVllVCcnJ1qu8qog6boghJCrV68SOzs7YmlpSaZNm8Z4DUgSHbx37x4pLi4mRUVFVABs\nea5duyZW+HD06NHE1dVVotJtcXExcXV1JaNHj2b8XEdHh/To0YOsW7eOPH/+XKydipD1nBw4cIB0\n7dpVrAKuqakpCQ0NldgGWc4HIfJxn8l6jxFCyNSpU8moUaPEtlFIRdc3E9ybSBWwtbXFoUOHqGmH\n27dvY9KkSWjcuDGGDRsGoHR9ZcKECThw4AAEAoFI/adPn2Ls2LGoXbs2/vzzTxw7dgzHjh2Du7s7\nbG1tqXIFBQVi3e1CQ0Oxb98+WFlZQV1dHQEBAcjLy0NQUBCmT58OgUCA2NhY7N27F8bGxhg0aBDN\nxurVq2FkZISGDRsCAI4ePYqNGzeib9++VHnhHHyzZs1gZWVFsxEdHU3lnRDaBCAyLXPv3j3MnTsX\n69ato+Vu3rx5M4qKihAQEABVVVWsW7cO06dPR05ODi5cuIDWrVvj1atXmDZtGvz8/LBhwwZaG06d\nOoWFCxeid+/e6NmzJ5SVlXH37l2cPXsW8+fPR6NGjRASEoKxY8fi6NGjldYGkpbbt29j2rRpaN26\nNfT09HD//n3Y2dnBxcUFrq6uUtkQBsEKefLkCbp37y6yLyUlRewv3wULFsDR0RFWVlawsrKCtrY2\n1NTUAADZ2dl4/vw5zp07h/fv3yMoKEhsO9TV1REUFISgoCB06NABw4YNw6BBg6ChoSFVP2Q9JwcP\nHsSqVasokUVtbW3KQSArKwsvXrzAyZMn4eXlBR6PhzFjxtDawMb5kIf7TNZ7DABMTU1x5MiRCvv7\nxx9/MCbBk0ilhpzfgLCwMKn+PD09pZJhJ6Q0b8jQoUNFchR8+/aNDB48mLi4uNDqT5s2jYwePZqS\nLMjMzCTz5s0jfD5fJGeGpF9IQ4YMEZG5Pn/+PNHV1SW+vr4i5dzd3cm4ceOk6oe1tTWZOXMmY3vH\njBnDaEMgEIjIhhsZGZHTp0/TyoWFhRFTU1PafjYkqgcPHkw8PDxo+w8fPkzMzMxIUVERKSgoICNH\njiRubm6MNtzc3KT6c3BwEHtOJkyYQKZOnUq9BRQUFJANGzYQPp8vIqEj7rz6+fnR/k6ePEkrZ2tr\nKzHnQ2JiInFxcSEGBga0X+/6+vrExcVFooy68LpIS0sjW7duJf369SM6OjpEIBCQmTNnksuXL1eY\n00PWc9K/f3+yceNGid9BSGmujX79+jF+Juv5IEQ+7jNZ77Hqpsa9ibi5uVVqgUkaHjx4AC8vLxEp\ngfr162Py5MmMCqOPHj3CypUrKe8vdXV1bNiwAe3bt4evry+ysrJoi6nlefPmjcivk+7du1NrCmUx\nNzfH5cuXperHy5cvMWfOHNr+YcOGiXxXWRo1aiTytlRSUsLolaapqcmojMyGRHVKSgqjfEf//v2x\nYsUKpKSkoG3bthg3bhzWr1/PaCMsLAyqqqoVSkkwiRkKefbsGXx8fKCoqAigVJF43rx50NbWxpIl\nS/D9+3esXbtWbH1pfx0fP35c4ud8Ph9bt25FUVER3rx5I+I52KpVK4lphsvSvHlzuLi4wMXFBffu\n3UNYWBguXLiAS5cuoUGDBhg8eDBsbGwYY05kPSdpaWmMWQLL061bN7FvVLKeD0A+7jNZ77HqpsYN\nIurq6rCwsKgwCPDatWv4+++/pbJZXFzMqHvzxx9/ICcnh7b/+/fvVFxJWaZOnQo1NTV4enoiJydH\n4mulgoKCyEAofPiVt6uiooLs7Gyp+lGnTh1q6qMsqqqqYmXY+/bti507d6Jv376oV68ezM3NceTI\nEdpNdvToUbRv355WXyhRLZSQKStRXVaGRpJEdYMGDfDq1SvatM/Lly/B4/GoBcvmzZuLPRbNmzeH\nmZkZvLy8GD8Xcv78ecydO5fxs8LCQsYHtLW1NVRUVDBnzhzMmDEDkydPlvgdbKGkpMRKjnIA6Nix\nIzp27Ijly5fj8uXLOHHiBDXVw+TJI+s5adGiBW7dulWhtFB0dLRYzSk2zoc83Gey3mOSePnyJZ4+\nfQoA0NfXr5IDSo0bRAQCAd6+fStW+19IRd4Whw4donIKqKioMCa3SU9PZxwsmjdvjoSEBMbI47Fj\nx6Ju3bpYtmyZRLn4Jk2a4M2bNzAzMwNQKqG+fft2KlBNyPv376m5WCbc3d2pN6ji4mK8evVK5OEt\ntCGUmy7PzJkzER0djcGDB2PUqFHo0aMH1qxZg2HDhlH9i46ORnJyMvz9/Wn12ZCoHjBgADZu3AgV\nFRX06tULtWrVwv379+Ht7Q0+n089ZD59+iRW2r8y8vziaNmyJe7du0d7cAKl7ro7duyAi4sLnj9/\nXuH3yCvKysoYOHAgBg4ciC9fvuDUqVOM5WQ9J5MmTYKHhwc+fPiAIUOGMK7tnDp1CidOnMDKlSsZ\n28DG+ZCH+0zWewwA9u3bh+LiYkycOBFAaVDqggULEBERIRLIamtrCy8vL+rtTRpq3CCip6eHkJCQ\nCstpamrSTnJZjh07JrJ99epVDBw4UGRfbGws2rRpQ6vbtWtXhIWFif0FZGNjAxUVFYmudoaGhrh9\n+7bIQqwwJ0dZIiMjoa+vz2ijvJuqQCDAhw8faOUuXrwIHR0dRhtqamo4cOAA1q9fj+3btyM/P5+S\nXhDKLwgEAuzcuZNxesLZ2RmvX79GYGAgiouLYWNjg+XLl0NFRQVLly6lph75fD5jcB0AzJs3D8nJ\nydRUpZD//e9/IlMl7969o4L2ymNhYYHw8HDGz8rSrl07zJgxg/Gz7t274/jx45g2bRrjTditWzcE\nBwfLpGIAyIfEBgA0bNiQeiiVR9ZzIlTU3rhxI06cOEH7nBCCBg0awN3dXUR9uyxsnA95uM9kvccA\nYP/+/XB0dKS2161bh+vXr2PevHmUG/m1a9ewbds2NG/eXOqpVYALNqxWgoOD0aZNG/Tp00dkf0pK\nCm7evFmht0t8fDxiY2MrdULLExUVhZYtW8o0rXH79m00bty4wre3vLw8JCQk4NOnTygpKYGGhga0\ntbWl8oZiQ6L69u3bePjwIRQUFNCmTRv06NGjUr+oZOXz589ISEhA586dJaodvHz5Eg8ePBDxxKsM\nS5cuBSGE0QuHDRtxcXHQ09MTORdVRdZzUlxcjHv37onEiaipqUFbWxtGRkYS40T+q/MB/Hf3WVXv\nMUNDQ+zcuZOaHuzevTumTJmCSZMmiZQLCAjAwYMHERkZKXW7uUGEg4OD4zena9eu+Pvvv6ksjwKB\nALt27aK9Jd26dQvOzs7U2qQ01LjpLHlC+Fpap04dat+zZ8+QnJyMJk2awNjYmJXv+PLli1TJlMrX\n8/X1hZ2dncTFti9fvkicC64MaWlpjJHJsiQsKi4uRnp6Oho2bCjxVyub/QCqpy8/g0+fPuH58+fI\nysoCj8eDlpYWBAKByDX7X3L9+nVqIV9fX18qDy5A/vohRJi3XpqUESkpKSgpKRF524mMjKSeF5aW\nlmKj3k1NTXHs2DFqENHT00NsbCxtEImJian0s6LGxYlMnTqVJCQkSF3+x48fJDg4WCQyVlYbubm5\nZO7cuURPT4/o6uoST09PQgghHh4eIhG9I0aMINnZ2VJ/DxNViUAlhJDs7GzC5/NF/NOZ4PP5ZNSo\nUSQ0NFRiGlJJREVFkSFDhjBGJvP5fGJtbU2uXr0q0cbu3buJtbU1sbW1pWIrDh06RDp16kT4fD7p\n1KmTxMh7NvrBVl+YyM3NJTNmzKhSBPn379/JgQMHiLu7O3F3dydHjx4l+fn5EuvExcWRkSNH0qLN\nhdHVHh4eFR4nWVQZfH19RdImZ2RkkOHDh4scV2GK6tzcXLntByGERERE0O7j8PBw0qdPH6otffr0\nEZva9suXL2T06NFU2alTp5L8/Hzi7Owscn316dOHfPz4kdHGixcvSKdOncjMmTPJnTt3yM2bN4mJ\niQn5+++/yZUrV8iVK1eIl5cX0dPTI7t27ZJ4PMpT46azvL29cfDgQejq6sLa2hrGxsbQ0dER+ZX6\n8eNHPHr0CJGRkbh06RIaN26M1atXU9GhstrYuHEjdu3aBQcHB8ojycLCAmfOnIGbmxv09fXx4MED\nrF27FmPGjKkwZkQSFy5cEJsfgGmBUAghBB8/foSmpiaUlZXB4/Eob7Sy8Pl8aGhoIDMzE8rKyujT\npw9sbW3Rq1cvqdJsXrx4EbNnz4apqSmGDBmCdu3aUetEmZmZePHiBcLDwxEXF4dNmzbB0tKSZiM8\nPByLFi2CkZERNDQ0cOPGDSxbtgxeXl4YOnQoFVV86dIl7NixgzFPjKz9YKMvkmT/s7Oz0bVrV+zZ\ns4f69cjUriVLlkBFRYXSSEpNTYWDgwPevXtHrQvk5OSgXbt22Lt3L2O8wY0bNzBt2jT873//g5mZ\nGZSVlXHv3j3cuXMHM2fOhIKCAo4ePQplZWWEhoYyuqs+ffoUo0ePplQZhB5Q5VUZHjx4gDFjxtCu\nz7/++gszZ86k3NwXL16Mq1evwsPDQ2Qh2NPTE0OHDsWyZcvksh8AoKurK6JwERERAVdXVwgEAgwY\nMABAaZrcxMREBAUF0bzJvLy8cObMGcyZMweqqqrYtm0bWrZsicePH2Pt2rUwMDDA/fv34ebmBgsL\nC3h6etLaAJS6z7u5uVGJ+AghIk4PtWrVgrOzc6XXYGvcIAKUBhDt2bMHp06dwrdv38Dj8VC/fn0o\nKysjOzsbhYWFIITAwMAAY8eOxZAhQ2iLgbLY6N+/P0aNGkV5S9y6dQuTJ0/GokWLRBa6AgMDcfTo\nUZw/f57WBzaknfl8PmP+baBUcuXMmTPo0aMH5e7MtAjL5/Nx6NAhFBYWUsFo379/R8OGDWFtbY2h\nQ4eCz+eLbR8b+epHjhyJdu3aUe07dOgQvL29MXLkSLi7u1Pl5s6di6ysLAQHB7PeDzb6oqurK7Fe\n2Zuex+MxelaZm5tj8eLFlHTGjBkzkJiYiI0bN9IkMrp168Z4TkeNGoVmzZph06ZNIvu3b98ucmyG\nDx+Onj17Mj7Ap0+fjoyMDAQGBqJ+/frIysqCp6cnzp49i7lz58LZ2RmA+Ievvr4+du3aRXlImpiY\nYOHChRg5cqRIuf3792P79u24fv26XPYDoKszCwel3bt3Uz8EiouLYW9vjzp16tCuz7/++gtOTk6U\nh9jDhw8xatQoeHp6inimhYaGIjg4WGISO0IIYmJicPfuXXz69AmEEGhoaFDpK6SVtSlvtMaSn59P\nYmNjSUBAAPH29iYrVqwgvr6+JCwsjKSmplabDQMDAxIbG0ttf//+nejo6NCmjmJiYoihoSGjDeHr\nPJMwHdM0ChOnT58mZmZmxNHRkfaKnpWVRXR0dEhcXJzE/peXdPjx4wc5deoUmTx5MiVQN3ToULJ7\n925GcTh9fX2RYyGO2NhYoq+vz/hZ586dSVRUFLWdkZFBdHR0yPXr10XKXbx4kfTq1ata+sFGX4TC\nh76+vjT5k3Xr1hEdHR3i5uZG7WOCDYkMAwMDcu3aNdr+r1+/Eh0dHfLy5UtCSKmEibm5OaMNMzMz\ncunSJdr+7du3Ex0dHbJ27VpCiHjJETMzM3Lu3DmRfsXExNDK3bhxQ+x1IQ/9IIR+bQkEAnLhwgVa\nudOnT5MuXbow9qPsfSjpeWFgYMDYhuqkRi+sKysrw8TEpMKoWLZtaGpq4v3799S28P/yvuOSgvzY\nkHYeNGgQevXqhbVr12LIkCFwdHSEs7MzNX1VFWrXro3Bgwdj8ODB+Pz5M06ePImTJ09i9erVWL9+\nPc3rQ5ivvqLjJylffVFRkYgEtjDAs/yx09DQECudIms/2OhLaGgoPDw8EBkZiZUrV4o4Vnz79g2B\ngYEYNmyYRBl6NiQy6tWrhy9fvtD2f/78GTwej3qjbt26NWM5QHZVhp49e2Lfvn3o168fFBQU0KVL\nF1y8eJEWoHvx4kVa4J889YMJRUVFRicOLS0t5ObmMu5/+fIldd6F01HlAxZfvnxZJUl6WanRg8jP\nwsTEBH5+ftDS0kL9+vWxbt06GBsbY8uWLTA0NETLli3x9u1bbN++HUZGRow2BAIBnjx5UqHPfUWf\nq6qqwsvLCzY2NvDw8MCpU6fg7u5OvXrLgpaWFpycnODk5ISEhATGoDE28tVraWkhLS2N2lZUVMSK\nFStoXiYfPnyglGDZ7gcbfenUqRPCwsIQGBgIR0dHDBw4EIsWLRL7Q4IJNiQyevfuDV9fX7Ro0YJ6\nSL18+RLLli1DixYtqH5lZGSI9WiTVZVhzpw5GDlyJMaMGQN7e3uMHj0ay5Ytw4cPH6g1g+vXryMq\nKopRn05e+iHEz8+POo+1atVCamoqzfvy48ePjNNJvXv3xqZNm6CgoID69etj27ZtsLS0hJ+fH1q1\nakWpOvj7+zOu9wHAtGnTMGvWLEadMyby8/MRGhqKOnXq0JSly8MNIj+BOXPmYOLEidSaSOvWrREa\nGorZs2ejX79+UFNTQ3Z2NlRUVMQucrEt7WxsbIwTJ04gICAALi4u6NatGytJiYTo6elBT0+Ptp+N\nfPUdOnTArVu3MHz4cGrf+PHjaeWio6Olvokq2w+2+qKkpIRp06bBysoKHh4eGDBgAObPn4/+/ftL\n1T42JDIWLVoEBwcHao5eSUkJOTk50NDQEFmLS0hIYJQUAWRXZWjSpAkOHDgAd3d3LFy4kFIuuHz5\nMiV02KhRI6xevRpDhgyR234ApQNRcnIyta2qqopHjx5h6NChIuWioqLQrl07Wn1XV1c8fvwYK1as\nAFAaAb927VosX74cEyZMoO5TLS0tzJw5k7ENLVq0wKhRo6rkDFQRNXJhXR4QZvMrLCxE9+7doays\njIKCAhw5coTK825ra/tT4gpev34Nb29vJCcniyzIMhEWFobevXtX6tcyE7Lkq//+/TsKCwsrXBQ8\ndOgQ2rdvj44dO1ZbPwDZ+lKekydPYs2aNWjcuDGePXuGvXv3VphVMTs7G+vXr0d4eDgVi1QWgUCA\n+fPnS4yxEOZiLxttbm1tzTi1wwSbqgxv377FnTt3aAvBRkZGFb5py1M/KuLMmTNo0aIFDA0Nxbal\nsLAQ2tra1L6rV69SzwtLS0uJkflsOBQxwQ0iHBxyTlZWFv79918kJydj2bJlFXqJCZFFhobj96Wg\noAD379/HgwcP8OnTJ+Tn56NBgwZo06YNunTpUukfrtwgwsHB8Z9SUFCAgwcPon///lUa0GStz5YN\njlKki6LiYJVp06ZVSj01Pz8fu3btwoEDByr9XRcuXKgw/uBn2/j48SP8/PywfPly7N69G9++faOV\nSU5OhoODg1j78mIjIiIC06dPx8yZMxEbGwugdK7bysoKAoEAAwcOxLlz58TWF0dxcTE+fvwoNq9L\nRVy/fh0BAQEICAjArVu3KixfXf0ASq/n1atX4+3btz+lfmVsPH36FPn5+SL74uPjYWdnB0NDQxga\nGsLe3h53796tNhtstKE64d5EfgJsRM1Li6SIdXmwkZqaiuHDhyM7OxuampqUhtX69etF5uwlBXPJ\ni42oqChMnToVTZs2haqqKlJSUvDvv/9i3rx5MDQ0hEAgwJ07d/D48WOEhoYyet7t2bMS2V84AAAO\nNklEQVQHx44dg5KSEiZOnIghQ4bg8OHDWLNmDXJzc1GvXj24uLiIyHqX5d9//4WioiJmzZoFoDRS\nXuhVRsrkjTA1NYW/vz+j1hIb/WBybBBSXFyM+/fvo3379lBVVQWPx6OlZ5C1Pls2ykeb3759GxMn\nTkTjxo1hbm4OoHRdIj09HQcOHIBAIGDdBhttqE4476yfwPLly+Hg4IA9e/Zgy5YtFS5yLV26lLbI\nJc7NtDyS1DjlwcbGjRvRsGFDhIWFUV4sHh4emDJlClavXg1ra+sKbcuLjcDAQPTu3Rtbt26FoqIi\ntmzZgsWLF+Ovv/7Cv//+C6A0YtjR0REBAQHYtm2bSP3w8HCsXr2akm9ZunQpvn//TpNvWb9+PbS1\ntRndOU+fPi3ioSP8tb1hwwaaXIivry9jlLas/QCAO3fuoFGjRoz5dISDmYKCglhJGVnrs2Wj/G9s\nPz8/tGvXDvv376ek8ufPn4+xY8fC398fW7duZd0GG22oVv7LyEYOOlWNmmcjYl0ebPTu3ZsWUV1U\nVERWrFhBdHV1yf79+wkhkiOC5cWGqakpuXz5MrX9+fNnoqOjQxNcPHPmDOnTpw+t/ogRI4ibmxu1\nffDgQSIQCMiqVatEys2ZM4dMmjSJsQ3lI9a7dOlCDh8+TCsXEhJCevToUS39IISQHTt2ECMjI+Lu\n7k4TOJRGDUHW+mzZKB9tbmhoSMLDw2nljh8/LlYBQFYbbLShOuHeRH4yVY2aZyNXvDzYyMjIoKVH\nVVRUhKenJ9TU1ODl5YWcnBzGYC95s5GXlyfiYil0Fy4fna6lpUVJgJclJSVF5C2if//+8PDwgIWF\nhUg5KysreHt7M7ZBXV1dxHZeXh5jkqM///yTcj9mux9AaczMwIEDsXLlSgwYMACLFi2i4pWkiT+S\ntT5bNspTXFzMKJX+xx9/ICcn5z+xwUYb2IQbRH5R2MgVLw82mjRpgqSkJMbYhwULFkBFRQW+vr5i\nI3HlyUbDhg1FpGsUFBQwadIk2sP38+fPjHEKbMi3sCEXIms/hLRs2RJBQUE4deoUfHx8cPToUaxa\ntUpsnnu267Nl49ChQ5SCtYqKCj59+kQrk56eLvFYyGqDjTZUF9wg8ovCRq54ebDRuXNnnDp1Suwi\n6PTp06GioiIxclZebPD5fMTFxVER1DweD4sXL6aVu3v3rkjAmBA25FvYkAuRtR/lsba2hrm5Odas\nWQMbGxuMGDGiUm8CstaX1caxY8dEtq9evYqBAweK7IuNjWVce2HLBhttqDb+8wk0Do4yPHz4kPj4\n+IhVxhVy+vRpkfUCebSRlpYmVdIoPz8/kTUHIbNnzybz58+vsL6bmxtxdnYW+3lqaiqZPHmy2PUq\nMzMzEhYWJra+rP2QRHx8PLGyspJqPaI66rNlozxBQUEkMjLyp9pgow1VgXPx5eCQE9iQbymLLHIh\nHBzSwg0iHBwcHBxVhotY/0VhI+pdHmzIQxvkyYY8IA/HQh7aIC825P264hbWf1HYkHaWBxvy0AZ5\nshEREUFFrNvZ2cHU1JRaBH/z5g1atmyJWbNm0RZVyyKrDXk4FvLQBnmxUZ0y7mzATWf9wvzsXPFs\n2ZCHNsiDDTbkRtiwIQ/HQl7aIC82qkvGnQ24QeQ3gA1pZ3mwIQ9t+Jk27O3toaKiIiI3smvXLvTq\n1YsmN1KnTh1GuRE2bMjDsZC3NsiLDbZl3FnhP/MD4+DgkAgbciNs2ODgqAzcwjoHh5zAhtwIGzY4\nOCoDN4hwcMgJbMiNsCVZwsEhLdwgwsEhJwjlRoQI5UbKZ96TJDfChg0OjsrAufhycMgJy5cvR25u\nboXlNDQ0xGZXZMMGB0dl4LyzODg4ODiqDDedxcHBwcFRZbhBhINDTpAHiQ0OjsrCrYlwcMgJ8iCx\nwcFRWbg1EQ4OOUIeJDY4OCoDN4hwcMgh8iCxwcEhDdwgwsHBwcFRZbiFdQ4ODg6OKsMNIhwcHBwc\nVYYbRDg4ODg4qgw3iHBwcHBwVBkuToTjl8TCwgLv3r3DH3/8gcuXLwMAvn37hj179gAoFSLs27dv\npWw+ffoUERERAIC+ffuCz+czfmfz5s0RGRnJQi/kh7CwMCxZsgQA4OPjAxsbm0rbkPX4c/yacIMI\nxy8Lj8cT2c7OzsaWLVsAALa2tpV+iCUmJmLLli3g8Xho0aIFbRDh8Xjg8XhQUPg9X+CF/asqsh5/\njl8TbhDh+GUp750u3JblQSgJ4RvP74itrS1sbW1lslHdx59DPuHiRDjkirNnz+LIkSN49eoVMjMz\nUVxcjCZNmsDMzAyzZs1Cw4YNAdCns/z8/LB161bweDza4GJra1uhrIe9vT3i4+MZ6wund5ims8pO\nA7m4uEBBQQEHDhzAjx8/0LdvX7i7uyMpKQmrV69GUlISWrZsidmzZ9N+pV+7dg179uzB48eP8f37\ndzRu3BgWFhaYMWMGlZ2wfL99fX3h4+ODxMRE1K9fHzY2NpgzZ46IxElaWhq2bduG6OhofP78GXXr\n1oWenh4cHBxgYWFBlRM3nVX2+/7991+sW7cOjx49QoMGDWBlZYW5c+dCSUlJquOfmpqKzZs3Iz4+\nHl++fEHt2rXRtGlTCAQCLFy4EJqamhLPEYd8wr2JcMgVsbGxiImJEdmXlpaGQ4cOIT4+HuHh4SIP\nSSHlp2LE/S8Jpjrl64qb8uHxeDhw4AAyMzOpfeHh4fj06RPu37+PHz9+AACeP3+OOXPm4OzZs2jV\nqhUAIDg4GGvXrhWx+/79e4SEhCAqKgqHDh0SecDyeDx8/foVEyZMQH5+PoBSIcXAwECkp6fDx8cH\nAJCcnIyxY8ciOzubsp2Tk4OYmBjExMRg3rx5cHZ2FnsMyn+fnZ0dCgsLqfYFBwdDVVUV06ZNk+r4\nT506FcnJydR2YWEhXrx4gRcvXsDR0ZEbRH5Rfs/JXY5fFmtraxw+fBgxMTFISEjAzZs3qWmWV69e\nISoqirGeq6srIiIiQAgBj8eDjY0NEhMTkZiYiH/++afC7923bx/++ecfqv7q1auRmJiIJ0+eiCwy\ni3txJ4QgPz8fBw4cQGRkJOrVqwcAuHXrFoyNjRETE4NFixYBAIqLi3Hu3DkAwIcPH+Dr6wsej4ee\nPXviypUrePDgATZs2AAASE1Nhb+/P+27fvz4gREjRiA+Ph5HjhyhHsAnT57Es2fPAADe3t7UADJ9\n+nTEx8cjJCQEampq4PF42Lx5s0gqXXEIv2/w4MGIiYnBtm3bqM9Onjwp1fHPzMykBhB7e3vcv38f\ncXFxOHr0KGbPns2l6v2F4QYRDrlCS0sLe/fuhY2NDQwMDNC9e3ccP36c+vzVq1c/sXXi4fF46Nu3\nL4yMjNCsWTO0bduWeqA6OTlBXV0dffr0ocq/e/cOAHD9+nUUFRUBKJ3S6t27NwwMDDBv3jwApQ/w\nmzdv0r5PSUkJCxcuRP369SEQCDBixAjqs1u3biE/Px9xcXHg8XhQV1eHq6sr6tevD2NjY9ja2oIQ\nguLiYty4cUOq/ikqKmLp0qVUPzQ0NEAIofpREerq6lBTUwMAREVFwd/fH1evXoWysjKmTZtGS9/L\n8evATWdxyA05OTkYO3Ysvn79SptOEr4BCKeF5JGygoa1a9em7a9Vqxa1r6CgAADw5csXap+4abes\nrCzaPg0NDZHvaN68OfV/RkYGtZ7E4/HQuHFjEY+ysmW/fv1acccANGzYEPXr16e269Wrh8zMTKof\nFcHj8bBu3TqsXLkSb968wY4dO6hzqq2tjcDAQG4g+UXh3kQ45IaYmBhqAOnWrRtu3ryJxMRELFu2\nTKr6snoFyVpfnJy6JJl1oaMAAMyZM4eaAir7x/QmkpmZSa2HABB5I2jQoAE0NDSo7/306ZPINNz7\n9++p/6Vdh2BahypPRcfP3NwcV65cwfnz5+Hv7w9XV1coKirixYsXIlNkHL8W3CDCITeUfVApKyuj\ndu3aeP78Ofbt2ydVfQ0NDer/169fIy8vr1LfX7Z+UlISiouLK1W/KvTo0QNKSkoghCA4OBjXr1/H\njx8/kJOTg7i4OLi7uyMgIIBWr6ioCOvWrUNOTg4ePnyIo0ePUp91794dtWvXRteuXUEIQVZWFvz8\n/JCTk4M7d+4gLCwMQOnx7tGjB2t9qej4e3t749atW6hbty569OgBS0tLKCsrAxAd2Dh+LbjpLA65\noVOnTtDU1ERGRgauXr0KY2NjAMCff/4pVf169epBW1sbL168wN27d9GxY0cA0kdg6+rqolatWigq\nKkJwcDCCg4MBAJGRkSJTQGzSrFkzzJkzBxs2bEB2djamTJki8jmPx8OMGTNo++rVq4cTJ04gJCRE\nZL+NjQ3at28PAFi6dCnGjRuH7OxsbNu2TeTXPo/Hw+zZs9G0aVPW+lLR8T9w4IBIe8u2pWfPnqy1\ng+O/hXsT4ZAb1NTUEBgYCGNjY9StWxdNmzbFrFmz4OzszOhyy+Ruu27dOnTu3BmqqqqVji5v0qQJ\n1q5di3bt2qF27dqM9Zm+U9w0jqSyZfc7OTkhICAAvXr1QoMGDaCkpAQtLS106tQJs2bNogUBEkKg\noaGBvXv3okuXLqhTpw4aNWoEJycneHt7U+Xatm2LsLAwjBgxAs2bN4eSkhLU1NTQtWtXbNu2DU5O\nThW2t7L7JR1/Z2dndO7cGY0aNYKSkhIVs7J8+XLY29szHkMO+YcLNuTg+IVg0gzj4PiZcG8iHBwc\nHBxVhlsT4agRlBdTLM/Tp0//o5bIjqxCiRwcbMINIhw1AkkP3V/pgfy7SdBz/PpwayIcHBwcHFWG\nWxPh4ODg4Kgy3CDCwcHBwVFluEGEg4ODg6PKcIMIBwcHB0eV4QYRDg4ODo4q8/8ALrZjTZ76/UIA\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5dde582ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.boxplot(data = spline_model['time_betas'],\n",
    "          x = 'alt_timepoints',\n",
    "          y = 'exp(beta)',\n",
    "           whis=[2.5, 97.5],\n",
    "          fliersize=0,\n",
    "          )\n",
    "_ = plt.ylim([0, 4])\n",
    "_ = plt.hlines(1, -1, 20, linestyles='--')\n",
    "_ = plt.xticks(rotation=\"vertical\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T05:44:23.456814",
     "start_time": "2016-08-18T05:44:16.296Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RSPDuu+8CKL999e9//xtubm549tlnsXHjxloLkoiIGiatk0jFVcikSZPkZd7e3li1ahVu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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5dde5a1490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.boxplot(data = spline_model['time_betas'],\n",
    "           x = 'alt_timepoint_end',\n",
    "           y = 'exp(beta)',\n",
    "           whis=[2.5, 97.5],\n",
    "           fliersize=0,\n",
    "           )\n",
    "_ = plt.ylim([0, 4])\n",
    "_ = plt.hlines(1, -1, 20, linestyles='--')\n",
    "_ = plt.xticks(rotation=\"vertical\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### tvc with random-walk tvc\n",
    "\n",
    "Goal of this model is to assess sensitivity to knot position & consequences of using a linear spline. \n",
    "\n",
    "Using a random-walk to model the nonlinear shape of the beta over the range of timepoints allows for more flexible modeling of the variance over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T05:44:23.457194",
     "start_time": "2016-08-18T05:44:16.298Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../utils/stan/logistic_model.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_custom_coefs_expressed_missense_and_neoant_mutations.stan\n",
      "../utils/stan/logistic_model_by_group.stan\n",
      "../utils/stan/pem_survival_model_unstructured_varcoef.stan\n",
      "../utils/stan/pem_survival_model_unstructured_varcoef_hsprior.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_tvc.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_alt.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_bspline_est_xi.stan\n",
      "../utils/stan/pem_survival_model_varying_coefs3.stan\n",
      "../utils/stan/pem_survival_model_randomwalk.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_custom_coefs_rate_only.stan\n",
      "../utils/stan/pem_survival_model_gamma.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_custom_coefs_missense_and_neoant_rates.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_tvc2.stan\n",
      "../utils/stan/pem_survival_model_varying_coefs2.stan\n",
      "../utils/stan/pem_survival_model_varying_coefs4.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_bspline_est_xi2.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_bspline.stan\n",
      "../utils/stan/pem_survival_model_randomwalk2.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_bspline2.stan\n"
     ]
    }
   ],
   "source": [
    "## load stan models again (for convenience)\n",
    "models = survivalstan.utils.read_files('../utils/stan', pattern = \"*.stan\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T05:44:23.457573",
     "start_time": "2016-08-18T05:44:16.300Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NOT reusing model.\n",
      "Ran in 133.885 sec.\n"
     ]
    }
   ],
   "source": [
    "tvc_model = survivalstan.fit_stan_survival_model(\n",
    "    formula = '~ missense_snv_count',\n",
    "    df = dlong,\n",
    "    timepoint_end_col = 'end_time',\n",
    "    sample_col = 'patient_id',\n",
    "    event_col = 'end_failure',\n",
    "    chains = 4,\n",
    "    iter = 10000,\n",
    "    model_code = models['pem_survival_model_randomwalk_tvc.stan'],\n",
    "    model_cohort = 'random-walk prior - randomwalk tvc',\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T05:44:23.457995",
     "start_time": "2016-08-18T05:44:16.302Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#print(tvc_model['fit'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T05:44:23.458369",
     "start_time": "2016-08-18T05:44:16.303Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'diff': 0.9011200537314632, 'se_diff': 1.895223005890716}"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stanity.loo_compare(spline_model['loo'], tvc_model['loo'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T05:44:23.458717",
     "start_time": "2016-08-18T05:44:16.305Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{{{time-varying-timepoint-id}}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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iREQkGUOEiIgk4z0RItKgUCgg5OaavFKhoMyC4mlB6TtSpcKWCBERScaWCBFp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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5dddfa3bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('{{{time-varying-timepoint-id}}}')\n",
    "tvc_model['time_betas'] = extract_time_betas(tvc_model)\n",
    "sb.boxplot(x = 'timepoint_id',\n",
    "           y = 'exp(beta)',\n",
    "           data = tvc_model['time_betas'],\n",
    "           whis=[2.5, 97.5],\n",
    "           fliersize=0)\n",
    "_ = plt.ylim([0, 2])\n",
    "_ = plt.hlines(1, -1, 20, linestyles='--')\n",
    "_ = plt.xticks(rotation='vertical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{{{time-varying-timepoint-id-overlap}}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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FYmIipk+fjjt37mD79u3aBnv48CG++OILrF692qyBkq7K/nVY9q9Ml2XLTP48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\nTExERGQqTExERGQqTExERGQqvwBBfrkdFPv57AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5dcd6c1850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('{{{time-varying-timepoint-id-overlap}}}')\n",
    "f, ax = plt.subplots(1, 1)\n",
    "x_min = min(np.log(tvc_model['time_betas'].end_time.drop_duplicates()))\n",
    "x_max = max(np.log(tvc_model['time_betas'].end_time.drop_duplicates()))\n",
    "time_varying_timepoint_id_overlap_plot = tvc_model['time_betas'].boxplot(\n",
    "    column='exp(beta)',\n",
    "    by='end_time',\n",
    "    whis=[2.5, 97.5],\n",
    "    positions=np.log(tvc_model['time_betas'].end_time.drop_duplicates()),\n",
    "    ax=ax,\n",
    ")\n",
    "f.suptitle('')\n",
    "_ = plt.ylim([0, 2])\n",
    "_ = plt.hlines(1, -1, 20, linestyles='--')\n",
    "_ = plt.xticks(rotation=\"vertical\")\n",
    "_ = plt.xlabel('Days')\n",
    "_ = plt.ylabel('HR for # Missense SNV / MB')\n",
    "_ = plt.title('')\n",
    "_ = time_varying_timepoint_id_overlap_plot.xaxis.set_ticks([2, 3, 4, 5, 6, 7])\n",
    "_ = time_varying_timepoint_id_overlap_plot.xaxis.set_ticklabels(\n",
    "    [r\"%d ($e^%d$) \" % (int(round(np.exp(x))), x) for x in [2, 3, 4, 5, 6, 7]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{{{time-varying-log-time}}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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piczMTAiCAA8PD1hZWQEArl69CuCfhENERNWLyp7IuHHjUKNGDZSUlEg9DkEQMH78eGmf\nAwcOAADatWun5zCJiKgyUtkTcXJywsaNGxEWFoa//voLDRs2xIgRI9ChQwcAQHZ2Nh49eoR27dqh\nZ8+eBguYiIgqD7Uz1j08PODh4aF0m5WVFTZt2qSXoAxp7dq1OHXqFAAgKysLAGBtbQ0A8Pb2RkBA\nwEuLjYiosmPtrOfk5+cjPz//ZYdBRFRllLt21qsmICBA6m0MHz4cABAeHv4yQyIiqjLYEyEiIq0x\niRARkdaYRIiISGtMIkREpLVy3Vi/efMmTp8+jfT0dHz++edISkoCANSrVw81alT7e/RERNWOxj2R\nOXPm4P3338fSpUuxfv16AMC0adPQs2dP/Pzzz3oLkIiIKi+NksiuXbuwZcsWaSEqGT8/P4iiiOjo\naL0FSERElZdGSWTbtm0QBAF9+vSRa/fy8gJQumwuERFVPxrdyLh9+zYA4Ouvv5ZbxbBOnToAgMeP\nH+shNCoPdeVbAJZwISL90KgnIruEZW5uLteemJio+4iowli+hYgMRaOeSJMmTRAfH489e/ZIbY8e\nPcLs2bMBAK+99ppegiPNsXwLEb0MGvVE+vTpA1EUMXv2bAiCAADo2rUrzpw5A0EQ8M477+g1SCIi\nqpw0SiKjR4+Gm5ub3Ogs2c+urq4YOXKkXoMkIqLKSaPLWaampggPD0d4eDiOHz+OtLQ01K5dG927\nd8fw4cNhamqq7ziJiKgS0niaubm5OcaMGYMxY8boMx4iIqpCNEoiaWlpSEtLg7W1NerXr4+UlBSs\nWrUKycnJ8Pb2xieffKLvOImIqBLSKIksWrQIe/fuxeTJk/HZZ59h9OjRiI+PBwCcOnUKRkZGGDp0\nqF4DJSKiykejG+vXrl0DUDph7datW7h16xaMjIxgbm4OURSxe/duvQZJRESVk0ZJ5OHDhwCApk2b\nSiVOxo0bh4iICABAQkKCnsIjIqLKTKPLWQUFBQBKR2ndvn0bgiDA1dUVLVu2BAAUFRXpL0IyGHWl\nU1g2hYiU0agnIquRtWTJEql2VvPmzZGWlgYAqFWrlp7Co5eFpVOISBMa9UQ8PT2xb98+bN68GQDQ\noEEDNGvWDOfOnQPAsievCpZOIaLy0qgnMmnSJLRs2RKiKMLa2hqzZs0CAERFRcHY2Bienp76jJGI\niCopjXoiDg4OOHDgANLT02FrayvVz/ryyy/x5Zdf6jVAIiKqvMq1MDrvfVBVwAECRIajcRL57bff\n8NNPPyEpKUkarSUjCAI2bdqk8+Co+tH14lqywQHPn4OIdEejJLJ3717MmDFD6TZRFKXLW0S6pG0C\n4AABIsPRKImEhoZKJeBfBVOnToWZmZlCu2yZX9kHz4vq1q2LpUuX6jW26o4JgKhq0SiJJCYmQhAE\njB49Gv3794eFhUWV7n08SU1FfWtbhXYzI2MAQElmtsK2tPxcvcdFRFTVaLw87u3bt/HZZ5/B0tJS\n3zHpXS0zCyzr/X65jvnXEdYHIyJ6kcYrG4qiiJ9//lnf8RARURWiUU/k/PnzsLGxwaxZs7Bz5040\nb94cNWr8c6ggCJg7d67egiQiospJoySyZ88e6R7I9evXcf36dYV9mESIiKofjeeJqBudVZVvshMp\nwwmLRJrRKIlERUXp9EFTUlIQGhqK69evIy4uDvn5+YiOjkajRo3KPLawsBDBwcHYv38/srKy4Ozs\njGnTpsHDw0OnMRLJcMIikWoaJZHGjRvr9EHv3r2LI0eOwMXFBR4eHjhz5ozGx86YMQOnTp3C9OnT\n4eDggK1bt2LUqFHYvn07nJycdBonVV+cr0KkmXLVzoqMjMTp06eRkZGB4OBgxMbGQhRFtGnTplxD\nfzt06IDTp08DAHbu3KlxEomLi8OBAwcwf/58DBgwAEBpmXpfX1+sWLECq1atKs/TqdL+/e9/IzU1\nVek2TpokIkPRKImUlJQgMDAQMTExUpmT4OBghIWF4cSJE/j6668xZMgQfceKqKgomJiYoE+fPlKb\nsbExfH19sXbtWhQVFcHExETvcVQGqampePQoBTYWittq/D1wOz8rRWFbZp6eAyOiakWjJLJp0yYc\nP35cof2DDz5ATEwMjh8/bpAkcvv2bTg4OCiULHF0dERRURHu3bsnLdlbHdhYAOP7lqsziVUHi/UU\nDRFVRxpNNpQN8R0xYoRce7t27QCUfrgbQkZGBmxtFcuVyErUp6enGyQOIiIqpdHX2Hv37gEoXeHw\n+ZLvNjY2AKDy2nxl1/7LKUrbf50TrLR99+7dOHnypEL7X3/9pXR/VcsG62r/A0dO4uQpxeHVEfO6\nK93fb8ZxZOWKEIyM5Z6HqvM/ePBAaUwv6/lqs3+LFi0Uji3v+bt06aKzeLg/969q+8vuX6uiURIx\nMirtsDx79kyuXdYDeX72uj7Z2NggKSlJoV3WA+GiWfQqsbe3l4YVP/83KBswwfkqpC1l86BkX7iy\nsrKkwTma0OjTv2XLlrh+/TrCwsKktkuXLmHOnDkAgNdff13jB6wIR0dHREZGoqCgQO6+SHx8PExM\nTNC0aVONzpNekId/HdkNb29vpduVFVtMy89Fr169sGfPHo3jVZXxdbW/b+8u5bonEjGvO1YdLIa5\ndX2Nhqs6ODggOjpa4/Pr+/lqs7/sA1eT56Hq/KpGuek7/jFjxkh/6LI/ant7e52dn/tzf+CfeVAO\nDg4AFL+cPHjwQO3xGn0CDRw4ENeuXUNoaKg0O/3jjz8GUDpbvX///hoFW1E9evRASEgIDh06JA3x\nLSkpwaFDh9C5c+dqMzKLqgfOVSF90eV7S6Mk8vHHH+PMmTNKv81169ZNq5FZR44cAQBcu3YNoiji\nxIkTqF27NmrXrg1PT08kJSXBx8cHgYGBGD9+PADA2dkZffv2xbx581BUVAQHBwds27YNiYmJ5Zr3\noG0peGNrq3IdQ0T0qtMoiQiCgO+++w4HDx7E8ePHkZaWhtq1a6N79+7o27evVrWzJk+eLB0nCAK+\n/fZbAKWTB8PDwyGKovTvefPnz0dwcDCWL1+OrKwsODk5Yd26dZytTkT0Emh8QV0QBPj6+sLX11cn\nDxwXF6d2e+PGjXHz5k2FdlNTUwQFBSEoKEgncVRnnPVORBWlURL566+/cPfuXdSvXx9OTk6Ii4vD\n4sWLkZycDG9vb3z++ecwNjbWd6ykY7JZ75ZKZr0b/z2DKEfJrPccznonor9plERCQkJw8OBBBAUF\noXXr1hg/fjySk5MhiiISEhJga2uLcePG6TtW0gNLC2BI//Jdjty2T/WyAERUvWg0Y122CFWnTp1w\n48YNJCUlwcLCAg0bNoQoijh48KBegyQiospJoyQiuz7euHFj/PnnnwCA8ePHY/PmzQCA+/fv6yk8\nIiKqzDSu4guUrm4YHx8PQRDg5OSE+vXrA1CcyU76l5WVhby88hdUzMwDipClp6iI9EPdSpMAZ++/\nTBr1ROrWrQugdEGoffv2ASidIv/kyRMAgJ2dnZ7CIyKSl5+fL82yppdPo56It7c3tm3bhmPHjkEU\nRbRo0QKNGjWSysPLaq6Q4VhbW8MEuVqVgjevZMu8qhpqzGHGJMPZ+5WXRp9AkyZNQmJiImJjY+Hg\n4CDVzLp8+TKaNm2K7t2VV40l0kRqaipSHqXA7IXFMYW/R42n5ygOMy7IMUBgRFQmjZKInZ0dQkND\nFdqnTJmCKVOUl1MnKg8zS6DdYM33/227/mIhIs1pXcP9ypUrSEpKQvv27VGvXj1dxqR3siq+L8op\nKgQAWJqYKmxLy8+FvQ1rZ1VGnHlPVcXzAwQAxUECVXGAgEZJZMOGDThw4ADeffdd+Pv7Y/bs2fjh\nhx8AABYWFti8eTNcXFz0Gqgu1albF8YvLLELAAV/f+DYKEkW9jZW0gADqlxkl8NgpWTSpHHpxMiU\n3EeK27I5aZJeLtkAAetKdp+yPDRKIlFRUbh+/TomTpyItLQ0RERESIURc3Nz8d1332HVqlV6DVSX\nlixZItXOfx5v2FVhVgKMPrEse7/nPNvMGytkWM8PEABejc8cjYb4yhY3cXZ2xtWrV1FSUoKuXbti\n0qRJAEpvsBMRUfWjUU8kIyMDAFCnTh0kJCRAEAT069cPb7/9NlasWIHMzEy9Bkn6IZuwWN5aWDl5\nwDNOWCQiaNgTsbIqvUdw/fp1/PrrrwCAZs2aoaCgAABQs2ZNPYVHRESVmUY9kRYtWuC3337D4MGl\nYzBNTU3RunVrJCQkAECVG51FpaytrWGEXK2q+FpW4RuBRKQ7GvVERowYAUEQpJUGP/zwQ5iamkpD\n1dzd3fUaJBERVU4a9UTefvttRERE4Ndff4WDgwN69eoFAGjbti0WLlxYpYb3EhGR7mg82dDNzQ1u\nbm5ybZ6enjoPiDSXqaKKb17pnElYKM6ZRGYeYM4rUUSkIyqTyMWLFwGUJgrZz+owoRiWuomPWX9P\nmjS3tlfYZm6t/tjqirPeibSjMol88sknMDIywo0bN/DJJ59AEFTffBUEATdu3NBLgKScug+tqjaB\nKSsrCwV55auHVZADZD3T3TBjada7pWIlAxiXvvdTctIVt+UU6CwGoqpI7eUs2az0F38meiVZmsF4\nWIdyHVKy5YKegiGqGlQmkQkTJki9j8DAQIMFRNWPtbU1Soxyy13F19qSN3eIXjaVSWTixInSz0wi\nRESkjEbzRIiIiJRR2ROZMWOGxicRBAFz587VSUBE5ZWVlQXkieWvypstIquENcCIKkJlEtmzZ4/a\nEVkvYhIhIqp+ypxsqMmorPIkGyJds7a2Rq5xnlbriVjX5M15ooooM4kIgoDGjRtj8ODBcHJyMkRM\nZEA5KkrBF/w9691Myaz3nDyAA6P0Q9WkR054pMpKZRL5/PPPsWPHDty9excPHjzA0qVL4e7ujo8/\n/hjvvPMOTE2VfLpQlaJu5nru3x9alkpmvVty1rvelE56fARYvrC8grExACAlJ1vxoJxcA0RGpJzK\nJDJq1CiMGjUKp0+fRkREBGJiYnD58mVcuXIFc+fOxQcffAA/Pz80adLEkPGSDr1Ks95fKZY1YTLk\nA413L9q2S4/BEKlX5uWszp07o3PnzkhJScHOnTuxefNmpKenY/369bh37x5CQkIMEScREVVCGs0T\nKSkpwaVLl3Dx4kVpKVxRFGFubq7X4IiIqHJT2xNJTk7G9u3bsWvXLqSmpkIURRgZGaFLly4YMmQI\nunbtaqg4iYioElKZRD777DOcOnUKz549gyiKqFu3Lj744AMMGjQIjRs3NmSMRERUSalMIjExMdLP\njRs3Rs+ePVFUVIStW7cq3X/69Ok6D84Q1q5dKy3z++IwSm9vbwQEBLy02KqTghzFUvDFf1dZr6Gk\nOntBDoDyTQshIj1QezlLNokwKSkJmzdvVnuiqppEnsd7PC+HquHCj3NLk3otS8VhxrDU7TDj0tIp\nBeUv7Z5ToNN1TYiqGo3XE1GnKs9YDwgIYG/jJVM11JjDjIkqP5VJZN68eYaMg+ilsra2Rq5RiVaL\nUnFdE6rOVCaRgQMHGjIOIiKqgrieCBERaa3MGetEVUK2ivVE8v++r2eu5L5dtgjUVGwmIs0xiVCV\np26U1uOc0hFe9jWVjPCqyUKSRBXFJEJVHgtJvppYFr9qYBIhokopNTUVjx49hrllbbl2I+PS2aeZ\nOSUKx+TnpBkkNvoHkwgRVVrmlrXRa5jmvYpjW/6tx2hIGY2SyN69ewEAAwYM0GswpD115VsAlnCp\nKkpnzueVb42QnFxkPdNsYjCRrmmURP7zn//AyMhISiIrV66EIAiYMGGCXoMj7bB8CxEZisok0rt3\nb3h5ecHT0xOAfAkUJpHKh+VbXg2lM+eFcq9saG1ppceoiFRTmUSKioqwY8cO7Ny5U2qbPHkyOnXq\nZJDAiIio8lOZRKKjo3H//n388ssv+OqrrwAAR48exZEjR6R9Ro0aBU9PT3To0AHt2rXTf7RERFSp\nqCx7kpOTgyZNmuCjjz6S2s6cOYMFCxZIv1+8eBHLli3D0KFD9RslERFVSip7Ih06dMAbb7yBDh3+\nqWpau3ZtvPfeewgKCgIAxMbG4tdff8WFC+Vcg4GoMspRsZ5IQXHpf82U/LnkFHBxLKrWVCaRN998\nE5cuXcLqCjA9AAAgAElEQVTly5eltr59+6Jjx47S76ampnjrrbfw1ltv6TdKIj1TWzrl78Wx7C1r\nKW7U8eJYRFWNyiSybt06lJSU4Pfff4efnx8AICMjA1u2bJEWoerUqRM8PDzg5eWFjz/+2DARE+kB\nS6cQaUdtKXhjY2O0bdtW+v3MmTPYvXu3NNzXzs4OR44cwezZs/UbJRERVUrlLnvSpk0b6eeff/4Z\nqampvCdCRFRNaZRE4uLi5H5v1KiRdEmrbt266Nu3r+4jIyKiSk+rAozR0dG6joOIiKogLo9LRERa\nYyl4IiI9UrW4FqB+ga2qsrgWkwgRkR6lpqbi8aPHsDOzU9hmKpgCAIoziuXanxY8NUhsusAkQkSk\nZ3ZmdljSaZHG+08987keo9Et3hMhIiKtldkTKSoqkkqftGrVCra2tnoPiqhay8lVXNmwoLD0v2am\nSvcH1xOhl6TMJGJiYoIRI0YAAI4fP84kQqRHqupw/VO/S0mysLR6Jet3ZWVlIT8vv1zrpufnpEF4\nxpU9DUmjeyINGjRAcnIyrKz4bYdIn1SNxmH9LqqsNEoigwYNwvLly7F3716uHUJEBmFtbQ3RqCZ6\nDdN8mOuxLf+GtaWxHqOiF2mURAoLC2Fra4s5c+YgOjoabdq0gZmZmdw+gYGBegmQDGft2rU4deoU\nAMXx697e3lzDnYgUaJREVq1aJdXKOnv2LM6ePauwD5PIq8XcnNeViahsGs8TkZV/V0aWYDT18OFD\nzJ07F2fPnoUoiujYsSO++OILNGzYsMxjnZyclD7+nj17lG4jzQUEBLz03oa63hCgWY+IPSoiw9Eo\niejyZl5+fj6GDx8OMzMzLFy4EAAQHByMESNGYN++fRp9A/7ggw8wePBgubbmzZvrLEaqHHTRG2KP\niki/NEoiz6+zXlHbt29HYmIiDh8+jCZNmgAonX/Su3dvREREwN/fv8xz1KtXD25ubjqLiSoPXfSG\nKkOPiqi6KFfZk8uXL+PEiRN48uQJ6tSpg27dusHd3b1cD3j8+HG4u7tLCQQAHBwc0K5dO0RFRWmU\nRIiIqHLQOIl8/fXX2Llzp1zb6tWr4efnh5kzZ2r8gPHx8ejZs6dCu6OjI44cOaLRObZt24awsDAY\nGxvD3d0dEydOhIeHh8YxEBGRbmiURHbv3o0dO3Yo3RYREQF3d3cMGDBAowdMT09XOuvd1tYWmZmZ\nZR7/3nvvoVu3bqhXrx6SkpKwbt06+Pv7Y8OGDfD09NQoBiIi0g2NkogsgTRq1Aj+/v5o1KgRkpOT\nsXHjRiQmJiIiIkLjJFJRCxYskH5u3749evTogX79+mH58uXYsmWLQWIgIqJSGiWRP//8E4IgYPXq\n1WjVqpXU7uXlhf79++PWrVsaP6CtrS0yMjIU2jMyMmBjY6PxeWQsLS3RtWtX7N69u9zHEhFVB9ou\njAUANWvWVHtujZJIUVERgNIaWs+T/S7brglHR0fEx8crtMfHx6Nly5Yan4eIiDRTujDWI9Q2t1bY\nZmZUmgZKMvMUtqXlZ8GmjKK7GiWRhg0b4v79+1iwYAGCgoJgY2ODrKwsaZ6HJpMEZXr06IFFixbh\nwYMHcHBwAAA8ePAAly5dwrRp0zQ+j0x2djZiYmI45JeISI3a5tYI9inf0PcpkWtRXMY+GiWRbt26\nITw8HLt378bu3btRs2ZN5ObmAiidLd69e3eNgxo0aBB++OEHjB8/HpMnTwYArFixAo0aNZKbQJiU\nlAQfHx8EBgZi/PjxAID169fj7t278PLyQt26dZGYmIj169cjNTUVS5Ys0TgGIiLSDY2SyLhx4xAZ\nGYmkpCQAQE5OjrStcePG+OyzzzR+QAsLC2zatAlz585FUFCQVPZkxowZsLCwkPYTRVH6J9O8eXNE\nRkbi6NGjyMrKgpWVFdq3b4958+bB1dVV4xiIysLSKUSa0SiJ2NnZYceOHVi2bBlOnDiBp0+fonbt\n2ujWrRsmTZqEWrVqletBGzRogBUrVqjdp3Hjxrh586ZcW/fu3cvV6yHSBZZOIVJNZRKR1csaPnw4\nLl68CACYM2eOYaIieslYOoVIMyqTyNy5c2FkZIThw4fjk08+gZGREW7cuGHI2IiIqJIzUrVBEASI\noojCwkIA6kvBExFR9aSyJ2JnZ4enT59i4MCBUpuqySiCIGDTpk26j46IqIrLyspCfn4+pp75XONj\nnuY/hblR1bgXpzKJuLm5ISYmBgkJCQBKeyKyeyPPE0Wx3ItSERFpIj8nDce2/FuuraigdHSoiZml\n0v1tLO0NEhuVUplE/vOf/yAtLQ03btxASUkJAF7SIiLDqVu3rtL2x7kFAAAbS8UySTaW9iqPe1ms\nra1h8cwCSzot0viYqWc+Rw3rcq3U8dKojPK1116TCi86OTlBEATExcUZLDAiqt6WLl2qtF12WV2X\nK66S9jRKdfPmzdN3HEREVAVplESev7lOREQko3KILxERUVmYRIiISGtMIkREpDUmESIi0ppOBiJf\nvXqVi0IR6Zi6cvQAS9JT5VChnkhCQgImTpwot5gUEemeubk5S9JTpaS2J7JlyxZs3rwZycnJqFOn\nDt59911pNcKFCxfihx9+kGazE5FusRw9VQUqk8iePXswZ84cqZpvcnIywsLCkJmZidzcXPz8888A\nSkuhlHdRKiIiejWoTCKykifP18sSRRG7du2Slq21sLDA8OHD+W2JiKiaUnlP5NatWxAEAYGBgYiN\njUVsbCwmTJiA4uJiPHv2DF5eXjh8+DCmTJkCKysrQ8ZMRESVhMokkpNTWm45ICAAVlZWsLKykutx\nLFy4EPXr19d/hEREVGmpTCKyy1hmZmZS2/OjQ5hAiIiozHkizs7OCm2iKMq1C4LA9deJiKqhMpPI\niwtRyVYx5AJVRESkNokoSxRMHkREJKMyiXAVQyIiKgsLMBIRkdaYRIiISGsqL2cpG5WlCkdnERFV\nTyqTiCiKUt0sIiIiZdRezlI2vFc2xJeIiKhco7OcnJwgCAJHbhEREQAdrWxIRESVV1ZWFvLz8zAl\ncm25jkvLz4JRkbHafZhEiIj07GnBU0w987lCe05RaaFbSxNLhf3tYW+Q2CqKSYSISI/q1q2rclvh\n40IAgK2trVy7PezVHlde1tbWqCnWQLBP+dZ+mhK5FsVm6meCqEwiK1euVHmQsm2BgYHlCI2IqHpY\nunSpym3Dhw8HAISHhxsqHJ1Tm0ReHIkl+/27775T2J9JhIio+il3AUZlOOyXiKh6UplE2LMgIqKy\nMIkQEZHWWICRiIi0xiRCRERaYxIhIiKtMYkQEZHWmESIiEhrTCJERKQ1JhEiItIakwgREWmNSYSI\niLTGUvBEVOmtXbsWp06dAgA8fvwYwD8VcAHA29sbAQHlK3NOusEkQkRVirm5+csOgZ7DJEJElV5A\nQAB7GpUU74kQEZHWmESIiEhrTCJERKQ1JhEiItIakwgREWmNSYSIiLTGJEJERFpjEiEiIq0xiRAR\nkdaYRIiISGtMIkREpDXWziIiqgbS8rMwJXKtQntOUT4AwNJEsbBlWn4WbMxs1Z6XSYSI6BVXt25d\nldsKHmcDAGxsLBS22dtYoGbNmmrPzSRCRPSKW7p0qcptsnVZwsPDlW5/8OABjh8/rvJ43hMhIiKt\nMYkQEZHWmESIiEhrTCJERKQ13lgnIjKQtWvX4tSpU9Lvjx8/BvDPzW1vb+8qtwwwkwgR0Utibq44\nN6OqeSlJ5OHDh5g7dy7Onj0LURTRsWNHfPHFF2jYsGGZxxYWFiI4OBj79+9HVlYWnJ2dMW3aNHh4\neBggciIi7QUEBFS5nkZZDH5PJD8/H8OHD8edO3ewcOFCLFq0CH/99RdGjBiB/Pz8Mo+fMWMGdu3a\nhX/9619Ys2YN7O3tMWrUKMTFxRkgeiIiep7BeyLbt29HYmIiDh8+jCZNmgAAWrVqhd69eyMiIgL+\n/v4qj42Li8OBAwcwf/58DBgwAADg6ekJX19frFixAqtWrTLEUyAior8ZvCdy/PhxuLu7SwkEABwc\nHNCuXTtERUWpPTYqKgomJibo06eP1GZsbAxfX1+cPn0aRUVFeoubiIgUGTyJxMfH4/XXX1dod3R0\nxO3bt9Uee/v2bTg4OMDMzEzh2KKiIty7d0+nsRIRkXoGTyLp6emwtVWsCmlra4vMzEy1x2ZkZCg9\ntlatWtK5iYjIcKrVEN+SkhIApaPDiIiqq4iICFy8eBEA8OTJEwDA4MGDAZTeZ/bz85P2lX1eyj4/\nX2TwJGJra4uMjAyF9oyMDNjY2Kg91sbGBklJSQrtsh6IrEeiimxiz9ChQzUNl4ioWrh//z4A4PLl\ny1i7VnHdkcePH6NZs2YK7QZPIo6OjoiPj1doj4+PR8uWLcs8NjIyEgUFBXL3ReLj42FiYoKmTZuq\nPd7V1RVbt26Fvb09jI2NtXsCRETVSElJCR4/fgxXV1el2w2eRHr06IFFixbhwYMHcHBwAFBar/7S\npUuYNm1amceGhITg0KFD0hDfkpISHDp0CJ07d4aJiYna483NzTkpkYionJT1QGSMZ82aNctwoQCt\nW7fGwYMHceTIEdSrVw937tzBzJkzYWFhgTlz5kiJICkpCV5eXhAEAZ6engAAe3t7JCQk4IcffkCt\nWrWQmZmJxYsX4/fff8fixYvVrt5FRES6Z/CeiIWFBTZt2oS5c+ciKChIKnsyY8YMWFj8szyjKIrS\nv+fNnz8fwcHBWL58ObKysuDk5IR169bBycnJ0E+FiKjaE8QXP6WJiIg0xPVEiIhIa0wiRESkNSYR\nIiLSGpMIERFpjUnkFZGXl4eUlBSkpKQgLy+vwucrLi5GVFRUta1HpuvXk+hVxdFZVVhKSgrCwsIQ\nFRWF5ORkuW0NGzZEz549MXr0aNSvX7/c587KykKHDh2wefNmrSdoFhcX48SJE2jfvn2ZJWmUyc7O\nlio7t2rVSm4IuD7o+vV89uwZ7t27h4yMDAiCgHr16qFBgwYVirGwsBARERHo3bu3Vv9fqypZle7n\nSxw1bdq0zAnGuj6HNuLi4tC8eXO5KhsXL17E8uXL8fvvvwMA3NzcMGXKFLRr167c53/Z74lqm0Ty\n8vJw6NAhpKSkwNHRET179oSRkXzH7P79+1i1ahXmzZsn1963b1/07NkTAwYMKLNUizq///47fvrp\nJ9SoUQMfffQRWrZsievXr2PZsmW4d+8emjZtinHjxil9Y/35558YPnw4RFFE9+7d4ejoKFU4zsjI\nwO3btxEdHQ0A2Lx5M1q1aqVwjunTp6uMrbi4GAcPHkSnTp1Qp04dCIKABQsWlOv5aZqIDh48iMLC\nQqkKwbNnz7Bw4UJs3boVxcXFAAAzMzMEBARgwoQJ5YrhRSdPnsQ333yjsHaNLl5PmYSEBISEhCAm\nJkZhtc6GDRtiyJAhGDlyJGrUKP80LU1eU3d3d+n92blzZ4X3tS4dOXIE//rXv3Dz5k29HB8XF4cV\nK1YoXS/IxMQEnTt3xqRJk9TOE6vIOXSRAJydnbF9+3a4ubkBAGJjY+Hv74969eqha9euAICYmBik\npqZi27ZtKsuLqKLp35m+klm1TCJpaWkYPHiwVHAMAF5//XUsXbpUbq2TK1euwM/PT+ENLnuzCYIA\nFxcXDBw4EL6+vuX6tn358mUMGzYMgiDA1NQUgiAgNDQUY8eORe3atdG6dWtcu3YNqamp2LVrl8Ia\nLCNHjkRxcTG+//57WFlZKX2M7OxsjBs3DiYmJli/fr3CdicnJ1hbW8Pa2lphmyiKePjwIerUqSPF\np2zRMF0kov79+2PQoEEYNmwYAGDlypX4/vvv8dFHH6Fz584ASj/8d+3ahRkzZkj7aUPVh5YuXk8A\nuHbtGoYPHw4LCwu0b98epqamuHr1KhITE+Hv74/s7GwcPnwYrVq1QlhYmMLaOID6AqElJSW4fPky\nWrVqBWtrawiCgC1btsjt4+TkhBo1aqCkpAR16tRB//79MWDAALWJT1v6TCKxsbEYNWoUGjZsCF9f\nXzg6Osot+xAfH49Dhw4hMTER69atU/oBWtFz6CIBODk5YceOHdI5RowYgYyMDGzduhWWlpYASt9b\nQ4YMQdOmTfHdd98pnKOi7wldPRdlqlUpeJkVK1agoKAAW7ZswRtvvIHz589j7ty58PPzw6pVq+Dl\n5VXmOZYuXYo7d+5g3759mD17NubPn4/u3btj4MCB6NKlS5kFHletWgVXV1esW7cOFhYW+PbbbzFp\n0iS4uroiNDQUJiYmyMvLw/Dhw7FmzRosXrxY7vjLly8jJCRE5QceAFhZWWHMmDGYNGmS0u2DBg3C\n/v374efnh1GjRsnFnJmZiQ4dOiA4OFgqO6PMvn371CYiQRDwxx9/SIlImfv378v16H788UeMGTMG\nkydPltp8fHxgY2ODLVu2KE0isrLWZbl165bSdl28ngCwcOFCuLi4IDQ0VLr8JooiZs+ejV9++QW7\ndu3C+PHj8eGHH2LNmjVKz/Xrr7+ibt26aN68ucI22Xc+IyMjtT2MsLAwPHz4EHv37sXGjRuxYcMG\nODs74/3334evry/s7OxUHgsAe/fuVbtdRvYNVtfHA8DixYvRpUsXLFu2TOXf0/jx4zFlyhQsWrQI\n27dv1/k5XvyOHRISAkdHR7kEMHXqVAwZMgTff/+90gTwoitXrmD27NnS8UDpe+vTTz9V2dvXxXtC\nH89FduJqx8fHR9y5c6dcW3Z2tjhmzBjRzc1NjIqKEkVRFC9fviw6OTkpHN+6dWvxypUr0u+xsbHi\nV199JXp6eopOTk7iW2+9Jc6dO1e8ceOGyhg6deokHj58WPo9MTFRbN26tXjs2DG5/fbs2SP26tVL\n4XgvLy/xwIEDZT7XAwcOiB06dFC5PTY2Vnz33XfFvn37ihcuXJDaMzMzxdatW8u1KfPVV1+Jbdu2\nFdesWSMWFxfLbcvIyNDoHB4eHuKJEyek39u0aaP0mLNnz4qurq5Kz9G6dWvRycmpzH+y/V6kq9ez\nbdu24vHjxxXaU1JSRCcnJ/HevXuiKIpieHi40v+voiiKa9asEdu2bSt+/fXXYkZGhtw2TV7TF9+f\nycnJ4urVq8U+ffqIrVu3Fl1dXcUJEyaIx44dE4uKilSeQ/Z6lfVP1d9IRY4XRVF0c3MTz507p/J5\nypw9e1Z0c3PTyzlefC3d3d3Fffv2Key3e/du0cvLS+m5XzyHq6urGBsbq7Df+fPnRRcXF6XnqOh7\nQlfPRZlq2RN59OgRXnvtNbk2S0tLrFq1CtOnT8ekSZMwb968MkvLy7Rv3x7t27fHl19+icjISOzd\nuxdbtmxBeHg4WrVqhZ9++knhmMzMTNSpU0f6vV69egCgcOO1cePGSElJUTi+Z8+eWLhwIezt7VX2\nFGJjY7Fo0SL4+PiojX3Pnj0ICwtDQEAAevfujaCgII1vNn777bd47733MGvWLPz000+YNWuWFI+q\nnseL3N3dcfjwYXTp0gUA0KJFC1y/fl3heV2/fl1lkU1LS0t06tQJQ4YMUftYFy9exPfff6/QrqvX\ns0aNGgr3QQCgoKAAoihK1+Rff/11lYujjRkzBn369MGsWbPwzjvvYPr06dL9Ik1f0+c1aNAAY8eO\nxdixY3H16lXs2bMHBw8eRGRkJOzs7HDu3DmFY2xtbdGjRw+MGzdO7blPnjyJ//3vfzo/HgCsra3x\n4MEDtccDpVXAlfWEdXWO55WUlKBRo0YK7Y0bN0Z2drbK47Zv347jx48DKH2vPnr0SGGf1NRUlTHo\n+j0BaP9cXlQtk0jdunXx119/KVz/NDY2xuLFi1GzZk0EBQXh/fffL9d5TU1N0bdvX/Tt2xdPnjzB\nvn37VHbra9WqJfdGMjY2xttvv61wX+Xp06dKRyUFBQVh7NixGD58OOrVq4fXX39d7kZwfHw8UlJS\n4O7ujqCgILVx16hRA5999pncm3T06NEavzkrmogCAwMxbNgw2NjYYOTIkZg2bRo+//xzCIKAjh07\nAgBOnz6N7777Dv7+/krP0aZNG2RnZ+Ott95S+1iqlmDW1ev51ltvYcWKFXB1dZWWOsjIyMCcOXPk\nLkdkZ2erXYStSZMmWLduHfbv34/58+fjxx9/xDfffCN92dCWm5sb3Nzc8MUXX+D48eMq35+urq64\nf/9+mV+k7O3t9XI8APTr1w8LFy5EjRo10KdPH4X7RwUFBTh06BAWL16s8m9VF+eoaAIAgF27dsn9\nHhMTgz59+si1nT9/XunlKhldvCd08VxeVC2TSNu2bXHo0CF8+OGHCtsEQZCuV27cuFHrLF+nTh2M\nHDkSI0eOVLq9VatW+O2339C3b1/pcVesWKGw37Vr15S+sWxsbLBt2zZERkbi+PHjiI+PlwYK2Nra\nomPHjujRowd69uyp8XNo1qwZNmzYgJ9++gkLFixQuIaqTkUSUdu2bbFy5Up88cUX2LRpE2xtbVFQ\nUID58+dL+4iiiIEDB6ocneXq6ordu3eX+VgWFhZo2LChQruuXs/p06djyJAheOedd9CsWTOYmJjg\n7t27KCkpwZIlS6RjL168qNGNy379+qFr165YsGABBgwYgA8//FDr9+TzTExM8Pbbb+Ptt99Wut3F\nxUXpzdkX1a5dW+kN7YoeDwBTpkzBo0eP8J///AdfffUVHBwc5BL7gwcPUFRUhL59+2LKlCl6O0dF\nE0BcXJzqF+A5zZo1Q7du3crcryLvCV0ksxdVy9FZ586dQ0REBGbNmqX2BmNoaChOnTqFzZs3y7XP\nmDED48ePR5MmTbSO4fr168jMzCzzm/PXX38Nd3d3fPDBBxqf+9mzZ/jzzz/RrFkzredWZGdn48GD\nB1qfQ5aI0tLSsHnzZrU352VycnJw6NAh/Pbbb3j06BFEUUStWrXg6OgIHx8fhRFqLx6bnp6Oxo0b\nlztWXUtPT8cPP/yAq1evwsjICM2bN4efn5/c+6W4uBiCIJRrhc3Y2FjMnDkTt2/fVvuarly5Eh99\n9NErM48kLi4OUVFRuH37trS0to2NDRwdHdGjRw84Ozsb5BzqrF+/Hs2bN0f37t0rdJ7yio2Nxddf\nf42EhASN/87KUt7nUi2TyKtOFxMFdXGO3NxcPH36FPb29jA1NdXqHKmpqQBQ4QXHnjx5AltbW63m\nZhDpS0Un5FYG1fov6vz580hJSUHLli3h4uKisD0lJQU7d+5EYGCgXo5PTk7GkSNHYGxsDF9fX9Su\nXRtJSUkIDQ2VJht++umnSq8rL1++XOXzKiwshCiK2LlzJ86cOQNBEJQOJdXFOZRJS0vDli1bcPXq\nVQCll6uGDRum8o/k/PnzyM/Pl8aqA6UT+tasWYMnT54AKL05PHnyZOlmojIRERHYu3cvRFGEv78/\n+vTpg59//hn/+9//kJ6eDjMzMwwZMgTTp09X2v0vKirCjz/+iMjISPz555/IyMiAkZER7O3t0b59\newwZMgTu7u4avQYyeXl50n0YGxsbvc+6N4T09HTcu3cP9evXf2V6O+Wlq2oKeXl5CAwMrNKVIapl\nTyQnJwejRo3ClStXpLkMHTt2xNy5c+X+KFRNNqzo8QBw+/ZtDB48WBoFUa9ePWzcuBEjR45Ebm4u\nmjZtioSEBJiYmGDv3r0KoyicnJwgCILK+xbPbxMEQWkMujhHhw4dsGHDBimJJicnw8/PD6mpqdII\nuDt37qBBgwbYsWOH0h7Fhx9+KN1DAYCtW7di9uzZ8Pb2RqdOnQAAp06dwtmzZ7FkyRLpPtLzdu3a\nhf/+979o27YtrKys8Msvv+Cbb77BzJkz8c4778DNzQ1XrlzBwYMHMXPmTPj5+ckd/+TJE/j7++PW\nrVuoVasWTE1N8fjxYxgbG8Pb2xt3797FnTt3EBAQgH//+99KXy8ZXZRPqUg1g+zsbFhaWsolyjt3\n7mD16tVyiX3s2LEKoxRlSkpKsGzZMikpf/rpp/j0008RFhaG5cuXS5UEevXqhcWLFyvtaVakKgRQ\nOSpD6KKawqteGaJa9kTWrFmD27dvY968eXjjjTdw4cIFhISEYNCgQVi3bh0cHR31ejxQOtGnQYMG\nCAkJga2tLWbOnIlx48ahbt262LhxI6ytrZGamopPPvkEoaGhmDVrltzxnTp1wh9//IEvvvhC4UNV\nNlGwrGukujhHZmYmSkpKpN8XL16MoqIi7Ny5E23atAFQ+kccEBCAkJAQfPPNNwrnuHPnjtw16U2b\nNmHIkCGYOXOm1Obv748vv/wSa9asUZpEtm7disGDB0vn37FjB2bNmoUhQ4bgv//9r7Sfra0ttm/f\nrpBEFixYgJycHPz444/SDe/ExEQEBQWhZs2aOHjwIE6ePIkJEyagRYsWKntEmpRP2bdvH/bt26ey\nfMqL1Qx+/PFHpdUM/P39lVYz8PT0lJuZ/Oeff0pDn9u3bw8AOHr0KKKjo7F9+3aliWTTpk0ICwtD\nnz59YGVlhZCQEOTn52PVqlUYNWqUlJTXr1+PDRs2YOzYsXLHa1oVIi0tDXv37lWaRBISEpCQkICw\nsDCdVYYo72u5evVqDBo0SPp91apV2Lx5s0I1hVWrVsHW1lbpRFhdTMgtKxGJoojvv/9ebSLSxXNR\nSuMZJa+Q3r17i5s2bZJre/jwoThw4ECxQ4cO0oQcVZMNK3q8KIpily5dxJ9++kn6/c6dO2Lr1q0V\nJrxt27ZNfOedd5SeY//+/WKnTp3ETz/9VPzrr7+kdk0nCuriHC9OYOrQoYPCayOKorhu3TqxW7du\nSs/Rtm1b8ezZs9Lvbdq0EX/55ReF/U6fPq1ysuH//d//yZ1DFv+LE81Onz4ttmvXTuH4Dh06yP3/\nkImPjxednZ3FJ0+eiKIoikuXLhUHDhyoNAZRFEV/f39x2LBhYlZWlsp9srKyxGHDhokjR45Uuj0g\nIEAcPHiwmJ2dLZaUlIgzZ84UO3XqJPr7+4uFhYWiKIpibm6u+OGHH4pTp05VOP7F/yefffaZ2KNH\nD4d8QQ0AABFISURBVDE5OVlqS0xMFLt37y5OmzZNaQy+vr5icHCw9PvRo0dFZ2dnccWKFXL7BQcH\ni++++67C8TNnzhS9vb3Fixcvivn5+eKJEyfE3r17i+3atZP7f6vub0T297By5Urx7bffliZKTpw4\nUYyOjlaY3KpMRV/LF9+bXbt2FZctW6aw36JFi8TevXsrjUEXE3Jbt24tenh4iN27d1f4161bN9HJ\nyUns1KmT2L17d7FHjx5Kz6GL56JMtSwFn5ycrDAao379+tiyZQtatWqFkSNH4vz583o7Hij9Bvb8\nJSrZqCLZ3AKZ5s2bq5yU9u677+LAgQNo1KgR+vfvjxUrVqCwsFDt4+rjHM/LysqSeiDPa9OmDR4/\nfqz0GBcXF5w8eVL6vVGjRnLfYGXu378vfat/kbm5uVzJdtnPBQUFcvvl5+crrVeVn5+v9BuunZ0d\nnj17Jt2b8fDwQEJCgtIYgNJvvmPHjtWofMqlS5eUbr9x4wZGjhwJS0tLGBkZYcyYMUhNTcXQoUOl\nuTcWFhYYOnSodHlKnYsXL+Kzzz6Tm8jaqFEjBAQEKJ1oCJT2wp4fOfjWW2/h2bNnePPNN+X28/Ly\nUjqZ78yZM5g0aRI8PDxgZmaGLl26YNeuXfDw8MCYMWOkYpZlcXBwwIQJE3DkyBFs3boVAwcOxC+/\n/ILx48fD29sb8+bNU1u3q6KvZY0aNeSKNj5+/Fiau/S8Tp06ITExUWkM3377LcLCwrB//370799f\nrkSPpkNzBw0ahOLiYvj5+eHYsWOIjo6W/v30008QRRHBwcGIjo5WWuNOV89FmWqZROzs7JR+MNes\nWRNhYWFo3749xo4di5iYGL0cD5ReVklLS5N+NzY2houLi8KHT3Z2ttpJe7a2tpg9ezbWrVuHo0eP\nwtfXFydOnCjXXIKKnuP333/HuXPncO7cOdSuXVvpbNfs7GyVN+wCAgKwefNmbN68GYWFhRg/fjyW\nLl2KyMhI5ObmIjc3F0ePHsWyZcvQu3dvpedwdnbGpk2bpNnioaGhUmKXXe8tLi7GDz/8oPRyo4uL\nCyIiIvDs2TO59vDwcJibm8sNz1U30szMzEzlhMbnZWVlqTxPRasZvCgvLw8tWrRQaG/RooXK9WKs\nrKzktsl+lg2Pfb5dWcJUVxXCx8cHkyZNwv79+8uM/Xnt27fHt99+i9OnT2PJkiVwdXXFli1b8P77\n7+O9995TekxFX0tZNQUZWTWFF6mrpiCLfc+ePejXrx8CAgIQFBQk9/dfFl0kIl09lxdVy3siLi4u\nOHbsGPr166ewzczMDKtWrcLUqVPx/fffK/0fVNHjAaBly5a4cuWKNNnLyMhIYSIQAPzxxx8azUfx\n8PDAnj17sHbtWrl7AOWh7TnmzJkD4J8CbxcuXFCYNHXz5k2lJRYAoGvXrvjyyy8xb948LF26FC1a\ntEBBQQEmTpwot1+HDh1U3tQeP348Pv30U3h6esLExASiKCI8PByTJk1C37590bp1a/zxxx+4f/8+\nQkNDFY6fNGkSRo8ejT59+qBjx44wMTHBlStXcPXqVYwbNw7m5uYASr/ZqrvnpYvyKRWtZgAA0dHR\n+PPPPwGUfkl4+vSpwj4ZGRlyRQCf5+7ujtWrV0uVnhctWgRHR0eEhobizTffhJWVFbKysrBu3Tql\nPU99VYUADFsZQhfVFGRehcoQylTL0VmHDx/Ghg0bsHr1apWTDUVRxKxZs3Dq1CmFrndFjwdK/2dl\nZGTA19dXbayBgYFo27atNHJJE8nJybh//z7atGmj9rKKLs5x4cIFhTZra2uFy32ff/45Xn/9dYwZ\nM0bluRITE/Hjjz9Kkw2fPXsGOzs7ODo6olevXnJDgJX5448/cODAARQVFeH999/H66+/jrt372LJ\nkiW4desW6tati2HDhqnszcTGxmLlypW4cuUKjI2N0bx5cwwfPlzuy8LNmzdhYmKiMpFkZmZi7Nix\nuHz5cpnlU0JDQ5WWPhk9ejRee+01fPnll2qf79KlS3Hx4kVs27ZNrl3Zuhh+fn4KgzMWLlyIixcv\nYufOnQr7x8fH45NPPpF6IHZ2doiIiMCECROQnJyMpk2b4t69e8jPz8cPP/wg3cSXmTp1KtLT07Fu\n3TqV8c+fP1+qCqFq9ODzJdS1UdHXEiid1f3FF1/g6dOnsLW1RV5entwlX/HvagqzZ88u1zwkbSbk\nyty9exezZs3C9evXMXr0aAQHByM8PLzMc+jjuVTLJEKkb8+XT5F9ENva2kozpNWVT6loNQNl17NN\nTU0V6lQtWLAAjo6OKqshPHr0CMePH0dxcTHeeecd1KlTB2lpaVi7di1u3boFe3t7+Pn5KZ07U9Gq\nEEDlqgxRkWoK6lS1yhDKMIkQEb0kr0JliGp5T4ToZbt48f/bu/+Yquo/juPPA2ggTJm1xNR+LDeu\nAsmgJJaGkygoxd8/JkMiNiUZw8kfpptSa7M2mFRObEOCzKmjjBri4uZGFjnsDjVwXtqk6aYxK9ad\no646L/f7x9357B44/PDeq/D1vh9/HeFzPnwOf/D2nPs5r7eNffv2cejQoTGb40Gswd9Uh0DM4U8y\nhPf5YWFhvP7666bn5+fn89RTT5me/7AlQwwkdyJCjAF/28oGYo77uYZApDqMh2SI0Z4/ceJEGhoa\nTDePPEzJEGbkTkSIAPrjjz9GNW647Z3+zjEe1hCIVIfxkAzh7/nwcCVDmJEiIkQALV68eFRbNvX/\nWd+POcbDGqxWK8XFxeqxyLPPPqs6Hebk5FBdXT3irqtAzHH+/HlKS0tVf4zS0lIyMzPZu3eviiF5\n7LHHyMvL4/PPPw/4+QA1NTWcOHGCPXv2cPz4cXbv3q0effnaG6a1tZWioiLD9uqEhAQ2bdpkukkB\nPFlZ3tll169fJzMzc9C4rKws026sQ5EiIkQAhYeH8/zzzw+5jVh38eJF6uvr78sc42ENw6U6bN68\nmfz8fKqqqtT7N2YCMYe/yRCBSJYATzLEwoULqaioIDs7m4KCAgoLC4ccPxJ/kiH0nWp6MkRKSoph\n3HDJEGakiAgRQBaLhdDQUNasWTPsuMmTJw/5B9zfOcbDGkZKdSguLlaFYCiBmMPfZIhAJUvoc73/\n/vssW7aMd999l8bGRkpKSu4pGeLff/8F8DkZoqioiCeeeIJ169axZcsWysvLiY6ONrxs+NFHH434\n/pq3oIw9EeJ+iYuLM42SMDPUB63+zjFe1vD999+bjtdTHdLS0jhw4MCQ8wZiDj0ZQqcnQwyMgRkq\nGcLf883oyRArVqy452SIt956i/z8fP7++2/TF31HkwxRUVFBSkoKhw8fVskQycnJJCcnU1JSQmxs\n7IjtDrzJ7iwhAujGjRtcvXqV+fPnj9kc42ENgUh1GA/JEPczWQL+P5MhBpIiIoQQwmfyOEsIIYTP\npIgIIYTwmRQRIYQQPpMiIh56O3bswGKxYLFYRv0mtr/KysqwWCzk5uaqr/3yyy9qHRaLhbi4OJKS\nksjIyODtt9/m5MmTg5pijaW2tjYsFguJiYnDvgMhgpsUERE0fH07+F51d3fz1VdfoWkaW7ZsMV2H\npmn09/fjdDq5du0aLS0tbNu2jby8vEHdA8fKiy++SGJiIrdv36aysnKslyPGKSkiQgRYbW0tLpeL\nGTNmDNnH4oUXXsBut2Oz2fj000+ZPXs2mqZhs9koKSl5wCse2tq1a3G73TQ1NRk6BAqhkyIigpLT\n6eSTTz5hyZIlzJs3j8TERFasWEFdXZ0h6A48vRZKSkpISkoiJSWF3bt309LSoh5L7dixQ43977//\naGpqQtO0UQXYRUVFkZaWRk1NjYrvOHv2LK2trWpMWVkZK1euJDU1lfj4eJKSklizZo2hC19XV5da\nz8DwvcOHD6vvnTx5Uv2MgoICFixYQHx8PKmpqaxbt46KigrDua+++iphYWG4XC7T9s1CSBERQcfp\ndJKTk0NVVRXd3d3cuXOH27dv09XVxYcffmh4BHXnzh3efPNNmpubcTqd3Lx5ky+//JKysjL1WMpb\ne3s7TqcT8PTEHq1p06YZ2vC2tLSo44aGBux2Ow6HA5fLhdPppLOzk/fee4/9+/cDnpgSPQW2sbFR\nrQGgqakJ8PQbz8jIoKenh82bN3PmzBl6e3txuVw4HA46OjoGFYqoqChiY2Nxu9389NNPo74eETyk\niIigU1dXx6VLl9A0jYULF/Lzzz9z6tQp9fbvjz/+qP7wfvPNN1y+fBlN04iPj+eHH36gubmZqKgo\n07iPzs5OdWzW63w43lEa3i1uP/jgA6xWK+fOnePixYt8++23xMTEABiaQeXl5QGePhyNjY2A543o\nCxcuoGkay5cvZ8KECXR2dnLr1i3A01u8s7OT1tZWamtrycnJGbQu/Tq8r00InRQREXROnz6tjrdt\n28bUqVOZMWMGRUVFg8a0tbWprxUWFjJt2jTVyc6M3mYUGLa3uJnhmhbt3LmTRYsWkZCQQHZ2ttot\ndfPmTRUQmJ6ervKbjh07BnjuQvR5V69eDRgTaI8ePcpnn33G+fPneeaZZ0y7A+rXcffuXdUvXgid\npPiKoPPPP/+o4+nTp6tjPeYbUO1Cvcd6B9t5nxcov//+uzrW/9A3NTVRWlpqeGymH+vFQb+r0DSN\n3Nxc9uzZg91up6OjQ30GMm/ePNXEae7cuWzdupWDBw9is9mw2WxqroyMDD7++GND3wlvkpIkBpI7\nERF0pk6dqo57enrUsfc7JI8++ihgvJu4ceOG6Xne9PPAWIBG0tPTw4kTJ9S/09PTAVQRANi1axcd\nHR3Y7fZBwXu6VatWqSC/8vJy9dhu7dq1hnGFhYWcPXuWhoYG9u7dS3Z2NgCnTp3iu+++M4zVryM0\nNPSe767Ew0+KiAg6ixYtUseVlZX09vZy7do19SG19xjvLbrV1dX8+eefXL16ldraWtO5ExIS1HFX\nV9eIa+nr66OlpYWCggKcTqfqI67/3NDQUDU2MjKS/v5+jh8/PmRf9MjISFatWoXb7cZmswGe/htZ\nWVlqTHd3N/v27ePSpUvExMTwyiuvGK5z4AuZ+nXEx8ePeD0i+MjjLBF0Nm7cSHNzM3a7ndOnT/PS\nSy+p72maRlpamtqeu2zZMg4dOsTly5dpb2/n5ZdfBuDxxx83nKNLTk4mIiKCW7du0d7ebhqr7Xa7\n1dvr3nNomsb8+fMNL/ZlZGRgtVoB2L59O9u3byciIoKYmJgh74Zyc3P54osv1KOnN954w9CoyOFw\nsH//fkPR1IWEhBh+H319ffz2229ommb4uhA6uRMRQcF7O25ERARHjhyhqKiI2bNn88gjjxAeHs7c\nuXN55513qKqqUudNnDiR2tpaXnvtNSZNmkR0dDTr169n69atakx0dLQ6joyMJCsrC7fbbXgUNXAd\nmqYRGhrKpEmTePLJJ1m8eDGVlZXU1dUxefJkNX7p0qXs3LmTmTNnEh4eznPPPUd1dTWzZs0y3WIM\nns9T0tPTVREZ2J1w1qxZbNiwgTlz5jBlyhTCwsKYMmUKqampHDx40PCozGq1cvfuXUJCQtQH80J4\nk34iQozg3LlzPP300+qzlL/++ovi4mK1dba6upoFCxao8d3d3WRnZ9Pf309NTY1qPfqguFwuNm7c\nSHt7O3FxcX69JLh+/XouXLjA0qVLKS8vD+AqxcNCHmcJMYK6ujqsVivR0dFMmDCB3t5e+vv71Vvp\n3gUEPC1VV69eTX19PQcOHHigRSQzMxOHw4HD4UDTNIqLi32eq62tjV9//ZXw8PB7apcqgovciQgx\ngq+//pr6+nquXLlCX18fkZGRxMbGsnLlSpYvXz7WyzOYM2cOISEhTJ8+nU2bNg3alSVEoEkREUII\n4TP5YF0IIYTPpIgIIYTwmRQRIYQQPpMiIoQQwmdSRIQQQvhMiogQQgif/Q/iCoP/IxvJ/gAAAABJ\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5ddc1d0d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.axes as ax\n",
    "print('{{{time-varying-log-time}}}')\n",
    "sb.boxplot(x = 'alt_log_timepoint_end',\n",
    "           y = 'exp(beta)',\n",
    "           data = tvc_model['time_betas'],\n",
    "           whis=[2.5, 97.5],\n",
    "           fliersize=0)\n",
    "_ = plt.ylim([0, 2])\n",
    "_ = plt.hlines(1, -1, 20, linestyles='--')\n",
    "_ = plt.xticks(rotation=\"vertical\")\n",
    "_ = plt.xlabel('log(Days)')\n",
    "_ = plt.ylabel('HR for # Missense SNV / MB')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{{{time-varying-time}}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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sUwo+M7+oC62NpYXa+k72RgmNiMhoZJPI/PnzjRlHuVLcXOv/zNNeVaXdyV51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\nSvdqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5ddc1db090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('{{{time-varying-time}}}')\n",
    "sb.boxplot(x = 'alt_timepoint_end',\n",
    "           y = 'exp(beta)',\n",
    "           data = tvc_model['time_betas'],\n",
    "           whis=[2.5, 97.5],\n",
    "           fliersize=0)\n",
    "_ = plt.ylim([0, 2])\n",
    "_ = plt.hlines(1, -1, 20, linestyles='--')\n",
    "_ = plt.xticks(rotation=\"vertical\")\n",
    "_ = plt.xlabel('Days')\n",
    "_ = plt.ylabel('HR for # Missense SNV / MB')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## plot estimated first-diff in beta for each timepoint "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# plot first-difference of timepoint-specific betas\n",
    "tvc_model['time_beta_deltas'] = extract_time_betas(tvc_model, element='beta_time_deltas', value_name='beta_delta')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Dhvj5558xcuTIPPtWKBSYNWsWVq5cic2bN8PW1ha9evXSGPKibNmyCAkJwfLly7F161bE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\nEEJYJA2EEEIIi6SBEEIIYdH/AUsUKKXXSsuDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5ddc1c6290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tvc_model['time_beta_deltas'].boxplot(\n",
    "    column='exp(beta_delta)',\n",
    "    by='end_time',\n",
    "    whis=[2.5, 97.5],\n",
    "    positions=np.log(tvc_model['time_beta_deltas'].end_time.drop_duplicates())\n",
    ")\n",
    "_ = plt.ylim([0, 2])\n",
    "_ = plt.hlines(1, -1, 20, linestyles='--')\n",
    "_ = plt.xticks(rotation=\"vertical\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7e6Np06b6jI+oyFjK5s2gvY5YJgDAysJKbZuVhQPriJWwIpU9cXd3V07rJSoN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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5ddc1d0150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.boxplot(x = 'alt_log_timepoint_end',\n",
    "           y = 'exp(beta_delta)',\n",
    "           data = tvc_model['time_beta_deltas'],\n",
    "           whis=[2.5, 97.5],\n",
    "           fliersize=0)\n",
    "_ = plt.ylim([0, 2])\n",
    "_ = plt.hlines(1, -1, 20, linestyles='--')\n",
    "_ = plt.xticks(rotation='vertical')\n",
    "_ = plt.xlabel('log(days)')\n",
    "_ = plt.ylabel('change in exp(beta)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## compare estimated betas from each model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T05:44:23.459109",
     "start_time": "2016-08-18T05:44:16.308Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HIVVEREREnI5CqoiIiIg4HYVUEREREXE6CqkiIiIi4nQUUkVERETE6SikioiI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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5dfd33a290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.boxplot(x = 'timepoint_id',\n",
    "           y = 'exp(beta)',\n",
    "           data = tvc_model['time_betas'].append(spline_model['time_betas']),\n",
    "           hue = 'model_cohort',\n",
    "           whis=[2.5, 97.5],\n",
    "           fliersize=0)\n",
    "_ = plt.ylim([0,4])\n",
    "_ = plt.hlines(1, -1, 20, linestyles='--')\n",
    "_ = plt.xticks(rotation='vertical')\n",
    "_ = plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T05:44:23.459460",
     "start_time": "2016-08-18T05:44:16.310Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JKrt8NGvWTL///rsKCwvNtg8YMEArV67UN998o3vuuce0ffjw4Zo/f76OHDkiSfrpp5/M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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5dbf5fe710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.boxplot(x = 'alt_timepoint_end',\n",
    "           y = 'exp(beta)',\n",
    "           data = tvc_model['time_betas'].append(spline_model['time_betas']),\n",
    "           hue = 'model_cohort',\n",
    "           whis=[2.5, 97.5],\n",
    "           fliersize=0)\n",
    "_ = plt.ylim([0,4])\n",
    "_ = plt.hlines(1, -1, 20, linestyles='--')\n",
    "_ = plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "_ = plt.xticks(rotation=\"vertical\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T05:44:23.459824",
     "start_time": "2016-08-18T05:44:16.311Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BU0aj0fRTpEWLFtq4caO+/PJLXbx4Ud7e3oqIiNDMmTMVFhZm68sAAACAFdlt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\nSCWEEEIIIe0OBamEEEIIIaTd+T/qfb9dhpm/cQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5ddc0adf90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.boxplot(x = 'alt_log_timepoint_end',\n",
    "           y = 'exp(beta)',\n",
    "           data = tvc_model['time_betas'].append(spline_model['time_betas']),\n",
    "           hue = 'model_cohort',\n",
    "           whis=[2.5, 97.5],\n",
    "           fliersize=0)\n",
    "_ = plt.ylim([0,4])\n",
    "_ = plt.hlines(1, -1, 20, linestyles='--')\n",
    "_ = plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "_ = plt.xticks(rotation=\"vertical\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### tvc with linear spline; estimated knot position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T05:44:23.460173",
     "start_time": "2016-08-18T05:44:16.314Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../utils/stan/logistic_model.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_custom_coefs_expressed_missense_and_neoant_mutations.stan\n",
      "../utils/stan/logistic_model_by_group.stan\n",
      "../utils/stan/pem_survival_model_unstructured_varcoef.stan\n",
      "../utils/stan/pem_survival_model_unstructured_varcoef_hsprior.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_tvc.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_alt.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_bspline_est_xi.stan\n",
      "../utils/stan/pem_survival_model_varying_coefs3.stan\n",
      "../utils/stan/pem_survival_model_randomwalk.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_custom_coefs_rate_only.stan\n",
      "../utils/stan/pem_survival_model_gamma.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_custom_coefs_missense_and_neoant_rates.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_tvc2.stan\n",
      "../utils/stan/pem_survival_model_varying_coefs2.stan\n",
      "../utils/stan/pem_survival_model_varying_coefs4.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_bspline_est_xi2.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_bspline.stan\n",
      "../utils/stan/pem_survival_model_randomwalk2.stan\n",
      "../utils/stan/pem_survival_model_randomwalk_bspline2.stan\n"
     ]
    }
   ],
   "source": [
    "## load stan models again (for convenience)\n",
    "models = survivalstan.utils.read_files('../utils/stan', pattern = \"*.stan\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T05:44:23.460541",
     "start_time": "2016-08-18T05:44:16.316Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NOT reusing model.\n",
      "Ran in 253.696 sec.\n"
     ]
    }
   ],
   "source": [
    "spline_model2 = survivalstan.fit_stan_survival_model(\n",
    "    formula = '~ log_mut_centered',\n",
    "    df = dlong,\n",
    "    timepoint_end_col = 'end_time',\n",
    "    sample_col = 'patient_id',\n",
    "    event_col = 'end_failure',\n",
    "    chains = 4,\n",
    "    iter = 15000,\n",
    "    model_code = models['pem_survival_model_randomwalk_bspline_est_xi2.stan'],\n",
    "    model_cohort = 'random-walk prior - linear spline estimating knot',\n",
    "    stan_data = {'H': 1, 'power': 1},\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T05:44:23.460894",
     "start_time": "2016-08-18T05:44:16.319Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#print(spline_model2['fit'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T05:44:23.461301",
     "start_time": "2016-08-18T05:44:16.320Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'diff': 1.4327601373406302, 'se_diff': 2.513869849498461}"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stanity.loo_compare(spline_model['loo'], spline_model2['loo'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T05:44:23.461666",
     "start_time": "2016-08-18T05:44:16.322Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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cvXpVrszPzw9+fn5V1iUiIsNRm0QqN6nVRIcOHapdh4iI6q860WOdiIjqp2r1E8nJycG+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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5dbf266110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "spline_model2['time_betas'] = extract_time_betas(spline_model2)\n",
    "sb.boxplot(x = 'alt_timepoint_end',\n",
    "           y = 'exp(beta)',\n",
    "           data = spline_model2['time_betas'],\n",
    "           whis=[2.5, 97.5],\n",
    "           fliersize=0\n",
    "          )\n",
    "_ = plt.ylim([0,4])\n",
    "_ = plt.xticks(rotation=\"vertical\")\n",
    "_ = plt.hlines(1, -1, 20, linestyles='--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T05:44:23.462047",
     "start_time": "2016-08-18T05:44:16.325Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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USeF2kUiELl26oEuXLpgwYQIyMjIQGxur8gmPHz9ea1AdOnTAjRs3ZMp8fHzg4+NTa10i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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5dfc17ab50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.boxplot(x = 'alt_log_timepoint_end',\n",
    "           y = 'exp(beta)',\n",
    "           data = spline_model2['time_betas'],\n",
    "           whis=[2.5, 97.5],\n",
    "           fliersize=0\n",
    "          )\n",
    "_ = plt.ylim([0,4])\n",
    "_ = plt.xticks(rotation=\"vertical\")\n",
    "_ = plt.hlines(1, -1, 20, linestyles='--')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### tvc model with 2 breakpoints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T05:44:23.462434",
     "start_time": "2016-08-18T05:44:16.327Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing model.\n",
      "Ran in 312.013 sec.\n"
     ]
    }
   ],
   "source": [
    "spline_model_h2 = survivalstan.fit_stan_survival_model(\n",
    "    formula = '~ log_mut_centered',\n",
    "    df = dlong,\n",
    "    timepoint_end_col = 'end_time',\n",
    "    sample_col = 'patient_id',\n",
    "    event_col = 'end_failure',\n",
    "    chains = 4,\n",
    "    iter = 15000,\n",
    "    model_code = models['pem_survival_model_randomwalk_bspline_est_xi2.stan'],\n",
    "    model_cohort = 'random-walk prior - linear spline estimating knot',\n",
    "    stan_data = {'H': 2, 'power': 1},\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T05:44:23.462779",
     "start_time": "2016-08-18T05:44:16.329Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# print(spline_model_h2['fit'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T05:44:23.463123",
     "start_time": "2016-08-18T05:44:16.331Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'diff': 1.4938997121271338, 'se_diff': 2.4096078316405589}"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stanity.loo_compare(spline_model['loo'], spline_model_h2['loo'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T05:44:23.463466",
     "start_time": "2016-08-18T05:44:16.334Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "spline_model_h2['time_betas'] = extract_time_betas(spline_model_h2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T05:44:23.463807",
     "start_time": "2016-08-18T05:44:16.336Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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iIv1RmUSqT6lVR+fOnWtdh4iIGi6DWLFOREQNU60WG+bk5ODAgQO4c+cOiouLZY6JRCK1\nFgwSEdGLQ+0kcv/+ffzP//wPnjx5IndMEAQmESKiRkjtJBIZGYns7GxdxkJERA2M2mMiV65cgUgk\nwgcffACg6vLVN998A1dXV7zyyivYvHmzzoIkIiLDpHYSkfRCpk6dKi3z9PTEqlWrcPfuXZw7d077\n0RERkUFTO4mYmJgAACwtLWFqagoA+Pvvv6X3RT948KAOwiMiIkOm9phI8+bN8ejRI+Tk5KBNmzZ4\n8OABAgMDpQmltLRUZ0ESEZFhUrsn0qVLFwDAnTt30K9fP+mWJ7du3YJIJIKbm5vOgiQiIsOkdk/E\n398fbm5uaNq0KUJCQnDlyhWkpaUBqFqp/tlnn+ksSCIiMkxqJ5E33ngDb7zxhvTxTz/9hD/++APG\nxsbo1KkTjI2NdRIgEREZLrWTiJ+fH0QiEXbs2AGgaoqvk5MTAGD9+vUQiUSYNWuWbqIkIiKDpHYS\nuXz5snQL+OcxiRARNU513oAxLy9PG3EQEVEDpLInEh8fj/j4eJkyPz8/mccZGRkAAGtray2HRkRE\nhk5lEklPT5e5jCUIAq5cuSJzjuTug6+++qqOQiQiIkOl1piIZJdeyffVNWvWDK6urpziS0TUCKlM\nIiEhIQgJCQEAODk5QSQSISUlRS+BERGR4VN7dlZ4eLgu4yAiogZI7SQyevRoAEBycjISExORk5OD\nTz/9VDqwbmdnhyZNanWjRCIiauBqNcX3q6++wpgxY7B69Wps3boVADBnzhy89dZb+Omnn3QSIBER\nGS61k8j+/fsRGxsLQRBkBtcnTpwIQRBw8uRJnQRIRESGS+0kEhcXB5FIBG9vb5nyvn37AgAH3ImI\nGiG1k4hkx94FCxbIlLds2RIAkJWVpcWwiIioIVA7iUguYZmbm8uUp6enazciIiJqMNROIi+//DIA\nyGyD8vjxYyxevBgA8Morr2g3MiIiMnhqJxFvb28IgoDFixdLV68PHjwY58+fh0gkwrBhw3QWJBER\nGSa1k8j06dPh4uIiMztL8r2zszMCAgJ0FiQRERkmtVcHmpqaIiYmBjExMTh16hSePn2KFi1awNPT\nE35+fjA1NVWrnSNHjuDgwYO4efMmnj17hrZt2+Kdd95BUFAQLCwsVNYtLS1FREQEDh48CLFYjO7d\nu2POnDlwd3dX92UQEZEW1WqJubm5OWbMmIEZM2Zo/ITbtm1D69atERoaijZt2iA5ORmRkZG4fPky\ndu/erbLuvHnzcO7cOcydOxf29vbYtWsXpk2bhj179kjvskhERPpTqyRSUlKCvXv34vz583j27Bma\nN2+OAQMGYOzYsTAzM1OrjY0bN6J58+bSxx4eHrC2tsa8efNw6dIl6bqT56WkpODQoUNYunQpRo0a\nJa3r6+uLdevWYcOGDbV5KUREpAVqJ5GsrCz4+/vj9u3bMuWnT59GXFwctm/fjlatWtXYTvUEItGr\nVy8IgoDMzEyl9U6cOAETExOZxY7Gxsbw9fXF5s2bUVZWBhMTE3VfDhERaYHaA+tff/010tLSpIPp\n1b/S0tLw9ddfaxyE5MZXnTt3VnpOWloa7O3t5Xo8jo6OKCsrw/379zV+fiIi0ozaPZGzZ89CJBLh\n1VdfRUhICNq2bYtHjx5h/fr1+PXXX3H27FmNAsjMzERkZCT69euHnj17Kj0vNzcXNjY2cuXNmjUD\nAOTk5Gj0/EREpDm1k4ixsTEAYM2aNbCzswMAdOzYEZ07d8bgwYOlx2ujsLAQM2fOhImJCZYsWVLr\n+kREVL/Uvpw1aNAgAPK3x5UYPHhwrZ64pKQEQUFBSE9PR3R0NFq3bq3yfGtra+Tm5sqVS3ogkh4J\nERHpj8okkpGRIf2aMmUKbG1t8fHHH+PixYu4e/cuLl68iH//+99o3bo1/Pz81H7S8vJyfPjhh7h1\n6xY2b94MR0fHGus4Ojri4cOHKCkpkSlPTU2FiYkJHBwc1H5+IiLSDpWXs7y8vKRbnEhkZWVh6tSp\nMmWCIGDChAm4detWjU8oCAJCQ0Nx+fJlbNq0CS4uLmoF6uXlhcjISCQkJEin+FZUVCAhIQEDBgzg\nzCwionpQ45iIsstXmp63aNEiHD16FDNnzoS5uTmuXr0qPdamTRu0bt0aGRkZGDJkCEJCQhAcHAwA\n6N69O3x8fBAeHo6ysjLY29sjLi4O6enpWL16tVrPTURE2qUyiUjuq65N586dg0gkwsaNG7Fx40aZ\nY7NmzUJISIjM9OHqli5dioiICKxduxZisRhOTk6Ijo7manUionqiMomEh4dr/QnVuY1u+/btkZyc\nLFduamqKsLAwhIWFaT0uIiKqPbVnZxERET1PZRJZv3498vLy1G4sLy8P69evr3NQRETUMNSYRDw9\nPTF//nwkJiaisLBQ7pzCwkIkJiZi3rx58PT0xH//+1+dBUtERIZF5ZiIo6MjUlNTER8fj/j4eBgZ\nGaF9+/bSTRSfPXuG9PR0VFZWAqiaodWlSxfdR01ERAZBZRI5cOAA9u3bh+joaNy7dw8VFRW4f/8+\nHjx4AEB2Wq+DgwOmTZuGcePG6TZiIiIyGCqTiJGREcaPH4/x48fj0qVLSExMxPXr15GdnQ0AaNmy\nJXr16oUBAwbg9ddf10vARERkONTegLFv375KbxhFRESNk8ZTfMvKyrQZBxERNUC1uj1uamoq1q1b\nh/Pnz6OwsBAWFhbo168fZs+erdYmikRE9GJRuydy7do1jBs3Dj///DMKCgogCALy8/Px888/Y9y4\ncbh27Zou4yQiIgOkdk8kPDwcRUVFVZWaNEGzZs2Qk5OD8vJyFBUVYenSpfj22291FqiubN68GefO\nnQMAiMViAICVlRUAYODAgQgMDKy32IiIDJ3aPZGbN29CJBLh/fffR1JSEhITE5GUlITJkydLjzd0\nxcXFKC4uru8wiIgaDLV7ItbW1njy5Ak++ugjmJubAwDMzc3x8ccfIzY2tsHeWTAwMFDa25DcWCsm\nJqY+QyIiajDU7olIbgR1+/ZtmXLJ43fffVeLYRERUUOgdk/EwcEBzZo1w8yZMzFu3Di0a9cOGRkZ\n2LdvH1q3bo127drhhx9+kJ4vSTpERPTiUjuJLFiwQHqr3E2bNskd//zzz6Xfi0QiJhEiokagVutE\n1L0FLhERNQ61muJLRERUndpJRBf3WyciooZN7dlZO3fuVHqstLQUixYt0kY8RETUgKidRL7++mt8\n8MEHePbsmUx5SkoKRo8ejT179mg9OCIiMmy12sX3zJkzGDFiBC5evAgA2L59OyZMmIC0tDSdBEdE\nRIZN7TGRkJAQbNy4EVlZWZg2bRocHR3x119/QRAE2NjYYMGCBbqMk4iIDJDaPZGQkBDs2bMHXbp0\nQWVlJf78808IgoD+/fvjwIED8PX11WWcRERkgGp1OauoqAhFRUXSRYcikQgFBQUoLS3VSXBERGTY\n1E4iX331Ffz8/JCeng4jIyP06dMHgiDg6tWrGDFiBOLi4nQZJxERGSC1k0hsbCwqKyvRrl07xMbG\nIiYmBitWrICFhQWKioqwePFiXcZJREQGqFaXs4YPH44ff/wRr776qvRxfHw8evfuzS1RiIgaIbVn\nZy1fvhwjRoyQPi4uLoa5uTlefvllfPvtt4iMjFT7STMzMxEVFYWbN28iJSUFxcXFOHnyJNq1a1dj\nXScnJ7kykUiE+Ph4hccaC1V3aAR4l0Yi0g21k8iIESNw9+5dLF++HBcuXEBpaSlu3bqFr7/+GgUF\nBQgICFD7Se/du4ejR4+iZ8+ecHd3x/nz52sV9LvvvosJEybIlHXs2LFWbbzIJHdnrJ5EiIh0Qe0k\nkp6ejgkTJiAvLw+CIEhnaDVp0gTx8fGwtbXFJ598olZbffr0QWJiIgBg7969tU4idnZ2cHFxqVWd\nFx3v0EhE9UHtMZH169cjNzcXTZrI5p1hw4ZBEARcuHBB68EREZFhUzuJJCYmQiQSITo6Wqa8a9eu\nAICMjAztRqZCXFwcevXqBVdXV0yZMgVJSUl6e24iIvo/al/Okmy8KJmZJSGZlZWbm6vFsJQbOXIk\n3nzzTdjZ2SEjIwPR0dHw9/fHtm3b4OHhoZcYiIioitpJxMbGBk+fPkV6erpM+cmTJwEAzZo1025k\nSixbtkz6vZubG7y8vDB8+HCsXbsWsbGxeomBiIiqqH05y9XVFQAQGhoqLVuwYAHmz58PkUgENzc3\n7UenBgsLCwwePBjXr1+vl+cnImrM1E4igYGBMDIywq1bt6Qzs/bu3YvS0lIYGRlh6tSpOguSiIgM\nU616IitWrIC1tTUEQZB+2djYYPny5ejdu7cu41QqPz8fp0+f5pRfIqJ6oPaYCAD4+PjAy8sLv/76\nK548eYKWLVvi1VdfRdOmTWv9xEePHgUA3LhxA4Ig4MyZM2jRogVatGgBDw8PZGRkYMiQIQgJCUFw\ncDAAYOvWrbh37x769u2LVq1aIT09HVu3bkV2djZWrVpV6xiIiKhuapVEAMDc3Bz9+vWr8xN/9NFH\nMlvKf/nllwAADw8PxMTEyPR2JDp27Ijjx4/j2LFjEIvFsLS0hJubG8LDw+Hs7FznmIiIqHZqnUS0\nJSUlReXx9u3bIzk5WabM09MTnp6eugyLiIhqoVa7+BIREVVXbz0RMjyqdgLmLsBEpAh7IqRQcXGx\ndDdgIiJl2BMhKe4ETES1xSRCBoU31yJqWHg5iwwWL6kRGT72RMig8JIaUcPCnggREWmMSYSIiDTG\nJEJERBpjEiEiIo0xiRARkcY4O4teONy+hUh/2BOhFxrXmhDpFnsi9MLhWhMi/WFPhIiINMYkQkRE\nGmMSISIijTGJEBGRxphEiIhIY0wiRESksUY5xTc0NBRmZmZy5VlZWQD+b1ro81q1aoXVq1frNDYi\nkseblRmuRplEnmRno7WVjVy5mZExAKAiL1/u2NPiQp3HRYaDq94Nl2TxaPUkQvWnUSaRZmZNsWbo\nmFrV+fjo9zqKhgwdf2nVPy4gNVyNMokQ1YS/tIjUwyRCZKA4DkANAWdnETUA3EiSDBV7IkQGipfU\nqCFgEmmg/v3vfyM7O1vhMU5VJiJ9qZckkpmZiaioKNy8eRMpKSkoLi7GyZMn0a5duxrrlpaWIiIi\nAgcPHoRYLEb37t0xZ84cuLu76yFyw5GdnY3HjzNh3VT+WJN/LlIWizPljuUV6TgwImpU6iWJ3Lt3\nD0ePHkXPnj3h7u6O8+fPq1133rx5OHfuHObOnQt7e3vs2rUL06ZNw549e+Dk5KTDqA2PdVMg2Kd2\n/4UbDpfrKBoiaozqZWC9T58+SExMxKZNmzB06FC166WkpODQoUOYP38+xo4di9dffx1r1qxB27Zt\nsW7dOh1GTEREijSo2VknTpyAiYkJvL29pWXGxsbw9fVFYmIiysrK6jE6IqLGp0ElkbS0NNjb28vt\ne+Xo6IiysjLcv3+/niIjImqcGlQSyc3NhY2N/J5XzZo1AwDk5OToOyQiokatQSURIiIyLA1qnYi1\ntTUyMjLkyiU9EEmPRF1un32isPx/v4pQWP7999/j7NmzcuV3795VeP4rr7yisFxb5x86ehZnz4nk\nyneHeyo8f+K8UxAXChAZGePs2bN49uwZKioqMGaM/GaUWVlZKCwshKWlpdyxMWPGKFxrouvXq8n5\nnTp1kqtb2/YHDRqktXg0Pb/666jP95PnG97nU53zs7OzgUoB586dQ9IXihesui+UX1f2tEgM3+H/\nUni+RINKIo6Ojjh+/DhKSkpkxkVSU1NhYmICBweHeoyu4amoqEBFRQUKFKwnMTYCsrOzIFRWyB17\n/Fj+fCJqnBpUEvHy8kJkZCQSEhIwatQoAFW/CBMSEjBgwACYmJjUqj1lPQ5lxowZU6ttJ5T9haCt\n832HDqrVOpHd4Z7YcLgc5latERMTAz8/PxSIMzFphHxvporDP1+y4g4ICs/W9PU+v/pestL++ZX3\nz/cIalp5f/fuXWndkydPqh3P85St/Nf1/2/189V5HfqMh+dr53xtfD7VOd/Pzw8VeUWIGKJ8w05F\nPZRPjm9GTSvL6i2JHD16FABw48YNCIKAM2fOoEWLFmjRogU8PDyQkZGBIUOGICQkBMHBwQCA7t27\nw8fHB+EbZuWPAAAgAElEQVTh4SgrK4O9vT3i4uKQnp7ObTwasOzsbGQ+zoSZhWy5qOoeYcgpkO/5\nlBToITAiqlG9JZGPPvoIIlHVX8AikQhffvklAMDDwwMxMTEQBEH6Vd3SpUsRERGBtWvXQiwWw8nJ\nCdHR0bVarZ5TUlTrm0w9LS6EuUjxX+BUd2YWwGsT1D//1z26i4WI1FdvSSQlJUXl8fbt2yM5OVmu\n3NTUFGFhYQgLC9NVaEREpKYGNSaiLZreHtfYSn6mEhFRY9Yokwi9WLgtPlH9YRJpoMRiMYqKar8r\nb14RUAaxjqKqH5KBeVgqmGVmXDWOlVn4WP5YPse4iOqKSYReDJYiGL1vUfN51VTu5BQvorpiEmmg\nrKysYIJCje4nYm5lpaOoiKix4d5ZRESkMSYRIiLSGJMIERFpjGMijZhkhpeyvbCUKSgCKl+wGV5E\npBn2RIiISGPsiTRiVlZWMEKhil18FYs7IMBCizO8xGIxSopqtx9WSQEgrmRviKi+sSdCREQaY0+E\n6p2VlRUqjAprvYuvlYX2ekPcOoVIM0wiRKi2dYqFmfxB46rLfZkFOfLHCkp0HBmRYWuUSUTZ/UQK\nykoBABYmpnLHnhYXwtaau/gaIrFYDBQJtd/GJF+AuKLauIqFGYwn96lVExWxl2v3nEQvmEaZRFq2\nagVjM/m/OEv+uWxhrSBZ2FpbolWrVjqPjYioIWmUSWTVqlWwt7eXK5dc867NfdTrU56SXXyLqjpU\naCrfoUJeEWD+gm2dZWVlhULjIo02YLR66QV7M4j0rFEmkReBql6R+J8elbmVrdwxcyvVdYmIaoNJ\npIFSNRuoofWoiKjh4joRIiLSGHsijVyBkr2zSv4ZVzFTMK5SUARocYkGEemYWCxGcXERPjm+uVb1\nnhaLYVRmrPIcJpFGTNXYSOE/4yoWCsZVLDiuQkT/YBJpxAxpXKWkQH7vrPJ/1vE1UbD+r6QAQO0m\nYxE1WlZWVnhJaIKIIYG1qvfJ8c0oN1M96sEkQvVOWa8mq7CqN9TMQr43BAv2hogMAZMI1TtlPSJ9\n9oaqVr2X1H4FekEJdxOmRo2zs4iISGPsidCLIV/J3lnF/8w8M1dwz5R8AXip6lsrKysUGlVotHeW\nNncTJmpomESowVM1NpJVUDWuYvuSgnGVlziuQlRXTCLU4BnSLDOixqZeksjff/+NJUuW4MKFCxAE\nAf369cP8+fPRtm3bGus6OTnJlYlEIsTHxys8RtSQKLs5Fm+MRYZK70mkuLgYfn5+MDMzw/LlywEA\nERERmDJlCg4cOABzc/Ma23j33XcxYYLsbfA6duyok3iJ9Knq5liPAYuXZA8YV60azizIl69UUKiH\nyIgU03sS2bNnD9LT03HkyBG8/PLLAICuXbti6NCh2L17N/z9/Wtsw87ODi4uLjqOlKieWLwEk0nv\nqn16Wdx+HQZDpJrek8ipU6fQu3dvaQIBAHt7e7z22ms4ceKEWkmEiKihUHaJElB9mbKhXKLU+zqR\n1NRUdOnSRa7c0dERaWlparURFxeHXr16wdXVFVOmTEFSUpK2wyQi0ors7GxkPc5CeW653JepyBSm\nIlO58qzHWUoTj6HRe08kJycHNjY2cuU2NjbIy8ursf7IkSPx5ptvws7ODhkZGYiOjoa/vz+2bdsG\nDw8PXYRMRFQnzc2aY1X/FWqfH3r+Ux1Go10NborvsmXLpN+7ubnBy8sLw4cPx9q1axEbG1uPkVGD\nV6Bk25OSf25BbKbgx6WghBtB6ghnqjUMek8iNjY2yM3NlSvPzc2FtbV1rduzsLDA4MGD8f3332sU\nz+bNm3Hu3DkA8h/OgQMHIjCwdrteUsOkcsHiPxtB2lo0kz/IjSB1Jjs7G48fZ8HcooVMuZFx1bbO\neQUVcnWKC57qJTb6P3pPIo6OjkhNTZUrT01NRefOnfUdjgx1phcbKlXJEGBCrAkXLBomc4sWeHuy\n+r2Kn2P/rcNoSBG9JxEvLy+sWLECDx8+hL29PQDg4cOH+O233zBnzpxat5efn4/Tp09rPOU3MDDw\nhfvl2pCTIRE1LHpPIuPHj8e3336L4OBgfPTRRwCAdevWoV27djILCDMyMjBkyBCEhIQgODgYALB1\n61bcu3cPffv2RatWrZCeno6tW7ciOzsbq1at0vdLMSjaSIa8tEdEtaX3JNK0aVPs2LEDS5YsQVhY\nmHTbk3nz5qFp06bS8wRBkH5JdOzYEcePH8exY8cgFothaWkJNzc3hIeHw9nZWd8v5YVWX70ZXpYj\naljqZXZWmzZtsG7dOpXntG/fHsnJyTJlnp6e8PT01GVojZqhXdrjZTkiw9fgpvjSi83QEhkRqcY7\nGxIRkcbYE6EXDicIEOkPkwi90BrauIpYLAaKimq3M29BIcSVQs3nEekAkwi9cDiuQqQ/TCJEBsTK\nygqFRqJa30/EysJSh1ERKceBdSIi0hiTCBERaYxJhIiINMYxESIySGKxGMVFxbXambe44ClElQ1r\nRl5Dx54IERFpjD0RIgW4YLH+WVlZQTB6qdb3E7GyMNZhVPQ8JhGiGjS0BYtE+sQkQqQAFywSqYdJ\nhMjQFBTKb3tSUlr1r5mpwvPBxYZUT5hEiAxIq1atFJZnFVaNy9gqShYWlkrrUf0Ti8UoLi5G6PlP\n1a7zrPgZzI0axmVUJhEiA7J6teJBZMmgfkxMjD7DIaoRkwgRkQ5ZWVmhaWVTrOq/Qu06oec/RROr\nhvHrmetEiIhIY0wiRESkMSYRIiLSGJMIERFprGGM3BBRo1Rc8FRuA8aykgIAgImZhcLzrS1s9RJb\nQ/O0WIxPjm+WKy8oKwYAWJjITyl+WiyGtZmNynaZRIjIIClfM1MCALC2sJY7Zm1hyzUzCqh6T0qy\n8gEA1tZN5Y7ZWjfFSy+9pLJtJhEiMkhcM6M9yt5LoOb38+HDhzh16pTS+hwTISIijTGJEBGRxphE\niIhIYxwTITJQqm6MBfDmWA3Js5JnCjdgLCirmmlmYWIhd74tGsYss3pJIn///TeWLFmCCxcuQBAE\n9OvXD/Pnz0fbtm1rrFtaWoqIiAgcPHgQYrEY3bt3x5w5c+Du7q6HyInqB2+M1XCpmhlVmlW1xb+N\njew0Wls0nFlmek8ixcXF8PPzg5mZGZYvXw4AiIiIwJQpU3DgwIEaf1jmzZuHc+fOYe7cubC3t8eu\nXbswbdo07NmzB05OTvp4CUR6wRtjvRjqMjOqIdB7EtmzZw/S09Nx5MgRvPzyywCArl27YujQodi9\nezf8/f2V1k1JScGhQ4ewdOlSjBo1CgDg4eEBX19frFu3Dhs2bNDHSyAion/ofWD91KlT6N27tzSB\nAIC9vT1ee+01nDhxQmXdEydOwMTEBN7e3tIyY2Nj+Pr6IjExEWVlZTqLm4iI5Om9J5Kamoq33npL\nrtzR0RFHjx5VWTctLQ329vYwMzOTq1tWVob79++jc+fOWo2XiOofJxkYLr0nkZycHLlBJKBqYCkv\nL09l3dzcXIV1mzVrJm2biF5snGRgWBrVFN+KigoAVbPDiKjh8Pb2lrmMrcjDhw/1FI3mdu/ejStX\nrkgfP3nyBAAwYcIEAFVjvBMnTtRrHDXFIPl9Kfn9+Ty9JxEbGxvk5ubKlefm5sLaWn5Dteqsra2R\nkZEhVy7pgUh6JMpIusHvvfeeuuESEencgwcPAAC///47Nm+W32nXEGLIyspChw4d5Mr1nkQcHR2R\nmpoqV56amlrjeIajoyOOHz+OkpISmXGR1NRUmJiYwMHBQWV9Z2dn7Nq1C7a2tjA2NtbsBRARNSIV\nFRXIysqCs7OzwuN6TyJeXl5YsWIFHj58CHt7ewBV3dDffvsNc+bMqbFuZGQkEhISpFN8KyoqkJCQ\ngAEDBsDExERlfXNzcy5KJCKqJUU9EAnjRYsWLdJfKEC3bt1w+PBhHD16FHZ2drhz5w4WLlyIpk2b\n4quvvpImgoyMDPTt2xcikQgeHh4AAFtbW9y+fRvffvstmjVrhry8PKxcuRLXr1/HypUrG8wKTyKi\nF4XeeyJNmzbFjh07sGTJEoSFhUm3PZk3bx6aNv2/m6IIgiD9qm7p0qWIiIjA2rVrIRaL4eTkhOjo\naK5WJyKqByLh+d/SREREauJW8EREpDEmESIi0hiTCBERaYxJhIiINMYk8oIoKipCZmYmMjMzUVRU\nVOf2ysvLceLEiUa7H5m230+iFxVnZzVgmZmZ2LJlC06cOIFHjx7JHGvbti3eeustTJ8+Ha1bt651\n22KxGH369MHOnTs1XqBZXl6OM2fOwM3NrcYtaRTJz89HWloagKp7zlSfAq4L2n4/Kysrcf/+feTm\n5kIkEsHOzg5t2rSpU4ylpaXYvXs3hg4dqtH/a0Ml2aW7+hZHDg4ONS4w1nYbmkhJSUHHjh1ldtm4\ncuUK1q5di+vXrwMAXFxc8Mknn+C1116rdfv1/ZlotEmkqKgICQkJyMzMhKOjI9566y0YGcl2zB48\neIANGzYgPDxcptzHxwdvvfUWRo0aVaet569fv44ff/wRTZo0wbhx49C5c2fcvHkTa9aswf379+Hg\n4ICZM2cq/GD9+eef8PPzgyAI8PT0hKOjo3SH49zcXKSlpeHkyZMAgJ07d6Jr165ybcydO1dpbOXl\n5Th8+DD69++Pli1bQiQSYdmyZbV6feomosOHD6O0tFS6C0FlZSWWL1+OXbt2oby8HABgZmaGwMBA\nzJo1q1YxPO/s2bP44osv5O5do433U+L27duIjIzE6dOnUVxcLHOsbdu2mDRpEgICAtCkSe2Xaanz\nnvbu3Vv6+RwwYIDc51qbjh49io8//hjJyck6qZ+SkoJ169YpvF+QiYkJBgwYgNmzZ6tcJ1aXNrSR\nALp37449e/bAxcUFAJCUlAR/f3/Y2dlh8ODBAIDTp08jOzsbcXFxSrcXUUbdnzNdJbNGmUSePn2K\nCRMmSDccA4AuXbpg9erV6NKli7Ts6tWrmDhxotwHXPJhE4lE6NmzJ0aPHg1fX99a/bX9+++/Y/Lk\nyRCJRDA1NYVIJEJUVBSCgoLQokULdOvWDTdu3EB2djb2798vExcABAQEoLy8HN988w0sLS0VPkd+\nfj5mzpwJExMTbN26Ve64k5MTrKysYGVlJXdMEAT8/fffaNmypTQ+RTcN00YiGjFiBMaPH4/JkycD\nANavX49vvvkG48aNw4ABAwBU/fLfv38/5s2bJz1PE8p+aWnj/QSAGzduwM/PD02bNoWbmxtMTU1x\n7do1pKenw9/fH/n5+Thy5Ai6du2KLVu2yN0bB1C9QWhFRQV+//13dO3aFVZWVhCJRIiNjZU5x8nJ\nCU2aNEFFRQVatmyJESNGYNSoUSoTn6Z0mUSSkpIwbdo0tG3bFr6+vnB0dJS57UNqaioSEhKQnp6O\n6Ohohb9A69qGNhKAk5MTvvvuO2kbU6ZMQW5uLnbt2gULCwsAVZ+tSZMmwcHBAf/973/l2qjrZ0Jb\nr0WRRrUVvMS6detQUlKC2NhY9OrVC5cuXcKSJUswceJEbNiwAX379q2xjdWrV+POnTs4cOAAFi9e\njKVLl8LT0xOjR4/GoEGDatzgccOGDXB2dkZ0dDSaNm2KL7/8ErNnz4azszOioqJgYmKCoqIi+Pn5\nYdOmTVi5cqVM/d9//x2RkZFKf+EBgKWlJWbMmIHZs2crPD5+/HgcPHgQEydOxLRp02RizsvLQ58+\nfRARESHddkaRAwcOqExEIpEIf/zxhzQRKfLgwQOZHt2+ffswY8YMfPTRR9KyIUOGwNraGrGxsQqT\nSPXttVX566+/FJZr4/0EgOXLl6Nnz56IioqSXn4TBAGLFy/GL7/8gv379yM4OBhjx47Fpk2bFLb1\nv//7v2jVqhU6duwod0zyN5+RkZHKHsaWLVvw999/44cffsD27duxbds2dO/eHWPGjIGvry+aN2+u\ntC4A/PDDDyqPS0j+gtV2fQBYuXIlBg0ahDVr1ij9eQoODsYnn3yCFStWYM+ePVpv4/m/sSMjI+Ho\n6CiTAEJDQzFp0iR88803ChPA865evYrFixdL6wNVn62pU6cq7e1r4zOhi9ciabjRGTJkiLB3716Z\nsvz8fGHGjBmCi4uLcOLECUEQBOH3338XnJyc5Op369ZNuHr1qvRxUlKS8PnnnwseHh6Ck5OT8MYb\nbwhLliwRbt26pTSG/v37C0eOHJE+Tk9PF7p16yb8/PPPMufFx8cLb7/9tlz9vn37CocOHarxtR46\ndEjo06eP0uNJSUnCv/71L8HHx0e4fPmytDwvL0/o1q2bTJkin3/+ueDq6ips2rRJKC8vlzmWm5ur\nVhvu7u7CmTNnpI979OihsM6FCxcEZ2dnhW1069ZNcHJyqvFLct7ztPV+urq6CqdOnZIrz8zMFJyc\nnIT79+8LgiAIMTExCv9fBUEQNm3aJLi6ugoLFiwQcnNzZY6p854+//l89OiRsHHjRsHb21vo1q2b\n4OzsLMyaNUv4+eefhbKyMqVtSN6vmr6U/YzUpb4gCIKLi4tw8eJFpa9T4sKFC4KLi4tO2nj+vezd\nu7dw4MABufO+//57oW/fvgrbfr4NZ2dnISkpSe68S5cuCT179lTYRl0/E9p6LYo0yp7I48eP8cor\nr8iUWVhYYMOGDZg7dy5mz56N8PDwGreWl3Bzc4Obmxs+++wzHD9+HD/88ANiY2MRExODrl274scf\nf5Srk5eXh5YtW0of29nZAYDcwGv79u2RmZkpV/+tt97C8uXLYWtrq7SnkJSUhBUrVmDIkCEqY4+P\nj8eWLVsQGBiIoUOHIiwsTO3Bxi+//BIjR47EokWL8OOPP2LRokXSeJT1PJ7Xu3dvHDlyBIMGDQIA\ndOrUCTdv3pR7XTdv3lS6yaaFhQX69++PSZMmqXyuK1eu4JtvvpEr19b72aRJE7lxEAAoKSmBIAjS\na/JdunRRenO0GTNmwNvbG4sWLcKwYcMwd+5c6XiRuu9pdW3atEFQUBCCgoJw7do1xMfH4/Dhwzh+\n/DiaN2+OixcvytWxsbGBl5cXZs6cqbLts2fP4uuvv9Z6fQCwsrJS60ZTDx8+VNgT1lYb1VVUVKBd\nu3Zy5e3bt0d+fr7Senv27MGpU6cAVH1WHz9+LHdOdna20hi0/ZkANH8tz2uUSaRVq1a4e/eu3PVP\nY2NjrFy5Ei+99BLCwsIwZsyYWrVramoKHx8f+Pj44MmTJzhw4IDSbn2zZs1kPkjGxsZ455135MZV\nnj17pnBWUlhYGIKCguDn5wc7Ozt06dJFZiA4NTUVmZmZ6N27N8LCwlTG3aRJE3zwwQcyH9Lp06er\n/eGsayIKCQnB5MmTYW1tjYCAAMyZMweffvopRCIR+vXrBwBITEzEf//7X/j7+ytso0ePHsjPz8cb\nb7yh8rmU3YJZW+/nG2+8gXXr1sHZ2Vl6q4Pc3Fx89dVXMpcj8vPzVd6E7eWXX0Z0dDQOHjyIpUuX\nYt++ffjiiy+kf2xoysXFBS4uLpg/fz5OnTql9PPp7OyMBw8e1PiHlK2trU7qA8Dw4cOxfPlyNGnS\nBN7e3nLjRyUlJUhISMDKlSuV/qxqo426JgAA2L9/v8zj06dPy92p8dKlSwovV0lo4zOhjdfyvEaZ\nRFxdXZGQkICxY8fKHROJRNLrldu3b9c4y7ds2RIBAQEICAhQeLxr16749ddf4ePjI33edevWyZ13\n48YNhR8sa2trxMXF4fjx4zh16hRSU1OlEwVsbGzQr18/eHl54a233lL7NXTo0AHbtm3Djz/+iGXL\nlsldQ1WlLonI1dUV69evx/z587Fjxw7Y2NigpKQES5culZ4jCAJGjx6tdHaWs7Mzvv/++xqfq2nT\npmjbtq1cubbez7lz52LSpEkYNmwYOnToABMTE9y7dw8VFRVYtWqVtO6VK1fUGrgcPnw4Bg8ejGXL\nlmHUqFEYO3asxp/J6kxMTPDOO+/gnXfeUXi8Z8+eCgdnn9eiRQuFA9p1rQ8An3zyCR4/foz/9//+\nHz7//HPY29vLJPaHDx+irKwMPj4++OSTT3TWRl0TQEpKivI3oJoOHTrgzTffrPG8unwmtJHMntco\nZ2ddvHgRu3fvxqJFi1QOMEZFReHcuXPYuXOnTPm8efMQHByMl19+WeMYbt68iby8vBr/cl6wYAF6\n9+6Nd999V+22Kysr8eeff6JDhw4ar63Iz8/Hw4cPNW5DkoiePn2KnTt3qhyclygoKEBCQgJ+/fVX\nPH78GIIgoFmzZnB0dMSQIUPkZqg9XzcnJwft27evdazalpOTg2+//RbXrl2DkZEROnbsiIkTJ8p8\nXsrLyyESiWp1h82kpCQsXLgQaWlpKt/T9evXY9y4cS/MOpKUlBScOHECaWlp0ltrW1tbw9HREV5e\nXujevbte2lBl69at6NixIzw9PevUTm0lJSVhwYIFuH37tto/ZzWp7WtplEnkRaeNhYLaaKOwsBDP\nnj2Dra0tTE1NNWojOzsbAOp8w7EnT57AxsZGo7UZRLpS1wW5hqBR/0RdunQJmZmZ6Ny5M3r27Cl3\nPDMzE3v37kVISIhO6j969AhHjx6FsbExfH190aJFC2RkZCAqKkq62HDq1KkKryuvXbtW6esqLS2F\nIAjYu3cvzp8/D5FIpHAqqTbaUOTp06eIjY3FtWvXAFRdrpo8ebLSH5JLly6huLhYOlcdqFrQt2nT\nJjx58gRA1eDwRx99JB1MVGT37t344YcfIAgC/P394e3tjZ9++glff/01cnJyYGZmhkmTJmHu3LkK\nu/9lZWXYt28fjh8/jj///BO5ubkwMjKCra0t3NzcMGnSJPTu3Vut90CiqKhIOg5jbW2t81X3+pCT\nk4P79++jdevWL0xvp7a0tZtCUVERQkJCGvTOEI2yJ1JQUIBp06bh6tWr0rUM/fr1w5IlS2R+KJQt\nNqxrfQBIS0vDhAkTpLMg7OzssH37dgQEBKCwsBAODg64ffs2TExM8MMPP8jNonBycoJIJFI6blH9\nmEgkUhiDNtro06cPtm3bJk2ijx49wsSJE5GdnS2dAXfnzh20adMG3333ncIexdixY6VjKACwa9cu\nLF68GAMHDkT//v0BAOfOncOFCxewatUq6ThSdfv378d//vMfuLq6wtLSEr/88gu++OILLFy4EMOG\nDYOLiwuuXr2Kw4cPY+HChZg4caJM/SdPnsDf3x9//fUXmjVrBlNTU2RlZcHY2BgDBw7EvXv3cOfO\nHQQGBuLf//63wvdLQhvbp9RlN4P8/HxYWFjIJMo7d+5g48aNMok9KChIbpaiREVFBdasWSNNylOn\nTsXUqVOxZcsWrF27VrqTwNtvv42VK1cq7GnWZVcIwDB2htDGbgov+s4QjbInsmnTJqSlpSE8PBy9\nevXC5cuXERkZifHjxyM6OhqOjo46rQ9ULfRp06YNIiMjYWNjg4ULF2LmzJlo1aoVtm/fDisrK2Rn\nZ+P9999HVFQUFi1aJFO/f//++OOPPzB//ny5X6qShYI1XSPVRht5eXmoqKiQPl65ciXKysqwd+9e\n9OjRA0DVD3FgYCAiIyPxxRdfyLVx584dmWvSO3bswKRJk7Bw4UJpmb+/Pz777DNs2rRJYRLZtWsX\nJkyYIG3/u+++w6JFizBp0iT85z//kZ5nY2ODPXv2yCWRZcuWoaCgAPv27ZMOeKenpyMsLAwvvfQS\nDh8+jLNnz2LWrFno1KmT0h6ROtunHDhwAAcOHFC6fcrzuxns27dP4W4G/v7+Cncz8PDwkFmZ/Oef\nf0qnPru5uQEAjh07hpMnT2LPnj0KE8mOHTuwZcsWeHt7w9LSEpGRkSguLsaGDRswbdo0aVLeunUr\ntm3bhqCgIJn66u4K8fTpU/zwww8Kk8jt27dx+/ZtbNmyRWs7Q9T2vdy4cSPGjx8vfbxhwwbs3LlT\nbjeFDRs2wMbGRuFCWG0syK0pEQmCgG+++UZlItLGa1FI7RUlL5ChQ4cKO3bskCn7+++/hdGjRwt9\n+vSRLshRttiwrvUFQRAGDRok/Pjjj9LHd+7cEbp16ya34C0uLk4YNmyYwjYOHjwo9O/fX5g6dapw\n9+5dabm6CwW10cbzC5j69Okj994IgiBER0cLb775psI2XF1dhQsXLkgf9+jRQ/jll1/kzktMTFS6\n2PDVV1+VaUMS//MLzRITE4XXXntNrn6fPn1k/j8kUlNThe7duwtPnjwRBEEQVq9eLYwePVphDIIg\nCP7+/sLkyZMFsVis9ByxWCxMnjxZCAgIUHg8MDBQmDBhgpCfny9UVFQICxcuFPr37y/4+/sLpaWl\ngiAIQmFhoTB27FghNDRUrv7z/ycffPCB4OXlJTx69Ehalp6eLnh6egpz5sxRGIOvr68QEREhfXzs\n2DGhe/fuwrp162TOi4iIEP71r3/J1V+4cKEwcOBA4cqVK0JxcbFw5swZYejQocJrr70m83+r6mdE\n8vOwfv164Z133pEulPzwww+FkydPyi1uVaSu7+Xzn83BgwcLa9askTtvxYoVwtChQxXGoI0Fud26\ndRPc3d0FT09Pua8333xTcHJyEvr37y94enoKXl5eCtvQxmtRpFFuBf/o0SO52RitW7dGbGwsunbt\nioCAAFy6dEln9YGqv8CqX6KSzCqSrC2Q6Nixo9JFaf/6179w6NAhtGvXDiNGjMC6detQWlqq8nl1\n0UZ1YrFY2gOprkePHsjKylJYp2fPnjh79qz0cbt27WT+gpV48OCB9K/655mbm8ts2S75vqSkROa8\n4uJihftVFRcXK/wLt3nz5qisrJSOzbi7u+P27dsKYwCq/vINCgpSa/uU3377TeHxW7duISAgABYW\nFjAyMsKMGTOQnZ2N9957T7r2pmnTpnjvvfekl6dUuXLlCj744AOZhazt2rVDYGCgwoWGQFUvrPrM\nwTfeeAOVlZV4/fXXZc7r27evwsV858+fx+zZs+Hu7g4zMzMMGjQI+/fvh7u7O2bMmCHdzLIm9vb2\nmOySxsoAABMSSURBVDVrFo4ePYpdu3Zh9OjR+OWXXxAcHIyBAwciPDxc5b5ddX0vmzRpIrNpY1ZW\nlnTtUnX9+/dHenq6whi+/PJLbNmyBQcPHsSIESNktuhRd2ru+PHjUV5ejokTJ+Lnn3/GyZMnpV8/\n/vgjBEFAREQETp48qXCPO229FkUaZRJp3ry5wl/ML730ErZs2QI3NzcEBQXh9OnTOqkPVF1Wefr0\nqfSxsbExevbsKffLJz8/X+WiPRsbGyxevBjR0dE4duwYfH19cebMmVqtJahrG9evX8fFixdx8eJF\ntGjRQuFq1/z8fKUDdoGBgdi5cyd27tyJ0tJSBAcHY/Xq1Th+/DgKCwtRWFiIY8eOYc2aNRg6dKjC\nNrp3744dO3ZIV4tHRUVJE7vkem95eTm+/fZbhZcbe/bsid27d6OyslKmPCYmBubm5jLTc1XNNDMz\nM1O6oLE6sVistJ267mbwvKKiInTq1EmuvFOnTkrvF2NpaSlzTPK9ZHps9XJFCVPVrhBDhgzB7Nmz\ncfDgwRpjr87NzQ1ffvklEhMTsWrVKjg7OyM2NhZjxozByJEjFdap63sp2U1BQrKbwvNU7aYgiT0+\nPh7Dhw9HYGAgwsLCZH7+a6KNRKSt1/K8Rjkm0rNnT/z8888YPny43DEzMzNs2LABoaGh+OabbxT+\nB9W1PgB07twZV69elS72MjIyklsIBAB//PGHWutR3N3dER8fj82bN8uMAdSGpm189dVXAP5vg7fL\nly/LLZpKTk5WuMUCAAwePBifffYZwsPDsXr1anTq1AklJSX48MMPZc7r06eP0kHt4OBgTJ06FR4e\nHjAxMYEgCIiJicHs2bPh4+ODbt264Y8//sCDBw8QFRUlV3/27NmYPn06vL290a9fP5iYmODq1au4\ndu0aZs6cCXNzcwBVf9mqGvPSxvYpdd3NAABOnjyJP//8E0DVHwnPnj2TOyc3N1dmE8DqevfujY0b\nN0p3el6xYgUcHR0RFRWF119/HZaWlhCLxYiOjlbY89TVrhCAfneG0MZuChIvws4QijTK2VlHjhzB\ntm3bsHHjRqWLDQVBwKJFi3Du3Dm5rndd6wNV/1m5ubnw9fVVGWtISAhcXV2lM5fU8ejRIzx48AA9\nevRQeVlFG21cvnxZrszKykruct+nn36KLl26YMaMGUrbSk9Px759+6SLDSsrK9G8eXM4Ojri7bff\nlpkCrMgff/yBQ4cOoaysDGPGjEGXLl1w7949rFq1Cn/99RdatWqFyZMnK+3NJCUlYf369bh69SqM\njY3RsWNH+Pn5yfyxkJycDBMTE6WJJC8vD0FBQfj9999r3D4lKipK4dYn06dPxyuvvILPPvtM5etd\nvXo1rly5gri4OJlyRffFmDhxotzkjOXLl+PKlSvYu3ev3Pmpqal4//33pT2Q5s2bY/fu3Zg1axYe\nPXoEBwcH3L9/H8XFxfj222+lg/gSoaGhyMnJQXR0tNL4ly5dKt0VQtnswepbqGuiru8lULWqe/78\n+Xj27BlsbGxQVFQkc8lX+Gc3hcWLF9dqHZImC3Il7t27h0WLFuHmzZuYPn06IiIiEBMTU2Mbungt\njTKJEOla9e1TJL+IbWxspCukVW2fUtfdDBRdzzY1NZXbp2rZsmVwdHRUuhvC48ePcerUKZSXl2PY\nsGFo2bIlnj59is2bN+Ovv/6Cra0tJk6cqHDtTF13hQAMa2eIuuymoEpD2xlCESYRIqJ68iLsDNEo\nx0SI6tuVK1cQGRmJmJiYemtDHzHUdVcHbbRRl50hqtdv0qQJfHx8FNYPCAhAhw4dFNZ/0XaGeB57\nIkT1oK63ldVGG7qMQRu7OhjCzhDq1jc1NUV8fLzCySMv0s4QirAnQqRFGRkZap2nanpnXdswhBi0\nsauDIewMUdf6wIu1M4QiTCJEWuTl5aXWlE3JX9a6aMMQYjh27Bg+/PBD6WWRzp07S+90+N5772Hz\n5s01zrrSRhu//fYbQkNDpffHCA0NxbBhw7B69WrpNiStWrXClClTsGPHDq3XB4Do6Gj89NNPWLJk\nCfbv348FCxZIL31pem+YxMREzJo1S2Z6da9evTBjxgyFkxSAqr2yqu9dlp6ejmHDhsmd5+3trfBu\nrMowiRBpkbm5Odzd3ZVOI5a4ceMGvvvuO520YQgxqNrVISgoCAEBAdiwYYN0/Y0i2mijrjtDaGNn\nCaBqZ4iBAwdi5cqVGDFiBKZNm4YPPvhA6fk1qcvOEJKZav+/vbMNiqoK4/jvDijC8LKCpmgfmrFJ\nFISFxSBABaJsSkZh8IUxCImKAoVxMqUaetGsgLIBogkdIKUcwgnSwikdRYzcdCRgRqBsNBrNJEBA\nDFaC7QOzt112F2EBNTq/T3funnvOcw6H+9z73HP+j04Zws/Pz6DccMoQphBORCAYR9zc3LCysmL1\n6tXDlnN0dDR7Ax9rHXeDDbdSddi4caPsCMwxHnWMVRlivJQldHVt376dlStX8vrrr3Po0CFSUlJG\npQxx48YNAIuVIZKSkpgzZw5r167lhRdeIDMzE4VCYbDZ8IMPPrjl/jV9/peyJwLBROHu7m5SSsIU\n5j60jrWOu8WGI0eOmCyvU3VYtmwZH330kdl6x6MOnTKEDp0yxFAZGHPKEGO93hQ6ZYiIiIhRK0PE\nx8ezYcMGWltbTW70HYkyRFZWFn5+fhQXF8vKECqVCpVKRUpKCvPnz79lugN9xOosgWAcuXr1Ks3N\nzTz44IN3rI67wYbxUHW4G5QhJlJZAv6byhBDEU5EIBAIBBYjwlkCgUAgsBjhRAQCgUBgMcKJCAQC\ngcBihBMRjIrQ0FDc3Nx4+OGH5XPXr18nNzeX3Nxcjh49Ouo609LScHNzw83NbcQ7pSeSpqYmuT9N\nTU1Gv+vGIDQ09A5YN7GUlZXJfwtzOTpuxVjnw+1Gv8+5ubl32pz/HGKfiGDUDF3X3tXVJf/zRURE\nmE22NNp67xSNjY3k5uYiSRL33nuvUX4OSZKQJMlg9+9kQtc/Sxmv+XC7uVvm338N4UQEo2bogj59\n8bj/A+ZyWE8GIiIiiIiIGFMd/7f58H9ncj5KCUZNRUUFGzZsIDg4GKVSyaJFiwgLC+O1116TZaJN\nkZOTQ1hYmKxEqh8aSEtLG5NNPT09ZGdns2LFCry8vFAqlURERFBUVGQgRgeD+RBSUlLw8fHBz8+P\n9PR0jh8/PmpbYmJiSEtLk/uzbds2o/COqXCWfr+zs7PJzc0lMDAQlUrF1q1buXHjBj/++CNr1qxB\nqVQSHh5uMtRTVVXF008/jZ+fHx4eHoSGhrJjxw6j9Lb6YcW6ujqio6NRKpUEBQWRlZUl55XXcfny\nZV555RVCQkLw8PBg8eLFxMXFGe2tMBfO0m+vvr6emJgYlEolISEhZGZmyu2N13yora0lKSmJwMBA\nPDw8WLJkCWlpaUYJt2JiYnBzc2PBggVcuHCBxMREfHx8CAoK4tVXX5V3eOu4cOEC8fHxeHl5ERQU\nxK5du4zGSjA6xJuIABjMNaBWqw3OXb58mZKSEs6cOcPBgwdNpsscGvowdzxaenp6WL9+PQ0NDQb1\nNDU10djYyKlTp/j444+BwZwMcXFx/PLLL0iSRE9PD6WlpVRWVlpkg6k+DK3HXMhHkiT2798vZzME\nOHjwIC0tLdTW1tLb2wvA+fPnSU1NpaKiQs5jUVBQQEZGhkG9V65cobi4mBMnTlBSUoKzs7NBW+3t\n7Tz11FNoNBoANBoNe/bsobW1lXfeeQcYlDOPjo6mq6tLrru7uxu1Wo1arWbz5s1Gm9PM9a29vZ0n\nn3ySvr4+2b6CggIcHBxITEwcl/lQUVHBli1bGBgYkM+1trZSVlbGsWPHKCkpkeXP9etdt24d169f\nBwbnz4EDB5Akie3btwPItre3tyNJEm1tbeTn51uUiEnwL+JNRABAeHg4n3/+OWq1mnPnzlFdXS2H\nNS5evMiJEydMXpecnMzRo0dlNddVq1bR2NhIY2MjO3futNieoqIi2YEsWbKE6upqjh49Ku/Qraqq\n4uuvvwagvLxcdiAeHh5UVlbyzTffYG9vb1bWwxz79u1j586dcn/efvttGhsbaWhoMEjUY65erVaL\nRqNh//79HDt2DDs7O2AwXaxKpUKtVvPSSy8B0N/fz+HDhwH4448/eP/99+X+Hj9+nLq6Ot577z0A\nLl26ZCTvodVq6e3tJSoqSs6VrnMyX375JT/99BMwKJehcyDPP/88Z86cobi4GEdHRyRJIjs7e1jx\nwKHtrVixArVaTV5envybTvV1rPOht7eXN954g4GBARYuXMjhw4epr6/nk08+YcqUKXR1dZGRkWFk\nF4CXlxfV1dWUlJTIOlaHDh2SyxUWFsoO5JFHHkGtVlNWVjbqOSIwRDgRAQAzZ85k7969rFq1Ck9P\nTwICAvjiiy/k3y9evHhb7dF3Wps3b8bZ2Zm5c+eSlJRkVEb/DSoxMZFZs2bJ2eZuN5IkERYWhlKp\nxNXVlXnz5sk31ISEBJycnAgJCZHL61ajnTx5Ug6rVFVVERwcjKenp6xhpNVqqa6uNmrP2tqaLVu2\nYG9vj4eHB1FRUfJvp06dQqPRcPr0aSRJwsnJieTkZOzt7VGpVERERKDVaunv7+e7774bUf+srKx4\n+eWX5X4oFAq0Wu24raqrqamhs7MTGMyP/thjj7Fo0SJiY2Pp6+tDq9Xy/fffm7x269atODs74+np\nKecJ12g0cjj2hx9+kMsmJyfj5OSEm5vbLUUmBcMjwlkCuru7iY6Olp/S4N8Qge4pTReGuV3ofwNw\ndXWVj3VS3IB8c9Avqy8+p3/d7UTfRhsbG6Pz+mqvN2/eBDD47mQu7KO7ueqjUCgM2tDv/7Vr1+jo\n6KC/vx9JkrjnnnsMVpTplx0uQZU+Li4uBhpPdnZ2dHR0yP0YKyMZh5s3b9Lb22skAa/L+aGzS4cu\n1KcfYpw9e7bJY8HoEU5EgFqtlh3IQw89RFZWFs7OzhQXF7Njx45bXj8Rq3CcnZ1pbm4GBuPuuvwG\n+k+8Li4uAAbifFevXpVDXleuXLGo7bH2x8rKalTn4d++AKSmpvLcc8+NqK2Ojg40Go3sSPTHZ/r0\n6SgUCqysrBgYGKClpcUgiZT++Oh/axkOU9/FhjKW8dMfh9WrV/Pmm2+O+NrhxhcGx+O3334DBsOH\njo6O8rHAckQ4S2BwY5g6dSo2NjacP3/ebIa0oSgUCvm4ubmZnp6eMdsUHBwsH+/atYu2tjYuXbrE\nhx9+aFRGl2QHYPfu3bS0tNDc3ExhYaFFbev35+effzZaCTYRBAUFYW1tjVarpaCggJMnT9Lb20t3\ndzenT58mPT2d/Px8o+v+/vtvMjMz6e7upr6+ngMHDsi/BQQEYGNjg7+/P1qtls7OTnJycuju7ubs\n2bOUlZUBg3//oKCgcevLWOaDt7c3Tk5OaLVaysvL+eqrr/jrr7/o6emhrq6Od999l7feessiu/ST\nL+Xk5NDR0UFDQwOlpaUW1ScYRDgRAT4+PvKTaGVlJSqVivDw8BE/UdrZ2ckx6JqaGry9vce04xkg\nNjYWd3d3YPDbR2BgIGFhYZw7dw5Jkli2bJmcA3rlypVy+2fPnmXp0qUsX77cIGnPaJ6OFyxYIIec\nCgoKcHd3n/Dd9K6urqSmpiJJEl1dXTzzzDMolUp8fX2JjY2ltLTUKGQkSRJ2dnaUl5fj6+vLmjVr\naGtrkz9oP/DAAwDyNwyAvLw8fH19Wb9+PZ2dnUiSREpKyriGdMYyH2xtbUlPT8fKyoq+vj5efPFF\nfHx88Pb2Zu3atRQVFZlMxjQS4uLi5DedI0eO4O/vT2RkpMEqMMHoEU5EgKOjI3v27EGlUmFra8vs\n2bPZtGkTzz77rMklrqaWt2ZmZuLr64uDg4NFu7mH1mlra8unn35KUlIS999/PzY2NkybNo2FCxey\nbds2g5VBU6dOpbCwkOXLl2NnZ4dCoWDdunWkpqbKZfSfjm/FrFmzyMjIkNs11R9TY2DOUQ1XVv98\nQkIC+fn5LF26lOnTp2Ntbc3MmTPx8fFh06ZNRpsAtVotCoWCvXv3snjxYqZNm8aMGTNISEgwCEPO\nmzePsrIyoqKimDNnDtbW1jg6OuLv709eXp5RDozhli+P9PxY5sMTTzzBZ599xqOPPsqMGTOwtrbG\nxcVFziEeHx9vkV3Ozs7s27ePgIAAeazi4+Nl5y2wDJFPRDApqKmp4b777pPfqP788082btxIbW0t\nkiSxe/fucQ3Z3GlCQ0P5/fffmTt37qTeQS+4+xEf1gWTgqKiIr799lsUCgVTpkyhra2NgYEBJEni\n8ccfn1QORCC4mxBORDChDBUvHIoplVxLCA4OpqWlhV9//ZVr167h4ODA/PnziYyMNNgkeLvsuR2M\nVSjxTjCZxl8wiHAigglluJvceN4AIyMjiYyMvGvsmWhM5RP/LzBZxl/wL+KbiEAgEAgsRqzOEggE\nAoHFCCciEAgEAosRTkQgEAgEFiOciEAgEAgsRjgRgUAgEFiMcCICgUAgsJh/AMHIbmmMVwvJAAAA\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5dfc5d6fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.boxplot(x = 'alt_log_timepoint_end',\n",
    "           y = 'exp(beta)',\n",
    "           data = spline_model_h2['time_betas'],\n",
    "           whis=[2.5, 97.5],\n",
    "           fliersize=0)\n",
    "_ = plt.ylim([0,4])\n",
    "_ = plt.hlines(1, -1, 400, linestyles='--')\n",
    "_ = plt.xticks(rotation=\"vertical\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-08-18T05:44:23.464155",
     "start_time": "2016-08-18T05:44:16.338Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vv1/Ozs6mY3bs2FE//vijGjduXKn2FcTg6empVatWKTw8XA4ODmYj6xQWFBSk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\niIiqOAb1RERERERV3P8Du310yYph6kgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5dde3053d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.boxplot(x = 'alt_log_timepoint_end',\n",
    "           y = 'exp(beta)',\n",
    "           data = spline_model_h2['time_betas'].append(spline_model['time_betas']),\n",
    "           whis=[2.5, 97.5],\n",
    "           hue = 'model_cohort',\n",
    "           fliersize=0)\n",
    "_ = plt.ylim([0,4])\n",
    "_ = plt.hlines(1, -1, 200, linestyles='--')\n",
    "_ = plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "_ = plt.xticks(rotation=\"vertical\")"
   ]
  }
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