Example_1_Individual_estimation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING (theano.configdefaults): install mkl with `conda install mkl-service`: No module named 'mkl'\n"
     ]
    }
   ],
   "source": [
    "import os, errno\n",
    "\n",
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "import pymc3 as pm\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import glambox as gb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def make_sure_path_exists(path):\n",
    "    try:\n",
    "        os.makedirs(path)\n",
    "    except OSError as exception:\n",
    "        if exception.errno != errno.EEXIST:\n",
    "            raise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "make_sure_path_exists('examples/example_1/figures/')\n",
    "make_sure_path_exists('examples/example_1/results/in_sample/traces/')\n",
    "make_sure_path_exists('examples/example_1/results/in_sample/model_comparison/')\n",
    "make_sure_path_exists('examples/example_1/results/out_of_sample/traces/')\n",
    "make_sure_path_exists('examples/example_1/results/out_of_sample/predictions/')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example 1: Individual gaze biases"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Our first example is based on the study by Thomas and colleagues (2019). Here, the authors study the association between gaze allocation and choice behaviour on the level of the individual. In particular, they explore whether (1) gaze biases are present on the individual level and (2) the strength of this association varies between individuals. In this example, we replicate this type of individual model-based analysis, including parameter estimation, comparison between multiple model variants, and out-of-sample prediction of choice and RT data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Simulating data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we simulate a dataset containing 30 subjects, each performing 300 simple value-based choice trials. We assume that in each trial participants are asked to choose the item that they like most out of a set of three presented alternatives (e.g., snack food items; similar to the task described in Krajbich & Rangel (2011)). While participants perform the task, their eye movements, choices and RTs are measured. Before completing the choice trials, participants were asked to indicate their liking rating for each of the items used in the choice task on a liking rating scale between 1 and 10 (with 10 indicating strong liking and 1 indicating little liking). The resulting dataset contains a liking value for each item in a trial, the participants' choice and RT, as well as the participant's gaze towards each item in a trial (describing the fraction of trial time that the participant spent looking at each item in the choice set)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_subjects = 30\n",
    "subjects = np.arange(n_subjects)\n",
    "n_trials = 300\n",
    "n_items = 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To simulate individuals' response behaviour, we utilize the parameter estimates that were obtained by Thomas et al. (2019) for the individuals in the three item choice dataset by Krajbich & Rangel (2011) (for an overview, see Fig. S1 of the manuscript). Importantly, we assume that 10 individuals do not exhibit a gaze bias, meaning that their choices are independent of the time that they spend looking at each item. To this end, we set the $\\gamma$ value of ten randomly selected individuals to 1. We further assume that individuals' gaze is distributed randomly with respect to the values of the items in a choice set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.random.seed(1)\n",
    "\n",
    "# load empirical model parameters (taken from Thomas et al., 2019)\n",
    "estimates = pd.read_csv('resources/individual_estimates_sec_nhb2019.csv')\n",
    "kr2011 = estimates.loc[estimates['dataset'] == 'krajbich2011']\n",
    "gen_parameters = dict(v=kr2011['v'].values,\n",
    "                      gamma=kr2011['gamma'].values,\n",
    "                      s=kr2011['s'].values,\n",
    "                      tau=kr2011['tau'].values,\n",
    "                      t0=np.zeros(len(kr2011)))\n",
    "\n",
    "# define participants with no association between gaze and choice:\n",
    "no_gaze_bias_subjects = np.sort(np.random.choice(n_subjects, 10, replace=False))\n",
    "gaze_bias_subjects = np.array([s for s in subjects if s not in no_gaze_bias_subjects])\n",
    "gen_parameters['gamma'][no_gaze_bias_subjects] = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The resulting distribution of generating model parameters looks as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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fJFG2ON8z6rbiXC/ufZbE/qhwqe6MNbNZwNkEbe1vAa+5+0kFvD7xztjGLTuY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\nYlOR3iZaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 389.764x433.071 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(4, 1, figsize=gb.plots._plots_internal.cm2inch(9,10), dpi=110, sharex=True)\n",
    "\n",
    "for subject_set, color, label in zip([gaze_bias_subjects,\n",
    "                                      no_gaze_bias_subjects],\n",
    "                                      ['C0', 'C1'],\n",
    "                                      ['gaze-bias', 'no gaze-bias']):\n",
    "    # v\n",
    "    axs[0].scatter(subject_set,\n",
    "                   gen_parameters['v'][subject_set],\n",
    "                   color=color,\n",
    "                   s=10)\n",
    "    axs[0].set_ylabel(r'$v$', fontsize=7)\n",
    "    \n",
    "    # sigma\n",
    "    axs[1].scatter(subject_set,\n",
    "                   gen_parameters['s'][subject_set],\n",
    "                   color=color,\n",
    "                   s=10)\n",
    "    axs[1].set_ylabel(r'$\\sigma$', fontsize=7)\n",
    "\n",
    "    # gamma\n",
    "    axs[2].scatter(subject_set,\n",
    "                   gen_parameters['gamma'][subject_set],\n",
    "                   color=color,\n",
    "                   s=10)\n",
    "    axs[2].set_ylabel(r'$\\gamma$', fontsize=7)\n",
    "\n",
    "    # tau\n",
    "    axs[3].scatter(subject_set,\n",
    "                   gen_parameters['tau'][subject_set],\n",
    "                   color=color,\n",
    "                   label=label,\n",
    "                   s=10)\n",
    "    axs[3].set_ylabel(r'$\\tau$', fontsize=7)\n",
    "    axs[3].set_xlabel('Subject', fontsize=7)\n",
    "    \n",
    "\n",
    "axs[0].set_title('Generating GLAM parameters', fontsize=7)\n",
    "axs[0].set_ylim(-0.1, 1)\n",
    "axs[1].set_ylim(-0.1, 0.5)\n",
    "axs[2].set_ylim(-1.5, 1.2)\n",
    "axs[3].set_ylim(0, 5.1)\n",
    "axs[-1].legend(loc='upper left', frameon=True, fontsize=7)\n",
    "for ax in axs:\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.tick_params(axis='both', which='major', labelsize=7)\n",
    "    ax.set_xticks([])\n",
    "    \n",
    "fig.tight_layout()\n",
    "fig.savefig('examples/example_1/figures/Supplementary_Figure_1_generating_parameters.png', dpi=330)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are the subjects that we defined as having no association of gaze allocation and choice behaviour:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3, 10, 14, 17, 19, 20, 21, 22, 24, 26])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_gaze_bias_subjects"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first instantiate a GLAM model instance using `gb.GLAM()`and then use its `simulate_group` method. This method requires us to specify whether the individuals of the group are either simulated individually (and thereby independent of one another) or as part of a group with hierarchical parameter structure (where the individual model parameters are drawn from a group distribution, see below). For the former, the generating model parameters (indicated in the following as `gen_parameters`) are provided as a dictionary, containing a list of the individual participant values for each model parameter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "np.random.seed(2)\n",
    "\n",
    "glam = gb.GLAM()\n",
    "glam.simulate_group(kind='individual',\n",
    "                    n_individuals=n_subjects,\n",
    "                    n_trials=n_trials,\n",
    "                    n_items=n_items,\n",
    "                    parameters=gen_parameters,\n",
    "                    value_range=(1, 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then access the simulated data as part of our GLAM model instance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = glam.data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject</th>\n",
       "      <th>trial</th>\n",
       "      <th>repeat</th>\n",
       "      <th>choice</th>\n",
       "      <th>rt</th>\n",
       "      <th>item_value_0</th>\n",
       "      <th>gaze_0</th>\n",
       "      <th>item_value_1</th>\n",
       "      <th>gaze_1</th>\n",
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       "      <td>0.382790</td>\n",
       "      <td>7</td>\n",
       "      <td>0.407891</td>\n",
       "      <td>condition_0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   subject  trial  repeat  choice        rt  item_value_0    gaze_0  \\\n",
       "0      0.0    0.0     0.0     0.0  1.678472             9  0.649142   \n",
       "1      0.0    1.0     0.0     2.0  1.307034             3  0.332040   \n",
       "2      0.0    2.0     0.0     2.0  1.324605             3  0.423884   \n",
       "3      0.0    3.0     0.0     1.0  1.145805             5  0.195771   \n",
       "4      0.0    4.0     0.0     2.0  1.065713             8  0.209319   \n",
       "\n",
       "   item_value_1    gaze_1  item_value_2    gaze_2    condition  \n",
       "0             9  0.224745             7  0.126113  condition_0  \n",
       "1             9  0.312176             8  0.355783  condition_0  \n",
       "2             2  0.192437             6  0.383680  condition_0  \n",
       "3             5  0.454379             6  0.349851  condition_0  \n",
       "4             4  0.382790             7  0.407891  condition_0  "
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     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As this example is focused on the individual level, we can further create a summary table, describing individuals' response behaviour on three behavioural metrics, using the `aggregate_subject_level_data` function from the `analysis` module. The resulting table contains individuals' mean RT, their probability of choosing the item with the highest item value from a choice set and a behavioural measure of the strength of the association between individuals' gaze allocation and choice behaviour (indicating the mean increase in choice probability for an item that was fixated on longer than the others, after correcting for the influence of the item value on choice behaviour; for further details, see Thomas et al. (2019))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "np.random.seed(3)\n",
    "\n",
    "subject_data_summary = gb.analysis.aggregate_subject_level_data(data, n_items)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">rt</th>\n",
       "      <th>best_chosen</th>\n",
       "      <th>gaze_influence</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>se</th>\n",
       "      <th>q1</th>\n",
       "      <th>q3</th>\n",
       "      <th>iqr</th>\n",
       "      <th>mean</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subject</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <th>0.0</th>\n",
       "      <td>1.292315</td>\n",
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       "      <td>0.142598</td>\n",
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       "      <td>0.027597</td>\n",
       "      <td>1.025087</td>\n",
       "      <td>1.458560</td>\n",
       "      <td>0.433473</td>\n",
       "      <td>0.750000</td>\n",
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       "      <th>1.0</th>\n",
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       "      <td>0.878345</td>\n",
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       "      <td>6.317563</td>\n",
       "      <td>0.050796</td>\n",
       "      <td>1.413609</td>\n",
       "      <td>2.499227</td>\n",
       "      <td>1.085618</td>\n",
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       "      <td>0.202899</td>\n",
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       "      <td>1.742749</td>\n",
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       "      <td>0.697514</td>\n",
       "      <td>0.062293</td>\n",
       "      <td>5.375163</td>\n",
       "      <td>0.040338</td>\n",
       "      <td>0.967926</td>\n",
       "      <td>1.705945</td>\n",
       "      <td>0.738018</td>\n",
       "      <td>0.936667</td>\n",
       "      <td>-0.006805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>2.217397</td>\n",
       "      <td>0.755107</td>\n",
       "      <td>0.005503</td>\n",
       "      <td>4.586606</td>\n",
       "      <td>0.043669</td>\n",
       "      <td>1.662276</td>\n",
       "      <td>2.623872</td>\n",
       "      <td>0.961595</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.062939</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               rt                                                              \\\n",
       "             mean       std       min       max        se        q1        q3   \n",
       "subject                                                                         \n",
       "0.0      1.292315  0.477199  0.142598  4.708733  0.027597  1.025087  1.458560   \n",
       "1.0      2.040758  0.878345  0.523410  6.317563  0.050796  1.413609  2.499227   \n",
       "2.0      1.742749  0.720414  0.137617  4.912813  0.041663  1.261841  2.002483   \n",
       "3.0      1.427180  0.697514  0.062293  5.375163  0.040338  0.967926  1.705945   \n",
       "4.0      2.217397  0.755107  0.005503  4.586606  0.043669  1.662276  2.623872   \n",
       "\n",
       "                  best_chosen gaze_influence  \n",
       "              iqr        mean                 \n",
       "subject                                       \n",
       "0.0      0.433473    0.750000       0.189652  \n",
       "1.0      1.085618    0.716667       0.202899  \n",
       "2.0      0.740643    0.856667       0.033876  \n",
       "3.0      0.738018    0.936667      -0.006805  \n",
       "4.0      0.961595    0.850000       0.062939  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subject_data_summary.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Exploring the behavioural data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In a first step of our analysis, we explore differences in individuals' response behaviour. To this end, we plot the distributions of individuals' scores on the three behavioural metrics, and their associations, using the `plot_behaviour_associations` function implemented in the `plots` module:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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7la7ydbfum+S4fv6trbXXbNH219O9DiTJ/avq2D3sd7Kq6hFJ7tnf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\nYjD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ve/DUU+40U72ZXs404emn3WXsWHeKqRtvdMNCRzhvUgklET+1MZMVuxp4/4wyejyz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iV9fSHEvGeh8wJOaUjMbnsWhIlUNJpu9aPphIHGCyNNpfLSeN/HRKtUTeTriGPuOhnivq0n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J8gw48fHytWNYmJx046XTtcyWExcn1/ev65+s43bLD3zU17csY1xVxeWniKg/ehCAN1B4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\ncF+AXf1ok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      "text/plain": [
       "<Figure size 2338.58x909.449 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(4)\n",
    "\n",
    "fig, axs = gb.plots.plot_behaviour_associations(data=data)\n",
    "\n",
    "fig.savefig('examples/example_1/figures/Figure_1_individual_differences.png', dpi=330)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The resulting plot shows that individuals' probability of choosing the best item, as well as the strength of their behavioural association of gaze and choice, are not associated with their mean RT (D-E). However, individuals' probability of choosing the best item increases with decreasing strength of the behavioural association of gaze and choice (F)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Likelihood-based model comparison"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In a second step of our analysis, we want to test whether the response behaviour of each individual is better described by a decision model with or without gaze bias. To this end, we set up the two GLAM variants:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One GLAM variant that includes the gaze bias mechanism:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating single subject models for 30 subjects...\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(5)\n",
    "\n",
    "glam_bias = gb.GLAM(data=data, name='glam_bias')\n",
    "glam_bias.make_model(kind='individual')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And one without a gaze bias (as indicated by `gamma_val=1`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating single subject models for 30 subjects...\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(6)\n",
    "\n",
    "# for the no-gaze-bias variant, we set the gamma-parameter to 1, indicating no influence of gaze allocation on choice behaviour\n",
    "glam_nobias = gb.GLAM(data=data, name='glam_nobias')\n",
    "glam_nobias.make_model(kind='individual', gamma_val=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Subsequently, we fit both models to the data of each individual and compare their fit by means of the Widely Applicable Information Criterion (WAIC; Vehtari et al., 2017): "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `fit` method defaults to Metropolis-Hastings MCMC sampling (for methodological details, see the Methods Section of the manuscript). The `draws` argument sets the number of samples to be drawn. This excludes the tuning (or burn-in) samples, which can be set with the `tune` argument. In addition, the `fit` method accepts the same keyword arguments as the PyMC3 sample function, which it wraps (see the PyMC3 documentation for additional details). The `chains` argument sets the number of MCMC traces (it defaults to four and should be set to at least two, in order to allow convergence diagnostics)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_tune = 5000\n",
    "n_draws = 5000\n",
    "n_chains = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 30 model(s) using MCMC...\n",
      "  Fitting model 1 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1310.10draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 2 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1318.24draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 3 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1318.39draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 4 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1306.96draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 5 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1318.35draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 6 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1308.01draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 7 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1314.01draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 8 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1309.53draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 9 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1313.33draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 10 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1317.36draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 11 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1304.60draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 12 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1309.36draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 13 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1300.42draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 14 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1313.52draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 15 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1301.97draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 16 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1307.51draws/s]\n",
      "The number of effective samples is smaller than 25% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 17 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1316.78draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 18 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1304.21draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 19 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1292.46draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 20 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1311.52draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 21 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:29<00:00, 1335.10draws/s]\n",
      "The number of effective samples is smaller than 25% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 22 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1306.03draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 23 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1308.38draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 24 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1311.45draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 25 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1306.84draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 26 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1307.22draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 27 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1293.38draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 28 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1308.93draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 29 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1307.66draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 30 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1298.39draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/!\\ Automatically setting parameter precision...\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(7)\n",
    "\n",
    "glam_bias.fit(method='MCMC',\n",
    "              tune=n_tune,\n",
    "              draws=n_draws,\n",
    "              chains=n_chains)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 30 model(s) using MCMC...\n",
      "  Fitting model 1 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:20<00:00, 1977.11draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 2 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:19<00:00, 2004.20draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 3 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:20<00:00, 1977.98draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 4 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:20<00:00, 1975.45draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 5 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:20<00:00, 1986.99draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 6 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:20<00:00, 1963.78draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 7 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:22<00:00, 1816.57draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 8 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:20<00:00, 1974.83draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 9 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:22<00:00, 1795.38draws/s]\n",
      "The number of effective samples is smaller than 25% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 10 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:20<00:00, 1918.71draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 11 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:21<00:00, 1830.05draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 12 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:20<00:00, 1956.02draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 13 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:20<00:00, 1975.46draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 14 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:20<00:00, 1958.79draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 15 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:20<00:00, 1973.48draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 16 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:20<00:00, 1982.15draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 17 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:20<00:00, 1996.80draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 18 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:20<00:00, 1967.96draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 19 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:20<00:00, 1984.74draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 20 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:20<00:00, 1994.98draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 21 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:19<00:00, 2028.99draws/s]\n",
      "The number of effective samples is smaller than 25% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 22 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:20<00:00, 1972.42draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 23 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:20<00:00, 1987.80draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 24 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:20<00:00, 1994.49draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 25 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:20<00:00, 1997.07draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 26 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:20<00:00, 1953.03draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 27 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:20<00:00, 1942.56draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 28 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:20<00:00, 1976.93draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 29 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:20<00:00, 1989.45draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 30 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:20<00:00, 1970.76draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/!\\ Automatically setting parameter precision...\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(8)\n",
    "\n",
    "glam_nobias.fit(method='MCMC',\n",
    "                tune=n_tune,\n",
    "                draws=n_draws,\n",
    "                chains=n_chains)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convergence check:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def check_convergence(summary, varnames=['v', 's', 'tau'],\n",
    "                      n_eff_required=100, gelman_rubin_criterion=0.05):\n",
    "    varnames = [varname + '__0_0' for varname in varnames]\n",
    "    enough_eff_samples = np.all(summary.loc[varnames]['n_eff'] > n_eff_required)\n",
    "    good_gelman = np.all(np.abs(summary.loc[varnames]['Rhat'] - 1.0) < gelman_rubin_criterion)\n",
    "    if not enough_eff_samples or not good_gelman:\n",
    "        return False\n",
    "    else:\n",
    "        return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.all([check_convergence(pm.summary(trace), varnames=['v', 's', 'gamma', 'tau']) for trace in glam_bias.trace])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.all([check_convergence(pm.summary(trace), varnames=['v', 's', 'tau']) for trace in glam_nobias.trace])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Saving traces and traceplots for visual inspection:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for subject, subject_trace in enumerate(glam_bias.trace):\n",
    "    gb.plots.traceplot(subject_trace)\n",
    "    plt.savefig('examples/example_1/results/in_sample/traces/sub_{}_gaze_bias_model_trace.png'.format(subject), dpi=330)\n",
    "    plt.close()\n",
    "    pm.trace_to_dataframe(subject_trace).to_csv(\n",
    "        'examples/example_1/results/in_sample/traces/sub_{}_gaze_bias_model_trace.csv'.format(subject))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for subject, subject_trace in enumerate(glam_nobias.trace):\n",
    "    gb.plots.traceplot(subject_trace)\n",
    "    plt.savefig('examples/example_1/results/in_sample/traces/sub_{}_no_gaze_bias_model_trace.png'.format(subject), dpi=330)\n",
    "    plt.close()\n",
    "    pm.trace_to_dataframe(subject_trace).to_csv(\n",
    "        'examples/example_1/results/in_sample/traces/sub_{}_no_gaze_bias_model_trace.csv'.format(subject))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After convergence has been established for all parameter traces (for details on the suggested convergence criteria, see the Methods Section of the manuscript), we perform a model comparison on the individual level, using the `compare` function:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The resulting table can be used to identify the best fitting model (indicated by the lowest WAIC score) per individual."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject</th>\n",
       "      <th>model</th>\n",
       "      <th>WAIC</th>\n",
       "      <th>pWAIC</th>\n",
       "      <th>dWAIC</th>\n",
       "      <th>weight</th>\n",
       "      <th>SE</th>\n",
       "      <th>dSE</th>\n",
       "      <th>var_warn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>glam_bias</td>\n",
       "      <td>523.6</td>\n",
       "      <td>5.75</td>\n",
       "      <td>0</td>\n",
       "      <td>0.94</td>\n",
       "      <td>50.25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>glam_nobias</td>\n",
       "      <td>645.09</td>\n",
       "      <td>3.64</td>\n",
       "      <td>121.49</td>\n",
       "      <td>0.06</td>\n",
       "      <td>44.15</td>\n",
       "      <td>23.56</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>glam_bias</td>\n",
       "      <td>1097.86</td>\n",
       "      <td>3.69</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>40.32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>glam_nobias</td>\n",
       "      <td>1185.02</td>\n",
       "      <td>2.85</td>\n",
       "      <td>87.16</td>\n",
       "      <td>0</td>\n",
       "      <td>38.22</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>glam_bias</td>\n",
       "      <td>832.43</td>\n",
       "      <td>4.02</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>41.15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>glam_nobias</td>\n",
       "      <td>847.56</td>\n",
       "      <td>3.02</td>\n",
       "      <td>15.12</td>\n",
       "      <td>0</td>\n",
       "      <td>41.33</td>\n",
       "      <td>7.74</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>glam_nobias</td>\n",
       "      <td>690.75</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0</td>\n",
       "      <td>0.79</td>\n",
       "      <td>45.73</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3</td>\n",
       "      <td>glam_bias</td>\n",
       "      <td>691.47</td>\n",
       "      <td>3.37</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.21</td>\n",
       "      <td>45.74</td>\n",
       "      <td>2.26</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>glam_bias</td>\n",
       "      <td>927.23</td>\n",
       "      <td>3.61</td>\n",
       "      <td>0</td>\n",
       "      <td>0.99</td>\n",
       "      <td>39.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4</td>\n",
       "      <td>glam_nobias</td>\n",
       "      <td>966.35</td>\n",
       "      <td>2.68</td>\n",
       "      <td>39.12</td>\n",
       "      <td>0.01</td>\n",
       "      <td>37.55</td>\n",
       "      <td>12.09</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5</td>\n",
       "      <td>glam_bias</td>\n",
       "      <td>1047.15</td>\n",
       "      <td>3.85</td>\n",
       "      <td>0</td>\n",
       "      <td>0.99</td>\n",
       "      <td>42.42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5</td>\n",
       "      <td>glam_nobias</td>\n",
       "      <td>1460.1</td>\n",
       "      <td>2.99</td>\n",
       "      <td>412.95</td>\n",
       "      <td>0.01</td>\n",
       "      <td>33.1</td>\n",
       "      <td>30.37</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>6</td>\n",
       "      <td>glam_bias</td>\n",
       "      <td>1821.62</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.98</td>\n",
       "      <td>44.58</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>6</td>\n",
       "      <td>glam_nobias</td>\n",
       "      <td>1883.22</td>\n",
       "      <td>3.33</td>\n",
       "      <td>61.6</td>\n",
       "      <td>0.02</td>\n",
       "      <td>41.92</td>\n",
       "      <td>15.77</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>7</td>\n",
       "      <td>glam_bias</td>\n",
       "      <td>1042.19</td>\n",
       "      <td>3.7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>43.31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>7</td>\n",
       "      <td>glam_nobias</td>\n",
       "      <td>1354.08</td>\n",
       "      <td>2.1</td>\n",
       "      <td>311.89</td>\n",
       "      <td>0</td>\n",
       "      <td>29.78</td>\n",
       "      <td>26.01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>8</td>\n",
       "      <td>glam_bias</td>\n",
       "      <td>295.23</td>\n",
       "      <td>3.34</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>49.43</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>8</td>\n",
       "      <td>glam_nobias</td>\n",
       "      <td>424.61</td>\n",
       "      <td>2.55</td>\n",
       "      <td>129.38</td>\n",
       "      <td>0</td>\n",
       "      <td>48.14</td>\n",
       "      <td>21.22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>9</td>\n",
       "      <td>glam_bias</td>\n",
       "      <td>966.07</td>\n",
       "      <td>3.82</td>\n",
       "      <td>0</td>\n",
       "      <td>0.96</td>\n",
       "      <td>44.74</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>9</td>\n",
       "      <td>glam_nobias</td>\n",
       "      <td>1267.56</td>\n",
       "      <td>2.97</td>\n",
       "      <td>301.49</td>\n",
       "      <td>0.04</td>\n",
       "      <td>34.8</td>\n",
       "      <td>31.21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>10</td>\n",
       "      <td>glam_nobias</td>\n",
       "      <td>732.99</td>\n",
       "      <td>2.81</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>42.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>10</td>\n",
       "      <td>glam_bias</td>\n",
       "      <td>734.6</td>\n",
       "      <td>2.96</td>\n",
       "      <td>1.61</td>\n",
       "      <td>0</td>\n",
       "      <td>42.55</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>11</td>\n",
       "      <td>glam_bias</td>\n",
       "      <td>683.41</td>\n",
       "      <td>4.09</td>\n",
       "      <td>0</td>\n",
       "      <td>0.99</td>\n",
       "      <td>51.38</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>11</td>\n",
       "      <td>glam_nobias</td>\n",
       "      <td>879.43</td>\n",
       "      <td>2.9</td>\n",
       "      <td>196.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>44.38</td>\n",
       "      <td>26.28</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>12</td>\n",
       "      <td>glam_bias</td>\n",
       "      <td>957.48</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.95</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>12</td>\n",
       "      <td>glam_nobias</td>\n",
       "      <td>1065.61</td>\n",
       "      <td>2.76</td>\n",
       "      <td>108.13</td>\n",
       "      <td>0.05</td>\n",
       "      <td>39.32</td>\n",
       "      <td>21.55</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>13</td>\n",
       "      <td>glam_bias</td>\n",
       "      <td>927.51</td>\n",
       "      <td>4.13</td>\n",
       "      <td>0</td>\n",
       "      <td>0.99</td>\n",
       "      <td>45.41</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>13</td>\n",
       "      <td>glam_nobias</td>\n",
       "      <td>1049.05</td>\n",
       "      <td>3.03</td>\n",
       "      <td>121.54</td>\n",
       "      <td>0.01</td>\n",
       "      <td>42.55</td>\n",
       "      <td>21.65</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>14</td>\n",
       "      <td>glam_nobias</td>\n",
       "      <td>483.89</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>48.24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>14</td>\n",
       "      <td>glam_bias</td>\n",
       "      <td>485.6</td>\n",
       "      <td>3.06</td>\n",
       "      <td>1.71</td>\n",
       "      <td>0</td>\n",
       "      <td>48.22</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>15</td>\n",
       "      <td>glam_bias</td>\n",
       "      <td>1760.66</td>\n",
       "      <td>4.02</td>\n",
       "      <td>0</td>\n",
       "      <td>0.94</td>\n",
       "      <td>43.82</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>15</td>\n",
       "      <td>glam_nobias</td>\n",
       "      <td>1899.37</td>\n",
       "      <td>3.12</td>\n",
       "      <td>138.71</td>\n",
       "      <td>0.06</td>\n",
       "      <td>40.82</td>\n",
       "      <td>25.18</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>16</td>\n",
       "      <td>glam_bias</td>\n",
       "      <td>1180.79</td>\n",
       "      <td>4.42</td>\n",
       "      <td>0</td>\n",
       "      <td>0.91</td>\n",
       "      <td>39.88</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>16</td>\n",
       "      <td>glam_nobias</td>\n",
       "      <td>1268.26</td>\n",
       "      <td>2.87</td>\n",
       "      <td>87.47</td>\n",
       "      <td>0.09</td>\n",
       "      <td>36.97</td>\n",
       "      <td>20.29</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>17</td>\n",
       "      <td>glam_nobias</td>\n",
       "      <td>935.04</td>\n",
       "      <td>3.04</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>42.46</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>17</td>\n",
       "      <td>glam_bias</td>\n",
       "      <td>936.51</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1.47</td>\n",
       "      <td>0</td>\n",
       "      <td>42.52</td>\n",
       "      <td>1.23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>18</td>\n",
       "      <td>glam_bias</td>\n",
       "      <td>747.64</td>\n",
       "      <td>4.01</td>\n",
       "      <td>0</td>\n",
       "      <td>0.96</td>\n",
       "      <td>38.14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>18</td>\n",
       "      <td>glam_nobias</td>\n",
       "      <td>842.02</td>\n",
       "      <td>2.67</td>\n",
       "      <td>94.38</td>\n",
       "      <td>0.04</td>\n",
       "      <td>34.93</td>\n",
       "      <td>19.44</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>19</td>\n",
       "      <td>glam_bias</td>\n",
       "      <td>1191.53</td>\n",
       "      <td>3.91</td>\n",
       "      <td>0</td>\n",
       "      <td>0.65</td>\n",
       "      <td>38.44</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>19</td>\n",
       "      <td>glam_nobias</td>\n",
       "      <td>1192.6</td>\n",
       "      <td>2.94</td>\n",
       "      <td>1.07</td>\n",
       "      <td>0.35</td>\n",
       "      <td>37.99</td>\n",
       "      <td>3.79</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>20</td>\n",
       "      <td>glam_nobias</td>\n",
       "      <td>3024.94</td>\n",
       "      <td>2.17</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>170.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>20</td>\n",
       "      <td>glam_bias</td>\n",
       "      <td>3026.69</td>\n",
       "      <td>2.54</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0</td>\n",
       "      <td>170.54</td>\n",
       "      <td>1.63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>21</td>\n",
       "      <td>glam_nobias</td>\n",
       "      <td>904.11</td>\n",
       "      <td>2.78</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>42.13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>21</td>\n",
       "      <td>glam_bias</td>\n",
       "      <td>905.02</td>\n",
       "      <td>3.14</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0</td>\n",
       "      <td>42.11</td>\n",
       "      <td>1.83</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>22</td>\n",
       "      <td>glam_nobias</td>\n",
       "      <td>1051.61</td>\n",
       "      <td>3.29</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>37.47</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>22</td>\n",
       "      <td>glam_bias</td>\n",
       "      <td>1053.29</td>\n",
       "      <td>3.61</td>\n",
       "      <td>1.68</td>\n",
       "      <td>0</td>\n",
       "      <td>37.34</td>\n",
       "      <td>1.27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>23</td>\n",
       "      <td>glam_bias</td>\n",
       "      <td>1432.37</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.95</td>\n",
       "      <td>43.26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>23</td>\n",
       "      <td>glam_nobias</td>\n",
       "      <td>1474.78</td>\n",
       "      <td>2.99</td>\n",
       "      <td>42.41</td>\n",
       "      <td>0.05</td>\n",
       "      <td>41.99</td>\n",
       "      <td>13.38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>24</td>\n",
       "      <td>glam_nobias</td>\n",
       "      <td>1695.8</td>\n",
       "      <td>3.36</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>39.82</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>24</td>\n",
       "      <td>glam_bias</td>\n",
       "      <td>1697.75</td>\n",
       "      <td>3.64</td>\n",
       "      <td>1.94</td>\n",
       "      <td>0</td>\n",
       "      <td>39.81</td>\n",
       "      <td>1.03</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>25</td>\n",
       "      <td>glam_bias</td>\n",
       "      <td>1158.37</td>\n",
       "      <td>3.84</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>42.29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>25</td>\n",
       "      <td>glam_nobias</td>\n",
       "      <td>1452.21</td>\n",
       "      <td>2.67</td>\n",
       "      <td>293.85</td>\n",
       "      <td>0</td>\n",
       "      <td>29.41</td>\n",
       "      <td>28.28</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>26</td>\n",
       "      <td>glam_nobias</td>\n",
       "      <td>451.16</td>\n",
       "      <td>3.74</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>46.43</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>26</td>\n",
       "      <td>glam_bias</td>\n",
       "      <td>452.31</td>\n",
       "      <td>3.99</td>\n",
       "      <td>1.15</td>\n",
       "      <td>0</td>\n",
       "      <td>46.15</td>\n",
       "      <td>1.91</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>27</td>\n",
       "      <td>glam_bias</td>\n",
       "      <td>867.65</td>\n",
       "      <td>3.52</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>42.55</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>27</td>\n",
       "      <td>glam_nobias</td>\n",
       "      <td>895.33</td>\n",
       "      <td>2.6</td>\n",
       "      <td>27.69</td>\n",
       "      <td>0</td>\n",
       "      <td>43.06</td>\n",
       "      <td>9.67</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>28</td>\n",
       "      <td>glam_bias</td>\n",
       "      <td>1175.13</td>\n",
       "      <td>4.43</td>\n",
       "      <td>0</td>\n",
       "      <td>0.98</td>\n",
       "      <td>39.55</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>28</td>\n",
       "      <td>glam_nobias</td>\n",
       "      <td>1445.24</td>\n",
       "      <td>2.87</td>\n",
       "      <td>270.11</td>\n",
       "      <td>0.02</td>\n",
       "      <td>32.8</td>\n",
       "      <td>29.11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>29</td>\n",
       "      <td>glam_bias</td>\n",
       "      <td>685.52</td>\n",
       "      <td>4.33</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>45.94</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>29</td>\n",
       "      <td>glam_nobias</td>\n",
       "      <td>1024.66</td>\n",
       "      <td>3.09</td>\n",
       "      <td>339.15</td>\n",
       "      <td>0</td>\n",
       "      <td>39.26</td>\n",
       "      <td>27.55</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    subject        model     WAIC pWAIC   dWAIC weight      SE    dSE var_warn\n",
       "0         0    glam_bias    523.6  5.75       0   0.94   50.25      0        0\n",
       "1         0  glam_nobias   645.09  3.64  121.49   0.06   44.15  23.56        0\n",
       "2         1    glam_bias  1097.86  3.69       0      1   40.32      0        0\n",
       "3         1  glam_nobias  1185.02  2.85   87.16      0   38.22     18        0\n",
       "4         2    glam_bias   832.43  4.02       0      1   41.15      0        0\n",
       "5         2  glam_nobias   847.56  3.02   15.12      0   41.33   7.74        0\n",
       "6         3  glam_nobias   690.75   2.9       0   0.79   45.73      0        0\n",
       "7         3    glam_bias   691.47  3.37    0.72   0.21   45.74   2.26        0\n",
       "8         4    glam_bias   927.23  3.61       0   0.99    39.4      0        0\n",
       "9         4  glam_nobias   966.35  2.68   39.12   0.01   37.55  12.09        0\n",
       "10        5    glam_bias  1047.15  3.85       0   0.99   42.42      0        0\n",
       "11        5  glam_nobias   1460.1  2.99  412.95   0.01    33.1  30.37        0\n",
       "12        6    glam_bias  1821.62   4.5       0   0.98   44.58      0        0\n",
       "13        6  glam_nobias  1883.22  3.33    61.6   0.02   41.92  15.77        0\n",
       "14        7    glam_bias  1042.19   3.7       0      1   43.31      0        0\n",
       "15        7  glam_nobias  1354.08   2.1  311.89      0   29.78  26.01        0\n",
       "16        8    glam_bias   295.23  3.34       0      1   49.43      0        0\n",
       "17        8  glam_nobias   424.61  2.55  129.38      0   48.14  21.22        0\n",
       "18        9    glam_bias   966.07  3.82       0   0.96   44.74      0        0\n",
       "19        9  glam_nobias  1267.56  2.97  301.49   0.04    34.8  31.21        0\n",
       "20       10  glam_nobias   732.99  2.81       0      1    42.6      0        0\n",
       "21       10    glam_bias    734.6  2.96    1.61      0   42.55   0.97        0\n",
       "22       11    glam_bias   683.41  4.09       0   0.99   51.38      0        0\n",
       "23       11  glam_nobias   879.43   2.9  196.02   0.01   44.38  26.28        0\n",
       "24       12    glam_bias   957.48     4       0   0.95      42      0        0\n",
       "25       12  glam_nobias  1065.61  2.76  108.13   0.05   39.32  21.55        0\n",
       "26       13    glam_bias   927.51  4.13       0   0.99   45.41      0        0\n",
       "27       13  glam_nobias  1049.05  3.03  121.54   0.01   42.55  21.65        0\n",
       "28       14  glam_nobias   483.89  2.76       0      1   48.24      0        0\n",
       "29       14    glam_bias    485.6  3.06    1.71      0   48.22   1.25        0\n",
       "30       15    glam_bias  1760.66  4.02       0   0.94   43.82      0        0\n",
       "31       15  glam_nobias  1899.37  3.12  138.71   0.06   40.82  25.18        0\n",
       "32       16    glam_bias  1180.79  4.42       0   0.91   39.88      0        0\n",
       "33       16  glam_nobias  1268.26  2.87   87.47   0.09   36.97  20.29        0\n",
       "34       17  glam_nobias   935.04  3.04       0      1   42.46      0        0\n",
       "35       17    glam_bias   936.51  3.18    1.47      0   42.52   1.23        0\n",
       "36       18    glam_bias   747.64  4.01       0   0.96   38.14      0        0\n",
       "37       18  glam_nobias   842.02  2.67   94.38   0.04   34.93  19.44        0\n",
       "38       19    glam_bias  1191.53  3.91       0   0.65   38.44      0        0\n",
       "39       19  glam_nobias   1192.6  2.94    1.07   0.35   37.99   3.79        0\n",
       "40       20  glam_nobias  3024.94  2.17       0      1   170.6      0        0\n",
       "41       20    glam_bias  3026.69  2.54    1.75      0  170.54   1.63        0\n",
       "42       21  glam_nobias   904.11  2.78       0      1   42.13      0        0\n",
       "43       21    glam_bias   905.02  3.14    0.91      0   42.11   1.83        0\n",
       "44       22  glam_nobias  1051.61  3.29       0      1   37.47      0        0\n",
       "45       22    glam_bias  1053.29  3.61    1.68      0   37.34   1.27        0\n",
       "46       23    glam_bias  1432.37   4.2       0   0.95   43.26      0        0\n",
       "47       23  glam_nobias  1474.78  2.99   42.41   0.05   41.99  13.38        0\n",
       "48       24  glam_nobias   1695.8  3.36       0      1   39.82      0        0\n",
       "49       24    glam_bias  1697.75  3.64    1.94      0   39.81   1.03        0\n",
       "50       25    glam_bias  1158.37  3.84       0      1   42.29      0        0\n",
       "51       25  glam_nobias  1452.21  2.67  293.85      0   29.41  28.28        0\n",
       "52       26  glam_nobias   451.16  3.74       0      1   46.43      0        0\n",
       "53       26    glam_bias   452.31  3.99    1.15      0   46.15   1.91        0\n",
       "54       27    glam_bias   867.65  3.52       0      1   42.55      0        0\n",
       "55       27  glam_nobias   895.33   2.6   27.69      0   43.06   9.67        0\n",
       "56       28    glam_bias  1175.13  4.43       0   0.98   39.55      0        0\n",
       "57       28  glam_nobias  1445.24  2.87  270.11   0.02    32.8  29.11        0\n",
       "58       29    glam_bias   685.52  4.33       0      1   45.94      0        0\n",
       "59       29  glam_nobias  1024.66  3.09  339.15      0   39.26  27.55        0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comparison_df = gb.analysis.compare_models(models=[glam_bias, glam_nobias])\n",
    "comparison_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualising the individual WAIC differences:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-121.49,  -87.16,  -15.13,    0.72,  -39.12, -412.95,  -61.6 ,\n",
       "       -311.89, -129.38, -301.49,    1.61, -196.02, -108.13, -121.54,\n",
       "          1.71, -138.71,  -87.47,    1.47,  -94.38,   -1.07,    1.75,\n",
       "          0.91,    1.68,  -42.41,    1.95, -293.84,    1.15,  -27.68,\n",
       "       -270.11, -339.14])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dWAIC = []\n",
    "for subject in subjects:\n",
    "    comp_s = comparison_df.loc[comparison_df['subject'] == subject]\n",
    "    dWAIC_s = comp_s.loc[comp_s['model'] == 'glam_bias', 'WAIC'].values - comp_s.loc[comp_s['model'] == 'glam_nobias', 'WAIC'].values\n",
    "    dWAIC.append(dWAIC_s[0])\n",
    "dWAIC = np.array(dWAIC)\n",
    "np.save('examples/example_1/results/in_sample/model_comparison/dWAIC_in_sample.npy', dWAIC)\n",
    "dWAIC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# identify subjects better described by each model variant\n",
    "gaze_bias_idx = dWAIC < 0\n",
    "no_gaze_bias_idx = dWAIC > 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 779.528x259.843 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=gb.plots._plots_internal.cm2inch(18,6), dpi=110)\n",
    "\n",
    "ax.bar(subjects[gaze_bias_idx], dWAIC[gaze_bias_idx], color='C0', label='gaze bias')\n",
    "ax.bar(subjects[no_gaze_bias_idx], dWAIC[no_gaze_bias_idx], color='C1', label='no gaze bias')\n",
    "ax.set_xlabel('Subjects', fontsize=7)\n",
    "ax.set_ylabel('WAIC( gaze bias )'+' -\\n'+'WAIC( no gaze bias )', fontsize=7)\n",
    "ax.legend(loc='lower left', frameon=False, fontsize=7)\n",
    "ax.set_xticks([])\n",
    "ax.tick_params(axis='both', which='major', labelsize=7)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "fig.savefig('examples/example_1/figures/relative_model_comparison.png', dpi=330)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this comparison, we are able to identify those participants whose response behaviour matches the assumption of gaze-biased evidence accumulation. In particular, we find that we accurately recover whether an individual has a gaze bias or not for 29 out of 30 individuals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3, 10, 14, 17, 19, 20, 21, 22, 24, 26])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_gaze_bias_subjects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3, 10, 14, 17, 20, 21, 22, 24, 26])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subjects[no_gaze_bias_idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[True, True, True, True, False, True, True, True, True, True]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[s in subjects[no_gaze_bias_idx] for s in no_gaze_bias_subjects]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the individual parameter estimates (defined as MAP of the posterior distributions), we find that the individually fitted $\\gamma$ values cover a wide range between -0.8 and 1 (A), indicating strong variability in the strength of individuals' gaze bias. We also find that $\\gamma$ estimates have a strong negative correlation with individuals' scores on the behavioural gaze bias measure (B)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2kt/vb2hbUlKCz+fDWssrr7yS6McWERFJGiWTRERERLqmEdCw0ViZtXZ3Avd8GXc8KvkhHRpefvllAE488UQ8nj3zhkJeLz8fOJCbgEgrfaQBDwM3R6NEYkvUyoCa2PUUaKibZGLHzS2k8/l8DYmiSCRCTU1Nw/u62AwoY0yj2kndu3cHYMmSJa1/YBERkSRrbdatiIiIiHRO8Wu2tiZ4T3y71td87QdjTJP1mhKQn8w4rLUNCZiBTcxCKtq+nbuBL4CncJJALbneWkYA1wMVQCVOIioTZ4e3HkBp7Fw3nMF3Zdz90WgUYwxer5dwOEwkEmnYtQ2cQt31/H5/Qx2l+iTYsmXL9uPTi4iIJIeSSSIiIiJdU2bccXWC98S3y2y21YEpSHJ/B2T79u3s2rULgKysrH2u19ctWgRMAF5yuSiIRlvs82TgFZxt877EmZ0UBbJwpoZ1B8qBIJARO1fWRD8ejwdrLdFolGjsmdG4Z8cvf6ufsVRaWtpibCIiIu1By9xEREREuqb4VVXBZls1Fog7Tmu2VSdWXl7ecBxtIklkrW043gickZ7Oa57Wf38dCswHTo29D+DMSIriDLi7Af7YtVScGUumiX48LTwrvr5SJOIsxKvf+U1ERORg0swkERERka6pNu7Y12yrxvxxxzXNtjowqw7wvnyar1+93+KTL/GJo3opKY0fVW0MN/j9XBMMckMw2OIvsZnAY8DdwJ+BEFDCnuVt2Tizkqpx/obkxq7vHZ/b7W4yWRQfb30iLCcnp4WIRERE2oeSSSIiIiJdU3xpnvQE74mfjVTZbKsDYK09oILesVpLSVsi16NHj4bjioqKRu8BevXqxebNmxveR6NRMIaHUlLYZgy/rqtrdf3fT4EjgdlAFU7CKAenhlL9MrcKnIF4LlBtLaG4pFFzyaRQKNQ4LuDoo49O5GOLiIgklZa5iYiIiHRN2+OO+yV4T/9m7u8ycnNz6d/f+ZhlZftWLurbt2+j9/VJHYAPPR6uSU1lQwJLy07FWfY2FLA4CaW62LVUnOQSOImlHkBKE7OkoPFSvNraPZPNqqud8lbjxo1rNRYREZFkUzJJREREpGtaw54d7nOMMbkJ3DMs7vhAd1875I0fPx6ALVu27HNt6NChjd7XF7qut9Xl4nyfjwUJPOcInITSSbH3ZeypcO7DSSJ5XS4MTpHu9FhCKT6BFX9cVVXV6Lzb7eaMM85IIBIREZHkUjJJREREpAuy1tbRuE7R8S21N8a4gOPiTn3WHnEdCs477zwAli1bts9uaBkZGRxxxBEN7yPRaKOEDjhL164A7krgWdnAP4Hr4+6tiB17gFyXC3csiZQFZO71vGg0irWWYDDYcN7tdgNwzjnn0KdPnwSiEBERSS4lk0RERES6rhfjjr/TStup7Fl9tdla22WTSTNnziQ315mo9eGHH+5zfeLEiY3e19bW7lOs2+IU2f4+ToKoJS7gF8BDOEWpanFmKQG4jaE74Iv1nxIO01RJ7V27djUc1yeVbrrpplaeLCIi0j6UTBIRERHpup7E2Z0e4DvGmJFNNYrNSrot7tQ/2zuwjuT3+7nhhhsA+Pjjj9m0aVOj6wMGDGDYsD0r/qLWNlruFj+Afg04A9iQwHO/DbwEDMKpn1Tp8RAFDM6Ob75wmKi1+IHebnfDc8rLywmHw436mj17Nsccc0wCTxUREUk+JZNEREREOhljzGRjjK1/NdfOWrsG+EfsrQ94zhgzaK++3MBfgEmxU7twdrfv0m6++WbGjBkDwIsvvrhPMe6ZM2fi8ezZ+DgYClEXDGKtJdvlIjV23gDrcRJKbyTw3JHAK8BkY4i4XJTgFLaKRiKkRaNkxPpMcbnIBXzGUF5e3qiP/Px87rzzzv3/0CIiIkmiZJKIiIjIQWKMeXbvF5AX1+Q3TbT5bhsfexOwOnY8ElhljHncGHObMeZuYC17SvqEgVnW2oom+ulSfD4fc+bMwe/3U15ezpw5c9i9e3fD9ZSUFC655BLi5wPtDIWoqq3FhVPfKBtnmZrBqYM0Cycr15oc4HFruSoSIWIt20MhamK7tmUAfdPSiITDuIHu1uKLu7dPnz688cYbpKWlteXji4iItImn9SYiIiIikiTntXJ9UhPnVrTlgdbaUmPMqcBTOAW204CLm2haAlxlrX21Lc/rTMaOHctzzz3HOeecQ1lZGQ899BBTp07lmGOOwRjDwIEDOfvcc1n0/PNkAEFgp7V0q63FB/gBr7WUAyGc9YR/xNkG716cL7o5buAXkQgjgJ8BpThL3bqnphKJRMi2lvrS4D1waiwNHTWKV199lQEDBrTDtyEiIpI4zUwSERER6eKstYXAROBCnLI9hTi5kWLgU+BXQIG19vkOC7KDnH766bz66qvk5eURDod57bXXuP/++1m8eDFVVVWMHj2asy6+mHJjsDiFt0uBytixG+hO48TRy8AM4KsEnn8uMBfoh5Mw2lFbS6CuDi9OEsmNs+ztNzfeyNK33lIiSUREDglm750pJHmMMd2Ai4DTgTE4YwIPUA6sA94H/mmtbdMvjslmjFkJFAAUFBSwcuXKDo5IRKTtBt86v819bL7zjLbcbtocgMhh6GCNS3bv3s3111/P008/3eh8ZmYmPXv2xBhDUWEhqbFED4CX2FK32PsgTkKofnTdDfh/wIkJPL8YuAb4AGfGUxbOfzSiQLXbzdARIxg9ejTHT53KGRdfTIrff+AfVkRE4xJpI81MaifGmHNw6jHej5NM6g+k4ow7cnGmmf8M+MIY81djjLe5vkRERESkfeXm5vLUU0/x4YcfMmvWLHw+p1JRZWUlGzZs4Msvv6S6ro7dQHXsnhCwGwjE3vtwfjmsr3FUBvw0L4+nunVr9fk9cNYhXhHrrxQnkeQCMiIRNqxcyf8+9RTXX3UV3+zfn1/fdhtVVVXJ+OgiIiL7TTOT2oEx5ts4M5brk3U1ODOeN+LsBDsQOA3oHXfb09ba8w9mnM3RzCQR6Yo0M0mkc+qocUlFRQVLly5lyZIlrF+/nkAggNfrZejQoUyYMIF0t5ulb77Jo488QtH27aQCuT4fWRkZ9MjLI6t3b/qPGEF6ejoAI1avZtrChXgjkVaf/W/gydGjGTB4MBmRCOFAgKrKSjYXF/PFxo2AswNc1uDB/O2xx5g8eXK7fQ8i0mVpXCJtomRSkhljXDhL5PvHTs0DrrDW7t6rnR+4Dfhl3OlJ1tp3D0qgLVAySUS6IiWTRDqnQ3pcEolQsmED586Ywbr16/EAU8eNY8LYsbjcbqJuN4GUFKzL+X2x544dnDVvHlmVla12vb1XL+adeSaVGRmkBgK4Ykmo3VVVfLJuHUuWLCEcK9J91333MXv27Hb8oCLSBWlcIm2iZW7Jdwx7EklVwCV7J5IArLUBa+1tOHWT6p12EOITERERkWRwu+k+fDhPv/IKI0eMIAy8tmQJ/3rhBb7avBlCIVJra3GHwwDs7NWLJy66iM19+7bade8dO/juP/6B+fRTiqNRwh5nE+bcjAxOnTCBC88/n955efQAbvnRj7j//vvb73OKiIjsRcmk5BsUd7zCWlvRSvv4mUg92iEeEREREWlHvYYNY+6773LRJZcAsLGkhKcXLuRf//oX773zDl8tW8buzZsp2raN1Tt3cnZNDY8k0G+3UIjZ775L1UMP8benn2bp2rWEgkFckQiDs7M5f+ZM+vfrRzfgF7Nn88EHH7Tr5xQREann6egAuqCauOPcBNrnxR0nsoOsiIiIiBxiMnNz+Z9//IMZZ5/Nn//wBz759FO+qqmhbPVqvKtXA07B7jKcwtq/BlYAd+Ls3tYcL/BHYExxMb98+23eW7yY0447jmFHHEG2y8V5Z57J4089BaWl3HDppbzzxRekpqW174dtQigUIhAI4PP5SElJOejPFxGRg0szk5LvQ5yxAsAwY8yZzTU0xgwFvhN7G8LZxENEREREOiOXixPPPZcX//MfFi1cyFXXXMOw8eOxfiddVL+lb33y6N/AucD2BLq+CHgW8AWDPPfWWyx47TWCgQBZkQhnTJ2KAb7esIGH//AHiEaT/9n2snv3bu69917OO+88Bg8ejM/nIysrC7/fT9++ffn2t7/NHXfcwdatW9s9FhEROfhUgLsdGGPuA+qrINYBDwNzgE3s2c3tLOAnODOTQsDV1tp/HPRgm3BIF7oUETlAKsAt0jl12nFJMAilpRCJEIlEqNi1i5eefJJ7/vxnwJnKHnC7GTJ0KEMzMrh5/XpGVFW12u1OY7jcWj4HjujRgzPPOAO/38+r777LJ6tX069vXz789FPceXngSf4ihC1btvCrX/2Kp59+mrq6ulbbu91uzjzzTH77299y5JFHJj0eETlgGpdImyiZ1A6MMR7gL8APW2kaBhYBv7XWftgOcRzoaCsfSIFONmgTEWmBkkkinVOnTSaBM0OovBxqawE4dsIEqrZtwwtkZmQwKD+fgN9P1Bg80SiXb97MqTt3ttptEPg5zpT2kb16ce6MGZSUlvLk889TDvxzzhxOnjYNcnIgSUvOrLX87W9/46abbqIythud3+/nuOOOo3fv3mRkZFBXV0c4HGb37t3s3r2b1atXU1PjVIDwer3cfvvt3HLLLXjaIcklIvtN4xJpE/2XvB1Ya8PAdcaYJ4Bf0fwubVuA94A17RRKQTv1KyIiIiKtcbmchI7Px/L332fLtm0A5Pr9DBs2DJfLhS8UosbtJuh28/DQoWxKT+eKzZvxtvCDrw+4GxgN3L5jBx8vX86xY8fSLycHd2kpH37wASeffDIUF0O3btDGGkrhcJgrr7ySOXPmANCtWzfOP/98evXq1WT7YcOGATBt2jRWrFjB22+/TUVFBbfddhvvvPMOzz//POnp6W2KSUREOpZqJrUTY8xxOPUSTwOqcZbF/x/gNuARYBswFPg9sNIYc0zHRCoiIiIi7cmmpTHrpz8lHHvfb/hwanw+rDEYID0SIT0cBmtZ1KsXtxcUUOr1ttrvZcDTwGcff8yOykryevbEC2xasgTCsaeVlUFFa5sLNy8ajXLFFVc0JJJOPvlkfvaznzWbSIrn8/k4+uij+cEPfsDYsWMBWLhwIWeddVZCS+REROTQpZlJ7cAYcynwGE6ybhFwobV2915tvDiJpJ8BfYEFxpgx1trCJIay6gDva1jmJiIiIiJt89577/HZihUAdHe5SEtLIwSUG0NGJIInGsUXjeK2lmq3m7WZmfxs9GhuXreO4a3UUToWeBX4/UcfkTZgAKxdy5aNG6GkBLKznWVuVVVOciknB8z+rWx54IEH+Oc//wnAhRdeyMiRIwkGg/vVh9/v56yzzqJ79+68+eabvPHGG/zyl7/krrvu2q9+RETk0KGZSUlmjPkGTsFtF07B7bP3TiQBWGtD1tqbgWdip7oBv01mLNbaUQfyAjYkMw4RERGRw9mDDz7YcFzj81Hl8WABawyVHg+1LmdI7raWzHCYlEiEUp+PXxUU8EZeXqv99wXu3riR/9q1i1KgurbWqddUWtpQr4lAAHbvhkgk4bg3bNjArbfeCsD06dMZOXIk4foZTwdg4sSJHHOMMxn/7rvv5oMPPjjgvkREpGMpmZR8s9kzq+cha21NK+3/HHd8tjFGf09EREREuoi6ujqef/75hvfWWurcbsp9PsKxWUIBj4dKj4dobNlbWmzZW9gYHhw6lL8NHtzQtjl+4JpVq/gFUOv17tnJrbwcYgWzCYVg1y5np7kE3HLLLdTU1NCjRw8mTZrUpkRSvalTp9KjRw8AZs+ejTYDEhHpnJS4SL5vxR0vT6D9F3HH3YDc5IYjIiIiIh1l+fLljZaF1dXVEYlEiBhDuddLwO0GIOxyUeHxEIrNUvJFo2SFw7itZUHv3vxm5EjKE9gF7Wrg7/VJH5/P+Wt1tTNLyVpnxlJx8Z4ZS83YunUrc+fOBeCSSy5JWo0jr9fL9OnTAVi6dCmffPJJUvoVEZGDS8mk5MuMOz6Qn1qiyQpERERERDrW0qVLAUhJ2VOOsqq+DpIxVMdmJdUve6vyeKh1u7E0Xva2KiuLm0eP5ssEdkE7uqYGzjgDtm2D1FTnZF2dU0cpEnGSSqWle2YsNeHRRx8lEomQlSQ9oKAAACAASURBVJVFTk7OgX78JuXn59O9e3eg8RJAERHpPJRMSr74+khjEmj/zbjjOqAkueGIiIiISEfZsWMHAN26dSM9lggqLi5u1Ca497I3t5uqvZa9ZYTDFPt8/GrUKN7KTWAi+9atcNZZ8NZbkBn7rTMUcmYl1c+UqqzcM2NpL2+99RYAU6ZMIRpN7m+dxhjGjHGGya+//npS+xYRkYNDyaTk+yju+GpjTGor7X8Sd/yetVYzk0RERES6iEis4LXL5eKb33R+QywqKtonQdPasjdvNEpWKEQE+L/5+Tw6aBCtltIOBOCHP4R773USSsbsW5i7ttZJMMXFY61tmFHVq1evtn0BzejXrx8AX3/9NaWlpe3yDBERaT9KJiXfY+xZqjYUeN4Y02PvRsYYnzHmLmBm3GnN8xURERHpQjJjs4JqamoYP348AOFwmMLCwn0b1y9783qxxjQse6uJLXtzgbPsLRrl+e7dmd2jBwmlYR58EK66ClwucLudmUjl5VC/3C4YdApzx2ot7dixg/LycoCG2VTJFp+kWrt2bbs8Q0RE2o+SSUlmrf0C+FPcqenAZmPM/xpjfm2M+YUx5mFgE/DTuHZPW2ufR0RERES6jCOPPBKA8vJy/H4/Rx11FACbN2+murq6yXuCLhdlXi/h2KykOre7Ybe3cDBI0bp1bPzsM14sLmY6sDKRQN5+m8i3v+0kjeoLc1dVQVmZk1yKRJxrgQC1ccW5k1V4e2/xNaSa+x5EROTQpWRSO7DW/hz4JVC/dUcGcD7wG+D3wFVA3/rmwH3ApQc5TBERERFpZ+PGjWs4LiwsZNq0aWRkZADOTm/NJWuixlAeK8YNEHG52FhWxpKVKykrLycFZwvgYo+HM4F5CcTiLiwkPGMGLF4Mfr9zMhBwlr1Fo05SqaQEXyjUcE/9Mr1ki+/XHfuMIiLSeSiZ1E6stXcA+cBtwBtAERAAQsAu4APgj8AIa+0N1tpgc32JiIiISOeUm5vL0UcfDcBnn32G3+/nvPPOwxhDXV0dn332GZXN7apmDDWxZW+FX3/Nug0bKLGWSpzlc5mpqWTElqZdC7wydmyrWwl76urg6qvhoYcgLc05GQw6dZNifeX5fPTweADYvXt3c121SXy/gwcPbpdniIhI+1EyqR1Za7daa39vrT3ZWtvXWptqrfVZa3taa4+31t5qrV3X0XGKiIiISPv5wQ9+ADi1gUpLSxk8eDDf/e53GxJKS5cuZdOmTYRjyZy9bdy2jSWFhQQBn9dLavfufF1TQ0VtLQbIAobn5bF6yhSeP+ccAnFLyJp1330we7ZTR8kYZ5lbcTHU1eHz+Rg3YgTdgbKS9tlouKioCHBqMg0aNKhdniEiIu1HySQRERERkXZ0wQUXkJubi7WWefPmYa1lxIgRzJo1q6FA95YtW1i8eDHr1q2juLiYYNCZtF5VVcWGDRuIAuVuN5XWUlxSQm0kQjFQv0hu/OjRpNXUsGXgQJ688EJ299hn/5d9vf46zJwJJSVOUslaZ8lbTQ3jxo0jBdi+YgWmHZa6rVmzBoBvfOMbGGOS3r+IiLQvJZNERERERNpReno69913H+AU3l68eDEAgwYN4rrrrmuoqxSNRikqKmLFihUsXryYd999lyVLljT0E45EKAmHKQHcsWVoZUB2v34Mzc/HWEtqbS1VGRn864ILWD9sWOvBbdgAZ58NS5dCrE8qKjj/9NMBKNm1i12rV+NOYkJp9+7dbNq0CYDvfe97SetXREQOHiWTRERERETa2fnnn8+5554LwKJFi/j0008BZ1ezGTNm8JOf/IRJkyaRnZ3dcE80Gm3UhzGG7OxsBh5xBLuNoQ7wejyceNppBNLSiMZm+KTU1eGORJg3YwbvHXdcq3WUqKqCK66Axx9vSCiNzs/nhIICDLD4/fdJqarCG1eY+0BZa3n99dcByMjI4Prrr29znyIicvApmSQiIiIi0s6MMTz22GOMHz8egPnz5zN37lwCgQDgFNSeMmUKN954I7fccguzZs0iJycHcGY2TZgwgYkTJ9K7d282btxIIBSi1Bimz5xJVlYW1u2mNi2NSGxnNHckQmogwCcTJjD3rLOo8/laD/Kuu+DWW8FajDH86Npr6Q7s2L6dTz/5BH8gQEozu88lasWKFaxduxaAK6+8krT6IuAiItKpKJkkIiIiInIQZGVlsWjRIiZNmgTA559/zgMPPMD7779PTU1NQzu/309mZialpaWAs9tZTU0Nn3/+OWvXriUSieD1ern4kkvo841vUJuaijUGjCGQmkrQ58MCrmiU1NpatgwcyL8uvJCSWHKqRa++ChdeCKWlnHD88Zz77W/TA/j4/fdZv349vmCQ1Npap77SftqyZQvz5s0DYPjw4fzpT3/a7z5EROTQYOwB/I9AujZjzEqgAKCgoICVK1d2cEQiIm03+Nb5be5j851ntOV2VZgVOQBdcVwSDof5n//5H26//XZCsaVjLpeLvn370rdvX3Jycti2bRvLly8HwOPxNNrpbciQIZx11lmNlsSZaJTUQKChtpErEiElEMAVG+uHPR4scPqCBeRv3Nh6kNnZcO+9VA4dymWXXcaGTZuoBL41ZQpjx44l6nY7SSxXYr9Nr1q1ihdeeIFwOExGRgZvvvkmEyZMSOheEWkXGpdImyiZJPvoioM2ERElk0Q6p648LlmzZg133303TzzxRMNyt5b079+fY489loKCgqZ3QLOWlLo6fPW1jazFH5dgirpcBFJS+NaHH/Ktjz5qPUBj4Oab2XnSSVxz9dVs/uorqoGc/v2ZOnUqObm51KamEo0trWtKeXk5CxcuZNWqVYBTJ+mZZ55h+vTprT9fRNqTxiXSJkomyT668qBNRA5fSiaJdE6Hw7iktLSUefPm8emnn7J06VK2b99OUVERtbW1dOvWjQkTJpCfn0+vXr0S6s8TCuEPBBr+o+ONTzABAb+fIZs2Me3ll0lN5M8CM2ZQOns2/33HHfzn7bepw9lFbkD//owYOZKsQYPI6dMHl8uFtZbi4mK2bdvGqlWrGuojAeTn5/Pkk09yzDHHJP7liEh70bhE2sTT0QGIiIiIiBzOcnJymDVrFrNmzWo4N2PGDObPn09+fj7HHXfcfvUX9nqpcbtJra3FFY0SSkkh4nbjr6vDxGYrbRk4kBuGDOHnGzcypLUOX36ZnA0buPvee3lt2jTuvvtuXCUlfL11K4VbtwJQAdR5PEQiEfb+sdrv93P55Zdzzz334EukELiIiBzyVIBbREREROQQ4/f7AQgGgwd0f9TlojotjZDX67z3eKiJW5LmDYXYbi0zgJU9e7be4erVmO99j+l9+/LKq69yzx//yGkTJpAeSw5lAenhcEMiye12M2LECG6//Xa2b9/OAw88oESSiEgXoplJIiIiIiKHmOHDhwNQXFx84J0YQ8DvJ+J2kxIIYFwuav1+fHV1eMNhKktK8ACvf/e7jLIWHnig5f7KyuCKK/DeeiunnHcep5x8MpFolE0lJWz4+mtqa2tx+f0M+OY3OXLMGLyxRJaIiHQ9SiaJiIiIiBxixo0bB8D27dsJBoNtmtUT8nqJuFzOsjcg6PdTU1VFaXk5LuCo/Hw4/XQYNQp+8hNoqRh4NAp33AGrVsGtt+L2ehmWm8uwIUMgPd1p43Y7xbtFRKTL0jI3EREREZFDzHHHHYfL5SIajSal6HjU7aYmPZ2wx/ktecWXX1IMWGMYPWYMVFXBxInw4oswYEDrHc6dC1deCbt3O+8rK6G8HKyFSAR27Wo5KSUiIp2akkkiIiIiIoeYPn36cOaZZwLwySef7FPU+kBYY6hNTSXg87Fs2TIiwDGnn05Onz5Og2AQevVyEkXHH996hytWwIUXwhdfOO9ra6G01Jm9ZC2UlDhJKhER6XKUTBIREREROQRdd911ABQVFbF06dKk9fv+55+ztqSECPD9yy+H7GznZYwzq8gYeOQRuOqq1jsrLnbavfCCk0AKBp1zoZBzvaLCqbWUhGSYiIgcOpRMEhERERE5BE2dOpUZM2YAsGjRIkpKStrc5+7du1m0aBEhYOI553DsiSc6F1JToXt3p96RtVBTAzfeCPfeCykpLXcaDsPvfgd33ukkkyIRZ1ZS/TK3mhonwRSNtjl+ERE5NCiZJCIiIiJyCDLG8NBDD9GtWzfq6up44oknKC8vP+D+SktLeeKJJwiHw+Tk5PDgX/8KPXpAZqbTwOt13tcnjwIBmDIFnnkG6pfCteTZZ+Haa506StY6M5Lql7kFg04dpfoZSyIi0qkpmSQiIiIicojq27cvTz/9NF6vl9LSUh599FE2bNiw3/2sX7+ev//975SXl+Pz+Xjuuefo2bOnczEz05mV5HI5r5ycPQmmcBj693fqKB17bOsP+vxzuPhip54SOMmk+mVukYiTaKqt3e/4RUTk0OLp6ABERERERKR506ZN44UXXmDmzJlUVFTwxBNPMHbsWI477jjy8vJavHfnzp28//77fBErku33+5k7dy5Tpkxp3NDvh7w8Z3laKATp6c5MpfJyJwnk8cBDD8E998A//tFywLt2Ya+8kqLvf5/lgwdTFwziTU2l1ze+QcHo0fitdZJU9QkrERHpdJRMEhERERE5xJ1xxhl89NFHzJo1i2XLljW8Bg4cyODBg+nTpw+ZmZlYa6msrKSoqIjNmzdTWFjY0Me4ceOYM2cOo0aNavohbjfk5jpFs6urwedzlr2VlTnL1MJh+MlPoKAAbrvNOdcMEwrR9+GHeRv4MxAGIkCVMYwYM4YLLriAcy64gIwBA5yC3yIi0qmYZGwzKl2LMWYlUABQUFDAypUrOzgiEZG2G3zr/Db3sfnOM9pyu/60JHIANC5pLBQKcc899/CXv/yFbdu2JXRPv379+PGPf8wNN9yAx5Pgb8m1tY13YausdBJM4CyF27ABrrsOduxotaslxvALYKe1WKASqAUy0tO58aabuObnP8fdWpFvEUk2jUukTVQzSURERESkk/B6vdx8881s3ryZZ555hksuuYSCggJcrj3DemMMo0aN4tJLL+X5559n8+bN/PSnP008kQTO7m55ec7yNnCWpHXr5iSSolEYMgSeeYbgmDGtdjXOWp5LS+PGU07hlKlTGTtkCFlAVXU1v/vNbzjzuOPYtGbNfn4TIiLSkTrFMjdjzH8Dj1prN3V0LCIiIiJtZYwZClwJnA4MBPxAEfAR8IS19pUkPmt/p6HPttben6znS/vwer3MnDmTmTNnAhAIBKioqMAYQ2ZmJn6/v+0P8XichFJZmTNTye936iiVlUEoxNdlZVy9cycXAhe10lVGdTUXvfkmb0+axPpTTuG46mre+fBDPt2wgc+WLuXM44/n3wsWMHLChLbHLSIi7a6zzEz6JbDeGPO6MeZ7xhhfRwckIiIiciCMMT8EVgA/B74J5ACpwFDgAmC+MeYFY0xWx0UpnY3f76dnz57k5eUlJ5FUzxhnd7fsbOfY7Ybu3SkJBLj22mvZtn0797jd/KuggIir5T9aeCIRpv7nP3xr8WIyU1M5bepULpw2Db/bTXFJCd+dPp3C5cv3LK0TEZFDVqeYmRRjgCmxV6kx5nHg79baFR0bloiIiEhijDHXAg/EnfoCWADU4CSWvo0zPjsbeMEYc5q1tvkqx/unFLgjgXbvJel50pWkpzsFuUtKIBLh1jvuYOW2bfRwuznttNOo6NuX50eM4PQFC0ivqWmxq28uX06PkhJeP+kk8gcP5tIZM3jipZcoLinhR1deyfMvvYTp0cNJXImIyCGpsyST7gIuBnrH3ncHfgT8yBjzCfA34ClrbXUHxSciIiLSImNMPvCXuFM/t9beuVebo4BXgV7AScBPgT8kKYQKa+1dSepLDkdeL+TlMXfOHF6e72xqMG7aNHr37w/RKMW5uTxz3nlMX7iQ3q0U5u7/9decO3cur51yCvTqxVmTJjHvrbf46OOPefzRR7n40kvZUFpKUXEx4XCY9PR0RowYQXZ2dkMfoVCIFStWsGTJEr766itCoRB+v58jjjiCcePGMXz48Ea1pEREJHk6zW5uxhg3cAZwBXAaexJh9R+gGvg3zmylxQc/wq5Du6aISFek3dykoxljngQujL39l7W2yTIzxphpwGuxtxXAIGttWRueWz9W+spaO/hA++koGpccWqy1FBQUULhmDceOHMn06dMxQEoggCcSAcAViXD8Bx8wavXqVvsLu93858QT+TI/nwULFrC+sJBqt5sUv5/K6mrKcXZ+q3fEEUcwduxYrLUsWLCAqqqqZvvu1asXV1xxBddccw0DBw5s2wcX6Xo0LpE26TSpemttxFo7z1p7Fk6hyl8A63H+JTBABvB94D1jzApjzI3GmB4dF7GIiIiIwxiTAZwbe2uB3zbX1lq7EPgw9jYLZ8mbyCHhrbfeYs2aNVQDI044wdndzRjqUlOp8/mwQNTt5t0TTuCtSZOImpb/vOqJRDjlzTcZ+vLL7Ny2jRQgKxKhproaA3TD+Zeg3vr163nmmWd49tlnGxJJKSkp9OnThwEDBtC7d2/cseVxO3bs4I477mDo0KHccsstBAKBdvhGREQOT51lmVsj1trtwJ3AncaYE3B2Q5kJpMWajATuBv5gjHkRZ7bSog4JVkRERAROxdmxDeALa+3aVto/AxwbOz4H+Ec7xSWyXx5//HHAmSHUo08fqq0lNRDAHYkQ9vmIuN34AwFcwJoRIyjNyeHUhQtJq61tsd9Ti4pIB24DyoGhWVmccPrpZPTogcvlYu2mTTy1cCGB4L4lxI499lgmTZrUsKQtEomwc+dOvvjiCz7//HNqa2v505/+xEsvvcSzzz5LQUFBcr8UEZHDUKeZmdQca+171trLcOopXQt8zJ7ZSinAd4AFxphNxpjbjDH9OyxYEREROVwdFXecSIHrd5u5ty26G2PmG2OKjDFBY0ypMWaVMebvxpjTkvQM6eI++ugjAMaMGYMxButyUZOWRtDnbLZs3W5q09KI1M8O6tWLZ889lx15ea32fQLwpNtNPhAMBBiYlUWGMXz11Vcsmj+frGAQnzEMHTqUgoICfLFnvv322zz33HNEYsvs3G43ffr04dRTT+XGG2/kmGOOAWD16tVMnDiRZcuWJflbERE5/HT6ZFI9a22VtfZha+2xwGjgPiAcu2xwlsb9BthkjHnRGHNSB4UqIiIih59RccfrE2j/ZdzxAGNMVrMtE5cJnI7zA5wXZwXRSOBy4BVjzCfGmCOS8JwmGWNWHsgLyG+vmGT/VFdXszpWByknJ6fRtbqUFGpTU51ipsYQSE0l5PUCsAt44Kij+KRPn1af0S8S4Z/A8cEg1dXVlBYW8va8eUStJT0lhZPGjCG/b1/y8vKYMGECPXv2BGDVqlXMnTuXvevB+nw+pk+fzqxZs0hJSaGkpIRp06ZRWFjY1q9DROSw1imXubXEGDMI+C7OlPD6/UQtewqMuYEZwAxjzNvAVdbaDQc9UBERETmc9I473tpaY2ttqTGmGkiPneqFU4z7QEWBT4DPgG2x9/2AyTgJJYDxwCfGmBOstSva8KzmaG1RJ1dUVNSQrKmfFRQv7PFQk55Oam0tu4qKWPb552zbsAFPbS0GeAlnkH4zewbpTUnDqVfx1scf85uiIlKAPl4v/QsK8KSkQDhMwFqqPR5GjBiB1+vl66+/ZsWKFQwZMoSjjz56nz4HDx7MpZdeypw5c9i1axdXXXUVr776KqaVmk4iItK0LpFMMsb4cIpaXgFMYU/iqP6vy4BHgJ7ALGBQ7PxkYLEx5mhrbasDOxEREZEDlBl3XJ3gPTXsSSZlttSwFbcCj1lrdzZ10RhzJs44KQ/IBuYZYwqstapWLI2Ew+GG4/oi13vbUVzMyy+9REVhIan1bXGmwXlxioF9CdwF5DTZwx6T1zuT+H4F9Bs6lHSXi6polLDLhT8SwW0tlR4P+fn51NbWUlJSwoIFC8jPzyc7O3uf/vr27cvpp5/O3Llzee2115gzZw6XXXbZ/nwFIiIS06mXuRljxhpj7gOKgCeBk3A+k8EZqD0CHGOtPdpa+6C19v8AQ4GzgC9i3fQAfn2wYxcREZHDSmrc8b4VhJsWn8xJa7ZVK6y1f2wukRS7Pg84mT1JriHANQf6vBasOsBXXTvEIgcgNXXPP8Z1dY3/tlhr+eCDD3jooYfYUlhIGRBMSWFA//6MKChg6PjxHDl+PGNGj2YpcCGwJoFnTgb+5XZzhMuFsZaMcJiUWG0kbzRKt1AIj7UMHz4ct9tNKBTi3Xffbba/MWPGMGzYMADuuOMOotHofn0HIiLi6HTJJGNMtjHmh8aYJcAS4DqcHzbqi24vwRkA9bXWXm2t/ST+fut4CTgGWB27Z8rB/AwiIiJy2Infymrf9UFN88cd1yQxln1Ya78A/hJ36rvt8IxRB/ICVI7gENG/f3/S053JclVVVQ3nrbXMnz+fRYsWEYlESElJoaCggFHHHEPmkCFkZmfj8Xio9njYXFwMwHbg1rw83vT7m3pUIwMiEX6xdi1jyssxQFokQno4DNbispbsUIhMj4cBAwYA8Pnnn++T7KpnjOHEE08EYP369bz55ptt+EZERA5fnSaZZIyZYox5Amed//8FxrIngVQB/BU42lo7wVr7N2ttVfO9gbW2Dvh37O3A9otcREREhMq44/RmWzUWPxupstlWyfPvuOP/MsZ0mnGiHBxut5ujjnI2F9y1a1fD+YULF7JkyRIAcnNzGTduHHl5eRhjCLtclHm9hF3OP05biospBfx+P0G3m7uys3k4M5PW5gelRqP8aMMGZhQVYazFF42SFQ7jshYDZIbDDIvtGBcOh1m5cmWzffXr14/evZ0yZk8//fSBfRkiIoe5TjFIMMZsAF4HLsCZJl6fRPoIp05SX2vtD621+7vP5+7YX7tE7SgRERE5ZG2PO+7XWmNjTDaNk047kh7RvuJ3kPPglAIQaWTy5MkAfPTRR4RCIdavX8+HH34IQK9evSgoKMAb28WtXtQYyj0eKiIRAnV1BIHatDTCxoAxzE1P57acHCoTKIZ9dlER127a1FAzKSscxhNbqpbldjM0KwsDbN3afDlUYwz5+c4mgZ9++un+fwkiItI5kkk4a/frE0hlwP3AGGvtt6y1j1lrD3Tq93achNTHyQlTREREpEnx0ySGJ9D+iLjjQmttW3ZyS5Rt5b0IV155JcYYKioqWL58OS+99BIAWVlZDB8+vPnd0YxhRyBAGc4/WF6/nzJjqIu1X5by/9m77zipqvPx459zp28vwFJ26UWKEASUaCyxJCgIIpbECoollvA15mfMN0YTNYYkptoSY++iWFBB8auioCiCIEVYuiDLwtbZMn3m/P44M7tL2cKysMzyvF+v+5o7d+4998wkL9x55jzP4+K61FTWt2AOoyor+XVhIV0CAZTWpDeoo5SdmkpnoLSoqMkxunXrBsDq1asbTYkTQgjRuGQJJgF8iunE1l1r/fO2aFmrtZ4dD0h9/+CnJ4QQQgjRqK8a7J/UgvNPbrC/vI3n0piGAawoUH6Y7iuSSK9evZg4cSIA77//PtXV1SilGDhwIJbV9FeLQCCAH6hQCm1ZoBTVlkWtZaG1Zn1tLdOA91swjx6BAL9Zt46+xcXU1NaiamoI7t6Nr7YWGxDZtYu3Z83io48+orCwkNraPZsoZmVlASYlrrKy8sA/CCGEOMolS3rXUK312vaehBBCCCFEK83HFOH2ACOUUgO11k0twrigwf7rh3Rm9S5ssP+l1lraXIn9uv/++5k/fz5+v6kr37Vr17rC3E1JdE6LWBZllkVWLIZDa3xAideLQ2uiwIPdu/NNUREzmhkvNRbj9qIiHgCeih+z4lsMKFq7Ft/atXyCSW0bNGgQY8aMoU+fPnsEvqLxVU1CCCFaLilWJkkgSQghhBDJLN4YJBEUUsBvGztXKXUmcGL8aRXwxqGdHSilhsIe391fOdT3FMmrf//+TJ8+ve55JBJB6+azIhMBnFgshlaKCsvCF0+ZqwwEKANyOnfGk5bGU8BNNF953sL8H/ePmEitA1PsywlkZ2XRyeMhF1Bas27dOp599lleeuklSktL68ZIT09v8XsXQghhJEUwSQghhBCiA7gTCMX3L1NK/b+9T1BKjQCeaXBoptZ6nxwcpdRpSimd2Bq7oVJqqVLqQqVUo/3XlVLjgQ+BtPihbcAjzb8dcTTr0aO+jnxJSUmLag95PB4AtNZEo1GisRiby8rYXlODBtIzMsjs0wcrvsrpU+BSYHML5jMOeBJT3d4CsoFQZSV5eXmcNHo0Y/v2JSc+7vr165kzZw5g0vYkmCSEEAcuKdLclFIpwMuADXhLa92iP3CUUjcAEzB/uF2ktQ41c4kQQgghxCGhtd6klJpBfaDmz0qpS4F3AR8wAjgXs7gC4CPgrwd521HALKBGKfU5sBoow2QBdQd+CAxpcL4XmKi19h/kfUUHt3y5KeWVnZ1NRUUF5eXlLF26lIKCArp167ZPRzfYcwVQRUUFPp+vbkVTWlYWAwcNQilF0OXCb7PhiUbZjima+gebjVOaSUcbBDwP3IbprpMGVGzdit/no3efPuR17szm0lJWb91KJBIB9gyKCSGEaLmkCCYBU4DxmOYPvzmA677AdH7TwGRMQEoIIYQQol1orf+tlLIBfyFePym+7e1N4Mo2/CEsDTgzvjXms/g9N7bRPUUHtn37dgAGDx5Mp06dePfddwmFQmzZsoWtW7eSnZ1Neno6qamp2Gw2otEoPp8Py7KIxWJ7FMTu27cv+fn5VAEZkQg15eVURaP4gSwg4nLx56wsNnm9TAsEmpxXJvAw8IjLxXOxGITDhHfv5jvLoqB3b/p17kyGx8PC1aaXz5IlS1i+fDkjR448JJ+TEEJ0VMkSTBoXf9yktW5xRxOt9TKl1CagL3AO6nJ19AAAIABJREFUEkwSQgghRDvTWj+klJoHXAucDfQE3EAx8DnwrNZ6bhvdbiQwNr4NAToDnQAXUAl8G7/nLK31wja6pzgKhMNhAOx2OyNHjqRv3758/PHHrFq1ikgkQnl5OeXlTTcEzM3NpV+/fvXpb0BJLEbhhg24gDBmGZ0tFiOiNQ8EAnwN/AFoqty3DbgpGOQYt5t7lcIbChEqLsaXkUFKTg7+8nI6Y9oVRiIRpk6dypdffonT6TzIT0UIIY4eyRJMOg7z35dPW3HtIqBffAwhhBBCiHantd4M3B7fWnP9Akwh7+bOWwGsAP7dmvsI0ZhEAChRJykzM5OJEydy1llnsXr1arZt28bOnTspKyuruyYrK4uuXbuyfv16YrEYlmXVjZOwYeNGyqNR3JhVSTGgOBxGV1UB8DFwjcvFn0IhCpop+n1mIEBPu51bLYvvYjHKNm/GaVnsKCrCDpzYvz9fbtzIypUr+ctf/sLUqVOpqqpCKUXnzp3Jzc1to09LCCE6nmQJJuXHH7e04trENT3baC6topRKAy4CJgHDgK5AFNgFbAU+Af5Pa724veYohBBCCCFESxxzzDF88skn+6w+8ng8jBkzhjFjxgCm2LbWGqUUSpn45+LFi5k/fz4lJSXs3r2bLl26AFBdXV3XZc2TnU1pRQU5mJVGuwIBPEAnh4Pv7Haus9m4IxjkxGbqKA2MRHhWKX4BLI9EKNqwgWwgkpLCaaeeSm1NDRuKi/ntHXdwxx137HFtQUEBo0eP5sILL2TKlCmyckkIIRpIlm5uiX+5w624NnGNp8mzDiGl1MVAIfA4MBGTdpcCpAP9MfUL7kba8AohhBBCiCQwatQoAHbs2EEsFmv0PKUUlmXVBZIATjjhBAoKCgBYt24dFRUVABQVFQFgs9lwu91EgBLqWyD6gVqHgxhQoxS/cbl4ej+FvveWpTWPAhcAkWgUJzAwN5fnn3ySncXFpAE57PvFaPv27bz++utccsklFBQU8Kc//akuvU8IIY52ybIyqRzoAnRrxbWJa7xtN52WU0r9GrivwaHNmO4s32FS93oAfYATD//shBBCCCGEOHCnnnoqAD6fj8LCQgYPHtziay3LYsqUKTz++ONUV1ezatUq+vbty65duwDT9S1RoFtZFl6lSIlGSQUq/X7Cbjc5lkVMax6IRFgJ3EvTvxzbMV18jgH+phTfbd9OOiaAFHW5IBhkbN++HD9uHFGbjaqqKioqKti6dSsrV65k9+7d3H777cyaNYunnnqKY4899oA/MyGE6EiSZWXSVkxdgNNbcW3imm1tNpsWUkpNpT6QVAFcDPTXWk/XWv9Oa/17rfW1WuuzMD+IXHi45yiEEEIIIcSBGjRoUF1AaenSpehm6hftLTMzk6lTp5KZmYnWmk2bNtWN4fF4qK6uBqBr165EolGqMBXjo1pT7fezvbaWMr+fqNZ8CFzrdrPTZmv2vlOAh7UmF+iVn8/J3/sefeO1kSrKysh3u8lLS6N3796MHDmSyZMnc/PNN9etxPrqq6/4/ve/zwcffHBA71cIITqaZAkmfRR/HKyUOq+lFymlJmE6l+gGYxwWSql84J/xp37gNK31LN3If2m11kGplySEEEIIIZLFjTfeCMDmzZtZs2bNAV+fk5PDNddcs8+qpuLi4rrAktvtrjuuUlIowxQdjWHSDqoBh93OtzYb17rdfKGarUvPCOBFIOO771i3bh2OaJRcoNrrJRgM4g4Gcfv9EJ9DTk4OEyZM4NJLL61bNXXuueeyaNGiA37PQgjRUSRLMOkZzH8zAJ5QSh3f3AVKqdHAk/GnGnj6EM2tMb8EMuL7d2utVx7m+wshhBBCCHHITJkyhdNOOw2Ad999l6p4x7UDkZqayoUXXsigQYPqjjX87XXz5s11+z6fjzBQSn0dJR9QEolQVVvLd7W13KQ1T7Xgvp2BJzAd34p37cIO5AIxnw8ARyRCqs+H1aAeVP/+/Zk6dSoZGRn4/X4uvvhiKisrD/g9CyFER5AUwSSt9TrgKUyqWxawUCn1kFLqeKVUXd0npZQtfuxBYFH8XA08r7Vedbjmq5TyAFPjT4NIO14hhBBCCNHBWJbF448/TmpqKrW1tTz77LN16WkHQilFdnb2PmOrvVYZ2e12srOz6d23LwNOOIHegwfTo3t37PEVS5H49k/gdiDQzH0dmA44t2E6xinAv3MnzmDQzCEWI6W2FnuDots5OTlccskl2Gw2ioqKuOWWWw74/QohREeQFMGkuJuApfF9O3A9sBjwKaV2K6V2YdLJFgM/o74D3Ffxcw+nsUBmfH+x1rpSKTVQKfVPpVShUsqnlKpUSq1WSj2glBp6mOcnhBBCCCHEQevbty/PP/88NpuN0tJSHn/8cbZs2XJAY2itKS0trXvudDq57rrr+N///V8uu+yyuuMjR45k+PDhFBQU4HK7cXbpgpWdTa3PRxTTsSexYuk9YLplsbMF9/8p5pffLGD+/PnU7N6NJ57mpgBPIIArEKhLe8vLy6tbkfXUU0+xcqUkIAghjj5JE0zSWgeA0zApzqrBZgc6xTd7g+PEzz1Na+0/zNP9foP95UqpnwErgZ8DAzHNJjKBoZgg2Uql1J+UUm36v4dSak1rNqBfW85DCCGEEEJ0XJMmTWLWrFk4nU68Xi/PPPMMb775Jrt3727yOq01W7du5bnnnmPjxo11x6+88kq6dOmC3W6nV69eWJb5E7mmpmaP6wOBAEvXrqUMUw9DYzreBC2LVI+Hb91uLgOWtOA9jMZ8cegTizFnzhxigQApPh9WJAKAMxwmxedDxdPeTjzxRDIyTEWLRx55pAV3EEKIjsXe/ClHDq21D7hUKfU34EZMp7ae8ZcTAaRvgQ+Bh7XWyw7/LAHo32D/+0Bi/esu4A1MZ7kc4BxgMCaodxuQhnlfbWVIG44lhBBCCCHEfp1//vl8/vnnTJ06lZUrV7JixQpWrFhBfn4+PXv2pFu3bqSmpqK1xuv1snPnTjZv3kxZWdk+Y9kadGWz2+106dKF4uJiKisr6dKlC2ACUevXrycWixECSjA1j9JcLqIOBzWAJxKhBrgBuEUpLm2m41xX4FngrmCQd+fPZ8L48XgCAUJOJ2GnE1ssRqrPR8DtJmK3M3r0aD788EOeeeYZ/v73v+9RLFwIITq6pAomJcSDRFcBKKVSMf/tACjTWte228TqNUz6Hht/nA1M1VrX/aSilLoN+DVwb/zQDUqpd7TWcw/PNIUQQgghhGgbI0eO5Msvv+Qf//gHDzzwAN99913d1pRhw4Zx22238T//8z+Ul5ezatUq8vLy6l4fNGgQxcXF7Nq1i379+mGz2SgrK6OioqLunBhQ43aT4nCgMPWSfLEYnvjr/1CKNVrzO+prYeyPC5gJPL1jBxu3b6dHQQHOUAhbNErA7TZpb34/IaeTYcOG8eGHH+Lz+fj666854YQTWvGpCSFEckqaNLfGaK1rtdbb4tuREEgCSN/r+TfAJQ0DSQBa65jW+g/s2Wnu1204j29auQXbcA5CCCGEEOIo4XQ6ue2229iyZQuvv/4606dP57jjjsPhcNSdk5KSwg9+8ANmzJjBwoULWblyJZdffjlXX301AMuXLyfcoOj1qFGjAIjFYhQVFQGwY8eOfe7rdLmotCxq4oW7A9EoZUAUiMZizAOuBHbvVdh7f64EJr73HvZ4MW5bNIrH50NFo+Z+oRDdnU5SXC4Ali1rr4QIIYRoH0m5MikJ7F2j6X6tdWi/Zxr3Yf6bBXCSUipba13RxPktorVuVWHveN0kSZETQgghhBCtYrfbOe+88zjvvPMAEwgKBAJYloXL5dqnUxvA9ddfz/3334/P52PBggWcddZZAKSnpzN8+HBWrlzJli1bSEtLo7Kysu46pdQeKWY1ShEBbLEYGlOYOx1wA1ucTq622bg3GGREvP5RY8bEYvSfNYt3zz0Xb1YWltZ1q5IiTif2WIzBnTqxaseOPWo+CSHE0SDpVyYdofbuifphUydrrdcDiZ9XFDDyUExKCCGEEEKI9mBZFikpKbjd7v0GksB0hpsxYwYAixcvZtu2bXWv/ehHP8Lj8aC1prCwcI/r9hecCihFKWZVUgzwAjWAw27Ha1n83Onk5RbMO9vv58LZs+m9dStaKRTgCoVwxbu9Oe12cgFdVdXCT0IIITqGpAwmKaUcSqmRSqlxSqkLlFIXtWQ7jFPcuwvpjv2etaeGyeSd2nAuQgghhBBCJIU//OEPDBgwAK01L730EsXFxQCkpqZy7rnnAhAM7lmRwencfxWkiFKUAHXpASkpeC2LaCxGdSDAH4E7gfB+r65nj0YZN38+xy9Zgm5wzOP3o+PpeGlaQ1kZNLPaSQghOoqkSnNTSo0E7gDGA45mTt+bBma1+aT2b3Urrmn4c0rTrSaEEEIIIYTogFJSUnj11Vc59dRTqays5KmnnmL8+PEMGzaMwYMH8+Mf/5j33nuv7nzLsva70ikajRKLB3bKgDyXC8uyqAmHqQwGyQJswCcZGfwsHGam39/sr7mjli+nc2kp7/7oR8RsNlM/qbwcN9CrVy8IBqGkBLKzoZEAlxBCdBRJszJJKXUV8AVwHqYJg2rFdrgs3et5fguu6dFgv6QN5yKEEEIIIUTSGD58OO+//z65ubkEg0Fee+01Xn75ZYqKihg7dixZWVl158ZiMWpqagiFQmiticVi+P1+amr26HuDVyl21tYSCAaJYAJMyuHAW1XFMr+fi4CvWzC3ntu3c/Err5BeXU1NbS2BQIBM4LgBA0BriEbNCqXaI6UvkBBCHBpJsTJJKTUM+A/mBwQwq1EXAoWY+kRH1EoerfUqpdQGYED80OnAE42dr5QaRH0wKQp8dWhnKIQQQgghxJFr9OjRLF26lKuvvpoPP/yQwsJCCgsL6d69e92Ko4RoNIrf78fv37sHTr1AIEAAk/LW2WYjGo1SHA6TAXgAn9PJTUrxy0iESfGObY3JrKriolde4ZF+/QgBKTYbg3v1gvJyyMoCmw28XgiFzPMWdI8TQohkkxTBJOBWTCBJA+8CV2mtd7XvlJr1JKZLG8CtSqnnmujo9r8N9v9Pay0V/IQQQgghxFGtd+/evP/++zz++OPMnDmTzZs3U1RU1OrxEh3fdgYC5GK+XERTUlCWhVOb36b/YrOxLhLh1lCoyS9KjkiEnxcWYgdWn3IKHrcbwmGzKikjA9xu8PvNsexscBxohQ4hhDiyJUua26nxx+3A5CQIJAH8AzNfgCHA80qptIYnKKUspdSvgSvih2LA3YdvikIIIYQQQhy5LMvimmuuYcOGDcybN49p06bRtWvXfc5zOBykpaWRlZVFTk4O2dnZdOrUaY+UOKUUgUCAKFDldOJOT8dms+FTikql0IClFHNsNqYB5S2Y3w3AHysrE5M1BbgrK6E63tw5EoHSUvD5DvKTEEKII0uyrEzqhlmV9FoTq3uOKFprv1Lqp8D7mNWzFwAnK6VeB7YBOcA5mEBTwl1a688O+2SFEEIIIYQ4glmWxbhx4xg3bhyLFi3i5JNPBiArK4uhQ4dit+//a000GmXZsmV7pMG53W5cLhflWpMJeLQmpBTlQFa87tJqzB/vDwBDm5lb5vLlcP758NhjkJdnViPV1prHzEyT9lZZadLeMjMl7U0I0SEky8qkRAW9ZFiRVEdr/Smm81xihVIecD0m/e2X1AeSAsD/aK3vPeyTFEIIIYQQIomceOKJ5OTkAFBVVYXNZmv0XJvNRr9+/fY45nK5zI5SeJWiKh7ciSpFuVLUBIMAVGDSBxZmZzc/qW+/hUmTYPFiSEkxx0Ihk/YWHw+fz6xSikRa/F6FEOJIlSzBpI3xxy7tOotW0Fp/hPlB42bgI6AIU0C8HNP17T5ggNb6n+02SSGEEEIIIZKEZVlccYWpEhGLxZqto1RevmfCWnV19R5FvH3xIJIGQpEIZUCilLfT5eKJPn14tqCAPct+74fPB9ddB488AunpZgVSLAYVFZDoLhcOQ0mJqackhBBJLFmCSS8DCjirvSfSGlrraq31g1rr07XWPbTWTq11rtZ6jNb6N1rr79p7jkIIIYQQQiSL3//+9zidTgA2b968T4e3hEgkQnFxMQCpqamACUBVV1cTDAbR8cLbIaXYrTXVgQAAVZiW0Xl5ecRCIZ4JBLgCqGzJ5P71L/jZz8BuNxuYYFJ5uQkuaW0CTJWVZl8IIZJQsgSTHgM2AUOVUtPaezJCCCGEEEKI9pORkcGMGTMAExz6+uuv6wJDDZWUlNQFmnJzc+nUqRMqntYWCASoqqrC5/MRDAap8vkoBRIFWv3Atqoqvl69mtKSElYDP3E6KUpPb36CH3wA551nAkjxLnJ1aW+h+B18PrNKSdLehBBJKCmCSVrrGmAKsBt4RCl1k1IqKeYuhBBCCCGEaHt/+tOf6NmzJ2BqJ61atYrIXoGZynintbS0NJRSpKam0qNHD1ISdY2AcDhMIBAgFouhgTLAh+n+U1xZSXl8f0C/fpxzySW8+dOfsr5//+YnuHkznHsuLFlSX3g7GjUBpkTaWyRiAkrS7U0IkWSSopubUuq2+O7rmALW/wR+pZSaD3yLKWDdLK31nw/NDIUQQgghhBCHk1KKBQsWMGjQIMLhMBUVFSxZsoSBAweSm5uLUoqaeNDGnVgdhCnK3blzZyKRCGVlZQQC+36V8JoTKcjMpH///ow+7jiyASu+ymn+WWexu0sXTvzss6Z/na+pgWnT4Je/hBtvhKoqE0Cqqanv7pbo9hYMQlaWdHsTQiSFpAgmATMxPwgQf1RAD2DqAY4jwSQhhBBCCCE6iD59+jB37lzGjRtHNBolHA6zZs0a3G433bp1wxdf8ZPo+BaLxQgGgwQCAWpqavZbayk7O5uLL76YLl264IhGcfv9KMCvNe5AAFs0ij0aZeWIEezu1Ikz58who7mJ3n8/rF4N//gHOBymAHci7S0zE1wucywchuxsc44QQhzBkilVTDXY9nesuU0IIYQQQgjRwZx55pnMnz+/rsA2mHpIW7ZsqXu+a9cuvv32W7Zv387u3bupqqrabyApPz+f6667jry8PJRSROx2fCkpxJQCpQh4PITjgR4rGqW4e3cudDjqWk836d13YeLE+gBSw25v1dXmnEgESkuhtvZgPhIhhDjkkmVl0s/aewJCCCGEEEKII9Ppp5/ON998w/Tp03n//fcP+HrLsvjhD3/IiSeeiGXt+Xt7zGbDl5KCx+/HFosRcrmIWRbOYBAVjVKuNZOBd4cMocc33zR9o/XrYfx4eOghOOUU8HrNaqTaWvOYSHvzeuvT4Kxk+v1fCHG0SIpgktb6P+09ByGEEEIIIcSRq2fPnrz33nu88sorPPDAAyxatKjZa2w2G8OHD+ekk04iNze30fO0ZZmAUiCAPRIh4nAQsyyipaVEIhHsQNFvfkOPL780qWxNqaqCK66AX/3K1FGqqTEFuBNpbxkZpgNcIhUuJ0fS3oQQR5ykCCYJIYQQQgghRHOUUlx00UVcdNFFrFq1iiuuuIIVK1bgcrlwu91YlkVaWhrdu3ene/fuDBw4cI/i3M0Mjt/jwRUM4gyFiNlsbPZ6iQAOYFivXjBqFAwbBjNmNJ2qpjXMnGnqKP3972YFUlWVSXurrITUVEhLM93fSkshPd08F0KII4QEk4QQQgghhBAdzrHHHsu0adOYMWMGWmuuv/76lgeOmhCMp7m5AwEKN26kHBjSrRsej8cEkMaOhTlz4OqrYevWpgd7+23YtAkefxzy800gKZH2FgqZ7m42mwk0JZ5L2psQ4ggg/xIJIYQQQgghOqRLLrkEl8tFKBRixYoVbTZu2OGgJBZjbWEhGjh10iTweMyLwSB07gxz5hA95ZTmB1u7Fs45Bz791KS0JQqJh8Mm7c3vN88DASgpMUElIYRoZ0kZTFJKna+UekoptUYptVspVauU+vl+zjtbKXWOUuq49pinEEIIIYQQov106tSJiy66CIBFixbh8/nabOz5H3/Mbq1Rdjs33HijSVVLT2fHjh38869/5YLzz2fgJ5/wYEsGq6yESy+F//zHpLMlViDFYqYYd1WVSY1LpL3V1LTZ+xBCiNZIqmCSUmqYUmo18ApwOXAM0Alws/+UvRuBt4B3lFK2wzZRIYQQQgghxBHhzjvvxOPxUFtbyzvvvIPW+qDH3LBhA8uXLycKjLvsMnK6daOoqIjpM2Zw8sSJPPn002zauJF04G/A9UCzYaxYDO65B26+2QSOcnPB6TSv+XxQXg6RiHleVWVWLcViB/1ehBCiNZImmBRfXfQZMBhQ8a25kPxD8fO6AGcc0gkKIYQQQgghjjj9+/fnvvvuA+Cbb77ho48+OqiAUlFREbNnzwagoKCABx9+mKfmzGHs6aczd948AoDf7WbosGH8+Kyz+NlPfsKA667jxZ/8hPKWFNF+/XU47zzYuROysxtPewsGYfdu8yiEEIdZUgSTlFJO4DUgDRMc+jfQT2ud2cyl7wOV8f0fHboZCiGEEEIIIY5UP//5zznvvPMAWLhwIe+88w6hVtQeWrt2LU8//TTBYBCPx8Nzzz3Hvffey7SrrmJ7dTUhl4szzziDq669lpFnnkmvfv1Iz8ggVWv8OTm8eNllfNuzZ/M3Wr0azj4bFi82ndyys03am9Ym7c3rNfuxmAkwVVcf8HsRQoiDkRTBJOBqoCeggd9orW/QWm9p7iKtdQT4AhOAGn1opyiEEEIIIYQ4ElmWxYsvvsiECRMAWLZsGY888gjr169v0Sql8vJyZs+ezaxZswiFQqSkpPDSSy+xcOHCulVPffr04YobbmDg2LHY7HawLPweD1GbqbZhj0RQSjF78mSWjhrV/KTLy+EnP4EnnjDpbg3T3vx+E0RKpL1VV5taStHogX84QgjRCvurM3QkmhR/3AzMPMBr1wA/Bvq36YyEEEIIIYQQScPtdvPaa6/xu9/9jpkzZ1JZWcmLL75IVlYWI0aMoEePHnTt2hW32000GqWsrIyioiLWr1/Pxo0b68bp27cvzzzzDLFYjN/+9rcADBo0iAsuuAC73U4E8KWk4PH7sYCAx4MzGMQRDmNFo7iDQT45+WR2d+7MWe+/j6OpAFA0Cr/9rVmpdN99pttbTY3ZIhETUMrIMJ3kQiHT7S07G1yuQ/thCiGOeskSTDoWsypprj7wBOfy+GN2205JCCGEEEIIkUwcDgd/+MMfmDx5MjfddBNffPEFlZWVfPzxx81em5KSwtSpU/nb3/5GNBplxIgRaK3Jy8tjypQp2O31X61iNltdQMkWixFyuYhZFs5gECsWw+P3UzhoEGW5uUyaM4eM5tLUXn4Z1q+HRx+F7t3B4TBFuKNRk/IWDJpucom0t7Q0kx6n1MF+ZEIIsV/JEkzKjT8WteLaxL+gyZLSJ4QQQgghhDiERo8ezeeff16X7jZ//ny2b9++z3kej4cBAwZw0UUXMWPGDNLiBbQffPBBNm7ciFKKc889F4fDsc+12rJMQCkQwB6JEHE4iCmFOxhEaY3H76c8J4fnLr2Uc996i4IdO5qe9PLlcM458N//wpgxJu2tstKsSAoEzEqlzEwTaKqpMcezs8HWuqbW3333HUuXLuWrr75i9+7dxGIx0tPTGTZsGKNGjWLo0KHYWjm2ECL5JUswqRbIAlJbcW2P+GN5k2cJIYQQQgghjiqjRo3iscceA6CyspJ169ZRU1ODzWajoKCAfv36ofZa3aO15uGHHwZg+PDh9OjRY59x6yiF3+PBFQziDIWI2e34LQu334+lNa5gkJDTyasXXMBpH3/MyBUrmp5wSQlceCHccw9cfrlJe6utNTWTIhFTZyk9HVJS6tPesrLA7W7R5xEMBpk9ezYPP/wwn376aZPn9ujRg+uuu45rrrmGrl27tmh8IUTHkSzBpCJMMOnYVlz7Q0yK3KY2nZEQQgghhBCiw8jKymLs2LHNnrdkyRLWrl0LwHHHHdeisYMuV92qJG1Z+BM1lWIxnKEQYYeDD08/nV1dunDG++/jaKqyRzgMt98Oq1aZoFJqqlmN5PWatLeqqvq0NzABptRUU1upibS3Tz/9lGnTprFhw4a6Y0op8vLySE9PRymF3++nuLiYcDjMjh07uPPOO/njH//Ifffdx8033ywrlYQ4iiRLMGkBMBT4kVKqk9a6tCUXKaXGA4MwwaQFh2x2QgghhBAHQCnVF5gOnIPpWOsGdmK60D6ntZ57CO5pARcBPwW+B+QBVcAWYA7wmNZ6V1vfV4iO5rPPPgNM8KmgoKDF14WdTmKWhcfvN4EZjwd3IIAtGjXFuWMx1gwdyo6UFM554w26NTfg889DYaGpo5SXZ9LeEvWTgkFTOykz03SAq62tT3uz7/kVMBaLcccddzBz5sy6znYDBw7kuOOOo0+fPjgTHeQanL9r1y6WL1/O119/jd/v55ZbbmH27NnMnj2bLl26tPgzEUIkr2QJJr0A3Ij5Q+vfSqkLmyvErZQaBDwWfxoDnjm0UxRCiP3rffs7Bz3G1pnj22AmQrQtpVS+1vq79p5HslFK3QDcD3j2eqlvfPupUuoN4EqtdVUb3TMfeAk4aa+XOse344FfKKWma61fb4t7CtFRLVu2DDBpXnunwDUnarfv2+ktEMARiWCLRvH4/Xh792aKx8O//H5GNzfg0qX1dZSOO84Ei3w+k/YWjUJFhSnGnZpqVjQl0t485p+fWCzGddddV5fql5eXx4QJE8jPz2/0lpZl0a1bN7p168Zpp53GvHnzWL16NYsWLeKUU05hwYIFjaa9lZWVsWTJEpYtW8bmzZsJhUK4XC769evHqFGjGDNmDDk5OQf0mQoh2kdSBJO01ouVUm8Ck4DJwDyl1K+01l/vfa5SqiswDfgVkIFZlfSM1nrj3ucKIYQQ4qCsU0r9Gfiz1jrQ3pNJBkqp64E8igD5AAAgAElEQVSHGhxaCbwL+IARwLmYv8/OA15XSp2ttQ4d5D2zgfnA4PghP/AaUAjkABMxQawcYJZSaqLWet7B3FOIjmzr1q0AZCbSyA7QPp3e3G5ioRCuUKiu05vKzeWC777jlYEDGbN+fdMDFhfDlCkwcyZcfLGpl+RwmOLc0agJLIVCZpWSZZkAUygEGRn8/ve/rwskjRgxggkTJuzRla45KSkpTJkyhf79+zNnzhwKCws5++yz+fzzz3G5XICpMbVgwQIeeugh3njjDaLRaKPjORwOzj//fG644QZOPvnkAw7WCSEOn2TqcDYVWIfpznYW8JVSquFS7BuVUhuBHcC9mEASwGrg5sM4TyGEEOJokQLcBRQqpS5q78kc6ZRS/YB/Njj0a631CK31r7TWv9dan49ZIZT4++Z04NY2uPX91AeS1gFDtNaXaa3v0VrfAgwEHo6/bgeeVkpl7GccIQQQCpn47oEEXfaW6PQWidcYijidBNxuNKC0plM8iPLyqFEmSLSfbnF7TQp+8Qu44w6zAsnhMGlvicLbibS3+NypreWr+fP54z33AKaQ+KRJk1r9nkaMGMH5558PwIoVK7j77rsB0xFu/PjxnH766cyePbsukNSpUycGDhzI0KFDGThwILm5pnl3OBzm5Zdf5tRTT2Xy5MkUFxe3aj5CiEMvKVYmAWitvUqpk4HngB/HD3fGrDwC6B1/bBi+/gC4SGvtOyyTFEIIIY4uu4EuQD7wolLqRmCG1rqZdkRHrbuBRPGRF7TWM/c+QWu9XCl1BfBe/NDtSqlHtNaVrbmhUuoYzA9yAGHgAq311r3uGVVK3QwMB36A+fvql8CdrbmnEB2dOx6gSQSVWi1RNykYxBEOE7Xb8Xs8eAIBwuEw2UC63W66th1zDFxzjUlTa8qTT8K6dfDvf0OnTialrWHaW3k5pKUR9Xj4xc03k6s19txcJkyYcNCrgIYOHcq2bdtYsmQJM2fOpEuXLtx11114vV4AunbtyujRoxkyZAgez95ZvuDz+VizZg1Lly5l9+7dvPnmm3zyySe88MILjBs37qDmJoRoe8m0MgmtdZnW+mxgPDAPqMUEjxpuIUyx7Sla67O01hXtNF0hhBCioxsA/BUTpFCYQMSXSqlHlVKd23VmRxilVBpwfvypxgSW9ktrPR/4PP40A5Py1lqXUf/33qta6zWN3DOGWdmdcMVB3FOIDm3AgAEAVFS0wdcMpQi43QQTKWE2Gz63m5L42IO6dTOBoDFjYN48GDmy+TEXL4azz4aVK83zlBTIyakvvF1Tw8dvvMGGDRtQwAVnnEF6NApNl6RtkTPOOIPMzExisRi33HILXq8Xt9vNxIkTufbaaxk1atR+A0lmmimMGTOG66+/nvHjx+N0OqmoqODcc8/ljTfeOOi5CSHaVlIFkxK01vO01uOBLMyy7ZOBH2J+UcvRWp8uxSOFEEKIQ0trXa21/n/AscBcTEDJBlwNrFdK/UIplTSroA+xH2MaiQCs1FoXNnP+Kw32Jx/EfSc12J/VzLnvA4kVUL2UUi341irE0WfUqFGASeFqpidQi4WcTvzxNDdvTQ1FwSAhYMjgwaYTW2UldO0Kr75q6iI1p6gIJk+G114zz/dKe3v1hRfoBPTt3p38/Hwc4TCpPh9WE/WMWsLpdDJo0CDA1ErKzc3l+uuvZ+TIkS1e+aSUYvTo0Vx//fXk5uYSiUS4+OKL+fLLLw9qbkKItpWUwaQErXVUa12otf5Ua/2x1nq1pLQJIYQQh5fWeoPWegKmzX2ivmEm8BdglVLqnPac3xGiYWBmUQvOX9jItS2mlHJRXyup2fvGVyd9drD3FaKjO/nkkwGora1l48a26/ETcTjwp6SwZu1aNBBwuRgwfLh5MRAwhbOdTvjrX+HeeyFeb6lRgQDcfDP8/vcQiYBSkJVFlVIs/vxzLOCEgQNxBIMAWLEYKT4fjnC41e8hFApRWFgfKx89enSrC5VnZ2dzxRVXkJWVRSgU4sorryQQkF4PQhwpkjqYJIQQQogjh9b6Xcwq4VsBLyaoNBB4Syk1Vyk1sD3n186GNtjf0ILzG35DLWhlQexjMCvFACq11qUHeN+hjZ4lxFHs2GOP5YQTTgBg2bJlbTp2CPho1SoiwJQLLsDTtSukpcVfDJmaR9EoTJsGL71k0tea8+ijcNll5lpg9aZNlAIRoEd+Ps5wGI/fD7EYCnAHArj9/lalvS1YsKCuRhJAefyerZWRkcHkyWZx5tq1a7nvvvsOajwhRNuRYJIQQggh2ozWOqK1/jumntJjmPpACpPmtUop9dejtFNY1wb73zV3crzmY22DQ3mH+p77Oa9ro2e1glJqTWs2oF9bzkOItnDDDTcAUFhYyPr169ts3E8//ZSK6mpKganXXmsOpqVBZqZZWRSJmKBQKAQnnmjqKA0b1vzACxfC+PGwZg2rV68mCoTT0nBmmH+OrWiUFL8fKxIBwBGJHHDaWzAYZOnSpfEpmwBYW3Rj69mzJ2PGjAHgwQcfxOeTRBQhjgRJEUxSSnVpi62934cQQghxtNBal2qtrwVGY9KrFOAA/gfYoJS6Rh1s66Dkkt5gv7bRs/bU8BtTeqNnte09G57Xmns2ZUgrN1cbz0OIg3bJJZdw/PHHAzB37tw2CXDs3LmTjz/+GIBpV13FkJNPhkSxao/HdGazLIjFTMqb3w/5+fDGG3D++U2MHLdtG0yaRJcvvgAgNS2NYIPi30prPIEAzr3T3lrYte7rr78mHA6jlCInvmKqtral//Q07aSTTkIpRUVFBS+99FKbjCmEODhJEUwCioGdB7kVHfZZCyGEEEc5rfUKrfUpwE+B7ZigUmfg38AypdTJ7Tm/w6hh+6KW9hNvWBwkJUnuKcRRwW638+STT+J0OvF6vTz//PP4/f5Wj1daWsrzzz9PLBaje/fu/P3vfzcrkbKz69PcXC6T1mazmRQ0r9cU5/Z44F//gjvvNMGmpvj9nPfuu/ya+hzYiMOBLyWFWPxax95pb8EgHr8f1Uza27p16wDIzc3FHu8c11YFyjMzM+u66L3+uvRZEuJIkEwdVo6mXy+FEEKIDkVr/bJS6m1MEOlSzH/XvwcsUEqtApYDKxKPWuuqdpvsodHwW6azhde4G+y3ZtlDe9yzKd+08rp+yOokcQQaMmQIjz32GFdccQVFRUU88cQTTJ48me7dux/QOOvWrWPOnDn4/X48Hg+vvfYaGRkNsoEzMkwAyesFu910ZauogHAYqqtN6ltmJlx3HQweDD/7men+1oSbgRElJawMBAi63WjLwu/x4AyFcITDdWlvQZeLqN2OPRIhpbaWgMdDdD+Fv7XWFBWZ3+6zs7PrAmtutxutNdu2bWPbtm0UFRVRUVFBJBLBZrORnZ1Nt27dKCgooHfv3lhNBMP69OnD+vXr27xOlRCidZIlmLQEU3OhOSlAPpAdf66BZcDB9bgUQgghRIsppTyY4s97bwMwQYGG/01XwLHx7YoGY3wLLNdaTzlM0z7Uqhvsp7bwmoYrg6obPevIumejtNatKugdr5s0pC3nIkRbufzyywmHw1xzzTWUlpby2GOPMXbsWMaOHbtnQGg/du3axcKFC1mzZg0AqampzJs3r6649x5SU01AqaLCrD7KyTEBo2DQpLtFo2YV0ymnwNy5cPXVsHZtk/c/JRpl2PPPM2fSJMo6dQKlCLlcRG02XMEgSmvcgQBhu52Qy4UFpPh8BF0uQs4949OVlZUE4+lx6enplJSUAGBZFg8++GCjhbh3795d1/0tMzOT0aNHM2bMGFyufePH3bp1A0w6YHFxMV27tmlZNyHEAUqKYJLWeuyBnK+U+j5wL/BDoAS4WGtdcyjmJoQQQhzNlFLpwATgBOqDRvnsf0VxY6uM93e8N9CrDaZ4pGhYhbZHcycrpTLZMwC061DfMy6/keuFEI246qqr6NevH9OmTWPLli0sXryYzz//nP79+9OrVy+6detGamoqWmu8Xi87d+5k8+bNbN++vW6MsWPH8vTTTzNwYBNNL91u6NSpvqNbdjZUVYHPV9/pLSsLevWCOXPgllvg7bebnHuO18slL77IvHHj2BhPI4va7fgtC3cwiBWN4ohEsMViBOIrmFzBILZIhIDHg46XvmuY4udwOKiqMotLd+zYUXc8LS2NzMzMutVK0WgUn89HTU0Nfr8fr9fLBx98wNKlS5k4cSJ9+/bdY64Ng3MVFRUSTBKinSVFMOlAaa0XK6XOBJ7GLKV/HpjUvrMSQgghOhal1FDgPaBbw8ONnB4GNgHrgcK9HqPASEzaW2IbRPLUdmyJNcB58f0mvi3WGdBgf3sr0/7WYT5bG5CtlOqktS5t5pr+DfbXtOKeQhyVTj31VFauXMndd9/No48+itfrZcOGDWzYsKHJ6/Lz8/nlL3/JTTfdhG0/6WP7cDjqA0rhsEmBs9tNUCkcrg8opaTAv/8NDz0EM2eaGkuNcIbDTHrrLRafcAKfnXgiKGXS3tzu+rS3WAxPfFVS1OHAHo2SWluL3+0mat/zK+XGjRuJxWJ1z/v06UNubi4pKSk01nehurqaoqIiiouL8Xq9PPvss5xyyimcdtpp+72mrWoxCSFar0MGkwC01lop9TNgHDBBKXWB1vrV9p6XEEII0YH8Cdi7OMhOTJBo74DRFq11U2nn/xffAFBKuYAW9LtOGl812D+pBec3LEy+vDU31FoHlVLfYFIIE/d9s7HzlVIWcOLB3leIo1VaWhp//vOfueuuu3jxxReZO3cuy5YtY9u2bXucd8wxxzB69GimTJnChAkT6opVt5jNVh9QCgZN4MiyTE2laLQ+oORywU03wZAhcOONJuDUhO9/8QVdSkqYe/bZhFyu/ae9BYNEolGCLhcKSPH7CTmduBukpZWVlcWnaWs0ZW1v6enpDBo0iB49erBu3Tpqa2v55JNPiEQinHnmmSil9ugMl5mZeWCfmRCizXXYYBKA1rpWKfUWMC2+STBJCCGEaDtjMfWPqjD1XN/SWnvbYmCtdRBT97CjmI8piO0BRiilBmqt1zdx/gUN9g+mddGb1AeTLqSJYBJwBvV1J7dqrSWYJEQrpKamMn36dKZPnw6YekJVVVVYlkV2djapqS0tYdYEpUzdJK/XpLm53fU1lWIx85iZaTq9nX46vPMOXHUVNLNSqt/mzVz6wgu8OWkS5Tk5wL5pb/ZIBCsWI+ByoW02nKEQ3Z1OnJZFqMGKpCFDhrQokNRQWloaxx13HN988w1lZWV89tlndOrUiZEjR9YV+O7UqdMBFzkXQrS9jrR8vDGb4o/D23UWQgghRMeTE398WGv9XFsFkjqieO3GRFBIAb9t7Nx4qn5ihVAV8MZB3Pp5IPHt7kKl1OBG7mkBdzQ49MxB3FMI0UBWVhY9e/YkPz+/bQJJCUqZFUiJWkIOh+n0lljp5PWabm8AffvCW2/BuHHNDptTUcElL7xA382b644lur0lCm9bsRgevx97KARAxO8nNxarawfZpUsXcnJy9h66RSzLYsiQIWRlZQEwb948vF4v3377LQCjRo1qNF1OCHH4HA3BpEQ4vHO7zkIIIYToeE4FfkGD9DTRpDuBUHz/MqXU/9v7BKXUCPYM5MzUWu/T41spdZpSSie2xm6otV4HPBV/6gRmK6X2KGyulLIB/wROiR8qAf7asrckhGh3aWmmGLdSZnVSTg4kuq3V1pqub1pDejr8979w663NDukKhTjvjTc44fPP96i3FHY68ccLb6v4eW6/n/nvvQeYpY2ZQO9eB9c/wbIsjjnmGGw2G+FwmLfffpt169YBMGHChIMaWwjRNo6GYNJp8cd9/hATQgghROtprRdqrf+ptf6oveeSDLTWm4AZDQ79WSm1Qik1Uyl1p1JqNvAl9QXNP6Jtgjq/BBI9wgcD3yilnlVK3aGU+iumrtVN8dcjwJWtLPgthGgvHo9ZlWRZZsvONqlvAIFAffqbZcEvfgFPPmmCUE1QwA8++4xz334bRyhUdzxms+FLSSFqsxGLRln62WdUbt5MPHxFChAtLsbWIOWtNVwuV11Ht0RR75SUFC6//PKDGlcI0TY6dM0kpdQ1mAKWGljaztMRQgghxFFOa/3v+EqgvxCvnxTf9vYmJqgT2s9rB3rPCqXUj4GXMOlzKcBl+zm1HLhGaz3vYO8phGgHTmd9Ye5IxKTA1dSYLRSqL8xtt8OPfgRvvw3TpsGWLU0OO3DDBnLKy3lj0iS88dSzYCjEisJC1n31FeHKStMyEujSpw+rtmxh5/btdM7JQaenE2hJl7pG5OXlsWnTprrucNOnT5fi20IcIZIimKSUOr6lpwJpmJa75wOnN3jtibaelxBCCCHEgdJaP6SUmgdcC5wN9ATcQDHwOfCs1npuG99zu1LqZOBi4KfA94A8oBrYggle/Vdrvast7yuEOMzs9vqAUihkVh/ZbKaTWyRSH1ByOmHAAFOY++ab4YMPmhy2U1kZP3n6ae7Lz2ee309JSQmxePqbHZPadtKYMRwzeDDlO3eyMxBgw/r1DB06FIfTSY3djm5FnaNYLFZXH8lms3HPPfcc8BhCiEMjKYJJmD+sGq0H0ITEv1gvaa1fa8P5CCGEEEK0mtZ6M3B7fGvN9Quo/zunpdfEgBfjmxCio7Isk/JWWQl+v0mBsyxTkDvR6S0jwxzPzISnnoK//AX+9a8mh02LRrnn229JAR4GbJbFOeecw4IFCyirqSG3oAC3282Zp5zC3PnzqfL7Wbt2LYMHDyZLa6rtdiJWy6ushMNhVq5cSTQaBUBrjTuRuieEaHfJVDNJtWKrxhS7vKId5iuEEEIIIYQQh59Spm5Soi6Sy2UKc9tspqC212uKc4MJNP3qV/Doo5CS0uSwNkzbxyWjRrFs0SLuvvtuqmtqALDn5BB0uejZpw8/+P73yQJUbS1fr1iBt7yczHAYTyTSoumXlpby5ZdfUhMfG8wqpfXr1x/gByGEOFSSZWXSyy08TwM1wC7gK+A9rbXvkM2qlZRS3YE1QFaDwz+M/8oohBBCCCGEEAcvI8MEkLxekwKXm2tWJoXDUF1tUt8SNYjGj4e+feHqq+Hbb5scNn/ZMpg+nW/vuqvuWCQSIeJwELXZGPq972G321m4cCGOSIQNhYWkZ2aSl5dHRm4uNQ4Hsb3S3rTWlJWVsWPHDiorTe8ky7IYN24cc+eazF+v19uGH44Q4mAkRTBJa/3T9p5DG/sPewaShBBCCCGEEKLtpaaagFJFhVmFlJNjUuCCQZMGF42aVUxKweDBpo7SDTfAJ580Pe4335B/7bWcBHza4LC2LPxuN/2HDycvL4+PPvwQe3k51V4vhV4vllJ4UlOJpKURttmIxWLU1tZSXV2N1vWVTbp3787kyZPxeDx1wSTVirpLQohDIymCSR2JUupKYEL8aRhwtON0hBBCCCGEEB2d211fmDsRPKqqAp9vz05vNpt57dlnYeZMeOSRJoe1eb28CNwNVHi95OTkmBeUIuRykdK9O5OnTGHThg2sXrOGbSUlVGlNbbzLXC1QtdeYBQUFHH/88QwZMgTLsigqKqp7rUuXLm35qQghDoIEkw6jeHrbP+JPnwDOAHq134yEEEIIIYQQRwWHoz6gFA6bFDi73QSVwuH6gJLDYY7fcQcMGwa33gqBQKPD2jHBpEWLF7MsP5+Io/638qjdjj8tjb6DBzNg4EAqKyspKi5mS3k5pVVVpri23Y6tc2fyevSgoKCA3NzcPcZPBJM8Hg/9+vU7FJ+MEKIVJJh0eP0Xk962E7gVWNG+0xFCCCGEEEIcNWy2+oBSMGgKbic6vUWj9QEll8ucf9550L+/qaP03XdNDv2D4mL6z5rFnIkTqU5Pr39BKQIeD45QiKysLLKyshgChJxOwk4nYArfBl2uuucNbdq0CYD+/ftLmpsQR5Bk6uaW1JRSU4Fz4k9v1FpXtuN0hBBCCCGEEEcjpUzdpETnNrfbPLcs0+mtosLUUkoYNgzmzYMTT2x26K67dnHp88/TYz+Bp7DTid/jIWaZr6DOUAi33w+xGApwB4N4/H5Ug7pJVVVVFBYWAjBp0qTWv2chRJtLipVJSqm5h/gWWms9/lANrpTqAfw9/vQVrfXrh+peQgghhBBCCNEkpcwKpESam8NR3+ktEjErlSIRSKwwysmBF16Ae+6Bxx9vcuhUn48LX32Vj047ja9HjDD3iovZbPg9HlzBIPZIBFs0SorfT9DlImq3Y49ESKmtJeDxELXZWLRoEVpr3G43t95666H8RIQQBygpgknAOMzqx0NBHcKxEx7FpLeVAzcd4nsJIYQQQgghRPPS/j979x0fVZX/f/x1ZtImBRIQqSJIcSkBVNy1oC4KiLIqoriiruBvLYh817Iqruvq2gB7WQUUCyi4uiKoYAfEriggCFhQVJoU00hIT87vjzOTTELKJJlU38/HYx5z751zzzk3jjzIh8/5nHi39C093b0HdnrLz4d9+9zSt9atXUAoMhJuuw2Sk2HKFLdMrhLe4mKGLV9O+927WXbiiRRFBP3aaQx5MTEUFRQQnZeHsZaY3FwKIiLIj47GA8RmZ7Np504+//xzAM4++2wSE7UZtkhT0pyWuZmgV/nzil6htKn3RbfGmIsoXd52lbV2d32PKSIiIiIiEhKfz2UleTzulZTkroErvJ2WBsXFpe3HjoWFC6Fjx2q7Tl6/nnNefJG4rKz9PiuMjCQ7NrZk2VtkYaFb5lZcTEZGBstfeYW2QOcOHZg1a1Y4nlREwqi5ZCb18b//FVe42gAfAguBdcCv/mttgIHAaOB4oBi4H6g6F7OelFve9qa19tkGHn9DLW/VNgkiIiIiIr8VUVGlhbkLC102ktcLWVkuSylQmDuQYTRokKujdOmlsHJllV13+uUXLpg/n1dPO41fOnUq85n1eMjx+YjKzyeyoABPcTE527bxyhtvkLVvH7FeL3Pvv584r7e+nlxEaqlZBJOstd8aY/6FCySlAedba9+qpPm7wIPGmOHAc/579llrb22Y2ZYxG2gNZAKXNcL4fRthTBERERERaW4iIkoDSvn5pUvg9u51AaZAQCmw41q7dvDCC/Dvf8PcuVV2Hb9vH3/+3/9YdtJJfJWcXPZDY8iPjqbAGL5ZuZJVK1fiAdp4PNx0110MPeEEN3ZcHLRqVaYGk4g0nmaxzM0Y8wfgFlym0WlVBJJKWGvfAU7H1UP6lzHm6PqdZVnGmP8HnOI/vcFau6UhxxcREREREakRj8cteQssc/P5XADJ43FL3crv9BYVBVOnwj33uJpKVfAWFzPinXc4aelSPEVFAFhrSU1N5ZNPPmHW00/z2sqV5AIJcXE8dNddnDtsmAtsgavhtGePC2yJSKNrFplJwOW4wNcb1tpPQr3JWvuJMeYtXFDnMiDke+vCGNMFt7wO4ANgZkOMW4GNtbyvBxAdzomIiIiIiEgzYIyrmxRY5hYd7Qpzp6W5gtwZGS6wFBdXes9550Hv3m7Z265dVXY/aN067DffcF2rVnyfkUF+QUGZzwccfzyP3H03baOi3HipqS5LKj7eBZL27HEZSsHjNwO//PILq1evZvv27RQWFhIXF0efPn0YMGAAMTExjT09kRprLsGkIbgMo89rce9KXDBpSFhnVLXA8rZc4GJrbX3vFlcha22/2tznr7WkJXIiIiIiIr9VrVq5gFJGhlsC17atCygVFEBmpgv0tGpV2n7wYHj9dbjkEli9usquD8vP55lff+WvwFogJjqaESNGMHnyZPr37+8aFRS4sQsLS2s3BWo5ZWS43eQCWVNN1E8//cTjjz/Os88+y7Zt2ypsExERwTHHHMPEiRM566yziAosIxRp4pru/3llBSq1FVfZqmKBQE712w2EgTHmLGCk//Tf1trvGmJcERERERGRsIqLc1lJxrigTZs2LlMJIDvbBZeC/928QwdYsADGjau2607AkogIVl53Hd9++y2zZs0qDSSBWzYXvOQuPx9SUtwOc+De9+xxQaUmJj09nYsvvphDDjmEadOmlQkkxcXF0apVK6L9P8fCwkLef/99zjvvPLp168aCBQsaa9oiNdJcMpP24ZZdDa7FvYF7ssM3nSodFHQ83RgzPcT73jWlxeTmWmsnhHVWIiIiIiIiNRUT4wpzp6S45W1JSS4zKCfHBXLS0spmCEVHuxpKyclw881V1jjyFhbS5Z573FK2f/1r/7pLxrhspKgoVwi8uBjS0yE2FhISXHZUSoo7jo9vEsW5ly9fzoUXXsj27dsB8Pl8DBo0iN69e9OxY8eSIJK1lvT0dLZt28batWv54Ycf+OWXXxg7dixjx47liSeeoFVw5pdIE9NcgkkbgOOBkcaYw621VedN+hljjsAtcbPUvn6QiIiIiIjIb1dkpNu9LSXFBYcCy80Cy88CO71F+H+9NAbGj4dDD4XLLoNff626/yefhK+/hscec9lP5fl8LqCUkeHGy852y+Bat3ZjZma6wFag1lMjWbhwIeeeey4FBQV4vV5OOOEEjjrqKCIrKE5ujCEpKYmkpCSSk5P59ddfWbx4MVu2bOHFF19k8+bNvP3227Sp6Och0gQ0l2Vu//W/e4HXjTHHV3eDMWYIsMR/D8D8eppbeV/7xwrltS/ovqVB1z9uoLmKiIiIiIhUz+t1GUqBmj7x8S6YY4wLMKWmlu68FnDUUa6O0sCB1ff/8cdwyimwfn3l4ycllRbeLihwwa1s/wKU/HzYvbvsbnMNaPny5SWBpDZt2nDppZdy3HHHVRhIqsgBBxzAhAkTGDZsGACrVq1i1KhR5DXBZXwi0Hwyk57A7cY2EDgQtyRsGfAyrmZbir9dW2AAMBoYFnT/Wn8f9c5a+xbwVihtjTE/AYFtCO601q6op2mJiIiIiIjUjcfj6hilp7ugjc/nrqWnuyVoaWkuwBS8O1nnzlb6NbIAACAASURBVPDSSzBlinuvyrZtcMYZcN99MHr0/p8b45a0BZa9FRW597w8N67H4+YQOG+gZW8ZGRlceOGFJYGkCRMmkJCQUON+jDEce+yx+Hw+Fi9ezKeffsodd9zB7bffXg+zFqmbZpGZZK0tAk4FAsWsDXAS8B/gfdwyuA3+40dwgSTjf30HjLLW1qZ4t4iIiIiIiAQY4zKE4uPdeXS0W5rm9bpi3OnppdlCAT4fPPQQ3Hpr9cvQcnPhiivgjjtcsKgi0dEuqBUoBp6X57KUAplR2dmuOHdBQe2fswauvfZatm/fjtfr5c9//nOtAknBDj/8cI488kgApk6dyupqdscTaQzNIpgEYK3dCRwJzAAKKA0WVfYqAGYCR1prf2mMOYuIiIiIiLRIrVq57B9wNZXatCktoL13r6tjFMwYuPhieO45F4yqzsyZ8Je/uEyjing8rp9WrVzfRUVuqV1Wlvu8sNDVagqc15Off/6Zp556CoDjjz+eAw88MCz9Dhs2jMTERIqLi5k6dWpY+hQJp2YTTAKw1mZZaycDXYArgf8BXwI/+19f+q9dBRxkrb3CWptZWX8iIiIiIiJSS3FxLohkjMs4atOmtKbSvn0uS8nasvcMGQJvvAF9+1bf/3vvwahRrjh3ZWJj3biB4t9ZWS6oVFTkxt67t3Qnunrw+OOPU1xcjM/n4+ijjw5bv1FRUQwZMgSARYsWlewOJ9JUNKtgUoC1do+19j/W2nOttYdba7v7X4f7rz1srd3d2PMUERERERFp0WJi3JIzj6d0CZzP5z7LzXWZReUDOQcdBK++6uojVefnn+H002HJksrbREa6OQTGzc93AaRAMe68PFecux6KWc+bNw+AgQMHhlxsO1TJyclER0dTXFzM888/H9a+ReqqWQaTWgprbTdrrfG/VjT2fERERERERGosKsrt9BYR4QJKrVuX1lTKzy/NFArm88Gjj8JNN7lAVFWys+Gyy2D69MrrKAXGTUx0/RUXQ0aGe1nrzlNSXKZS+WypWtq1axdbtmwB4NBDDw1Ln8GioqLo1q0bAJ9//nnY+xepCwWTREREREREpG4iIlxAKbDMLT6+tKZSYaEL5JQviG0MXH45zJvngkDV+c9/4KKLXICoMoFMqcA8cnLKjp2V5WopFRbW7PkqsGrVqpLjDh061Lm/inTq1Gm/sUSagmYZTDLGRBlj/miMuc4Yc7cx5lFjzImNPS8REREREZHfLI/HBXJiYty5z+eWvRnjMoNSUyteanbCCfDaaxBKds+yZfCnP8GmTZW3CdRvCmRHFRa6sQO7zBUUuN3eyu86V0M7duwAIDY2lpjAM4dZmzZtAFQzSZqcZhVMMs4UYCuwDJgO/B2YCAyqoP1nxpjdxpilDTtTERERERGR3yBjXCAnLs6dR0e7c4/HLS9LSyutZRSsWzdXR+nUU6sfY/NmF1B6++2q28XHu7G93tJi3IEaTta6AuEV1XQKUaE/u8nr9dbq/lAE+i4on9Ul0siaTTDJGOMDlgJTgXaACXpVZh5wADDUGBP+RawiIiIiIiKyv9atoVUrdxwokB3YcS0jwy03Ky8+Hh5/HK6/3gWlqpKV5Za83X9/1cGgqKiy2VJ5eW6ZWyBDKifHZSnl59fs+YA4f8AsNzcXG6Y6TOXl+ANvgbFEmopmE0wC5gBDccGjncDtwFnV3PM8EPiTZVS9zUxERERERETKio8vXeYWWHoWqGWUlVVaHDuYMXDllTBnDiQkVD/GfffBxRdDZmblbTweV5OpdevSJXdpae4ea11R719/rbqPCvTp0wdwWUNpaWk1ujdUO3fuBKBfv3710r9IbTWLYJIx5nhgLGCBD4E+1tpbrLWLqrrPWrsHWO0/Pa5+ZykiIiIiIiJl+HwuM8jjca+kpNIsoZwcF9SpKKtn2DBXR6lnz+rHeOstOO00+OGH0OYSGenO9+1ztZQCxbgzM11QqbId48pJTk4m0t/Xtm3bQrqnpgK1kgYPHlwv/YvUVrMIJgHj/e85wFhrbRXl+/ezBpfN1CfssxIREREREZGqRUW5nd68XpcZlJhYWlMpP98FdCoK4PToAUuWwIgR1Y+xaZOro7RsWdXtIiJchlRsrDsvKHC7vQXqOOXnu2VvubnVDhkdHc2QIUMA+PLLL6ufYw3t3r27pMj3iSdqvylpWppLMOk4XFbSq9baXTW89xf/e/3s1SgiIiIiIiJVi4iAdu1Ks4ISEkprKhUUlM0QCpaQAE8+CVdfXf0Ye/fC+PHwn/9UnO0UYIwbOzGxtDB4RoYryF1cXLrzXHp61f0AEydOBODHH39k9+7d1c+xBlauXAlAp06dGDVKVVukaWkuwaRAIGhjLe4NbBUQG6a5iIiIiIiISE15PC5DKbDMLTbWBXSMcZlJqamlhbHL33fttS6oVF0hamth+nS47DK3jM2vqKiI7OxscnJySotlx8S4+QTqOOXmuiylQDHu7GyXpVTFTmqjR4+mU6dOACxZsoTiWu4MV97WrVtZtWoVAJMmTSIiULxcpIloLsGkwF6LtSmRn+R/3xumuYiIiIiIiEhtGFN2mVlMjKuj5PG4jKD09NIlZ+WNHAmLF0O3btWP89pr7B06lDsvu4zhw4fTvXt3evbqRY+ePenduzejR4/mlltu4YvVq7FJSS4DKjioFdhtrrDQ1VEKCkwFi4qK4pFHHgFcAOijjz6q4Q9kf7m5ubzyyisA9OrVi2uuuabOfYqEW3MJJgXyBQ+uxb2H+993hmkuIiIiIiIiUheJiaW7tUVFuQCT11u65KyS4A2HHuoKc4dQQ6jV9u1MWrKEths3UhhUk2lfdjYrP/+c2U88welnnMHJI0fy6rJlLqgUyADKyiqt5RSYU0qKC3iVc+aZZzJu3DgAli9fzhdffFGzn0WQ3Nxc5s+fT0pKCgBz5szB5/PVuj+R+tJcgkmBItrDa3KTMeYg4I+4jKZPwj8tERERERERqZWEBBdUAhfECd5pLTPT1UCqSGIizJkDkydXO0QS8Bwwc+BAxp51FmefdRYnjxjB7488kvYHHgjA+vXrmXj55fx14kR2FxW5Xd/ALXcLLs6dlwe7d1e4FO+xxx7jyCOPBOC1115jyZIl5FW0ZK8K27Zt44knnijZGW7GjBkcc8wxNepDpKE0l2DSYv97V2PMpaHcYIzxALOAwOLSV+pjYiIiIiIiIlJLsbEuiGSMW+rWpg1ER7vPsrMhLa3iItheL/zjHzBzZmnwpxIe4Iy1a5m4YQM9OnWif//+HHfccZx//vmcfdZZHNy1KwBvvvUWJ48cybc7d0Lr1qVL7zIy3Mtad56S4gJdQfNKSEjg7bff5thjjwVg1apVzJw5ky+++KLaoNLu3btZvHgxTz75ZElG0owZM7j88stD/CGKNLzmUsVrHnAz0BV4yBizz1o7v7LGxpiuuEDSSFxW0kZr7ZIGmamIiIiIiIiELjraFcIOLCtLSnLBm5wclwWUlla681p5p5/Oim3b6H7nndXWROnzzTe0SU3lldNPJ7NVK4wxHHzwwXTt2pV169bx3nvvsWv3bs4++2wWLVpEz+7d3Tzy891cCgpckCky0i2Dy8tzc/UvjUtMTGT58uXceeed3HnnnWRkZPDaa6/xzjvv0K1bNzp16kSbNm3wer3k5uayc+dOtm3bxi+//FIyx169ejFnzhxlJEmTZ2w1Wx02FcaYPwJvAv68R74C3gKuwwWMXgTWA8cCQ/3tDJALDLHWrm7gKTdbxpgNQF+Avn37smHDhkaekUjz1u2G1+rcx0/TtR1sXTWB/w6mzhMQ+Q3S30tEfkOKilzWT2GhO8/KKi2EHRHhAkrldjX7/vvvGTFiBL68POb6fAyurHh3kGyfjyWjRrHVn5EUsGPHDl566SXyCwo4tHdv3nzzTaKjo8vOwxiIjy/dVc4YF2CKLbt5+Nq1a5k6dSovvfQSRUH1mirTuXNnJk2axNVXX91QNZL09xKpk+ayzA1r7QpgHJCF++InA9dSusPbWOBWYAQQ5W+TBfxZgSQREREREZEmzut1GUpRUe48Pt4FaoxxAabUVJcd5FdUVMQ111xDbl4enjZteHv8eL444ohqh4nNyeHsl17isDVryixV69SpE6effjoA3373HQ888EDpPIILhGdmurkUF7vz9HSXPRVUnHvgwIG88MILbN26lQceeIBx48bRu3dv4uLiiIqKIikpiSFDhnDllVfyyiuv8NNPP3HjjTeq2LY0G81lmRsA1tpFxpivgDuBMYC3iuaLgSnW2m8aZHIiIiIiIiJSNx6Pq6GUnu6Wlvl87lp6ugvWpKa6AFNMDAsXLuSLVasAOOWUU4iOjeW9E05gd7t2DH/nHSKryAjyWMuJ777Lgbt3s/SkkyjyZzwdfPDBHDZoEGu+/JJHHnmECy64gC5durgAV9u2rlZSbq5b+vbrr24u0dFurvn5btlbIBgGdOzYkauuuqp+f2YijaDZZCYFWGu/t9b+GTgIOA+4C3gSmAvcD1wCdLfWnqFAkoiIiIiISDNjjAvKxMe78+josplB/kDTnDlzADhs0CDat29fcvvXffvy/Lnnsjchodqh+m/YwJ9feIH4zMySa0OGDCEmOppia5k3b15pY4/HLbULZEsVF7uMpMxMN6+iIhdgCupLpKVqdsGkAGvtTmvt89baf1hrL7HWXmStvdZa+6S19ufGnp+IiIiIiIjUQatWLnADruh1mzYlNZO+WbmS7778EoBDDz10v1t3t2/PvPPPZ2uXLtUO03HXLi6YP5/O27cDEBUVRf/+/QGYP3/+/jWPfD6XpRTpL+e7b5/LmArUesrMdEGlEGoliTRXzSKYZIy53v+6orHnIiIiIlIXxpgBxpiHjDEbjDEZxpgsY8x3xpjZxphjwzxWN2OMreHrT+Gcg4hIncTFuSCSMS4zqU0biIpi9apVxAMHtWpFp44dK7w1JzaWBWedxepBg6ofJjubsS++yIC1awHo06cPACmpqXz//ff73xAR4eYSKMRdUOCKhwcKgOfnw549peciLUyzCCYB04FpgPZHFBERkWbJOLcDq4G/4XYoawXEAb2Ai4EPjTGPGWMiK+9JROQ3JibGZQJ5PO6VlMTa774DoHvHjvjy8soU0g5W7PXy7okn8uaIERR6qyq5C97iYoYvW8awd96hfWIiEf7269atq/gGYyAhwS3J83jcHDIySus7BZbBpadXOj+R5qq5FODOBOKBrxt7IiLSsJrAdu5NQl1/Di3hZyDSAkwDpgSdfwy8BxQCfwCG43ajvRSINcZcaG1Yf/vYDMwMod3GMI4pIhIeUVFup7eUFCgqYv3WrewDklq3xltUhC8nh9yYGKyn4nyJDf37k9K2Lae/+ioJ+/ZVOdTAr77igJQU3kxKYv2vv1acmRQsOtrNLT3dZSTl5rpMpdat3byzs0uLc0fq3wqkZWguwaRfcP9ip//zREREpNkxxhxHaSCpCBhvrZ1frs0w4GVcptIFwOvAf8M4ja3W2nvD2J+ISMOKiIB27SAlhdzcXLKALH/wyFNcjC8nhxyfr9KA0s6OHZl3wQWcvngxnXfsqHKozjt2MM/j4SIgJ5Slah6PW/aWne1qJhUVuTpK8fHuVVjo6iglJJQWFhdpxprLMrcPcP9Sd3hjT0RERESkFqYHHd9VPpAEYK1dClwTdGmqMaa5/F1NRKRheDxwwAHYqCgAsoHc6GgsYKzFl5ODp4rC19lxcfxv7FjWJidXO9SBxcUsBI7YtCn0+cXGlikUTlaWCyoVFbmlbnv3uuyq4uLQ+xRpgprLX1CeBiww3BjTu7EnIyIiIhIqY0wPSus+ZgNVZQc9BWzzH3cDjq+/mYmINFPGkHDwwWQDaWlpFEVGkuvzYY0pCSh5AzurVaDY62Xp8OG8M2wYRZVkMQVEA2e8/z78859u6VooIiNdjSefz53n55ctzp2XB7t3h96fSBPULIJJ1tqPgYeBKOBVY8whjTwlERERkVCNDjp+x1qbVllDa20hsCjo0pn1NisRkWbsiMGDyQB+/PVXwAWIcnw+io0BICY3l4hqgjXrBgzgf2PHsi82tvoB58yBc891S9VCYYyrmdS6tcumKi52xbkzMlyGUqA4t0gz1SyCScaYA3Hp4TOB3sBXxpjHjTGjjTGHGmM6GmMOrO7VuE8hIiIiv1GHBR1/GEL7Dyq5t656GWOWGWN2G2PyjTG/GmO+NMY8aow5NozjiIjUuz/84Q8A/JSSwtbMTACsx+MCSv5so+i8PKLy8qrsZ0fnzsw7/3x+6dCh+kE//RROOQUq292tIj6fy1LyL8sjJ6c0iFRYqOVu0mw1i2ASsBPYAUzELXfzAX8FXsLtOLINV6S7qlfVFdZERERE6ke/oONQCm8EbxvUr9JWNdcJOBFoh9vUpC0wEJgEfGiMedMY0z6M44mI1JuTTjqJTp06AfDxl1+6wtsA/oBSkdcLQGRBAdG5uS4bqBJZCQm8cM45rO8Xwh+5O3bAmWfCSy+FPlmv19VRiokJ/R6RJq657OYGrgB3VeciIiIiTVHwP3dvq7RVxW3aGGMirbV1LaxRAHwCrAV2AV6gKzAMONjf5mTgc2PMUdbasP8jnDFmQy1v7RHWiYhIixAREcFll13GLbfcwpo1a/jDH/5Au8REYnNyMECuz0d0bi4RhYVEFBbisZacmBi3/KwCRRERvDViBLvat+eEd98loorgE7m58Le/wfr1rpZSRIi/VofaTqQZaC7f5pW4jCQRERGR5iYh6HhfCO3Lt0kAUms5dhYu82i+tXZv+Q/9u8VNwNWmjAMOAhZQWjA8nPrWQ58i8hs2efJkZsyYwa5du3j55Ze56KKL2BcbS2xODp7iYvJiYijOyyOqoABPURG+nBxyY2KwlRXdNoYPevRg9vvv82hhIW2rm8Djj8PGjTBzpss8EvkNaRbBJGvtUY09BxEREWl5jDE3AReEsctHrLWPlLvmCzrOD6GP3HLnsdQymGSt/RVXc7Kyz4uBp4wxW4E3cSUQjjbGnGGtfaU2Y4qINJQ2bdowa9YszjzzTLZt28aSJUs47bTTyI6NdTu6FRVREB2N9XiIzsvDU1xcElAq9i+DC5aTk8OiRYvYU1jIGVFRLO/Wjajvvqt6Eh9+CKeeCk8+CaEskxNpIZpFMElERESknnQADg1jfwdUcC0Hl/UDbmfa6pQvqpFdpxmFwFr7jjHmeeA8/6VzgHAHkzbW8r4euN25RUT2M3r0aCZNmsSMGTNYs2YN+fn5/OlPf8L6fPj8y9wKIyOxxhCdm4uxtiSgVBS07Cw1NZVXX32VlFQXu594++1EnXUWXH89LFxY9SS2boXTT4f774czzqjPxxVpMhRMEhEREalfmZQGk+KqauhXfo/qzPBOp1L/ozSYdHS4O7fW1uqf7P21lrRETkQq9fDDD5Odnc2cOXPYsGEDW7Zs4dRTT+XQQw8lxr/MrSgighx/gMlYS0xuLnnR0eQAX375JR999BFFxcUY4MYbb+SCCy4IdA79+8Mdd1S981puLkyaBBs2wJQprui2SAvW5IJJxpj7/YevWGvfa9TJiIiISItmrZ0MTK7nYXZSWoS7M7CqmvZdgo5Tw1B8O1TBu8hpVzcRaTa8Xi9PPvkkvXr14pZbbiEzM5MXXniBpKQkDj/8cHp26MDBSUlERUWR7fNBaiq/7tzJ9h07WPXtt6QVuD9m4+PimDp1KmeffXZp58bAZZdBnz5w+eWQnl71ZB591NVReuQRSEysx6cWaVxNLpgEXIUrtr0NqDCYpICTiIiINCMbgEH+494htO9Z7t7GoI1PRKRZ8Xg83HjjjZx22mlceumlfPrpp6SlpbFs2TKW4dYPt/V4wFqstSQBkbi1x62Aw44/nv/85z8ccEBFq5WB44+HN96A//f/4Ouvq57Mu+/CqFGujtLvfhfOxxRpMiopY9/kXQVcCRzR2BMRERERqcbqoONjQ2h/XNDxmjDPpSrBQazdDTiuiEjYJCcn88knn7By5UouvPDCkuBQLrCnuJgia7G4XQ1at2vHmWecwdJXXuG/M2ZwQNtq9m/r2hVefRX+9KfqJ/LTT3DaafD663V8IpGmqSlmJomIiIi0JK8A9/mPhxtjEq21Fa6TMMZ4gdFBlxbV9+SCjA06/qwBxxURCbsjjzySuXPnArBjxw6++uor0tPT8RQV0SkmhgH9+pGQkAAZGZCTA3l5kJbmlqZ5qsi5iI2FWbPcMra77gJbRSJndjZccglcdRX8/e9hfkKRxqVgkoiIiEg9stb+YIz5GDgGV4D778C/Kml+EdDVf/wT8H69TxAwxpwIjAu69GJDjCsi0hA6depEp06dSi8UFUFKChQWQuvWrlh2Vhbk50NqKiQlVV1A2xj4v/+Dfv3giitg796qJ/Dgg7B+PUyf7u4VaQGa6zI3ERERkebkhuBjY8y48g2MMScBDwRdutFaW+HWQcaYCcYY63/9VEmbg40xy4wxpxhjKvwHRGOMxxgzHpc9Ffh74UoaNiNKRKRheb1wwAEQFeXO4+NdUAlcgCklBQpC2PvgxBPhtdegV6/q2y5dCmPHuuVvIi2AgkkiIiIi9cxa+wFwl/80AnjOGPOBMeYOY8ytxpg3gXeAeH+becDzdRzWACcCrwO7jDGvGWPuNcb80xjzL2PM48APwJygcbcBZ1lb1boNEZEWwOOBtm3B53PnPp/LSDIGiotdhlJeXvX9HHIILFkCI0dW3/bHH+HCC2HFijpNXaQp0DI3ERERkYbxD6AImAJ4gSH+V3mzgSvCHNBpA5zqf1VmCXCxtXZXGMcVEWm6jHEBJI8H9u2D6Gho08bVTioudu+tW5cGnCoTHw+zZ8NDD8G991bddt8+t0Tu55/hyivD9ywiDUyZSSIiIiINwDr/xO1G+wjwNZAJZAPfA08BQ6y1l1prQ1hfUa0twFG4XXCfx+0qtxXIAfKAXcDHwL3AIGvtaQokichvUuvW0KqVO46MdBlLEf68i4wMFwCqjscDV18NTz/tgkvVufdeuOwyyMys/bxFGpEyk0REREQakLV2LfB/dexjDm55WlVtinG7sn0GPFSX8UREWrz4eFdLKT3dvbdp447z813Ap6ioNOBUlREjXB2liy6CzZurbvv663DMMfDKK9CzZ3ieQ6SBNOVgUg9jzPFhaFPCWtsgO6KIiIiIiIhIM+PzuQyjtDR3npTkAkp5eZCd7QJKiYnV78jWs6cLKE2eDMuWVd1240Y48kh4/nk4+eTwPIdIA2jKwaSJ/ldFbAhtKrqnKT+viIiIiIiINKboaLfTW0qKCx4lJcHevS6YlJfnAk2JiS7oVJVWrWDOHLec7aFqkkPT0+HUU2HaNLjuuuqDVSJNQFOumWSqeIXSpqr76p1x/uDfMWWJMWazMWafMSbPGLPTGPOeMeY2Y0y3hpyXiIiIiIiIVCEiwgWUAnWTWrWChAR3nJ/vdnorKqq+H48Hrr8eHn8cYmOrbltcDFOmwLhxodVoEmlkTTFTZwulmUfNkjHmdGAG0LmSJu39r+OBG40x9wD/stYWNtAURUREREREpDJerwsopaW5jKS4OBcc2rsXCgtd5lJSkivYXZ1Ro6BHD5gwAbZurbrtCy/AN9/AokXQvXtYHkWkPjS5YJK1tltjzyEM+lI2kPQj8BHwM27HloNwW/N2xW0NfANwiDHm3DBvAywiIiIiIiK14fGUFuLOySmtqZSe7jKJUlPdkrfo6Or7+t3vYMECuPJK+PTTqtuuXQuDB8P//gcnnRSeZxEJs6a8zK25ywIeBAZaaw+x1v7FWnuTtXaqtfZyoAdwW1D7c4DxjTFRERERERERqYAxLgMpPt6dR0e7AJPXC9a6zKWcnND6SkyEhx92O71VJzXV7Qz3wANuHJEmRsGk+vE60M1ae7W1dl1FDay1hdbaW4DHgy5PbpDZiYiIiIiISOhatYLWrd1xZKQLKAVqKmVkQFZWaP14vXDNNTBjBsTEVN22uNi1vfDC0ANWIg1EwaR6YK1dZ61NCbH5g0HHRxhjqvkTRURERERERBpcXJzLUjLGBYXatIGoKPdZVpYLKoXqjDPglVfg4IOrbztvHgwZAlu21G7eIvVAwaTGt7nceVKjzEJERERERESq5vNB27audpLH44JLgZpJOTlu2Vuoy9L694eVK2Ho0Orbrl7t6ii9/37t5y4SRgomNb7gQt0WCDWjSURERERERBpaVJTb6c3rLa2pFBvrPsvLcwGl4uLQ+jrgAHj7bbjqqurb7tnjCnI/+qjqKEmja3K7uf0GnRN0vNJam99oMxEREREREWlkKSkpfPnll6SkpGCtJTExkYEDB9KhQ4fGnlqpiAgXCEpNhYICV1PJ64XMTMjPL93pLSKEX7kjIlyh7cMPh0sucQGpyhQWwuTJLlNpxozQdpITqQcKJjUiY0wH4PqgS49X1raW/W+o5a09wjkPERERERGRqmzatIlZs2axcOFCfvrppwrbdOrUidNOO41JkyYxYMCAhp1gRbze0oBSXp6rqeTxwN69LugTCCgF6ipV5y9/gT594MwzYdu2qts+9RRs2AAvvQSdO1fdVqQeaJlbIzHGRADPUlojaRUwJ8zD9K3lS+FtERERERGpd9u3b2fMmDH07t2b+++/v0wgKSIigoigzJ4dO3bw2GOPMXDgQIYNG8b333/fCDMuxxhXiNvnc+c+nwsgGeOWuqWlQW5u6P0NHgxffAHHHVd9288+c+0//rh2cxepA2UmNZ5HgGH+4wzgHGttiAtrRUSan243vFan+3+aPipMMxEREZGm4LnnnmPSpElk+HdBi4+PZ9CgQXTv3p2OHTvi8wdo8vLy+OWXX/j5559ZvXo1e/fuZdmyZSQnJ3P33XczefJkjDGN9yCBukler9vVLTraBZjS06GoyL23bh16f+3bRk2cnwAAIABJREFUw9KlcPXVbilbVXbuhD/+0dVRuuSSOj2GSE0omNQIjDF3Apf5T/OAsdba8ru6hcPGWt7XA2UniYiIiIhIPbnrrru44YYbAIiJieGkk07isMMOw+v17tc2Ojqabt260a1bN4477jg2bNjAm2++SXZ2Nn/729/48ccfue+++xo3oASldZMyMiAy0gWU0tLckreMDPdZqKKiXIDo8MNh0iRXh6kyBQVw+eVwwgnQu3fdn0MkBAomNTBjzG3Ajf7TfOBsa+079TGWtbZfbe7z11rqG+bpiIiIiIiIMHPmzJJAUteuXRkzZgytQ8zc8Xg8JCcn06NHDxYtWsT333/PAw88QEJCArfeemt9Tjs0cXEuaJSW5t4DGUr5+S5Lqab++lfo1w/GjIFffqm83YMPKpAkDUo1kxqQMeZ24F/+00AgaUkjTklERERERKTBbNiwgauuugqA7t27c8EFF4QcSAoWGxvLueeeS58+fQC47bbb+OCDD8I611qLiYG2bV0xbo/HLYGLial9f0cdBatWufeKXHQRXHFF7fsXqQUFkxqIMWYacJP/NB+3tG1xI05JRERERESkwRQVFXHRRReRn59P69atOeecc4iMjKx1f16vlzFjxnDAAQcAMGHCBLKzs8M13bqJinI7vXm9rqZSYiLExta+v44dYcUKl6kU7Pe/d3WVGnuJn/zmKJjUAIwxdwM3+E8DGUmvNuKUREREREREGtSrr77K559/DsCoUaOIqUu2jl9ERASjR4/GGMPmzZuZO3dunfsMm4gIaNfO1U8CV1MpIaH2/UVHw+zZLngUEeEKdS9cWLesJ5FaUjCpnhlj7gOu85/mA2cpI0lERERERH5rZvh3JuvevTu9evUKW7+dO3emb19X8vXRRx/FWhu2vuvM43EZStH+/Y3i4uoWUDLGFdtevtwFkjp3Ds88RWpIwaR6ZIx5ELjGf5oHjFGNJBERERGRutm8eTM33ngjgwYNok2bNsTGxtKjRw/OO+88Xn/99XobNysri4cffpiRI0fSqVMnoqOjadu2LQMGDODaa69l/fr1te73qaee4owzzqBHjx7ExcXRqlUrevXqxfDhw7n99tv55JNPwvw0oRkzZgzGmEpfCQkJ9O7dm/Hjx/Pee+9V2s+OHTtYunQpAEcccUTY53nkkUcCribTmjVrat1PvXy3jHGFuAPL3AKZSpGRLthUm7GPOw6OOabWz2KMObXmj2EOMcZMNcZ8aYxJNcZkG2N+MMY8V9P+jDEHGGNONsbcaIx5yRjzszHGBr3+WNP5ScPSbm71xBjzMPB//tM84Exr7RuNOCURERERkWZvxowZXHvtteTk5JS5vnnzZjZv3sx///tfRo8ezdy5c2nVqlXYxl2yZAkTJkwgJSWlzPXU1FRSU1P56quveOihh7j++uu54447Qt6m/oUXXuCaa65hx44d+32WmZnJ999/z9KlS3nsscfYtm1bWJ6lJlavXl3l51lZWWzatIlNmzbxzDPPcMkllzBr1iw8nrJ5CytXrgTAGBPWrKSArl27EhMTQ25uLitXruTwww+vcR/1+t0K1E2KjITsbFdLKajweLjHrq4/4DVjzMvAeGvt3uqnbyYB9wK+ch8d4n+NC7U/Y8xEYGa1DyFNmoJJ9cAY8w9KA0kAy4F+xph+Idz+grV2a/3MTERERESk+Zo1axZXBO1aNWDAAEaOHElsbCxr165l8eLFFBYW8vLLL3PmmWfyxhtvEBUVVedxFyxYwDnnnFOyfKpdu3acfvrpdO/enZycHD777DOWLVtGYWEhU6dOZd++fTz44IPV9jtt2jRuvPHGkvNDDjmEoUOH0qVLF4wxbN++nR9//JGPP/64zs9QGykpKfz8888l59OnT8fr9Zac79u3j++++45FixaVBC1mz57NIYccwg033FCmr1WrVgHQvn37sPw3Kc8YQ8eOHfnxxx9LxqqJBvtuxcW5Vz2OHWp/wGhgkTHmFGttfmX9+YM/jwZdWge8CWQDA4HTcLGFkPoDKirylOm/XvuK7NKgFEyqH4eWOz/F/wrFF4CCSSIiIiIiQX744QeuvPLKkvNp06btF7BYs2YNp5xyCrt27WL58uXcd999/OMf/6jTuLt27eKiiy4qCSSNGzeO2bNnE1cuIPDxxx8zevRo9uzZw0MPPcTw4cMZNWpUpf3OmTOnJJCUlJTErFmzGDt2bIUZTXl5edVmCNWH4KBM9+7dmTJlSoXttm7dygknnMCPP/4IwD333MN1111XJvC0dav7FScxMbHe5tu2bVt+/PFHtmzZUqP7Guu7VR9jh9rf4YcfvgtoD5wI/B2YVlF/xpgewENBl/5hrZ1ers1hwBuh9OeXBbwHrAp6fQf8CBxcxX3ShKhmkoiIiIiINHk333wz+fku2eG8887b7xdkgMMOO4xnnnmm5Hz69Omkp6fXadyHH36YrKysMv2XDyQBHHPMMcybN6/k/Nprr620EPS2bdtKfuH3+XysWLGCc845p9KlcdHR0Rx99NF1eo7aCA4mVbVs7KCDDuK+++4rOU9NTWXjxo1l2hQUFACUCTCFU2FhYcl/68D3JFSN9d2qj7FD7Q+4MOjSDcaYyqJ8twGBNKjnygeSAKy1a2rQH9baJ6y1f7TW/t1a+5y19lvbpKqmSygUTKoH1toJ1lpTy9eKxp6/iIiIiIRuypQpJcWIn3/+eQA2bdrEP/7xDwYOHEhiYiLGGI4//vhGnmnzlZWVxcKFCwG3nOnmm2+utO2IESM46qijANi7dy8vv/xyncZ+443Ssqf/93//R0RE5Ys7RowYQXJyMgDffPNNpUWz7733XvbudWVlbr75ZgYMGFCnOdaX4Gyo6opm//73vy9zvmfPnjLnsf7i03l5eWGaHVhr2bJlC4sXL+a+++7j+++/LzNWKBrzuxXusWvSn7X2beBT/2kr3BK1Mowx8cCYwC24wFKt+5OWRcEkEREREZE6+PLLL0uOk5OTue222+jfvz/Tp09n3bp1ZGRkACXZAFILb731Frm5uYCr/3LooeWrSpQ1duzYkuNFixbVaWx/sWIgtF3Igtu89NJL+32ek5PDnDlzAJdxNHHixDrNrz6FmpkE7JdV1TqouDRQ8t+sfJCpNlJSUnj33Xd5+OGHefrpp1m9enXJ9wOgT58+IffVmN+tcI9d0/6AF4OOz6zg85MprW+0zlr7bR37kxZENZNEREREROogsA15dHQ0999/P0899RQRERGMHDmSI444gsjISDZu3Ngoy5RaiuCt3ocMGVJt++OOO67Ce2sjeDcsn6/8Rlb7C25TUSHoTz/9tCTAePTRR5OYmMh3333Ho48+yptvvsnWrVuJioqiS5cuDB06lIkTJ9KvXyj7+IRXWlpaSQ0kqD6YtH79+pJjYwzdunUr8/ngwYMByMjIIDMzk4SEhBrNJzs7m/Xr17Nu3Tq2b9++3+etW7cu+bkGxgpFY363wj12TfsDPgg6rijaHXztwzD0Jy2IgkkiIiIiIrW0ffv2kkyLvLw8nnrqKQ4//HDmzZtXo+wIgAkTJjB37tywze2WW27h3//+d9j6a0wbNmwoOQ5lW/mePXuWHG/dupW9e/fWfCt3v7Zt2/LLL78AsGXLlmrHDy7+HBxgCQhe+nbYYYcxc+ZMrr766jLLv3JycsjIyGDDhg0lW7xPmzYNj6fhFpYEB8IOOugg2rVrV2X7p59+uuT497//PW3bti3z+eDBg4mNjSU7O5u1a9eGFOwoLCzku+++Y926dWzatIni4uIyn/t8Pvr27cuAAQNISUnh1VdfBUIOpACN+90K99g17Q/4Puj4IGNMK2vt3qBrwVHMTWHoT1oQBZNERERERGqpfHbA4MGDWbFiRYUFmqX2du7cWXLcpUuXatsnJSURFxfHvn37ALcjW21/4T/yyCNLghQvv/wyJ510UqVt09LSeO+990rOU1JSKCgoIDKydLfzQF0fcIGlBx54AID27dszevRounbtSmpqKq+//jpff/01xcXF3H333WRlZfHoo8G7s9ev4GBSdcv7Zs+eXVIvDOD666/fr018fDznn38+s2fPZtWqVRx99NEVFuO21rJ161bWrl3Lxo0byyxfA4iIiODQQw8lOTmZnj174vV6sdby5ptvAjBq1KiQviMBjfndCvfYNe3PWptmjNkHBP7Aag8EB386BB1vC0N/0oIomCQiIiIiUkvBwSSfz8eLL75Y60DSueeeS//+/cM1NY455piw9dXYMjMzS45D/fnGxsaW/NIdfH9NjRs3riSY9Pjjj3Puuedy7LHH7teuuLiYK664omTnt4DMzEzatGlTcp6WllZy/Omnrl7xWWedxZw5c4iPjy/57O6772batGncdNNNAMyYMYNRo0Zx6qmn1vpZaiK4+Hb5JW5FRUWkpqbyxRdf8MQTT5QUfQb4y1/+wpgxY6jIpEmTmD17Nunp6Xz44YeccMIJJZ+lpKSwbt061q1bt99OZcYYDjnkEJKTk/nd735HdHT0fnMNZI9Nnjy5Rs/ZmN+tcI9dm/6AbEqDP+XXHgaf7wtDf9KCKJgkIiIiIlJLwcGkyy+/fL86MTUxcuRIRo4cGYZZhd8dd9xRZtv7upo8eXKNfukPrlsUFRVVRctSMTExJcfZ2dmhT66cc845hwcffJDPPvuM/Px8hg8fzpQpUxg3bhzdunUjJyeHlStXMnXqVFasWEF0dHSZJWvZ2dllgknlAwB9+/blueee2++5PB4P//znP9m0aVPJ8sdp06Y1WDApODPp5ptvrnJnMHABn0mTJvHQQw9V2mbQoEFccsklzJ49m/fff59evXqRmZnJBx98UGEdpE6dOpGcnEz//v3LBNqCpaam8vbbbwMuK+nkk08O5fFKNOZ3K9xj16Y/IDj1q/w2eMFFwvLD0J+0IAomiYiIiIjUUnAw6a9//WsjzqR+7dy5k2+/rW4jp9D9+uuvNWofXNQ6Pz+032mDl0fVZKv48jweT8nyto0bN5KTk8O///3vCutReb1e/vOf/3DppZeWXCu/BKp8Ee9rr722yl/8b7zxxpJg0kcffURaWhpJSUm1fp5QpKenl9nFripRUVGcfvrpXHXVVRVmbJV377338tZbb7Flyxbmz59Ply5dygSSEhMTGTBgAAMGDNiv7lJF83z22WfJz8+ndevWzJ49e79d5arTmN+tcI9dm/4o3a0NXFZRsJyg41CjU1X1Jy2IgkkiIiIiIrWQnp7OTz/9BEDnzp3p27dv406oBQve+SuwxKc6wVkbNd05rLwOHTrw+eefc/vttzNr1qz9lmGBWwo2c+bMMpkjXq93v7HLn5944olVjt27d286d+7M9u3bsdayZs2aau+pq9WrV2OtBdyz//3vfy/5zBiDz+cjMTGRHj16MGjQoP2WnVWlVatWLFy4kKFDh5KZmVmmhhS4/24ZGRmkp6eTlJRUadHx7777jldffZV9+/YRGRnJggUL6NixY42ftTG/W+Eeuzb9UTZ7qPyaveDzUNfNVdWftCAKJomIiIiI1EJwVtJRRx1V5/7efPPNCnf/qq1jjjkmbHWTHnnkER555JGw9FUbHTqU1gGuaDlUeRkZGWV+mW7fvn2d5xAbG8u0adO47bbb+Pzzz/n222/Zu3cvbdu25bDDDqNfP7fx1fz580vu6dmz536ZMuUDHp07d6527ODsnZpmddVGcL2kY489lmuvvTas/R9xxBG88847nHrqqaSmpgIuSGWtJT8/n7Vr17J27Vri4uLo378/ycnJJT+nHTt28PHHH5fsXBYTE8OiRYsYNmxYrebSmN+tcI9d0/6MMa0pGyTaVa7JzqDjar+oIfQnLYiCSSIiIiIitfDll1+WHJcvUFwbzz//fMlypnC45ZZbWkwR7n79+vHyyy8DLiOlOps2le5iftBBB9V6t62KREZGVhmo++STT0qOKwoy1qbIeiBLCKjxMq7aCK6XdNhhh9XLGH/4wx9YvXo1F198MUuXLi15xsjISAoKCgCXXfPZZ5/x2WefERMTQ0RERJkC50ceeSRz586lT58+tZ5HY363wj12TfsDegUdb7XWlt95bQMw2n/cOwz9SQtScc6giIiIiIhUKTgzKRzBJKlc8M/3o48+qrb9Bx98UHJcX8GQihQVFbFgwYKS84oKqg8ePLjM+bZt1e64XibLpF27dnWYYWgaIpgEcPDBB/P222/z9NNPlywTDQSSysvNzS0JJEVFRTFmzBgWLVpUp0ASNO53K9xj17Q/4Lig4zUVfL466Lj6gljV9yctiIJJIiIiIiK1EO5g0pw5c7DWhu1VUYHo5mrEiBElxYXXrl1bbdZFcEDnzDPPrNe5BXv22WfZtcut7Gnfvj1jxozZr01ycjK9epUmcCxfvrzKPr/99tuSYJLX6633wOXevXvL1DGq72CcMYYJEyawfv16VqxYwd/+9jeOPfbYMsWlPR4PkZGRJef5+fksXLiQgw8+mJNPPplnnnlmv13yQtWY361wj13T/oCzg44XVfD525QW4R5ojKkuO6m6/qQFUTBJRERERKSGcnNz+eabbwBX8+bAAw9s5Bm1bPHx8SW/PFtruf322yttu3TpUj7++GPAFXsePXp0pW3DafPmzVx//fUl5//85z8r3aXtoosuKjm+7777qtx5a+rUqSXHw4YNC+uSvYoEF99u3759rYpa14YxhhNOOIGHHnqIDz/8kKysLHJzc8nJyaGwsJCcnBzeeOMNzjvvvJJAU1FREW+//Tbjx4+nffv2nHvuuXz11Vc1Grcxv1vhHrsm/RljhgGBtZp7gZfLt7HWZlEaFDLAv+rSn7QsCiaJiIiIiNTQV199RWFhIaAlbg3ltttuKwnOzJs3j3vuuWe/NmvXruXCCy8sOb/hhhtITEystM8JEyZgjCnJjqnMa6+9xvvvv1+mdlGw5cuXM3ToUPbs2QPA0KFDmTx5cqX9XXXVVRx00EEAbNy4kfPPP79MLSCA4uJipk2bxjPPPAO47Jybb7650j5XrFhR8ix1qasUXHy7IZcIlmeMITo6mpiYGIwxeL1eRo4cyfz589m1axdz5szhpJNOKtntLScnhxdeeIFly5bVeKxwf7dC/V7Vx9ih9gc8E3RpurV2/y0KnZuBQLTzAmPMdeUbGGMG1qA/aSFUgFtEREREpIZUL6nh9ejRg4ceeojLL78cgOuvv5758+czcuRIYmNjWbt2LYsXLy6puTN06NAyW9rXxSeffMKdd95Jly5dOP744+nZsyfR0dHs2rWL9957L/DLOeAKSy9cuLDKgI7P5+O///0vw4cPJycnhwULFvDBBx9w5pln0rVrV1JTU3n99dfZuHFjyT233nprgxRUb6h6SXURHx/P+PHjGT9+PNu3b2fevHk8++yzJTu81VRjfrfCPXao/QGBlLN3gfsq689a+4Mx5kpgpv/S3caY84E3gWxgIHAaEFiHWGV/AcaYBRVcDi4IdqsxZk+5z/9nrf1fdX1Lw1AwSURERESkhhRMahwTJ06kqKiI6667jpycnJIt5Ms744wzmDt3bqXLzGpr27ZtPPfccxV+5vV6ueyyy7jrrruIj4+vtq9jjz2W1157jfHjx7N161Z27drFrFmz9msXExPD9OnTufLKK6vsLzhrKpCtUxvNIZgUrHPnzkyZMoUpU6awZs2aSrPHqtOY361wjx1qf8ArwHhrbeXrLAFr7SxjjBe4B/DhAkgDa9uf31nVfH58BdfWh9CvNBAFk0REREREakjBpMZzxRVXcMopp/D444/zxhtvsGXLFnJzc+nQoQNHHXUUf/nLXzj11FPDOubll19O165deffdd1m/fj27du0iPT2dtm3b0rVrV04++WTGjRtX453Fhg4dyoYNG5g7dy4LFy7k22+/Zc+ePSQkJHDIIYcwYsQILr/8crp06VJtX8FZOWPHjq3xMwJkZWWV2X6+OQSTgtV1vo3x3aqvsavr7/nnnx9lrX091P6stY8aY94ALgVOAboCMcBO4FPg2Zr0J82fqW3kVlouY8wGoC9A3759a50uKhIO3W54rc59/DR9VBhmUnvheIa6auyfAdT951DXZ2gC36XaF7AQ+Q3T30tEQjN27FgWLFiAMYa1a9eSnJzc2FOSpk1/L5E6UQFuERERERGRZu79998H3FIoBZJEpL5pmZuIiIhIAzDGdAKO8L8G+987BDXpbq39qR7HjwYuBMbiMn3aAanAJmAh8JS1dm99jS8i9efrr79m9+7dANx0002NPBsR+S1QMElERESknhljpgNTGnH8/sDzQL9yH3Xwv44DrjbGnG+t/bCh5ycidfPee+8BcMopp3DEEUc08mxE5LdAy9xERERE6l9MBddSG2JgY0w34B1KA0npwJPAv4AZuOKp4Iqpvm6MaV4Vd0WEiRMnYq3l9ddV/1hEGoYyk6TFauxiv+HQEgo3N4VnqCs9Q9PQEp5BftPScAGdVYGXtfZHY0xD7ITyJKXL6T4CzrDWpgQ+NMZcCzwHjAYSgHnGmAHW2qIGmJuIiIg0QwomiYiIiNQza+2tjTGuMWY4cKL/NBU4KziQBGCtzTHGnA+sBXri6imNB55qyLmKiIhI86FlbiIiIiIt1/ig48estbsqamStzQbuDbp0Yb3OSkRERJo1BZNEREREWiBjjAf4U9Cl/1VzywIgsOzuOGPMAfUyMREREWn2FEwSERERaZkOAVr7jzOBdVU19i9/+9p/6gEG1N/UREREpDlTzSQRERGRlqlf0PEP1triEO75HlczKXD/8nBNxhizoZa39gjXHERERCQ8FEwSERERaZk6BB1vC/Ge4HYdKm1VO32rbyIiIiLNgZa5iYiIiLRMCUHH+0K8J7hdQqWtRERE5DdNmUkiIiLym2WMuQm4IIxdPmKtfSSM/dWFL+g4P8R7coOOY8M4F4CNtbyvBxAdzomIiIhI3SiYJCIiIr9lHYBDw9hfU9oBLSfoOCrEe2KCjrPDOBestf2qb7U/f60lLZETERFpQrTMTURERKRlygw6jgvxnuBspMxKW4mIiMhvmjKTRERE5DfLWjsZmNzY86gnO4OOO4d4T5dK7hcREREpocwkERERkZZpQ9BxD2NMKH/v61nJ/SIiIiIlFEwSERERaZk2Axn+41ZAclWNjTFtKK1NVAysrb+piYiISHOmYJKIiIhIC2StLQYWB10aW80tZwHGf/yBtTalXiYmIiIizZ6CSSIiIiIt1zNBxxONMQdW1MgY4wOuC7o0t15nJSIiIs2agkkiIiIizYwxZoIxxvpfP1XWzlr7DrDcf9oWeMm/nC24Lx8wD+jlv7SRskEoERERkTK0m5uIiIhIPTPGtANmVtNshjEmu9y1R6y1K+o4/F+BT4AOwBDge2PMAuBn/7WzgI7+tlnABdbaojqOKSIiIi2YgkkiIiIi9S8OF7SpyikVXFtS14GttT8ZY0YAz+MKbCcBl1TQdCtwvrV2TV3HFBERkZZNwSQRERGRFs5a+5Ux5nBgPK4Qd1/gACAN2AQsBJ601u5tvFmKiIhIc6FgkoiIiEg9s/b/t3ff4XZU9RrH3xcIEHroCkgg0kFAULGAQUABjSh2RQGvFxUL6pWrYKGIiL0iXEGNKKhIFZSmEpqFqgjBICUUkRpaCKGE3/1jzXavc9hl9tn9nO/neeY5s+esWbOm7Zn57TVrxVxVe0rrRH4zJc1scZ4nJP2gGAAAAMaMBrgBAAAAAABQGsEkAAAAAAAAlEYwCQAAAAAAAKURTOoy2+vbPtL2X23Ps73A9s22T7K9e7/LBwAAAAAA0Aoa4O4i2/tL+pqkyaP+tX4xvMP2GZL2pvcUAAAAAAAwDAgmdYntD0g6Opt0raRzJS2QtKWkGUrb/w2STre9W0Q82fOCAgAAAAAAtIBgUhfYnibp29mkgyLiqFFptpZ0jqQ1JL1K0v9I+lLPCgkAAAAAADAGtJnUHYdLWrIYP2l0IEmSIuIaSe/JJn3a9kq9KBwAAAAAAMBYEUzqMNvLSdqz+BhKgaWaIuJ8SX8uPq6g9MobAAAAAADAwCKY1HmvkbR0MX5tRMxpkv5X2fgbu1MkAAAAAACAziCY1HlbZ+OXlkh/SZ15AQAAAAA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TD5ObF/ktp5FtaeQP8xsp/XrclO3JSm2ErFtM+rWkT9ZJvlM2fl6Z/JVqCG0gaXXb60abbdiUtEipDZJG5mTja3SxLN3U0v6IiCdt36AUbHyh7cUjYlGd5FeVuP4124Z5+WY2K18ttldWapNISl3Q31Fitr9m4y0FVrPlrqf0upmUHjrPbZQ+Ip6yfbqkDxSTXiHp9w1m+W2T/BbZvknpvF5K6b5kXomil/Fj2z8uke67EXFcjelrZ+P/bpRB0b7S9tmkmSWWW0bD7VeYI6nSy94aSo11V8q1lKqBHalcj3QnK7VlJKX9+8MS8+Ty8+G0iGj4fV8yn06f9/1wgqrn6buVGtkfoTgfK21X3aXG51ZlnuWV2mGbpnTNXkrVdqqkFDCXRrbj1W/d3rf5+bpO3VR9QDAJwDDJb4D/UzPJ9lZK71tL6df0b/eyUACGxuiHgNEN1nZM8dBzgNJD6nolZ5tSMm8r3chXGty+WtI7i5pOtWyZjV+ZZm/JaipXk6Fd90fEY03S5AHAru2/bilqqmyeTbq/xf2xuNJxcn+d/5fZT8224abZ+OUlyzXaC1R9ANzc9uhaMs2s1jxJTRtk43+Pcg0u5w1RN/vVfyzbt1PBpGb+IulLRc3CWvJg+oI6aSrWVeosQJLui4i5bZatot3jc12lVzUl6e6IuLtEfq3s31raPh96cN73wy8kfUOpbbQ32d4/Ih4flWYvVb8HTmxwjZLtrZVqVe6q6j5upNT1stt6tG/z6+LyrWTebQSTAAyT0TWTKr6javXS/2nyigSAiWv0a22jX3sbISJu0shfRCVJzR6MbU9R+nWy1doVS5dMd6Sqr2v8S6mL4UZBmFVbLMdoyzZP0hELS6TJt/0wNtcwRe2Xe1nVf/DoxDbMz4taPQyW0a9jLn/ALPvgfV82vnKTtP08Rn8p6crs8yKlwMtdkq6JiIa1jTTyua9ZkK0Tx0At7W6/bu/fWjqxLbp93vdcRNxv+xyl17SWV2pAe3QPyntl47V6cZMk2d5bqcZYK6+FLtVC2m7qxb7Nz9dJbS6rowgmARgmtypVb52kIphk+22qVsU+Pwar+1QAg+Vupd7cVio+b60aVfM74DuqBpLmSfq+UvX+m5UCWgsrv9Da3lepl6tSbO8j6dPFx/mSXtesDSSNvN/7jFLvXK2o2T03xiTfFwtUbeuvFQ92qCxltFqjqCJfz7+rwYNkHQ+Pcbl58HcsZR/r+vbCuRExs43589pIZQPX0mBtk37v306cD8Nw3pf1U1Xb/Hm3smBS0dZUpSbYNRFxXa0MbG+s1DteJZB0QZHv1UqB0sfyH4lt3ybpeR1ch3b1Yt9Ozsab1d7tKYJJAIZGRDxt+2alQNIGtpeT9JXi308rdX0NADVFxDO2L1b15ne6pK93chm2V1XqQUhKr9VtGxG3NphlpQb/G533dEk/KD4+o/Rq21/rz/EfDyj1ZCdJp0TEjY0S41nyB8gy7y8s0+B/ee24yUq9apWprdFLD6ja7tDqatJQfYM8KuZHxNfaLlU5+StlZWtH5ekG8YG9U1qpoZPvv9W7UJax6sf+7cS2GIbzfizOUtqmUyS92vYaWZttZRve/qCqtW2+GxEfbbLM0tfMBobtOz0/X++rm6oPhrF6MICJrfIQtJSkY1X9deLoiLihP0UCMETy9kR2s71u3ZRj82JVf2E9tUkgSRrZ1kJdtjeUdJqqN92faKEmZv7d+PK6qVBP3jNPmXaa6v5qXmF4hqMAAAqlSURBVPQsdnPx0ZJe2ka5uuX6bPwlY8wj7/lra9uNHsY66Z/Z+BYle1N7YTY+ngOt+XfR2nVTJXNVrQGxuu2pXSjPWNymas3K5xQ9WjbT7v5t+3wYkvO+ZRHxhFID51K67r1D+k8D7m8rpi+SdFKDbPJt0TDobHsdPbsjjbEYtu/0vFfDZvcUPUUwCcCwyRvhflfx93416AoZADI/k1TpWWpxdb7B/rxNj4aNw9peWtKMZhnaXkXSb7K8vx8RrZT7gmz8v+qmQj1572gNG/C1vbpGPrzWku+P9461UF2U97j0nrFkUPTeVgkoLa1qbb2uKoK3ld6/llNqzLcu20tIemM26dIuFW0QXJuNN+wJq2i4/OJs0piOg04rghd5u1Fvrpc289ZsfCz7Nz8f3lj0NjYWg37ej1Ve66hSG2k3VWuEnd+kh8nS10yN3JftGLbv9Lyd2L91If8xI5gEYNjU+lXpcxHxUM9LAmDoFG0vHJhN2sP2VzyGLs7qyKu8b1U3VfJ5Nem1yvaSkk6X9Pxi0rmSmr0GMNqPVK1l8HLbpQNKHdwuwyzvDer1RQCinsPUvIHUo1V9zeKdtl9dtiA92h8/llTplWlH22MNBH03Gz/C9lp1U47S5nr+JBv/QlFLop5PSFqzGJ8raVYbyx10f8nGt6ybqur72fiBRe3IQTAzGz/I9or1Etp+q6Rtio/zJZ3S6sIi4nJJVxUfV1CT2jMNDPp5PyYRcZmqNXNeaHsTlWx4u1Dqmmn7uaq2F9iuoflOL/7/guLjE5LKvNreMwSTAAybOaM+X6vUcB8AlBIRv9TItpIOlPQ72y9rNJ/tybY/2CT7P6n6GsZutQI3tifZPkTSQWreoOvxqnYy8HdJb42IRU3mGSEi7lO6Ia441vZBRc2ommxvZft7kr7cyrLGo4iYreqrgs+T9KzgY7FPD5P0gRL5XSfpmOLjYpJOt/2+eq9kOXm57Z+q9UBiy4rj5ahs0k9s79+gfBvYPrjGv45X9cFnTUmX2N6h3nJtL237jbZnqXlNgEa+o+oD6taSfmH7Wa/GFOfmkdmkw1o9t4ZJRNyt9B0ipXYnm70i9htVOyhYTtIs2zvWSlgco9sXHQR0288k3VSMryXpbNtrjk5k+7VKgdGKr0fEWBt2P1CpnTpJ2s/2sbWOqWK5K9v+39Hbd9DP+zb9LBv/iKo1bh/RyFfLa7koGz+6zr7cTOlYXFUdaBB+yL7TX6BqO1EXDVpbWzTADWDYjK6ZdMB4vvkD0DUHKnWrfYhSOwevkvQq27crvQpxp1LDopOUuobeWCmok7f9cqNG9ToVEQ/bPkbSAcWk44sA1CVKDWeuLem1Sjew85XafvtkrQIWwa28EdNZkt5f4kfqhyNiRJA9Ir5a9JrzXqX7vyMlfdz275TamXlc0oqSpim1+1RpS+roZgubIA5RtW2Qj0va2fZvlB6W1pb0OqV9er3Sjx57NsnvY0rb+jVKx9Rxkg4p9sdcpYDkSkrH3UskVR5Me/WKwxFKgZg3KJ0DR0v6X9vnS7pd6ZxZW9J2Sg87t2lkYEYR8aTtPZRel1pX0nqSLrL9N6Xz4d9Kr5quImkLpfVctph9zDUxIuIep14ST1M61veUtIPtM5S27QqSdinWr+LENntKGxZnKG1rSdpJDdqyiYgoaqVdplQz8jmS/mD7aqV9eq/SK4zPl7SD0vHwE42sOdRxEfF4Ua5ZSsfLKyTdaPtMpXNvstJ39fbZbLOUjumxLvNC2weq+iPE+yW93fZ5Sq9zLlRqnHtrpXbpllD1+yI36Of9WJ2gai9mH1D1/D0lIh6vPct/fFfS/koBy20l3WT7dKWA4RLFtF2UvitmStpR1etTO4blO32nbLxZYK73IoKBgYFhaAZVf5UISdf2uzwMDAzDPSj16HZJ9r1SZvin0q+vk+rkuaSkXzfJ426lANY+2bRDa5StlXJVhrkN1vcApQBYmXwWSvpom9t3Vpbf1DppmpY7Szs1Sz+rQbpSeZbNr0h7RJPtdaXSw8fMbNr0BvktLumLxXYusz8elfTmOsdwJc3MEtuw7jFXo3xHKT0ENT0nGuSzilJgp+zxe6ekaR04t3dVahul0bKelvQNSYs1yKfU/mzlmB/j+bNPB7bJhll+p5ScZ2WlXrvK7Lvjasx/aCvr0ML5s62kW0qU6URJk9s9H4q071Bqp7PZMp+RtHaD86rt876NY2luu3nVyf+yGmVver4U875GKYjTaDv8VKnznbmVaXXymprNM6vJcgfyO73Odn1S0ird2HftDNRMAjBs8l8Sr+lbKQCMCxExS9L2tl+k9PC5g6T1lR6Al1W62Zun9OvzFUqNif6pSZ6VGhlvU3pQ2UbpF8l5Sj2xnCnp+Ii4r0evheRl+7btn0jaW9LOSrVKVlWqffKo0o36tUqNzv4mIsZzV+ktiYjP2r5A0oclvUxpuz2sdGycJOlHxb4vm98iSZ+x/V1J+yoFFzdROvYWK/K+RelVsQsknRsRj9XJruOK8n3a9rFKNdp2UfrlfYrSg81dSsfKeapdC6OSzwOS9rS9pVJbKjsoPfBNUQrmPKAUoL28yOuiiHimdm4tlf9c29Mk/bdSbcDNlLbtY0qN8P9eKfgxu91lDYuIuNH2hUq1O3a3vVI0aXMyIuZJmmH7pUr775VKr5ctr7Qtb1P6bjy7GHoiIq4salu+R6kG3VZK5+STSo2wX6QUXP1zB5f5c9tnK52vuyl9f65S/Pt+pVenLpT0i4i4s04eA33et+EEpe/Fits18hW2uiLiPNubK9UQ2lWp5tEzSsHgP0k6ISLOl6RONh816N/pTr0oVnqHO7X4Lh0oLiJeADAUimrGXyk+fiIivtnP8gAAAAwL27srtYckpZqH322UHuND0RbZKyXdFhFT+1salGH7CEmfKT6+KCKubJS+H2iAG8CwyXt6oGYSAABASRHxW6XXdyTpgHqNBAPoH9vLKLXNJUnnDGIgSSKYBGD45MGkgeoeEwAAYAgcVPydJukt/SwIgJr2U3rt7hlVaycNHIJJAIZG0Y31RsXH25q95w8AAICRIuJ3Sj27SdLhtmlHFxgQtpeXdHDx8fiIGNg3MfjiADBMNlfqKUGiVhIAAMBYfVTVbsmnKnXFDqD/pkn6fjE+0G2a0QA3AAAAAADjFA1woxt4zQ0AAAAAAAClEUwCAAAAAABAabzmBgAAAAAAgNKomQQAAAAAAIDSCCYBAAAAAACgNIJJAAAAAAAAKI1gEgAAAAAAAEojmAQAAAAAAIDSCCYBAAAAAACgNIJJAAAAAAAAKI1gEgAAAAAAAEojmAQAAAAAAIDSCCYBAAAAAACgNIJJAAAAAAAAKI1gEgAAAAAAAEojmAQAAAAAAIDSCCYBAAAAAACgNIJJAAAAAAAAKI1gEgAAAAAAAEojmAQAAAAAAIDSCCYBAAAAAACgNIJJAAAAAAAAKI1gEgAAAAAAAEojmAQAAAAAAIDSCCYBAAAAAACgNIJJAAAAAAAAKI1gEgAAAAAAAEojmAQAAAAAAIDSCCYBAAAAAACgNIJJAAAAAAAAKI1gEgAAAAAAAEojmAQAAAAAAIDSCCYBAAAAAACgNIJJAAAAAAAAKI1gEgAAAAAAAEr7fwhcBFiLd3saAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1169.29x584.646 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(10)\n",
    "\n",
    "fig, axs = plt.subplots(1, 2, figsize=gb.plots._plots_internal.cm2inch(9, 4.5), dpi=330)\n",
    "\n",
    "axs[0].hist(glam_bias.estimates['gamma'], bins=np.linspace(-1., 1, 20))\n",
    "gb.plots._plots_internal.plot_correlation(subject_data_summary['gaze_influence'],\n",
    "                          glam_bias.estimates['gamma'],\n",
    "                          ax=axs[1],\n",
    "                          ylim=(-1.2, 1.2))\n",
    "\n",
    "axs[0].set_ylabel('Frequency', fontsize=7)\n",
    "axs[0].set_xlabel(r'$\\gamma$', fontsize=7)\n",
    "axs[0].tick_params(axis='both', which='major', labelsize=7)\n",
    "axs[0].spines['top'].set_visible(False)\n",
    "axs[0].spines['right'].set_visible(False)\n",
    "axs[1].set_ylabel(r'$\\gamma$', fontsize=7)\n",
    "axs[1].set_xlabel('Gaze influence on P(choice | value)', fontsize=7)\n",
    "axs[1].tick_params(axis='both', which='major', labelsize=7)\n",
    "for ax, label in zip(axs.ravel(), list('AB')):\n",
    "    ax.text(-0.2,\n",
    "            1.1,\n",
    "            label,\n",
    "            transform=ax.transAxes,\n",
    "            fontsize=7,\n",
    "            fontweight='bold',\n",
    "            va='top')\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "fig.savefig('examples/example_1/figures/Figure_3_gaze_bias_estimates.png', dpi=330)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Out-of-sample prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have identified those participants whose response behaviour is better described by a GLAM variant with gaze-bias than one without. Yet, this analysis does not indicate whether the GLAM is a good model of individuals' response behaviour on an absolute level. To test this, we perform an out-of-sample prediction exercise."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We divide the data of each subject into even- and odd-numbered experiment trials and use the data of the even-numbered trials to fit both GLAM variants:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_even = data[(data['trial']%2)==0].copy()\n",
    "data_odd = data[(data['trial']%2)!=0].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Replaced attached data (9000 trials) with new data (4500 trials)...\n",
      "Fitting 30 model(s) using MCMC...\n",
      "  Fitting model 1 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1290.64draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 2 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1309.52draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 3 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1300.43draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 4 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1294.37draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 5 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1311.43draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 6 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1298.52draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 7 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1295.00draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 8 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1302.20draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 9 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1298.66draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 10 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1304.15draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 11 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1300.47draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 12 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1293.10draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 13 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1291.89draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 14 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1292.09draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 15 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1302.03draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 16 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1299.49draws/s]\n",
      "The number of effective samples is smaller than 25% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 17 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1297.38draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 18 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1302.35draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 19 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1297.33draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 20 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1307.26draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 21 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1328.15draws/s]\n",
      "The number of effective samples is smaller than 25% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 22 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1299.94draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 23 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1310.99draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 24 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1304.29draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 25 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1298.89draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 26 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1300.38draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 27 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1297.61draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 28 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1295.92draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 29 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:30<00:00, 1302.98draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 30 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [gamma]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:31<00:00, 1288.66draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/!\\ Automatically setting parameter precision...\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(11)\n",
    "\n",
    "glam_bias.exchange_data(data_even)\n",
    "glam_bias.fit(method='MCMC',\n",
    "              tune=n_tune,\n",
    "              draws=n_draws,\n",
    "              chains=n_chains)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Replaced attached data (9000 trials) with new data (4500 trials)...\n",
      "Fitting 30 model(s) using MCMC...\n",
      "  Fitting model 1 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:20<00:00, 1948.87draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 2 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:20<00:00, 1971.92draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 3 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:20<00:00, 1951.15draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 4 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:20<00:00, 1968.04draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 5 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:20<00:00, 1973.10draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 6 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:20<00:00, 1963.97draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 7 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:20<00:00, 1986.74draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 8 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:20<00:00, 1981.61draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 9 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:20<00:00, 1984.35draws/s]\n",
      "The number of effective samples is smaller than 25% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 10 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:20<00:00, 1983.64draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 11 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:20<00:00, 1960.01draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 12 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:20<00:00, 1971.24draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 13 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:20<00:00, 1970.88draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 14 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:20<00:00, 1955.98draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 15 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:20<00:00, 1974.73draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 16 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:20<00:00, 1976.54draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 17 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:20<00:00, 1973.73draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 18 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:20<00:00, 1957.15draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 19 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:20<00:00, 1974.88draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 20 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:20<00:00, 1972.80draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 21 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:19<00:00, 2031.58draws/s]\n",
      "The number of effective samples is smaller than 25% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 22 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:20<00:00, 1966.89draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 23 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:20<00:00, 1952.92draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 24 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:20<00:00, 1963.30draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 25 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:20<00:00, 1975.71draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 26 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:20<00:00, 1990.65draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 27 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:20<00:00, 1942.32draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 28 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:20<00:00, 1962.68draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 29 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:20<00:00, 1944.58draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Fitting model 30 of 30...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Metropolis: [tau]\n",
      ">Metropolis: [s]\n",
      ">Metropolis: [v]\n",
      "Sampling 4 chains: 100%|██████████| 40000/40000 [00:20<00:00, 1976.92draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/!\\ Automatically setting parameter precision...\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(12)\n",
    "\n",
    "glam_nobias.exchange_data(data_even)\n",
    "glam_nobias.fit(method='MCMC',\n",
    "                tune=n_tune,\n",
    "                draws=n_draws,\n",
    "                chains=n_chains)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, we check all parameter traces for convergence, before proceeding further in the anlaysis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.all([check_convergence(pm.summary(trace), varnames=['v', 's', 'gamma', 'tau'])\n",
    "        for trace in glam_bias.trace])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.all([check_convergence(pm.summary(trace), varnames=['v', 's', 'tau'])\n",
    "        for trace in glam_nobias.trace])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for subject, subject_trace in enumerate(glam_bias.trace):\n",
    "    gb.plots.traceplot(subject_trace)\n",
    "    plt.savefig('examples/example_1/results/out_of_sample/traces/sub_{}_gaze_bias_model_trace.png'.format(subject), dpi=330)\n",
    "    plt.close()\n",
    "    pm.trace_to_dataframe(subject_trace).to_csv(\n",
    "        'examples/example_1/results/out_of_sample/traces/sub_{}_gaze_bias_model_trace.csv'.format(subject))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for subject, subject_trace in enumerate(glam_nobias.trace):\n",
    "    gb.plots.traceplot(subject_trace)\n",
    "    plt.savefig('examples/example_1/results/out_of_sample/traces/sub_{}_no_gaze_bias_model_trace.png'.format(subject), dpi=330)\n",
    "    plt.close()\n",
    "    pm.trace_to_dataframe(subject_trace).to_csv(\n",
    "        'examples/example_1/results/out_of_sample/traces/sub_{}_no_gaze_bias_model_trace.csv'.format(subject))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then evaluate the performance of both models in predicting individuals' response behaviour using the MAP estimates and item value and gaze data from the odd-numbered trials. To predict response behaviour for the odd-numbered trials, we use the `predict` method. We repeat every trial 50 times in the prediction (as specified through the `n_repeats` argument) to obtain a stable pattern of predictions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_repeats = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  0%|          | 0/4500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Replaced attached data (4500 trials) with new data (4500 trials)...\n",
      "Generating predictions for 4500 trials (50 repeats each)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 4500/4500 [56:17<00:00,  1.33it/s]\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(13)\n",
    "\n",
    "glam_bias.exchange_data(data_odd)\n",
    "glam_bias.predict(n_repeats=n_repeats)\n",
    "glam_bias.prediction.to_csv('examples/example_1/results/out_of_sample/predictions/gaze_bias_model_predictions.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  0%|          | 0/4500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Replaced attached data (4500 trials) with new data (4500 trials)...\n",
      "Generating predictions for 4500 trials (50 repeats each)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 4500/4500 [55:53<00:00,  1.34it/s]\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(14)\n",
    "\n",
    "glam_nobias.exchange_data(data_odd)\n",
    "glam_nobias.predict(n_repeats=n_repeats)\n",
    "glam_nobias.prediction.to_csv('examples/example_1/results/out_of_sample/predictions/no_gaze_bias_model_predictions.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To determine the absolute fit of both model variants to the data, we plot the individually predicted against the individually observed data on all three behavioural metrics. To do this, we use the `plot_individual_fit` function of the `plots` module. This function takes as input the observed data, as well as a list of the predictions of all model variants that ought to be compared. The argument `prediction_labels` specifies the naming used for each model in the resulting figure. For each model variant, the function creates a row of panels, plotting the observed against the predicted data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2338.58x1559.06 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(15)\n",
    "\n",
    "fig, axs = gb.plots.plot_individual_fit(observed=data_odd,\n",
    "                                        predictions=[glam_bias.prediction,\n",
    "                                                     glam_nobias.prediction],\n",
    "                                        prediction_labels=['gaze-bias', 'no gaze-bias'])\n",
    "\n",
    "# We'll change the xlabels to \"Simulated observed\", just to be clear that these are simulated data!\n",
    "for ax in axs.ravel():\n",
    "    xlabel = ax.get_xlabel()\n",
    "    ax.set_xlabel('Simulated o' + xlabel[1:])\n",
    "\n",
    "fig.savefig('examples/example_1/figures/Figure_4_absolute_fit.png', dpi=330)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both model variants perform well in capturing individuals' RTs and probability of choosing the best item (A, D, B, E). Importantly, only the GLAM variant with gaze bias is able to also recover the strength of the association between individuals' choice behaviour and gaze allocation (C)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. References:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thomas, A. W., Molter, F., Krajbich, I., Heekeren, H. R., & Mohr, P. N. (2019). Gaze bias differences capture individual choice behaviour. Nature human behaviour, 3(6), 625.\n",
    "\n",
    "Vehtari, A., Gelman, A., & Gabry, J. (2017). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Statistics and computing, 27(5), 1413-1432."
   ]
  }
 ],
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