arosh/hotbath.ipynb

## hotbath.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "afece28c",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4863f177",
   "metadata": {},
   "outputs": [],
   "source": [
    "from cmdstanpy import CmdStanModel\n",
    "import seaborn\n",
    "import scipy\n",
    "import numpy\n",
    "import scipy.stats\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "seaborn.set_context('talk')\n",
    "seaborn.set_style('ticks')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1cf63207",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:cmdstanpy:found newer exe file, not recompiling\n"
     ]
    }
   ],
   "source": [
    "model = CmdStanModel(stan_file='model.stan')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "efa3bc25",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:cmdstanpy:CmdStan start procesing\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c827999ab48e467cba463d84bb932e8b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "chain 1 |          | 00:00 Status"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0f98458bd4a34b90b10aeb9264421c9b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "chain 2 |          | 00:00 Status"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6ae0f7ce074c470089b1ac9b18042faa",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "chain 3 |          | 00:00 Status"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "98f2556dbbf44cfb9ad54e66bae96dc9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "chain 4 |          | 00:00 Status"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                                                                                                                                                                                                                                                                "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:cmdstanpy:CmdStan done processing.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "data = {}\n",
    "data['Y'] = [2, 5, 10, 11]\n",
    "\n",
    "mcmc = model.sample(data=data, seed=2021)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0ae43c47",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing csv files: /var/folders/tg/syws6pks733dt6xx_t_rdddr0000gn/T/tmpq7lr2brn/model-20220102145356_1.csv, /var/folders/tg/syws6pks733dt6xx_t_rdddr0000gn/T/tmpq7lr2brn/model-20220102145356_2.csv, /var/folders/tg/syws6pks733dt6xx_t_rdddr0000gn/T/tmpq7lr2brn/model-20220102145356_3.csv, /var/folders/tg/syws6pks733dt6xx_t_rdddr0000gn/T/tmpq7lr2brn/model-20220102145356_4.csv\n",
      "\n",
      "Checking sampler transitions treedepth.\n",
      "Treedepth satisfactory for all transitions.\n",
      "\n",
      "Checking sampler transitions for divergences.\n",
      "No divergent transitions found.\n",
      "\n",
      "Checking E-BFMI - sampler transitions HMC potential energy.\n",
      "E-BFMI satisfactory.\n",
      "\n",
      "Effective sample size satisfactory.\n",
      "\n",
      "Split R-hat values satisfactory all parameters.\n",
      "\n",
      "Processing complete, no problems detected.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(mcmc.diagnose())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6968aa0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mean</th>\n",
       "      <th>MCSE</th>\n",
       "      <th>StdDev</th>\n",
       "      <th>2.5%</th>\n",
       "      <th>10%</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>90%</th>\n",
       "      <th>97.5%</th>\n",
       "      <th>N_Eff</th>\n",
       "      <th>N_Eff/s</th>\n",
       "      <th>R_hat</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>lp__</th>\n",
       "      <td>-35.000</td>\n",
       "      <td>0.02600</td>\n",
       "      <td>1.000</td>\n",
       "      <td>-38.000</td>\n",
       "      <td>-37.000</td>\n",
       "      <td>-36.000</td>\n",
       "      <td>-35.000</td>\n",
       "      <td>-35.00</td>\n",
       "      <td>-34.00</td>\n",
       "      <td>-34.00</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>8500.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mu</th>\n",
       "      <td>41.000</td>\n",
       "      <td>0.00660</td>\n",
       "      <td>0.280</td>\n",
       "      <td>41.000</td>\n",
       "      <td>41.000</td>\n",
       "      <td>41.000</td>\n",
       "      <td>41.000</td>\n",
       "      <td>41.00</td>\n",
       "      <td>42.00</td>\n",
       "      <td>42.00</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigma</th>\n",
       "      <td>1.300</td>\n",
       "      <td>0.00810</td>\n",
       "      <td>0.310</td>\n",
       "      <td>0.840</td>\n",
       "      <td>0.950</td>\n",
       "      <td>1.100</td>\n",
       "      <td>1.300</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1.70</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>8100.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>theta[1]</th>\n",
       "      <td>0.091</td>\n",
       "      <td>0.00110</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.082</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.21</td>\n",
       "      <td>2148.0</td>\n",
       "      <td>11674.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>theta[2]</th>\n",
       "      <td>0.190</td>\n",
       "      <td>0.00083</td>\n",
       "      <td>0.039</td>\n",
       "      <td>0.110</td>\n",
       "      <td>0.140</td>\n",
       "      <td>0.160</td>\n",
       "      <td>0.190</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.27</td>\n",
       "      <td>2190.0</td>\n",
       "      <td>11902.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>theta[3]</th>\n",
       "      <td>0.300</td>\n",
       "      <td>0.00170</td>\n",
       "      <td>0.066</td>\n",
       "      <td>0.180</td>\n",
       "      <td>0.220</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.300</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1482.0</td>\n",
       "      <td>8053.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>theta[4]</th>\n",
       "      <td>0.420</td>\n",
       "      <td>0.00200</td>\n",
       "      <td>0.087</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.310</td>\n",
       "      <td>0.360</td>\n",
       "      <td>0.420</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.59</td>\n",
       "      <td>1862.0</td>\n",
       "      <td>10119.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y_pred[1]</th>\n",
       "      <td>2.600</td>\n",
       "      <td>0.03900</td>\n",
       "      <td>2.100</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>4.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>2769.0</td>\n",
       "      <td>15048.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y_pred[2]</th>\n",
       "      <td>5.200</td>\n",
       "      <td>0.04200</td>\n",
       "      <td>2.300</td>\n",
       "      <td>1.000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>4.000</td>\n",
       "      <td>5.000</td>\n",
       "      <td>7.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>3123.0</td>\n",
       "      <td>16972.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y_pred[3]</th>\n",
       "      <td>8.400</td>\n",
       "      <td>0.05800</td>\n",
       "      <td>3.000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>5.000</td>\n",
       "      <td>6.000</td>\n",
       "      <td>8.000</td>\n",
       "      <td>10.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>2658.0</td>\n",
       "      <td>14445.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y_pred[4]</th>\n",
       "      <td>12.000</td>\n",
       "      <td>0.06900</td>\n",
       "      <td>3.500</td>\n",
       "      <td>5.000</td>\n",
       "      <td>7.000</td>\n",
       "      <td>9.000</td>\n",
       "      <td>12.000</td>\n",
       "      <td>14.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>19.00</td>\n",
       "      <td>2630.0</td>\n",
       "      <td>14292.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Mean     MCSE  StdDev    2.5%     10%     25%     50%    75%  \\\n",
       "name                                                                        \n",
       "lp__      -35.000  0.02600   1.000 -38.000 -37.000 -36.000 -35.000 -35.00   \n",
       "mu         41.000  0.00660   0.280  41.000  41.000  41.000  41.000  41.00   \n",
       "sigma       1.300  0.00810   0.310   0.840   0.950   1.100   1.300   1.50   \n",
       "theta[1]    0.091  0.00110   0.052   0.018   0.033   0.054   0.082   0.12   \n",
       "theta[2]    0.190  0.00083   0.039   0.110   0.140   0.160   0.190   0.21   \n",
       "theta[3]    0.300  0.00170   0.066   0.180   0.220   0.250   0.300   0.34   \n",
       "theta[4]    0.420  0.00200   0.087   0.250   0.310   0.360   0.420   0.48   \n",
       "Y_pred[1]   2.600  0.03900   2.100   0.000   0.000   1.000   2.000   4.00   \n",
       "Y_pred[2]   5.200  0.04200   2.300   1.000   2.000   4.000   5.000   7.00   \n",
       "Y_pred[3]   8.400  0.05800   3.000   3.000   5.000   6.000   8.000  10.00   \n",
       "Y_pred[4]  12.000  0.06900   3.500   5.000   7.000   9.000  12.000  14.00   \n",
       "\n",
       "             90%  97.5%   N_Eff  N_Eff/s  R_hat  \n",
       "name                                             \n",
       "lp__      -34.00 -34.00  1600.0   8500.0    1.0  \n",
       "mu         42.00  42.00  1800.0  10000.0    1.0  \n",
       "sigma       1.70   2.00  1500.0   8100.0    1.0  \n",
       "theta[1]    0.16   0.21  2148.0  11674.0    1.0  \n",
       "theta[2]    0.24   0.27  2190.0  11902.0    1.0  \n",
       "theta[3]    0.39   0.43  1482.0   8053.0    1.0  \n",
       "theta[4]    0.53   0.59  1862.0  10119.0    1.0  \n",
       "Y_pred[1]   5.00   7.00  2769.0  15048.0    1.0  \n",
       "Y_pred[2]   8.00  10.00  3123.0  16972.0    1.0  \n",
       "Y_pred[3]  12.00  14.00  2658.0  14445.0    1.0  \n",
       "Y_pred[4]  16.00  19.00  2630.0  14292.0    1.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mcmc.summary(percentiles=[2.5, 10, 25, 50, 75, 90, 97.5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "068262c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lp__</th>\n",
       "      <th>accept_stat__</th>\n",
       "      <th>stepsize__</th>\n",
       "      <th>treedepth__</th>\n",
       "      <th>n_leapfrog__</th>\n",
       "      <th>divergent__</th>\n",
       "      <th>energy__</th>\n",
       "      <th>mu</th>\n",
       "      <th>sigma</th>\n",
       "      <th>theta[1]</th>\n",
       "      <th>theta[2]</th>\n",
       "      <th>theta[3]</th>\n",
       "      <th>theta[4]</th>\n",
       "      <th>Y_pred[1]</th>\n",
       "      <th>Y_pred[2]</th>\n",
       "      <th>Y_pred[3]</th>\n",
       "      <th>Y_pred[4]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-35.7757</td>\n",
       "      <td>0.893461</td>\n",
       "      <td>0.749683</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.9863</td>\n",
       "      <td>40.8681</td>\n",
       "      <td>1.003070</td>\n",
       "      <td>0.086296</td>\n",
       "      <td>0.270521</td>\n",
       "      <td>0.378823</td>\n",
       "      <td>0.264359</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-34.7812</td>\n",
       "      <td>0.992877</td>\n",
       "      <td>0.749683</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36.0217</td>\n",
       "      <td>41.2299</td>\n",
       "      <td>0.988498</td>\n",
       "      <td>0.040055</td>\n",
       "      <td>0.190076</td>\n",
       "      <td>0.377527</td>\n",
       "      <td>0.392342</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-36.1462</td>\n",
       "      <td>0.588617</td>\n",
       "      <td>0.749683</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36.2116</td>\n",
       "      <td>41.4127</td>\n",
       "      <td>0.910017</td>\n",
       "      <td>0.017782</td>\n",
       "      <td>0.140151</td>\n",
       "      <td>0.380264</td>\n",
       "      <td>0.461803</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-36.1251</td>\n",
       "      <td>0.925479</td>\n",
       "      <td>0.749683</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36.8918</td>\n",
       "      <td>40.8130</td>\n",
       "      <td>1.449780</td>\n",
       "      <td>0.182550</td>\n",
       "      <td>0.231972</td>\n",
       "      <td>0.267669</td>\n",
       "      <td>0.317810</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-34.5705</td>\n",
       "      <td>0.961779</td>\n",
       "      <td>0.749683</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.8591</td>\n",
       "      <td>41.2827</td>\n",
       "      <td>1.069410</td>\n",
       "      <td>0.047761</td>\n",
       "      <td>0.184363</td>\n",
       "      <td>0.348396</td>\n",
       "      <td>0.419479</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3995</th>\n",
       "      <td>-36.3557</td>\n",
       "      <td>0.843098</td>\n",
       "      <td>0.638960</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.4146</td>\n",
       "      <td>40.8633</td>\n",
       "      <td>0.909066</td>\n",
       "      <td>0.066853</td>\n",
       "      <td>0.277867</td>\n",
       "      <td>0.413446</td>\n",
       "      <td>0.241834</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3996</th>\n",
       "      <td>-37.3628</td>\n",
       "      <td>0.873068</td>\n",
       "      <td>0.638960</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.6741</td>\n",
       "      <td>41.2288</td>\n",
       "      <td>2.141720</td>\n",
       "      <td>0.209770</td>\n",
       "      <td>0.157043</td>\n",
       "      <td>0.183562</td>\n",
       "      <td>0.449625</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3997</th>\n",
       "      <td>-35.7357</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.638960</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.4274</td>\n",
       "      <td>41.2382</td>\n",
       "      <td>1.730500</td>\n",
       "      <td>0.157578</td>\n",
       "      <td>0.177259</td>\n",
       "      <td>0.225282</td>\n",
       "      <td>0.439881</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3998</th>\n",
       "      <td>-34.6662</td>\n",
       "      <td>0.996881</td>\n",
       "      <td>0.638960</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.8680</td>\n",
       "      <td>41.4199</td>\n",
       "      <td>1.309920</td>\n",
       "      <td>0.071368</td>\n",
       "      <td>0.169886</td>\n",
       "      <td>0.283118</td>\n",
       "      <td>0.475627</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3999</th>\n",
       "      <td>-34.5854</td>\n",
       "      <td>0.603051</td>\n",
       "      <td>0.638960</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.1177</td>\n",
       "      <td>41.3517</td>\n",
       "      <td>1.337800</td>\n",
       "      <td>0.083163</td>\n",
       "      <td>0.179027</td>\n",
       "      <td>0.281958</td>\n",
       "      <td>0.455852</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4000 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         lp__  accept_stat__  stepsize__  treedepth__  n_leapfrog__  \\\n",
       "0    -35.7757       0.893461    0.749683          1.0           1.0   \n",
       "1    -34.7812       0.992877    0.749683          2.0           3.0   \n",
       "2    -36.1462       0.588617    0.749683          1.0           1.0   \n",
       "3    -36.1251       0.925479    0.749683          2.0           7.0   \n",
       "4    -34.5705       0.961779    0.749683          2.0           7.0   \n",
       "...       ...            ...         ...          ...           ...   \n",
       "3995 -36.3557       0.843098    0.638960          2.0           7.0   \n",
       "3996 -37.3628       0.873068    0.638960          3.0           7.0   \n",
       "3997 -35.7357       1.000000    0.638960          2.0           3.0   \n",
       "3998 -34.6662       0.996881    0.638960          3.0           7.0   \n",
       "3999 -34.5854       0.603051    0.638960          3.0           7.0   \n",
       "\n",
       "      divergent__  energy__       mu     sigma  theta[1]  theta[2]  theta[3]  \\\n",
       "0             0.0   35.9863  40.8681  1.003070  0.086296  0.270521  0.378823   \n",
       "1             0.0   36.0217  41.2299  0.988498  0.040055  0.190076  0.377527   \n",
       "2             0.0   36.2116  41.4127  0.910017  0.017782  0.140151  0.380264   \n",
       "3             0.0   36.8918  40.8130  1.449780  0.182550  0.231972  0.267669   \n",
       "4             0.0   35.8591  41.2827  1.069410  0.047761  0.184363  0.348396   \n",
       "...           ...       ...      ...       ...       ...       ...       ...   \n",
       "3995          0.0   37.4146  40.8633  0.909066  0.066853  0.277867  0.413446   \n",
       "3996          0.0   38.6741  41.2288  2.141720  0.209770  0.157043  0.183562   \n",
       "3997          0.0   37.4274  41.2382  1.730500  0.157578  0.177259  0.225282   \n",
       "3998          0.0   35.8680  41.4199  1.309920  0.071368  0.169886  0.283118   \n",
       "3999          0.0   38.1177  41.3517  1.337800  0.083163  0.179027  0.281958   \n",
       "\n",
       "      theta[4]  Y_pred[1]  Y_pred[2]  Y_pred[3]  Y_pred[4]  \n",
       "0     0.264359        3.0        7.0        9.0        9.0  \n",
       "1     0.392342        4.0        3.0       14.0        7.0  \n",
       "2     0.461803        0.0        3.0       13.0       12.0  \n",
       "3     0.317810        5.0        9.0        8.0        6.0  \n",
       "4     0.419479        0.0        2.0       13.0       13.0  \n",
       "...        ...        ...        ...        ...        ...  \n",
       "3995  0.241834        1.0       10.0       10.0        7.0  \n",
       "3996  0.449625        6.0        3.0        7.0       12.0  \n",
       "3997  0.439881        5.0        3.0        9.0       11.0  \n",
       "3998  0.475627        1.0        7.0        8.0       12.0  \n",
       "3999  0.455852        2.0        4.0        4.0       18.0  \n",
       "\n",
       "[4000 rows x 17 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ms = mcmc.draws_pd()\n",
    "ms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3a2a913d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def MAP(ms):\n",
    "    from scipy.stats import gaussian_kde\n",
    "    lo, hi = numpy.percentile(ms, q=[0.25, 99.75])\n",
    "    kde = gaussian_kde(ms)\n",
    "    xs = numpy.linspace(lo, hi, 200)\n",
    "    ys = kde.evaluate(xs)\n",
    "    return xs[numpy.argmax(ys)]\n",
    "\n",
    "best = {}\n",
    "for par in ['mu', 'sigma']:\n",
    "    best[par] = MAP(ms[par])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c4d4684a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='mu'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "boxplot_whis = [2.5, 97.5]\n",
    "seaborn.boxplot(x=ms['mu'], whis=boxplot_whis, orient='h', width=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0016165d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kCx9/39uyLmzB63s83qZNGwIDA/n4449p164dCxYsAAqeDJTUWi88b0mtfCHEw/li2zmupmbh62rHoAB3tcMxCOamJsx8tgUmGojYHcuxxNtqhyREmelN8oXv4hMSEnTKC8e83/uuHuD06dOsX7++WLmPjw9JSUkANGzYkNu3b5OSklLsvK6urlhYWJTyEoQQJTl7LY0lf8Wi0cD/9W2OaTWam/5R+brWZlSnhmgVeGftSZnyVhgsvUnew8MDV1fXYmPit2zZgqenJ87OzsWO2bdvH2+++abOjUF+fj779u3D27tghq0OHToAFA2pg4Ke99u3by/aJoQoG0VReGftSfK0CoMC3PFzq612SAbn9e7eONrW4MSlFH47flntcIQoE73v5AEmTJhAWFgYdnZ2BAcHs23bNjZu3Eh4eDgAycnJJCQk4OXlhbW1Nf3792fFihWMGzeOiRMnYmlpycqVKzl79iwREREAuLi40K9fPz788EMyMzPx8PBg6dKlpKamMmrUqIq7YiGqgbVHL3EgNhkHKwveCmmsdjgGqaaFKW/2aMzUn44za1M0IT5OWJqbqh2WEA+lVEm+f//+5OTkEBERwZo1a3Bzc2PmzJk8+eSTAGzfvp2wsDCWL19Ou3btqF27Nt9++y2ffvopH3zwARkZGfj6+vLNN9/odI774IMPsLW1ZeHChWRmZuLj48PSpUuLhucJIR5eyp1cPlofBcD00CbUriWvvsrq2TauROyOJepqGsv3xvFK58fVDkmIh6JRjGii5sIbCH298IUwZv/+9RTL9sTRxsOeNWMCq9U68ay6e62Dyu9rbXt0EsOWHsTW0oydU7vKTZOoUvTlPZn2Sggjcj6poMVpcrezXbVK8BWki3ddgrzqkJqVx9w/zqsdjhAPRZK8EEbk081n0SrwYoA7zZxt1Q7HKGg0GqaHNkGjgeV740i4mal2SEKUmiR5IYzEscTbbDp1FUtzk6JZ20T5aO5iR7+WLuTmK8zaHKV2OEKUmiR5IYzEfzdHAzC0gyeOtpZ69hYP682QxliYmbDu+BWOygQ5wkBIkhfCCOw5f4O/zt/AxtKMcV2kB3hFcKldkxEdCyb/+s+GM3qn5haiKpAkL4SBUxSFmXdb8WM6N5Te3xVofNfHsa9lzoHYZLaeSVI7HCH0kiQvhIHbcvoaxxJvU8faguEdi08zLcqPraU5k+72d/hk4xny8rUqRyTEg0mSF8KA5WsVPr3bin+1qxdWNUo1v5V4BC+188DzsVpcuJ7B9wcT1Q5HiAeSJC+EAVt75BLnktJxqV2Tge1klbnKYGFmwtReTQCYvfUs6dmy5ryouiTJC2GgcvK0hK/bBcDrPbypYSbzqleW0OZOtHKvzY30HBbuuKB2OELclyR5IQzU9wcTuHjHlkY14unXykXtcKoVjUbD2082BWDRrlhupGerHJEQJZMkL4QByszJ44ttBVOsvun0rawVrwJ/TweeaFKPO7n5LN4Vq3Y4QpRIkrwQBmjp7jhupGfjVzOaENu9aodTbRX2tF++N47kjByVoxGiOEnyQhiYlMxcFtx9DzzVaTkaacSrxs+tNsGN65KZk0/EX9KaF1WPJHkhDMzXOy+QmpVHR6/H6GhzTO1wqr2J3Qpa88v2xHE7U1rzomqRJC+EAUlKzWLp7oIW41shTVSORgC08bCnU6M6pGfnEbE7Tu1whNAhSV4IA7JwZwxZuVp6NnOkpVtttcMRdxW+m1+6O5aUO7kqRyPE3yTJC2EgkjNyWLk/Afg7qYiqoa2nA4ENHyMtK49v9sSpHY4QRSTJC2Eglu2O5U5uPl0b16W5i53a4Yh7FN54LfkrlrQsac2LqkGSvBAGIC0rl2V3W4gTunqpG4woUfuGDgR4OpByJ5fle+PVDkcIQJK8EAbh230JpGbl0a6BA/6eDmqHI0qg0Wh4rXtBa37xrhgyZE57UQVIkheiisvKzWfJXzGAtOKrug6PP0YbD3tuZeby7T5pzQv1SZIXoopbfTCRG+k5tHCxo1OjOmqHIx5Ao9EUvZtfuDOGzBxpzQt1SZIXogrLydMWzW43oasXGpnersrr3KgOfm61uZmRw6q7oyGEUIskeSGqsLVHL3E5JYtG9azp2cxR7XBEKWg0Gl57ouC1ytc7YsjKzVc5IlGdSZIXoorK1yp8tb2gFT++6+OYyEpzBqNr43q0cLHjRno23x2Q1rxQjyR5IaqojSevEHsjAzeHmjzt66x2OOIh/PPd/Nc7LkhrXqhGkrwQVZCiKMz/s6AVP7bL45iZyp+qoenetB7N6ttyLTWbnw5fVDscUU3JN4cQVdCf0UmcuZJKPZsaPNvaVe1wRBloNBrGBj8OwOJdseRrFZUjEtWRJHkhqhhFUZj3x3kARndqiKW5qcoRibJ6srkTrvY1ib2Rwe+nr6kdjqiGJMkLUcXsi0nmcMJtatcyZ1A7d7XDEY/AzNSEkUENAFi484LK0YjqSJK8EFXM/D8LWvHDOzTAqoaZytGIRzWgrRu1a5lzOOE2kXHJaocjqhlJ8kJUIUcTb/PX+RtYWZgyrIOn2uGIclDLwoyX23sAsGBnjMrRiOqm1El+3bp19O7dG19fX0JDQ1m7du0D979+/TrvvPMOXbt2pVWrVvTv35+NGzfq7HP16lUaN25c7Oepp54q08UIYei+2l7Qih8c6IFdLXOVoxHlZUigJxZmJmw9c40L19PVDkdUI6V6FrhhwwamTJnC0KFDCQoKYuvWrUybNg1LS0t69epVbP+cnBxGjRpFWloakyZNol69emzevJnJkyeTn59flMSjoqIAiIiIwMrKquh4S0vL8rg2IQxK3I0Mtpy+hrmphpEdG6gdjihHde+OkvjuQAKLd8XwcX9ftUMS1USpknx4eDihoaGEhYUB0KlTJ1JSUpgzZ06JSX7nzp1ERUWxZs0afH0LPswdO3bk8uXLLFq0SCfJ16lTh44dO5bX9QhhsCJ2x6Io0LelC/Vs5UbX2Izu1IDvDybw0+FLvN7Dm3o28v9YVDy9j+sTExNJSEigZ8+eOuUhISHExMSQmJhY7BgrKysGDBhAixYtdMobNmxIQsLfUzyeOXOGxo0blzV2IYzG7cwc1kQWTJgyqpO04o1Rw7rW9GjqSE6elm/2xKkdjqgm9Cb5mJiCjiINGuh+8Xh4FHQkiY2NLXZMYGAgH3zwgc6KWbm5uezYsYNGjRoVlUVFRZGVlcXAgQNp0aIFHTp04LPPPiM3N7dsVyOEgVq5P4E7ufl0alSHJk62aocjKsiYLg0B+HZfAhnZsgytqHh6H9enpaUBYG1trVNe+A49Pb10nUj++9//EhcXx/z58wG4c+cOCQkJpKSk8NZbb/H666+zb98+Fi5cSFJSEjNnzix2Dn9/f72x2tjYlCoeIaqK7Lx8lt1t2Y3u1FDdYESFauPhQBsPew7F32L1wURGBMlTG1Gx9CZ5RXnwVIwmJg9+GKAoCv/973/55ptvGDlyJN27dwfA1NSUiIgIXFxccHcvmPAjICAAc3NzZs+ezbhx4/D09CzlZQhhuH47doXradk0cbKhU6M6aocjKtgrnRsyZsUhlvwVy5BAD1mXQFQovUm+sGWckZGhU17Ygn9QyzknJ4fp06ezfv16Ro4cydSpU4u2WVhYEBgYWOyY4OBgZs+eTVRUVLEkHxkZ+cBY9bX0hahqFEVh8a6CV2IjgxrovOISxqlHU0ca1rEi5kYG609coW9LF7VDEkZM7y1k4bv4f3aYA4iPj9fZfq/09HSGDx/Oxo0b+de//qWT4KGgQ9/q1atJTtadASorKwsAe3v7Ul6CEIbrr/M3iLqaRl2bGvRpKcvJVgcmJhpGdy54LbNwZ4zep6VCPAq9Sd7DwwNXV1c2bdqkU75lyxY8PT1xdi7+xZSfn8+4ceM4duwY4eHhDB06tNg+qampvPfee6xbt06nfMOGDdjY2NCsWbOHvRYhDM6iXQUdV4d18KSGmSxEU130a+VCHesanLqcyp4LN9UORxixUo2TnzBhAmFhYdjZ2REcHMy2bdvYuHEj4eHhACQnJ5OQkICXlxfW1tZ8//33HDhwgAEDBuDk5MTRo0eLzqXRaPDz88PHx4du3boRHh6OVqulUaNG7NixgxUrVjB9+nTpQCeMXvTVNHaevU5Nc1NekoVoqhVLc1OGdfDg0y1nWbAzho5e0hdDVIxSJfn+/fuTk5NDREQEa9aswc3NjZkzZ/Lkk08CsH37dsLCwli+fDnt2rVj8+bNAKxevZrVq1frnMvU1JTTp08D8Nlnn/Hll1+yYsUKkpKScHd35//+7/94/vnny/MahaiSCt/FP+/vSu1aFipHIyrb4PYefLn9AjvPXufMlVSa1pehk6L8aRQjeiFU2PFOXwc9IdSWlJZF0Cd/kqvV8uebwXjWsdJ/UElW3e2oN8ho/owfjYHVx79/PcWyPXH0b+3C5y+0VDscYYD05T0ZuyGECpbviScnX0vPZo5lT/DC4I0MaoCJBtYdu0JSWpba4QgjJEleiEqWmZPHt/sLRqeMkslvqjU3h1r0aOZITr6WlfsS9B8gxEOSJC9EJfvp8CVuZ+bi51Ybfw8ZKlrdjbi74uDK/fFk5earHI0wNpLkhahEWq1CxF8Fw+ZGd5LJbwQENHDAx9mWG+k5/HbsstrhCCMjSV6ISrT1zDVib2TgUrsmvXyc1A5HVAEajYbhd1vzEbvjZHIcUa4kyQtRiRbfnfxmRFADmbNcFHnarz51rC04cyWVfTHJ+g8QopTkW0aISnLyUgoH4pKxqWHGgLZuaocjqpAaZqYMbl+wfHfE7uLLdwtRVpLkhagkhV/eL7R1w7pGqeahEtXIS+08sDA1YeuZa8TfzNB/gBClIEleiEpwPS2bdceuoNHA0EBPtcMRVVDhIkWKAt/siVc7HGEkJMkLUQlW7U8gJ19L96aOuD9WS+1wRBU1vKMnAD9EJpKWlatuMMIoSJIXooLl5GmLJr8p/BIXoiQ+zna0b+hAenYeayIvqh2OMAKS5IWoYOtPXOZ6WjaNHW0IbPiY2uGIKq5wON2yPXHka2U4nXg0kuSFqECKorB0dxxQ0IqXyW+EPt2bOuLmUJOE5Ey2nbmmdjjCwEmSF6ICHU64zfGLKdjXMueZVi5qhyMMgKmJhmEdCifHkeF04tFIkheiAi29+yU9MMAdS3NTlaMRhuIFf1esa5ixLyaZU5dT1A5HGDBJ8kJUkCspd9h48iqmJhpeDvRQOxxhQGwszXne3xWg6HWPEGUhSV6ICrJibzz5WoXQ5k7Ut6updjjCwAzr4IlGA78eLei4KURZSJIXogJk5ebz3YGC9cFl2JwoC4/HrOjetGCt+VX7Za15UTaS5IWoAGuPXOJWZi6+rna0dpc140XZFN4grtgXT06eVt1ghEGSJC9EOVMUhWV74gAZNiceTWDDx2jiZMON9Gw2nLiidjjCAEmSF6Kc7Y25SdTVNOra1KB3C2e1wxEGTKPRMKyDJwBL7944CvEwJMkLUc4Ke0MPbueBhZn8iYlH07elC3Y1zTmWeJsjCbfUDkcYGPkGEqIcJdzMZOuZa1iYmjConbva4QgjUNPClBcD3ACKXgMJUVqS5IUoR9/sjUNR4Cm/+tS1qaF2OMJIvNzeAxMNbDhxhaTULLXDEQZEkrwQ5SQjO48fDiYCMOLuIiNClAdX+1r0aOZIbr7CShlOJx6CJHkhysnPhy+Slp2Hv4c9zV3s1A5HGJnC+exX7k+Q4XSi1CTJC1EOdIfNSStelL/2DR1kOJ14aJLkhSgHf52/wYXrGTjZWtLTx1HtcIQRkuF0oiwkyQtRDpbdHTb3cqAH5qbyZyUqhgynEw9Lvo2EeETxNzP4IzoJCzMTXmzrpnY4wojJcDrxsCTJC/GIlu+NR1Ggj58zj1nLsDlRsWQ4nXgYkuSFeAQZ2Xn8EFkwbK7wfakQFUmG04mHIUleiEfw85FLpGXJsDlRuWQ4nSitUif5devW0bt3b3x9fQkNDWXt2rUP3P/69eu88847dO3alVatWtG/f382btxYbL9vvvmGHj164OvrS79+/dixY8dDX4QQalAUhW/uvhcdKq14UYlkOJ0orVIl+Q0bNjBlyhSCgoKYP38+AQEBTJs2jU2bNpW4f05ODqNGjWLPnj1MmjSJefPm0bx5cyZPnsy6deuK9lu8eDEzZ86kX79+zJ07Fzc3N8aPH8/Ro0fL5eKEqEi7z9/kfFI6jrY16NXcSe1wRDWi0WiKbixlOJ14ELPS7BQeHk5oaChhYWEAdOrUiZSUFObMmUOvXr2K7b9z506ioqJYs2YNvr6+AHTs2JHLly+zaNEinnrqKTIzM/n6668ZMWIE48ePB6Bz5868+OKLzJs3j8WLF5fXNQpRIQp7Nw9uJ8PmROV7pqULn2yMKhpO18rdXu2QRBWk95spMTGRhIQEevbsqVMeEhJCTEwMiYmJxY6xsrJiwIABtGjRQqe8YcOGJCQUdBQ5duwYaWlpOufVaDT06NGDvXv3kpOTU6YLEqIyJNzMZFtUwWpzA2W1OaECGU4nSkNvko+JiQGgQQPdqTo9PDwAiI2NLXZMYGAgH3zwARqNpqgsNzeXHTt20KhRI53zNmzYsNh58/LySrx5EKKqWLHv79Xm6siwOaESGU4n9NH7uD4tLQ0Aa2trnXIrKysA0tPTS/WL/vvf/xIXF8f8+fN1jis8z73nzcjIKHYOf39/vbHa2NiUKh4hyiozJ4/Vd1ebG95B5qkX6ikcTrf51DVW7k/g9R7eaockqhi9LXlFUR58ApMHn0JRFGbNmsU333zDyJEj6d69e6nO+8+nAEJUJf87conUrDxau9emhasMmxPq+udwuuy8fJWjEVWN3pZ8Ycv43pZ1YUv8QS3nnJwcpk+fzvr16xk5ciRTp04t8bz/fErwoPNGRkY+MFZ9LX0hHtU/h80Nk9XmRBVQOJwu6moaG05coV8rV7VDElWI3pZ84bv4wg5zheLj43W23ys9PZ3hw4ezceNG/vWvf+kkeH3ntbCwwNnZuZSXIETl2XvhJmevpVPPpgahMmxOVAE6q9PtjtP7lFRUL3qTvIeHB66ursXGxG/ZsgVPT88Sk3F+fj7jxo3j2LFjhIeHM3To0GL7tGrVilq1arF58+aiMkVR+P3332nbti0WFhZluR4hKlTRsLn2MmxOVB19W7pQu5Y5xy+mcCTxttrhiCqkVOPkJ0yYQFhYGHZ2dgQHB7Nt2zY2btxIeHg4AMnJySQkJODl5YW1tTXff/89Bw4cYMCAATg5OelMbqPRaPDz86NmzZqMGDGCL7/8ElNTU/z8/Pjpp584deoUy5cvr5CLFeJRJCZnsvXM3WFzATJsTlQdNS1MebGtO1/vuMCy3XG0ljHz4q5SJfn+/fuTk5NDREQEa9aswc3NjZkzZ/Lkk08CsH37dsLCwli+fDnt2rUrap2vXr2a1atX65zL1NSU06dPAwU3D6ampvzwww8sXrwYLy8vvvzyS9q0aVOe1yhEuVixLx6tAr1961PXRobNiarl5UAPFu68wIYTV3i7d1McbS3VDklUARrFiF7gFHa809dBT4iHlZmTR/v/bCM1K49fJnTEz6222iEVWHV3FMogo/kzfjTVvD7GfXuIjSevMumJRrwhw+mqBX15T14qClEKPx/+e9hclUnwQtyjsAPeqv3xMpxOAJLkhdBLUZSiDnfDZdicqMICGhSuTpfD+uOyOp2QJC+EXn+dv8H5pHScbC1ltTlRpWk0GoZ39ARkOJ0oIEleCD2W7Y4DCjo2ybA5UdUVDqc7cSmFwwm31Q5HqEy+sYR4gLgbGfwRnYSFmQkvtnVTOxwh9LI0Ny0a4imr0wlJ8kI8wDd7C1abe6alM4/JanPCQAxu74GpiYaNJ65wNUVWp6vOJMkLcR9pWbmsibwI/L0IiBCGwKV2TUJ8HMnTKqzcH692OEJFkuSFuI8fD10kPTuPdg0caOZsq3Y4QjyUwhvTVfsTyMqV4XTVlSR5IUqg1f692lxhb2UhDElbT3ua1bflZkYO62Q4XbUlSV6IEmw/m0TczUxcateke1NHtcMR4qFpNBqG3b1B/WaPDKerriTJC1GCpXeHzQ0J9MBMhs0JA9XHzxkHKwtOXErhUPwttcMRKpBvLyHucT4pjV3nblDTvGBlLyEMVcFwuoKhn4U3rqJ6kSQvxD0Kxxb3a+2CXS1zdYMR4hG93N4TMxMNG09e4eKtTLXDEZVMkrwQ/5CSmctPhy4BMPzuYh9CGDInO0t6+9ZHq8DyvTKcrrqRJC/EP/wQmcid3HyCvOrQyNFG7XCEKBcj7i6s9N2BBDKy81SORlQmSfJC3JWvVfhmbxwgw+aEcfFzq42/hz1pWXn8eOii2uGISiRJXoi7tp65xsVbd/B4rBZdG9dTOxwhytWIoILW/NLdsWi1MpyuupAkL8RdS3fHAjA00BMTE43K0QhRvno2c8Sldk3ibmbyZ3SS2uGISiJJXgjg5KUU9sUkY13DjOf8XdUOR4hyZ2ZqwrC7nUmX/BWrbjCi0kiSF4K/v/QGtHXD1lKGzQnj9EJbN2pZmLLnwk3OXElVOxxRCSTJi2rvakoWvx27jImGopaOEMbIrqY5z7cpeFJV+HpKGDdJ8qLaW743jjytQmjz+rg51FI7HCEq1LCODdBoYO3Ry9xIz1Y7HFHBJMmLai0zJ4+V+xOAv3sfC2HMGtSx4okm9cjJ07JyX4La4YgKJkleVGs/Hb5Eyp1cWrnXpo2HvdrhCFEpCifHWbEvnuw8WWvemEmSF9WWVqsQcbfD3aighipHI0TlCXz8MZo42XAjPZvfjsla88ZMkryotv6ISiL2RgYutWsS4iNrxovqQ6PRFL2eivgrVtaaN2KS5EW1tfivGKBgCltZM15UN338nKljbcHpK6nsj01WOxxRQeSbTVRL/5z85oW2bmqHI0SlszQ35aV2HoBMjmPMJMmLailCJr8Rgpfau2NhasLWM9eIv5mhdjiiAkiSF9XOtdQsfpXJb4Sgno0lT/s5oyiwdHec2uGICiBJXlQ7hZPf9GruJJPfiGpvRJAnAD9EJpKSmatuMKLcSZIX1co/J78ZKcPmhMDH2Y4grzpk5uTz7f54tcMR5UySvKhWfjp8iduZMvmNEP/0SueCG96lu+PIypXJcYyJJHlRbfxz8puRMoWtEEU6NapD0/q23EjPZu2RS2qHI8pRqZP8unXr6N27N76+voSGhrJ27dpS/5KZM2cybNiwYuWRkZE0bty42M+YMWNKfW4hSuvP6L8nv+nl46R2OEJUGRqNhjF3W/MLd8Wg1crkOMbCrDQ7bdiwgSlTpjB06FCCgoLYunUr06ZNw9LSkl69ej3w2BUrVhAREUFgYGCxbdHR0dSqVYulS5fqlNva2j7EJQhROot3FbTiZfIbIYrr7VufWZuiiLmewbaoJHo0k1kgjUGpknx4eDihoaGEhYUB0KlTJ1JSUpgzZ859k/y1a9eYNWsWGzZswMbGpsR9oqKiaNSoES1btixb9EKU0omLKeyNuYmVhalMfiNECcxNTRgR1IAP159h4c4LkuSNhN7mTGJiIgkJCfTs2VOnPCQkhJiYGBITE0s8Ljw8nNOnT7N06VKaNm1a4j5nzpyhcePGZQhbiIfz9Y4LAAxq5y6T3whxHy8GuGNjacbBuFscTrildjiiHOhtycfEFMzv3aCBbkclD4+C6RBjY2NxcyveMho1ahQNGzbExMSE+fPnF9uen5/PuXPnsLe3p1+/fpw7d446deowZMgQhg8fjkajKXaMv7//A2NNS0u771MDUX3F3chg48krmJtqZNicEA9gXcOMwe09+Gr7BRbuiOHrl9uoHZJ4RHpb8mlpaQBYW1vrlFtZWQGQnp5e4nFeXl6YmNz/9HFxcWRlZREbG8vo0aNZtGgR3bt3Z9asWcydO7fUFyCEPgt3xaBV4JmWLjjZWaodjhBV2vAOnliYmrD59FVib8hUt4ZOb0te3xKED0rkD+Lo6MiiRYto2rQpdevWBSAwMJCsrCwWLVrEiBEjit1YREZGPvCc+lr6ovpJSsvix0MXARjTRVrxQuhTz9aSZ1o580PkRRbviuGjfi3UDkk8Ar0ZuvDxd0aG7h1dYQu+rI/Hra2t6dy5c1GCLxQcHExOTg6xsbIqknh0y3bHkZOnpWczR7zqyascIUqjcHKcHw9d5EZ6tsrRiEehN8kXvotPSEjQKY+Pj9fZ/rCio6NZtWoVubm6cyVnZWUBYG8vs5GJR5OWlcuKfQWf07HBj6scjRCGw6ueDU80qUd2npble2WqW0OmN8l7eHjg6urKpk2bdMq3bNmCp6cnzs7OZfrF8fHxzJgxg507d+qUb9iwAVdXV1xcXMp0XiEKrdqfQFpWHgENHGjtLjeNQjyMwtb8ir1x3MmRqW4NVanGyU+YMIGwsDDs7OwIDg5m27ZtbNy4kfDwcACSk5NJSEjAy8ur2Hv0+wkODsbHx4d3332X5ORknJyc+O233/jjjz+YO3duib3rhSit7Lx8ltydwnZcF2nFC/GwAho44OdWm2OJt1lzKJEhgZ5qhyTKoFS95vr378+MGTP466+/mDBhAgcPHmTmzJk8+eSTAGzfvp0BAwZw6tSpUv9iCwsLFi9eTPfu3Zk3bx7jx4/n/PnzzJs3jx49epTtaoS4a+2RSySlZdPEyYbgxnX1HyCE0PHPqW4X74olX6a6NUgaRV/3eQNS2LteXy98Ydy0WoXu4TuIuZ5B+AA/+rVyVTukirPq7hOvQUbzZ/xopD7KVb5Wodtn24m/mcn8Qa3p7Vtf7ZDEPfTlPZnAWxidLaevEXO9YCGap3zL1mdECAGmJhpG3V2xceHOC3qHVIuqR5K8MCqKohRNYTuqUwPMZSEaIR7Jc23ccLCy4NjFFPZcuKl2OOIhyTegMCr7Y5M5mngb+1rmDJCFaIR4ZDUtTBl5tzU/949zKkcjHpYkeWFUClvxQwI9qWVRqsEjQgg9hgR6YGtpxr6YZA7GJasdjngIkuSF0ThzJZXt0dexNDdhaAdPtcMRwmjYWJoz7O7f1Lw/zqsbjHgokuSF0Shsxb/Y1h0HKwuVoxHCuAzv2IBaFqbsOHud4xdvqx2OKCVJ8sIoJCZnsu74FUxNNEXvD4UQ5cfeyoKX2xcsMS6tecMhSV4Yha92XCBfq9DHzxk3h1pqhyOEURrZqQE1zEzYcvoaUVdT1Q5HlIIkeWHwLt2+w5rIRDQamNBVprAVoqLUs7FkYIA7APP/vKByNKI0JMkLg/fV9vPk5is85essy8kKUcFe6dwQc1MN649fJuZ6utrhCD0kyQuDdiXlDj8cvIhGA5O6eakdjhBGz7l2TZ5r44pWga+2S2u+qpMkLwzaV9svkJOv5ckW9WnkKK14ISrDuC5emJpo+N+RSyQmZ6odjngASfLCYF1NyeL7A4kATOrWSOVohKg+3B+rRV8/Z/K0Cgt2Smu+KpMkLwzW1zsKWvGhzZ1o7CSteCEq0/iuj6PRwA8HL3ItNUvtcMR9SJIXBikpNYtVBxIAmPSEtOKFqGxe9WwIbe5ETr6WhTtj1A5H3IckeWGQvt4RQ06elhAfR5rWt1U7HCGqpQldCzq7rtwfz830bJWjESWRJC8MTlJaFiv3xwPSihdCTT7OdjzRpB5ZuVqW/BWrdjiiBJLkhcFZuCOG7DwtPZo54uNsp3Y4QlRrE+4OXV2+N56UzFyVoxH3kiQvDMr1tGy+vduKf01a8UKorrW7PUFedUjPzmPRLnk3X9VIkhcGZdHK/5KVq6V703o0d5FWvBBVwes9vAGI2B3LDXk3X6VIkhcG40Z6NivimwLyLl6IqqSNhz1PNKlHZk4+X8qc9lWKJHlhMBbtiuGOYklXm4P4utZWOxwhxD+82bMxAN/uj+dKyh2VoxGFJMkLg5CckcOKvXffxTt+p3I0Qoh7NXO25Snf+uTkaflim6w3X1VIkhcGYeHOGDJz8uliE0nLWmfVDkcIUYLXe3hjooEfIhOJu5GhdjgCSfLCAFxLzWLZnoIxuJMdV6kcjRDifh6va81zbVzJ1yrM3io341WBJHlR5c3eeo6s3ILZ7VpJK16IKm3SE40wN9Xwy7HLRF9NUzucak+SvKjSLlxP54fIREw08FZIE7XDEULo4Wpfi0EB7igKfLYlWu1wqj1J8qJK+3RzNPlahRf83fCqZ612OEKIUpjQzQtLcxO2nL7GscTbaodTrUmSF1XW0cTbbDx5lRpmJkzu7q12OEKIUqpnY8mwDg0A+FRa86qSJC+qJEVR+GTjGQCGd2yAk52lyhEJIR7G2C4Nsalhxq5zN9gXc1PtcKotSfKiStpx9jr7YpKxtTRjXJfH1Q5HCPGQateyYHTnhkDBazdFUVSOqHqSJC+qHK1WYeamgkd8E7p6YVfLXOWIhBBlMSKoAQ5WFkTG32J79HW1w6mWJMmLKue345c5cyWV+naWDO3gqXY4Qogysq5hxvjggidxn26JRquV1nxlK3WSX7duHb1798bX15fQ0FDWrl1b6l8yc+ZMhg0bVqw8Ly+P2bNn06VLF/z8/Bg0aBDHjx8v9XmF8cnJ0xZ11JncvRGW5qYqRySEeBSD23vgaFuDU5dT2XjyqtrhVDulSvIbNmxgypQpBAUFMX/+fAICApg2bRqbNm3Se+yKFSuIiIgocdtHH33EsmXLGD16NOHh4ZiamjJs2DASExMf7iqE0Vi1P57E5Dt41bPm2dauaocjhHhEluamRatGztwURXZevsoRVS+lSvLh4eGEhoYSFhZGp06dmDFjBqGhocyZM+e+x1y7do0333yT//znP9jY2BTbfvHiRVavXs20adMYPHgw3bp1Y8mSJdjZ2bF48eKyX5EwWOnZecz9o2Bhi7dCGmNmKm+ThDAGA/zd8Ha0JiE5k2W749QOp1rR+y2amJhIQkICPXv21CkPCQkhJibmvq3u8PBwTp8+zdKlS2natGmx7fv27SM/P5+QkJCiMgsLC4KDg9m5c+fDXocwAot2xnAzI4fW7rXp2cxR7XCEEOXEzNSEd3o3A2DuH+e5npatckTVh94kHxMTA0CDBg10yj08PACIjY0t8bhRo0axfv162rdvf9/z2tnZ4eDgUOy8ly9fJisrS3/0wmjcSM9m8a6Cz9r00KZoNBqVIxJClKfO3nXp1qQe6dl5fP67rEFRWcz07ZCWVrDAgLW17pSiVlZWAKSnp5d4nJeX1wPPm56eXuyc/zxvRkYGlpa6E6D4+/vrjbWkVwOi6pv3x3kycvLp1qQeAQ0c9B8ghDA4/3qyKTvPXmf1wQRebu9BM2dbtUMyenpb8vomMDAxKdt7U33nlZZc9XE+KY1v98Wj0cDUXo3VDkcIUUG86lnzcqAHWgX+b91pmSCnEuhtyRe2jDMyMnTKC1vwZW05W1tbFzvnP89bUis/MjLygefU19IXVY+iKPz719PkaRUGBrjTxEnu7IUwZq890Yj/HbnE3pib/H76Gj19nNQOyajpbYYXvotPSEjQKY+Pj9fZ/rAaNmzI7du3SUlJKXZeV1dXLCwsynReYVg2n7rKX+dvYFfTnLdCpBUvhLGrXcuCyXeH1P1nwxly8rQqR2Tc9CZ5Dw8PXF1di42J37JlC56enjg7O5fpF3fo0AGAzZs3F5Xl5OSwffv2om3CuN3Jyef/1hUsQvNmT28crOTGTojq4KX2Hjxe14q4m5ks3xundjhGTe/jeoAJEyYQFhaGnZ0dwcHBbNu2jY0bNxIeHg5AcnIyCQkJeHl5lfiYvSQuLi7069ePDz/8kMzMTDw8PFi6dCmpqamMGjWq7FckDMbXOy5w6fYdmjjZMCjAXe1whBCVxNzUhHeeasbwpQeZs+0c/Vq58Jh1DbXDMkqlSvL9+/cnJyeHiIgI1qxZg5ubGzNnzuTJJ58EYPv27YSFhbF8+XLatWtX6l/+wQcfYGtry8KFC8nMzMTHx4elS5cWDc8TxisxOZOvd1wAYEYfH5n4RohqpmvjenTxrsuOs9cJ33qWD59poXZIRkmjGFH3xsKOd/o66An1jVkRyeZT1+jj58wXA1uV/sBVd0ddDDKaj+2jkfrQJfVhUM5dS6PXnF0oisLG1zrT2EmGQD8sfXlPmk+i0u06d53Np65Ry8KUfz1ZfDZEIUT10MjRhpfaucuQugokSV5Uqpw8Lf/+9RQAr3bzwsnOUs8RQghjNrm7N7aWZvx1/gZ/RCWpHY7RkSQvKtU3e+K4cD2DBnWsGBlUtuGXQgjj4WBlwWvdvYGC1nxWrqxSV54kyYtKk5SaxZxt5wB476lm1DCTteKFEPByew+8Ha2Ju5nJvLsrUYryIUleVJpPNkWRnp3HE03q0bVJPbXDEUJUERZmJnzcv6B3/dc7LhB9NU3liIyHJHlRKQ7FJ/Pz4UtYmJrw7lPN1A5HCFHFtPFwYHB7d/K0CtN/Po5WK53wyoMkeVHh8rUK79/tbDe6cwM861ipHJEQoiqa2qsJ9WxqcCThNiv3x6sdjlGQJC8q3LI9cZy8lEp9O0smdH3wEsRCiOrL1tKcD/r6ADBzUzRXU7JUjsjwSZIXFSr+Zgb/3RwFwAd9m1PLolSTLAohqqkQHyd6NHMkPTuP9389qXY4Bk+SvKgwWq3C1B+Pk5WrpW9LZ3o0c1Q7JCFEFafRaPigrw9WFqZsPnWNTSevqh2SQZMkLyrMygMJ7I9Npo61Bf9+2kftcIQQBqK+Xc2ipaf//esp0rJyVY7IcEmSFxXi4q1MPtlQsIzsB32bYy/LyAohHsLLgZ74udXmamoWn26OVjscgyVJXpQ7RVEI+/kEGTn5hDZ34skW9dUOSQhhYExNNHzSvwVmJhqW74vncMIttUMySJLkRblbE3mRXeduULuWOR/0ba52OEIIA9W0vi2jOzdEUSDspxPk5mvVDsngSJIX5epqShb/t/40AP9+2oe6NjVUjkgIYchee6IRHo/VIvpaGgt3xqgdjsGRJC/KjaIovP2/E6Rl5dG9aT36tnRWOyQhhIGzNDflo2cKpryds+0c567JlLcPQ5K8KDe/HL3MtqgkbCzN+PCZFmg0GrVDEkIYgaBGdXi+jSs5eVomfX+U7DxZqa60JMmLcnE9LZt//1Ywde27vZvJOvFCiHL1fh8fPB6rxZkrqdLb/iFIkhfl4r1fTnI7M5dOjerwvL+r2uEIIYyMdQ0zZg9oiamJhkW7Ytl17rraIRkESfLikf127DIbT17FysKUT571lcf0QogK0crdnslPNALgzR+OkZyRo3JEVZ8kefFI4m9mEPbzCQD+1bspLrVrqhyREMKYje/qRVtPe5LSspn203EURZakfRBJ8qLMsvPyeXXVEdKz8wht7sSgAHe1QxJCGDlTEw2fv9ASmxpm/H76Gt8fTFQ7pCpNkrwos483RHHiUgpuDjXlMb0QotK4OdTiw34FE2198NtpLlxPVzmiqkuSvCiTzaeusmxPHOamGuYNbI1dTXO1QxJCVCN9W7rwTEtn7uTmM/n7o+TkyWx4JZEkLx5aYnImb605BsC0Xk3wc6utbkBCiGrpg2ea42pfkxOXUgjfelbtcKokSfLioeTma5n43RFS785qNzKogdohCSGqKVtLc2YPaImJBr7ecYE9F26oHVKVI0lePJT/bo7maOJtnO0s+fR5P3kPL4RQlb+nA692a4SiwBurj3E7U4bV/ZMkeVFqf0RdY+HOGExNNHwxsBW1a8ka8UII9U3q5kUr94K15yd+d4Q8Wa2uiCR5USpXUu7w5g8F7+Hf7OmNv6eDyhEJIUQBM1MT5g5shYOVBbvO3WCWTHtbRJK80CsvX8uk745wKzOXzt51Gdv5cbVDEkIIHa72tfjypdaYmWhYuDOGtUcuqR1SlSBJXuj16ZazHIy7RT2bGnz+gh8mJvIeXghR9bRv+Bjv9/EBYNpPxzl+8ba6AVUBkuTFA/146CJf77iAiQbmvNiKOtY11A5JCCHua3A7dwYGuJOdp2XMikMkpWWpHZKqJMmL+9p74SZhPx8HYEYfHwIff0zliIQQ4sE0Gg0z+vjg72HPlZQsxn17uFqvP1/qJL9u3Tp69+6Nr68voaGhrF279oH7Z2RkMGPGDDp27EirVq0YPXo0cXFxOvtERkbSuHHjYj9jxowpy7WIchRzPZ2x3x4iN19hRMcGvBzoqXZIQghRKhZmJnw1uA317Sw5FH+L9385VW0XsjErzU4bNmxgypQpDB06lKCgILZu3cq0adOwtLSkV69eJR7z+uuvc+LECaZOnYqVlRXz5s1jyJAhrF+/HhsbGwCio6OpVasWS5cu1TnW1tb2ES9LPIpbGTmMWHaQlDu5dG9aj7d7N1U7JCGEeCh1bWqw4OU2PP/1Xr4/mIiPs221bKyUKsmHh4cTGhpKWFgYAJ06dSIlJYU5c+aUmOQjIyPZsWMHixYtonPnzgD4+/vzxBNP8N133/HKK68AEBUVRaNGjWjZsmU5XY54VNl5+YxZcYi4m5k0q2/LnBdbYSod7YQQBsjXtTYzn/Vl8uqjzPjtNF71bKrda0e9j+sTExNJSEigZ8+eOuUhISHExMSQmFh8mb/du3djZWVFx44di8ocHBxo27YtO3fuLCo7c+YMjRs3fpT4RTlSFIWwn05wIC4ZR9saLBnmj1WNUt0HCiFElfRMKxfGdG5InlZhwqrDJCZnqh1SpdKb5GNiYgBo0EB3jnIPDw8AYmNjSzzGw8MDU1NTnXJ3d/ei/fPz8zl37hxXr16lX79+NG/enODgYCIiIqrtuxO1zfvjPD8fuURNc1OWDG1LfbuaaockhBCPbGqvJnTxrktyRg7Dlh7gZnq22iFVGr3NtLS0NACsra11yq2srABITy++jm96enqx/QuPKdw/Li6OrKwsYmNjeeONN7C3t2fbtm3MmjWL9PR0Jk2aVOx4f39/vbEWvu8XD+eXo5f47PezaDTwxcBWNHexUzskIYQoF4VTcQ9YsJeoq2kMiTjAd6+0x9bS+JfI1pvk9bWqTUyKPwx40DGF+zs6OrJo0SKaNm1K3bp1AQgMDCQrK4tFixYxYsSIEm8URPk7FJ/MWz8WDJV7p3czejRzVDkiIYQoX3Y1zVk+MoDnv97LqcupjFx2kOUj2lHTwlT/wQZMb5IvbBlnZGTolBe2yEtqOVtbW3Px4sVi5RkZGUWJ29rauqhT3j8FBwezZs0aYmNjadGihc62yMjIB8aqr6Uviou6msqobyLJydMyuL07Izp6qh2SEEJUiHo2lnw7sh0vLNjLwbhbjP32EIuG+GNhZrxTxui9ssJ38QkJCTrl8fHxOtvvPSYxMbFYiz4+Pr5o/+joaFatWkVubq7OPllZBbMT2dvbl/YaRBlFX01j0KL93MrMpVuTevz7aR9ZOlYIYdTcHGqxYmQ7HrOyYMfZ60xebdyr1ulN8h4eHri6urJp0yad8i1btuDp6Ymzs3OxY4KCgkhNTWXPnj1FZcnJyURGRtKhQwegIOHPmDFDp7c9FIzJd3V1xcXFpUwXJErn7LU0Bi3aR3JGDl286xYs7GBqvHezQghRyKueNd+MCMCmhhkbTlwl7OcTaLXG2eG7VOOjJkyYQFhYGHZ2dgQHB7Nt2zY2btxIeHg4UJDAExIS8PLywtramrZt2xIQEMAbb7zBlClTqF27NnPnzsXGxoaBAwcCBY/lfXx8ePfdd0lOTsbJyYnffvuNP/74g7lz50qLsgKdu5vgb2bk0KlRHRa83AZLc+N+LyWEEP/U3MWOiOFteXnJftYcuoiNpTnvPtXU6HJPqZpu/fv3Z8aMGfz1119MmDCBgwcPMnPmTJ588kkAtm/fzoABAzh16lTRMfPmzaNbt27MmjWL6dOn4+TkxLJly7CzK+i1bWFhweLFi+nevTvz5s1j/PjxnD9/nnnz5tGjR48KuFQBcD4pnYGL9nMjvSDBLxriLwleCFEttfV0YMHL/pibaojYHcvsrefUDqncaRQjGpRe2PFOXwe96urC9XReXLiP62nZdPR6jMVD2hpez9JVd++yBxnNx/bRSH3okvoQZbDxxBUmrDqMVoG3n2zK6M4N1Q6p1PTlPXkJW03EXE9n4N0EH9jQQBO8EEJUgNAW9Zn5rC8AH204w+e/nzWaSdkkyVcDsTcyGLhoH0lp2bRv6MCSYf6S4IUQ4h+e93dj5rMtMNHAF9vO8a//nSTfCDrjSZI3cueupTFw4T6upWYT0MCBiGFtqWUh89ELIcS9BrR15+vBbahhZsJ3BxIYv/IQWbmGvRa9JHkj9te5G/T/ag9XU7MI8HRgqSR4IYR4oJ4+TqwY2Q5bSzM2n7rGkIgDpNzJ1X9gFSVJ3kh9fyCBYUsPkJaVR2hzJ74ZESArygkhRCkENHDgh7GBONrW4EBsMgMW7CUpNUvtsMpEkryR0WoVPtkYxfSfT5CnVRjTpSHzB7WWd/BCCPEQmjjZ8tO4DjSsa0XU1TT6f7WH2BsZ+g+sYiTJG5Gs3Hxe/e4wX++4gKmJho/7tyAstCkmJsY1uYMQQlQGV/ta/Di2A35utbl46w7PfbWH4xdvqx3WQ5EkbySup2UzYOE+Npy4ik0NM74ZHsDAAPdSHXvy5EmGDx9Oy5Ytad26NWPHjiUmJqZoe0ZGBpMmTcLPz48+ffqUOB5z1apV9OzZk7y8vHK7psqwa9cuBg0ahJ+fH61atWLYsGEcPXpUZ5/ExEReffVVAgICCAgIYOrUqSQnJ+vsYyx1tHfvXgYOHEirVq3o1KkTH330UbHFqUpTZ8ZSH4WioqJo3rw5c+fO1SmvTp+N6srByoJVo9rR2bsuNzNyeHHhPtYdv6x2WKUmSd4InL2WxjPzd3Ms8TYutWvy0/gOBDWqU6pjY2JiePnll4mOjmb8+PGMHTuWY8eOMWjQIK5duwbAggUL2LNnD5MnT8bZ2Znx48eTmppadI6cnBwWLFjA+PHjMTMznPf+Bw4cYPTo0aSlpfH6668zYcIEEhISGDx4MMePFyy9e+vWLYYOHcrRo0cZNWoUw4cP548//mD48OHk5OQUncsY6mjv3r2MGDGC3NxcpkyZQt++fVm9ejWjRo1Cqy1YwKM0dQbGUR+F8vLyCAsLK7aYVnX6bFR3VjXMWDzEn/6tXMjMyefVVUf496+nyMkzgIVtFCPSpk0bpU2bNmqHUam2nr6qNH9vk+IxbZ3Sd95fSlJq1kMd/9577yne3t7KqVOnisqOHTumeHt7K5988omiKIryxBNPKJ9++qmiKIqSlpam+Pj4KL/++mvR/suXL1d69uyp5OXllcMV6bGSgp9y0LdvXyU4OFjJzMwsKrt+/brStm1bZdiwYYqiKMrnn3+uNG3aVDl//nzRPrt371a8vb2V1atXF5WpVkflWB/9+vVTunbtqty5c6eo7Ntvv1W8vb2V7du3K4pSujpTFOOoj0Lz5s1TfHx8FG9vb+WLL74oKq/ynw1R7rRarbJsd6zi9a/1Rd+5F29l6j+wAunLe9KSN1BZufm8/8tJRn4TSVp2Hk+2cOL7V9pT16bGQ53n4sWL2Nvb06xZs6IyX19fateuzdmzZwG4du0arq6uAFhbW1O7dm2uXr0KQHZ2NgsXLmTChAmYmhpO576UlBSioqLo1asXNWvWLCqvU6cObdu25ciRIwCsX7+egIAAHn/88aJ9OnToQIMGDVi/fn1RmaHXUXZ2Nvb29rzwwgtYWloWlQcEBAAFS0OXts7A8OujUHR0NF999RXjx48vtq26fDbE3zQaDUM7ePLDmEBcatfkaOJten+xiz+jk9QO7b4kyRug6Ktp9J23m2/2xmNuqiEstAnzBrYu00IzHh4epKSk6LxHvH37NmlpadSrVw8Ae3t70tLSANBqtaSnp2Nvbw/Ad999h7W1NU899VQ5XFnlsba2ZtOmTQwbNqzYtlu3bmFqakpKSgqJiYn4+PgU28fHx0dnQSZDr6MaNWqwZMkSxo4dq1N+5swZAJydnUtVZ4UMvT7g78f0HTt2pE+fPjrbqtNnQxTXyt2edRODCG5cl9uZuQxfepDPtkRXyRnyJMkbEEVR+GZPHE/P+4voa2k0qGPFz+M6MqbL42XuQT9q1CicnJx44403iIqKIjo6mjfffBNzc3NefvllANq2bcvPP//M+fPnWbZsGbm5uQQEBJCVlcXixYuZMGECJiaG9VEyNTXF09MTR0dHnfKoqCgOHz5Mq1ativok3LsPQN26dUlLSyv68ja2Orp06RI///wzH330Ed7e3vTo0aNUdVbIGOpj0aJFxMfHM2PGjGLbqvNnQxSwt7IgYmhbpvT0xkQDc/84z8tL9nM9LVvt0HRILw8DcTM9m6k/HmdbVMFjoQH+brz3dLNHnuDG2dmZMWPG8H//93/07dsXKEiAX3zxRdEj/MmTJzNixAh69+6NiYkJ06ZNw93dnYiICGxtbYuWHDZ0GRkZTJs2DYBXXnmlqFf5Px9NF6pRo+C1SGZmJjY2NkZVR7dv36Zbt25AwbW/8847Rdd7r3vrrJCh18e5c+eYP38+7733Hk5OTly8eFFne3X9bAhdJiYaXu3WiNbu9kz6/gh7Ltyk9xe7+PR5Pzp711U7PECSvEHYefY6b645xvW0bGwtzfjkWV+ebFG/XM49e/ZsvvrqKwICAnjhhRfIz8/n+++/Z/LkyXzxxRd069YNNzc31q9fz9mzZ3F0dKRu3bpkZmayePFi3nnnHUxMTFi7di1ff/012dnZ9O/f3+BaJ3fu3GHcuHFERUUxZswYAgICOHz4sN7jNJqCJyjGVEcajYbw8HBycnJYsWIFw4cPJzw8nJCQEJ39SqqzQoZcH/n5+UyfPp02bdrwwgsvlLiPUooVyozxsyFK1sGrDusndWLiqiMciEtmSMQB+rVy4Z3eTXnM+uH6SZU3SfJVWGpWLp9vOcuyPXFAwVSLswe0xLl28dZDmc6fmsqSJUto3rw5y5YtK3qn2rt3b5577jneffddgoKCsLCwwMLCgubNmxcd++233/LYY48RGhrKuXPnmD59Ou+++y7u7u68+eabODo63vcLsqpJTU1lzJgxHD58mGeffZbXX38dgFq1agEFnaPuVVhmbW1dVGYsdWRnZ1fUuuzVqxdPPfUUH3/8sU6Sv1+d/ZOh1seSJUuIjo5m1apVRX1VCoe83blzh+Tk5Gr72RD352hryarR7Vi0K5bZW8/yvyOX2B6dxDu9m9G/tUvRTV9lk1vFKkirVfjx0EW6fbqdZXviMDXR8FZIY74b3b7cEjxAXFwcOTk5PPXUUzqdpszNzXn66ae5ceOGzqQ4hTIyMliyZAkTJkxAo9GwadMm3N3deemll+jUqRO9evXS6V1cld28eZMhQ4Zw+PBhBgwYwEcffVT0x+js7AzA9evXix2XlJSEra1t0Zf9vYyljiwtLQkODubKlStFCe9BdXY/hlQfu3btIjc3l+eff57AwEACAwPp168fUHADEBgYWNSSr86fDVGcmakJ44IfZ/PkznT0eoxbmbm8ueYYQyIOkHAzU52YVPmt4r5OXkrhvV9OcjjhNgBtPOyZ0ceH5i525f67LCwsgILHk/cqnPyk8N9/WrFiBfXq1Stq2d24cQMHB4ei7bVr1+bgwYPlHm95S09PZ+TIkZw5c4Zhw4YRFhams93W1hZXV1edntKFTp8+rdMyu5eh1dGFCxcYPXo0I0eO5KWXXtLZlpGRgUajwcLCQm+d3Y8h1ce0adN0JquBgnjfeust+vbtyzPPPEPDhg2rzWdDPDzPOlZ8O7IdPx2+xIfrT7Pr3A16zt7B5O7ejApqgJlp5bWvpSVfRdzKyOHt/53g6Xl/cTjhNnWsa/D5C378ODawQhI8QKNGjahXrx7/+9//dB47Zmdns3btWuzt7WnUqJHOMenp6SxdupSJEycWtd7q1q3LlStXilo3Fy9eLLHXcVXzwQcfcObMGYYMGXLfZNWzZ0/27t3LhQsXisr27NlDbGzsfTtMGWIdeXh4kJaWxvfff68zW9ulS5fYvHkzbdu2xdraulR1di9Dq4/mzZvToUMHnZ/WrVsDBe/XO3ToQI0aNarNZ0OUjUaj4bk2rmx9owvPtHQmK1fLJxuj6DNvN+eupVVaHNKSV1m+VuH7gwn8d3M0tzNzMTXRMLyjJ691b4SNpXmF/m5TU1Pee+89Jk2axHPPPcdzzz2HVqvlp59+IiYmhlmzZmFurhvDsmXLcHJyokePHkVl3bt3Z968ebz99tu4ubmxZcsW3n///QqN/VFduHCBX375BVtbW5o2bcovv/xSbJ++ffsyevRofvnlF4YNG8aIESPIzs5m8eLF+Pj4FI1GuJch1pGZmRnvvPMOU6dO5eWXX6ZPnz7cunWLlStXYmJiwrvvvlvqOruXIdZHaVSXz4Z4NHWsazD7xVY808qFd9ae5PSVVP6z4QxLhwfoP7g8VMa0e5XFkKa1zc/XKltOXVWenLNT8Zi2TvGYtk4ZuHCvEn01tdJj2bNnjzJ48GDFz89P8fPzUwYOHKjs2LGj2H6pqamKv7+/8vvvvxfb9uOPPypdu3ZV2rdvr3z++edKfn5+xQRbTtOWrlq1SvH29n7gT6ELFy4oo0aNUlq2bKm0b99emTZtmnLz5s0Sz1vpdVTO07iuX79e6devn+Lj46O0bdtWmThxohITE6MoysPVWSFDr49CiYmJxaa1VZQq/tkQVU5Gdq6y9K8Y5UjCrXI7p768p1GUUowFMRD+/v4AJa7yVFXkaxU2nrzCvD/OE3W14JFNfTtL3undjCdbOKnWA9NgrLpbP4OM5mP7aKQ+dEl9iGpGX96Tx/WVJC9fyy9HLzN/+3lirhdMpOFoW4MxnR9nYIA7NS1k3mohhBDlS5J8BcvOy+fnw5f4cvt5EpPvAOBqX5NxwY/zXBtXaphJchdCCFExJMlXkCspd/gx8iKrDiRwJSULgIZ1rBjf1Yu+LZ0xr8QhFEIIIaonSfLlKDdfyx9RSaw+mMj26CQKFyRq7GjDhG5e9G5RH9MyLiQjhBBCPCxJ8uUg7kYGqyMT+fHQxaIViMxNNYQ2c2JAWzeCvOqUeZU4IYQQoqwkyZfRjfRstp25xv+OXGJfzN9rsT9e14qBAe70a+Wi+sIEQgghqjdJ8g8h7kYGv5++xpbTV4mMv0Xh4ENLcxOe8nXmxbZutPGwl2FwQgghqgRJ8g+gKAonL6Wy5fRVtpy6RvQ/piK0MDWhg9djhPg40du3PrYVPDudEEII8bAkyT/AkIgD7Dp3o+i/bWqY0bVJPXr6ONLFu26FTzsrhBBCPApJ8no421nSrWk9ejZzon3Dx7Awk6FvQgghDIMk+QdYMbKd2iEIIYQQZSbNUiGEEMJIlTrJr1u3jt69e+Pr60toaChr16594P4ZGRnMmDGDjh070qpVK0aPHk1cXJzOPnl5ecyePZsuXbrg5+fHoEGDOH78eFmuQwghhBD3KFWS37BhA1OmTCEoKIj58+cTEBDAtGnT2LRp032Pef3119m0aRNTpkxh5syZXLt2jSFDhpCW9ncP9Y8++ohly5YxevRowsPDMTU1ZdiwYSQmJj76lQkhhBDVXKneyYeHhxMaGkpYWBgAnTp1IiUlhTlz5tCrV69i+0dGRrJjxw4WLVpE586dgYLl8J544gm+++47XnnlFS5evMjq1at59913GThwIABBQUGEhISwePFiZsyYUV7XKIQQQlRLelvyiYmJJCQk0LNnT53ykJAQYmJiSmx17969GysrKzp27FhU5uDgQNu2bdm5cycA+/btIz8/n5CQkKJ9LCwsCA4OLtpHCCGEEGWntyUfExMDQIMGDXTKPTw8AIiNjcXNza3YMR4eHpia6i6j6u7uzsaNG4v2sbOzw8HBodh5L1++TFZWFpaWljrb/P39Hxhr4asAffsJA5bzeMG/n8v/Y0Dq415SH6KaSUtLe+Asq3qTfGHitLa21im3srICID09vdgx6enpxfYvPKZw/wftAwUd9+5N8qVRnlPKFl67jY1NuZ3TkFWJ+rCord7v/ocqURcg9XEvqY8qR+pCV3nXh0ajwcTk/g/l9SZ5pXCC9vso6eQPOqZwf33nLSlZR0ZGPvCY8lb4RKCyf29VJfXxN6kLXVIfuqQ+/iZ1oauy60PvO/nCu42MjAyd8sIWeUl3I9bW1sX2LzxHYev9fvsUnrekVr4QQgghSk9vki98F5+QkKBTHh8fr7P93mMSExOLtdbj4+OL9m/YsCG3b98mJSWl2D6urq5YWFg8xGUIIYQQ4l56k7yHhweurq7FxsRv2bIFT09PnJ2dix0TFBREamoqe/bsKSpLTk4mMjKSDh06ABT9u3nz5qJ9cnJy2L59e9E2IYQQQpRdqcbJT5gwgbCwMOzs7AgODmbbtm1s3LiR8PBwoCCBJyQk4OXlhbW1NW3btiUgIIA33niDKVOmULt2bebOnYuNjU3RmHgXFxf69evHhx9+SGZmJh4eHixdupTU1FRGjRpVcVcshBBCVBOlSvL9+/cnJyeHiIgI1qxZg5ubGzNnzuTJJ58EYPv27YSFhbF8+XLatStY1GXevHl88sknzJo1C61WS5s2bZg9ezZ2dnZF5/3ggw+wtbVl4cKFZGZm4uPjw9KlS4uG5wkhhBCi7DSKvm7u1Zj0CtUl9fE3qQtdUh+6pD7+JnWhq8r1rhdCCCGEYZKWvBBCCGGkpCUvhBBCGClJ8kIIIYSRkiQvhBBCGClJ8hTMo79s2TJCQkLw9fWlT58+/Pbbbzr7bN68mWeeeYaWLVsSEhLCsmXL0Gq1KkVccfTVRX5+Pl9//TXdunWjRYsW9OnTh7Vr16oXcCV69dVX6dGjh07ZX3/9xbPPPoufnx/dunUjIiJCpegqX0n1UejatWv4+/tXqx7VJdXHxo0befbZZ2nVqhVdunQhLCyMmzdvqhRh5SmpLn799Veefvpp/Pz8CAkJYfny5XrXMDEWD/pbAfjPf/5Ds2bNKuR3S5IHFixYwKxZs3jmmWdYsGABHTt2ZMqUKWzYsAEo+CKfNGkSjRs35ssvv+SZZ55h5syZRvmFrq8uPvzwQ2bPnk23bt34+uuv6devHzNmzGDZsmXqBl7BfvnlF37//XedssOHDzN27FgaNmzI3Llzefrpp5k1axZLlixRKcrKU1J9FEpKSmLUqFFFq21VByXVx4YNG5g8eTI+Pj7MnTuXyZMns2/fPoYNG0ZOTo5KkVa8kurit99+46233qJjx45F3xuffPIJixcvVinKyvOgvxWAgwcPsnz58ooLQKnmcnJylLZt2yoffPCBTvngwYOVgQMHKoqiKG+88YbSvXt3JT8/v2j7W2+9pXTt2rVSY61o+uri5s2bSpMmTZR///vfOttXrlyp+Pn5KSkpKZUZbqW5evWq0rZtW6Vz585K9+7di8qHDh2qPP/88zr7zpo1S/H391eys7MrO8xKc7/60Gq1yrp165QOHTooAQEBire3t3Lw4EEVI60c96uPPn36KKNHj9bZ9+jRo4q3t7fy+++/V3aYleJ+ddG7d29lzJgxOvtOmzZN6dy5c2WHWKnuVx+F0tPTlSeeeELp3Lmz0rRp0wqJodq35E1NTVmxYgWvvPKKTrm5uTnZ2dkAZGdnU7NmTZ1ldWvXrs3t27crM9QKp68u4uPj0Wq1BAcH62xv27Ytd+7c4cCBA5UYbeV555136NixI4GBgUVl2dnZREZG0rNnT519Q0JCSE1N5fDhw5UdZqUpqT4ALl26xLRp0wgNDWXmzJkqRVf5SqoPRVHo0KEDL7zwgs6+DRs2BIov+GUs7vfZmDt3Lm+//bZO2T+/Y43V/eqj0KxZs6hTpw79+/evsBiqfZI3MTGhcePGODo6oigKN27cYOHChezZs4cBAwYAMGjQIGJiYlixYgVpaWns3buXn3/+mb59+6ocffnSVxeFixFdvnxZ57jExESdf43JmjVrOHXqFO+++65OeWJiIrm5ucVWYSyckjk2NrbSYqxM96sPAHt7ezZt2sQ777xDzZo1VYiu8t2vPjQaDdOmTaN79+465Vu3bgXAy8ur0mKsLA/6bDRo0AA3NzcAbt++zZo1a1i7dm3Rd6wxelB9AOzevZtffvmFjz/+WKcBWd5KNXd9dbFlyxYmTZoEQHBwMH369AEgMDCQESNG8OGHH/Lhhx8C0LFjR8LCwlSLtaKVVBeWlpZ06NCBL774AicnJ/z9/Tl37hyffvopJiYmZGZmqhx1+bp06RIff/wxH3/8MQ4ODjrbCt83W1tb65RbWVkBkJ6eXjlBVqIH1QcUXHvh9VcH+urjXgkJCcycORMfHx+CgoIqIcLKU9q6OHnyJM8++ywAzZs3Z/jw4ZUVYqXSVx9paWm8/fbbTJo0qcTl2stTtW/J/1OzZs349ttveffddzl8+DCvvPIKiqLw/vvvExERwauvvsqKFSt4//33OX36NK+99prR9g69X13MmjULHx8fxo4di7+/P6+++iqvvvoqGo3GqFpviqLwr3/9iy5duhASElLi9gepyDtzNeirj+rmYevjwoULDBkyBDMzM2bPnm1Un4+HqQsnJyeWL1/Oxx9/zPXr1xk0aBBZWVmVFGnlKE19/Oc//8HJyYlhw4ZVeDzSkv8HNzc33NzcaNu2LdbW1kybNo3Dhw/zww8/MGHCBCZOnAhAQEAA7u7ujBw5ku3bt9O1a1eVIy9/JdXFkSNHaN26NYsXL+bWrVvcvHkTDw8Prl+/Tn5+vs4Kg4Zu5cqVREdH89tvv5GXlwf8ndjz8vKwsbEBICMjQ+e4whZ84XZjoa8+TE1N0Wg0aoZYqR6mPvbv38/EiROpVasW33zzDe7u7qrFXREepi7q1KlDnTp1gILvmMGDB/P777/z9NNPqxN8BdBXHzt37mT9+vX89NNPaLXaop/C7SYmJuV6E1jtk/zt27fZvn07gYGBODo6FpUXjlnct28fiqLQunVrnePatm0LwLlz54wmyeuri6SkJNavX0+jRo3w9vbG3t4egNOnTwPg4+NT+UFXkM2bN3Pr1q0SH6v6+Pjw73//G1NT02IdqAr/u6IfwVU2ffXx8ccfV2jnoaqmtPWxYcMGpk6dSoMGDVi8eLHO35WxKM3fio2NDc2aNSvqeAi63yvGRF99uLi4kJ2dzVNPPVXi9ldffbWoQVkeqn2S12q1TJ8+nfHjxxe9g4aCThEAQUFBzJ8/n0OHDtGxY8ei7UeOHAHA1dW1cgOuQPrqwtvbm4kTJ9K8efOi3tNarZbly5fj5uaGt7e3KnFXhBkzZhRrpc+fP58zZ84wb948XF1d2bhxI1u2bGHo0KFFLZXNmzdjY2ND8+bN1Qi7wpSmPqqT0tTHrl27mDJlCm3atOGrr74q1n/DWJSmLnr27Em3bt10Rl3883vFmOirD41GU+x13w8//MBPP/3E6tWrqVevXrnGU+2TvIODA4MGDWLhwoVYWlrSokULDh06xIIFC3j++efx8/Nj8ODBLFy4EI1GQ0BAALGxscydO5cmTZo8cBYjQ6OvLho2bMhLL73Ehx9+iJeXF82bN+eHH37g4MGDzJ0716jeM/6zxVGodu3aWFhY0KJFCwDGjRvH8OHDef311+nXrx9HjhxhyZIlvPnmm0bVPwFKVx/Vib76yMnJ4e2338bKyoqxY8dy/vx5nX3r169vNK360nw2xo4dy6xZs6hbty4dO3YkOjqaefPm0bFjR6PrhFiWv5Xt27cDVMjfUrVP8gBhYWHUr1+fH3/8kblz5+Lk5MSkSZMYOXIkANOnT8fJyYnVq1ezYMECnJyc6N27NxMnTsTc3Fzl6MuXvroYOHAgWVlZrFy5kuTkZBo1asTXX39Nly5dVI688gUGBjJ37ly++OILJkyYgKOjI1OnTmXEiBFqhyZUduzYMa5duwZQ4ufhtddeY/z48ZUdlmpGjhyJra0ty5cvZ/ny5djb2/Piiy8yceLEatWXQw2ynrwQQghhpIzn+aoQQgghdEiSF0IIIYyUJHkhhBDCSEmSF0IIIYyUJHkhhBDCSEmSF0IIIYyUJHkhhBDCSEmSF0IIIYyUJHkhhBDCSP0/oIY4Xj1Znu0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "\n",
    "norm = scipy.stats.norm(loc=best['mu'], scale=best['sigma'])\n",
    "\n",
    "ax = plt.gca()\n",
    "thresholds = [39.5, 40.5, 41.5]\n",
    "for th in thresholds:\n",
    "    p = plt.Line2D(xdata=(th, th), ydata=(0, norm.pdf(th)), color='orange', linewidth=2)\n",
    "    ax.add_line(p)\n",
    "\n",
    "xs = numpy.linspace(38, 44)\n",
    "ys = norm.pdf(xs)\n",
    "plt.plot(xs, ys, linewidth=2)\n",
    "\n",
    "plt.ylim(ymin=0)\n",
    "\n",
    "pct = 100 * norm.cdf(39.5)\n",
    "ax.text(39, 0.02, '{:.0f}%'.format(pct), size=18, horizontalalignment='center')\n",
    "pct = 100 * (norm.cdf(40.5) - norm.cdf(39.5))\n",
    "ax.text(40, 0.02, '{:.0f}%'.format(pct), size=18, horizontalalignment='center')\n",
    "pct = 100 * (norm.cdf(41.5) - norm.cdf(40.5))\n",
    "ax.text(41, 0.02, '{:.0f}%'.format(pct), size=18, horizontalalignment='center')\n",
    "pct = 100 * norm.sf(41.5)\n",
    "ax.text(42, 0.02, '{:.0f}%'.format(pct), size=18, horizontalalignment='center')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "\n",
    "ax = plt.gca()\n",
    "thresholds = [42.5, 43.5]\n",
    "for th in thresholds:\n",
    "    p = plt.Line2D(xdata=(th, th), ydata=(0, norm.pdf(th)), color='orange', linewidth=2)\n",
    "    ax.add_line(p)\n",
    "\n",
    "xs = numpy.linspace(38, 44)\n",
    "ys = norm.pdf(xs)\n",
    "plt.plot(xs, ys, linewidth=2)\n",
    "\n",
    "plt.ylim(ymin=0)\n",
    "\n",
    "pct = 100 * (norm.cdf(43.5) - norm.cdf(42.5))\n",
    "ax.text(43, 0.02, '{:.0f}%'.format(pct), size=18, horizontalalignment='center')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "77fc5c78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 9))\n",
    "seaborn.swarmplot(data=ms[['Y_pred[1]', 'Y_pred[2]', 'Y_pred[3]', 'Y_pred[4]']].sample(120, random_state=2022))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b2a97b2b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def traceplot(mcmc, par, inc_warmup=False):\n",
    "    \"\"\"\n",
    "    Parameters\n",
    "    ==========\n",
    "    fit : cmdstanpy.stanfit.CmdStanMCMC\n",
    "    par : string\n",
    "    inc_warmup : bool\n",
    "    \"\"\"\n",
    "    import seaborn\n",
    "    import matplotlib.pyplot as plt\n",
    "    from matplotlib import gridspec\n",
    "    alpha = 0.6\n",
    "    # `pars` is ignored\n",
    "    ind = mcmc.column_names.index(par)\n",
    "    trace = mcmc.draws(inc_warmup=inc_warmup)[:,:,ind]\n",
    "    gs = gridspec.GridSpec(1, 2, width_ratios=[3, 1])\n",
    "    ax1 = plt.subplot(gs[0])\n",
    "    ax1.set_title(par)\n",
    "    for i in range(trace.shape[1]):\n",
    "        ax1.plot(trace[:, i], alpha=alpha, label='chain %d' % (i + 1))\n",
    "    ax1.legend(loc='best')\n",
    "    ax1.set_xlabel('iteration')\n",
    "    ax1.set_ylabel('value')\n",
    "    ax2 = plt.subplot(gs[1], sharey=ax1)\n",
    "    for i in range(trace.shape[1]):\n",
    "        seaborn.kdeplot(y=trace[:, i], alpha=alpha, ax=ax2)\n",
    "    ax2.set_xlabel('density')\n",
    "    plt.tight_layout()\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "traceplot(mcmc, 'mu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ffa0e7e9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}

## model.stan
data {
  int<lower=0> Y[4];
}

parameters {
  real mu;
  real<lower=0> sigma;
}

transformed parameters {
  vector<lower=0>[4] theta;
  theta[1] = normal_cdf(39.5 | mu, sigma);
  theta[2] = normal_cdf(40.5 | mu, sigma) - normal_cdf(39.5 | mu, sigma);
  theta[3] = normal_cdf(41.5 | mu, sigma) - normal_cdf(40.5 | mu, sigma);
  theta[4] = 1 - normal_cdf(41.5 | mu, sigma);
}

model {
  Y ~ multinomial(theta);
}

generated quantities {
  int<lower=0> Y_pred[4];
  Y_pred = multinomial_rng(theta, sum(Y));
}
	data {
	int<lower=0> Y[4];
	}

	parameters {
	real mu;
	real<lower=0> sigma;
	}

	transformed parameters {
	vector<lower=0>[4] theta;
	theta[1] = normal_cdf(39.5 \| mu, sigma);
	theta[2] = normal_cdf(40.5 \| mu, sigma) - normal_cdf(39.5 \| mu, sigma);
	theta[3] = normal_cdf(41.5 \| mu, sigma) - normal_cdf(40.5 \| mu, sigma);
	theta[4] = 1 - normal_cdf(41.5 \| mu, sigma);
	}

	model {
	Y ~ multinomial(theta);
	}

	generated quantities {
	int<lower=0> Y_pred[4];
	Y_pred = multinomial_rng(theta, sum(Y));
	}