{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8ab6398f",
   "metadata": {},
   "source": [
    "# 11 - A cravada de Deus e Inteiros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2cea1405",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "from scipy import stats\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.gridspec import GridSpec\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "import networkx as nx\n",
    "# from causalgraphicalmodels import CausalGraphicalModel\n",
    "\n",
    "import arviz as az\n",
    "# ArviZ ships with style sheets!\n",
    "# https://python.arviz.org/en/stable/examples/styles.html#example-styles\n",
    "az.style.use(\"arviz-darkgrid\")\n",
    "\n",
    "import xarray as xr\n",
    "\n",
    "import stan\n",
    "import nest_asyncio\n",
    "\n",
    "plt.style.use('default')\n",
    "plt.rcParams['axes.facecolor'] = 'lightgray'\n",
    "\n",
    "# To DAG's\n",
    "import daft\n",
    "from causalgraphicalmodels import CausalGraphicalModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "449b2877",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add fonts to matplotlib to run xkcd\n",
    "\n",
    "from matplotlib import font_manager\n",
    "\n",
    "font_dirs = [\"fonts/\"]  # The path to the custom font file.\n",
    "font_files = font_manager.findSystemFonts(fontpaths=font_dirs)\n",
    "\n",
    "for font_file in font_files:\n",
    "    font_manager.fontManager.addfont(font_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2774f695",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To make plots like drawing \n",
    "# plt.xkcd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6f4405d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To running the stan in jupyter notebook\n",
    "nest_asyncio.apply()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "119c6e36",
   "metadata": {},
   "source": [
    "### R Code 11.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d9635532",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>actor</th>\n",
       "      <th>recipient</th>\n",
       "      <th>condition</th>\n",
       "      <th>block</th>\n",
       "      <th>trial</th>\n",
       "      <th>prosoc_left</th>\n",
       "      <th>chose_prosoc</th>\n",
       "      <th>pulled_left</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   actor  recipient  condition  block  trial  prosoc_left  chose_prosoc  \\\n",
       "0      1        NaN          0      1      2            0             1   \n",
       "1      1        NaN          0      1      4            0             0   \n",
       "2      1        NaN          0      1      6            1             0   \n",
       "3      1        NaN          0      1      8            0             1   \n",
       "4      1        NaN          0      1     10            1             1   \n",
       "\n",
       "   pulled_left  \n",
       "0            0  \n",
       "1            1  \n",
       "2            0  \n",
       "3            0  \n",
       "4            1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./data/chimpanzees.csv', sep=';')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0271609",
   "metadata": {},
   "source": [
    "- `pulled_left:` Outcome ${0, 1}$ - indicator if the animal pulled the left-hand lever.\n",
    "\n",
    "\n",
    "- `prosoc_left:` Predict ${0, 1}$ - Indicator if left-hand lever was(1) or not(0) the prosocial options.\n",
    "\n",
    "\n",
    "- `condition:` Predict ${0, 1}$ - Indicator if there is a partner (1) or not (0) in the end table. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f78cb4b",
   "metadata": {},
   "source": [
    "### R Code 11.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "702eae88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>actor</th>\n",
       "      <th>recipient</th>\n",
       "      <th>condition</th>\n",
       "      <th>block</th>\n",
       "      <th>trial</th>\n",
       "      <th>prosoc_left</th>\n",
       "      <th>chose_prosoc</th>\n",
       "      <th>pulled_left</th>\n",
       "      <th>treatment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   actor  recipient  condition  block  trial  prosoc_left  chose_prosoc  \\\n",
       "0      1        NaN          0      1      2            0             1   \n",
       "1      1        NaN          0      1      4            0             0   \n",
       "2      1        NaN          0      1      6            1             0   \n",
       "3      1        NaN          0      1      8            0             1   \n",
       "4      1        NaN          0      1     10            1             1   \n",
       "\n",
       "   pulled_left  treatment  \n",
       "0            0          1  \n",
       "1            1          1  \n",
       "2            0          2  \n",
       "3            0          1  \n",
       "4            1          2  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['treatment'] = 1 + df['prosoc_left'] + 2 * df['condition']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3a22540",
   "metadata": {},
   "source": [
    "- treatment= $1$: prosoc_left = $0$ and contition= $0$: Two food items on **right** and **no partner**.\n",
    "\n",
    "\n",
    "- treatment= $2$: prosoc_left = $1$ and contition= $0$: Two food items on **left** and **no partner**.\n",
    "\n",
    "\n",
    "- treatment= $3$: prosoc_left = $0$ and contition= $1$: Two food items on **right** and **has partner**.\n",
    "\n",
    "\n",
    "- treatment= $4$: prosoc_left = $1$ and contition= $1$: Two food items on **left** and **has partner**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "928c0d2f",
   "metadata": {},
   "source": [
    "### R Code 11.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "69c1346d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>condition</th>\n",
       "      <th>prosoc_left</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>treatment</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>126</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>126</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>126</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>126</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>504</td>\n",
       "      <td>504</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           condition  prosoc_left\n",
       "treatment                        \n",
       "1                126          126\n",
       "2                126          126\n",
       "3                126          126\n",
       "4                126          126\n",
       "All              504          504"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot_table(['prosoc_left', 'condition'], 'treatment', aggfunc='count', margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fe1a1fff",
   "metadata": {},
   "outputs": [],
   "source": [
    "def logit(p):\n",
    "    return np.log(p) - np.log(1 - p)\n",
    "\n",
    "def inv_logit(p):\n",
    "    return np.exp(p) / (1 + np.exp(p))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "630e267e",
   "metadata": {},
   "source": [
    "$$ L_i \\sim Binomial(1, p_i) $$\n",
    "\n",
    "$$ logit(p_i) = \\alpha_{ACTOR[i]} + \\beta_{TREATEMENT[i]} $$\n",
    "\n",
    "$$ \\alpha_j \\sim \\mbox{to be determined} $$\n",
    "$$ \\beta_k \\sim \\mbox{to be determined} $$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecbb0a3e",
   "metadata": {},
   "source": [
    "**Entendimento:**:\n",
    "\n",
    "- **Likelihood**:\n",
    "\n",
    "    Para esse caso os dados da resposta, *outcome*, são do tipo binários $0$ ou $1$. A função de likelihood apropriada para esses caso é a $Bernoulli(p)$, ou no caso $Binomial(1, p)$.\n",
    "\n",
    "    Como $p$ é uma probabilidade, está entre $0$ e $1$, usamos uma *função de ligação* para **transformar** os resultados dos preditores para a escala de probabilidade. Nesse caso a função que faz essa transformação é:\n",
    "\n",
    "$$ logit(p) = log(\\frac{p}{1-p}) $$\n",
    "\n",
    "\n",
    "- **Parameter $\\alpha$**:\n",
    "\n",
    "    Na função de ligação o parâmetro $\\alpha$ é na verdade definido no modelo como um vetor (*vector*). O que estamos modelando aqui é que existe um parâmetro $\\alpha$ para cada chimpanzé, ou seja, para cada *actor*.\n",
    "    \n",
    "    $$ \\alpha[1] := \\mbox{ Para o chimpanzé } 1 $$\n",
    "    \n",
    "    $$ \\alpha[2] := \\mbox{ Para o chimpanzé } 2 $$    \n",
    "     \n",
    "    $$ \\alpha[3] := \\mbox{ Para o chimpanzé } 3 $$    \n",
    "    \n",
    "    $$ \\alpha[4] := \\mbox{ Para o chimpanzé } 4 $$    \n",
    "    \n",
    "    $$ \\alpha[5] := \\mbox{ Para o chimpanzé } 5 $$    \n",
    "    \n",
    "    $$ \\alpha[6] := \\mbox{ Para o chimpanzé } 6 $$    \n",
    "    \n",
    "    $$ \\alpha[7] := \\mbox{ Para o chimpanzé } 7 $$\n",
    "    \n",
    "    Cada um dos parâmetros $\\alpha$ tem um priori $Normal(\\mu_i, \\sigma_i)$ e essas prioris são idênticas.\n",
    "    \n",
    "    O que estamos inferindo com cada um desses $\\alpha$'s é a localização ($\\mu_i$) de cada uma das prioris e seu desvio padrão ($\\sigma_i$)\n",
    "    \n",
    "    \n",
    "    \n",
    "- **Parameter $\\beta$**:\n",
    "\n",
    "    O $\\beta$ é a representação de um vetor de $\\beta$'s que mapeiam cada um dos tratamentos:\n",
    "    \n",
    "    $$ \\beta[1] := \\mbox{ Para o tratamento } 1 $$\n",
    "    \n",
    "    $$ \\beta[2] := \\mbox{ Para o tratamento } 2 $$\n",
    "    \n",
    "    $$ \\beta[3] := \\mbox{ Para o tratamento } 3 $$\n",
    "    \n",
    "    $$ \\beta[4] := \\mbox{ Para o tratamento } 4 $$\n",
    "    \n",
    "\n",
    "    No experimento existe uma hipóstese a ser testada: **Os chimpanzés tendem a puxar mais a alavanca do lado pro-social quando tem um outro chimpanzé do outro lado da mesa**, o *partner*?\n",
    "    \n",
    "    Os $\\alpha$'s são independentes entre eles, porém quando colocamos os $\\beta$'s estamos medindo qual é a inteção conjunta dos chimpanzés em cada tratamento.\n",
    "    \n",
    "    A variável tratamento (*treatment*) é a construção de uma **variável sintética que indexa** o conjunto de duas variáveis *{prosoc_left, condition}*, isto é, a **interação** entre elas. Então temos que a variável $treatment = 1$ indica que o experimento foi realizado usando dois pratos com comida no lado direito e que não tinha parceiro.\n",
    "    \n",
    "    Portando $\\beta_{treatment = 1}$ terá o valor do *efeito de todos os experimentos* que os chimpanzés fizeram nesse tipo de tratamento.  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdf56906",
   "metadata": {},
   "source": [
    "### R Code 11.4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0956231e",
   "metadata": {},
   "source": [
    "$$ L_i \\sim Binomial(1, p_i) $$\n",
    "\n",
    "$$ logit(p_i) = \\alpha $$\n",
    "\n",
    "$$ \\alpha \\sim Normal(0, \\omega) $$\n",
    "\n",
    "\n",
    "$\\omega = 10$ to start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "03e2cb48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['pulled_left', 'actor', 'N', 'qty_chimpanzees'])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating data list to model\n",
    "data_list_partial = df[['pulled_left', 'actor']].to_dict('list')\n",
    "data_list_partial['N'] = len(df)\n",
    "data_list_partial['qty_chimpanzees'] = len(df.actor.unique())\n",
    "\n",
    "data_list_partial.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bb1c4a96",
   "metadata": {
    "tags": [
     "remove_output"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mBuilding:\u001b[0m found in cache, done.\n",
      "\u001b[36mMessages from \u001b[0m\u001b[36;1mstanc\u001b[0m\u001b[36m:\u001b[0m\n",
      "Warning in '/tmp/httpstan_7jdqpv18/model_vneh7nsw.stan', line 5, column 8: Declaration\n",
      "    of arrays by placing brackets after a variable name is deprecated and\n",
      "    will be removed in Stan 2.33.0. Instead use the array keyword before the\n",
      "    type. This can be changed automatically using the auto-format flag to\n",
      "    stanc\n",
      "Warning in '/tmp/httpstan_7jdqpv18/model_vneh7nsw.stan', line 6, column 8: Declaration\n",
      "    of arrays by placing brackets after a variable name is deprecated and\n",
      "    will be removed in Stan 2.33.0. Instead use the array keyword before the\n",
      "    type. This can be changed automatically using the auto-format flag to\n",
      "    stanc\n",
      "\u001b[36mSampling:\u001b[0m   0%\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m   5% (400/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  11% (900/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  21% (1700/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  46% (3700/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  66% (5300/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  85% (6800/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m 100% (8000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[32mSampling:\u001b[0m 100% (8000/8000), done.\n",
      "\u001b[36mMessages received during sampling:\u001b[0m\n",
      "  Gradient evaluation took 8.5e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.85 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 8.6e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.86 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 8.1e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.81 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 0.000132 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 1.32 seconds.\n",
      "  Adjust your expectations accordingly!\n"
     ]
    }
   ],
   "source": [
    "model_partial = \"\"\"\n",
    "    data {\n",
    "        int N;\n",
    "        int qty_chimpanzees;\n",
    "        int pulled_left[N];\n",
    "        int actor[N];\n",
    "    }\n",
    "    \n",
    "    parameters {\n",
    "        vector[qty_chimpanzees] alpha;\n",
    "    }\n",
    "    \n",
    "    model {\n",
    "        vector[N] p;\n",
    "        \n",
    "        // Prioris\n",
    "        alpha ~ normal(0, 10);\n",
    "        \n",
    "        for (i in 1:N){\n",
    "            p[i] = alpha[ actor[i] ];\n",
    "            p[i] = inv_logit(p[i]);\n",
    "        }\n",
    "        \n",
    "        // Likelihood\n",
    "        pulled_left ~ binomial(1, p);\n",
    "    }\n",
    "\"\"\"\n",
    "\n",
    "posteriori_partial = stan.build(model_partial, data=data_list_partial)\n",
    "samples_partial = posteriori_partial.sample(num_chains=4, num_samples=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "957f4268",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_5.5%</th>\n",
       "      <th>hdi_94.5%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alpha[0]</th>\n",
       "      <td>-0.343</td>\n",
       "      <td>0.241</td>\n",
       "      <td>-0.705</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.003</td>\n",
       "      <td>3440.0</td>\n",
       "      <td>2702.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha[1]</th>\n",
       "      <td>11.235</td>\n",
       "      <td>5.286</td>\n",
       "      <td>3.743</td>\n",
       "      <td>18.571</td>\n",
       "      <td>0.140</td>\n",
       "      <td>0.106</td>\n",
       "      <td>1788.0</td>\n",
       "      <td>1566.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha[2]</th>\n",
       "      <td>-0.645</td>\n",
       "      <td>0.251</td>\n",
       "      <td>-1.047</td>\n",
       "      <td>-0.245</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.003</td>\n",
       "      <td>3976.0</td>\n",
       "      <td>2309.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha[3]</th>\n",
       "      <td>-0.637</td>\n",
       "      <td>0.244</td>\n",
       "      <td>-1.034</td>\n",
       "      <td>-0.259</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.003</td>\n",
       "      <td>3263.0</td>\n",
       "      <td>2551.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha[4]</th>\n",
       "      <td>-0.345</td>\n",
       "      <td>0.242</td>\n",
       "      <td>-0.700</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.003</td>\n",
       "      <td>3926.0</td>\n",
       "      <td>2410.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha[5]</th>\n",
       "      <td>0.580</td>\n",
       "      <td>0.251</td>\n",
       "      <td>0.198</td>\n",
       "      <td>0.987</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.003</td>\n",
       "      <td>3797.0</td>\n",
       "      <td>2384.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha[6]</th>\n",
       "      <td>2.134</td>\n",
       "      <td>0.376</td>\n",
       "      <td>1.560</td>\n",
       "      <td>2.717</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.005</td>\n",
       "      <td>3644.0</td>\n",
       "      <td>2528.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            mean     sd  hdi_5.5%  hdi_94.5%  mcse_mean  mcse_sd  ess_bulk  \\\n",
       "alpha[0]  -0.343  0.241    -0.705      0.062      0.004    0.003    3440.0   \n",
       "alpha[1]  11.235  5.286     3.743     18.571      0.140    0.106    1788.0   \n",
       "alpha[2]  -0.645  0.251    -1.047     -0.245      0.004    0.003    3976.0   \n",
       "alpha[3]  -0.637  0.244    -1.034     -0.259      0.004    0.003    3263.0   \n",
       "alpha[4]  -0.345  0.242    -0.700      0.055      0.004    0.003    3926.0   \n",
       "alpha[5]   0.580  0.251     0.198      0.987      0.004    0.003    3797.0   \n",
       "alpha[6]   2.134  0.376     1.560      2.717      0.006    0.005    3644.0   \n",
       "\n",
       "          ess_tail  r_hat  \n",
       "alpha[0]    2702.0    1.0  \n",
       "alpha[1]    1566.0    1.0  \n",
       "alpha[2]    2309.0    1.0  \n",
       "alpha[3]    2551.0    1.0  \n",
       "alpha[4]    2410.0    1.0  \n",
       "alpha[5]    2384.0    1.0  \n",
       "alpha[6]    2528.0    1.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(samples_partial, var_names=['alpha'], hdi_prob=0.89)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4a7bf10c",
   "metadata": {},
   "outputs": [],
   "source": [
    "samples_partial = az.from_pystan(\n",
    "    posterior_model=posteriori_partial,\n",
    "    posterior=samples_partial,\n",
    "    observed_data=['pulled_left', 'actor']\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c727da7f",
   "metadata": {},
   "source": [
    "### R Code 11.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "83eb2d03",
   "metadata": {},
   "outputs": [],
   "source": [
    "# extract prior from quap\n",
    "omega = 10\n",
    "N = 1000\n",
    "\n",
    "norm_prior = np.random.normal(0, omega, N)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c6f5bfd",
   "metadata": {},
   "source": [
    "### R Code 11.6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "31dbb1b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = inv_logit(norm_prior)\n",
    "\n",
    "omega = 1.5\n",
    "\n",
    "better_p = inv_logit( np.random.normal(0, omega, N) ) \n",
    "\n",
    "az.plot_density([p, better_p],\n",
    "                data_labels=['a~normal(0, 10)', 'a~normal(0, 1.5)'], \n",
    "                figsize=(15, 6))\n",
    "\n",
    "plt.title('')\n",
    "plt.xlabel('prior prob pull left')\n",
    "plt.ylabel('Density')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fc03a480",
   "metadata": {
    "tags": [
     "remove_output"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mBuilding:\u001b[0m found in cache, done.\n",
      "\u001b[36mMessages from \u001b[0m\u001b[36;1mstanc\u001b[0m\u001b[36m:\u001b[0m\n",
      "Warning in '/tmp/httpstan_4k2t7wqj/model_2lk2lql7.stan', line 5, column 8: Declaration\n",
      "    of arrays by placing brackets after a variable name is deprecated and\n",
      "    will be removed in Stan 2.33.0. Instead use the array keyword before the\n",
      "    type. This can be changed automatically using the auto-format flag to\n",
      "    stanc\n",
      "Warning in '/tmp/httpstan_4k2t7wqj/model_2lk2lql7.stan', line 6, column 8: Declaration\n",
      "    of arrays by placing brackets after a variable name is deprecated and\n",
      "    will be removed in Stan 2.33.0. Instead use the array keyword before the\n",
      "    type. This can be changed automatically using the auto-format flag to\n",
      "    stanc\n",
      "\u001b[36mSampling:\u001b[0m   0%\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  11% (900/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  36% (2900/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  61% (4900/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  86% (6900/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m 100% (8000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[32mSampling:\u001b[0m 100% (8000/8000), done.\n",
      "\u001b[36mMessages received during sampling:\u001b[0m\n",
      "  Gradient evaluation took 6.8e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.68 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 6.2e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.62 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 5.9e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.59 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 5e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.5 seconds.\n",
      "  Adjust your expectations accordingly!\n"
     ]
    }
   ],
   "source": [
    "model_partial_better = \"\"\"\n",
    "    data {\n",
    "        int N;\n",
    "        int qty_chimpanzees;\n",
    "        int pulled_left[N];\n",
    "        int actor[N];\n",
    "    }\n",
    "    \n",
    "    parameters {\n",
    "        vector[qty_chimpanzees] alpha;\n",
    "    }\n",
    "    \n",
    "    model {\n",
    "        vector[N] p;\n",
    "        \n",
    "        // Prioris\n",
    "        alpha ~ normal(0, 1.5);\n",
    "        \n",
    "        for (i in 1:N){\n",
    "            p[i] = alpha[ actor[i] ];\n",
    "            p[i] = inv_logit(p[i]);\n",
    "        }\n",
    "        \n",
    "        // Likelihood\n",
    "        pulled_left ~ binomial(1, p);\n",
    "    }\n",
    "\"\"\"\n",
    "\n",
    "posteriori_partial_better = stan.build(model_partial_better, data=data_list_partial)\n",
    "samples_partial_better = posteriori_partial_better.sample(num_chains=4, num_samples=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "af2add12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_5.5%</th>\n",
       "      <th>hdi_94.5%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alpha[0]</th>\n",
       "      <td>-0.333</td>\n",
       "      <td>0.232</td>\n",
       "      <td>-0.693</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.002</td>\n",
       "      <td>6592.0</td>\n",
       "      <td>2975.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha[1]</th>\n",
       "      <td>3.937</td>\n",
       "      <td>0.734</td>\n",
       "      <td>2.767</td>\n",
       "      <td>5.084</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.008</td>\n",
       "      <td>5582.0</td>\n",
       "      <td>2874.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha[2]</th>\n",
       "      <td>-0.628</td>\n",
       "      <td>0.242</td>\n",
       "      <td>-0.998</td>\n",
       "      <td>-0.241</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.002</td>\n",
       "      <td>7867.0</td>\n",
       "      <td>2996.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha[3]</th>\n",
       "      <td>-0.624</td>\n",
       "      <td>0.247</td>\n",
       "      <td>-0.984</td>\n",
       "      <td>-0.203</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.002</td>\n",
       "      <td>7082.0</td>\n",
       "      <td>3107.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha[4]</th>\n",
       "      <td>-0.337</td>\n",
       "      <td>0.236</td>\n",
       "      <td>-0.695</td>\n",
       "      <td>0.067</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.003</td>\n",
       "      <td>6557.0</td>\n",
       "      <td>2961.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha[5]</th>\n",
       "      <td>0.559</td>\n",
       "      <td>0.249</td>\n",
       "      <td>0.159</td>\n",
       "      <td>0.950</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.002</td>\n",
       "      <td>6864.0</td>\n",
       "      <td>3103.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha[6]</th>\n",
       "      <td>2.006</td>\n",
       "      <td>0.366</td>\n",
       "      <td>1.421</td>\n",
       "      <td>2.578</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.004</td>\n",
       "      <td>6111.0</td>\n",
       "      <td>2551.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           mean     sd  hdi_5.5%  hdi_94.5%  mcse_mean  mcse_sd  ess_bulk  \\\n",
       "alpha[0] -0.333  0.232    -0.693      0.050      0.003    0.002    6592.0   \n",
       "alpha[1]  3.937  0.734     2.767      5.084      0.010    0.008    5582.0   \n",
       "alpha[2] -0.628  0.242    -0.998     -0.241      0.003    0.002    7867.0   \n",
       "alpha[3] -0.624  0.247    -0.984     -0.203      0.003    0.002    7082.0   \n",
       "alpha[4] -0.337  0.236    -0.695      0.067      0.003    0.003    6557.0   \n",
       "alpha[5]  0.559  0.249     0.159      0.950      0.003    0.002    6864.0   \n",
       "alpha[6]  2.006  0.366     1.421      2.578      0.005    0.004    6111.0   \n",
       "\n",
       "          ess_tail  r_hat  \n",
       "alpha[0]    2975.0    1.0  \n",
       "alpha[1]    2874.0    1.0  \n",
       "alpha[2]    2996.0    1.0  \n",
       "alpha[3]    3107.0    1.0  \n",
       "alpha[4]    2961.0    1.0  \n",
       "alpha[5]    3103.0    1.0  \n",
       "alpha[6]    2551.0    1.0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(samples_partial_better, var_names=['alpha'], hdi_prob=0.89)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3bbfde00",
   "metadata": {},
   "outputs": [],
   "source": [
    "samples_parcial_better = az.from_pystan(\n",
    "    posterior=samples_partial_better,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "397534d5",
   "metadata": {},
   "source": [
    "### R Code 11.7"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c23e52e3",
   "metadata": {},
   "source": [
    "$$ L_i \\sim Binomial(1, p_i) $$\n",
    "\n",
    "$$ logit(p_i) = \\alpha_{ACTOR[i]} + \\beta_{TREATEMENT[i]} $$\n",
    "\n",
    "$$ \\alpha_j \\sim Normal(0, 0.15) $$\n",
    "$$ \\beta_k \\sim Normal(0, 10) $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8b290a30",
   "metadata": {},
   "outputs": [],
   "source": [
    "# quap code to get priori"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc8d3a1f",
   "metadata": {},
   "source": [
    "### R Code 11.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "58ae0c25",
   "metadata": {},
   "outputs": [],
   "source": [
    "p = []\n",
    "\n",
    "alpha = np.random.normal(0, 1.5, N)\n",
    "betas = np.random.normal(0, 10, (4, N))\n",
    "\n",
    "p = [inv_logit(alpha + beta) for beta in betas]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "987a3928",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1224x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_density(np.absolute(p[0] - p[1]), figsize=(17, 6))\n",
    "\n",
    "plt.title('')\n",
    "plt.xlabel('prior diff between treatments')\n",
    "plt.ylabel('Density')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7693fc47",
   "metadata": {},
   "source": [
    "### R Code 11.9"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "074c25ed",
   "metadata": {},
   "source": [
    "$$ L_i \\sim Binomial(1, p_i) $$\n",
    "\n",
    "$$ logit(p_i) = \\alpha_{ACTOR[i]} + \\beta_{TREATEMENT[i]} $$\n",
    "\n",
    "$$ \\alpha_j \\sim Normal(0, 0.15) $$\n",
    "$$ \\beta_k \\sim Normal(0, 0.5) $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "31963fb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "better_p = []\n",
    "\n",
    "alpha = np.random.normal(0, 1.5, N)\n",
    "betas = np.random.normal(0, 0.5, (4, N))\n",
    "\n",
    "better_p = [inv_logit(alpha + beta) for beta in betas]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "968a7152",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_density([\n",
    "        np.absolute(p[0] - p[1]), \n",
    "        np.absolute(better_p[0] - better_p[1])\n",
    "    ], \n",
    "    data_labels=['b ~ normal(0, 10)', 'b ~ normal(0, 0.5)'],\n",
    "    figsize=(15, 6))\n",
    "\n",
    "plt.title('')\n",
    "plt.xlabel('prior diff between treatments')\n",
    "plt.ylabel('Density')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a48821a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.10167149527022139"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(np.absolute(better_p[0] - better_p[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d28fa6f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WL6ZrziJeK5rDLzLVNGVO//uvCjrZuCDEtQtCVAb1/2MyZfOGfz8Q5cl3uklEE1QkeylP9FCZ7OHyQJI1vjj+3m47/HV1QTIBydTkDGV0uSAUhGCorwJoEBMM2j1k7w1qZWX2UMYLRSSCtX07bH0D6623hh1abKqrMR/8IOaD6+1KlyLnUG8qxzstSba3JHm7JUlzZOz3BIcFNSEXc0rcNIQ9NJS4mdMX2C60E3IKcKNTgJNp58jGKd//Uyr2PXnaAtbxsotpW3orkdqrNLRvKhiDr+dAX5jbPEaY+wC99R8iXr5cYW68Uim7h23PXqy9e2Df6HPYjMcNS5dilq+ga+FFvEAVLx5LcqQ7c9qxLgdcMTvAhxeGuLTGh0P/P6ZUPJPnX3b38ovdvUNKbPtcFp+6uJhPXVyMb/Bis7mcXYq/v4hHMgmpNGQz2H2lfQb/szmddlALhey5ZYXaW3auZTLw1ls4XtgMb7152lBL47Bg5UryH9oAa1aDW0OKZfJlcoY9bSnebE6wvTnJwc70qMVFnBbML/WwtNLLonIPc0o81Be7tGh1HwW40SnAybSxcinKDv2Kyr1P4Ep1D7ktWrWWtqV/TqxytYLbuWIM3p6DhBs3E256EW/k2LCHZfyVfUsTfJB4+QqFucFisb5y/nuw9uy112MbpSfGOJ2wcCFmxXLM8hVE5i7gleYMLx2Osas1NeyHf0PYzYcXhvjgvCBh38wcOnM+64xn+Yd3enj+YJTBOaHM7+SWS0vYMD94RhXXZJJ0dGC99DLWi5uxWk5fL9MUF2Ou+zDmox8dWBtQ5ExFUjm2NiV4rTHBmycSow6JLPM7uajSy5IKL0srPCwo8yisjUIBbnQKcAWkNdXKscQxHDhwWA6clhOn5Rz4OegMEnaHCTqD5/eE1XyWkqPPUrX7MTyJoR+wsYqVnFx+B/GKS6apcQLYYa730EABFF/k6LCHZXwV9py5ug/a/2YXUpjL5ewqh4cOwbv23ByOHx+10qFxOGDePMzFF2NWrICLltLr8PDa8ThbjsZ5uyXJcMv1lPqcvH9ugA/OC7KgzHN+//++QBzpSvPYm1282Tx0XtzcEje3rSllVe0FNJzxfJTPw+49WJtfwHrtNaz00F5s4/Nirr0Wc8MNGl4pExJJ5XjlWJzfHY2z82SSEWrrEHRbrKjxsarGz8paH7OKXHrvngAFuNEpwBWQf2/9dx4//viYxzlxUuwuJuwKU+IuocZXw2z/bOp99dT76wk4A+egtcMwecKNm6na/Xen9e4kSpdwctkdRKsvU4/becjbc4hw44sUN23G13tk2GMyvnJ669bT098zN5PmKvaHtYOH4NBBO7QdOYKVHH1dK+PxwOJF9tpVF11kV8rz+WjqzbK1KcHWE4kRvwD43RZXzg6wfl6QS6p96tU5T207keDxN7tOG+a6ZpaPz68uZW6Jii1Nu1gMa8sWrBdewDp0eMhNxuXEfGA95qYbobZ2mhoo57t0zvB6Y5yXjsR4oylBdphCkQ4LllZ4WVXrY1Wtn0XlHr1vn4XxBri8yZPIJUjkE8RzcXs/Z+8n80lyJocxhjx58iZPnvyQn+2Fw/OnOkUsB07s/ctKL6PEXXLuXvQEKMAVkGdbn+WJ40+c9eOUucuo99dT76tnfnA+C4MLqfJUTd2ZIWMoPvEyVbv+Dl/voSE3JYvm0LrsC/TWrVdwKxDjCXNZTzHRmvcRqb2KSPXl5D0FMlTJGLsaYf/C0MeOnwpr41iE2ISC9hy2iy6yA9u8eeB2k84ZdpxM8kZTgjeaEjRHhx9W6XFarJ7l4wNzg1xW59fwmgKRyxteOBTlx9t76EqeqiLqsGDjghC3XFpCqV/DXaedMbBnD45f/ItdAGXwTQ4L874rMJ+8yf5/Kxe8vLHft186HOOVY/Fh12Hz9r1nv68+wNo6v4a1n6VUPkVHuoOOdAff2vIuOUc3LleSDQtdp8LZe4JaMj911YGDziCPrnx0yh7/bCjAFZCdvTu5f//95MjhwMEVZVfYZxNMnqzJEsvF6Mn00JPtIZ6LT+ixi13FLAwuZHFoMcuKljE/MB/H2Q6HM4ZQyx+o3vUI/u53h9yUDtTQevFtdM+5Diy94RUqb+/hgQIovt7Dwx5jLCexikvtMFd7Femi2ee4lcNIpaC9HZqb7aDW1GQvInyiCSsaG/v+2GfuaWjAzJ8PCxZgli6FurqBsuVtsawd2E7YE9pTw42NxA5ta2f5uWpOgHV1fgJuhbZClcjk+cUIhU4+fXExn3xvoROZPocOYf3LL+3hle/5vmJWryZ/8+dg/vxpapxMp5ZohhcOxnj+UJS22OnLurgcsHaWn/Xzgqyt8+v/9AQkc0na0+20p9tpS7fRnrIvW1OttKXb6M32TncTh/BYHp5YffYdJ1NBAa7A/Me3/iOpfAqvw8uPVv1oxOMy+Qy92V460h00JZtoTDTSmGykMdFIZ6ZzzOcJOANcFLqI5cXLWRVeRbW3evyNNIZg61aqdz1CoHP30Hb5K2ldeivd8z6OcagS2Ezi7T1CceOLFDVvIdC1d8TjUkUN9NZeRaT2arui5WQPtTQGolFoa4f2Nqy2Nmhrw2pvt69ra8PqndiHhHG57LC2YD7Mn2+HtoaGIdXscnnD3vYUW/t62YarHNmvKuhkXZ2ftbP8rKjxXZBfAHImRywXI5aNEcvFiOfipPIp0vk0yVySdD5NxmTI5DMjXubM0C9XVl9JR4flwO1w47JcuC03Lod9Odx1LsuF2+Em4AwQcoUochVR5CrC7/Cf8aiEjniWf3i7m+cPxoYUoinzO/nzlSV8cJ4KnZw3mpqwfvUrrJd/h5Ub+veUv+IKzM2fs0/MyIyWzOb5w7E4vz0Y5Z2Tw4+2WFblZf3cIFfPCVA0Q9demwx5k+dk6iSNyUaOJ47TmGikOdlMe7qdaC46qc/ldXjxO/z4nX4CzgB+59B9n8OH03JiYeGw7HoRA/t99SMc2J+//UMqcybHT0/8lKzJjvldezopwBWY8Qa40cSyMZqSTRxNHOVg7CD7Y/s5kTwx6n1m+WaxOryaVeFVLA4txmUN/6U70Lad6l0/JNj+9pDrM94y2pf+GZ3zb8Q4VRp7pnMl2ihq/gNFzVsItb6BIzf8B2LWXWQPtZx1tT3U0vLZC/Ymk5BI9G1JrOSpfRIJ+/Z43J7bEo9BzN6nb9/KjByeRmMcFlRVQ10dpm6WfTlvHsyefVrp8VzecLQ7w662JLtbU7zVnCSaHmZiBPZQuosrvayt87Ouzk9D2D0jJ7MbYwZOHHWkO+jIdNCebqcz3UkkGyGajRLLxYhmoyTyIy+ncD5w4iTkCg0JdUWuIvtn56D9vutL3aX4nL4hj3G4r9DJW+8pdDK/1M1/Wl3KShU6OX+0t2M9/TTWb5/HSqcHrjYOC7N+Peazn7UXQ5cZ5WBnmn/bH+F3R2LDDpGsL3bxofkhPjA3SHVoBs3rniTZfJZjiWMcjh/mSOIIR+NHOZY4Rio/9pSD97KwKHGXUOGpoNxTziuHPGRSYVwE+b+vmn1aQPM5fSN+Fz1bk/Fde6opwBWYqfqjimajHIgdYE9kDzsjOzkcP4wZYQWTgDPApcWXsiq8ipXhlfbZ6o6dVO96hFDrG0OOzXrCtC/5D3Qs+BTGpS8rFwRj7IAVjUI0itXbje/EO3hP7MTTehBicXJpx3s2i1zagclOfU+UsSwoLYGKSkx1X1irr7PPstfUjLhGVDyTZ39Hij1tKXa3ptjTniIxzAd+vxKfgzWz/Kyt87Oq1k/IMzN62bImS2uqlRPJEzQnm2lONdOaah0IbRlzZuF5JvA7/JR6Silzl1HpqaTaV021p5quSCm/3uHmePfQs/ZrZ/n5/OoS5qjQyfmjqwvrF7/A+u1vsbKneuSMy4X58EbMpz4FJSXT1z45a8YY3m5J8vPdvaedXAHwuyyumRtk44IgSyu8M/Jk25kwxtCWbuNA7MDAdiR+ZNzv+RYWZe4yKrwVVHhObVXeKio9lVR4KnAPGpl1zqpQmhyuVDfOZBeObAJHLsGfND1E0mTxWS5+XvWfSAdrSZYunbo2nAEFuAJzrs4KRLNR9kT38HbP27zZ8yZdma5hj3NgsSLnZkPPST4QTzAvk8UCcu4Q7Yv/lI6FnyXvDk5ZO2UaZLPQ3GyfsW5vt+eStQ3a7+wcde2zqWYCfigutgNaZaV91ryiAlNZYe+Xl4+5kG/eGE70ZtnbnrK3thTHejIjlovut6jMw9o6O7QtKvcU9OLaeZOnNdVqn1mNH6Ex2ciJ5AlaU63kGb6ncbyclpOQM0TQFRy4DDgD+Bw+vA4vHodnYOsf+jhw6XDjsTy4HW6c75k/a4wZqCiWMfZQy2w+O/TSZAeGYQ6+LpaNEc1FB3oKI9kIsdz45kOOl88qJhkvJ52sJJ+uJJ+qgkwl186p55ZLylTo5HzS2or11FNYL788ZGFw4/NiPvYxzCc+YS+6LgUjlze8cizOz3f3crAzfdrty6u8bFwQ4qo5gQtyWPt75U2epmQTeyJ72BPdw97IXrqz3WPez2W5qPPVMds/e6ACep2/jnJP+YR6zCYrwDkyUbyRY3gjx3DHTuBOtuNKdOBKtuNOtONKdWG9Zzj+ZXPqSTgc+PN5Xj/aSN7hZfenNp9xG6bCVAU49TOPpaMDa8cOu/KiZQGWPc7K4bBLj3u9p7Zw2P5C6jh3byghV4h1JetYV7IOYwxHE0d5s+dN3up5i4OxgwO9c3kMbzvTvF1WynfLSpmdyXKZby4XN/wJi0tXTVn39rlijCGRMUTTeTJ5Qy5vyObtL/hel4Og2yLoceBxWjPvLJ0xdiA73ojV2GivcXb8OBw9esZDFEdjOQ1OTx6nJ4/DZXC48jjcxt53G4zXS66khkxpA+mK+WTKGiAUsr9E9W9+Pzgn9iXYGEN7PMfBzjTvdqR4tz3N/s40sRGGQ/ZzWDC/1MPFVV4urvSyrMpXsF/A8yZPc7KZQ/FDHIkfGQhtEx3m6HV4KfeUU+4uty8HbWFXmJArRNAZxOsojLPaOZMjmo0OBLpILjIk4PXv92Z76cp00ZXuIsfpRQ/6JU0v+Hvx+IcW/Hkl72bLmxXM8tVzRdVClobnMsc/h7A7PNUvUUZSVYX5v/4vzI034vjnn2C99hoAVjKF9fNfYJ59DnPTjZiPfAR8vjEeTKZTKpvn+YMx/mVPLy3vqQDsd1l8eGGIjy0pYlbRhT0v3xhDS6qFnZGd7Ordxa7IrjHnrLktN/MC85gfnM9c/1zmBuZS56vDNR3LCOWz+HoO4u/cjb/7XbyRo3gix3Gnxq77MBbLTN9J6XNNPXBjeestnP/zb8Z9uHE6oaSEQ94uOkKG3iInH1r8WSgrxVRU2HN0zsGwDl/nHlzv/j3bu7fxu4CfV/w+4iMEy5AzxOqS1awrWcclxZfgcZxfw4T6e1mOdKfpSOToTuToSebpSubo7tt6knnSI1QPHMzlgIDbQdDtIOBxEPT0hbu+n0t8TiqDTqqCLqqCLsr8zvOriEEmYwe0w4fh0GH78tgxrOTEy/Maf18PWCgEoRAmGISi0MDPhEKYUAiCoVPXB4NYTkOwdRvFzVsInnwDb6xp1OfJesLEKi4lXrmKWMVKkiULxqxoGk/nOd6b4XhPhsNd6b4tQ2SMsAb2oqxLKrwsqfCyrMrL4gpvQVaMNMbQmm5lb2TvQGA7mjg67jkLfoefWb5Z1PpqqfXVMss3i2pvNZWeSoLOYEEEs6mSN3ki2QidmU5aU62cTJ2kJdXCyeRJTqZO0pHpmNDjlbhLmOOfw5zAHBr8DdT56pjlm3XevZdeEA4exPFP/4S1fej8blMSxnz605hrrx2zZ1/OrUgqx7++G+HpvRF6UkPf40t8Dj6xtJiPLgoRuoALknSmO9kVscPazt6dY75H1XhrWBRcxMLgQhYGF9IQaJiyE/Wj9sAZgyfaiL9rN/7OPQQ6d+Pr3o8jf3rP6liy7iKy/gqyvnLyrgB5l48PO98lQX5QD5yb3Z96aXJe2CTREMrp8uabOP/mf03qQ5riYpgzB9PQAAvmYxYutOf0nG3PnclT1PwHKt79J4Ltbw25KenwsHneB9hcUs226G7a0+3DPoTX4eXS4ku5rPQyVoVXnfPFxNM5w9HuNIc60xzqSnOwM8OR7jTJ7PT8STktqAg4qQy6qAm5mFXspq7YRV2Rm9oi19Sv/9XdDbv3YO3ZjbVnLzQeHzLfYzTG7Ya5c+35Y5UV9vDEikHDEydpWJEr0Uaw7S2CbdsJtG/HFzk66vE5dxGxikuIVayktXg575oGDkedHO+xA9vx3gwd8fG9RocFs8NullR4WVrh5aJKL3XFroIdEtmR7mB3ZPfAB/VI/08Hc+Cgzl/H/MB85gbm0uBvYJZvFmFX+IIOaWcjlU/RkmyhKdnEieQJTiRPcDjWyMlUC8Ya/zySWl8t8wPz7S04nzn+OacVUJEpsmuXHeT27htytamuJv9nfwaXX1a4a5oagzPVjSfWhDvRhjPd27f14EpHBvYdmRhWPouVzwy6zIBlYRwejNND3unBOLx9l6euy3lLyHrLyfjLyfoGb2WTVpm6NZblV3t6ee5A9LTP+NoiF5+6qJgNC0J4nAX673QW0vk0e6N7eavnLXb07qApOfqJ0tn+2VwUuoiLii5iaWjpOV3UenCA+/lnqgh07ibQsQN/5y4CHTtxpXvG9ThpfzXpotmkihpIh+rJ+KvI+CrI9v0NDldYr3+6kj9veP3ocXJOP3s++fxkv8SzogA3XRobcfzF17ByOYzTifnI9faQtXwe0mlIpbCSKbsYRE8PdHWOe32qwUwgYK9RtXCBHegWL4bS0nHd15nqoeTov1F6+NenfXnOWy665n2ctqW3kg1U2c9lDMcSx9jWs41t3ds4FD803MPitJwsK1rGZSWXsaZkzZS8IXQnc7zdkuTtliTvtqc43pNhHB1pp/G5LEp8Too8DtxOC5cDXA4Ly4Jk1hBL54ln8sQyhng6P0LZl4mrDDiHhLr+/eqga8Seu7zJ05PtoTPdSUe6g86MfdmT6cGKRKjd10b9oW7mHo5R3T72cICsEzrKvXTXhIjUlJKsqyRXX4t7VgM1wXpqfbXntCfAmewk2L59INS9d1H498oZiyOmht1mDrvzc/su59BGyZDjLGBWkYv5ZR4WlXtYXO5lQZkHfwH2rvXryfQMCWwtqZZRj3dZLmb7ZzMvMI+5gbnMC8yjwd+gnp5zJG/ybD3ZxM8O7OVg9CgOXzNO7wkcnu5x3d/Cot5Xz/zgqVCnf78pZAy89RaOJ5/EOjL0s9FcfDH5//gfYf55uhi4yeOOt+KJNuKJNeGJNuGJNfZdNuHMTl912IyvnHRoNqlQPenQbNKhOlKh2aRD9RjX2Ccojvdk+OnOHn53JHba5/2iMg+fXlbMFbMD59fol3OgLdXG9t7tbO/Zzq7IrlFHW1R7q1letJxlRctYVrSMYnfxOWzpIMbw//zk91xu3mG98x0ud72LIz/6Sa68w0OyZDHxsotIlF1EqngeqdDsMyqgpwA3yWZUgAMct9yClUxhfF7y//APY98hleLrr3yBYG+aqpiLu4pvtgtGtLTYw91OnhzX85rKSsyiRbB4MWb5cpjTcOqMoTEE296k9PDTFDe9dNp/mJwrSOf8m+hY9Fmy/tFLKren23mj+w22dm1lT3TPsFUtLSwWBxezrtSeb1flrRrXa3ivZDbPO32B7e2W5KhrcPVzOaAh7GZ+qYeaIhclPuepzW8Pe5zIROa8MSQzhlgmb2/pPPF0nmgmT1ciR2ssR2s0S2ssS1ssS2yUCoYjcVqG6nCckvBJPIEW8q4WMo4u4qaLnsyp+TeOvGHhCVh5KM+lhwwLmmG0V5J0w5FqOFxtcbjG4nC1RWMF5EY5Q2lhUe4pZ5Zvlj2kzltLnb+OOf45hFyhCb+24WRyhtaY/Ts7GT21nYhkifV2sCK/l8sde7ncsYeLraM4rLF/p11WCc3e+USKFmJVLCJYPR9K5hR0ldT+okO7I7vZ1buL48njox5f5aliWdEyFoUWMTcwl9m+2dMzZ0FOc7AzzU929PDq8Tg44jh9LTi8zTi9rZSG28m5Wkjkxz6Z58TJbP/sgVA3LzCP2f7ZQ6q8yVnK57H+8Aesf/onrJZTn7/GsjAfXI/50z8d9wnTqWDlUnh7D+Pv3o+vf+s5gDMbn/TnMn3D199bDGKyZPyVpEL1JMOLSJQuJVm6hFTRbLCcNEcyPPlODy8fiZ1WaGp1rY/PLAuzorow5t1OhrzJczB2kK3dW3mz581Re9nCrjDLi+3AtrxoOZXe6Vsqw5nqInTydUItrxM6+fqoc9cMFqniuSTKltmBrfRikuH5k7aurALcJLvgAxxjVKFMJOyCE0eOwMEDWPsP2EPjxiidZ8JhrIsXE6jOUO59m5B1+n/2dKCajoV/Qte8G86oqmRvppdtPdt4o/sN3ul9h+wIE0Ln+OfYxVNK1zHbN3vUN9xUNs+2E0l+fzTG1qbEqMMh/W6L+aUeeyvzsKDUw+ywG/c0DqGIpfO0xrK0RLM09WY40ZulKZKhqTdDdzIPZHF4W/vOxjfj8DXj8DbjcA3/4VsaMaw8ZG8rjhhCo0xf6w7CntkO9jU42TfHxfFyQ8bKjbh0xERVe6vtic19vQHzAvOGHTabzNrh9mRfsG2N5U4FtViWznhu3C0qJsZaxz4ud+xltfsISzlCkRn/oqHpQA2pojmkiuaQLmogHZxFOlRHOlAz+YuNn6VELsG+6L6BHrYj8SOj/tuVucsGzqheXHTxtH5Iy/gc6Urzk509bDkaf8+/rOHSugSXzm0n6z7OofghDscPE8+N/aXcaTlp8DcM9LTOD8xntn+2eurOViaD9Zt/w/r5z7Dip3qwjM+H+dSnMB//GHim9nfsSEfwd+3F170ff48d1ryRYxMKVHnLRSZYSzpURyZQTdYTJucJk/MU9V0Wk/WEybuDGKcH43BhHG6M5Tp1ErhvSKUjl8bKp3HkUlj9+9kkrlQXrmQHrmQH7mQnrmS7vd9XDXAisk4/h53z2BJv4J38PN4yCzlianBYFtfMCfDpZWHml14Yf9s5k2NPZA9bu7eytXvrKJXDHSwJLWFleCUrwyvH/J41pUweX/e7FDW/SlHzH/B37cEa4XMs7/AQL19BvOIS+7LsYvKeoilrmgLcJFOAO4NlBJJJOHwY68AB2L8f6939drn3UbiDWYLVKQI1GfKXrKRn2SeJ1lw+ZoGI8UrkEmzv2c7W7q1s79k+YqW7am81q8KruLjoYi4KXUTIFSKdM2w7kWDL0RivNyZIjBDair0OLqn2cWmNj0tqfNQWnd/zlyLZCEfjRzmaOMrR+FGOxI9yItk0akU7V9Zw0XHDpYft0NbQNvLjR90edtTOZX/9YloalpKvmUNl0EuZ30mZ30nI48DvduBxGlzOPG5nDsuRIZaL0puN0JvpJZqN0JuN0JFppyXVzMlUM5Fc7/heoLHwmipcmdmQqicdqyManUU6e+Z/Uy4HzCpyU1/sZnbYTUPYvqwr7ptDaAzuRCu+7gP4ek6dgR6rOMrpTXeSCVTbgS5YRzpYSyZQRcZfTSZQRdZfOWlzN0aSzqd5N/ruQGA7FDs06t9GsauYi4suHghtNd6aC+bs80xzrNsOcr8/Gj+td2FBqYePLQlx9Rw/Pbk2DsUPcSh2aCDUjacwjRMns/yzaPA3DJT9bvA3UO4u19/MRPX0YP3zT7BeeH7o0gNVleRv+TO44n2TNj/OHW8h0L6DQMc7BNrfwddzcMQvv4MZy0mqqMGeDxSss09SBev7QlvVpH3OnwlHNm4P7Yw24YkexxttxBM5jifaOO5Kgr2OMMnKS8lVX0q84lISJYvOuxNwkyWWjfFO7zu81fMWb/W8NWLFyBJXCZeG7bV7VxSvOOc1CAZzZGKEWrdS1PwHQi1/wJ0cuWjKflPHy7lLeM26hK9++sPjGkY7WRTgJpkC3FmuA5fPEujYSWD/73Dt2kb2cBvxkx5SPaN/+TTz52EuvRRz6UpYsnhSK21l8hl2RnaytXsr27q30ZsdKRBYBPL1RHvmkowsIJeYB/lTE08dlr2Gy9q6ACtrfMwtdZ+XgS1v8rSkWjgaP8qxxLGBwNaZGd+HU00nXH0sxKUHYd7hCJ7M8F/i81jsL6lnW9UStlUvYW9pA3nHFHwwO+I4PO12T6GnHae3BYe/EYdr7J4vk3eRS9aTi88hl5hLLj4H8kM/WMJeB1Uhu9BLVdBFdcjeavq2M5nL4MjE+oYS7cfXexhv7zG8kSMTPvs78DqwyPrK7InR/koygWpy3lKy3v6z12Gy3hJynmJy3vC4wl42n+Vg/KAd2Hp3sT+2f9TFUgPOABeHLmZZsd3DVu+rx2EV7hw+OV1LNMMv90T47YEoqfdM8Al5HFy7IMhHF58qhT54aYiDsYMciR/hSOLIuKuNBpyBIYFutn82s32zCbq09tmYjh7F8aMn7KWCBjFLl9rz4xYumNjjmRzensME+8JaoP0dPImxp0zkXEGSJQtJliwiEV5EsmQhqeJ5wxZtON85MjGyHYfYv/cd8q17uIjDLLGO47ZG72HMO33Eyy7u67m5hHj58oJdm9YYw4nkCd7qeYs3e95kX3TfiGty1vnqWFeyjrUla5kXmDetnweeyDG7l63lDwTatuMYYQRWzh0iWn3ZwHbjr1PnZiHvYSjATTIFuAkEOGNwJdvx9RzC23OQYPvbBNu2DTtBOZNwEGsN0h2pJ9FkoGvkXhXj88Ky5X2B7hKYNWvSzijmTZ590X283v06b3S/MWqlPGMc5JO1hK0GlocX8sFZS7i4ZM55M4/HGENnppPmZDMnkicGwtrxxPFxf4HyOrzM8c5mbVuYS/YlqXunCW/LKL+TcJjEshU0L1zOvrqlHM77aY/bwxA7Ezm6ErkzKuQycQbL1YvD14TT34jT14jD1zji0M/Bwo5aZnsXcVHREtaUXkxDsPqc9QI40714eo/ijRy1z/zG7En97ugJXJnIpD1PzhUk5w2fGp7kDZN2F/Gux8kbxHgr38muTAupUdad8Tq8LA0tHRgSOd0f0HLu9CRz/ObdCP/6bqRvqPVQq2t9bFgQ4vJ6/2lzePMmb1e+jB8e2I7Ej5DMj3+5kHJ3+dBQ559NpbcSv8N/QfTY5U2eVD5FIpewt3yCeC5+6udcwj7ZYgw1OxtZ8YtthNqGvn8cv2w+u29YTbLsVJBwW258Dh8+pw8fDsLRFkp7j1DWdYCyrn0UpaP4jBlxLnPWXUS8fAXJ0iUkShaRLFlEJlBbuBUxB4ml8/xyTy+/3NtLYtDccQ8ZPlvbxs3VTdQl9xPo2DFmtWKDg2TJAjvMVVxCrOLSMefzT6d0Ps2eyB7e7HmT7T3baU23jnjs/MD8gSkodb66c9jKoaxcmkD79oHQ5o02jnhssngukZoridReSbx8xZDe0slayPtMKMBNMgW4oQHuyUXfxpmO4Ez34I6fxB0/iSfegjvWbPcopEcf3pZzBYlWr6O3/oNEaq6wz0oZAy0tWDt2Yr3zDuzcMWr1S1NZiVm5ErNqFaxYbi+kfJYOdKT41b5etjQfBN9BnMGDuAKHsZyjf8lwW24a/A3U+mqp8lZR5ami0ltJtbeaUnfppH/BTefTA4v3dmQ6OJE8QXOy2d5SzeMOamB/KZoTmMMc/xzmuWax8FCCsu0HsLZtw+oevlSucTphyWLMylWYlSth7pxRl4zIG0NvKk9nPEdHIkdnPEtnwg538UyeRMaQzBmSmTzJrCGZzWMBlmX1Xdo9nYN/HrgeuzKn0wFFHidFXgfFXgfFXnu/yGNhXN10myO0ZQ9zPHWIQ/GDo/YqgT30Y0loCYtDi1kSWkKDv2FaijA40r14YifsoT3xFtyJVtzxVtyJk7jjrRPuvUsDe7we3vF6ecvnZavPS/coC5B78oaVqRSXJZNclsxwUc6FwxUg7/Lb69a4A+RdAXKuAHl3kJw7SN4VHLKfcwfJuwPk3CHyTj95l88+E6/gV5AyOcOrx+L867sRdred/l7jc1lcMTvA+nlBVtb4Rq1e25Zu43jiOMcSxzieOM7xxHGak80jnt0fjs/ho8xTRpm7bMhlubt84OciV9F5EfJyJkcsFyOWjRHLxYhmo0N+HrKfjRHNRYllY8RzcZL55ITmCjtzhuu3GT6zJU9w0D9TygVPX27xq/c5SHnG/zvx5/ME8gY/Doqdfko85RQH6ikKzKbUU0apu5RyTzmVnsqCX1oimc3zzL4IP9/Ve9panWtn+bnl0jALy4f2JjpTXfZIo/a3CbbvwNe1d8Qen36pUD2xipXEK1cSq1hFJlgz6a9lIjrTnQPDIndGdo74XcJjeVhevJxV4VWsCq+i3FN+jlt6ijvWTKjlNYpO/pFg6xsjVjPNOzzEqlYPhLZMsHbEx1SAG50C3DSaaIALtm7jc0e/Q8LhGFhccCIMDhJlFxOtvoxI9WUkyi4ae2x4LgeHDmFtfxvr7bfh3Xex8sN/qBuXE5ZehFm1CrN6FdTXj/vsXzZvfxl5el+EPcN8GakrdrBmbhfFJYc5mtrL3ujeCZ01dlpOKj2VVHmrKHOXEXAG8Dv9pzaHH4flIG/y5EyOPPZZ1mg2am+56Kn9bJTebO+I481H47Jc1PvqB8Ja/wK9oYSxw9rrW+Htt7FSw79hm/JyzOrVmFUrYflyCEzfWPazlclnOBw/zLvRd9kX3ce+2D4i2dF7ulyWiwZ/w0BxlNn+2dT76qf9i4qVS+NOtOJKtNmXqR6cqW6c6V7S6S6OZDo4ZKIcJMkel2Gfx01mlP8bLmNYnkpzWSLJ5ckkl6ZSeKfo3S/n9PcFQXvLeYrIesvI+UrJesvIekvJ+kr7Lu2fC7li50x0qCvNb/ZFePFw7LThlWAPRb56ToDL6gOsqPaNa/2rdD5NU7JpIND1h7uRiiOMh9tyU+oupdRditfpxefw4XF48Dq8eB1efM6hP3scHuzTRQxcDpY1WdL5NFmTJWMyZPP2ZSafIZFPkMwlB3rFBgezkeZdT6WiuB3iPvymwTnon6grCP/8AQcvrbAwk1zePugMUuGpoNJTSbmnnArvoH1PxXm7pmM6Z3h2f4Sf7uw5rZf5khoff3ZpmIsqx/eeb+VS9kLPHe8QbH+HQMcOnJnRP7vTgWpiFav6At1K0qHxf5c5E/1VI9/seZO3et7iaGLkXsRKT+VAYLu46OJpK0BkZRME298h1PIaoZN/HLXnM+2vJlJ7BdGaK4lWrRn3XDYFuNEpwE2jiQY4f8dO/vTQ/xhXgDNYpEP1JMPzSYbtMfCxipVnX7UnFoNdu7DeftsOdaMsXWDKyjCXXgqXXoq5ZAUUn76eSE8yx7P7o/zruxE6E0PHsjsseN/sAJ9YUsSyqqElgPuHAg2etH8kfmTMHp1zxWW5qPZWD5TXr/fboW2WbxYuqy80t7Vhvf66Hdr27B6xWqhpaMBctg5z2WUwb96MGBIzHGMMzalmO8z1bWOtX9avwlNBva+een/f5qunxltDwBk4Z19QErkELakWTqZO0pho5FjiGMcSxziZGt/yHvMdYVZbYdbmvazMuwll0ziyCRzZJFbOvrR/juPMxqesXPd45Jz+voBXOijg9YW9QCXpQI09F9BTMmP/Xs9HsXSeV47FeOlwjB0nU8P2E/lcFitrfVxW52ftLD9lgYkNO49kI0MC3YnkCTrTnXRmOkesLjxTuCzXqZN/Dv/pJwL7rnc73AOB02DsKQ2pLtzR45QcOczCzR2UHh3a4x4tz7HnijTHGyDudBHxhYl6QkRdPuIOJ4l8imQ+SSqfmtDojtG4LTeV3kp7KRjvLGp9tQPLwkzWUjATkc0bnj8Y5Z939NAeH/r+trTCy5+tDHNpzVmePDJ5vL2HCbS/Y08tad+OOzFKBTAg6ykhXr7MLllfvpxE2UXkXWd+8tQYQ0uqhd2R3eyO7GZHZMeIJy8dOFgcWszq8GpWhVdR56ubltBtZRMEOnb0rcP6FoHO3SN+BhkcxMuXE6m1e9lSxfPP6HNAAW50CnDTaKIBzhM5yi17vz4Q4F6KlJDzhMi5i+wCCsGavi9ONaSDs85NxZ7mZqw338J66y3YvQsrPXyAMpYF8/qLoVzKwap5PH0wzsuHY2Te06EX8ji4flGIjy4qoio0/i8XOZOjJdnCyfRJWlOttKXaaE210ppupTXVOqEeu7E4cFDiLrHPJntKKXOXUe2tHvgArPRUnj5kM52GPXuwtm+3w+/x4dfqMg4HXHQRZt06zLq1UF09ae0uNN2ZbvZH97M3utcuxDCBIgwAfod/4GxzhadiYL/YXTzw5SvgDOCxPOTJY4yxLzHkjX1pjBkYctWb7SWSjdiXmQhdmS5aU620pFroyQ4/1HU4HsvD/OB8FgUXsTC4kKWhpRNbMNUYu1R3Nt63JXBmYvZ+JoYzG8ORifddF7MvB/ajdhjMJfvua+9PhbzTa1fw7At0GX81mWANGX816WANGX/VjK0QN93a41l+d8R+jz3YlR7xuEXlHi6r87Ouzs/8Ms8ZF38yxhDJRujIdAwEuiGX6U46Mh2TFj7OlNtyE3QGCbqCBJ1BQq7QkJ+H7LuChJyhgZ8n0tvhSrQRat1GsHUroZNv4E4Onb8ca/FwcnuYVPfQIeHWojlk/8OtsHzFiI+dN3mS+SS9mV66M932UP5MF92ZbjoznXSkO2hPt9OZ7pzQMNjBil3FpwKdt2+tz77pCc5JrlSZyxtePhLjyXd6aIkOPQmwoNTDLStLWDvLNzXBxRg8sSYCbdsJtm8n2LYdT7x59Lv0z6MrW06ifDnx8uWkg3UjhpTBga1/vc7RerJDztBAL9uK4hXTEqadyU4Cnbv7hqNuHzWwAWR8FURr3kek5n1Eq9aQ95z9AuAjBjhjIJ+HbNYeJTZ4C4XAe/ZFehTgJtmFHuAwhv/0xp+ScFj484bH1v3z1DdyIlIp2LUb66237B66EydGPDTh9PBOxQLeqlrM2xULOFpcQ0OplxuWFLF+XnBCC2iPhzGGSC5Ca6qVnkzPwOTz/uE1/WsoOSwHDhw4LAceh4eQM0TIZW9FrqKBnwPOwNhz6oyBEyfswPbWdti9Gys9/Bcp4/XCypV2aFuzGoqmbn2TQpYzObvnNWaXSW9MNtKYaJxQeDrXilxFzPHbQ2UbAg3M8c9htn/2pH8JOism19fDF8eZ7sWV7MKV6uxbs6kTV6obZ7Lv51QnrmQXjvzIoWDcT2s5SQdrSYdm26XNQ7NJ95U4z/oq1Hs3SY73ZHi9Mc7rTQn2tKVOW46gX9jr4JIan70ES62P2pBrUr80G2NI5BP0ZHpI5pOk82m7RymXGvJz/366729s8Jyzwd8tXA4XbsuN2+HGZZ3ad1tufE7fwAkan8NHwBkg5ApN2ZAzRyZGsG27Hdhat+LrPTLy7wEHidKlRCrX0HvQQ+aZV7F6hr6HmUsuIX/zzbB40Rm3KWdydGW6aE+105ZuGwh2bek22tPttKfaSZuJ/T92Wk5qvDUDgW5w791Eg0be2FMn/vGdHo73DD352xB28x8uDXPF7MA5ryjtjrcQaHubYPtbdmGUUf4t+2U9YbvaZ8liYuGFHAiUsote9kT3jhnYwF4Dtz+0LQwuPKdFqRzZOL6ufQQ6d+Pv3IO/azee+OgjR/JOH7GKS4hVrSVS875TvWzG2MtXRaOntlgMK9K3n4hDKg3pFKTSWH2XpNP2d8h0eiCYtfemcOZzuEyOIhd9IS2LlR05SJpggPwTT5z170QBbpJd8AEO+E9bbz5/A9x7tbZivf0O2be2wzs78CRHrkKYDYRwLL8Yli/HLF9mz58bpSDHeckYu0dy1y57mOmu3VhdI79pm5IwZtVqzOWXwYoVk3LW6EIVyUZoTDQOBLqmZJP9BSXdTu4cDTUsc5dR462h2ldNrbd2ILCVuErOy3kmZ8UYHNk4rlQXzveEPbuY0knc8Rbc8ZNnHPRyTj/potl9oc4OeOlQA6mi2eTd5/6M9EwRSeXYdiLJ1qY4b5xIEkuP3ENT5neyuNzD4govi8s9LCr3EvQU2PvyFLGySQKdu+whZa3bCHTuGrWHIlXUQLRqLdGqdcQqVw2dzpBIYD3zr1hPP40VH/o5adasIf+5z8H8eZP+GowxdGW67AJcKbticv/Wke6YULEWsHvt+oNdjbeGam/1wOXgecrGGLY2JfiHt7s51DU0uNWGXGy6NMz75wTPaJmYqeBIRwZ6o/ydOwl07MKZtYu7JS2L/W43e7we9nrc7PV4eNfjJjXG95cabw0XF108sM5tmafsnLwOT7wZT+Q4vt5DeHsP4+s5hCfahDVMT60xkM9Y5FIOMlkfMc98Eq56UlSQyXghEsMaHNT6Nis3fcP7jdtN/p+ePOvHUYCbZApwhRXgDneleXpvhJeOxMhmcyzuOs7q1n2sbn2XpV3HcJqRvzgYnw/mzsXMmwfz52HmzYf6OnCdJ8Ot8nk4eRLr8GG7yMuhQ3DosP1mNgK7auQSzKqV9pp6Y1SNlLOXN3l6sj10pDtoS506Ax3NRQd6XuO5ONl8FsuycOAY8TLgDFDkKqLYXWxfuuzLam811d7qaZtMfl4zBme6G3esBU/iJO5YS1/F3BY88ZN4YidwnsESDRlv2aBw10CyeD6p8Hx7SOZMC8tTKJc37GlL8XpTgjeaEhzrGXsOcX2xiwVlHuaV2tv8Ug+l/vOoN3mKODKxvnlT9jC7saobZr2ldmCrXkesai2ZwDiGwkejdoj7199gJYcOazaXX07+c38CDedmLlA6nx5YBue94e5MhsGGXWF7+GWuhOOdQTp6Q5hsCflMCSYTptxbzJ+uKGXDghCu8yS4DZY1WdpT7ZxMnaQp0cix3p0ciR+mMdtDbhzNnZPJsDaRYl0yydpkijJ3CZlANVlPCTlvMTmPvVbocOuH5t0BDA67YrDJ4cilsfJpHLkUVi6NlUvhzMRwZiJ2ZfJMpG8URQeuRAeuZAfuRNtA6OyXz1pkYk57izsH7btIJ9zkEhZnOAJ3UuWwcLhd4HSe2lxOcLpO7TedwDJGAW4SKMCNw0wMcLm84Y+NCX69t5edrae/yc8Ou7lhSREfqrHw7d2NtXOn3VN1dPS1W8A+s0JDA2bWLKiqhMoqTFWVvV9ePqkLjJ96QTlob7d71ppOQPMJrGPH4chhrPjYlcxMTQ3m0kvswDZJyyyIzBh9Ac8bOY4ncgxv9DjeyDE80WN4ok048hMrSpRzh/rC3DySxQtIhueTCi8gNwnzMS4EXYkc75xM8k5LkndOJmmOjK8oSYnPwdwSDw0lbmYXu5ld4qYh7KbYW7jBzpnqHhrYuvcP20vRL+/0EatcSbRqHdHqdWdcuAGA3l6sX/0K69+eHTLs3lgWrF5N/mMfsz9PpuFkxeBeu/6tP+CNtnbrWFyWizJ3GeWe8oG5ymWevp/d9nVBZ3BKRjMYY4jmonSmOwfmE3ZluuhMd3IyZc+pb0+3j3s+ocdYLMjBskSUtYkEa5MpqqehVyqXtgNaOuY6Fc76wlo25iKXnvwTyCYYtOeh9V2aUAhCfdeFiuzK2V6PPW3E4wWPB7weewSSx2Nvbjc4ndz88xPE8hZet3PMIiZn8n16NBdygDtPuknkXImkcvz7AbuaZGts6BuVBayr8/OJpUVcWjNoIvJll2Euu8wepBGJ2PPndu3E2rMXjh8/rRveymTg4EGsgwdPe37jsKCszA515eX2HLKiIigKQTCEcbnsRcqcTnA4IZuxS/Wn0/b462gUenugp9eei9Dba/8ciWKN82SEsSyYNQtz8UWwbBnm4ovtNonI8CyLnLeUuLeUeMUlQ28zOdyxFrzR43gix/FGj9nhLnIcT2L4+RnOTJRgxzsEO94Zcn3GV04yvIBU8Tw74BXPI1U81177UgaU+p18YG6QD8y1fy89yRz7O9K825Hi3Xb7sjd1+pfY7mSe7S1JtrcM7TUq8TmoL3bbwS5sh7qGsIcSn+P8GlZsDO7EyVPrh7W9ha/38Kh3yTu9xMtXEKu4lFjlKhLlyzGTtUZlcTHmz/4M8/GPY/3iX7B++1usbNb+LNq2Dee2bXZ14o99DHPN1faX3nPEsix7XT9PGcuLlw+5LZVPDayFejJ1kpOpkxyKNNOUaCHnHH2ectZk7YJjoyxU7bbcA6Mf3A73aSMlLCwclgOLU/uDb0vn0wOVPJO55MB8y1QuRY4zC1gBZ4A5/jnMDcwd2AaqTeezeGLNJKLHaY4cwxM9bp+sijaO+B42EcZALuUgHXWSibpIR52kIy7SEReZ6NkHNBMI2NXDi4ugqBhTXATFYQiHIVyMKSq2v2OF+rZAwP6ONUmyThdT0Rkko1OAu0Ac6U7zzN7h1x8KuC02Lgjx8SVF1BaN8cFWVATvuxzzvsvtQJfJwLFjWIePwOFDWIcOw9GjIxYBsfIG2jugvWOY1YImn3E4oL4es2A+zJuPmT8P5sxRD5vIZLGcZEJ1ZEJ1UPO+oTdlk3iijXao6z2Cr/cw3p5DeKPHh52H5E524E52UHTy9SHXpwM1p0JduO+yaM65qeBbAMI+J2vr/Kyts9/XjDG0xnIc7kpzqCvN4a40h7syp1UO7NedzNOdTJ02GiPocdAQPhXqZvdtlQHnOQl2Vj6Dr3s/gY4ddmjr2DFmKfmcK0i84hJiFSuJVa4kWbpk8gLbSEpLMbf9J8yNn8D6xS+wXnppoNKzdewY1v/5P5h//EfMdR/GfPjDUFo6te0Zg9fhZW5gLnP8c3i7Jclr7/awq//f3krjcHfj8/WwpiFNQ3mMSK6Ljow9rL0jPXaF0ozJ2FVNM53n4NUMFXQGB4bL13hrmBOwQ1uVp2rkv1mHi3SRPYc3WnvlkJusbBJXsgNXugdnqse+7Nt3pnvstUTT3TgT3eR7UmR6jb1FHGSiFpmIRbY3z5munDRw0ruiAlNRCZX2Zior7P2KCvDpffBCpAA3g+XyhjeaEvxqX4R3Wk4vP15f7OLjS4r50PwgAfcZngFyu2HBAsyCBcAGO9TlctDSYs89a22D1v7LVntNtcjE59KMxliWHSzLyjCzamHWLLuHbdYsmD1bRUdEpolx+UiVLCRVsnDI9VYuhTdyDG/PIXy9h/D1HMTbe2jEimqeeAueeAtFLX849dhYZAJVpIP1pIvq7Xl2oXp7C87COC/c//eWZVEdclEdcvG+2afWwYqn8xzryXCsJ0Nj3+XxnjQnY8P3asTSefa0pdjTNvQLu99lUf+eYNcQdlMVdJ1xQQtHJoqv56D9t9B36e/aN2ZxnawnTKziUuKVq4hVrCRZsgCmq3JsRQXm9tsxN9+M9fwLWM/+G1anXSDL6u3FeupnmH/5F8xVV2Ouvw4WLpy24ZVbmxL8ZGcP+9qH/n6DLi83Ll3EJ5YWExqmEI4xhlguRke6g45Mh335nq0n2zPpy1C4LBdeh5diV/HAskD9i82Xukup8lZR7a2e9FL+xuWzT1BRZ3eldXVBYxNWUyM0OrBOJOCkgfYMVh7ssUz9/6ambxvl8R2WHcKqqjF9U06GBLSysvOnpoCcV/RXMQNF03l+eyDKM+9GODnMGde1s/zcsLSIVbW+qSn763RCXR3U1Q28dQ15C0sk7DDX1Y0VjdjDMvtK2J62VojbfWr8tcdjj9EuLsaE+4YHFBfbQwImcTiAiEwt4/SSLFlEsmQRgwdsOTJRvD2H7Z663kMDPXbu1Oln8i2MXWAlfhLatg19fIW7YQU8DpZWellaOfT1J7N5GnsyHB8IdfbWHM0Ou5RBImvY35Fmf8fQL/8ep0VdsYuGsJs5JR4WlnlYWO4ZOscun8UbOTY0rPUeHLMcer9UsI54+Qri5cuJV64kVTTHLhpxPikuxnzqk5gbPo712mt25cq+KQVWNof18svw8suY+nrMB9dj3v/+c9Ir178cwE929nD4PVUlg26LGy8qHjG49bMsa2DJnjnMGfG4dD5NJBsha7JD1ursX7+zf3/wbQb7Oo/lwef04XV48Tl8eJ1ee6jjuZLP23Pqjx3vC2qNWI1N0NR0WvXR8TAuJ1RV2SGtphqqazC1tVBbY18/FXUBZMZTgJtBjnWneebdCC8cPH2YpN9tce18e5hkXfE0v1n4/fYwxjlzJlj8WERmsrw7RKJiBYmKoYsjO1M9dintQSW1vZEjuFLdwz6Owt3E+FwOFpZ7WVg+9LVncoam3gzHezMc6z4V8E5EMmSHqRORzhkOd2U43JXBRxfzrBbmW81c4m1muaeF+TRRlT6Oc5zjyfIOD4nSpXZY6wttOV8BzVd2uzHXXIO5+mrY9y6OZ56B1/9oTyUArMZGrB//A+Yf/xEuXYlZ/wHM2rWTPmokkzP87miMn+3qPW0dt2Kvg5suKuZji4smdekJj8NDuad80h5vShgD3d32XP5jx+H4sb7L46dVFx3zoULBIQGN6v79arsXTSeZZZIpwBW4dM7wyrEYz+6PnhrDPkhtkYsblhRx7fwQAa0LJCIFKOcNE69cSbxy5ZDrHekInlgT3mijXTQl1og30ogn1jgp4S4dqCXjryLrryDjryTjr8S4Lpz5s26nxdxSD3NLPQzubMnmDS2RLI1dMWLtjZjuo/iijZSkmmigmbmOFmZZg3pNDTDGiLqUt4J0yUKS4fkkwwtJhfvmOTpnwLIflgVLl5BfusSeRvDb32K9/Dusjg775ryBt97CeustjM+LWbMWc8UVcOklZzVfuzmS4dn9UX57MHpaUZtSn5NPXVzMRxaH8Llm+HcDY6Cnx+5Ba2waGtQmMKXDOBxQU23Pq6+rh/o6TF0d1NTYI4FEziEFuAJ1vCfDs/sjbD4UIzLMAq+ra33csLSYNbOmaJikiMg0y3uKSHqWkixdetptQ8JdtLGvstyZh7t+OXcRGX/FQLDLeorJu4vIeYrJeYrIuYvIuUP2ft/POArno9bKpXFkY7hSPbiSnfbi78nOU/uJdtzJdpYn2nGl31OxcBydDHHjZZ+Zzd78bPaahoH97mQRpSkn8zNu5uc8LLQ8LHI5qAya86sS5tmqrMRs2oT53Odg506sF1/Cev2Pp4qeJFNYr7wCr7xiD71behFm9WrMqpVQXz/mnLlMzvB6U5xn90d5q/n0XqTKgJPPLAuzcWEIj3MG/V7BnnbR2moHtaamvrlqTXCiCSsaG/v+fYzTCXWzMLMb7LBWX2f/7mtqNNxRzhuF86kixDN5/tgY57n90WHXbgu6LT44P8THFhcxO6w3GRG5cI0a7jLRvuqY4w93/ZwZewHescrXD5Zz+cm57YBnh71T4S7nKSLv9JF3+TBOL3mnD+NwAQ67QJPltC/7Fgg2lgMsC2M5AWvodX3HOHJJHJk4jmwMZybWdxnHMbAfw5GND7otNnD8RNf0G0nWW0oqNJtU8Ry6fQ0cNLN4O1nNtkgp+7uytMdPL5zSlcyx7USObSdOBY8Sn4OLq3ysqPKyvNpHQ9h9xoVSzitOJ1x6KebSSzGxGNarf8B6ZQvs3n1qiGU2Z4e8nTvh7/8eU1mJWb0Ks2oVLF8+pPpgU2+G5w5E2XwoSnfy9JO6i8o9fGxxER+YG8Rd6MEtEoHmFqyWllNhranJXgc2O741EaGvAFp1NTTMtsNaw2xMQ4OCmhQEBbjzXDKbZ2tTgt8fifPGiQTp3OmzxpZWeLl+UYir5wRm/lAIEZGzlHeHSJaOHe7c8ZO4Em24E+24E60DvU/DLYEwGmc2gTObgElYU+p8YXCQ9ZWSCVSTDs225xIWzSYVqicdmn3a2n21fdv1fT93JXIc7ExzoDPFoc4MB7vSwxbd6k7mefVYnFeP2cUj/G6LxeVellZ4WVrhYXGFl7CvwOcXBYOYjddiNl4LXV1Yf3wda9s22LVzoGcOwGprw3ru3+G5f8e4XOQWLqSxeh4ve2bxnLOOLl/xkIf1uSw+MDfIRxaFTpvfeF5LJqGjAzo6sTra4eRJaOkLbC0tE+pNg75Kj1XVp4Y8zp6NmT3b7lVTlWopUApw56FUNs+2E0l+fzTG642J0wqSgL122wfnhbh+UYh5pTNgjoCIyHlgtHAHgMnhSnbZwS7ZjjNt98gNvnQMd90Y5fCnm8Ei7w6ScwXJuwPk3EVkfWX25i07te+rIOOvIOstPauhoaX+oWvXgV1B+UhXmoOd6b4qlymaIkNDXSJjeLslyduDlsaZVeRiSYWXxRUeFpd7mV/qKdxeptJSzPXX2csMpFKwazfWW29ivfkW1slTJwCsbBbX3r3M3buXucCtwEl/CXvL5tBRN5+aFQtZsWYhgcrSaVmqYFjGQDzeF846sDo67f3OTns+YGdfaItNLKANPLzXaw99nFXXN09tll0Ru7ZWPWoy4yjAnScyOcObJxL8ri+0JbKnhzanBStrfbx/TpCr1NsmInLuWU577pu/gonUqbNyKZzp3vcEviiOdMQe8phLYuVSOHIprHwWTB7L5IE8ljFg8vZ1GDC591yXB2PsnkFjyLt85F0BO4i5gkOC2an9odflnb5pL8cf8jhYXu1jefWpoYE9yRy7WlPsOJlkd1uKw13p05Y2OBHJciKS5cXD9hd/lwPml9q9c4vK7VBXV+wqvPngXi9m1UqOzl/G9is+w8HdRwnveps1J/eyov0QnvzQcFud6Ka6qRua3obXgb8DEwxCdRVUVPatM1YJ4RJMMADBoL0F+vY9EzwZbAxks/YWj0Mkai8JFI1g9fQMCmf9Qa0DK3l268MZh2WvlVZTg6mpgdpae45aXR2Ul4ND34vkwqAAN426Ejm2NiXY2hRne3Ny2NDmsOCSah/XzAlwRUNg6Ho6IiJSEIzTS9ZfSdZfOd1NKShhn5MrGwJc2WAvSJ7M5jnQkWZve8re2lKnzfnK5uHdjjTvDlqnLuC2WFTuZXG5HezmlNgLj7vOs/l0ubyhsTfDvvY0O04m2d6cpCvZP2Q3DAvez78seD/uXJZFkSauzZ5gZeQ41U2HcbS3nfZ4ViwGhw7DocOM9UqNywWecfRUGSCXhUzWPpEwiYzTaa+JV16OKS+zS/BXVfeFtRp70Wv1pokowJ1LyWyeXa0p3ukb/nGgc/ghNRawvNrLNXOCXNkQoKTQx/eLiIhMAp9raC+dMYa2WI697Sn2d6TZ157iYGf6tKkH8WGGXjosqAq6qC3q20Lugf2akAvvFI9yyeQMLdEsR7vtsPluX9uHO5nbz+u0WFvn58qGAOtmzRtYHsgAua4u2L8fa/9+rOP2AtS0nhwoijIWq783bYoYtxvKy6CsHFNebu+X9+2X2fsUF2vNNJFxUICbcoYfb+9mx8kk+9pTDDOdDbA/SC6q9HJ1Q4CrGgKUBfRPIyIiMhrLsqgKuagKuXj/XLtwSi5vONqT4d32FO/2zaU72p05behl3kBLNEtLNMtbzac/dnnASW2oP9C5KfI6CLgdBNyWfemx9/0uB5ZlP14ub0jnDKmsIZHN05uyt0gqZ+8n87TFs5zozdAWz53WpuHUF7tYVetnZY2PS2t9I0+fKC2Fyy7DXHYZAw+bzdrDF9vasdrboK3NruIYi9u9c/EYxOLQv58dZ4EelxNcbrs3zO0GnxdCRZiiIigKQVHxqV608nJ7C4XOn/l4IgVOKWGS9CRzHO5Kc7grw5HuNLgN9A1Y+MnOnmHvE/I4WDPLx7q6AGtm+SjS8EgREZGz4nRYzC/1ML/Uw/WL7OuS2TwHO9O8224HuhORLM2RDLHMyAmqI56jI54bdtmeqeK0YG6ph8XlHpZWeLm0xkdF8Cy+qrlcdqn86mre+0ond/CjiJxLCnATYWBvW4rmaIbmSJbmiH3mrjmSOW0MftWS0+/udVpcXGW/Ia+o9rGwzDMz1rMRERE5j/lcDpZV+VhWdapAijGGSDo/8HneHMnQHM3SEslyYpjP9clW7HVQW+RiVpGbReUeFpV7mV/qnvKhmyJS+C64AGeMIZOHRCZPIpMnnjHEs3kS6TzxrDl1XSZPVyJHZyLHvVmDF0hmDV97rmXCz/kfLglzSY2PxeXewi1tLCIiMoNYlkWx10mx18mSitPXA0tk8gOB7mQ0SzyTH7SZgf1EXy+ew7I3r8uB12nhc1kUeR0Ue50Dl2GvgxK/k1lFLo26EZEzdkEEuL1tKf6/LW3EM/aY9OwET6qNZ4x6ZdDJvBIP80o9zCt181hr/y0Wf3pJyQRbLCIiItPJ73YMDMUUETmfXBABzrLgZGycE3PHeJw1s3zUDKpUVRtyUT1MtarHWtXTJiIiIiIik+uCCHAB99BwVRl04nc58PdVkvK7HQRcln3p7r90EPY5KPO78P6bBTnwuiz++4eqp+lViIiIiIjIhe6CCHCzilz4XBbJrMHnsnj8k/UTur+q3oqIiIiIyPnggih1pEqPIiIiIiIyE1wQAU5ERERERGQmUIATEREREREpEBfEHDgRkelijMEYQz6fJ5+f2oWBRaaTZVk4HA4cDgeWJo+LiEwZBTgRkSlijCGXy2GMweFw4Ha7AfTlVmYUY8zAZT6fJ5PJ4HK5cDg0yEdEZCoowImITJH+3jeXy6XQJjNW/992fw9cPp8nm80qxImITBG9s4qITJF8Pq/hZHLBcTgcOJ1ODRkWEZkiCnAiIlOkP8CJXGj6e+JERGTy6ZuFiIiIiIhIgVCAExGZQho+KRci/d2LiEwdBTgREREREZECoQAnIiIiIiJSIBTgRERERERECoQCnIiIiIiISIHQQt4iIiLjtGLFCgDuvPNO7rrrrmluzZlJJBLs2bOHXbt2sXPnTnbv3s3Ro0cxxjBr1iyee+656W6iiIiMQgFORETkAvL444/zf/7P/5nuZoiIyBnSEEoREZELkNPpZOHChXziE5+grq5uupsjIiLjpB44ERGRcdqxY8d0N+GsffCDH+R973sfF110EX6/H4DPf/7zNDU1TXPLRERkPBTgRERELiAXXXTRdDdBRETOgoZQiohIwXr44YdZsWLFQHGRlpYW7r//fj760Y+ydu1arrnmGu688062bNky7P23bt06cP+tW7eSTqf50Y9+xKZNm7j66qtZsWIFP/7xjweO7z/24YcfHrFN0WiURx55hE2bNnHVVVexevVqNmzYwJe//GU2b9486uu57rrrWLFiBffeey8Ar776Kl/5ylfYsGEDq1at4jOf+cxEf0UiIjLDqAdORERmhN27d/PFL36Rrq6ugetSqRRbtmxhy5Yt3HrrrXzta18b8f49PT1s2rSJffv2nXEbdu7cyd13301HR8eQ61tbW9m8eTObN29m/fr1fOc73xkYvjiShx56iB/+8Idn3BYREZmZFOBERGRGuOeeezDGcN9993HNNdfg8XjYsWMHDz74IAcOHOCJJ56gvr6em2++edj733///XR1dXHnnXdy3XXXUV5eTnNzM5Zljev5m5ubueOOO+jt7cXn8/GFL3yB6667jnA4zKFDh3j88cd56aWXeOmll7j33nv57ne/O+Jjvfbaa7S2tnLVVVdx2223sWDBApLJJAcOHDij342IiMwcCnAiIjIjdHR08OSTT7Jo0aKB69avX8+aNWvYtGkTR44c4cEHH+SGG24gGAyedv+TJ0/y0EMPsX79+oHrSkpKxv38DzzwAL29vViWxd/+7d9yxRVXDNy2evVqVq1axTe+8Q1+85vf8Nvf/pbf//73XHPNNcM+VmtrKxs3buSBBx4YEiBnzZo17vaIiMjMpAAnIjKNfvhGJ4c609PdjCkzv8zD7WvLzslzfe5znxsS3voVFRXx1a9+lS9/+ctEo1GeffZZPv3pT5923FVXXTUkvE1EV1cXzz//PADXX3/9kPDWz7IsvvnNb/Liiy+SSCT42c9+NmKAczqdfP3rXx9375+IiFw4VMRERERmhGuvvXbE297//vfj8/kAePPNN0c85kxt376dXC4H2IVIRhIOh7nyyisB2LZt24jHLVmyhJqamjNuj4iIzFzqgRMRmUbnqnfqQrBgwYIRb3O5XMyZM4d9+/bR2Ng47DH19fVn/NyD11AbrhdwsEWLFvHCCy/Q09NDNBolFApNaltERGRmUw+ciIjMCIFAYFy3x+PxYW8fqyrkaGKx2LjbMXj+3eD7DdbfWygiIvJeCnAiIjIjjBTM3nv7WAHrTAwOZWO1Y3BoG66YioiIyGgU4EREZEY4ePDgiLdls1mOHj0KTM3wxLq6uoH9sUr9998eDoeHHT4pIiIyGgU4ERGZEfqrQA7nd7/7HclkErBL+k+2lStX4nQ6AXjuuedGPK63t5dXXnkFgDVr1kx6O0REZOZTgBMRkRnhJz/5Cfv37z/t+mg0yve//33AHrJ4/fXXT/pzl5aWsnHjRgCeffZZXn/99WGPu//++0kkEgB89rOfnfR2iIjIzKcqlCIiMiOUl5dz22238V/+y3/hmmuuwePxsGPHDh588EEOHz4MwFe+8pUpm3d2zz338Oqrr9Lb28vdd9/N7bffzoc//GHC4TCHDx/mscce48UXXwRg48aNXH311VPSjrGk02n27Nkz5Lr+eXnpdJq33357yG0LFizQUE8RkfOIApyIiMwIDzzwAHfccQff+ta3hr391ltv5eabb56y56+treUHP/gBd999Nx0dHTz44IM8+OCDpx23fv16vv3tb09ZO8bS1tbGLbfcMuxt7e3tp9322GOPsW7dunPRNBERGQcFOBERmRGWLVvGU089xWOPPcaWLVtobW3F7/ezfPlybrnllnPS47V8+XKeeeYZnnzySV566SWOHDlCIpGgtLSU5cuXc+ONN7Jhw4Ypb4eIiMxcCnAiIjJj1NbWcu+99477+HXr1rFjx45xHz+eY0OhELfffju33377uB+332gFUCZLXV3dhF6ziIicX1TEREREREREpEAowImIiIiIiBQIDaEUEREpENlslnQ6PeH7ORwOfD7fFLRIRETONQU4ERGRAvHMM89w3333Tfh+a9eu5fHHH5+CFomIyLmmIZQiIiIiIiIFQj1wIiJSsO666y7uuuuu6W7GOXPTTTdx0003TXczRERkGqkHTkREREREpEAowImIiIiIiBQIBTgREREREZECoQAnIiIiIiJSIBTgRERERERECoQCnIiIiIiISIFQgBMRERERESkQCnAiIiIiIiIFQgFORERERESkQCjAiYiIiIiIFAgFOBERERERkQKhACciIiIiIlIgFOBEREREREQKhGu6GyAiIlIoVqxYAcCdd97JXXfdNc2tOTMnT57k5ZdfZuvWrezbt4+WlhYymQzhcJglS5awYcMGPvGJT+Dz+aa7qSIiMgwFOBERkQvECy+8wFe/+lWMMafd1tHRwauvvsqrr77KE088wfe+9z0WL148Da0UEZHRKMCJiIhcIOLxOMYYysrK+PjHP86VV17JggUL8Pv9HDt2jH/+53/m17/+NceOHeMLX/gCP//5z6moqJjuZouIyCAKcCIiIuO0Y8eO6W7CWSkvL+e+++7jk5/8JG63e8htK1asYMWKFdTX1/Pwww/T2dnJo48+yje+8Y1paq2IiAxHRUxEREQuEFdeeSV/8id/clp4G+w//+f/THFxMQAvv/zyuWqaiIiMkwKciIgUrIcffnig5wigpaWF+++/n49+9KOsXbuWa665hjvvvJMtW7YMe/+tW7cO3H/r1q2k02l+9KMfsWnTJq6++mpWrFjBj3/844Hj+499+OGHR2xTNBrlkUceYdOmTVx11VWsXr2aDRs28OUvf5nNmzeP+nquu+46VqxYwb333gvAq6++yle+8hU2bNjAqlWr+MxnPjPRX9GEud1u5s6dC0Bra+uUP5+IiEyMhlCKiMiMsHv3br74xS/S1dU1cF0qlWLLli1s2bKFW2+9la997Wsj3r+np4dNmzaxb9++M27Dzp07ufvuu+no6BhyfWtrK5s3b2bz5s2sX7+e73znO/j9/lEf66GHHuKHP/zhGbflbPS3PxQKTcvzi4jIyBTgRERkRrjnnnswxnDfffdxzTXX4PF42LFjBw8++CAHDhzgiSeeoL6+nptvvnnY+99///10dXVx5513ct1111FeXk5zczOWZY3r+Zubm7njjjvo7e3F5/PxhS98geuuu45wOMyhQ4d4/PHHeemll3jppZe49957+e53vzviY7322mu0trZy1VVXcdttt7FgwQKSySQHDhw4o9/NROzatYumpiYAVq5cOeXPJyIiE6MAJyIyjWq2fx9/99R/KZ8uiZKFtKz8yjl5ro6ODp588kkWLVo0cN369etZs2YNmzZt4siRIzz44IPccMMNBIPB0+5/8uRJHnroIdavXz9wXUlJybif/4EHHqC3txfLsvjbv/1brrjiioHbVq9ezapVq/jGN77Bb37zG37729/y+9//nmuuuWbYx2ptbWXjxo088MADQwLkrFmzxt2eM2GM4Tvf+c7Az5s2bZrS5xMRkYnTHDgREZkRPve5zw0Jb/2Kior46le/Ctjz05599tlh73/VVVcNCW8T0dXVxfPPPw/A9ddfPyS89bMsi29+85sDQyd/9rOfjfh4TqeTr3/96+Pu/ZssDz30EG+++SYAH/nIR7j88svP6fOLiMjY1AMnIjKNzlXv1IXg2muvHfG297///fh8PpLJJG+++Saf/vSnhz3mTG3fvp1cLgfYhUhGEg6HufLKK3nhhRfYtm3biMctWbKEmpqaM27PmXj66ad55JFHAJg7dy5/+Zd/eU6fX0RExkc9cCIiMiMsWLBgxNtcLhdz5swBoLGxcdhj6uvrz/i5++eMAcP2Ag7Wf3tPTw/RaHTS23ImXn755YHAVltbyw9/+EMVMBEROU8pwImIyIwQCATGdXs8Hh/29rGqQo4mFouNux2D598Nvt9gPp/vjNsyUX/4wx+45557yGazVFVV8eijj1JbW3vOnl9ERCZGAU5ERGaEkYLZe28fK2CdicGhbKx2DA5twxVTOZe2bt3Kl7/8ZdLpNOXl5Tz66KM0NDRMa5tERGR0CnAiIjIjHDx4cMTbstksR48eBaZmeGJdXd3A/lil/vtvD4fD0zpMcfv27dx9990kEglKS0t59NFHmTdv3rS1R0RExmdcAc6yrF9YlvUxy7IU+ERE5LzUXwVyOL/73e9IJpOAXdJ/sq1cuRKn0wnAc889N+Jxvb29vPLKKwCsWbNm0tsxXjt37uTOO+8kHo8TDod55JFHWLhw4bS1R0RExm+8gexhYBOw37Ks+y3LWjKFbRIREZmwn/zkJ+zfv/+066PRKN///vcBe8ji9ddfP+nPXVpaysaNGwF49tlnef3114c97v777yeRSADw2c9+dtLbMR779u3jjjvuIBqNUlRUxA9/+EOWLNHHuohIoRhXgDPGPG+M+Q/AauAI8LxlWa9alvV5y7LcU9lAERGR8SgvL+e2227jqaeeoqWlhc7OTl5++WX+/M//nMOHDwPwla98Zcrmnd1zzz0UFxeTz+e5++67efTRRzl27Bg9PT1s376dL33pSzz99NMAbNy4kauvvnpK2jGaw4cPc/vtt9Pb24vf7+d73/sec+fOJR6Pj7iJiMj5ZdzrwFmWVQ7cAvwZ8Bbwj8DVwK3A+qlonIiIyHg98MAD3HHHHXzrW98a9vZbb72Vm2++ecqev7a2lh/84AfcfffddHR08OCDD/Lggw+edtz69ev59re/PWXtGM2//du/0dnZCUAikeA//+f/POZ9duzYMdXNEhGRCRhXgLMs61+AJcCPgRuMMc19N/3Esqw3pqpxIiIi47Vs2TKeeuopHnvsMbZs2UJrayt+v5/ly5dzyy23nJMer+XLl/PMM8/w5JNP8tJLL3HkyJGBIiHLly/nxhtvZMOGDVPeDhERmbnG2wP3iDHmN4OvsCzLa4xJGWPWTkG7REREJqy2tpZ777133MevW7duQj1M4zk2FApx++23c/vtt4/7cfuNVgBlMtx1113cddddU/ocIiIytcZbxGS4sR5/mMyGiIiIiIiIyOhG7YGzLKsGqAP8lmWtAqy+m4qByV8JVUREREREREY01hDK64D/CNQD3x10fQT45hS1SURERIaRzWZJp9MTvp/D4cDn801Bi0RE5FwbNcAZY54AnrAs69PGmJ+fozaJiIjIMJ555hnuu+++Cd9v7dq1PP7441PQIhEROdfGGkJ5izHmH4C5lmXd897bjTHfHeZuIiIiIiIiMgXGGkLZv9ppaKobIiIiMlEXWlXFm266iZtuumm6myEiItNorCGUP+i7/O/npjkiIiIiIiIyknEtI2BZ1ncsyyq2LMttWdYLlmW1WZZ1y1Q3TkRERERERE4Z7zpwHzbG9AIfB44AC4H/OlWNEhERERERkdONN8D1D7X8GPCUMaZnitojIiIiIiIiIxiriEm/ZyzL2gskgDsty6oEklPXLBEREREREXmvcfXAGWO+AVwJrDXGZIAYcONUNkxERERERESGGm8PHMBS7PXgBt/n7ye5PSIiIiIiIjKCcQU4y7J+DCwAtgO5vqsNCnAiIiIiIiLnzHh74NYCFxtjzFQ2RkREREREREY23iqUO4GaqWyIiIiIiIiIjG68PXAVwG7Lsl4HUv1XGmM+MSWtEhERERERkdOMN8D99VQ2QkRERERERMY2rgBnjHnZsqw5wCJjzPOWZQUA59Q2TURE5PyyYsUKAO68807uuuuuaW7NmXnjjTfYunUru3fv5siRI/T09BCJRPD5fNTV1bF27Vo++9nPsmDBguluqoiIDGO8VSi/ANwOlGFXo6wD/n/AhqlrmoiIiEy2733ve7zzzjunXR+NRtm3bx/79u3jJz/5CXfeeSe33377NLRQRERGM94hlP8XcBnwRwBjzH7LsqqmrFUiIiIyJSorK7nppptYtWoV8+bNo6KigqKiIlpbW9m2bRtPPPEETU1NPPTQQ5SXl/PpT396upssIiKDjDfApYwxacuyAOhbzFtLCoiIyAVlx44d092Es/b9739/2OtLSkpYvHgxH/nIR/jUpz5FW1sbjzzyiAKciMh5ZrzLCLxsWdY3Ab9lWRuBp4Cnp65ZIiIiMh1KSkq49tprAWhqaqKnp2eaWyQiIoONN8B9A2gDdgB3AL8B/p+papSIiMh4PPzww6xYsWKguEhLSwv3338/H/3oR1m7di3XXHMNd955J1u2bBn2/lu3bh24/9atW0mn0/zoRz9i06ZNXH311axYsYIf//jHA8f3H/vwww+P2KZoNMojjzzCpk2buOqqq1i9ejUbNmzgy1/+Mps3bx719Vx33XWsWLGCe++9F4BXX32Vr3zlK2zYsIFVq1bxmc98ZqK/ojPidruH3RcRkek33iqUecuyfgn80hjTNrVNEhERmbjdu3fzxS9+ka6uroHrUqkUW7ZsYcuWLdx666187WtfG/H+PT09bNq0iX379p1xG3bu3Mndd99NR0fHkOtbW1vZvHkzmzdvZv369XznO9/B7/eP+lgPPfQQP/zhD8+4LWcqmUzy4osvArBgwQICgcA5b4OIiIxs1ABn2ZPe/gq4m77eOsuycsBDxphvTX3zRERExueee+7BGMN9993HNddcg8fjYceOHTz44IMcOHCAJ554gvr6em6++eZh73///ffT1dXFnXfeyXXXXUd5eTnNzc30z/8eS3NzM3fccQe9vb34fD6+8IUvcN111xEOhzl06BCPP/44L730Ei+99BL33nsv3/3ud0d8rNdee43W1lauuuoqbrvtNhYsWEAymeTAgQNn9LsZSzabpb29nbfffpu/+7u/4/jx4zgcDu65554peT4RETlzY/XAfRW4ClhnjDkMYFnWfOD/WJb1VWPM96a6gSIiIuPR0dHBk08+yaJFiwauW79+PWvWrGHTpk0cOXKEBx98kBtuuIFgMHja/U+ePMlDDz3E+vXrB64rKSkZ9/M/8MAD9Pb2YlkWf/u3f8sVV1wxcNvq1atZtWoV3/jGN/jNb37Db3/7W37/+99zzTXXDPtYra2tbNy4kQceeGBIgJw1a9a42zMea9asIZ1On3b9nDlz+G//7b+N2D4REZk+YwW4PwM2GmPa+68wxhyyLOsW4N8BBTgRkbPwxPEnOBo/Ot3NmDJzAnO4dfat5+S5Pve5zw0Jb/2Kior46le/ype//GWi0SjPPvvssJUVr7rqqiHhbSK6urp4/vnnAbj++uuHhLd+lmXxzW9+kxdffJFEIsHPfvazEQOS0+nk61//+rh7/yZTWVkZt9xyCytXrjznzy0iImMbq4iJe3B469c3D06zmkVE5LzRXzlxOO9///vx+XwAvPnmmyMec6a2b99OLpcD7EIkIwmHw1x55ZUAbNu2bcTjlixZQk1NzRm3Z7x+//vf88c//pFXX32Vp59+mr/6q78iGAzyP//n/+RP/uRP2L9//5S3QUREJmasHrjTx1WM7zYRERmHc9U7dSFYsGDBiLe5XC7mzJnDvn37aGxsHPaY+vr6M37upqamgf3hegEHW7RoES+88AI9PT1Eo1FCodCktmUiBhcoKSoqYu7cuXzkIx/h85//PHv27OHOO+/k17/+tQqZiIicR8bqgbvUsqzeYbYIsOJcNFBERGQ8xgoZ/bfH4/Fhbx+rKuRoYrHYuNsxeP7d4PsN1t9bOB2CwSB/8Rd/AdjzAp999tlpa4uIiJxu1B44Y4zzXDVERETkbMTjcYqKika9HcYOWGdicCgbKSD2Gxzahiumcj645JJLBvb37NkzjS0REZH3Gu9C3iIiIue1gwcPjnhbNpvl6FG7WMxUDE+sq6sb2B+r1H//7eFweNjhk+eD/vl8IiJy/lGAExGRGaG/CuRwfve735FMJgG7pP9kW7lyJU6nPWjlueeeG/G43t5eXnnlFcAu4X++2rp168B+Q0PDNLZERETeSwFORERmhJ/85CfDVk2MRqN8//vfB+whi9dff/2kP3dpaSkbN24E4Nlnn+X1118f9rj777+fRCIBwGc/+9lJb8dYWlpaxhzi2dnZObDIuMvl4kMf+tC5aJqIiIyTApyIiMwI5eXl3HbbbTz11FO0tLTQ2dnJyy+/zJ//+Z9z+PBhAL7yla9M2byze+65h+LiYvL5PHfffTePPvoox44do6enh+3bt/OlL32Jp59+GoCNGzdy9dVXT0k7RvPaa69x/fXX8zd/8ze8+OKLHD9+nJ6eHrq6uti9ezd/93d/x6c+9SmOHDkCwO233z5keKiIiEy/sZYREBERKQgPPPAAd9xxB9/61reGvf3WW2/l5ptvnrLnr62t5Qc/+AF33303HR0dPPjggzz44IOnHbd+/Xq+/e1vT1k7xtLV1cU//dM/8U//9E8jHuPxePjiF7/IF77whXPYMhERGQ8FOBERmRGWLVvGU089xWOPPcaWLVtobW3F7/ezfPlybrnllnPS47V8+XKeeeYZnnzySV566SWOHDlCIpGgtLSU5cuXc+ONN7Jhw4Ypb8dINmzYQDAYZOvWrezcuZO2tja6urowxlBcXMyCBQtYu3YtN954I7W1tdPWThERGZkCnIiIzBi1tbXce++94z5+3bp17NixY9zHj+fYUCjE7bffzu233z7ux+03WgGUyVBUVMTGjRsH5uuJiEjh0Rw4ERERERGRAqEAJyIiIiIiUiA0hFJERKRAZLNZ0un0hO/ncDjw+XxT0CIRETnXFOBEREQKxDPPPMN999034futXbuWxx9/fApaJCIi55qGUIqIiIiIiBQI9cCJiEjBuuuuu7jrrrumuxnnzE033cRNN9003c0QEZFppB44ERERERGRAqEAJyIiIiIiUiAU4ERERERERAqEApyIiIiIiEiBUIATEREREREpEApwIiIiIiIiBUIBTkREREREpEAowImIiIiIiBQIBTgREREREZECoQAnIiIiIiJSIBTgRERERERECoQCnIiIiIiISIFQgBMRERERESkQCnAiIiLjtGLFClasWMHDDz883U2ZdK+88srA61uxYgW//OUvp7tJIiIyDAU4ERGRC1wsFuO///f/Pt3NEBGRcVCAExERucB973vfo7m5mdmzZ093U0REZAyu6W6AiIhIodixY8d0N2HSbdu2jZ/+9KeEw2G+9KUv8V//63+d7iaJiMgo1AMnIiJygUqlUvzVX/0Vxhj+4i/+gvLy8ulukoiIjEEBTkRECtbDDz88UHQDoKWlhfvvv5+PfvSjrF27lmuuuYY777yTLVu2DHv/rVu3Dtx/69atpNNpfvSjH7Fp0yauvvpqVqxYwY9//OOB48dTxCQajfLII4+wadMmrrrqKlavXs2GDRv48pe/zObNm0d9Pddddx0rVqzg3nvvBeDVV1/lK1/5Chs2bGDVqlV85jOfmeivaFT/+3//b44ePcrll1/OJz/5yUl9bBERmRoaQikiIjPC7t27+eIXv0hXV9fAdalUii1btrBlyxZuvfVWvva1r414/56eHjZt2sS+ffvOuA07d+7k7rvvpqOjY8j1ra2tbN68mc2bN7N+/Xq+853v4Pf7R32shx56iB/+8Idn3Jax7Nq1i7//+7/H6/Xyl3/5l1P2PCIiMrkU4EREZEa45557MMZw3333cc011+DxeNixYwcPPvggBw4c4IknnqC+vp6bb7552Pvff//9dHV1ceedd3LddddRXl5Oc3MzlmWN6/mbm5u544476O3txefz8YUvfIHrrruOcDjMoUOHePzxx3nppZd46aWXuPfee/nud7874mO99tprtLa2ctVVV3HbbbexYMECkskkBw4cOKPfzXtlMhn+8i//klwux913301DQ8OkPK6IiEw9BTgREZkROjo6ePLJJ1m0aNHAdevXr2fNmjVs2rSJI0eO8OCDD3LDDTcQDAZPu//Jkyd56KGHWL9+/cB1JSUl437+Bx54gN7eXizL4m//9m+54oorBm5bvXo1q1at4hvf+Aa/+c1v+O1vf8vvf/97rrnmmmEfq7W1lY0bN/LAAw8MCZCzZs0ad3tG8+ijj/Luu++yaNEibr311kl5TBEROTcU4EREppH1+ONYh49MdzOmjJk3F/P5z5+T5/rc5z43JLz1Kyoq4qtf/Spf/vKXiUajPPvss3z6058+7birrrpqSHibiK6uLp5//nkArr/++iHhrZ9lWXzzm9/kxRdfJJFI8LOf/WzEAOd0Ovn6178+7t6/iTh48CCPPPIIDoeDv/7rv8btdk/6c4iIyNRRERMREZkRrr322hFve//734/P5wPgzTffHPGYM7V9+3ZyuRxgFyIZSTgc5sorrwTs8v0jWbJkCTU1NWfcnpHk83n+8i//kkwmw80338wll1wy6c8hIiJTSz1wIiLTyHz+85jpbsQMsWDBghFvc7lczJkzh3379tHY2DjsMfX19Wf83E1NTQP7w/UCDrZo0SJeeOEFenp6iEajhEKhSW3LaH784x/zzjvvUFNTw5e+9KUpeQ4REZla6oETEZEZIRAIjOv2eDw+7O1jVYUcTSwWG3c7Bs+/G3y/wfp7CydTe3s7//t//28A7r333mHnAYqIyPlPPXAiIjIjxONxioqKRr0dxg5YZ2JwGBopIPYbHNrOZYhqb28nkUgA8F/+y38Z8/j77ruP++67D4DHHnuMdevWTWn7RERkfNQDJyIiM8LBgwdHvC2bzXL06FFgaoYn1tXVDeyPVeq///ZwODzs8EkREZHRqAdORERmhOeff56VK1cOe9vvfvc7kskkYJf0n2wrV67E6XSSy+V47rnn+NCHPjTscb29vbzyyisArFmzZtLbMZq5c+fy1FNPjXrMrl27+Ou//msA7rrrLj74wQ8CaJ04EZHziAKciIjMCD/5yU+48cYbTysiEo1G+f73vw/YQxavv/76SX/u0tJSNm7cyLPPPjuwTMFll1122nH333//wDDGz372s5PejtH4fD6WLl066jGRSGRgv7a2dszjRUTk3NMQShERmRHKy8u57bbbeOqpp2hpaaGzs5OXX36ZP//zP+fw4cMAfOUrX5myeWf33HMPxcXF5PN57r77bh599FGOHTtGT08P27dv50tf+hJPP/00ABs3buTqq6+eknaIiMjMph44ERGZER544AHuuOMOvvWtbw17+6233srNN988Zc9fW1vLD37wA+6++246Ojp48MEHefDBB087bv369Xz729+esnaIiMjMpgAnIiIzwrJly3jqqad47LHH2LJlC62trfj9fpYvX84tt9xyTnq8li9fzjPPPMOTTz7JSy+9xJEjR0gkEpSWlrJ8+XJuvPFGNmzYMOXtEBGRmUsBTkREZoza2lruvffecR+/bt06duzYMe7jx3NsKBTi9ttv5/bbbx/34/Z77rnnJnyfyTTR34eIiJx7mgMnIiIiIiJSIBTgRERERERECoSGUIqIiBSIbDZLOp2e8P0cDgc+n28KWiQiIueaApyIiEiBeOaZZ7jvvvsmfL+1a9fy+OOPT0GLRETkXNMQShERERERkQKhHjgRESlYd911F3fdddd0N+Ocuemmm7jpppumuxkiIjKN1AMnIiIiIiJSIBTgRERERERECoQCnIiIiIiISIFQgBMRmULGmOlugsg5p797EZGpowAnIiIiIiJSIBTgRESmiGVZ6omQC5IxBsuyprsZIiIzkgKciMgUcTgc5PP56W6GyDmXz+dxOPQVQ0RkKujdVURkivQHOIU4uZDkcjmMMQpwIiJTRAt5i4hMEcuycLlcZLNZnE7nwBdaDS2TmaR/mLAxhnw+jzEGl8ulv3MRkSmiACciMoUcDgcul4t8Pk8mk5nu5ohMGcuycDgcOJ1OhTcRkSmkACciMsUcDoeGk4mIiMik0DcKERERERGRAqEAJyIiIiIiUiAU4ERERERERAqEApyIiIiIiEiBUIATEREREREpEApwIiIiIiIiBUIBTkREREREpEAowImIiIiIiBQIBTgREREREZECoQAnIiIiIiJSIBTgRERERERECoQCnIiIiIiISIFQgBMRERERESkQCnAiIiIiIiIFQgFORERERESkQCjAiYiIiIiIFAgFOBERERERkQKhACciIiIiIlIgFOBEREREREQKhAKciIiIiIhIgVCAExERERERKRAKcCIiIiIiIgVCAU5ERERERKRAKMCJiIiIiIgUCAU4ERERERGRAqEAJyIiIiIiUiAU4ERERERERAqEApyIiIiIiEiBUIATEREREREpEApwIiIiIiIiBUIBTkREREREpEAowImIiIiIiBQIBTgREREREZECoQAnIiIiIiJSIBTgRERERERECoQCnIiIiIiISIFQgBMRERERESkQCnAiIiIiIiIFQgFORERERESkQCjAiYiIiIiIFAgFOBERERERkQKhACciIiIiIlIgFOBEREREREQKhAKciIiIiIhIgVCAExERERERKRAKcCIiIiIiIgVCAU5ERERERKRAKMCJiIiIiIgUCAU4ERERERGRAqEAJyIiIiIiUiAU4ERERERERAqEApyIiIiIiEiBUIATEREREREpEApwIiIiIiIiBcI13Q0QERE5G5FIhJ/+9Kf86mf/TLSjm7wxWA4nvWQxTguPx8MVq9/H1//rf6W2tna6mysiInJWFOBERKRg/eqX/8Lf/I//gclmuaYoyE1lFZQ7nXTkcvw01s0fYnF87wvyx/bXue5j13HTDTfxrf/+relutoiIyBlTgBMRkYL0q1/+C9+9/38QtvL8sGEuC7zeIbevD4U4mErxH7ccJ7ypmurPVvLr//U0gEKciIgULM2BExGRghOJRPjO//qfZFNZfljbcFp467fA6+Xx6tm0/bgZd6mbed+Yyy+f/iUnT548xy0WERGZHApwIiJScH79618xPwxXh4Ijhrd+C71e3hcK0r2lC1+dj9CyYu7/f//fc9RSERGRyaUAJyIiBed3//6vWJksHw+WjOv4P/EXk30tAkDpB8L84c3XprB1IiIiU0cBTkRECk4k0ksyC+VO57iOL3e6yMdzALjCLrKZzFQ2T0REZMoowImISMEpKirG54KOXG5cx3fksjgCdtjL9mRxud1T2TwREZEpowAnIiIF5/0f/hjG7eKZWPe4jv9pohfX+4oA6Hq5hytWv28KWyciIjJ1LpgA53NZA5uIiBS2T3ziRg71wJZojIOp1KjHHkileC0ao+TqUpJNSaK7evnGf/tv56ilIiIik+uCCXD/8JnZ/OzmBv7hM7MnfmevD+Pzgtc37rv4jcGfN/iNmfjziYjIqIqKivj6/30vLq+L25uPcWCEEHcgleLzJ49T+We1ZDozHL7/CDfdcBPV1dXnuMUiIjPPhDpIzuD79KgP5/DidXjxG0PO6ce4JudxC4FlpiBg7Ny5U6lFRESm3K9++Uv+5n98C5PNck1RkBtDYcqdLjpyWX4a6+G1WJzQ+hLSbTmiu3q56YabtIi3iIicE8uXL5+SoX8KcCIiUtAikQhPPfVTfvXUPxPp7MbkDTic9JIFp4Xb4+HKNVfw377+dfW8iYjIOaMAJyIiIiIiUiCmKsBdMHPgRERERERECp0CnIiIiIiISIFQgJP/f3t3HjVHVadx/PuQICEJjCgEMEcIAQ2gQNgFB4URFTeCG+CGcR1FAR1lhKNHEFFBHHEQBdTRMCKgogaVGRCRKJtAlCUSRLYgjiiyCQGzGH7zx73NW770nu7qrs7zOeeeruXWvVWdX6r6vvdWlZmZmZmZVYQbcGZmZmZmZhXhBpyZmZmZmVlFuAFnZmZmZmZWEW7AmZmZmZmZVYQbcGZmZmZmZhXhBpyZmZmZmVlFuAFnZmZmZmZWEYqI3hcqXQhs2MWmGwL39Xh3zLrleLRh4Vi0YeFYtGHieLRh0SgW74uI/XpdWV8acN2StDAidhn0fpiB49GGh2PRhoVj0YaJ49GGRdmx6CGUZmZmZmZmFeEGnJmZmZmZWUUMWwPuK4PeAbMCx6MNC8eiDQvHog0Tx6MNi1JjcajugTMzMzMzM7PGhq0HzszMzMzMzBroeQNO0gslfVvS3ZKWS7pH0o8l7d+j8teWdJikKyXdL+kxSbdKOkXSzF7UYaOhX7Eo6emS3irpTEk3SnpY0kpJ90laIOlISRv06jis+vp9XqxT33aSVkiKnI7tRz1WTWXEo6QJkg6R9CNJv5e0TNJfJN0g6auS5vSqLquuEn4zPkfSqZIWSXqkcK2+TNJRvlabpJmSDpR0kqRL82+62rVzbo/rUj4vXiLp3nxevFPS1yXN7qisXg6hlPRp4ChADbJ8E5gbEY93Wf404CJgdoMsjwJvjoj53ZRvo6NfsShpZ+CXwMQWWf8EvCEiFnRSvo2efp8X69Q3gRSjxccZfyIiju1F+VZtZcSjpK2Bc4EdmmS7ISJmd1uHVV8Jvxk/BJxA8+v1X4ADIuLKbuqw6pPUrCH0toiY16N6JgPnA/s2yLIS+EBEfLmd8nrWAyfpUOBo0n/EhaQdnAbsCJyds70F+EyX5a9FOvDZedEpwDbAxsAc4DZgCnCOpJ26OggbCX2OxXVJF4OHSTesvhrYAnga6cfK54FVwCbABZKe2/WBWOX1+7zYwL+RGm+397BMGwFlxKOkWcAC0vnwIeCjefrpwHTgxcCXgHu7rcOqr4TfjHOAz5Gu13cD7wK2znXsSorBVcBGwA8lbdjtsdjIeAC4GPhOn8qfx1jj7WxSe2ZaXrYQWBs4VdIr2iotIlY7ARsADwIB/A6YUifP2Xn9SmCrLuqYm7cP4FN11m8K3J/XX9qL43KqXup3LJJ+iBxVr9xCnrcWYvX8QX8nToNJZZwX65T3LOAxYDnw8kIcHjvo78NpsKmk6/QE4Npcxl3AjEEft9PwpZJi8bK8/dJG25P+uFA7Rx4x6O/FaTAJOBCYWZjfuxAXc3tUR7HMb9VZPwW4tfB/YmKrMnvVA/cW4Kl5+uMR8WidPP9O+mvHROC9XdRxeP58CDh+/MqIuIf01xaAvSVt10UdVn19jcWIuCEiTmhQbi3PmcBv8uxLJbUabmmjqYzz4hMkCfgaqZf4BGDx6pRnI6eMeDyEsaG7cyNiSRdl2OgrIxZ3zJ9XRMRtDfJ8ozC9dRd12AiIiO9ExB19rqbWhlkFfKTOPjwKfDzPPgt4WasCe9WAq92MvBz4Qb0MEfEH4Io8e0AnhUvajLH/jOdHxN8aZD23MN1RHTYy+hqLHag14NYBPDRjzVR2LL4HeAHwW+DTq1mWjZ4y4vE9+fO6iLi0i+1tzVBGLC7Ln83unyuu85Be6wtJk4CX5tnLc2zX8wPS/wloI+Z71YCr/cXt1xGxvEm+2k2iMyU9tYPyd65TxpNExJ2kh0eM38bWHP2OxXZtXJh+uA/l2/ArLRYlPRM4kTT84t0t6rM1U1/jMd9DtFuevXDcuom5h9gMyjk3/ip/7pk7Aep5Y2H6fzss36xd2wCT83SzNswy4Lo827INs9oNOEmbAuvn2VY3zRe7KLfpoJpi13a7dXRSvo2AkmKx3f3YK89eHxGP9bJ8G34DiMUzgPWAr0bEZV2WYSOqpHjctTB9U37dysmSfg+sAFZIWizpM5I26qBcGyElnhuPJQ1XWx+4WNLrJT1D0rqSZkk6njTUHOBzEfHLDss3a1c3bZhZrf7o1Yt7c4on4j+3yFtc38mwsm7q8LC1NU8ZsdiO2pOvAL7Y47KtGkqLRUmHkMbL/4k6Y+vNKCceNy9MTyMNI9+ksGwi6Uf4NsDbJO0fEdd0UL6NhlLOjRFxlaRXku5zezb1nyx4OXBKRHy3k7LNOtRNzE8CpgKPNMrYiyGUUwvTyxrmSor3rk1tmKs3dXRSvo2GMmKxKUnvZGxYxhXAmb0q2yqllFiUtDFwcp49LCIe6mR7W2OUEY9PLUyfQBpG/nlgK9K9wFsCnyUN892Y9Oj2jbE1TWnX6Yi4EHgN6b7gejYFpnt4r/VZX2K+Z++By1q9FbwXbw0vow6rvtLjRNI+pHfLQLoh+g0RsarX9Vjl9DMWTyW9g/BHEXHeapRja45+xWPx98RTgGMi4kMRcXtErIiIOyLiI6RHt0NqxB3ZZV02Gvp2bpQ0WdI5pHuOpgLvBGaQzpc7kt4lPJP0B7Bz8ruGzfqtZzHfi4BdWphet0Xe4vqlDXP1po5OyrfRUEYs1iVpV9KL5p9CetXFfhFx9+qWa5XV91iU9BrgdaQhFoe2v2u2Birj3Fgc6vMQcFKDfJ8nvTAX4LUdlG+joazr9LnAwcBfgD0i4r8i4q6IeDAiro+II4D35bwHMfYEVbNe60vM96IBd19helqLvMXhEvf3uY5OyrfRUEYsPomk7UlPXVuP9CNmv4i4rvlWNuL6GouSnsJYb+9HmzyW2AzKv05fnZ+o9iT5qYNX59kZkny7w5ql77EoaWfgVXn2S03Oj6cDS/L029st36xD3cT8cvrdgIuIPzL2mPQtW2SfWZi+uYNqiuOX262jk/JtBJQUi/9A0rbAT0nDMh4FXh4RVzffykZdCbE4mbEHRJwiKcYn4M5C/mMK6+a2WYeNiJLOjcUXxz/YIu8Dhen1G+aykVNSLP5zYfraJvsSwMI8+5wOyjfrRDdtmFtyfDbUqzG/tfdt7CRpnSb59syfd0REqxN80cLC9B6NMkmaQbopdfw2tubodyw+QdIs4BLSE4b+BrwyIi7vpiwbSaXFolkb+h2PNzM2jPLpLfIWnyj4UAd12GjodyxOLky3e89Rsxd+m62OxUDtdVLN2jDrADvl2ZZtmF414Obnz0k0eHu4pOmM/VVkfr08jUTE7xl7ud0B+a3m9RxUZ59szTI/f/YlFgtlzCQ13jYhPVVoTkQs6KYsG1nz82c/YvER0o34zdIrCvnPKCz/YQf12OiYnz/7dZ1eAVyQZ3eXNLlePknrArvn2Vv8nsw10vz82a/r9B8L07s0ypSfPllbf1eHdZi1JQ8nvyjP7pVju54DSE/shTZivlcNuG8y9le04xqcuE8EJgB/J4077lTtfVobMPYUqydI2oSxJ1r9IiIWdVGHVV/fY1HSM0mNt+mkF9S+JiIu7mpvbZT1LRYjYlW+Eb9h4h+HtP2psO6BBsXaaCvjOv2F/Lk+8LEGeY5m7JUDZ3dRh1Vfv2PxEsZ61N6fXx5ez7uBLfL0/3RYh1knam2YiYy9QP4J+f/AcXn2NtJzFZqLiJ4k0lPQIqdrgH1IwyS2B84qrDuxwfa19QsarJ8AXFXIdzIwi3RD4KuA3+XlfwN26tVxOVUv9TMWSTeY1mLt76R3vk1tkTTo78RpMKnf58UWdc8obH/soL8Lp8GnMuKR1Ntby3d6LnsDYDvSg3dq624B1hv0d+I0mFTCb8bTCnnuAuYCm5H+eLAD6Wmoq/L6B4BNB/2dOA0mke5Le14hFWPzk+PWbdugjCU5/5Im9XynUO5ZOdY3zLF/TV7+OPCKtva7x1/CZ3Ll0SD9N7BWg23buTBMIw2lbFT+UuDVgw4Gp8GnfsVivgg0KrNRmjHo78NpcKnf58Um9c4obH/soL8Hp+FIJVyn1yY9wr3ZOfEmYOagvwunwaZ+xiLplT6t4jBIwy33GPR34TS4BMzr4PfcggZlLKF1A24ycHGTslcAh7a73z19cWFEHE1qSX4X+L+8M38mjYs/ICIOiYiubxSNiHtJY+ePAH5JetLVMlJ346nA9hHxg9U6CBsJ/Y5Fs3Y5Fm2YlHCdXhkRBwNzSPdb3gOsJPVy/Bx4P7BzRNyxWgdildfPWIz08viDgX1JvR23kR4k8XfSY91/TrrtZtuIuGp1j8WslUj3+76E1BFwKSkOl5N6iOcBu0XEl9stT7lVaGZmZmZmZkOupz1wZmZmZmZm1j9uwJmZmZmZmVWEG3BmZmZmZmYV4QacmZmZmZlZRbgBZ2ZmZmZmVhFuwJmZmZmZmVWEG3BmZmZmZmYV4QacmZmZmZlZRbgBZ2ZmZmZmVhFuwJmZjTBJx0nat4flLc2fz5B0XmH5OZJulPRBSVtLul7SdZK2rLd9B/UdIGnb3ux99yR9QNLkYS1v0PWYmVl5FBGD3gczM+sDSRMiYlUvt5G0NCKmjlu2CXB5RGyV548CJkbE8e1s32J/5gE/jojzWuXtJ0lLgF0i4r4667r5nhuW10tl1WNmZuVxD5yZWcVImiHpt5K+JelmSefVelkkLZF0oqRfA6+XNE/S6/K6F+VesUWSvi5pnXrbjKtrC0lX5W2OH7cPv8mzPwGm5163Y4APAO+VdGmD/T9Z0k2SLpG0UV62paQLJf1K0mW5F29PYH/gpFz27pJ+lfPvICkkbZbnb5c0WdJGkr4n6dqcnp/XT8nHfE3+Dubk5XMlfT/Xfaukz9bZ38OBZwCX1o5J0lJJ/yHpBmAPSW/OZV8v6QxJE3K+0yQtzMf7iRblnZTz/VTSbpIWSLpD0v45z4Sc59rc2/mvefneOe95hbjQ+Hry9vMk/Sb/e36wdbSZmdmwcQPOzKyaZgFfjohtgIeBQwvr7o+InSLi3NoCSZOAecBBEbEdMBF4b7Ntsv8ETsvb3NNgX/YHbo+I2RHxCeB04OSI2KdO3inAwoh4DvBz4Ji8/CvAYRGxM/DhfGxXAj8EjsxlXw1MkrQ+sBewENhL0ubAvRHxWN7fkyNiV+C1wNdy+R8FfhYRuwH7kBqFU/K62cBBwHbAQZKeWdzhiDgF+COwT+GYpgBXR8QOwP15++dHxGxgFfCmWr0RsQuwPfBCSds3Ke9n+Xt5BDgeeDHwauC4nOcdwF/zse0KvEvSFnndjqSG87bAzLwv4+uZDUyPiOfmf89v1Pn3MTOzITdx0DtgZmZduTsirsjTZwGHA5/L89+uk38WcGdE/C7Pnwm8D/hCk20Ank9qCAF8EzhxNfYZ4PFCXWcB35c0FdgT+K6kWr51Gmx/Zd6nFwCfBvYDBFyW1+8LbFsoZ/1c/kuA/SV9OC+fBGyWpy+JiL8CSFoMbA7c3eI4VgHfy9MvAnYGrs31rgvcm9cdKOndpOvtpqQG1o11ylsBXJinFwHLI2KlpEXAjLz8JcD2tR5V4J+AZ+Vtr4mIP+RjuD5vc/m4Ou4AZkr6InABqefUzMwqxg04M7NqGn8Dc3H+0S7Ka7ZNP2+WDtJokIdy71UrvyD1vm0OnA98JJdxQV6/FvC8iFhW3EipZfXaiLhl3PLdgeWFRato79q4rHDfm4AzI+LocWVvQepN3DUiHlS6n29Sg/JWxthN6Y/X9ikiHpdU2x+ReikvGlfP3u0cQ96HHYCXAu8BDgTe3vpQzcxsmHgIpZlZNW0maY88/Uae3Nsy3i3ADElb5fm3kIYwtnIFcHCeflOzjG1aC6j1IL2R9PCTh4E7Jb0eUmMrNzQgDSdcr7D9ZcCbgVsj4nHgAeDljB3/T4DDapklzc6TFwGH5YYcknbscL/H70fRJcDrJE3LZT8tD+tcn9Qw/qukjYGXtVleIxeR7i1cO9fz7MIw0Jb7LWlDYK2I+B7wMWCnDus3M7Mh4AacmVk13QK8T9LNwAbAac0y5x6pt5GGKS4i9fKc3kY9R+R6FgHTV2+XgdSg2U3pASj/wtj9XW8C3pEfCnITMCcvPxc4Mj94ZMuIWELqifpFXn85qffuwTx/OLBLfsjHYlJPE8AngbWBGyXdlOc78RXgQtV5MEtELCY1iH4i6UbgYmDTiLgBuA74LXA2qTHcsrwmvgYsBn6dv78zaN1bWKxnOrAgD7E8Czi62YZmZjac/BoBM7OKkTSD9Gj95w56X8zMzKxc7oEzMzMzMzOrCPfAmZmZmZmZVYR74MzMzMzMzCrCDTgzMzMzM7OKcAPOzMzMzMysItyAMzMzMzMzqwg34MzMzMzMzCrCDTgzMzMzM7OK+H8fG0qAipyjYQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the 4 prior\n",
    "\n",
    "az.plot_density([\n",
    "        better_p[0], \n",
    "        better_p[1], \n",
    "        better_p[2], \n",
    "        better_p[3], \n",
    "    ],\n",
    "    data_labels=['prior_1', 'prior_2', 'prior_3', 'prior_4'], \n",
    "    figsize=(15, 6))\n",
    "\n",
    "plt.title('')\n",
    "plt.xlabel('prior diff between treatments')\n",
    "plt.ylabel('Density')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a4b71b6",
   "metadata": {},
   "source": [
    "### R Code 11.10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "267a6870",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['pulled_left', 'actor', 'treatment', 'N', 'qty_chimpanzees', 'qty_treatment'])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_list = df[['pulled_left', 'actor', 'treatment']].to_dict('list')\n",
    "data_list['N'] = len(df)\n",
    "data_list['qty_chimpanzees'] = len(df.actor.unique())\n",
    "data_list['qty_treatment'] = len(df.treatment.unique())\n",
    "\n",
    "data_list.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a321f9b2",
   "metadata": {},
   "source": [
    "### R Code 11.11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "69fb5982",
   "metadata": {
    "tags": [
     "remove_output"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mBuilding:\u001b[0m found in cache, done.\n",
      "\u001b[36mMessages from \u001b[0m\u001b[36;1mstanc\u001b[0m\u001b[36m:\u001b[0m\n",
      "Warning in '/tmp/httpstan_p8_n_hcy/model_vigmi4t7.stan', line 6, column 8: Declaration\n",
      "    of arrays by placing brackets after a variable name is deprecated and\n",
      "    will be removed in Stan 2.33.0. Instead use the array keyword before the\n",
      "    type. This can be changed automatically using the auto-format flag to\n",
      "    stanc\n",
      "Warning in '/tmp/httpstan_p8_n_hcy/model_vigmi4t7.stan', line 7, column 8: Declaration\n",
      "    of arrays by placing brackets after a variable name is deprecated and\n",
      "    will be removed in Stan 2.33.0. Instead use the array keyword before the\n",
      "    type. This can be changed automatically using the auto-format flag to\n",
      "    stanc\n",
      "Warning in '/tmp/httpstan_p8_n_hcy/model_vigmi4t7.stan', line 8, column 8: Declaration\n",
      "    of arrays by placing brackets after a variable name is deprecated and\n",
      "    will be removed in Stan 2.33.0. Instead use the array keyword before the\n",
      "    type. This can be changed automatically using the auto-format flag to\n",
      "    stanc\n",
      "\u001b[36mSampling:\u001b[0m   0%\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m   4% (300/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  10% (800/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  19% (1500/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  44% (3500/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  65% (5200/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  84% (6700/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m 100% (8000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[32mSampling:\u001b[0m 100% (8000/8000), done.\n",
      "\u001b[36mMessages received during sampling:\u001b[0m\n",
      "  Gradient evaluation took 6.4e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.64 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 6e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.6 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 8.7e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.87 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 8.7e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.87 seconds.\n",
      "  Adjust your expectations accordingly!\n"
     ]
    }
   ],
   "source": [
    "model = \"\"\"\n",
    "    data {\n",
    "        int N;\n",
    "        int qty_chimpanzees;\n",
    "        int qty_treatment;\n",
    "        int pulled_left[N];\n",
    "        int actor[N];\n",
    "        int treatment[N];\n",
    "    }\n",
    "    \n",
    "    parameters {\n",
    "        vector[qty_chimpanzees] alpha;\n",
    "        vector[qty_treatment] beta;\n",
    "    }\n",
    "    \n",
    "    model {\n",
    "        vector[N] p;\n",
    "        \n",
    "        // Prioris\n",
    "        alpha ~ normal(0, 1.5);\n",
    "        beta ~ normal(0, 0.5);\n",
    "        \n",
    "        for (i in 1:N){\n",
    "            p[i] = alpha[ actor[i] ] + beta[ treatment[i] ];\n",
    "            p[i] = inv_logit(p[i]);\n",
    "        }\n",
    "        \n",
    "        // Likelihood\n",
    "        pulled_left ~ binomial(1, p);\n",
    "    }\n",
    "\"\"\"\n",
    "\n",
    "posteriori = stan.build(model, data=data_list)\n",
    "samples = posteriori.sample(num_chains=4, num_samples=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "91a29c95",
   "metadata": {},
   "outputs": [],
   "source": [
    "samples_chimpanzees = az.from_pystan(\n",
    "    posterior_model=posteriori,\n",
    "    posterior=samples,\n",
    "    observed_data=['pulled_left', 'actor', 'treatment']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d1473b96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_5.5%</th>\n",
       "      <th>hdi_94.5%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alpha[0]</th>\n",
       "      <td>-0.449</td>\n",
       "      <td>0.332</td>\n",
       "      <td>-0.976</td>\n",
       "      <td>0.082</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.006</td>\n",
       "      <td>1419.0</td>\n",
       "      <td>2459.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha[1]</th>\n",
       "      <td>3.891</td>\n",
       "      <td>0.751</td>\n",
       "      <td>2.774</td>\n",
       "      <td>5.142</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.009</td>\n",
       "      <td>3817.0</td>\n",
       "      <td>2940.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha[2]</th>\n",
       "      <td>-0.750</td>\n",
       "      <td>0.345</td>\n",
       "      <td>-1.293</td>\n",
       "      <td>-0.220</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.006</td>\n",
       "      <td>1593.0</td>\n",
       "      <td>2295.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha[3]</th>\n",
       "      <td>-0.751</td>\n",
       "      <td>0.341</td>\n",
       "      <td>-1.293</td>\n",
       "      <td>-0.210</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.006</td>\n",
       "      <td>1454.0</td>\n",
       "      <td>2483.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha[4]</th>\n",
       "      <td>-0.443</td>\n",
       "      <td>0.328</td>\n",
       "      <td>-0.954</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.006</td>\n",
       "      <td>1390.0</td>\n",
       "      <td>2326.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha[5]</th>\n",
       "      <td>0.482</td>\n",
       "      <td>0.337</td>\n",
       "      <td>-0.029</td>\n",
       "      <td>1.034</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.006</td>\n",
       "      <td>1565.0</td>\n",
       "      <td>2318.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha[6]</th>\n",
       "      <td>1.965</td>\n",
       "      <td>0.418</td>\n",
       "      <td>1.342</td>\n",
       "      <td>2.670</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.006</td>\n",
       "      <td>2113.0</td>\n",
       "      <td>2613.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta[0]</th>\n",
       "      <td>-0.044</td>\n",
       "      <td>0.290</td>\n",
       "      <td>-0.523</td>\n",
       "      <td>0.402</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.006</td>\n",
       "      <td>1287.0</td>\n",
       "      <td>2170.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta[1]</th>\n",
       "      <td>0.480</td>\n",
       "      <td>0.288</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.932</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.006</td>\n",
       "      <td>1256.0</td>\n",
       "      <td>1841.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta[2]</th>\n",
       "      <td>-0.387</td>\n",
       "      <td>0.284</td>\n",
       "      <td>-0.808</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.006</td>\n",
       "      <td>1285.0</td>\n",
       "      <td>2112.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta[3]</th>\n",
       "      <td>0.370</td>\n",
       "      <td>0.286</td>\n",
       "      <td>-0.074</td>\n",
       "      <td>0.840</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.006</td>\n",
       "      <td>1349.0</td>\n",
       "      <td>2159.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           mean     sd  hdi_5.5%  hdi_94.5%  mcse_mean  mcse_sd  ess_bulk  \\\n",
       "alpha[0] -0.449  0.332    -0.976      0.082      0.009    0.006    1419.0   \n",
       "alpha[1]  3.891  0.751     2.774      5.142      0.012    0.009    3817.0   \n",
       "alpha[2] -0.750  0.345    -1.293     -0.220      0.009    0.006    1593.0   \n",
       "alpha[3] -0.751  0.341    -1.293     -0.210      0.009    0.006    1454.0   \n",
       "alpha[4] -0.443  0.328    -0.954      0.085      0.009    0.006    1390.0   \n",
       "alpha[5]  0.482  0.337    -0.029      1.034      0.009    0.006    1565.0   \n",
       "alpha[6]  1.965  0.418     1.342      2.670      0.009    0.006    2113.0   \n",
       "beta[0]  -0.044  0.290    -0.523      0.402      0.008    0.006    1287.0   \n",
       "beta[1]   0.480  0.288     0.018      0.932      0.008    0.006    1256.0   \n",
       "beta[2]  -0.387  0.284    -0.808      0.100      0.008    0.006    1285.0   \n",
       "beta[3]   0.370  0.286    -0.074      0.840      0.008    0.006    1349.0   \n",
       "\n",
       "          ess_tail  r_hat  \n",
       "alpha[0]    2459.0    1.0  \n",
       "alpha[1]    2940.0    1.0  \n",
       "alpha[2]    2295.0    1.0  \n",
       "alpha[3]    2483.0    1.0  \n",
       "alpha[4]    2326.0    1.0  \n",
       "alpha[5]    2318.0    1.0  \n",
       "alpha[6]    2613.0    1.0  \n",
       "beta[0]     2170.0    1.0  \n",
       "beta[1]     1841.0    1.0  \n",
       "beta[2]     2112.0    1.0  \n",
       "beta[3]     2159.0    1.0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(samples_chimpanzees, var_names=['alpha', 'beta'], hdi_prob=0.89)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eeab6b4e",
   "metadata": {},
   "source": [
    "### R Code 11.12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7bcefe63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = az.plot_forest(samples_chimpanzees, \n",
    "                    combined=True,  # Joint all chains \n",
    "                    kind=\"forestplot\", # or ridgeplot to plot densities\n",
    "                    var_names=['alpha'], \n",
    "                    transform=inv_logit,\n",
    "                    hdi_prob=0.89,\n",
    "                    figsize=(15, 6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "90316218",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = az.plot_forest([samples_chimpanzees, samples_parcial_better, samples_partial], \n",
    "                    combined=True,  # Joint all chains \n",
    "                    kind=\"forestplot\", # or ridgeplot to plot densities,\n",
    "                    model_names=['samples_chimpanzees', 'samples_parcial_better', 'samples_parcial'],\n",
    "                    var_names=['alpha'], \n",
    "                    transform=inv_logit,\n",
    "                    hdi_prob=0.89,\n",
    "                    figsize=(15, 6))\n",
    "\n",
    "plt.title('All predicts to alpha')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00bd75ab",
   "metadata": {},
   "source": [
    "### R Code 11.13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "40f03c17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = az.plot_forest(samples_chimpanzees, \n",
    "                    combined=True,  # Joint all chains \n",
    "                    kind=\"forestplot\", # or ridgeplot to plot densities\n",
    "                    var_names=['beta'], \n",
    "                    # transform=inv_logit,\n",
    "                    hdi_prob=0.89,\n",
    "                    figsize=(15, 6))\n",
    "plt.axvline(x=0, c='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36d0435c",
   "metadata": {},
   "source": [
    "### R Code 8.14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "097e17ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "beta_0 = samples_chimpanzees.posterior.beta.sel(beta_dim_0=0)\n",
    "beta_1 = samples_chimpanzees.posterior.beta.sel(beta_dim_0=1)\n",
    "beta_2 = samples_chimpanzees.posterior.beta.sel(beta_dim_0=2)\n",
    "beta_3 = samples_chimpanzees.posterior.beta.sel(beta_dim_0=3)\n",
    "\n",
    "diff_beta_0_2 = az.extract(beta_0 - beta_2)\n",
    "diff_beta_1_3 = az.extract(beta_1 - beta_3)\n",
    "\n",
    "az.plot_forest(\n",
    "    [diff_beta_0_2.beta.values, diff_beta_1_3.beta.values],\n",
    "    combined=True,  # Joint all chains \n",
    "    kind=\"forestplot\", # or ridgeplot to plot densities\n",
    "    # transform=inv_logit,\n",
    "    model_names=['db13', 'db24'],\n",
    "    hdi_prob=0.89,\n",
    "    figsize=(15, 6)\n",
    ")\n",
    "\n",
    "plt.axvline(x=0, c='gray')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d34cb0ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = az.plot_forest(samples_chimpanzees, \n",
    "                    combined=True,  # Joint all chains \n",
    "                    kind=\"forestplot\", # or ridgeplot to plot densities\n",
    "                    var_names=['beta'], \n",
    "                    # transform=inv_logit,\n",
    "                    hdi_prob=0.89,\n",
    "                    figsize=(12, 6))\n",
    "\n",
    "plt.axvline(x=0, c='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9e54642",
   "metadata": {},
   "source": [
    "### R Code 11.15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "6717efa6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>actor</th>\n",
       "      <th>treatment</th>\n",
       "      <th>pulled_left</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>actor</th>\n",
       "      <th>treatment</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">1</th>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.277778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.555556</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 actor  treatment  pulled_left\n",
       "actor treatment                               \n",
       "1     1            1.0        1.0     0.333333\n",
       "      2            1.0        2.0     0.500000\n",
       "      3            1.0        3.0     0.277778\n",
       "      4            1.0        4.0     0.555556"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pl = df.groupby(['actor', 'treatment'])[['actor', 'treatment', 'pulled_left']].mean(['pulled_left'])\n",
    "\n",
    "pl.loc[pl.actor == 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9aabd4a1",
   "metadata": {},
   "source": [
    "### R Code 11.16 and R Code 11.17"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "141e3660",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# R Code 11.16\n",
    "\n",
    "plt.figure(figsize=(18, 6))\n",
    "\n",
    "plt.ylim(0, 1.3)\n",
    "plt.xlim(0, 7)\n",
    "plt.axhline(y=1, ls='-', c='black')\n",
    "plt.axhline(y=0.5, ls='--', c='black')\n",
    "\n",
    "\n",
    "for i in range(7):\n",
    "    plt.axvline(x=i+1, c='black')\n",
    "    plt.text(x=i + 0.25, y=1.1, s=f'Actor {i + 1}', size=20)\n",
    "    \n",
    "    RN, LN, RP, LP = pl.loc[pl.actor == i + 1, 'pulled_left'].values\n",
    "\n",
    "    if not i == 1:\n",
    "        plt.plot([0.2 + i, 0.8 + i], [RN, RP], color='blue')\n",
    "    plt.plot(0.2 + i, RN, 'o', markerfacecolor='white', color='blue')\n",
    "    plt.plot(0.8 + i, RP, 'o', markerfacecolor='blue', color='white')\n",
    "    \n",
    "    if not i == 1:\n",
    "        plt.plot([0.3 + i, 0.9 + i], [LN, LP], color='darkblue')\n",
    "    plt.plot(0.3 + i, LN, 'o', markerfacecolor='white', color='blue')\n",
    "    plt.plot(0.9 + i, LP, 'o', markerfacecolor='blue', color='white')\n",
    "    \n",
    "    # Labels for only first points\n",
    "    if i == 0:\n",
    "        plt.text(x=0.17, y=RN - 0.08, s='R/N')\n",
    "        plt.text(x=0.77, y=RP - 0.08, s='R/P')\n",
    "        plt.text(x=0.17, y=LN + 0.08, s='L/N')\n",
    "        plt.text(x=0.77, y=LP + 0.08, s='L/P')\n",
    "    \n",
    "plt.title('Observed Proportions')\n",
    "plt.ylabel('Proportion left lever')\n",
    "plt.yticks([0, 0.5, 1], [0, 0.5, 1])\n",
    "plt.xticks([])\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n",
    "# R Code 11.17\n",
    "# ============\n",
    "\n",
    "plt.figure(figsize=(18, 6))\n",
    "\n",
    "plt.ylim(0, 1.3)\n",
    "plt.xlim(0, 7)\n",
    "plt.axhline(y=1, ls='-', c='black')\n",
    "plt.axhline(y=0.5, ls='--', c='black')\n",
    "\n",
    "\n",
    "for i in range(7):\n",
    "    plt.axvline(x=i+1, c='black')\n",
    "    plt.text(x=i + 0.25, y=1.1, s=f'Actor {i + 1}', size=20)\n",
    "    \n",
    "    alpha_chimp = az.extract(samples_chimpanzees.posterior.alpha.sel(alpha_dim_0=i)).alpha.values\n",
    "    \n",
    "    RN = inv_logit(alpha_chimp + az.extract(samples_chimpanzees.posterior.beta.sel(beta_dim_0=0)).beta.values)\n",
    "    LN = inv_logit(alpha_chimp + az.extract(samples_chimpanzees.posterior.beta.sel(beta_dim_0=1)).beta.values)\n",
    "    RP = inv_logit(alpha_chimp + az.extract(samples_chimpanzees.posterior.beta.sel(beta_dim_0=2)).beta.values)\n",
    "    LP = inv_logit(alpha_chimp + az.extract(samples_chimpanzees.posterior.beta.sel(beta_dim_0=3)).beta.values)\n",
    "    \n",
    "    # To R/N and R/P\n",
    "    # ===============\n",
    "    if not i == 1:\n",
    "        plt.plot([0.2 + i, 0.8 + i], [RN.mean(), RP.mean()], color='black')\n",
    "    \n",
    "    # Plot hdi compatibility interval\n",
    "    RN_hdi_min, RN_hdi_max = az.hdi(RN, hdi_prob=0.89)\n",
    "    RP_hdi_min, RP_hdi_max = az.hdi(RP, hdi_prob=0.89)\n",
    "    plt.plot([0.2 + i, 0.2 + i], [RN_hdi_min, RN_hdi_max], c='black')\n",
    "    plt.plot([0.8 + i, 0.8 + i], [RP_hdi_min, RP_hdi_max], c='black')\n",
    "    \n",
    "    # Plot points\n",
    "    plt.plot(0.2 + i, RN.mean(), 'o', markerfacecolor='white', color='black')\n",
    "    plt.plot(0.8 + i, RP.mean(), 'o', markerfacecolor='black', color='white')\n",
    "    \n",
    "    # To L/N and L/P\n",
    "    # ===============\n",
    "    if not i == 1:\n",
    "        plt.plot([0.3 + i, 0.9 + i], [LN.mean(), LP.mean()], color='black', linewidth=3)\n",
    "\n",
    "    # Plot hdi compatibility interval\n",
    "    LN_hdi_min, LN_hdi_max = az.hdi(LN, hdi_prob=0.89)\n",
    "    LP_hdi_min, LP_hdi_max = az.hdi(LP, hdi_prob=0.89)\n",
    "    plt.plot([0.3 + i, 0.3 + i], [LN_hdi_min, LN_hdi_max], c='black')\n",
    "    plt.plot([0.9 + i, 0.9 + i], [LP_hdi_min, LP_hdi_max], c='black')\n",
    "\n",
    "    plt.plot(0.3 + i, LN.mean(), 'o', markerfacecolor='white', color='black')\n",
    "    plt.plot(0.9 + i, LP.mean(), 'o', markerfacecolor='black', color='white')\n",
    "    \n",
    "    # Labels for only first points\n",
    "    if i == 0:\n",
    "        plt.text(x=0.08, y=RN.mean() - 0.08, s='R/N')\n",
    "        plt.text(x=0.68, y=RP.mean() - 0.08, s='R/P')\n",
    "        plt.text(x=0.17, y=LN.mean() + 0.08, s='L/N')\n",
    "        plt.text(x=0.77, y=LP.mean() + 0.08, s='L/P')\n",
    "    \n",
    "plt.title('Posterior Predictions')\n",
    "plt.ylabel('Proportion left lever')\n",
    "plt.yticks([0, 0.5, 1], [0, 0.5, 1])\n",
    "plt.xticks([])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0bf458c",
   "metadata": {},
   "source": [
    "### R Code 11.18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "08c0dcb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['side'] = df.prosoc_left + 1  # Right 1, Left 2  - Because R start index in 1\n",
    "df['cond'] = df.condition + 1  # no partner 1, partner 2 - Because R start index in 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b42e1219",
   "metadata": {},
   "source": [
    "### R Code 11.19"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "024bbbb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['pulled_left', 'actor', 'side', 'cond', 'N', 'qty_actors', 'qty_side', 'qty_cond'])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating data list 2 to model\n",
    "data_list2 = df[['pulled_left', 'actor', 'side', 'cond']].to_dict('list')\n",
    "data_list2['N'] = len(df)\n",
    "data_list2['qty_actors'] = len(df.actor.unique())\n",
    "data_list2['qty_side'] = len(df.side.unique())\n",
    "data_list2['qty_cond'] = len(df.cond.unique())\n",
    "\n",
    "data_list2.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "0f0856a6",
   "metadata": {
    "tags": [
     "remove_output"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mBuilding:\u001b[0m found in cache, done.\n",
      "\u001b[36mMessages from \u001b[0m\u001b[36;1mstanc\u001b[0m\u001b[36m:\u001b[0m\n",
      "Warning in '/tmp/httpstan_77qezkt0/model_f2p3ngm3.stan', line 8, column 8: Declaration\n",
      "    of arrays by placing brackets after a variable name is deprecated and\n",
      "    will be removed in Stan 2.33.0. Instead use the array keyword before the\n",
      "    type. This can be changed automatically using the auto-format flag to\n",
      "    stanc\n",
      "Warning in '/tmp/httpstan_77qezkt0/model_f2p3ngm3.stan', line 9, column 8: Declaration\n",
      "    of arrays by placing brackets after a variable name is deprecated and\n",
      "    will be removed in Stan 2.33.0. Instead use the array keyword before the\n",
      "    type. This can be changed automatically using the auto-format flag to\n",
      "    stanc\n",
      "Warning in '/tmp/httpstan_77qezkt0/model_f2p3ngm3.stan', line 10, column 8: Declaration\n",
      "    of arrays by placing brackets after a variable name is deprecated and\n",
      "    will be removed in Stan 2.33.0. Instead use the array keyword before the\n",
      "    type. This can be changed automatically using the auto-format flag to\n",
      "    stanc\n",
      "Warning in '/tmp/httpstan_77qezkt0/model_f2p3ngm3.stan', line 11, column 8: Declaration\n",
      "    of arrays by placing brackets after a variable name is deprecated and\n",
      "    will be removed in Stan 2.33.0. Instead use the array keyword before the\n",
      "    type. This can be changed automatically using the auto-format flag to\n",
      "    stanc\n",
      "\u001b[36mSampling:\u001b[0m   0%\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m   1% (100/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m   2% (200/8000)\n",
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      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  30% (2400/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  34% (2700/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  52% (4200/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  71% (5700/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  85% (6800/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m 100% (8000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[32mSampling:\u001b[0m 100% (8000/8000), done.\n",
      "\u001b[36mMessages received during sampling:\u001b[0m\n",
      "  Gradient evaluation took 0.000106 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 1.06 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 0.000146 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 1.46 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 0.000148 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 1.48 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 0.000144 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 1.44 seconds.\n",
      "  Adjust your expectations accordingly!\n"
     ]
    }
   ],
   "source": [
    "model2 = \"\"\"\n",
    "    data {\n",
    "        int N;\n",
    "        int qty_actors;\n",
    "        int qty_side;\n",
    "        int qty_cond;\n",
    "        \n",
    "        int pulled_left[N];\n",
    "        int actor[N];\n",
    "        int side[N];\n",
    "        int cond[N];\n",
    "    }\n",
    "    \n",
    "    parameters {\n",
    "        vector[qty_actors] alpha;\n",
    "        vector[qty_side] bs;\n",
    "        vector[qty_cond] bc;\n",
    "    }\n",
    "    \n",
    "    model {\n",
    "        vector[N] p;\n",
    "        \n",
    "        // Prioris\n",
    "        alpha ~ normal(0, 1.5);\n",
    "        bs ~ normal(0, 0.5);\n",
    "        bc ~ normal(0, 0.5);\n",
    "        \n",
    "        for (i in 1:N){\n",
    "            p[i] = alpha[ actor[i] ] + bs[ side[i] ] + bc[ cond[i] ];\n",
    "            p[i] = inv_logit(p[i]);\n",
    "        }\n",
    "        \n",
    "        // Likelihood\n",
    "        pulled_left ~ binomial(1, p);\n",
    "    }\n",
    "\"\"\"\n",
    "\n",
    "posteriori2 = stan.build(model2, data=data_list2)\n",
    "samples2 = posteriori2.sample(num_chains=4, num_samples=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8547fc0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "samples_chimpanzees_2 = az.from_pystan(\n",
    "    posterior_model=posteriori2,\n",
    "    posterior=samples2,\n",
    "    observed_data=['pulled_left', 'actor', 'side', 'cond']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e1a6d1b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = az.plot_forest([samples_chimpanzees, samples_chimpanzees_2],\n",
    "                    combined=True,  # Joint all chains \n",
    "                    kind=\"forestplot\", # or ridgeplot to plot densities\n",
    "                    var_names=['alpha', 'beta', 'bs', 'bc'], \n",
    "                    model_names=['samples_chimpanzees', 'samples_chimpanzees_2'],\n",
    "                    transform=inv_logit,\n",
    "                    hdi_prob=0.89,\n",
    "                    figsize=(17, 8))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e54c425",
   "metadata": {},
   "source": [
    "### R Code 11.21\n",
    "\n",
    "To viewing data from rethinking packages"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44e73c71",
   "metadata": {},
   "source": [
    "### R Code 11.22\n",
    "\n",
    "Comparing two models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07fc4e32",
   "metadata": {},
   "source": [
    "### R Code 11.23"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1915213b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9314303282351553"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beta_4 = az.extract(samples_chimpanzees.posterior.beta.sel(beta_dim_0=3)).beta.values\n",
    "beta_2 = az.extract(samples_chimpanzees.posterior.beta.sel(beta_dim_0=1)).beta.values\n",
    "\n",
    "np.mean( np.exp(beta_4 - beta_2) )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1d31e89",
   "metadata": {},
   "source": [
    "### R Code 11.24"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ab2dff83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>treatment</th>\n",
       "      <th>actor</th>\n",
       "      <th>side</th>\n",
       "      <th>cond</th>\n",
       "      <th>left_pulls</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   treatment  actor  side  cond  left_pulls\n",
       "0          1      1     1     1           6\n",
       "1          1      2     1     1          18\n",
       "2          1      3     1     1           5\n",
       "3          1      4     1     1           6\n",
       "4          1      5     1     1           6\n",
       "5          1      6     1     1          14\n",
       "6          1      7     1     1          14\n",
       "7          2      1     2     1           9"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = pd.read_csv('./data/chimpanzees.csv', sep=';')\n",
    "d['treatment'] = 1 + d['prosoc_left'] + 2 * d['condition']\n",
    "d['side'] = d['prosoc_left'] + 1  # right 1, left 2\n",
    "d['cond'] = d['condition'] + 1  # no partner 1, partner 2\n",
    "\n",
    "d_aggregate = d.groupby(['treatment', 'actor', 'side', 'cond'])[['pulled_left']].sum('pulled_left').copy()\n",
    "d_aggregate.rename(columns={'pulled_left': 'left_pulls'}, inplace=True)  # Rename columns\n",
    "d_aggregate.reset_index(inplace=True)\n",
    "d_aggregate.head(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "66023cc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(d_aggregate)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa3e127e",
   "metadata": {},
   "source": [
    "### R Code 11.25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c2031373",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['left_pulls', 'treatment', 'actor', 'side', 'cond', 'N', 'qty_actors', 'qty_treatments'])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat = d_aggregate[['left_pulls', 'treatment', 'actor', 'side', 'cond']].to_dict('list')\n",
    "dat['N'] = len(d_aggregate)\n",
    "dat['qty_actors'] = len(d_aggregate.actor.unique())\n",
    "dat['qty_treatments'] = len(d_aggregate.treatment.unique())\n",
    "\n",
    "dat.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "0587006c",
   "metadata": {
    "tags": [
     "remove_output"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mBuilding:\u001b[0m found in cache, done.\n",
      "\u001b[36mMessages from \u001b[0m\u001b[36;1mstanc\u001b[0m\u001b[36m:\u001b[0m\n",
      "Warning in '/tmp/httpstan__vz9egg2/model_vjg6bgap.stan', line 7, column 8: Declaration\n",
      "    of arrays by placing brackets after a variable name is deprecated and\n",
      "    will be removed in Stan 2.33.0. Instead use the array keyword before the\n",
      "    type. This can be changed automatically using the auto-format flag to\n",
      "    stanc\n",
      "Warning in '/tmp/httpstan__vz9egg2/model_vjg6bgap.stan', line 8, column 8: Declaration\n",
      "    of arrays by placing brackets after a variable name is deprecated and\n",
      "    will be removed in Stan 2.33.0. Instead use the array keyword before the\n",
      "    type. This can be changed automatically using the auto-format flag to\n",
      "    stanc\n",
      "Warning in '/tmp/httpstan__vz9egg2/model_vjg6bgap.stan', line 9, column 8: Declaration\n",
      "    of arrays by placing brackets after a variable name is deprecated and\n",
      "    will be removed in Stan 2.33.0. Instead use the array keyword before the\n",
      "    type. This can be changed automatically using the auto-format flag to\n",
      "    stanc\n",
      "Warning in '/tmp/httpstan__vz9egg2/model_vjg6bgap.stan', line 28, column 30: Argument\n",
      "    18 suggests there may be parameters that are not unit scale; consider\n",
      "    rescaling with a multiplier (see manual section 22.12).\n",
      "\u001b[36mSampling:\u001b[0m   0%\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  25% (2000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  50% (4000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  75% (6000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m 100% (8000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[32mSampling:\u001b[0m 100% (8000/8000), done.\n",
      "\u001b[36mMessages received during sampling:\u001b[0m\n",
      "  Gradient evaluation took 1.7e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 9e-06 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.09 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 1.2e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.12 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 1e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.1 seconds.\n",
      "  Adjust your expectations accordingly!\n"
     ]
    }
   ],
   "source": [
    "model = \"\"\"\n",
    "    data {\n",
    "        int N;\n",
    "        int qty_actors;\n",
    "        int qty_treatments;\n",
    "        \n",
    "        int left_pulls[N];\n",
    "        int actor[N];\n",
    "        int treatment[N];\n",
    "    }\n",
    "    \n",
    "    parameters {\n",
    "        vector[qty_actors] alpha;\n",
    "        vector[qty_treatments] beta;\n",
    "    }\n",
    "    \n",
    "    model {\n",
    "        vector[N] p;\n",
    "        \n",
    "        alpha ~ normal(0, 1.5);\n",
    "        beta ~ normal(0, 0.5);\n",
    "        \n",
    "        for (i in 1:N){\n",
    "            p[i] = alpha[ actor[i] ] + beta[ treatment[i] ];\n",
    "            p[i] = inv_logit(p[i]);\n",
    "        }\n",
    "    \n",
    "        left_pulls ~ binomial(18, p);\n",
    "    }\n",
    "\"\"\"\n",
    "\n",
    "posteriori = stan.build(model, data=dat)\n",
    "samples = posteriori.sample(num_chains=4, num_samples=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "6e71b7d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_chimp_bin = az.from_pystan(\n",
    "    posterior_model=posteriori,\n",
    "    posterior=samples,\n",
    "    observed_data=['left_pulls', 'treatment', 'actor'],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "e4da92ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_forest(\n",
    "    [model_chimp_bin, samples_chimpanzees],\n",
    "    var_names=['alpha', 'beta'], \n",
    "    model_names=['Model Binomial', 'Model Bernoulli'],\n",
    "    combined=True, \n",
    "    transform=inv_logit, \n",
    "    figsize=(16, 6),\n",
    "    hdi_prob=0.89,\n",
    ")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "60ae24f0",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for t in range(4):\n",
    "    p_chimp_bin = []\n",
    "    for i in range(7):\n",
    "        alpha_chimp_bin = model_chimp_bin.posterior.alpha.sel(alpha_dim_0=i).values.flatten()\n",
    "        beta_chimp_bin = model_chimp_bin.posterior.beta.sel(beta_dim_0=t).values.flatten()\n",
    "\n",
    "        p_chimp_bin.append(inv_logit(alpha_chimp_bin + beta_chimp_bin))\n",
    "\n",
    "    az.plot_density(p_chimp_bin, figsize=(17, 5))\n",
    "    plt.title(f'Treatment = {t + 1}')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "677e1e4a",
   "metadata": {},
   "source": [
    "### R Code 11.26"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "425326b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "models_to_compare = {'model_chimp_bin': model_chimp_bin, 'samples_chimpanzees': samples_chimpanzees}\n",
    "\n",
    "# az.compare(models_to_compare)  # or use 'elpd_waic' to WAIC\n",
    "\n",
    "# XXX: Need to make posteriori in model to read in arviz"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "570b5d54",
   "metadata": {},
   "source": [
    "### R Code 11.27"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "d0187d9b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11.790482659407832\n",
      "20.652116257094463\n"
     ]
    }
   ],
   "source": [
    "# Deviance of aggregated 6-in-9\n",
    "agg = -2 * np.log(stats.binom.pmf(k=6, n=9, p=0.2))\n",
    "print(agg)\n",
    "\n",
    "# Deviance of dis-aggregagted\n",
    "k = [1, 1, 1, 1, 1, 1, 0, 0, 0]\n",
    "\n",
    "not_agg = -2 * np.sum(np.log(stats.bernoulli.pmf(k=k, p=0.2)))\n",
    "print(not_agg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10b717e1",
   "metadata": {},
   "source": [
    "-----------\n",
    "\n",
    "### My Tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "18c93130",
   "metadata": {},
   "outputs": [],
   "source": [
    "N = 100\n",
    "p = np.random.rand()\n",
    "\n",
    "y = np.random.binomial(1, p, N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "3bd7f494",
   "metadata": {
    "tags": [
     "remove_output"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mBuilding:\u001b[0m found in cache, done.\n",
      "\u001b[36mMessages from \u001b[0m\u001b[36;1mstanc\u001b[0m\u001b[36m:\u001b[0m\n",
      "Warning in '/tmp/httpstan_9ps1k5ca/model_2mkr3rt2.stan', line 4, column 8: Declaration\n",
      "    of arrays by placing brackets after a variable name is deprecated and\n",
      "    will be removed in Stan 2.33.0. Instead use the array keyword before the\n",
      "    type. This can be changed automatically using the auto-format flag to\n",
      "    stanc\n",
      "\u001b[36mSampling:\u001b[0m   0%\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  25% (2000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  50% (4000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  75% (6000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m 100% (8000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[32mSampling:\u001b[0m 100% (8000/8000), done.\n",
      "\u001b[36mMessages received during sampling:\u001b[0m\n",
      "  Gradient evaluation took 1.4e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 1.3e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.13 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 1.2e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.12 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 2.2e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.22 seconds.\n",
      "  Adjust your expectations accordingly!\n"
     ]
    }
   ],
   "source": [
    "model = \"\"\"\n",
    "    data{\n",
    "        int N;\n",
    "        int y[N];\n",
    "    }\n",
    "    \n",
    "    parameters {\n",
    "        real alpha;\n",
    "    }\n",
    "    \n",
    "    model {\n",
    "        vector[N] p;\n",
    "        \n",
    "        // Priori\n",
    "        alpha ~ normal(0.5, 1);\n",
    "    \n",
    "        for (i in 1:N){\n",
    "                p[i] = inv_logit(alpha);\n",
    "        }\n",
    "    \n",
    "        // Likelihood\n",
    "        y ~ binomial(1, p);\n",
    "    }\n",
    "\"\"\"\n",
    "\n",
    "data = {\n",
    "    'N':N,\n",
    "    'y': y,\n",
    "}\n",
    "\n",
    "posteriori = stan.build(model, data=data)\n",
    "samples_test = posteriori.sample(num_chains=4, num_samples=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "6aa6ecdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "samples_test_az = az.from_pystan(\n",
    "    posterior_model=posteriori,\n",
    "    posterior=samples_test,\n",
    "    observed_data=['y']\n",
    ")\n",
    "\n",
    "az.plot_density(\n",
    "    samples_test_az, var_names=['alpha'], \n",
    "    transform=inv_logit)\n",
    "\n",
    "az.plot_density(\n",
    "    samples_test_az, var_names=['alpha'])\n",
    "\n",
    "az.plot_density(inv_logit(np.random.normal(0.5, 0.5, 1000)))  # Priori\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "4d7221dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.21214535724578476 0.199584411979789\n"
     ]
    }
   ],
   "source": [
    "print(p, np.mean(inv_logit(samples_test_az.posterior.alpha.values)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "6361e9b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_density(inv_logit(samples_test_az.posterior.alpha.values) - p)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "6e09d9f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Binomial\n",
    "\n",
    "N = 100\n",
    "p = np.random.rand()\n",
    "\n",
    "y = np.random.binomial(5, p, N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "a206180e",
   "metadata": {
    "scrolled": true,
    "tags": [
     "remove_output"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mBuilding:\u001b[0m found in cache, done.\n",
      "\u001b[36mMessages from \u001b[0m\u001b[36;1mstanc\u001b[0m\u001b[36m:\u001b[0m\n",
      "Warning in '/tmp/httpstan_73ddg024/model_raar4edp.stan', line 4, column 8: Declaration\n",
      "    of arrays by placing brackets after a variable name is deprecated and\n",
      "    will be removed in Stan 2.33.0. Instead use the array keyword before the\n",
      "    type. This can be changed automatically using the auto-format flag to\n",
      "    stanc\n",
      "\u001b[36mSampling:\u001b[0m   0%\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  25% (2000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  50% (4000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  75% (6000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m 100% (8000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[32mSampling:\u001b[0m 100% (8000/8000), done.\n",
      "\u001b[36mMessages received during sampling:\u001b[0m\n",
      "  Gradient evaluation took 1e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.1 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 1e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.1 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 1.3e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.13 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 1.3e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.13 seconds.\n",
      "  Adjust your expectations accordingly!\n"
     ]
    }
   ],
   "source": [
    "model = \"\"\"\n",
    "    data{\n",
    "        int N;\n",
    "        int y[N];\n",
    "    }\n",
    "    \n",
    "    parameters {\n",
    "        real alpha;\n",
    "    }\n",
    "    \n",
    "    model {\n",
    "        vector[N] p;\n",
    "        \n",
    "        // Priori\n",
    "        alpha ~ normal(0.5, 1);\n",
    "    \n",
    "        for (i in 1:N){\n",
    "                p[i] = inv_logit(alpha);\n",
    "        }\n",
    "    \n",
    "        // Likelihood\n",
    "        y ~ binomial(5, p);\n",
    "    }\n",
    "\"\"\"\n",
    "\n",
    "data = {\n",
    "    'N':N,\n",
    "    'y': y,\n",
    "}\n",
    "\n",
    "posteriori = stan.build(model, data=data)\n",
    "samples_test = posteriori.sample(num_chains=4, num_samples=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "5c3d0b3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "samples_test_az = az.from_pystan(\n",
    "    posterior_model=posteriori,\n",
    "    posterior=samples_test,\n",
    "    observed_data=['y']\n",
    ")\n",
    "\n",
    "az.plot_density(\n",
    "    samples_test_az, var_names=['alpha'], \n",
    "    transform=inv_logit)\n",
    "\n",
    "az.plot_density(\n",
    "    samples_test_az, var_names=['alpha'])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "ac6e60ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.376926619935282 0.39083492880300796\n"
     ]
    }
   ],
   "source": [
    "print(p, np.mean(inv_logit(samples_test_az.posterior.alpha.values)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "f6fe01ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_density(inv_logit(samples_test_az.posterior.alpha.values) - p)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da479218",
   "metadata": {},
   "source": [
    "-----------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3513e54",
   "metadata": {},
   "source": [
    "### R Code 11.28"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "963188cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dept</th>\n",
       "      <th>applicant.gender</th>\n",
       "      <th>admit</th>\n",
       "      <th>reject</th>\n",
       "      <th>applications</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A</td>\n",
       "      <td>male</td>\n",
       "      <td>512</td>\n",
       "      <td>313</td>\n",
       "      <td>825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A</td>\n",
       "      <td>female</td>\n",
       "      <td>89</td>\n",
       "      <td>19</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B</td>\n",
       "      <td>male</td>\n",
       "      <td>353</td>\n",
       "      <td>207</td>\n",
       "      <td>560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
       "      <td>female</td>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>C</td>\n",
       "      <td>male</td>\n",
       "      <td>120</td>\n",
       "      <td>205</td>\n",
       "      <td>325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>C</td>\n",
       "      <td>female</td>\n",
       "      <td>202</td>\n",
       "      <td>391</td>\n",
       "      <td>593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>D</td>\n",
       "      <td>male</td>\n",
       "      <td>138</td>\n",
       "      <td>279</td>\n",
       "      <td>417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>D</td>\n",
       "      <td>female</td>\n",
       "      <td>131</td>\n",
       "      <td>244</td>\n",
       "      <td>375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>E</td>\n",
       "      <td>male</td>\n",
       "      <td>53</td>\n",
       "      <td>138</td>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>E</td>\n",
       "      <td>female</td>\n",
       "      <td>94</td>\n",
       "      <td>299</td>\n",
       "      <td>393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>F</td>\n",
       "      <td>male</td>\n",
       "      <td>22</td>\n",
       "      <td>351</td>\n",
       "      <td>373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>F</td>\n",
       "      <td>female</td>\n",
       "      <td>24</td>\n",
       "      <td>317</td>\n",
       "      <td>341</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   dept applicant.gender  admit  reject  applications\n",
       "1     A             male    512     313           825\n",
       "2     A           female     89      19           108\n",
       "3     B             male    353     207           560\n",
       "4     B           female     17       8            25\n",
       "5     C             male    120     205           325\n",
       "6     C           female    202     391           593\n",
       "7     D             male    138     279           417\n",
       "8     D           female    131     244           375\n",
       "9     E             male     53     138           191\n",
       "10    E           female     94     299           393\n",
       "11    F             male     22     351           373\n",
       "12    F           female     24     317           341"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./data/UCBadmit.csv', sep=';')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "dc19d60d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentual total male = 44 %\n",
      "Percentual total female = 30 %\n"
     ]
    }
   ],
   "source": [
    "total_male_applic = df[df['applicant.gender']  == 'male']['applications'].sum()\n",
    "total_male_admit = df[df['applicant.gender']  == 'male']['admit'].sum()\n",
    "print('Percentual total male =', int(100*(total_male_admit / total_male_applic)), '%')\n",
    "\n",
    "total_female_applic = df[df['applicant.gender']  == 'female']['applications'].sum()\n",
    "total_female_admit = df[df['applicant.gender']  == 'female']['admit'].sum()\n",
    "print('Percentual total female =', int(100*(total_female_admit / total_female_applic)), '%')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d4f3efd",
   "metadata": {},
   "source": [
    "### R Code 11.29"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40cdc110",
   "metadata": {},
   "source": [
    "$$ A_i \\sim Binomial(N_i, p_i) $$\n",
    "\n",
    "$$ logit(p_i) = \\alpha_{GID[i]} $$\n",
    "\n",
    "$$ \\alpha_j \\sim Normal(0, 1.5) $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "f225c88a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'admit': [512, 89, 353, 17, 120, 202, 138, 131, 53, 94, 22, 24],\n",
       " 'applications': [825, 108, 560, 25, 325, 593, 417, 375, 191, 393, 373, 341],\n",
       " 'gid': [1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2],\n",
       " 'N': 12,\n",
       " 'qty_genders': 2}"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['gid'] = np.where(df['applicant.gender'] == 'male', 1, 2)\n",
    "\n",
    "dat_list = df[['admit', 'applications', 'gid']].to_dict('list')\n",
    "dat_list['N'] = len(df)\n",
    "dat_list['qty_genders'] = len(df.gid.unique())\n",
    "dat_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "d6045a7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DEPARTMENT ---> A\n",
      "The dept A has 62% acceptions rate for male\n",
      "The dept A has 82% acceptions rate for female\n",
      "\n",
      "\n",
      "DEPARTMENT ---> B\n",
      "The dept B has 63% acceptions rate for male\n",
      "The dept B has 68% acceptions rate for female\n",
      "\n",
      "\n",
      "DEPARTMENT ---> C\n",
      "The dept C has 36% acceptions rate for male\n",
      "The dept C has 34% acceptions rate for female\n",
      "\n",
      "\n",
      "DEPARTMENT ---> D\n",
      "The dept D has 33% acceptions rate for male\n",
      "The dept D has 34% acceptions rate for female\n",
      "\n",
      "\n",
      "DEPARTMENT ---> E\n",
      "The dept E has 27% acceptions rate for male\n",
      "The dept E has 23% acceptions rate for female\n",
      "\n",
      "\n",
      "DEPARTMENT ---> F\n",
      "The dept F has 5% acceptions rate for male\n",
      "The dept F has 7% acceptions rate for female\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for dept_i in df.dept.unique():\n",
    "    \n",
    "    print(f'DEPARTMENT ---> {dept_i}')\n",
    "        \n",
    "    for gend_i in ['male', 'female']:\n",
    "    \n",
    "        applications = df[(df['dept'] == dept_i) & (df['applicant.gender'] == gend_i)]['applications']\n",
    "        admit = df[(df['dept'] == dept_i) & (df['applicant.gender'] == gend_i)]['admit']\n",
    "        perc_admit = admit.values / applications.values\n",
    "\n",
    "        print(f'The dept {dept_i} has {int(100 * perc_admit[0])}% acceptions rate for {gend_i}')\n",
    "        \n",
    "    print('\\n')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "08cdf06c",
   "metadata": {
    "tags": [
     "remove_output"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mBuilding:\u001b[0m found in cache, done.\n",
      "\u001b[36mMessages from \u001b[0m\u001b[36;1mstanc\u001b[0m\u001b[36m:\u001b[0m\n",
      "Warning in '/tmp/httpstan_30ejw6o9/model_zdkc2poh.stan', line 6, column 8: Declaration\n",
      "    of arrays by placing brackets after a variable name is deprecated and\n",
      "    will be removed in Stan 2.33.0. Instead use the array keyword before the\n",
      "    type. This can be changed automatically using the auto-format flag to\n",
      "    stanc\n",
      "Warning in '/tmp/httpstan_30ejw6o9/model_zdkc2poh.stan', line 7, column 8: Declaration\n",
      "    of arrays by placing brackets after a variable name is deprecated and\n",
      "    will be removed in Stan 2.33.0. Instead use the array keyword before the\n",
      "    type. This can be changed automatically using the auto-format flag to\n",
      "    stanc\n",
      "Warning in '/tmp/httpstan_30ejw6o9/model_zdkc2poh.stan', line 8, column 8: Declaration\n",
      "    of arrays by placing brackets after a variable name is deprecated and\n",
      "    will be removed in Stan 2.33.0. Instead use the array keyword before the\n",
      "    type. This can be changed automatically using the auto-format flag to\n",
      "    stanc\n",
      "\u001b[36mSampling:\u001b[0m   0%\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  25% (2000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  50% (4000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  75% (6000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m 100% (8000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[32mSampling:\u001b[0m 100% (8000/8000), done.\n",
      "\u001b[36mMessages received during sampling:\u001b[0m\n",
      "  Gradient evaluation took 5e-06 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.05 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 5e-06 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.05 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 8e-06 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.08 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 5e-06 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.05 seconds.\n",
      "  Adjust your expectations accordingly!\n"
     ]
    }
   ],
   "source": [
    "model = \"\"\"\n",
    "    data {\n",
    "        int N;\n",
    "        int qty_genders;\n",
    "        \n",
    "        int admit[N];\n",
    "        int applications[N];\n",
    "        int gid[N];\n",
    "    }\n",
    "    \n",
    "    parameters {\n",
    "        vector[qty_genders] alpha;\n",
    "    }\n",
    "    \n",
    "    model {\n",
    "        vector[N] p;\n",
    "        \n",
    "        alpha ~ normal(0, 1.5);\n",
    "        \n",
    "        for (i in 1:N){\n",
    "            p[i] = alpha[ gid[i] ];\n",
    "            p[i] = inv_logit(p[i]);\n",
    "        }\n",
    "        \n",
    "        admit ~ binomial(applications, p);\n",
    "    }\n",
    "\"\"\"\n",
    "\n",
    "posteriori = stan.build(model, data=dat_list)\n",
    "samples = posteriori.sample(num_chains=4, num_samples=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "a32819bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "app_gender = az.from_pystan(\n",
    "    posterior_model=posteriori,\n",
    "    posterior=samples,\n",
    "    observed_data=list(dat_list.keys()),\n",
    "    dims={\n",
    "        'alpha': ['gender'],\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "522dfebf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alpha[0]</th>\n",
       "      <td>-0.221</td>\n",
       "      <td>0.039</td>\n",
       "      <td>-0.296</td>\n",
       "      <td>-0.152</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3410.0</td>\n",
       "      <td>2607.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha[1]</th>\n",
       "      <td>-0.830</td>\n",
       "      <td>0.052</td>\n",
       "      <td>-0.927</td>\n",
       "      <td>-0.735</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>3592.0</td>\n",
       "      <td>2698.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  \\\n",
       "alpha[0] -0.221  0.039  -0.296   -0.152      0.001    0.000    3410.0   \n",
       "alpha[1] -0.830  0.052  -0.927   -0.735      0.001    0.001    3592.0   \n",
       "\n",
       "          ess_tail  r_hat  \n",
       "alpha[0]    2607.0    1.0  \n",
       "alpha[1]    2698.0    1.0  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(app_gender)  # In scale inv-logit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "65e54287",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_forest(app_gender, var_names=['alpha'], combined=True, figsize=(15,5), transform=inv_logit)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5f65953",
   "metadata": {},
   "source": [
    "### R Code 11.30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "0df1d526",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alpha</th>\n",
       "      <td>0.609</td>\n",
       "      <td>0.063</td>\n",
       "      <td>0.486</td>\n",
       "      <td>0.724</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>3651.0</td>\n",
       "      <td>2731.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  ess_tail  \\\n",
       "alpha  0.609  0.063   0.486    0.724      0.001    0.001    3651.0    2731.0   \n",
       "\n",
       "       r_hat  \n",
       "alpha    1.0  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alpha_1 = app_gender.posterior.alpha.sel(gender=0)\n",
    "alpha_2 = app_gender.posterior.alpha.sel(gender=1)\n",
    "\n",
    "diff_a = alpha_1 - alpha_2\n",
    "diff_p = inv_logit(alpha_1) - inv_logit(alpha_2)\n",
    "\n",
    "az.summary(diff_a)  # Shark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "1b7677ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alpha</th>\n",
       "      <td>0.141</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.113</td>\n",
       "      <td>0.166</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3641.0</td>\n",
       "      <td>2919.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  ess_tail  \\\n",
       "alpha  0.141  0.014   0.113    0.166        0.0      0.0    3641.0    2919.0   \n",
       "\n",
       "       r_hat  \n",
       "alpha    1.0  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(diff_p)  # Deer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1de2f992",
   "metadata": {},
   "source": [
    "### R Code 11.31"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "eec13014",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18, 6))\n",
    "\n",
    "plt.ylim(0, 1.3)\n",
    "plt.xlim(0, 5.8)\n",
    "plt.axhline(y=1, ls='-', c='black')\n",
    "\n",
    "depts = df.dept.unique()\n",
    "\n",
    "for i in range(6):\n",
    "    plt.axvline(x=i+1, c='black')\n",
    "    \n",
    "    dept = depts[i]\n",
    "\n",
    "    plt.text(x=i + 0.25, y=1.1, s=f'Dept {dept}', size=20)\n",
    "\n",
    "    perc_male, perc_female = df.loc[df['dept'] == dept, 'admit'] / df.loc[df['dept'] == dept, 'applications']\n",
    "\n",
    "    plt.plot([0.2 + i, 0.7 + i], [perc_male, perc_female], color='blue')  # Line\n",
    "    plt.plot(0.2 + i, perc_male, 'o', markerfacecolor='white', color='blue')  # point male\n",
    "    plt.plot(0.7 + i, perc_female, 'o', markerfacecolor='blue', color='white')  # point female\n",
    "    \n",
    "    plt.text(x=0.5 + i, y=max(perc_male, perc_female) + 0.05, s=dept)\n",
    "    \n",
    "plt.title('Posteriori Validation Check')\n",
    "plt.ylabel('Admit')\n",
    "plt.xlabel('Case')\n",
    "plt.yticks([0, 0.2, 0.4, 0.6, 0.8, 1.0], [0, 0.2, 0.4, 0.6, 0.8, 1])\n",
    "plt.xticks(np.arange(0, 5.9, 0.5) + 0.2, np.arange(1, 12.1, 1, dtype=int))\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# Need put points with open points from samples"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3bbf673",
   "metadata": {},
   "source": [
    "### R Code 11.32"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e71794aa",
   "metadata": {},
   "source": [
    "We want ask:\n",
    "\n",
    "    - What is the average difference in probability of admission between women and man within departments?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09de1a7e",
   "metadata": {},
   "source": [
    "$$ A_i \\sim Binomial(N_i, p_i) $$\n",
    "\n",
    "$$ logit(p_i) = \\alpha_{GID[i]} + \\delta_{DEPT[i]} $$\n",
    "\n",
    "$$ \\alpha_j \\sim Normal(0, 1.5) $$\n",
    "\n",
    "$$ \\delta_k \\sim Normal(0, 1.5) $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "f189efb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'admit': [512, 89, 353, 17, 120, 202, 138, 131, 53, 94, 22, 24],\n",
       " 'applications': [825, 108, 560, 25, 325, 593, 417, 375, 191, 393, 373, 341],\n",
       " 'gid': [1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2],\n",
       " 'dept_idx': [1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6],\n",
       " 'N': 12,\n",
       " 'qty_genders': 2,\n",
       " 'qty_dept': 6}"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['gid'] = np.where(df['applicant.gender'] == 'male', 1, 2)\n",
    "\n",
    "df['dept_idx'] = [ord(dept_i) - 64 for dept_i in df['dept']]  # ord('A') = 65\n",
    "\n",
    "dat_list = df[['admit', 'applications', 'gid', 'dept_idx']].to_dict('list')\n",
    "dat_list['N'] = len(df)\n",
    "dat_list['qty_genders'] = len(df.gid.unique())\n",
    "dat_list['qty_dept'] = len(df.dept_idx.unique())\n",
    "\n",
    "dat_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "d920dd20",
   "metadata": {
    "tags": [
     "remove_output"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mBuilding:\u001b[0m found in cache, done.\n",
      "\u001b[36mMessages from \u001b[0m\u001b[36;1mstanc\u001b[0m\u001b[36m:\u001b[0m\n",
      "Warning in '/tmp/httpstan_j8lnz9tc/model_zo74hsu3.stan', line 25, column 60: Comments\n",
      "    beginning with # are deprecated and this syntax will be removed in Stan\n",
      "    2.33.0. Use // to begin line comments; this can be done automatically\n",
      "    using the auto-format flag to stanc\n",
      "\u001b[36mSampling:\u001b[0m   0%\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  25% (2000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  50% (4000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  75% (6000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m 100% (8000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[32mSampling:\u001b[0m 100% (8000/8000), done.\n",
      "\u001b[36mMessages received during sampling:\u001b[0m\n",
      "  Gradient evaluation took 7e-06 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.07 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 1.3e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.13 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 8e-06 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.08 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 4e-06 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.04 seconds.\n",
      "  Adjust your expectations accordingly!\n"
     ]
    }
   ],
   "source": [
    "model = \"\"\"\n",
    "    data {\n",
    "        int N;\n",
    "        int qty_genders;\n",
    "        int qty_dept;\n",
    "        \n",
    "        array[N] int admit;\n",
    "        array[N] int applications;\n",
    "        array[N] int gid;\n",
    "        array[N] int dept_idx;\n",
    "    }\n",
    "    \n",
    "    parameters {\n",
    "        vector[qty_genders] alpha;\n",
    "        vector[ qty_dept ] delta;\n",
    "    }\n",
    "    \n",
    "    model {\n",
    "        vector[N] p;\n",
    "        \n",
    "        alpha ~ normal(0, 1.5);\n",
    "        delta ~ normal(0, 1.5);\n",
    "        \n",
    "        for (i in 1:N){\n",
    "            p[i] = alpha[ gid[i] ] + delta[ dept_idx[i] ];  # O Delta calcula a taxa média de admissão para o departamento i\n",
    "            p[i] = inv_logit(p[i]);\n",
    "        }\n",
    "        \n",
    "        admit ~ binomial(applications, p);\n",
    "    }\n",
    "\"\"\"\n",
    "\n",
    "posteriori = stan.build(model, data=dat_list)\n",
    "samples = posteriori.sample(num_chains=4, num_samples=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "a3e3b750",
   "metadata": {},
   "outputs": [],
   "source": [
    "applic_gender_2 = az.from_pystan(\n",
    "    posterior_model=posteriori,\n",
    "    posterior=samples,\n",
    "    observed_data=list(dat_list.keys()),\n",
    "    dims = {\n",
    "        'alpha': ['gender'],\n",
    "        'delta': ['department'],\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "9280ea18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alpha[0]</th>\n",
       "      <td>-0.547</td>\n",
       "      <td>0.523</td>\n",
       "      <td>-1.556</td>\n",
       "      <td>0.379</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.023</td>\n",
       "      <td>254.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha[1]</th>\n",
       "      <td>-0.446</td>\n",
       "      <td>0.525</td>\n",
       "      <td>-1.387</td>\n",
       "      <td>0.549</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.023</td>\n",
       "      <td>252.0</td>\n",
       "      <td>253.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delta[0]</th>\n",
       "      <td>1.127</td>\n",
       "      <td>0.526</td>\n",
       "      <td>0.199</td>\n",
       "      <td>2.147</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.023</td>\n",
       "      <td>257.0</td>\n",
       "      <td>292.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delta[1]</th>\n",
       "      <td>1.083</td>\n",
       "      <td>0.529</td>\n",
       "      <td>0.161</td>\n",
       "      <td>2.106</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.023</td>\n",
       "      <td>254.0</td>\n",
       "      <td>307.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delta[2]</th>\n",
       "      <td>-0.135</td>\n",
       "      <td>0.527</td>\n",
       "      <td>-1.057</td>\n",
       "      <td>0.881</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.023</td>\n",
       "      <td>254.0</td>\n",
       "      <td>268.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delta[3]</th>\n",
       "      <td>-0.166</td>\n",
       "      <td>0.528</td>\n",
       "      <td>-1.137</td>\n",
       "      <td>0.820</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.023</td>\n",
       "      <td>257.0</td>\n",
       "      <td>277.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delta[4]</th>\n",
       "      <td>-0.610</td>\n",
       "      <td>0.531</td>\n",
       "      <td>-1.548</td>\n",
       "      <td>0.418</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.024</td>\n",
       "      <td>256.0</td>\n",
       "      <td>251.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delta[5]</th>\n",
       "      <td>-2.167</td>\n",
       "      <td>0.539</td>\n",
       "      <td>-3.100</td>\n",
       "      <td>-1.133</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.023</td>\n",
       "      <td>280.0</td>\n",
       "      <td>381.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  \\\n",
       "alpha[0] -0.547  0.523  -1.556    0.379      0.032    0.023     254.0   \n",
       "alpha[1] -0.446  0.525  -1.387    0.549      0.033    0.023     252.0   \n",
       "delta[0]  1.127  0.526   0.199    2.147      0.032    0.023     257.0   \n",
       "delta[1]  1.083  0.529   0.161    2.106      0.033    0.023     254.0   \n",
       "delta[2] -0.135  0.527  -1.057    0.881      0.033    0.023     254.0   \n",
       "delta[3] -0.166  0.528  -1.137    0.820      0.032    0.023     257.0   \n",
       "delta[4] -0.610  0.531  -1.548    0.418      0.033    0.024     256.0   \n",
       "delta[5] -2.167  0.539  -3.100   -1.133      0.032    0.023     280.0   \n",
       "\n",
       "          ess_tail  r_hat  \n",
       "alpha[0]     259.0   1.01  \n",
       "alpha[1]     253.0   1.01  \n",
       "delta[0]     292.0   1.01  \n",
       "delta[1]     307.0   1.01  \n",
       "delta[2]     268.0   1.01  \n",
       "delta[3]     277.0   1.01  \n",
       "delta[4]     251.0   1.01  \n",
       "delta[5]     381.0   1.01  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(applic_gender_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "26a67406",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1224x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_forest(\n",
    "    applic_gender_2,\n",
    "    var_names=['alpha', 'delta'],\n",
    "    combined=True,\n",
    "    transform=inv_logit,\n",
    "    figsize=(17, 6),\n",
    "    hdi_prob=0.89,\n",
    ")\n",
    "\n",
    "plt.xlim((0, 1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa84b646",
   "metadata": {},
   "source": [
    "### R Code 11.33"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "4246c2fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_5.5%</th>\n",
       "      <th>hdi_94.5%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alpha</th>\n",
       "      <td>-0.101</td>\n",
       "      <td>0.082</td>\n",
       "      <td>-0.233</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>4107.0</td>\n",
       "      <td>3513.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        mean     sd  hdi_5.5%  hdi_94.5%  mcse_mean  mcse_sd  ess_bulk  \\\n",
       "alpha -0.101  0.082    -0.233      0.027      0.001    0.001    4107.0   \n",
       "\n",
       "       ess_tail  r_hat  \n",
       "alpha    3513.0    1.0  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alpha_male = applic_gender_2.posterior.alpha.sel(gender=0)\n",
    "alpha_female = applic_gender_2.posterior.alpha.sel(gender=1)\n",
    "\n",
    "az.summary(alpha_male - alpha_female, hdi_prob=0.89)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "4a65bd43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x295.2 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_forest((alpha_male - alpha_female), hdi_prob=0.89, combined=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "f18b2dc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_5.5%</th>\n",
       "      <th>hdi_94.5%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alpha</th>\n",
       "      <td>-0.022</td>\n",
       "      <td>0.019</td>\n",
       "      <td>-0.054</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3187.0</td>\n",
       "      <td>3078.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        mean     sd  hdi_5.5%  hdi_94.5%  mcse_mean  mcse_sd  ess_bulk  \\\n",
       "alpha -0.022  0.019    -0.054      0.005        0.0      0.0    3187.0   \n",
       "\n",
       "       ess_tail  r_hat  \n",
       "alpha    3078.0    1.0  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(inv_logit(alpha_male) - inv_logit(alpha_female), hdi_prob=0.89)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "ccf7f2a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x295.2 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_forest((inv_logit(alpha_male) - inv_logit(alpha_female)), hdi_prob=0.89, combined=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "68fbeab1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.88</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.12</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           A     B     C     D     E     F\n",
       "male    0.88  0.96  0.35  0.53  0.33  0.52\n",
       "female  0.12  0.04  0.65  0.47  0.67  0.48"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pg = {}\n",
    "pg['male'] = {}\n",
    "pg['female'] = {}\n",
    "\n",
    "dept_i = 'A'\n",
    "\n",
    "for dept_i in df['dept'].unique():\n",
    "\n",
    "    total = df[df['dept'] == dept_i]['applications'].sum()\n",
    "    total_male = df[(df['dept'] == dept_i) & (df['applicant.gender'] == 'male')]['applications'].sum()\n",
    "    total_female = df[(df['dept'] == dept_i) & (df['applicant.gender'] == 'female')]['applications'].sum()\n",
    "\n",
    "    pg['male'][dept_i] = round(total_male / total, 2)\n",
    "    pg['female'][dept_i] = round(total_female / total, 2)\n",
    "    \n",
    "pd.DataFrame.from_dict(pg).T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1334e465",
   "metadata": {},
   "source": [
    "## Poisson Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97b5e08e",
   "metadata": {},
   "source": [
    "### R Code 11.35"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "2e080d53",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean is 9.9965 and variance is 10.14188775\n"
     ]
    }
   ],
   "source": [
    "N = 10000\n",
    "p = 1/1000\n",
    "\n",
    "y = np.random.binomial(N, p, 10000)\n",
    "print(f'The mean is {np.mean(y)} and variance is {np.var(y)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee432076",
   "metadata": {},
   "source": [
    "### R Code 11.36"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "7a669fba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>culture</th>\n",
       "      <th>population</th>\n",
       "      <th>contact</th>\n",
       "      <th>total_tools</th>\n",
       "      <th>mean_TU</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Malekula</td>\n",
       "      <td>1100</td>\n",
       "      <td>low</td>\n",
       "      <td>13</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tikopia</td>\n",
       "      <td>1500</td>\n",
       "      <td>low</td>\n",
       "      <td>22</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Santa Cruz</td>\n",
       "      <td>3600</td>\n",
       "      <td>low</td>\n",
       "      <td>24</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Yap</td>\n",
       "      <td>4791</td>\n",
       "      <td>high</td>\n",
       "      <td>43</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lau Fiji</td>\n",
       "      <td>7400</td>\n",
       "      <td>high</td>\n",
       "      <td>33</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Trobriand</td>\n",
       "      <td>8000</td>\n",
       "      <td>high</td>\n",
       "      <td>19</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Chuuk</td>\n",
       "      <td>9200</td>\n",
       "      <td>high</td>\n",
       "      <td>40</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Manus</td>\n",
       "      <td>13000</td>\n",
       "      <td>low</td>\n",
       "      <td>28</td>\n",
       "      <td>6.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Tonga</td>\n",
       "      <td>17500</td>\n",
       "      <td>high</td>\n",
       "      <td>55</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Hawaii</td>\n",
       "      <td>275000</td>\n",
       "      <td>low</td>\n",
       "      <td>71</td>\n",
       "      <td>6.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      culture  population contact  total_tools  mean_TU\n",
       "0    Malekula        1100     low           13      3.2\n",
       "1     Tikopia        1500     low           22      4.7\n",
       "2  Santa Cruz        3600     low           24      4.0\n",
       "3         Yap        4791    high           43      5.0\n",
       "4    Lau Fiji        7400    high           33      5.0\n",
       "5   Trobriand        8000    high           19      4.0\n",
       "6       Chuuk        9200    high           40      3.8\n",
       "7       Manus       13000     low           28      6.6\n",
       "8       Tonga       17500    high           55      5.4\n",
       "9      Hawaii      275000     low           71      6.6"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./data/Kline.csv', sep=';')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23298ef7",
   "metadata": {},
   "source": [
    "$$ y_i \\sim Poisson(\\lambda) $$\n",
    "\n",
    "The parameter $\\lambda$ is expected value of the outcome y."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "146b7c33",
   "metadata": {},
   "source": [
    "The *link function* for poisson is the $log$ link.\n",
    "\n",
    "$$ y_i \\sim Poisson(\\lambda_i) $$\n",
    "\n",
    "$$ log(\\lambda_i) = \\alpha + \\beta(x_i - \\bar x) $$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93380509",
   "metadata": {},
   "source": [
    "### R Code 11.37"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "c715dc8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>culture</th>\n",
       "      <th>population</th>\n",
       "      <th>contact</th>\n",
       "      <th>total_tools</th>\n",
       "      <th>mean_TU</th>\n",
       "      <th>log_population</th>\n",
       "      <th>P</th>\n",
       "      <th>contact_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Malekula</td>\n",
       "      <td>1100</td>\n",
       "      <td>low</td>\n",
       "      <td>13</td>\n",
       "      <td>3.2</td>\n",
       "      <td>7.003065</td>\n",
       "      <td>-1.361332</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tikopia</td>\n",
       "      <td>1500</td>\n",
       "      <td>low</td>\n",
       "      <td>22</td>\n",
       "      <td>4.7</td>\n",
       "      <td>7.313220</td>\n",
       "      <td>-1.147433</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Santa Cruz</td>\n",
       "      <td>3600</td>\n",
       "      <td>low</td>\n",
       "      <td>24</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.188689</td>\n",
       "      <td>-0.543664</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Yap</td>\n",
       "      <td>4791</td>\n",
       "      <td>high</td>\n",
       "      <td>43</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.474494</td>\n",
       "      <td>-0.346558</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lau Fiji</td>\n",
       "      <td>7400</td>\n",
       "      <td>high</td>\n",
       "      <td>33</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.909235</td>\n",
       "      <td>-0.046737</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Trobriand</td>\n",
       "      <td>8000</td>\n",
       "      <td>high</td>\n",
       "      <td>19</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.987197</td>\n",
       "      <td>0.007029</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Chuuk</td>\n",
       "      <td>9200</td>\n",
       "      <td>high</td>\n",
       "      <td>40</td>\n",
       "      <td>3.8</td>\n",
       "      <td>9.126959</td>\n",
       "      <td>0.103416</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Manus</td>\n",
       "      <td>13000</td>\n",
       "      <td>low</td>\n",
       "      <td>28</td>\n",
       "      <td>6.6</td>\n",
       "      <td>9.472705</td>\n",
       "      <td>0.341861</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Tonga</td>\n",
       "      <td>17500</td>\n",
       "      <td>high</td>\n",
       "      <td>55</td>\n",
       "      <td>5.4</td>\n",
       "      <td>9.769956</td>\n",
       "      <td>0.546861</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Hawaii</td>\n",
       "      <td>275000</td>\n",
       "      <td>low</td>\n",
       "      <td>71</td>\n",
       "      <td>6.6</td>\n",
       "      <td>12.524526</td>\n",
       "      <td>2.446558</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      culture  population contact  total_tools  mean_TU  log_population  \\\n",
       "0    Malekula        1100     low           13      3.2        7.003065   \n",
       "1     Tikopia        1500     low           22      4.7        7.313220   \n",
       "2  Santa Cruz        3600     low           24      4.0        8.188689   \n",
       "3         Yap        4791    high           43      5.0        8.474494   \n",
       "4    Lau Fiji        7400    high           33      5.0        8.909235   \n",
       "5   Trobriand        8000    high           19      4.0        8.987197   \n",
       "6       Chuuk        9200    high           40      3.8        9.126959   \n",
       "7       Manus       13000     low           28      6.6        9.472705   \n",
       "8       Tonga       17500    high           55      5.4        9.769956   \n",
       "9      Hawaii      275000     low           71      6.6       12.524526   \n",
       "\n",
       "          P  contact_id  \n",
       "0 -1.361332           1  \n",
       "1 -1.147433           1  \n",
       "2 -0.543664           1  \n",
       "3 -0.346558           2  \n",
       "4 -0.046737           2  \n",
       "5  0.007029           2  \n",
       "6  0.103416           2  \n",
       "7  0.341861           1  \n",
       "8  0.546861           2  \n",
       "9  2.446558           1  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['log_population'] = np.log(df['population'])  # log population\n",
    "df['P'] = (df['log_population'] - np.mean(df['log_population'])) / np.std(df['log_population'])  #std log population\n",
    "df['contact_id'] = [2 if contact_i == 'high' else 1 for contact_i in df['contact'].values ]  # CID 1:low; 2:high\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "903da70d",
   "metadata": {},
   "source": [
    "#### Model - Poisson Regression\n",
    "\n",
    "$$ T_i \\sim Poisson(\\lambda_i) $$\n",
    "\n",
    "$$ log(\\lambda_i) = \\alpha_{CID[i]} + \\beta_{CID[i]} log(P_i) $$\n",
    "\n",
    "$$ \\alpha_j \\sim \\mbox{to be determined} $$\n",
    "$$ \\beta_j \\sim \\mbox{to be determined} $$\n",
    "\n",
    "\n",
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41cb68d3",
   "metadata": {},
   "source": [
    "$$ T_i \\sim Poisson(\\lambda_i) $$\n",
    "\n",
    "$$ log(\\lambda_i) = \\alpha$$\n",
    "\n",
    "$$ \\alpha \\sim Normal(0, 10) $$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2f295fc",
   "metadata": {},
   "source": [
    "### R Code 11.38"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "553ac44f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(17, 6))\n",
    "\n",
    "N = 200\n",
    "x = np.linspace(0, 100, N)\n",
    "\n",
    "y = stats.lognorm.pdf(x=x, s=10, loc=0, scale=20)  # See the docs to understanding the parameters\n",
    "\n",
    "# R Code 11.40\n",
    "y_new = stats.lognorm.pdf(x=x, s=0.5, loc=3, scale=20)  # See the docs to understanding the parameters\n",
    "\n",
    "plt.plot(x, y, c='black')\n",
    "plt.plot(x, y_new, c='blue')\n",
    "\n",
    "plt.ylabel('Density')\n",
    "plt.xlabel('Mean Number of Tools')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dc1d31a",
   "metadata": {},
   "source": [
    "### R Code 11.39"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "22c86c7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11155436105.390947"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.random.normal(0, 10, 1000)\n",
    "lambda_ = np.exp(a)\n",
    "np.mean(lambda_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1393dc22",
   "metadata": {},
   "source": [
    "### R Code 11.40"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "d0dffda4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ploted in R Code 11.38"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "045f9679",
   "metadata": {},
   "source": [
    "### R Code 11.41"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "7d1198b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 100\n",
    "a = np.random.normal(3, 0.5, N)  # prior to alpha\n",
    "b = np.random.normal(0, 10, N)  # prior to beta\n",
    "\n",
    "x = np.linspace(-2, 2, 100)\n",
    "\n",
    "prioris = []\n",
    "\n",
    "plt.figure(figsize=(17, 6))\n",
    "\n",
    "for i in range(N):\n",
    "    plt.plot(x, np.exp(a[i] + b[i] * x), c='blue')\n",
    "    \n",
    "plt.xlim(-2, 2)\n",
    "plt.ylim(0, 100)\n",
    "\n",
    "plt.title('Poisson Priori \\n beta ~ Normal(0, 10)')\n",
    "plt.xlabel('log population (std)')\n",
    "plt.ylabel('total odds')\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9953f366",
   "metadata": {},
   "source": [
    "### R Code 11.42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "14f0f74f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 100\n",
    "a = np.random.normal(3, 0.5, N)  # prior to alpha\n",
    "b = np.random.normal(0, 0.2, N)  # prior to beta\n",
    "\n",
    "x = np.linspace(-2, 2, 100)\n",
    "\n",
    "prioris = []\n",
    "\n",
    "plt.figure(figsize=(17, 6))\n",
    "for i in range(N):\n",
    "    plt.plot(x, np.exp(a[i] + b[i] * x), c='blue')\n",
    "    \n",
    "plt.xlim(-2, 2)\n",
    "plt.ylim(0, 100)\n",
    "\n",
    "plt.title('Poisson Priori \\n beta ~ Normal(0, 0.2)')\n",
    "plt.xlabel('log population (std)')\n",
    "plt.ylabel('total odds')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bf2dcda",
   "metadata": {},
   "source": [
    "### R Code 11.43"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "70ceb090",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 100\n",
    "a = np.random.normal(3, 0.5, N)  # prior to alpha\n",
    "b = np.random.normal(0, 0.2, N)  # prior to beta\n",
    "\n",
    "x_seq = np.linspace(np.log(100), np.log(200000), 100)  # log(population)\n",
    "\n",
    "lambda_poisson =  [ np.exp(a[i] + b[i] * x_seq) for i in range(N) ]\n",
    "\n",
    "plt.figure(figsize=(17,6))\n",
    "\n",
    "for i in range(N):\n",
    "    plt.plot(x_seq, lambda_poisson[i], c='blue')\n",
    "\n",
    "plt.ylim(0, 500)\n",
    "plt.xlim(min(x_seq) - 0.1, max(x_seq))\n",
    "\n",
    "plt.title('a ~ Normal(3, 0.5), b ~ Normal(0, 0.2)')\n",
    "plt.xlabel('log population')\n",
    "plt.ylabel('total tools')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25611c39",
   "metadata": {},
   "source": [
    "### R Code 11.44"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "d1f30db4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#N = 100\n",
    "#a = np.random.normal(3, 0.5, N)  # prior to alpha\n",
    "#b = np.random.normal(0, 0.2, N)  # prior to beta\n",
    "\n",
    "x_seq = np.linspace(np.log(100), np.log(200000), 100)  # log(population)\n",
    "\n",
    "lambda_poisson_ =  [ np.exp(a[i] + b[i] * x_seq) for i in range(N) ]\n",
    "\n",
    "plt.figure(figsize=(17,6))\n",
    "\n",
    "for i in range(N):\n",
    "    plt.plot(np.exp(x_seq), lambda_poisson_[i], c='blue')\n",
    "\n",
    "plt.ylim(0, 500)\n",
    "#plt.xlim(min(np.exp(x_seq)), max(np.exp(x_seq)))\n",
    "\n",
    "plt.title('a ~ Normal(3, 0.5), b ~ Normal(0, 0.2)')\n",
    "plt.xlabel('population')\n",
    "plt.ylabel('total tools')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e03ffb4",
   "metadata": {},
   "source": [
    "#### Curiosidade do python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "b0049940",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Sl8n/wQC4PSIOlOB9mJlVpGm7dtGTcRcOFBD2ABGxHlg/pO3W1PAtwC0jLLsWWDuJGs3MpqbeXhpfeYXDl1+edSX+BK2ZWak07t1LXV9f5idnwWFvZlYy03bvBqD7zDMzrsRhb2ZWMtMGL7tcuDDbQnDYm5mVzLTdu+mdOzfTu10OctibmZXItN27K6K/Hhz2ZmalEeGwNzOrdvX795M7fNhhb2ZWzY5eieOwNzOrXoNX4vRUwGWX4LA3MyuJxt276Z8xI9OHjKc57M3MSmDa7t30LFwIdZURs5VRhZlZlZne2loRn5wd5LA3Myuyhtdeo6Gtjc7zzsu6lKMc9mZmRTZzyxYAOi+8MONK/oPD3sysyGY+8wz9s2Zx5N3vzrqUoxz2ZmZFNuvpp+k8/3yoL+iRIWXhsDczK6K6jg6mt7ZWVBcOOOzNzIpq5rPPAtB50UUZV3Ish72ZWRHNevppor6ezve8J+tSjlFQ2EtaLulFSa2Sbh5m+hck7ZD0nKSfSjojNa1f0tbkta6YxZuZVZqZzzxD1+LFxIwZWZdyjDHDXlIOuBu4ElgMrJK0eMhszwDLIuK9wEPAV1PTuiLiguR1dZHqNjOrOOruZsa2bby9dGnWpbxDIUf2FwOtEbErInqAB4AV6Rki4rGI6ExGNwGnFbdMM7PKN2P7dup6eyvu5CwUFvanAq+mxvckbSO5EXgkNT5dUoukTZKuGX+JZmZTQyV+mGpQUS8ClfTfgGXAh1LNZ0TEXklnAj+T9HxEvDTMsquB1QDz588vZllmZmUxa8sWjpx5Jv3NzVmX8g6FHNnvBRakxk9L2o4h6Q+BLwJXR0T3YHtE7E2+7gIeB4b9kxcRayJiWUQsa67AHWVmNhr19jJz69aKPKqHwsJ+M3COpEWSGoGVwDFX1Ui6EPgW+aBvS7U3S5qWDM8B3g/sKFbxZmaV4rinniJ3+DCHL78861KGNWY3TkT0SboJ2ADkgLURsV3S7UBLRKwDvgYcB/xAEsAryZU35wLfkjRA/g/LHRHhsDezqjP7xz+mb/Zs3nrf+7IuZVgF9dlHxHpg/ZC2W1PDfzjCck8BlXOPTzOzEqjr7OT4xx+n/WMfIxoasi5nWP4ErZnZJDU99hh1XV10XHVV1qWMyGFvZjZJJ/z4x/S8610Ve3IWHPZmZpOSO3CA4556Kn9UXyHPmx1O5VZmZjYFzN64EfX3c7CCu3DAYW9mNimz16/nyNln011BT6UajsPezGyCpr/wArO2bOHgRz8K+cvOK5bD3sxsgubdeSd9s2dz4Lrrsi5lTA57M7MJmNnSQtMvfsGbN97IQFNT1uWMyWFvZjZeEcy78056Tz6Z/atWZV1NQRz2Zmbj1PTEE8zaupW2P/szYvr0rMspiMPezGw8+vqYd9dddJ9+Ou3XXpt1NQVz2JuZjcO8u+5i+s6d7Pvc56BC74MzHIe9mVmBjt+wgbn/+I/sv+46Dl1xRdbljIvD3sysANN27uTUv/kbOs8/n9f/+q+zLmfcHPZmZmOof/NNTv/85xmYNYtX/u7vKvY2xqNx2JuZjWL6iy9y5qpVNLS18erXv07fySdnXdKEOOzNzEbQ9MQTLPrkJ9HAALvuu4/OpUuzLmnCCnpSlZlZLcm9+Sbz7r6b5h/+kCPnnstvv/GNKXtEP8hhb2aWqDt8mBMffJC53/42dT097L/+evZ99rPEzJlZlzZpDnszq229vczavJnmH/2I43/6U+q6uzl0+eW8/oUv0LNwYdbVFU1BYS9pOXAnkAO+ExF3DJk+DfgucBGwH7guIl5Opt0C3Aj0A38RERuKVr2Z2XhEUP/GG0xrbWXGjh3Mamlh5pYt5Lq66G9qov2aa2i/9lqOLFmSdaVFN2bYS8oBdwMfBvYAmyWti4gdqdluBNoj4mxJK4GvANdJWgysBJYApwD/JundEdFf7DdiZjWkvx/19qKeHuq6u1F3N3VdXdS9/Ta5zk7qDh2ivqOD3MGD1O/fT8Prr9Pw+us07tlD7vDho6s5cvbZHFyxgrcuuYS3Lr2UmDYtwzdVWoUc2V8MtEbELgBJDwArgHTYrwC+lAw/BHxTkpL2ByKiG9gtqTVZ3y+LU/6xzrruOtTdXYpVm1WXiJKvS+n2ocPJuFLDDAxABBoYyI/39+eHBwZQf39+fDDkx1F/f1MTvfPm0TtvHp3nnUf3WWfRffbZHDnnHPqbm8f1VqeyQsL+VODV1Pge4PdHmici+iR1ACcl7ZuGLHvqcBuRtBpYDTB//vxCan+H7kWLUE/PhJY1qznFfLLSCOsKafhp6XaJGHxQd11dflyCujoil8uP19dDLkfU1RENDUR9ff5rYyMD06fnv86YwcDMmQzMmkV/UxP9J5xA/+zZRGNj8d7nFFYxJ2gjYg2wBmDJkiUTOuzYc8cdY89kZlaDCvlQ1V5gQWr8tKRt2Hkk1QOzyZ+oLWRZMzMrsULCfjNwjqRFkhrJn3BdN2SedcANyfDHgZ9FRCTtKyVNk7QIOAf4dXFKNzOzQo3ZjZP0wd8EbCB/6eXaiNgu6XagJSLWAf8X+KfkBOwB8n8QSOb7Z/Inc/uAz/hKHDOz8iuozz4i1gPrh7Tdmho+AnxihGX/FvjbSdRoZmaT5BuhmZnVAIe9mVkNcNibmdUAh72ZWQ1QFPNj00Ui6Q3gtxNcfA7wZhHLKRbXNT6ua3xc1/hUY11nRMTckSZWZNhPhqSWiFiWdR1Dua7xcV3j47rGpxbrcjeOmVkNcNibmdWAagz7NVkXMALXNT6ua3xc1/jUXF1V12dvZmbvVI1H9mZmNoTD3sysBlRN2EtaLulFSa2Sbi7D9hZIekzSDknbJX0uaf+SpL2Stiavq1LL3JLU96Kkj5SqdkkvS3o+2X5L0naipI2SdiZfm5N2Sbor2fZzkpam1nNDMv9OSTeMtL0Ca/qd1D7ZKumQpL/MYn9JWiupTdK2VFvR9o+ki5L935osW9AjoUao62uS/j3Z9sOSTkjaF0rqSu23e8ba/kjvcYJ1Fe37pvzt03+VtD+o/K3UJ1rXg6maXpa0NYP9NVI2ZPszFhFT/kX+1ssvAWcCjcCzwOISb3M+sDQZbgJ+Aywm/yzevxpm/sVJXdOARUm9uVLUDrwMzBnS9lXg5mT4ZuAryfBVwCOAgEuAXyXtJwK7kq/NyXBzEb9frwNnZLG/gA8CS4Ftpdg/5J/ZcEmyzCPAlZOo6wqgPhn+Sqquhen5hqxn2O2P9B4nWFfRvm/APwMrk+F7gD+faF1Dpn8duDWD/TVSNmT6M1YtR/ZHH4oeET3A4EPRSyYiXouILcnwYeAFRni+buLow9cjYjcw+PD1ctW+ArgvGb4PuCbV/t3I2wScIGk+8BFgY0QciIh2YCOwvEi1/GfgpYgY7VPSJdtfEfEk+ecuDN3epPdPMu34iNgU+d/K76bWNe66IuLRiOhLRjeRf9rbiMbY/kjvcdx1jWJc37fkiPQ/AQ8Vs65kvf8VuH+0dZRof42UDZn+jFVL2A/3UPTRgreoJC0ELgR+lTTdlPw7tjb1r99INZai9gAelfS08g9yB5gXEa8lw68D8zKoa9BKjv0lzHp/QfH2z6nJcLHrA/hT8kdxgxZJekbSE5I+kKp3pO2P9B4nqhjft5OAg6k/aMXaXx8A9kXEzlRb2ffXkGzI9GesWsI+M5KOA34I/GVEHAL+ATgLuAB4jfy/kuV2aUQsBa4EPiPpg+mJydFAJtfcJv2xVwM/SJoqYX8dI8v9MxJJXyT/tLfvJU2vAadHxIXAF4DvSzq+0PUV4T1W3PdtiFUce0BR9v01TDZMan2TVS1hn8mDzSU1kP9mfi8i/gUgIvZFRH9EDADfJv/v62g1Fr32iNibfG0DHk5q2Jf8+zf4r2tbuetKXAlsiYh9SY2Z769EsfbPXo7tapl0fZI+BfwRcH0SEiTdJPuT4afJ94e/e4ztj/Qex62I37f95Lst6oe0T1iyrv8CPJiqt6z7a7hsGGV95fkZK+SEQ6W/yD9ecRf5E0KDJ3+WlHibIt9X9n+GtM9PDX+efP8lwBKOPXG1i/xJq6LWDswCmlLDT5Hva/8ax54c+moy/FGOPTn06/iPk0O7yZ8Yak6GTyzCfnsA+JOs9xdDTtgVc//wzpNnV02iruXkn+E8d8h8c4FcMnwm+V/2Ubc/0nucYF1F+76R/y8vfYL2f060rtQ+eyKr/cXI2ZDpz1jJwrDcL/JntH9D/i/2F8uwvUvJ/xv2HLA1eV0F/BPwfNK+bsgvxReT+l4kdfa8mLUnP8jPJq/tg+sj3zf6U2An8G+pHxoBdyfbfh5YllrXn5I/wdZKKqAnUdss8kdys1NtZd9f5P+9fw3oJd/feWMx9w+wDNiWLPNNkk+qT7CuVvL9toM/Y/ck8/5x8v3dCmwBPjbW9kd6jxOsq2jft+Rn9tfJe/0BMG2idSXt9wL/Y8i85dxfI2VDpj9jvl2CmVkNqJY+ezMzG4XD3sysBjjszcxqgMPezKwGOOzNzGqAw97MrAY47M3MasD/B5vHdUeQf+InAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0, 10, 100)\n",
    "y = np.exp(2 + 4 * x)\n",
    "\n",
    "plt.plot(x*2000, y, c='red')\n",
    "plt.show()\n",
    "\n",
    "plt.plot(np.exp(x), y, c='red')\n",
    "plt.show()\n",
    "\n",
    "plt.plot(x*2000, y, c='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7eeea48e",
   "metadata": {},
   "source": [
    "### R Code 11.45"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "930728c5",
   "metadata": {},
   "source": [
    "#### Model:\n",
    "\n",
    "$$ T_i \\sim Poisson(\\lambda_i) $$\n",
    "\n",
    "$$ log(\\lambda_i) = \\alpha_{CID[i]} $$\n",
    "\n",
    "$$ \\alpha_j \\sim Normal(3, 0.5) $$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "d99d1734",
   "metadata": {
    "tags": [
     "remove_output"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mBuilding:\u001b[0m found in cache, done.\n",
      "\u001b[36mSampling:\u001b[0m   0%\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  25% (2000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  50% (4000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  75% (6000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m 100% (8000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[32mSampling:\u001b[0m 100% (8000/8000), done.\n",
      "\u001b[36mMessages received during sampling:\u001b[0m\n",
      "  Gradient evaluation took 3e-06 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.03 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 4e-06 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.04 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 4e-06 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.04 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 4e-06 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.04 seconds.\n",
      "  Adjust your expectations accordingly!\n"
     ]
    }
   ],
   "source": [
    "model_11_9 = \"\"\"\n",
    "    data {\n",
    "        int N;\n",
    "        array[N] int total_tools;\n",
    "    }\n",
    "    \n",
    "    parameters {\n",
    "        real alpha;\n",
    "    }\n",
    "    \n",
    "    model {\n",
    "        // Priori\n",
    "        alpha ~ normal(3, 0.5);\n",
    "        \n",
    "        // Likelihood\n",
    "        total_tools ~ poisson(exp(alpha));\n",
    "    }\n",
    "\"\"\"\n",
    "\n",
    "data_list = df[['total_tools']].to_dict('list')\n",
    "data_list['N'] = len(df)\n",
    "\n",
    "posteriori = stan.build(model_11_9, data=data_list)\n",
    "samples = posteriori.sample(num_chains=4, num_samples=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "cd18cda1",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_11_9_post = az.from_pystan(\n",
    "    posterior=samples,\n",
    "    posterior_model=posteriori,\n",
    "    observed_data=list(data_list.keys()),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "8ac01c77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alpha</th>\n",
       "      <td>3.542</td>\n",
       "      <td>0.053</td>\n",
       "      <td>3.444</td>\n",
       "      <td>3.645</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>1572.0</td>\n",
       "      <td>2036.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  ess_tail  \\\n",
       "alpha  3.542  0.053   3.444    3.645      0.001    0.001    1572.0    2036.0   \n",
       "\n",
       "       r_hat  \n",
       "alpha    1.0  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(model_11_9_post, var_names=['alpha'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "b5943f5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x295.2 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_forest(model_11_9_post, var_names=['alpha'], combined=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f211d335",
   "metadata": {},
   "source": [
    "#### Model:\n",
    "\n",
    "$$ T_i \\sim Poisson(\\lambda_i) $$\n",
    "\n",
    "$$ log(\\lambda_i) = \\alpha_{CID[i]} + \\beta_{CID[i]} \\times log(P_i) $$\n",
    "\n",
    "$$ \\alpha_j \\sim Normal(3, 0.5) $$\n",
    "\n",
    "$$ \\beta_j \\sim Normal(0, 0.2) $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "236c417c",
   "metadata": {
    "tags": [
     "remove_output"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mBuilding:\u001b[0m found in cache, done.\n",
      "\u001b[36mSampling:\u001b[0m   0%\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  25% (2000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  50% (4000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  75% (6000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m 100% (8000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[32mSampling:\u001b[0m 100% (8000/8000), done.\n",
      "\u001b[36mMessages received during sampling:\u001b[0m\n",
      "  Gradient evaluation took 6e-06 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.06 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 6e-06 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.06 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 7e-06 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.07 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 1.1e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.11 seconds.\n",
      "  Adjust your expectations accordingly!\n"
     ]
    }
   ],
   "source": [
    "model_11_9_new = \"\"\"\n",
    "    data {\n",
    "        int N;\n",
    "        int qty_cid;\n",
    "        array[N] int total_tools;\n",
    "        array[N] real P;\n",
    "        array[N] int contact_id;\n",
    "    }\n",
    "    \n",
    "    parameters {\n",
    "        array[qty_cid] real alpha;\n",
    "        array[qty_cid] real beta;\n",
    "    }\n",
    "    \n",
    "    model {\n",
    "        vector[N] lambda;\n",
    "        \n",
    "        // Priori\n",
    "        alpha ~ normal(3, 0.5);\n",
    "        beta ~ normal(0, 0.2);\n",
    "        \n",
    "        // Likelihood\n",
    "        for(i in 1:N){\n",
    "                lambda[i] = alpha[ contact_id[i] ] + beta[ contact_id[i] ] * P[i];\n",
    "                lambda[i] = exp(lambda[i]);\n",
    "        }\n",
    "        total_tools ~ poisson(lambda);\n",
    "    }\n",
    "\"\"\"\n",
    "\n",
    "data_list = df[['total_tools', 'P', 'contact_id']].to_dict('list')\n",
    "data_list['N'] = len(df)\n",
    "data_list['qty_cid'] = len(df.contact_id.unique())\n",
    "\n",
    "posteriori = stan.build(model_11_9_new, data=data_list)\n",
    "samples = posteriori.sample(num_chains=4, num_samples=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "f630d538",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_11_9_new_post = az.from_pystan(\n",
    "    posterior=samples,\n",
    "    posterior_model=posteriori,\n",
    "    observed_data=list(data_list.keys()),\n",
    "    dims={\n",
    "        'alpha': ['contact_id'],\n",
    "        'beta': ['contact_id'],\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "40b799f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alpha[0]</th>\n",
       "      <td>3.321</td>\n",
       "      <td>0.088</td>\n",
       "      <td>3.144</td>\n",
       "      <td>3.478</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.001</td>\n",
       "      <td>3353.0</td>\n",
       "      <td>2928.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha[1]</th>\n",
       "      <td>3.610</td>\n",
       "      <td>0.073</td>\n",
       "      <td>3.473</td>\n",
       "      <td>3.747</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>3865.0</td>\n",
       "      <td>2920.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta[0]</th>\n",
       "      <td>0.360</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.265</td>\n",
       "      <td>0.453</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>2984.0</td>\n",
       "      <td>3238.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta[1]</th>\n",
       "      <td>0.192</td>\n",
       "      <td>0.153</td>\n",
       "      <td>-0.086</td>\n",
       "      <td>0.486</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>3968.0</td>\n",
       "      <td>2923.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  \\\n",
       "alpha[0]  3.321  0.088   3.144    3.478      0.002    0.001    3353.0   \n",
       "alpha[1]  3.610  0.073   3.473    3.747      0.001    0.001    3865.0   \n",
       "beta[0]   0.360  0.050   0.265    0.453      0.001    0.001    2984.0   \n",
       "beta[1]   0.192  0.153  -0.086    0.486      0.002    0.002    3968.0   \n",
       "\n",
       "          ess_tail  r_hat  \n",
       "alpha[0]    2928.0    1.0  \n",
       "alpha[1]    2920.0    1.0  \n",
       "beta[0]     3238.0    1.0  \n",
       "beta[1]     2923.0    1.0  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(model_11_9_new_post)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "376bf3f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_forest(model_11_9_new_post, var_names=['alpha', 'beta'], combined=True, figsize=(17, 5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bb96eee",
   "metadata": {},
   "source": [
    "### R Code 11.46"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "502c7bac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Need to make a generated_quantities to compute compare\n",
    "\n",
    "\n",
    "dict_to_compare = {\n",
    "    'model_11_9_post': model_11_9_post,\n",
    "    'model_11_9_new_post': model_11_9_new_post\n",
    "}\n",
    "\n",
    "# az.compare(dict_to_compare)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51dacca5",
   "metadata": {},
   "source": [
    "### R Code 11.47"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "8158cf3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ADR/WH374YR566CG6urro6Ojg/vvv5/bbb5+wx/ubv/kbkskk73rXu9i9ezd//Md/jOM4XHPNNfzsZz/jG9/4BgC9vb28733v49ixY9x000184AMfOOO+du/ezac+9SkKhQLRaJQvfOELhMNhPvGJT/Dcc88RDof5wz/8Q6666iq++c1v8uMf/5hisTjmPj/zmc9QKpW4++67WbVqFX/+53/O7/3e79HV1UW5XObtb387b37zmwH42c9+xmc/+1k8z2PWrFl8/OMf56tf/SqhUIjvfOc7fPjDH2b79u0T9rMTEREREZH6GR3SjTEK6ROsocP6ww8/zAMPPECxWATg1KlTPPDAAwCXFNir4bcqlUpxww03nHG7j370o3zsYx9j69atfOYznxnzuT179vC1r32NaDTK61//et72trfR0dFR+3ylUuEP/uAP+PSnP81ll11GNpslFovxD//wDxhj+MY3vsHBgwd573vfy3e+851z3uf73/9+/umf/omvf/3rtfv+xCc+QVtbG8Vikbe+9a38+q//Or7v88ADD/B3f/d3dHZ2kkqlaGtr45577qm9+CAiIiIiIjPP6SE9Ho+r0/skaOiw/tBDD9WCelWxWOShhx66pLAei8XGhN9vfvObPPfcc2Nuk06nyeVytfLx2267jUcffbT2+Ve84hW0tLQAsHLlSk6ePDkmrB86dIh58+Zx2WWXAdDc3AzAk08+ydve9rba1y1atIjDhw+f131W/cM//AOPPPIIAF1dXRw5coTBwUG2b99OZ2cnAG1tbRf3wxERERERkWmjVCrR3d2NtVYhfZI1dFjv6uq6oOsnUyQSqb0fCoXwPG9S7vPxxx/nF7/4Bf/rf/0vEokE7373uymXy5f82CIiIiIiMr3kcjl6enqIxWKEww0dHeuioV8WOduu8ktdP55aW1tpamrimWeeAeB73/veBX39ihUr6O3tZffu3UDwF8l1XbZt28bDDz8MwOHDhzl16hQrVqx4yfsKh8NUKhUAMpkMra2tJBIJDh48WFvfli1b2LlzJ8ePHweC0n6ApqYmcrncBa1dRERERESmtlQqRXd3N/F4XEG9Tho6rN9///3E4/Ex18Xjce6///5JefyPf/zjPPDAA9x9990UCoVaKfv5iEQifPrTn+bP/uzPuOuuu3jPe95DuVzm3nvvxfd97rzzTv7wD/+QP/mTPyEajb7kfd19993cddddfPCDH+S6667DdV3e8IY38Jd/+Zds2bIFgNmzZ/Oxj32M97///dx11138wR/8AQDXX389jzzyCHfffTc7d+68+B+GiIiIiIjUnbWWgYEB+vv7SSaThEKhei+pYRlrbb3XcNE2bdpkv/KVr4y5rlwuv2w4HW2yu8GPls/nSSaTAHzhC1+gr6+PD33oQ5Py2JPlQv88RERERESkPjzPo7+/n3w+P207vZfLZeLxOHPnzq33Us7L5s2bd1prd5ztcw1fz3D77bdPWjg/3aOPPsoXvvAFPM9j4cKF/Mmf/Eld1iEiIiIiIo3NdV16enqoVCq1DUWpr4YP6/V0yy23cMstt9R7GSIiIiIi0sDK5TLd3d0AJBKJOq9GqhTWRUREREREGlSxWKSrq4tIJDJmepTU34xsMDedz+HPJPpzEBERERGZukqlEqdOnSIWiymoT0EzLqwbY/B9X0Gxzqy1+L4/LZtSiIiIiIjMdOVyma6uLmKxmDq+T1Ezrgw+HA7jui6e59V7KQ3PGKOZjCIiIiIiU0ylUqGrq4twOKzf16ewCfuTMcZ8CXgd0GOtvWz4uv8HeD1QBg4A77bWDg1/7sPAfYAH/J619l8v8nFVwiEiIiIiInIW1aDuOI5y0xQ3kWXwfwec3ur8+8Bl1totwIvAhwGMMRuBe4FNw1/zN8YY1WKIiIiIiIiME9d1a13fo9FonVcjL2fCwrq19lFg4LTr/s1a6w5/+Augc/j9O4AvW2tL1tpDwH7gqolam4iIiIiISCPxPI+enh583ycWi9V7OXIe6tlg7reAfxl+fzFwbNTnjg9fJyIiIiIiIpfA9316e3txXZd4PF7v5ch5qktYN8b8/wAX+IeL+Nr3GGOeMMY8MTg4OP6LExERERERmSF836evr49SqaSgPs1Melg3xryLoPHc2+3IfLUTwJJRN+scvu4M1trPW2t3WGt3zJo1a0LXKiIiIiIiMl1Za+nv7yefz5NIJOq9HLlAkxrWjTG3AH8EvMFamx/1qW8B9xpjYsaYFcAa4FeTuTYREREREZGZolr6nsvlSCaT9V6OXISJHN32T8ANwFxjzHHgYwTd32PA940xAL+w1r7PWvucMearwPME5fH/yVqrQekiIiIiIiIXqFKp0NvbS6VSUVCfxiYsrFtr33qWq7/4Erf/JPDJiVqPiIiIiIjITFcqlejp6QFQ6fs0N2FhXURERERERCZPLpejt7eXSCRCJBKp93LkEimsi4iIiIiITGPWWlKpFIODg8TjcUKhUL2XJONAYV1ERERERGSa8n2fgYEBMpkMyWSS4d5gMgMorIuIiIiIiExDruvS19dHsVikqamp3suRcaawLiIiIiIiMs2Uy2W6u7vxfV8d32cohXUREREREZFpwlpLNpulr6+PaDRKLBar95Jkgiisi4iIiIiITAOe59Hf3082myWZTOI4Tr2XJBNIYV1ERERERGSKq5a9W2tpbm6u93JkEiisi4iIiIiITGGZTKZW9q756Y1DYV1ERERERGQKUtl7Y1NYFxERERERmWJU9i4K6yIiIiIiIlNIoVCgu7ubSCSisvcGprAuIiIiIiIyReRyOXp6eojH44RCoXovR+pIYV1ERERERGQKqDaSU1AXUFgXERERERGpu6GhIQYGBtRITmoU1kVEREREROrEWsvQ0BCDg4M0NTVhjKn3kmSKUFgXERERERGpA2st/f39pNNpBXU5g8K6iIiIiIjIJPN9n76+PvL5vEazyVkprIuIiIiIiEwiz/Po7e2lWCySTCbrvRyZohTWRUREREREJkm5XKa7uxtrrYK6vCSFdRERERERkUmQzWbp6+sjHA4Ti8XqvRyZ4hTWRUREREREJpDv+wwMDJBOp0kkEpqhLudFYV1ERERERGSCVCoVent7KZfL6vguF0RhXUREREREZALk83l6e3sJhUI6ny4XTGFdRERERERkHFlrGRoaYmhoiHg8rrJ3uSgK6yIiIiIiIuPEdV36+/spFAokk0mVvctFU1gXEREREREZB8Vikd7eXo1lk3GhsC4iIiIiInIJrLWk02kGBweJRqOEw4pZcun0LBIREREREblInufR399PNpslmUziOE69lyQzhMK6iIiIiIjIRSiVSvT09GCtpbm5ud7LkRlGYV1EREREROQCZTIZ+vr6iEajRCKRei9HZiCFdRERERERkfOksneZLArrIiIiIiIiL8N1XQqFAkNDQ/i+r7J3mXAK6yIiIiIiIudQLpfJZDJkMhkAYrEYoVCozquSRqCwLiIiIiIiMorv+xSLRVKpFMVikVAoRCKRwBhT76VJA1FYFxERERERISh1z+VypNNpXNclGo3S1NRU72VJg1JYFxERERGRhlcsFunt7cX3faLRKLFYrN5LkgansC4iIiIiIg3LWks6nWZgYIBYLKaQLlOGwrqIiIiIiDQk13UZGBggl8uRTCZ1Jl2mFIV1ERERERFpOKVSiZ6eHnzf17l0mZIU1kVEREREpGGo7F2mC4V1ERERERFpCJ7n0d/fTzabJZlM4jhOvZckck4K6yIiIiIiMuNVy96ttTQ3N9d7OSIvS2FdRERERERmrNFl79FolEgkUu8liZwXhXUREREREZmRqt3eVfYu05HCuoiIiIiIzDjFYpHe3l6Vvcu0pbAuIiIiIiIzhrWWVCrF4OAgsViMcFiRR6YnPXNFRERERGRGcF2Xvr4+CoUCyWQSY0y9lyRy0RTWRURERERk2isUCvT29gLQ1NRU59WIXDqFdRERERERmbY8z2NoaIh0Ok08HicUCtV7SVIn1kI26+B5DnPn1ns1l05hXUREREREpqVCoUBfXx++76vsvcG5LgwNhRkY8Jk/f2Y8DxTWRURERERkWhm9mx6LxYjFYvVektRRsWjo7w9hLcRitt7LGTcK6yIiIiIiMm1oN12qfB/S6RDptEM8bnEcyOXqvarxo7AuIiIiIiJTnnbTZbRKJdhNd11DImGZia/ZKKyLiIiIiMiUpt10qao2kRscdIhEIB6fOWXvp3Mm6o6NMV8yxvQYY3aPum62Meb7xph9w29nDV9vjDGfNcbsN8Y8Y4zZNlHrEhERERGR6aFSqdDT00NXVxehUIhEIqGg3sBcF/r7wwwMOMTjEInUe0UTa8LCOvB3wC2nXfch4BFr7RrgkeGPAW4F1gxf3gP89wlcl4iIiIiITGG+7zM0NMTx48cpFos0NTURDqsouJFVKoaenjDFIjQ1gTORSXaKmLBv0Vr7KDBw2tV3AH8//P7fA28cdf3/tIFfAO3GmIUTtTYREREREZl6rLXk83lOnDhBKpUikUgQj8frvSyps3ze4dSpEI4DjfR0mOyXpxZYa08Nv98FLBh+fzFwbNTtjg9fdwoREREREZnxyuUyAwMD5PN5EokEoVCo3kuSOrMW0mmHVCpU6/beSOpWS2KttcaYC+4GYIx5D0GpPAsXavNdRERERGQ68zyPVCpFKpUiEonQ3Nxc7yXJFOB5MDgYJp9nxnZ7fzmT/dpEd7W8ffhtz/D1J4Alo27XOXzdGay1n7fW7rDW7pg1a9aELlZERERERCZOsVjk5MmTZDIZkskk0Wi03kuSKcB1g/PppRIkkzRkUIfJD+vfAt45/P47gX8edf07hrvCvwJIjSqXFxERERGRGcRay9DQEKdOnVKXdxmjUDCcOhUcgYjF6ryYOpuwMnhjzD8BNwBzjTHHgY8BnwK+aoy5DzgC3DN88+8CtwH7gTzw7olal4iIiIiI1E+lUqGvr49SqaSZ6VLj+8H89KGhxjyffjYTFtattW89x6duOsttLfCfJmotIiIiIiJSf9lslr6+PsLhMMlkst7LkSmiVDIMDIRwXdOw59PPRsMKRURERERkQnmeR39/P9lsVp3epcbzIJUKkc06RKOWROKC+4/PaArrIiIiIiIyYYrFIr29vVhr1eldgGAkW6HgMDgY1LprN/3sFNZFRERERGTcWWtJp9MMDAwQi8UIhxU9JOj0PjQUIpcLSt51Nv3c9DdGRERERETG1eiy92QyiaNE1vB8H3I5h6Ehh1AImppU8v5yFNZFRERERGTcVCoVuru78X1fZe8CBLvp/f0hymVDLKbd9POlsC4iIiIiIuMil8vR29tLOBwmHo/XezlSZ9ZCPu/Q3+8QiaAGchdIYV1ERERERC6JtZahoSGGhoaIx+Pq9i64LsNn0x2dTb9ICusiIiIiInLRXNdlYGCAXC5HMpnEqK13wysWg7np1ups+qVQWBcRERERkYtSKpXo6enB932amprqvRypM9+HdDpEOu0Qj2s3/VIprIuIiIiIyAUZPZYtGo0Si8XqvSSps0olaCJXqRjNTR8nCusiIiIiInLeqmXvGssm1kKpZMjlHHI5oyZy40xhXUREREREzkuhUKC3txdAY9kaWKViyOeDkO66EA5DIoF208eZwrqIiIiIiLwk3/cZGhoilUqp23uDcl0olRwyGYdy2eA4lmgUotF6r2zmUlgXEREREZFzKpfL9Pb24rquur03IN8PRrBlswZjDNGoJZlUqftkUFgXEREREZEzWGvJZrP09/cTDodJJBL1XpJMMs+D/v4wpVK1aZxC+mRSWBcRERERkTFKpRKDg4MUCgUSiYSayDUg14W+vjCep6Zx9aKwLiIiIiIiQFDyPjQ0RC6XIxKJaHZ6g3JdQ09PCGtBU/nqR2FdRERERKTBVSoVUqkU2WyWUCikkN7AKpUgqDuOmsfVm8K6iIiIiEiDcl2XdDpNKpXCcRwSiYQayDWwUikI6pFIMI5N6kt/BCIiIiIiDcb3fdLpNENDQziOoy7vQqFg6O0NEYuBJvNNDQrrIiIiIiINpFwu09PTg+u6xONxNY8TcjmHgYEQ8bhFT4epQ2FdRERERKQBVEex9fX1EYlESCaT9V6S1JnvQzbrMDSkoD4VKayLiIiIiMxwrusyMDBANpslmUxqN73BWQvFomFwMITvV2eo13tVcjqFdRERERGRGaxYLNLb24vv+zQ3N9d7OVJnlUoQ0guFIKQ7jmaoT1UK6yIiIiIiM5C1lqGhIYaGhojFYsQ0MLuheR5kMiHSaYdIxNLUpJA+1Smsi4iIiIjMMJVKhb6+Pkqlkjq9NzhrIZ93GBoKjj6o5H36UFgXEREREZkhfN8nk8kwODhIKBRSE7kGVyoFJe/lMsTjqIHcNKOwLiIiIiIyA+Tzefr7+/E8TyPZGpzrwtBQiHzeIRq16DWb6UlhXURERERkGiuXywwODpLP53U2vcGNjGJzCIUgmdS59OlMYV1EREREZBryPI9UKkUqlSISidDU1FTvJUmdBKPYHAYHHTzPaGb6DKGwLiIiIiIyjVhryeVyDAwMYK1VA7kGd/ootmhUu+kzhcK6iIiIiMg0US6Xa13e4/E4oVCo3kuSOtEotplPYV1EREREZIqz1pJKpRgcHFTJu1AsGgYGQvi+0Si2GUxhXURERERkCiuXy/T29lKpVEgkEury3sA8D9LpEJmMQyymkveZTmFdRERERGQK8n2fdDpd203XzPTGpt30xqOwLiIiIiIyxYzeTVcDucam3fTGpbAuIiIiIjJFaDddRtNuemNTWBcRERERmQJKpRJ9fX3aTRdcN+j0rt30xqawLiIiIiJSR77vk0qlGBoa0m56g7MWcjmHoSEHY9BueoNTWBcRERERqZNisUhvby++72s3vcGVSobBwRDlMsTjoKb/orAuIiIiIjLJPM8jlUqRSqWIRqPEYrF6L0nqxHUhlQqRyzlEoxYVVkiVwrqIiIiIyCQqFAr09fXheZ520xuY748teU8mdS5dxlJYFxERERGZBK7rMjQ0RCaTIRaLaTe9QVkbdHkfHAzheYZYzKrkXc5KYV1EREREZAL5vk8ul2NgYABjjHbTG1Q1pKdSwbn0WAx1eZeXpLAuIiIiIjJB8vk8/f39eJ5HPB7H0RZqw6mG9KGhEJVKENJ1Ll3Oh8K6iIiIiMg4K5fLDA4OksvliMfjKnlvQEFID86kK6TLxVBYFxEREREZJ67rkkqlSKfTRCIRmpub670kmWS+D6WSw+BgENLjcYV0uTgK6yIiIiIil0jn0hub50G5bMjnQ+TzBmvt8Jn0eq9MpjOFdRERERGRS6Bz6Y0pCOgOuZxDoRCUvUciEI9b9DqNjAeFdRERERGRi1AulxkYGKBQKGgUW4OwFgqFkYBujCEctsTjKKDLuFNYFxERERG5ANVz6ZlMhnA4TFNTU72XJJOg2tG9XA520INz6Bq9NpX4PmQyEdra6r2S8aGwLiIiIiJyHnzfJ5PJMDQ0hDGGRCKhc+kNoFIJZqPncoZYzKpZ3BT2F3+xkWIRPv7xF+q9lHHxsmHdGNMEFKy1vjFmLbAe+BdrbWXCVyciIiIiUmfWWgqFgs6lNxjXhUwmRCbjEA5bmpq0iz7VVCqGn/50Addc00si4XHTTV2kUj52hvxRnc/O+qPAK40xs4B/Ax4H3gK8fSIXJiIiIiJSb6VSicHBQfL5vOalNwjfh1zOIZVyMAYSCTWMm2rKZYdHHung299eQn9/DGMsN97YzbZtA+RyJYyJ13uJ4+J8wrqx1uaNMfcBf2Ot/a/GmKcmeF0iIiIiInVTqVRq59I1L70xVJvHDQ05eF5Q8q4CiqnF9+E73+nkO9/pJJWKsmHDEO9974ts2TJY76VNiPMK68aYawh20u8bvi50KQ9qjHk/8B8IOjI8C7wbWAh8GZgD7AR+01pbvpTHERERERG5EL7vk06na+fSNS995rMWikWHwUEH14V4HKLRGVJHPUOUy4ZoNHjx5KmnZrNsWZY77zzGxo2pei9tQp1PWP994MPAN6y1zxljVgI/utgHNMYsBn4P2GitLRhjvgrcC9wGfMZa+2VjzOcIXhj47xf7OCIiIiIi58taSz6fZ2BgQOfSG0QQ0kc6vAchvd6rktEGBqJ85zud/OQnC/h//p+dzJ5d5oMf3E0s5td7aZPiZcO6tfYnwE9GfXyQIGxf6uMmjDEVIAmcAl4NvG34838PPIDCuoiIiIhMEGstrutSKpVIpVKUSiUSiYTOpc9w1ZCeSgUhPRYDTd+bWrq64nzrW0v4yU8W4PuGa6/twfeDCpdGCerwEmHdGPNtXmJwoLX2DRfzgNbaE8aYTwNHgQJB07qdwJC11h2+2XFg8TnW9R7gPQALFy68mCWIiIiISINyXZdyuUw+nyefz+N5HsYYnUtvAGcL6RrDNvWk0xH+83/egTFw441dvOENx5k/v1jvZdXFS+2sf3oiHnC4q/wdwApgCPgacMv5fr219vPA5wE2bdqkwyQiIiIi8pJKpRKFQoFcLke5XMYYQygUIhqNqtS9QZTLQbl7oaBZ6VPRnj2tPPdcO3fddZTW1gq/8zt72bQpxaxZjd3C7Jxhfbj8HQBjTBRYO/zh3kucsf4a4JC1tnf4vv8PcC3QbowJD++udwInLuExRERERKTBlcvl2tg1x3GIRqM0qd65obguDA2FyOcdIhHNSp9KfB927ZrNP//zEvbubaO1tcxrX3uS5maX667rrffypoSXPbNujLmB4Az5YcAAS4wx77TWPnqRj3kUeIUxJklQBn8T8ARB07q7CTrCvxP454u8fxERERFpYNWxa9lslnA4rIDegDwPslmHdNrBcSCZVEifSg4fbuKv/3o9x441MXdukXe/ez833tjVUOfRz8f5dIP/C+Bma+1eAGPMWuCfgO0X84DW2l8aY74OPAm4wC6CsvaHgS8bY/5k+LovXsz9i4iIiEhj8jyPdDpNKpXCGEMikdDYtQbj+5DPO6RSDtYa4nGLngJTQ6nkMDAQZeHCInPmlIjFPH73d/dwzTW9hMN6MeVsziesR6pBHcBa+6IxJnIpD2qt/RjwsdOuPghcdSn3KyIiIiKNx/d9stksg4ODAArpDapQMAwOhvC84Fy64ygATgXpdJh/+7dFfO97i5g3r8Sf/ukuWlpcPvnJp+q9tCnvfML6E8aYLwD/a/jjtxOUrYuIiIiI1I1mowuMbR4Xj1uiUYX0qaCnJ853vrOYH/2og3I5xLZt/dxxxzFVOlyA8wnrvwP8J0Zmq/8U+JsJW5GIiIiIyEuw1lIoFOjv78d1XeLxuGajNyDXhXQ6RDar5nFTibVgDDz7bDs/+MFCXvnKHl7/+uN0dubrvbRp52XDurW2ZIz5a+D7BHPXL7UbvIiIiIjIBauG9MHBQcrlMrFYTCG9AXke5HLBuXRjIJHQufR6sxaefnoW3/52Jzt29HPrrSd55Su7ueKKAWbPbuzxa5eiHt3gRUREREQuSDWkF4tFYrGYOrw3IGtHmseNnEuv96oam+saHntsHt/+didHjjQza1aJ667rASAatQrql2jSu8GLiIiIiJyvYrHI0NAQ+XyeWCxGc3NzvZckk8xaKJWCc+nlMsTj6Fz6FPHZz67nl7+cR2dnjve9by/XXddDJKI/m/FSl27wIiIiIiJn4/s+5XKZQqFANpvFdV0ikYhCegNyXSgWg1nplUqwk55M1ntVja2vL8a//MsiXv/647S3V7jtthPceGMXl18+qCqHCaBu8CIiIiJSV5VKhUqlQjabJZ8PmlAZY4hGozqT3oDKZUMm45DLGYwJQrp20uvr4MFmvvOdTh57bB4Aq1dnuOaaPtavT9d5ZTPbxXaD/28TtiIRERERmfGstaTTabLZLOVyGWMM4XBYM9IblOcFu+iZjEO5bAiHLYkEGKOQXk+ua/jUpy7j2WdnkUi43HbbcW699SRz55bqvbSGcD5h/X3W2geBB6tXGGPuBx6asFWJiIiIyIxlraW3t7d2Dl3N4hpXtbN7Ou1grSEatSSTCuj1VCo5PPdcO9u2DRAOWxYtyrNlyyCvec0pkkmv3strKOcT1t/JmcH8XWe5TkRERETkJVlr6e/vJ5/Pk9QB5Ibl+yOd3ash3XEU0utpcDDKv/7rIr7//YVksxH+6q9+xfz5RX7rtw7Ue2kN65xh3RjzVuBtwApjzLdGfaoFGJjohYmIiIjIzFIN6plMRrvpDcpaKBQchoZGj19TSK+n/v4oX/nKcn72s/n4vmHHjn5uv/048+YV6720hvdSO+s/B04BcwnGt1VlgGcmclEiIiIiMrNYaxkcHCSdTquze4MqFoPxa6USJBIav1ZPvg/pdIT29gqRiOXJJ2fz679+iltvPUFHh0L6VHHOsG6tPQIcAa6ZvOWIiIiIyEw0NDTE0NCQdtQbULkchPRCIdhJ11OgfgqFED/+8QK+973FtLaW+cQnnqa1tcJ//++/1Hz0Keh8zqyLiIiIiFy0oaEhBgcHaWpqUqf3BuK6kEqFyOUcIhFLU5PCYL309sb43vcW8cMfLiSfD7NmTZpbbz2JtWAMCupTlMK6iIiIiEyYoaEhBgYGFNQbiOdBNhs0j3Mc1N29TqwNLo4DTz45m+9+t5NXvKKXW289wdq1mXovT86DwrqIiIiITIhUKqWg3kBO7/CeSFj0xz75ymXDY4/N51/+ZRE33dTFr//6Ka6/vpvt2wcaZj66tTPjBaKX6gb/LHC279IA1lq7ZcJWJSIiIiLTlu/7pNNpBfUGYW3QPG5wMKQO73U0MBDl+99fyA9+sJB0OkpnZ47W1goA8bhPPD5zg7q1lsOH9/HEE//Or371M9785t9k3bo31XtZl+yldtZfN2mrEBEREZFpz1pLPp9nYGAAz/NIJpMK6jPc6OZx8bhVh/c6euihDezd28q2bQPceusJLrtsaEZXNrhuheeee4onnvgZO3f+O/39vRhjWL16E7FYvN7LGxcv1w1eREREROQlWWspFAoMDAxQqVSIx+PEYrF6L0smULlsSKWCkK7mcZOvUjH84hfz+MEPFvKf//NztLa6vPOdB0gm3Rk9ei2bzfDUU7/giSf+naee+iWFQp5YLM6WLTu455772LbtGkKhBG1tMzysVxljXgH8FbABiAIhIGetbZ3gtYmIiIjIFFcsFhkYGKBYLBKPxzWabYYrlQzp9EhIV/O4yVUtdX/kkYWkUlEWLszT1xentTXLypXZei9vQpw6dZydO/+dnTt/zp49z+L7Hu3ts/m1X3s127dfy+bN24lGR14czOVmTrn/+TSY+2vgXuBrwA7gHcDaiVyUiIiIiExtpVKJwcFB8vk8sViM5ubmei9JJlCpFOykF4uNE9J/+tN5fPnLK+jvjzFnTol77z3EK1/ZW7f1pFIRfvd3r8L3Ddu2DfDa155k8+ZBHKduS5oQnufy4ovPsXPnz9m58+ecPHkUgKVLV3LHHW9l27ZfY/XqDTgz7Rs/i/PqBm+t3W+MCVlrPeB/GGN2AR+e2KWJiIiIyFRTLpcZGhoil8sRiUQU0me4RgzpEAT1z39+LeVyCIC+vjif/3ywXzlZgb1UcvjZz+Zz6lSC3/iNQ7S1Vbjvvv1cdtkQCxbMrFL3fD7L008/zs6dP+epp35JJpMiFAqzadNWbr75jWzf/mvMn7+w3sucdOcT1vPGmCjwlDHmvwKngJn/MoaIiIiI1IwO6eFwWOXuM1i1u3sqFaJUMkSjjRPSq7785RW1oF5VLof48pdXTHhY7+qK8/3vL+RHP+ogl4uwfHkG1zWEw5abbuqa0MeeTKdOHefJJ3/Ok08+xgsvPI3neTQ3t3LFFVezffu1XH75VSSTjf3vzPmE9d8kCOe/C7wfWAJM/z74IiIiIvKyKpVKrdw9FAoppM9gngeFgkM67eC6EI3SsI3j+vvP3iDxXNePl0cfnc9/+2/rCYV8rryyn1tuOcH69ekZ0dXddV327HmGJ598jCeffIxTp44B0Nm5gte97i1s23YNa9ZsJBQ6r+LvhnA+P4k3WmsfAorAxwGMMfcDD03kwkRERESkfiqVCkNDQ2SzWUKhEMlkst5LkgniupDLOWQyDtYGc9Kj0Xqvqr7mzCnR13dmR/E5c8a3eVk6HeZHP+pg2bIcW7cOctllQ9x992FuuqmL2bPL4/pY9ZBKDfLUU7/kyScf45lnniCfzxIOR9i06QpuueVNbNt2TUOWt5+v8wnr7+TMYP6us1wnIiIiItNcpVIhlUqRyWRqIV2z0memctmQTjvk8wbHCXbSHacxd9JPd++9h8acWQeIRj3uvffQJd+3tXDgQAv/+q+LeOyxeVQqDq973TG2bh1k9uwyb37z0Ut+jHrxfZ/Dh/fx5JOPsWvXLzhwYA/WWtrbZ3P11a9i+/ZfY/Pm7cTjevHvfJwzrBtj3gq8DVhhjPnWqE+1AgMTvTARERERmTyVSoV0Ok06ncZxHIX0GaxSCc6j5/PBOWgVTZypei59IrrBP/TQeh57bD7xuMuNN3Zx880nWbIkf8n3Wy/5fI5nn93Jrl1BQB8aGsAYw6pVG3jzm9/NFVdcw/Llqxuie/t4e6md9Z8TNJObC/zFqOszwDMTuSgRERERmRwK6Y3DdSGdDpHLOYRCjdc07kK98pW94xLOjx9P8KMfdfCWtxwhGvXZsaOfDRtSvOpVPSQS3jisdHJZazl+/DC7dv2CXbt+wd69z+J5HslkM5dffiVXXPEKtm69mra2WfVe6rR3zrBurT0CHAGuMcYsAK4c/tQL1lp3MhYnIiIiIhOjUqmQyWRIp9MYYxTSZzDPC86kDw05OA7E43ZGNCybylzX8KtfzeX731/I88+3Ew77bN8+wMaNKa67rn6z2i9WsZhn9+5dtYDe398DBLPPX/e6t7B16ytYu3YT4bCaw42nl/1pGmPeDHwa+DFggL8yxvyhtfbrE7w2ERERERlnruuSyWQYGhrCcRwSiYRC+gzl+5DPO6RSDr5viMctqkSeeAMDUT70oW2kUlHmzy/wtrcd5MYbu2ltrdR7aefNWsuJE0fYteuXPP30L3nhhWdw3QrxeILNm7dz113vYOvWq5kzZ369lzqjnc9LH/8FuNJa2wNgjJkH/ABQWBcRERGZJkaXu6tx3MxWnZM+OBjC84Lu7mocN3E8D558cg79/TFuueUks2aV+bVf6+Hyywe5/PLBafMCSaGQZ/funcMB/Vf09XUD0Nm5nNe+9k6uuOIVrF+/mUikwUcFTKLzCetONagP6yeYuy4iIiIiU1yxWCSdTpPP51XuPsOdPic9FoNoVCF9ovT1xfjhDzv44Q87GByMsXBhnptvPonjwLvedbDey3tZ1lqOHj3IU08F4XzPnmfxPLe2e/7GN/4GW7dexbx5HfVeasM6n7D+PWPMvwL/NPzxW4B/mbgliYiIiMilsNZSKBQYHBykXC4TiUQ0J30Gc11DLmfIZBzAEI1qTvpE+/73F/LFL64G4PLLB7nvvv1s29Y/5XfRM5kUzz67k6ee+iXPPPMEg4N9QHD2/Pbb38zll1/F+vWbCYcjdV6pwHmEdWvtHxpj3gRcN3zV562135jYZYmIiIjIhfI8rxbSXdclGo3S1NRU72XJBLA2mJOeyYQoFAzGWGIxMEY76ROhry/Gj360gMsvH2Tt2gzr16d44xuP8epXn2L+/FK9l3dOvu+xf/8LPP304zz11K84cOAFrLU0NbWwZcsOLr/8SrZsuVJnz6eo82kw9+fW2g8C/+cs14mIiIhIHVlrKRaLZLNZ8vk81lpisRixWKzeS5MJ4HlQLAal7pWKIRKxJBIK6BPB82DXrtk88shCdu2aDUA06rN2bYYlS/Lce+/h+i7wHPr6unn66cd5+ulfsXv3k+RyGYxxWL16PXfd9U4uv/wqVq9ej+OE6r1UeRnnUwb/68DpwfzWs1wnIiIiIpPAWku5XCafz5NOp/F9n3A4TDwe13n0GchaqFQM2axDLmeolrprTvrEsRY+8pErOHy4hVmzSsO76F3Mn1+s99LOUCzmef75p3nmmcd5+unHOXnyKACzZ8/jyiuvY+vWq9i8eQfNza11XqlcqHOGdWPM7wD/EVhpjHlm1KdagH+f6IWJiIiIyFiVSqUW0F3XxXEcYrEYzlQ/KCsXxXVHdtFd1xAOW+JxlbpPBNc1PPHEHJ54Yg7/8T/uxXHgtttOkky6bNvWT2gKbUL7vs/hw/t45pkneOaZx2uN4aLRGBs3Xs5rXvN6Lr/8KhYvXqYX76a5l9pZ/0eCRnJ/Bnxo1PUZa+3AhK5KREREpIG5rovnebiuS6lUolwuUy6X8X0fx3GIRCIqc5+hRp9Fz+fBmGD0mrq6T4zjxxP86EcdPProAtLpKHPmlOjtjbNgQZHrr++u9/Jq+vp6ePbZx3n66SfYvXsnmUwKCBrD3Xbb3WzZciXr128mGtW/CzPJOcO6tTYFpIC3Tt5yRERERBpPuVwml8vVgrnnebUdsVAoRCgUIhaLaZdshiuVgtno5XL1LLp20SfSnj2tfOxjWwmFfHbs6OfGG7umzFz0QiHP888/xTPPPM4zzzxRK21vb5/N1q1Xc/nlV7J583ba2+fUeaUykc7nzLqIiIiIjDPf9ykWi6RSKYrF4pgdc4XyxlKpGFKpEPm8zqJPFGvhwIFmfvSjDubOLXHnncdYsybNu961n2uu6aW9vVLX9bmuy/79L/DsszvZvfsJ9u17Hs/zaqXtN930OrZsuZIlS1bo34cGorAuIiIiMokqlQq5XI5MJqPxag3OdSGTCZHJOITDCukTIZ2O8NOfzufHP17A0aPNRKMer33tSQBCIbj11pN1WZe1lhMnjvDss0/w7LM7ef75pygU8hhjWLlyHa9//b1s3ryDdesuIxKJ1mWNUn8K6yIiIiITzFpLqVQinU6Ty+VqjeF07rwxeR7kcg6plIMxkEhYtFk6fnyfWin73/3dKv793+ezalWa//Af9nHttT0kk15d1tXf38Pu3U8O754/yeBgHwALFizmuutew+bNO9i06Qp1bZcahXURERGRCeL7PoVCgaGhIcrlMpFIhGQyqTLWBuX7UCg4DA05+H7QOG4qnI+eKbq64vz4x0GzuI985Bk6Owu86U1HeeMbj7J0aX7S15PNZnjuuV3s3r2TZ5/dyalTxwBoaWnjssu2sXnzDjZv3s78+QsnfW0yPSisi4iIiIwzz/PI5/MMDQ3hui6xWEyl7g3MdYOQnsk4eB7EYuA4KnkfD+Wy4bHHgjL3559vxxjL1q0DuG7wKkhn5+SF9GKxwN69u3nuuWD3/NChF7HWEosl2LBhC695zeu57LLtLF26UuMW5bworIuIiIiME9d1yWazpFKp4V/SVereyFzXkMk4ZLMGqI5gq/eqpj9rIZWK0N5ewXUd/vZvVzNnTpm3vOUQ11/fzZw55UlZh+tW2LfveXbv3sVzzz3Jiy8+h+e5hEIh1qzZyF13vZPNm7ezevUGwuHIpKxJZhaFdREREZFLVKlUSKfTpNPp2nl07Zw1rlKpOifd4DiWeFwj2MZDX1+Mn/xkAT/5yQKamlz+7M92kUx6/Nf/+iQLFxYm/Ny/73scPPgizz23i+ee28WePc9QKhUxxrBixVpuu+1uLrtsO+vXX0Y8npzYxUhDUFgXERERuQhnaxqn8+iNy/ehVAqaxlXnpKu7+/h45pl2vvWtJeze3Y61hk2bhrj++i6sBWNg0aLChDyu7/scPXqQ5557kuee28ULLzxDPp8FoLNzOTfeeBuXXbaNDRu20tzcMiFrkMamsC4iIiJyAXzfr51Hr1QqRCIRnUdvYK5ryOdN7Tx6NIpC+iXyfdizp40lS3K0tLj09cXp6kpw111HuP76HubPL07I41bHqVV3zp9//ikymRQAHR2LueaaG9i06Qo2bbqC9vY5E7IGkdEU1kVERETOQ6VSIZvNkk6na+fRozqA3JCsHSl1LxSCUvdoNGgcJxevuzvOo48u4NFH59PTk+Bd79rPrbee5FWv6uaGG7rGvXO+tZaTJ4/Wgvnzzz9FKjUIwNy5C9i27ZrhcL6NuXPnj++Di5wHhXURERGRczjXfHSdR29Mo7u6uy5EItpFHw+ViuFP/3RzrZv7ZZcNcc89R7jyymAOeTg8Pj/jajh//vmneO65ajgfAGD27Hls2bKDjRuDnfP58xfqSIvUncK6iIiIyGlGz0cvlUpEo1GVujewSsWQTjvkcgbHMUSj6up+KTwPnnlmFsePN/H61x8nErHMn19ky5ZDvPKVPcydWxqXx6mWtQe75k/zwgtPMTQUhPNZs+ayefN2Nm7cyqZNV7BgwSKFc5lyFNZFREREhrmuSy6XI51O1+ajNzc313tZUieViiGVcigUHBzHkkioq/vFshYOH27ipz9dwM9+Np9UKkp7e4nXvvYE0ajld37nxUt+DN/3OX78MC+88HStW3u1rH327Hls2rSNTZu2snHjFXR0LFY4lylPYV1EREQaXnX0WiaTAdB89AZXLhvS6WD0WjhsSSQU0C/Vv/3bQr70pTWEQj7bt/fzqlf1cMUVA5dU4u77HkeOHOCFF57hhRee5oUXnq41hJszZz5btlzJxo1b2bjxchYsUDiX6UdhXURERBpWsVgknU6Tz+cxxpBIJPQLfQMrlw2pVNA0LhzW6LWLlc+H+NWv5vLTn87n5ptPcvXV/WzfPoAx+7jmml5aWtyLul/XdTl06EWef/4p9ux5hj17nq2NUps/fyHbtl0zHM63Mm9eh/4uy7RXl7BujGkHvgBcBljgt4C9wFeA5cBh4B5r7WA91iciIiIzW6VSYXBwkFwuRyQSIZlM1ntJUifBfPSgs3uxqPnoF8v3Ydeu2fz0p/N54ok5VCohFiwo4HlBM8a5c0vcfPOpC7rPcrnEvn3Ps2dPsHP+4ovPUSoFY9sWLVrKNdfcyIYNl7Nhw+Xq1i4zUr121h8CvmetvdsYEwWSwEeAR6y1nzLGfAj4EPDBOq1PREREZiDf98lkMgwMDBAKhdQ0rkFZG+yi5/NB0zjfV2f3i2Et9PbGmD+/hDHw//1/K8lmI7z61V1cd10Pa9ZkuJDN7Vwuw4svPjdc0v4MBw7swfNcjDEsWbKSG2+8jQ0bLmf9+i20t8+euG9Mpi1rgwaGM8Wkh3VjTBvwKuBdANbaMlA2xtwB3DB8s78HfozCuoiIiIyTfD5Pf38/nueRSCQ0fq0BVSqGfN6Qyzm4blDqHotxQYFS4MSJBD/72Xz+/d/nk82G+X//318QiVg+9KHdzJ1bOu9z6IOD/ezZEwTzPXue5ejRA1hrCYVCrFq1nttvfzPr129h3brNNDe3TPB3JdOV50GlAr5vMAZiMWhq8uu9rHFRj531FUAv8D+MMZcDO4H7gQXW2mptTBewoA5rExERkRmmWvKezWaJx+NqHNdgRs9G9zyDMcHYtWhUu+gX6tln2/lf/2sFhw+31Oah33lnT+3zHR3Fc36ttZZTp47xwgvPsHfvs+zZ8wzd3ScBiMXirFmzibvuehcbNmxhzZqNxGLxCf9+ZHryfSiXg7fVcN7W5hOLWcJhi+t6xGIz4+93PcJ6GNgG/F/W2l8aYx4iKHmvsdZac465GMaY9wDvAVi4cOFEr1VERESmKc/zSKfTDA0NEQ6HNYKtgXgeFIsOuZxDqRTstgWz0WfGL/CTJZcL8ctfzmPlygzLl+eIxTxCIcs737mfa67pY9as8jm/1nVdDh/eV2sEt3fvs6TTQwC0tLSxfv0Wbr75jaxfv4Xly9cQDqvvtZzbSEAPKmJaWnzi8SCch0L1Xt3EqcffiuPAcWvtL4c//jpBWO82xiy01p4yxiwEes72xdbazwOfB9i0aZP+xRUREZEaay2lUolMJkM+n8daSzKZVFfoBhD8Mm/IZkMUCgBBsziNXbswpZLDk0/O5t//fT67ds3GdR3e9KYjLF+eY+3aDH/6p0+d9evy+SwvvvhcLZjv3/8C5XIJCDq1b916NevXb2HDhi0sXLhEfyflZVkbVMZUKuA4hpYWj2TSEok0zt/pSQ/r1touY8wxY8w6a+1e4Cbg+eHLO4FPDb/958lem4iIiExP5XKZXC5HNpvFdV3C4TDxeFyBYAarNpLyPEOhEIR037dEIhCPwzmKNOUsrA3Kia2FD3xgB319cWbNKnHzzSe59toeVq3KnnZ7S19fN3v3PsvevbvZs+dZjh07iLUWxwmxfPlqbrrp9axfv5l16zYza9acOn1nMh15XvDCW/BiK8ya5RGNWhqxzUi96k3+L+AfhjvBHwTeDTjAV40x9wFHgHvqtDYRERGZBiqVCsVikVQqRaVSwXEcYrGYzqTPQNVQ7nmGcjnY/a3+Mg/gODTsL/MXy/fhhRfa+Pd/n8+RI038yZ88hTFw772HmTWrxMaNqdrPs1rS/uKLz9UC+uBgHwDxeIK1azdx9dXvYt26zaxZs4F4XKMQ5fz5/kiDOIBIxDJ7tkcs5tPopyPq8u1ba58CdpzlUzdN8lJERERkmikWi6TTaXK5HI7jEI1GiUaj9V6WjDPPg1zOIZt1hkcxBb/IO44lHIZYzKqL+0U4cSLBD36wkMcem8fgYIxYzGPHjn6KxRCJhMcrX9lDNpvm6aefrwXzAwf21Oabz527gI0bL2fdus2sXbuJpUtXEgo1eKKS81atiHHdkQZxkUjQvT0WC0rcGz2gj6YfhYiIiEx51loKhQKDg4OUy2UikYhmpM9Qngf5vEMq5WCtIRYLureDytovhrVw+HAT7e0VZs0qc/x4kn/7t0Vs3TrAtdceZOvWXgYGDvPYY8/x4ou7efHF5zhx4ghAraT91a++nXXrLmPt2suYM2d+nb8jmW6qzeGsrY5WszQ1+USjM79B3KVSWBcREZEpy/M88vk8Q0NDuK5LNBpVSJ+hfH9sSA/K2hXQL9axY0kee2weP//5PE6dSnLPPYe5666jbNx4nPe//wccOfIsjz76HF/4wnPkchkAmptbWbt2E6985a+zdu1lrFq1nng8UefvRKajYPa5wVoIhYLu7YlEsHOuipjzp7AuIiIiU06lUiGbzZJOp7HW6iz6DOb7wRz0VCqYgx6LKaRfCt+Hj3zkCg4dagF81qzZyQ03/JDu7sf4wz/czbFjh7DWB6CzcwWveMX1rFmziXXrLlOXdjknzwueW8A5w3bQvd0AllgM2tu92ng1uTgK6yIiIjIlVMeuVc+jh0IhYrEYjrqGzUjVWeijQ7rmoF+4U6fiPPbYPLq7E7zznbs4cGAP8fi36ex8gsHBXezbl2LfPkgmm1m9egNXXfVK1qzZxJo1G2hqaqn38mWKC/6ejhxHqQb2s3GcIKBHImoMN170YxQREZG6cl2XQqFQK3XXefSZqzqSKZcbmYWukH7h+vpi/PSnc3n00X5OnnwW+AXR6M/5yU9eqHXIX7x4GVdddS1r1mxi7dpNLF68TC98yXnzfSgWg+Zv8+e7xOMqX68HhXURERGZdNVd9EwmQzabxRijUvcZ6syADuGwZqFfqP37C/T0PM3Ro7v55S8PcPLkbiANQDLZytq161mz5l2sWbOJ1avXa9dcLorvQ6lkCIUsc+f6JBK+QnodKayLiIjIpPE8r9bV3XVdwuEwyWRS52RnGGuD0tmzB/T6rm06qFTKHDq0jyef3MfOnfs5efJZXPcwAMY4dHau5LrrbmLLlvWsWbORjo5O7ZrLJamGdMexzJrlkUz66ClVfwrrIiIiMuHK5XJtF10N42Yu1w2axWUyDpUKRKMK6C/HWktX1wn273+e/ftfYP/+Fzh8eD+uWxm+RSfNzdtYu/Yt3HDDMi6/fKU6tMu48P3qzPOgKVxbm0dTk69RalOIwrqIiMg4evjhh3nooYfo6uqio6OD+++/n9tvv73ey6qL6mz0VCpFsVjEcRzi8bh20WegSsWQzTpks4aRc+j1XtXUlE4PsW/f8xw48AL79r3AgQN7aqPTQqEk69at5bbb7ubgwZtYv349N94YZu7cUp1XLTNBME5tpElcOBzMPG9t9YnF1BRuKtIfiYiIyDh5+OGHeeCBBygWiwCcOnWKBx54AKChArvruuRyOdLptGajz2DVUvdMJkSxGJxx1Tn0sYrFPAcP7uPAgT0cOBDsmvf2dgFBOXtLy3p8/27gWuBKNm1ayEc+8sKoSgRv+CJyYXw/qHTxvJEXR6vBPBoNxqkpnE99+iMSEREZJw899FAtqFcVi0UeeuihaR/Wq7vkuVwOf3hbxlpbe9/3/VoXas/zMMYQjUZV6j4DVSpmOKQ7uG5Q6p5MKqC7rsuxYwfZv39PLZwfO3a4NtN87twOFizYzE033cm6dRt46qnX8O1vr2fTpiGuvrqPK6/so739hTp/FzIdnRnMgyCeSFhiMZ9IxBIKWZ1Bn4YU1kVERMZJV1fXBV0/HZy+Sx6JRGpl7MaY2vuhUKj2vgL6zFOpGAqFoNTddYMmVNEoDVvq7vs+J08e5cCBPRw8uJcDB/Zw+PB+KpUyAC0tbaxatZ4dO15FNLqdrq5X8vTT63nuuRi3376bjRsH6Owc4nWve4zWVrfO341MN9Vw7rpBP4hIJAjm8bhPOKxgPpMorIuIiIyTjo4OTp06ddbrp5PqWLV0Ok0+n9cueYM6d0BvrF10ay29vV3Du+XB5dChFykU8gDEYglWrlzLa197J6tWrWf16g3Mm9dBb2+cj3xkG5lMhGjUY+vWAa66qp8NG1IAtLZWXuphRcbwfSiXwVqD40AyGYxVi0YVzGcyhXUREZFxcv/99485sw4Qj8e5//7767iq81cdqzY0NESlUiEcDpNIJNQQroE0ekC31jIw0FvbLT9wYC8HD+4lmw3mmYfDEZYvX80rX3kzq1atZ9Wq9SxevJRSKcpTT83il7+cy8GDJX7jNw4xd26Ja67p5bLLBrn88kHicb/O351MJ9aOdGq3FkIhS0uLTyJhiUSsJiw0CIV1ERGRcVI9lz6dusF7nkepVCKbzVIoFLDWqiFcg3HdkYBeqTRWQB8Y6OPQob0cPPjicEDfSyo1AIDjhFi6dAVXXfVKVq5cz6pV61i6dCXhcKT29T//+Tz+4R/m8+yzs3Bdh9bWMq9+ddfw18N99+2vy/cl089IOAffNxgTNIRrb/eIx4OGcNJ4FNZFRETG0e233z6lwzlApVKhVCqRy+VqZe7hcFhj1RqI60Kx6NQCujFBOI9EZm4gGBzs5+DBajAPStkHB/uBoDP74sXL2Lr1KlauXMeqVetZtmwV0ejYox89PXF27ZrFzTefwhjYvbudEyeSvPa1J7nyyj7WrUurJFnOSzWcVyoj/+bG45bm5pFu7XouicK6iIhIA6hUKhSLRdLpNOVyGWMMkUhEO+gNxHWhVHLI5RyKRYZ7EVgSiZkV0K21DA72cejQixw8+GLt7eBgH1AN5ku57LLtrFq1jhUr1rF8+Wri8cRZ7gsOHWrmiSfm8Pjjczh6tBmADRtSLF2a553vPEA06qskWc7J2pGGcL5vADtc1h78/Wtu9hTO5ZwU1kVERGao6hn0TCZDqVRSQG9AngflcrCDXigw/BywJJMA0z+kW2vp7+/h4MG9HDq0r/a2Wso+Esy3sXLlOlauXDsczJPnvE/XNVQqDomEx1NPzeJTn9qMMZZ169L85m8e4Mor+1mwIOhLEYvpHHojsbY6Im2kVP20W5xx+1Ao+DvX1OQTjTIcyjXjXM6PniYiIiIziLWWYrFINpsll8sBEIlESCbPHU5kZjlbQA+Hp39A932f7u6THD78IocO7ePQoeBtJhN0V3ecEJ2dQSn7ihVrWblyHcuWrTrrjvnp8vkQTz89i8cfn8uuXbO57bbjvPnNR9m0KcX73reX7dsH1L29AY2MSAtSueME58ibm31CIUaF9eDvVfVjY4KLQrlcKj19REREZoByuUwulyObzdbmoauTe+MIArohlwtRKABM74DueS4nThzl0KEXOXx4//DbfbVxaaFQmKVLV7Jjx3WsWLGmFsxPP2P+cqyFv/iLjezaNRvXdWhpKXPVVX1s2hS8ABCN+tx4Y/e4f38y9Yxt8BZcFw4H58gTCZ9IJJhfrn9SZTIprIuIiExT1TL3VCpFuVzGcRxisZjmoTeIMwN6NVyAMdMnoJfLJY4cOcDhw/s4dGgfhw/v4+jRg1QqZQCi0VhtXNqKFWtZsWINS5asGNOV/XxUz5/v3DmH7u44v/u7ezEGZs8uccstJ9ixo18N4hpE9Rx5pRLMLQdqTRZbW0cavGlXXOpNT0EREZFpxFpLqVQik8nUytw1aq1xvHRAr+/azkcmk+Lw4f0cPryfI0f2cejQfk6cOIK1wVZmU1Mzy5ev4eab31gL5osWLcFxQhf9mAcONPPjH3ewc+cc+vtjtfPnlUpwlvi3fuvAeH17MkVVy9k9L5hZ7jhBMG9p8YenIKBdc5mSFNZFRESmAdd1KRQKDA0N4bou4XBYZe4NwnWhUgnOoBeDvmZTPqBba+nt7eLw4X3D4Tx429/fU7vN7NnzWL58NVdf/UqWL1/D8uVrmDev45Kf00NDEXbtms0VVwzQ3l7h4MEWfvKTBWzZMsg99xzmiisGaGvT+fOZrLprXi1nD4VUzi7Tk8K6iIjIFOW6LuVyudYszhijMvcGcbYxa+GwnZIBvVwucfz4EY4c2Tdczr6fI0cOkM9ngZGO7OvXb2H58tXDwXw1ra3t4/L41sLRo03s3DmbnTvncOBAC9Yafud39nLDDd286lXdXH99N9GoOrfPVNVw7nnB349QCJqafGIxSySicnaZvvTUFRERmSKstVQqlVqZe7kcnNkNh8Mkk0ntos9gQXMrQ7FoyGYdyuWpOWYtlRrkyJEgjB85EpSznzhxFN/3AIjFEixbtpJrr72JZctW186Xx2LxcV1HueyQyYSZM6fM4GCUP/qj7QCsWpXmzW8+wvbt/Sxblhtek0L6THN6OA+Hobk5COc6ay4ziZ7KIiIideT7PuVymUKhUOvk7jiOxq01gGp5e6FgKBQcPC84SzsVArrnuZw8eWxUMA/C+dDQQO021TL2HTuuZdmyYMd8wYJFOBPUoa2/P8qTT87hySdns3t3O5s3D/FHf/Qcs2eX+cAHnmft2jSzZpUn5LGlPnx/pBmc5wXvWzsSzoPz5grnMnPpqS0iIlIHlUqFXC5HOp3G8zxCoRCRSEQl7jNYEM4NpZIhnw/CORhCoSBw1OuPPpNJ1QL50aPB2+PHD9e6sYdCYTo7l7Nly5UsW7aKZctWs2zZqnErYz8Xa0dK/v/qr9bxs58tAGD+/AI33tjFlVf212579dV9E7oWmTijm78FL1CNvA2FgiAejUI4rHAujUdPdRERkUlUKpVIp9Nks9naqLWJ2omU+nPdkdL2YExUcJ42EgkCyGTunruuy8mTRzl69ABHjx4c3jU/yODgSNBta5vN0qUreO1r76wF88WLl17wmLSLlcmEefrpWezaNZsXXmjjL//yCaJRny1bhli2LMe2bQMsXpyfcuf25fydHs5DIUgkLLGYj+MEHxtTfVvv1YrUl8K6iIjIBPN9v9bJvVwu6wz6DDc2oBuMCUrbE4nJeXxrLYOD/bVQHuyWH+TEiSN4nguM7JZfdtm24Z3ylSxduor29tmTs8jTvPBCK//4jyvYt68Vaw2trWW2bh0knw8Rjfpcf313XdYll250Z/ZqCXsiYYnHPe2Si7wM/fUQEREZB9ZafN/HH54V5Ps+1lqKxSLpdBrf9zUPfQY7d0Cf2J3zYjHP0aOHOHbs4HAwDy7ZbLp2mzlz5rFkyUquuOJqlixZydKlKyd1t/x0+XyIZ5+dxa5ds7j22l42bx4iFvNxXYc3veko27YNsHJlBhWcTE/WBjvnrhu8GBkKWXVmF7lI+usiIiJygTzPI5/P10K4Fxw+HsNaizGmNm5Npe4zj+tCseiMCejR6MQEdNd16eo6Xgvj1XDe03OqdptYLMGSJSu4+upXsXTpKpYuDYJ5c3PruK/nQlUqhu9+dzG7ds3mxRdb8TyHZNJl9eoMmzfDypVZ/uzPdtV7mXKRquHc94MmiYmEpb3dIxLxFc5FLoH++oiIiJynSqVCNpslnU5jrSUajaopXIPxvGD+eTY7Mv98PAO67/v09XVz7Nih4VB+iGPHDnHy5FFctwKA44RYtGgJq1Zt4IYbbquF8nnzOqbMi0LV3fNi0eH663sIhy3/8i+LaWur8PrXH2fr1gHWrMkQDk+NkXRy/oIxg0FA932DMUFzxPZ2rzY6TSd8RMaHwrqIiMhLGF3KXigUMMYQj8d13ryB+D6USoZcLkQ+Pz7zz621pFIDHD16iOPHD3Ps2EgwLxYLtdvNnbuAJUtWcMUVV9PZuZylS1exePFSIpHo+Hxz4+jo0SRPPDGHp58Ods9939DZmeP663swBv7yLx8nHtfM8+mmWtbuecHOuTEQj4+UtofDVkcWRCaIwrqIiMhZ+L5PPp9ncHAQ13U197zBVGeg53LBDrrvBx3cE4mgU/WFyGRSHDsWBPIgmAehfPS58paWNpYsWcENN9zKkiUrWLp0JZ2dy0kmm8f7Wxs36XSE3bvbecUrenEc+N73FvPIIwtZuTLDHXcc4/LLg93zKgX16WH0rjkEZe2xmKW11SMa1c65yGRSWBcRERnm+z7FYpFcLkc+n8daSywWU5l7A7A2OFddLBpyOQfXBTCEw0GJ7/mEk2w2w/Hjhzh27DDHj1d3zA+TSg3UbpNINA2fK7+eJUuWs2TJSpYsWUFb26wJ+97Gi+sa9u1r4emnZ/P007M4dKgZaw0dHQVWrszypjcd5Z57DtPeXqn3UuU8jR2jFohGLc3N1ZnmQYM4hXOR+lBYFxGRhnZ6QPd9n0gkolL3Gc73g4DiuoZ83qFQqDbHskSjwS76uUrcg1B+mBMnDo8J5oOD/bXbxGIJOjuXsXXrVSxZsoLOziCYz5kzb1o9r7q740SjPrNmlXn22XY+9anNOI5l9eo0b37zEbZuHWD58iwAc+eW6rxaeTmjO7Vba4erRSzxuE84bAmFVNIuMpUorIuIyIxhraVcLlOpVGqd2IHa+6Mv1WZxhUJBAX2GCxpiGSoVQ6kUNIgrl0f+nIPd8zN3D7PZNMePHz7jMjaUx1m8eBmbN+8YDuRBMJ87d8GUafZ2IfL5EM8/38bTT8/mmWdm0dWV4O67D/PmNx9lw4YUH/jAc1x22RBNTWdOQJCpaWT3PKgQqTaDU6d2kalPf0VFRGTaK5fL5HI5stksruueV+AOdpUU0Gcq1zUUCsGu+ehg7jgju4lQbfQ2OLxTfqQWyE+cODqmfL26U75ly5V0di6vXaZrKK/yPEilosyeXcbz4D/9p6vJ58PEYh6bNg1xyy0n2L49+DnE4z5XX93/Mvco9XZ6t/ZQCJJJn0QiKG2fxk9XkYajsC4iItNSpVKpzTr3PG94hFZU58sbmOcFc89zOYdSyeA4lnA4COa+79Pf31ML5cHlKCdOHBnT6C2ZbKazcxnbtl1DZ+cyFi9eRmfncubMmT+tQ/loPT1xnnmmnWeemcXu3e3Mn1/kU5/aRSgE73jHAebNK7JuXZpIRGPVpoPqzrnrGoyp7p6PdGuPRHTmXGS6UlgXEZFpo1Kp1MaolctlQqGQ5pw3OGuhXDZksw75vMF1PQYGDtPVdXRUKD/CyZNHKZWKta9raWmjs3M5r3jFDcOhPNgpnzVrzoyrtMjnQySTQdn6F76wmu9/fxEAc+YUueqqfi6/fKSC4MYbu+uyRjk/Qa+F4FIdoxYKjZw7j0SsGsKJzCAK6yIiMqVVKhUKhQKZTKZ2Fj0ajdLU1FTvpUmdWAupVIEDB45x4MBRjh07Snf3EU6dOkp39wk8b+Q89Zw581m8eBk33fR6Fi9eyuLFwW55a2t7/b6BCVapGPbubeXZZ2fx7LPtHDzYwn/7b79kzpwyO3b0s3hxnssvH2ThwoJC3RRl7dhO7cGfkyUUCs6cR6N+bYxaKFTv1YrIRFFYFxGRKedcAV1zzhuLtZa+vj6OHj3K4cPHOHLkKEePHuP48WMMDvbWbhcKhejo6KSzczlXX/0qFi9exqJFS1m0aCmJxMx/zlQ720cilqefnsWnP72RcjmE41jWrElz111HCYWCkvatWwfZunWwziuWs/F9KJeDc+aOM3qEGurULtKgFNZFRKSurLW4rovrupRKJXK5nAJ6gymVShw/fpzjx49z9OhRjh07VrsUCoXa7RKJJhYtWsbmzdtZtGhJ7Uz5/PmLCDdQW2trg5Fqu3e38+yzs3juuXbe/ObDvPa1p1iyJMerX93Fli2DbNiQqpW/y9RkLVQqwQ664xhaWjwSCZ0zF5FA4/zPJiIidVcN5pVKhVKpVLtYG+z6KaDPXNZa+vv7OXbs2BmhvLu7u/YcAJg7dwEdHUu57rotLFq0lCVLgktb2+wZd578fLmuIRy2lMuGP/iDHXR3JwCYPbvEtm39LFmSH/64zLvffaCeS5XzUKkEF8cJzpvPnu2fdXygiDQ2hXUREZlQnudRKpXIZrPk83mstbVZ5xqdNvMUCoVaID9+/HgtkB8/fnzMLnk8Hqezcwlr1mzkuutuZd68pSxcuIyOjk6SyRjhMA0dXLLZMC+80Mbu3e3s3t3O3LklPvzh3USjlh07+lmwoMDmzUM6dz5N+H4QzqvtFBKJYNZ5LKbSdhE5N4V1EREZd6cHdIBwOEwikVAwnwE8z6Orq6sWxke/7evrq93OGMOCBQtYsmQJl112GYsWLWH+/KXMmbOMpqZ5QHA2NxwOOlo38lOjUjG1UWmf//wafvjDDqw1RKMe69en2Lp1pGP7O95xsF7LlPM0ujmctcEIwebmkVFqagonIudDYV1EpqSHH36Yhx56iK6uLjo6Orj//vu5/fbb670seQkK6DOLtZaBgYHaDvnoy8mTJ3Fdt3bblpYWOjs7ueKKK1iyZAmdnZ0sWbKERYsWEwrFKRaD0WrVOdAKK8G4uRdfbOX554Od88OHm/n85x8jHvdZty7NrFllNm0aYs0azTufDkaHc4BQyA6PU/OIRIKwLiJyofRPh4hMOQ8//DAPPPAAxWIwE/nUqVM88MADAArsk8xai+/7+L6PtRbP87DWUqlU8Dyv9tZ13dptIpGIAvo0kk6nOX78OCdOnBgTyE+cODGmbD0SibB48WKWLVvGtddeWwvknZ2dtLW11W4XzIA2FAqGgYEgoDuOJRqloUOn6xqsDX4GP//5PP7mb9ZRqTgYY1m1KsMtt5ygXHaIx32uv16zzqcya4PnuesG78Pps859hXMRGRf6p0REppyHHnqoFtSrisUiDz30kML6OPI8rxa0R78d/b7v+2O+ZvR5c8dxMMYQCoWIRqM4Ong5ZeVyuVoYP3HixJhgnslkardzHIeOjg46OzvZvHkznZ2dtVA+b948fN/B8wy+b4ZDObiuQ0/P6F1FCxiMCQJ6NNqYAd11DQcOtPD88208/3w7e/e28ju/s5drrulj6dIcN998kk2bhtSxfZrwvOCogu8HxzViMUtbm2adi8jEUlgXkSmnq6vrgq6Xc6t2X6+G8FKpRLlcplwujwnipwdwx3EIhUIK4NNIPp+vBfHRl+PHjzM0NDTmtvPnz2fx4sVcf/31LF68uBbKOzo6iEajtdu5bhA6SyVDd7dDpVI9Vx6EcWOCbtbVc+cjwbzxArrrGvL5EK2tLoODUe6//0pKpSDBLV2a5YYbuujoCCoVOjvzOnc+xVk78vwHSyw20hAuHFbXdhGZHArrIjLldHR0cOrUqbNeL2fn+35tR7w6r7xcLlOpVLDW1nbEHcchHA4Ti8VUpj4NVXfIT548eUYoHxwcHHPbOXPm0NnZyTXXXMPixYtroXzRokXE4/Gz3r/rQrEYhPN83qmV+YZCEIkEl0DjhfHTua5h//4WXnihjeefb2Pv3jauuqqP3/3dvbS3l7n11hOsXJlhw4YUra3uy9+h1N3oju3GGBIJS3u7p7J2Eakb/dMjIlPO/fffP+bMOgRjnu6///46rmryjA7cruuOmT89+v3qzni5XMZ13Vr4rpamh0IhjUWbhtLpdC2Mn/729B3yOXPmsHjxYq6++mo6OztroXzRokUkEomXfaxqae/Lh3Mplw3d3YnaPPP/8l+2cuhQCwCdnTluuKGLbduCju3GwFvferheS5WXYe1IQzjfrx7dAMcxJJM+iURQ3q7CIhGpN4X1SVLd5aqWlI7+pbr69mzXVZ3+eZWmykxWPZfeCN3gq2XqlUqFQqFAPp/HGx7EO7os/VyMMbWdcpkerLX09/dz8uRJTp48yalTp8YE8mw2O+b28+bNY9GiRVx77bUsWrSoFsbPN5CPFuwcjoTzcjkIlsYw3ABuPL/T6a1Ucti3r7W2c75vXyvRqMcXvvAYjgN33HEMx7Fs2JCmtbVS7+XKOYzt0m6HX4wKxuQ1NflEoxAOWxzHNvz4QBGZehTWJ0kmkyGVShE6zw4k1ZLVs103upx19DnT6hnT0c71S371LGo4HD7jrOroy+n3cbbrxsPobtOnd572fb+2ztHnaUd/31Pd6O+luht6+s/75UJZo7n99tunfTh/qee167qUy2WKxWJttzwUCil4zxCu69Ld3T0miFeD+cmTJymVSrXbOo7DggULWLx4Ma9+9avHhPGFCxee9/Oh2qHa2qAJlu+b2pnb4DLSvdpxgnCeTE7UT2D6yedD7N3byqZNQ0Sjlv/9v5fyz/+8FGMsy5dnufnmk2zcOFTr/n3NNX0vfYcyqc7cLQ9EIpZkcmwoV0m7iEwX+udqEsVisTGNey5V9Rxq9Rf9agA4H9XQMLqk9kIetxoqRwfO6sdnC53Vxxm95tGX6nrg7C8OvNw6Rz/m6BB/ehg+/XK2yobRaz0fp7+AUT07XD0vXB1tdbaf4dlelKm+6BKJRIhEIrUu29Wy5vMJ9ef6eY/+/OiPT3+R5vQXbKab0c/v059jF/vnevoLWKPDd/XPePSZ8dP/zE+/7+pzUmXq01culxsTwKvvnzp1iu7u7jEN/GKxGAsXLmTRokVs3769FsYXLVrEggULCF9CeiiXDZmMQz7vjPo3ZaQje7UBXLW0XQL5fIhnn53FCy8Eu+dHjjRjreFjH3uajRtTXH99Nxs2pFi3Lq1u7XVQbfA20tTw5Rii0WC3PBZjuEO7StlFZHqrW1g3xoSAJ4AT1trXGWNWAF8G5gA7gd+01pbrtb7p4PQwdb679uPl9EBYdXooeqnQA4zLjvLZAunp6zj9NhfzQsXoxztX5QOMfcHgQndKq+uu7rqe/kJG9Tyy4zi1MDD6+zp91NbFfC+jjR7PVX3RYPTHoysbRlcOnP4izOgd5rMF6epjjf4+X+rFn9PvZ/T14+3lfkanV3tEIhHtjs8AnufR19c3JoRXg/mpU6dIp9Njbt/W1sbChQvZsGEDN910Uy2cL1q0iDlz5ozrizK+H5Rpp1IO5bIhHLbE49UO1Y3bkf1crIXe3jgvvNDKkiV5Vq7McuxYEw8+uJFIxGPt2gx33XWUDRuGWL06GGW3eHGBxYsLL3PPMp5GB3THCeaWz5rlEw6//HNZJewiMhPVc2f9fuAFoHX44z8HPmOt/bIx5nPAfcB/r9fi5OVNVEn8xZjOu8CnqwbWc734MjqQVm9zrhdCxsPpobpavTE6LJ/rqMTZ+i6cvkN9elXD6S+mjA78Z7uvif7+Z6of/OAHfPGLX6S3t5d58+Zx33338ZrXvKbey5p02WyWkydP0tXVNSaQV3fHR1crVcvVFy1axKte9So6OjrGlKs3NzdP+HpdF/J5h0zGwferpewK5WfjuoZHHulgz5429uxpY2AgeAHtjjuOsnJlllWrMnziE7tYuTJ7XmFQJk6lMjIiLZEIRqRFo5pdLiJSl7BujOkEbgc+CXzABL9dvxp42/BN/h54AIV1kTNUd7Un8/EmKwArbE+OH/zgBzz44IO1c9M9PT08+OCDADMusJfLZbq7u8eE8er7XV1dZDKZMbdvaWlh0aJFrF69mle+8pW1IL5w4ULmz58/IX/3gvPlp19bfQFr5DZBqbvBcYw6VZ+mXDYcPNjCnj1tRKMet912klDI8rWvLSMctqxfn2LDhhTr16dZsiQHBGXSa9dmXuaeZTxVn+ueF5wrD57fowO6r4AuIjJKvXbW/xL4I6Bl+OM5wJC1trqFcRxYXId1iYjMeF/84hfHNDiDYGLFF7/4xWkX1qul6tVmbqODeFdXF319fWMqMyKRCAsWLKiVq1eDeEdHx6TtjledvkteDehnY23QFCuRAGO0C1z18MOL+eUv53LgQAuuG7x6ccUV/dx220mMgQcf3ElLS0Xl0XVQnVlebfZmrSUUCl5oCkajQShkhy91XqyIyBQ16WHdGPM6oMdau9MYc8NFfP17gPcALFy4cHwXJyLSAHp7ey/o+nqy1jI4OFgrS6+G8Gog7+npGVOqboxh7ty5dHR0sHXrVhYtWkRHR0ctjM+ZM6fuEyRcN2gIl8mcvkuuEH421kJXV5y9e9vYu7eV48eT/N//99MYA6dOJbAWbr31BOvXp1i7Nk1r68jzQSPVJk81nHtecHY8HIbm5mBeeThMbTSaiIicv3rsrF8LvMEYcxsQJziz/hDQbowJD++udwInzvbF1trPA58H2LRpk36zERG5QPPmzaOnp+es1082ay1DQ0NjgvjoS3d3N+Xy2F6j7e3tdHR0sHbtWq6//vraTnlHRwfz588f16kb46lcNqTToeFSdqtd8nOoVEyti/ePf7yAf/zHFaRSwZ9pU1OFtWvTFAohkkmP++7br13zOhk9v7xa+dHU5BOPW8JhjUcTERkPk/5PqbX2w8CHAYZ31v/AWvt2Y8zXgLsJOsK/E/jnyV6biEgjuO+++8acWYdgtNh999037o9V3RmvBu/T33Z3d59Rkt/S0sLChQtZvnw5r3jFK2o74x0dHSxYsIBEIjHu65wo1kKpFIT0YjHo2q6GcGMNDUXYt6+VvXtbefHFVg4ebOGjH32GdevSzJ5d4vLLB1m3LhihtnhxfsxZfQX1yTE6mAeCXfJEwhKPe0QiCuciIhNhKv3T+kHgy8aYPwF2AV+s83pERGak6rn08egG73ke/f39teBdvYwO45XK2FLk1tZWOjo6WLZsGVdddVUthFffNjU1jcv3WS/B6ClDPu9QKDj4vlXX9mG+D8eONZFIuMyfX+LFF1v46EevACAc9lm5MsNrX3uClpbgObNlyxBbtgzVccWNZ+yOeXBdJGJJJCyxmF+bX66SdhGRiWcuZdZ0vW3atMl+5StfqfcyzktfXx/FYnHKlmeKiJxNuVymp6fnjDBeva63txfP88Z8TbVMfcGCBWdcOjo6SCaTdfpuJkYwG9pQLAYBPajaN7VS4Ebe/fV9eOaZWbz4YrBrvn9/C4VCmDvuOMrb3naYUsnhe99bxLp1aVauzBCNTt/fSaajajAPxqYFz9VIxBKP+8Ri1IK5Jg+IyHRSLpeJx+PMnTu33ks5L5s3b95prd1xts9NpZ11ERGZRNZaMplMLXiPDuXV9wcHB8d8jeM4zJ07l/nz57Np06Yzwvj8+fOJx+N1+o4mT3X3vFAIds+rTbWCTtfQiM3ifB9OnEjy4outhMM+118f9EX47GfXk8+HWbYsx3XX9bB2bZqNG1MAxGI+d9xxvJ7Lbhijg3n1BaRo1JJMKpiLiExVCusiIjNUpVKhr6+Pnp6e2mV0EO/p6aFYLI75mmg0Wgvd11xzTe39ahifO3cu4QY7nGptUBJcLhsKBUOx6NTmoodClkjEEovVd4319L3vLWLnzjns2xfsmgOsW5fi+ut7cBz44z9+ho6OAvH4GcPkZYKcXspe3TFXMBcRmV4a6zcuEZEZwlpLKpUaE8RPvwwMDHD6Uaf29nYWLFjAsmXLuPLKK5k/f34tjM+fP5/29nZMg9Ztex5Ya/D94H3PC4J5sRhcF4yjCsJ5o4Uc1zUcPdrE/v0t7NvXSnd3nI9/PBifdvBgM+l0hOuu62HNmgxr1qRZuLBQ+9rly3N1XPnMN/r5GjxPLZFItfmbr2AuIjKNKayLiExBuVyO3t5eenp6am9Hv9/b23vGSLNoNFoL39UgPm/evFoQnz9/PrFG3gKmGmwMpVKwU+66Bs8LwmjwwoYZHqcWvA2FIBazDXfuvL8/SltbhXDY8i//soh//McVlMtBR7G2tjJr1qQplRzicZ/f+Z0XG+7nUy/Wjswzr76A5DgQj0Ms5g3PNFcwFxGZKRTWRUQmWbFYpLe3d0zwPv39XG7sbqTjOMyZM4d58+axZs0arr32WubNm1cL4fPnz6etra1hd8XPJSgHDsJ5dZccgpATCtnht8GOefCjq1YiNM6Z81LJYf/+ltpl375WBgdjfPKTT7J6dZbOzjyvec0pVq8Ods3nzSuNCed6yk2MkR3zajA3tb4Ira1+LZg32KkUEZGGon/iRUTGUbFYpK+vrxa6Rwfx6vnxTCZzxte1t7czb948Fi1axNatW2tBvPp27ty5hDQr6SX5Pvi+GdP8rRrOHScINfF44+2Sj+Z5wei0/ftbWL06w/LlOfbta+UTn9gCwIIFBTZtGmL16gxz5gSVG5s3D7F581AdVz2zVUN59fkLwTGXcBhiMYhG/eE55kGlRyM/f0VEGo3CuojIeSoUCmMC+OhQ/lJBvK2trRa6N23aVOumPm/evNpFYx3PXzXUVCrBrnm5HIxLC0rZgzO7QUMtzTYHKBYdvvrV5Rw40MLBg821cvZ77jnM8uU5Vq9O86EPPcvq1RlaWtw6r3ZmCpoUBk3frDUElRtB6q7ujkejPtEowzPMNcdcREQU1kVEsNaSTqfp6+urXU4P5H19fWeUpkOwIz56lFk1fFevmzt3bsOfE78U1cZZ1TnmpZKhUjG1kvVqGXsoFHS7bmSpVIQDB4JS9gMHWli+PMtb33qYaNTn0Ufn09FR5KabgnL21aszLFgQTAKIx32uuGLwZe5dLsTIufKRYxexmCWRGAnkjqOdchEReWkK6yIyo7muS39//5ggfnoo7+/vP6NZmzGG2bNnM3fuXJYsWcK2bdtqZ8ZHB3HtiI+f6qipSiUI5aWSqXVor54xD4cVygHy+RB9fTGWLs0D8LGPXc6ePW1AUFnQ2Zln7do0EDQg+/znf6GmYxNkdDf2YPhC0I29uTk4Vx6JjPRHEBERuRAK6yIyLVlryWaztdB9rkA+NDR0xviySCTC3LlzmTt3Lhs2bKi9X73MmzeP2bNnN9w88ckyUhJcDeYOpRK12eVgRpUGQyM1ezuXI0eaeO65Ng4eDHbNT55MMmdOkb/5m18BcNVVfezY0ceqVVlWrsycMdNcQX38jO7GDma4G7tVN3YRERl3+k1URKacUqlUC98v9bZUKp3xta2trbXQvWbNmjOC+Ny5c2ltbVXX9EkSBPPgfHm5XD1fbvD9IIBXy9gjkdGBsnHDebkczDM/eLCFQ4ea+Q//YR+hEDzySAf/+q+LmTWrxKpVGa67rodVqzLDZ/Th9ttP1HvpM1J113x0P4Sxu+bqxi4iIhNH/8WIyKSpVCoMDAwwMDAwJnSf/v7ZmrRFo9Fa2F63bl3t/Tlz5ox5q7L0+gk6WpvhUWnBSLBy2dQCpTHV3XKVBAO1s/fhsOXJJ2fzla8s49ixJjwveNWipaXMnXceY/78Im94w3He+MZjzJ5dfpl7lYs1tpw9eMEoFAo6sre0aNdcREQmn8K6iFwy13VrAbx6GRgYoK+vb8z1Q0NDZ3yt4zi1s+GdnZ1cfvnlzJkzpxa+q0G8ublZu+FTSDXUVM+XF4vB+fJqh+vq+fJEonF3yUdzXcORIyM75gcPNnP0aBMf+tButmwZIhr1aW11ef3rj7NyZYZVq7LMmTMyz3zu3DOrSOTCWDtyBGNkTJod7olQHZVmicWCUWnqyC4iIvWmsC4i51Qul8eE7er7p193rhA+a9asWlO2DRs21EL46Et7ezuOtqqmrJFwc2YwrzZ+q+f5ct/3sdbH90cu1TU4TohIJILjTG7iCkrZmzl0qJlly7KsXZvh6NEmPvKRbQA0NVVYsSLL7befqO2UX3bZEJddNjSp65zJfH+kYWHAYowhFArK2CMRf/johR2eJqAdcxERmXoU1kUajLWWXC5XC9yDg4NnBPHq5Wzl6NWd8Dlz5tDR0cHGjRtrwbt6fTWEh7QtdV5+8IMf8MUvfpHe3l7mzZvHfffdx2te85pJXcNICXsQcMrlIJxXKiMlwdWwM1nB3FqLtT6e540J4sZU12RwHEM4HCEajRIOR4hEojiOg7WWYrFAoZDD84IRZeFwhHA4clEVGtZafD9Yh+d5WOsPd/4OdmUhyv/4H5s4dKiZ48ebaiO73vCGY6xdm2Hp0hy///vPs3JllvnzizoGMM6sDcJ59Wx5KASJhE8i4RGJBEcw9M+RiIhMNwrrIjOE53kMDg6OCdvnupytMVskEqkF7qVLl7J169Za+B4dwtva2rQTPo5+8IMf8OCDD9b+THp6enjwwQcBJiSwV8t/KxWGZ5c7VCqMKmEfO7s8Fht9vvziw7nnucOh2wvuyZ7r/kbWAA6hUGg4hAdBOxQKEQqFCYUcjHFe8rmYSCSZNWsOlUqZcrlMPp+jWCwM3//I1wYvCljA4vt2OHyPpOnqWkqlBMePt3P0aNtwKXsTq1YV+MAHDlAs5jl0KElbW54tW7pYuTLH6tUF5s8Pds7DYcs11/Th+x6eV60C8GovOoz8fC3gYIyprS94Ozrd29q6R7893ejrR7/gUr3v4P2X/hlOVSMTBYLnszFBR/bWVo9YzGpUmoiIzAgK6yJTmLWWTCbDwMDAGUF89MeDg4OkUqmz/tLe0tLC7NmzmT17Nhs3bqy9f3oQb2pq0pnwOvjiF794xosnpVKJL37xi5cc1sfOLXcol0eamsHIbuN47pRba/E8D89zxwTzSCRCPJ4gGo0BDIdFqIbQaoisfs5xQuNWmRGJRIlEojQ1NeP7PpVKmVKpiOu6OM5IYB0dXAcHIxw61EQqFeWWW9KEQiHe+97l7N8fB2DevAqrVxfZvLlEU1MzTU3NfOlLJ3DdCq5boVDID+/su7WdfjCEQg7hcJR4PE44HB1+8SH4Pqsl/UEQdYd/hra2k19lTGj45zTyswt2+M1pAXXkZ1r9+VYrA6ovnLhuGc8bOTowcn+B6j8p1o4Nv5FIUMkwGUYavwXvBy82BI0Km5p8YjE1fhMRkZlJYV1kkllryefztZBdvYwO4KOvc133jPuIRCLMmjWL2bNns3DhQjZt2jQmhFcvs2bNUnf0Ka63t/eCrj+X0eXrwdxyg+fZ4S7sIzvl4bBfK+euBmvXtbVQ5jhmzIs+xlAr9x4d2ILr7ajrg7eOEyIajRKPNxOLxQiFIoTD4Smze+s4DrFYnFgsCN2+PzIy7l//tZUf/aiVffviDA0F/z02NXncdlsOgN/6rV5CIcvq1SXa270z7tsYU3thIJFoAsB1K/i+JRQKXoCYqi+IjT77P/Y1vzN3733fI5NJk89ncRyHaDQ+Ln++1QZwI2fNq70HDNGopbnZHx6VFrzINEV/lCIiIuNGYV1kHFTPgY8O3+e6DAwMUC6fOX6p2pCtelm+fHktcFeDefVjdUafOebNm0dPT89Zrz+XINAEwbxQCIJ5dW65tT7GuBjjYYxHEHgMrmtx3bFnvIMS8+Btded1ZJd2pPz69OfaSHA7M8iFQuEp+9wslw2HDsXYvz/GgQNx9u+PcehQjK985QDJpE93d4TBwTBXX51l9eoSq1cXWbVqpCP71VfnLvgxJ2v3+VIFf9bOeZ/rTiSaqFSCowXZbBrP84lEgp4B52N0A7jqiz3GQCRiiceDjuxBKNcccxERaVz6L1DkHHzfJ5PJvGTwHhoaqr1fqVTOuA/HcWhra6sF7sWLF9eC9+lvW1tbp8zuo0ye++67b8yZdYBYLMZ9992H67q1nU7XhVIJikWHQgF8P9gNrwYaY6rdrh0ikSjRaJJwODq8qx2a8ju7421wMMSBA0Eof/Wr08yb5/Ld77bxV3/VAUAy6bFqVYnXvjZFqWRIJuEd7+jnHe/or/PKp49IJEpbW5SWljZKpSKZTJpCIYsxzhkvUgTHMYKQDsHs8ljMEo1CJBIcxQiHTy/jFxERaWwK69JQyuUyQ0NDY0L22T6uvvV9/4z7GL0D3t7eztKlS8fsiI++tLW1qSO6vKTqufRqN/i5c+fyG7/xm+zY8WsUCg6uG6NYjBCc7TZEItDcbIbPdTvDO+AhHMchFJo65eaTJWgyZojFLIcPR/nc5+Zz8GCM/v6RsLhkSZl587K84hU55sw5zqpVJTo6KjrjPE4cxyGRSBKPJ6lUgvP6+XxxeNc8eOEzFrM0NVmiUUskYjEm+Ld15DiGpVIJrqvutJ+v4PjFSFO+0e+Pvp/qY53+9lwN+mDs+f3R9zm6+V+jvAAmIiKTT2FdprXRu9/V0D06bJ/+cS539jLWaDRaC9jz589n7dq1YwL56ADe0tLScIFIJobr+pRKHtu2Xctll12L50UIh1uIRBI4ToxQKEQsZmlrU/MsCKoLnn8+wYEDcQ4ciHHwYFDG/tu/3cub3jRILGYZGAizbVu+VsK+cmWRtrYgBHZ0VOjoOLMCRs6t2nXd88yYmeUjRoKq44AxUeLxKC0tlkTCJx4Pwvn5Pn9Hh+fT3z/XbapN81zXxXXd2vunN26sNvMLh8NnvD/6vs/2ONXRgdXmfNVL9fHP9n2c/qLB6BcSREREzofCukwp1lqy2WwtZKdSqVrQrr6fSqVqATydTp/1FyVjDG1tbbS3t9Pe3s6aNWvGhO/qpXpdPB7XL1AyYay1uK5HuexRKvkUi0E5u+87xOPNJBJJmpoixONhQiFGBZszn9uNwPPgxIkoBw/GOHAgxpIlZW6+OU2lYvjAB5ZiraGtzWXVqhJveMMQ69YFc9QXLqzw+c8fru/ip7Fq1/XqrPJqEI9GfZqaPOLx0ccugq+pNjAcL6OnAoyH6v8PE/kCazWwj96xH/0CQjXYVyoVyuUyvu+P6dJffeFAQV5ERE6nsC4TqrrzXQ3Y53o7+n3PO7PLMkBTU1MtYC9evJiNGzfWSs1H74C3tbXR2tqq8nOZdCPd1V1c18d1zXAjuBCeF4zqikRiNDWFmDPHIRqdus3YJsvQUIihoRDLlwdNFz/4wU6eeSZJuVwtM7bcdtsQN9+cJpGwfPrTx1i8uMzcua7ON1+CoMGbwfNGurCHQoZYzKe52SMSscNnyaf3vPLJqIIaHbhfzshow7EBvnqpHgGoNoAUEZHGprAuF8R1XdLp9MuG7+rbc+18QxC+q7vfHR0drFu3bsyud3t7e+3zbW1tGkEmU0bQ8M1SqXiUyyPB3PNChEIxHKeVUChGOBwhHg/R2upoDvSwxx5r4umnkxw8GOfgwRiDg2FWrizyt397GIAVK0qsWFFi5crgsnRpmWh0pDR569Z8nVY+tVXL1YPgbWoBvFrV7fvV5m3BFaEQxOOWeHxkHJq6rk88YwzhcJjwWX7YQQWOSz6fJ51OUywWh0cNxnT0SkSkQem/5gZWHTd2evhOp9O190+/ZLPZc95fS0tLLVgvXryYTZs2jSlFHx28Fb5lKqueRfU8n0rFp1IJwnkwvxx8P/jFORqNE422EovFSSZDxGKRWhn72N+tz93AaqbxfTh1KsKhQyNnyvv6wnz2s0cxBn74w1Z+9rMWli8vcdVVWVauLLF69cjZ4ve978Lmyze6SgUqFQewxGJ2+PlnR11Gno/GMDwKzZ73iDaZPMYYIpFIrTqsXC7XgrvvV0fjRRq+GkdEpJEorM8gxWKxFqqrAfz0j6tBvPqx67pnva/qLwzVy4IFC2hvb6e1tXVM4K5ep67nMp2MbkpVqXi4ro+1QQMt33fwvBDWRvC8CKFQZLgRlUNbW4hw2BCNNtYYtLOxFvr7wxw+HOXQoRhveMMQsZjli1+cx5e/PAcAYywLF1ZYsaJEuRx0bL///m4+9KFTCouXwPOgXHawFmIxn7lzXRIJXz/TGcQYQywWIxaL0dbWRqlUIpPJkM/na5+v7tBP93+HfN9X5YCIyDkorE9B1lqKxeKYgH2ut6NDeblcPuv9GWNoaWmpBeyFCxeyfv36MUG7GsCrHyeTyWn/C4AIUAvkpZKH6zK8Sx7G2hCO0zw8izwI5NFomFDIIRQy2n0cJZ12iMWCndtdu5L8z/85l0OHYmQyIz+grVvzrFlT4pWvzNDZWWbFihLLlpVIJMZWFTQ3N2bTvJdSLgdl6yNN20aOTFQbuFkb3M7zgudme7tLMhl0WpeZLRiNlyCRSOB5HqVSiXK5TKlUolgs1rrWB+Mbgw73Uz38+r5PqVTC931CoVCt6V4kEjnrEQERkUalfxEnQT6f5/jx4/T29lIoFGoh+/TQPfrjSuXc44VaWlpqoXrevHmsWrWqFrSr14++NDc3a9dbGkLwi6xLuRycIS+VDK4bJhRqJhZLEA5HaGkJEQ6HCIdHSoPP1LgBKJUK8bOfNXPkSFDCfuRIlP7+CJ/4xHF+7deyhEIWz4NXvSrNihVlli8Pzpi3tweNIdevL7J+fbHO38XUV90d9307HLp9fN9gral1Zff96vnz4Ena1OTR3OwTjU7vpm9y8UKhEMlkkmQyCYycc3ddl3K5TLFYpFQq4XneS77gProb/egO/C/38aWqVCpUKhUcx6G1tZWmpiai0SiVSqW2SVGtHqiW/YuINDKF9Unwta99jU9/+tNnXF/9z6p66ejoYO3/v707D5LrLO89/n26zznds0uzj2TJMmCJ2LENBjvYMnivAkywgdxgxzchRIFKws0VW4JzSRUmkBTUDSpErk3iG1FcnMTgDZzEcXKjXINDCieYJbbxEuNYsixLmtE6+/T23j/ec7rPLFrGkqZ7Zn6fqqnp6enpPjOa1vTvvO/zPOvXVwP3XO/b2toUvGXZK5ehWKwwNVViaqpMuWwUChnMcoRhS1xLHtHamiUMTSvkM0xMGDt35tixw4fxHTtyXHvtEa66aoTDh7Ns2TJALlfhzDOnuPDC8eoqOcD550/wpS+9WOfvYPEqFIxiEYKAeHW8gvKIvFLJanQYhjQ1NdHR0QEwbYzczI/T4+Xmun6uUXTp+fNpSff6pCN+Mks+vbKfrKI758jlcvT29pLP56fdJvke2traKBaL1W3/Y2Nj1S3/c923iMhSp7C+AC677DLCMCSfz9PV1VUN5y0tLfqjIzJDrau1HytVLsPUVIWpqQoTEyXKZd9MKwhy5HLN5HJNNDcHhGGWMMyo43rK+HiGF1/0Ybyzs8TFF48xMWH8/M+vr67WhmGFNWsK1VFpZ5xR4M47n6e/v9iwP8vt29vYtq2XoaGAnp4SmzYNcs01I/U+rKOq1Zj7VfSurjL5vFbH5fRJXlucqpP7yVb79AmAZAxdpVKprpiXSiWKxWJ1BKtzjmw2S3t7O62trSe0Up4E99bWVkqlEpOTk0xMTFRH3c0c75qs+mezvo9IEuhVyiciS4HC+gJItqlPTk6qA7oItUBeqVi8Kg5TU8bUVJlisRK/AExqd40wjGhqaqKnJ0cUBYRhqBNdKaOjGYaHs6xa5ctnPvOZVTz1VBODg7UXxm9+8wgXXzxGU5PjN39zkL6+ImeeOcWqVcVpOw+yWar304i2b29jy5YBpqb8v//gYMiWLQMADRPYkxnmpVISInyNeUtLRePRZFFKb4Wfzzz5SqVyUjX0QRDQ2tpKa2vrnPedvC8UCtXZ9ckJg/TY2GQHgO9NEinIi8iioZcNInLaJKG8XDaKRf/mtwEb5bKvs/SdgH0oz+VCWlubiKIcQRCQzWbJZhd/t+NT7TvfaeOJJ5rYubNWU75hwwS3374TgDB0nHfeOGeeWeDMM6eqoTzxnvccqtehn7Rt23qrQT0xNZVh27beuoX1ctmH83LZ/55mMtDUVGblSkcU+VFp+hWW5STZur5Q993S0jLrdjO3+5fLZcbGxhgfH9coPBFZNBTWReSkJavkpZIPLYVChkKB6sqic5V4dnmhOvs5ikI6OprJ5fIEQUgQ6EVT4sCBLM8/n+fFFyNefNEH8tHRLNu2vQDAww+389hjzaxdW+DCC8dZt26K17ym1tTtllv21OvQT7uhobn/bB3t+lMtOflUKlm1jjcIoLm5QlNThTB0qkEXaQDJSnp6J0BzczOVSoXJyUkFdxFZFBTWRWRO6a3qSVfqpIa8VMpUL/ut7C7VPdhhVsG5IlCKrzNaWnI0Na2MV83DZd8osViE3bsjdu7M8eKLEbt2RXz843uJIsddd3XxzW92AtDRUWLt2gLnnjtOuey3qX/iEy8v25rnnp7StO396etPBef8NnYg7spuVCpW/VwYVsjnK+RyfmxaEDhtbRdZRDKZTLWjfhLcR0dHGR8fr26XTyTb/5Nt/OlaeNXFi8hC0EsMkWVsZu14sQjF4sxVcR/AweIg7rf5ZjI+OAaBo1JJtrSXqFR8TWNTUxP5/ErCMFrWKxbDwxl27fKB/NJLR+noKPPggx188Yv91RAI0Ntb5NChLH19Jd7xjsO85S0jrF1bqI5ES5s5u3w52bRpcFrNOkAuV2HTpsHjfm0yEs2viqd/H138e27xOD9HNuviFfMyuRxxKHcN23RPROYvHdyTBnbpBnpJqVZSG5/eUp9utncsSdhPVvrT4V9E5HgU1kWWuEpl+gqhD+I+kJfLta28zhmZjCOb9SE8DN2M+ylTqZTjJj4VSiVXXYUIw4iWllZyuTxh6Le0LyflMuzdG9LWVqa9vcIzz+T50z/tZdeuiMOHa//Nfv7zL/LGN47zmtdMcdNNB1i7tsDatVOsWVOYFsDXrSvU49tYFJK69GN1g09Wx2t15P5n6/siVGhtLROG/nc9Cef+Pctyt4KIMGvL/IlKd8lP/p6mA32pVKo2v0s65qe75c91Inuu6+da5dcoO5GlT2FdZBGbOebMr5BDsWhxN2ofWMxqf/yTmvHZgdxf9o3fyvGLieTzRjabIQxz5PO+AVwyUzebfeWdfhebZBv64cNZ7r13Jbt25di1K2L37ohSyfjYx/bw9rcfIZer4Bxceukoa9b4QL52bYG+Pt/kbcOGSTZsmDzOo8nRXH31CFdeORKfgPK/92NjSbfq5ARShebmCvm8XxFPVspFRE6lmSH6RCUhP315ro/T8+6ToJ90xC8UCtXPp+fdv9ITDyLSePTSRaSBTd+m7oN3EsSLRYtra/0W9dof69qKYRBAFM0O5OBXyguF0qxQHoZhNZCHYRiH8uyyCeTgf+aPPtrKrl2+lvyll/zb9dcf4ld+5QBmcPfdXaxeXeCMMwpccokP5RdcMA7AWWcV2Lr1xTp/F4tfsm29Vjfuf0/9C1LfzC2KKkQRqV0hDr1GFZFGlx6HdzJmjqwrFApMTk4yNTU17+PJZrPLumxNpBEprIvUWXq8WVI3XihkKBaZtoU3qRlPgngYputn3Yz3s5VKJUolX4/n7yeIQ3meIAjiUWnLZ0za0FAwLYi/9FLE2WdP8v7378cMPvvZVRQKGVauLLFmTYFLLx1l/Xq/Gt7RUebv/u5ZrdSeIrXff79LJOmTkMlALueqdeM+iPvnwDL5NRUROaZkFT2KomnXJ1vxgVQDWJt1uVKpVEP++Pg4k5OT8esEPyJP4V2kvvRSU2SBzB5vVlslT7a+Qa1u/Fir4sfj6+R8bVzydVGUo62tvVpXns0u7ae/c3DkSJbdu6N4q3pIPu+4+eYDAHzsY2vZvdu/uMnnK5xxRoHXvjapbYbbbttJb2+R1ta5GwgpqM9P+qRUpZL8zvsXgEHgu6tHUSXusK4VchGRk3Gi9eyZTIZcLkcul6OtrQ3n3Jzh/UQcrQb/RCSlBH7hQP/5iyT0clPkFEkauSWd1Uslv0JeKvka8lo48R3Vk0ZuuVx6BNcr6/Lt68x9N3Yf+DNEUZ62tg6iKCIIwiW7jX10NMPu3T6MDw9nueGGwwD8/u+fwaOPtlZvl806LrhgvBrWP/ShfUSR44wzCnR3l2at1L7qVfPbQii1xm4zV8nBkc0aUVQhn5++Sh4EWiUXEWkUZkYURURRRGtr66x6+sTMj9NfP9f75GuSGvvkcvKx3/1XmrWFP1nhD4Lls/NPJE1hXeQ4podwH0CS1XHfbTrdVd2H8Zmd1WuB/JWP3PJ/3KaPjknuz3djbyGXa6qumi+lP2pjYxl27w7ZsyfiLW8ZwQy+9rUuvvWtlRw5UvtvLJer8M53HiaTgauuGubCC8eqdeX9/cVpq+E/93NjdfhOlo7k996P+EtG+9XmkEdRbQa5VslFRBanU1Vbn9zXiSwcJOE9WeGfmppiamqKcrmMmX+95RvcqpmeLH0K67LspbenO+drxkulTDWQVypJA7fp88aTutlk1vj0MD7/UO5DeDme51qrM0tvkU+2h0VRLn47uVXz7dvbjjkCayGNjmZ4+eWQdesKRJHjkUfauPfelezePX382T33PEdnZ5mBgSKXXTbK6tWF6tuqVcVqHf/VVw/X5ftYSmafqPLPkXQoz+VcPAZNq+QiInLy/ESPkDAMaW5url6fHoNXKBQoFovVID/XfcwcdZecdEhfFml0Cuuy5M1ne3pSM56MNouiuVbEX/nqOBBvV58+Gs05CIIsYZijqamZMIymzVA9Hd3Yt29vY8uWAaam/P0ODoZs2TIAcFoCu3MwPJwll/PjtP7jP3Lcd18nu3dH7NkTVgP5l7/8AuvXT8U/Ez/+bNWqAqtXF1m9ukB7uz+Rce21w1x7rQL5yUo3d6tUrHpCyv/8XbXjei5XWymvnZwSERFZGMl2eGBaiE9W4pNdhzPfJ6Pu0h/PrMNP19snnfFnNu0TqQeFdVn0SqXpW9Rnr4zDXB3V596efnJB/OjHWKRYLGAGUZQnn28mDHMEQVC3WeXbtvVWg3piairDtm29rzisVyr+Zx5FjqGhgAceWMGePX4O+csvh4yNZfn0p1/isstGGRvL8vjjzaxeXWDjxtoK+cCAn0V++eUjXH55fVb5l5qZEweSE1Mws7mb37KelHAs0TYHIiKyhCQr8fMxs2Y+fblcLjMxMcHY2Fi1hj9QV1mpE/3myaLhA4dVV8QnJzMUCka5XNuafvSV8eq9LNjxzgzonZ095PP5hunCPjQ093Ec7fqELweAiQnjoYdW8PLLIS+/7FfH9+wJ+eAHh3j3uw8xOZnh7ru76O8vsmpVgXPPnWDVqkK1cdvrXz/OXXc9f8q/r+UuXUvuVwogkzHCsEJLS5ko8ivj2rYuIiLLVbJ6frR69/b2dkqlEhMTEwwPDzM+Pl4N7qqRl4XUGKlBJJasADpX25ZbLPoV30Kh1lnazG+RDkNHLuevi++hbsfut1f5WeaNGtDTenpKDA7OPhPd01MC/L/Fww+3sWePXxVP3l999TAf+MAQALfd1kc+X2HVqgJr1xZ405tqs8hXry7w0EPPqrHYaVQr5agl7iCYXkuebF0XERGRExcEAW1tbbS1tVEsFhkfH2d4eJjJyclqg7ukLj6pkVctvJxqegknCyrdrCpp6pbMGi+VmLFFt7ZSHgQzR5zVT1Jv7uueyiTbiZPGb+3tKxo2oKdt2jTIH//xAMViba+zmWNgoBBfhi99qZ+RkSxdXUUGBoq87nXjnH22D+NNTY57732OFSvKc/67aAv1qeUcqdIOv5Mkihzt7eU4mCdb2Ot9pCIiIktLGIZ0dHTQ3t5enUGfrpNPmt8dT7rxXRLsk0bC6Vr6ZLxd+vbJTgCdEFheGjtNSMPYvr2PbdtexdBQnp6eSTZt+k+uuWYfUJutnHSKThq6JSPOisW5Oqsn/wHVamODwG9b9+q/Ug6z55cnjeByuTxRlCMI/Ji0pPa80Tz3XI4dO3LxFvWIvXtDOjpK3Hrry1xzzQjbtvUwOBjh52DDWWdNcsklo9Wvv/32HXR2lsjn5/53WLny+H+YZP7S88r9dnb/HMnnK7S3l4kiv2regL9yIiIiS5aZkcvlyPltnbOk69+BabXwSSO8mc3u0iv02Wy22lw4HeanpqaqY+zSzfHS2/lPdDTeXMebPjmQ3G/6MZL3OlGw8BTW5ZgqFR/Uv/jF1zI15fczDw428YUvvJbDhzNccsn+VAO32n8qtSd2rZnb7M7q9Q3iaUlDkXK5VB2bBr42yc8vz5PNhg0Xyl96KeT55/Ps3Ruyd6+vGS8WjS98YRcAX/1qD48+2oqZo6urxMBAkd7eUvXrP//5l2hpKbNyZXnO4LdqVXGhvpVlyQdyH8orlWQ3SdIsx29nz+ddNZzrb6SIiEjjOl4t/CvV0tJSvZzs7kzPoi8Wi9UTAenX4enLMyUnCjKZDEEQVMcFJ28zV/mT66IomndDP3nlFNaXsbnqw8vl2rb0YtFf9+d//upqUE8UClnuuecsLr98KBUgTt14s9PB/ydTm2Ne+w/MARmiKCKfbyWXq62Y1zuY79sX8NOf1sL43r0hg4Mht9++g2wW7r23k7/5m5UAtLWV6e/3482SJnAf/OAgv/Ebg/T1FVO7FmrWri0s9Le0bCWd8ksl/zuXjKaLIkdzc5lczm9j929q/CYiIiKzJScD5hotN1dn+/SqeRLOk1X4+bzOdc4xMTHB4cOHGRsbIwxDjbdbAArrS1R6tnjyvlisbatN6sPTM8ZnbksPQ8hkHAcPzr3V58CBXEMGCv+fU6kayv33V8EsSxiG5PNNhGFYDeSn4wzoiTp4MMszzzSxb58P4vv2BezbF/KZz+ymu7vE9u0dfOUrPYDfAt3fX6S/v8jERIbW1gq/8AsHecc7DtPfX6S1tTLr/s88U2F8oSUlIbXZ5f76IIB83tHR4bex+47s9T1WERERWTpO18p+ct/Nzc00NzczOTlZ7ZKfyWTI5XLaIn+aKKwvMslqOExv0uZXxDPVmeNJbXj8VaTni8+3Pryra4r9+/NzXl8vyVnDSqUcnz30icgMzDKEYUQ+30Qul0sF8oX/dT9yJMMTTzSzb1847e0jH9nLhg2T/OAHLXzuc6sAyOUq9PUV6esrUij4f7trrjnCG94wxsBAkfb22Y3czjhD29QX0uxt65A8v5Kz1v755bux5/MVdWQXERGRJSWfz5PP5ykUCgwPDzM6Okom43epzndXarKtP9kFkJZusjdX5/1XUqe/2OjlYwNIN2RLb0tPQkGyQucDAjO2b/sQnslQnS0eBDNrw2denp8bb3yBO+5YT6FQO0sXRWVuvPGFV3yfJ8I34ihSLpeqOwB8KDey2QxBEFa3rPu5l0E1mC+UsbEMjz3WEq+KJ28B73//fjZuHGXHjhyf+tQZgF8Z7+31YTxx0UVj3HbbDvr6inN2Ve/rK9HXV0IWVu25R2osmn+e5XJ+23oUMe0EWHJZJ5ZFRERkOYiiiO7ubjo6OhgdHWVkZGRaA7wTEQRB/Hrev08C+czGfElTviTUJ9clt0vX6TvnaGpqOh3f8oJb8LBuZmuArwF9+AR5h3Nuq5l1At8A1gE7gF90zh1a6OM7XUolmJgwCoUM5TKUSrUO6eVy7QV+EkozGR+4fQggDuPp7s8L16TtzW/2M7W//vWzOHAgR1fXFDfe+EL1+lOpVCpSLBbjn4eRz+dpa+uobunx9TXZ034WLan5LhSMhx9uY3Bw+sr4u951iHe96xBHjmT5gz9YDUBLS5neXj/irKnJ/0e1fv0kt9/uw3hHx+wwvmJFmRUr1FF9oSQr40l5iA/iDucMs9qJsGRlPIqSlXGqteQiIiIiUhOGIStXrmTlypWzOswnl9OhemZju5M18zGS5nlLgc3cbnDaH9BsABhwzv3QzNqAHwA3AL8KHHTOfc7MbgFWOuc+caz7Ovfcc903vvGN033Ip8Rzzx1iaGgqPmPkqqvgftt2vY+uPpLGF6VSsfrEyufzNDe3xp0mo9NS/+Kcb6IXRb7J1wMPrGDfvrAayAcHA664YoTf+q1BCgXjbW/bAMDKlSX6+/3K+FVXDbNx4yjlMuzYkaOvb+6acamPpFwkmUnur3PV8g+/Ld1/nDwf089LEREREZGFcN555/3AOffGuT634Cvrzrk9wJ748oiZPQ2sBq4Hrohv9n+AbwPHDOuLiXMQho6mpsbrkn66pbuwl8tl/EqmI5PxDd/a2zvI5fKE4fzrXOZSKBijoxk6O/2K9X33rWTHjhyDgwGDgz6UX3TRGLfeuhszuPPObsbGMvT2lujrK3LRRWP8zM9MAD7Y3Xnn83R3l+bspp7NwqtfXb/a/eUsPYs82a7uz9gmc1ArtLX5Luu+mZtWxkVERERk8ahrzbqZrQNeD/wr0BcHeYC9+G3yc33NB4EPAgwMDCzAUcqJSjd8S0ajpUN5Pt9EFOUIw7Da8G2+K+flMuzfHzA6mq2G5Lvv7uTJJ5sYGvJh/PDhgA0bJrj99p0A/NM/tbNvX0hfX5Ezz5zioovGeO1rJ6r3+dWv/ietrZWj7nDQrPH6SIfxZKJB0qchmV6QbFf37/3qeNJlfbnuWBERERGRpaFuYd3MWoH7gA8754bToc0558xsziVo59wdwB3gt8EvxLHKdJVKhVKpWA3kiUwm6cKeJ4ry1TrzEw3l5TIcPBiwf3/AoUMBl146Cvgw/sgjbQwNBRw8GFCpGJ2dJe6556cAPP98jt27I3p7i5x99iR9fSXWrKmNLPuTP9l5zBXVtjZtX6+HZKt6rXGiVXsF+GZu08N4EFAN4pnM8gzkDz74IFu3bmXv3r309/ezefNmrrvuunofloiIiIicBnUJ62YW4oP6Xzrn7o+v3mdmA865PXFd+2A9jk2mq1TKcbfFchyk/Ep5Lpcnl2siCAIymSzHm+mYBPGhoYChoZD9+wPe+c5DhCHcc89K7r+/k/37g2p9sZnjoYeeJQx9fXlTU4U3vGGMnp4SPT1FentrHdJ/7/f2HO1hAdUg11My6SCZapAec5bNGlFUIZerEEW1bepJEF/ikzjm7cEHH+TWW29lcnISgD179nDrrbcCKLCLiIiILEH16AZvwDbgaefcltSn/hp4H/C5+P0DC31sy9X0meXlaqdG35ArjOeV5+Pt67NHoxWLsG+fD+D+LWRoKOCmmw7Q2VnmW99awW239aXmUnsbN47S31+ku7vEBReMV0N4d7d/n4S1m28+wM03H1ioH4fM09ECebI6HkUV8vkyYci0pm46iTI/W7durQb1xOTkJFu3blVYFxEREVmC6rGyvhH4ZeAJM/txfN3/wIf0u81sE7AT+MU6HNuSVS7X5hM6V6luN3YOslm/fT2KfE15snU9WS0fHc3w1FNNqTDuA/n73jfE+vVT/PM/t/GHf7h62uPl8xWuvnqYzs4y69dP8ku/dIDubr8q3tPjA3l7u99+fuWVI1x55Ug9fixyFDPrxZ1LN3CrzR33YwaZtkKuQH567N27d17Xi4iIiMjiVo9u8N/FL7vN5eqFPJalJN1xfebquFmGbDZbHYcWhiHlcoBZlqamDCMjAdu3d7B/f8CBA7UwvmnTEJdfPsILL+S45ZY11cfq6CjR3V1iYsIvfZ977gS/8zt76O72q+Td3SVaWmoN2845Z5Jzzpmc67CljtIr4rWZ48RNAY1sdnq9uK8Rd9X3ftzZ8qsbr5f+/n727Jld8tHf31+HoxERERGR062u3eBl/tIj0JyrUOuMnSEIAqIoz8REM8PDzbS0wJo1ZSYnQ/7sz/qmhfHDhwN+/dcHuemmg0xMZLn99j7CsEJXlw/bZ589SXu7H3326ldP8cUv7qS7u0RX1+wRZn19Jd761iML/8OQY5o7jCc145DNOqLIxavivmY8vSKuEN5YNm/ePK1mHSCfz7N58+Y6HpWIiIiInC4K6w3IOVddIa/NJYexsYjh4SZGRloZGWmmp8dx8cXjZDJZPvrRVzE0FHLgQBDXDcMNNxzit397H1Hk+N73Wuns9GF7w4ZJuruLnH++H1/W01Pk/vufo729PGdAa26ucN55E7M/IXVTLlMdZzY7iPtdFdmsIwx9GE+2p6uB2+KV1KWrG7yIiIjI8qCwXkflcqkaynfvbubgwWYOH444ciTPkSPN9PSUeO979xMEAb/2az/D3r25aV9/6aUjXHbZbgC6ukqsWpVsQy/S1VVi3To/vsx3XP/pUY8jm4WOjvLp+0Zl3pIw7lfFpwfxZIt6GEIYlslm/b9xbUVcK+NL1XXXXadwLiIiIrJMKKwvkO9/v5Of/CRgdLSpGsjb2kp88pNPEUU5/uiPfpZnn22p3r61tcyFF47R0uK3vL7nPYdxzofy9FviU596ecG/Jzk56RnjtUAO4OeLhyE0NZWJImaNNVMQFxERERFZ2hTWF8h3v7ua7353Be3tpep29HXrKnR19QLwoQ/tp1LZXw3hudz0uvB3v/tQPQ5bTkJSM16pWLxSbpglY81m1oz7jzMZXzeuMC4iIiIisrwprC+QT3xiH7/7u/toaXFzfv7cc1UTvlgkY82SWvFyOV0zDnM1cAtDRxgqkIuIiIiIyIlRWF8gra0VJifV0avRJbXi6RBemy3ucM7i7eiVOHz7IO7Dtxq4iYiIiIjIqaGwLsvK8WaLZzIuDt+1EF4L37XZ4iIiIiIiIqeTwrosGT58z2zaBkkndfCB3NeI+3Fmmi0uIiIiIiKNSGFdFp2ki7rfpg5JwzZfC+7I5fzKeBCku6gn9eJ1PXQREREREZETorAuDSlp4paskpfLFq96+/Cdzzvy+QpB4OI3rYqLiIiIiMjSobAuCy5p4lap1Lar10aa+feZDNW68WS0WRLMtTouIiIiIiJLncK6nLRkFRxqndRr1xmVyvSa8UzGjzQLw3LcUT3dvE214yIiIiIiIgrrMqf0LPFkBXz6GDNIr4L7sWXpZm0VsllmdVNXEBcRERERETk+hfU62769jW3behkaCujpKbFp0yDXXDNy2h4vHcKPN0s8m/VN2sKwTDYLYVibJV57r/AtIiIiIiJyqims19H27W1s2TLA1JQvwh4cDNmyZQBgXoE9CeD+vVUv+xni6Vpw4m3olXj7uWaJi4iIiIiINCKF9Tratq23GtQTU1MZtm3r5corR6rhuxbGa9vPnUuasgEY2awjDP2bD+Gza8EVwkVERERERBYHhfUF5Nz0TuhDQ3P/+IeGAkolIwh8I7YgqNV/m9VCtwK4iIiIiIjI0qSwvkCCwK+GVyquWgfe21tk375o1m37+4usWVOsw1GKiIiIiIhII9Ca7AJZsaLC2rUFVq8u0ddXorOzwkc+so98vjLtdvl8hc2b99XpKEVERERERKQRaGW9jq677ggAW7f2sXdvSH9/kc2b91WvFxERERERkeVJYb3OrrvuiMK5iIiIiIiITKNt8CIiIiIiIiINRmFdREREREREpMEorIuIiIiIiIg0GIV1ERERERERkQajsC4iIiIiIiLSYBTWRURERERERBqMwrqIiIiIiIhIg1FYFxEREREREWkwCusiIiIiIiIiDUZhXURERERERKTBKKyLiIiIiIiINBiFdREREREREZEGo7AuIiIiIiIi0mAU1kVEREREREQajMK6iIiIiIiISINRWBcRERERERFpMOacq/cxvGJmNgTsrPdxyGnXDeyv90GINAg9H0Rq9HwQqdHzQaRmMT0fznTO9cz1iUUd1mV5MLPHnHNvrPdxiDQCPR9EavR8EKnR80GkZqk8H7QNXkRERERERKTBKKyLiIiIiIiINBiFdVkM7qj3AYg0ED0fRGr0fBCp0fNBpGZJPB9Usy4iIiIiIiLSYLSyLiIiIiIiItJgFNal4ZnZfzGzn5hZxcwWfVdHkVfCzN5qZs+a2U/N7JZ6H49IPZnZV8xs0MyerPexiNSTma0xs4fN7Kn4tdLmeh+TSL2YWd7M/s3M/j1+Pny63sd0shTWZTF4Eng38Ei9D0SkHswsC9wGvA04B7jJzM6p71GJ1NVXgbfW+yBEGkAJ+Jhz7hzgTcCH9PdBlrEp4Crn3AXA64C3mtmb6ntIJ0dhXRqec+5p59yz9T4OkTq6GPipc+4/nXMF4OvA9XU+JpG6cc49Ahys93GI1Jtzbo9z7ofx5RHgaWB1fY9KpD6cNxp/GMZvi7pBm8K6iEjjWw3sSn38EnoxJiIiKWa2Dng98K91PhSRujGzrJn9GBgE/tE5t6ifD0G9D0AEwMy2A/1zfOqTzrkHFvp4RERERBYLM2sF7gM+7JwbrvfxiNSLc64MvM7MVgDfNLOfdc4t2v4mCuvSEJxz19T7GEQa2G5gTerjM+LrRERkmTOzEB/U/9I5d3+9j0ekETjnDpvZw/j+Jos2rGsbvIhI4/s+cLaZnWVmEXAj8Nd1PiYREakzMzNgG/C0c25LvY9HpJ7MrCdeUcfMmoBrgWfqelAnSWFdGp6ZvcvMXgIuAR40s3+o9zGJLCTnXAn4b8A/4JsH3e2c+0l9j0qkfszsLuB7wAYze8nMNtX7mETqZCPwy8BVZvbj+O3t9T4okToZAB42s8fxCx3/6Jz72zof00kx5xZ1gzwRERERERGRJUcr6yIiIiIiIiINRmFdREREREREpMEorIuIiIiIiIg0GIV1ERERERERkQajsC4iIiIiIiLSYBTWRURETiMzG633MZwoM/u2mb3xOLf5sJk1pz7+u2Su7Sl4/A+b2a8c4/NXmNmlx/j8aPy+x8z+/lQck4iISL0orIuIiMh8fBiohnXn3Nudc4dP9k7NLAB+DfirY9zsCuCoYT11TEPAHjPbeLLHJSIiUi8K6yIiIgvAvP9pZk+a2RNm9t74+oyZ3W5mz5jZP8Yr1b8wx9d/28y2mtmP4/u4OL6+08y+ZWaPm9mjZnZ+fP2tZnanmX3PzJ4zsw/E119hZn+but//ZWa/OsfjfdnMHjOzn5jZp+Pr/juwCnjYzB6Or9thZt3x5Y/Gx/akmX04vm6dmT1tZv87vq//a2ZNc/yIrgJ+6JwrJY9lZk/F39fXzWwd8BvAR+KfwZvN7Kz4+3vCzD474/6+Bdx8Yv86IiIijSeo9wGIiIgsE+8GXgdcAHQD3zezR4CNwDrgHKAXeBr4ylHuo9k59zoze0t8m58FPg38yDl3g5ldBXwtfhyA84E3AS3Aj8zswXkc7yedcwfNLAv8k5md75z7kpl9FLjSObc/fWMzewPwfuDnAAP+1cy+AxwCzgZucs59wMzuBt4D/MWMx9sI/CD18S3AWc65KTNb4Zw7bGZ/Cow65/44fsy/Br7snPuamX1oxv09BswM8CIiIouGVtZFREQWxmXAXc65snNuH/Ad4KL4+nuccxXn3F7g4WPcx10AzrlHgPa4Vvwy4M74+v8HdJlZe3z7B5xzE3Gwfhi4eB7H+4tm9kPgR8C5+JMJx/v+vumcG3POjQL3A2+OP/eCc+7H8eUf4E9OzDQADKU+fhz4SzP7r0DpKI+5kfhnQvwzSBnE7wIQERFZlBTWRUREFg93nI9P5PYlpv/9z8/8IjM7C/g4cLVz7nzgwbluNw9Tqctl5t7ZNzHjMa4DbgMuxO9CONpuwKP9DPLxfYqIiCxKCusiIiIL45+B95pZ1sx6gLcA/wb8C/CeuHa9D99E7WiSOvfLgCPOuSPx/d4cX38FsN85Nxzf/nozy5tZV3y/3wd2AueYWS5emb96jsdpB8aAI/ExvS31uRGg7Sjf3w1m1mxmLcC74utO1NPAa+LvIwOscc49DHwC6ABa53jsfwFujC/PrE9fDzw5j8cXERFpKKpZFxERWRjfBC4B/h2/Gvy7zrm9ZnYfPjA/BewCfggcOcp9TJrZj4AQ3zkd4FbgK2b2ODAOvC91+8fx29+7gc84514GiOvGnwRewG9zn8Y59+/x4zwTH9O/pD59B/D3Zvayc+7K1Nf80My+ij8BAfDnzrkfxY3hTsRD1LayZ4G/MLMOfP37l+Ka9b8B7jWz64HfBjYDf2VmnwAemHF/V+J3BIiIiCxK5tzxdtCJiIjI6WRmrc650XgF/N+AjXH9evo23wY+7px77ATv81ZSzdgWAzP7Jv4kxnOn4L4eAa53zh06+SMTERFZeFpZFxERqb+/jbekR/gV8L3Huf1SdQu+0dxJhfW4zGCLgrqIiCxmWlkXERERERERaTBqMCciIiIiIiLSYBTWRURERERERBqMwrqIiIiIiIhIg1FYFxEREREREWkwCusiIiIiIiIiDUZhXURERERERKTB/H8SnsLuwo4+xwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1224x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 100\n",
    "a_low = model_11_9_new_post.posterior.alpha.sel(contact_id=0).values.flatten()  # CID = 0 (low contact) \n",
    "b_low = model_11_9_new_post.posterior.beta.sel(contact_id=0).values.flatten()  # CID = 0 (low contact)\n",
    "\n",
    "a_high = model_11_9_new_post.posterior.alpha.sel(contact_id=1).values.flatten()  # CID = 1 (high contact) \n",
    "b_high = model_11_9_new_post.posterior.beta.sel(contact_id=1).values.flatten()  # CID = 1 (high contact)\n",
    "\n",
    "x_seq = np.linspace(-1.4, 3, 100)  # log(population)\n",
    "\n",
    "lambda_poisson_low =  np.array([ np.exp(a_low + b_low * x_seq[i]) for i in range(N)])\n",
    "hdi_low = np.transpose([ az.hdi(lambda_poisson_low[i]) for i in range(N) ])\n",
    "            \n",
    "lambda_poisson_high =  np.array([ np.exp(a_high + b_high * x_seq[i]) for i in range(N)])\n",
    "hdi_high = np.transpose([ az.hdi(lambda_poisson_high[i]) for i in range(N) ])\n",
    "\n",
    "plt.figure(figsize=(17,8))\n",
    "\n",
    "plt.plot(x_seq,lambda_poisson_low.mean(axis=1), c='blue', label='Low', ls='--')  # Low\n",
    "plt.fill_between(x_seq, hdi_low[0], hdi_low[1], color='blue', alpha=0.1)\n",
    "\n",
    "plt.plot(x_seq, lambda_poisson_high.mean(axis=1), c='black', label='High')  # High\n",
    "plt.fill_between(x_seq, hdi_high[0], hdi_high[1], color='black', alpha=0.1)\n",
    "\n",
    "# Plot the points\n",
    "plt.scatter(df[df['contact_id'] == 1].P, df[df['contact_id'] == 1].total_tools, label='Low contact', color='blue')\n",
    "plt.scatter(df[df['contact_id'] == 2].P, df[df['contact_id'] == 2].total_tools, label='High contact', color='black')\n",
    "\n",
    "plt.title('')\n",
    "plt.xlabel('log population (std)')\n",
    "plt.ylabel('total tools')\n",
    "plt.legend(loc='upper left')\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eda1e60f",
   "metadata": {},
   "source": [
    "### R Code 11.48"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "2009a830",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 100\n",
    "a_low = model_11_9_new_post.posterior.alpha.sel(contact_id=0).values.flatten()  # CID = 0 (low contact) \n",
    "b_low = model_11_9_new_post.posterior.beta.sel(contact_id=0).values.flatten()  # CID = 0 (low contact)\n",
    "\n",
    "a_high = model_11_9_new_post.posterior.alpha.sel(contact_id=1).values.flatten()  # CID = 1 (high contact) \n",
    "b_high = model_11_9_new_post.posterior.beta.sel(contact_id=1).values.flatten()  # CID = 1 (high contact)\n",
    "\n",
    "p_seq = np.linspace(-5, 3, 100)\n",
    "\n",
    "# 1.53 is sd of log(population)\n",
    "# 9 is mean of log(population)\n",
    "pop_seq = np.exp(p_seq * 1.53 + 9)\n",
    "\n",
    "lambda_poisson_low =  np.array([ np.exp(a_low + b_low * p_seq[i]) for i in range(N)])\n",
    "hdi_low = np.transpose([ az.hdi(lambda_poisson_low[i]) for i in range(N) ])\n",
    "            \n",
    "lambda_poisson_high =  np.array([ np.exp(a_high + b_high * p_seq[i]) for i in range(N)])\n",
    "hdi_high = np.transpose([ az.hdi(lambda_poisson_high[i]) for i in range(N) ])\n",
    "\n",
    "plt.figure(figsize=(17,8))\n",
    "\n",
    "plt.plot(pop_seq,lambda_poisson_low.mean(axis=1), c='blue', label='Low', ls='--')  # Low\n",
    "plt.fill_between(pop_seq, hdi_low[0], hdi_low[1], color='blue', alpha=0.1)\n",
    "\n",
    "plt.plot(pop_seq, lambda_poisson_high.mean(axis=1), c='black', label='High')  # High\n",
    "plt.fill_between(pop_seq, hdi_high[0], hdi_high[1], color='black', alpha=0.1)\n",
    "\n",
    "# Plot the points\n",
    "plt.scatter(df[df['contact_id'] == 1].population, df[df['contact_id'] == 1].total_tools, label='Low contact', color='blue')\n",
    "plt.scatter(df[df['contact_id'] == 2].population, df[df['contact_id'] == 2].total_tools, label='High contact', color='black')\n",
    "\n",
    "# Limits graphs\n",
    "plt.xlim(-10000, 300000)\n",
    "plt.ylim(-3, 90)\n",
    "\n",
    "plt.title('')\n",
    "plt.xlabel('population')\n",
    "plt.ylabel('total tools')\n",
    "plt.legend(loc='upper left')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91fb5f51",
   "metadata": {},
   "source": [
    "### R Code 11.49 - Overthinking: Modeling tool innovation - Pg 356"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c6f8a0d",
   "metadata": {},
   "source": [
    "Expected number of tools in one time step is:\n",
    "\n",
    "$$ \\Delta T = \\alpha P^\\beta - \\gamma T $$\n",
    "\n",
    "\n",
    "- $T$: Number of tools\n",
    "\n",
    "- $\\alpha, \\beta, \\gamma$: Parameters to be estimated\n",
    "\n",
    "The equilibrium number of tools $T$, just $\\Delta T = 0$ and solve for T.\n",
    "\n",
    "$$ \\hat{T} = \\frac{\\alpha P^\\beta}{\\gamma} $$\n",
    "\n",
    "The noise around the outcome will still be $Poisson$, because that is still maximum entropy distribution in this context - `total_tools` is a count with no clear upper bound. The linear model is:\n",
    "\n",
    "$$ T_i \\sim Poisson(\\lambda_i) $$\n",
    "\n",
    "$$ \\lambda_i = \\frac{\\alpha P^\\beta_i}{\\gamma} $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "e04afa42",
   "metadata": {},
   "outputs": [],
   "source": [
    "N = 100\n",
    "\n",
    "p_seq = np.linspace(df.log_population.min(), df.log_population.max(), N)\n",
    "\n",
    "a = np.random.normal(1, 1, N)\n",
    "b = np.random.exponential(1, N)\n",
    "g = np.random.exponential(1, N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "ab9c2d1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1224x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(17, 6))\n",
    "\n",
    "plt.hist(a, alpha=0.3, label='alpha')\n",
    "plt.hist(b, alpha=0.3, label='beta')\n",
    "plt.hist(g, alpha=0.3, label='gamma')\n",
    "\n",
    "plt.legend(loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "e69eeb99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "()"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(lambda_[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "bbb4714f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(17, 6))\n",
    "\n",
    "lambda_ = [(np.exp(a[i]) * (p_seq ** b[i])) / g[i] for i in range(N)] \n",
    "\n",
    "for i in range(N):\n",
    "    plt.plot(p_seq, lambda_[i], c='blue')\n",
    "    \n",
    "plt.ylim(0, 100)\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(17, 6))\n",
    "for i in range(N):\n",
    "    plt.plot(np.exp(p_seq), lambda_[i], c='blue')\n",
    "    \n",
    "plt.ylim(0, 100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "881a941b",
   "metadata": {
    "tags": [
     "remove_output"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mBuilding:\u001b[0m found in cache, done.\n",
      "\u001b[36mSampling:\u001b[0m   0%\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m   5% (400/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  30% (2400/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  55% (4400/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  80% (6400/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m 100% (8000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[32mSampling:\u001b[0m 100% (8000/8000), done.\n",
      "\u001b[36mMessages received during sampling:\u001b[0m\n",
      "  Gradient evaluation took 8e-06 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.08 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 6e-06 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.06 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 1.1e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.11 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 6e-06 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.06 seconds.\n",
      "  Adjust your expectations accordingly!\n"
     ]
    }
   ],
   "source": [
    "model = \"\"\"\n",
    "    data {\n",
    "        int N;\n",
    "        int qty_cid;\n",
    "        array[N] int total_tools;\n",
    "        array[N] real population;\n",
    "        array[N] int contact_id;\n",
    "    }\n",
    "    \n",
    "    parameters {\n",
    "        array[qty_cid] real alpha;\n",
    "        array[qty_cid] real<lower=0> beta;\n",
    "        real<lower=0> gamma;\n",
    "    }\n",
    "    \n",
    "    model {\n",
    "        array[N] real lambda;\n",
    "        \n",
    "        // Prioris\n",
    "        alpha ~ normal(1, 1);\n",
    "        beta ~ exponential(1);\n",
    "        gamma ~ exponential(1);\n",
    "        \n",
    "        for(i in 1:N){\n",
    "            lambda[i] = (exp(alpha[ contact_id[i] ]) * population[i] ^ beta[ contact_id[i] ])/gamma;\n",
    "        }\n",
    "        \n",
    "        //likelihood\n",
    "        total_tools ~ poisson(lambda);\n",
    "    }\n",
    "\"\"\"\n",
    "\n",
    "data_list = df[[\"total_tools\", \"contact_id\", \"population\"]].to_dict('list')\n",
    "data_list['N'] = len(df.total_tools)\n",
    "data_list['qty_cid'] = len(df.contact_id.unique())\n",
    "\n",
    "posteriori = stan.build(model, data=data_list)\n",
    "samples = posteriori.sample(num_chains=4, num_samples=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "9f78dc39",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_11_11 = az.from_pystan(\n",
    "    posterior=samples,\n",
    "    posterior_model=posteriori,\n",
    "    observed_data=list(data_list.keys()),\n",
    "    dims = {\n",
    "        'alpha': ['contact_id'],\n",
    "        'beta': ['contact_id'],\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "d2abb88d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alpha[0]</th>\n",
       "      <td>0.917</td>\n",
       "      <td>0.685</td>\n",
       "      <td>-0.346</td>\n",
       "      <td>2.219</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.012</td>\n",
       "      <td>1681.0</td>\n",
       "      <td>1625.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha[1]</th>\n",
       "      <td>0.922</td>\n",
       "      <td>0.857</td>\n",
       "      <td>-0.682</td>\n",
       "      <td>2.488</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.015</td>\n",
       "      <td>1923.0</td>\n",
       "      <td>1648.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta[0]</th>\n",
       "      <td>0.261</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.198</td>\n",
       "      <td>0.323</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2336.0</td>\n",
       "      <td>1623.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta[1]</th>\n",
       "      <td>0.292</td>\n",
       "      <td>0.106</td>\n",
       "      <td>0.084</td>\n",
       "      <td>0.481</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.002</td>\n",
       "      <td>1601.0</td>\n",
       "      <td>1392.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gamma</th>\n",
       "      <td>1.164</td>\n",
       "      <td>0.776</td>\n",
       "      <td>0.107</td>\n",
       "      <td>2.514</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.013</td>\n",
       "      <td>1518.0</td>\n",
       "      <td>1454.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  \\\n",
       "alpha[0]  0.917  0.685  -0.346    2.219      0.017    0.012    1681.0   \n",
       "alpha[1]  0.922  0.857  -0.682    2.488      0.020    0.015    1923.0   \n",
       "beta[0]   0.261  0.033   0.198    0.323      0.001    0.000    2336.0   \n",
       "beta[1]   0.292  0.106   0.084    0.481      0.003    0.002    1601.0   \n",
       "gamma     1.164  0.776   0.107    2.514      0.019    0.013    1518.0   \n",
       "\n",
       "          ess_tail  r_hat  \n",
       "alpha[0]    1625.0   1.01  \n",
       "alpha[1]    1648.0   1.00  \n",
       "beta[0]     1623.0   1.00  \n",
       "beta[1]     1392.0   1.00  \n",
       "gamma       1454.0   1.00  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(model_11_11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "59826de1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1224x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_forest(model_11_11, var_names=['alpha', 'beta', 'gamma'], combined=True, figsize=(17, 6))\n",
    "\n",
    "plt.grid(axis='x', color='white', alpha=0.3, ls='--')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "8f76a5c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 100\n",
    "a_low = model_11_11.posterior.alpha.sel(contact_id=0).values.flatten()  # CID = 0 (low contact) \n",
    "b_low = model_11_11.posterior.beta.sel(contact_id=0).values.flatten()  # CID = 0 (low contact)\n",
    "\n",
    "a_high = model_11_11.posterior.alpha.sel(contact_id=1).values.flatten()  # CID = 1 (high contact) \n",
    "b_high = model_11_11.posterior.beta.sel(contact_id=1).values.flatten()  # CID = 1 (high contact)\n",
    "\n",
    "g = model_11_11.posterior.gamma.values.flatten()\n",
    "\n",
    "\n",
    "p_seq = np.linspace(df.log_population.min() -1 , df.log_population.max() + 1, N)\n",
    "\n",
    "pop_seq = np.exp(p_seq)\n",
    "\n",
    "lambda_poisson_low =  np.array([ (np.exp(a_low) * pop_seq[i] ** b_low)/g for i in range(N)])\n",
    "hdi_low = np.transpose([ az.hdi(lambda_poisson_low[i]) for i in range(N) ])\n",
    "            \n",
    "lambda_poisson_high =  np.array([ (np.exp(a_high) * pop_seq[i] ** b_high)/g for i in range(N)])\n",
    "hdi_high = np.transpose([ az.hdi(lambda_poisson_high[i]) for i in range(N) ])\n",
    "\n",
    "plt.figure(figsize=(17,8))\n",
    "\n",
    "plt.plot(pop_seq,lambda_poisson_low.mean(axis=1), c='blue', label='Low', ls='--')  # Low\n",
    "plt.fill_between(pop_seq, hdi_low[0], hdi_low[1], color='blue', alpha=0.1)\n",
    "\n",
    "plt.plot(pop_seq, lambda_poisson_high.mean(axis=1), c='black', label='High')  # High\n",
    "plt.fill_between(pop_seq, hdi_high[0], hdi_high[1], color='black', alpha=0.1)\n",
    "\n",
    "# Plot the points\n",
    "plt.scatter(df[df['contact_id'] == 1].population, df[df['contact_id'] == 1].total_tools, label='Low contact', color='blue')\n",
    "plt.scatter(df[df['contact_id'] == 2].population, df[df['contact_id'] == 2].total_tools, label='High contact', color='black')\n",
    "\n",
    "# Limits graphs\n",
    "plt.xlim(-10000, 290000)\n",
    "plt.ylim(10, 90)\n",
    "\n",
    "plt.title('')\n",
    "plt.xlabel('population')\n",
    "plt.ylabel('total tools')\n",
    "plt.legend(loc='upper left')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb962c4b",
   "metadata": {},
   "source": [
    "## Example: Exposure and the offset:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4849e3ee",
   "metadata": {},
   "source": [
    "Implicity, $\\lambda$ is equal to an expexted number of events, $\\mu$,  per unit time or distance, $\\tau$.\n",
    "\n",
    "This implies that $\\lambda = \\frac{\\mu}{\\tau}$.\n",
    "\n",
    "$$ y_i \\sim Poisson(\\lambda_i) $$\n",
    "\n",
    "$$ log \\lambda_i = log \\frac{\\mu_i}{\\tau_i} = \\alpha + \\beta x_i $$\n",
    "\n",
    "$$ log \\mu_i = \\log \\tau_i + \\alpha + \\beta x_i $$\n",
    "\n",
    "The $\\tau$ values are the \"$exposures$\". When $\\tau_i=1$, then $log(\\tau_i) = 0$ and we're back where we started.\n",
    "\n",
    "Then the model with different exposures just by writing a model like:\n",
    "\n",
    "$$ y_i \\sim Poisson(\\mu_i) $$\n",
    "\n",
    "$$ log \\mu_i = log \\tau_i + \\alpha + \\beta x_i $$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02367d9d",
   "metadata": {},
   "source": [
    "### R Code 11.50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "4657d50c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 0, 0, 4, 3, 0, 2, 2, 2, 5, 0, 1, 3, 1, 4, 3, 3, 3, 3, 1, 2, 0,\n",
       "       1, 1, 2, 1, 3, 4, 0, 0])"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_days = 30\n",
    "\n",
    "y = np.random.poisson(lam=1.5 ,size=num_days)\n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6aea078",
   "metadata": {},
   "source": [
    "### R Code 11.51"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "173c11e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 2, 3, 3])"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_weeks = 4\n",
    "\n",
    "y_new = np.random.poisson(lam=0.5*7, size=num_weeks)\n",
    "y_new"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "494fc8ee",
   "metadata": {},
   "source": [
    "### R Code 11.52"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "bb90b77b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "      <th>days</th>\n",
       "      <th>monastery</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <th>9</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
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       "      <th>12</th>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <th>13</th>\n",
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       "      <th>14</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    y  days  monastery\n",
       "0   4     1          0\n",
       "1   0     1          0\n",
       "2   0     1          0\n",
       "3   4     1          0\n",
       "4   3     1          0\n",
       "5   0     1          0\n",
       "6   2     1          0\n",
       "7   2     1          0\n",
       "8   2     1          0\n",
       "9   5     1          0\n",
       "10  0     1          0\n",
       "11  1     1          0\n",
       "12  3     1          0\n",
       "13  1     1          0\n",
       "14  4     1          0\n",
       "15  3     1          0\n",
       "16  3     1          0\n",
       "17  3     1          0\n",
       "18  3     1          0\n",
       "19  1     1          0\n",
       "20  2     1          0\n",
       "21  0     1          0\n",
       "22  1     1          0\n",
       "23  1     1          0\n",
       "24  2     1          0\n",
       "25  1     1          0\n",
       "26  3     1          0\n",
       "27  4     1          0\n",
       "28  0     1          0\n",
       "29  0     1          0\n",
       "30  4     7          1\n",
       "31  2     7          1\n",
       "32  3     7          1\n",
       "33  3     7          1"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_all = np.concatenate([y, y_new])\n",
    "exposure = np.concatenate([np.repeat(1, 30), np.repeat(7, 4)])\n",
    "monastery = np.concatenate([np.repeat(0, 30), np.repeat(1, 4)])\n",
    "\n",
    "df = pd.DataFrame.from_dict(dict(y=y_all, days=exposure, monastery=monastery))\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4390532d",
   "metadata": {},
   "source": [
    "### R Code 11.52"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "dd77a94d",
   "metadata": {
    "tags": [
     "remove_output"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mBuilding:\u001b[0m found in cache, done.\n",
      "\u001b[36mSampling:\u001b[0m   0%\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  25% (2000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  50% (4000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  75% (6000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m 100% (8000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[32mSampling:\u001b[0m 100% (8000/8000), done.\n",
      "\u001b[36mMessages received during sampling:\u001b[0m\n",
      "  Gradient evaluation took 1.4e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 2.1e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.21 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 1.6e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 2.4e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.24 seconds.\n",
      "  Adjust your expectations accordingly!\n"
     ]
    }
   ],
   "source": [
    "model = \"\"\"\n",
    "    data {\n",
    "        int N;\n",
    "        array[N] int y;\n",
    "        array[N] real log_days;\n",
    "        array[N] int monastery;\n",
    "    }\n",
    "    \n",
    "    parameters {\n",
    "        real alpha;\n",
    "        real beta;\n",
    "    }\n",
    "    \n",
    "    model {\n",
    "        array[N] real lambda;\n",
    "        \n",
    "        alpha ~ normal(0, 1);\n",
    "        beta ~ normal(0, 1);\n",
    "        \n",
    "        for (i in 1:N){\n",
    "            lambda[i] = log_days[i] + alpha + monastery[i] * beta ;\n",
    "            lambda[i] = exp(lambda[i]);\n",
    "        }\n",
    "        \n",
    "        y ~poisson(lambda);\n",
    "    }\n",
    "\"\"\"\n",
    "\n",
    "df['log_days'] = np.log(df['days'])\n",
    "\n",
    "data_list = df[['y', 'log_days', 'monastery']].to_dict('list')\n",
    "data_list['N'] = len(df)\n",
    "\n",
    "posteriori = stan.build(model, data=data_list)\n",
    "samples = posteriori.sample(num_chains=4, num_samples=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "d7f425ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_11_12 = az.from_pystan(\n",
    "    posterior=samples,\n",
    "    posterior_model=posteriori,\n",
    "    observed_data=list(data_list.keys())\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99ef1649",
   "metadata": {},
   "source": [
    "### R Code 11.54"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "2f0a6df4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x374.4 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_forest(model_11_12, var_names=[\"alpha\", \"beta\"], combined=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "68663296",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x295.2 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alpha</th>\n",
       "      <td>1.871</td>\n",
       "      <td>0.244</td>\n",
       "      <td>1.444</td>\n",
       "      <td>2.34</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.004</td>\n",
       "      <td>2097.0</td>\n",
       "      <td>1945.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  ess_tail  \\\n",
       "alpha  1.871  0.244   1.444     2.34      0.005    0.004    2097.0    1945.0   \n",
       "\n",
       "       r_hat  \n",
       "alpha    1.0  "
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alpha = model_11_12.posterior.alpha\n",
    "beta = model_11_12.posterior.beta\n",
    "\n",
    "lambda_old = np.exp(alpha)\n",
    "lambda_new = np.exp(alpha + beta)\n",
    "\n",
    "\n",
    "az.plot_forest(lambda_old, var_names=['alpha'], combined=True)\n",
    "plt.show()\n",
    "az.summary(lambda_old)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "c9f70f29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x295.2 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>x</th>\n",
       "      <td>0.479</td>\n",
       "      <td>0.126</td>\n",
       "      <td>0.251</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>3170.0</td>\n",
       "      <td>2567.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  ess_tail  \\\n",
       "x  0.479  0.126   0.251     0.71      0.002    0.002    3170.0    2567.0   \n",
       "\n",
       "   r_hat  \n",
       "x    1.0  "
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.plot_forest(lambda_new, combined=True)\n",
    "plt.show()\n",
    "\n",
    "az.summary(lambda_new)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b7f771d",
   "metadata": {},
   "source": [
    "## Predictors matched to outcomes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "94781378",
   "metadata": {},
   "outputs": [],
   "source": [
    "def softmax(scores_list):\n",
    "    return np.exp(scores_list) / np.sum(np.exp(scores_list))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e67aadc",
   "metadata": {},
   "source": [
    "### R Code 11.55"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "1fb801d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Career choiced:  [2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 2, 2, 3, 3, 3] \n",
      "Income:  [1 2 5] \n",
      "Score:   [0.5 1.  2.5]\n"
     ]
    }
   ],
   "source": [
    "# Simulate career choices among 500 individuals\n",
    "\n",
    "N = 500  # number individuals\n",
    "income = np.array([1, 2, 5])  # expected income of each career\n",
    "score = income * 0.5  # scores for each career, based on income\n",
    "\n",
    "# next line converts scores to probabilities\n",
    "p = softmax([score[0], score[1], score[2]])\n",
    "\n",
    "career = [np.random.choice([1, 2, 3], size=1, p=p)[0] for _ in range(N)]\n",
    "\n",
    "print('Career choiced: ', career[:20], '\\nIncome: ', income, '\\nScore:  ', score)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45b68423",
   "metadata": {},
   "source": [
    " ### R Code 11.56"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "201450e7",
   "metadata": {
    "tags": [
     "remove_output"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mBuilding:\u001b[0m found in cache, done.\n",
      "\u001b[36mSampling:\u001b[0m   0%\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  25% (2000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  50% (4000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  75% (6000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m 100% (8000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[32mSampling:\u001b[0m 100% (8000/8000), done.\n",
      "\u001b[36mMessages received during sampling:\u001b[0m\n",
      "  Gradient evaluation took 1.4e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 1.9e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.19 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 3.1e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.31 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 2.8e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.28 seconds.\n",
      "  Adjust your expectations accordingly!\n"
     ]
    }
   ],
   "source": [
    "model = \"\"\"\n",
    "    data {\n",
    "        int N;  // number of individuals - Here is 500\n",
    "        int K;  // number of possible careers - Here is 3\n",
    "        \n",
    "        array[N] int career;  // outcome\n",
    "        vector[K]  career_income;\n",
    "    }\n",
    "    \n",
    "    parameters {\n",
    "        vector[K-1] a;  // intercepts\n",
    "        real<lower=0> b;  // association of income with choice\n",
    "    }\n",
    "    \n",
    "    model {\n",
    "        vector[K] p;\n",
    "        vector[K] s;\n",
    "        \n",
    "        a ~ normal(0, 1);\n",
    "        b ~ normal(0, 0.5);\n",
    "        \n",
    "        s[1] = a[1] + b * career_income[1];\n",
    "        s[2] = a[2] + b * career_income[2];\n",
    "        s[3] = 0;  // pivot\n",
    "        \n",
    "        p = softmax( s );\n",
    "        \n",
    "        career ~ categorical(p);\n",
    "    }\n",
    "\"\"\"\n",
    "\n",
    "dat_list = dict({\n",
    "    'N': len(career), # 500\n",
    "    'K': len(score),  # 3\n",
    "    'career': career,\n",
    "    'career_income': income,\n",
    "})\n",
    "\n",
    "posteriori = stan.build(model, data=dat_list)\n",
    "samples = posteriori.sample(num_chains=4, num_samples=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b50f7c1",
   "metadata": {},
   "source": [
    "### R Code 11.57"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "25b56228",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.16525252892399064"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(samples['a'][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "92299214",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_11_13 = az.from_pystan(\n",
    "    posterior=samples,\n",
    "    posterior_model=posteriori,\n",
    "    observed_data=list(dat_list.keys()),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "6e905d9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a[0]</th>\n",
       "      <td>-2.073</td>\n",
       "      <td>0.165</td>\n",
       "      <td>-2.423</td>\n",
       "      <td>-1.808</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.005</td>\n",
       "      <td>668.0</td>\n",
       "      <td>608.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[1]</th>\n",
       "      <td>-1.730</td>\n",
       "      <td>0.216</td>\n",
       "      <td>-2.145</td>\n",
       "      <td>-1.361</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.008</td>\n",
       "      <td>436.0</td>\n",
       "      <td>429.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>0.122</td>\n",
       "      <td>0.096</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.299</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.004</td>\n",
       "      <td>431.0</td>\n",
       "      <td>569.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  ess_tail  \\\n",
       "a[0] -2.073  0.165  -2.423   -1.808      0.007    0.005     668.0     608.0   \n",
       "a[1] -1.730  0.216  -2.145   -1.361      0.011    0.008     436.0     429.0   \n",
       "b     0.122  0.096   0.000    0.299      0.005    0.004     431.0     569.0   \n",
       "\n",
       "      r_hat  \n",
       "a[0]   1.01  \n",
       "a[1]   1.00  \n",
       "b      1.00  "
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(model_11_13)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddd49704",
   "metadata": {},
   "source": [
    "obs: This model not fitting like a book!\n",
    "\n",
    "*TODO*: Read this in future!\n",
    "\n",
    "Explanations:\n",
    "https://github.com/pymc-devs/pymc-resources/blob/main/Rethinking_2/Chp_11.ipynb\n",
    "\n",
    "The model described in the book does not sample well in PyMC. It does slightly better if we change the pivot category to be the first career instead of the third, but this is still suboptimal because we are discarding predictive information from the pivoted category (i.e., its unique career income).\n",
    "\n",
    "In fact, it is not necessary to pivot the coefficients of variables that are distinct for each category (what the author calls predictors matched to outcomes), as it is done for the coefficients of shared variables (what the author calles predictors matched to observations). The intercepts belong to the second category, and as such they still need to be pivoted. These two references explain this distinction clearly:\n",
    "\n",
    "Hoffman, S. D., & Duncan, G. J. (1988). Multinomial and conditional logit discrete-choice models in demography. Demography, 25(3), 415-427 [pdf link](https://www.jstor.org/stable/pdf/2061541.pdf)\n",
    "Croissant, Y. (2020). Estimation of Random Utility Models in R: The mlogit Package. Journal of Statistical Software, 95(1), 1-41 pdf [pdf link](https://www.jstatsoft.org/index.php/jss/article/view/v095i11/v95i11.pdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7966c14",
   "metadata": {},
   "source": [
    "### R Code 11.58"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "afaf2887",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x453.6 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "arviz - WARNING - Shape validation failed: input_shape: (1, 4000), minimum_shape: (chains=2, draws=4)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>x</th>\n",
       "      <td>0.038</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.101</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.001</td>\n",
       "      <td>436.0</td>\n",
       "      <td>554.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  ess_tail  \\\n",
       "x  0.038  0.033     0.0    0.101      0.002    0.001     436.0     554.0   \n",
       "\n",
       "   r_hat  \n",
       "x    NaN  "
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = model_11_13.posterior.a\n",
    "b = model_11_13.posterior.b\n",
    "\n",
    "\n",
    "s1 = (a.sel(a_dim_0=0) + b * income[0]).values.flatten()\n",
    "s2_old = (a.sel(a_dim_0=1) + b * income[1]).values.flatten()\n",
    "s2_new = (a.sel(a_dim_0=1) + b * income[1] * 2).values.flatten()\n",
    "\n",
    "p_orig = [softmax([s1[i], s2_old[i], 0]) for i in range(len(s1)) ]\n",
    "p_new  = [softmax([s1[i], s2_new[i], 0]) for i in range(len(s1)) ]\n",
    "\n",
    "# Summarize\n",
    "p_diff = np.transpose(p_new)[1] - np.transpose(p_orig)[1]\n",
    "\n",
    "az.plot_forest([np.transpose(p_new)[1], np.transpose(p_orig)[1], p_diff], model_names=['new', 'old', 'diff_new_to_old'])\n",
    "plt.show()\n",
    "\n",
    "az.summary(p_diff)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7b72c30",
   "metadata": {},
   "source": [
    "So on average a $5\\%$ increase in probability of choosing the career, when the income is doubled."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94979d33",
   "metadata": {},
   "source": [
    "## Predictors Matched to observations (pg 362)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bad7f0f",
   "metadata": {},
   "source": [
    "### R Code 11.59"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "184eb05a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Career choiced:  [3 3 1 3 3 3 3 2 3 3 2 3 2 3 3]\n",
      "Family Income:  [0.88220597 0.26090131 0.41481564 0.95091294 0.31800893]\n"
     ]
    }
   ],
   "source": [
    "N = 500\n",
    "\n",
    "# Simulate family incomes for each individual\n",
    "family_income = np.random.uniform(0, 1, N)\n",
    "\n",
    "b = np.array([-2, 0, 2])\n",
    "career = np.empty(N)\n",
    "\n",
    "for i in range(N):\n",
    "    score = np.array([1, 2, 3]) * 0.5 + b * family_income[i]\n",
    "    p = softmax([score[0], score[1], score[2]])\n",
    "    career[i] = np.random.choice([1, 2, 3], size=1, p=p)\n",
    "    \n",
    "career = career.astype(int)\n",
    "print('Career choiced: ', career[:15])\n",
    "print('Family Income: ', family_income[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "e877f5ae",
   "metadata": {
    "tags": [
     "remove_output"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mBuilding:\u001b[0m found in cache, done.\n",
      "\u001b[36mSampling:\u001b[0m   0%\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m   0% (1/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m   0% (2/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m   0% (3/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m   0% (4/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m   3% (203/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m   5% (402/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m   8% (601/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  10% (800/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  11% (900/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  12% (1000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  14% (1100/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  15% (1200/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  16% (1300/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  18% (1400/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  19% (1500/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  20% (1600/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  21% (1700/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  24% (1900/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  26% (2100/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  29% (2300/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  31% (2500/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  34% (2700/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  36% (2900/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  39% (3100/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  41% (3300/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  42% (3400/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  58% (4600/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  72% (5800/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  86% (6900/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m 100% (8000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[32mSampling:\u001b[0m 100% (8000/8000), done.\n",
      "\u001b[36mMessages received during sampling:\u001b[0m\n",
      "  Gradient evaluation took 0.000368 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 3.68 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 0.000398 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 3.98 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 0.000579 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 5.79 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 0.000367 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 3.67 seconds.\n",
      "  Adjust your expectations accordingly!\n"
     ]
    }
   ],
   "source": [
    "model = \"\"\"\n",
    "    data{\n",
    "        int N;\n",
    "        int K;\n",
    "        array[N] int career;\n",
    "        array[N] real family_income;\n",
    "    }\n",
    "    \n",
    "    parameters {\n",
    "        vector[K-1] a; // intercepts\n",
    "        vector[K-1] b; // coefficients on family income\n",
    "    }\n",
    "    \n",
    "    model {\n",
    "        vector[K] p;\n",
    "        vector[K] s;\n",
    "        \n",
    "        a ~ normal(1, 1.5);\n",
    "        b ~ normal(0, 1);\n",
    "        \n",
    "        for (i in 1:N) {\n",
    "            for (j in 1:(K-1)) s[j] = a[j] + b[j]*family_income[i];\n",
    "            s[K] = 0;\n",
    "            p = softmax( s );\n",
    "            career[i] ~ categorical( p );\n",
    "        }\n",
    "    }\n",
    "\"\"\"\n",
    "\n",
    "dat_list = {\n",
    "    'N': N,\n",
    "    'K': len(b),\n",
    "    'career': career,\n",
    "    'family_income': family_income,\n",
    "}\n",
    "\n",
    "posteriori = stan.build(model, data=dat_list)\n",
    "samples = posteriori.sample(num_chains=4, num_samples=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "5e782228",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_11_14 = az.from_pystan(\n",
    "    posterior=samples,\n",
    "    posterior_model=posteriori,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "d48d7066",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a[0]</th>\n",
       "      <td>-1.306</td>\n",
       "      <td>0.266</td>\n",
       "      <td>-1.802</td>\n",
       "      <td>-0.812</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.004</td>\n",
       "      <td>2149.0</td>\n",
       "      <td>2219.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[1]</th>\n",
       "      <td>-0.639</td>\n",
       "      <td>0.201</td>\n",
       "      <td>-1.019</td>\n",
       "      <td>-0.270</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.003</td>\n",
       "      <td>2279.0</td>\n",
       "      <td>2078.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b[0]</th>\n",
       "      <td>-2.276</td>\n",
       "      <td>0.550</td>\n",
       "      <td>-3.329</td>\n",
       "      <td>-1.255</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.008</td>\n",
       "      <td>2176.0</td>\n",
       "      <td>2278.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b[1]</th>\n",
       "      <td>-1.536</td>\n",
       "      <td>0.383</td>\n",
       "      <td>-2.263</td>\n",
       "      <td>-0.826</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.006</td>\n",
       "      <td>2294.0</td>\n",
       "      <td>1994.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  ess_tail  \\\n",
       "a[0] -1.306  0.266  -1.802   -0.812      0.006    0.004    2149.0    2219.0   \n",
       "a[1] -0.639  0.201  -1.019   -0.270      0.004    0.003    2279.0    2078.0   \n",
       "b[0] -2.276  0.550  -3.329   -1.255      0.012    0.008    2176.0    2278.0   \n",
       "b[1] -1.536  0.383  -2.263   -0.826      0.008    0.006    2294.0    1994.0   \n",
       "\n",
       "      r_hat  \n",
       "a[0]    1.0  \n",
       "a[1]    1.0  \n",
       "b[0]    1.0  \n",
       "b[1]    1.0  "
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(model_11_14)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe05e691",
   "metadata": {},
   "source": [
    "## Multinomial disguise as Poisson"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19d581bd",
   "metadata": {},
   "source": [
    "### R Code 11.60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "f4dd6b2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dept</th>\n",
       "      <th>applicant.gender</th>\n",
       "      <th>admit</th>\n",
       "      <th>reject</th>\n",
       "      <th>applications</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A</td>\n",
       "      <td>male</td>\n",
       "      <td>512</td>\n",
       "      <td>313</td>\n",
       "      <td>825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A</td>\n",
       "      <td>female</td>\n",
       "      <td>89</td>\n",
       "      <td>19</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B</td>\n",
       "      <td>male</td>\n",
       "      <td>353</td>\n",
       "      <td>207</td>\n",
       "      <td>560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
       "      <td>female</td>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>C</td>\n",
       "      <td>male</td>\n",
       "      <td>120</td>\n",
       "      <td>205</td>\n",
       "      <td>325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>C</td>\n",
       "      <td>female</td>\n",
       "      <td>202</td>\n",
       "      <td>391</td>\n",
       "      <td>593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>D</td>\n",
       "      <td>male</td>\n",
       "      <td>138</td>\n",
       "      <td>279</td>\n",
       "      <td>417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>D</td>\n",
       "      <td>female</td>\n",
       "      <td>131</td>\n",
       "      <td>244</td>\n",
       "      <td>375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>E</td>\n",
       "      <td>male</td>\n",
       "      <td>53</td>\n",
       "      <td>138</td>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>E</td>\n",
       "      <td>female</td>\n",
       "      <td>94</td>\n",
       "      <td>299</td>\n",
       "      <td>393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>F</td>\n",
       "      <td>male</td>\n",
       "      <td>22</td>\n",
       "      <td>351</td>\n",
       "      <td>373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>F</td>\n",
       "      <td>female</td>\n",
       "      <td>24</td>\n",
       "      <td>317</td>\n",
       "      <td>341</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   dept applicant.gender  admit  reject  applications\n",
       "1     A             male    512     313           825\n",
       "2     A           female     89      19           108\n",
       "3     B             male    353     207           560\n",
       "4     B           female     17       8            25\n",
       "5     C             male    120     205           325\n",
       "6     C           female    202     391           593\n",
       "7     D             male    138     279           417\n",
       "8     D           female    131     244           375\n",
       "9     E             male     53     138           191\n",
       "10    E           female     94     299           393\n",
       "11    F             male     22     351           373\n",
       "12    F           female     24     317           341"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./data/UCBadmit.csv', sep=';')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "e4948e82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([512,  89, 353,  17, 120, 202, 138, 131,  53,  94,  22,  24])"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.admit.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "124bc6be",
   "metadata": {
    "tags": [
     "remove_output"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mBuilding:\u001b[0m found in cache, done.\n",
      "\u001b[36mSampling:\u001b[0m   0%\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  25% (2000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  50% (4000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  75% (6000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m 100% (8000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[32mSampling:\u001b[0m 100% (8000/8000), done.\n",
      "\u001b[36mMessages received during sampling:\u001b[0m\n",
      "  Gradient evaluation took 2e-06 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.02 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 3e-06 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.03 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 3e-06 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.03 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 3e-06 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.03 seconds.\n",
      "  Adjust your expectations accordingly!\n"
     ]
    }
   ],
   "source": [
    "model_bin = \"\"\"\n",
    "    data{\n",
    "        int N;\n",
    "        array[N] int admit;\n",
    "        array[N] int applications;\n",
    "    }\n",
    "    \n",
    "    parameters {\n",
    "        real a;\n",
    "    }\n",
    "    \n",
    "    model {\n",
    "        real p;\n",
    "        \n",
    "        a ~ normal(0, 1.5);\n",
    "        p = inv_logit(a);\n",
    "        \n",
    "        admit ~ binomial(applications, p);\n",
    "    }\n",
    "\"\"\"\n",
    "\n",
    "dat_list_bin = {\n",
    "    'N': len(df),\n",
    "    'admit': df.admit.values,\n",
    "    'applications': df.applications.values,\n",
    "}\n",
    "\n",
    "posteriori_bin = stan.build(model_bin, data=dat_list_bin)\n",
    "samples_bin = posteriori_bin.sample(num_chains=4, num_samples=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "d669b873",
   "metadata": {},
   "outputs": [],
   "source": [
    "m_bin = az.from_pystan(\n",
    "    posterior=samples_bin,\n",
    "    posterior_model=posteriori_bin \n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd2041e6",
   "metadata": {},
   "source": [
    "### R Code 11.62"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "37a724e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.388</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.374</td>\n",
       "      <td>0.402</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1343.0</td>\n",
       "      <td>1691.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  ess_tail  \\\n",
       "a  0.388  0.007   0.374    0.402        0.0      0.0    1343.0    1691.0   \n",
       "\n",
       "   r_hat  \n",
       "a    1.0  "
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.plot_forest(\n",
    "    m_bin,\n",
    "    var_names=['a'],\n",
    "    combined=True,\n",
    "    transform=inv_logit,\n",
    "    figsize=(17, 3),\n",
    "    hdi_prob=0.89,\n",
    ")\n",
    "plt.show()\n",
    "\n",
    "az.summary(inv_logit(m_bin.posterior.a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "78c2b417",
   "metadata": {
    "tags": [
     "remove_output"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mBuilding:\u001b[0m found in cache, done.\n",
      "\u001b[36mSampling:\u001b[0m   0%\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  25% (2000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  50% (4000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  75% (6000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m 100% (8000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[32mSampling:\u001b[0m 100% (8000/8000), done.\n",
      "\u001b[36mMessages received during sampling:\u001b[0m\n",
      "  Gradient evaluation took 5e-06 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.05 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 7e-06 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.07 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 4e-06 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.04 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 7e-06 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.07 seconds.\n",
      "  Adjust your expectations accordingly!\n"
     ]
    }
   ],
   "source": [
    "# RCode 11.61 - Poisson model\n",
    "\n",
    "model_poi = \"\"\"\n",
    "    data{\n",
    "        int N;\n",
    "        array[N] int admit;\n",
    "        array[N] int rej;\n",
    "        array[N] int applications;\n",
    "    }\n",
    "    \n",
    "    parameters {\n",
    "        real a1;\n",
    "        real a2;\n",
    "    }\n",
    "    \n",
    "    model {\n",
    "        real lambda_1;\n",
    "        real lambda_2;\n",
    "        \n",
    "        a1 ~ normal(0, 1.5);\n",
    "        a2 ~ normal(0, 1.5);\n",
    "        \n",
    "        lambda_1 = exp(a1);\n",
    "        lambda_2 = exp(a2);\n",
    "        \n",
    "        admit ~ poisson(lambda_1);\n",
    "        rej ~ poisson(lambda_2);\n",
    "    }\n",
    "\"\"\"\n",
    "\n",
    "dat_list_poi = {\n",
    "    'N': len(df),\n",
    "    'admit': df.admit.values,\n",
    "    'rej': df.reject.values,\n",
    "    'applications': df.applications.values,\n",
    "}\n",
    "\n",
    "posteriori_poi = stan.build(model_poi, data=dat_list_poi)\n",
    "samples_poi = posteriori_poi.sample(num_chains=4, num_samples=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "c29f3654",
   "metadata": {},
   "outputs": [],
   "source": [
    "m_poi = az.from_pystan(\n",
    "    posterior=samples_poi,\n",
    "    posterior_model=posteriori_poi \n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4a436d3",
   "metadata": {},
   "source": [
    "### R Code 11.63"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "14c97864",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>x</th>\n",
       "      <td>0.388</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.374</td>\n",
       "      <td>0.401</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3787.0</td>\n",
       "      <td>2203.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  ess_tail  \\\n",
       "x  0.388  0.007   0.374    0.401        0.0      0.0    3787.0    2203.0   \n",
       "\n",
       "   r_hat  \n",
       "x    1.0  "
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lam1 = np.exp(m_poi.posterior.a1)\n",
    "lam2 = np.exp(m_poi.posterior.a2)\n",
    "\n",
    "resp = lam1 / (lam1 + lam2)\n",
    "\n",
    "az.plot_forest(resp, \n",
    "               combined=True,\n",
    "               figsize=(17, 3),\n",
    "               hdi_prob=0.89)\n",
    "plt.show()\n",
    "\n",
    "az.summary(resp)"
   ]
  }
 ],
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