Chapter_8.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "717a1c89",
   "metadata": {},
   "source": [
    "Manatees and bombers examples: in both cases damage seen is conditional on survival. We are already looking at a subset of population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "6ea52595",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pymc as pm\n",
    "import arviz as az\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "221d31fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "rugged = pd.read_csv('./data/rugged.csv', sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "a4986ede",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['isocode', 'isonum', 'country', 'rugged', 'rugged_popw', 'rugged_slope',\n",
       "       'rugged_lsd', 'rugged_pc', 'land_area', 'lat', 'lon', 'soil', 'desert',\n",
       "       'tropical', 'dist_coast', 'near_coast', 'gemstones', 'rgdppc_2000',\n",
       "       'rgdppc_1950_m', 'rgdppc_1975_m', 'rgdppc_2000_m', 'rgdppc_1950_2000_m',\n",
       "       'q_rule_law', 'cont_africa', 'cont_asia', 'cont_europe', 'cont_oceania',\n",
       "       'cont_north_america', 'cont_south_america', 'legor_gbr', 'legor_fra',\n",
       "       'legor_soc', 'legor_deu', 'legor_sca', 'colony_esp', 'colony_gbr',\n",
       "       'colony_fra', 'colony_prt', 'colony_oeu', 'africa_region_n',\n",
       "       'africa_region_s', 'africa_region_w', 'africa_region_e',\n",
       "       'africa_region_c', 'slave_exports', 'dist_slavemkt_atlantic',\n",
       "       'dist_slavemkt_indian', 'dist_slavemkt_saharan', 'dist_slavemkt_redsea',\n",
       "       'pop_1400', 'european_descent'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rugged.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "f9294a2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "rugged = rugged.dropna(subset = ['rgdppc_2000'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "f2a8e683",
   "metadata": {},
   "outputs": [],
   "source": [
    "rugged['log_gdp'] = np.log(rugged.rgdppc_2000) / np.mean(np.log(rugged.rgdppc_2000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "ccf5470e",
   "metadata": {},
   "outputs": [],
   "source": [
    "africa = rugged[rugged.cont_africa == 1]\n",
    "rest_of_world = rugged[rugged.cont_africa == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "2f3fd02b",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'standarized_rugged'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/hy/9qct0wrx5pl34rzz153ys6sw0000gp/T/ipykernel_69191/2206372337.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mafrica\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog_gdp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mafrica\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstandarized_rugged\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrest_of_world\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog_gdp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrest_of_world\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstandarized_rugged\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/personal/statistical_rethinking/venv/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5485\u001b[0m         ):\n\u001b[1;32m   5486\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5487\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5488\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5489\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'standarized_rugged'"
     ]
    }
   ],
   "source": [
    "plt.scatter(africa.log_gdp, africa.standarized_rugged)\n",
    "plt.scatter(rest_of_world.log_gdp, rest_of_world.standarized_rugged)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "204b3a18",
   "metadata": {},
   "outputs": [],
   "source": [
    "rugged['rugged_scaled'] = rugged.rugged / np.max(rugged.rugged) \n",
    "rugged['rugged_standarized'] = rugged.rugged_scaled - np.mean(rugged.rugged_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "4b24be80",
   "metadata": {},
   "outputs": [],
   "source": [
    "with pm.Model() as m1:\n",
    "    alpha = pm.Normal('alpha', 1, 1)\n",
    "    beta = pm.Normal('beta', 0, 1)\n",
    "    mu = pm.Deterministic('mu', alpha + beta * rugged.rugged_standarized)\n",
    "    sigma = pm.Exponential('sigma',1)\n",
    "    y = pm.Normal('y', mu=mu, sigma=sigma, observed=rugged.log_gdp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "91f4a27c",
   "metadata": {},
   "outputs": [],
   "source": [
    "with m1:\n",
    "    m1_prior = pm.sample_prior_predictive()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "83343845",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rudgeness_to_plot = np.linspace(-0.1,1.1,100)\n",
    "az.plot_hdi(rudgeness_to_plot, m1_prior.prior['alpha'].values.T + m1_prior.prior['beta'].values.T * rudgeness_to_plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "5c0e1f78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 100)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(m1_prior.prior['alpha'].values.T + m1_prior.prior['beta'].values.T * rudgeness_to_plot).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "d4c377f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "with pm.Model() as m1bis:\n",
    "    alpha = pm.Normal('alpha', 1, 0.1)\n",
    "    beta = pm.Normal('beta', 0, 0.3)\n",
    "    mu = pm.Deterministic('mu', alpha + beta * rugged.rugged_standarized)\n",
    "    sigma = pm.Exponential('sigma',1)\n",
    "    y = pm.Normal('y', mu=mu, sigma=sigma, observed=rugged.log_gdp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "2aed388a",
   "metadata": {},
   "outputs": [],
   "source": [
    "with m1bis:\n",
    "    m1bis_prior = pm.sample_prior_predictive()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "ad191d3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'a')"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rudgeness_to_plot = np.linspace(-0.1,1.1,100)\n",
    "az.plot_hdi(rudgeness_to_plot, m1bis_prior.prior['alpha'].values.T + m1bis_prior.prior['beta'].values.T * rudgeness_to_plot)\n",
    "plt.xlabel(\"a\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "ca549b57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, (ax1, ax2) = plt.subplots(1, 2, figsize=[7, 4], constrained_layout=True)\n",
    "\n",
    "n = 100\n",
    "rugged_plot = np.linspace(-0.1, 1.1, n)\n",
    "\n",
    "# Prior 1\n",
    "prior = m1_prior.prior.sel(draw=slice(None, None, int(len(m1_prior.prior.draw) / n)))\n",
    "reglines = prior[\"alpha\"].T.to_numpy() + rugged_plot * prior[\"beta\"].T.to_numpy()\n",
    "for regline in reglines:\n",
    "    ax1.plot(\n",
    "        rugged_plot,\n",
    "        regline,\n",
    "        color=\"k\",\n",
    "        lw=1,\n",
    "        alpha=0.3,\n",
    "    )\n",
    "ax1.set_title(\"a ~ Normal(1, 1)\\nb ~ Normal(0, 1)\")\n",
    "\n",
    "# Prior 2\n",
    "prior_t = m1bis_prior.prior.sel(draw=slice(None, None, int(len(m1bis_prior.prior.draw) / n)))\n",
    "reglines_t = prior_t[\"alpha\"].T.to_numpy() + rugged_plot * prior_t[\"beta\"].T.to_numpy()\n",
    "\n",
    "for regline in reglines_t:\n",
    "    ax2.plot(\n",
    "        rugged_plot,\n",
    "        regline,\n",
    "        color=\"k\",\n",
    "        lw=1,\n",
    "        alpha=0.3,\n",
    "    )\n",
    "ax2.set_title(\"a ~ Normal(1, 0.1)\\nb ~ Normal(0, 0.3)\")\n",
    "\n",
    "for ax in (ax1, ax2):\n",
    "    ax.set_xlabel(\"ruggedness\")\n",
    "    ax.set_ylabel(\"log GDP (prop of mean)\")\n",
    "    ax.axhline(0.7, ls=\"dashed\", color=\"k\", lw=1)\n",
    "    ax.axhline(1.3, ls=\"dashed\", color=\"k\", lw=1)\n",
    "    ax.set_ylim(0.5, 1.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "edeaa089",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [alpha, beta, sigma]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [8000/8000 00:03&lt;00:00 Sampling 4 chains, 0 divergences]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 21 seconds.\n"
     ]
    }
   ],
   "source": [
    "with m1bis:\n",
    "    m1bis_trace = pm.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "ba2f068f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>1.000</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.981</td>\n",
       "      <td>1.020</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5859.0</td>\n",
       "      <td>3134.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta</th>\n",
       "      <td>0.001</td>\n",
       "      <td>0.056</td>\n",
       "      <td>-0.107</td>\n",
       "      <td>0.108</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>6464.0</td>\n",
       "      <td>3118.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigma</th>\n",
       "      <td>0.138</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.124</td>\n",
       "      <td>0.152</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5383.0</td>\n",
       "      <td>3086.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mu[0]</th>\n",
       "      <td>1.000</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.979</td>\n",
       "      <td>1.020</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5898.0</td>\n",
       "      <td>3447.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mu[1]</th>\n",
       "      <td>1.000</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.961</td>\n",
       "      <td>1.045</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>6024.0</td>\n",
       "      <td>3098.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mu[165]</th>\n",
       "      <td>1.000</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.980</td>\n",
       "      <td>1.021</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5765.0</td>\n",
       "      <td>3214.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mu[166]</th>\n",
       "      <td>1.000</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.975</td>\n",
       "      <td>1.029</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5780.0</td>\n",
       "      <td>3023.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mu[167]</th>\n",
       "      <td>1.000</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.980</td>\n",
       "      <td>1.022</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5744.0</td>\n",
       "      <td>3128.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mu[168]</th>\n",
       "      <td>1.000</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.976</td>\n",
       "      <td>1.022</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5995.0</td>\n",
       "      <td>3636.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mu[169]</th>\n",
       "      <td>1.000</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.982</td>\n",
       "      <td>1.021</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5841.0</td>\n",
       "      <td>3054.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>173 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  \\\n",
       "alpha    1.000  0.010   0.981    1.020      0.000    0.000    5859.0   \n",
       "beta     0.001  0.056  -0.107    0.108      0.001    0.001    6464.0   \n",
       "sigma    0.138  0.008   0.124    0.152      0.000    0.000    5383.0   \n",
       "mu[0]    1.000  0.011   0.979    1.020      0.000    0.000    5898.0   \n",
       "mu[1]    1.000  0.022   0.961    1.045      0.000    0.000    6024.0   \n",
       "...        ...    ...     ...      ...        ...      ...       ...   \n",
       "mu[165]  1.000  0.011   0.980    1.021      0.000    0.000    5765.0   \n",
       "mu[166]  1.000  0.014   0.975    1.029      0.000    0.000    5780.0   \n",
       "mu[167]  1.000  0.011   0.980    1.022      0.000    0.000    5744.0   \n",
       "mu[168]  1.000  0.012   0.976    1.022      0.000    0.000    5995.0   \n",
       "mu[169]  1.000  0.010   0.982    1.021      0.000    0.000    5841.0   \n",
       "\n",
       "         ess_tail  r_hat  \n",
       "alpha      3134.0    1.0  \n",
       "beta       3118.0    1.0  \n",
       "sigma      3086.0    1.0  \n",
       "mu[0]      3447.0    1.0  \n",
       "mu[1]      3098.0    1.0  \n",
       "...           ...    ...  \n",
       "mu[165]    3214.0    1.0  \n",
       "mu[166]    3023.0    1.0  \n",
       "mu[167]    3128.0    1.0  \n",
       "mu[168]    3636.0    1.0  \n",
       "mu[169]    3054.0    1.0  \n",
       "\n",
       "[173 rows x 9 columns]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(m1bis_trace)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "cdadc963",
   "metadata": {},
   "outputs": [],
   "source": [
    "with pm.Model() as m2:\n",
    "    alphas = [pm.Normal('alpha_africa', 1, 0.1), pm.Normal('alpha_nonafrica', 1, 0.2)]\n",
    "    beta = pm.Normal('beta', 0, 0.3)\n",
    "    alpha = alphas[0] * rugged.cont_africa + alphas[1] * (1-rugged.cont_africa)\n",
    "    mu = pm.Deterministic('mu', alpha + beta * rugged.rugged_standarized)\n",
    "    sigma = pm.Exponential('sigma',1)\n",
    "    y = pm.Normal('y', mu=mu, sigma=sigma, observed=rugged.log_gdp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "e0d755b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [alpha_africa, alpha_nonafrica, beta, sigma]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [8000/8000 00:03&lt;00:00 Sampling 4 chains, 0 divergences]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 19 seconds.\n"
     ]
    }
   ],
   "source": [
    "with m2:\n",
    "    m2_trace = pm.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "fe56025c",
   "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>rank</th>\n",
       "      <th>loo</th>\n",
       "      <th>p_loo</th>\n",
       "      <th>d_loo</th>\n",
       "      <th>weight</th>\n",
       "      <th>se</th>\n",
       "      <th>dse</th>\n",
       "      <th>warning</th>\n",
       "      <th>loo_scale</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>m2</th>\n",
       "      <td>0</td>\n",
       "      <td>126.195308</td>\n",
       "      <td>4.092346</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.968645</td>\n",
       "      <td>7.407369</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>log</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>m1</th>\n",
       "      <td>1</td>\n",
       "      <td>94.438598</td>\n",
       "      <td>2.564519</td>\n",
       "      <td>31.75671</td>\n",
       "      <td>0.031355</td>\n",
       "      <td>6.472848</td>\n",
       "      <td>7.349211</td>\n",
       "      <td>False</td>\n",
       "      <td>log</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    rank         loo     p_loo     d_loo    weight        se       dse  \\\n",
       "m2     0  126.195308  4.092346   0.00000  0.968645  7.407369  0.000000   \n",
       "m1     1   94.438598  2.564519  31.75671  0.031355  6.472848  7.349211   \n",
       "\n",
       "    warning loo_scale  \n",
       "m2    False       log  \n",
       "m1    False       log  "
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.compare({'m1':m1bis_trace, 'm2':m2_trace})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "46b1b37d",
   "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_africa</th>\n",
       "      <td>0.880</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.849</td>\n",
       "      <td>0.909</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>6731.0</td>\n",
       "      <td>3197.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha_nonafrica</th>\n",
       "      <td>1.050</td>\n",
       "      <td>0.011</td>\n",
       "      <td>1.030</td>\n",
       "      <td>1.069</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>6612.0</td>\n",
       "      <td>2992.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta</th>\n",
       "      <td>-0.046</td>\n",
       "      <td>0.046</td>\n",
       "      <td>-0.134</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>6240.0</td>\n",
       "      <td>2780.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigma</th>\n",
       "      <td>0.114</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.103</td>\n",
       "      <td>0.126</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5819.0</td>\n",
       "      <td>3305.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mu[0]</th>\n",
       "      <td>0.884</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.853</td>\n",
       "      <td>0.913</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>7260.0</td>\n",
       "      <td>2915.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mu[165]</th>\n",
       "      <td>1.047</td>\n",
       "      <td>0.011</td>\n",
       "      <td>1.028</td>\n",
       "      <td>1.068</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>6781.0</td>\n",
       "      <td>3383.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mu[166]</th>\n",
       "      <td>1.042</td>\n",
       "      <td>0.012</td>\n",
       "      <td>1.019</td>\n",
       "      <td>1.065</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>7040.0</td>\n",
       "      <td>3294.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mu[167]</th>\n",
       "      <td>0.877</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.846</td>\n",
       "      <td>0.907</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>6337.0</td>\n",
       "      <td>3252.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mu[168]</th>\n",
       "      <td>0.886</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.856</td>\n",
       "      <td>0.917</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>7350.0</td>\n",
       "      <td>2906.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mu[169]</th>\n",
       "      <td>0.881</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.850</td>\n",
       "      <td>0.910</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>6878.0</td>\n",
       "      <td>3135.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>174 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  \\\n",
       "alpha_africa     0.880  0.016   0.849    0.909      0.000    0.000    6731.0   \n",
       "alpha_nonafrica  1.050  0.011   1.030    1.069      0.000    0.000    6612.0   \n",
       "beta            -0.046  0.046  -0.134    0.037      0.001    0.001    6240.0   \n",
       "sigma            0.114  0.006   0.103    0.126      0.000    0.000    5819.0   \n",
       "mu[0]            0.884  0.016   0.853    0.913      0.000    0.000    7260.0   \n",
       "...                ...    ...     ...      ...        ...      ...       ...   \n",
       "mu[165]          1.047  0.011   1.028    1.068      0.000    0.000    6781.0   \n",
       "mu[166]          1.042  0.012   1.019    1.065      0.000    0.000    7040.0   \n",
       "mu[167]          0.877  0.016   0.846    0.907      0.000    0.000    6337.0   \n",
       "mu[168]          0.886  0.016   0.856    0.917      0.000    0.000    7350.0   \n",
       "mu[169]          0.881  0.016   0.850    0.910      0.000    0.000    6878.0   \n",
       "\n",
       "                 ess_tail  r_hat  \n",
       "alpha_africa       3197.0    1.0  \n",
       "alpha_nonafrica    2992.0    1.0  \n",
       "beta               2780.0    1.0  \n",
       "sigma              3305.0    1.0  \n",
       "mu[0]              2915.0    1.0  \n",
       "...                   ...    ...  \n",
       "mu[165]            3383.0    1.0  \n",
       "mu[166]            3294.0    1.0  \n",
       "mu[167]            3252.0    1.0  \n",
       "mu[168]            2906.0    1.0  \n",
       "mu[169]            3135.0    1.0  \n",
       "\n",
       "[174 rows x 9 columns]"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(m2_trace)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "c2543cef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Contrast between intercepts for Africa and Non Africa')"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ys7PxyiuvwNHR0WRdQ/v4sWPHEBcXh4cffhhpaWnG7eTm5mLAgAHYtWtXuZEuM2bMMPn9mWeeAfD3++duPDw8cPDgQSQmJla5blVu3LiBY8eOYfLkyfDy8jIuj4yMxKBBgyqs587n426+++47KBQKjBkzxrjsoYcewsaNG5Genm7WY2g0GkyZMqXK9dasWQMfHx/jvrzd7X0Zbj8upKenIzMzE3369DGrf8ft983NzUVqaip69uwJIQRiY2PLrV/Va3fDhg1QqVR48sknjcuUSmWFf0NVtFotZs6ciXXr1lVYCwBs3boVRUVFmDlzJhSKvz/2pk2bBnd393LHZ1dXVzzyyCPG39VqNbp27WrW+y8tLQ2bNm3CQw89ZFw2ZswYSJKEH374wey/6873ZEXMeZ8C1X/+LA2DSiXc3d0BANnZ2Watn5CQAIVCgebNm5ss9/f3h4eHBxISEkyWN23atNxjeHp6mn0QA0qDSkX27t2LgQMHwsXFBR4eHvD19cWrr74KAMagEhMTgzFjxmDevHnw8fHBqFGjsGLFChQWFpq9/cqEh4eb/O7q6oqAgADjMOu4uDgAQP/+/eHr62vys3nzZmMHw7tp1KgRwsPDjaFk9+7d6NOnD6Kjo5GYmIiLFy9i79690Ov1xqCSkpKCjIwMLFu2rNx2DR8Ihm3HxcXh1KlT5dZr0aKFyXoGdfF83s7R0RG+vr41erwLFy5ACIHXX3+9XP1z5sypsP47X0vx8fEAgLZt21a6HcPzOGnSpHLbWb58OQoLC42vN4M7XxthYWFQKBRmDcF/5513cPLkSQQFBaFr166YO3dujYOg4f0YERFR7rZWrVoZA9ftKnu/VeTrr79G165dkZaWhgsXLuDChQvo0KEDioqK8OOPP5r1GI0bNzar02x8fDwiIiKgUqnuut769evRvXt3ODo6wsvLC76+vvjkk0/KPUcVuXLlijHUubq6wtfXFzExMQBQ7v7mvHYTEhIQEBBQbghxRc+HOZ577jl4eHhUOpVBZc+3Wq1GaGhoueNzkyZNynVYNff99/3336O4uBgdOnQwPve3bt1Ct27dKuxQXRlzXm/mvE+B6j1/lujur2w75u7ujsDAQJw8ebJa9zO3N3ZlSVkIYfa2bk/JBvHx8RgwYABatmyJ9957D0FBQVCr1diwYQMWLVpk/IYrSRL+97//4cCBA/j111+xadMmPProo/jvf/+LAwcO1OscBIYaVq1aBX9//3K3V3XANejduze2bduG/Px8HDlyBP/+97/Rtm1beHh4YPfu3Thz5gxcXV3RoUMHk+0+8sgjmDRpUoWPGRkZaVy3Xbt2eO+99ypcLygoyOT3ung+zXk8cxj+zn/+858VnnEDUC5QV/RaMnc77777LqKioipcp6rXUXVGL4wbNw59+vTBzz//jM2bN+Pdd9/FwoUL8dNPP+Gee+4x+3Fqytx9FBcXh0OHDgEoH8wAYPXq1Xj88cfrbHvm2L17N0aOHIno6Gh8/PHHCAgIgIODA1asWIFvvvnmrvfV6XQYNGgQbt26hZdffhktW7aEi4sLrl+/jsmTJ5c7aybHCDbDWZW5c+fWyRmC2ryfDWGkV69eFd5+8eJFhIaGVvk4dfX8V/f5s0QMKncxfPhwLFu2DPv370ePHj3uum5wcDD0ej3i4uLQqlUr4/KbN28iIyOjRhNM1WQI2q+//orCwkKsW7fO5Ft+Zc0p3bt3R/fu3fHWW2/hm2++wT/+8Q989913mDp1ao2HwMXFxaFfv37G33NycnDjxg0MGzYMAIxNJH5+fhg4cOBdH+tuNfTp0wcrVqzAd999B51Oh549e0KhUKB3797GoNKzZ0/jQcfX1xdubm7Q6XRVbjcsLAzHjx/HgAEDLHYoYGV1GQ6CDg4OVf6dlTE8RydPniwXau5cx93d3eztxMXFmXxTvHDhAvR6PUJCQozL7ra/AwIC8NRTT+Gpp55CcnIyOnbsiLfeeqvaQcXwfjx37ly5286ePQsfH58aT7y2evVqODg4YNWqVeU+8Pbs2YMPP/wQV65cqfAsXE2EhYXh4MGDKC4uNmlyvd2aNWvg6OiITZs2QaPRGJevWLGiysc/ceIEzp8/j5UrV2LixInG5Vu2bKlxzcHBwdi2bRtycnJMwmxFz4e5Zs6ciffffx/z5s0rN+T39uf79pBQVFSES5cu1fh9cqdLly5h3759ePrpp41nLAz0ej0mTJiAb775Bq+99lqdbM+c92l9PH8NjU0/d/HSSy/BxcUFU6dOxc2bN8vdHh8fjw8++AAAjB/Cd06BbPhGfu+991Z7+4YDZVVTat/OcGC8PflnZmaWOyClp6eX+3Zg+FZsaP5xdnau9vYBYNmyZSb9Xj755BOUlJQYP0yGDBkCd3d3vP322xX2j0lJSTH+/277wNCks3DhQkRGRkKr1RqXb9u2DYcPHzbpB6RUKjFmzBisWbOmwjNlt2933LhxuH79Oj777LNy6+Xn51vEXCOVPT9+fn7o27cvPv30U9y4caPc/W7/OyszePBguLm5Yf78+SgoKDC5zfC66dSpE8LCwvCf//wHOTk5Zm3no48+Mvl98eLFAGASNFxcXMr9TTqdrtwpaj8/PwQGBtaouTIgIABRUVFYuXKlybZOnjyJzZs3G9/PNbF69Wr06dMHDz74IMaOHWvy8+KLLwIAvv322xo//p3GjBmD1NRULFmypNxthudKqVRCkiTodDrjbZcvXzZrBuCKjilCCOOxryaGDRuGkpISfPLJJ8ZlOp3O+HqoCcNZlV9++aXcJRIGDhwItVqNDz/80OTv+Pzzz5GZmVmj43NFDGdTXnrppXLP/bhx4xATE1Ot5p+qmPM+rY/nr6HxjMpdhIWF4ZtvvsGDDz6IVq1aYeLEiWjbti2Kioqwb98+/Pjjj5g8eTIAoH379pg0aRKWLVuGjIwMxMTE4M8//8TKlSsxevRokzMM5oqKioJSqcTChQuRmZkJjUaD/v37w8/Pr9L7DB48GGq1GiNGjMD06dORk5ODzz77DH5+fiYfWitXrsTHH3+M++67D2FhYcjOzsZnn30Gd3d340HayckJrVu3xvfff48WLVrAy8sLbdu2rbI9tKioCAMGDMC4ceNw7tw5fPzxx+jduzdGjhwJoPQb+CeffIIJEyagY8eOGD9+PHx9fXHlyhX89ttv6NWrl/Gg26lTJwDAs88+iyFDhkCpVGL8+PEASpsv/P39ce7cOZNOeNHR0Xj55ZcBwCSoAMCCBQuwfft2dOvWDdOmTUPr1q1x69YtHD16FFu3bsWtW7cAABMmTMAPP/yAJ554Atu3b0evXr2g0+lw9uxZ/PDDD9i0aRM6d+5c9ZNYj+72/Hz00Ufo3bs32rVrh2nTpiE0NBQ3b97E/v37ce3aNRw/fvyuj+3u7o5FixZh6tSp6NKlCx5++GF4enri+PHjyMvLw8qVK6FQKLB8+XLcc889aNOmDaZMmYLGjRvj+vXr2L59O9zd3fHrr7+aPO6lS5cwcuRIDB06FPv378fXX3+Nhx9+GO3btzeu06lTJ2zduhXvvfceAgMD0axZM0RERKBJkyYYO3Ys2rdvD1dXV2zduhWHDh3Cf//73xrtv3fffRf33HMPevTogcceewz5+flYvHgxtFptjS/bcPDgQVy4cAFPP/10hbc3btwYHTt2xOrVq42v0dqaOHEivvrqK8yaNQt//vkn+vTpg9zcXGzduhVPPfUURo0ahXvvvRfvvfcehg4diocffhjJycn46KOP0Lx5c/z11193ffyWLVsiLCwM//znP3H9+nW4u7tjzZo1Ne5/BQAjRoxAr1698Morr+Dy5cto3bo1fvrpp1r3l3juueewaNEiHD9+3OSMmK+vL2bPno158+Zh6NChGDlypPHY1KVLF5OOs7WxevVqREVFlWsaNhg5ciSeeeYZHD16FB07dqz19sx5n9bH89fgGnqYkTU6f/68mDZtmggJCRFqtVq4ubmJXr16icWLF5sM/ywuLhbz5s0TzZo1Ew4ODiIoKOiuE77dqaKJnD777DMRGhoqlEqlyVDlyh5DiNKhupGRkcZJ3BYuXGgcwmgY1nb06FHx0EMPiaZNmwqNRiP8/PzE8OHDxeHDh00ea9++faJTp05CrVZXOVT5zgnfPD09haurq/jHP/5hMgTUYPv27WLIkCFCq9UKR0dHERYWJiZPnmxSQ0lJiXjmmWeEr6+vkCSp3FDlBx54QAAQ33//vXFZUVGRcHZ2Fmq1utyQPSFKh47OmDFDBAUFCQcHB+Hv7y8GDBggli1bZrJeUVGRWLhwoWjTpo3QaDTC09NTdOrUScybN09kZmYa10MFQwyFKH2OqpoK/m4Tvt3JMEz2dnd7fuLj48XEiROFv7+/cHBwEI0bNxbDhw8X//vf/4zrVHXZg3Xr1omePXsKJycn4e7uLrp27Sq+/fZbk3ViY2PF/fffL7y9vYVGoxHBwcFi3LhxYtu2beVqP336tBg7dqxwc3MTnp6e4umnny73HJ09e1ZER0cLJycn4yRohYWF4sUXXxTt27cXbm5uwsXFRbRv377CiQzvVNnwZCGE2Lp1q+jVq5fx7xsxYkSlE76lpKRUua1nnnlGABDx8fGVrjN37lzjkOy7DU9u06ZNhfev6DiRl5cn/vWvfxmPPf7+/mLs2LEmdXz++efGCeFatmwpVqxYUeFrqiKnT58WAwcOFK6ursLHx0dMmzbNOAS/pq/dtLQ0MWHCBOOEbxMmTKj2hG8VPaeGbVVUx5IlS0TLli2Fg4ODaNSokXjyyScrnfDtTpMmTRLBwcGV1nTkyBEBQLz++uuVrnP58mUBQDz//PNCiLsPT67oPVnZhG9VvU/Nff4slSREDXv7ERGZae7cuZg3bx5SUlLg4+MjdzlEZEXYR4WIiIgsFoMKERERWSwGFSIiIrJY7KNCREREFotnVIiIiMhiMagQERGRxbK6Cd/0ej0SExPh5uZmsVObExERkSkhBLKzsxEYGGhyFeuqWF1QSUxMrHTWPyIiIrJsV69eRZMmTcxe3+qCipubG4DSP9Td3V3maoiIiMgcWVlZCAoKMn6Om8vqgoqhucfd3Z1BhYiIyMpUt9sGO9MSERGRxWJQISIiIovFoEJEREQWi0GFiIiILBaDChEREVksBhUiIiKyWAwqREREZLEYVIiIiMhiMagQERGRxWJQISIiIovFoEJEREQWi0GFiIiILJbVXZSQiGzHuaRs7LmQiqTMfDirVWjk7ohuoV4I83WVuzQishAMKkTU4M7cyMK8X0/hwMVbFd7eoakHpvRqhuHtAqBQVO9Kq0RkWxhUiKhB/X4yCc9+G4sinR4OSgm9m/uguZ8r8ot1uJSaiwMXbyH2SgZir8Ti890X8drw1ugS4iV32UQkEwYVImowa2Ov44Ufj0OnF+gX4Yu37muHQA8nk3WSswvw7cGrWLYrHsevZeKBpfsxOioQrw1vDR9XjUyVE5FcJCGEkLuI6sjKyoJWq0VmZibc3d3lLoeIzLQ/Pg2PfH4QOr3A2E5NsOD+dlApK+/Pn5xdgEVb4vDdoSsQAtA6OeBfw1rhgc5NIElsDiKyNjX9/OaoHyKqd5n5xXjuu1jo9AKjogLxzpjIu4YUAPBzc8T8+9th7VO90DrAHZn5xXhpzV8Y9+l+/HUto2EKJyLZMagQUb2bv+EMkrML0czHBQvuj6xWB9n2QR5Y93QvvDqsJZwclDh0OR0jl+zFzO9icS09rx6rJiJLUO2gcv36dbz//vsYPHgwmjZtCrVaDX9/f4wZMwYHDx6s8D5ZWVmYNWsWgoODodFoEBISghdffBE5OTm1/gOIyLLtu5CK7w5dBQAsHBMJJ7Wy2o+hUirweHQYtr0Qg/s7NAYArD2WiP7/3YkFG88iM6+4TmsmIstR7T4qr7zyChYuXIiwsDD07dsXvr6+iIuLw9q1ayGEwDfffIMHH3zQuH5ubi569+6NY8eOYfDgwejQoQNiY2OxefNmdOnSBbt27YKjo6PZ22cfFSLrodcL3Lt4D87cyMKE7sF4c3TbOnnck9cz8X+/nTYOb3bTqDCxZzAe6R6MAK1TFfcmIjnU9PO72kHlp59+gre3N2JiYkyW7969GwMGDICrqytu3LgBjaa0d/6cOXPwxhtv4OWXX8aCBQuM6xsCz9tvv43Zs2ebvX0GFSLrsf6vRDz9TSzcNCrseqkfPF3UdfbYQghsPZOM/24+h7NJ2QAAhQQMaNUIj/Zqhh5h3nW2LSKqvQYLKnczZMgQbN68GYcOHULnzp0hhECTJk2QlZWFpKQkuLi4GNfNzc2Fv78//Pz8EB8fb/Y2GFSIrEOJTo/B7+/CxZRczBwYjpkDW9TLdvR6gU2nkvDlvss4eOnvCeR6hHpj1uAWnIOFyEJYxKgfBwcHAIBKVTo9S1xcHBITE9GrVy+TkAIALi4u6NWrFy5evIirV6/WZRlEZAF+O3EDF1Ny4ensgMd6N6u37SgUEu5pF4Dvp/fA1lnReKR7U6iVCuy/mIYHlu7HhM8P4nJqbr1tn4jqV50FlStXrmDr1q0ICAhAu3btAJQGFQAIDw+v8D6G5Yb1KlJYWIisrCyTHyKybEIIfL7nEgDg0V7N4Obo0CDbbe7nhv8b3Q7bX+yLh7s1hUohYXdcKu79cDfWHLnWIDUQUd2qk6BSXFyMCRMmoLCwEAsXLoRSWdqrPzMzEwCg1WorvJ/h1I9hvYrMnz8fWq3W+BMUFFQXJRNRPTqckI6/rmVCo1Lg4W5NG3z7jT2c8PZ97fDHC33RrZkXcot0eOHH41i05TysbI5LIrtX66Ci1+sxefJk7Nq1C9OmTcOECRPqoi6j2bNnIzMz0/jDZiIiy/f57tKzKfd1aAxvGae9b+rtjG+mdcez/ZsDAD7YFoc31p9mWCGyIrW61o9er8ejjz6Kb775Bo888giWLl1qcrvhTEplZ0wMzTiVnXEBAI1GYxxBRESW73pGPjafTgIAPFqPfVPMpVRImDU4Aj5uGsxZdwor9l6Gl7MazwyouEmaiCxLjc+o6PV6TJkyBStXrsRDDz2EL7/8EgqF6cNV1Qelqj4sRGR9/nf4GvQC6B7qhRaN3OQux2hijxC8Map0Hpf3tp7HvvhUmSsiInPUKKgYQspXX32FBx98EKtWrTL2S7ldeHg4AgMDsXfvXuTmmva6z83Nxd69e9GsWTP2OyGyEXq9wI9HSptnH+xiee/rCd2DMb5LEIQAnv/+GNJyCuUuiYiqUO2gYmju+eqrr/DAAw/g66+/rjCkAIAkSZg6dSpycnLw5ptvmtz25ptvIicnB9OmTatZ5URkcfbFp+Faej7cHFW4p22A3OVU6N8jWiPM1wU3swoxZ90pucshoipUe8K3uXPnYt68eXB1dcVzzz1nnDPldqNHj0ZUVBSA0jMnvXr1wvHjxzF48GB07NgRR48eNU6hv3PnTjg5mT/lNSd8I7Jcz3wbi1+PJ+KR7k3xf6PbyV1OpU5ez8TIJXugF8BXj3ZFdAtfuUsisnk1/fyudmfay5cvAwBycnLw1ltvVbhOSEiIMai4uLhg586dmDt3LtasWYPt27cjICAAL7zwAubMmVOtkEJEliu7oBibT5V2oh3X2fKafW7XtrEWk3s2wxd7L+H1X05i08xoODpU/2KJRFT/6nQK/YbAMypElumno9cw64fjCPV1wbZZMZAkSe6S7iqnsAQD/7sTSVkF+NewVpgWHSp3SUQ2zSKm0Cci+7XueCIAYERkoMWHFABw1agwa3Dp9YeW7oxHXlGJzBURUUUYVIio1m7lFmFPXOlw35FRgTJXY777OzRGsLcz0nKL8NX+BLnLIaIKMKgQUa1tPHkDJXqBNoHuCPN1lbscs6mUCjzTv3Qep095VoXIIjGoEFGtrTtW2uwzsr31nE0xGB0ViKZezkjPK8ba2ES5yyGiOzCoEFGtJGUW4M/LtwAAw60wqKiUCkzsEQwAWLnvMq8DRGRhGFSIqFbW/5UIIYDOwZ5o7GGd0w080DkITg5KnLuZjf0X0+Quh4huw6BCRLXy+8nSuVOGR1rmTLTm0Do54P6OjQGUnlUhIsvBoEJENZacXYAjV9IBAEPa+stcTe1M6hkCANh6Jhkp2bwGEJGlYFAhohrbcvomhADaB3kgQGudzT4GLRq5oX2QB3R6gV+OXZe7HCIqw6BCRDVmaPYZ2sa6z6YYPNCpCQDgf0eusVMtkYVgUCGiGsnMK8b++NKOp0PaNJK5mroxIjIQapUCZ5OycSoxS+5yiAgMKkRUQ3+cu4kSvUCLRq4ItaJJ3u5G6+yAwa1LQ9f/jlyTuRoiAhhUiKiGbK3Zx8Aw+ue3Ezeg07P5h0huDCpEVG35RTrsPJ8CwPpH+9ypd3NfaJ0ckJJdiD8v3ZK7HCK7x6BCRNW283wKCor1aOLphNYB5l+u3RqoVQpjn5vfTnBKfSK5MagQUbVtOvV3s48kSTJXU/eGR5ZeCmDjiSSU6PQyV0Nk3xhUiKhaikr02HrmJgBgqI01+xj0DPOGp7MD0nKLcOAim3+I5MSgQkTVcuBiGrILSuDjqkGHpp5yl1MvVEqFMYQZzh4RkTwYVIioWn4v++Ae3KYRlArba/YxGNy6NKiUzr7L0T9EcmFQISKz6fQCm0+VNfvY2LDkO/UI84azWomkrAKcuJ4pdzlEdotBhYjMFnslHak5hXBzVKF7qLfc5dQrRwclYlr4Aig9q0JE8mBQISKzGSZ5G9iqEdQq2z98DC4bpmw4i0REDc/2jzREVCeEEMb+KUNsvNnHoF+EH5QKCeduZiMhLVfucojsEoMKEZnl9I0sXEvPh6ODwtgkYus8nNXo1swLAJt/iOTCoEJEZtlU1uwT08IXTmqlzNU0nEFlFynczKBCJAsGFSIyy6ZTtj3JW2UMQeXw5VtIzy2SuRoi+8OgQkRVupiSg3M3s6FSSOgf0UjuchpUE09nRDRyg14Auy+kyl0Okd1hUCGiKhnOpvQI84bW2UHmahpe34jSPjk7zibLXAmR/WFQIaIqGS9CaGfNPgYxZUFl5/kU6PWcpZaoITGoENFd3cjMx7GrGZCkv/tr2JvOwV5wUSuRlluEk4mcpZaoITGoENFdGSY769TUE35ujjJXIw+1SoHe4T4AgB3nUmSuhsi+MKgQ0V3Ze7OPQd8IPwDA9nPsp0LUkBhUiKhSt3KLcPDSLQD2MxttZQwdao9dzeAwZaIGxKBCRJXaevomdHqBVgHuCPJylrscWQVonRDRyA1CALvi2PxD1FAYVIioUutP3AAA3NvOvs+mGPRtWTZMmf1UiBoMgwoRVehWbhH2lk1wdm9koMzVWIa+LUr7qeziMGWiBsOgQkQV2nQqCTq9QJtAdzTzcZG7HIvQOcQTrhoV0nKLcOI6hykTNQQGFSKq0Pq/EgEA90YGyFyJ5XBQKtCruTcAjv4haigMKkRUTmpOIfbHpwEAhrdjs8/tDMOUd51nPxWihsCgQkTlbDyZBL0AIpto0dTbvkf73Cm6xd/DlDPzimWuhsj2MagQUTm/lTX7DGezTzmNPZwQ7ucKvQD28GrKRPWOQYWITCRnFRgneRvWjkGlIoazKjvPs58KUX1jUCEiExtPJkEIICrIA0082exTkZgWf19NWQgOUyaqTwwqRGTit79KJ3ljs0/lujbzgkalwM2sQpy/mSN3OUQ2jUGFiIySMgtwKIHNPlVxdFCie2jpMGU2/xDVLwYVIjLacOIGhAA6B3si0MNJ7nIsmqH5Z9d5dqglqk8MKkRk9CsneTOboUPtn5duIa+oROZqiGwXgwoRAQCu3spD7JUMSBJwL5t9qhTm64LGHk4o0ulx4GKa3OUQ2SwGFSICAKw7Xno2pUeoN/zcHWWuxvJJkmQ8q8LmH6L6w6BCRACAX8uCysj2nDLfXLcPUyai+sGgQkQ4fzMbZ5Oy4aCUcE9bNvuYq2dzb6gUEi6l5uJKWp7c5RDZJAYVIsK6Y6VnU2Ja+ELr7CBzNdbD3dEBHZt6AgB2xvGsClF9YFAhsnNCCGP/lJFRjWWuxvrERBj6qTCoENUHBhUiO3fsagau3MqDk4MSA1v5yV2O1YkOLw0q+y6koqhEL3M1RLaHQYXIzhnOpgxq3QjOapXM1VifNoHu8HZRI7dIhyMJ6XKXQ2RzGFSI7JhOL7C+7No+HO1TMwrFbcOU2U+FqM4xqBDZsYMX05CSXQitk4Pxw5aqL7qFDwBg5zkGFaK6xqBCZMcMzT7D2vlDreLhoKb6lPVTOX0jC8nZBTJXQ2RbeGQislOFJTpsOFHa7DOCzT614uOqQdvG7gCA3ZyllqhOMagQ2ald51ORVVACPzcNujXzlrscq8dZaonqB4MKkZ0yNPsMjwyEUiHJXI31i2lROrR7d1wKdHohczVEtoNBhcgO5RWVYOvpmwCAkVFs9qkLHZp6wFWjQnpeMU5ez5S7HCKbwaBCZIe2nL6J/GIdgr2d0b6JVu5ybIKDUoFezUub0DhLLVHdYVAhskO3XylZktjsU1ei2U+FqM4xqBDZmcy8YuMHKSd5q1uG6fRjr2YgM79Y5mqIbAODCpGd2Xw6CcU6gYhGbghv5CZ3OTYlyMsZYb4u0OkF9l3gMGWiulDtoPL1119j+vTp6Ny5MzQaDSRJwpdfflnhunPnzoUkSZX+XL58uZblE1F1GeZOGdYuQOZKbBObf4jqVrWvQPbaa68hISEBPj4+CAgIQEJCQpX3mTRpEkJCQsot9/DwqO7miagWMvOKsTuu9Jv+vZH+Mldjm2Ja+GLF3svYdT4FQgj2ASKqpWoHleXLlyM8PBzBwcFYsGABZs+eXeV9Jk+ejL59+9akPiKqQ5tOJ6FEL9DS3w3N/djsUx+6h3pDo1IgMbMAF5Jz2LxGVEvVbvoZOHAggoOD66MWIqpnbPapf44OSnRt5gWAzT9EdaHaZ1RqYteuXTh48CAUCgXCw8MxcOBAuLq6NsSmiahMRl4R9pQ1+zCo1K+YFr7YHZeKnedTMLVPqNzlEFm1Bgkqc+bMMfndw8MDH3zwASZOnFjlfQsLC1FYWGj8PSsrq87rI7IHm0/dvK3Zh18U6lNMC1/8329ncPDSLeQX6eCkVspdEpHVqtfhye3bt8cXX3yBixcvIj8/H5cuXcLixYshSRImT56MdevWVfkY8+fPh1arNf4EBQXVZ8lENmt9WbPP8EieTalvzf1cEah1RFGJHgcvpcldDpFVq9egct9992HKlClo1qwZHB0dERISgqeffho//vgjgNIRRFWZPXs2MjMzjT9Xr16tz5KJbFJ6bpFxXg82+9Q/SZIQE8FhykR1QZYJ3wYMGICwsDCcOHGiyqYcjUYDd3d3kx8iqp7NZaN9WgW4I9SXzT4NwTBLLa/7Q1Q7ss1M6+PjAwDIy8uTqwQiu7H5VOmVku9py7lTGkrP5j5QKiTEp+Ti6i0e54hqSpagkpubi1OnTsHFxcUYWIiofuQVlWBPWbPP4DaNZK7GfmidHNCpqScAYPu5ZJmrIbJe9RZUsrOzcf78+XLL8/PzMW3aNGRnZ2PcuHFQqRpk4BGR3dp1PhWFJXoEeTkhgpOPNah+Lf0AAH+cZVAhqqkazUy7Z88eAMCJEyeMy3bs2AEA6N27N6ZOnYq0tDS0bNkSXbp0QatWreDv74+bN29i69atuHbtGtq1a4d333237v4SIqrQltOlzT6DWvlzOvcG1r+lHxb+fhb749M4TJmohqodVPbs2YOVK1eaLNu7dy/27t1r/H3q1Knw8vLCU089hT///BMbNmxAeno6nJyc0KpVKzz77LN4+umn4eTkVPu/gIgqVaLT44+zZUGlNZt9GlqLRq5o7OGE6xn52BefigGt+BwQVVe1g8qXX35Z6dWSb+fu7o4lS5bUpCYiqiOHE9KRnlcMD2cHdAnxlLscuyNJEvq39MOqAwn442wygwpRDcg26oeI6p+h2ad/hB9USr7d5dC/rJ/K9rPJEELIXA2R9eGRi8hGCSH+7p/CZh/Z9AjzhqND6dWUzyZly10OkdVhUCGyUedv5uDKrTyoVQpEt/CVuxy75eigRM+w0mkYOPqHqPoYVIhs1JbTSQCA3s194KLhNABy6ndb8w8RVQ+DCpGNYrOP5TD0Uzl6JR3puUUyV0NkXRhUiGxQcnYBjl/LBAAMaOUnczXU2MMJLf3doBfArjhe+4eoOhhUiGzQjnOlH4aRTbTwc3OUuRoCOEstUU0xqBDZoB1l15bpF8GzKZbC0Pyz41wKSnR6mashsh4MKkQ2plinx+7zpRchNHyLJ/l1CPKA1skBmfnFiL2aIXc5RFaDQYXIxhy+nI7swhJ4u6gR2VgrdzlURqVUIKZsmDibf4jMx6BCZGMMzT4xEb5QKHgRQkti6NjMYcpE5mNQIbIxhm/r7J9ieWJa+EIhAWeTsnE9I1/ucoisAoMKkQ25eisPcck5UCokRIdzNlpL4+GsRsempReH5FkVIvMwqBDZkB3nS4cld2rqCa2zg8zVUEU4TJmoehhUiGyI4Vt635Y8m2KpDMOU98WnoqBYJ3M1RJaPQYXIRhQU67AvvmxYMvunWKyW/m4I0DqioFiP/fFpcpdDZPEYVIhsxIGLaSgo1iNA64iW/m5yl0OVkCTJeFaFzT9EVWNQIbIRhmnz+0b4QpI4LNmS3R5UhBAyV0Nk2RhUiGyE4WJ3MS3Y7GPpeob5QKNS4HpGPuKSc+Quh8iiMagQ2YBr6Xm4mJILpUJCz+becpdDVXBSK9EjrPR52naGzT9Ed8OgQmQDdpVd26dDkAfcHTks2RoYmn84nwrR3TGoENmA3WXNPtEtOCzZWhhGZh25ko6MvCKZqyGyXAwqRFauRKfHngulZ1T6hPvIXA2ZK8jLGS0auUKnF9hZNlEfEZXHoEJk5Y5fy0R2QQm0Tg6IbOIhdzlUDQNbNQIAbGU/FaJKMagQWbldZd/Gezf3gZJXS7YqA8qCyo5zySjW6WWuhsgyMagQWbldxv4pbPaxNlFBHvB2USO7oASHLt2Suxwii8SgQmTFMvOKcfxqBgCgD6+WbHWUCsl4kUI2/xBVjEGFyIrtjU+FXgDN/VwR6OEkdzlUAwNblQaVbWdvcpZaogowqBBZMeOwZJ5NsVp9wn2hViqQkJaH+BTOUkt0JwYVIislhDBO9NaH/VOslotGZZyldstpNv8Q3YlBhchKxafk4npGPtQqBbo347T51szY/HPmpsyVEFkeBhUiK2Vo9uka4gUntVLmaqg2+pcNUz56JR23cjlLLdHtGFSIrJRh/hTORmv9Gns4oVWAO/SC1/4huhODCpEVKizR4cDF0nk3OCzZNtw++oeI/sagQmSFjiSkI79YBx9XDVoFuMldDtUBw3T6O8+loLBEJ3M1RJaDQYXICu2JKx3tEx3uA0nitPm2oF1jLXzdNMgt0uHgRc5SS2TAoEJkhXaXBZXe7J9iMxQKCQNacvQP0Z0YVIisTFpOIU4mZgIovRAh2Y4Bt11NmbPUEpViUCGyMnvj0yAE0NLfDX7ujnKXQ3Wod3MfaFQKXM/Ix7mb2XKXQ2QRGFSIrMyeOA5LtlVOaqXxLNnW02z+IQIYVIisihDC2D+Fw5Jt0+3NP0TEoEJkVeJTcnAjswBqlQJdm3nJXQ7VgwFl86kcv5aBlOxCmashkh+DCpEVMZxN6RriBUcHTptvixq5O6JdYy0EZ6klAsCgQmRV/m72Yf8UW2Y4q7KVw5SJGFSIrEVhiQ7749MAsH+KrTPMUrs7LhUFxZylluwbgwqRlTiakGGcNr+lP6fNt2VtAt0RoHVEfrEO++JT5S6HSFYMKkRWYs+F0mHJvZt7Q6HgtPm2TJIk41mVzafY/EP2jUGFyEpwWLJ9GdzGMEz5JnR6zlJL9otBhcgKpOcW4cT10mnz2ZHWPnRr5g03RxVSc4pw7Gq63OUQyYZBhcgK7I1PhRBARCNOm28v1CoF+kWUjv7ZzFlqyY4xqBBZgd3nOSzZHhmafzafusmLFJLdYlAhsnCl0+aXXd+nBfun2JOYFr5QKxW4lJqL+JQcucshkgWDCpGFi0/JRWJmAdRKBbqGcNp8e+Lm6IAeYd4A2PxD9otBhcjCGa6W3KWZJ5zUnDbf3tze/ENkjxhUiCwchyXbt0Fl86kcu5qBm1kFMldD1PAYVIgsWFGJHvsvlk6b37s5O9LaIz93R0QFeQDgtX/IPjGoEFmw2CvpyCvSwdtFjdYB7nKXQzJh8w/ZMwYVIgtmaPbpHe7DafPt2ODWpUFlf3wasguKZa6GqGExqBBZMMOwZDb72LcwX1eE+rigSKfHzvMpcpdD1KAYVIgs1K3cIvxlnDafHWntmSRJGMTmH7JTDCpEFmp3XAqEAFr6u8Ffy2nz7Z2h+Wf7uWQUlehlroao4TCoEFmo7WeTAQB9y673QvYtKsgTPq4aZBeU4OClNLnLIWowDCpEFkinF8a+CP0i2OxDgFIhYVDr0tC6hbPUkh1hUCGyQH9dy0B6XjHcHFXoGOwpdzlkIQa15kUKyf4wqBBZoO3nyi5CGO4DByXfplSqZ5gPnNVKJGUV4ERZR2siW8cjIJEF2nmO/VOoPEcHJfqWNQX+fjJJ5mqIGgaDCpGFSc0pxPFrpd+W+7Zg/xQydU/bAADAhhM32PxDdqHaQeXrr7/G9OnT0blzZ2g0GkiShC+//LLS9bOysjBr1iwEBwdDo9EgJCQEL774InJycmpTN5HN2lXWibZNoDv83DksmUz1a+kHtUqBy2l5OJuULXc5RPWu2kHltddew7Jly5CQkICAgIC7rpubm4uYmBgsWrQILVu2xPPPP4+IiAj85z//Qf/+/VFQwCuBEt3J0D+lH5t9qAKuGhViys60bWTzD9mBageV5cuX4/Lly0hJScETTzxx13XfeecdHDt2DC+//DI2bdqEBQsWYNOmTXj55Zdx6NAhLFq0qMaFE9miEp3eeEalL4clUyXuaesPANh44obMlRDVv2oHlYEDByI4OLjK9YQQWL58OVxdXfH666+b3Pb666/D1dUVy5cvr+7miWza8WsZyMwvhtbJAVFBHnKXQxZqQKtGcFBKiEvOwYVkNv+Qbau3zrRxcXFITExEr1694OLiYnKbi4sLevXqhYsXL+Lq1at3fZzCwkJkZWWZ/BDZqu1nS8+mRLfwhYrDkqkSWicH9Cq7UOXGE2z+IdtWr0EFAMLDwyu83bDcsF5l5s+fD61Wa/wJCgqq20KJLMiO82XDkjnah6owrGz0D/upkK2rt6CSmVk6vFKr1VZ4u7u7u8l6lZk9ezYyMzONP1WdgSGyVsnZBTh5vfSMYQz7p1AVBrVuBKVCwukbWbicmit3OUT1xuLPLWs0Gri7u5v8ENmiHWWjfdo30cLHVSNzNWTpPF3U6BHqDYBnVci21VtQMZxJqeyMiaGvSWVnXIjszbYzpRea42y0ZK572pWO/vn9JEf/kO2qt6BSVR+UqvqwENmTgmIddp1PBfD3heeIqjK4tT8kCTh+LRPX0vPkLoeoXtRrUAkMDMTevXuRm2vafpqbm4u9e/eiWbNm7BxLBGB/fBryi3UI0DqiTSCbN8k8vm4adA3xAsBr/5DtqregIkkSpk6dipycHLz55psmt7355pvIycnBtGnT6mvzRFZlS1mzz4BWfpAkSeZqyJoYJ39jUCEbparuHZYvX449e/YAAE6cOGFctmPHDgBA7969MXXqVADASy+9hF9++QULFy5EbGwsOnbsiKNHj2Lz5s3o0qULZs6cWTd/BZEV0+uFsX/KwFZs9qHqGdo2AHN/PY0jCelIyiyAv5bXhyLbUu2gsmfPHqxcudJk2d69e7F3717j74ag4uLigp07d2Lu3LlYs2YNtm/fjoCAALzwwguYM2cOnJycalk+kfU7mZiJm1mFcFEr0SPMW+5yyMr4ax3RKdgTRxLSselUEib1DJG7JKI6JQkru054VlYWtFotMjMzOVSZbMJ7W87jw21xuKetPz55pJPc5ZAVWr77Iv7vtzPo1swL30/vIXc5RBWq6ee3xc+jQmTrtp5msw/VztCyfiqHLt9CSnahzNUQ1S0GFSIZXc/Ix+kbWVBIQL+WnD+FaqaJpzMim2ihF8CmU+xUS7aFQYVIRoZOtJ2CPeHlopa5GrJmw9qVXvvnt784+RvZFgYVIhltYbMP1ZF7y4LKwUtpSM4ukLkaorrDoEIkk8z8Yhy4mAYAGMCgQrUU5OWMqCAP6AWw8QSbf8h2MKgQyWT72WQU6wSa+7miuZ+r3OWQDRgeWXpW5dfjiTJXQlR3GFSIZLKx7EJyhplFiWrr3rKgcjghHYkZ+TJXQ1Q3GFSIZJBXVIKd51MAAEPaMKhQ3QjQOhmv/bPhBDvVkm1gUCGSwa7zKSgo1iPIy4kXIaQ6Nbx9WfMPR/+QjWBQIZKB4QJyQ9v48yKEVKfuaRsAhQQcv5qBq7fy5C6HqNYYVIgaWGGJDn+cSQbw94yiRHXF102D7qGl14z69S92qiXrx6BC1MD2xachu7AEfm4adAjylLscskHDIwMBAOuPs/mHrB+DClED+71sjoshbfyhULDZh+re0Lb+UCkknL6RhYspOXKXQ1QrDCpEDahEp8eWsmnzOSyZ6ouXixq9mvsAANazUy1ZOQYVogZ06HI6buUWwcPZAV2becldDtkwTv5GtoJBhagB/V42ydugVo2gUvLtR/VncBt/qJUKxCXn4FxSttzlENUYj5REDUSvF9h0qqzZpx2bfah+aZ0cEN3CFwCwnqN/yIoxqBA1kGPXMpCUVQBXjQo9w3zkLofswIj2fzf/CCFkroaoZhhUiBrIb2WdGvu39IOjg1LmasgeDGzVCE4OSlxOy8Pxa5lyl0NUIwwqRA1ArxfGoGLo5EhU31w0Kgxq3QgAsDb2uszVENUMgwpRAzickI6krAK4OaoQE+ErdzlkR0Z3KJv87a9ElOj0MldDVH0MKkQNwNCZcXBrf2hUbPahhtMn3Beezg5IzSnCvvg0ucshqjYGFaJ6VqLTY8OJ0mYfQ+dGoobioFTg3rLmxrXH2PxD1odBhaieHbx0C6k5RfB0djDOFkrUkEZHNQYAbDqZhPwinczVEFUPgwpRPTPMDDq0bQAcOMkbyaBTsCeaeDoht0iHrWWXcCCyFjxqEtWjohI9fj9VehHCERztQzKRJAkj25d2qv3lGCd/I+vCoEJUj/ZeSEVGXjF8XDXoFuotdzlkx0Z3KG3+2Xk+GRl5RTJXQ2Q+BhWievRr2Wife9v5Q6mQZK6G7FmLRm5oFeCOYp3Abyd4RWWyHgwqRPWkoFiHzWXX9hlRdtqdSE6jotj8Q9aHQYWonuw8n4KcwhIEah3Rsamn3OUQYWT7QEgS8OelW7iekS93OURmYVAhqieG0T73RgZAwWYfsgCBHk7oGuIFAFjHsypkJRhUiOpBXlEJtp1JBsBmH7Ishk61v3DyN7ISDCpE9WDbmWTkF+vQ1MsZ7Rpr5S6HyOietv5wUEo4m5SNs0lZcpdDVCUGFaJ6sK6s2Wd4ZAAkic0+ZDk8nNXoG+EHgJ1qyTowqBDVsfTcIuw4V9rsYzjNTmRJDFPqrzuWCL1eyFwN0d0xqBDVsfV/JaJYJ9Am0B0tGrnJXQ5ROQNa+cFNo8L1jHwcuMQrKpNlY1AhqmM/xZZ2UryPZ1PIQjk6KI1XVF5zhJ1qybIxqBDVoYspOYi9kgGFBIyM4mgfslxjOjUBAGw8eQO5hSUyV0NUOQYVojq0tuxsSp9wX/i5OcpcDVHlOgd7ItjbGXlFOvx+MknucogqxaBCVEeEEPi5bG6K+zuy2YcsmyRJuL9D6VmVNUevyVwNUeUYVIjqyOGEdFy9lQ9XjQqDW/vLXQ5RlQyBev/FNE6pTxaLQYWojvx0tPRsytC2/nBSK2WuhqhqQV7O6NbMC0L83WxJZGkYVIjqQEGxDr/9VTp51v0c7UNWxNCpds2RaxCCc6qQ5WFQIaoD288mI6ugBAFaR3QP9Za7HCKzDWsXACcHJS6m5iL2aobc5RCVw6BCVAcMc6eMimrMKyWTVXHVqDC0bWmfqjVH2KmWLA+DClEt3T5lPkf7kDUa07G0+efX44koKNbJXA2RKQYVolrilPlk7XqEeSNA64isghJsO5MsdzlEJhhUiGqJU+aTtVMqJOMFNDmnClkaBhWiWriQnI3YKxlQKiROmU9WzdD8s/N8CpKzC2SuhuhvDCpEtfD9oasAgP4t/ThlPlm15n6uiArygE4vjHMCEVkCBhWiGioq0WNN2QF9fJcgmashqj3D6/j7Q1c5pwpZDAYVohraduYmbuUWwc9Ng5gWvnKXQ1RrI9oHwkWtxKXUXBy4eEvucogAMKgQ1dh3Zc0+D3RuApWSbyWyfi4albGv1XeHrshcDVEpHl2JaiAxIx+74lIAAOM6s9mHbMf4Lk0BABtPJiEjr0jmaogYVIhq5MfD1yAE0CPUG8HeLnKXQ1RnIpto0SrAHUUlenaqJYvAoEJUTXq9wA+HS5t9HmQnWrIxkiThoa6lr+vvDl1hp1qSHYMKUTXtjU/F9Yx8uDv+fY0UIlsyKqoxHB0UOH8zhxcqJNkxqBBV0+oDpZ0MR3doDEcHpczVENU9rZMDhrULAAB8e5CdakleDCpE1XA9Ix+bTycBAB7pHixzNUT15+GupZ1qf/0rEZl5xTJXQ/aMQYWoGr45mAB9WSdaXoCQbFmnYE+0CnBHQbEePx65Knc5ZMcYVIjMVFiiw3d/lh6wJ/bg2RSybZIkGV/nqw4kQK9np1qSB4MKkZk2nLiBtNwiBGgdMah1I7nLIap3o6IC4eaoQkJaHnaWzRtE1NAYVIjM9NX+BAClbfeciZbsgbNaZZzQ8Kt9l+UthuwWj7ZEZjhxLROxVzLgoJQwvqyTIZE9MHQa33E+BQlpuTJXQ/aIQYXIDF/tvwwAGNYuAL5uGnmLIWpAzXxcENPCF0IAXx9IkLscskMMKkRVSM0pxLrjiQDYiZbsk+F1/8Pha8gv0slcDdmbBgkqISEhkCSpwp++ffs2RAlENfbVvssoLNGjfRMtOjb1lLscogbXN8IPQV5OyMwvxs+xvP4PNSxVQ21Iq9Vi5syZ5ZaHhIQ0VAlE1ZZXVIKvyk53T48JgyRJMldE1PCUCgmTeoTg/347g+V7LmJ8lyAoFHwvUMNosKDi4eGBuXPnNtTmiOrED4euIiOvGMHezhjShtf1Ifv1YJcgfLA1DhdTcrH9XDIGtOIQfWoY7KNCVIkSnR7L91wCAEztEwolv0GSHXNzdMBD3UpHvH22+6LM1ZA9abAzKoWFhfjyyy+RmJgId3d3dOnSBd26dTPrfoWFhcbfs7Ky6rNMIqMNJ5NwLT0f3i5qPNCpidzlEMlucs8QfLHnEg5cvIWT1zPRtrFW7pLIDjRYUElKSsKUKVNMlnXp0gXffvstwsLCKr3f/PnzMW/evPouj8iEEALLdsUDACb1DOFVkokABHo44d7IAPxyLBGf7b6ID8Z3kLsksgMN0vQzZcoUbNu2DTdv3kRubi5iY2MxYcIEHDp0CAMGDEB2dnal9509ezYyMzONP1ev8uJYVP/2xafh5PUsODkoMYFXSSYymtYnFACw/q8bSMzIl7kasgcNElTmzJmD/v37w8/PD87OzoiKisJXX32FCRMmICEhAZ999lml99VoNHB3dzf5Iapvi/+IAwCM69wEni5qmashshxtG2vRPdQLOr3Al5xWnxqArJ1pp0+fDgDYu3evnGUQmdgfn4YDF29BrVTgib6VN0sS2SvDWZVvDl5BZl6xzNWQrZM1qPj4+AAAcnN5/QiyDEIILNp6HgAwvmsQArROMldEZHn6RfghopEbcgpLsLLs8hJE9UXWoHLw4EEAnPSNLMf++DT8ean0bMqTPJtCVCGFQsKM/s0BAF/svYTcwhKZKyJbVu9B5ezZs8jLy6tw+csvvwwAePjhh+u7DKIq3X425SGeTSG6q3vbBaCZjwsy8oqx+iAvVkj1p96DynfffQd/f38MHz4cM2bMwEsvvYTRo0cjMjISSUlJmD17NqKjo+u7DKIq7YpLxaHL6VCrFHiyb3O5yyGyaEqFhCdjSs86frb7EgqKebFCqh/1Po9Kv379cObMGcTGxmL37t3Iy8uDj48Phg0bhqeeegqDBw+u7xKIqqTXCyzYeBYAMKF7MPy1jjJXRGT5RndojA+2xeF6Rj5+PHwVE3qEyF0S2aB6DyoxMTGIiYmp780Q1craY9dx5kYW3BxVeLofz6YQmUOtUmB6TCj+/cspLN15EeO7NoWDkldmobrFVxTZvYJiHf67ubRvypN9wzhvClE1jOscBB9XDa5n5OPn2Otyl0M2iEGF7N7XBxJwPSMf/u6OeLRXM7nLIbIqjg5KPB5d+r75ZEc8dHohc0VkaxhUyK6l5RTiw22ls9DOGtSC1/QhqoF/dAuGh7MDLqXm4tfjiXKXQzaGQYXs2n+3nEdWQQlaB7hjDK+QTFQjLhqVcbbaD7fFoUSnl7kisiUMKmS3TiVm4ts/rwAA5o5sA6VCkrkiIus1qWcIPJ0dcDE1F+t4VoXqEIMK2SUhBOatOw0hgOGRAejazEvukoismqtGhcejS+dV+YBnVagOMaiQXVr/1w38efkWHB0UeHVYK7nLIbIJE3sEw9tFjYS0PPzEEUBURxhUyO7kF+kwf8MZAMCTMc0R6MGp8onqgotGhSfKZqv9cFscinlWheoAgwrZncV/xCExswCNPZwwPSZU7nKIbMoj3YPh46rBtfR8/O/INbnLIRvAoEJ25VxSNpbtuggAmDOiNYcjE9UxJ7XSeOXxJX9cQFEJz6pQ7TCokN3Q6wX+9fMJlOgFBrduhMFt/OUuicgm/aNbU/i5lc5W+/3hq3KXQ1aOQYXsxveHr+JwQjpc1ErMHdlG7nKIbJajgxIzyq6ZtXhbHPKLeGVlqjkGFbILKdmFxg60LwyOYAdaono2vmsQmng6ITm7ECv2XZK7HLJiDCpkF/7vt9PIKihB28bumNQzRO5yiGyeRqXErEEtAABLd8QjM69Y5orIWjGokM3bduYmfjmWCIUEvH1fO85AS9RARkU1Rkt/N2QVlODjnRfkLoesFIMK2bSsgmL86+eTAICpfUIR2cRD3oKI7IhSIeHFIREAgC/3XsaNzHyZKyJrxKBCNm3+hjNIyipAiLcznh/YQu5yiOxO/5Z+6BLiicISPT7YGid3OWSFGFTIZu27kIpv/ywdGrlwTCSc1JwzhaihSZKEV+5pCQD44fBVXEjOkbkisjYMKmST8opK8PJPfwEAHuneFN1CvWWuiMh+dQr2wsBWjaAXwH83n5O7HLIyDCpkkxZsPIurt/IRqHXEy0Nbyl0Okd17cUgEJAnYeDIJx65myF0OWREGFbI5u86n4Kv9CQCABWMi4eboIHNFRBTh74b7OzQBACzceBZCCJkrImvBoEI2JTOvGC/9r7TJZ2KPYES38JW5IiIyeH5QONRKBfZfTMPuuFS5yyErwaBCNuXf604iKasAzXxcjB34iMgyNPF0xoQewQCAhb+fhV7PsypUNQYVshk/x14zTuz23rj2cFar5C6JiO4wo19zuGpUOJWYhfUnbshdDlkBBhWyCQlpuXitbGK3ZweEo0NTT5krIqKKeLmo8Xh0KIDSEUBFJXqZKyJLx6BCVq+oRI9nv41FbpEOXUO88HTZVVuJyDI91rsZfFw1SEjLw/eHr8pdDlk4BhWyeu9tOY/j1zLh7qjCovFRUCn5siayZC4aFZ4dUPqF4sNtccgrKpG5IrJkPKKTVdsTl4pPd8UDKJ19trGHk8wVEZE5xndpiqZezkjJLsRnuy7JXQ5ZMAYVslqJGfl47rtYCAE81DUI97QLkLskIjKTWqUwXrBw6c54JGUWyFwRWSoGFbJKhSU6PLn6KNJyi9A6wB1zRrSRuyQiqqbhkQHoHOyJ/GId3vn9rNzlkIViUCGr9Mavp3H8aga0Tg74dEInODrwgoNE1kaSJPx7RGsAwE+x1xF7JV3misgSMaiQ1fn6QAJWH7wCSQLeHx+FIC9nuUsiohqKbOKBsZ1Kp9Z/Y/1pTq1P5TCokFXZeT4Fc9adAgD8c3AE+kX4yVwREdXWi0Mi4KxWIvZKBn45lih3OWRhGFTIapxNysKM1Ueh0wuM6dgET/UNk7skIqoDjdwdMaNs/qMFG89yuDKZYFAhq3D1Vh6mrDiEnMISdA/1wvz720GSJLnLIqI68ljvZmji6YSkrAJ8uvOi3OWQBWFQIYuXnFWARz4/iBuZBWju54qlj3SCWsWXLpEtcXRQ4tVhrQCUDldOSMuVuSKyFDzak0W7lVuERz4/iIS0PDT1csbqqd3g4ayWuywiqgf3tPVH7+Y+KCzR47W1J9mxlgAwqJAFS8oswIOf7sf5mzlo5K7B6qnd0MjdUe6yiKieSJKEN0e3hVqlwO64VPz6F6+uTAwqZKEup+Zi7NJ9iEvOgb+7I1ZP7c5hyER2oJmPi/HCom/8ehqZ+cUyV0RyY1AhixN7JR1jl+7HtfR8hHg7439P9kBzP1e5yyKiBjI9JhShvi5IzSnE/A1n5C6HZMagQhbll2PX8eCyA0jNKUSrAHf8+ERPNPHkmRQie6JRKbHg/khIEvDdoavYfi5Z7pJIRgwqZBFKdHos2HgWz313DEUlegxs1Qg/PtEDvm4auUsjIhl0beaFKT2bAQBeWfMXMvPYBGSvGFRIdokZ+Ri/7ACW7owHADwRE4ZPJ3SCq0Ylc2VEJKeXhkYg1McFN7MKMffXU3KXQzJhUCFZbTxxA8M+3I3DCelw06iw5OEOeOWellAqOJkbkb1zdFDiP+PaQyEBP8dex7rjnF7fHjGokCwy84ox87tYPLn6KDLyihHZRIvfnu2D4ZGBcpdGRBakY1NP4yig2Wv+wqVUTgRnbxhUqMHtPJ+CIe/vwtpjiVBIwNP9muN/T/REU292miWi8p4dEI6uzbyQW6TDU6uPoqBYJ3dJ1IAYVKjB5BWV4LW1JzDpiz+RlFWAUB8XrHmyJ/45JIJT4hNRpVRKBT4c3wFeLmqcuZGFeb+e5qy1doSfDtQgjiTcwj0f7MbXB64AACb3DMFvz/ZBh6aeMldGRNbAX+uI98a1hyQB3/55BV/svSx3SdRAGFSoXhWW6LDw97N4YOl+JKTlIUDriNVTu2HuyDZwUivlLo+IrEjfCD/MvqclAOD/fjuNzaeSZK6IGgKDCtWbMzeyMGrJXnyyIx56AdzfsTF+nxmNXs195C6NiKzUtD6heKhrUwgBPPfdMRy7miF3SVTPGFSozun1Ap/ujMeoJXtxNikbXi5qLH2kI94bFwWtk4Pc5RGRFZMkCW+MaoM+4T7IL9ZhwucHEXslXe6yqB4xqFCdSs4uwMQv/sT8jWdRpNNjYCs/bJoZjaFtA+QujYhshINSgU8e6YSuIV7ILijBxM//xFGGFZvFoEJ1Zuf5FAz7YDf2XEiFo4MCC+5vh88mduY0+ERU51w1KqyY0gVdm3khu7AEE5YfxLYzN+Uui+oBgwrVWlGJHvM3nMGkL/5Eak4RWvq7Yf0zvTG+a1NIEmeYJaL64aJR4cspXdCruTdyi3SY+tVhLNsVz6HLNoZBhWrlSloeHli6D5/uuggAmNA9GGtn9EJzPzeZKyMie+CsVmHF5K7GDrZvbziLZ787xosY2hBJWFn0zMrKglarRWZmJtzd3eUux66tO56If/10AtmFJXB3VOGdse0xtK2/3GURkR0SQmDlvst4Y/1p6AXQyF2DBWMi0S/CT+7SqExNP78ZVKja8opKMG/daXx/+CoAoHOwJ94fH4UmnpwCn4jkdfRKOv7543FcTCm9JtDQNv54+Z6WaObjInNlxKBCDeLEtUzM/D4W8Sm5kMqu0/PcgHColGxFJCLLUFCsw7ubzmHF3kvQC0ClkPBwt6Z4PDqUX6hkxKBC9UqnF1i6Mx6LtpxHiV7Az02D9x+MQk9O3kZEFur8zWzM33AG28+lAACUCgkjIgMwqWcIooI82Nm/gTGoUL25eisPs344hkOXS+cpGNrGH/PvbwdPF7XMlRERVW3fhVR8vCMeey6kGpe1aOSKcZ2DMLpDY/i4cgqFhsCgQnVOrxdY/ecVLNx4FjmFJXBRKzF3ZBuM7dSE30SIyOqcvJ6Jz/dcwoYTN1BYogdQ2izUr6UfhkcGYECrRnDVqGSu0nYxqFCdupCcjVfWnMDhhNKzKJ2CPbFoXBSaerN9l4isW2Z+MX49nogfD1/F8WuZxuUalQJ9I3xxb2QgBrT0gwtDS51iUKE6kVtYgk93xmPpzoso0unhrFbixSERmNgjBEoFz6IQkW05l5SNdcev47e/buByWp5xuUalQJ9wXwxp0wgDWjWCF5u6a41BhWqlRKfH/45cw3+3nEdKdiEAoH9LP7w5ui0aezjJXB0RUf0SQuD0jSz89tcNbDhhGloUEtC1mReGtPHH4Db+PCbWEIMK1Uh+kQ4/HrmKZbsu4lp6PgAg2NsZrwxtiaFt/dkXhYjsjhACZ25kY9OpJGw+fRNnbmSZ3N62sTuGtC4NLS0aufI4aSYGFaqWq7fy8L8j1/D1gQSk5RYBALxd1Hiybxgm9giBWsV5UYiIgNJLhWw+nYTNp27iUMIt3P6pGeLtjCFt/NG/pR86NPXksfMuLD6oHDp0CHPmzMG+fftQXFyMdu3aYdasWRg3bly1HodBpebScgqx5fRN/HT0Ov68fMu4vImnE6ZHh+KBzkFwdFDKWCERkWVLzSnEtjM3senUTey5kIqistFDAOCsVqJbMy/0au6DnmE+iPB3Y9++21h0UNm+fTuGDBkCR0dHjB8/Hm5ublizZg0SEhLwn//8By+88ILZj8WgYr6cwhIcv5qBw5fTseN8Mo5dzTB+E5AkoHdzHzzQOQjD2vpzZlkiomrKKSzBznMp2Hw6CXviUo1npw2c1Uq0baxFVJAH2jfxQItGrmjq7QyNyvK+EBbr9Mgr0qGwRAedXsDPzbHOQ5bFBpWSkhK0bNkS165dw4EDBxAVFQUAyMzMRNeuXXH58mWcP38ewcHBZj0eg0p5+UU6JGbm42JKLi4k5+BCcg7O3MjC2aQs6O94dlsHuGNkVCBGRzWGv9ZRnoKJiGyMXi9w7mY29l5Ixe64VBy+fAu5Rbpy60kS0NjDCU29nOHjqin9cVPDx1UDZ7USaqUCapUCGpUSapUCCgnQi9J+M3oB6IWAXgiIsv8XlehRUKxHQbEOBSU64/8Li3UoKNEjv0iH/GId8opKkFekQ16RDvlFOuQWlSD/tt+LdHqTOg+/NrDOJ8Kr6ed3vQ8S/+OPPxAfH48pU6YYQwoAaLVavPrqq5g8eTJWrlyJf//73/Vdyl2t2n8ZBcV6qJQSVEoFHBQSHJQKqJQS1EoFNA6lLxzHO/7VqBTQOJT9q1LUuFOVXi9QUKIzvqgM/+YUliAjr7j0J7+o7P9FuJVbhMSMAtzIzEf6XS5n3tjDCR2DPdE91Av9W/ohQMve6kREdU2hkNAqwB2tAtwxtU8odHqBiyk5OHY1A8evZeCva5m4mJKLnMISXEvPNw5esDSSVDoJnu7Ob7kyqvegsmPHDgDA4MGDy902ZMgQAMDOnTsrvX9hYSEKCwuNv2dlZVW6bm18+McF47Dc2jAEFkcHpTHcqBQS9EKgRC+g15f+qyv7KdbpkV9cmoJrw1mtRIi3C5r7uaK5nytaNHJFVJAnz5oQEclAqZAQ3sgN4Y3c8EDnIAClZ0VSc4pwOS0X19LzkJpdhNScQqTmFCEtt9B4ZqOopPSnsEQPAQGFJEEhSZAklP2/9F8AUJd93jg6KOFo/L/pMie1Cs5qJZzUSjirlXBRq4z/L/35+3a1suZfuOtLvQeVuLg4AEB4eHi52/z9/eHq6mpcpyLz58/HvHnz6q0+g3vbBSAzvxjFOj1KdKUBolgvUKLTo1hX+oIpLNajoERX7t/bG88Ky15cWQUlNa7F0UEBJwclnByUcNao4OnsAK2TGp7ODvBwdoCHsxqezmoEaB0R4OGIAK0T3B1VFvfiIiKiv0mSBF83DXzdNOgS4iV3OVaj3vuoDB48GFu2bEFcXByaN29e7vbGjRsjJycHmZmZFdy74jMqQUFBFtNHRQiBYp1AYVnb4O3/FpaUthWW6ARUCgnK235UCgUUCsBBWRZKypKto0oJBXuJExGRjbHYPiq1pdFooNFY7pUtJUmCWiVBrVLAja0sREREdarex6RqtVoAqPSMiSFhEREREd2p3oOKoW9KRf1QkpKSkJOTU2H/FSIiIqJ6DyoxMTEAgM2bN5e7bdOmTSbrEBEREd2uQSZ8i4iIwPXr1yud8O3cuXMICQkx6/E44RsREZH1sdjOtCqVCsuXL8eQIUMQHR1d4RT65oYUIiIisi8NMuqnX79+2LNnD+bMmYPvv//eeFHChQsX4sEHH2yIEoiIiMgKNdjVk+sKm36IiIisT00/v3nJXCIiIrJYDCpERERksRhUiIiIyGIxqBAREZHFYlAhIiIii8WgQkRERBbL4q+efCfDaOqsrCyZKyEiIiJzGT63qzsritUFlezsbABAUFCQzJUQERFRdWVnZ0Or1Zq9vtVN+KbX65GYmAg3NzdIklTu9qysLAQFBeHq1aucEM4M3F/Vw/1VPdxf1cP9VT3cX9Uj9/4SQiA7OxuBgYFQKMzveWJ1Z1QUCgWaNGlS5Xru7u584VYD91f1cH9VD/dX9XB/VQ/3V/XIub+qcybFgJ1piYiIyGIxqBAREZHFsrmgotFoMGfOHGg0GrlLsQrcX9XD/VU93F/Vw/1VPdxf1WOt+8vqOtMSERGR/bC5MypERERkOxhUiIiIyGIxqBAREZHFYlAhIiIii8WgQkRERBbLooJKVlYWZs2aheDgYGg0GoSEhODFF19ETk6O2Y8RFxeHt99+G9HR0QgMDIRarUZQUBAmTpyIs2fPVnifyZMnQ5KkSn8qs3r1anTt2hUuLi7w9PTE8OHDcfTo0Wr/3TUlx/7Kzc3F119/jXHjxqFFixZwcnKCh4cHYmJi8O2331a4jR07dtx1/3755Zc13QXVItfrCwBu3LiBxx57DAEBAXB0dERERATeeustFBcXV7h+YWEh3njjDYSHh8PR0RGBgYF4/PHHkZycXO2/u6bqYn8BwOLFizFlyhRERkZCpVJBkiTs2LGjwnUvX75819eKJElQKpUm97Gl1xdQvf0F2PfxC6je/rL34xdQ/dcXIP/xy2KGJ+fm5qJ37944duwYBg8ejA4dOiA2NhabN29Gly5dsGvXLjg6Olb5OOPHj8f333+Ptm3bonfv3nB3d8eJEyewceNGODk54ffff0d0dLTJfSZPnoyVK1fiueeeg4eHR7nHnDt3brllb731Fl577TUEBwdjzJgxyM7OxnfffYeioiJs27YNvXr1qumuMItc++v333/HPffcA29vbwwYMAChoaFITk7GTz/9hIyMDMyYMQNLliwx2caOHTvQr18/xMTEoG/fvuVqGD16NKKiomq7S+5KztdXUlISunbtimvXruG+++5DeHg4du7ciQMHDmDkyJFYu3atyQeKXq/HsGHDsGnTJnTv3h0xMTGIi4vDzz//jGbNmuHAgQPw9fWt8310u7raXwCMf1tAQACEEEhKSsL27dsrfC1kZGTg/fffr/BxDh8+jN9++w333HMPNmzYYFxuS68voHr7C7Dv4xdQvf1l78cvoPqvL4s4fgkL8e9//1sAEC+//LLJ8pdfflkAEG+//bZZj7NixQpx9OjRcsu//fZbAUC0bt263G2TJk0SAMSlS5fM2sb58+eFSqUSLVq0EBkZGcblsbGxQqPRiFatWgmdTmfWY9WUXPsrNjZWrFq1ShQWFposT0pKEsHBwQKAOHjwoMlt27dvFwDEnDlzzKqpPsj5+po4caIAID755BPjMr1eL8aPHy8AiG+++cZk/S+++EIAEA899JDQ6/XG5Z988okAIB5//HGzaq2NutpfQgixfv16cePGDSGEENOnTxcAxPbt26td0/DhwwUAsWbNGpPltvT6EqL6+8uej19CVG9/2fvxS4jqv74s4fhlEUFFr9eLwMBA4erqKnJyckxuy8nJEa6uriI0NLTW22nRooUAIFJSUkyWV/eNPnv2bAFArFy5stxtkydPFgDEzp07a11vZeTeX5V5++23BQDx7rvvmiyX+40u5/7KysoSGo1GhIaGmrxphRDi8uXLAoDo16+fyfIePXoIAOLy5cvl/o7Q0FDh4uIi8vLyal1vZepzf9U0qFy/fl0olUrh5+cnioqKTG6z5ddXfQQVWz5+1SYI2+Pxq6r9ZSnHL4vooxIXF4fExET06tULLi4uJre5uLigV69euHjxIq5evVqr7Tg4OAAAVKqKLxq9fv16zJ8/H++99x42btyIoqKiCtcztOcNHjy43G1DhgwBAOzcubNWtd6Npeyv6q4fFxeH999/H/Pnz8eqVatw/fr1WtVnLjn31/79+1FYWIhBgwaV6y8QHByMiIgI7N27FzqdDgBQUFCAgwcPIiIiAsHBwSbrS5KEQYMGITc3F4cPH65VrXfTUPurOr788kvodDpMnDjRuJ/vZOuvr6rw+FU79n78qoilHL8sJqgAQHh4eIW3G5Yb1quJP//8E6dOnUKXLl0qbMcFgGeeeQavvvoqXnjhBQwbNgwhISHYtGlThfW6urrC39+/XmqtiqXsr9vpdDp89dVXkCQJAwcOrHCdb775Bs8//zxeffVVTJw4ESEhIZg1a5bxRV5f5Nxf5my7qKgICQkJAID4+Hjo9fp6rbUqDbG/qkMIgS+++AIAMHXq1ErXs+XXlzl4/Ko5ez9+1WbbDXH8soigkpmZCQDQarUV3u7u7m6yXk0ef9KkSVAoFHjnnXfK3R4dHY0ffvgBV65cQX5+PuLi4vDGG28gIyMDI0eOLJf+MjMz661Wc8i9vyry+uuv48SJE5gyZQratm1rcpuvry8WLFiAkydPIicnBzdv3sTatWvRvHlzLFq0CC+99FKN6jSXnPurutuu71rNYQk13G7nzp2Ij49H7969ERERUe52W399VYXHr9qz5+NXXW67vmo175y+mV544QUUFhaavf5zzz1XafKqK/n5+bjvvvtw9uxZvPXWWxX2bH700UdNfm/evDlef/11NG7cGI899hjeeOMNrFu3rs5rs9b9daelS5di/vz56NChAz744INyt7dp0wZt2rQx/u7i4oJRo0ahW7duiIyMxIcffoiXX34Zfn5+d92OreyvhmKJ+6smPv/8cwDAY489VuHttvz6MgePX7Vjz8cva1GnQeXTTz9Fbm6u2euPHTsW4eHhxvRVWcrKysoCUHlKq0xBQQFGjRqF7du3Y/bs2Xj11Verdf9JkyZhxowZ2Lt3r8lyrVZbJ7Xawv5avnw5nnrqKbRr1w5btmyBq6ur2dvz9/fHqFGjsHz5chw8eBAjRoy46/rWuL+qu+26rNXS9ldNZGZmYs2aNXB3d8e4ceOqdV9rf33Vlr0dv2rCno9f5pDz+HW7Og0q1Z14xqCqdquq2skqkp+fj1GjRmHLli146aWX8Pbbb1e7LqVSCQ8PD6Snp5erd//+/UhKSirXzludWq19f3322WeYPn06WrdujW3btsHb29vs7Rn4+PgAgFlvYGvcX+ZsW61Wo2nTpgCA0NBQKBSKOqnVkvZXTa1evRr5+fmYOHEinJ2dq31/a3191QV7On7VhD0fv+py2/V1/DJRrTFC9cSc4VfNmjUz+/Hy8vLEoEGDBADxz3/+s8Z1JSQkCAAiIiLCZPkrr7xi8cP76nt/LVu2TEiSJFq3bi1u3rxZrfpv169fPwFA7N+/v8aPURU591dmZqZQq9XVGt7XvXt3ix+eXJ39dbvqDh/t2LGjACAOHTpUo+1Z4+vrdrUZbmsvx6/bmbu/7Pn4dbuq9pelHL8sIqgIUf0JbXJzc8WZM2dEQkKCyfL8/Hzjh8isWbOq3O6NGzfEtWvXyi1PT083vgjnzZtnctu5c+esbsKkutpfQgjx2WefCUmSRKtWrURSUlKV6x8+fLjC5e+//74AIMLDw0VJSYlZ264pOfdXZRMmPfTQQzYz4Vtl++tO1fngjY2NFQBEZGTkXdezpdfXnaraX/Z+/LqTOa8vez5+3ak2E7415PHLoqbQ79WrF44fP47BgwejY8eOOHr0qHGK4J07d8LJycm4/u3TGt9+nQLDdNL+/v6YPn16hduaPHkyQkJCjI8zaNAg9OzZE+Hh4fD19cXVq1fx+++/Iy0tDf3798dvv/1WbnpiS5iCWo799ccff2DgwIEQQmD69OkVDnGMiorC6NGjjb+HhITAwcEBnTt3RpMmTZCbm4sDBw4gNjYWHh4e2LRpE7p27Von+6Uycu0voPQ6Gd26dcO1a9dw//33o3nz5sYpqEeMGIFffvmlyimoL1y4gJ9++gkhISE4ePBgg0yhXxf7CwAWLFhgvA7S/v37cf78eQwZMsT42hk9erTJ68XgmWeewZIlS/Dhhx/imWeeqbRWW3p9AdXbX/Z+/AKqt7/s/fgFVP/9aBHHr2pHm3qUkZEhZs6cKYKCgoSDg4No2rSpeOGFF0RWVla5dQ2zBcbExJgsj4mJEQDu+nN7erxy5YqYOnWqaN++vfD29hYqlUp4eHiI6OhosXTp0rsm5a+//lp07txZODk5Ca1WK4YNGyaOHDlSV7ujSnLsrxUrVlS5/qRJk0y2sWDBAtGvXz8RGBgoNBqNcHJyEi1bthQzZ84UV69erYc9UzE59pdBYmKiePTRR0WjRo2EWq0W4eHh4s033yw3lbdBQUGBmDt3rggLCxNqtVr4+/uLqVOnmvUNsK7Uxf4Soup9VtGMn/n5+cLT01M4OjqKW7du3bVOW3p9CVG9/WXvxy8hqre/7P34JUTN3o9yH78s5owKERER0Z0sYsI3IiIiooowqBAREZHFYlAhIiIii8WgQkRERBaLQYWIiIgsFoMKERERWSwGFSIiIrJYDCpERERksRhUiIiIyGIxqBAREZHFYlAhIiIii8WgQkRERBbr/wH0fzk/nXInhgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_kde(m2_trace.posterior['alpha_africa'].to_numpy() - m2_trace.posterior['alpha_nonafrica'].to_numpy())\n",
    "plt.title('Contrast between intercepts for Africa and Non Africa')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e11cb08",
   "metadata": {},
   "source": [
    "This means that given a country with average rudgeness, if the country\n",
    "is African it will have lower GDP."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c60558b",
   "metadata": {},
   "source": [
    "So just adding the African alpha parameter allows for a little better\n",
    "prediction just because of the average behavior, only one slope, so there's little\n",
    "descriptive power just yet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "071c3990",
   "metadata": {},
   "outputs": [],
   "source": [
    "with pm.Model() as m3:\n",
    "    alphas = [pm.Normal('alpha_africa', 1, 0.1), pm.Normal('alpha_nonafrica', 1, 0.2)]\n",
    "    betas = [pm.Normal('beta_africa', 0, 0.3), pm.Normal('beta_nonafrica', 0, 0.3)]\n",
    "    alpha = alphas[0] * rugged.cont_africa + alphas[1] * (1-rugged.cont_africa)\n",
    "    beta = betas[0] * rugged.cont_africa + betas[1] * (1-rugged.cont_africa)\n",
    "    mu = pm.Deterministic('mu', alpha + beta * rugged.rugged_standarized)\n",
    "    sigma = pm.Exponential('sigma',1)\n",
    "    y = pm.Normal('y', mu=mu, sigma=sigma, observed=rugged.log_gdp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "32c77204",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [alpha_africa, alpha_nonafrica, beta_africa, beta_nonafrica, sigma]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [8000/8000 00:04&lt;00:00 Sampling 4 chains, 0 divergences]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 22 seconds.\n"
     ]
    }
   ],
   "source": [
    "with m3:\n",
    "    m3_trace = pm.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "b02197ae",
   "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>rank</th>\n",
       "      <th>loo</th>\n",
       "      <th>p_loo</th>\n",
       "      <th>d_loo</th>\n",
       "      <th>weight</th>\n",
       "      <th>se</th>\n",
       "      <th>dse</th>\n",
       "      <th>warning</th>\n",
       "      <th>loo_scale</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>m3</th>\n",
       "      <td>0</td>\n",
       "      <td>129.727836</td>\n",
       "      <td>4.850946</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.874558</td>\n",
       "      <td>7.304150</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>log</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>m2</th>\n",
       "      <td>1</td>\n",
       "      <td>126.195308</td>\n",
       "      <td>4.092346</td>\n",
       "      <td>3.532528</td>\n",
       "      <td>0.125442</td>\n",
       "      <td>7.407369</td>\n",
       "      <td>3.251605</td>\n",
       "      <td>False</td>\n",
       "      <td>log</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>m1</th>\n",
       "      <td>2</td>\n",
       "      <td>94.438598</td>\n",
       "      <td>2.564519</td>\n",
       "      <td>35.289237</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.472848</td>\n",
       "      <td>7.443459</td>\n",
       "      <td>False</td>\n",
       "      <td>log</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    rank         loo     p_loo      d_loo    weight        se       dse  \\\n",
       "m3     0  129.727836  4.850946   0.000000  0.874558  7.304150  0.000000   \n",
       "m2     1  126.195308  4.092346   3.532528  0.125442  7.407369  3.251605   \n",
       "m1     2   94.438598  2.564519  35.289237  0.000000  6.472848  7.443459   \n",
       "\n",
       "    warning loo_scale  \n",
       "m3    False       log  \n",
       "m2    False       log  \n",
       "m1    False       log  "
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.compare({'m1':m1bis_trace, 'm2':m2_trace,\n",
    "           'm3':m3_trace})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a6e33bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "So the interaction adds predictive power. The dse between m3 and m1 is as big as the difference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "d7d60afd",
   "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_africa</th>\n",
       "      <td>0.887</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.856</td>\n",
       "      <td>0.916</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>6417.0</td>\n",
       "      <td>3221.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha_nonafrica</th>\n",
       "      <td>1.051</td>\n",
       "      <td>0.010</td>\n",
       "      <td>1.030</td>\n",
       "      <td>1.068</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5527.0</td>\n",
       "      <td>3507.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta_africa</th>\n",
       "      <td>0.133</td>\n",
       "      <td>0.075</td>\n",
       "      <td>-0.007</td>\n",
       "      <td>0.272</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>5990.0</td>\n",
       "      <td>3366.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta_nonafrica</th>\n",
       "      <td>-0.143</td>\n",
       "      <td>0.055</td>\n",
       "      <td>-0.239</td>\n",
       "      <td>-0.037</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>6013.0</td>\n",
       "      <td>3581.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigma</th>\n",
       "      <td>0.112</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.124</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>6289.0</td>\n",
       "      <td>3576.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mu[165]</th>\n",
       "      <td>1.043</td>\n",
       "      <td>0.010</td>\n",
       "      <td>1.024</td>\n",
       "      <td>1.062</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5787.0</td>\n",
       "      <td>3375.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mu[166]</th>\n",
       "      <td>1.028</td>\n",
       "      <td>0.013</td>\n",
       "      <td>1.004</td>\n",
       "      <td>1.053</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>6109.0</td>\n",
       "      <td>3333.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mu[167]</th>\n",
       "      <td>0.896</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.864</td>\n",
       "      <td>0.930</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5874.0</td>\n",
       "      <td>3154.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mu[168]</th>\n",
       "      <td>0.870</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.838</td>\n",
       "      <td>0.903</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>7512.0</td>\n",
       "      <td>3473.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mu[169]</th>\n",
       "      <td>0.884</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.854</td>\n",
       "      <td>0.913</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>6658.0</td>\n",
       "      <td>3361.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>175 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  \\\n",
       "alpha_africa     0.887  0.016   0.856    0.916      0.000    0.000    6417.0   \n",
       "alpha_nonafrica  1.051  0.010   1.030    1.068      0.000    0.000    5527.0   \n",
       "beta_africa      0.133  0.075  -0.007    0.272      0.001    0.001    5990.0   \n",
       "beta_nonafrica  -0.143  0.055  -0.239   -0.037      0.001    0.001    6013.0   \n",
       "sigma            0.112  0.006   0.100    0.124      0.000    0.000    6289.0   \n",
       "...                ...    ...     ...      ...        ...      ...       ...   \n",
       "mu[165]          1.043  0.010   1.024    1.062      0.000    0.000    5787.0   \n",
       "mu[166]          1.028  0.013   1.004    1.053      0.000    0.000    6109.0   \n",
       "mu[167]          0.896  0.018   0.864    0.930      0.000    0.000    5874.0   \n",
       "mu[168]          0.870  0.017   0.838    0.903      0.000    0.000    7512.0   \n",
       "mu[169]          0.884  0.016   0.854    0.913      0.000    0.000    6658.0   \n",
       "\n",
       "                 ess_tail  r_hat  \n",
       "alpha_africa       3221.0    1.0  \n",
       "alpha_nonafrica    3507.0    1.0  \n",
       "beta_africa        3366.0    1.0  \n",
       "beta_nonafrica     3581.0    1.0  \n",
       "sigma              3576.0    1.0  \n",
       "...                   ...    ...  \n",
       "mu[165]            3375.0    1.0  \n",
       "mu[166]            3333.0    1.0  \n",
       "mu[167]            3154.0    1.0  \n",
       "mu[168]            3473.0    1.0  \n",
       "mu[169]            3361.0    1.0  \n",
       "\n",
       "[175 rows x 9 columns]"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(m3_trace)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "61d3469d",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'm_8_3_posterior' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/hy/9qct0wrx5pl34rzz153ys6sw0000gp/T/ipykernel_69191/2439247771.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;31m# extract posterior samples of parameters\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0ma0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mm_8_3_posterior\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"a\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m \u001b[0ma1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mm_8_3_posterior\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"a\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0mb0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mm_8_3_posterior\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"b\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'm_8_3_posterior' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, axs = plt.subplots(\n",
    "    1,\n",
    "    2,\n",
    "    figsize=[8, 4],\n",
    "    sharey=True,\n",
    "    constrained_layout=True,\n",
    ")\n",
    "\n",
    "ax1, ax0 = axs\n",
    "\n",
    "# extract posterior samples of parameters\n",
    "posterior = m3_trace.posterior\n",
    "a0 = posterior[\"alpha\"][0, :].to_numpy()\n",
    "a1 = posterior[\"alpha\"][1, :].to_numpy()\n",
    "b0 = posterior[\"beta\"][0, :].to_numpy()\n",
    "b1 = posterior[\"beta\"][1, :].to_numpy()\n",
    "\n",
    "rugged_plot = np.linspace(-0.1, 1.1)\n",
    "\n",
    "ax0.scatter(\n",
    "    dd.loc[cid == 0, \"rugged_std\"],\n",
    "    dd.loc[cid == F0, \"log_gdp_std\"],\n",
    "    label=\"Not Africa\",\n",
    "    facecolor=\"w\",\n",
    "    lw=1,\n",
    "    edgecolor=\"k\",\n",
    ")\n",
    "# calculating predicted manually because this is a pain with categorical variabiles in PyMC\n",
    "pred0 = a0 + rugged_plot.reshape(-1, 1) * b0\n",
    "ax0.plot(rugged_plot, pred0.mean(1), color=\"grey\")\n",
    "az.plot_hdi(rugged_plot, pred0.T, color=\"grey\", hdi_prob=0.97, ax=ax0)\n",
    "ax0.set_title(\"Non-African Nations\")\n",
    "\n",
    "ax1.scatter(\n",
    "    dd.loc[cid == 1, \"rugged_std\"],\n",
    "    dd.loc[cid == 1, \"log_gdp_std\"],\n",
    "    label=\"Africa\",\n",
    "    color=\"b\",\n",
    ")\n",
    "# calculating predicted manually because this is a pain with categorical variabiles in PyMC\n",
    "pred1 = a1 + rugged_plot.reshape(-1, 1) * b1\n",
    "ax1.plot(rugged_plot, pred1.mean(1), color=\"k\")\n",
    "az.plot_hdi(\n",
    "    rugged_plot,\n",
    "    pred1.T,\n",
    "    color=\"blue\",\n",
    "    hdi_prob=0.97,\n",
    "    ax=ax1,\n",
    "    fill_kwargs={\"alpha\": 0.2},\n",
    ")\n",
    "ax1.set_title(\"African Nations\")\n",
    "\n",
    "\n",
    "ax.set_xlim(-0.1, 1.1)\n",
    "ax0.set_xlabel(\"ruggedness (standardised)\")\n",
    "ax1.set_xlabel(\"ruggedness (standardised)\")\n",
    "ax0.set_ylabel(\"\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "e1d79d71",
   "metadata": {},
   "outputs": [],
   "source": [
    "tulips = pd.read_csv('./data/tulips.csv', sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "74208d40",
   "metadata": {},
   "outputs": [],
   "source": [
    "water = tulips.water - tulips.water.mean()\n",
    "shade = tulips.shade - tulips.shade.mean()\n",
    "blooms = tulips.blooms - tulips.blooms.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "40c2e43d",
   "metadata": {},
   "outputs": [],
   "source": [
    "with pm.Model() as tulips1:\n",
    "    beta_water = pm.Normal('beta_water',0,0.25)\n",
    "    beta_shade = pm.Normal('beta_shade',0,0.25)\n",
    "    alpha = pm.Normal('alpha',0.5,0.25)\n",
    "    sigma = pm.Exponential('sigma',1)\n",
    "    mu = pm.Deterministic('mu', alpha+beta_shade * shade + beta_water * water)\n",
    "    y = pm.Normal('y',mu, sigma, observed=blooms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "cc720d30",
   "metadata": {},
   "outputs": [],
   "source": [
    "with pm.Model() as tulips2:\n",
    "    beta_water = pm.Normal('beta_water',0,0.25)\n",
    "    beta_shade = pm.Normal('beta_shade',0,0.25)\n",
    "    beta_interaction = pm.Normal('beta_interaction',0,0.25)\n",
    "    \"\"\"\n",
    "    The motivation to choose this prior is that\n",
    "    we believe that a lot of shade makes water have an oposite effect, but proportional, so we set the same\n",
    "    sd as beta_shared\n",
    "    \"\"\"\n",
    "    alpha = pm.Normal('alpha',0.5,0.25)\n",
    "    sigma = pm.Exponential('sigma',1)\n",
    "    mu = pm.Deterministic('mu', alpha+beta_shade * shade + beta_water * water + beta_interaction * water * shade)\n",
    "    y = pm.Normal('y',mu, sigma, observed=blooms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "d3ff1611",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [beta_water, beta_shade, beta_interaction, alpha, sigma]\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
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       "\n",
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       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [8000/8000 00:04&lt;00:00 Sampling 4 chains, 0 divergences]\n",
       "    </div>\n",
       "    "
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       "<IPython.core.display.HTML object>"
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     },
     "metadata": {},
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 22 seconds.\n",
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [beta_water, beta_shade, beta_interaction, alpha, sigma]\n"
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    {
     "data": {
      "text/html": [
       "\n",
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       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
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     "metadata": {},
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    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [8000/8000 00:04&lt;00:00 Sampling 4 chains, 0 divergences]\n",
       "    </div>\n",
       "    "
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       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 20 seconds.\n"
     ]
    }
   ],
   "source": [
    "with tulips1:\n",
    "    tulips1_prior = pm.sample_prior_predictive()\n",
    "    tulips1_posterior = pm.sample()\n",
    "with tulips2:\n",
    "    tulips2_prior = pm.sample_prior_predictive()\n",
    "    tulips2_posterior = pm.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "42231bf5",
   "metadata": {},
   "outputs": [
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       "\n",
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       "\n",
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       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
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       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
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       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
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       "\n",
       ".xr-attrs dd {\n",
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       "\n",
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       "  vertical-align: middle;\n",
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       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:           (chain: 4, draw: 1000, mu_dim_0: 27)\n",
       "Coordinates:\n",
       "  * chain             (chain) int64 0 1 2 3\n",
       "  * draw              (draw) int64 0 1 2 3 4 5 6 ... 993 994 995 996 997 998 999\n",
       "  * mu_dim_0          (mu_dim_0) int64 0 1 2 3 4 5 6 7 ... 20 21 22 23 24 25 26\n",
       "Data variables:\n",
       "    beta_water        (chain, draw) float64 0.1828 -0.2361 ... 0.05864 0.315\n",
       "    beta_shade        (chain, draw) float64 -0.2383 0.2289 ... 0.2646 -0.03908\n",
       "    beta_interaction  (chain, draw) float64 -0.1202 0.1199 ... -0.122 0.1972\n",
       "    alpha             (chain, draw) float64 0.6228 0.3237 ... 0.4405 0.2692\n",
       "    sigma             (chain, draw) float64 113.9 108.4 114.7 ... 111.6 99.94\n",
       "    mu                (chain, draw, mu_dim_0) float64 0.5581 0.44 ... 0.7422\n",
       "Attributes:\n",
       "    created_at:                 2023-07-12T18:38:43.603748\n",
       "    arviz_version:              0.12.1\n",
       "    inference_library:          pymc\n",
       "    inference_library_version:  4.1.4\n",
       "    sampling_time:              21.504027843475342\n",
       "    tuning_steps:               1000</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-a0fd8144-85a0-4616-80c3-c34ffa1f5bdb' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a0fd8144-85a0-4616-80c3-c34ffa1f5bdb' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li><li><span class='xr-has-index'>mu_dim_0</span>: 27</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-02274e56-9f82-42db-8086-eb563de37481' class='xr-section-summary-in' type='checkbox'  checked><label for='section-02274e56-9f82-42db-8086-eb563de37481' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-dbf8c5a2-4d88-4718-a26b-4627914a67be' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dbf8c5a2-4d88-4718-a26b-4627914a67be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-671771ff-f7be-48b4-882e-78c8da369005' class='xr-var-data-in' type='checkbox'><label for='data-671771ff-f7be-48b4-882e-78c8da369005' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-a500e879-ca44-477e-b974-650097d04e59' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a500e879-ca44-477e-b974-650097d04e59' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3939822e-0a82-4b8d-8451-2870941eb4ca' class='xr-var-data-in' type='checkbox'><label for='data-3939822e-0a82-4b8d-8451-2870941eb4ca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>mu_dim_0</span></div><div class='xr-var-dims'>(mu_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 21 22 23 24 25 26</div><input id='attrs-a621a84d-a982-478a-8073-08c62e02101e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a621a84d-a982-478a-8073-08c62e02101e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9462a29d-fd2f-418a-b5fd-5e58530b6dd0' class='xr-var-data-in' type='checkbox'><label for='data-9462a29d-fd2f-418a-b5fd-5e58530b6dd0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ccbc9585-040f-47ea-a524-6a5d7d102da6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ccbc9585-040f-47ea-a524-6a5d7d102da6' class='xr-section-summary' >Data variables: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>beta_water</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1828 -0.2361 ... 0.05864 0.315</div><input id='attrs-08fa94dc-63cb-46cd-998f-f23ac2fc4798' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-08fa94dc-63cb-46cd-998f-f23ac2fc4798' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-afd16127-7d18-4b30-99d0-6bf2432be237' class='xr-var-data-in' type='checkbox'><label for='data-afd16127-7d18-4b30-99d0-6bf2432be237' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.18281457, -0.23610624,  0.20625259, ...,  0.28627327,\n",
       "        -0.50468091,  0.32211673],\n",
       "       [ 0.11563475, -0.29217924, -0.05691681, ...,  0.18489186,\n",
       "        -0.16852364,  0.0900956 ],\n",
       "       [-0.11112641, -0.17960343,  0.10373916, ..., -0.11499779,\n",
       "         0.04312297, -0.11028005],\n",
       "       [ 0.4585537 , -0.44347219,  0.15609319, ..., -0.08249421,\n",
       "         0.05864087,  0.31498589]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta_shade</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.2383 0.2289 ... 0.2646 -0.03908</div><input id='attrs-3c67250c-a09c-4b59-ac48-8ef07ff9800e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3c67250c-a09c-4b59-ac48-8ef07ff9800e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe50c075-bef9-40e3-8574-f357400ffe15' class='xr-var-data-in' type='checkbox'><label for='data-fe50c075-bef9-40e3-8574-f357400ffe15' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-2.38317862e-01,  2.28926165e-01, -1.77251822e-01, ...,\n",
       "         1.34890309e-01,  3.82684471e-02, -2.04717283e-01],\n",
       "       [-3.74078556e-04,  7.19627702e-02, -3.47336524e-01, ...,\n",
       "        -3.75148835e-01,  3.77625624e-01,  6.16955006e-01],\n",
       "       [-4.10195732e-02, -1.26557884e-01,  1.48083736e-01, ...,\n",
       "        -1.76575869e-01, -4.54841177e-01,  4.69964814e-01],\n",
       "       [-4.84237092e-01,  4.69677409e-01, -3.70085326e-02, ...,\n",
       "        -4.17196031e-01,  2.64568026e-01, -3.90836147e-02]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta_interaction</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.1202 0.1199 ... -0.122 0.1972</div><input id='attrs-abe587b7-951b-41f2-aa69-7d5fc80443ca' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-abe587b7-951b-41f2-aa69-7d5fc80443ca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-88cf589c-3c42-41c1-bab5-99c3156dc938' class='xr-var-data-in' type='checkbox'><label for='data-88cf589c-3c42-41c1-bab5-99c3156dc938' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-0.12021225,  0.11991891, -0.1115511 , ...,  0.23587864,\n",
       "        -0.17548376, -0.07888478],\n",
       "       [ 0.22151749, -0.23881256,  0.17281725, ..., -0.28376986,\n",
       "         0.31924822,  0.09405611],\n",
       "       [-0.01024265,  0.08230117, -0.09859808, ..., -0.59326504,\n",
       "         0.77714106, -0.78203849],\n",
       "       [ 0.06257639, -0.0795148 , -0.13981925, ...,  0.0235585 ,\n",
       "        -0.12196208,  0.19715572]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>alpha</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.6228 0.3237 ... 0.4405 0.2692</div><input id='attrs-91a310ca-780a-4a70-a8b2-4deae3c4a7c9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-91a310ca-780a-4a70-a8b2-4deae3c4a7c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2708a74c-ac67-46c3-9997-40525c671676' class='xr-var-data-in' type='checkbox'><label for='data-2708a74c-ac67-46c3-9997-40525c671676' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.62277024,  0.32366999,  0.6177387 , ...,  0.71071711,\n",
       "         0.12348892,  0.41888762],\n",
       "       [ 0.60920053,  0.50217578,  0.7334964 , ..., -0.05608742,\n",
       "         0.69914336,  0.35934019],\n",
       "       [ 0.97063473,  0.16688565,  0.77058231, ...,  0.48135592,\n",
       "        -0.18933595,  1.13703742],\n",
       "       [ 0.46464274,  0.55912704,  0.59039444, ...,  0.4299877 ,\n",
       "         0.44052162,  0.26918858]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sigma</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>113.9 108.4 114.7 ... 111.6 99.94</div><input id='attrs-176e9c3e-2596-4bf9-a4e4-44a305c18002' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-176e9c3e-2596-4bf9-a4e4-44a305c18002' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca62f491-4085-499c-b296-c776b88d1227' class='xr-var-data-in' type='checkbox'><label for='data-ca62f491-4085-499c-b296-c776b88d1227' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[113.86645605, 108.41167516, 114.6523914 , ..., 106.97889768,\n",
       "        108.27042422, 118.30901392],\n",
       "       [106.85150265, 109.48094318, 109.36207469, ..., 105.44366428,\n",
       "        117.26646028, 116.13695079],\n",
       "       [113.69913368, 109.7089831 , 112.13516426, ..., 107.47354934,\n",
       "        110.95419994, 105.68946512],\n",
       "       [117.22401365, 107.00329715, 113.58073224, ..., 112.21363594,\n",
       "        111.56811893,  99.93722785]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mu</span></div><div class='xr-var-dims'>(chain, draw, mu_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5581 0.44 ... 0.5842 0.7422</div><input id='attrs-0245c776-47ce-488c-a9e6-b047935c3680' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0245c776-47ce-488c-a9e6-b047935c3680' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34419a01-549b-46d6-961a-d5a0e58bb36a' class='xr-var-data-in' type='checkbox'><label for='data-34419a01-549b-46d6-961a-d5a0e58bb36a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 0.55806128,  0.43995567,  0.32185005, ...,  1.16411493,\n",
       "          0.80558482,  0.44705471],\n",
       "        [ 0.45076898,  0.55977623,  0.66878348, ..., -0.26128133,\n",
       "          0.08756375,  0.43640883],\n",
       "        [ 0.47718683,  0.41148611,  0.34578539, ...,  1.11279421,\n",
       "          0.82399128,  0.53518836],\n",
       "        ...,\n",
       "        [ 0.52543217,  0.42444384,  0.32345551, ...,  0.62622143,\n",
       "          0.99699038,  1.36775933],\n",
       "        [ 0.41441761,  0.62816982,  0.84192203, ..., -0.24397668,\n",
       "         -0.38119199, -0.5184073 ],\n",
       "        [ 0.22260339,  0.09677089, -0.02906161, ...,  1.02460641,\n",
       "          0.74100435,  0.45740229]],\n",
       "\n",
       "       [[ 0.71545735,  0.49356578,  0.27167421, ...,  0.50369187,\n",
       "          0.72483528,  0.94597869],\n",
       "        [ 0.48357969,  0.79435502,  1.10513035, ...,  0.37684634,\n",
       "          0.20999655,  0.04314676],\n",
       "        [ 1.31056698,  0.79041321,  0.27025943, ...,  0.85109887,\n",
       "          0.6765796 ,  0.50206032],\n",
       "...\n",
       "        [ 0.17966454,  0.59635371,  1.01304288, ...,  1.13619904,\n",
       "          0.36635813, -0.40348278],\n",
       "        [ 0.99952332, -0.23245892, -1.46444115, ..., -0.46851286,\n",
       "         -0.14621298,  0.1760869 ],\n",
       "        [-0.00468584,  1.24731746,  2.49932076, ...,  1.33883104,\n",
       "          1.02675737,  0.71468369]],\n",
       "\n",
       "       [[ 0.55290253,  0.00608905, -0.54072444, ...,  1.34485713,\n",
       "          0.92319644,  0.50153574],\n",
       "        [ 0.45340702,  1.00259923,  1.55179145, ..., -0.27450775,\n",
       "          0.11565485,  0.50581746],\n",
       "        [ 0.33149053,  0.43430124,  0.53711196, ...,  0.92331541,\n",
       "          0.74648763,  0.56965985],\n",
       "        ...,\n",
       "        [ 0.95323644,  0.51248191,  0.07172738, ...,  0.74113102,\n",
       "          0.34749349, -0.04614404],\n",
       "        [-0.00464936,  0.38188075,  0.76841086, ...,  0.35655656,\n",
       "          0.4991625 ,  0.64176844],\n",
       "        [ 0.19044202, -0.04579731, -0.28203664, ...,  0.42610238,\n",
       "          0.58417448,  0.74224658]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-859effae-a234-4513-ab66-409276229f62' class='xr-section-summary-in' type='checkbox'  checked><label for='section-859effae-a234-4513-ab66-409276229f62' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-07-12T18:38:43.603748</dd><dt><span>arviz_version :</span></dt><dd>0.12.1</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>4.1.4</dd><dt><span>sampling_time :</span></dt><dd>21.504027843475342</dd><dt><span>tuning_steps :</span></dt><dd>1000</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:           (chain: 4, draw: 1000, mu_dim_0: 27)\n",
       "Coordinates:\n",
       "  * chain             (chain) int64 0 1 2 3\n",
       "  * draw              (draw) int64 0 1 2 3 4 5 6 ... 993 994 995 996 997 998 999\n",
       "  * mu_dim_0          (mu_dim_0) int64 0 1 2 3 4 5 6 7 ... 20 21 22 23 24 25 26\n",
       "Data variables:\n",
       "    beta_water        (chain, draw) float64 0.1828 -0.2361 ... 0.05864 0.315\n",
       "    beta_shade        (chain, draw) float64 -0.2383 0.2289 ... 0.2646 -0.03908\n",
       "    beta_interaction  (chain, draw) float64 -0.1202 0.1199 ... -0.122 0.1972\n",
       "    alpha             (chain, draw) float64 0.6228 0.3237 ... 0.4405 0.2692\n",
       "    sigma             (chain, draw) float64 113.9 108.4 114.7 ... 111.6 99.94\n",
       "    mu                (chain, draw, mu_dim_0) float64 0.5581 0.44 ... 0.7422\n",
       "Attributes:\n",
       "    created_at:                 2023-07-12T18:38:43.603748\n",
       "    arviz_version:              0.12.1\n",
       "    inference_library:          pymc\n",
       "    inference_library_version:  4.1.4\n",
       "    sampling_time:              21.504027843475342\n",
       "    tuning_steps:               1000"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_, axs = plt.subplots(2, 3, figsize=[9, 5], sharey=True, sharex=True, constrained_layout=True)\n",
    "n_lines = 20\n",
    "pred_x = np.array([-1, 1])\n",
    "\n",
    "for i, shade in enumerate([-1, 0, 1]):\n",
    "    ind = d.shade_cent == shade\n",
    "    for ax in axs[:, i]:\n",
    "        ax.scatter(blooms.loc[ind, \"water_cent\"], d.loc[ind, \"blooms_std\"])\n",
    "     # top row, m_8_4\n",
    "     ax = axs[0, i]\n",
    "     ax.set_title(f\"m8.4 post: shade = {shade:.0f}\", fontsize=11)\n",
    "     pred_y = (\n",
    "         m_8_4_post[\"a\"][:n_lines].to_numpy()\n",
    "         + m_8_4_post[\"bw\"][:n_lines].to_numpy() * pred_x.reshape(-1, 1)\n",
    "         + m_8_4_post[\"bs\"][:n_lines].to_numpy() * shade\n",
    "     )\n",
    "     ax.plot(pred_x, pred_y, lw=1, color=(0, 0, 0, 0.4))\n"
   ]
  },
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