GStechschulte/plot_comparisons_v1.ipynb

## plot_comparisons_v1.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import arviz as az\n",
    "import itertools\n",
    "import bambi as bmb\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn.objects as so\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "\n",
    "from bambi.plots import plot_cap\n",
    "from bambi.plots.plot_cap import make_main_values, make_group_values\n",
    "from bambi.plots.plot_comparisons import plot_comparison\n",
    "from bambi.utils import listify, clean_formula_lhs, get_aliased_name\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### mtcars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load data\n",
    "data = bmb.load_data('mtcars')\n",
    "data[\"cyl\"] = data[\"cyl\"].replace({4: \"low\", 6: \"medium\", 8: \"high\"})\n",
    "data[\"gear\"] = data[\"gear\"].replace({3: \"A\", 4: \"B\", 5: \"C\"})\n",
    "data[\"cyl\"] = pd.Categorical(data[\"cyl\"], categories=[\"low\", \"medium\", \"high\"], ordered=True)\n",
    "data[\"am\"] = pd.Categorical(data[\"am\"], categories=[0, 1], ordered=True)\n",
    "#data[\"drat\"] = pd.Categorical(data[\"drat\"], ordered=True).codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [mpg_sigma, Intercept, hp, drat, hp:drat, am, hp:am, drat:am, hp:drat:am]\n"
     ]
    },
    {
     "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 23 seconds.\n"
     ]
    }
   ],
   "source": [
    "# Define and fit the Bambi model\n",
    "model = bmb.Model(\"mpg ~ hp * drat * am\", data)\n",
    "idata = model.fit(draws=1000, target_accept=0.95, random_seed=1234)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "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>Intercept</th>\n",
       "      <td>13.199</td>\n",
       "      <td>13.415</td>\n",
       "      <td>-13.335</td>\n",
       "      <td>37.366</td>\n",
       "      <td>0.354</td>\n",
       "      <td>0.251</td>\n",
       "      <td>1435.0</td>\n",
       "      <td>1685.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hp</th>\n",
       "      <td>-0.014</td>\n",
       "      <td>0.084</td>\n",
       "      <td>-0.179</td>\n",
       "      <td>0.141</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>1331.0</td>\n",
       "      <td>1775.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drat</th>\n",
       "      <td>3.735</td>\n",
       "      <td>3.797</td>\n",
       "      <td>-3.023</td>\n",
       "      <td>11.266</td>\n",
       "      <td>0.101</td>\n",
       "      <td>0.073</td>\n",
       "      <td>1410.0</td>\n",
       "      <td>1726.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hp:drat</th>\n",
       "      <td>-0.012</td>\n",
       "      <td>0.025</td>\n",
       "      <td>-0.057</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1348.0</td>\n",
       "      <td>1806.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>am[1]</th>\n",
       "      <td>2.029</td>\n",
       "      <td>14.087</td>\n",
       "      <td>-22.234</td>\n",
       "      <td>30.039</td>\n",
       "      <td>0.358</td>\n",
       "      <td>0.253</td>\n",
       "      <td>1550.0</td>\n",
       "      <td>2032.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hp:am[1]</th>\n",
       "      <td>0.055</td>\n",
       "      <td>0.089</td>\n",
       "      <td>-0.112</td>\n",
       "      <td>0.220</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>1469.0</td>\n",
       "      <td>2046.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drat:am[1]</th>\n",
       "      <td>0.357</td>\n",
       "      <td>3.614</td>\n",
       "      <td>-6.505</td>\n",
       "      <td>6.869</td>\n",
       "      <td>0.098</td>\n",
       "      <td>0.069</td>\n",
       "      <td>1357.0</td>\n",
       "      <td>1737.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hp:drat:am[1]</th>\n",
       "      <td>-0.013</td>\n",
       "      <td>0.025</td>\n",
       "      <td>-0.056</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1378.0</td>\n",
       "      <td>2069.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mpg_sigma</th>\n",
       "      <td>3.079</td>\n",
       "      <td>0.449</td>\n",
       "      <td>2.311</td>\n",
       "      <td>3.940</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.006</td>\n",
       "      <td>2715.0</td>\n",
       "      <td>2410.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 mean      sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk   \n",
       "Intercept      13.199  13.415 -13.335   37.366      0.354    0.251    1435.0  \\\n",
       "hp             -0.014   0.084  -0.179    0.141      0.002    0.002    1331.0   \n",
       "drat            3.735   3.797  -3.023   11.266      0.101    0.073    1410.0   \n",
       "hp:drat        -0.012   0.025  -0.057    0.037      0.001    0.000    1348.0   \n",
       "am[1]           2.029  14.087 -22.234   30.039      0.358    0.253    1550.0   \n",
       "hp:am[1]        0.055   0.089  -0.112    0.220      0.002    0.002    1469.0   \n",
       "drat:am[1]      0.357   3.614  -6.505    6.869      0.098    0.069    1357.0   \n",
       "hp:drat:am[1]  -0.013   0.025  -0.056    0.035      0.001    0.000    1378.0   \n",
       "mpg_sigma       3.079   0.449   2.311    3.940      0.009    0.006    2715.0   \n",
       "\n",
       "               ess_tail  r_hat  \n",
       "Intercept        1685.0   1.00  \n",
       "hp               1775.0   1.01  \n",
       "drat             1726.0   1.00  \n",
       "hp:drat          1806.0   1.00  \n",
       "am[1]            2032.0   1.00  \n",
       "hp:am[1]         2046.0   1.00  \n",
       "drat:am[1]       1737.0   1.00  \n",
       "hp:drat:am[1]    2069.0   1.00  \n",
       "mpg_sigma        2410.0   1.00  "
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(idata)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Like `plot_cap` the user must also pass the `model` and `idata`. However, for `plot_comparisons`, the user needs to pass a `contrast_predictor` and `conditional` argument. `contrast_predictor` is the variable whose contrast we are interested in computing comparisons (the difference between predictions) for. `conditional` is the variable that we are conditioning on.\n",
    "\n",
    "Currently, the `plot_comparisons` function allows the user to pass either `str`, `list`, or `dict` into `contrast_predictor` and `conditional`. A `dict` is used to compare predictions of user defined contrasts. I think this data structure is a good representation since the key, value pair reads as \"predictor\" : \"contrast\". If a `str` or `list` is passed, the function will use a grid based on the observed data.\n",
    "\n",
    "To compute the contrasts, the cartesion product (cross join) is used to compute all pairwise combinations of the `contrast_predictor` and `conditional` variables which is stored as a dataframe. This dataframe, like the `create_cap_data` dataframe, is used as the new data for the model to perform predictions. To compute the difference in predictions (comparison) of the contrasts, multiple pandas methods are chained:\n",
    "1. `.groupby()` is used to group the dataframe by the covariates in the model (excluding the contrast predictor)\n",
    "2. `.diff()` is used to compute the difference between the predictions of the contrasts\n",
    "3. `.dropna()` is used to remove the first row of each group since the difference is computed between the current row and the previous row\n",
    "4. `.reset_index()` is used to reset the index of the dataframe\n",
    "\n",
    "The result of steps 1-4 are saved in a new dataframe—`contrast_comparison`. Conditional variable values are added to the dataframe and the dataframe is passed to `plot_comparison` to plot the comparisons."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "main: am, group: drat, panel: None\n"
     ]
    },
    {
     "data": {
      "image/png": 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g9u3bOkVZaT9Dtra2xX6GHlfYz47ffvsNCQkJ2LNnj9wrBgD37t0rVQ5UNbAgo0rl6tWr+N///gcrKysMHTq03PbbpUsXrFu3DlqtFi1btizzPrZv3w4vLy9YW1s/dU716tWDl5cXli9fjrFjx8p/2T/K1NQUISEhOHbsGAICAors4auM1q5di7Fjx8q/wK5cuYL9+/fjrbfeAgB4e3vD0dERP/zwA8aOHStvd/XqVezfv7/UBfDp06dx4sQJnSGnNWvWwMLCAk2aNAGQ97WdPn06bG1tSyxsNBpNkT0aoaGhmDBhAiwsLORhSTMzM7Rq1QoLFixAQkKCzvp6rVu3Ro0aNXDmzJkCy7c8ysjI6Jl9DiRJKvBZ3LZtG65fv446depU2HHzjw2gwPEfveL6SYWEhGD27NlYvXo13nvvPbl9zZo1T7R9YZ+h0nqS83q0h6+yLrVDhWNBRnrr1KlT8jyY5ORk/P7771ixYgVUKhU2b94s9yCVh169emH16tV44YUXMGrUKLRo0QJqtRrXrl3D7t270b17d7z00kvF7uOTTz5BdHQ0goKC8N5778Hb2xsZGRm4fPkytm/fjq+//rrY4cfCfPXVV+jatStatWqFMWPGoFatWrh69Sp+/fVXrF69GgAwf/58tGnTBm3btsXw4cPh4eGB+/fv4+LFi9i6dSt+++23Mr8vSkpOTsZLL72EwYMHIyUlBeHh4TA2NsaECRMAAAYGBpgyZQqGDh2Knj174u2338a9e/cwZcoUODk5wcCgdFNknZ2d0a1bN0yePBlOTk5YtWoVoqOjMWvWLHl9qNGjR2Pjxo147rnnMGbMGAQEBCA3NxdXr15FVFQU3n//fbmQ9/f3x549e7B161Y4OTnBwsIC3t7eAPLmkWm1WuzatQsrV66UcwgNDUV4eDgkSdKZX2Rubo4FCxagX79+uHPnDnr27Al7e3vcvHkTJ06cwM2bN7F48WIAz+5z0KVLF0RERMDHxwcBAQE4evQo5syZ88Sf7bLw8fGBl5cXxo8fDyEEbGxssHXrVnkOXlmEhYXhueeewwcffIC0tDQ0a9YMf/zxB77//vtSbT969GgsX74cL774IqZOnQoHBwesXr0af/31V6lzCAoKgrW1NYYNG4bw8HCo1WqsXr1aHp5/VP5UjVmzZqFz585QqVRV7o+x6ooFGemtAQMGAMj7679GjRrw9fXFuHHjMGjQoHItxgBApVJhy5YtmD9/Pr7//nvMmDEDhoaGcHV1RXBwcKnmqzk5OeHIkSP49NNPMWfOHFy7dg0WFhbw9PREp06dytRr1rFjR+zduxeffPIJ3nvvPWRkZMDV1VVnonL9+vURFxeHTz/9FB999BGSk5NRo0YN1K1bFy+88MITH1NfTJ8+HbGxsRgwYABSU1PRokULrFu3Dl5eXnLMkCFDIEkSZs+ejZdeegkeHh4YP348fv75Z1y9erVUx2nUqBEGDBiA8PBwXLhwAc7Ozpg3bx7GjBkjx5iZmeH333/HzJkzsXTpUsTHx8PExAS1atVCaGiozppn8+fPx4gRI9CrVy+kp6cjODhYnvTfuHFj2NnZ4datWzo9YfkFWePGjQvMZXrzzTdRq1YtzJ49G0OHDsX9+/dhb2+PRo0a6awf96w+B/Pnz4darcaMGTPw4MEDNGnSBJs2bcJHH31UbscoilqtxtatWzFq1CgMHToUhoaGCA0Nxc6dO1GrVq0y7dPAwABbtmzB2LFjMXv2bGRlZaF169bYvn07fHx8Stze0dERMTExGDVqFIYPHw5TU1O89NJLWLhwIbp3716qHGxtbbFt2za8//77ePPNN2FmZobu3btj/fr1ci9tvt69e+OPP/7AokWL8Mknn0AIgfj4+Cdad4/0kyTK+3I1IqKnsGfPHoSEhODHH38sdjJ7Ue7du4d69eqhR48eWLp0aQVkSERU/thDRkSVVlJSEqZNm4aQkBDY2triypUr+Pzzz3H//n2MGjVK6fSIiEqNBRkRVVoajQaXL1/GO++8gzt37sDU1BStWrXC119/jQYNGiidHhFRqXHIkoiIiEhhXKmfiIiISGEsyIgUtn79ejRo0AAmJiaQJAnHjx+vsGNFRERAkiQcOXKkwo6hpISEBEyePLlC38OSrFmzBl988UWp4yVJkh9z587Vee2jjz5Cly5d4OLiAkmSdK6qfNTp06fxzjvvIDAwEGZmZiXezmndunVo1KgRjI2N4ezsjNGjR5d4y6Li7Nu3D4MGDULTpk2h0WggSRIuX75cIC7/81fUY+bMmaWKfXzR1UaNGsmvPX6rJqLKggUZkYJu3ryJvn37wsvLC5GRkThw4ECBe2RS6SUkJGDKlCmVqiADgIEDB+LAgQMF7qf4+eef4/bt2+jWrVux60wdOXIEP/30E2xsbNC+fftij7V69Wq88cYbaN68OXbs2IHw8HBERETg5ZdffqKcH7Vr1y556YmgoKAi41588UUcOHCgwKNDhw4AUOhafytWrCgQ//jSIN9//z0OHDhQ6tsdEekjTuonUtD58+eRnZ2NN998U+eWKU8jPT1dXtCUiqcv75Wrq2uht0a6f/++vMBtcQuV9u3bV76V0IYNG7B169ZC47RaLf7v//4PYWFh+OabbwDkrTRvYWGBPn36YMeOHTo3fy+tSZMmITw8HAAwd+7cInvnatasWWANwbS0NBw4cABt2rSRF9B9lJ+fH5o1a1bs8fPXCSzsbhZElQV7yIgU0r9/f7Rp0wYA8Prrr0OSJJ37c27ZsgWBgYEwNTWFhYUFOnTogAMHDujsY/LkyZAkCXFxcejZsyesra11Fk4tyv379zF8+HD5Hp4vv/wyEhISdGI8PDzQpUsXbN68GQEBATA2Nkbt2rXx5Zdflur8cnNzsWDBAjRq1AgmJiaoUaMGWrVqhS1btujEzJ49Gz4+PtBoNLC3t8dbb71V4P6d7dq1g5+fH2JjY9G2bVuYmpqidu3amDlzJnJzcwHkrV/WvHlzAHmLCucPYU2ePFl+v83NzXHy5EmEhYXBwsJC7k2Kjo5G9+7d4erqCmNjY9SpUwdDhw4tcEPumzdvYsiQIXBzc4NGo0HNmjXRunVr7Ny5U85z27ZtuHLlis4QW1mV9m4DpY07ePAgEhMT5UWX87366qswNzfH5s2bnzjHJzl+YdavX48HDx5g0KBBZd4HUVXAgoxIIZMmTcJXX30FIG9V+gMHDmDRokUA8oa9unfvDktLS6xduxbLli3D3bt30a5dO+zbt6/Avl5++WXUqVMHP/74I77++usSjz1o0CCo1WqsWbMGs2fPxp49e/Dmm28WiDt+/DhGjx6NMWPGYPPmzQgKCsKoUaMKzHUqTP/+/TFq1Cg0b94c69evx7p169CtWzeduUXDhw/HuHHj0KFDB2zZsgWffvopIiMjERQUVKAYSkpKQp8+ffDmm29iy5Yt6Ny5MyZMmIBVq1YBAJo0aYIVK1YAyJt7lT+89egv+qysLHTr1g3PP/88fv75Z0yZMgUAcOnSJQQGBmLx4sWIiorCxx9/jEOHDqFNmzbIzs6Wt+/bty9++uknfPzxx4iKisK3336L0NBQ3L59GwCwaNEitG7dGo6OjjpDbPri1KlTAICAgACddrVaDR8fH/n1Z2nZsmWwtLTEq6++WujrXbp0gUqlgo2NDV5++WVFciR6JgQRKWb37t0CgPjxxx/lNq1WK5ydnYW/v7/QarVy+/3794W9vb0ICgqS28LDwwUA8fHHH5fqeCtWrBAAxDvvvKPTPnv2bAFAJCYmym3u7u5CkiRx/PhxndgOHToIS0tLkZaWVuRx9u7dKwCIDz/8sMiYs2fPFprLoUOHBAAxceJEuS04OFgAEIcOHdKJrV+/vujYsaP8PDY2VgAQK1asKHC8fv36CQBi+fLlReYkhBC5ubkiOztbXLlyRQAQP//8s/yaubm5GD16dLHbv/jii8Ld3b3YmEcBEOHh4SXGmZmZiX79+pUY9+OPPwoAYvfu3QVemzZtWoGvc76wsDBRr169UmRcvDlz5ggAIj4+vsTY/M/A0KFDC7y2Y8cO8eGHH4qtW7eKmJgYsXDhQuHq6irMzMwKfCbzubu7ixdffPFpT4FIEewhI9Iz586dQ0JCAvr27aszFGRubo5XXnkFBw8eRHp6us42r7zyyhMd49F7YQL/9ZhcuXJFp71BgwZo2LChTlvv3r2RmpqKuLi4Ive/Y8cOAMCIESOKjNm9ezcAFLhysEWLFvD19cWuXbt02h0dHdGiRYsCeT+ec0kKe6+Sk5MxbNgwuLm5wdDQEGq1Gu7u7gCAs2fP6uQWERGBqVOn4uDBgzq9Z5VJUcOoTzO8WhbLli0DgEKHKzt16oSpU6eiS5cueO655zBixAj8/vvvkCQJH3/88TPNk+hZYEFGpGfyh7+cnJwKvObs7Izc3FzcvXtXp72w2OI8fpVa/mTohw8f6rQXdtVaflt+noW5efMmVCpVsVe9lXSej+//8Zzz83485+KYmprC0tJSpy03NxdhYWHYtGkTPvjgA+zatQuHDx/GwYMHAei+J+vXr0e/fv3w7bffIjAwEDY2NnjrrbcKLMOgr/Lfw8K+dnfu3IGNjc0zyyU7OxvfffcdGjZsWOKk/XweHh5o06aN/LUhqkpYkBHpmfxfmomJiQVeS0hIgIGBAaytrXXaK6pno7BCI7+tsAIpX82aNaHVaostVEo6Tzs7uydNt0SFvU+nTp3CiRMnMGfOHLz77rto164dmjdvXuj52dnZ4YsvvsDly5dx5coVzJgxA5s2bSpyfTB9k3814smTJ3Xac3Jy8Ndff8HPz++Z5fLLL78gOTn5iSfzCyGe6iICIn3FTzWRnvH29oaLiwvWrFkD8cidzdLS0rBx40b5ystn4fTp0zhx4oRO25o1a2BhYYEmTZoUuV3+0gmLFy8uMub5558HAHlSfr7Y2FicPXu2xPW0ClNUT19x8ou0x5dMWLJkSbHb1apVCyNHjkSHDh10hm+ftNfuWWrZsiWcnJwQERGh075hwwY8ePDgqdYie1LLli2DsbFxgbXXihMfH48//vij0CVCiCo7rkNGpGcMDAwwe/Zs9OnTB126dMHQoUORmZmJOXPm4N69ezqrmVc0Z2dndOvWDZMnT4aTkxNWrVqF6OhozJo1q9iisG3btujbty+mTp2KGzduoEuXLtBoNDh27BhMTU3x7rvvwtvbG0OGDMGCBQtgYGCAzp074/Lly5g0aRLc3NwwZsyYJ87Xy8sLJiYmWL16NXx9fWFubg5nZ2c4OzsXuY2Pjw+8vLwwfvx4CCFgY2ODrVu3Ijo6WicuJSUFISEh6N27N3x8fGBhYYHY2FhERkbqFDL+/v7YtGkTFi9ejKZNm8LAwKDUQ3KPi4mJwc2bNwHkrSF25coVbNiwAQAQHBwsr+mVnp6O7du3A4A8nBcTE4Nbt27BzMxMLpBVKhVmz56Nvn37YujQoXjjjTdw4cIFfPDBB+jQoQM6deqkc3xJkhAcHFzsqv9A3hB1TEwMgP9633bs2CGvO/b4GnsJCQmIjIzE66+/XqC3N19oaCiee+45BAQEwNLSEidPnsTs2bMhSRI+/fTTUr1/RJWKwhcVEFVrhV1lme+nn34SLVu2FMbGxsLMzEy0b99e/PHHHzox+VdZ3rx5s1THy7/KMjY2ttA8Hr0yL/+KtQ0bNogGDRoIIyMj4eHhIebNm1eqY2m1WvH5558LPz8/YWRkJKysrERgYKDYunWrTsysWbNEvXr1hFqtFnZ2duLNN98U//zzj86+goODRYMGDQoco1+/fgWuaFy7dq3w8fERarVa5wrGfv36CTMzs0JzPXPmjOjQoYOwsLAQ1tbW4tVXXxVXr17V2T4jI0MMGzZMBAQECEtLS2FiYiK8vb1FeHi4zhWnd+7cET179hQ1atQQkiSJkn7MopirLPOvLi3s8ejXKj4+vsi4wq74XLNmjQgICBBGRkbC0dFRvPfee+L+/fs6Mffv3xcARK9evYrNX4j/Pj+FPYKDgwvE51/t+dtvvxW5z9GjR4v69esLCwsLYWhoKJydncWbb74pzp07V+Q2vMqSKjNJiEfGRIiI/uXh4QE/Pz/88ssvSqdSpUmShEmTJuHjjz+GSqV65lc6FmX79u3o0qULTpw4Ic8901darRZCCNSpU4efWaq0OIeMiEhhn376KdRqNT777DOlU5Ht3r0bvXr10vtiDACaNm0KtVr9xEugEOkTziEjIlJQbGys/G83NzcFM9E1Z84cpVMotTVr1shr89WoUUPZZIjKiEOWRERERArjkCURERGRwliQERERESmMBRkRERGRwliQERERESmsWl1lmZubi4SEBFhYWOjNWj9ERERUNQkhcP/+fTg7O5d4D9ZqVZAlJCTo1WXlREREVPX9888/cHV1LTamWhVkFhYWAPLeGEtLS4WzISIioqosNTUVbm5ucv1RnGpVkOUPU1paWrIgIyIiomeiNNOkOKmfiIiISGEsyIiIiIgUVq2GLImIiJSgzdUiLjkON9NvoqZpTTSxbwKVgUrptEiPsCAjIiKqQDuv7MTMwzNxI/2G3OZg6oDxLcYj1D1UwcxIn3DIkoiIqILsvLITY/eM1SnGACA5PRlj94zFzis7FcqM9A0LMiIiogqgzdVi5uGZEBAFXstvm3V4FrS52medGukhFmREREQVIC45rkDP2KMEBJLSkxCXHPcMsyJ9xYKMiIioAtxMv1mucVS1VYqC7PLlyxg4cCA8PT1hYmICLy8vhIeHIysrS+nUiIiIClXTtGa5xlHVVimusvzrr7+Qm5uLJUuWoE6dOjh16hQGDx6MtLQ0zJ07V+n0iIiICmhi3wQOpg5ITk8udB6ZBAkOpg5oYt9EgexI30hCiIKfkkpgzpw5WLx4Mf7+++9Sb5OamgorKyukpKTw1klERFTh8q+yBKBTlEnIu5XOvHbzuPRFFfYkdUelGLIsTEpKCmxsbJROg4iIqEih7qGY124e7E3tddodTB1YjJGOSjFk+bhLly5hwYIF+Oyzz4qNy8zMRGZmpvw8NTW1olMjIiLSEeoeihC3EK7UT8VStIds8uTJkCSp2MeRI0d0tklISECnTp3w6quvYtCgQcXuf8aMGbCyspIfbm5uFXk6REREhVIZqNDcsTleqP0Cmjs2ZzFGBSg6h+zWrVu4detWsTEeHh4wNjYGkFeMhYSEoGXLloiIiICBQfH1ZGE9ZG5ubpxDRkRERBXuSeaQKTpkaWdnBzs7u1LFXr9+HSEhIWjatClWrFhRYjEGABqNBhqN5mnTJCIiIqpQlWIOWUJCAtq1a4datWph7ty5uHnzv0X0HB0dFcyMiIiI6OlVioIsKioKFy9exMWLF+Hq6qrzWiVdtYOIiIhIVimWvejfvz+EEIU+iIiIiCq7SlGQEREREVVlLMiIiIgqWHp2OvxX+sN/pT/Ss9OVTof0EAuycpKelQOP8dvgMX4b0rNylE6HiIiIKpFKMamfqDoTWi3SjxxFzs2bMKxZE6bNmkJScVFJIqKqhAUZkR5LjYrCjekzkJOUJLcZOjrCYeIEWIaFKZgZERGVJw5ZEump1KgoXB81WqcYA4CcGzdwfdRopEZFKZQZERGVNxZkRHpIaLW4MX0GUNjSLv+23Zg+A0KrfcaZERFRRWBBRqSH0o8cLdAzpkMI5CQlIf3I0WeXFBERVRgWZER6KOeR24OVRxwREek3FmREesiwZs1yjSMiIv3GgoxID5k2awpDR0dAkgoPkCQYOjrCtFnTZ5sYERFVCBZkRHpIUqngMHHCv08eK8r+fe4wcQLXIyMiqiJYkBHpKcuwMLjM/wKGDg467YYODnCZ/wXXISMiqkK4MCyRHrMMC4NF+/ZcqZ+eWHpWDup//CsA4MwnHWFqxB/3RPqM36FEek5SqWDWsoXSaRARUQXikCURERGRwliQERERESmMBRkRERGRwliQERERESmMBRkRERGRwniVJRERUQUzVZviZL+TSqdBeow9ZEREREQKY0FGREREpDAWZEREREQKY0FGREREpDAWZEREREQKqzQFWbdu3VCrVi0YGxvDyckJffv2RUJCgtJpybS5Qv73ob/v6Dwnehq56ek46+OLsz6+yE1PVzodIiKqAJWmIAsJCcEPP/yAc+fOYePGjbh06RJ69uypdFoAgMhTiQidFyM/HxARizazfkPkqUQFsyIiIqLKotKsQzZmzBj53+7u7hg/fjx69OiB7OxsqNVqxfKKPJWI4avi8Hh/WFJKBoavisPiN5ugk5+TIrkRERFR5VBpesgedefOHaxevRpBQUGKFmPaXIEpW88UKMYAyG1Ttp7h8CUREREVq1IVZOPGjYOZmRlsbW1x9epV/Pzzz8XGZ2ZmIjU1VedRng7H30FiSkaRrwsAiSkZOBx/p1yPS0RERFWLogXZ5MmTIUlSsY8jR47I8f/3f/+HY8eOISoqCiqVCm+99RaEKLr3acaMGbCyspIfbm5u5Zp/8v2ii7GyxBEREVH1pOgcspEjR6JXr17Fxnh4eMj/trOzg52dHerVqwdfX1+4ubnh4MGDCAwMLHTbCRMmYOzYsfLz1NTUci3K7C2MyzWOiIhIn+Wmp+Nck6YAAO+4ozAwNVU4o6pD0YIsv8Aqi/yesczMzCJjNBoNNBpNmfZfGi08beBkZYyklIxC55FJABytjNHC06bCcqCqz8DUFL5/nVU6DSIiqkCVYg7Z4cOHsXDhQhw/fhxXrlzB7t270bt3b3h5eRXZO/YsqAwkhHetX+hr0r//D+9aHyoDqdAYIiIiIqCSFGQmJibYtGkT2rdvD29vb7z99tvw8/NDTExMhfaAlUYnPycsfrMJHCx183C0MuaSF0SkGC5WTVS5SKK4WfFVTGpqKqysrJCSkgJLS8ty3ff9jGz4T44CAKzo3xzP1avJnjEiUkTkqUSEbzmNG6n/TelwsjJGeNf6/CORngrnkD2ZJ6k7KkUPWWXwaPHVsrYNizEiUkT+YtWPFmPAf4tV8w4iRPqJBRkRURXBxaqJKi8WZEREVUSVXKw6Kw2YbJX3yEpTOhuiCsOCjIioiuBi1VTRhFYr/zvtyFGd5/R0WJAREVURXKyaKlJqVBQuvdhFfn5tyBBcbB+K1KgoBbMqu+xMLb4a9hu+GvYbsjOVLyxZkBERVRH5i1UXdUmRhLyrLblYNT2p1KgoXB81GtrkZJ32nBs3cH3U6EpblOkTFmRERFUEF6umiiC0WtyYPgMobJWsf9tuTJ/B4cunxIKMiKgK4WLVVN7SjxxFTlJS0QFCICcpCelHjj67pKogRe9lSURE5a+TnxNa17HjYtVULnJu3izXOCoce8iIiKogLlZN5cWwZs1yjaPCsSCjgrjuDxER/cu0WVMYOjoCUhFFvSTB0NERps2aPtvEqhgWZERERFQkSaWCw8QJRbyYV6Q5TJwASaV6hllVPSzIiIiIqFiWYWFwmf8FVPb2Ou2GDg5wmf8FLMPCFMqs6uCkfiIiIiqRZVgYzAIDcb55CwCA69KlMG8dxJ6xcsKCjIiemexMLZaOigEADJkfDLWGP8iJKpNHiy+zZk1ZjJUjDllSQbmPLO53eb/ucyIiIip3LMhI15ktwFfN/3u+pifwhV9eOxEREVUIFmT0nzNbgB/eAu4/tiJzamJeO4syekq5uf/deiXhwl2d50RE1RnnkJUTUyNDXJ75otJplF2uFogcB6CwX5ACgAREjgd8XgQMOGeAntylY8nYu+68/PyXhX/CrIYGbV+vC6/G9sVsSURU9bGHjPJc2Q+kJhQTIIDU63lxRE/o0rFkRC45hfSULJ32tHuZiFxyCpeOJSuUGRGRfmBBRnke3CjfOKJ/5eYK/L7+QrEx+364wOFLIqrWWJBRHnOH8o3TA+nZ6fBf6Q//lf5Iz05XOp1qK/HCPaTdyyw25sHdTCReuPdsEiIi0kMsyCiPexBg6QygqBsQS4ClS14c0RNISy2+GHvSOKpmuAwPVRMsyCiPgQroNKuIF/8t0jrN5IR+emJmlppyjaNqhMvw6B0DU1P4/nUWvn+dhYGpqdLpVCksyOg/9bsBr30HWDjqtls657XX76ZMXlSpOdWtAbMaxRdb5tYaONWt8WwSosqBy/BQNcOCjHTV7waMiP3vee8NwOiTLMaozAwMJLR9vW6xMW1eqwsDg6KGy6naKXEZHuQtw8PhS6pCKl1BlpmZiUaNGkGSJBw/flzpdKqmR4clPYI4TElPzauxPToN9SvQU2ZurUGnoX5ch4x0cRkeqoYq3cKwH3zwAZydnXHixAmlUyGiJ+DV2B6eDWvmXXWZmgkzy7xhSvaMUQFchoeqoUpVkO3YsQNRUVHYuHEjduzYoXQ6RPSEDAwkuHhbK50G6bsquAwPUUkqTUF248YNDB48GD/99BNMeWUHEVGxKvXt3PKX4UlNROHzyKS817kMD1UhlWIOmRAC/fv3x7Bhw9CsWbNSb5eZmYnU1FSdBxER6Tkuw0PVkKIF2eTJkyFJUrGPI0eOYMGCBUhNTcWECROeaP8zZsyAlZWV/HBzc6ugM6lijMyAySl5DyMzpbMhouqIy/BQNSMJIRS7gdytW7dw69atYmM8PDzQq1cvbN26FZL03+RfrVYLlUqFPn36YOXKlYVum5mZiczM/1b/Tk1NhZubG1JSUmBpaVk+J0F6Kz07HS3XtAQAHOp9CKZqDnUTVToZqcDMf/+Y7r0BqPM8e8aoXGRnarF0VAwAYMj8YKg15f+5Sk1NhZWVVanqDkXnkNnZ2cHOzq7EuC+//BJTp06VnyckJKBjx45Yv349WrZsWeR2Go0GGg1X/yYiqrS4DA9VE5ViUn+tWrV0npubmwMAvLy84OrqqkRKRBVKq9UiOztb6TSIlJeVCZj/20OWkQnk/leQqdVqqFQs0KhqqBQFGVF1IYRAUlIS7t27p3QqRPpB5AKtP8v797VEQNKd+lyjRg04OjrqTGkhqowqZUHm4eEBBae+EVWY/GLM3t4epqam/CVDlKsFbv07F9jOQx6yFEIgPT0dycnJAAAnJyeFEiQqH5WyICOqirRarVyM2draKp0OkX7I1QKG//5hYmysM4fMxMQEAJCcnAx7e3sOX1KlVinWISOqDvLnjHHhY6LSy/9+4ZxLquxYkBHpGQ5TEpUev1+oqmBBRkRERKQwFmRERADatWuH0aNHK50GEVVTLMiIiMrJ5MmT0ahRI6XTqFoMVIBz47wHF4WlKowFGRFRCbKyspROgYiqOBZkRFTtpKWl4a233oK5uTmcnJzw2Wef6bzu4eGBqVOnon///rCyssLgwYMBAOPGjUO9evVgamqK2rVrY9KkSfLVfREREZgyZQpOnDgBSZIgSRIiIiKe9akRUSVVpnXIrK2tC72yRZIkGBsbo06dOujfvz8GDBjw1AkSEZW3//u//8Pu3buxefNmODo6YuLEiTh69KjOcOOcOXMwadIkfPTRR3KbhYUFIiIi4OzsjJMnT2Lw4MGwsLDABx98gNdffx2nTp1CZGQkdu7cCQCwsrJ61qdGRJVUmQqyjz/+GNOmTUPnzp3RokULCCEQGxuLyMhIjBgxAvHx8Rg+fDhycnLkvyyJiPTBgwcPsGzZMnz33Xfo0KEDAGDlypUF7ov7/PPP43//+59O26PFmYeHB95//32sX78eH3zwAUxMTGBubg5DQ0M4OjpW/IkQUZVSpoJs3759mDp1KoYNG6bTvmTJEkRFRWHjxo0ICAjAl19+yYKMiPTKpUuXkJWVhcDAQLnNxsYG3t7eOnHNmjUrsO2GDRvwxRdf4OLFi3jw4AFycnJgaWlZ4TkTUdVXpjlkv/76K0JDQwu0t2/fHr/++isA4IUXXsDff//9dNkREZWz0t4H18zMTOf5wYMH0atXL3Tu3Bm//PILjh07hg8//JAT/omoXJSpILOxscHWrVsLtG/duhU2NjYA8ibNWlhYPF12RETlrE6dOlCr1Th48KDcdvfuXZw/f77Y7f744w+4u7vjww8/RLNmzVC3bl1cuXJFJ8bIyAharbZC8iaiqq1MQ5aTJk3C8OHDsXv3brRo0QKSJOHw4cPYvn07vv76awBAdHQ0goODyzVZoieRm5sr/zvuRhwCnQOh4jpG1Z65uTkGDhyI//u//4OtrS0cHBzw4YcfwsCg+L9P69Spg6tXr2LdunVo3rw5tm3bhs2bN+vEeHh4ID4+HsePH4erqyssLCyg0Wgq8nSIqIooUw/Z4MGDERMTAzMzM2zatAkbNmyAqakpYmJiMHDgQACQJ7sSKWHnlZ3o/nN3+fnwXcPRcWNH7LyyU8GsSF/MmTMHzz33HLp164bQ0FC0adMGTZs2LXab7t27Y8yYMRg5ciQaNWqE/fv3Y9KkSToxr7zyCjp16oSQkBDUrFkTa9eurcjTIKIqRBKlnVBRBaSmpsLKygopKSmciFuF7byyE2P3jIWA7kdbQt5SLfPazUOoe8E5kErLyMhAfHw8PD09YWxsrHQ6RJUCv2+orLIztVg6KgYAMGR+MNSa8h9BeZK6o0xDlgCg1WqxefNmnD17FpIkwdfXF927d4ehYZl3SfTUtLlazDw8s0AxBgACAhIkzDo8CyFuIRy+JCIivVGm6unUqVPo3r07kpKS5EvFz58/j5o1a2LLli3w9/cv1ySJSisuOQ430m8U+bqAQFJ6EuKS49DcsfkzzIyIiKhoZZpDNmjQIDRo0ADXrl1DXFwc4uLi8M8//yAgIABDhgwp7xyJSu1m+s1yjSMiInoWytRDduLECRw5cgTW1tZym7W1NaZNm4bmzdnrQMqpaVqzXOOIiIiehTL1kHl7e+PGjYLDQsnJyahTp85TJ0VUVk3sm8DB1EGewP84CRIcTR3RxL7JM86MiIioaGUqyKZPn4733nsPGzZswLVr13Dt2jVs2LABo0ePxqxZs5Camio/iJ4llYEK41uML/S1/CJtXItxnNBPRER6pUxDll26dAEAvPbaa5CkvF9y+atndO3aVX4uSRJXraZnLtQ9FPPazcOMQzOQ/DBZbncwdcC4FuP0cskLIiKq3spUkO3evbu88yAqV6HuoWjl2AqB6/JuIL24/WKu1E9ERHqrTAVZcHAwMjIy8OeffyI5OVnnFjUA0K1bt3JJjuhpPHornCYOTViMERGR3irTHLLIyEjUqlULrVq1Qrdu3dCjRw/58dJLL5V3jkSk52bMmIHmzZvDwsIC9vb26NGjB86dO1fsNv3794ckSQUeDRo00Im7d+8eRowYAScnJxgbG8PX1xfbt2+vyNMhInrmylSQjRw5Eq+++ioSExORm5ur86ioOWMeHh4FfnCPH1/45G2i6k6bK3Dg0m38fPw6Dly6DW1uxd4hLSYmBiNGjMDBgwcRHR2NnJwchIWFIS0trcht5s+fj8TERPnxzz//wMbGBq+++qock5WVhQ4dOuDy5cvYsGEDzp07h2+++QYuLi4Vej5ERM9amYYsk5OTMXbsWDg4OJR3PsX65JNPMHjwYPm5ubn5Mz0+UWUQeSoRU7aeQWJKhtzmZGWM8K710cnPqWKOGRmp83zFihWwt7fH0aNH8dxzzxW6jZWVFaysrOTnP/30E+7evYsBAwbIbcuXL8edO3ewf/9+qNVqAIC7u3sFnAERkbLK1EPWs2dP7Nmzp5xTKZmFhQUcHR3lBwsyIl2RpxIxfFWcTjEGAEkpGRi+Kg6RpxKfSR4pKSkAABsbm1Jvs2zZMoSGhuoUXFu2bEFgYCBGjBgBBwcH+Pn5Yfr06bx6m4iqnDL1kC1cuBCvvvoqfv/9d/j7+8t/ueZ77733yiW5x82aNQuffvop3Nzc8Oqrr+L//u//YGRkVCHHItIHQgg8zC5d8aHNFQjfcrqQ26oDAoAEYPKWM2hdxw4qg8IXzn2UiVolL2vzJIQQGDt2LNq0aQM/P79SbZOYmIgdO3ZgzZo1Ou1///03fvvtN/Tp0wfbt2/HhQsXMGLECOTk5ODjjz9+4tyIiPRVmQqyNWvW4Ndff4WJiQn27Nmj80NbkqQKKchGjRqFJk2awNraGocPH8aECRMQHx+Pb7/9tshtMjMzkZmZKT/nQrVU2TzM1qL+x7+Wy74EgKTUDPhPjipV/JlPOsLU6Ml/RIwcORJ//vkn9u3bV+ptIiIiUKNGDfTo0UOnPTc3F/b29li6dClUKhWaNm2KhIQEzJkzhwUZEVUpZSrIPvroI3zyyScYP368ztICT2ry5MmYMmVKsTGxsbFo1qwZxowZI7cFBATA2toaPXv2xKxZs2Bra1votjNmzChx/0RUft59911s2bIFe/fuhaura6m2EUJg+fLl6Nu3b4EebycnJ6jVaqhU/y1Z4uvri6SkJGRlZbGHnIiqjDIVZFlZWXj99defqhgD8v6S7tWrV7ExHh4ehba3atUKAHDx4sUiC7IJEyZg7Nix8vPU1FS4ubmVLVkiBZioVTjzScdSxR6Ov4P+K2JLjIsY0BwtPEue22WiLv26bUIIvPvuu9i8eTP27NkDT0/PUm8bExODixcvYuDAgQVea926NdasWYPc3Fz558358+fh5OTEYoyIqpQyFWT9+vXD+vXrMXHixKc6uJ2dHezs7Mq07bFjxwDk/QVdFI1GA41GU6b9E+kDSZJKPWzYtm5NOFkZIyklo9B5ZBIARytjtK1bs1RzyJ7EiBEjsGbNGvz888+wsLBAUlISgLwrKU1MTADk/YF0/fp1fPfddzrbLlu2DC1btix0vtnw4cOxYMECjBo1Cu+++y4uXLgg30uXiKgqKVNBptVqMXv2bPz6668ICAgoMKl/3rx55ZJcvgMHDuDgwYMICQmBlZUVYmNjMWbMGHTr1g21atUq12MRVVYqAwnhXetj+Ko4SIBOUZZffoV3rV/uxRgALF68GADQrl07nfYVK1agf//+APIm7l+9elXn9ZSUFGzcuBHz588vdL9ubm6IiorCmDFjEBAQABcXF4waNQrjxo0r93MgIlJSmQqykydPonHjxgCAU6dO6bxWlquySqLRaLB+/XpMmTIFmZmZcHd3x+DBg/HBBx+U+7GIKrNOfk5Y/GaTAuuQOVbwOmRClLzwbERERIE2KysrpKenF7tdYGAgDh48WNbUiIgqhUpxc/EmTZrwBzJRKXXyc0KH+o44HH8HyfczYG9hjBaeNhXSM0ZEROWjTAUZEek3lYGEQK/CL3YhIiL983SXSRIRERHRU2NBRkRERKQwFmRERERECmNBRkRERNVObu5/V4cnXLir81wJnNRPRERE1cqlY8nYu+68/PyXhX/CrIYGbV+vC6/G9orkxB4yIiIiqjYuHUtG5JJTSE/J0mlPu5eJyCWncOlYsiJ5sSAjIiKiaiE3V+D39ReKjdn3wwVFhi9ZkBEREVG1kHjhHtLuZRYb8+BuJhIv3Hs2CT2CBRkRPbXFixcjICAAlpaWsLS0RGBgIHbs2FHsNjExMWjatCmMjY1Ru3ZtfP311wVivvjiC3h7e8PExARubm4YM2YMMjIyCtkbEVHJ0lKLL8aeNK48cVI/UVWUqwWu7Ace3ADMHQD3IMBAVWGHc3V1xcyZM1GnTh0AwMqVK9G9e3ccO3YMDRo0KBAfHx+PF154AYMHD8aqVavwxx9/4J133kHNmjXxyiuvAABWr16N8ePHY/ny5QgKCsL58+flG5V//vnnFXYuRFR1mVlqyjWuPLEgI6pqzmwBIscBqQn/tVk6A51mAfW7Vcghu3btqvN82rRpWLx4MQ4ePFhoQfb111+jVq1a+OKLLwAAvr6+OHLkCObOnSsXZAcOHEDr1q3Ru3dvAICHhwfeeOMNHD58uELOgYiqPqe6NWBWQ1PssKW5tQZOdWs8u6T+xSFLoqrkzBbgh7d0izEASE3Maz+zpcJT0Gq1WLduHdLS0hAYGFhozIEDBxAWFqbT1rFjRxw5cgTZ2dkAgDZt2uDo0aNyAfb3339j+/btePHFFyv2BIioyjIwkND29brFxrR5rS4MDKRnlNF/2ENGpM+EALLTSxebqwV2fACgsKuDBAApr+esdrvSDV+qTQGp9D+UTp48icDAQGRkZMDc3BybN29G/fr1C41NSkqCg4ODTpuDgwNycnJw69YtODk5oVevXrh58ybatGkDIQRycnIwfPhwjB8/vtQ5ERE9zquxPToN9cPeded1lr4wt9agzWvKrUPGgoxIn2WnA9Ody2lnIq/nbKZb6cInJgBGZqXeu7e3N44fP4579+5h48aN6NevH2JiYoosyqTHij0hhE77nj17MG3aNCxatAgtW7bExYsXMWrUKDg5OWHSpEmlzouI6HFeje3h6mODb8fsBQB0GRkAt/q2ivSM5WNBRkTlwsjISJ7U36xZM8TGxmL+/PlYsmRJgVhHR0ckJSXptCUnJ8PQ0BC2trYAgEmTJqFv374YNGgQAMDf3x9paWkYMmQIPvzwQxgYcMYFEZXdo8WXc11rRYsxgAUZkX5Tm+b1VJXGlf3A6p4lx/XZkHfVZWmO/RSEEMjMLHzibGBgILZu3arTFhUVhWbNmkGtVgMA0tPTCxRdKpUKQgi5N42IqKpgQUakzySp9MOGXs/nXU2ZmojC55FJea97PV/uS2BMnDgRnTt3hpubG+7fv49169Zhz549iIyMBABMmDAB169fx3fffQcAGDZsGBYuXIixY8di8ODBOHDgAJYtW4a1a9fK++zatSvmzZuHxo0by0OWkyZNQrdu3aBSVdwSHkRESmBBRlRVGKjylrb44S0AEnSLsn+74jvNrJD1yG7cuIG+ffsiMTERVlZWCAgIQGRkJDp06AAASExMxNWrV+V4T09PbN++HWPGjMFXX30FZ2dnfPnll/KSFwDw0UcfQZIkfPTRR7h+/Tpq1qyJrl27Ytq0aeWePxGR0iRRjfr+U1NTYWVlhZSUFFhaWiqdDlWw9Ox0tFzTEgBwqPchmD7lEFxFy8jIQHx8PDw9PWFsbFz2HRW6DplLXjFWQeuQESml3L5vqNrJztRi6agYAMCQ+cFQa8r/j9UnqTvYQ0ZU1dTvBvi8+ExX6icioqfDgoyoKjJQAZ5tlc6CiIhKideNExERESmMBRkRERGRwliQERERESmMBRkRERGRwipVQbZt2za0bNkSJiYmsLOzw8svv6x0SkRERERPrdJcZblx40YMHjwY06dPx/PPPw8hBE6ePKl0WkRERERPrVIUZDk5ORg1ahTmzJmDgQMHyu3e3t4KZkVERERUPirFkGVcXByuX78OAwMDNG7cGE5OTujcuTNOnz6tdGpERERET61SFGR///03AGDy5Mn46KOP8Msvv8Da2hrBwcG4c+dOkdtlZmYiNTVV50FUHWhztYhNisX2v7cjNikW2lxthR5v8eLFCAgIgKWlJSwtLREYGIgdO3YUu01mZiY+/PBDuLu7Q6PRwMvLC8uXL5dfz87OxieffAIvLy8YGxujYcOG8s3K802ePBmSJOk8HB0ddWKEEJg8eTKcnZ1hYmKCdu3aFfhjLjMzE++++y7s7OxgZmaGbt264dq1azoxd+/eRd++fWFlZQUrKyv07dsX9+7d04m5evUqunbtCjMzM9jZ2eG9995DVlaWTszJkycRHBwMExMTuLi44JNPPsHjd7CLiYlB06ZNYWxsjNq1a+Prr78u8P5t3LgR9evXh0ajQf369bF58+YCMYsWLZJvKdS0aVP8/vvvevvenDt3DiEhIXBwcJDP+6OPPkJ2dnaB8yKqkoSCwsPDBfLugFzkIzY2VqxevVoAEEuWLJG3zcjIEHZ2duLrr79+4v2npKQ8i9MjhaVlpQm/CD/hF+En0rLSlE6nRA8fPhRnzpwRDx8+fKr9RF+OFu1/aC+fu1+En2j/Q3sRfTm6nDItaMuWLWLbtm3i3Llz4ty5c2LixIlCrVaLU6dOFblNt27dRMuWLUV0dLSIj48Xhw4dEn/88Yf8+gcffCCcnZ3Ftm3bxKVLl8SiRYuEsbGxiIuLk2PCw8NFgwYNRGJiovxITk7WOc7MmTOFhYWF2Lhxozh58qR4/fXXhZOTk0hNTZVjhg0bJlxcXER0dLSIi4sTISEhomHDhiInJ0eO6dSpk/Dz8xP79+8X+/fvF35+fqJLly7y6zk5OcLPz0+EhISIuLg4ER0dLZydncXIkSPlmJSUFOHg4CB69eolTp48KTZu3CgsLCzE3Llz5Zi///5bmJqailGjRokzZ86Ib775RqjVarFhwwY5Zv/+/UKlUonp06eLs2fPiunTpwtDQ0Nx8OBBOWbdunVCrVaLb775Rpw5c0aMGjVKmJmZiStXrujle3Pp0iWxfPlycfz4cXH58mXx888/C3t7ezFhwoRCPz/5yuv7hqqfrIwcsXDoLrFw6C6RlZFT8gZlkJKSUuq6Q9GC7ObNm+Ls2bPFPh4+fCh+++03AUD8/vvvOtu3aNFCTJw4scj9Z2RkiJSUFPnxzz//sCCrRqpjQRZ9OVr4R/jrFGN+EX7CP8Jf+Ef4V2hR9jhra2vx7bffFvrajh07hJWVlbh9+3aR2zs5OYmFCxfqtHXv3l306dNHfh4eHi4aNmxY5D5yc3OFo6OjmDlzptyWkZEhrKys5D/m7t27J9RqtVi3bp0cc/36dWFgYCAiIyOFEEKcOXNGANApeA4cOCAAiL/++ksIIcT27duFgYGBuH79uhyzdu1aodFo5J85ixYtElZWViIjI0OOmTFjhnB2dha5ublCiLxC1MfHR+c8hg4dKlq1aiU/f+2110SnTp10Yjp27Ch69eolP2/RooUYNmyYToyPj48YP368Xr43hRkzZoxo06ZNka8LwYKMyk7fCjJFhyzt7Ozg4+NT7CO/q12j0eDcuXPyttnZ2bh8+TLc3d2L3L9Go5GHUPIfRJWJEALp2emletzPvI8Zh2dAQBTcz7//zTw8E/cz75dqf0IU3E9paLVarFu3DmlpaQgMDCw0ZsuWLWjWrBlmz54NFxcX1KtXD//73//w8OFDOSYzMxPGxsY625mYmGDfvn06bRcuXICzszM8PT3Rq1cveYoDAMTHxyMpKQlhYWFym0ajQXBwMPbv3w8AOHr0KLKzs3VinJ2d4efnJ8ccOHAAVlZWaNmypRzTqlUrWFlZ6cT4+fnB2dlZjunYsSMyMzNx9OhROSY4OBgajUYnJiEhAZcvX5ZjHs0lP+bIkSPy8F1RMfm5ZGVl4ejRowViwsLC5Bh9e28ed/HiRURGRiI4OLjQ14mqmkpxlaWlpSWGDRuG8PBwuLm5wd3dHXPmzAEAvPrqqwpnR1RxHuY8RMs1LUsOLKUb6TcQtC6oVLGHeh+Cqdq01Ps+efIkAgMDkZGRAXNzc2zevBn169cvNPbvv//Gvn37YGxsjM2bN+PWrVt45513cOfOHXkeWceOHTFv3jw899xz8PLywq5du/Dzzz9Dq/1vPlzLli3x3XffoV69erhx4wamTp2KoKAgnD59Gra2tkhKSgIAODg46BzfwcEBV65cAQAkJSXByMgI1tbWBWLyt09KSoK9vX2B87C3t9eJefw41tbWMDIy0onx8PAocJz81zw9PQvdj4ODA3JycnDr1i04OTkVGZN/nFu3bkGr1RYbo2/vTb6goCDExcUhMzMTQ4YMwSeffFJg30RVUaWY1A8Ac+bMQa9evdC3b180b94cV65cwW+//VbgBwURKcPb2xvHjx/HwYMHMXz4cPTr1w9nzpwpNDY3NxeSJGH16tVo0aIFXnjhBcybNw8RERFyL9n8+fNRt25d+Pj4wMjICCNHjsSAAQOgUqnk/XTu3BmvvPIK/P39ERoaim3btgEAVq5cqXM8SZJ0ngshCrQ97vGYwuLLIya/J7I8Yh5vK6+Yx1XUewMA69evR1xcHNasWYNt27Zh7ty5xeZCVFVUih4yAFCr1Zg7dy6/OalaMTE0waHeh0oVe/TGUbyz650S4xa1X4SmDk1LdewnYWRkhDp16gAAmjVrhtjYWMyfPx9LliwpEOvk5AQXFxdYWVnJbb6+vhBC4Nq1a6hbty5q1qyJn376CRkZGbh9+zacnZ0xfvx4eHp6FpmDmZkZ/P39ceHCBQCQr7hMSkqCk5OTHJecnCz32Dg6OiIrKwt3797V+QMvOTkZQUFBcsyNGzcKHO/mzZs6+zl0SPdrdffuXWRnZ+vEPN4jlJycDAAlxhgaGsLW1rbYmPx92NnZQaVSFRujb+9NPjc3NwBA/fr1odVqMWTIELz//vs6hThRVVRpesiIqiNJkmCqNi3VI8g5CA6mDpBQeO+GBAmOpo4Icg4q1f5K6iUpiRACmZmZhb7WunVrJCQk4MGDB3Lb+fPnYWBgAFdXV51YY2NjuLi4ICcnBxs3bkT37t2LPGZmZibOnj0rFxienp5wdHREdHS0HJOVlYWYmBi5oGjatCnUarVOTGJiIk6dOiXHBAYGIiUlBYcPH5ZjDh06hJSUFJ2YU6dOITExUY6JioqCRqNB06ZN5Zi9e/fqLPcQFRUFZ2dneSgzMDBQJ5f8mGbNmkGtVhcbk5+LkZERmjZtWiAmOjpajtG396YwQghkZ2eXeT4jUaVS7pcU6LEnudqBKr/qfJXl41daVvRVlhMmTBB79+4V8fHx4s8//xQTJ04UBgYGIioqSgghxPjx40Xfvn3l+Pv37wtXV1fRs2dPcfr0aRETEyPq1q0rBg0aJMccPHhQbNy4UVy6dEns3btXPP/888LT01PcvXtXjnn//ffFnj17xN9//y0OHjwounTpIiwsLMTly5flmJkzZworKyuxadMmcfLkSfHGG28UurSDq6ur2Llzp4iLixPPP/98oUs7BAQEiAMHDogDBw4If3//Qpd2aN++vYiLixM7d+4Urq6uOks73Lt3Tzg4OIg33nhDnDx5UmzatElYWloWuuzFmDFjxJkzZ8SyZcsKLHvxxx9/CJVKJWbOnCnOnj0rZs6cWeSyF8uWLRNnzpwRo0ePFmZmZnr73qxatUqsX79enDlzRly6dEn88MMPwsXFReeq2sLwKksqK327ypIFGVVZ1bEgE6LwdchCfwit0CUv3n77beHu7i6MjIxEzZo1Rfv27eViTAgh+vXrJ4KDg3W2OXv2rAgNDRUmJibC1dVVjB07VqSnp8uv79mzR/j6+gqNRiNsbW1F3759dZZNEELI62ap1Wrh7OwsXn75ZXH69GmdmNzcXBEeHi4cHR2FRqMRzz33nDh58qROzMOHD8XIkSOFjY2NMDExEV26dBFXr17Vibl9+7bo06ePsLCwEBYWFqJPnz46xaEQQly5ckW8+OKLwsTERNjY2IiRI0fqLHEhhBB//vmnaNu2rdBoNMLR0VFMnjxZXvLi0XNv3LixMDIyEh4eHmLx4sUF3vMff/xReHt7C7VaLXx8fMTGjRsLxHz11Vfy16VJkyYiJiZGb9+bdevWiSZNmghzc3NhZmYm6tevL6ZPn17i9wMLMiorfSvIJCGqT19wamoqrKyskJKSwiUwqoH07HT5CsUnvWJQCRkZGYiPj5dXVn8a2lwt4pLjcDP9Jmqa1kQT+yZQGXAODlU95fl9Q9VLdqYWS0fFAACGzA+GWlP+PyOfpO6oNJP6iaj0VAYqNHdsrnQaRERUSizIqMoyVZviZL+TSqdBRERUIl5lSURERKQwFmRERERECmNBRkRERKQwFmRERERECmNBRkRERKQwFmRERERECmNBRkRERKQwFmRERERECmNBRlQFCa0WaYcOI+WXbUg7dBhCq63Q4y1evBgBAQGwtLSEpaUlAgMDsWPHjmK3+eqrr+Dr6wsTExN4e3vju+++03n9m2++Qdu2bWFtbQ1ra2uEhobi8OHDFXkaRESK4Ur9RFVMalQUbkyfgZykJLnN0NERDhMnwDIsrEKO6erqipkzZ6JOnToAgJUrV6J79+44duwYGjRoUCB+8eLFmDBhAr755hs0b94chw8fxuDBg2FtbY2uXbsCAPbs2YM33ngDQUFBMDY2xuzZsxEWFobTp0/DxcWlQs6DiEgpvLk4kZ4oj5skp0ZF4fqo0cDj39aSBABwmf9FhRVlj7OxscGcOXMwcODAAq8FBQWhdevWmDNnjtw2evRoHDlyBPv27St0f1qtFtbW1li4cCHeeuutCsubKhfeXJzKijcXJ6JSE0JAPHxYulitFjemTitYjOXtCJCAG9OmwywwEJKq5B88kokJpH8LuSeh1Wrx448/Ii0tDYGBgYXGZGZmFvjlaWJigsOHDyM7OxtqtbrANunp6cjOzoaNjc0T50REpO9YkBHpMfHwIc41aVpOOwNybtzA+eYtShXuHXcUkqlpqXd/8uRJBAYGIiMjA+bm5ti8eTPq169faGzHjh3x7bffokePHmjSpAmOHj2K5cuXIzs7G7du3YKTk1OBbcaPHw8XFxeEhoaWOiciosqCBRkRlQtvb28cP34c9+7dw8aNG9GvXz/ExMQUWpRNmjQJSUlJaNWqFYQQcHBwQP/+/TF79myoCum9mz17NtauXYs9e/ZwWIqIqiQWZER6TDIxgXfc0VLFph85gn+GDC0xzm3pEpg2a1aqYz8JIyMjeVJ/s2bNEBsbi/nz52PJkiUFYk1MTLB8+XIsWbIEN27cgJOTE5YuXQoLCwvY2dnpxM6dOxfTp0/Hzp07ERAQ8EQ5ERFVFizIiPSYJEmlHjY0a90aho6OyLlxo/B5ZJIEQwcHmLVuXao5ZE9LCIHMzMxiY9RqNVxdXQEA69atQ5cuXWBg8N9qPHPmzMHUqVPx66+/olkpikgiosqKBRlRFSGpVHCYOCHvKktJ0i3K/p2c7zBxQoUUYxMnTkTnzp3h5uaG+/fvY926ddizZw8iIyMBABMmTMD169fltcbOnz+Pw4cPo2XLlrh79y7mzZuHU6dOYeXKlfI+Z8+ejUmTJmHNmjXw8PBA0r/LeJibm8Pc3Lzcz4GISElcGJaoCrEMC4PL/C9g6OCg027o4FChS17cuHEDffv2hbe3N9q3b49Dhw4hMjISHTp0AAAkJibi6tWrcrxWq8Vnn32Ghg0bokOHDsjIyMD+/fvh4eEhxyxatAhZWVno2bMnnJyc5MfcuXMr5ByIiJTEHjKiKsYyLAwW7dsj/chR5Ny8CcOaNWHarGmFDlMuW7as2NcjIiJ0nvv6+uLYsWPFbnP58uWnzIqIqPJgQUZUBUkqFcxalm55CyIiUl6lGLLcs2dP3uTmQh6xsbFKp0dERET0VCpFD1lQUBASExN12iZNmoSdO3fyyisiIiKq9CpFQWZkZARHR0f5eXZ2NrZs2YKRI0eW6dYuRERERPqkUhRkj9uyZQtu3bqF/v37FxuXmZmpsw5SampqBWdGRERE9OQqxRyyxy1btgwdO3aEm5tbsXEzZsyAlZWV/CgpnoiIiEgJihZkkydPLnKyfv7jyJEjOttcu3YNv/76KwYOHFji/idMmICUlBT58c8//1TUqRARERGVmaJDliNHjkSvXr2KjXl0oUgAWLFiBWxtbdGtW7cS96/RaKDRaJ4mRSIiIqIKp2hBZmdnV+BGwsURQmDFihV46623oFarKzAzIiIiomenUs0h++233xAfH1+q4UoiIiKiyqJSFWTLli1DUFAQfH19lU6FSK/l5gpcP3cX52OTcP3cXeTmipI3KkczZsyAJEkYPXp0sXFfffUVfH19YWJiAm9vb/nm4/k2bdqEZs2aoUaNGjAzM0OjRo3w/fffV2DmRETKqFTLXqxZs0bpFIj03qVjyfh9/QWk3ftvyRezGhq0fb0uvBrbV/jxY2NjsXTpUgQEBBQbt3jxYkyYMAHffPMNmjdvjsOHD2Pw4MGwtrZG165dAQA2Njb48MMP4ePjAyMjI/zyyy8YMGAA7O3t0bFjxwo/FyKiZ6VS9ZARUfEuHUtG5JJTOsUYAKTdy0TkklO4dCy5Qo//4MED9OnTB9988w2sra2Ljf3+++8xdOhQvP7666hduzZ69eqFgQMHYtasWXJMu3bt8NJLL8HX1xdeXl4YNWoUAgICsG/fvgo9DyKiZ40FGZEeE0IgO1Nbqkfmwxz8vv58sfv7ff0FZD7MKdX+hHjyYc4RI0bgxRdfRGhoaImxmZmZMDY21mkzMTHB4cOHkZ2dXSBeCIFdu3bh3LlzeO655544NyIifVaphiyJqpucrFwsHRVTbvtLu5eJb8fsLVXskPnBUGtUpd73unXrEBcXh9jY2FLFd+zYEd9++y169OiBJk2a4OjRo1i+fDmys7Nx69YtODk5AQBSUlLg4uKCzMxMqFQqLFq0CB06dCh1XkRElQELMiJ6av/88w9GjRqFqKioAr1eRZk0aRKSkpLQqlUrCCHg4OCA/v37Y/bs2VCp/isELSwscPz4cTx48AC7du3C2LFjUbt2bbRr166CzoaI6NljQUakxwyNDDBkfnCpYhMu3MMvC0+UGNdlZEM4161RqmOX1tGjR5GcnIymTZvKbVqtFnv37sXChQvl3q1HmZiYYPny5ViyZAlu3LgBJycnLF26FBYWFjrrExoYGKBOnToAgEaNGuHs2bOYMWMGCzIiqlJYkBHpMUmSSj1s6FbfBmY1NAUm9D/K3FoDt/o2MDCQyitFAED79u1x8uRJnbYBAwbAx8cH48aNK1CMPUqtVsPV1RVA3rBnly5dYGBQdDEohEBmZtHnSERUGbEgI6oiDAwktH29LiKXnCoyps1rdcu9GAPyhhX9/Px02szMzGBrayu3T5gwAdevX5fXGjt//jwOHz6Mli1b4u7du5g3bx5OnTqFlStXyvuYMWMGmjVrBi8vL2RlZWH79u347rvvsHjx4nI/ByIiJbEgI6pCvBrbo9NQvwLrkJlba9DmtWezDllREhMTcfXqVfm5VqvFZ599hnPnzkGtViMkJAT79+/XuX9tWloa3nnnHVy7dg0mJibw8fHBqlWr8PrrrytwBkREFUcSZbm2vZJKTU2FlZUVUlJSYGlpqXQ6RDoyMjIQHx8PT0/PUk+ML0purkDihXtIS82EmaUGTnVrVEjPGJHSyvP7hqqX7EytfBX7k15VXlpPUnewh4yoCjIwkODiXfzCrEREpD+4MCwRERGRwliQERERESmMBRkRERGRwjiHjIiIiKodtUaFEV8/r3QaMvaQEemZanThM9FT4/cLVRUsyIj0hFqtBgCkp6crnAlR5ZH//ZL//UNUWXHIkkhPqFQq1KhRA8nJyQAAU1NTSBLXDiMqjBAC6enpSE5ORo0aNYq9PRdRZcCCjEiPODo6AoBclBFR8WrUqCF/3xBVZizIiPSIJElwcnKCvb09srOzlU6HSK+p1Wr2jFGVwYKMSA+pVCr+oiEiqkY4qZ+IiIhIYSzIiIiIiBTGgoyIiIhIYdVqDln+AoKpqakKZ0JERERVXX69UZoFjKtVQXb//n0AgJubm8KZEBERUXVx//59WFlZFRsjiWp034nc3FwkJCTAwsKCC25WE6mpqXBzc8M///wDS0tLpdMhomqMP4+qHyEE7t+/D2dnZxgYFD9LrFr1kBkYGMDV1VXpNEgBlpaW/AFIRHqBP4+ql5J6xvJxUj8RERGRwliQERERESmMBRlVaRqNBuHh4dBoNEqnQkTVHH8eUXGq1aR+IiIiIn3EHjIiIiIihbEgIyIiIlIYCzIiIiIihbEgoypr0aJF8PT0hLGxMZo2bYrff/9d6ZSIqBrau3cvunbtCmdnZ0iShJ9++knplEgPsSCjKmn9+vUYPXo0PvzwQxw7dgxt27ZF586dcfXqVaVTI6JqJi0tDQ0bNsTChQuVToX0GK+ypCqpZcuWaNKkCRYvXiy3+fr6okePHpgxY4aCmRFRdSZJEjZv3owePXoonQrpGfaQUZWTlZWFo0ePIiwsTKc9LCwM+/fvVygrIiKiorEgoyrn1q1b0Gq1cHBw0Gl3cHBAUlKSQlkREREVjQUZVVmSJOk8F0IUaCMiItIHLMioyrGzs4NKpSrQG5acnFyg14yIiEgfsCCjKsfIyAhNmzZFdHS0Tnt0dDSCgoIUyoqIiKhohkonQFQRxo4di759+6JZs2YIDAzE0qVLcfXqVQwbNkzp1Iiomnnw4AEuXrwoP4+Pj8fx48dhY2ODWrVqKZgZ6RMue0FV1qJFizB79mwkJibCz88Pn3/+OZ577jml0yKiambPnj0ICQkp0N6vXz9EREQ8+4RIL7EgIyIiIlIY55ARERERKYwFGREREZHCWJARERERKYwFGREREZHCWJARERERKYwFGREREZHCWJARERERKYwFGREREZHCWJARERERKYwFGREREZHCWJARERERKYwFGRFVe5GRkWjTpg1q1KgBW1tbdOnSBZcuXQIAXL58GZIk4YcffkDbtm1hYmKC5s2b4/z584iNjUWzZs1gbm6OTp064ebNmwqfCRFVVry5OBFVexs3boQkSfD390daWho+/vhjXL58GcePH8fVq1fh6ekJHx8ffPHFF6hVqxbefvttZGVlwdLSElOnToWpqSlee+01hIaGYvHixUqfDhFVQizIiIgec/PmTdjb2+PkyZMwNzeHp6cnvv32WwwcOBAAsG7dOrzxxhvYtWsXnn/+eQDAzJkzERERgb/++kvJ1ImokuKQJRFVe5cuXULv3r1Ru3ZtWFpawtPTEwBw9epVOSYgIED+t4ODAwDA399fpy05OfkZZUxEVY2h0gkQESmta9eucHNzwzfffANnZ2fk5ubCz88PWVlZcoxarZb/LUlSoW25ubnPLmkiqlJYkBFRtXb79m2cPXsWS5YsQdu2bQEA+/btUzgrIqpuWJARUbVmbW0NW1tbLF26FE5OTrh69SrGjx+vdFpEVM1wDhkRVWsGBgZYt24djh49Cj8/P4wZMwZz5sxROi0iqmZ4lSURERGRwthDRkRERKQwFmRERERECmNBRkRERKQwFmRERERECmNBRkRERKQwFmRERERECmNBRkRERKQwFmRERERECmNBRkRERKQwFmRERERECmNBRkRERKQwFmRERERECvt/StNgwO+J4C8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 700x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# user defined contrast for `hp`\n",
    "# grid is determined for `am` and `drat`\n",
    "(fig, axes), comparison_df = plot_comparison(\n",
    "    model,\n",
    "    idata,\n",
    "    contrast_predictor={\"hp\": [110, 175]},\n",
    "    conditional=[\"am\", \"drat\"]\n",
    ")\n",
    "fig.set_size_inches(7, 3)\n",
    "plt.title(\"Difference in mpg between am and drat\\nfor hp contrast [110, 175]\");"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Palmer penguins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"https://vincentarelbundock.github.io/Rdatasets/csv/palmerpenguins/penguins.csv\", index_col=0)\n",
    "data = data.dropna(axis=0, how=\"any\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [body_mass_g_sigma, Intercept, flipper_length_mm, species, flipper_length_mm:species, bill_length_mm, flipper_length_mm:bill_length_mm, species:bill_length_mm, flipper_length_mm:species:bill_length_mm, island]\n"
     ]
    },
    {
     "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 146 seconds.\n"
     ]
    }
   ],
   "source": [
    "model = bmb.Model(\n",
    "    \"body_mass_g ~ flipper_length_mm * species * bill_length_mm + island\",\n",
    "    data, family=\"gaussian\"\n",
    ")\n",
    "idata = model.fit(draws=1000, target_accept=0.95, random_seed=1234)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "main: species, group: None, panel: None\n"
     ]
    },
    {
     "data": {
      "image/png": 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nq6++woABA9CxY0dMnjwZjRs3xo0bN7B//35s3rwZALB8+XJ07twZXbp0wXvvvQdnZ2c8ePAAV69exZ49e3Do0KFqH5fnsX37dujo6KBXr144f/485syZg5YtW2Lo0KEAADc3N4wbNw5ffvkltLS00LdvX6SmpmLOnDlwdHTE5MmTATw+Czlt2jTMnz8fY8aMwRtvvIG0tDRERkZWeDlu5MiRmDlzJo4dO4bZs2e/kELXz88PDRs2xLvvvou5c+dCV1cXmzdvxp9//qkUd/bsWUyaNAlvvPEGmjVrBj09PRw6dAhnz57FjBkzAACrV6/GoUOH0L9/fzRu3Bj5+fnSGa0nJ9Qur6rH9FlUJd8y9vb2CAoKQmRkJOzs7LBp0ybExsZi4cKFMDIyAgBERERg27Zt6Nq1KyZPngxvb2+Ulpbixo0b+PXXXzF16lT4+PigS5cuCA4Oxvz583H79m0EBgZCX18ff/zxB4yMjBAWFqY236p+lqt7jIlU1PGgDKJ6q2yUa9lLT09PWFtbi27duokFCxaIO3fuqKzzvKNchRCiqKhILFmyRLRs2VIYGBgIExMT0aJFCzF+/Hhx5coVKc7JyUn0799fbe6ZmZkiPDxcuLi4CF1dXWFubi7atm0rZs2aJXJzc4UQ/xtN+eRUDmUAiLlz5yq1xcfHi759+wq5XC709fWFq6urygjSlJQUMXr0aNGoUSOhq6srrKyshJ+fn5g/f77aPJ9U0SjX//u//1PZBgCxfv36Svsr+1qcPn1aDBgwQJiYmAhTU1Px5ptvitu3byvFlpSUiIULF4rmzZsLXV1dYWlpKUaOHKkyhUZpaamIiooSjo6OQk9PT3h7e4s9e/aojHB8UkhIiNDR0RE3b9586jF4cv/Kf10qOh7qRmPHxcUJX19fYWRkJKysrMSYMWPEmTNnlI7b7du3RUhIiGjRooUwNjYWJiYmwtvbW0RHR0ujnuPj48WgQYOEk5OT0NfXFxYWFqJbt25i9+7dT92Pqh7Tqo5yrUq+Qvzvc/HTTz+JV155Rejp6QlnZ2exdOlSlT5zc3PF7NmzhZubm9DT05Om3Zk8ebLIyMhQ2pfo6Gjh6ekpxfn6+oo9e/ZIMeq+B6ryWX6eY0z0JJkQNTxMj4iIADyeWNbZ2RmdO3fGjz/+WNfpvBScnZ3h6emJn3/+ua5TIXqheMmViKiGZWZm4tKlS1i/fj1u376tckmQiKimsaAjIqphe/fuxTvvvAM7OzusXLmyylOVEBFVFy+5EhEREWk4TixMREREpOFY0BGV88cff6Bbt26Qy+WQyWRYtmxZrW4vNTUV/fv3h7m5OWQyGSIiIpCamgqZTIYNGzZIcRs2bIBMJkNqamqt5lObZDIZIiMj6zoNAMCjR48QGRmp8jg1AIiMjIRMJqv20y3qyoIFC7Bz5846235cXBwiIyPVTmKsjr+/P2QyGWQyGQIDA5WWfffddxg+fDjc3NygpaWldp4/4PHjxcr6UPcqe+RcSUkJli5dij59+sDBwQFGRkZwd3fHjBkzqpyvOtu3b8ebb76Jpk2bwtDQEM7Oznjrrbdw5coVldhZs2ahdevWMDc3h4GBAZo0aYJx48apfYzd7NmzERgYiEaNGkEmk6k8Rq1MRESEtK8mJibV3g/SfLyHjqic0aNH4+HDh9i6dSsaNmxY4S+SmjJ58mQkJiZi3bp1sLW1hZ2dndpnxPbv3x/x8fHPNFs9VezRo0eYN28eANTo0z7q0oIFCzBkyBAMHDiwTrYfFxeHefPmISQkBA0aNKjSOq1bt8bKlStVJsHeuHEjMjIy0KFDB5SWlqKoqEjt+nPmzMG7776r0j5gwADo6+tLExvn5eUhMjISb775JsaMGQNLS0ucOXMG8+fPx549e3Dq1CkYGho+2w4DWLhwIWxtbTFr1iw0adIEaWlpWLBgAdq0aYOEhATpkXvA46d1vPnmm3B3d4epqSkuXLiA+fPnY/fu3Th//rzSY+6io6Ph7e2NoKCgCp9mATz++TF8+HB88sknKs9pppcLCzqics6dO4exY8dKD7N/XkVFRZDJZNDRUf9xO3fuHDp06KD0S1jdWTgrK6sancS4NjxtX6n+yMvLg4GBgdrHx71IZmZm6Nixo0r7/v37oaX1+CJSYGAgzp07p3b9smefPuno0aO4e/cuZs+eDW1tbQCAoaEhUlJSlIomf39/NG7cGG+88Qa2bduGkSNHPnP+e/bsgbW1tVLbq6++CmdnZ0RHR+Pbb7+V2r/66iulOH9/f7i4uKBfv37YtWsXRo8eLS178OCBtP8bN26scPtOTk5wcnKq9z8bqPbxkivR/1d2SbO4uBirVq2SLmOUOXfuHF577TU0bNgQBgYGaNWqFf773/8q9XHkyBHIZDJs3LgRU6dORaNGjaCvr4+rV6+qbK8s9urVq/jll1+k7VV0SVXdJVd/f394enrit99+Q8eOHWFoaIhGjRphzpw5KCkpkeLKLuEuWrQIn376KRo3bgwDAwO0a9cOBw8eVNnWlStXMGLECFhbW0NfXx/u7u4qv4yeZV8rk5GRgfHjx8PBwQF6enpwcXHBvHnzUFxcrJL/kiVLsHTpUri4uMDExAS+vr7SJbUnffPNN2jevDn09fXh4eGBLVu2ICQkRDrbmpqaKv0CnDdvnnTsy1/Wun37Nt58803I5XLY2Nhg9OjRUCgUVdqvmJgY9OjRA3K5XLq8FxUVpRSze/du+Pr6wsjICKampujVqxfi4+OVYsou/54/f77SXGQyGR4+fIj//ve/0v6UnXks+9759ddfMXr0aFhZWcHIyAgFBQW4evUq3nnnHTRr1gxGRkZo1KgRBgwYgL/++kspj9LSUsyfPx9ubm4wNDREgwYN4O3tLT1EPjIyEtOnTwfw+Lm9ZTmou6RdFWXFTHWsXbsWMplMqUDS1tZWKubKdOjQAQCQlpZWrW2VL+aAx0+qcHBwqFKfZd+H5f8Iep79p5cT/4wm+v/KLmn6+vpiyJAhmDp1qrTs0qVL8PPzg7W1Nb744gtYWFhg06ZNCAkJwe3bt/Gf//xHqa+ZM2fC19cXq1evhpaWltof+m3atEF8fDwGDRoEV1dXLFmyBABgZ2eH9PT0KuedkZGB4cOHY8aMGfj444+xd+9ezJ8/H1lZWVixYoVS7IoVK+Dk5IRly5ahtLQUixYtQt++fXH06FH4+voCAC5cuAA/Pz80btwYn3/+OWxtbbF//36Eh4fj7t27mDt37jPva2W5d+jQAVpaWvjoo4/g6uqK+Ph4zJ8/H6mpqVi/fr1S/FdffYUWLVpI9zXOmTMH/fr1Q0pKCuRyOQDg66+/xvjx4zF48GBER0dDoVBg3rx5KCgokPqxs7NDTEwM+vTpg9DQUIwZMwYAVM5yDB48GMOGDUNoaCj++usvzJw5EwAqvQQGPC4oxo4di27dumH16tWwtrbG5cuXlc4ybdmyBW+99RZ69+6N77//HgUFBVi0aBH8/f1x8OBBdO7c+ZlyiY+Px6uvvoru3btjzpw5AB6f/XrS6NGj0b9/f2zcuBEPHz6Erq4ubt26BQsLC3z22WewsrLC/fv38d///hc+Pj74448/4ObmBgBYtGgRIiMjMXv2bHTt2hVFRUW4ePGidP/ZmDFjcP/+fXz55ZfYvn27dGuAh4dHpceqpikUCvz000/o0aNHpc9oLVP2SLonL40+r2vXruH69esVXvouLi6Wjl9ERASaN2+O119/vca2Ty+pOn1OBVE9BEBMnDhRqW348OFCX19f3LhxQ6m9b9++wsjISGRnZwsh/vd4pq5du1Z5e+oe4aXu8VZlj3hKSUmR2rp16yYAiF27dimtP3bsWKGlpSWuX7+u1J+9vb3Iy8uT4nJycoS5ubno2bOn1BYQECAcHByEQqFQ6nPSpEnCwMBA3L9/v9r7inKPFBs/frwwMTGR8iyzZMkSAUCcP39eKX8vLy+lxzydOHFCABDff/+9EOLxI5psbW2Fj4+PUn/Xr18Xurq6wsnJSWrLzMxU+4gzIf73OKpFixYptU+YMEEYGBiI0tLSCvfxwYMHwszMTHTu3LnCuJKSEmFvby+8vLxESUmJ0rrW1tbCz8+vWrkYGxsrPT6tTNn3zttvv11h3mWKi4tFYWGhaNasmdKj3QIDA0WrVq0qXXfx4sUq36OVqeyRaU/q37+/0teuMqtWrVL6nqjMzZs3hY2NjWjXrp3S1+F5FBUVCX9/f2FmZqby80IIIdLT05UeKejj4yP++eefSvus6Ov6pIoeMUgvD57TJaqCQ4cOoUePHnB0dFRqDwkJwaNHj1Qukw0ePPiF5WZqaoqgoCClthEjRqC0tBTHjh1Tan/99ddhYGCgtO6AAQNw7NgxlJSUID8/HwcPHsSgQYNgZGSE4uJi6dWvXz/k5+erXOJ8nn39+eef0b17d9jb2yttq+z+xfI3effv31+6JwoAvL29AUAaJXjp0iVkZGRg6NChSus1btwYnTp1eub8yh9Xb29v5Ofn486dOxWuExcXh5ycHEyYMKHC+9MuXbqEW7duITg4WOnSmomJCQYPHoyEhAQ8evTouXMpT93Xqri4GAsWLICHhwf09PSgo6MDPT09XLlyBcnJyVJchw4d8Oeff2LChAnYv38/cnJyqrzdF2nt2rWwsLDAoEGDKo27f/8++vXrByEEfvjhhxq5xCmEQGhoKH777Td89913Kj8vAMDS0hInT57E8ePH8c033+D+/fvo3r37M52VJ1KHBR1RFdy7d0/t6FJ7e3tp+ZNe5EhUGxsblTZbW1sAqnmVtZdvKywsRG5uLu7du4fi4mJ8+eWX0NXVVXr169cPAFSm8niefb19+zb27Nmjsq2yy1/lt1X+Hih9fX0Aj2/wf3J/1R0TdW1P87TtqZOZmQkAcHBwqDCmLM+KvqdKS0uRlZX13LmUp257U6ZMwZw5czBw4EDs2bMHiYmJOHnyJFq2bKnU98yZM7FkyRIkJCSgb9++sLCwQI8ePXDq1Kkqb7+2nT17FqdOncLIkSOl46NOVlYWevXqhX/++QexsbFo0qTJc29bCIExY8Zg06ZN2LBhA1577TW1cTo6OmjXrh06deqEMWPG4NChQ7h27Ro+++yz586BXm68h46oCiwsLNT+BX3r1i0Aj//qftKLHDl4+/ZtlbaMjAwAqkVAWXv5Nj09PZiYmEBXVxfa2toIDg7GxIkT1W6v/H1Jz7OvlpaW8Pb2xqeffqp2eVnBXFVl+1vZMaltZffh3bx5s8KYsjwr+p7S0tJSmcajJqj7Wm3atAlvv/02FixYoNR+9+5dpalHdHR0MGXKFEyZMgXZ2dk4cOAAPvzwQwQEBCAtLQ1GRkY1nu+zWrt2LQBI90Sqk5WVhZ49eyIlJQUHDx6UzvI+j7Jibv369Vi7du0zjZZ1cHCAvb09Ll++/Nx50MuNZ+iIqqBHjx44dOiQVMCV+e6772BkZKR22oUX5cGDB9i9e7dS25YtW6ClpYWuXbsqtW/fvh35+flK6+7ZswddunSBtrY2jIyM0L17d/zxxx/w9vZGu3btVF7qRgpWV9l0FK6urmq39awFnZubG2xtbfHjjz8qtd+4cQNxcXFKbdU5w1UVfn5+kMvlWL16tdr5BMvybNSoEbZs2aIU8/DhQ2zbtk0a+fqs9PX1n3l/ZDKZytmsvXv34p9//qlwnQYNGmDIkCGYOHEi7t+/L428rq1jWhUFBQXYtGkTOnToAE9PT7UxZcXctWvX8Ouvv6J169bPvV0hBMaOHYv169djzZo1eOedd55p/atXr+LmzZto2rTpc+dCLzeeoSOqgrlz50r3e3300UcwNzfH5s2bsXfvXixatEgaYVkXLCws8N577+HGjRto3rw59u3bh2+++QbvvfceGjdurBSrra2NXr16YcqUKSgtLcXChQuRk5MjTbALAMuXL0fnzp3RpUsXvPfee3B2dsaDBw9w9epV7NmzRxoVWBM+/vhjxMbGws/PD+Hh4XBzc0N+fj5SU1Oxb98+rF69utJLl+VpaWlh3rx5GD9+PIYMGYLRo0cjOzsb8+bNg52dndJ9UqampnBycsKuXbvQo0cPmJubw9LS8rknkjYxMcHnn3+OMWPGoGfPnhg7dixsbGxw9epV/Pnnn1ixYgW0tLSwaNEivPXWWwgMDMT48eNRUFCAxYsXIzs7u9qX37y8vHDkyBHs2bMHdnZ2MDU1lUapViQwMBAbNmxAixYt4O3tjdOnT2Px4sUqx33AgAHw9PREu3btYGVlhevXr2PZsmVwcnJCs2bNpO0Dj7+HRo0aBV1dXbi5ucHU1PSZ9+XChQu4cOECgMdnVx89eoSffvoJwOORs+VHz+7cuRP379+v8OxcXl4eAgIC8Mcff2DZsmUoLi5Wuh/UyspKaT47f39/HD16tMKivEx4eDjWrl2L0aNHw8vLS6lPfX19qWg8e/YsJk+ejCFDhqBJkybQ0tLCX3/9hejoaFhYWGDatGlK/R49elS6fF9SUoLr169L+9+tWzfOO0eq6m48BlH9BDWjXIUQ4q+//hIDBgwQcrlc6OnpiZYtWyqNQhXifyM//+///q/K23veUa6vvPKKOHLkiGjXrp3Q19cXdnZ24sMPPxRFRUUq/S1cuFDMmzdPODg4CD09PdG6dWuxf/9+lZxSUlLE6NGjRaNGjYSurq6wsrISfn5+Yv78+c+1r1AzqjQzM1OEh4cLFxcXoaurK8zNzUXbtm3FrFmzRG5urlL+ixcvrlKfX3/9tWjatKnQ09MTzZs3F+vWrROvvfaaaN26tVLcgQMHROvWrYW+vr4AII0kLBtZmpmZqRSv7mtQkX379olu3boJY2NjYWRkJDw8PMTChQuVYnbu3Cl8fHyEgYGBMDY2Fj169BC///67Usyz5JKUlCQ6deokjIyMBABpBGlZ7MmTJ1XyzMrKEqGhocLa2loYGRmJzp07i99++01lBOrnn38u/Pz8hKWlpdDT0xONGzcWoaGhIjU1Vam/mTNnCnt7e6GlpSUAiMOHD1d4jCob5Vq23+pe6kYm9+rVSxgbG4ucnBy1/ZV9D1X0Kj+KtG3btsLW1rbC3Ms4OTlV2OeTI3MzMjLEyJEjhaurqzAyMhJ6enqiSZMm4t1331U7GrZsBLu6l7pjylGuJBPiKX9+EFG95e/vj7t371Y4i36Z1NRUuLi4YPHixSpnAl4G2dnZaN68OQYOHIivv/66rtOh/8/f3x9CCBw8eBBaWlr1ZjLdBw8ewNzcHMuWLavwXtL6orS0FKWlpQgNDcW2bduQm5tb1ylRHakfnx4iohqSkZGBsLAwbN++HUePHsV3332H7t2748GDB3j//ffrOj0q59ixY9DV1VWZlqUuHTt2DI0aNcLYsWPrOpWnmjJlCnR1dfHdd9/VdSpUx3gPHRH9q+jr6yM1NRUTJkzA/fv3pUErq1evrtGnAdDzW7NmDR48eAAASiNq61r//v3Rv3//uk6jSqZOnSqNqn1yjkZ6+fCSKxEREZGGq9NLrlFRUWjfvj1MTU1hbW2NgQMH4tKlSxXGjx8/HjKZTHqOY5mCggKEhYXB0tISxsbGCAoKUpkDKisrC8HBwZDL5ZDL5QgODpaeQUhERESkyeq0oDt69CgmTpyIhIQExMbGori4GL1798bDhw9VYnfu3InExES181JFRERgx44d2Lp1K44fP47c3FwEBgaipKREihkxYgSSkpIQExODmJgYJCUlITg4uFb3j4iIiOhFqFeXXDMzM2FtbY2jR48qTYj6zz//wMfHB/v370f//v0RERGBiIgIAIBCoYCVlRU2btyIYcOGAXg807qjoyP27duHgIAAJCcnw8PDAwkJCfDx8QEAJCQkwNfXFxcvXnzqPE1ERERE9Vm9GuWqUCgAAObm5lJbaWkpgoODMX36dLU3NJ8+fRpFRUXo3bu31GZvbw9PT09pZvj4+HjI5XKpmAOAjh07Qi6Xq8weT0RERKRp6s0oVyEEpkyZgs6dOys9tmXhwoXQ0dFBeHi42vXKnkNZ/rmHNjY20rMbMzIyYG1trbKutbV1hc93LCgoQEFBgfS+tLQU9+/fh4WFxQt9TicRERG9fIQQePDgAezt7as0R2O9KegmTZqEs2fP4vjx41Lb6dOnsXz5cpw5c+aZiyghhNI66tYvH/OkqKgopcchEREREb1oaWlpVXoEYr0o6MLCwrB7924cO3ZMKenffvsNd+7cUXoeZUlJCaZOnYply5YhNTUVtra2KCwsRFZWltJZujt37sDPzw8AYGtri9u3b6tsNzMzEzY2NmpzmjlzJqZMmSK9VygUaNy4MdLS0mBmZvbc+0xERERUkZycHDg6Olb5Wch1WtAJIRAWFoYdO3bgyJEjcHFxUVoeHByMnj17KrUFBAQgODgY77zzDgCgbdu20NXVRWxsLIYOHQoASE9Px7lz57Bo0SIAgK+vLxQKBU6cOIEOHToAABITE6FQKKSirzx9fX3o6+urtJuZmbGgIyIioheiqlco67SgmzhxIrZs2YJdu3bB1NRUup9NLpfD0NAQFhYWsLCwUFpHV1cXtra20shUuVyO0NBQTJ06FRYWFjA3N8e0adPg5eUlFYPu7u7o06cPxo4dizVr1gAAxo0bh8DAQI5wJSIiIo1Xp6NcV61aBYVCAX9/f9jZ2UmvH3744Zn6iY6OxsCBAzF06FB06tQJRkZG2LNnj9JjUDZv3gwvLy/07t0bvXv3hre3NzZu3FjTu0RERET0wtWreejqs5ycHMjlcigUCl5yJSIiolr1rHVHvZqHjoiIiIieHQs6IiIiIg3Hgo6IiIhIw7GgIyIiItJwLOiIiIiInsGjwmI4z9gL5xl78aiwuK7TAcCCjoiIiEjjsaAjIiIi0nAs6IiIiIg0HAs6IiIiIg3Hgo6IiIhIw7GgIyIiItJwLOiIiIiINBwLOiIiIiINx4KOiIiISMOxoCMiIiLScCzoiIiIiDQcCzoiIiIiDceCjoiIiEjDsaAjIiIi0nAs6IiIiIg0HAs6IiIiIg3Hgo6IiIhIw7GgIyIiItJwLOiIiIiINBwLOiIiIiINx4KOiIiISMPVaUEXFRWF9u3bw9TUFNbW1hg4cCAuXbokLS8qKsIHH3wALy8vGBsbw97eHm+//TZu3bql1E9BQQHCwsJgaWkJY2NjBAUF4ebNm0oxWVlZCA4Ohlwuh1wuR3BwMLKzs1/EbhIRERHVqjot6I4ePYqJEyciISEBsbGxKC4uRu/evfHw4UMAwKNHj3DmzBnMmTMHZ86cwfbt23H58mUEBQUp9RMREYEdO3Zg69atOH78OHJzcxEYGIiSkhIpZsSIEUhKSkJMTAxiYmKQlJSE4ODgF7q/RERERLVBJoQQdZ1EmczMTFhbW+Po0aPo2rWr2piTJ0+iQ4cOuH79Oho3bgyFQgErKyts3LgRw4YNAwDcunULjo6O2LdvHwICApCcnAwPDw8kJCTAx8cHAJCQkABfX19cvHgRbm5uT80tJycHcrkcCoUCZmZmNbfTREREpFEeFRbD46P9AIALHwfASE+nxrfxrHVHvbqHTqFQAADMzc0rjZHJZGjQoAEA4PTp0ygqKkLv3r2lGHt7e3h6eiIuLg4AEB8fD7lcLhVzANCxY0fI5XIppryCggLk5OQovYiIiIjqo3pT0AkhMGXKFHTu3Bmenp5qY/Lz8zFjxgyMGDFCqlYzMjKgp6eHhg0bKsXa2NggIyNDirG2tlbpz9raWoopLyoqSrrfTi6Xw9HR8Xl2j4iIiKjW1JuCbtKkSTh79iy+//57tcuLioowfPhwlJaWYuXKlU/tTwgBmUwmvX/y/xXFPGnmzJlQKBTSKy0trYp7QkRERPRi1fxF32oICwvD7t27cezYMTg4OKgsLyoqwtChQ5GSkoJDhw4pXUu2tbVFYWEhsrKylM7S3blzB35+flLM7du3VfrNzMyEjY2N2pz09fWhr6//vLtGREREVOvq9AydEAKTJk3C9u3bcejQIbi4uKjElBVzV65cwYEDB2BhYaG0vG3bttDV1UVsbKzUlp6ejnPnzkkFna+vLxQKBU6cOCHFJCYmQqFQSDFEREREmqpOz9BNnDgRW7Zswa5du2BqairdzyaXy2FoaIji4mIMGTIEZ86cwc8//4ySkhIpxtzcHHp6epDL5QgNDcXUqVNhYWEBc3NzTJs2DV5eXujZsycAwN3dHX369MHYsWOxZs0aAMC4ceMQGBhYpRGuRERERPVZnRZ0q1atAgD4+/srta9fvx4hISG4efMmdu/eDQBo1aqVUszhw4el9aKjo6Gjo4OhQ4ciLy8PPXr0wIYNG6CtrS3Fb968GeHh4dJo2KCgIKxYsaJ2doyIiIjoBapX89DVZ5yHjoiIiADOQ0dEREREtYAFHREREZGGY0FHRERE9AxKSv93t1ritftK7+sKCzoiIiKiKoo5l46eS49K79/ZcBKdFx5CzLn0OsyKBR0RERFRlcScS8d7m87gdk6BUnuGIh/vbTpTp0UdCzoiIiKipygpFZi35wLUXVwta5u350KdXX5lQUdERET0FCdS7iNdkV/hcgEgXZGPEyn3X1xST2BBR0RERPQUdx5UXMxVJ66msaAjIiIiegprU4MajatpLOiIiIiInqKDizns5AaQVbBcBsBOboAOLuYvMi0JCzoiIiKip9DWkmHuAA+1y8qKvLkDPKCtVVHJV7tY0BERERFVQR9PO6wa2QY2ZvpK7bZyA6wa2QZ9PO3qKDOg5p8mS0RERPQv1cfTDp2aWsIr8lcAwPqQ9uja3KrOzsyV4Rk6IiIiomfwZPHm08S8zos5gAUdERERkcZjQUdERESk4VjQEREREWk4FnREREREGo4FHREREZGGY0FHREREpOFY0BERERFpOBZ0RERERBqOBR0RERGRhmNBR0RERKThWNARERERabg6LeiioqLQvn17mJqawtraGgMHDsSlS5eUYoQQiIyMhL29PQwNDeHv74/z588rxRQUFCAsLAyWlpYwNjZGUFAQbt68qRSTlZWF4OBgyOVyyOVyBAcHIzs7u7Z3kYiIiKjW1WlBd/ToUUycOBEJCQmIjY1FcXExevfujYcPH0oxixYtwtKlS7FixQqcPHkStra26NWrFx48eCDFREREYMeOHdi6dSuOHz+O3NxcBAYGoqSkRIoZMWIEkpKSEBMTg5iYGCQlJSE4OPiF7i8RERFRbZAJIURdJ1EmMzMT1tbWOHr0KLp27QohBOzt7REREYEPPvgAwOOzcTY2Nli4cCHGjx8PhUIBKysrbNy4EcOGDQMA3Lp1C46Ojti3bx8CAgKQnJwMDw8PJCQkwMfHBwCQkJAAX19fXLx4EW5ubk/NLScnB3K5HAqFAmZmZrV3EIiIiKhee1RYDI+P9gMALnwcACM9nRrfxrPWHfXqHjqFQgEAMDc3BwCkpKQgIyMDvXv3lmL09fXRrVs3xMXFAQBOnz6NoqIipRh7e3t4enpKMfHx8ZDL5VIxBwAdO3aEXC6XYsorKChATk6O0ouIiIioPqpWSfnFF1+obZfJZDAwMEDTpk3RtWtXaGtrV7lPIQSmTJmCzp07w9PTEwCQkZEBALCxsVGKtbGxwfXr16UYPT09NGzYUCWmbP2MjAxYW1urbNPa2lqKKS8qKgrz5s2rcv5EREREdaVaBV10dDQyMzPx6NEjNGzYEEIIZGdnw8jICCYmJrhz5w6aNGmCw4cPw9HRsUp9Tpo0CWfPnsXx48dVlslkMqX3QgiVtvLKx6iLr6yfmTNnYsqUKdL7nJycKu8LERER0YtUrUuuCxYsQPv27XHlyhXcu3cP9+/fx+XLl+Hj44Ply5fjxo0bsLW1xeTJk6vUX1hYGHbv3o3Dhw/DwcFBare1tQUAlbNod+7ckc7a2draorCwEFlZWZXG3L59W2W7mZmZKmf/yujr68PMzEzpRURERFQfVaugmz17NqKjo+Hq6iq1NW3aFEuWLMHMmTPh4OCARYsW4ffff6+0HyEEJk2ahO3bt+PQoUNwcXFRWu7i4gJbW1vExsZKbYWFhTh69Cj8/PwAAG3btoWurq5STHp6Os6dOyfF+Pr6QqFQ4MSJE1JMYmIiFAqFFENERESkqap1yTU9PR3FxcUq7cXFxdLZNHt7e6WpRdSZOHEitmzZgl27dsHU1FRaVy6Xw9DQEDKZDBEREViwYAGaNWuGZs2aYcGCBTAyMsKIESOk2NDQUEydOhUWFhYwNzfHtGnT4OXlhZ49ewIA3N3d0adPH4wdOxZr1qwBAIwbNw6BgYFVGuFKREREVJ9Vq6Dr3r07xo8fj2+//RatW7cGAPzxxx9477338OqrrwIA/vrrL5UzbuWtWrUKAODv76/Uvn79eoSEhAAA/vOf/yAvLw8TJkxAVlYWfHx88Ouvv8LU1FSKj46Oho6ODoYOHYq8vDz06NEDGzZsUBqUsXnzZoSHh0ujYYOCgrBixYrq7D4RERFRvVKteegyMjIQHByMgwcPQldXF8Djs3M9evTAxo0bYWNjg8OHD6tMJ6LJOA8dERERAfVzHrpqZVB2X9ulS5dw6dIlCCHQokULpcuX3bt3r07XRERERPSMnqukdHNzq/QeNDMzMyQlJaFJkybPsxkiIiIiqkStPimiHj1VjIiIiOhfq149+ouIiIiInh0LOiIiIiINx4KOiIiISMPVakH3tOetEhH9mz0qLIbzjL1wnrEXjwpVJ2MnIqopHBRBREREpOFqpKArKSlBUlISsrKylNp/+eUXNGrUqCY2QUREREQVqFZBFxERgbVr1wJ4XMx169YNbdq0gaOjI44cOSLFde7cGfr6+jWSKBERERGpV62C7qeffkLLli0BAHv27EFKSgouXryIiIgIzJo1q0YTJCIiIqLKVaugu3v3LmxtbQEA+/btwxtvvIHmzZsjNDQUf/31V40mSERERESVq1ZBZ2NjgwsXLqCkpAQxMTHo2bMnAODRo0fQ1tau0QSJiIiIqHLVepbrO++8g6FDh8LOzg4ymQy9evUCACQmJqJFixY1miARERERVa5aBV1kZCQ8PT2RlpaGN954Qxr4oK2tjRkzZtRogkRERET1iZGeDlI/61/XaSipVkEHAEOGDFF6n52djVGjRj13QkRERET0bKp1D93ChQvxww8/SO+HDh0KCwsLODg44OzZszWWHBERERE9XbUKujVr1sDR0REAEBsbi9jYWPzyyy/o06cPpk2bVqMJEhEREVHlqnXJNT09XSrofv75ZwwdOhS9e/eGs7MzfHx8ajRBIiIiIqpctc7QNWzYEGlpaQCgNG2JEAIlJSU1lx0RERERPVW1ztC9/vrrGDFiBJo1a4Z79+6hb9++AICkpCQ0bdq0RhMkIiIiospVq6CLjo6Gs7Mz0tLSsGjRIpiYmAB4fCl2woQJNZogEREREVWuWgWdrq6u2sEPERERz5sPERERET2jas9DBwAXLlzAjRs3UFhYqNQeFBT0XEkRERERUdVVq6C7du0aBg0ahL/++gsymQxCCACATCYDAA6MICIiInqBqjXK9f3334eLiwtu374NIyMjnD9/HseOHUO7du1w5MiRKvdz7NgxDBgwAPb29pDJZNi5c6fS8tzcXEyaNAkODg4wNDSEu7s7Vq1apRRTUFCAsLAwWFpawtjYGEFBQbh586ZSTFZWFoKDgyGXyyGXyxEcHIzs7Ozq7DoRERFRvVOtgi4+Ph4ff/wxrKysoKWlBS0tLXTu3BlRUVEIDw+vcj8PHz5Ey5YtsWLFCrXLJ0+ejJiYGGzatAnJycmYPHkywsLCsGvXLikmIiICO3bswNatW3H8+HHk5uYiMDBQ6SzhiBEjkJSUhJiYGMTExCApKQnBwcHV2XUiIiKieqdal1xLSkqkka2Wlpa4desW3Nzc4OTkhEuXLlW5n759+0pTnqgTHx+PUaNGwd/fHwAwbtw4rFmzBqdOncJrr70GhUKBtWvXYuPGjdJceJs2bYKjoyMOHDiAgIAAJCcnIyYmBgkJCdKkx9988w18fX1x6dIluLm5VecQEBEREdUb1TpD5+npKT2z1cfHB4sWLcLvv/+Ojz/+GE2aNKmx5Dp37ozdu3fjn3/+gRAChw8fxuXLlxEQEAAAOH36NIqKitC7d29pHXt7e3h6eiIuLg7A46JQLpcrPcGiY8eOkMvlUow6BQUFyMnJUXoRERER1UfVKuhmz56N0tJSAMD8+fNx/fp1dOnSBfv27cMXX3xRY8l98cUX8PDwgIODA/T09NCnTx+sXLkSnTt3BgBkZGRAT08PDRs2VFrPxsYGGRkZUoy1tbVK39bW1lKMOlFRUdI9d3K5XHrUGREREVF9U61LrmVnyACgSZMmuHDhAu7fv4+GDRtKI11rwhdffIGEhATs3r0bTk5OOHbsGCZMmAA7OzvpEqs6QgilPNTlVD6mvJkzZ2LKlCnS+5ycHBZ1REREVC891zx0TzI3N6+prgAAeXl5+PDDD7Fjxw70798fAODt7Y2kpCQsWbIEPXv2hK2tLQoLC5GVlaV0lu7OnTvw8/MDANja2uL27dsq/WdmZsLGxqbC7evr60NfX79G94mIiIioNlTrkmt+fj4WL16Mfv36oV27dmjTpo3SqyYUFRWhqKgIWlrKKWpra0uXe9u2bQtdXV3ExsZKy9PT03Hu3DmpoPP19YVCocCJEyekmMTERCgUCimGiIiISJNV6wzd6NGjERsbiyFDhqBDhw7Vvsyam5uLq1evSu9TUlKQlJQEc3NzNG7cGN26dcP06dNhaGgIJycnHD16FN999x2WLl0KAJDL5QgNDcXUqVNhYWEBc3NzTJs2DV5eXtIlWXd3d/Tp0wdjx47FmjVrADweLRsYGMgRrkRERPSvUK2Cbu/evdi3bx86der0XBs/deoUunfvLr0vu2dt1KhR2LBhA7Zu3YqZM2firbfewv379+Hk5IRPP/0U7777rrROdHQ0dHR0MHToUOTl5aFHjx7YsGEDtLW1pZjNmzcjPDxcGg0bFBRU4dx3RERERJpGJsqe2/UMPDw8sHXrVnh7e9dGTvVSTk4O5HI5FAoFzMzM6jodItIAjwqL4fHRfgDAhY8DYKRXY7ctE9G/3LPWHdW6h+7zzz/HBx98gOvXr1dndSIiIiKqQdX6c7Fdu3bIz89HkyZNYGRkBF1dXaXl9+/fr5HkiIiIiOjpqlXQvfnmm/jnn3+wYMEC2NjY1Ojcc0RERET0bKpV0MXFxSE+Ph4tW7as6XyIiIiI6BlV6x66Fi1aIC8vr6ZzISL6Vykp/d+Ys8Rr95XeExHVpGoVdJ999hmmTp2KI0eO4N69e3yIPRFROTHn0tFz6VHp/TsbTqLzwkOIOZdeh1kR0b9VtaYtKXt6Q/l758qej1pSUlIz2dUjnLaEiKoq5lw63tt0BuV/uJb9xFw1sg36eNq96LSISIM8a91RrXvoDh8+XJ3ViIj+9UpKBebtuaBSzAGAwOOibt6eC+jlYQttLQ4oI6KaUa2Crlu3blWKmzBhAj7++GNYWlpWZzNERBrnRMp9pCvyK1wuAKQr8nEi5T58XS1eXGJE9K9WrXvoqmrTpk28p46IXip3HlRczFUnjoioKmq1oKvG7XlERBrN2tSgRuOIiKqiVgs6IqKXTQcXc9jJDVDR3XEyAHZyA3RwMX+RaRHRvxwLOiKiGqStJcPcAR5ql5UVeXMHeHBABBHVKBZ0REQ1rI+nHVaNbAMbM32ldlu5AacsIaJaUa1RrkREVLk+nnbo1NQSXpG/AgDWh7RH1+ZWPDNHRLWiVs/QjRw5kpPwEtFL68nizaeJOYs5Iqo11SronJ2d8fHHH+PGjRuVxq1atYpz0BERERHVsmoVdFOnTsWuXbvQpEkT9OrVC1u3bkVBQUFN50ZEREREVVCtgi4sLAynT5/G6dOn4eHhgfDwcNjZ2WHSpEk4c+ZMTedIRERERJV4rnvoWrZsieXLl+Off/7B3Llz8e2336J9+/Zo2bIl1q1bx4mFiYiIiF6A5xrlWlRUhB07dmD9+vWIjY1Fx44dERoailu3bmHWrFk4cOAAtmzZUlO5EhEREZEa1Srozpw5g/Xr1+P777+HtrY2goODER0djRYtWkgxvXv3RteuXWssUSIiIiJSr1oFXfv27dGrVy+sWrUKAwcOhK6urkqMh4cHhg8f/twJEhEREVHlqlXQXbt2DU5OTpXGGBsbY/369dVKioiIiIiqrlqDIp5WzBERERHRi1Plgq5hw4YwNzev0quqjh07hgEDBsDe3h4ymQw7d+5UiUlOTkZQUBDkcjlMTU3RsWNHpQmNCwoKEBYWBktLSxgbGyMoKAg3b95U6iMrKwvBwcGQy+WQy+UIDg5GdnZ2lfMkIiIiqs+qfMl12bJl0v/v3buH+fPnIyAgAL6+vgCA+Ph47N+/H3PmzKnyxh8+fIiWLVvinXfeweDBg1WW//333+jcuTNCQ0Mxb948yOVyJCcnw8DAQIqJiIjAnj17sHXrVlhYWGDq1KkIDAzE6dOnoa2tDQAYMWIEbt68iZiYGADAuHHjEBwcjD179lQ5VyIiIqL6SiaqMVnc4MGD0b17d0yaNEmpfcWKFThw4IDaM21PTUQmw44dOzBw4ECpbfjw4dDV1cXGjRvVrqNQKGBlZYWNGzdi2LBhAIBbt27B0dER+/btQ0BAAJKTk+Hh4YGEhAT4+PgAABISEuDr64uLFy/Czc2tSvnl5ORALpdDoVDw+bREVCWPCovh8dF+AMCFjwNgpPdcM0UR0UvkWeuOat1Dt3//fvTp00elPSAgAAcOHKhOlypKS0uxd+9eNG/eHAEBAbC2toaPj49SsXj69GkUFRWhd+/eUpu9vT08PT0RFxcH4PGZQ7lcLhVzANCxY0fI5XIphoiIiEiTVaugs7CwwI4dO1Tad+7cCQsLi+dOCgDu3LmD3NxcfPbZZ+jTpw9+/fVXDBo0CK+//jqOHj0KAMjIyICenh4aNmyotK6NjQ0yMjKkGGtra5X+ra2tpRh1CgoKkJOTo/QiIiIiqo+qdf5/3rx5CA0NxZEjR6R76BISEhATE4Nvv/22RhIrLS0FALz22muYPHkyAKBVq1aIi4vD6tWr0a1btwrXFUJAJpNJ75/8f0Ux5UVFRWHevHnVTZ+IiIjohanWGbqQkBDExcWhQYMG2L59O7Zt2wa5XI7ff/8dISEhNZKYpaUldHR04OHhodTu7u4ujXK1tbVFYWEhsrKylGLu3LkDGxsbKeb27dsq/WdmZkox6sycORMKhUJ6paWlPe8uEREREdWKat+h6+Pjg82bN9dkLkr09PTQvn17XLp0San98uXL0jx4bdu2ha6uLmJjYzF06FAAQHp6Os6dO4dFixYBAHx9faFQKHDixAl06NABAJCYmAiFQgE/P78Kt6+vrw99ff3a2DUiIiKiGlXtgq6kpAQ7d+5EcnIyZDIZPDw8EBQUJE0VUhW5ubm4evWq9D4lJQVJSUkwNzdH48aNMX36dAwbNgxdu3ZF9+7dERMTgz179uDIkSMAALlcjtDQUEydOhUWFhYwNzfHtGnT4OXlhZ49ewJ4fEavT58+GDt2LNasWQPg8bQlgYGBVR7hSkRERFSfVaugu3r1Kvr164d//vkHbm5uEELg8uXLcHR0xN69e+Hq6lqlfk6dOoXu3btL76dMmQIAGDVqFDZs2IBBgwZh9erViIqKQnh4ONzc3LBt2zZ07txZWic6Oho6OjoYOnQo8vLy0KNHD2zYsEGpsNy8eTPCw8Ol0bBBQUFYsWJFdXadiIiIqN6p1jx0/fr1gxACmzdvlp4Mce/ePYwcORJaWlrYu3dvjSda1zgPHRE9K85DR0TV9ax1R7V+uhw9ehQJCQlKj/mysLDAZ599hk6dOlWnSyIiIiKqpmqNctXX18eDBw9U2nNzc6Gnp/fcSRERERFR1VWroAsMDMS4ceOQmJgIIQSEEEhISMC7776LoKCgms6RiIiIiCpRrYLuiy++gKurK3x9fWFgYAADAwP4+fmhadOmWL58eU3nSERERESVqNY9dA0aNMCuXbtw9epVXLhwAQDg4eGBpk2b1mhyRERERPR01R5ytXbtWkRHR+PKlSsAgGbNmiEiIgJjxoypseSIiIiI6OmqVdDNmTMH0dHRCAsLk57lGh8fj8mTJyM1NRXz58+v0SSJiIiIqGLVKuhWrVqFb775Bm+++abUFhQUBG9vb4SFhbGgIyIiInqBqjUooqSkBO3atVNpb9u2LYqLi587KSIiIiKqumoVdCNHjsSqVatU2r/++mu89dZbz50UEREREVVdlS+5lj1nFQBkMhm+/fZb/Prrr+jYsSMAICEhAWlpaXj77bdrPksiIiIiqlCVC7o//vhD6X3btm0BAH///TcAwMrKClZWVjh//nwNpkdERERET1Plgu7w4cO1mcdLjw/xJiIiouqq1j10RERERFR/sKAjIiIi0nAs6IiIiIg0HAs6IiIiIg3Hgo6IiIhIw7GgIyIiItJwnBuDiKiWGOnpIPWz/nWdBhG9BHiGjoiIiEjDsaAjIiIi0nAs6IiIiIg0HAs6IiIiIg3Hgo6IiIhIw9VpQXfs2DEMGDAA9vb2kMlk2LlzZ4Wx48ePh0wmw7Jly5TaCwoKEBYWBktLSxgbGyMoKAg3b95UisnKykJwcDDkcjnkcjmCg4ORnZ1d8ztEREREVAfqtKB7+PAhWrZsiRUrVlQat3PnTiQmJsLe3l5lWUREBHbs2IGtW7fi+PHjyM3NRWBgIEpKSqSYESNGICkpCTExMYiJiUFSUhKCg4NrfH+IiIiI6kKdzkPXt29f9O3bt9KYf/75B5MmTcL+/fvRv7/yfE4KhQJr167Fxo0b0bNnTwDApk2b4OjoiAMHDiAgIADJycmIiYlBQkICfHx8AADffPMNfH19cenSJbi5udXOzhERERG9IPX6HrrS0lIEBwdj+vTpeOWVV1SWnz59GkVFRejdu7fUZm9vD09PT8TFxQEA4uPjIZfLpWIOADp27Ai5XC7FEBEREWmyev2kiIULF0JHRwfh4eFql2dkZEBPTw8NGzZUarexsUFGRoYUY21trbKutbW1FKNOQUEBCgoKpPc5OTnV2QUiIiKiWldvz9CdPn0ay5cvx4YNGyCTyZ5pXSGE0jrq1i8fU15UVJQ0iEIul8PR0fGZciAiIiJ6UeptQffbb7/hzp07aNy4MXR0dKCjo4Pr169j6tSpcHZ2BgDY2tqisLAQWVlZSuveuXMHNjY2Uszt27dV+s/MzJRi1Jk5cyYUCoX0SktLq7mdIyIiIqpB9bagCw4OxtmzZ5GUlCS97O3tMX36dOzfvx8A0LZtW+jq6iI2NlZaLz09HefOnYOfnx8AwNfXFwqFAidOnJBiEhMToVAopBh19PX1YWZmpvQiIiIiqo/q9B663NxcXL16VXqfkpKCpKQkmJubo3HjxrCwsFCK19XVha2trTQyVS6XIzQ0FFOnToWFhQXMzc0xbdo0eHl5SaNe3d3d0adPH4wdOxZr1qwBAIwbNw6BgYEc4UpERET/CnVa0J06dQrdu3eX3k+ZMgUAMGrUKGzYsKFKfURHR0NHRwdDhw5FXl4eevTogQ0bNkBbW1uK2bx5M8LDw6XRsEFBQU+d+46IiIhIU8iEEKKuk9AEOTk5kMvlUCgUtXL59VFhMTw+enwp+cLHATDSq9cDkImIiKgWPWvdUW/voSMiIiKiqmFBV0+UlP7vRGnitftK74mIiIgqw4KuHog5l46eS49K79/ZcBKdFx5CzLn0OsyKiIiINAULujoWcy4d7206g9s5BUrtGYp8vLfpDIs6IiIieioWdHWopFRg3p4LUHdxtaxt3p4LvPxKRERElWJBV4dOpNxHuiK/wuUCQLoiHydS7r+4pIiIiEjjsKCrQ3ceVFzMVSeOiIiIXk4s6OqQtalBjcYRERHRy4kFXR3q4GIOO7kBZBUslwGwkxugg4v5i0yLiIiINAwLujqkrSXD3AEeapeVFXlzB3hAW6uiko+IiIiIBV2d6+Nph1Uj28DGTF+p3VZugFUj26CPp10dZUZERESagg8MrQf6eNqhU1NLeEX+CgBYH9IeXZtb8cwcERERVQnP0NUTTxZvPk3MWcwRERFRlbGgIyIiItJwLOiIiIiINBwLOiIiIiINx4KOiIiISMOxoCMiIiLScCzoiIiIiDQcCzoiIiIiDceCjoiIiEjDsaAjIiIi0nAs6IiIiIg0HAs6IiIiIg3Hgo6IiIhIw9VpQXfs2DEMGDAA9vb2kMlk2Llzp7SsqKgIH3zwAby8vGBsbAx7e3u8/fbbuHXrllIfBQUFCAsLg6WlJYyNjREUFISbN28qxWRlZSE4OBhyuRxyuRzBwcHIzs5+AXtIREREVPvqtKB7+PAhWrZsiRUrVqgse/ToEc6cOYM5c+bgzJkz2L59Oy5fvoygoCCluIiICOzYsQNbt27F8ePHkZubi8DAQJSUlEgxI0aMQFJSEmJiYhATE4OkpCQEBwfX+v4RERERvQgyIYSo6yQAQCaTYceOHRg4cGCFMSdPnkSHDh1w/fp1NG7cGAqFAlZWVti4cSOGDRsGALh16xYcHR2xb98+BAQEIDk5GR4eHkhISICPjw8AICEhAb6+vrh48SLc3NyqlF9OTg7kcjkUCgXMzMyee3/Le1RYDI+P9gMALnwcACM9nRrfBhEREWmGZ607NOoeOoVCAZlMhgYNGgAATp8+jaKiIvTu3VuKsbe3h6enJ+Li4gAA8fHxkMvlUjEHAB07doRcLpdiiIiIiDSZxpwGys/Px4wZMzBixAipUs3IyICenh4aNmyoFGtjY4OMjAwpxtraWqU/a2trKUadgoICFBQUSO9zcnJqYjeIiIiIapxGnKErKirC8OHDUVpaipUrVz41XggBmUwmvX/y/xXFlBcVFSUNopDL5XB0dKxe8kRERES1rN4XdEVFRRg6dChSUlIQGxurdB3Z1tYWhYWFyMrKUlrnzp07sLGxkWJu376t0m9mZqYUo87MmTOhUCikV1paWg3tEREREVHNqtcFXVkxd+XKFRw4cAAWFhZKy9u2bQtdXV3ExsZKbenp6Th37hz8/PwAAL6+vlAoFDhx4oQUk5iYCIVCIcWoo6+vDzMzM6UXERERUX1Up/fQ5ebm4urVq9L7lJQUJCUlwdzcHPb29hgyZAjOnDmDn3/+GSUlJdI9b+bm5tDT04NcLkdoaCimTp0KCwsLmJubY9q0afDy8kLPnj0BAO7u7ujTpw/Gjh2LNWvWAADGjRuHwMDAKo9wfRGM9HSQ+ln/uk6DiIiINFCdTlty5MgRdO/eXaV91KhRiIyMhIuLi9r1Dh8+DH9/fwCPB0tMnz4dW7ZsQV5eHnr06IGVK1cq3fN2//59hIeHY/fu3QCAoKAgrFixQhotWxW1PW0JERERUZlnrTvqzTx09R0LOiIiInpR/tXz0BERERGRKhZ0RERERBqOBR0RERGRhmNBR0RERKThWNARERERaTgWdEREREQajgUdERERkYar0ydFaJKy6fpycnLqOBMiIiL6tyurN6o6XTALuip68OABACg9gYKIiIioNj148AByufypcXxSRBWVlpbi1q1bMDU1hUwmq5Vt5OTkwNHREWlpaXwaBdG/BD/XRP9Otf3ZFkLgwYMHsLe3h5bW0++Q4xm6KtLS0oKDg8ML2ZaZmRl/8BP9y/BzTfTvVJuf7aqcmSvDQRFEREREGo4FHREREZGGY0FXj+jr62Pu3LnQ19ev61SIqIbwc03071TfPtscFEFERESk4XiGjoiIiEjDsaAjIiIi0nAs6F6wyMhItGrVqsrxqampkMlkSEpKAgAcOXIEMpkM2dnZtZIf0ctKJpNh586dFS7nZ4+I6jMWdDUgLi4O2tra6NOnT61vy8/PD+np6c80Nw0RARkZGQgLC0OTJk2gr68PR0dHDBgwAAcPHqzS+jX92XvWP+6IqHoyMjLw/vvvo2nTpjAwMICNjQ06d+6M1atX49GjRzW2HX9/f0RERNRYf8+KEwvXgHXr1iEsLAzffvstbty4gcaNG9fatvT09GBra1tr/RP9G6WmpqJTp05o0KABFi1aBG9vbxQVFWH//v2YOHEiLl68+NQ+6uqzV1RUBF1d3Re+XaJ/g2vXrkmf/QULFsDLywvFxcW4fPky1q1bB3t7ewQFBdV1mjVD0HPJzc0Vpqam4uLFi2LYsGFi3rx5SsujoqKEtbW1MDExEaNHjxYffPCBaNmypVLMunXrRIsWLYS+vr5wc3MTX331lbQsJSVFABB//PGHEEKIw4cPCwAiKytLivn9999Fly5dhIGBgXBwcBBhYWEiNze3tnaZSOP07dtXNGrUSO3nouyzBEB88803YuDAgcLQ0FA0bdpU7Nq1S4or/9lbv369kMvlIiYmRrRo0UIYGxuLgIAAcevWLaV12rdvL4yMjIRcLhd+fn4iNTVVrF+/XgBQeq1fv17KY9WqVSIoKEgYGRmJjz76SBQXF4vRo0cLZ2dnYWBgIJo3by6WLVumtB+jRo0Sr732moiMjBRWVlbC1NRUjBs3ThQUFNTswSTSIAEBAcLBwaHC34mlpaVCCCGys7PF2LFjpc9O9+7dRVJSkhQ3d+5c0bJlS/Hdd98JJycnYWZmJoYNGyZycnKEEI8/f+U/0ykpKUIIIY4cOSLat28v9PT0hK2trfjggw9EUVGR1Hd+fr4ICwsTVlZWQl9fX3Tq1EmcOHHimfeVBd1zWrt2rWjXrp0QQog9e/YIZ2dn6Rvkhx9+EHp6euKbb74RFy9eFLNmzRKmpqZKBd3XX38t7OzsxLZt28S1a9fEtm3bhLm5udiwYYMQ4ukF3dmzZ4WJiYmIjo4Wly9fFr///rto3bq1CAkJeWHHgKg+u3fvnpDJZGLBggWVxgEQDg4OYsuWLeLKlSsiPDxcmJiYiHv37gkh1Bd0urq6omfPnuLkyZPi9OnTwt3dXYwYMUIIIURRUZGQy+Vi2rRp4urVq+LChQtiw4YN4vr16+LRo0di6tSp4pVXXhHp6ekiPT1dPHr0SMrD2tparF27Vvz9998iNTVVFBYWio8++kicOHFCXLt2TWzatEkYGRmJH374Qcp/1KhRwsTERAwbNkycO3dO/Pzzz8LKykp8+OGHtXBUieq/u3fvCplMJqKioiqNKy0tFZ06dRIDBgwQJ0+eFJcvXxZTp04VFhYW0ud/7ty5wsTERLz++uvir7/+EseOHRO2trbS5ys7O1v4+vqKsWPHSp/p4uJicfPmTWFkZCQmTJggkpOTxY4dO4SlpaWYO3eutP3w8HBhb28v9u3bJ86fPy9GjRolGjZsKG27qljQPSc/Pz/pL+WioiJhaWkpYmNjhRBC+Pr6infffVcp3sfHR6mgc3R0FFu2bFGK+eSTT4Svr68Q4ukFXXBwsBg3bpzS+r/99pvQ0tISeXl5NbWbRBorMTFRABDbt2+vNA6AmD17tvQ+NzdXyGQy8csvvwgh1Bd0AMTVq1eldb766ithY2MjhHhcSAIQR44cUbu9sr/41eURERHx1P2aMGGCGDx4sPR+1KhRwtzcXDx8+FBqW7VqlTAxMRElJSVP7Y/o3yYhIUHtZ9/CwkIYGxsLY2Nj8Z///EccPHhQmJmZifz8fKU4V1dXsWbNGiHE48+rkZGRdEZOCCGmT58ufHx8pPfdunUT77//vlIfH374oXBzc5NO9Ajx+OdE2ecyNzdX6Orqis2bN0vLCwsLhb29vVi0aNEz7S8HRTyHS5cu4cSJExg+fDgAQEdHB8OGDcO6desAAMnJyfD19VVa58n3mZmZSEtLQ2hoKExMTKTX/Pnz8ffff1cph9OnT2PDhg1K6wcEBKC0tBQpKSk1tKdEmkv8/7nTZTLZU2O9vb2l/xsbG8PU1BR37typMN7IyAiurq7Sezs7Oyne3NwcISEhCAgIwIABA7B8+XKkp6dXKed27dqptK1evRrt2rWDlZUVTExM8M033+DGjRtKMS1btoSRkZH03tfXF7m5uUhLS6vSdon+jcp/9k+cOIGkpCS88sorKCgowOnTp5GbmwsLCwul36UpKSlKv4udnZ1hamoqvX/y816RsjrgyRw6deqE3Nxc3Lx5E3///TeKiorQqVMnabmuri46dOiA5OTkZ9pPDop4DmvXrkVxcTEaNWoktQkhoKuri6ysrKeuX1paCgD45ptv4OPjo7RMW1u7SjmUlpZi/PjxCA8PV1lWm4MziDRFs2bNIJPJkJycjIEDB1YaW37wgUwmkz6nVY0XTzx8Z/369QgPD0dMTAx++OEHzJ49G7GxsejYsWOleRgbGyu9//HHHzF58mR8/vnn8PX1hampKRYvXozExMRK+3kyL6KXTdOmTSGTyVQGPTVp0gQAYGhoCODx71E7OzscOXJEpY8GDRpI/3/Wnw/A45qg/OfvyT8yK/qDU916T8MzdNVUXFyM7777Dp9//jmSkpKk159//gknJyds3rwZ7u7uSEhIUFrvyfc2NjZo1KgRrl27hqZNmyq9XFxcqpRHmzZtcP78eZX1mzZtCj09vRrdZyJNZG5ujoCAAHz11Vd4+PChyvLanleudevWmDlzJuLi4uDp6YktW7YAeDxqtqSkpEp9/Pbbb/Dz88OECRPQunVrNG3aVO1Z/D///BN5eXnS+4SEBJiYmMDBwaFmdoZIg1hYWKBXr15YsWKF2s9+mTZt2iAjIwM6Ojoqv0ctLS2rvD11n2kPDw/ExcUp/aEXFxcHU1NTNGrUSPpdffz4cWl5UVERTp06BXd392fYWxZ01fbzzz8jKysLoaGh8PT0VHoNGTIEa9euxfvvv49169Zh3bp1uHz5MubOnYvz588r9RMZGYmoqCgsX74cly9fxl9//YX169dj6dKlVcrjgw8+QHx8PCZOnIikpCRcuXIFu3fvRlhYWG3sNpFGWrlyJUpKStChQwds27YNV65cQXJyMr744guV2yJqSkpKCmbOnIn4+Hhcv34dv/76Ky5fviz9kHZ2dkZKSgqSkpJw9+5dFBQUVNhX06ZNcerUKezfvx+XL1/GnDlzcPLkSZW4wsJChIaG4sKFC/jll18wd+5cTJo0CVpa/FFPL6eVK1eiuLgY7dq1ww8//IDk5GRcunQJmzZtwsWLF6GtrY2ePXvC19cXAwcOxP79+5Gamoq4uDjMnj0bp06dqvK2nJ2dkZiYiNTUVNy9exelpaWYMGEC0tLSEBYWhosXL2LXrl2YO3cupkyZAi0tLRgbG+O9997D9OnTERMTgwsXLmDs2LF49OgRQkNDn21nn+mOO5IEBgaKfv36qV12+vRpAUCcPn1afPrpp8LS0lKYmJiIUaNGif/85z8qN0Jv3rxZtGrVSujp6YmGDRuKrl27SjdxVmXakhMnTohevXoJExMTYWxsLLy9vcWnn35aG7tNpLFu3bolJk6cKJycnISenp5o1KiRCAoKEocPHxZCPB6MsGPHDqV15HK5NJ1IRdOWPGnHjh2i7MdqRkaGGDhwoLCzsxN6enrCyclJfPTRR9IAhfz8fDF48GDRoEEDlWlLyueRn58vQkJChFwuFw0aNBDvvfeemDFjhtLPkrJpSz766CNhYWEhTExMxJgxY1Ru9CZ62dy6dUtMmjRJuLi4CF1dXWFiYiI6dOggFi9eLA0iysnJEWFhYcLe3l7o6uoKR0dH8dZbb4kbN24IIdQPYoqOjhZOTk7S+0uXLomOHTsKQ0PDZ5q2JC8vT4SFhQlLS8vnmrZEJsQT5wGJiEgjhYSEIDs7u9LHlxHRvxfPwxMRERFpOBZ0RERERBqOl1yJiIiINBzP0BERERFpOBZ0RERERBqOBR0RERGRhmNBR0RERKThWNARERERaTgWdEREdSgkJAQDBw6s6zSISMNx2hIiojqkUCgghECDBg3qOhUi0mAs6IiIiIg0HC+5EtFL76effoKXlxcMDQ1hYWGBnj174uHDh9Ll0Hnz5sHa2hpmZmYYP348CgsLpXWFEFi0aBGaNGkCQ0NDtGzZEj/99JNS/+fPn0f//v1hZmYGU1NTdOnSBX///TcA1UuuT+svKysLb731FqysrGBoaIhmzZph/fr1tXuAiKje06nrBIiI6lJ6ejrefPNNLFq0CIMGDcKDBw/w22+/oezixcGDB2FgYIDDhw8jNTUV77zzDiwtLfHpp58CAGbPno3t27dj1apVaNasGY4dO4aRI0fCysoK3bp1wz///IOuXbvC398fhw4dgpmZGX7//XcUFxerzedp/c2ZMwcXLlzAL7/8AktLS1y9ehV5eXkv7HgRUf3ES65E9FI7c+YM2rZti9TUVDg5OSktCwkJwZ49e5CWlgYjIyMAwOrVqzF9+nQoFArk5eXB0tIShw4dgq+vr7TemDFj8OjRI2zZsgUffvghtm7dikuXLkFXV1dl+yEhIcjOzsbOnTvx8OHDp/YXFBQES0tLrFu3rpaOCBFpIp6hI6KXWsuWLdGjRw94eXkhICAAvXv3xpAhQ9CwYUNpeVkxBwC+vr7Izc1FWloa7ty5g/z8fPTq1Uupz8LCQrRu3RoAkJSUhC5duqgt5sq7cOHCU/t77733MHjwYJw5cwa9e/fGwIED4efn91zHgIg0Hws6InqpaWtrIzY2FnFxcfj111/x5ZdfYtasWUhMTKx0PZlMhtLSUgDA3r170ahRI6Xl+vr6AABDQ8Mq51KV/vr27Yvr169j7969OHDgAHr06IGJEydiyZIlVd4OEf37sKAjopeeTCZDp06d0KlTJ3z00UdwcnLCjh07AAB//vkn8vLypMIsISEBJiYmcHBwQMOGDaGvr48bN26gW7duavv29vbGf//7XxQVFT31LJ2Hh8dT+wMAKysrhISEICQkBF26dMH06dNZ0BG95FjQEdFLLTExEQcPHkTv3r1hbW2NxMREZGZmwt3dHWfPnkVhYSFCQ0Mxe/ZsXL9+HXPnzsWkSZOgpaUFU1NTTJs2DZMnT0ZpaSk6d+6MnJwcxMXFwcTEBKNGjcKkSZPw5ZdfYvjw4Zg5cybkcjkSEhLQoUMHuLm5KeVSlf4++ugjtG3bFq+88goKCgrw888/w93dvY6OHhHVFyzoiOilZmZmhmPHjmHZsmXIycmBk5MTPv/8c/Tt2xc//PADevTogWbNmqFr164oKCjA8OHDERkZKa3/ySefwNraGlFRUbh27RoaNGiANm3a4MMPPwQAWFhY4NChQ5g+fTq6desGbW1ttGrVCp06dVKbz9P609PTw8yZM5GamgpDQ0N06dIFW7durfXjRET1G0e5EhFV4MkRqERE9RknFiYiIiLScCzoiIiIiDQcL7kSERERaTieoSMiIiLScCzoiIiIiDQcCzoiIiIiDceCjoiIiEjDsaAjIiIi0nAs6IiIiIg0HAs6IiIiIg3Hgo6IiIhIw7GgIyIiItJw/w/1uRd68fbouwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 700x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# User defined contrast for `flipper_length_mm`\n",
    "# grid is determined for `species`\n",
    "# Not specified: bill_length_mm --> held at mean and island --> held at mode\n",
    "(fig, axes), contrasts_df = plot_comparison(\n",
    "    model=model,\n",
    "    idata=idata,\n",
    "    contrast_predictor={\"flipper_length_mm\": [172, 231]},\n",
    "    conditional=[\"species\"]\n",
    ")\n",
    "fig.set_size_inches(7, 3)\n",
    "plt.title(\"Difference in body mass of species \\n for flipper length contrast [172, 231]\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "main: island, group: species, panel: None\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(fig, axes), contrasts_df = plot_comparison(\n",
    "    model=model,\n",
    "    idata=idata,\n",
    "    contrast_predictor={\"flipper_length_mm\": [172, 231]},\n",
    "    conditional=[\"island\", \"species\"]\n",
    ")\n",
    "fig.set_size_inches(7, 3)\n",
    "plt.title(\"Difference in body mass of species on islands \\n for flipper length contrast [172, 231]\");"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Titanic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "dat = pd.read_csv(\"https://vincentarelbundock.github.io/Rdatasets/csv/Stat2Data/Titanic.csv\", index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "dat[\"PClass\"] = dat[\"PClass\"].str.replace(\"[st, nd, rd]\", \"\", regex=True)\n",
    "dat[\"PClass\"] = dat[\"PClass\"].str.replace(\"*\", \"0\").astype(int)\n",
    "dat[\"PClass\"] = dat[\"PClass\"].replace(0, np.nan)\n",
    "dat[\"PClass\"] = pd.Categorical(dat[\"PClass\"], ordered=True)\n",
    "dat[\"SexCode\"] = pd.Categorical(dat[\"SexCode\"], ordered=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "dat = dat.dropna(axis=0, how=\"any\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Modeling the probability that Survived==1\n",
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [Intercept, PClass, SexCode, PClass:SexCode, Age, PClass:Age, SexCode:Age, PClass:SexCode:Age]\n"
     ]
    },
    {
     "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 16 seconds.\n"
     ]
    }
   ],
   "source": [
    "titanic_model = bmb.Model(\n",
    "    \"Survived ~ PClass * SexCode * Age\", \n",
    "    data=dat, \n",
    "    family=\"bernoulli\"\n",
    ")\n",
    "\n",
    "titanic_idata = titanic_model.fit(draws=1000, target_accept=0.95, random_seed=1234)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "main: SexCode, group: None, panel: None\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(fig, axes), contrast_df = plot_comparison(\n",
    "    model=titanic_model,\n",
    "    idata=titanic_idata,\n",
    "    contrast_predictor={\"Age\": [50, 70]},\n",
    "    conditional=\"SexCode\"\n",
    ")\n",
    "fig.set_size_inches(7, 3)\n",
    "plt.title(\"Difference in survival probs. for \\n Age contrast [50, 70] given SexCode=[0, 1]\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "conditional: {'Age': [50], 'SexCode': [0, 1]}\n",
      "main: Age, group: SexCode, panel: None\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x300 with 1 Axes>"
      ]
     },
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   ],
   "source": [
    "# user can also pass a dict into conditional to define the \n",
    "# values used for the conditional variables\n",
    "(fig, axes), contrast_df = plot_comparison(\n",
    "    model=titanic_model,\n",
    "    idata=titanic_idata,\n",
    "    contrast_predictor={\"PClass\": [1, 3]},\n",
    "    conditional={\"Age\": [50], \"SexCode\": [0, 1]}\n",
    ")\n",
    "fig.set_size_inches(7, 3)\n",
    "plt.title(\"Difference in survival probs. for \\n PClass contrast [1, 3] given Age=50 and SexCode=[0, 1]\");"
   ]
  }
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